diff --git a/INSTALLATION.md b/INSTALLATION.md index b218fe97..c8bbecb8 100644 --- a/INSTALLATION.md +++ b/INSTALLATION.md @@ -42,8 +42,8 @@ for more details. ## [PyFFTW][pyfftw] If installed, PyMKS will use [PyFFTW][pyfftw] to -computed FFTs instead of [Numpy][numpy]. As long as [Numpy][numpy] is -not using [Intel MKL][MKL], [PyFFTW][pyfftw] should improvement the +compute FFTs instead of [Numpy][numpy]. As long as [Numpy][numpy] is +not using [Intel MKL][MKL], [PyFFTW][pyfftw] should improve the performance of PyMKS. To install [PyFFTW][pyfftw] use pip @@ -56,11 +56,11 @@ See the [PyFFTW installation instructions](https://github.com/hgomersall/pyFFTW# ## Installation on Windows We recommend you download and install the [Anaconda Python Distribution](http://continuum.io/downloads) -for Python 2.7 (x64) and then download and install PyMKS using the [windows installer](https://github.com/materialsinnovation/pymks/releases/download/version-0_2_1/PyMKS-x64-anaconda27.exe). +for Python 2.7 (x64) and then download and install PyMKS, using the [windows installer](https://github.com/materialsinnovation/pymks/releases/download/version-0_2_1/PyMKS-x64-anaconda27.exe). ## Installation on Mac OS X -We recommend you download and install the [Anaconda Python Distibution](http://continuum.io/downloads) +We recommend you download and install the [Anaconda Python Distribution](http://continuum.io/downloads) for Python 2.7 (x64). Once Anaconda has been installed, follow the above procedures to install SfePy. Finally, install PyMKS using `pip` as described above. @@ -74,7 +74,7 @@ use your terminal or shell to install PyMKS using pip. ## Requirements -The [REQUIREMENTS.md](https://github.com/materialsinnovation/pymks/blob/master/REQUIREMENTS.md) file has a list of required +The [REQUIREMENTS.md](REQUIREMENTS.html) file has a list of required packages in a Python environment used to run tests and examples for the current release of PyMKS. diff --git a/LICENSE.md b/LICENSE.md index a8de0c41..72768f14 100644 --- a/LICENSE.md +++ b/LICENSE.md @@ -2,7 +2,7 @@ The MIT License (MIT) -Copyright (c) 2014, David Brough, Daniel Wheeler, Tony Fast, Surya Kalidindi, Andrew Reid +Copyright (C) 2014-2015 David Brough, Daniel Wheeler, Tony Fast, Surya Kalidindi, Andrew Reid Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal diff --git a/README.md b/README.md index a119ab66..3bbfc7ef 100644 --- a/README.md +++ b/README.md @@ -5,7 +5,7 @@ The Materials Knowledge Systems (MKS) is a novel data science approach for solving multiscale materials science problems. It uses techniques from physics, machine learning, regression analysis, signal processing, -and spatial statistics to create structure-property-processing +and spatial statistics to create processing-structure-property relationships. The MKS carries the potential to bridge multiple length scales using localization and homogenization linkages, and provides a data driven framework for solving inverse material design @@ -32,16 +32,16 @@ See these references for further reading: ### PyMKS The Materials Knowledge Materials in Python (PyMKS) framework is an -object oriented set of tools and examples written in Python that -provide high level access to the MKS framework for rapid creation and +object-oriented set of tools and examples, written in Python, that +provide high-level access to the MKS framework for rapid creation and analysis of structure-property-processing relationships. A short -intoduction of how to use PyMKS is outlined below and example cases can +introduction to how to use PyMKS is outlined below and example cases can be found [in the examples section](EXAMPLES.html). Both code and -example contributions are welcome. +examples contributions are welcome. ### Mailing List -Please feel free to ask open ended questions about PyMKS on the +Please feel free to ask open-ended questions about PyMKS on the list. diff --git a/REQUIREMENTS.md b/REQUIREMENTS.md index 01df0cbe..98f1a695 100644 --- a/REQUIREMENTS.md +++ b/REQUIREMENTS.md @@ -26,8 +26,9 @@ Packages Required to Run Tests and Examples - pytz==2015.4 - pyzmq==14.6.0 - setuptools==17.0 + - scikit-image==0.11.3 - sfepy==2015.1 - six==1.9.0 - sympy==0.7.6 - tables==3.1.1 - - tornado==4.2 \ No newline at end of file + - tornado==4.2 diff --git a/doc/EXAMPLES.rst b/doc/EXAMPLES.rst index 1f0f481f..0e377ce5 100644 --- a/doc/EXAMPLES.rst +++ b/doc/EXAMPLES.rst @@ -7,6 +7,7 @@ Examples :maxdepth: 2 spatial_stats.rst + structure_analysis.rst homogenization.rst localization.rst diff --git a/doc/MKSmodels.rst b/doc/MKSmodels.rst index 591f6219..7ca918c1 100644 --- a/doc/MKSmodels.rst +++ b/doc/MKSmodels.rst @@ -1,6 +1,11 @@ MKS Models ========== +MKSStructureAnalysis +---------------------- +.. autoclass:: pymks.mks_structure_analysis.MKSStructureAnalysis + :members: + MKSHomogenizationModel ---------------------- .. autoclass:: pymks.mks_homogenization_model.MKSHomogenizationModel diff --git a/doc/_static/pymks.css b/doc/_static/pymks.css index 50a8898f..fa59860f 100644 --- a/doc/_static/pymks.css +++ b/doc/_static/pymks.css @@ -65,8 +65,8 @@ a:focus { /*START OF Change to code reference text in documentation */ code { - font-size: 90%; - color: #762a83; + font-size: 100%; + color: #1f78b4; } /* START OF Change for code blocks*/ diff --git a/doc/bases.rst b/doc/bases.rst index f4629cfc..af6be366 100644 --- a/doc/bases.rst +++ b/doc/bases.rst @@ -1,17 +1,22 @@ Microstructure Bases ==================== -DiscreteIndicatorBasis ----------------------- -.. autoclass:: pymks.bases.DiscreteIndicatorBasis - :members: - -ContinuousIndicatorBasis +PrimitiveBasis ------------------------ -.. autoclass:: pymks.bases.ContinuousIndicatorBasis +.. autoclass:: pymks.bases.PrimitiveBasis :members: LegendreBasis ------------- .. autoclass:: pymks.bases.LegendreBasis + :members: + +FourierBasis +------------- +.. autoclass:: pymks.bases.FourierBasis + :members: + +GSHBasis +------------- +.. autoclass:: pymks.bases.GSHBasis :members: \ No newline at end of file diff --git a/doc/datageneration.rst b/doc/datageneration.rst index 159c2e77..5b5eac4e 100644 --- a/doc/datageneration.rst +++ b/doc/datageneration.rst @@ -1,38 +1,46 @@ Data Generation =============== +make_microstructure +------------------- +.. autofunction:: pymks.datasets.make_microstructure + make_delta_microstructures -------------------------- .. autofunction:: pymks.datasets.make_delta_microstructures -make_elastic_FE_strain_delta ----------------------------- -.. autofunction:: pymks.datasets.make_elastic_FE_strain_delta - -make_elastic_FE_strain_random ------------------------------ -.. autofunction:: pymks.datasets.make_elastic_FE_strain_random +make_checkerboard_microstructure +-------------------------------- +.. autofunction:: pymks.datasets.make_checkerboard_microstructure make_cahn_hilliard ------------------ .. autofunction:: pymks.datasets.make_cahn_hilliard -make_microstructure -------------------- -.. autofunction:: pymks.datasets.make_microstructure +Data Generation that Requires SfePy +----------------------------------- -make_checkerboard_microstructure --------------------------------- -.. autofunction:: pymks.datasets.make_checkerboard_microstructure +make_elastic_FE_strain_random +----------------------------- +.. autofunction:: pymks.datasets.make_elastic_FE_strain_random + +make_elastic_FE_strain_delta +---------------------------- +.. autofunction:: pymks.datasets.make_elastic_FE_strain_delta make_elastic_stress_random -------------------------- .. autofunction:: pymks.datasets.make_elastic_stress_random + Simulations ----------- +Cahn-Hilliard +------------- .. autoclass:: pymks.datasets.cahn_hilliard_simulation.CahnHilliardSimulation :members: +Finite Element Elasticity +------------------------- .. autoclass:: pymks.datasets.elastic_FE_simulation.ElasticFESimulation :members: diff --git a/doc/homogenization.rst b/doc/homogenization.rst index ae6857ca..ec34759d 100644 --- a/doc/homogenization.rst +++ b/doc/homogenization.rst @@ -4,4 +4,5 @@ Homogenization .. toctree:: :maxdepth: 1 - rst/stress_homogenization_2D.rst \ No newline at end of file + rst/homogenization_stress_2D.rst + rst/homogenization_fiber_2D.rst \ No newline at end of file diff --git a/doc/intro.rst b/doc/intro.rst index 9df111b9..f2a98030 100644 --- a/doc/intro.rst +++ b/doc/intro.rst @@ -2,11 +2,11 @@ Meet PyMKS ========== -In this short introduction, we will demonstrate the functionality of -PyMKS to compute 2-point statistics in order to objectively quantify -microstructures, predict effective properties using homogenization and -predict local properties using localization. If you would like more -technical details amount any of these methods please see the `theory +In this short introduction, we will demonstrate the functionality in +PyMKS. We will quantify microstructures using 2-point statistics, +predict effective properties using homogenization and predict local +properties using localization. If you would like more technical details +about any of these methods please see the `theory section `__. .. code:: python @@ -18,20 +18,23 @@ section `__. import numpy as np import matplotlib.pyplot as plt + Quantify Microstructures using 2-Point Statistics ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -Lets make two dual phase microstructures with different morphologies. +Lets make two dual-phase microstructures with different morphologies. .. code:: python from pymks.datasets import make_microstructure + X_1 = make_microstructure(n_samples=1, grain_size=(25, 25)) X_2 = make_microstructure(n_samples=1, grain_size=(15, 95)) X = np.concatenate((X_1, X_2)) + Throughout PyMKS ``X`` is used to represent microstructures. Now that we have made the two microstructures, lets take a look at them. @@ -43,6 +46,7 @@ have made the two microstructures, lets take a look at them. + .. image:: intro_files/intro_5_0.png @@ -58,9 +62,10 @@ function. from pymks import PrimitiveBasis from pymks.stats import correlate - prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1]) - X_ = prim_basis.discretize(X) - X_corr = correlate(X_, periodic_axes=[0, 1]) + + p_basis = PrimitiveBasis(n_states=2, domain=[0, 1]) + X_corr = correlate(X, p_basis, periodic_axes=[0, 1]) + Let's take a look at the two autocorrelations and the cross-correlation for these two microstructures. @@ -74,6 +79,7 @@ for these two microstructures. draw_correlations(X_corr[0]) + .. parsed-literal:: (101, 101, 3) @@ -89,6 +95,7 @@ for these two microstructures. + .. image:: intro_files/intro_10_0.png @@ -99,7 +106,7 @@ Predict Homogenized Properties ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this section of the intro, we are going to predict the effective -stiffness for two phase microstructures using the +stiffness for two-phase microstructures using the ``MKSHomogenizationModel``, but we could have chosen any other effective material property. @@ -111,15 +118,17 @@ types of microstructures, totaling to 600 microstructures. from pymks.datasets import make_elastic_stress_random + grain_size = [(47, 6), (4, 49), (14, 14)] n_samples = [200, 200, 200] X_train, y_train = make_elastic_stress_random(n_samples=n_samples, size=(51, 51), grain_size=grain_size, seed=0) + Once again, ``X_train`` is our microstructures. Throughout PyMKS ``y`` -is used as either the prpoerty or the field we would like to predict. In -this case ``y_train`` is the effective stress values for ``X_train``. +is used as either the property, or the field we would like to predict. +In this case ``y_train`` is the effective stress values for ``X_train``. Let's look at one of each of the three different types of microstructures. @@ -129,10 +138,11 @@ microstructures. + .. image:: intro_files/intro_16_0.png -The ``MKSHomogenizationModel`` uses 2-point statistics, so we need +The ``MKSHomogenizationModel`` uses 2-point statistics, so we need to provide a discretization method for the microstructures by providing a basis function. We will also specify which correlations we want. @@ -140,15 +150,18 @@ basis function. We will also specify which correlations we want. from pymks import MKSHomogenizationModel - prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1]) - homogenize_model = MKSHomogenizationModel(basis=prim_basis, + + p_basis = PrimitiveBasis(n_states=2, domain=[0, 1]) + homogenize_model = MKSHomogenizationModel(basis=p_basis, periodic_axes=[0, 1], correlations=[(0, 0), (1, 1), (0, 1)]) + Let's fit our model with the data we created. .. code:: python - homogenize_model.fit(X_train, y_train, periodic_axes=[0, 1]) + homogenize_model.fit(X_train, y_train) + Now let's make some new data to see how good our model is. @@ -158,23 +171,28 @@ Now let's make some new data to see how good our model is. X_test, y_test = make_elastic_stress_random(n_samples=n_samples, size=(51, 51), grain_size=grain_size, seed=100) + We will try and predict the effective stress of our ``X_test`` microstructures. .. code:: python - y_pred = homogenize_model.predict(X_test, periodic_axes=[0, 1]) + y_pred = homogenize_model.predict(X_test) + The ``MKSHomogenizationModel`` generates low dimensional representations of microstructures and regression methods to predict effective -properties. Take a look at the low dimensional representations. +properties. Take a look at the low-dimensional representations. .. code:: python - from pymks.tools import draw_components + from pymks.tools import draw_components_scatter - draw_components([homogenize_model.reduced_fit_data, homogenize_model.reduced_predict_data], - ['Training Data', 'Testing Data']) + + draw_components_scatter([homogenize_model.reduced_fit_data[:,:2], + homogenize_model.reduced_predict_data[:,:2]], + ['Training Data', 'Test Data']) + @@ -188,11 +206,12 @@ Now let's look at a goodness of fit plot for our from pymks.tools import draw_goodness_of_fit - fit_data = np.array([y_train, - homogenize_model.predict(X_train, periodic_axes=[0, 1])]) + + fit_data = np.array([y_train, homogenize_model.predict(X_train)]) pred_data = np.array([y_test, y_pred]) - draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Testing Data']) + draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Test Data']) + @@ -217,18 +236,22 @@ First we need some data, so let's make some. from pymks.datasets import make_elastic_FE_strain_delta + X_delta, y_delta = make_elastic_FE_strain_delta() + Once again, ``X_delta`` is our microstructures and ``y_delta`` is our -local strain fields. We need to discretize the microstructure again so +local strain fields. We need to discretize the microstructure again, so we will also use the same basis function. .. code:: python from pymks import MKSLocalizationModel - prim_basis = PrimitiveBasis(n_states=2) - localize_model = MKSLocalizationModel(basis=prim_basis) + + p_basis = PrimitiveBasis(n_states=2) + localize_model = MKSLocalizationModel(basis=p_basis) + Let's use the data to fit our ``MKSLocalizationModel``. @@ -236,26 +259,31 @@ Let's use the data to fit our ``MKSLocalizationModel``. localize_model.fit(X_delta, y_delta) + Now that we have fit our model, we will create a random microstructure -and compute its local strain field using finite element analysis. We +and compute its local strain field, using finite element analysis. We will then try and reproduce the same strain field with our model. .. code:: python from pymks.datasets import make_elastic_FE_strain_random + X_test, y_test = make_elastic_FE_strain_random() + Let's look at the microstructure and its local strain field. .. code:: python from pymks.tools import draw_microstructure_strain + draw_microstructure_strain(X_test[0], y_test[0]) + .. image:: intro_files/intro_40_0.png @@ -266,12 +294,12 @@ and compare the predicted and computed local strain field. from pymks.tools import draw_strains_compare - y_pred = localize_model.predict(X_test) draw_strains_compare(y_test[0], y_pred[0]) + .. image:: intro_files/intro_42_0.png diff --git a/doc/intro_files/intro_10_0.png b/doc/intro_files/intro_10_0.png index e7d6f414..7d625426 100644 Binary files a/doc/intro_files/intro_10_0.png and b/doc/intro_files/intro_10_0.png differ diff --git a/doc/intro_files/intro_16_0.png b/doc/intro_files/intro_16_0.png index 88fc9a1a..0a197fe2 100644 Binary files a/doc/intro_files/intro_16_0.png and b/doc/intro_files/intro_16_0.png differ diff --git a/doc/intro_files/intro_26_0.png b/doc/intro_files/intro_26_0.png index 851f4cf7..fb785f8a 100644 Binary files a/doc/intro_files/intro_26_0.png and b/doc/intro_files/intro_26_0.png differ diff --git a/doc/intro_files/intro_28_0.png b/doc/intro_files/intro_28_0.png index ef8b1d53..7bc7c3f6 100644 Binary files a/doc/intro_files/intro_28_0.png and b/doc/intro_files/intro_28_0.png differ diff --git a/doc/intro_files/intro_40_0.png b/doc/intro_files/intro_40_0.png index 7c820eda..53de1d54 100644 Binary files a/doc/intro_files/intro_40_0.png and b/doc/intro_files/intro_40_0.png differ diff --git a/doc/intro_files/intro_42_0.png b/doc/intro_files/intro_42_0.png index d4d98035..238393a5 100644 Binary files a/doc/intro_files/intro_42_0.png and b/doc/intro_files/intro_42_0.png differ diff --git a/doc/intro_files/intro_5_0.png b/doc/intro_files/intro_5_0.png index ea2c471c..7434ad8e 100644 Binary files a/doc/intro_files/intro_5_0.png and b/doc/intro_files/intro_5_0.png differ diff --git a/doc/intro_files/intro_9_1.png b/doc/intro_files/intro_9_1.png index fcb40ba4..0e69f7ed 100644 Binary files a/doc/intro_files/intro_9_1.png and b/doc/intro_files/intro_9_1.png differ diff --git a/doc/localization.rst b/doc/localization.rst index 3ccbb277..ec85378b 100644 --- a/doc/localization.rst +++ b/doc/localization.rst @@ -4,9 +4,10 @@ Localization .. toctree:: :maxdepth: 1 - rst/elasticity_2D.rst - rst/elasticity_2D_Multiphase.rst - rst/elasticity_3D.rst + rst/localization_elasticity_2D.rst + rst/localization_elasticity_multiphase_2D.rst + rst/localization_elasticity_3D.rst rst/filter.rst - rst/cahn_hilliard.rst - rst/cahn_hilliard_Legendre.rst \ No newline at end of file + rst/localization_cahn_hilliard_2D.rst + rst/localization_cahn_hilliard_Legendre_2D.rst + rst/localization_elasticity_polycrystal_hex_3D.rst \ No newline at end of file diff --git a/doc/spatial_stats.rst b/doc/spatial_stats.rst index 8c5b4687..978d7535 100644 --- a/doc/spatial_stats.rst +++ b/doc/spatial_stats.rst @@ -4,4 +4,5 @@ .. toctree:: :maxdepth: 1 - rst/checker_board.rst \ No newline at end of file + rst/stats_checker_board.rst + rst/stats_steel.rst \ No newline at end of file diff --git a/doc/structure_analysis.rst b/doc/structure_analysis.rst new file mode 100644 index 00000000..7e719263 --- /dev/null +++ b/doc/structure_analysis.rst @@ -0,0 +1,8 @@ +Structure Analysis +================== + +.. toctree:: + :maxdepth: 1 + + rst/structure_ising_2D.rst + rst/structure_md_2D.rst \ No newline at end of file diff --git a/notebooks/cahn_hilliard.ipynb b/notebooks/cahn_hilliard.ipynb deleted file mode 100644 index 01d19c22..00000000 --- a/notebooks/cahn_hilliard.ipynb +++ /dev/null @@ -1,2990 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Cahn-Hilliard Example\n", - "\n", - "This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation as well as details about calibrating and validating the MKS model. The example demonstrates how to generate sample data, calibrate the influence coefficients and then pick an appropriate number of local states when state space is continuous. The MKS model and a spectral solution of the Cahn-Hilliard equation are compared on a larger test microstructure over multiple time steps." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Cahn-Hilliard Equation\n", - "\n", - "The Cahn-Hilliard equation is used to simulate microstructure evolution during spinodial decomposition and has the following form,\n", - "\n", - "$$ \\dot{\\phi} = \\nabla^2 \\left( \\phi^3 - \\phi \\right) - \\gamma \\nabla^4 \\phi $$\n", - "\n", - "where $\\phi$ is a conserved ordered parameter and $\\sqrt{\\gamma}$ represents the width of the interface. In this example, the Cahn-Hilliard equation is solved using a semi-implicit spectral scheme with periodic boundary conditions, see [Chang and Rutenberg](http://dx.doi.org/10.1103/PhysRevE.72.055701) for more details." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKS\n", - "\n", - "In this example the MKS equation will be used to predict microstructure at the next time step using \n", - "\n", - "$$p[s, 1] = \\sum_{r=0}^{S-1} \\alpha[l, r, 1] \\sum_{l=0}^{L-1} m[l, s - r, 0] + ...$$\n", - "\n", - "where $p[s, n + 1]$ is the concentration field at location $s$ and at time $n + 1$, $r$ is the convolution dummy variable and $l$ indicates the local states varable. $\\alpha[l, r, n]$ are the influence coefficients and $m[l, r, 0]$ the microstructure function given to the model. $S$ is the total discretized volume and $L$ is the total number of local states `n_states` choosen to use.\n", - "\n", - "The model will march forward in time by recussively replacing discretizing $p[s, n]$ and substituing it back for $m[l, s - r, n]$.\n", - "\n", - "###Calibration Datasets\n", - "\n", - "Unlike the elastostatic examples, the microstructure (concentration field) for this simulation doesn't have discrete phases. The microstructure is a continuous field that can have a range of values which can change over time, therefore the first order influence coefficients cannot be calibrated with delta microstructures. Instead a large number of simulations with random initial conditions are used to calibrate the first order influence coefficients using linear regression.\n", - "\n", - "The function `make_cahn_hilliard` from `pymks.datasets` provides an interface to generate calibration datasets for the influence coefficients. To use `make_cahn_hilliard`, we need to set the number of samples we want to use to calibrate the influence coefficients using `n_samples`, the size of the simulation domain using `size` and the time step using `dt`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pymks\n", - "from pymks.datasets import make_cahn_hilliard\n", - "\n", - "n = 41\n", - "n_samples = 400\n", - "dt = 1e-2\n", - "np.random.seed(99)\n", - "X, y = make_cahn_hilliard(n_samples=n_samples, size=(n, n), dt=dt)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `make_cahnHilliard` generates `n_samples` number of random microstructures, `X`, and the associated updated microstructures, `y`, after one time step `y`. The following cell plots one of these microstructures along with its update." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEsCAYAAABT+wIrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlYVHX7xm9gYIZtQDZBQRHRUFzTSMVQyyUT9zSxzPqp\n", - "Zfb2Zqa+r5VLLmmrmhllWImlgaJp5pJpuORCkmjuGyooqIjszAzM8PvDi3nFeZ7D4kbT87kurgvu\n", - "meecM2ebh3POfX9tysrKyiAIgiAIgiD8Y7B90AsgCIIgCIIg3F+kARQEQRAEQfiHIQ2gIAiCIAjC\n", - "PwxpAAVBEARBEP5hSAMoCIIgCILwD0MaQEEQBEEQhH8Yqge9ALWJ+Ph4bNmyBUuXLn1gy3Do0CFc\n", - "unQJTz31VJXeX1JSgk2bNmH37t3IyMgAAPj7+yM8PBw9e/aEg4PDvVzce8qZM2dw8OBBDBky5K5N\n", - "s7S0FGvWrEFYWBgCAwPN+tWrV/Haa6/hP//5Dx5++OG7Nj9BSExMxC+//IL09HTY2NigUaNGiIyM\n", - "RPv27as9rcuXL2P37t2IjIyEk5PTPVjams1j37592LJlC86fPw+DwQAvLy+0a9cOffv2RZ06de7J\n", - "ct4PcnNzsWXLFnTr1g3e3t53bbq//vor3Nzc8Mgjj1TQX331VXTs2BHPPffcXZuXIHBIA3gbNjY2\n", - "D3T+hw4dwv79+6vUABoMBsyePRtpaWno06cPQkJCAAAnT57EunXrYGtrW+VGsjZy5swZrF69+q43\n", - 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This leaves the number of local states in local state space as a free hyper parameter. In previous work it has been shown that as you increase the number of local states, the accuracy of MKS model increases (see [Fast et al.](http://dx.doi.org/10.1016/j.actamat.2010.10.008)), but as the number of local states increases, the difference in accuracy decreases. Some work needs to be done in order to find the practical number of local states that we will use. \n", - "\n", - "### Optimizing the Number of Local States\n", - "\n", - "Let's split the calibrate dataset into testing and training datasets. The function `train_test_split` for the machine learning python module [`sklearn`](http://scikit-learn.org/stable/) provides a convenient interface to do this. 80% of the dataset will be used for training and the remaining 20% will be used for testing by setting `test_size` equal to 0.2. The state of the random number generator used to make the split can be set using `random_state`. " - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import sklearn\n", - "from sklearn.cross_validation import train_test_split\n", - "\n", - "split_shape = (X.shape[0],) + (np.product(X.shape[1:]),)\n", - "X_train, X_test, y_train, y_test = train_test_split(X.reshape(split_shape), y.reshape(split_shape),\n", - " test_size=0.5, random_state=3)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We are now going to calibrate the influence coefficients while varying the number of local states from 2 up to 20. Each of these models will then predict the evolution of the concentration fields. Mean square error will be used to compared the results with the testing dataset to evaluate how the MKS model's performance changes as we change the number of local states. \n", - "\n", - "First we need to import the class `MKSLocalizationModel` from `pymks`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks import MKSLocalizationModel\n", - "from pymks.bases import PrimitiveBasis\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Next we will calibrate the influence coefficients while varying the number of local states and compute the mean squared error. The following demonstrates how to use Scikit-learn's `GridSearchCV` to optimize `n_states` as a hyperparameter. Of course, the best fit is always with a larger value of `n_states`. Increasing this parameter does not overfit the data." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "text/plain": [ - "GridSearchCV(cv=5,\n", - " estimator=MKSLocalizationModel(basis=,\n", - " n_states=2),\n", - " fit_params={'size': (41, 41)}, iid=True, loss_func=None, n_jobs=1,\n", - " param_grid={'n_states': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])},\n", - " pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n", - " verbose=0)" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "from sklearn.grid_search import GridSearchCV\n", - "\n", - "parameters_to_tune = {'n_states': np.arange(2, 11)}\n", - "prim_basis = PrimitiveBasis(2, [-1, 1])\n", - "model = MKSLocalizationModel(prim_basis)\n", - "gs = GridSearchCV(model, parameters_to_tune, cv=5, fit_params={'size': (n, n)})\n", - "gs.fit(X_train, y_train)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MKSLocalizationModel(basis=,\n", - " n_states=10)\n", - "0.99999908222\n" - ] - } - ], - "source": [ - "print(gs.best_estimator_)\n", - "print(gs.score(X_test, y_test))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAawAAAEnCAYAAAD1v3e3AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtAVGX++PH3MAMMd7mIqCCkUiiWmoqYqGx2WW3rW6lk\n", - "dtsu7lZmu9/a6re7pdluW1sZ25rad0Pt4u4GYoVZVpsFmpuieCO85AUQVO4CjjAMM3N+fwCjI6MM\n", - "ODAz8nm1LXPOec4znzPafDjnuakURVEQQgghXJyHswMQQggh7CEJSwghhFuQhCWEEMItSMISQgjh\n", - "FiRhCSGEcAuSsIQQQrgFSVhCCCHcgsbZAfQGpaWlrFixgsLCQgIDA7n33ntJSEiwWba5uZl//vOf\n", - "/PDDDxgMBiZOnMiDDz6IWq3uVF2ZmZmsWbOGF154gREjRjjsWoxGI2+99RZHjx6lqqqKhQsXMnz4\n", - "cIfVL4QQFyJ3WA6UkZHBmjVrrPaZTCZef/11xo4dy6pVq/jVr37FkiVLOHnypM06Pv30UwoLC3nz\n", - 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" score_label='R-squared', param_label='L-Number of Local States')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As expected the accuracy of the MKS model monotonically increases as we increase n_states, but accuracy doesn't improve significantly as n_states gets larger than signal digits. \n", - "\n", - "In order to save on computation costs let's set calibrate the influence coefficients with `n_states` equal to 6, but realize that if we need slightly more accuracy the value can be increased." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model = MKSLocalizationModel(basis=PrimitiveBasis(6, [-1, 1]))\n", - "model.fit(X, y)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Here are the first 4 influence coefficients. 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"###Predict Microstructure Evolution\n", - "\n", - "With the calibrated influence coefficients, we are ready to predict the evolution of a concentration field. In order to do this, we need to have the Cahn-Hilliard simulation and the MKS model start with the same initial concentration `phi0` and evolve in time. In order to do the Cahn-Hilliard simulation we need an instance of the class `CahnHilliardSimulation`." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets.cahn_hilliard_simulation import CahnHilliardSimulation\n", - "np.random.seed(191)\n", - "\n", - "phi0 = np.random.normal(0, 1e-9, (1, n, n))\n", - "ch_sim = CahnHilliardSimulation(dt=dt)\n", - "phi_sim = phi0.copy()\n", - "phi_pred = phi0.copy()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In order to move forward in time, we need to feed the concentration back into the Cahn-Hilliard simulation and the MKS model." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "time_steps = 10\n", - "\n", - "for ii in range(time_steps):\n", - " ch_sim.run(phi_sim)\n", - " phi_sim = ch_sim.response\n", - " phi_pred = model.predict(phi_pred)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the concentration fields." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAn4AAAElCAYAAAB6ce/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt0VeWZP/Bvcm45uZyEJIRbuIjoRONdSlmCiNJ26ug4\n", - "tgitTG2romN17LQUbZ1ZKtSqVVtp1SkVcWxZq7Wm4LS2tnRZWYC31lEHuYgXEBTkGgK5n3t+f/Az\n", - "I83zfcM+HEKy/X7WYi3ynrOvZ+8nb96zn+ct6Orq6oKIiIiI+F7hsd4BEREREekb6viJiIiIfEyo\n", - "4yciIiLyMaGOn4iIiMjHhDp+IiIiIh8T6viJiIiIfEwEj/UOyKFWrlyJP/7xj9i1axcKCwtRU1OD\n", - "+vp6fPnLXwYA7NmzBzfeeCO+/e1v46yzzuqTfZo3bx5isRjmzJlz2Mvs2LEDzz//PC6++GIUFxd3\n", - "t69cuRILFy7EkiVLEIlEjsbuisgx1NDQgGXLlmHo0KH48Y9/3OP1r3/969i9ezcuu+wyzJgxAw0N\n", - "DfjTn/6ERx99tPs92WwWDz30EP7617/i29/+Nk477TTE43H85je/wUsvvYR9+/YhGo1ixIgRmDJl\n", - "Ci644IK+PESRAU0dv37kv//7v9HQ0IB/+qd/Qn19PVKpFDZv3oznn3++u+NXWVmJO++8E8OHD++z\n", - 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"\n", - "draw_concentrations((phi_sim[0], phi_pred[0]), labels=('Simulation', 'MKS'))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The MKS model was able to capture the microstructure evolution with 6 local states. \n", - "\n", - "##Resizing the Coefficients to use on Larger Systems \n", - "\n", - "Now let's try and predict a larger simulation by resizing the coefficients and provide a larger initial concentratio field." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "m = 3 * n\n", - "model.resize_coeff((m, m))\n", - "\n", - "phi0 = np.random.normal(0, 1e-9, (1, m, m))\n", - "phi_sim = phi0.copy()\n", - "phi_pred = phi0.copy()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Once again we are going to march forward in time by feeding the concentration fields back into the Cahn-Hilliard simulation and the MKS model. " - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for ii in range(1000):\n", - " ch_sim.run(phi_sim)\n", - " phi_sim = ch_sim.response\n", - " phi_pred = model.predict(phi_pred)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the results." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAn4AAAElCAYAAAB6ce/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfWuUZVV57TzPenY1NA2CtoQgmsZGTZQQBxhFiXmZGBOE\n", - "qFeTGwWD8SY3EtCYXB+JwRf3hjxMCAaDYSQaWjEGY4JDZSBGNFz1AoLEB2JEBA229rOqzvP+2Guu\n", - "x1xrnXOqu6opyzV79Nh19tlnn73XXnud/c01v/nVhsPhEAUFBQUFBQUFBRse9Yf6AAoKCgoKCgoK\n", - "Cg4PyoNfQUFBQUFBQcH3CcqDX0FBQUFBQUHB9wnKg19BQUFBQUFBwfcJyoNfQUFBQUFBQcH3CcqD\n", - "X0FBQUFBQUHB9wmaD/UBFIS48cYb8a//+q944IEHUK/Xccwxx2DHjh34lV/5FQDAt771Lfzmb/4m\n", - 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" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/cahn_hilliard_Legendre.ipynb b/notebooks/cahn_hilliard_Legendre.ipynb deleted file mode 100644 index 1ba92f4e..00000000 --- a/notebooks/cahn_hilliard_Legendre.ipynb +++ /dev/null @@ -1,7091 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Cahn-Hilliard with Primtive and Legendre Bases\n", - "\n", - "This example uses a Cahn-Hilliard model to compare two different bases representations to discretize the microstructure. One basis representaion uses the primitive (or hat) basis and the other uses Legendre polynomials. The example includes the background theory about using Legendre polynomials as a basis in MKS. The MKS with two different bases are compared with the standard spectral solution for the Cahn-Hilliard solution at both the calibration domain size and a scaled domain size. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Cahn-Hilliard Equation\n", - "\n", - "The Cahn-Hilliard equation is used to simulate microstructure evolution during spinodial decomposition and has the following form,\n", - "\n", - "$$ \\dot{\\phi} = \\nabla^2 \\left( \\phi^3 - \\phi \\right) - \\gamma \\nabla^4 \\phi $$\n", - "\n", - "where $\\phi$ is a conserved ordered parameter and $\\sqrt{\\gamma}$ represents the width of the interface. In this example, the Cahn-Hilliard equation is solved using a semi-implicit spectral scheme with periodic boundary conditions, see [Chang and Rutenberg](http://dx.doi.org/10.1103/PhysRevE.72.055701) for more details." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Basis Functions for the Microstructure Function and Influence Function\n", - "\n", - "In this example, we will explore the differences when using the\n", - "Legendre polynomials as the basis function compared to the primitive\n", - "(or hat) basis for the microstructure function and the influence coefficients.\n", - "\n", - "For more information about both of these basis please see the [theory section](THEORY.html)." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKS\n", - "\n", - "###Generating Calibration Datasets\n", - "\n", - "Because the microstructure is a continuous field that can have a range of values and changes over time, the first order influence coefficients cannot be calibrated with delta microstructures. Instead a large number of simulations with random initial conditions will be used to calibrate the first order influence coefficients using linear regression. Let's show how this is done.\n", - "\n", - "The function `make_cahnHilliard` from `pymks.datasets` provides a nice interface to generate calibration datasets for the influence coefficients. The funcion `make_cahnHilliard` requires the number of calibration samples given by `n_samples` and the size and shape of the domain given by `size`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import pymks\n", - "from pymks.datasets import make_cahn_hilliard\n", - "\n", - "length = 41\n", - "n_samples = 400\n", - "dt = 1e-2\n", - "np.random.seed(101)\n", - "size=(length, length)\n", - "X, y = make_cahn_hilliard(n_samples=n_samples, size=size, dt=dt)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The function `make_cahnHilliard` has generated `n_samples` number of random microstructures, `X`, and returned the same microstructures after they have evolved for one time step given by `y`. Let's take a look at one of them." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAoAAAAEsCAYAAABT+wIrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlYlOX+xm9gVsABAXEDRbTEMFckSzM9VmqZaZa5VpaW\n", - "ZZ2MspN1Sm052aqtlNEilQtpmbnW0VBblDLNcjeXxEDFBcSZYYDx98f5SdJ8vy8gLjjdn+vyupx7\n", - "5n7fd553mYfnfe/vE3D8+PHjIIQQQgghfxsCz/UGEEIIIYSQsws7gIQQQgghfzPYASSEEEII+ZvB\n", - "DiAhhBBCyN8MdgAJIYQQQv5msANICCGEEPI3gx3A08CqVaswceJEDB8+HEOGDMH999+P9PR0HD58\n", - "uErLGT16ND788MOy12+88QbGjRtX9jozMxM333wzioqKTtu2/5U//vgDGRkZcDqd5fSzse5zuT6J\n", - "7du345NPPjln6yd/T3g9OXP8+OOPmDhxIm699VYMGzYMjz76KDIzM09pWfn5+cjIyMCBAwdO70ae\n", - "5XWQvy+mc70B5zvp6elYuHAhunXrhuuuuw52ux179uzBV199hf379+Ohhx6q0vICAgLK/n/jjTei\n", - "uLj4dG+yITk5OZgzZw7+8Y9/IDg4uExv164dnnnmGVgslrO6PeeS7du3Y/bs2bjpppvO9aaQvwm8\n", - "npw55s6dixkzZuDyyy/H9ddfD5PJhB9//BFvv/02tm/fjhEjRlRpefn5+ZgzZw5atmyJOnXqnJFt\n", - 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As mentioned above, the microstructures (concentration fields) are not discrete phases. This leaves the number of local states in local state space `n_states` as a free hyper parameter. In the next section we look to see what a practical number of local states for bases would be. \n", - " \n", - "### Optimizing the Number of Local States\n", - " \n", - "Below, we compare the difference in performance as we vary the local state when we choose the primitive basis and the Legendre polynomial basis.\n", - "\n", - "The `(X, y)` sample data is split into training and test data. The code then optimizes `n_states` between `2` and `11` and the two `basis` with the `parameters_to_tune` variable. The `GridSearchCV` takes an `MKSLocalizationModel` instance, a `scoring` function (figure of merit) and the `parameters_to_tune` and then finds the optimal parameters with a grid search." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.bases import PrimitiveBasis\n", - "from sklearn.grid_search import GridSearchCV\n", - "from sklearn import metrics\n", - "mse = metrics.mean_squared_error\n", - "from pymks.bases import LegendreBasis\n", - "from pymks import MKSLocalizationModel\n", - "from sklearn.cross_validation import train_test_split\n", - "\n", - "train_split_shape = (X.shape[0],) + (np.prod(X.shape[1:]),)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X.reshape(train_split_shape),\n", - " y.reshape(train_split_shape),\n", - " test_size=0.5, random_state=3)\n", - "\n", - "prim_basis = PrimitiveBasis(2, [-1, 1])\n", - "leg_basis = LegendreBasis(2, [-1, 1])\n", - "\n", - "params_to_tune = {'n_states': np.arange(2, 11),\n", - " 'basis': [prim_basis, leg_basis]}\n", - "Model = MKSLocalizationModel(prim_basis)\n", - "scoring = metrics.make_scorer(lambda a, b: -mse(a, b))\n", - "fit_params = {'size': size}\n", - "gs = GridSearchCV(Model, params_to_tune, cv=5, fit_params=fit_params, n_jobs=3).fit(X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The optimal parameters are the `LegendreBasis` with only 4 local states. More terms don't improve the R-squared value." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "MKSLocalizationModel(basis=,\n", - " n_states=4)\n", - "1.0\n" - ] - } - ], - "source": [ - "print(gs.best_estimator_)\n", - "print(gs.score(X_test, y_test))\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAjAAAAEnCAYAAAC3ynnRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xl8k2W6+P9PlrZp06Yr0JZuQIHSsjpQ64AcFJcDKi7I\n", - "6ijjCI6IzHHgzDiL6IvR0fnOHOQoM+BRwNERBwqoID8YGReKyjaDrAUBWUoLXWgL3dI0TfL8/ggJ\n", - "DUlpWtKmodf79fLV5nnu586VIOTK/Vz3fasURVEQQgghhAggan8HIIQQQgjRWpLACCGEECLgSAIj\n", - "hBBCiIAjCYwQQgghAo4kMEIIIYQIOJLACCGEECLgSAIjhBBCiICj9XcAXUFRURErVqzg9OnTGAwG\n", - "fvSjH5Gdne2xbWNjI6tWrWLnzp2YzWZGjhzJ448/jkajaVVf69atY+3atSxYsICBAwf67LVYLBZe\n", - 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In order to further compare performance between the two models, lets select 4 local states for both bases." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Comparing the Bases for `n_states=4`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "prim_basis = PrimitiveBasis(n_states=4, domain=[-1, 1])\n", - "prim_model = MKSLocalizationModel(basis=prim_basis)\n", - "prim_model.fit(X, y)\n", - "\n", - "leg_basis = LegendreBasis(4, [-1, 1])\n", - "leg_model = MKSLocalizationModel(basis=leg_basis)\n", - "leg_model.fit(X, y)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's look at the influence coefficients for both bases.\n", - "\n", - "First the `PrimitiveBasis` influence coefficients" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAABOAAAAEfCAYAAADoR7kbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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} - ], - "source": [ - "from pymks.tools import draw_coeff\n", - "\n", - "draw_coeff(prim_model.coeff)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now for the `LegendreBasis` influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAABNYAAAEdCAYAAADAYupvAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtU3NW9//8Xw20IBMLF3BwKxqSlIauSGHFOi5g2erTt\n", - "EaskOSG2plaw1ksvscv+7KmmqD2NnuRgqwZriR7rJUIuNml6Tq2rGlJtWiptxETjJQGEJCQhMcQB\n", - "BgLD7w++jE5mPsCQIWzg+ViLtTJ7Np/PZgbf4Iv357PDenp6egQAAAAAAAAgKLaRXgAAAAAAAAAw\n", - "GhGsAQAAAAAAAENAsAYAAAAAAAAMAcEaAAAAAAAAMAQEawAAAAAAAMAQEKwBAAAAAAAAQ0CwBgAA\n", - "AAAAAAxBxEgvoE9FRYVefPFFrVu3LqjP27hxo1566SWdOHFCl156qW655RY9+uijamxs1M9//vNh\n", - "Wu3I+utf/6oXX3xRdXV16uzsVEpKii688EJdddVVSkxMDPn5Ar3GgcYlqaGhYdCv+3C+T3/5y1/U\n", - "2dmpBQsWhPzYGBuoOYNHzRkYNQcDoeYMHjVnYNQc9Id6M3jUm4FRb4CBGROsSVJYWFhQ8/ft26cN\n", - 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"\n", - "In order to compare the difference between the two bases, we need to have the Cahn-Hilliard simulation and the two MKS models start with the same initial concentration `phi0` and evolve in time. In order to do the Cahn-Hilliard simulation we need an instance of the class `CahnHilliardSimulation`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets.cahn_hilliard_simulation import CahnHilliardSimulation\n", - "np.random.seed(66)\n", - "\n", - "phi0 = np.random.normal(0, 1e-9, ((1,) + size))\n", - "ch_sim = CahnHilliardSimulation(dt=dt)\n", - "phi_sim = phi0.copy()\n", - "phi_prim = phi0.copy()\n", - "phi_legendre = phi0.copy()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the inital concentration field." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAVEAAAEiCAYAAABAwOEtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJztnXlcVPX3/1/s++KwKZuIoCyiYooLrmXuVqbimuYHt4/l\n", - "JzOttMwlzczUDMtSzK+mGChWpuZaiDspoYAoghvIOqwiDMsMvz/8OUlz3mzX0ZLz9MHjIa+597zv\n", - 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Below, we plot the difference between the two MKS models and the simulation." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAn4AAAElCAYAAAB6ce/JAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt4VPWdP/B35j7JZJIMIYQQbgFsMAremqpQtbZbtVrt\n", - "LhKFFSkKXdSy20Wotc9Wg9ha9Vm00jZbGmubVi0RbEW0WFeKsEKL0AJykUu4CJIASSC3uc/k9wdP\n", - "5kfM53NCQgiTk/frefI8zPdc55wzw2e+53w+35TW1tZWEBEREZHpWS70DhARERFR72DgR0RERNRP\n", - "MPAjIiIi6icY+BERERH1Ewz8iIiIiPoJBn5ERERE/YS1tLS09ELvxIWwZs0alJWV4Xe/+x1WrFiB\n", - "DRs2oLq6GuPHjwcAHD9+HPfddx9GjRqFwYMH98o+lZaW4u9//zuuueaas15mx44dmDNnDq655hp4\n", - "vV5Eo1EsW7YMaWlpyMzMPI972z1tx3XZsmVYtmwZVq5ciY0bNyItLQ1Dhw49b9t96KGHcPLkSYwb\n", - "Nw4AsH79elRVVWHEiBHt5uvOOSACzPOdcvToUfzpT3/CyJEjYbfbE+1r1qzBI488gttvvx02m+18\n", - "7G63XIjjej7df//9CIfDKCoqutC7QiaVPJ/eXvSHP/wBlZWVuOOOO1BUVIRIJIKqqir83//9H+69\n", - "914AgM/nww9/+EPk5eX12n6lpKR0eZmCggL88Ic/RE5ODgAgGo1i+fLlGDRoUIegJplMmzYNhYWF\n", - 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"mse = metrics.mean_squared_error\n", - "from pymks.tools import draw_differences\n", - "\n", - "draw_differences([(phi_sim[0] - phi_prim[0]), (phi_sim[0] - phi_legendre[0])],\n", - " ['Simulaiton - Prmitive', 'Simulation - Legendre'])\n", - "\n", - "print 'Primative mse =', mse(phi_sim[0], phi_prim[0])\n", - "print 'Legendre mse =', mse(phi_sim[0], phi_legendre[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The `LegendreBasis` basis clearly out performs the `PrimitiveBasis` for the same value of `n_states`." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Resizing the Coefficients to use on Larger Systems \n", - "\n", - "Below we compare the bases after the coefficients are resized." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "big_length = 3 * length\n", - "big_size = (big_length, big_length)\n", - "prim_model.resize_coeff(big_size)\n", - "leg_model.resize_coeff(big_size)\n", - "\n", - "phi0 = np.random.normal(0, 1e-9, (1,) + big_size)\n", - "phi_sim = phi0.copy()\n", - "phi_prim = phi0.copy()\n", - "phi_legendre = phi0.copy()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the initial large concentration field." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEiCAYAAABuhcC/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXdgF1X2/UnvvYcEUigp9N6kSFERBARUEAuLvayuq1/X\n", - "irqi61rW7rqKFZCuICLSQXozEBJaAklII7338vvj3PuZT9Q1QszuD3fuP5Bk5s3Me2/mnXfuvefa\n", - "NDc3N8M000wzzTQAgO1/+wZMM8000/5/MvOjaJpppplmZeZH0TTTTDPNysyPommmmWaalZkfRdNM\n", - "M800KzM/iqaZZpppVvY/+1FctmwZ5s6de8HnvfPOO3jssccsP6ekpGD58uW/WfvXX3891q9f3+px\n", - "VVVVWLp0Kf70pz9h9uzZuOWWWzBv3jxs2bIFTU1NF3zd/5/syJEjWLdu3W/aZmlpKZYtW4b8/PwW\n", - "v09KSsL111+PzMzM3/R6pl26Zv/fvoH/ptnY2FzwOdOnT0d9fb3l55SUFKxYsQIzZsxocdzYsWMx\n", - 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"metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "draw_concentrations([phi0[0]], ['Initial Concentration'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's look at the resized coefficients.\n", - "\n", - "First the influence coefficients from the `PrimitiveBasis`." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAABOAAAAEfCAYAAADoR7kbAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90VPWd//FXfjETCBMhCcY4KSOFOjZq0aYxhZxAqxW3\n", - "RY8VYU2UViRBC0u1UOyxP5aw1oUWMGcBm24NuoJVk6J79Nj91rWiSWvVtLg0iIkVCSEhBAhqYAiT\n", - "IT++fyRzZZLJzEACudw8H+dwkrn3cz/3k4l5J77mPfdGdXd3dwsAAAAAAADAORE93AsAAAAAAAAA\n", - "rIwADgAAAAAAADiHCOAAAAAAAACAc4gADgAAAAAAADiHCOAAAAAAAACAc4gADgAAAAAAADiHCOAA\n", - "AAAAAACAcyh2uBfgV15erldeeUWbN28+o+O2bdumV199VZ9++qlmzJihxYsX67HHHlNjY6NWr159\n", - "jlY7vN5++2298sor2rdvn3w+n5KTk/XlL39ZN998s8aNGzfk5wv2HAfbLkkNDQ0RP+/n8vv0l7/8\n", - 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" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "for steps in range(time_steps):\n", - " ch_sim.run(phi_sim)\n", - " phi_sim = ch_sim.response\n", - " phi_prim = prim_model.predict(phi_prim)\n", - " phi_legendre = leg_model.predict(phi_legendre)\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA6sAAAEoCAYAAAC+WWfaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfX24pVVZ/r332XufrzlnZg4zwzgiGoKCCCgllNbPj7Q0\n", - "yywFMiUuNRQjr4wsyi4TJTKywBTLRNIwCEbR1DTKIsoiTUDJzA9ALZRgGIb5OJ/78/fHu+718bxr\n", - "7XedmT2HGXzu6zrXu8/7udb7rvfZ937u9TxPbTAYDKBQKBQKhUKhUCgUCsUhhPrD3QCFQqFQKBQK\n", - "hUKhUCgk9MeqQqFQKBQKhUKhUCgOOeiPVYVCoVAoFAqFQqFQHHLQH6sKhUKhUCgUCoVCoTjkoD9W\n", - "FQqFQqFQKBQKhUJxyEF/rCoUCoVCoVAoFAqF4pCD/lg9xHDzzTfjwgsvxDnnnINXvvKVuPDCC3H1\n", - 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However, the Legendre polynomial basis looks to be better. Again let's look at the difference between the simulation and the MKS models." - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAn0AAAElCAYAAACRRlTKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXuUXFWZ/v90Xbqq+n5L50IIGEBCAgFBIIICgjOOilwM\n", - "lyESWBpgCQyjgzDgmt8SkK8LESdcVKIjLjCKSkNQYkRQiUEkznBHAQWJRAgEOp1OX6vr1tW/P/Z+\n", - "zr6cU90JCdBdvM9aWafr1Lnsvc+pyqnPft/nrRkbGxuDSCQSiUQikaiqFXunGyASiUQikUgkeusl\n", - "D30ikUgkEolE7wLJQ59IJBKJRCLRu0Dy0CcSiUQikUj0LpA89IlEIpFIJBK9CyQPfSKRSCQSiUTv\n", - "AlX1Q9+6detw2WWX4eyzz8ZnPvMZXHbZZVi5cmXwfnd3N04//XQ88cQTb1ubrrzySixfvnyH9nn2\n", - "2Wdx+umnY9OmTQCAUqmErq4ubNy48S1o4c6L48p/Z599Nr70pS/hj3/844T7+n3dGa1btw6nn346\n", - "8vn8Du13zz334LnnngutP/3003H//ffvdLtE1alq+b557bXX0NXVhWw266x/s5+nt1rvxLi+lVq2\n", - "bBnuvPPOd7oZoipV4p1uwFuln/3sZ+jq6sKJJ56IBQsWoFgsYsOGDfjDH/6As846CwDQ1taGr371\n", - "q5g1a9bb1q6ampod3mfu3Ln46le/is7OTgDqoW/VqlWYPn069txzz13cwl2npUuXYt68echms/jd\n", - 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}, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Primative mse = 4.43233838563e-23\n", - "Legendre mse = 2.53128987275e-28\n" - ] - } - ], - "source": [ - "draw_differences([(phi_sim[0] - phi_prim[0]), (phi_sim[0] - phi_legendre[0])], \n", - " ['Simulaiton - Primiative','Simulation - Legendre'])\n", - "\n", - "print 'Primative mse =', mse(phi_sim[0], phi_prim[0])\n", - "print 'Legendre mse =', mse(phi_sim[0], phi_legendre[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "With the resized influence coefficients, the `LegendreBasis` outperforms the `PrimitiveBasis` for the same value of `n_states`. The value of `n_states` does not necessarily guarantee a fair comparison between the two basis in terms of floating point calculations and memory used." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/checker_board.ipynb b/notebooks/checker_board.ipynb deleted file mode 100644 index bdb1962c..00000000 --- a/notebooks/checker_board.ipynb +++ /dev/null @@ -1,4365 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Checkerboard Microstructure\n", - "\n", - "##Introduction - What are 2-Point Spatial Correlations (also called 2-Point Statistics)?\n", - "\n", - "The purpose of this example is to introduce 2-point spatial correlations and how they are computed using PyMKS.\n", - "\n", - "The example starts with some introductory information about spatial correlations. PyMKS is used to compute both the periodic and non-periodic 2-point spatial correlations (also referred to as 2-point statistics or autocorrelations and crosscorrelations) for a checkerboard microstructure. This is a relatively simple example that allows an easy discussion of how the spatial correlations capture the main features seen in the original microstructure." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## n-Point Spatial Correlations\n", - "\n", - "###1-Point Spatial Correlations (or 1-point statistics)\n", - "\n", - "n-point spatial correlations provide a way rigorously quantify material structure using statistics. As an introduction n-point spatial correlations, let's first discuss 1-point statistics. 1-point statistics are the probability that a specified local state will be found in any randomly selected spatial bin in a microstructure [1][2][3]. In this checkerboard example discussed here, there are two possible local states, one is colored white and the other is colored black. 1-point statistics compute the volume fractions of the local states in the microstructure. 1-point statistics are computed as\n", - "\n", - "$$ f[l] = \\frac{1}{S} \\sum_s m[s,l] $$\n", - "\n", - "In this equation, $f[l]$ is the probability of finding the local state $l$ in any randomly selected spatial bin in the microstructure, $m[s, l]$ is the microstructure function (the digital representation of the microstructure), $S$ is the total number of spatial bins in the microstructure and $s$ refers to a specific spatial bin. \n", - "\n", - "While 1-point statistics provide information on the relative amounts of the different local states, it does not provide any information about how those local states are spatially arranged in the microstructure. Therefore, 1-point statistics are a limited set of metrics to describe the structure of materials.\n", - "\n", - "###2-Point Spatial Correlations\n", - "\n", - "2-point spatial correlations (also known as 2-point statistics) contain information about the fractions of local states as well as the first order information on how the different local states are distributed in the microstructure. \n", - "\n", - "2-point statistics can be thought of as the probability of having a vector placed randomly in the microstructure and having one end of the vector be on one specified local state and the other end on another specified local state. This vector could have any length or orientation that the discrete microstructure allows. The equation for 2-point statistics can found below.\n", - "\n", - "$$ f[r \\vert l, l'] = \\frac{1}{S} \\sum_s m[s, l] m[s + r, l'] $$\n", - "\n", - "In this equation $ f[r \\vert l, l']$ is the conditional probability of finding the local states $l$ and $l'$ at a distance and orientation away from each other defined by the vector $r$. All other variables are the same as those in the 1-point statistics equation. In the case that we have an eigen microstructure function (it only contains values of 0 or 1) and we are using an indicator basis, the the $r=0$ vector will recover the 1-point statistics. \n", - "\n", - "When the 2 local states are the same $l = l'$, it is referred to as a autocorrelation. If the 2 local states are not the same it is referred to as a crosscorrelation. \n", - "\n", - "###Higher Order Spatial Statistics\n", - "\n", - "Higher order spatial statistics are similar to 2-point statistics, in that they can be thought of in terms of conditional probabilities of finding specified local states separated by a prescribed set of vectors. 3-point statistics are the probability of finding three specified local states at the ends of a triangle (defined by 2 vectors) placed randomly in the material structure. 4-point statistics describes the probability of finding 4 local states at 4 locations (defined using 3 vectors) and so on. \n", - "\n", - "While higher order statistics are a better metric to quantify the material structure, the 2-point statistics can be computed much faster than higher order spatial statistics, and still provide information about how the local states are distributed. For this reason, only 2-point statistics are implemented into PyMKS. Let us look at an example of computing the 2-point statistics for a checkerboard microstructure." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##2-Point Statistics for Checkerboard Microstructure\n", - "\n", - "Let's first start with making a microstructure that looks like a 8 x 8 checkerboard. Although this type of microstructure may not resemble a physical system, it provides solutions that give some intuitive understanding of 2-point statistics.\n", - "\n", - "We can create a checkerboard microstructure using `make_checkerboard_microstructure` function from `pymks.datasets`." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets import make_checkerboard_microstructure\n", - "\n", - "X = make_checkerboard_microstructure(square_size=21, n_squares=8)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's take a look at how the microstructure looks." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEaCAYAAACGrEV/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAE3lJREFUeJzt3X+I23cdx/FX7tqYfnPdpH/UWc5yXwJx7LsQbVXIRFwm\n", - 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Let's start by looking at the periodic autocorrelations of the microstructure and then compute the periodic crosscorrelation. This can be done using the `autocorrelate` and `crosscorrelate` functions from `pymks.states`, and using the keyword argument `periodic_axes` to specify the axes that are periodic. \n", - "\n", - "In order to compute 2-pont statistics, we need to select a basis to generate the microstructure function `X_` from the microstructure `X`. Because we only have values of 0 or 1 in our microstructure we will using the `PrimitiveBasis` with `n_states` equal to 2." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.stats import autocorrelate\n", - "from pymks import PrimitiveBasis\n", - "\n", - "prim_basis = PrimitiveBasis(n_states=2)\n", - "X_ = prim_basis.discretize(X)\n", - "X_auto = autocorrelate(X_, periodic_axes=(0, 1))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We have now computed the autocorrelations.\n", - "\n", - "Let's take a look at them using `draw_autocorrelations` from `pymks.tools`." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAswAAAEyCAYAAADuoYbuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXmUXFWd/+e+pbauVLqTJnQg0QQSaBKRLbIkRFCBc3Ab\n", - 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We have just computed the periodic autocorrelations for a perfectly periodic microstructure with equal volume fractions. In general this is not the case and the autocorrelations will be different as we will see later in this example.\n", - "\n", - "As mentioned in the introduction, because we using an indicator basis and the we have eigen-microstructure functions (values are either 0 or 1), the (0, 0) vector equals the volume fraction. \n", - "\n", - "Let's double check that both the phases have a volume fraction of 0.5." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Volume fraction of black phase 0.500000006869\n", - "Volume fraction of white phase 0.500000006869\n" - ] - } - ], - "source": [ - "center = (X_auto.shape[1] + 1) / 2\n", - "print 'Volume fraction of black phase', X_auto[0, center, center, 0]\n", - "print 'Volume fraction of white phase', X_auto[0, center, center, 1]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can compute the cross-correlation of the microstructure function using the `crosscorrelate` function from `pymks.stats`" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.stats import crosscorrelate\n", - "\n", - "X_cross = crosscorrelate(X_, periodic_axes=(0, 1))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the cross correlation using `draw_crosscorrelations` from `pymks.tools`." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWQAAAEuCAYAAAC52GgqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsfXuUHGWZ/lNd1Zfp6ZnMTIYwgUQDJDAkIrcQkCSCCpzF\n", - "26KESBDkFhBhWVlQjuxvBbkpsitxdZfsASLLRSAs7Lp4FNQFCRCQgIAhgVwwARKSyWRumenpqe6u\n", - "y++Pqu/73q+7aqan+xtoSD3n5KS6prv6e6qq33rvr+a6rosIESJEiPCBI/ZBLyBChAgRIniIBHKE\n", - "CBEi1AkigRwhQoQIdYJIIEeIECFCnSASyBEiRIhQJ4gEcoQIESLUCSKBHCFChAh1gkggR4hQA957\n", - "7z288cYbH/QyInxEYHzQC5hI/OlPf8Lvfvc7vP322ygUCmhvb8fRRx+NL33pS2htbf1A1/bv//7v\n", - 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The (0, 0) vector has a value of 0. This statistic reflects the probablity of 2 phases having the same location. In our microstructure, this probability is zero as we have not allowed the two phases (colored black and white) to co-exist in the same spatial voxel.\n", - "\n", - "Let check that it is zero." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Center value 7.48243552237e-17\n" - ] - } - ], - "source": [ - "print 'Center value', X_cross[0, center, center, 0]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Compute Non-Periodic 2-Point Statistics\n", - "\n", - "We will now compute the non-periodic 2-point statistics for our microstructure. This time rather than using the `autocorrelate` and `crosscorrelate` functions, we will use the `correlate` function from `pymks.stats`. The `correlate` function computes all of the autocorrelations and crosscorrelations at the same time. We will computed the non-periodic statistics by omitting the keyword argument `periodic_axes`." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.stats import correlate\n", - "\n", - "X_corr = correlate(X_)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "All or some of the correlations can be viewed using the `draw_correlations` function from `pymks.tools`. In this example we will look at all of them." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAABDQAAAE4CAYAAACkFKOmAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXmQZlV5P/65+7v12z09PRsMys4IGmQJKIuQCFSp0agM\n", - "KCoaIhCXUKFELZNyCWIqmqiY0oDfErQgagk/qBgtxSWoKIgCQYQBZgZlWIaZYZae6e1d7/L749xz\n", - "znPOPbeXYYbuds6naqrv3H77vud57jnP85xnO06WZRksLCwsLCwsLCwsLCwsLCwsFhHc+R6AhYWF\n", - "hYWFhYWFhYWFhYWFxVxhHRoWFhYWFhYWFhYWFhYWFhaLDtahYWFhYWFhYWFhYWFhYWFhsehgHRoW\n", - "FhYWFhYWFhYWFhYWFhaLDtahYWFhYWFhYWFhYWFhYWFhsehgHRoWFhYWFhYWFhYWFhYWFhaLDtah\n", - "YWFhYWFhYWFhYWFhYWFhsehgHRoWFvsJzz33HB577LH5HoaFhYXFAQsrhy0sLCzmF1YOW+xv+PM9\n", - "gPnCb37zG/z4xz/GU089hV6vh5GREZx00kl44xvfiCVLlszr2P7zP/8Tmzdvxr/+67/O+m9+/etf\n", - "o9fr4eyzz37Bz7LYN/if//kfDAwM4Nhjj53voVhYLBhY2WvxYsLKYQuL6WFlssX+hpXDFvsbB2SG\n", - "xs0334wvfelLWLlyJa644gp8/OMfxxve8AY88sgjuPHGG+d7eHuFe++9F3fddVfh/tq1a/HBD37w\n", - 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"9Tn/A3Cv4x8aDFSrAAAAAElFTkSuQmCC\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_correlations\n", - "\n", - "correlations = [('black', 'black'), ('white', 'white'), ('black', 'white')]\n", - "draw_correlations(X_corr[0].real, correlations=correlations)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Notice that the maximum values for the autocorrelations are higher than 0.5. We can still show that the centers or the (0, 0) vectors are still equal to the volume fractions." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Volume fraction of black phase 0.5\n", - "Volume fraction of white phase 0.5\n" - ] - } - ], - "source": [ - "print 'Volume fraction of black phase', X_corr[0, center, center, 0]\n", - "print 'Volume fraction of white phase', X_corr[0, center, center, 1]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The non-periodic statistics are different from the periodic 2-point statistics along the diagonal vectors, but in both cases the probability of (0, 0) vector is still the volume fraction." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## References\n", - "\n", - "[1] S.R. Niezgoda, D.T. Fullwood, S.R. Kalidindi, Delineation of the Space of 2-Point Correlations in a Composite Material System, Acta Materialia, 56, 18, 2008, 5285–5292 [doi:10.1016/j.actamat.2008.07.005](http://dx.doi.org/10.1016/j.actamat.2008.07.005)\n", - "\n", - " \n", - "[2] S.R. Niezgoda, D.M. Turner, D.T. Fullwood, S.R. Kalidindi, Optimized Structure Based Representative Volume Element Sets Reflecting the Ensemble-Averaged 2-Point Statistics, 58, 13, 2010, 4432–4445 [doi:10.1016/j.actamat.2010.04.041](http://dx.doi.org/10.1016/j.actamat.2010.04.041)\n", - "\n", - "\n", - "[3] D.T. Fullwood, S.R. Kalidindi, and B.L. Adams, Second - Order Microstructure Sensitive Design Using 2-Point Spatial Correlations, Chapter 12 in Electron Backscatter Diffraction in Materials Science , 2nd Edition , Eds. A. Schwartz, M. Kumar, B. Adams, D. Field, Springer, NY, 2009. " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/derivation.ipynb b/notebooks/derivation.ipynb index 5e6c4422..5506e60e 100644 --- a/notebooks/derivation.ipynb +++ b/notebooks/derivation.ipynb @@ -15,7 +15,7 @@ "source": [ "### Definitions\n", "\n", - "Let $C(x)$ be the local stiffness tensor for a two phase material with stiffness tensors $C_A$ and $C_B$. The stiffness tensor at location $x$ can be represented at a perturbation from a reference stiffness tensor.\n", + "Let $C(x)$ be the local stiffness tensor for a two-phase material with stiffness tensors $C_A$ and $C_B$. The stiffness tensor at location $x$ can be represented at a perturbation from a reference stiffness tensor.\n", "\n", "$$C(x) = C^R + C'(x)$$\n", "\n", @@ -25,7 +25,7 @@ "\n", "where $\\bar{\\varepsilon}$ is the average strain and $\\varepsilon '(x)$ is the local strain perturbation from $\\bar{\\varepsilon}$.\n", "\n", - "The constitutive equation is therefore.\n", + "The constitutive equation is therefore\n", "\n", "$$\\sigma_{ij}(x) = \\big(C^R_{ijlk} + C'_{ijlk}(x) \\big ) \\big (\\bar{\\varepsilon}_{lk} + \\varepsilon'_{lk}(x) \\big )$$" ] @@ -36,7 +36,7 @@ "source": [ "### Equilibrium Condition\n", "\n", - "The equilibrium condition is defined below.\n", + "The equilibrium condition is defined below:\n", "\n", "$$\\sigma_{ij,j}(x) = \\Big [\\big(C^R_{ijlk} + C'_{ijlk}(x) \\big ) \\big (\\bar{\\varepsilon}_{lk} + \\varepsilon'_{lk}(x) \\big )\\Big ]_{,j} = 0$$\n", "\n", @@ -46,7 +46,7 @@ "\n", "$$F_i(x) = C'_{ijlk,j}(x)\\bar{\\varepsilon}_{lk} + \\Big [C'_{ijlk}(x) \\varepsilon'_{lk}(x)\\Big ]_{,j} $$\n", "\n", - "Using the definitation of $F(x)$ above, the equilibrium equation above can be rearranged in the form of an inhomogenous differential equation. \n", + "Using the definition of $F(x)$ above, the equilibrium equation above can be rearranged in the form of an inhomogenous differential equation. \n", "\n", "$$C^R_{ijlk}\\varepsilon'_{lk,j}(x) + F_i(x) = 0$$\n" ] @@ -57,13 +57,13 @@ "source": [ "###Strain, Displacement, and Green's Functions\n", "\n", - "By using the relationship between strain and displacement, the equilibrium equation can be rewritten as follows.\n", + "By using the relationship between strain and displacement, the equilibrium equation can be rewritten as follows:\n", "\n", "$$ \\varepsilon_{kl}(x) = \\frac{\\big (u_{k,l}(x) + u_{l,k}(x) \\big)}{2} $$\n", "\n", "$$C^R_{ijkl} \\frac{\\big (u'_{k,lj}(x) + u'_{l,kj}(x) \\big)}{2} + F_i(x) = 0$$\n", "\n", - "The solution to the displacements can be found using green's functions.\n", + "The solution to the displacements can be found using Green's functions:\n", "\n", "$$C^R_{ijkl} G_{km,lj}(r) + \\delta_{im}\\delta(x-r) = 0$$\n", "\n", @@ -73,7 +73,7 @@ "\n", "$$u'_l(x) = \\int_V G_{il}(r) F_i (x - r)dr = \\int_V G_{ik}(r) \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ]_{,j}dr$$\n", "\n", - "therefore the strain can also be found interns of green's functions.\n", + "Therefore, the strain can also be found in terms of Green's functions:\n", "\n", "$$\\varepsilon'_{kl}(x) = \\int_V \\frac{\\big (G_{ik,l}(r) + G_{il,k}(r) \\big)}{2} F_i (x-r)dr = \\int_V \\frac{\\big (G_{ik,l}(r) + G_{il,k}(r) \\big)}{2} \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ]_{,j}dr$$\n", "\n", @@ -87,7 +87,7 @@ "source": [ "### Integration by Parts\n", "\n", - "The equation above can be recast with all of the derivatives on the green's functions by integrating by parts.\n", + "The equation above can be recast with all of the derivatives on the Green's functions by integrating by parts.\n", "\n", "$$\n", "\\varepsilon'_{kl}(x) = \\Bigg [ \\int_S \\frac{\\big (G_{ik,l}(r) + G_{il,k}(r) \\big)}{2} \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ] n_j dS\\Bigg ]_{r \\rightarrow 0}^{r \\rightarrow \\infty} - $$ \n", @@ -102,7 +102,7 @@ "source": [ "###Principal Value Singularity\n", "\n", - "In the equation above, the surface term tending to zero is a principal value integral because of the singularity in the green's functions at $r = 0$. As a result, the integrand is not differentiable. Torquato shows that by excluding a sphere at the origin and using integration by parts and the divergence theorem we can arrive at the following equation [1].\n", + "In the equation above, the surface term, tending to zero, is a principal value integral, because of the singularity in the Green's functions at $r = 0$. As a result, the integrand is not differentiable. Torquato shows that, by excluding a sphere at the origin and using integration by parts and the divergence theorem, we can arrive at the following equation [1].\n", "\n", "\n", "$$\\varepsilon'_{kl}(x) = I_{ikjl} - E_{ikjl} + \\int_V \\Phi_{ikjl}(r) \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ]dr $$\n", @@ -111,7 +111,7 @@ "\n", "$$\\Phi_{ikjl}(r) = - \\frac{\\big (G_{ik,lj}(r) + G_{il,kj}(r) \\big)}{2} $$\n", "\n", - "is the green's function terms, and \n", + "is the Green's function terms, and \n", "\n", "$$I_{ikjl}^{\\infty} = \\lim_{r \\rightarrow \\infty} \\int_S\\frac{\\big (G_{ik,l}(r) + G_{il,k}(r)\\big)}{2} \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ]n_l dS $$\n", "\n", @@ -119,11 +119,11 @@ "\n", "are the contribution from the surface integrals at $\\infty$ and from the singularity.\n", "\n", - "Finally let \n", + "Finally, let \n", "\n", "$$\\Gamma_{iklj}(r) = I_{ikjl}^{\\infty}\\delta(r)-E_{ikjl}\\delta(r) + \\Phi_{ikjl}(r)$$\n", "\n", - "the strain can then be written in the following form.\n", + "The strain can then be written in the following form:\n", "\n", "$$\\varepsilon'_{kl}(x) = \\int_V \\Gamma_{ikjl}(r) \\Big [C'_{ijlk}(x-r)\\bar{\\varepsilon}_{lk} + \\big [C'_{ijlk}(x-r)\\varepsilon'_{lk}(x-r)\\big ]\\Big ]dr $$\n" ] @@ -136,7 +136,7 @@ "\n", "$$\\varepsilon'(x) =\\int_V \\Gamma(r) C'(x-r) [ \\bar{\\varepsilon} + \\varepsilon'(x-r)]dr $$\n", "\n", - "By recursively inserting $\\varepsilon'(x)$ into the LHS of the equation, we get the following series. \n", + "By recursively inserting $\\varepsilon'(x)$ into the RHS of the equation, we get the following series: \n", "\n", "$$\n", "\\varepsilon'(x) =\\int_V \\Gamma(r) C'(x-r) \\bar{\\varepsilon} dr +\\int_V \\int_V \\Big[ \\Gamma(r) C'(x-r)\\bar{\\varepsilon}\\Big ]\\Big [\\Gamma(r') C'(x-r') \\bar{\\varepsilon}\\Big] dr'dr + ...$$\n", @@ -158,17 +158,17 @@ "\n", "$$ C'(x-r) = \\int_H h m(h, x-r) dh$$\n", "\n", - "where $m(h, r)$ is the microstructure function which is a probablity density that spans both the local state space $h$ and real space $x$. The expectation of local state variable for the microstructure function is the integral over the local state space $H$ and discribes the expected local state $h$ which is equal to $C'(r)$. \n", + "where $m(h, r)$ is the microstructure function, which is a probablity density that spans both the local state space $h$ and real space $x$. The expectation of local state variable for the microstructure function is the integral over the local state space $H$ and describes the expected local state $h$ which is equal to $C'(r)$. \n", "\n", - "Also let \n", + "Also, let \n", "\n", "$$\\alpha(h, r) = \\Gamma(r) h \\bar{\\varepsilon} $$ \n", "$$\\alpha(h, h', r, r') = \\Gamma(r) h \\bar{\\varepsilon} \\Gamma(r') h' \\bar{\\varepsilon} $$\n", "$$ etc... $$\n", "\n", - "where again $h$ is the local state variable. \n", + "where, again, $h$ is the local state variable. \n", "\n", - "Plugging these definitations into the Weak Contrast Expansion recasts the series in the following form." + "Plugging these definitions into the Weak Contrast Expansion recasts the series in the following form:" ] }, { @@ -177,7 +177,7 @@ "source": [ "$$\\varepsilon '(x) =\\int_V \\int_H \\alpha(h, r) m(h, x-r) dr dh + \\int_V \\int_V \\int_H \\int_H\\alpha_(h, h', r, r') m(h, x-r) m(h', x-r') dr'dr dh dh'+ ...$$ \n", "\n", - "The discrete version of this equation is the MKS localization." + "The discrete version of this equation is the MKS localization:" ] }, { diff --git a/notebooks/elasticity_2D.ipynb b/notebooks/elasticity_2D.ipynb deleted file mode 100644 index f6780bc5..00000000 --- a/notebooks/elasticity_2D.ipynb +++ /dev/null @@ -1,4486 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Linear Elasticity in 2D\n", - "\n", - "##Introduction\n", - "\n", - "This example provides a demonstration of using PyMKS to compute the linear strain field for a two phase composite material. The example introduces the governing equations of linear elasticity along with the unique boundary conditions required for the MKS. It subsequently demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients for all strain fields. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared\n", - "with the finite element data for a large problem.\n", - "\n", - "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Elastostatics Equations\n", - "\n", - "For the sake of completeness a description of the equations of linear elasticity are included. The constitutive equation that describes the linear elastic phenomena is Hook's law.\n", - "\n", - "$$ \\sigma_{ij} = C_{ijkl}\\varepsilon_{kl} $$\n", - "\n", - "$\\sigma$ is the stress, $\\varepsilon$ is the strain, and $C$ is the stiffness tensor that relates the stress to the strain fields. For an isotropic material the stiffness tensor can be represented by lower dimension terms which can relate the stress and the strain as follows.\n", - "\n", - "$$ \\sigma_{ij} = \\lambda \\delta_{ij} \\varepsilon_{kk} + 2\\mu \\varepsilon_{ij} $$\n", - "\n", - "$\\lambda$ and $\\mu$ are the first and second Lame parameters and can be defined in terms of the Young's modulus $E$ and Poisson's ratio $\\nu$ in 2D.\n", - "\n", - "$$ \\lambda = \\frac{E\\nu}{(1-\\nu)(1-2\\nu)} $$\n", - "\n", - "$$ \\mu = \\frac{E}{3(1+\\nu)} $$\n", - "\n", - "\n", - "Linear strain is related to displacement using the following equation.\n", - "\n", - "$$ \\varepsilon_{ij} = \\frac{u_{i,j}+u_{j,i}}{2} $$\n", - "\n", - "We can get an equation that relates displacement and stress by plugging the equation above back into our expression for stress.\n", - "\n", - "$$ \\sigma_{ij} = \\lambda u_{k,k} + \\mu( u_{i,j}+u_{j,i}) $$\n", - "\n", - "The equilibrium equation for elastostatics is defined as\n", - "\n", - "$$ \\sigma_{ij,j} = 0 $$\n", - "\n", - "and can be cast in terms of displacement.\n", - "\n", - "$$ \\mu u_{i,jj}+(\\mu + \\lambda)u_{j,ij}=0 $$\n", - "\n", - "In this example, a displacement controlled simulation is used to calculate the strain. The domain is a square box of side $L$ which has an macroscopic strain $\\bar{\\varepsilon}_{xx}$ imposed.\n", - "\n", - "In general, generateing the calibration data for the MKS requires boundary conditions that are both periodic and displaced, which are quite unusual boundary conditions and are given by,\n", - "\n", - "$$ u(L, y) = u(0, y) + L\\bar{\\varepsilon}_{xx}$$\n", - "$$ u(0, L) = u(0, 0) = 0 $$\n", - "$$ u(x, 0) = u(x, L) $$" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKS\n", - "\n", - "###Calibration Data and Delta Microstructures\n", - "\n", - "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2% or less when comparing the MKS results with those computed using finite element methods [1]. \n", - "\n", - "Because we are using distinct phases and the contrast is low enough to only need the first order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first order influence coefficients [2]. \n", - "\n", - "Here we use the `make_delta_microstructure` function from `pymks.datasets` to create the two delta microstructures needed to calibrate the first order influence coefficients for a two phase microstructure. The `make_delta_microstructure` function uses SfePy to generate the data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAnEAAAEaCAYAAAB+VgE8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAFBFJREFUeJzt3V1opPXd//FPNjqNk1WLB4td6pIhkErHIe32AWIpNRYP\n", - "hIoV2tDt0pJOtUf1wJNSuq1pyipioScKPVlj0C1IpFsphVICIn04qNVSSelByMNqZKUVbFmYDFnj\n", - "zn3wh7ndW5OsNv9krsvXCwZm5pf5XteC4JvfNQ99nU6nEwAACuXAfp8AAADvnYgDACggEQcAUEAi\n", - "DgCggEQcAEABiTgAgAK6Yr9PAADgg+Tf//53Hnroobz66qt58sknc+DA/+6pvfHGG3nkkUeyubmZ\n", - "iYmJNBqNLefYiQMA2EMHDx7M/fffn5GRkXesPfPMMzl27FhOnDiRM2fObDtnx524vr6+93+WQOkd\n", - 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Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Generating Calibration Data\n", - "\n", - "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields with the boundary conditions given above.\n", - "\n", - "In this example, lets look at a two phase microstructure with elastic moduli values of 100 and 120 and Poisson's ratio values of 0.3 and 0.3 respectively. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. Note that `make_elasticFEstrain_delta` does not take a number of samples argument as the number of samples to calibrate the MKS is fixed by the number of phases." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets import make_elastic_FE_strain_delta\n", - "from pymks.tools import draw_microstructure_strain\n", - "\n", - "elastic_modulus = (100, 120)\n", - "poissons_ratio = (0.3, 0.3)\n", - "macro_strain = 0.02\n", - "size = (n, n)\n", - "\n", - "X_delta, y_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", - " poissons_ratio=poissons_ratio,\n", - " size=size, macro_strain=macro_strain) \n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - 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Because we have 2 phases we will create an instance of MKSLocalizationModel with the number of states `n_states` equal to 2. Then, pass the delta microstructures and their strain fields to the `fit` method. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks import MKSLocalizationModel\n", - "from pymks import PrimitiveBasis\n", - "\n", - "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", - "model = MKSLocalizationModel(basis=prim_basis)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first order influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.fit(X_delta, y_delta)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it, the influence coefficient have be calibrated. Let's take a look at them." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApIAAAEiCAYAAABKqjKOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9UVPed//EXMMAgFKJQg3bGoVUblJzGtIRMo6VuzA+r\n", - "1W9blAXTbaKF3e1a26zpOXt2tys12dTuJn45bqq02xHXNM0GImnx0Gw8OTFi06ahYWtMbchPoUwU\n", - "FU2lI94Zgfn+4ZepyMDMAF7G6/ORM+c4dz73fX+gNy/e98ckBIPBoAAAAIAYJU72CgAAAODqRJAE\n", - "AADAmBAkAQAAMCYESQAAAIwJQRIAAABjQpAEAADAmBAkAQAAMCa2yV6BSOrr67Vv3z7t3Lkzpvn2\n", - "7Nmj559/Xn/84x/12c9+Vn/3d3+n7du3y+v1asuWLVdobSfXr3/9a+3bt0/t7e0KBALKycnRpz71\n", - "Ka1YsUJTp06d8OWF28fhpktSZ2dn1Pv9Sv6cfvWrXykQCGjx4sUTXhtXP4430eN4ExnHG1wL4j5I\n", - "SlJCQkJM49999109/fTTKi8vV0FBgbKysq7QmsWPxx9/XM8++6z+4i/+QitWrFBaWpo6Ozv1/PPP\n", - "6+TJk/rWt741ocsbaR+PND0QCERde9WqVbpw4cKEru+gl19+WT6fb0IO7L/73e/0xBNPKDU1VZs3\n", - "bx5XLcMw9MQTT2jmzJnq6urSDTfcoIULF457HRE7jjeRcbyJTrwebySpr69PzzzzjHJzc1VcXDzu\n", - 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The constant-valued influence coefficients may seem superfluous, but are equally as import. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predict the Strain Field for a Random Microstructure\n", - "\n", - "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two phase microstructure and compare it with the results from a finite element simulation. \n", - "\n", - "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAosAAAEoCAYAAAAwirvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3X1cVGXeP/APAyPDQ4PCgEgDQz4Uiim6SrNKREtpKdEW\n", - "gmDdPazgtvWyLQrv29oyKrc2bcl8wIJste7dINgNl0q3VUG3zVgpJA3SEpBRkQcVGodhGOD3hz9m\n", - "HWdguNgRx3N/3r14vZxzvue6rjkz2NfvOee63Pr6+vpARERERGSH7EoPgIiIiIhcF5NFIiIiIhoQ\n", - "k0UiIiIiGhCTRSIiIiIaEJNFIiIiIhoQk0UiIiIiGhCTRSIiIiIaEJNFJ3vhhRewePHiKz0MIiIi\n", - "IqfwuNIDcCUXJ3lvvvkmxo4dazcuOzsb3377LQDgV7/6FeLi4iz73NzcLusYL6cXXngBNTU1KCgo\n", - "uNJDscvVx0dERCRFTBYvIZPJ0Nvbi927dyMtLc1m/6lTp/Dtt99a4i5NDh977DGYTKaRGi4RERHR\n", - "ZcVk8RKjR4/G6NGjUVZWhsWLF0Mms75Sv2vXLgDAT37yE/zrX/+yOV6lUo3IOImIXF13dze++OIL\n", - "fP311zh37hxMJhOMRiMiIyOxYMECBAcHX+khEtEQMFm0Iz4+Hnl5eaisrMTs2bMt281mM8rLy3HD\n", - "DTdArVbbTRYHu1R68OBBfPrpp/j+++/R2dkJpVKJ6667DnfeeSduvPFGAMDhw4fx4osvYtGiRZgx\n", - "YwY+/PBDHDlyBAaDARs3boRKpUJ3dzc+/vhj7Nu3D83NzZDJZAgPD8cdd9yBn/70pzb9HjhwAJ98\n", - 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] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "strain_pred = model.predict(X)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally let's compare the results from finite element simulation and the MKS model." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApUAAAEqCAYAAABEJauaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtclGX+P/4Xw0kOKikHkdPgCQUVD0iJFhhatlqpC8qm\n", - "kZ91axNFViz1Iz81TLeNbdc1K/1sJ9ktA7LMQ4aQpqJhSqYQqHgiGQFnOCgOKDjD/P7wy8g4zAyX\n", - "FuDt69mDx8O5533d73tmmKs3131f92Wl0+l0ICIiIiK6B7KOPgAiIiIiuv+xqCQiIiKie8aikoiI\n", - "iIjuGYtKIiIiIrpnLCqJiIiI6J6xqCQiIiKie8aikoiIiIjuGYvKTkapVGLGjBl47733OsV+2tP9\n", - "eMxERER0i01HH8CDYsaMGWafnzt3LiIiIn7z41AqlYiPj0d4eDji4uJ+01yWXjMArFy5EoGBgb/p\n", - "cXR27fmZEN0vWvYfb7/9Njw8PFqNS05ORlFREQDjfrR5H+np6UbtKioqsGbNGiiVSkydOhUxMTEA\n", - 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"RHnpNQYA7MKfNU2A/Nonvc5IrzEAoIGPKC+9xgDAvffKrmZtbT+K8lptH1GevIeP0+nkDQhERERE\n", - "dEd8Pf0AiIiIiOjvH5tKIiIiIrpjbCqJiIiI6I6xqSQiIiKiO8amkoiIiIjuGJtKIiIiIrpj/w8k\n", - "NhjXe8qsYgAAAABJRU5ErkJggg==\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_strains_compare\n", - "\n", - "draw_strains_compare(strain[0], strain_pred[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, let's look at the difference between the two strain fields." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEhCAYAAAC5hYFyAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtUU3e6P/43ECSBGDQgIiYSC1gKos6U0mgtpSC9AnZK\n", - "cVCZ6bFCa0udWi8dOz2KtHrUQUqn/pRvz4mXsc5UKfQcmCxPz9cpCjPqkJHKQL11bIUmg2CBFhrD\n", - "TuTy/cNfdo1JSD6wdz2dPq+1WMvs/d5PLurj4yc7Oz7Dw8PDIIQQ8r+a7+1+AIQQQjyjZk0IId8D\n", - 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"YII=\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_differences\n", - "\n", - "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Resizing the Coefficients to use on Larger Microstructures \n", - "\n", - "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger random microstructure and its strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(63, 63)\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAosAAAEoCAYAAAAwirvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlcVdX+/h/m0SMiICIi4gTiLCopKqWVU2bO2pxDZdlg\n", - "Wde6ZWRWtzQzS+2KluWtHFNzaHJAK5UcEWcTUEBlEpDhgAy/P/x54Nn76D4bTmrn+3m/Xr5e5zl7\n", - "WGuvvfdhufeznmVXWVlZCUEQBEEQBEEwg/2troAgCIIgCIJw+yKdRUEQBEEQBOG6SGdREARBEARB\n", - "uC7SWRQEQRAEQRCui3QWBUEQBEEQhOsinUVBEARBEAThukhnURAEQRAEQbgu0lm0Mm+99RZGjRp1\n", - "q6shCIIgCIJgFRxvdQVuJ6p38j755BM0aNDA7HoxMTE4evQoAODpp59GdHS0aZmdnd3fWse/k7fe\n", - "egvHjh3D8uXLb3VVzHK7108QBEEQbBHpLCqwt7dHRUUFtm7dijFjxqiWnz9/HkePHjWtp+wcPvPM\n", - "MygtLb1Z1RUEQRAEQfhbkc6iAi8vL3h5eWH79u0YNWoU7O35Tf2WLVsAAJ07d8aff/6p2t7Hx+em\n", - 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" poissons_ratio=poissons_ratio, size=size, \n", - " macro_strain=macro_strain)\n", - "\n", - "draw_microstructure_strain(X[0] , strain[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The influence coefficients that have already been calibrated need to be resized to match the shape of the new larger microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the 'resize_coeff' method." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.resize_coeff(X[0].shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now take a look that ther resized influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAo4AAAEfCAYAAADP3EebAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9UVPed//EXMMAgFCJQg3ZGaNUGxdOY1pBptNRE86Om\n", - "+m2LskC6bWxhd7tWm9Wes2c3Xa3JpnY38ctxrdJuR61p6gYiafHQbt0kRmzaNCRsjakN+SmUiaJB\n", - "E+kId0Zgvn/4ZeLAyAwCl7nm+eiZc7x3Pvd971zqzcv3/cwlLhAIBAQAAABEED/RBwAAAABrIDgC\n", - "AAAgKgRHAAAARIXgCAAAgKgQHAEAABAVgiMAAACiQnAEAABAVGwTfQCD1dbW6sCBA9q5c+eIttu3\n", - "b5+efPJJvffee/rsZz+rv//7v9f27dvl8Xi0efPmcTraifX73/9eBw4cUGtrq/x+v7Kzs/WpT31K\n", - 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"metadata": {}, - "source": [ - "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApUAAAEqCAYAAABEJauaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd8T9f/x1+SyBQiUyKLkBAhRuyV2Fu1RlqqWrS2mlW+\n", - "ZosOrdJBq0Nau9RWYs8oMZJIBEFIJGRJIokkMn5/+Em8zg3RUiF9Px8Pj4dX7ufec8e553M+977O\n", - "65TJz8/PhyAIgiAIgiA8BTolvQOCIAiCIAjCy490KgVBEARBEISnRjqVgiAIgiAIwlMjnUpBEARB\n", - "EAThqZFOpSAIgiAIgvDUSKdSEARBEARBeGqkUykIgiAIgiA8NdKpfMGIi4tDv3798N13370Q23me\n", - "vIz7LAiCIAjCffRKegf+K/Tr1++xy4cPHw5vb+9/fT/i4uIwevRotG7dGiNGjPhXyyrumAFg5syZ\n", - "cHd3/1f340XneV4TQXhZeLj9WLx4MWxsbIr83OzZsxEWFgZA244+2MbatWs16928eRNz585FXFwc\n", - "evXqBV9fXwBAXl4e9u3bh8OHD+P69evIzMxEuXLlYGZmBhcXF3h5ecHLy+tZHaYglCqkU/mc6dOn\n", - "T5F/r1KlCgDAwsICCxcuhLGx8VOV86jtlClT5qm2+0941DEDgJWV1XPckxeTkrgmgvAyoKOjU9DJ\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, let's look at the difference between the two strain fields." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWsAAAEcCAYAAAABYZUBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXtYlNe99n8DAzPDUTnJYZBRQBHEUxBHQ4jxlDRBczAa\n", - "THzf7KSS2lj3azS2v3TvaEx0p2lqbJtG3u6iNqltolFbDNudQxPFRI1EFMFzVECQk4CCwzAMp98f\n", - "loH7y8nDTPt2Z32ui+viO8/zrOc4a9Zzr++6l0tHR0cHFAqFQvH/NK7/6ANQKBQKxcCoylqhUCj+\n", - "CVCVtUKhUPwToCprhUKh+CdAVdYKhULxT4CqrBUKheKfAM0/+gCczfbt27Fz584enyckJOAHP/gB\n", - "fvSjH+EnP/kJJkyYcNNlnjx5Eq+++irWr18Pg8GA1tZW7Nq1C0lJSTAajQ457ieeeKLXz4cMGYJf\n", - "//rXAIB33nkHZWVleP311x2yz78nBw8ehM1mw9SpUx1W5jvvvIP9+/dj9OjRePnll2mZzWZDeno6\n", - "rFYrfvjDH9r329s1tNlseP3111FSUoJVq1bBaDSioaEBH374IfLz81FXVwdvb28MHToUs2bNwsSJ\n", - "Ex12DgpFX/yPr6wBwNPTE//2b//W47PBgwdj3bp1CAsLu6Xyhg8fjnXr1iE4OBgA0Nraip07d2LI\n", - 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"source": [ - "As you can see, the results from the strain field computed with the resized influence coefficients is not as close to the finite element results as they were before they were resized. This decrease in accuracy is expected when using spectral interpolation [4]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## References\n", - "\n", - "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", - "\n", - "\n", - "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", - "\n", - "\n", - "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi;10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", - "\n", - "\n", - "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/elasticity_2D_Multiphase.ipynb b/notebooks/elasticity_2D_Multiphase.ipynb deleted file mode 100644 index 6ee1c756..00000000 --- a/notebooks/elasticity_2D_Multiphase.ipynb +++ /dev/null @@ -1,4419 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Linear Elasticity in 2D for 3 Phases\n", - "\n", - "##Introduction\n", - "\n", - "This example provides a demonstration of using PyMKS to compute the linear strain field for a three phase composite material. It demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared with the finite element data for a large problem.\n", - "\n", - "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Elastostatics Equations and Boundary Conditions\n", - "\n", - "The governing equations for elasticostaics and the boundary conditions used in this example are the same as those provided in the Linear Elastic in 2D example. \n", - "\n", - "Note that an inappropriate boundary condition is used in this example because current version of SfePy is unable to implement a periodic plus displacement boundary condition. This leads to some issues near the edges of the domain and introduces errors into the resizing of the coefficients. We are working to fix this issue, but note that the problem is not with the MKS regression itself, but with the calibration data used. The finite element package ABAQUS includes the displaced periodic boundary condition and can be used to calibrate the MKS regression correctly." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKS\n", - "\n", - "###Calibration Data and Delta Microstructures\n", - "\n", - "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2% or less when comparing the MKS results with those computed using finite element methods [1]. \n", - "\n", - "Because we are using distinct phases and the contrast is low enough to only need the first order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first order influence coefficients [2]. \n", - "\n", - "Here we use the `make_delta_microstructure` function from `pymks.datasets` to create the delta microstructures needed to calibrate the first order influence coefficients for a two phase microstructure. The `make_delta_microstructure` function uses SfePy to generate the data" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n = 21\n", - "n_phases = 3\n", - "from pymks.tools import draw_microstructures\n", - "from pymks.datasets import make_delta_microstructures\n", - "\n", - "X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, n))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at a few of the delta microstructures by importing `draw_microstructures` from `pymks.tools`." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - 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Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. The number of delta microstructures that are needed to calibrated the first order coefficients is $N(N-1)$ where $N$ is the number of phases, therefore in this example we need 6 delta microstructures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Generating Calibration Data\n", - "\n", - "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields.\n", - "\n", - "In this example, lets look at a three phase microstructure with elastic moduli values of 80, 100 and 120 and Poisson's ratio values all equal to 0.3. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. The number of Poisson's ratio values and elastic moduli values indicates the number of phases. Note that `make_elasticFEstrain_delta` does not take a number of samples argument as the number of samples to calibrate the MKS is fixed by the number of phases." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets import make_elastic_FE_strain_delta\n", - "from pymks.tools import draw_microstructure_strain\n", - "\n", - "elastic_modulus = (80, 100, 120)\n", - "poissons_ratio = (0.3, 0.3, 0.3)\n", - "macro_strain = 0.02\n", - "size = (n, n)\n", - "\n", - "X_delta, strains_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", - " poissons_ratio=poissons_ratio,\n", - " size=size, macro_strain=macro_strain)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - 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Let's also take a look at the $\\varepsilon_{yy}$ and $\\varepsilon_{xy}$ strain fields." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Calibrating First Order Influence Coefficients\n", - "\n", - "Now that we have the delta microstructures and their strain fields, we will calibrate the influence coefficients by creating an instance of the `MKSLocalizatoinModel` class. Because we are going to calibrate the influence coefficients with delta microstructures, we can create an instance of `PrimitiveBasis` with `n_states` equal to 3, and use it to create an instance of `MKSLocalizationModel`. The delta microstructures and their strain fields will then be passed to the `fit` method. " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks import MKSLocalizationModel\n", - "from pymks import PrimitiveBasis\n", - "\n", - "prim_basis =PrimitiveBasis(n_states=3, domain=[0, 2])\n", - "model = MKSLocalizationModel(basis=prim_basis)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first order influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.fit(X_delta, strains_delta)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it, the influence coefficient have be calibrated. Let's take a look at them." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA78AAAEgCAYAAAB8T8QlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XF0VOWd//HPTCZkQtJJEyINdEJSwRINR6GlcUppyhYU\n", - "FgvbFskS2BWBxLXr0rpot2e7FUutxS1gjlVJ3Y2hqLUmgPsLJ/VIOUWCbtUIu0hriVYlaUYIEKDQ\n", - "IZmJmZnfHyxTQxJm7s2EyU3er3NyDnPnme88dybzhM98752xhcPhsAAAAAAAGMbsiZ4AAAAAAACD\n", - "jfALAAAAABj2CL8AAAAAgGGP8AsAAAAAGPYIvwAAAACAYY/wCwAAAAAY9gi/AAAAAIBhz5HoCfSn\n", - "trZWu3bt0pNPPmnodtu3b9fu3bv1pz/9SV/60pf0j//4j3r88cfl9Xq1fv36QZptYr322mvatWuX\n", - "mpub1dXVpezsbH32s5/VggULlJmZGff76+sx7mu7JLW2tsb8uA/m8/Sb3/xGXV1dmjVrVtxrY3hg\n", - "zYkda050rDmIhjUndqw50bHmALEZsuFXkmw2m6Hx7733nrZt26bS0lIVFhYqIyNjkGY2dDz11FN6\n", - "4YUX9Fd/9VdasGCBUlNT1draqt27d+vEiRO6995743p//T3G/W3v6uqKufatt96qDz/8MK7zvejV\n", - "V1+Vz+eLyx+F3/3ud3rmmWeUkpKidevWDaiW3+/XM888o/Hjx6utrU2TJ0/WF77whQHPEeaw5kTH\n", - 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The constant-valued influence coefficients may seem superfluous, but are equally as import. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predict of the Strain Field for a Random Microstructure\n", - "\n", - "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two phase microstructure and compare it with the results from a finite element simulation. \n", - "\n", - "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAosAAAEoCAYAAAAwirvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtclGX6P/DPgCPDIZCjqCCk5aJoYSlhEFF4WA3dFPFA\n", - "btsa2NZmCWS7br/N6PCtbwqUmVjStlq7BUqmUbZrHtDdNJNNWU3zyElFRBQdcTjO7w+/zDLOwHCN\n", - "j4jPft69fL2aZ67nvu45gJfXc7g1RqPRCCIiIiIiKxxu9ASIiIiIqPtisUhERERE7WKxSERERETt\n", - "YrFIRERERO1isUhERERE7WKxSERERETtYrFIRERERO1isaiwl156CdOnT7/R0yAiIiJSRI8bPYHu\n", - "pG2Rt2TJEvTu3dtqXHp6On788UcAwJNPPomYmBjTcxqN5rrO8Xp66aWXcODAAeTm5t7oqVjV3edH\n", - "RESkRiwWr+Lg4ICWlhZs3rwZM2fOtHj+1KlT+PHHH01xVxeHv/3tb9HQ0NBV0yUiIiK6rlgsXqVX\n", - "r17o1asXtm7diunTp8PBwfxI/aZNmwAAd999N77//nuL/X18fLpknkRE3V1jYyN27NiBH374AefP\n", - "n0dDQwMMBgNCQ0MxYcIE+Pv73+gpElEnsFi0IjY2FitWrEBRURFGjhxp2t7U1ITCwkL87Gc/Q0BA\n", - "gNVisaNDpXv37sWGDRtw5MgRXL58Ge7u7rj11lsxfvx4DBs2DACwf/9+vPzyy5g6dSqGDx+O1atX\n", - "49ChQ6irq8O7774LHx8fNDY24ssvv8T27dtRVVUFBwcHBAcH4+c//zlGjRplkXf37t346quvUFFR\n", - 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" poissons_ratio=poissons_ratio, size=size, \n", - " macro_strain=macro_strain)\n", - "draw_microstructure_strain(X[0] , strain[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with**\n", - "\n", - "Now to get the strain field from the `MKSLocalizationModel` just pass the same microstructure to the `predict` method." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "strain_pred = model.predict(X)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally let's compare the results from finite element simulation and the MKS model." - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApUAAAEsCAYAAACSfEiHAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - 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"iIiIiCbsf/niOMrWAjC9AAAAAElFTkSuQmCC\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_strains_compare\n", - "\n", - "draw_strains_compare(strain[0], strain_pred[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's plot the difference between the two strain fields." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWMAAAEcCAYAAAASttX1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X9cVHW+P/AXMMgMjIMiKtKMjIFFjqTtIk1GZFpuGbHd\n", - "DBdd264FbSvXvaa5e2tXk7qu3jXjliltj1HLbTchcReWbzfvtiS26krSJTDFtlZoJkEEV2iCM8Ov\n", - "7x8uEyMDM5/Dx7XdXs/HYx4POed1PnNm9PH2zWfO+UxQX19fH4iI6IoKvtInQERELMZERF8JLMZE\n", - "RF8BLMZERF8BLMZERF8BLMZERF8Bmit9ApdbUVERiouLB21PSkrC97//ffzbv/0bfvzjH+Mb3/hG\n", - "wGN++OGHePrpp7FlyxYYjUZ0d3dj3759SElJgdlslnLe3/nOd3xunzhxIl544QUAwLZt2+BwOLBx\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Resizing the Coefficeints to use on Larger Microstructures \n", - "\n", - "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger random microstructure and its strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(63, 63)\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAosAAAEoCAYAAAAwirvKAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlcVVX//RczAoIiICoKOYUzmho5EE6ZpqaSs2Xl0GiW\n", - "U4+ZmWblkxoNmhZaT1qZc5pDaU5ZaeacsyagOCEOIOBl/v3hV2Dtc3Tfi7e0+/u8e/V63XXPuWfv\n", - "s8++l+05a6/tVFBQUABBEARBEARBMMH5TldAEARBEARBuHuRwaIgCIIgCIJwU2SwKAiCIAiCINwU\n", - "GSwKgiAIgiAIN0UGi4IgCIIgCMJNkcGiIAiCIAiCcFNksCgIgiAIgiDcFBks2pk333wTvXr1utPV\n", - "EARBEARBsAuud7oCdxPFB3kfffQRypcvb7rfhAkTcPDgQQDAc889h+jo6MJtTk5Of2sd/07efPNN\n", - "HDp0CAsWLLjTVTHlbq+fIAiCIDgiMlhUcHZ2Rn5+PjZs2IA+ffoYtp89exYHDx4s3E8dHL7wwgvI\n", - "zs7+p6orCIIgCILwtyKDRYUyZcqgTJky2LRpE3r16gVnZ35Sv379egDAfffdhz/++MPw+YCAgH+k\n", - 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"print size\n", - "\n", - "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", - " poissons_ratio=poissons_ratio, size=size, \n", - " macro_strain=macro_strain)\n", - "\n", - "draw_microstructure_strain(X[0] , strain[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The influence coefficients that have already been calibrated on a $n$ by $n$ delta microstructures, need to be resized to match the shape of the new larger $m$ by $m$ microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the 'resize_coeff' method." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.resize_coeff(X[0].shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's now take a look that ther resized influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA7oAAAEdCAYAAAAighsSAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90VPWd//FXkgmZkHRiQsSAE5IqlmiyCm0apyxGtqCw\n", - "WvKtRVMiuyIlce26tC7K9mxXUWopboHmuCqpuxGNWpcEcL/h0B5TTpGgWzWFXaClRKuSmBECBCh0\n", - "mMzE/Pj+4TdTZjLJfMIEkhuej3PmHO6dz3zu587AJ7zyvp87MT09PT0CAAAAAGCUiB3uAQAAAAAA\n", - "MJQIugAAAACAUYWgCwAAAAAYVQi6AAAAAIBRhaALAAAAABhVCLoAAAAAgFGFoAsAAAAAGFVswz2A\n", - "c9XU1Kiurk7PP//8oF63efNmbd++XX/84x9188036+///u/17LPPyu12a/Xq1RdotMPrnXfeUV1d\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApUAAAEqCAYAAABEJauaAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXtcj+f/x186n5B0pJOiKCnkEDkkNscZiow5zA7ONsM2\n", - "5jjGzIYZNr6MGcpx2sQyx5hDOZVCSJSiQlLp7PeHn/K678jGRHs/99jj4fX5fO77ug/Xfd1X9/26\n", - "Xlel+/fv34cgCIIgCIIgPAMa5b0BgiAIgiAIwquPdCoFQRAEQRCEZ0Y6lYIgCIIgCMIzI51KQRAE\n", - "QRAE4ZmRTqUgCIIgCILwzEinUhAEQRAEQXhmpFMpCIIgCIIgPDPSqXzJSElJQZ8+fbBkyZKXYj0v\n", - "kldxmwVBEARBeIBWeW/Af4U+ffo88fthw4ahbdu2//p2pKSkYNSoUWjTpg2GDx/+r5ZV1j4DwNSp\n", - "U+Hi4vKvbsfLzos8J4LwqvBo+/Hdd9/BwsKi1N9Nnz4dMTExANTt6MN1BAUFqZa7fv06Zs2ahZSU\n", - "FPTo0QMBAQEAgKKiIuzZswdhYWG4evUqcnJyYGRkBGNjYzg6OsLT0xOenp7PazcFoUIhncoXjL+/\n", - "f6mf16pVCwBQvXp1zJ8/HwYGBs9UzuPWU6lSpWda7z/hcfsMAGZmZi9wS15OyuOcCMKrgIaGRnEn\n", - 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"\n", - "draw_strains_compare(strain[0], strain_pred[0])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Again, let's plot the difference between the two strain fields." - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAWMAAAEcCAYAAAASttX1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlclPe59m/WmWEZFJB1BkZARRFXRFxCjEaTGCVtjFbp\n", - "OclJhDSJ9TRL056kjYlNbdKTquckp/KmL8amMYl7j4aksWlMcAeXILjghsMim4AwDsMwbO8fvszM\n", - "dYOMjo8nPc39/Xz4fLh59mV+/OZ6ruf6efT09PSQIAiC8K3i+W3vgCAIgiCNsSAIwt8F0hgLgiD8\n", - "HSCNsSAIwt8B0hgLgiD8HSCNsSAIwt8B3t/2DtxptmzZQtu3b+/z9+TkZPrRj35EP/7xj+nnP/85\n", - "TZgw4abXeerUKfrVr35Fq1evJp1OR52dnbRjxw5KTU0lg8GgyH7/4Ac/6Pfv4eHh9PbbbxMR0e9/\n", - "/3uqqqqiN954Q5Ft/k9y8OBBstlsNGPGDMXW+fvf/5727t1Lo0ePpldeeQWm2Ww2ys7OJqvVSk8/\n", - "/bR9u/2dQ5vNRm+88QaVl5fTihUryGAwkMlkoq1bt1JRURE1NTVRQEAAxcTE0Jw5c2jSpEmKHYPw\n", - 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This decrease in accuracy is expected when using spectral interpolation [4]." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## References\n", - "\n", - "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", - "\n", - "\n", - "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", - "\n", - "\n", - "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi;10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", - "\n", - "\n", - "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/elasticity_3D.ipynb b/notebooks/elasticity_3D.ipynb deleted file mode 100644 index e05c3081..00000000 --- a/notebooks/elasticity_3D.ipynb +++ /dev/null @@ -1,2121 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#Linear Elasticity in 3D\n", - "\n", - "##Introduction\n", - "\n", - "This example provides a demonstration of using PyMKS to compute the linear strain field for a two phase composite material in 3D, and presents a comparison of the computational efficiency of MKS when compared with the finite element method. The example first provides information on the boundary conditions used in MKS. Next, delta microstructures are used to calibrate the first order influence coefficients. The influence coefficients are then used to compute the strain field for a random microstructure. Lastly, the calibrated influence coefficients are scaled up and are used to compute the strain field for a larger microstructure and compared with results computed using finite element analysis." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Elastostatics Equations and Boundary Conditions\n", - "\n", - "A review of the governing field equations for elastostatics can be found in the Linear Elasticity in 2D example. The same equations are used in the example with the exception that the second lame parameter (shear modulus) $\\mu$ is defined differently in 3D.\n", - "\n", - "$$ \\mu = \\frac{E}{2(1+\\nu)} $$\n", - "\n", - "\n", - "In general, generateing the calibration data for the MKS requires boundary conditions that are both periodic and displaced, which are quite unusual boundary conditions. The ideal boundary conditions are given by,\n", - "\n", - "$$ u(L, y, z) = u(0, y, z) + L\\bar{\\varepsilon}_{xx} $$\n", - "$$ u(0, L, L) = u(0, 0, L) = u(0, L, 0) = u(0, 0, 0) = 0 $$\n", - "$$ u(x, 0, z) = u(x, L, z) $$\n", - "$$ u(x, y, 0) = u(x, y, L) $$\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "import timeit as tm\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKS\n", - "\n", - "###Calibration Data and Delta Microstructures\n", - "\n", - "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2% or less when comparing the MKS results with those computed using finite element methods [1]. \n", - "\n", - "Because we are using distinct phases and the contrast is low enough to only need the first order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first order influence coefficients [2]. \n", - "\n", - "The `make_delta_microstructure` function from `pymks.datasets` can be used to create the two delta microstructures needed to calibrate the first order influence coefficients for a two phase microstructure. This function uses the python module [SfePy](http://sfepy.org/doc-devel/index.html) to compute the strain fields using finite element methods." - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAnEAAAEaCAYAAAB+VgE8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAFBJJREFUeJzt3V9opHe9x/FPknZMJ9tWerHUxS4ZArE4HaLrH0hFbCq9\n", - "KFhqQYProsSx9cpe9EbE1cbItpQK3rTgzTYN7QolxbWIIBIoxT8X1lYsES9C/mybskULVRYmQ7bp\n", - "zrk4MKd7bJJVc5J5nvN6wcDM/JLv8wQW9s3vmT99nU6nEwAACqX/oE8AAIB/nYgDACggEQcAUEAi\n", - "DgCggEQcAEABiTgAgAK66qBPAADg/5O///3veeSRR/L666/n6aefTn///+ypvfXWW3nssceytbWV\n", - "ycnJNBqNbefYiQMA2EeHDh3Kgw8+mNHR0X9ae+6553L8+PGcPHkyZ8+e3XHOrjtxfX19//5ZAqV3\n", - "4sSJnDlz5j+ec/78+T04G6Csjhw5ctCnsGeuvvrqXH311e+5tr6+3o27wcHBtNvtXHPNNe/5s3bi\n", - "AAB6xKVLl7r3q9VqWq3Wtj/rNXEAAHtsfn6+e79er6der1/R77379XHtdjuHDh3a9mdFHABQavv9\n", - "co0jR45kcnLy3/rdo0ePZmlpKUePHk273c7g4OC2PyviAIBS63Q6B30Kl3nnnXfy8MMP59y5c3no\n", - 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"source": [ - "n = 9\n", - "center = (n - 1) / 2\n", - "\n", - "from pymks.tools import draw_microstructures\n", - "from pymks.datasets import make_delta_microstructures\n", - "\n", - "X_delta = make_delta_microstructures(n_phases=2, size=(n, n, n))\n", - "draw_microstructures(X_delta[:, center])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Using delta microstructures for the calibration of the first order influence coefficients is essentially the same as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Delta microstructures are composed of only two phases. One phase is located only at the center cell of the microstructure, and the rest made up of the other phase. " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "###Generating Calibration Data\n", - "\n", - "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields with the boundary conditions given above.\n", - "\n", - "In this example, lets look at a two phase microstructure with elastic moduli values of 80 and 120 and Poisson's ratio values of 0.3 and 0.3 respectively. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed Time 137.266396999 Seconds\n" - ] - } - ], - "source": [ - "from pymks.datasets import make_elastic_FE_strain_delta\n", - "from pymks.tools import draw_microstructure_strain\n", - "\n", - "elastic_modulus = (80, 120)\n", - "poissons_ratio = (0.3, 0.3)\n", - "macro_strain = 0.02 \n", - "size = (n, n, n)\n", - "\n", - "t = tm.time.time()\n", - "X_delta, strains_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", - " poissons_ratio=poissons_ratio,\n", - " size=size, macro_strain=macro_strain)\n", - "print 'Elapsed Time',tm.time.time() - t, 'Seconds'\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - 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Because we have 2 discrete phases we will create an instance of the `PrimitiveBasis` with `n_states` equal to 2, and then pass the basis in to create an instance of the `MKSLocalizationModel`. The delta microstructures and their strain fields are then passed to the `fit` method. " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks import MKSLocalizationModel\n", - "from pymks.bases import PrimitiveBasis\n", - "\n", - "prim_basis = PrimitiveBasis(n_states=2)\n", - "model = MKSLocalizationModel(basis=prim_basis)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first order influence coefficients." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.fit(X_delta, strains_delta)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "That's it, the influence coefficient have be calibrated. Let's take a look at them." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAokAAAEiCAYAAACV5EiRAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X90VHV+//HXJBPm5scmEiINmF8KroH4VdyNyRQwpgWF\n", - "4hfOcflREqyKEtet0h9st/1+rQtFVrEFTo5dTbRGFGWtCcQ9yTfbI+tpJGDXbFZajGUJqJCYESIE\n", - "FDoMMzGZ+f7ByaxhksxkvUzC5fk4Z87JvfOZ9/3MjXx85XPvfMYWCAQCAgAAAL4mZrQ7AAAAgLGH\n", - "kAgAAIAQhEQAAACEICQCAAAgBCERAAAAIQiJAAAACEFIBAAAQAj7aHcgnJqaGu3atUsvvfTSiF63\n", - "c+dOvf322/ryyy91++2368///M/13HPPyeVyaePGjZeot6OrublZu3btUnt7u3p6epSWlqbvfve7\n", - "WrhwocaPH2/68QY7x4Ptl6TOzs6Iz/ul/D396le/Uk9Pj4qLi02vjcsf403kGG/CY7zB5W7Mh0RJ\n", - "stlsI2r/ySefaMeOHSopKVFeXp5SUlIuUc/GjldffVX/9m//pj/6oz/SwoULFR8fr87OTr399ts6\n", - "ceKE/uZv/sbU4w11jofa39PTE3HtJUuW6KuvvjK1v/3ee+89ud1uUwbt//7v/9b27dvlcDi0fv36\n", - "b1TL6/Vq+/btmjx5srq6unTDDTdo1qxZ37iPGDnGm/AYbyIzVscbSert7dWbb76p9PR0FRUVfeN6\n", - 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The constant-valued influence coefficients may seem superfluous, but are equally as import. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Predict of the Strain Field for a Random Microstructure\n", - "\n", - "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two phase microstructure and compare it with the results from a finite element simulation. \n", - "\n", - "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed Time 67.0600619316 Seconds\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAoMAAAEoCAYAAAAjXfs+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XtUVPe99/EPl5HhcvA2KNpBiE1SDEmKVuk0IYQ8pKZJ\n", - "OLYNYtDkpMkJ2DZddrVmkbV8uqIhbc7J05pDE43YQi+x55wGlLRaczkXDejpSsqJDaEYjWkFZFQC\n", - "eIGO4zByef7IYuo4IFujm3H7fnWxlrP3b+/vbwbt+uS7929PxNDQ0JAAAABwVYoc7wkAAABg/BAG\n", - "AQAArmKEQQAAgKsYYRAAAOAqRhgEAAC4ihEGAQAArmKEQQAAgKsYYfASe+qpp3T//feP9zQAAAAM\n", - "iR7vCYSTs0PcCy+8oOnTp484rqysTO+//74k6Zvf/KZyc3MD+yIiIi7rHC+np556Svv27VN1dfV4\n", - "T2VE4T4/AACuRITBc0RGRmpwcFA7d+7U0qVLQ/YfPXpU77//fmDcueHvW9/6lvx+v1nTBQAA+EQI\n", - "g+eYNGmSJk2apLq6Ot1///2KjAy+kr5jxw5J0uc+9zn97//+b8jxDofDlHkCQLg7c+aM3nrrLb37\n", - "7rs6efKk/H6/fD6fMjIydM899yg5OXm8pwhAhMER5eXlqbKyUnv27NGCBQsC2/v7+1VfX6/PfOYz\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with**\n", - "\n", - "Now to get the strain field from the `MKSLocalizationModel` just pass the same microstructure to the `predict` method." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed Time 0.0036780834198 Seconds\n" - ] - } - ], - "source": [ - "t = tm.time.time()\n", - "strain_pred = model.predict(X)\n", - "print 'Elapsed Time',tm.time.time() - t,'Seconds'\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Finally let's compare the results from finite element simulation and the MKS model." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - 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"ayKiWwDDmojoFsCwJiK6BTCsiYhuAf8/o9epJGNSNZgAAAAASUVORK5CYII=\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_differences\n", - "\n", - "draw_differences([strain[0, center] - strain_pred[0, center]], ['Finite Element - MKS'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Resizing the Coefficeints to use on Larger Microstructures \n", - "\n", - "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger $m$ by $m$ random microstructure and its strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "m = 3 * n\n", - "center = (m - 1) / 2\n", - "t = tm.time.time()\n", - "X = np.random.randint(2, size=(1, m, m, m))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The influence coefficients that have already been calibrated need to be resized to match the shape of the new larger microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the 'resize_coeff' method." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.resize_coeff(X[0].shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elapsed Time 0.0222690105438 Seconds\n" - ] - }, - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAoMAAAEoCAYAAAAjXfs+AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtclHXeP/4Xh5Hh0KByEHQINC2UNDSlKYnopnOsWyKK\n", - "dNwE29p1d8PV+/bbLw2r73rv2pJ5wA3KtPveAqHStXQPGui2FkUhYeAhAUVAwAM4DsMIw/cPf0yO\n", - "M3C9rVmZrl7Px4PHQ67rPe/rMwfwzee6rs/bo7e3txdERERE9KPkOdgDICIiIqLBw2KQiIiI6EeM\n", - "xSARERHRjxiLQSIiIqIfMRaDRERERD9iLAaJiIiIfsRYDBIRERH9iLEYdLHnn38ec+bMGexhEBER\n", - "EYl4D/YA3MnFRdyrr76KESNGOI3Lzs7G119/DQB46qmnkJiYaNvn4eHxbx3jv9Pzzz+P6upqFBQU\n", - "DPZQnHL38REREf0QsRi8hKenJ6xWK3bt2oW5c+c67G9qasLXX39ti7u0+PvFL34Bi8VypYZLRERE\n", - "9L2wGLzE0KFDMXToUJSUlGDOnDnw9LQ/k75z504AwI033ojPPvvM4fHBwcFXZJxERO7u/Pnz2Lt3\n", - 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"\n", - "t = tm.time.time()\n", - "strain_pred = model.predict(X)\n", - "print 'Elapsed Time',(tm.time.time() - t), 'Seconds'\n", - "draw_microstructure_strain(X[0, center], strain_pred[0, center])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "## References\n", - "\n", - "[1] Binci M., Fullwood D., Kalidindi S.R., *A new spectral framework for establishing localization relationships for elastic behav ior of composites and their calibration to finite-element models*. Acta Materialia, 2008. 56 (10): p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", - "\n", - "\n", - "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, *Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems*. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", - "\n", - "\n", - "[3] Marko, K., Kalidindi S.R., Fullwood D., *Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals*. International Journal of Plasticity 24 (2008) 1264–1276 [doi;10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", - "\n", - "\n", - "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., *Crystal plasticity simulations using discrete Fourier transforms*. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/filter.ipynb b/notebooks/filter.ipynb index 7b371c7c..1c486ae8 100644 --- a/notebooks/filter.ipynb +++ b/notebooks/filter.ipynb @@ -36,7 +36,7 @@ "\n", "$$F\\left(x\\right) = e^{-|x|} \\cos{\\left(2\\pi x\\right)} $$\n", "\n", - "We want to show that if $F$ is used to generate sample calibration\n", + "We want to show that, if $F$ is used to generate sample calibration\n", "data for the MKS, then the calculated influence coefficients are in\n", "fact just $F$." ] @@ -50,180 +50,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAX0AAAEACAYAAABfxaZOAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3X3YHHV97/H3l0ACgUAIgQBD8AYMCtYnVKTaeqKCjXKK\n", - "Ts85BmqVquieWqzt1SpU25LTXlbtqdZ6Ye0UHw72QaTq0igPEqm32IqUlAcRkiZRIslCQniIhIdA\n", - "At/zx28WNpvZ3Zmd3XufPq/ruq/szM5vZ3Jn89nffuc3vzF3R0REJsM+gz4AERGZOQp9EZEJotAX\n", - "EZkgCn0RkQmi0BcRmSAKfRGRCVI69M3si2a21cxub7PNZ8xsvZndZmYvLbtPERHpTi96+l8ClrV6\n", - "0szeBDzX3ZcA7wU+14N9iohIF0qHvrt/H3iozSZnAZem294IzDezRWX3KyIixc1ETT8CNjUsbwaO\n", - "mYH9iohIk5k6kWtNy5r7QURkAPadgX3UgMUNy8ek6/ZgZvogEBHpgrs3d6xbmonQXwmcD1xmZqcB\n", - "2919a9aGRQ5c2jOzFe6+YtDHMeqiJP6zh7+55o92fGut3ps9ovdmbxXtMPdiyOZXgB8AzzOzTWb2\n", - "LjOrmFkFwN2vAn5qZhuABHhf2X2KzKADAKIknj3oAxHphdI9fXc/J8c255fdj8iALGj4c8sgD0Sk\n", - "F3RF7viaHvQBjImFc05cCLBw0AcyRqYHfQCTTKE/ptx9etDHMCYOm/O8wx04bNAHMi703hwshb5I\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -261,6 +90,7 @@ "source": [ "import scipy.ndimage\n", "\n", + "\n", "n_space = 101\n", "n_sample = 50\n", "np.random.seed(201)\n", @@ -273,7 +103,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "For this problem, a basis is unnecessary as no discretization is\n", + "For this problem, a basis is unnecessary, as no discretization is\n", "required in order to reproduce the convolution with the MKS localization. Using\n", "the `ContinuousIndicatorBasis` with `n_states=2` is the equivalent of a\n", "non-discretized convolution in space." @@ -290,8 +120,8 @@ "from pymks import MKSLocalizationModel\n", "from pymks import PrimitiveBasis\n", "\n", - "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", - "model = MKSLocalizationModel(basis=prim_basis)\n" + "p_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "model = MKSLocalizationModel(basis=p_basis)\n" ] }, { @@ -358,241 +188,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAX0AAAD7CAYAAACG50QgAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8G9d1L/DfmRmsBCguokUKkkxZGy1vki3LS+xYjndZ\n", - "toN0ifOaZo/Rxf20zWv2NHHTvCbpy0ubNImDpGnjNomz04vkvY68xY6taLE2apcoQZREiSIBEsQy\n", - "M+f9AVAGwcEmgAIJnO/no485yx1c0sPDO3fuPZeYGUIIIeqDUu0KCCGEOHck6AshRB2RoC+EEHVE\n", - "gr4QQtQRCfpCCFFHJOgLIUQd0apdgTFEJGNHhRCiRMxMpZw/ZYI+UHrlhTUieoCZH6h2PWqF/Dwr\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -601,7 +199,7 @@ ], "source": [ "plt.plot(x, F(x), label=r'$F$', color='#1a9850')\n", - "plt.plot(x, -model.coeff[:,0] + model.coeff[:, 1], \n", + "plt.plot(x, -model.coef_[:,0] + model.coef_[:, 1], \n", " 'k--', label=r'$\\alpha$')\n", "l = plt.legend()" ] @@ -618,7 +216,7 @@ "\n", "$$ \\sum\\limits_{l=0}^{L-1} m[l, s] = 1 $$\n", "\n", - "Thus, the regression in Fourier space must be done with categorical variables, and the regression takes the following form.\n", + "Thus, the regression in Fourier space must be done with categorical variables, and the regression takes the following form:\n", "\n", "\n", "$$ \\begin{split}\n", diff --git a/notebooks/homogenization_fiber_2D.ipynb b/notebooks/homogenization_fiber_2D.ipynb new file mode 100644 index 00000000..71582a39 --- /dev/null +++ b/notebooks/homogenization_fiber_2D.ipynb @@ -0,0 +1,419 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Effective Stiffness of Fiber Composite\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "This example demonstrates the use of the homogenization model from pyMKS on a set of fiber-like structures. These structures are simulated to emulate fiber-reinforced polymer samples. For a summary of homogenization theory and its use with effective stiffness properties please see the [Effective Siffness example](http://materialsinnovation.github.io/pymks/rst/stress_homogenization_2D.html). This example will first generate a series of random microstructures with various fiber lengths and volume fraction. The ability to vary the volume fraction is a new functionality of this example. Then the generated stuctures will be used to calibrate and test the model based on simulated effective stress values. Finally we will show that the simulated response compare favorably with those generated by the model. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Generating Structures\n", + "\n", + "These first lines inport important packages that will be used to run pymks. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are defining the parameters which we will use to create the microstructures. `n_samples` will determine how many microstructures of a particular volume fraction we want to create. `size` determines the number of pixels we want to be included in the microstructure. We will define the material properties to be used in the finite element in `elastic_modulus`, `poissons_ratio` and `macro_strain`. `n_phases` and `grain_size` will determine the physical characteristics of the microstructure. We are using a high aspect ratio in creating our microstructures to simulate fiber-like structures. The `volume_fraction` variable will be used to vary the fraction of each phase. The sum of the volume fractions must be equal to 1. The `percent_variance` variable introduces some variation in the volume fraction up to the specified percentage." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "sample_size = 100\n", + "n_samples = 4 * [sample_size]\n", + "size = (101, 101)\n", + "elastic_modulus = (1.3, 75)\n", + "poissons_ratio = (0.42, .22)\n", + "macro_strain = 0.001\n", + "n_phases = 2\n", + "grain_size = [(40, 2), (10, 2), (2, 40), (2, 10)]\n", + "v_frac = [(0.7, 0.3), (0.6, 0.4), (0.3, 0.7), (0.4, 0.6)]\n", + "per_ch = 0.1\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we will create the microstructures and generate their responses using the `make_elastic_stress_random` function from pyMKS. Four datasets are created to create the four different volume fractions that we are simulating. Then the datasets are combined into one variable. The volume fractions are listed in the variable `v_frac`. Variation around the specified volume fraction can be obtained by varying `per_ch`. The variation is randomly generated according a uniform distribution around the specified volume fraction." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets import make_elastic_stress_random\n", + "\n", + "\n", + "dataset, stresses = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size,\n", + " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio,\n", + " macro_strain=macro_strain, volume_fraction=v_frac,\n", + " percent_variance=per_ch)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Now we are going to print out a few microstructres to look at how the fiber length, orientation and volume fraction are varied. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_microstructures\n", + "examples = dataset[::sample_size]\n", + "draw_microstructures(examples)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Creating the Model\n", + "\n", + "Next we are going to initiate the model. The MKSHomogenizationModel takes in microstructures and runs two-point statistics on them to get a statistical representation of the microstructures. An expalnation of the use of two-point statistics can be found in the [Checkerboard Microstructure Example](http://materialsinnovation.github.io/pymks/rst/checker_board.html). Then the model uses PCA and regression models to create a linkage between the calcualted properties and structures. \n", + "Here we simply initiate the model. " + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSHomogenizationModel\n", + "from pymks import PrimitiveBasis\n", + "\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "model = MKSHomogenizationModel(basis=p_basis, correlations=[(0, 0)], periodic_axes=[0, 1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we are going to split our data into testing and training segments so we can test and see if our model can accurately predict the effective stress. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "\n", + "\n", + "flat_shape = (dataset.shape[0],) + (dataset[0].size,)\n", + "data_train, data_test, stress_train, stress_test = train_test_split(\n", + " dataset.reshape(flat_shape), stresses, test_size=0.2, random_state=3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use sklearn's GridSearchCV to optimize the `n_components` and `degree` for our model. Let's search over the range of 1st order to 3rd order polynomial for `degree` and 2 to 7 principal components for `n_components`." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "\n", + "params_to_tune = {'degree': np.arange(1, 4), 'n_components': np.arange(2, 8)}\n", + "fit_params = {'size': dataset[0].shape}\n", + "gs = GridSearchCV(model, params_to_tune, fit_params=fit_params).fit(data_train, stress_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the results." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Order of Polynomial 3\n", + "Number of Components 5\n", + "R-squared Value 0.894170873165\n" + ] + } + ], + "source": [ + "print('Order of Polynomial'), (gs.best_estimator_.degree)\n", + "print('Number of Components'), (gs.best_estimator_.n_components)\n", + "print('R-squared Value'), (gs.score(data_test, stress_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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1Wq1m9OjRjB49ukb1CCGEcCyOHJdMS5AsXLjQagmSYcOGWZR9+OGHzf/29vam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\nERERERG1IHa6iIiIiIiIWtD/A6Wx1gdJd3IxAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores_matrix\n", + "\n", + "draw_gridscores_matrix(gs, ['n_components', 'degree'], score_label='R-Squared',\n", + " param_labels=['Number of Components', 'Order of Polynomial'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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LlQX05O3YsgRpVjUoIqwqGSEhEXwNlqXbVDKswXKrKfZ7stE2k78tXRozrWL58lV+dess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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores\n", + "\n", + "gs_deg_1 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 1]\n", + "gs_deg_2 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 2]\n", + "gs_deg_3 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 3]\n", + "\n", + "draw_gridscores([gs_deg_1, gs_deg_2, gs_deg_3], 'n_components', \n", + " data_labels=['1st Order', '2nd Order', '3rd Order'],\n", + " param_label='Number of Components', score_label='R-Squared')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Our best model was found to have `degree` equal to 3 and `n_components` equal to 5. Let's go ahead and use it." + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model = gs.best_estimator_" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Structures in PCA space\n", + "\n", + "Now we want to draw how the samples are spread out in PCA space and look at how the testing and training data line up. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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oQhzcAPhLFLQsuuRNOmvNEAgEHWfEA7N49YO/8LrLMsArx+oZ8dScG6rN1ggK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_components_scatter\n", + "\n", + "\n", + "stress_predict = model.predict(data_test)\n", + "draw_components_scatter([model.reduced_fit_data[:, :3],\n", + " model.reduced_predict_data[:, :3]],\n", + " ['Training Data', 'Testing Data'],\n", + " legend_outside=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like there is pretty good agreement between the testing and the training data. We can also see that the four different fiber sizes are seperated in the PC space. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Draw Goodness of fit\n", + "\n", + "Now we are going to look at how well our model predicts the properties of the structures. The calculated properties will be plotted against the properties generated by the model. We should see a linear realtionship with a slope of 1. " + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9dQDKy8u5ceMGhw8f5ujRo7z22mu0adOmVuPt27eP/v37C9H5jWNp/0Yzti3l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\nly9HTk4OJxv2vQRfKLIhrwJnclWYtXgB5i5e2GRYrzNh1CkZ6Zi7eCG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_goodness_of_fit\n", + "\n", + "\n", + "fit_data = np.array([stresses, model.predict(dataset)])\n", + "pred_data = np.array([stress_test, stress_predict])\n", + "draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Testing Data'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "Yay! There is a good corrolation between the FE results and those predicted by our linkage. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/homogenization_stress_2D.ipynb b/notebooks/homogenization_stress_2D.ipynb new file mode 100644 index 00000000..7d4ae4f8 --- /dev/null +++ b/notebooks/homogenization_stress_2D.ipynb @@ -0,0 +1,765 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "#Effective Stiffness of Composite Material\n", + "\n", + "##Introduction\n", + "\n", + "This example uses the `MKSHomogenizationModel` to create a homogenization linkage for the effective stiffness. This example starts with a brief background of the homogenization theory on the components of the effective elastic stiffness tensor for a composite material. Then the example generates random microstructures and their average stress values that will be used to show how to calibrate and use our model. We will also show how to use tools from [sklearn](http://scikit-learn.org/stable/) to optimize fit parameters for the `MKSHomogenizationModel`. Lastly, the data is used to evaluate the `MKSHomogenizationModel` for effective stiffness values for a new set of microstructures.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Linear Elasticity and Effective Elastic Modulus\n", + "\n", + "For this example we are looking to create a homogenization linkage that predicts the effective isotropic stiffness components for two-phase microstructures. The specific stiffness component we are looking to predict in this example is $C_{xxxx}$ which is easily accessed by applying an uniaxial macroscal strain tensor (the only non-zero component is $\\varepsilon_{xx}$). \n", + "\n", + "$$ u(L, y) = u(0, y) + L\\bar{\\varepsilon}_{xx}$$\n", + "\n", + "$$ u(0, L) = u(0, 0) = 0 $$\n", + "\n", + "$$ u(x, 0) = u(x, L) $$\n", + "\n", + "More details about these boundary conditions can be found in [1]. Using these boundary conditions, $C_{xxxx}$ can be estimated calculating the ratio of the averaged stress over the applied averaged strain.\n", + "\n", + "$$ C_{xxxx}^* \\cong \\bar{\\sigma}_{xx} / \\bar{\\varepsilon}_{xx}$$ \n", + "\n", + "In this example, $C_{xxxx}$ for 6 different types of microstructures will be estimated, using the `MKSHomogenizationModel` from `pymks`, and provides a method to compute $\\bar{\\sigma}_{xx}$ for a new microstructure with an applied strain of $\\bar{\\varepsilon}_{xx}$.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generation\n", + "\n", + "A set of periodic microstructures and their volume averaged elastic stress values $\\bar{\\sigma}_{xx}$ can be generated by importing the `make_elastic_stress_random` function from `pymks.datasets`. This function has several arguments. `n_samples` is the number of samples that will be generated, `size` specifies the dimensions of the microstructures, `grain_size` controls the effective microstructure feature size, `elastic_modulus` and `poissons_ratio` are used to indicate the material property for each of the\n", + "phases, `macro_strain` is the value of the applied uniaxial strain, and the `seed` can be used to change the the random number generator seed.\n", + "\n", + "Let's go ahead and create 6 different types of microstructures each with 200 samples with dimensions 21 x 21. Each of the 6 samples will have a different microstructure feature size. The function will return and the microstructures and their associated volume averaged stress values.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets import make_elastic_stress_random\n", + "\n", + "\n", + "sample_size = 200\n", + "grain_size = [(15, 2), (2, 15), (7, 7), (8, 3), (3, 9), (2, 2)]\n", + "n_samples = [sample_size] * 6\n", + "elastic_modulus = (310, 200)\n", + "poissons_ratio = (0.28, 0.3)\n", + "macro_strain = 0.001\n", + "size = (21, 21)\n", + "\n", + "X, y = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", + " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", + " macro_strain=macro_strain, seed=0)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The array `X` contains the microstructure information and has the dimensions \n", + "of `(n_samples, Nx, Ny)`. The array `y` contains the average stress value for \n", + "each of the microstructures and has dimensions of `(n_samples,)`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1200, 21, 21)\n", + "(1200,)\n" + ] + } + ], + "source": [ + "print(X.shape)\n", + "print(y.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lets take a look at the 6 types the microstructures to get an idea of what they \n", + "look like. We can do this by importing `draw_microstructures`. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_microstructures\n", + "\n", + "\n", + "X_examples = X[::sample_size]\n", + "draw_microstructures(X_examples[:3])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In this dataset 4 of the 6 microstructure types have grains that are elongated in either\n", + "the x or y directions. The remaining 2 types of samples have equiaxed grains with\n", + "different average sizes.\n", + "\n", + "Let's look at the stress values for each of the microstructures shown above.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Stress Values [ 0.25577371 0.24376877 0.2610072 0.25344437 0.24913381 0.2492148 ]\n" + ] + } + ], + "source": [ + "print('Stress Values'), (y[::200])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now that we have a dataset to work with, we can look at how to use the `MKSHomogenizationModel`to predict stress values for new microstructures.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MKSHomogenizationModel Work Flow\n", + "\n", + "The default instance of the `MKSHomogenizationModel` takes in a dataset and \n", + " - calculates the 2-point statistics \n", + " - performs [dimensionality reduction](http://en.wikipedia.org/wiki/Dimensionality_reduction) using [Prinicple Component Analysis](https://en.wikipedia.org/wiki/Principal_component_analysis) (PCA) \n", + " - and fits a [polynomial regression model](http://en.wikipedia.org/wiki/Polynomial_regression) model to the low-dimensional representation. \n", + "\n", + "This work flow has been shown to accurately predict effective properties in several examples [2][3], and requires that we specify the number of components used in dimensionality reduction and the order of the polynomial we will be using for the polynomial regression. In this example we will show how we can use tools from [sklearn](http://scikit-learn.org/stable/) to try and optimize our selection for these two parameters.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKSHomogenizationModel\n", + "\n", + "In order to make an instance of the `MKSHomogenizationModel`, we need to pass an instance of a basis (used to compute the 2-point statistics). For this particular example, there are only 2 discrete phases, so we will use the `PrimitiveBasis` from `pymks`. We only have two phases denoted by 0 and 1, therefore we have two local states and our domain is 0 to 1.\n", + "\n", + "Let's make an instance of the `MKSHomgenizationModel`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSHomogenizationModel\n", + "from pymks import PrimitiveBasis\n", + "\n", + "\n", + "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "model = MKSHomogenizationModel(basis=prim_basis, periodic_axes=[0, 1],\n", + " correlations=[(0, 0), (1, 1)])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the default values for the number of components and the order of the polynomial." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Default Number of Components 5\n", + "Default Polynomail Order 1\n" + ] + } + ], + "source": [ + "print('Default Number of Components'), (model.n_components)\n", + "print('Default Polynomail Order'), (model.degree)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "These default parameters may not be the best model for a given problem; we will now show one method that can be used to optimize them.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Optimizing the Number of Components and Polynomial Order\n", + "\n", + "To start with, we can look at how the variance changes as a function of the number of components.\n", + "In general for SVD as well as PCA, the amount of variance captured in each component decreases\n", + "as the component number increases.\n", + "This means that as the number of components used in the dimensionality reduction increases, the percentage of the variance will asymptotically approach 100%. Let's see if this is true for our dataset.\n", + "\n", + "In order to do this we will change the number of components to 40 and then\n", + "fit the data we have using the `fit` function. This function performs the dimensionality reduction and \n", + "also fits the regression model. Because our microstructures are periodic, we need to \n", + "use the `periodic_axes` argument when we `fit` the data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.n_components = 40\n", + "model.fit(X, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now look at how the cumlative variance changes as a function of the number of components using `draw_component_variance` \n", + "from `pymks.tools`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Me89o7/nAMTJabPiosKzyygre3jublEvHae7elE9uf43m7k0ZNmCIraMJIRoR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\n2L9/P1OnTqVjx47A1X+/rKwsvv/+e15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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_component_variance\n", + "\n", + "\n", + "draw_component_variance(model.dimension_reducer.explained_variance_ratio_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Roughly 93 percent of the variance is captured with the first 5 components. This means our model may only need a few components to predict the average stress.\n", + "\n", + "Next we need to optimize the number of components and the polynomial order. To do this we are going to split the data into test and training sets. This can be done using the [train_test_spilt](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) function from `sklearn`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(960, 441)\n", + "(240, 441)\n" + ] + } + ], + "source": [ + "from sklearn.cross_validation import train_test_split\n", + "\n", + "\n", + "flat_shape = (X.shape[0],) + (X[0].size,)\n", + "X_train, X_test, y_train, y_test = train_test_split(X.reshape(flat_shape), y,\n", + " test_size=0.2, random_state=3)\n", + "print(X_train.shape)\n", + "print(X_test.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We will use cross validation with the testing data to fit a number \n", + "of models, each with a different number \n", + "of components and a different polynomial order.\n", + "Then we will use the testing data to verify the best model. \n", + "This can be done using [GridSeachCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) \n", + "from sklearn.\n", + "\n", + "We will pass a dictionary `params_to_tune` with the range of\n", + "polynomial order `degree` and components `n_components` we want to try.\n", + "A dictionary `fit_params` can be used to pass the `periodic_axes` variable to \n", + "calculate periodic 2-point statistics. The argument `cv` can be used to specify \n", + "the number of folds used in cross validation and `n_jobs` can be used to specify \n", + "the number of jobs that are ran in parallel.\n", + "\n", + "Let's vary `n_components` from 1 to 11 and `degree` from 1 to 3.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "\n", + "params_to_tune = {'degree': np.arange(1, 4), 'n_components': np.arange(2, 12)}\n", + "fit_params = {'size': X[0].shape}\n", + "gs = GridSearchCV(model, params_to_tune, fit_params=fit_params).fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The default `score` method for the `MKSHomogenizationModel` is the [R-squared](http://en.wikipedia.org/wiki/Coefficient_of_determination) value. Let's look at the how the mean R-squared values and their \n", + "standard deviations change, as we varied the number of `n_components` and `degree`, using\n", + "`draw_gridscores_matrix` from `pymks.tools`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mzaUnfDKZ7Ll32EqbiPhFCQ8P19ux9f3/86233tLr8UeOHKm3Y6vL+uuLehoH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\n/vjjaDQa/vWvf9GtW7dar63GRtvNmzfNvkGV1yQQQgjRcKz9RNOWSC4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores_matrix\n", + "\n", + "\n", + "draw_gridscores_matrix(gs, ['n_components', 'degree'], score_label='R-Squared',\n", + " param_labels=['Number of Components', 'Order of Polynomial'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "It looks like we get a poor fit, when only the first and second component are used, and when we increase\n", + "the polynomial order and the components together. The models have a high standard deviation and \n", + "poor R-squared values for both of these cases.\n", + "\n", + "There seems to be several potential models that use 4 to 11 components, but it's difficult to see which model \n", + "is the best. Let's use our test data `X_test` to see which model performs the best.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Order of Polynomial 2\n", + "Number of Components 11\n", + "R-squared Value 0.999808062591\n" + ] + } + ], + "source": [ + "print('Order of Polynomial'), (gs.best_estimator_.degree)\n", + "print('Number of Components'), (gs.best_estimator_.n_components)\n", + "print('R-squared Value'), (gs.score(X_test, y_test))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the parameter range that we searched, we have found that a model with 2nd order polynomial \n", + "and 11 components had the best R-squared value. Let's look at the same values, using `draw_grid_scores`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OneK3v/0tt99+Ozt37uTLL7/EbrczatQorrrqqoTPwdatW3nnnXcoLy+nR48e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\nemVYUtJa1Tpl0vZJs8nouo4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BPs999Fi4rmuu6z2MMJ/5fr/X95tFYmIiERER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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores\n", + "\n", + "\n", + "gs_deg_1 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 1][1:]\n", + "gs_deg_2 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 2][1:]\n", + "gs_deg_3 = [x for x in gs.grid_scores_ \\\n", + " if x.parameters['degree'] == 3][1:]\n", + "\n", + "draw_gridscores([gs_deg_1, gs_deg_2, gs_deg_3], 'n_components', \n", + " data_labels=['1st Order', '2nd Order', '3rd Order'],\n", + " param_label='Number of Components', score_label='R-Squared')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we said, a model with a 2rd order polynomial and 11 components will give us the best result. Let's use the\n", + "best model from our grid scores.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = gs.best_estimator_\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Prediction using MKSHomogenizationModel\n", + "\n", + "Now that we have selected values for `n_components` and `degree`, lets fit the model with the data. Again, because\n", + "our microstructures are periodic, we need to use the `periodic_axes` argument.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.fit(X, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's generate some more data that can be used to try and validate our model's prediction accuracy. We are going to\n", + "generate 20 samples of all six different types of microstructures using the same \n", + "`make_elastic_stress_random` function.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "test_sample_size = 20\n", + "n_samples = [test_sample_size] * 6\n", + "X_new, y_new = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", + " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", + " macro_strain=macro_strain, seed=1)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's predict the stress values for the new microstructures. \n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y_predict = model.predict(X_new)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can look to see, if the low-dimensional representation of the \n", + "new data is similar to the low-dimensional representation of the data \n", + "we used to fit the model using `draw_components_scatter` from `pymks.tools`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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U3r9/P4MHD8bT05NOnTq1+LrVhxA1AoHALQ3lYmlt58y29uNpSl4ZZz+asZ3t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\nSQrVsHK8vI/MwhF9CQ1RMbJbjPR6W0Ii4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_components_scatter\n", + "\n", + "\n", + "draw_components_scatter([model.reduced_fit_data[:, :2], \n", + " model.reduced_predict_data[:, :2]],\n", + " ['Training Data', 'Test Data'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The predicted data seems to be reasonably similar to the data we used to fit the model\n", + "with. Now let's look at the score value for the predicted data.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R-squared 0.999731315593\n" + ] + } + ], + "source": [ + "from sklearn.metrics import r2_score\n", + "print('R-squared'), (model.score(X_new, y_new))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks pretty good. Let's print out one actual and predicted stress value for each of the 6 microstructure types to see how they compare.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Actual Stress [ 0.2494416 0.25038939 0.23247306 0.25266295 0.23964323 0.24461285]\n", + "Predicted Stress [ 0.2494777 0.25035744 0.23256064 0.25261754 0.23954181 0.24463568]\n" + ] + } + ], + "source": [ + "print('Actual Stress '), (y_new[::20])\n", + "print('Predicted Stress'), (y_predict[::20])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, we can also evaluate our prediction by looking at a goodness-of-fit plot. We\n", + "can do this by importing `draw_goodness_of_fit` from `pymks.tools`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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kkrKpBwWY7inIL9BgmVbCrMefe+ATCoikUq2IgyGE/fEbcl0pWqkMn8nTGTB4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\nlYpKpdKUh0QiwZUrV7otk5KSgpCQEM3vqampyMz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_goodness_of_fit\n", + "\n", + "\n", + "fit_data = np.array([y, model.predict(X)])\n", + "pred_data = np.array([y_new, y_predict])\n", + "draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Test Data'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the `MKSHomogenizationModel` has created a homogenization linkage for the effective stiffness for the 6 different microstructures and has predicted the average stress values for our new microstructures reasonably well.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##References\n", + "\n", + "[1] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", + "\n", + "[2] Çeçen, A., et al. \"A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells.\" Journal of Power Sources 245 (2014): 144-153. [doi:10.1016/j.jpowsour.2013.06.100](http://dx.doi.org/10.1016/j.jpowsour.2013.06.100)\n", + "\n", + "[3] Deshpande, P. D., et al. \"Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels.\" 2 World Congress on Integrated Computational Materials Engineering. John Wiley & Sons, Inc. [doi:10.1002/9781118767061.ch25](http://dx.doi.org/10.1002/9781118767061.ch25)\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/intro.ipynb b/notebooks/intro.ipynb index 2c876298..831f0766 100644 --- a/notebooks/intro.ipynb +++ b/notebooks/intro.ipynb @@ -6,7 +6,7 @@ "source": [ "# Meet PyMKS\n", "\n", - "In this short introduction, we will demonstrate the functionality of PyMKS to compute 2-point statistics in order to objectively quantify microstructures, predict effective properties using homogenization and predict local properties using localization. If you would like more technical details amount any of these methods please see the [theory section](THEORY.html)." + "In this short introduction, we will demonstrate the functionality in PyMKS. We will quantify microstructures using 2-point statistics, predict effective properties using homogenization and predict local properties using localization. If you would like more technical details about any of these methods please see the [theory section](THEORY.html)." ] }, { @@ -31,7 +31,7 @@ "source": [ "###Quantify Microstructures using 2-Point Statistics\n", "\n", - "Lets make two dual phase microstructures with different morphologies." + "Lets make two dual-phase microstructures with different morphologies." ] }, { @@ -44,6 +44,7 @@ "source": [ "from pymks.datasets import make_microstructure\n", "\n", + "\n", "X_1 = make_microstructure(n_samples=1, grain_size=(25, 25))\n", "X_2 = make_microstructure(n_samples=1, grain_size=(15, 95))\n", "\n", @@ -66,168 +67,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAnEAAAEaCAYAAAB+VgE8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3V1oZGcdx/Ffk+2YnaSt7MValzRkGBmL0yG6W4VUxKbS\n", - "i6KlCnZwtyhxbL2yF70p4rYbI9ulvuBNe5mmoV2hjLQWEUQCRXy5sFKxRLwY8rIvoUUKW1mYDNnu\n", - "ZrxY5niye86ceTlnzvM88/1AYDInOXPmvDzzn///PM9zS7PZbAoAAABWGUl7AwAAANA9gjgAAAAL\n", 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -258,9 +100,9 @@ "from pymks import PrimitiveBasis\n", "from pymks.stats import correlate\n", "\n", - "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", - "X_ = prim_basis.discretize(X)\n", - "X_corr = correlate(X_, periodic_axes=[0, 1])\n" + "\n", + "p_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "X_corr = correlate(X, p_basis, periodic_axes=[0, 1])\n" ] }, { @@ -286,996 +128,9 @@ }, { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAABDQAAAEsCAYAAAA8U+MrAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXuYFMW5/78zswvLxUUREAkg3uKCJsFECQpefwpGjx6N\n", - "QBRNRDQeTczlmMRf9DEq3jUqkih4uKiJF0DFBJ/kHE1+GBU1HiIalQ0IkVVQQG6CWZfdZXfm98dM\n", - "dVd1v/12dU/P7gz7fp6HZ5fu6uqq7urq3qrv+61ULpfLQRAEQRAEQRAEQRAEoYJId3YBBEEQBEEQ\n", - "BEEQBEEQoiIDGoIgCIIgCIIgCIIgVBwyoCEIgiAIgiAIgiAIQsUhAxqCIAiCIAiCIAiCIFQcMqAh\n", - "CIIgCIIgCIIgCELFIQMagiAIgiAIgiAIgiBUHDKgIQiCIAiCIAiCIAhCxSEDGoJQJB9//DH+8Y9/\n", - 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j9JkcKEblmqiHBvU0YDwS9JhoXg31HpVzw3CddOXciOehweX+0M8s9WoI8pwY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\nzuPK67lyj9BxqMwEFaZsoM+MgyuZWCxVjPN462H4mvvMqLG5SnBSy59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KcPN3u/mcnQEA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N7vTHIyEKYWVZGTtUTgEIOXGo2ZW/KUgtRv8wwvJUNIqfSxMTxMhfdGTC8h08V65E+OBjNJnDLG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\nbRqqqqrQ0tKCsrIyWSYvLw/9+/dHS0tL1EJtXYsPAMIeHckbQ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Rc6/QGZrVKhllX95bZltmEKta9iZRJq/sBErtRqxIycdlXUHpckT6wDyZwsj3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\n2lp873vfC3Sed955B0OGDMGaN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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2141,7 +185,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In this section of the intro, we are going to predict the effective stiffness for two phase microstructures using the `MKSHomogenizationModel`, but we could have chosen any other effective material property. \n", + "In this section of the intro, we are going to predict the effective stiffness for two-phase microstructures using the `MKSHomogenizationModel`, but we could have chosen any other effective material property. \n", "\n", "First we need to make some microstructures and their effective stress values to fit our model. Let's create 200 random instances 3 different types of microstructures, totaling to 600 microstructures." ] @@ -2156,6 +200,7 @@ "source": [ "from pymks.datasets import make_elastic_stress_random\n", "\n", + "\n", "grain_size = [(47, 6), (4, 49), (14, 14)]\n", "n_samples = [200, 200, 200]\n", "\n", @@ -2167,7 +212,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Once again, `X_train` is our microstructures. Throughout PyMKS `y` is used as either the prpoerty or the field we would like to predict. In this case `y_train` is the effective stress values for `X_train`. Let's look at one of each of the three different types of microstructures." + "Once again, `X_train` is our microstructures. Throughout PyMKS `y` is used as either the property, or the field we would like to predict. In this case `y_train` is the effective stress values for `X_train`. Let's look at one of each of the three different types of microstructures." ] }, { @@ -2179,167 +224,9 @@ "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA6EAAAEdCAYAAAD9zGENAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3V9oZOd9P/6Ptba6Hm3s7l4saSqLHVTU0IlQa7cFpZRG\n", - "prkwbXALragbWlSR9Kq5yE3/bWtVZWNCCr1JeucoIkmhKMQ1pVCKwIT+uahLSo1KL4T+2Fo1oYR1\n", - "imEktJal70X4zW+PdY5mZmeemTkzrxcIpHNmnnNmzr/nrWfOZx45Pz8/DwAAAOiBsX6vAAAAAKND\n", - "CAUAAKBnhFAAAAB6RggFAACgZ4RQAAAAekYIBQAAoGeEUAAAAHrm0WYPeOSRR3qxHiOt6D0+PDzs\n", - "8ZrQLUVfvzs5OdnjNem969evx9tvv/3Qz//Od75zYVre+/nUU09dmNbOMZPXZort0+nxXbQvtfr6\n", - "29kX89a1TOehFPtJq8tpZ9/Je5/v3r2b+9gU698rne677fjQhz700M/NO+fk6dU5o+z0aXqnn/vk\n", - "qG/nTs45Zff9738/Pv/5z8fh4WF87Wtfi7Gx/39M8+23344vfvGLcXp6GouLizE7O1vYjpFQAAAA\n", - "mrp27Vq8+OKLMTMzc2Heq6++Gi+88ELcvn07XnnllUvbaToSCgAAAI899lg89thjufPu3r3bCKdX\n", - "r16N4+PjePzxx3MfayQUAACAjpydnTV+r1QqUa/XCx9rJJTSa/U+MAB4WJ3eyzuM2nlPeHjt3F8N\n", - 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -2354,7 +241,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The `MKSHomogenizationModel` uses 2-point statistics, so we need provide a discretization method for the microstructures by providing a basis function. We will also specify which correlations we want." + "The `MKSHomogenizationModel` uses 2-point statistics, so we need to provide a discretization method for the microstructures by providing a basis function. We will also specify which correlations we want." ] }, { @@ -2367,8 +254,9 @@ "source": [ "from pymks import MKSHomogenizationModel\n", "\n", - "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", - "homogenize_model = MKSHomogenizationModel(basis=prim_basis,\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "homogenize_model = MKSHomogenizationModel(basis=p_basis, periodic_axes=[0, 1],\n", " correlations=[(0, 0), (1, 1), (0, 1)])\n" ] }, @@ -2387,7 +275,7 @@ }, "outputs": [], "source": [ - "homogenize_model.fit(X_train, y_train, periodic_axes=[0, 1])\n" + "homogenize_model.fit(X_train, y_train)\n" ] }, { @@ -2425,759 +313,28 @@ }, "outputs": [], "source": [ - "y_pred = homogenize_model.predict(X_test, periodic_axes=[0, 1])\n" + "y_pred = homogenize_model.predict(X_test)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "The `MKSHomogenizationModel` generates low dimensional representations of microstructures and regression methods to predict effective properties. Take a look at the low dimensional representations." + "The `MKSHomogenizationModel` generates low dimensional representations of microstructures and regression methods to predict effective properties. Take a look at the low-dimensional representations." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAi0AAAEmCAYAAACuxqAsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXlAVOX+/18zDKtssruvgIILbqUmZpmllkupmJZambe6\n", - "3r7V7d76WWnZduvbbf/anqWmJpapJO5J4pYrogwYibjjsDogArP9/hjnOMOcgWEHfV7/AOecec7n\n", - "POcwz/t8ns/n8yhMJpMJgUAgEAgEgmaOsqkNEAgEAoFAIHAGIVoEAoFAIBC0CIRoEQgEAoFA0CIQ\n", - "okUgEAgEAkGLQIgWgUAgEAgELQIhWgQCgUAgELQIhGhp4UydOpWFCxc2tRmNRlJSElOnTiUpKamp\n", - "TamW+Ph4pk6dilqtbmpTBM2IRYsWMXXqVPLy8praFIGgxaFqagPqk6lTpwKwatWqJrbEedLS0nj9\n", - "9ddttrm5ueHl5UVYWBjh4eEMGzaMzp07N42BzRSFQtHUJtQ78fHx/PzzzzbbVCoVgYGB9OrVi/vv\n", - "v5/g4OAmsu7G5bXXXiM9Pb3evjcs9/HVV18lKirKbv+N+OwKBI3FDSVaWjLBwcGMGDECAL1ej1ar\n", - "JSsri4SEBBISErjtttv429/+hoeHh83nPvzwQ9zd3ZvA4qbhlltuISIiAn9//6Y2pcGIiooiOjoa\n", - 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oDLRaLV26dGHixIk89dRTgHXgqA7pWUnCzhaTyUR6erriuLogvSs33nij3b6mKNM/e/Zs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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3185,10 +342,12 @@ } ], "source": [ - "from pymks.tools import draw_components\n", + "from pymks.tools import draw_components_scatter\n", + "\n", "\n", - "draw_components([homogenize_model.reduced_fit_data, homogenize_model.reduced_predict_data], \n", - " ['Training Data', 'Testing Data'])\n" + "draw_components_scatter([homogenize_model.reduced_fit_data[:,:2],\n", + " homogenize_model.reduced_predict_data[:,:2]], \n", + " ['Training Data', 'Test Data'])\n" ] }, { @@ -3200,479 +359,16 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAZQAAAEpCAYAAAC0kdQLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4lGXa+P3v9GSSTHqhQyCFBAQEESFApAjiUhWWsoCi\n", - "vKvrFv25Pq66gKiw7rO71sX+KEVE6UWQKoFQYiAQwEyCEUggQJj0STLJZNr7xziTDJlACKlwfY7D\n", - 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LXC6nc+fOZGVlSecagtJidnr8ftpDG+Kezly5cgWj0Sh1kNt1796d/fv3U1BQ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\nL+xYZaqHKNS6JQGujY0o4Pr5phaJGMfEYgS3nxo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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3682,11 +378,11 @@ "source": [ "from pymks.tools import draw_goodness_of_fit\n", "\n", - "fit_data = np.array([y_train, \n", - " homogenize_model.predict(X_train, periodic_axes=[0, 1])])\n", + "\n", + "fit_data = np.array([y_train, homogenize_model.predict(X_train)])\n", "pred_data = np.array([y_test, y_pred])\n", "\n", - "draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Testing Data'])\n" + "draw_goodness_of_fit(fit_data, pred_data, ['Training Data', 'Test Data'])\n" ] }, { @@ -3716,7 +412,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -3724,6 +420,7 @@ "source": [ "from pymks.datasets import make_elastic_FE_strain_delta\n", "\n", + "\n", "X_delta, y_delta = make_elastic_FE_strain_delta()\n" ] }, @@ -3731,12 +428,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Once again, `X_delta` is our microstructures and `y_delta` is our local strain fields. We need to discretize the microstructure again so we will also use the same basis function." + "Once again, `X_delta` is our microstructures and `y_delta` is our local strain fields. We need to discretize the microstructure again, so we will also use the same basis function." ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 16, "metadata": { "collapsed": false }, @@ -3744,8 +441,9 @@ "source": [ "from pymks import MKSLocalizationModel\n", "\n", - "prim_basis = PrimitiveBasis(n_states=2)\n", - "localize_model = MKSLocalizationModel(basis=prim_basis)\n" + "\n", + "p_basis = PrimitiveBasis(n_states=2)\n", + "localize_model = MKSLocalizationModel(basis=p_basis)\n" ] }, { @@ -3757,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 17, "metadata": { "collapsed": true }, @@ -3770,12 +468,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now that we have fit our model, we will create a random microstructure and compute its local strain field using finite element analysis. We will then try and reproduce the same strain field with our model." + "Now that we have fit our model, we will create a random microstructure and compute its local strain field, using finite element analysis. We will then try and reproduce the same strain field with our model." ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 18, "metadata": { "collapsed": true }, @@ -3783,6 +481,7 @@ "source": [ "from pymks.datasets import make_elastic_FE_strain_random\n", "\n", + "\n", "X_test, y_test = make_elastic_FE_strain_random()\n" ] }, @@ -3795,267 +494,16 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAoMAAAExCAYAAAAHhKhtAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xt8lOWd9/HvTEiYTA6SAEIhmZxwBENAUNQgghTUivog\n", - "IFHqiaVP11qrXXVtZRFCIN1dq60iiKeWZbGeUkSsVXEjntBilS5nowkJJIMogQQ5zAyQkDx/8DAS\n", - "cpPkutUh7f15v155vWDu6zvXNYcMP35zH1zNzc3NAgAAgCO5T/UCAAAAcOpQDAIAADgYxSAAAICD\n", - "UQwCAAA4GMUgAACAg1EMAgAAOBjFIAAAgINRDH7HZs+erWuvvfZULwMAAMBSl1O9gM7s+CLukUce\n", - 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SXfwvyf7jprSsLqb27zz1mrmP3G/aP9cTu/aaa7odl2euue/O/zDX3DLlJ+aa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\nvfafm8ZPvjTXDL3pUlP7gt6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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4065,6 +513,7 @@ "source": [ "from pymks.tools import draw_microstructure_strain\n", "\n", + "\n", "draw_microstructure_strain(X_test[0], y_test[0])\n" ] }, @@ -4077,374 +526,16 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAApkAAAEwCAYAAAD8VGrpAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtYVPXaP/43Aw4EqKgcFAHHIwIiHnFLntpo7cpKDJW2\n", - "Sf52u3bigR2W+ejjAdMs89HH3Jo7e3bSQYHMPJaKmIaKKR4Qz6iRoOIMHlBAQYb5/eGXyWFghluN\n", - "weX7dV1cypr7XvdaDCxuPuvwsTMYDAYQERERET1EKltvABEREREpD5tMIiIiInro2GQSERER0UPH\n", - "JpOIiIiIHjo2mURERET00LHJJCIiIqKHjk0mERERET10bDLrMa1WixEjRmDp0qX1Yj116VHcZiIi\n", - "Ivqdg6034HE0YsQIi6+PGTMGAwYM+MO3Q6vVYvz48ejfvz9iYmL+0FrW9hkAZsyYgcDAwD90O+q7\n", - "unxPiB419x5HPvnkE3h5eVUbFx8fj+PHjwMwP55WriMpKcksLz8/H3PmzIFWq0VERASioqIAABUV\n", - 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hCAkJAQBotVr07dsXq1atQkVFBVxdXbF9+3YUFBSYXKgDAGfOnEFBQQEqK++8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\nzTcxePBgAMCTTz6Jzp3/X3t3F9tUHcZx/Le2dKX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -4454,7 +545,6 @@ "source": [ "from pymks.tools import draw_strains_compare\n", "\n", - "\n", "y_pred = localize_model.predict(X_test)\n", "draw_strains_compare(y_test[0], y_pred[0])\n" ] diff --git a/notebooks/localization_cahn_hilliard_2D.ipynb b/notebooks/localization_cahn_hilliard_2D.ipynb new file mode 100644 index 00000000..152f591c --- /dev/null +++ b/notebooks/localization_cahn_hilliard_2D.ipynb @@ -0,0 +1,490 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Cahn-Hilliard Example\n", + "\n", + "This example demonstrates how to use PyMKS to solve the Cahn-Hilliard equation. The first section provides some background information about the Cahn-Hilliard equation as well as details about calibrating and validating the MKS model. The example demonstrates how to generate sample data, calibrate the influence coefficients and then pick an appropriate number of local states when state space is continuous. The MKS model and a spectral solution of the Cahn-Hilliard equation are compared on a larger test microstructure over multiple time steps." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Cahn-Hilliard Equation\n", + "\n", + "The Cahn-Hilliard equation is used to simulate microstructure evolution during spinodial decomposition and has the following form,\n", + "\n", + "$$ \\dot{\\phi} = \\nabla^2 \\left( \\phi^3 - \\phi \\right) - \\gamma \\nabla^4 \\phi $$\n", + "\n", + "where $\\phi$ is a conserved ordered parameter and $\\sqrt{\\gamma}$ represents the width of the interface. In this example, the Cahn-Hilliard equation is solved using a semi-implicit spectral scheme with periodic boundary conditions, see [Chang and Rutenberg](http://dx.doi.org/10.1103/PhysRevE.72.055701) for more details." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKS\n", + "\n", + "In this example the MKS equation will be used to predict microstructure at the next time step using \n", + "\n", + "$$p[s, 1] = \\sum_{r=0}^{S-1} \\alpha[l, r, 1] \\sum_{l=0}^{L-1} m[l, s - r, 0] + ...$$\n", + "\n", + "where $p[s, n + 1]$ is the concentration field at location $s$ and at time $n + 1$, $r$ is the convolution dummy variable and $l$ indicates the local states varable. $\\alpha[l, r, n]$ are the influence coefficients and $m[l, r, 0]$ the microstructure function given to the model. $S$ is the total discretized volume and $L$ is the total number of local states `n_states` choosen to use.\n", + "\n", + "The model will march forward in time by recursively replacing discretizing $p[s, n]$ and substituing it back for $m[l, s - r, n]$.\n", + "\n", + "###Calibration Datasets\n", + "\n", + "Unlike the elastostatic examples, the microstructure (concentration field) for this simulation doesn't have discrete phases. The microstructure is a continuous field that can have a range of values which can change over time, therefore the first order influence coefficients cannot be calibrated with delta microstructures. Instead, a large number of simulations with random initial conditions are used to calibrate the first order influence coefficients using linear regression.\n", + "\n", + "The function `make_cahn_hilliard` from `pymks.datasets` provides an interface to generate calibration datasets for the influence coefficients. To use `make_cahn_hilliard`, we need to set the number of samples we want to use to calibrate the influence coefficients using `n_samples`, the size of the simulation domain using `size` and the time step using `dt`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymks\n", + "from pymks.datasets import make_cahn_hilliard\n", + "\n", + "n = 41\n", + "n_samples = 400\n", + "dt = 1e-2\n", + "np.random.seed(99)\n", + "X, y = make_cahn_hilliard(n_samples=n_samples, size=(n, n), dt=dt)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `make_cahnHilliard` generates `n_samples` number of random microstructures, `X`, and the associated updated microstructures, `y`, after one time step `y`. The following cell plots one of these microstructures along with its update." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VHHYFiw27nZ7mwdjDKMHZ0AD8daZkd8NN47YZ4O1eONuW69PofeUscgDA31iR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\nyR3jyU3dAaC4fUczvvuV9TQnXmFXJLluQn74+XafnI/++QH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_concentrations\n", + "\n", + "draw_concentrations((X[0], y[0]), labels=('Input Concentration', 'Output Concentration'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibrate Influence Coefficients\n", + "\n", + "As mentioned above, the microstructures (concentration fields) does not have discrete phases. This leaves the number of local states in local state space as a free hyperparameter. In previous work it has been shown that, as you increase the number of local states, the accuracy of MKS model increases (see [Fast et al.](http://dx.doi.org/10.1016/j.actamat.2010.10.008)), but, as the number of local states increases, the difference in accuracy decreases. Some work needs to be done in order to find the practical number of local states that we will use. \n", + "\n", + "### Optimizing the Number of Local States\n", + "\n", + "Let's split the calibrate dataset into test and training datasets. The function `train_test_split` for the machine learning Python module [sklearn](http://scikit-learn.org/stable/) provides a convenient interface to do this. 80% of the dataset will be used for training and the remaining 20% will be used for testing by setting `test_size` equal to 0.2. The state of the random number generator used to make the split can be set using `random_state`. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import sklearn\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "split_shape = (X.shape[0],) + (X[0].size,)\n", + "X_train, X_test, y_train, y_test = train_test_split(X.reshape(split_shape), y.reshape(split_shape),\n", + " test_size=0.5, random_state=3)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are now going to calibrate the influence coefficients while varying the number of local states from 2 up to 20. Each of these models will then predict the evolution of the concentration fields. Mean square error will be used to compare the results with the testing dataset to evaluate how the MKS model's performance changes as we change the number of local states. \n", + "\n", + "First we need to import the class `MKSLocalizationModel` from `pymks`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSLocalizationModel\n", + "from pymks.bases import PrimitiveBasis\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next we will calibrate the influence coefficients while varying the number of local states and compute the mean squared error. The following demonstrates how to use scikit-learn's `GridSearchCV` to optimize `n_states` as a hyperparameter. Of course, the best fit is always with a larger value of `n_states`. Increasing this parameter does not overfit the data." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "GridSearchCV(cv=5, error_score='raise',\n", + " estimator=MKSLocalizationModel(basis=,\n", + " lstsq_rcond=2.2204460492503131e-12, n_jobs=None,\n", + " n_states=array([0, 1])),\n", + " fit_params={'size': (41, 41)}, iid=True, loss_func=None, n_jobs=1,\n", + " param_grid={'n_states': array([ 2, 3, 4, 5, 6, 7, 8, 9, 10])},\n", + " pre_dispatch='2*n_jobs', refit=True, score_func=None, scoring=None,\n", + " verbose=0)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.grid_search import GridSearchCV\n", + "\n", + "parameters_to_tune = {'n_states': np.arange(2, 11)}\n", + "p_basis = PrimitiveBasis(2, [-1, 1])\n", + "model = MKSLocalizationModel(p_basis, n_jobs=4)\n", + "gs = GridSearchCV(model, parameters_to_tune, cv=5, fit_params={'size': (n, n)})\n", + "gs.fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MKSLocalizationModel(basis=,\n", + " lstsq_rcond=2.2204460492503131e-12, n_jobs=None, n_states=10)\n", + "0.99999908222\n" + ] + } + ], + "source": [ + "print(gs.best_estimator_)\n", + "print(gs.score(X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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UlA4fZ3fCmjZtml3lOtJeodVqSUhIYNWqVTz22GMUFBSQm5vLn//85zZlJ06c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\nGhoyxW50Oh2mp6fN5Iv9ftbi7e2NK1euoKurCy0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores\n", + "\n", + "draw_gridscores(gs.grid_scores_, 'n_states',\n", + " score_label='R-squared', param_label='L-Number of Local States')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the accuracy of the MKS model monotonically increases, as we increase `n_states`, but accuracy doesn't improve significantly as `n_states` gets larger than signal digits. \n", + "\n", + "In order to save on computation costs, let's set (calibrate) the influence coefficients with `n_states` equal to 6, but realize that, if we need slightly more accuracy, the value can be increased." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = MKSLocalizationModel(basis=PrimitiveBasis(6, [-1, 1]))\n", + "model.fit(X, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here are the first 4 influence coefficients. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "draw_coeff(model.coef_[...,:4])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Predict Microstructure Evolution\n", + "\n", + "With the calibrated influence coefficients, we are ready to predict the evolution of a concentration field. In order to do this, we need to have the Cahn-Hilliard simulation and the MKS model start with the same initial concentration `phi0` and evolve in time. In order to do the Cahn-Hilliard simulation, we need an instance of the class `CahnHilliardSimulation`." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets.cahn_hilliard_simulation import CahnHilliardSimulation\n", + "np.random.seed(191)\n", + "\n", + "phi0 = np.random.normal(0, 1e-9, (1, n, n))\n", + "ch_sim = CahnHilliardSimulation(dt=dt)\n", + "phi_sim = phi0.copy()\n", + "phi_pred = phi0.copy()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to move forward in time, we need to feed the concentration back into the Cahn-Hilliard simulation and the MKS model." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "time_steps = 10\n", + "\n", + "for ii in range(time_steps):\n", + " ch_sim.run(phi_sim)\n", + " phi_sim = ch_sim.response\n", + " phi_pred = model.predict(phi_pred)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the concentration fields." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZxIvy9yWUIu9zkl/jHXvdpV6iUfevB+spCoszfmMMwONMyogXLM74jTEAv89Z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\nePXVV5FOGxU0AKxYsQK///3vcfvtt6O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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_concentrations_compare\n", + "\n", + "draw_concentrations((phi_sim[0], phi_pred[0]), labels=('Simulation', 'MKS'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MKS model was able to capture the microstructure evolution with 6 local states. \n", + "\n", + "##Resizing the Coefficients to use on Larger Systems \n", + "\n", + "Now let's try and predict a larger simulation by resizing the coefficients and provide a larger initial concentratio field." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m = 3 * n\n", + "model.resize_coeff((m, m))\n", + "\n", + "phi0 = np.random.normal(0, 1e-9, (1, m, m))\n", + "phi_sim = phi0.copy()\n", + "phi_pred = phi0.copy()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again we are going to march forward in time by feeding the concentration fields back into the Cahn-Hilliard simulation and the MKS model. " + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for ii in range(1000):\n", + " ch_sim.run(phi_sim)\n", + " phi_sim = ch_sim.response\n", + " phi_pred = model.predict(phi_pred)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the results." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PNjy8AcQo1TnRSDbpqpzD6b+l31eCacXMvL+8WSalu4w3Xhom2LTVZatmjdF4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wAAAg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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_concentrations_compare\n", + "\n", + "draw_concentrations_compare((phi_sim[0], phi_pred[0]), labels=('Simulation', 'MKS'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MKS model with resized influence coefficients was able to reasonably predict the structure evolution for a larger concentration field. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/localization_cahn_hilliard_Legendre_2D.ipynb b/notebooks/localization_cahn_hilliard_Legendre_2D.ipynb new file mode 100644 index 00000000..0f63390a --- /dev/null +++ b/notebooks/localization_cahn_hilliard_Legendre_2D.ipynb @@ -0,0 +1,739 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Cahn-Hilliard with Primtive and Legendre Bases\n", + "\n", + "This example uses a Cahn-Hilliard model to compare two different bases representations to discretize the microstructure. One basis representaion uses the primitive (or hat) basis and the other uses Legendre polynomials. The example includes the background theory about using Legendre polynomials as a basis in MKS. The MKS with two different bases are compared with the standard spectral solution for the Cahn-Hilliard solution at both the calibration domain size and a scaled domain size. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Cahn-Hilliard Equation\n", + "\n", + "The Cahn-Hilliard equation is used to simulate microstructure evolution during spinodial decomposition and has the following form,\n", + "\n", + "$$ \\dot{\\phi} = \\nabla^2 \\left( \\phi^3 - \\phi \\right) - \\gamma \\nabla^4 \\phi $$\n", + "\n", + "where $\\phi$ is a conserved ordered parameter and $\\sqrt{\\gamma}$ represents the width of the interface. In this example, the Cahn-Hilliard equation is solved using a semi-implicit spectral scheme with periodic boundary conditions, see [Chang and Rutenberg](http://dx.doi.org/10.1103/PhysRevE.72.055701) for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Basis Functions for the Microstructure Function and Influence Function\n", + "\n", + "In this example, we will explore the differences when using the\n", + "Legendre polynomials as the basis function compared to the primitive\n", + "(or hat) basis for the microstructure function and the influence coefficients.\n", + "\n", + "For more information about both of these basis please see the [theory section](THEORY.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKS\n", + "\n", + "###Generating Calibration Datasets\n", + "\n", + "Because the microstructure is a continuous field that can have a range of values and changes over time, the first order influence coefficients cannot be calibrated with delta microstructures. Instead, a large number of simulations with random initial conditions will be used to calibrate the first order influence coefficients using linear regression. Let's show how this is done.\n", + "\n", + "The function `make_cahnHilliard` from `pymks.datasets` provides a nice interface to generate calibration datasets for the influence coefficients. The function `make_cahnHilliard` requires the number of calibration samples, given by `n_samples`, and the size and shape of the domain, given by `size`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import pymks\n", + "from pymks.datasets import make_cahn_hilliard\n", + "\n", + "\n", + "length = 41\n", + "n_samples = 400\n", + "dt = 1e-2\n", + "np.random.seed(101)\n", + "size=(length, length)\n", + "X, y = make_cahn_hilliard(n_samples=n_samples, size=size, dt=dt)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The function `make_cahnHilliard` has generated `n_samples` number of random microstructures, `X`, and returned the same microstructures after they have evolved for one time step, given by `y`. Let's take a look at one of them." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AgGtToRi/bMh1ao7W7iWutty2BQDMUXLbhmlZL6g5Dc9rJsY//kJuPwIA3t1y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\npDcfsyngYyE7Oxu33Xabua2goAALF763TnNpaSlKS3nt4ve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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_concentrations\n", + "\n", + "\n", + "draw_concentrations((X[0], y[0]),('Calibration Input', 'Calibration Output'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Calibrate Influence Coefficients\n", + " \n", + "In this example, we compare the difference between using the primitive (or hat) basis and the Legendre polynomial basis to represent the microstructure function. As mentioned above, the microstructures (concentration fields) are not discrete phases. This leaves the number of local states in local state space `n_states` as a free hyperparameter. In the next section, we look to see what a practical number of local states for bases would be. \n", + " \n", + "### Optimizing the Number of Local States\n", + " \n", + "Below, we compare the difference in performance, as we vary the local state, when we choose the primitive basis and the Legendre polynomial basis.\n", + "\n", + "The `(X, y)` sample data is split into training and test data. The code then optimizes `n_states` between `2` and `11` and the two `basis` with the `parameters_to_tune` variable. The `GridSearchCV` takes an `MKSLocalizationModel` instance, a `scoring` function (figure of merit) and the `parameters_to_tune` and then finds the optimal parameters with a grid search." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.bases import PrimitiveBasis\n", + "from sklearn.grid_search import GridSearchCV\n", + "from sklearn import metrics\n", + "mse = metrics.mean_squared_error\n", + "from pymks.bases import LegendreBasis\n", + "from pymks import MKSLocalizationModel\n", + "from sklearn.cross_validation import train_test_split\n", + "\n", + "\n", + "train_split_shape = (X.shape[0],) + (np.prod(X.shape[1:]),)\n", + "\n", + "X_train, X_test, y_train, y_test = train_test_split(X.reshape(train_split_shape),\n", + " y.reshape(train_split_shape),\n", + " test_size=0.5, random_state=3)\n", + "\n", + "prim_basis = PrimitiveBasis(2, [-1, 1])\n", + "leg_basis = LegendreBasis(2, [-1, 1])\n", + "\n", + "params_to_tune = {'n_states': np.arange(2, 11),\n", + " 'basis': [prim_basis, leg_basis]}\n", + "Model = MKSLocalizationModel(prim_basis)\n", + "scoring = metrics.make_scorer(lambda a, b: -mse(a, b))\n", + "fit_params = {'size': size}\n", + "gs = GridSearchCV(Model, params_to_tune, cv=5,\n", + " fit_params=fit_params, n_jobs=3).fit(X_train, y_train)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The optimal parameters are the `LegendreBasis` with only 4 local states. More terms don't improve the R-squared value." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MKSLocalizationModel(basis=,\n", + " lstsq_rcond=2.2204460492503131e-12, n_jobs=None, n_states=4)\n", + "1.0\n" + ] + } + ], + "source": [ + "print(gs.best_estimator_)\n", + "print(gs.score(X_test, y_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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v2IDJ1oC/wo/Hps+9oWLb9hjTl65NkK43QzNy5Ejee+89jh07RkVFhfPyEUDP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\nLrXJ22lNv4XZs2fbfXgBIWxFEpgfsVGjRjUa8XX//v2UlJQ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_gridscores\n", + "\n", + "\n", + "lgs = [x for x in gs.grid_scores_ \\\n", + " if type(x.parameters['basis']) is type(leg_basis)]\n", + "pgs = [x for x in gs.grid_scores_ \\\n", + " if type(x.parameters['basis']) is type(prim_basis)]\n", + "\n", + "draw_gridscores([lgs, pgs], 'n_states', data_labels=['Legendre', 'Primitve'],\n", + " colors=['#f46d43', '#1a9641'], score_label='R-Squared', \n", + " param_label = 'L - Total Number of Local States')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see the `LegendreBasis` converges faster than the `PrimitiveBasis`. In order to further compare performance between the two models, lets select 4 local states for both bases." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Comparing the Bases for `n_states=4`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "prim_basis = PrimitiveBasis(n_states=4, domain=[-1, 1])\n", + "prim_model = MKSLocalizationModel(basis=prim_basis)\n", + "prim_model.fit(X, y)\n", + "\n", + "leg_basis = LegendreBasis(4, [-1, 1])\n", + "leg_model = MKSLocalizationModel(basis=leg_basis)\n", + "leg_model.fit(X, y)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's look at the influence coefficients for both bases.\n", + "\n", + "First, the `PrimitiveBasis` influence coefficients:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "draw_coeff(prim_model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the `LegendreBasis` influence coefficients:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(leg_model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, let's do some simulations with both sets of coefficients and compare the results." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Predict Microstructure Evolution\n", + "\n", + "In order to compare the difference between the two bases, we need to have the Cahn-Hilliard simulation and the two MKS models start with the same initial concentration `phi0` and evolve in time. In order to do the Cahn-Hilliard simulation, we need an instance of the class `CahnHilliardSimulation`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets.cahn_hilliard_simulation import CahnHilliardSimulation\n", + "\n", + "\n", + "np.random.seed(66)\n", + "phi0 = np.random.normal(0, 1e-9, ((1,) + size))\n", + "ch_sim = CahnHilliardSimulation(dt=dt)\n", + "phi_sim = phi0.copy()\n", + "phi_prim = phi0.copy()\n", + "phi_legendre = phi0.copy()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the inital concentration field." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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OkaGkrqvnlzHfqqkm9ZyXDrHvaQ62dKLz5s1DTEwM0tLScOXKlSbTow4dOoTr16+3WUk/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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_concentrations([phi0[0]], ['Initial Concentration'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In order to move forward in time, we need to feed the concentration back into the Cahn-Hilliard simulation and the MKS models." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "time_steps = 50\n", + "\n", + "for steps in range(time_steps):\n", + " ch_sim.run(phi_sim)\n", + " phi_sim = ch_sim.response\n", + " phi_prim = prim_model.predict(phi_prim)\n", + " phi_legendre = leg_model.predict(phi_legendre)\n", + " " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the concentration fields." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EREQGgMGUBvzW35U2NDSgoaGh220KhQJuuukmJJNJXHjhhc79VFZWorPzn992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\nVISQyQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_concentrations\n", + "\n", + "\n", + "draw_concentrations((phi_sim[0], phi_prim[0], phi_legendre[0]),\n", + " ('Simulation', 'Primative', 'Legendre'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "By just looking at the three microstructures is it difficult to see any differences. Below, we plot the difference between the two MKS models and the simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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XL0dlZSVKSkqQmpqK9evXY86cOZg/fz7cUnLrGWox8OviUc8iZfsmC9m+iU71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\nrmbtdSFk9UrHTDj2p5ZhVi8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primative mse = 5.28643237759e-23\n", + "Legendre mse = 7.11271905397e-30\n" + ] + } + ], + "source": [ + "from sklearn import metrics\n", + "from pymks.tools import draw_differences\n", + "\n", + "\n", + "mse = metrics.mean_squared_error\n", + "draw_differences([(phi_sim[0] - phi_prim[0]), (phi_sim[0] - phi_legendre[0])],\n", + " ['Simulaiton - Prmitive', 'Simulation - Legendre'])\n", + "\n", + "print 'Primative mse =', mse(phi_sim[0], phi_prim[0])\n", + "print 'Legendre mse =', mse(phi_sim[0], phi_legendre[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `LegendreBasis` basis clearly outperforms the `PrimitiveBasis` for the same value of `n_states`." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Resizing the Coefficients to use on Larger Systems \n", + "\n", + "Below we compare the bases after the coefficients are resized." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "big_length = 3 * length\n", + "big_size = (big_length, big_length)\n", + "prim_model.resize_coeff(big_size)\n", + "leg_model.resize_coeff(big_size)\n", + "\n", + "phi0 = np.random.normal(0, 1e-9, (1,) + big_size)\n", + "phi_sim = phi0.copy()\n", + "phi_prim = phi0.copy()\n", + "phi_legendre = phi0.copy()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the initial large concentration field." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9vVF8oMLNDBYhifLyMrNNyf7SHPGeLvZTc7EHm3vwedjIyPDYB/2NmIUULbPM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\nNtHtjMbXbUaWNL8Amj6W23FXEROHeSXidJyPIW1FyxJLloBSP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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_concentrations([phi0[0]], ['Initial Concentration'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the resized coefficients.\n", + "\n", + "First, the influence coefficients from the `PrimitiveBasis`." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(prim_model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, the influence coefficients from the `LegendreBases`." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(leg_model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Once again, we are going to march forward in time by feeding the concentration fields back into the Cahn-Hilliard simulation and the MKS models. " + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "for steps in range(time_steps):\n", + " ch_sim.run(phi_sim)\n", + " phi_sim = ch_sim.response\n", + " phi_prim = prim_model.predict(phi_prim)\n", + " phi_legendre = leg_model.predict(phi_legendre)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lafdn2FOYOlmBXWU1KZhLnf/O4rzarUoNcuwNW5IHV3tlqfxB2y0ZROuytorF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alduYfzRCkdHAvHXumYObTPPX5VZZWUDPaoi7fsJHpe02PjTHn7TryC+6wdHMOnjy2eA8Wmreg+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\nykoYFmofbef9arN0or0J/V1P7eAszeuh3jeg/M1gu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8bvVCML9kCe+6FSOoZiajOK32dl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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_concentrations((phi_sim[0], phi_prim[0], phi_legendre[0]), ('Simulation', 'Primiative', 'Legendre'))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Both MKS models seem to predict the concentration faily well. However, the Legendre polynomial basis looks to be better. Again, let's look at the difference between the simulation and the MKS models." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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NVb2eXIeXObfRfeL9urK4AdJEhSotZzaiAvU7eX8ckiXjSt6yOJtDq0SGtspb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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Primative mse = 4.43146361392e-23\n", + "Legendre mse = 3.97762730375e-29\n" + ] + } + ], + "source": [ + "draw_differences([(phi_sim[0] - phi_prim[0]), (phi_sim[0] - phi_legendre[0])], \n", + " ['Simulaiton - Primiative','Simulation - Legendre'])\n", + "\n", + "print 'Primative mse =', mse(phi_sim[0], phi_prim[0])\n", + "print 'Legendre mse =', mse(phi_sim[0], phi_legendre[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the resized influence coefficients, the `LegendreBasis` outperforms the `PrimitiveBasis` for the same value of `n_states`. The value of `n_states` does not necessarily guarantee a fair comparison between the two basis in terms of floating point calculations and memory used." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/localization_elasticity_2D.ipynb b/notebooks/localization_elasticity_2D.ipynb new file mode 100644 index 00000000..5a8b02ec --- /dev/null +++ b/notebooks/localization_elasticity_2D.ipynb @@ -0,0 +1,608 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Linear Elasticity in 2D\n", + "\n", + "##Introduction\n", + "\n", + "This example provides a demonstration of using PyMKS to compute the linear strain field for a two-phase composite material. The example introduces the governing equations of linear elasticity, along with the unique boundary conditions required for the MKS. It subsequently demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients for all strain fields. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared\n", + "with the finite element data for a large problem.\n", + "\n", + "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Elastostatics Equations\n", + "\n", + "For the sake of completeness, a description of the equations of linear elasticity is included. The constitutive equation that describes the linear elastic phenomena is Hook's law.\n", + "\n", + "$$ \\sigma_{ij} = C_{ijkl}\\varepsilon_{kl} $$\n", + "\n", + "$\\sigma$ is the stress, $\\varepsilon$ is the strain, and $C$ is the stiffness tensor that relates the stress to the strain fields. For an isotropic material the stiffness tensor can be represented by lower dimension terms which can relate the stress and the strain as follows.\n", + "\n", + "$$ \\sigma_{ij} = \\lambda \\delta_{ij} \\varepsilon_{kk} + 2\\mu \\varepsilon_{ij} $$\n", + "\n", + "$\\lambda$ and $\\mu$ are the first and second Lame parameters and can be defined in terms of the Young's modulus $E$ and Poisson's ratio $\\nu$ in 2D.\n", + "\n", + "$$ \\lambda = \\frac{E\\nu}{(1-\\nu)(1-2\\nu)} $$\n", + "\n", + "$$ \\mu = \\frac{E}{3(1+\\nu)} $$\n", + "\n", + "\n", + "Linear strain is related to displacement using the following equation.\n", + "\n", + "$$ \\varepsilon_{ij} = \\frac{u_{i,j}+u_{j,i}}{2} $$\n", + "\n", + "We can get an equation that relates displacement and stress by plugging the equation above back into our expression for stress.\n", + "\n", + "$$ \\sigma_{ij} = \\lambda u_{k,k} + \\mu( u_{i,j}+u_{j,i}) $$\n", + "\n", + "The equilibrium equation for elastostatics is defined as\n", + "\n", + "$$ \\sigma_{ij,j} = 0 $$\n", + "\n", + "and can be cast in terms of displacement.\n", + "\n", + "$$ \\mu u_{i,jj}+(\\mu + \\lambda)u_{j,ij}=0 $$\n", + "\n", + "In this example, a displacement controlled simulation is used to calculate the strain. The domain is a square box of side $L$ which has an macroscopic strain $\\bar{\\varepsilon}_{xx}$ imposed.\n", + "\n", + "In general, generating the calibration data for the MKS requires boundary conditions that are both periodic and displaced, which are quite unusual boundary conditions and are given by:\n", + "\n", + "$$ u(L, y) = u(0, y) + L\\bar{\\varepsilon}_{xx}$$\n", + "$$ u(0, L) = u(0, 0) = 0 $$\n", + "$$ u(x, 0) = u(x, L) $$" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKS\n", + "\n", + "###Calibration Data and Delta Microstructures\n", + "\n", + "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure, as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met, we can expect a mean absolute error of 2% or less, when comparing the MKS results with those computed using finite element methods [1]. \n", + "\n", + "Because we are using distinct phases and the contrast is low enough to only need the first order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first order influence coefficients [2]. \n", + "\n", + "Here we use the `make_delta_microstructure` function from `pymks.datasets` to create the two delta microstructures needed to calibrate the first order influence coefficients for a two-phase microstructure. The `make_delta_microstructure` function uses SfePy to generate the data" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 21\n", + "\n", + "from pymks.tools import draw_microstructures\n", + "from pymks.datasets import make_delta_microstructures\n", + "\n", + "X_delta = make_delta_microstructures(n_phases=2, size=(n, n))\n", + "draw_microstructures(X_delta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using delta microstructures for the calibration of the first order influence coefficients is essentially the same as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Generating Calibration Data\n", + "\n", + "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields with the boundary conditions given above.\n", + "\n", + "In this example, lets look at a two-phase microstructure with elastic moduli values of 100 and 120 and Poisson's ratio values of 0.3 and 0.3 respectively. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. Note that `make_elasticFEstrain_delta` does not take a number of samples argument as the number of samples to calibrate the MKS is fixed by the number of phases." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_delta\n", + "from pymks.tools import draw_microstructure_strain\n", + "\n", + "elastic_modulus = (100, 120)\n", + "poissons_ratio = (0.3, 0.3)\n", + "macro_strain = 0.02\n", + "size = (n, n)\n", + "\n", + "X_delta, y_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio,\n", + " size=size, macro_strain=macro_strain) \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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lypXKzc1VTU2NJCkcDmvp0qXq6OjQxIkTNXPmTO3Zs0crV66Uw+FQUVGRvv/9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\nADAD08S2mCYGAAAwGJ1BAAB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructure_strain(X_delta[0], y_delta[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calibrating First Order Influence Coefficients\n", + "\n", + "Now that we have the delta microstructures and their strain fields, we can calibrate the influence coefficients by creating an instance of the `MKSLocalizationModel` class. Because we have 2 phases we will create an instance of MKSLocalizationModel with the number of states `n_states` equal to 2. Then, pass the delta microstructures and their strain fields to the `fit` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSLocalizationModel\n", + "from pymks import PrimitiveBasis\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", + "model = MKSLocalizationModel(basis=p_basis)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first-order influence coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.fit(X_delta, y_delta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it, the influence coefficient have be calibrated. Let's take a look at them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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N++Jq/9e/xXd7gigd5YPYRvBs1ZQ4TzYpca4j3voAgARIsqu2rc7yQRIAAFxC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\n1CsZjhWu2k62K5KT8Zco2Y6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "draw_coeff(model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients for $l=0$ have a Gaussian-like shape, while the influence coefficients for $l=1$ are constant-valued. The constant-valued influence coefficients may seem superfluous, but are equally as important. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict the Strain Field for a Random Microstructure\n", + "\n", + "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two phase microstructure and compare it with the results from a finite element simulation. \n", + "\n", + "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VCAsLAwBMmjQJ8+bNQ0FBgcMYurq68PLLL+Ohhx6CVquF2WzG6dOnBx23zHMv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\nc3MHHDeXoYmIiEhyvO+W3EBwcDCWLl3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s1jWLHtlZz1VmQ/8vLp6efM52MZf/vzm4cDVV\nP7/cSffYX9hL1a8uDtI9utFO1fc6S+keJ859TdUvnzyTql8bXE7VR0QU7dy+xuBUP91jbeVSqr7f\nys1gjojYXTibqt/v5u6piIjFdu7ePahwPaJIfpYlXx5c7Z/PHZ+pm/th0jYAACVMcAEAoJSwCABA\nKWERAIBSwiIAAKWERQAASgmLAACU+gl9d5CRAIo4vAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_random\n", + "\n", + "np.random.seed(99)\n", + "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio, size=size, \n", + " macro_strain=macro_strain)\n", + "draw_microstructure_strain(X[0] , strain[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with.**\n", + "\n", + "Now to get the strain field from the `MKSLocalizationModel` just pass the same microstructure to the `predict` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "strain_pred = model.predict(X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally let's compare the results from finite element simulation and the MKS model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MV4X7+GrFBqH8ujX/K9xHVOzTQvnMf30j3MfR4nyhvL5B/ALy/xo3Rii/beO3\nwn3YtYMHix3NOdLq7CsJrwjv3/fxAKF80Yafhfuwd3OyHmpC8aT5VSzM0QiOM1mHfhTK+/bxE8oD\ngJfgOLP/q0zhPl6cITbOfLXiM+E+1n/yqVB+RNwzwn3s+ke69VAThaePC/dxXa8Tyj879r+E+9j+\n+XdCeTuZnVjeTizfFGcqbYszlURERCQJLCpti0UlERERSQKLSttiUUlERESSwKLStlhUEhERkSQ0\ntKOHnz+IWFQSERGRJHCm0rZYVBIREZEksKi0LRaVREREJAksKm2LRSURERFJAotK22JRSURERJLQ\nwKLSptrBWhpEREREdL/jTCURERFJAk9/25bVovKJUVFCO7yovSyUL9l5TCgPABD8pfEbFyKUd+nQ\nUSgPANr9KqG8nb/4Grveci+hfM21WuE+XAaJ9fFLgdja8ABwveyKUD7qZfG1affv3iuU79C3y230\nsU8oL3O2F+7jzVenCeUDo4cI95H3VbZwm7vtiejIVmcv14j9/gDAmR1HxRrcxv+XFBMGC+VdO7gI\n93Fm31mhvEOf3kL5rg+5C+UBoLbumlDeZbDYGAMAhwt/FcrXq8R/R6JeHi2UzxYcYwCgQ0BXsT72\niPch6yA2V/Q/L/9VuI+g6KFC+WNfiI2V+iv1Qvmm2ltRWVlZidTUVOTn58NgMGDAgAGYMmUK5HK5\n1bb19fVIS0tDdnY2amtroVAoMGnSJAQGBhozZWVlyMjIQH5+PqqqqtCxY0f06dMHEydOhJ+faZ3x\n008/ITc3F6dPn0ZVVRUiIyORmJhotu///Oc/2L9/P6qqquDi4oI+ffrg7bffhoOD+d8xzlQSERGR\nJLSnorKurg6LFy+Gk5MTpk+fDgBQKpVYtGgRli9fDmdnZ4vt165di99++w0vv/wyPD09kZGRgaVL\nl2LJkiVQKBQAgLy8PBQUFGDEiBHo06cPampqsHXrVqSkpGDx4sXo3fvmPyz379+PK1euICQkBDk5\nOWb71el0eP/996HRaDB+/Hj4+Pjg0qVLyM/PR0OD5YfLs6gkIiIiSTDczmmGe2T37t1Qq9VYuXIl\nvLxuzND36tULycnJyMrKwpgxY8y2LSkpwYEDBzBt2jRERUUBAIKCgjBz5kykp6dj9uzZAIBhw4bh\nmWeeMWnbv39/vPnmm9i5c6exmAWAlJQU2NnZAQCOHjV/9mb79u04c+YMVqxYga5db86uP/bYY1Y/\nM2/UISJttYn9AAAgAElEQVQiIkkwGBra7Mua3Nxc9OvXz1hQAoCnpycCAgKQm2v50rHc3FzY29sj\nIiLCuE0mkyEiIgLHjh2DTqcDAHTq1KlZWxcXF3h7e6O6utpke2NBac2uXbsQHh5uUlC2FmcqiYiI\nSBLa0yOFSktLERYW1my7j48PDh48aLGtSqWCl5cXnJycmrXV6XSoqKiAj49Pi221Wi1KS0sxYsQI\n4WOurKzEhQsX4OnpibVr1yInJwc6nQ6PPPIIXn75ZeNpd3M4U0lERESSYDAY2uzLmpqaGri6ujbb\n7ubmhpqaGotttVqt2baN75vz2WefAQBGjxa7+QwALly4AADYsmULNBoNZsyYgeTkZFy+fBmLFi1C\nZWWlxfacqSQiIiJJaE836tjC5s2bjddiNj3t3lqN378OHTpgzpw5xpnSPn364K233sKuXbswadIk\ns+1ZVBIREZEktHVRmZ6ebvxzcHAwgoODja9dXV1bnJHUarXGGUdzXF1dW5wVbJyhbKl9ZmYmlEol\n4uPjjTf3iGq8RjMgIMDk1Hu3bt3Qo0cP/Pnnnxbbs6gkIiIiSWjrojIuLs7se76+vigtLW22XaVS\nmb0esmnbw4cPo76+3qS4U6lUcHBwQPfu3U3y+/btw/r16/Hcc89h/Pjxgp/iJk9Pz2bXcYrgNZVE\nREQkCQ0GQ5t9WRMaGori4mKo1WrjNrVajRMnTmDoUMsPkA8NDYVerzd5nmTj65CQEJMHkB86dAhr\n1qxBdHQ0Jk+efBvftZscHBwwePBg/PHHH6irqzNur6ysRFlZGfr06WO5/R31TkRERNROtKdrKqOj\no5GRkYFly5YhPj4eAJCWlga5XI5Ro0YZcxqNBklJSYiJiUFMTAwAQKFQIDw8HBs3boRer4eHhwcy\nMzOh0WiQnJxsbFtYWIiVK1fCz88PUVFRKCoqMr7n6OgIf39/42uVSgWV6sbqf3V1ddBoNMa70IOC\ngvDQQw8BuDH7Om/ePPz973/HmDFjUF9fj2+++Qaurq549tlnLX5mFpVEREQkCe2pqHR2dsaCBQuQ\nmpqKVatWAYBxmcamq+mYu5s8MTERSqUSSqUSNTU1UCgUmDdvnsljfQoKCqDT6XDmzBnMnz/fpL2H\nhwc++ugj4+ucnBx88803xteFhYUoLCwEACxcuBBBQUEAbjy2aMGCBdi0aRP+9a9/wd7eHv3798fs\n2bONhac5LCqJiIhIElrzUPK2JJfLMWvWLIsZT09PpKWlNdvu5OSEhIQEJCQkmG0bGxuL2NjYVh2L\nSPbhhx/GwoULW5VtympRqWvQC+2w8mKVUF534ZpQHgDsnO2F8k8/9heh/IaVnwjlAaDDI92E8md+\nK7IeukXYpCFC+c5unYX7yMs8JJQ/W3pZuA+Xwd2th5r4efc+4T5etvCXsCWfLV8j3IdOc1UoH/nK\nfwn3UV51XihfsEPs5wcA4a8+LdzmbhMZZ6ouXRDff6XYz0rWQWyMAYBRj0YJ5TeuWifcR4dgD6F8\n8a9/COVj483fdGBOZ1fLMxe3Opb5i3AfJX+KjTOuQ72F+/j5x2yh/ORJ4teubfjH/wrlRccYAIia\nIvZsQnW15ecOtuT3HWI/w/C/io0xih4BQvmm2tNM5YOIM5VEREQkCSwqbYtFJREREUlCA1hU2hKL\nSiIiIpIEzlTaFotKIiIikgQWlbbFopKIiIgkgUWlbbGoJCIiIklgUWlbLCqJiIhIElqzfCLdOywq\niYiISBI4U2lbLCqJiIhIEtrbijoPGhaVREREJAmcqbQtFpVEREQkCSwqbctqUVl6XiW0w76+fYTy\nout4A4CdzE4oX6o+J5SvPyu+nnVQwjChfKCin3AfaRu/FMr/ddrrwn3oqsTWmm24LrY2PAA88ZrY\n2rRllRXCfXy16xuhvIOnq3AfdvZiv4fFpaeE+8g7WSCU7xzWU7iPPytKhdvcbarzrf872sfHX3j/\nouOMnb1MuI9zleVC+fqSS8J9DHw1Sijf17e3UP7rz78SygPAq//zmlD+uqZWuA9Dvdg4E/nGGOE+\nRMcZZdZ3wn04dhccZwT/XwcAJ/4sFsofK/5duI/Oj4qNM2cF64jqTo8I5ZtiUWlbnKkkIiIiSeDd\n37bFopKIiIgkgTOVtsWikoiIiCShvRWVlZWVSE1NRX5+PgwGAwYMGIApU6ZALpdbbVtfX4+0tDRk\nZ2ejtrYWCoUCkyZNQmBgoDFTVlaGjIwM5Ofno6qqCh07dkSfPn0wceJE+Pn5mezvp59+Qm5uLk6f\nPo2qqipERkYiMTGxxb4PHTqEb775BufOnYO7uzuio6Mxbtw4yGSWLw0Sv3CIiIiIqB0ytOF/1tTV\n1WHx4sUoLy/H9OnTkZSUhIqKCixatAh1dXVW269duxZ79uxBfHw85s6dC3d3dyxduhQlJSXGTF5e\nHgoKCjBixAjMmTMHU6dOxeXLl5GSkoLTp0+b7G///v3QaDQICQlBx44dzfZ79OhR/POf/8TDDz+M\nlJQUPPvss/j222/x1VfWr7nmTCURERFJQnuaqdy9ezfUajVWrlwJLy8vAECvXr2QnJyMrKwsjBlj\n/oaykpISHDhwANOmTUNUVBQAICgoCDNnzkR6ejpmz54NABg2bBieeeYZk7b9+/fHm2++iZ07d2L6\n9OnG7SkpKbCzu3Hz19GjR832/eWXXyIwMBCvv/66sd9r167hu+++w+jRo+Hu7m62LWcqiYiISBIa\nDA1t9mVNbm4u+vXrZywoAcDT0xMBAQHIzc212tbe3h4RERHGbTKZDBERETh27Bh0Oh0AoFOnTs3a\nuri4wNvbG9XV1SbbGwtKSyorK/Hnn3/iiSeeMNn+5JNPQq/XWyxGARaVREREJBEGg6HNvqwpLS2F\nr69vs+0+Pj5QqSw/ZkmlUsHLywtOTk7N2up0OlRUmH8EllarRWlpKXr2FH/EXONx3Xrcnp6ecHJy\nsnrcPP1NREREktCeTn/X1NTA1bX5s0nd3NxQU1Njsa1WqzXbtvF9cz777DMAwOjRYs+Ebrpfc31b\n6hdgUUlEREQS0Z6KSlvYvHmz8VrMpqfd74bWfG9ZVBIREZEktHVRmZ6ebvxzcHAwgoODja9dXV1b\nnJHUarXGGUdzXF1dUVlZ2WJbAC22z8zMhFKpRHx8vPHmHlGNM5QtHXdNTY3V42ZRSURERJLQ1ivq\nxMXFmX3P19cXpaXNl8FVqVTw8fGxuF9fX18cPnwY9fX1JtdVqlQqODg4oHv37ib5ffv2Yf369Xju\nuecwfvx4wU9h2i9w43rQvn37Grer1WrU19dbPW7eqENERESS0J5u1AkNDUVxcTHUarVxm1qtxokT\nJzB06FCrbfV6PXJycozbGl+HhITAweHmnOChQ4ewZs0aREdHY/LkybfxXbtJLpfDz88P2dnZJtuz\ns7Ph4OCAwYMHW2xvdaZSVVAidEBPhY0Qyju4dxDKA4C9u7NQfl/OfqF8x2DrT7q/1ZnDx4XyI4Y+\nYT10i8l/fUUo/7//WC3ch10Hsclrh46Own0E+vUTyvfvHWg9dIv1H30ilDdc1wv3YdBbf6REUy9M\nfU64j9WL/imUn/P3+cJ9/L+/fSDc5m4r/aOk1dm/hD4pvH97wXHGoav4uLTvYLb1UBMd+3sI91H8\nS4FQftjAx4Ty8VMmCeUBYN0/PxbKywTHGACwcxEbZ/r1eli4j0BFgFB+w9pPhftoqBccZ3RiYwwA\njJ9i/tmHLVmz5F/Cfby9ZJ5QfsXyfwjla1wvCuWbak/XVEZHRyMjIwPLli1DfHw8ACAtLQ1yuRyj\nRo0y5jQaDZKSkhATE4OYmBgAgEKhQHh4ODZu3Ai9Xg8PDw9kZmZCo9EgOTnZ2LawsBArV66En58f\noqKiUFRUZHzP0dER/v7+xtcqlcp493ZdXR00Gg0OHjwI4MazKB966CEAwIsvvoi///3v+OSTTzBs\n2DCcOXMG3333HZ599ll07tzZ4mfm6W8iIiKShPZUVDo7O2PBggVITU3FqlWrAMC4TKOz883JMXMz\nn4mJiVAqlVAqlaipqYFCocC8efOgUCiMmYKCAuh0Opw5cwbz55tOKnh4eOCjjz4yvs7JycE333xj\nfF1YWIjCwkIAwMKFCxEUFAQAGDx4MGbNmoWvv/4ae/fuhbu7O1544QW88MILVj8zi0oiIiKShPZU\nVAI3TifPmjXLYsbT0xNpaWnNtjs5OSEhIQEJCQlm28bGxiI2NrZVxyKSDQsLQ1hYWKuyTbGoJCIi\nIkkwtGKlG7p3WFQSERGRJDSgfc1UPmhYVBIREZEktLfT3w8aFpVEREQkCSwqbYtFJREREUkCi0rb\nYlFJREREksCi0rZYVBIREZEktPUyjWSKRSURERFJAmcqbYtFJREREUkCi0rbslpURj87ylrExLqP\n/lco79jTTSgPAA1XdUL5l8e/KJT/7ON1QnkAsH9IbD3yz7/bJNzH2oUrxfpw/Ey4jwZ1vVC+55hg\n4T5+yN0rlHeQ2Qv3oXhMbB3fU1l5wn1AcPDSXq0R7qJBJ7ZWcOXFKuE+dOfFj+tui356ZKuzn60W\n//vp5NtJKC86xgDApLHxQvmNt7F2tExwnNm0VSmU/+fsvwvlAeBLx8+F8vqKOuE+fJ8fIJTfc0Rs\nHXYAcLQXm2PxD3tEuI/iXUfFGtxGgVRbd1Uo33BdcD1yANVXxNbmri/TCuX1l8X+P9QUH35uW5yp\nJCIiIkngTKVtsagkIiIiSWBRaVssKomIiEgSePe3bbGoJCIiIkngTKVtsagkIiIiSWhvRWVlZSVS\nU1ORn58Pg8GAAQMGYMqUKZDL5Vbb1tfXIy0tDdnZ2aitrYVCocCkSZMQGBhozJSVlSEjIwP5+fmo\nqqpCx44d0adPH0ycOBF+fn7N9vnDDz9g+/bt0Gg08PDwwOjRozFqlPkbss+fP49Zs2bh+vXr+PDD\nD+Hl5WXxmGVWPxURERHRfcDQhv9ZU1dXh8WLF6O8vBzTp09HUlISKioqsGjRItTVWX8Kwtq1a7Fn\nzx7Ex8dj7ty5cHd3x9KlS1FSUmLM5OXloaCgACNGjMCcOXMwdepUXL58GSkpKTh9+rTJ/n744Qes\nW7cOjz/+OFJSUvD444/j008/RWZmptlj+PTTT+Hq6mr1WBuxqCQiIiJJMBgMbfZlze7du6FWq/HO\nO+8gNDQUoaGhmD17NiorK5GVlWWxbUlJCQ4cOIBXXnkFf/nLX9C/f3/MnDkTcrkc6enpxtywYcPw\nj3/8A2PHjkVwcDDCwsIwb948ODo6YufOncacXq+HUqlEZGQk4uPjERQUhPj4eERFRSEtLQ16ffNH\nS+3fvx8lJSV4/vnnW/39Z1FJREREktBgMLTZlzW5ubno16+fySljT09PBAQEIDc312pbe3t7RERE\nGLfJZDJERETg2LFj0OluPEu3U6fmz+B1cXGBt7c3qqurjduKiopw5coVPPHEEybZJ598ElqtFseP\nHzfZrtVq8fnnnyMhIQEuLi5WP6vxGFudJCIiImrH2tNMZWlpKXx9fZtt9/HxgUqlsthWpVLBy8sL\nTk5OzdrqdDpUVFSYbavValFaWoqePXuaHAuAZsfj4+MDADh37pzJ9i+++AI9e/ZsVoRawxt1iIiI\nSBLa04o6NTU1LV6P6ObmhpoayyuZabVas20b3zfns89urKY3evRok/01bW9pf3/88Qeys7OxbNky\ni8fYEhaVREREJAnt7e7vtrZ582YcOHAA06ZNs3qndkt0Oh0++eQTjB492mSms7VYVBIREZEktKei\n0tXVtcUZSa1W22zGsKW2lZWVLbYFms84AkBmZiaUSqXxBpymms5Iuru7m93fjh07UFtbi2effdZ4\n7I13ql+9ehVXr15Fx44dzR631aLyh227rEVM6C9Zv02+qSfGRgvlAeDI8aNC+U27vhbKO8jNf8PM\nsbMXuzzVs0d34T7Sd/9HKD/6pXHCfWz/92ahfGVRmXAfot8r2NsJ99GKpz2YGPs/ccJdbPtU7Pcq\nbcc3wn24DhH7PfkyU7wPJ39366F7TGSc0V+8Jrz/4WP/Syh/tChPuI+vsr4VyjvIW3/xu5Hg3wW5\nl6dQfvPe7UJ5AHgmfqxQ/vsvxMYxANCcOGc91ISdg/jtAnaC39vbqV3GvB4rlN+xTvzvc9pOsTau\nQ8X/XyT6u+7s31kob9+1g1C+qbYuKpveiR0cHIzg4GDja19fX+O1jE2pVCrjtYzm+Pr64vDhw6iv\nrze5rlKlUsHBwQHdu5v+3Pbt24f169fjueeew/jx45vtr7G/0tJSk6Ky8drOptdWXrx4EW+88Uaz\nfcyZMwcKhQIffPCB2ePmTCURERFJQlsv0xgXZ35CIjQ0FP/+97+hVqvh6XnjH3hqtRonTpzApEmT\nLO43NDQUX3/9NXJychAZGQngxmOBcnJyEBISAgeHm+XboUOHsGbNGkRHR2Py5Mkt7i8gIACdOnVC\ndnY2BgwYYNyenZ0NNzc3BAQEAADGjRvXbJbz6NGj2LJlC5KSktCjRw+Lx82ikoiIiCShPZ3+jo6O\nRkZGBpYtW4b4+HgAQFpaGuRyuckqNhqNBklJSYiJiUFMTAwAQKFQIDw8HBs3boRer4eHhwcyMzOh\n0WiQnJxsbFtYWIiVK1fCz88PUVFRKCoqMr7n6OgIf39/AIC9vT0mTpyITz/9FF27dsWAAQPw+++/\n48cff8TUqVNhb28PAOjRo0ezwlGtVgMA+vbta/U6TRaVREREJAntqah0dnbGggULkJqailWrVgGA\ncZlGZ2dnY87cI4oSExOhVCqhVCpRU1MDhUKBefPmQaFQGDMFBQXQ6XQ4c+YM5s+fb9Lew8MDH330\nkfH1qFGjYGdnh23btmHbtm2Qy+WYOnUqnnrqqbv2mVlUEhERkSS0p6ISAORyOWbNmmUx4+npibS0\ntGbbnZyckJCQgISEBLNtY2NjERvb+mt1R44ciZEjR7Y6DwBRUVHNTombw6KSiIiIJKG9FZUPGhaV\nREREJAkG0Ud/0F3FopKIiIikgTWlTbGoJCIiImng6W+bEn9CLBERERHRLThTSURERJLAiUrbYlFJ\nRERE0sCq0qasFpWG63qhHcoc7YXyY4c/I5QHgIM5OcJtROgviq1fDgANNfVC+cixE4T7qLpULZS/\nXKsV7qP38CCh/Knd4msk2z/kbD3UhK76qnAf46e9JJTfuuk74T4MOrHBq0/vPsJ9XK65IpTv5OIm\n3EdB7i/Cbe42kXFG5iT+b+FnH48Wyv9y6N5/T3S3sYZ5g1ZsnHlydPM1gC25cPmiUB4ArgiOM32e\n6C/cx8kfjgnl7TuLjTEAoLsgNs6MTxQbYwBg65di44xB3yDcR78+fYXyoj8/AHDt6CqUzz8k9v/s\nhr7i/w+m9oEzlURERCQNnKi0KRaVREREJA08/W1TvPubiIiIiO4Yi0oiIiIiumM8/U1ERETSwLPf\nNsWikoiIiCTBwGsqbYqnv4mIiIjojnGmkoiIiKShnU1UVlZWIjU1Ffn5+TAYDBgwYACmTJkCuVxu\ntW19fT3S0tKQnZ2N2tpaKBQKTJo0CYGBgSa57du34/fff8fp06dx6dIlxMTEIDY2ttn+6urq8NVX\nXyEnJwdarRbe3t4YN24chg8fbpJraGjAzp078eOPP0KtVsPFxQV9+/ZFXFwcevXqZfGYOVNJRERE\n0mBowy8r6urqsHjxYpSXl2P69OlISkpCRUUFFi1ahLo66w94X7t2Lfbs2YP4+HjMnTsX7u7uWLp0\nKUpKSkxyu3fvxpUrVxAWFgYAsLOza3F/y5cvx08//YTx48djzpw5CAgIwKpVq5CdnW2SUyqV+OKL\nLxAWFoa5c+diypQpOH/+PBYtWoQLFy5YPGbOVBIREZFEtJ+pyt27d0OtVmPlypXw8vICAPTq1QvJ\nycnIysrCmDFjzLYtKSnBgQMHMG3aNERFRQEAgoKCMHPmTKSnp2P27NnG7IoVKwDcmGHMyspqcX/H\njx9HXl4eEhMTERkZCQAYOHAgqqqq8MUXX2DYsGGQyW7MM+7duxcRERGYOHGisb2fnx9mzJiBX3/9\nFSNHjjR73JypJCIiImloRzOVubm56Nevn7GgBABPT08EBAQgNzfXalt7e3tEREQYt8lkMkRERODY\nsWPQ6XTNP7qFm5SKiooAAIMHDzbZPmjQIFy8eBHFxcXGbTqdDi4uLia5xtfWboRiUUlERETS0I6K\nytLSUvj6+jbb7uPjA5VKZbGtSqWCl5cXnJycmrXV6XSoqKiwfgBNNM5COjiYnqBufF1aWmrc9vTT\nTyM7Oxu5ubmora3F+fPn8emnn6Jbt24IDw+32I/V098Rz0YKHfjejTuF8tsP7BLKA4C7dzehfNeH\nugjl/8iw/C+Ilhh0DUJ5B3vxKw+qr1y8530UbRb77P5jBwn3Ye56D3POfJ8n3MeOrduF8o7dXYX7\ncOzhJpQ/fa5EuI8G7XWhvMZe7HsLAPadnKyH7jGRcWbvBrExBgAyD/0olJf38BTuw71TZ6F8wc5D\nwn0YrouNM/aCY8DlmstC+dvp48R34p/74fFDhfJ2EP97cHL7b0L5HVu2Cffh6CU2zjgJjjEAcFJ1\nRiivv1Iv3Iedg9j3V/aQ2Bhj1+FOrsxrP6e/a2pq4Ora/Gfu5uaGmpoai221Wq3Zto3vi+jZsyeA\nGzOWgwbd/P924wxm0/3FxcXB3t4ey5cvN85Ment7Y+HChcb+zeFMJREREUmCwdB2X/eTkJAQ9OzZ\nExs2bEBRURG0Wi327NmDn3/+GcDNmUwAyMzMxObNmzFhwgQsXLgQM2bMQMeOHbFkyRJUV1db7IdF\nJREREUlDOzr97erq2uKMpFartTrj5+rq2uJsZOM2a+1vJZPJMHPmTDg7O2P+/PmYOnUq0tLS8NJL\nLwEA3N3djftPTU3F2LFjERsbi6CgIDz++ONISUnB5cuXsXXrVov98O5vIiIikoi2nUJMT083/jk4\nOBjBwcHG176+vibXKjZSqVTw8fGxuF9fX18cPnwY9fX1JtdVqlQqODg4oHv37sLH6uPjg2XLlqGy\nshLXrl1Djx49cPDgQQDAI488AgAoKyuDTqdD7969Tdq6ubnBy8sLZWVlFvvgTCURERFJQxvPVMbF\nxRm/mhaUABAaGori4mKo1WrjNrVajRMnTmDoUMvXCYeGhkKv1yMnJ8e4rfF1SEhIsxtuRMjlcvj4\n+KChoQEZGRkICQmBp+eN68gbZyxPnTpl0kar1aKiogJduli+R4UzlURERER3WXR0NDIyMrBs2TLE\nx8cDANLS0iCXyzFq1ChjTqPRICkpCTExMYiJiQEAKBQKhIeHY+PGjdDr9fDw8EBmZiY0Gg2Sk5NN\n+jl16hQ0Gg0aGm7cyFdaWmqcgRwyZIhxpnPz5s3w8PBAly5dUFlZiV27dqGqqgrvvfeecV+enp4Y\nMmQItm7dCjs7OwQGBuLKlSvYunUr9Ho9nnrqKYufmUUlERERSUM7uoHG2dkZCxYsQGpqKlatWgUA\nxmUanZ2djTmDwdDi8x8TExOhVCqhVCpRU1MDhUKBefPmQaFQmOR27dqFvXv3Gl8fPHjQWFSuXr3a\nuCRkXV0dlEolqqur4eLigsGDB+Ptt99G165dTfY3Y8YMbNu2DQcOHMC2bdvg4uICf39/vPbaa81O\ni9+KRSURERFJQzu7LVsul2PWrFkWM56enkhLS2u23cnJCQkJCUhISLDYPjExEYmJiVaPJT4+3jhj\naomTkxMmTJiACRMmWM3eikUlERERSUL7KikfPCwqiYiISBpYVdoUi0oiIiKShnZ2+vtBw0cKERER\nEdEdszpTuU9wnd3ov44Vyu/btlsoDwDOvd2F8hc1F4TyE96YJJQHgPR/bBTKOzuKr7d8ePfPQnnZ\nbazp7DLYSyh/vrRcuA80iP1L0rmP2M8bAHSaWqF88NBg66Fb1NXXCeWv1l0T7qNkj9h6xP4vDBHu\nw7v/QOE2d9ve9TtanR35P+PE97/9B6F8xz5drYduUXVeI5Qf97r1C+Zv9e0/PhfKi44zvwiOMQAg\n6+QolHcZIv7Q5rKz58Qa6MVnq5z7Wn7+3q1ExxgAGPDoAKF8Xb34utxX68XGmVNZucJ9PBz3qFC+\ne/8QoXwvb8t3GFvEiUqb4ulvIiIikgae/rYpnv4mIiIiojvGmUoiIiKSBk5U2hSLSiIiIpKEllam\nobbD099EREREdMc4U0lERETSwIlKm2JRSURERNLAotKmWFQSERGRRLCqtCUWlURERCQNrCltikUl\nERERSQOLSptiUUlERESSYGhnVWVlZSVSU1ORn58Pg8GAAQMGYMqUKZDL5Vbb1tfXIy0tDdnZ2ait\nrYVCocCkSZMQGBhoktu+fTt+//13nD59GpcuXUJMTAxiY2Ob7a+urg5fffUVcnJyoNVq4e3tjXHj\nxmH48OHGzNWrV7Ft2zYcPXoUFRUVMBgM8PHxwdixY/Hoo9aX57ReVDrbW400lZ3xo1D+dkx+Jk4o\n39G5g1B+TeonQnkAcFZ0FsqvT90g3EdDvV4orz93RbiP6f93hlD+w0XLhfuwcxT7nRo95QXhPq4J\nrrP9w/qtwn3Ez/pvofxX//hMuI+Ivz4jlD+oFP/7N/ydx4Xb3G12HVv/79t9GXvu4ZHcED9S/HfO\n2clZKP/pJvHfB+fe7kL5jf9OFco3XNMJ5QFAf0ns79q0OcnCfax+759CedExBgDG/PcEobzoGAMA\nWZ9uEcq/+Parwn18uXy9UP7JaWOE+ziw6QehfMQ7U4Xy3TqJrcNuoh3VlHV1dVi8eDGcnJwwffp0\nAIBSqcSiRYuwfPlyODtbHjPWrl2L3377DS+//DI8PT2RkZGBpUuXYsmSJVAoFMbc7t274eLigrCw\nMGRlZcHOzq7F/S1fvhzFxcWIj49Hjx498Msvv2DVqlUwGAx44oknAAAajQZZWVmIjIxEXFwcZDIZ\n9opKHg8AABhvSURBVO/fj+XLl+PVV1/F008/bfGYOVNJRERE0tCOisrdu3dDrVZj5cqV8PLyAgD0\n6tULycnJyMrKwpgx5gv6kpISHDhwANOmTUNUVBQAICgoCDNnzkR6ejpmz55tzK5YsQIA0NDQgKys\nrBb3d/z4ceTl5SExMRGRkZEAgIEDB6KqqgpffPEFhg0bBplMBi8vL6xevRpOTk7Gto25LVu2WC0q\n+fBzIiIikghDG35Zlpubi379+hkLSgDw9PREQEAAcnNzrba1t7dHRESEcZtMJkNERASOHTsGna75\nWQVLqwkVFRUBAAYPHmyyfdCgQbh48SKKi4sBAM7OziYFZSN/f39UV1dbPGaARSURERFJRfupKVFa\nWgpfX99m2318fKBSqSy2ValU8PLyalbg+fj4QKfToaKiwvoBNCGT3Sj3HBxMT1A3vi4tLbXY/o8/\n/kDPnj2t9yN0VERERETtVTsqKmtqauDq6tpsu5ubG2pqaiy21Wq1Zts2vi+isSBsnLFs1Pja0v5+\n+OEHnDx5EuPGjbPaD4tKIiIikoh2VFW2IyEhIejZsyc2bNiAoqIiaLVa7NmzBz///DOAmzOZtyoo\nKMCGDRsQGRlpcpe4OSwqiYiISBraUU3p6ura4oykVqs1zjhaatvS7GHjNmvtbyWTyTBz5kw4Oztj\n/vz5mDp1KtLS0vDSSy8BANzdmz9Z4uTJk1i2bBkGDBiAN954o1X98O5vIiIikgQL96rcE+np6cY/\nBwcHIzg42Pja19e3xWsVVSoVfHx8LO7X19cXhw8fRn19vcl1lSqVCg4ODujevbvwsfr4+GDZsmWo\nrKzEtWvX0KNHDxw8eBAA8Mgjj5hkz549i6VLl8Lf3x+zZs0yO5N5K85UEhEREd2GuLg441fTghIA\nQkNDUVxcDLVabdymVqtx4sQJDB061OJ+Q0NDodfrkZOTY9zW+DokJKTZDTci5HI5fHx80NDQgIyM\nDISEhMDT09P4fnl5Od577z10794dc+fOhaOjY6v3zZlKIiIikoa2nqq0IDo6GhkZGVi2bBni4+MB\nAGlpaZDL5Rg1apQxp9FokJSUhJiYGMTExAAAFAoFwsPDsXHjRuj1enh4eCAzMxMajQbJyaYLCJw6\ndQoajQYNDQ0AbtzJ3TgDOWTIEONM5+bNm+Hh4YEuXbqgsrISu3btQlVVFd577z3jvi5duoQlS5ZA\nr9cjNjYWZ8+eNemrd+/eFgtaFpVEREQkDe2npoSzszMWLFiA1NRUrFq1CgCMyzQ2XU3HYDC0+IzJ\nxMREKJVKKJVK1NTUQKFQYN68eSar6QDArl27sHfvXuPrgwcPGovK1atXG5eErKurg1KpRHV1NVxc\nXDB48GC8/fbb6Nq1q7GtSqVCZWUlAOCDDz5odkxN99cSFpVERERE94BcLsesWbMsZjw9PZGWltZs\nu5OTExISEpCQkGCxfWJiIhITE60eS3x8vHHG1Jzg4OAWj6W1WFQSERGRNLSj098PIqtFpX2n5sv1\nWKK7WCeUHzVZfDH7vJMFQnnvbl7WQ02M/MtIoTwAbP843XqoCXu31l/4amzTuYNQXn+1+TJO1lRf\nuSjWR9U14T56x1q+QPlWKvU54T4U3r2E8g4eHYX72LJ7h1C+Y7D5Uwbm5J0Q+1136iH2mAkA+G7H\nf4TyHz0+23pIkL1b68cZXbXYGAOIjzP5pwqF+/CWi40z0SOihfvYvlpsBkF0/BYdYwBAJzjOiI4x\nAKDTXBXKB7z4mHAf5zTlQnlfzx7CfYiOM1v2bBfuo2N/D6H80T/yhPtw6tH8odyWiI4xHQaOAYLH\nCrUxYk1pU5ypJCIiIklgTWlbLCqJiIhIGnj626ZYVBIREZE0sKa0KT78nIiIiIjuGGcqiYiISBp4\n+tumWFQSERGRNLCmtCme/iYiIiKiO8aZSiIiIpIGzlTaFItKIiIikgQDq0qbYlFJRERE0sCa0qZY\nVBIREZE0sKi0KatFZd2ZS0I77B0ntq5z926eQnkA+OHr74Xyz8Q/J5SvrRNbZxYADILr3xqc7IX7\neDFhklD+3yvXC/fx9dZvhfIdB4qtMwsAl/9/e/cfE9WZ7gH8y0AHGEBAhoFtQWYFBaRosZRe8KLu\nqrf1ltbWIOC6oNlm23UuhlQaq5jQQOrSaFNrvCbc3tSI0RbQYlm9Lsid3jXIQivb6mLjWhV/DCLL\ngC11YJmRH/cPwyiCMzxindnp95OQOMfnPc85w/Dy8L7nnLf3pii+u+eGOMc3fz4tiv/ly8+Jc/y5\n5UtRvE/AFHGOW4Oyz1Xnn66Kc8Ss/Bdxm4fN3Drx9aCjfpUk3r8mULbuuv5gjTiHtJ/p7e8T5xgS\n9jMKT9m4QVb2SlE8AOzbsVsUX3XkkDiHao7s98T3ph/EObp6ukXxLfV/EedY+NISUfzJs1+Jc3j7\ny9YXHxwcFOe4/s0VUfys7Hmi+MCgQFH8aKwqHYkjlUREROQaWFM6FItKIiIicg1OVlR2dXWhrKwM\nLS0tGB4eRnx8PNasWQO12v7sicViQUVFBerr69HX1wetVotVq1YhNjZ2VNyRI0dw5swZtLa2oqen\nB+np6VixYsWY/ZnNZlRXV6OhoQHd3d3w8/NDXFwcMjMzERw8etbRYrHgs88+w4kTJ9Dd3Q2VSoXI\nyEi8+eab8PC4f+nIopKIiIhchPNUlWazGcXFxVAqlcjNzQUAlJeXo6ioCO+99x48PT1tti8tLcXX\nX3+N7OxsaDQa1NTUYMuWLXjnnXeg1WqtcXq9HiqVCklJSairq4Obm9t999fc3IyMjAxERkbCaDSi\nsrISxcXF2LZtG7y8vAAAAwMD+P3vfw+j0YhXXnkFYWFh6OnpQUtLC4aGhmweM4tKIiIicgnOtEqj\nXq9HZ2cnduzYgZCQEADAtGnTkJeXh7q6OqSlpd237eXLl9HQ0IC1a9di4cKFAIBZs2Zh/fr1qKys\nxIYNG6yx27dvBwAMDQ2hrq5u3P2ZzWY0NjZi2bJlePHFO9d/+/v7o6SkBN9++y1mz54N4PbI56VL\nl7B9+3ZMnTrVGvvss8/aPWeuqENERESuYfgRftnR3NyMmTNnWgtKANBoNIiOjkZzc7Pdtu7u7khJ\nSbFuUygUSElJwenTpzEwMPamvWEbFfXQ0BCGh4ehUqlGbR95ffcIZG1tLZKTk0cVlBPFkUoiIiKi\nh8xgMCApaezTKsLCwtDU1GSzbVtbG0JCQqBUKse0HRgYQEdHB8LCwiZ8LN7e3khNTcXRo0cRFRVl\nnf7et28ftFot4uPjAdy+BvTGjRvQaDQoLS1FY2MjBgYGEBMTg+zs7FHT7uNhUUlERESuwYnmv3t7\ne+Hj4zNmu6+vL3p7e222NZlM92078v9SOp0Ou3fvRnFxsXVbVFQUNm/eDHf32485vHHj9iP8qqur\nERUVhTfeeAMWiwUHDhxAUVERtm3bZvMmI05/ExERkWtwoulvZ1NeXo4TJ04gOzsbRUVFyM3Nhclk\nQklJCcxmM4A7U+heXl5466238NRTTyEpKQkbN26ExWJBbW2tzRwsKomIiIgeMh8fn3FHJE0mk3XE\n0Vbb8UYjR7bZa38vg8GA6upqrF69GmlpaYiJiUFqaio2bdqE1tZW6PV6AICfnx8AIDo6etTUe1BQ\nEB5//HFcuWL7wfec/iYiIiLX8IinvysrK63/jouLQ1xcnPV1eHg4DAbDmDZtbW12r4cMDw/HyZMn\nYbFYRhV3bW1t8PDwQGhoqOg4r169vdpaZGTkqO2hoaFQqVRob28HcPtGonuv45TgSCURERG5hkc8\n/Z2RkWH9urugBIDExEScP38enZ2d1m2dnZ04d+4cnn7a9pLWiYmJGBwcRGNjo3XbyOs5c+bYfAD5\neAIDby99eeHChVHb29vb0dfXZ73T28PDAwkJCTh79qx1Shy4fQNPe3v7mKL0XhypJCIiIpfgTJc6\nLlq0CDU1Ndi6dSuysrIAABUVFVCr1Viy5M468EajEevWrUN6ejrS09MBAFqtFsnJydizZw8GBwcR\nHByMY8eOwWg0Ii8vb1Seixcvwmg0Wh8LZDAYrHeXz507F0qlEjExMYiIiMDevXthMpkwffp0dHV1\noaqqCiqVCgsWLLDuLyMjAwUFBXj33XeRlpYGi8WCgwcPwsfHB0uXLrV5znaLymd/+28Tee+svjrc\nIIrviJghigeApKX/KoqvO1wjS/AAw+fuAbafjH+v6YvixTnOXjonih+6abYfdI/nFz8nij/0wX5x\njpkJs0TxoYEacQ791/8jin8mNkGc49iealH8wt+OXTbLnj9sl72/s7Pni3O07K+XNXhVnMKu5N89\nP+HY5j+cEO9/Rth0UXzicyn2g+5x7PAfZQ2G5P2MR4CXKD5yyWxR/N8unxfFA8CgySKKl/YxAHBo\n+z5RfGzik+IcmoBg+0F3+d+vjohzzI2eI8ux97A4x4LXMkTxn70ve28BIOE3vxDFnyo7LopPem4a\n8EtRkzuc6O5vT09PFBYWoqysDDt37gQA6zKNd6+mMzw8PO4zJnU6HcrLy1FeXo7e3l5otVoUFBSM\neaxPbW0tjh+/8x43NTVZi8pdu3ZBrVZDoVCgsLAQVVVV0Ov1qKyshJ+fH6Kjo5GZmYmgoCBr+7Cw\nMBQWFmL//v344IMP4O7ujieffBIbNmzAlClTbJ4zRyqJiIjINThPTQkAUKvVyM/Ptxmj0WhQUVEx\nZrtSqUROTg5ycnJsttfpdNDpdHaPxdfXd0L7A24/aujtt9+2G3cvXlNJRERERJPGkUoiIiJyDU40\n/f1TxKKSiIiIXANrSofi9DcRERERTRpHKomIiMglcPbbsVhUEhERkWtgVelQnP4mIiIioknjSCUR\nERG5Bg5UOhRHKomIiIho0jhSSURERK6B11Q6lN2i8nTdF6Id/ib/d6L4Pf/536J4AHDzdBfFpzy/\nwH7QXf60W7ZuNABk5K8RxX9aKl8z+9JjsoHlZ3+9WJzjSodBFD8jba44x2nhOrCRBavFOeavlK0v\n3PldlziHm5ubKL7mkPxz5RWrFsW3trbKc8QE2Q/6kX19rGnCsWvWvybef9muj0TxCmEfAzxAP/OR\n/POQni/7WTj0Xx+L4t2U8vNOzl4iir/692viHNHLnhHFf7X7/8Q5Xi6wv3Td3RasnPh69SO6e26I\n4t0g62MA4I9VsjXJvWfJ+hgAuHDxgijea5asj/EI8RHFk/PgSCURERG5Bg5UOhSLSiIiInINnP52\nKBaVRERE5BJYUjoWi0oiIiJyDawqHYpFJREREbkGJ5v+7urqQllZGVpaWjA8PIz4+HisWbMGarX9\nG6QsFgsqKipQX1+Pvr4+aLVarFq1CrGxsaPijhw5gjNnzqC1tRU9PT1IT0/HihUrxuzPbDajuroa\nDQ0N6O7uhp+fH+Li4pCZmYng4OBRsV9++SUOHjyIa9euISAgAIsWLcLLL78MhcL2DcN8TiURERHR\nQ2Y2m1FcXIzr168jNzcX69atQ0dHB4qKimA2m+22Ly0txeeff46srCxs3LgRAQEB2LJlCy5fvjwq\nTq/X4+bNm0hKSgJw/yeTlJaW4vDhw1i8eDEKCgqQlZWFs2fPori4GP39/da4U6dO4f3330dUVBQ2\nb96MpUuX4tNPP8Unn3xi95g5UklERESuwYkGKvV6PTo7O7Fjxw6EhIQAAKZNm4a8vDzU1dUhLS3t\nvm0vX76MhoYGrF27FgsXLgQAzJo1C+vXr0dlZSU2bNhgjd2+fTsAYGhoCHV1dePuz2w2o7GxEcuW\nLcOLL75o3e7v74+SkhKcO3cOc+bMAQB8/PHHiI2NxWuvvWbN29/fj6qqKrzwwgsICAi473FzpJKI\niIhcw/Aj/LKjubkZM2fOtBaUAKDRaBAdHY3m5ma7bd3d3ZGSkmLdplAokJKSgtOnT2NgYGDsqduY\n+h8aGsLw8DBUKtWo7SOvR9p2dXXhypUrSE1NHRU3f/58DA4O4tSpUzaPm0UlERERuQjnqSoNBgPC\nw8PHbA8LC0NbW5vNtm1tbQgJCYFSqRzTdmBgAB0dHXbz383b2xupqak4evQovvnmG/T398NgMGDf\nvn3QarWIj4+35gUw5rg1Gg2USqXd4+b0NxEREbkEZ7pPp7e3Fz4+Y1cH8vX1RW9vr822JpPpvm1H\n/l9Kp9Nh9+7dKC4utm4buW7S3d191H7vl9teXo5UEhERkWtwnoFKp1NeXo4TJ04gOzsbRUVFyM3N\nhclkQklJyYRuHLI1vT6CRSURERG5COepKn18fMYdkTSZTNYRR1ttxxsVHNlmr/29DAYDqqursXr1\naqSlpSEmJgapqanYtGkTWltbodfrrXkBjHvcvb29dvPanf62dNoeor2Xu8JdFN9/4TtRPABMy0wQ\nxX++s1oUn/L686J4APiHpd9+0F1uXZMPXU957uei+B96b4pz3PhB9v3wVY0dIn/YbvzwvbhNeMgT\noniVp7c4h5tS9lkf6JD9LAGAZn6kKP6H7h5xjseekHVOPwbL3yf+3ige4G9h83nZ53r6r54R5/h8\nx2ei+Hm6fxfn6Bf2MxaDrA/wf0H2eQPk/cx3N+U/z77esn5mIiMq9/pO2PeFaWR9DAB4eXqJ4t28\nZH0MANwS9jOhC2eIc/TckH0PVdPuf7fweJSB8v7Y6hGPIFZWVlr/HRcXh7i4OOvr8PBwGAyGMW3a\n2toQFhZmc7/h4eE4efIkLBbLqOsq29ra4OHhgdDQUNFxXr16FQAQGTn6Zzw0NBQqlQrt7e3WvMDt\nInTGjDufjc7OTlgsFrvHzZFKIiIicg2PeKAyIyPD+nV3QQkAiYmJOH/+PDo7O63bOjs7ce7cOTz9\n9NM2TyMxMRGDg4NobGy0bht5PWfOHHh4yG6JCQwMBABcuHBh1Pb29nb09fVh6tSpAAC1Wo2IiAjU\n19ePiquvr4eHhwcSEmwP6vFGHSIiInIRznOx46JFi1BTU4OtW7ciKysLAFBRUQG1Wo0lS5ZY44xG\nI9atW4f09HSkp6cDALRaLZKTk7Fnzx4MDg4iODgYx44dg9FoRF5e3qg8Fy9ehNFoxNDQEIDbo4xN\nTU0AgLlz50KpVCImJgYRERHYu3cvTCYTpk+fjq6uLlRVVUGlUmHBggXW/a1cuRLvvvsuPvzwQ8yb\nNw+XLl1CVVUVli5dCn9/f5vnzKKSiIiIXIPz1JTw9PREYWEhysrKsHPnTgCwLtPo6elpjRseHh73\nkg2dTofy8nKUl5ejt7cXWq0WBQUF0Gq1o+Jqa2tx/Phx6+umpiZrUblr1y6o1WooFAoUFhaiqqoK\ner0elZWV8PPzQ3R0NDIzMxEUFGRtn5CQgPz8fBw4cADHjx9HQEAAli9fjuXLl9s9ZxaVRERERD8C\ntVqN/Px8mzEajQYVFRVjtiuVSuTk5CAnJ8dme51OB51OZ/dYfH19J7Q/AEhKSrIu+yjBopKIiIhc\ngjM9p/KniEUlERERuQZWlQ7Fu7+JiIiIaNI4UklERESugQOVDsWikoiIiFwDp78ditPfRERERDRp\nHKkkIiIi18CBSoeyW1TGpSeLdtjQ8oUo3t1HaT/oHsaWsWtp2uIR4Gk/6C6mf8jXaL547bIo/sk1\n88U5/lbTLIqPm7fEftA9Dn1ULoq/4fuYOMdLb/1aFF+zX7Z2OwC8np8rij975VtxjvB50aL4QF/b\nKxGM56/HvhTFu7nLJx8eZJ3khy1uxcT7mS/O/kW8f4VK9jm9fvqSOIdH4I/fz7QK+5nZry4UxZ89\nelIUDwCz/mOxKF7axwBAl5/s98RLG2V9DADUfnJYFP9q3uviHOeunBfFT5sXI84R4DNFFP/XOvn3\n3M3dTRRvHpL1MWZ/kyj+bs7Qn/2UcfqbiIiIiCaN099ERETkGjhQ6VAsKomIiMg1cPrboTj9TURE\nRESTxpFKIiIicg0cqHQoFpVERETkGlhUOhSLSiIiInIRrCodiUUlERERuQTep+NYLCqJiIjINbCo\ndCgWlUREROQiWFU6EotKIiIicgmmL647+hB+0uwWlT/3e0K0Q4VC9ujLwYiZongAeOxnvrIcXv2i\neOk5A4D/sEoU/yDrQCvCekTxP/MMEueIfWKGKF7hLf+75AmvYFF87ONR4hzB7rL3t99bI84x4G8W\nxU/xln1uAeCW8Nz/Wdf+lvzMuT/AOd6KkK3TrhT2McAj6meGZP1MgLCfcQv7XhQPyPsZaR8DAAqV\nrJ+R9jGAvJ8J9ggQ57AI+5nhKbfEOfyE/Yy0jwEAN4Vs7W9pHxPirxbFk/NwG3aG3yhERERE9E+N\nK+oQERER0aSxqCQiIiKiSWNRSURERESTxqKSiIiIiCaNRSURERERTRqLSiIiIiKatP8HeXB24MQo\n5R0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_strains_compare\n", + "\n", + "draw_strains_compare(strain[0], strain_pred[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Lastly, let's look at the difference between the two strain fields." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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xhPWqVatG3BYdHY3e3l40NjbKUxh1dXUwm83Das1mM2pra2G1WuW6sLAwhIaGQqPReO0z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\n8EnHJaH6WQax/rUusX9s2j1iA4NTrf5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_differences\n", + "\n", + "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Resizing the Coefficients to use on Larger Microstructures \n", + "\n", + "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger random microstructure and its strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(63, 63)\n" + ] + }, + { + "data": { + "image/png": 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lq3HdIvaxqmsoVwllL6yagbj8h0WaMoa8+CTpOcqa13HD40m3frg9\n6d9mcLZmjXCt1/3Yes7WVOuVeegM6cpd2M/pUcabtJtF69nfto77SdfXOynn4DILs/ieFxeyJ/yf\nb79CespczhwFAA//Eh/rtfIRmu2OIINF5yFPFgVBEARBcDmKNf+zERxFBouCIAiCILgcxcXaldME\nx5DBoiAIgiAILofMhnYehoPF0r4tPf+TWQ+ViiOeLLO+SKMy7cknNCrTEW+fWU+cUQafPd5Lo/Y1\n8nfeadvrncORtjM6p3oOs3mFgHEWo5FXVsWez496D83mear3w56+bXYNcr11zu+UMoGBtp+t/pzR\ndm4+r/NbY3Ar0qnntNeYb+XMQo9AzvW7cvQc6cbtu5P+7ewK0r5N2KNYPoj9bwBQxo9zFE+msQf5\nwkkuE+5ut9V+TUI1ZWxJ2k66xSPseZv3FXvkmvbmjL29C9mLFtNYm+P3/tgPSau5l1Xa1iN9NYf9\noJkreF3hKn14HW4AOPUjewwbDOF67j7KvrzQ1jVIL9ywnHREJf5slg0oqynz5Fm+H8GNuJ+fXcO5\niSdO8/rV1vPcDnW7NtOUsecY91VLVc5AXLaZfZKVlTzQh/7BntRVc9grC2hzLT0CvDT7lGbcZ5+T\nbtGF29rP21dzzOqv2Y+5/Df2Uka04uzNY4vY56rmMKZfzCCtWd8dQNF1q+1n1cPqKPeiZzE7OxsT\nJ07E/v37ERgYiEGDBiEmJkZ332XLlmHJkiXIy8tDdHQ0Ro4cCU9PT1itVkyePBlJSUnIzs5GxYoV\nER8fj6ZNmwIArFYrvvjiC5w8eRKZmZl4++230aBBAzr3yZMnMWPGDCQnJ8Pb2xuPPPIIunfvrlcN\nAPJkURAEQRAEF+ReHCxOmTIFFosFU6ZMQXJyMj766CNERERoHhTs3bsXixcvxttvv41y5cphzJgx\nSExMRHx8PAoLCxEcHIyEhAQEBwdj9+7dGDt2LMaMGYOQkBv/sa1fvz569OiBsWPHauqQlZWFDz/8\nEMOGDUN0dDSsVisuXLig2a802mlQgiAIgiAIf3OKi4v/0n9G5ObmYvv27Rg4cCC8vb0RGRmJli1b\nYuPGjZp9N2zYgE6dOqFKlSrw9/dHv379sH79egCAt7c34uLiEBx8I6mgefPmCA0NRXLyjdV9PD09\n0b17d0RGRuquULds2TI0adIEMTEx8PT0hI+PDypXrqzZrzTyZFEQBEEQBJfjXnuyePbsWXh4eCAs\nrCQeLCIiAgcPHtTse/r0abRu3dqmq1evjitXriA7OxsBAWx5uXz5MtLS0nStaHqcOHEC1apVw5tv\nvolz586hdu3aGD58uG3wqYepwaKed+xO8+0c8ZKZzVE0u7/eMUbZjkbeMUfWhjZqG3vW1TY6h9k6\n2INRhuGdZljqHaNilMPoiE/VKBPRGZmhRp8fo7WkVexZz/3PWGPcLKP6DLP9/N+336Nt3jV5jeXz\nB7mNXhn8T835XnnjNdJq1l/9mKakj/6hZByW5Sy6nD3nSbcbzr4vAKhWkb+op34xiXTNmCjS6r1J\nvnCdtPVSrqaM9GMXSUd05/4Rdn9t0rvmbCDdYTivTTx3zUJNGbUbsSfxyFpew/rsgRTSasZeiyce\n4DrMXK8po+xD7EHMusZZmqoH0eLJ92/XbL6uB15vR7pCWa2n9Le1m0i7eXC//+f7r5L+evwE0h7l\n2IdXrgz3S0CbnZuXzGsqFyvrOKde4XWeH1a8s+u82SsIAEW5VtJ1GrF/8Ep2Fuk/ViWR3r15B+lR\nI0ZpytgQyr7hvYfZYwrlq8wjkD8v9Trz5+t0Bq+RnXswU1Nm6fYttORptv9dSEwsWe87KioKUVEl\nn/vc3Fz4+rJH1MfHB7m52s96bm4u/Pz8bPrmcbm5uTRYtFqtGDduHGJjYxEeHq45jx4XLlxAcnIy\n3nzzTVStWhWzZs3CF198gffee++Wx8iTRUEQBEEQXI67MRv60UcfveU2Hx8fXL/O/ynMycmBj482\ngF/dNycnx/b7mxQVFWH8+PGwWCwYPny43XX08vJC69atUbNmTQBAXFwchg8fjuvXr2sGszcRz6Ig\nCIIgCC7HveZZrFSpEgoLC3HuXEkyQmpq6i1XEEtJSaH9ypYta3uqWFxcjEmTJiErKwsvv/yyrjfx\nVui96TRCBouCIAiCILgc99pg0cfHB61bt8bcuXORl5eHI0eOYNeuXWjfvr1m3/bt22Pt2rU4ffo0\nsrOzsWDBAsTGxtq2T548GWfOnMFrr70Gi0UbRVRQUID8/BtLilqtVtvPABAbG4vt27cjJSUFVqsV\n8+fPR2Rk5C2fKgLyGloQBEEQBBfkXlzBZcSIEZg4cSJGjBiBwMBAjBw5ElWqVEFmZiZGjx6NsWPH\nokKFCmjatCl69+6NhIQE5OfnIzo62vaKOyMjA2vWrIHFYsGoUSWe01GjRtkyG1988UVkZt7whn7w\nwQcAgK+++grBwcFo2LAhBg0ahI8++gh5eXmoX78+XnjhhdvW263YYDhc+tGmkZkeMDb5O2LIVzEK\nPzYKGVYf1+pN3DE7ucFokoY9GAUqq+dUHyU7MmnDaJKG2Uk2ehjN0DK6n3ptqd7DO21/eyY5qZOB\njPqEPWHiKmYDyR2ZrHKnbfNnhHI/uf4d28+Lv55L29z9+X/NnmXYTP+/CRwiDQAvjx7Nx4T4kQ6o\nyJMTruexjyj/FE+4KL7OkwrcvTkYGQDinxxKOi+fTfrzps3mcxbxfegW/zDXKTdHU8baWRwW7lOr\nHGl18oM1k6/rtXfeIP35lHGaMqxX2GzfseeDpH+ZsYS0pRLPzIQycaQ4XxuwbAnzV/bhPvXC0H+Q\n/uSDj0gXXua27fhET9I5uXzdgHYSzdkLPGkpPDiMdKUKFUmvW8STTTz8tU9ziq18TwvOcmB5ZM+W\npH0Vr9qBNRxu3WtIX00Z+QUcOL9q/jKulzI5Ky/lCum2AzqT3rpkvaYMNwv3775D2Yfn7cVB4HO/\n/4G0uy8/hypSQrbVdgGAVv062H6u7l8J3/R4R7OPWYatefOOz2GGGZ1uPUHk7448WRQEQRAEweW4\n16Jz/s7IYFEQBEEQBJejCDJYdBYyWBQEQRAEweWQJ4vOw3CwaDb8WM/7dSf72xNkrGLkZzPymulh\n1Omc4e0zG2iu7q9XR7Oh6GY9cPa0nepTNdtW9gRLG/k9jXAk0NzsF5E94eJmPYpGHlO9z5cz2sbZ\n/LR6le1nSxUOLi68wv60pvez72v5Zm1wsXpMRDQHF19Wgotb1OMQ4U17VpH2rMCzBFW/IQAkLppH\n+vM3PyG9YE4i6SLFB+npwT4xq443tPAC+wkbPNKQ9O6FHDztVTWQ9BczviL9r3+8oinjvx//l/S6\nlatJq32sQxcO4V6TuJL31/Es9hsQR9rPm9t3zCfcdm6e/J2u+iS37uegaRRq78+Y0R+QfuldDuE+\ncYYDtNsPu490RofmpPcmclsDgEd5vg6vGmVJJx85wdVU+mmTzm1IX72m9fbVrBxBuiCDva194vuT\nnj9hFultyzjQ3Hpe642t1rsx6QtZHAYfFMDX1aNvL9JLZy4grX5e1M8TABw8cdj2c2EFbUi1I8hg\n0XnIk0VBEARBEFwOGSw6DxksCoIgCILgcshg0XnIYFEQBEEQBJfjbiz356qYGizq5eWZzRs060/T\n83kZ7WPk/VOPt+e6jPIhjTL27PHdqRj5z+zxkqnZgOo57KlnaRzxdzrbx6pXDyOvnyOe0jvt20Z1\n0MOofdXrNGorvUxEs3mef0XOYvaWM7afW43sQtsKrOzt27V+G2l3T+1CVCP/8xzpb7/9lnco5GsI\natyatJqr2Koj+9c2jluqKTNyRBPS363k7LnaLRuQLleGsx6Xfb+QtCVcyS8E4NcslPSBzbtJu3vz\nV3pozXDSFy+z92ztzo2aMu7vzKtJbJjD/k0PJedy7byfSPce1o/04kmcmwkAPt6cL5h+OZN00dV8\n0lA+Fm5efP86tu5A+lDyEU2Z4+dPJu0byt5YD3duu5Sz/LlIWs9treYZAlp/5pOPPU76m88m8DkC\nOK/wSOoxPqGON/aRDj1Ie5bjtvT34wxLdx8l81DJ4vSsxPsDQNrW46Rb1mdP79E/2HtZPpDzPlt1\nb0d66/y1pNVcTQCo2bbEJ1m5TLhmuyPIk0XnIU8WBUEQBEFwOe7FFVz+rshgURAEQRAEl0OeLDoP\nGSwKgiAIguByyGDRedzxYNHIE6diNtdPz4tmj6ftdmWa3a6H2Xrbk7Fn1iumei31/G5qPYzWzTa6\nDiPPo94+Kn9Gbp89XsrS2HPP79R7ac86z0btaXQOs/cLMN+P/oqcxdI+uBqVeM3zRVPZ8xbckrdf\nvcyZiQCw4zD7y8Ib8DFXsnm93IUfziRdscftcxk9yrDXDADCyit+wt8PkVb7h5oHm3+a1y6u0Fjr\npa4ayr/bMXsN6fse43Wcd63bqjlHaQojtRmIBxW/n09d9qMFBZcnferHfaTVbMCKMbU0ZRxJYW/e\n9sXsnazbkT1yB6ZzNmD7F3kd7eqV+HOyei1nQwKAeyDfM4sn//m7lsl9Yu0abluPIPYoFhforA2d\nx37A4KAKpN3c+bNlvcR5gs1jOWcx+/o1TRlz1ywiHdO7I+nvxrI38/44Xgt63ZfK8f9gDyQAbP6G\nszIvXeW2OTDvN9KtBnMd1LYNi6mtKUPl4E8lWZlFlesAD99mZzuRwaLzkCeLgiAIgiC4HDIb2nnI\nYFEQBEEQBJdDniw6DxksCoIgCILgcshg0XkYDhZL+5f0PFfVq7MXyKwPT8XIk+VIGY54sMz6z9Qy\n1f1Vb6c99TTKdjSb86dXhlFbqvfXKM9Q7xwqZtcDt2fdZr2szNvhyPFmv3hUb6A998eoTLNrdeu1\nnVH7G/mQ/4ycRTdLiX9v2YLFtO3xF0aRnjFxKh/rxWsqA0D4fZVIh5YLIb16znLSvvXYh3ctgz1a\nJ5IzSHf7J69tDGjb5eKxs7yD0n0ys/mz9MAzvUlvmv+Lpoy6/dj/1yqevWJbZnAmYrUevMZv3ao1\nSW9cyr48QLu+cWEWr1/8SP+hpMf/fJh0vep1SG9YoS3j4olzpPsMH0B6yQ+8trBnsB/pvALOYfxl\nG+f4RdTh6wS0HlH1s3Jw23bShYqfsExH/vy6eWjzPb292Nc452e+Du867P8sVPIkz2Rwn8lXrhMA\nCgvZZ3ru4nnSXhG8bvPvaSmkVb9t4zq8vjgAbK/yK+lfFyv+TeUce1ewh/HhJx8l3bBmfdJTx3De\nJAB0f6Ikn7OyT4hmuyMUqx86wWHkyaIgCIIgCC6HPFl0HjJYFARBEATB5SiSUG6nIYNFQRAEQRBc\nDnmy6DwMB4ulG9sRz5XqeTPy4amY9aLpYZRdZ8+6wKqPy8irqZah+mXsqZeKPbl9d4p6Xep129N2\n6j026ym1J7tTLdfsmsn2YDbz0J62UTFqmztdH1yv3xn1VfV+/BVfuMWFJWWEN6pB205npJEuSOMc\nvwGvD9ecb8lS9j26ed/+q65Sowguw1pA+oqSs3hZyZ0DgG1K5mHbIZx5uHUpZwW6+3FO328rePvj\nL7JXEwBmTf6WdP+hA0nv8t9E+vxRvtddWseSLjivzfHrP2wQ6cRvZpG+rGRUqrby5DT+/Kr5kQAw\n9I2nuIxZvI724OHDSM/8nLMDk3ZwtmPRZfZVDnnmCU2ZBYr/b/a4b0nfN4D9n5smsK81ezN/rzV8\n9H5NGQG+vM5yTt510tUq8ef36AHOA+016HHS33yh9fZ1Gs7rYCdOn03aEsL+zoxDXO/6j3CW44rf\ntN7Yyq04FzF1xQHSnkrmJCz8PbPsxyWk33szgbS6hjkA1Agv+dsT7BGo2e4IMlh0HvJkURAEQRAE\nl0MGi85DBouCIAiCILgcMlh0HjJYFARBEATB5ZAVXJyHDBYFQRAEQXA55Mmi8zAcLJY2v+tNqDCa\nlKEG1ZoNZLanTCOTv9EECr1JNEb1UK/LbDi5PceoGIU82zNR507LUNG7LrMTcRwJF1dRJ3Ko12k0\nqcaefmYUWG7Utnp935HA+DvFaAKLMyYHmaXNwAdsP2+bs462tXqpGWm1vheyLmrO51GWDfhFuRxk\nXK4Zf+bP/HaM9KhX/kl62nfTSO/J3KUp01KNTflbF/F1VI2J5P3d+ev3yJwtpM9f5CBwAGjYoQXp\nxG/nkPbw50kzAwbxBJjvps0g7akEcAPaSRqPjhpCet7suXyOirz/mp94woS7r/bPTH4BTyBq8UA0\n6avXeFJM0TXev1bDuqQPLuC2O5x8VFPmH+d5oodPXQ7IvpB1ibRnBW4bNYj62Bae9AEAXfv3Iu3v\nw5NN5o7n9q/YlkPWV2zhtnvgEZ4kBQDzZyeSLi7g7+x6UdzPKlUII/3LghWk3X2098dNmbCithWK\n+DOo/hUoVO7Xii2rSQc1rqwpc8ehksk+1f3DgHqaXUwjg0XnIU8WBUEQBEFwOe7FwWJ2djYmTpyI\n/fv3IzAwEIMGDUJMTIzuvsuWLcOSJUuQl5eH6OhojBw5Ep6enrBarZg8eTKSkpKQnZ2NihUrIj4+\nHk2bNrUde+DAAUydOhUXLlxA7dq18eyzzyI4OBgAUFBQgOnTp2PHjh0oLCxEvXr1MHLkSJQvX163\nHgCgzdUQBEEQBEH4m1NcXPyX/rOHKVOmwGKxYMqUKXjuuecwZcoU3UjBvXv3YvHixXjrrbcwYcIE\npKenIzHxxlPlwsJCBAcHIyEhATNmzMDAgQMxduxYZGTceBuRlZWFTz/9FAMHDsT06dNRq1YtjB07\n1nbuFStW4Pjx4/j000/x9ddfw9/fH9OmTdPUoTQyWBQEQRAEweUoLi76S/8ZkZubi+3bt2PgwIHw\n9vZGZGQkWrZsiY0bN2r23bBhAzp16oQqVarA398f/fr1w/r16wEA3t7eiIuLsz0pbN68OUJDQ5Gc\nnAwA2L59O6pWrYro6Gh4enoiLi4OqampSEu7kVmbkZGBJk2aIDAwEBaLBW3btjXMwDYVyq16nezB\nrCfLyBcGmA9LNgoydsSL6UiQtMqdesXs8ReabSsVo/8t6ZVpFORtNphdrw5GYe1G/U5ta71+ZuQ7\nVc/hDF+k0WdMrYMjofVqGWZD1P8MPDxLvoqKrrPfydubvWLu3h6kG9eO0pzv19UccF2seKyystgP\n2CWevWZffzSO9JAXOfh75phvNGW2j3+I6zCffVqntrIvsnv8w6SPum8jna+ESAPAvpVbSUd2bk76\n5O8nSS/4mcPJPQLZy1m2XiVNGcdO/U5a7Q/B9cJJn17E3r3qfZuS/uOY1tunhlXnKdd67mI66Vrd\n+XHUuTEAACAASURBVJyHF28n/eirHMK9cAb7+gDAI1DpR4q/88SmJNKVOtQhfeUKh5HXr8neQABY\n/vU80u2GdCXdoGsr0kfWcCi3mxf/SW43kr2cAOBfg/2DFQL51eHBdXzO1s+OJF2Uw58vPQpSOKw9\nuGOtW+x5A4sH11v1vW5epQTSW7TPqUIalbRNOZ8gwzraQ5HGTXl3OXv2LDw8PBAWVuIjjYiIwMGD\nBzX7nj59Gq1bt7bp6tWr48qVK8jOzkZAQADte/nyZaSlpdn+Hpw6dYr+pnl7eyMsLAynT59GeHg4\nOnbsiOnTp+PSpUvw8/PDpk2b0KwZe8NVxLMoCIIgCILLca95FnNzc+HryxOnfHx8kJubq7uvn1/J\nBKmbx+Xm5tJg0Wq1Yty4cYiNjUV4+I3/yOXl5SEwkCfc+fr64vr1G/9JCwsLQ4UKFfD000/D3d0d\n1apVw/Dh2pWwSiODRUEQBEEQXI67MVi86SsEgKioKERFlbz58PHxsQ3YbpKTkwMfHx/NedR9c3Jy\nbL+/SVFREcaPHw+LxUKDPR8fH9v+pY+/OeCcMmUKrFYrpk2bBm9vbyxevBgffvghPvjgg1telwwW\nBUEQBEFwOe7GYPHRRx+95bZKlSqhsLAQ586ds72KTk1N1bX6VK1aFSkpKYiOjrbtV7ZsWdtTxeLi\nYkyaNAlZWVl4/fXXKT6uSpUq2LCh5NV/bm4uzp8/b3tNnZqaikGDBsHf/4ZdoGvXrkhMTNR9xX0T\nw8Hinea+GWXoqT4u1Ttm5KkDjL17Rn5CvWtUf2fkPzPyONrTjma9Yka+Sb16GbWF2eu05/6o12WU\n82dPfqSRn1A9h5rDaI9v0sg/aOSLtMd/a/Y6jPy2RvcTMN8HzHpMHWHnhhIvnpeSV6iuwuDmxZ7F\n3Uf3a87XrC17w36bvJJ0vzceJ71s/hI+gWKpOnkmmXTrfh00ZW6cwWU88vxg0kt/WEj6l7XsafSq\nzte9Zjbn4QFAs15tSYeVDyV9dMM+0m4+3FZQ7m3LjlqfkocHH+PlwV6/jTP5Ojs+/wjptRMXka7c\nXespvXCFszH3reCcxI/GjiH95qcJpC2V+Q+anw+/2rNe0r7WK7zCvxv8ygjSc77iDMQLJ8+RVvMi\nO7Zspyljz0+/kd62hT2mqm/SI4ifKKne2tDyIZoycs5mkb72O7flyOefIT1lymTS7srn54E+2izH\nVePmk774K/f/Pv+MJ33pKvs5N/7wE+l2A9jPW8ZPOyBZtbSkX9WvWAu4/wXNPma5115D+/j4oHXr\n1pg7dy6efvppJCcnY9euXXj//fc1+7Zv3x4TJkxATEwMgoKCsGDBAsTGxtq2T548GWfOnMGbb74J\ni4X9t61bt8asWbOwbds2NGvWDPPnz0dERITtNXWtWrWwYcMGNGjQAF5eXli1ahXKly9/y4EiIE8W\nBUEQBEFwQe7F5f5GjBiBiRMnYsSIEQgMDMTIkSNRpUoVZGZmYvTo0Rg7diwqVKiApk2bonfv3khI\nSEB+fj6io6NtTy0zMjKwZs0aWCwWjBo1ynbuUaNGISYmBoGBgXj55Zcxbdo0jBs3DnXq1MGLL75o\n22/o0KGYNm0aXnjhBVitVlSrVg2vvPLKbestg0VBEARBEFyOe+3JIgAEBATg1Vdf1fw+ODgYM2fO\npN/17NkTPXv21OwbEhKCuXPnan5fmkaNGlG2olqH559/3kStZbAoCIIgCIILci8OFv+umBosOpLj\np3qsnLGWtJFXzCi7Ti3DnrWh1TKM1iI2Oh9gfq1hsx5GvTLM1tvofH/GeseOfMDNrtOs4oyMSqPt\nar8D7jzDUG1rZ6x5bXSP9a7jTrl+oCT3sOsrA2jbnLG8ssDAFzlTb/6MHzTn6zWIfXSqF+zYH5wl\n6KV44PzrVCT964xVpHs8q2Nid+e2z83P4zJqlCXt7cWZhxePsPfMq2oZTRGHDh0ifbIse8ks4Xwd\n1gs88/LBPt1I/zx/uaaMvo/xtakexuJ8Xmc7NCiYtHcEZ+RduXBZU8auVM5RdFMyD5PT2Ffbuk0b\nroNyP+d89z3XWfEGAkBRjpV0odKPiwtZq3mELz37IunPxmmf2qjrSRek8RrXfXsPJT37f9lP2GUU\n99txn32hKePRYYNIz0yYSHrTPvZ/lqnB9+f6VZ4lm6Wsww0AUD/iyvdK5RDO2lwynfMl1TzJk2dS\nSIcH83rVAFCneX3bz1XLaNeOdgR7grIF+5Ani4IgCIIguBzyZNF5yGBREARBEASXQwaLzkMGi4Ig\nCIIguBz34mzovyumBot6o3QjP6CRl8wZmW5GPi2jHDlHMhDNrketh1FOolHb2bOusFov1Z9ptu3s\nwWhtaKM+YJQ96Ei91P3V67Snbxtdl9Hxev3MrA/SyE9o1E/1MPLG/hX/Oy+dN5d9ndelLVB8dwVW\n9p71ju+nOd/i7zknzq8J5xH+fojXafYoy/7B7Kxs0p5hfqR/XsJZgwDQ7jFeB1hd2zlX8STmK7l9\nqt/t4X7sXwOAuf+dQjqkD+ckVgllr5enO/sNV83mPMnY/px/BwDzJ7H/76W3XyOtttWiHxaQ7taf\n19nOyuZcQABY9/VS0pW68jrLa3duJN2qPl/n9+Onk+43inP/fpyhnSmq+lIXLubcy6Jc9mJGPtSQ\n9B/n+W9TwSmt1y+qN3sr98/5lXRFJTfREsZ1WjefvbEPDeK2BIBzF9jv6eHP/syjW3gt7gFD2eM4\nawz3Ie/GvL44ABRb+e+4el0L1nE/8qlbgbQ6Dji1itfdrvGENnO2asWSv02VvCtotjuCPFl0HvJk\nURAEQRAEl0MGi85DBouCIAiCILgcxZDBorOQwaIgCIIgCC6HPFl0HoaDxdKeJ0fWiTa6WUZeMnty\n/IzWl3ZGBqLZjEN71o428ijas+5yafTa2mymnpG/0B5PnCO5lrc7Xu/+GfUTs2tY2+OLNDqHM9aG\nvtOMSmd4fs3mezqDFo+WrLW85XteM9kjkD1yvt6s1XVpAaBQWRu4x8CHSS8eN4d05S71SVsVX+TZ\nzdyuwxL+oSkzMTGRtGd5XvfXXVmn2ZrOeXdtBnYk7eWlzQr0COBrP7s/hXSPp0eSnjZ5KulnXuVV\nGyZ9MV5ThppheDo9jXThBW7bqu3Yb3hN8ZxmKutAA0CN3k1Jn9p8hPSlEH/SbaJakrZe5gzL6/lc\np+rR9TRlpp3i67CU4bbMS+Z+dGT1HtLBQeyji+zBdQKAQz/v5DIqstf1cAp7ZRvHch7hnsXscawW\npv2unDqZPYfw5M9vv8Gckzln6izS6trr25X1qwFtxmf8g/1Jv/v5fzXHlCYlWcn/rKKUuYmzIAFg\n5JMlfTfYI1Cz3RFkgovzkCeLgiAIgiC4HPJk0XnIYFEQBEEQBJdDVnBxHjJYFARBEATB5ZAni85D\nBouCIAiCILgcMlh0HoaDxdKmfnsmgpgNeTY7wcIejDqIPWWYnbBiNJlBb3KE2VBnFaPJEXrl3mn7\nmp08ZA9GE0P0Jliox5id2KG2g97xZoPYjbbb88VlNjTdbB3t2ceeIG9ns2/tdtvPljCe3FB0lcOt\nS4f3AsDsb2Zqzle9CwcqHz91knR4Z56UcX5XCmn3AAtpj0CebOJj4ckrAJCXfJl0XBxPNpn7HU+q\nUSca7Nm4g/SDrz6gKcPN4k66UTsOVJ701ljSbYZ0Ju3pwZNs3Hy0fwK8ynM4+MJFHF7tXbcc6YzT\n50g/3oNDoD8a+7GmDM3knzLcvt17dSc9e8Z3pD2U/csHBpFuGckh3gDw497fSecr9+u+ATzBaNtS\nDgbfun4z18GP+wig/byOGs0ToaZ/P4P3d+f9X3jnVdLjP/lCU0bjzq1J75y9jnRaJt+P/FSeuBPc\ntS7psPIcWA8Ah45sI71q21rS97W9j/T6mStIdxvJQfnLPue+HzOC7y8ApJ4r+U4v8A4GtHOUTCOD\nRechTxYFQRAEQXA5ZDa085DBoiAIgiAILoc8WXQeMlgUBEEQBMHlkMGi8zA1WNTzjhl5qIy8e6o/\nSg2qdiSU2wh7QqKd7RWzx0+oXquRf83I6weY9xiqdVDLVO+Pnr/N6NrN+ijVMvXqadY/qPYBe4Kn\njfq2Uds44h80Chs3CtB2RqC2I0HfZim8XBKqPOCFx2nbvEkcKpxyltuofgetPy1pEQcN9/rHANJL\nlFDu4Adqkc7LV0Kf96WTPnDykKbMopwC0leuXSXdrAN7zXy92be38ftVpDfvZ98YADTt3Zb03uUc\nbuymeC13/czbm9drTDq6Y4ymjMvZ7HFLWsleSje2PeL5f3LQ9/9+9Snpomz2nAJA3ZaNSAc1Lkt6\nyfT5pKO6cAD2vjmbSIeUCyY9btw4TZnxw4aQnjnmG9I7Vv9GullX9uVtnfQT6cghvB0AThziIO/0\nS5mkK9Xmz6Ma/r41iUO9VX8iAOyat4F07V7c/9d+/iOX2TuK9Pmfj5KOflEbLp7Xg/v/+inLSN//\n+EOk63Zn7+wf55XvDOVPU7O63A8BYNJnJQHx9cNrA/c/r9nHLDJYdB7yZFEQBEEQBJdDBovOQwaL\ngiAIgiC4HMXqI03BYWSwKAiCIAiC6yFjRadharCo+rwAY4+bkX/NKCvQnhw/R44pjd6jaiNPopGP\nyygj0d59SmPk59TzxBl588zmYtqTH6li1JZG+6tl6pVr5K008u458rpCbVujtrHHt6pyp58nvbY2\n+/kw208dwc29JD9Qbdc2j8Te9tgjm/ZpfudVnTMMf/mZ/YC+kRVIXzvLmXtFuewl865TnvSe39hb\nBgCeSj5hGb8A0ldzskn7+/iRbhfPPrBf17MvDwA8g7xJewRw3mBhNvsmrVdzSRcoHjm9jL3Nizm3\nr/vjj5BePmke6d/PJJOuUov7XFYYezcBYO9cvraHX4gnXe+BpqQPLGHvpbs//+lKy+BswWqN2YMK\nAEdTj5Mu7ZMFgJiBXUhvXf0raa8qZUj//st+TRlt4juR3nv8AOkKgdyP1Cdfah6o3pOxRn3YK5m0\nlL2tnqHcry7u579VaoZo5ZBKmjLWrllD2jeS6713N3szE176H9Kv/+vfXKdy/Nlwd+O8UAAovFzi\nkywqU6DZ7hDyGtppaO+YIAiCIAiCIPx/5DW0IAiCIAguhzxYdB4yWBQEQRAEwfW4B0eL2dnZmDhx\nIvbv34/AwEAMGjQIMTHaCCsAWLZsGZYsWYK8vDxER0dj5MiR8PT0hNVqxeTJk5GUlITs7GxUrFgR\n8fHxaNq0xL5x4MABTJ06FRcuXEDt2rXx7LPPIji4JGJq1qxZWLfuhuWkY8eOGDx48G3rbThYNOv1\nMvI3Gfns1O32+KvuNPPQnvw7Iw/cnfom7amX0XrUjmTqOdszp1cvs21j5D8EjPMGza6rbU+ep1Gu\npVonZ/hWjT4fjqCWeS/ESwS1KbmuDXt4Dd7KoeypUrPp8k9lac7XZHB70vt/YI/coDd43ebEccr6\n0p5KG+UWkm7aqY2mzF0/sNevTtWapH9csID0+2+8S/qNt/9DukM3XqsY0K77e+TIbtLuvvyVXpjF\n9VZ9lN9OmKIpo4WSL/jbge2k/Zqxz/HClUuk2zbmbMBZX07TlAFljevaVbitlicuJu1ZgT1vxTnc\nB5rW4bXAF61aoiny9B/s3fOuwetJb9/E2Zzxjz9GesaHk0h7BGjXho6qyWuOz/yS27fba5wdeCmL\n227fz0q2pof2+6HnSPa2Ji3lert7cR9wV9ewVtajnj5+sqaMB/pxGbuOsi/Y4sFlJJ08TLrYyr7j\n4gLuh9fzrmvKRGGR/s8uxpQpU2CxWDBlyhQkJyfjo48+QkREhCZXee/evVi8eDHefvttlCtXDmPG\njEFiYiLi4+NRWFiI4OBgJCQkIDg4GLt378bYsWMxZswYhISEICsrC59++imefvpptGzZEj/88APG\njh2LDz74AADwyy+/YOfOnfjkk08AAO+//z5CQ0PRpUsXTX1vIp5FQRAEQRBcj+K/+J8Bubm52L59\nOwYOHAhvb29ERkaiZcuW2Lhxo2bfDRs2oFOnTqhSpQr8/f3Rr18/rF+/HgDg7e2NuLg425PC5s2b\nIzQ0FMnJNyabbd++HVWrVkV0dDQ8PT0RFxeH1NRUpKWl2c7dq1cvlC9fHuXLl0evXr1s574VMlgU\nBEEQBMH1KC7+a/8ZcPbsWXh4eCAsLMz2u4iICN23badPn6aEjerVq+PKlSvIzs7W7Hv58mWkpaXZ\nnk6eOnWKjvX29kZYWJgtxUXv3EYJLzJYFARBEARB+JPJzc2Fry9bKnx8fJCbm6u7r59fSQzSzePU\nfa1WK8aNG4fY2FiEh4cDAPLy8ujYm8dfv379lufWq0NpDD2LRv4ys74tI4+cI/6pOy1DL4vObDag\n6qszWtdZr55my1T/J6DXdkYetzv1rzmyXrjZrEejdtArw8jTqKJ3f4x8kGa9mHpt62zvpT0+ViPf\nsHoOtS/r5a3eKVmHz9t+tlTyp22XkzNIt3+K10f2rsXeMwA4ujOJtFdVzl20ePJXX1E+e6rc3Xm7\nV1XO2Dt4+KCmTE+l3lsO8JrKDVtxduDCDbzebplavL6xtxdnKgLA4Z93kY7swmvypqbxvby6nvvH\n7mOcDehbj/MmASC3gNcFvryLv2fUHL92TdnjeE3Jk1T9ngA0jyny8nn96PwU9qE2HHg/6YOL2du3\nXVmTuWp17XeG6ve8ti2NdIthD5BWfXgo4s9F/1HaCQHffTOdtJq92boB369R/36WtJviOXWzaJ/n\nzF+9iMsI4fthzWQ/YEyXdqQ3zlxJ2r0MZ3UCQLUw/syvX/wzH6Pke/rG8nXWi1VyMr9jz/Cuo3s1\nZTYdUFLPiIBwzXaHuAt27MTERNvPUVFRiIoqWZvbx8fHNmC7SU5ODnx8eJ14vX1zcnJsv79JUVER\nxo8fD4vFguHDh9OxN/cvffzNAafeufXqUBqZDS0IgiAIgstxNybvPfroo7fcVqlSJRQWFuLcuXO2\nV9Gpqam3/E99SkoKoqOjbfuVLVsWAQE3JqkVFxdj0qRJ/6+9N4+rqtzb/y/mUVDAGWfNATNLJUwj\nTHMky1DTBm1AszqdOnbqPPWcBk89pzpWlnokjTSnSgU7IKU5gphTkkOamAM44zwhbjaw+f7RL+C6\n18J7L9yWZ/8+7169XlystddnDffa3K513deNixcv4pVXXoFnlYkNIiMjkZWVVaFtNhtOnDhR8Q//\n37bdqlWrq+5DVeQ1tCAIgiAIwnXG398f0dHRWLBgAYqLi5Gbm4ucnBzExsYa1o2NjcXq1atx5MgR\nFBYWIjU1FXFxcRXLP/30Uxw9ehQvv/wyfHx4xHt0dDQOHz6MTZs2wW63IyUlBc2bN694TR0bG4uM\njAycPXsWZ8+eRUZGBm3bDHmyKAiCIAiC+/HHp4IZSExMRFJSEhITExESEoIxY8YgMjISp0+fxvjx\n4zFp0iSEh4ejc+fOGDx4MCZMmAC73Y6YmJiKp5anTp3CqlWr4OPjg7Fjx1Zse+zYsejZsydCQkLw\n4osvYubMmZgyZQratGmDF154oWK9e+65BydOnMBf//pXAEDv3r3Rp0+fq+63R7nmOe3Ro0crfnZm\nnlmr8+VazUQErOcL1mTeYKtzJlvNywP0XjyrWY1mx6HLPNT5HtVtquub+dd0eZCu8Cha/YwzuYoq\nutFh1+qTNNuGiu7+UHHmuHS+SN0+NW7cWFvDKvWfq/RyJVb58gOAT96bQtpLyY27/5Ghhu0tTv6S\n9H1P8Kuh9DkppNW5oMvOsW+vcXwU6eAA9icCxjl2sxevJH3X0L6kS8vYy7cudQXpex8zHtf2feyV\nDPRjr9iurzlzr0lf3u9blDzCpV9wniEAeNdm71LJSfY+dejXhfRNTXkeZnW+6RkfJxlqqH/EH3pq\nFOn5U9n7V17GH7h1MHsYjyt+xHp12P8JANtTOb+z3l2tSYeH8vzHu1LZF3nXkwNJ20vYZwkA62ew\nH3Dwy4+QVs/VlMncttW/IiUnLxtq9HlsMOnMdG43nkFXz1V0XOL97pswyFBj2Vz2RZYpn+kyIo50\nj06cOzpjtpLdqHzHePp5GWo6rlTeg+3qtcSaF780rGOVxm+ah11fL46+uU6/0n8p8mRREARBEAQ3\n5AZ8tPhfinQWBUEQBEFwP6Sv6DKksygIgiAIgvshnUWXcc1zQ6tUHb4NGP2BNfEoqlj1E6oeLNVH\naeZNs5p5qKLuk1rTbB1dDav5hGaf0WXoWT23Og8doPdFquemJvNqq9Qk81BFd27UGq7I2rzW+8OZ\nuaStenxdcT10tOrSvuLngwVcT/U3OUrZ67ck3TgP8MsTeJ7lSZ98zNtUfF3lSoaebzPOZTyxJY90\npxHxhpq+Ppw91+N+nts5e3kmaccVPo5ewweQ3nNwn6FGeAj76nIWZpEe+dKTpFPmfkW6S9tbSNv3\nnzfUCLq3Dem2t7HvcftCzszze5jzIE+c5VzMNj1vNtSIUPyBX0ybTfrex9mv+fXEuaSb1OMcvq1L\n1pM+6ZNvqDnujRdIz5zNvsizxex79GnA+YW9urD/7Z2J7xpq+NRnL+tlG/s91+/gebZ9G/Fc3V7K\nvM4lBUbP4qq5nM/Zrj97SEMCORN0w+eckZj4D56fet4X8ww1VI+hl5LFqM5hrfpUo27tRPrgcSUP\n1qQXdzKrMtfySvMQw/KaIb1FVyFPFgVBEARBcDv+gJhFt0VyFgVBEARBEIRqkSeLgiAIgiC4H/Jk\n0WVIZ1EQBEEQBDdEeouuQttZrGpuNxsUoBvwoAtorsmgDV34sarV9XWDbqqrWxVdYLOKWXi1bmCH\n1fBqdXCR2TZ1A16sDuwxG3ChOw4V9dw4E0StOw5du9K1EbN1dDXU9WsSNq47F7rBJmobMFu/JuHh\n15u8Pfsrfs7fzQM7Bjx0H+klU3jQRkRn44CdmUvYtO8ZzAb9ei14gMTB9O2k4566l3TWbA5bXrNs\nlaHmvya8Q/pvE14l/fgTT5BO/mAa6VaRLUhnpnHYMgB4+PJgH69wDtCuUyuU9AMPcxj54vkLSfs2\nNQ4kuJJ3jvRtcXwudob8QPrHrzmIuPtwHtgTqQxGAYCSMg5BbxnLg2i8PPk4vZWBI98s4kFNXkH8\np6z0rM1Q88Ax/t7v2etO0iuTOIi60YAOpL9cnsr7FMbnHgCad+LBQWsX8OCSsS89Q/qHDB4spA7m\nGjRumKHGksnc/vdt/pn0X8aPJ70pdQ3pXw7z/dW8I4eTA8DelXw/qANeVDPgqhUcQP/XZ/9CeuLU\nD0l7hfCgKAAI6FS34mf/+nUMy2uE9BVdhngWBUEQBEEQhGqR19CCIAiCILgf8mTRZUhnURAEQRAE\n90Oyc1yGR7kmnfjo0aMVP5t5rlT/n+rD03n/rHrNzLDqy1O3aXYKVO+XLhxZdxzOhEDrauq8l2bB\n3+q5UT1xVkPXVcx8krr9trpPZjWshr1fj3Zldf2atDOrAebOeE5196TuejVqZPSgXSsRoys9a0+8\nMI6WzZo0nXT/UfeTXp7OfkIAKC/lNlV2yU767hEcgL1hG4cMBwSxR64g5SfSt47rY6hZp1Zt0mcv\nsvcvonY46UC/ANIrv2V/W7mdQ7sBoOToJdJtBnMgc6O6DUkfPXmM9JmLZ3kfMzlsHADuf+Fh0kum\ns8/x9mG9SG+Yz341nwgOsx73lz8ZauzO20NavR8PHMsnrQZ9n89g313vFxNIqx5TAPBtwNf04SdH\nk953iLeZPXsZ6bgn2bu55tMlhhodh3YnnX+Ev6fq1eXw6n0L2P/ZY9xA0mbfU+smc936Q9jvWT+s\nLukWjfg7Y9lC/rynv/GZUc++d5FelZTGKyhfyf2f5RD1zBWrSZddLCb957+ypxEApkyeXPFz+wat\nkPVaimEdqzR8Jeaat2GF4+9s/F3r/Z7Ik0VBEARBENwPebDoMqSzKAiCIAiC+yGvoV2GjIYWBEEQ\nBEEQqsXSk0VnPFc6L5ku002Xh+cMOn+gM/5BnY9Ot5/OZOxZzfFT0fndzFD322qWoyuuh9UsR2fa\nnS7b0WoupjPoauquL6A/dqseYHUf1PvRrKa6TbXtR0ZGXnW5Syiv3O/GSi6fR7AP6eWp35Ae+cSj\nhs3NfucT/oVi/aofzt4x+9FC0qWB7LHyaRRMum1TztMDgJPn2Fd35gL7A0+dO026bp0IrlGfvX5t\nmrYy1Pjpu82k8zfmkr77T7Gkv8/gjD3VB9n1Yc5EBIDLV4r4F958r/24KYe0f9sw0o5LJaTbNzOe\nqxnJM0hPemsi6RdefZG0ut++kbVId2zFmYiZV4x+wgadmpPe9ssO0o0i2O/pGcjZnBvWcJ5k4M3s\nDQSAA3vY91hewvt95Oh+0r4NuF09EBdP+o1JbxtqBMXw/XHpCLezy8fZK9unG/sPy4t5nx556nFD\njTnTZ5H2DjdmSlZl1SL2iHaP53a49tNvSf+0n7MhAeCe+yp9xI39jee2RsiDRZchr6EFQRAEQXA/\n5DW0y5DX0IIgCIIgCEK1yJNFQRAEQRDcD3mw6DK0ncWqvrmazA2t8wfWxKPozNzBVmo44yXT5fbp\n5gk2Oy6df9AVc/bqro9un6wuB67PNbeK7no4g85raXVec7McTF27UnHm/FfFmRxM3fW41ixOZ4i+\nr9LjlLKaM93u6Kf4n774jvSlIs4eBIxT2XrVYc/V4m95HmCHjecq7nXvPaRXzmefpBmNIhqQXv9N\nJu+Tcin6PcF+wZ+XbyEd1Yv9awDgPZD9m9tWcz7knMXzSXsG8Fe8V132Re7+hT2PAODhyzvqHcF5\nkA4ls3Lsn54mPWNKEunCK5cNNRzFfL73HeW8R8dl9j16BvFxe/jxcant3E/xUQLA6RMnSZeUco1f\n8tlv6KPkMpae4/mmhyU8ZKix6FM+/+pc3mr+Z3kZ6+ztnNN3a5fbDDVsxbwfW7/+nvTdozirQxm+\n3AAAIABJREFUccob75PuPpIzQh0m39dl57mGZyCff6/aiodROY4t234k7aN4TDMXc6YoAIx5vjJf\nta5XqGF5TXBmfILgHPIaWhAEQRAEQagWeQ0tCIIgCIL7IQ8WXYZ0FgVBEARBcD+ks+gytHNDV/WC\nmPnddPP4Wp1T2ZlcPzXn7Vqz6MzQeR10XkD1OMxq6j5jdbmZt8zqvNhWsxudyY+0er2uR7ajVa+f\nGepx6K6HM+1S3S9dDTUvUnf/mNXUnV91m2rO4vXwAU3I/azi52lT/k3LHnqccxR/3s8+ux9XbDBs\nz1HIfjSHknc38sUnSKfMW0DaK4gz9kY9zPswd/EXhpoGo6S6WMkr9PDk9YvzL5B+Zvxzhm0kKeem\n3R2dSB89dZz0qSV8rno+wz7ITV9nGmoMf4aP9cv3kkn3eKw/aTVP8vwlPo72LdoaapQ7uA1tWLGW\ntOoPfHz8U6TnJHMOIJRptPsk8NzfAPBd8tekX37vNdKTpn2s7CTvo1ctX2WxidfvLO93eZny/ap4\n/xxF3E7V74M/v/C8ocaUqVNIJ4wYRjp1caqyTf58uU3JWXxslKHGZ29zO1O3MewFnld70dS5pLsM\nYZ/xD1/wXNFeIX6GmsPGVc5J3tA3HG9EP2VYxyoN/tL1mrdhhYJJW/Qr/ZciTxYFQRAEQXA/bsAn\ni4WFhUhKSsKOHTsQEhKCkSNHomfPnqbrZmRkID09HcXFxYiJicGYMWPg7f1rt23ZsmXIzMzE4cOH\n0aNHDzzzzDP02VWrViEtLQ3nz59Hu3bt8PTTT6NOnToAgPT0dGRlZeH06dOoVasW+vbti8GDB191\nv2WAiyAIgiAI7kf57/y/EyQnJ8PHxwfJycl47rnnkJycbDrD2LZt25CWlobXX38d06ZNw8mTJ7Fw\n4cKK5WFhYUhISECvXr0Mn921axe++uorvPzyy5g5cybq1auHjz/mJ+fPPfccZs2ahVdffRXfffcd\n1q9ff9X9ls6iIAiCIAhuR/nv/J8Om82GzZs3Y8SIEfDz80O7du3QtWtXrF271rBuVlYWevfujcjI\nSAQFBSEhIQGZmZkVy6Ojo9GtWzcEBwcbPpuTk4OYmBhERkbC29sbCQkJ2L17N06e/DU+avDgwWje\nvDk8PT3RqFEjdO3aFbm5xhitqmhfQ1f1ZZj503Rz0Vr1n+nmpa1JTdW3ZbZNXQ3d3M+qP80Zn53u\nOFyRb2fV42b1XJrNE6zLONT5IHXzcpttw2o7c2Z+anUbOj+n2gacuZ66dmbVr6nzPAL6LFSdT/J6\nUFhUmcV39+C+tMzhYI/VDymZpNsNNPqSdqfxHMrqHL3qPM2hN9Unrc5nPOvfn5KOva+3oeaqmTwf\ncfQIzlHcfyyfdNP6jUlv28Z5eTM+nW6o8Zfx40l/9PFHpD2VebR9m4aQ3rQ4k3Svh43ePrUNeQWz\nV0+da/i9j3heZ9UTFzvkMUONd/75T9LdlSzN7HmcpXnoBLfBtt3Zq7nzP5xPuHIJfx4AvOpyXuSB\no/mkB8ZzPmHadPaxxo26l/Sq1GWGGmquYukpnmc7btQg0uu+YS+fRxD/SZ78keKjBJDw8HDSKfN5\nP/sN4RpHThwlvS2NnyDNnT3HUMO3GbebsjNXSPv5cpuIGhRNOudr7vx4Ke2y+wNxhpoLp1buR4fI\nNi7xLN5or6GPHz8OLy8vNGhQmcnavHlz7Nq1y7DukSNHEB1deV6bNWuGCxcuoLCw0LSDWBUPDw/6\nXv/t50OHDqFevXq0bnl5OXbv3o2+ffl7V0WeLAqCIAiC4H7cYK+hbTYbAgL4Hy3+/v6w2Wym6wYG\nVobo//Y5s3VVOnfujI0bN+LQoUOw2+1ISUkBANjtdsO6ixYtAgDExcVddZsywEUQBEEQBDfk93+0\nWNVXGBUVhaioqArt7++PK1f4KW1RURH8/ZUZcUzWLSoqqvi9jptvvhnDhg3DBx98gKKiIgwaNAgB\nAQEIC+OZjZYtW4bs7GxMmDChYuBMdUhnURAEQRAE9+MPeA09fPjwapc1bNgQZWVlKCgoqHgVffDg\nQVOLUpMmTZCfn4+YmJiK9UJDQ7WvoH+jX79+6NevHwDg2LFjSE1NJYvR6tWrkZaWhgkTJhg6kWbI\na2hBEARBENyPG+w1tL+/P6Kjo7FgwQIUFxcjNzcXOTk5iI2NNawbGxuL1atX48iRIygsLERqaiq9\nKnY4HLDb7XA4HHA4HCgpKanwG5eUlODQoUMoLy/H6dOnMWPGDAwaNKjitXZ2dja++uor/P3vfzd4\nGKvD0pNFsxBS1aCvDgLQmfx1od3OBIHXZGCBroZq8ndmQMTVcGYAhS4wW0U9LrP1dQNU1M/ozuX1\nGPygG5xidv10A3PUQUy6gSTq512xDWfaiG6wj4or7h8VXRtxRaC5DntppZdGHWChuw/2rt9p+F3H\nB7qT3vYZDyRQRy6e28GDACKj40h36cvbWzHtP4aatz3CAz/q1okgvflL3oebn+hA2nGllD/fhgfA\nAMCGnTxwp+T0ZdLDH+SA5QUfcHh1eSmf2yIbvxIDgOOnd5Nu2/c20unZS0nHxN5BOvPTDNJrtxlD\n06N63kp63YIVpJ+d8CLp5LmfkS6383F41+FXc47LHHYNAPGPDiH99ZyFpAeMvI/0PaM5d27F3HTS\ntbsav5eKi4tJ2w9eJJ29ZBXpjnF8bu0lvN97Vm011Fj0GQfCd7+X212DMP7jv/TLNNIePvw3uvSM\n0QM34lmlHU3n0O3Ietw2v/j356TLbdyWh774GOm0hYsNNb2qBJZ7+rvqpecNNsIFQGJiIpKSkpCY\nmIiQkBCMGTMGkZGROH36NMaPH49JkyYhPDwcnTt3xuDBgzFhwgTY7XbExMTQU8uUlBSkplYGsGdn\nZ2PYsGEYOnQo7HY7pkyZgoKCAgQEBKBXr1548MEHK9ZdsGABCgsL8corr1T8LjY2FomJidXut7yG\nFgRBEATB/bjx+ooIDg7GSy+9ZPh9REQE5szhkenx8fGIj483rAv8+rq7ulfeQUFBmDhxoukyAJg6\ndaqFPf4V6SwKgiAIguB2XIeZSf9/i3gWBUEQBEEQhGrRPlms6leqibdP9cBZ9Vg54/tSa+q8fzqf\npTP7ofrV1Bo1CdRW98tqoLZZgLO6jq6GDmdCn3XnRuc3dCbgXOdbtRrKrX7ebBu6a6wLH3cm1F5X\nw2o7M/MbqttwJgT9auu7gqperdVffkvLHn2efTSq56rvMA4hBoCVGRzK7NeiNuk187lGhwHdSJ+5\ncJa0lxIrUV7MwdMAEOgfSNpmZy+Yut/fL80k3e/ZBNIr539jqHHUYx/pPo9yUPT5wgukVS+fVzhn\nvG3f/ZOhhuM8++6eHctzzk6eOIn0c399gfTGthz6/MPOHEMN9fWgl7Kfew/vJ92iXWvSuct4mx7g\ndj4wkc8lAHwz52vSgx8bSjp9birpB8c8QrpDP24judnbDTXKy/je8PDm/XrgUX5tmPY1e19VL+2D\nT7N3EAC+/GAm6Y1Ls0nf9WoP0mXnuB2WFbIvsvMo9jwCxsDy8Fv5OyFrKwfIlzvUC8ptPe84fxf6\nNDKO6PXyrbzH/P+/OYyvGXm06DLkNbQgCIIgCO6H9BVdhryGFgRBEARBEKpFniwKgiAIguB+yGto\nl2Gps1gTb58ue071eak1nPFJ6vIKdb48sxw3q15LHWbHoeb4qehy/XTHbVZXV1O3DzofpTM1dB5G\nZzIt1f2y2s50vj2gZr7Tqrgin1Dnt62Jd1b9jCuyMq+VBuGV2XAeIb60bP7nnPE27pXnSc943xgD\n8chzT5KeN419Xqqn6pf17N179B/sLXvtH2+QDurawFBz+14l77GUr4VnEB9X/wSOxFiWsoTXDzB+\nPTuusN+sWQO+vjPfTyLd4u6OpAuLOJexWwfO+QOA5Us4R/GTVD533g2CSCfNmk564L3sIc3N32uo\ncdlWRNojnDMpVy3ifVA9cbW6NiJdVsy5fqvXrTHU9G7I+52zhz2HdW/l74zd+XtI/7x8C+mYIb0M\nNTYsWmX4XVX2H8snHdA4hHRocCjpXw6xdxMAHMqxqm3iwuVLpNVsTQ9fL9JRLdoZalwqKiS95dt1\npHMOnCI95qVnSc/85FPSP+fu4n3wNvYlbIcr/bZ2e6FheY2QvqLLkCeLgiAIgiC4HdJXdB3SWRQE\nQRAEwf2Q19AuQzqLgiAIgiC4H9JXdBnazmJVz5uZ/0k317DqsdJlujnjw9NxrV5AwLpHUbdNs89b\n9ZtZ9RuaoZtnV+cFVPfJGZ+kbj7pmmRUqjWszg/ujG/Paq6lK7I2VZxpq1XR+T8B19wfrmb6rBkV\nP496fDQt++ztf5P29/Ujbch4A3DkBM/13LJHFOlf0tl/BiUDcecBnh+5eN850i2G8NzGAHBByThs\nUL8+19zL2Y2rN2aRVrMGHRftUPGpx1mOXy5N4eUNOb+u4EQB6bJLvE3vm9m/BgA3deVzdfIs+9Mu\nXT7PH/DjbRw+cYy0Okc2AISW8H5s+ornza4Vw3MPl5VxruWFLL7/h/6V20z6V5yZCBiPvWEX4zWs\nysZ5K0nf8Whf0pu+4XxDAOgzinMvM9OWk/55F3v3ypXMw5f/xn7c1175X5M94/uzQUwr0is3Z3IN\n5f7wrsttqGPL9oYKE959i3Spkr15S78Y0mty+FzU7cjffRcu8r0R2cA47/mBQz9X7rPdmGMq/LHI\nk0VBEARBENwPeQ3tMqSzKAiCIAiC+yF9RZchodyCIAiCIAhCtWifLDozN3NVdD4vq5mHzsw9fK0e\nKzNfl86bp3rmrK7vzGfU47C6PqD30en20+qcy85sU+f9cyb3T+et1HkYVd+eM95L3T6oOHOurM4F\nrZ473T46M1+4znupnqvrMTd065srs96272NfV9kl9kut/2kz6fue5ExEAPgmg+dV9lR8dd4R7Nvq\nHh9L+ot/f07arw3PVVuw39hGPWtxjuLBS/mkPXx4H7rezBmHFy6xr2tLEvvdAKBeAvsJG0Vw3uPO\nH3he5i49+pH29uSv/IzPjd4+NaPS4WD/2BfT5/ByJfcvKr4tb9Dk+3XuxGTSI8Y/Tjp1wSLeRAm3\nOZ+6PMf1NxkZpMc9x7l/ADDt3Y9Ib13PvlV1Hmd1/uIfN/xA+v7Rwww10ufz+eydMID0oQJuN3uW\nbyVdXMJt3dPH+DzHpz633dN72SN6WskwVDMNI29vQ/rbDSsMNTrefgvpnL2ZpIOUedB/XLiW9Ph3\nXiE99RP2Hecd+8VQ8+4h/Sv3MaCeYXmNkCeLLkNeQwuCIAiC4HaUS2/RZUhnURAEQRAE90P6ii5D\nOouCIAiCILgf0ll0GZY6i2b+J6s+O132nDNePxV1mzo/oTNYzSPU1TTbB7WGM35Nq+s7c/6qosvF\n1PndnN0vK/tghlpD51HU7YO6PqD3D+qyG1Wc8d/q2lFNroeK1TxI3b3gCrq061zx8xezeS7ogA6c\n0/dj1iZe3i/OsD01j7BWIPvP/Jtzztu6+ezbclxhH16He7qS3j6XMxIB4LbHeK7gvXk8r69vY96H\nPQd5zuSq82MDgHc9nssYAM6t53baaRR7GAO7NiSdd4zXbxjB2Y9+bcMMNZZv4szDOrVqk+7Srzvp\nDZ99Rzr/OLdBNRcTML4eLDh7knSbLh1I/5zBfkH1+tx0F3vs9h/JM9Q07IMyZ3JZIW8TyvI74/uQ\nTpvNvkoA6DuC5/uOqM3nd/ncdNKNe95Eesm6ZaQ79Yo21Ngyl6+PQ8lqHDRmKG/z3wtIH87OJd1m\nZEtDDW8v7ho0HXQz6awZPI95eRlfz115XKNpFNewl/I+A5ylqeZq1hzpLboKebIoCIIgCIL7IX1F\nlyGdRUEQBEEQ3A/pLLoM6SwKgiAIguCGSG/RVUhnURAEQRAEt0Nm+3Md2s5iVXO7MwNF1MEJasCy\n1ZBhZwY7WB3EYTW0G9APCtDtd00Gguj205mBJNd6PXTrm7UJdb+v9TjMrq+6jm5Ai26QhjpAxqyu\nbsCL1YEjZnh6coCueu6s3i/ODE75PQaw6Ni5f3fFz/7NeEBFiRLK3b59O9Lrlq4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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = 3 * n\n", + "size = (m, m)\n", + "print size\n", + "\n", + "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio, size=size, \n", + " macro_strain=macro_strain)\n", + "\n", + "draw_microstructure_strain(X[0] , strain[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients that have already been calibrated need to be resized to match the shape of the new larger microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the `resize_coeff` method." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.resize_coeff(X[0].shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now take a look that ther resized influence coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bWcqUyB/94DGLg8dEnj8f+Q0K5wcdTMS2AQAwC8HRVARHAABgXbyOx1QERwAA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\n4kJwBAAAQFwIjgAAAIjL/wMPDrq5cKZvOgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hKpmZmQgMDERYWBjS09Ph7u6OIUOGoF69h9PaXrlyBSEhIYiMjERycjLKlCmD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\n6RVzdebj7Whfepba78HSuV/d/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "strain_pred = model.predict(X)\n", + "\n", + "draw_strains_compare(strain[0], strain_pred[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, let's look at the difference between the two strain fields." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3erfUxc70yLeAWKNmrwxezi9htpIGfJNdam2s24VsjzRi1j2zrKw7TmzxmkOl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\nNf7CDHOAYQZk5FRGRh2Ugvi3bjZhN5YaNc1OyzZNrUaJSWu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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the results from the strain field computed with the resized influence coefficients is not as close to the finite element results as they were before they were resized. This decrease in accuracy is expected when using spectral interpolation [4]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## References\n", + "\n", + "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", + "\n", + "\n", + "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", + "\n", + "\n", + "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi:10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", + "\n", + "\n", + "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/localization_elasticity_3D.ipynb b/notebooks/localization_elasticity_3D.ipynb new file mode 100644 index 00000000..e99080c1 --- /dev/null +++ b/notebooks/localization_elasticity_3D.ipynb @@ -0,0 +1,529 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Linear Elasticity in 3D\n", + "\n", + "##Introduction\n", + "\n", + "This example provides a demonstration of using PyMKS to compute the linear strain field for a two-phase composite material in 3D, and presents a comparison of the computational efficiency of MKS, when compared with the finite element method. The example first provides information on the boundary conditions, used in MKS. Next, delta microstructures are used to calibrate the first-order influence coefficients. The influence coefficients are then used to compute the strain field for a random microstructure. Lastly, the calibrated influence coefficients are scaled up and are used to compute the strain field for a larger microstructure and compared with results computed using finite element analysis." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Elastostatics Equations and Boundary Conditions\n", + "\n", + "A review of the governing field equations for elastostatics can be found in the [Linear Elasticity in 2D](elasticity_2D.html) example. The same equations are used in the example with the exception that the second lame parameter (shear modulus) $\\mu$ is defined differently in 3D.\n", + "\n", + "$$ \\mu = \\frac{E}{2(1+\\nu)} $$\n", + "\n", + "\n", + "In general, generating the calibration data for the MKS requires boundary conditions that are both periodic and displaced, which are quite unusual boundary conditions. The ideal boundary conditions are given by:\n", + "\n", + "$$ u(L, y, z) = u(0, y, z) + L\\bar{\\varepsilon}_{xx} $$\n", + "$$ u(0, L, L) = u(0, 0, L) = u(0, L, 0) = u(0, 0, 0) = 0 $$\n", + "$$ u(x, 0, z) = u(x, L, z) $$\n", + "$$ u(x, y, 0) = u(x, y, L) $$\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import timeit as tm\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKS\n", + "\n", + "###Calibration Data and Delta Microstructures\n", + "\n", + "The first-order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure, as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met, we can expect a mean absolute error of 2% or less, when comparing the MKS results with those computed using finite element methods [1]. \n", + "\n", + "Because we are using distinct phases and the contrast is low enough to only need the first order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first-order influence coefficients [2]. \n", + "\n", + "The `make_delta_microstructure` function from `pymks.datasets` can be used to create the two delta microstructures needed to calibrate the first-order influence coefficients for a two phase microstructure. This function uses the Python module [SfePy](http://sfepy.org/doc-devel/index.html) to compute the strain fields using finite element methods." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_microstructures\n", + "from pymks.datasets import make_delta_microstructures\n", + "\n", + "\n", + "n = 9\n", + "center = (n - 1) / 2\n", + "X_delta = make_delta_microstructures(n_phases=2, size=(n, n, n))\n", + "draw_microstructures(X_delta[:, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using delta microstructures for the calibration of the first-order influence coefficients is essentially the same as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Delta microstructures are composed of only two phases. One phase is located only at the center cell of the microstructure, and the rest made up of the other phase. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Generating Calibration Data\n", + "\n", + "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields with the boundary conditions given above.\n", + "\n", + "In this example, lets look at a two-phase microstructure with elastic moduli values of 80 and 120 and Poisson's ratio values of 0.3 and 0.3 respectively. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed Time 156.211350918 Seconds\n" + ] + } + ], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_delta\n", + "from pymks.tools import draw_microstructure_strain\n", + "\n", + "\n", + "elastic_modulus = (80, 120)\n", + "poissons_ratio = (0.3, 0.3)\n", + "macro_strain = 0.02 \n", + "size = (n, n, n)\n", + "\n", + "t = tm.time.time()\n", + "X_delta, strains_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio,\n", + " size=size, macro_strain=macro_strain)\n", + "print 'Elapsed Time',tm.time.time() - t, 'Seconds'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructure_strain(X_delta[0, center, :, :], strains_delta[0, center, :, :])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calibrating First Order Influence Coefficients\n", + "\n", + "Now that we have the delta microstructures and their strain fields, we can calibrate the influence coefficients by creating an instance of a bases and the `MKSLocalizationModel` class. Because we have 2 discrete phases we will create an instance of the `PrimitiveBasis` with `n_states` equal to 2, and then pass the basis in to create an instance of the `MKSLocalizationModel`. The delta microstructures and their strain fields are then passed to the `fit` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSLocalizationModel\n", + "from pymks.bases import PrimitiveBasis\n", + "\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2)\n", + "model = MKSLocalizationModel(basis=p_basis)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first order influence coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.fit(X_delta, strains_delta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it, the influence coefficient have been calibrated. Let's take a look at them." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ATGMqrkm8WhESAQCAeUzGxQYmRUgEAADmwUiiYQiJAADAPLhwxTCERAAAYBqs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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "\n", + "coeff = model.coef_\n", + "draw_coeff(coeff[center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients for $l=0$ have a Gaussian-like shape, while the influence coefficients for $l=1$ are constant-valued. The constant-valued influence coefficients may seem superfluous, but are equally as important. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict of the Strain Field for a Random Microstructure\n", + "\n", + "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two-phase microstructure and compare it with the results from a finite element simulation. \n", + "\n", + "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed Time 78.8157169819 Seconds\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_random\n", + "\n", + "\n", + "np.random.seed(99)\n", + "t = tm.time.time()\n", + "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio, size=size, macro_strain=macro_strain)\n", + "print 'Elapsed Time',(tm.time.time() - t), 'Seconds'\n", + "draw_microstructure_strain(X[0, center] , strain[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with.**\n", + "\n", + "Now to get the strain field from the `MKSLocalizationModel` just pass the same microstructure to the `predict` method." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed Time 0.33176612854 Seconds\n" + ] + } + ], + "source": [ + "t = tm.time.time()\n", + "strain_pred = model.predict(X)\n", + "print 'Elapsed Time',tm.time.time() - t,'Seconds'\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally let's compare the results from finite element simulation and the MKS model." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CwNBpGslrfmf2fiGuWP609Y+S13Re9v+Q1YEWMm645DXFEKSS+Dgz1fczxcTw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\nYlgkIiIiIq8YFomIiIjIq/8PWhb2foa3PeoAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_strains_compare\n", + "\n", + "\n", + "draw_strains_compare(strain[0, center], strain_pred[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's look at the difference between the two plots." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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sdjuefvrpqX4sADhnTUSiqXSDUZIkpKWlYf/+/XC5XDhz5gzq6+uRlZU1pm1WVhaqq6vh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\nIDMzE7NmzUJlZSWys7MnVSs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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_differences\n", + "\n", + "\n", + "draw_differences([strain[0, center] - strain_pred[0, center]], ['Finite Element - MKS'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Resizing the Coefficeints to use on Larger Microstructures \n", + "\n", + "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger $m$ by $m$ random microstructure and its strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "m = 3 * n\n", + "center = (m - 1) / 2\n", + "t = tm.time.time()\n", + "X = np.random.randint(2, size=(1, m, m, m))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients that have already been calibrated need to be resized to match the shape of the new larger microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the 'resize_coeff' method." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.resize_coeff(X[0].shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed Time 0.0531430244446 Seconds\n" + ] + }, + { + "data": { + "image/png": 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Kf7MO760RjeXjK/u7MCJW8Nn3yf6tCtYGKWYCR2hFYykZimJwwYIFA27TarXo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\nceM8v7ecGSQiIiL1GYJqsKSkxPnfCQkJSEhIcN7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_strains\n", + "\n", + "\n", + "t = tm.time.time()\n", + "strain_pred = model.predict(X)\n", + "print 'Elapsed Time',(tm.time.time() - t), 'Seconds'\n", + "draw_microstructure_strain(X[0, center], strain_pred[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## References\n", + "\n", + "[1] Binci M., Fullwood D., Kalidindi S.R., *A new spectral framework for establishing localization relationships for elastic behav ior of composites and their calibration to finite-element models*. Acta Materialia, 2008. 56 (10): p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", + "\n", + "\n", + "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, *Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems*. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", + "\n", + "\n", + "[3] Marko, K., Kalidindi S.R., Fullwood D., *Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals*. International Journal of Plasticity 24 (2008) 1264–1276 [doi;10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", + "\n", + "\n", + "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., *Crystal plasticity simulations using discrete Fourier transforms*. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/localization_elasticity_multiphase_2D.ipynb b/notebooks/localization_elasticity_multiphase_2D.ipynb new file mode 100644 index 00000000..e8a3508f --- /dev/null +++ b/notebooks/localization_elasticity_multiphase_2D.ipynb @@ -0,0 +1,601 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Linear Elasticity in 2D for 3 Phases\n", + "\n", + "##Introduction\n", + "\n", + "This example provides a demonstration of using PyMKS to compute the linear strain field for a three-phase composite material. It demonstrates how to generate data for delta microstructures and then use this data to calibrate the first order MKS influence coefficients. The calibrated influence coefficients are used to predict the strain response for a random microstructure and the results are compared with those from finite element. Finally, the influence coefficients are scaled up and the MKS results are again compared with the finite element data for a large problem.\n", + "\n", + "PyMKS uses the finite element tool [SfePy](http://sfepy.org) to generate both the strain fields to fit the MKS model and the verification data to evaluate the MKS model's accuracy." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Elastostatics Equations and Boundary Conditions\n", + "\n", + "The governing equations for elasticostaics and the boundary conditions used in this example are the same as those provided in the [Linear Elastic in 2D](elasticity_2D.html) example. \n", + "\n", + "Note that an inappropriate boundary condition is used in this example because current version of SfePy is unable to implement a periodic plus displacement boundary condition. This leads to some issues near the edges of the domain and introduces errors into the resizing of the coefficients. We are working to fix this issue, but note that the problem is not with the MKS regression itself, but with the calibration data used. The finite element package ABAQUS includes the displaced periodic boundary condition and can be used to calibrate the MKS regression correctly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Modeling with MKS\n", + "\n", + "###Calibration Data and Delta Microstructures\n", + "\n", + "The first order MKS influence coefficients are all that is needed to compute a strain field of a random microstructure as long as the ratio between the elastic moduli (also known as the contrast) is less than 1.5. If this condition is met we can expect a mean absolute error of 2% or less when comparing the MKS results with those computed using finite element methods [1]. \n", + "\n", + "Because we are using distinct phases and the contrast is low enough to only need the first-order coefficients, delta microstructures and their strain fields are all that we need to calibrate the first-order influence coefficients [2]. \n", + "\n", + "Here we use the `make_delta_microstructure` function from `pymks.datasets` to create the delta microstructures needed to calibrate the first-order influence coefficients for a two-phase microstructure. The `make_delta_microstructure` function uses SfePy to generate the data." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.tools import draw_microstructures\n", + "from pymks.datasets import make_delta_microstructures\n", + "\n", + "\n", + "n = 21\n", + "n_phases = 3\n", + "X_delta = make_delta_microstructures(n_phases=n_phases, size=(n, n))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at a few of the delta microstructures by importing `draw_microstructures` from `pymks.tools`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructures(X_delta[::2])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using delta microstructures for the calibration of the first-order influence coefficients is essentially the same, as using a unit [impulse response](http://en.wikipedia.org/wiki/Impulse_response) to find the kernel of a system in signal processing. Any given delta microstructure is composed of only two phases with the center cell having an alternative phase from the remainder of the domain. The number of delta microstructures that are needed to calibrated the first-order coefficients is $N(N-1)$ where $N$ is the number of phases, therefore in this example we need 6 delta microstructures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Generating Calibration Data\n", + "\n", + "The `make_elasticFEstrain_delta` function from `pymks.datasets` provides an easy interface to generate delta microstructures and their strain fields, which can then be used for calibration of the influence coefficients. The function calls the `ElasticFESimulation` class to compute the strain fields.\n", + "\n", + "In this example, lets look at a three phase microstructure with elastic moduli values of 80, 100 and 120 and Poisson's ratio values all equal to 0.3. Let's also set the macroscopic imposed strain equal to 0.02. All of these parameters used in the simulation must be passed into the `make_elasticFEstrain_delta` function. The number of Poisson's ratio values and elastic moduli values indicates the number of phases. Note that `make_elasticFEstrain_delta` does not take a number of samples argument as the number of samples to calibrate the MKS is fixed by the number of phases." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_delta\n", + "from pymks.tools import draw_microstructure_strain\n", + "\n", + "\n", + "elastic_modulus = (80, 100, 120)\n", + "poissons_ratio = (0.3, 0.3, 0.3)\n", + "macro_strain = 0.02\n", + "size = (n, n)\n", + "\n", + "X_delta, strains_delta = make_elastic_FE_strain_delta(elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio,\n", + " size=size, macro_strain=macro_strain)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at one of the delta microstructures and the $\\varepsilon_{xx}$ strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Kg7YIgwAAwAhd3IHXWEwTAwAAGIzKIAAAMAOFQVuEQQAAYAamiW0x\nTQwAAGAwKoMAAMAMFAZtEQYBAIAZCIO2CIMAAMAQpEE7hEEAAGAE9o/YYwMJAACAwagMAgAAM1AZ\ntEUYBAAAhiAN2iEMAgAAI5z44EBPDyEtsWYQAADAYIRBAAAAgxEGAQAADEYYBAAAMFiXG0iKiopS\nMQ4AV4i+ffsm3XdgxlWXcCS4VJwOR08PoUfFuFJx2srJcPf0EK4IjnicP+UAAACmYpoYAADAYIRB\nAAAAgxEGAQAADEYYBAAAMBhhEAAAwGCEQQAAAIP9PxVlayafuCBOAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructure_strain(X_delta[0], strains_delta[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because `slice(None)` (the default slice operator in Python, equivalent to array[:]) was passed in to the `make_elasticFEstrain_delta` function as the argument for `strain_index`, the function returns all the strain fields. Let's also take a look at the $\\varepsilon_{yy}$ and $\\varepsilon_{xy}$ strain fields." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Calibrating First-Order Influence Coefficients\n", + "\n", + "Now that we have the delta microstructures and their strain fields, we will calibrate the influence coefficients by creating an instance of the `MKSLocalizatoinModel` class. Because we are going to calibrate the influence coefficients with delta microstructures, we can create an instance of `PrimitiveBasis` with `n_states` equal to 3, and use it to create an instance of `MKSLocalizationModel`. The delta microstructures and their strain fields will then be passed to the `fit` method. " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSLocalizationModel\n", + "from pymks import PrimitiveBasis\n", + "\n", + "\n", + "p_basis =PrimitiveBasis(n_states=3, domain=[0, 2])\n", + "model = MKSLocalizationModel(basis=p_basis)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now, pass the delta microstructures and their strain fields into the `fit` method to calibrate the first-order influence coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.fit(X_delta, strains_delta)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "That's it, the influence coefficient have been calibrated. Let's take a look at them." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "\n", + "draw_coeff(model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients for $l=0$ and $l = 1$ have a Gaussian-like shape, while the influence coefficients for $l=2$ are constant-valued. The constant-valued influence coefficients may seem superfluous, but are equally as important. They are equivalent to the constant term in multiple linear regression with [categorical variables](http://en.wikipedia.org/wiki/Dummy_variable_%28statistics%29)." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict of the Strain Field for a Random Microstructure\n", + "\n", + "Let's now use our instance of the `MKSLocalizationModel` class with calibrated influence coefficients to compute the strain field for a random two-phase microstructure and compare it with the results from a finite element simulation. \n", + "\n", + "The `make_elasticFEstrain_random` function from `pymks.datasets` is an easy way to generate a random microstructure and its strain field results from finite element analysis. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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F/Px8aLVa5Ofn49ChQ3jxxRcRGhqKoKAgi9zOnTuxatUqLFy4ED4+Pli8eDEK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\nIy8vD2VlZfDy8sLMmTMRExNjM1tcXIz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.datasets import make_elastic_FE_strain_random\n", + "\n", + "\n", + "np.random.seed(101)\n", + "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio, size=size, \n", + " macro_strain=macro_strain)\n", + "draw_microstructure_strain(X[0] , strain[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**Note that the calibrated influence coefficients can only be used to reproduce the simulation with the same boundary conditions that they were calibrated with.**\n", + "\n", + "Now, to get the strain field from the `MKSLocalizationModel`, just pass the same microstructure to the `predict` method." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "strain_pred = model.predict(X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Finally let's compare the results from finite element simulation and the MKS model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fbD/oFk7OKvtBt3FtI+tngjoEiXMcPXVcFG+qlv0+tQu0PcG2LRypdCyOVBIR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\nrRhffdTE2PcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_strains_compare\n", + "\n", + "\n", + "draw_strains_compare(strain[0], strain_pred[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's plot the difference between the two strain fields." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cXzacMyYiuXjrgiYsYyKSjGWsBcuYiOTimbEmLGMikmok1xl/lbGMiUgunhlrwjImIrlY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\nokXIzMwcMl9RUYHy8nK43W7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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_differences\n", + "\n", + "\n", + "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The MKS model is able to capture the strain field for the random microstructure after being calibrated with delta microstructures." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Resizing the Coefficeints to use on Larger Microstructures \n", + "\n", + "The influence coefficients that were calibrated on a smaller microstructure can be used to predict the strain field on a larger microstructure though spectral interpolation [3], but accuracy of the MKS model drops slightly. To demonstrate how this is done, let's generate a new larger random microstructure and its strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(63, 63)\n" + ] + }, + { + "data": { + "image/png": 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uueduWQd70JWRns65eGrbqF5MZ+BVr+Kxys5Ezu1z92N/4LnUM6Trt2J/MQAc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\nPTiE74kuHTiHsVunrqQX//VvpN39OHcxtBd7aQGget+pxp9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ch2XnZTL55uyy5ZrjdnNG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "m = 3 * n \n", + "size = (m, m)\n", + "print size\n", + "X, strain = make_elastic_FE_strain_random(n_samples=1, elastic_modulus=elastic_modulus,\n", + " poissons_ratio=poissons_ratio, size=size, \n", + " macro_strain=macro_strain)\n", + "draw_microstructure_strain(X[0] , strain[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The influence coefficients that have already been calibrated on a $n$ by $n$ delta microstructures, need to be resized to match the shape of the new larger $m$ by $m$ microstructure that we want to compute the strain field for. This can be done by passing the shape of the new larger microstructure into the `resize_coeff` method." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model.resize_coeff(X[0].shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's now take a look that ther resized influence coefficients." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(model.coef_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Because the coefficients have been resized, they will no longer work for our original $n$ by $n$ sized microstructures they were calibrated on, but they can now be used on the $m$ by $m$ microstructures. Just like before, just pass the microstructure as the argument of the `predict` method to get the strain field." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cc9u7z9+LX375BQAwciSXufbw8ED//v3h6OiIwsJCxMfH48cff8T169cxblx1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\nO8c1Y8BU9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cDbUGl66VUUs+bY4sf6tYJjpnfh941HC99QmWfW7WYta/RA1gofTObrOzKE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\nHIn169ejoaEBFgsP4aQu2xFt/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L/xfehni14y2vzkAy4W8dx8LqCwfMtKil0tnEP25XF+\ndvYU90vrPVho66CyXdEnlYqiKIqidBB0VNme6KBSURRFUZQOQeX+r9p7E/6laXNQ2TfwzqsmR6+o\n7FauCxvgw6+afYJ5mUALvzrpG9iDYhdv3qQGZ/bQAgBXP/buagD/Rr66DPfkVydBruyh1btLhNGG\nrB9a22T6nLVEepQ11fPy9Xb2j5PbAABRIewp52RhvYGTeA3ZJLz2wjrxKztrgChADsDHl9v1C+XX\nXyFWfjUS1ZW3yd+X2/D35ldIDcFcDxswz1eYB78yjw5jyYRrIP8+xI23qZdvN6MN/zDuZ6Hu/Jrf\nbmdPuqgg7nfS107WggYAr87sI1gR5MU/kK/QhV9cXQgfqyZRt7mbF7/6AoAai/CQC+XzEeDCr76i\ngnu0+n1ff66jDgD+zrxffbpEUlzVlb//IaA848LHsUEcAz8ffr1qC2bJTLCVjzNgnm9nL76W7CLP\ntJljxLkFzDwTILbDUZ5pSbXd3G6Ji9juxnq+xuvrWs8zUcEyx/CxNmpRA2j04msj0ptf87vY+Hs/\nHxvF3qF87Tk8PyLPyPuIzYtzQI3o523mmHDTA9YawK/MZZ7p6RNOcecw3s+ubryfDS78PcD9GgAa\nvfhVtKs/SyA6+fA2BQSLPOTgHiz7Ym1XcXztreeZOtc6ihtkjrGY3sMyzxj3dZFnZB6SOaZS+HEG\n+XD+Vn48ODVp9XVFURRFURTlHrkHNayiKIqiKIqi3EYHlYqiKIqiKMo9o4NKRVEURVEU5Z7RQaWi\nKIqiKIpyz+igUlEURVEURblndFCpKIqiKIqi3DP/Hwg4SYdqLOcWAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "strain_pred = model.predict(X)\n", + "\n", + "draw_strains_compare(strain[0], strain_pred[0])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Again, let's plot the difference between the two strain fields." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Y3HQCphU4cF+tFjyV7GM9O4y5IoPtmMVwSyb6gL2teCzyXaihfngGfc7LCvFc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\nwVx8/bj9FLZ3L1rxJ1D7DuMrrW2kSXNsI9uPEkn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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_differences([strain[0] - strain_pred[0]], ['Finite Element - MKS'])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you can see, the results from the strain field computed with the resized influence coefficients is not as accurate as they were before they were resized. This decrease in accuracy is expected when using spectral interpolation [4]." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "## References\n", + "\n", + "[1] Binci M., Fullwood D., Kalidindi S.R., A new spectral framework for establishing localization relationships for elastic behavior of composites and their calibration to finite-element models. Acta Materialia, 2008. 56 (10) p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", + "\n", + "\n", + "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", + "\n", + "\n", + "[3] Marko, K., Kalidindi S.R., Fullwood D., Computationally efficient database and spectral interpolation for fully plastic Taylor-type crystal plasticity calculations of face-centered cubic polycrystals. International Journal of Plasticity 24 (2008) 1264–1276 [doi:10.1016/j.ijplas.2007.12.002](http://dx.doi.org/10.1016/j.ijplas.2007.12.002).\n", + "\n", + "\n", + "[4] Marko, K. Al-Harbi H. F. , Kalidindi S.R., Crystal plasticity simulations using discrete Fourier transforms. Acta Materialia 57 (2009) 1777–1784 [doi:10.1016/j.actamat.2008.12.017](http://dx.doi.org/10.1016/j.actamat.2008.12.017)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/localization_elasticity_polycrystal_hex_3D.ipynb b/notebooks/localization_elasticity_polycrystal_hex_3D.ipynb new file mode 100644 index 00000000..0cec8744 --- /dev/null +++ b/notebooks/localization_elasticity_polycrystal_hex_3D.ipynb @@ -0,0 +1,557 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Linear Elasticity in 3D for Polycrystalline Microstructures\n", + "\n", + "Authors: Noah Paulson, Andrew Medford, David Brough\n", + "\n", + "## Introduction\n", + "\n", + "This example demonstrates the use of MKS to predict strain fields in a polycrystalline sample. The Generalized Spherical Harmonic (GSH) basis is introduced and used for a material with hexagonal crystal symmetry. The effect of different levels of truncation in the GSH basis functions are examined, as well as the effect of selecting an incorrect crystal symmetry.\n", + "\n", + "## Modeling with MKS\n", + "\n", + "### Obtaining Data for MKS Calibration and Validation" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n" + ] + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "To start off we need to obtain data from somewhere. In order to make things easy the pymks_share package is used to import data." + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(50, 21, 21, 21, 3)\n", + "(50, 21, 21, 21)\n" + ] + } + ], + "source": [ + "from pymks_share import DataManager\n", + "\n", + "\n", + "manager = DataManager('pymks.me.gatech.edu')\n", + "X, y = manager.fetch_data('random hexagonal orientations')\n", + "\n", + "print(X.shape)\n", + "print(y.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The X variable contains a set of 3 Bunge Euler angles at each spatial point, corresponding to the crystal orientation. The y variable is the '11' component of the strain tensor as obtained by a finite element simulation ($\\epsilon_{xx}$). We can visualize this by plotting a slice of a 3-D microstructure ($\\phi_1$ angle only) and its corresponding strain response." + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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9wjV6H7ZXOGfLps2xhA7x3xu7dtslnNO1S9dQ/JL658M1Gt/ZGs7JNcWutl1W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\nhwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_microstructure_strain\n", + "\n", + "\n", + "n = X.shape[1]\n", + "center = (n-1) / 2\n", + "draw_microstructure_strain(X[0, center, :, :, 0], y[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This may not mean much, but trust us that the $\\epsilon_{xx}$ field is rather expensive to calculate. In principle we could visualize this in 3 dimensions using a package like mayavi, but for this tutorial we will just look at a single slice down through the center.\n", + "\n", + "In order to ensure that our models are valid, we need to split the data into \"calibration\" and \"validation\" sets. The idea here is that we train the model on a subset of N_cal datasets, then test the model on the rest. This is a crude form of \"cross validation\", and will give us confidence that we have not over-fit the model." + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(40, 21, 21, 21, 3)\n", + "(10, 21, 21, 21, 3)\n" + ] + } + ], + "source": [ + "N_cal = 40\n", + "X_cal = X[0:N_cal, ...]\n", + "X_val = X[N_cal:, ...]\n", + "y_cal = y[0:N_cal, ...]\n", + "y_val = y[N_cal:, ...]\n", + "print(X_cal.shape)\n", + "print(X_val.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that we have 40 calibration sets, and 10 validation sets. Ideally we would have a lot more data to validate the model, but at least the 10 should give us an idea of how transferable the model is.\n", + "\n", + "Next we need to set up the MKS \"localization model\" which will be used to compute all the parameters we need for the machine to \"learn\" how the input microstructure field is related to the output strain field. In order to capture the orientation dependence we are going to use a basis set of \"generalized spherical harmonics\". A quick Google search of \"generalized spherical harmonics\" will tell you that these are pretty trippy functions (nearly all the results are from technical journals!).\n", + "\n", + "In the GSH basis n_states refers to the set of basis functions we want to work with. In this example we want to use the first 5 basis functions, so we assign a list containing indices 0-5 to n_states (we could alternately pass the integer 5 to n_states and PyMKS would automatically know to use the first 5 basis functions!). If we only wanted the 5th basis function we would simply pass n_states a list with only one entry: n_states=[5].\n", + "\n", + "We also need to specify the symmetry we want (and the symmetric domain) of our basis function. PyMKS makes this very easy; we can simply give domain a string specifying the desired crystal symmetry. For example, passing 'hexagonal' specifies a hexagonal crystal symmetry, while passing 'cubic' specifies cubic symmetry. If we pass \"triclinic\", or don't define the domain at all the non-symmetrized version of the GSH basis is used.\n", + "\n", + "### Calibrating First Order Influence Coefficients" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks import MKSLocalizationModel\n", + "from pymks.bases import GSHBasis\n", + "\n", + "\n", + "gsh_hex_basis = GSHBasis(n_states=np.arange(6), domain=\"hexagonal\")\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now we have selected the basis functions, perhaps we want to know more about what we've selected. Let's ask for the l, m and n indices of the GSH basis functions we've selected (Note that this is an advanced feature and may only be useful for the most seasoned materials scientists!)." + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 0 1]\n", + " [ 2 -2 1]\n", + " [ 2 -1 1]\n", + " [ 2 0 1]\n", + " [ 2 1 1]\n", + " [ 2 2 1]]\n" + ] + } + ], + "source": [ + "print(gsh_hex_basis.basis_indices)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now all of the complexity of the GSH basis set will be taken care of by pyMKS from here on out. We just need to fit the model:" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "model = MKSLocalizationModel(basis=gsh_hex_basis)\n", + "\n", + "model.fit(X_cal, y_cal)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "and then we can look at the \"influence coefficients\" which capture the connection between the input and output fields. Notice that there are 6 (complex valued) influence coefficients, because we had 6 \"states\" in the basis set. In other words, there are 2 fields of influence coefficients for each basis set. Again, we are only looking at a slice through the center in order to avoid the complexities of visualizing 3D data." + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Bfc3zgSPLR2NfNbTsgIhYNbJ8HS2zwixJkiRJpZbJKdmZeVdEXAisiYjXAUcA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\nLgZmgJOAD9Sxr42Iy4HTIuJU4KXAIVS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "\n", + "coef_ = model.coef_\n", + "draw_coeff(np.real(coef_[:,center, :, :]), figsize=(2, 3))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "we also want to plot the imaginary components of the influence coefficients, you can't forget about these when using the GSH basis!" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JHpLkB3coOktycSnl40k+keQ3kzy71npiL8+/v3oTAACAvp6V5CW11o9k5ym6b0pyUa31\ngiSPSvKYJD+x1yeXGQUAAOhjbf/spltKeUCSr0ty8fxH2764Wuv7N/z/3aWUK9INRp+zlzYYjAIA\nAOxDpZTLN3x7Za31yg3ff22SL0jygVJKkpyf5FAp5b611kt6VL/nkbnBKLCaptPF1b3g9SCTpnuU\ntcX56XSnGTbja3qtrce09Ti1lm9pzz5bQ7STlmM6W67T8bS0vqfa3t/t5dmHlix+LPJzZLLjLNCt\nHrBcfZNkOdu0g1rr5Tv8+sVJXjX//1qSH083OH3S5oKllG9K8o5a6zWllPsk+dkkda/tMxgFAAA4\nYGqtNyW56eT3pZTrk9xUa/37UsqFSa5Oct9a64eSPDTJy0op5ye5Jt0GRr+41zaszXa+bDm79tpr\n9/ocwJI5evRosoDtufeoLd6scGZ0kWRGByQzumez2SzHjh1LVjjeLDozCssW+1Y1M7q+vp7jx48n\ni403s6v/4cMWWP2wLvrzP0yWL/6ewqcnAAAAozNNFwAAoA+zHwYlMwoAAMDoZEYBAAD6sEfAoPQm\nAAAAo5MZBQAA6ENmdFB6EwAAgNEZjAIAADA603QBAAB6WHNrl0EZjAKrqWXNxnS6uHYsuv7GtSmT\nxg/JW249sdD6J1ngh/Ytt7aVP/ustvINfT+dztrqbjSdtdV/+JCJT2fMouPNAVmv1vqeao1NC9dy\nHizZMW3py+bYt2SvlTPPYBQAAKAPA+pB6U0AAABGJzMKAADQx5pc3pD0JgAAAKOTGQUAAOjBbrrD\nkhkFAABgdDKjAAAAfdhNd1B6EwAAgNHJjAIAAPQhMzoovQkAAMDoZEYBAAD6sJvuoAxGgf2vdUrN\ndLrY8i3tueXWtrrPPquxKW0fqreut73Ww4f6v9bZZ25uqnvtTuc0lW89TuuzlqobCqe93zlz2o+V\nYzuE6azxPbVs/W4q55bESjYzGAUAAOhhzYWGQelNAAAARiczCgAA0IfM6KD0JgAAAKOTGQUAAOjD\npkqDkhkFAABgdDKjAAAAfVgzOii9CQAAwOhkRgEAAHpYW5PLG5LeBAAAYHQyo8DyWF/vX/bwAsPX\ndNpYftZY/kTvorNbbm2qenbTTU3lP52zmsrf5fxzmsq3WGs8ptNPXddU/oZzjjSVb3H40GKv7S66\n/gOp7/t82daHtcSnZWt7A+f8apgsemfZlvN91vhZfLrspjso73QAAABGJzMKAADQxwrPOFhGehMA\nAIDRyYwCAAD0sCYzOii9CQAAwOhkRgEAAPqwm+6gZEYBAAAYncwoAABAH9aMDkpvAgAAMDqZUQAA\ngD6sGR2UzCgAAACjkxkFlsfhJQlJretBpieais+m0/5lT6w31X3iox9vKn/eddc3lX/NJw81lf+u\nh92/d9nrXv36prrf98D/p6n8nc9tO07H7nKkd9nDh9rOmYkr62feqq77WtV2w+loOd8bPlv3wn1G\nh6U3AQAAGN2SpCEAAACW3Jpc3pD0JgAAAKOTGQUAAOjDmv9ByYwCAAAwOplRAACAPuymOyi9CQAA\nwOhkRgEAAHpwn9Fh6U0AAABGJzMKAADQh910ByUzCgAAwOhkRgH2qvEq6VrLdcBDbWF67bxzm8pf\n/4rfayr/wJf8TlP52TXv6F/3K9/fVPcTbjjaVP57v+3Lm8pPGo7rocZzYLLWWN6V+DNmOp01lW8+\nVtNpW3nr1bZ08y3rTeXPOXvBfwKvN7Tn8AH6c3w/nO/L2KYVpjcBAAAY3QG6FAMAAHD67KY7LL0J\nAADA6GRGAQAA+rCGf1AyowAAAIxOZhQAAKCPfbZmtJTyW0m+Lsm5Sf42yXNrrS/dpuxlSX5yXvbV\nSZ5ca71lL8+/v3oTAACAvp6d5F611rsmeWSSny+l3OFeaKWUhyf5qSQPTXLPJPdO8sy9PrnMKAAA\nQB9r+yuXV2u9etOPZukGmptvFP69SV5Sa31PkpRSrkjy20l+ei/Pv796EwAAgN5KKS8qpdyQ5D1J\nPpLk9VsUu1+S/7Xh+z9Pco9SyrG9PLfBKAAAQA9rk7WV+ddXrfWHk5yf5EFJXpNkq3Wg5yf51Ibv\nPz3/eufT7MokpukCAADsS6WUyzd8e2Wt9cqtytVaZ0muKqU8NsmTk/zqpiLXJ7nLhu/vOv963V7a\nZzAKsFnrTnkL3Flv7eyzmsofPv/cpvJ3/4WfbCp/7Fk/3lR+reF2bH/1un/eVvds2lR+v+2AyDgm\ni76n4DKdl9Mlek81tmX9xKyp/DlNpbNcfbPK9kO/rNhrqLVe3viQs9KtGd3s6iQPSLeLbpJ8WZJr\naq2fPP3WGYwCAAAcOKWUC9Ld1uV1SW5K8vVJHj3/t9krkry8lPLKdLeAeXqSl+21DQajAAAAfaxY\nZnQXsyRPSvLv0u0l9H+TPLXW+rpSyoXpsqH3rbV+qNb6hlLKc5O8McmRdBnSZ+y1AWuz2Y7TGmbX\nXnvtXp8DWDJHjx5NkgXPPWsm3pwJbTPbMm2cqjY51P9De+ePozsyTXf5zWazHDt2LBFvVsMyTUVt\nbMsNN59oKn/ekbYlEM1900JsGsT6+nqOHz+eLDbezP76n1++wOqHde9fvjxZvvh7CplRAACAPha9\njvyAcSkGAACA0cmMAgAA9LBmWvWg9CYAAACjkxkFAADoQ2Z0UHoTAACA0cmMAgAA9GE33UEZjAJs\nMp023vCytf6GG2reeNOtTXVfd+MtTeVf/p/e0VT+Za95Z1P5977ux3qX/b8XXNxU912f/Nim8kd/\n+PFN5dfufF7vspM7ndNUdw4faiu/SI1TzlreH7vcy5zT0NL/k0X/0dx4781pw+0OJ43n5XlHFjzZ\nb4mmZrZ+Ri38PGjQ3PaWm2GLNyvJYBQAAKCHtbXluTCxH+hNAAAARiczCgAA0McSTdneD/QmAAAA\no5MZBQAA6GOJNoTaD2RGAQAAGJ3MKAAAQB/WjA5KbwIAADA6g1EAAABGZ5ouAABAD2um6Q5KbwIA\nADA6mVFg35tOZ23lZ23lW926Pu1dtrUtn7n51qby3/K1X9JU/kfv/Kmm8pND/a95vuNVr2yq+xF3\nO9FUfnrd9U3lD51/pHfZ2S1t/b7WemuAJboSP2lo+4LfSnsz7fk+XKK+T9r6vzX2TRb9Whvb01Z3\n/7iaJNO0vQdb+n3Rlqktrdrb3lC+8Rw4bSvc/8touSIsAAAAB4LMKAAAQB9LNlti1elNAAAARicz\nCgAA0Ic1o4OSGQUAAGB0MqMAAAA9uM/osPQmAAAAo5MZBQAA6GNNLm9IehMAAIDRyYwCAAD0YTfd\nQcmMAgAAMDqZUWAlTaezM92E09ZyUfXQobZrhp919/Obyt/r3Buayk+++1ubyrd45EPv21T++htv\naSp/NLc2lV871PARuegr5dNpW3m7Pe5fTedC43nZeJ5NG+ufLPJ90njOL9U7ZMne3y2frws9pmlr\ny2ykPwvspjssvQkAAMDoZEYBAAD6kBkdlN4EAABgdDKjAAAAfdhNd1AyowAAAIxOZhQAAKAPa0YH\npTcBAAAYncwoAABAD+4zOiy9CQAAwOhkRgEAAPqwm+6gZEYBAAAYncwosO9NWq9iThuLz2ZN5dfW\n+rfnyDltYfrwrLHxd7prU/HZZ25uKr927pHeZU9M2/rx6J3v1FR+MunfliTJ+nr/so1ttxvjEljV\nY9DQ7vZX2BYrl6kHp43vwebPhQNkmfqmpS3Txo+/07a2TGf+6tObAAAAjE5mFAAAoAe76Q5LbwIA\nADA6mVEAAIA+lmhN7X4gMwoAAMDoZEYBAAD6sGZ0UHoTAACA0cmMAgAA9CEzOii9CQAAwOhkRgEA\nAHpYs5vuoGRGAQAAGJ3MKLCSJit8ZfLwof7XAdtfZ+M1xvX1puJrdzqnrf7ptHfRsw4v2fXRwwv8\niGzolyTWKK0Sx5ZWreeAc+zM0p+D0psAAACMTmYUAACgD5nRQelNAAAARiczCgAA0McK71mxjGRG\nAQAAGJ3MKAAAQA9ra/snl1dKeUqSJyS5f5JX1VqfuE25JyR5aZIbN/z4W2qtb9prGwxGAQAADp4P\nJ3lWkocnObJL2atqrZcO3QCDUQAAgD720W66tdbXJEkp5ZIkn7dL8YUsljUYBQAAOLh2G2jOklxc\nSvl4kk8k+c0kz661ntjrE++foT0AAMAiTdZW519/s11+/6YkF9VaL0jyqCSPSfITp9mDp5AZBfa9\n6XS3GHuqSeu27dO24s31L5MFTk+aLPo4rbJp20k2bZhNdaD6kUEtPLYuSd3Jcr1WaFFKuXzDt1fW\nWq/cotiOJ2yt9f0b/v/uUsoV6Qajz9lr+wxGAQAAelhbsTWjtdbLexRru9rSGeSKi8EoAADAAVNK\nOZTkrHRjwkOllHOSrG9eC1pK+aYk76i1XlNKuU+Sn01Sh2iDwSgAAEAfK5YZ3cXTk/zchu8fm+Ty\nUsrLk1yd5L611g8leWiSl5VSzk9yTboNjH5xiAaszWY7ZmVn11577RDPAyyRo0ePJgvaonsPFhZv\nFr3WZ6XXEq2vt5U/vLhrmCvdj60a14A2V78ka0Zns1mOHTuWHKB403xsV/gP24P0nl2q13qAzrEW\n6+vrOX78eLLYeDP729f+4QKrH9ZnP/JhyfLF31PIjAIAAPSxwhdVltHBuFQCAADAUpEZBQAA6OOA\nTHsei94EAABgdDKjAAAAPazafUaXnd4EAABgdDKjAAAAfazZTXdIBqPAvneg7hvaev+5Bd43tNWB\nOk6LnubV2DcM6ABN4Vuq99RBuvfmgtveEluX6hxgJS3PXyEAAADLbJUvZCwhvQkAAMDoZEYBAAD6\nMDV5UDKjAAAAjE5mFAAAoAf3GR2W3gQAAGB0MqMAAAB9yIwOSm8CAAAwOplRAACAPuymOyiZUQAA\nAEYnMwoAANCD3XSHZTAKsMnEFBxWnHN4QNNpW/lV/kN1lV/rgtvS+p5aP9G/Lw8fWqJ+zHLFj+l0\n1rvsrH9RlojBKAAAQB/LdBFmH9CbAAAAjE5mFAAAoI8lmsa8H8iMAgAAMDqZUQAAgD7W5PKGpDcB\nAAAYncEoAAAAozNNFwAAoIc1t3YZlN4EAABgdDKjAAAAfbi1y6BkRgEAABidzCjAftK6lmU6XWz9\nDabTWVP5iavTw1iic2ApHaTXe5Be64IdPqQvh9AS51tD2WnzPhmU3gQAAGB0MqMAAAB9yIwOSm8C\nAAAwOplRAACAHtbsVzAomVEAAABGJzMKAADQhzWjg9KbAAAAjE5mFAAAoA+Z0UHpTQAAAEYnMwoA\nANDD2prddIdkMApwkC3RdKOJ7fKHM532L9t4Dkyns95lZ7P+ZTkAFnheAqvJYBQAAKAPF0oGpTcB\nAAAYncwoAABAHzKjg9KbAAAAjE5mFAAAoA+b7Q1KZhQAAIDRyYwCAAD0sGbN6KD0JgAAAKOTGQUA\nAOhDZnRQehMAAIDRyYwCAAD0YTfdQe06GD169OgY7QAQb4DRiDcAZ95ug1FDf2As4g0wFvEGOD3W\njA5KbwIAADA6a0YBAAB6cJ/RYelNAAAARiczCgAA0MeaJedDkhkFAABgdDKjAAAAfeyzNaOllLsl\neWmSb0jyd0l+utb6qm3KXpbkJ5Ocm+TVSZ5ca71lL8+/v3oTAACAvl6Y5DNJPivJ9yT5d6WU+20u\nVEp5eJKfSvLQJPdMcu8kz9zrk8uMAgAA9LCfdtMtpZyX5DuSXFRrvTHJVaWU/5zkcUl+elPx703y\nklrre+aPvSLJb29RrslSDUZLKZcn+ZFa6wWNj/u5JD+U5LOT/Eat9ftKKS9P17FfMXhDl0Ap5VFJ\nfiTJxUmOJPmbJK9L8rxa60cX8Hx36OMtfv6KefH79+33RR6nUkpJcqTW+htD181qE2v6E2t61S3W\nsC3xpj/xplfd4g1D+uIk67XW/2/Dz/5XkgdvUfZ+SV6z4fs/T3KPUsqxWusnT7cBSzUYnZu1FC6l\nXJLk8nSj8iuTfOx061oVpZRfSvLUJL+e5JeSfDrJRUmelORe6a5wDPl8W/bxDn1/pKH6K5LcaZCG\n3lFJcvckew7YpZSHJHl2kptrrV+7x7rul+S7ktyS5P5Jfr/W+tt7bSPNxJpdiDW9LWWs2VTvfZI8\npdb6lKHCm8tJAAAc1ElEQVTqpIl4swvxpreljDellK9L95r/Yv710iSvrrVeu9d2Lp3JvtpN9/x0\n77WNrkty523KfmrD9ycfd+ck+2ow2nqE7zP/+qJa63V7rGvplVIekeSyJN9Xa335hl+9uZTy4nSL\nj4e2XR/v1Pe91Fr/ek8tG0mt9Y2llD9Kct4A1b0iyWW11jeXUr4kyV+WUj5aa33jAHXTn1izA7Hm\nzBg41mz0b5L87cB10p94swPx5swYON48KMnPzf9/Q5J/uS8HovvP9Unusulnd003IN2t7F3nX0/r\nfXLSMg5Gb3NymkOSn0l3lezeSd6Z5IdqrX85//3j58U/1c1cyINrrW/aoq4rk3y81vpdG3724CT/\nLd3Ui7+c/+xBSX4+ySVJbkryH5P8i1rr9X3btaHcpekW9l6S5MS8zGW11nf1fa4tXJbk7ZuCdZKk\n1jpN8oZNr7skeXqSL0p3de8VSZ5Raz2xocy27dimjx+S5Ik7/PyUqSk79cNWU1mGOAbz33/HvOx0\n/rDLa61XlFIumj/mK5Kck+QDSV5Qa33R5j7d5EFJ/u0uZfqYJXlAkjenm4K0Nm+LwegZItZsSazJ\nyseak6/h4UnWh6qPvRFvtiTeZOXjzSzdNM6jSf58vv5wX5qt2PWg+VKBk66stV654fv3JjlcSvnC\nDVN1vyzJu7eo6up0f7++ekO5a/YyRTdZ8sFouhP7wiTPTfKsdDs9PS/Jf0jypemmQXwgyc+mCxY3\nJXnPDnXtOLWllPKPk/xxugDxqCTHkzwnybF00yr7tuvkh8EfJfmTdIHthiT/OMnnJHlXw3NtbN9Z\nSb5q/ly7KqU8LMnvpJvK8S/TnTTPSje948k9X/N2ffzBbX7+xGzo52364WtO9sOG/jxZfqhjcEWS\nz0931eaH54/50Pzrf0n3hvqeJDenuwq61XSEjX15droA/5adyvWxaQ3JPedf37HXetkTsebU9ok1\nt1vZWDOv73C64/H2JF8wRJ3smXhzavvEm9utdLyptf7vIephWLXWy3f43Q2llP+Y5IpSyg8k+fIk\nj0j3ntzsFUleXkp5ZbqZNk9P8rK9tm/ZB6NrSe6W5Ktrre9LklLKJMlrSilfXGt9bynl5FSIt+1y\nFabPZYznJHlLrfUxJ39QSvlwkj8ppVxUa726b7vSzcF/Z631GzfU/4c9n+t+G69CbnD33H61q48r\nkryx1vrEk88/v8r37FLKs2qtH+nTjm36+Lqtfj6vf2Nf79YPm8sPdgxKKZ9MslZrfeuGuo6n+4Ps\nERvq6pORvCTJB2utH+9RtsX3J3lFrfWPB66XNmLNqcSa/RNrHpfkN5P84ED1sXfizanEm30Sb0op\nT023H8aFSd5da33lEPUum+ls3y3b/uF0a7U/lu4+o0+qtb6nlHJhugsc9621fqjW+oZSynPTnVtH\n0mVIn7HXJ1/2wWiSvP/kG3Lu5NXBz0uXWh5EKeXcJF+Z5EfnV5JPuirJrUkemO6A7NqueYD5R0l+\n7DSf65IkWwXsk3Z9F5RSDqXbje6pm35Vk/zrJF9VSvmvu7Tjgbu0Y7c2nJcd+mGL8oMdg2x/bnwi\n3ZXPXyul/Eq66Qof26bsRg9K8qdbtPn8dNNbdtvn++pa621XfUspD0h35em+6a64cuaJNXck1nRW\nMtaU7kbmR2qtHy2lrNa8sv1PvLkj8aazkvEm3Wu4ah5vJkmuLqW8t9b6th7t4AyaT7P99i1+/oFs\nyq7XWp+f5PlDPv8qDEY3L36+Zf516F3KjiU5lORF838bzdIFgb7tOpbu6tZ225C3PtdJf59u6sWF\n2/x+o+NJzkpyzaafn/z+bunm9e/Ujs/v8Tw72a0ftio/1DHYUq11Op/i8wvprgIdKaVcleTHTq53\n2calSV61RX3Xp8tuNpk/17tKt7Pu1aWUb6i1/nlrPQxKrLmdWHOqVY01T0zygvn/992l/BUn3txO\nvDnVSsabWuurN/x/Wkp527yOfTcYnU2F0yGtwmB0qKu5N6WbBrLRsQ3/vzZdUHhGktdv8fjNQWen\ndn0yyTTd2oGttD5XkqTWeus8uHxjbt+xbDt/l+6K22dt+vk95l8/kW575p3a8ZFdnmM3u/XDZkMe\ng23VWv8qyXfOr7Bemu5q6u8n+dytys+v8H11bl+fMZj5VKGPprsC+ZCh66eJWDMn1qx+rCml3DfJ\nB2qtN89/JDO6XMSbOfFmX8Sb89JtQvW8Wutn5j9ey+37YsC2VmEwOtTlhw+le3Nu9LCT/5kv4P2z\nJPeptf78Xto1r+t/pFvU/oJtft/yXBv9cpLXllIeX2t9xcZfzAPLw2qtf1BrPVFKeXu6+1H92sZi\n6YLof99jO3a1Wz9sU36QYzB3S3a4L1jtdt17Yynl+UleWUo5WrfehvxLk1xfa/2bUsrX1FpvW+jf\nOpWllPI16TYw+KZa69s3tPOu2z+UkYg1pxJrbrdysSbJ1yb5glLKl89//nVJ7lRK+cUkv1BrvWGX\nelgs8eZU4s3tVjHe3CfJT6TbVOqD89/9g2y9I+vK24drRs+oVRiMDnX18DVJvr+U8m/SXZl6SJKH\nbyrzk+kWk0+T/F66++ZcmOSbkzyt1vp/Gtr1r5L88XztwouT3JhuZ6q31Vp/v/G5blNrfd38Nby0\ndLuzvTbdfX/uk+7G0H+d5A/mxZ+R5A2llF/PqTuxvXi+wL/1NZ+O3fphsyGPwXuSPLKU8m1JPjz/\nd0G6nel+J8n7011B/qkk79omWCdd3/5ZKeVzsml6z2lMZbkh3ZXs65Lb1nTdP93r5swSazYQa1Y7\n1tRa/9+N35fulhCzWuvP9K2DhRJvNhBvVjvepNtB+Pm11g8mSSnls5L8w9g4jR52u+Ixts1blG+3\nZfnmn21X5raf11pfn+6+Td+ZLjP1+ekWwG8sc1W6K4wXpNu++LXprvR8IKeuT9i1XbXWN6e7SfO5\nSX4rXYB4UOZXjBqe6w5qrT+e5LvT3V/rlel2cLss3TbjT95Q7o+SPDrdpgGvTbfY/nlJnnKar3kr\nWx2L3v2wRfnBjkG6tRl/mG79xFuT/LN002H+NsnT0n1wvzDdovtHbvP6km5x/9lJfiDJ7+5Qble1\n1nemOw7fXUp5VpJ/n+Sna627Xl1lUGKNWLOvY81JpZQjpZQXpuubbyilvKCUsm1WhYUQb8SbfR1v\n5tnY3y2l/FIp5efT7Tb8zbXWv97loStpOp2tzL9VsDaTagYAANjN7JqP/f2ZbkNv9/isuydLvmfA\nKkzTBQAAOOOsGR3Wsk3TBQAA4ACQGQUAAOjBEsdhyYwCAAAwut0yo4b+sH8t24J28Qb2L/EGGMtC\n482q7FK7KnadpnvttdvdnghYVUePHj3TTdhSS7xp+TCYTJbt72A4GGazWY4dO3amm7Elf9/sM9Np\nW/mJyYH7zfr6eo4fP36mm0Eja0YBAAB6sJvusFwWAgAAYHQyowAAAD20zghnZzKjAAAAjE5mFAAA\noAf3GR2WzCgAAACjkxkFAADowX1GhyUzCgAAwOhkRgEAAHpwn9FhyYwCAAAwOplRAACWS+vNHCfy\nK/tRy/rMsRKW1owOyzsXAACA0cmMAgAA9OA+o8OSGQUAAGB0MqMAAAA92E13WDKjAAAAjE5mFAAA\noAe76Q5LZhQAAIDRyYwCAAD0YM3osGRGAQAAGJ3MKAAAQA8za0YHZTAKLI/ptHfRycTEDuD09d2E\nZJK2PzynWWsqP5m0lW/SEFOTJWv7klnkpjXN/dh4XLPCn5ctfdPaLSwHg1EAAIAeJEaHtbqXSgAA\nAFhZBqMAAACMzjRdAACAHha5fvggkhkFAABgdDKjAAAAPUxnMqNDkhkFAABgdDKjAAAAPcysGR2U\nzCgAAACjkxkFAADowZrRYcmMAgAAMDqZUWB5TJbj+ljrPcQmk7UFtYQzan29f9nDPk5XTf/3beP7\nuzF+iDfbWJLPg5Na+32h96Jcsr5ZqOm0f9mRMpbuMzqsA3Q2AwAAsCxcygUAAOjBmtFhyYwCAAAw\nOplRAACAHmYyo4OSGQUAAGB0MqMAAAA92E13WDKjAAAAjE5mFAAAoAeJ0WHJjAIAADA6mVEAAIAe\nrBkdlswoAAAAo5MZBVZSy5XJyWStqe7W8uxTk+W5XrvIK/GTNNa9RP0yhkVnQRYabxqPVfORnU5b\nH7E4ja+19bgu8nNk0W1ZaS3HdaTz0X1Gh3WwPlEAAABYCjKjAAAAPVgzOiyZUQAAAEYnMwoAANDD\n9ICtGS2lPCXJE5LcP8mraq1P3KHsE5K8NMmNG378LbXWN233GINRAAAAtvLhJM9K8vAkR3qUv6rW\nemnfyg1GAQAAejhoa0Zrra9JklLKJUk+r8dDmrZ7NhgFAABgJ30GmbMkF5dSPp7kE0l+M8mza60n\ntnuADYwAAAB6mM1mK/Nv6Jfeo8ybklxUa70gyaOSPCbJT+z0AJlRAACAA6aUcmWS7dZ3vmXT2s9d\nM6O11vdv+P+7SylXpBuMPme7xxiMAgAA9LBqa0ZLKZdv+PbKWuuVJ7+ptT64oarTfeE7DmINRgEA\nAPahWuvle3l8KeVQkrPSjRsPlVLOSbK+1TrQUso3JXlHrfWaUsp9kvxskrpT/QajwP43nS62/snB\nWX7fekV4MmnaVG+xWs+DJTqurf3YdJyW6HUuo4X2/WlYZP3N79fWc+eWWxvqXnBbGt1y67b7r2zp\n7LMO9S7b3O+L/kxrqf/wwRtKHLT7jCZ5epKf2/D9Y5NcnuSKUsqFSa5Oct9a64eSPDTJy0op5ye5\nJt0GRr+4U+VruyxunV177bWn33RgKR09ejRp3Hp7BE3xpuUPsslpzyzp+wQH5495g9HV0PT+WOAx\nms1mOXbsWLLi8abFot8jSzUYbbXIwWjjoKi1H9dPtMWPlsFoM4PRLa2vr+f48ePJYuPN7D//8bsW\nWP2wvu3rH5AsX/w9xfKcQQAAAEvsAGZGF2p1L/sCAACwsmRGAQAAepgteJb0QSMzCgAAwOhkRgEA\nAHqwZnRYMqMAAACMTmYUAACgh0XfS/igkRkFAABgdDKjAAAAPVgzOiyDUWAlTSZrDaVbyrKT1g/h\nyTL1/cRkoK20Tjlre+8dPK3909r/Le/ByVpbW2659URT+bPPOtRUPg19M7vl1qaq1w63/Ul7c+Nr\nPafxtbYc18m0rS1pnSbqPcsSMxgFAADoYWbN6KBcJgYAAGB0MqMAAAA9WDM6LJlRAAAARiczCgAA\n0IP7jA5LZhQAAIDRyYwCAAD0YM3osGRGAQAAGJ3MKAAAQA8zmdFByYwCAAAwOplRAACAHqbTM92C\n/cVgFFgeLRF+skQTO1o/mZap7Y0OH1rdth8kk8na4ipvOd+XeDrb+ol+r2PR53zrZigtt5WYpq3u\nW9fbYtlnbl5vKn/+zTf2Lju5652b6m51qPE98ukbbm4qf5dZ/75pHdusHT7UWL7xz/3G+mEvDEYB\nAAB6sJvusFziBgAAYHQyowAAAD20TJVndzKjAAAAjE5mFAAAoAf3GR2WzCgAAACjkxkFAADowZrR\nYcmMAgAAMDqZUQAAgB7cZ3RYMqMAAACMTmYUAACgB2tGh2UwCiyPyYpO1ljVdsPpaDnfl3g62+FD\ny/G+naytNZWfpn+ftt6C4sSJaVP5a/7++qby77vxlt5lv/C//mlT3Xd+7D9pKv9f3vi/m8p/693b\n+ubEnc/vXfbQ3Y821Z3Dh9rKT9rOMRiTwSgAAEAP7jM6rOW4LAgAAMCBIjMKAADQgzWjw5IZBQAA\nYHQyowAAAD24z+iwZEYBAAAYncwoAABAD5aMDktmFAAAgNHJjAIAAPQwkxodlMwoAAAAo5MZBQAA\n6MFuusMyGAVWUstNpyeTtdbK28pPTDLZzs23rPcue87ZC/5IWu/fliTJ4QPyEel8Xykt8az10B46\n1HZszz1ydlP53/mDd/cu+1uv/Zumuv/qe9oGCBd/z+Oayl//pO9pKn/XH/yn/Qs3vqfWWmNT63vW\ne5wROdsAAAAY3QG57AsAALA3LTOz2J3MKAAAAKOTGQUAAOjBBkbDkhkFAABgdDKjAAAAPcysGR2U\nzCgAAACjkxkFAADowZrRYcmMAgAAMDqZUQAAgB7cZ3RYBqPASppM1hZZ+eLqbjWdtpVfdNsb27N+\nov+H9jkLbstSHddlol/OqNZYNklD+UNtbTn7rLYHnHfkrKbyT/+hr+1d9md/4EFNdWetrR/v/bG3\nN5VvHX8s9DMK9hGDUQAAgB6sGR2Wy6EAAACMTmYUAACgB4nRYcmMAgAAMDqZUQAAgB7spjssg1EA\nAADuoJTyW0m+Lsm5Sf42yXNrrS/dofxlSX5yXv7VSZ5ca71lu/Km6QIAAPQwnc1W5t9Anp3kXrXW\nuyZ5ZJKfL6V8+VYFSykPT/JTSR6a5J5J7p3kmTtVLjMKAADAHdRar970o1m6QeY7tij+vUleUmt9\nT5KUUq5I8ttJfnq7+g1GAQAAejiIa0ZLKS9KN9A8km4Q+vptit4vyWs2fP/nSe5RSjlWa/3kVg8w\nTRcAAIAt1Vp/OMn5SR6UbrC53RrQ85N8asP3n55/vfN2dcuMAgAA9DBbsRuNllIu3/DtlbXWKzf8\n7sokl27z0LfUWm/7Xa11luSqUspjkzw5ya9u8Zjrk9xlw/d3nX+9brv2GYwC7FHrlJ3JZG1BLUky\nnbYVT1tbJpO2CTXnHVngBJzGtizSUp0DjZrbnobyK/ZH2xnR+J5dZN3TG25sK//pG5rKf+rXXtm/\n7L/7raa67/WxrZavbe8Lv/VXmso/8dsvbir//d/+wN5lzz/v7Ka6zznrUFP5w4eWJ1Y2x76Wc1i8\n2VKt9fIdfvfg06jyrHRrRrdydZIHpNtFN0m+LMk1203RTQxGAQAAejlIa0ZLKReku63L65LclOTr\nkzx6/m8rr0jy8lLKK9PdBubpSV6203Msz6USAAAAlsUsyZOSfDDJJ5I8N8lTa62vS5JSyoWllOtK\nKZ+XJLXWN8zLvDHJ/03yviTP2OkJZEYBAAB6GPD+nUuv1vp3SR68w+8/kE2bE9Van5/k+X2fQ2YU\nAACA0cmMAgAA9HCQ1oyOQWYUAACA0cmMAgAA9LBq9xlddjKjAAAAjE5mFAAAoAdrRoclMwoAAMDo\nZEYBAAB6kBgdlsEosDym0/5lJ8szsWMyWWsq3zLFZ7Lo17noT9WGYzpNWz+29vsiLVNbWrW3vaH8\nQdzooyWOnU75BrNbbm17QGM8mN30maby53/7w3uX/dMHf3NT3fc+1BYr3/nYL2gq/74v/+Km8tff\neHPvsucdOaup7lvX286Z1vf4ZG2J4lnLZ+AC30ssjsEoAABAD9ODeJFtgZYntQAAAMCBITMKAADQ\nw8yi0UHJjAIAADA6mVEAAIAerBkdlswoAAAAo5MZBQAA6KHl9mzsTmYUAACA0cmMAgAA9GDN6LBk\nRgEAABidzCgAAEAP7jM6LINRgL2aThsfsLawuqctdSeZTNrKN5v0n4CzdFN1Wvq+4XWejtYNMxZ5\nXFvaMtsP09ma399LpPW8PHSorfhnX9BU/rp7fHbvsv/kzndqqrvVXb7rW5rKf9l11zeVv/6cc3uX\nPXSo7TgtOm63TEOdrC34M4R9z2AUAACgB2tGh7V0F6IBAADY/2RGAQAAenCf0WHJjAIAADA6mVEA\nAIAeJEaHJTMKAADA6GRGAQAAenCf0WHJjAIAADA6mVEAAIAe3Gd0WDKjAAAAjE5mFFgekxW9PtbY\n7rbSawuse/Fa7sc2mbS91oNkmfqmpS0HMoGw6Dg2nfYuutbYlrVz79RUfn2trf6ja/3PnZtvPdFU\n95Fz2v6kna2vN5WfHLtrU/m7NPT9+on+xzRpv8/lpKHf2Z3M6LCW7e8WAAAADgCZUQAAgB5aM9Ps\nTGYUAACA0cmMAgAA9DCzZnRQMqMAAACMTmYUAACgB2tGhyUzCgAAwOhkRgEAAHpwn9FhyYwCAAAw\nOplRAACAHqwZHZbMKAAAAKOTGQX2v+m0rfzEdTrSdh44x/avVT5Whw+1lW98ra1/RK6f6P8+Oees\ntra3ZqsmZ5/VVH6RDh9qPMcaD2tz30zW2p7ggLFkdFgrHGEBAABYVTKjAAAAPVgzOiyZUQAAAEYn\nMwoAANCD+4wOS2YUAACA0cmMAgAA9CAzOiyZUQAAAEYnMwoAANDDzG66g5IZBQAAYHQyowAAAD1Y\nMzosmVEAAABGJzMKsI9MG9eyTCZrC2rJ4utfttcKSZLptK38ZIF5gUXWvWCLf7829s0K92Wrltg6\nSWOW8AD1I/0YjAIAAPTQeiGUnbk8AQAAwOhkRgEAAHqwgdGwZEYBAAAYncwoAABADzNrRgdlMAoA\nAMC2SilflOQvkvxurfVx25R5QpKXJrlxw4+/pdb6pu3qNRgFAADo4QCvGX1hkrcmu97P56pa66V9\nK7VmFAAAgC2VUh6d5JNJ/iTJbjcBbrp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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(np.imag(coef_[:,center, :, :]), figsize=(2, 3))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can see that the coefficients for some basis sets have significant values, while others are mostly zero. This means that in principle we could probably describe the system with fewer basis states. We also notice that when there are non-zero components, they are typically centered near zero. This is intuitive, since it tells us that the elastic response of the material is local, as we would expect (and as can be seen in the other elasticity tutorials).\n", + "\n", + "## Prediction of Strain Fields for Validation Microstructures\n", + "\n", + "Now we want to use these coefficients to predict the response of the validation set, and ensure that the results are in line with the outputs of the full simulation." + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "y_predict = model.predict(X_val)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First let's simply compare slices of the $\\epsilon_{xx}$ strain fields for one of our validation microstructures" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VaFt8PJKrVTFfbp9NlfaXXit6FjC5eGNSW1R8ioiISC68R1c4iq1e8A70Y2bj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\nLwJXu/udZfZ5IXAZMAJYCkx195szmc7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yQcVnU1DxKSIiIhmh6rMZqPgUERGRTDj/1v+/\n612QFahbfF7bNeR6wlLB1xO5WPb3ot7S7ev7en70eld+tLjZlQe4pnOTK79jeKt7jFzR10O50Nvl\nHsN7vIfy/e4xyl3zrvxqfkZyzr7ZIwVfz2yAWb505Tt7/H/r9Sz4jnl5seweYzVf+1obc7ymC/l2\n9/OXFn2vhc3O1zP4X9Pe8xjAoPNY7Rhynmdy/n7lbRt8x2OsZ8Q9xlC+z5XPd7iHYK500ZX3f6dg\ntGPQle91/twC5Eu+PZvt9J3HAPI537nM+3WX2npc+YTVHBb5DrQs1l6dq/lrEalmLU/yOs+ISDUq\nJjOq3p8vOvAi8m3TeUZE5HtE7TVFREREpGFUfIqIiIhIw6j4FBEREZGGUfEpIiIiIg2j4lNERERE\nGuZrLFhBI7DP+p8AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_strains_compare\n", + "\n", + "\n", + "draw_strains_compare(y_val[0, center], y_predict[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So it looks like the MKS is working pretty well in this case, but it is worth seeing if we can do even better.\n", + "\n", + "### Improving the MKS Results\n", + "\n", + "The next thing to try is to include even more basis functions:" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3AeWGKsnmalXsySx33H3Lbc/MjgB+DywC/qFabyao0BQREZGc2Rc9mu4+ucLq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\n1UOl+U6xeciPP+Y9oTzASaNPCOXvf2p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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gsh_hex_basis = GSHBasis(n_states=np.arange(20), domain='hexagonal')\n", + "model = MKSLocalizationModel(basis=gsh_hex_basis)\n", + "model.fit(X_cal, y_cal)\n", + "y_predict = model.predict(X_val)\n", + "draw_strains_compare(y_val[0, center], y_predict[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Clearly now the results are very good. You might ask if we have too few or too many basis functions? First, let's look at the influence coefficients and what is going on." + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_coeff\n", + "\n", + "\n", + "coeff = model.coef_\n", + "draw_coeff(np.real(coeff[:,center, :, :]), figsize=(4, 5))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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nvGUK/QAAAAAAMIVSyp8kuV+SGyX5SpIX1lpfs07bM5P85qjt25I8sdZ67TTb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\nNIItN1hentVVVQAAAAAANGj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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_coeff(np.imag(coeff[:,center, :, :]), figsize=(4, 5))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "If we look carefully at the influence coefficients we notice that they appear to be identically zero for the 15th basis function and beyond. If we wanted to be thorough we would want to check the influence coefficients for even more basis functions, but for the purposes of this example we can be satisfied that we only need the first 15.\n", + "\n", + "Let's redo the study once more with only the first 15 basis functions and hexagonal symmetry." + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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c6GLFZ2eJTaOwE/oxswnAr0lusNINRyIiIvLu0NnLa5rZxSWfznX3uSWfr6X8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\nfnf/WZ7jfR64GzgPOAj8Dlji7gW77dXzKSIiIqW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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gsh_hex_basis = GSHBasis(n_states=np.arange(15), domain='hexagonal')\n", + "model = MKSLocalizationModel(basis=gsh_hex_basis)\n", + "model.fit(X_cal, y_cal)\n", + "y_predict = model.predict(X_val)\n", + "draw_strains_compare(y_val[0, center], y_predict[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we expected the results look great, probably even better than the previous iteration.\n", + "\n", + "### Selection of the Wrong Crystal Symmetry!\n", + "\n", + "Finally let's take a look at what happens when we choose the wrong crystal symmetry." + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gEREReW/ZH4WmmV1b9O0sd59V9H0DpXsu+7Gzx7E9DcDQdtpS1L4BKDXKfzZX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\nY/0egZ+OejRFREQkXzpeh+alwAhgjLs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ZautwxXuvqfcaud+zacRaoPxoy9j0lLcSuDG1\nzcBrcH8BbjzTcW+pVlYtnobTTN65745KM3NfvO5kfYXnogJoFu4vAK4S31PzgjmaAAAgX8gzcyNr\n6BwAAKCVXKvEhVGYq0CPJgAAyBMSvhyhMhAAAACagkQTAAAATUGiCQAAgKYg0QQAAEBTkGgCAACg\nKf4HlfdHkdnakUcAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "gsh_cube_basis = GSHBasis(n_states=np.arange(15), domain='cubic')\n", + "model = MKSLocalizationModel(basis=gsh_cube_basis)\n", + "model.fit(X_cal, y_cal)\n", + "y_predict = model.predict(X_val)\n", + "draw_strains_compare(y_val[0, center], y_predict[0, center])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As you might expect, when the wrong gsh basis functions are used for our problem the results are pretty bad!\n", + "\n", + "## References\n", + "\n", + "[1] Binci M., Fullwood D., Kalidindi S.R., *A new spectral framework for establishing localization relationships for elastic behav ior of composites and their calibration to finite-element models*. Acta Materialia, 2008. 56 (10): p. 2272-2282 [doi:10.1016/j.actamat.2008.01.017](http://dx.doi.org/10.1016/j.actamat.2008.01.017).\n", + "\n", + "\n", + "[2] Landi, G., S.R. Niezgoda, S.R. Kalidindi, *Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems*. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", + "\n", + "\n", + "[3] Yabansu, Y.C., Patel, D.K., Kalidindi, S.R. \"Calibrated localization relationships for elastic response of polycrystalline aggregates.\" Acta Materialia 81 (2014): 151-160. [doi:10.1016/j.actamat.2014.08.022](http://dx.doi.org/10.1016/j.actamat.2014.08.022).\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/microstructure.png b/notebooks/microstructure.png deleted file mode 100644 index 7f801f17..00000000 Binary files a/notebooks/microstructure.png and /dev/null differ diff --git a/notebooks/stats_checker_board.ipynb b/notebooks/stats_checker_board.ipynb new file mode 100644 index 00000000..26994132 --- /dev/null +++ b/notebooks/stats_checker_board.ipynb @@ -0,0 +1,395 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Checkerboard Microstructure\n", + "\n", + "##Introduction - What are 2-Point Spatial Correlations (also called 2-Point Statistics)?\n", + "\n", + "The purpose of this example is to introduce 2-point spatial correlations and how they are computed, using PyMKS.\n", + "\n", + "The example starts with some introductory information about spatial correlations. PyMKS is used to compute both the periodic and non-periodic 2-point spatial correlations (also referred to as 2-point statistics or autocorrelations and crosscorrelations) for a checkerboard microstructure. This is a relatively simple example that allows an easy discussion of how the spatial correlations capture the main features seen in the original microstructure. If you would like more technical details about 2-point statistics please see the [theory section](THEORY.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##2-Point Statistics for Checkerboard Microstructure\n", + "\n", + "Let's first start with making a microstructure that looks like a 8 x 8 checkerboard. Although this type of microstructure may not resemble a physical system, it provides solutions that give some intuitive understanding of 2-point statistics.\n", + "\n", + "We can create a checkerboard microstructure using `make_checkerboard_microstructure` function from `pymks.datasets`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.datasets import make_checkerboard_microstructure\n", + "\n", + "X = make_checkerboard_microstructure(square_size=21, n_squares=8)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Now let's take a look at how the microstructure looks." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUUAAAEaCAYAAACGrEV/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npSVVq1V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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(1, 168, 168)\n" + ] + } + ], + "source": [ + "from pymks.tools import draw_microstructures\n", + "\n", + "draw_microstructures(X)\n", + "\n", + "print X.shape\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Compute Periodic 2-Point Statistics\n", + "\n", + "Now that we have created a microstructure to work with, we can start computing the 2-point statistics. Let's start by looking at the periodic autocorrelations of the microstructure and then compute the periodic crosscorrelation. This can be done using the `autocorrelate` and `crosscorrelate` functions from `pymks.states`, and using the keyword argument `periodic_axes` to specify the axes that are periodic. \n", + "\n", + "In order to compute 2-pont statistics, we need to select a basis to generate the microstructure function `X_` from the microstructure `X`. Because we only have values of 0 or 1 in our microstructure we will using the `PrimitiveBasis` with `n_states` equal to 2." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.stats import autocorrelate\n", + "from pymks import PrimitiveBasis\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2)\n", + "X_auto = autocorrelate(X, p_basis, periodic_axes=(0, 1))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have now computed the autocorrelations.\n", + "\n", + "Let's take a look at them using `draw_autocorrelations` from `pymks.tools`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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B+Iy4i7H3Anf2maoqY+ZdfSwaXBcyszdtbBA2djVv9jnra6zc6wRj6NKbh+bg\nLj5LcCOkCk2vj2leEve9zL0pn8H1QPq4IVKzYC5oxuiTRW7Ht0Kjn1VFCDWUboZwDJMl3MVkCWXB\nVAB1blgAQXw8kMwagSKNLxEMFI1/RtylIfYGd6ZEwlEWBTW8gdG4j5U3+3yXSrKK+2i8ATQF9zCs\nVdIJUyIhFXgz6WOal/H/xD3qZi9wb5ZncD2QPm4M2koQCAQCgUAgEAijIDVvmLv7RnbL4T9RIpI5\n8Eu108Mg4KUhJ8XhXyqP6fmB5OAfIoh3llGwg6gnP1HBDsR919wnJeCwirfgGef9DEPpWgQTHuhR\nzT1kv6vnizcYKeVu6HXejJAJMHVoSn1M85K4TyX3JnwGz5qJWpA+boj0LJh7R8acUoalbZGjUVWE\nfJKGFReQ0sXw464LVKTj40wpw9IqsdQtQDR5mRnE9wNU4lQ5ruvDjcf1/JC3o+O7n06HuI+PO0+d\nU3F3O6UdTKOGNxCZv5iCqngB5+N6PlfWrsRzT1IJBYaWiDxnJs/Q8zkXSLzSyj2XqaNyKMgkdWhG\nfUzzkrhPJfdmfAbXBenjhkjNgplAIOybUHRSQwQCgZAGkD5ujNRcme6+wpjKYiZKTkrRqGoYCHOH\n6yIsRWUfg3IZYSkqwRyWK1K7zM8Zc1nM2ISkAMJsoWvcwd7zQ757LVd8lF0vbnu89Gei7XrjKglK\n3MfPPSzHPEtlXoo7LI2zLHcQihALyRwYKKp4c+H6VdwYfz/ZdkV7LOVQq3kDUeQ5DyrxfPE2o1RB\nwDmmk3sYWKhBnTKvhKlFU+pjmpfEfSq5N+EzuC5IHzdEehbMvSPI2DoyVmRusC0d2Ux0s/i+nkjN\nIvKRJ6M5ma9UWHHFJC2UEBaLAICgUEI4UojaxRLCQik6p1iEkskAABTHhhpXAlKyDtS47jqk2uyq\nqoik4NL4QShMH2XXQ7EUKY1S2UOxHN2cI8UKSnG7WHZRLEVt4j453APGc6SAoFiquS5KJgMl5qlm\nbChZJ2o7NvexCxjvaPAa3kBk6mMKqFhyJZ4eRooV6Vq48TkeMnY0/TKWAduK2tmMCd9ni5Ek92re\njDszZwblcuJ3TSP3OhQIKUQz6mOal8R9Krk34zOYMD6kZsFMIBD2UVCQCYFAIKQDpI8bIjUL5u29\nw8g7JpzYHJPLmlLEp8nPU1XheB9FfLL8jaEUjeoL00exiGBoBAAQDBcQxDu8cGiYt4PBEagt2aj/\nrIMwn4toSV36AAAgAElEQVTans/7VAHuDB9KpTYRhDzPoRyZW654fCc3VKigEO9qR0ouhkeidqFU\nwVAhahP3yeEeDMc8RwoIh4Z5OxiMrovakoUav8UI87mIdzSIyLmoKgilMqP8TYciR1+H3NRXKnuc\nW6FYwUi80x8eqaAQm+GGChXknYifY5vIZdnbu6CGNxAFV4no7hA8t6fvi6CSUlm8zUgp96xdq3IU\nUtCpQ1PqY5qXxH0quTfhM7geSB83RmoWzN29IyhXfOSd6EbwA5EiBUAitYtlsujPKpMIS9PiutxX\nKiiUxCQdGELAJmlVW/an4pNUjiLVNKixM3xou3XrsHt+IEwiFZ+bQQrFCgZHov6HCxUMDkftoUIF\ng8Mlfj5xn3juwcBQ9P/QcN227EumSumAGG8AUHUdoe0mxpJ5A7EJMPYNK5Y9rqAGR8oYjpX14HCZ\nK+7B4RI/P+/4PL1RNXeWusgy/RreTB4ebV2ucJNnWrm35ev4MFOi/NSh2fUxzUvivre5N+MzuC5I\nHzdEehbMfQW4UioY2V9Ik25UQ1fhupHYvh/wUpNhIDndV1yxwxsRu7pgaBhB30D03b4BPkn9vgFo\nUrqXhGe/lO8xZGldMnYizyKrnuP7geR0L3ylRkoun6T9gyX0D0UKZHCkzNvEfXK4c6XUNwA/5h8M\nDPG2JqU3qubO0hiFphHxBhJ5RUNVpDGKgkyEnxh/i1GooH8w4tk/VOKKq3+oxNP7uK5fw5v9ZemN\nXFfnY4VhKHKber5IVVQqCx+5lHKf2eGgBvRGI3VoRn1M85K4Tyn3JnwG1wXp44agK0MgEAgEAoFA\nIIyC9Lxh7h1J+sBB+AtpqsoT6JctkXhb3ohFB+K3GxWXmziCYkn4Skk72aBvAH5vPwDA7+njvkLJ\nvDEq39UGpsGjd6NdoJCTDx+Ay1au+NyHaHhEmEH6h8QOr2+wiN6ByL+LuE8Od3kXz3f3vf0R72jw\nGt5AtKMPWDJ5x5beeiS5s6+6XsBNeqWyx/3EBofFm/T+oRL6BiPOvQPFpA+cxJ1VVzN1DWVLJNmv\n4R3LwwsAlMsi8jyl3IfqvNVQKFF+6tDs+pjmJXHf69yb8BlcD6SPGyM9C+a+EW7OA+JgEoPlP9R4\nyhfHNXiFGj8IxH0TBEkfIpbjsFBKmkTYhO3th9/dG7W7e5I3IJukpiFyQmYyCLMV0T/zIQoCBIrG\n5WGylV2RskYOcBscKfMbtae/gJ39cRAAcZ8U7rKfGFdQ3b0Rb6CGO6+4ZJs81U+YrQifPM/jSi1Q\nNO7rVpHTGJXdRDAJM331DRbRE3Pe2V+o4Q1EqYpY3s+MrcNxDd4/GysIwWVI+AuWKjxVUVq5DxXq\nmAG11KghQoym1Mc0L4n7FHJvxmdwXZA+bgi6MgQCYUpBbzQIBAIhHSB93BipWTDv7It2OwqvniMc\n7C1T4+mNyhVRC96TTYZBiJCldZFMImGxyFPWyCYRv6eP72r9HT1CEFXh0biKafCqO2rWEX1WXD6W\nnNbFSzjd+7wwh5wRon+oxF0RdvYXOG/iPjncZRMgM/v53T21vKPBxRsNyxLpjcplYWKsSmPkJYJM\n4qjskpeIvmYmsN6BIn+rXs1dDqJibzQc2+R9uq7PxwqCUJj/fD9h/uQpnFLKvZ5LBgWZpA9NqY9p\nXhL3KeTejM/guiB93BCpWTB3tjvobHPQ0RqZXdryNlpykc9O3jG5CdAyNW4a1DVVVPwJFSh1bjQl\nk+E5DsNymUejQppoAKBNnxb9ndYOrb0VAKC25vl3lUxG9GkafCyoCpdB19S6Zsu8I000L0hE13L+\nxH1SuDMlo1VcyU8syV2b1h6121uhtuYjai1ZUX3JsoTi1jSu0FVV4em1qk13LKdnuSJ8POUsAow3\nAHS0ZtCWjzi35Gz+3YwtyrIahsbHUlUFCBUuj/xQYTKnlXs+WyetHCF1aEp9TPOSuE8h92Z+BhPG\nhtQsmAkEwj4KeqNBIBAI6QDp44ZIzYJ5ensWHa0ZtLdEu8iWrJWovJOxoh2lZeg8QltTVW69iSJK\nIzqKITnLO7Yw5ZQq9XM8qorY1Xa0iV1tPse/qzi26NMw+FiQZJCjxy1D5zI7tskLAFTneGQmT+I+\nOdx58EV1fksW8T+tHVpHW3SoNQ+VVVnKOjwqWbFNKKyylK5zhaIqSERQW0YkR8YyuMk67/h1c9kq\nisLf3rW3ZNASv3mVq6tlLIP3aeoaH0tVIEWP60I22+Qyp5V73ql9w8wiwQnpQVPqY5qXxH0KuTfj\nM7geSB83RnoWzB3ZyOwX36iRCTBq57ImbEsyAerMFFLVSXznJHx/Mnay1GSdlDXQtIQZRG4r7LsZ\nO2ESEU+GZHeynx+TOZc1RURtdcqa2KxD3CeHO6+aVM09NmnJZj+5reRzUOPk+LIJsJo7k0X2dbMt\nXZRWDRqk5tJUYfbLWpIJ0OLftS3JBKir9Tf+qpL084tlTiv3ui4Z9EYjdWh2fUzzkrjvde5N+Ayu\nC9LHDUFXhkAgEAgEAoFAGAXjesP8yCOPYNmyZbAs8Zboy1/+Mg499FD+/6pVq3DPPfdg586daGtr\nw2c/+1l0dXXtsu/p7U7Cwb4lZyEXt7O2gWwmalumzh3bNU3lZn1FVUUNdNOAYse7saxTvza7nOPR\nNJJmkHomkazD+4RpiJKVqpqMJOdO9zqX2feFo300tMjxyHaExH2SuFfxZpwZ/4TZr9oEyPjbVsQb\niEq0sn7kaGpDg2VG0ymbMTnnau6Mg6GriSAq/vbOMZG1Dd4P69MwND6WoihcBugal02xLS5zWrnn\n47c1CVAao90C6eOkTqJ5SdynlHsTPoPrgvRxQ4zbJaOrqwvf+MY36n72/PPP4+c//zn+6Z/+CQcf\nfDD6+vrGHIk5vSOb8BfKZcWN6mRMZBImQBGVLSPhQ2RF/aiOLaJxoy/F52rCJ8iyeDSqmnWEGSTr\nQM05vB/WZ8KHSIKuqVw2y9Tg+9E5ni8WCZqmct8nyxSRvMR98rkz3yzFNoXJrCUrlJKkoNScE/UB\nQLGqfOaqeAOAoQs+vq/X8AYiHzN2jmVqCZ9QZvbL2gacDPOZk02AWg1vJg+XzTK5zGnl3lLHJUOh\nRPm7DdLH9XUSzUvivre5N+MzuB5IHzfGuK/MaAp3xYoVWLJkCQ4++GAAQHt7++5LRiAQ9g3QG43d\nBuljAoEwoSB93BDjXjBv2rQJl1xyCXK5HN7znvfgzDPPhKqqCIIAGzduxNFHH41//Md/hOu6eNe7\n3oXzzjsPplnHDFuFmR05ZGwR2WlbwqSQsXQpyESHrgkThJz3kyf81jVhvghC4aitiV1tYBoiP2TW\nETkeHVsEF2SlXW0mI8wsusbHgqpAlUwiTDbL1BMO9mw3qmkikjVj6yICl7hPDnfps4CZwDIZ8RYj\nkxFR3BlbmA8dW1wX2xKRw3LeT8kEqGsKN9dVc2fnmLpUUtgWuWwzlsF5ZqW3d1GQic77Z/3IOU/l\n31WxLZ7bM63c67tkUCjF7oL0saSTaF4S96nk3oTP4LogfdwQ41owH3roobjpppswffp0vPbaa7jl\nllugaRrOOOMM9Pf3w/d9/OlPf8J1110HTdPw3e9+F//zP/+Dc845Z5d9T+/IRmZ6Q5j62M2ZaBs6\nNzuwG4WB+1CZBlRW/Sc6MTpf1xGySerYojZ7uSwiUCXzkGJbwgxiW1DlxOF1bipVUbhsyWDYZOWg\nshWZaBzX4AnFifvkcFdj01VoGlwRh9kKT6CvWFbCNMaVkmXytionyq/izmQxdK2GN5CskFa2fDhu\n1E+54gtzoCFMfZZZ1Y6vi6FrNby5PMz/LwjBRUgpdypcMnEgfdxYJ9G8JO57m3szPoMJ48OoC+bH\nHnsMt912GwDgkEMOwTXXXMM/e8tb3oIlS5bgvvvuwxlnnMHfWpx66qloa4vyKH7oQx+qq6DXrl2L\ntWvX8v+XLl06MWwIBEJTYMWKFQCiuV9P+RNqQfqYQCBMNJguBkgf7wqjLpgXL16MxYsXj9oB86HL\n5XLo6OgY06ALFy7EwoULE8emtzswDI2bCwxdlSJYNW5qMHS5FKswR0CRolQDAyJ/voIwDhAIbRdg\nOSErLkLX5W2+YzVExC5MQwouEOU+YYgoVagqVLDSlAoCKfJU5ITUYJkscbjOS3NWPJFEnbhPDvfQ\njhPFZ2yeND50Xd5WZJ6mIaK7DUOYFU0j4h0NzsmpULh81dxF8IUP142mmesFqHgigTzjY+oiY0iU\nPUTwZO2oFKswPUKRItKDWGaINylp5Z7LRP8nFmWUKH9MIH28C51E85K4TyX3JnwGA3U2yKSPG2Jc\nLhl/+ctfcOCBB6KtrQ1vvPEG7r33Xhx33HH88/e+97148MEHccQRR0BVVTzwwAM46qijxtT39I5s\n5F/Ebn4Vkj+S8DtSFTEpEj5zUJIVavjdYkCJo1RDzxOpXYIw+h9A6PuiDryuC6d3XRPRqJqWSJuj\nJG5YdooK5h2oKiJlje8H8HzRZuYSPwjgxeleiPvkcGc84fncly70PITxdVE0iaeqSFHMuvBJk1IG\nKQkFLWQ1IZkDDY2n8fF80Q4C8ATynh8IvzJVJMGP/NCEvxnrs8ZnLlaSNdyZgk0pd0OvE1BCQSa7\nBdLHVTqJ5iVxn0LuzfgMrgvSxw0xrgXzmjVrcOutt6JUKqGtrQ2LFy/GRz/6Uf75WWedhcHBQVxx\nxRUwDAPHH3984nMCgUCoBpkAdw+kjwkEwkSD9HFjKOFYE3NOMt7Y2g1VVRKbG0VyqpfLOLJdXsPS\njkGQ9HqXokXDxPFAfM76km6WxI0jj6WqifOSQ8fO/tJllaNV5csdhOIz4i762Svc2WeqMnbe8t+a\nocMx82afMx5j5T4qb/mvPBDSxT0MA+y/34zE91865/K6/U4U5t9166T2/2bEm0kf07wU//OxiXtC\npAnn3iTP4FkzO2v6IH3cGKnJUO34bpSapdENE8p3MTte/4YNoCBQxHcDRbphpBsvUDT++S4ni9yG\n0rCmuIp4ERhKEyMcg6Ig7mLsvcCdfTbqoqCKd/S3PlSEY+cNxKvHXSjJGu6NeUe80s/dR539udKo\nZ8JU4c2kj2lesgGJO7B3uDfNM7geSB83RGoWzAQCYR8FBZkQCARCOkD6uCFSs2D2duzcPYd/qUSk\nJ9WL93xhmhAO/8GEO/wnymN6HkKpbjzfzU1WsANx3yV3+bOJCjis5h3x8XlARxgEvBzqpAR6VHEX\nv7EwQ6aVe2AawP5IgHzm0odm1Mc0L4n7VHJvxmcw9p+FapA+bozULJj97TvHlFIGpgGWsgYQP24A\nhd+MFS/gKctcz+c3ryulMtuTlDKBoSWiUZkZJPR8nh4HUqqc0PN5ChlU3N1Op0Pcx8/dlXjuSUo7\nlq5Hjr5WEXIFFVZcQEoTxI+7LlCRjo8zlRC7FtXcmcnT9wNUYl4Rx2jctHIP81nUgKKyU4dm1Mc0\nL4n7VHJvxmdwXZA+bgjaShAIBAKBQCAQCKMgPW+Yd+wcW1nMIBSu9pI5MFBUsZNzfV52uex6KFci\nc0S54ifbrmiPpSxmotwkr98uBRd4vtjVlioI4nKfYamMsFyR2mV+zrhKghL3cXPn/8tt1xtXWe5q\n7pomBVYwE5jrIixFPINyGWEp5lmuSO0yP2fM5VBj85nCeAOArvGgEs8P+VuL5O+aUu51Ak0UPTVq\niBCjGfUxzUviPpXcm/EZXA+kjxsjNVfG374TSibD68urGRtK1onajs39bgJI6V0MIxGnynylXE/c\njMWSi1I5bpc9jBSjm6RU9lAsu/E5HjJ2dCkylgHbitrZjAnfZ5NURJpGudhrHePDIODmjqBcRlgo\nRceLRQSsPVJAUIzbhRLCYhEAiPskcS/G/EeKFelauCiWonbG1pGxIvOabenIZiLl6Pt6De9o6CR3\n5icWVlyhoCRuQaGEcKQQtYulxHVRMpmIv2NDjStAKVkn4h0RA9ORqqqIAgBV3JmZs+x6KJai3yC1\n3I06KodMgKlDs+tjmpfEfW9zb8ZncF2QPm6I1CyYCQTCPgoKMiEQCIR0gPRxQ6Rmwext7YbakoUa\nv8UI8zmozLzi+8LZWqrNrvgi4jNQwkRkLjN9lMoehgrRrq5QrGAk3u0Oj1RQiE0SQ4UK8k60k3Vs\nE7ks29UG8HzmXi/epBi6XF4zFHkOfV8EF5TKYlc7NIJgON7VjhQQDg3zdjA4EvVN3CeFeyHe0Y+U\nXAyPxNeiVOHXJe+YcGKzcy5rSn0lubOAiyi6m+WPDqXoa1+Y+opFBEMRt2C4gCB+oxEODfN2MDgC\ntSUKgFOzDsJ8Lmp7Pu9TjXkDQCiVWEUQ8tyeckR2ueLxtxlp5a7kMqgB5f1MHZpRH9O8JO5Tyb0Z\nn8F1Qfq4IVKzYPZ37Ez4FKlSWhgAPI2RqusIbRb96SX6YGldXE/4EBXLHp+kgyNlDMc37+Bwmd/I\ng8Mlfn7e8Xm6F3ZTAsm0Npbp89rsMkLPE1G35Qo3gwTDBQQDQ1F7aLhum7hPDvfBkUhpDhcqGByO\n2kOFCgaHI/nKFR95J/qOHwQ1vCMRVFgmi/SuMgGytESuy/3EgkJJKKiBIQRMQVW1ZV8yvhip4q7G\n/mSh7dbwZty5+bPic5NnWrmrHW01HAjpQ7PrY5qXxH1vc2/GZzBhfEjNgplAIOyb4CnKphDDw8NY\ntmwZnn/+ebS0tODcc8/FCSecUHPeqlWr8N///d/o6+uDaZo44ogjcPHFFyMT+z+ef/75iQpdlUoF\np5xyCi6++GLs2LEDn//852HFAUUAcMYZZ+CjH/3o5BMkEAiEMYD0cWN9nJoFs7d9JzQpR2AiJFRK\nEh6aBhA75MvJuUNV5EGMolRFcAE3gxQq6B+Mdl39QyW+2+0fKvGckK7rJxzsNWlXy/Ijuq7OxwrD\nUCQI93yR47FUFsEFIwWxk+0bgN83ELUHhnibuE8Od7aj7x8soX8o4j84UuZtV8qZWc2d8Td0Fa4b\nTRXfDxDGJrAwkKKyK64wAY4Is18wNIwg5un3DfDdvd83AE3KB1rNG4gUV8jyfmbsRCJ+VmLV9wMp\nIl0ElaSW+6wZqEEKfOZuv/12GIaB22+/HZs2bcKNN96IuXPnYvbs2YnzFixYgG984xtobW1FqVTC\nD3/4Q9x111246KKLAAA/+clP+LmlUgmf/OQncfzxxyf6uOOOOxJKPI1oRn1M85K4TyX3ZnwG1wXp\n44ZIzYLZ37Ez8hVK5E6JfjhF16IKYYgiWIUSDxN9sK+6XsBNHKWyx32lBofFxOwfKqFvMPLx6R0o\nJn2C+PAKrzpk6hrKlkg6XrcMexCKpODlsohGlcwgft+AmLC9/fB7+uILQNwngzsz+/UPCQXdN1hE\n70AxHjqo4Q1EFZdYAv2yJZLs13BnVZMqLjfpBcWS8BOTNgZB3wD83v5o3J4+7htXzZ0tRgLT4FkK\nIiWe5M2+ymQrV3zuL5hW7spgHTPgFEdll0olrF69GjfddBMsy0JXVxeOPvpoPProo/j4xz+eOLez\nszPxv6qq2L59e91+//jHP6K1tRVdXV2J42EYpn7B3PT6mOYlcd/L3JvxGVwXpI8bIjULZgKBsG9i\nqvN+bt26FZqmYb/99uPH5s6di7Vr19Y9f/369bjxxhtRLBZhmia++MUv1j1v5cqVOPHEE2uOX375\n5VAUBYcddhjOP/985PP5iSFCIBAIewjSx431cWoWzP72nckdm6qKUpW2yXMkhtmKcGz3PL6tCxSN\nO8tX5DyIZTcRjcrMIH2DRfT0RyaLnf0FbtaJho6jUQ2NJw7P2Doc1+D9s7GCEFyGhNN9qcLzHCZM\nIgNDYlfb3Qu/uweiI+I+0dx5YMVIme/oe/oL2Bnzr+bO8ltapsZzYzquwcuY+kEgxA2CZJAJS4Jf\nKCVNgGx339sPv7s35t9TwxuIy4SzYhGZDMJsRfTPgkyCAIGicXmYbGVX5PZMK3d1sIEZcApRKpW4\nzxuDbdsolernKe3q6sJ//dd/obe3Fw8//DCmT59ec053dzfWrVuHyy+/nB9raWnBt7/9bcydOxdD\nQ0NYvnw5vv/97+MrX/nKxBKaADSjPqZ5SdynknszPoPTiDTr4/QsmHfEiydmDtA0oaAtS6Q3KpeF\n2aEqjZGX8CGKo1RLXiIalZlEegeKfJLu7BPpVRRFSfhKsRvWsU3ep+v6fKwgCIUZyPcTJhGe1mVw\nJGESYa4IfneP4E3cJ4U7i77uHypxs9/O/kIN72ho4SdmmRpPb1Su+NyvzpNNhkEYyYKkCTAsFnnK\nPNkE6Pf08U1CDfc464BiGrzKlJp1RJ8Vl48lp3DyEv6CPi8AkFbuWr0F814wAa5YsYK3Fy5ciIUL\nF/L/bdtGMb5fGQqFAmzbHrXPjo4OHHHEEbjlllvwne98J/HZo48+ikMOOSShvG3bxkEHHQQAaG1t\nxcUXX4xPfepTKJVKuxxrb6MZ9THNS+I+ldyb8RlcF6SPG46VmgUzgUDYN6HshbyfS5cubfjZrFmz\n4Ps+tm3bxs2Ar776KubMmbPLfn3fr+sz9+ijj+LMM88ck2zy2yUCgUCYSpA+bqyPU7Ng1mZMgzZ9\nGrRp7dH/7a1QWyNfErUlK8pWWpZ406FpfDekqgrP01htymBJwcsVESwgR9cCQGdb9MakozWDtny0\nu2jJ2fy7GVvUdTcMjY+lqgoQKlwe+S0Mk1ltyfKdqVZxpeACKRqWuE8Kd74r94JEZLHMvaM1GqMt\nb6MlF/HPOyY3AVqmxk2DuqaKUsChEsmC5JsIJZPhSfDDcplHX0N6A8d4A4A2rR1ae2vEpzXPv6tk\nMqJP0+BjQVW4DLqm1jVbppW72lLHP2yKo7Jt28YxxxyDu+++G5/+9KexadMm/PnPf8YNN9xQc+7j\njz+Orq4udHZ2oru7G7/4xS9w2GGHJc558cUX0dvbi2OPPTZx/G9/+xscx8F+++2HkZER/PjHP8bC\nhQtrzI9pQDPqY5qXxH0quTfzMzgB0scNZUvPgnlmZ3SjxoUN1NY8VFZtJ+vw6FTFNqGwylK6zn9c\nVUEiotQyRG12ZsrJO37dlDWKovBJ2t6SQUvWis8XVYcylsH7NHWNj6UqkKLHdSGbbXKZ1awj/Kmq\nU9awKGDiPincWRL86nQ9zOzX0ZpBe0vEvyVrJSouZaxoPMvQeYS2pqrCYqWqPEBCMSRfN8cWJutS\npX5qLlURi5GONrEYyef4dxXHFn0ahgjGkGSQo8ctQ+cyp5V73QVzCvJ+XnrppVi2bBkuvfRStLS0\n4LLLLsPs2bOxc+dOfOELX8DNN9+MadOmYfPmzfjZz36G4eFh5HI5HHnkkTWR2ytXrsS73/3uGrPe\n9u3b8Ytf/AIDAwNwHAfveMc7cMUVV+xNmmNGM+pjmpfEfSq5N+UzuB5IHzdEahbMBAKBMFXI5XJ1\no6s7Oztx55138v/POeccnHPOOaP29clPfrLu8UWLFmHRokV7JiiBQCC8yZFWfZyaBbM2ozNh9pPb\nSj4HNU6OL5sAq53TmSVBdpa3LV3UZg8a5HjUVGEGyVqSScTi37UtySSiq/WtFqqSDIyJZQ7zOVFq\nszqvaWzWIe6Tw51HE1fnt4xNWm15m+/oIxNg1M5lTdiWZALU1cRYMm+gKjgkYyNkb+O8+rlsoWkJ\ns5/cVth3M3bCBFgvGENVkQiMYTKnlbvakqvhoKQgUT4hiabXxzQvifte5t6Uz+A6IH3cGKlZMOsz\nO5Nmv2oTIDOL2BbAFLSu8R83EV1qaLDMiFo2Y3JfKbk2u5yyxtDVhK8Un6SOiaxt8H5Yn4ah8bEU\nRRE3mK5x2RTb4jKrnp+sSS+nrInPJ+6Tw72aNzuPKVzZT6wlZyEXt7O2gWwmalumzq+XpqncfKio\nqjBfmUYkIwAl6wgFVcWdKzTTSJr96pkAsw7vE6YhKk6pajKSnPsL6lzmtHKvt2Ce6kT5hFo0oz6m\neUncp5J7Mz6D64L0cUOkZsFMIBD2UaTAZ45AIBAIIH08ClKzYNZmdEJtyYrdnLSzU3MOVBZkYlUF\nmUjgUaq6iFL1fR2eb4pxWG12VZhNLFNLBBcwM0jWNuBkmNO9bBIRUaoyEk73lsll5lGpkZC81KZi\nm8JsRNwnnTsLlLBMcZ4cWJHLih29kzGRSZgAtcRYMm8gDgKxon5Ux67hHZ2riaARy+LR12rWEWa/\nrAM15/B+WJ+JIJMq7kw2y9Tg+9E5aeWukEtGU6DZ9THNS+K+t7k34zO4HkgfN0ZqFsz6rOlR2hYW\n2ZmxhUnBsUUaI9viPzjkNEaSSUTXFG6+kH2GdE3l55i6VDnIFilrMpbBfaWy0iSNfIh03j/rR07r\nAk2+GS2etkVlsiKapAEzm2Qy4oFE3CeFO1MsmiYitzO2LqKPbRHFbVvCfJixdMlnToeuCXOjnMZI\n5sbNdUEIrnKk6xKYhkj1k3UEN8cWPqFZaTGSyQizoq7xsaAqUCXzJ5PNMvWa3zx13OtlySCkDs2o\nj2leEvep5N6Mz2DC+JCaBTOBQNhHsRcS5RMIBAJhDCB93BCpWTBrMzujiGvJRMJ3c5bJ26qcKL/K\ndMB2uIauVQWD1pbaLFs+r81ervjCPGLokqmkqh3nQTR0jY8lQ1FV7nSvBiG4CKoCNTbfhKbB39qE\n2QpPKE7cJ4e7XGa0bEWmKcc1eAJ9yxQ5MyPTYJ22oXMTYDV3LotpQGVlgWPeAKDqOkImt2MjzMb5\nMMtlEXEtuacotiXMfrYFVU6UX8dUpioKl60puFPQX1Og2fUxzUvivre5N+MzuC5IHzdEehbMMzqj\nm5D54JiGiPg0DGFqMA3AEFHZPFE+FG6mCQzhtB6luGE3nQ/XjSi7XoCKJ5Kos4hVUxcRu1H0LvOV\nEkogqrQjTI98R6ZrQBDLDDFRQsNAaMfJ0jO2qPfuurxN3CeHu2UynjqvslTxRAJ5w9C4adDQVSly\nWQy2BA4AACAASURBVPA0dLmylDA9QpGisgMDIn++gjCWNbRdgKX3qbgIXZe3FZkni1iWr4UuKjfB\nEFHZUFWoYFW1lBreTOY0cg/zWVSjnh8kYWrRjPqY5iVxn0ruzfgMrgfSx41BV4ZAIEwt6I0GgUAg\npAOkjxsiNQtmbWZnVAudl5tUpGhWXTitS7kWlcQbDRGBakIyjxgaz3/o+aIdBOBJ1D0/EMEIqkgK\nHgUviCAF1meN0328l+XBL9EH/M2L4vsIPQ/xYNwZP/Q8hHEEK3GfHO6eH33H9wNuJvODAF58LXQp\nWjuqpaKK44ynIt4eJIJMoNTyjgaHEnMLPU/kwAxCfi1C34eiSTxZn7p0LTQtkWNTSbzRYKeoYLHO\nqiJye6aVOws4IaQbzaiPaV4S96nk3ozPYML4kJoFsz6jM7pJJb+ghI+QvOthx6t8iHRpQmmaiE6V\nI1XDUDoeis/ZpJOHUSQ/IVX6QFWUxP9CAD05oaoHAhDKzk1BID4j7mLsvcCdfaaqyph5Vx+LBteF\nzGzhwAZhY1fzZp+zvsbKvY6/oC4p0ubgLj5LcCOkCk2vj2leEve9zL0pn8H1QPq4IVKzYCYQCPsm\nKO8ngUAgpAOkjxtjXAvmRx55BA899BC2bt0Kx3GwaNEifPzjH4caX+AdO3Zg+fLl2LBhAwzDwLHH\nHosLL7yQfz4aCpox+u5Sbsd7p0a9qgihhtLuKRzD7jLcxe4ylAVTAdTZ4QEI4uOBlJolUKTxJYKB\novHPiLs0xN7gznbd4Shv0Wp4A6NxHytv9vku3ypUcR+NN4Cm4B6Gdd5qaFrtMcIuQfqYfwCal7Ug\n7lPHvVmewXVB+rghxrVgrlQquPDCCzF//nwMDAzgu9/9Lu677z6cccYZAIDly5ejtbUVt912G4aH\nh3HDDTfgN7/5DU499dRd9t3dN7Jb/kuJijfMH0mqnR4GAa90Myn+S1JEqecHkr9SiCCeKJHvlqgn\nP1G+W8R919wnxX+6irfgGacxCkPpWgQT7rdWzT1kv6vnC4WcUu6GXqvo6Y3G7oH0cZVOonlJ3KeS\nexM+g2fNRA1IHzfGuBbMp5xyCm93dHTghBNOwNq1a/mxHTt24NRTT4Wu62hra8MRRxyB119/feKk\nJRAIBAIA0scEAoGwN7FHPsx//etfMWfOHP7/aaedhlWrVuHQQw/F8PAw/vKXv+Ccc84ZU1/dvSNj\nzsHI8hzK0agqQr6rDSsuIOVX5MddF6hIx8eZg5HlIWW5DoFoN8bMIL4foBLnlnRdH248rueHvB0d\n3/38k8R9fNx5rsmKu9s5oGEaNbyByPzFdvQVL+B8XM/nbzdcieee5N4MDC0Rec5MnqHncy6QeKWV\ney5TR+VQGqMJwb6uj2leEvep5N6Mz+C6IH3cELu9YP7973+PTZs24fLLL+fHurq68Lvf/Q4XXHAB\ngiDAiSeeiHe9611j6q+7rzCmKj+JCjpSNKoaBsLc4boIS1EVm6BcRliKKsqF5YrULvNzxlzlJzYh\nKYDwNdI17i/k+SGfjOWKj7LrxW2PVzJKtF1vXBWOiPv4uYflmGepzCsLhqVxVhkMQuExJpkDA0UV\nitj1q7gx/n6y7Yr2WKo7VfMGoshz7iPn+UI5lyoIOMd0cg8DCzWgRPl7DNLHNC+J+xRzb8JncF2Q\nPm6IUa/MY489httuuw0AcMghh+Caa64BAKxevRq/+MUv8PWvfx25XFTqNggCfOtb38LJJ5+MG264\nAaVSCbfeeit++tOf4rzzzkv0u3bt2oTpcOnSpRNKikAgpBsrVqwAEM19hd5ojAmkjwkEwkSD6WKA\n9PGuMOqCefHixVi8eHHi2LPPPosf/vCHuOaaaxLmv+HhYfT09OADH/gAdF1HLpfDSSedhLvvvrtG\nQS9cuBALFy5MHOvuHUHG1pGxInODbenIZqLdle/riVyGIh95MpqTBReEFVfsagslhMUiACAolBCO\nFKJ2sYSwUIrOKRahZDIAAMWxocalM5WsAzWuuw6pNruqKiIpuDR+EArTR9n1UCxFu+xS2UOxHO3m\nRooVlOJ2seyiWIraxH1yuAeM50gBQbFUc12UTAZKzFPN2FCyTtR2bB6UEjDe0eA1vIHI1Md27MWS\nK/H0MFKsSNfCjc/xkLGj6ZexDNhW1M5mTPg+e3uX5F7Nm3Fn5sygXE78rmnkzigkFmUUZDImkD4e\nXSfRvCTuU8m9GZ/BQJ0NMunjhhjXu/c1a9bg+9//Pq6++mrMmzcv8VlLSwtmzJiB3/72tzj99NNR\nLBaxcuVKvPWtbx1T39t7h5F3TDixOSaXNaWIT5Ofp6rCjyiK+GTpaEIpGtUXpo9iEcHQCAAgGC4g\niG/YcGiYt4PBEagt2aj/rIMwH72lUT2f96kC3LcnlCoHIQh52hY5Mrdc8fiNOVSooBBP0pGSi+GR\nqF0oVTBUiNrEfXK4B8Mxz5ECwqFh3g4Go+uitmShxko5zOci3tEgIm2QqiCUqiZxxa3I0dchN/WV\nyh7nVihWMBIrruGRCgqxGW6oUEHeifg5tolcli1GghreQOQrKqK7Q/BURb4vfORKZaGcU8o9a5O5\nb6JA+rhKJ9G8JO5Tyb0Jn8GE8WFcT697770XxWIR3/rWt/gx2TR41VVX4Y477sAvf/lLqKqKww47\nDBdeeOGECkwgEN5koDcauwXSxwQCYcJB+rghxrVgvvbaa0f9fO7cubs8pxG6e0dQrvjIO9HOyQ9E\nTkEAiVyIlsmiP6tMIiyvoevy4IKgUBK72oEhBGxXW9WWAxD4rlaOItU0qLEzfGi7deuwe34gTCIV\nn5tBCsUKBkei/ocLFQwOR+2hQgWDwyV+PnGfeO7BwFD0/9Bw3bYcfKFK+TMZbwBQdR2h7SbGknkD\nsQkwDqYolj2+ox8cKWM4frsxOFzmbzoGh0v8/Lzj83yg1dxZrk/L9Gt4M3l4tHW5wk2eaeXelq8N\n+lMoUf5ugfRxY51E85K4723uzfgMrgfSx42RGvtod18BrpQKRvYX0qQb1dBVuG4ktu8HvDZ7GEhR\nqhVXmERGhBkkGBpG0DcQfbdvgE9Sv28AmpTuJREKKyVID1lal4ydSEzOquf4fiBFqQpfqZGSyydp\n/2AJ/UORAhkcKfM2cZ8c7lwp9Q3Aj/kHA0O8rUnpjaq5szRGoWlEvIFEIv5QFWmMoqhs4SfGzX6F\nCvoHI579QyWuuPqHSjy9j+v6NbzZX5beyHV1PlYYhqIYgOeLVEWlsvCRSyn3mR0OakBvNFKHZtTH\nNC+J+5Ryb8JncF2QPm4IujIEAoFAIBAIBMIoSM8b5t6RZNAIhIO9pqo8gX7ZEom35Y1YdCB+u1Fx\nuYkjKJZEcIG0kw36BuD39gMA/J4+7lyfTLSo8l1tYBo8ejfaBQo5+fABuGzlis+d7odHhBmkf0js\n8PoGi+gdiAIiiPvkcJd38Xx339sf8Y4Gr+ENRDv6gCWTd2zprUeSO/uq6wXcpFcqezywYnBYvEnv\nHyqhbzDi3DtQTAaNSNxZOWJT11C2RJL9Gt6xPLwAQLksIs9Tyn2o3lsNSmOUOjS7PqZ5Sdz3Ovcm\nfAbXBenjhkjPgrlvhJvzgDj62mAJwzWe8sVxDV6hxg8Ccd8EQdKHiCUFL5SSJhE2YXv74Xf3Ru3u\nnuQNyCapaYgk6pkMwmxF9M98iIIAgaJxeZhsZVekrJEzQgyOlPmN2tNfwM7+OGqWuE8Kd9lPjCuo\n7t6IN1DDnVdcsk2e6ifMVoRPnudxpRYoGvd1q8hpjMpuIvqamb76BovoiTnv7C/U8AaiVEUsUX7G\n1uG4Bu+fjRWE4DIk/AVLFZ6qKK3chwq1C2aFEuWnDk2pj2leEvcp5N6Mz+B6IH3cGHRlCATC1ILe\naBAIBEI6QPq4IVKzYN7ZF+12FF5uUjjYW6bG84GWK6IWvCebDIMQIcuDKJlEwmKR53iUTSJ+Tx/f\n1fo7eoQgqsKjcRXT4GUq1awj+qy4fCw5D6KXcLr3eWEOOSNE/1CJuyLs7C9w3sR9crjLJkBm9vO7\ne2p5R4OLNxqWJfKBlsvCxFiV99NLBJnEUdklLxF9zUxgvQNF/la9mrscRMXeaDi2yft0XZ+PFQSh\nMP/5fsL8yXOeppR7fZcMCqVIG5pSH9O8JO5TyL0Zn8F1Qfq4IVKzYO5sd9DZ5qCjNTK7tOVttOQi\nn528Y3IToGVq3DSoa6qo+BMqPB2KfKMpmQxPCh6WyzwaFdJEAwBt+rTo77R2aO2tAAC1Nc+/q2Qy\nok/TEKlXVIXLoGtqXbNl3pEmmhckoms5f+I+KdyZktEqruQnluSuTWuP2u2tUFvzEbWWrKi+ZFlC\ncWsaV+iqqvD0WtWmO5YEv1wRPp5yFgHGGwA6WjNoy0ecW3I2/27G1nmfhqHxsVRVAUKFyyM/VJjM\naeWez9amlSOkD02pj2leEvcp5N7Mz2DC2JCaBTOBQNg3odAbDQKBQEgFSB83RmoWzNPbs+hozaC9\nJdpFtmStRKnKjBXtKC1D5xHamqoKdxtV5c7qiiE5yzu2MOWUKvVzPKqK2NV2tIldbT7Hv6s4tujT\nMIRjvCSDHD1uGTqX2bFNXgCgOscjM3kS98nhzoMvqvNbsoj/ae3QOtqiQ615qKwsadbhUcmKbUJh\npVh1nZusVAWJCGrLiOTIWAY3Wecdv24uW0VR+Nu79pYMWuI3r3I54oxl8D5NXeNjqQqk6HFdyGab\nXOa0cs87dd4wU6L81KEp9THNS+I+hdyb8RlcF6SPGyI9C+aObGT2i2/UyAQYtXNZE7YlmQB1Zgqp\n6iS+cxK+Pxk7WZu9TsoaaFrCDCK3FfbdjJ0widRzjFdVJPz8mMy5rCkiaqtT1sRmHeI+Odx51aRq\n7rFSkM1+clvJ56DGyfFlE2A1dyaL7OtmWzpy2Ui5+UGD1FyaKsx+WUsyAVr8u7YlmQB1tb5rmaok\n/fximdPKva5LBgWZpA7Nro9pXhL3vc69CZ/BdUH6uCFSs2AmEAj7JsgESCAQCOkA6ePGSM2CeXq7\nk3Cwb8lZyMXtrG0gm4nalqlzx3ZNU7lZX1FVXkYSpgHFjndjWad+bXY5x6NpJM0g9UwiWYf3CdMQ\nJStVNRlJzp3udS6z7wtH+2hokeOR7QiJ+yRxr+LNODP+CbNftQmQ8betiDcQlWhl/cjR1IYGy4ym\nUzZjcs7V3BkHQ1cTQVT87Z1jImsbvB/Wp2FofCxFUYRS0zUum2JbXOa0cs/Hb2sI6UYz6mOal8R9\nSrk34TOYMD6kZ8HckU34C+Wy4kZ1MiYyCROgiMqWkfAhsqJ+VMcW0bjRl+JzNeETZFk8GlXNOsIM\nknWg5hzeD+sz4UMkQddULptlavD96BzPF4sETVO575Nlikhe4j753FnVJMU2hcmsJSuUkqSg1JwT\n9QFAsap85qp4A4ChCz6+r9fwBiIfM3aOZWoJn1Bm9svaBpwM85mTTYBaDW8mD5fNMrnMaeXeUs8l\nQyfFnTY0uz6meUnc9zb3ZnwG1wXp44ZIzYKZQCDsmyATIIFAIKQDpI8bIzUL5pkdOWRsEdlpW8Kk\nkLF0KchEh64JE4Sc95Mn/NY1Yb4IQvCfXxO72sA0RH7IrCNyPDq2CC7ISrvaTEaYWXRNRJKqClTJ\nJMJks0w94WDPdqOaJiJZM7YuInCJ++Rwlz4LmAkskxFvMTIZEcWdsYX50LHFdbEtfu0g5/2UTIC6\npnBzXTV3do6pSyWFbZHLNmMZnGdWensXBZnovH/Wj5zzVP5dFdviuT3Tyr2uSwYp6NShKfUxzUvi\nPpXcm/AZXBekjxsiNQvm6R3ZyExvCFMfuzkTbUPnZgd2ozDwnZFpQGXVf6ITo/N1HSGbpI4tarOX\nyyICVTIPKbYlzCC2BVVOHF7nplIVhcuWDIZNVg4qW5GJxnENnlCcuE8OdzU2XYWmwRVxmK3wBPqK\nZSVMY1wpWSZvq3Ki/CruTBZD12p4A8kKaWXLh+NG/ZQrvjAHGsLUZ5lV7fi6GLpWw5vLw/z/ghBc\nhJRypywZzYFm18c0L4n73ubejM/guiB93BC0lSAQCAQCgUAgEEZBet4wtzswDI2bCwxdlSJYNW5q\nMHS5FKswR0CRolQDAyJ/voIwDhAIbRdgOSErLkLX5W2+YzVExC5MQwouEOU+YYgoVagqVLDSlAoC\nKfJU5ITUYJkscbjOS3NWPJFEnbhPDvfQjhPFZ2yeND50Xd5WZJ6mIaK7DUOYFU0j4h0NzsmpULh8\n1dxF8IUP142mmesFqHgigTzjY+oiY0iUPUTwZO2oFKswPUKRItKDWGaINylp5Z7L1KocRUuNGiLE\naEp9TPOSuE8l9yZ8BtcD6ePGSM2Vmd6RjfyL2M2vQvJHEn5HqiImRcJnDorwa2IdAIBhQImjVEPP\nE6ldgjD6H0Do+6IOvK4Lk4SuiWhUTUukzVESNyw7RQXz0FQVkbLG9wN4vmgzc4kfBPDidC/EfXK4\nM57wfO5LF3oewvi6KJrEU1WkKGZd+KRJKYOUhIIWspqQzIGGxtP4eL5oBwF4AnnPD4RfmSqS4Ed+\naMLfjPVZ4zMXK8ka7kzBppS7odcx95EJMHVoSn1M85K4TyH3ZnwG1wXp44ZIzYKZQCDso6AgEwKB\nQEgHSB83RGoWzNPbs1BVJbG5USSnermMI9vl1ZR2ZHkQ5R0owHeyABDKuyrWDkKxq5JuloRjvTyW\nqta9qXRpB6ppYkw5WjUMpeOh+Iy4i772Cnf2maqMnbf8lw0tvWkbK2/2OeMxVu51S5nqevLNQ/VA\nSBf3MKzzVoMUdOrQ7PqY5iVx3+vcm/AZXBekjxsiNQtmx3ej1CyNbphQvovZ8fqmgwAKAkV8N1Ck\nG0a68QJF45/vcrLIbSgNoyVVxItAeWEQjkFREPf/n713jZXkuO7Df139npk7dx93l+RqCW4kGpC0\nsCELCqN/RMiCA8T5FMiOSZj0AwFJKYYVI4YME+AHOyLgRLIciYmkmLJF24ifIiMjiZAP+WLDFiA5\nJkLFkbSyYtmmFEqklnv3PubZz+r/h+o6VT2P3XuX997p0Z4fsNiamZ7q+s3tOl3V55zfMec+Ae76\nsxsuCmZ4q/8XQ6A6OG+gXj3exEjOcV/OW/FqP/cSy6w0o034brLHPC/1CZk7cDLc1+YezDgUWrNg\nZjAYtycasX8MBoPBWBnYHi9HaxbMxavbtxbwb5WILKx68UVZ6yBWlRXwL4884L9RHrMoUFl142k3\nd1zJDsz9ptztz44q4XCWt+JTUkJHJSWVQz2WRI8Z7uZvXEHWTxPayl0GPnABTbALsHVYR3vM85K5\nr5L7Ot6DceEuzIHt8VK0ZsFcXt0+kKQMAp8kawDjMpFw6GLMCkmSZXlR0sWbW1Jmr0VSRvpuIxtV\nu0GqoiR5HFhSOVVRkoQMsvyW5XSY++G55xbP1yJpp+V67OxrgYoMVJXlgCUTRO/nOZBZ7x9SSkj/\nFrPctcuzLCWympfiqM7bVu7VRhezsF2PjHZgHe0xz0vmvkru63gPXgS2x8vRmgUzg8G4TeGxGWIw\nGIxWgO3xUrTmlylf3T5YWUxZmVB7yx0oHWF2cnlJZZfTvECaKXdEmpXNdm7aBymL2Sg3aWWjUoB9\nUZpdbZJB1uU+qyRFlWZWO6VjDlUSlLkfmju9ttt5caiy3LPcdQayqKRx7+U5qkTxlGmKKql5ppnV\nTumYA5dDrd1njuYNAJ5LSSVFWdFTi+bftaXcOdFkLbCO9pjnJXNfJfd1vAczDof2LJivbsOJY6ov\nL+IITrej2p2I4m4kLHkX32/kqepYqbwwF+M0yZGkdTstMJ6qiyRJC0zTvD6mQBypnyIOfUShanfj\nAGWpJ6nJNFVa7POB8ZWU5O6QaYpqkqj3p1NI3R5PIKd1e5Kgmk4BgLkfE/dpzX88zazfIsc0Ue04\n8hCHyr0WhR66sTKOZenN8VanbnLXcWJVlhsDZXGTkwTVeKLa06TxuzhxrPh3Ioi6ApTT7Sjeihi0\njRTCMQUAZrhrN2eaF5gm6m/QWu7+ApPDQvmtw7rbY56XzP2kua/jPXgh2B4vRWsWzAwG4/aEw0km\nDAaD0QqwPV6O1iyYi1euQfS7EPVTjGqjB6HdK2VpdAet2uxOaTI+pVM1MnO16yNJCwwnalc3mWYY\n17vd0TjDpHZJDCcZNjpqJ9uJAvS6elcrUZQ6vN48SfE9u7xmZXQOy9IkFySp2dUOx5Cjelc7nqAa\njqgtB2PVN3M/Fu6Tekc/TnKMxvVvkWT0u2x0AnRqt3OvG1h9NbnrhAuV3a31oysr+7o0rr7pFHKo\nuMnRBLJ+olENR9SWgzFEXyXAiW4H1UZPtYuS+hQ1bwCorBKrkBVpe9oZ2WlW0NOMtnJ3ejHmwAa6\ndVhHe8zzkrmvkvs63oMXgu3xUrRmwVy+ut2IKRKWLAwAkjESnocq0tmfRaMPLeuSFyaGaJoWNEkH\n4xSj+uIdjFK6kAejhI7f6JQk96IvSqApaxMGJdVmt1EVhcm6TTNyg8jRBHJ/qNrD0cI2cz8e7oOx\nMpqjSYbBSLWHkwyDkRpfmpXY6KjvlFLO8VZDEAgDnek94wLUskR5TnFicpIYA7U/hNQGaqZtx5LR\nYmSGu6gTMKoon+OtuZP7MyvJ5dlW7uLMqTkOOu6V0R6suz3mecncT5r7Ot6DF4Lt8VK0ZsHMYDAY\nq8JoNMLTTz+NL33pS+j3+3jooYdw//33zx33p3/6p/gf/+N/4JVXXkGn08E73vEOPPzwwxD1U5kP\nfOAD+PrXvw63XlCePXsWTz31FH3/y1/+Mn7zN38T169fx7333ov3ve992NraOhmSDAaDsQZoqz0+\n8gXzQYnOori6DdfSCGykhFoi4VXgA3VAvi3OXQmjg6iyVE1yAblBJhn2BmrXtTdMaLe7N0xIEzLP\ny0aAvWvtarU+Yp57dK6qqoxAeFEajcckNckF44nZye7uo9zdV+39IbWZ+/Fw1zv6vUGCvaHiPxin\n1M4tzcxZ7pq/7wnkuZoqZSlR1S6wSlpZ2VluXIBj4/aTwxFkzbPc3afdfbm7D9fSA53lDSjdz0rr\nfsZRQ4hfl1gtS2llpJukktZyv+s8ZtGGmLlnnnkGvu/jmWeewYsvvogPfehDuHTpEi5evNg4Lssy\n/PN//s/xPd/zPdjf38eHP/xhfPazn8W73/1uAErD9NFHH8UP/uAPzp1jMBjgIx/5CH76p38ab3vb\n2/DpT38aTz31FP7Nv/k3x8brdrLHPC+Z+yq5r+M9eBHYHi+3x0e+YD4o0VmUr26rWKGGdor6wzme\nqyqEQWWwGiNeNfrQX80LSS6OJC0oVmowMhNzb5hgd6BifHb2p82YIDq9Q1WHAs9FGhrR8YXqWLIy\nouBparJRLTdIubtvJuzOHsrru/UPwNyPg7t2++0NjYHeHUyxsz+tTy3neAOq4pIW0E9DI7I/x11X\nTcpycunJaWLixKyNgdzdR7mzp857fZdi42a568WIDHxSKVBGvMlbf1WPLc1KihdsK3dnsMANuGKh\n/CRJ8Pzzz+OjH/0owjDEG9/4RrztbW/D5z73OTz88MONY//xP/7H1D5z5gzuv/9+XLly5UDnef75\n53H33Xfj7W9/OwDggQcewKOPPoqXX34ZFy7Mlj88Gty29pjnJXM/Ye7reA9eCLbHS+3xkW4lNNEf\n+7EfmyPKYDAYi+B43rH+uxleeeUVuK6LO++8k967dOkSXnrppZt+96tf/Sruvvvuxnt/8Ad/gEcf\nfRS/+Iu/iK9+9av0/ksvvYR77rmHXodhiDvvvPNA57kVsD1mMBiHBdvj5ec50ifMy4geZMVfXt1u\n7tiEMKUqo4A0EqtuZgLbi4K2ddJxKVg+s3UQ07yRjardILuDKa7vKZfF9t6E3Drq1HU2qu+ScHgc\neejkPvWvzyUr0BgaQfdJRjqHDZfI/tDsaq/toLx2HaYj5n7U3CmxYpzSjv763gTbNf9Z7lrfMgxc\n0sbs5D6VMS2lNMOVsplkokXwJ0nTBah39zt7KK/t1Pyvz/EG6jLhulhEHKPqZqZ/nWQiJaTj0nj0\n2NLcaHu2lbsYLHADrlj3M0kSxHFTvSOKIiTJEp3SGn/yJ3+CF198ET/zMz9D7/34j/84Ll68CM/z\n8PnPfx6/8iu/gl/91V/F+fPnkaYp+v1+o484jm96nlvF7WaPeV4y91VyX8d78EKwPV56jiNdMN8q\nUQAoX60XT/qP5brGQIehkTdKU+N2mJExKhoxRHWWalI0slG1S2Rnf0qTdHvXyKs4jtOIldIXbCcK\nqM88L+lcUlbGDVSWDZcIyboMxg2XiA5FKK9dN7yZ+7Fw19nXe8OE3H7be5M53urUJk4sDFySN0qz\nkuLqCttlKCs1FjRdgNV0SpJ5tguwvL5Lm4Q57nVSghP4VGVKdDumzyync9kSTkUjXrCkAgBt5e4u\nWjCfAJ577jlqX758GZcvX6bXURRhWl+vGpPJBFEULe3v+eefxx/+4R/il37pl9Dr9ej9e++9l9o/\n8AM/gM9//vP44he/iH/yT/4JoijCZNKUcppMJnM286hwu9ljnpfMfZXc1/EevCqsqz0+0gXzQYle\nuXKl8ZTjwQcfPMphMBiMlkMbzAcffBAVjv+Jxo1szF133YWyLPGd73yHnsZ+85vfnHPtafzlX/4l\nfuM3fgNPPPHE0mMW4eLFi/izP/szep0kCa5evXrTeOJbBdtjBoNxM9iLV7bHN7bHR7pgPijR2R0F\nALjnz8I9dxbu2dPq9elNiM0NAKqwBZWtDEPzpMN16QmIEA7pNM66MrQoeJqZZAE7uxYAtk6pJyZn\nNmOc2lA3lH4vou/Gkanr7vsunUsIB6gcGo/9FEaPWfS7tDN1s9xKLrCyYZn7sXCnXXkhG5nFNvcz\nm+ocpzYi9HuK/0YnIBdgGLjkGvRcYUoBV44aC5pPIpw4JhH8Kk0p+xrWEzjNGwDcs6fhnt5U1gj8\nEgAAIABJREFUfDY36LtOHJs+A5/OBeHQGDxXLHRbtpW76Ku/rW0wpTWmVSCKItx333149tln8dM/\n/dN48cUX8cILL+CXf/mX5479yle+go997GN4/PHH8YY3vKHx2WQywV//9V/jzW9+M1zXxRe+8AX8\n1V/9FR555BEAwH333Yff+73fw1/8xV/g+7//+/GZz3wGly5dOraEv9vNHvO8ZO6r5L6u9+DZxSvb\n4+X2+MifMB+U6CzcO7bUhVoXNhCbGxC62k63Q9mpThTA0ZWlPI9ijYSDRkZp6Jva7NqVs9EpF0rW\nOI5Dk/R0P0a/G9bHm6pDcehTn4Hn0rmEAyt73DNjiwIas+h2TDzVrGSNzgJm7sfCXYvgz8r1aLff\nmc0Yp/uKf78bNiouxaE6X+h7lKHtCmFCvISgJAbHt2LdOpFxWSfZYmku4ZjFyJlTZjGy0aPvOp3I\n9On7JmHCGoOdPR76Ho25rdz1gtmGLdC/Kjz22GN4+umn8dhjj6Hf7+M973kPLl68iO3tbbz//e/H\nU089hbNnz+KP/uiPMJ1O8W//7b+l777pTW/CE088gaIo8Oyzz+Lll1+GEAKve93r8Pjjj9Nitd/v\n4+d//ufxW7/1W/j4xz+O7/me78HP/dzPHRun280e87xk7qvkvpb34AVge7wcRy4rt4wog8FgLIJ9\n81gVer0efuEXfmHu/a2tLfzO7/wOvf7X//pfL+2j3+/jgx/84A3P873f+70N4fzjBttjBoNxGLA9\nXo4jXzAvI3ozuOe3Gm4/u+1s9CBqcXzbBTibzan1tu1g+Sj0TG12uUTj0RXGDdINLZdISN+NQssl\n4onF5daF00yMqcdcbfRMqc1ZXdParcPcj4c7ZRPP6lvWLq1TGxHt6JULULV73QBRaLkAPdE4l80b\nmEkOiSNU+mlcsVjLFq7bcPvZbUd/N44aLsBF2ctCoJEYo8fcVu6i3wPj5HDb2mOel8z9hLmv5T2Y\ncSi0pjS2d8dW0+036wLUbpEoBLSB9lyqStPILvVdhIGi1o0DcjHYrgZbssb3RCNWiiZpJ0A38qkf\n3afvu3Qux3FMZRzPpbE5UUhjFkXZrElvS9bUxzP34+E+y1sfpw2uHSfW74Xo1e1u5KMbq3YYePR7\nua4g96EjBFWBQuCrMQJwuh1joGa4k0EL/Kbbb5ELsNuhPhH4puKUEM1McooX9GjMbeW+aMG86pg5\nxjzW0R7zvGTuq+S+jvfgRWB7vBytWTAzGIzbE22ImWMwGAwG2+MboTULZvf8FkS/a3Zz1s5O9DoQ\nOskknEkysUBZqp7JUi1LD0UZmPPo2uzCuE3CwG0kF2g3SDfy0Yl10L3tEjFZqjYaQfdhQGOmrFQ1\nSCq16USBcRsx92PnrhMlwsAcZydW9LpmR9+JA8QNF6DbOJfNG6iTQELVj+hEc7zVsa5JGglDyr4W\n3Y5x+3U7EL0O9aP7bCSZzHDXYwsDF2Wpjmkrd4efMK8F1t0e87xk7ifNfR3vwYvA9ng5WrNg9u46\np2RbdGZnHBmXQicyMkZRSH9w2DJGlkvEcx1yX9gxQ54r6JjAsyoHRUayJg59ipXqWpNUxRB51L/u\nx5Z1gWtfjCHJtgg9VqhJKrXbJI7NDYm5Hwt3bVhc12Rux5Fnso8jk8UdhcZ9GIeeFTPnwXONu9GW\nMbK5kbtOVqbmvPW7yMA3Uj/djuHWiUxMaNdajMSxcSt6Lp0LwoGw3J96bGHgzf3NW8d9gUrGTZK2\nGSvAOtpjnpfMfZXc1/EevAhsj5eDo74ZDAaDwWAwGIwboDVPmN07tlTGteUiod1cGFBb2EL5M1me\neofre+5MMuh8qc00LKk2e5qVxj3ie5arZKZd6yD6nkvnsuEIQUH3QlagIQgHonbfVIFPT22qbkaC\n4sz9eLjbZUbTULmmOrlPAvphYDQzlWtwQdv3yAU4y53GEvgQuixwzRsAhOeh0uPuRKi6tR5mmpqM\nays8xYlC4/aLQghbKH9BVrNwHBrbWnBfEJJRsQuwdVh3e8zzkrmfNPd1vAcvAtvj5WjPgvn8lroI\ndQxO4JuMT983robAB3yTlU1C+XDITSPr7FMAtcSNvuhK5LminBcSWWFE1HXGauCZjF2VvatjpYwR\nUJV2jOsRjpWlKusxw0yUyvdRRbVYehyZeu95Tm3mfjzcw0Dz9KjKUlYYAXnfd8k16HvCylw2PH3P\nrixlXI9wrKxs6VNBUSEcVPVYqygHtLxPlqPKc2o7Nk+dsWz/Fp6p3ATfZGVDCAjoqlrOHG895jZy\nrza6mEXBSSatwzraY56XzH2V3NfxHrwIbI+XozULZgaDcXuiDUL5DAaDwWB7fCO0ZsHs3rGlaqFT\nuUnHymb1TNC6pbXoNJ5omAzUAJZ7xHdJJqUoTVtKkIh6UUqTjCCMKLhKXjBJCrrPuaD7ei9LyS/q\nA3ry4pQlqqJAfTIKxq+KAlWdwcrcj4d7UarvlKUkN1kpJe2iPStbW9VSEeZ9zdMxTw8aSSZw5nmr\nk8OpuVVFYTQwZUW/RVWWcFyLp+7Ts34L121obDqNJxr6EAGd6ywco+3ZVu464YTRbqyjPeZ5ydxX\nyX0d78GMw6E1C2bv/Ja6SK24oEaMkF1VR78/E0PkWRPKdc0uyd4x2fE5+m0pK5p09mkcK05IWB8I\nx2m8NgPwmhNq9kQAKju4SUrzGXM35z4B7vozIZwD8559T53cM2PWCwd9En3uWd76c93XQbkviBf0\nLEO6Htznn15wzFz7sPb2mOclcz9h7mt5D14AtsfL0ZoFM4PBuD3Bup8MBoPRDrA9Xo7WLJgnrn/j\n3aXdrvdO83tb/XkFUVm7p+oAu8vqJrvLyh6YALBghwdA1u9Lx3xXOtb5LYLScekz5m6d4iS46113\ndYOnaHO8gRtxPyhv/flNnyrMcL8RbwBrwb2q5p9qcJJJ+/DdZI95Xtavmbvq5gS4r8s9eBHYHi9H\naxbM13bHtxS/1Kh4o+ORrNrplZRU6eZY4pesaj9FKa14pYp2aip2y9STP6rYLeZ+c+7HEj89w9vw\nrGWMqsr6LeSRx63Ncq/037UojUFuKXffmzf0FSeZtA5raY95XjL3VXJfw3vwXXdgDmyPl2PZJonB\nYDAYDAaDwWCgTU+Yd8YH1mDUOod2NqpARbvaKssBS1+R3s9zILPeP6QGo9Yh1VqHgNrtajdIWUpk\ntbZknpfI6/MWZUVt9f6t608y98NxJ63JLL9lDWgE/hxvQLm/9I4+KyTxyYuSnm7kFs/Xor0pfbeR\nea5dnlVREhdYvNrKvRfPmxx+oNE+rKM95nnJ3FfJfR3vwYvA9ng52rNg3p0cqMpPo4KOlY0qKmnc\nHXmOKlFVbGSaokpURbkqzax2SsccuMpPfSU5gIk18lyKFyrKiiZjmpVI86JuF1TJqNHOi0NVOGLu\nh+depTXPJKXKglVyyCqDsjIRY5Y7UDrCGOK8nOGm+ZfNdm7aB6nuNMsbUJnnFCNXlMY4JxkkcWwn\n90qGmAXHzLUPa2mPeV4y91VyX8N78CKwPV6O1iyYGQzG7QmOmWMwGIx2gO3xcrRmwXxtZ4w48hCH\nyt0QhR66sdpdlaXX0DI0euTNLE+dXFBludnVThJU0ykAQE4SVOOJak8TVJNEHTOdwoljAIDTiSDq\n0plOtwNR112HVZtdCMeIglvnl5VxfaR5gWmidtlJWmCaqt3ceJohqdvTNMc0UW3mfjzcpeY5nkBO\nk7nfxYljODVPEUdwuh3V7kTkm5Katzr5HG9Aufr0jn2a5BbPAuNpZv0WeX1MgThS0y8OfUShanfj\nAGWpn941uc/y1ty1O1OmaePv2kbuCyiwjFELsY72mOclc18l93W8By8C2+PlaM2C+erOCBudAJ3a\nHdPrBlbGZ0DHCWHiiFTGp5ajqaxs1NK4PqZTyOEYACBHE8j6gq2GI2rLwRii31X9dzuoNnqqXZTU\npwAoe7WyKgdBViTbYmfmpllBF+ZwkmFST9JxkmM0Vu1JkmE4UW3mfjzc5ajmOZ6gGo6oLQfqdxH9\nLkRtlKuNnuKtTmIyYoWDyqqaRIbbsbOvK3L1JWlB3CbTDOPacI3GGSa1G244ybDRUfw6UYBeVy9G\n5BxvQMWKmuzuCiRVVJYmRi5JjXFuKfdu1BqTw7gB1tIe87xk7qvkvob3YMbhwHcvBoOxUthPbhgM\nBoOxOrA9Xo7WLJiv7YyRZiU2OmrnVEqjKQigoYUYBjr7c8YlonUN85ySC+QkMbva/SGk3tXOtO0E\nBNrV2lmkrgtRax5WUb6wDntRSuMSyUpyg0ymGQZj1f9okmEwUu3hJMNglNDxzP3oucv9oXo9HC1s\n28kXwtLP1LwBQHgeqihvnMvmDdQuwDqZYpoWtKMfjFOM6qcbg1FKTzoGo4SO3+iUpAc6y11rfYZB\nOcdbj4eyrdOMXJ5t5X5qYz7pr7QzaRitwLrbY56XzP2kua/jPXgR2B4vR3sWzLsT5JYUjL3Lca0L\n1fcE8lwNuywl1T2vpJWlmuXGJTI2bhA5HEHu7qvv7u7TJC139+Faci+NVFhLIL3Ssi5x1BAm19Vz\nylJaWaomVmqc5DRJ9wYJ9obKgAzGKbWZ+/FwJ6O0u4+y5i/3h9R2LXmjWe5axqgKfMUbaAjxV8LI\nGKmsbBMnRm6/SYa9geK5N0zIcO0NE5L3yfNyjrf+X8sb5blH56qqyhQDKEojVZSkJkaupdzvONPB\nLPiJRvuwjvaY5yVzXyn3NbwHLwLb4+VozYKZwWDcnmD7zGAwGO0A2+PlaM2C+drOuJk0AhNg7wpB\nAvppaIS35zwHutRmlpOLQ04Tk1xg7WTl7j7KnT0AQHl9l4Lrm0KLgna1MvApe1ftAuevKilBY0uz\nkoLuR2PjBtkbmh3e7mCKnX2VEMHcj4e7vYun3f3OnuKtTj7HG1A7eqnF5DuR9dSjyV1/NS8kufSS\ntKDEisHIPEnfGybYHSjOO/vTZtKIxV2XIw48F2loRPYXespkZQoApKnJPG8p9+GSpxqMdmHd7THP\nS+Z+4tzX8B7MOBzas2DeHZM7D6izr30tGO6S5Esn96lCTSmluW6kbMYQaVHwSdJ0iegJu7OH8tqO\nal+73rwA9SQNfCOiHseoupnpX8cQSQnpuDQePbY0N5I1tiLEYJzShXp9b4LtvTprlrkfC3c7TowM\n1LUdxRuY404Vl6KApH6qbmZi8oqCjJp0XIr3ymwZozRvZF9r19fuYIrrNeftvckcb0BJFWmh/Djy\n0Ml96l+fS1agMTTiBZOMpIrayn04mV8w23GCjHZgLe0xz0vmvkLu63gPXgS2x8vRmgUzg8G4PcEx\ncwwGg9EOsD1ejtYsmLd31W7HoXKTJsA+DFzSA00zUwu+sF2GskKldRAtl0g1nZLGo+0SKa/v0q62\nfPW6GYhwKBvXCXwqUym6HdNnltO5bB3EohF0X1JhDlsRYm+YUCjC9t6EeDP34+FuuwC126+8dn2e\ntzq5eaIRhkYPNE2Ni3FG97NoJJnUWdlJ0ci+1i6wnf0pPVWf5W4nUeknGp0ooD7zvKRzSVkZ919Z\nNtyfpHnaUu6LQjJYKL99WEt7zPOSua+Q+zregxeB7fFytGbBvHW6g61THZzZVG6XUxsR+j0Vs7PR\nCcgFGAYuuQY9V5iKP5UDZ8GF5sQxiYJXaUrZqLAmGgC4586q/8+ehnt6EwAgNjfou04cmz4Dn84F\n4dAYPFcsdFtudKyJVshGdi3xZ+7Hwl0bGTfLrTixJnf37GnVPr0JsbmhqPW7pvpSGBrD7bpk0IVw\nSF5r1nWnRfDTzMR42ioCmjcAnNmMcWpDce73IvpuHHnUp++7dC4hHKByaDz2TUWPua3cN7rzsnL8\nRKN9WEt7zPOSua+Q+zrfg22wPV4OcfNDGAwGg8FgMBiM2xetecJ87nQXZzZjnO6rXWS/GzZKVcah\n2lGGvkcZ2q4Q5L1RGaWKjuNbwfKdyLhykmyxxqNwzK72zCmzq93o0XedTmT69H06F6wx2Nnjoe/R\nmDtRQAUAZjUetcuTuR8Pd0q+mNW31Bn/Z0/DPXNKvbW5AaHLknY7lJXsRAEcXYrV8ygpQzhoZFCH\nvhpHHPrkst7olAu1bB3Hoad3p/sx+vWTV7sccRz61GfguXQu4cDKHvfM2KKAxtxW7hudBYVLOMmk\ndVhLe8zzkrmvkPs63oMXge3xcrRnwXymq9x+9YWqXICq3esGiELLBehpV8hMJ/WV04j9iaNmbfYF\nkjVw3YYbxG47+rtx1HCJmDtDszs7zk+PudcNTEbtrGRN7dZh7sfDnaomzXKvXVq2289uOxs9iFoc\n33YBznLXY7Fj3aLQQ6+rjFspl0hzucK4/bqh5QIM6btRaLkAPTHPux5PI86vHnNbuS8MyeCYudZh\n3e0xz0vmfuLc1/AevAhsj5ejNQtmBoNxe4INNIPBYLQDbI+X49AL5qIo8KlPfQpf+cpXMBqNcMcd\nd+Dhhx/GW97yFgDAF77wBfzn//yfsbOzg7Nnz+Khhx7C3//7f/+m/Z473WkE2Pd7IXp1uxv56Maq\nHQYeBba7riC3viMElZFE4MOJ6t1Yt7O4Nrut8Rj4TTfIIpdIt0N9IvBNyUohmpnkFHTv0ZjLUjbc\nHLbGo94RMvdj4j7DW3PW/Btuv1kXoOYfhYo3oEq06n7sbGrfRRio6dSNA+I8y11z8D3RSKKip3ed\nAN3Ip350n77v0rkcx6ExwHNpbE4U0pjbyn2jflpjoyzZQN8q2B6DbBLPS+a+Uu5reA9eBLbHy3Ho\nBXNZltja2sKTTz6Jra0tfPGLX8RTTz2Fj3zkIxBC4BOf+AQef/xxvOUtb6HP/uN//I/o9/s37Pfc\nmW4jXqjXNRdqJw4QN1yAJivbRiOGKFT9iE5ksnHVl+pjXRMTFIaUjSq6HeMG6XYgeh3qR/fZiCGy\n4LmCxhYGLspSHVOUZpHguoJin8LAZPIy9+PnrqsmOVFgXGb9rjFKloESvY7qA4ATzsTMzfAGAN8z\nfMrSm+MNqBgzfUwYuI2YUO3260Y+OrGOmbNdgO4cbz0eGlsY0Jjbyr2/ICSDcetge7zYJvG8ZO4n\nzX0d78GMw+HQC+YwDPHAAw/Q67e+9a04f/48/u7v/g6nT59Gt9ulpxtvfetbEYYhrl69elMDzWAw\nbk+wjNGtg+0xg8E4SrA9Xo7XHMO8t7eHl19+GRcvXsSdd96J173udXjhhRfw/d///fhf/+t/wfd9\n3HPPPTft544zPcSRyeyMQuNSiEPPSjLx4LnGBWHrfpLgt+ca94WsjHaea3a1MvCNPmS3YzQeO5FJ\nLuhau9o4Nm4Wz6VzQTgQlktEjy0MvMaFp3ejrmsyWePIMxm4zP14uFufSe0Ci2PzFCOOTRZ3HBn3\nYScyv0sU0m8HW/fTcgF6rkPuulnu+pjAs0oKR0bLNg594tm1nt6pJBOP+tf92Jqn9t/ViULS9mwr\n90UhGRwzd3S4re0xz0vmvkrua3gPXgS2x8vxmhbMRVHg4x//ON71rnfhwoULAIB3vvOd+A//4T8g\nz3N4nof3v//9CIL5m+Qszp3pKje9b1x9+uJstH2P3A76QtGgGKrAh9DVf9SB6njPQ6UnaScytdnT\n1GSgWu4hJwqNGyQKIWzh8AXpwcJxaGzNZNhm5aA0VC6aTu6ToDhzPx7uonZdVYFPhrjqZiSg74Rh\nwzVGRikMqC1sofwZ7nosvufO8QaaFdLSsEQnV/2kWWncgb5x9YXBTLv+XXzPneNN49Hxf7ICDaGl\n3BepZFT8RONIwPbYgOclcz9p7ut4D14EtsfLccsLZiklPvGJT8D3fTz66KMAgC996Uv4/d//fXzg\nAx/A61//evzt3/4tPvzhD+OJJ57ApUuX6LtXrlzBlStX6PWDDz546wwYDMba4bnnngOg5n7Bup+v\nGWyPGQzGrUDbYoDt8c1wSwvmqqrwyU9+EoPBAE888QREvdv5xje+gTe96U14/etfDwB4wxvegHvv\nvRdf/vKXGwb68uXLuHz5cqPPc6c78H2X3AW+J6wMVpdcDb5nl2I17gg4Vpaq9GH08x1UdYJAFeWA\n1oTMclR5Tm3asfomYxeBbyUXmHKf8E2WKoSAgC5N6UBamadGE9JFGGjhcI9Kc2aFEVFn7sfDvYpq\nofg4ItH4Ks+p7dg8A99kd/u+cSsGvuKtTk7kBBwa3yx3k3xRIs/VNMsLiawwAvKaT+AZxRClHmJ4\n6rYqxWpcj3CsjHRZjxnmSUpbufdi9ZoXZUcHtsc+z0vmvnrua3gPBtgWHwa3tGD+1Kc+hW9/+9v4\nxV/8Rfj6wgVw77334rOf/Sy+8Y1v4NKlS3jxxRfxta99DT/0Qz900z7Pnemq+CJ98QtY8Ugm7kg4\nZlI0YubgmLgm3QEA+D6cOku1Kgoj7SIr9RpAVZamDrznkQtFZfLWP5HrNmRznMYFqw8R0M5O4RjJ\nmrKUKErT1u6SUkrazTH34+GueaIoKZauKgpU9e/iuBZP4VhZzJ6JSbMkg5yGgTZjDWC5A32XZHyK\n0rSlBAnIF6U0cWXCiOCrODQTb6b7nIuZq43kHHdtYFvK3ffmXbgcM/fawPbY5XnJ3FfOfR3vwYvA\n9ng5Dr1gvnbtGv74j/8Yvu/jve99L73/3ve+F/fffz9+9Ed/FB/96Eexv7+Pfr+PH/7hH8b3fd/3\nHemgGQzGdw8qNtC3DLbHDAbjKMH2eDmcqiW/zrdfuQYhnEbFRy3IDcB6cmF2jstKO0LKZtS7FcRe\nNd6X5nPdlxVM3wist88lROO45qnrYH/rZ7WzVe2fW1bmM+Zu+jkR7voz4Ryct/3/3KmrA/PWn2se\nB+V+Q972//aJ0C7uVSVx4c7zje//1mf+fGG/R4VHfvT/O9b+vxvx3WSPeV6a13Ru5t4Y0pFzX5N7\n8F13bM31wfZ4OVpTGrtT5kqaZdkFU9lXsX5/8QUr4UA65rvSsS4Y68KTjkuf33Sy2G04WHy5AgL1\nIrCyJkZ1AEPB3M25T4C7/uyGi4IZ3ur/xRCoDs4bqFePNzGSc9yX81a82s+9xPz+nJNM2ofvJnvM\n81KfkLkDJ8N9be7BC8D2eDmW/e4MBoPBYDAYDAYDLXrCXLy6fWsB/1aJyMKqF1+UxjVhAv7lkQf8\nN8pjFgUqq2487eaOK9mBud+Uu/3ZUSUczvJWfEpK6KikpHKox5LoMcPd/I2NG7Kt3GXgAxfQQEui\nwhgW1tEe87xk7qvkvo73YFy4C7Nge7wcrVkwl1e3DyQpg8CHlqwBjMtEwqGLMSskSZblRUkXb25J\nmb0WSRnpu41sVO0GqYqS5HFgSeVURUkSMsjyW5bTYe6H555bPF+LpJ2W67GzrwUqMlBVlgOWTBC9\nn+dAZr1/SCkh/VvMctcuz7KUyGpeiqM6b1u5VxtdzIJLsbYP62iPeV4y91VyX8d78CKwPV6O1iyY\nGQzG7QmOmWMwGIx2gO3xcrRmwVy+un2wspiyMqH2ljtQOsLs5PKSyi6neYE0U+6INCub7dy0D1IW\ns1Fukuq3W8kFRWl2tUkGWZf7rJIUVZpZ7ZSOOVRJUOZ+aO702m7nxaHKcs9yd10rsUK7wPIcVaJ4\nyjRFldQ808xqp3TMgcuh1rt9R/MGAM+lpJKirOipRfPv2lLuCxJN2AXYPqyjPeZ5ydxXyX0d78GL\nwPZ4OdqzYL66DSeOqb68iCM43Y5qdyKKu5Gw5F18v5GnqmOl8sJcjNMkR5LW7bTAeKoukiQtME3z\n+pgCcaR+ijj0EYWq3Y0DlKWepOYiUlrslkh5jUpKcnfINEU1SdT70ymkbo8nkNO6PUlQTacAwNyP\nifu05j+eZtZvkWOaqHYceYhD5V6LQg/dWBnHsvTmeKtTN7nrOLEqy42BsrjJSYJqPFHtadL4XZw4\nVvw7EURdAcrpdhRvRQzaRgrhmAIAM9y1mzPNC0wT9TdoLXe/NSaHcQOsuz3mecncT5r7Ot6DGYcD\n370YDMZKwTFzDAaD0Q6wPV6O1iyYi1euQfS7EPVTjGqjB6HdK2Vp9O+s2uxOaTI+pVM1MnO16yNJ\nCwwnalc3mWYY17vd0TjDpHZJDCcZNjpqJ9uJAvS6elcrUZQ6vN48SfE9u7xmZXQOy9IkFySp2dUO\nx5Cjelc7nqAajqgtB2PVN3M/Fu6Tekc/TnKMxvVvkWT0u2x0AnRqt3OvG1h9NbnrhAuV3a31oysr\n+7o0rr7pFHKouMnRBLJ+olENR9SWgzFEXyXAiW4H1UZPtYuS+hQ1bwCorBKrkBVpe9oZ2WlW0NOM\ntnJ3ejFmwaVY24d1tMc8L5n7Krmv4z14EdgeL0drFszlq9uNmCJhycIAIBkj4XmoIp39WTT60MHq\neWFiiKZpQZN0ME4xqi/ewSilC3kwSuj4jU5Jci+lFfxuy9qEQUm12W1URWGybtOM3CByNIHcH6r2\ncLSwzdyPh/tgrIzmaJJhMFLt4STDYKTGl2YlNjrqO6WUc7zVEATCQGd6z7gAtSxRnlOcmJwkxkDt\nDyG1gZpp27FktBiZ4S5qyaIqyud4a+7k/sxKcnm2lbs4c2qOQ1mygW4b1t0e87xk7ifNfR3vwYvA\n9ng5WrNgZjAYtyfa8ERjNBrh6aefxpe+9CX0+3089NBDuP/+++eO+3//7//hd3/3d/F3f/d3GI1G\nePbZZ+mzoijwqU99Cl/5ylcwGo1wxx134OGHH8Zb3vIWAMCrr76Kn/3Zn0VYJxQBwLvf/W78yI/8\nyPETZDAYjAOA7fFye9yaBXNxdRuupRHYSAm1RMKrwAfqgHxbnLsSRgdRZama5AJyg0wy7A3Urmtv\nmNBud2+YkCZknpeNGB7X2tVqfcQ89+hcVVUZgfCiNBqPSWqSC8YTs5Pd3Ue5u6/a+0NqM/fj4a53\n9HuDBHtDxX8wTqmdW5qZs9w1f98TyHM1VcpSUhZxJa2s7Cw3LsCxcfvJ4Qiy5lnu7tOEPL+LAAAg\nAElEQVTuvtzdh2vpgc7yBpTuZ6V1P+OoIcSvS6yWpbQy0k1SSWu533UebcQzzzwD3/fxzDPP4MUX\nX8SHPvQhXLp0CRcvXmwc53ke/uE//If4oR/6Ifzqr/5q47OyLLG1tYUnn3wSW1tb+OIXv4innnoK\n/+7f/TucO3eOjvtP/+k/NcrethHraI95XjL3VXJfx3twW9FWe9yaBXP56raKFWpop6gLxPFcVSEM\nKoPVGPHmTkh/NS8kuTiStKBYqcHITMy9YYLdgYrx2dmfNmOC6PQOVR0KPBdpaETHF5Zhl5URBU9T\nk41quUHK3X0zYXf2UF7frX8A5n4c3LXbb29oDPTuYIqd/Wl9ajnHG1AVl7SAfhoakf057rpqUpaT\nS09OExMnZm0M5O4+yp09dd7ruxQbN8tdL0Zk4JNKgTLi8zt/KUFjS7OS4gXbyt0ZzLsBV51kkiQJ\nnn/+eXz0ox9FGIZ44xvfiLe97W343Oc+h4cffrhx7IULF3DhwgV85zvfmesnDEM88MAD9Pqtb30r\nzp8/jxdffLFhoKuqav2Cee3tMc9L5n7C3NfxHrwIbI+Xoz0L5qvbzQtQCFN5JwpI8qXqZiZOpyjo\nKpWOS7E/mS3rkuaN4Hq9q9sdTHF9T+3AtvcmDe1BCq73XdJBjCMPndyn/vW5ZAUaQyOGKMlItqWx\nw9sfmkl6bQflteswHTH3o+ZOcWLjlAzU9b0Jtmv+s9y1XE8YuCT108l9qspUSmmGK2UzZk5rek6S\n5hMNbax29lBe26n5X5/jDdRVD7X2bRyj6mamfx0zJyWk49J49NjS3EgVtZW7GMw/1bBj9VaBV155\nBa7r4s4776T3Ll26hCtXrrymfvf29vDyyy/PPRX5mZ/5GTiOg+/93u/FT/7kT2JjY+M1nec4sI72\nmOclc18l93W8By8C2+Pl9lgs/YTBYDBOALKqjvXfzZAkCeK4qd4RRRGS5NZ1SouiwMc//nG8613v\nwoULFwAA/X4fH/zgB/Frv/Zr+NCHPoQkSfCxj33sls/BYDAYRw22x8vRnifMr9ZPG7UIvuuaJxph\naOSN0tS4HWZkjIpGDFGdpZoUjWxU7RLZ2Z/SrnZ718irOI7TiJXSO7xOFFCfeV7SuaSsjBuoLBsu\nEZJ1GYwbLhEdilBeu254M/dj4a6zr/eGCbn9tvcmc7zVqU2cWBi4JG+UZiXF1RW2y1BWaixougCr\n6ZQk82wXYHl9l56qz3GvVQecwKcqU6LbMX1mOZ3LlnAqGvGCJRUAaCt3d8ET5pNwAT733HPUvnz5\nMi5fvkyvoyjCtL5eNSaTCaIouqVzSSnxiU98Ar7v49FHH22c5/Wvfz0AYHNzE4888gj+xb/4F0iS\n5JbPdVxYR3vM85K5r5L7Ot6DF4Ht8XJ73JoFs3v+LNxzZ+GePa1en96E2FSPxkW/a6rwhKEx3K5L\nBl0Ih2RnZl0ZWuMwzUzsk50sAABbp9QN4MxmjFMb6sfq9yL6bhyZMpW+79K5hHCAyqHx2DcVPWbR\n79JEc7PcipWygvuZ+7FwJyNTyEaihM39zKY6x6mNCP2e4r/RCcgFGAYuuQY9V5jKZpWjxoKmYXXi\nmDQ9qzSlZBJYCwrNGwDcs6fhnt5UfDY36LtOHJs+A5/OBeHQGDxXLHRbtpW76K8m/ODBBx9c+tld\nd92Fsizxne98h9yA3/zmN3H33Xcf+jxVVeGTn/wkBoMBnnjiCQhxcydeG0vRrqM95nnJ3FfJfZ3v\nwSeNdbXHHJLBYDBWilW7AKMown333Ydnn30WaZria1/7Gl544QW8853vXHh8lmUo6vjFPM+R13GD\nAPCpT30K3/72t/H444/Drwt6aPzN3/wNXn75ZUgpMRwO8du//du4fPnynPuRwWAwVgW2x8vtcXue\nMN+xpXZ2dWEDsbkBoavtdDuUnepEARxdWcrzKDhfOGhklIa+qc2uXTkbnXKhZI3jOLSrPd2P0e+G\n9fGm6lAc+tRn4Ll0LuHAyh73zNiigMYsuh2TgDArWaOzgJn7sXDXIvizcj3a7XdmM8bpvuLf74aN\niktxqM4X+h5laLtCkJdaZVCr8zm+lRzSiYzLOskWS3MJxzy9O3PKPL3b6NF3nU5k+vR9OhesMdjZ\n46Hv0Zjbyn3RE+Y2COU/9thjePrpp/HYY4+h3+/jPe95Dy5evIjt7W28//3vx1NPPYWzZ8+SdqfG\nT/zET+DcuXP4xCc+gWvXruGP//iP4fs+3vve99Ix733ve3H//ffj6tWr+MM//EPs7++j0+ng+77v\n+/Cv/tW/WgXdm2Id7THPS+a+Su5reQ9eALbHy9GeBfP5rYbbz247Gz2IWuvTdgGamVK/rJ+X27E/\nUeiZUpNyiWSNK4wbpBtaLpGQvhuFlkvEE3Su5gCcZpxfPeZGWdlZmabarcPcj4c7ZRPPyvXULq1T\nGxEZKOUCVO1eN0AUWi5ATzTOZfMGZmLd4qhZWnWBNBdct+H2s9uO/m4cNVyAs7x1d3acnx5zW7mL\nfm+OQxuE8nu9Hn7hF35h7v2trS38zu/8Dr0+f/58Qxzfxrlz55Z+BgDveMc78I53vOO1D/YEsPb2\nmOclcz9h7mt5D14AtsfLwSEZDAaDwWAwGAzGDdCaJ8zeHVtNt9+sC1C7RaIQ0E80PBeOdkfY2aW+\nizBQ1LpxQMkFtr6grfHoe6KRXEC72k6AbuRTP7pP33fpXI7j0BjguTQ2JwppzKIomzXpbY3H+njm\nfjzcZ3nr4/QTCjuxot8L0avb3chHN1btMPDo93JdQe5DRwiqAoXAV2ME4HQ7Zkc/w52eAAR+0+23\nyAXY7VCfCHxTcUqIZiY5Jdh4NOa2cl/4hHnFQvmMeayjPeZ5ydxXyX0d78GLwPZ4OVqzYHbPb0H0\nu+bitC5U0etA6Ji5cCZmzgJlqXomS7UsPRRlYM6jS00K4zYJA7cRK6XdIN3IRyfWMUS2S8Rkqdpo\nxBCFAY2ZslLVIKlykBMFxm3E3I+du477CgNznB0n1usaA9WJA8QNF6DbOJfNG6hj2kLVj+hEc7zV\nsa6JgQtDyr4W3Y5x+3U7EL0O9aP7bMTMzXDXYwsDF2Wpjmkrd2fBgnnVQvmMeay7PeZ5ydxPmvs6\n3oMXge3xcrRmwcxgMG5PtCFmjsFgMBhsj2+E1iyYvbvOKZ1DndkZR8al0ImM7mcU0g4Jtu6n5RLx\nXIfcF7Z7wXMFHRN4VqnNyGg8xqFPyQVda1ergu496l/3Y+sgwrV3byHpHAo9VqhdrdRukzg2T3CY\n+7Fw1ztx1zWZ23HkmezjyGRxR6FxH8ahZyWZePBc4260dT9tbuSuk5VJDrB+Fxn4Rhuz2zHcOpFJ\noupaT+/i2LgVPZfOBeFAWO5PPbYw8Ob+5q3jvkAlg12A7cM62mOel8x9ldzX8R68CGyPl6M1C2b3\nji2VcW25SOjiDANqC1sofybLU09Y33NnkkHnKwelYUm12dOsNO4R37NcJTPtWtbF91w6lw1HCIoh\nErICDUE4ELX7pgp8uglV3YwExZn78XC3qyaloXJNdXKfBPTDwEgAKdfggrbvkQtwljuNJfAhdJWz\nmjcACM9DpcfdiVB1a3mfNDUZ11Z4ihOFxu0XhRC2UP6CrGbhODS2teC+ICSD0T6suz3mecncT5r7\nOt6DGYdDaxbMDAbj9kQbK90xGAzG7Qi2x8vRmgWze35L7dp00Hrgm4xP3zeuhsAHdMUWz6WMTwGH\n3DSyzj4FUGtC6l1aiTxXlPNCIiuMiLrOWA08k7Grsnd1coHZNavSlMb1CMfKUpX1mGF2lpXvo4pq\nsfQ4MvXe85zazP14uIeB5ulRWdKsMALyvu+Sa9D3hJW5bHj6nl2K1bge4VhZ2dKH0c93UNVjraIc\n0HqYWY6qrkJUZbl5MucbxRDYv4VnSp3CN1nZEAICugytM8dbj7mN3KuNLmZRtEAon9HEOtpjnpfM\nfZXc1/EevAhsj5ejPQvmO7ZULXSqnuNY2ayeicGxpGOchoE2GagBLPeI71LWZ1GatpQgEfWilCa2\nShhRcBWLZWKudJ9zMUT11KRYPvUB3UicskRVl25EYerXV0WBqs5gZe7Hw70o1XfKUpKbrJQSRf1b\neFa2tqqlIsz7mqdjjGEjZg7OPG91cjg1t6oojKSPrOi3qMoSjmvx1H161m/hug3JIKdhoPUhAjrX\nWThGqqit3HX8nA1OMmkf1tEe87xk7qvkvo734EVge7wcrVkwMxiM2xMVJ5kwGAxGK8D2eDlas2D2\nzm+pXZ0VSN8IqrfLUOr3Z4LuPWsH6rrmj25nfdrxOfptKSvapdqncazAemF9IByn8doMwGvuQGdP\nBKCyswGkNJ8xd3PuE+CuPxPCOTDv2ffUyT0zZv2kTZ9En3uWt/5c93VQ7gsSbDzrycN6cGdjvA5Y\ne3vM85K5nzD3tbwHMw6FW1owf+ADH8DXv/51uLUL4+zZs3jqqacAAGma4nd/93fx53/+5yjLEvfc\ncw+efPLJm/Y5cf0bTxa7XV8K81NVf15BVNbFUB1gslQ3mSyVPTABYMEFC0DW70vHfFc61vktgtJx\n6TPmbp3iJLhrI1LdYFEwxxu4EfeD8taf39RIznC/EW8Aa8G9quaNNLsAXxvYHt/YJvG8rF8zd9XN\nCXBfl3vwwj7YHi/FLS2YHcfBo48+ih/8wR+c++zXf/3XUVUV/v2///fo9Xr4xje+8VrHyGAwvotR\ncGWp1wS2xwwG46jA9ng5jjQk49vf/jZeeOEF/Pqv/zqiSGWi/r2/9/cO9N1ru+NbCvhvlIjUAfxW\n7fRKSioNeSwB/1Z5zKKUVoB/RTs1lexg6skfVbIDc78592NJOJzhbXjWup9VZf0W8sgTPWa5V/rv\nWpTmCUZLufve/JMRjpk7Htx29pjnJXNfJfc1vAffdQfmwPZ4OW55wfwHf/AH+P3f/31cuHABDz30\nEN785jfjb/7mb3Du3Dk8++yz+NznPofTp0/jgQcewD/4B//gpv1d2xkfWFJGy7bY2agCFU3SKssB\nSy6G3s9zILPeP6SkjJZV0tItgJq82g1SlhJZLZWT5yXy+rxFWVFbvX/rcjrM/XDcSTony29Z0g6B\nP8cbUO4vbaCyQhKfvCjJWOcWz9ciJSR9t5F5rl2eVVESF1i82sq9F8+bHHYBvnawPeZ5ydxXy30d\n78GLwPZ4OW5pwfzjP/7juHjxIjzPw+c//3n8yq/8Cj784Q/j+vXreOmll/D2t78dv/Ebv4H/+3//\nLz70oQ/h4sWLeN3rXnfUY2cwGIzbHmyPGQwG4/hxSwvme++9l9o/8AM/gM9//vP43//7fyMMQ7iu\nix/5kR+BEAJvfvObcfnyZfyf//N/bmqgr+1ODlQWs1Fy0spGFZU07o48R5Woso8yTVElqgRzlWZW\nO6VjDlwWs3ZVOIAJzvdcCrAvyop2r2lWIs2Lul1Q6c9GOy8OVRKUuR+ee5XWPJOUSnFXySHLcsvK\npFhY7kDpCPPkIi9nuGn+ZbOdm/ZByqHO8gZU5jkllRSleZqRZJDEsZ3cKxliFiyU/9rA9hg8L5n7\n6rmv4T14EdgeL8eRxjDfc889C9+3M00B4MqVK7hy5Qq9fvDBB3FtZ4w48hCHyt0QhR66sbpYytJr\nSLMYPfJmlqeOlaqy3EzSSYJqOgUAyEmCajxR7WmCapKoY6ZTOHGsxtqJIOpKQE63A1HXXYdVm10I\nx4iCW+eXlXF9pHmBaaKMRpIWmKbq4hxPMyR1e5rmmCaqzdyPh7vUPMcTyGky97s4cQyn5iniCE63\no9qdiGLspOatTj7HG1CuPm2Apklu8SwwnmbWb5HXxxSIIzX94tBHFKp2Nw5Qlnox0uQ+y1tz1+5M\nmaaNv2sbuWsKzz33HAA19zlm7nhwu9ljnpfMfZXc1/EeDBhbDLA9vhkOvWCeTCb467/+a7z5zW+G\n67r4whe+gL/6q7/CI488gnPnzmFrawv/5b/8F7z73e/G17/+dXz1q1/FT/7kTzb6uHz5Mi5fvnxk\nJBgMxnrhwQcfpDbHzN062B4zGIzXAtsWA2yPb4RDL5iLosCzzz6Ll19+GUIIvO51r8Pjjz+OO++8\nEwDw+OOP45Of/CT+63/9rzh//jz+5b/8l7hw4cJN+726M8JGJ0Cndsf0uoGV8RnQcUKYwHuV8an1\nGysrG7U0ro/pFHI4BgDI0QSy3uFVwxG15WAM0e+q/rsdVBs91S5K6lMAlL1aWaU2ISvSObQzc9Os\noJ3ccJJhUu9qx0mO0Vi1J0mG4US1mfvxcJejmud4gmo4orYcqN9F9LsQ9VOMaqOneKuTGJ1N4aCy\nyozSkw7Hzr6uyNWXpAVxm0wzjOud/micYVK74YaTDBsdxa8TBeh19dM7OccbUMlVJru7Aml7lqVJ\nKklS8zSjpdy7ESf9HSXYHls2ieclc18l9zW8By8C2+PlOPSCud/v44Mf/ODSzy9evIhf/uVfPvRA\nru2MkWYlNjrqQiilkUgB0JB2CQOd/TnjEtEyLXlOsVJykphJuj+E1JN0pm3HU9EktbNIXReilnCp\nonxhHfailMYlkpXkBplMMwzGqv/RJMNgpNrDSYbBKKHjmfvRc5f7Q/V6OFrYtmPJhCUHpHkDgPA8\nVFHeOJfNG6hdgHVs2DQtyEANxilGtbEejFIy3INRQsdvdEqSN5rlrqWLwqCc463HQ9nWaUYuz7Zy\nP7UxH8PMuHWwPV5sk3heMveT5r6O92DG4dCa0tgMBuP2hH1TYjAYDMbqwPZ4OVqzYL62O0FuaSfa\nAfautbPzPYE8V8MuS0m12StpZalmuXGJjI0bRA5HkLv76ru7+7SrLXf34Vr6iI1UWEsgvdI6iHHU\nECbX5SbLUlpZqia5YJzktKvdGyTYG6od92CcUpu5Hw932sXv7qOs+cv9IbVdSw90lrvW/awCX/EG\nGkL8lTC6nyor2yRWkNtvkmFvoHjuDRPa6e8NE9LDzPNyjrf+X+uB5rlH56qqyhQDKEqj7ZmkJqmk\npdzvONPBLCQnmbQO62iPeV4y95VyX8N78CKwPV6O9iyYd8bNGDiYeCFXCBLQT0MjvG1fV+qN2lhn\nObk45DQxsVLWxJS7+yh39gAA5fVdihVq6sYImqQy8Cl7V13U8xeVlKCxpVlJMUSjsXGD7A3NBbs7\nmGJnX8V3Mffj4W4bJTJWO3uKtzr5HG9AGSipxeQ7kWXEm9z1V/NCkksvSQuKExuMzMZgb5hgd6A4\n7+xPmzFwFnddXS3wXKShEdmf412PhwoApKnJPG8p9+ECI80xc+3DuttjnpfM/cS5r+E9eBHYHi9H\naxbMDAbj9kTJup8MBoPRCrA9Xo7WLJiv7Y7JnQfU2de+Fgx3SSOxk/tU0rGU0my0pGwG3WtR8EnS\ndInoHe7OHsprO6p97Xpzx6Z3tYFvRNTjGFU3M/3roHspIR2XxqPHluZG49FWhBiMU9rZXd+bYHuv\nzppl7sfC3U6soB39tR3FG5jjTiVKo4C0MatuZpJYioKeAkjHpeSQzNb9TPNG9rV2fe0Oprhec97e\nm8zxBpS2pxbKjyMPndyn/vW5ZAUaQyPBJslI27Ot3IeTxW5ARruwlvaY5yVzXyH3dbwHMw6H1iyY\nt3fVH8+h6jkmXigMXJI3SjNTC76wXYayQqVlXSyXSDWdkmSN7RIpr+/SJC1fvW4GIhzKxnUCn6ru\niG7H9JnldC5b1qVoxBCVVJjDVoTYGyYUirC9NyHezP14uNsuQO32K69dn+etTm4MdBgaeaM0NS7G\nGRmjohEzV2dlJ0Uj+1q7wHb2p7RJmOVux4RqA92JAuozz0s6l5SVcf+VZcP9SRJOLeW+MCSDY+Za\nh7W0xzwvmfsKua/jPXgR2B4vR2sWzAwG4/YEx8wxGAxGO8D2eDlas2DeOt3B1qkOzmwqt8upjQj9\nngpy3+gE5AIMA5dcg54rTInMyoGzYGfmxDGJgldpStmosHamAOCeO6v+P3sa7ulNAIDY3KDvOnFs\n+gx8OheEQ2PwXLHQbbnRsXamhWxk1xJ/5n4s3PWu3M1yK7Giyd09e1q1T29CbG4oav2uKVcahuZJ\nh+vSExAhHNKjnXXdaRH8NDNJUbaKgOYNAGc2Y5zaUJz7vYi+G0ce9en7Lp1LCAeoHBqP/RRGj7mt\n3De68zrMbKDbh7W0xzwvmfsKua/zPdgG2+PlaM2C+dzpLs5sxjjdV5Oi3w0blXfiUE2Q0PcoQ9sV\ngrw3KqNU0XF8K/anExlXTpItlqwRjpmkZ06ZSbrRo+86ncj06ft0LlhjsLPHQ9+jMXeigAoAzErW\naJcncz8e7hRLNivXozP+z56Ge+aUemtzA0JXWep2KCvZiQI4urKU51GMmXDQyKAOfTWOOPTJZb3R\nKRdKczmOQ4uR0/0Y/XohaVdXi0Of+gw8l84lHFjZ454ZWxTQmNvKfaMzv2Bm3c/2YS3tMc9L5r5C\n7ut4D14EtsfLIW5+CIPBYDAYDAaDcfuiPU+Yz3SV26/e2SkXoGr3ugGi0HIBetoVMtNJvdVqBMvH\nUbM2+wKNR7huww1itx393ThquETMo5Rmd3ZijB5zrxuYjNpZjcfarcPcj4c7lRmd5V67tGy3n912\nNnoQtTi+7QKc5a7HYieHRKGHXlc9DSjlEi1bVxi3Xze0XIAhfTcKLRegJ+Z51+NpJMbUY24r94Uh\nGZxk0jqsuz3mecncT5z7Gt6DF4Ht8XK0Z8F8utOIF+r3QvTqdjfy0Y1VOww8itNxXUFufUcIqoqD\nwIcT1RdXt7O4NrstWRP4TTfIIpdIt0N9IvBNBR4hmpnkFEPk0ZjLUjbcHLZkjb7AmfsxcZ/hrTlr\n/g2336wLUPOPQsUbUBWndD92NrXvIgzUdOrGAXGe5a45+J5oxITSYqQToBv51I/u0/ddOpfjODQG\neC6NzYlCGnNbuW/UNx8bHDPXPqyjPeZ5ydxXyn0N78GLwPZ4OVqzYGYwGLcnOGaOwWAw2gG2x8vR\nmgXzuTPdRoB9r2t2dp04QNxwAZqsbBuNoPtQ9SM6kcnGVV+qj3VNEH0YUjaq6HaMG6Tbgeh1qB/d\nZyPo3oLnChpbGLgoS3VMUZqnaq4rKFkgDEwmL3M/fu66zKgTBcZl1u+aXby1oxe9juoDgBPOJJnM\n8AYA3zN8ytKb4w2opAx9TBi4jSQq7fbrRj46sU4ysV2A7hxvPR4aWxjQmNvKvb8wJGPuLcaKse72\nmOclcz9p7ut4D14EtsfL0ZoF8x1neogjk9kZhcalEIeeFTPnwXONC8KWMSLBb8817gtZmcxG10xS\nGfhG7qbbMZI1ncjESnWtSRrHxs3iuXQuCAfCconosYWB14gF0pPLdU0maxx5JgOXuR8Pd+szqV1g\ncWyMchybLO44Mu7DTmR+lyik3w62jJHlAvRch9x1s9z1MYFnVUiLjDRXHPrEs2stRlTMnEf9635s\nCSf77+pEIUkVtZX7opAMRvuwlvaY5yVzXyX3NbwHMw4H/sUYDMZKwTFzDAaD0Q6wPV6O1iyYz53p\nKje9b1x9ejfXaPseuR30zkqDkg4CH0KXy1QHquM9D5Xe1XYiU5s9TU0GquUecqLQuEGiEMIWDl+Q\nHiwch8bWTIZtltpMQ+Wi6eQ+CYoz9+PhLmrXVRX49OSi6mYkoO+EYcM1Rrv4MKC2sIXyZ7jrsfie\nO8cbaJYUTsMSnVz1k2alcQf6xtUXBjPt+nfxPXeON41HJ8zICjSElnJfpJJRcVZ267Du9pjnJXM/\nae7reA9eB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_autocorrelations\n", + "\n", + "correlations = [('black', 'black'), ('white', 'white')]\n", + "draw_autocorrelations(X_auto[0], autocorrelations=correlations)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that for this checkerboard microstructure, the autocorrelation for these 2 local states in the exact same. We have just computed the periodic autocorrelations for a perfectly periodic microstructure with equal volume fractions. In general this is not the case and the autocorrelations will be different, as we will see later in this example.\n", + "\n", + "As mentioned in the introduction, because we using an indicator basis and the we have eigen microstructure functions (values are either 0 or 1), the (0, 0) vector equals the volume fraction. \n", + "\n", + "Let's double check that both the phases have a volume fraction of 0.5." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase 0.500000006869\n", + "Volume fraction of white phase 0.500000006869\n" + ] + } + ], + "source": [ + "center = (X_auto.shape[1] + 1) / 2\n", + "print 'Volume fraction of black phase', X_auto[0, center, center, 0]\n", + "print 'Volume fraction of white phase', X_auto[0, center, center, 1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute the cross-correlation of the microstructure function, using the `crosscorrelate` function from `pymks.stats`" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.stats import crosscorrelate\n", + "\n", + "X_cross = crosscorrelate(X, p_basis, periodic_axes=(0, 1))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the cross correlation using `draw_crosscorrelations` from `pymks.tools`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TyAUXq6lpIR80cepYNlsZFA0LRslGseR/zhdNGMF2sWShaPjbtSquOU64khfL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\n6+UdFBSXyla2ZLwieEt8B2SF9FNj3SRURa5qYqmXN0mcSOi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NMVmqYzy5zrJ5U7FLzU67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\n/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_crosscorrelations\n", + "\n", + "correlations = [('black', 'white')]\n", + "draw_crosscorrelations(X_cross[0], crosscorrelations=correlations)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the crosscorrelation is the exact opposite of the 2 autocorrelations. The (0, 0) vector has a value of 0. This statistic reflects the probablity of 2 phases having the same location. In our microstructure, this probability is zero, as we have not allowed the two phases (colored black and white) to co-exist in the same spatial voxel.\n", + "\n", + "Let's check that it is zero." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Center value 3.74121776119e-17\n" + ] + } + ], + "source": [ + "print 'Center value', X_cross[0, center, center, 0]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Compute Non-Periodic 2-Point Statistics\n", + "\n", + "We will now compute the non-periodic 2-point statistics for our microstructure. This time, rather than using the `autocorrelate` and `crosscorrelate` functions, we will use the `correlate` function from `pymks.stats`. The `correlate` function computes all of the autocorrelations and crosscorrelations at the same time. We will compute the non-periodic statistics by omitting the keyword argument `periodic_axes`." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks.stats import correlate\n", + "\n", + "X_corr = correlate(X, p_basis)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "All or some of the correlations can be viewed, using the `draw_correlations` function from `pymks.tools`. In this example we will look at all of them." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eHwNJqj8HAIWX2goCWJH4HXtwQHnHBvtVtMJgP+x+YZm36zXqN8vzYLn6WAAA\nz7XheeJ3olD0Sa3tkccrTdX7sG01JgbqAQb6xDPcQ9Zf89FXE+M7DDwEnluI4NBvElyX2mSFPqyi\nH+z+PuSdWPQJ6w8A9Lw9OKDkHOwn67Aj+2GgTvPBDn1YnpoPWaYUNvlePM8h+euRh3as5E+0PhAy\nSBkH6gH66up6oE+NAzlmosCFV4wD17FhFd+R5TkcR/ym5XmwakJ+avfgABwpf9Fm+Z6r+mSh0dfX\nh49+9KNd95cvX47rrruO/v/888/H+eefX/kdK1aswPXXX7/X2vRCcDF5opYN0zi06jX1Pj0PVvGe\n0zyn9+86No2LKHDR7ojxEtfEeMvYxsK2LXjFe+bcvWyIcXB/SHOyHnk0nj3P0fiHxj/jVzsU/ZDX\na7DjWD6ohLUtmkNWFBEPce+SPTKseKi/Tz1bfLflOoArl9JUcZDnEE+EgYe+mhjzaZohl+Petoh7\nfNch/uYRF9I7N1APUCv6Jwpd4jfPtTUOIu5h64tdrxH3IMuoD4iLXUdxahQqr9Rgv+Iexkd2f534\n3QoC1edsYyjfje85CHzxeS3y0I5FvyWsH8SrkP1mY6CuIsUkJ/FxINYjcV/yUeC71JdF54o2+Z5a\nN6OI1tO81eniYdl/8j3b2hqkuNgeEBxt1ULYtZB+B3wtKuaDo63LLq2jQwMhzQU5di3bguNILmbr\nT92na7Euid/srwU0ZwLfpT7X1iTHUfIzLiZ5OjHxcE/s4YZ/b2KxRMtx7EseBkRkBiC8zTx6shbK\nse/S3HIdxgElXQwo5nWfaBfK+odcr4MAWTGv03pEOrnLotCc4UHFg7VQfb/bvZ1wHYvaFwYu6sXY\nj5NU01c0zpRzJXS1CGG5HgxqOqnoBz73bcsCpG7usrEfBGrud2J9HSjp71YQIJVrwYplKhphREVk\n2f19am8SBkp+S/GxZVukKwe+Q3O/U/R/muk6mFoLld421B9qfMjHgdTRapFH/ex7Dq3FxAOWTe2z\nwkDpYq2O0kmzDI5sj1CKhfyDA0pmvh5QP9TVHiUI1N7Edeg92Bbbd/kuvbd2J6W+4Poo50EuO+dA\nvjeR4yrsMR8ANT6tIIBVcLbd3wfIdVHOiVwfFw5bC7V1QUaqDPbTvLIiNR/gONp6KNsk2xcFLs3j\ngb4Ucakfhge6Ix5eTDy8q+858cQT8e1vfxu//e1vceyxx+L73/8+DjnkkDlTA+dl0JChebsTnnf2\n2WfP56uBh9cnAAAgAElEQVThHniASi9gYZ76ZFEbOMu2IenQZqThuw7CQIiVpBlTFNgmz5UE69FE\nmG0GWkgnbWrrAfpravHwXbWB05RItoETbVXkqSkLts02u/rEkvLbgyy0iU+iKIQVFAtkxSbesW14\nriQNh2QrLxycWKJAbXYHK9INJIH01QJNcaxUnPjiEfqwiklup4nqA9tSyj97VvaVtmHv72MbiTpb\nRCPqN/iutpBaFUpkWLS7HqVIU9EO7q1xHZvGRD1Shos+ZsTorwWoSyUiVKTp2MqYZnEDFx8HxcKI\nTow8ScR1pjaAcNhmoi9S77u/Txk3ZF9q5MkMXK6DtLRRBMRYl5uqWuSrhbRkzJAy9NdEO2qhmht9\nNbWp6KurxSPwXeo3h82HLMvhMAVByp8XciFJkZc3lQN96MIiIO/FiheCiymNgKdB9ffR++QbuCzL\nFQ+x+RT4SonVDBnMuCzHZxR6qEXCCDzEQun5prYW+cqY6DqaUVl+Px97ZExM0i5FhZ4Ji7lXCxUP\n8fBSZmSlz7niwjZEFptLjs2Ul9BFmhbGlQxkVC73FW12K7i4vxagHjGjKjOuc+4hPvRd4kkrjkUf\n0AuQhgzFxXJjntVrsPqUEVbxkeJgu69G/G6Fvq7Eyp+wqjb0njJssXXJKhm2+Pvur+DjvlrAeEj8\nnefacOwe61KxbtpRCBQydBkz2AZQk5+tR4BYo+i6bFx31FrEx7jUG6LAJXnyLKc+UIZ4xdf1yCPF\nvx56tHnpq/m0FtUij/Qd31WOBj4vLNtmeolP7QYZ1xPVB7a+CaDvWCTG5cUWLbcveRgApZnwVIOB\neoD+YiyEoaevwfL92ZamZwLCyYZUdyQAYu4pg1fIdNK6lm5ly/VgcEB3shXfT3PftnvzWzFuNZ20\nvJEvxnNfzdfTrAYUJwJi7MtnPVfpvtrYdx3SM+1aSJv3Li5kuqh8lnQvnnLBDZz9dbUW9tibuOW1\nsDA+cv3L0nhSrReyr/rrOu+R06lP7U3qoVoPPNfRDbvo5gDSSfvrTCfNaANueR5syRUDfbBkX5TW\nA3mPdPZaqPqyZNyV78dzbXpv7chDUuiCeZ5rexnOg0J2H301ZeDlzjfZV2WdlArxMgONFQZKL6h3\nyJDBjetyLbADto/R1gK2BgywPqmFSp/gBh27ez6EoYf+utLH+ZxwHZscGRwvJh6e63sAYGBgAJde\neim++c1v4itf+QqOOOIIXHLJJXO2a14GDQMDA4MXDKYqvIGBgcHCY5EYlxdjtJyBgYHBC4IXEQ/P\n9T0Sr3zlK3HllVfudrsWjUHDWb2ChbYOaNY/CukMA6CwQpY9QdIzE/gOklRY9PKS11B6Schj3Y7R\nKqx8zXZM1r++mk9W4H5mFaxFvuYN0jzy8tpnYV2F5c/KcmU11sI/lUcwm22Q/Bb3zPf3KU9YLVR/\nKz1Btg0gLbrEYqHeXpcVXDzDIxdcNMK5rZ+8T7hHTFoZuzxBPLyP0goyESIDYWG0ytbweg3ZbIOu\nyfrfV1MW8HoN9oDyitnSK+Z71Pf8fXOvmHxnUibRVmUZ91iotwgJLqzekUch3vVIpdxwa7hfkXKS\nZbnyjviesvByD7FoJD2Thio0nDwkvC/YPRkWb0XcGuwqbx8LY/dcZvVPXKRpQD9PnmTNS17MjdBX\nqSqhr6VdhQEfBzb1N4cWhVT2ijCPoFVELFVGaBgsCCQX21qoJUv3ihQfk8eAOTeEV6qbj7k3msan\nZ9N4ilodmm8DfYGWXiAjh3iItefaKszYtuj7XbeCg5MENosKIm+V71XykDPYD4t5oijsVKYa9NW0\n6Kiq8GmfhU/XuyKiup+JAleLDpP/qrVIeecC36mMFszznPjQ8j3iSdRLIeYsHQMQkQtSdqvEO+SV\nqteUJ45zMwsz5iG2WpSYLyPgvK6UIykDcVCgp0LWKbWkmpvls57n6GHGUkbH1T3QhZfazjItNF2m\nJ6VhoMsvU2vqEckuPXyah9p3tQgHi+RXHFyLfC1gk0cUShkk70aBir6IWDQpjyzlfRX4Ts8IFa6X\nABDpVzwNVM4ZGWljsCggC2DycPu+uk9jP/Cqo3I0jzRLt7IkBwMqbZtHqbG5nzWbivf663q6mVwP\nolDjQUDnANexqX1R4OopZhVRpFHo0nznOnk5BRYQunl1uhXrQNdRablBALuPpRVInnRdTW8FgDyK\ntKiEKt1cS7/k6WasAdwjHwUupRPIbrAsC67NUw4Lngh99NU6dM11MRW9prgxDHm0ip6KSW3iOqls\nd5KqdZHp7/A9pXNGkc77khsZH1LUcBCo/tb2aIrjRGqp+E0eqQKA+KvMgyRvyGX3qH84H/osDVrC\n4pHxLGoYSaKnHqGIWCrmQ6rxf03tRdk8sfvUusijpkXKjf4eeDZB4Dk0j8vp357rYKi/d/FLg2os\nGoOGe8BKpSzywVIL1WTi5MnCuhxH1UsIMreylgAP55IKdDtO0GoldN1r8xqGSmHQamiwEHu7rBAk\nKaxioti2hZzlNWdy8WhFyNuCtOxGS5FCPWIKVaTnp5XD+1AdzhT4GbLco8+JKFwbYSBIPQpctIu8\n7nackPKkb2BVzrb8XOQrlnL0xP+osN3QJ6KwRSMAADlfPGUqQrsDu9Eq5A2Vohyy2hq1kCmRocpf\n9zwVNt1RG2X+vuWGisNimyquLAa+LievFyEXjDD02KaqpERDhL6rTZVPG/ksy0D0xkgz57LVVFqR\nXYu0zSNQzAG65+uLR8rGvVwkPYcWfb6R4PJ7rqMpxUp21Q9csebPSHK2LIvGYZ7ngMdqaMi282Pr\n5DtzHfEuB6pqaCwaetqvILlYS/3ii7ecv2zu5Z2E3r9lqUU7rQirdxxliAh8B81AzNt65KHdEXOl\nl8JSizwaz76nNnC6AKyGhDRWZDXkLM0ilwZRxjGchzT5We453QsCVkPD1dK9JB+IjbxUYBUH8ZpP\nnqdSJIX8CckM6KHHIgVB8TKlvZWVV1fNPYTSqK64x7ItZGRUlgp8qAyvjZZKVQl82DXOu9U8RJsj\npswr47JF62aW5Vo9JzJ8eYpj+usqrSjwHc2Aytcj+Z2cix1m4FL94agxm6Swebg9U3JzWpciTX4u\ns+wzzahX8LXle8RZ5RQs2VZRz8qjrqI6I2zTQ2uR5yIslPrAc3tyM7+WelC5thU3XIn2sbWIheXL\nMd0FEy23IJCnmdRCn9JMRDqo5AcPAatnpmo3sM1pwSlW6NPmNee6mu8hl+O90UJe6OF2o6WnmMkN\nbJ1tYGth9wbWtvSx70k9zC2F1Xc7lMLQRV9NtLfVTtTmvbQGAIIDaex7qoaGxWpocGMm0kDp5FBO\nwaxk1AaEMYfWgrpKr+tyuFXVjuBGXcvquTcpukqvsdEW39Fsx2i1ZXphSQ9lxiy5N9FSTjy7y8mG\nsrNRSzmSzkZXrQtBQKdVaUbd0Fc6OddZOR9yp6v87jzXahemgUo7tKqM2qELXhdD9E+1Pl7W2bX5\nINeB3NL1Amms6ZWGyvcaNWW4t9m1tjchfcLXnSUlA7M2H0IPeckRRM/4DoYGlPORYHh4Tpgdg4GB\nweJEj3xuAwMDA4MXDsa4bGBgYLCwMDw8NxZN7zirVygPSC1SXo8w0D0jzBpMnp5SiD1CFZmgQp9d\n8n7FSVb8m6ITp3RPVZ/VvdCBp655iL0e4ldYhGX7wpyOAssdB3lhtcyDgMK98k5MhbmydluXU1r8\naqFKr+DWP60Yn+wSG37Rvjx3VbGhUtElKX+741Fl3U6c9fQAyb+ja08vvENgoXv68TI2bJLfB1jY\nVvHjyOSJMNzz6XvM8qnevRX66r6vUmuynIcwWyzUr6Kathbel6Le5oUubXat+o33hVZ4qLTxzrJc\nebySBKipcZpLQmKnf+RRqHl+qYhV6KviotJLGKi0q7JXQBZ3EiH9Sk7lFfBLXkM13puRGIf89Bbe\nDz67VsWnVEQQj1LRQqp9D1amV2u2LUv3jEYh8iKMU39wceQL7m+QXCzHp+6NZ95pFp6eaZ4GNfcy\nLQ0soc/lGGp3EtSigo/jFO1YPMPD7TkfcY8kj46yLUsV+rWZp4WnmbACmLksrswiADUeYvJrUQps\nXSKu9z1kMT/FqTtiTraxfF+cAqPWJXldFZXAOSgKXG3uSd7Lcpba43vqFCGAIlRyxqvSM5t3Ytgt\nES2YtdsqVcVXJ8WARVvx6Bd+ighcFyjCZ3kRbq0GcEUqCi+I2u6k8D2bPaP4pupaFQRnKUgW6wfX\nIR61ayGL1HGQM37NpUeyv67JTx5Hvv6wv9M+5xEq/NQfV0UD8rRIniIKFKfAFFFKnqvC9T3XKa1L\n/AQZ1SdaClau5Od6CSDGQya9h+y95pHO1UoYY1xeCMiUkyhQJ/IFLOoy8PSTxgi2pdKSZfQB1Dvn\nczYLA9JDrXpbFYuNY8X13DvNTniy+WlPPQri0hjP88r7gt8F73USX+PDXpFIgDzRQkUlVEZnsYKn\nYFFJueuyue/T3KeI6VYbeXEqFvfGd+1HKqLULNtmUQcWRV9Vpdv4rsM430WtmPvtTkJ6Ouc6nmoo\ndLHufYrP10UqEmsj59HTsh0A3YfvqWjFeg12sRbyKFsr8LVID+oHuscKRJe4UEUl2giLKDUeKcxP\n+apHHqUb9dJDPZa6oenmbD5QpEoObT7IcZABsOWBDnI+BIGKRhqoa+t81bpocZ2d718ct4s3tXGf\n8ohpaPI02x76ahXpf4aH58SiMWi4B6zUjiRVg8jVFAZSSm2bFKdyfpZtCVJwbJtCfpM0QxyrsE9A\nhEPTdZqRglhWJFRYqBqMIryPC1BMiopKuZbrqIrqUUpEmScJ5bHaSdpbfhZCrCmOgLaTcNhksVia\nie85ZLjheXxxklEOW5Jm9Lc+U5740XuqSrGDqtx1OGBVeAOouhmuqqQcBHQtc5nRURX4eS4iP5K1\n6z7rh4znn2mbBmjQDVwZ5eiJjZRPslF+O3v3fEzwDQlXoiXynIXZh4HamPP6KYGvjsuq12BLhYKH\n//LnWV4eD2mT40CEMqs2WGzTwOV35H3PofkQR2ojxTdJfKzLRdRhm1VewZuHu8r2AIDre0qJYBsq\naeCzCmNGVqFIl4+8Mnhh4B6wsjt0km9kWZ5tshtzz3HkBld8HvguO23HQxzL48pyMrDyTZs4/rSY\nb57DeNrRjIly/GtjT6ZBuQ5yX8wxJ/Rp45Z34p48pKUSSJl5n0gFKMu7Tk0Sv5lDLrHddW3UuqP6\ngskv1x9N3vIGt3ruqY2sC0tOK55Dnfhk0MmTQpFnNRXsTqx43Pdg0TrmKmOq62oh67z6Oq/lQ/1R\n6ND8iGzPTdkpW6of4jjVwm+5E4GvRyrfuvtzDa4Ly9dPGgMA23cBadiJY3UCApffdbT1V90rPudr\nlOtq67Iqm6U2JPoR8xniQn65PvclPo0Bnprl8PHjOey+rYzX7BhjTTex7W7Pnm0JQw8g1uRYrUWV\nMFy8IJDHlnInis/4kPODY1tsM69qaCArOAugd557MUv9Vaev2QnXT9PKjSpfAyzfU6c8sVNOJGyr\npINwhxI7pjJmHMhPYuPcVzZgcoeK5zm02eXHtlquW7a2kwzkZEzU6Sd04kUck87O9XHtCOjA19MU\nKmpoWNp8d5Wx21Yc0JF6WKJO4eskKenmvfSvMgf6bD2wulJOlBMJLP3RcpWz1QoDpZMyPrS4QZTX\nwOO8yNYF5XRVKSd2ltP7AVwtHVG+z07sIgyUMYvzICD1cTV+eMo050Cq71fiQD4faJbYFhm2cmmI\nqNfIsJWztUCk2CquJ52A6+nMeM5raFBZAOZcRqgcga5jI2B7tFonReireaPJYdATpncMDAwMDAwM\nDAwMDAwMDAyWHBZNhIazapnyIjiO8s66+nWVhcqyLLL+JcwimrMCZEmaUciXvJdl6jrPc7Jquo5e\n1IgX3eSWRfl8luewpc2PWepkuHOesNDnLEcur/OMnYGczUv+jMki22Hb0Lx2rqMK78jwbXHeMehv\nc+qLnFkRbRZUoKzeVHSJeYu4FVQrjuq6yiLMZU5SXX75uYzacFWYFg/Z4vfBTvTIUhVlUi4wJCFl\n4UX6klSPTpGWcafHu3eZ1dt1LK1f5PNUJDZnXmLHFlEaKLwZ8n0niYraSdPdk192OCs8l7EIo/I5\n1mX5y0VjNfmLv5URHNyjzMMCeYFZ21bjg/9eluVa9Wq3FGViJYkKbU5S5GmCymJHboWF2mCfw1m1\nTHicZIgm90gzT29SmnsS5bHnZcqjJJ+V8yPP1XWW5do45F56/bq7uChvAx97dpF+YtdcWIkMPVZ8\ngywX4690X4tAYF567oWUv8ejo2zLQs7TuljocVJwTOC7usxU+T7XeEj2JZ9jveae5L3y3Nsd+QGI\nPpDXSaLzjYy04ycn2LbGx+V3IP8UEF4zzsueK9oXBi4bP0CaqT7hBWZttr5pvFs6IYTfsyyVcoEM\nlPJouQ71Q57p6y9Sxc3amsvll4JRIT2mkzDdhK/Lvmsj4dFLPd49IKKU5Jpcll0VfOTrr0V9y1MN\nuzy05ZMoWGFcbU1OU1TBRMstDGTKiaZzsPB9wQml9IICVnn9dB0a71bo62NfhbepscB1UtfRdRHG\njdpcEA0hT6mIFlARW1L/SNMMWa6iS/NMjX+uk8+la5T18croLJ5yYlsUPZD7CaXC5oniPpDOntK1\nrns6vfX0iggVfS1UbfRdNe/5utWLB3hqJR8HlfsUxg/sx7X0ZLm251mm0s1YxLi2RrATQsB4n68R\n6tRFS187GEh2sEgdzynp4TLqUa0HllXBbyUO1OYGmw9qTlj6fOARdqHifSk719NpfPMoD9uqXiOq\n5gMDjxLX03AcbRwkaY6q4Wx4eG4sHoPGimWaMsRrAcvrLM8pzYRDDFo5sXWlalfgocJdSkAFyouG\nRFLZLhm2u+uNmcNTN6DLTxX0c3TJr53owiYzAOTu3IN/rm6aK1WrVz9leY6M5YXJSQ4HZOhAjyLq\nst1cHn5yQJbl1Ck5O82klwxaGLbM5y99H/9/uaDyEHYexm33uF+un9ElQ5ZSX1qW2hzYQQAE6vt6\nyV8ey3mWU9Vy/hGXx7It2MUw8V0HmSRN9gdZnms5nbwv5pKx3CdVz2V5ztbFjHJBtX4oFkDbK3LK\nU/VO6fssQ94LAcnFKTN28rlXHndAsYGTikSmTm3IHJuN4e552GsMlsdV1VwsP6OUQfl9PB1Gtdmy\nbMDz6bv49/B5WOYLAHSSUJ5U94NsCwB4tqPkc53q70N1f1TNud3lINLNk6wkN01AlQtc8X3ltYja\nxq45N1etyfz3HAtwirUwz3ME6F4P93Qt4r/T9Z1kZYK+LhWbMdt1K9ej3ZGf36P3V+oHrkzLNPUs\nywFvbn1gvjpJ9+/pqNRNeApKjzVZgzEuLwiWDamjG3vN/8r3btv6RqwAPVk6qrJqAuZZpm+gtNoU\nVcaD7nuuY0va78l55c/o95lRUHy9Lmf5JI/KfmDjnG8wrd2UH8Du90GF/LZtwbeVIb+MXmsCAM2g\nUf5O1ZxumXdrPEgDp/hh3iD1+1Xyix+t/v6qa9Ym6fQVp1vxn6zug17yy+/Tm7SL+QCosgDl+TDH\nWOiaA+oHyw2qeGbX80GmjlX1QV5ul/iC7nsGhEVj0DAwMDDQsKudjIGBgYHBPocxLhsYGBgsLAwP\nz41FY9DYMTarhU5yL5weYiWet0rPaFY5S4XMVVkc0SP9g4dNadZM+d3Myim+RnknyaomvZq5/nle\n8WzZM9dL/ur7u9EPqqHVMpcts0xOmjjzkD0tFaacr/wSexoloaV/2BZZQrXIgB7fRacE9PAiVFmO\nxTPdlnUeqVL2QFd9f176+6qoj/lGi/DwS097nv1OheeX2tbDWyDaXf0c76+UUmHm9sbIseHawIoV\nXc0wWADsGJsFoM8rib099jjK47DMqcDzG3tzeQiB3vNwd6JD6PMSB9lshZ2Lf8r3qziyzEG9+oLS\neXaDy+bDwTyahUfN2KV3XOaVrnUp616f81xfo6o8oXOtxWXZeq1LPPKGp6SW788VITPXmmxV9FWX\nbjLXWsxToCy7Os2l7DGuSnkpycb1EnmPp7vwfhsZGe7qV2NcXhi4zSaA7rGvjQtUe8f56W/le5z+\nhA7D/1/OA8Cy8+JnLNgy4MkCLHbCIP101n2Pf7HN5rsWIZFlcKr0dNGASvm77okPevaBklP+v1XS\n1aFB6CXFk7Y6ncVm1GSxTrRtq1p+1gd2aT9C/VB83tUHqgHVUSJ2j/vYFTeW1xKw+3PLL/uA/5xl\nWRS5yPuhqy94P9C9nKXjq3FQll/804P3+Gd7MB+4/LKZveYA7wON1nvMh15jgfqBzwc2DvIsQ2Xc\no+HhObF4DBrjDe1kDTpRwrYrq5iLmhlMYeA1GopcqDxJtbxYnh8m7+UyZ5RVdO6qWquduqFyCuWx\nrEmsahDwU1P0a65c5RXP5Jr8PE/Q73G/aMYe9wPPE8zTVFWz9/VqvtQnrKIzhaxmQAZFFPOVX/bZ\nfGtY8GfcgsAym+VX2pb2t+IBljMds+s0QSbHhkjWLL5D5cy5Wv6cyndLUhXWnTAjBq/douRUimWv\nGh6WpdrLc+y4clyVV2pbFvVDr3zKrvFRVDMvK9Fl2Xvlyyesbkea5ZAMrOeC6vIDet0EOU5qoano\nvFiwY7wBx6muYcHzefl9O0PPPNaufN4kUbxTrmNQKDGW49J3iNMjqmtXpKSF6HwCoKhJUV2jgXMQ\n/z6p3MwlP6DnbGvHwzl6HYPec49ds/oFmbymWkhuTw7idTioa7V89EzbtFbNv6r87TTNK2t4cOdC\nWX7tWurbfEMv16JOXFk7CEmqKuyztUjL3y7nr5fXKLYmZxrX5iWuFdedpMzB4jpOsp7yi5+uruNS\nruniehXrTpIo3k1S6gs6ZSFV/QDX0U+YIQ6eox9khX/bVkb1PEcnUXwr5Y2LMZhmGZ2ywuuvGCw8\nsrEJceE4+jygk9BcpqvpNdb4aX7yXtV459xQ1s/0mmxzj3nJ1y6U/oEsU/Nd1suS15qumqrne+nk\nTH4hb686R1D9kOea/qHL3K2fVulnvPbYnLxXJT+vlVZRq2YufZz2K4wH5uTDqnoerN5ar9Mdq/oh\nLp2y0mv9l7L34kaqLVjmwF5rAL/PeBCA6ANen4Lppzk9s3vzoZf8ohk5WwvSnuufLnMP3Rts/S/6\noWo+5J1YHwdpiqx8TKPBLmF6zMDAYFGiqqiSgYGBgcELDGNcNjAwMFhYGB6eE4vGoLFzooHAlycw\nuFRULvAdBJloJj+LWpxmwr6AeTroHO1OrJ2rjI66DwgrKFkBO7Go/AzACgJ1RrfvifOmAcBP6dx6\nCwD87nAmsuzFKXlF4iRFXHhA4iSjc7bjONW8RTISI/BdktNzbcRFtErgucg88TuyjpfrOMo7mGVK\ntk4M9OqHHn1FMnue6gtPnVEtCwkhy2H5RYiV7wFkXM/nLb/sM/msqIKvLL8eReqo86o1izkL+/Ns\nh0XjMc8w65NdjQ2wM7ct1608XxuuQ1Xzfc8h7xYoUkVZgONYnacdJyk9y8/Z5n0lrP5CCHHutuoL\nQIwHilLyHHL6ep5NnhUHzFvi2LCLduWtjhrv7Jxx3i+9InLALODSa+KygqNA1uUprpJfvI6UCoXS\n2Mj0IoUADHkvEHZONOC7au55rkNz0mfnvHMHgs+MT1pUEPIK3k10Dqo67cfzlBfS9+hsd8v3aO6J\n3+iOupD/xnFGURkdxrVxontlJB+lqZq3c8mv7in5yUMDFcXhe47yuLU6LDosVl6pTqIi6Rg3U9G2\nUj9YrB/k6UEJMi0tJ2HepSqPWydOyWsrOThJVfHeJM20qEDZDz7rB4+Ng9xlIcaW1X3qQolr6brV\nVnzc6rDrtpKZya9xs+9RH+XsnnzWtiy5LGnvOI4ztOOEZJcytzsJ2kWxZb4Wl+UHBAfzsVGWH4BW\nnFuLPmm32XVH6wv6vNURf8fksVjUKErjQFuX5W8y3YR7HKWMcZLRdbuTUv+0O93FmQFjXF4opDtG\nAXTrpLIwOEIfyDx63qLIBPSY40wPY/qXnAcdxhOCB5RO7jH9lNYAzyYdyeb5/ey0kJ46h4zE4Pww\nl07Oxj8AUdi4+G3kvA+skmdecaDsi05J/0rY2iCfVWuBQ/M98Bxt7ss9S+46VOzXttmphyJsWTxT\nkl/eo/WRRc3mHaaTch7wPDUXe/CAJRoBjizPtfGg5nuq9UM7TruecbUIFVtbA2Q/+Nq9Qle0ckr7\nFpEJap1T7zhB1m4X1zHy4jrvxBoPShm1KPIe3Fg1H3iKEZeN66fV3JioPYhja2sA18OpT9hYAWzY\nMl2FsgJK86GQsWtdbLeR10KUYXh4biwag4aBgYGBBpMvaGBgYLDwMMZlAwMDg4WF4eE5sWgMGqMT\nDUSBaE4UeggDeUa1pzy/oQfbEtY0hx8HaJWsf+Tt6Giej0xa/ApLWNZu0+eIE1iRsIhZUQi7Jq8j\nKlpjZzkrwMXOtwb3ChbWvjjVPCDK+5Oi3VEWQXkdJynJH/guXYehh0DLaxX3pUdQqzHEIzSYp6fL\nK1ThDco7MaxCZjsI6Jr6JE1hZ8xiKPPbS32QsIgTbuWUll8uM+8faSX1XAe+1+0JCHyHrOeBr2pY\nwHfhOKp2BfWSY6v3XCVv+brREvKEvpLJ8/SoFWkRDtU5d5bvVRbV5Dl4JGdpTDTbMV3L+1x+4QVW\n8osmOWQlzvOcPrdZ/RDx/4WXFHmP+VCaG1L+Ks+o79Exj7nvwiry/qwgIG85P/M7TvKe8gt59THQ\nbCdUJ8dg4TE60dA4SESMiXGWZnphW0mHmWNTAVBxRFsxJ0teFwDdXpkK771dC2HJaLgwUFFyaaCK\ni4XBLudezDxRPDKM866KHFLeGi5zFHia/AC6+qBq7vXkYx4p1mrrkXTFM8S/QVCKHCyus4xKn7m+\nR3gis50AACAASURBVDLr9Xty5nXSZebXon8SdGLVb5x3ZLSgdp1myHzZBy4VyeQFYSV43Yi81UbW\nbNF1Lq8bLRoTeaOlcW3VOLBiD1Yi76v1kTxYtqOKvOU51YtoxwmarYJ34xSt4rrZVhEazXbSU35A\neGzVvQx5rtSoXcvf0eTMi6KPkn/zZov6BJ5L64/wRhdcG/oUKcrng/hQ1p1Ra6SQX3nmAaDZitFs\nS3ljtOR10R8GiwPpqKih0aWTFjygKsmh60hHXhsIKPSMmOlkPfiAdNU4QxQWOjnjwCjJSD/PclfV\nyqGaArnyRSSp4rdSFJZcF7JWZ9c6eejDisQRtnZxL0tSWFwnlcUjfbVnSLNc4zhN9475eqDrKB22\nRkShi8Bz1bUv+8TVj1hmtRNkZEKeZSWZ1RpQ7gfODSJaoVgLAl+LSuHrAULJA6nOAzKitvhfXkOi\nk6Q036X+Rdct2Q/qOvAdikwMPMV9PGol9eV40AuCyvdgA1q9IFoLmy1631lT8WHe6mg8CAj+p+PG\nfU/wICC4kFX2pPnAjqfldWKSNNPGQYvxoLindPNmK+m5H6laI3QqrnDIZZk2HzJNXrUW5M0WEPd1\n/73BnFhUBo1aJAZfPwsJzjN9gsiCiFpFelZoJWdEkDWausLEBg+gT6a83YZVr4nf6asRCdlJSlV5\nMwC2XDQcR6vUXC58yAmx2Y6JHJrtmO632rFGJlL+euizwnZKVp5GIT/3WCXdvKOKf6KTKKJstJQS\n2WzS5j1vd5A1mnRtF/Ln9Qi2TEEo+tXOcqq6a7PCmbwP8kzfyHKiqOqLVrtbmey1kYgTly2iuuEi\nTVmxtKr0G02BFLJnzRbymUZxrcZJeQMh+wFBIJTHEizbpgKp/IQCHuYoF8lmK0ZDKtBMoWw0YyJQ\nLn/guZr88vM8YIuoVCJSGx4bJ0Sm5c3EbIP6gsbEbIPGAW0kWIirHfrIir60olCzosmQPtf3SFnO\nspzVP8w0JRoQ71u++9lC9v5aNxVZ7qKhp/0KkovbnYKPIg9JWigGWc4U1wxe5tB9CW5M1Dbykmub\nLU2Z1Qys8u+akeJjbmwF2KbN1eaeUuCrFRfOtZqxOe42PEeBR3wcxymiUMkvYbNNfM+5x5TVnG/k\npfyNlv5McW01C3mjCFZf0Q+JrrTmrEAaP0ElZek0UpnnHNtq6/KLz2Otr7jSHgZC9ih0qXhxygqo\ninWpWINK6zKgh9lmzRatP9lsA3nBR1mjiXy2Sfel4cKuRcqIUQu7NjUACyt2XdID7NAtFY9VKRdq\nXUow2xRjs9GMMdtS11Xy83WJr0HEwbatimrydCMuf7ut1p3ZhuJj2Q8zqk84B+dRqAzpnQhWTcjJ\nDVvoWpfVO5FzghttGoXss60YjaZo33SjjUqYaLkFQTo6DkDopFK3s7Jc6aS2rXTSJNU2sFwXA3Rj\nXqtkwKu6bndS1AsObEfqunwCmssK6Aqwsc918zhW+ibnAeZQmksnl7qoLDBs55laC2xbFSzPcjro\ngm/keSpBs52QztVqMyNnBR/WI484IE481KLuwrmObcN1Cpldu7o4fyfWNu8AkDeaPQ28tBYwB2Me\nhbDahV4WxbBSqZ+HoqK8eBGqr+R7yHk6uN4PGgc2ZZ+o68B3EDEOlGsh6eMsxUMUsld7E0KWV+9N\nmk1kpIe3evIgIAwauTRw1UKgU9yvpcrpXIMy7HmZ2p9YdqWTrcXlbymdlLixGWulEKqczmnqdjk4\nxGuwkWXF/8j1kRU+5eNek316RvRJxUlshofnhtkxGBgYLE5UWbgNDAwMDF5QGOOygYGBwcLC8PDc\nWDS9MzrRQFyEkIoj6dXZvw4riBj46tglArP+cSto3myR9S+fbShLKLcIyqiNRguWTElJUirikgFk\ncrNdRxUgC31m/WRFQbNuK2CzlaDRUhY/aRkWnnnppe+gXVgc0zTX5SsgimHK1IyKEH0WlaBZ/2Yb\nKjJhhnmFeP80m9Rvdiem/lQlH5VHMufpCMwTxMO6uBe02Uo0mbknDNC9RWHgUZhjHGcIQ5l6VPYK\nyGKZKjwarI3i+K/CMs4icjJmAc6mZ8T92SZdi9BO4QW06hEVdNKswJZFltLcU0UK6TUwa3iHhTMK\na7jshw6mG6ofZgrPGJc/CjxEiS5/VgpxpAKintMV7gfo4X2ad3R6RvXF9CzJbxceUCsKYcnolE7Q\nwyNoIXeKcEDf04zHVfNAejwazQ5mC2v4zGwHM40Olg10F0Ay+YILg9GJBtqdAHFNcS3nY9cR79Nx\nVKFewNHev1b8TXqlGO/08s5RFF1/H2w6zi1RPGTbdDRn7rja3KOz42X6X6LSTLhHrtlKtOiwqvt9\ntYAVC/UpKMliR5HKwo+e22PuJYkWWlzpnWyyyAQWUWj391H/SW8sSt74XEZTZRn1SfEYyc+LXsrI\nBL4GtYpoudlWR4ucq0V+8XcuarwfKOXGY5E6FnGwvi6rIwq1fmBRCZJ3sinFQdn0DEVi5O0OhZ3b\nfF1mfZGx9ZlSkxjSVBVAFOHEsh8E9wDATKODqZl2cd2ulD9KiiKk/F1bJQ7O2BiUgybPdPmbbF2e\nni3kF7Knk9PI5VrEI1JabYrUQcLWIqhIHX7cPLKcDMKiIKBaj4RcKmJJcjAATM22uvpPNMYYlxcC\nMuUESUrjSbxzlfKbe0X0ZEknLRe6nCs6qdnujp5sthK0YzEPOklaGZ1mWapQcuCzdDw5XEpzX4uY\nnlWRSrqu2q2T53EsItTED8hGwC70j9x1YWWirTmLYkvZ8cR8zHMdpKyfA0X0NKVf+Ogr9J+8pJdL\nDvTcFF7anQauHUses/TLhopGI/6fUbLzPrFqIey2mPtWqyOidaBzYG5bKs0k8agPKGJHGw8qeny2\n2SE9fKbRoQitmUYHM7OiT6LQRTMQfSGiNfVUpl5R9L7Lo7czNTZZMcyMjwOmh2ZTsxoPAiLVypaR\nwnEM1PU9iugHFqXGdQOWiqlH6qio6elZxYG0Lsx2SB8PAw/tjtLNeRQ9l18V8GfFuivWAq0I6mwD\n2eR0IfsM0snpauOF4eE5sWgMGmNTzdJmTfzrsLOeA9+lQST/ldBqaPAUA0aamVQeuJGDKVdEmLn6\nbtu26Izj3PeQh+oMeTWSLZa3rPIVeY7qLCMNuZDMNBSZzLY6XTIBcgMh5XcofFCGefE+yxN2djWf\nLM2WIooZ1Q+aQjnTUAtPKbSZ+kFWlQ9ZlX6uOLF8xS7SqCBNfk8aOephirhQHMUZ0XKRUnmZFttU\nea463aPcF1olaYgQRgpvm55RBDI9i2xySshZ5+lG6jQGvpHPXZfIJg9SLe1GtlUPc1QpRtJwM11S\noKdmxXUUJGjHMtQ9Y6RZUUnfsaieRtfYofPMk8rNRMYJdHKarvO6aJ/daiuDRr1agbZcF7lf1NOQ\n4wH6BjhlefxcaZqhxaONqZkWGq0aDBYHxqaaSNKsx5hT58wHvgq11DaySULGRHQSpcQyo2rON3UV\nIacoc1CFUTkPk665J/5UplzkzJimcoIbrQ4zLOoGVjlGE3Y+PQcPr/bY3OuVaiFPI8jbHV3+2Yq0\nA25slqkTbF4B0Crc07wOUsCTynSurUVazZ6WMqaS/EyR52sR5dTXfKa4VveF5/F1iakUVNmdpb21\nWa7w9AyyCcE76eQ0cXA2OU1KO+IYllQAs4waYQO0LpPiGqhwXgtqTGYZ2LqkDKzCkCzkn5huYWpG\nvJ+p2bZKv4k8bTzRe+CGLVqfM3Q5WgARdl4hfz7bUIaMiUnxJxPTyIprq16DXVc593aqNnRqI2NT\nP8D31LrMwMcypf8x4/psq0OGjInpHgYNY1xeEGTjk903HUfbtMm0JNL9CnCnCqCn1HFjXpkDeQi+\nmtfVm1bPVae8EU/kuQqXz7JKZ2PGdc/ZBrIpppNynVzqIIx8ZIpZ7rpKJ/U9NfezTNMJuTFPT7lS\naVZcF5X9Ize6/ISoXFuSLO00xjStOIEiZyk3rQ7VylHpvk3igLKTiVIu6jVVW6oT0/piQxlzNeNO\nEMAqkzWq67q12okyZs60iQfEtXhXtbaHeqROxFHvuUh9mcPpTCfvdZUFYOmns2w9kDrp+CRdU9pR\nXZ0KZaeJph9oztaQGb7kGgQ1Pvkpe82W4kEu++S0WgtqbZl6y/cmeUk/Ev9yp3PgVYyHRJUF4LXr\nspmGZtTOxifJkKPB8PCcWDQGDQMDAwMOy5C3gYGBgYGBgYGBgcEcWDQGjdGJBl1zi5/PznpudxLE\ncWGFZBZjHtaFTkynmWRNVXBHD2ea6b43PQuHe/tkiIjr0FnPeRAARfhrXrKGy/aoauK6FVRZgNuU\nalAO9aSwbtuCiiK1VDVh30UU6KktpUaUChCp4qgqMkFFI2jyT05rsVOyYjOFsXmekB8QfSCtrVkG\nOMo7qAoPpexUi5Q8ANONtmYRFn2iPETtWspCvTPNSSvHhDgP2i6eUb8JsMrC3DvaYgX4tPFQROyM\nTyItPCG6J0yFDdvME5Z7ngr3LleXLvdDKfWIR+pIC/jkdAsT08JK3VcLyCuSlrzkEuQZdR3qqyB1\nVPFYC6pNSapbw6vkn5wm+W2WKrArj2Dme3Bkobosh8XOoecVtTuFNbzV7vaMTs20MDHdIm9ISdDu\newb7HKMTDd0jx6LEXMemgomdJNWiGHhBXskPWbutRcwBpYix6dnKiDnxw8r7RBFRvpp7SELlrbIU\nB6ds7PFwYx6NoFIN2swrp1K/hAdG/rzFQosd+pdOPinPU55qwSvc81RIOfe0tIsZul+KYxX967rI\nZLV7z1MnXSQpbF+lWqQsoiCuSDEoyw+IiDF+L66rSMhekZO+V3BPrDyYWpQc4yDwsHPulaL1ZwrZ\nuFiX0rFx1W9J2hWlUjSArcvy2aTaM5llFF2hpZy0OiT/TKNN0QnjU81K+WX4cPd8KAo3x6keqSP7\nYQ75U+mRlJEqY+OUZmC3VJFcO0nK7mEUDaBK/k4QV67LeaaPA/mvTC3g6Ta9IjSMcXlhkI6N0zXp\nZL5K+c3CAFZUjC2mhwjVRefDOEnBC5HzCAWeeiWvpxttWgcsdoqZ7zqU8iwKRrra73BokbJtdsIR\nT/+d6qGT9tDJrSISAZ5LKWZ5EHRFqAAisqSjnTyoUhB5pLSMGFX6eJsiiflaaLEUM8+1yQvfiV2V\nFlwqhklcFuspBkCZA6eJ/9PJKbq2W20lG5fRtlVkgusglwWE41hfP4o26QcWqFONZpvqfXMeGJsS\nOml/LSD+5KkbPGqT1kUvRRB3R/XwtmuROnxvMjVLEUnp+KTGg/LvqtYCy3WRsWi9vEjdL+/ReLFs\nPXq8+91LHhybaqK/Jr4vZodV9NKPPM/R1sJyF+S8LAArFK7pAeOTSMfGYfd3Ry0bHp4bi8agMTbZ\nog0rD+cNPJdVGK4OwddCizoxUx46KpyZhfRUhdpn0zO0YFiOS6G9eegjk0d31kKV/5SkagGxna68\nslgLb1MhfbPNmEiDh3VNzbSJIFyWZuOxI+KiwKUNLOXqZrm+eeXpFU2Wsz1bkWoxOa0Uqskppa3a\nFm0gtIVTVlruxIossowdUaVSDRKWt9xiKScNLj+TXRJIkurHQqrjAC062lP0SbXCLSGqaxfkJ/uE\nLaIiZ61QoMcnVWgnTyUqH4NVSZoJWzws+rOqsO9mW4W0zzTaFOI8Md3E2KSSP+1BmvJftXg4iFKX\nnu1KP0Lxrqry+Gd0+WU19d1SoFtKgabvLinQOVOmuBIN6BuJqVkR3ldp0DDkvSAYm2zBtpQC6zo2\nzb3Ad+g9pqlH77lsYOZ5omRgbvNw42JDPz1TaWAGoCrYOw4yedJF4FOlc42HbFXHImOGDXmCSbPN\nUk6Y4WJqps0UeLWxA3jReEtT4OW/dPJLnus2TZ5qUaHM8zSTbHKKwq2FQsvkL/rAYgZESq+ohcp4\nXWFQBQoOpvx5dhQdW4+mtXBj1SdUtiOvVty4oyFJM309ooao1JvKTQ0fB8yomo6Oq/fKU49sm9aD\n3PfIuCNP/8hjZWhHptKAsiyndVMYdtRaxOWXYcZjky1tPa9U4GktssnJIlI7qg06veTPp0s6yfgk\nsoKL0Yk1eQiWDctRxnXIDW0n1teuYl3mx6nHbDyQcX1WGdd7ppwY4/KCQOolluOyo4wD5DIcvVNK\n/6Uxr+pu8XdepZNyg5ZIAVVGPi3VsFiPxelrYsy3AxW+rzgDyKXukJWdbGojK3XzvKSTcp1cwqqo\njWCFPvKi3hnSRO+HAmmaaU42zoGNVvf4V/3Q0tYCfqKVzx2MoUqxrjTqsrRl3cnIN7JyLZwlDswm\np6gfoOmYYLqYq3TSIEAesb1JaeOvO9kydfJeO1H9wJxsY1NNjE82qQ+rUkv5Jp6vj9xwwBqg2pQm\n6vhabthiMqejEzoPFnLJQWbZNtWRyTwPdnG0ax6FbBxkxMGZ5bDaUjwNPNF4END3I+OTTXKK8HVO\nvAa1N1FOdxdxIJ9nexlWzITaV66hwdbCbHwS1sgQumB4eE4sGoOGgYGBAQd5pAwMDAwMFg7GuGxg\nYGCwsDA8PCcWjUFjcrpJaQQ8KqPWjsnLFicpUmaBJjCrV56m5AkUVfOrvUFA4ZlnnhGeXiG9PlYt\npKI0eSdGnrKQTm79kx7pon0dZgVsthKt6BAPbaLiMzMtLZVARWV4qHWY/D2KolKbKqzBWkXp6RkW\n1jZNHoBsfFJFZbiOZgUHRIVhWTBShLQxczy9hupimOIkgaRbfhbeNlmkXPSqIu3a3DKeIC5O/4hZ\n9e0sy8lqmrFwb+kh1sJ9WT9kk9MqQqEctihP8fA8VYwwCJDX5QkMLFLHUtZTGaIXx5lWiK1yHMy0\nNfnLVaMBnm5jsRBnl0KJexYFTVIVodLuqAJMrBBXxsbB7ngEqR+iUPekOopOeKQSlx8opd7Mdnqn\nnBgsCCanm0VUQneUXDNIUIvUu63iIbCz1rVTp2TKyUyDhZmWvHOysrnrUkqBFQRU+C6vRWo8a957\nNfe0sRd3e+ZFhFAv76RoI5ff9xwEnjp/HgDqkaqen5ZOYNI883JdanWU/I2WHmarrUdThTxFeDWL\nyhBeOOGZzVodVbQ3TRXvsSg5ccqJDLdO9JSvWZ2Dy2sRB48c9BgHh4HyTlZHTqpoAZV609FONNDC\nrWVhzNGJHh5Jh6JVrCCgFMjKqEmGhHtpk+rCgNwrNznd1E8yYZFK8l8aD55LUTApi5LLcigOzjKl\nN5QLIzLvJFBEy+0YE8+W5alIPbLCAJk8BSCtVXqps0zpJXw8qPkQU4X/yakeERoGCwLpsbcCFSFr\n1Wuw5AkY9bYqZt+rKCgriNtsd4997pkXxXFlKmxTixSWBTCj0KWTH/oSX6UIV6VBi+M1AMiUi0IX\n42O/R/prNj5JYz71PFhyvteKyI4oRF5X6QigaEE9oqCyQHuLF8WNu1KuuE5qW6r4J9fNw8BFuyOj\nx1OVmsajpnt55LV9iSwGOaV4YHSCdDJNJ7XVCXsWS7/Ma6GKTOcRHcRB/JSPUqQOHwfy3U+1qAyA\niDRQTXBYZAZQROx4Uh/X1wJKo88y9X5KOkFOUdNNpBNqP8Z5UMmvOFCLmObpNqTD6usyFYdl66KI\n1CmlnLC1YHSiUXnCoH6aiYOgKI4fhXrKfBfYwQ0ZLwpa2qOl45NAOVrTYJdYNAaNsamWIorQQz0S\nE7VV8+louQ5ToLM8Z+HO1QqDOCaKbeDK4Z1sI5uNT5LSnAYBrJo6vtLuL8inparsImWpCY7aiPOj\nkfikIeWh2dHCnTmBSgU6CjxEoVdcu9QX7Y5S0PO8e5IJ8uw+Iirjx7PyGhos1SIdHadcXGHQKSoY\nS2MOz4UvG3bkz5cUJ2mIanfUsax6uoFSIMcKRUocmqLSK6QRw/P4QpJoG/myEYQaw+qqCBk6+rGt\nPMy3yN8GWGSAbamUG99joc1x9abKUUcXJhUKdLuT0FFhPPVI5Csq+Xk7uEIBiFDvtraRkPndqhmW\nZakvKh+ZWDkfptTxcLvI3bfCADnvBzYOaFOV89Sj7vztRktVEJchrnKOa3AWDT3tVxibaulG1dBD\nJE8hijw293JNcaR8Xl7ZPu5W4jItBW5W52OpwPse5UdbtZCUeV5XgHOwZVk0/iUdJGmuhVvzDf0s\n5Y/ryrzkY54XHPguwqJOQ424OCF+70p7qzImcj6ebZRSAOXpHqqOBE/5o36IInX6R690BOhrkWbQ\noSNaYy3MGOheiyRsm59moupFRIG7S0eDXBvyVE+54DVVeqWccFAqqOsgk0auKFIpkBoX6wZ22R8x\nq+fUYkc3Vhm2xqZa2jG9LsuZB0RYddiWhi1PGwd5lUFH42D9tBvuXACAbHxKGddZZ1riRYhrvhZF\nodJ3Susy1xF4hX9AbOh4P1DI/awKs+cw0XILA8kHGRvvdr2GfKAuHujEavxrNTTUBlZurNqdhNV1\ni7UaEjwNeryonTAxrXRykV4hrmtNj44xbXeSrhTZLMspXVU74ajVUca8ZsnJVpH+m46OM2Ourwx3\nkTLsyFNQcp56w/uvh06q105oz6mT8rVQ1LErjDk1X50G1aOOENdD+XGl1A8z3KipG3M0HiSd1NYM\n3LIv8laH+rnrhDCUUk5YLZF2J9VqSFTppMXPAigM/Z5MOxT/RoGLMFQOxkqnM6CnHklnI9ubaGth\nWf5SP1iuAxTvxAoDWLTn67D1QJ2Eklm2norKTv/ipz0BunGfrwXi5/kpZywNtxgT2nzIeRq40o3o\nPXWlnDDj/vgUbFlPi8Hw8NwwOwYDA4NFCUPeBgYGBosAxrhsYGBgsLAwPDwnFk3vTE63UC+iEmqR\nh9mmiBBoaiknKnRU84TkzBufJEDxfN5WKQZ5qRAdoEcoZBNTSFlURlZ4wuz+OoUF2XEsvh/oLrpT\nqrBftoLysC4K951tUXjv5HSLKiZHoUeeQGEFLiJUkrSr+Gjx44XsrCgoTzlpNiu9QrpXbIKiMrJI\npdlI67HVaGknYFB/57pXQDvrmRUe4tW1pytTToo+tkBVpLk1OPAdNAuvWK3jkec1TXNUpt+wQmxU\njLDRpIidfLaphfrxauK86JLsEyv0kTcKa3i92ktsF7MpZ9bw/8/eu4Xalh1lwDXGvM+11t773Lo7\n/jEEzY+dtEpEEzUxXl4CXh6SGGKU9sF4JaJBIaAooiJ4gxh8MKCJVzQaL2BAxIeAtgZUEEFsVH6x\nMSoa093ntvdel3kZ/8McVfXVXHPtc46m++zEWXDY86y99lpzjDlGVY2qr74aiPmUgGgDpKCnE4R0\nPAdE09lBzIwidG48B5IdNcSEu+n9gCUneBOYERRy2JzcYtgnJisy6i4wjdTREqy70l3ggpKTuV7w\noQjrokr08ZbqUlFiU5l5I+NuS7wPZe+dj0rg4j5EfVyX5KphnfllTUGQYoqSC217sMSA70/IIBvN\nTo5RckrOq3qoyBKqYplNVSpKjrPa2OGlg8z80GFoGh2lXV7WBm5tsvOshzj7VJeKkluuhdTXbbeq\nj0dQc70vi1BhlOBm28D+QxI8tUWsg8ZIFZ6T9balZdTvY+SkSAC7BLbDEOJhdg5skelwM5WRXK8p\nbLQUlGgMN9Z1gWSdu1HJCWepkdnejt9JJhLnoY4ICURNInkeykAM2Mo99lPdfrDs6BaiBRklp91M\nfKmlR2GxFX+HgIAQ9THaI1kPWHIyIsmdkjm4/HBEkAuLivxqQGX06zV5XkONJWgXu99bX5SIaNeM\nyqA32OVEy0xwHzAaoSwy8c+XdSFrB0sMJssPQ7Dlh1so/z2NaNFx+e+UT76oqV8Oe8WvlsNnbLfq\n4wKU/yKfFPe+lEGf7S70SdEWlmVKy3rY+5ttI/alPeCHUh+m0eNIEH0HCLKn9MAYnRVLb1xRCGIv\nAHIxdBapxqLPKYBP2sg83D3fGrSi9clVByKSnOeSy+IvtAW8DtpOn9t2C533RuXwt2z5pbEFMA9h\nUeu6QqLwEXIb16mWQTdmHRCNyw+tXz6NVsyoLnWPoT3g8nEpI+97i6JnW7hWklwuPWLbiDLr4Yvl\n0gQ0Hrm6oBtXB4V97aSmk9VgsJd1Ic50ketmcs5JaYJhgM4zA0lzceP4tpMWUNiSVMpM6orSx24Q\nEVHy6HVKrl4Z/u54RT7C+3xV6vdkmZ48ifZKA6oyo7IcFMyiymjbxM1nWuDZ2rxHry3j+CsZ/9Gi\noGVsG1QVmRzwuX5ruDGoMZZuAKDstqDs0Pl1TuvUy4KSR64NY7lxlZJrJzJ+IiJ/tNB5LQtl24dW\nnVhriM7vss5NicjY6cOuNierUsZ+fFTS1aMK5iGX+Syjoc1Sb+eChwbP1rPzt1oaxZcAGzI7jv7K\nsYw5gWt/fET+aHg+ri7VqGSZGBuEKXN9YQ4HIxOcarrJTgInq5KOloVcH8e5wP2wiM40so1zwINo\nWGPiCEP5kFtUMp6w3VIitecanDPjnZqH1ZJ8dDJ8Xer6gcixd46SuDdwHXCQ7mhZyKGYDwE85lke\nvjxydUGPXlvStZPhOeOarKtMnmeRp/KcpV0yRX0c14WvSwrrGACLjui41hvLKxhWnFy3OojXn1tU\nup6zTNY56lWFgmpt76LMDVs5izdBw0SCyjeuLujayaA/rxxVdLQYvpN1UFVkphyObVEfyHIxFdoh\ni/VH6DpzCMGaaC4lSG5clXnwV47iPByJM++rUudhxHyutb1e9OSiymi7Gz576uDtnZMuBlma0NXj\n4Zkdr1QHH69KMw+4Dljv4zqQNos56KCqFHsa1ivVQR0Ep7yn5OR4uLxyRMm1aIvHeijaN3Xwc2WB\nh2Aodumpikzs6XqjQS7kYiIiM37Wvfza0bKQAFdZpnLYydJE/IA4GcN9ZRoQ9uPx44GMyARikivH\n8uyH6zgnJ8fk4jpwi1rtMvKtJKn6R9AZi53wutR5OFm1oI8nApSj+ZzlxZPkkevDzxvX5PknJng2\nVgAAIABJREFUoAcc+qRpqnbfBxOAI7KB2W2TSzKk74PpZoJd9dgnvX5S0ZXjfV+sLHTvs97x3hEF\n6e+s94fBWSwdDD0cWrXMtysL9clvXFU9ADoAfVK6D5+Ufadt3U3yj2FJAc/DjSsLsYVXjS0oQAfq\n2cTowDQBHzsnx4nSmGRLdo3Z8w64k7qoM5NrJ5Tw3r96RexicuVYglxuWSvfHfikOC7UAeqTFlKK\nOC4f4hLSk1VJV47UF8B1MHxGrj7pRbYAypb5ufnVUg71yXZ7UA8SWVuAutGvlrKuXK37gZJE1tW4\nQ1cJZUM4fqLBFoSRT05kbcEJ2sJFLvuqKtEnT7RcUkpl4KxalbKPk/UG5mE3tGKP4zYy6+EL5dIE\nNGaZZZZZjMzKe5ZZZplllllmmWWWWWa5QC5NQOMlN1Z048oQZbt6VEFGOqdFpVkxzr5xVo2IGBNF\nRBaW6pf1JAzNQeeKrtDMUfLoEA1Pb0xnxUxmPk0h+qfRSMkOZolA9A51A0gTb6LHHAVGhMrRspDM\ndlXY6N/w3ZAZhe4kvi4pbGMmHWFYpFFgRKi4utTI7/VrGhU9higoR5fLXOcbIsFDq3DOzCdCIrVo\nlDgN0QgJREx5XEeLQrLBR8tCoqBHy4JWgFSRecg0K+b9KDIumYE4D9udkPDs9faOayI5WZGLUe8E\nUAp+tZBSC7+oZd4oTXQ+48cZhEKWCGyzrjJq2uHvBgI5vN3hM5Z1biK/On7dDzVEg3U/eHv+R4gi\n74eqkiz50KkGbyCVcQ4/l4pMOj6C1xdSjuQKQOp4ZzKch7LERERNW+wRia0W+wiNceZ5lhdHXnJj\nRddOKsnKXD2qZE9ehI7iZ5lAls0VhawXD4gok5GTbia5lJkk105sZl6QCZXJSNNI/xDZtVcJWVk2\nCYX13tmSiqizrxxVkpEfI1SIoi6OCI3EO6uHJ/SrX9SSkfRAVoZkj74sxB6JLr52RXXxaqGohLrS\nvWfmwBkWeuyWtar3yzVZZ6IOrspM9M7xCDG2XChKjue2yBPR+6iDZP8myWAziCgsAR682U0y0rs0\nBaTYihK2QVeOjD0SfVwDQgHnwgHyBrLOiBSbskveO2N3ePzHBjUZ56HMZB1kqZfv9E4nw6WpReqw\nDgaS8RRL9tivOF5RMoGY8yfw+hIQGkUhdhnRo0PHHkWoEBEtqlyg1kMnKiYTnREal0mSlzxCRINP\nOqkHikJQuTRa+6wHUAdsqwiNB0TSmHxcydczun7AJ+WMvPHFJuy1A9Swr0oKi2mfVNBcSWJ9ckaq\n3bhK/uoINbyotfNLnkknNuc9dCbCtZ/SVnyQznShEF0BqDvu7HX9pFK03nFlbAH7NFmWiN1x3k0i\nTlxVkV8oQphoQI4bhAZ0cWKdYZHCK/DNV4LU8otabKfLAT0uiJ1e7WKWyH2vt5mMp+3tfPCaOFoW\nBjGO64CIaIU+aZGaM5oXhIJ2ynN5ZgluGTXctoKix3sXpPDxSvX/IVtQlYpSH63HqdLJRZnTdqld\ngIhsdxLnnJ5HDpxNBp98fz8kibcolWFSplH06wUlm4jIiCVK7P8YmfXwhXJpAhqPXV+KA3kFHGg8\n0Bd5IjBfs1CwtqosxMEJTUPeto0YfrDSKHM57PbLWstMEOZ6vFI4U1VNH2RJAyzaWlSd47ZTtlvv\nLbSXF39dZQbOdIwBHeh4wgGQZEp5IrytKMhHBYft7Jx3CvEuc+qZN2NRK6QR4K2iPFFhmoOszsF+\nZ4AIa4NuCN6p86/BnESe8aLKxVlc1nq9qtWJrKtMoLN5mkweqga4txpSosGI+AkmbAx+DMoxHsCO\nFnqQWi0A5lmpUsJDVYS0ez+9DlZtL3G14UzHME9txbqocimxWi4UyoflNhwgKbJUDPBYeToMaHDQ\nalmT7yY4L9Do8tgXta77Za0BjUUt84mlRy5N5aDkDhyquO4c2ygO0+8nAxqz8n448tj1pSn9OlrC\n3ivzyWCi806fP8Iqy0K4HrC0SRytJLGHPQ6aHq+sPjpCaG18Px5gezKwab4/Ljmpis4eWsGBlfeU\nGS3rnYxZnbiSjhbDtQRVS3Vc8iw5HEzEDiUT7ecoTYikDbIGaxRervPgj1e6P9Fxy7LJYOIQpIgl\nBpV9jxvP1cgWTeldXAfLuhD9XuSp6H1bcgElJ3JIUaeexvMBzrxZB6CDHepjsEc8D1hy4ryOUVpO\nFunBRAMGQDiJMg4w82vLA+sAEy2TiYPxoWbEAZM6L2var5ZG7/LYk+OVlj8uag3olNN2GbuF4Xo4\nisH1Drr0mKQAyBxcfjiScsnJtRPy4pMemWAeJhX4IOsD1PtDNwY+yKIuQJ80TyERVWZSXnCyKk3J\nFe8Fm1TRYJ5zsPaNbx590q6DoK71SQ/65KAHiVgHcDAvl88Yl4Jn2f5Bdrzm91qR5pqQPDmypb94\nkC/FF5suOUFb6MtcuG/EJuL+Rz1Raptev1raMbMONGeTUoLGg120RztTcpJpycWqzie7s2Cgf7lQ\nfXe0KGi1sD7puPwQfVK4AX3GRS7PjVYLaDVtk87ok++NHa+PFrKujA6EM5onux+k7fAil0BOmEi2\nZmmiQfwSziYLvT5alLKvhtKj/YAO8VSAr22Tzkc2mOz8ZMnJrIcvlksT0JhllllmMTITIM0yyyyz\nPHyZg8uzzDLLLA9XZj18oTxQQONP//RP6X3vex8VhWZTf+AHfoBe9apXyf8/+tGP0u/93u/Rs88+\nSycnJ/Td3/3d9Pjjj9/zsx+9tpRMyMmREq6sAKFwKBPkvLcZaWbHbzvimJ+Hrg2BI5l1CeQ0i2lC\nxOOVZkmQgAlhXb2SvhiEApAusaSJN9AvRiacrw+XGjCcaRj/OBo+IuMDAiYXoUu4BXpgSx8g3jHC\nfXau0N7VAuBcWmpgMvM836NMEI8NSy1CAISG34dClmUKZUWpjBfh7YsSYW0Ic/SSbfTOSZQ5HZHv\nEO3D+0Q5QAbR16VBKSA0zhAPATGhKple7gMj/jyGMSFqAmSE0lGhUGTLAggYEdIn8LZSYe9pohnJ\nPsDYsow879fFaPyMWBqRFxLRAO2XLBBkjhHmWeSSXaY0IWp1/FPrYKpDUZJ4ShIv0e5Z7k9eaF28\nrHPJRC3rXJAJFiGk6KgxERphGQmvKf6995Kt6YvclL1xNxOTmUek1KLW9Qx7r+96Wf+49rqYiRmj\nM+Q9uSI0FtvmvtBhRAMZ3iF0lCATkJC368hP6B7cewF1zxGUfmEZGGTkEB3VBUXAOSh/ZD3ZAUqO\nSLa+RZIBUZog5kDvLqpMUFb3sw6wQ4eU3uy0U5glw0MywFzG6Re1EumBDvarpbxuYeeWnJjIws6L\nXPUnJsTGpUdoazj7hiilBayDKRK4eAN6X0AKOjV+g2Zh/6QCxNKi1rWxrK2NAqj5lF0eIyeJhuy7\n6GMgANyDSM9yobyQepiIKHk0ErVfOTY+mUENZ6oHWLAzhZSctP0eMoMoIrkEpZZSvVbCyKnSq7FP\njhB7ooj+QrQeoIbdYt8ntSgO3fv92CdnPSgE0bWgOQZSUEZojAiBAQksCJWRDzImzV2UGS3rAUWB\nqAQsN7OkoKn65CMdYFDTNRChxt/bva9E9j2is6b0AJQe+TFR/SgZ5L2SXxd5ShV0CUT0tNpO8EOh\n6+KiQtvAvmk+QihYkth4A9q5qyz0ubUjuwgE4awH0e+W60WtYwdUtSuKyf3gRiVYRSSERT2I77Xl\nKVoyjfYPzyl1qfbClpyQ/WzcD1UF6FWkBBjOqu7qBCnoLBfKAyM0Hn/8cfqxH/uxyd/93d/9Hf3W\nb/0Wfd/3fR+94hWvoJs3b9r2qhfIo9cWAmuqq4xWCG1l5XmILwFbTBY5ubhBPNRqByjH4DILt6jJ\nr2LbnPONdSInlMbAoREPXsCg60KQzZuAo9wHPaTZmr7hXssipc02sk7Xrdks6kBPO08GzsWChqEt\nBOLc43xhu6OqUkV5vtFDw7I2fBHy2tRB1jvljhg5hd0ExDnPPMDAI8R5m9EmKvqhVCWVazzoYx10\nAbWOCPMVg42HKjYS2GEG56pQI+qKQhVpVcGYAQIIh4kxmzTRoJDwUNX3EVI+YhNH5n01CNqZocgT\ngHVriyi+LvJUDiQHD1V5RsRj6Ht7qGQumTJXyB7D2MvCXkMwa6rkhLynHspYhB8FWp4d5E/JUjkw\nm3mce25fKC+kLsZDW13lhgeGn2ee2a4Ost2hBJC6QtZcz/DPNKEAey8wx81mK/wKblFpScGytgd5\nOMBiMHFccpJnieigMZP/FOM7tnaty1xgqTgXPA9VkU0GE41gKVtfQnBd5yeAvgmLWtjvkQcKDy9i\ni8rCBBP7DoPmUE4Dh1b9vcKw1/mgd8sCOiDsWtE7VQE6uEzt6xOJhj27PNyI6pK6JB8szJrIJiLw\nGRvOlLpU3VNXADfXEqQpfYydG1qAmuMtpok3hzrlEbA2aPg9BNqrXPRxAskK5xT+T0lyePyopylC\n7isoJ5jSu9jSGPeD6fJi7XIC9oiIqC0zsghnm5DZkxktd1BeKD1MRMLrdtAnLfPJrg7ok6K/yeK9\no0RKn6GzSZnSso6tXSHAuyhzgN5nwBmQHjjAOvlpupzEX6NP6rJMbIBb1NLG06FPjgkl8EllT2Ra\nZuG8+kJDJ0H1syZ5vkxp4vBzXbW0jK1psYvHwJuh1xX4qnI2weYeSUpBStwLPZvIGzS4H8DPCoua\nXOx6gXoAk0uuKrXcbMzrNvKdxgGuqTITtItlkYr9qwodZ1lk5nWesxLsIs+3A24pB0kMl2VacjLq\n8HJID+p41U8VvVdV011OoASLApn9wOOxCcaYLPbQkrVMCTvZ8HU5sov4Ou4HPvcYW4DBu66W7zdl\nqHlGNBXQmPXwhfLAJ4aLlPGHPvQheutb30qveMUriIjoypUJUpNZZplllvuRWXlfKLMunmWWWV4M\nmYPLh2XWw7PMMsuLIbMevlgeeHaeeeYZ+tZv/VZaLpf05V/+5fTmN7+ZvPfU9z39y7/8C33RF30R\nfe/3fi81TUOvec1r6Mknn6Q8vzec/MaVhYn6akYwtxnp1CIhiGjICHK2Ku+ECLSnIRtIRBTyjMII\nVh92zcC0TkS+ae6ZAfFVBTBfjYZT1wHRZUSEBAt9FQLMLKGqHCLg211G2x1fQ1asTCejnyYTxmUW\n0F3Ce09BUAlAdpYm8HohxERhuyO/2cr1ZOSXs2ZlrhlTKDmhNCWK8K0hQ6XkqCHsQ9lyzAbs0jj2\nTjKjWFaD4y1yJa3DjEKW2iyxOBc+Ic+Ep6TC8xZGRHUhzoMh0cwA/ovwcST7y6F7AkD3JPuRJRAF\nz0ckfJGgrexoscVsp/7tGAqJ3Qiy1EM0WAmI+j7IPaV5JlFwT0QBosCBx7BWqL8pqcr0GrOgZk7y\nAyRjUE6jv8tlfnBcZZFNl5zM9YIXygupizETU2QpZCywq4OiozAz3/eBPGdJ+l4CU57RclkmCI1Q\nlUTNkAkLu0bY3/dQQaC7D+09RMHx/Vk47aBjssyL3q3KlJqGWc570UMlIKHGqKjhZ6IlJ5ARPLT3\nzPiTRMoeQ1EISi5stmb8RNH+sN7B8pyqFP1G3iuxGllCsz6fIjpTvcF2drtrqYkw5O2uNeOUayD4\nHaDmUScBSs7YZc7IpQkRj6cPilSBEsl+1J3sIFKM10GZm+thYJmUWvSjMopxyQW/jllagVhvs4Pj\n17Hv/z5PD6DkoMzT95WO33mdI0EFltQz+TTaH9TBoznxqJsFOemJImpnIEUd7qXLtQQLO7Loesho\nUubg8kF5ofQwkZKC3rdPGsUSXcY9AT6pg2de5Alti0EHLttcdGDT9ibzrl2NUpOx58836GnOzCcp\nrP1gSsADoNS4+5xbVMYnF2J3LHsF31T3gXbesx1+FI02+GG5fD8SgFaF+uTDT9WHiJQ1yDRAn+ap\nfs+AzvJyLw5sIWvHAMgsRoyHqhRkdNjsyEe7aPRAUahtMDqwMHZxr8uJC3J/XddTKHWfY1cwROow\nahZfR72PfiiiGDJALopgJ8oyV58U6AJ69L0r7QYlJRqARsJ5QLvooWGBS1IZvw9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sAAAg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Oc9VJYlPf744/TBD36QHnzwQXr1q1/9YqlyW8K2OJo5p4gozsBQVel7LndkIVaKRlBbsxEb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\nISGJ5edA1HADZoa3rgJ0aOreoC743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g98kRP9zM/QJl9cfqSAxt/8m3+TvvnNb1JRFPQH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\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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_correlations\n", + "\n", + "correlations = [('black', 'black'), ('white', 'white'), ('black', 'white')]\n", + "draw_correlations(X_corr[0].real, correlations=correlations)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that the maximum values for the autocorrelations are higher than 0.5. We can still show that the centers or the (0, 0) vectors are still equal to the volume fractions." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase 0.5\n", + "Volume fraction of white phase 0.5\n" + ] + } + ], + "source": [ + "print 'Volume fraction of black phase', X_corr[0, center, center, 0]\n", + "print 'Volume fraction of white phase', X_corr[0, center, center, 1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The non-periodic statistics are different from the periodic 2-point statistics along the diagonal vectors, but in both cases the probability of (0, 0) vector is still the volume fraction." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## References\n", + "\n", + "[1] S.R. Niezgoda, D.T. Fullwood, S.R. Kalidindi, Delineation of the Space of 2-Point Correlations in a Composite Material System, Acta Materialia, 56, 18, 2008, 5285–5292 [doi:10.1016/j.actamat.2008.07.005](http://dx.doi.org/10.1016/j.actamat.2008.07.005)\n", + "\n", + " \n", + "[2] S.R. Niezgoda, D.M. Turner, D.T. Fullwood, S.R. Kalidindi, Optimized Structure Based Representative Volume Element Sets Reflecting the Ensemble-Averaged 2-Point Statistics, 58, 13, 2010, 4432–4445 [doi:10.1016/j.actamat.2010.04.041](http://dx.doi.org/10.1016/j.actamat.2010.04.041)\n", + "\n", + "\n", + "[3] D.T. Fullwood, S.R. Kalidindi, and B.L. Adams, Second - Order Microstructure Sensitive Design Using 2-Point Spatial Correlations, Chapter 12 in Electron Backscatter Diffraction in Materials Science , 2nd Edition , Eds. A. Schwartz, M. Kumar, B. Adams, D. Field, Springer, NY, 2009. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/stats_steel.ipynb b/notebooks/stats_steel.ipynb new file mode 100644 index 00000000..f6945002 --- /dev/null +++ b/notebooks/stats_steel.ipynb @@ -0,0 +1,581 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Low Carbon Steel Optical Micrographs" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Introduction\n", + "\n", + "This page is an example of how to obtain 2-point statistics using PyMKS tools. The workflow is the following: import image dataset, display the images, threshold the images if necessary, and calculate 2-point statistics for the images.\n", + "\n", + "First, we will go through the process of calculating 2-point statistics on the full-size images at different magnifications and then perform the same procedure on cropped versions of the original images. Since sometimes images are very large, and we want to crop them to reduce the effort of computing the 2-point statistics. \n", + "\n", + "The dataset that we are importing is optical micrographs of chemically etched low carbon steel. It is etched to display some features of the microstructure, which otherwise would not be visible." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Load Data\n", + "\n", + "First we are going to import the image dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks_share import DataManager\n", + "\n", + "\n", + "manager = DataManager ('pymks.me.gatech.edu')\n", + "X = manager.fetch_data('Low-Carbon Steel Optical Micrographs')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Now, we will display the images using the PyMKS `draw_microstructure` tool." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "data": { + "image/png": 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d4p133iEnJ4fs7Gzsdjs5OTkiqKzMPaFQSOytBgYGxPsbN24cGRkZOBwO3n33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\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q6vjWt77F559/zvnz57l16xYuLi5cuXJFFQuiqD18+LAqLm1sbLC2tlYlX2trK0ePHmXu\n3Ln86Ec/4sSJE+zcuZNr166RnJzMunXr6OrqwsrKiqtXrxIVFcXChQvV09Hd3V3DkufNm4ex8R0/\n13379vH666/z1FNPERMTQ1dXF7a2tiQkJPDBBx8QERGhivny8nJVTL700ktkZGSQmJhIb2+vgpiW\nlpZ89tln7N+/X4vcrKwsnnzySeLi4hgYGFBrmpqaGvbs2UNqaiqurq6kp6djY2PDQw89RHFxMVu3\nbsXc3JygoCDNa3jggQcoLS0lKSmJt956i2vXrlFRUYGXlxfHjh3j7//+77G3t+fDDz/ExsaG/Px8\n/uEf/oGMjAy+9a1vER8fT1FREenp6fziF7+gp6eHxMREfvOb33DmzBm+/e1vq0dgf38/S5cu5eDB\ng3pvvb29+eKLL1i9ejXXrl3j5s2buLi4kJWVxcjIiLaB/v73v2f+/Pm8+eab2NjYMD4+zo0bN/S5\nWrx4Mc7Oztja2rJhwwZaWlqoqanRtsj09HTy8/Nxc3PjwIEDuLq64u/vr/6+iYmJREREqIrl2LFj\nREdHMzo6SkhICKdOnSIpKUmVrENDQ9jY2HDy5Ena29tZuHAhDg4OPP300+zdu5fk5GQ+//xzIiMj\nsba2pru7m6mpKa5fv05KSooWOhMTE/ziF78gMTGRW7duER0dTU9Pzz175H7wwQdMTU1x+PBhYmNj\nsbS8k3wvtkP33Xcfb775JnV1dWqzJSCIu7s7/f39zJ8/H2tra/z9/XF1dSU1NZWMjAxWrFhBWVkZ\nTk5OtLW1YWZ2J9CtpaUFa2trzR7ZsmUL3t7e2NraYmZmhr+/P3Z2dvz4xz+mv7+fH/zgB3R1dXHu\n3Dn27dunCvobN27g6+ur3rhubm4kJiaq3YZ8lpCQEK5du8Ybb7zB7t27mTt3Lk888QT+/v709PTw\n4IMP0t/fz4ULFzAYDMzMzHDgwAFqa2vZunUrc+bM0Ra+Rx99FE9PT2xsbNRqSJRfn3/+OSYmJkRH\nRzMxMaE+uRYWFrz66qtkZWUxPT1Neno6FRUV2rURFRVFWVmZdvUsWrQIExMTLl68yPbt2+nq6qKy\nspKHHnqIuro6jhw5QkFBATExMSQlJTEzM8O5c+dYv349zs7OfPjhh0xOTpKfn8/k5CTr16+nsbER\nPz8//P39OX78OIWFhaxevVqBRSsrK4qKihgdHcXT05O2tjaefvpptm3bhpOTEx4eHixZskQ7tADO\nnz9Pamoq+/btIzQ0VH0Pr1+/zv3338/y5cu5du0ahYWF2rZsYWGhgIqHhwfPPPMMq1evJi4ujomJ\nCT755BO2bduGsbExL7/8Ml5eXjz77LMkJydrrklgYKACijU1NZiZmZGQkEBraysPPPAApqamDA8P\nc+DAAQ3S9fLyYnp6mhdeeAFLS0t27tzJxMQEH330EZ2dnQoQJycns3r1an7729/S19fHfffdp23t\n7e3tjI+PU1BQwMTEBN/+9rdZvHgx//7v/47BYFB7vZGREQ0umzdvHh0dHczMzDA0NIS5uTmtra1Y\nW1trB5h49ebl5alS/OTJkzQ3NzNv3jxsbW3p7e1V//KKigpqamp0Pa6oqMDBwYGWlhZeeOEFVcCH\nh4fT39/PBx98QGNjI52dnfT393Po0CHa29vJzc1l1apVCohIgTM0NMQ777xDSkoK586dIzw8HGtr\nay0+4+LitCXXwsJCVZ33MsrKytTSTJSrfX199PT0EBAQoIC+AIeAKm5dXFw0uNDExISuri4FF0U5\nm5OTQ0JCAi4uLnrYFtsyX19fVWePjY3h4+OjYL4ARAKWSQt3ZWWlhlaK4mtwcFAVzoD6XYsyVQCx\nmZkZ3S+km0pIcFtbW+zs7PD09KS2tpbCwkIsLS358ssv6ezspLS0lKamJhobG2lra2NkZIT6+nry\n8/O5cOECJSUlpKSk4O/vr6IAAToE4G9ubr7LPqG9vV0BIHt7e723ooL18vKira2NW7duKWDS0NBA\nYGAgc+fOZeHChXh7e1NVVcXY2BhlZWXcvHkTW1tbPDw8VF0pYL2EqYnyXfYwuGPT1NLSxzGFZQAA\nIABJREFUomCl2OJI2K2npydTU1NcvHjxLoAa0JDYhoYGrl+/Tl5enlp1VFRUaL6MKOFF2CGCGymm\nRSktn0vABRMTEyUYpNNAlNXi297Q0EBLSwvd3d1kZ2ezYMECBTANBoMGaEvRK6piKfrkOs2225Iz\n7mzrnNk5ErOfjYmJCd555x0qKiro7OxkxYoVrF69WpWZrq6uSnxIGLvslwKCTkxMqO2EnAFmB6LK\n8ydZSh0dHYyNjXHp0iXS09OxtbXF2dmZoKAgjI2NWbdunRK/IyMjCgwK2DubwJCfb968iZeXF3Pn\nzr2L5AgMDGR8fJzGxka9Vl1dXRocm5CQcE/rjnwXEZ80NjYSGhrK5s2bmZ6eVoV1TEwMlZWVhIeH\nq8Dn1q1b2oWVnp5OQ0MDGRkZXLp0iaGhIcbHx3n66afx9vZWQkzAkuPHj+Pj48OLL76Ir68vJiYm\n+Pn50dPTQ2ZmJra2tgpMjo6OqmWO+LXL/Ovr61MiRubNbJJrenpaiRP5WQgAmUOWlpbU1NRw5swZ\n8vPzMTIyoqKigry8PL7zne/g6OhIaGiorg2yTllbWxMSEoKbmxvOzs50dHRgZGSkYKmQFrOtWZYs\nWaIh3V1dXUxOTjI5OYm3tzf19fWaxdHV1aUh7EIIwx21sHRLiNpTnq++vr67yOvx8XG6urr46U9/\nSmNjI8XFxXpeXbhwodqA3b59m9/+9resXLkSExMT6uvrMTIyUoBKLFvl96an74RCd3Z28tprr6nC\ntaOjgzlz5tDR0cF3v/td7T6WsMihoaG7lPcSCFlTU6P1rVg0zl6fq6qqNFhecgbMzc1pbm7m1KlT\n/OpXv2LLli00NjaqXZbsk1L7yXo22/JnNskoZKX8ku4OWQ8dHR354IMP1O6upaUFFxcXPeuKklfU\nq9KZMRv4l/kmJKYA9Pb29jg6OuLp6ald7bW1tZw7d46zZ8/y2WefceXKFQ1CPnXqFA4ODvz93/89\n27ZtU6W6dAnI95O5N5tscXV1ZfPmzerpL8+/zEM5+xgZGeHu7q5EpNhviFWfsbExJ0+eZM6cOUxM\nTBAUFHRP605mZib5+fl88MEHpKam0tDQgKurq17XP9fR5O/vr2thR0cHS5cu1Xki3/svEQB9fX0K\njs6ehzJX7OzsGBoaIigoiHXr1jE0NKQuBBIOe/XqVbWnu3DhAmFhYXh5edHZ2UlfXx9+fn4EBwdz\n5MgR7RT7uvFVkLWnp4ebN29ibGzMwoUL+eY3v8nGjRsxNjamoKBA55sMUUzn5uZq5+DU1BQeHh7k\n5uYSGxv7V1X+QgB8Fcj/a+P111+/y3pu9vvU1taSkZFBQEAAzz77LD4+PtpZKf740l0i55rZQ86m\nPj4++Pr64uDgoNZhp0+f5siRI8TExLBt2zYV4P6lIfPf2tqanp4eSkpKVDT41QDcrxtGRkbs37+f\n2tpali1bRmhoqH5nEYbIWTwwMBAzMzP17jcYDGq3Iu/V29uLvb09HR0dap/T3d3NK6+8wrZt29Sq\narYVHNzZs2T9s7a2xtnZmeDgYOzt7SkqKqKkpERrOwHyP/30Ux5//HGMjY25ceMGjY2NACqEtra2\nxmAwMDAwwOnTp8nKyuL27dv6Pf4SmTXbSi4mJobCwkKsra1pbW3V+qi/v19rB8m3sbS0VCI1Pz8f\nT09PPD097xJX9vf38+tf/5qamhrmzZtHSEiIzhu5J1+1ovpL439JgK8f/0+SAB9++CELFy5UFeZn\nn32Gt7c33t7enDlzhpCQEP7whz9w4MAB3cj7+/vp6enhl7/8JY899pgWDUuXLmXt2rVERUVx/fp1\nZmZmSEpKYv/+/eq9ZWFhoQDnE088QV5eHkNDQ8yfP1/DXvLz81m8eLG21J84cYKoqCjy8vLIysrC\n19eXxMREioqKaGhoICIiAltbW7KyshgfH2fBggW4u7ur55ePjw+Dg4OUlpYqgBgWFsbu3btV4RcS\nEkJlZSU+Pj44Ozszb948Pv/8cx5++GF8fX3Jyspiz549ZGdnExAQwNTUFIODg/T09JCSkoKlpaUG\nIovq87777uPEiRNEREQQHx/PiRMnGB0dZePGjRgZ3fHvNzMz4w9/+APFxcXk5uZiZmZGcXEx69ev\nx9LSkqamJnp7e5mYmCAzM5Pf/va35ObmMjU1RWpqKmvWrOHSpUvMmTOH0tJSSktL1TvtP//zP2lo\naFDvSrESkUXZ2dmZ3t5eTp48qUrNjIwM7djYtGkT77zzjob3irXO0aNHFYhpbGwkOjqaK1euYGdn\nx+DgIAEBAezYsYOjR4/i7+/Pk08+SWZmJsXFxSQlJZGenq5sr7m5Ofn5+bi6uvLTn/6UpKQkCgoK\nNNCmubmZK1euEBcXR0NDg1omdHZ2cv/992MwGHj44YeJi4ujra0Nd3d3Pv30U9577z2srKzYuHEj\nx44d04T4mZkZDT8NDw/Xw/ubb77Jhg0b6Ozs5IUXXiAnJ4eSkhIOHjyIubk5GRkZPPHEE/j4+HD8\n+HF2797NqVOnGB8fZ/fu3TQ2NvKNb3yDVatWqZJ+eHiY8vJyWltbVVW0a9cunn/+eS5cuMDly5c1\n6AjAycmJ4uJi9uzZQ1JSEsuWLcPBwYGLFy/S3t6uQGdKSgo+Pj6MjY1x7tw5nJ2dlSS6ePEifX19\n6kFsb2/Pyy+/zLVr17h8+TLPPPOM+mFLmLe0DoeFheHt7U1tbS1eXl78/ve/17Coqqoq/Pz8mJyc\nJCcnh4sXL/L8889z+fJlkpOT7xmMq6qq4vXXX+eBBx4gOjpa7X5SU1OZnp7WtnkTExPeeustBgcH\nSU9P57333iMvL48jR47g5+en9jmijnzooYeIiYmhu7sbe3t77O3t8fDwUOudixcvcuHCBQXNy8vL\nqauru2tDd3Jyoqmpic2bN6v628zMjOrqary9vRkaGsJgMJCTk4Ofnx+fffaZbrSi4PH396e0tJQj\nR45o0LKJiQmrV69maGiIOXPmaNBldXU1ly5dYtu2bXR2duLi4kJxcbGuawEBAaSnp5OcnIy1tTXH\njx9Xe5Jf//rXerhoaWkhOzubt99+m4yMDO1iMjExobGxkTfffJO+vj4trg0GA729vVy/fh0rKyvW\nr19PUlISbm5uqiQODAyksrKSxMREtm/fzpEjR6isrOTKlSuUl5fz3e9+l46ODlX0f/bZZzzyyCNK\n0AiZKerokpISVqxYweDgIJaWlqq0Ky8vZ8WKFRQXFxMfH4+VlZWG2IpqVZSqhYWFbNmyRbubXF1d\nyczMZPv27Xh4eDAzM6NWSRIievnyZWxsbFi6dCkvvPACa9as0SyE0tJSVqxYoQTnsWPH2Lx5s3ba\n9Pf3Mzo6SlVVFdevX1fP5bCwMC5evMiePXswNzfn1VdfVRurtWvXasj6v/3bv7Fx40bi4+NxdHTk\nX//1X/nhD3/I4sWL1TNeAA0J7K2vr+fgwYP09vbS1NREUFAQTU1NrFmzRhWAq1evJiEhgZqaGhwc\nHNTbd9GiRTQ3N/PRRx9RVFREXl4eJiYmhIWFYW5uTnBwMM7OzoyOjpKWlsbChQsZGRlh//797Nq1\nS20n/vjHP6rtUF9fHxs3bmTDhg14eXnh6+uLv78/tbW1ODs7c/78eRwcHEhJSaG5uZmEhATOnj1L\nQkICYWFhVFdX89RTT/Hpp5+yatUq9QgeHBxUuwCxr+rr61Mbpv7+fmZmZsjLy9PAdwERTU1N77ko\nrqmpITIyksjISPz8/Fi6dKkSJBYWFri4uGjLPKBgl4mJCSUlJWrX5+7uruA13FG4j4yMMDV1J3hT\nuvgEfBbQSD57X1+fnodEsSiqSwE6xBquqalJlYICxAno09vbqyGTVlZW6vlvZWWlgJiodEUdKgop\naeF2dnbW9XNgYEAJOWNjY6KjoykuLla7xYCAAObPn09SUpJatswG32dbzLi4uDA4OKhrjnQciLJW\nCEvJOWhtbaW1tZWcnBxu3rxJeXk58+bNIzg4GEdHR803iY2NxcrKioqKClXeh4WF6Toh3Sxwp9PV\nwcFBySgHBwcNBxdbEdl7BRyTcGBR8kZFRWn2RlVVFZ988gkxMTFYWVkREBBAREQEoaGhjI6O0tLS\nAtzxOJYsDlGyCogpoL6ApYAC8KLOF+Xe7Lbz6elpBgcHmZmZITw8nKqqKvr6+hgeHlZhR319Pebm\n5lRUVNxlhSMAqXxv+VlUf93d3XeBmjJHW1patLiXDgMJS7a2tubWrVt4eXmRkpKiQY0CBNrY2ODk\n5MT4+Dj19fU0NzfT2tpKc3Mzvb29mJiY4OjoiJeXF5OTk3d54MvnGxoa0mfGxMSE1NRUampqmJyc\nZN++fURFReHv74+HhwenTp2it7dXVZsyH6WLRYD/2eBdXFycZkbMtkqSIlq6O4KCgkhKStIA5b/F\n73j2EOsxIYCsra1xcXGht7eXvr6+u4JSbW1t1Ut87dq1zJ8/n9jYWM1aEDBMOhwtLS3Ztm2b5j5M\nT09riPvOnTtZtWqVrkNS/Ds5OSmxWFJSwuuvv05tba12fs/OwDh69CgDAwOUl5fT0dGBq6vrXd00\n4oks4LHYyIgqXT6XzLMtW7YQHx9PTEwMGzduVNsHWQ8F5JUuHRsbG4aHh/Xc4u7uzn/+538SHh4O\ncNfcludN/KkFvLKyssLPz0+JIxEgyJlChDrS6SRrhXjCy9olFlniW19VVaUCC6nf3NzcMDO7E2wc\nHx+vqulLly7xyCOP6DMtwKWQoWNjY/T19WkocGNjI9XV1bz11lu6Fsi68Z3vfIdvfOMbajU2W1Bm\nbW1NR0eHepn7+voyOjqqAOHo6Ki+jgBPGRkZ9Pf36zpmampKV1cXx48fJzIykgcffFABt+DgYLWy\nAe7qxJL1YbbnvfwsalZZn8X2TAB7+bdJSUlqi2dsbExUVJR2swlZMztrSkB/uf8yh2TPy87OxtnZ\nmdraWrVjkz+fnp7m+PHj9Pf3634v2SLSPbBgwQIVk8j8EpsZ+W6NjY0cOHCAixcvan0tterk5KSS\nzAKmC9go12VycpKmpiYl24RUk3yMmZkZ7OzsNOT+bx2yds2ZM4f169cTHR2t9zQyMvJr7XTkbGFu\nbq52Qn9uiMAE/mSLJGuorLnSgWVnZ8crr7zCypUrdf/o7+/H0tJS94XBwUFycnJ45pln9PWkO146\nN8zNzTU/ITo6Gjs7O27evKlnoK+O6elpBgYG6O7uJicnh8HBQVatWsVDDz3EkiVLcHZ21k6nsrIy\nFi1a9N9eQ+wefX19KS0tZWxsjIKCAlVNfxVgn/3eV65c0XPq30IATE1NqXBVCICOjg5GR0fveh9X\nV1eampp49NFHcXJy0mf5qzkFpqamagvzdUCznD/Fbq+wsJAf/OAHmssTGBj4Fz/z6Oio3ufKykoC\nAgK06zsrK4vQ0NC/CHLPfp1XXnkFuGMVLq8xM3Mnoy41NRVra2sVsglYHxgYqLXKbOujyclJamtr\n8fX1paGhgddee428vDwGBgZU3Ajo2l9VVYWHhwfDw8Pk5+djb2+vFmkiFhTbxt/85jdcv36dw4cP\nU1ZWxuLFi7VOOHv2LCYmJoSGhjIzM8PIyIiGIFtaWmJvb8/09DQNDQ1cuXKFW7dusXTpUl0X/1zW\nhNRlTk5OrFq1ioMHD6rYore3V+22bt++recU6cqXPfXq1au6ro2OjjI9PU1qairJyck0NTVx3333\nqdXexMSEEiP30glQWFj4N//d/2lDLKX+b46/SgI8//zz2NrasmjRIsbGxtTT+Nq1awpOzp8/n/r6\neoKCgli+fLkeLiTQTPyVN2zYoMqVlJQUPvroIzZv3szcuXO5cOECJ06cYNeuXepV2dzcjI2NDVFR\nUepzLoFgpaWlunicPXuW2tpaTE1NiY6OZv369Qog9Pf309DQgLm5OUePHmXr1q0EBQXR0NDAkiVL\n8Pb2Vt+rgYEB/vjHP9Lf38/ChQtxcnJiZGSErKwsBgYGdLL7+fnh5OSEu7u7HgQLCgpob29X8ES8\nwffv369hlgK+/uY3v+Ghhx5SIHbu3LnU19fj5+fH3r17OXr0KGFhYbz77rt4enoyMDDAM888w6VL\nl6itrcXBwQEjIyPOnz9PcnIyY2NjeHt788ADD6j9i5+fH2VlZaSmpioQWFRUxCOPPKJewnv37iU8\nPJyDBw+ybds2mpubefPNN/niiy/w8/NTpr2jo0MVTrKQ+fn56UFLAqEiIyOZmJggJiaGJUuWEBAQ\nwJYtW3j++ed57LHHuH37NjExMVhaWnLixAkcHBxUnVVXV4e5uTne3t7s2rWL+vp6iouLNfi3qKiI\nnTt38tZbbxEfH096ejpPP/00sbGxGAwGjh8/jpWVFeHh4fzTP/0T3//+9/n3f/93KisrsbGxITY2\nlujoaIKDgzXc1NXVlenpaVXlSatbWloaK1as0LAtMzMzDh06RGlpKeHh4VRXVxMUFMTSpUv5P//n\n/xAYGMilS5fo6uri3XffZXBwkMHBQVpaWnjttdcYHh4mLS2NiIgIhoaG+PLLL4mIiOBf/uVfaG1t\n5bHHHuPEiRM0NjZSWVmJk5MT+/btY8OGDVhbW1NXV0daWpp6ycfFxakVRlVVFRcvXuTZZ59l06ZN\nREVFkZaWxuXLlzVMt6ysjPPnzzM8PMzLL79MREQE2dnZmlzf2tqqbZ/iOR4ZGUlhYSH29va8+eab\nREREKOMtoJ+EfopVixB1R48eZXp6mh07dtDe3o6Xl5faOPyt49y5czQ0NPDFF1+wceNGXF1dNbym\npKSE06dPc/jwYR5//HGio6NpaGigpqaGgYEB9ScOCAjgj3/8o7LrXV1dxMTE4OnpSWtrK6GhoXh6\netLX18fQ0BA3btxQ0EusWPbv3097eztr165VEMPHx0cVKqKArKys1GA7FxcXPD091bLr4MGD5OTk\n8NFHHzE1NaVqNiFvzMzM2L59uyqWqqur1a7h7bffJiQkRImpzs5OnJyc2LNnD6+++iqLFy+mt7eX\nQ4cOkZiYiJWVFcHBweTm5vLiiy/y2GOPERYWxo4dO4iNjWXevHnk5uaSmJjItm3bSE5O1tBnV1dX\n7OzsCAoKorCwkIiICAYHBykuLqajo4PCwkJWrFjBuXPnSEpKUhXXyMgIIyMjnDhxgtDQUB5++GHy\n8vLUezYqKgoTkztZGnl5eTQ0NDBv3jwFePv7+7G2ttZnRsJcGxsb+fDDD/n000+pr6/H3t6ewMBA\nAgICmJycJC0tjejoaDo6OqipqdFCzNHRER8fHyoqKlQVaGtry40bN5g3bx4TExPU1tYyPj7O+++/\nT1JSEvHx8Xh5eeHh4UFYWBhpaWkcOnSIDRs2KAjU0tKiyuTk5GSKi4uZmZnhtddeY+vWrQQHBxMQ\nEKBK09DQUNatWwfcURfV1NSoulDAYVtbW5YvX463tzcvvfQSc+fOVaJCgCCZW8PDw7i4uPDss8+q\nZ70Ebpmbm7Ns2TJV8soQG4e2tjZdd86fP8+pU6eYmJigqamJ9vZ2GhoaGBkZISAggM7OTs6cOUNH\nRwcPPPCAgiXm5ub8x3/8B5GRkVo4Hjt2jNjYWN1TpXPGwsKCkZERJaPS0tIIDQ1lw4YNNDQ0EBoa\nqn62kZGRLFiwgNu3b2ueTXt7u1qbXbx4kY8++ogdO3bQ2dmptmhig2BpaUl5eTnz58/XwtLMzIza\n2tp7BuPKysoU7JucnNQ5IdYTAQEBqlqTomZgYEDBzNkBr1L8ClgldjYCggpAJ8pu+BOpILYLUjCL\nLYKAnbNVkg4ODppdIJYy9vb2dHd3q7IT7qjbjYyMVD1qY2ODpaUl1dXV2NraKskpGQSi1BPLgLq6\nOv2c5ubm7Nixg9DQULy8vIiNjSUiIkJBJycnJyXyvmonIkOAqZmZGVVuCujf1tbGjRs3yMvL07k3\nOTnJjRs3VMlqanonr0MC3eT1xNpKOumWLVumhYsA1GKZIt/TwsJCVcpSTNnY2GjXggBCPT09FBQU\n3NXKLsrM0dFRPvroIxYuXIi/v/9dJI6trS1eXl6aQyKEm6iJLS0t1RdcgFgByoyNjVUVL8DWbMsa\nAY5GR0cpLCzEz89PladFRUXY2Ngwf/589XQX8LWiokIBSVG4y+uK0l4sPmRdmZqaUhCwra2NtLQ0\nSkpKlPyvqanByclJ1/X6+nrGxsZITk7WwlVU06ImE7DfxcUFR0dHJUPkPYUck+sh97C7u1vnhih6\nTU1N6evr4xvf+AYeHh46ry0sLMjLy2NwcJD58+cr0DbbD1pAVPn/2f7hXyWwZiuWbW1tqa+v189s\naWl5z8VcU1OT3k8hpmUu29ra0tXVRX9/v5LBEkgu4JPYowUFBRESEoKHhwclJSUEBwfT09ODl5fX\nXcV+SEiIri3S5SGWbwIyyrpkYWFBamoqfX19lJWVERISQmZmJl1dXSpKkFwde3t7KioqVEEpcxPQ\nvVPspgRgETLSYDBw+/ZtPD09NYdBlNFCborVi8FgUFXp4OAgXl5ejI6OMjQ0RG9vL1evXlXBlVh5\nCfgrinPJ3pDuBkBBR7ku4v0/G0Cenp7WfCVZj21sbLQLy8HBQQlXFxcXzYcpKytTMMnY2BhfX18V\nkMzMzBAUFKQAsoAsY2Nj2pVUX19PQUEBGRkZDAwMMDAwgJubG8HBwdTV1SnY/9vf/hYHBwfMzMz0\nWs+ev+Pj4zg5OTE8PMzMzIwSS319ffq5Z4eM9/f309fXR3BwsK4rpaWlXLt2jQ0bNuDj46O2KLKH\nmZqa6llVAPjZnT4mJne87WevM7PVpaLS7+vrU3W0kCVTU1NcuHCB/Px8bGxsiIyMVHsi+XNHR0dy\nc3Nxc3PDxcVF75PM6dmB12Jv5OPjo8+HiDuGhoa4cOGCEufj4+M4Ojrqfjs6Osr999+v1hsC5Ev3\njlzHN998k02bNrFmzRq11RkfH1eSQ4SSgJLAouAdHx/n2LFjJCUl4ejoSGlpKX19fQT+l1XgbDDO\n39//ntYdsfBycnLCx8eH3t5ePD09WbRoEWFhYV/77yYnJ+nt7cXd3R34k/d7e3v7XUD08PCwZjGK\nDdjXAd1VVVV4eXlx5coVBSdv3bqFqakppaWlJCYmUlFRwbJly7Czs9P9ob29ncDAQD1n+Pj46Joj\nc9/b2xsrKyut5xsbG7l16xYVFRVcv36dl156iaysLAoLCwkICFBiX8LhR0dHVcn954aZmZlmBrq4\nuGjNKetuT0+PhhbP7kowMjK6J6FKamoqBw4cYMWKFTpvhoaG+PDDD9UaWPZZExMTIiIiFFD+awTD\nX1Ljt7e388knn2gQsouLi977v0YAwN3EQ1VVlZ6DjY2NVfjr6+t7l9XcnxtDQ0NcunRJf5aad2xs\njJaWFmpra1m+fLme3SSnSmxxRNgiQ+yJBMSvrKxkYGCAqakpNm7ceJe9lKWlJR4eHpqjZ25+J5BX\nMkiku0nO376+vhw9elT3rUceeUTtgsrLy5VQtrKyYsuWLURHR1NdXa1r0+joKE8//TQbN25UwWN/\nf//XhvDK/iR7q6enJ1lZWdrdLuuohYWFCnJljREbaOkCLCoq4ssvv1TLY3t7e9ra2li0aJF+T+nQ\nFku1v3X8Lwnw9eOvnRt7e3t54YUX+OCDD7j//vuZmZnh97//PWfOnOHq1auaHZOZmck777zD9evX\nSUhI+LOkkYy/SgIYGxurn6CRkZH62SUlJdHY2MjatWtxcXGhvLycqKgoPD09qauro7Kykm3btlFe\nXk5gYCBFRUU4Ojri7e3NwYMHVRkbFhbGxMQEJ06cYPPmzVrwdnZ2EhUVpYcfGxsbjh07RmFhId3d\n3Xz55ZcAREZGkpOTw+rVq7G0tCQ/P5/w8HAOHDjAjh07aG1t5dNPPyU+Pp7CwkKysrK4fPmyttBX\nV1drG/2pU6f43e9+x3333acq9by8PCYmJrhx4waBgYEaiCbKEICf//zn/PSnP2VoaIjs7Gwefvhh\n2tvbFcAfHh4mKioKX19fbt26RXd3N76+vpw5cwYfHx+qqqro6upi6dKlFBQUkJ2dTWNjI9/73veI\njo5m3759qv7ZunUrLi4uLFu2jKVLl2rxdOXKFVWVyEKwfPly1qxZw9DQEKampjQ2NuLk5ERAQAAe\nHh5MT0/j4ODAihUrlPVzcnIiJiaG8+fPc+bMGd59912eeuopxsbGaG9v5/z583R3dxMZGYmnp6eC\nMy0tLRw5cgRj4zvZDEVFRdp9ID66c+bMYWxsjLa2Ng0EXbt2Lbm5uaSkpJCUlERgYKB6Mo+Pj2Nl\nZcXExAS+vr7qlX748GGcnZ3ZuHGjBvds3LiR6Oho3nrrLf7lX/6Fjz/+mKKiIvr6+vjmN7/J0NAQ\n5eXlZGZmasu1KMxGRkYUnP/DH/7A4OAghw4dYnJykrVr1xIXF6cAS0hICNHR0SQkJBAcHExjYyNf\nfPEFxsbGbNiwgQcffJBbt27h6enJCy+8oGzv8uXLqaioIDIykoGBAT7//HPa29s148LBwQGDwcDb\nb7+tXr0xMTH87Gc/o7y8nLa2NrXokRCcCxcuMDY2hpWVFbGxsQpuNDQ0KLDz+uuv89hjjxEbG0tp\naSnNzc3s37+foKAgvL29NSdg+fLl2j7p5+enuQHiLxcTE4Obmxu//OUvWbhwIQcOHOC5557jww8/\nxN/fn8cff1w9c7Ozs5mcnKSmpgY7Ozveeecdvvvd797LWqgA8uXLl5W0MTU1xcfHh8TERObPn6+q\nKQmCLC8v56mnnqKiooKXXnqJOXPmsHr1auLj4xUsjoyMxNbWluzsbGXxfXx8yM3NJTc3lzlz5mjQ\nswREbtmyRT2ZxfbJ3t5eyZ7ZgYaiJnNxcWFmZoZf/vKX/OM//iMXL17ExcWFffv2YW9vzw9+8AP2\n7NlDZmYmjzzyCP7+/trm7eTkREZGBt7e3hw7dozy8nJefvlltcqKjo7G1dWVoKCMa/6cAAAgAElE\nQVQgnnvuObKysgA0dHFoaAhvb28efvhhwsPDtViAO4eZhIQEsrKyiIuL00N5UFAQ77//PmvXrmVi\nYkIDl6Rw++lPf0p7ezsDAwN3gX1ScImiaXBwECMjIzZv3syNGzcIDw8nIiKCnp4erKysePfddykt\nLaWyspLly5erVcqZM2doa2tj165dDA0NUVBQgJWVFcnJyaxZs4bz58+rvYyXlxdffPEFqampnD9/\nngsXLpCTk8OaNWvo6elRME3AkM8++4zu7m7i4+Oxs7PD3t4ef39/fHx8mJycJCoqCvhToJm3tzf+\n/v4UFRWRkpKiXuhyyLKwsNDuMTc3N1atWsXZs2c1FNPMzIwbN25oMOn777/P8ePHiY6O1uByHx8f\nfHx89PDU3d3Ntm3byMjIICoqiszMTA2NvXz5MhEREdoSnZOTw+TkJEuXLmXdunVUVlayY8cOBTpF\n/dbQ0MDY2Bg3b97ktddeo6mpCSMjI/7u7/6OyspKfvKTn5Cbm4uDgwNubm6MjIxQW1tLZWUllZWV\n3H///Tg7O2MwGLjyX8HyEvLr6emJhYUFc+bM4Sc/+QnXr19X/1UBW6QwhDskyO7du7l9+7Zm0URH\nR2NsbIzBYNCsjKioKBYsWEBYWBi3bt3SboaMjAysra3Vomrfvn2sWbMGOzs7LCws2LdvH05OTnfZ\nkQgpfC+jsrJSW/EFpBWgLzU1lYSEBAXuASWRxfart7dXQSQ5YEsRIEBSdXU1rq6uCrrJ4VyeUbFs\nkDBS8c4VSxgBZ+RgZ2pqyuDgIL29vTg7O6vqVggYAaekaJNnQNRf4pktQNDY2Nhd4NOlS5fIz89X\n2xU5cyQlJWFtbY2bmxuOjo6qFDMyMtKgaQG5Rd0vYJR4jkoRmJGRQV5env6SsF9ra2vq6+u101HU\noQA7d+7Ezc1NiQZRqguQKWukADMCugwPD6uaysrKiuLiYoKDg7XQgzvKbFE3DQ8P097ejpWVFZmZ\nmaSkpNwFYMt3mpmZUbBCgkoBDaYTot3IyIiAgABsbGz0rCv2IAJ2yzMkwDCg64so9eXeS9eEgMEC\nDIlFk52dHUuWLNFrI8SEh4cHxcXFuqaZmZmpdZQUv0L6iApXLIB6eno4c+aMApWtra16na5fv65B\nkp6enixYsAAvLy+9VjJ3Z+driNJe7C4EDJ2amqKpqUkLX+kCGB8f5+zZswQEBOi8MzY2xtnZWW0z\nZnvYm5ubEx0dTUREBOXl5UqISKeEZHGJf/1swmI24C+dAvJ8y72RXCUPDw8mJyeVyP5bR2trq85N\nc3NzJdG6u7vp6+ujs7OTuLg4BUnEn1zILhMTE5ydndXf3NnZWe2X1qxZQ1dXFx999BGrV6++a5+Q\n6y5rmYAlAtZLl4fBYKCgoIC6ujpOnTpFU1MTWVlZrFixQpXAzs7OTExM4OLiQkVFhRI9cjaQLhJR\nYMs8kvOTAHriwS5+1XK+kHktxBSgnT7SBWNmZkZHRwc5OTlkZmYqICS2aqLgHhoa0hpDwB07Oztc\nXV3VslAISbkuAnJLF4lkS9jZ2WndJWHecn3b2tr42c9+pnWf+ERL7SvEoDzLkrFWWVnJyZMnMRgM\nGvBuMBhISEhg4cKFhIWFUVZWpvktnp6eui+6u7srmTv7eZP6QM6ZPj4+ADqvzczMGBgYoL29XT2p\nra2tyc7OJjQ0lLa2No4fP46joyOxsbEkJiZqXTbbsgfQDg/Zv+RcJOuVdDPIEAtEORMMDAzwq1/9\nipaWFvbv309VVZVa0wwMDHDs2DH8/f1Zvny5hlULcSPK/5KSEsLDw/V7zyalBcCXTDf5jLa2trS3\nt6sA8ciRIzz44IOkpKTg4eHByMiIWqHKHjAwMEBcXBzj4+Nq89nW1saRI0f413/9V86ePYudnR0r\nV67Uay3kpsFgoLu7+y7wdbbt2Pj4OIcPH2bfvn24ubmp2l/2kYKCAgXlbt++/ReB+z832tra7rJn\nsre3p729nblz5/7FfzcwMMCVK1dITk7G0tKSsrIyJiYmcHd3V2xH1sqRkRHS09P56KOPqKurIyMj\ng8OHD5Oamkp+fj4BAQE4ODjg6uqqQe8DAwN0dHRo13l0dDRHjx5l586d+Pn5qXBIPrOApqOjo7pX\nSCD17BBYM7M72WiSn5GRkcGVK1eUFDQ1NaW1tZWVK1fi5OSk86W0tJRdu3Z97fUQ4k6ep5mZGe2G\nFcsbIblzcnLw9fW9p/s0Pj7OuXPneO+991R8efjwYSIjI3n77bdpbm6mvr6ey5cva6h3RESEkoGy\nRv45tb38/lcJgOrqar2WJiYmGvbc3d19z+fq2WN0dBQXFxeMje/4yjs6OhIcHExbWxuXL19WMvSr\nY2pqipaWFs6fP8/Y2BgdHR0MDQ0RGRlJR0cHL774Ii0tLQQHBytJO5uQMjc3p6+vT4l9uLPmSgeQ\nhYUF0dHRKq7auHHjf/sMIsawsrJSslfOJLOJJzlLxsbGkpeXx65du/Dz82N4eJjCwkJMTEx45JFH\n2Lp1K2vXrlXL4sWLF5Odna3E/N/93d+pvbo8o/X19bi4uPy3eyndoGIf6eXlxcqVKzlx4oTOz7Gx\nMf0Oo6Oj2NnZaXfB0NAQHR0deu6cO3cuGzZsYHp6muHhYW7dukVoaKhm5km3H3BPJEBBQcHf/Hf/\np42/lpNhamrKsmXLqKioIDk5mfr6eurq6vjxj3+MiYkJVVVVBAcH8/777yv+ODtL7c+Nv0oCfO97\n32NoaAg3NzfOnz+PnZ0dkZGRTE9PMzk5iYeHhy76AirV1NQo4+rn58evfvUrHn30Ud58801OnjzJ\nq6++yjvvvIO7uztRUVFqwSMPYXNzs7ZGVldXs3z5cgwGA76+vnz729/Gw8ODqqoqNmzYwNy5c6mr\nq8PDw4PIyEji4uK0eO7v79cQ1ampKZ544gkCAwMpKChg2bJlGAwGQkJCeOONN0hNTcXExIS2tjby\n8vLYu3cvP//5z8nNzaWnp+eulOz+/n6qq6vp6urSAqu5uVnBdQmazMzMxNXVVW0idu/eTVxcHKtX\nr+YPf/gDDz30EBEREcooenh40NjYyJUrVzAzM2Pv3r04Ojry5ZdfcvPmTXbu3Kn+qZmZmdr619PT\nw+HDh9ViA+4UCcJGf/zxxxw5coT77ruPZ599VrstJGSqqqqK9PR08vLyGBsb4/HHH2diYoLc3FxM\nTU25desWcXFxCkQ98MADVFVV0d7ejsFg4PDhw4yPj1NeXs769etpaGjg5s2bnDt3jpiYGJYuXcqZ\nM2dYtGgRHR0dHDp0SD3TBVQShZu0/nV2drJs2TLWrFnD/PnzCQ0NVRAjNzdXlciNjY3k5OSQn5/P\nqVOniImJISQkRK0namtrCQ4OxtPTk4KCAnbs2IGLiwsBAQG88cYbZGdnk5CQgIeHh4a/paamMjk5\nyYsvvkh1dTWOjo4MDg4SGxurnmouLi66Gdrb29Pc3IyjoyPz58+npKSEnTt34uXlpcX9rVu31PLg\n7NmzeHt7ExoaSklJiR7uDAYD09PTJCQkcOHCBVxdXdmxYwd1dXX09vZqC35gYCDDw8McPnwYa2tr\n8vPzuX79ujLQ0v5lYmLCiy++qAehuLg4mpqa+OEPf0hcXBwXLlwgICCAjo4ODaq1sLDAxsaG8+fP\ns27dOsLCwrhy5QoPPPCAdhSsXLmSmzdv4ufnR3p6uhZ6dXV1jI6O0t3dzYoVK7C3t2f58uUaUHov\nIycnBy8vL/Wcjo6OxtfXFzs7OwWV5BAgh/4rV67g6upKSkoKubm5BP6X7YZsnt3d3XzyySdaqK9d\nu1YBvMnJSXJzc3F3d2f+/PnY2tpqMS6bpqWlpR7oRF02PT3NK6+8ws6dO9XSabY3cmdnJ7a2tmRk\nZPDcc8/p4Sc+Ph4XFxe2bNlCdXW1dmAI0GIwGHjjjTc0uHP16tV0dnayZMkSenp6cHJywsnJibVr\n15KYmMjJkyc5dOgQSUlJfPzxx8ybN09VbhJQJ6rBmZkZkpOT2b9/v6qnxefXx8eH3/3udyxZskQV\nW729vSxYsIAFCxbw7rvv8uWXX7J+/XotxgUEsrS0VEVVcXExTz31lPq9urq6MjU1xa5du7jvvvso\nKChgzZo1dHZ2auFw+vRpQkNDuXHjBuvWrcPBwYGamhq8vLxYtGgR169fVxLaxMRErQKkM+eLL77g\n9OnT5Ofns2PHDgXVBgYGGB8fp6mpSUPcBJz08vLik08+ISLi/2Pvu8ObPM/1b29bMrIl27IlW95D\nXtjYxhMwy9gmQCBASAg5adOkSUPWaUZP07RN0uS0IaMJpbQ5GayGhBU2GDBhe+GB95T3ki1Zsqxh\ny+v3B32eiow2nPP775z3unKFXBHSp0/v977vcz/3iMLw8DAD2a+88gp7MFutVrz++utYtWoVVCoV\nA6bU6Dl37hxcXV2xZ88e1NTUsFqLGCbp6ekcXj87O4u4uDi4u7szMGG1WnkNUyqV+OSTTzA8PMwe\nnoGBgXBwcEBDQwPEYjFWrFiBiYkJtiQiQJ7ABOB2Mf7SSy+hqakJAoEACxcuxOOPP46UlBS8/fbb\n6O3tRXZ2NoxGI/r6+vDyyy9DqVSiqqqKg4l/9atfISEhAW+99RYGBgawZMkSNDU1QalUskUF+Ve+\n8sorbMtTUlLC+SMkmx0aGkJJSQl6e3vxk5/8BGfOnMGiRYtYfk9h6rbesocOHWKAy8XFBSaTCfPn\nz0dTU9Mdxa6XlxcDGmRfAtz2baYGzw8dDQ0NfMCldYUYwa2trQgODmY2qa3dBbH36O+RxJxAkfb2\ndkilUmg0GvT29vIcJvCawEZS1tAaRwUcfTdaH4ixTX8mOzIqDtzd3ZnxPz4+zgxBo9HILGIKRaVC\nj9j6FDzZ09ODS5cuMRjr5OSExMRE9rilgDwaBPYBYBYWMSSp0LQtXKihQRZZLS0tEAgEiIyMRFxc\nHIcTR0REoKenB1qtluXOMTExiIiIgMlk4meZ9jkADCrazqeWlhYGDQjsJwCd9hWDwYDx8XFWF1AY\nsKenJ9RqNfR6PQIDAxk0tvWJJWl/WloaN49o/6H7Q4PmCQBmUxPbFAADKORzTYGvZMFA67otqFRd\nXQ1/f3/Mzs7yOVIkEjFRh+49gT5k+djQ0ACdTsfWKMTMJasTAqXMZjNGRkbuaPj4+PhwQ6O7u/sO\n2yuTyYSgoCD4+/tzCDSB+25ubtzgoPlhMplgMpkYQKV5SMpXepbodyosLMTIyAhiY2PvsOUCwFYm\nVJDTPCsvL0doaCiuXbvGPv9kOUdgr63nNs0jYuzRb0r30WKxYGpqCgKBgMFggUDw31ICELOTbJW0\nWi28vLzYYkYkEsFkMvGcJiCysrKS7Rp0Oh3fI3d3d1YoeXp6wmAwICAggOcmNeNs75utnzkpLBwd\nHZnwolarIRaL4eXlhY0bN8LX15ebaaR4mjNnDrPfNRoNEwbouiYnJxlAJTIVfa/m5mYEBgbydyQV\nDV3bzMwMP4MEatDZRqvVsrVUeXk5rFYrGhoaEBcXx41Ud3d3biBQA4Tyh+g5cnJygkwmg6enJ27e\nvMm5AKQOo3lMCh76XgT+E/ub5reTkxMqKio464nOAenp6fD39+fGCeXqnTp1itX3AQEB/LzI5XI+\nX/b29iItLe2OLJ+goCC0tLRwoLttU9lsNvMZgWyOuru7eY2hhoxYLEZ3dzdnLmi1Wri6uuLMmTOQ\nyWTIyclhhTF9b2pm2qq9yMLH9r4Su52uiRoCMzMzaGxshLe3N8xmM8RiMfR6PaqqqqDVamE2mxEV\nFYXx8XGEhYVhcHAQOp2OiUxyuZw/j5o6VquVaxRae2ndozW/oaGB90XbfB6yCiWcISMjA66uroiM\njERycjKCgoLY8pOaC3K5HL6+vggODsbQ0BD279+P8+fPc26DyWRCVFQU5HI5Z7uoVCrs2bMHZ8+e\nRV1dHZqamjA8PMw5fwTMtbW1MYFOIBBAp9Pho48+QnZ2NiQSCaqrq9HX18dq/bsZ1BSiMTk5yflW\n/2zQ/SOlpZ2dHUpLS7kZTffUzs6OiYc3b96EWq2GRqPhMwXZeIWHh2N8fBzXrl2DXq+HTqeDyWRC\nTEwMvLy8MDw8jNWrV/P+SJZWdI4+ffo0hoaG7shiIQcIW796jUaDixcvor+/H+3t7XBwcGCygqOj\nI4xGI1avXo3w8HA+WwwMDKCjo+M7gW9SPtgC6LRfzJkzB1988cW39oKAgACuJ3/IGB8fR3FxMf78\n5z/DbDbDwcEBvb29DJqTgorqUbJD/aYq5Pvsduzs7L51PbSuUk1BZ93+/n60tLT8UwsoGnQ+/eaY\nnZ3lMxudC81mMwfbEmn3m4POugcOHGBbtXXr1qGurg6lpaXo6enBM888g8DAQCbhfvPv075DjcbO\nzk7IZDI+9xJpatOmTd+ywurr64OnpyeGhoZQV1fH/u225AbbQcoTqVTKJAyVSoWvvvoKTz31FHx8\nfLiBRXu5r68vFixYgIyMDNx///13qGfpnNTV1YXW1laujWyt1qheIbIA7Rujo6MwGAzcCKeg86Gh\nIbaU9fT0hKOjI7y8vJCXl4eVK1fyml1QUAC1Wo2UlBRuSFCjkhrhP3T8XxPg+8e/agIQYYbsnx0c\nHFBdXY3U1FQ0NDRgzpw5bNWXnJwMsViMc+fO/dPQ8X/ZBHjzzTchFAqxbNkyhISEoKCggCWOfX19\n6O/vh0qlQlFRES5duoTU1FS88cYbWL9+Pezs7BAZGYmFCxeiqakJWVlZiI6ORmRkJLOjtm3bhlWr\nVkEkEuHChQuorq5mD+fU1FQkJSWhubkZw8PDWLduHVpaWuDg4IC0tDRcvXoVZrMZNTU1yM7OxqlT\np3hjAG53uU+cOIGJiQksXryYGXaFhYUMShL4sGzZMoyOjmLjxo0oKyuDRqPhYEE7Ozs888wzyMrK\nQmBgIKqrq3H9+nUsXLgQ3t7eOHPmDDQaDQdszp07F5WVlYiLi0Nvby/WrVuH9evXM2NZIBBg//79\nmJ6eRkBAAF599VW0trayz/ns7Cyef/55fPrpp/Dx8UFVVRXWrVvHBzMPDw8EBARAp9NBp9PhZz/7\nGXp7ezEzM4PIyEgGINrb26FQKBAeHo7Lly9j5cqVGBwcRGZmJqanp1FeXo6JiQmoVCq2zvjDH/4A\ns9mMiooKzJ07Fxs2bGA/7d/85jfMiiYGIDGG7e3tMXfuXMTHx6OyshKbN29GfX093NzcEBERgfnz\n5+Pjjz9GZGQk/Pz8kJGRgbVr10KtVuONN97A3LlzERoaitnZ24Flhw8fZq9fjUYDmUyGv/71r3B1\ndUVLSwuWLl2KhoYGJCcnIz4+HlKpFK6urrhy5QpycnIwMDDAG2JlZSVSU1PZM/0Xv/gFEhISsHr1\navT19SE8PBzh4eHQarUc4vfZZ59BIBBg9+7dOHDgAB555BEEBgZiZmYGKpUKBw8exK1bt+Dt7Y3S\n0lJMTEzgkUcewbvvvot7770Xe/fuRWlpKTOgJyYmcOzYMWYUazQa1NXVsf/a+Pg4Fi9ezKqDgoIC\naLVaxMbGYtmyZVi0aBFCQkIwNjaGffv2cU7C1atXkZubi9DQUA76vHnzJgQCAWdLODk54dKlS5ia\nmsLhw4chFAoxZ84cxMXF4Y9//COysrJw48YN9PX1oaqqCjk5OVi+fDlnSKxduxalpaUICgrC0aNH\nMT09jR//+McwGAxYsGABXFxckJ2djZmZGZSWlqK6uhpxcXF82Pbx8bnrolitVmNmZgYBAQF47LHH\nIJVKYTAY2At+165dKCgowMjICCYmJhAWFoZ169axV2ZAQAB7exJgduDAARiNRpSWljJATQc2iUSC\njo4ObNq0CU1NTZDJZAxQODo6QiKRYGxsjGXABoMBr776KsrKyuDr64t58+Yx44waP+Pj4+ju7saB\nAwewatUqZpWQJHB6ehrHjx/HyMgIh2G9/PLL+Oqrr1BXVwdfX19otVr8/Oc/x8TEBK5cucIhuCRV\nnp2dxXvvvYdf/epXEIlEfFBcuHAhnJ2d+TuSn+x7772HvXv34uDBg3j11Vf5sN3U1ASVSoWYmBhk\nZ2fj/PnzCA4OxrZt2/DEE08wi2LFihVISUnBwYMHsXv3biQnJ+ONN97AqlWrEBAQgJaWFkilUkRG\nRsLFxQUikQhms5lZOwSwLFu2DI6OjuzpL5PJ0Nvbi4SEBAbiZ2Zm4OnpCbPZjJmZGWRlZSEhIYEb\nhO3t7ejs7MSSJUuQn5+P5ORk9pgPCgqCxWLhBs7KlSvh4+PDtgnEdOvv78fk5CT6+vqwYsUKmM1m\nODs7QyaT4d5774VIJMKRI0cwMjLCa9i6deuYkTg2NoY//vGPyMvLQ3JyMsrLy/Hqq69CoVDA29sb\nr7zyCs/T+fPnY/Xq1aipqcEXX3yBq1ev4uzZsygpKcGDDz6IsLAw9oltaWlBe3s7rly5grCwMISG\nhmJqagqRkZHw9PTkXAS9Xs8SZFtQmIDVjRs3IjY2lg+cBN5evHgRDz74IGZnZ/H444/Dzc0NJ06c\nwObNm5npkZqaihMnTgC4DR6Hh4cjNDQU+/btQ1JSEjNgKHtFKBRCoVAgMjKS7fPef/99XL16ldm7\nTzzxBNra2rB+/XoGB/R6PZycnNhq4u2332ZGtUgkYvVJaWkpDAYDsrOz77BIUalU8Pf3h5ubG4NY\nr7/+Omft3M1obGxkAJQOxvTf169f599dJpOxj2dJSQlbdZBFhy2gev36dbS2tjL7OjY29o5GIfAP\nFigV/9S4sz3kExBPjGV6f+D24dDb2xvd3d3coOnr6+NAN71ez/YRxGICbnviWywWZgfbXlN/fz8M\nBgMUCgV8fX3R0NCA0NBQ/p19fHz4GkhmTQ2bvr4+toUkZQHNTWLiEtudAJzExERERkaygkcikbC6\nTCQSofPvQZnZ2dl3ZKaQQoIsmEwmE3Q6HQfZETGCro0A78rKSmadDQ4OwsfHhxsC5FlPDQOr1YrG\nxkbY29+2g6OwY2Luk68rhWbbsu9smwXUNLp+/To3giik18XFBRaLBcA/2Ln03FAQJdl+DAwMsHWD\nnZ0dN/C1Wi0uXrzIvsQPPvggP88EztlaAwwODiI+Pp5/J7Vajba2tjsAgL6+PlaXUbOC7BqImapQ\nKGA0Gjko18XFBd7e3ujo6EB/fz8qKyuRkpLC4L7ZbIbBYGB2OFnxEChLQLQtGExNMmqIFBYWsu0e\nNU0IXLR9PwKPaf4LhUJIpVI0NTVxQ5gAXCrmyYaDfmMCzem3pjMvAYdUxJMlWmxs7F2tO1evXoWP\njw+fF+n3IiBVLBbj5s2b7MdsNBqZOCCTyQDc9oD28PDgMwgpg8hOSKVSoaysDK6urggICGAVKd1T\namzaris0T+zs7JCZmYnm5mbo9Xq8+uqrfPanayXgi0BmunbyICb7M7IvoXtr20j78ssvkZiYyEHE\n9va3rfVsVTKOjo7o6+tj8Lmurg47d+7E0aNHUV5ejvPnz3POEq3Tcrmcbe2IZUnf1TYsmRQLxB4P\nDAyEi4sLrFYrK3J7e3t5Xaa5Rv+2nYP0/JA1BKlaaJ9+6KGHOGOKnp3W1laukezs7JhMR+AnPXu0\n3rm5ueF3v/sdrl69ynYpqampcHC4HUxub2/Plnm0r5D9GtnakO2Sn58fhoeHERISwvaYt27dQmxs\nLBYsWMAZW46Ojuzdb6scoueD7iFdK4FSBEDZ2io5OjqitrYWp06dQmNjI8rKyrBo0SI4ODggKyuL\nr2vlypWIjIxktZDJZMLlv+fMUWAyNeoAsNJQp9NhZGQEbW1tvL4ODg7i7bffxoULF5CXl8frBDWK\nRCIRhoaGMDw8jDVr1gAA1xJEbExPT0d6ejpnp1VVVUGn0+Ho0aM4cuQIVCoVK6jJ2qmxsRFtbW0Q\ni8XYt28fysrK4OXlBTc3N3R1daG3t5cJfdXV1SgsLERNTQ2Cg4MREhLCyjRiZVNjNjw8HD4+Ppid\nnb3r7LWxsTH+MzVkfoiaYGpqCs3NzVzXCQQCKJVKeHl5oa6uDiEhIbzfGAwGSCQSjI6O4saNG7w3\nkipm8eLFuHnzJo4cOcLkveC/27ESMOng4MDfn54fd3d39PX1sb857eGETen1eq5hb9y4gYsXL+Lo\n0aOcaTQ4OMiqnK6uLkilUlYhhISEQKVSQa/Xo6SkBM7OzlAqlXfcA2qu0qAGOJFjPD09v7Pura2t\nZWLp99l0rF27Fg888ACmp6fR1dWF999/HwaDAd7e3twsp9qazjnR0dEQCAR466237rre/mZDwnb9\nokEEirCwMBQWFiI0NJT/33dZCX0XMA7cnr+nT59m9SW9ls5pcrkcdXV13MCzHW5ubliyZAns7e3x\nwAMPQKFQIDExERkZGVi4cCGTaimn8ZvPA13T0aNHERgYyDlvlCtD5NWHH34YarWa91yyyxsdHYWH\nhwdiYmJ4z/s+C6WZmRlUVlair6+Pc2Y+//xzPProo5wpRf/QWYfUx97e3nfMDdqPx8bGcOzYMRQU\nFKC8vJxDfkl1R6pXOg/Rb2s2m5GQkIDU1FS0tbXdoYSYmppCdnY2kpOTERcXhy1btjDWRPv1hQsX\n0NHRgfvuuw8CgYDJKXTtd9MEqKqq+sGv/d82fqh9LTUBXFxcUFRUhIMHD6KzsxP/9m//hqGhIWg0\nGt57KJfz+8a/bAJ4e3ujtrYWy5Yt4wV7x44dSEpKQnJyMj766CPk5ubCz88PNTU1HCRJYacxMTFw\ndXWFwWBAa2sr5HI5uru7kZmZiZKSEl6k161bB7lczkxqoVDIheC1a9cgEolw8eJF3HPPPQwWDA0N\n4dq1a7zo1tfX48knn8T4+DguXboEvV6PZ599ltkmDQ0NaG9vx9KlS9HV1QWj0YidO3fC09MTGzZs\nQFRUFK5fvw65XM4MwoSEBHR3d0Or1XIQ0uDgIHp6emCxWHDq1CkIBALMm6moUa8AACAASURBVDcP\nSqUSHh4eOHHiBDMUsrKyUFVVhR07dmD16tXcwVUqlTh79iwfAp599ll88cUXSEtLw9TUFD755BNo\nNBo0NDTgxRdfhLe3N6qqqrjY+slPfoLs7Gx89tlnUCqV7I9PnX1XV1cUFRVhz549kEgkcHV1hZ+f\nH7RaLZRKJdrb23H48GFYrVbk5+djxYoVyMjIgFarhV6vR2pqKsRiMcrLy7Fq1SoIhUJkZGTAz88P\nJpOJLQJMJhPEYjEsFgsKCwsBAA8++CA3BchHXSwWo6GhAcXFxZDL5Wwb8u677yI5ORlTU1MIDg7G\n5OQk/va3v6GtrQ2RkZHMuiR2+e9//3t88MEHiI2Nhbe3N1paWiCTybBt2zZERkZy8HNYWBiampqg\n0+nw29/+Fh0dHaiqqsLMzAwWL16MrVu3orW1FTk5OezlfOLECTg5OWHt2rXs82Zvb49r165h+fLl\ncHJywu7duxEWFsZAt9lsho+PDwICAiCXyyGVSiEQCDgsNSoqim1kiouL0d/fj/HxcfZb12q1sFgs\nmJ6exsDAAMrKynDp0iWsXbsWISEhzHQwm82sUhkcHMRPfvIThIaGwt/fHwsXLkRMTAyWLFmC4OBg\nfPTRR4iIiEBlZSUcHR3h6+uLvXv3Yvny5ZicnERSUhKuX7+Ouro6LF26FGFhYdi2bRtSU1Mxd+5c\nzMzMoKWlhcGKdevWQSaTYdeuXUhISIBCoUBwcDCGh4ehUChQUlICqVQKpVIJBwcHPhzs2bMH9fX1\nuHz5Mp599tkftLjRaGxshEajQXBwMLMxBwYG0NPTA5FIhM8//xxWqxVbtmyBQqFAT08PhEIhGhsb\nERUVhV//+tcoLCzEypUrodPp2G4lLy8PaWlpbM8wOTnJVljUwCO7JXt7+zuYl9Rpr6urg8lk4jBA\nFxcXDA0NcZEwOzsLtVrNwW0NDQ149NFH2ceturqaDxRJSUnw9/eHp6cnHnvsMZhMJoyPj+Oee+5B\nVVUVwsLC2Iefij7yhrVarfjoo4/g6OiIpUuXIioqCjdv3uTiu7W1Ff7+/uyT+9FHH6G9vR1isRgC\ngQA3btxAcHAwPDw8UFRUhNzcXGa8eXt747/+678gl8uRkZHB7D2DwcBs5atXryInJweXL19GTk4O\nLBYLjhw5gsHBQSxYsIALyblz50KlUjFIR6AAybfpev/yl7+gpKQEx48fR1ZWFrOrAWDnzp2QSqWI\nioqCm5sbHBwccOrUKSQkJHBx6OHhAblczuFplNlw69YtDlGWy+XYu3cvsrOzWWXR3t7OQbV9fX1w\ncXFBb28vM+e8vLxw+vRpNDU14bnnnuOGxujoKEZGRnDp0iWMjY1hcHAQeXl5CAkJYVu74uJiZusR\nO/fhhx9GQUEBWltbuSk4d+5c9PT0YGJigr28lyxZgs2bN2NwcBAhISGQSqV8+ALAYCp5s5LqAbh9\nAPXz8+NgLJI5i8Vi/PKXv8Tk5CSUSiXCw8MxMzMDo9HIoWbEJhsdHUVZWRnWr1+PvLw8jI+Pc1gu\nBdKTl/LIyAjvTeQRLBQKodfr0d/fD2dnZ2aRFhQUIDExkS1aZmZmMDAwAE9PT1RXV0OlUsFoNGLt\n2rVQKBTo6+tDZWUlgw4hISHsa0w5E/Pnz0dLSwtEIhETDEJCQu46i4SsQgjMoQaRh4cHmpqaGGQm\nX9C6ujr09/ejr6+Ppd9kz0PPTHFxMe/75JlOhQUBItQAsA3vtWWpEihKTHtbj2Rau2Zmbod4ent7\nY2JigkFCKh6pMUFMd5IIi8XiO0BraiI1NjZiamqKm6+9vb2YO3cuh3Lasixp3SMbAFKHUmFDns22\n9irEeLZVWxAAR+szKa7Ifk2v1yMrKwt2dna8DszMzNzhY+3k5MQWIgR60+tnZ2e5AUvgH30G2UpQ\ns4CaXAQ6k98sARN07bZs8c7OTiiVSl7XbBsftg3ktrY2xMTE8PXNzs4ySEZg58TEBCwWC06cOAFv\nb28olUpmuYWGhkIkEnHxODQ0BKvVisOHD/PneHh4sCLMFuCk+0yWiARmUoFMDDGz2Yzu7m6EhIRw\nvoGtPzutG93d3Whvb4dKpWLVAAFyxGpdtmwZfHx8eJ7SGdXWOsVWNWHrh00AHYGrMzO3w0uLioow\nPT3NhB7bhgu9L93f6elpDA8Ps+qloaEBFRUVsLOzY8CKro3mNP2bGjf0e9NnUQFMjFYClsnK8W7G\n6OgoM7aHhoZ4LlLtRKHTZ8+eRWtrK6Kjo5mFb8tCJysjW1URhXlfunSJ7UtIzWPrGU/NRZrLBCbY\nWpC5urrC398f0dHRPMeoYUU1HwHBFosFcrkcFy9ehEKh4MYj/R70/sTMt7e3x8WLF5Gamsrgv0gk\nwsTEBEZHR1k1NzExwZYYbW1tsFgsaG1tZSa4k5MT3n//fSQnJyMxMZHPOLRvU3OF1iOaJxSaS6A2\n2eCJRCIcPHgQGzZsQGhoKPR6PS5cuHCH/zKBmgB4rhLASAxMUjlSNgKFHTs6OqKkpAQtLS1IT09n\nD3taB+jMRn+PGpMTExOwWq3QaDRsmxIVFcVKinPnziEsLIxZt4ODg7BYLGhpaYGzszNKS0vh4eGB\n4eFh6PV6VvnQ+9bW1nLOHKlyHBwc7vCvJ9ATwB1WhBSMS2oIaujQe9B8MhgM2Lt3LwQCAaurKOxT\nIBBAKpUiISGBnwd63pydndnOpbi4GEFBQZBIJHc8n46Ojrxv0BweHx/HgQMH2N5u6dKl3EgmdZRe\nr0d7ezsiIiIgFAp5/STrTAJghUIh59+VlJQwqYts29zd3ZGWloaamhpMT09zrVdaWorBwUFuzJAl\nnNlsxtjYGMxmM38u/b4AuOlG+5bFYuEztO2cuptBTYDz589DKBR+y2/c1ubGdlgsFqjV6m+drxwc\nHBASEsIWa2S75+TkhKCgIAbWx8fH+fpNJhPkcjk2bdqEefPmwcHBgb347exuW6TZsrIrKipY+SkS\niaDX6zkTZPfu3Xj33Xdx9OhRNDc387wlFVBHRwfs7OzQ1dUFiUSCoaEhDmWm6ydCj5+fH7s0fJeS\n3VYF2dXVxYGw9vb2/Pt/1/D19UVJSQnCw8O/twnwwAMP8LO/fft2zogKDw+H1WqFp6cnN3knJibw\n0EMP4cEHH8TChQvZCu9/OqgBaTto75bJZGhvb4e9vT00Gg12796NhoYGPltRA/Cbw2q1wmg0wmq1\noquri+crrZW0Jp8/f57Dfb85HB0dUVNTg6ysLFbfUWOSFEnu7u6clUX3Qq1W89pERDNScdA+0Nvb\nC4VCgYSEBJhMJrz55ptQqVQICQmBj4/PHWQ2nU7Hje3vGrOzswgKCuIapaenB2q1GsnJyWwpR4P+\nTHvidz1ztH5duXIFXl5eHO7u5+fH9m96vZ7XMyL+7dmzh/dVwq7c3Nzw05/+FOPj41AqlfjZz36G\n6Oho6PV6bshQY1Gr1TJWmJCQAB8fHyYN0HX/XxPg/8+YN28eDh48yLg4AHZAsR3UBKiursbIyAh+\n+ctfQiQS4ebNmwgNDUVjYyOSkpJgNptRXV39P1MC6PV65OfnY3BwEB9//DFWrlyJJUuW4MCBAygq\nKsJbb70FkUiEkJAQrF69GnFxcWhoaEBGRgY2btyI4eFhDrpKTU1laZWnpye2b98OuVzOFizk35ie\nns4sjP3798PJ6bbHskwmY9ZsWVkZVCoVoqOj4e/vj8jISCQlJcHd3R0SiQRHjhzBf/zHf7A37Ouv\nv441a9Zg/vz5cHFxwY0bN3Dt2jX4+PjgN7/5DYqLi3H58mUMDAxgeHgY7e3t+OlPf4qrV69i/vz5\n8Pf3R1ZWFtra2vD8889j/vz5SEtLw40bNxAQEICqqipUV1fj3//93xEbG4uhoSGMjo7CweF2yN/z\nzz+PEydOoLS0FLm5ufD19cW9996LmpoaFBQUYN26dSgtLYVGo0Fubi6qqqrw8ssvo66uDunp6ais\nrMTp06fZq3zDhg2QyWTw8/NDXl4etm/fjlWrVmH//v2oqqpCYmIiLly4gM2bN2POnDkwGAzsUT06\nOgq5XI7GxkZ0dnZCLBajvr4eu3fvRm1tLS5fvsxWMWfPnoWj423P8N27dyMxMRGlpaVwdnZGXl4e\njEYjBAIBenp64OzsjI6ODly+fBkLFizASy+9BI1GA7FYjJ6eHqxYsYI/4+GHH+ZNbd++fWhqasLK\nlSshEAiQlpaGFStWoLCwEGq1GgKBAG+++SZMJhO2bt2KoKAgPPvss3xAKCsrw5YtW1BXV8fMrqqq\nKoyNjWHJkiWorq7G2bNn4eTkhODgYPznf/4nHnnkEWzZsgUGgwGRkZFoaGhASkoKKisrERUVhdDQ\nUJw7dw4pKSlITk7GJ598grCwMAwPDyMvLw85OTnw9fXFjh07UFxcjMWLFzP4ERsbi0OHDrEP3NTU\nFLZv346hoSGeG15eXpBIJDCZTFi/fj3mz5+PBQsWcNBtVlYWioqKIJFIoNFosG/fPlitVigUCjg7\nO6O3t5cDpY8dO4bo6GhYrVacPHkSzz//PGJjYzFv3jxoNBpotVr09vZi/vz5iI+Px9DQENauXcus\n6pGREZSWlmLRokUwmUxQKpVwcrodFtXV1YW0tDTs3LkTnZ2dMBqNiIqKwuHDh9HS0oJLly7hvvvu\n40P722+/jSVLliAwMJC9yB577LG7DuisqqrCzZs3Wb4tFAohk8mgUCjQ1taG2tpavPbaa8wEi4yM\nxIsvvog1a9agoKAAq1evxubNm3Hw4EHU1dWxx6VarWYrA2IzUuFYWFiIyMhIDgeSSCS4dOkSIiIi\ncOPGDXzwwQdoampCWVkZ0tPT0dHRgbKyMmzYsAGHDx/GokWLGKR1cnLi5tOzzz6L8PBwJCUlYdeu\nXZiYmEBqaioEAgH7LtbX1+Ppp5/Ghg0bUFBQgOeeew4/+tGPkJSUBIFAgMuXLyP479kqbW1tWLt2\nLfz9/eHg4IBly5axzDg3Nxfnz5/nw8zs7CwqKyuhVqvR2NiI1157DVu2bIHJZEJRUREqKysRGxuL\n2tpaLFmyBL6+vti+fTsKCwvR0tKCqKgodHV1oaWlBX/7298gkUgQFBSEhoYG9Pb2orCwEFNTU9i/\nfz8KCwvh6+uLFStWwNvbG3q9HtHR0RgfH0dDQwPq6+sRGxuLF154Afn5+TCZTAwm2dvbQ6VSISsr\nC97e3vjb3/6Gy5cvw8XFBRcuXEBCQgID1X5+fqirq0NsbCxiY2OxZ88e+Pn5cVj24OAgKisrERwc\nzBYuEokEu3fvxscff4ycnBzOcwgPD0dsbCwX1Z9//jnKy8uxYMEC1NfX4+uvv8bixYsBAE8//TRU\nKhW6urqg0+kQHBwMkUiEoqIizM7OorOzExs3bmSri9HRUQ6+d3V1xVNPPYW8vDw4OjoiPz8fy5cv\nx4oVK3Ds2DHk5ubi66+/Rl1dHdu8AbdDt6hIIuCR2P8uLi5ob2+Hs7MzmpqaOJhsdHQUn376KYeP\nUuFILN78/HzMnz8fra2tOH/+PJqamhAQEID9+/cjMzMTYrEYzc3NeOONN9DW1oacnBy8+eabkMlk\n8Pf3x+7du5GWlga5XA6hUIi//OUv0Gq1iImJgVwu56KKwMJFixYhMDAQ09PTuHr1Ktzc3FBRUcG+\nzD09PdBoNCgoKGC58X333QexWMz2gqdPn0ZUVBQyMjLQ0NCAoqIi6PV67N+/H9HR0SgoKIBSqYSj\noyMGBwcxPT0NHx+fu/Yuraur44M45fAQ4z0tLY1taCQSCSQSCSvY7O1vBz2qVCoMDw8z454ai0lJ\nSQgNDUVKSgpmZmZw4sQJKJVKtjYhJjg1JYmtSkA02b8QiEUAJIU76nQ6NDU14dKlS6isrGTfemLq\n0tyh9yIwZc6cOQyYEBhNFgQEQHR2dqK3t5eb0cRMJxYryZ7pvYlhTKxrug47u9te1FSU0fcl1ih9\nF/puBOoRe7e8vBxjY2MMzhDhgUArsiMRCoV3sItt7ZRIySEQCODg4MD+zleuXIFCoUB/fz+OHz+O\nkJAQBrkNBgOam5uRkZHB1jRkDUIF28zMDLRaLWZnZ9luzjbcEfhHAVxSUsIBh1RwUSNErVYzI3t6\nepr3eWoczZkzB+7u7tDpdKisrIRMJsO1a9fQ09OD9vZ2brSSjY9CoWAbQlKGEcBJeRq24DvtjWTf\nFhsby80GAu0JMJ+enkZrays3E+VyOQYGBjA0NARfX18mAQUGBsLDwwNisZj9w2nOEVA4PDzM94vs\nCaxWKxMxqDlNgNfExAQuXLgANzc3zJs3jwHzbzbJaJ7X19cjKCgILi4uGBkZwcmTJ1nZFxUVBbFY\nfEdDDgCD2QTIAuD5T9779NsTUEjX969k3d8carWarYkI4KMGK4Xf9vb2Muhy/fp19Pf3w2g0oqys\nDLW1tTh48CD6+/shkUjQ3t6O9vZ2jI6OoqWlBc3NzZiensbo6ChGR0exdOlSbg7Ts0lse/oeZFdF\nv5m9vT0UCgW6u7tZYWAymfgaidBEknUAzLotKSlBbGws70dUA9IzSp+XkpKCzz77DIGBgWxpSY3u\n8fFxlJSU3GH3oFarYTAY4OHhgdraWkilUrzxxhtspUd2IKTCpLMGrS00l6lpTY0geo3ZbMarr76K\n0dFRVFdXY8GCBfD390dSUhL8/Px4vy0vL4dEIuG/b5vdYDab2Tqtra0Nvb29kEgkuHDhAhOjMjMz\nER8fj8nJSbY3GhgY4MwiYmQKBAJuOtFrg4KCsGzZMnh6ekIqlUIkEsHR0RExMTGc+9Ld3c3PZGBg\nIEZHR+Hs7AyBQMC5K3Z2duju7sbExARGRkaQm5t7B7uULELIPmh6epqfSVrvqElD9/SbgJZtpsfQ\n0BB27dqF8PBwjI6OQiQSwdPTE0ajEXFxcbBarZBIJNwQp0F7Y1BQEE6dOgWz2Yza2loYjUYOIyZA\nTKPRYHJyErW1taioqMDp06cxODgIDw8PJCQkcKagWq1m9YVQKERgYCAsFssd9jO0LpBlKtk3jY+P\nIzU1Ffn5+SgpKcFzzz2HVatWYc2aNTh37hzGx8dhZ2fHQeWU0Tc2NobHHnsMcrkcUVFRzMT19/dH\ncXExW8BSFldOTg7vC/SMUUOlra0NoaGhrAr6oYOaAGFhYd8J5H0fy3liYgKtra2IiYn5zv9PwCg1\nAGiEh4fjwoULzLx2dnZGd3c3xsbGsHLlSn4dsfypeUfj+vXr7E1Pw83NDT09PVAoFPjwww+RkpIC\njUaDoaEhODg4ICgoCGKxmM/QpD4jVZ3JZGLLX/LclsvlnGuxb98+pKWl/dP7SIoA2zPHd4HgU1NT\nqK2tRV9fH+bNm3eHRc/g4CA0Gg2Hk9NeuHz5cqxevRohISGor6/nxiK5B7z88ssIDw9n8J8cAf47\njYDOzk7s3bsXxcXFKC4uxujoKNtbE1BPz6q3tzfq6upw7tw5lJeXQ6PR4PTp07h8+TIaGxu54Vhf\nX4+ysjJcuHABzc3NnL1gsVhQXV0NpVLJTXSr1YqWlhYEBAR871weGhrCyZMnkZ6ezlZCRBqhvYqe\n49LSUri6usJkMmFiYgJSqRQmk4ntH+l5onttb2+Pjz76CMuXL4ePjw8efPBBLFu2DBKJhFn/Dg4O\n0Ov1nBlhO2yVAXq9HkajEZ9//jk6OjpQVFQEpVKJpUuXfisAmc67wPc/c7Qv9/T04JFHHsHChQth\nsViwZ88enDx5EuPj4+jo6IBcLufsQsIghEIhMjMz2crnxRdfREREBLKzs5GSksKNaoVCAYVCwc1J\nvV6P48ePMzG7pqYGDg4OCA4OvsNy7m6aAJWVlT/4tf/bRlJSEuMasbGx39kAAP7RBKC8t7i4OK6l\nMzMzcfLkSWRnZ6OiooJttr5v/MsmAMm6PTw8UF5ejiVLlrDtR3x8PMRiMS+Ag4ODLCOqqalhf0Gz\n2YzAwEAcOHCAO5cffPABAgMD8fOf/5wtfVatWoWLFy8iMTGRA85qamoQHh7O/vPe3t6orq7G8PAw\nBwWlp6ejpKQEEomE5Ys9PT2YN28exsbG0N7ejrS0NCQnJ7OVyNatW5GSkgJvb2+EhobC19cXp0+f\n5uDGe+65hyWT+/btw/nz5yEWi5Geno6vv/4a9fX1bEHj4eGBZ599loMuDx48yEDBkSNHMDs7i7S0\nNISFheHChQvsJ6bX61FWVsayZIlEgoyMDLi4uKC+vp7Bp0OHDiEyMhJXrlzhYFur1coMXmdnZ3z5\n5ZfYvHkzS/dPnjyJ9vZ2ltKnp6djx44dzPKOiIjA9u3b8fLLL/O9Xr9+PcbGxtDV1cXdRYvFgujo\naFy9ehUTExOIj4/Hrl27mIlDjN+PP/4YBoMB77zzDtavX8/+wUePHsWKFSvg7u6O7du344UXXkBd\nXR3Wrl3LEvr29nb09/dj7dq1LF3q6+tDVFQUdzsbGhqQlpYGo9EIhUKB+fPnQ6lUYsGCBUhOTsax\nY8c4gPG+++6Dj48P4uPjceLECZSVlTFr/JVXXkFOTg4CAwNhb2+P4OBgzMzMoKKiAnPmzEFLSwvc\n3NzQ2tqK+Ph4uLm54erVq7h16xby8vLg6uqK4OBguLu7Y2xsDCEhIdiyZQvUajU+/PBDPPTQQ2hu\nbsbFixdhtVqRmJgIg8GA69evIzc3F7m5uTh16hRbATQ2NkIkEmHevHmIjIxEb28v/2bEnOvp6cGj\njz6KqKgo+Pr64ujRowgLC8PZs2cRHR2N1atXQ6FQ4JVXXsHMzAwyMzNhsVhQW1uLpqYmWK1WDA0N\nYc2aNVCr1aiqquI8Dzc3N/z2t7/FSy+9hMnJSS6oqdt74sQJJCQksDWKUqlEb28vlEolVq1ahZs3\nb6K7uxtz587F9evXkZyczADY0aNHMTMzg+jo6H95iPrmUKlUCAwMxGuvvYaenh5cvHgR4eHhbHux\nePFiPPXUU7BarRgcHERKSgpmZ2fh4+ODgwcP4kc/+hHc3NwQEhICf39/nDx5EkFBQSgpKYFKpYKf\nnx+z/ghIImbtoUOH+ICr0+ng6OiIo0ePsgx91apVfLBMT0/H8PAwpqam4OPjgx07dmBsbAzvvPMO\ny8iJ3eXk5ISYmBhcvHgRQUFBmJqa4uK/oqICUVFRcHBwwPHjx3H//ffzOhoWFgaj0Yj09HQYDAbU\n1tZi0aJF6OjoQEFBATZu3MiHvunpaeTl5WFkZAT33nsvqqqqMG/ePGRkZCA3Nxeenp6YmpqCTqdD\nbm4u1qxZA3d3d7Yw8fHx4QDY++67D6mpqfD09MQ777wDZ2dnLF68GAKBAH5+fvjqq68YwBKLxXji\niScQHh4Ob29vDjujAxipkEJDQ7FmzRoGfIgdNjMzgytXriA9PR2zs7O8Zh05cgQBAQGYN28eYmJi\n7rCDoKyB6OhonD59GhERERgdHYVUKsVnn32GJUuWICIighlFBoMBo6OjuHbtGjw8PJCZmcnsLlJv\nFBUVwWw2Y8OGDfDy8sKNGzcwOjqKuLg4ZkrOzs6itbWVAZD169fD3t4ejY2NyMvLg52dHUZGRuDv\n7w97e3uo1Wr4+/ujrKyMs1DII1ulUuHJJ5/Evn37sHDhQuTm5kIgEMDd3R2urq4QCoVckAC3D5Yi\nkYjnrlqtxunTp6FWq1kWrFarkZOTg2PHjsHV1ZWzC2zBN2KyhIeHY9WqVbC3t0dzczMWL16M/v5+\nVj/96Ec/Yg/mjIwMnD17Frm5uRCLxXw9rq6u3ACSSqXo6+tjq5CBgQFYrVbU19dj06ZNWLFiBZKT\nkyESibhZFBISAg8PD7S3t3MoV01NDTOr3dzc4OXlhYSEBIyNjSE+Ph5KpRLHjh1jFlR0dDQkEgmO\nHz+OHTt2oLS0FHK5nPfQHzrq6+uZ5ULMfAI7yGKE7C6I+Uugq1QqhZeXF4RCITOi3N3dGRgiENTF\nxQUymQxqtRoODg4oKiqCt7c3qwwIZCHmPrEUqcghsJ3AfLVaDa1Wi+7ubrYSiY6OZr9R+i62zENq\nVAJgJufk5CQHums0GmRkZCAkJAShoaEICQlBXFwcKisrIRAI4OLigqqqKvj7+/OzTMUXKWgI4Kdn\nhhpDVHhZrdY7JOck47YFiug+zM7OIiQkhNeW1tZWBolNJhNqa2tx5coVCIVC+Pn5cQFuC+gSk5sY\nTKOjowBuKyNKS0vR2dnJ6kbaJ9ra2tDY2AgXFxcO6ra16qH9w2Kx8Nylz6G1kVQPxDiTSqWYmZmB\nQqG4Q1FA6x7NPwIoyTudwFqyUXJxcYFAIOBinNZSIheYzWYEBAQwu5MaLbT+kwWUnZ0dhoeHucCl\nTAovLy8GEenvk7qFmNNkiUDrkoPD7WBZd3d3aDQaGI1GxMbGoq+vj0Fimrs0Nzo7OxmgpHXN0dGR\nmw70+9H3B8Ce84ODg+yNajabmXlKQCw1Y4k9CQAdHR24desWz9n4+Hj+TnQPKYCaPo8CmY8fP46K\nigokJCSwTZjtHLdYLNDpdMjKyrqrdYds6cRiMe8NNNeJIV1SUoKIiAhMTEwgJiYGDz/8MIKCghAR\nEYHk5GQsWLAASqUS7777LlpbW5GYmAixWMwqyKSkJEilUlgsFiiVSm4C2IIOBDASu5IacjQnCTSk\nRhc1W2dmZjA0NMS2CfRcETuzvb0d8fHxvGbRfO7p6WFAhtYpg8HAzHf6p7+/H+Xl5Zw7FBwczM96\nYGAgOjs7MT4+jqeffhoymYyt7wBwc4jWFQJ7aG+iz6f5R4xw2jMvX74MhUIBi8UCgUBwR2YBWUUG\nBgZieHiYA3BJHWQwGGA0GjFnzhzU19ezlaOTkxOrlyQSCcRiMSvoyC+dQMvq6mquV0ZGRvj3EQgE\nrOoisJ4C5e3t7VlVRWQXCkqltZAaXwBw6tQpdHR04Pz587h16xY2b97M70kZMnQGsQW+bG0lzGYz\nPztk4TQ5OYnx8XEMDw9znh5lSJE97NTUFNzd3bkBf/36dSQkJDCISswuPQAAIABJREFUSc10Akat\nViu0Wi3s7e1RW1vL97ulpQV9fX0QCAQQi8Xo6+vD73//e9y6dQt1dXXM/h4ZGWEWv0KhYJ9/288i\nTIIahzQfaB2i55zWYLlcDmdnZ3R1dSE3N5ev1dvbG+3t7bw+aDQatgtdtWoV5s2bB4lEguDgYPj6\n+iIwMBAymQyJiYmIiIhAeHg4GhoaIJFIkJOTw/fTdg0l68KQkJBv+aD/q0HP83exvqkx/V2DGg/f\n1wT4rkH7y+nTp3k/7Onpgdlsxvj4OO6///47Xj8yMnKHRRkAbrx989rIcikvLw91dXWYmJhAQkIC\ntFotbty4AT8/P7ZVI6Wvg4MDhoaG2HWiq6sLU1NTrD6pqKiAXC5ngsfdDGJk2w6LxYLOzk4AQGFh\nIdu30fdzd3eHp6cn2xGSDRipRI8cOYKxsTFeW5RKJZ577jm2s6Oxc+dOyGSyb3na/6sxPT2NDz/8\nENeuXUNLSwu6u7sxMjICnU6HgwcP4uTJkwgMDIREIuG1RSqVIvjvOZsdHR2cAUX2pykpKQgODkZq\naioSEhKQnp7OAdCUcWIymdiTn6yYqDFj+xtPTU1Br9ejuLgYGo2GsRgiufT397NlFH2f8fFx9PX1\nISQkBEKhkJ8XssAkJRNZpbm7uyM7OxvvvvsulixZcsd9pfMBeerT72t7FqQacXh4GE899RTOnDnD\ndarFYsGTTz4JHx8fJjrQWkrryj8bs7OzGBsbQ2dnJzusUF1mtVpZcUz2pJ9++ikMBgOr9/Py8pig\nRqoispaijDvaf0i9VF1djStXrqC/vx/+/v7w9/fHV199xSQ4Iuj8XxPg/8/4V/a109PTeOutt9DR\n0YG6ujq2ID5//jzq6+vx4IMPsur6008/xcDAADZv3vy9iiPgBzQBtmzZgpycHGZGhISEQK1WQyKR\n4OzZsygsLERFRQX27t3LG/qePXvQ3t6OtWvXQiaTwcnJCX/+85/R2trKzGyywIiOjkZtbS0rBIgV\nR4e4hoYG9pjNyMjAihUroFarcf/99+P06dPMNE1NTUVERAT8/Pxw4sQJjIyMIDg4GPv378eyZctw\n9epVjI2NwcvLCw0NDYiMjOQD9969e6HX6xkAvnHjBu6991709/fzgUwikbC/2PT0NFauXMle2rdu\n3UJoaCi8vb3xzDPPYHZ2FkuWLMG2bdvwwAMPIDQ0lIOE7rnnHg5scHd3R3JyMlasWIGmpiYcOXKE\nmVXEij1y5AgqKiowNjaGoaEhPPHEE/jggw9QUFCA/Px8qNVqDkyk4MEjR45gamoKDzzwADQaDUpL\nS9HX14f7778fO3bswMsvv4ydO3fi5ZdfRmxsLLRaLcLDw+Hu7o7PPvsMSUlJ2Lp1K5KSktDe3o6e\nnh5kZ2fj6tWraGxsxNatW1FUVITHH38cdna3g3/Wrl2L++67D05OTujv74fJZIJarUZcXBy++OIL\nSKVSzJ07FyEhIVi2bBmuXbuG5557Dp6ensyKlclkePjhh/H111+jtbUVBoMB+fn5mJ2dZcA5KCgI\n/f398PLygsFggEajgZ2dHfr7+xEaGor58+fDzu62R+6FCxcQFxeHTZs2ITs7Gz4+PsjJyeHmFfkY\n+/r6cjZAcXExHnjgAfzpT3+Co6Mj3n//faxbt44Ll0WLFmFoaAhOTk74xS9+gQ0bNmB4eBhvvfUW\nNmzYgISEBExOTrIUt7+/H/Hx8fD390dmZiZMJhMaGxuRn5+P2NhYNDY24pFHHoGdnR08PT1RWVkJ\nrVaL3/3udzh27BjEYjEyMzMxO3s7ZPYPf/gD3njjDczOziI3Nxfx8fEMJKhUKixevBhubm44efIk\nFi1ahJiYGGi1Wjz00EMYGxvjgoPmLVkT6fV6VgVQULNQKIS/vz/ndMjlcggEAgQHBzM7e8GCBYiM\njMSnn37KTG+FQgGhUMg2He7u7nddFK9atQqjo6PYunUrFi1ahPj4eOzbtw9Llizhg+H169cxPT2N\nn/3sZyxN9fT0xObNm6HRaNDd3Y2nnnoKW7duRWJiIjo6OtDc3Iz6+npkZGSwSufq1ascUK1Wq3H9\n+nU8//zzuHjxIjfeCGDbsmULxGIxF//vvfcebt26hUceeQReXl5YsWIFzp8/j8jISGbq1dbWorKy\nEsuWLYPRaERaWhqqq6uhUCjwwgsv8PqRmZmJU6dOQalUQqlUshR1ZGQEGo0GBw8exOrVq2E2m3Ht\n2jUsXLgQAwMDkMlk8PLyur2g//0wHBkZCb1ej/j4eAZFyBJjenoaTU1NfHig8MCIiAhotVqIRCLs\n2LEDixYtYmXVxo0b2ULG0fEfAVonT55k9QitE2TfMT09jT/96U+cnSGXy5mxOzY2htHRUX6vQ4cO\nwdHRkdU/CxcuREpKCnJzcxEYGIiAgAAGF3/9619z0QWA2bEeHh5obm5GUVERxGIxzpw5wzZeFACd\nmJiIyclJLtYIFKAguY6ODi72kpKS0Nvbyyyibdu2sQcfMSdaW1sRFRWFsLAwKJVKvPfee5icnERJ\nSQkyMjLYQ1KtViM1NZUblWazGcPDwwgODmZAobq6GtHR0Zxdk5aWhtraWqhUKsjl8jv8mhsbG1kK\nKJFIUFhYiObmZmRnZyMyMhJubm6IiYnBwMAAbt68iZCQEGbm0XtQkeDi4oKGhgZcuHABZ8+eRVZW\nFuLi4hjcunHjBsLCwlBTU8OqkuTkZNTX18PPzw/BwcHIz8+HVCrF4cOHAYAzCvz9/SEUChEbGwuJ\nRAInJyf88pe/xNNPPw3gNrj2+9//HmFhYVi6dClWrFiB/Px8rFy5EqWlpSguLkZHRwf6+vpw7733\nQiAQ4NChQ5g7dy7S0tIQFxfHgIezszNSUlLg4+OD6upqREZGIjc3967WnVu3bnGhQKGYVHQS+EJ/\nBsAsSPKspbMLyd1JPkyMSQJCdDodent70dPTg5KSEsjlcpw7dw5FRUWoq6tDUVEREhMTudlFljKU\nj0EyXSo+DAYDysrKuPFIfsrAbdaere8xNeYcHR35udDpdDhw4ABUKhWcnJy4eUIkB/IaT0hIQH9/\nPzQaDebMmYNr166hr6+Pmxu2vupU5JB8moolWysV2+YBFSsODg7o6+tjpirwD3sHd3d3BAcHQyaT\nQSwW4+LFi8x6zszMxLx587hApXlua59FljvEXDUajayWo2eVlGEmkwnd3d1wcHDA5s2b75COk00C\nWdaQzdGlS5cglUoZQKJrobWXLB60Wi0kEgmHzdJ9AcCqDAJkVSoV5x/QGu/g4MD7lYeHB27dusUF\nKlnpWSwWJhlIJBK26KD7SAUoNZBt7TxsgTdi3tK9HB8fx+TkJO/3IyMjbDklkUjYgmXevHkMPPv5\n+aG1tRU+Pj7MZCZvZ1JlHD16FFVVVdy8ICCf/G4pyI/UAc7OzliwYAFmZ2dx7Ngx3Lp1CyqVCuHh\n4dwAILUC5TlZrVbs3LmTv5uzszOio6O5OCZrKAIGCHCjOSSTybgAt7WMIpBveHgYZ8+exY9//OO7\nWncIbJFKpcwcNhqNmJiYgLu7O4aGhuDm5obGxkZkZ2cjLi4Oc+bMYdurOXPmoL+/n73wly1bhuDg\nYCY5kSJHJBIhKSkJBQUFzB61bULRGkPKBnpm6IxJSivK1iCg1mg0sjrNZDKx1RAB6k1NTUhMTPzW\nWurh4cHqB9qbgoOD0dXVhaamJsTExEAkEqG/vx91dXWoq6uDVCrl3ABix8/MzCA5ORn9/f0ICAhg\nWyZbhQOdg+g5ef3113kvs7XlIiszWnfKy8vR1NTEYbUEYtk256g5QYxUCsem5pyfnx98fX3h5+cH\no9HIwCQxntesWQOz2Yy2tjZWJ1G2g6urK1uLEAN/amqKLWNMJhMuXLiAr776CufOncOiRYt4ndHr\n9Ty3PTw8cObMGQgEAhgMBkxPT+PQoUM4ePAgjEYjpFIp6uvrYW9vj66uLoSGhrJND+0/lGtw69Yt\nhIWFMThOrFo7OzsMDAzg9OnTkMlkDMLRmRC4bX31ySefYMuWLRw8bGdnB29vbxgMBnh5eaG5uZmJ\nSmQ/R0G5BNyazWZ8+eWXXAtmZGTg0UcfxezsLJ577jm0tbUxCZEU67Tm5ufnY9myZbwmU8NHr9fz\nnCdsgprck5OT0Ol0vB9TZg1dCzEyyVKJWMqBgYHsO0/e+M888wyrfCn8k/ZOBwcHKBQKSKVSBp+p\nsTAyMoKioiJ8+eWXKCsrY1uhxMREDha9m0GB5N/FGrez+37P86mpKTQ1Nd1V9gmdk06dOoXBwUEm\nTpGly/33389AqE6nY+zIdtiuE7agKZEzNBoNli5dylY6BOwODAygqqoKPj4+aG5uxtjYGLq7u+Hk\n5AQvL687yAkWiwV5eXk894kgejfWkgRYCwQCtsv59NNPce3aNbS1tXF2j9FohEwmYxCVmv703dRq\nNTw9PXHixAnOlly+fDlCQ0OhVCrh7+//LfC4vLycia13M3p6enD06FFW+9D11NTUYPPmzZBKpdi9\nezdOnz6NS5cucV1cVFSE5cuXw8HBAf39/Ziensann36K0NBQthIjNTL9hnQO8ff3x+joKPbv38+v\nn5mZwZw5czA8PAyRSISenh4mpzo43M6OUiqVvMbQ/lhYWMhWaXq9ntU9AwMDnNsRGRnJig9yA6Cm\nsG195ebm9i0VMa291PijQedd2/Pt4OAgvL29malNSpX8/Hzel76pIqDn47sGNSCGhobg7e3NayUR\nfYKDg7F3715otVqMjY2huLiYyWiJiYl4/vnnIZPJ4OrqCq1WC6PRyGQDIm1OTEzg5MmTjL0KhUIo\nlUqkpKSgsLAQMTExUKlUEAqFaG5uRlNTEzu5EP7wQ0ZFRcUPfu3/tmEbbP5dw97eHtnZ2Vi7di0H\nw2dkZGDx4sVYvHgxE8eCgoKwdOlSLFiw4J82AIAf0AQ4fPgwJ0lfvHgRkZGRDKIkJydzKJhEIuFC\nlPyN77//fnh4eEAoFKK9vR2NjY3YtGkTTp48iS1btkCj0bDXlru7Oxb/Pbw3LS0NOp0Ozz33HD78\n8EOEh4ejpaUF165dw6ZNm1BfX48jR44wI21kZIQDH8+fP88ydk9PT3h6erJX/Oeff85Fd0VFBWJi\nYvDaa6/hnXfegYeHB6fXBwQEYP78+XjxxRdhNpuxdetWLF68mIuG6elp/PWvf8W5c+eQmpoKf39/\nzJ07l8PZVq9ejcOHD0On08He3h5JSUk4duwYjEYjPvzwQwZegX94ob3//vtcHC5cuBAKhQJisZjv\njU6nw7Zt2xAeHo6YmBh8/fXXqKmp4bDLzs5ObN++HS0tLYiLi4NOp8PNmzfxyCOPwGw24/jx47Cz\ns8PTTz+NyspKREdHIyoqig9UXV1d8PHxgVKpREZGBrNMTCYToqKicOvWLaxZswZDQ0OIiIjAE088\ngbKyMgiFQkxPT8PNzQ1jY2NobW3FSy+9hLNnz2Ljxo2oq6tDdHQ0Tpw4gczMTPT09MDPzw8nT56E\nTqfDggULeI6oVCo89NBDEIlE2LhxI86ePYvm5mZIpVL86le/gpubG7RaLTQaDYKCgrBr1y6kp6dj\nenoa3d3d6OrqwvXr15GWlsbhTDk5OfDw8GCv5c7OTlitVrS2tuLKlStoampihqNGo0FlZSXS0tJg\nsVgQGRmJ2tparFu3DidOnGCLEDs7Oxw9ehT33XcfPvnkE3h7eyMrKwuxsbEoLS3lYvTxxx/HnDlz\nIJVKuZDatWsXQkNDER0dzR6M9vb2aG1tRWtrK2ZnZ5GYmIjOzk5YLBbk5ubi+eefR3V1NQoKCpCW\nlgYPDw+WdRFgMDY2hjNnzrBUctu2bdi4cSNGRkZw5swZ+Pn54Y9//CNcXV0hkUhgNpuRlZUFkUiE\nmJgYnDlzBoGBgXBwcOBA5t7eXjQ1NWHhwoXYv38/ZDIZWlpakJCQgL6+PohEIsjlcgwODqKgoAD+\n/v6QSqUYGRnB0NAQtFotzp07h5s3b+LFF1/8oesggNuHny+//JJls7t27cILL7zAMlwCec6ePYuH\nHnqID5S0sZGX95NPPgkXFxeMj4/Dx8cHk5OT0Gg0cHJyQmlpKbq7u5GQkABfX19UVVXBz88P99xz\nD4RCIaKjo1FZWckMq02bNiExMRFCoRBeXl7YuXMn3NzcWJ7o5+cHPz8/HDp0CM8++yxWrlyJS5cu\n8QaZmZkJNzc3SCQSyOVyHDx4EE899RTy8/Mhk8lgNBqRmZmJS5cuITk5GcePH0dcXByzL6Ojo/GX\nv/wFT/0/9t48uuk63x9+NW3apm2apGnTpDtN933fgEJRoBRkF5HZVBy96sxczziOekdHZ8brjDOj\nKMcNFARBBBSQHUoLtFCWlq50pfvepk3SNE3TdEmfP3rf7wkuV/md53nOPed3P+d4zjDQNPnm+/0s\nr/Xpp5Gdnc3qVPru6PBNKpKBgQEu+CYCiOacQ4cOcen5zMwMDh06xBur8PBw5OfnIy8vj6MQJicn\noVKp8PTTT+PcuXNYvnw5O1GGh4fR19eH2dlZLF68GDdu3ODM4NraWhiNRixevBifffYZFi9ezCrx\nJ554ArW1tVwGFhUVhQULFvBcTgVqmZmZaGxs5JK+8vJyLF68GD4+PqisrIRUKkVrays/40uWLIGv\nry+TthR34eHhgX379uHJJ59Eeno6Ojo6OELH3t4e3t7eCAoKwq1btxATEwMvLy/uuqGeEbPZjNTU\nVEgkEmg0GqxZs4YzfI8cOYKnnnoK3t7emD9/Ps+Ng4ODyM/P582vXC7nsjqKImpra4OnpyerRAYH\nBxEcHAyFQsHW+KmpKXz99deYmppCQ0MDjEYjK+AffvhhZGRkQCwWszqdyA9PT09+XgmYIeWsQqHA\n7OwsSktLOX6grq4OaWlpKCgo4ENtVFQUK3pSUlLQ0NCAiooKJCcn86ZZKBSisLAQy5cv5zmJnkcC\n3aanp5nIs1qtePPNN/Hyyy9zvjVFwxB5JpFI8Otf/xq5ubk4duwYl7IWFxfDbDYjKCgI3d3d8PPz\n43uZFK7Lli37VpnbD43bt28zkUYRZwAYkKQDFx3WCVQSi8XQaDS8gZ6ammJFKwHE09PTMBqNKCkp\nwfXr1zE4OMi5onK5HF1dXRxpQ7EK09PTqKqq4gKzEydO3KWmJmCioKCA76/58+fzfU1RJaRqphgJ\nEjfQge/OnTvo6OiAwWBAeno6A3UUD2I0GtHQ0MAEOAFNtnEd5eXlHHlgb2/PMR3ktLK3t2dyxTZz\nnQBvcltQV4BQOFeAOzY2xmA5bXIpCoGur1qtRlhYGP89AccEGtDnJ1CQDm46nQ46nQ5tbW0QCoUc\nISOVSu+KJ1q4cCG/V5ovZmdnodPpUFhYyPFB1BugVqsZEKR7hciD2dlZVtPRd0j3PpXlEigkFApx\n7do1BAQE8GemQUotIrDr6+sxOTnJ8ZuTk5MMRpAKke5LV1dXBu3oPdJ/FEND14nAEboWBK4TyEDr\nIb0eKZOJrLe3t4fJZIKHhweTLnv37uXog+DgYC73PH/+PBobG3HfffdhaGiI7yXKmNdqtejv74dQ\nKMTg4CDc3NxgsVg4DmN4ePguNdXw8DCTckVFRVzKKJPJ2MGZkpLCroOJiQk4ODhAp9OhtraW+w6o\nNFkgEHCRs63bAPiXUk+v12Pz5s33NO+Qs3Z8fBxarZaBW6PRiNbWVrz33nsoKytDY2Mjq+a9vb3v\nyqXv7u7GnTt3kJyczM8+AfO2MVhCoZD347YZ6pOTkzAYDBCLxXwvEBhk269B8UQUL0e9WBQDQwQ5\nqfpbW1tRX1/PLlNam8iJQ4rz0dFRtLW1wWKxoLu7mwHbqakpLpf28vLC6tWrOZeeoiI1Gg2kUil8\nfX15DaRnlD4ffQ4CW7Kzs7kbgeYeAHztaI5SKpW4fPkyd9jcvn2bo1jpeaRnRSAQoK+vD97e3hAI\n5jqlCMwH5khVLy8vFBQUYGJiAqGhoRgdHcWGDRswOzvL+7iRkREEBQUx+XXnzh3o9Xp24Lm5ufE8\nPj09jdOnT6OhoQH+/v4cN+Hg4ICioiJER0fzc+vl5QUnJyfcuXMHO3bsQH19Pa85CoUCnp6emJyc\nxNDQEOrr65mItlgsGBkZwcGDB1FcXIyBgQGOvKK4ICqfP378OJYtW8b7gra2NsTFxfF909zczBEe\ndH9arXO9QBs3bkRSUhIyMjIQGhoKsVjMIDyB5HZ2digtLcXMzAx6e3s55sNisbDSWC6Xo7y8nBXg\n5Owh8jA7OxtyuZzXb7qWtsXRdnZ2HNukUql4XSBCjUgRmt/Gx8e5T4m+l66uLvj7+/NZu7W1FY6O\njkhPT4evry+voUSa0XpNRI+9vT06OjowNDQEgWAuMvPKlSuwWCwwmUxoa2vDokWLGMS7104Ai8WC\nmzdvfu/PfR8oSb/7XkgAugcLCwv52ZXL5ZBIJJiamkJOTg4/JxQ3ZAtkT09PcyrF96mmSaFNZ4mE\nhAT4+vri5s2b7DAlgkYgECAkJARGoxEqlQoCgQAjIyNQKpUIDAzkziOr1Yqurq4fXbpMsYN1dXWY\nnJzECy+8wOTy1NQUenp6mARvaGhAfX09VqxYAeBuYsM2jiowMJDPBg4Ocz1/5DIh5S+NefPmYWho\nCBKJ5Fvlvv/d6O/vR3FxMV/zp556ikH2K1euYGBgAO7u7ux81+v1qK6uRnNzM1pbW9HU1AQPDw+8\n9NJL90RAyGQyRERE4NNPP8XU1BQLZ4RCISv1x8bGMG/ePMhkMiZfQ0ND8eGHH3LeOXWIEilPpeJW\nqxU7d+6EUqmEk5MTPv/8c5hMJigUCt4vfhOAn52dhaenJ4C5+W18fBxlZWU8f37XoD0lOb2VSiWS\nk5ORn58PZ2dneHt7Iycn5ztB2cnJSSbMv+uZI0KRyo5p/0aOMq1Wi+vXr/Oewc3NDVqtFhkZGXj2\n2Wfh4+MDg8EAV1dXmM1mdi3TXtNqtWLHjh3IysriOEKa60h0RusaCd9IuJ2SknJXhO0Pjf91Anz/\n+CES4P+L8aM6AVxcXFBXV4fHH38cQqGQLU0eHh4oLy/Hhg0bmAyoqanBpk2bOLdbIBDg6tWrMBgM\nWLhwIRfKnT59GgKBgK1varUav/71r/H444/jlVdegbu7O1auXImamhoEBAQgICAAMpkMcrkcCQkJ\nMBgMWL9+PcbGxtDf34+4uDgcP36cy0mXLFmC3bt3IyoqCrW1tZw1v2DBAsjlcsTFxWHbtm347LPP\nMDY2hk8//RQvvvgiZmdncfbsWTQ0NGDVqlV49NFH77L1Dw4OoqqqinMB79y5g5qaGixbtgx9fX0I\nCAjA7t278fTTT2NqagonT57EY489Bl9fX6SmpuLw4cNs97IFfBobG/Hb3/6WQVMnJye88847ePjh\nh6FSqTAwMID4+HjOUgOA69ev8+KUk5ODxsZGzoMvLy9HS0sL4uPjsW3bNrz88stYsGABzGYzAgMD\nUVlZyQsKuRImJibw5ptvIiYmBvv27cOePXvYqjdv3jz4+PggJiYGhw4dglqthqOjIxQKBdrb21Fe\nXo6YmBi88sorUKlUeO+996DValFRUYHAwECOGqqsrERzczMyMzOxfv16DAwMYHZ2Fr6+vvD394ev\nry+3qBPr/fzzz2Pjxo04c+YMnnvuOZjNZrS1taG8vBzNzc0oKyuDSCSCXq+Hs7MzSktL0dnZiXXr\n1sHDw4Oth6RcfOedd5CdnQ2xWMyESUZGBl5//XVkZmbi5MmTWLZsGc6dO4fJyUnO3qLDZVxcHFQq\nFSQSCd5++2385je/QWdnJ0pLS9Hd3Y0NGzawpXDv3r1obW2F2WzGrl27EBcXB61WC6VSiVOnTnG8\nFZWLPvbYY5wV/9hjj2FsbAwLFy6Eu7s76urqkJKSwq935MgRXLx4Efn5+Th79iyeffZZuLi44Nq1\na+jr68Pp06exfv16JhwSExMxb948DA8Ps8L73XffhUwmQ3p6Otzc3NDR0YHx8XE0NTVBq9WiubkZ\nBQUFaG1t5Yig0NBQ1NTUoKOjAx0dHbh06RLmz58PgUCArKwsdHV1wdHREVlZWVi+fDnmz5//vSU/\n3zesViseeughCAQCpKSkoLu7G++88w5WrlyJgYEBuLq64saNG5iYmMCaNWswMzODnTt3IiIiglWq\nZrMZGo0GhYWFMBqNCAoKws6dO9nWToQQ9Rw0NDSgoaEBaWlpDFRFRUVh4cKFSE9PR2xsLBobG9np\nEBERgdzcXCamsrOzcfv2bS54vXDhApYuXYqYmBgUFxcjMDCQixJ7enqwfft22NvP5VJTGa7ZbMaB\nAweQnJzMWYlCoRBvvvkmUlJSkJuby2oHApeSkpJw+vRpuLq6cqkxRYaVlpYiOTmZ1XIzMzO4c+cO\nVqxYgSNHjiAqKgotLS0AwA6Ep59+Gk8//TQ8PT0xOztXLGo0GrF9+3Zs3boV8+bNw9dff4377rsP\nUVFRHJvi7OyM0NBQyGQyfPnll0yszc7OYuXKlaivr0dxcTEr1Y8ePQqDwYCf/exncHZ2ZgBGqVRi\ncnISOp0OsbGxrAwlAK+2tpY3xVarFVeuXMGePXtQUVGBpqamuwD88vJy1NfX4+zZszh8+DDeeOMN\nzM7OwmAwcOEYKTyIjF21ahVSU1PR3d0NhUKBjIwMVnxRCTjFYuXn58PDwwMDAwNITU3F9u3bsXjx\nYi4G6+vrg16vR2xsLHbt2sUFv3q9HvPmzWPg7D//8z9x+/ZtFBcXM6Hm4+PDmeu0HjzyyCPw9PSE\nTqdjJXd4eDju3LmD3//+9wgKCuLcS2DuEEcdLUKhECUlJfD09GS1Kdld9+zZgzfffBNLlixBc3Mz\nLl++jM2bN6O6uhptbW04efIk/Pz8cP36dSgUCvzxj3/E888/j7a2NkgkElbqnT59Gp6enhyPQEra\nsbGxu8oNSf0ZExPDB++pqSnugCEQoq+vDxkZGezy+Prrr+HXnlANAAAgAElEQVTn5we5XI6vvvoK\npaWlGB8fR1RUFBfROTk5ISYmBiKRiEvKf+z4xz/+gZGRESYS6bBIcxLwrw07/f+2cT0E1FIZLm34\nKfqru7sbN2/exLJlyyAWi6FUKrlrgSILk5KS4OzsjKamJhQXF8NkMqG8vBzp6elwdnaGVCrFmTNn\nkJ+fz/P1ggULEBoaym4bLy8vBiiHh4cZaLNVLQuFQoyNjaGoqAgNDQ3o7++Hm5vbXTFZlAu6f/9+\nTE1NYdmyZQgJCeFS5sDAQERHR8NkMsFoNOLy5cvszCFSbmJiAhqNBsXFxQycNzY28mHpm/M+gf40\nn1HGNb0f2zJEJycnvt+oeJSeKTpM2ea9EjgoFAphsVggkUjQ39+Pnp4ePhBRqSe5WUwmExYuXHhX\nDjMRCpRNbWdnB41Gg7S0NC5pA/6lDqP7m8gQIoVorqb7iLKnidCyWq0oLCxEamoqA1YA+HtUKBRo\naWlBYGAg1Go1bt26xd+t7fvs7+9Hc3MzjEYjGhsbERsbywdtil0C5kAUAt3IqUUAKb2nmZkZLrim\na2nrNtNoNKxcpuLgQ4cOoaqqCn5+fti7dy+cnZ2h1+s5torWpqmpKfj5+XFGO32O0dFRFlRQbEJw\ncDAkEgm8vLwQFRUFlUrFpcy0V29paUFfXx+CgoIQGhqKqKgoZGVlISkpCRKJhBXcBHjTfzdu3EBN\nTQ3HbLW1taGlpQX9/f2Ij4/nz0ufmb4bZ2dn7ie7l0GxGOQ6dnZ2hkajQUlJCb766iuMj4+z85D2\n76Q4tFUfUqTgiy++iLy8PI7cIuKAgMuGhgZ2ZZBj0Zaco+goIgmpR0IkEmF8fBylpaUQCATcl9LQ\n0MCgKUXUEMCyfft2dHZ2QqvVQq1WM6nT29uLkZER7rgaGBhAREQEpFIpBgcHOb5RqVRyMfvWrVt5\n3SPFJxHyer0ecrmcYySoIJueA9teBxJK0LWheYIcngSE0M+3tLSwO0MkEvE+Lz4+nsltetZoHejv\n74fZbOYYy7GxMe5caW5uhqOjI3Q6HdatW8eFn9PT03B3d0dnZyc8PT05co1cdLS3NpvNqK+vZxFK\nYWEhpFIpXnnlFQwNDaGzsxPz5s2DXC5HdXU1R4R1dXVhx44duHLlCrtOvL29sWHDBhQXFyM+Ph51\ndXWcQ19YWIiTJ0/ixIkTOH/+PNrb2/n9rVq1iu9/Us8fP34c6enpmJ6e5mgTilJydHSEXq9HbW0t\n2tvbGUSkbr+NGzdCJpPdRSLSa9NcT39uamrCwoULIRKJUFhYiJUrV2J4eBjz58/n/qP58+ejsbGR\ngWFvb2+4u7tDJBKhpqaGf54GOeaIEAPmwLfg4GDo9XpYrXMF9ET005xx9epV+Pn54eLFiwgODuaf\nr6+vh6OjI8LCwuDk5AQ/Pz9UVFTAyckJS5Ys4fmTnFd0j1KpLv3drVu32PFGMWZ6vR5isRi9vb3s\nEhAKhQgICLineYeEKN83bKNObIfVakVDQ8O3CtCJBPy+stSpqSk0NTVhYGCA1xdfX1/u+gPmiNv+\n/n7ucaNhMpkYiPy+QYC42Wxm9+L4+DjS0tIwOjoKnU6Hvr4+WCwWjI+Pw8fHBx0dHRwXYzAYkJSU\nhMzMzLuU6z9EAFChu6urK8rKyvDPf/4TRUVFuHDhAry8vDi2ViaTQSAQQCaTISgoCAkJCXjxxRe/\nExim2KmTJ08iJSXlW39nb2/P8WJEhpFz09XVlUmtHzu8vLxYkPboo4+yWHHRokVoampCf38/u0jJ\n0U1Co/HxcXh7e+PZZ5/91vf2Y4ZIJEJGRga8vb3R3t6Ozz//nEUpExMTCAsLY2KOPjtFih45cgRq\ntRrOzs4s+qB1AJgjmSmOyMvLC2q1Gv/4xz9QVlaG1tZWxMfH3wXsz8zMQK/XM7EtFArZmUKv+V2D\nnhV6nskF4eXlBb1ez32S3/VdE8lJ5zFboQowJxI4deoU9uzZ860ILhJb7Nq1i501UqkUW7duxYMP\nPsguE7FYDJFIhIMHD3LUakNDA0wmE1pbWzmmFQAXsdNeWqlUoqGhgSP+BgYGuOctMDDwnmLB/tcJ\n8P3jfyQJ8MUXX7Dl1Wq14syZMzh//jwWLVoEYE7ZQA/S9PQ0Dh48iMuXL3NGI7HdAsFcISHlgmZn\nZyMjIwMymYyjTOrr6+Hs7IycnBzO4Dxw4AB8fX1hNptRXFwMBwcHyOVybN++nVUrV65cwenTp/H4\n44+jqKgIeXl5mJiYwMKFC3H58mWEhYXhgQcegJ2dHd59911kZGTA398fo6OjUKvVOHLkCGJiYjA7\nOwuJRILly5fjoYce4sOGVquF1WpFc3Mzjh49iu7ubjz55JMcKbJy5Ur87W9/g0qlgslkQk5ODjfW\nFxcXo76+HosXL8a1a9e4HFKlUmF4eJizgCUSCS5evAiRSIQnn3wSAPjgMz4+DqVSiba2Nri7u8Pb\n2xteXl5Yu3YtNm3aBBcXF7S3t6O9vR2JiYkYHBxET08PwsPDERsbi8jISFRXV0MqlfKGyMnJCYcP\nH2YAprCwEPX19di0aROEwrlS2AsXLmBoaAiBgYE4c+YMampqOA9aIpFgenoax44dw5UrV/i7cHR0\nxPr16zl3LyIiAu+++y6ioqJgsViwe/du/OpXv4Jer4e/vz/a29shl8vh7e3Nh9HOzk4cOHCAN8uL\nFi1CY2Mj59jGx8fjww8/hNFohL29PWcq+vr6YunSpUhKSkJYWBjs7Ozw1Vdf8YMlEAiwa9cumM1m\nJCYmoqenB5mZmaipqcHY2BjKy8uxZs0aZGdnY+fOnVi3bh0aGhoQHx/PG2oXFxe8/PLLXNrR3d0N\nFxcX9PT0oLi4GGq1mmN+PDw8cOrUKbS2tsLOzg56vZ7LOioqKvi9qtVqeHt746GHHmK10SeffAKr\n1Yp58+ZhYmICR48ehUKhQGVlJeLj43HmzBmIxWLodDoGTz08PJhgkUgkaG1thdFoREBAAHx8fBgA\ncHNzw9GjR/kwXFlZiQULFsDNzQ2XL1/mGJLk5GQkJCQgISEBFRUVWLduHXp6ejA+Pg6DwQCr1QqT\nyYSBgQEkJiZCKpXC3d2dN5ZjY2PMxt/LIgHMxXKIRCK2LlPRdVRUFD755BPk5uZCKpUiISGBgUCy\nuA0ODjJQr9Vq0dLSgvT0dDz//PNs+e/t7YWdnR02b97MtuGLFy9ylwQdHL28vFBaWorw8HAmxBQK\nBZNxdD/a2dkhMTERKpUKnp6eyMrKQlNTEx5++GHOCr1x4wZWrFiBxsZGvPPOOxCLxWhubkZpaSl/\n1/v27YODgwNu3ryJBQsWYHJyEm1tbRgcHIRarYZCoUBHRwcDjBaLhRXXnp6ed9n2qaSSwMJbt27B\nzm6u/C00NBTOzs7YsWMHRCIRxsbGkJyczCDMe++9h7y8PAgEAmzbtg0FBQXc70DxAmFhYbBarQgP\nD+cCUZFIhI6ODuj1elZ9btu2jTc0dOCzt7dHbm4uBAIB4uLiWCFIFnapVAq5XM4qHbPZjNLSUhw7\ndgz9/f2QSqWQyWSQyWQ4deoUXF1d4ebmBkdHRxQVFSEsLAxCoRAhISEIDQ1FamoqRyhQbjEpk4gA\ntbe3R3FxMaxWK9LS0uDn58cqLQJ4pqamoNVq4eTkhNraWqhUKoSHhyM8PJzV7gUFBfDz80NNTQ0X\n9bi5ueHSpUt3lfNRpITFYsHy5ct5A3779m24uroiODiYv2eTyYQPPvgA69atg1AoxPDwMAYGBpCR\nkYHJyUl89NFHePfddzFv3jycPHmSI+iMRiMmJibg5+fHttFr165xJAiBfVSq7u3tjYiICAwMDKCj\nowM1NTV45plnMDg4iMLCQty8eZPdMSEhIXcp3+rq6nDz5k2sX78eX3/9NcRiMcLDw+Hs7IyTJ08y\nAESALpEDBMKRzbarq4vVW48++ijkcjl6enqgVCpx584d3LhxA1qtFnq9nqM73NzcGNTq7+9nta9a\nrb6neeeVV15Be3s7zxO2cS6k+qM/E6hMahyyytJGnlxaZrMZ4+PjMJvN8Pb2RlJSEjw9PaFWqxEQ\nEABfX1/4+flxvAMBWFTGNTs7i6VLl/LBZ2BgAGazGbGxsViwYAGCg4Ph7u7O+bIUMUD3q0QiYZCV\nMu1HRkZgsVg4a3tsbIxzUZOSkvgQYrFYUFZWhp6eHmzatIlf1zZChtae2tpa7tUZGBiAxWLB0NAQ\nmpubIZVKuUfF3t6eyYrS0lLodDqIxWJ28xCBQICPbbEvAX8Ud+Hi4sI52LaHKHpmyb5tq+4lxxR9\nt9QZ4uXlhba2NnYcBAcHIywsDN7e3ujo6IBEImGQge4BUscTWUq9D7YOAJpnyA1osVhgMBg4QoXm\nIiIgiLwgIMpoNDKpRMCErUqf4sIIwBofH2cXqtlsZuKEDqUikYjLym3LguleJWUvHfYJYCJCr7m5\nGWq1GiMjI3xQpEzp2dlZVqnSdRUIBCgtLYWDgwM8PT2hUCgwOTkJX19fVuGTi7i0tBQTExO85pPi\nm3J/LRYLd3OQI4PAXnrmyMFKOeDLly9nIpXmcbrGYrEYVVVVd6lDR0dHERQUBLVazWIPioUSi8Uc\n80QkEM0V5MKjyIJ7GZcvX2Y1Ko1bt26hoaGBi0SfffZZ+Pn5cX788PAwqquroVQqMTQ0hICAAEil\nUohEIqxcuZLnVuBfRFhFRQU8PDy4eJai1pqamni9GBsbg6urK0ZGRuDq6oq+vj64uLjc1c8xMDAA\nlUrFcwCJDghkpeswNDTEALVer+dM6ZmZGTQ1NeGjjz5CS0sLlEoll63T83fr1i2oVCqOy4iIiOAc\nbZPJxKrpiYkJ1NTUICoqis+atsSMLVkLgJ97InWJdKO/I6CJ5szjx4+jpqaGu4hCQ0M5r7u0tJT3\nmkTsUaxMU1MT9xjRvUXrQ0tLCzo6Ojgbm8pxqTSTAG8PDw/ei5JToLm5mUUTbm5u6OnpQV1dHTZu\n3Mh9Mz4+Puz4oWi1mpoaHDt2jKNlySFtb2+PBQsWoLe3FzU1NdDpdDy/G41GvjYymYxJCoqVorWA\n5nL6vDKZDB0dHUwS2V4buVyOsrIyuLu74+GHH0ZZWRliY2N5T0TXnnCHwcFBLrCn76q5uRkDAwPQ\naDTo7e1FfHw8xsbG2D1A+5uQkBDo9XqYTCa+d8bHx5GZmYmgoCCOKaF1RqvV8nxMqlzqGmloaEBA\nQADHBxFxRXNpZGQkvy+KSsnLy8PIyAiTQBQ1GhoaymAm/X6a04ikn5ycxOTkJPz8/Dj2hSLKaO4K\nDAzElStX2KH0Y9XqNPr6+u6aI2xdc7YxaN8cY2Nj6Ozs/Na57tq1awgNDf3Wv//iiy84uvbUqVOY\nnZ2FSqXCpk2b0N3djTVr1jA46uLigra2tm/l8JP7ieYynU7HjinbedPOzo7dSM7OzjAajfDy8sLX\nX38NtVoNnU7HxCI5411cXGAwGDAwMICNGzfCz8/vW+RHU1MTE9PfHOQw+vTTT3Hr1i2OlQoICIDR\naER8fDwmJydhMpkgk8kQFRWFvLw87N27F1u2bOG9ge2YnZ3Fvn378Itf/OL7vj4AYHf5xMQEWlpa\nGJSnveC9jKmpKVy5cgVJSUm8ntEZTalU4tq1aywgoihZcu+6ublhwYIF/MwDuCv3/rv+bDtobVYq\nlVi8eDEiIyPh5+eHQ4cOQSwWw9fX91s/Mz09DbFYzF0b5Mql1wPmvhtyQQ8PD+Mf//gHn9/HxsYQ\nFRXFbn2apzv+q6eI9lTU+fXfDdvfR4PW7uXLl3NsGUX42Lo+iBgn0TU9gzR27NiBs2fPsrC2oKAA\ncrkc169fR0JCAqanpxEdHQ2xWIysrCxs3bqVBTT0vdLcQgX3UVFR/DPe3t5wdXVl8lGv17MoBZib\n+5OSknD58mUMDg5iYmKCI2MTExPvad65devWj/63/7eNb5J9/3+MHyQBdu/ezdl8BQUFqK6u5jKq\nqakpREVFQSKR4J133kFERAR+9rOfISwsDOfOneMIjvr6ely9ehUXL15EcXExx1pkZWVBp9OhoKAA\ne/fuxS9/+UsIBHOFJ52dndi+fTv+8Ic/cMHgunXrEBgYyIUYaWlp2LlzJ+Lj47FixQouS3VxccGx\nY8dQWFjI8QuRkZFwc3NDREQEH4iSk5MhFAqRnJyMgIAAjI6O4k9/+hMmJiZ486hWq/H666/j4MGD\n+Pzzz5GZmclRHZSrm5SUhPHxcXz88cfo6+tDS0sL/P39UV1dzYtqeHg4Tp48iczMTIyMjODEiRNc\nPkoA1I0bN+Dk5ITY2Fj09PRALBZjZGQEERERCAgI4EnkjTfeQGNjI5YtWwahUIj33nsPoaGh2LJl\nC8RiMUZHRxEVFYXY2FgoFAoIhUKUlZXh6NGj+PLLLzFv3jycO3cOzz33HIqKirBw4UIkJCRwvjiV\nzLq4uGDbtm1ISEjA/fffj76+Pnh6esLJyQlDQ0OQy+X461//CqFQiMTERNTU1GBychL33XcfJiYm\n8NZbbyE/Px/vvfceq8zz8vLw7rvvoqGhAUqlkiMwBgcHcfjwYRw6dAirVq3CqlWr4O/vj/z8fM57\n/Ld/+zccPHgQKpUKjz/+OKqqqjA+Po6nnnoKzs7OXBhIwP1nn32GrVu3QqvV8mH2yy+/RGpqKsrK\nyrBw4ULOg4+KiuK85oMHD8LT0xPnzp2DVqvFxo0b4eLigtdeew2+vr44dOgQamtrcenSJbS1tUEq\nlSIvLw95eXnIzs6Gu7s7amtrUV9fj61bt2LTpk1cglNXVwdnZ2ds2bIFcrkclZWVDCzQ5n92dhah\noaHw9fXFm2++iQsXLsBisWDnzp04fPgwHnvsMYyOjuLJJ5/E+vXr8emnn0Kn0+Ghhx7iMtHz58/j\n2Wefxa5du2Aymbjgt6mpCYcPH8aWLVuYzImJiUF/fz8mJibQ1tYGjUaDCxcuYPXq1XBwcGCVZX19\nPWJjY3HkyBGkpKRgyZIlGB4eRkZGBitgTp48yUXSycnJHGtyr5vT/v5+jI+Ps9W3v78fly5dglAo\nxHPPPQe9Xo/3338f9fX1eOqppzhbcHh4GL29vWhsbERfXx98fHwQHx/PpaMGgwGZmZnQaDQwm81Y\ns2YNL4bLli1DfX09Dhw4AAcHB/j5+XER3IcffsgRNYODgzhx4gTbpwWCuQxVsuk5Ozvjo48+wvPP\nPw+r1YoTJ06gsLAQOTk5iIuL4+dhZGQEb731FpYsWYJHHnmEbajR0dEMav39739HQEAAXFxckJKS\nAldXVy5fdXV1xejoKP72t7+ho6MDLS0t2LFjBxYvXozLly8jMDAQcrkcHh4e+PTTT1FZWYmioiJW\navn6+iI2Nhaenp44fvw4Vq9ezbEupNoTCoVYuHAhSkpKEBwcjNDQUCgUCrz77rsIDg7GzMwM/P39\nMTExgevXr8PX1xdpaWkIDw/H6tWrsWrVKlYTUM5zcHAwurq68P777zNgQsCcXq/HX/7yF9TU1ODk\nyZPcF/PXv/4VRUVFSElJwW9+8xsuu3V3d+dycCp5W7duHZycnLhIkNQ3UqkUx48fZ5U05U7SBv7t\nt9+GyWRCf38/NBoNAgIC0N3dzcC/RCLhe0wmkyE4OBi+vr7w8vKCRqPBzMwMPD098fHHH2PDhg1M\nLjg6OuLjjz/G1q1bcePGDfT396O8vBzJycl8vSsrKxEYGAgfHx+Eh4cjNTWVyxibm5uRn5+PrVu3\nYnZ2Fl1dXSgtLUVXVxdnXm7ZsoWzUg8cOIBdu3aho6MDFy9eRG5uLpeHEWC2bds2BAUFcblnf38/\nIiIiIBKJeE6wWCzYvHkzR2j94he/gJubGxoaGiASiXDmzBmUlJRgcHAQUVFREAgEDBRTFjgRYGlp\naWhtbYVKpWI1qoODA27dusUFiHQIFQjmSpZ7enrQ0tICV1dXDA0NQalUwt/fH/39/XBxccELL7zA\nCtP6+npotVoud6M4lnt1AnzyyScwGo3o6+tDXV0dWltbGeQi9S8BXKRMB+YObBQPQaDw2NgYq8+o\nnIyUWrbFwnQoogM3/WxNTQ0yMjK4wNCWPAkLC4Ovry87VGxBUUdHx7uy+EdHR1mhR0DWtWvXUF5e\njvLycgafyN1ob2+P8+fPY2hoCHfu3EF1dTUcHBzQ1tbGOeJEMhKwalu2SznunZ2d7MxRq9XsYCJX\nEsXazczMsFPL3t7+LgKArgeBNQSckCjlm//eFvyj74minej+oogNAhFFIhEXjKakpCA5OZnL0f39\n/REUFMRdHWfPnuWibtv3JxAIIBKJ+HVtv08AfKijnH4HBweOedDr9XyP0fu3JZqUSiWOHDmChIQE\n9PX1cW4uAe5EZNJna2lpgdlsZvs3xUyoVCo8/PDDSExMxJ07dxAUFMTvjwgLKhy2dbfQHnZychIt\nLS0IDw9nhxplgms0Go55EQgELNCws5vrUiIhRWVlJVavXo34+HhERETA2dkZvb29uHjxIgYHByEU\nClkJHhkZyapaAr/od9CzSP0yMzMz3JFRXV2Na9euwcXFBWvXrr1L0UbxI/T5hEIhRyu4uLhALBZz\nVBPlOZeVlcHFxYXVzAqFgkEFineiAzu5MOLi4u5p3iGCxWw28+d1cnJCRUUFF5BmZWWxmq+zsxOh\noaHIysqC1WrFtWvX2PE3Pj7OBO/s7FxZMYGq7u7uGB4ehtFo5E40chARINbe3s5CCjc3NwZ66JoL\nhUI0NjbC3d0dCoWC3Uak4AYArVYLe/u5bo87d+7Aw8MDXl5e6OzsRGVlJcrKyljJDgDNzc2IjY1l\nIIi6p7Zv3460tDQWk1A80N69e3H16lVkZGTAwcEBBoOBo+cITKO4LbqHCIy3BTvJfUHPs8Vi4TWI\nQJPt27dzRCaV7ppMJuh0OuTl5aGgoAC7d+/mvd/k5CTee+89LF++nAEpctTQnNzR0YH+/n788pe/\nhLu7O27evMn3AZEh9Ho075GTjsBLkUiEwcFBHDhwgLtAMjMz2WnY1dXFvUW7d+9GZWUlBgcHodPp\nsGnTJp7bgoODcfv2bSQlJaGhoYGV3BQNJpFIeA4FwKR9WVkZ4uLiYGc3V/Ds5eUFuVwOk8kEsVgM\nlUoFBwcHFBYW8n6NXBxBQUEcbfXYY48hJCSEizyBf8VPtbW1Qa1WM0E6NTXFwLuPjw/UajUEAgEa\nGxuxceNG+Pj4wGQy8RpkZ2eH2NhYVFRU8HccEhKCvLw8zM7OciwLdf1IpVI4OTmxU5G+MycnJ8yb\nNw9TU1M4cOAAKioqkJiYCIVCwfOuQCCAv78/vL29ER4ezi7ZiYkJnD17Fvn5+fjrX/8KuVzOQCp9\nTlrPyB1GfUL0/BGxMzIygoSEBEgkEnR0dECr1UIsFmP9+vXw9fX9PyoGbm9vx7Fjx/CXv/wFly5d\nwoULF5Cdnc0dJwDuUkrTtbp69SrS0tLuer3vcyKQY8DOzg4LFixAVlYWampqYDKZEBMTww4mGt8k\nAMbHx3kuo/dC663JZPoW2E2u0NHRUXh4eKC0tJRJpY0bN/IZSqvVMj40NTUFk8mE2NjYu9ZGGlTS\nTr9/amoKhw8fhlwuR1tbG9544w3ObO/v70dCQgJiYmKwYMECTE9PIy8vD2q1GoGBgXBzc0NsbCwa\nGhqg1+sZyB0bG+PXLy8vx+rVq3/wO7T9zCqVCo6Ojtw7Rp0y3zVs1y0aly5dwurVq79FgJDjcdWq\nVVi6dCny8/Ph6OjIri8iIrKysu5Sy38T8P8+AsB2EPZGbtSkpCRUVVUhMjLyrvc8OTmJjo4OVv1f\nuXIF169fZ5eM2WzmPTP121itVpw/f55jCx944AGEhYXxug3M3aPt7e0IDAzkPZ6tY5VIqB+6plqt\nFlqtls9VgYGBGBsbw+Dg4F3xObZuQupbtX19i8WC6OhoqNVqdHV1wcHBAS+99BISExORlpbGfWhy\nuRzh4eEcIUyCGqvVygLgnp4eqNVqiMVieHl58TnG3t6eSX8iPoiAnpiYgKurK9zd3ZGeno5ly5ah\nra0NTU1NGBoagkwmw8KFC3/we6XxvyTA94//kSSAQqHAzZs30dPTgy1btuDSpUtYtGgRTpw4AbVa\njenpabS1teHo0aPQaDSc2evi4oKMjAysXbsWISEhiI+Px+rVq/HAAw9Ao9Fg0aJFOHXqFNzd3VFY\nWIiXX34ZX375JdauXQuxWIzjx4/j6aefxv79+1FQUIAHH3wQH3/8MXbt2oWAgADU1dXhww8/hJ2d\nHdLT0+Hg4IC//e1vmJ6eRl9fH7q6uqDX65GZmcmqit27d8Pe3h6ffPIJ0tLSUFVVhfr6eqhUKhw/\nfhzFxcX44x//iMLCQlRUVCA3NxcjIyMIDQ3FvHnz8Mtf/hKXLl2CyWTCokWLsHfvXjg5OSEgIICL\n0p5//nlcunSJD0DPPfcckpKSsH37dkxPT6OpqQn29nMFKo888gh+97vfYXp6GocOHcJvfvMblJSU\nYHR0lBXZFLOwc+dOXLhwgXPsenp6sHjxYpjNZhw/fhwDAwOIi4vjjgEPDw+8/vrriIiIgF6vx40b\nN/iwdP36dTz66KP8vs+fP4/o6GhWd7u6uuKLL77AqlWr4OHhAalUCoPBAB8fH+zfvx9msxlqtZoV\nJlu3bsXBgwcxMjICnU4He3t7XLt2DQMDA3j11Vd5A9zZ2YmwsDAcPHgQbW1tyMzMREpKCiQSCeRy\nOZYsWYJFixbB0dERIyMjMBqNaGtr4wNRa2srnnjiCRw8eBBhYWF48MEHsWbNGpjNZs7l7e7uxsTE\nBJYtW8Zqoddffx0tLS1obGyERqPBq6++yoU2TU1NWLFiBXp7e3Hy5Enk5OTAarWivb0dzzzzDIC5\nojIHBwfs2rULjo6OeO2115Ceno758+cjPDwcYWFh+PTTT2EwGBAaGgonJyd0dnbC29sbTU1NmJqa\ngo+PD/z8/NDZ2YmWlhbk5OTAzs6O8w0vXLiAzz//HB3hXdgAACAASURBVElJSdBqtaiqqsLo6Chn\nfK5fvx6Ojo6oq6vD7OwsMjIyYG9vjzNnziA3Nxc+Pj4ICwuDTqdjpU1MTAzq6+sxPT3NhdqUV6xU\nKrkQdmRkBNPT02hvb0ddXR2r5JOSkjhyxWAwwNfXl50vtFCYzWbEx8fj4sWLaGhowP3334+hoSE+\nNJPbY8GCBfc0MVEGrJ+fH2ZmZrBjxw7Mnz8fxcXFTLrRwen++++H1WrlyJeuri709PSgqakJZ8+e\nxbx583DkyBFYrVYsWbIEarWay0/t7OzQ1NQEYC77NTY2FsuXL8f+/fvR1dXFKp+4uDi88847UKlU\nAMB57QRoCQQCVqVu27YNf/7znzkKbdmyZaitrcXo6Cg6Ozvx6KOPYunSpZBKpQgPD4dMJoPBYMCx\nY8ewaNEi3oxqNBrExcWxUqGjo4PL8CinLygoCB0dHfjVr36F9PR0CIVCfPbZZ6ioqMCyZcu4DHDF\nihWYmZnB0NAQvLy8GJwUi8Woq6tDd3c3srOzMTk5iV27duG3v/0tPDw80N3dzZ0BnZ2dsFqtSE1N\nhaurK4KCgvgwqlQqkZ2djcjISJw+fRo3btxARUUFFixYADs7O7z22muYmZlBTEwMPv74Y1RUVGBy\ncpJjuwgQLSoq4lzYhQsXYtWqVYiMjMStW7cwMzODF198ERKJBEajkUFAUsf29fUhNDQUFy9ehIuL\nCz744ANUVVUhJiYGDg4OqK2txdKlS3H27FnuWfH39+dC7NzcXOTk5MDLywvHjx9HaGgoCgoKEB0d\njd27d7NCnVTMVLJktVrh6ekJqVTKMUYUMaZSqXDixAl4enoiJCQECoUC+/fvR3p6OoKDg2FnN5fT\nHhkZySC+TCZj+zkBzZ9//jmampowPDyMzMxM7N+/n10kzzzzDBPP09PT2LdvH6xWK5YuXYqhoSGU\nl5fDzs6OD7EdHR2cj11SUoK6ujosXrwYQqEQrq6uaGlpgcViQX19PYKDgzl+QK1Ww9XVFUuWLEF2\ndjby8/OxadMmPPzwwwwg06G6srISwNzhkTbktbW1nDVPz2hhYSEyMjIYmKPS6wsXLuChhx7ClStX\nkJaWxooVT09PzJs3j0sLFy1ahNLSUpjNZrzwwgsICQnB/v37OXvz/4QEIMCDnjMiDEdGRuDl5cUg\npG1BnW1kEBUL0xpIUXcEUNFBgYo16fBLQDGpMKuqqhAREQE3NzfuyaB8aVvAn2KLgH+pWulQQ8pw\nWtMuXrwIvV4Pb29vhIWFMQDk6+vL4of4+HjI5XJER0dDpVKxNdrOzg4GgwHh4eEMytPvI5U0xZHR\nNcnMzGRy1PbwRmCiwWC4K+aBMstJBU/3P4Ek9PtsQTpbiziRS3QtaI6g78o2f9nBwQHj4+OsNqe5\njO7R8fFxVu7aglfXr19HQEAAgzj0+lNTUzxPUocJ/T7KQO7s7GRCh4gKiukg9SFFiwDg/93V1QWF\nQgGpVIrx8XGMjIyg4786TajzRiqVsuKrp6eHFdF08M7NzYVcLufrQ/cRXRciHenz0zXVaDSwWq2o\nrq7mmBv6XggE7+np4e+RvhvgX90MRqMR9fX1nPtM965Op8OtW7eg0WgYzJmYmIDFYkFNTQ2GhoZQ\nXFzMTlbbeCJ6TknxOTExgaamJkgkEi6PJYLI1k1CJI3tswiACQy6Hg4ODpBIJIiLi4PVakV3dzfc\n3NyQnJzMBC85DwhAoAN9fHz8Pc07VJRJWfv0PgcGBqDVahEUFIQFCxbwIZ0iSql3JjIykjs5pFIp\nF7HS3qSgoABXrlxBREQE9u3bB4VCAWdnZ4yNjXE2+tTUFJc4k6qUInPI/UIgvF6vZ0EQuYpsiVyh\nUIi+vj4cOXKEQQaNRsOAEcUP0f1jMpmgUqng7u7OwLmjoyPUajV3JNGesri4GGVlZXB0dEROTs5d\nLhSRSITu7m5WdhJR6eTkhOnp6bsINtsYwPr6evj6+mJmZoaVrfS+vvzySwwNDcHf3x+PPvooenp6\nkJKSgunpaaxcuRKBgYEwm804evQoLl26hOvXr+Oll16Co6MjO60J3CXi6vr16/D29maxRWBgIGpr\naxnYlcvld5F9RqMRwJw7vL+/H8PDw+jp6cHp06e5S8zBwQEhISEcw9PxXx0gX3/9NTQaDZycnJCS\nkoJnn30WCQkJiI+Ph1qtRlRUFJN05Cine4AcDiKRiPc3SqUSo6OjiIyMhEajQXt7O7q7uxEVFcWx\nILR+UqY3icDMZjN6enru6uigvYmtQ4fWV1vXLZGLtbW1vNcRCoUICgpCeno6vz+a+8ViMQYHB+Hs\n7Izi4mJ2ujzxxBOwWCxc/ErxbURq2s4FdI4jgojcuxQlVFdXh6CgIBQVFSEwMJDV87RvqK2txeXL\nl9Hb24uHHnoI/v7+cHJyYreMwWDgOZPISjrDkziF3g+5YeRyOaKiolBQUMACTTpH32sZbHV1Nd58\n8020tLSwotpkMqG0tBQnT55ERUUF/P39mVygrhQvLy8MDQ0hMjLynn4ffZbm5macP38eIpEI7u7u\nTAAC4OcZAM8Trq6uGBsbY0KGhm3vzDeHg4MDBgcH8emnn7KDTSKRoKKiAlKpFCMjI/D29oZCoYBG\no+H0Buou+Wb2u8FgwLZt25CdnQ0HBwe0trbi3XffhV6vR2lpKYxGI4KDgzE+Po4tW7ZwFK9CoWCC\nTiqVwsPDgzPWKUUgKSkJU1NTvNekNZQiDmnYknHfN+zs7ODm5sZF0nSNvuvf2Q7qO/m+OB/ag0kk\nEiiVStTW1nJ5LwDExMRg6dKl3wuQf9ewdaJ816B159q1a0hJSblrz03xzxT7FBUVhbS0NDg7O+PK\nlSuYmprC888/D6VSCS8vL/T09GBmZgZVVVVwcHCAo6MjHnroISa5gbn1YmRkBIWFhUhLS/uWIv+7\nrhsw973QfguYu4e7urrg4uLCpAg5wci1QfcX/QylWtCw7QeYmZmBi4sLioqKIBQKsXnzZna90bpG\npDb1S9F3Tvsp2jPNzMzwOYb2pkKhEKOjozzXUyQmkWkuLi537QvpHlYoFBCJRFi2bNmP/s7Lysp+\n9L/9v23ca2z2/xvjB0mAM2fOIC4uDhcuXICHhwciIiJ4orS3t0dNTQ1ycnIYpGxqakJqaiqCgoKY\nVac8PrK5f/jhh1i4cCErWdetW4e+vj5cv34d1dXVGBwcxE9/+lP4+/sjOjoaYWFhkEqlWLJkCS5d\nuoTKyko88MADbDkmJWFiYiJ+/vOfIy4uDqmpqaipqUFeXh66u7uxe/duZn+feOIJREREcCRGeXk5\n9Ho9Xn75ZQiFQmRlZaGzsxOzs7NQKBSQyWScU52bm4uqqipMTk4iNTUVHR0diI6O5ugcUud1d3cj\nIyMDv/71r+Hs7MzRCdSq3dbWhoKCArzwwgvIz8/HI488AoVCgQceeAAuLi7Q6XQoKipCd3c3hoeH\nsWHDBvz0pz+FQqFAUFAQ8vLyOKO0pKQE5eXl+Oqrr1BbW4s1a9bgjTfeQF1dHTZv3oy6ujrIZDKs\nX78eixcvxpo1azAyMgKDwYCYmBi8//77uP/++7lDQCaTYe3atZBKpYiNjYXJZMKxY8e4/DgmJgZt\nbW0ICwtDbm4uXF1d4efnx5ZmAlinp6fx/vvvIz4+Hq+88gqMRiOysrLg5+eHxx57DDKZjIEW20ME\n5bQbjUb4+vpixYoVuHbtGtavXw+BQMD5ZXSQJwvzzMwMFwHRJvqTTz5BRkYGfvrTnyI0NBTZ2dms\nDg4PD+d2bbPZzC33VCxsZ2eHyspKxMXFQS6X4/jx43jiiScQFBQEpVKJ9vZ2VFdXIz4+HjqdDps3\nb0ZzczOOHTuGoqIiLF26FG5ublAqlRgbG8PAwAAKCwtZLUmT/969ezlnPzMzE66urqiursapU6cA\nAE888QRGR0fxwQcfoKOjgwHdmzdvIj4+HiUlJSgpKUFqaio8PT1hb28PtVrNNuwtW7YgKCiI83mj\noqKYPf7nP/+JwsJCSCQSNDc3Q6/XY8WKFWhqaoLJZIJWq0VMTAwEAgFOnDgBrVbLhXS0uPT29iI2\nNhYqlQr19fWcT+zl5cXZhOnp6fc0MdXW1uLvf/87bt68yUrglStXIikpCd7e3qitrUV2djYW/1dh\nNx2AW1paIBAI2P2hVCoZPE5MTISzszNnopP67dixY8jLy2OQztHREUuWLIHBYIBWq+WMYirEcXNz\nY1sz5W77+fmhq6sLXl5eKC4uRkxMDKqrq5Geno7BwUEu76K5hGzblF9JZVaOjo5cHkqgi7e3N6qq\nqnDmzBkMDQ3hxo0bKC0thZubGyorK/GTn/wEY2NjaGxsRG9vL7q6umA0GnHjxg0sXbqUiydTUlLg\n5+eHzMxMhIWFISAgAF5eXkzW7NmzB0qlEg888ADEYjGDQC4uLvwzFJv29ttvY9GiRRw5YlsmmJaW\nhqGhIeTl5bFy+bPPPsO///u/QyicKxWNj49ngiotLY0LFVUqFa8dGo0GdnZ2cHFxgY+PD1avXo1T\np07Bx8cHbm5unFFJJdSDg4O4dOkSQkJCEBkZiZiYGERHR+OLL75AXl4e/P39IRAIEBwcDJFIhJKS\nEnh4eODQoUN8H4lEIvj7+7MaKCcnhzfHRM7eunWLLf9RUVEYHBxEWVkZZDIZnJ2dMX/+fNy+fRs+\nPj744IMPMDIyguXLl8PHxwclJSWoqamBSqVi8JsOvBRF4eDggD179iA4OBjd3d1wcHBAbm4u8vLy\n0NvbC41GwyrVnTt3ore3l10ZVqsVly9fxu9+9ztkZWUhOzsbUqkUBQUFSExMxMDAAIKCgri/RyqV\nslqWALfs7GwoFAo0NjYiISGBFbhSqZQVdufOncOrr77KSjxSdFKGNJGhBM7o9Xps374dTz/9NMfu\nWa1WLjwlMPvQoUNYt24d7rvvPlYlHzx4EFevXuXnt7+/nxU6Op0ODz/8MFasWAGhUIimpia4ubnx\n2nCvh9Rt27ZxjAlFYBD43d/fj4aGBggEAnh6esJisXA3AwHuFE8yPT0NjUaDlJSUu+z+wL+KJ22z\n4G0jH0ZHR+/KG6Y5i1ShBBQQCE1/pgIzcq0QuCAQCPDll19yOfTMzAxiY2NZDZSTk4PIyEgGrfz9\n/TE0NMRl4BQ3IhAIEBYWxoSA0WjE5OQkjEYjRkZGWCHt7+/P5XQxMTEYHR1l4I/IAoqhIeUtAHbo\n0MGH/g1F99Ahy/ZgZEsA0OGZwCFSWJL7hZ4xyn627RewdTb09vby2mVL0ABzwEJwcDAGBgZQWVnJ\nEVRUUnnnzh10dnZidHQU8+fPZwWrg4MDx6P5+vryIV2lUjHAYzabOf6LPisRY97e3ujv7+f3a7FY\nmMTX6XQYGhqCSqXC9PQ0nJ2d0dLSwgdCeq3ExES4uLigpKQELS0tTFzSdaT7k8hHun9cXFxgsVgQ\nGRl5FwhDEW719fW4ceMGenp6eG9AxAi5EShuhvKxCeQYHx9HQUEBdDodHB0dOWqNMt97e3uh1+sh\nFApx+/ZtDA4OIiQkhN8fkSRjY2M4c+YMsrKyWI1Lzws9cwQy2RaNUvySrRqOVNAUP0VRG76+vnB0\ndIRMJsPIyAj6+/uZ9KXYCXqmExMT72neoXmEiCki5Dw9PbFq1SrExMTAZDLB3d2d3zOBAHRf+vj4\nQK/Xw2KxwNPTEwaDgcF3Pz8/JCYmQigUwtfXlwvhIyMjIRKJ+F5wdHTE4OAgfH19MTs7i7feeovn\nViLjpqenIZFIuLh9dHQUJSUlTLT7+PhgZmYGH374IZf/0vXt7e3F1NQUx8JSDAaJU6iLg+ZMiUSC\nwMBAVFdXM9EsFouRmJiInJwcVsJTt4NIJMLw8DB27dqFyMhIjmuiNYwcAXR96fmQy+WYmZmByWRi\nh8fk5CTa29uxZs0arFy5EosXL4ZEIkFaWhq7MAjgprg/i8UClUrFzjiBQICuri4ubiXXU01NDZqb\nmzmnuq+vjx1THR0dKCsrY6CSIlwrKyuZJAgICMD09DTq6uowNDTEc9/t27eRlZWFiYkJtLe3Y9++\nfRgaGoKzszOefPJJPPDAA9wBR88Yda4A4MhRo9HIjsrQ0FD8x3/8B5YvX47c3FxoNBqMjY0xcVRb\nW4vU1FQ4Ozvj9u3bEAqFaGlpQVlZGY4cOYKqqiqcP38eV69eRVlZGfLz81FSUgKZTAahUIgbN26g\nrKyM3dPj4+MYHh6GXq/H6OgoNBoNDAYDz9PUi0P4Aj0vdE4YGBhAe3s77+/1ej3q6+vh7++Pn//8\n55iZmcGtW7cQGRnJe36KNqM9C0UIkSuICDqa5ymmKjAwkPf/JAyh3ps//OEP3K3w1FNPsRua9grk\nZKX5nCJ4aO4hYsdkMjFpQPtpd3d3JCcnIzU1FQUFBejs7ERMTAzHM/7Y8cgjj2B0dJSfU7VaDZlM\nBq1WyxFely9fRn5+PlJTU2GxWKBUKmEymRAdHc3z648dExMTuHDhAs6ePcsi0vj4eCiVSnYkWq1W\n1NXVQalUorW1Ff7+/kxmk6uN8AJy8tOg53ZkZATnzp1Dfn4+1q5dy3FTp06dwvDwMMbHx1lsYzKZ\nEBgYiIyMDGzYsAFfffUVMjIyeG8xNDSEc+fO4fDhw2hoaMCDDz7IjjWJRAK9Xo+FCxciMTERCQkJ\nWL16NUJDQ3nOq6ysxNjYGJKSkuDu7g4PDw8kJCQgIiKCuyI7OjoQGBjIQDAJWb45fogAsB0Uy6XV\natHb28tFt983jh49igcffPAHX5NIu5UrV2LVqlUYGRlBX18fnn/++XuOH/rvCAAAvLeKjY1lhf/t\n27fZtWa7RwPm9mlSqRTR0dEICgrC6tWr4e3tjRs3bmDfvn04e/YsxsfH8eCDD+Kxxx6DQCDg3hwA\nHN3Z19eHY8eOIS0tDS0tLZBIJCz0+GZsEwDev9Oa0dzcjJSUFC5yp0EAvdlsZocfvf/p6WlotVru\ncaA9Cu1/HR0d4e/vj8zMTKhUKr7Pb926xftGjUbD5D/tO2nv6+Liwk53wmOJaKSYN3KNtLW1wWAw\nQCqVMhkNzO2dKKZ56dKlMBgMiIyMvKf4w/8lAb5//I8kAV599VU0NzfjpZdegk6nYwZobGyMowAo\ni/ujjz7C6tWr+XCwY8cOhISE4E9/+hNu374NDw8PjIyM4OrVq+js7IROp4NOp4NGo0FsbCxiY2MR\nEhICkUiEmZkZyGQyfP7556iqqsIXX3wBk8kEe3t7vPbaa7BarWhpacHAwAAaGxvxxBNPICAggHMk\ne3p6kJOTw4qGRx55BMnJyfjiiy+QnZ2NlpYW1NTU4OjRo5DJZHjmmWe42PDjjz9GXV0dVq9eDYVC\nwYvG0NAQ/Pz80NPTg5s3b3LRmLe3NxcRfvnllxgeHmaVyaJFizA9PQ2FQgF/f3+sWrUKcXFxnP0/\nMTHBCpiuri64urpCp9MhLCyMlTJ//vOf4e3tzVbf3//+9zAYDIiKikJgYCC++uorCAQCpKamYu3a\ntfjoo49QV1cHsViM0NBQPlzTJlqj0WBwcBD19fU4evQoNm/ejH379iEwMJAb0AcGBlBUVMQ2vZs3\nb2Lx4sVQqVRobW1FWVkZoqKiOIe8v78fQUFBEIlEWLFiBef1Uo7+T37yExw+fBgbNmxAQ0MDhEIh\nbt68iZ07d+LSpUuYnZ3F119/jcrKSlRVVcFgMHBRqpeXF44cOYL6+nqOuIiNjUVdXR2io6Px1ltv\n4Re/+AUMBgPHXtBhoLa2FmlpadwR8NZbb0GpVPK1nJiYQEBAABQKBWprazkv/dKlS0hLS0NSUhJS\nUlLQ0NAArVaLsLAwSCQStkq2t7dj165dnGccHByM1NRU7NmzB/Hx8cjPz4dEIkFZWRmmp6fZuTA2\nNgalUom3334bjzzyCEQiEaqrqxEQEIA9e/bAx8eHrV/JyckQCATw8fHB7Ows4uLi4OzsjCNHjiA/\nPx8///nPWZmRmpqK27dvs6r9zJkzqKqqQm9vL9LT01FVVYWEhAS4urpi27ZtqK6uZttcbm4uVq5c\niXfeeQd9fX2sNKMSwb/85S+Ijo5GSkoKTCYT22flcjk++eQTfP7553j88cdZ8Tw7OwsvLy/cvn0b\nS5YsuaeJ6erVq/jJT36C4OBgREdHIyMjg3Ns3dzcoNFo0NjYyLbV9vZ2yGQybNiwAevWrcPk5CTH\nC1DhN20iPTw8cODAAY4X8fb2ZlCGwCFgzm7q5eWFM2fOICQkBImJifDx8UFjY+P/w96bR0V93vvj\nrxkGhmWAWYABhn3f90VBNApqRIySuESzp8Y0rbe9N12+TU7am5zmpr01bbNVs6dVE2NcIoqoiAjI\nIiA7qOw7OMAAAwMzA8zw+8P7fndM0zZ+f9/zPfec733O8TRVlpnPPJ/n8zyvFTU1NRy9RGDCrVu3\nOIPf1dUV8fHxDHRJpVKoVCq8+eabMJlMOHLkCBITE7nwr7y8HEqlEo6OjpzHrtVqkZWVBRcXF4SG\nhmJ2dhYpKSlYWFjAww8/DBcXF8TExODKlSvo7+9Hd3c3nnzySWzatAnFxcWYm5tDXFwcrKzuljfO\nz8/jxo0brGw8ffo0wsLCcPPmTRgMBuTm5kIguFsiScofkUiEjz/+mO+/zZs34+TJk3B1dcWaNWsY\nYFhYWMDt27eZdGloaIC9vT1bG21tbVntVVRUhKCgIAbcioqKYGNjg9TUVC5Rsra25mzJpaUlnDt3\nDiqVCv39/RwzU1lZyWW9Tk5OCAkJYUI0Li4OHh4e+OSTT/DGG2/whooU0jY2NggODsahQ4fQ0dGB\nnJwcvk6Uoy6RSKDX6zExMQGBQIDa2lpotVrI5XIsLCwgISEBEokEP/vZz7B7924IhUJUV1dDJpPh\n7NmzaG1tRW1tLX7yk58AAJfebd26FVqtFoODg6w8JtXGH//4RyQnJ/MBQ6vVMphsNpsRHBwMb29v\nfP3111heXkZWVhZkMhnm5uawuLiIK1euoK+vD+Hh4XxApSz9kJAQSCQSjriQy+UYGRlBbm4uPvzw\nQ6xbtw4hISEMClDWOB2GSE05MTGB1NRULtAC7m7WqbS5uLgYfn5+DMZaWVnhk08+4Q05KYGNRiP0\nej0UCgUmJibg5OTEYDRttG/evInu7m489dRTOH78OKRSKWZmZmBjY4Pq6mq4u7vDy8uLexIWFhZw\n+PBh1NbWIigoCCtWrLivdeett96CjY0Nq87oPRDZpdfrodFo+Lp0dnYyuWSZEy8QCFBTU4OgoCBe\nd4jkpn+nP5aAr8Fg4EPJ0NAQZmZm0NTUxMQKAQMEWhNoSApEUmBKpVIIhXcz4ZeWllBbW4sHHngA\n0dHRnGlNnwORCHl5eUxGUtkzOQMDAwMREhLC5DqpWbu6ujA3N4eOjg6EhoYyWA8At2/fhp+fHxYW\nFiCRSDguhMCO+fl59PT0QKlUwtramjs9iOChCBcCROj9kdqfHAWWwL6l4pZAOPo+OqgBuAcAtHQL\nkIXakmygdYOUqgTAymQy5OXlwc/PDwaDgSOkSOFJhXNE+lBZJ4G39HMpA/b8+fOIjY1lMoKKZynX\nf2RkhMkkilGSSqV88HZ1dWXggXojAHA8jFarhZ+fH1xdXRESEgKTycRKZeCvVnYCe6kjghT/ANjR\nQQdek8mEgoICfmYuLCxAqVQyYEdza3R0FOnp6Rw7RZ8rxXsQwEaROJSlS8ApPb9EIhGmpqYQHBzM\nh+D5+Xk0NjZiw4YNfOime/bbHCL0XizjtwAwCWZJLlj+9/Ly3ULSgYEBODs7M4gpEolQX1/PheJU\n1H0/o++/en4oKo0UuVQKS1FF1JVGBNfMzAyLEshd09DQwKA3zVMCEWhtmZ+f57lBDgtSXMvlckxM\nTODChQvIyclh8oMcMwaDAUtLS7h06RIXwAcEBOCll15CY2Mjtm7ditnZWRQXF/O9otVqOWeYwAeF\nQoHFxUV4e3ujqqqKSUYnJyfOor99+zb/7rq6OoyOjmJgYADe3t7o7u5GY2MjEhMTWWUrEAjg4uKC\nyMhIHD58mMVq9BnPzMwwwUsRDOQUsYx4GxgYwJ07dzjGhNYm+jrLLg0ioerq6mA2mzE+Pg4HBwf0\n9/dDpVLxOQ64S/Z0dHTg6tWreOmllxAVFcV7T2dnZ7i4uMDX1xcxMTH4/PPPYW9vj+7ubpw/fx5D\nQ0NcAE/zgQqLiRRcXFxksoBc5WazGVFRUXj00Uf52U7gD63Xg4ODsLW1hVarxZUrV/DMM88gLS0N\n7u7uWLduHRP9U1NTaGlpwdLSEhenz8zMoK+vj/s+qqqq0NDQgI6ODgwPD0MqlcJsNsPV1RUbN26E\nlZUVJicn4erqisHBQVhZ3e1I2rdvH6KiouDt7Q0fHx+IxWLuGSMQjtYsAu0IcFcoFLyey+VyKBQK\nXmcWFxfR29uL7OxszM7OIjAwEEFBQbyeWEaDkQqf3AFECNEaurx8t7/Qy8uLn2e0vhOBQFhBSUkJ\nx4vRWZnIVrru5GIiBxY5CQnst3Qp0XPL0dGRI4js7OzQ0NCAkZER5OTk3Hcx8CeffAKtVstqX3oP\nRKbQOkElrC4uLhwNYvmM/C5Dp9Ph3LlzyMvL4/vMZDIhIyOD94XA3TMHnT8s8+VpWDq7qEyYypI/\n//xzXL58GWfPnkVISAh2794NV1dX2Nvbw8vLC+fPn2dxId1/69evh7+/PyIiIliQU1FRgd7eXnR3\nd+P48eOoqqqCi4sLlEollpaWEBoaCqFQCH9/f+j1epw/fx7bt2/n+Wo5mpqaYGVlhdDQUP47itoi\nRTY5C4l07u3tZaf5/98hkUiwsLDAefR/b0RGRrK77bsMKysraLVaqNVqrFy58m9Kov9PDNqbUI/f\n2NgYn2OAe10jlsPyvhGLxfD09ER8fDyXs2/evJnXTWvrv/YdzczMoKurCwUFBfjxj3+MwcFBODk5\nYWxsDIcOHUJJSQlHc39zaLVaDAwMQKfTISkpo+mZKgAAIABJREFUideFb3MOkGiFenvoHGApgqHn\nlqXQxd3dnQlxirgNCAjA2NgYCgsL2RlkNBrh5OTEjsDW1lYmkn18fHDz5k34+/vz76LzC+2raU2l\ndZbIdNp70L0rFosRGBj4N/Fd/2jU1NR856/9f218M17t/8b4pyQAZW16eXlhenoax44dY5Y4OTkZ\ns7OzeO6557CwsIDKykqYTCa4u7tjZGSEreRhYWF46KGH8MUXXyAgIABJSUlYXl5GRUUF58aWl5ej\noaGBoyGo8DIuLg4+Pj7IycnBzZs3YW1tjcjISDQ3N2P79u0YHx/ncg8Cuz/77DPk5ORAJpPh448/\nxlNPPYXp6Wm2RKanp7OrQSKRYO3ataxMDAwMRH19PVauXInKykr09vaivLwcJSUlkMvlbPEmNXda\nWhrbbJqbm7Fz5074+fkhOTkZkZGRkEql+OSTT7Bx40YolUqcPn0aEREReP3117F582YsLS2huLgY\nAoEAK1aswG9+8xvOmyRFRG5uLuzt7TE1NYW9e/dienoa1dXViIiIYOtnUlISLly4gB07dqCzs5PL\njwQCAc6ePYs9e/agrKwMXV1dXCa1bds2LgPdtWsXfHx8EB4eDrVajZmZGSiVSrz44ovw8/PDgw8+\niNHRUY4JSk1NxbFjxxAeHs7xL5RVmp+fz4Wqe/fuRd9/layQVexPf/oTpqamcPv2bTz77LMcxbO4\nuAiVSoVnnnkGYWFhTIgcP34cjzzyCHp6erBp0yZkZ2dDqVSipaUFEomE3SVNTU3Ytm0bSkpKOK/7\nq6++QmRkJEJDQ6HX61FbW8sW1sTERJSVlaGkpISVRAEBAWhuboZarUZUVBQ0Gg1OnDiBoaEhrF69\nGteuXYNcLoenpyfKysqQlJSE4OBg7NixAzExMWhra8OBAwfw+OOPY2BgAM899xzOnj0LmUyGc+fO\nwdfXF8PDw3jooYcwODiI+Ph4LpXctWsXhoeHkZmZCYlEgtzcXJSXl2Pr1q0QCoUoLS1ldSHFjQQH\nB+PkyZNcEkWRFfn5+dBoNIiNjUVNTQ32798PiUSC0NBQHDhwAHfu3EFPTw8OHToEhUKBnJwcCAQC\nfPXVV8jJycGzzz6L4OBgtlu3t7ejp6cH5eXlWL9+PQPWlZWVaGlpwebNm9HY2IjS0lLk5ubCZDKh\nvLwcnZ2dSE9P/9ayqH80GhsbuUBnamqKi5siIiLQ1dWFY8eO4aWXXuKHp1KpxK1btxASEgIrKyvM\nzMzgqaeewsqVK2FjY4PR0VGOh3rqqaewc+dOREZG8oONVJNGoxFWVlZ47LHHsGfPHuTn57P1l0pc\nCbRSqVR4+eWX+RoRcHbu3DlIpVKEh4fDaDTiz3/+M/70pz8BAKsQ1qxZg8HBQfj6+qKsrAyxsbE4\nceIEwsLCcPbsWQQHB8PBwYG7Ta5cucLlomvXruWHsUwmQ3d3N9avX4/U1FQGwqiEndh9OtQQGCwS\niRAZGYmTJ0/yZ5efn49HHnkEnp6eXOp87do1PPzww/j444+xf/9+VFVVYceOHYiIiODiUrVaDa1W\nCzc3N8zPz+P69etwcHDAxYsXUVpaiq6uLuzYsQNHjhyBh4cHYmJicPXqVaxevRpJSUlcgEhF65SZ\nDty1i7766qscZQIAZWVlOH78OJdvCYVCjIyMQCwW49y5cxxlJBKJEBsbyxsxOuQTqKjVamFtbY3U\n1FRYWVnxxpKyRw8cOIDi4mJ0dXXhe9/7HsxmM5RKJROhaWlprKwVi8WIj4+HSqXC5OQk4uPjcfny\nZUxNTWHbtm0wm83QaDTw8PDA5OQkHBwcsHLlSiYBLl26BLlczqRufn4+hEIhKisrkZGRwZZSAqWT\nkpJw9OhRBhCUSiVnCYtEIoyNjcHX15dtnORyIrdDZGQkXFxcOL6B4iGOHDmCoKAgGI1GuLq6Qi6X\nY3R0lLtlRkdH+QA4NTWF1tZWvP/++9iyZQvEYjHc3NxY6RcTE8PFxCtWrGDlHhEDHR0dHFVDv58A\nloWFBWi1WtTU1OD555+HTCbD4cOH0dHRgZGRESQmJiIrK4st+HV1dVysWVlZyRvo3Nzc+1p33nnn\nHSwvL3MBnGW2KIGj5MCxs7NjxSzwVzUQHaJGR0d5c0wuDUuAlVQ6BKQQgECHcR8fH4SEhGBwcBDd\n3d388yxLvkZGRhhQsLGx4TWO1iraH01OTqKzsxMLCwvw9PRkYIYUdURoUkfFV199hYaGBgYY4uLi\nOAed7rmBgQE+YN28eRNxcXEMlppMJvT09MDf3x9ubm6wsrLirGZ6n+RwICUmiUlojpNCCQAD0lSq\nq1aroVarsbS0dI9K0jIejIB0SwDFkhiw7H4CwKAgAbtEDlh+tmTHp8/T1dUVjY2NaGlpwa1bt+Dq\n6gpXV1eIxWKMjo7yQWp2dhYNDQ2slKcYFHotYrGYo6woq1Wn06G1tRV37tzB1NQUrl27BqVSCW9v\nb1hZWWF2dpavQU9PDxYWFhhU8PDw4NhMApTUajVGR0fh6emJsbExXL9+Hb6+vuyIINcBgbQA2JlJ\nJaT0/wFwAfCNGzfg5OQEvV7PTqbh4WHMzs4ycaZSqdgtZm1tzSIC4O4Bc3BwEFqtlpWBLi4umJub\ng6enJwwGAxITE9Hf348VK1awor2lpQXnzp3jmAyy2VPEliWIRoo/uub0WdPzkvYR9O8A7nGhELl0\n9epVTExMID09HS4uLgxQq1QqyGQynDlzBrOzs9i1a9d9rTsTExPQaDTo6+vD559/jsjISEgkEpSU\nlCAoKAgjIyNQKBRM6qnVari5ucHR0ZHXDFLoSSQSLC4usmLf8nD/yiuvcKybo6MjqwatrO6W+JJL\n4ujRo6yy9fX15dihpaUlLmCMjIxEaWkpBAIBrly5goGBAZjNZqSkpKC1tZWvMc0FyvUmkJM+D4qM\nImU0qaOFQiFOnjyJvr4+FllVVFSgtrYW5eXlMJvvlpj39vayUIU+M3oenzhxAsHBwexCaGtrw3/8\nx3+w+OP27dtceEtxHGq1GtevX8eNGzeQkJDAzwJyEFjmzdM1UavV2LFjB0eSlpeXY3BwEAUFBfyH\nHG3Ozs7Yt28fPDw8mPSSy+UsTqBoiYSEBIyMjMDKygoVFRX4wQ9+ALPZzM4To9GIzz//nKOCKEqJ\n3isRAK+99hq2b99+T7SP0WgEAJ7btIcqLS2FSqXiM6evry+D3UajEWNjY2hra8Pk5CRu3bqFjRs3\nYvv27UhPT4e/vz/c3d1hNBoRERGB2dlZiEQivqepx4aU0STaysjIwP79+zm6kpx8BH6Ro4j2ILTO\nExlFnU/0R6vVoqWlhaNk2trasH79ety6dQsqlYrd0rQXJPctfT/tF+3s7KDX6zmnm4hNiqeRSqUM\n0hFhS/uZwcFBtLW1cd9fcHAwxyQR2G/p5KNnEfBXwN/a2pp7SOgetXT30fdaW1sjOjoaoaGhHFf6\nXUdPTw/+/d//HY8++iiio6MxPj7OvXUkqnB0dGTF/MqVK/l7/xHI+W3j3XffZcEPAAwPD2NkZAQP\nPfQQnJ2dMTg4iCtXrvxT9yY5GoG7hO7U1BTeeustlJWVoa+vjx3Fc3NzSE5O5jgtg8GAM2fOYPfu\n3Sy8c3d3x+LiIvc5VVdXY3l5GTk5OQgLC0NISAji4uJQWFiIjIwMDA0NYe/evbw/of3G2rVrIRbf\nLZ4lspT2gUVFRXj88cf/5n2QwprK2mkeCYVCREREoLi4GAEBATwvvjlu3brFBPA/GxQPRopxmq/0\nsy3J0O866N6MjY3934qF+q6D3NBvvPEGHn300XuIke/y3unrKNovIiICX3/9NaRSKVpaWjAyMoKD\nBw+yg3J0dBQ/+tGPWABjb28PZ2dn1NTUoLGxEUNDQ1ixYsU9LhgS2nh5eSEoKOhvHKTfHBQ/OT8/\nz4kmtO8FwAXllt9P7tvOzk54eXlxkgqdV2JiYqDVajE+Po6GhgY4OTnxfoZiNs+fP4/U1FT4+vqi\nsbGRy+Hn5ubQ3d3NTtKWlhYuWCcnAT2zqVuABDFKpfJbybq/N/6HBPj7478lCfDmm29CJpPh008/\nhUgkYotgbm4uPDw8sGbNGgwNDeHAgQPYtm0bPDw88MYbb+DmzZtobGyEvb09rl27hvb2dvzoRz/i\nQ4eLiwvUajWeeOIJhIaGore3F6+//jrMZjOOHDmCwsJCBAYGor+/n7PhS0pKkJmZCZFIhNWrV8Pa\n2hq+vr5ITk7GpUuX4OXlhbfffpuLy86dO4f5+Xls3rwZGo2GbwoqMTWbzSgqKmJLLB1wPvzwQwQG\nBuLpp5+Gj48P6uvr8eKLL2JpaQnvvvsuUlNTce7cOXh4eCAkJAT9/f0wGAy4ePEiNmzYAJVKBbVa\nDb1ej1OnTmF6ehpbt25FWVkZOjs74ebmxrncYWFhmJiYQHJy8j1Wow0bNkAgEMDNzQ2hoaF4/vnn\nORNZo9HgP//zP1FaWsqAX2NjI+fRkUNg48aNrPSi32EymXDr1i20tbVhbm4OdXV1WL16NY4ePYqS\nkhIGqAYHB6HT6aBUKlFaWgpnZ2e8+eabyM7ORmRkJACgo6MDZ86cwdDQEHx9fTE7O4vg4GCO5SA7\nGtl+b9++jcLCQmzatAk5OTlISkpCUFAQMjIysGrVKkRFReHDDz+En58fW+aLi4uh0WgwMDDABa90\nGGtsbMTatWvZOUGM9MWLF/Hwww9jaWkJQUFBXE5L1sbW1la88sorqKiogJ+fH06ePIn169dzjAjZ\n31etWoXx8XE4OzujpKQEKSkpmJmZQUBAAJaWlnD9+nUuFKKC0d///vfs7ti+fTuOHz8OX19fjI6O\ncvRQWVkZjh07hps3byI8PBwLCwu4c+cOl9PpdDpWTi8tLaG7uxshISE4deoUb37z8/NRVlYGLy8v\nrFmzBnq9ngGfS5cusaNj/fr1XPo1NzeH3/72t3jhhRd4sx0VFQWTyYTe3l6UlpZiamoKvr6+KCws\nxMDAACIjI9HY2IiPPvqI4zGsra3ZIXPx4kW2pmk0Gqxfvx5ubm4wm81obGyEl5cXFAoFQkJC7mth\n+vWvf80Zo15eXryZNhgM8Pb2BvBXdRYBMx988AFqa2sxNDSEEydOYOvWrXwIlslkrMiOiIhAcnIy\nKioqUFxcjNHRUXz00UdYsWIF5ufnMTg4yHFYpNx87bXXkJ2dzVFdoaGhqKioQG5uLgoLCxEdHc2H\nl88++ww/+clPsLS0hJKSEuzevRvAXVVLTk4O3NzcoNVqoVQqMTAwgDNnzkAoFKKvrw+3bt3C448/\njoKCAuTm5sLPzw9+fn7QaDQYGxvDxo0bOW7I2dkZOp0Or7/+Oh555BE+YCwtLUGv1+O9995DaWkp\nHnnkkXsUytTtYDabERYWhoGBAVRXV2Pfvn14//33ER8fj/HxcX5/CwsL8PLyQlhYGKKjo2FtbY2r\nV6+yqjw+Pp4L88rLy7kEaXh4GHV1dejo6EBYWBgWFxeRkJCAmZkZBAUF8YaDsrLLysqQnp7OamZS\nZ5w7dw4CgQA7duzA3r174eHhgSeffBJHjx7lDSGpEQsKCqDT6biAicqdaeNLimmhUIi8vDx2l4SE\nhHCUx7lz56BUKjlS7rnnnoONjQ0OHjyI3bt3o6urC6tXr4a3tzdqa2vR3t6O69evc1dGZWUlPvjg\nA0xPT/PGKS4ujlVrRUVFnLNKB3U3Nze0tLRwoSV1h7zyyiuYmpriQ8/U1BQfQnNychASEoITJ05w\nR4hYLEZISAirZQi47OvrQ1xcHJPrHR0d7GJra2uDs7Mz/P39sWbNGrS1tXHp4/LyMpcDGo1G5Ofn\ncwRZQUEB2tvbMTIywnFKOp0Obm5uaG9vx4kTJ3Dq1CkmqYhYoGzf5eVl3L59G5GRkRgbG2OFNBE2\ndnZ2UCqVOHv2LL788kv+O3t7eyQmJnLOZXNzM27evIlbt27Bx8eHi0fff/99/PSnP72vdeedd96B\nSCTiLHgCBYxGI+bn52FnZ3dPjrVCocDIyAiX5ZHVVyS6W/JeVlYGpVKJ8fFxdj2S3ZeUjdPT03wI\nJMcAHfaBuwC4Wq3G1NQUdDodwsLCGMyxdFXQ9xOwR9m+BFxqtVr09fUxSAP8VSllZWUFqVSKiYkJ\nXLp0ifuBxGIxCzEIVKfX5unpCXt7e1RXV2Pnzp0MqtBrsbGx4egfAjwsP1+KnaH7sa6ujsFb6kLS\n6/XcWzM5OYn29vZ7yj4HBwe5nJ1UVBSpQBZmOqzSgYgiQeggb6mKpc/REgym90ugsslkYoKFSlfv\n3LnD5eZ0kKMS4ODgYIyNjTEhoNFoWD1M14IAL/rehoYG3icTSU3OU0dHR1RXV2NxcZGVh15eXlCr\n1VwMSEAROTnpIE2Z4rTWpaWl4dSpU0hOTr5HiUh2crPZjM7OTgZWbW1t7ymaplxoijcLDQ2Fk5MT\nfH19Gag0m80MHItEIu4eoM/CbDZjZGSEwWAiCZydnZnwcnR0xOLiIiIjI6FQKNiF09/fz/9u6eaj\nz4xcejRnLAkAmuP0mZJLh+YERYGRC8VkMqG7u5vXOMorJ0BRLBajtrYWc3Nz2Lt3732tO4ODg6iq\nqsJHH32Ep59+Gh4eHjCbzQgKCoLZbOb4FQJVqfSRQFH6b3KsqNVqlJeXIyAggMEmoVCIuLg4TE5O\nshvW8l4hoLG3txcXLlzA3NwcXF1dERMTw8QlAcj080jNSLF4QqEQPT09DLRSrKclcUQOO4PBAL1e\njy1btiA5ORmxsbGoqqri/jGRSISIiAhERUWhuroaly5dQmtrKyIjIyEUCrF3716YzWZ4enreA7CK\nxWKObggMDMTly5eh1Wrx8ccfo7y8HOPj41i3bh3fO0ajEYuLi5idncXQ0BB3b7i5ucHHx+eeuDZ6\nJljOKyK17ezs4OnpyeeEzs5O3nMQEUMxbXFxceyYHxwc5Gct3SO0byA18LZt2+Du7s4uCY1Gw/GP\nRqORI5gEAgGrz2dnZ/Hqq69CLpczyUJgvuXaS8+Kjz76CDExMXB0dOTYLnre0T0yPDyM9vZ2NDQ0\n4NChQ1CpVHBwcGB1vpXV3fhIIn8bGxt535mWlgYHBwfU1tYy6O7p6YkdO3ZwLBHNQ7qutCbQ9V5c\nXMTQ0NA9BZakqiWylCIhqd9henoafn5+TCRRqTk5tqytrZkgofVpcnKSCRpbW1uOCSUydXp6muco\nEUF0HwoEApSWliI7OxtRUVFobW3FwsICF6GT+47uIVp36HlDrjZam8iBSX8sHW10vSUSCRQKxX0X\nA1dVVSE9PR12dnZc/url5YUbN25gYWGBndH0HM/MzLzn+2n9+GdjcnISn376KTsLKKt+enoaubm5\ncHJyQkFBARoaGpCVlfUPf5almn18fBzvv/8+Ojo6oFarYTAY4OrqCg8PD6jVapw/fx6XL1/GlStX\nUFVVhd/+9rdISkpiV4dOp7snsuX69ess9KFhY2PDhDrF41KP27Vr16DT6RAeHo75+XnGTCzB9Obm\nZu7SsRx6vR4lJSVQKpVoamqCXC6/p8Q3ODgYX3zxBUeLfRNQnpiYuC/SR6vV4vbt22hra0NdXR2L\ntSje8X4HxXX9IwBYo9EA+O5g/TcHiTx6e3tRX1+P4OBgyGSye6L77mfQc5r2xn/5y1/Q0NCAqakp\nXL58GXv37uX4Y4FAACcnJ35+urq6oq2tDQkJCewwo2E2m3Hx4kWsXLnyO71XelbZ2dlhenoaAoGA\nzwVEvFkOWiMWFhYQExOD/Px87uukWEhy8b/zzjuorq5GVVUVQkNDoVAooNFocPbsWaxYsYL3ZsXF\nxewmpTMOPbclEgmqq6v57EAl5+QudXJyuidOzrIM+p8N6nL8n/G3435js/9PjH9KAnR1dcHf3x+z\ns7PIyMjA+fPnsbS0xIvaiRMn8OWXX2LNmjVQKBSYmpqCVCrF7t27UVNTg507d6Krqwu7d+/G1atX\nAdyNPIiIiEBgYCAMBgMqKyuRkpKC/Px8HD58GE888QSeeeYZ2NnZobW1FXq9HitXrsSePXuwsLAA\nf39/zq8kBczc3BwOHjwIqVSKpKQkFBYWIjMzEwUFBdBqtYiLi4PJZMLFixc545uKxJqampCSkoIL\nFy7AYDCgrKwMP/rRj9De3g5vb2+UlpZi5cqVcHV1RUJCAjPj169fR05ODo4cOcLZ3QKBAL/5zW/w\nwAMPIDg4GGfPnsX+/ftRU1ODixcv4tFHH2XFG9kFQ0NDMT4+Djs7OwwPD/ODmDY/zc3NePzxxxET\nEwN/f38IhUIG6sbGxhAcHMxlLuXl5ZzjFRERATc3N+Tl5WHXrl2wsbnb5p6SkoKsrCwUFhbixRdf\n5NKimJgYKJVKPtyPjY1hYGAAu3btwvLyMmpqaqBUKhEeHg6z2YyCggIMDQ1heXkZSqUSPj4+qKys\nxMTEBPbu3YuRkRHO0K2vr4dOp8Pp06cREBAAFxcXzM/P8+Ha3d0dH3/8MX72s59BJpPh3//93yEQ\nCLB69WqEhobi888/x8LCAnx9fSGXy/ma3LlzB25ubli7di3nuCcmJmJhYQH5+fmQyWT44osvOJql\nvLwc//Zv/4aOjg7Y2Njg5MmTeOGFFyCVSuHn5wej0chKf7JYzc/PY+vWrayGmJqagrOzMxISEvDT\nn/6UgaAPPvgAr7zyClatWoXy8nLk5+djy5YtUCqV6O/vR3FxMRYXF9HV1QUA2L9/P8xmMwYGBpCW\nloaCggLo9Xq0tLRwCd309PQ9mbgqlQr19fUwmUx4+eWXYTab8fXXX+PBBx/E8PAwF7RERESw2ryo\nqIhJGScnJ4yOjuLKlSvo7e1FWloarK2t0dbWhrCwMLY8urq6cs7z6dOnERcXh5CQEGRnZ3P26Cef\nfAKj0YjMzEyOBiJV9aFDh5CQkIDo6GgoFIr7sosBgLOzMxeNNzU14Xe/+x3Ky8tx5coVWFlZ4caN\nG4iLi0NnZyeampp4I/foo48iJSWFsx3n5uZQVlbGudXFxcUYGBjA2rVr0dDQgB/84AeIiorCtm3b\ncODAAVy5cgVdXV1QKBTs+qmvr0dmZibkcjkDrRMTE2hsbGTlLJXo1NbW4tlnn4WrqyuampowOjqK\nuLg4zkp9++23+WDf39+P+fl55OfnIyEhATk5ORCLxTh9+jRnx4aGhmJqagqbN2+Gra0t/vznP2No\naAhnzpxBW1sb7ty5g6eeeooLheiwdvDgQfj5+SEmJgaenp4Qi8Vs2T548CACAgIYNKirq0NOTg6M\nRiO8vLzwq1/9CqWlpfj5z3/O3QqFhYVYtWoVH7A7OjoglUrh6+uLixcvIjIykjfV8fHxUCgUUCgU\niImJwdjYGM6ePcuuqtOnTyM5OZnnREREBLKysuDp6Yny8nJcuHABg4OD8PHxgVwuR05ODrZt24b4\n+HgsLy+jtLQUSUlJ2LFjBzIyMhAZGckH96ioKCQlJQEA3n77baxZs4at5BQvQsq6DRs2IDw8HJ6e\nnpibm4OPjw/c3d3h7+8PBwcHdpv9/Oc/x8qVK/k5QnbJ3/3ud6ioqICbmxt0Oh2Xffv4+GDDhg04\nefIkjEYjOjo6UF1djerqamRkZCAiIgLu7u4MKlhbW+PAgQMoLCzE66+/jujoaCQnJ+Ohhx6CTCaD\nQqHAwsIC9u/fj9u3byMzMxN37txh1e/o6CjCw8P5EGpra8tEJgCOH4iOjsbY2BhGR0fh4+ODgYEB\nSCQSBAQEcBnl7OwsZ+nS88doNGJhYQFHjhzB008/DYFAAC8vL8TGxuLKlSswm804f/48BAIBAgMD\nYW1tjcbGRri7u8PFxQXZ2dmYmZmBh4cHH44mJiZw6NAhzM/Pw93dHSqVilVtBAKMjo6ira2NMzBV\nKhV6e3uRnp6O+Ph4dHV1wdXVFT09PRgfH0dubi6cnZ1ZQTM5OXnfitw333wTLi4urGImBTTlRTs7\nO7NycuPGjUxYk6Ojt7eXSwcpA//mzZuYmJjgUsClpSU0NTVheHgYdnZ2HJdBxAPZlEmNTpnwer0e\nVlZW7PAgoGh+fh41NTW8PhHRLxAIEBoailWrViEsLAzp6elYuXIlQkND+fMdHx/nA8fy8jIkEgnq\n6uqg0+k4d/nBBx9k0IQ+P1KtOjs7IzIy8h5Qnz5H6ogigIreG4Efs7OzrC4ntTgJISxBXLPZzMWU\n3t7e/LoI9KitreW5RcAtAW8E0AB/LbYjkJy6CACwupeAIMuYJsuIHAAcNdfa2sqfvb+/P2JiYhAa\nGgpvb28MDAzA0dERg4ODaG9vh5WVFWJjY+Ho6HhPBm1nZycXqxoMBshkMn6f7u7unH9NcTs+Pj6s\n9Pf29mbyleJvqNSN9sZEhKSnpzMY6+DgAL1eD71eDz8/P6Snp/N1ItKQQHD6eyrtJfW+QCBAR0cH\nAgIC4O7uzq+V1h4Cp+zt7eHk5MSFdp2dnZDJZPc8R86ePQu5XH5PPj8BkMPDwyw6GB0dRU9PD+Li\n4jhSo66uDhqNBiMjI6yOpwMt2dbpHiKyh4qVCUy0tr7b7/Luu+8iISGBy4RpTliSReTEIUCRrsXC\nwgLGxsbQ2NgIoVCIffv23de6U19fj7y8PIyNjWHr1q2c00/XkyLTJiYmuNB0eXkZnZ2dfN9YxlvZ\n2Njg0KFDLC4SCO72mkxOTmJ2dhY+Pj73ZIyTWMdoNKKqqgp9fX2Yn5/Hli1boFAoGOSmeWgJSgUG\nBiIgIABr1qzB+vXrkZSUxHvylJQUaDQaTExMICQkBG5ubnBwcMAzzzyD3NxcbNq0Cd7e3kwYJSQk\nwNvbG0VFRejt7UVAQAB0Oh0MBgOys7MRFxeHxsZGVvK2tbXBy8sLcrmcgVRL5wOtWw0NDSgpKQEA\nziEnQH1oaAi//vWvcfbsWVRVVSE+Ph7+/v4ICQnB4uIilyOS6pfeN80RofBu4aOtrS2vfTExMVi3\nbh3y8vJ4flCU59zcHAuIyJlF8xUAr/vq3Mh4AAAgAElEQVR9fX1oaWlBUFAQZDIZk5O0dp8+fRrP\nP/88nn32WWzatAnp6elITU1FVFQUVqxYAZVKhfj4eL7mRJwS+ERii6GhIRw/fhyrVq1CcnIyQkND\neY2iPSK9rtOnTwMAXnzxRbi7u3N8Fj0XaJ9lb2/PoPnIyAgCAwPh5uaG/v5+dgBQfn16ejocHBwY\nSCfAneYxPTNoXXZyckJ9fT2vM2q1Gi0tLSykCgwMhL+/P1xcXNDZ2cm/W6/XQyKR8FpAMYykkF5c\nXIRGo7lHkS8SiZCXl8cdMcPDw5ibm2PixrK4l8Byg8GAsLAwKJVKKBQKxMXF4dSpUwDAHTD0Xuh+\nonXa0plmSUhbqoTJ7UDiJ61Wi6WlJUilUnh6et7XuuPn58f3MpEdMTEx2LRpE8fhrl+/Hk8++SS8\nvb3R2dnJnR3AXTD/m5E1k5OTvDbSsLOzYzGnRqPh+KH5+Xk8+eSTEIvFiI6O/ocEAMVsLSws8N5s\n7969mJiYYKW7q6sru3l+8Ytf4OGHH8bGjRvx0EMPYfPmzeyUIVCVgFeaExQ3Y/mehEIhVCoV0tLS\nkJaWhhMnTiAxMREajQaJiYnw9fWFRqPBzMwMlwxToa9areY9C43l5WUMDAygvLwcmzdvhouLC4KC\ngv7mOgoEdwtY//SnP3E/lFqtBnAXRL7fM7VCoYCfnx98fHw4hozice93EHbj5ub2D79ucHAQAwMD\n9/1aiTjUarX47LPP8P7770MoFDLpQ30UlqQJcDdyis4Ff29MT09zBHRpaSl3BxJBR1E+ExMTLESg\n+S6RSKBSqe7poaCzyqFDh5Cbm/udCQ/LOELLQnFaG2hMTk5icHAQrq6uLJwjIvzy5cvo6urixISD\nBw/CZDJhfHwc8/Pz6O/vh5eXF4aHh+Hg4ICoqCgoFAoIhUKEh4dDLBbjrbfe4thBim4sKirCl19+\nCXt7e9y+fRvNzc1IT0/H1NQUr9UAYG9vzw7U7zr+hwT4++O/JQlAm6fU1FQYDAbExcXh6NGjiImJ\ngbW1NeLi4lBbW4uSkhL09/fj0Ucf5WxHOqA//fTTmJ6exszMDD799FM88MAD8PPzw6lTp/DAAw8g\nOzubGeyoqChERUXBwcEBb7zxBnJzcxEREcF2446ODgad6CBZUlKCtLQ0REREYG5uji2cH374Iezs\n7Hij/M477/DNcOvWLbZM63Q6BAUF4dVXX0VlZSXWr1+PoaEh2NraIiwsDGazGV988QUyMzM5M7a8\nvBwhISH49NNPuVTG398fzzzzDGcQOjs7cyYdbQy8vLwQHR2Nt956CyaTCatWrYK3tzcmJydZMapQ\nKHDr1i3OpIyOjsbx48cRGBgIR0dHBpcIRJDJZPwQm56exqZNm9iCLxQK+aBIysHCwkJotVooFAqE\nhYUxaUDKPQLLXF1dYTQaERAQwHnrExMTUKvVuHjxInQ6HbZv3w6dTseFzQ0NDUhMTMQf//hHhIaG\noqmpiVXg1dXVGBkZgUajQVlZGaytrREbGwuTyYRLly5BrVYjPj4eMpkMfn5+GBgYQGpqKtvUn3rq\nKS5njYmJQVFREfLz8xEcHIyampp7DkNPP/00jEYjdu7cifr6ejz44IMwGAxwcnJCTEwM59jq9Xo8\n+uijkMlkfHhsb2+Ho6MjxsbGkJCQgIGBAQiFQqxbtw7BwcEIDw9Hb28vbt68iZSUFDQ3N3NhjZWV\nFcrKyjA/Pw+5XI7h4WF+WPb29mJkZASPPfYY5HI5srKy4O3tjcLCQoyNjWHXrl0oKCjA/v37ERgY\niNOnT2Pr1q0IDg5GQUEBW8FeeOEF2Nvbc0GzQqFAcXExkpKSoNfrsWLFCnh7e6O9vR2jo6Po7+/n\nhXrPnj24evUqzGYzFxQJhULcvn0bk5OTXPQSEBDApaBRUVGwtrbG7Owsrl69yqqWoaEhbNmyhZXH\npNq2tbVFcnIy8vLyuGiV1PvfdVC0gtlsxrlz5+Dm5gaJRIKXX34ZAwMDqKqqwubNmzmu5NixYwx2\nDQ8Po7OzE76+vrhx4wZsbW2RlJSEgIAAtLe3QygUwt3dHRkZGVwGJhQKUVVVxfEfS0tLrNzx9/fH\nZ599xoWEQ0NDGBsbw7p161jl+9FHH0GlUiEmJgYSiQQGgwGnTp3CM888g6NHj+Kxxx6Dra0tEhIS\ncOzYMSQnJyM4OBgtLS34/ve/j5CQEC5YI5tfV1cXKisrcfv2bcjlcnz55ZcICwtDQ0MDVCoVfvWr\nXyEsLAxHjhyBn58fLl++jLCwMP58aO14++230dDQgLS0NIhEIlRXV6OhoYFzwLds2YKJiQkuB1y7\ndi1kMhmTFRTFZKn4MplMCAkJQVNTE3bu3Angr9ZHyjjs7e1FbGwszp49i+TkZExPTyM6Ohq/+MUv\nIJVKcefOHc5WXFpagqOjIytQN27cCA8PDwYwCGQld5SjoyPa29uhUChYnUdZokR2FRUV4YEHHuCN\nPwF6FHnR09PDqlO5XM65h1Twa2tri/b2dri6uuLgwYNQKpVoaGhgxdlDDz2EJ598Et3d3RgZGUF/\nfz+SkpLYJt7a2oqpqSlWPG7atIkJC1JG2tvbQ6PR4NSpU3BwcOA8baVSiY8++gjJyckQCoVQq9VI\nT09HcXExVq1axYpCAgtInUZFnwSCTk9Pw8PDAxs2bIDRaERpaSmKior4sDc7OwulUnmPGl0kEuHC\nhQuIiIjgnEuFQgGz2QxnZ2cGzsgS39LSAqPRiKamJmzfvh3Ly8tITExEcHAwNm/eDH9/f3h4eKC1\ntRVSqZRzkauqqjgKgaL8qFhvfn4e9fX1mJycREpKCpRKJQIDAxEZGYmamho4OTkhLi4OarUa9fX1\n2LNnDy5dugRPT09WtU1MTDAp+l2HwWBASEgI7OzsWG1LIBDdA+RISkxM5MMtESZTU1Po6uriTTLF\nwwQHB3NsDe0h5HI5Ojs7YWdnh88++wyrVq1iEMASaGpvb4dcLkdbWxuysrK4P8bFxYXLnOngQQpU\nb29v7s6gn0fADzlEAHD8De0VFhcX0djYCL1ez/c6lT4SYEigKN1PlvEpFHVAYNzly5fZ4UaHKlIB\nE/hPZICdnR2TLJY55kKhkIkVAPw66fWTAp9+/9TUFBMBi4uLqK+v5w4HKtCk+4MOYpbkA4ErdDiz\ndAKYTCYsLCygvr4eGRkZfF0oKokOSEqlEjdu3ODYow0bNsDZ2ZnJEXK9ULa8wWCAVCqFRCKBWq1m\n5SVl3i4uLrIqjWzYBIwB4M+L/tcy65riPBwcHHi/QI7PqKgozM/P4+uvv2aHGKlhzWYz9z/Mzc3x\nukjOhKmpKXaIEHBHJKuVlRU0Gg2Da2fOnMHw8DC7IFQqFd9zN2/e5N/l4eHBWf4zMzMMNN65cwc+\nPj7QaDTsKiMinaJvxsfHodFo4OrqytF5RHz0/VfHjlgsxq1bt5Cfn4/GxkYEBgbCxcUFBoMBNTU1\nXJgtEAhQWVnJgiFSIVtZWXHnGHUwEEly5coVDA8PQyaT3bcTYN++fTAYDBAIBAgODoaHhwfPOyIl\nxGIxF/FS7wdwl8CanJxkpb5Go2HwtbS0lPe5MzMzMBqNHMdq2TtB5OHc3BxOnToFjUYDW1tbPPHE\nE5DL5bx2UQwhqedNJhMDXuQiobWSymejo6Ph5eWF3t5euLu744UXXoCLiwucnZ35ZxLBJBAIIJFI\nEBgYCCcnJ/z+97+Hr68voqOj4eDgAIlEwsKV+vp6nD9/Hn5+flyqSWppikCgqLOhoSFel0UiEfr6\n+iASiVBVVYVr165hdHSU10FnZ2dERUVxdBlFB9L6QHPccq0j4B4Ag7ZE1phMJuzduxfR0dHYtWsX\nUlNT0dDQwGu1m5sbq1vNZjOam5thb2+PTz75BAUFBZienmZXrF6v58zq8PBwpKWl8f6GnAWU2e7r\n63uPs4wIdiIC6I+TkxNcXFz4PVs68QwGA8+VpaUlXLx4EUqlEunp6bxvJgcWkdT0DKNrXV9fj7Cw\nMFy8eJH39CqVCpmZmSw8suyCsVSX07UkJxI5RMl9PDU1Bb1eD6lUyl02dN6dnZ3FjRs3IBaLGXyn\n3G+z2czkPil9KQ2AAPY7d+7AZDIhNTWVSTdaT+g5SCrq2dlZfpZSHx4p/oVCIYaGhlBXVwc3NzcW\nDn2TCLDstSEShIgtS8cSXSOR6G6Hx9jYGBYWFuDi4nLfYCsVLn9zHjs4ODAJSV0VNjY2uHz5MsfP\nWVlZobu7+55cftpnfNvw8vLCl19+CaFQyPufubk5BAUFwdvbm+//vzdGRkYgk8lw48YN7vtra2vj\nzhraO0okEuzbtw8BAQH8LLR0TwB3wWLL10n7p7GxMSauv23Y2Njg+PHjSElJuYdwsbe3h1QqRXd3\nN5qamrizRqfTQaVS3eOWGBgYQGlpKXbs2PE375fmOg3aM8THx3M0GzlMCwoKEBoa+nevt+Wg8zTw\n15J2Ozs7XLp06b5KXQGwKOgf9QvQcHFx4cjH+xl0XdRqNd599134+/tjfn4ezc3N7NTNyMj4m+9b\nXl5mt/G3jcXFRbz66qtITU1FbW0t6uvrodVqeZ9OPWgqlQpubm4QCoWwtbXFwMAApFIphoeH+ffS\nPSMUChnnoqhdyzEzM8PrIg1LAQp1nBHpqtFouAuutrYWLi4u8PPzAwAmI2hvFxgYiKtXr+Ljjz9G\nUVERALCD3mg0Ijs7G1VVVRAIBGhubsaqVavg6OjIz6fFxUX09PRwUgZFn4WFhaGiogIKhQJarRbt\n7e3IyMiAs7MzJiYmWIBG4pX7IR//hwT4++O/JQnw0EMPwWAwQCwWIyoqiq2mEokEISEhWFpaQmpq\nKmfMy2QyhIeHY2RkBO3t7WhsbGQ1s0AgQEpKCh/MqXyGcu3JihodHQ21Wo3w8HAuumloaMCHH36I\n2dlZdHd3Iz09Hc8++ywSExPR2toKR0dHHDp0iJvZSfGu1+vx0ksv4fe//z1eeuklTExMYOvWraiv\nr8eNGzfg5+fHakuNRoPMzExkZmZieHgYaWlpHG3z3HPPQSqVorW1FR0dHXjyySdhNBrx+OOP44sv\nvkBrayvOnDmD119/HRKJBLt27eKIHFtbW8763Lp1K+bm5nDt2jXMzs6itrYWp0+fRkVFBYqKihAb\nG8uFyfHx8bwx9/f3xy9/+UvMzMxg586d3IyekpICmUyG559/HoWFhYiIiGD10pkzZ/jAcPnyZRw+\nfJg7Bs6fP4/bt28jOjqa8zopxz0iIgK1tbXw9PTE+fPnsWHDBnzwwQcoLS1FWFgY2tvbIRAI0NbW\nhuTkZMjlcuzZswft7e3IycmBQqFAVlYWlpeXERgYiLCwMDQ1NaGiogLOzs7IycmBXC6HXq+Hu7s7\nOjs74eTkhK6uLkxNTeHo0aPw8PBAUFAQTp48CV9fXwbwKXM6IiICeXl5mJ+fR3R0NAICAvDee+/B\nz88PR48exdNPP409e/awevXatWv4+uuvERERgaCgIHR3d2PLli24dOkSrK3vlpQdPnyYAevKykp4\neHhgYGAA69evh4+PD5577jkUFxdzKSuBHDt27IBYLMZLL70EiUQCZ2dnBAYGIiEhAQkJCThw4AD0\nej2GhoYQFRWFjRs3YsWKFXB0dMSNGzcgEom4zV6hUODTTz9lACIsLAyDg4OQSqVob2/Hvn37mH0l\nwJ5AFlJzEdBUX1+PFStWYPPmzRyXJBaLMTk5ifXr1+Py5cvw9fVFUFAQiouLERUVhbCwMJw8efKe\nTa9CoYBMJoO3tzcMBgPWrl2Ld955B9PT07h+/ToSEhJYyVVdXY20tDTodDoUFBQgPT0ddXV1/9Ti\n+c1Btt6amho0NzcjODiYWXaj0YiamhpUV1ejuLgY8fHx2LlzJytFyA5tWeLk5OQEHx8fZGZmIi8v\nD62trbh27RqCg4OZCEhLS4Ofnx9qamrg7+/PYINIJEJ8fDwOHz6M06dPIysrC52dnbC1tcX09DR3\npBiNRo5quHHjBvbv34/BwUGMjY2xtV8ulyM2NhZubm58aJ+fn8cf/vAHdl7I5XLExMRg9erVeOCB\nB9DQ0IAHH3wQO3bsgI+PD65evQo3NzesXLkSDg4OCA0NxcsvvwydToeQkBB2Yy0vLyMvLw8ajQbl\n5eUoLCxEXl4evv/972P9+vUQiUQc40Jlp9PT01hcXMTo6Cg8PDxYPRgdHY3KykpER0djYWEBAwMD\nCA0NZWUqgUyk4BwfH0dfXx+sra2xc+dOTE5OYsOGDVi3bh3s7e2hVqu5sIuUPHTgcXR0xPnz5xET\nE8M2Sb1ezwDd9PQ0RxQBdzfOlClNIHp7ezueeOIJ9PT08KaQ4hw6OzsxNDQEsVjMG1Ra/wikIlWt\nVCpFaWkpenp64OzsjCeeeAJr166FWq3G6tWrsbS0hJSUFGzZsgUuLi544403kJOTg6WlJWRmZuLC\nhQvcm/Kv//qvvMmje+XmzZvw8PDAjRs3oNVqERwcDF9fX9jZ2SE2NpYJGAIMN23axArZmZkZNDc3\nQ6fTITg4mOeTXq9HaWkpZy/rdDpWkFZUVCA7Oxtzc3OIjIzE4OAgk6eUZatQKNDa2or8/HwuBi8v\nL8e6deswPj6OiYkJJpm9vb2xatUqFBcXw9/fHwEBARCLxZDJZAzCAGD1sslkgkKhgFKpxAMPPID+\n/n4MDg5y9mZbWxuOHz+OiooKrFy5EklJSQxoLi8v486dOwwQ1dXVQSQSMbm1tLTEzwaRSARvb+/7\njiHr7u6Gi4sLZDIZEhIS4OLigv7+fgYhxsfHAdw9pMTExHAuc3BwMLRaLY4ePQqTycRqRIpnoHxf\nIlCkUimsra0hl8sxMzPDiiwCACiGRKfTYXR0FGFhYTCZTGhqamKAdmpqClNTU7CxsUFkZCRUKhVc\nXV3h6ekJvV7PpD+B6aRUpWgIGxsbBkS+aV+XyWSIiopCZmYmnJycGCz/ZlQRXXcA98ToEPEwMDDA\nmafA3QNKR0cHZmdnMTMzwwXYloCbg4MDK83oj6Uai34v5S+Tw0+tVkOn06GwsBDT09OwsbFBf38/\nE41arRYzMzMMXJH6imKPSHlJoAiBRvQeCUidmJhg9yYAnrsUM0QWdbo3XV1dER4efo/ikqJE6NlD\n69/k5CQ8PT0ZkLGyssL8/DwfItvb2zE3NweBQMAdC/SaSclIgBYplynSY2lpCV1dXRgaGoJKpcKG\nDRvg6OiIgwcPQiwWo6SkBC0tLSy8IFGEwWBgUn14eJiLEDdu3Mjvl+YEuTdo/hJg7OTkhMTERExN\nTSEhIYHn5fLyMoKDg9nBSv0YFNWydu1aBunJaZuYmAiJRAIrKyuEh4ezwIjA8oqKCrS0tKCzs5OJ\nHpFIxKDf+++/z6AP9QuYTCakpaVBIpEwgUUEAIFvNC+6u7tZhUhOiUuXLmF8fJwFTps2bbqvdefY\nsWPs/omOjr6nrJccXcDdngsq0SQSbXp6mvd7rq6uaG1txTvvvAOtVovHHnsMAQEBcHZ2hlwuh7+/\nP8cL9fT08Hqs0+mY5LKxsUFVVRX27t3L4Iu9vf09xB3NTSIuaI2h+9fPz4/nn1gshr+/P1JTU7kH\nhyL6LN1BdF9YEkp0nqSvp3Mi9cXU1dVxKTDFaBIoRmuG2Xy3y4eAS1tbW3ZcT05OYnh4GA8//DC6\nu7sZ9KBnCs1nIvsInLJ0NtG8EIlEmJubY0KYgFmdTsfE/cLCAiYmJpCUlITq6mp4eXnB19cXlZWV\nmJ2dhUwmQ09PD4qKitDT0wNHR0cYjUacOnUKJ0+exNmzZ9HQ0ICJiQkEBwcjNjb2nrWC9mEA+H7Q\n6XS8hpIogIhVAqDo7+izIJKD1lxSl2ZkZLAoyFKZbnn/0/0iEom4jNLDwwNSqRRqtRo7d+7kCEma\nMxR1QfEz5OqyJNnUajVHeFKcqVQqhYeHB/R6PRMe1tbWmJiYwJEjRzgudmhoCF5eXkwOkegLuCta\nmZ2dRV9fH5ycnNDT08PArdlsZkHg9PQ0HBwc4OvrC5FIxEXMFGvm6OjI5at0Lege9vPzw9DQEL7+\n+muOtKMzJJV9E4lCz1Mi3efn57lPY2lpCQsLC5ibm8PS0hJaWlrw2Wefobq6Grdu3WJBzncdc3Nz\nAP4KulpZ3e0GIaGAVCrF/Pw8f1YTExOYmZnBli1bIBKJ0NLSgtDQUCbHAGBoaOhbAWKBQIDMzExU\nVVXBaDRCo9HAzc0NfX190Gg03OtRUlKCiooK+Pv7w2Aw8POUYuC8vLzg6ekJhUKB3bt3Y/fu3di5\ncye2bt2K7du3Y/PmzfDy8uK9cEVFBX/2RLj8vRiZzs5O+Pv7M8j6zUE4FzkZvjlcXV0RERGB69ev\nw9vbGz09Pbx+6/V6XLp0Cf7+/tzt983xbeA17RvJrUu9KStWrMDS0hLef/99zM/PQywW43vf+x7y\n8vJQVVXFONtf/vIXXL9+HatXr77n51J0aXJy8rdei28b8/Pz0Gq1cHFx+c7f810IgMXFRUxOTuLS\npUvcTdXe3g6NRoOKigr+GrFYjNdffx0pKSl831jG8tCa/Y9GbGws/vCHP+Ds2bMsDFhYWOBowmvX\nrsFgMCAhIQECgQBLS0twcXFh4oPWtcnJSRgMBkxNTWF6ehpZWVnf6gIgstRy0DppMBjQ3NwMf39/\nTidxdnbG9PQ08vPzuWyaxtTU1D2iN9r7Z2VloaysjB2bZrMZ/+t//S9UVVXh+eefR2pqKrKzsyGR\nSHhNGh4eRktLCx555BE+k1GpsMlkYhEbickoYm15eRkjIyM4c+YMRkdH7yEpvsu4fv36d/7a/9fG\nihUr/q//zn9KArz33nuwtrbGhg0bIJfLcfz4cZSVlfEDraurC5OTk3j22WcZTBAKhejq6kJfXx9i\nYmKwYsUKaLVafPTRR+jq6kJ0dDTm5ubw1VdfQSKR4M9//jMSExPx3nvvYe3atbh9+zZsbW1x48YN\nfPHFF1CpVDhy5Ah2794NR0dHrFq1Cj/96U/h5eUFk8mEqKgoVFRUwGg0oqioCJWVlWhubkZubi5m\nZmYYWAkKCuJcLLlcjrq6OqxatQqXLl2ClZUVSkpK8MQTT8DJyQmHDx+GQqFgxbufnx+6u7vx5ptv\nIiYmBv39/fj6668REhKCkydPIjY2Fq+99hr6+/sB3GWUm5ubOcc2KioKWVlZqKmpgUKh4HgQsViM\n4eFhvPzyy1hcXERmZiZMJhM8PDwYTLSysoKPjw9UKhXGx8dhMplw4sQJNDY24tixY/D390dfXx+y\ns7OxceNGvPzyy7hy5Qqsra05BiAlJQVffvkl1q1bh3fffRdpaWno6elBbm4upFIpXn31VeTl5SE9\nPR3T09M4ePAghEIh/9z09HR4eXmx42N6eho//OEPUVJSgu7ubo6HEovFKCoqQnd3N3bs2IHFxUWc\nPn0aq1at4vy97OxsODg4wMfHB+3t7Vi9ejWcnZ1x7tw5iEQi+Pj4YNu2bRgfH+eN8po1azjOZXR0\nFFFRUVi5ciUGBwchFAoZgCIlWVBQEJycnBAbG4u33noLGRkZ8Pf3x/DwMPLy8tDV1YXs7GxkZ2ej\nu7sbvb29iIqKwhtvvIG8vDwAdzeHFGk0PT2NdevWYXBwEBcvXkRXVxdvvKOioliJ5OrqipKSEigU\nCvz2t7/Fnj17sHLlSuzatQsSiQSZmZno7OyEn58fnJyc8PLLL0MkEmFychKBgYHQ6XTIyspicPLk\nyZOc/VZVVcXKfEvmenFxEREREfiXf/kXJCUlwdXVFW+//Ta7FQoKCtipolKpEB0djZs3b0IikbDC\nIzExEQAYRHrvvfdw+fJl1NbWwtramhUfBGY4OTnhxz/+MdasWQM7OzvOpn/llVdgb2+Pt956Cw8/\n/DAA8Ly+n9HQ0IADBw7gl7/8JZKTk5GYmAidTsd23qysLNjb2+OHP/whfH19cfjwYdy5c4czcI1G\nI44dO4bMzExIpVLuxqDDWnNzMzIyMvDggw/yv5EKbHx8HI899hjbvAncDwsL40icwMBAyGQyzhul\nfODIyEhERkZCJpOx6kepVCIoKIhB7CNHjsDKyoo7OeLj4/Hggw8iNTUV3d3duHDhAjIyMvDaa6+x\nmsnd3R1yuRw1NTXYt28fYmJiOEPV1tYW2dnZWL16NWdvDg0NIT4+Hh4eHvj/2Hvz6KjP+97/pX0d\nzUga7dJoF1pBQkhCEhICA2ZzANsQHJvacZY6uY2TXrdp0jRtncRufGvHCfF1wTGOjVswxgYv7DsI\nbYD2BbTv+zIjjTSSRtvvD/p8IhI7Nvf8Tk/Pufc5p6eJI0sz3+/zfb7P814rKyv5xje+waZNm9i1\naxfPPfccZ86cYfXq1bS1tQmorMrOz507x1e+8hVaWloYGRkhOjqaubk5goODZcMVGRkpLgGVp6w2\nTNPT0/z93/+9FBGrA+WaNWuEWPLx8ZHcfpUlb2NjQ3FxMYmJidjZ2bFkyRLJdFV29oWFBSoqKoC7\nKpMzZ87Q3NyM2WwmKSlJADFF4F29epX8/Hw+/vhjqqqquHnzJrt27WJhYYGmpiY53Nva2kr+q4qy\n+PnPf86RI0dobm7GYrHg7u7OsWPHZBOsbPYqJzsgIICenh5mZmY4deoUFosFT09PORS1trZSUVHB\nypUrqa6uZuPGjURFRaHVasnLy2Pjxo3Mzc3xu9/9juXLl+Pt7S3kQ2lpqQCFqpBdrT2lpaXExsby\n0ksvERAQgL+/vwBUZrOZ06dP09HRgb+/v4BpqvPF19dXejPMZjOOjo74+/tz48YNkpOThbyLiYmR\nqKGCggI5gMPdDW1mZqao6P38/EQt5+bmhtFopLKyEj8/P/r6+tBqtQLWqELn9vZ2oqOjxXa/evVq\n9Ho9NTU1QhhrtVqZ82azWTbOSumrruPly5eJjo7GwcFBIge+7Kirq5PDmbOzM15eXkRGRlJRUXHP\nYS4qKor4+Hj0ej0JCQk4OTkxM2SRVXkAACAASURBVDNDT08PGRkZhISESA761NQUnZ2dol5UilOl\nci8vLycyMlJiUFTm8+IDgqOjo+QXR0dHi5I2ICBA7rlSHSqngcoRVwq5xQp9BeCp++7q6srs7Czj\n4+M4OTmRl5cn4OFi8GtoaEhKG9XvUmC6mgvqc1itVvkcfX19NDQ0sGzZMnl3KWWain2Yn5+X7hdH\nR0dqamokckH1IqhoF6XYvHz5MmVlZUxOTtLS0kJ7ezvp6ekEBQVJiXBlZSVFRUWUlpZSWFhIb28v\nIyMjAiQDEsehCtFVbIydnR0zMzPS0dHU1ERZWRmJiYnU19fLHFRElclkEkVuW1ub9IKo2Cf1c87O\nzveoStXfVVELSk0KyGFRKUsHBgaEfLWxsaGyslLAeAXqKbWv+vcXZ6UPDw/zxBNPyPWPjo6mrq6O\nTZs2iZLNxcVFMn8/+eQTZmdnCQoKYnJykqKiIh555BEpjlNAoZojCwsLQhxcuXKFa9eukZOTg6en\np+yFFVCongWz2Uxvby8ajYb4+Hji4+MJDw+XzHblmFGRWiqGQinkg4KChHjLzc0lJSWFjIwMDAYD\n7u7uaDQaWT8DAwPx8PBg1apV1NTUMDg4yPXr18UtsjhKaLECHJDiPZUR3dTURH5+PqmpqcTFxREa\nGkpAQIBE0n3ZcfToUQGBv/rVr+Li4iLzSBFV6nlTIKG61vb29iKmam5u5t1335V9zKZNmwgKCpIC\nQOXgUaCecsbZ29tTU1MjSvTBwUEeffRRyQhWyu6pqSm5Z3A389nFxUU+p1Lzqt4aRbApdTEgZJ8i\n2hS4Y7VaGRwclDVeORgUMKHmAkB+fj4LCws888wzUl6rXH0K7GtqaqKlpQUXFxfJuZ+YmCAoKIjc\n3FwefvhhUlJSGBkZYWJigm3btpGVlYWjo6PsJRb3SKg5rtxySs2pAGv1flTr4Pz8PB4eHlgsFgwG\ng4jdFIgUExPDsWPH5KyiXKaFhYVotVqJwlXPsXJfqDmwdetW/Pz8ZL1V11WtnwsLC0K2qbmyOGt6\nYWFBHIUKRFRiAHVv1NpjNBrx9fWVKCKlnlfkLfyBGFEAuFrbLl++THNzM7GxsURERMj7TO0VW1tb\nCQoKwsnJSeaXxWJhamqK27dv4+TkxKlTp3BwcCAyMpLc3FyCgoJENa3cWep9piLGNm3aJNGmKt60\npqYGvV4v6nEFvqs9qOotGhwcFGdLSEgIVquV0tJSBgYG8Pf3Z2JiQvZm6v43NzeL60rNGaXud3Fx\nITk5mbS0NLq6uqirq6OgoABnZ2chG11cXMS9ZrFYqKmp4fr167S2tnL79m06Ojrk3VRQUMDBgwfF\nUaKI9j179tzXuqNKpRcP9a5Y7OxVYODIyAi1tbVkZ2dLBJsSvqnxeQpxFe+1fv16bty4wS9+8QvW\nrVtHWFgYIyMjaLVaXnrpJY4fP05paSk1NTXcuXOH4uJiHBwc7nGPATL31OdU80a5MNQ/C/vPyCNA\n3Jx/PBTJe+XKFZYsWUJDQ8OfqJsHBwcldlC9mxcLBxYPvV5PSUkJBw8e5MiRI9TV1eHl5cWaNWtE\n2KP2f180lNP/8uXLImpRv+PUqVNs27aN0dFRpqamSEpKwtbWlomJCdavXw9AcnLynxAAalitVhHa\nftFQDrP7iX65n7F27VqsViunTp3i+PHj7Ny5U7CJTz75BGdnZ/7hH/6B2NhYNBoNbm5uHD58mKCg\nILq6ukTs8edGS0sLP/zhDyVOVUU3ubm5MTExgU6n41e/+hVeXl6cOnVKOgHV3lCJ4mZmZnj11Vc5\ncuQIS5YsISkp6XOJpZ6enj/J+Ie754i5uTlOnz4txNPin2tvb/+TLglFPqn3kHJt1dXVsXv3bmJj\nY9m4cSObN29Gr9dL+oiKFVT9LgsLCwwODrJ8+XJ5jha7VFU5fXFxsQhXSkpKqKyspLi4mImJCRYW\nFsjNzSU8PPyeXo0vGv+PBPj88d+SBCgsLCQuLo60tDQmJycZHh5m9+7dFBUVMTExIWWPnZ2dYsma\nn5/n2LFjBAUFERUVxeTkJBcvXuRv//Zv+fDDD3nqqafQarWEhYWh0+lYt24dJSUlfPOb3+TUqVMM\nDg6Snp5OUVER3/72t3n99df5i7/4CyIiIigqKkKv1/Od73yH1NRU/P39iYmJQafTceHCBV588UX6\n+/t5/PHHGRoaIjU1VRRcRqORhx9+WBTRdnZ2LF26lP7+fubm5njooYeE2MjOzmblypWiLGhtbeXc\nuXNs376d/fv38xd/8RdoNBoCAgLIyMhg06ZNzM7OUl5eTnt7Oxs3bsTf319KaKxWK88++yxWq1WK\nZ3bu3Mmbb77J22+/zdjYmORuW61W9u7dK58hJCRELKzqAPjxxx+zf/9+kpKSeP755yWf2NbWlj17\n9jAxMYFer+e9994T5UthYSG7du2iuLiYuro6urq6ePTRR+no6BCFxsjICL/61a/YuXMn/f39rF+/\nHp1Ox40bN7C1tSUjI4P+/n7WrFkjpYYqauLYsWPk5eXh6OjIihUrGBgYEMD19OnT/PznP8dgMNDY\n2MhHH31Ec3OzqBKdnZ1ZunQpSUlJfPjhh0LaGI1Gzp07Jwqumzdv8vzzz7N//35qa2vlsKTy4ZUy\nyN3dncLCQi5fvsyePXv4+OOP0Wg0XLlyhd7eXlatWkVlZSUDAwNERETIRu/mzZtSkPm73/2OyMhI\n8vPzaWlpoaCggMjISB5//HEpZzQYDLS1tWG1WgkICMBsNtPZ2cnly5cxGAwkJSUxPT2NVqtldHSU\nvXv3sm7dOpycnLBYLBw7doyMjAxycnKoqanB09NTDtoJCQlYrVZWr16Np6cn169fF7Y6NTVVSlU7\nOztFvZmbm8u1a9dEpevr60tVVRXe3t78+te/JjMzE71ezy9/+UuqqqrYunUrZ8+eJSAggPHxcby9\nvenq6uL69ets2LCBr3/96yxZsoTi4mK0Wi3Dw8PU1dWRmpoqBwRXV1deeOEFyUQ2mUwMDg4SGRlJ\nYmIib7/9Nt/61rfua2F66KGHeO211xgYGMDPz4+xsTFxJNnZ3S2xvHjxosz58PBwQkNDef7557l+\n/bocwg4dOsTmzZvlhTk2Nsb7779PcnIymzdvvuflpTaToaGhcsBRFj0FOtTW1hIUFMT8/Dwmk4n5\n+XnJRVTF4+pQ5uTkJEU6dXV16PV63N3dGR0d5cSJE9KpcfHiRbZu3Sqbgu7ubmZmZigqKqK3txeL\nxUJLSwvp6ens37+fXbt24eLiQlVVFQaDQQA0pdDOzc0lMTERq9UqEUX9/f0kJSUxPDzMqlWrqK2t\n5fz58/LcmUwm3nnnHU6ePElfXx8XL16ksrKStrY24uPjpVxu//79eHh4iE3V1taW4eFh3NzcGBoa\nApAi276+PnkuTp48SWpqqiiYhoaG+OlPf0plZSUZGRmibAD4yU9+wpYtW/D29hYgGe4eWC5cuCBE\njCJjmpubMZlMJCQkiNKnqamJCxcuSMZtUlISmzdv5sqVK8THx+Pu7k5KSgpTU1P85je/ob29HX9/\nf+mXsbOzIzs7m9OnTzM9Pc3u3bvJy8tj/fr15OXliRqsq6tL4mdOnDhBfX09U1NTfO973yMqKoqk\npCQuX77Miy++yOHDh6VQOywsTJTDiwtNDx06xNTUFOvWrRNVpk6nw8bGBr1ez4EDB/jmN7+JjY0N\n5eXlTE9PSyH6+vXreeONN4C7imur1SqK8NbWVh544AGioqKws7MjPT0df3//exSMFy9eJCkpSQ7G\nubm5cuBRz8/AwABFRUWsXbtWQAer1cqHH36Ip6cnH3zwgRCKi7OCldpZbS5VXr4qEezo6CA0NJSC\nggKSkpKESFAki4q/q6qqoqenB4PBgI+Pj+RbLy4FNRgMki++OLvzy4za2loBf9S7yd7+boFgY2Oj\nAMObNm0S1aTaiE9MTIizRwGPTU1N6PV6fHx8pIBdgSTq+Tl//jz5+fncuXMHo9EogNbQ0JAoDjs6\nOqirqyM8PJzw8HA0Go0APO7u7qKaVIC/Akfc3d3/RFmolNrqADs2Niag9KVLl1i5ciVarVa+/+LI\nB0WYKpBDqUUXxymoaz85OcnNmzcFcJyenmbJkiWYzWYBSNT7bPE+QIGdSpFqY2PD9evXGR8f58yZ\nM7S3t9Pc3ExFRQVjY2PAH+z909PT5OTkiBPMy8uLmJgY6QWZmpqir6+PtrY2jEYjcPdwOzIywvDw\nsJCR/f39or7s7OykoKCA8vJyRkZG0Gg0pKSkYDAYmJmZkT2vs7Mz7u7umM1mZmdniYmJISkpCS8v\nL4kGPH36NImJiZJPrfaWBw8eJDQ0FE9Pz3tAe3UdtVqtXI/bt28TExMjCmW9Xo9GoxHVqMlkoqGh\nQZxWivBR0RdeXl5idZ+fn5fOgvT0dCHM1bu9sbGRsbExBgYGqK+vp7a2Fo1GI5n4ixX/iuBQ8WeX\nL19mfHycvLw89Hq9xHnGxsbekzeuiA0VN6qcNM7OzgwMDHD58mWJOVH7aVWGqaIBnJ2d8fHxkb4B\n9bsVqKiiMs1mswBDn376qfQJqJzv+vp6GhoaMBgMdHV1cefOHcLCwlhYWJD3gr+/v+RSh4SEMDk5\nSUBAACaTSdxP92vr/rd/+zecnZ157LHHZJ/g7Ox8T2yA6pFQYgL1HdVe8sqVK9y4cYOuri68vLyk\nAFcJHAYGBvDy8pJnUcUBmEwmETyYzWZKS0vlva/clH19fRQVFREWFiYkk4pcMRqN8gwoclCRPH8c\nD2Nra8sbb7xBSEiIAOQKnFDrgAKIp6amRIChSASVje7v78+SJUuE3JienhaVN9zNUX7ppZeorq7G\n09NTipA9PDzIz88nLS2N0NBQmXsNDQ00NTURGRnJypUrJS5GCX1UQS0gn08RUFNTUwKkLP7fFEDZ\n1dUlvUzqLKrizubm5rhz5w5+fn4YjUaGh4fZsGEDer2eqqoq4K4wRzn5VD+Ii4sL3/jGNyS2cHG8\nmSpTV4pQRQiq50JFvigCXb2j1d5pMWmwOI5RPd/q3QL3gqCLy7fVvbdYLFy6dAk7OzuioqLu6dJQ\n+dInT54kMjJSSMqpqSmamprYv38/kZGRaLVaQkJCJLd6fHxcHJUuLi6yXqnehOHhYbKzs9FoNFJu\nPTc3J+9VBcwrMkO5D1Q0oY3N3Sx2RcLr9XqampqwtbXFx8cHZ2dn+vr6+PjjjyktLeXatWu4ubnJ\ne1PFc6n3promSvRnMBgoKCigt7eX7u5uoqKi0Gg0mEwmHBwcOHbsGFNTUwQHBxMREUFYWJj09E1O\nTvLOO+9QVFREX18fQ0NDArqHhYXdtxPgs0gAdR8WRzJ1dXWh1Wrp7++nt7eX1NRU+fv+/v5fqhhU\ngbRTU1M88sgjIm7TaDQEBgby6quvEhISQk9PD+7u7hKhaGtrS1lZGSkpKTJ31Lvwsz77Zw11fxeT\nFYuH+q6tra0iuFSuUOAz/922tjZxWKrPpN6b5eXl91y35cuX8+CDD4q4YWFh4UuB1oDEu6ampnLr\n1i327dtHSEgIo6OjvPfee2i1WsxmM+fOnaOvr4/Vq1czNzcn+/A/N1QM4xeNsbEx6aa6n9HU1PSl\nOgfU+0F16oyOjvLkk0/i7e1NW1sbdXV1/PjHP5YuADU/4+Pj+fTTT7G1tZVYKuVU+2OnxfT0NC+9\n9JLE4qkzemRkJI899hienp488cQTeHl5ERQUhKurK6WlpbJWq8+n3gWVlZXiGFD7qc+ak4uBfeXg\nVZFgKoVDCd4Wx9k1NzdL16cai50xaoyPj4sYxcXFBW9vbxwcHCReU/VcLnZFnjhxgszMzD8hoWxs\nbDhz5oz0wObn5zM/Py/RYIosDQgIYM+ePdKl8Fkkx+eNoqKiL/2z/7eNzMzM//K/+YUkwNzcHF5e\nXpw/f57m5mZ27NghC7PKNCsqKqKiooKdO3dib2/P22+/TUREBF5eXgICf/vb32Zubo73338fo9FI\nXFycqB9iY2OlgC8nJ4fAwEAKCwt59tln6e3txdnZmZSUFN566y0eeeQRKWP76KOPJDJhfHyc4uJi\ntm3bhlarFaXU2bNnOXLkCDExMcTGxuLg4MDJkycpLS0lNDQUq9VKSEgI2dnZcsjNysrCzc2NPXv2\ncPnyZTQaDZcvXyY9PV2KpIxGIxs2bMBisZCWloa9vT1ms5nDhw8TFhYmjJ2npydnzpwR1fKqVato\nb2/n7NmznDt3jqCgIInQeeSRR3jqqafYuXMnzc3N5Ofns2rVKkZGRvinf/onHn30URISEvjhD3/I\nCy+8gJ+fH25ubrz//vtoNBrJQM/MzKS6upqHHnqI+vp6TCYTDz30EDk5OVy6dIng4GDa29tJSUmR\nzHNlJS4tLZWCV1WOpTI6z549i4uLC7/73e/IzMykvLyc5ORkfvvb37J06VK2bNnCsWPH2LlzJ9PT\n05w6dYqsrCzs7OwoLi6mvLycpqYmWltb+cd//EdWrVrFBx98QH9/P9evX8fLy4tXXnmFbdu2kZ6e\nDiBxG2lpaVy5coWf/vSnstHasmULU1NTzMzM4O3tjVarFUBWr9eLsqKiooKenh4qKyv52c9+xu7d\nu/Hz82N8fJzY2FgcHR0JCQmhtLQUFxcXtm/fTk1NjXzvr371q6xdu5bAwEAuXLjArl278PHxYWxs\njDfffFOKFCMiIggODubq1at861vfwt7eHq1Wi4+PD42Njdja2goxduXKFXp6evjOd76DRqNhaGiI\n2dlZsrKyZHOrSllramrIysoiLS1NIrfs7e05ePAgzs7OeHt7k5GRQVZWlhwygoODKS8vx8/PD09P\nT2pqanj55ZeZn5/n17/+Nc899xyPPfYYFy9eJCEhAZPJhKenp6gh4uPjycvLk74Ig8HA7du3MRgM\nvPPOO6xevZqZmRn+8R//kYqKCr773e+yevVq3NzcqK2txd3dnbNnz+Lt7c358+d57rnn7mthUnmH\nWq0Wd3d3jh8/LpZbi8XC7Owst2/fprOzk9jYWNzd3XF0dOSBBx4gISEBPz8/fvvb37Jv3z5mZ2fp\n7u6W76LAsPj4eCYnJ0UFa2Njw/e+9z02bNhAbW0t7733HtHR0Xh4eAiIoGIF3N3dJf7m008/xdvb\nm1deeYW+vj5GR0dlDTCbzbS2toq1VlnTN2zYwMMPP8xDDz3E6OiouJp+/vOf8w//8A/09/dLofRf\n/dVfkZmZyYEDB7BaraSkpBAYGEhISAjj4+NcunSJsLAwfvWrX0lWpMlkYvny5XK4f+edd0hLSxPL\nX2ZmJmFhYezYsYP29nbeffddkpOTefrpp/Hw8ODZZ58lLy+PvLw8nnrqKbKzs7G3t5eyvL1797Js\n2TKxYKss5AMHDpCVlcWxY8ck0uidd95h/fr1TExMSPfLM888Q2ZmJj/60Y/k2k9OThIYGMjKlSv5\nxS9+webNm2UDqNPpOHToEDt27JBnOjg4WMqj1f2/cOGCqNhUjNfY2BixsbGiUjOZTKSkpAjgEBsb\ny9KlS4mLi6OyspKRkRH8/f2ZmZlh3bp1ODs7k56eTkREBC+++CIlJSU0NjYKoWZjY0N1dTWZmZks\nXbqUI0eOsHXrVjmYnzp1CoPBQHx8POfOnaOqqoqxsTGKiorkpT86OsqlS5f4+te/TmxsrOQJK0W/\ni4sLfX19VFdXk5ycjFarFfL71KlTZGZmSoxcY2MjOp2OzMxMWltbMRqNzM/Ps2LFCkpLS4mJiRHS\nSB0gjUajHNBnZ2c5evQoK1euFHWlyWTi6NGjaDQazp49S2dnJ1NTU+j1eoxGI8ePHycnJ4eOjg4S\nEhIkr1YdToKCggRMKCgoIDQ0VK5PQEAAgYGBHD58mICAAHE5DA8PYzQauX79OkajUVwTnZ2dLF26\nFFvbu90JU1NTwB9K6ubn5zlz5gwGg4HY2Nj7Wndu374N3M0iVeCh1Wrlxo0bTE9P4+vry8MPP4zB\nYJC858nJScl/HR0dRaPRyKFAr9djsVjQ6XSYTCYpslfdAjMzMwwNDeHu7o7RaKS7u5v+/n7a29up\nra0Vd1FAQADx8fF4enpSUFAgTgfljlGgkwJn5ufnJWYIEDAHEAJGkRhqnaqrq2PdunXy2eAP5WUK\n3FOAkYpSAATsWKw+V3F1QUFBBAYG4uvrK5nzHh4ecq9sbW0lTkYBJuqQ5ObmRldXF2azmYWFu4Xg\n6rCoon08PDwkasJoNAr4FBkZKfnAyt2ybNkyUlJSCAkJISMjA09PT1pbWykoKKC9vZ2uri6am5u5\nePEiVVVVVFVVUVtbS0dHBwMDA3h4eGBjY8OaNWvw9/cXkEy9l1R/gFJum0wmfHx8CA0NlTLjzs5O\nWlpaxFE3Pz/P+fPnpf9IxZ+pezs9Pc3k5CTFxcVCnkVHR6PT6eQeTk1NyUFe9TqpuDZlMVeEk729\nPXq9npGREby9vWXtjYmJEZWyKoBVpIy6bopA9PLywmAwCMCrDpcKBFR5tJcvX+aRRx5henqa3t5e\n8vPz6e/vp6Ojg+XLlwvAqg6ZExMTLF26lICAAPndirRZtmyZxEYo14yah66urgLwKVAPkHeTAkVr\namq4du0alZWVuLi4sGrVKqKjo+np6cHX15clS5bQ2dnJxMQE165do66ujsHBQWJjY5mYmODw4cN0\ndXUB4OnpiVarpaysjKmpKcLCwujr65OS5/vtIjl06BDf//73paxvMYCh5pQSQZjNZiGF1Pusra2N\nkydP0tnZiY2NjfQ13L59W56poKAgiaBafH3VM9jT08Pk5CTnzp1jYWGBwsJCwsPDaWho4NVXXxWR\ngSpPVWvze++9R35+vgCxCqRQf0OBxwqg/td//Vfc3d2lV06t3y0tLej1eiEtx8fHZY1YPBRAr+bO\nYieCmn9Go5Hy8nLKysqorq4WwZiPjw/Lli0T0FmROitXrsTOzo7AwEDpoVBxE+o9oL6PIlBV5I9a\nI5U6Uq1/qlxXRStOTEwIEKz2Hg4ODty+fZvo6GhsbGykZ0Sn07Fp0yZWrFhBYGAgjo6OBAUFScmp\nv7+/xKkoh5CKGFPuMRUbBtzzblDrlIpLUuu3InSnp6cFkHZwcODOnTsSxaLimtS7QxFJs7OzQgKr\n66T2YaWlpRIrGh8fL+uLirqqr6/Hz8+P0tJSGhoamJycxMfHh+zsbGJiYnB0dMTDw4OZmRnq6+sl\nTk+5CVVHSXd3N4cPH+bSpUtkZWVJxJ76Tvb29vj4+ODi4sLY2BglJSU0Nzfj7+8va4Z6xuzt73Zn\nHT9+XPYUav4UFBRw/fp16Q7YsGGDOKLV+1S9D8rKyu5Zz9Q5IDs7m02bNpGYmEhxcTG/+93vyM/P\nR6PRsHbtWnG0q3eiq6srVVVVvPHGGwKCGgwG0tPTWbNmjXSzbN269b7Wnc8iAeDufnRx8bm3tzfN\nzc1CAgcEBEiHnOpg+TIZ8fCnUTxOTk4YjUYOHDggBI+KVw0JCRGXTXV1NSUlJQwNDdHe3k5UVNQ9\nz9+fG8od80UjLi6Oo0ePijBQjc+LeTGZTPK9r1y5Io4Wf39/UZy3trbyyCOPMD4+LsIERS4oBf/n\nqcjVZ1d7vNDQUDZs2IDJZOKjjz6itbWVW7duMTw8TGdnJxaLhcrKSuLj4/8EQP6sofqN2tvb5Z2q\nHGJqmM1mbty4IV1s9zPup3Q4JSWFHTt2sG3bNvbs2XNP3NrAwADbt2+/h7BU5zml1r9y5QpdXV10\ndHQQGRn5J7//9u3bvPXWW0xNTfHkk0/y9NNPs2PHDrZs2SJ7F6VqVw7ZAwcOEBMTg42NDT/+8Y8Z\nGBggJydHiqAjIyMxGAz3iHr+3HxUwii176qvr2fJkiV4e3tLtJ5S4ldWVhIWFnYPSbN4DVZDOY9U\nz4MiNVUEqdrfqPew6uD6vHsTGhqKxWKhsbGRW7duERAQwNjYmERVzs3N8cQTT4gAET7f/fNZ4/+R\nAJ8//luSAGNjY2g0Gm7fvk1YWBhOTk4SU5Cenk5iYiLz8/NERkYKA/XWW29JyW1hYSH19fX4+/tT\nUVFBW1sb3/nOd0TJNz09zfz8PP/0T/9EeXm52O1UiZyvry9RUVFYLBZ+//vfs3btWlpbW+nu7pbM\n7p6eHn7xi1+wf/9+PvroI1FMKMYvKSkJBwcH2cAbDAaKi4sFuB0bG6O/v5/bt29z7tw5GhsbcXd3\nZ926dRQUFJCamsrjjz/O3r17Jfrm6aef5qWXXsLPzw+DwSAq4/z8fAYHB0lKSuLdd99l+fLlpKWl\ncfDgQWJjY7l16xY3b95kyZIl9Pb2EhERQXp6Oi0tLTg5OREfH8+FCxfYuXMnS5cu5fz587z88sus\nWbOGlStX0traKsoKLy8vPD09CQwMpKamhrCwMLZv386RI0dYWFggNjaW+Ph43nvvPR5++GECAgIA\neOCBBxgbG2P16tVcv36d8vJyKT3JyMjAz89P2D0V3eDs7Ex8fDzvvPMOQ0NDtLa2kpmZyeDgIHV1\ndYyNjYl1yd/fHw8PD0pKSli3bh1DQ0OSEf7Xf/3XtLa2kp2dzdWrV/nGN76Bv78/wcHBREVFERkZ\nKeCiyWTi+PHjdHd3s2zZMg4dOkR0dLTMGS8vLwICAqitraWgoAB/f3+ZZ/Hx8aJKdHFxITAwkPz8\nfB5++GFMJhNnzpwhKytLlKQlJSX4+PiQnJzM3r17cXNzk2JdLy8vQkNDmZmZITY2VgqNXnzxRcbH\nx0lOTuaJJ56QzNq9e/dy/vx53N3dSUxMpKioiIGBAQIDA/Hx8cHX11dIFZV9bzabWb58uURf9Pf3\n88Ybb/D0009z5swZMjIypPTy4MGD7Nu3j7/5m7/h5s2bUhY5Pz/P3/7t32K1WpmenmbPnj3o9Xrq\n6uqE1AkICKChoYEPPviA3Nxc9u3bR2RkJMnJyXh6enLx4kXWrl0rjg+l+J6bmxMgLy4ujtraWslw\nLSwspKurS6KOjh49KgBGAWt9SwAAIABJREFUd3c3P/jBD1i2bNl9LUzKWq2UT1FRUbz88suicjSb\nzUxOTlJdXS2xGLa2tjz33HO0tbXh7OzMjh07JK9Z/Z/VaqW9vZ26ujpyc3PlEDQ0NCROJmU1Vmrs\nxVEAr7zyCuvWrcNkMokN+dKlSzKfvLy8qKysJCoqSmy9BQUFoqoeGxtjcHAQBwcHYekzMjL4/e9/\nT2lpKTMzM2i1Wl599VW+//3v8/bbb5OVlUVXVxebNm1i586djIyMSGzNzMyMEA4WiwVfX19+8IMf\nEB0dLYfl6upqtm/fzqFDhwTQGBkZwcnJCR8fH+zs7BgZGWH37t1Sijc/P09/fz/e3t5cuXKF7du3\nYzQaBXArKyvj3//931m/fj1Wq5VXXnlFgG6TycSdO3ek5Fur1ZKUlERISAjvvfcee/fuJTU1le9+\n97tSyqvAO6Usb2trY/Xq1eh0Olk/srKyhPA1mUwyPwYHB0Udl5ycjEaj4eOPPyYqKgo3NzeJu5md\nneX9999ny5Ytoph2dHSUUloF5hQWFgrQr0DkiIgIrFYrxcXFWCwW1q5de48qUuW729jYcPbsWSnO\nU0BaRUUFW7ZsITU1VXoHVEdEXV2dFMcrW7cCeZQienZ2Fjc3N44fP87s7Kxs8B0cHEhMTOTHP/4x\nn3zyCY899hgNDQ10dnaSnJxMUFCQ5AaXlJRIebhaP5WKr7y8HEdHR/r6+vDw8CA6OprW1la5N6+8\n8gq3b9+mqqoKDw8Pvva1r6HRaIS8+OY3v4nBYGD58uWcOHGC2tpa5ufnee211yQX/tatWyQkJODl\n5SWZ/kuWLGF2dhatVsuyZct4/fXXmZqawt7eHqPRiKenJ8XFxaSlpYkCJzAwkH379omNVxEXFotF\nCvreffddZmZm7vtQXF1dDSCK4ampKXp6erhx4wZTU1PiyoK7iqOZmRnGx8cZHBwUtaVSXfv7+4v6\nUt1XpVRWAO709DQXL17EaDRKHJM6BMzNzbFp0yaZVyrTPygoiNraWlEMK0BHAW6Ly2yVulb9d1U0\nqzo2lBK1r6+PzMxMAZQAUT+rWA/4gwJPAXWqLFUdUhUIAohqXqmjlWpcORaam5vR6XQ4OTkxPj4u\nsTrKDWBjc7ckdGJiAg8PD1xdXWltbWV0dBSdTseqVatYtmyZZLaqLNX+/n7Z58EfivDU39+3bx+5\nubmEhYURFhZGfHw8XV1d9PT0CCC6OFvcwcGBqKgoAgMDWb16NYGBgZIlOzw8jJ+fnxBaKk91fn7+\nHuBsMUEwMDDAp59+Krn3sbGxZGVl4efnJ5nq8IcYn+rqagFpVVG1IqAWdzyouJDe3l5RfLm5uQnA\nZ2NjQ29vL97e3uKucXJyEpJbAbXqZ1V/gqenJw4ODri7uxMeHk5kZKTkj6s5rOagUhcrta2K9Llx\n4wZDQ0PiVsjJyZH1zd7enoGBAezs7OT5UPN1enqauro6YmNjiY6OprKyEo1Gg4eHB93d3eJYUaps\nVVZrtVqxWCxCtlZWVlJfXy9KO41GI24DGxsbcUZFR0cTEhJCWVmZlDGr/ay/vz9BQUHiALG3t6e+\nvh69Xi/l4Uoter8kwLvvvktSUhJ6vV7U2mqfq94BCkBQkThKaT05OUlHR4cUAKo1YHx8HJ1Ox9at\nWwkJCZG9sE6nY3R0VBxe6u+5uLhQWlpKeXk5Xl5e2NraUlhYSFNTEz09PTz77LMkJyfj7+9PZ2cn\nw8PDnDlzBrPZTF9fH01NTVRXVzMzMyN7VUVUKOW5jY0NDz/8MPHx8ff0b0xPT/P8889LFv/09DQ9\nPT0i5lHArMr9HxwclM+uCBJ1LxUorQAx5chWGeGurq4yP9WzqtwEJpNJOnkWPyMq9kz9f7i3F0Dt\nX9Q/U/uRmZkZOjs7mZmZoaCggKioKClBVm7Phx56CA8PDxEzKIIB7ip1/f39SUpKIiUlhaCgICn9\n7evro6enh5CQECFRFxYWCAkJkfVmsVtEPdfKCbDYsaG+z+JOhvHxcVpbW+nv7yc0NFTul/o96vlU\nQ5Ewi++5ra0t3d3dzM7OsmHDBnx9fe9ZL1Q2vALFDAYDISEh+Pj4iEr5+PHjxMbGSmG86u1Q93xy\ncpKKigquXr1KXV0dX/nKVwgICJD3psVikb3O+Pi4iHfUXuLixYt4e3tLya+6N4cOHcLGxobu7m56\ne3ul43B0dBSLxSJumG3btokLWL1r1PdxdXXFxcWF0dFR2Qsu/jkVNdLQ0CDYSlxcnDz3imiysbER\n0qG9vZ24uDiSkpLYsGGDxKAlJCR8Jvj558bnkQDKhbMYnFaAcVRUlMxtW1tbenp6vjD+7M+B9TY2\nNrS0tHDjxg3s7e2lo0EJcSYmJrC3t6exsZH+/n42b97MypUr5X36RUPFCX6ZbHqLxUJwcDB37twR\nXOfzhoODAyUlJUK8rVmz5h7xxPXr18WF8uCDDxIUFCTl6GqovcbnDbW/Uvs2NfR6vYgGBgYGiIuL\no7W1VUjQ+vp6Hn300T/7Xbu7u3FxcREgW0VZqfmoegFHRkZoaWkRHO3LjpGRkXsAbOV0/7z7oIB0\nBaara3/o0CERyqp/tvh3LHZftbW13eNAU3uD+fl5Ll++LHHK27dvZ2BgQBwsExMTEgmtiJmWlhbB\nCZUAbc2aNURERGAwGIiOjmbJkiX8/ve/JzQ0VPaof27OANTU1DAzM4O7uzttbW0Ss2ZnZ4ednZ10\nwZSXlws5rYbVar3nmqp9ASB7m6GhIXp7eyktLeX06dPY2tpiMBiE/KypqSEmJuYz76Vav7u6uvjN\nb34ja/ziLgQVyaY+s+qp+LKjsLDwS//s/20jKyvrv/xvfiEJcOfOHYaHhyVbSr20Dh48yJIlS+RQ\ns2zZMhwdHXnzzTcJCAggKCiI8+fPA/Dtb3+bixcv8pWvfEViWBRTfvToUU6fPo3JZGLLli1kZGRw\n9uxZdDqdqJkNBgPPPfecWJWV7dLV1ZXi4mIiIyMxGo0kJCSQl5fHoUOHiIqKoqOjQw5Z/v7+1NbW\nsnTpUpqamnBycuKJJ56gsLCQoKAg0tLSGB8fZ9myZSQkJDAxMcHg4CAPPvigbEoeeughtm/fzubN\nmwGoqKgQRcbVq1e5fPkyvb29vPzyyzzzzDP88Ic/xMfHR+zRb775Jt3d3fzoRz/Cz8+P9evX09jY\nyPHjxwkNDeXXv/41jz76KKWlpbIBSk5OZvv27bi6ugqIcu7cOZ555hlqa2uZnJwkPj6enJwcLl68\nKCoXnU7HRx99xOHDh/nlL38pMSR79+7l6tWrPPnkk7i6unL06FEefPBBtmzZQkBAgKgU33rrLSoq\nKnBzc6Ompob4+HhmZmYoLy/H1dWVZ555hrfffptdu3aRlZUlESk2NjbSNH7gwAFWrVrFhx9+yNq1\na1m5ciWurq7U1dXJvImJicFqtdLS0sL8/Dzt7e2Ul5fz/vvv09nZSXx8PL29vRQUFPDiiy/KRvrw\n4cNYLBbeeOMNdu/eTW9vL4WFhWzfvh1vb29KSkro6emRTXZQUBCVlZV0dHRw+vRpdu7cyQ9+8AO+\n/vWvY7FYuHDhAh0dHcDd3OPExEQCAwPZsmULdXV1wF217OnTp0lOTmb//v0sLCyQlJQkhx0FBpw6\ndYqf/OQnUqQZGhrKJ598IrnFOp2OiooKKTO1t7entraW5uZmAS3/9//+35KXunv3bkwmE35+fmg0\nGhISEigrKxMrtLIXDg8PExMTw9atWzEYDAQGBkoshbIHq/LnqakpfHx82L59u9j5R0dHSUlJwc3N\njZaWFhITExkYGKCnp4ewsDB5ZnU6HSEhIRITlZOTQ1pamiiTkpOTuXnzJqGhoQwNDWEwGO6b4ezp\n6WF0dFSeg7m5OVGj79y5k/DwcOLi4jAajdja2vLuu++Sl5fH6tWrGRgYwM3NDV9fX/R6PXfu3JGO\nhampKVF2qlJAlaHt5OREX18fDg4OxMfHY2try+HDh1m+fDljY2O8/vrr/OhHP8LOzo7f/va3YlFd\nsmSJqLPi4+PJzc3FYrEwPj5OW1ubkJJ79+6V7PqrV69KLMTMzAw3btxg9+7drF+/noiICDo6OiQj\n8NFHHxXHyu3btyVD9+DBgxw6dIgrV67g7u7O6tWrpZhXqWhV+dj8/DwrV65kcnKSkZERLBYLQUFB\ncohWmbBKfaRi0BwdHaVvJTU1le7ubi5dusTTTz/Ntm3bGBgY4NVXX2XFihXcuXOHt99+m2PHjhET\nE0NycjInTpyQyLjz588zPT3Nd7/7XWpra0lNTRWXWUdHB2+99RbZ2dm8++67rF27Fk9PT4aGhjh9\n+jSrV6/Gz8+PkZERPD09CQ4OpqSkhCNHjnD27Fk5hL344os88MADrFq1ipMnT4qK49ChQ/T09JCS\nkiLKiNnZWdn02tnZidMjIiKCpUuXyt/77W9/S1FRESdOnGBwcBCNRsOePXuEqBwdHSUwMFCiBXx9\nfYmPj8fV1VXKh/38/AgPDxc128jIiGQjtra20tDQQFZWFhaLheHhYTmQqs+n7k99fT2rVq0C/lD8\nNzc3R1FREX/3d39HREQEy5cvp7u7m/fee4/Y2Fh8fX3Jz89n165deHp6CiCqnAN9fX04OjqSmJiI\ns7Mz+/btIycnB1dXVw4ePMhrr73Giy++yObNm8nLy2N2dpYLFy6QlZUl2cyJiYlYLBacnZ25du0a\nWVlZsrZHREQwMjLC4OAg8fHxaLVaysvLuXbtGkajkerqauzs7GhtbeXrX/86aWlpqNLa4OBgli9f\njtVqJSwsTOalk5MT+/btk44Xs9lMRESE5Mbr9XoCAgLIycm5r3VHFTQODQ1RW1vLJ598QllZGc7O\nzmzbto3AwEC6urro7++nu7tbCgvd3d0ZGBggKSmJ6Oho3NzcaGxs/BNgG7gn53l0dJTCwkJGR0el\naFCBWE899ZTcLwUMKcDO19dX1JpqbqifU3ZiRWypjbwiIBcW7hYs37lzh+bmZlJTUwkLC5MDzPz8\nPIODg5w4cYKqqio5/KnfryKEent7xZWm7NUKfJqYmODq1auiUFSxL2NjYxJRo9frpY9ARV8pRZOK\nWuvo6MDBwYFr165RXl7OzMwMf/mXf0lcXByBgYECGCi7clJSEnfu3KGnp0ccNQosV+BVTk6OXC+1\n/ickJJCSkiKgtdqjrFu3jqVLl5KcnIyXl5cQioq40Ol0zM3NSbm5IhGUM0SVmar9pNlsprCwkIWF\nBerr66mqqiIvL0+A+MUAnSIujx49yuTkJFVVVbS3t1NZWUlLS4tEYlgsFoaGhuSdExYWhp+fH9XV\n1URGRgroBncPiq2trej1evr6+uT9qeKM1Bxtbm7Gzs4OjUZzT5+BctItzoVX91b9d1Ve6evrKwX1\nGo2Gmpoa1q9fLz08i6NWFCmuXJDq+pWVlTE7O0toaCizs7M0NjaSmpoqgN7AwICo0RS5/R//8R80\nNDRw6dIlampq0Ol0tLa2Sjl1ZmYmDzzwAM7Oznh4eODp6YlGoxHiR6vVkp6eLl1awcHB0oGiFOgq\n576rq0u+y/j4OBMTE4yPj7Njx477WnfefPNNtm7dKtdyMbmiYt1aW1uprq6murqa+vp6Acd7e3ul\nq0KpI+3s7Hjsscek40ztJVVXha+vrzwPVqsVo9GIu7s7Y2NjNDc3i5taqSNVVIfaIyyOLOrs7JSC\n8enpaek4MRgMTE9PCyADf8jUVwCeivexWq1s3boVR0dH+vv7yc/Pp7a2luXLlwvooWLLTCYTVVVV\nVFRUoNPppDRYrYUKIP7000/p7e0F4OrVqzQ1NTE1NcXg4CADAwMMDAyI43IxqB0cHIyTk5MA3IrI\nW+xIWPxuXkwCKDJTdZYogiQoKIiw/yxPnJmZEXI5LS1NgGK1/trZ2UnEGyDqTuUcUCpPRRgpcH58\nfJyZmRnJ7lbPISAEsAJtAHEbqbVBgd8LCwsMDQ3x2muvSexZTU2NrAGKNFiche7k5MTY2Jh8d/V3\nVFyo6s1Qz+rIyAjvvPOO9Pht375dSDkVY6cA+bi4OMm0Bu5xSs3PzzM8PMwnn3xCVVUVNjZ3o9yC\ng4NFTa/OD0pIoeadchcoQkq54Xt7e2lra6OqqoqBgQEsFovElZnNZukFcXZ25sknn0Sv19Pb24uH\nh8c9+0n1vKl3jHK7Lia5Z2dnmZ+fp6SkREQtwcHB0kWmCJmFhbv9Dl5eXgwNDQmR5ufnJ50XWq32\nHoD5y4zPIwFUsbxa09V8npmZITw8nN7eXlFCK5fEn8u3V9d8YGDgHqBUFWXv27dPSq1VqoG3t7eI\nHb28vIiOjsbOzo7Nmzd/KVW/un5KHHH27FmioqL+7L+j3tcajUbOB39uKPHrZ3VPrV27lrKyMtra\n2khNTRXB5pchI9TnV9/zswDb8vJyHnjgAbZs2YKjoyN1dXVCHKt4KhX3pdymgPQWeXh4fOZ1bGxs\nFCJUOTfr6+s5f/48ycnJX5oI+OOYIbW/uZ+h3MbBwcH3iFP+GGivra0lPj6eyMhIKYE2m81yxlHd\nXQ888ADp6ek4Ozuj1WpF4GBjY0NjYyNLlixhbm6Ot956i/fff5+QkBAeeeQRcnJyyM3NJSAgQM7H\nCwsL6HQ6EhMT6e/v5ze/+Q0pKSlfCIj7+/uj0WhkrR8cHMTf35/Z2VkcHR3x9vamp6eH1tbWPylt\n/uNrr9ZIo9FIWVkZL730EmfPnmVgYIDs7Gyio6NpamoiLCxM+kvUtfosssLGxgaz2czExASnTp2S\nLkKr1SrngO9973uCEyhB2f10Avw/EuDzx39LEqCxsZGPP/6Yt99+m76+PjmIubq60t3dTUlJiWz4\n/f39uX37thS9KpZNFRXq9Xq6urpEofmTn/yEXbt2iQWzs7OT999/n3Xr1pGRkUFVVRWnTp1ixYoV\nfPrpp5IVaLFYBAy/evUqJSUl/OAHP8Df35/Dhw9z4cIFYmNjJf6io6ODO3fuEB8fz+HDh8nLyyMq\nKgqTycTHH3+MxWKRiISf/exnzM/Pk5ycLNl+rq6uvPjii2i1WpKTk2lvb6e4uJiEhAQ++OADrl27\nhl6v50c/+hE5OTkMDQ2xefNmKUHr7e3l5MmTfP/73yc1NZX5+XlRWb7zzjtSurqwsMCNGzdoamqS\nguHExES6urr45JNP2LBhA7Ozs3z66ad0dHRI2ZJWq+Xtt98WQKKiooJf/epXvPDCC2zevJnAwEBm\nZ2dpaGjgxIkT7N+/nxMnTpCdnc2FCxfuKYQyGo2EhIRQV1eHj48PCwsL7N+/n02bNnHnzh1u3LjB\nY489hsFg4MSJE3z1q18VVWd3dzcxMTG4ubnh5eXF8PAwmZmZWK1WYmJiROWel5dHYGAgL7zwgqhO\nDx48yJo1a4iOjubAgQN85zvfIS8vj3/5l3+hvr6eJ554goiICHQ6HUFBQZw4cYInn3wSPz8/Kioq\n2Lx5M8PDw2RkZHD+/Hm2bdtGSEgIg4OD3LhxgxdeeIGAgACeeuopYmNjJQIqICBAFLb9/f1SPqvX\n6ykrKyM8PJyqqiquX7+OXq9n/fr1uLq6EhgYiMFgICsri4iICLy9vfn9739Pd3c3zs7O5OXlMT09\njdFoRK/Xs3LlSiorK/nWt74lqpTc3FwyMzOJjIwkKyuL5cuXU1NTg729PUlJSWzdupWkpCRef/11\nampqWLVqlWwIN27cSEREhJS61tTUYDabpdzuvffeE7bX3d2dw4cPs3HjRhwdHQWwVJtVpW7x9/en\np6dHNpOTk5OUlJQQGxvL9PQ0TU1NaDQaUborlczmzZvx8PDA2dmZ//k//ycbN25kYmKC5ORkPvjg\nA+rq6virv/qr+1qYfvzjH4vN19PTk/z8fEwmE5mZmbIhPnLkCA4ODvz7v/87JpOJhx9+WApa09PT\ncXFxEUub+j1ms5kPP/yQH/7wh3Lws7W1FaXK9PQ0fn5+AsQsBsczMzNFcWUwGBgaGsJsNrNkyRJs\nbGwEIHJ0dESr1dLc3ExYWBhBQUF4e3vLnNHr9SxZsgSA69evc+DAAdasWcORI0eEONq0aRNXrlzB\narWSmZkp4H5gYCATExOcPXuW4uJiBgYGANixYwc+Pj6yYVaqCjc3N+nmUJv6n/zkJ6KkUUC/g4OD\nOIKU+hvubt4//vhjvv3tb+Po6MjQ0BC//OUv2bVrF05OTuh0OlpaWqS07+bNm+KiiY6O5tatW6xb\nt47p6WlWrFjB2NgYFy5cYMWKFYSGhuLu7k5ISAi/+c1vGBwc5KOPPmJiYoLly5dz+fJl6YhRijil\nZFPgurJcr1u3jpSUFDZv3oyrqyvOzs5ERETQ19cnFvH169dz/fp1wsPDBZxSqhMFsKjSUDs7O4aG\nhoRQVa6KiYkJUUj09vZSU1PD0qVLReE1NDRETEyMgKVKcdnb23tPCXd4eDjt7e3s3r2b7Oxs4uPj\n+eijj8Qurw7bCphQh+PExES0Wi2vv/46aWlpGI1GdDod58+f5+TJk2RkZGBvb09WVhapqakSSRAR\nESGKblWI9/d///cMDQ2xdu1aIRLNZrMQ/mNjY5SXl2NjY8ODDz4oB8Lo6GjOnTvHtWvXWLNmDTk5\nOUKguLm5kZ+fz6ZNm4TQGBwcFNVdRUUFgYGBxMfHExcXR3R0NAaDgfb2dtzc3GhubhYQZ3JyUoCN\n3t5e3N3d8fb2pq+vj6ioKE6ePInFYuHZZ5+VqIXg4GDOnTsnBPv9KnL37dtHUVER5eXl1NXVYTab\ncXFxIS0tTRw3oaGhhISEEB4ejo+PDx4eHvT09JCYmCiHcAV0KzXrYhBucnKS2dlZBgcHaW5upqur\nS8DqmZkZUlNTyc7OltJH9ewuBghnZ2dxd3enqKgIb29vUYHDH+zc8/PzjI+PYzabKSgo4Pbt29TW\n1mIymSgqKkKj0bBy5UrJR168Fl68eFH2IH+cfa+y5UdGRgRgVPN9bm6O0dFRamtr6e/vp6GhQfK/\nbW1tpY9pcbSQAg8UOaWU846Ojpw5c4bx8XEhOl1cXMjIyECr1QpgpdZopdS9desWs7OzkhO/+DA6\nMjKCTqeTOaaukyKWXFxc8PX1xWq10t/fj8FgIDIyUkA/QDoiFBmgwAulvlfgmMViEfu8Wmenp6cp\nKysTp4eKGlsMjirgQgGkCuRVRGdcXByurq40NDRQW1uLp6cnUVFREnfh7OzM1NQUFouFwMBAAaRU\nRIkCmPv7+zl37hwlJSVMTk6i1+sFnGpra5P3yOIeCJW3q4hlBTgvVqvPzMzQ19cnSlxVLGpvb096\nejru7u7ysyruSK15N2/epLm5mevXr0v3h42NDSaTiampKVJSUqR3Qd3fkZERysrKJEIsMDBQioET\nExPRaDSsWLECHx8fcnNzZV9ka3u3QFGRN+r+qWgZdW/V//bHYPD09DRtbW2sWrUKd3d3cXqGh4f/\nyeH9i8a1a9fIyMgQBbmjoyPDw8NMTk5y9epViWIMDg7G29sbLy8v6SxQxHRzczNms1nm8QMPPIDB\nYBDluVJdq2dLrW3qe9vY3I12bW9vF4dBQ0MDfn5+LF++/J4oCEXYubi4UFxcLOSfj48PYWFhAuir\nsTiPWCk3FQm3OM6qu7ubf/3Xf+XKlStkZmYSERGBnZ2dRLIMDAzQ2NjI6OgoH3zwASMjI4SGhuLl\n5SWOOUDcgY2NjTI3zWYzFRUV3Lx5UzpefH19CQkJEfLOaDRKF5ECARerUpUjSoGaSrGtgF1FeExP\nTzMyMkJDQwOnT5+mt7dXHBgvv/wyRqMRq9UqsW4KEFJrh1qX1GdQToo//vuTk5NYLBY6OzupqKig\nt7cXFxcX2tramJ6eZnBw8B5FrBLCLB6L3VyTk5PiTt6zZ48AzIt/Tv1ntU4pIGpxjJUisufm5vi3\nf/s3xsbGWLZsGfb29rS2tmKxWKTUXZ3H2/6z804BsUpEoUhBtfbY2trKZ1EAf3NzM/39/YyMjDA2\nNkZcXBwRERFCcikRlK2trSQCqHJWNRd9fX0ZHx/nxo0bXLp0ib6+PpycnATfUPNUCaimpqbYvHmz\nREqp37W4B0CVDSun0NjYGFarVWIeFYHe3d1NV1eXKPxtbGzk31XvJnVmU/1XyuWsHBj29vb/v5EA\ngJwF4W5x+cDAAFlZWTg4OJCQkICnpyeHDx9Gr9czOjoqSQOfNzo7O8W9pYioubk5ysvLqaysJD09\nHavVSldXFx4eHmRmZkr03tKlS4mOjiY8PPxLf0e191bjiwgAuOsEUKSN6lb5Px329vbSWdbc3ExE\nRITs6z9L/T83N0dPTw8NDQ1SQPznhopXVKKQ2tpa+vr6mJ6elv1hWFgY8/Pz6HQ6iWtUHRqfNxaL\nMuDus3zkyBFxvN6vq///dCwsLEgSw2Li6I8/u9rTzc/Po9VqpftFp9Oh1+ule0Gtf4vdBIrAbWpq\nYmJiguDgYNra2jh8+DA2NjZER0ezY8cOIRYWD0X4OTo64uvri1arpa+vj7BFRdSfN9T+rra2VtZv\nRQQrEjQ2NpYPP/xQnNPAPY4Q5azo7Ozktddeo6ysjLm5OZYtW0ZfXx+bNm3CbDZTVVXFrVu3yM7O\nBu6+wxaLANRQBLbaI6gEC7j7XPT39/PCCy8A3NNjZ2dn96U6QdQoKCj40j/7f9tQ9+i/cnwhCVBW\nVkZVVRXPP/88S5cuRafTMTk5yeTkJKtXryYnJ4eDBw+ydetWZmZmGBgY4MCBA9jb25OWlkZubi6H\nDx/m+PHjLF26lNdee002PImJiXzwwQcsLCzw0EMPMTQ0hMlkIjc3l8bGRrKzs7l48SJnzpzh2Wef\nZcWKFeTn56PT6diwYQM9PT0kJSWxfft2OcCkpKQwPj5Oe3u7TObAwEB5qaenp4uiZXx8nMuXL/PC\nCy/Q3d2Nr68vy5Ytk2ielpYWPv30UywWC4mJiaSmpqLRaBgfH+f8+fNs3LiR7OxsZmdnSUhIoLOz\nk7i4ODw9PQXcnpqqhsydAAAgAElEQVSa4sCBA7K5Tk1N5ac//Sm7du3i+PHjZGdn841vfINbt26R\nmpqKnZ0dTz31FGlpaRQWFhIfH091dTUeHh6sWrUKHx8frl27Jox6X18f4eHhZGVlMTg4yFe+8hUq\nKirkwDM9Pc3+/ftJSEjAwcFBLGRZWVlSctrQ0MCVK1dEkenh4UFHRwcNDQ1oNBqMRiNHjhzBx8eH\nXbt2odPpqKmpYceOHTz++OPs3r2bEydO8LWvfY2jR48KY3j79m18fHwwmUzk5eXR1dVFeHg4f/3X\nf81HH33Ezp07OXHiBOPj45Kdv3fvXilreeGFF8TGnJubS0NDAwkJCYyPj5OVlcXevXt59NFHpZDt\n2rVr9Pf3c+3aNVavXi0Hg4yMDC5evMgvfvEL/uVf/oWbN28SGRmJr68v/f39zM7O0tLSQn19PV5e\nXpKfvHTpUiYmJti4cSNbtmwhOTlZsocbGxu5dOkSZrMZX19fBgcHiYmJwd7+boGz1WolLi4Ok8mE\nTqfjn//5n3F3d5cyy8bGRoKDg5mYmKCuro5Tp05x4cIFHnzwQYk4GBgYIDg4mPXr15ObmyuHjWPH\njhEZGcnIyAhDQ0OcO3eOr33ta1IEVFBQwPnz51mzZg1FRUWEhIRw/vx5enp6mJub42c/+xn/43/8\nD8LDw5mbm+PSpUtcvHhRYlIUIHfmzBlSUlKkyOzKlSvU1NSQl5fHs88+i4eHBw0NDURFRUmWpru7\nO8eOHeP69eukp6eTmZnJjh07SEhIuK+FScW+RERE8Pjjj9Pc3ExWVpbEEw0PD5OUlERMTAw7d+7E\nbDazYsUKDh06REZGhmQEf/jhh3zzm98kMDCQl19+mezsbClVNhqNeHl5iV20tbVVCmLd3NwEvFD5\nsf/rf/0v8vPzhdQKDw8XcunKlSuipFbK+n/+539my5YtTPx/7L13VJR3vj/+YqhDGRgYei9DlyIg\nggVEiqJRicZodlPWJJtszW6yJ3c3+aZs9p67yWZTNuZmNUWjJhrUJBoLgoCCgPTekWEoQxlggIEZ\nBmZgfn943+8LuZts/J57fuee873POXtWI2XmmefTXlWjgVwu5xx6mUyGxsZGODk54fLly3jttdcw\nMzODTZs24ejRo7wQxMXFISIiAuPj4xzLBoAje7Zu3YqQkBBER0fzwYE2l0qlkpUfBMySgkoqlaK7\nuxufffYZUlNTOfZpbGwMfn5+rOSlIqTCwkJWNTc2NqKvrw8HDhxgQiouLg4tLS1wdXVFa2srdDod\njEYj6urq2GVFii0qwVYoFPD19YVarUZJSQlaW1t5U+Hr64u9e/fC3t6eN55KpZKJFFIfODg4sPp7\n165dTA7QgUggEODkyZNoa2uDq6srBgcHsWPHDrz++utcMEXP0rFjxzj+a35+Hl1dXZDL5RAIBJBK\npRwdtWHDBo54amhowN69e7lsbmFhAUKhEG+88QYcHByYtPj666+xZcsWBgpWHmKDg4NhZWWF7u5u\nvvcVFRU4fPgwrl69iqamJly+fBkFBQU4deoUHnroIVhYWDCh4OzsjIKCAuzduxe7d+9Ga2srd92Y\nmJjAw8MDc3NzsLS0ZNWqicndDgOlUolf/vKXuHz5MnJzczE6OoqCggLcuXMHNjY28Pb2xtatWxEX\nF8dAtVAoxMjICDZt2oS6ujrExsZicHAQXl5enFtbV1eH8PBweHt7w97eHiUlJXj66acRGxvLBwi5\nXA47Ozs4OjpiZmaGAXTaQE9MTODmzZu4fPkyqqurERsby8BYS0sLrly5AldXV+zduxd+/1Gc5+Tk\nBL1eD6lUCmtra/T29mLbtm33NO/87ne/w/j4OObn5xkYIgLAxcWFHUOkVm9oaICTkxMXGNKhjNSc\nEokEk5OTDFqamZlhfHwcTU1NKC4uxp07d3gzrtFooNPp8NBDD3H5KJViroyToT+Tm4diG4iIpK+f\nnJxEf38/CgsL+e/UQUBkOIH4BCSo1WqUlpZCLpcjNTUVmzdvZjBGp9MxOEoxMTTeCGRbXFxEX18f\nmpqakJqaChcXF3YMUHQW2cTpXpE6kg4hBO7T+y8pKWEg22AwwMHBgYt+FQoF3Nzc0NPTAz8/P1bC\ni8VidrH6+voy4Et5qQSu0GsnNxzFLxiNRsjlcoyPj8PPz48JNJ1Oxwd6+ryJ0CAAcnl5mYts6ZBJ\nz3RJSQnPpTY2NggJCeE+HlL70rUyu3vXrl2Ij49HXFwcIiMjERwcDKlUCqPRiPXr16/KGKcYp8nJ\nSQZmiEwih8H8/DxKS0sxNzfHopTOzk7o9Xp4eXnBxcWFgdCVsSkE+hEovhKcpPtHgD89J0ajEU1N\nTUhISGBn2spccQLDlpeXGXgKDQ1FdHQ0g9gBAQGslKVMWwcHB352fXx80NnZicHBQWzZsgWOjo5c\nvkcRIiKRCHZ2dvwcjo6Ooq2tDW5ubquAcDoYazSaVQQAAZ3AXbeDXC6Hs7MzXF1dGRCgsueYmJh7\nmndef/11Vr1WVlaiq6uLAT5yQ1GMExH15LTw8/ODt7c3KisrodFokJ2djaysLAQGBjK5QnE6HR0d\ncHFxYdU2dQSQGj8vLw9LS0usgAbuqjGTk5MZaCHxBIEJmzdvxp49e7Br1y4kJydj+/btfJ9WupcA\nMCBP0YsrwSYCcRISErgHav369QxaLy0t4aOPPkJhYSF2794NExMTNDQ08NmGiARLS0t+ZjIyMnD7\n9m1+vqampiCVSqFUKjE+Pg5vb2/u57C0tGTRmL29PbRaLSu4aZ5Y6bii/06OHdr30L2xsbGBs7Mz\nkpKSOJP5xIkTiI+PR2RkJMLCwthZv3Jup2giuof0vBJZSUp7ExMTdndFRkYiPDyc+/K8vb3h5uYG\nc3NzXLx4kfeH9JkPDg4y2QHcBUynp6dx4sQJuLi4MFmn1WphZmbG0WVGoxEdHR04e/YsfH19VxEB\nKpWKn5fl5bvdY2NjYxyXmJ+fj/LycnR3d6OmpgaLi4uwsbHB7t27IZfL4e/vz70/9vb2q9wiJNAh\nh4ZCoYCzszOXUstkMgwMDLA7bvv27ZBIJNDr9ZiammIXM7nCZmdn4e7uvmo8k6ObXqOdnR0rqcmN\nRHN6dHQ0Hn/8ce56oQx9WmeI2CHREBGnVGhOfQ1E7Pf19XHEX3BwMLq6upj8zM3N5TXCzMyMzyjU\njUefoUAg+KdA/Lev4eHh71S7015iYGAAcXFxvMchAsjW1haJiYlwc3PD9evX0dbWBqlUCoPBwCps\n4C6xd+zYMZw8eRKlpaVobm5GTU0NpqamUFRUxAkLsbGx2Lx5M7Zt2wZ/f39ERUWtciTMzc1xtNPK\nWKqVFwlHjEbjd5YAf9+1Umnd3t7OfQ7/t5dIJEJmZiZCQ0Px3HPPIS8vD+fOncO+ffv45965cwd5\neXlob2/Ha6+9hsbGxn8a5QOsjoIhPG14eBjy/4jDJSJJKBTi6tWrSEhI+EHvhQQLwF2S6K9//SsU\nCgVHPxUWFt6zuIZ+1pUrVxAWFvaDvt7ExAS/+c1v8OCDD676nGmvNjU1BZVKhby8PN6jrfz8vovo\noLWehAB6vR4vvvgiJBIJrly5ggsXLgC4K15+8cUXv7cHgeY+ExMTiEQiTExMoKenBwEBAd9LBMzN\nzeHWrVtITk5GUFAQfH19WYxA4jxLS0sEBQVxvxKVg5Owgvb0Tz31FASCu91A0dHR6OvrQ1paGoRC\nITo7O5GZmYnU1FSOd3JxcVm1z6GL1hnad/b29uKFF15AWloaduzYgZSUFI5dXvk95Bj9odf/kgDf\nff2PJAHokFpcXMwq1LKyMoyOjnJkRn19PbZt2waVSoWKigpkZWVxeYZer4dMJsMf//hHvPTSS6wQ\nppKRtLQ0nDhxAg4ODkhPT4dOp0NCQgIWFxehVCrx6KOPws/PD11dXVheXkZqaiq6u7tx/fp1mJqa\n4tChQ/jqq69QU1ODvr4+eHl54fbt23j88cchkUjwwgsvoKmpCT/72c9YRVRTUwM3Nze8/vrr2L59\nO1QqFavXtVotTp06hfj4eMhkMuj1evz2t79FSUkJZ6FSLvKXX36J1tZWqNVq1NXVYdeuXWwjo43g\n6dOn8dRTT2F6ehre3t44c+YMHnroIeh0OtTX13OmmFQqhZ+fH65evYqbN28iOjqameHY2Fg4Ojpy\nFIalpSXKysrwt7/9DdHR0VwyQ8ATbVjs7Ozw97//HdnZ2RCLxejo6EBnZydkMhmGh4cxOzuLlpYW\npKWlwc3NDQMDA/D09IRKpUJxcTGkUil+/OMfY2RkBF5eXmhsbMSWLVsgk8nQ0dGBoaEheHh4oLKy\nEqGhobh+/TrH3VBOHSkrwsPD0dPTg9LSUrz00kvYuHEjbGxs8Mgjj8DGxgZarRZr165l0OHEiROI\niorCwYMHMT4+jgMHDsDFxQUtLS3w9fXF7Owszp8/j1u3bmF0dBQ1NTXQ6XR47LHH0N7ejszMTCws\nLGBgYIAdKDExMQgICIBYLEZhYSEqKytRUFCAS5cuITExkQ8flPtPNlPKN9doNFhaWsIHH3yAgwcP\nwsrKCl1dXYiPj4ednR1HSYSEhCA+Ph56vR7u7u5obW3F/Pw8kpKS4OzsjMrKSmzevJk3iBcuXMCh\nQ4dgZmaGwcFByOVy2NvbIzw8HG1tbRAIBCgsLOQsRnILUDxIU1MTrl27Bp1Oh/7+fnR1dXFskKur\nK4qLi7Ft2zacPXsWFRUVcHd3R0JCAgOwO3fuhIeHB1tLyQ1BmeWLi4tMUhmNRrz66qvYu3cvOjs7\nUVtby7bvwcFBWFtb48yZM/jzn//Mh6Lp6WmOMPmh18DAAAQCAY4fP4777rsPt2/fxu7duwHcPZy/\n++67yMjIAHD3cKjRaBAQEMBFkGTPv3jxIpKSklgttG7dOs4MJru9wWBAXl4eTp06hcceewz29vbM\nsjc2NiIiIgLLy8vYsmULUlNTUVdXh/LycqSkpDCA4e7uzs4ZpVKJmzdv4vnnn+d8xfXr10Or1cLa\n2hrnz5+HiYkJPvzwQ7zyyivo7OzkZ87Pzw9vvvkmYmJiWBkwNjaGsLAwVmoTaUEFmyKRCI6Ojqxa\nEggEHFug0+nQ1dWF8fFxiMVi7m+gjNXbt2/D398fXV1d7HIiQrWxsRF/+tOf8Pnnn8PU1BRqtRr5\n+fkwNzfHzp072W1gNBoREBDAAC2BJR999BFOnjzJeeiffvopAgMDYWNjAy8vL8zPz3N+4e7du3Hr\n1i2o1WpkZ2ejvLwcrq6uSElJQUFBAbZt24Z33nkHWVlZEAgEEIlEDErZ2tqira2N1Vl0UFtcXERQ\nUBCqqqrw9NNPw9XVFQqFghWn6enpGB4exujoKBISElBTU8P5tFZWVti1axcEAgFKS0shFAoxNjaG\ndevWwdzcHGvWrMHGjRsZHAPuxvoMDQ3h7NmzqK+vh1QqhUwmg5nZ3UJl4D9Vz2q1mp+Vjo4OBhXz\n8vKY/HV0dER0dDR27NiB5uZmhIaGIiIiAkqlksEJimSYnp6Gm5sbfH19cfHiRURFRUGr1WJhYYFV\nV4cPH8a6detQUFCA8PBwpKSkoLa2FmVlZXj11VfR2NjIDpbq6mp88803CAwMhLOzM65cuYKYmBjO\n3F5aWsLAwABHGFVWVqK7uxtisZjBUB8fH8zMzODSpUtIT09fddiUSCQQi8XssFh5eLaysmICa2Ji\nAhqNhg8yg4ODSEhI4P0FdQuR2nViYoIjJaytre/ZXnn27FkGITZt2gQnJycmKyhegYonx8bGEB8f\nD7FYzLn3BGR/W1msVCqZjKH1rq2tjftlaD6ysbHB5s2bWU1KoA/NaVqtlsFWAu4sLCwwMDDAAB/9\nvhs3bqCurg4uLi7Q6/VwcnLC7OwsxGIxwsPDWR1JiuzJyUkmKBcWFpCWlsZAg8FgwPXr1xEcHMx7\nm5WxJQRgjYyMwM7Ojm3RYrGYYxnIPXPs2DH4+voycTY9Pc2A69TUFDQaDWZnZ9HU1ISamhooFAoG\nl8ViMRobG9HS0sL/RtFpBoMBTk5OaG5uZnWYpaUlE8djY2Mc30OHQAJiCJykteTWrVu8H6KSYcpq\nJVUYfQ4AVoGCNB/QuKM15vr16+jo6GB1IpWgktMhMTGRnQ0rlcaTk5OrVJ/0+4C7ZK+Pj8+qjhg6\nwI+Pj//D+AEqWRwZGeE1cGlpCUlJSVCr1VAoFBgeHoanpye6urr4tUxPT7P6e+X7W5lbTyXTBKgT\ncXPz5k2sXbuWiY6VHTDUdbG8vAw7OzvuRKD/TqAEuRlWFt+Rc4VAvKioKNja2jLoRgASPZ8EBPf1\n9cHT0xN1dXXw8PBgYoIIm9nZWVYa0zpHYCD9zo6ODib+qTuDQO2oqKh7mnc+/vhj+Pr6wsPDA/b2\n9nB1deUSSVKjErBDY4+eYSJ9vLy8sLi4iB07dsDLywv29var1Ol035ycnLjElsDkxcVFTE9P48aN\nG5iZmYFGo4GpqSk0Gg0XW4eFhTFAsfJe0TxHfyYyitbnleQTjQfK25+cnOTPayUp4enpicjISB6r\n5Dywt7eHSqVCdHQ0WltbMTQ0BL//6PVYGUND4OrQ0BC6u7t5jqKxQOQzdfvQPCAUCjE6OsrqWlJs\n0hxIID89C+RAEgjuZt+T+0WtVvNzTASFnZ0dhoeHER4eDicnJyZSCZimZ5ScVQQIkcNrZTeBpaUl\n5ufnce3aNY62mp+fZwKEwDByAtP8S5+5ra0tz5lzc3Po7e3FlStXcPDgQbi6uvJ7JNKMPjej0cj3\n7vr16xgfH+coWLon9LvJQTYxMYGpqalVBKuFhQVsbW3x85//HLa2tiyQItctEQQU6bKy44AInZVr\n1/T0NGpqaiAUCuHq6oqsrCwsLCww6UhzIzmbLCwsMDIygrm5OUgkEu6gsLa2Rk9PD+7cubMq2s7L\nywuDg4PYsGED9u/fj/DwcIjFYhZbEJlDzzGR4yvHH82z9AyZmpqitbUVAoEA58+fx6FDh+Dh4YGO\njg4MDg5yR0hERAT0ej3HaKxfv57PGiMjI+xKUyqVCA8Pv6d5R6fTobS0FH5+fgxgurm54fPPP4eT\nkxN3nhFhBdyNeSF3Ln0m1tbWaGhogFwuR2VlJWQyGSoqKtDQ0ICWlhbcunULBoOBi37J2fPzn/+c\n97L0jBFxSxetA3Nzc/Dx8UFvby8TASvdKQD4XHQv2fXfdRHZ8W3V/j8iH/7ZJRKJmNDQ6/UICQmB\ntbU1Wltb8cEHH8BgMKCrqwve3t7413/911UOhu+6vv0aqqurcfv2baxfvx779u3Djh074PcfMTCx\nsbGriNjve/005uhnUpQrcHe/sbCwgOzs7HuO9rl58yZOnTqFBx544Ad/z/Hjx/HAAw+sAp5pj//5\n55/js88+Q2dnJywtLREbGwudTveDP3siLpubm5GVlcX4mE6nw49+9CMsLCxwdOQ/uug+keuLziMy\nmQxzc3Pw8vLiOYTuN62BZWVlHN/7j14XXTR/Ozg4YHR0lNcN2mtfvXoV9fX1LDohvOHAgQPsJCYn\nrqmpKa8RwH86uwwGA581DAYDNBoNlpeXcenSJezatYt71pycnPgZXklaCwSC/yUB/puu/5EkwHvv\nvYcLFy7A1dUVNjY2OHLkCObm5vDiiy9iaGgIFRUV8Pb2xqVLl3Dnzh08+eSTmJiYgKenJxYXFzE5\nOYk9e/bA1tZ2VVZzbGws6urq4Ofnh8rKSvziF7+ARqPBmjVrUF5ezlEELS0t+PTTTwHcdSUUFxfj\nV7/6FYqLi/HGG2/wZu/ChQuce7t27VqEhYVBqVRCLBajra0Ne/fuhYnJ3XKdjo4OSKVSZGZmwsTk\nbilNWVnZqjwxo9GIjIwM6PV6qFQq3L59G0lJSZDL5TAYDKiurkZOTg4KCwvxzDPPsPVrcnISCoUC\nRUVF6OnpwY0bNzA9PY3R0VHs2rULu3btglgsxjfffAO1Wo2nn34a8fHxqK+v54xlvV6P/Px8ZGdn\no7q6GqGhofDx8UFpaSmsra2hVCq5AOjw4cM4efIk1qxZw3bL0dFRLqXx9vZGTEwMZ6JGRkZCIpHg\nL3/5CwoKCuDq6ors7Gy0traiu7sboaGhqKysZBJh48aNmJycRE5ODiorK9Hc3AxLS0vs2LEDJ06c\nwK9+9Sts2rQJAQEBsLOzQ2FhIS8ON2/ehF6vR3Z2NlQqFZRKJTw9PaHX63Hq1Cl0dXUhNjYWf//7\n35GYmIg333wT69at46xfhUKBrKwsODk5YW5ujjNi165di3/7t3/D2NgYfvWrX/FiSnZknU4HkUiE\nDz/8EElJSTh69CiamppQVVWFH//4x1i7di2cnZ2xc+dO3H///bh69eqqzLNvvvkGw8PDOHDgAL75\n5hu88sorMDU1xalTp6DVahEfH89xVxKJBMePH4efnx8XVZ0/fx6+vr64dOkSQkND8dFHH2Hnzp0Y\nHR3lTbdQKMTFixfx2WefYXR0FB0dHdi+fTsSEhJgMBjg6OiI3Nxc+Pr6shKrsbGRD5impqaora1F\nWloaTE1NOS+/oKAAe/bs4agVZ2dnBAQEoKysDI899hi0Wi38/f1RXV3NNn9HR0eEh4ez8s/c3By3\nbt1CaGgoOjo6cOzYMQwPD8PZ2RkpKSkICAjgErOcnBxcv34d8fHxKC8vx9mzZ+Hq6govLy9IpVKo\n1Wr4+vpizZo19zQxPfvssxw1olKp8MQTT8DGxgYikYjZbToQP/vss9i/fz+7dMbHx2FmZobKykoU\nFhaioKAAc3NzvHleXFzEqVOncOzYMeTm5qKnpwe3bt3CoUOH2E5IwEN3dzeCg4M5qsPKygoRERG4\nefMmNm/ejJCQENTV1aG3txcmJiZwdXWFRCJBaGgorl27huDgYHh6erIypbu7m23u7u7urHadmpqC\np6cnHBwckJGRgQ8//HBVZj+BQXV1dfD394dKpUJ5eTnWrFmDN998E6GhoTzu6L7QYq5QKJCUlMTZ\n9ORYGR4eRltbG2pra1FfX4/CwkK0trbC398fra2tOH78OBYXF7F//36EhIQgLCwMV69e5bgMygGl\nA393dzd++ctf4r777sPu3bvx+uuvw9raGnv27IGZmRl27NiBd999FykpKbC1tcWRI0dw9uxZPPfc\nczwPkYMnMTERLi4u0Gg0KCoqglwu5zJYOpBTBrFQKMS5c+dQXFyM4uJixMXFobu7G2+99RbS0tKw\na9cuTExMYHl5mXtjOjo6EBcXBxMTE/j7+8Pa2hrp6enIzs7G6OgoHnzwQY47cnV1xV/+8hc89NBD\nTMTSoXelDd7Ozg6Tk5N45plnsHPnThw9epTHdnp6OkcMNTc3czSOo6MjIiIiOE4pLi4O69at4wLG\nwMBAng8EAgH8/f1XgQdU5KfT6eDm5sZ23/b2dgQFBaG2thZubm4YGhpCdnY2xsbGuNSJMnsfeugh\njuoQCoXo6OiAjY0Npqen4e/vD6lUirCwMNy8eZPf7+LiIjZv3oz29nZERUXxQeOjjz5CS0sL+vv7\nOdM5NDSUQQedToe+vj5YWFjAzs6OD86Li4vQ6XRQq9Uc00IAYW9vLxYWFuDh4YHW1lbExcWhoqIC\nxcXFGB8f5zlzZGQE7u7umJubw4kTJ3DgwAEEBATc07yjVqvZYeTn5wcvLy9YWVmhrq4OYrEYCoUC\nRqMREokE/v7+sLGx4bJJOsDS/L5S1U6W5rm5Ody5cwezs7PcF6NQKLhU7MCBA2wFJ+cAqX4o35k2\n8URIAHeBWA8PD8jlchQXF3NUGB1SHnnkEXh6euLWrVvYvXs3d/8A4NiBEydOQCaT8c9OTU3l9yUQ\n3C0Xowx/AhQJ1KDXMDc3B19f31XZ8UToEBirVqshk8lQVVUFmUyGnp4efPPNNygtLWVXYnV1NTo6\nOnicE+i1tLTExZ70c6empjjHWSaTwcXFhSPYHBwcuACPgAcCM+j+USn8yjiM5uZmJlkSExO5WF6n\n08HBwYEPQt+OCiF1OKlhiby4c+cOqqqq8Nxzz2HLli3YvHkzgoKC4OTkhM7OTpibmyM+Pn7VM9Pb\n28tzBe0tCPiYmppi5x2BTtQVQ8+xTqfj/RYRN6QeNDc3R2NjI//Z3d0daWlpCA8P5/W9pqYGmzZt\nYoAxLy8Pra2tHKtA94AOkPPz85ienoZKpWI1eX9/P3p7ezm2JCQkhHOgVSoV5ufnWeVMAB2RbN3d\n3QgMDIS5uTkfpoeHh+Hq6spq2snJSeTl5aG+vh7u7u6s7qV4Szqo9vT04NNPP0VraytaW1uRlZXF\nsY7FxcWora3l/TP1JqzMuV7pvtHr9ejv7+e+DCKOiIAWCAT3HJlw7tw5zhy2trbmMkYqa6QzCa3x\nExMTTPxYWlpCo9Ggu7ubOwCo/JT2DxQHROt+YGAgZmdn0dHRAT8/P8zMzKCsrAxPPPEEtm7diuDg\nYMhkMnh4eHAOclhYGCwtLZmAIvDt2+CyWq3msnYC5OnrVwL0AGBnZ8ekF4HdRBoQ6UkCB1oHKcs9\nKioKHh4eUKlU3LdD45v2JBS/NTAwsGoPNz8/jz/+8Y+QSqUMqtMYEQqF6Ovr41jBb79Hep3082nM\nOjo68vO2MsINAK+b4+PjAMAunpUAPY1fep00bun7AfBY0Ol0qKyshKWlJUJDQ1kZS2QYuTQ1Gg3U\najU+/PBDdHR08Bii+KDbt29jeXkZzs7OCAsL46i0lfnbtPYQAWdhYQFfX1+EhYWhoKAAN2/eREVF\nBfz9/RlYo+fx3LlzGB0dhUwmw+TkJMzNzeHr64uf/vSnSEtLg52dHZ9TKAqD5nsixleCV+Tmor4G\nip+jiF47OzuYmpoiKSkJtra27FCmcanVapmkoo4PAo6p40mlUqGvrw8eHh6YnZ1lZ1lgYCBycnLg\n4ODAMUR2dna8D1wJpNHzQQSK0WjkMlAilshJ09nZiZCQELi5uWF8fBw9PT3Yvn07P/cE8Hl7e0Mq\nlfJewdramlxbjXoAACAASURBVHPNLS0t4ebmBldX13uad+bm5mAwGHDmzBmsWbMGZmZmsLe3R1RU\nFEQiEd5//33s2rWLI1XoojFDa9+f/vQnft6vXbvGUbVEvpP7RyqVQiwWc9zvD1XZr1RHSySSVQp4\n4G7UEAlYSBT5Xde3+z2+63J0dERLSwtcXFxWvU5aB6jP74e+B6VSif379yMtLQ1//vOfUVdXh8uX\nL8Pd3Z3nwb1790Iqld4zyVBZWYnExERkZGRg06ZNjBHZ2NjwXnTl6/++i+azwcFBREdHw8XFBXv3\n7kVwcDAKCwvh5OSEoqIihISEcLQfrePf19Xg7++PtLQ0tLe3w9PTEwC4o2VkZIRFDiQIIQxwx44d\n/DN6enogkUgwPj6OV155hSPCurq6kJmZ+YPBaBqPs7OznM+flJSEzMxM7Ny5E+Hh4ejr68O6deu+\n8/MdGhrC7OwsTExMeHxSNwqthd8uCqb5ipyNP+QisZe9vT2Ki4vR0NCA1tZWHD58GPPz85idnYVW\nq4WTkxO6u7tha2sLf39/SCQSFgjSnDM3N8cEE61l344XMhgM+OCDD/DUU08x8D81NQVXV1fcvn2b\no3TJTWYwGO7JdVNWVvaDv/b/tetexbL/Hdc/JQFeffVVbNy4ERkZGbh16xYUCgW8vb05xsTZ2RmJ\niYmwtbVFUVER4uPj2QLu6urK4C0BYVSsY29vj4iICHh7e+PmzZtQKpVYXFxEfn4+7rvvPvj6+mLt\n2rUIDw/H1atXoVKpIBAIkJqaisDAQHR3dyM7OxsmJneLgNLT07Fr1y58+eWXyMzMhIODAywsLFBS\nUoIXXngBs7Oz0Ol0eP755/HrX/8aCoUC1tbWyM/Ph6WlJR588EFcvnwZsbGxq9TQPj4++OyzzxAY\nGIiNGzdieHgY+fn5sLCwgEKhwPz8PB588EGcOHEC4+PjuHjxItLT06FSqRAaGorJyUnExcVx5jrl\nhoaGhjLbTyDfyZMnsXHjRmg0Gvz+97/nTLczZ85wJmpxcTF6e3s5X9vc3BzPPPMMA9jDw8M4ffo0\nLC0t4ePjg9jYWFbL9vX14fDhwygrK8P999+PyMhIPPLII/jiiy+wf/9+2NvbIykpCWNjY3jmmWew\ndu1a5Obmoq+vD+Pj4/jFL34BBwcHqFQqBAYGoqCgAE5OTqitrWVws6KiApmZmUhOToaFhQWGhobw\n1ltvob+/H8HBwYiPj0d7ezvUajV+9rOf4cKFCxAKhZifn8dPf/pTGI1GDA8Pw9vbm7sjzp8/j+np\naYSHh+Pw4cNQKBQ4dOgQEhISMD8/j4qKCkxPT+PgwYP44IMP8Pzzz0On06G6uhqXL1/m3M9t27Yh\nODgYRUVFSEhIYIVeeHg4ZDIZ7r//fvj7+yMlJQV5eXlITEzEfffdx89tUFAQFhcX0drayqo3f39/\nzvHr6enB008/jeTkZFhaWkIqleLGjRtwcHBAZWUlq9nNzc3R2dmJoaEhdHR0ICIiAk888QSsra25\nnNdoNCIxMRFisZjzno1GI9555x0kJydjeXkZly9f5tgngUCAt99+GxkZGTweaJNIxcCkCqyoqMAv\nf/lLlJWVISsrC3/961+RnZ3NcRcEmFB5TmpqKiwtLREdHQ1ra2uIxWJW8t66dYtJmri4OAQEBODn\nP/85GhoacOfOHS6PioyMvKeJiTLmFhYW4OjoyIqkqqoqzsRvaWmBs7MzLl68CLVaDTc3N7S3t+O9\n995DYWEhGhoaANy1rMlkMqjValbKqFQqZGVl4dChQ4iOjoaJiQm2bNmCL7/8kgt0VCoVGhsbOdbr\n8ccfx3333ccK+o0bN8LU1BQVFRVYXl7G2NgYuzXm5+dx+fJlhIeHQ6PRALjrYLhy5QpEIhEr9uPi\n4tDe3s72XjpYlpWVoaqqClFRURAIBHjyyScRExODzZs3s1ImMDAQb731Fnx9fZGbm4vi4mL4+/tj\ncXERzc3NePbZZ3H58mU8/vjjDGKMjIzA3NwcAwMD2L59O7KyspCVlYXt27fD398f165dw6FDh+Dj\n44O8vDzodDqOvDEzM0N8fDy++uorJCUlITc3F2q1Gi0tLUww0tcJhUJs3LiR45PoXmi1Wj4Am5ub\no7W1FXv27GFlqLW1NV577TWcPXuWy0ljYmKg0+mwceNGVrEQoLWwsICzZ89i3759OH36NGZnZzE3\nN4ePP/4YTzzxBEcMUGfFyMgI55UTKURqO8ptX1hY4AMiAYaOjo7o6+tDR0cHR6oNDw9DrVbDyckJ\ng4ODEIlE3CuiVquRkZGBuro6BAUFITo6moEuUmO++eab2LdvH5ycnNgS7uXlxQdZNzc3KBQK2NnZ\nYXR0FBs3bsQLL7zAZJNWq4VWq0VLSwsfHAj8+Oqrrzjeoaenh+dPUmUDQFdXFx8i6RArlUrR1tYG\nb29vzMzM4LHHHmPl3enTpxEUFIRPP/2UP4uamhpYWVlxbEdaWho2bdqE/Px86HQ6XL16FbOzs9Dr\n9WhuboZcLodYLF6lINXpdJDJZBgcHERgYCAUCgVu3LjBrqm1a9ciNjYWO3fuREZGBtzd3eHt7Y3q\n6moMDAwgKSkJKpWKD4lvv/02XnnlFQBgsvyHXq2trZypLxKJWOXn6emJhoYGPhQR0LgSJKf/EalB\n6n26TE1NWd14+/ZtTExMMPC0bt06bNu2Dc7OzqzupX+jAxEBvouLi6zkVavVkMvlmJycxNDQEIOR\nlLWekJCAmJgY/rnd3d2wtLSEl5fXKpv90NAQhoaGeBza2NggNjZ2VUyKQCBYdaihzGcArK6mYjWd\nTseABwHPpDx1dXVFb28vpqenMTExAQ8PD3R1dUEgEEClUq0qQtTr9XBzc+M4JYpXIseIiYkJQkND\n0dPTw8rniIgIhISEwNvbm3tSCFwj1eHc3Bx3f9B71Ov1GB8fR0dHB6t6DQYDYmNjufiRug1WjjVS\nQ5mYmLDTgMAnvV6Pmpoa5OXl4Wc/+xkDtwBY3b+wsIDQ0FAm8SjjnUoilUoli1QMBgO0Wi2mpqY4\nO5ai6+g9AmC1LeVg07xJLp7l5WW0t7dzvImVlRViY2NZJbvy4KrX65n4IMJFIpHA3t4eCwsL+Nvf\n/oYNGzagq6sLly9fZvDc1dWVx2ptbS36+/sZaKfoDYo+6ejoQGlpKateadyNj48zgEdAqkwmg42N\nDTo6OlBeXs4xYIGBgewQI/UtvW8qyZbL5UhOTuaYh+XlZY6AiYiIYKEQ9c0QCElrztDQEPr7+5mI\nDg0NxfLyMqanp3mPYmlpec+ihzNnziAqKor3ETKZDDdu3OBIAHpm6DVTdBc5GyorK5Gfn4/l5WV4\ne3tDpVLxOkbvwWg0cl+HVCrleXtpaQmFhYXYvHnzKlB2w4YNHJmakJCA4OBgHps01wFgclKpVHIk\ng6mpKWZmZnitod9PLjuKWyM3DgGpBKCSmv7o0aO83lHUEuUW09pNYAdlrdPzT3Nnd3c32tvbMTEx\nwUSKUqnE9u3bmWRZ6ZawsLDAzMwMz//k8gHA6xb9HHo/NO/r9fpVaktSWtLzNzY2xtnv5BYg0Ijm\nEwIW5XI5HBwc+PtJAUrk5blz57Bz507uyDh69CgaGxuxdetW/lwMBgNmZmbg6+uL2NhYBmFFIhF8\nfX0hlUrh7e3N0VpyuZzz6kkpSi63ldFvtH8zNTWFQqFg8VtNTQ1HNh45cgSLi4vYvn07wsPDERER\ngbCwMOzbtw/29vb8e2xtbTm/neYw+l3W1tb8/ukcRmsWOQYaGxtx5coVaDQaWFpaQqvV4sCBA7C2\ntuYz8sr5Y3FxEVqtlteklWS2paUlSkpKoFQqmWCjCL/t27ez087MzIx/H3329NwSYU3jZOXPp3WU\niCISXbW0tGBychKfffYZXn75ZTg4OPDro/WXXBc0B6xcfwjIu9dOgJmZGZw7dw4PPPAAOjo6EBQU\ntMqJMjg4yH1OKy8rKysolUp+9uvq6mA0GuHu7s5zko2NDSYmJmBtbc1RgTMzM3j++efh7Ox8T68T\n+MfluHQRCPl9BACt0ysJgH9GCLS1tcHHx4f3qTqdDh0dHXj33Xdx9uxZODg4MFAKAN3d3f+FMKGL\n+olKSkoQGhqK5uZmjqGmsZqSkvKd3/9d1/T0NI4fP75K3GNubs4ij/+ba35+Hi4uLvx3EliamJiw\nMIE6PU6ePIkLFy7AwsICQUFBfE9pHzQ9PY0jR46gpqYGTU1NCAkJgUQiQUVFBU6cOIH33nsPcrkc\ndXV1GB0dhUKhwDfffIPc3FxMTk4iJiaGHVY0zkxNTTE7OwuBQMDdHjt37oSVldUPInlIeEDxOCRA\noDXcysoKkZGR/7Acenh4GDqdDk5OTjAajezyWFpaglwu57n16tWrPJ7oEggEuHXrFnfd/NDrzp07\ncHJygo2NDYaHh/HJJ5/wmdTLy4vvSVhYGHeoBQcH856Y1lPaVwFYRQCQ81gul2NiYgJVVVXYtm0b\nu+7J6SQWi9ktRPFFAP6XBPhvuv5HkgBmZmZISUnB+Pg4Tpw4AW9vb/yf//N/sLCwgJiYGI6u6erq\nQmdnJ3p7e9HQ0IC0tDQAQFVVFSoqKuDi4oLBwUEEBQWxjTE3NxfR0dEICgpCQEAAtFotoqOjOevL\naDTCzc0N7u7u2Lx5M3JycngjpVKpkJCQAJVKhZmZGfj4+MDNzQ2xsbG4fPkyTEzully5urrC1NQU\nf/jDH/D111/D1NQUu3fvxtLSEt555x08+uijiImJwfvvv4+NGzfiyJEjyM7ORmhoKP72t7/B1dUV\nDz/8MNtfzp07h4MHDyIjI4NtpLGxsYiLi0NRURFefPFF2NraoqGhAbW1tTAxMYG7uzu8vLwwOTkJ\nU1NTFBQUwNPTE2vXrsV7772HkydPYmhoCA8//DD8/f0xPj7OJch6vR73338/x0NQ9vsXX3yB0dFR\n7N+/n0vUJicnOYYnPT0dn376KaRSKTo6OnDx4kVkZmbCz88PAJCZmYl3330XO3bswObNm/H6668j\nPT0d5ubmKCsrg42NDauq29vbMTw8DD8/P5SXl8PR0RFqtRqPP/44PvnkEzzzzDMoLy9Hbm4uFhcX\ncejQITQ2NnIWpkgkQm9vL3bv3o0//OEPSE5ORnZ2Np577jmUlJQwmxwWFobf//73iIiIgE6nQ3l5\nOQoKClBfX4+nn34avb29yM7ORkZGBv70pz+hsrIS+/fvh7u7O+6//34cP34cf/3rX1FeXo6YmBhs\n2LABBQUFnGUpl8sxOzuLoqIiGAwGvPHGG0hPT4dIJEJgYCC0Wi16e3sRGBiI1tZW3Lhxg8GKpqYm\nhIWFIS4uDnNzczhz5gweeeQR9Pb2IisrC4mJibCysoK/vz+cnJxQXV2N2dlZZGZmor+/H/v27UNc\nXBxeeOEF3L59Gz/96U+RmJiIrq4ubNu2DV9++SVEIhE0Gg2srKxgZ2cHuVyOixcv4uzZsxgcHISb\nmxtCQkLQ29uLgoICSCQS+Pr6QigUor6+Hs888wwSEhIQGxuLTz75BOHh4RgcHIS5uTmKi4shEong\n7OyM7OxsGAwGlJeXr1J+ymQyuLu7szKE4kVogaOi3tnZWVapvfPOO/jJT37C4yk2Nhbt7e1ISUlB\nfHw8nJyc8MUXX+Chhx66p4lpZGSEbeC0AJ07dw7Z2dnw9vZGYmIiZ3Tu2LEDGzdu5Kz4yspKmJub\nQyQSwdXVFUeOHMHAwAD0ej1u3LiBM2fOoK6uDiMjI2hubsbRo0cxPT0NvV6PCxcuoKenB0VFRfj6\n668xPz8PpVKJqqoq7Nq1C2NjY/j6669ZzU5RPl5eXhCJRHjnnXewYcMGLrAcHh7GG2+8gaGhIQwM\nDCAkJAQODg749NNPkZGRgaWlJS77o+xTKq/73e9+xyVlf/zjH1lptlIFk5CQwOVCb7zxBiYnJxEe\nHg57e3s8+uij8PT0hIuLC8zNzXH06FF29gBgGzcdgt3d3ZGTk8P515WVlQzgUhSAra0t9u3bB5FI\nhJKSEmzZsgXh4eFwdnZmkI1AGIPBwDFjkZGRCAoKwieffIJbt27B1tYWk5OTSE9P50xVihI4duwY\nBAIBDh48iL179yIxMREpKSkYGhpCWVkZg6FarZYt7NevX2cVRFJSEn7729/C398fQqEQpaWlOH/+\nPEZHRyGRSBAdHY3a2lp4eHhgYmICjo6OrJh2cHBg9Spl3hqNRvj4+OD999+HTCbDli1b0N/fD5lM\nBnt7e0xOTrJN08Tkbqmmr68vRCIRNmzYgNzcXJw/fx5RUVHo7OyESCTC+Pg40tLSYGFhgcrKSvj7\n+0MsFvM6QyQDRTht2LABNTU1+MlPfsLxZdeuXYOLiwvHD5WUlODDDz/Egw8+iHXr1nG+8pdffgm9\nXg8bGxv4+fmxotHR0ZG7SQg0sbS0xPr16xEYGIivv/4aU1NTiImJwdzcHGQyGXbt2oUdO3YwYKJQ\nKKBQKBAZGcngtL29PS5dugSNRgOJRILp6WkEBgYiLS0NAQEB0Gg0mJqagomJCcRiMY9vOuDW19cj\nJSWFSbvPP/8cqamp3NdCkSFTU1MIDw+HVCqFmZkZj2s3NzcEBARgfn7+nrtIGhoaVkUTzczMYH5+\nHmKxGD4+Pvjqq68QEhLCykGtVstujJWHFBrLAFZlXhOoJBAI0NraCpFIhPvuu4+VOwR+0PdSxrFO\np4NCocD169fR09PDSnmKxRkZGcHIyAjGxsYwNTWF5eVlREVFYdOmTXB1deW4pKioKHR3d0Or1TLw\nIxAIuFzazs4OYrEYBw4cYIUUARmkQqX3TEQ23Qt3d3cGRijTeSUJQGCJUCiEWq3G4OAgJiYmGGgg\n0JrmmR/96EfIyMhATEwMkpOTsWnTJpibm3McxfT0NCtgNRoNRkdHYW1tjcnJSQQFBUEikTC4R8QG\ngZL19fWoq6tDf38/5HI5SktLUVNTg+7ubqhUKnZSESASGRnJwG9fXx/3AZD6mCIraGwREDQzM4Oz\nZ8/iySefhJOTE5dG0pxramrKZb50EFteXkZvby8WFxcxNTWF4uJi9PX1oba2FlVVVWhoaGDRQUtL\nyyoFL+XEkpOPiBQibCiSY3BwEF1dXQDA3xceHs7znUAgQG9vL+rq6rjklsQr3t7esLKygpWVFSws\nLJCQkMBEASnhqPCUHHWkDh4YGGDHaGRkJIaGhlBQUMAiEUdHRya3SNVMjjZytpDCjqIT77vvPqxZ\ns4YJAL1eD51Ox8pfihGam5tDRkYGpFLpqpgfsViMyMhIdHZ2snior6+Pu88IQKQxTZGD5IwhRxOJ\nLZaWlrif6Ydeer2e49Pc3Nzg4OCANWvWIDQ0lMchuS2Au4BWfX09PvnkE5SWlqKxsZFdU/X19Rxn\nRT1EpCq/dOkSTE3vdtHMzc1hYmICly5dwvbt2zmOhdYxoVDIRaVxcXG8H6M5jOY9+pxXOpQWFxcx\nOzsLuVzO3QIAOBqB5h167yRSIHBmZmYGJ0+eREZGBk6dOsXilubmZgQHB7Pjgwjz06dPs3NQp9Ox\nqtTU1JQLtMmNptFoMD8/jyeffJLH6uLiIiub6fUTEbmSyKB7Q8QKAdHm5nd7gWiuIaKRfv7s7Cza\n2tpw+PBhKJVKBstoTqL9B93bpaUlzoCn10eAr06nQ1tbG9atW8eEhpubG+Lj41mYQmTx2NgYnJ2d\nMTQ0xAX1NH5oH0XrLwFqS0tL7LYgMlsgEHCUKMXimZiYwMfHByUlJZibm8Pw8DC6urpw+/ZtlJSU\n4KmnnoKzszNSU1MRHh6O6OhorF27FtXV1bzuA3eFPiv3vxQRRc8LOYco5pEiy/R6PRobG3H9+nX0\n9/cjKSkJe/bswf79+zkO09raGouLi3yGAYD8/HwUFBTA2dkZZmZmUKvVqKmpgYWFBTo7O1FaWsqR\noPQcG41G7Nmzh0mu2dlZKJVKJpEIzCeCmggh2hNptVqOiSPAl2JEjEYjIiIi4Ofnxw4GWjdpX6HR\naCASiVaRQStjhQjQ/DZY/8+uqakpDA8PY2JiAqGhoZyeQPsYHx8ffPTRR0hJSfkvoKWtrS3Pd+Xl\n5aiqqoKfnx87/JRKJaanp7nfxMTEBG+88cY99xZ81/XtKCCdTvedETXz8/P/UKX+fWDx0tISPD09\nOQ70lVdewYkTJ1BZWYmenh5YWFiguLgYVVVV3E3k6en5X14DkQ8EuFIGfFBQEG7evMlxWRqNBvv2\n7eNx8UMuo9GIl19+Gc888wwrtGkuo3np2xft2b7v+i5Ff3BwMNLS0rC4uAgPDw8sLS0hKioKAQEB\nqKqqws2bN1FSUoJz587hwoULyMvLw/Hjx+Hi4oKQkBCYmJigpqYGY2Nj+PjjjxlkXl5eZkFTZ2cn\nGhoaMDc3BycnJ6SkpEAmk0EoFLIYoba2FnK5HBKJBL///e85uolctCv7Eui6cOECQkNDAdzFNcl9\nsDLmij4nciGv7AwZHx9nV5K9vf0qd6mpqSlu374NHx8fPm8rFAoEBgby86DT6TA/Pw9bW1uO+Puh\n0VKk5ifHQWRkJBQKBVQqFe/3TU3vRpUrlUosLS3hwoULKCoqYhxSKpVyhBeJRgAwKWowGHD8+HHU\n1tay8Iocv0RMk+Oou7sbbm5u3LdyL3FAt27d+sFf+//atWnTpv/ff+cPigOKiYmBnZ0dW3oTEhLg\n7OwMtVqNdevWYWhoCLGxsbh27RqEQiHu3LmDrKws3LhxA1ZWVmhpaYGNjQ3WrVsHlUqFzz77DAsL\nC3jggQfQ29vLcRTr16/H+fPncf36dZSVleGhhx7iw0F9fT0MBgNGR0fR2tqKtWvXIiQkBC0tLfww\nEqPa1NQEBwcHnD59GqmpqVhcXGQbS1hYGBQKBS5dusRq84WFBURHR7PNLiAgAAaDgSM3EhMT2Urf\n3t6OPXv2cGHmwMAAvL29cfToUY6UoAkoJycHMTEx6Orq4hxuJycndHR0wMPDA4uLi5BIJEhOTua8\nL61Wi/z8fERERKC6uhoymYxjifr7+zla4Pz58+jr60NBQQEeeeQReHl54a233kJMTAyqqqoQGhqK\nGzduoKioCIWFhWxNDA4ORnJyMqysrFBbW8uASktLC6ysrKDX61FXVwehUAgvLy+UlZVh3759GB8f\nR1hYGC5duoTk5GSEh4fDxORuwaSHhweOHDnCqsOYmBhW7vX29kKpVGL9+vVYWFhAVlYWpFIp7O3t\nER0djT179kClUqGsrAxNTU0wGAwctUBWWQIsgoOD4evrC6VSyRvO9PR0jjfp6elBZGQk3N3dMTs7\ni5dffhnj4+P48Y9/jMDAQOzfvx8KhQLr1q3D6dOnIRQKkZmZCWtrazz55JNYXl5GeHg4JBIJEzCh\noaGQSqXw9fWFj48Puru7cfjwYWRmZnKPBNmubW1t8frrr2PdunV44403UF5ezpmx/f39uH79On79\n619j165d6O3tRVhYGCoqKphE8vLywssvvwyRSASJRAJnZ2dERkbCy8uLXS4UeVBVVYWgoCAIBAJc\nuXIFcrkcCQkJmJmZgV6vR25uLuLj4+Hp6QmZTIaWlhYsLS1hzZo1sLW1xfPPP4+AgABs374d586d\n4xgKc3NzLsbMzc1ldY5er0dJSQnKysrQ3d2Na9eusVWcHALR0dHshgkLC4NIJMLU1BS2bNmCkJCQ\ne5qYOjs7eeNORN/s7Cy8vb2xtLSEr776CvHx8ax8oAXc1tYWAQEB6Ovrg1qtxptvvslxSYmJiejv\n78fTTz+NiYkJPPjgg7hx4wYTXtXV1QCAbdu2YW5uDq6urpDL5Thw4ABiY2Nhbm6OY8eO4de//jXc\n3d3x0Ucf4fLlyzA1NUVqaioGBwexsLDA0UwmJiZwdHREXFwcfH198f777+ORRx5Bf38/hEIhRw+R\nCmx8fBxvv/02+vv78dJLL7EFnwqcyV5LB/S5uTmeOx977DHO0jUxuVsQev78eaxduxaNjY1wc3ND\nVFQU2tvbsXbtWvj6+mJ4eBhGoxHl5eWYnp6GSCSCTCZjFeRHH30Ea2trBAYGIioqigEuijuJj4/H\nwMAAgoODodFoOJuQDsampnfLUzs6OpCRkYHa2lo8//zzSEhIQGhoKIqKiuDo6Ahvb28mHsbHx+Hu\n7s4Z1QQ8EAhJBXeFhYUoLi7G4OAg0tPTUV5ejqeeegrFxcVcJE4HJrJRGwwGjjIZGxtDcnIyK7MJ\nyCTbfWhoKHQ6HWZmZmBjY4Oenh6Ym5vj+eefx9DQEP7yl79gaWkJ3d3dGBoaQmtrK2e41tXVISQk\nhO/V119/DRcXF1y5cgXLy8u4evUqO7tEIhHc3d3Z0q3VavmQuTLOys3NDTt27EBpaSnc3d1ZMb9+\n/XpWzC4uLuLVV1/lDFUrKysIhULExsaiuLgYQUFBCAoKgtFoRF9fH+7cucPZpAsLC9BqtXw/hEIh\nAgMDkZeXhw0bNsDKygoNDQ1ISUlhdYxarUZtbS2rXU6cOIGAgACYm5ujsrISFhYWmJqawo4dO+Di\n4oK2tjZ4eXlxRImXlxdUKhWDowKBAC0tLVi/fj1nbI+Pj7O4ID09nUEouncEhFDcnUQiQXh4OIaG\nhuDq6nrPGbmtra0wGAyYmJhgh4RIJIJareYYgjt37sDX15eBLFIlEkC0Mh+e1Hwrszc7OzvR3NwM\no9GITZs2wdnZGba2tgyq0c9RKpVQKpUwNzfH7du3ceXKFYyNjTHZr1arcf/998PPzw8KhYJBazs7\nO2zfvh1xcXHsagLAecV2dnbIz89ntTkVdFIsAUWl0Hsg8J7+PDY2Bp1OBy8vL7i5uTGAT+4Jcq0R\nSEHRDSuBcvq6gYEB3puQSt3Z2Rk5OTmQSCQMuFLuKCl0k5OTERAQAKPRCIVCwQCSVqvFgw8+iKqq\nKkilUiYWSb1J4AwRIV1dXRgdHYVKpYK1tTXvcQkwtLe3h4uLC7u7hEIhrly5wkIIjUaDubk5ju+g\nQzf1IFtG3wAAIABJREFUPPT09MDf358PwHSYoq8lgLC6uhpNTU2oqKiAr68vuwLIjba0tMQqT/pd\ndD+dnJzg5ubGjsHx8XEGSwmgJTs4gesFBQWropxCQ0MREBDAYOT09DRGRkaQmZkJpVLJB3WaU+jz\nog4CelaGh4dRU1PDv5s+u8DAQKxZswZBQUEoKipCRkYGNBoNrl27BrVajfn5eWg0GiZbCHi3s7OD\nTCaDhYUFCgoKeLybmJjg7NmzHBtHRBnFMJF4gIBdvV7PZOTKdXQlQC4SiTA5OYnl5WUYDAaUlJRw\n/rCrq+uquYd6QCimj57dmzdvQigUYsuWLfc078zMzLCitLe3F8HBwQwY2NjYrJoXqKy1qakJbW1t\nmJ2d5blWp9MhMDAQU1NTUCgUyM7OZlDaaDRCKpVCo9FALBZjamoKk5OTiI+Px+DgIMcPkkqZlNE0\nboRCIUfW0OdD/08kCZ2blEolioqK4OnpibGxMeTn5zPoSnseIgSIIATA6k6VSgWpVApnZ2fcuXOH\nu2wGBwcZnKD+FWtra7i5ubEIhGKZqDvJaDTixo0bDOzb2dlBKBQiJyeHlehEDpiZmWFmZgZ9fX3Q\naDRcrrtyLqTni1TxFAFEIC8BstR7QMSUmZkZqqqqYGJigqamJo63XKlGX+koo9JFAjDp/SgUCly7\ndg07duzA7OwsALBzEfjPskbK1bezs8P09DSsrKyYDKa1iX4uCXyoEJzID4p1oj0lAI7UoXxoe3t7\nlJaWwsbGhgV5v/nNb2AwGBAcHIzl5WU0NjbC398fpqZ3SyQbGxvh6enJxCTNT0QAr4zMoT0gOU6G\nhoZ4z/zuu+9ifn4eBw8exPr167mAmUC2la6mpaUlzM/P4/r165BIJOjo6MDAwAD6+/sxOTmJ8vJy\nyOVyjI6OMqhFc+3CwgL27t3Le1tLS0solUp4eHgw2UTzOz2XwN1zTHl5OZqbm9HV1cUki1Ao5L0e\ndSIQwWlmZsZkX0NDA8RiMYs4SOVNxPyVK1e4b8rKymqVevuHXBMTE8jLy8Pk5CR0Oh2Cg4N5LwOA\nxTbu7u7/EJwuLS3FV199BZVKhenpaUxPT/N5XSAQwMrKCl5eXnjggQcwPDyMrVu33hPITZdarf4v\niuyVBMDExMT3Atv32hFAc5lOp0NTUxNsbW1x+vRp/jdzc3MmmMlZ7eDgwD2ZK69/RDTQ83v16lX8\n6Ec/Qk5ODvLz85GSkgIAP+ge1dfXQ6FQIDQ0FCEhIbwHovm6v7+fxwEAFhV8n1vkgw8+YNL/H12m\npncLuqOiorhv8Pbt2xgaGoKNjQ0SEhLg6+uLpqYmjgc0MzPD7OwsGhsbIRaLsWXLFuTm5kKj0bBb\nzNraGqOjozA1NUVQUBDGxsYQEhKCPXv2IDw8HH5+frzfoTWY8KLk5GQW1wYGBq5y0awE2KnbaeVa\nQ1n5K5+P5eVlVFVVwdvbG8DdfkLqZSI36Mqvp+dwYWGBnUKLi4vIzc1FamrqqrFEhCFF/nV0dDDZ\nQfMf3ZP5+fn/8tzS+6G98N69e1FTU8MuT4lEAjc3NyaNqci7vb0d+/bt43X622OJyryrqqogl8uR\nk5PDawA9V7SmEBlDZzfam//Q639JgO++/keSAIWFhZDL5Qx+Ozs7o6qqiktRDQYDl1hSFqtcLkdB\nQQEcHR2xc+dOZuCFQiFEIhEf+tevX4/S0lK0t7djbm4ONTU1CAsLw/bt27F161aUlJTAz88P7e3t\nmJqaQk9PDzZu3AgXFxfExMQAuFtcQhnbZE/U6/XQarXIzMxEdXU1W/gPHjyI8PBwHDt2DEtLS0hP\nT0dAQAAef/xxNDQ0QKPRIDExETU1NTh+/DhkMhmMRiM2btyIuro6fPbZZ2xJ3L17N9atWweFQoEz\nZ84gIyMDarWaVRA5OTmsrjYajTh27BhCQkJQUFCAnJwcvPbaa8jPz8ejjz6K8vJyREVFwcrKCp98\n8gkefvhhhISEcLmhWCzGF198AX9/f4SGhmJ0dBTffPMNEhIS8Nvf/hanT59Gfn4+/v3f/x0SiQQJ\nCQlYWFhAeno6UlNTIRQK8eyzz0IsFrNqydLSklVWVVVVqK2txVNPPYXjx4+jq6sLL7zwAtuCJRIJ\n1Go1Pv74Y24iP3LkCOLj4xl8vXr1KiYmJtDd3Q21Wo0NGzbg/fffR1tbG7Zt2wZLS0sunOvv70d/\nfz8uXLgAqVQKlUqFf/mXf4FYLEZsbCx6enpQXl6O4eFhGAwGJCUlYXBwEHl5eZDJZNy98Ic//AEC\ngQB///vfsWbNGoyPj+PKlSsoKiqClZUVMjMz8fDDD+PEiRPYuXMn5HI5pFIpPv30Uzz99NNISEhA\nf38/bt68ieXlZfj6+sLLy4sz9Pz8/ODu7o6rV68iNTUVra2tcHZ2ZuA+PDwcVlZWOHPmDNra2mBm\nZga5XA5zc3OUlJTAxMQEjz32GDw8PKDVarFv3z5mnqVSKaanp7mQVKvVwsvLC0lJSfj444+hVCqR\nnJwMg8GAqKgojgvZunUrWlpaMDU1hZmZGdTW1uKll15CQkICZ4zOz88jNTUVFRUVXBq4uLiIrVu3\nYm5uDjMzMygpKUF3dzdycnIYZD516hTMzc3R3NyMiIgISCQSdHV14dy5c1hYWEBOTg6Sk5O5VPbE\niROQSqWIjIxES0sLRkdHsW3bNtjZ2WHz5s3MeOfn5yMrK+ueJqa2tja8/fbbvLh4eXkxyz85OQm1\nWs1/p3gx2iC4uLj8f+x9d3jT573vR5ItyVvykLxkWzbeA28wGBuDwVBWzExygIS0TUna5jlP2tOV\nJiVw2jRt0yTN3skhIYQkDAMGGzAGDF544r33kuUl2ZJs2bp/+H6/FRlNuPc+959z3ufJQ0JsWf7p\n/b2/9/1MtLS0YHx8HOvWreODbX9/P5doUfwRWVnpkGc2m/Hzn/8cZWVl2LRpEx566CEYjUbuRFm7\ndi2rTc+dOwc/Pz9UV1dj5cqVUKlUKCkp4ZgmFxcXvPrqq1izZg0EAgEyMzPR2dmJVatWITg4GE5O\nTmhqauJ8T1tbW6xcuRIrVqxgoJ02KR9++CF8fHwglUrvisL5wx/+wIDh8PAwKx5nZmY4r5gynEkp\nQdmrlFH7xhtvICwsDHK5HN7e3nwtcnNzOWswNDQUL7zwAs6fP4+ioiIMDw9DqVRyiWZCQgKWLFkC\nG5vFThKLxYLf/OY3OH78OGZmZrgLxcZmsfx6YmICCwsLcHBwYAX49PQ03Nzc8PLLL/N6QCQgAcJ0\nPebn57FhwwYGhZKSkuDs7IzU1FR8+umn2LNnDwDwhkwikSAgIABFRUXo6OhAREQEK6iKi4v5sKrR\naNh6SiDcc889h82bN6O9vR3BwcFwd3dHcnIy3n//fcTHx6OtrQ1BQUFQKBQYGxtDeHg46uvrIRKJ\nkJubi9///vfYsGEDgoKCuNz22WefRVRUFN544w2sWrUKRUVF6Ovrg0ql4gOzXC5HVlYWtm/fDl9f\nX+Tk5GDTpk3cb3Dr1i309vYiJiYGpaWleOqpp2A0GtkCqtFoYDKZuKCturoabm5ubCu9desWQkJC\nIBKJMDY2xiCCvb09JiYm4OHhgfXr1+PIkSNMKFKsDLAIBhQXF2NkZITjoBYWFvCb3/wGa9asQW1t\nLTZv3oxly5ZBKBSyc486gWQyGW/sJycn2a306quvwsnJCWazGSdPnsSDDz6IqqoqLCwsoKqqigFA\nIgUqKioQHByM0NBQuLm5QaPR4M0330RISMg9Fy3V19fzhpgAIyKHqJx5aGgInp6evGGnCBXKBifw\n02KxQK/Xs4JzamqK3UekNu3o6LirxHt6eprVi9PT07x/IPCHrr1cLkdwcDB8fX2hVqsRHR2NgIAA\nZGRkYMWKFZzLT4AS7YsI7JRKpcjPz8fc3ByDOzqdDqmpqUyU0ZphDYDpdDqO3yBw3ToOgQ5ERAKS\negjAXREaQqEQXl5eaGxs5HVXLBbD2dmZHYsE/hCQTwpcAjtkMhkiIyMRFBTE6ub9+/czOT87O8su\nH7qfCZwnIi02Nhb+/v5ITU1Feno6bGxsOCrSYDDgwIEDUCqVOH36NMbHxxEYGAiNRgO1Wo0bN24g\nLi4OBoMBpaWl6O7uxujoKMeGlZaWcuE6kS/WzgiTyYTR0VGcP38eq1evRmxsLIPa9PVKpRKrVq1C\nYmIiDAYDtFotg1y+vr6IiopCdHQ0ysrKWGwwNjaGyMjIu9wFer0eTU1N+PDDD1FYWIiBgQGO6iTX\nEeVbi0Qi9PT0ICEhgeeNXq+Hs7MzHyDpM5FIJDAajdDr9bBYFjs/AgICeF4Rue3m5sYgWXBwMC5c\nuID8/HwmxoTCxW6i8PBwjuI0GAzw8PDgzpxVq1ZxMTYAJCUlcReGwWCAq6srk6nWJaqzs7MoLy9n\nlTTNBYp/I4JIKFwsPZZKpSgtLeVse5lMhoKCAggEAi6cpmgackPU1NQAAJdr79+//57WncbGRhYh\nGQwGzM3NQaVScaEqzRmKSjKbzbhz5w7a2tqYKCRhzcTEBMcSXrhwAbGxsayipjiqK1euID4+Hkql\nEnK5HD4+PnepIAm0NhgMEIlEGBkZgZubGytMaV2xzvHX6XQYGhpCc3MzvL29ERcXdxcRTftjUqfT\n95pMJpjNZtTX18NiWexbIXGSVquFyWRiJXlSUtJdwC6JVCgrvKWlBcnJyQxqNzQ0wGAwoL6+npXu\n5FihWD87OzsGmGmdov22Wq3m+U6/J/3e1pnvFMlC2ecAOMOe+moKCgrQ1NTEZMSdO3dw4sQJXLx4\nEZ988gkKCwuRnp7Ozw9yUBDZRvdXTk4ODh48yPn4YrGYXV1E4BApRWSOvb09xwbS82h8fJw/c4rH\noZ4RIpr0ej2am5vR3t4OhUIBqVQKZ2dnXifoe27dusWZ+H/729/g5eWFgIAAKJVKtLa2IiIiAjKZ\njF1JROLTnpCcFQBYQd/d3Q25XA6DwYCTJ0/Czc2Nyai2tja89tpriI2Nxe7du3Hx4kVcvnwZDQ0N\nGBsb45hdYHEPSPF5n3/+Oe677z4EBwfzupGZmYnY2FhWeJMDkJ6DQ0NDAICMjAwWQ1q/dxrW9wyw\nuD8icVZERASio6M5hmxycpKLRGkuCwQCjm8jcYGHhwccHBzYeU0uGnqWh4WF8WdA0Wb3Mt5//31I\npVLuBLx58yauXbvGZJxUKoVKpeI9OLkQJiYmcPjwYahUKnz66ad4+umn8dBDD0GtVqO5uRkODg6Y\nm5tDYGAg9u3bh/DwcI7ZvdfSdGAx898a0P7q+D5Fut93EDnk7+8PgUCA5557Dvn5+dBoNOzamZqa\n4khOR0dHeHl5wd3dHUNDQwgODv5eP0coFHKPppeXF3bt2oVXXnkFtra23yvG0svLC6+//joeeOAB\nvh+tx9jYGF8zes79q+t0584d+Pn58Rz66U9/io0bN/L8pD0dYT5SqRRpaWnYsWMHsrOzkZGRgeDg\nYLi5uaG0tJT3zxaLBYcPH0ZGRgbWrFmD559/HkqlEr/4xS+wf/9+tLW1cWyZwWBAWFgY1q5di927\nd3MkJe1h6d+dnZ2RkpKCxMRE3LhxA8ePH8fY2Bhyc3MRFhYGpVL5NRKAvpcGuZkA3HUfl5aWQiwW\nw8vLi0m7559//lsLjbVaLcrLyxEfH8+E+OTkJLKysr7RmWJvb4++vj4Wg/X39yMmJoYJcTqvfhtx\nRX0NKpWK4/omJydZhAksxvPo9XoAi/v6v/3tb0wAf5XwoJiuF198EV1dXUhJSUFGRgbGx8f5WUV7\nLooho3lA6/a9xAFdv379e3/tf7eRlpb2//1nficJ8NJLL0Gv1yMrKwsODg4oLS3Fxo0bMTk5iXPn\nzrFNc2ZmBu7u7igqKoKNjQ0OHTqE9PR0ZrP6+/tZdf/iiy/iRz/6EVpbW2E0GrF06VJs2bKFwatN\nmzbBwcEBhw4dwqZNmxAVFQWLxcJlJ6SOCAgIgEQiQXNzM4M98/PzvPkoKiqCQqGAxWJBfHw8BgcH\nERYWhoGBATz44INYtmwZpqenUV1dzXl2Dg4OOHXqFJydnREbG4uqqioEBwfjnXfe4Q2dWCzGypUr\nIRKJMDw8DIvFgszMTCQkJHBRk6urKxoaGtj6uHr1anR3d2P//v3o7++HWq1GR0cHNm/ezPnUXl5e\nyMnJYZtRXV0dqqqqUFhYiJ6eHuzZswdzc3MoLCxESUkJDh8+DLFYjLS0NPT19fEG+fnnn4e3tzfM\nZjNkMhkqKyuhUqng4+ODmzdvoq2tDXFxcXBzc8OZM2fw8MMPIzo6GgaDAf7+/ujq6kJeXh4cHR3x\n/PPPIycnBw0NDfjjH/8IX19fhISEQCgUYnBwEL29vfjoo494sxkbG4uWlhYkJSUhICAAOTk5rEg0\nm81obW1FXl4eampqkJ2dDVdXV4SGhsLJyQkqlYqt2V5eXsjPz0dAQADq6+uhVCpRU1ODrKws6HQ6\nnDp1CqtWrcLExAQcHR1x48YNtLS0IDo6Gp2dnbxxIVBtyZIlUKvVKCoqQnV1NbZs2YK6ujq88MIL\n2L9/P8bHx1FZWYnt27cjLy8PQ0NDUKvVGB8fx/Xr1xEVFYVXX30VQ0ND6Ovrw44dOyASiXiTCgBn\nzpyBt7c37OzsUFxcjEOHDrEtXqVSQavVQqfTwdPTE8PDw5iYmMBzzz2HFStWwM3NDUFBQejt7YVQ\nKIRCoYC3t/ddRWGkNI6Pj8fly5fxxBNPoKKiAqWlpbh48SJmZmawZMkSjIyM4OLFi7h27RpHdrW2\ntuLNN9/kjN60tDSoVCrU1tZyh0Fqaipee+01pKen4/Lly1i1ahUCAgKwZcsWtLa2srrD09MTvb29\n8Pf3R05ODjtlgoKCsLCwgMHBQYyNjcHZ2Rl6vR5FRUXf+gD9tpGVlYXOzk709PRgx44dmJub47gH\nKju+dOkSFAoFNBoNAweNjY145513kJmZieLiYo7rKS8vh4ODA/z8/BiYVqvVqK+vR0ZGBu677z5k\nZ2ejoKAAX3zxBeLi4vg15HI5d5xcu3aNnScWiwXbtm3jtnuKMhscHMTOnTvxyiuvoK6uDuvXr4fJ\nZEJfXx+Sk5NZqS0Wi/kzIYDMzs4Ok5OTdwEZtra2XNhKdtN3330XSUlJ2L59O7Zu3Yrk5GT4+fmh\nqamJCQjKpxYKhdw3YJ1TOjw8jOHhYZjNZrS1tWFhYYEVnxMTE8jOzkZoaOhdsTUbN27E+vXrsWzZ\nMszOzvJBPjw8nA+/pHAtLS3F6OgoIiIicPnyZfT396OrqwuZmZkQCoX461//iscee4wdH1ScnZmZ\niStXrnAWJ0UMUalzSUkJK7x1Oh20Wi3/rp9//jmampqwZcsWVilqtVp2i0ilUrz99tu4ffs2ent7\nUVhYiN7eXqSnp0MgWCyJp5gkg8GAX/ziF/jNb36Dc+fOobKyEm5ubgz2FhQUYGBgACtXrsS1a9fg\n6emJo0ePorOzE+fOnUNUVBRWrVrFET+Tk5PIy8vDW2+9xUVgNEfPnj3LP3vp0qUMsFEsh7OzM8LD\nwzE/P4+Kigp88MEHGB0dZZWuQqFAYGAgWltbuaPD3d0dZ86cwdmzZ/Huu+8iODgYarWac8WXL18O\nkUiE1tZWnD59GvHx8RwNMDg4iLq6Oo6tiYyMhFqtxtDQEORyOfR6PYxGI5YtW8Y5lAUFBejv78fe\nvXtRUFDAkUwzMzO4efMmHB0dIZfLERYWhmvXrnGPwOHDh1FWVgZnZ2ccP34cGo2G440eeughBAUF\nIT4+Hg4ODncdvPz8/CAUChmU1+l06O7uxvvvv4/Z2Vmo1ep7Jh9Pnz7NamJSWRO4Q/nTAoGAwXA6\nGBMhQPOQ5g8BzxMTE2hvb2cXIIFJ/v7+uH37NmpqatDY2AhgkfCcn59HTU0NYmNjYW9vj+LiYjg7\nOyM+Ph5Lly5FZGQklEolFAoFZ30SOE6bfFLY0j1PhwxgMY+ebNVms5ldAbShJ/cCKYfo4Et2YlJP\nEfFKP8tkMrF6mA5hpPa0jkIiJW1wcDBGRkag1+vh7e2NAwcOsPWZ1L7WByIifeVyOb+mnZ0dPDw8\nIBKJ4O7uDplMxrF15CKi7yXikdYCOzs77rehQzQVEj/44IOs2szNzeWoFh8fH3h6eiIkJAQCwWIh\nOJX7nT59GjMzMxgeHuZSSXqOW6uvyIJdU1OD5ORkjmKh60igjnU8C3UJkYLf1tYWISEh7Jg4c+YM\n+vv7sWXLFiY/rly5gu7ublRUVKCwsBCTk5MclUNEQ2BgILKzs+Hr64vx8XF2rRLYR4XIHR0drC4m\nkosOh9ZOPDr8k+uC3GE0V0QiEdzc3DA6OspkcGBgILsriXCiyCEAvM+iP8lF6+HhwXOFyCqKTCHQ\noLm5GTExMazEI1cOKbxJ9UkkhcVi4c+jp6cHjo6OGBkZQXNzM/dQOTs736X+9vLyQlNTE8xmM1at\nWnXPiq7BwUGek46OjhgcHISnpyffAx0dHfyZ0ud3/fp1aDSau4iygIAALFu2jEsC9Xo9SkpKmAyd\nnZ1Ff38/x1dafy70JwFKBDALBAIGVWhuEsEyPT3Nz4yuri7U1dXxmkWEw8TEBIOCjo6O6OnpQX9/\nP2QyGRYWFnhP7evry2IIeq/T09Nobm5GYGAgzylSxBNxSvOQQFvap9KctFgsqK6u5hgeig3Kzs5m\nQojmy/T0NDQaDaamprhnghwftHbSnAbAc5XAUSIpKXJxamoK7e3tePrpp9He3g5HR0de/ykWcHJy\nElNTU9DpdMjIyIC9vT3H6hEgRK9/9epVbNq0CXZ2dqwmt1a+0vu0ji4i0oYU6nQPkLKcnH9EZBAJ\npNfrMTg4iPHxcXYDjo2N3bWW0VpESvAdO3bcJaSie4nIcPpdyMVK6lyac2azGdPT0/wcEggEmJiY\ngNls5sJ1iUSCCxcuwMfHB2lpaVyurlQqsbCwWE6fmZnJqmhS89fV1WFkZASJiYmsXqVCabFYjMrK\nSnR2dnIXwcjICJdfksNGqVTeFftFHTM0z8gp09jYyEIc6+cUgWfz8/M4ceIEl23TOkqkGwDeM1IJ\nra+vL881ej4TKUgExL2SAF9++SWGhoYwPz8PhULB+5ru7m6cP38ey5cvx+joKNrb27mnS6fTob+/\nnyOR9+zZA6VSCTs7O3h7e0OtVmPjxo3YuHEjZmdnERcXx3sKShD4VzE8Op3ua8/K2dlZVvpb34vf\npJb+vx0eHh5cXNvV1YVPP/0Uw8PDsLFZLE0mgQjNWYpg3b17N5qamlh4911jdnYWWq2W3c0CgQBJ\nSUk4deoUvLy8/iXpASw+M/Ly8rB169a7/p6uyfz8PPcCfJUg+KbR19eH/v5+dpZs2rTpayC69aA9\nBM1vkUiEqakpKBQKpKenw9/fH0FBQWhsbMTu3bshFovR09ODoKAgqFQqNDU1ISoqCrm5uZiamsLU\n1BR2796NXbt2cbzf3NwcSktL2b1Fg8QyNjY2+OCDD3iNmJ6exsGDBznG8V/93iaTCWfPnkVkZCSD\n9YODgzCbzYiOjuY9yMzMDDo7O5GUlPQ1UN9iWSzIXVhYYBf+/Pw8enp6OL73q2N2dhYFBQUwmUy8\nZkdFRfHXflWl/9VhsVhw/PhxxMXFcRerq6srnJ2dcefOHWRnZ6O6uhqZmZn46U9/igceeIDJxa8S\nISaTiQuYr127Bq1Wi6ysLKjVan62klODOpyIhLTuh/muuWo9/ocE+PbxXSTA+Pg4Dh06hA8//JD3\nLw899BBqa2tx7do1xoBv3LiBd999F2VlZUhISPiXa+R3kgAEMDQ1NUEgEHC8CWWAkjKQAPabN2/C\nz88PMTExmJmZwXvvvYf4+HjEx8fzgYmKVW/fvo3y8nI+gF2+fBkpKSnIy8uDRqPB7373O+j1eoyM\njEAsFsPDwwO5ublIS0uDRqNBSUkJYmJiuCjDw8MDSqUSOp2Os6xfe+01VFZWYmRkBFlZWWhoaMCS\nJUsYaCgvL8fDDz+M5ORkfP7557h+/TqGhoYwMzODxsZGWCwWrFmzBtHR0fj5z3/OGeRdXV1YWFgs\n801LS4PZbIafnx+GhoawcuVKtLS04NChQ2xfv3HjBmpra6FUKlFWVoYbN27g8OHD+OCDDzAyMoJ3\n3nkHW7ZsQXZ2Nuc3pqSkYNeuXeju7saaNWtga2uLK1euoLa2FiKRCBkZGawIXrp0KYRCIU6ePHnX\nAeKll15CdnY2AgMDASwWB5WWliIoKAhffvkl9u/fj+LiYiQnJzNj++KLLyIsLAx///vfcfDgQTg6\nOuLHP/4xpqam2FZK0TebN2/Gpk2bcOXKFTz55JOor6/H1q1b8f7776OoqAiPPfYYcnNzoVAo4Ovr\ni7Vr18Lf3x8HDhyAm5sbF6jOzs5yvmlMTAwr+YgI2Lt3L1paWrBlyxYAwJo1a3Djxg2EhoaitLQU\nP/7xj9HW1oaf/vSniIiIgNlsxqVLlzjW4NNPP0V5eTmXtuTn5+OBBx5ATEwMjhw5ws6S2NhYjI6O\nMnhFfQZVVVVYsWIFdu3ahZSUFBiNRnh7e0MqlUKn06GiogL79u1DQkICjEYj1q1bx8w7kVMikQiv\nvfYaIiIi2FL20UcfYcWKFVxeqlKp4OTkhDfffJMzmi9evIjk5GTY2dmhtLQUSqUSLi4uaGtrQ2Ji\nIurq6vDoo48iKioK7733Hvr6+rBu3TpkZmYCAL/f3/3udwgLC8OFCxdY8a3RaLBkyRIIBAJ88cUX\nyMzMRF1dHZRKJS5dugRHR0dcv34d3d3dWLJkCQYHB3Hjxg1UVlYiNDQUMzMzGBwchJ+fH8RiMf78\n5z9j9erVDDYeOXIEarWaP7fvOw4fPsxl4qTyVigUvKkRCoXw8/PDM888w3MkPz+fCcVf//rXrGSs\nqKiAWq3m65aZmYmYmBhUVVXh3LlzCAgIQFRUFJycnBAdHY3e3l50d3djfn4eFy5cwMzMDLy9vSGx\n4PZWAAAgAElEQVSTyaDT6Th6bHh4GA4ODigoKEBlZSV2794Nk8mE++67D0NDQ4iOjkZLSwvOnTuH\nsrIyHDhwgAv9LBYLuru7ERQUxDm2r7zyCs6fP49Vq1Yx6OTj4wODwQCZTIb8/HwcO3YMS5cuxfr1\n6/lAR1myjo6O6O7uZlfU22+/jfDwcAiFQmb7x8bGIJfLMT8/j+vXr/OBcGpqCunp6aiurkZTUxMm\nJyfR3d3NBAodkjZv3gyJRIK+vj7odDqcO3cOiYmJuHDhAm9+6dClUqkQHR0NrVaLX/7yl0hLS0NC\nQgLfC9u2bcPk5CQ0Gg1nM7u6usJoNCIuLo6zDInIoALwtLQ01NXVoa+vj8sg9Xo96urqsHnzZi6T\n9/X1ZcBmamoKTz/9NFavXo21a9ey02X37t2oqqpCcnIyOjs7OS7HOqPXz88PgYGBTCSWlpais7OT\nX+vOnTtISEhAT08PDh48yEWVpaWlaGlpQW9vL0fMRUVFwc7ODkajEaOjozh27BiOHDkCpVKJtWvX\n4oMPPmBwhHo7yFZaWloKhUKB8PBwBoLJhUME4qeffoqGhgYUFhYiLi4OarUat27dwoYNG7g/ID09\nHUePHkViYiK0Wi0CAgKQlJSEtrY2uLu7w2g0suq1pqYGe/fuhdlshpeXF27fvo2+vj5YLBbuCxGL\nxXB1dUVcXByuXr2KGzduICMjgwHy6elp+Pr6ckzI8PAw/Pz8cPPmTeTk5OAnP/kJ4uPjERAQgJqa\nGkgkEo7l2L17N288bWxs4O3tDXd3d45g6u7uRnJyMiYmJtDZ2cngyq9+9StERERArVbf07pjHYdF\nalUi7Chj3tHRkUu/rPN4FxYWUFlZiZycHMzOzsLJyQkjIyPIyclBX18fK57c3NxQUVEBW1tb6PV6\nxMXFYWhoiONIHBwc4OLiArVazV/T1dWF1tZWbNmyhRXDCoUCIpEIMpkM7e3tHOdGhzMCzIhwoH9I\nzUPdNdPT0+yc8vb2ZvCIDgu0gSTgiwBgawCMCAOKlyAAhpRC1qphs9nMBInJZIKTkxMqKysxMTGB\nuLg4uLq68v6NlGSkbJVKpZiYmODfG1gE98fHxzlDnkBRylDX6/WQy+X8tdYWZwLereNPKO82MjKS\nIxovX77MzoQlS5ZwzBLF5KnVao6NuXPnDtauXYsNGzYgNjaWOxkIuCQHyK1bt9DU1IQNGzbwZ0TX\nlhwWtHbPz8/jo48+YnLD2dkZS5YsQXh4OHcrhIeHIy4ujhX79vb28PHxgaOjI8rLy5ksEggE8PT0\nxA9+8AOEhIQgISGBM6spWojcKdZuDnqtgYEBBh7pc6ZYFNrjkzJMIBBgaGgIw8PD/FokKKBc9K6u\nLixduhShoaF84AT+WYLb09MDAFyqKhQK+dxBILX1/CYSmnodKNKF5iGBJCaTidWNRLLMz89zOa2L\niwtqamrQ09PDKt2wsDBeYwhcpd/dzc0Np0+fRmJi4j2TAFqtltXfNEfJfSGRSDiSjtYHjUaD06dP\nc9yMUCjE7t27sWPHDhZBVVVVwWg0cvcOqZQLCwvh4ODAUSJ0rajU1dotQb+j9f1EpKjRaOT7sra2\nFjKZDGFhYbC1teU4NxIKCIVCVFZWori4GFeuXOHCV5VKBYFAAH9/f4hEInb6ESkjFovZVWtdREgF\n3RQvQ4QFESVEDBiNRnR2dmL9+vW4//77sXbtWsTFxWHFihW4ffs25HI5RkdHMTQ0hGeffRY3b97E\n+fPnUVpaivXr1zMRBoB7sWitIyKO1JK0LwXA4p19+/ahoqICCwsL3H3k4OAAR0dH7k6h9fjgwYPc\nQ0RrHc3Tubk53tMEBAQw2UWK/qmpKc6wtiZl5+bmIJVKMT09jfr6epw9e5YBbIPBwGsm7dvIlUN/\n5+7uzv0Ls7OzDLxZR94Bi3FifX192L59O2xsbHifTiIOivihOSWXy9lVQ6I+imMi0crQ0BB6enpg\nY2MDmUwGo9GIixcvwmw289zu7e1FdHQ0Vq1aheXLlyMtLQ2pqal3OdjoWr355ptIT0/nZxwBtfb2\n9ujq6mIBgMWy2DMTHh6Of/u3f4OLiwv27t2LsbEx+Pr6QiKRQCqVoqWlBR4eHvx8IkIRWMz/vnLl\nCmJjY/n/0fpiNBpRVlbGkYMlJSU4deoUSkpKkJCQgJdffhktLS1obm7me3ZqaopJVno9IuXoHGAd\nGfd9R2lpKX74wx8iIyMDUVFRUKvV7ETbsmULrl27hra2NjQ2NrJK3NnZmcVI//jHP7gjitZ7KvEW\ni8Xw9fXlZy+5bMmR821AOd0PNKqqqjjLnX4Gjf/XBMBXR1lZGaqrq3n+0v1PMSi05hw+fBjj4+OI\niopCR0cHCwz+1cjJyUFycjLvY8jRkZSUhLNnz2J2dpYj1L46Jicn8fHHH+PIkSNf+3+2trYYHR2F\nUCiE0Wjk92EwGNDV1YXbt2+jrKwMVVVVGBwcxOTkJLy8vCAWixkH+bZBz8dvA9cJb7GxsYGfnx8k\nEgl8fX0xODgIgUCAyspKfPbZZ7hz5w66u7tx9uxZTExMwM3NjdcWOiPRHra/vx8VFRWIjo7mPZR1\n+XBiYiKysrKwfPlyHDx48K4Ol381iLSimNbBwUE4OTmxM10gEGB4eBhPPvkklEolkzUtLS0sLh4b\nG4NAIEBISAi0Wi2LY2tra7mr7KtDJFrsPaOINV9fXxZcWcev0fnTOtpIq9Xi7Nmz2L59O+bm5vD6\n66+zEFmj0bCQrbe3Fz09PXj00UdZhEF7IHqt8fFxlJSU4NixYzh58iRsbGywf/9+LFu2DPPz8xx1\nTf1bcrmc+1rosyGy5V5IgGvXrn3vr/3vNigO7NuGjY0NUlNT0dzczMLFqqoqPPvss1i9ejWfvz78\n8EMcOnQIIpEIdXV1/zKO+ztJgLfffpvLCQ0GA1QqFZ566ikEBARwLEhzczPi4uIwPDyM+vp6KBQK\nqFQqXL16FRKJBDKZDFqtlnMuL126hH379mFkZARPPfUUYmJi4OLigtTUVLz99tt47LHHIJFIEBgY\nCJlMBnt7e3zxxRestEhNTWW14/T0NPz8/LBt2za89NJLeO211zA6OgqtVouYmBj09/djfn4ecXFx\nMJlMqKur401UXV0d+vv7sXnzZjg6OmL58uWYmJiAUCjEkSNHOC5kcnISgYGBkEql6OjoQFtbG0pK\nSvDRRx8xgEjKApVKBZFosXQxNzcXAwMDEIlE+NnPfoaIiAgulty0aRPMZjPUajUsFgsSExMBACqV\nCp6envj444/R2tqK+fl5xMfHc647qQYoTqeqqgq7d+/mQ2ZjYyNSUlLg7e0NZ2dn3Lx5ExaLBXFx\ncZiYmACw6O6wtbVFVlYWPvvsMzg4OLDabu3atbCxsUFLSwsCAwORk5PDGc4WiwXt7e3IyclhEO7g\nwYNwdXXFhg0buKwxPz8fJpMJTz31FM6fP4/BwUFER0dDr9djdnYWFy5cQGBgIMrLy+Hh4YHBwUEs\nX74ct27dQnp6OsrKyjA0NMSFUQsLC1izZg1aWlo4QiM7OxsKhQJXr15FXFwcPD09cfz4cWRmZjKT\nf/nyZbS2tqKnpwcGg4Ffa8uWLcjPz0dtbS0kEgm2bdvGasiioiJ4eXnh1KlTnJccEhKCO3fuID09\nHcXFxQy+kF382LFjeOSRR+Dk5ITS0lJUV1djxYoV0Gq1qKur41zrwcFBjniIjIyEUCjErl27YLEs\nFmCTktTOzg69vb3Q6/XYs2cP/Pz84OPjw+pbFxcX3kR7eXnB2dkZoaGhKCoqQnd3N6RSKeLj43ku\nvvrqq0hOToZCocCXX36J5cuX49q1a2hvb+dCyoKCAt4c7Nu3DykpKQgJCYFKpcK6deuQlJSEmpoa\nxMXFwc7ODpmZmXwd9Ho9XF1d0d3djQcffBC//e1vERsbi/Lychw8eJBLUO9lvPvuuxz7IxQKERUV\nxQcbepABi6TW7du3WUno4+MDk8nE8SFTU1OwtbXFtm3bMDIywtePsi6zs7MRHh6O8fFxyGQyzM7O\nYmpqCpOTk6irq8OTTz6Jt99+my2XAQEB+PzzzxEfH4/e3l7Y29tj2bJl2LRpEzs+xGIxR9n4+/tj\nw4YNvLEhZj0/Px+xsbG4desWTp48CRcXF3R1daGvrw+bN29GUlISrz/kApBKpXzocXV15Rzrubk5\nzrAtKSnBsmXL0NPTw1ZJtVoNGxsb/Pa3v0VDQwPS0tIgFArZeVNUVITi4mLs2LGDycLg4GBotVq4\nuLgwsUqAIoFapO4+duwY6urq2P547NgxfPbZZzCbzewmIHUwlYLa2NiwnZ0AEIqvMBqNMJlMKCgo\ngJOTE7q7uyGRSDgn0tbWFq2trWhpaUFdXR1cXV0xPT2NhoYGSKVS+Pn5wd/fn/skqLxxeHgYSUlJ\nEAgEGBwc5PgIUi/X19dj6dKlAMAF9HRfarVa/noqEA8NDcXY2BhCQ0NRXFyM3bt3w9vbG3K5HMuX\nL8eyZctQUFCAoqIiVFZWwtPTE3q9HlVVVTh16hTi4+Px6KOPYnx8HJGRkZzf6+LigtHRUfT397Pq\nKCIigh0KFBNFnRyxsbFoa2vj8vC6ujqMj48jJCQEvb296OzsZKKaSjzpGXX+/HkuBP3www9x7tw5\nTExMIDAwEEePHuV1lWKk/P398eabb+L++++/S7kHLIK7crkcHR0drBSbm5uDs7MzF9bOzMygqqoK\neXl5UKlU0Gg0nK8tFArR0dEBo9HIwGdsbCwmJiZQVVWF7u5uuLi4cFH3mjVrEBUVBY1Gw6p6V1dX\njsYaHh5GamrqPa07TU1N3AVBhzPgnyV0BHx2dHRArVZDKpVCIFgs7svPz4e/vz/nohLBVlNTwy4m\nymem+I7R0VFoNBru7KD+EFK1Etje0NAAsVjMrjnagBN4Q6ova7UlrZHWsRAAGIgGFsHHrq4uBrW6\nurqY6CAiiHJMxWIxgyQEUlMMEh1UvvoPKfEp25lAGVI39vb2ora2FlqtFs7OzhAIBAgKCmIwl4BH\nil2h0ldSwNJnYzAYWGlLBx6tVgsnJyd24Mnlcj440j0+OTkJoVDI+cyUJ+7r63tXTwUdtKgniNxJ\ns7OzrADv6upCWFgYOjs7WdE7NTXF4DewqAIjBd/OnTsRHh4OJycnLvGzVtdbA4tmsxnl5eV84HJ3\nd2clmMlkwvj4OIPodHAk8NXW1hbe3t4YGxuDg4MD5HI5srOzcefOHb6+Dg4OmJqa4vg9IhKsLfgE\nuFNMC4ELpMz8alwNzcGPP/4Yk5OTaGxs5HuVMul7e3sxMDCAkZERJCUl8f6WCKSRkRHuIyksLIRU\nKkVycjL3UNB1I+CIDt1UwtfQ0HAXsG19HxDoSEWntIZRdMjExAS6urq4v2VwcBBLliyBn58fk7TW\nqriBgQHMz8+jubkZBw4cuKd1p729/S5lnVAoxMjIyF0kGwGCpLouKipiYk4oFGLt2rXw9PRkIFWv\n16OhoYGzvukenJqaQmxsLJYuXQpXV1eed0qlkucpRQfQWkLXlUgKiv+xs7Njt2xoaCgroen+sH7e\ntbS0YHJyEpOTkwgNDUVCQgKfmwCwc4DWE2ARuGppaeEIUOq4sgb76XtpzplMJp4ber2eSxxdXV3h\n5OQEZ2dnjsz77LPP8Omnn6K0tBQuLi7sBHB1deVcZHItWZMLRJSSS5f+m94DuckuX76M0dFRXtOo\nANS6MHZ8fBwbNmxASkoKJicnmfyhIRAIYDAY0NTUhNWrVzP5+P777+OTTz7B/fffzxFv1msHlfrO\nz8+jt7cXL7zwAvbt2wdXV1cmpTs7O3k/Tfuz+fl5/pyJVKC5QJ8PrYUEVIlEIjQ2NiIpKYnnkzUA\nRoIja+fc2NgYEwvkMqP3PT09jZqaGsTExHCEUGNjI+bm5pjcv3r1KiIiInjOE8BM5wSDwcCCE71e\nj56eHiQnJ7NrmNY1o9GItrY23L59G3q9HrGxsdi+fTt8fHywZMkS6HQ6Pq8VFxcjIiKCSRoi0Mhx\nQXNkYWEB/f39CAwMhIODA4OjRqMRQ0NDyMvLw5IlSxAbG4uwsDCEhYWxcIIcmenp6TxX5XI5hoaG\noNPpMDU1xS4JIononEQK9u87iOihPaWPjw/8/f05Kiw8PByRkZHw8PDA0NAQhEIhSkpK8PLLL8PZ\n2ZlV118dAsFiXyCdg+heoflF/SLfBNR+9fX6+/vR0dEBf3//b/wdvhr7ci+jra3tWwFMi8WCkydP\nMgktl8vZpUDrok6nw0MPPcR9Kh4eHrh06RJ3Gn7bmJubQ05OzjeK40QiES5cuICoqCgYjcZvfH8f\nf/wxC5O+OsbGxnDq1Cl4eHjgjTfe4Ohhi2Wx4ygyMhLR0dGIiYlBQEAA5ufnIZPJuOwV+HaHBT2L\nvmsQKUTEPcVVRkVFITExERkZGbh8+TI2btyI4OBgPus8/vjjOHnyJJRKJf/eKpWKHbhXrlxBV1cX\nwsPDmZgmIp76JO9lkJhmZmYGQ0NDGBgYwNjYGMeYnTx5EmazGevXr+dI1r6+PrS3t0OlUnEXhYOD\nA9ra2ngPVFhYiBUrVnwjCWCxWDA0NMTR5nq9nuMtGxsb0d3djf7+fnZK6nQ6ODg4YHp6GkePHsWm\nTZvg5OQEjUaDs2fPwtHRkc8A5IKVSCSIiYlBVlYWu5XoPqF1qK2tDX/84x85dlutVmPt2rV3lRAT\nuUzYnpubG4B/niForbU+K33X+B8S4NvHd5EA5NC8du0akwAff/wxKisrGWcdGBiARqNBQkIC5HI5\n8vLysGLFim9/ze8iAb788kt8/vnn+NGPfgStVouCggL84Ac/wNGjR+Ho6IiJiQmEhITgrbfe4mJV\nDw8PVFRUYMmSJfj4448hFArx0UcfwdHREW+++Sb+8pe/wM7ODrW1tVi2bBkrNjw9PZGUlMT2q9jY\nWEgkErz88svo7e3laKDy8nJ4enpyxn5NTQ2rIskuTnmHqampWL16NfLz83H+/HnU1tbi5s2bWLdu\nHdsKS0pKkJubC5lMBpVKhfz8fISEhKClpQXT09PYu3cvnnnmGSxbtgzOzs4ICAhASUkJ/vKXv2Dd\nunVwc3NDdXU1wsLCYLFY0NbWhj/84Q8IDQ2FTCbDAw88gC+//BJKpRI7d+7km4tyHKuqqrjIcnJy\nEmKxGJGRkbh69SoeeeQR+Pj4wNfXF1euXOGs0Z/97Gec2b1z506Mj4+jpaUFX3zxBaampjA8PIxT\np07h6aefhtFoxPj4OKRSKf7rv/4Lo6Oj2LVrFzo6OnDlyhXMz8+jsrKSMx+rq6tx+vRpbN68GT/8\n4Q/R09MDPz8/5OfnY+/evUhISEBxcTH++te/orm5GQEBAWxdDQwMxA9+8ANs3rwZAoEA8fHxcHV1\nxcsvv4ydO3fi+PHjePzxx/ng9sorr6C7uxt5eXlYunQpVq9ezZFAV65cYaU79U+cOXMG69evh0ql\nws2bN3H16lXs3bsXNTU1CAsLg4eHB4aHh/Hcc8/h6aefxsLCApYvX86qnMLCQuTm5mLHjh3w8fHB\ntWvX0NjYCIPBgP3793N8x5kzZ9DR0cE25h/96Ee4c+cOfHx8MDExAVdXV7z11luYnJxET08Purq6\nMDc3h7y8PPj6+uKtt95Cc3Mzdu7cCS8vL5SXl8PX1xcODg7MWFMZn0KhwJkzZ5CSkoLW1lbk5uZC\nKpXi5z//Oebm5vDSSy8hICAAP/vZz9DQ0MClVOHh4VzQ6+DggBdffBGJiYm4//77kZOTw2DVgQMH\n+OFeUlKCN998E83NzXBzc0NkZCRycnIgkUjwyCOPYHZ2FiqVCp2dnXj33XdhMpng5eUFgUCA9957\nD5GRkSgpKeHIqqCgIISGhiIvLw+xsbEcS/LOO+9g3759zD7HxcXd02JYVlaGgYEBREdH8zUlgB/4\nZ06qQqHAzZs30dHRgZSUFDg7O8PJyQmpqanIycnB4cOH0dTUhOTkZD4szM7OsqKG4j7o4PzLX/4S\nAwMDSE5OxqOPPoo7d+6gsbERGRkZEAqFKC0tRXFxMU6dOoWgoCCo1Wq+tnQQICXj1atXAQChoaFw\ndXXFjRs3ODZm6dKlsLW1RUdHBx5//HEGxkgROTIygoqKCrz33nvo6OiARqPB1q1bkZCQgNraWi7t\nJeX2kSNHYDQa8fjjj/Ph0tXVFSEhIbh58ybu3LmDiYkJSCQSeHl5obq6Gq6urpBKpXjttddw6NAh\nqFQqJgBv3LjB+fdkNQwJCYGbmxs+/PBDVFVVYceOHaipqcEzzzyDxsZGnDx5Eq6urpxVHxERwRE2\nZWVlWLJkCavpBAIBOjs74eTkhBs3bkAkEsHX15dVp/b29mhra8OaNWtgZ2eHp59+Go899hhHqwQH\nByMpKQmhoaF48sknsWPHDkRGRkIsFqO7u5t7Z+rq6jj2bP369XjllVewefNmyOVyNDU14d1338V9\n990HYLE4igCI6upq2NnZQafTob6+Hu7u7tx54OPjg7CwMLz++uuorKzEgQMHEBYWxopTGsPDwygp\nKcGjjz6K++67DyqVCgEBAYiLi0NMTAzc3d1ZVUaHxzfeeAMbN26En58fRCIR3nnnHfz2t7/F6Ogo\nxsfH4eHhgcrKSly6dAm+vr5wcnKCo6MjgoODERISgpSUFIyMjGBmZgYPP/wwAgICsHbtWpw4cQLB\nwcEwGAw4cuQI0tPT8emnn2Lt2rX8PmJjY6FSqRgQaWlpwa1bt/Dkk0+it7cXTk5OePHFFxEeHs7Z\nwmKxmA+iDg4O8PLywsqVKxEVFcWgy/j4OIBFgP3YsWMYGRnh+UbPzKNHjyInJweJiYkoLy/H9PQ0\nFAoFrly5gvLycrS1tTFZ29LSgvT0dPj4+ECr1aK2tpY7Hqanp9HW1sY9BPHx8fe07vzpT3+Cv78/\nhEIhF1jm5+ezEo3UhVqtFoODg7C1tcXp06fh5OTEa4x1dm5zczOGh4dZGTY4OIiUlBRoNBoMDg4y\nyZeSkoLQ0FDI5XIuYCXLv1QqRWRkJJYuXcogNwE3Y2NjXAbc1NQElUrFQKI1OEXgEPBPJf7IyAgq\nKyshk8ng5OQEkUjERb/9/f3o7+9HdXU1W/nFYjGLJOj5TZEopEAhwNA6CojWRXICUFHkyMgIioqK\n0NnZyerYmZkZFBUVwdbWFuPj47h48SIT+ETWEbDu6OjIQBeBVaReMpvNnOFLarqKigo4OTmxmo1c\niDTH+vr64OjoiOLiYkRFRaGurg5yuRy1tbUYHh5mYmRiYgJjY2MMZkdHR8PNzQ3BwcGws7PjfNfW\n1lZIJBIWPzQ3N6O1tRWzs7PIyspi9x8RLNZODXI/ETEhFou5yyM+Ph5RUVFwcHDA/Pw8SkpKsGbN\nmruU8AR4W5fzhoaGIiUlhQ8HISEhaGhogL+/P2QyGRQKBSYmJjA0NARvb2+2gNOwBkAHBgb4wE2f\nL33+RN6TyjgyMhJzc3MICAhARUUFent7UVFRgbq6Oi79JYCb4kEohqq2thadnZ0oKSmByWTC9PQ0\nkpKSGGAhtwA9v+l9WCwWNDY2Ii4ujvcgtE6Ril2n06G3txdffPEFysrKMDExwYAkkVKRkZFQKBRo\naGhg9XpoaCivezSnKcJkfn4e9fX1eOKJJ+5p3Xn22Weh0WgwMjKC9957DzY2NvD09ERjYyPHL0kk\nEnh4eGBqaoqJxaeeegphYWEICgqCh4cH3NzcOOJHq9UiJSUFq1at4ljQ8vJypKSksNqX9kOkPicS\nxppAJHCbFM9ms5lBUFKob9q0CcA/4wVMJhPq6+vx6quv4vr16+jp6YGdnR27rJVKJZKTk+9ybtD7\nIHCQ9q8UBUPPO61WywIJAKxGBMCgLsVmEdnv6urK6yLdE25ubvDw8EBDQwPc3Nw4AojyvSMjIzn2\nicpy6TUozovcQWNjY3BycmIwikCS4uJi7qHq6elBSkoKu6Hd3NxYHHLhwgV0dXXhzJkzKC0tRW5u\nLvr7+9nRffToUezbt4/n+fT0NF599VWYTCZs27aNnRA0SLRFz6va2lrExcVxHJK3tzdUKhU/xwsK\nCjjCiXpwhEIhWlpaIJfLOS6JiFhS21KXkEgk4n4JayCcyAOz2czuGeuICSL2SdBH3R70s+gZ0tDQ\ngE8++QTV1dW4evUqqqqqEB0djfvvvx9OTk5MDtPzYG5uDmfPnoVSqYRWq8X169exY8cO9PT0sEOF\nrk9LSwteeukl/Od//if27duHlStXIiQkBGFhYZBIJBzpSmd7corR+mrtBKEYwJmZGczPz3OkJUVY\n9fb24tSpU1hYWMB9993HPRu0D6T5TB0q9N90rhGJRNzfQlFhtB+YnZ39VqD82wY9M2gQqEd/0l5c\nqVQiIiICL7/8MvffzczMYNu2bejt7WWlPM2JmpoaqNVqfh0imOhnAmAHCEU8fdOwWBZjPK2dANYq\ncHqv/6fDGmD/KikhEAgQGBiIPXv2YM+ePfDx8eEeS4vFwvHQ//Ef/8HriUAggLOzM0pKSiCRSL6V\nYJicnOT1xfp3JZA2LS0N165dg4+PD59XrUdDQwO2b9/+tdednZ1FUVERd40E/O84X5lMBgcHBxiN\nRp5nRN65uLigsrISISEhTGx/EwGwsLCAycnJu0QN3zSsSZn+/n52TRORLZfLIZPJsHLlSlgsFly/\nfh1arRZzc3PQarWQSqW4evUq1q1bB2CR0CfRo5ubG0fB0nugvSKRxfc6aO5ev34dJ06cQFNTE8rK\nylBRUYFdu3YhNTUVubm5OHHiBObm5tDU1ISpqSkcP36cxW+2trbw9PTk+zIpKQmXL1+Gv7//166l\n2WxGRUUFvL29+br29PRgZGQEaWlpLB4kUROJ3E6ePIl9+/bxPsTJyQm1tbXsDjWbzZBKpQgODsbz\nzz+PjRs38r1nTQIYDAbU1dVBIBDg6NGjjOWEh4djw4YN0Ol0LMJQKpWMSyoUCjQ2NkIoFC8gz44A\nACAASURBVDKWQGeUe+kEKCwsvOfP6L/LWL169ff6OmsSIDMzE+vXr0dxcTHvzzUaDUfRFRQU/Ety\n4TtJgNHRUaxYsQKurq6Ym5vD9PQ0zp07h+zsbMTHx/PBp729Hffddx/KysqgVCq5QLi/vx9arRYP\nPPAALly4gLi4OFa6hYWF8caX2MoXXngBnZ2d8PX1RUREBP70pz8hOzsb5eXlWLFiBfLy8pCUlASj\n0Yji4mJkZGRAo9GgpaUF1dXVbHPx9PREVlYWR1MQg//jH/8Yt2/fRmlpKWxtbbF161bIZDL8+te/\nhpeXF6amplBUVIQHH3yQN2tPPfUUZDIZsrOzYTKZ0Nvby4VldPimaIfGxkZcunQJzz77LFxcXODu\n7o7Ozk5s3LgRBQUFiIyMRENDA1QqFW9uKKv/zp07iI2NhZeXFyYmJvDJJ59g+/btMBqN6OnpQUlJ\nCedi+/j4ID4+HsXFxdi4cSMuXLiAf/zjH/D09MT999+PhIQEuLq6QqFQoL6+HidOnIDRaMS2bdvg\n5+cHi8WCpqYmVoV99tlnCA0NhYeHB6uwt2zZgpmZGSQkJODixYsICQlBdXU18vLy8OCDD+Lq1auI\njIyEXq+/K5+TrKDNzc0YGxuDn58fBgcHMTExgeXLl0Or1cLW1pZLp3fv3o3W1lYkJiZyhJFQKMTG\njRsRFhaGuro6/OQnP0FGRgaGh4dRXl6OyspK3Lp1C0899RRvki5cuAB3d3dIpVK0traivLwcBoMB\na9euhUKh4GzbZ555hlVxbW1t2Lt3L4OzpBCih1VQUBDHypBDZOfOnXj55Zfh7e2N1NRUpKWlISUl\nBZ999hnuv/9+dh9s374dzzzzDJqamiCRSFBRUYFHH30UWq0WjzzyCLy8vPCXv/wF6enpqK+vx5Il\nSyCRSDgui/5OLBYjISEBFRUV2Lx5M3p7e7F582a4u7sjJycHRqMRx48fx9zcHP785z9jbm4OcXFx\neP3117Fnzx7OLRSLxaivr0dXVxeeeOIJnD59GqWlpUhNTYW7uzuamppw+/ZthIaG4tChQ/j973+P\noqIifPHFF2htbcWOHTsQGhoKOzs7hIaGoqCgAG1tbYiIiOBDnlC4WLS7detWaLVadHd3Iycn556L\n8iwWC1QqFcLDw5GSkoKmpiaOFAPAG4qGhgZUVlZyBBPFR9jY2LAa3tPTk231RqMRHh4erNRRKBQY\nHBzk8uSxsTGYTCY88sgjmJycRHBwMC5evIht27ahpaUFy5cvR319PSQSCaqrq6HVatHc3AyFQsHW\ny4GBAYyOjkIikSA0NJTByTfeeANisRjJyckQCAT48ssvOb9XLBajs7MT+fn56OzsxLp161j1c+DA\nASQkJGBgYAD+/v6IjIzEmjVrcPLkSczPz+PSpUv49a9/jcjISM5LJhC5t7cXXl5eePHFFzE9PQ29\nXo/29naIxWK88cYb0Ov1XEwlkUhY2SCTydDf38+bwv7+fiiVShw/fhwHDhyASCRCYWEhtm7dCjs7\nO2RkZCArK4s3xAaDAY899hhkMhlMJhMKCwu5xJdATgcHBy4g++STT5CYmMiH68nJSbz11lvYsmUL\n5ubmcOPGDeTn56O+vh4XL17kjEjaYA0ODiI0NBQGgwEKhYJViH/84x+xZ88e5OTkYOvWrbwZIks4\nka+urq6QyWT44IMPEBERAT8/P45S8/b2RmFhIdzc3NDb2wtbW1t2IT388MNQKBR8ACAQb2ZmBr/8\n5S/R09ODH/7wh6wGswZVbGxskJubi7y8PC60DAwMxNjYGLy9vTn6ITg4mCPYgMWYitjYWM6YpcMy\nHa7Ly8s5F5fUYv7+/nj66aexfPly6HQ6tLW1Yf/+/VySSM/269evY3p6mkserR0zSqUSn3zyCZ54\n4gm+zwgQIrCEbKlUok4KXTs7OxQWFjKRsWHDBlgsi9nh7u7uSE9PR2JiItrb2/HII48gISEB1dXV\nEIlE8PLywvz8PHQ6HW7fvo3a2lq4u7sjMDAQBoMB3t7eDExotVqMj48jOjoacrmcnS3fdxw5coSV\n+aRMDwgIAPBPG/rs7CxGRkZYZZuamspuG1p7ZmdnYTAYcPnyZVboE9A5MDDABIOTkxPWrVvHBByB\nYnTQpYMOqc4py5/INIqVm5ubY6CLQGB6vwTIUOGo2WzG0NAQrl27BolEgq6uLri6urJTwcPDg505\ndIhtbm6Gj48Pz2MC3cgeT0okWpuBfx72rSO9qBTywoULHDsmkUgwMTGBmZkZjoKorKy8q6TZxcWF\nwRIiPug6dHR0sKvIZDKhp6cHhYWFGBoagpeXF3Q6HTsH+vr6MDQ0hJGREbS0tMDX1xeRkZGwtbVF\nXV0dpFIpVq5cye5VNzc3qNVqhISEIDU1FfHx8bC3t0d0dDQ8PDyg1Wo5x5xIFwJsZTIZz3F7e3vI\nZDL4+vpCp9NxfBvl0ZPamByZBKBRTjeBkBSHRb9PZWUl75sIGKH3QopLk8mEgYEBnDhxAitXrrzL\nFq5Wq3H79m0mqMRiMbq6upgIo3lInyWtBzT/KHKE5oJer8exY8c4ai4gIACOjo7w9PTk6I/29nYA\n/wSY6d6emZlBcHAw7O3tYTKZuO+psrKSr1FYWBhHtgFgAp/ATroG9CymPYnZbL6rL2BqagqXLl1C\nb28vgMWsVVpriZCi+18ul6OnpwdjY2PYuXMnk7YGg4HveYomUSqVaG9vx+OPP35P6051dTWCg4Ph\n4+OD1tZWLqOmZ+TQ0BDGxsZgMBhgb2+PkpISJCUlIT4+HgqFAsHBwVy0Tvefr68v3NzcWOEfFhYG\nPz8/1NbWIjw8nEEgWmcIUKPvpz9pHtI8oHV+YWEB3d3dCA4O5rx/Ozs7Fou99957PBfpuWA2m+Hg\n4MDl3/RsoM+ass1JrUigFZX0UvkprXH0nqyJI3ICdHZ2Ijw8HGKxmCNpaC9GKu7Z2VncunULLi4u\nHB1oNBq52LqoqIgJDyIQrJXzUqn0ayAm3dP0ue3ZswdqtRp79+7l2LuYmBj4+/sjICAAoaGh0Ol0\nDLAQSWVjs9jpIZFIWIBBCmqKTwwKCuIoWOv13rpLzGAwIDc3F2vWrMH4+DgWFha42JqeNf7+/mhv\nb+eSZFrTyDVr7cCVSCSYmZnh/dfMzAy71ug60fuhtYuea3TfkGKWOsjodZ2dnaHT6dDQ0IC2tjZ4\ne3vD1tYWly5dQltbG0wmE6vLY2Ji4OvrC5lMhra2Npw6dYqFCBS7R8AzxSxZg65U5Prhhx/iT3/6\nE+RyObt1KRaJnuWk+Pfy8kJubi4DLORwnZyc5Ditnp4eSKVSdm4CgF6vh8FgwNGjRzEwMIAHHniA\nXcO0x7YGVgmwIyCe1l6RSIT6+nqEhISgtrYW9fX1aG9vR19fH1pbW783iERDp9N9r6+jfSY5SPR6\nPXbt2oXIyEg+M1PEDxEACoUCbW1tHB39TYOedVQqbTKZvuYgIYKfBkW4TU9Pf2d+uvWgZ9k3jZmZ\nmW+MJ7K3t2fRn6+vL1QqFQurSFCwZcsWWCwWFn9JpVI0NzcjIiLiW4t4xWLx14DTr5IZwcHB6Ozs\nxNTUFHc8kls2OTn5a+Ay4S1vvfUWhoeH8e677yIlJYXfg3Xni/UYHx9Hf38/k11fHfS5CgT/LJYH\nvt2BYf13zc3N0Ol07LCmQXtfLy8vXlsCAgJYCDk6OooNGzbc5UKjNdxisXytKNrBweFbnQC0/lgP\n614JwqxcXV2Rn58Pi8WCRx99lEVhdnZ2qKysxMzMDA4ePIjTp0+jvb0dAoEAvr6+WL9+PX821u6c\n7u5uCIVCdjHQe5mbm4O7uzvc3d3h5uYGmUwGpVIJf39/VFZWQi6X85mB9oSzs7O4ceMGli9fzs8X\noVCI8PBwfPDBB+xQP3ToEOO1FMlFa63FYsHU1BQqKiowPT2N1157jZ179IwJDw+Hg4MD3wskFKDX\noc/BycmJ4wAtFgs7BL7P+B8nwLeP1atX48SJE6ivr0d9fT0AQKFQfO3rrEkAIv9FIhG6u7sREBCA\nxsZGxMfHc+/Y/5UT4K233kJkZCRkMhk0Gg1CQkKQlZUFb29v1NfXIzAwECKRCG1tbVi5ciW8vb0x\nMzMDHx8fKJVKnDt3DiEhIdi9ezcuXboEW1tbFBcXc1yHQCBAfn4+vLy8OEtNo9FAJBKxFXjjxo0o\nKyuDvb098vLy0NDQAI1GA4PBgIiICN58NDQ0oL29HVqtFtnZ2dDr9VyYQDnawcHByMzM5CLd4OBg\n9PX1wdvbG/39/bh06RIWFhZQUVGBtLQ0ODk5ISMjg4v4JBIJ5HI5Tp8+jRMnTiAzMxPNzc24evUq\n/v3f/53jXPr6+hAREQFXV1cUFBTAzs4OZWVlzOS3tLSgp6cH7u7umJqawszMDHbt2oXq6moMDg4i\nJycHvr6+SExMRGdnJw4dOsQ3fVBQEAQCAYKDgxEfH4/nn38eRqMRTzzxBJYvXw4HBwd0dnbi73//\nO4KCgpCamsoHjP7+fsTGxmJkZAQajQZVVVVQqVSQyWQoLS3Fe++9B6PRiF/96lccL3H27FlkZWUh\nMjISRqMRW7ZsQX9/P+RyOWZn/xd7/x1d5Xnm/eIf9d5776ihAkIgIUAgBAiIjQGXmOBJQsYTJx5n\nZpLMOE6ZvI7jN/NmMhPX5Ql27LGBgE0xHQM2AkkUCSEJJCQkUO9dW13akvb5g1yXtxwnsX/rd846\na5251/KysTZbez/lfu77W6c5ePAgJ0+eZM+ePQQGBlJbW8s777xDa2srZWVlnD59mpmZGVJTUzl/\n/ryez1OnTuHp6UlmZiY5OTk4OTkxMTFBbGysljC9++676jBxdnYmNjaWzs5OvvOd72iG+YEDB7hy\n5Yoq5B0dHVm9erXanS9dusTu3bsxGo383d/9nSpQ3N3dycvL45NPPmHNmjXEx8frgre7u5vk5GQS\nEhKor6+npaUFW1tbSkpK2LFjB2NjYzzyyCMcO3ZM2+F37drF5cuX8fPzo66ujvHxcR544AEWLVpE\nVlYWcXFxWFpacvPmTX1oSYb7r371K6Kiojh48CB9fX384z/+I6dPn8bHx4dVq1ZhY2PDxYsXeeSR\nR+jr61PAt7KyUu3LL730EtbW1rz22mvcvHmTv/u7v+N73/seRqORgwcPEh0dzZ07d0hPT6e2thaj\n0ciqVau4ceMG69evp7S0VHNQCwsLuX37Nps2bSI9PZ2QkBDS09P1uEn8wL1793jvvfeor6/HYDBQ\nVlbGxMQEFy5cYNu2bURERODg4KDFdF901NfXKylz8OBBMjMz+cUvfqEbWwFNfH19Wbp0KWfPniUs\nLIzAwEB1C1hYWDA8PExsbCz79+/XRZVYcwsLC4mLi1PAoKurSyN3MjIy1D3z6KOPqtrIycmJ5ORk\nenp6+Pa3v63lqqJSm5mZ4dKlS6xcuVIVVePj47S0tFBaWkp3dze5ubk0NTVhMBgUJIH7hZRPP/00\nCxcuVAVOd3c3ixYtYnR0VHMTDQYDDg4O+rCXolYBkp2dnVWdK/b3jRs3Ul5ervFklZWVzM7OkpOT\nQ0tLC0lJSczOztLU1MQLL7xAcXExERERqpiX8ui0tDRMpvvFsT4+PppbLouB+Ph4rl27xuo/FqEv\nWLCAyclJVfOJbbm2tlY3ZkFBQWRlZWEwGLRn5fjx47z88svY29vj5uZGRkYGK1euVNJl+/btVFdX\nc/fuXUJDQ4mLi+Ptt9/WPNrf/e531NfXK7j4zDPP4OjoSG9vL3/4wx/w9/cnNDSUU6dO8frrr9PT\n04OFhQWbN2/G3t6eubk5oqKiqK2tZXR0lJKSElJTU3Fzc6O3txdnZ2fGxsZYvXr1nyyQh4aGtCxc\nFl6SvSxWTQsLC27duqVRYR999BFRUVGanymRComJiXR0dCgJMDc3x927d9m/fz8lJSXz8itnZmYY\nHh5WMjEsLEw35GIx3rdvHz/96U9JTk7G1taW7u5uJicnOXToEKmpqcTHx7N8+XKuXLlCc3Mzo6Oj\nPPvss/T09Gge7CuvvEJMTAyurq4MDAzonCkKl66uLry9vdX9IcBtXFwcK1asICoqioCAAAXpJAKh\npaWF6upqVVIODg7yrW99Czc3N1xcXMjJycHb25ubN2/S2tqKvb09Fy5cID09XaMWLl26xCOPPKLA\nTWho6Jead1555RWNVnByctIsdenUEPBrcnKSxMREwsLCVFUrGw1RJEshsoBw4+PjAJqVm52dTUpK\nioIP5lmnooATcFNAHSlzlM2a+cZudHR0HiE5MzPDwMCARrGIc2BwcJD8/HyNaJBjFB8fr4t/IYdm\nZ2eZnp7G3t6ekpISQkNDFbAQFbb5Z5brUFSy5nEZVlZWKmZYt24dXV1d6lgQW7eDg4PG/8jmx9nZ\nmYGBAY2DuHbtmhLWBQUFBAUF6TkSZ+fw8DDt7e1UVVVpv4Mo+3x8fPD391cXnlyHS5YsISoqSsEo\nyWY9ePAgOTk5GocxOTmpSmQBywT8l2NiTsZItIoonj09PXFwcFCngIuLyzwFpkTqmIObY2NjHD16\nVLNeh4aGqKmpUVBfMnS7urpwc3ObFy1kbX2/0HDp0qVKngspNT09TX19vW7IBdg3mUxKYsi5Gx0d\nVRC4ublZiypFXS9E3PDwMLa2tkrECYAkz+v4+Hjy8/M18svcvRAaGoqTkxMNDQ1qq5fYxRUrVrB2\n7Vrc3NwYHx/XDbfsIySbf3p6mpGREe0tMM9xt7Gxobm5mfPnzyvYNzg4yKOPPsqSJUtUFSdAtMRQ\nmUdUCdgsxwbg7t27GkGXkZHxFzddnzdmZmZwdnbG0dGRtWvXEhkZSWZmJgEBASqyaW5u5s6dO8TE\nxJCWlsaCBQsYHh5WYlCUyfI95TuIqlvIi7a2NgXgZWMv/5Z5XK5zueZlzoP7gObU1BSFhYW6xjA/\nli+99BI1NTX4+/vj4OCg5IX5/ikhIUHJHFHSy7EWAkBiENzc3HjzzTf1WrKyslKwVs6DzMui2u/r\n68PX1xd/f38lJ0UEIvOoKLbFrfPEE09QXl6ue1CJV7x58ya9vb1kZWUp6WB+T0xPT2vMiQCUvb29\n/Md//AdPP/001tbWREdHq+NK5lUBAh0cHIiOjmb79u2sWrWKbdu2sW3bNhUYXbp0iQ0bNszrOrG0\ntCQ8PJyEhATdi8l5MhgMOvdaW1szMDCg60Rxvop6Vq4P83m3srJS18jmEVvirpd1sTiz5JprbGzE\nzc1NyVlZl8g6SPZVsm+cnZ0lLi6Os2fPahGo9GNZWVmRn59PWloabW1tfPjhhyxatIjw8HDtJikr\nK6OgoEDXa7dv39Y4nJ6eHlXY7t+/n8zMTAW3JC6uqalJ55akpCS9H+SaF8JFrkl5Hri6unLgwAGN\nchNSVOZvV1dXvL2950X67d27lw8++ECjnh577DEVbsjfE0IAUFeMOYgsz0pHR0euXr2qpJtciykp\nKVrq+kXHFyUBABWZ7d69m+9+97skJSXN+7msHU6cOMGqVauwsLjfD/DXokLEmdPW1qZdSnDfUSBr\nIPPxWXLkiw5zIFje2/wzTExMYDAY5h1zS0tL3nvvPRwcHLRzydvbm/Xr17NkyRIef/xxbt68SVBQ\nkL6fvEYy4j+vG0D2cH9pWFlZceXKFby8vBQsh/txzqKSlyHuSkkgeO6550hLS/uT7/JZAuDWrVvY\n2Njw0UcfkZ6e/rkKf3NyRGKF5F4xJ1Y+L95pYGCAvr4+kpOT9f+ZP3NkbszIyCAlJUUz8jdv3vwn\n7oehoaF5HWTSVyNzrru7Oz09PfT399PT04OHh4deK+ZEQHNzMz/+8Y81MlX2ry4uLly4cIF//dd/\n1dQBcYsFBgaqyDA6OpqCggKMRiM///nPtYhevr8A6xJd19HRoYTp2NgYzc3NmEwm7YURoN1gMBAR\nEaHRtvX19VqGXlFRgZeXlwrcxLHo5+fH5s2b8ff3586dO3h6epKenq7xeCaTicLCQmpqaggJCVHH\n0b59+xgYGNC9BdyPu6qtrdVuPwsLC5r+WIw9OjpKeHg4Q0NDzM3N0dTURHl5OUeOHKG9vf2vxtiY\nj/8hAf78WL16NYmJifrP5xEA8CkJIPeAhYUFFy9eJDg4mLi4OE6cOEF2djY3btxQ0e6fG3+VBPjD\nH/7A1NQUYWFhvPDCCxQUFLB06VLCw8Pp6enh1KlTpKWlUVxcrOWa9vb2uLq60tTURGRkJOfOncPO\nzo7s7Gza2to0L7mzs5M33ngDe3t7/Pz8iIqKYmZmhs7OTrKysuju7ubBBx/UfEXJip6dnaWjo4On\nnnpKFVNRUVFMT0+zfft2Ll26RHFxMbm5uWrVEzVxaGgo9+7do729nZMnTxIbG0tSUpLGBXV1dbFy\n5UrGxsa01FIArldeeYULFy4oIbFx40bKysqIjIykpqaGdevWqfVPAPaLFy/i7+/P7du36ezs1KiD\nzs5OVaFL+aso0M6ePUtzczP/8A//gKurKz4+PmRnZ/PQQw8RGxvLnj17qK+v5+7du3h7eytzmpKS\ngr+/P1//+te1PCIqKoqzZ89SWFhIWloaAJGRkZSWlmrO5uXLl7l37x5///d/z+XLl3F3d1eFVHx8\nvGbN3rlzh7NnzxIYGEhdXR1tbW0sXbpU7ZFSBpqYmEhfXx/FxcUEBQWRlpZGc3MzV65cIScnh/j4\neNzc3Pjoo4/YtGkT4eHhdHR00NzcTFZWFq+++qqWIc7NzVFTU0N9fT3Z2dlMTk7yzjvvYGtrS3l5\nOceOHeOJJ54gJyeH5cuX09PTo90VUqzj7e3N4OAgN2/e1PLRsLAwOjo69LyPjo4SGBioUTwxMTEa\nA9Ld3c3c3Bz19fU88sgjqsS1tLQkIiKC9vZ2Vq9eTW1tLR4eHvj6+lJRUcHY2BilpaUa79HR0UFx\ncTF5eXm8+uqrJCcna954YGAg+fn5rFmzhoyMDGxsbFi3bh2BgYF0dnbi6urK3bt38ff35w9/+IOW\niD7xxBMUFRWxfft2jYLo7+9n9erVXLx4kX/5l3/BxsaGsrIygoKCOH/+PKtWrSI1NZWNGzfi4OCg\nxYZ+fn68//775OXl0dXVxbZt23QDv2DBAhwcHDh37hyJiYn84Ac/IDg4GIBHHnmE4eFhli9fjqur\nK4sWLcJgMBAaGsrAwAC9vb1fWqFy4MABqqur2b9/P87Ozjz44IN8/PHHPPbYY9y5c4fAwMB5KuOs\nrCxee+01MjMzVUUUFBTEm2++SVJSEsuWLePNN98kISFBlcWSLy8xF1evXiUmJob333+ftLQ0YmNj\nMZlMFBUVadlue3u7KhmFXffz89MMcolmEGBQgLGpqSm6u7t59tlnNStWrPuivBIlhkSClZaWkpKS\ngp2dnRZRAWrDk029lJU5OzsruSRgjGwcenp6OHjwIE899RRZWVk8+eST5OXlqZVwdnaW3bt38/HH\nHxMUFER3dzfbtm3D29ubkJAQJicn6e3tZWhoCCcnJ7y9vbXIXTYu4iSoq6vjySefxMrKCicnJ/bv\n34+vry//9V//RW9vLyMjI6SlpVFUVER4eLhuNh0dHfHy8mJycpL6+nrWr1+vC8W2tjaMRiNpaWl8\n5StfUaW2LOb9/f3x9vbW8r1Tp05pLEJ1dTVf/epXMZlMvPjiiwrku7m58cYbb+Du7k53dzfr1q3T\nbos7d+5QX19PRkYGLi4uREREUFdXR0hICCbT/eI4FxcXPUdisxSltYWFBWlpaWRmZuLv78+5c+c0\nv9PKyoq+vj4tDExLS9PCd4mukhgDUZLdvn2bvXv3EhgYyHvvvYfJZNJnQ3V1NT09PZhMJg4dOqSb\nU1Hxye/09vamsrKSLVu24O7ujtFo5H//7/+Nt7c3ubm5ukk/e/YsAwMDREVF8Q//8A8YjUZVYf73\nf/83bm5uLFmyRG29kjtcVVWlxFZoaKhGWdy6dUvBvZ/+9KfcunVLo1vm5ub41a9+xeTkJHl5eSQm\nJhIXF0d/fz8rVqzAw8MDg8GAm5sb/f39GAwGvva1rxEUFMQrr7yihNTJkyc5efIk3/72t9WV0dnZ\nycKFC7/UvHP48GGsrKwoLCykqqqKzMxMzd43GAzcvn2bK1eu4OrqioODgwKN5sdZAJje3l6qq6tV\nKdff38/k5CTR0dEkJSVpz4K5ItM8gxk+3SiZ23nldwlYJ5uPqqoqenp6NLZmenqa999/X4ut/f39\nsbS05ODBgwqW+vj44Ovrqz0D0g8kGzwBciTmQdYUAi7LnCRrJCsrK1Xbe3h4qFpaNo3iaLCwsKC2\nthYrKysSExOxsLBQd56oAU0mk+Y6p6Sk0NXVpSIPcdUImNTf309KSgodHR3AfWWf0WhkfHyczMxM\nTCYTt27dws3NjYCAAD2Gct92d3cTEBCg1nCJRuns7MTJyUmLECUHW4Cz0dHRebEVQr6KOl4APzmO\novQVsEkiP+BTx4ZsSoUMECX/J598otdXcHAwaWlp887B4OCgbijluS3nRIBKQDesFhYW9PT04ODg\noD0vAk6WlZVpaamAAEIOdHV10dzcrBvliooK2tvbCQ4OZm5uTmNGvL295ymn5TsChIaG0tbWpqBB\nX1+fkjGhoaEYDAbq6+vp7e3FaDQSGxvLsmXL5hXPS2eNgP+SzT8xMUFNTY3GnEm+sclkYnx8XPcj\nBoOBsbEx1qxZQ3R0tPaSyPPI3HU4Pj6u2cGenp44OTnh4OCgZJefnx8TExOqOl+2bNmXmndGRkY0\nX13U86LQc3BwoKGhgYCAAHJycvDx8cHJyQkbGxslAERBbzQa54FLch+J2tjS8n4Gv3mWsvw3oO8z\nNzenSlu5Z+V+LCgooKamhoKCAhYvXszw8DCtra288847XL9+nerqaiVpxFng4eHB8PAwc3NzODk5\n4eTkpOsvAUDFzWNpaanr+A8//JAlS5bMuxdMJpNGlMl8KPdUZ2cnx44dU3Je1hXiH2ZjyAAAIABJ\nREFUkPksCGZtbU1vby+zs7MsXLgQHx8fLl++zNzcHAsXLtTeA6PRyMqVK1Xp/lmnlRxnITTefPNN\nvv/97+t1J/O89JmYl5OLo8DKygofHx8F42VtK1G5AmrJPSPfW1SYQgjLPSL3XVVVFX5+fnp9SZ+G\nkCEyV8ix9/X1pbGxUR2yjo6OGn0ia8+Ojg58fX11/rCwsKC5uVnj5GRfbk6IGgwGXZs6OzvT29uL\nt7c3CxYswNLSkqKiInW2dXd3ExcXR3NzM/v27SMiIoJ79+4RHh7O1NQUAwMD6taQvpzExETi4+OB\n+4KayspKamtrGRsbY/369Trfz83NaRfPiRMnyMvLw9XVVedtIVumpqbo7+/H3d1dFcF2dnYEBgbi\n4OBAUVER8fHxGI1GKisrNddbiHqZ82dnZ6msrFRhwA9/+EN1nAiZZX7vDQ4O8sYbb+Dr66vZ//J+\nMhc1NTWxdOlSXFxc8PPzUyGf7Mm+zLzzRUZ5eTnOzs489NBD+Pj4/FlF/eXLl1m2bNlfjAcxj7sz\nH9IdNTk5qU4V6XwxH3/ud3/RIQ6/zw4535+N8Ons7GTVqlX6ZwsLC+1KEyerRDHK6/39/Tly5Ahh\nYWGfeyw6Ojq+UJmqONqF3Ons7GRkZGQeASOk7P79+zUOT5y7f27MzMxoR570VoyPj887LkVFRX8i\nopF5SYb5ufg8AkGiZf5aTJWFhQUjIyPavSPg5yeffKJ9FfJck9fm5+erk3xwcJC+vj46OjrUfT8y\nMqJEgvnn7Ovr4+rVq2zdupXvfe97ZGVl4ebmxuzsrK6Fx8fH8fb21tgdHx8fFcZYW1uTlpbGrl27\ntFMH0Gt1fHyciYkJnJ2dNS2koKCA1tZWJicn2b17N46OjoSEhOizCdB1t62tLb6+vnh6enLx4kWa\nm5sxGo2kp6fT0dGhhL64Iuzt7bW7rry8XOObjUYjZ86cITMzk7CwMFxdXTGZTJrmIY4CER/Z2NhQ\nV1enzlURCe3du5empiZ12u/Zs4djx45RXV1NTEwMGzZsIPyPbukvMv4nDujPj7+Gk83OzvLiiy/S\n2NhIVVUVoaGh/PrXv6aoqAgrKyu2bt2qpPDvf/97Ojs72bFjx+c6fGT8VRLA0tKSlJQUZmZmyM7O\npqioiODgYK5du0ZNTQ11dXWsWLGChIQEenp6eOGFF8jNzcXOzo6SkhK6u7vp6OjAyckJd3d3Hnvs\nMV2YHT16lL6+Pnbu3Mnx48cpKCggKiqKixcvsn37dj744AOsrO7n1EqObnR0NKOjozz66KMKcszO\nzmIwGEhKSqKpqYk7d+7g6OhIREQEycnJHD9+nIiICEpKSpidneX06dMAPPHEExgMBhYuXEhiYiLL\nli2jqamJu3fv4uvry5o1a/D39+fixYu8+OKLPP7440RFRWkz94cffsjo6CjBwcFkZGRw4MABXn31\nVW7fvk1UVBQLFy5k586duvmIioriJz/5CUuXLmVkZIT169dz/PhxysrKyMnJ0ZObnp5OdHS0spsS\n5yJldb///e9xdHRk/fr1Gqnzla98hevXr5Ofn8/y5ctxc3PjoYcewsnJiaioKPz9/TV3f3Z2lk8+\n+YSxsTFSUlI0/1rikrKysoiIiODMmTMcOnQIf39/8vPzsbGxobe3l8bGRrZv305xcTHXrl0jOjqa\ndevW8fbbb3P16lVKS0t1o/bcc89ptNBjjz3Gz372M2V8ReWXmJhIQ0MDKSkpHDp0iLKyMr773e/y\n+uuvYzKZ+Na3vkVAQAD+/v6aY/3uu+/y4x//WIuoDh8+jKurK0ePHmVwcJC2tjbNb/X19eXq1atY\nWFiwY8cO/v3f/526ujpycnJISEigu7ubnp4edSUUFhZy5MgREhISOHr0KLOzs4SHh+Pp6cng4CCh\noaGcOXOG3/zmN5w4cYInnngCLy8vSkpKyMjIwGg0UlRUxNDQEMHBwZw/f14XDKKUjYqK4saNG8TF\nxZGZmcnChQuVJFu2bBmOjo4MDg5iZ2fHd7/7Xezt7RkaGqK3t5fExESCg4MpKirivffeY82aNUxN\nTREeHs709LRe3xs2bGBkZISwsDDtV7C3t9coA7EFHz16lHv37lFdXc3jjz+Og4MDUVFRODg40N3d\nzZUrVxgYGKC2tpZ169bxv/7X/+L555/n0qVLqjaWhby7uzsTExMaJXP58mWsra3ZtGnTl5oM//mf\n/5mtW7eSk5OjMTJVVVWsWbMGa2vreZnA8iBct24ds7OzWsr561//mh/96Ec4ODjQ3t7Ohx9+qArb\nubk5amtrcXd3p7y8HBcXF65cuUJ6ejoFBQVcvXqViooK/P39VQFmMBjYv38/XV1d1NTUEBwcTHh4\nuG5cXnnlFYaHhzl79qyWOcN91vYXv/gFcXFxBAcH09bWptb8qqoqAHx8fHjppZeYnp4mMzOT6elp\nfve739HW1kZ0dDS3b9+mpaVFs8TNJ3UBwyR7VUDqTz75hJCQEE6cOEFRURG//vWviYyMJCIiQjeM\nohrOz8+nrq4OZ2dnXnzxRXV1iRujpaWF8fFxXF1dcXR0ZN++fZSXl5OcnMyvfvUrjWXz9vYmNDSU\n9957j0cffRQPDw8yMzPx8PBg48aN2oUQEBCAp6enltB1d3erKjw0NFRLZYXwdXNz45133mHPnj10\ndXWRlJTEyMgICQkJ2NnZUVlZqaW8ZWVlLFu2jOzsbCIiIkhJScHX11fjnurq6picnMTS8n4xd3x8\nPC0tLeTn59PX18e7777L9PQ0O3fuVEXwH/7wB3WqLVmyRKNqVq1aRXd3t4JdU1NTvPzyy2RlZSk5\nMjg4SHh4OIWFhTQ2NiqpbTAY9Jna39/Pt7/9bdavX4/JZOLKlSt4eHgQHh7OwMAAixcvxs7OjiVL\nlhAeHk5rayvf/OY3iYmJITIykpMnTxIcHKw9CuJUcHV1VXDo8uXLfP3rX1eg+Oc//zkBAQGsWrVK\nCfa+vj6mp6c5d+6clk/fuXOHkZERfH19uXLlCmNjY8TExODn56cb1OHhYQ4fPqxxJeXl5axevZqp\nqSmuXbtGfHw8586do6KiAoDw8HCWLl2Kk5MTy5cvVxeWABtCfMH9qAzpGlm8eDF1dXV4eXkxMDBA\nf38/GRkZhISEqB3eycmJlpYW3NzcvrQy7vLlyzg5OXH37l1GR0dZv3695sw7OTkRHh5OVFSU5uVL\n0biUHovqe3R0VEkec7Xz1q1bSUpKUrBEgDW5nwU0MAePJYfbPOpE1I/y+unpaY2Hq6+vp66uTuPj\nZN0wNTWFwWBQO7+UxFZVVTEzMzOveFrmESlNldJnub+8vb01asLcCSBAhnmxsPxMVKyy0UpLS1Ph\nQlNTkxJzAuxZWloSEBCgsRd1dXUkJyezadMmbGxsVN0/OzvL6OgobW1t2muxZMkSmpqaWLt2LUFB\nQVr4KhnMQujcu3ePBQsWEBAQoGCYAHqWlpYcO3YMk8lEZGSkRppUVFRo3rGcIwEChQQ0V+3Ozc2R\nn59PaGioKq1FfeXp6UlVVRWurq50dXXpBlRiOSRm4ubNm6r2tLGxISgoSImZ7u5uVViLW0g25KIU\nkrgX8/gJiWaxsrJiYGAAk8lEf38//f396qKQzaqcaylTbmxsxNfXl71795KQkKBCi6amJvz8/LC3\nt6e9vV07leSzyHUg16NEOYl4RvKkJbJpaGiIpUuXkpOTMy8ay9HREWdnZzo6OjS2R8jXmZkZzp49\nS0xMDIC6EATgj4iIYHp6mvb2dnJzc0lISJgHKsu9Z2lpqVm7cn9+8sknGI1G+vr6aGtro6enBx8f\nH+DTMuWZmZkv7QSora3FxcWFiYkJVVe3trbi5OREeXk5ixcvVvJKwGw5ZtJPIK4TceKIcl9AfBkC\ndklEoswr7e3t895L5hi5vg0GA8XFxaSkpGA0Gjl16hRVVVUUFhbS3NysJeKzs7MMDg7qtSbqZXEr\n9vT0MDw8TF5enr5enCyilgQ0okCiXSIjIykrK6O1tVWLOOX7jY2NceXKFX1GiGJUQF9nZ2dGR0c5\ncOAAixcvZmBgQCOAjh07xs6dO1UpPzg4qJF8w8PDPPXUU5rfbk7KSsGvnZ0dXV1d6vTp6OjQ9bO4\nBqampuYRNfb29vT19TE2Nsb169cJDQ1VQFj6m3x9ffXPCxcunNcbYq4e/2wZN3xa3Dg8PMxbb71F\ndnY2lpaWek2Ic0NIEIkfkmePu7s7rq6u1NbW0tvbqw4GIS3F/SHEm9FoZGBgQME7KSwXgkDmH1m3\nC5koxLO1tTWRkZH09/dz8OBBOjs7uXLlClevXlUnmpeXF729vfT39xMWFsZXv/pVHnroIVauXMnc\n3Bznzp2jpaWFGzdusHTpUtLS0oiLiyM7O5uRkRFGR0fZv38/N27coLGxkeXLl+Pt7a29ROYl9kLy\nyPpJXFNC0Lm5uVFbW0tFRQUBAQFERESoq8PCwkLPpTwfrKysuHTpEi+88IK+xpysEbHH7OwsXV1d\nlJSU6D4M0NjF2dlZjbZ0c3NjZmZGwUmTyfT/NxJAusoefvhhAO2F+NnPfkZubu6fkGmA3ksi8vtz\nQ8hw8yHOB2dnZ8Vgurq69Nyb9138/zKmpqb43ve+x4YNG/5sYa+tra32gkikMaBxRhLhae56BObF\nMcL9LjBPT0/S0tLIz8/XPZP5d/4iBADcd2QeOXJE98B79uxh165d1NbWaozNnTt32L9/vxbFS7LC\nn3NKCFEuPUByPc/Nzc2Ly/prLlrzIvbPux7kew4PDwP8xaJkQJXpR44cISYmBjc3N1pbW0lMTNRn\nkDyr9u/fz8KFC0lJSdEIMXt7e/7P//k/LFy4UAUvra2tXLlyhbCwMO388fT0ZNOmTRiNRkJDQ7VP\n7N69e6SlpTE4OIjBYJgnInBzc9POEV9fX0JCQrC3t1cRgMFg0GNnZWWlAj0RC4SGhjIzM0N0dDQp\nKSkcP36ckydPavRuc3MzHR0dKu6SZ4U4rsXtH/7H/k1xK8r++dq1a0RGRrJjxw6NAhoZGcHPz4/J\nyUmCgoIwGo2MjIzwH//xHzpny9rL2tqa5uZmxsbGuHHjBocPH+bixYvaFzkwMEB1dTWHDx9WUfhX\nv/pVNm/ejLu7+5eKA5KuxP8ZfzrWrFnzF39uaWmpgvDs7Gw8PT1Zt24da9asIT09XeeksLAwcnJy\nWLFixV8kAOALkABFRUWafZafn09gYCBubm7k5ubS2NjIU089RUFBAXDfgif2p6KiIj7++GMmJibY\ntGkTUVFRvPfee/T09HDkyBE+/PBDVq9eTW9vr7oAbt68qbmiN27c4Fvf+pa2loeGhuLl5UVhYaGC\nSgIwBgYGajRKVFQULi4ubNu2TfPkV65cyeTkJGFhYUpg7Nq1i71799LQ0MCaNWv0QRMbG8vu3btp\nbGxk2bJlTE1NcebMGR555BFdXCcmJuom5JFHHuHkyZM4OTnR1tbGli1bsLGxYfPmzbS3t5OSkoK7\nu7uCgjU1NczMzHDu3Dnq6+txc3MjJiaG69evExYWRnd3NzU1NTzxxBP09vaqldBgMLBgwQJGR0dV\nndj0xyzflpYWiouLMRqNel6EjQ8KCuI3v/kN09PTVFZWkpaWpgrHe/fusWrVKl2QicLH2tpac/Aa\nGhoYHh7mG9/4BsePH2fFihW6wBsYGGDhwoUcOXKERYsW8cEHH6jNtr29neeff56///u/57nnnlP1\nyblz51i9ejV+fn5ERkZq8UhsbCxjY2NUVlayZMkSPvjgA1auXKmT2tzcHMePH8dkMnH16lXtUVix\nYgV2dnYcP36c27dv84tf/IJNmzbh7OzMb3/7W/z8/KioqKC0tBRPT09OnDiBhYWFlhO3tLRozEda\nWhr29vacOHGChQsXcvz4ce7evUtycjJHjx7lm9/8JmNjY3h6emJtbc2jjz7Kgw8+SHd3Nx9//DFZ\nWVl4eXmpVaqtrY2nn36anJwczX8MCwsjKSmJyMhIYmNjaWtrw9vbW8EePz8/xsfH1ep59uxZHnvs\nMRISEujt7WXjxo1UVlayZ88eZmZm+MY3vkFzc/M8x8R3v/tdtdnGx8fT0NDAV77yFe1neO6550hN\nTeWVV15hzZo1HDt2DFtbW55//nkcHBy4cOECPj4+usCVcs29e/fi5OTEmjVrVHUtWdh3794lISEB\no9FIYmIiixYtwt/fn8rKSjZu3PilFbmiSq6rqyMgIAB7e3uKi4tZsmQJIyMjGmsjGyLzTbEo0EVN\nf+zYMSorKxkdHeWf/umf8PPzw8HBgR//+McaGVNXV8fXv/514P7ip7i4mIaGBnJycrC2tqagoICQ\nkBDy8/P5p3/6JwIDA6moqODGjRscOHCAS5cuMTo6SmNjI9u2bdNCQom3euihh6iuriY5ORlHR0de\nf/11tcUJSLd69Wru3r1LUFCQqhVGRkZ4+OGHlXDw9fVVkGVycpIjR46oivH27duUlZVx9+5d6urq\niI+P1w1dQkICk5OTuLi4aK9LSUkJYWFh7N69m97eXgC+//3vc/bsWX14jI2NMTw8TEBAgMbsCDEm\n9knpZzlw4AAlJSWkpKTQ3NxMRkYGgMYZ/Mu//Avp6em4urqqIurtt9/m8OHDXL16lSNHjrB9+3ZV\ngvT19WlsTlFRkSrIf/7znyugJcrlkpISVTMnJCQwNzfHvn37sLGxIScnRzeBDQ0NWii6c+dOIiMj\nCQ0NJScnh8TERN566y1CQkIYHBwkKyuLU6dOERMTo0T42NgYTk5OvP/++/NUmUajkX379vHGG2+w\nY8cOOjs7efXVV7lw4QKZmZkKin744Yd8/PHHZGRk4OHhQUhICGlpaWzatImAgADdMCQlJXHt2jVd\n9O3du5elS5fq4jAqKkoja0wmE93d3QrMt7a24urqytTUFK6urhpb4O/vj5eXF4cOHeLatWsMDg6y\na9cuQkJCNDYqLCxMN7Umk4nU1FRVygKkpKSwfPlyXn/9dd555x2ysrJwdHTEw8ODlJQUpqamuHnz\nJh988AFnzpzBwcGB4OBg9u7dy9WrV5Us8fX1JS4uTkEm834HcwVqSUkJiYmJTE1N4e/vr2C0wWCg\nqKiIp59+mo6ODiYmJtixYwfFxcXaw+Pm5qYlYl90vP7662RkZBAfH8/GjRtxc3NT9aeASuIMkTgg\ncXi5u7vj4eGh56OwsJCtW7eSnJxMYmKiXvsCtAkRZx5VIiC8KLdl49jf38/bb7/N0qVLVUUqQJDc\nC6LcESdOeno6Pj4+mtksAGdwcDCRkZGsWrVKM/Ht7OxoaGigra1Ny4Dv3r2r64DJyUkqKys1EsjW\n1pauri6qqqq4ceOGWukFRJdy5PLyclU1NzY2zssaFZWzOHmWLl1KVVUVvb29CoQLqbxgwQJdV0h0\nm1znYWFhdHV1kZWVRVhYGC4uLlRUVGiMTnBwMNbW1nh7e2NpaanqrYiICKKiohS8kw2+nJO5uTn8\n/f1pa2ujpaWFvr4+YmNjiYyM1MgCuWZlc2quqBUiRXor5LWyYRYwV8A1IRhKS0vp7e3FwsKCtrY2\nDAaDxiOKi1FUc/39/UxMTCgI2dPTo6AQfBrDIf+enp7WDaDJZKKhoYHTp09TX19PY2MjNTU1wH1A\ne8uWLQQFBel3Gx0d1WeBFL6L68fd3V0z19vb27W03twdJf0E8l6nT5/WOBu5PwQgnpubw93dnR07\ndpCamqo2dvPYJLl2hGgzB6vFsSMFynIuzO+XhIQEfHx8FDg3J9XGx8dpaGigtLRUxQBNTU0YjUbt\nJOnu7tZ1nXl0irOz85cuJG9tbVXStr+/n76+PoKCghQkTk1NBeZHKQiZJJ/b1tZWC7jFKSDzhAhu\npqammJiYoLS0VAF2+bmovkU1L3OSEIGiqnZycsLf35/6+np6enp48MEH1S0sxxo+zT8eGxvTyChx\nItja2moxpIuLi/5c4jxdXV2xtLTE1dUVZ2dnFerY2toyOTlJU1OTgqLNzc3A/QxtFxcXJVzlWSOA\nrqWlJcnJyXrcLC0tGR0d5eOPP9b1Wk9PD3fu3MHd3Z0nn3yS9evX4+/vr45RGaKMFzBG3DeTk5MU\nFxer800IJCFAhCycmppSYsTT01NFOaLyFJLD0tKSW7duaUmtucJc7iu4r9T29PRU0kP2qg0NDVy8\neFH3rPI9rKys5hFG8lyS/btECQm409nZydjYGKGhoUrmvfvuuyxatEgdChMTE0pMCtBoaWmpBeZC\noJjHVZnnqM/OzuLp6UlsbCy1tbXa72NtbY2Pj48KKOLi4khPTyc8PFw/p6enJ9nZ2bz++us8+eST\nKroaGRnBaDTi4eHB7t27SUlJYd26ddTU1ODi4qLrJ1H7yzw5MzNDR0cHIyMjSmZZW1szNjamZNu1\na9f42te+pt9fen7k+wO6rysuLmb79u14enoq1iC9VXK85Tp1d3dnxYoVhP/RJQvovC2Rj7Gxsfp6\nk8mkz2lxlX3R0dfX97kAroeHBw8//PA8t430LkriwWfHoUOHiI2NxWg0aunpXxvj4+NcuHBBr3O4\nnz4gJHdLS4veLxLT9EWGqKv7+/spKSnh5ZdfJjY2lszMzL/498TJYT5sbGx0bwH3j5kopQFdU0in\njnmER1BQEKdOndL72sLifgyt5Nt/3vH4rNK+q6sLR0dH6urq2Lp1K3D/+dza2sr4+DjFxcVcunQJ\nDw8PbGxsWLFiBV5eXjg5Oc2LUhofH+fs2bN6Lcl1Oz09rfORueMH4Mc//jFr16793GMla1MBoz/r\njpI/T05O8sknn5CSkqLuoM9zc0gPj7+/v8b0REVF8Z3vfIfNmzfr3xkZGeHw4cM8/PDDek6cnZ3p\n7Ozk/Pnz3L59W89RYGAgcXFxTE9P09/fr3E8MzMztLe3MzU1xaFDhzh9+jRGo1G7VwCdD2XdLPOv\n+brbwcFBO2bKy8v1GjL/fvJsE4LV19dX8bPS0lI2b95MQECAdstJDJ/MOV1dXQQFBVFdXU10dPQ8\np5+Iu0JCQvD29tb1p6QCyHzT1NTE2NgY9fX1JCcnc/78ee2ZEvxEnEpCLsneemZmhoiICGZmZvD0\n9GTbtm089thjKvy0tbX9q5Ff5uN/SIA/P/4aCfB/x/irJEBtbS3T09NUVFRoeUtZWRlGo5Ho6Gha\nWlp0QXX+/Hnu3r3LnTt3mJ2d5datWwQEBJCcnEx4eDgpKSmsWrWKlpYWsrOzuXDhAh0dHWzfvp3f\n/va3BAYGsnPnTpYtW8aaNWsIDg5m//79lJaWsnLlSt24vPnmm+Tl5eHl5cXNmzdVmTY8PKxlUhMT\nE1hZWaldYs2aNczMzLBnzx5+9rOfUVxczK5du1i0aJGqGiYmJrhz5w5PPfUUeXl5vP/++7S1tXHh\nwgXOnTvHzMwMwcHBlJSUsGDBAoqLi4mKiiI5OZmf/vSneHl5kZ6eztq1a3XhIazgJ598gslkwtPT\nU2/EvLw8zap/5plnsLOz4+TJk6xdu5YDBw5gZ2fH7t27+f73v090dDT79u0jKSmJyclJBdLu3LlD\nY2Oj9gnY2dmpglXAOR8fH7KyssjPzyc/P58dO3bw2muvERkZSVVVFUlJSVy8eJHvfOc7jI6O4uPj\no1al1tZWrK2tSUlJwWAwcPnyZZqbm8nMzKSlpYWQkBAeffRRrl69SmtrK97e3iQmJvLggw9y7Ngx\nnn/+eS1rmZqa4oMPPmDt2rW6iRYr5dWrVzl8+DCRkZFs2bKFxMREysrKsLa21sgdyV1ftmwZBoOB\n2tpaHnjgAY4ePcpDDz2ElZUVXV1dhISE6HHeuXMntra2pKamatbkk08+CdxvnY+IiGBoaIg333yT\nkZER9uzZoxnikmF7/vx5+vv7SU1N5V//9V/x8PCgoaGBubk5fHx8eP/99wFITEzk5ZdfprGxkWee\neYbFixdr3rh0TPT09BASEqJumGeffZb09HQAvU5tbW1pbm7GxsaGxsZGMjMzGRgYIDY2lo6ODrq6\nurTjwd/fn6SkJBwcHFi6dClBQUH4+vpiY2PDvn37OHDgAFu2bOHChQuEh4cTFhZGSUkJhw8fxsLC\ngoSEBNauXcumTZuwsrKiuroaS8v7Wcf+/v68//77mkebl5dHVVUVqampzM3NaQSUu7s7b775Jh99\n9JGW6cqmSMDTxMTELzUxSYFxcXExycnJjI6OkpSURHl5OW+88Qbp6emcPXtWrYyTk5OqIBOVYUBA\nAGlpaWzYsAFXV1c2bNiAk5MTtbW1HD16FDc3N42ICg8PJzg4mJ6eHnbv3s13vvMdCgsLGRkZobW1\nlezsbC5evMiKFSuYm5tTECQ1NZUHHniAubk5qqurmZub42//9m91QSbgh5WVFa6urvj5+fGf//mf\nGtt09+5dSkpKWLZsmbLxArgODg7S09NDZmambn6LiopwdnZWpU1HRwfu7u6q3jt79iy5ubkkJibS\n09NDcXExdXV1jI6O6mIsKiqKuLg4li9fTmNjI2NjY7S1tWEymdiyZQsrV65kaGgIgHv37nH16lW8\nvb3x9PTk/PnzPPPMM4SFhZGQkICFhQUBAQFs2bKFtLQ00tPTNbc7JydHgXopZD558iSOjo6cP3+e\n8vJytVZLFMj69etpbGzUTEUhxtrb22loaMBgMLB9+3ZsbW25e/cu7e3tlJeXa4xXVFQUtra2FBcX\n09TUpERoc3Mz+fn5tLa2MjU1xQsvvMCNGzeIjY1VhZybmxsRERHcvHlTY0AiIiIIDg5menqa3t5e\nurq6qK6u5plnnqGxsREfHx9MJhPT09OcP3+e+vp6nJycuHDhAjMzM3R3d9PU1KRZsJcuXcLR0ZG8\nvDy1PcsmUxaWAswMDAzQ2NiIu7s73t7eODg48NZbb1FSUsKGDRvw8vJSJZmtrS0+Pj4KDAhJ6OLi\nQl9fn7oYJMpJei/i4uKwt7fXTgRR9ly5ckVz4+Pj43VRL1mb169fp6enh9hjIPL5AAAgAElEQVTY\nWKKjozXiwN3dnVOnTtHY2MiOHTuIjo4mKytLP9s3v/lNzSEXZ5JYsAUgrKurA+4v9k0mk8aKNDc3\nMz4+zksvvURYWBhr165lYGCAZcuWERAQwIULF1RUcPz4cTo7O9m2bduXmncka1fmD1nbTExMzMvf\nlX8EKHF1deXevXt0d3fj6OjImTNn2LFjh0Z+CTAlxKWAlgJOAgocyJ8lXsZoNNLV1cWFCxdYt27d\nPJDa/PXT09PcunWLbdu24ePjM+/3yvU1NzeHi4uLdnMIkCqK79DQUFXY19XVMTg4qBuF8PBwVq5c\nqcWcTk5OhISEEB4erqSMvb29lm4LiO7o6EhHRwcff/wxg4ODqmiWjbqADLa2tqSlpZGamoqrq6u6\nUuLj43F0dNS+iP7+fhwcHLQkdmBgQDe+4pooLi7Wjbd06Pj4+GhvimykzDercqwkL1+A1djYWGJi\nYlT9JSpPARolqlI2h/Ke8jPZAMp5MndNGI1GhoeHmZiY4Pjx40xNTdHa2kpXV5ceu9jYWAICAvDz\n81NlmaWlJSdPnqS+vp7i4mLa29vp6OggLS0NOzs7xsbG9POMjY0pUCyglMw5//Vf/6VAnHz24OBg\n1q5dqwCnOEKkl8YcJBSSTO4HAWpGR0extrbm7NmzFBQUkJSUpMD1xMSEFlpaWVnh5eXFunXriIyM\nJDw8nPDwcCorKwkLCyM9PX2eq0IACgHaZN0O6D0xMTFBRUUF9vb2lJWVkZycrK+T72MwGBSwFAW6\n+RgaGqKwsJDa2lq6u7vVXezl5cWyZcsICgoiLi5uHuAjETiWlpYsWbLkS8070uXg6OioqmwR+hQU\nFBAbG4u7u7tGB8q9azKZ9HqTqBBRPwqBI9ecgC9WVlbcunWL6OhojYUxjwcyd24I2GxhcT8f+MSJ\nE1RUVKhzNScnh7i4OPz9/UlOTiYuLg4XFxemp6d1b+jh4UF3d7fGAg0MDODl5UV0dDQeHh76+2dn\nZ7X42Mbmfrmi+V7uo48+oqioiMHBQQXWg4ODCQ0NVQBE1kFyfZgDVfJnAT7FsSDua1GCDwwMcPv2\nbVasWKEFjfIcEBDLvARcrj0BwGTtbi5MEbJb7imj0cjQ0BBWVlacP3+eoKAgdf5KxrilpSVDQ0O4\nuLjQ1tamaw15RsN99XVDQwMxMTGMjo7qfSzu+JMnT7Jlyxbi4uJUfS7AlQA8VlZWSu6Im9HR0VGB\nwKGhIYaHh7G3t9c+ACsrK12zAnpfizvfYDAoyCnPMbn35FjI+8g1JufI2tqahoYGjfxxcHBQ16d0\nV61evVrdCTLPSoSY9MvZ29ure83S0lIjGaOioliwYIGWDMuzx9zZNTU1pXObXDdClonDorGxkcWL\nFyvRI9ewPFdE3W5+jiQHXCLZzGPy5H42XysIkSaEweDgIBEREQBKAMo6RGLdvszYt2+fRih93jAH\nkV9++WW2bNmipfEy/5or4qurq/Hy8lIX9F8aEmmcnJzMyMgI9fX1LFy4kLGxMQXdp6amVGQzMDCg\nBBR8Sqabj+npabq6uqirq1MHe11dHdevX8fDw4OMjIw/+XvybATUuWT+GukVk+vZnACQUVZWRlhY\nmPZzyLCzs9NIZknFyM3N1Sioz77P56l2vb299XkqApjm5mZef/11Tp06xZ07d1RkJOsMcQ7Ld5Po\nvOjoaBobGzGZTHqt3Lp1S9exQuTJ+HMEwGeHrDXkeJqD4CJQdHV1pb6+/nPdKnKv9PT08NJLL5Gd\nna3HwpwAgPuE4b1793T/KeINESc7OjqqQOvVV18lODh4nvNcYjCDg4MxGAwcPXqUgYEBgoODVXAA\n95/nEsUsrqba2to/ucdExPHss88yNjZGamqqro9EPCSCjaCgIMbHxxkdHaWurg5PT08yMjLmZfNL\n7wLc3wP19/djNBqprq4mJSVF74uJiQl1RUdHR+Pm5oazs7M+TyUmEO6Xyx45coTQ0FD8/f0pLy9X\nPEw+K6DPUBsbGwIDAzEYDPj7++Pv788PfvAD8vLyiIqK0vlVzvmXIQEuXLjwhV/7/7WRk5Pz//jv\n/KskwMWLF1Vpn5GRQUJCghYWdHV10d/fz8zMDLdv32bp0qXk5eWRk5NDVlaWbrg++ugjPD099eL3\n9PRkyZIluLi4kJWVRWVlJVZWVuzatYuYmBi6urqYm5sj/I/FR3l5edjb23Pw4EFyc3NZvHixss6S\n01dQUMDIyAgbNmwgOjoab29vfvKTn3D79m2eeOIJ/u3f/o3Lly/z3HPPkZ+fz7Jly9i3b59mpNva\n2mIwGPjJT37Ctm3blBiQgpvMzEySk5MpLy8nNjaWK1euUFNTo2VP27Zto7CwkK6uLn7/+9/T3t6O\nu7s7e/fuBe4DzgsXLiQwMJADBw6wceNGVYXs2rWLs2fP8rvf/Y6dO3eyZcsWVq1aRUREBNXV1axc\nuVInu9raWhYsWMCpU6eoqKjgW9/6FomJiZw7d45//ud/ZsmSJezfv59jx45x9epVHn/8cdrb27l0\n6RLf/OY3KSgowMHBgR/84AfExsZy+fJlxsfH+Zu/+RvGxsYoLCwkPj6erq4uzp07x6ZNm5idnSUw\nMJDc3FxSU1OZmJjg97//PXFxcSxdulTtz8uXL6esrIyvfe1r+Pv7ExMToxmqdXV1BAUF4e3tzZIl\nSxgaGuKjjz7i0KFDhIeHs3nzZmV4Dx8+rOWPGRkZXLx4kY6ODoKCgvj3f/93/P39uXXrllq4RkZG\nNAc2MzMTa2trLl++zPbt25mZud8YPz09TVRUFK6urvzyl79k+/bt3Lhxg5MnTyoI39TURHV1tS6g\nx8fHSUtLo7Ozk2984xssW7YMJycnVq5cSVhYmFpVk5KSWL58uZIt2dnZvPPOO7i7u7Nr1y5ycnJI\nSUnhl7/8JefPnyc3N5cLFy5w9epVvvKVr1BYWEhpaSnLly/XQsQ7d+4QERFBUVERL7zwgmZvOzo6\ncufOHXJycnB0dMTBwQFvb29eeOEF5ubmuHTpkjbIBwcHExISQlBQEC4uLjQ2NtLc3ExXVxc/+tGP\n6OzsZMWKFapqd3Bw4MaNG5SVlREfH8/g4CC9vb0KuIha7+7du/znf/6nlsJNTU1x/vx5/uZv/obH\nHntMNzkDAwM4Oztz9uxZNm/e/KUmpra2NoqKimhtbSUnJ0cXMq+++irf//73SUhIYOnSpUxNTXHu\n3Dmsre8XIpeXl3PlyhUKCgoUYLh16xYhISH827/9G0eOHKGiooLy8nJ27NjBwMAADz/8MPX19ZpB\nLnmL2dnZpKen62bnN7/5Db6+vkRHR1NaWkpcXJw6NpKSkuju7uZ73/segYGB8wo1zTP/amtruXHj\nBj/60Y/YunUrX/va13jggQd4/vnnyc3N5ebNm6o4MplM+Pr6kpycjKXl/TLd//7v/+bKlSusWLFC\nFxcvv/wyeXl5ODs7Ex8fz9tvv01hYSFFRUWMjo7y9NNPKzi9du1a7SsA+PrXv87s7Cw//OEPOXPm\nDKWlpWRnZyvgI7mkvb29ODk58eCDD+Lp6anRO6J8DA8PJzAwkHfeeQcPDw9SU1MpLS0l/I99H/v3\n71ey0tvbm0WLFvHQQw9RXl6Ora0tgYGB9PX10dzcTE5ODl5eXnR3d2NhYcGhQ4eYmJhQEnDPnj2s\nWLECT09PQkJCKCws5IEHHuCtt97io48+oqamhvLycuLj40lJSSEmJoaRkRHGxsb427/9W8LCwpie\nntY896mpKerq6vQeq6+vZ8OGDXh6evLyyy9z+vRprKysKCoqYtGiRWqHDAoK4sCBAwQHB3Ps2DEt\n2jMvkDUYDPzmN79RR5jRaKS+vp7ly5cTFRVFVVUVISEhjI2N0d/fD9wnwF577TUtZba2tmbhwoV4\ne3uzatUqfXZK1rDJZOLcuXMEBgby4YcfcuvWLYKDg1m+fDkODg5cv36d2NhYzeyWObi1tZVf//rX\nxMbGMjc3R3d3N21tbezevZuuri4tOC8oKODGjRsEBwfj6OjIvXv3ePTRR9m5c6duyiS2TtwjP/vZ\nzzRmw87OjoMHD/KP//iPmEwmqqur+eEPf8js7CxjY2MKfgno1NPTg5WVFbW1tTg6Ouqma25ujrfe\neotf/vKX2mEipLwQwhkZGXR0dLB161ZSU1OVUPui4/bt2zg4OODh4aGFrcC8qBhRpZnHMoh139HR\nkYKCAjZt2qRqXPO/D59uOgVUMl+wy3sKOCCbCDs7Oy2Il4W7eQTK6Oiogp8pKSmabS9gtPw9KcYU\nQYUAtgIwubu763eJj49neHgYFxcXli5dqmCKuKwk/mh6epr8/Hy6urqUTJXzJarjkydP0tfXp7ml\nqampOgcJUCLRJk5OToSFhZGWlkZISIhunqWfQ4DmtrY2wsLCiI+Px8/PT7+vq6urui6zsrJISkpi\ndHRUHWUCAMp3EyBDAHvzTgIBnG1tbVXBKde5RO2IwktA2ImJCX0vycOWrgC55iVHvaenR4FVKRlN\nSEggJyeHqKgoLfI0J6BE/S4FkQJOjY6OkpCQoJ9XAL7p6WndyJorSqempiguLlZnnDiFcnNz9ffJ\n8aivrycoKEive1EUmxMmQ0NDSgQ6ODgwODhIUVERMzMzVFVV0djYiLW1NRcuXKC5uRlbW1uefPJJ\nLZ0HCAgI0M6RhQsX6nvDp64GuS/M1fvd3d0agVZWVkZ7e7uqgF1dXeflgItKrrGxEQsLi3kZ3rL5\nrqiooKamRhW+0lsWExOjZc5C6Mm/4b6oxdnZmUWLFn2peaeyshInJycGBwe1F+P8+fOcPn2a2dlZ\nrl+/zqpVq7Czs1OyVAhjKUKuqqqiq6uL999/H3t7e+rq6khJSVGAU65zAegTExNxdnZWwuGz3Q0W\nFhYMDw8zNjZGX18f58+fZ2BggIiICAYHB0lLS9OSayELRe0uxYYm0/0CRHGJrF69mh/84Ads3LhR\nBRlGo1EV/ObONfncQoKeOHFClbEbN24kNzdXXZECoAqobK6mlOtGyDhHR0clFsbGxhTMlf4LDw8P\nVZJ7enoq2C/vbzAY9DNPTU3pvWQeuSQ/l3Nz5swZVaxLhJcQFyJQkj4JWfcJUevu7s7NmzeJjo6e\nB1DNzc1RXFxMfHy8Am89PT08//zzXL9+nY8//piGhgY2b96MjY0Nw8PD+Pj40Nraqo46+V6SLy3R\nfs7OztqZUVdXx8qVK/H29mZqaorbt2+ru0OIUpmPzPuhhBiUmCgh6eQaFoWs+TN1aGgIa2tr3nrr\nLdra2rSA3WQyERAQQGZmpoJmd+/eVUdqTU0NAQEBLFy4kIGBAXx8fDRLXECuU6dO4e7uTlRUlD5v\nAgIC1CUjAKq4NiQ6Ce4TGUKqy+uqqqpIS0vT54GQqzIfCDgpZKkor+Wahk8dZDKPyH+LaEcEOIOD\ng7z66qua0x4YGKiiNylKd3Z2/tIkgEQSyb0m3/XzlNq5ubmEhoaqGt/CwoLS0lKNFXNycqK4uBgv\nL68/yYAXYhOgoKCA5uZmvLy8iIqKoqWlBR8fHzIyMhgfH9d4tcbGRlUhu7m5qaq5p6eH7u5u6uvr\nGRkZ0Zjcjo4O7aMUkZL0L506dYpdu3YRGho6j7iATwHoubk5Ll68qM8TFxcXOjo6qKysVMJUhnmE\nI9x3TsjvFuelDDs7O+Lj49UFbp65/pfGyMiIOrsiIiJwd3fn1q1blJWV8dvf/pbJyUksLCy0/0hI\nxYaGBg4dOsTJkyc5cOAA4+PjJCcnYzQaKS8vJzMzc951EhAQoOp16XKCT+N+ZEicpIgGxNHX2dmp\nThpzMsD8WNXX15OUlERERIS+5+3bt5VEl3VKS0sLDQ0NWnxs7i6QISB5eHi4zodC6Pr4+BATE6MR\nliKWkiH9LkJ2iFOgsbGRyMhIkpOTmZyc5MUXXyQ6OlojC0XUJ25Sc8JD5vLVq1ezfPnyeYSMEIom\nk4kbN26wYMECddItWbKEGzdukJSUhK2trfYbCKEhe/tLly6pWFFiBuW55uXlpXOBucNUPpus/8VZ\n7OXlhcFgYGJiQh0Rcp6tra1xdXWlp6cHuL8GiIqK4qc//Sl5eXl6DKRTSPZ+ggN90fE/JMCfH/+v\nJAFqa2vx8fFRRWZbWxunT5+msrKSqakpcnJySE5O5t133+Xxxx9X1dfAwACdnZ20tLSwdetW+vv7\nVbE9Pj6Ov78/o6OjLF68mMHBQTZu3Mjo6CjOzs68/PLLhIWFaWbt3NwcQ0NDJCYmYm9vj729PaWl\npZw4cYKysjLu3bunC8r+/n61/0xPTxMXF8fMzAwlJSXk5uZqpu7g4CDLly/n8uXLRERE4OLiQl1d\nHcHBwfT39+Pr68vJkyextLSkoqKCuro6BUCHh4dVHd3U1MS5c+cYGhoiJycHe3t7Fi1aRHR0NJcu\nXSIoKIi2tjZ27NhBeXk5Tk5O5ObmMjU1hY+PDzU1NXh4eHDmzBmGh4d59tln1X5UUVGhhVW1tbW8\n9tprJCcnExwcTEJCgpZEim16amoKW1tbWlpa2LhxI21tbQQHB9PV1cXy5ct55plngPuRDvHx8RQU\n/F/svXdUm/e9P/6ShNhDCITYey8DZhiD8QAPPOIkxXaWWzfNbNqmvbdNk5ubcdvMtkmbOmmTNMtJ\n7CSOHW8MeADGTANmiyHMRiAEQkICJIZ+f/i+3xFuepv8zj3fc8+593OO/0iMxSPpeT7jNa+w1d7N\nzQ39/f2oqamBTCbD5cuXYWtri+9973t44403uATy6tWrnIOdnJwMBwcHaDQamEwmBAYGoqOjA9eu\nXeNN9NmzZ1FTU8P/feLECWzduhV2dnZISkqCUqlEQUEBnJ2d4eXlBa1Wi6ioKPzqV7/CwMAAduzY\ngT//+c84cOAAzpw5A7FYjBs3bvDBcWlpCeHh4Wx3FYlEuHbtGsLCwnD58mVoNBq88847UCgUKCws\n5LiICxcuoLq6Gvfccw/HvPT19eFvf/sbPv74Y9x+++1IT0/H9PQ09u7dCw8PDygUCgQHB+PkyZNI\nSUlBYmIihoeH8eqrr2L9+vUoLi5GamrqiqJSUi2ZzWbEx8dz5uOuXbsgl8tZMW82m7F9+3bI5XK8\n//77TFhUV1ezwmNhYQEymQxHjhxBc3Mztm/fDgcHB3z44YdYWlpCamoqnJyc4OrqCldXVxiNRlZt\nXLlyhRfH+vp6XLx4EY899hjef/99jkUi0E+tVmPv3r3sNJBKpTh58iQCAgK4oIxU8QkJCVhYWMCG\nDRvg5+fHRY2Ojo747W9/i8LCQnR1deFnP/vZd5qY+vr6MDMzg4cffhg2NjZwcXGBj48PNBoNsrKy\nYGdnx6o4kUiEF154Aa+++ipmZ2exc+dOZGVl8cEyLCwMS0tLKCgowO23347z58/j+eef5ziSpqYm\nPPTQQ1haWkJzczPs7OygVqsREREBmUwGnU6HpaUlVteSwotiVDo6OpjYdHZ25nIjUsMQOGI2mzE8\nPIyamhps2LCBcyYtFgtOnjyJ1tZWSCQSnDx5Env27GH3BUU+0DX39fXhjjvugI2NDRcCUUSHxWLh\nzbROp+N5My0tDV5eXjh8+DAX2VLcy4YNGzi3nmKzwsLC2CIoEAjw+OOP48KFC9i6dSsfdkUiEUZH\nR3Ho0CHOmN2+fTsiIyMRFBSEqKgo1NbWQqVSYffu3XBwcEBkZCRmZmbY7r5hwwY0NTXBxsYGTz75\nJDZu3MgEL5HI09PTuOOOO6BWq/Hggw/i3nvvhaurKwOCSUlJHEOzY8cOPPTQQzh+/DhUKhUeeugh\nWCwWXLt2DbfddhuD80ajkSMMqEi8p6cHH3/8Mb7//e+zAri7uxu/+MUvOJf4+PHjCAgI4M4WNzc3\nFBcXczRZa2srZmZmGAB97733VsS3UHxETEwMhEIhlzArlUo0NTVhcHAQGzZswMaNG1FVVQWTyYTp\n6WkuiKayKyIv6P10dXWxu2f79u3w8PBg4D44OJhLmPV6PYNiAoEANTU16OrqwrFjxzA0NITIyEhk\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ToaysjAvMqXiW9hLWymOxWMwxBwkJCTzXkqqPnmvraKDZ2VkYjUZkZWUhNDSUy9Ld3Nx4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\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jGYimuC86+KhUKhw5cgS+vr6IiIhAUVERlyVSkzwVawN3DrMfffQRsrKy4OvrixMnTiAo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\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a2srZEPfffz9KSkqQnp7O4P6+ffvQ39+PTz/9FO7u7sjMzFxmUWIymdgCiw5dVqsVaWlp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BzR0dE4fPgwwsLCcOnSJeTk5OiibPm8yYLdmpoaVFZWagWnBHCysrIQFBSkre7SBWdldWPsn9yg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\nuMGePXs0ySqfIzkny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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import skimage.io as io\n", + "import matplotlib.pylab as plt\n", + "import numpy as np\n", + "from pymks.tools import draw_microstructures\n", + "\n", + "\n", + "draw_microstructures(X)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The images were taken at different magnifications (increasing left to right), 50x, 100x, 200x, 500x, and 500x with higher exposure value. Although the images were obtained using the same equipment, there are some variations in brightness, contrast, exposure, etc., to account for variation in image collection process." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##Image Segmentation\n", + "\n", + "The imported images are in grayscale. We need to make them black and white, since we know that there are only two different particles in the microstructure. We do this by thresholding the image using Otsu's method. By thresholding, each pixel in the image will become either black or white, giving us only 2 local states." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from skimage.filters import threshold_otsu\n", + "\n", + "\n", + "X_thresh = np.array([threshold_otsu(x) for x in X])\n", + "X_binary = X > X_thresh[:, None, None]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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jfI9isRiSySTKysoQCoWQTqeZvJpMJsjlclRVVeH+++9nW/b973+fgyCxWAy5XI5CoYDF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PAzMtHk5OQIXyg5ORn9/f1oaWkRJTVH0UQc5ubmcPDgQbEm4bvJ59fX1wej0YipqSkZgbrd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\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HDuj1eun2BYNBKbx4bz7r+t+e4H2mTco777wjwYqH7i233ILu7m58/PHHKCwsxMMPPyyG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\nBALo7+/HwMCA8FqZIdPpdHA4HPjKV76ChoYGGeQ+PT0tkhOxsbHIyMhAQ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WlCh+bDHLudNSsX0MbfX11v/g4OCQRyJSjxEHnOc29G4/AdjtOYSC3Duuc4hY8xyi9O3x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\nG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructures(X[2:4,:,:])\n", + "draw_microstructures(X_binary[2:4,:,:])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We plotted two original images at 200x (top left) and 500x (top right) to compare with their thresholded image (bottom). The original images have 256 shades of gray and the thresholded images have only two shades, 0 or 1, seen in the scale bar.\n", + "\n", + "In the original images, we can clearly see grain boundaries, whereas in the thresholded images, some of the grain boundaries are removed from the image, better seen in the 500x image (bottom right).\n", + "\n", + "##2-Point Statistics\n", + "\n", + "Now we are going to compute correlations using PyMKS `correlate` tool, which computes auto- and cross-correlations for all phases in the microstructure. We have 2 phases in our microstructure `n_states=2`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of X_corr\n", + "(5, 520, 694, 3)\n" + ] + } + ], + "source": [ + "from pymks.stats import correlate\n", + "from pymks import PrimitiveBasis\n", + "\n", + "\n", + "prim_basis = PrimitiveBasis(n_states=2)\n", + "X_corr = correlate(X_binary, prim_basis, periodic_axes=(0,1))\n", + "print(\"Shape of X_corr\")\n", + "print(X_corr.shape)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "After computing the correlations we have three correlation results for each of our 5 microstructures. Note that the array of each correlation plot is the same as the original image. For each image we obtain 3 correlation plots.\n", + "\n", + "We will plot the 2-point statistics below. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gDM1uctLw943LtFo5Vq9ue+MAVO9WcmgwfrNhvYd1TbD7839sLKPP1z7mmGMw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\nDn1q11NDTz+AwXBgtQ20sCdEEVeaUlTtuj2rBhBW7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K+lNUU1A4w0ZahGvCbbhNMDOU+b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jzYKztnMCHXnvTaBgL5cJFnoUWnp+8N6jqCq7eYHWc/E19jDmtrlP88KxsLhade4IviVFtBZi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\nFBUV4c4770T//v0BxJ0Njz32mOb2pqYmPPjgg7jhhhtQUVGBaDS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.394543879546\n", + "Volume fraction of white phase\n", + "0.605456087097\n" + ] + }, + { + "data": { + "image/png": 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RwYSPS1IA+lrOSKHMBfTOtvyv2hmPoEgBdsRD1kSlnxs/Ex4jkBBONmuHx2Gz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IjUZttHCE1LKZnj8splRSs2Go1w0kkn4fTTT8dFF12UfX7ZZZfF/+/cuROve93rcNJJJ6lrtm/f\njuuuuw4bN25c0UBZ8ZUfaatgpTxibaTKZ03TItR5IU6gUyDatntfGdwOMSHjEYWLw1CVZ8eQDHKt\niIxJ+pF7RnX3Y2PDYT2vUMmjw7UuWGlWVc+NAitrF0Kqzi4BaJxu4qWQ2P5LYclshFslS3tr+zER\n4cGKaRpvG9sSJVWRNq2/Xh6ZMY1kiASRo/jzGso9iRzJlshtR9YgGYlprw5F94jUdVCRNGP4HVvF\nX0LV5bWs6QT8TNLalIrT2jm5EXNz1nlVZU9icrd3wiAmy3VsBHqYzMIkg4fJqe9qKiZbWS1Mjqds\neNEXpYglwoQSJnO/qc7CMCbz2Pl+T7zxWkyO40E7FZNL7Zcw2Y2WmILJHgYqoiP+ltFv7xRM9vaG\nt3a8X6ZhskdSWExGSCQO/6YNYTIT/hL58lAx2b1mjsmrKnsSkzvjsjP0xbgq1aGAeJ6RF4XMREAo\nBCA0ffumMCankfQiRpkccerWe7CRCzQuQKcQwBq8oaL3qpwUKcxXRKXo2IgRnosiPfr6Fmiicdo2\nVTq1w46d+7fEDn9mxoWm7aI8uJBoAx21IDdYAsqZe3xevJZqbausjWKxTCctJBYL7d5Q5JY7Z7P+\nxVog9n3aL6oWh/xYemvBe8zucYlWketlvexzDBUk+kSid9qmAZbSmqgoEVuwlGVU52RRNu85Lq9U\nVkRobN68GZs3b8b27dunXnvdddfhMY95DI444gj1/tatW3H22Wfj0ksvXdlIkZQLm+ssSgoAk0ec\n9qcoFaxI2gKZksvKOcPct1V02VhlIkWNhUgJ8SLKHKy3TsYXDVEZW5OUavmMx2K9Qqy0WKUJhZoU\nmjQATLCUqSNCtTJMhIXtN/c0piJ3/ExLY7aRBa0ZZ6zpEZKHNXtOxvBhI0bGUTolwZsTz5XX0fOC\n2bbYQwgkZVfu5XlFA4OMNx3ZA3U/R7FwOLnn+VNjbNvCHvDnwXuOv4s8/6JRVQLvuTwk2ZOYzGSr\nh8ny/1kxmWtkiFpUwuRodE/FZHNqxSphsvQ7qkMs/LvamKyvBUqYDEiExTAmRwJpBkzmmhd2zLZg\npZ5rmIrJXmqoxWQhxb2TZEqYLJ8NYbJNkRrCZOssGcLkFBUq+nA7EyYX8das0SyYbPe6nf8ckx8e\n2aN6MkUhWGM0OyWCazsg1UuInucQOuOQC23CkBQs0eg25IPcw15qFdVR0TjNkads8PH+HU/iPGFz\nYEd1IjeUsQ4y6g15w959Gbvcn0VwpEgQ8fZ782rHE01IBE00xfk50R/yPLKIBUsQyP3qGNKgohKq\nUYociSfZqLUJLpllI3piWochrbq5jtV9to4Ft29JHpsixVEuEgWj2g1pf7dNkwqp8lz6McVjWPtj\nZitJrwm0PzKyjJ8vEzhmjWw0UIlIMkReZeu/lGSOyyuW3RbTsm3bNjz72c9W7335y1/GwsICjj32\n2BW3l7wYnQIg3p/lZfFM6C+j9TIB2hMlyp0UX5PrlyWcjhRKzuVlpZsLbuZKe6/sO05yuS/OTYx6\nxxvFSgt7t0LtKZyp/bgOxgBnj5ldLx5/CBUCtAcwhd1qZZ/nxetsFfQxe+uMwcDzbduWnqt+FrG4\nXS/xyFBZuyonQaxYz6YouVH5RFpnmKr+IvYZifIdQoUxmqics7KtFc1KKc6sjLLyWorOSO9XsYo+\nr6NXXLGk0LJhakXu4SM2eQ095V/a9GpozAzmc1l1WW1MBuSEDh+Ts/S4AUwWnOXvZQmTAY07q4XJ\nQqpETzsZ9iVMltSs1cZkr6j1MCanL3AJk70aISVMtvV/pmEyj2kIk6MBb0gK24asSxjVqmZICZPt\nvSVMtjVbhjBZxpDmlNq2WGpJhGmYPE3k+8C/HyIlTGbxIqWYWFJjnyvOe0x2Bybz6RRsXPPrKNFI\nd/ZB04BTUdT1gsl8D9eEsESIGNK9pEKMQsDkoKzuY097Zti3+n4hLYREiAZ0253U0TSaLACU8Snj\nyrzn8t3hCADpA2mdUioE6TpOrZEsHUfa6z37KfLA9N+PrxUig4kCIoJUhIEQSkzONKktJgL4Ho7y\nAXqSYHFB3x+q4kkmSiyhQGvBZEIW1QJLFI3j+OLqxAiitD81iWBMXUopyogIXoMQ9DPioqUsal1b\nt003IsOJGuK+57Iy2S0rdtddd+HGG29UQP3AAw/g8ssvx7nnnvuQ2oxKiDGq5HX81+Y/2EBSePiv\neJG8lBJ7r1K0Qx5hED8j41RChO0clDeRPufq9TyXOEd6LfMXBV7GxEqMJYCkXStjGqdnfNvXHHkR\nAlXZN4obK45N26r7eB5S7Iz7EkWOFX+rtLLkhezyPcFzl7UqrUkcc8FrK+tdG2LKI1PiPjP/eJ28\nZ8bvy95ozTpGY4o9z0D2fGK7jpIrfwN5Ie098RoaJ68hkIxb7jeTKuy+f3Mpyp7A5JZwy8NW/l7z\nfquq1cVkADNhsldQuoTJqm2a32pgsjU8hzCZUwGnYbI15ocwmU/okPunYbI18j1MtvMtYbInJUwO\ndN8smMwYNQsmyz1DmMwyDZOlvZbX2sFkK9MwWcbP48kwGU7bdb37/s2lKLsDk9kwVBEGksIgBlTT\nZgasiBjasRZH02rjmu5rjQGryQ+bcmD6kz6EgLH3WYNeRE4pUf9s22l+kgrCtQ7YsOSTVarRKBbN\nVKkv1vC0US+9qLQTFV1CfUl6ghlHnIc1mBV5Uqm+kgHdKkIlraVZP45GYNLDmZuQC5V9jrY9J3KB\nybOM3JE50ZjtuOKzzwg4J0JFrWHrF4uNBFel7zdz0afTpOdUIh7Sd8SsHz13Sy5J+pW6zpM5Jq9Y\nBiM0vvjFL+KjH/0oAODII4/EBRdcMFOj11xzDY488khs2rQpvnfVVVfhlFNOwYEHHhjfKxmoN9xw\nA2644Yb4+hWveAWATllZGNVRMQWSMpC8XW0M4QWSN8zLL55MmlhIjRU/VgZtqLOXulBVlaKGROno\n74i1CFSobNsCHBXQew+tssfRIerIvLaNx2bauab/63WV6AoJ6WYlTfpnb2TT6Necs8trWReiGGwB\nVh6nyFDurxXpxyrXJQUzKdCpDU6d8cbTkCfTRi7oE1m0d9Q7ipJztmUNpnn2hEyw9/F84rg9D3jQ\n/fB62es4nFvaWhjVGPffCzZYAe0t5vz4UlSJzO3KK68E0H2P5xEauyaPNEyu66DC8QFtoHkYWcJk\niZhr2zbzbLfqu2cIgAFMVlEHM2JyHFPvPPQwmaMs4v6fgskSScJiMTm7byomA4LlvJa7isn2vZIw\nkTANk7v+8hQLD5NZZsFkG6UzhMmWyJ6GyWn80zHZFkq1mMz7QT2jAiaH4Ecdephs5+BiMuaYvNry\nSMPkLi0gaGOLyAF1gkQC5dxIj+kQJg2lF8+THsWSHmMpnOlHHUSvexYl0oJAGQTK5rr0vk0TyAxO\nllGdCm+SVH2NAyVxnsFfrzhGHS2hjFvbHoAYlRHn4Yg9McRK0wChVtEEKuXIEFvxhJOm7dJmQpWl\nfWRHoar+Wp1a0pixNW1cWxWx4hwP7BaNNREj2WknDaWqGJIjG7PFt1BB7SF+lpwaJWRRFpETgKby\niRO5Xv5PEutsmD5l/ILJwByXH6oMEhqnnHIKTjnllBU3es011+DFL36xeu+b3/wm7r77bnzuc58D\nANx33324+OKL8aIXvQhnnHGGuvaoo47CUUcdlbWblAlLGujQ2pKixmLDTW1b4rFrqN1ZFDzP2yaE\nS6aUVBVCq4vbccirDRudTFpVnX2MRuXxynzlPVaetPLGCnL+Y2nnafOO7dwArVhZw90qgQDUmL2o\ngqZJIcCsgDJxZb15nrfMzisZBE6YsjGMujZ1+zJXjYd6H7ESbO/12vI8d6xAe4YL4JNBTdMX9Jx0\n99occnUcMRtr8bdV+gu01o0ySJmwYE8pG5C810XR6iY8r9y8K/JIw2RAf59KmMwYUsJRi0dFTMaw\nAcziheQPYfIEfd2BBoOYDGgjWubmYTKAFWGyh8vTMNkjE0qYXIoGsGO293iYzOtT+o2w0SJ2Xh4m\nC47aOdt+4hrW2oAvYbKdm7ffLCaP0dD+9jGZyV/7+2cxWeFmqIqY3KDFqAdduz4eJouoFKI5Ju92\neSRiMhvPyQveqtM4shMoQshP5ciiBRxDtCd/i6dosBDxoN9PXn4ptMkkTJcykCIx2GhVBiwb0dL2\nGMk4VYRH/x7PkYzMjJTwCA65x/PiWzIhVOC6JfF9bsussRfVkhEkAWibKq0Brw+M0Dyyoq50TUQ3\nIg94/4hk5IFas1qTOB7x483N20McYQMAY6QaF9w2PzeO2rDREI1O2dG1WkJMl+I16O5DspqDrveS\nvjuGnAERMkKmyF6QvQ6DycAclx+CrHjFlpaWMO7Z1OXl7lzjhYV0/MzNN9+Me+65ByeeeKK67x3v\neAcmk/585rbFBRdcgFe96lU45phjVtS/rf7N7zetrljPXkJrfNkz7rnIIXvHrPJmFU2bf8wncHgK\nsFU6eR7sTReFhZUorltQOUqKXCsexBRdovPKpcCoDb2OCrXMV5Qh53QVFvaaiRIlnqrFUCOGCjvK\nPD8brm3C5BVfx2HaHlHChs5Q+HLXX55TDiAr0idezahQ9+toPcDsjZP+PcIhi1Bp05qzkSR56wsL\ntXomPGcv6sN6wOUajjbi9eZrbBSKCHu+h7zkpRQBkWoGUnAuK5M9icmyL4cw2Rre0l+Gya3+3uwJ\nTLZRAEOYbKPySphcUVSEvLfamOwSpgaThawMUqhzAJMBHUGwGpgsxEKJiGJMrmk/yXy5D8ZkSzQP\nYbL0Y9fKw2S5n/WFWTDZRttIn0xg8XhsRKja29l+gLqPf8Ns5M4ck/ec7FE9WZ4nn8jAnzWtHylg\njXcx+JEMMo6k4IKVWS0IY5RWlO6R1V8IQUVVVCOgpaiJRCykcfJ4IqlhQDmrfWCF3ovXstFsvfNq\nbQwhwgZqYQ2UYa0M2w63INETDonkG9fJSK/MPdlRptSOKsbakxQuEWXIqlSDtSOfsj5ClYgA2WNe\nVA6RS6ovtVZOBE48wrZSe0hFHMV9piNB3JN+7FwNWQekOiJqj5r9wN8lHf0EvV9k7fgv9WVljssr\nlxURGnfeeSfOO++8+Pqcc87Bpk2bcMkll8T3tm3bhqc//elYt26dunf9+vXqdQgB69evz66bJt6P\nsxjPAbryeMmw9E7W8I5EzfpucuWCSQRui70irAhyQU3PYyXtWQ8YV4C3SqMYwTbsV4WCmzoVeh6p\nHckHHxkvpfX22TbiGomCTh4wvs/OxYb/cmhxyStZMuDl/9I+e/vYEwhARd/ENvqxZ97UoNuNc+7X\nZ2T2zjQPq6dcl5RO61VtJm1m6HknAbCnt0tj0Xtd7pd18sifVHSOc/99At377vjHtg78qMxlxbKn\nMbmUMsaYzCczDWGy/c7Pgsl6/GVM7r4nIRszY3IpcmRXMdmTEiZb7BnCZPvdLxntfGKGJS6nYTKT\nw9MwmT/zMFmiYrr3p2Py0LowJiuMXAkmFyIcvLVj/J+GyaXTWZgwi+lZfRslTObf0RCqIiZHIg5Q\n92Tr6O3xOSavquxpTHaPi4wkR2+YinFoogpUmL7j2dYh+QVlQE9An1oCEJmRaivYdJbMa27ESyPh\n97LaFDJeGYPXppyWEdeK/jLRENcmRZWklAQiOrhvWo9IvMR7JTJB3yO1RapRrVIyVFSEtwYyR7t+\nxviWucUjYjn1ZBZMsOsiR7UC8cScrp2GyKEm3etEMpTfZ7KsTURQJHd6Ap8ILEW+2dNZiLSTdWxj\nWk+3SkLYuMVymajo90MlhF+/lpUqZutHn7RNg1Ba6zkur1iqdpa43UeAbH72/zUekE5qoxSWlDv7\n4249hPZ6L2y4bbUnT14vLNRK+ePj2mx1d6vocPE7GwkA5HneC6M6m4+tSG/nLffzvRLq6hrXxivI\nOb1eCLe3hkAK3bX9Sai3jMeO3c6P5+EZ4aX+vfQj+8ztPmDx9huTRnKPKM/xSCJVLmoAACAASURB\nVGBRvmn9oifYeVZW+fc+BwDPePHWiu+3e9ZeAyAjSKQvGbs3FktAeWHopx5/KC6/+FWqr9v/8m+w\nu2TzWS/abW3PJZeDTnlnhjGAxmT53gO7B5MBObJ0GJOrKhXqnIbJANR32MNk7jfWa1hlTG7bNot+\nKGGy1499T96XsZYw2SOvS5jMMgsmy3XxdJgCJss6l0gbq7bYfqdhMs/h4cRkuY4JDe/3h6PpmCAp\n9cdtD2Hyzz3xAHzx47+l+ppj8t4j/77u8Gg4ZadcMKnh1WwoGFwuOSDXi5Fo7zOnqrRSQ8NEeUBO\nvSgVcmT8iAZ0Fa/3+onRA2xQArotZ94qCsKerMKefnuihyUxSoTGtHQKMXr7NISYEjGqu5NFbJu2\nPecZ6YgM5xnSdenkD4dMakxtEjPHYgoQ9ytzlPUUYoixT+ZkfzcsIVCKBioRSt5a2egVIjSmzpMJ\nEmdfltbORoq0TYPq0fvi2J+kWjgiuwuX92ZMXlNJOnymu1UeVajlDF49VkpFaWQlRbw9HLa7PJ7E\na6JCuqCPlbNhx6x0RCWflFKey6TVHj1RZFSObd+mPeqUx88iXjDpD402MlhYmZxMWlUstWlSMTzv\nPn4/epeov6ZJaRqTSZs8uEzoDBgyknZpFVlvDWTNOHTZXm/XxpsHK6ac7iPjmEx0njd7D9V7Ex0W\nXaq6b9M6+D0AsZChvN89rzTekjeY27Kh+mwI2jnx90odH1lVdBymTm/hsXhcabWXV1n+aRL5rsle\ncDG59fek11ZyZuT1HkqYzDhYwmQ1JvouljAZSPhdwuSuUGVvjPbe8dXGZPG6z4LJ9jevhMn8WQmT\n2XCejsnOKR8DmCyyQKHdJUwu4qnBZMbEaZisyOUVYDKQoihKmGw/A3JMlvVyIyWgMRkQYqMrXM7H\ntFpMBkARJwOY7JxyMsfkvUgo/UEfjYpkSIaCfuxEE1T8mTVQ4/8VKKujRJMBWOv7YrstYoFKbtsa\niwCRGNoojMRI0wKB62NoIz2bR2mu3lpwX0wWNS2qEdL8u9DAYfLCS4NQXv/++Y2BViw0ZThPIZfg\npDF40SpsgAMdaWKvt3PnaAy6VtbOM9jBJBBLJApaIjv68Y3qnHSjdUPcV6ndLELHeT+bS99uK+vu\njVGejUSf9NEfat/Jc89SlCoAqa6HdypOSea4vHJZU4SGKCMBVXa2vRX2UkiQGXvC0t/uenWmewDk\nK+oV1Mqqk8e2EcdjPXpN02Z55nxdUv78uctceBxWaRVRyioQFc/Yd5MrqNJeyShmpVrGI+/bInnS\nZjxBptVrqXJ+jULGr72ccpszrKrCG8XYtilGkY1M6eal63dwXntcN1TxFBK+j8O5Y359v+4SeRef\n+SSFMDdVeiaeQWKVdbsOrPgy2WL3hCUXPK+tJXY6I0ferLLPvHZ0e4W9NA+j26tkFky232sPk+O1\n9LqEyex99+o9MCZzIV32enuYzEasHtPwGuwpTJaxzorJ0l6DdhCTh8Sv86HnOR2TNXEyDZPj/QVM\nzn9Hy5gsfalIhwImd2NIazsNk5lokc9LmCzX2d9/u1ag4sw6UignpkspoTy+ygu4n2Py3iViIAYk\nQwswBjARBTCRGMU0CTinVPREhfRVKqZIBje/L9EaAKVQ8H4UEsN47VNKQ9WPz+z5rJaFT2JAGbI0\nTyYQLNmioh7yZj3DOivAGscFIkHaNCdek2mSpcA4p6mY560+A9U4kTYoUoSfR140lkiWUCkSjUmf\n7j4ifvv1iBEvQoJFkmOSxgGK7KB1Y3JJ0kp4P/G+VcVSmaQwogrbFgipakSEEe93S8bJeg605ZEc\n6vO5rEjWDKFhiQigbMCKl6K7L/3AWwWJjVh1FFzTqmr10r4oNyoaxPFCy7XSby0V2Nv8fvHG13Ua\nJ+fP6qMOoT63RcGaJuVc8xqJsRFri1CRPM+YKClfdo7aAEAca1Wl4mZ8HRdW45xfzxO3uNABbDNJ\n4+7WKbjPTT1/JOWX72UPLYc1W5F2S8a79p51imaqjK8VTXvEI4DO2wZKxzFebmvUiUGiQrAVkTb9\nxB5rdMheiffAHEFJbfB6lL6Dmde7RMyxF2Aua1pmxWQAak8MYXLEigFMFuNX9jWnVQEak3lfTsNk\nua5pgIWFoN63mGy/c0OYzGtjCaAhTOYoiGmRHuL1H8Jk7/tdwmQej0gJkxnfuM/VwGRLEE/D5NBf\nU8JkuY/nDfiYDCAWX50Fk7XeMVy4lo8gT89SY7KNzLQ6iIfJ6nswx+SfPlGe/p6giIay2QC98Qvo\nb0ixwKdHDLAh2vcphmpmNFrjzqYXcNP8fZEIgJETmWBeZ+H8IRUMVYQNR5soAqhK80LQ68lrZtfZ\njlXWoBSRgWQ8Z0SSFxzCxBT3F7+7/TrGiBQy9rub8nsVaUNGftOCo2bskbqKnFFr4RAO6MknGZvU\nTFlc6I/ypfQd+S3j+0NLhT57m0kIsVIKirzm6Ii+jyoEtDAn/PQi7WapMtS3EBjFI4t533p7xJKK\ncgSwI3NcXrmsGUIDIGWGIiHUZ4CrALoGGrSSxxXsM2OZlRYgIyas4tC0qVI7e2lEEeoM4aDuAYBQ\np1QJwChzNA+rDPFnlmCx7ylltvHnwUox0IdZFxRq9oB6hrS0w4q0V/jMSvSiOZ9l5MoA2cLGAXth\n81zvdL81SoRwGPfpSUmRT3n6Em7uGR9273Xktz7dhIkzHmMIXYSLGG427Fzusd5n/kzP0zf+xCjk\nAonSpzcXa4jOLM4+msvalSFMlnQ+r+BnCZMBTMVkMfw8j7rFMoBSFadgsuAZG+JDmMwRJEOYzHP0\ncLqEySqKw8NkZaTy97yMyXaNPUy214oMYbK9ZzUwmecxhMkabytUVRmTvbFPw2Q7x2mYnNJ4fEy2\nKa7TMDmEdL+XymcxuUSAFWWOyXuXROKh0e+FkApfsgGb3Zv/P5IDfKoITJFPSStpKlQwx5NaEcO2\nj+6Ix8gyIbG0jKyeQ6iQnSZC4+MIksxAtdfSuihiBTCGvyF0rAdfxh7rlIQ0t74tVcBzCgFjjeQs\nAoCF0zJKaSJxTuZ77o2JySnbhpmHIoq6H1NpOB+DrFEMoKDn6dVPaaSwJvTpJp49EMmLRBLFSBLe\nNwg6goPbEGKF94UiIlqac4sWaQ/FPnkteonjKDyfQeyd4/KKZc0QGurH31Svl/eBpERYLxmgjWyr\nQItwYbmYI23rMFQpv3hUh1jFH9BKUKuU7ORxUTm+jkHL47NGf6w90StzOvxW39eQwurNm5VMVr5k\nHWQtlpcn6pg6q5SLQe0VNxNl2XoPpQ+ec5a3bdbSjdqitjmEmOfIbZVOa7FEQrceLToGtawIp/D0\n7lqev057Sv3wetgIDr6+qnTeuZePzZ95NSu051nvIc6V78aUtyvzACqzdn77fHqDq1TPw+j2Gol7\nuIjJGo9WC5PdsQxgMqCNROnLw2T2kk/DZE6vYwO7hMnx/4Z0LxEEggm8DozJQjR7GFjCZHleexqT\n+bMiJleVihwbwmRezyFM5rHzXIcwuWs/f+68Xmp9qZ6Wh8l2nrIGgI/J9jsj4y9hMqe5WEy2NaX6\nBXDHOJc1KNE4J4++vA+UDVKSUhHQ1hi5neGY6mVUMDn/bIDa41Dpr0pNECMWnAZBxrLcS+NT9SK4\nKCOTH5R2kZ0OwkSFSa+I60HX2nWQNW+XllOtkFDlRAkb9lxTwosgkTF4EQOyrg4Z5UUexHvk+kiC\nEJFgx8oFO5lwCBXQMME1oRM9iAgxRnx2kgrvMXeuNIu4HkGtf8X30Bpn86fvgkop8a5jsWk440mM\n8Kic70d8zVE4PDdal0jujOoycTHH5RXLmiE0AK1QJtEGO4cVN21SLplE4HYkhQHwPWquAkCfN5Wu\nPM7t8/+lLR4Hi1dpH8i97bZgW8xDpvBkWR9WOGWNxCPJeeysMFqDXISVIm9+HHrLCp8lZFqjEK/U\no2QjYngMVkl0DYbsN6Aczi1j9AwxS4bE/9PzmkCfwMDjV97agiFnc7+lXx6TNXi8Mdn1AsjY4vBv\n6ssSXNETT2vGJIwYH7Y2gZWZckLnsubEw2Qg3+/L44mLydwO7+tilEPbggsO8+ceJrPMgsmhV9ym\nYbJqt4DJgCaMhzA5Swkx+GTxgVMc7PxUilqrcYavG8Lk0hqWZFZMVp9NwWRLslhM5vt438Q++boA\nLC93xb3tepcwWfqUvx4mA1ARRXbsdkze62mYbAkui8lW5pj80y2Z4S3CxmuoukgIjpgAFBmhUjfo\n/u69SUolaFr0oOwYeu2wx5l1oGLYfx91ovS5NJZiu1mUQm9kc/2OeH0TCQlVk0FFM6S5WAIhi/7g\ndvt2UtHS1lknIpuyucjaOpjsrTl/VhoPf67ep8iEocgPWYfxBAjOfmMCiJ8/Ez4hxGicwVou5nVL\na6V2FvdtIorU2Lz5T8NCJkyInHDFZ/uBhtJmCs4Md5xzmSpritCwiocY4d2PeSBPS389WNnVYcOA\nDuMEkCkMcu140gATaG9Y73GyYa02DLjpWdC6DkoBBbQnS15HZbiuMpIkKom2UrnxVnGYtreGI9CJ\nFaGiMRE7TQqwjF2KfNr2lDLdamVUPFtWPGVUThSwih7PhZVOAMpLyn+tUmoVXHuf/T/AewsA8iKc\nrHjXFJXQNG12ygobFtY7WPI05mkx+rqFhRDXl8k3z2hQ95r9I9dz/jYMAVg0LONvvCY5iobQvHLz\nXiPWALSYLPujMx7THpuGyWpfOZgs7zdogQkdD+1gMgCEWtc4mIbJLNMwWQzcWTCZT9RSl1pMVr8h\nq4PJ8j6P24r3e+GRoTIWa6x3pE2OrRaT7fsyZ3uf/S0pYXJJn/QwWd4XkQgHD5OH1siL2nkomBwJ\npFkwud+/cq99zmnsUOs3x+SfIolGL0cGVJ1h3hvi0VMsBiidzBC97bbYYTAnngCa4JD3G6AH5X4c\nEzJO6bognunOwG+XluNYo3jeenlNpIKNdogGro1QMQSCRBhUI2f/hy4KLBmviQipqLsYpYIuokSl\nO9j2OI0hPptCpMmQ2HUvSUgpQtlxoqXUCq9N/twQHJKGpKJxQlUkV2JaDu/RptHPIDspZHiNXPJL\nrm1aYMR1XRIJ7BI5sn9C0PuH/6/2i67R4ZJH6rvTKiJk0I07x+UVy5ohNGJqBhmWnjeblWgAMZ+W\nlS/rBYt9hJQXK59bD45UiOcaAuJ9Y8zmNpk4UUZAr5Dnk0Xfng6j5fHze+ytkrkyMcCkz1Iz0cSM\nKFeOsuP1ndZJH2Eq4wAoR5iMABlDKUJF5mXDZT3voSelSAubjx9TdoIox8kgknZkPZiQsGvAn8W5\nOntqcaHO3mPPKQAVotwpxLknLoSKjAZ9hKDMRyKOvJN54jjNusqejMVkZd/03mkxrjgCSNqx62/H\n7HkN53mBe49Ekq6AyWzQ6fD+YUy2RKTF5PQ+ERgFTK6IkOA2PUyWtgD0NRJYqUpz4nkDGpc8TJb3\n6wx7yphcijB5qJissG9GTNbG+jAme+14mMzRFdMwmecFTMdkmWdckwImj5z5ephsyQsdZaMx2f4e\nyH0Wk23KDu97i8kcIROqFMmzK5jMR8rSQuXvzWVtSvT+AzG0HzqtIxYKZkNQedIp6oENce5DIjSQ\nvw+gT/NoddqGMhZNxAaNVbUjbQFAsBENxpCUeUNHSthTRzjiJBrSYpg3LRAaVNJPJGYabbSrNS98\nf4zhak/PiDU/AMgxu4p4KpELMj/po39fRSzIM7LHpbLRjbRW8XnycaQAujSRvi9aK2JNsxNfLOkw\nWMeD19kK9yPkF38GHWVj5yb1WXSbldob9pl6ESIZ6cXEjRwvS4RX/L+z1i4p4+2p2M5cViJrhtAQ\nr5v1yHCeqSgko4VQzHO1Hnnr6egUlDYal56wYmPDQq1YA5yjOFjptsqMTdVg5UqUXU6v4e8N3yue\noqZtgX4NJ5O2q94+0cqSqrpPCqqMTRSz5PmROZdraEj7jaPw2WcAgNpM69e2TqgttHEBJGNElEgh\npFJKEZRxE5XJPnydx2OVcf17XsV5SZ8cxcMKtEcqxLHX5LVuW2jFNPcac3FCacMaUjzGEULmrS56\nvCncmbMQeb14j7Dw3k2RK+6052F0e5E0vXJcwmQWIfaaVuMZkGOyah85JnukxjRMtrUJWKwha0kB\nD5OZhATSd9jD5BRVoQnm1cBkIQRmwWSVrrjKmCzzssSAxWRJR2KcKGGypDfOgsnyDLhPD5OHIhUs\nJksb0q9X+8rDZF4ju64BFcZIRJLMqxSF5EZ2zDF5LiURr1rQtQQy8gHoTsigSI4sVaLg+RZxi1Uq\nQzA/QlPX52ijQZmdqqIMSGSkgK0FwhEZsS2u6yDffyQjtRsX0ljjtUAbelJhDKjjVEc10FTpWl5f\nS1T03nghHjKkZWKHSQyOIDAkk6ydIjOcMYio4phBj1tqeTDZhKYveCnHrwaqdxHHylFAudGu5inP\nUdbbIafahvpjYTIjBAB9ZA8dJ6sIG8FKSxAYMktdL+s9RiKA+nm5qTJ2vooo7K+TsRo2pbXjaPqa\nMaVoljkur1jWDKEhJ4MkUoOV2qSUcnTA8niiFTpSiljBEWXZ9ShDe6pYeeExsWfRU9DFCx5fOznA\nfI9nQLOk0O08ciO1QWvUH7Vn887bti8e2up72zalrVil2BuXXWcA0XMbx0tGuyVshsSrfyJtLC83\n6hSW1ozPKoUSom3HKh4vXrOhPHzbh/TNHrBIQjneOXltjwaWMSliJPRRJPSMrFGnUnLqlHol93B7\npdBy66Xm59gtXpqzfO4p4t5+jWsyP4pqr5FZMdl+/0qYLK8tkWDFfudmxWTve+ZFZ5VS9UqkpkgJ\nk23awWTSrgom23lxH9K3h3NAhz2rjcliPJcwWUc1cN2rXcPkSGBThNkQJnuRI3EuBpOl31kwmedY\nwmRb48NeWxJbNHUIkz2ZY/JPh3TeZPSGmo6KUIYtGUsqRcILmSfjThm/8trckx/72tLpKhTt4aVe\nsJBBP2TcuUUzRUY1qqbpiIzxBAh1ThrI2EMVSRFrxCbPPX2/QshJFjsvNa7cW99/0L9uNQnjXjsg\nIWSkVDSeS6k1MbqFolDAe8LuByFG6BkKUdJfF9dDTtOR/Ub7w5JBHjES7xlPOkvVrikTKzw2eY7q\n2RKhFufeas7BRAhNJRRozt1f+cCk95Senax1idCY4/KKZc0QGvYHWRRMViCCCUWVgp/F/WSUVL6X\nlXHxqHCYso0Y4D6nERvee7o2Q/d58jqmavZN72FqeoVQctNteCygK6d7ChMXAR1SYmOOetNGkojX\niHN1eR1LobYy3pKCWgqh9desC52WE2m8sYvSbo0fCeGNfQa9n4SQsdX2AejUDLSqLfaIiedVigTa\nufKxg/ZEAWlL8tLt3rEEXTRI2hZNz4RbwkSuE4PEO36VjxHm0PSk2NfUHoySPqyYu4rLXNakzILJ\nCEBd9SRzGMbkEqHgYXJTtco7PoTJbKQPYbI6zWMKJret9tgPYbLtd2FUFzGZay/MgskAsLQ8UekL\nQ5jcOT/T7wKL3C/EjFfoumQYa8N8GJNDVXXRKAOYzLVJFPk1gMlxfAOYzHU3Sr8/CpPNX2mrhMky\nzmmYLOvP8yhhciSCqjyFMz3jRCx2bcwx+adS7Pcz5EUMgYBq1Ed6AbH4ZXb6BzAToZAM1iqeBCH3\nKGPNIUbiuLw5SGQFGYacRqJTSlog2NNDWkgNjwp91IVZG5FqcUH3RaJqLzg/XIrIENJgaVmnLxij\nn9uugPwUF74uRnw0/udmLuq9JhVv5Vof+bVVJJwyQqonWdrxOEXCsLEur3vs9Ytu9teNutSauBca\nOvkE0GvlEV88VyblZB3pJJSKryFSQ80vRozInmvjPNRpNN73IpKDiYRq6UQYRSxyG0EXhi1+t+a4\nvGJZM4RGDE9lRYSUGwTEEF6+p+SN5r/e51UlAIbYvnwmSkpdp5NBOHfbesY4uqGkWMv7Qoh0Xiye\nU+iUGaME8nGDJSkVtLPKqfWkeeKFa3fkd6PmxV4x9i6x4m3F8xTaKIt8/Uy4mxkfK7QiVqG3nsmm\naeNznUzaWHSPQ4Tj3ggV0D8nUaBlb3DqC+c68/NjwkW8btbLqtbaSMkjC0ClxLCC3Y1DxhmyvWNr\nDtR1QF3zkcgp911yzWfZO/2gyp/NZc0J11fwMBkAlseTZMTN4I22hni6T3/vuM7FECZbzFkVTG4Q\nx7MSTI5kYQGTvfW1Y7br4kaPOJisbIsBTLZkBq+hxWT7W9q14WOytD8LJts+AUzHZKA74WYKJnPk\nhFcHxUau8Nh5PvZ97xlNw+RE3viY7DlsZN4Wk4XUkH7nmPzTKZkxJ0LfTTlVAoAiClQb/J4hO6Sg\nZofH3d6FRDDYSI3QF8/0CAlrcELaM++ZMWSGZk/U8F97Wkq16BA2cV2QGad2zrw2cZyhUnU5eNzq\nWyVG/ZiKpNpraN6tN0/z+5BHwpgxswE9MmOHia7pDXGdkpGTC1yfA0A8tjcSHXIfR2E0PeHVgXLc\nh90eRb52jTM/eUbqCFkipMw+cQt1etdK21w0VQriConDESzcZtN0+4X7XUx1UFS0FAzBJ/t0SOa4\nvGJZM4RGUp46bwRXBY9embbNagaoa3opKSrWw4T+vHsWGzIqF0ifrGCxghgjFpyx8f2swNR1iARN\n28/NHq3KCn0n+kvA0QkcMYHAeFfFe2370kYsEkmG8YTyvTnKAIA6JYNP9xDvY4x+IU8Tj4PXb4j8\nkM+5HgmvObcTP8vCiXPDQeVLkyePi+1JWLgowHaspeifkljvL+8JLmTnHcHoGoGOx9rz2PK45fNR\nX6uAvYm2NoL9rskpDl7Uh0hVrxnImcsMMoTJgU68KO1Z3U7+WQmTG/JAT4zh7mGytDUNky3ulDC5\naVuEVtfasHPhCBGOrsv6J0wGuqKpaT7DmMwyhMkhpEhDea+EyTxnfTpMjslZlEuTPxcPk+WaEibL\nvBnDS5gMJLJjeTxBXYepmJyI90L4Zi8rwWSZo90/LDzPVEx8GJPl/Th3B5PT2ur75pj8UyhNn5vv\nPVYpYthflykpZHyVPksbtEpGWwfK8Tp13CmQRYkoQ53azYgOew0by2wojtAbopUuwOm1Eccixrom\nEnRqToookP7VyR7cV9N0xjtJLLgp/VFtje5Z6PXsnp1d90Q8xXHJ6Rre2tjnxGNHTiDECJS+r0or\neX2fad4pYqXS0R6WFJNimUv987anl9CY+H6uvZKRNtIugBiRY/dqXC+HGHOjkAh/pW9+r0AQdu9L\nodTUZ6xX07TuvcVoKCNzXF65rLkV85TNsdXsVtqmMvK1wjGZQCkypfxqfdRePk4gV9pFMmWe2vLC\nl0UBFbEFzOJ15H2K8wFVvKf5RiWpoPSEUEUjgsdjIwsyz1ad3ot1HdhTKtE1jSZLvEJrISQPGxsL\n8tcaFpy/3im82tsqCjF75DjfXa0lzUH6kdBjjrBQod9BGwFeWLxNk7IGgnw2njTJi6kMg6Cus55a\nXlNu33pNrad7aXlSNDiHiuCV9nga1Jx13tukhMkclTHoITaijEUHkzmVgY1iO6aVYrIlPFR7BpNt\nibchTOa/IiVM5rSXmTDZ+V66mEzFStn49jBZxj+eNHH9hzDZI3DkbwmTOS3Pw2QgRbowweNhsnzG\nMoTJdjwlTLYyKybz/hzCZH6P183iLqetlMYzVNNkjsk/hSLPVHm++0gKeb2SkPamoUKd2hjrDFcT\nDeGlHTRNNMbV+/w592fH6RmAvZFcAR2pocbcInOCc40K9vYjERPqVBQhXkykwGCagI2CkLFzBAOQ\nilvKWPvrim1HMsqJQJE+zP/9+h76Xo6iQdMXBZV6J0ayyJghw9ziiqx7Y8blRFBkqUnBSZOxc5bX\nci2TNUFHBwGGZCuQIkNHwsbTepzxZCes0PszH887x+UVy5oiNIRYYEOUxSuO5imubGjaGg+d0UwK\nb9uihs5rVWHWzqZbqfLujZdDU+0aDIlHCnAIa+cVMp5UWgNWtuI6VSmSwoYn66Psuns4d1lCakXY\nI8cGD4/HHi3HY2Jledo6pHGlYyXVfpDfmrbNjB9r6ABw5wFAkTJJifSVVzvuWMhuVGdrxSIKdGoT\nGfmS6hPkYeg2TNoamexR9dbPFiO0xot4VdkgqbytOs8L3GtEjNMhTGaZFZNFSpisDcnu2mmYPItk\nGDwFkxmLvNNC7Nx2ByZ74/UwmTGKCU6eh8LkdnZMHiL7vbVI40IRkwHtaOB58v8fCibL/ZZIiG0L\nJtc5IczCmCztzILJXr8uJlddCpeQZZZw8zBZ5pp+ozUmu9+NOSbvNRINqejJ76VkEJa8+fKZJT7k\nOyje/t4TjfEkEgrSa6prIOOZbZ/ZVBE1rgK5YQ1398jOOIeUMqAMezLi3ZQD9vyzAczrFKNf9HjZ\n8x8jZ0Y1IjnUNMrQzYtXVtp4LkXQmDF55Ihf4wLxeWZH7Kpr5Yk6z4FJLkukxHvT0bBt06RisXI/\nP79+bO14nE5BGdVZYVrbZ4wIgdkHZk3kGdu0ELUudv+HqiOm5LeqEGFU+hvJPruXPJnj8oplzRAa\nfHpJ9MzEPe4f5ee9tgXeuLp6bKcO0SuEASNTeT7IkwVoQ08UC8/TJdeodkWhchRJmYP0YQ1dK+yV\nlIG2lDrREZp5ZIXMz0ZdNE0bj7P1IhaaBkiezELKh7RlIkgYnzzPlvwteVJ5LHJyDRMA3j3Rs1bp\n9bNGiP3L19gfTq6nYvciP/sYIt6HnXNBPJ6zqAhijNjIiwmdmNM06eQEMTZtWLX+DaKCc32xQtVP\nbxhMJmWDjD3KdR0Q+j3piVuQai5rUvi5lzCZjTL+7lpMlj0ciWOL7YTJodX3epED3YW7H5OtwVvC\nZLmPiz9Ow+RsPtCYLK853XAIkz3DfAiTu/eGI8C8NSthcpzbDJgsY1kptDsktwAAIABJREFUJsf7\nC5jMkRSTyXRMTsSInXOOyfK3i+z0MbnTUXV/si48fnnG3ik8Q5jMayzXTsPkueK8FwlHEohH3EQX\nJKMsGfaegRwNT3viRTyKE1QHgfa0NYS57SZdAxgPeAiZsZq1Y8V6yKk/e0xm/42g9SDD3URiRLIG\nRMyQ51+PwZI+efHT6JkPVfqs0eMUcaMuGmOA96JII08KhJUiT+S6EFR6hEqL4OdXerbQzz6fh37f\nT3vpa3LQvWoPhjoSI4osM+2psQWpa0HpRNJf03RpStRfFsEhbUpEhpAqqq9uTGialGqDFNHC69ql\nMvUpPEvL+X4y85rL7LJmCI2ScR1C56nyvP0irBBYDzMr0xwmzR4Wvt4qtKyQpHzktBG5uCIUPuQK\nnHsPdGExPXetqLGhIJ83DbTXtH9PcmuHvJmeB1/+2nVk8fKSbXE0ngsbGqWcZhmv9WLZsbLHlvtg\nBc96KjPPHkCe0TR+QBf8G/I0i4iHriTdtR2xlk6TSZ+xt1aMnFIkR1ybgqHg9404XxZW7GX+YnSV\nooRSXYFyn3OQ3nskPudgcSlhcqno5a5gsvVIryYml743FpNlPGxczoLJQJdCUcLk2H+BRLfYx0a9\nF7UgoqM2UsTaECbLV7VE6NsC2KWxMiZ7NSKGMLlp0lrNgsl2HtIPrw3XnfCEMTlGH0Hv7xImWwfE\nECaXMFy9NuRdCZOlToslLWbB5DnJvBdJNI4cYzQeudk61yMzfjk8PtaCCKGr+xDJkdzr7RVRtORC\nZ5hz/QVqb0zGpPGAZ9Kf/pEIA/F+J6M1Rh1we3FsbXpffQ+oVkYcy4BeQ8JeeY9AiOtg2xZD3upI\nvHbw2+N+M4KFRZ4XnWiSRdIA6Xl4qiuvVaOjVSJ55J0OYokGM+4Y7WP6UqkwY6Rn3FC7iiwQwm2i\nnkE+jn6f0HtuOkh2P2GyfC+EuOhPDIoSqiy1SVK/2jhe/1nNcXnlsmYIDcBXQFgJlTDR+JkJofWq\nl7Mxy/nMNrKBozrYO833sxIoShSHYTfGs+fNiYkTuV6UQKl7gb5wHBsAErrLxINI9BaSx6ltq6gg\nTaDrhMSxGy8qtzeqgwp3lWtsCLANxS55aDl8m8fPxESMmgERD8ijCdhLmfrLCRq+jsOuVR8R73SO\nNxM7PB8u9JY8tBqwSnnNdm+y0cP7yZ7iwCJ70BqJTPp5z8Q+tzSOhvaGvt4aM02j69m0zm9DVc9B\nem+TUhQB8NAwGdBRTruKyfbEoRImy3ht3Q4Pk+O8+69o3UeQeJgc254Bk+XeyaSdiskyhpSOgFXB\nZK8WRgmTrWPXRuR4mKwiUQYwWdZ1FkzuPtQEtofJXZtSYNSmkea/xeRYBFDGZBuh4+kF9rnE004K\nz4TXWr+fY3IIlTpSna9nTPZ+d+aYvBeKjbbgKAM2+oBoTCcJriEWSYveYEupGORNl/4klcCJxEhR\nHVSrofd8d9eQsSqvbd0OEwXinR7BRru6RwNW+n8sgFlF4iemRrDhakkDazTL7wKRKd4pMmpdrU1r\niBcvpYYjAaRfITwUCnBEDqCfF89P7itEYKiTVxhHDDGgUip0eFtcq0hixAKfiQywtS5SpFGtI21E\n4r7J9wjP0Y3CINKgbRp92kkwbZZIj0anJql0maXlPG1H9uh4OR3H68gcl1cua4rQsEZUMsS0wgxA\nKc3ZZ6GK93iVxjkPNrZFSplVmtjQLUVgiNIe76lSlfmYGiFttC2C5L5Wem7sGRLjVtZGKuZz6gCL\nV1Ss5G23ijQrSgujWhXZTKKN25KwAS2Gts4RJsCN3lnEeVolUwwAWzzP5jTHuZHn2KZ3qGfbaoVY\n5scnubAyzZErNYWMe2spRhbPgRV865XmGiaeR9SSQnKve+oB9WFrAlhCjIkyj4iSHO10/CDc8UUZ\nrSnImcuM4mGy4NVKMFnvJR+To9d+ZkzOyQUXk0O6nvF0CJPj/KtqEJNFPA+6ek3RE0wM2bkl4zzE\nv6uFyUz6pLbKmCxzlWcur0uYzPOJ/zeYLL9N9pjTEiZLG50tNozJMkcmoz1M5mtVVIaDyek6TSZ7\nmBz36gyY3I07KOz3MFnmECMv55g8F2u8A4m8ECyInnjzftMm46wnN9RpGND1FgComhDqfRUdUfnX\nyOfUdzLq22QEj0ZAqFJNhb59t5BmT9zwKSXRsO0N2Qp1wYOOeL2KnvCiH0xqhRjJ1ahGO57oIpv9\nesbxF4xZ+UwVn2watCNKY7BfW/Mdt4UrmbQopoYMRdA2fVQNk13UvnpGakw5wZHqljCxpqN7IrGB\nBlkNGBWNo8cYP+9fc4SOzFtFUti9ytFCgC4k24siywKlrBiiS0gbXjd18snAb/Icl1cua2bFspxg\nJIONvXjyPpB7nrgta8jXdSh4QxB1OauAAYjeKK4qz/eyF0+FH/eKL3u5Qq2VcL7HhpuK2NxhKWQG\nMvrZsxcVM85Jd5TmbA2cdbX/Z7EGjhdNAABt261hXbhXXtuQ8jyKrFUKqY2ciOtg8rJtDrk37zgu\nQy5ln9kitWZt7DP1vHm2ffEIl9aRDbesjSathdTTkLHaI465Ro38vFslnBVo9qKWivG6oepDivVc\n1pTY2giAxmQA0aBlsmAIk3kvDWGyV+RwlzGZvOLdHm+LmAxIjRzpPI2xhMkWN4cwmfsZwmT+fLUw\n2aYJpUizHJNthKOdu8XkrK8CJteUylGaN6Ax2f1sAJPtM9hVTGbSbQiT5Z5pmNy1GVTKyxAmy/U8\nVrk29o05Ju/NktVGsJ+xp5oMZYRgjFQyTul7V41q7aG2kQlyv0iodNvcH98vXqpCekAkFnpPfSRf\nxpPO4KToEfpFoHFoI5nfU2MxJEy1uKAJD7V2A2REbFsII+6n1df1n7tFOM06qLbMGnonk3DEA4sX\ntaDab/K5RvKB14PWa6bTO5jgYPKhFO0SUq0Md7xMzPGpMTCRL5ZIAlRaTEqV4nnTHlPrMAJHEdkT\nZNA0PQki/VB6jhWKjvHXai4rkTVDaFjPDnslmOzor1aKwYQ8PJ0iQV4zqsvQNDrP194vfS2M6vgZ\nn3cvxcAA7SXzqownDxHi2K0nisORJ44SlVJQUh9SfMyT2Le0Xad+RFiBZE+ZjYSxhJBHfKT/a0Wa\njZKq6sae3k91PVgxTAa3nza0sBDiWCXCgOfI6+Yp6Z6RZYUr2sc5s0cQ+bNmI8saGSklJnnhlpcn\n2drKuG10UvweQO9DGYcX6t+0eXE4NSejzPOzkAKNchSimjPSd0DG6UX/FH+w57LmxBaGHcJkJjym\nYnLUM3cdk+211oCUMci1IiFUg5gs3xP5bsq+9zB5PGkwKpTc31VM1gVGNb54xAeAbH0sJuv7hzE5\nEQR6DYcwWfqZBZO9ebBYTOa1jPN2njWnrrAwJnOkhSVvdhWTOWppFkyW74KaA2GyRKPI2pcw2TuF\naI7Je48oo5YMdjYK3WMr5f2mBQIZyHEfpoKR4uGHvZ9SUdqm6cgANg65wLBNW+FrxJC0hl7oCmpW\nUqLARn40rTLUozEvEROLC/31QoQUFtFGh4iHnnGmN3JVWkT/ORcYLRIG3BfQFZo0URuSAtGCCYQW\nbTNRJ3NoAomeuSU8KPoh7gmOSGgoEsaSPR75480pPjeHeOD1k9dNW9wjai1Meo0l59oxzZHXJD7H\nnqRhUkae66juInWYHGEig4VJqWAKk/L+DQEIrdnniQThKI24L43McXnlsmYIDau0aGUhRTEAnEbS\nfep6+vqQWjSIHr34Wa/QSKE3uVaUmPGkQWgr1ZcoN7Z+RRy/UeSSly8pgGn8vfJEERa2JoK0xW3H\nsZvQWlk/ViDlGhtGa/voPHF6TtMUTRtarkOAW6XktS0w6YkYLuyaKakFDySHHst7MbycyAY2sLiI\nnCjRHK5c8iTb6CA2RuI4yVCQOfOayWec6iLjlZBivrZUUNR6ZWWdXa8gRVLYZ2aNPWsQltpN7aTi\neWxwFj2nexikd+zYgQ9+8IP4xje+gQ0bNuCss87CySefnF33gx/8AJdddhm++93vYseOHbjiiivU\n56985SuVgbO0tITTTjsNr371qwEAX/rSl3DVVVfhnnvuwQEHHICzzjoLxx9/PADgf//3f/Gxj30M\nX//61wEAp512Gl7+8pfvrinvVuH952EyoOs4DGEyt7PUTIqYDKRUNTE8S5gs93Z96j1ZwmQhUaZh\nssg0TLbvK5JkFzG5FAVmhYkIZfAXMBnoSdgZMdmOrRONybwOPCcPk9U61LNhsvRv+7GYLOSUnYvF\nZB4jExdDmMx/ZQ4eJvOzmAWTI3lhiBArUt8qOXT2HkwGgDvuuAMf+9jHcOONN2I0GuHUU0/FOeec\nAwD4u7/7O3zhC1/Arbfeimc+85l4/etf77bxyU9+EldddRV+93d/F0cffTQA4F3vehduuummeM14\nPMbmzZvxx3/8x6s824dB+HnGkP4+4qB/5RIbSGkSmXd4VANLxnveG9dCgEgNihjiP54Aof+Oj8eI\nKQv9vTQsGntvDDbkFYdOk9D1MrSnXaQ141TzaCb6/VD50SlyvVxDn2d9ROO80oY/j8tGdUQjtzUG\nf4jrlSZUiGbIIhac90IqQKpJjDxiJKs3YtuM60BpGGZe2ckrXmSKvFZrYz+n6AVLZkCTYRWE9ICe\nv0f6AInMUOOkZ2EjXXhOTO547fRjj9IfaSwFTXMSpCB7EJdXS08eaue2227DJZdcgjvuuAMA8OQn\nPxnnnnsuDj74YADA3/7t32Lbtm348Y9/jP322w+nnXYazjjjjMFxrxlCw4b7AkCgH3yOYrAiHiRW\nlrhyOqA9LgDX6mgQUCnlIqZ/uP2kTSj9DXlfvCJe3TUOQW0UFPG8iPdf7hWlxmvXUyy7N3QfrMhJ\n2LA93cSLmpG2ozLKynlf6E7WcmFUo21bPLg0xvK46TxK8SSVNs5NzYGxqkr57N7+kLWQaAG5xqvO\nL+vhP6vhPuSzWaI82CPKp8wsL3c/qJz6AYDIAus5NWsOXTeA52U9z9ZI6sailXE+CpnXJvReaK8w\noPKiF4xVXUn84ZdLL70UCwsLuPTSS/G9730P73nPe3DooYdGEBUZjUY46aSTcPrpp+Oiiy7K2rns\nssvi/3fu3InXve51OOmkkwAA99xzDy655BKcf/75OOaYY/Bv//ZvuPjii/GBD3wAGzZswF/8xV9g\neXkZH/jAB3Dvvffine98JzZt2oTnPOc5u3Xuu0M8Uo0xWaI2PMPXYrK8z/vTw+QJFSVeCSazcTiE\nyUCeNtJdozGZMXAaJntE4RAmN02L0Fa0nj4ms0E/hMnxPfu5g8kAekyeYNK0AIYxmfsZ1aGIyXzt\nNExmDPEjTXJSvUS4PFRMlpRQee/hxuROnzXHiZu9Eom9/rdQfvdlX+xNmDwej/EHf/AHeN7znoc3\nv/nNCCHg9ttvj5/vv//+eOlLX4qvf/3rWFpacvvavn07rrvuOmzcuFF9V972trep637/938fT33q\nU1dxlg+jWKM2BEDODB73nysDuhOJZsg8w8aIlyKXsQClkAxyr4r44BSXRArE+g9sHEaPuCYz1Pji\nHIU8aFMBUhhD3IkcifOXdbIGaaPnFE/ViOvaRtJFSJbYdkhEERNHpfoKaX3pOfRkTkp/WIjr2u58\nsDvmczJJJ2Rw/zwH/rGyz5QjKPjaELK0FV5bRTrkxomO6mCigsWswVDai0qf4poVQowpQiDtKVuQ\nVo1HmhxR2hKQkxjOnheiLzuJRsZAe1Ler0JAK+Pq94WKgPG58TTGPSSrpScPtbP//vvjzW9+MzZt\n2gSgI6Tf//73q3bOO+88bNmyBdu3b8cf/uEf4sADD4x6tidrLqZlMmmwvDzpCnIZLzIX4IpKSEiV\n5BcXauVxEq+FKHJybObCQh2VCBtWat/3Iic4hHfSF+Zi749VKr3oB++1VYI8pVv6Yi+g7WtUh/gv\n3t8mhdQz2EXp5blKW56oqIA2FVBrmhaLCzUeu986/J+fezxOO+lwHHbI/njUugX1bOzatQXlViQp\n92kdPE+azE2u7dKEGqpnkuY+oTFzNIeMQ+5no4XX2VtHmYc9Mndhoc7ek1Bjvpc9id68+F5AGzAy\nRhlbyv3On7klM6wizekkPCb1DJx8bVGGdse/abJz50589atfxZlnnol99tkHRxxxBI477jhcc801\n2bWbN2/GqaeemgG4J9dddx0e85jH4IgjjgAA3H333Xj0ox+NY445BgDwC7/wC9hnn30iE3399dfj\njDPOwOLiIjZt2oTnPve5+PznPz+1n0ea8Pe0hMn8PZiKybR39gQmM/k6DZMt/sr70zDZI3AsJnOx\nSGA6JodQTcVk/j5Pw+RfesZh+MWnbMZj91uHOqweJsv99h4Pk/nvNEyWNmbBZLse0q+sJ19Tq6Ko\niOtXwmQ71yFMtmPxMLniPVlVbjvqdUjRfkVMdh7VWsHkL3zhC9h///3xghe8AIuLixiNRtiyZUv8\n/IQTTsDxxx+P9evXF/vbunUrzj77bNQDJwjceeeduPHGG/HsZz976vgfcRJD9Mdol5Y7A7iPnIhh\n72QIxlM7IhHRJmMP0IZe6MPje2NSiiuCn7fxiNtaDWykqmNNx2MVNZLaSSRIZkT2BiQTGB4Zo/Yi\nGanxVAuPAOK5ynrwNVS3g+dVjUZxHdRc+HoTYZKiAlpdByIEVPuuw+Jhh+JRz38O1j39GISNjwFq\nE+XStHE+8dQQSziQtELy0Npn98SxtokU4vt47uOxak+10T9j9bk8d/u8nAgb9ez6/ZYdZxoJhKDW\nmE+nUWPiucX3zfOVOfb9q+KfcU8SvpdI80j0tPE58fyG7l0LmDykJ09r51GPehQe97jH9b9ZnUNj\n+/bt8f4zzjgDhx56KEII2Lx5M4477jgVRefJmonQkDBK8dZ1ipX28LHnhiX+4FMVdq/ImyfWwyKF\nxqRfz9vP93I6jNSh4CP7pDioKOKSdmDDpdmQHhJVrDHz+vSKX9tivOyHLFvPpZ0PK5kcMTE0TrlP\nZKmvE/HUwx+Hl5//W7jqwkvw/1/zbdx1z/8qI8Qqk5ZM8LygIch96X2OLLGF4jjixSqqVmzRO8Cv\n7ZLa1m3YNrkiPRuCk0mD5cbmxyfh8GwO1wYA64WzCjhAyrETBST9DZ3GwKSGHFVpn38pvLla8PMF\nHw750Y9+hLqucdBBB8X3Dj30UNxwww271O62bduU8nvYYYfhCU94Aq6//noce+yx+Nd//VcsLCzg\niU98YrzGGts/+MEPdmkMe0LS9xEoYTKga22IeJgMwCViS/3G17sBkzl6wsNka0gPibTB9SJWC5Pl\nd3EaJjeNrtMwhMlvOvHxuPZ/F/De/+9LeHDpJ1imXGKvLg6vgS1qyZjMa6vS94qYTL+/8Ilcm14q\n6+2NLbWfxjYLJksby5NJEZM5Wsh+7mGnvaaEybJeo4V05PBQu12bYa/E5G9/+9vYtGkT3v3ud+OW\nW27Bli1bcO655ypSY0i+/OUvY2FhAccee+zgdddccw2OPPJIHHjggSubzCNBhEgAunSQpu1JjbE6\nNcErIBnFvj92Iib6fWb75c+lj/i3ZExFoy8ZodGD7dTUyAxMGTOTKQMGPbfBfeUpOBJx0OakR78m\nKgVGZNSfnBI99hQRweslhAq9F8mFfk4VgPpnNuGe89+ELd/5Fu551wew/GAfqSFzEbKkaVI3vAZy\nHK01okfd2sopMDqtonu+1bougqXq25S/Ug8im3sw6S203mrudh2I7HCLZHqkU9OgXZokgsMj0Xje\nirCgaApFbDiRF+aknLgXRwv92Fvdrow35ZujGlFhW257IApjT+HyaunJs7bzq7/6q3jwwQfRNA1+\n+Zd/2W2rbVvceOONOO200wb7XDOEBqeGWI9N21a5EmK9F5WveEbFyiiBIp63i4u32bBnrzr6NKJF\nlKSJUThLYr1LHPbcST5X6znkEO2mD01dblosLNRqTHxt8ozqsViiQcYlIrVIksLX4if3PoBt//pf\nuOuCC3HHLXdiMmmwuFBjqU+9KM3fC+1Nym+qn2HvV+tklNVRHYAaUWGU9to278t7xhmpYMgcqzg3\nTYuFhY50AdI+kJB0b42tcJ41t8+G3CzGoY3SsR5BGwYv85f7RNEe91EuIkUjbGAsu1t27tyJfffd\nV723bt067Ny58yG3edddd+HGG29U+dohBDzrWc/C+9//fiwvL2M0GuFNb3oTFhcXAQDHHHMMPv3p\nT+M3f/M38d///d/4/Oc/XwyRfqSL7AkPk2WfMKlmMXpXMNkruDiEyUJeADOQ3/149yQmA8DyZFLE\n5C5i1TGgC5ic1mAYk3/wo3vRtC123L+EuidMSvPnmiDqfQeTbWHkWTGZ2/Mw2ZNZMJllGibb+dv7\n7X4rYfJQBIdIhsn8W2fIb+5H0oX2Vky+5557cMMNN+Ctb30rjj76aFx99dW46KKLcPHFF2M05YjD\nBx54AJdffjne/va3Tx3Ttm3b8LKXvWxlE3kkiRiFxoOsimQ2LREAVW5oWWdMNHZNlIGId5JDqBBr\nLVA7TG7YFJFUPNExEuUeNvpt1INZh2wO5KGvrJEPuKSLNdDbpYkq5KjIIU4jUOsvHv+OCOB+VU0Q\nNrQBNP+zA0v/cRM2/v678T9Ng+a+HV1/LslEz7NU0DJGktRprCbSJK6BEDNy76hGD8rxvUruoTom\nLsljr5P5OmNzhVOD+P/8e0InutjUp4y0CgHuXvZIN+5XXiuixO5XSreSfTJaQNUTWG1PdrWgAree\n7CFcXi09edZ2/vzP/xwPPvggtm3bViSRr7rqKgDAc57znME+1wyhAWilJimDyQsfRrUqthXvM94T\noFMEpLCaLfaormt1ZXt5z1OmkiKd+pA22TvVpTggKqpW+bFSUp48pdyPOknKuXi98vvkO5rfn59m\nkA0xG5utCM8ezfGkwf07l/Gft96D2+64D1VVYXk8UYq4DbEdOgYvzpIMF1uVXt73nuc4evnKBseI\nFHueJ4/D89qWrlFRI6LsGs8wt2XXnb2+nmgSpVL7vWEDj1KPmr44oHiKefxZ+D6RH7I+9p7KMRhb\n573VlCuvvDL+/6ijjsJRRx0VX69btw4PPPCAuv7+++/HunXrHnJ/4s2TPEAA+MY3voGPf/zj+L3f\n+z08+clPxn/+53/iwgsvxAUXXIBDDz0U5557LrZu3Yo3vOEN2G+//fDMZz4T11577UMew54SKaQ7\niMkhx0651/sO6TD5vp9dwGQROWljGiazETwLJnM0yDRMjutG1wxjsjFmDSbra4pTV99LxsgSJv/X\n7f+NhVGNyaRRp4h4mCz381hLmNzNN/9tWA1MZgJDR4f4mGyjDbux7RomyzXTMLlp0gkxQ5hcVRXQ\np3F5tVdsP7Z2lovJDvyuFUxeXFzEkUceGVP5zjjjDHzqU5/C7bffPjVK46qrrsIpp5yiFGbvOd10\n00249957ceKJJ842uUeaiGcdOmxf6l2040lOGrBxmHnyGxUmnzz4Nn2iN8Rtuw5hwOkZKhWBw/ll\n/OOJSjHQBinPO407GrYxaiHk9ziRBZFkkfv7e/P6FFXZ+Ob7vM8NOWPJHZXO0LTA8hjjH96Byd3/\njWpxoVsvibiQ6/iZcftJqVfjzdJ/TPRM2zR9RMIkjaNpuvojztqp2hmjFNFhiQ7v3rQu3f2xGGrf\nryowG+eV9lVGhjnt/z/23j7m0qo6H77ufc4zjDqZKgLqCKJ0+v5A+uHbKlICiFrS1mojitQPGtsw\nmrQN2trGt5pai21Tq/6YmtLSBBM1Wl8haqI/bG0bLSM1saYWXz8ixgbQoEUFVDpB5nnO2fv9477X\n3tdae+37nId5hplnOCuBec45972/7n2usz6utXbLwZL7HBxJ4uRRtTLMPMqeTZUTg9cvi5ymIsL1\nTMZ+tHFkcfmh0JM3084JJ5yAiy++GPv27cP+/fuxe/fu/NknPvEJ3HzzzbjqqqsWOq63jUNDlB2m\nolqFzypGKjd7Xlf59iIkNrrvVavnI998B0IRjgrK65axWKJEI+sQOjIk/Ar3XBCMlW2Vd26o1YBW\nPmWNPQo0j9/Ozx+bNkiEQjybR8x+tK7WWNg27ty7UpDOi7iVcWHc2E/9MY8QpdkY67wWqn+at94D\nyO+zIcb3eAUL2eHAc7JGoI1+wyl9o09ksMZAGVCvTHfVHFnZ5+ffkhhTP4zYr5+cGOQdo6juazyT\nrZLLLrus+dkTnvAEzOdz3HXXXZkG941vfAOnnXbag+7v05/+NC655BL13h133IGzzjoLZ5xxBoA+\nBWXv3r340pe+hCc/+cnYtWsXXvOa1+TrP/CBD+AnfuInHvQYjraMYnLsT11ahMn83bbiYbJtq4XJ\nPC6PqSGvPUaXtNXCZMbXMUy2LBE7psPBZDbYuT+LyXXqWRuTN2YxFynu+25jMssymMxrq+41mCzv\nieOB14KF25d5cNqGh8l5BboOMHvOYjL3swiTrZOkhckxJQTjoPAweU41aeT+Rc8hpnTcYvLpp5+O\nr33ta/l16/fdky9/+cu455578M///M8AgPvuuw/79+/HC1/4QlU5/6abbsIzn/lMnHDCCUu3fUwK\npwdYYzdGIFBRRGZlRDnOUozZRvuZcRD0/dXf1hmgx1UxNbIxS9HvQdQRqdMGJjMLQ/onp4aMw9b1\nqNbKslcaDgFlzItw6gv3x+/Za4HKIO+dUJO+COiPHlBpJgiesyCp9VKfOfPrKD3ENfjz+Ib9kvsI\nutBq477i/CpOtjw340QR6Z0P5phaKT5rjVl2zOV/Q7Xe7CSp+hRWTIxIkUYo+i3YYTNRp8/kNVhg\nZPfXD/1MJ+Xv1trl244cLj8UevJm24kx4tChQ7j33nuzQ+NTn/oUPvrRj+Kqq67CiSeeuLDPxVrK\nMSYc5RClgT8DSMkhpVReSwQphC4X/fIUpdExpNo5ImIL4Un/XICG2pI1AAAgAElEQVSN62WwstY0\n5INu01Oq+NquK4qVLVQm4+E2bPt2PlMT+WIRem1zPMM62cJ2ISvqyBEojpbyezJn7wveWgOeq517\n6HT+uDjBZO28Ipk8Zhux5Mry/K8owaHrCxxyET87JmtceP+yAif7d206ycUThXJsn4mNOlpFMBtH\nRpne2JgrB08VWY0lL98WhGwJF2Hd6v8Wyc6dO3HOOefg+uuvx6FDh3Drrbfi85//PC688EL3+vX1\ndcyGH62NjQ1sbGyoz7/2ta/h3nvvraJ5e/fuxa233oo77rgDAHD77bfj1ltvzTU0vvOd7+B//ud/\nEGPELbfcgk9+8pN40YtetHD8x6qMYXKOwBuDsMLktDwmi7Fmawh4mJy/g853zsNkoBSeXITJfPrF\nGCZrp8RiTFZj30JMzu8twOSuK3g7j4sx2WegoHrP1oXicVlMlvHJfJbBZMsYAxqYHMr8F2FyNbcG\nJnNh0bW1cUwW9gX3O4bJ7BxcBpOZ5XI8YfIFF1yAr3/96/jSl76EGCM+/vGPY/fu3XjiE584rE/E\n+vo6YoyIMWJjYwNxMNT++I//GFdffTXe/va3421vexse85jH4NWvfrXKyV5fX8dnP/vZhbTmbSFE\ne8/Ra9lrkQwqZlSI5MKS/TV9IcZp23iVaHV1Ygg7OIgtIP26xiXR8kHOg4Gm3xciNUepyt8hUJFT\nmpfjRFAshZhU8UjNyEjVvdxedXSrzMNIVUPCXiuvTbFRAH0R0K4r6zMfUhaYKcDPzDhgqjWwThRv\nni1GheAZr7W6j565YdzIvVVhWF7T6QTdwGLouDitjMWwTfp+jGNuGIcq1LpjrRR53bHWOzqEjSH9\net8Tu2Z5bxUnUlrf0Cey2P0h3zeg1FaR+YzIdsBkoK0nL2rni1/8Iu644w7EGHH//ffjve99L3bt\n2pULjN5888344Ac/iD/6oz/CKaecsnDcwDZiaNgoQ0oJoQuDS6ZsoByhkEghKdBeXq3HPOC8YJv3\nyyktXlTQRhltugaPVWolWIXKRuptwTovQglQ7m3oo2rSh6xXZ5QjO28el1pPWj/5DlomQ/7cGBW5\nSFzSa5DkOaGcNGONmJajxFM+vcgngBz54nt5DRUtmaKsLZHnoZ5zkH7LW3k8k9oJFbqg8ptlD+T5\nmOid7MFesS/rzesqFPGyNmWf8dgKLpc0FEwC4lyfCjMZjma1zhBmmtg9xWPmf73Pjpbs27cP1157\nLfbt24fdu3fjVa96FU499VTcfffdeN3rXof9+/fjsY99LL773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qL4xbo6vo3YqYJo5ISQ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KGk4VP8WD2DYt51CE7oPWRj8/qD2Qjf91s37ybwi9M0jmSuySyinnKcqD2JoYlZPMm/fQZ4pz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.282115929406\n", + "Volume fraction of white phase\n", + "0.717884037238\n" + ] + }, + { + "data": { + "image/png": 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sgfF4jA0bNsRwumuvvTZLByH6yU9+go997GN4+9vfLtpM089NN92EE044AUcc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.202374743859\n", + "Volume fraction of white phase\n", + "0.797625222784\n" + ] + }, + { + "data": { + "image/png": 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AmGA5DmUuuPNzWWDk1DDkEKbjNjrPj9FknsGWwaiNvj8vm1FCsvExyayMKCtG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\npCN7XB6VEtpEPvej2JWX54upcWgMnI/cEP7liiZTLIdy28nkeGahoYLT0wo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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.199789397016\n", + "Volume fraction of white phase\n", + "0.800210569628\n" + ] + }, + { + "data": { + "image/png": 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VV+JVr3pV4uGrGXf23HNPPPe5z8VFF12ED37wgxvFw9aMyxT2HnCFng1TXqPA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.174321098153\n", + "Volume fraction of white phase\n", + "0.82567886849\n" + ] + } + ], + "source": [ + "from pymks.tools import draw_correlations\n", + "\n", + "\n", + "for x in X_corr:\n", + " draw_correlations(x, correlations=[(1, 1), (2, 2), (1, 2)])\n", + " x_center = (X_corr.shape[1] + 1) / 2\n", + " y_center = (X_corr.shape[2] + 1) / 2\n", + " print('Volume fraction of black phase')\n", + " print(x[x_center, y_center, 0])\n", + " print('Volume fraction of white phase')\n", + " print(x[x_center, y_center, 1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "The above plots indicate the states on which the 2-point statistics have been calculated (black-black, white-white, black-white). In this case, the black phase is labelled as 1 and white phase as 2. The grain pattern in the original images seem random and the grain shapes are different. From the correlation plots, we cannot tell any microstructure features or patterns that clearly stand out. \n", + "\n", + "We can focus on the volume fractions for each image. For images at highest magnification (500x) the volume fraction of the black phase is minimum at 17.4%, and at lowest magnification (50x), it is 39.5%. \n", + "\n", + "The difference in volume fraction is because the lower magnification images retained a higher amount of grain boundary information in the image after thresholding. Grain boundary information is not desirable since it is not part of a particular phase. A grain boundary can separate two phases that appear white, and the statistics treat the grain boundary as a black phase, which will not be accurate.\n", + "\n", + "Since we see less grain boundaries in the higher magnification images (500x), these images are more reliable for computing 2-point statistics. The images with 500x show a volume fraction of black phase of 20.0% and 17.4%. The image of 200x shows a volume fraction of 20.2%, which is in good agreement with the statistics of 500x images compared to 50x and 100x, 39.5% and 28.2%, respectively. \n", + "\n", + "The volume fractions vary slightly in 200x and 500x images, which is expected since the images were taken at different locations. However, the etched images at higher magnification are more reliable for 2-statistics since they do include as much grain boundary information as the lower magnification images." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2-Point Statistics on Cropped Images\n", + "Now we are going to crop the image to the first 300 pixels in horizontal and vertical directions and repeat the process of computing the 2-point statistics. Our new images are going to be 300x300 pixels." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Shape of X\n", + "(5, 250, 250)\n" + ] + }, + { + "data": { + "image/png": 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R4MqVK2QyGdbX11lcXFSCZ9OmTcDN4L9er9PR0UEul9OALxwOc/LkSaLRqBJcAPv37+dH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BICb3k53YyLVVg8vFksFqliIsiiHkzpd15TUyM9Svx+PwwGAxoaGtDf34+2tjbs2bMHhYWFQuB4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\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kgv/85z9Hc3Mzdu3aherqaszNzUmToEAggMXFRQG/5+bmUF9fjz179oihGhgYwODgIGZn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\n1NXV4ZVXXsH09DRef/11qULJy8tDPB5HY2MjnE4ngHTZFdnYwWAQr7/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25SCwODk5KSSm\nP+bg8xoMBlRXV6O4uDijnw2JI0zYMTmvgrh/rOdiDMH7WJV1A9L3CyUQKQ1os9mQSCTg8Xhkjfx+\nf8YdTwIgPysYDGYA1alUuo8T5YtnZ2cRCoXEp2hqaoLdbpfE/o0cvGM1Go3IVBuNRrnP/pCGuD6f\nD319fVi7du07gPiPIllks9ky5HwWFhYwPDwsGJI63m9eVPKKXq9HJBIRXwoANm/eLIlam80Gt9v9\njsbEHCqhkbEI7SR/TglJElDfDcOgPxAIBFBWVobm5maMj49j//79WLVqFe699175XZPJhMnJSXl2\nJurot/y/HlNTUyJ9GQgE0Nra+o4G1bSdS5NHS8k3f8i4mQD46IMYOIfNZpM4LRqNyp6rrKyERqPB\nihUr8MMf/vB9P/MDEwCTk5OYnZ3Fs88+CyANzFdXV6O5uRlzc3Po6upCW1sbampqcPjwYQDpi6C6\nulqCUwKDDz74IEKhEMbHx3H69Gls3boVK1asgEajgcvlwujoqLzQpz/9aZhMJvT09AiDwOfzScDs\ndrvFSVy/fj0A4PTp0+jt7UV+fj4qKipEP9vhcCCRSOCWW25BMBjEmTNnJHgrKCjA2rVrYbPZYLFY\n0N/fj5dffhmrV6/G7bffLo7q3r17ZfI7OjqEPcvsIfUC2TTo1KlTKCgoEH1gAOJoNzc348///M/x\nox/9CF1dXXLAZmdn8eCDD6KkpCQj43z16lUMDQ0hPz8fmzdvlmCeAW55eTkGBgYk27x582a88MIL\nSKVSWLt2LbZv346pqSnJ3tHBUgFAghsXL15Ef38/nE6nOEd8v7y80mOL9gAAIABJREFUvIzkCh0U\n6uyRDZiVlYWSkhJ4vV4JLletWgUAOHjwIBKJBPr7+2EymfDGG29geHgY7e3t2L9/P3JyclBdXY3X\nXntNvmdsbAwrV65ETk4Odu3aJb0C5ubmcOnSJej1enHA+D5btmzB3NwcfvzjHyM7Oxtmsxmf/vSn\npaJkampKdD/b29tlbz/99NN444034Pf7YTQahXVNZsn8/DzWrl2bkT2njmAgEIBer8eOHTtw7Ngx\n7NmzR+YkkUgImDkxMSFNd8fGxjA3N4fh4WGsWbMGubm5AuCtW7cOPT09ANKVNzU1NXjkkUfQ1dWF\ngYEBFBYWwm63o7S0FF6vV8AmOsoMJCsqKoS5PzIygtHRURw4cABA2gG4cuWKGP+hoSFotVoYDAbc\neuutWLduHWw2mzjzgUAABw8eRG5urjCMnn/+eaxevRqf/exnJUHwcUdHRwd27twpwG44HJbgAEiz\nz3mRRiKRDO1RJgjIIuMa5efnIx6PY82aNWhtbcW3v/1tAOkAKRaL4fOf/3yGxvro6Kg0keTn8jvZ\nWwFIOxjUeI3FYqivrxegSm1m7fV6pWqKzj4dZpfLBYPBgOnpaZSUlIjtPH36tGik1tXVYWpqSoDx\n/fv3IxKJ4O677wawyMCcnp5GMpluEMqGaQywCGjwdwi4EExghQD1RWnPCJwwYAQgSRNV25rJufLy\nckSjUdFI51D1q/n3WVnpBlwPPvggjh07Bp1Oh7/5m78R8A5YdCapI83AKTs7G+FwWFg8DLKBNKBy\n6tQprF+/Xppa/+53v8MjjzyC8+fPo6mpCRcvXkRTU5P0NSEQNDU1BafTidnZWWnqdPr0aTQ1NaGi\nogLr1q2TprMNDQ2IxWI4fPiwNEhTm+QNDw/D7XbjySeflHNP1ggZtUxgcm/5fD4UFBRk9E8JBAK4\ncOGCVGuQGXb27FkkEgls375dAOYnn3wSQDoRRmCZrFzq/w4NDeH8+fMSPDz44IMA0gmA+++/XyrS\nJiYm0NvbK+AQ+6TY7XaxNa+//jruvvtuAUvZY4FNPmkr8vLyBDBQ9cPJoNJoNBLIcw3r6+tlz8Vi\nMelJo9PpJAFG4PVGDOp2Wq1Wac7M52cTTADS2JWBPMFKrg2D4KWsT9pSAmAzMzMIh8OS0KVfoyZd\nCPLyv0UiEUlUETxQKzU5HyqzlCAcAWcChksb0HJtCMCRocrksMp+57nnzxg8z8/Py9yp7EZgsfEr\nGdvsn2K1WoUlxe/nHHCOyQRbWpFIIExlzDc2Nop+vfpOtGO5ubmoqamB2WyWBNjY2BiGhoakfwif\nnTq5ZM6qwasK0pvNZmGEqr6S2nSZfwOkE5tsQplMJgXI5b5S97WaLGE1FvcrsMiQ5mfxvQmM8hwF\nAgFZL84XE6t8D/bk4Hrn5uYiGo2K38lAVgV5mHxlYoas8FgsBq/Xi+HhYYyOjiIajcr31NbWoqqq\nKgNY5yDAplaSqHuNIAz3LZP8BoNB+r2od7dqk0n2UaupuD/UZAPvRAI5ajKLZ+7d2HUfZeTl5cHj\n8YgGusqk5XcReOOz5OXlSSKHfggAaZDM5yN4UlRUlLG3ampqMqp5ent7pQpZrSRkgiWVSjcCzc7O\nFkBt7dq1aGpqyuhloQI4BLfVRCi/j3aI/537gHubfZwASHJcBRi5z8ngJAmA70TwMpFIQKvVwmg0\nIisrS+ym2+2GXq/PaIBdV1eHtrY27NixQ2IeFdQicYeJSCCdMFObaKoVCUw+EhhX9xafnQQTAEKc\nYHWOqs1PwI/+JfcvQWICmlarFdXV1QAgfUjIWi0oKJAkfTKZblZeWVmJhoYGiYVIyhobG8Po6CgS\niYRU3HFP2mw2afrKPcckAO/DWCwm68b3VP1FJos8Ho+sodFoxMzMDCoqKtDS0oJLly5hampKfFUm\nPlhNTo16VnYRCNbpdJIIYq9B2jImW7imfK7i4mLxwzo6OlBYWIitW7dKHE+7zjuL1bwkQPG71EpB\ndW15/0ej0Yz+c4yxeF7VhDDPRjwex9TUlDyfRqMRctHHHWqykUPt/8J1SSaT8Hq9GUAmz6Ma93F+\naZdv5GBsQtuiPgP/We2D5nA4EI1GUVJSApfLJVVBNpsNwWBQ7lt1jXQ6HTwej5B8aLvUytRkMinz\nbzKZkJ+fj+LiYtx6663YsGGDnMkbPbi/mJjzer0fudIgHA4L+QRYrNAGPloFh1qRwp5Ck5OTqKur\nQzAY/IP6A6rkP9W3BRZ7qjA245pyndReALTRag9BFYQnQYH7KZVKCZlWxRU471QeYMWAx+NBfn5+\nBimG55X78EZo8N/Iwd55TKKwpwFHLBYTJYcb2bvgZgLgxg32r/nGN76B7OxsfPvb3xZCIJCOTz+o\nOvUDPddnnnlGHAwA2L17N0pLSxGNRrFv3z7s3bsX27dvR0dHBzZv3oxAIIDLly9jcHBQQNwNGzag\nqqoKxcXFMBgMGB8fx/j4OID04e7s7BQWKeVM6urq4HK55LvcbjfKy8vR3NwMk8mEVatWwW6348yZ\nMyLLsXbtWkxOTqKvrw+Dg4Ow2+3CdF+7dm2G5AcBn6amJiwsLEiS4bnnnhPngZfM0aNHcfLkyYzS\nYDK2CfzfdtttiEQiwkosKytDbW0t5ufnMxyDcDiMuro6acyxfv16jI+Pi5NYWVkp5ZJAmsXNxMfq\n1atRWFgoYDIDYVY+0DE4fPgw9u7dC41Gg4GBAaxYsQJ+vx/19fXiKC0NfsLhMDo6OhAIBOD3+/Gl\nL30Jr732Go4dO4bvfve7eOqpp1BWVibgFJBmo1y+fFlAY7/fj/HxcZhMJgGReVFSnoiZeqfTibfe\neguNjY24//770dLSgsOHD+NXv/oVenp6UFVVldEI9fXXX8fWrVvl3xlYMuFgsVjwxBNPyLPF43H0\n9vZiz5490Gq1GBkZQTgcFrZ+Tk4OjEYj+vr6Mth/TCzNz8+L1NJnPvMZZGdnw+fz4ezZsyguLpaL\nn6Cyw+GQhElDQwNcLpc4yWfPns1g5J0/f17ATIvFgnPnzolDrtVqpVyZjF4g7ZQ8+uijcDgcGBwc\nlAoZBoYajUaCDAZPMzMzsFgsWFhYkLkaGBjAW2+9BY1Gg5UrV6KhoQF+vx8HDx4EkAYM6+vrhZFb\nXl6Oubk5jI+PC6tp586duOWWW2QOXnzxRfT09ODMmTNobW3F4OAg9uzZ80Gm5X1HQUGB7KWZmRlM\nTk5icHAQt956KwoKClBWVgaXy4VgMAiDwZDBQuFgVVFzc7MYS40m3eR1cnJSggM2R6KkTTKZlAbi\nPGMEAri/CIpPTEzA4XBAq9XiwIEDaG5uxujoqIAg+fn54oDYbDZUVlYKGBYIBMSBffvtt3HhwgX8\n8z//cwaTZXZ2VsoLyVIiEBWNRjE1NYWpqSkJ7Hk+3G43srKycODAAeTl5WHHjh1SncFgiM1eeZZZ\nccHARAWZKKugslAtFksGm2lmZgY///nPsXv3biSTSWmYvW7dugxWFcEEJlQIFCQSCbS2tsocUd4G\ngAB8XFc2P3O73ZiZmcH09DRcLpf8DwAefPBB3H///TCZTGhqasLk5CTGxsbw/e9/H1NTUxgZGcHn\nP/95TE1NSTULAPzqV79CWVkZvvnNbyKZTKK7uxs+nw+Tk5P4wQ9+gM2bN2NhYQFvvPEGgHTCxWg0\nIhQKSXDLxt9FRUUwGAwYHh6G0+kUm2a32yXQYOCu0Wjg8XiQm5uLhoYGXLlyJaPEPh6PC7uV4HdO\nTg5qa2uFUUd5BgKaakM0MmSYMH3kkUfw4osvIhaLIRqNYv/+/QAgjcwp1ZRIJKRSwmq1SvLN7/fL\nPi0rKxOAiDJ1lDRiU06PxwOTySQOpdVqFbt19uxZ3HXXXYjFYhgYGBCpoN7eXrS3t0vg8NOf/hSB\nQABf/epXhdFDsP3d2D8fZeTn54uUTDAYxNzcnNwFZGUCEEYwA3eeE84JAxe1YTnXiPObnZ2NWCwm\nMi5qNY1aYcRgmuBYIpFAeXm5VMaQ1cShBk1arVaYqATICGCqALMql0AAjzJ1PL98D7UhJ3+X4DgZ\nlmT0MujiyMrKwsTEBC5fvox4PC77rLKyEtXV1bBYLBngLrDI0iY4wrVWgzaCJ3xWjUaTAYyphAbe\ns3Nzc9J4EoDIQ7CRHr+D30lbR1AdgDAM6RcyCej3+yXJx2dcmqAh85CJUto4gnxq2bsKbhJE4r3A\nMnkmeFTmoRpIEwANBoNIJBIiCUI/g9+v1+tRV1cHt9st50xtNst35jpxHigdMzc3B6/Xi7m5Ofh8\nPgwPD2NsbEzsNqXD6urqkJubK2AzgSUmewhUq9URDPDJ5OfdzEQAnzUUCgnzkHuDwJyanOM7qQCQ\neoYIZPKskX3JPX2jgtqSkhKp7AmFQmLzuD+4liT+sHqU4L5aAeTxeOB0OpGTkwO73S73AeUsz5w5\ng3g8jtLSUkniBgIBAUH5Xrw/pqenEQwGZR1VWTP6SqwQVW2gmrTi3cOfM9nONed7sbqHyUf6XKrM\nCBMgwGLzaQa79F04X/wbgq6qZCYJSgAEAPnkJz+JyspKWK3WDJ9Dq9VKRWVeXl6GVAQrb1R7SKCJ\nd5zBYMioaOAdrp5dPjP9PNp9vkdxcbGw2WOxmNhoNTlMH5iA+a5du3Dx4kUEg0GpeJqbm5Nzyube\ns7OzAuDp9Xo0NDRI0i8UCglxampqCtevX4fL5YLX65XEdUlJiTQmZTIzKytLqlf5c5VIwzni97N6\ng8mR8vJyDA8Pw+v1SpxvsVhQUlIifhAryYxGoyQEWBnIOWCsQjvDwdiciT0VVA8Gg9i1a5fIclLe\nRQWaiY2oOstqk1/ubdoZJm46OzsxPDyMbdu2AYA0y2UFCACp0OG9wXiP38WKj487KNHGdVcTIvR1\n1D2Yk5OD/Pz8jAa4S4dqr2/04L3+XtrWTAKrz11YWIiqqioho7AxNe/LeDwu606barVa0dzcjL6+\nPmnAzHfn77Da3mq1Ii8vT0itZrNZqndu9FD3FIFm1XfjvszKysK+ffsApG3bjh073vFZtbW1QjYD\nILEU8PEknLiPz549C6vVCp1Oh+vXr6OoqEhwjXdLOr3bUH0t9a7lPUG/jJgiiTvq7wGLFe5App/O\nWJPrm5ub+w4fHkjvu2AwiHA4jKqqKszMzKCyshJ/8Rd/gerqarEr9Kmi0aj4VX+MffBxhiodR38m\nFApJnKDGaTdy3EwAvP/45S9/Kf+8fPlyqTB6t9Hd3Q2TyYR/+7d/w+nTp/Hyyy9j06ZNGRUv9G3e\na3xgAuCpp57CsWPH5FI6cOAAVq1ahaamJmg0GgwODmJiYgKVlZX4xCc+IRni/fv3o7e3F0BaF2z3\n7t2orKzEW2+9hfr6emzfvh2RSARTU1M4cuSIlLbxoB85cgSxWAwbNmyQoEKr1QrANj4+LiWcDQ0N\n8rw0Zj/84Q9RU1ODoqIi7Ny5U1jBnZ2dOHr0qBxISuTk5OTg0KFDyM7OFpbk0NCQGNH77rtPEgin\nTp0SYJs6xzU1NXjppZdErqO6ulrKwnlR2u12HD58GGVlZRK41dTUYHx8XBxfi8UCv98vF4vNZkND\nQwMmJyfh9/vh9Xpx+fJl2O12DA8PIz8/HwaDIUPCoaKiAitXrkQqlUJHRwcOHDiAuro6hMNh0b9b\nWFjIYLD87Gc/w7lz57B8+XLodDqUlZWhqqpKgoPnn38ef/Znfwaj0Shg2Y9+9COsW7cOn/vc5/DD\nH/4Qer0e4+PjyM/Ph06nQ2FhIaamppCXlyfVD7fddhuampqg0+mwatUq5Obmyncw+FnqUIyOjiIU\nCuHKlStYtmyZ6O1fvHgRXq8Xa9euRSgUQiKREJ3IaDQKj8cjwOerr76Knp4eYcDY7Xb4/f6My+XU\nqVPi2DPbf9ttt8Fut2NoaAiHDh2SEj8GEaFQCAcPHhSHldIm9fX1GB0dRW1tLXQ6HZ577jl5tvLy\ncpHW8Hg8mJiYwLVr11BVVYWOjg5s2LBBPp/lfFu2bBHNUValsLKFJcepVEqeHQAuXLiAhoYGaLVa\n9Pf3o7+/H1NTU/jmN7+JhYUFBINB9Pf3o76+Xs630WiEy+VCV1eXSDfREWcwYrVaRb8dSPcAOH78\nODo6OhAKhfDb3/4W3/jGNz7ItLzvKC4uRkdHhzCuW1tb0dTUJIyoaDSaIZ+klkY5nU6YzWZZS4Ip\nZGrq9XpUVVXJ85NJd/XqVbS2tqK8vBxWqxWlpaWSaLLZbIjFYhmAOVlOGo1GemycOnUKoVAIO3bs\nkOBqqR4uAFy7dg3l5eWYnZ1FNBrF8ePH8eCDD0Kj0cDtduPs2bPybNzPAwMDwjRJJpPYvHkzwuEw\nfvGLX6C6ulpk1XJycuD1eiXgdLvdmJ2dxR133IGGhgYBmlj+B0CAKoKTZFioLH01kAUgz+7xeNDT\n04OzZ8+ioaEBmzZtElaBCtQBi0AMg1VgkXmp0+kwNDSEmpoaAIua18CiY0qQgAEh9UgnJyfR09OD\nQCCAp556CkA6sUPt65ycHNGJJguUlURarVYkl0ZHR/HVr34VjY2Nwgq64447hF22b98+/OY3v0Fx\ncbE4yNPT03A6naipqZFy3IcffhjxeFwu4W3btuHVV1+Vc11YWIhkMilyRnSMcnNz4XA48Nhjj+HY\nsWM4cOCA7LnNmzdj5cqVGBgYgFarRSKRQFVVVQYYRYY0A18GCWRxXb16FRUVFcjKysKqVatgtVrx\n3//93xgcHJREaG9vL77whS9g48aNSCbTfWQSiQSKi4vFsaWuOgHaVCqFyspKHD9+HEVFRSgqKoLb\n7YbH4xH5Jb1ej6amJtkL0WgU8XgcFy9exL59+/C1r30NxcXFGBoawptvvolIJAKfz4cXX3xR9hGr\nYQ4ePAi73Y4VK1aIXviN0pzNyckRGQzKXfj9fhgMBtjtdikNHx8fl3JLVYpCBYG4v1XgnPZIBTGZ\nqCWIqcoDcH051wRNuKcJ4ADIAFZ4j/KZSGxYGtwsZf0zCchAUmUV0+ehkzk5OSngAedlenoaVqsV\ny5Ytg9VqzQi6+GxZWVmSQIpGo8Le9nq9WL58OUpKSt7BCOdgskPtS8AkB9mYHKzco+Y3e8qoFRtq\nJQrZ10xmkZHM71KTHQSPmFDg9/Eu9nq9AlazIo1zTUAIWAyKVAkFVS5JZTPymcbHx+Hz+dDa2ip+\nJvedyqDmO3Ee1Z4LkUgkY+5Ulh1BtMLCQkmy0v6ryWLeC0DapsViMakEiEajGB0dhc/ng9PpFMCl\nuLhY/BrKnTHw5rNzLVXwn/PE/c/kFn1xgmUFBQUCHDIxSn+OiTX2LojFYvLOfHY1GcI9yKQ42e3T\n09MZifIbMfLz81FeXo5EIoHJyUnpa0RWNs8ugQZW5vKOVpm2TLi63W4UFRVh69atYgfGx8flPmPP\nNSBdmUrgLxQKSYUA920sFkMikUBZWRkSiQRWrlwJIA24UMqU+4EVCGRdM8lG0gXvPPVMcP/yjNHf\n5F4mW592nvaITHnVrvL8qNVCTBQQgOJ+NJvNKCkpwYYNGwCk+yExMc89SdCctpN+qFoFGg6H5fvo\np3Bv8PlYIQAsyloymcFqBlYnAYtVcqpUDCt4CJpzDmkruIYquzw3Nxder1d6YHA/MdnANVErJzh/\nfA6z2Yxly5ZhaGgIbrcbFRUVsNls4kPSF2Eyjj/nHUVgTLVHOp1O5ppAOwA5q9XV1fB6vZKAAtJV\ngWVlZbKGqVRK7HVOTo6sQSAQkPmnJCjXilIgqsRlNBpFOByW2Lu9vR1FRUXvkONiUpBJB96ZHBqN\nRmJ+Vj6oVVKDg4N4+eWX5e4BIBU/vIP5nFlZaam2iYkJYRRv2bIFADIq1z7OoEyWuidUuTq+N/fB\nUpkdNTnwXiMSiUg8BEASfh/m2Vhp1NbWJj9/v79ViUskkhgMBpSUlOD69eswm82wWq1CmvJ6vSI1\nxe9kMvPatWvwer0yF8Si2KeFz2a1WrF9+3a0tbXBZDJJHPVx7ob3mlfeYawOWZqUAhb73fAO+SAg\nkIN3xHsNysR9mM8ZGhrCzMyMJLkKCgqk2lVNar9bhYj67u82h9yDTDLabDbk5eVhbGwMFotF1kv9\nnJmZGbl/2DeStp12Q7V3HKlUSiqxx8bGUFBQgJKSEszMzKC4uBjZ2dnvkMmhlBDVC96tB8OHGcPD\nw7Db7ZKIu5EjJydHZAVPnTolVcX0n1itfiPHzQTA+w9W4X/YwRiEuHNpaSmcTidSqRQuXrwoMvzv\nNT4wAVBXV5ehR3flyhV4PB4UFxdj69at6Orqwq5du4QhSLAhkUhg9+7dANI6/729vWL08/PzodFo\nsHbtWpHAGRkZgcFgkIvzxIkT+OxnP4uysjJxtq9duyYO+8TEBCwWCzZt2iTGra+vD3Nzc+js7ASQ\ndoIpgUFwuKamBg899JCUtxw4cAANDQ3CuPnmN7+J7u5uvPTSS8jNzcWuXbuQm5uLsbExebYVK1Yg\nGAyKXuIDDzwAk8kEn8+HZDIpbOWlJdKXL18W5zgrKy2Tc+3atYzGMn19fcjLy5PLaGxsDFqtFj6f\nD7FYDM8++yxKS0tFbojsfqfTKYmQZDKJ4eFh9Pb2wu12IxQKIRKJoKCgQPSUyWYki7W5uRmBQAAD\nAwP4+te/jmAwCIfDgdtvvx1Op1NAmpaWFnHEzGYz1q1bh/7+ftxyyy0YGBhAMBhEV1eXvDOd2v7+\nfgDA/fffL06O3W6XcsIjR44gEolgzZo10iCYGev+/n688cYbsufYZGbv3r14+umnMTw8jFQqhR/8\n4Ad45JFHAKSDOZPJhEAgIDrrTz/9NLZv3w5g8WJIJpPSoPjIkSP4whe+gNWrV4v0DHU+ue+WLVsm\nPSuAdBJkeHhYQBga1NHRUdjtdhgMBgwODkqVBwB85jOfQXFxMVwuF6xWq2ig79u3T9iD99xzj+hL\nAumg12azIR6Pw+fzobi4GAsLCygqKkIsFhMwUa/XC7BdXFyM1157Tc7jkSNHpLF2PB4XkMbpdOLY\nsWMA0nImo6OjksSYn5+X3gwAUFRUJGAsz90DDzyA5cuX4+mnn0Z3d/cNaXxUU1ODCxcu4IUXXsCd\nd96JwsJCWCwWmYNwOIyRkRFs3Lgxg41GsJ4BaDwex6lTpyTJkZ+fD5vNhtHRUdnHKlP30KFDeOih\nhwBAJIQYlGVnZ0tQ4/P5UFlZKQzZVCqtAx8KhbBnzx4BWihRwsEyRTbOMhgMuHLlCmZnZ7Ft2zYk\nEgm89tpr4rgtLCygvLwcsVhMAotUKoXt27dj586d0Gq16OzsxFtvvSWJuR07dsDhcODq1avwer1S\nvcLAa3Z2VmR+aGfcbreUVRPgmJ6ehtlsFps+OzuLCxcuSPNnVjo0NjaioqICe/bswblz58TOu1wu\nqRJQAwg6rCyVZNBfWFgojHeCNCobk04V7eW6devEiYzFYigtLcX3v/99vPrqqwCAu+66CzabTQJF\nrgU11MvLy/HLX/4SsVhM9Bv/8R//UWwSG98yuZefn4+HH34YO3bsgNPpxM9//nNZV7vdniG5MjIy\ngurqarlX6uvrsXPnTpHJ+/SnPy1yRslkUhK5er1eGEzbtm3Da6+9hrVr1wIA7rjjDgDpBBerTsLh\nMKLRKMxms8hHtLW1CQtwfn5eSurn5uZgsVhgNBqFXVhZWYmHHnoI3/ve9yTBvbCwAI/Hg2AwKHfR\n1q1bUVVVhenpabS1tYntfPHFFwFAqtqqq6vR1NSEZDLdm8FisSAUCuHo0aNYWFjASy+9JM47JbJy\nc3PxD//wD/B6vRgcHMTGjRtRXV2NV155RRKkaiIsEomgv78fBoMBAwMDuPXWW1FdXX3D9HDZk4cs\nTAJK4XAYVqtVALhAICABuJqAZEBBkEuVFOHeZ0UNmWRqoo1/Q5B9dnY2Q0aCiSUGgAx41YBzqe/B\nf+dncK8yAAIgfsnSZAa/W5XT4FyTWcrvcDgcCIfDAkwyEUXAhMPhcGQw6fh5TG6zCkKVfQGQAXSq\nIKH6+ep8qxVKZDZxTflcTJoBaTtTU1MDr9crwT2TbVyLSCQCrVabcV64rpQno3waK65isZiAXZwr\nlSFKubRwOCy+BJPVfHdVOoN7TgXG1f4EaiBPFjeTGKw8MRqN0ixQlYZaOijlRQCHCSDqwnOP6XQ6\nqfihtvDk5KTMnUaTlnlasWJFhnQD5cLUzyJYynlV75Cla6+CH0yiFBUVYWxsTCrIzGazyFiqFX3c\nIzwTlC0CIIl2Mq4BiJ49pZrIWr4RgyArn4tVsx6PB3V1dZI0okyhWvlBm8O5djgcuHLlCoLBoNio\nrKwseDweDA0NZVTUVVZWAkjb8OHhYYTDYaRSKcRiMUxMTKCwsFASeUzUjI+PizyGXq/H6OioVO2y\n8pc+Bu80VQKIMSXXj8kcJrK4z9X9yIb1TIiOjY1hZGQETU1N8mz8HNrj4uJika4pLS0V+RjaEfqx\n7e3tkpTiO/I8c6/y71SGNu+CvLw8uFwuadqqnmH6OWqSElhkodKmUJaIpBNWUalVQKwUIuGEJAv1\n/HIOOXd5eXloaWnBmTNn4Ha7pfl4KBRCdnZ2BhFMBebpL6qJWMYhZN7y2flsyWQSHo9H7AOw2J8F\ngFRgcR+QtERJSu4HVjYAkBiX52FsbAxjY2Mig0JWv5og5FCTtLzbmHCmbWXykFXjHJSd5X5iYpxJ\n3dzcXLGLXC8O+kesHFJJL7S5kUhE+jSoPUZYXXX9+nVMTU0hmUw3i16xYgXWr18vpEVW2X/ckZOT\nI+S0DzOYxGL8p7Kp322kUmnZMFaCAIu9nD5oqGoISwf9GVWei1J2apKU7H6/3y84k81mE8yGtoT3\neVFREZLJpCSvmRSmxCsxhXg8LuTFXbt2wWKxSFNt2qGPM5aC/9zX6llkvwv6EvxvlDsjGerDPote\nr39fsPm9wH8C636/H+Xl5cjKyoLP50NbWxvKy8ul4TLvT1blLuNdAAAgAElEQVTZAxAZOzWhRH9I\nfe53Sz5Q2i07OxvBYFA+h8lx4hzAIgufUlaRSAQdHR3o6emB1WrF7bffjrq6uneQTuhDut1u+P1+\n6eNGW82kmTroixFH+qiD/TX/mEOn0yEQCMDlcmHPnj2CM6jkLpWU8nHGzQTARx/JZBLf/va3cf36\ndfzTP/0THn74YYyPj+Nb3/oWFhYW8JWvfAXZ2dn4P+y9eXCb53U1fgDuGwCCAAFu4L5T3CVZG2VZ\nthTJtrylSZykSupmmUmTziRNO8k0bafLH5102mk6k0w76aSe1Eu8xY7tWLYsydolkqLE3dx3AARB\ngNi5AcTvD+RcvZCVeM335fuNnpmMYokE3vdZ7nPvueeee/DgQfzd3/0dMjIy5Pz/tvG+K8oDxoNX\nVlaGxsZGlJWVob6+HoWFhaipqRFDu7CwIE4JHapwOKbZb7FYMDMzg8uXL8PpdKKvrw8ejwdHjx6F\n1WrFyy+/LHrWjz76aJyWV0pKClZWViSoLCkpwVtvvYXExEQBvrlha2pq0NvbC7fbjdraWpGU0Ov1\n0Ov1qKioEEd5enoaN27cQFFRETo6OoRlHYlE8Pbbb+PChQuilcjMO51AlUoFg8EgpfsNDQ0i7cCm\ntmQ/ABDWsVod0/yORCIoKiqC0WhEbW0tRkZG8Itf/AIWi0U0h3noNBoNcnNzce+99wpQ8NRTT2F9\nfR379+/Hrl27RCLn5MmTSE1NhVarRV5eHr72ta9hbm5O5JJCoRCWl5cF/ANiTlZxcTF27dqFgoIC\nDA4OQq1Wo7e3F/feey927twpDi3fp7CwECUlJXC5XJJ17ezshMViQVtbGyKRCGw2G2ZnZ6XkmyW0\nLMFj34aEhASYTCaEw2E0NzejoKBAHIzDhw+jvr4eqampqKysxMLCAnp6etDS0oJHH30U//mf/4nH\nHnsM/f39+MlPfiLfw3Xv6OiQkrzR0VG0trbC7/fjhRdeQG9vrzg/Dz/8MA4ePAiVSoVAICBaqBsb\nG2hqasLKygra2tqQl5cXx9g1m804evQoUlJSpEJFr9fjwoULcLvd4kQrGQw2mw25ubnwer1wOBzC\nOopEIhgdHZUEBkvZAoGAZM09Ho80lOUlGQqFsLm5KU2ZgZjhvnDhAubn50WCiKXaZWVlUKlUOHny\nJILBIP72b/8WQIxd093djc3NTbjdbiQnJ2N6elqcZTKGWA7P76mpqUFJSQkCgcAnkgDg2UpMTER/\nfz/Ky8sFTA6FQhgfH0dTU5OwtJWMgMXFRYyPj2P37t3Izc1FQUFBXKkw1+no0aMAYpJZCwsLMJvN\nsNvtmJiYQFFREaamppCXlyeAOPtxALHy4LKyMmHiko2lLKlzuVyitQfcbEq7tbUlwV1ycjKuXLmC\nRx99VDRXZ2dnZW5zc3PR1tYGvV6PS5cuYXl5GXv37hX5iuTkZLS2tqK4uBiXL18GEKtkyc/Px8WL\nFyVYpmYh9xAZgHRcWOJMx4xAIzWqX3/9dXR1dWHfvn2SmGOZPB0tm82Gra2YPmdaWhoyMzOFJacE\nWxg4kdFIFl9aWpo0yKY8gBL4JXje2toq68I1ValUKCsrw65duzA+Pi424Atf+AKysrIQCARQWFiI\n5eVlpKWlCZAQDAZRXV0ttpPyJSwr7enpgcfjwY4dO4ShxsoNJcuEjj8d0fT0dJGzaWhowOrqqjjG\nAIR9RhCP54jzwmDxgQcewNDQEABg7969SEtLQ2lpaZxcAkENgjZKBgzvqlAohMHBQbS2tsr9AcTs\nV11dHXbs2CHzu23bNnR1dWF6eloqSPbv3y/PxKTkyZMnRabi7rvvht/vlwQTQUgGqaurq6isrMTO\nnTvjZEaU+qMFBQXQarUCCjz00EM4f/48duzYIWfIarVCp9PBarVibm4O/f390qujvr7+Y0uPATHJ\nwkgkpo1M283AaH19Xao4bmWok4FIBqRSmkcpFxIOh+H3+8XJTkhIgN/vR0JCrOE455kBy+bmpjS4\nLy8vF+CKQGAwGBQggL9DQJCgCgEM/hsZpNFoVHwhZUKB/g2/n/aPQB7vGKUsGSuOVldX0dDQAK1W\nG1d5xc+jjNLevXslsef3+8U/Y0WHMqgieAbcbIDLuSeIk5iYGAf+0Obczv4SyCYLmb/HJEFTUxNW\nV1fh9Xrl/Qi2KhmzysF5UyZvWLVJcJY+jZKpz/uDdwntEytc+X2U1AJi/qDJZJKqhnA4jNnZWSwu\nLiIlJQVGo1ECR2Xigc9Jv4FJVqW8EnCz6iQxMRF6vR5Op1PuG+6rQCAg4BZ/Z3V1VRKIy8vL8Pv9\nYtMyMzNRVFSErKws8WtYLcq15T4hUKgEdZV7m76IskKCew2AEAYoH0MWHfcV7bdSfo0yYjwPBAqi\n0ZuNQilZRcIF2dufxOD5S0tLExIHBxM3yvuOZB5lfxG+f15eHg4fPgy3241AIICJiQlsbW1hdnZW\nErUEgglcJicnw2w2w2AwIDs7G1NTU9K3iDaPhKTi4mIBIlUqFcbGxnD27Fm0trYK8UWZBFXaEu4V\nzq2SjANAZHJ45nhmKFWn0+kQDAZx9uxZdHV14fOf/7w03GQSij59Q0MDRkZGJAHGNSOjv7i4GA8+\n+CAKCwsF+CIJhz4vQWmCIMokqfI8U36P55E29tbqFp4X2kWeRVZ98RzSVvHnOPLy8kQ+knaf88oz\nSzkZ/h3/e3p6WiRaWdVDyTXe28BNSSM+V1JSkryf0WhEQ0MDlpaW4ohA6+vr4iN5PB5JSPAe9Hq9\n75E0Sk5OxuLiojSyzs3NFXvOeU5ISIgjni0sLGBychJerxdVVVVSkcykOvEK2hEA4qNmZmbG7Tmu\n1dLSEubm5uBwOMS/pW3kurMChIAeK4e9Xq/YYZ5D+mPKJD7n12g04sCBA1hYWBCGJvvR8f4gbkCZ\np23btuGRRx6RHhjATdv5ccfvAv9vl4jj+/D+e78EKH37QCAgn6P0X37XMJlM72lOCkAAb94Nm5ub\nUgnm8/kkaeBwOKRn48zMjBCbent7kZGRIeeMhA8gdgetrKwIsYuNw41Go9gk3k87d+4EcFPe7JPS\n/VfKh3EoZdWAm9UNTFQS/Oa80K/4qOPWpNbvGnxOxgPz8/MAYlgccLORtdvtjqvyAyDniX6KskJK\n+Sw+n+89YDgJEPPz84L1UeaayXJKwvK8MCH75ptv4uzZs0hLS8PExATuuuuu274b+0GxAp94H2XN\nNjc3b5uc4FmmT/RRBqv/fp9DpVLhgQcekITF1taWkC+AGEZqs9lw4MAB+Z1Pyue5Mz74SEhIwN/8\nzd/E/d13vvOd9/xcR0fHbeW+bjc+kHXQ6XR44YUXAMTKo44fPw61Wo233noLhYWFmJ6eRnZ2Nvx+\nP/r7+zE7OysHDQD27NmDp556Cv/1X/+FlpYWPPTQQ+KAAbHNVFRUhJKSkrhGMmq1GgsLCxgZGcHK\nygoKCgriyrH27duH2dlZCSY8Ho98L3XZjx49KkEqhzJoYQl/VlYWMjIy4PP5ROrD5/MhEong6NGj\nuOeeeyTL6XK55JnIzmSTtYSEBPT09KC4uBhNTU3CSAJiWVI65Ovr69i3b5/IDUSjUbz99tvSDJKl\nIL/85S9hNptRUlKCK1euSILimWeeQTQahUajQWlpqRxezks0GkV7eztOnjyJ4eFhGAwGRKMx3erK\nykpkZWWJdiMADA4OYmZmBg0NDRgcHEQoFMLLL7+Mv/iLv0BKSgpu3LiB7du3o7CwUIIqOoYEat99\n9108+OCDolvFPgWVlZXyOxcvXkRpaSmKi4vh8/ngcDgwPDyM9fV1HDx4ENXV1VhYWIBWq0VnZyeA\nWJ+GsrIyaX5pMpngcrkwOzuLtbU1VFRUIC0tDUePHpXyTV6ATqcTfr8fBoMB9913H86dOweNRoNn\nnnkGs7Oz2L9/P/7kT/5E9hsdm2g0ptHPIHBlZQVZWVnIzc0VfTsgFmDcddddyMjIkMoWymyQwUGA\nippqZPxSTy4SiWBgYAAHDhyQ6oGZmRls27YtjtEZiURw8uRJHDx4EBqNBgkJCbDb7eJsG41GqVYA\nbjZjUwYvOTk5IpfS3d0tsjAEFvhdXq8X586dQ2NjIwKBgIC9BA+5V4DY5byxsYG7775bmu993MGk\ny9DQEJaWllBXVwe73Y6RkRGYTCaUlJQI04PMDCDGyk1OTkZjY6MEHzwzBAhYbUPnbW5uDn6/H1VV\nVXA6nRgcHETJb5rjsdyaTAI2Vtu/f38cI5blutQpZYDLRlrATb1qtVotzPxwONYXRKfTYWVlBVar\nFVqtFo899hiAWCUE5UhYRtvS0gKn0yksPu7ZQ4cOAYiV205OTkojayDGtiYbnaCrUqdzcXERpaWl\n4uxEIrGG4G+//TbKy8tx6NAhHD9+PI5xyl4kZB7r9XqYTCacP38ehw8fFhan1+sV55XOGRs6Kys3\nkpOTYbfbYbFY5J3oyM/MzODNN98Udgh7UBQUFEjArNVqUV1djdbWVgCxKjKbzYa8vDzk5ubikUce\nQTgcFiYZWYrj4+NSccHEWGpqKrKzs0WihglJSvYwOQbEwCU2U8rJycFnP/tZaUxdV1cnGufKRBUH\nA0f2JeHZo65yaWkppqenZU8xKGBwwl4OycnJmJiYQE5Ojuwx4GYVCxMQPC8AJFBQq9Vob2+XpDNB\nIKPRKM2TlQ6f2+3GmTNnEIlE8Ed/9EfybCqVCvn5+XGBYTQaxcDAAFJTU/HYY4/F3fvKCg86yUxO\nUWaoqakJbrdbfsdisWD//v1YXl7GxkasOe/S0hJGR0fx5ptv4p/+6Z/e37i8z2A5cDQalQQjGzMS\nUOAze71e0RumjSawqNQcZVBD+7O6ugqn04nZ2VlUVlZCq9UKuEjwiDaNCU9qvpeVlYncilLiQqvV\nxrHhOJTVAUqJDCYslQHlrYkKBlFra2twOByYmZmRZwViAXZmZqaAFS6XC0ajUUrouQeUZfkExQhy\nEoDmnmBgrpTcYNCoTFzQdjDxQDCHf69kG9NuKeULbn0m5WdlZGSgvLxckvEMrJWJVGW5PSVAyGbL\nzs7G2tqaSA7xexiMAjf9DcoJ8dySeU5wROknezwe+P1+FBcXCwudNnJ0dBSLi4tISEhAfn6+VIQ1\nNjbKOzK5yvXl/cTKK+4bgqn875ycHJHkImB0q1QAq7Z8Pp+QEegDpqWloaKiAg0NDcjIyJDKkJSU\nFAHtuKYEy5TAKOeZ60n/jv6Ncq9xXfLy8nDjxg0AMbJKenq6AItseMwKDM6Nsp/Q1taW+Bj8Ln4f\nq8oY5H8Sg74Vk2yMa1iZQP+etol+HfcGmd8AZO/19fWJ7IVOp0M0GsX+/ftRWloKr9crMgjATekL\nVslUVlaiv78f/f39WFhYQFZWliRF77rrLmG/RqNRWCwWXL16FaWlpcjNzUViYqIASGR0K0FEnj/l\n3xF8oB29FTDnncR+bXy+zs5OmM1m+V5WfQOxBob79+/H2NiY2Ffut9bWVuzdu/c9Uge8v5jk4XPR\nrjPRxcQ216SgoACBQED6TRFE5/15a4WSkrVPsJqSL5TQ4V3NP5WVFdwzrPyjPadsnZIRHwqFYLPZ\nYLfbkZOTg4SEBJEoTUtLg06nE6ILACwvLyMvL08IgIwfWKFVX18vZDImIex2O/Ly8pCZmYm1tTX4\nfD4hR5Blz+oGpR1mciYvL0+qH/l3ACQ+J6GQzUWdTqckFlNTU6WfCpPq7HHA96GNY58XAu5er1eq\n9rdv3y77QemvKxPMPKesDNva2oLf75cEBYFBZcU91zcajUlvtre3Y9euXXHyJFzDUCgEt9sNv9+P\nlJQU5OXlob29HUajURJjHB+ERf9xBs8iQVp+t7LK5oPYv4KCAgH9gVgc4Pf743pU3W5Q3guIkeZ4\nd3AteI8xAbS0tISlpSXx8Rkfc9/RZjGm53nOzMyUZ6Pci7LnA5O+KSkpIp3X2NgooLHZbI7rxfG7\n3umDDFaBKOdH6VfTp7Db7VJxS+kxj8cjCUIlxvPbBuUiaZ/4fXa7/QPrwCvtFHv2NTQ0xNl3qhNQ\nKYO+NKvbExJivVUoL8j7jvbndkx4rk1mZqZUggWDQVy8eBFFRUVISEjAlStX5L3q6upQWVkpMTCl\nmM1mM/Ly8t6TvCGh7cyZM1hdXcXhw4fFpyMZkjZBiTHyvqCt/qR8hA86/H7/h8JhmOBlfB6NRoUg\n3NXVhZmZGWzfvl3w2d9V8fO7xp0KgD+s8b4JADoodDjI4Ont7cWOHTuwvLyMX/3qV9i1axeam5ux\na9cukQih7vvBgwfx1a9+FZOTk7BYLFCpYvqvDQ0NsFqtcjl2dHSIA8sGGmSJdHR0YHh4GAMDAygv\nL4fNZsO2bduwsbEhetnRaBQ6nQ43btyQcmMaUmWDHSUjlYGh1WqFz+cTSZTnnnsO7e3toi/vcrmk\nv8Dy8jKKiopw5coVdHd3S4m3x+PBCy+8gPn5ecnsV1VVieNiMplQWloqpYHr6+vSZ+Cll17CkSNH\nEAqFMDo6Ks5PRUUF5ufn0djYCLvdjh/+8IfQaDRYXFyEXq+H1+tFX18ftFqtZF8Z6GRlZeGxxx7D\nxMQErl69CgBob29Ha2srqqqqJKADgDfffBMWi0VA9Z6eHjzxxBNQqWKak2+88YY4uQwAo9EoFhcX\nYTKZcPHiRVgsFmzbtg1+vx9ra2uYmppCcnIyKisrZb6Xl5dRW1srTFmWG+/cuRPZ2dnSuXpkZASX\nLl0CEAs+jUYjpqamYLFYxACfPHkSVVVVaG1txWuvvQbgZhf6rKwslJWVwWq1wuPxCLuDzTkPHTqE\nV155BQcOHBAninO2vLyM9fV1kZtxu91YWVmRhpgqlUpKN3ft2oWioiIB3pOSklBbW4unn34a7e3t\nmJqaQigUQm5urjSepi6r2+2WEmutVouRkRG0tLTg3nvvxcmTJzE5OSnP1tDQAJfLBY/Hg0cffRRr\na2vSOIqNchmQ8OJ87bXXYLfbkZubKwZ7aWkJ165dQ0lJCb773e+K/ALXh7Ix0WgUs7OzWFlZkYs6\nLy8PHR0d2Lt3LyoqKuTC40Wo0WhQW1srjtTHGQwqOjo60NPTg/HxcRw7dgwvvfQSrl27hgcffFDO\nNpnTAERWh7JXBNEIllNvNBAIyGXG0nsGl8PDw6KvmZKSgqKiIkkAcJ76+vpgNBqRnp4On88nJcyN\njY1Slkk2GM8LdWMdDocAfENDQ5idnYXb7UZnZydyc3Pxne98R4Je7kXKNRUXF4sEAZmpoVAIs7Oz\nEoDs2rUL7e3tWFxcRHd3twSxb775Jo4dOyY65gzUAEjQub6+jtHRUTz33HN4+OGH8b3vfU/kZhhM\ncU8SJOJnsHfJfffdJw4PEyNKtgfXjc41g3wmBDUaDbKysuDz+XD+/HnZy5TLOXv2LJxOJ+6//35h\nJjJgzs3NFXvb1tYmQE9SUhJqampw/PhxnD59Gjdu3BDglHJnQCyJTBa3RqPBvn37pMcBK922tmLN\nO/newWAQ7e3t2Llzp+ie09EhUEg2G/c1q2jobFHmbm1tDd3d3Th16pSU2tOmsdkzg1JqUvKu7Ovr\nkwQU72uyy3t7e6XZtRK4ACA2guWCc3NzSExMlN4nw8PD6O3tFfYTn+3QoUNy7ijlw33CO251dRWv\nvvoqvv/974sWq5KxywQ5mziyIS4ZKNXV1XHOKKX3rl27hsbGRlRUVKCurg779u2LS2J+nMH9SWY6\nEwIEQRgk8H4jYMaECoFl7nsCrnzn1dVVWK1WjIyMCHhD+UOCJgkJCbKWSlmS5eVlGI1G+XmDwSDs\ne1YrAIiTuVECT8rqAvpDShYp2WQEhgk+Li4uor+/Hy6XKw44z87Ohl6vF5YrWX1KUJfngM9DybaM\njAwBYqiZqwyYeG44lCAi7QZlX5T+3K1MLLL9OQd8ToI4THjyZ/nMZWVl0iBcyVi9HSuXgT+lAZR3\nE2VuVKqbDRzpRxCwpL452bkOh0N0ofns6+vrGB4eFt+WVUesTCkvL0dWVhb8fj+WlpaEhWc0GoWt\nrWRKAjF/gJWSyuCMQBQBCILk7BWjlKpQAhNqtVqaxbLHVXp6OioqKpCbmytBPtdRKWuVnp4uEhsE\nBAieEZjkXJOZyCSRkvFIf4BVFACEBKI8i0pwmt+jTHBz36Snp0sig0xPgtO85z+JQdCAxAHaGVbS\nUTaHOtRsespksN/vl2e5fv26VMIdPnwYOp1OEohsnJ6SkoLs7Gw5/wS1qY+sBD+vXbsm81NZWYm6\nujr5vUgkguzsbJSXlyM3N1dY1kzy8Ge4r4B4qS++K4kWTCTxnHEeNBqN/FxWVhZMJpNUuba1tQko\nwyQ9EGtOf+jQIezatQtut1vsqE6nE+mjjIyMuESTkjHPSqhbk2NpaWmyJzgo67KxsSH2mex4JpCU\nlV0AJCHOSj0m3SjLwvPPiisl8ELJW/qoZADzvNAm+v1+dHd3y/3Y398PlUqF+fl5DA0Nobm5GWVl\nZdjY2JBzRcb25uamJBSoQb+1tQWtVovW1lZMTU3J+igrP2mfOR/UWmdcRTCPgP3y8jKmpqakwpEV\nU/QP2Fydz6bT6eByudDf3w+r1SoJSvauYLUl4zSDwQCz2RynZkCi4MzMDCYmJqTPGIcSAFYm8Ln/\nNjc3ce3aNRQXF0Oj0Yg/yL1LUI12ktV6nI+kpCR4vV55p62tmG778PAwTp8+LX6RwWBAcXFxnC/L\nffp/AlBT2nqPx/OeCj3e4awIS0pKum1lAvccEEsI0G9RNmy93VBWKXPtaMc3NjZgtVpht9uxsLAA\nn88Hm80m/qJSReLQoUNob2+XKoHFxUXpaTU5OSkxAHBTqpFnjKxxEi8OHjyIw4cPS48ZnhHl836c\nQRujHMqKHv7MxsYGRkdHUVFRIeSIzc1NjI6OQq/Xf6AEwNLSUhzA/mH21O2qOObm5pCamvoeJQDO\naUpKCnw+H5xOJ4BYBb7f74dKpUJpaSk6OjokYfSTn/wEJpMJn//85yXBD8T7tB6PR/qP0AfT6XRi\ncxnrEHNj0qeiogJWqxWBQAA7duy4LWAeCoWwsLCAS5cuoba2Vu4jZcWkMtnNwbWiTaZf/0moI7zf\n8Hg8uHr1Kg4fPvyR9iLPLu/3vXv34q677hJMJSMj433P7G8bdxIAf1jjfRMA165dg0ajwaOPPgog\nphNpt9vFWI6Pj2NxcRGLi4tQq9UwmUw4fPgwzp49Kw2mnnzySdx7772oqKiQ7t+lpaUCYng8Hmmw\nR634/v5+2Gw2+Hw+PPTQQ/B4PFCr1airq5OkQUNDQ5ycz9jYmMibdHd3Y2VlBYODg7jvvvuEAaBk\n2QAx0IpO9NjYmDC+CwoK8LWvfQ0ejwdOpxMjIyMCGJrNZumuvnPnTmRmZsJoNKKzsxMFBQWoqanB\n7t27sbGxgZdeekmyzsXFxfL91LGenZ3F0NAQvvKVr4h2+bZt2/CrX/0KAETShdlTs9mM0dFR1NXV\nCfO8oKBAnB8A0hSVjrxOp8OuXbuwubmJkZERNDY2SlMmXpQVFRUoKSkRlrFKFZPBWVxcxBtvvIFw\nOAyDwSC62EAMVL106RJaWlowMzMjzFc2e1lbW8POnTsRCASEXTo9PY3c3FxkZ2fj5MmTCIfDsFgs\nMJlMaGhoQHJyMsLhMC5fvoz29nYANy9Ut9uNSCQiJVgbGxuoqqqC2+2WJmM0wk6nE8FgENu3b8fi\n4iK6urqQmJgoz1hWVoazZ89iYmJC1pWXCAOjaDTWKNfj8cDtduOtt95CTU0NTCaTBFrJycninFEb\n+Y033oDBYMDExISUfH71q1+VLvUsNd/a2oLT6cRrr70GnU4nesgulwtjY2Oora0Vx//06dPQ6/XY\nv38/ZmZmpPx/aGgIY2NjWF5elrJ89jSgNMjx48dhNBpF85eXGPXrlSyDUCiE3bt348qVK/jGN74B\ng8EAm82G6elp9Pf342c/+xkCgQAef/xxCabo7HZ1dSE7O1sSUR9n8IItKChAf38/ZmZm0NvbiwMH\nDuCpp55CMBiUXh9FRUVysVC/EYDIWyhZSnQAlDqRlGlZWVmRZN6VK1fQ0tKCuro6bG1tSUNZ9gAA\nYkGk3W4XZ4SMC1YFEIwig5vAXUZGBi5fvoznn39e2LRk0X3/+98HAGnIGggEoFarpeEtKxC4Txl8\nJyUlSeWNsgSeQA5ZynNzc3j77bdRVVUFh8MRB14sLy/jP/7jP7B//3788Ic/jGOeKNmXSjBTpVLB\n6/UiGo3ixRdfxF/91V8hMTFRzs3s7CwsFos4Vx6PB3q9XkpoGawvLCxgdHQUmZmZIpt18eJFvPji\niwBuBkgApNrI5/PhW9/6lswDJVIY+DLgorzR1tYWTCYTHnnkERQVFYkNN5lMAlYw2PR6vQL+HT16\nFH6/X0q2k5OTUVdXJ+uanp6Oo0ePSkADQPSZk5KSMDk5KYAUbSdL/SnrEAwG8eKLL8JmsyE1NRWZ\nmZmYm5uDSqWSZ7tx4wYqKiqQnZ0Nk8mE5ORk6WkxODgIu92OxsZG6HQ6OZvRaBTXrl3Dyy+/jEgk\nAq1Wi8985jNSOaFkM/Eera6uFgDBbDbD6XSip6cHubm5+PWvfy0MzJdffllY3EwI+v1+AVdzcnLg\ncrlQVlYmAbyyCoL7hz0TCGoyoOTPRKNRKQPXarVS0UfwgADwJ9UDIBwOY3p6WjSnWdWglA0AIAni\n9fV10RVWsqbJGuQ6ABBQi31htra2BJBhAEdggnOglAViQ0Qy0ggWEchWsrJpQ5Va07xfabOV8kR8\nZgKorHBgtR17Y6hUKgGU9Xq9BIC8Az0eD+bn55GYeLMnDnBTj5kBNZ+F88N3UALAfDYG/Lwjlawq\nJg94tyn3Ds89AQuuH5OxXBelTeNcJSQkoLS0FEtLS3A4HMIUpA2n7aQsEBMwlG8gMMukISsbAMSB\n2wR6eH8TxKRkBH20jIwMlJaWSlJVKUWSmJiIiooK5HiYnTsAACAASURBVOfny/5VVgKura0hEomI\ndBYlyvr6+nD//fcjPT09LvmhlPHivrgVtCI4zDVSrhclQHmmyY5UJv84CDARdOYZ4NkGEMfqZoKJ\nSaJbn5VrGIlExD45nU6sra3JHnQ4HHC73TAajdBqtXFnRfnOtFWsVmICkGC9VquVZ/y4g+A+7T/v\nOyaqmSRlwlCZfKWsJ/vzTE9Po7a2Ftu2bRPmPnCzB4/T6ZTGmEp7pmQxc72BmO/NBoi7d++O02xm\nEpt9Fijbw4QiwXMlmE/giH9PcIb9rCijovwd4GalA6VnWltbUVFRAYvFIgkL2mruBZ1OB41GI1XQ\nTOAo5Vz431xr5Z7mz5ONTHtCHx64ab/S09Phcrnkzk5OThYfT7l3lO+irHphkvDWaiVl82QOVgzQ\n5hiNRvkMZbXa2NiYAOFra2u4fv26VIBmZWVhYmJCKtoJaBYUFGDPnj1ISEjA2tqaSCYySZCYmCh6\n2fRVgRjIRmLTwMAALBaLJAxJhtFoNHGVV7OzsyLTxEQE54SEJqUfRF8gEolgamoKVqtVmvEuLCxA\nrVZjYmIC0WhU5sBut+PatWtobm7GPffcI2doaGgIKysraG1tRXV19XvIKrxbmPyixjzvwZqaGrFZ\njHsIVBKApB+orMRmoph+MWXjxsfHcfnyZWHHr66uCumSe4Fz93ElXj7sYOURpSuVJJN3330X6+vr\nqK+v/62yRBaLJa46Lycn50M9v1IKiGu7srKCpaUlnDp1SvAKAvUAxC88duyYKB+wSo8VIuFwOG6O\nNRoNfD4fBgcH4fV6pRKGsc/Bgwexe/du2csA4mzU72so/Roy2HNycjA+Pg673Y7a2lqo1TFJr+bm\n5g98L91aAUV/7neB1Q6HA729vWhsbIxLANBfvJ2cjrLabmNjQ8hdV65cQXl5OYAY29xsNqOgoACb\nm5sio6skBgA3JaBYVdLQ0CD/lpycjB07dgjwzmb1wWBQiLm0vRaLBdFoFHV1dbdltW9sbGB8fFwk\nc5V+Of003g9KKSO9Xo/k5GS43W4YDAYsLS1heXlZqmWZlPp9DJ1OJ/1XP+7nADeTGSQ5s7r3o4w7\nCYA/rPG+1tfr9WJsbAyPP/44gJizqdFooNVq8eMf/xgLCwuiT88MrUajkewaEHP2nn/+eej1etG0\namhokEPT09OD7OxsDA4OSsYyOzsbnZ2dSExMRG9vL9bX13H69Gl8/etfR0tLC1588UU8++yz2LNn\nj/xOd3c3RkZGUF5eDrPZLE12o9EofD4f7HY7MjMzsXPnTrnkz5w5g+3bt8Pn84nRT09Px6c//WkB\nlVnm19/fDyCW4aSe6eTkJKqrq3H9+nUYjUaUlpbi5MmT2Ldvn7CEWKFgNBqRkpICm80meonl5eWS\nfSRwkpSUJI1Ie3t7cfXqVfz3f/83bDYb0tPTodVqYTabkZ+fj5SUFMzMzEjpLRA7qNTsDYfD0vjR\n5/NJM2E6sEqtNo1Gg+3bt0v3+Ndffx3Xrl0TtrnT6ZSsLXAzqBoaGsL3vvc9CYyp69je3i6l+SdO\nnAAQc67ZaKm5uRklJSUAYuxyllQnJSWhuLhY9srAwABcLpf0ekhOTobf78dnPvMZYQt5vd64TvdJ\nSUlYWlqSeZ2fn5cGwePj46Lb1t7eLplvOn/KUs3NzU08++yzcDgcKCgowJe+9KU4GYizZ89KRUNy\ncjJefvll5OTkwGKxwOv1SuKJSQUg5jCNjIzA5XKhtrYWbW1twgRi+XdxcbHI6vDcMajq6enB8PAw\nZmZm0NHRgfb2dpmDgYEBaXC9vLyMQ4cOQafTyV4hy1ir1QoLXa/XSyXIkSNHpGF2TU0NgsEgiouL\nUV5eDp1OhzNnzsBms8Hv98vFz+CI5fOfhDPEag+1Wo2amhrYbDbMzMxIFQZBDAK2BOZ//vOfQ6fT\nwWAwoK2tDaWlpXGNVqkBPzk5GVfySXkXp9OJwsJCLCws4MSJE5ifn8e2bduwbds2kYbgPn7uueeQ\nlJSEhx56CCpVTKLHbrfDZDIhNTUV+fn5cWzLiYkJCTySk5ORn58vElCFhYX43Oc+J4xWfk9lZSVs\nNhs2NjZE55DJOyYu1Wo1WltbBQD1+/0iXcQS7NXVVWi1WnGATp48CavVim984xsAYo7OD3/4Q/z9\n3/+9aOCTDUmg5VZNSoI2ycnJ+PnPf46vf/3rEhxlZWWhvb0dJSUluHz5sgQFJSUl2NzclN4IBHFv\n3Lgh8zA6OoobN27g/Pnz4oSywoWa0tRtpNRBMBjE0tISmpqa5HxarVb89Kc/xYMPPohdu3bJGU1N\nTUV9fT2uXr0qQAmZRQz2yCAhELG4uIiCggJcvXoVhYWF0Gq10jfgzJkzOHHiBPbt2ydOIefM7XZL\ngtvhcMSxJjnHZP7pdDrYbDa43W4BSAj6AsCvfvUraLValJWV4Wtf+xp8Ph/6+/uhVqtx4sQJfPe7\n3xVmIp3/xcVFXL58GampqcKY/pd/+RdpIl1TUyMVfgQ0l5aWUFJSItIkVqsVpaWlcckYm80Gm80m\n+6G4uBhZWVlITEwUW8IeKP/4j/8owAzZMjx3rB7Kzc0VYGFzc1PYz7cyJl0ul0hNhcNhjI6OCtPu\nxIkTIuf2cQZtJO1CTk5OXFKR85SWlibnkP9GQJZ/8r2VbEyfz4fExESYzWa43W74fL64ZBWBz1uZ\nogkJCbDZbMIO1Wq1cRrZwE1nneAcQRsClvyTQDkBJABiJ+kn2Gw2OBwOeL1ekapJTU1FWVmZMCWV\nLNVQKCSl/QQzNBqN+Crck7Tb4fDNhr4ExIH45pgcBE+U60A7w4od/r1SOoJyEAQVCcIp10cpM8JE\nA4M6g8GA8vJyAScJgCiBPAZzvJ+pRc37nCAQ7yAAUjnFJISyrwDfe3l5WfTOAYjdZLXW/Py8vD9Z\n8bQnTU1NcVImTEhMTk7C5XIhKSkJZrMZ+/btQ25ublziCkDcfChZyVy/paUlkaajDUhISBDiRGZm\npgTBXA8ScOgvcJ9yT/L9lWvMc6b8HsqS8Ewo9w3vKK41P2NqakpsjEqlQm5urlSf8t24jlxX7g0l\n6KtM7nFOPynwh40PlXIMQMxmcA5J2iBpYGNjA0tLS5iamsLU1JTsndraWrS3t0sDVsYDbGaZmpoq\n/httBRNXBPpoV3U6HZqamhAKhVBSUiJ9f/j7PM+RSER6vXEuCVjyDHCw6oMJJNrFyclJOYNJSbHm\n6MomxrRbXq8XNpsNDQ0NKCoqkvuW1RL8LhK6lPePMgnPs0SyFBAPzPNnybRnYoAAMOebkjP044Gb\ndwWlgOhbcC0JavFzaZM4d7R5Kysrsu9Yuco4kL2rWM1Npvbp06cFwCHAFQqFUFBQAIfDgZWVFUkc\naTQaFBQUYHFxUWL2mZkZAJD1ZryYkJAgEimUd6Vdf/fddyWmHxoaek9FCStQKKfKObDb7YhEIlI5\nyWSQ3+/H2NgYEhISYLFYxA6yzyC190mSYaJAeX/ybGZnZ2N8fBw9PT1YWFhAWlqayCUeOXIkTv5D\nKUOsvIO5L5VJTKXUnRIQp80Ih8NxEnBMZNJ+0D55PB54vV7cuHFD7BulD5lkJ3jKoWTh/74H9yT9\nV95xwE09/+zs7PdoxrN6k4QVAr1KCS3lOn2QwaQQiQbd3d3SYJb3MM8LK7cpH5abmyvJHKW0FyWg\n+cx6vR79/f2or6/HoUOHJC7X6XTQarVIT0+P68fwYcet0oPvNyifxefzeDwi0zU5OYlXX31VcDm1\nWi3ni+e4uLj4Q++V35YACIfDguHc2mB3fHxcKqtuHayiWF9fh81mE1UK+oKUi52bm8PmZqzB8be/\n/W0hifl8Ptl/VFt4+umn4fF4sH37dknQ0L8FYskc4kjKComlpSW89tprmJycRF1dHdra2n4rqE0f\ngLLYjY2NKCoqkv1OAhrtPqthjUYjJicnJa5V+tq/L/D/kxx8RiaWie1ubGxgYWFB8LgPM+4kAP6w\nxvtan6amJvT19YnBYjZbpYpp3CUlJeFTn/oUampqBKRhgMSGvs8++yxGR0dRWFgIo9GIzMxMucCd\nTidOnjyJ1tZWPPbYY5KNJDups7MTS0tLIg/DzuLHjx/H2NgYXnzxRezfvz/2Mr/JrIdCITzxxBMo\nLi7G/Pw8fvrTnwKAlKn19vbKBaHX63HlyhW0t7ejrKwMU1NT6OnpwcGDBxEIBLC0tIQrV65genpa\nNM8tFgv8fr8wL+i0abVaDA8Px4E+Xq8Xp06dAhBrsHrs2DGsra1JOZHX643rZM7ghBfDjh070NjY\niB//+MdQq9U4fPgwHA4HduzYISWoRUVFWFxclEoDAlbUogUgQCP1tTc2NhAMBkX7es+ePXC73cKC\nysjIwH333QeHw4Gamhp4PB5hHpIlcv/998Nms6Grq0sYGKurq3jrrbfkezIzM3HmzBm5iGjIp6en\ncfjwYczNzaGnp0dAvfn5ebz77rswGo3CZsrOzsb8/Lz0EnC73dDpdFhcXBR5I6/Xiz179uDgwYMA\nYlUa4+PjAqgR7Pf5fNBqtbh06RL8fj+cTqdcrNQW5N6MRCL42c9+hvn5eUQiETzyyCMIBoPC0gJi\nxv7q1avIzc3F1atXUV1dLQxcg8GA/v5+YXQxwHjjjTeQnp6O5uZmkXHKyckRx5OB7QsvvCAatgQ5\nh4aG0NjYiAceeABPPPGEsPuys7NFnolMia6uLilTJHBBgJIBzK1NJFkZwL3OLDo1fbOyspCfn4+p\nqSk5qwwSi4qK4Ha7YbVapcHuRx2UenrttdfQ2NgoFRUjIyMIBAK4dOkSEhIS8PDDDyMUCuEf/uEf\nAMTY3AMDA6ioqMDs7Cyi0aiwNd1utzQx93q9+Pd//3cAkEQVAxs6HwAkaDCZTDCbzeJYkOmcnZ2N\nlpYWOWMWi0VAQSZJeMaWlpZk3Wtra0XvkKy7hISY/ns4HBaW5fr6ugR8LPkHIH1DCDarVCpx+tbX\n1zE/Py9SWQkJsaaPrM5pbGwUbeC5uTkAseDt+PHjMBgMwgBWliUTIFGyJAhEXL9+HbW1tXGyDPyf\nTqfD0aNHBcQeGBhAZ2cnVKpYE8OSkhLk5OQgKysLnZ2doqOvUqnwp3/6pxJs6XQ6DAwM4JlnnpHe\nLmy6TkmPuro6XLt2TXoArKysxAXUvMPC4bBIPPX19YmGPxBL7IRCIWH3ENDU6/Win2u32+FwOIRZ\nEg6HcerUKbhcLhw+fBh6vV4SibTPGxsb6OrqEs18glN0Fjc3N1FdXY0dO3ZIE+uenh5hBwMxwMLh\ncEgj3c3NTdhsNoyNjeEHP/iBaA+Hw2EJ5BksU16FjmtzczMqKiqkZ87m5qbcH9SwJQhSX18vgA9t\nCINe2o1f//rXuPfee5GTk4O2tjaMjo7C6/Vi37598nsEjLiHOc8sy1fqGNPpZ2DN+4MgK8G4U6dO\nwWKx4MiRI9I34+MOvV4vuvycEwb/SiCSVWhMlFEygmeEa0TbAkBkFdrb27G1tSUyP0lJSXA4HLBa\nrSKboCz1JlDp8/mwsLAg/TyUkhvKZ+NevxUwJ0CilGBhIE8GusPhkCaLycnJcj60Wi3y8/NhNBrl\njqHPRdmmzMxMmM1m8S88Hg+sVivW19cl2U97QtkNAvjKagXuL74PwRTl89MGKlm4ymQLARPOjbKf\ngFJznuAuAOn5oCwlb2xslKCXkhRbW1txyQsmQfid1IQNBALIycmRBIlSwoi/Q5Ywq5hoV1mar6xq\npK4+KxwrKytRXFws8ltcD55LriuBQVZArK6uorm5GWazWfwBzhMHE0fcI2Tq8yzzXZQMQK4jExOc\nZ84JqyK4FkoQi38qATz2imFS7da9zLuPn8Xn5vyy8pTNzJVNpg0GgzwP5QmUFTRcS0o50UdnfMM9\n8lHL4W83+DysZlHOL/dXKBRCIBCAx+OBy+XCzMwMfD4fDAaDgC8lJSXC4qdNYsLd7XbHJTyU+t68\nC3iWONesMrRarThz5gxaW1sFAFDKVajVajidTphMJlkP5foqbRp92tXVVfh8PoyMjODs2bPyPDxD\n7G2kBHJOnTqFU6dO4bOf/SzMZjNcLhfMZrPYYtoNxlGU/rpVWoyJIqWtUf43E3oElej7K2VJuDbR\naFSkYOgzJycnS9UGKxGViTaClmSGhkIhsUm0a+FwWHqHcL0o70E/C4glx8PhMAYHB6FWq4UQRokv\nAqdLS0twOp1wOBwijbq5GWswTknF1dVVXLx4EePj46iqqpL7kMQN+hTRaFRAXfblof8cCoWwsrKC\n5eVlZGVloeQ3cmBbW1sS6/BMqlQx2SKHwyH9j3p7e+FwOJCRkYGsrCz5nszMTInjmYDjn5STov3j\nXsrJycHy8jJsNhuCwSCKioqQkpKCAwcOSBUM7TltACtu6DsywaS0kQT6lbaD95ryXuE5pjReOByO\nk2Hc2tqSGJt2h/r/rDDnPruVhPNxhzIBfruhlHlh0mpra0tilJSUFEkU3e6zlTGDslqa9wJjjQ/y\nnPRRAoEAXC4XOjs7MT8/L8oFvOfon6yvr6OsrAxms1kq4JWS0LTlrIjl91CKLCcnJ052k3v+VqLF\nrXfn+40PW7lBUgpws6dHSkoK8vPzUVJSIn4j7QeJH/wdv9+P9PT093yvEsv4oGN1dRXFxcXQ6/Vi\n/xhvMJl4u8HzEA6H0dXVJXuhqqoKTU1NiEQiQthlsjchIUFsSDQalQp4nudQKCTVqOwPSlIKB4lx\n0ehNWTr6UlqtFkajUWyQcr75O2lpacjIyMDKygoMBgP0er1U4NntdvFnGacVFRUhMzMTJSUlcDgc\nWFxclH5wH1U7///2YPUF4z8Shz/suJMA+MMa/+fqx+6MO+POuDPujDvjzrgz7ow74864M+6MO+PO\nuDPujDvjzrgz7oz/X487CYA/rPG+CYC1tTWsrKyI9Et1dbUwSZqamuB2u1FcXCxlwYmJiXjjjTdQ\nVVUljNRwONadneWZ1DXV6/UYGxtDXV0dHnzwQfj9fmH6JScno6ioCCMjI9jY2IDP54NGoxH2eUJC\nAsrKytDX14d33nkHAKQ0KxgMoqamBhMTE0hPT0d1dTW6urqE5en1eiUTSnmNnp4eJCcnw+v1IhQK\nYWJiAhMTE9jc3MTU1BTGx8flfVpaWmAwGFBQUCBlS8XFxdjc3JTfefLJJ6HRaGCz2eS7JicnEQgE\n0NzcDJ/Ph6eeegp+vx87d+4EAJFpIGMSgDRMIVugrq4OFRUVUnI0NjaGmpoamM1mXLx4EUCMLUvt\nOpagkwFDXbLs7Gw8//zzeOCBBwBA5I78fj8SExNhMpnwzjvvoK2tDXV1dZifn0d2dja2b98uh5j6\nmLOzs5Kdd7lcKCkpkYwuS487OjoA3MzGjo+PCxuFDODe3l688sorSElJQUVFhWRxz5w5I1n71tZW\nZGVl4erVq8LKbmlpwYkTJ2C1WqXxdH5+Pnbs2CE65yqVCsvLy7KPCgoKMDw8LDIuQKzZidlsFhmG\nQCCA+++/H7/85S8xPT2N7u5u0djj6OrqwsjICCwWCx5//HEYDAYsLCxIiWxycjKGhobwb//2b8KC\nXllZQUZGhjDPWQWSk5MDg8GAjIwMNDU1IS8vT7LSlNBaXFzEF7/4RVRVVQlDimvHckqe1bq6Oik5\nZTO7cDgs/TRYknbt2jV5J7Vajddffx2NjY3CrktPT0cgEEBPTw++9a1vIRKJwOl04syZMwCA0tJS\nFBQUID09Ha+++iqmp6fxl3/5l+9nWn7ncDgcmJqaEhZMRUWFyBvl5eWhsrJSGus8+eSTcYxW6kOq\n1bFmidT2Ly8vh8/nw7lz57CysiLMB0qIOZ1OKTsPBoNybiixQPYOEGN+GI1GRKNRvPrqq8J0pgwD\ndVztdrs04xsbG5MKFIvFgoaGBoRCIdESZym9sox8bGwMBQUFcLvdKC0tFRaRw+GQRq1KSQsAwuI6\ndeqUlLeTNUImYFVVFQYHB9HT0yPv3tbWJlq1ZFiyqRcZUCyF59ja2sLVq1fxZ3/2Z8JuI4OR7M20\ntDRhSXZ0dGBtbU009qempuRcRaNRjI6OwmKxCMuYrCK1Wo1t27ahpqYGBw8eFIYj2TopKSlSFsqK\nnrKyMvz1X/+12FWuG9l1arUa5eXlcdIJNpsNmZmZyMrKEqYrmz2TATEzM4NLly7h8OHDAICHH34Y\nk5OT6O3txdzcHPbu3YuMjAxUVlaipKREdEo1Gs17SmY3NjakkbTZbIbBYEBOTg66u7tx8OBBrK6u\nivQc5brINBkYGEAoFMIPfvCDuHfi/AOxu2BychJqdazJF7VlKRFFNi6Zz0A8o5ll7m1tbejv70dn\nZyfcbrfYBTLYx8fHMTc3B4PBgOPHj0Ov1yMvLw/Hjh0TlmUkEoHD4ZD9mZ2dLdIsZK+rVCosLS2J\nFMbq6iqmp6elqoNn1Ov1IisrC0888YTYuE+KXZOYmCh7jwxopd64clAblnILfJ9IJBJna+k7kDXF\nzzIajdJA3GQyobi4GFNTUwgEAiJrFgwG4fV6pcEh+zasra3BarXCYDAIK5U2QNkEGICwqfhvZBor\nNU1XV1cxOTkpEm+FhYVS5kzmMRmKtJ1K6RY2S+b5VqvVyM/PF/1wMtlzc3OFDUbmJO96PjcZ58r+\nBJubm+K7KCvWlJUBSjYbWUu36tSTdUj2ubJJMBnEyj4DCQkJqK2tRXZ2NmZnZ6VqkD6aUhotLS1N\n9LUpT0GZNJ4p5XPzMziPtLWhUEj8Ps6b3++XHgG8I1JTU+MawpL9qqzWYtl7SkqKaFZnZmYiLy9P\n5GB4fpQ9AMj2ZAULqxFSU1OlqkGpmc/9Qf+GTX053ywjV+rHK9nYZDNTdohs21slNjiUUimUVFJK\ncG1txRrjAjF/kFV5SpkKrrff75d143ngs1Oqg5I79Af4+7fKXnzUQdY0m0gr97KS5e1yubC8vAyn\n04nl5WUkJydj586dyMnJkbtBKTvDtdfpdGJTKLvDijHuE1YVUqucvWoyMzNhMpkwOzuLGzduwOPx\nCCu9tbUVaWlpUn3N3jh6vV7kdBh/KKuNuD7Uix8eHhZ7xLXUarUYGBgAELszS0pKZJ/W19cjISEB\nPT09OH/+PHbv3o377rtPpHiAm9KBfDdKzAAQ/4YVt8o9QT+CdpV3K+22UnYMgMwbzyvP9fr6uvSa\n4F7js/GZlLKj/CxWhScnJws7HoC8i8vlEmksnjdWD1ksFlRVVYkPwD9pu/V6PaqqqqRS9MKFC3j3\n3XeFXQ3E7oLV1VW43W7Mzc1hZGQEpaWlaG5uFtvCyiHeM9u2bcPMzAy8Xq9IqAWDQZHbYTNtpX48\n98HKygpmZmZEem9paUnma2lpCc3Nzdi9e7fMd19fn1RYsm+XshfIysoKsrOz4+T5yPBn1WNiYqL0\nDGNFIatggNi9S6Y4q39oh4iB8A5RykXR/vBZKE2itLGUxGJc7/V6RWZueXlZfpcxvLKfEG0BJfw+\n7nC5XFItcrvPU/pVSvkjsvZ5Xn7b796uMoB9wig7BkBipls/h+csEAjA7/djenoa586dk4oJlUoF\nl8sV1xOIa0h5ydzcXPl8vnNKSor0lOJ78NkCgQAikYhUAVPKjmvNz+Fa/La+B5/E4Bnj+5Htr1ar\nEQgEoNVqYbVaMTY2Jj3K1tfX4xpnq1Sxno6sfuP4sOx/IFYhrKyEoSoCAJGYvd2g7WT/ADL3I5EI\nlpeXRdKrublZKnKSkpLQ3d2Nc+fOISMjQ2wAe641NTWhrKwMJpNJ+iDeKsvE92W1FTGcgoIC6HQ6\nHDx48Lf2LdzY2IBGoxF/vqGhAWVlZVIZXFRUJJKZ7HnqcrlQWloKg8GA6upqTE1NwWazwWQyQafT\niQ3/f22wKikpKekjyf8AdxIAf2jjfXehz+fDtWvXxNhbLBYBlXJzc+OCmoSEBDzzzDOoqKgQPT8g\ndkiSkpKwc+dOAYCcTiempqYwOTmJvXv3in64srFVSkoKjh49CoPBAKvVioSEBAmko9EoAoEADh06\nhJ/85CcAYiV7Go0GJpMJdrtddN+Ki4sxNzcnmrvz8/NxOp9qtRqhUAgulwuBQAAlJSVISEjAtWvX\nUFVVhT179sj3A7GmJUajEbt370ZWVpZoer/yyitipF0uF3bv3o3GxkaMjIwAiDXS8vv9WF5exiuv\nvILOzk5UVFRI2SIBJwZfHCMjI/D7/Thw4ACysrIQDAZht9sFiBkdHUVVVZV8t9frRWFhoZQmMdAP\nhUIYGxvD9evXkZiYiJqaGkmorK+v44033oDH4xHH6+zZs8jIyMDIyAgOHDgg5cMcycnJGBkZwaFD\nhxAOh+Hz+eB2u9HY2Cj65FtbW3C5XCJzYjabEQ6H8dWvflXkMcbHxzEwMIC+vj5J4Jw7d04kpO69\n915otVpoNBrMzMzAbrfDbDbjyJEjSE9PlwtCKTPB5sfcU+fOncPo6ChaWlqkcY7L5UJzczMOHDgA\nINYMh7JKNptNNG6j0Sgefvhh9Pb2or+/H9/+9rclKLl06RKysrKkrJZN7yhZpVKp4Ha7pXyMzxYO\nhzE2Nob6+nqkp6fjRz/6EQ4fPiygot/vR15enpwHt9uNY8eOiePLwNbj8Uip/enTp1FQUCDljz09\nPdi/f7+ABnRGWcbFYGhgYADHjx8HADz33HNob2+XdUpJScHAwACuXLmC3NxcXLhwAQcOHIDJZJJy\nc+oqrq+v4/HHHxfppo8zCHS63W6Mjo6KZqbL5YLJZEJbW5uUTaemporM1tTUlICc9fX12LZtG9Tq\nWCNmpZxRZ2enJBtnZ2cRCAQQDAYlSIlEIlhdXYXFYsGjjz4qIM7w8DCAmB771tYWNBoNNBoNOjs7\nEQ6H8eyzz6KhoUEa3/7yl7+UQNlqtUKj0eDFF1+UBKJOpxNtvbm5OSlxpOM2ODiI0tJS0dV95ZVX\nkJ2dLY1gleX9HJwr9tQg2EuJq3A4jJaWFrz2vjGfvwAAIABJREFU2muSMGM5IxuVAzHbr/xsOtBK\nLcrnnnsOx44dE7kdBguUe2CDVn4m7WVSUhLy8vJQU1MjPQ9OnDgBu92O5eVlcdC5l4PBILq6umC3\n2/Hkk0+ivr4ejzzyiOjLMpjLz8/H66+/DiAWCBw8eDAukKcM3KlTp6Rp8uXLl/HCCy8AiDXaveee\ne/C5z31OtB1ZqgxAgkClw7e5uYkvfelL4oT/+te/lsCHklAJCQloaGgQu2GxWCR4VavVCAaDuHjx\nIgwGAy5fvoxvfvObSEtLw8WLF+VsvvXWW0hLS5Nk5OjoKL7zne9ITxee6ZWVFQks33zzTRgMBqyt\nrYneLteUSR6WQdOhJ9ihlI/QaDT49Kc/jby8PLz88svSiF3ZaGx5eRnr6+v43//9X7S1tcFsNgvg\nlJ6eDrvdjoKCAvELKEdGgJ1Nj3n39fb2IiMjA0VFRXHamcFgUAA7yh4Bn1wQppSDSE1NFe1i7n0C\nSBsbGxgdHZUkCTWzeX9T6kAJSPFzlXsqOztbzktSUhIyMjLkvgZige+7774LlUolCQWuJ/X5b22u\nyrUFboLKBKV5FxDEZpPZubk52O12rK2tIT8/H3q9HhqNRuwiE5tGozFOz1857/QHExMTodFoZB+x\nrxDnlFIZDNbphynlNnj2ONj/gyAKgW2CLwQaOcckm9AuKQFcnheul1JGhgE+QR4mREn4YKNTJVC8\ntbUlICgD9lv1oikFwnkiKEt5OmqBM4m9tLQUl8yjNBsTOGlpaQLEhsNhTE5OIjExUXTRlcE9JbaY\nWOW7EwDlvcDnY0NvSlZFIjebzXN9GKArm2a6XC709fWhqalJbD2TMNw/SgCC68UeJARaSFZR7gHO\nJYNxpcQMZQzpW3HNOAwGA/x+PxYXFwX0odY+9w73ilLWhYlJJYh3O5DhkxiUe6O0Kt+bdyj9vt7e\nXgQCAWRnZ6O2thY5OTkoLi6OO/O0rUyEKKWneC6Y7OA8KWVr2BOJgJxWq8Xdd9+NGzdu4Pr165iY\nmIDP5wMQ85sLCwulp1lubq4kInjf8jxz3gh+vvTSS+jr64u7a7nGXCMmmcPhMFZWVsTHKCsrg9Vq\nxeLiIqLRKCYnJ3HXXXdhfX1d4kSl1JHSBpNwQbujlKXa2NgQvXsAcZrSyoQA5bYAiJQRzxXtBAFj\nj8cjCSbOAfcSv4P+NvtkAJCEAgFagtSUT1Kr1bhy5QpCoRAOHjyI2tpa5OXlxX0PpZcIPKWnp0t/\nOMrEZmdnY2JiQnzVaDQqMj2U43W73YhEIsjPz0deXh7S0tJE+hWA3Mfj4+MIBALiP5pMJmxtbWFi\nYgLz8/Oor6+X33G73ejq6sLi4iJWV1exsrIi/iVtsU6nQ2FhYZx8DPsSKBPEJpNJYq1IJIKKigqx\nZz6fDw6HQ96rtbUVra2t0Gg0kkig1JfSrvD8M0FAm84kAONFrif/pK3gn1tbWwgEAnL3MHHIPenz\n+ZCZmYnm5mZcuXIFDocD5eXlqK6ujpOq8/l8kqRhb4SPO5Q9RtivTjluB1YqJU+YyP6wNpFkBuVZ\n5TnleVNiIcFgEIuLi7h48SLm5+dht9slKc0kLiVf6f8q/S3avo2NDczOzopUC4C4fgw8f2tra3C7\n3XIfKXvMcCiTgL+vQVkt2tuFhQUB8+fm5jA2Nib+yPDwMEwmE/Lz8+MkISlFqbRrHF6vV6RxPujg\nOWHcWFpa+oF+T6WKSYcXFxdLbKBSqfDuu+/C5XLhnnvuQXNzs9hVNnpm/07eBVxHvV6P0tJSkf65\n3V7lu/FPnt26ujpUVVVJjxnlUMpB0Sdua2tDeXm5nBHu0czMTOTn56O2thZAzKaNjIwgEokgMzMT\n9fX1GBsbQ29vr/Si4334+0wcfdChTEjSr6NNVP47iTEAPrLs4Z0EwB/WeN8EwI0bN7Br1y4x9sPD\nw6irq8P6+joCgQCqqqqkieqZM2fgdrvhcDhQXV0thruwsBB333035ubmpFHUO++8g6WlJczMzMDt\ndiMrKwtarVYM8sbGBrq7u9HY2Ija2lrk5+djcHBQmoisr69jcXERMzMz2Lt3L4AYG7ujo0N0tOmA\ndXR0oLCwEBcvXhRWNL+HziH1xO6++27k5ORIMxnqLX/hC18QsHxoaAglJSXCbPL5fLh06RKcTidc\nLpcwfGtra7GysoK6ujoAkOZMs7OzGBwcFCNGRoderxcwhIA5HdT19XXMzMzg9OnTmJmZwd13342K\nigosLS0hEolgdnZWMqpTU1MCIDCYHRgYwNNPPw0glly59957sX37drnArl27hmAwiL179+L69esI\nBoPYtm0bFhcXUV9fj3379mFjYwM2m02aGm9tbaG9vV0a/LCBkdVqxfT0NLZt24bs7GwJWoBYs98j\nR46gsrJSnG4+36OPPopLly4hLy8PJpNJ9LJ9Ph88Hg/C4bAkOp566ilcvHgRR44cQUNDA775zW/i\n/PnzshepZcyeFLW1tRgdHYXT6cSlS5eQmZmJ6upqPPzwwwLMsynf/Py8OM2nTp3CkSNH0NHRgb17\n9+L5559HZ2enNEHcuXOnNGMkGLWysoKqqirMzs6KI0J9ayDGzM/Pz4fL5cLTTz8trI9z585Bo9Hg\n4MGDwhJlQ1+9Xi8NI19//XU88sgjyMrKwuTkpCTT+vr68Od//ucCIs/Pz+PEiRN46KGH5NmCwaB8\nX0pKCkZHR7F37165jPPy8lBaWirB2okTJ6DRaHD06FF0d3djfn5eGvDe/ZsGxeFwGH6/HxqNBmlp\naVLt8XGG0+lEZmYmXnvtNdTV1QmAn5qaitnZWfT09OBTn/oUVCoV8vPz8cUvfhEA0NnZiQsXLsDj\n8UhAyDMQiUQEhCfzHYix7HkhM1GTlpaGL3/5y2hpaUF/fz/S0tJQVVWFiooKADebq+7cuRN6vR5v\nv/22AKZdXV24cOGCJKQ4t9nZ2eKUXb9+XXqhFBQUCKOGTLBr164BiDHmU1JS4HQ6MTIygtzcXBgM\nBlRWVkrTVQKTygZJ1OUlOJieno6vfOUrcubS0tLwwAMP4Ny5cwBiTss777wDq9WK9PR02Gw25OXl\nob6+HqWlpQKyKB2Wf/3Xf8VnP/tZhMNhafgYiUTimnCZTCZZNwACcJENkp6ejqeffhp79uxBW1sb\n/ud//gcGgwH79+/H66+/jvPnzwOIgZNkZjscDvT09KCvrw9tbW0oLi6WhFAoFJL7gJq/rP6hw8qk\n1tGjR5GamoqJiQnZD/X19bjvvvsE/AEgCVqy91g1RocyNTUVVqsVbW1tWF9fx/j4OMbHx3H58mVk\nZWVh//79wr5nwqW3txdJSUmwWCzY2trCyZMnsbi4iLy8PHz3u9+VZnnHjh2T5ygqKsIvfvELjIyM\noKurC5/5zGfgdDqFbcuES1JSEiYnJ2WfKhnBZH56vV4MDQ1hz5498Hq9AtQB8bq2ymaNbOKWm5sr\n+q+cAzYeZpK4u7sbLS0twhynjjTBFwACllADloE173WtVoudO3diY2NDNEa5fmq1GnNzc5idncXk\n5CRUKhVycnLw0EMPfSR7oxxKNiABMQaEwWBQzvPs7KxUNpKJtrq6imAwiPLycgEplJrUSmeaoA8D\nElaNKSt2gBh4mZmZGcfYZ68Kv98v1UrUTOf3EFAnw5VgH9eERAwmpci65P8PBoNobGwUNieT67cy\n7anzqwSCCY7xvQoLCyUBMD8/L/cn9xpBc841gyQlwMNEHwdZ/Bx8TwJIZHre2gMEuAm+kVnJf1Nq\n1CuBZI1GI9WDlZWVUt0I3ASkgdj9Sf17Nmhl8zoC+HxWABJI8lnYR4LBFhnEAGQNSCxg8+i1tTWM\njo5K36lAICDrrhxqtVpY+QS+uT+VNo7rQgAsIyNDKhuU+vu3MqrZk8Tr9cr+ZlKDLHnaIiUrj//L\nzMyUigT2HWBlEvcgB5M9vP949/J+ZcUCA1XevcvLy5IYJ7Cr0+kkua1k2iq/k6DdrUxcJmQ+iUHG\nd0pKithcjs3NTQwNDaGvrw/RaBSNjY0oKCiQ+eTdTDAtNTVV3lG5zgCE2Ur2Lc8Y9w6rAkjCov+U\nlJQkfQb8fj+2b98OAKisrBQ/noQIfldGRoacDyVrnnu8uroaVqtVqlZZ2cJkkFarlWSj0+lESkqK\nEHQASJ8ltVotleqZmZmyvzQajbwD4yg2/+U+pk/DtVZWQPLvybYlC/jWajBlopOV0axe4bniPlb2\nqqCNo6/ItWQymfaA9yxj2+TkZKysrGBwcBBmsxlVVVWwWCxxlR9KRjrXV1llwKSg2WzG3XffjcLC\nQmnMOT09jZSUFBiNRni9Xng8HmxubuLixYvQ6XQoKSlBcXExzGaznDH22WB/Ad6hra2tkvDh3HHP\nWa1WzM3NSUNu+r+bm5vIyclBcnKyNCnnYJKOjPr19XVheK+srEgySaVSoeQ3fWdITiARkfHs8PCw\nJLgyMjKgVqsxMTEBIGYDCwsLhcGuTKIpE8a3augrwVEmW+gDE9BOSIg1jabtYPKtpaUF5eXlWFpa\ngtFohNFolHtsdXUVJ0+eFN+rra1NCDIfZ7zzzjtCggNuxg8ZGRm/07bRj6OdViZlP8hQ9i5idSh1\n+rnflD36XC4XJiYmYLVa46rF9Hq9VNrxzqKdSU1Nlb3F/aJWq4V85fP5hMiqHPxZMtpZmXJrNcPv\nm8nNpM/c3Jz4wKFQCMFgEC6XC3Nzc9KbMRAI4Pz58zh8+DAqKiriquaYAGQVuHKdmAxRKhvcbrAS\nkBURW1tbGBkZgdFo/MDzkJSUhPz8fPzxH/+xxJ52ux3r6+tobGzE448/LiQDIBZfdHR0iL1ub2+X\n9wmHw6ipqREs79bE1e2G3++H2+1GamoqmpqapKfJrYN2MhQKYXl5GXq9Hi0tLTCbzdBqte/p7wHc\n3AsajUbUBvgOhYWFGB8fx+nTp1FbW4uysjLxJ4D/O02B2XuH9xNjCSAWcyjvN5I9lATbO+P//fG+\np7a0tBQdHR3SjHRoaAg5OTkoKCiQS3drawuXL1+GxWJBYWEhLly4gOzsbAEIy8vLYTab8c///M/4\n0Y9+BKvVirW1NSkzZDnQ1tZWHPtvfX0dnZ2daGlpgdFoREZGBmZnZ3Hq1ClcvXoVTU1NOHbsmGxa\nfuaJEyfw5S9/GUtLSwiFQlhYWMDly5fx8MMPw+l0or+/P87JLygoQFFREebn5+H1elFTU4O+vj5U\nVVUhEAigu7sbdXV16OvrAxAzCq+++ioaGhr+P/a+NLjN87r6gOAOgFgIcAO47+IiLtqozZJsRfIa\nKbbjWHEdJ5N24iZt0qbL3/zpNE2nP5p2mkyayVbbSWzHdex40WJJlrWLIimKpLjvC8AFK0kQJAB+\nP5Bz+YBx4jh2Ovlm9Mx4bEsE8b7Pcp97zz33XFRXVyMpKQkNDQ3o6elBXl4erFYrDh8+jPHxcWzb\ntk0ME1l8R48ehcPhwI9//GP5fYmJiVLq73a7xUHyer2oqKjAgw8+iMnJSbz44otITU3F8PAwysrK\nhMU/Pj4uxl6v1+P06dM4cOAAfD4fPB4PnnvuOQEADh8+DL1ej+vXr8tF6fV68cADDyArK0suvbGx\nMUQiEWRlZcHtdgsThc0vJycn5RJ//fXXkZubC6vVivX1dXR3d+Pll19GSkoK7rvvPnHY2ehwcnIS\nFotFgtfs7GxhcDQ0NMBsNoujQ2B5cXFRMt4dHR0oLy+Xsq/q6uo4wL2+vh5TU1Nwu90CpEciEdy8\neRPRaBQGgwH33nuvNCwEYpdTZ2cnkpOTMTExgVu3bqGqqgrNzc1YW1tDXl4e/uqv/govvfSSGO7m\n5ma0tLRgbW0N58+fR1VVlTgkBHXz8vLw+c9/Ps7It7e348aNG3C5XNLktru7GxcuXIBWq0VeXh50\nOh1ef/11ABvNU4EYs39gYAAZGRmwWq2Ynp7G8vIyKisrMTIyInunpKQEy8vLeOedd5Cfn4+cnBxM\nTEzge9/7HrKysiQxtXXrVgnQtVotxsfHYTQa8eKLL6K+vh7Z2dnw+XwYHBwUuSGWYgMxJ95mswkw\ntLCw8EFm5QNHTk4OQqEQPvvZz6KjowOTk5PCtolEIujo6EBVVRXKysriSn337duHYDCIS5cuobW1\nFe3t7fj7v/97pKSk4PLly5iZmUFzczOsVqsAUnRoQqEQ1tbW8NRTT8l7LiwsSGCrBrBPP/20MJzd\nbjeOHDkidg2I2SKDwSCyU/wzXqKDg4N44IEHkJiYCJ/PJ84aQQwGVWwITLvERAgrbNgMdH19XeaA\ndmB6elpAsZycHGEO+3w+XLt2TRJtwIZjdPbsWayvxxppGo1G3LhxA//4j/8oUkVMvgHAkSNHJNkU\nCoVEuoPONO0NA02+DwE2ghRPPvkkMjMzJWHLoLKlpUWqNFJSUnD06FGYzWa4XC788pe/xPDwME6f\nPo3c3Fz09PSgpKQEBQUFsq8JkKlNJHt7e3Hu3DnU1NQgHA6jsLAQDzzwAH7xi18AiIEMP/rRj1Be\nXo7KykosLy9LlQ5LnZuamvDiiy/KurJMtKOjA0tLS9i5cydqampw9epVLC4uCtulp6cH9fX1AGIJ\nvenpaYyOjqK/vx99fX34xje+ITJdQ0NDMBgMcc3R2BCuvb0daWlpmJ2dxeLiorBXOa9svsp1CQaD\n8Pl8KC8vF0al2WzG1NSUsIvISAYgQEl6ejoCgYBUaLDaoqamBocOHUIwGJQm0hcvXpSmfGQn3rhx\nQyS79u/fj8zMTCwuLgrgPD8/j5qaGmFs3blzBwaDATabTfb/2toavF6vJEJzcnJw4cIF+Hw+ZGVl\nwWazob6+HhaL5bc2H/uwg6AQ9zJBYUoaUNJrYmJCymGDwaAErbW1tXGsf1V+R20mTJCUATDPLWU3\nNjNS3W43tFothoaGxA7Ozc1henoaBQUFcVIW6jsQuOX7cM0DgQAmJibEXicmJkpwyUa4BQUFKC8v\nF9YhzzmfjQG3ymTkmVdZTkajUVhig4OD0lBeLftX7asqz8F9oNfr4xpwcm0IlhEA4D4m2EdJDZVV\nTYCIEjZMaLA6Tk0W8D4l07CoqEgYtEAsgI1GowLGsmKEoDkroFi1yt9FeUTuC61WK0CDOjdqQzpW\nDUSjUUxOTsJsNiMajWJ2dhYGgwElJSXIzMwUcgqwERhzH6oB72YpMg6yVWn3yI5VEyabm1l6PB6R\nEiFgTCCSgD3ngj47f5asfIJ//B6+O9dPnSsmMdT55ecpXcr1JkkjLS1NEjKZmZlxYAP3lEra4O8n\nwKWCxOpzfByDwPPi4qK8B989EAjg3LlzyM/PR1NTE3Jzc8VGMDlPiSwgBnzx7ktMTERqaqpIh7E5\n8OLiIqxWq+wTJgTdbrc0fFWrKwwGA+x2OywWCwYHB6UpKwBJ9nHdCdBQBpOg6WYgl3IP7e3tGBkZ\nEQkrrpHJZBKfiueYe1OVFiSBy+PxyP7nPLDRJcF5PgMTlZurE3iXMvGhrrFaQcU9DcTIBrRFAIQI\nMT8/LyAwQTjufdp5ylHSNtNPW15eFl+M+45kj+HhYUSjUZSVlaGoqAhpaWny8yr4z8+olYyqjaRN\nCIfDqKmpkbmx2WxwOp2wWq1x1UvLy8viVwYCAQwODgooyia+Ho9HEjMejwerq6tSqczG4zxjZWVl\nSElJwbVr1zA8PCzPk5qaCqPRiPX1dbjd7rjqbib7mXBhgmd8fFzIgUlJSZKMBGIJ7dXVVRw+fBiH\nDx9GMBjE66+/jr6+Pql24XvwPOTm5uLw4cNSyaZKGVIKgwlR9b7gfKrJaO43JtxY3UCbS1k0k8mE\ntbU18YNYVcGkWFtbm9zXlGSlhPAfOnp6ekQCxW63IykpSSruVRlPdZDoAEDmgskX7hcmzlUpJJ4x\nnptoNIr5+Xn09/eLvSMxcnM1DImAqhQTfz8AIauoNkar1WJkZAQulwtWq1XIFlReUFnNHLy3iDvw\nzKjgv1ql8MccrPyijAywQYQoKChAdnY27ty5g5GREXi9Xuzfvx9VVVXiS6nYGBOl6mAl1O/ThJlJ\nFCYtL1++DJPJJMTY33ekpKSgoKAAn/3sZwFsxCjcZ+ozRiIRVFRUwOFwxMlF0S+w2Wzv+x207ZsT\n9iRazs/Po6WlBVu3bo2r+FXnBYjhVRMTEzhw4ADKy8thMpk+8L7n3UeFAq1WKza6q6sLHR0diEQi\nKC4ulpididv/qxEMBrGwsICZmRmMjIygv79fbDVjc967lAGtqKgQGWLuqw877lYA/GmND0wAVFVV\n4dKlSwIQMuO4uLgIn8+Hjo4O1NfXY2hoCA8++CACgQCOHTsGl8slrA673Y5IJIKWlhacOnVKgDy1\n7Lavrw/9/f2SzUtPT0dubi6Wlpbw8ssvIzc3V2RvbDYbHnnkEZGLoDGYmZlBeno6jh8/LqwJp9OJ\nkZERPPPMM9Dr9XjvvfcwOzuLPXv2AIhpJE9NTUlpoMlkgtVqxaOPPipd1R966CGcPn1anOuOjg48\n9NBDKC4uxsTEBFJSUlBeXo7S0lJoNBrY7XYYDAaUlZXFsTF/+ctfwmw2S5k0HVq/3y+d669cuYJL\nly6J/A2zqwSJU1NTkZeXh3fffReBQABjY2PweDxwOBxSyl9YWIi6ujq5+C5evAi9Xo/y8nIcOnQI\nJSUleOedd9Df3499+/YBAO6//35Eo1F0dnZiy5Yt6OzsRH9/P+677z7pbeBwOOD1eqXXgJql3bJl\nC2pra0Vv12w2Y21tDdu2bYPf7xewbHFxETMzMyKppNPpUFtbi/T0dJFM8Pv9qKmpEQNkMplE9mh8\nfByzs7P43Oc+h6amJgkUyICizj7ZK9FoFENDQ7h06RKcTifuvfderK2t4c6dO6ioqIgz/kwYtbe3\ny15bW1vD8ePHZV+ur6/j4MGDIjOSnp6OqakpFBcXIykpCWNjYzAYDOjp6YHVaoXf7xcNejqJZ8+e\nhdlshtVqxaFDh+BwOHDz5k10d3djenoaV69exbZt2+IANoLr1PkPh8NyxoxGI+x2O3Q6HUpLSyVB\nw3L4kydP4rXXXkNSUhLMZjMcDgdWV1dx6NAhjI2NScDNs3ry5ElMT0+jsLAQJSUlcLlcaG1tFQCk\nsrIyTjOPTiMZmVyDjzJYykkplbKyMszPz6O3txcJCQnw+/0iUaGCAwaDAXv37hWQjGy21dVVVFdX\nw2AwoLCwEMePH5fPeL1eRKNRNDU1STk5v3tmZkZAlqmpKakwSU1Nxfbt2+F2u6VfxLFjxwDEGDSh\nUEikV376058CiFXmkLnh9Xrh8XhQVlaGpaUl9PT0CDBLLXkA4vQ8++yz0tvDZDJBp9Ohu7tbJB82\nl6XeuXNH9hvZNQw+EhMTUVZWJvYNiCUNWX7N4HZxcVFYIwSCCPABsUoWSrYwwCTLaWVlBZOTkyLj\nQ4CNwR2dfY1Gg6ysLPlMamqqAKmU3ABiQXlJSYmUkN5///2Ym5vD6dOnodVq0d3djZmZGQwODuLF\nF18EEAtI8/Ly8MADD8BoNOJXv/oVGhsb8Q//8A+yp2tqatDY2IgrV64AiMkBDQ4O4vbt2zCbzTh2\n7FiczrVGo8GVK1cQCARE6mr//v1ITU1FX18fRkdHcfjwYbjdblgsFrS0tCASiUgFDm1mWloaSktL\nUVZWhoaGBjgcDtHOXVlZgdFoRF1dnQCuAETOieDj5OQklpaWhB1OxmA4HJYqCN4RwWAQ8/PzWFxc\nFJClpKREElU3b94USS/KExFwJCM1GAzi7NmzeOSRR5Cbm4uFhQU5Q6wo0ul0ovNZWVmJQ4cOYXx8\nHD/84Q+Rk5ODyspKAdCBWAUV9Y4rKyvlXRgQhkIh6HS6uL4WjY2NaGhokDVeWFhAXl7exybLwaCe\nIDuBWZfLhba2NrFv/Lm5uTkBSHbt2gWTySSgIc+fyvjmPuCdoEqkECxTS+oJnDU0NEifI5fL9RvV\nXBaLJS6YZaC4tLQkLG4G12Q2zc/Pi02bmZnBwsKCnG8Cj6x8SE9PF5YYzwMZ/kxe0rejzAwrE9bX\n1+Usp6WlYW5uTnrLkDFMgERlonPQDqpSHQTmNktGqPq/BIUJ6gGQ5AbBCj67OrhOauWAKrdWUlIi\nbOvbt28L697v90uijL0TDAZDnAwR549AAys7yIDlfUwQkDaDe8btdiMzM1O0jYPBIAwGA4qLi2Ey\nmSQpyPlmsoPAIKsTVJY/wTQCZmRiEoQnsKLKoXBd6H+zx9WWLVsEvFM131XpKp4NAtSMBTYnXhjE\nqwx2/j6yYjkIHiYlJWFxcVGYyECM0ebz+YQpSv9UlVfiXlCTW9xPrNCIRCKSHAUQB1B91EFQidU6\n6p3T2dkppCruQT6/mhhhjEKQl4xN1f9kpR7fm4MADGMnknl4B7D/w+LiIjo7O+NsAOed54jrqe4B\ndd/wrmJV2eTkpEg9UtpFrW7i510ulyTR8vPzhUXN/hMEhLmutLWqVJ2aaGVii5I+HLzz+bOsYKBN\npW3hs7ECTD3jnE+PxwOj0SjSlOog05p2IBKJyHsHAgG4XK44kD0hIQFTU1NCxHI4HJI8oIwDWfRq\nQkOdeyYEmLBRZclI/kpPT0dPTw98Ph+CwSAsFov4ZkxystKH9jYUCondYc8Dn8+HgYEBqUJl0pYS\npvRXLBYLXC4XtFqtSMyx2j8xMRHV1dUSc7CvgMViQWJiovTCMBgMsq9TUlIwMzMTV8XhcDhQU1OD\ntLQ0LCwswOl0ig30eDwCsNbW1gKIxYN9fX0oLCwUW0j7oCbsaU/V/aUCaPx5lYHNnlX8Hbm5udI3\nh/KKBDdZ5bS2FuvjRnKHzWaTO+ijDJ/PJ/3cLBYLqqqqUF5eDo/HI2trMpmQmZkpcefS0pLYGibh\n5ubmZE+trq5KhbKaAFCrBlhltLKygqysLBQVFYk8IIF3xnrARg+ciYkJjI6Owmq1Ym0t1mdH7emj\n3ucajUaIg4WFhXEgPhP0mwcTdbTDXAPc0m49AAAgAElEQVR1/DHBfyaw6Bc6nU74fD7B0zIyMiQJ\nDGzEVPSZmSSkzQYgpA5KiXEkJiYKrvR+Q026qj2r2traoNVqBWv4sIOSSsCGnVSrTDm4PjqdTioQ\nAAj54bcN3o/EX4DYuRwYGMDQ0BAWFhbkrJWXl/+GhK6KKSYlJWH37t3IzMz8vWMMVpCQFBIIBJCf\nnw+DwYCxsTF0d3cDgCRP+H2/TyLmo4719ZhM7HvvvYcbN27A6XSKCgsAIRfSxzGbzTAYDJibm8PV\nq1dRWVmJlJQUkau+O/7/HR+YACCLmQy89vZ2JCcn4/HHH8fExAQGBgag0WgwPT0trJvCwkIEg0Fx\nvtnk9eDBg2htbYXP54PZbMbs7Kw4LzQAPKy1tbUik9Lc3CzOPqVuDh8+LDIcDMjppNCpIeNz586d\nIkvxyU9+Mk57Kz8/H9u3b4dWq8Xc3BwuX74cp5FOwL6rq0tAuYMHD+Izn/kMlpeXMT4+LrrEPp8P\nzc3NaGhokOaF58+fF4mZr33ta/D7/bh27Rq6urrECXj++eflEpycnMTy8rJI5tx///0CvK6uriIn\nJ0dkT6qrq8UZSEpKEmbA8vIyhoeH0dfXJ4FzUVERHnvsMQFSDh06BLPZLMEzS1inp6fhcrlw48YN\n7Nq1S/TJh4aG8K1vfQuBQEAO/q5du1BQUCCONJ06As3bt29HTU0NTp8+LYElM/IsF29sbMTAwABq\nampEO7KiogInT56Uy+v48eOix2+xWLB//34J4BYXF+H1ejE2Nobbt2/jwQcfBBBjo3k8HjGoHo8H\nfr8fExMTmJ+fR1ZWFrKzsyXgBmIB7NLSEgoKCvDggw8KQ5wME7LMcnJy8IlPfAJATHKGTRxtNhtO\nnjyJcDiMnJwcXL58GS6XCzqdDmfOnJE5aGhoQH19PZaWliQw6+rqQlVVFaampoT9rjaJWl5eRkFB\nARYWFgQkYTNFJpF27NghOu9AzIn/yU9+Ao1Gg4MHD8JsNqO2thYXL17EyZMnhcVFlhEQM/bDw8NS\nBTA3N4fs7Gy0tbUhISEBu3btgkYT07xW2aYM6JaWluJK1//QEQ7H+oyoDbYzMzNRUFAAu90uoKTX\n6xVgAohd3jMzM9i3bx/MZjPee+89vPrqqzh+/Ljo7vt8PtGPBCBVAY2Njbh9+zaysrIkgWQ0GgU4\nWF5eFpA0HA5Lo1C3243V1VW0trZi//79ohFIMOyZZ54BAHzve9+LkwlzuVzIysqCx+PBpUuX4Ha7\nsb6+jrKyMpG/YmBKkGh2dlYcl4SEBGRnZ0tZsVpp0NbWhlAohOzsbNTV1WHbtm1wu91YXFxEbm6u\nyLh86UtfkjV84403cOnSJSwtLUkA6XQ68fLLL+Opp57CysoK3nnnHTmXZLCqch08b9TNpBPLvc8/\nI5hFh51JBKvVitHRUfT19UkQAgA7duxAUlISXC4X1tbWYLVaUVhYiLGxMUxMTKC6uhoFBQUYHh6O\nk8wYHBzEd77zHWRlZeFrX/uaBPLJycnSQ+G9994TCaAHH3wQ0WgUN27cwNzcHH7+859LUEQpBb/f\nLxU7QOw+OHPmDM6fP4/CwkJ0d3dLEoksyUgkIo2rgA0N+NXVVWRmZqKqqgpOpxM6nQ7T09NSVaQ6\no9TDzM7OlvJXlWkWCASEechg4fOf/7x89gc/+IE0/GZFEAONLVu2CHBBOSnaRdq+//iP/0BmZiZK\nS0sxNzeH9vZ2vPzyywAgyQA22/zEJz6BJ554AgkJCWhoaMCuXbswNjYGr9crTP22tjZ8/etfR2Nj\no0hLkGHT2dmJkZER7NmzB3q9XvYVzzHlGBiU0P58HINgJeV/mHi5ffs25ubm4oK/YDAoGqgOh0OY\nwpxXFSDh71Y/T4BRBTjpc/F91F4RlZWVcDgcGBoawsjICAwGAxYXF0WWSmVMM0FBoITygmw8T8Yt\n/ZPBwUEB0BITE6UPCwFbyjSx5xH3MYEwNtwjqExAHtgAMQBITyebzSbSOARgVXkYFbQke5t7ncAk\ng1/aH5XxRdCb867qbfOZybhVmbsqY5bni89FwkBycrIANPn5+XC5XFheXhbWM/sEcF7oO1CygdI4\nZHbSxvN5+dmMjIy4feD1eoVRm5+fj4yMDAFazGYzZmZm4PF4kJ6eLutqt9uFMU5bq1ZFcY/Ozc3J\n9zORRzkR7guW4rOagRrA3D/0TSn3xSowsv8jkYgksNX5BhB3L/BZyaZTWdgE2NRgfDMLd21tDb29\nvXFSZUwiswcN+3rwczyvqlyXOvhsq6ur0Ov18rwfF6uN+5XrTd98aWkJ7e3taGxsREZGhuwbgvWq\ndNBmAJ53DAEerjsACfhVcEuv10tVJftAcC8R/CfAxLuZvbOYzOEzqckUrj/tBv0GJn8MBkNcZRTl\nUFVGLytdKI+kPvfExATC4TBsNpuQdQAIU53Vhaxko30lwKjVauWOUZO1PPcE17lOZIGrzHw12cjf\nr0pcARDdff63Oies3PN4POjs7MTAwADGx8clwcLvPnz4MFpaWqQRJ+2RWmVDuS+uL6WLeP9sttEp\nKSlxGtparRbZ2dno6OgQYNzpdEpiinETKzz4PezxQ3ucnJwMt9uN0tJSLC8vi4wl55oVHrQns7Oz\nkmAJBoNYWVlBbm5uXJUvJfLKy8vR09Mj7+D1eqWaizaKc0AAvqysDOnp6cjJyUFJSQnGxsZgNBqx\ntrYmPVeYaCB5gglVFaCk70Fwn74h11FdLyacAcT5xFwD9ezxO9icmAnbtbU1mEwmHD16VH6+qKjo\nY6l6pFwaAKkgHRoaitt3Ho9H4kxWRPJ+YaVAdXW17Cn1fAExgNPtdgtJcXFxUXrl6fX63wCmaR/W\n1tbi9PxTU1ORmpoqNml2dlYIJJTQIkkCgJAqurq6UFdXB4vFIskbNQGv3iUEpx0OR1wC/v9qqEmJ\n5eVlIa1yFBUVoaqqCmlpaXH9z8bGxnD16lX4fD7U1dVJQgWAVH1tHqxu3/xnwEYC3O12w+12w2Qy\nYX5+Hj09PUhPT0dzc/NHek91XtWE9/s9I+0aewCwJ8D7sfc5eFfTfvX19Ync2OrqKtrb21FUVCR+\nOwBJUpHcNTExgfvvvz+uB8kHDUo08i4mTqHX6xGJRJCdnY3BwUFcuXJFiFq8O1X53j9GlQml27u7\nu/Hcc88hMTFRfDq/3x9XfcyKXSZsZ2dnhXCgJjw/zLhbAfCnNT7QsrW2tuLatWtxelbd3d1wOByY\nm5sTZqDf78e5c+dw+/ZtaDQaGAwGfOYznwEAAYZV+YdnnnkGV69exc2bN+HxeJCcnIzl5WU8+eST\nAIBHH30UXq9XNE/ZbK+oqAj33Xcf1tc3dDC//e1vA4CA8F6vF1lZWQKAOxwOGI1GAcvz8/MxOTkJ\nAJJQyMvLg8lkEl01lri1tbXBZrOhpKREHLc9e/ZgfHwcFosFo6Oj2LlzJ1pbW6UzNrPa58+fx/Hj\nx4WtSDBnYmICmZmZ4kD39/cjMTERs7OzAjgQwGEpHVkKQMxpCgaD6OvrQ2pqKlJSUnD+/HmRaSLr\nLBrdaKy2uLiIgYEBjI6OirxHf3+/ZCDNZjOGhoZw4cIFkaEIBAJSKmQ2m3H06FH4/X7RW9+shRiN\nRjE8PIxQKISamhqUl5dDq9Vi//790p/g+9//PmZnZzE1NYXBwUEMDAxImeGrr76Kb33rW+I8MOHS\n3d0No9GI0tJS1NTUCPOdWpt8dr4XAHEcNRqNgDaZmZmorKxET08Pdu/eLY7F6dOnAQDFxcWYnZ3F\nY489hnA41vGdgQ+NP51BOl4HDx6E1+vFxMQEfvGLX6C4uBif+MQnUFJSgtbWVnR0dMBqteLYsWNy\nEbndbmGhMJOdm5srAMrx48dF14/O6E9+8hO43W5YrVbRQafkFYGCxsZGZGVlyT6fnp5GWloavvjF\nLwrYuri4iNraWrS3tyMajeK73/0ubDYbqqqqAEDYPizJu3LlCnJzc+H1epGXl4fGxkZh99KY03n3\ner1xUjQfZdCZS0lJERmnnJwc2Gw2bN++PY4VMz8/H1cGmpGRgfz8fOh0OtjtdszNzeH8+fMoKyuD\nXq/HwMAApqamUFdXByDm3M7Pz4veYE9PD8xmszBs9Ho9RkdHpb8DAGHDRSKxniY/+9nPsGvXLoyP\njyMvL08CheHhYblIH374YVy+fFkYiBcvXsT4+DhMJhP279+PUCiEvr4+lJaWxjlHJSUlomFaWFgo\nbI6ysjKsrq6iq6sL9fX1woggezgUCmHXrl3YsWOHgEeJiYno7e3F6OgoCgsLZV+npaXh+PHj0lDZ\n5/NJufuVK1cwPz8PjUaDPXv2iLPe09ODrKwsSUbQ3jCwYXkugQruFYL9tLHhcBgdHR147rnnkJ6e\njv379yMhIQEXL16UJPKBAweQmJiI7OxsJCTEmub+9Kc/hUYTk1TbunUrbDYb3G639E8gi25+fh77\n9+8XrXaW9waDQbz00ksIBoP493//dwAQzeHs7GycOXMGMzMz+M53voNPf/rT8ufHjh1DUVERvvWt\nbwGIJQFfe+01pKWlobOzEwsLC/D5fPjLv/xLLCwsQKvVQq/XC5jFc8ZghsDUyMgI3nrrLTz00EMA\nNhqTqSxEICY7xpJNVo0wyUImHe9rJgT0ej1OnDiBaDSm25meni5gDUFrlU1LzUfeAwT/v/CFL0iT\nqx/84AdxCQ0+Y0tLC44fPy7gDoNam82G2tpaeR/aeILXb731Fv76r/8aNTU1MJvNKCwsxN69ewVY\nASBEAAJ3lKkgUPlxDAI/BKZZZUD2I5+f68qqQY1mo6Eyn4cBCs+zyrhk+bkKyKhMdBWIJJDM+a6s\nrERCQkzrnPt6bGxM7kxW4nm9XpjNZjQ3NyM3N1dY+iMjI5ifnxcQGYCc46KiIhgMBmRnZ0uinXc0\nAAHpuE/Ue4B2mfabdkgFShlIzs3NwWKxSIUCQU0Cz5x/rgn9ElVTnqA+QTAVUFaZ45xz/j6V3Q1s\nyPwQ7GfwSLCGv5sgKwM0INbnhOxH1SdSexaQTc3nZuJKo9FgeHgYCQkJok1L0JYSEKpcDtnC2dnZ\nIr/G+SE4azAY4PV6Bfw2m80CclLqi2A3k/cEz5ig0Gq1wthNTU2V9eGdQFu0srKCnp4eABDyT05O\njlSaUKZKr9fDaDRKfwTOOxM5PFeMFagdryb1VF+T+1tdSwKqTJanpaWJn+9yudDY2Cj9vdTqDyYo\n1tfXRTYO2JC1oWQH/4xyO3q9XiqqPs6hSo4BsRhMr9dj27ZtQr4gIUE9H2qzWFaOkWzFuaTPotok\nNcHM+UxNTRUmKglcTHBRRohzW1NTI6CtOlcE8Ngslb4qsNHbLDk5GVarFQUFBcjLyxMyRXp6OmZn\nZ+MAckp+ELDW6/XS18rj8cBut4ukJ98tFApJdQH7A+Xn54tNAjZYlwTpWWnEd6C902hi/TDUs6Sy\n/dU7kHuM+2dlZeV9Kzt4P/IdKFFx48YNIZKoybw9e/agqqpKkp4kvHGeeLZV4IiVSyqZhOc+FApJ\nAlS9Q1mdUVxcjOHhYQGseY5ZXcLqMACoqKjAjh075H0oL6LX6xEIBMRGkcDCuQZi4LfVaoXL5Ypj\nL2u1WszMzIhOOYdWG2uAy9iM9yztcmJiovg4/PmMjAyx6Xq9Htu3b8fMzAzm5uYQjUalGSrB40Ag\nIHc5zw3vAO4HJvtVW0WfjvIt3Oc8G7yX+Dk+H+ddlS5R2dyJiYnYs2eP2D01sfBRRlpamjQwramp\nQUVFhRAm+S4kQeh0Ouj1euTn58uzMznP+1QlhAEbbGidTgeHwyF7n+/Gniw8Q/R1AMTZjJWVFWRk\nZCA5ORmBQEAIepwzrmtxcbEA34uLi1haWooD1VViAp9v80hJSREyj8fj+Q3t/z/moCQn9xTtNd+h\n6NdyMtxT2dnZSEtLQ2pqKiYnJ9HW1oZbt26hrq5O1rW6ulp8GLXHCO9pVmQwKQLEzuTS0pIk5qam\npjAzM4OCggLs3r37Y3/n35Vo4XoxrqEUoiortXnQ9yAuduvWLfl5s9mM+fl5aT5OH9rv96Ojo0MS\nDQ8++CDy8vLiiCkfNChFFQqFRK5xenoaFotFZPl27tyJU6dOSaKBDdVZjUmc9P2SNupgpdQHNeVl\nbOd2uzE8PIzXX389LoltMBhEyWBtbQ2lpaVyL9IWOJ1OJCcno7e3VxRUPuy4mwD40xofmAAoKioS\n+RUAGB0dxfz8PLq7u6VE/9q1awL6s4Gpz+eTZrHMeFVWVqK2tlZYAJ/61KeQmJiIs2fPIjExEXq9\nXgKQlZUVyeZGo1GUl5fD6XTiwoUL6O3thV6vF4bu3/3d3wGIOcpvvPEGduzYAZ/Ph3PnzuGRRx6R\n30cGxNTUlDDFo9GYlqrL5UJfX5/oqZGlRKb/u+++K4yvd999F0ajEdPT08jIyEBfXx8GBgZgtVox\nMDAAl8uFaDSKQ4cOobS0VBwkXlJ79+6FVqvFu+++i7m5OaSlpYmmMcuh2OyErCNqeFosFszMzODA\ngQOoqKiA0WjE8vIyrl+/jomJCQAQlhgHAVqWp5tMJinlooEJh8Mwm814+umn8fbbb2N4eBiDg4N4\n+OGHBZjIycmR6gIg1txZvagY4CYkJKC6ulqAH61WKxf/l7/8Zbzyyiu4evUqUlJSxJG4c+cOmpqa\nRBu4oKBAgplAIIDOzk6UlpbC6/VKPwI6k6FQSBo8k/0TDofx2muvYc+ePdBqYw2XRn/dRJlsV7fb\njWvXrgmYX1JSgrffflvKgq1WqwS+3EOhUAgjIyPiJGZmZsLr9eJf/uVfsLy8jK1bt0rzZ6fTCYvF\nIsmcgoICALFglCxlgi29vb3Q6XR44IEHMDExIc4qx3333Yc7d+5gampKwPn19XUJdPkskUisITQA\nvPnmm/jSl74klysBnNLSUpSUlODUqVNobGxEdXW1gOHBYBAHDhyQhnMXLlyQgCYcDqO1tRVbt26N\nYyazMWVaWhrGxsZ+g83xhww2L2UyIjU1FfPz8xgYGEBvby+amppE89VgMEjw5vf7odFo4PP5RBYk\nOTkZGRkZeP755/HQQw+hrq4OJ06ckEtxdHRUtK+zs7NlfzArv7q6CrvdDqfTKfIlOTk5cDgcCAQC\ncDqd+MpXviIgYdGvG4+xEoRMO1Y0kSHjdruh0cQkcKqrq0XOIycnR/ZxcnKyMHg5t263W6oRpqen\nJenDBBP1+o8dO4aGhgZx2s6dO4e2tjYcP34c9913nzRt42fS0tJQVlaGV155ReQ+Zmdnhen12GOP\nobi4WBwhn8+H3t5ezM3NoaGhQTS62ZjS6/X+RqBHYIJA3srKCt544w1cvnwZ4XAYJ06cwN69exEO\nhzEwMCCON/c8wZ3bt2+jra0Ne/fuFaDHYrFAr9cLS2ptbQ0DAwMIhUKoqqpCJBLTLp+bm0NXVxd+\n/vOf47HHHkNzc7P0QuB5uueee7C+vo7Lly9Ls+6SkhI8+uijSElJQVlZmTRBpCQa2XjDw8NITU3F\n2bNncfjwYXGwExIS4kq2GSByfquqqrC4uIjs7GzZz3TQAQjjtrm5GU1NTbh06RJefvll7Ny5E/X1\n9UhKSsKFCxfQ1taGp59+GsBGsjgpKQl2ux12ux3T09NSNUfmR2JiotxvBGvILO7q6pLAbHJyEn6/\nH//93/8dV45OVmAwGMSRI0fiwGuCHpmZmSKtAsTsrV6vh9PplATu9PQ0mpubsbCwAIPBAI/HIyXh\n6u9jXwIG7QS3Po7BZCkTTVwDvV4vCUFggylGsI53wmbwSA1SmMwggKoCoQCENa0C6xqNRsBvtdl3\nUVERpqen41jq9NNYCu71ekXisKGhASkpKSL9Q51hkgD27dsnbFwChAz8VX17Vq3w2VSpFgYVfDcV\nEFTn12azYWpqCl6vV2Tc6EuojC+1goDrQpCLAMRmsIHzRgCFYKNaUUCpMlYU8G5U9cs1Gg0CgYAk\n4RiUqc3VAUgCn1KPqk/EQDspKUmATg7uBQL2y8vLyMjIQCAQgN/vR15entzxQOxuW1hYQG5uLrZs\n2SJMTY1GA4fDIcnUSCQSJ+XU1taGLVu2SDDK5CfZrn6/X8gTKjgJbFTDmEwmAQnUCj9KmACx+433\nwOLiokg2RKNR2O12Ad/VKhiCS9wjbCSuakdz7rl2er1egDW14pCydaFQSOKHsrIyALGEmdlsjmOk\nE4TiWk1OTsLn88Wxk7OzsxGJRERehslSn8+HUCj0gYH3hxkkNPC5GDd0dnbi4MGDUuGiJjDUHi5q\nlQvtF5MA1BhnJQX/YdAPbFSd0I/kncUEDSVwSIJhU8rp6Wnk5ubKPQNsgOC8N5hApO0iUUCVAiGx\nQT3zWq1WpC4IilKSgM+YkZEBu90ujNeEhAS5P5eXl9Hf34/r168LccXhcMSxq5lk47NvlgJSm+dy\n3zBGIOlCrRqiPZqamoJOp4vrK0Jtc2Cj3wSTzFNTU7hx4wauX78u80ZZLuq819bWCrjN9SOoTzYv\nZTtUySfuK74DK4H4Z+pdA2zIrubm5gqpwGQyyToyeREIBOKIcTabTe4es9kse5p2VE0cArGz7HA4\nMDo6img0KlVIjGt59tWkBvc72auqlAsrOngPMHbjc6ua/Ha7HRUVFbhw4YKQl6xWq8RmJHzwTiAh\nQk2AMj5QEy70URh7cB8xaaLKy9HuqRUtaqJO9UX4vXxXVcrro4xDhw6hpaUFAARgVytEmABJT0+H\n3++HzWaLq5QiQEh2MeO/zdWPQLx0DhNYkUhEKojUv19dXRUZUiBG6BkbGxO/ymKxCEmR95DNZoPd\nbhcf0+/3o7S0FPv27UNxcbHMp9vtFp///QabT/f39wv7/eNO9P62wdheTRxSyg2AVGdxT5SXl8vP\nE9e4fPkybty4gd7eXgCxpt70/9WYOC0tDYFAQO5qv98v94HaS0xN7qyvr6OrqwuFhYUfiwQV9/Bv\nq+BlQpxxCQBs375d4nN+Vk1e0t8jCQyI+euUy6SyBWUWFxYWEAgE8M4774iMORDDbDb3iHi/sZmt\nz2Q47QDjYMYMGo0GO3fulJh9YGAAhYWFco4yMjIEg+S6v98g3vG7nou4AgC89dZbGBsbw+zsrPxu\nn88nlQeslNyM4ZDMlJeXhx07dvzBfUfuJgD+tMYHJgAqKipgNpuFWUYpFWaDqRe5a9cubNmyBV6v\nV5qtkL3Z2tqKlpYWFBcXi9wBA+lHHnkEHo8H/f39iEQiArBVV1dLQMIs/LZt25CTk4ObN2+KXAYA\nYRyzs/aPf/xj0YR85ZVXsG/fPjQ1NUGr1WJxcRFOp1MMGysJ6ITt2bMHvb296OrqwrFjxzAzM4OO\njg5oNBr09/cDiDnKzNbV19eLRIHT6cTc3BympqZw5MgRNDQ0xF0azK7qdDo0NTVhYGAAfX19KCsr\nw5e//GXpeXD27FmZ7/n5eezduxc2mw3FxcXCZlWZlGlpafJ9AHDq1Cn86le/EtbDQw89BI1Gg3vv\nvVccp0uXLmFyclIC0vLyciwvL2N2dhZHjhwRfWkG3jQAGRkZkrUcHR1FSUkJkpOT0dPTI9JGJ06c\nwJYtW0QqIBQKCTNfr9fjsccek/LO9fV12Gw2Yf1Go7GmQOfOnYsLLIeHh3Hz5k2RZEpPTxc5AsqC\nDA4OyqVeV1eH2tpahEIhdHd3w+VyISkpCdu2bYPJZILb7cZzzz2H9fV11NTUAAB6e3tx+/ZtzM/P\nIzMzE5/61Kfk+ekYsSqFjiUBVDalmp2dFakVJljISqEzaLVaMTQ0hGg0ipmZGTQ2NsJoNCIzMxNL\nS0uyvw8dOiQlYsFgEHV1dfjBD36AUCgkgZMaGA8ODiIvL08amrIihg4DL9L09HR85StfwQsvvCAa\n5CoT5Zvf/CYef/xxfPKTn0QoFMKFCxfE+fR6vaiqqpJKEp4HnU6HpaUldHR0xDF1/tBB8InsDa1W\ni/z8/Lj+AmTRzM3NSUBBZ5zvyqDDaDQiJycHhw8fFrCKgX5+fj56enpw/fp1HD9+HDabTWQyjEaj\nANpra2vCDKiqqpLg22g0oqSkBIFAAKurq7h9+zaqqqqEsUv7lJGRgSNHjuCll15CSkoKDh48iEOH\nDolzyUbcBoNB2Jvnz5/H0aNHYbPZMD4+Lg5xQkKC6GdrtVq8+OKLAkDW19fjkUceEZDPbDbj3/7t\n31BQUIB//ud/lsAM2LiQ/X4/urq68LOf/QxPPfUUgsEgrl+/LmWLW7duxa1bt1BaWhoX/CwvL+PV\nV1/F22+/DYPBgKamJpSWliI/Px+9vb3CqKIdZJAcCARw8uRJvPPOOzh69ChOnDiBffv2CbNNq401\nwqad7u3tRU1NDaxWq8gSRSIRPPTQQxgdHUVvb680lFOZomzIPTAwgLq6OoTDYVy5cgXXr1/Hs88+\ni23btsUFx2S8rq2t4Z577sHU1JToEDudTpSWloqD9OyzzwIA/vVf/1XknoAN5+/WrVtwOBzS4Dw7\nO1u+JyUlBe3t7UhISEB9fb0w9Qn0kgWiOkwswXc4HCK3c+fOHbz66qu4ceMGUlNTEQ6H8bd/+7fC\nDqZME2VIbDYbgsEgCgsLJahjTwHVAff5fOLwNzU1ISEhAS+88AIASL8EtREU94LJZBKQjEApNSV5\nHjgHTDyQabaysoIXXngB/f390Ol08Hg82L59O5qbmyWg49yoe5CsGVUC5KMM7lUy+vlPfn6+MBqB\n2H0+Pz8vzb4yMjKEqUdQnIGB2muDYBoDAX4XgeL3kxUhY5XvycA6JydHmh9S9g7YYH0SEB0ZGREQ\nmkSA/Px8qcwDgKysLFlzzgFtO2UOmXjhfUHAi4AA15efJ6uUuroABDhzuVwYHx9HaWmpyHOwUScA\naT4KIM4uqOAkx2ZWH7+H/2YSgKAZ2ekEuPmdKogJxFc4bA4wVcCQCWKv1yvPqjITExISYLPZxEbT\nJyDJxeFwSHDNO4t3O/cIZb7y80Ir4lgAACAASURBVPPFttGnZEKDfWZU+UBKujHZUVJSIjIYHKqE\nBbBhp7mXucZMQpHsYTKZsGPHDgAQkMLr9aK/vx8ul0uqTNgfSgVS1e8mYMfqEP6/Cihz/jcD8ZOT\nkygvL5cEFXuUzc/Py/5mIo7sZ41GI74FASGPxxO3T8nQZdKLPgbXjufwtwEXH3bw91DrmHY2NTUV\nxcXFAlbo9XqRlCIQTl+ac6SCzaFQCE6nUxpL87MEGGnPyP4j0EtAibad8+bxeKDX6wUo7e7ulkQ9\n9y/Xmn6j2uAZgLDCExNjzaonJiZExoMVA5vnPRQKYWFhQeLLsbExrKysSNNfyo/xvHEekpOTJfmj\naulzLxkMht+QmOJ+4fypVQ20U7xv+fsI7K6vr2N2dha9vb2oqqqCXq/HwsJCHEua30OQyuPxoKOj\nA6Ojo9Jk0efzSfUwARdWFag9FAiUWiyWONk+njEmCdRKus0Sf5slW2gvkpKSoNfrMTExIfc0bST7\nA5Csxvmkz5uUlCTSTmRnc1+RFNXX1ycECa1Wi4qKCrhcLlkXvV4vlbyqzCcJC/feey9efPFFURFg\nXEmbQr9Bq9XCbDaL78tK4f3790On02FyclJYsNzXlKhRzyXfRwX8mIDh4NzRPqjJON5fXDf1/uf8\nc/+pfiDtHyVtuG5qxeAfOo4ePRrn89AmsjqL/z07OysxBeVXOLes8FBZy2rlzO8aqqyc+rNMYhGQ\nTkxMRE5OjvQjok1h5QclXplQBGLnsqysTKSTExPj+4FwqHufw2QyCQH2dzHN/xiDYHd7ezt8Ph80\nGo0QNXj30T7x/5mMYvUm+18CwNTUlDDc2d+OFUm8w8LhsDRTB2L3X2FhoVTOs8nz2toaxsbGEAgE\nYLPZJMn+uwaJb2rvQA76iO+nf09/w+/3Y3h4WBKuRqNR/Dc1aU6/gXGBXq8XfMfv92NqagoJCQko\nLi6WWJr+0u3btzE0NIRjx47JXP++gPXmPU6pPmCj18Ti4iLMZrP0t6CUKxCTGurv74fX64XX60V2\ndjbKyspQUVHxOxNQm6UQNw/6Qbdv3wYQw2JXVlbg8/kkrsnMzITJZBLihEajiUu8MYaz2Ww4duwY\nHA6HVKR92HE3AfCnNT4wAZCWlob8/HxxEq9cuYK8vDwcP34coVAIr7zyimjxT05OCjtMr9fLYm/Z\nsgVlZWXiaNDQ8FIjOG+328Vh7+3txZYtW6TUlgedbKsbN24gEAigurpaKg2ysrJES/jWrVvQarUY\nGBhANBqVEiheJixf3bFjB0wmk5RAswzZ6XTi+eefh9FoRHNzM7xer4C9BC1Yjmu320V7mg0cKysr\nMT8/j9bWVtHm37ZtG9LT00Xepb6+HocOHZLDptPp8MMf/hBarVYqFM6cOQOv1wubzYbk5GQcOXIE\nBQUFv1FuGAwGJaDj2rCxj91uh8lkwqVLl5CWloaamhrYbDY0NjaKoevp6cH3vvc91NTUoLq6Gk1N\nTfIdSUlJuHjxInQ6HSYmJqTSgFISWq1Wyr71ej06Ojpw3333xTW+UsuM8/PzceLECZkvGnWyFaem\npmA0GiV7TYY2gYvx8XGEw2FMTU1hfHwcOp0ORUVFKCsrwz333CP7VqvVore3F2+++SYSExNx4sQJ\nYe4UFhYiGo3iwoULsk9v376N6upqWCwW9Pf348c//jGKi4uRnJwMj8cjYNrS0pKUQKWlpaGurg5f\n//rX8eqrryISieD06dM4deoU8vLypIqETAcAwg46efKkBAzj4+PYtm0bent7YbFYRIaIlzHZ2QUF\nBZiYmJDkjNFolJ4Lb7zxBmpra3Ho0CEAMWmjoaEhZGRkYGlpCWazOa40zWq1oqurC9u3bxewYH19\nHc888wzy8/OxurqKPXv24Pr16yK1k5GRIeAEwfi3334bnZ2dyM/Px8zMDAwGA7761a9+kGn5nYM6\nx06nE1VVVQL+NDY2YnZ2VvTq1tdjerHnzp0DEAO+WIZMB8HpdMLv98Nut8NqtYoTS+cIiGkkulwu\nCVLJUOe8rKyswG63S3KDl6Ra5rd161a8++67mJmZkSZ6tF18JyYTp6en8fDDD0sgxhJOBlA8E0eP\nHhV2bHZ2tjS95pmnPNITTzwRJwGk0Whgs9mQlpaGM2fOAAAOHz4Mk8kUdwnTeTAajdi3bx8mJibg\ndDqxfft2tLe3Y3l5GQ6HAyUlJTAajaIxDcQqWX7yk58gKSkJHo8HkUgE7733HlpbW4UlHwqFJKEG\nQCqw/umf/gmLi4vYsWMH7rnnHiQmJsLv94tsBt+ZLMNIJIJbt25JyeLu3buxvLwMl8uFhYUF3Lhx\nA3v37kVubq68X3JyMhwOhwAJWq0WPT09aGtrQ3NzM2pra+Hz+ZCeni5gNhPBXC+tVouvfvWrcLvd\nKC4ujksw0EEqKCgQPXX2qggEAgiHwzh16hSi0Sjy8/PxxS9+Ma7C7erVq8jLy8POnTuFWUkJIgaZ\nan8Lt9uNiooKkQ4hU6y/vx+Tk5PweDywWq1x7I309HQBbHU6HQ4cOICZmRncuXNH5C6o7Uw2Cp07\nBhgpKSloamrC5cuXceHCBQnACQoBGw1xjUajBKkqKEVghyATEGMmj4+PC9hCuZnBwUFotTF96Jdf\nflkST8CG3nRqaqr4DZtZyx91kLnO8tqRkRGEw7FGhZmZmXENQCkTYLVaBcCnXSJbj+A+150BlyoT\nxDkkOKeCCiqoxnkhGMOSYp4/VgAsLi4Kc5fASHd3N0wmE4qLi2G325GVlRUHYql7mnNgNBoRDAal\nqSttD/ekWh1GEI3MXgIWfFc16cgeK0NDQ8jKykJycrIkhAhWUVYE2ACJORjw8Tn5d2oFAIA4MEUl\nk7B6jBr69E84Z3w/AuAq+43Pxp8JBoPIzMzEnj17ZB2cTmdco0TK8xD8I4iovitBCFXaRl0bsqEJ\nlGg0GmFHJyQkYHZ2VtinFotFkshOpxPLy8vSM+jGjRuSROD3sxJTJQKsr69LxZa6r5lY4l7lnllb\nW4tjk/MO5vry/VUQVAUpCWhmZ2fLz6rrqSZ2eUezEphVfx6PRxrFrqysyHkgwBEKhYTdyaR7UlKS\nSM+oZ1VtQB+JROKqIFgdpe7rjzq0Wi28Xi/W1tYwMjKCtrY2AMDu3bsRCARE8obzqjKBgY0qGQCS\nUGXihlItq6ur0jyTwAnXIjMzEzMzM1hcXJT9xfkgE57yhouLi0LQ6enpQU9Pj1RI0RZQyoeEDNU+\ns18NKysoTUF7GQgE4kBurqHRaMTq6qoAvcFgUJIHxcXFwgZWpbd27NghPXyoUc815nOqUguqLeO+\n83q9ApwBiANGAcQldaPRWN+VAwcOQKvVStxC5j7PSFJSkvTNO3fuHPr6+uTPV1djDXAfe+wxHDhw\nIK6yQ60a4/rQTrMqH4CQrjh3fF9gQ4KJVUpMjnHPE/TRarVwu93o6emBxWKRu4T34759+8QnHh8f\nl74ECwsLKCgoEOIUzwn1semj8F5KSUlBfX09SktL0drainA4jIqKCiFsqXNPuWD6RvTRSQTIyMgQ\nKTU1CUxZK5XZnpWVhUOHDsn7Ly0tiS+o0WhgMpniekpw3pngp49I/weA7PX09HSxO0yicu5pH1UG\nM/vtABs+fDQajWtyT7+f4+PQpyeYyudQ71r1zqXvSnuiSgBFIrH+XerPfxjAnGeeFVV+vx/RaFSk\nSwFI8oW+2K1btxAKhWCxWGQ9WTHHzxQVFaG2tjYu2U3pzc3fv3kwSTQzMyP9Hv4vBslrAwMD6Ozs\nFAUMAtk5OTlyN7MXFuW/VDkzi8WC+vp6ABvAKyutSFJ0Op2IRCIoKCiAzWaTpuIAZO+RyMneHVRH\n8Pl8mJqaQk9Pj/ghlORUx/DwMJ577jkUFxfj2LFjkuhVfY3fJnXDqserV68iISFB8B3acI1GI9X0\nQ0ND8Hq9qK2tFSKSzWaTeHVhYUEqB3k3jYyMiA8/PDyMLVu2SF9LPtvvMzwej5CXi34t0eR0OqUq\nfn19XRQrmJxQY4mMjAxMT0+L/WYyYG1tDYWFhZL0VZ9ps8wZ/Rv6vEysX7t2Dbdu3ZK9xTuDa+nz\n+WSvRyIRqbBUZfSsVisOHjyIkpISWK3W39mA+XeNuwmAP63xgbcHg02CAlarVTJ+169flyacZBOW\nlpZieHhYmlACMbZsUVERCgsLRcJldHQUBoNBmmG0tLTA5/PFNZ545513kJaWhqqqqjgtdhr92tpa\nBINBMT7UjayqqsL09DTm5uaECeV0OiUrt76+LuXaSUlJKCsri8vKt7a2or+/Hzt37sTRo0cRCARw\n5cqVuIx/OBzGo48+ivLyckxNTQlTSKuN6e//6Ec/kiCYl1FLSwt0Op0wIouLi4VNPj09jbfffhv7\n9u3DysqKBI5kfZ45cwYtLS0S2FHXk0aUEkJA7JA9/vjjWFhYQGdnJ9xuN4p+LRfQ2NgIi8UCm80m\nbAQgxsSoqKjAI488IkEGger//M//RDAYlNJxDp/Ph3A4jKKiIgwNDeHP//zPMTExgTNnzqC3txfb\ntm2TIFN1jrRaLUpKSuByuaT/QUZGBt566y3ce++9eOqpp6RKAYiVQM/NzeGFF15AQ0MDCgoKUFhY\niN27d8PhcIgMz3vvvScacWQKORwOPPPMM5idnZWf7e7uFskTs9ksjrff74fRaMQ999yDz33uc/B4\nPHjttddw9epVGI1GmM1mlJSUYHZ2Fu+99x6AGKhKiZWMjAwUFhbi4sWLyMjIgM/nE+bB+vq6PFtv\nby8uX76M1NRUSZTl5+cjGAyitLRUGhWpgQwNul6vh9/vx5e//GWUl5djZGQEv/jFL/DVr34VQ0ND\nGBoakvcpKyvD22+/jTfffBNPPvkkysrK8P3vfx9JSUl44IEHkJ2djZ6eHmFUABCWjNPpFC04u92O\nqakpaeT07W9/GwUFBdLzwul04hvf+AZu3bqF//3f//2dJWu/71hYWEBiYiJGR0eFWUsWcWtrK4aG\nhmCz2bCyshKnub9161ZhBlI6Z2ZmBtPT07j33nsRCoUwODgoPQKAWOnyt7/9bVitVmHN8SKkw83y\najWRxQCqsLBQys5HRkaQmpqKmzdvorS0FEVFRbLut27dgsfjEUeGzG+eNQYvm8trVbmnixcv4ujR\nowI+ksm5vr4ujo4aKEUiEVy5cgV79uxBWVmZBKl0gsiGIQP3oYcewuuvv46TJ09KBYZGo8HZs2dR\nVFQEr9criZ/p6Wk8+uijUjGVmpoqJaSrq6tYWFhAOBzG3r17ZX/19vbim9/8pjgvwWAQP/3pTwXU\nZZOx7Oxs7N69W0COvXv3Sjk9EzcZGRl44YUXkJmZifr6emFjc74JFDHZHAqF0NPTA6fTibq6OgHg\nVC1Vl8uFkZER0fM/ceIEUlNTkZOTg8zMzDhQlgAbK8BYlsx9wWTy2toann76aeTl5cn+SUtLw7PP\nPitB3fz8vOiE9/f3o7KyEj6fD9nZ2SLp1dfXh6effjoOnNHr9airq4PX68XMzAwOHz4sDGTOAUvf\nqeV++PBhDA0Nobu7W8qAmbwFYo7kxMSEJHBpw7dv344rV65geXkZqampceeB0km8V/heBJ1UzWIC\nIJSvycvLQzgcht/vF1tEuQgmegnkzc3NCaiYl5cnxAK1tP+jDgaZrHiamZmBw+EQ6QkOArYEOMj+\nV+UKCAypvgPtOlm6XCdgo3qEzFiuB4EaDpWBbjAYkJ6eLpJJ/H0rKyvyd0wmmUwm5OXliXyiCvqQ\n6UqAkeeH72wymSRYItBIPWSC+qpd5L/D4bDIKPB52ZtAq9Wio6MDTU1NEhAR3FTBVQJW1BveDNrx\nblAZ8/Q76OeozwRAfJrNg8GhOtdMcnAQ1FHnQK/Xy3/n5OQIY5/3EeUR+byqvCDnlDZbBefpo/HZ\nNydfaGtSUlJQWloqZ5f7Y35+XkroOTdOpxNpaWnwer1SDs93ACCsQzIt6csSlCV4CWzIeVCeJSUl\nBUajEQsLC1hfX0dRUZHse3Uf8DPAxl1FYg0BVhWM5fwzkcP34770+/24ffs2XC6X/C7eVYFAQFjM\n7KFEEEJlOlssFqkipTwCk2lMHDF4f78qrY8y6GcEAgGMj4/L3iSzm0l9AghqM27OI+eEgKcqFRGN\nxrSIdTpdXDUrv0en0yEnJ0dYg2Rfq/sSgJxTde34vJWVldKElYkT3uGqv8EYgxW6AwMDSEtLk4QA\n94jKyKa/7vf7RT6U/efYL4tnXZWMYxUuzybvFdqU1dVV6HQ6sbdkx6pVTjyHBJ2Z/OLac2/wM4yJ\nyWRXq6L4M8FgUO7arq4ukQPi+XriiSdw3333xa2zutZ8DzUpQJunPgvPEu0H/Vv1ebhH1EpIMovJ\nMub7cM4oH6pWNvMuDAQCIg9M37mwsBA+n0/6GgAxMJNxjN1uR2pqqsT1VqtVCGbq0Go3Gjbn5+ej\noKAAfX198j2cV7Uvw+rqKvr7+9HY2CgVlKzk4XxoNBqRwOAckEhGAF+V9uE9vVkGiGeQlVd8Vq4r\n9x6ry/kZ+gJc383kAH6fWq32cbDS1So32lhKuvIcEzQkA1/1FzUazUeO+WjzmbRkgocALxBLVCwu\nLsLtdiMSicDj8cTJpXA+1WTLPffcg5ycnLjz835VCZv/nzGY1WpFe3s7hoaGBEz/MIPM/A/7mVAo\nJL0o6+vrUVNTg6JfS8uqdiwhIQFOpzOuMhXAbyQ4OCf0ASl7TIkctZm3Wuno9XqlEpMycuz3l5iY\niLq6OsHzgI0qDfbEAmK4ChvIMslDwi2fVT3jTC4xOdrW1obOzk7U1NTEVSipCViXy4U333wTPp8P\nBoNBklHsFQBA8ILV1dU4UqPFYhHbxZjlw56rwcFBwYRIwqIPRttLxj0xMCZRgZhPXFhYKFjY+vo6\n5ufncfbsWTgcDuzevVswWJ4HJuW5vqxqJbmFVYQej0diVZ7jjIwMmM1m8alnZmYwNTUlfXRGRkaE\nCOdwOHDvvfeioqICFoslLrH8YcfdBMCf1vhAy0QmDp2JsrIyjI2N4fnnn0ckEkFFRQWefPJJpKSk\nwOVywWw2Y+fOnVIGDABdXV145ZVXUFFRgdbWVsk+8nJ1u90Ih8Nx7FSDwYDMzEy8/fbbovOn1Wql\nKS2lidTAv7e3V1hRu3fvFumHpKQkvPjii3C73eI8E7y02WwCnnu9XqysrGBgYEDKryhDoF5EJpMJ\nR44cwerqKmZmZmC329HW1ga73Y6ZmRkMDg5ifX1dmjh9/etfBwABh4eGhpCQkICRkREYjUb4fD5M\nT0/jiSeegN1uRzAYlKoGdmBns+VQKCRMATpo4XBMO5lGnY7Epz71qTjHwW63w+fzxbG4eVFu27YN\njY2N0giZz/jWW2/hs5/9LObm5jAxMYHi4mIx3N/97ncxMTEBvV6PlpYW2Gw2WCwWaDQadHR0QKfT\noaqqCm+++Sa2bt0KIFbRsGPHDhQXF6OsrAx37tzBlStX0NDQgMrKShw8eBB6vR4rKyuSDAoGgxgY\nGEBLSwseeeQRKQmlQ762toa8vDzY7XapGti1axcWFxclcURwhNn/paUlNDc3w2AwSNNQj8eD27dv\nCyPeZrPhc5/7HEwmE86fP4/09HRh7TKoOXXqFJKSkqRHhcFgEMc8IyMDLpcLExMTWFlZwX/9138B\n2Ci1ZqMrk8mEnJwceL1eWCwWAQbm5uaEdZGSkoLZ2VmcPn0au3btQmNjI8LhWN+G8vJy2Gw2OJ1O\nfPrTn8bg4CAASOJmaWkJJpMJ//M//4NHH31UGjA3NzcLQ7uiogIAhGFus9lkH9ERDgaDWF5eFsmV\nd999FwDwN3/zN5J84vp/1HHp0iXs378fKSkpWFhYQGlpKaxWKwYHB7Ft2za8/vrrKCoqQlNTE7xe\nrzQ7YukjG9YtLy/LGcrJyUE0GkVOTg58Pp/IpGi1WszNzeGxxx4DsMGoY5kz/0y9vAgEzM/PIy0t\nDadOnYLVasX27dtx69YtpKamYnR0NK7c9JOf/KQAhdFoTMtep9MJ24rBExm0ACQgXFtbw507dzA0\nNISf//zncDgcOHLkiIAfZFEDwJ07d7CysiIM90AggO3bt4u+IN9lM9txZWUFer0eFRUV2L59O7q7\nu6HRxJpUjo+PS0k6G0Bt3boVKSkpIp0QjUbR19eHM2fOYHBwELOzs7h69SqGhoZkrjs7O2GxWGC3\n26HVxiTKCgoKhDnocrnwxhtvYGRkJI6pV1RUJGCGzWbDrVu30N/fj9zcXPzFX/yFMHQikYh8FxOp\nBoNBGkM3NTXhueeeQ1tbG1wul0jh0AZ0dHTIPObn50ugzzkj+0mVGSB7Zn5+Hj6fT8ASJputVisK\nCwtFFgfYkBoiyOx2u5Gfn4+lpSVJUK2vr2NyclJArLy8PAly6KyGw2GRvrv//vuRn58ft38YtBMA\nBWJJvqKiIpw5cwaFhYUoKCgQ5j4QcyytViuuXbsm1XwFBQVoaWlBMBjEd77znbjyY+7T1NRUFBQU\nCBsuOTkZMzMzAlAQnGBCfHl5WXp8+Hw+rK6uIi8vL67pHdnRDExpN+fn5/HGG28gJycHO3fuRE5O\nzseSeAQ2AJ+1tTWpNmNfDjWw4jOzsala+q5WRqgADedKZfQTmGXAT1BXBbLVSgc+G5MstCHRaFSk\nIoqLi6XhNYE47oe8vDxJPKvBqSrFQsCQZfNqEz1WwgEbzdoJym/+PJ97M1CqApas8lFL03nu+M60\ngTzj6veooJUKFBO8IOCiStAkJiaiqKhI/pzPQ39XlUnZvA9VDWx+D5No/DueB4KrlIejzVCTZFwb\n/g4ypgkwb2Yn83lIhiHbm8FZXl6eaCNzzdUKPzKnmSxyOBwYHByEzWYTMo26n5kQo11hEoBgC4EH\nBtqsylOToVwngslqlYO6z/lvNcGjrhnnQF0rVun09vYKW1Oj0Ui1DRDza+iT816g/7W+vo6SkhJk\nZmZKnwIOAk6scGWik4lx2uGPY9BXWFhYwPDwsNhJp9MpwCQZzLQBBIMILKrnigDD8vIyEhMTkZqa\nKlVWWm2sybNqzwhWEqjhmaUMGGXq+L2U02E1BJMABQUF8vvV5CyrZ/nfBKm7urok9uJaM2ZZWVmR\nCmqeR4L91O/PysqSJKXf74fD4YjTXifYwsTeyspKHNv6/aRUWCFB+5aZmYm1tY0eDJv15FXbSdIc\n559VFWxayXs4EolgeHgYb775puwnkgYefvhhHDlyRBKiqv1m3MNnoe9HWRb6lyoRQm0syeekLWOS\nFtiIHWlb09PT0dDQAJ/Ph9nZWbFbGRkZUj1FjW02LU9ISJB5Li4uxsTEBHw+H+bn5zEyMiK947hP\n1fkguMSKOvXZVZka/pv94hivcp8T6KP/rtVqMTY2hjfeeANra2toaWmR+VMbgvM5gA2pL/5DwI3P\nrP4d7x4AAiBy35FNyzOr3vX0b2nvVJ9ATYpyf6jSO8CHY9n/tqHaDdV+qAkuyr4RCFbHx+V3MRHj\n8/mkCkl9ttTUVCwsLGB6elp6k6lJLWJEXq8XBw4cABCrGOUZUsl0m9978+DPpKeno6KiAu3t7aip\nqfm9GeEcHwb8J3jLviWDg4PYvXs36uvrYbPZZK+oPlFaWhpyc3PFRv628duqF3Q6nSQS1PUGNhJt\nrPDgXuN6M+6z2+1yFwwPD+POnTsoLi4W4l5+fj4ef/xxRKNRIcilpqaKv0BZOwDSYD09PR1utxuX\nLl2C2+3G/Py8VH0BkOQQ541yY7xfmNTV6XSCoTzwwAPo7+/HhQsXMDU1hVAoJD5MVlYWysvLUVhY\niLy8vDgM4/0SRptHfX29+E7sd8nehLxb3G43ZmdnkZ2djYSEmIQv142KDvRbZmdnEQgEBPvj/dHY\n2CjfmZiYiIKCAvFvV1ZWpDl2a2urYKv0G/gZVipzvflu1dXVOHDgAOx2u8T+QCy+rayshNVqjYsD\n/pBxNwHwpzU+ev3Y3XF33B13x91xd9wdd8fdcXfcHXfH3XF33B13x91xd9wdd8fdcXfgbgLgT218\nYAKAjB1m3qqrq4W5rtPpkJ2djbNnz6K0tFRKrZjpJss+NzcXP/zhDzExMSHMooyMDDQ0NGB4eBh+\nv1/YN6qen9lsxrZt2/Duu++ip6cHO3bsQCAQwMDAAMLhMGZnZxEOh0Xy5ObNmwCAL3zhC6iuroZG\nE2tYNTo6ipmZGWFl6fV6YXxu3boVWq0W4+Pj6O3txZUrVxCJRFBeXi6M1JdeeimOLftnf/ZnOH/+\nPDIzM0WjzOl0IicnBwUFBdDr9Th79iwi/4+99w6O8zqvh88uymILymKBXWDReyEAgiBhdlESRckq\nFCUrpsaKIlnVsT22ZzyOJ844djJjz6TYSZzYEyeTIlpjWRrJUqhGkaIoVpEgIRIEQIBoRF/U7RVY\nYPf7Y3/n4V2oW0o+/+E7w2HDvvu+9733uU855zyrq6ivrxcqzVtvvYWMjAx87WtfQywWw9jYGP75\nn/8Zfr9fKPBE9Pf19QFIoFBPnjyJaDSKwsJCaXaTlZUl1W+9Xo9NmzZJxY4o07S0NGzbtg0DAwM4\nfvw4tm7dCiDRWDQej8Nut0t1NBgMYnR0VHoCUIv5/vvvh06nw+zsLFJSUtDY2Iju7m4ACQmOffv2\nITMzU5ga9fX1aG9vRzAYxIkTJ5CamppUUW1oaEBqaqpQke+8805oNBq89dZb0Gq1cDqdKCkpgdVq\nlXtrbW1FNBpFR0cHzp8/j23btiEYDCbpyJHuzT4Nu3btkiZCRBNdvnwZzz77LILBoCC9Kisrce7c\nOQAJimFhYaEwOVwuF2w2G3bt2iW65g888ABaW1sFKRMKhfDyyy/j8uXL2Lt3rzQc27RpE6anp3Hq\n1CkcPHhQ0PNAgm1RXV2NmZkZpKSkoKWlBRkZGcjOzsbPf/5zmM1mVFRUICcnR5pl+f1+dHV1SaPt\nubk5ZGdnY2lpCZ/73OdEsLOXHwAAIABJREFUB29ubk76aDQ1NeHkyZO44YYb4Ha70djYiJycHESj\nUVy8eBG1tbUYGBjAtWvXZL+fOXMGf/qnfyqIgI6ODtEZTEtLE0mAWCwmlESi7aPRKFwuF9ra2j7K\nrHzkuOGGG9Dd3Y3a2lrYbDZBEHNfp6en47nnnhMJL1W+hDTtvLw8TE5OSrWfaHnqbdLWsH8BWSqU\nhCHSOhKJYG5uLkmygYycaDSKzs5OdHZ2SkPlvLw86PV6LCwsIBQK4dZbbwUAkeKgJiARS6zEDw4O\nYmZmRqj3AETKCEj0Lfn617+OxcVFrFu3DvF4HB6PBykpKZiampImraSQtrW14ciRI9iyZYvI1xCd\nRvSoSk8m8rSyslLm9eabb8bo6ChisRjq6+ulSan6GcoEAAmkfmZmJv7zP/8zie7Pg//GG29EcXGx\noEaMRiNMJpPQpS9evIi3334bkUgEV65ckWu0t7ejoKBAELO9vb1IS0vDTTfdJAgVygpxEOXLZw6H\nwzAYDNi6dSuOHj0qkj1ZWVliG69duybMD6/Xi8nJSZSXlwsLQKfTSU8TStn09fXhzjvvxOLiIi5f\nvoypqakkfXBK0akoaKLzgMTerqqqgsfjQV5eHoxGIw4fPoympiZkZmZKz5Ubb7xRNGotFosg/0hD\nLywsFAorEZGkb5PF53a7EYvFcPvtt+P73/8+Dhw4gAcffBDr1q0TlAiZHpQnULVAt27diqGhIZw4\ncQIul0sQe1VVVdIImSgWoozD4TCqq6tF4oI2mhR2j8cjDEDaSOoP8zzkfJKWrtFo0Nvbi/7+fgwN\nDeHRRx/91OgUDqLpnU4nQqEQMjMzkxovcnC/EGWv6uGraDMiR/k+aFsptaP2DODfVcSWKvtCzWH+\nPxGlqamp0owYgGg18x4oOUC0FxGKa6WF+H38d16b8jAqeo7fT/YQEe2qvIaKpCeCjP9GzWX2ZlGR\nnpQRUWVfeH3K4alSCfSFiAoHkhHiKSkpwrghE4BIahVNxrnj59Xv5n4jilOdC55PvKYq40YkHRtj\nchC5pgZFPBP4cypCizIJKhqWKFm9Xg+r1SoIaTasBBJ7MyUl0VQ9HA5jfHwcTqdTzpZQKISuri5k\nZWUJwqyioiJJzorsW6JqY7GY+KBc20SGq5IRfPfq/KrsFj7fWokmzh8/q7I3KJfBPUPfdXFxUZC/\nXG/qfiUjl3uS32GxWJCXl4fMzMykfhK0ZyaTSeRyeI4uLS0hOztb3v1nMdhDbXx8XFCPANDR0SHI\nY9p9astTA53PweflHqOMH+VU+My0uSpymYjjeDwuchv8eUouce6JHueabWxsxOTkJPr7++HxeFBb\nW5vEmoxGoyJNwPsLBALo6+uDw+EQ9L+q667VauFyueSZjEaj6BQDSGI0kq01OjoKq9WapJdMJLu6\nFlWbQcaxymajX0MWE3+Wz6QytfgZzgWl3ciaZJyRn58vLAoA0peroqICq6urcDgciMViaG9vx43/\nr38Aba/6PCpDjGwX/pl+Y2ZmpqDf2VCZWs58xyobkTJffA7u35WVFdTX10Ov1+P48eOYmJiA3++X\nHjIq45f978jS0mg0aGpqgsViQSAQQFpamswB15zD4YDf7xfkMwDR7+efyZBT9xnlPQwGAxobGyWe\n5xzxeVQEtlarxcLCApxOp8gh8oygHSLbmWuWe4KSWyr6n3uC38PYgOfJysqKyFZyTrk+aOsYR/I6\nKouQeyYcDmN0dBSrq6vIzc2VGF2VCfysBtc1cL3ZMv0BVXIEuM4WURnFn3aQNUFJ5UgkgtLSUgAJ\nn5Cyw+FwGJWVlcKqYzzncrmQm5srLGDKc9JmcAwNDaGnpwe33377+zafVe+nqqoKw8PDwsj/3+oF\nQObH/Pw8zp49i6qqKmzZskXOeHX9891wHxQVFYlN+DBWCPeS6ivzuu/3/rgfVFlIIGFfMjMz5bs4\nh4wjuC49Ho+c7X19fZiZmUFWVhays7MlZs/Ly0NBQQHcbrf4tmT0TUxMSM8ZsuR5r7zflJQUlJeX\n49ZbbxUZZXWd8t4oOanRaIQRqNfr5RznfKtSSpS0/Kh3rtPpBPnPwd4ajPnZ28fpdCIrKwuBQEB6\nkU5MTGB+fl56nDHu4rnd09OD5eVl9PX1iV+fk5MjkoA8o6LRKK5du4aFhQV4PB4UFBTAZrOJQoPH\n4xH1AqpDLC8vo76+HnfccQc2bNiA1NRUFBcXiz1LTU1FTk7OZxZf/WH8/oyPLABQ81VtEFhTU4Pt\n27cLddLhcGB4eBhjY2PSiEnVhTt8+LAYKlL7c3JyUFtbK1252ZWaDsiWLVug0+lQXl6O+vp6nDhx\nAgcOHEA8HkdTU5Nc7/Tp03JvPPSPHj2K+fl5aLVa3HLLLZJgOnfuHM6dOyeUWABC33/66aeliQep\nPADk8CctGUjIvqSnp2Pnzp3Q6/Xo6OhAaWkp1q9fj3fffRdDQ0OwWCwi4UD94scffxxlZWXSdK6m\npgZ2ux0DAwPo7e3FsWPHYLVaMTk5ifb2dgAJg+pwOHDu3DmcPn0aLpcL9957rzgupDxFIpEkijQP\n7bKyMhw/fhx2ux2vvvoq7rnnHrhcLqEJ0ZlgM8ba2lo0NjYiNzdX9Lx9Ph/Onz+PL3zhC3A4HDh9\n+jSARBKrqakJpaWlmJmZwcGDB3HixAlUVlZiy5YtCIVC+PnPf47W1lZJSvt8PpHzyczMFAob5YzY\nhBSAFJCYjCkrK0M4HMa5c+fQ2toKu92OrKwsoSdSvgi43muAyf+MjAx4vV5JNpCqdubMGdHYrqio\nEKpvXl4elpaW4HA4cOzYMXzlK19BaWkpLBZLkma+z+eDx+PBj3/8Y2RlZcHlcqGzs1P0MUdHR5GV\nlQWbzYZbbrkFQKJw4vP5YDKZ8M4770iS/8yZM3j00UeF0udyuSSAqqqqQk1NDTQaDX72s5/h3/7t\n37CysoLCwkLcdtttuHTpEurr62G1WuWAaGpqwsrKiuizf+lLX5Kkr8vlwlNPPSVSOJSP2r9/v6x3\nJmhaW1ulFwf7aDz//PPYvXs3gIQj2N3djaamJkSjUWzatOmjzMpHjpaWFkSjUVy5cgWxWAxms1mo\nnkCCZpeZmYm+vj40NDQk0ezsdjvq6uoQDAbF0Wtvb0c0GoXX68X58+dRUlIiFMTDhw8jMzMTU1NT\nQpGPRqMoLS2VZGZ2djaCwaDMLan3AwMDuHTpkuy706dPY+/evXKwsuAFJBKeZrM5qeFhMBhEeno6\nnE4nfvrTn8p1qPen0WikwVtaWhpGRkZEjgBINHreuXMn8vPz8eijjwJIOC0WiwVOpxNXr17F7bff\nLkmZUCgkSSRVm5xze+jQIYyOjmLHjh2w2WzIyspCUVGR7CEWX7jHVCdscXERS0tL6O7uFsfn3nvv\nRVNTk9gZg8GApaUlzM/PS/MqBlORSAS9vb3Ys2cPXnnlFdGFByDUZyZPqVH86quvori4GPn5+Who\naBCHiO+I7w+43nizoaEBQ0NDuOeee4TmXVdXByARkHZ0dCAtLU30+8fGxqSJGIOy+fl5dHV1AQAO\nHTqE+vp6aSDV3d2N8+fPSwIpFouJ3BCfJxgM4oUXXkBJSYkU5iiv0dLSgqeeegojIyPYuHGjOG/Z\n2dnIyMiA3+/HmTNnpNk8dTz5PtVGgGazWZIrlJ1i09iCggJJygSDQQmSKA1RVlYmTeuHhoZQUlIC\nvV6PvXv3orCwEEeOHMHc3BwAYH5+HpWVleLsMgliMpkwPj4un43FYlJwJFU+Ly8PbrcbNpsNpaWl\nuHjxImw2m1C+1QZslEC64YYbpP/KhQsXkpLFn3ZQ4oaNLpm0UaU3gETQzmZYDBLUAJp7nEljdZ8x\nUGMym9dQk+W0/fSJqNHPZA/3MANBJiwASCNVVQ6HPgMLRgzaVUefiRYmf1SpGiab+F3AdUkZBoJM\nhrCAyqSUKrPB72VAv3XrVtmHfDa1+TWQ3ABWLbDwudRigJoU4Xcy2Q4gSV6KWvZrC6G8lirXoEpu\n0K8Arhd2KYOmFgJUaQmTyZS0RtUCEgf3KZ9JLU6oElGcCw4WOPhzRUVF4n+z1wkLNVarFTMzM+jt\n7UU8HkcoFBLdXZ5l1B3n3mMB8erVqxgcHMSOHTtEZ5eFUPoMalJNLTKt1VTn4PywWMMCOedSLXjw\nnVKOiTIr/MXkBgvjnCPKlak9CCjjkpmZiZycHOkhRhudnZ0NnU4nTd1TU1Nhs9lEUs3n831m8j9A\nopA8NjaGYDCY5Ou4XC6Mj48jMzNTGl6yQKb27VALzPyd0k2UvVH1o9n4WC0a0EYDCTlH7mM14c33\notoN+qJ+vx8zMzOIRCIoLCwU6Tv2w6Cve/nyZUxOTkqRgWuD642JUxbbAYg80fLyskj6ZGdnJ0mU\nLSwsiI44gKQeBmoSn8+oymxxb7JwxX5QPFvUouvahDTtJYEBY2Nj8Hq9qK6uhsVikfOBtpSDUraX\nLl3Ciy++iPr6euzfv1+aVKuyfMB16UDuId4r50MtUtBuOJ1O+P1+ZGRkoLKyMqm3jCqvpxYtOV8q\n2Ofzn/88BgcH0dfXJ4k9riXgevKY980zw263SyzD2JvfSQmS5eVlKepxf/Ns4ZrlvXH98fq0Q4yB\naVNZDOXzGI1GlJWVoby8XGwbdc0paajaZ75T+rlqYZsFIwIU1p4vlP7geaL2JFB/VrXl6nlPmxkM\nBnH06FEsLi5KI/ebb74ZQCIXw6LJpxnqOauuab4n7q+1cjYf1Lj104xoNCrNpqlXTx9rfHwcoVBI\n7HN+fj6mp6elAEhftKqqShpT0wdSixh6vR6Tk5OYnJz8SJ+RMmQmkwkDAwPYtGnTRyaDT506BZ1O\nh8997nOf6Nl9Ph+WlpYwPj4Or9eL5uZm8QG4HwBIfM71HYvFxMf6qPNobW+ATzK4RtW9+H6DIN9I\nJAKj0Qin04lIJCKydrOzs+IfARAwJsFNOp0Ok5OTiMfjKCoqEptXXl4uyXnVD+KcMIZTfQ3guu08\nc+YMRkdHBdCYmZkJq9WKkpIStLe3Y8OGDe+Rs/o0Se/V1VUpdHs8Hni9XrhcLvj9fkn8U+Zvfn4e\ni4uLcDgccDgcEn/F43EMDw9LIXlyclLiwenpaQGm8jw3m80CGDSbzQIoY/xKyWfG4QUFBbjttttQ\nX18vvRRTUlJgMpmSQEufVfL/DwyA36/xkQWA1157DZcuXUJjYyOAxKGzZcsWLC8vIxwOC+qdwR5R\nKcFgUPTYFxYWZBF94QtfELTa6OgoMjMzkZKSgs7OTtxzzz1Jul50XJlEZ7fq1NRUQRWwASIA7Ny5\nUwxUXl4eKioqkJ+fL1rbbW1t0mzpoYceApDYEC6XC9PT05LYrampkWTqyMgInnnmGWzduhUdHR0A\nEkbhtttuk8PEZDKhvLwci4uLqKqqwtDQEMrKyqTZGPXVWXVzu91yoO7btw9dXV24fPky7HY7MjMz\nsW3btqTAta2tDQ0NDRgZGcFTTz0Fi8WCG2+8EYFAQFDIatKHzfg4b0Rgsnnt2NgY+vr6YLFYpNDQ\n0NCA0tJSRKNRdHd3C1tjYmICb7zxBqxWK44dO4aBgQEJ+EKhkCTm2WT2t7/9Lbq6ulBXV4fl5WX4\n/X4cPXpUdMMDgQDKysqkkY9Op0NBQQG2bNmCEydOiMNE/TIAGBkZwa233gqTyYT/+Z//QSgUwttv\nv4377rtPgp6VlRVYLBa8+eabABIBRn19PZaWljA3NyfNSz0eDwwGA+68805EIhFcuHAB+fn5ABKB\n/MTEBC5fvozNmzcjOzsbo6OjiEQiqKurk0BfdWDfeecdPPDAA9JAFkgUY8bGxrC8vIzh4WH8+Mc/\nxrVr16RQVVZWJo6M1WoVZ4dNtNmgKj8/Xwoa1DgcHh4W9khOTg5mZmaQl5cnDjGbzALXiwZ///d/\nj8XFRdH/NBgMuOGGG3Ds2DF8+ctfxqZNm6SQxKQH9W5bW1tx00034ezZs7Db7YK0sNlscqgUFRXh\n9OnTiMViWLdunfz7pxnsL5KWloaenh6kp6ejtbU1CaFEZ39hYUEKGGqC7NKlSzh06JAwfI4cOYKF\nhQXcdddd0mgUAB588EH84he/wLPPPos77rgDZrMZy8vLWFhYgNVqlYJNb2+vfP/Y2BieffZZlJSU\nSMFwYWEBN910E/Ly8jA1NYW0tDQUFhbKWmHQodo4ol0MBoP0xFhdXZWiYWpqqjSxZeBLrXedTod9\n+/aJk8qiFJGmhw8fRm1trTA14vFEkzQVfaTqngYCAWzatEkKVezTojbUU9c+UUu8DguyFy5ckMRE\nd3c3Nm/eLI4n13FqaipmZ2el7wF1+Ldu3Yq0tDS8+eab8Hq9ouXZ1NQkiVmr1YrHH38cR48eFQZL\nNBqF2WzGd7/7Xbk/BgF0XsjaeOedd2QN0IbTdtLZZgG3u7sbDQ0NOH36NLZt24acnBxotVpUVFTI\nM7322mvo7e1FYWGhNFK+evWqnIXRaBQej0fsAwBcuXJFEvkdHR3Yv38/ysrKEI0mGiAXFxfj6tWr\nuHDhAvbv3w/gukZ0NBpFfn4+7HY7otEoJicnRfOburh03oiq0Wg08Hg8YgMY0Pz85z+XhDTXTzye\naHDMAOONN97Arl27pElhdnY2du/ejdbWVvzDP/yDfE96ejoqKytlnnNzcyXwp13XaDSii07kJRPt\ni4uLWF1dxYMPPgi/34+8vDzpmcMzPj09HQUFBbjnnnsQi8XQ1NSEkZERvPnmm7BYLFKU/DTj4sWL\n0Ov1MBgMsFgsUvgi8kxNmFAnWkWFcl+rWueqDSB6iz+rJtTVIEtFPHN/qVqeaqGBn1WTxUzgr0Xt\n86wge1C9R+pqA5DEH303FSHGP/P5ASQhxFTGJBPB/B4i1L1eL/R6vWiIq6hKnuucA1WjltdiUM9r\ncn75vSpSk4UDFRHOe1ZtGs8/PhvfjZqg4d9VdgITbqqWMm0gP7sWXUn2hJqsZZGOP8OCMf+uBlBM\nSKkIYSYAMzMzJVimbjyvzUa9ZE3y+j6fTwJTt9stDEzOM88Mh8MhmtTAdfvKd6Mi9tkXYW2Cei1D\nhvuLTAP+P9GE8Xhcvofvi8k0+qFkkXGPcC6ABOiC30GtcT6P2WxGWlqa9HAhc4IsgYyMDElKcR8S\nfc618FkMNomenZ1FLBaT8yUtLQ25ublITU2Fz+eTpumpqanweDzIz8+XRL+6vnm/jA/UHhrsueP3\n+4XFRT+C74Nrmj0kCOri2UKbrIIIiouLkZeXh2AwCIfDIfPpdDoxMDAg/T5mZ2extLQEs9mMUCgk\n+8NkMiElJQXZ2dkoKChI6mXh8Xjes9/NZjPMZjNSU1Ph9/uFuafaHrV4G4vFJFHMJD/XNueb+4pJ\nLBZEuR7UOeaf6c/TRyksLER9fT1MJpP0tIlGo5K853zH43GYzWZs2LABFosFubm5AkJR2Vpqo3b2\njNLr9ZKkZsyg2g769Onp6SgsLBSGEJk6PE/Ud82hMjLIfGSvI7vdjmPHjmFkZERAMgDEF2EDTCbK\n9Xq9rE3q//P9kSVLpiCfhfueBdC1jCveM/d6eXk55ufnxcayiEwbaLVa0dbWhsbGRlRWVgrLQE1q\nq9/NeVOLmjxHVGYAk/lq4ZjvQP2s+nNqEUi17VybKttwYGAAExMT0Ol0cLlcWFhYkH5n6j74rIbK\nZAEgRT8W0T5ofFr0P4s97EHH4kxqaqqwhgjKUBvX8h3y5wOBQJKSRGZmphRKyVTPyclBd3e35KU+\nbPj9filmzszMwOFwoLq6+kOfNy8v7xMnOmmnvV4vRkdHodfrkZOTI4X492NY8NyjTTcYDB+7IM1C\n68d9b4FAQM7QjzrvtFqt9AWhzv2bb74Jp9OJQCAgsXdVVRWARJwWCoXEF3G73QJWYIPj5uZmAQUD\neM89rGVI0OeivQSAmZkZrKysoKamRvZhSUkJtm/fjh07dsBkMn2mBX2y9NxuN2ZmZjA3N4dgMChx\nEZl0wPUiLZnT5eXlSEtLw9zcHLRarTCnotGofEbtw8MiKXv0RKNRya2y/xTfDQGNLS0t2Lt3L4qL\ni8VOrlUIACBAxc+i18cfCgC/X+MjCwCbN29GLBZDf38/gES1rr+/XxI3NCSRSEQa8sRiMZw9e1Y6\ng9fU1CAnJwdNTU1oamrCoUOHsG7dOrS1taGjowO7d+9GZ2cnLl68KJ3WeSD8zd/8jSRjJyYmEIvF\ncPHiReh0OlRWVorcBgDcdtttACCIWpvNJpTkaDSK4uJi7Nu3Dy+++KIEOoFAAKdPn0Z5eTnuvvtu\nkadgd/mMjAzccccdSE9Pl4TSPffcA7vdLgGO0+lEfn4+zGYzfv3rX0Oj0aC6uhrBYBAbNmyAzWZL\nTHZqKkKhELKyshAMBuH1euHz+bC4uIgNGzYIwlyVsdBqtYIIKioqwn333YcjR47gc5/7HFJSUqRy\naDQapXHw5OQk3G431q9fj/T0dLS0tCAWi2Hjxo2YmJjAhQsXoNfrUVxcLPdWWloqzqnBYEBJSQmq\nqqoQjUbxxBNPIBQK4bnnnsPs7KwY9yeeeALV1dW4fPky2trapLnv3/3d3+Gdd95BZmYmDAYDrFYr\n7rjjDgAJx398fBzFxcUieVRYWIjCwkKUlJTg1KlT4vCw6FRWVoasrCx0dXWho6MDjz32GN599138\n93//N+688045HE+dOiXUP9KmKZv08ssvw+PxYO/evTh79iwmJyeRkpJo/trc3AwgcRAFAgFcvHgR\n+fn5yMzMhMfjwW233QaNRoNgMCiIMR4UTqcTpaWl0sjS5XJh/fr1GBgYwG9+8xt8+9vfxtjYGOrq\n6qQAwAOODmVeXh4OHz6MxsZGoZyxAMZqrxoY79+/H9euXUNXVxeWlpaQn58vDrbb7U5yRG02G4xG\nIyYmJjA9PS3IIlL96fCqB+vS0pI47pmZmQiFQqirq4PX68XMzAyys7OxuLiIF198EUCiuU5qaiqy\ns7MFSfhph8FgkOJaZWUlnE6nzJterxdHbnJyEuvWrUuiIq6srMi7CwaDKCkpkYLA17/+dZjNZgkK\ngUQQ+cQTT+Dpp5/GmTNn8PDDD4ttGx8fh9VqxezsLJ566il5h1lZWfjud78re2RwcFDYBmNjYygt\nLZVG1HyHdPKIsmNgxyTE/v378a//+q8YHx+X52FgkJKSaIx09epVQXE3NzdLIoIyPZw7SqXt2bMH\nbrcbubm5krBnUKk2h+R3mEwmSRA6nU6RFWCyUpXyUJFkTBa+8cYbkpCiPMNa9CepxEtLS7h27ZpQ\nNicnJ3HvvfdicXERZWVlWF1dxa5duwBAip1+vx+5ubmora1FWVkZ3n77bXR0dCAajWJ+fj4pQKKU\nhoouPHbsGHbu3CnJfwb9tLlmsxkajQY2mw2xWAxHjhxBS0sLampqMD09jXg8Dr/fnyRr1tzcjHff\nfRd33XUXIpEIlpaW0NjYKM2109LSMD8/j5mZGUG/X7t2TZLkXq8Xv/71r/GNb3xDiiPt7e2YmZmB\nx+PBoUOHAACPPfaYBIlerxcFBQXIyspCfn6+FOisViuWlpaSkN8ZGRkSvNTX1yMQCODpp5/G9u3b\nYbPZBOnIeYrFYnA4HJiamkJXV5c0It67dy9ycnIkGVdZWYnt27cDSLDi2GyZycFAIACbzSasKqJo\n6+vrASSaVTudThiNRilUA0Bvby/MZjMGBgbEh2CRlnPS29uL8vJysVUfFZR9ksEgJTs7W5IlalCu\nImaZQGNigIhxImxWVlaSmiWryHsVLc+EKQNtJo+B68E1nWcmblSUNO+TRSI1IGJiVUW58l6Y1OJg\nkpjJp2AwKCw7lX6tShLRF1Sld5hsVJ+BdoOJfAbWDFRU5Pjae1ULlbw2k0BrCx3qOlARmGsLL5wb\nFRHM7yDyTk2G8mzkWas+G39GLeio88+zXpUvISCEjAEWbXiPtFFq0YDXo41WUbwqIlW1e2qTP87T\n0tISmpub4XK5MDo6ivn5eZGYARJ+DZvqmc1maahXUlKC3bt3S4M7g8Eg98d3QlAKi4r0L1h04tpX\n16m6DtW1kpqaKo3EaQM4d3z3RqNRmE5qoUEtapIZwP3CeybCdGVlRdgRamGNxXQmo2gro9EojEYj\n3G73ZybDsbq6Kj7o4uKizFFZWRkaGxvlWdfuFZfLJc2M1WJgMBiUohtwXSqLtoMyDNzLfr9fEmxl\nZWVJ6557kp9lUphDo0nIrdrtdknGs5EhABw9ehR9fX1JTK5YLCb3qLIywuGwIBeJFgUS69jn80kC\niYk9FhQpoXH16lXx6cmWVNkxKgBDRbmvLQ7SdhOkocpGxWIxAT8ACZYG9yALOVznjE/JRlDtIP1V\ns9ksCXo1YUz7qjbxphSNagfV4s7KygoGBwfFXpeXl8sz8+dXV1elELK0tCTXUxPiPJtoMyhjUVNT\ng2AwiFdffRWzs7OSPFpdXZWCD5vMc/6Ki4sRDoflLOT7NhqNsu4cDgdaWloEKa9KuqhyUqoMHmU1\nDAYDcnJyBNAHJHw6Mk/37NmDDRs2SBNb2ngWeNQzkPOmsuDoh6tFNrUooDLpVGYB/WKTySTsB9XX\n4h5iQVNN6NNuEpiWlZUl/heQYO1fu3YN+/btw6cdRNVz3pl8DgaDcDqdWF1dRVFRUdKe532QVbyW\nIfBhY3h4WBrF8/mvXr0qQL5AIIDi4uIkVDvXLZH7WVlZwv5g8Yg5Bw7adTJnaTO2b98uNmbt4Hf6\nfL73yKsNDQ0hLS1NQFXvN9QCzccZbI7u8/kwPj6O6elp3HfffTCZTEnzrQI4CLAgayUQCHyid0AA\nxcdlBKysrMhe/LAkeTwex8TEBFwulzR5J+iVPkdra6tIigLX5YS8Xi/8fr+wBPr6+uD3+9Hc3Iw9\ne/YgOzv7E/n4Go0myW7s2LEDWVlZWFlZwfbt25GZmYmKigphG3/Wg7YjGo0K4HVwcFBkzTwej9jO\n+vp6OZtdLhfS0tK/WDqgAAAgAElEQVREpo9MHxbg+RkW6+PxuJxzZMEUFxdLjL26uir/Hg6HUVZW\nhptuugk33HCD7D8Aknei/0M7uLy8LMV3giN+1/GHAsDv1/hIa1FdXY2Ojg7s2LEDQIIW29fXh4WF\nBUHDZGZmor6+HisrKzhz5gy6u7vR2tqKL37xiwASi+7s2bPIyMiAx+OBxWJBTU0NXnvtNUxMTKCy\nshJGoxFTU1PYvHkzAEiylSiKQCAgB0Y4HEZ9fT08Hk8S/ZOUVepGLi4uoqSkRBCHNHhZWVk4e/Ys\ngMQmraysxJ49e+DxeKDVatHf3w+/34/i4mKkp6eL7v7OnTsBJJAo7GI+OTmJgwcPwm63Iz8/Hzqd\nDj/60Y+Qk5MDn8+Hzs5OWfRms1kqm3SORkdHMTQ0JMhgnU6HcDicpEOr0paLi4uRlZWFCxcuICUl\nBb29vRgfHxcEJpDQin744YdhMBgwODgoFelXXnkFc3NzcLvdorFPGZScnBwJHomIWl5eRmNjIzQa\nDUZHR3Hfffdhx44dopl/3333AQDeeOMN6aWQm5srqGer1YrKykrRp+P6Ie1TRS0ZjUZs2bIFDQ0N\n6O/vx8DAgGhFt7W1ISMjA2azGe3t7YIqPXbsGGpqalBTU4Ouri4UFBRIwnBkZATj4+OYnJzEs88+\ni3g8ju9973vQ6/WYmppCSkoKdDodFhcXxUH3+/0oKSmB1+vFM888g/3794vUlcfjgU6nk4BYrY4S\nxXrt2jVkZ2ejuroa0WgUZWVlKCkpweDgIIDraA0Wp8g6+dWvfoXOzk585zvfEcppeno6FhYWkqQJ\nXC4XSktLYTAY0N7ejo0bN+K1117DmTNnUFVVBZvNhtTUVGENzM3NyT4IBAI4fvw4br31VgmEq6ur\npWjEg5UJ/IWFBczPz0tQYjAYYDQaBaGwtLQkcgE6nQ5VVVWYnZ1NQl99mhEOh5Mq+kSRkRZoMplk\nPzEo5zwxsCOinknCyspK1NTUwOPxwO/3S/FL1cEbHh5OescrKys4e/asFB9/9rOfAUgkJMfGxgQJ\nSmkph8OB3Nxc1NTUSJFHDSx5yPO+ibAKh8Ow2+2SaFHXF509FoEcDgeeeeYZkbzJzs4WyTMg4dy9\n9tprsFqtqKqqkkCOv5jIV4MZ6sX6fD7U1tZKEEOt7XA4LPJPaoGH1P6hoSG88MILSShzg8GA1dVV\nKZAC14MmrVaL+vp6cdxHR0dF5sdsNuMb3/gGTp48KRTnqakpjI+PY2lpCXv27EEsFkNBQQEeeugh\n3H777fiXf/kX9Pf3IyMjQ9YNEzZMEPT09GDfvn2IxxP9FqinqiJTCwsLkZ2dDb1ej6amJoTDYfzq\nV7/Ct7/9bQCJ5LDf709CGt91110YHh7G+fPnsXnzZszPz2NiYgLf+ta3hDWzsLAgxTMASfswPT0d\nnZ2dOH36NO644w5kZGSgoqICjzzyCLq7u2WfDQ8Po62tTTQdmWicn59HeXm5BMF+v1+cRDL17HY7\nysrK4PF48Oabb8Lj8eAv//IvAUBon1wL0WgU5eXleOutt3D58mXk5eVhdnYWFy9exN133w2XyyX7\nk4h7h8OB5eVlXL16FWVlZTI3GRkZKCgogMPhwPT0NDZt2iTsFhZ0AoEAYrGYFNwvXboEq9WKzZs3\nSw8aJpJ+8pOfIBAI4Kc//ak4uSUlJTAYDFJY+LSjoaEB4XAYk5OTMBqNglBX+w/w/omkBK47zHTK\nVcQc1ySTLKo8ABOe1EHOysqCTqeTz/A9q4UsJk4pm8AEhPoZ4HoSS00qsci89h6B60kpAFLIIZJR\nLRSpCO21SHR+F5PuakKW3xGPx4UdqZ4XtEnvl5ih7eBQixJMhK9FyKvfyf3GAgWT/Kq8h4owVm23\ninJmwYMBuZpwX4v2J6JKTVgD13tcqGhV4Dqzg9dhYUedN7XQodVqk+SQeA018UsfWS1aUO+e5/rU\n1BSGhoZkjYVCIfT29mJ5eRkFBQVobm6W+Sd6nFI4KnNoZWUFoVAIDodDEicstrMIpZ4fahKWjCOe\nmwygMzMzRYqUz8N3q9VqYTabUVdXJ4kGonstFosErOwLQPZqJBKB2WxOslUsYHKOmOwhG0xd9wAk\n9qFk5acdk5OT8Hq9CIfDSWh3u90urA4iQtViv8vlkmLdWtks7l8mGJlIVsEHnNdIJCJ2lntXlUDl\n2qMNoZ0IBALQahNSbjzDs7KypB9DOBxGaWkphoeHJdHIhDJtEwDpfcTnI8qXa9lgMAjAzOPxYGlp\nCW63G16vFxkZGUnSOFyTPMvV51GlWsisVBkmtLXchyxqMTaj/JPf75e9qNfrhVkTCASSioi0OSza\nrWXB0M+iDeE8046oxQn6NLQT9KU4p/Td/+M//kOY5w899JAUTYgwp20B8J754LVUO8Z5NRqNEhee\nO3cOU1NT4kfH43FBCU9NTaG4uFhsAs8IVWaO6zAlJQUWi0WYRexlk5aWhlAoJGtIZWmxsBgKhXDh\nwgU4HI6k84CsNfoEDQ0NMBqNYlv4c7T/ZGPxs3weFn7ItqDdVhlnPBfXzh3nVUXscg1yX61F2tP+\n0CeqqKhIilNZwAQS7InPggFAHwFInCEqO5rvPxQKweVyJckjq8lYMsbVPkHvN2h/3njjDbS1tWHb\ntm2IxRISvz6fDxqNBgsLC2Lf2eeOc52VlSVJea5L7jXa9vT0dEnoquxEFs9YdFQLBe83WADSarVS\n2FpcXMTQ0BDS09ORnZ0ttp9KBqqdVJOpHzR45oVCIQQCAVy4cAG1tbWSEFbnWmU18Rc/W1pa+olk\nWlwul9jMjzOYW/ugoTJ6vF4vNBoNpqenodPp0NzcjFtuuQWRSET6Fa79bE5OjsjsRiIRVFVVwWKx\nYGBgQGRXVZ/m446lpSV5z+yTyf3L/fZR86b6Zx9nqP54eno6QqEQFhYW4Ha7EYlExM/Pzc0VueSy\nsjIpEg4MDMDlcomNZyE0LS0NNptN5iEUCiEWS/SM0el0wpSmfB7tWnp6uhSsiouLUVVVhaqqKuTn\n538sVg97THwW4w8FgN+v8ZEFAFI/1YYdmzZtQk9PD86dOweTyYTS0lJcuHABk5OTaG5uxkMPPYTi\n4uKkQ4XNfvPz81FQUACXyyX641evXhXkGRv6ms1mZGdno76+Hs8//zzKyspw//33w+l04uzZs9II\nlQ0LgcRBRtppNBrF0NAQmpubYTQaxYlfWlpCQ0MDWltbAQCjo6O4cuUKXnnlFaSmpmLr1q0oKiqS\n5LrP55MmoKQsMXHh8/nwy1/+UpCQS0tLKCoqSqL7Z2RkoKenRz7X0tICm82G1dVVTE1NYefOnRgb\nG0NnZye2bduG0tJSBINBOViysrLEcXc4HIhGo8jOzpZGV2NjY9JTgMn8bdu2iSPu8XhQWVmJwcFB\nbN26FfPz8zh58iT+5E/+RJpGAkg6bNLT07G4uIiGhgZxXufn51FbW4vMzEzZxCoimIGqVqvFDTfc\ngOHhYdTU1ECr1SIrKyvJ4aKuNBOfDBS8Xi+mpqZw+PBhmEwm6QEAJIzQ9PS09GzYtWsX0tPTcfDg\nQXHevvnNb8pBVFRUhKysLLz44ouIx+P4i7/4C0GK5eXl4fz588KcOHLkCIBEoJWXlyeH0fj4ONat\nWydI6aGhIdTU1Ehlm3Nw/PhxNDU1obi4GGazOcnZHx8fF201Jv8mJiYQj8cxOTkp///EE0+I9r/J\nZBLaOR2I5eVlRCIRdHd3Y+PGjZLUrq2thcvlwpEjR/DlL385yakxm804fPgwHA4HNBoNHA4Hnnrq\nKdx8883SQIlVZK5ZauoODQ3BbrdL5d5ms8mePH/+PDZt2iT7LhaLYXR0FBaLBdPT03j99dfxzW9+\n86NMy4cOzl9GRoY4ugzEJiYmYLfbUVRUhLfeeksoxgBEtmp2dhZFRUVSsHryyScFhUT0Gh1L9uPQ\n6/UYHR3Fa6+9hvvvvx+5ubmYmprCtWvXMDIygh/84AfiMDHI5NrPzc2F2+3Grl27xDlTERrc/0RG\nMWlEJCwLA/w3lVbNoI+JWr/fj5WVFfT09GB2dhbl5eVoaWmRQzotLQ2PP/64UKJZpFLRaJFIJEku\nSkUlEHnAJCTRdTMzM9JIic/D4u6JEycwOjqKtLQ0QU7H43EptrChodPpRE1NTZKD7nK5MDQ0hKqq\nKkE56PV61NTU4MqVKwAgBWYWwIBE0cJoNCIvLw9NTU2SHFE11cncmZiYwI4dO0SX0mw2SzCjFlhN\nJpMwZ0ZHR1FYWIj+/n688MILeOKJJyQYJBIYSNiNr3/963jmmWek380jjzyCdevWSbBNtImKZCYa\nJD8/H5s3b8Zvf/tbzM7OoqKiAmlpacjMzMSWLVukSHf48GHYbDZpig1cD9rZFDoWi8FisQhtOj09\n0ZScjeQ6Ozvx3HPP4fnnn5eEMQMpBmd+vx9Wq1US3wzCybDg/lQTYmazGSdOnEAkEsFDDz2UhOou\nKCjAzMwMRkZGsH79evT29gJIMPYsFgtSU1MxOTkJm82GyclJVFdXY2RkBDfddJMEzVyndHrZjM3p\ndOLWW29FS0uLBICfdmRkZGB+fh7T09PSzJKJKhU97Ha74Xa7UVJSIsl8Ji4ZpHJ+1POVCDMm7Yhm\nPX/+PBwOB9rb22W9A9cDPq5n2i6VKaYmjPl96rtVk8SqVIRa0KDN4plPbVEiEVXJBHX9MXlLfW4G\nv5SgYGJOZZhwffK6sdj1niQqK0ItaqrSK2qSTE228Pr8XhYuiMZU55NsCTXJSLYAf+f7VovMwPWC\nAwfnWC1Aq++PSCx+5+DgoMghZmVlwWg0yhpjApLX571RLojndVpamvhSBoMhiea9dr2pRU4yvVZW\nEj1WMjMzUV1dDZ1OJ8U59rthMoqFXLK9+P5VVgNt+szMDLq7u2EymaSvTmNjIwwGg6Ax1WCea5kJ\nTT4j55S9T1SGCc9QzlleXp70yiFjIS8vT+ZbLQ4tLSUa+JaWlsJutyf5wAzQ+WxE36nyHSkpKSJT\nwYLIZzHm5uYQCoUEqcr7oi1mwY5zRx+EjTHV/c+kNv0onuVMWDI5pe4xo9GIubk5kRvifqEEGvc1\n1zbXG88RIqoZsxQUFIjfaTAYYDAYkmSDVLkRnU4Hq9Uqa9vr9YrN497mfs/IyBBdfTIWvF6voGBj\nsRi6u7sBJM6/pqYmaDQa6SFAdgolyFR2I3C9sTZ9Mq4FFqvn5uaECczECueX0gv0rfl9nGMVDLL2\nDF1bKOB6UxPFalKRe4Y9D8jGz8rKwi233CLxhvr+mfiivVWvQ7CHen+0W5wXFuXI6L9y5YqgS30+\nH3bv3i3rSy1cq+9QPXNomzUaDcxmM8bGxpCeni7gNLIpKA8DJPwTxiVMpjHZlZWVhVAolOTX8llU\n2R+18M5zlfOvzi/fkXo+MM7lYDFYjXG5F3kN1f7yeqp/wPNNZRewCE4Wezgclsa4QCKZ92FI9I87\n1Htfi+5OS0v0OVILo2sHpXEzMjJkfX5QEYDxx0MPPSQMSjKtOS/9/f0oKioSP1+V1aMNjEQimJiY\nEO17JjN9Ph+8Xq+c1TxPqAbA9bj2PFcH3xNtA5vL5+bmoqKiAsPDw+ju7kZxcTFef/11AIn39/DD\nD6OwsBCx2PV+VCpDRJ0/NYlL6Ze+vj643W788R//sdyzChKhP0UfDrje42tpaSmJzeByuaSX1/s9\n3wf934eND3qn8Xhczkw22CZoZ+vWrVJo0Ov1yMrKksKoavcIzFVtM4EGFy5cQE1NTVIzW9owgkFZ\nUOZe5lzwO9V5p//zYc+kjk/a64K+psvlEjYSc5O0J+np6cjNzZXYbnBwEFVVVVhdXUVvb6/4xS6X\nS1QPwuEw5ubmkgq2QKIXAGM8NkUvKChAbm4uqqqqBNTGz2i1WmE0/l+PPxQAfr/GR64A6lBRjiUn\nJwdlZWXQarUYGBjA/v37UVRUJA3FSA2emJhAdXU1AAhVOBgMYmRkRJLTd955J6ampgRNPzo6igMH\nDgBIaLh/9atfxfnz52Gz2fDFL34RgUAATU1NGB4eTkKF0JD5fD4sLycaDzKRMzQ0BJ1OJ92tI5EI\niouLxUAMDAzAYDCgoKAAdXV10qFcq9WipqYGy8vLeOmll5CamioH0vz8PI4fP47R0VFUVlYiLS0t\nSfs8GAyKs2m1WvHss88CAE6ePImioiJ8+9vfFiQ/K7HLy8twOp2CbuBGCQQCooNmMpng9/vR0NCA\nhYUFKRjwZ4g0ZzDIgysWS+g2V1ZWik4lEbmcBzqrKSkpyM/Px5UrV5KaK23YsAGhUEjmBkhIONTU\n1CA3N1ecvEgkIvd38uRJfPGLX5TEAADRgRsdHUV6ejrsdrs44B0dHTh69ChCoRAyMjJEpoYJqN7e\nXhgMBthsNly+fFnYGauriT4J1MEErjtqmZmZaGpqwqVLl6RHRHt7u9CfY7GYrDnen9FoxNatWwWJ\nQAQa+wUYjUa89dZbABL02itXrqCiokKa3VRVVSEnJwctLS14+umn8f3vfx+Tk5PCnHC5XLjxxhuR\nk5MjCJWhoSGcPHkSmzdvxsrKiqxrFUVSUFAgqDwikah36PP5RFqLe6O5uRm7d+/GyMgIUlJSsGPH\nDszNzWF4eBjFxcWSyGcDPc41m7kWFRWhv79fZGcikQjWr18vRRH2xGCle+/evbjtttuEgfBphsPh\nQElJiewDr9eLqqoqBINBzM3NYXx8XIJFOglAQlpleXkZt912G0pLS3Ho0CHcddddqKqqkoBkLZKU\nbILs7GwUFxeLdFAwGMTCwgLC4TCefPJJmM1mCYZ4EGdkZEhgS314Ht5MlPAZ6PASGc+5AyCFiY0b\nN2JkZETujUGo2WyGz+dDeXk5du3ahXXr1uHQoUNSUCgtLRXqKZ3ivr4+XLhwQYqflBgzGo3o6elB\nOBwWqnw8HsdLL72E7du3i/xTWloavF4vFhcXxS6qAeH4+Dg6OzvR29uL2dlZpKWlSbFCDaovXryI\n4uJiABBWFQNQoumI9mGwFIslGs+98847AIDu7m4UFBTI3NFO8p0GAgFMTEzglVdekeZbNTU1CAQC\nMJlM2Lx5M7xeLyYmJtDQ0ICioqIklI56nqysrGBiYkKYM0w2URbO7XYnIV+BhEM6MjKCf/qnf4LH\n40FtbW2SNIhOp5OiJ9dCdXW1OPr5+fm488478V//9V/Izc3FLbfcIgkUzl1HRwcGBweRk5MjQQWD\nWZPJJPPg9/tRWloq62d1NaGT/cYbb+D06dM4ePCgFM2IamPBAYA43l/4whdgMBjQ19cHs9mMr371\nq1heXkZnZ6cw8Ohc79mzB7W1tfjNb36D48eP44/+6I9w4cIFGAwGrFu3Du3t7SgpKcHx48fxwAMP\nALjOuBodHYXZbIbL5YLFYoHNZsP4+HhSwMeiAYNMn8+H9evXY/v27aivr8fq6ipeeOGFz4QOz8Ro\nSkoK7Ha7JA5YTGPQwL2pJtMYsLFfkc1mS0p28fpEHnLtra6uShOw+fl5NDU1yT7jtZmcYIKDCSIm\nE1R0oIoWJ6KeyWxKX60NftQglfuTaFMmtjSaRI8JJsZI8eY5zDVJySeCIJig5Lwx6GMClwl32gYm\ndNTEPgulqg1Q51xN6gDXbasqkaPqePNzTLQAEBum2mzuezVprdp12iAiNPk+eH/8znA4LEE8kcuh\nUAh2ux0lJSVJ70WVm+A6YFGJSQXeHwtCXCf0DcigzMvLk8CPRRYmpCjxp9FokpLhV65ckfteWVnB\nwMAAvF6vNGTU6/UilaUm1nW6RI+XhoYGBAIB9Pb2QqNJSBmWl5e/h2auShypyHb13arJEHXwedVk\nDoN7SsipvY2Iso1Go6isrER5ebn0+iG6WU3SkB1GsAWTFpSjYz+dz0o+YGJiQpLaJpNJ5JiYvFQL\nICqLiLZcq9UmFfY4h3xHqs2IxWLSo4a+H+eZIATaPMYGqhyKKtvDtaom2LnnMzMzkZ+fj3g8jra2\nNokDgsGgJKQ1Go2wp6enp2EymYQpDUAK9dHo9T5nTOYxrmS8E41GpccaAPT09ECv16OiogLx+HV2\nE4AkP2xtsZFrhjrNKSkpUmTQarUoLy+HxWKRz5P5wuIWfR8yJQisWWtjCYLi2mciS2Vq0F4B10Ek\nan8HnU4nyfl4PI6cnBzcdddd8j1qYVoFyvBZ1zKnOPjca9cN56W1tVXsAtcPC9pZWVnS/DI1NVWK\nQnxuDvrZGk2iNxDzBlyLfD5K/gCJpKfb7cbw8DBOnToFjUaDvLw8FBUVybnNtUP/hOcn7R8lZNh0\nlM9HmwNcZ4GpxU6eB2rRUGU4qM+39hzhnlJtHhPFAJLYtipTaWZmRkBQdrtdfDuulU871ALH2qS4\nWvxZi9zm4PNTcpNr4MMSp3wv4XAYMzMzCAQCwjTNycnBpk2bkuSRAIgcEQvRLEpnZ2dLryaywvkO\n1J4OjM2MRiPq6uoEMLl2qHuBOQr2XSHYsKenB4cPHxZ75vf7MTg4KLaWvkosFpP8iCrdQqaVx+NB\nKBQSyWyqIzCXsri4iIyMDJH2BBKxp9VqldiTvSPVwYLl2sIO9y/Plvcbqt/5cUYkEhGgFvNUXV1d\nws7jHuZe4xnN7yETTS0cEeBaVVWFgYEBnD17FoFAQPJW/PzMzAxcLpdIRhcVFYlUN4Ak//H9/v6/\nNcLhMMbHxzE3Nwefz4fBwUH4/X4Btni9XgwODsraZk8svn+fz5cUk3HdLS4uynvZv38/nE6nqK6U\nlJTgkUceQVlZGVJSUoTtpAKA1vo4/9fjDwWA33243W78zd/8DaampvD0009Dq9Xi4YcfRmVlJQDg\nO9/5DoxGI06dOoUjR47AaDTiW9/61gfabeBjFACee+45uFwuQW5QHqaurg41NTXo6elBX18fLl68\niJycHJEr0Ov1uHjxIoDr1OCysjJcuXIFlZWV2LVrFzIyMuBwOGTjR6NR3HnnnQCA559/Hq+88gqG\nh4cF3WOxWCSYmJiYEJ1DGvKhoSHk5+eL076ysiKoqbNnz0qArCYWTSYTPB6PVJzn5ubw6quvYsOG\nDdi6daugnzdu3CjPlpGRgXXr1okuX2pqqjQ/pJMZDocRCoWQk5OD733vewCAX/ziFxgZGZEGvCx8\npKeno7S0VLTl1yJGiMTMzs5GSUkJFhcXMTU1JWibxcVFccD5TBkZGXA6ndi8ebM8KxuMsFDCRjMc\nHo8nCSVENDo16qgzysLO7Owsurq65L5XV1dhNBoxPT0trAM6PFw/VqsVBoMBZ86cQX19PeLxOC5f\nvoxz584hHo+juLgYPp8P2dnZgvhgYOP3+3Hp0iWMjIzgz/7sz2AwGAQVqNPpMDs7m1SEmZ+fl6TH\nmTNncOXKFWg0Gmzfvh1tbW3w+/1ISUmRJHx3dzfcbjfKy8sRjUbR2NiI8fFxGAwGFBYW4mtf+xr0\nej3m5+dlLXz1q19FWloaOjo6oNPpBIGg1SYo1j6fD3/7t38r65zv9KabbhLK1+TkJF588UXs3bsX\nTqcTBQUFkujhYGJo+/btcLvdgjKirvbu3btx5MgRXL58WYLez3/+89i1a5doqTocDmzcuBFvv/02\nLl68KE03VQ3bjIwMTExMiAMaDofR3d2NUCgEm82GeDwujda+/OUvy7q5cuUKLl26hJaWls+kWcyr\nr76Kxx57TAIaAILEJfWSevxMUAMQJLjVasXg4CBmZmawfv16QX6zsa2K0CTt2Gg0YsOGDTh48CBC\noRBqamqkwTJRC1zHLNKQlUAdTCbM6JgTCQYkaK+tra0oKytDRkaGMEm4LlicVJNLOp0Oe/bskQCa\nkgWpqanIz8+HVqtFY2Nj0sFO+h+d8ZycHJhMJrzzzjsoLCxES0sLamtrpbgHAC+99BLeffddCcQt\nFgtCoRD+/d//HUNDQ7jrrrtw//33Y3BwUIotO3bsQCAQED1+MiZGRkaEEqzRJOTDxv6flnt7e7sg\neIk6o4693++HxWKBTqeTRCMdQibKKHPAZ6OGsdfrxeOPP56knbyysiK2fmFhARqNBlVVVfD7/eJg\nE82vFihbW1tx8OBBCQCXl5cFUZeSkoKBgQFotdcbXS0tLeGVV17BL3/5SxQUFIjkmsFgkEQjkSd0\nerlGVDSYRqPBzMwMKioqJGkXCAREFm9kZASDg4OwWq2w2+3IycmBRqORBnqFhYWCtl7bBC0cDuP0\n6dM4cOCA/NsHocQZoFosFtx77724+eabBWkUCARw6tQpNDQ0oKSkJMmhNhgMuPvuu3Hx4kWEw2FU\nVFRI4K/VanHu3Dncc889Yh+YdKuqqhLmRnp6Orq7uzE7Owun0ynU466uLpm3eDyO7373u1i3bp28\n+9OnTwud9tOO5eVlGAwGlJeXSzGPyX41qGUiVV1vaoKdsnpr0V9ECPFs4hrftGkT0tPTpSjHf6eN\nVpN9DCSYNCJCWfUb6HdRTkFN5Kja/fwMkxS8J56tfB6udQDveR6VRUDbozLYWGzjZ7VaLYqKioS1\noyZnmahWi420wbx/+hv8nYhLlaHBz6poU16LiX/eDxMG3KcqWj4lJUXsjhrA8LqcfyYzyYiIx683\nGWUih8Vxk8mEwsJCLC0tyZnCIisLIyrLCIAkZvmdakFDZTFlZmaKFA4HA0H6l1y7TE4ywCSrz263\nY2FhAaurqyKb4PV64XA4oNPppO9RNBqVoFyn0yE/Px95eXmSjCFbjXaANobvhrrYKitF1Tjme1GT\nhkww89pLS0sC1mCCzGg0iiwI5zsSiSAYDKK+vh6FhYVISUmR+3I6neLr09/gOmTiORwOS0J4ZWVF\nfKQPC7A+yaC+d2trKzZs2CDrmLKnlBtTzxGVbawmePl/3Ns8M1WGDte9WiQpKCgQG8c5VxuTLi8v\nw+fzvUduhb8zMc17t9lskrBeXV3FxMQEgATzmoUF/h+BXUTREmDBfct9BFyXJ2NSRAVbhEIh8TfG\nx8fFj7ZarfRwstEAACAASURBVLJGuO41Go2c89xbTOBx/bndbiwvLwuLj7aUcQdwvVeTymaJxWLC\nziCbSj0vVNkw+o3cH0yKAclsCWpYA9dtk1rg5WfUAi8LqiwCMg5mQTEajcozqXI16nerBWgVdNDc\n3IzLly8DSMgt5efnJ6HsdTodxsbGpFeIVqtNimso28XiWjAYFL9YZZWpBQAifVNSUoSp5/V6sby8\njOLiYkSjUfT09KCiokJ6CvI9cx2rTC+ykjintKNkv6k2iOtR7bfA98DB/cmkOFkeBCzxjKFvuPZz\n9LPIbmBcQIlXrpFPKk3yQcNoNIrtXTu4B2hv3m+o64bX+7hjZWVFUM7UGm9oaJA4nkAG/qzH45Fz\nhLFVeXm5sOy4TlS/dG2RhH73B40PQkbTvuTm5qKoqAinTp2SGCAjIwMXLlxAT08PCgoKYDKZpEEv\nJVf4fpeWlqQ4f+nSJfT392N8fBwAsHv3bmg0GslNUAoqEAgkgTUppc38EAeL8ER8rx3cz2uZHmuf\nE4D4EDzf6HOuBZkMDQ2JXa6srMTU1BTcbjc2bdokc0aGIwEC6rpVgQn8nffa1NQEvV6Pjo4OOBwO\nYSBnZ2ejqqoKRUVF0uewublZAAr/f45wOAyn04mFhQVcu3YNly5dSgIdUkouHo8nNSmnPxOLxQRc\nxXXOGEKVNI/H4wgEAlhZWcGGDRvw6KOPwm63J4HTmKf4sPf9fzn+UAD43YfJZMIPfvAD/OQnP5F/\nKysrww9/+EP5+8rKCo4ePYq//uu/xrlz5/Dmm2/i7rvv/sBrfmQBQKtNaDXToSosLJTDj7IFQ0ND\naG1tRVtbGyYnJ+FwOBCLxfDggw8CSKB3L126JJXL0tJSaLVazM3NYXV1VQ7r+vr6JE27gwcPQqPR\nIDc3F/Pz84JUttvtKCgoQDwex9WrV5OkL7xerzANiNJmQ43q6mqYzWbYbDahOvt8PqEQDwwM4IUX\nXpDEDVEjGzduFB0vIJHE+u1vfwufz4eRkRHcfPPNWF5exmuvvYaRkREUFRVh165dOHXqFHbt2iVB\nFdG958+fR0NDA1ZXV0WX/0tf+hI0Gg2GhoZgNpuTElJnz56F0WjEtm3bMDs7i5qaGuzYsSPJyVD1\npVkpp4yLx+PBmTNnYDKZYLfbEY1GJcih4abRvXz5Mqanp+HxeOD1evHyyy9Dq9XixhtvRDweR2dn\npzQOocTMHXfcAb1eL46d3W7H66+/LkGHiv4LhUKiverxeHD8+HFUVVXha1/7mqCml5aWMDc3J4fk\n6OioaLevrq4K+iYajWLLli3o7+/H+vXr0dPTg5dffhkABM29bt06vPXWW5LQ1+l0OHnyJPr6+pIk\nqvg8vEeTyYTy8nL09/cjHA4jPz9fHL2cnBz86Ec/ApAwaPn5+ZiYmMChQ4fEeV1cXMT4+LhIoKgO\nSW1tLVpaWuDz+fD666/D4/Ggra0Nzz77rPTNYA8MFSF+4sQJrK6uYsuWLSgqKpL5npubw+nTp5Gf\nn4+BgQFxeM6cOYO+vj5BGIXDYbz77rtwu90wGo2w2+3vcUCCwSCsVisikQja29tRWlqKRx55BIOD\ng5iamsKxY8ckKc21U1FRIU2O33rrLUQiEUH5/q5jx44dktAhspSSBBkZGaL/rdFocPjwYamCtra2\nSnFkYGAAd911lyCg6Lytrq7C7/fL/PE7yNx58sknUV1dLQ5/UVGR2BAVoWgymcRhMRgMOH36NCwW\nC6qrq5MaY9JhKi0tFZ1IBjek0jOp99xzzwlinGuzoKAA7e3tkiSmsz01NYWbbroJ2dnZss8ASJCi\n1+sFmZ+Xl4fW1lYcO3YM/f394iRyfVEK6uzZs+ju7obFYkFdXZ0E4qmpqRgaGoLH48H+/fvlPT3+\n+OPyLAxEPR4Prl69ioGBAbz77rsYGRkRmS32SlAb26WkJBpejoyMCPqL80znk2h4BmgrK4lmYcvL\ny5iZmYFerxfdTNW553tn8Yf0RxVZtrCwIPNdWVkJnU6Ho0ePirPMpLrL5UJOTg5uvPFGzM7O4umn\nnwaQON8OHDggLI1YLCYJp/T09CTnSw34mBykY33gwAFoNBoUFBRAr9fD7/djaWlJkoZPPvkkhoeH\n8dJLL8n7DQQCKCgogNVqRXZ2tiDUuTeJwnS73bj77rtlb1Pah8hl1UarQR/XOQvDqamp2Llzp0jw\nqMGxXq+H3W5HZ2cnzp8/j5tvvlmC3AMHDmDdunXQaDQib0eKq1arhcvlQlZWlmiO19bWSlM/Ul+B\nRKLl/vvvR3V1tSRd+vv7YbVapQH8px0GgwFlZWVyfvH9qRqsvP+8vDwBG3DdqjIcnDeer0wQMfnE\nva7RaFBdXY3S0lIJjrj2GeizgM9Em5p4VnX9geso+5SU683j1QKPinxVk4ZMBvEaqlQZ52Atmp+2\nk4EG/49N4VlAURHDwWBQJBtYNGBSRy1+qol4UqfVOVbvnyASdaiFRjVxsva66jU4VDYT7Zsq/aPS\n/Hkv6j3RflB+i01SOY8sKlFnfnl5WaQtLBaL9N/h9zD4ZhFILQCxEMA/Z2ZmJgXuqqwRWQRMbhNx\nSMAM98Dc3JyccZFIBLOzs5idnZWEG202E+EpKQmZuO3btwtisba2FgaDQe5ZlUUCEvR1m80m36s2\n7eN+4nvg/BIlz/0QCAQwOTmJlJSEXAaDacqFAZACRmNjo0ixqQkdNiPNy8uTz/B8ZsLI7XaLvSbq\nem0S79OMW265RRovsxAPJJgBLH7RZgAQXxlAUqKZc+dyuZCdnS3+Ov0gVW+ePXIACBOktLRUmGFq\nA2eevTyXmLxg7wS1OMU9wHdG8BJ16VdXV9HX1yea6n6/X5qPz83NIRwOS+GDflosFhNgwpUrV2Aw\nGJCbm4tYLIa8vDwEg0F4PB5kZ2fLOyG61uFwoK6uDiUlJUmFFNpFFtuBZFb5wsICnE4nqqurhTGs\n2kVVdotFOfqD7E2g2gW+QyCxXyYnJ6HT6ZCTk4NYLPaeoisZ3Kp9Un3itT6yetaoBU61GKki+ZmU\np03kc7NQocra0P4yTtPr9WhsbMTw8LCsYYJNaCt5JnAthsNheL1eiVXT0tKkgSXvj3kCnr9r/ROn\n04nnnnsOzc3NuPHGGzEzM4O3334bly5dwsLCghSOdu/eLYkw+ntEXxMYSGDPByVF+Z084wg4IfuG\nP6OeO2pRiEVctVCtnne0e2qx2+/3Y2JiQtZReXm5xC0E1PG9flaIZt4H2Xzqs6ggpfcbapHokyQa\no9EonE4nPB4PSktL4fF4YDAYhPEKQNir/PP09DS0Wq0wouibEtTY3t6OxsZGOaNVcAIHAUG/S1NZ\n2k1KrbzyyisAIIwwNinX6/UiwzI/P49oNCqMssHBQZGFvnLliuQMeGbr9Xrxjc1mM0KhEHw+n+SR\namtr4fV6sbS0hMXFRdGPB97L4Hi/Z/iwZ1b9P7KrVAk4tThCCS7aXCChjjE4OIiGhgbU19dLTs7n\n86GhoUEAH2vZne9XdCFbgzI2kUhEnrOgoAArKysSZ9En/qgxPz+PQCAg+YKPMz5q/XPQ3k5MTGBm\nZgaTk5Po7u6WQqjK+CXgSY0t9Hq9+Cn038mOp19ARjoAdHV1IRAIiNoECyEqy/J/o7kxgPf42R93\n/KEA8LuPtYVNIOE///CHP0RdXR0eeOABzM7OoqSkBFqtFi0tLfjlL3/5odf8WD0ADAYDOjs7ASQa\nEdpsNmmUx4p/e3s7KisrUVdXh2AwiK6uLkxNTQFIbNadO3fihRdeQE1NDSoqKrC4uIhTp04JKqeg\noEAokUAiqbhv3z48++yzGBoawoEDB9DW1obm5maRU/D5fKipqcFjjz0GIFGcmJmZQVpaGkpKSvD2\n228DSGzgb33rW8jNzUVPT0+SJvX58+fx53/+59DpdHA6nSgsLMSGDRtQU1OD0dFR+Hw+KQDQAPf2\n9sJoNGLfvn144403MDc3h46ODnR1dcHtdiMUCsFqtaKxsRF2u10MA+Vy3G43/uqv/go2mw233347\ndu7cibKyMgSDQfT19SVtWqKR2SzUYrGIDiU1l6PRKIqLi/H5z38eQMJpPX/+vOiw/eM//iM8Ho8c\nJs3NzUkJCSDh8DF57nQ6MTExgZ/+9Kfw+/1S8aZeM52q5557Dl/5yldgtVqlEs2goru7G7feemtS\nNRlIJKvr6upQVlYGl8uF4uJi5Obmit4ef95gMEhC/sUXX8TFixdRU1MDq9WKm2++GQBEbzwjIwNG\noxFNTU3yXtnMlTIljY2NaG5uRlpaGmZnZ+FyuWA0GqVjPZAoXGg0GszPz6O9vR1paWmCKFGTsky8\nAZBgo7i4GI8++ijeffdddHR0YGTk/2PvzYPbPK/r4cOdBAkQC0ESBPd9EReRoiSLkixZluTYcuzI\nTr0ltZM6ybhtpknaiSeTtvkrmabJTJs2mbbOJG6T2HHqOHZsy5FtLdYW7RJFifu+gSQAYiUAggTI\n7w/8zuUDOrGdWOl45tMz47ElE8T7Pst97j333HOHMTMzI86eChxs374dfr8f165dg81mw2OPPYax\nsTGcOnUKp06dkqYuBH34rsz6W61W6alRU1OD8vJymEwmvP7668JeAmJBid1ux1133YWzZ89iaGgI\nu3btwrZt20Qz0ePx4Pnnnxc2/caNG3Ht2jV0dHSgq6sLp0+fRlNTE7Zv3466ujp873vfw6OPPirA\nDrDWZJBSWuzj8WEGmc0MyF0uF6amptDc3CyOd1ZWFu677z5cvHhRWBBGoxE+nw8zMzPyvk6nU9gS\nDJbsdruAirfffjsCgQBOnjyJhIQEYVwTmKVtCAQCKP1/mpsOh0N6JDAoKCoqgsVikQoJo9EoZZpA\nrDcHL3+V9UBniAG3yv5aXFzEkSNHYLVaRY4kEAigq6tLdAT57gwMCNiwjNBut+P2229HVlYWPvOZ\nz4hTbTKZ4th+c3NzeOuttzA2Nobx8XHY7XY5V5cvX8bGjRuxbdu2uECf9puyY3TK29ra0NraCpPJ\nhEOHDqG3txdATMaGMi2qk8KeG6xYWFlZkaoTABgbG4PX68XKSqxJMKt0FhcXcfLkSWEBqYyh9PR0\nLCwsSDlyZmYment7pem83+/H1NQU8vPz4+bQZDKhubkZJ0+eBBBLEh87dgyf+tSnxAmdmJgQ2/mv\n//qvSElJEfkdrjGdPrWMXHW+6RguLi5iZGQE/f39yMjIwODgIAKBAMrKytDd3S3zRUZITk6OOOMm\nkwkOhwO//e1vkZCQgPr6egm0gZgtHx8fR0ZGBvbv3y/VDrQRQCzg1Ov1YgcJWpO1QlkkNq/fsGFD\nHLMNWJNLKCsrw6OPPopvf/vbmJ2dlV45bN68sLAgc8116urqkh4p/H0ej0dKtn/605/Ks7FKAojJ\n47322msiM/eHBHPvNSiNQYaNyq4neABApAZJTCCooAJxAATs5/MTRCSQRBCMzECyTVUZCDLbuQbq\n/Kt7a30Vx+rqqlT0qexO6lwDa8A1meQENXkfsyKAoJCaZOPPEwwjWE6mo8oOpK1RmZDAWrUCEN8s\nmc/OP6s613wnFXyhPVV132lHEhLWNHTVu5igF+8UAmhqgoM/pwI+KlFFBeD5D5MckUgEPT09cndw\n73CPsLEhwYWkpCS43W7Y7XYEg0Hxi/g+aqCvJhq4RiqbWQVB1PemP0J/m3uP4AYAaQ5MkIlMWLvd\njpSUFLhcrrgeQkDMF6JuN3+XXq+X7yQAqiaUWAHLJBFBJH5+fVKL+5XAwtzcnPRzYuk8g2UyabnH\nSkpKUFlZKXPE5yKgRP9VBfDW63ozcOd8kI18M0Z1dTWSkpIwPj4uto9r7XA4RE6MAKzT6Yzrf6Yy\n77kHCUCy6oj7k8Ay3wGIBZNqgo9yPtwD3I9kRnNuOBe0NyoZAVirSE5OTsbWrVsBrPlpTqdTQCSy\nQ2lHaCfU6hzeuUzmR6Oxxs5LS0vCHjcajQJIZWZmIi8vD4mJibh06RK6u7vR09ODzMxMWK1WbNiw\nQWQqVBCUlQRMYnH9ec+ooDsAkaFSAQ7aFtoX/sNzSelWrVYrVYO8F3gP0DaqZ5n30/Hjx5GSEuuH\nxoQG921Cwpo8DMFqlblOcJ92mOC2mtQkMM7fR9KAWqFjMBjQ1tYGADh69CgCgQCMRmOcfBDfTa2Q\n4t3GyoCxsTE4HA5JFpDxzH2l3u0+n08qdoxGY1x/q1AoBIfDId+pSgHxrlXlRvgPE8UqqK6uB0mB\nXGveu+o5UJNOvHu4bgT11CqM1dVVkU9is1D2NmRSWKfTidSYmtTnZ2724LszgbO+uuF3DSZkOY8f\ntGGoy+USaSOn04nu7m7s2LFDEpYA5B4EYtgBm8LyrlxeXsbs7CyysrKwd+9ebN68WXqY/L7BPoR/\nrL9IaRbVxrE6gRWgi4uLMJlM0q9vYGAAw8PDiERiWvC8l4hlMQk/NTWF/v5+1NXVSexDf5+2JTU1\nFRaLBQsLC8jNzY3zRT7MUP1UPhuJD6rfyoQWMTi1txzlQpuamoQQ1t/fD4PBILJDJCC832CFFxMR\narWqKv/8h1Tg/epXv8L8/Dw++9nPxjW0VhMf9BmBNVtK+8/KMdXvpIweiVpDQ0OYn58XfI69j2h3\n6UfQJgBrDb2DwSBCoZDgSiQ1UCJocXERo6Ojsj5FRUVYXFwU4hrvYHWu/hTjj0083koA3Nzxb//2\nb8jMzMQzzzyDS5cuQafTCd6kkrJ/33jfBEBHRwcaGhrwyCOPAIhpbB87dgzPP/88zGYzqqursWfP\nHlitVszNzYlMiMoQMZvNiEajqK+vx8jIiJS6UjokEAjg+vXruPPOOwVg6+/vx+pqTK/9jjvuEOkY\nlutOTExg8+bNUjIHxA5uUVGRlCd+8pOfxGuvvSZsoObmZqSmpuLEiROiX97U1CSsu+3bt2NyclJY\ndnl5eYhEInA6nZiZmRGQlKz/mZkZmEwmXL9+HRqNBp/4xCdw9uxZXL58GcnJyXjwwQeFwQjEEhRt\nbW04d+6cMGmcTqfMWX5+PgwGA3p7e3Hq1CkAkKREMBjEqVOnhDXgcrmwb98+1NXViZPFgCEnJwca\njQYmkwkzMzPYs2cPjh07JpI4FosFTqdTtDaBWBOSpKQk7Nu3L65pqMvlwtDQEK5cuSJBHA1fZWWl\nOEEpKSno7u7G0NCQMKDLysrgcrlw4cIFSSBxbYxGI5aXl5GVlSXOMwNYrheDUUoRNTY2oqKiAjU1\nNcKu4GX77LPPxgVjPT092LRpEwYHB5GcnIyBgQH84Ac/EFmK4eFhpKamIjc3VxypgwcPSsWEx+NB\nTU0NAEg5JnXR1XIuGnOWpDc2NiIQCODSpUuig0qW0YEDBwDEtPl9Ph/OnDmDe++9FwMDA0hJScGf\n/dmf4ejRo/ja176GlpYW0dMDYkmn7u5u/O3f/i0SEhJgtVqlcd/8/Dzq6+tx+vRprK6uYtu2bQBi\n0jh2u12Sa16vF2azWZo/M6lAViIAvPPOO/j617+OxsZGjI+Po6enB729vTAajXj55ZelFwAQD44s\nLS2JJiuD0g8zzGYzfD4fRkZGcPbsWSQmJuLy5cv4xje+IdqtkUgEBoMB4+PjMreUYBocHERraysW\nFhZQUFAgQRTXq6ioCGVlZQBiduPq1at4++238Z3vfEe0+BwOhzQE56VKJ7+wsBBTU1OwWCwij0I2\nUXFxsYDsamkty8oJ3pB9x7UgYK0yVFki+z//8z8oLy+H2WyGXq+H3+/H7t27hR2lNiZbXFzEc889\nh/T0dExNTeEv//IvcfHiRbF1lHdRtUiTk5ORn5+Pe++9F2fOnEFnZyc8Hg+mp6dRWVmJgwcPoqys\nLK4kWk2I8RmWl2NNyt1uN1JSUvCxj30MJ0+eFJvR2dmJvXv3SlNDOljsU0CNw2AwCJfLJU7V5cuX\nUV5ejqysLPT19eHq1atobm6GwWDA8PAwLBYLfvazn8X1GyktLUVeXh7a29tFxqO8vFzK3yORiDBB\nCCyw6qajowO9vb0YGhpCZmYm+vr68P3vfx/t7e3QarUYHR3Fww8/LGsExAK/L33pS9BoNPja176G\nhoYGsWF0AOk4EfxXmXwMwI8ePYqsrCwkJibiySefjANaeC543vx+P3Q6HWpqanDo0CHk5OTEsdNo\nh9WAh8w6Bh8q6AysaZozAF5eXobH45Fgl6BvZmZmHDszGo3Keh04cAAvvvgiQqGQSPf9y7/8C1pa\nWkTmj+D59u3b4fF4BORSNU9TUmI9iNTzkJCQgJGREbzyyis4cOAALBaLgCI3Y7jdbjgcDqkWpN5o\nKBSKC+rZT4fAA1nOvMcIzKosYY1GI6CcCnpzP3DuKZ+lDgY91CAniElwRj3P3AMMCmhrCJJzXXl2\nuSfVygSVxc5gm4As34cAHQM0/l7uMZ579X35nvTdmBBVgyomRlUZDM4Bwav1jHaVJQ6sATFMygAQ\nxjiBTcqd0Q9iUpIAznpAhPeIKgekzhntI8FIu92OcDiM4uLiOEY014XPRbYzbSG1+R0Oh6wp2bUA\nRO6Ac05wl8+tBn+qFBL10slSJEjMqjE1gZSVlYWEhFjlHYFDrpXf7xcZFgb2JSUlqKioQHZ2tlRd\nEfxkRSOTQ7w7KPXA51UBOTWZQvCLY2FhQfwDt9uNhIQECZAJxLpcLvFVWHXJNV1cXITL5ZI9Sp36\n9SAuwQ+CRpTC5FyR+X0zhk6nk7gjJSVFJB/IIh0bGxP5QFbN0cfUaDRis7nmlDpUJU64T2gLuHeA\ntaaXtLFMzNMfof+fnZ0dl2RmskWtUFRlcLiGSUlJIhfFRr6Tk5MYHR3FlStXEA6HpVqWspvsGQZA\n7jzahKSkWINOr9cblzBW5Zl0Op1IamVlZYlPHAqF0N7ejoqKCuntxD24srICv98Pm82GqqoqZGZm\nytlVq5DUPUkwivsrGAwiMzMTkUhEpLK4vnz+1NRYg2Im4RhbqWeX66lKOqjVVOvtEe01WeqcJwJY\nXFu+C+8P7hUOVlmpfiUTZWpFGWMRIHaWh4eHpQ+gTqcTkgqw5v8mJCTEAWzhcBhDQ0OSjHK73XI/\nqCC3Wkn79NNPx1XQLi0tSV+IpKQkeDwezM/Px2mGs/pFrQSjT8P7itWu6jwAsXuD66km73kfMSHC\nd6JdZ+xIAgptmipHxHNJklFKSorIVKkEC36Xuh43e/D7CFrr9XqJgd7LzlmtVrjdbszPz7+riuB3\nDSadaLMvXryI/Px8kRdWx+TkJIBYDMAqcK4Bz0dTUxO2bNkiMp3vNz5okuJ3DfoNZP0DsbuZyYqE\nhATo9XqR/mEFrt/vh8fjQX5+vpARxsfHhfCzsLCAmZkZacReVlYWl1xS4232E1ETUxzqz6oJJlVq\ncWhoCHq9Hrm5uUJ0YY9J3s0q+SIxMVFif/ZDcrlc0l+QawQAra2tkkjr7++H0+nEnj17xK9Swfb3\nGzwvSUlJv1O//oOsNUc0GpUzvH7O1OdhshyIYU+sVFVtEueX369WKff19WFiYkK+R8VFKXNGu8Xf\nQ2IWk408b6w+J9mLFfZALLkdCARw2223oaGhQTC0m+WP3Bof/UEfcfPmzRgdHUV7e7vcrSTvv9d4\n34i5uLhYmvEAMYZwWVkZOjo68Otf/xqnTp0S1jyzghcvXkRXV1dcdisvLw9erxdlZWU4ffo0Hn74\nYTzyyCPw+/3o6enBb3/7W5w8eVKAbzoydXV1yMrKktKdkZERJCYmwuFw4PHHH8e5c+fkQLC8NCEh\n1lDonnvuQWNjozDIS0pKkJaWhp6eHsmums1m9PX1QavVSonw6dOnYTAYUFdXB71ej6GhIXGkgTVt\nwPLycrhcLinTIUBGNuezzz6LtrY2uSSGh4el43t7ezvuu+8+mEwm0Zelc2KxWIQpo9PpRHdV1cLc\nsWMHqqqqkJ2dLQ4XnQ4C42lpacjLyxPphx/96EdwOBw4fvw4RkZG8OSTT8pFdObMGezbt08uNmZ+\nyZS5ceMGdu7cibS0NBw+fBgAcPHiRTQ2NkqjUTrULNs9dOgQvF4vcnJysHfvXgAxg/qzn/0MFosF\nW7ZsQWJiIjo7O1FTUwODwYDExERMTk7ijTfekNLShIQEfO5zn4t7/6WlJQFCLly4gMcffzyusc2R\nI0cQiUSwY8cOdHZ2wm63w2AwIDU1FQMDA3jiiSekEoJ7bnk51kSzq6sLxcXFMp+jo6PCICcooQIt\nLFEnK43AGJ/H4XDgrrvuwv79+2V9uru70dHRIYFka2srPB4P0tPT8dxzz6Gjo0NkAYDYQb9x4wYm\nJydRW1srMiV79uxBNBqV0sM77rhD9g4DwmAwiL/6q7+Cz+dDU1OTBCPDw8OIRmMav2wgy8awlBZ5\n6qmn4HK5MDAwgPT0dGzYsAFbt26N003n+cvIyBCZpg87xsfHcfXqVTmXvb29WFpawokTJ1BZWYnx\n8XGMjY1h69at+OY3vynA46uvviqJq82bNyMvL0+cHvViXO8819XVYe/evVheXhYnwWq1YnV1VQIZ\njUYjmo9kout0OgmI6KQTWGOAqAIwdDxU8IaAanJysiSE+D7RaBRzc3NwOp1wuVzCeqK95Rqr+r/L\ny8vIy8uTxqlmsxlbt26VJsr8fu5bYE0WhI2ea2pqcO7cOVy8eBHNzc2IRCI4f/48srKypMkSgyc6\n4nR+6SQtLy8Li5+yL+Pj4yL7kpiYCKPRKFI0ra2tePHFF1FfX4/+/n4Eg0G0tLQAAJ5++mlpxLW0\ntISpqSkcPnwYN27cQG1tLQwGA5xOJ2pqaiT4YBXE1NQUcnNzBZhl9Q8QCy4onQOsNeccGBgQSSiC\nZA6HQ/rDZGdnS48bp9OJHTt2YGJiAisrMc1go9GIpaUluTuZuFUdz+XlZfmnu7sbDzzwgIDDVVVV\nGBkZQU1NTZyePyVArFarlINmZ2dLtdfhw4fx2GOPSfUUmVKrq6vw+/0i6bG6ugqbzYaUlBQBFVUm\nNdmBj0MAKAAAIABJREFUkUhMY/vYsWMYHh7Gli1bsHPnTrkD1aaTWVlZ0Gg0mJ+fh16vR0ZGBp56\n6ikJgN1uN44fPy69XR588EGYzWZpDkbgcGxsDIuLixgbG0M0GkV7e7s416FQCG+88QZmZmbw2c9+\nVvY0beDNGGxATCCeifOVlZW4ZrAsgS4oKEAwGIRWqxVpF551gtC8L5jgVv+OjNn19kBlSRLcUfu1\nEGzjs3HPq79TrUbhHuf/4+dpN/jcKrDN71EBMjJz+T5sOkvZCgJ+aqDHII57hT1QuP/5XATCaLP5\nGZVtx/dVkyxs1KoSQpiEpW4w2dts5rmysiKNxTkHlK9Qq5zUhA2TKbRvnA8+B30I2p20tDRJwJvN\n5jhGjsred7lcAlIR0GXQTps9PT0t7FaV6aPeJWzozb/nupKpT6CKCSLaYSaROA98F/43mZ1OpxPD\nw8MwGAzSN4ukHa1WC6PRGMfS4x7hmvEfVQqI4CS/n/9frQhQB/cciUCsOiPoTF1x9nMCYvc7ATud\nTgev1yt9pSi1pFZbcF15x9HOct1JqFCbM37YQVCVGvj0GwjeBwIBnDlzBvX19aitrRWGdDAYFDvA\nz8zNzUGj0UjVWGJiosjTqUkx2jAgBmJpNJq4iicC1qqcElmQvDeYOKScFPtXcNC3SExMlM+Q8JCb\nmytkjAsXLqC/v1+qqSjPqibNgNi5zs3NRTAYhM1mi5NHoo4y34n7mAkv+u45OTkYHR3F4OCgVBGT\nWUl5qfLycuj1+jiwh89D32s92KLaaBXcWVmJ6TqzWpRzwLXg71SZ44zj1Aql5ORkkbjcu3evyBly\nqP2VeP4oPUMbxt9HO0BQXq3s4p7h+tGX5TMCa7EP16W9vR3nzp3DxMQE9Hq9+Jb0hfkcwFpCg3PQ\n0dEh1S8zMzMSC6uVJWoFEFm3fC9WhJjNZlitVvT398PhcMTFNRkZGdLDkAlN3jcOh0PkZNTGwUwM\nMGFCe025KiZt6SMCkHuGd29WVpb0EuLvJdmCfo3X65XqquzsbHk2riXXQLXRXJs/xbDZbPj+97+P\nXbt24eMf/7ic2/caBoPhAzcZJcZQU1MjPSK2b98ep36gVisBsSpglY3OhL9Wq0Vtba1oy/9fAKA8\nrwTYub9LS0tRU1ODmpoaSUjQ36Fsjd/vl3MWCoVw7do1iTv9fj9mZ2ffJYNEeSQAcRJBGRkZ71ob\n9b7k/pidnZXmsAsLC7hy5QoqKiqQm5sragKVlZVx1efcw/RTgsEg5ufnpdn6wMAAtFotUlNTMTw8\nDCBWTc+9PTk5if7+frS0tMgz/yGAPQCpFlIrVv/YkZSUhHvvvRfBYDCO/a+OlZUV2Gw2OZfs7UjJ\n6dLSUhQXFyMnJ0f8rFAoBKfTiXPnzgGI4WIksfG+UatVeY7VfgUkwqgEq8XFRVgsFgQCAcFsVElS\nSilt3LgR4XBYiNUf5QTArQqA9x7/+7//K//d0NCAhoaG3/uzJMglJiair68PJSUlsFgsmJycxMrK\nCrq6ugQT/33j5lDmbo1b49a4NW6NW+PWuDVujVvj1rg1bo1b49a4NW6NW+PWuDVujf/fj1sJgPce\nan/F9SMajeJb3/oWxsbG8M1vfhOPPPIIfvjDHyI9PR15eXl46KGHkJCQgD179uAb3/gGMjMz8Td/\n8zfv+X3vmwBwu91x2Wuv14toNIqioiLs2bMHU1NTGBoaQmpqKnw+H44cOQK3242ysjLJCnq9Xill\nrqysRE9PD86ePYt9+/ZBq9XCarWitbVV9M8A4Pjx46irq8OBAwewvLyMvr4+/Pa3v4VOp0NxcTFK\nS0ulVIasXGbEyJZISUlBbm4u8vLyMDIyghMnTmBqagq1tbXSN4AMDEoSmUwmXLt2DQ0NDaLtCwDP\nPfecZN6am5tRW1uL1NRUNDY2YmJiAi+99JKUZZeVlYkO6eHDh9He3g4AuOeeezAzM4O0tDTs2LFD\nmBwszSLbwGAwSLZTo9FgamoKoVAIk5OTyMvLw9WrV4UFRs1SSukAsdJ3Ni6l/hj1WSlTkJ6ejhdf\nfFGYuQ0NDcLoJLvp+vXrOHv2LIaHh7F3717k5OQgGo2iqakJAPDCCy/g2LFjqKmpQWZmJqampnDx\n4kVhofn9fhgMBuzZs0cYtwaDATt27BD5p5GREWzduhWhUEga5nZ1deHChQvC5H366adRXV2NoaEh\n0ZOmfNKZM2eg1+uF3U82zKZNm/D666/jvvvuQ1pamjCA//3f/x2NjY2orKyU0leWoXd2dmJsbAx7\n9uzBwsICrl+/DoPBIP0IyNJkuTMQM2hkai4uLiIcDuPKlSsiI0Q2j9lsRldXF4BYo2U2b2XFSDgc\nRnZ2NqqqqpCXlwe73Q6tVitZ/+bmZpSWlmJkZEQy8nfccYcwHicmJmC32zE8PIzbbrsNwJrmLzPN\nZrMZXq8XJpMJly9fhsViQU5ODpKSkuTZtFot/H6/lKkuLCwgEAjgyJEj0hthfHxcWFScA+qpX7ly\nBdFoFF/4whfez7S85zhy5Ag+//nPy3x/73vfw8zMjFQCdHR04K677oqTzwFi/RX+6Z/+CVu2bEFF\nRQXcbjcikQgKCwvFNlAfT9VMTU5Oxvbt26WUHogx7vj7XS6XMNqBtSaOrLxZWVkRhjbZddTdJkND\nZdCqTYDILsrMzMQ999yDbdu2xZVInz59GufOnZNqDZ/Ph+vXr2N+fh4mkylOpgQAZmZmkJWVJY1U\naQe4f1lqTo1ZICZnQhmPrKws6PV6FBQUIDMzU6oNent7MTIyIpe41WoVOTIgxn4j+4NSQGlpadiw\nYQMGBwcBrGmdXrhwQUpcaQPLy8vx61//GqOjo7j33nulETOAOL3q5ORklJaWIj8/H+fPn0dJSQk2\nbdqEkydPIhAIYMOGDQBiTO7jx4+LXBp1j8l49Xg8GBgYQHFxsVSE9fb24sc//jGsVqswzDQaDfR6\nPXw+n+h1kxkOxHqHJCUloa6uDl/+8pdRXV0t1QZkvahsPM4V7eTCwgL6+/vx1a9+VSqwWNZO3Un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Y1GcerUKUxMTKC7uxtATCMuNzcXCwsLqK2txfz8vCSInn/+eYyOjgpAwUwtm8fReL7wwguw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\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Vy63ZM4SBLe2TCsCq2up2u10qAdXqGlYpRKNR2aMq+GcwGOSe4/oSCF9eXpb+JGqFCb/G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\nBB566CEkJycLm6m2tha7du3C6OgoRkdH8fbbb4uRaHJyMp555pkEpiAQN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ZSUFDz99NMY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alJwAUSAvgeVCkjXp+/Uys+CGio383k1Grw5KMOvV4vSRf1ef1+v6wDo9GIiooKlJaWoqCg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\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kfr9fgHb6I+Xl5XjkkUdEam8l0P/7rguQshsqYUkd/Fz2elx519psNvFhgsGgJHl+2/zT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\npplmmmmmmWaaaaaZZppp9lto/xeYcIkwC+725wAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_crop = X[:,0:250,0:250]\n", + "print('Shape of X')\n", + "print(X_crop.shape)\n", + "draw_microstructures(X_crop)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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6dswG9hS0yOKlgARZ5PwfOATrO3OMfb4TLVqTLRtywAc7bBRbkWFzygyU4ccCq05TdMGa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\n2pmbiXrP1K1mzNU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SNRwtxzWP0VrHE+9UErkubzkoQMS/2J3T733sz1JGwbGT/tYYxYCP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\nfBnbcY/rw7Hp73tFivhHYKRZzQLlETI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Tj0lCGePNGe9Ccc+RVyL2s3085bid2khqzwHO\n6w3WIePMLdXValMPuL6p7FMZOydYZt9k4NjqTfJvLYJtb9A1ZM226+WkZR1+7wbwFhDp4VMJf/07\nk//cKJHHS5Ujiz2QI7VlbBlPaue17Hxs1bu9AIhMOTJ1XKLd9oBjrdfa2JosywJ1+SwPwsFq2fVG\n5/NcwWM9wTzFHGtDq4wd2C8mnfEfwZuovL1O5PqxCK15ZYSeF7WoAodJf45qF8kfv6HgyG+MGLBI\nJiXSRWzS1osQ9+rLdc8uuvqcRzih4XDnzh3zeCB1UljR7l6duG4o2Et9HDlEOPLeGnc8Rmsv/yzV\nSeRwB0KPow3QmWKVwyK4vXGTVRywbeaIxMuQnEoYq/NQJEdy1EZQeIYxz1krkoCdeC3w2gHJWR7L\ntYqNtXsHSWuPaMd3plxShc5M/FxD/PVS5ixiH9MuOUwZ3hE5niyMHAW19bOcyx5xxrLTcoDzLi7c\njRChdu3JYunI/yk7ho4FLNPmjOTfAwm2daxNssyR/xgXOZSis62L06fmVdM3S43NGjtpLWSDrdZG\nC1E0JShjC30jsk45UOfa6ngYqMMc5LzFU+0NeLxptg61R6LuMWinp67j7Wjfyjo8EOP111+XH/7w\nh/LMM8/Iyy+/fPX9v//7v8vf//3fi4jIb/zGb8hv/uZvhunMRvx7sCYek2Y1sAZyTRkwf/6tRZHF\ndzU9Lz9M34vAtvK2dgh4igFH9ZSidvkIHNxmZAGJFyUDLGeD5o+7AqwLWKy6WKSVBexHL6Ket22q\nM0FErpFailqli3cn4GWamI/2OT7Pu0GQ0OZx0jL29Tm9kNfzrmcUdevCzZOTfmf8Z8h/izDuBZ7/\nkcKGu4W4PBmy1iLe9W9vZwPuTsJ3NK1aQ8tqR4s4YIdcdndDpu4Ma5dMhCmOB5HyReatwDEk4kf5\n83hpneNe3pETGREdIYdpch4ROL0pZECU7lK4qYY4j9G9GFADAzcVpSMjpyJrK9UEaCyBOUi4KZh6\nt89SmKKHDNTh5ORk1iOgagOnBqZjLp1pa/1Xq+O3Bj4eIzySvuVd/g3/5pM6LO6pF8Ya0I4f/OAH\n8pOf/ES+8pWvyNe//nX5/ve/L88++6yIiHzrW9+SP/iDP5Ann3xS/uRP/qQf8c8EZGZbf00kSUTG\nI1CQWMQcP5t1JHjCgyeJSB0R7O1mULKutOshuxsimlDWkQhR+1p56nfWe7xNi9+ztipZfWNFUYsc\nHvsUkVeWEGNgu/P40XJaEboRvGhrdOowmXv//v2r760IGkxDRA52iGBepV0QUTt4fY35cz2tRZzr\n0GMh1vbJHA2iz84Bb15Zjjq8i4PLOCUvPgqHSX/OB8d37aVAlhOWxwkrBL2IA2sNmMMIthxoU3dl\nKNjRaf2mf5fKVlrfWsuHiOSG9z2vJfw7yi39bM1P/a2XA2AJZPrwJqB33cdOioGB/qiN6KslF0QO\nd+Rm096S7NxSWUS2V54e2JqDRcQPtumZrvVb7b1Za9k4A/vA1qP99zi+kBNaQ25FO7Vr0hCx5Qzb\n9967vbHHsbAVfO9735NPfepTIiLy/PPPy9tvv31F/H/yk5+U9957Tz72sY/Jz//8zxfTKhL/HgGq\nHciEJw4wPGO0loC3lFUerN7EiM79sojOmkhIEbl2N0HpndJgV+HiOVUyk6XWQRARvlwO/K50nr3n\nNIjKgp89wgwJwMhJhMZI5KCxCFNr3GSVM64LR2F7eXqf+TvLAcXt4kWF1zoFrIjxrLOvF9SZlEmz\nNUqg1K9IcrIB7TkkWqNySiQ3H2tUOlZHx5+1i6UkqyLZiQ6hJZTMOfKwIpZ7nvdY6heR2ImG96VY\n61tPRbSlfUt3x0Tfefnv6bzNrREYLdhiXaw1dEvlGxjYI2pJ/+j5Vvuktiy9MByK28JSR9ZEtiQH\nYOmzmSAZawcs/ibi3zXIz9VAA7XGkT8DHrY4LtAG3WL5PGzBLplCyGc4Mz6CPMshTsEg/tvx3nvv\nySc+8QkREbl169bBfPqVX/kV+Yu/+As5PT2V3/7t3y6mVST+veMdFBliAsmO0nnVHBHCC3hpcdUt\ncTULL0fGYpQiRh5b5cA6ZklSy0GhaUQRoyWUysSGPpcT649RBmgMWBELqAxlLnn0YDkd8Dervtad\nAiKHx8+UiG/riCLMw4oKseYDE61empyWl58HL3JKFymeI5FzLiqftehl5m4vzGkkZqO8cS5weXov\nkqX6np6eXsmlbNvo1nFrp4iVPzr4+FisbDlrgGOn5KzriYzjrRYoB722Q7ADuRSFEX3vPTcX6WE5\n5XGnAj+XdQBsHcekuHo727ZEuGfHxRYdGQPzYEvjc+9gPSgz32pl4BQytRYZx8QYP+2Y0le9Ayw8\n4Bhg+5fzZ1vXKx+uLyW7bQ4doRQYNBWoL465MQ/mtJNrdnUtDd6xvies0aZTnNUeN+Q9a+WdLZ8e\nw1uDY7KflsatW7fkgw8+EBGR999/Xx577LGr3/7hH/5BvvrVr8rHP/5x+bM/+zP5tV/7NXnkkUfc\ntBY/439gYGBgYGBgYGBgYGBgYGBgYGBgYOA4MYj/GG+88cbV33fv3pW7d+9efX7uuefk29/+tnzm\nM5+Rt956Sz772c9e/XZ6eiq3bt2Sn/3Zn5WTkxP5v//7vzCfIvFvRVIrLM9U6Xx075Ka6PgKjiAs\nRZVPifDg6PU7d+5ceVO57nzsDbZVKeKTdyZgFH1rVIQVXRlFtmK74OWyXrpYL30Hj6Q4OTm5dt6/\ntashE2GN+URnG2q+XplLlyLpFkrNx9ptER2PxDtetKxaLm+nheX5xy1XUTtFx4QweDtX9D7DGite\nW6+5JQ7zj3YUlWSG1ybeu94iFo03bwdJFi0RCBjVVNoCzTscoh1IUyP1uC30O+/ZqdEyVtvPEYVj\nrWPWThttV5QrJcWId6d5wLWgZx1Lx0Rp3hpBV8p7atmWOsrhmCLK545ObAHLmaxMGUd53ExsJbpx\nSvTbVoByu/Rc7VzLRKPiDuuofNbOQ8sWjGTa1qNj94Apa8bc7c7R/mi7Rv1eOloUbQxrt2PplIEe\nmKvtcN2d8xLhm4y59JS96KV7jvpfAy2yZIm1baqs24q9sVW89NJL7m/PPPOMPPLII/Lqq6/K008/\nLc8++6y89tpr8sorr8jnP/95+dM//VM5OTmRX/7lX5aPfOQjYT5F4t87gkdhGWi8rc5yDkRHAnnA\ngc1GooiEJHPNZNB0+AgYb8G3BBkS2CWws4EJ+VrU1JXvYbCcJvwZSSU+/oiPE+E2xCOU9BlPob9z\n585VPggck9H5cUhAeeC2ji6hRIUSjRFMwyLYLVI8IjhLAjwi2q3jm7CeepxKhpDjc+FL9wWsCXb0\n1BiEeG4+P99CkHnyjduwdBRVb1hEPs5ldvRYf2eODMqAnVyZsuMdB7VtxvlhX3Mdssp0REjo/+rA\ntI5n4natQWlLOq+9llyY0oYl4LFUJVmVdWZE5ZoTloNq78B5YMnJ2gsIeyCaHwgey9j/ezYsex55\nMjA/liL8toCWumaJCF2zrMAanAuejs+XybPNxfPqWGT4GsjatGvD6ucMv1Dz+xoOdF77XnzxRXnz\nzTcnpcn1GuT/PMjaOtH7IjbnsAecnJyY92NmMTVo7pgx99rWS9btZaxuFS+//PLB51deeUVERD79\n6U/Lpz/96XQ6ReI/Ip4zBKUHa2dANJk5L4s00ej8KUoeR/Iz8anfZUhlLmcmP31eCfnSDosMMu9p\nxIMVnR7t4LDIA4wsxmh5ETlQ8PVdjDjCM/K9hQ7TK3mSs6Qiwopwxohk71x/LSO+j4s91qdE+k5V\nUrUeCDzvPTLikBye+7KXOWDd8+EZhCJyRU6yTMkYup7jM2o7lGE1ZLblOGiJ9vAuZ7Xyq9npkIVX\n7lIboPOwNj+th46Bk5N4N06UBxIJIofyTuSQfMzM49p7UTwF2CKoLVncAi8NzgfHspL/URvw+tBS\npqVwLIYGykIvyGCrCnppLG+t3FljdS+RewMf4iaR/orWKMRSmtlx76Wleq33HMu7KWUYsAlwEVm9\nDbEf53burDH/rTznyn9v5P8WSWFLP+0R7LXXNQd5BWybmrZArugYHbglm4btvSgoe46yDRwHUmf8\nY4SzFbE6JVJP32UCXOEpbRhNidvUNLKylfRHYYRRmlZEsBJoU4HtwLDq7JVb+8iK1it5my8vL68c\nJyJyoEBZxKbIQ6Lgzp071xRvFPLYdnicjrVYW/16eXkZOiDYkywyjaTxIjtwrKJw1nZD4gvroAs9\nl62HEoDpaNmjhQD7JtpajUcW4bulMmxJ6eLPpQWVn7VIzoxzCcd+6VmVIZmxoPMokklZZAzyOQwb\nJNaZfMc5YhnjWt9WA9NaE1guchtb5bfaBeeS1WZW/0YyL0I0Dlk+8Q65KfMzmg/s3MRnMsiWC+UM\nOo3nwpJkwpLAulhBBmsjkod7MUCs4IdIhyk5yW4yUD6Ugm2Wwl7GoYfsetPiYM3K817jfkqgzBQ9\naimwzTVVF+qJ1oCMOcoxtwxlu3RqWtmddWs4GrZO/rNcyqyzDOZIRPxg12xwVimAQttVpI6n0HFt\nHd+7F2DQmR6PN2W37zEiE2yo/88d5S/yMJDN4zRa0hxYF0XinwcWLu5INGAEd/b8cCQNoonvDWzv\n+ymkPzo4svn1WhwzZc484y2AljFtLXYc8WoRmyKHxwNZhBq2IR4pxJ5fFmAR6YZlyCyyJViLJxO3\nOK55rGIdmHjzHDVM3DIZWjuWNJ2a4yiw/TLkSuQAQuJ2LZSUIi4/jk8rYtqrf3b+Wbt4PIKfSVOv\nbNl7HTLwxibXo5RGts9xrLDzwtuxYynIGkXeAs9BU3rOgrd7xMuXd9BEMs8rV4mExt/5KCNrPLbC\ncy6iAyNS5j0Dq+Rs0Wei86B7gvM9dmytjq0G8pZgOaAZg/TPAdfJLRNSe0IUENRKctYSElsY91so\nQwQk1zFoaktzoaSTrFWG3uhJXqHOb/VjT0fDlPJtDRzckn3W+i26t6/0rPdcj2c84DpYCujcInjH\nOQZ2lmxKtKWPDZngxF72fw1K97bWYKvy5KYhfbkvfmYCgo1xKzrBI1hKUVDeQshkZ1TmLJCEZrLE\nejZaTLzLW5X46nlRpQerjaIz0LU/s0ZoyWHDSo3mYbVtRMp5x41YCz97KfHZUjn5WSSR+YgiTUsX\nMIzq5zFUWsguLy8PSI6W8TtlzHvp4RhhEgwNOyQZb9++LU888YQ8ePCgqTxTwd5wLL91rIvIQzLa\ni6hp8azzmBLJHZumf2ubsrzriV5KQ40ssxTVk5PrZz9GjolSuTPtjWlZYzt63hoL7HBiOcHPe30Z\njbXoN8vRhX/XHifkoTQX2CixHM1syJbWEG98ZZxXragxLOfKn51/NxEaFYbr654j3jzszXi3sCTJ\nt5YBWQrqOAb0IBuPYTxvEXxcp8h2yZQtOie2DMtJvESUf8nW3hJ4zc/YZUvI6rV0kZZAwS0AT8to\ndS4fIyI+Zq7ofuZKaspVi62uVTcNqaN+GBYJhQOSFywlQ6O0mPSMov+RTFGCVURCko/LxvloWni8\nT8nDi4PY8hpbkb1Kqmukd+1igeXn6HIrotlqd+8MdHzWiz7w2tU7xoKFOhMqHLFfOrOs5PXUv/ns\ndIuM84DtYpWf68Dkv5ajlqyd4r3H8VvzDl+OXIpyjRx4+t53v/vdlip0B/aBbi205rM1H6y0pgLl\nYSkPJlB7laFUvhp5lDXsSiSmKuaWQzBLyHvlz2xPzCrP6Lj1dm1klCh83ipH1M/Rb5H8wDtcWpFV\nPrENLKeq51zDZ/B7dERHDo5eUBmh7bm0kYHjbOBD4LEDezH6MrJgr4a7hTmc0x6yEYJz4Zh2aKg8\n5t3BvfPYwrE0x4C9jDvUvY+B8FmK3F3DqbPlMWXxO7VE6Nz1s5wL3nipsTcsboef2atcrQ2k27pT\nagpKY8XiZXr2uXXv3xw4hnXgGJAi/i1j2zse4+zsrOq8YiSomUjxlHuLILPSteAJUS/CPztQcTHS\nz0wGY3m1fiKSJmSwTfHM/FKUKPcHl1NEDo6h8IAOC81b00FSVdPHS7esd2qi97jfSjsnsH2VvFEB\nWrNtiaNSLfIKhSamXWuUTjViuf+jPscdO+okwfc4rYyitRZRVoIVJWXBuki7tk96KygtDp0WsGzJ\n1gEdYhE0cjear54jKlt2zyloOeas/DN5ZPp3Csk+59xBB12tnKk1tDyZyM5mT4/gvBFTHKQZbEE5\nnbuOewSfOcwOwy0av55BxQEsx4I56uLJXAzCmLPveX1BOai/6fq2Z/SM6rOOtBuOzJsHvnh575hT\nN8B5swUdZC14divvot/auunZzBZ3lil7jYPgWBCt58esE6Pti9+xrjF3GebGMY3VPSMd8c+Re9Gl\nNEri1Cz4LAwx6o0vTrQufRR5SNyVBGbJWYAXtHp5WsYIlzmj7Oo7mahZPnoge6O31x8cVcikDJdR\n+4jJPnX24LNWOiIPL2DGciOp5kV2clq4c8KrM+7gQCdB1I+Wk8k631/f4ShEdBRkDB02KkvEunW5\nNj+Dc5PPBEUjTPPCc9MtArVUtjm88VOj+bitcA5E3nX827pUPJt3L7B8mPNCN5zPVtu3RrDg7yUy\nXMcizguR69ENnnFg9RUT8d67DK4v9mup/S2SPWpTnKuZMrXMjawTJHo/C1wHPLIf01QSz0pnHBNw\nM1CSzyI2+V/rlFoaKH+8sV+zs+kmIJK9jF6XUFprjhVsw3rCkv1V0le3Aqu/tjo/946SPb42Rr/n\nsRYxhhzKWtHVnK/FK1l/bxnWWlFT9ihYZ6mgsKVxU8lha1zod9jXa8n3HmvLTe3braHpqB8Fe2AV\nrRFY1nE5eLZ6Bq0GFBO6fAFlVmDzsSFRO3CefM61gkl/XQyUKLcWBs8hg2mWJiFHYXr9w9uevfJo\n+aOdFV7Ug6fclhweSJgiKR+B+52B59NFi24pH6tf+Xc8IgWdCZkIbbws2DraySurRUSU2rn3YmTt\nTsiAx6z1blZRaiH851CYtd9QtvQmQ61xYhFVKK+y81Dk4eXe5+fnxYtf0SFppW85QTP5Y97oEMse\n58ZlLDk70NHqzSN2LJYursV2Y6eBVW52FGravciyCFbkfgtBy5e8HlMEoYU55OmWo+IRlnOdYelJ\nWycCVGfw5NjeDKLMWldyYIrEZ0rXtEnPaHUMAMK0PcJ97rFnOUFwDauV40sRe1skoY8Vlv2EGH1x\nPPCCYXqlXVp/e8KSRXtZ0yN4DtoWmzI69nHPbeSB7dspwU5bg6eHZxz6lg65FHoF1+xNzz1WNBP/\nkfKYjXbWZ3GCW/no//psVrnJRFJxNLtXNy8K2hNIGMWeyZPra72ji7JVb6+9PaMADQevjlwery5W\nmbx6sKPCingViSN8s8Qnk29WeljnKArccmxhOhbxljXIItLfu0sD5wLvsGDFqeXoEZxvnrBHchH7\nrsflvphndueEiFybc1aEaK+LTjHvGgKnNDZYbllHEHlOwing6BSr3dnpVasI6PyPfr93714oH7M7\nnayya9/zLrJMuTPtHCmpXlnxe23fiKhFGRrJWZHDucNjf04lzDMeozaIjM3IaTkn1tpxMEcd96R0\nl84cVYJ2L7tBOJp8b2dfWzpxJnCkFLCzZP0jmZqpHwaOzH1sjUX2MyK9rkTuL9Xux0hMbQ0l3UTH\ngjcXa3TIvTiQjxEWBzFnXnOC5dNS9ZobkS04JZBsz21ioXZ9ivivvSDSMVAXjHiXPZP+IvuyQY4Z\nKeIfyScWbJ7SUDtIMkQcKh3WhcBW5HkpEsL7jSehBfSO19Q3YzhwlJEV6clpelGeUTlKwqZ0NIpX\nJs8oR4KHI6qQTC8pl9ZFrRHZFjmVNBo5IseZFLWiaKNyRIiI9dK40h0xpfSQ9IzmK28n85w6+Azu\nLJgD2IelI7esfvYu9cX0W8teImC9/LLEAcs0lcW9ova8yEtsayaz51Y+VC5Z0bHadi1lYIcd5heN\nKa/elrzltDPwDLrMGpEZuzjn57rEyXJ41J7vu9QFU7U4BmV1j9FSmTm+xM6VXkB9dI+kf0t5l6hj\nZmxjHTjoBJ/JAIMy5iBBM+2NNov+jXrFnsbXQDsyOlkmAKoURNISeFUDT+caOMRSxB/a6b362rJZ\nagJ4etk8veCVx7LHp7RfaWfxXudJaX3jgL091tPSMawAY7bltubg6FWeoZNsA6drF2BgYGBgYGBg\nYGBgYGBgYGBgYGBgYGBgoB/SR/3gESMYcVraPisSn3PvRbd4R3JghBdHvmYuPrXqZR2lgZ8z5zlb\nz0fRjnxki3emOraBFcmPkRr6zr1794rnVkfH2vB33OcYHc51x/dK59J527nw7FLNx6uHVy8r8l/P\n+Mbv8Dk8xoTT9S7mw3xbPLZW9EwmsqElCu/09FTu3bsXRnDguPTy5L6N3ukJPQbG250TofTMlLK3\neLFLEVrROMKdRjUROdG48nZKaNqRXNXvS3JeRA4iHLwoM2uuKlqi/TO71KxdFfhuVDdsJ007p7eP\n1gAAIABJREFUe6xW1G7ZKD7cuWU9xxe4e5c3Z8ulv2fX1xpsMep/jgjHNbC1KKIScIxu4diSVmTn\nin7f+wi3KZgS7b8kol2TIocR8LgDsLVeeN/OU089JRcXF03pWOXNtjc+o7tJljjGbWBbyMh17xm2\n7dg+8o4G7LnTisd8zY7YPcOS8VuKao92y9fC6uOW3cOZXfBzo0bOTqmnd9yp99xekB3jKmNUFqzd\n7xasumife2s579bDd7ZSx7n0z6GXbANNZ/wzSVJapCMlgSeJl4+XHhuHqLh4RET27OUonQyJljkm\nBY+fsCY/EiF8rrdnICCBjUIG82AHQolARAcAfhe1RasQY/JH65w54iQSLHjcjSdouU46vjMCC9Or\nWZC9tEtOiFpngUhMEurvJUJwTWUD+1Dk0HARyR0ZpkAH1taQmTs4TyKHlkg7gWPJbpX3fDSXd88F\nf4flY/nEz3KdpijQiFI6lmPWu1CUHbylY7U4nxYjl+UTO1SsfLT98J4DT8ZEa1t0hmmPueStmyL+\nnTU9YTkehsK6DrZiCLWiRe7OMdas4ITsO1se+9n6WMb3VGgavdunJr2InF0DaM/V3Ck1sB4s207n\nfjT/e5D/lozZI5lZgyiwJ2vnLoHaIBYPWBeLP9gDuD8ycrbFZrGC60pzbG2Z34JsmVXGbBkZYt9C\nrU2q6el87C0fPX6vJ/Y4Vo8RzZf7KiKDwiNG+D09K7MGTP5jupq3LlotEywCKitW5HcEJJItZd2L\n8EXo5ZeYr0f8Wd5HPjtNRFxyi5WwqO0wSrbUn9jv1s4OPB8ao1RrYI1NvBAtE3XttZuVV81Z39mx\nYn1necCzShpGU0dGs3cOriJ7x0JvlBxlLWh935N9U5T2WodRJnpbpD26yCKm2Tmm8sxrx1I0e8kR\npWm0zn/Orxals+q9eZpVcjIOYvwe1w1eiyyHLr6Ha21JPun73lqAz2Wik3pgCcXR2nVQkv8D84D1\nEwtb7ZutEOdYjtq2mlp2lkk9jfitRclNRU37oP68Vv29wAklkrc2H48FLU68DNieyBDQaAewE0q/\n84ILRPy7KNaWmXMgIvz5OQtrrHOqx7bmi+Onl6xesh2iQJcIU9cm3dmsa7ZXtmN2kCmWDPrphaxj\nqBatgWI16c+JY5Tre0Sa+MfJp0KNj0OwSGMR+/JDNkRaBoR3aSdHN9YKxwxZjr8joRI5MSwlKiJX\nvXf4SJqScsaT2YrUQLKDHQnRQouLjy5UeuGs9Q6WVfNETyO+ZzlVNNLdu3hUxxF6XPHdmkgkNGB4\njKM3Hts1OmLJajv9v2Z7vyectT2Z9NN30PmhCro1Z60xkCGN9Z0nnnhCHjx4UKxHLSxZUbNQcTRa\nDyVCCddsOTJ9nJGF3BaZ+ljRRbhbyitjVDcrIoPnZMbB5u0YyhhKJcwVhV56vjVfHZ+egmdFmHBb\nW4QpHk+h7zGxL3I4FlB+qtxjgkDzmeKYmYI5DCCrLnu6SPbYUBpbWzUotlIuLEeNQzJyuGTfR5k0\nJb0o7eg5kdhxtBXU2EGsZy+NzNp8LA6ZteHp2jV6ZwvQ/qnZZc1BCF7aImX791jQy0m59HqSDSiz\nMAfpj2nPiVbCX2RaX3trZlTOPcnZqUFxvcqwpsNkqsN2T/3N2Io+fNNRJP7Rmy8iJiHEykFmcpcM\nESsNj6C0nsGytRrsHFXP+SguLi5CoobrZSnNTDBH73D+vDh7EbbcRwwm/xFWGyJBhZEBWgaLWMb6\ncP3xd6/s1p0G3kLrkdW1HmRWgLmPuF3R4RSlyXdlIBGXPSrESheJPR5XSP7z1l5tE+t9izTmKOgp\nTrxs3fioJsyXn/XmAffP0gpAhgjPoIXMKZUlS9aLyDVZwc5CVLAiB4WORe5HdNJ5TkdvxwXmtaai\nVDuHsQ1qnUnW8/ydtk00f3hc6W/R/QoZYtbSCWpISBF/rqrTtddZ2x7Q0XWTHQA1Dsce6EkaR/DG\naa2xNpXMioItlgTvfrFkbY3T2NI7aqLcRR7Ou1qnfwtQ55gbtfmgnrYG9kxAbBW8/rNTfg1Eaz4H\nGul6kN2Jovo463+95B+WbSrh1ooWInip9S5blhpYenkPWTF3m3Dgy5Q5N6W+lk12TGhtV+aXpmCt\nIJ5eDsC9YhD/20CR+I8Ib+v7TFSuJ8BVyRaRgwluRY9ayoJXFqserBBYSndpkDIZrKRrSWnxPI4W\nuZEhgpAgi55jgk1JdM0T07EcEXzZCpcLCVU2GK1FtUbwYp2QgLLGhdbHIr2mkKUeQWkR/N5FwVwn\nngu4IE3d0oVtc3Jycu1iX8vZYrVJK9k3B7DNrPLjXI5IKd310BMlEgPnVHS+eg2ZNsWYiUiuDLw5\njijtgsH6ojMJnYpWmbXennNH5OGRaGuSZxkZY/W5V+Zo7cw+b80fz0nKv2dkdsZpr2llUDIgexuE\npXmszoUtELNrQfthyUsY57r4OXJ84XeRHGOndPZeoFK5egDnR8t6wWQ9zkErAKC0fvD7mT5tJZFK\nsibTHlYdNe2eY9Fqlzny6YGeJMxNR0mn4fGHNvYSa1A0Ry0bK2P7WOlwPj3kXylgYC6UHKDZNLYA\nXNtK0ec4RqJAkSllwbxa+rUkT3us20vOy2MD8kiWXSIiVSckWOlHR7dm0LIm33TSX2QQ/1tB6qif\nlmgn/syCkMlij9xmZZh/w7KpMhSdOWgZeVOUWDwzXvPk6HTrWBgkWlHRsSJk8bkoip2J0Mi5gMKV\n00No5Ia2Ix/tYUXvYlS5tgW+Uzovu6RkZDziGYKoZYHGCBVMA9PXMZidN5qm1htJbAsl4phJPvw7\n48ziy+JqdkZMXVCzYAUQiag7d+4cOGiy46gVKHMyC1tJYahdHFvq4TloWww2/d+LWETZZMkjlY9I\n9qM85eezBlVmvHuodcBMeV+dctZuJgueAzJKv1S2LFkXEb2es4DT0P8zdfDag9dKnfM9kN21NI7+\nWZ6csMZ+D+dn1kGXSacHUdvbqEfyvjcJNXUMZGTPVIM5Wucy6S7hcOTxjPLSk22evsdBO3NgGPF9\n4I1Hz47Q8aD9u8QaFN1zhOs5f1cLtmF71W2qjGol+nrnt5bDraRLW+WaQzdAmVsrf6JA0N6wODDU\nuZHfmdKPx0IiWzLOWvPRDply6XSPudkyhubur6l269wYOsM2kD7jPxqwOthKyilHG3tHhWTTxWf4\nOyWUmIjX3y1YUZCZxR7PTL9///61I3NKR4pgVBJHlnkR6hwlgm3HDgfOCyNsOaoICTxcyD3jo0S6\noxKrnzOGXu15+QxPAFpRyhwVFhndGaLKc6qUgKRGJrqw9Bvnn43cYHKlhuBaQhFhg4gVBp33UTuV\n5qU1H0plypTbctLVgud+5nlWOj3FpbZcWgZrlw+XQcRWdNnYU0x12GTHe/RuC5iUzDyv5zVn36np\nJyaoMnVDciGzmwzzyZZR5yiXDdezyIGNdZg6pxA1yukg/5cHO9/1u1b0MkY8Xa0VvdfSUnq9nCml\nPKy1qIQezoWpEbgWShe/10BlWER4MCK9vLdjYmA+eP3ryRHs3ykEWA0y6/lUsp7H7BaIolqir4fs\nLAXSrIFS8KFI/7P8LXBAYdYGaiVrW8BtxfpqJpAmk8eWSV5GyW7Nrnn6W0/HYA2ynMAamGK3LoEt\nyPM94/XXX5cf/vCH8swzz8jLL7988P29e/dEROSdd96Rb3zjG2E6ky735d9LAp+NZItktzyl2YWE\nz0xXoYjKeWRoRIKn5GlXsvr27dvXSF+P0LEcHQqrfZmA8TzrUdQZEqNs6OERKVgOjDyaExjxq5+n\npmeBiW0rSjUaJ9nIh9qyosOmVPfSoujlUXOxMb9bgtZ97st9S0a8jm1vq6CmoYISdwjx8+g81PdF\n2o4pwfxblRZ2HmTnJEc84rEUIrFTsuRkwF0+mTUgau9a0ryEUqRS6d3W9/Dy7Ay0jDXjAo3kjDOP\nndGlPFrqP/UdLJuW1ztXfwknYwaD/F8HPZynW8RcpPtW8rV0fA9Ty4Tr21zyole6Huk/Jf+5jsda\na4zeJGSIfQ06m3L0RYRacrEH8bSVdV0kTxjP4VgsOYPW0DlQ12FbaAnSX1FD/i9N+isivksxtb22\nTqRaelaPcbK009PKn8tSyw3MhS3JT8bWx+uW8YMf/EB+8pOfyFe+8hX5+te/Lt///vfl2WefFRG5\ncgK888478q1vfauYVor4x8lb03HWRCiRsfy+N7EtQooHPAteJJXR44qR9pxeNpoHBRFHLUbAKB+P\n9PLaIYoCijySSOR70c6lPLLAyFGR6xcmcxlLbRZFw9covty++p1+r9/xMVRRv1oKRo1Crg4bb5dL\nLWmMz3HZ+H2cD9i+TGJGbdDT8IgI06ziYBEMWmY8f1LrYznWap1e2l5oLFlKQu1OiqhemTJ5O1+8\nI3Wid73n9H8+p99yfODfFvkv0sewwfG7lJGEYylDakUO4BK4HzPP1hpBLHPnJn2s3V7Wufo8lpa4\n3JcxCLBtwNKdMuvjVrGW8TZXvrgWoE5VIgmnRDWiHN7DHJ2LOOudZquDYg3MrafOjcx5+Ur+z0WC\nteibPfOeoruh3Tk1DQ9rzYe11i9LP1uj/jUXxa/ZVjc1KITtbTxpoge2tP4gd1RzDOvAQBbf+973\n5FOf+pSIiDz//PPy9ttvXxH/in/+53+WX/3VXy2mVST+e3izkFiNSJiaiYwEW/QeLlKl6FVrcaiJ\nqL1z545J9jGRheXwzl0t7bAolcVzFETHbIhc3zVRs2hF7WvdVcCR1Low1DhLOO+W44EsogCVuehI\nAXay1ESzebi4uHCPt0DS2HIqYN9zuaNyKtTQ8Ehby/EyRxRuNOaiPDhi2PqdI+Uj0tv7jsdpZm4p\nkIC1FENUGrzva1GKNokIlpYIRByH3NYsY7w0em1Z1PLMBc+BmYElT2qNBD5qrvRsTaQM95fIoSPA\ncp7Vlh+d5tY9J156axM4WyDAphCjxwbcNSOyjcuXlxqjmZ1ZawLl+enp6dVuO29Xao869FxDlsBc\ncgT1xp5pbhkoA7wAkDnmyRzyONPWLRfqZvNeWp7weO2hu82l/62pA1xeXsoXvvAFefPNN1fJG3W/\nLTiqawIul8Le1qDe4IDPnuNkjaAukdyRRSzDtqqXLY0tB9tsHe+995584hOfEBGRW7dumePp3/7t\n3+S3fuu3imkViX8V6ijkLdKAyQCMQMAJoCSFhVqBbZHlVrQ0kxZM/LGS0Up2l26xZycE1wEJEAQe\nzcNkrxXpxlHqSKaUBJHngMi0gf7tldNKk8lHvKSx5GzyIqn591LksZUefra+Z1Jd+83auVGzC4Gj\nz9kBxOdaY1vrnONdLQo84igi/nR+lC5rxXr1Jh1aFQQt99yEmLVDiWVK6X124iB4vvQ0giz0VFBL\nba/jiy9gxu/Yobs0wVk7h5motpRDL02UXa2yQ8k0axs2p6NrSc2RQijzrPE45T4WLBMaCJYT3cKa\nxPcWSP+bbFgqrPU/irabgwz18lhqjERrwxYcA5aznXXB7JxvyfMmo2c7eGvHFp2Q3pzIHDk4ZafJ\nGij18RS7pDfYtrN0lB5A3mJqOh6pvLYOsDa2JGORdBVZ99iVOdd/jyvbkgxme24ux+Qac8DiERnM\nEWxpnqyFLcirveLWrVvywQcfiIjI+++/L4899tjB7//5n/8pTz75pDzyyCPFtNJn/A8MDAwMDAwM\nDAwMDAwMDAwMDAwMDAxEGMR/jDfeeOPq77t378rdu3evPj/33HPy7W9/Wz7zmc/IW2+9JZ/97GcP\n3v2Xf/mX1DE/IsmjfkQOt+xYx32wRxy3WHJ0ceT54oGR8ZJhPlbUfeR5tKKfcct4tBUZf+d8vQir\n0q4Gq76cLv/Gkdl8jj5/Pjn58GJTjRQXyV1qW4KVRjY9jCi1+i9zi7q1WwF3WHi7ELyjAaKdAtxu\nGsFd6r9s3TmqyooOtqJkSrtOvPS9Z60jN/Rd3NGzdoQBygAtS00kbEskZKadI9Scub7Egtk7KjAz\n7lkWRbsEWiObUX546VvjJ9snfD6/puHJMyyXwrqAHmWWSDlqTedrdoeI95sXoYM7oKxdWT2iib3d\nYB567lTJolcUYQ9k5tlNgXWRaTT+l2i3pfqGdyEiUJ6sjZJOPcbyPOi9w8UaS2vI4ilA/X/qjrUt\n7KjxYOkDNTv+WncKZMtTGje1u/AttMgVllUYTb7Vvp4LS0Rv98Tp6amIXD+SdSkssZ5Zut/WZDDO\nmd725Zq7XbNzf+gzh9iCDrplvPTSS+5vzzzzjDzyyCPy6quvytNPPy3PPvusvPbaa/LKK6+IiMi/\n/uu/yh/+4R+m8jm5LPTEj3/8YxE5NPA9ElYNLz6jnBUHJM75OUsB88hUvYgUf4+Mn7OzM7l3755L\n9HP6mke0vU/fxTMW8XlL4OkWdM7HOs7l9u3b185Y5jSziitvpfZIFlQOvYtmEZo/t4Gmi/XV7/hS\nHm+8KN59910zHasspfpz3zF5xenxmEUl1Hqez6jOXhCN0LqqAmMd32Tl35K+tYBhPax2w3ln1e+T\nn/xkU7kUKndqUSLClBi1LvJdC0jWspOLx6qihzKF86xljHppeg7YCDoeM7K8pUzZeur4YZlqrVs6\nB6010VsPFFHfefK8dI4vzkt22HlrkJWmljfT/pqP5tvaZ1adW84tnip3RHKOWpF+43MKBvF/HTXB\nI6xT9MLWiOxah9pcZbAcEFtrqxb0kDutOk8Neo53dARvjQjEdakVtesZrrlbANo1qMcj2O7zxoYl\nP6b0e0ZGs5289HrLwWKWjTw3sVsavy+++KJ85zvfmZRPrdzZm84x1xofYYnxqjzbFvRQD3M6ilDG\nz3XHSZS32lmRE3AvjrIWtOg83/jGN2YoyXHgd3/3dxfLK3XUj+X5tsBnVXOEICr+Sp4qeWJFUkeR\nSl5UI5KyOOmQLI2iVbCeSl5Z0TKchvVZ32VwpK9FFmN6JUdCBBZI+K5HnLcukp5xiUIZ+58vm8Wx\nY6XFEX013kNLecPxKRJHB7Bn2esDHUdThb2mf3FxISIPo4ozF1qXgMS314beuLHmXSlae0lkSDue\n6z0iiqbAitpQRFHZW406ajEMcG73qhMS9Oy4rr3ImOUROpA9hY8x9Qxb78xyK/qldLYkyheev9z+\nGeV1jnEYndE+8CH2ZIAvhZo2wXtvRPqeCbylvlmzLJ6uLjIP6X/MxvZU9G7nY0XLeral+Y6wgncs\nWOu+d474VGTaynNKzD2vORDMaret9vUS2FvdrZ2Ac2Ipm2wv/TDXOoG8TXRn4Zx5i+R3hg+MttgK\nisQ/e5x1EUSShkk0BZPV+B4+h8exMBlrgaOvEUzQIBmihHpWMKNHr/ScFUlRIkq43NY7GdLfckxE\neVngemKfeuRL1knAEdYih154ry85n0hxzSit2UhOK93sWBDxj/3RsdlqlPYiQzECiHfNlMhJbOc5\nyJKo3D2NeS8Ceg2yoJXEzygcSzoHpvYRHndTq9Ti3MSxrM5blY/az56zB9uMd1p5zsns+O+hqEdO\n65q8eC2O1hkrfS/NqNze2MhEGlrG+JywAgEG+sEKJlnT8Zpx/tdgbWfyVhBF+YvUB7LU5DswUAPW\ngfdCrHlgYqyWbOeoZZbZc8s4tmsxCGopuTqnfBrOyeWw1Fyew5HdCuRd1rRr58aSbc3zlmXkQIyh\nl20DReKfjV8+jkYnAhOCSrKIPIwIxmhlL6Kfo4iRfOE0RGyDkUkgL0JSJHe2fXawersNON8ahwAS\nENG7rLBh+0aKEhIquOMA02NSlAmvDEGCaUfbTT3wbhILVttl2sEj8CzSn6OZPQdDNnIm41QSkYNy\ntMAigS8vL+XOnTvu/MiMNU7//PxcXnjhBXnw4EFTOb0yZMnHqXlOfb+VmMQdQjXlUDLayxNJ3bmU\nP4vEa0XNuxjpzs4sTs+K0MD/rfVC+4PP3rfKwXVgB1rpjgF+n50OVrr8Tu345Ugoy7DuYaBmnLoi\nYraz5WxZinCI9I+BQ5R0Lu8d/HsLO8d6GpE3fcdKRPrPSa7WyttjRMt8vMmYe0yuCbRfpuxi8NbD\nOVF7b8tUMDcwF+mPwSea18B8KAVH9kpf/94KtkC0HtPYzgZZLeX06IWlHJFbGI8DCeK/tBU6I/DY\nwENiOSKN+agdJfxRieHF2TM2uLxeNDsqNrXn5nOeVhmigY/kKRIfqohYRylZ0fL8OToigndqIOnB\npBCXTeT6DhCvzaIIL488xzO0MT+v/Fo+fQeFmbcdjPNm7y2PLWxzzIfT5HZAhwHOF6v/Wp0jHnhO\nIBlqtXEWmg7Omdu3b8vjjz/eXFYEj9uWNqhZ0HoRii3GkDcWSs+LyEHbi+SPnmlB5OhCWbUEdPxy\n22WjXKx56l0Or/+XnAD8juL09FTu3buXNvTY4NZ3SnOgluSxDGvP0cuoUW7RoW8B68Zrj5W/dURR\nT2BZrMhHkWUjD7cGSx9kR1qL83Nt9CYIbir5n9HD58SUIIljQbb+GVss897ex/iWCLueQPu5R1rR\n5zng2Yu9x1vPAJZMXvh/qSxanr3PsbUwl21So5svjbXHC3JJW0Stc7yWHGe+ZatY0klx03WyrSB1\nxj8S9XgUQ3QsQ0QqIuHChpYnSJl88RZ/jPbxgEdKlJwImK5HOmeNDE+4YJnxKAouh0f2adn1OYyC\nRVIscmRYfYBOFy6r5qPPemd0Y3QIps1taLU1jzn9jQl9dk5gep7w9ZwekZDGcmH6Vr9GwpTHtUU2\n9FgsvPbl36P+ysCbM1PAziQuY7QA4++1UUml8uMcs2RPraLD4yQiS7x8ePxMudCuVMZIhs0Ja6xi\nvph/zZZwnr+WnOCdJ62oJaPYYM/mXVtG6/nSDiset9mdDNldRLqeaNt7RPMckeKl8b3G1t6lInJq\nYY3njMxlJ9qW6tb7TODMbkVE61j2ZOTSiEj/Jct2kyNq2Vkp0pdM5PeWdnD1Gutrz5WloLpHzz6a\ni4BnWPbRnONtqfqU1rzIJh7IoWQD98JW+2Xtcq2dv4dSYIL1PN+jdiy4KWvgwENUX+7L0Z0eGS7y\noQEl8pBUtkjGs7Mz09BSgWFFdZcIPSbESs9oOh6RxouulpsJI4w6LRFklmKO0Hf5iAEkvrUsmh47\nTbQOfMGlBesIJqxDify6vPzw2Bh2BqGjRuT6RbCs3OBRCp7Rxn3G5baAzhU+GqSGyOG6ZxWyaCzW\nRCPUCukooiTq01pCzVLOpwLLpONXyxwtwCwbPMckzjFFyZjRtKz8Wwks3A1SciR54w2dmfj8FJTI\n9uidORQJJXq99FnW4JwqjU9PptW2YW8lagnF2WubjNM06xBpMVzxrhTLCczleeqpp66cPlOQqUsP\nZByYIsvcoZJBzXzItBHrW1syEnuT/zWkQ4sMRUMW126R9c73ZSzZz+w0vamwxq+n756dnaV0CKsf\nl25r1Oe9ADAtV7Tmb03uzA0MWGp1tqK+sFQEp8pjkYdjuif5z8FCc8O7B87CTRynU8Bya64xugXC\ndCnn256AczkKWqsh/Tko9tjg2b8ife9VGxH/20CK+Be53mGqeFnRtRwxzKSURcyXJhRHxGPkNU5K\njvavMUCxrhaxz3VHUt1aaLLn+Wt+SlpZFzFpXfEi5FK6eExTSfjp71gPPfbi8vKyqGRpm3gkK6JE\n2rI31ssX744oGehIrqODoVb54jpZ7+OcyI5FJHyxnzGfrLIeEUaoKGB/lSJqrTZg8DyfCst5lVX2\nW+a9IpOHN85rdxewU9NT8tFB5S3S0RE3NQoijrPaSPfaNiiVwxrDUf+wAxRRS+Z55EIpcnKKctjq\nsJnq6PHmrqar9WfSv1TXqQa1tw7u/YItXONK0eC41q9h5C0RNbdFg2rNMlntHAWVsB60loE1p2ys\nxRbH1Nyw9DiPFGew/h45yy25vDRYd7VQ0klvwhhh+Y02ZOtuOdyRtxQ4kFCkbjeVV0+M5t2CfPJk\n6ICPDNmP/T9VX96SM4brOsWptySstcrqo0g+eXMFbdBoLGTHAAbnZYE805Lo1f9zBI4M4n8bSB/1\n4ylParyqQYrkSoYwZCcARrIzEabpMvmPR8/ou7WwJgs7F3inApLdaHQhgRsZ6lhHvM8gIrIxCjMb\niY3lsZwKnB6SiNiHTOhp+ZFELwGVLE/gYz35bwZeMh3laUWo1ghIXmA8oYiEDjoaIjLW+4xthE6O\nKA3+LTP3+B0uM5NUtWlOgaZ3cXFx1VdLLKSt0USZ+kdEmqdMlsZqhnBXR441/7BcmFa2P5ngzSht\npfSiiLJW54zlDI1gEQeevO9F+Ht1LtXXU04jxy8iWhfOzs5Mh2xGOWxRmiNYY71n+tn2mpo+Rl/W\nvLskSvMwemfrhmcJtcZhT1htWDIkM7LN0mPmGN+c9lrEMB43uvfxWAKvIbXrkrde4hzwxssc46gX\ntkDOrQ3PnuLgHkWmP09PTw/0ILbfam2r7BjiMZldmyw7nu3vtdGy3t50ZNqMZSFyWtZYjdbQLZH+\nFnoGX3mYSi579o41RzPwnNsRd8ncY6mctbi8/PAUjKXXxd7939u2GlgfReLfI/oi49VamKN0lIhi\ng0aVElQ6+SJRFMAojLxoOiTBRSQkoXHAa1lEDs/hxrKjYYaEpaUYYT76N7cftnMm6iZCRCZq2VAQ\n4nE4Ci/6tnT2GQtPvscA2xu/Q6PNMoL5nVLe3KdZstSKnsKxwA4TK6qg5GzhPPUILIt09Igpb5HK\njBN9xtp1ggTI0oKb89b6s1ORowd6GKLRBaJTiG3LuZdRJkttn+1n65JZHrs1sgVlOI8ZlP2YH7+P\nBljWoMv87t3FUIOIEBGRqx0pU8dbqT7eeNR6RhH7uE7XRvmhzCk5ZJcgFjVdK98eR/1oenOiNX1L\n9s2ByBGVMZTmvnx5Kaxp3GMbishBYI0HDfTIyII5wHmvTY6gbXCssOZqT3KK0/DS7NW8cW8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8MPP7Qf/ehH9vDhQ3vvvfdefP+Hf/iH9q//9b+2P/iDP7A33njD/uE//IdpOk3iHzeYZnnMYEWm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\nHg6H0GFuCVhjuUe9cf+Bxue1T4t7hu9aZ05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4moVlIJ1zDFjrgLX3ie6KWhNbk21rlwfH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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "X_thresh = np.array([threshold_otsu(x) for x in X_crop])\n", + "X_binary_crop = X_crop > X_thresh[:, None, None]\n", + " \n", + "draw_microstructures(X_binary_crop)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MBjIuc8nIvJgsv81pHSNgsjg5hNf5MblAawSTH/rQh6r6bN4fu4vtIQRs374d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vz2b/82fvEXfxHf8i3fAgB44QtfaG0FSyn4D//hP+CLX/wivvd7vxcA8Pu///v48R//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T+7aQJbeelVQXnV/lUW1Ta/lKcxBHWKZ1y9zdexfnR5COOoR0YTSCOkjkuOzF2AptagoII3h6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qpkCyDm9nx9X2gsnchc8XG3hjJ/54gKJXj8GnHbqOu1IpLeJL8zGPSd15onJphwGhAb2seU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\nZ1bgRl5z812orRlfP+zh0cc7jgceeABvfvObceutt+LUU0/FC17wA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.396191999\n", + "Volume fraction of white phase\n", + "0.603807998476\n" + ] + }, + { + "data": { + "image/png": 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/Xl2zb98+3HHHHXj2s5+N2WyGBz3oQXjmM5+JK6+8Uhk0brzxRtx555047bTT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Fww+V//9V/xZV/2ZXjVq16Ff/u3f8OjH/1o/MAP/IBxarzlLW/Bm9/8ZlxyySW45ppr8MQnPhEA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\nxx2HrVu34pBDDgEAbNu2DZ/73OewsLAAIPwAf/CDH8QRR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.287407999274\n", + "Volume fraction of white phase\n", + "0.712591998201\n" + ] + }, + { + "data": { + "image/png": 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ZuJfL38vlWctx5e5D5/NQd7f22/pf+BFYz4TeViF6p512wnA4xKpVq0I43a23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\n4yrXLAa4Q4cqiEF/jXQJ0Uo2tB0BDcTn0ufCo5WXA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yujcigSwWlEhi/JjDFJURqszeZx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pk+0Qp8KgPcJeu86InA3xcVnbFAZrhpDliIOrCkBtBIDbqvq/kAbbjS+JYMV5Ge4xM0GUht\nDVXkhF0vRWAJ453GIupO5Jy7Z7uP7IzSWJqvXPtR3xwhUtd2uy3p2pSmo0Po2hpUJrr8Wrc1VVEY\nMZbrYmwn1pA9RUejyDQWbsuTqEkPZz76xBhNJtnrDrKenBlCQ3vgnB9m4emQIcDyb1I4WUlGZZ3r\nXpvnhCQ8fjEDmIwikbRBqRXSokDLPo+rROgojHHxNKV4CqVLtZUyK1jyvtEpJ3xN0iHfrSBe0yZ1\nTnhTNGUECX0GFKzaztUj5v3mGg+vpzBzfzKPmDFKHHsnPIFcbM04KPY1PJZE5YKb17GGKnOdInFq\ng/yX1liGtLex9kUVSWkGSmi8HLM6krJ6OUeKvVxX+5NqjTY5P+lQ8doC/Ki67SE58IqRUQ0IlhUw\nGRFAxeUHIiZTmglfswIml/Gjfb4iJgOFUFoTk48jS5gsCSQ6FngNTJYE81qYDGhC6OSY3O/9AyZf\nOdJFn9nn7ZEZ1jCrJECrhzCrTZVRQuw54iBnJ2pibgZljCJ9opEass9jG3YqpSSrv60wETDqk3PK\ngrrHXuee4pFSS8Ph1Bdh9ApCRJ7yIseazbjydltfu6Bcgd2JQOBrRFRB7YsjC2SURhfRUMkP0Z1H\nLh01DUXX58jmdSOQKKUop1rTjYicXI7zlfdzlEaNMpEn4vB8KpEBlCiQtmYoZElRKFT9EisdOWQu\nU0SanKsch9NWeaPv74DJx5MzQ2gcV6wSKv+NoVXTZy9eEkXVUlMUpEfIFg0lspYVaDSFimoDOeR4\nG2P16LGXzygrVnGW0RmjPOSRN5DrkAglLAZfSR2JKmLmeIM8PU0p1VMzOngM0ogRIpVFOTfymk1T\nqKQBuShLDjxqaHAOAZupRESSt9V63DwF2laHHwmF+Gaxp+Q6j4SfdShj59N1ZN9V2c9EsqE9O1lY\nMLiGiKgjIIwG+pvG2HkQk9iHxgPdxm7WwJnqKKz/KMXqDnJ2ZJ8jNkmOhMnV+Js64/J0YbKNmlsT\nk/eR42CyHPtamFxe5wckJtN1a2MyvT5g8kGOJEchboXX3xaIDCIVg8CST6Iwxrc2cqtxCQCppE9I\nUoP7BZDlkdrS++6M0Yu4cMkMey1HcbRrhhEaliAgEmBAGLgSGylgyQr5nhV7/Cc/xUoOcZtw5mEj\nNVS60VTWP+VGkaa5zuv/b+/rY6Sqzv8/984sLKi8ClJclFhrQYo1RgkNL2IrLNZofAGsVGhYlFqq\naaMGaxujKGZNkBBE0bBai7VFUOrXBqNNf1hA2wqUalBEeRERRF5ky8sGlt2Ze35/3HvOfc65596Z\n3Z3dmVmfTyA7c1/Ofc6ZM595zvN2/GOaEYb8MEZ2TqHIJ43CcfQ2zB/wOKjPzIHjeeH2wLIN+HNK\nuEC4Zat+XhkzHDdisAorgnthO8SgEyliq1vewzlni9ZISFsJjzEnFxJlZ9CQyp6mzFq5L2oEoNf6\nbdiVHFOhVtOSeMPkeRF44N1UuAWePhl9b5ncvYfu4qMWj4GS4keIWPpCvJmyD7awZomIl8/wrJnH\nkxCnBIZF9fTxN99rOfIiftypAmgrAmgWOHMdx1qsTTih0gwXyjvoSiWUeI3NtuPGJYl36WdNI0fy\ngSf8QkX+Fk2u/89xImSWJF/StplyMScVZjfl+J5Y6uGNWTyYizZzwaYLFP7EUo+yYxmHQnhfGaUF\nWzRAqXMyEC5KC8PJMd+NAK3lZGvRzQJxsjxZSE4OLmZOlq9LjZMtw8Cc3AlhiwYAIMxtJA0jgK1g\nY6Innl4XtK0W7/4J33YhDSIpYhzRSCmQy2ZE9URYDNPzgEw2uVYDNcwExgzb9ZE+mQthdaHx3kY8\nceMTGAVMg0DEQEC/g06wG4xniQRxHUhjkH23FcsiHtCLaAaRM35kjdyhwwt3OwktrPow2J5Hxybf\nlBMaIZEH1Fhksv4OKC6M7W2NvpvPMc5H04LkmMrUFhcCGQCubhyJ+W3X05ziU5yU8U7KYHxWWpvM\nya1CWRk0tBDX4KXyTAVEaBYhA8JjpkdDtmHWjqBeQg2erqx4Qqgia34ObozSl9WVPhp+LYRAJusp\nxTmT9ZSiIl/LZ0m5TWOGDIWlsUs2RTmu6rn/CKHlZ9M+qgirrIic9z2Jep+poYnmyPtyxm+TRwty\nmjA9ZTYF0y9CF4SlwwWyntq1IA3AdVxksoH30ImGi2tF4IjH0FakTy3Cguv0qAm77LYietIj6Odt\n2xcvLi2IaLTpy2AfL3qPSk8JwsHp+MmFixOMXfhZIbLYoHNPzidayBDQo5xMsOW5cyGM7IL6W1BO\nBtTCr6CcDGgFRQvByYAeFVAITpbfpUJyMpXd/80rDCcDiPJKWzmZROMVkpNl24XkZH1M7M9rKSf7\n3x9//JiTGXlBLmaNv1pIGmAUhgQAsqhVh8giVRoiAggEO3zYPNHa98kLF/WZbNyXw/+bRfS8jMzI\nZEJjRjYL33MvgExGN2LIe4RxLJBNj7KILoJjd6KQEQKwLEaDcRLZbGghJ/c75nNBUkts3n1j0a3d\nG1fUEtHoBb/dqGFCuMEiPJ32d+hIpwG5e4zrAtlgnD3LQp5GfshUGvnXNGrIPgXXaLvHWOYBNSBo\nfKWiNFyrIcRxXGK0iRkTc1zkWGjjHujqSPsFbiPnwt13wugVB8IM8yRGtEgdFts8MsCc3DqUjUEj\n7seY5gW7rqPl6OYDqcDZjBoUrkZOeu6zphwSWYBQwVUF1xT/hV5A6mUxFWd5Tj0rJjIjzmsXhgHb\nFWfrmHh6oVCaw+uQMZYGilAG0obyukafJ0Oc42AqfmZfbDDHRFMcnbDavgx1VmG/lusBaIqz2ac4\nucJFnT5X6DE3IOp0sJ0rfZ7jhLvs2GDKFDcetvGzhSlLCEOZpuMlPYhydwJaRV/eC9D5HRYrdCw/\nPJkWfj8ZpYuO4GTHlVsdRyMISomTabs2+bTjLeBkT4Q7JBWKk22yFYKTTSWs1DlZHi8kJ8ehtZxM\nz/mGwjZysmVOMid3IiSF/dPFZQsR1nyQhmrpbU7YJtYI79cjN4zrzWgNuWiWqTA03SQI9afGDOUV\nVw+zGDOA2AWkaQRKGiO1EwcQLuDN5xvjEJfmYhpbNARpInFwHPsCPsmYEUHk+YFRC9C2y01sTy7U\nbW3qApPnIN6wRA1wqZR+nTSyJBjaadSQtX36nEiuqfwtt0UAyXkX/cycNPxda6SnwbEUiA0QKYjL\nnFwwlI1BwwZzO1Qz59QMzaTn4lIgVNGwVKhQS9DK8jbQPHCbhY1+f1Q+txGCLRVn6R2knqmkvGLz\neabSrMlpudYTCHfiAKyLEJdEm0iFVAsPTBgcU8GMCy03a3nkk/tsfZ7jxFbXlwqg9vthKIQtLQwa\npzjTnGjpEYxTrgF9DuUDKqccq9DbHDVW5N8fR22LKxelvkz+l8acj7KeiZQjFRPizaF03yy0lZOV\nUcMT30xOdsJolUJxshnpUChOpu3aUCqcLI+pehfMyS2WhVFm0IkuPK7l/xupGYbXPc6oEVl0G4s5\nE3ptjpjFbxiKpN0jZZbGDJHJyi+VigKIrfVge55tcU37b/QrNGAIKIIKPO56m7I2hb6Ij/SFti2f\npxkHhJXDZdSB/CuEp0ciKFmcZKNG0mcQfI6GGVZ719LCoInGDFKrwqFRIOQ+9dzA4GDtc45nRwqy\nEoNZq0ANFDISCVDzMi7iSTOMGWBObh3K2qBh7qQhYSvWRqGFsFIFmoRl5lpIJ52PSw/wlRihKbye\n8LfLkwozVZyzWS9QUqJesCTPWD6KMw1RVZ4ix/eYmf2QY5SF9DT5CmAabrgrAaIKt76Y949R5VF5\nxxzfO0Zll58PzX2m/aF58X6NEur9M2mXPNsDZDklmqtsetikAp003kleOaU4k/6G2/np19Bnep5c\nzBhGNgK5uAvvodbtUHGOA50v1BPYmr2vzbaA0DhmM3IzvhkoGCcHRg1ZtLJQnAxQI0dhOFn2wzxG\nj7eUk32yEIqXC8HJVB7XKRwn+9f6zyxpTibGjEJyMhA4QgrIya3h5UROZkX5mwnL5NPmlWnMAKyp\nBBGjRtBO7LoyZkHu32dJD6ApHLRwqBeknGSzvhGDGDNEkH5iTXUAIoth83iSMUMzvqioP5IyQPui\nZMgGNRI8CNeFk0ZUPosctO4JAJVaoopSug6cVLBkC63Q/nG6p2nkOWSxLo0tQbuRmipB2zJKA9ms\nUcwz2gfNqEF/D2wGGsuzVLvUAJROh9urRiJXAmOS62qGnEhUhZQ5FZ1T4S47CXyoGSIEVHSGNIC0\nBOR64Xl+3zwPvoWaOblQKGuDBhCdj7Yff1u4pxm2GqcHmR5Besym4Jjv064b8dKAKKeygr72N1Cc\nM1n/wtATQ/pNvIRhP4RS0l3X0RTkfBUZs1Cf9PpkEeb1poFwqz/XUXnctP8IrnHhaEozNXJQpdnM\n1ZaepbjfAFpYzVPeO5HoBTRD4f12LKHRSjmGktPmUctV8DTJ62cNezY/Y8PbBhheZMtvDFWkc33m\n9tQlkB9LhKkn0vubFbH3SjkTQ+hbawVnlA1KnZMlD9FjheBkIDq/C8HJsp2CcbLip8DIWiBO9mVz\n1Gdb6pxsoq2c7Mg5HVlbtY2TfXmgrELMyYzWIOIpNmDdyUKLFkhYlNvO0WgGugAHrO9pAVO6u4dt\npxK1wMxm5XZJMLeXjcpBFrEywoT0OXEnDwvMeh2ytoeSP50G4PmpCL6lV90bSdekxhVpvEiHxgtl\nyDDrZ8ghT/h+gxS8VIv+OAszoP+AEyNLUiFWAcPoQ0ENG6ZhDIjOm5jIDDo2evOWeU3mlRByy2Ag\n7HSM8cuGGH40DT0OfAMWyLausfNJGgrjHsmc3CqUtUGDKnj+e0M5kgYwLzQomDxKEZcTboOKDokY\nD+1KNN1XmIZdZ7KhJ8baR/mcwIuoKaeGMUP+pQq09EDaYCrYmsHei4avyt+EDPyJk4VQnsGw0fCl\nDHGVxox0ylWF2kyFMcJpFgVUGyNXwDQum/n6jqHYR9qQ1zk02oF8FvFGaXVfPrnTueaVJ0RiFX6q\nOIefuQcveD7Nn7fJKueBLDxnfq50/tm+F3S+SrR0QSbB+2t/M1AoTgby5+V8OVkeM1NLZBtt4WTb\nc9vEyWQxUEhOBqDqRhSMk4EILxeCk7VrCsHJrpOTl1vHyUHkTsotGCfnikJlTmbkA7WwkkYBCZcs\n9DwRLtK0SAkL8tWVPcsCEogaNoiMIiN3S5GGijAKIzZaSS1kYV30Rha6hlFDed9tMIwekTEUQjNu\nyMW9kEU2vSzUtk7yFtCFuv4sZcxIp4KtRp3w3ohjzGIUMPohyAJbXWvOAU9XppOiZzRDDtFH45hH\nKwQaBxmdkSNVKTG9xUwh8cUCXOEX+HRdICvTVCyftW/9DnbUMVJQLNEZ5hgJOrcCtNRIJsGc3DqU\npUEjadEFRL1/4XZ2DlFuo+3ZPH/RfFr5jKgMVo+doWCYW69Sj2CcAq3a8gRsBUvps6UiJBXouFBr\n1Z48lpBK55+3e6pcD8r75hCrryvHXBmZwzBmU2mOi2iIeFaNdBjpAYwDHVca5m4awpTSnOBdlX3K\n5e2yfTZJHmX1rGBxYoZUm4qz/HzTcH1+zoaLH8hw7zyUevlMfVGnj4vWJxeQ2y3Ke5wYZV310fLZ\ntCathVH6aC9OlqmAtGBlWznZlK2UOTkuEsE/3zpORkovEllITs6FlnKyOSaF4mQpC51XbeVk15Fj\nHSx+CsDJfp+j49BqTrZ8RMzJnROJW5siWoNOLuhiUwzIQi6yQ4NZ44AaAEzEfQ8iVjtPKwKq/U+C\nTC2w/yCEz6JGDdt3QF1L0gVAjBo2xI0DiNHAHLfAqKLtSuM6floCMWRYjRfkePh5W4wbFqOFdg2J\nfInsuqKMp8kRPvqzEqJ5EDMvZBvmnIvMQTf83OjzAGVwUMYH1wm3Dw76L3zLf2gsygWLsUsYY+P3\nyYlugRv0NZFhLWPJnNw6lI1Bg+5CklR8zCRpFf5KFGgK6iGUz6FKDs05Nud+Pp531aY6FvyNkT+y\nCIBlSzbjGgrZX1tobHhRvJInIZU1qoRZ87+FPqaaN1KGKEsPoBt6AFWaIMmbsyl9Hln0yM/Q92rp\nSq+5O4HWF+M47ZetQFvsIkgufLwwfNzz9IVFrjx/E3HX0jZtXreM52nbTkrvoNq1weBI0xNIFWfN\nE5hApDLEOQ3fI9yasLh8Fj2M8oDJyYB9TrSVk00UgpOl/P4xxMpu3tPRnEyLYRaSk6VcheRk/5wu\ne1s5mRZUtqGUOFkZNlSETNs5OdfcZE5maKCLPDmPbZ+v1UvvaUYNCmFboAbGXHU/YC8ImmRhk+2Y\n8stnxszNyFabnsWIEuFYx7hHxD47uMi64DUjBczFrm2xHokuoGkwtDCkjFIw0iv87VSdsH1Lek8Y\nXSP7ZolYkM+mhgt13NgtRgg1h7TjhkElAmoQoeklhFdbXkw0hr+1MbV6VfxIH5fMjYzfnnBBUlEC\nuczoDGrMoMYS/+IEmfyUI5HJtKpGBnNy61A2Bg0gVKBzbQOoCo25uX/gbbnctB0gVGTke6kIU4+O\nTSmn1/rHgr+5Fo1xJB4oa/oN4X0mosXBbEXc7EoaVRTN62wwFxL0ehmdYUIqzlRptoUz66G3gXeP\nKsuG14/ea4L2qyXV5iPtBUqz9plYFlhxsBWfpR5BKqMmN5lXZmi9rzD7SrRMRzHnheM4QYHDaEhz\nnPeYfp9UDrfwrd/mfMy56wRbnjsVypGT6fWlzsn0ukJycpx8beFkaWQuJCcnfS6lyMkAdF4uACfb\n+sqczIhFvuHqMu8fOcJzkWfovJzXZroKNYaYE7EVxoxc8tD6GzpiPPLmc2ExSsREXpj35zTemM83\nrnfcqIzSmKEZMiiH0AgKcyEujRlemEqhGSfiPlM59sKyJW7SPIhwiWHUkPfna8ygxiktYsbV35N+\na3IYUSsCwdzwAJqKEtktxXWATNaeZiLMtkNZ1feJ9B2exXjjOIljwJzcOpSNQcMW4koRt9WaeU1S\nKI/yZAk/f5ZGayR7PvIAABwRSURBVMjmzftt6QRJxgzA7s2U+eVZ+N5Jz0inCJVYQ2bDG+XL6qji\ndblyam2htLQPEW8jUY7kOVMBluc0BVEIwPIzQ9uPS9kJvXmC8HA0/9gzFE0Jmbdty1U2+yvlkHNB\n9cnTFV39/rCqv+eERfqyRo59YmG22Lb1awDp3QRciwKdtIgDovOBLuioMi1hemD15/geX3kLnReO\n68CxfN7ZbJ7KFqPk0RGcLO9zvqGcTN8XipNdwss2tJaTlZG5xDnZV4ZhjT4x+9kSTvbfQ9v+XMpu\nu0ciFyfT9oE2crKlu8zJnQdynjhxFR8jC3MLB0gPf+JzyNaXZuFGILrYi4vwsFyrjAW2CBPVfrAt\nqjAiDaRh29J/B0AkGkLV68jxHTCjE8xnRkIFwwWrQ405RnSFqhtBIiEcv5q+3hwdO1skH1l4m4YN\naszQolJM/g9+7xAZ0+j46vcIUqzUi44HNQbIMQmeoWqMpAEBsstJbJqgJdLFNhbGc8kvmm/UML8H\nkR1STJ2CRGXYjEGqDov5O0fSTYw5pIxXFsMGc3LrUDYGDYlYJczTFWjTq5akNEfygolSSGFrw9zD\nPu7aPH4jwmfSCvWuvv1dJD1CGtfdaD9MRYl6AuNCpK353a4MSbYox8G96ZQbGS9TGXQcAc8RwfdX\n94bJXHR6vSdEsLNANCzXlsfstxcRPzZ011S2kxR9uV1hnOU0k/UgC8B5nopqQzTMO3ovNaSZFfJN\nmfX7gja9cIcH/4S/IwEN47a1QdNM5FjL9+qzjpl74XaLep/ivOOlgoaGBjzzzDPYsmULevTogVtv\nvRWjR4+2Xrt69Wr89a9/xenTpzFy5EjccccdSKfTyGQyqKurw0cffYSGhgacc845mDp1Ki699FJ1\n75o1a/D666/j6NGjGDJkCH7xi1+gd+/eAICVK1fitddeQ0VFBQD/ezl//nz079+//QegHdCenCzb\n/yZzsnlOytBWTpayF4qT5etCcrLsayE52ZE8axjGtHFpBSer/ojCcbLNcPFN5uSDBw/ihRdewLZt\n25BOp3HVVVfhtttuy8nJhw4dwt13342uXbuqtm644QbcdNNN6v1nn32GZcuWYffu3ejatStuvPFG\n/PjHP27fzrcT4rjVMSZzxKCcQIjWNAubl9lmHLBEHsRdm1c0iFx80/aTFt4IvPMe9exLY6H+LH3B\nTI0phvyRxb0bSa0wF61OOhUdM5tF3HMCI1BKyWgzICkDRiartrGVsqlFuiX1xF5fJWaxbjM8xab1\nBMaYuM8v4+9EIxAWToXrwpE7Qbkp+33yWa5DDBSWcTMgjP7K+1SkhpvSx9Q6LoYxgxou5L20Pok2\nTk64va8tVaclaTcdiELoyfm08+GHH+L555/HkSNHcOGFF+KXv/wlzj77bHX+pZdewj/+8Q8AwA9/\n+EP89Kc/TZS7xQaNt956C2vXrsXevXsxatQozJ49GwCwfft2rFixArt374brurj44otRU1ODXr16\nASi8Ep9UCCxOybV5nylkBXhV8V1TNg2jY/DeFvqveWukwdF1lEddwdV3qIirE0zzmyMexBi4Qeip\nLazZ1oYZ5mtr23V0pZmGJJvHNMUtUA5lOLJLFWcZLus4ViVOKs42pdmUW8oo6SROCTbbAsLQcTP3\nGsYx63jl8rgKgZRUvImhwHX0MOfwBnsbLcmrC8dHPxbmtOvyy2J96lkx21IC4cLUVq+ARqPY9qbP\nZwHZnnjuuedQUVGB5557Drt378bjjz+OwYMHo6qqSrvugw8+wOuvv46HHnoIvXv3xhNPPIGVK1di\n6tSpyGazOPvsszF37lycffbZ+O9//4uFCxfiiSeeQL9+/bB161a8/PLLeOihhzBgwAD84Q9/wKJF\ni/Dwww8D8L+Lo0aNwl133VWQPpUKJwPx3NFaThae8LcWLRNOTkIpcTIQfO9LnJNlQU7N2FwATpbX\nlDwnC52X28zJ1oibvLvQLsiXkzOZDObNm4eJEyfinnvugeu62L9/PwDk5GSJZcuWWcfg+PHjqK2t\nxc9+9jOMHDkSmUwGR44caXWfSomTrdER8rgJm5XXspB3pEfe1Y2mtvoSaiEeswCNeN1dfX8huvj2\nfzOiYvvn3DAyI1cfjGdZU02sC/g8Ik+kYcOIyHDoexp9QuejNCJrRiZ/7FRkgZlOAoTGDNOQEWkH\nqp8iK6NTLAQgPzPDmJFkWIHxGUbGKw+i8aMroaW6aKk28rcB9rQiYfY5F9S1hpxmpIvsizRmUMNP\n3PdLjZNFUhW1oxvAImIVCYXQk3O1c/z4cSxYsAB33nknLr/8crz88stYuHAhHnvsMQDA3//+d/zn\nP//B/PnzAQDz5s1D//79MX78+Fi5W2we6tOnD26++WZcddVV2vGTJ09i/PjxWLJkCZYsWYJu3bph\nyZIl6rxU4l988UW8+OKLWLZsWYtI2hZmSyEVgFz5uoBPLErZM/6nU65SBulz7F4c/698rv58/Xn0\nuabX0nyWfG0r/kaLa8rjZl4ufe04diVG9YEoU1R5ku2kgm390ilHvU+nXFRUpJBKuahIp1BRkUI6\n5QbXSB4P+5zLMyQVt2zW871/nu8BbM4E72V+sReGKMvXkf4I83280ql5t8z55eiLATmO5nW2toWh\nmNI+Urk94e9SIv9TeancS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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.20191999949\n", + "Volume fraction of white phase\n", + "0.798079997985\n" + ] + }, + { + "data": { + "image/png": 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wziVFK3nyiXT9NVYlxrM0WnkCMqpPJjWTnhuZbFJr1UxwLaDJi2LC9aMSu2WF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\nQh0psuB1ABjPQ1vcpvzIC6TdsAAji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g==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.207167999477\n", + "Volume fraction of white phase\n", + "0.792831997998\n" + ] + }, + { + "data": { + "image/png": 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+9rGPYeedd8bFF1+Md7/73fjgBz+YpJosX74cl1xyCb7+9a/jaU972sSyfu1r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6bF2u4UCQE044whvcC6CKpoRkWppKaN460fjKnIEnoEVDpOEe9R+JImcgaFwCh9pOO8HW2DxZDJ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\nmTyh+vnUZIa1tJ9+Hswl9lYIPYdU7FATP3S/6VUQ3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375Po4jFosF3vOe9wAAfuInfgJ/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GB9bXnMCRLPpCgvRF6nyqVLKlsiS2LiAvN+8Q552YTYl5t1iLOZUHKIMZZK5XyaCooFBfrA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\n2+7OnTtx5pln5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase\n", + "0.168799999574\n", + "Volume fraction of white phase\n", + "0.831199997901\n" + ] + } + ], + "source": [ + "prim_basis = PrimitiveBasis(n_states=2)\n", + "X_corr_crop = correlate(X_binary_crop, prim_basis, periodic_axes=(0,1))\n", + "\n", + "for x in X_corr_crop:\n", + " draw_correlations(x, correlations=[(1, 1), (2, 2), (1, 2)])\n", + " x_center = (X_corr_crop.shape[1] + 1) / 2\n", + " y_center = (X_corr_crop.shape[2] + 1) / 2\n", + " print('Volume fraction of black phase')\n", + " print(x[x_center, y_center, 0])\n", + " print('Volume fraction of white phase')\n", + " print(x[x_center, y_center, 1])\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We have computed the 2-point statistics for the cropped images. The volume fractions for cropped images are very close to the volume fractions of the original resolution images, within 5% difference.\n", + "\n", + "At higher resolution images (500x) we can now better observe the distribution of the black and white phases. For example, in 2-point statistics result for last image, we can see the distribution of the black and white phases in a diagonal direction. It is not so evident for lower magnification images.\n", + "\n", + "We now have more meaningful 2-point statistics plots for the cropped images at higher magnifications (200x, 500x). However, for lower magnification images we do not yet have insightful spatial correlations. To improve the spatial statistics for 50x and 100x, we can crop the images further, which will approximately match the view field of the higher magnification images.\n", + "\n", + "Spatial correlations are a more rigorous way to analyze microstructure images rather than using intuition and expertise. It is important to have an image with a view field that is representative of the whole microstructure for 2-point statistics computations. Once two point statistics have been computed for a dataset, the spatial correlations can by further analyzed using principal component analysis (PCA). This can easily be done with either the `MKSStructureAnalysis` or if you have a material property you would like to correlate the microstructure with you can use `MKSHomogenizationModel`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/stress_homogenization_2D.ipynb b/notebooks/stress_homogenization_2D.ipynb deleted file mode 100644 index be15d6af..00000000 --- a/notebooks/stress_homogenization_2D.ipynb +++ /dev/null @@ -1,3726 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "#Effective Stiffness \n", - "\n", - "##Introduction\n", - "\n", - "This example uses the MKSHomogenizationModel to create a homogenization linkage for the effective stiffness. This example starts with a brief background of the homogenization theory on the components of the effective elastic stiffness tensor for a composite material. Then the example generates random microstructures and their average stress values that will be used to show how to calibrate and use our model. We will also show how to use tools from [sklearn](http://scikit-learn.org/stable/) to optimize fit parameters for the MKSHomogenizationModel. Lastly, the data is used to evaluate the MKSHomogenizationModel for effective stiffness values for a new set of microstructures.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Linear Elasticity and Effective Elastic Modulus\n", - "\n", - "For this example we are looking to create a homogenization linkage that predicts the effective isotropic stiffness components for two-phase microstructures. The specific stiffness component we are looking to predict in this example is $C_{xxxx}$ which is easily accessed by applying an uniaxial macroscal strain tensor (the only non-zero component is $\\varepsilon_{xx}$. \n", - "\n", - "$$ u(L, y) = u(0, y) + L\\bar{\\varepsilon}_{xx}$$\n", - "\n", - "$$ u(0, L) = u(0, 0) = 0 $$\n", - "\n", - "$$ u(x, 0) = u(x, L) $$\n", - "\n", - "More details about these boundary conditions can be found in [1]. Using these boundary conditions, $C_{xxxx}$ can be estimated calculating the ratio of the averaged stress over the applied averaged strain.\n", - "\n", - "$$ C_{xxxx}^* \\cong \\bar{\\sigma}_{xx} / \\bar{\\varepsilon}_{xx}$$ \n", - "\n", - "In this example, $C_{xxxx}$ for 6 different types of microstructures will be estimated using the `MKSHomogenizationModel` from `pymks`, and provides a method to compute $\\bar{\\sigma}_{xx}$ for a new microstructure with an applied strain of $\\bar{\\varepsilon}_{xx}$.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%load_ext autoreload\n", - "%autoreload 2\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data Generation\n", - "\n", - "A set of periodic microstructures and their volume averaged elastic stress values $\\bar{\\sigma}_{xx}$ can be generated by importing the `make_elastic_stress_random` function from `pymks.datasets`. This function has several arguments. `n_samples` is the number of samples that will be generated, `size` specifies the dimensions of the microstructures, `grain_size` controls the effective microstructure feature size, `elastic_modulus` and `poissons_ratio` are used to indicate the material property for each of the\n", - "phases, `macro_strain` is the value of the applied uniaxixial strain, and the `seed` can be used to change the the random number generator seed.\n", - "\n", - "Let's go ahead and create 6 different types of microstructures each with 200 samples with dimensions 21 x 21. Each of the 6 samples will have a different microstructure feature size. The function will return and the microstructures and their associated volume averaged stress values.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks.datasets import make_elastic_stress_random\n", - "sample_size = 200\n", - "grain_size = [(15, 2), (2, 15), (7, 7), (8, 3), (3, 9), (2, 2)]\n", - "n_samples = [sample_size] * 6\n", - "elastic_modulus = (410, 200)\n", - "poissons_ratio = (0.28, 0.3)\n", - "macro_strain = 0.001\n", - "size = (21, 21)\n", - "\n", - "X, y = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", - " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", - " macro_strain=macro_strain, seed=0)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The array `X` contains the microstructure information and has the dimensions \n", - "of `(n_samples, Nx, Ny)`. The array `y` contains the average stress value for \n", - "each of the microstructures and has dimensions of `(n_samples,)`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1200, 21, 21)\n", - "(1200,)\n" - ] - } - ], - "source": [ - "print(X.shape)\n", - "print(y.shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lets take a look at the 6 types the microstructures to get an idea of what they \n", - "look like. We can do this by importing `draw_microstructures`. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA54AAAEaCAYAAAB5MYgAAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAHNNJREFUeJzt3V9oZGf9B+DvZNuYTrZW9mLVkgk7jMTiGKKtClFEU/Si\n", - "qFRBg11R4th6ZS96I+JqY2QtouBN612NQVeQiLWIIBIo4p8LKxVLxIuQP+1OUKTQSmEyZJvu/C6E\n", - "+e2xm2TP+M4kM3keCMzMO+c97zlzznvOJ++ZOYVWq9UKAAAA6JKho24AAAAAg03wBAAAoKsETwAA\n", - "ALpK8AQAAKCrBE8AAAC6SvAEAACgq2466gYAAABwPL300kvx7W9/O7a3t+PHP/5xDA39/9jliy++\n", - "GI8++mjs7e3F7OxsTE5O7luPEU8AAACu6/Tp0/Hwww/HxMTEa8qefPLJuO++++LChQvxxBNPHFjP\n", - "oSOe//jHPzpvZZe0Wq3c04yNjXWhJf+7QqGQe5rt7e0utAQ6d/vttyerq9t9znHtPzrpC+r1eq73\n", - "l0ql3PPopL/Ju447aVfeZe90PnmXv5PtqxfL36t13It9pZN1vJ+8/U3eeR/Xc49eyduvnfTzm+N6\n", - "fDquenEOnfL8pp/dfPPNcfPNN1+3rF6vtwPpyMhINJvNuOWWW677XiOeAAAA5Hb16tX242KxGI1G\n", - "Y9/3+o4nAADACba8vNx+XK1Wo1qt3tB0137fs9lsxunTp/d9r+AJAACQ2HH8yuL13H777TE7O9vR\n", - 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The remaining 2 types of samples have equiaxed grains with\n", - "different average sizes.\n", - "\n", - "Let's look at the stress values for each of the microstructures shown above.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Stress Values [ 0.30279774 0.27063703 0.30712908 0.29559632 0.28195039 0.28474614]\n" - ] - } - ], - "source": [ - "print('Stress Values'), (y[::200])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now that we have a dataset to work with, we can look at how to use the `MKSHomogenizationModel`to predict stress values for new microstructures.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## MKSHomogenizationModel Work Flow\n", - "\n", - "The default instance of the MKSHomogenizationModel takes in a dataset and \n", - " - calculates the 2-point statistics\n", - " - performs [dimensionality reduction](http://en.wikipedia.org/wiki/Dimensionality_reduction) using [Singular Valued Decomposition](http://en.wikipedia.org/wiki/Singular_value_decomposition) (SVD)\n", - " - and fits a [polynomial regression model](http://en.wikipedia.org/wiki/Polynomial_regression) model to the low-dimensional representation.\n", - "\n", - "This work flow has been shown to accurately predict effective properties in several examples [2][3], and requires that we specify the number of components used in dimensionality reduction and the order of the polynomial we will be using for the polynomial regression. In this example we will show how we can use tools from [sklearn](http://scikit-learn.org/stable/) to try and optimize our selection for these two parameters.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Modeling with MKSHomogenizationModel\n", - "\n", - "In order to make an instance of the MKSHomogenizationModel, we need to pass an instance of a basis (used to compute the 2-point statistics). For this particular example, there are only 2 discrete phases, so we will use the `PrimitiveBasis` from `pymks`. We only have two phases denoted by 0 and 1, therefore we have two local states and our domain is 0 to 1.\n", - "\n", - "Let's make an instance of the MKSHomgenizationModel.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from pymks import MKSHomogenizationModel\n", - "from pymks import PrimitiveBasis\n", - "\n", - "prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])\n", - "model = MKSHomogenizationModel(basis=prim_basis, \n", - " correlations=[(0, 0), (1, 1), (0, 1)])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's take a look at the default values for the number of components and the order of the polynomial." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Default Number of Components 2\n", - "Default Polynomail Order 1\n" - ] - } - ], - "source": [ - "print('Default Number of Components'), (model.n_components)\n", - "print('Default Polynomail Order'), (model.degree)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "These default parameters may not be the best model for a given problem, we will now show one method that can be used to optimize them.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Optimizing the Number of Components and Polynomial Order\n", - "\n", - "To start with, we can look at how the variance changes as a function of the number of components.\n", - "In general for SVD as well as PCA, the amount of variance captured in each component decreases\n", - "as the component number increases.\n", - "This means that as the number of components used in the dimensionality reduction increases, the percentage of the variance will asymptotically approach 100%. Let's see if this is true for our dataset.\n", - "\n", - "In order to do this we will change the number of components to 40 and then\n", - "fit the data we have using the `fit` function. This function performs the dimensionality reduction and \n", - "also fits the regression model. Because our microstructures are periodic, we need to \n", - "use the `periodic_axes` argument when we `fit` the data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.n_components = 40\n", - "model.fit(X, y, periodic_axes=[0, 1])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now look at how the cumlative variance changes as a function of the number of components using `draw_component_variance` \n", - "from `pymks.tools`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAYIAAAEXCAYAAACgUUN5AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzt3XlcVPX+x/HXDDAsMsMioCESi+KCuKCpiaaZ5q6VXczS\n", - "yrqtVtfSdguVq/Wra1pki22mZkpqGS6VmvtuatLgjguIioiArMMyvz+ISZxBR4WZYebzfDx6PJg5\n", - "c868Odh8zny3o9Dr9XqEEEI4LKW1AwghhLAuKQRCCOHgpBAIIYSDk0IghBAOTgqBEEI4OCkEQgjh\n", - 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This means our model may only need a few components to predict the average stress.\n", - "\n", - "Next we need to optimize the number of components and the polynomial order. To do this we are going to split the data into testing and training sets. This can be done using the [train_test_spilt](http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.train_test_split.html) function from sklearn.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(960, 441)\n", - "(240, 441)\n" - ] - } - ], - "source": [ - "from sklearn.cross_validation import train_test_split\n", - "\n", - "flat_shape = (X.shape[0],) + (np.prod(X.shape[1:]),)\n", - "\n", - "X_train, X_test, y_train, y_test = train_test_split(X.reshape(flat_shape), y,\n", - " test_size=0.2, random_state=3)\n", - "print(X_train.shape)\n", - "print(X_test.shape)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We will use cross validation with the testing data to fit a number \n", - "of models, each with a different number \n", - "of components and a different polynomial order.\n", - "Then we will use the testing data to verify the best model. \n", - "This can be done using [GridSeachCV](http://scikit-learn.org/stable/modules/generated/sklearn.grid_search.GridSearchCV.html) \n", - "from sklearn.\n", - "\n", - "We will pass a dictionary `params_to_tune` with the range of\n", - "polynomial order `degree` and components `n_components` we want to try.\n", - "A dictionary `fit_params` can be used to pass the `periodic_axes` variable to \n", - "calculate periodic 2-point statistics. The argument `cv` can be used to specify \n", - "the number of folds used in cross validation and `n_jobs` can be used to specify \n", - "the number of jobs that are ran in parallel.\n", - "\n", - "Let's vary `n_components` from 1 to 7 and `degree` from 1 to 3.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "from sklearn.grid_search import GridSearchCV\n", - "\n", - "params_to_tune = {'degree': np.arange(1, 4), 'n_components': np.arange(1, 8)}\n", - "fit_params = {'size': X[0].shape, 'periodic_axes': [0, 1]}\n", - "gs = GridSearchCV(model, params_to_tune, cv=12, n_jobs=6, fit_params=fit_params).fit(X_train, y_train)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The default `score` method for the MKSHomogenizationModel is the [R-squared](http://en.wikipedia.org/wiki/Coefficient_of_determination) value. Let's look at the how the mean R-squared values and their \n", - "standard deviations change as we varied the number of `n_components` and `degree` using\n", - "`draw_gridscores_matrix` from `pymks.tools`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAA10AAADTCAYAAAB6KXlqAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3XtcVHX++PEX9xFwuIgmCIqIV1LJW7NpaPFtN3dNS9PU\n", - "spTAvGSZaf2qNbOvrVqutN7YTczVzQyKTc2yvJSQl7Twgte8DQqC4g1xhGFgZn5/+JjzdRwGBwJn\n", - "yPfz8eDxcM58zud8zoC8eX/O+3yOm9lsNiOEEEIIIYQQol64O3sAQgghhBBCCPF7JkmXEEIIIYQQ\n", - "QtQjSbqEEEIIIYQQoh5J0iWEEEIIIYQQ9UiSLiGEEEIIIYSoR5J0CSGEEEIIIUQ98nT2AETDNXHi\n", - "RC5evGi1zdPTk8DAQNq1a8ef//xn2rZtW+N+DQYD3333Hbt37yY/Px+9Xo+/vz9qtZrIyEg6dOhA\n", - "XFwcPj4+dXUqv3tPPfUUAGlpaU4eiRDC1Z09e5avv/6aQ4cOcenSJQDUajXBwcG0a9eO2NhYunTp\n", - "4uRR/jaW+LV48WJCQkKcPRwWL15MVlYW48ePp1+/fg7tk56eTkZGhtU2T09PfH19CQ4OJjIykm7d\n", - 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"YII=\n" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "from pymks.tools import draw_gridscores_matrix\n", - "\n", - "draw_gridscores_matrix(gs, ['n_components', 'degree'], score_label='R-Squared',\n", - " param_labels=['Number of Components', 'Order of Polynomial'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "It looks like we get a poor fit when only the first and second component are used, and when we increase\n", - "the polynomial order and the components together. The models have a high standard deviation and \n", - "poor R-squared values for both of these cases.\n", - "\n", - "There seems to be several potential models that use 3 to 6 components. It's difficult to see which model \n", - "is the best. Let's use our testing data `X_test` to see which model performs the best.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Order of Polynomial 3\n", - "Number of Components 3\n", - "R-squared Value 0.982073916103\n" - ] - } - ], - "source": [ - "print('Order of Polynomial'), (gs.best_estimator_.degree)\n", - "print('Number of Components'), (gs.best_estimator_.n_components)\n", - "print('R-squared Value'), (gs.score(X_test, y_test))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "For the parameter range that we searched, we have found that a model with 3rd order polynomial \n", - "and 3 components had the best R-squared value. It's difficult to see the differences in the score\n", - "values and the standard deviation when we have 3 or more components. Let's take a closer look at those values using `draw_grid_scores`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAiYAAAEWCAYAAABSXFx2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsvXl4HNWZt31XVXerW7slS7a1WKv33dgQg83rJSwOYJwF\n", - "g/EwEAOTycJ8IUwymRDgIpCQ701IJpOMSTJgliQshrAHYxsb8L5ghBdZXiR5kWzZlm3Zknrvqnr/\n", - "qO5St7ola1/PzWW66tSpU+e0urt+9TzPeY6k67qOQCAQCAQCQR9A7u0OCAQCgUAgEIQQwkQgEAgE\n", - "AkGfYVAKk9LS0t7uQrcixtd/GchjAzG+/s5AH5+gbyCEyQBEjK//MpDHBmJ8/Z2BPj5B32BQChOB\n", - "QCAQCAR9EyFMBAKBQCAQ9BkkMV1YIBAIBD1FIBBAVdXe7oagl1EUBYvFEvNY7NJBwKlTp3q7C91G\n", - "UlISDQ0Nvd2NbmMgj28gjw3E+Po7WVlZnW5DVVXOnz/fBb0R9GfS09NbFCbClSMQCAQCgaDPIISJ\n", - "QCAQCASCPoMQJgKBQCAQCPoMQpgIBAKBQCDoM/R68OsLL7zA0aNHKSgo4J577jHLjx07xnPPPYcs\n", - "yyxdupSxY8dSU1PDM888g67rTJw4kdtvv53S0lJWrFhBZmYmQ4cO5bvf/W7vDUYgEAgEA57a2lo2\n", - "bNjAsWPHOH36NEVFRXzve99rVxtnz55l9+7dzJ07F4fD0aZzduzYwZYtWzhz5gySJJGdnc28efOY\n", - 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"gs_deg_1 = [x for x in gs.grid_scores_ \\\n", - " if x.parameters['degree'] == 1][2:-1]\n", - "gs_deg_2 = [x for x in gs.grid_scores_ \\\n", - " if x.parameters['degree'] == 2][2:-1]\n", - "gs_deg_3 = [x for x in gs.grid_scores_ \\\n", - " if x.parameters['degree'] == 3][2:-1]\n", - "\n", - "draw_gridscores([gs_deg_1, gs_deg_2, gs_deg_3], 'n_components', \n", - " data_labels=['1st Order', '2nd Order', '3rd Order'], \n", - " colors=['#f46d43', '#1a9641', '#762a83'],\n", - " param_label='Number of Components', score_label='R-Squared')\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "As we said, a model with a 3rd order polynomial and 3 components will give us the best result,\n", - "but there are several other models that will likely provide comparable results. Let's make the\n", - "best model from our grid scores.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model = gs.best_estimator_\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##Prediction using MKSHomogenizationModel\n", - "\n", - "Now that we have selected values for `n_components` and `degree`, lets fit the model with the data. Again because\n", - "our microstructures are periodic, we need to use the `periodic_axes` argument.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "model.fit(X, y, periodic_axes=[0, 1])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Let's generate some more data that can be used to try and validate our model's prediction accuracy. We are going to\n", - "generate 20 samples of all six different types of microstructures using the same \n", - "`make_elastic_stress_random` function.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "test_sample_size = 20\n", - "n_samples = [test_sample_size] * 6\n", - "X_new, y_new = make_elastic_stress_random(n_samples=n_samples, size=size, grain_size=grain_size, \n", - " elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, \n", - " macro_strain=macro_strain, seed=1)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now let's predict the stress values for the new microstructures. \n" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "y_predict = model.predict(X_new, periodic_axes=[0, 1])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We can look to see if the low-dimensional representation of the \n", - "new data is similar to the low-dimensional representation of the data \n", - "we used to fit the model using `draw_components` from `pymks.tools`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAjUAAAEmCAYAAACav2EwAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VGXev+9pyaRNeoHQIQkkIKGIIAmigAJKUaS6VMXG\n", - "7qtu88eqKLq66+tr2XURbEhZRIMiEOkikRqpgZACISGUQHqZ1Mm03x/DnORkZuiBgM99XVxXOOU5\n", - "z5xz5pzPfKvCarVaEQgEAoFAILjNUd7qCQgEAoFAIBDcCISoEQgEAoFAcEcgRI1AIBAIBII7AiFq\n", - "BAKBQCAQ3BEIUSMQCAQCgeCOQIgagUAgEAgEdwRC1NzhTJw4kfnz59/qadw0kpKSmDhxIklJSbd6\n", - "KpclISGBiRMnkp6efqunImhBLFiwgIkTJ1JcXHyrpyIQ3Haob/UEbiYTJ04E4Ntvv73FM7ly0tLS\n", - "ePPNN2XL3Nzc8PT0JCwsjIiICOLi4ujQocOtmWALRaFQ3Oop3HASEhL4/vvvZcvUajWBgYF0796d\n", - "Rx99lODg4Fs0uzuXN954g4yMjBv23LBfx9dff53o6GiH9XfivSsQ3Cx+U6LmdiY4OJjBgwcDYDKZ\n", - "0Ov15OTkkJiYSGJiIgMHDuTpp59Gq9XK9vvwww9xd3e/BTO+NfTr14/IyEj8/Pxu9VSajejoaGJi\n", - 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} - ], - "source": [ - "from pymks.tools import draw_components\n", - "\n", - "draw_components([model.reduced_fit_data[:, :2], \n", - " model.reduced_predict_data[:, :2]],\n", - " ['Training Data', 'Testing Data'])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The predicted data seems to be reasonably similar to the data we used to fit the model\n", - "with. Now let's look at the score value for the predicted data.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "R-squared 0.99834842402\n" - ] - } - ], - "source": [ - "from sklearn.metrics import r2_score\n", - "print('R-squared'), (model.score(X_new, y_new, periodic_axes=[0, 1]))\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Looks pretty good. Let's print out one actual and predicted stress value for each of the 6 microstructure types to see how they compare.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Actual Stress [ 0.28985647 0.2831434 0.25138814 0.29399186 0.26338502 0.27548337]\n", - "Predicted Stress [ 0.29038894 0.28375754 0.25230674 0.29388488 0.26327469 0.27586485]\n" - ] - } - ], - "source": [ - "print('Actual Stress '), (y_new[::20])\n", - "print('Predicted Stress'), (y_predict[::20])\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Lastly, we can also evaluate our prediction by looking at a goodness-of-fit plot. We\n", - "can do this by importing `draw_goodness_of_fit` from `pymks.tools`.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": [ - "iVBORw0KGgoAAAANSUhEUgAAAZUAAAEpCAYAAABbU781AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\n", - "AAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xlc1HX+wPHXHAww3MgpqCACKuKdeeCNR5mmqaSWVpZb\n", - "bbXb3VoZUmZb/bIt86hcU8vcvI9UPFAMb1NRBPEAPBCR+xwY5vr9QTM6MigiCOjn+Xj0WPie7+93\n", - "cd7zuSUGg8GAIAiCINQBaUMHIAiCINw/RFIRBEEQ6oxIKoIgCEKdEUlFEARBqDMiqQiCIAh1RiQV\n", - "QRAEoc7IGzoAQWgsXnnlFQDmzZvXwJE0PidOnGDVqlWkp6dTVlZG9+7deeedd+76urGxsSxYsICX\n", - "X36ZAQMG3H2gQoMTSUWoFxkZGezYsYOkpCSysrIoLy/H1tYWb29v2rZtS58+fWjdunVDh1mFRCJp\n", - "6BAanaysLL744gvs7e0ZNGgQSqWS5s2b3/KcxMREPv7441se891335l+vvm9iwTfdImkItS5VatW\n", - "sXr1agBat25Nnz59sLe3p7y8nIsXLxIdHc3vv//O1KlTGTZsWANHK9xOQkICWq2WKVOm0KdPnzs6\n", - "193dvdoSiJ2dHT169CAoKAhnZ+cq+0WCb5pEUhHqlDGhuLm58c9//pOgoKAqxxQVFbF582bKysoa\n", - "IELhTuXn5wPg4uJyx+e6u7szbty4Wx6jVCprFZfQOImkItSZa9eusXbtWuRyOdOnT8fX19ficY6O\n", - 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] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##References\n", - "\n", - "[1] Landi, G., S.R. Niezgoda, S.R. Kalidindi, Multi-scale modeling of elastic response of three-dimensional voxel-based microstructure datasets using novel DFT-based knowledge systems. Acta Materialia, 2009. 58 (7): p. 2716-2725 [doi:10.1016/j.actamat.2010.01.007](http://dx.doi.org/10.1016/j.actamat.2010.01.007).\n", - "\n", - "[2] Çeçen, A., et al. \"A data-driven approach to establishing microstructure–property relationships in porous transport layers of polymer electrolyte fuel cells.\" Journal of Power Sources 245 (2014): 144-153. [doi:10.1016/j.jpowsour.2013.06.100](http://dx.doi.org/10.1016/j.jpowsour.2013.06.100)\n", - "\n", - "[3] Deshpande, P. D., et al. \"Application of Statistical and Machine Learning Techniques for Correlating Properties to Composition and Manufacturing Processes of Steels.\" 2 World Congress on Integrated Computational Materials Engineering. John Wiley & Sons, Inc. [doi:10.1002/9781118767061.ch25](http://dx.doi.org/10.1002/9781118767061.ch25)\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.10" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/structure_ising_2D.ipynb b/notebooks/structure_ising_2D.ipynb new file mode 100644 index 00000000..9dadc70b --- /dev/null +++ b/notebooks/structure_ising_2D.ipynb @@ -0,0 +1,385 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Structure Evolution of Ising Model\n", + "\n", + "In this example we will take a look at the simulated data. Data was obtained using SPPARKS kinetic Monte-Carlo simulator for 2 phase ising model. The model is 2D and the volume fraction of one phase is varied for different simulations. Microstructures are assumed to be periodic.\n", + "\n", + "###Data loading\n", + "\n", + "First, we load the data:" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "((201, 100, 100), (201, 100, 100))" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pymks_share import DataManager\n", + "import numpy as np\n", + "\n", + "\n", + "manager = DataManager('pymks.me.gatech.edu')\n", + "X = manager.fetch_data('2 phase ising model')\n", + "Y = manager.fetch_data('Ising 30%')\n", + "Z = manager.fetch_data('ising 10%')\n", + "R1 = manager.fetch_data('Ising 40%_Run#1')\n", + "R2 = manager.fetch_data('Ising 40%_Run#3')\n", + "X.shape, R1.shape \n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "`X` refers to 50% volume fraction and the rest of the data has its volume fraction in its name.\n", + "\n", + "Let's take a look how initial microstructures look like for three different volume fractions (50%, 30%, 10%) using `draw_microstructures`:" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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V1vuSy7xQmUc7SnLCvm5nvAfY7971R1EqkaRwaHzQV/XI+LHkkRHP7bE9+ue9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\nHcvC1JDcEUu8wpJ8sNA6lsTDwmSIsuFpQ22ykDAm18PCZy2Z68uknbz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import matplotlib.pylab as plt\n", + "from pymks.tools import draw_microstructures\n", + "\n", + "\n", + "X_con = np.concatenate((X, Y, Z, R1, R2))\n", + "draw_microstructures((X_con[::201]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "And the corresponding final microstructures after simulation (200 Monte-Carlo steps) look like these:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_microstructures((X_con[200::201]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "So, this is a microstructure evolution problem, and final microstructures look very similar to each other (just looking at them). Can we check it using PyMKS tools? We have 200 files (microstructure outputs) for each simulation at every fixed Monte-Carlo step, so we can also take a look at path each simulation takes.\n", + "\n", + "###Microstructure Statistics \n", + "\n", + "To get started, we are going to perform 2-point statistics first for couple of microstructures using `correlate` from `pymks.stats` :" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The correlations can be plotted using `draw_autocorrelations` from `pymks.tools`. Here 10th step is plotted since initial is completely random microstructure and its statistics does not look exciting. So, we are going to take a look at 10th Monte-Carlo step output. " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(201, 100, 100, 2)" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pymks import PrimitiveBasis\n", + "from pymks.stats import correlate\n", + "from pymks.tools import draw_autocorrelations\n", + "\n", + "\n", + "p_basis = PrimitiveBasis(n_states=2,domain=[1, 2])\n", + "X_auto = correlate(X, p_basis, periodic_axes=(0, 1), correlations=[(0, 0),(1, 1)])\n", + "\n", + "X_auto.shape\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fR0LNLJj96hauEko6QqDIMTiDqj9SmWSZ6ovUUJLqwuUAJiM4lJIjIKkUxTSf\nQixSKQuhpdaibG+CqslkmBNSmsqmBaZ271cuqPbMSupDo+bjsVvtlJ8JQc1nt1eoS3i+/tit0rsv\nOVXwWK/0tDqzwXHrKPyNs34a8xK4+yA5BxrVtaBy85xnguekrFSwNMzB7FUEqouPudEeclWwp3cO\n3EtqyVwuGB9cyvLoxVqVUn2perAroRgfu7gYCMnHrAjiEm5+UW0+5m0Mw8dSTGnJvCvOigjDx1bs\nadG0wJwNXOfiY9tZkhz8mFNjCD6WMuK6uBiIzsf8SVWSj6XMwJZZZ0Q+ltYVknMeh5+PLcs6MpO0\nbTIDbQzDx1I2WxHKx5FQMwtmhULRfRFTglYoFIqagPJxNNTMgpmcJGinxp0mpLBy/vz25GwCADHB\nqSIhOCQlBUcWv+G9FI6HO7RIu29TBpcOww6vw8uQ3BCkjFI5QcLs381KjgM8CRHtsJOCFJdu5TtY\n6rPkaGBlzMoGnf78jkKSVMaSgJLzDA8h55DypATnOGl8TWgloQ98V5/ynZMcXzgkaY90L0l3JAkB\nSXu4RIquSwlSIY6Mb45IGckkaUedUJYLtgNuoT3J4Htk/i9JmGsgDrMiPOrrktZzJQ6WwspZIc5C\n8LE9n4JOssQJEpe5+FjSTFllkHQYQc5x8bGUbS0ncJn0rrn42HqHhO9IVD7mPEAcIjnRu/iYl2Gk\nriU0oGH42Ao76uBjPxfnyy3Ox1xj6uJjLmkPw8d2lkVbY8qREebInuDj0I5+yseRoNsMhUKhUCgU\nCoXCgZqRMCsUiu6LWEz35gqFQlELUD6OhppZMPtjLYpOU1bWpoITXSrfhXgsqNIWtHEWPGeloBE/\nQXbCCII7w5DjAq+TYihKKjpS0+QEkxPJrKRU9j//MX59AkGnNFLOGGeUEuqlbMjYqVIWJFNunBz3\nguYtXO0rOS76M1vxvickTROZOAiqL676o3bSX2lMeZYy//X+/pg6KMapUXt61+wqZKDizyMjqO8k\nR1YpyxVBykJo2iiMpZRBUFJtulR/4vvkFRo8pqhZ1CXjoumEFAs/w52xQvCxi4vz54POgYRy+Zg7\nkVG9HSybXhg+lsztpPfVamcIPk4g+C5zhqokH0vvN4efj6VMu5YTspBRNRQfcxMHBx/zNobhY+l6\nFxcD4fjYio8vmJW4MsByhOHjUhkEo/KxCOXjSKiZBbNCoejGUCcThUKhqA2oDXMk1NyCWZKkSZK8\nrG8nnBLtZGw/AAAgAElEQVQmAD9idr/Cjs21s+O7akmSQOc708GdqJjtSpBamHOWMx/9Zjt4YWz8\nYY4ycEtDJNDYmHBDGUECw4pIFcQGknSGw+/gYG1qBWlkpyDdMM8+GZQMuCShlrNGKuhCIoXo8/fH\nkvgLQ0hzzmpvJjinSNJBkpo4c/DL1RccHdmxtCBdkCQeOZ+zkTUvHFkIOcR3oOAgYxyM2PCS04wU\n7jBYNzumBN3lIEnSJF7OCloiFx9LjnuSBkniARcf83PEx6X4Owwf21n4CtotYWykd6JcPpay71WC\njyVnMxcfdwrP1Hr2SVtrxs+7+NjFxbw8qS8uPubzzbQ3E5xTXOocho/TwjOVtAs569tdmo9dXMzb\nG08Ey3DxcdiwixolIxpqbsGsUCi6IZSgFQqFojagfBwJNbdglnZItCvkdsL0s7Ge7KuCUmJJcizV\nxXdx/uuSCNom8d16e0d+J9rJbFtb29OF8r1y/BJCvoGWEj1IdeVyQfvuepIMmLBExfuZPx/cufsT\nT1jPgKSNgiQBbNwk6Y13PQp98croSNMYBcvgfZASovj7mLDaEQxvJ80HgmX7laB2knSK9UGwv3QF\nkucgyQjda9v4FZ6pcJ8VxilXXDLhSmAiwbK5FmzEXddLx1zjq+i6cHEx4PEx//aG4WMXFwMeH4vh\nwRx8TFwMeHxMXJyvI/9XkhC6+FiSHhIX8/LquR12CD52cTFvb7l87OJiXq6Lj+2wnEHpZTJRvH8u\nPpakqByGj5mCIgwf25q34nzEpdRR+ZjPh0rysWQj7rpeOlZqfBW7h5pbMCsUim4IdTJRKBSK2oDy\ncSToglmhUFQdsaRSjUKhUNQClI+joWZGzR8yRg5hI6g44hRizLuf1GXZWDBMG4fkYOhXo3C1BoWw\n4VmsKAtSRnAu4RmjuLow0A6H+scKH2T6w5xbYtSX/F+ePcmEFCqhgqffxnGANceoUUs4E0hOaSlf\nfvsYK5jGUMrIF2ePgJw7bDMNu25JzcafolFtWmYdclkAUF8XdKKhn1JoI/6MpFBvdI/LKUcyy+Eh\nmaSsWARyUOHtkNSycgaseOBczGcGI/VZQioVzJzJCg1VhqI2UIyPbZVvND4u5fAsqai9OovzMc8Q\najhYyODn4mJ/Hf7/G16O8WsKjt9syMLwsfSNs97DiHwsmZzwcGph+Nh6HoVmckc7z0wjWL+Lj/n1\ncWPWgcD1HFH5WAr1xq+PyscuLgbC8bGLi/n5mGAGUy4fi1A+joSaWTArFIpuDHUyUSgUitqA8nEk\n1MyC2XMgKW68ziVXlOhE3GHmgteT84EozRUkHlLyCNqFcwc/MWh9YffGpYxekP+sdU2xdkgOBlQv\nd2ij8ElUnidjAVDY6Uq76gx3oDASj+K7Tmk3zttLbbISl7gkwSaIPytPCKWTEyQ6LlcKE9aK7a6l\ntrs26dJc8Z6z1z8SQrgcNDioOClBTdi2pR3aCN4OyfGFnjN/RpK0JBC2T3IaFZyCKNZVTnhAGsao\nayEZj4taKFubkj9Wx6RxYfiYJzqpJB+7uBhgySNywffaxceS8y3/BhD38dCiYfg4Z/FL/q+tDYzG\nx/z74Em13WX4+VgKM2nxFmkLirbQLpf4uFwuzrepNB9zgXAl+bhUUVH5mD9nekYuLub3uPjYdsIs\nzrnKx9Ggo6ZQKBQKhUKhUDhQMxJmhULRjaGJSxQKhaI2oHwcCTWzYDZqYARVf6k6iuEYvM9T1XH1\nklA+ZfoRMqVJajspb7wESQXpObwE4zs6Y2RyZzcE1VCuDFFeNqZg22zHOqHa9k6rfMv5wBEz1CrD\n9/x4XemCk42kXuJ9ygoqUykOs515y+fAKKjQqB0JwdRDdKDI2OYz+TqzgfZSTNo4U4PR2EmmEJQ9\ni5dBGcM6eaa/TFClJzlAUftIPcznccyMb1CtLsUHt55vxnaaoYxYvIyyY3yqCrBLIZvL2e9y4XkT\nFwPR+djKgilkSpNM2sLwsZuLAeJjySlOBDm7sWx9kkOZhDB87OJiXke5fGzxJ31P2eVh+NgyDxSc\n42lc/VwMuPmYtyMhmHpE5WMeH5z4mI+bZAoRho+leOIuLgbC8bE0RtK3EKx+5eM9j5pZMCsUiu6L\nsJ7dCoVCoagulI+joWYWzAFje8tgPuio5g9pxDdM5CQQk4zjGSgskWuHKzspBCdbHVNxZAUpo+RA\nYupwSAv4OHBHE1NvPGGVy0eFwuDY7SUJOnOgMI4shRBEzIOCHEj4DjohhCIjuELplHpJJemJdI+/\n3lLvviddKF4nb6/0jOKCtN5IkZJBSRyXeBinlVxQUuKqqxT80qBSZdH13ClIzEpF16XIkTMo4ZIk\nc04hR6JmqEYRAsX4mHOV5yAmhTAEu87mYxcXA27tT7l8nBWkjJJztVVHCD52cbFVFzsf5GMuQS9w\ng+XkXTk+Fh0iQ2aTo/EKw8X5e4sW69O8Fa83LEfSdZbENhmUuhIfWw6nIfi4XC7m5braza+nZ8ol\n4oaP+XXKx3scKpdXKBQKhUKhUCgc0G2GQqGoOlQFqFAoFLUB5eNoqNkFs6USEuMa2mo+rmpxGeVz\nNYmJ/cxU6nRvUnDqSAnq9lhHMDZizMTDLK4q5FmpvFiOXrlJBJ0vqK9SVjaCNUaCClJSB3YUHBzI\nmYCrckycUuEFSwtOGJJ61DgmdAafizTOvAz6zeuvj9tjI6kPrXakAlV480VUZQXHjSA5+2QEcwau\nOqaxIUcSKb5tnKtzC3ODPwfJySbjn6vM8VlyTjIZD4Vn2djgDRLdS3Um44IzSrlQJ5MuDWMuJcTu\nlRz2XHwsqcD5HCM+5veF4WPiYt7eGGtbVD5OIjj/Oe9KWdn854AgH0umch3M+TcqH1sZBCvIx9Y3\nrlBGvcANTj52cTFQWT7mmf4Ep/8wfJxl88JkbxQc0DPSXHXwsZXx0MHH1npF+XiPo2YXzAqFohtB\nCVqhUChqA8rHkVBzC2Yp/JvkmETHuGSAIDlkuHbapa4jSM4V0u5bCvVC+eVTqWThr7f9/Ki1o1CG\nVxftXHm0RNHBwRe+SJQCJILOKHHWtjrKBkVZmZJBpwIO2tVKGZJywvWZ4OMw10sZs6SwObwd9Byk\nSJIpYezl0DtBSQZlDpMci6SwchSySZL2SMfomUvl8+sb6/PShQ4ho6QUaoskYJKDH8/CZtotZJni\n4edSvnukEFO8C15YJBqPQJWIadzPLgcp/JvkmMSPheHjSnAxEORjSTMlZYxtZ1LcMHzMtTp0lej8\nK4QKc/GxxVGF33U8U2pEPubva1Q+5o7BxMfSd4/zYRg+lp6Hi4uBcHzMQyBWko+JiwGPj/mz94eh\nBaLzMZ+DxKV+LgbcfGyHDC3+/igfR0PNLZgVCkU3hEo0FAqFojagfBwJOmoKhUKhUCgUCoUDNSNh\nNmqRwhJeiivI1dGeei1duD4Y31FSw3B1BsXNjFnqftuRJSHE8eQwjhmCCQCzhAioHNMxr3+k9uGq\ny/pATcx5LM2dOuhvcfMAqW31TP2T8d0jZZbi5RoHyhh7HoW2p7PB50BlpIXx41XTs+FqXVJTSU69\n1M5Sz8hfPm8TV3HmEnQ+aPLhZRzk8TOL7ze5mo/UcOYQnxdurbNBR+Evf/ZhII15TDRZ8hrV6Ysj\nWl8XVN+JCbkypBoWGqJe2V0KuWzOEqe4+Ng2PYvGxzyGsZSlMgwfc1MuyQTAxEGWHOYcfOzi4vx1\n5PDsnQ/Dx7wdxMd+LgbK5+Mse7/p/Zfa5uJjzpXEx9xkoJJ8LJnSeFwMhOFjFxcDHh9zk4iofNzB\nflebjzuFNU+5fCxC+TgSambBrFAoui9iGihfoVAoagLKx9FQM6NGG6+0kDWKwHfkfkmGtZMlZwlh\n52rVae5hTh2FHasXHknIqMS8mkx+ebidFP1lWKij+9y7PmpuJ3Na8Wd7c2UtzLc92C9/KCHLsc3n\nVAgA6UIZre2dwXuEPrsgSRc4qJ1pdi5d8FrJpgpSA6HtLskGr0t0NqJnyvoSNm6lK6OW5Gbh6jOv\n00iWBOdSAg9J5T3TODuWv16SinCJUsrnEGKFAROGNdTQqESjSyGbc3Mx4M0/4mIgHB+7uRggPuY8\nFIaP+ftAfCxmT2OIyse8ucTHUrY3Fx9LWUalsG7l8rHVp4h87OIlXmeaeRBG5WPJYd46H5GPS2U3\nDMPHGaFOy5HT8R1x8TGfWy4+9nOxv03+YQ1Ns8rHkVAzC2aFQtGNoU4mCoVCURtQPo6Emlkw+0Og\n8F0c2ZLx3WF7RzDEC8GEwRF2rjz8DIHvfv2h2/i5WMFGLJlySy38iSoAb8coXW/ssQQbPEvyJ9j2\nmYQCOQqgXjwED+DloZdCIEk7aCkwOrVSkt5IkgkqNykcCxtiStrpS+GAJEmGl4jDTRI0b6RnJIHG\nxk66kJcIcOmcS2rjSnrAJTWSDTVJlIyEgpnCUR+43aFXlveb3iNemb+d2VxwvnEJiXmWhXGWooHF\nlKC7FIrxMfezoOdu5hDC8bHFPQ4+lkK3ufjYxcWAx8dZITQjh5+PeZ1G8if4vUi86eLjVMotOY7K\nx6USl1SSj/k3OQwf24k4inMCt0cPw8d8XLyEZB73ER+X0miG4WN+H/WBa1uj8jF/j6gyOYxgcT62\ntJIOqb7ycTToqCkUCoVCoVAoFA7UjIRZoVB0Y2igfIVCoagNKB9HQs0smL0sRYX/x4LqBK7ektQ/\nBM8Jw1NPSCHkpFBFOZ/KS8qqVwomzI+QkUdyipHUKeZcPKi2q08EM+EZNWZcUjEG2yiZdfjNO6Rz\nANCZzQSOmXsdOgtJHVXKOY/Ax8afLYmXQWpO2zGiEL6IqX9dzihenxG43sryVLiXxiNfF11ffK6I\njkYMpI60zFAKqnBuCuE9p3TRvlhqbSnLlGDe0uHIzOa9AyzElK9aQWusTiZdDLlsznIocvGxi4uB\nIB9LIeQ479L74edioHw+lniA81tUPrZCwiWCmfCi8rHIqWXysWXCUWU+ljLXufjY6gvIxNAdhi4M\nH/M5SOPBi4rKx9w0xJihpLkzX/AZheFjFxcDXn/8XAy4+djPxUWhfBwJNbNgVigU3RdqM6dQKBS1\nAeXjaKiZBXM6Y+/+ZQcKFtZG2FkSaOfc1uaFO0JD/g+X2BqnLUf4Its5LmbVzcEd/MgxhjeNdpud\nwo6xvSMYyJ7QWO89ooZCQH3purpCn9PCOb6Dlnb6FLpGcqzx+iIEXOdjQ44IbGj894gOeYL0RJYy\nlA6AD3jPgY+RF9xecLgQJLYUKYmPGzlylHJAyQjOj/7kK5aDihDsP2nCZTHHOsHpyp80hw8btYOX\nmxDKleYDMvY7aEmpsvZ9+TbZIZCkuYKYEnRXQjqTk99NSzobfCdC8XGDd474WHonYsI8LZeP+ftC\nTZP4jSMMHxMXF7suDB9L0m/OUVH5OM6cwuh9la538bH1/a0gH9uJn4rzsT3P8n/L5eOM4PwoJV9x\n8bGYNEYISCAlzXHxcUIoV5oPyAjvoIOPiYsBeXwNEmqSEQU1s2BWKBTdGGozp1AoFLUBlTBHgo6a\nQqFQKBQKhULhQM1ImCmOoRx3MOhcIqp9C5AyL5E6UHLk4LnZ/fVzRzFJXZUWVNpkXmKpA4XYoub6\nLJXL25R/NDw2Y8JhWhBLxoqe484zHfRDiHFq1E+sCJPNq8MzbzGqfR5j1Iw1K9cXY1WKZ2o50ZHK\nSzCn4MeoGEmlJs0ff53FrgvEDHXMMUCO6+qCUQcKWRZ5e8hBJSE6+CFwnaQSTiVJ1e0d8xxOvAds\nVIQIXueKA8udUfwxejNChji1metaaG3vdHIxwB21wzr95ecAN5Vz8bEYJ9fBx2lBpZ1mHGXMGYS4\n+xx+PubqduLjRAnTgjB83MEPCiYnkfk4EdTZl8o/4OfjHDcHE8wp6JiQ0LFsPg7Dxf5y/ZByHpRC\nGD7mHJwQHfwQuC4MH9vO2IlCGcx8DsHrwvCxlL9CgvJxNNTMglmhUHRjqFe2QqFQ1AaUjyOhZhbM\ntJNqp1BFbBdHRvY8pBZJDugYlyTkchT+he8cgyFsJGN7v3MA303W0zkmkZacDqSsfp6DTLB/UpY8\n2okmRQlMMJc8ldej0XNGIUEfz2lP1/HdJ41JGsF2SKGKKGsSz1RF0qa0IytVOuNJRfzjATCzKkEC\nZIX+8+3IeSgditYjZWoqlTGLYELOcccTBLUcYqgkoV7SntB1tiNeYSyTxbUcxUBtofLqmUOL5JBF\n81Zy3IoLEiXzXpQIg+eXZKTTgoQ5WTNUowiBTCZruBjweIO/VxL3huHjpCW1K+6IKjnOuvjYxcX8\nvO08XpqPsxa/FOdjKwxfCD7mddI7xLkhKh9zjkqbPgT5zsXHlgBS0I6a0H+CtqpcPi4lEQ7Dx2IY\nUQcX8+tcfFwuF/PyXHzM1xCSttU4GMaD74WLj11SZQ7l42jQUVMoFNWHSjQUCoWiNqB8HAm6YFYo\nFNWH2swpFApFbUD5OBJqZsHsV41xpwZS57QL93UKhvCSequzUJwU51LabJHqhDt8UAYfjpyg/qHs\nR+3Mq4P6JzmZSDDxK5nqhsYkLTop5P9vq0zz5+xYpMEYke2FwUkKLxGpQq2sUJ12WQCLDSlkYySH\nhFJ9l8wvvPjcQsasDKl4g+2uF8rnzhiSY6hpt0Olx0HqsnQHfx4I3OtXo/L2SuY7iUz+PM+iZZ4l\nn+dkjuNwvooLKj2pz2k2IWjGU3ul2LqSs5ZRyar0ossjnclZJgPEPZxfovJxJ6NRfwz4/L1Se0rz\ncU4wjeLvEPExNxcJw8fcoYz4mH+f0gIPheFjOxNsoY1scKLysRWnV8jGGIaPJfMLPm6V5GM+vlH5\nmPMR8TH3t5RMWcLwMXEx4M0lfj3xcSejyDB8LI0vB/Exn+1R+VhROZS1YH7mmWfwxBNPYOPGjejR\nowcmTZqE888/H/HCi/2jH/0Iq1atQqIQFLt///6YOXNm5VutUCi6FjRxScWhfKxQKCJB+TgSylow\nd3R04MILL8S4ceOwY8cO3Hzzzfjzn/+ML3zhCwDyu+lLLrkEJ510UtkNce12JYcT/31ZISua5QhA\nmfaE3axLIibtlpNxKRSRd6xDcHoiR5dcIQuTFDaHQ5JCmIx1lmSiMDZxchLwJBTk3yDtSHmXOzsL\nzjAFbw2eXdCMr5BtynLEE3bVfucL7o7g7fS966VQaF4beRizwp0OpxHeXuOIJIQ7s5xATWYt+j9v\nb7BNBHvukhTCO5LyOymysozkQ6hAam8pSRzBSJvYOFAWLTs8IwLXwcyl4hIN6ZgLsRpIXNLS0oI7\n77wTCxcuRFNTE6ZNm4Zjjz1WvHbz5s249957sWzZMiSTSZx44omYPn26OT9//nw89NBD2LJlC/r0\n6YNvfOMbaG5uBgAsWrQIs2fPxtatW7H//vvjG9/4Bvbdd18AQGdnJ+6991688soryGQyOPDAA3Hp\npZeiX79+ZfdnT/Cx5Iwt3efiY4tLBE1PJfnYxcVAOD6WNHRWxrpckPui8jFxMRCdj6Xwq5JjsouP\nU0kudQ0019OAskcVho8tx30HH/M5EJ2PuYYu/zclOSlWgI9LKdX8fMwzzHrfTna9P3Yquhcfh+Xi\nUkKB22+/HYsXL0Z7ezv69OmDz3/+8xbfVZqLy1owT5482fzu168fjj32WCxZsqScIhQKxd6IGrCZ\nmzVrFlKpFGbNmoU1a9bgpptuwujRozF8+HDrunQ6jRtuuAGnnnoqrr76asTjcWzYsMGcX7hwIR58\n8EH8x3/8B/bff398+OGHZiG5c+dO3Hrrrbj88stxxBFH4Pe//z1mzpyJn/70pwCAxx57DKtWrcKt\nt96KxsZG/PrXv8Y999yD73znO2X3R/lYoVBEwh7m47BcXEooMGXKFFx++eWoq6vDhg0b8KMf/Qij\nR4/GmDFjqsLFuzVqS5cuxYgRI6xjDz74IC655BL84Ac/wNKlS3eneIVC0V0Qj1X3Xwm0tbVhwYIF\nOO+881BfX4/m5mYcccQRePbZZwPXPvPMM+jXrx/OOOMM1NXVIZlMYuTIkeb83LlzMXXqVOy///4A\ngL59+xqpxIIFCzBixAgcffTRSCaTOOecc7Bu3Tqz4P7ggw9wyCGHoKmpCalUCscccwzWr19fiRFW\nPlYoFOHQRbh48uTJaG5uRiKRMEKBFStWmPMjRoxAXV2d+X8sFsP7778PoDpcHNnp7+mnn8aaNWtw\nxRVXmGNf/vKXMXz4cCSTScyfPx8///nPcfPNN2PQoEElyyPVBo8ZTDBqDLa8JzVfzKFCsTUoucB1\nVBfXLJKqTcroRI9FimMb1pGEyovFmIpMUJ/T7wTTnNAxK3ZxzHYmbGtjJg6poNqFyuAxlLOktiz8\nkeKZdqaD5i3pjDvmo1FvCRmSSIVkOxY5VF6WKUL+txSfU4KkyqJ7rUyDDvWsMV0Qszxx1V/QuYTm\nFJ2THE95nTQmfHjTgiqW5qjkxOOPz50vA4XrgmZJOcGUhhyG7JivdMxrmz+euTR+ezru58aNG5FI\nJDB48GBzbPTo0aJEduXKlRgwYABuvPFGvPXWWxg5ciQuuugijBw5EtlsFqtXr8YRRxyBf//3f0dn\nZyeOPPJITJ8+HXV1dXj33XcxatQoU1Z9fT0GDx6M9evXY+jQoTjppJNw77334sMPP0SPHj3w3HPP\n4bDDDtvt/lWDj11cnL8m/zcmmF+4+ZiXEeR9Ko6bPYThYym+cimnvjB8nLV4A4FjJnYxKyMMH/My\niI+z3ISkgnxsObuF4GMXF/PfdobS0nzMndP8ceQBlmkwx00yovExN/+QHPzC8HHO+tbn/6YFU0+e\nkTcMH/NwycTHlomjYEoTho+leOYS9iQfl8PFfkhCgVmzZmHevHno6OjAfvvtZ/i0GlzsHLXnnnsO\nd999NwDgoIMOwrXXXgsgv3L/3e9+hx/+8Ifo1auXuZ4kLgBwwgknYP78+fjnP/+JU0891Sp3yZIl\n1uCce+65zkYqFIquh7lz5wKojfe7ra0NjY2N1rGGhga0tbUFrt22bRuWLFmC733vezj44IPx6KOP\n4pZbbsFtt92GHTt2IJPJ4OWXX8ZPfvITJBIJ3HzzzfjDH/6A8847D+3t7WhqarLKa2xsRGtrKwBg\n8ODB6N+/Py6//HLE43GMHDkSl1xySag+KB8rFIooIC4G9vz7XQ4Xc0hCAQCYMWMGLrnkEqxYsQJL\nly5FsrAZqAYXOxfMxx13HI477jjr2BtvvIG77roL1157bWClHxYTJkzAhAkTrGOeU4AQYoUyL8WD\nUjtp90nXUaakwtHAdQlBemmkCrngzlFybJMcLfxtBLydnywNobK8dlD4pFKhYWiHmS2EOeJhlzzJ\nvFtNUl8IlST1gSQZkpSDw+V0EBOkEe3ZYLnSHJD6YDI0Ff5fJzlyWOHUglJRGn8uk/FLnaVQPVJo\nIx66iqRIrucmPQ8uyZAygRG4A5J/XHOx4Bhx6Z9UnuRQG3QEFJ6t1QfbaYX+WsT8MYSa4x8FP8c0\nNDQYoiTs2rULDQ0NgXLq6upw0EEH4dBDDwUAnHXWWfjDH/6A9957z5henHbaaejTpw8A4HOf+5xZ\nMDc0NGDXrl2BeugDMWvWLKTTadxzzz2or6/H//3f/+HGG280dnUufNx87OJiwJvH0vvKEeTj4lzM\nr7c0biH4WHJC5vBCcAbfExcfc051vdc8NGNUPq5nYUSj8nEpB7AwfGw7TQe/teYZ8W9h4a+LjzOW\ndhSFY8zR0XcfP18uH3OJflQ+lrLkchAf87aF4WMXF/PrREdAJx/zd8Y7HVgkV5mPK8XFhGJCAUIs\nFkNzczOee+45PPXUUzjttNOqwsVlyeUXL16M22+/Hddccw3Gjh0baMjKlSsxfvx4JBIJvPDCC1i2\nbBkuvvjicqpQKBTdELGPIYyRS3IyZMgQZDIZbNq0yagC161bJy4yR40aZdnJ8Q9Zr169nF7Uw4cP\nx7x588z/29rasHnzZuPMsm7dOkybNg09e/YEAJx66qmYO3cuWlpaxA+BC8rHCoUiCqrNx5XiYqA8\noUAmk8HmzZsBVIeLy1owP/zww2htbcXPfvYzc4xUg+l0GnPmzMGGDRsQj8cxbNgwXHPNNZadigsu\nuyfa4Uq7RLL9kux1rN1n4VYpYLgV8N638+IB6nO5/I61VJDwBII2s367MS6VSQhtz2SK7wB5v/xt\nsW1nhR25YGPr3sEXr196ZpIEKkth6/g5oVzPvitYRiIWlNabc0JIqoRgv8bbS+GbrHBLPrvHUtJ9\nkqRI48alzn6JjiT1lY51MGmyK2i/J73xjiWNVM8Dl+74IYXGc8GS/vnKEG3n9rBXdkNDAyZOnIg5\nc+bg8ssvx5o1a/Daa6/hhhtuCFx73HHH4ZFHHsGiRYswYcIEPPbYY2hqasKwYcMAACeeeCIef/xx\nHHrooYjH43j00Udx+OGHAwAmTpyIBx54AC+//DIOO+wwPPTQQxg9ejSGDh0KABg7dizmzZuH8ePH\no66uDk8++ST69etX9mIZ2DN8bPFWVD5mt0nvmklUIbxX5fIxcTHg8TEP6RWGj11cDLD3uoQE1M/H\ncYGj+LhVko8tDV0IPrZ9H4JlEB9bttEh+FiS7vNQeiYUqeAT5OJjLtV28bEkCXbxsaX5S2es+4oh\nDB+7uDh/vrg2RoLRjItliI0MVW41UA4Xu4QCO3fuxKJFi3D44Yejrq4OCxcuxPz58/Gtb30LQHW4\nOJaTdAN7AENPuL7oOb+Tl3UuHiQagqTSswjJoR5JCGqouoKKp4HFxXQt5iXVWKlFJsE12V0LZk4c\nXL3nh0vlJS2meTxTiaCpr+KCORsce7q3td37AJp2CCTMHTP8KjfJaSPSgtn30ZKeqfRcyl0w8/aW\nu2Dm9dMzoWOdgpMWVxNLTnmS+jtqhigq//Tj9sdd159nnVt73a2RygyL0T/+dslr/LE/zz//fEya\nNFCpVhoAACAASURBVAlbtmzB1VdfjZkzZ6J///4A8irABx54ADt27MCYMWNwySWXGMlEJpPBvffe\ni/nz5xvv6unTpxvbuUWLFuGee+7BBx98gHHjxlmxP1taWnDPPfdg0aJFSKfTGDlyJL7yla8EPgZ7\nGsX4WHLyss6H4GPJabqU6VAYPi61mI/Kx6UcB10L5nL5WFowl8vHvJ/igjkEH0vfSV4GcZhkjubi\n49ALZqHPleBjacHs4uNSC2bpeYThYylHhPSMdidbH9Wx/pn/CpyrJh9Xkot//OMfY/ny5UilUuZe\nEgrs3LkTv/jFL7Bu3Tpks1kMHDgQp512WiAOcyW5WBfM0AWzLpjt63TBXPkF87ofVzfD3Kjr/qOq\n5e9t0AWzB10w64IZ6F4L5mrycXfm4j0b64nBvyiWzAkkXwYTTiUZnFjS9VylR+DqESIYaRGbFEjN\nawcjkwJxZGPBBqQMCXrHKCyRHK4u+CJK9dLLxglBImHjmCVkwIoXTnZai8hg/yRIGaXgq4K3rbM9\nGHrHkLv18cz/5h9DPyEmLRVr4T6+WRI4R/q4dOZ8DS5BxtT2FDMXkUIV+dW+kkMid+aTSFj62Mfi\n9jvDQWPJX/BsNriZILg0dKWcRumdKjVHFF0H8VhMDJfIUW0+TgvcwBGGj7njV1Q+ljiq1DcgDB9b\nDl0V5GMp26qfi/1t8/OxJfgwi0fvehp7aXHq4mMXF/PrAlwMOPmYPw/iY94O4mMpc66LjyUBhYuL\ngXB8TFycv75yfMzfJ+XjyqNmFswKhaIb42OIkqFQKBSKEFA+joSaWTD7d018E5eMByVjxgRASKQg\nSTKkkGVe4hJpl+wIQ8MlCYXffEdOUg1JKkM7Ua6qS5m+eGXwgPcEE6LHsXO0Qjw5rG2k94XalhLU\nZilhF561QgQVpBBs8FtJhSWFiRIekpcvQ1CdOkIl8UQu5HQkqc3SgqOFS4IvOa9kO4NSDj7OMUE0\n4H9eWUFlKiUisKTkgqrSX35GOAZumiKoAwm8r8Gg/cE6M4IEykjtpXmXLK6OVtQeivFxMh6UjFkm\nACH4WApZZicuicbHVkIH0v4wCXNUPnZxsb9eV7s/Lj62NHSFvrRy864QfGzny6gcH0vJZex7i0vw\ny+XjMFwMuPnYSiglmBRF5eM4K9fVVymhlYuPJZMXEcrHkVAzC2aFQtF9IX28FAqFQvHxQ/k4GnTB\nrFAoqo+PIQ6zQqFQKEJA+TgSanbB7Io3CXjOA8aIXnCM4GgvqKSSglpHctaQInNIjhyk8rO8hQvq\nDpdxvh0JgjrBzseDzm7GcUBQ80mmIaSFk5wUuMc29TElqGnI+aFTcEqRvLEtBw5fdi7JM5hD0mya\neKpCVA9JTWscOFlXJNWfFDPYeG0X+hpjfa4TnJjoGeUEtR0Pn2kynUljlKUxCjo/8WcpxXz2qwOl\n5yep+1oFdWBHZ2fgOi/DV9Dbu2yozVyXhjP+L5vrYfi4nZkHSI5XfkdmfszFx9z8wkTSESI2SHDx\nMX/nqL383TTviaCqL5ePef+i8nFa4DRuJhWGj11czH9nhHHg8POxi4v5byuiUQg+5s8oJ5i0ER/z\nORCVj6WYz5JpRrl8bDkYKh/XJHSboVAoFAqFQqFQOFAzEuZADvs6r2l1SZdhfTjnA0KHEBsxy8rw\nx1XmklhpB007XTm7UVAqKoWcoTiUfEdM5bZng1ILyZi/QwiDQztcKT6pJVDw7Xrt7FRBSUJaCPsk\nST78oaBsZ56CZNzyDrLb6G+7HxTnUpJWc2mTa9ys5yHEhCVQSCErFmk8WC6NTSLBjpl4ycH6qQ88\nZqfoOEWSODakJNkTHU9M29izLOyPuXSmg8rioah8zi1S3GZJK2MkckJ7YsmaoRpFCKQzWfu9KvCx\ni4uByvKxxFsuPubzOiFIriWpaBg+5uW2m3fT7VwVho8tR1sqokS4ujB8zENUEh9LYfmcfMwu90K9\nuuVrYfi41LiZMRHiQHP4+Zg7QVK5fL4RH6eteMml+ThtfX8L5fNvfaE4rmUJw8dJJquk+dXBrpe4\nNwwfS99TCcrH0aCjplAoqg9VASoUCkVtQPk4EnTBrFAoqg/1ylYoFIragPJxJNTMgtk4Uwh6BFKL\nxOLF4x9yLZDnyBTMnsZV9VJZRsXhMOaXUlPXMxMSybDf7wBgOYgIZXhq8eA9rhiYksorXUIF6Xdc\n4G1LxIImHBlB9WeyY5WIL+lvR04Yh7igXuOj53d44c4grtS8YhxPKStWATnBTIGb/Zu4nCyDGI2J\nKwYmN70h1a3lzOdI11vniJ8pOTVJsZytuoTU4/70sGJWM1YGXUcxV+U5pgTdlZBMxJ1cDHh8LMa2\ndfCxZabg4OO4ME9dfMznNXFpGC7Ot7c4H9tq8eD1UfnYxcX83nL5mJsiSGmwJfj5WBp7qx2Fv5Lz\nYbl8LPG9xJ8uPrZ4LkbO2973KSofu7gYiM7Hduz+4HynOcr7HIaPufkK8bGEmMZhjoSaWTArFIpu\njIQStEKhUNQENKxcJNTMgpl2RknjYFA8fBbg7aiSiXwXEmzX1erY8WcEZ4KksBPzOz4BQBxBSYb/\nel6uJXmI29dJzih2ecVDx0jhi6RMP4SM4JwnSVkkL4G29nx4GylUkTTMtiNLcYm4FIrJBSnMjzcH\nuHMl1Sk4pYFLPoL99xwiyQGVnyVHpKB2QQp/JUkBJGlPWhgbybxMcrghaUXcZNPyzpGUiUvwzJzm\nmdYKN3UI8zfnkPakWYatOp9Dj5rHdX3EYjFrztG8d3Fx/rpofMwlY5XkY16umffx4HUuPi4VxkvK\nwhmGj7kEtNp8XEoi/nHxsS11Jce94lycb1NpPpa0ZtLz4AjDx+VyMRCOj6UsizyLZocwf8PwcZ3g\n7K6oHGpmwaxQKLov1CRDoVAoagPKx9GgC2aFQlF9qNhZoVAoagPKx5FQMwtmv6ORZKbAneL8ahHJ\nFICrf0iFxFUhOZ8TBsAzShXU0oIzQUqIRcrVawRJzSapvrwMPkHHkxSLAZrOFN8VUl1Sue0ZT31G\n42A7OtoqdSnGJx8HKd6m//nx+iFoG40aij83QVUoqZ9orOOC6YuXOYzXVfghZHni8NSohcsTwTHi\nkLJ5Sc+S+kDlc6ccT30YKN52UjSOdcVtgS0LIKFAl+MPNzWh9vnNpAC3WrvTxJsW6tG4n10KuVzO\nepddfCypqF18zNX5NHe4c1NaMGfYk3xsO2NRpj+3hC4MH/Nx8Bwdgyr1cvlYchSznlEIPi6Xi3l7\nXXxsmVXEg/xJyFjf5MJfBx/zuSJmxBVMeqLyMa8rKh+XcsIkPuZtC8PHfOwlh0yvAuXjKNBRUygU\nVYdkZ6pQKBSKjx/Kx9FQMwtm2j1R1hspu5Gd+ckncWATQA71EgwzJEmdaRcrORNIkhRyasix0GJ0\nj7VLjgclDgRph+1yzJBRkMoguNOWjP9j8aD0hsAlMemCNCQtOEZkBMcTOcRUsC9S2BxJikUZnzK5\noBZAyh4lleHKxmg9+0KbjCMSdyQxoQ2D0gVrHBwOThKMFEtwcg3rtEFNkjKp8bHvEKRupj8ZLhWi\nMFKFU0K5UrgsPveCN6jNXFdCLBazMtzFBemWl7GOOSaF4uNgCE5J6szfiTB8zOcp8TG/3miQmHQy\nDB+Xz8VAVD72czFQPh9LTuFWy0LwsYuLAY+PJWmri49LZWM0z571OQwfc0mvGYcyudiqX3ByrSQf\nO7kYMHzMNX9h+FhyipUbqXwcBTpqCoVCoVAoFAqFAzUjYU76dtZSmB9uz+u3tbJC9Ah2RZ7NV3CH\ny3fwUhKRMLASjBRCvOQSxXeYpcIzGVsqCBIS6d5CH6wyOimQPZMMkMSWHctlC2NJdlBCMg87dFr+\nryU9kKTkPttdbhMpSUMkGGmyJWWx64pb0qygNNmThAnhqoT6qbzOzuCcsuy2s8FnSZIJSYIgwSX9\nlualhA7BdliSOOQEKTnBsrfznS4lqTGJI2gcJEmMSjS6FIrxMZ+nxMeSTaWLj3PWfArO/5TAZVH5\nOMvCH0blY4v7IGgPI/JxRuBl4mJgN/jYwcW8rnL52JImF/onaUddfGxriSvHx1ZYN9LQsTkclY+l\nZCIuLs7XVZqPeXtdfCxVFTZRTswlEVc+joSaWTArFIrui5gmLlEoFIqagPJxNOiCWaFQVB8q0VAo\nFIragPJxJNTcgllSkVFOdCkskOu5c3WRlBu+sb6QlUpQDRk1s5DFzVIbCZFb6DzXAmVzdugf2+Sk\n4FAjmItwpzhSS8aEUD6eSs2rMy2o2RI+UxbAUyFJINURv94bLytGEAA5AxTdKzm0cEjOGpIq2J/J\nKSGo+STVn+RcIqmTc7GgWY6UMcvlUCOG/jGmId71Urg4L8RUUO3KEXiWQhi6LJsrkuqPrqtPBR2h\nsoV5IToACSA1fkJqrHpldzlIcz3FsvBF5WOLUwt1EBcDu8HHDi7O10/HvHciDB/z9hAfc26I+cJc\nAuH4OCFwhIuLgbB87JUblY8lR2bJTFLKcuri41KO11JYuzB8XMq5UQyXF4KP7fCr9N0JVCU+Sxcf\nu7gY8PjYMseJyMcilI8joeYWzAqFovshpnE/FQqFoiagfBwNNTNqkgSBkIwHpa0E2kGLwcd5WYUN\nc47t4kQHA187pPBB3BFPykdP91q7w0L7yKmA95OkBmkmIqFdZEzY4SaFpBgEKwkLtYONjRQwXZLi\n+tHYkArUYQXUb+3Mt00oI+mQ8EqQJPjZLJOq0/NNBh3xCHx8jdSXP/uitXuwHQfpmQalvlb1hYJ5\nWCT/M+LjRlI6Lg1wSR/4+HoB+mOF/7NmuMJDCeVK89HlOMhRynFT0fUQj8WcXAzsBh+zly8nae1Y\nGwhh+JjzgBSKzGhOWNvC8DF/h2KC9of4uFSIOj8fu7jYX68fLj4mLgYqzMesucTHloQ5BB9bUl96\n9kVrtuHiY+tTT1WwguNCIhmCi49LSYJpLO3kVdH4WJrvkjOji4+Vi6uLmlkwKxSKbgxVASoUCkVt\nQPk4EnTBrFAoqg91MlEoFIragPJxJNTMgpmyFVGDuCokXUhAXxcPxuI1qjpWlhfDkO2i4oEfYrxK\nPyRVj+SUxssltQiPG+mVV3Bg4KoZwTjfy0rlnevsDDrxeQ6GwTrbC7+TglqSj42/nZYzDzlyJbiq\nMH++vcO7rL6OnCSCWbz8beTlcSfMbDbYNuo/V3mRSjMmOJn4Yz9zsOZ6akk+RXxqPrsMIWaor438\nNy+XVHNS/E7jKCeo6riTizSXvD4UHFXYS0BxcNNW9rNg/eTcImVrq0f+HHdEkmKBU4xTE+c5UAsQ\nU4LuUkhncxanEs8SFwMeH0vO1U4+tqZC8djBElx8bL9fQSfdqHycEcwqOll8Z2PqYTkYluZjyalS\namO5fExcnC83mOE2DB9nBVMAPr7GgZGVEZWPk5ZzXOGvleGuNB9bZ6T42TFqd/A6jjB87OLifF2l\n+djFxfn6gyYZYfiYx5uWzOcIysfRUDMLZoVC0Y2hcT8VCoWiNqB8HAk1s2DOGkeLPPjuiHbz0o7J\ncybwjtHu13LWMLt5Li0o7qRAziu5rDexqC7JSUDaQXP4d7OSk6AUGkfK0sfhD0PEM2zRTjgrOhoI\nodsE55W6ZPBeaqd9Lj+VcoL03euD19b2juK7dO5MFBOc3QI9ERwd+DPNOqQmnfxZpsjho1AX4xQp\ng5kJmSQ4a3CQ9MqT7ruzXUkhkMy9gvxWksJLof+SCZIIBiVL3JkrG7PngTUvhfYaJylygJUEG2oz\n16WQzebAA5LRM+aS1ah8HLPeV9KkMSfZiHzMJbEuPpYki+XycRguBsrnY8vpLzIfe5/1nCB9D8PH\nnG+JX2OCs5sEFx9nS2gUiY+5tDUMH1sOgY4AAvWC9NvFx1J4UEszEJGPkwmuLSeHQDa+BT4mLrbb\nW5yP+TuZdkiYlY+jQeXyCoVCoVAoFAqFAzUjYVYoFN0XajOnUCgUtQHl42iomQWziTEsqDM8NR93\ndLCN4rmGQXLo4vcSzL2JoLqKVG7tQkYlyxEvHTQt8OoMxmH2VDNBJ6tWIYOflIEqI8RV9tRFwXZk\nBNVQTnA4cUHKYsVVXqQOlFSxXoYr75w0lmK9QjYoV6xJMRujADFOq8n+R3PQHRuWUDIbZAGSCptU\noTwOs1eG29nOFd+ZwFWMdN6ZAYrVQX9zCT72wedBdXSkC2pzaeyFrHCK2kU8HrOchaUsfTQXJIdR\nFx+7uBjw+FgyR3PxsYuL8/UG4zCH4WPJCZn3OSPEVQ7Dx1IGwTBc7L/Oz2XcbIPq52MZlY+luM2l\neDYMH4s5DKzsf9H42MWVQDg+tssImuO4+ufiY37OxcfW3AvBx3xOER+LUD6OhJpZMCsUiu4LlWgo\nFApFbUD5OBpqZsFspF9xn+MV++0yiuc7PNo7WZlzBAN/ctLICdJLaeeaMdLnYPslA3u+WffvLDus\n64rvMNPWLjXoDOZvmytjE+BJMiSHhDB9z99buI5VJdXrz07HxzmRCmZe8iSbXhmmLv5+Uza9OIXv\nkeZKULJqh30qHEvnAvdmMkGnkZwgUYkX2sEdoSikkzWnCk2hQzwskQnXZc2p4AQz97ILvXBd1CfB\nkZOV4coexUH3xoRzFFaM94HG10iWJWmSEnSXQiaTNVwMeO8G5+UwDqNAkI9j1jtP2omgM6HEDf42\n5v8G2+/iY4kbyufjoDOY1DYXH+cEPpJCspXLxy4uBsLxsS3ZtOsB2OvMs+mF4GM5vB07ls5Z9+Xv\nKc3HcdYO4mMeXs9cz2goHB8X52LA42N+LAwfuzKrcnCpfhg+5uMravq8goufUxSFjppCoVAoFAqF\nQuFAzUiYFQpFN4aGMVIoFIragPJxJNTMgpnUOFLsROnZ+lVHXJ1BagpJpZeB+xiBVISSo0PSykKU\n/ysZ+GctFYrttBXn8RUdcn5eronjyfrlj4HL4cqSx9Ha1pk/V8hkZDk6pIo7B/CyXFmFTOajVMJ5\njPrHzQ7IgYX3j0JYklMSz4iXEtprnIwkZ0lBbWX6L2SFsmI5k2OI5QQaVMVmfaY/kgqSo67gkMH7\nYpxsrH4VV+XR9aXig1MZKcEJhFR6drY0YZ7nbIcTyT4ulqgZqlGEQCoZL5uL8+dL87HlsIbixzjC\n8DGnIDcfB522yuVjrsanfpWKgevnYxcXA9H52MXFvDwXH/P+ER9zZ0LqH89/EYaPLYdPyVkyIh9z\nx33PCVRwwhRMf1x8XMd4kfpicbvQ7qh8zMuIyseWKZQjVrbycTToqCkUiupDJRoKhUJRG1A+joSa\nWzBLOdFJ0mHla0/a0j0pi5sUVoZLTajzktOIK8+8VK7oaGFt8OzwM9ZOOlNcysdB90hhbaR2Gycb\nIQQRrz/pCynk3OVD3iX7HQcBTyIhOZuZZypII/mO3zigCX0254RnxZ+HS+JiOWsU/tOeJW0AOxej\ncFLu9kp10VyWpM+m/JCh6ay2+8qRyuAZ1ChbWmN9MBOY9ExLZUTz12WylUlcrE4mXQ78nac5bPEn\n8UYyKN1z8TGf14YHWL1R+VjKImeXYWr1rovIx/x6KeSj1G4/H0uSVesbF5GPJcdBLh2OyseWA5rQ\n5zB8XEr6bRyZ2XVR+djFxUA4Pi4Vms6cC8npxMfExYDHx7y9UfnYnoPq9Fdp1NyCWaFQdEMoQSsU\nCkVNIKZxmCOhZhbMtFOUgs/TbiyX5DZX9FfYaVLCjGxxSSyHZPdKkHbh9q6zENye7f6ovbxNMSMF\nCe5+U0KINULaKdHjdoHBttN1jfVMytgZHF+yPaN2cAmkP3QZ4EmAeP9SqfxvbvNFtmypZFD6bMpn\n3auvo9BKfCyD7ZVs8AgJI3Wy7sjXxeaW13YWnsmXdIGPs8sejEPSOJAkwx1qSkow4j2HDkdSBhpz\nSbLUKTxvHsLLSL3iwWNkE8mfAb0DXLLhfw7JpC6OuzoSibiTiwGPj0tK/nx8XCo5R1Q+Ji4GPD7m\n7TU29kIyikrwsW3HGmy7n49dXMzbUS4fExcDHjdYdsUh+Ji4OF8ujWVxLubt5QjyMeOowvyyuZXm\nSnAsy+VjSePA7ZrD8LGdYCQ/hi4uBqLzsRUGNy4kqgrBx653R7H7qJkFs0Kh6L5QiYZCoVDUCFTj\nFwm6YFYoFNWHErRCoVDUBpSPI6FmFszGgSRGIWGCmcR4/nMvi07+Osn8wlKzFdRaVpi2bFA9QioO\nUmGVclKQkM0WVJUOT1Q7NEwhXBxXQwn3ShmwCAnf+PEyuGooKTjn+U0cSjkfSnU1NqQAAA1MfdjU\nqx4A0LtH/i/vH6lKP2JhlOiY5EQnZeKi67hDC13PHU9SxuTEu47C8fB5Ro4sntqOqX8FpxxJNSeF\n+eNqYcCegzHjDBJ83pI6mcP/fK0yqAoeAlFob9yo9JjDicNZhPrH2+OalwQpS5WidpFMxK33m94T\ny8mowBNpKzxaaT5OMY4wYdocXAxE52PiYiA6H7u42H8vIQwfJwWVvWTiUC4fExcDHh8TFwPh+Jhz\nj+RER3xshQ8Mwccpy+SkYIbJQqPRPONO3mH42MXFgMfHfi4GyudjFxfz8y4+dnFxvk0FZ2yX4x6C\nfFxqXhKUj6OhZhbMCoWiGyOpVKNQKBQ1AeXjSKiZUTNhyQRJQ0LYRSbJiL6wYZQcAqxc8rmgtIB2\n7lwaAl+28DSCTh4WMsXrd0k0rDrMrpDvqkOGQvNVwZ07xPoTwfskZ0Z/GVzKQcfqmYMBSTD67tNo\njg3o29M6x8vY1ZqXZGzdvssc27azNdCOlsJYtrZzSbQ9R/h4kEOLJNnhEpVMPCjR8cJfoSisMkyI\nPqF+QYKfFpxMSDPGpeqexIhVXDjPpW7xQngsnkiHVRool9rBJVvklGNLJuy+SmHAOIwzlSORisb9\n7FpIJOJOLgY8Pk5y6WwIPs7xd0PgF4+PhZBhLj7miS0E58OofOziYkB2xCM4+ZgJjumU9X5H5GMu\nTSY+Ji7m5118TFzM29HCxpL4WHIEdPGxlHiGuNi6zuLlQBXBMqwQfXbdgMfHkkTcxcfW/KFbuUSa\nvp0sVGEYPubtID7mzu6e1i7YVxcfW4EGXGFJlY8jQQ1ZFAqFQqFQKBQKB2pGwqxQKLovpHTZCoVC\nofj4oXwcDTWzYE4KhvR+SA57cmzk4s4apTL3kBqnU4iNKGXakzJFeSrHYP1GrcLb5GinHI+SlVc4\nnTD/965Pmhi7TP0TD5qL+GOLSmozruoh1Wqf3g3mWP8+PQAAg/r3MsdGD+0DABg+qAmArSrctiOv\n8lu3Ybs5trbwOys4LvA20viThY4UG5WbixjVGxsbE4uUO4YI5fghqf7szIvBe8x4FtrLY2Uax6JY\ncMx5a+qSpVVolnouI5Qbt8sv1l6/8042J81P7zcVlxRMPrwblKC7EsrhY+6wF5WPXVwMhONjK+5u\n4XfOKjcaH1tqfF+sdv6bq8/D8DHPqhf3qeyt+svkY+JiwONj4mIgHB+vZbwsmQJQOzn3heHjtPDt\n5M+Z6grDxbx+PvZe5sXg9dazDcHH1reo8DcMFwNuPs6y1ysrzCkCH4cwfMzHISmZxpkblI+joOIL\n5paWFtx5551YuHAhmpqaMG3aNBx77LGVrkahUHQl1EAc5nK4afPmzbj33nuxbNkyJJNJnHjiiZg+\nfXqoctrb23H//ffjxRdfRCaTwahRo/DjH/8YADB37lz88Y9/RCqVj2QQi8Vwyy23YODAgXu8zwqF\nYi/BHubjsLz0zjvv4P7778fq1avR0tKCOXPmWOcvuOACK+JHR0cHJk+ejIsvvhhA5bm44gvmWbNm\nIZVKYdasWVizZg1uuukmjB49GsOHD3feR9mMKHSLJEngUg/aeUmSENox2lIwIauRwyie7k2wUGQU\n6oU7SOUECaEpn19XqCtmpHBBpxgpZJIkyXPZ61uhdArtrRfGiE8ycv5oY451BBpfHn6NnlVjvRe+\nqGdj/veQfT0J8/4j+wEAmgfnj7UvWmHOjT3yE/m62e6eHEh2trSbY7vaOgpt88IBUVvShRRXkhMb\nd+RMCrtpqisrOCCJ0n3h2cgZoqgdPGRU/m+dcF8yFcw+5iqP95UkGJJULxcrzEvLAYWkecHyeWgw\n/1yW5qDdNltilhDGuxZUgGG5KZ1O44YbbsCpp56Kq6++GvF4HBs2bAhdzq9//Wvkcjncdttt6NWr\nF9auXWvujcVimDRpEq688sqa6rMfjfVJKwSXJAmmucilYGH4uFwu5ve6+DiXLT6HAe+941LnqHzs\ncrzmKJePuSNeVD4mLgY8PiYuBsLxMXfmIz4mLs63LR1oRxg+dnExIGsjwvCxi4vz7SDprHcsKh9L\nYVq5NDkcH/P1TbCOtKAhJLj4mDsOSmPttXHP8nFYXkomkzjmmGPw2c9+FrfcckugnPvvv9/8bmtr\nw2WXXYZjjjnGHKs0F1d01Nra2rBgwQKcd955qK+vR3NzM4444gg8++yzlaxGoVB0NcRi1f1XAuVw\n0zPPPIN+/frhjDPOQF1dHZLJJEaOHBmqnPfeew+vvfYavva1r6F3796IxWLYb7/9TNm5XM5nJlA9\nKB8rFAoRXYSLhw4dihNPPLHkBh8AXnrpJeyzzz5obm4GUB0urqiEeePGjUgkEhg8eLA5Nnr0aCxZ\nsqSS1SgUCkVZKIebVq5ciQEDBuDGG2/EW2+9hZEjR+Kiiy7CyJEjS5bz1ltvYcCAAZgzZw6effZZ\n9O3bF+eccw6OOuooAHmpxmuvvYaLL74Yffv2xWc/+1lMnjx5j/dZoVAoPg5Ui5fmzZuHE044wfy/\nGlxc0QVzW1sbGhsbrWMNDQ1oa2sLXYan7uBG/0FVcsrnRCCpgXj2MskJg2DFoI07zlHWuXRQhSTH\nSwzGwE0IMRql7HD+sgBu9C85AgZVpgnY56w6c8F+UVU8fiP1tTHBVaHkdBfsH4872q8QA7T9vca4\n/AAAIABJREFUtcUAgA2fn2HODf2/WQCAIeMPMsfe3bgDgK1S7NGQV5y1pDx1YDqTDPTVa1tB7ZkK\nxinlU4rUmK3M1IOeqqe+E1SFwlhK5hfxbPAZZc29QecZKcOXZFJkqwPzF5iYnYK+SIybzH7TeKUE\nVXdHurjZE4epv6B2zAnZqWJ7OFB+Ody0bds2LFmyBN/73vdw8MEH49FHH8Utt9yCmTNnlixn69at\nePfdd3H00UfjrrvuwooVK3DTTTdh+PDhGDZsGD71qU/hlFNOwT777INVq1bh1ltvRc+ePTFp0qQ9\n2mcJdkYzenmCquSU4GDr4mM+/1x8LM1nFx/bTn/2NfljwXctKh/zbHYeN3jXReVju735v+XyMS+f\n+Lgfi48fho+JiwGPj4mLAY+PiYv9ffXaZvOxlP2ukWV+JD7m0Z2j8rFlNiM41oXhYz63JZMiz1SO\nmV9E5GO+XiE+5sfC8LH1fsSKS0/3JB9XYp3oxwcffIBly5bhiiuuMMeqwcUVHbWGhga0trZax3bt\n2oWGhgbr2JIlS6zdxLnnnlvJZigUihrA3LlzARTe748hUD7VBwATJkzAhAkTzP/DchMA1NXV4aCD\nDsKhhx4KADjrrLPwhz/8ARs2bChaDn0A6urqkEgk8MUvfhHxeBzjx4/HhAkT8Oabb2LYsGGWavGA\nAw7AaaedhpdeeqkqC2blY4VCAdjc+HHwcaW4OCyeffZZHHTQQRgwYIA5Vg0uruiCeciQIchkMti0\naZMRt69btw4jRoywrvMPIIcXdiW4Y7Ovy/+lnT53WqJdOt/1ZTLBHaa7/vz/uUOC5ODXngm2zWRq\nE6S4RpKQC0o5+PUURklyPpDCHJlQOnEWIqdQHpeimuyHTNpKDhmSZN4LeeOVQTtc7gTyUSFTFHfY\n27ilBQAwaNLhAIChj93nteMTBwIAtm1qMcfIyUhyLEoIu+q6ZDBUETlXSNdzSYIkDTHOJblgqCKa\nN1zqRPNLknJIIYVMWexcIlZwMOJ9FiYp9TUuOJdI81KSbkhSLxPxjrWxs9PO3tXa6TkfSU4uXr12\n5iy+8MoJIb0qDddCLyw3AcCoUaOwYoXnEMWfTbFyiHxHjRol1l/KqbMa2F0+tueTPdfs67zfYfg4\nkwnOPwnSfHbxsYuLAY9fJWdsFx8TFwNuPubcEIaPeSZa4mPurByVj4mLAY+PiYuBcHzMHT4lp/tK\n8rGLi4FwfMy/9U4+FuaIi49dXMzvjQnrFRcfi2EX+Sej0E4/FwNuPrb77j0HPzdWm48rxcVh8eyz\nz2LKlCnWsWpwcUWd/hoaGjBx4kTMmTMH7e3tWL58OV577TUcf/zxlaxGoVB0MWRzuar+K4VyuOm4\n447DqlWrsGjRImSzWTz66KNoamrCsGHDSpYzfvx47LvvvvjjH/+ITCaD5cuXY+nSpTjkkEMAAK+8\n8gpaWlqQy+Xw1ltv4fHHH8eRRx5Z2cGO0GeFQrH3oKtwMZAPFZdOF6KydHais9OOHrNixQps27YN\nRx99tHW8GlxccUOWGTNm4M4778SMGTPQ1NSESy+9NJSHo0Kh6L7IhExEUE0U46YtW7bg6quvxsyZ\nM9G/f38MHToUV111Fe6++27s2LEDY8aMwTXXXINEIuEsBwASiQSuueYa/OpXv8Kf/vQnDBw4EFde\neSWGDh0KAHjhhRfwq1/9Cp2dnejfvz+mTJlS1QWs8rFCofBjT/NxWC5+//33cdVVV5n7pk+fjgED\nBuCXv/ylOTZv3jwcddRRAZOOanBxLPdxxTgqgcO+OBOAO86lFK+QznUwtRl3yCCQitBWRxfUGPGg\nOo7UKtycgVRNXC3J6yWITnGFOozTCHMmSAntJTWg5eQlqH/8E5/H8SSHD676k5wljEqzk+IbB535\nuMqroRDvk2eKGtC3JwBg5JB9zDHKLjWq8Jc7npCqcP3mneYYZZfavNVTH27dvgsAsP1fnkMAqWV3\nteZVkK6shfm2C1n9ssWfJakjuSpWUh2bDHfCSa6e9ccIlTKe8edI80GK92mpmH3zNyU6IgWfM38H\nOn195u012dKEF09SsZKDzMlHjcbM759tnftgy7bA9ZXEgH37lb5IERqHfXFmyRjwUfnYMteQ5mk8\n6CAdho9dXMyvlzLnufiY84Bx8hJMo6RFiIuPxW8RN/eLyMfExYDHxzzTXxg+5pn+iI+JiwGPj7mJ\nTBg+5mYdJqsfM0ORnmUYPrYy3Dn4WIrX7OJjPhekWPjG/FKYvy4+lkwyOoU+S1lvy+XjRf/37cC5\navJxd+bimkmNrVAoui9KJRZQKBQKxccD5eNoqJkFs38nahv9F3+43o5byi7Gdn2FIjKdLJd94ZY4\ngpKMsKCdPr+tM207+AFAZ6ctMf7/7Z17kB3Vde6/85oZCVkiBvEQAiugVIRkiIoAZUBgQh44iSsF\ntoyRLEyCQHEwJHUph7KwkwDJLQzEEgGCXEaYS1FABCZVUM7rD1xYBFdCFYZSwMjGRobwEA9jJGRp\nZs7r/nF67V6793f26WnOaM6M1q9KNaPTfXbv3t39Te+19lqL3bA0PVkp3J5nnRCQzpLrDZVmiKT3\nkb4wS3fDWcvTz6S6014VXPLz0t6gXdn+5s9/CQCYrdLFSRs6SPDtX3T2+8WuNIJWto+p4AcX+JKx\n2mv0GKXBhMFuHnkq3Pmpo8JjsYATdo9kKRFLsJ/+Krw2zmqR3L++JSxsw1UyJAEfrVZqCZP7xp2X\nik1qZLchtKw1SWBN3nvWGAy66XFMi/V+MT0uqyZEj3VQlNyfRbUYSJ810WIg1eN6PbQYx/SYeTt9\nrS6mxyz1pe5HUT0WLdbtaq3Oo8eixUCqx1qrRY91f/PosR9MGOyWtpWzwl2aVjU8VkyLex2/RDQ4\nTQ3bXYuBfHrsVTKM6LHW1jx6nHephelxMQbmhdkwjJnLVK+ZMwzDMDqYHhdjYF6YsxewrWZ22fRZ\ngFo7RNYfud2UpYtZxmolYl2IzJLdbFMnu3eJztVHxGohpdvdLJwk2S/3sC6471ILnvwM1zyxtbsg\nz0uaDifcpnHJ5cn6QH18Wecma+B0URM5l/f3plYLsWCMqnWK+0brQbvMciDIrF6n42EWB7buMOam\nyhaI0W2wdXl6gLOzeZrWSq9VS+4vXeAhZm1rsIuZJMBpt9JxkPWU2kIi5++vc5fvJmsMm+HY7xvz\nI5V7YRaN6UU3PWbps7x19Tn0uEGsZqLFgHoWiBdME+ixt9Y3+Yg8m2Vtzc6hxzEtBorrsV672089\nZuOrY0Dy6LG2JoseixbrdmNaDIR6zHScxeT0WjKQ1WPdBotZcSkviQ7F9Lil/taLQvbyfOTRY722\nXZ4f/cyl69zTe8/0eOoZmBdmwzBmLi0zaBiGYQwEpsfF6GseZsMwDMMwDMOYaQyMhVncSFUW2NYM\nXX8Cc5e1SHAHQ1wgXlW2lr+NpSnTiFtLBzOUIoEvacqkdBsLxJPt2v2TrXMP6KUrYWoaV9NetSEB\nJ9olJIEQ6TKFttrW6Qdb4pDW+QNK5c75792XnpekHNqjll0Icg4slc64imiRa8jdVdJW2q5LQaT6\n2yAuNBlzGlRBYK4/oeKlGUr2V/sxd6TrWzM8P+bWZn2U6yt90hWovPUccqxW6JZkFaLk3kj7lI5l\nM/mVLeuQwKEmMV8MSPZKIyeNZstfaiTXT2lDP/VY3//uGWuF22N6rJcYiA7oYNqieqy3pW7xMNVb\n21ue0FuPdTC2PHM6KK2oHosWA6ke6/RvefRY/z0TPdbXjy/lkrbSdrN6HNNi3W6vJQN59LiiNFCu\nJRs3TVaPY1qs29B/p/PosV6OI22w6qn673QePdbPUV1HhmYwPS7GwLwwG4Yxc+k18TQMwzD2D6bH\nxRiYF+ZmdmanZutiOdCzuOFZnc/YAn8346/oWVSyOF4XiEhmtnrWWZe69aQePA9oIYEvri/dLRra\nEsMsHzGYVZQV0Uhn89p6GVpWpZ+S7kk/TCwIg83M9/wysS4qa4hYDliREBbIKcnq9ZizaN5sERpt\nDckb9CfnE0szpMfSXWeEFisW+KLn9nKflYg1ud0O20gLh6j2iOUlvbzdrQXaAsSolplHx7eu6Hu7\nlmSi0tYQGX+XdqlMrIyW93Na0Wy2fK9Hcm21FVeeXdFiIK8ep/eO6HFNWRnl2RUt1u3F9Fjfwy4o\n3LvvJleP2bMZ02PRYt2G7mNRPRYt7uyfXCM1vnn0eLwRXvuYFut+xPSYBf3pc4ml4Izpsb5mMT3W\n91kePfbHGcH+2b+/XTufENPjapl5dELPY0yP9djHAjJNj4sxMC/MhmHMXMwFaBiGMRiYHhfDXpgN\nw5h0LI2RYRjGYGBLMooxcC/Mqacg/APLgkxi21iN+lnD4Sl7QVtBf0IXkiZvMEG2AhZb4M+WfNRb\n4cJ97VbKula8/kZeUrzjZ46rXXWxYI02cQtW6+F1iLnjGE2ErkJNdnzZcoYGcWV5LtMkIKKt8lyW\nItehEll+wfrGAoWca1Ptnx4z2qxr18vBGckZLuj93Z1f0W7iUDiZO9J91VWs0ufXuR8lOKhNnl1j\n+uFf/v7psdaNmB4z53VMQ/IG2urv9VOPe1UczaPH7JgT1WN9zH7qcR4tzraV1eMyWVbRUsFposel\nHksh8+gxW6LTJkuFJqrHul3RV1Y5lyH7e3d9oqkxLe7WblaP26pi63gk6M8oxsC9MBuGMfMwi4Zh\nGMZgYB6/YgzMC7NOvwMA1VpoX6jVwoX9MiPUa3JYgAibJTtLGgkuSYMP9P7JPupek8/0Yn6ZzbIU\nYGKNqCgrH6uYpdMAuWORFGCVmj9z7jXjzx4TAFqlzu8SzKCrBsl18asWigVSW2qSWbJ6MZIZLgsa\nkXHQaXMYzIqqA/V0+4AK8CNBaawin25XrokEl2hLvvRTtxt7CfT6JFUe22HQRlqBKrxGVWJZGaoS\ni0rONHQSbEUDb7TVpOUHX7LxZUYUuadYJaxe1bGMwWIieuxVv8yhx8yD5N9joebk0WP9GIge62eN\npQDLo8cxLdZ9r9RCr9JE9Vi0GCiux1ofRaO0tbGoHjMralaLdftAqMc6KI0FiLbc2Ku/LTn0uNeE\n3OmcrvI4pXpMxoiMm7Y6F9VjhulxMQbmhdkwjJmL6bNhGMZgYHpcDHthNgxj0rElGYZhGIOB6XEx\nBuaFuZJxz/jV06SKjV7i0HtBO3OHea4It2A+/ahc8d002kGVVkNKj82DD5LADNXF6pAfRcBc9hpx\nd3ru85Z/TI203yCVuFi1wIZahoLM0gntjqUPVvKRDowYGxdXFtm95bvlgDQHpj6XNK9x+t3UZZt+\nlq1qxJYY6P1dYA+pxKVheS7DbbqN0KWYHlPv1/ldKnHpMZVjDefM/aqPlT3/Wilsg+W51ktT2qXQ\npSdjJ67zlhpMeQb1OeQRX3MBTi8qlXJmuVLZ+wmk90IeLQZ66LF6XuR2K6vP8uixlwvfPeuq4mfy\na1aL/f1DPdZLAZ37XN3yRfXYe4ZEj8nSiYnqsWhxp79k9xx67Oc1TvYph9eIVfyM6bFuN9Xj7lqc\nPUZ2e6rH4XI7TarHabv91GOWmzmmxzrPtYxJWy3HYcuM8uhx3hdh0+NiTCxDu2EYhmEYhmEcYAyM\nhTk766XVdJrhzLVUDqvDsaAKl86lomdnoZVTiFV78irtJMEHbZLCRteyl2Ox1DwCCxJk2z0ri0uZ\n5v8fAJr18PzYDJQFfrljSiCX2iY2EG2xzZMIvVoJA0q8KkutMDBzqBpatvJE+OrhK5H7IWulBtLZ\nfI0EOMk2L2CoSqr0uSCM8FpmUwsCPEA120cgtaLpvkk7LH0QS+OEJrHkkGtfdfdvyet/Z3//e3r/\nsSRdFTmMRWVPM1qttq8lrJpdM7x38+ixl+qwEup9UT3WAWuiR76n0j+XTn9763FMi4FUj/2UaeFn\nWT2OaXH2GO6zHHqctyhFTI9ZFcChqh5LUiE0gjQX02J9fH1Z8uixrtTINI1dy6J6rD3M0jfdRi49\njmix7m/Vu39767Hef6ze3fNjelyMgXlhNgxj5mKlWA3DMAYD0+Ni2AuzYRiTTpMk5TcMwzD2P6bH\nxRiYF2ZxVUiuQ+0qozktMwEUOrdnyy3rCAOkmspLkbqCVD7Kql+1iVUZ8tyNLr9k6P7Qrr9swFW9\npRf9+8squh6L5MiUqk3MbSa5SHW74kJqEneguFiZ24y5D5krS+83lBxL2mt4bqjQ9efcfA29X+cW\nHSbXoUFcwtn7KNmh0y4JRmmo/Vxuz5Lk/QzdfOxYbLbuufRcjupm1/17VRpjeaDLVf+7rNqkuIEB\nfr+33bmm91Z2qFlgZqkRHkvGnKRmNYvGNKNcLnnPkNxjrLon1P2RR4+19oges1y4eolFHj32c693\n12MWcDVRPY5pMZBPj/Uz10891s+a7DekjpVHj70lcE6P09eFwnpMKv3p/UWP9bXPo8e9Ku25Y3g5\nqnvrMTsXr2+yXzX8bkyP2f3eVseSe4OtGIrpsbe0LrbcyPS4EAPzwmwYxszF9NkwDGMwMD0uxsC8\nMFezVkA1+x0jV1dmzvV6uNg9ag1RlNqS4kXPyP3gQDab023pKkxCmjYotHAzWMl3NzOv6Bl5ss2b\nOPrBa/qYzAIqFo9xhPB0PIkln1TuapHxLbdD6wa3AHUfDzbmY6rD8hkLpGQBQzLT94OTOr/PUpaX\nNKUSsS4QSyyrStWodypZsaAZZ7kiQSya9F5RFhViLcgjet59T9pleXLcM5D8vzoU9reqcgqOtjrn\nLOcZC6QypgfVcilz73R+j2kxkE+PY1oMpHrMggNjehzTYt1eTIs7+/n/188esz6nm8PgtZgea+tz\nP/XY86RJxTgvuLmYHnsBZeMIPsujx9rqmgbup/vPcpUX9XXurcesYqtoMcADyvPosX+vJEHeBbW4\ns5+834TtxrQYyKfHosWA6fBkMDAvzIZhzFx6vaQYhmEY+wfT42IMzAtz1urgr6/qPn0rZy3T4Clh\nYmlUvLVybkbe+b/OvDM23kAWmVkyaytbw5S1uupz8GbjLe+H14b+7kRv/NRK3b0NP50Ugr6x9EzI\n0Y1xNR6xlH4Vz5LRCD6rkxQ6sTaYpcalf1PbhklBA6GRrHEsN8N7S68xlJl+Qw2IrN2UY7G1316a\nKnJe7L7JpkrqlSpItmtrHrtuYnkZHurIA11DSYpVuNRjbLzNBzit6K7H8euYR4973acujsTzVnV+\nxvRY32LsuRI9bkW8YPoc3HNIZI6dX1Et7tXGhPU4Zzcmqsd6vF2KNZJekpEtTKZpEWt5TIuBfHqs\nvWCixzquKY8ex7QY4Cns8uix3ub0OKLFQHE9psc3PS7EwLwwG4Yxc7G8n4ZhGIOB6XEx7IXZMIxJ\nxywahmEYg4HpcTEG7oU5b2CGUC6HadLqkranGrpTvKpNZGlBNlVSvRHvh9x4uvoPqy+fppWT9DbK\nDUTS8TSJ65NVbxM3owQ/aHc7a5f1TdptZn4CfOmCuLDKZClLXlw6HpJOjQVaaH9VVVxTZVk6EAZB\n6Oss7eklMiyQRZB7sNEM3VxtbwlHpx86AMcFa6oIDrkN2ZIauW90oBELKpHTp/eqa0ttI/el9Les\nryUZu2wbyrOpgm3DfrggHtKWrZmbfuQNWtYU1mNSFZWlEY3psdYoue9bRAf8tHK99TimxUD6vGp9\nyaPHrG+63aJ6XMR6mNVjvmQvTIUmWgzk02PvvkjGiwVyavLo8bDqh+ixF6yZ9F3fgnn0OKbF+nzY\nmMf0WPdX9DimxbqNmB73Wpqa7bcxMeILhQzDMAzDMAzjAGdgLMwyi5TZYa+ZEp35uf27H4elvNHt\nyxwvlrxeWxKy1lnd9wapF8/7K4nkQ6sFSyCvi1Fkk6TTpO0kqTuzfmf30ej95RrpYMkaSeQv+5VI\nYITYIXsGaDqrepjOrVYNgzucBUZNBV2ACgly8QJOMlYCFvDRauvE92HAUM2lRQq+GqVUVuPrgkbi\n38nee+y+1NZvZikqkZyGcg5iDSl5QUfh/ZPHvWdr5qYXjWbbT11GrHFCTIs73+m+rUR0To6hH6E8\nesyss36AXzE9LpO/Gbq4kuhxiXj+Ynrcy/otTFSPY1oMfAA99v4+henc8uixF7ydjBG1oBO9iOkx\nC8SreSlDg69GET32U9N235/dezE9Zl7UmBYD+fQ471IL0+NiDMwLs2EYMxcTaMMwjMHA9LgY9sJs\nGMak08xp3TMMwzAmF9PjYgzMC3M7M+Nh+Wapu0jc1+ozcd9pdwoL8orljZxo5R42Y+tV3z62f4O4\nWFI3o/pO5ritRhjY1lIPh4yJdv25ZTCuQmHctVkphRUPQYII5Hykjdh4A6mrsEzch1UdwJGtHkXO\nuUnOmV2jYeWrC5dYxAMjJIiJBS6ya8lydrpr5AU4JcFzJD+pvjasoqTrhxv7dJvkXS171yqUAOf6\nlHNQbTSc6zh0H8o91bA8zNOebnrcM999Dj1mQV69tGGiVS1jepz3XpT9G2T5kb+MINmfHDOmx/rv\nkzw7rILfhPW4Zy7n3nqsc/3Kefn55rvnwo/pca/qu6LHbIlFTI91QCkLXGTXMpceqzzIpczfSd0e\nqyipyeqxzoGd6nFEizsd7vycoB4zTI+LMTAvzIZhzFzMBWgYhjEYmB4XY2BemJ0lMZnR6ZQsDQly\n0ylhklmWzHD17FdmeEPV0AJCAwe8FD1+cIleuO/2p4FUenvwEYaSGbALNCSRMHpGLOfVIO2z9GjS\nHkvdpK0A9aQikG/9To6f7DakAuwaOa0y7AHMplEaJ2Op+yHBKiViGdDBD6Wyf6/oIIhYsIhO5cMC\nhcRKUG+3vP50a1fuUeYNaRErAwsiEligzlg9razlvCzeufrteJZuhNdNrFdeSjixCOo0R5lKiqwy\nGrMUOeuYifG0p1QqeVXG5F5vqHtH9Fjf6/3UY338PHo8US3Wx4/psdbPmB57gYs59LiuqrOl1m91\n/IJ6HNNiIJ8eaz0qEa+ZS5vnef5667EeI9FjGhynUwTm0GP9vsAsx/3UY8/L4ipbxoNhs3qsPQku\nJZzWVEkBSt4JYnrsVRw0He47A/PCbBjGzKVtLkDDMIyBwPS4GPbCbBjGpMMsc4ZhGMb+x/S4GAPz\nwpzmX07cOsoVMVQLcz4Ksh/LM6mDj0rlxB2tFsezfI3yFTmWdi+1SRAGg7nhhqqhCysLy23bK5gg\nDdgL22DuzvQz7fvzx4tVkdJJUWV86yTQgLpFyTVK9w9dhX5OVn+b7tPIcC3oRyxAxHPpRQIB2bjF\nqiZp2PnHqgqKW7vXdWMWgWyAoR7LtCqU7re4BUnFLDUOY5n8qKw/+tnKBic1WdCfuQenFe1223Mz\nyzM/VAufIU0ePRYtBtJnl+Uy180X1eOYFmfby8Jy28YCbf2APb8NIHyO/P/Luji1lGUK9dirTeDq\nFaT7seUqRfXYW4rQRz2OaTGQT48nqsVAXj3WSyK76/EYqR0Q02MWkMgwPS7GwLwwG4Yxc7H1dIZh\nGIOB6XExBuaFOZtGjaVk8xbxZz7TMysWuCCzwrIKPGHWhTzpVvTMlKZ6I/XttRUbACoqoEVmp2Pj\n6WxSZunaypOOSfd0QN5Ml8zCpR+6bzKGLKAmVjGr7AX2hNaYbFo5FhTj9T0TzAek15BZneWhZ0F3\nvdZosaBKCS6pSNWrHveHjBczUul7TzLXseAkZi0YG28E50ADeTL3fl7rgr4XpZ96zGM4y5I66fHM\n8ZkWt8wDOK1oNFv0WWbWwF6fZfVY39eix0W1GEifZZrqTbUhmpfVYiCux9piKnrsa0MxPdb9kL61\n2qG+aPLoMUuDx9LKxfRY/1+0wde00OpcVI9ZUKVoMZBPj/VYxfRYV/zLo8eixfocYlqs+zRRPdbj\nW1SPx1thPxh5q14aPvHkl4ZhGIZhGIZxgDMwFmbDMGYuFmRiGIYxGFjhkmIMzAtz4CInf18991bG\nZdFC6IrQrpbx5Gelkn5WTQzsrVL3m8dzL0kBNNU3l+OXuNaHh8LgEhYswfKONpuhS6aaRsC5z7IV\nolj1vbLK48mWaUg/XZ5HvewgcUdq15TgLWcgASfZa+ivmwrHQQJ0fNdUmCdVuifH91yFYS/cONTJ\nS5u3PKHmu4xZVbNGW7t9O9t1RSUZS7akiAfZdK/oqNt1rspG6G5lblqWs5MtQ8nj+utV3c21kbiV\nqyTQytbMTS+qlbI/yYnocVaLO7t31+NxtZ/ocVU5O4vqsZfjN6LHevlHHj2OanGnEQBdqqdG9Dim\nxbpPE9XjPFqs+9jBHwcdLJku2wqXcOj3rlx6rP9mRPRY63IePdbb5DrosWRLivLosfcOIbnIvb97\n3fU7psdsGUreZRgxPfbaGOr+emd6XIyBeWE2DGPmYhYNwzCMwcD0uBjT4oVZZocsQEpmc8OVarC/\nnh2yNC2CV18+E6DiVc4hM90hFpRG+usCXkhQjKAtH1mLdKfBMPihQgJestvYuFVJkIuYA1jfdBtj\n4/XgmGzCWs4Ei7Ax8oKDiLWHBQNVXDXGJODDC8rpHmSirRbSbMkLhknukWYj6Ju2mAmuUhSxFPlB\nT52f40k/Wd/0R/tG68F2V4WwHgaGZvsD8EpVcn39ilnhbjIkkqpIi0QrvJVoG1lsScbMgGmPJo8e\nsxSGGlaBLY8eD7GgNNJfLxg8hx4zizS0fhK9L6rHvt6ja99iehzTYiCfHrM0njEtBorrsW625O4V\ndY/k0GPvb3hEj8tq6PPocUyLgVSPmVU/qsfqv+m9HTThjU0ePc77Hmx6XAwL+jMMwzAMwzCMCANj\nYc7ObLUloVahK1MB+AVOBJlps21srZFnQUB4/Cx6Bivf1RZbVqgim8ycrUNqE2srs5B4+xGrM+un\nUKt1twpJs/pc6uFSOXcsmuaHjJvs560pY9eGrPurwLd86N+ln3VvLKWtsN8M3Y+xJln6YbICAAAg\nAElEQVT7l8DWpVXIOmGXiqoUrhPOFsXx+lEOLTrjxFKjiVkJxHJWIWvlhsnaNpaKKWtpTjoS7O/G\nvGtvbM3cdKPVatP0YDEtBorrsbbIOu8awuMzpJ8l7xkKrb4sHiKPHrOiFLpdtx+xOrN+CjEt7vTJ\nPxdg/+mxlyZNtMQrthFa5vupxzEtBkI91tdDPvPStJbCdcJ59Fj/DR93qV4npsVAqMd6/37qsf6e\n6XH/GZgXZsMwZi62Zs4wDGMwMD0uhr0wG4Yx6VgpVsMwjMHA9LgYA/PCLBdQXDJeYNIEZ0NsgT+r\neMQW0dczdd29VEVDYbAECzgR16N2e8jxG8S1z9KICdpVJq4/FjQi6XpY39jyB5aeCU3pYzjeehmI\nnJ8+VilJBVUmSxYkRZEXXMGWkrTCvjHSsZN7hZxzK+wHC8TxAhcz16gX8t1elfuqmc/4chS2zCds\nq1wOA0lYkKtrl6RR0imp2NIfwS05Ic9f3XMd+9v5OE+9QO/ZswebNm3Ctm3bMHfuXKxatQorVqwI\n9nv88cexadMmDA8Pu8++/OUvY+nSpQCAa6+9Fi+++CIqyfKEQw45BBs3bgQA/PjHP8aWLVuwY8cO\nlMtlLF26FJdccgkOPvhgAMB3vvMd/Md//Ad2796NkZERnH766bjoootQLg9WOEmr7S/JcIFJBSxT\nWT1mVQC1m1n0OKvFQFyPWTC2XgbCAvyK6nGb6ItHQT32grgK6nFJpeUrkyULufRYnXpMj/1nv7ce\n9wqMdIGLOavksbZYJUOBampEj0tkqZz33XKYSi+PHnuBr4kes6U/mjx6nPdFeKr1OK8Wv/LKK7j3\n3nvx0ksvYc+ePdiyZYvb1mg0cOedd+K5557Dnj17cPjhh2P16tVYvnw5gMnR4gm/MMc6+dZbb+HK\nK6/0/tCcd955+NSnPjXRwxiGMYMYBIvG5s2bUavVsHnzZuzYsQNf+9rXsGjRIixcuDDYd8mSJbju\nuutoO6VSCWvXrsU555wTbNu7dy9+93d/F8uXL0e5XMZdd92FO+64A9dccw0A4JRTTsHZZ5+NOXPm\nYM+ePdiwYQP+9V//FZ/85CcLnZPpsWEYE2Wq9TivFlerVZx++uk499xzcfPNN3vbms0mDj30UFx3\n3XU49NBD8YMf/AAbN27E3//932P+/PmTosUTfmHu1smvf/3rbp977rmHBnjEcJZlkmomtjjffd+b\nkSUz6B43hbP6qs8kIX3LzYjD77GZJrNkaOSzmPVCz0glXY2e4Q5lLIqd/vlBh71m8C49GekjC8Bx\nwR06nROxlEjAC7MGyGXzkuFnioToNhjsAZev6ksk7elxk+9qSw1La9fKXCMvtiJy7/kW5nC/ZsW3\neFBLgu4bseALw7XQkyEpjZjljlm/vXOR4g80oKfp9xHKIucF+/j9nOizvz8YHR3FU089hQ0bNmB4\neBhLlizBySefjK1bt2L16tXB/kWDYsS6IZx77rnei/fhhx/uHaNUKuHNN98sdCxgcvWYpWHM+0xM\nVI89q2/yUxcHKarHMS0G8umxTuUoujJELIo6xVoePfbSk/VRj7WOFtXjmBZnj59tN6bH+nuix6zI\nE7P4T1SPY1rc2d5bj/W2mB7r5yuPHnueB9lPNV+mwe699Zg9C4PGRLR4wYIFWLBgAXbu3Bm0Mzw8\njM985jPu/yeddBIOO+ww7NixA/Pnz58ULZ7wC3O3Tr700ktYtGiRd3DDMAxg6oNM3njjDVQqFRxx\nxBHus0WLFuH555+n++/YsQNr167FnDlzcNZZZ+H888/3XHX3338/7rvvPixYsACrVq1yyzWyvPDC\nCzj66KO9z/7zP/8Td955J0ZHRzF37lxcfPHFhc/L9NgwjIkylXo8US3Oy3vvvYfXX3+degyB/mjx\nB17DzDp5+eWXo1Qq4YQTTsBFF12ED33oQx/0MIZhTGOYt2N/Mjo6ilmzZnmfjYyMYHR0NNh36dKl\n2LBhA+bPn49XXnkFt9xyCyqVCs477zwAwOc+9zksXLgQ1WoVTz75JG688UbcdNNNnsUCAF5++WU8\n/PDDuPrqq73PV6xYgRUrVmDnzp343ve+h7lz5/btPE2PDcPoxVTq8US0OC+NRgO33XYbzj77bCxY\nsCDY3i8t/kAvzNlOjo6O4oYbbsCiRYvw/vvv46677sKtt96Kr3zlKz3bcrkkXT5BbRERl4VyeREX\nj9BMPiuRNvjSidB9XyIViliFJOYCGU9ceCzozx1b/V8W/TN3lc6Vyc61WnY+9c4PUpWKube0+7Be\n94PXdP5f5+ZjgSrElcZyT7Nrxaor0eqGhLQaYzj2Mm4t5WLVYxg7Bzmu5MXUrthx5h6VylZqP/mu\n7zaT/aUyYHp8lrM7/Uy5A4c77e7dl1aeKpWbXp+Yq07fMuVyGPQkN06DZO10+6nYKxY41c48b8x6\nMdVr5kZGRrBv3z7vs71792JkZCTY97DDDnO/H3PMMVi5ciUeffRR98K8ePFit/3jH/84nnzySTzz\nzDP4xCc+4T7fuXMnbrjhBvzJn/wJlixZQvt0xBFH4Oijj8bmzZvxpS996QOdH9B/PfZzbct/9A3l\nP99AXj0OA9b8pROhXuTR45gW62Ow+zOmx3opl2hJVIuBXHrsL5dKllfVw+C1ieoxWy5V7vG3M6vH\ntLohwa/G2FuPY1qs29PHzKPH+lxkP53f2AWXKi3Lo8f+3/pkmc1w2q7osWix7lNMj0WLAX29VM78\ngnqsn6OYV2kq9XgiWpyHVquF22+/HbVaDWvXrg2291OLC78ws06OjIzg2GOPBQDMmzcPl1xyCf70\nT/8Uo6Oj3mA8//zznvn9ggsuKNoNwzAGlAcffBBA5/neHy5AOR4ALFu2DMuWLXP/P/LII9FsNrFz\n507nCnz55ZcDF103JrKm+e2338bf/u3fYuXKlTjzzDOj+zYajQ+0hlkwPTYMoxtaG/eHHk+mFmva\n7Ta+8Y1vYPfu3Vi/fn2Q4aLfWlzohblXJ9n+muwAAjqoI0wBls6qw7ZZ9TuZWekZ3lC1c6q+FVfa\nD9uToA6vGhJJ9yWWgZZOw5PspysNyfk0XKqg0FKpLRnp+YfVmDzLT/Ifds6SXqihykOJ5YBZeBls\nBp+9VkBatUr3LZsOSV/TBqtkCD+AUR9DfzebTk1Tl7Rr6nq0nfU73F9bUiqVzj0i101fI/luW6V/\na5GgFXd+5P6V75aJJYaNZZU8V3q/oeRncyhShZBUttSkFopgU2A51r+zRz5rzdIvXvvDohF70RsZ\nGcGpp56KLVu24Atf+AJ27NiBp59+Gn/3d38X7PvMM8/gV3/1V3HwwQfjtddew8MPP4zTTjsNQMcS\n8uMf/xhLly5FpVLB97//fbzwwgu45JJLAADvvvsurr/+enziE5/A7/zO7wRtP/bYYzjllFMwd+5c\nvPrqq3jkkUfwG7/xGx/ovCdLj1nAmhcgVVCPRYsBrX1pG9Kud6/n0GPtNZP7WT+b8lx7OpRDj/3n\nRoLSw3Hwtbe3Hmvt6ace6wqCrjocSSMW02Nd1S8WwMjSqWmyetz2rN/d9Vi0GMinxy0S0K1pkPs3\njx7rsYzp8ZD6LI8ex7S4s1+wubAeA6E2TrYe90uLAWB8fByNRueZqdc7Fv1arQYAuPPOO/Haa6/h\nr/7qr9xnwmRocaEX5m6d/MlPfoLZs2fjiCOOwC9/+UvcfffdWLZsWbBexTAMY39z6aWXYtOmTbj0\n0ksxd+5cXHbZZVi4cCHeeecdXHXVVdi4cSMOOeQQPPfcc7jjjjswOjqKgw8+GGeeeaZLxdZoNLBl\nyxa8/vrrKJfLOOqoo3D11Vc7S8ljjz2Gt956Cw899BAeeughAJ0/avfccw8A4Ec/+hH+6Z/+yQWZ\nnHbaabjwwgs/0HmZHhuGMZ3Iq8WSGlNYs2YN5s+fj9tvvx1vv/02HnvsMdRqNaxbt87ts27dOqxY\nsWJStLjUnmD+pLfffhtXXHEFarWaZ8lYt24dSqUSHnjgAezatQuzZ8/GiSeeiDVr1mDevHk92114\ndmd2MXtWR/CZhZmeALGYSoqgXuu2ZNbNLJrMoiFrovTaKGZhlrQyukBE1sKst8n+zMIs49GrT8yi\nIbPZsbpOnNfBTwzvz3r1mjnZT4+bzPi1xSG7Bl23O1QNCwswK3E69uF63rwW5n1jyfrDHpYMoUTa\nkPNj16/e0NapsHCJK/CgzjVbHKRGzk/fU3J9mUVDzk8fd99YPdgm9LIwZz0UGmbRyG7TyLn/wZmL\n8c2/9YXnKxv/Jdi/n/zf//OHk9r+oDKZeqy1h1mYGXn0mK151trHLJp59JhZmOtKy+R5ZhbmmB7r\n/WVMdFo56ZN+hovqMfsbMFE99tdN++fZ6XtvPfbHPlzPm9fCnNXjmBYDxfVYW59Z31jRmDx6rO+3\nmB7reySPHse0GOivHr/6+FeDbZOpxzNZiydsYZ4/f75XbSXLGWecUagjtFpSQmwZAfuMvby5B5G4\nbuKVjNTv5FjyEI2rN2aWS9Kt1ycvWXL8qhcP0X08WJ/SKlrh/vpBl3ybOoCiltwFY+ozQYSopMW1\n5h9bo18oXRAYzdmZCGhJuw+Tlzcimlr8dH7Y7LlIXsxGSwuouCzDQJZZw+lLgQvMkACKVnoc57L0\nqp91+q6X3sRokT+KkldWTxLkejEB1S7CtB/dA2nYvaJfIoTYSz+7zix4lj13aT+mNuhvprK/9bjX\nMoI8euw932RZU1E91joneszyrOuv5dPj+BIXVicgjx5rHRMNq6m/yP3UY/285tFjfQ1iepzVYn0u\nQKjHTU/7Qk0VPdYvwHn0WL/09lOP9T3FjAtF9Vhfq5ges5f+mB7rF2c2gcm2ZUyMgSmNbRjGzGWq\n8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sMOp9Uhw3b1OOgApOx3WUXJ2Plrl1o0lZC6LWVM0kCOMPVeyQsYIscnPtWs64+5D8sk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\nrrgCX/3qV93nr732G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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase 0.609899984593\n", + "Volume fraction of white phase 0.390099990145\n" + ] + } + ], + "source": [ + "correlations = [('black', 'black'), ('white', 'white')]\n", + "draw_autocorrelations(X_auto[10], autocorrelations=correlations)\n", + "\n", + "center = (X_auto.shape[2]) / 2\n", + "print 'Volume fraction of black phase', X_auto[0, center, center, 0]\n", + "print 'Volume fraction of white phase', X_auto[0, center, center, 1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's also plot 2-point statistics of the final microstructure for the same simulation. They do not look much interesting since it's mostly has black state:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bxcdcNuLjKFxcvNY+H0v5wa3fdfp/IeyC1FE+5u3WCm6ocflY0Xkomw2zQqFQ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\nJm67UhJfqewG50v9ICuAbVEZ+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Volume fraction of black phase 0.954199975895\n", + "Volume fraction of white phase 0.045799998843\n" + ] + } + ], + "source": [ + "draw_autocorrelations(X_auto[200], autocorrelations=correlations)\n", + "print 'Volume fraction of black phase', X_auto[200, center, center, 0]\n", + "print 'Volume fraction of white phase', X_auto[200, center, center, 1]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "###Reduced-order representations (PCA)\n", + "\n", + "Using `MKSStructureAnalysis` we can perform 2-points statistics and dimentionality reduction (PCA) right after. So we are not going to use whatever we have done in the previous section, it was just to show how 2-point statistics look like for our data.\n", + "So, total we have 5 simulations and they already have been concatenated into X_con." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(1005, 5)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pymks import MKSStructureAnalysis\n", + "\n", + "analyzer = MKSStructureAnalysis(basis=p_basis, periodic_axes=[0,1])\n", + "XY_PCA=analyzer.fit_transform(X_con)\n", + "XY_PCA.shape\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "R1 and R2 are two different simulation results with the same initial microstructure, but different seeds for random number generation for Monte-Carlo simulations. The hope is to see that the same initial microstructure will take two different paths and will end up in quite the same spot. Let's check it!\n", + "\n", + "So let's take a look at PCA plot:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ZsgQvLy/y8vLYtGkTL7zwgjJXR6VSUVpaSk1NDYmJiTQ2NpKens5f/vIXeYIohBADmAQK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\nQkRbyAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_components_scatter\n", + "\n", + "\n", + "draw_components_scatter([XY_PCA[0:201, :3], XY_PCA[201:402, :3], XY_PCA[402:603, :3],\n", + " XY_PCA[603:804, :3], XY_PCA[804:1005, :3]],\n", + " ['ising 50%', 'ising 30%', 'ising 10%',\n", + " 'ising 40% run#1', 'ising 40% run#2'], \n", + " view_angles=(30, 100), legend_outside=True, fig_size=(10,8))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Looks cool but not clear! Now, let's plot only initial and final structures." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JEyYwe/ZsAEJCQjhz5gz5+fk2gYKlIajX65Ux3PaG2lg3oPV6vU1j12QyMWjQIP70pz9x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\nWCywWCwoL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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_components_scatter([XY_PCA[:201:200, :3], XY_PCA[201:402:200, :3],\n", + " XY_PCA[402:603:200, :3], XY_PCA[603:804:200, :3],\n", + " XY_PCA[804:1005:200, :3]],\n", + " ['ising 50%', 'ising 30%', 'ising 10%',\n", + " 'ising 40% run#1', 'ising 40% run#2'],\n", + " view_angles=(30, 100), legend_outside=True,\n", + " fig_size=(10,8), title='Initial and Final microstructures')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The clustering on the top left side is final structures, while the spread is initial structures. Note: there are only four initial visible structures since two last runs start with the same initial microstructures. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these five simulations we can tell that different initial microstructures merge into similar ones after Monte-Carlo simulations although they take different paths." + ] + } + ], + "metadata": { + "celltoolbar": "Raw Cell Format", + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/structure_md_2D.ipynb b/notebooks/structure_md_2D.ipynb new file mode 100644 index 00000000..d9a8cfdd --- /dev/null +++ b/notebooks/structure_md_2D.ipynb @@ -0,0 +1,411 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#Phase Transition in Molecular Dynamics Simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Authors\n", + "[Alexander M. Lohse](https://www.linkedin.com/in/alexander-lohse-7b2a0468), Georgia Tech, MSE\n", + "\n", + "[Ross J. Verploegh](https://www.linkedin.com/in/rossverploegh), Georgia Tech, ChBE" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Introduction\n", + "\n", + "Classical molecular mechanics (MM) is a powerful tool in materials engineering. Some examples where molecular dynamics (MD) has been applied include large simulations of protein folding for drug design, diffusion of gas molecules through organic/inorganic nanoporous materials, and phase separation of polymers. Sometimes, however, analyzing the trajectory data (e.g. atom types and atomic coordinates) with respect to some 1-D observable, such as geometric distances or density, does not capture all the important information. The tools within PyMKS allow for analysis of MD trajectory data in an unbiased way.\n", + "\n", + "This example uses `MKSStructureAnalysis` to look at loading-induced thermodynamic transition of a metal organic framework (ZIF-8) from low to high loading configurations. This example is particularly interesting because it is not clear what 1-D or 2-D reaction coordinate could be used to describe this transition. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Molecular Dynamics Simulation of a Thermodynamic Phase Transition\n", + "\n", + "A molecular dynamics simulation was performed using [LAMMPS Molecular Dynamics Simulator](http://lammps.sandia.gov/). The flexible ZIF-8 framework and nitrogen gas molecules were described using the force fields described in [this manuscript](https://dx.doi.org/10.1021/ja401129h) [1]. For those familiar with the details of MD simulations, this simulation was performed in the NVT-ensemble with a 1.0 femtosecond timestep. The simulation was run for a total of 35 picoseconds with snapshots taken every 15 femtoseconds (total snapshots in trajectory: 2332). " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data Generation: Mapping MD Data to a Grid\n", + "\n", + "For use within PyMKS, atomic data needs to be mapped to a 2-D or 3-D grid such that 2-point statistics can be calculated.\n", + "\n", + "For our example, only the hydrogen coordinates from the ZIF-8 framework are mapped to a fixed 70x70x70 voxel 3-D grid, with a resolution of 0.5 A. For ZIF-8, there are hydrogens in two types of chemical environments: methyl-group hydrogens and those on the imidazole ring. Each grid space is either assigned a 0 (void space=no hydrogen), a 1 (filled by a methyl hydrogen), or a 2 (filled by an imidazole hydrogen). The 3-D grid is then mapped into a 2-D array by summing in the z-direction for visual purposes only; the following analysis of this data could have been applied to the full 3-D data as well." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below cells just loads the necessary extensions to python for plotting (MatPlotLib) and numerical analysis (NumPy). The data is loaded from a Georgia Tech server." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline\n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "from pymks_share import DataManager\n", + "\n", + "\n", + "manager = DataManager('pymks.me.gatech.edu')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "X = manager.fetch_data('Molecular Dynamics')\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As mentioned, there are 2332 snapshots at 15 femtosecond time intervals on a 70 x 70 pixel 2-D grid." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2332, 70, 70)\n" + ] + } + ], + "source": [ + "print X.shape\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can draw what the structures look like with draw_microstructures. From left to right the structure is changing as the system goes through a phase transition. Do you notice a difference?" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_microstructures\n", + "sample_size = 10\n", + "X_examples = X[::sample_size]\n", + "time = np.arange(X_examples.shape[0])[:] * sample_size * 15\n", + "\n", + "draw_microstructures((X_examples[::40]))\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup the model\n", + "\n", + "Now that we have microstructures we use `MKSStructureAnalysis`. We use the `PrimitiveBasis` because we will only have two states, voxel with Hydrogen or a voxel with no Hydrogen. Our data domain ranges from 0 to 255 corresponding to the grayscale (0 is black, 255 is white) as seen in the microstructures drawn above). Basically, we are reducing this from 255 (grayscale) states to a probability between 0 and 1. The statistics are then run on this basis we pass to `MKSStructureAnalysis`. As printed in the output, we are doing an autocorrelation from Hydrogen to Hydrogen (1,1) and a cross-correlation from Hydrogen to Void-Space (1,0). Change the corelations and rerun to see how the PCA plots further down change. Especially, note the increase in variance of PC1 with the addition of the (0,0) correlation." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[(1, 1)]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pymks import MKSStructureAnalysis, PrimitiveBasis\n", + "from sklearn.decomposition import RandomizedPCA\n", + "\n", + "\n", + "prim_basis = PrimitiveBasis(2, domain=[0, 255])\n", + "analysis = MKSStructureAnalysis(basis=prim_basis,correlations=[(1, 1)],\n", + " store_correlations=True, periodic_axes=[0,1])\n", + "analysis.correlations\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Polynomial Order\n", + "\n", + "Now that our model is setup we can calculate the statistics and regression with `analysis.fit`. Here we fit the model built above with a subset of our data, `X_examples`. We graph the variance versus the number of components in our PCA model. By default the `PrimitiveBasis` uses a polynomial degree of one." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "analysis.n_components = 40\n", + "analysis.fit(X_examples)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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MWO+O/u8eB7kzWgjRVFVaHMLCwggLC2P8+PEcOXKEHTt2sGvXLjZt2oSPjw/9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\nSds8PT2ZOXMmR48eNZlF8+abb+bOO+8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_component_variance\n", + "\n", + "\n", + "draw_component_variance(analysis.dimension_reducer.explained_variance_ratio_)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Thee graph above shows that over 92% of the statistics are captured with just 2 components." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting components\n", + "\n", + "We now can plot the principle components versus time. We see a distinct correlation beteen PC1 and PC2 and note that PC1 is correlated with time. This likely indicates the time of the phase transition in the simulation. We could next try plotting PC1 vs. various geometric order parameters to see if PC1 (or any other principal component) relates." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [ + { + "data": { + "image/png": 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KqaGT9Y6bHT6T2eEzm5x74/5tBpfttsXua/bYxoiJP8aHe76gZJIj2o/sxuyM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\nAABTogQBAABT+gMEierLFZ4pXAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_components_scatter\n", + "from pymks.tools import draw_evolution\n", + "\n", + "\n", + "analysis.fit(X_examples)\n", + "pcs_5 = analysis.reduced_fit_data[:, :5].T.reshape((-1))\n", + "times_5 = np.tile(time, 5)\n", + "\n", + "results = np.concatenate((times_5[:, None], pcs_5[:, None]), axis=1)\n", + "\n", + "labels = ['Component 1', 'Component 2',\n", + " 'Component 3', 'Component 4', 'Component 5']\n", + "draw_evolution(np.array_split(results, 5), labels)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we plot the first 3 principal components and note two general clusterings. This view captures roughly 95% of the variance in our data." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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AQECAy/PtCQsL4+WXX+aWW26hsbGRixcvEhkZyQ033AAge8Vqa2tFARyB37gm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\nhf6hhx7i3XffxcvLy6TzHY2o0iboLfqVpS79wzd165j24GJ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27FgAOjo67BZTIxA4A/0q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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "draw_components_scatter([analysis.reduced_fit_data[:, :3]],\n", + " ['X_example'], legend_outside=True)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Let's take a look at the two point statistics. We look at the images for principal components 1-4. Each image is the correlation for (1,1), or the Hydrogen probability. " + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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//3lsv/7+fq1du1bLly/XqlWrNG/ePK1atUoXXHCB1q1bp/7+E3urPRn+wM9x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\nH7YV9iEemz7BfsTEzIJ4OR7ssO3utb0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aaB1whyPeSlsO1ZOtZ5Zz40SuUJ6b4zrHYvpb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\nk7jP0MXUf6Al9rmvodEeu7HFpOm7ojOHDCJ2hOVIjyHrnm2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uuyPmtPaWWMcc131+Zmxw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fXEsqx199H0yTEfckzjdg7zuLcOx/tLPz5yvsJysW/zunkfxNjhfNa7fNmXV9XEWGA+pEc24cTF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\nSUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from pymks.tools import draw_correlations\n", + "from pymks.tools import draw_components\n", + "\n", + "\n", + "pcs = analysis.components_\n", + "draw_components(pcs[:4], fontsize=20)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "Sweet, the pictures are pretty. Now it's time to try and figure out if they mean anything...TBD." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##References\n", + "\n", + "[1] Zhang, Liling, Zhongqiao Hu, and Jianwen Jiang. \"Sorption-induced structural transition of zeolitic imidazolate framework-8: a hybrid molecular simulation study.\" Journal of the American Chemical Society 135.9 (2013): 3722-3728.[doi:10.1021/ja401129h](https://dx.doi.org/10.1021/ja401129h)\n", + "\n", + "[2] Kalidindi, Surya R., et al. \"Application of data science tools to quantify and distinguish between structures and models in molecular dynamics datasets.\" Nanotechnology 26.34 (2015): 344006. [doi:10.1088/0957-4484/26/34/344006](http://dx.doi.org/10.1088/0957-4484/26/34/344006)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.10" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/tech_overview.ipynb b/notebooks/tech_overview.ipynb index f58811ea..9a4d7808 100644 --- a/notebooks/tech_overview.ipynb +++ b/notebooks/tech_overview.ipynb @@ -8,7 +8,7 @@ "source": [ "# Technical Overview\n", "\n", - "This page provides very high level technical information about microstructure discretization, 2-point statistics, MKS Localization and MKS Homogenization. For more detailed information see the [MINED research group's website](http://mined.gatech.edu/)" + "This page provides very high level technical information about microstructure discretization, 2-point statistics, MKS Localization and MKS Homogenization. For more detailed information, see the [MINED research group's website](http://mined.gatech.edu/)." ] }, { @@ -17,22 +17,22 @@ "source": [ "## Microstructure Discretization\n", "\n", - "The first step in all of the MKS work-flows is to discretize the microstructures. To do this we create a probabilistic description of the microstructure by introducing the continuous local state variable $h$, the local state space $H$ and the microstructure function $m(h, x)$. The local state space $H$ can be thought of as all of the thermodynamic state variables that are needed to uniquely define the material structure at a given location. The local state variable $h$ is one instance of the local state space, or one configuration of state variables. The microstructure function $m(h, x)$ is a probability density function of finding a a local state $h$ at location $x$. For instance let $\\mu(x)$ be a microstructure that we plan to discretize, then $\\mu$ is the expectation of the microstructure function.\n", + "The first step in all of MKS workflows is to discretize microstructures. To do this, we create a probabilistic description of a microstructure by introducing the continuous local state variable $h$, the local state space $H$ and the microstructure function $m(h, x)$. The local state space $H$ can be thought of as all of the thermodynamic state variables that are needed to uniquely define the material structure at a given location. The local state variable $h$ is one instance of the local state space, or one configuration of state variables. The microstructure function $m(h, x)$ is a probability density function of finding a a local state $h$ at location $x$. For instance, let $\\mu(x)$ be a microstructure that we plan to discretize, then $\\mu$ is the expectation of the microstructure function.\n", "\n", "$$ \\mu(x) = \\int_H h m(h, x) dh $$\n", "\n", - "Now we will discretize the microstructure in space by averaging over small cubic domains in the microstructure function. The local state can be discretized using two methods one is to bin the microstructure using the primitive basis $\\Lambda$\n", + "Now, we will discretize the microstructure in space by averaging over small cubic domains in the microstructure function. The local state can be discretized, using two methods: one is to bin the microstructure, using the primitive basis $\\Lambda$\n", "\n", "\n", "$$ \\frac{1}{\\Delta x} \\int_{H} \\int_{s} \\Lambda(h - l) m(h, x) dx dh = m[l, s] $$\n", "\n", - "the other is to using a spectral representation using some orthogonal basis function $\\xi$\n", + "the other is to use a spectral representation using some orthogonal basis function $\\xi$\n", "\n", "$$ \\frac{1}{\\Delta x} \\int_{s} m(h, x) dx dt = \\sum_{l=0}^{L-1} m[l, s] \\xi_l (h) $$\n", "\n", - "In the notation above all of the round brackets are used continuous variables and the square variables are the discrete variables. The variables $s$ and $S$ represent a discrete position and the total volume, while $l$ and $L$ represent the discrete versions of $h$ and $H$. In PyMKS the Legendre polynomials are currently the only orthgonal basis functions available.\n", + "In the notation above, all of the round brackets variables are continuous variables and the square brackets variables are the discrete variables. The variables $s$ and $S$ represent a discrete position and the total volume, while $l$ and $L$ represent the discrete versions of $h$ and $H$. In PyMKS the Legendre polynomials are currently the only orthgonal basis functions available.\n", "\n", - "Either of these two discretization methods used to discretize the microstructure." + "Either of these two discretization methods are used to discretize the microstructure." ] }, { @@ -45,31 +45,31 @@ "\n", "####1-Point Spatial Correlations (or 1-point statistics)\n", "\n", - "n-point spatial correlations provide a way rigorously quantify material structure using statistics. As an introduction n-point spatial correlations, let's first discuss 1-point statistics. 1-point statistics are the probability that a specified local state will be found in any randomly selected spatial bin in a microstructure [1][2][3]. 1-point statistics compute the volume fractions of the local states in the microstructure. 1-point statistics are computed as\n", + "N-point spatial correlations provide a way to rigorously quantify material structure, using statistics. As an introduction to n-point spatial correlations, let's first discuss 1-point statistics. 1-point statistics are the probability that a specified local state will be found in any randomly selected spatial bin in a microstructure [1][2][3]. 1-point statistics compute the volume fractions of the local states in the microstructure. 1-point statistics are computed as\n", "\n", "$$ f[l] = \\frac{1}{S} \\sum_s m[s,l] $$\n", "\n", "In this equation, $f[l]$ is the probability of finding the local state $l$ in any randomly selected spatial bin in the microstructure, $m[s, l]$ is the microstructure function (the digital representation of the microstructure), $S$ is the total number of spatial bins in the microstructure and $s$ refers to a specific spatial bin. \n", "\n", - "While 1-point statistics provide information on the relative amounts of the different local states, it does not provide any information about how those local states are spatially arranged in the microstructure. Therefore, 1-point statistics are a limited set of metrics to describe the structure of materials.\n", + "While 1-point statistics provide information on the relative amounts of the different local states, they do not provide any information about how those local states are spatially arranged in the microstructure. Therefore, 1-point statistics are a limited set of metrics to describe the structure of materials.\n", "\n", "####2-Point Spatial Correlations\n", "\n", "2-point spatial correlations (also known as 2-point statistics) contain information about the fractions of local states as well as the first order information on how the different local states are distributed in the microstructure. \n", "\n", - "2-point statistics can be thought of as the probability of having a vector placed randomly in the microstructure and having one end of the vector be on one specified local state and the other end on another specified local state. This vector could have any length or orientation that the discrete microstructure allows. The equation for 2-point statistics can found below.\n", + "2-point statistics can be thought of as the probability of having a vector placed randomly in the microstructure and having one end of the vector be on one specified local state and the other end on another specified local state. This vector could have any length or orientation that the discrete microstructure allows. The equation for 2-point statistics can be found below.\n", "\n", "$$ f[r \\vert l, l'] = \\frac{1}{S} \\sum_s m[s, l] m[s + r, l'] $$\n", "\n", - "In this equation $ f[r \\vert l, l']$ is the conditional probability of finding the local states $l$ and $l'$ at a distance and orientation away from each other defined by the vector $r$. All other variables are the same as those in the 1-point statistics equation. In the case that we have an eigen microstructure function (it only contains values of 0 or 1) and we are using an indicator basis, the the $r=0$ vector will recover the 1-point statistics. \n", + "In this equation $f[r \\vert l, l']$ is the conditional probability of finding the local states $l$ and $l'$ at a distance and orientation away from each other defined by the vector $r$. All other variables are the same as those in the 1-point statistics equation. In the case that we have an eigen microstructure function (it only contains values of 0 or 1) and we are using an indicator basis, the the $r=0$ vector will recover the 1-point statistics. \n", "\n", - "When the 2 local states are the same $l = l'$, it is referred to as a autocorrelation. If the 2 local states are not the same it is referred to as a cross-correlation. \n", + "When the 2 local states are the same $l = l'$, it is referred to as a *autocorrelation*. If the 2 local states are not the same, it is referred to as a *cross-correlation*. \n", "\n", "####Higher Order Spatial Statistics\n", "\n", - "Higher order spatial statistics are similar to 2-point statistics, in that they can be thought of in terms of conditional probabilities of finding specified local states separated by a prescribed set of vectors. 3-point statistics are the probability of finding three specified local states at the ends of a triangle (defined by 2 vectors) placed randomly in the material structure. 4-point statistics describes the probability of finding 4 local states at 4 locations (defined using 3 vectors) and so on. \n", + "Higher order spatial statistics are similar to 2-point statistics, in that they can be thought of in terms of conditional probabilities of finding specified local states separated by a prescribed set of vectors. 3-point statistics are the probability of finding three specified local states at the ends of a triangle (defined by 2 vectors), placed randomly in the material structure. 4-point statistics describes the probability of finding 4 local states at 4 locations (defined, using 3 vectors) and so on. \n", "\n", - "While higher order statistics are a better metric to quantify the material structure, the 2-point statistics can be computed much faster than higher order spatial statistics, and still provide information about how the local states are distributed. For this reason, only 2-point statistics are implemented into PyMKS. " + "While higher order statistics are a better metric to quantify the material structure, the 2-point statistics can be computed much faster than higher order spatial statistics, and still provide information about how the local states are distributed. For this reason, currently only 2-point statistics are implemented in PyMKS. " ] }, { @@ -78,23 +78,23 @@ "source": [ "##MKS Homogenization\n", "\n", - "Homogenization can be used to determine effective or homogenized properties a material, and provides a way to multiscale from the bottom up. Below is some technical information on the MKS Homogenization work flow. \n", + "*Homogenization* can be used to determine effective or homogenized properties a material, and provides a way to multiscale from the bottom up. Below is some technical information on the MKS Homogenization workflow. \n", "\n", "###2-Point Statistics\n", "\n", - "The first step in MKS homogenization is to compute the 2-point statistics for each of the microstructures in the calibration dataset. For more information about 2-point statistics see the section above.\n", + "The first step in MKS homogenization is to compute the 2-point statistics for each of the microstructures in the calibration dataset. For more information about 2-point statistics, see the section above.\n", "\n", "###Dimensionality Reduction\n", "\n", - "Once we have computed the 2-point statistics for every microstructure in our calibration dataset, we need to determine which of these microstructure features are most important and need to be pass to the higher length scale. We can do this using dimensionality reduction techniques from machine learning. \n", + "Once we have computed the 2-point statistics for every microstructure in our calibration dataset, we need to determine which of these microstructure features are most important and need to be pass to the higher length scale. We can do this, using dimensionality reduction techniques from machine learning. \n", "\n", - "[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is the most common the dimensionality reduction technique used in the MKS Homogenization work flow, and provides an efficient way to find a small number of microstructure descriptors that capture most of the variance in the microstructures.\n", + "[PCA](http://en.wikipedia.org/wiki/Principal_component_analysis) is the most common dimensionality reduction technique used in the MKS Homogenization workflow. PCA provides an efficient way to find a small number of microstructure descriptors that capture most of the variance in microstructures.\n", "\n", "###Regression Model\n", "\n", "Once we have the low dimensional microstructure descriptors, we can use regression methods to map an effective property into the low dimensional space. \n", "\n", - "Multivariate [Polynomial Regression](http://en.wikipedia.org/wiki/Polynomial_regression) has been the most common regression technique used to connect the low dimensional microstructure descriptors to an effective property. \n", + "Multivariate [Polynomial Regression](http://en.wikipedia.org/wiki/Polynomial_regression) has been the most common regression technique, used to connect the low dimensional microstructure descriptors to an effective property. \n", "\n", "An effective property for a new microstructure is predicted by translating into the microstructure into the low dimensional space and using the regression technique." ] @@ -105,15 +105,15 @@ "source": [ "##MKS Localization\n", "\n", - "Localization can be used to determine how an applied boundary condition is locally distributed within a microstructure, and provides method to multiscale from the top down. Below is some technical information on the MKS Localization work flow. \n", + "*Localization* can be used to determine how an applied boundary condition is locally distributed within a microstructure, and provides method to multiscale from the top down. Below is some technical information on the MKS Localization workflow. \n", "\n", "### Influence Coefficients\n", "\n", - "Once the state space is discretized, the relationship between the response field $p$ and microstructure function $m$ can be written as,\n", + "Once the state space is discretized, the relationship between the response field $p$ and microstructure function $m$ can be written as \n", "\n", "$$ p\\left[s\\right] = \\sum_{r=0}^{S-1} \\sum_{l=0}^{L-1} \\alpha\\left[l, r\\right] m \\left[l, s - r\\right] + ...$$\n", "\n", - "where the $\\alpha$ are known as the influence coefficients and describe the relationship between $p$ and $m$. The localization requires periodic boundary conditions." + "where $\\alpha$ are known as the influence coefficients and describe the relationship between $p$ and $m$. The localization requires periodic boundary conditions." ] }, { @@ -122,13 +122,13 @@ "source": [ "### Convolution and FFTs\n", "\n", - "The first order influence coefficients in the MKS Localization equation can efficiency be found using DFTs and linear regression. The time complexity of the convolution is real space is $O(LMN^2)$ while the same results found using FFTs is $O(LMNlogN)$ where $L$ is the number of local states and $M$ is the number of samples. \n", + "The first-order influence coefficients in the MKS Localization equation can be efficiently found, using DFTs and linear regression. The time complexity of the convolution in real space is $O(LMN^2)$, while the same results found, using FFTs, is $O(LMNlogN)$, where $L$ is the number of local states and $M$ is the number of samples. \n", "\n", - "The convolution,\n", + "The convolution \n", "\n", " $$\\sum_{r=0}^{S-1} \\sum_{l=0}^{L-1} \\alpha[l, r] m[l, s - r] $$\n", " \n", - "can be deconvolved in Fourier space using the [circular convolution theorem](http://en.wikipedia.org/wiki/Discrete_Fourier_transform#Circular_convolution_theorem_and_cross-correlation_theorem).\n", + "can be deconvolved in Fourier space, using the [circular convolution theorem](http://en.wikipedia.org/wiki/Discrete_Fourier_transform#Circular_convolution_theorem_and_cross-correlation_theorem).\n", "\n", "If we write $P \\left[k \\right] =\n", " \\mathcal{F}_k \\{ p\\left[s\\right] \\}$, $M\\left[l, k\\right]= \\mathcal{F}_k\n", @@ -147,7 +147,7 @@ "source": [ "##More Information about MKS\n", "\n", - "More information about MKS work-flows can be found on [MINED research group's webite](http://mined.gatech.edu)" + "More information about MKS workflows can be found on [MINED research group's webite](http://mined.gatech.edu)" ] } ], diff --git a/pymks/__init__.py b/pymks/__init__.py index 2e4fab46..89d3edcc 100644 --- a/pymks/__init__.py +++ b/pymks/__init__.py @@ -4,6 +4,7 @@ from .mks_localization_model import MKSLocalizationModel from .bases.primitive import PrimitiveBasis from .bases.legendre import LegendreBasis +from .mks_structure_analysis import MKSStructureAnalysis from .mks_homogenization_model import MKSHomogenizationModel MKSRegressionModel = MKSLocalizationModel DiscreteIndicatorBasis = PrimitiveBasis @@ -36,4 +37,5 @@ def get_version(): 'MKSLocalizationModel', 'PrimitiveBasis', 'LegendreBasis', - 'MKSHomogenizationModel'] + 'MKSHomogenizationModel', + 'MKSStructureAnalysis'] diff --git a/pymks/bases/__init__.py b/pymks/bases/__init__.py index 6d72c8fb..48dd0fd0 100644 --- a/pymks/bases/__init__.py +++ b/pymks/bases/__init__.py @@ -1,7 +1,9 @@ from .primitive import PrimitiveBasis from .legendre import LegendreBasis +from .fourier import FourierBasis +from .gsh import GSHBasis -__all__ = ['PrimitiveBasis', 'LegendreBasis'] +__all__ = ['PrimitiveBasis', 'LegendreBasis', 'FourierBasis', 'GSHBasis'] DiscreteIndicatorBasis = PrimitiveBasis ContinuousIndicatorBasis = PrimitiveBasis diff --git a/pymks/bases/abstract.py b/pymks/bases/abstract.py index 4d60313b..cd48c2e3 100644 --- a/pymks/bases/abstract.py +++ b/pymks/bases/abstract.py @@ -8,18 +8,88 @@ def __init__(self, n_states=2, domain=None): Instantiate a `Basis` Args: - n_states (int): The number of local states + n_states (int, list): The number of local states, or an array of + local states to be used. domain (list, optional): indicate the range of expected values for the microstructure, default is [0, n_states - 1]. """ self.n_states = n_states + if isinstance(self.n_states, int): + self.n_states = np.arange(n_states) if domain is None: - domain = [0, n_states - 1] + domain = [0, max(self.n_states)] self.domain = domain + self._pyfftw = self._module_exists('pyfftw') + self._n_jobs = 1 def check(self, X): if (np.min(X) < self.domain[0]) or (np.max(X) > self.domain[1]): raise RuntimeError("X must be within the specified domain") + def _module_exists(self, module_name): + try: + __import__(module_name) + except ImportError: + return False + else: + return True + def discretize(self, X): raise NotImplementedError + + def _check_shape(self, X_shape, y_shape): + if not len(y_shape) > 1: + raise RuntimeError("The shape of y is incorrect.") + if y_shape != X_shape: + raise RuntimeError("X and y must be the same shape.") + + def _pred_shape(self, X): + """ + Function to describe the expected output shape of a given + microstructure X. + """ + return X.shape + + def _select_slice(self, ijk, s0): + """ + Helper method used to calibrate influence coefficients from in + mks_localization_model to account for redundancies from linearly + dependent local states. + """ + return s0 + + def _reshape_feature(self, X, size): + """ + Helper function used to check the shape of the microstructure, + and change to appropriate shape. + + Args: + X: The microstructure, an `(n_samples, n_x, ...)` shaped array + where `n_samples` is the number of samples and `n_x` is the + patial discretization. + size: the new size of the array + + Returns: + microstructure with shape (n_samples, size) + """ + return X.reshape((X.shape[0],) + size) + + def _reshape_localization_data(self, y, size): + """ + Helper function used to check the shape of the microstructure, + and change to appropriate shape. + + Args: + y: The localization fields, an `(n_samples, n_x, ...)` shaped array + where `n_samples` is the number of samples and `n_x` is thes + patial discretization. + size: the new size of the array + + Returns: + Localization fields with shape (n_samples, size) + """ + return y.reshape((y.shape[0],) + size) + + def _select_axes(self, X): + self._axes = np.arange(X.ndim - 1) + 1 + self._axes_shape = X[0].shape diff --git a/pymks/bases/fourier.py b/pymks/bases/fourier.py new file mode 100644 index 00000000..65ca9f01 --- /dev/null +++ b/pymks/bases/fourier.py @@ -0,0 +1,96 @@ +import numpy as np +from .imag_ffts import _ImagFFTBasis + + +class FourierBasis(_ImagFFTBasis): + + r""" + Discretize a continuous field into `deg` local states using complex + exponentials such that, + + .. math:: + + \frac{1}{\Delta x} \int_s m(h, x) dx = + \sum_{- L / 2}^{L / 2} m[l, s] exp(l*h*I) + + and the local state space :math:`H` is mapped into the orthogonal domain + + .. math:: + + 0 \le H \le 2 \pi + + The mapping of :math:`H` into the domain is done automatically in PyMKS by + using the `domain` key work argument. + + >>> n_states = 3 + >>> X = np.array([[0., np.pi / 3], + ... [2 * np.pi / 3, 2 * np.pi]]) + >>> X_result = np.array(([[[1, 1, 1], + ... [1, np.exp(np.pi / 3 * 1j), + ... np.exp(- np.pi / 3 * 1j)]], + ... [[1, np.exp(2 *np.pi / 3 * 1j), + ... np.exp(- 2 * np.pi / 3 * 1j)], + ... [1, 1, 1]]])) + >>> basis = FourierBasis(n_states, [0., 2 * np.pi]) + >>> assert(np.allclose(basis.discretize(X), X_result)) + + If the microstructure local state values fall outside of the period of + the specified domain, the values will be mapped back into the domain. + + >>> n_states = 2 + >>> X = np.array([[0, 1.5]]) + >>> four_basis = FourierBasis(n_states, domain=[0, 1]) + >>> X_result = np.array([[[1, 1], + ... [1, -1]]]) + >>> assert np.allclose(X_result, four_basis.discretize(X)) + """ + + def __init__(self, n_states=5, domain=None): + + r""" + Instantiate a `FourierBasis` + + Args: + n_states (int, list): The number of local states, or list of local + states to be used. + domain (list, optional): indicate the range of expected values for + the microstructure, default is [0, 2\pi]. + """ + self.n_states = n_states + if isinstance(self.n_states, int): + n_states = ((np.arange(self.n_states + 1) // 2)[1:] * + (-1) ** np.arange(1, self.n_states + 1)) + if domain is None: + domain = [0, 2. * np.pi] + super(FourierBasis, self).__init__(n_states=n_states, domain=domain) + + def discretize(self, X): + """ + Discretize `X`. + + Args: + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization. + Returns: + Float valued field of Fourier series coefficients in the following + order 0, 1, ,-1, 2, -2, with shaped `(n_samples, n_x, ..., + n_states)`. + + >>> X = np.array([[-1, -1./2, 0., 1./2, 1]]) + >>> f_basis = FourierBasis(4, [-1, 1.]) + >>> X_result = np.array([[[1, 1, 1, 1], + ... [1, 1j, -1j, -1], + ... [1, -1, -1, 1], + ... [1, -1j, 1j, -1], + ... [1, 1, 1, 1]]]) + >>> assert(np.allclose(X_result, f_basis.discretize(X))) + + """ + self._select_axes(X) + X_scaled = 2. * np.pi * ((X.astype(float) - self.domain[0]) / + (self.domain[1] - self.domain[0])) + nones = ([None for i in X.shape]) + X_states = np.zeros(X_scaled.shape + (len(self.n_states),)) + X_states[..., :] = np.array(self.n_states)[nones] + return np.exp(X_scaled[..., None] * X_states * 1j) diff --git a/pymks/bases/gsh.py b/pymks/bases/gsh.py new file mode 100644 index 00000000..c20f7e8c --- /dev/null +++ b/pymks/bases/gsh.py @@ -0,0 +1,203 @@ +import numpy as np +from .gsh_functions import hex_eval +from .gsh_functions import cub_eval +from .gsh_functions import tri_eval +from .gsh_functions import hex_basis_info +from .gsh_functions import cub_basis_info +from .gsh_functions import tri_basis_info +from .imag_ffts import _ImagFFTBasis + + +class GSHBasis(_ImagFFTBasis): + + r""" + Discretize a continuous field made up three Euler angles (in radians) used + to represent crystal orientation into continuous local states using the + Generalized Spherical Harmonic (GSH) basis. This basis uses the following + equation to discretize the orientation field. + + .. math:: + + \frac{1}{\Delta x} \int_s m(g, x) dx = + \sum_{l, m, n} m[l, \tilde{m}, n, s] T_l^{\tilde{m}n}(g) + + where the :math:`T_l^{\tilde{m}n}` are GSH basis functions and the + local state space :math:`H` is mapped into the orthogonal, periodic + domain of the GSH functions + + The mapping of :math:`H` into some desired periodic domain is done + automatically in PyMKS by using the `domain` key work argument to + select the desired crystal symmetry. + + >>> X = np.array([[0.1, 0.2, 0.3], + ... [6.5, 2.3, 3.4]]) + >>> gsh_basis = GSHBasis(n_states = [3], domain='hexagonal') + >>> def test_gsh(x): + ... phi = x[:, 1] + ... t915 = np.cos(phi) + ... return 0.15e2 / 0.2e1 * t915 ** 2 - 0.5e1 / 0.2e1 + + >>> assert(np.allclose(np.squeeze(gsh_basis.discretize(X)), test_gsh(X))) + + If you select an invalid crystal symmetry PyMKS will give an error + + >>> gsh_basis = GSHBasis(n_states=[3], domain='squishy') + ... # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + RuntimeError: invalid crystal symmetry + + >>> gsh_basis = GSHBasis(n_states=[3], domain='hex') # doctest: +ELLIPSIS + Traceback (most recent call last): + ... + RuntimeError: invalid crystal symmetry + """ + + def __init__(self, n_states=15, domain=None): + """ + Instantiate a `Basis` + + Args: + n_states (int, array): An array of local states to be used. states + requested. If an integer is provided, all local states up + to that number will be used. + domain (list, optional): indicate the desired crystal symmetry for + the GSH. Valid choices for symmetry are "hexagonal", "cubic" or + "triclinic" if no symmetry is desired (not specifying any + symmetry has the same effect) + """ + if isinstance(n_states, int): + n_states = np.arange(n_states) + if domain is None or domain == 'triclinic': + self.domain = 'triclinic' + elif domain == 'hexagonal': + self.domain = 'hexagonal' + elif domain == 'cubic': + self.domain = 'cubic' + else: + raise RuntimeError("invalid crystal symmetry") + full_indx = self._gsh_basis_info() + super(GSHBasis, self).__init__(n_states=n_states, domain=self.domain) + self.basis_indices = full_indx[self.n_states, :] + + def check(self, X): + """Warns the user if Euler angles apear to be defined in degrees + instead of radians""" + if (np.min(X) < -90.) or (np.max(X) > 90.): + Warning("X may be defined in degrees instead of radians") + if X.shape[-1] != 3: + raise RuntimeError('X must have 3 angles (in radians) in the ' + + 'last dimention') + + def _check_shape(self, X_shape, y_shape): + """ + Checks the shape of the microstructure and response data to + ensure that they are correct. + + Firstly, the response data "y" must have a dimension to index the + microstructure instantiation and at least one dimension to index the + local microstructural information. + + Second, the shape of X and y must me equal except for the last + dimension of X. + + Finally, the length of the final dimension of X must be 3. + This is because we assume that Bunge Euler angles are assigned for + each location in the microstructure + """ + if not len(y_shape) > 1: + raise RuntimeError("The shape of y is incorrect.") + if X_shape[-1] != 3: + raise RuntimeError("X must have 3 continuous local states " + + "(euler angles in radians) in the last axis.") + if y_shape != X_shape[:-1]: + raise RuntimeError("The X and y must have the same number of " + + "samples and microstructure shape.") + + def _pred_shape(self, X): + """ + Function to describe the expected output shape of a given + microstructure X. + """ + _shape = X.shape[:-1] # X has Euler angles, while output is scalar + if len(_shape) < 2: + _shape = (X.shape[0],) + (X.shape[1] / 3,) + return _shape + + def discretize(self, X): + """ + Discretize `X`. + + Args: + X (ND array): The microstructure, an `(n_samples, n_x, ..., 3)` + shaped array where `n_samples` is the number of samples, + `n_x` is the spatial discretization and the last dimension + contains the Bunge Euler angles in radians. + Returns: + Float valued field of of Generalized Spherical Harmonics + coefficients. + + >>> X = np.array([[0.1, 0.2, 0.3], + ... [6.5, 2.3, 3.4]]) + >>> gsh_basis = GSHBasis(n_states = [1]) + >>> def q(x): + ... phi1 = x[:, 0] + ... phi = x[:, 1] + ... phi2 = x[:, 2] + ... x_GSH = ((0.3e1 / 0.2e1) * (0.1e1 + np.cos(phi)) * + ... np.exp((-1*1j) * (phi1 + phi2))) + ... return x_GSH + >>> assert(np.allclose(np.squeeze(gsh_basis.discretize(X)), q(X))) + """ + self.check(X) + self._select_axes(X[..., 0]) + return self._gsh_eval(X) + + def _reshape_feature(self, X, size): + """ + Helper function used to check the shape of the microstructure, + and change to appropriate shape. + + Args: + X: The microstructure, an `(n_samples, n_x, ...)` shaped array + where `n_samples` is the number of samples and `n_x` is thes + patial discretization. + size: the new size of the array + + Returns: + microstructure with shape (n_samples, size) + """ + _shape = (X.shape[0],) + size + (3,) + return X.reshape(_shape) + + def _gsh_basis_info(self): + """ + Returns a an array with the indices used in the GSH functions for a + a specific crystal symmetry. + """ + if self.domain == 'triclinic': + return tri_basis_info() + elif self.domain == 'hexagonal': + return hex_basis_info() + elif self.domain == 'cubic': + return cub_basis_info() + + def _gsh_eval(self, X): + """ + Discretize `X` based on crystal symmetry. + + Args: + X (ND array): The microstructure, an `(n_samples, n_x, ..., 3)` + shaped array where `n_samples` is the number of samples, + `n_x` is the spatial discretization and the last dimension + contains the Bunge Euler angles in radians. + Returns: + Float valued field of of Generalized Spherical Harmonics + coefficients. + """ + if self.domain == 'triclinic': + return tri_eval(X, self.n_states) + elif self.domain == 'hexagonal': + return hex_eval(X, self.n_states) + elif self.domain == 'cubic': + return cub_eval(X, self.n_states) diff --git a/pymks/bases/gsh_functions/__init__.py b/pymks/bases/gsh_functions/__init__.py new file mode 100644 index 00000000..9276f78d --- /dev/null +++ b/pymks/bases/gsh_functions/__init__.py @@ -0,0 +1,9 @@ +from .gsh_hex_tri_L0_16 import gsh_basis_info as hex_basis_info +from .gsh_hex_tri_L0_16 import gsh_eval as hex_eval +from .gsh_cub_tri_L0_16 import gsh_basis_info as cub_basis_info +from .gsh_cub_tri_L0_16 import gsh_eval as cub_eval +from .gsh_tri_tri_L0_13 import gsh_basis_info as tri_basis_info +from .gsh_tri_tri_L0_13 import gsh_eval as tri_eval + +__all__ = ['hex_basis_info', 'hex_eval', 'cub_basis_info', + 'cub_eval', 'tri_basis_info', 'tri_eval'] diff --git a/pymks/bases/gsh_functions/gsh_cub_tri_L0_16.py b/pymks/bases/gsh_functions/gsh_cub_tri_L0_16.py new file mode 100644 index 00000000..21ba7812 --- /dev/null +++ b/pymks/bases/gsh_functions/gsh_cub_tri_L0_16.py @@ -0,0 +1,7074 @@ +import numpy as np + + +def gsh_basis_info(): + + indxvec = np.array([[0, 0, 1], + [4, -4, 1], + [4, -3, 1], + [4, -2, 1], + [4, -1, 1], + [4, 0, 1], + [4, 1, 1], + [4, 2, 1], + [4, 3, 1], + [4, 4, 1], + [6, -6, 1], + [6, -5, 1], + [6, -4, 1], + [6, -3, 1], + [6, -2, 1], + [6, -1, 1], + [6, 0, 1], + [6, 1, 1], + [6, 2, 1], + [6, 3, 1], + [6, 4, 1], + [6, 5, 1], + [6, 6, 1], + [8, -8, 1], + [8, -7, 1], + [8, -6, 1], + [8, -5, 1], + [8, -4, 1], + [8, -3, 1], + [8, -2, 1], + [8, -1, 1], + [8, 0, 1], + [8, 1, 1], + [8, 2, 1], + [8, 3, 1], + [8, 4, 1], + [8, 5, 1], + [8, 6, 1], + [8, 7, 1], + [8, 8, 1], + [9, -9, 1], + [9, -8, 1], + [9, -7, 1], + [9, -6, 1], + [9, -5, 1], + [9, -4, 1], + [9, -3, 1], + [9, -2, 1], + [9, -1, 1], + [9, 0, 1], + [9, 1, 1], + [9, 2, 1], + [9, 3, 1], + [9, 4, 1], + [9, 5, 1], + [9, 6, 1], + [9, 7, 1], + [9, 8, 1], + [9, 9, 1], + [10, -10, 1], + [10, -9, 1], + [10, -8, 1], + [10, -7, 1], + [10, -6, 1], + [10, -5, 1], + [10, -4, 1], + [10, -3, 1], + [10, -2, 1], + [10, -1, 1], + [10, 0, 1], + [10, 1, 1], + [10, 2, 1], + [10, 3, 1], + [10, 4, 1], + [10, 5, 1], + [10, 6, 1], + [10, 7, 1], + [10, 8, 1], + [10, 9, 1], + [10, 10, 1], + [12, -12, 1], + [12, -12, 2], + [12, -11, 1], + [12, -11, 2], + [12, -10, 1], + [12, -10, 2], + [12, -9, 1], + [12, -9, 2], + [12, -8, 1], + [12, -8, 2], + [12, -7, 1], + [12, -7, 2], + [12, -6, 1], + [12, -6, 2], + [12, -5, 1], + [12, -5, 2], + [12, -4, 1], + [12, -4, 2], + [12, -3, 1], + [12, -3, 2], + [12, -2, 1], + [12, -2, 2], + [12, -1, 1], + [12, -1, 2], + [12, 0, 1], + [12, 0, 2], + [12, 1, 1], + [12, 1, 2], + [12, 2, 1], + [12, 2, 2], + [12, 3, 1], + [12, 3, 2], + [12, 4, 1], + [12, 4, 2], + [12, 5, 1], + [12, 5, 2], + [12, 6, 1], + [12, 6, 2], + [12, 7, 1], + [12, 7, 2], + [12, 8, 1], + [12, 8, 2], + [12, 9, 1], + [12, 9, 2], + [12, 10, 1], + [12, 10, 2], + [12, 11, 1], + [12, 11, 2], + [12, 12, 1], + [12, 12, 2], + [13, -13, 1], + [13, -12, 1], + [13, -11, 1], + [13, -10, 1], + [13, -9, 1], + [13, -8, 1], + [13, -7, 1], + [13, -6, 1], + [13, -5, 1], + [13, -4, 1], + [13, -3, 1], + [13, -2, 1], + [13, -1, 1], + [13, 0, 1], + [13, 1, 1], + [13, 2, 1], + [13, 3, 1], + [13, 4, 1], + [13, 5, 1], + [13, 6, 1], + [13, 7, 1], + [13, 8, 1], + [13, 9, 1], + [13, 10, 1], + [13, 11, 1], + [13, 12, 1], + [13, 13, 1], + [14, -14, 1], + [14, -13, 1], + [14, -12, 1], + [14, -11, 1], + [14, -10, 1], + [14, -9, 1], + [14, -8, 1], + [14, -7, 1], + [14, -6, 1], + [14, -5, 1], + [14, -4, 1], + [14, -3, 1], + [14, -2, 1], + [14, -1, 1], + [14, 0, 1], + [14, 1, 1], + [14, 2, 1], + [14, 3, 1], + [14, 4, 1], + [14, 5, 1], + [14, 6, 1], + [14, 7, 1], + [14, 8, 1], + [14, 9, 1], + [14, 10, 1], + [14, 11, 1], + [14, 12, 1], + [14, 13, 1], + [14, 14, 1], + [15, -15, 1], + [15, -14, 1], + [15, -13, 1], + [15, -12, 1], + [15, -11, 1], + [15, -10, 1], + [15, -9, 1], + [15, -8, 1], + [15, -7, 1], + [15, -6, 1], + [15, -5, 1], + [15, -4, 1], + [15, -3, 1], + [15, -2, 1], + [15, -1, 1], + [15, 0, 1], + [15, 1, 1], + [15, 2, 1], + [15, 3, 1], + [15, 4, 1], + [15, 5, 1], + [15, 6, 1], + [15, 7, 1], + [15, 8, 1], + [15, 9, 1], + [15, 10, 1], + [15, 11, 1], + [15, 12, 1], + [15, 13, 1], + [15, 14, 1], + [15, 15, 1], + [16, -16, 1], + [16, -16, 2], + [16, -15, 1], + [16, -15, 2], + [16, -14, 1], + [16, -14, 2], + [16, -13, 1], + [16, -13, 2], + [16, -12, 1], + [16, -12, 2], + [16, -11, 1], + [16, -11, 2], + [16, -10, 1], + [16, -10, 2], + [16, -9, 1], + [16, -9, 2], + [16, -8, 1], + [16, -8, 2], + [16, -7, 1], + [16, -7, 2], + [16, -6, 1], + [16, -6, 2], + [16, -5, 1], + [16, -5, 2], + [16, -4, 1], + [16, -4, 2], + [16, -3, 1], + [16, -3, 2], + [16, -2, 1], + [16, -2, 2], + [16, -1, 1], + [16, -1, 2], + [16, 0, 1], + [16, 0, 2], + [16, 1, 1], + [16, 1, 2], + [16, 2, 1], + [16, 2, 2], + [16, 3, 1], + [16, 3, 2], + [16, 4, 1], + [16, 4, 2], + [16, 5, 1], + [16, 5, 2], + [16, 6, 1], + [16, 6, 2], + [16, 7, 1], + [16, 7, 2], + [16, 8, 1], + [16, 8, 2], + [16, 9, 1], + [16, 9, 2], + [16, 10, 1], + [16, 10, 2], + [16, 11, 1], + [16, 11, 2], + [16, 12, 1], + [16, 12, 2], + [16, 13, 1], + [16, 13, 2], + [16, 14, 1], + [16, 14, 2], + [16, 15, 1], + [16, 15, 2], + [16, 16, 1], + [16, 16, 2]]) + + return indxvec + + +def gsh_eval(X, Bvec): + + phi1 = X[..., 0] + phi = X[..., 1] + phi2 = X[..., 2] + + zvec = np.abs(phi) < 1e-8 + zvec = zvec.astype(int) + randvec = np.round(np.random.rand(zvec.size)).reshape(zvec.shape) + randvecopp = np.ones(zvec.shape) - randvec + phi += (1e-7)*zvec*(randvec - randvecopp) + + final_shape = np.hstack([phi1.shape, len(Bvec)]) + tfunc = np.zeros(final_shape, dtype='complex128') + + c = 0 + for Bindx in Bvec: + + if Bindx == 0: + tfunc[..., c] = 1 + + if Bindx == 1: + t189 = np.cos(phi) + t188 = t189 ** 2 + t193 = 4 * (-t188 - 1) * t189 + t186 = t188 ** 2 + t192 = 1 + t186 + 6 * t188 + tfunc[..., c] = (0.3e1 / 0.64e2) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((14 * t186 - 28 * t188 + 14) * np.exp((-4*1j) * phi2) + (t192 - t193) * np.exp((-4*1j) * (phi1 + phi2)) + (t192 + t193) * np.exp((4*1j) * (phi1 - phi2))) + + if Bindx == 2: + t201 = 4 * phi1 + t200 = np.cos(phi) + t199 = t200 ** 2 + t198 = t200 * t199 + t197 = t199 ** 2 + tfunc[..., c] = (0.3e1 / 0.16e2*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt((1 + t200)) * ((1 - t200) ** (-0.1e1 / 0.2e1)) * ((t197 + 2 * t198 - 2 * t200 - 1) * np.exp((-1*1j) * (t201 + 3 * phi2)) + 14 * (t197 - t198 - t199 + t200) * np.exp((-3*1j) * phi2) + (t197 - 4 * t198 + 6 * t199 - 4 * t200 + 1) * np.exp((1j) * (t201 - 3 * phi2))) + + if Bindx == 3: + t209 = np.cos(phi) + t211 = t209 ** 2 + t207 = t211 ** 2 + t215 = -1 + t207 + t214 = 2 * (-t211 + 1) * t209 + t210 = 2 * phi1 + tfunc[..., c] = (0.3e1 / 0.32e2) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.21e2) * ((14 * t207 - 16 * t211 + 2) * np.exp((-2*1j) * phi2) + (-t214 + t215) * np.exp((-2*1j) * (t210 + phi2)) + (t214 + t215) * np.exp((2*1j) * (t210 - phi2))) + + if Bindx == 4: + t223 = 4 * phi1 + t222 = np.cos(phi) + t221 = t222 ** 2 + t220 = t222 * t221 + t219 = t221 ** 2 + tfunc[..., c] = (0.3e1 / 0.16e2*1j) * np.sqrt((1 + t222)) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * ((1 - t222) ** (-0.1e1 / 0.2e1)) * ((t219 - 2 * t221 + 1) * np.exp((-1*1j) * (t223 + phi2)) + (t219 - 2 * t220 + 2 * t222 - 1) * np.exp((1j) * (t223 - phi2)) + (14 * t219 - 14 * t220 - 6 * t221 + 6 * t222) * np.exp((-1*1j) * phi2)) + + if Bindx == 5: + t229 = np.cos(phi) + t228 = t229 ** 2 + t227 = t228 ** 2 + tfunc[..., c] = 0.3e1 / 0.16e2 * np.sqrt(0.7e1) * np.sqrt(0.3e1) * ((35 * t227) - (30 * t228) + 0.3e1 + (5 * t227 - 10 * t228 + 5) * np.cos((4 * phi1))) + + if Bindx == 6: + t238 = 4 * phi1 + t237 = np.cos(phi) + t236 = t237 ** 2 + t235 = t237 * t236 + t234 = t236 ** 2 + tfunc[..., c] = (-0.3e1 / 0.16e2*1j) * np.sqrt((1 - t237)) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * ((1 + t237) ** (-0.1e1 / 0.2e1)) * ((t234 - 2 * t236 + 1) * np.exp((-1*1j) * (t238 - phi2)) + (t234 + 2 * t235 - 2 * t237 - 1) * np.exp((1j) * (t238 + phi2)) + (14 * t234 + 14 * t235 - 6 * t236 - 6 * t237) * np.exp((1j) * phi2)) + + if Bindx == 7: + t246 = np.cos(phi) + t248 = t246 ** 2 + t244 = t248 ** 2 + t252 = -1 + t244 + t251 = 2 * (-t248 + 1) * t246 + t247 = 2 * phi1 + tfunc[..., c] = (0.3e1 / 0.32e2) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.21e2) * ((14 * t244 - 16 * t248 + 2) * np.exp((2*1j) * phi2) + (t251 + t252) * np.exp((-2*1j) * (t247 - phi2)) + (-t251 + t252) * np.exp((2*1j) * (t247 + phi2))) + + if Bindx == 8: + t260 = 4 * phi1 + t259 = np.cos(phi) + t258 = t259 ** 2 + t257 = t259 * t258 + t256 = t258 ** 2 + tfunc[..., c] = (-0.3e1 / 0.16e2*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt((1 - t259)) * ((1 + t259) ** (-0.1e1 / 0.2e1)) * ((t256 - 2 * t257 + 2 * t259 - 1) * np.exp((-1*1j) * (t260 - 3 * phi2)) + 14 * (t256 + t257 - t258 - t259) * np.exp((3*1j) * phi2) + (t256 + 4 * t257 + 6 * t258 + 4 * t259 + 1) * np.exp((1j) * (t260 + 3 * phi2))) + + if Bindx == 9: + t269 = np.cos(phi) + t268 = t269 ** 2 + t273 = 4 * (-t268 - 1) * t269 + t266 = t268 ** 2 + t272 = 1 + t266 + 6 * t268 + tfunc[..., c] = (0.3e1 / 0.64e2) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((14 * t266 - 28 * t268 + 14) * np.exp((4*1j) * phi2) + (t272 + t273) * np.exp((-4*1j) * (phi1 - phi2)) + (t272 - t273) * np.exp((4*1j) * (phi1 + phi2))) + + if Bindx == 10: + t281 = np.cos(phi) + t280 = t281 ** 2 + t283 = t280 ** 2 + t284 = t281 * t283 + t287 = 4 * t281 - 4 * t284 + t277 = t281 * t284 + t286 = -1 + t277 - 5 * t280 + 5 * t283 + t282 = 2 * phi1 + tfunc[..., c] = -(0.13e2 / 0.256e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.33e2) * ((-2 * t277 + 6 * t283 - 6 * t280 + 2) * np.exp((-6*1j) * phi2) + (t286 - t287) * np.exp((-2*1j) * (t282 + 3 * phi2)) + (t286 + t287) * np.exp((2*1j) * (t282 - 3 * phi2))) + + if Bindx == 11: + t296 = np.cos(phi) + t295 = t296 ** 2 + t298 = t296 * t295 + t291 = t298 ** 2 + t302 = 3 * t291 - 5 * t295 - 10 * t298 + t299 = t295 ** 2 + t297 = 4 * phi1 + t292 = t296 * t299 + tfunc[..., c] = (-0.13e2 / 0.128e3*1j) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * np.sqrt((1 + t296)) * ((1 - t296) ** (-0.1e1 / 0.2e1)) * (6 * (-t291 + t292 + 2 * t299 - 2 * t298 - t295 + t296) * np.exp((-5*1j) * phi2) + (7 * t292 + 3 * t296 + 2 + t302) * np.exp((-1*1j) * (t297 + 5 * phi2)) + (-13 * t292 + 20 * t299 + 7 * t296 - 2 + t302) * np.exp((1j) * (t297 - 5 * phi2))) + + if Bindx == 12: + t311 = np.cos(phi) + t310 = t311 ** 2 + t312 = t311 * t310 + t313 = t310 ** 2 + t317 = 80 * t312 + (-88 * t313 - 8) * t311 + t306 = t312 ** 2 + t316 = -13 - 33 * t306 + 65 * t310 - 35 * t313 + tfunc[..., c] = (0.13e2 / 0.128e3) * np.sqrt(0.7e1) * ((66 * t306 - 138 * t313 + 78 * t310 - 6) * np.exp((-4*1j) * phi2) + (t316 + t317) * np.exp((-4*1j) * (phi1 + phi2)) + (t316 - t317) * np.exp((4*1j) * (phi1 - phi2))) + + if Bindx == 13: + t327 = np.cos(phi) + t326 = t327 ** 2 + t330 = t326 ** 2 + t329 = t327 * t326 + t332 = t329 ** 2 + t335 = 2 - 18 * t326 + 38 * t330 - 22 * t332 + t321 = t327 * t332 + t323 = t327 * t330 + t334 = -11 * t321 + 9 * t323 - 5 * t327 + 7 * t329 + t328 = 4 * phi1 + tfunc[..., c] = (0.13e2 / 0.128e3*1j) * np.sqrt(0.15e2) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * ((22 * t321 - 50 * t323 + 34 * t329 - 6 * t327) * np.exp((-3*1j) * phi2) + (t334 + t335) * np.exp((-1*1j) * (t328 + 3 * phi2)) + (t334 - t335) * np.exp((1j) * (t328 - 3 * phi2))) * ((1 + t327) ** (-0.1e1 / 0.2e1)) * ((1 - t327) ** (-0.1e1 / 0.2e1)) + + if Bindx == 14: + t344 = np.cos(phi) + t343 = t344 ** 2 + t346 = t344 * t343 + t347 = t343 ** 2 + t351 = 64 * t346 + (-44 * t347 - 20) * t344 + t339 = t346 ** 2 + t350 = 1 - 33 * t339 - 11 * t343 + 43 * t347 + t345 = 2 * phi1 + tfunc[..., c] = (0.13e2 / 0.256e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.15e2) * ((66 * t339 - 102 * t347 + 38 * t343 - 2) * np.exp((-2*1j) * phi2) + (t350 + t351) * np.exp((-2*1j) * (t345 + phi2)) + (t350 - t351) * np.exp((2*1j) * (t345 - phi2))) + + if Bindx == 15: + t361 = np.cos(phi) + t360 = t361 ** 2 + t364 = t360 ** 2 + t363 = t361 * t360 + t366 = t363 ** 2 + t369 = 2 - 26 * t360 + 46 * t364 - 22 * t366 + t355 = t361 * t366 + t357 = t361 * t364 + t368 = -33 * t355 + 79 * t357 + 13 * t361 - 59 * t363 + t362 = 4 * phi1 + tfunc[..., c] = (0.13e2 / 0.64e2*1j) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((66 * t355 - 126 * t357 + 70 * t363 - 10 * t361) * np.exp((-1*1j) * phi2) + (t368 + t369) * np.exp((-1*1j) * (t362 + phi2)) + (t368 - t369) * np.exp((1j) * (t362 - phi2))) * ((1 + t361) ** (-0.1e1 / 0.2e1)) * ((1 - t361) ** (-0.1e1 / 0.2e1)) + + if Bindx == 16: + t374 = np.cos(phi) + t373 = t374 ** 2 + t375 = t373 ** 2 + t371 = t373 * t375 + tfunc[..., c] = -0.13e2 / 0.64e2 * np.sqrt(0.2e1) * (-(231 * t371) + (315 * t375) - (105 * t373) + 0.5e1 + (231 * t371 - 483 * t375 + 273 * t373 - 21) * np.cos((4 * phi1))) + + if Bindx == 17: + t386 = np.cos(phi) + t385 = t386 ** 2 + t389 = t385 ** 2 + t388 = t386 * t385 + t391 = t388 ** 2 + t394 = 2 - 26 * t385 + 46 * t389 - 22 * t391 + t380 = t386 * t391 + t382 = t386 * t389 + t393 = -33 * t380 + 79 * t382 + 13 * t386 - 59 * t388 + t387 = 4 * phi1 + tfunc[..., c] = (0.13e2 / 0.64e2*1j) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((66 * t380 - 126 * t382 + 70 * t388 - 10 * t386) * np.exp((1j) * phi2) + (t393 - t394) * np.exp((-1*1j) * (t387 - phi2)) + (t393 + t394) * np.exp((1j) * (t387 + phi2))) * ((1 + t386) ** (-0.1e1 / 0.2e1)) * ((1 - t386) ** (-0.1e1 / 0.2e1)) + + if Bindx == 18: + t403 = np.cos(phi) + t402 = t403 ** 2 + t405 = t403 * t402 + t406 = t402 ** 2 + t410 = 64 * t405 + (-44 * t406 - 20) * t403 + t398 = t405 ** 2 + t409 = 1 - 33 * t398 - 11 * t402 + 43 * t406 + t404 = 2 * phi1 + tfunc[..., c] = (0.13e2 / 0.256e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.15e2) * ((66 * t398 - 102 * t406 + 38 * t402 - 2) * np.exp((2*1j) * phi2) + (t409 - t410) * np.exp((-2*1j) * (t404 - phi2)) + (t409 + t410) * np.exp((2*1j) * (t404 + phi2))) + + if Bindx == 19: + t420 = np.cos(phi) + t419 = t420 ** 2 + t423 = t419 ** 2 + t422 = t420 * t419 + t425 = t422 ** 2 + t428 = 2 - 18 * t419 + 38 * t423 - 22 * t425 + t414 = t420 * t425 + t416 = t420 * t423 + t427 = 11 * t414 - 9 * t416 + 5 * t420 - 7 * t422 + t421 = 4 * phi1 + tfunc[..., c] = (-0.13e2 / 0.128e3*1j) * np.sqrt(0.15e2) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * ((-22 * t414 + 50 * t416 - 34 * t422 + 6 * t420) * np.exp((3*1j) * phi2) + (t427 + t428) * np.exp((-1*1j) * (t421 - 3 * phi2)) + (t427 - t428) * np.exp((1j) * (t421 + 3 * phi2))) * ((1 + t420) ** (-0.1e1 / 0.2e1)) * ((1 - t420) ** (-0.1e1 / 0.2e1)) + + if Bindx == 20: + t437 = np.cos(phi) + t436 = t437 ** 2 + t438 = t437 * t436 + t439 = t436 ** 2 + t443 = 80 * t438 + (-88 * t439 - 8) * t437 + t432 = t438 ** 2 + t442 = -13 - 33 * t432 + 65 * t436 - 35 * t439 + tfunc[..., c] = (0.13e2 / 0.128e3) * np.sqrt(0.7e1) * ((66 * t432 - 138 * t439 + 78 * t436 - 6) * np.exp((4*1j) * phi2) + (t442 - t443) * np.exp((-4*1j) * (phi1 - phi2)) + (t442 + t443) * np.exp((4*1j) * (phi1 + phi2))) + + if Bindx == 21: + t452 = np.cos(phi) + t451 = t452 ** 2 + t454 = t452 * t451 + t447 = t454 ** 2 + t458 = -3 * t447 + 5 * t451 - 10 * t454 + t455 = t451 ** 2 + t453 = 4 * phi1 + t448 = t452 * t455 + tfunc[..., c] = (-0.13e2 / 0.128e3*1j) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * np.sqrt((1 - t452)) * (6 * (t447 + t448 - 2 * t455 - 2 * t454 + t451 + t452) * np.exp((5*1j) * phi2) + (7 * t448 + 3 * t452 - 2 + t458) * np.exp((-1*1j) * (t453 - 5 * phi2)) + (-13 * t448 - 20 * t455 + 7 * t452 + 2 + t458) * np.exp((1j) * (t453 + 5 * phi2))) * ((1 + t452) ** (-0.1e1 / 0.2e1)) + + if Bindx == 22: + t466 = np.cos(phi) + t465 = t466 ** 2 + t468 = t465 ** 2 + t469 = t466 * t468 + t472 = 4 * t466 - 4 * t469 + t462 = t466 * t469 + t471 = 1 - t462 + 5 * t465 - 5 * t468 + t467 = 2 * phi1 + tfunc[..., c] = (0.13e2 / 0.256e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.33e2) * ((2 * t462 - 6 * t468 + 6 * t465 - 2) * np.exp((6*1j) * phi2) + (t471 - t472) * np.exp((-2*1j) * (t467 - 3 * phi2)) + (t471 + t472) * np.exp((2*1j) * (t467 + 3 * phi2))) + + if Bindx == 23: + t485 = np.cos(phi) + t484 = t485 ** 2 + t486 = t485 * t484 + t489 = t486 ** 2 + t496 = (-t489 - 1) * t485 + t487 = t484 ** 2 + t481 = t485 * t487 + t495 = -56 * t481 - 56 * t486 + 8 * t496 + t478 = t487 ** 2 + t494 = 1 + t478 + 28 * t484 + 70 * t487 + 28 * t489 + t493 = 112 * t481 + 112 * t486 + 112 * t496 + t492 = 28 + 28 * t478 + 112 * t484 - 280 * t487 + 112 * t489 + tfunc[..., c] = (0.17e2 / 0.12288e5) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * ((198 * t478 - 792 * t489 + 1188 * t487 - 792 * t484 + 198) * np.exp((-8*1j) * phi2) + (t494 - t495) * np.exp((-8*1j) * (phi1 + phi2)) + (t494 + t495) * np.exp((8*1j) * (phi1 - phi2)) + (t492 - t493) * np.exp((-4*1j) * (phi1 + 2 * phi2)) + (t492 + t493) * np.exp((4*1j) * (phi1 - 2 * phi2))) + + if Bindx == 24: + t509 = np.cos(phi) + t508 = t509 ** 2 + t515 = t508 ** 2 + t502 = t515 ** 2 + t521 = 2 * t502 + t520 = 1 + t509 + t514 = t509 * t508 + t517 = t514 ** 2 + t513 = 4 * phi1 + t512 = 8 * phi1 + t511 = -7 * phi2 + t510 = 7 * phi2 + t505 = t509 * t515 + t503 = t509 * t517 + tfunc[..., c] = (0.17e2 / 0.3072e4*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(t520) * ((1 - t509) ** (-0.1e1 / 0.2e1)) * ((t502 + 6 * t503 + 14 * t517 + 14 * t505 - 14 * t514 - 14 * t508 - 6 * t509 - 1) * np.exp((-1*1j) * (t512 + t510)) + 198 * (t502 - t503 - 3 * t517 + 3 * t505 + 3 * t515 - 3 * t514 - t508 + t509) * np.exp((-7*1j) * phi2) + (t502 - 8 * t503 + 28 * t517 - 56 * t505 + 70 * t515 - 56 * t514 + 28 * t508 - 8 * t509 + 1) * np.exp((1j) * (t512 + t511)) + 14 * (t521 + (5 * t503) - t517 - (11 * t505) - (5 * t515) + (7 * t514) + (5 * t508) - t520) * np.exp((-1*1j) * (t513 + t510)) + 14 * (t521 - (9 * t503) + (13 * t517) - t505 - (15 * t515) + (13 * t514) - t508 - (3 * t509) + 0.1e1) * np.exp((1j) * (t513 + t511))) + + if Bindx == 25: + t534 = np.cos(phi) + t533 = t534 ** 2 + t540 = t533 ** 2 + t527 = t540 ** 2 + t539 = t534 * t533 + t542 = t539 ** 2 + t548 = -5 + 5 * t527 - 70 * t533 + 70 * t542 + t528 = t534 * t542 + t530 = t534 * t540 + t547 = -30 * t528 - 70 * t530 + 30 * t534 + 70 * t539 + t546 = -420 * t528 + 588 * t530 - 28 * t534 - 140 * t539 + t545 = -28 + 140 * t527 + 280 * t533 - 560 * t540 + 168 * t542 + t538 = 2 * phi1 + t537 = 4 * phi1 + t536 = -3 * phi2 + t535 = 3 * phi2 + tfunc[..., c] = (0.17e2 / 0.1024e4) * ((990 * t527 - 3036 * t542 + 3168 * t540 - 1188 * t533 + 66) * np.exp((-6*1j) * phi2) + (-t547 + t548) * np.exp((-2*1j) * (t537 + t535)) + (t547 + t548) * np.exp((2*1j) * (t537 + t536)) + (t545 - t546) * np.exp((-2*1j) * (t538 + t535)) + (t545 + t546) * np.exp((2*1j) * (t538 + t536))) * np.sqrt(0.13e2) + + if Bindx == 26: + t561 = np.cos(phi) + t560 = t561 ** 2 + t567 = t560 ** 2 + t554 = t567 ** 2 + t573 = 5 * t554 + t572 = 140 * t554 + t566 = t561 * t560 + t569 = t566 ** 2 + t565 = 4 * phi1 + t564 = 8 * phi1 + t563 = -5 * phi2 + t562 = 5 * phi2 + t557 = t561 * t567 + t555 = t561 * t569 + tfunc[..., c] = (0.17e2 / 0.3072e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt((1 + t561)) * ((1 - t561) ** (-0.1e1 / 0.2e1)) * (198 * (t573 - 5 * t555 - 11 * t569 + 11 * t557 + 7 * t567 - 7 * t566 - t560 + t561) * np.exp((-5*1j) * phi2) + (t573 - 30 * t555 + 70 * t569 - 70 * t557 + 70 * t566 - 70 * t560 + 30 * t561 - 5) * np.exp((1j) * (t564 + t563)) + (t572 + 210 * t555 - 266 * t569 - 454 * t557 + 150 * t567 + 310 * t566 - 30 * t560 - 66 * t561 + 6) * np.exp((-1*1j) * (t565 + t562)) + (t573 + 20 * t555 + 20 * t569 - 20 * t557 - 50 * t567 - 20 * t566 + 20 * t560 + 20 * t561 + 5) * np.exp((-1*1j) * (t564 + t562)) + (t572 - 490 * t555 + 434 * t569 + 286 * t557 - 590 * t567 + 130 * t566 + 150 * t560 - 54 * t561 - 6) * np.exp((1j) * (t565 + t563))) + + if Bindx == 27: + t586 = np.cos(phi) + t585 = t586 ** 2 + t588 = t586 * t585 + t591 = t588 ** 2 + t580 = t586 * t591 + t589 = t585 ** 2 + t582 = t586 * t589 + t597 = -260 * t580 + 260 * t582 - 260 * t586 + 260 * t588 + t596 = -3640 * t580 + 6136 * t582 + 456 * t586 - 3080 * t588 + t579 = t589 ** 2 + t595 = 65 + 65 * t579 + 260 * t585 - 650 * t589 + 260 * t591 + t594 = -36 + 1820 * t579 + 720 * t585 - 920 * t589 - 1456 * t591 + t587 = 2 * phi1 + tfunc[..., c] = (0.17e2 / 0.6144e4) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((12870 * t579 - 30888 * t591 + 23364 * t589 - 5544 * t585 + 198) * np.exp((-4*1j) * phi2) + (t594 - t596) * np.exp((-4*1j) * (phi1 + phi2)) + (t595 - t597) * np.exp((-4*1j) * (t587 + phi2)) + (t594 + t596) * np.exp((4*1j) * (phi1 - phi2)) + (t595 + t597) * np.exp((4*1j) * (t587 - phi2))) + + if Bindx == 28: + t611 = np.cos(phi) + t610 = t611 ** 2 + t617 = t610 ** 2 + t616 = t611 * t610 + t619 = t616 ** 2 + t621 = t617 ** 2 + t626 = 13 - 78 * t617 + 104 * t619 - 39 * t621 + t603 = t611 * t621 + t607 = t611 * t617 + t625 = 13 * t603 - 78 * t607 - 39 * t611 + 104 * t616 + t624 = -10 + 240 * t610 - 932 * t617 + 1248 * t619 - 546 * t621 + t605 = t611 * t619 + t623 = 364 * t603 - 728 * t605 + 432 * t607 + 4 * t611 - 72 * t616 + t615 = 4 * phi1 + t614 = 8 * phi1 + t613 = -3 * phi2 + t612 = 3 * phi2 + tfunc[..., c] = (0.17e2 / 0.1024e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((1 + t611) ** (-0.1e1 / 0.2e1)) * ((1 - t611) ** (-0.1e1 / 0.2e1)) * ((2574 * t603 - 6864 * t605 + 6204 * t607 - 2112 * t616 + 198 * t611) * np.exp((-3*1j) * phi2) + (t625 - t626) * np.exp((-1*1j) * (t614 + t612)) + (t625 + t626) * np.exp((1j) * (t614 + t613)) + (t623 - t624) * np.exp((-1*1j) * (t615 + t612)) + (t623 + t624) * np.exp((1j) * (t615 + t613))) + + if Bindx == 29: + t639 = np.cos(phi) + t638 = t639 ** 2 + t643 = t638 ** 2 + t632 = t643 ** 2 + t642 = t639 * t638 + t645 = t642 ** 2 + t651 = -13 + 13 * t632 + 26 * t638 - 26 * t645 + t633 = t639 * t645 + t635 = t639 * t643 + t650 = -26 * t633 + 78 * t635 + 26 * t639 - 78 * t642 + t649 = -364 * t633 + 676 * t635 + 44 * t639 - 356 * t642 + t648 = 4 + 364 * t632 - 104 * t638 + 464 * t643 - 728 * t645 + t641 = 2 * phi1 + t640 = 4 * phi1 + tfunc[..., c] = (0.17e2 / 0.3072e4) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * ((2574 * t632 - 5148 * t645 + 3168 * t643 - 612 * t638 + 18) * np.exp((-2*1j) * phi2) + (-t650 + t651) * np.exp((-2*1j) * (t640 + phi2)) + (t650 + t651) * np.exp((2*1j) * (t640 - phi2)) + (t648 - t649) * np.exp((-2*1j) * (t641 + phi2)) + (t648 + t649) * np.exp((2*1j) * (t641 - phi2))) + + if Bindx == 30: + t665 = np.cos(phi) + t664 = t665 ** 2 + t669 = t664 ** 2 + t668 = t665 * t664 + t671 = t668 ** 2 + t673 = t669 ** 2 + t678 = -1 + 4 * t664 - 6 * t669 + 4 * t671 - t673 + t657 = t665 * t673 + t659 = t665 * t671 + t661 = t665 * t669 + t677 = t657 + t665 - 4 * t659 + 6 * t661 - 4 * t668 + t676 = -14 + 392 * t664 - 1652 * t669 + 2184 * t671 - 910 * t673 + t675 = 1820 * t657 - 5096 * t659 + 4928 * t661 + 196 * t665 - 1848 * t668 + t667 = 4 * phi1 + t666 = 8 * phi1 + tfunc[..., c] = (0.17e2 / 0.3072e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * ((1 + t665) ** (-0.1e1 / 0.2e1)) * ((1 - t665) ** (-0.1e1 / 0.2e1)) * ((12870 * t657 - 30888 * t659 + 24948 * t661 - 7560 * t668 + 630 * t665) * np.exp((-1*1j) * phi2) + (t675 - t676) * np.exp((-1*1j) * (t667 + phi2)) + (t675 + t676) * np.exp((1j) * (t667 - phi2)) + 65 * (t677 - t678) * np.exp((-1*1j) * (t666 + phi2)) + 65 * (t677 + t678) * np.exp((1j) * (t666 - phi2))) + + if Bindx == 31: + t685 = np.cos(phi) + t684 = t685 ** 2 + t686 = t684 ** 2 + t682 = t684 * t686 + t681 = t686 ** 2 + tfunc[..., c] = 0.17e2 / 0.1024e4 * np.sqrt(0.3e1) * np.sqrt(0.11e2) * ((6435 * t681) - (12012 * t682) + (6930 * t686) - (1260 * t684) + 0.35e2 + (1820 * t681 - 4368 * t682 + 3304 * t686 - 784 * t684 + 28) * np.cos((4 * phi1)) + (65 * t681 - 260 * t682 + 390 * t686 - 260 * t684 + 65) * np.cos((8 * phi1))) + + if Bindx == 32: + t702 = np.cos(phi) + t701 = t702 ** 2 + t706 = t701 ** 2 + t705 = t702 * t701 + t708 = t705 ** 2 + t710 = t706 ** 2 + t715 = -1 + 4 * t701 - 6 * t706 + 4 * t708 - t710 + t694 = t702 * t710 + t696 = t702 * t708 + t698 = t702 * t706 + t714 = t694 + t702 - 4 * t696 + 6 * t698 - 4 * t705 + t713 = -14 + 392 * t701 - 1652 * t706 + 2184 * t708 - 910 * t710 + t712 = 1820 * t694 - 5096 * t696 + 4928 * t698 + 196 * t702 - 1848 * t705 + t704 = 4 * phi1 + t703 = 8 * phi1 + tfunc[..., c] = (0.17e2 / 0.3072e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * ((1 + t702) ** (-0.1e1 / 0.2e1)) * ((1 - t702) ** (-0.1e1 / 0.2e1)) * ((12870 * t694 - 30888 * t696 + 24948 * t698 - 7560 * t705 + 630 * t702) * np.exp((1j) * phi2) + (t712 + t713) * np.exp((-1*1j) * (t704 - phi2)) + (t712 - t713) * np.exp((1j) * (t704 + phi2)) + 65 * (t714 + t715) * np.exp((-1*1j) * (t703 - phi2)) + 65 * (t714 - t715) * np.exp((1j) * (t703 + phi2))) + + if Bindx == 33: + t728 = np.cos(phi) + t727 = t728 ** 2 + t732 = t727 ** 2 + t721 = t732 ** 2 + t731 = t728 * t727 + t734 = t731 ** 2 + t740 = -13 + 13 * t721 + 26 * t727 - 26 * t734 + t722 = t728 * t734 + t724 = t728 * t732 + t739 = -26 * t722 + 78 * t724 + 26 * t728 - 78 * t731 + t738 = -364 * t722 + 676 * t724 + 44 * t728 - 356 * t731 + t737 = 4 + 364 * t721 - 104 * t727 + 464 * t732 - 728 * t734 + t730 = 2 * phi1 + t729 = 4 * phi1 + tfunc[..., c] = (0.17e2 / 0.3072e4) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * ((2574 * t721 - 5148 * t734 + 3168 * t732 - 612 * t727 + 18) * np.exp((2*1j) * phi2) + (t739 + t740) * np.exp((-2*1j) * (t729 - phi2)) + (-t739 + t740) * np.exp((2*1j) * (t729 + phi2)) + (t737 + t738) * np.exp((-2*1j) * (t730 - phi2)) + (t737 - t738) * np.exp((2*1j) * (t730 + phi2))) + + if Bindx == 34: + t754 = np.cos(phi) + t753 = t754 ** 2 + t760 = t753 ** 2 + t759 = t754 * t753 + t762 = t759 ** 2 + t764 = t760 ** 2 + t769 = 13 - 78 * t760 + 104 * t762 - 39 * t764 + t746 = t754 * t764 + t750 = t754 * t760 + t768 = 13 * t746 - 78 * t750 - 39 * t754 + 104 * t759 + t767 = -10 + 240 * t753 - 932 * t760 + 1248 * t762 - 546 * t764 + t748 = t754 * t762 + t766 = 364 * t746 - 728 * t748 + 432 * t750 + 4 * t754 - 72 * t759 + t758 = 4 * phi1 + t757 = 8 * phi1 + t756 = -3 * phi2 + t755 = 3 * phi2 + tfunc[..., c] = (0.17e2 / 0.1024e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((1 + t754) ** (-0.1e1 / 0.2e1)) * ((1 - t754) ** (-0.1e1 / 0.2e1)) * ((2574 * t746 - 6864 * t748 + 6204 * t750 - 2112 * t759 + 198 * t754) * np.exp((3*1j) * phi2) + (t768 + t769) * np.exp((-1*1j) * (t757 + t756)) + (t768 - t769) * np.exp((1j) * (t757 + t755)) + (t766 + t767) * np.exp((-1*1j) * (t758 + t756)) + (t766 - t767) * np.exp((1j) * (t758 + t755))) + + if Bindx == 35: + t782 = np.cos(phi) + t781 = t782 ** 2 + t784 = t782 * t781 + t787 = t784 ** 2 + t776 = t782 * t787 + t785 = t781 ** 2 + t778 = t782 * t785 + t793 = -260 * t776 + 260 * t778 - 260 * t782 + 260 * t784 + t792 = -3640 * t776 + 6136 * t778 + 456 * t782 - 3080 * t784 + t775 = t785 ** 2 + t791 = 65 + 65 * t775 + 260 * t781 - 650 * t785 + 260 * t787 + t790 = -36 + 1820 * t775 + 720 * t781 - 920 * t785 - 1456 * t787 + t783 = 2 * phi1 + tfunc[..., c] = (0.17e2 / 0.6144e4) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((12870 * t775 - 30888 * t787 + 23364 * t785 - 5544 * t781 + 198) * np.exp((4*1j) * phi2) + (t790 + t792) * np.exp((-4*1j) * (phi1 - phi2)) + (t791 + t793) * np.exp((-4*1j) * (t783 - phi2)) + (t790 - t792) * np.exp((4*1j) * (phi1 + phi2)) + (t791 - t793) * np.exp((4*1j) * (t783 + phi2))) + + if Bindx == 36: + t806 = np.cos(phi) + t805 = t806 ** 2 + t812 = t805 ** 2 + t799 = t812 ** 2 + t818 = 5 * t799 + t817 = 140 * t799 + t811 = t806 * t805 + t814 = t811 ** 2 + t810 = 4 * phi1 + t809 = 8 * phi1 + t808 = -5 * phi2 + t807 = 5 * phi2 + t802 = t806 * t812 + t800 = t806 * t814 + tfunc[..., c] = (-0.17e2 / 0.3072e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt((1 - t806)) * ((1 + t806) ** (-0.1e1 / 0.2e1)) * (198 * (t818 + 5 * t800 - 11 * t814 - 11 * t802 + 7 * t812 + 7 * t811 - t805 - t806) * np.exp((5*1j) * phi2) + (t818 + 30 * t800 + 70 * t814 + 70 * t802 - 70 * t811 - 70 * t805 - 30 * t806 - 5) * np.exp((1j) * (t809 + t807)) + (t817 - 210 * t800 - 266 * t814 + 454 * t802 + 150 * t812 - 310 * t811 - 30 * t805 + 66 * t806 + 6) * np.exp((-1*1j) * (t810 + t808)) + (t818 - 20 * t800 + 20 * t814 + 20 * t802 - 50 * t812 + 20 * t811 + 20 * t805 - 20 * t806 + 5) * np.exp((-1*1j) * (t809 + t808)) + (t817 + 490 * t800 + 434 * t814 - 286 * t802 - 590 * t812 - 130 * t811 + 150 * t805 + 54 * t806 - 6) * np.exp((1j) * (t810 + t807))) + + if Bindx == 37: + t831 = np.cos(phi) + t830 = t831 ** 2 + t837 = t830 ** 2 + t824 = t837 ** 2 + t836 = t831 * t830 + t839 = t836 ** 2 + t845 = -5 + 5 * t824 - 70 * t830 + 70 * t839 + t825 = t831 * t839 + t827 = t831 * t837 + t844 = -30 * t825 - 70 * t827 + 30 * t831 + 70 * t836 + t843 = -420 * t825 + 588 * t827 - 28 * t831 - 140 * t836 + t842 = -28 + 140 * t824 + 280 * t830 - 560 * t837 + 168 * t839 + t835 = 2 * phi1 + t834 = 4 * phi1 + t833 = -3 * phi2 + t832 = 3 * phi2 + tfunc[..., c] = (0.17e2 / 0.1024e4) * ((990 * t824 - 3036 * t839 + 3168 * t837 - 1188 * t830 + 66) * np.exp((6*1j) * phi2) + (t844 + t845) * np.exp((-2*1j) * (t834 + t833)) + (-t844 + t845) * np.exp((2*1j) * (t834 + t832)) + (t842 + t843) * np.exp((-2*1j) * (t835 + t833)) + (t842 - t843) * np.exp((2*1j) * (t835 + t832))) * np.sqrt(0.13e2) + + if Bindx == 38: + t858 = np.cos(phi) + t857 = t858 ** 2 + t864 = t857 ** 2 + t851 = t864 ** 2 + t870 = 2 * t851 + t869 = 1 - t858 + t863 = t858 * t857 + t866 = t863 ** 2 + t862 = 4 * phi1 + t861 = 8 * phi1 + t860 = -7 * phi2 + t859 = 7 * phi2 + t854 = t858 * t864 + t852 = t858 * t866 + tfunc[..., c] = (-0.17e2 / 0.3072e4*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(t869) * ((1 + t858) ** (-0.1e1 / 0.2e1)) * ((t851 - 6 * t852 + 14 * t866 - 14 * t854 + 14 * t863 - 14 * t857 + 6 * t858 - 1) * np.exp((-1*1j) * (t861 + t860)) + 198 * (t851 + t852 - 3 * t866 - 3 * t854 + 3 * t864 + 3 * t863 - t857 - t858) * np.exp((7*1j) * phi2) + (t851 + 8 * t852 + 28 * t866 + 56 * t854 + 70 * t864 + 56 * t863 + 28 * t857 + 8 * t858 + 1) * np.exp((1j) * (t861 + t859)) + 14 * (t870 - (5 * t852) - t866 + (11 * t854) - (5 * t864) - (7 * t863) + (5 * t857) - t869) * np.exp((-1*1j) * (t862 + t860)) + 14 * (t870 + (9 * t852) + (13 * t866) + t854 - (15 * t864) - (13 * t863) - t857 + (3 * t858) + 0.1e1) * np.exp((1j) * (t862 + t859))) + + if Bindx == 39: + t883 = np.cos(phi) + t882 = t883 ** 2 + t884 = t883 * t882 + t887 = t884 ** 2 + t894 = (-t887 - 1) * t883 + t885 = t882 ** 2 + t879 = t883 * t885 + t893 = -56 * t879 - 56 * t884 + 8 * t894 + t876 = t885 ** 2 + t892 = 1 + t876 + 28 * t882 + 70 * t885 + 28 * t887 + t891 = 112 * t879 + 112 * t884 + 112 * t894 + t890 = 28 + 28 * t876 + 112 * t882 - 280 * t885 + 112 * t887 + tfunc[..., c] = (0.17e2 / 0.12288e5) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * ((198 * t876 - 792 * t887 + 1188 * t885 - 792 * t882 + 198) * np.exp((8*1j) * phi2) + (t892 + t893) * np.exp((-8*1j) * (phi1 - phi2)) + (t892 - t893) * np.exp((8*1j) * (phi1 + phi2)) + (t890 + t891) * np.exp((-4*1j) * (phi1 - 2 * phi2)) + (t890 - t891) * np.exp((4*1j) * (phi1 + 2 * phi2))) + + if Bindx == 40: + t907 = np.cos(phi) + t906 = t907 ** 2 + t913 = t906 ** 2 + t917 = t913 ** 2 + t899 = t907 * t917 + t920 = 1 - t899 + t919 = 34 - 34 * t899 + t912 = t907 * t906 + t915 = t912 ** 2 + t911 = 4 * phi1 + t910 = 8 * phi1 + t909 = -9 * phi2 + t908 = 9 * phi2 + t903 = t907 * t913 + t901 = t907 * t915 + tfunc[..., c] = (0.19e2 / 0.2048e4*1j) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt((1 + t907)) * ((-102 * t917 + 272 * t915 + 204 * t903 - 204 * t913 - 272 * t912 + 102 * t907 + t919) * np.exp((-1*1j) * (t911 + t908)) + (-170 * t917 + 272 * t901 - 476 * t903 + 476 * t913 - 272 * t906 + 170 * t907 - t919) * np.exp((1j) * (t911 + t909)) + (7 * t917 + 20 * t901 + 28 * t915 + 14 * t903 - 14 * t913 - 28 * t912 - 20 * t906 - 7 * t907 - t920) * np.exp((-1*1j) * (t910 + t908)) + (9 * t917 - 36 * t901 + 84 * t915 - 126 * t903 + 126 * t913 - 84 * t912 + 36 * t906 - 9 * t907 + t920) * np.exp((1j) * (t910 + t909))) * ((1 - t907) ** (-0.1e1 / 0.2e1)) + + if Bindx == 41: + t933 = np.cos(phi) + t932 = t933 ** 2 + t935 = t932 ** 2 + t934 = t933 * t932 + t937 = t934 ** 2 + t939 = t935 ** 2 + t944 = 8 + 152 * t932 + 56 * t935 - 280 * t937 - 64 * t939 + t925 = t933 * t939 + t927 = t933 * t937 + t929 = t933 * t935 + t943 = -9 * t925 - 188 * t927 - 182 * t929 + 55 * t933 + 196 * t934 + t942 = -136 + 680 * t932 + 136 * t935 - 1768 * t937 + 1088 * t939 + t941 = -306 * t925 - 680 * t927 + 2516 * t929 + 238 * t933 - 1768 * t934 + tfunc[..., c] = -(0.19e2 / 0.3072e4) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((t943 + t944) * np.exp((-8*1j) * (phi1 + phi2)) + (-t941 + t942) * np.exp((-4*1j) * (phi1 + 2 * phi2)) + (t941 + t942) * np.exp((4*1j) * (phi1 - 2 * phi2)) + (-t943 + t944) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 42: + t958 = np.cos(phi) + t957 = t958 ** 2 + t964 = t957 ** 2 + t968 = t964 ** 2 + t950 = t958 * t968 + t963 = t958 * t957 + t966 = t963 ** 2 + t952 = t958 * t966 + t965 = t958 * t964 + t974 = 56 * t950 + 112 * t952 + 40 * t958 + 16 * t963 - 224 * t965 + t973 = -952 * t950 + 2128 * t952 + 88 * t958 + 48 * t963 - 1312 * t965 + t949 = t965 ** 2 + t972 = -7 - 9 * t949 - 77 * t957 + 154 * t964 + 70 * t966 - 131 * t968 + t971 = -46 - 306 * t949 + 598 * t957 - 1852 * t964 + 1788 * t966 - 182 * t968 + t962 = 4 * phi1 + t961 = 8 * phi1 + t960 = -7 * phi2 + t959 = 7 * phi2 + tfunc[..., c] = (0.19e2 / 0.6144e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * ((t971 + t973) * np.exp((-1*1j) * (t962 + t959)) + (-t972 + t974) * np.exp((-1*1j) * (t961 + t959)) + (-t971 + t973) * np.exp((1j) * (t962 + t960)) + (t972 + t974) * np.exp((1j) * (t961 + t960))) * ((1 + t958) ** (-0.1e1 / 0.2e1)) * ((1 - t958) ** (-0.1e1 / 0.2e1)) + + if Bindx == 43: + t987 = np.cos(phi) + t986 = t987 ** 2 + t993 = t986 ** 2 + t992 = t987 * t986 + t995 = t992 ** 2 + t997 = t993 ** 2 + t1002 = -2 - 10 * t986 + 42 * t993 - 14 * t995 - 16 * t997 + t979 = t987 * t997 + t981 = t987 * t995 + t983 = t987 * t993 + t1001 = -3 * t979 - 30 * t981 + 28 * t983 - 9 * t987 + 14 * t992 + t1000 = 2 - 38 * t986 + 262 * t993 - 498 * t995 + 272 * t997 + t999 = -102 * t979 - 12 * t981 + 288 * t983 + 38 * t987 - 212 * t992 + t991 = 2 * phi1 + t990 = 4 * phi1 + t989 = -3 * phi2 + t988 = 3 * phi2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * ((-t999 + t1000) * np.exp((-2*1j) * (t991 + t988)) + (t1001 + t1002) * np.exp((-2*1j) * (t990 + t988)) + (t999 + t1000) * np.exp((2*1j) * (t991 + t989)) + (-t1001 + t1002) * np.exp((2*1j) * (t990 + t989))) + + if Bindx == 44: + t1016 = np.cos(phi) + t1015 = t1016 ** 2 + t1022 = t1015 ** 2 + t1026 = t1022 ** 2 + t1008 = t1016 * t1026 + t1021 = t1016 * t1015 + t1024 = t1021 ** 2 + t1010 = t1016 * t1024 + t1023 = t1016 * t1022 + t1032 = 40 * t1008 - 40 * t1010 - 16 * t1016 + 72 * t1021 - 56 * t1023 + t1007 = t1023 ** 2 + t1031 = 5 - 9 * t1007 - 5 * t1015 - 70 * t1022 + 126 * t1024 - 47 * t1026 + t1030 = -680 * t1008 + 1640 * t1010 - 48 * t1016 + 440 * t1021 - 1352 * t1023 + t1029 = -6 - 306 * t1007 + 150 * t1015 - 460 * t1022 + 252 * t1024 + 370 * t1026 + t1020 = 4 * phi1 + t1019 = 8 * phi1 + t1018 = -5 * phi2 + t1017 = 5 * phi2 + tfunc[..., c] = (0.19e2 / 0.3072e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.10e2) * np.sqrt(0.7e1) * ((t1029 + t1030) * np.exp((-1*1j) * (t1020 + t1017)) + (-t1031 + t1032) * np.exp((-1*1j) * (t1019 + t1017)) + (-t1029 + t1030) * np.exp((1j) * (t1020 + t1018)) + (t1031 + t1032) * np.exp((1j) * (t1019 + t1018))) * ((1 + t1016) ** (-0.1e1 / 0.2e1)) * ((1 - t1016) ** (-0.1e1 / 0.2e1)) + + if Bindx == 45: + t1045 = np.cos(phi) + t1044 = t1045 ** 2 + t1048 = t1044 ** 2 + t1047 = t1045 * t1044 + t1050 = t1047 ** 2 + t1052 = t1048 ** 2 + t1057 = -28 + 140 * t1044 + 28 * t1048 - 364 * t1050 + 224 * t1052 + t1037 = t1045 * t1052 + t1039 = t1045 * t1050 + t1041 = t1045 * t1048 + t1056 = -63 * t1037 - 140 * t1039 + 518 * t1041 + 49 * t1045 - 364 * t1047 + t1055 = -36 + 1044 * t1044 - 4796 * t1048 + 7532 * t1050 - 3808 * t1052 + t1054 = -2142 * t1037 + 2968 * t1039 - 532 * t1041 + 66 * t1045 - 424 * t1047 + t1046 = 2 * phi1 + tfunc[..., c] = (0.19e2 / 0.1536e4) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * ((t1054 + t1055) * np.exp((-4*1j) * (phi1 + phi2)) + (-t1056 + t1057) * np.exp((-4*1j) * (t1046 + phi2)) + (-t1054 + t1055) * np.exp((4*1j) * (phi1 - phi2)) + (t1056 + t1057) * np.exp((4*1j) * (t1046 - phi2))) + + if Bindx == 46: + t1071 = np.cos(phi) + t1070 = t1071 ** 2 + t1077 = t1070 ** 2 + t1081 = t1077 ** 2 + t1063 = t1071 * t1081 + t1076 = t1071 * t1070 + t1079 = t1076 ** 2 + t1065 = t1071 * t1079 + t1078 = t1071 * t1077 + t1087 = -56 * t1063 + 168 * t1065 + 56 * t1076 - 168 * t1078 + t1062 = t1078 ** 2 + t1086 = -7 - 21 * t1062 + 63 * t1070 - 126 * t1077 + 70 * t1079 + 21 * t1081 + t1085 = 952 * t1063 - 2408 * t1065 + 64 * t1071 - 696 * t1076 + 2088 * t1078 + t1084 = 2 - 714 * t1062 - 66 * t1070 + 484 * t1077 - 1428 * t1079 + 1722 * t1081 + t1075 = 4 * phi1 + t1074 = 8 * phi1 + t1073 = -3 * phi2 + t1072 = 3 * phi2 + tfunc[..., c] = (-0.19e2 / 0.1024e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.26e2) * ((1 + t1071) ** (-0.1e1 / 0.2e1)) * ((1 - t1071) ** (-0.1e1 / 0.2e1)) * ((t1086 + t1087) * np.exp((-1*1j) * (t1074 + t1072)) + (-t1086 + t1087) * np.exp((1j) * (t1074 + t1073)) + (-t1084 + t1085) * np.exp((-1*1j) * (t1075 + t1072)) + (t1084 + t1085) * np.exp((1j) * (t1075 + t1073))) + + if Bindx == 47: + t1100 = np.cos(phi) + t1099 = t1100 ** 2 + t1104 = t1099 ** 2 + t1103 = t1100 * t1099 + t1106 = t1103 ** 2 + t1108 = t1104 ** 2 + t1113 = -2 + 22 * t1099 - 54 * t1104 + 50 * t1106 - 16 * t1108 + t1092 = t1100 * t1108 + t1094 = t1100 * t1106 + t1096 = t1100 * t1104 + t1112 = -9 * t1092 + 22 * t1094 - 12 * t1096 + 5 * t1100 - 6 * t1103 + t1111 = 2 - 70 * t1099 + 358 * t1104 - 562 * t1106 + 272 * t1108 + t1110 = -306 * t1092 + 700 * t1094 - 544 * t1096 - 14 * t1100 + 164 * t1103 + t1102 = 2 * phi1 + t1101 = 4 * phi1 + tfunc[..., c] = -(0.19e2 / 0.1536e4) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.26e2) * ((-t1110 + t1111) * np.exp((-2*1j) * (t1102 + phi2)) + (t1112 + t1113) * np.exp((-2*1j) * (t1101 + phi2)) + (t1110 + t1111) * np.exp((2*1j) * (t1102 - phi2)) + (-t1112 + t1113) * np.exp((2*1j) * (t1101 - phi2))) + + if Bindx == 48: + t1127 = np.cos(phi) + t1126 = t1127 ** 2 + t1131 = t1126 ** 2 + t1135 = t1131 ** 2 + t1119 = t1127 * t1135 + t1130 = t1127 * t1126 + t1133 = t1130 ** 2 + t1121 = t1127 * t1133 + t1132 = t1127 * t1131 + t1141 = 8 * t1119 - 32 * t1121 + 8 * t1127 - 32 * t1130 + 48 * t1132 + t1140 = -136 * t1119 + 352 * t1121 - 8 * t1127 + 96 * t1130 - 304 * t1132 + t1118 = t1132 ** 2 + t1139 = 1 - 9 * t1118 - 13 * t1126 + 42 * t1131 - 58 * t1133 + 37 * t1135 + t1138 = 2 - 306 * t1118 - 74 * t1126 + 452 * t1131 - 996 * t1133 + 922 * t1135 + t1129 = 4 * phi1 + t1128 = 8 * phi1 + tfunc[..., c] = (0.19e2 / 0.3072e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.143e3) * np.sqrt(0.7e1) * ((-t1139 + t1141) * np.exp((-1*1j) * (t1128 + phi2)) + (t1139 + t1141) * np.exp((1j) * (t1128 - phi2)) + (t1138 + t1140) * np.exp((-1*1j) * (t1129 + phi2)) + (-t1138 + t1140) * np.exp((1j) * (t1129 - phi2))) * ((1 + t1127) ** (-0.1e1 / 0.2e1)) * ((1 - t1127) ** (-0.1e1 / 0.2e1)) + + if Bindx == 49: + t1148 = np.cos(phi) + t1147 = t1148 ** 2 + t1149 = t1147 ** 2 + t1145 = t1147 * t1149 + t1144 = t1149 ** 2 + tfunc[..., c] = (-0.19e2 / 0.512e3*1j) * t1148 * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.1430e4) * ((t1144 - 4 * t1145 + 6 * t1149 - 4 * t1147 + 1) * np.sin((8 * phi1)) + (-34 * t1144 + 88 * t1145 - 76 * t1149 + 24 * t1147 - 2) * np.sin((4 * phi1))) + + if Bindx == 50: + t1165 = np.cos(phi) + t1164 = t1165 ** 2 + t1169 = t1164 ** 2 + t1173 = t1169 ** 2 + t1157 = t1165 * t1173 + t1168 = t1165 * t1164 + t1171 = t1168 ** 2 + t1159 = t1165 * t1171 + t1170 = t1165 * t1169 + t1179 = 8 * t1157 - 32 * t1159 + 8 * t1165 - 32 * t1168 + 48 * t1170 + t1178 = -136 * t1157 + 352 * t1159 - 8 * t1165 + 96 * t1168 - 304 * t1170 + t1156 = t1170 ** 2 + t1177 = 1 - 9 * t1156 - 13 * t1164 + 42 * t1169 - 58 * t1171 + 37 * t1173 + t1176 = 2 - 306 * t1156 - 74 * t1164 + 452 * t1169 - 996 * t1171 + 922 * t1173 + t1167 = 4 * phi1 + t1166 = 8 * phi1 + tfunc[..., c] = (-0.19e2 / 0.3072e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.143e3) * np.sqrt(0.7e1) * ((1 + t1165) ** (-0.1e1 / 0.2e1)) * ((1 - t1165) ** (-0.1e1 / 0.2e1)) * ((t1177 + t1179) * np.exp((-1*1j) * (t1166 - phi2)) + (-t1177 + t1179) * np.exp((1j) * (t1166 + phi2)) + (-t1176 + t1178) * np.exp((-1*1j) * (t1167 - phi2)) + (t1176 + t1178) * np.exp((1j) * (t1167 + phi2))) + + if Bindx == 51: + t1192 = np.cos(phi) + t1191 = t1192 ** 2 + t1196 = t1191 ** 2 + t1195 = t1192 * t1191 + t1198 = t1195 ** 2 + t1200 = t1196 ** 2 + t1205 = 2 - 22 * t1191 + 54 * t1196 - 50 * t1198 + 16 * t1200 + t1184 = t1192 * t1200 + t1186 = t1192 * t1198 + t1188 = t1192 * t1196 + t1204 = -9 * t1184 + 22 * t1186 - 12 * t1188 + 5 * t1192 - 6 * t1195 + t1203 = -2 + 70 * t1191 - 358 * t1196 + 562 * t1198 - 272 * t1200 + t1202 = -306 * t1184 + 700 * t1186 - 544 * t1188 - 14 * t1192 + 164 * t1195 + t1194 = 2 * phi1 + t1193 = 4 * phi1 + tfunc[..., c] = -(0.19e2 / 0.1536e4) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.26e2) * ((-t1202 + t1203) * np.exp((-2*1j) * (t1194 - phi2)) + (t1204 + t1205) * np.exp((-2*1j) * (t1193 - phi2)) + (t1202 + t1203) * np.exp((2*1j) * (t1194 + phi2)) + (-t1204 + t1205) * np.exp((2*1j) * (t1193 + phi2))) + + if Bindx == 52: + t1219 = np.cos(phi) + t1218 = t1219 ** 2 + t1225 = t1218 ** 2 + t1229 = t1225 ** 2 + t1211 = t1219 * t1229 + t1224 = t1219 * t1218 + t1227 = t1224 ** 2 + t1213 = t1219 * t1227 + t1226 = t1219 * t1225 + t1235 = -56 * t1211 + 168 * t1213 + 56 * t1224 - 168 * t1226 + t1210 = t1226 ** 2 + t1234 = -7 - 21 * t1210 + 63 * t1218 - 126 * t1225 + 70 * t1227 + 21 * t1229 + t1233 = 952 * t1211 - 2408 * t1213 + 64 * t1219 - 696 * t1224 + 2088 * t1226 + t1232 = 2 - 714 * t1210 - 66 * t1218 + 484 * t1225 - 1428 * t1227 + 1722 * t1229 + t1223 = 4 * phi1 + t1222 = 8 * phi1 + t1221 = -3 * phi2 + t1220 = 3 * phi2 + tfunc[..., c] = (0.19e2 / 0.1024e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.26e2) * ((-t1234 + t1235) * np.exp((-1*1j) * (t1222 + t1221)) + (t1234 + t1235) * np.exp((1j) * (t1222 + t1220)) + (t1232 + t1233) * np.exp((-1*1j) * (t1223 + t1221)) + (-t1232 + t1233) * np.exp((1j) * (t1223 + t1220))) * ((1 + t1219) ** (-0.1e1 / 0.2e1)) * ((1 - t1219) ** (-0.1e1 / 0.2e1)) + + if Bindx == 53: + t1248 = np.cos(phi) + t1247 = t1248 ** 2 + t1251 = t1247 ** 2 + t1250 = t1248 * t1247 + t1253 = t1250 ** 2 + t1255 = t1251 ** 2 + t1260 = 28 - 140 * t1247 - 28 * t1251 + 364 * t1253 - 224 * t1255 + t1240 = t1248 * t1255 + t1242 = t1248 * t1253 + t1244 = t1248 * t1251 + t1259 = -63 * t1240 - 140 * t1242 + 518 * t1244 + 49 * t1248 - 364 * t1250 + t1258 = 36 - 1044 * t1247 + 4796 * t1251 - 7532 * t1253 + 3808 * t1255 + t1257 = -2142 * t1240 + 2968 * t1242 - 532 * t1244 + 66 * t1248 - 424 * t1250 + t1249 = 2 * phi1 + tfunc[..., c] = (0.19e2 / 0.1536e4) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * ((t1257 + t1258) * np.exp((-4*1j) * (phi1 - phi2)) + (-t1259 + t1260) * np.exp((-4*1j) * (t1249 - phi2)) + (-t1257 + t1258) * np.exp((4*1j) * (phi1 + phi2)) + (t1259 + t1260) * np.exp((4*1j) * (t1249 + phi2))) + + if Bindx == 54: + t1274 = np.cos(phi) + t1273 = t1274 ** 2 + t1280 = t1273 ** 2 + t1284 = t1280 ** 2 + t1266 = t1274 * t1284 + t1279 = t1274 * t1273 + t1282 = t1279 ** 2 + t1268 = t1274 * t1282 + t1281 = t1274 * t1280 + t1290 = -40 * t1266 + 40 * t1268 + 16 * t1274 - 72 * t1279 + 56 * t1281 + t1265 = t1281 ** 2 + t1289 = 5 - 9 * t1265 - 5 * t1273 - 70 * t1280 + 126 * t1282 - 47 * t1284 + t1288 = 680 * t1266 - 1640 * t1268 + 48 * t1274 - 440 * t1279 + 1352 * t1281 + t1287 = -6 - 306 * t1265 + 150 * t1273 - 460 * t1280 + 252 * t1282 + 370 * t1284 + t1278 = 4 * phi1 + t1277 = 8 * phi1 + t1276 = -5 * phi2 + t1275 = 5 * phi2 + tfunc[..., c] = (0.19e2 / 0.3072e4*1j) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.10e2) * np.sqrt(0.7e1) * ((t1287 + t1288) * np.exp((-1*1j) * (t1278 + t1276)) + (-t1289 + t1290) * np.exp((-1*1j) * (t1277 + t1276)) + (-t1287 + t1288) * np.exp((1j) * (t1278 + t1275)) + (t1289 + t1290) * np.exp((1j) * (t1277 + t1275))) * ((1 + t1274) ** (-0.1e1 / 0.2e1)) * ((1 - t1274) ** (-0.1e1 / 0.2e1)) + + if Bindx == 55: + t1303 = np.cos(phi) + t1302 = t1303 ** 2 + t1309 = t1302 ** 2 + t1308 = t1303 * t1302 + t1311 = t1308 ** 2 + t1313 = t1309 ** 2 + t1318 = -2 - 10 * t1302 + 42 * t1309 - 14 * t1311 - 16 * t1313 + t1317 = 2 - 38 * t1302 + 262 * t1309 - 498 * t1311 + 272 * t1313 + t1295 = t1303 * t1313 + t1297 = t1303 * t1311 + t1299 = t1303 * t1309 + t1316 = -3 * t1295 - 30 * t1297 + 28 * t1299 - 9 * t1303 + 14 * t1308 + t1315 = -102 * t1295 - 12 * t1297 + 288 * t1299 + 38 * t1303 - 212 * t1308 + t1307 = 2 * phi1 + t1306 = 4 * phi1 + t1305 = -3 * phi2 + t1304 = 3 * phi2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * ((t1315 + t1317) * np.exp((-2*1j) * (t1307 + t1305)) + (-t1316 + t1318) * np.exp((-2*1j) * (t1306 + t1305)) + (-t1315 + t1317) * np.exp((2*1j) * (t1307 + t1304)) + (t1316 + t1318) * np.exp((2*1j) * (t1306 + t1304))) + + if Bindx == 56: + t1332 = np.cos(phi) + t1331 = t1332 ** 2 + t1338 = t1331 ** 2 + t1342 = t1338 ** 2 + t1324 = t1332 * t1342 + t1337 = t1332 * t1331 + t1340 = t1337 ** 2 + t1326 = t1332 * t1340 + t1339 = t1332 * t1338 + t1348 = 56 * t1324 + 112 * t1326 + 40 * t1332 + 16 * t1337 - 224 * t1339 + t1347 = -952 * t1324 + 2128 * t1326 + 88 * t1332 + 48 * t1337 - 1312 * t1339 + t1323 = t1339 ** 2 + t1346 = -7 - 9 * t1323 - 77 * t1331 + 154 * t1338 + 70 * t1340 - 131 * t1342 + t1345 = -46 - 306 * t1323 + 598 * t1331 - 1852 * t1338 + 1788 * t1340 - 182 * t1342 + t1336 = 4 * phi1 + t1335 = 8 * phi1 + t1334 = -7 * phi2 + t1333 = 7 * phi2 + tfunc[..., c] = (-0.19e2 / 0.6144e4*1j) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * ((1 + t1332) ** (-0.1e1 / 0.2e1)) * ((1 - t1332) ** (-0.1e1 / 0.2e1)) * ((-t1345 + t1347) * np.exp((-1*1j) * (t1336 + t1334)) + (t1346 + t1348) * np.exp((-1*1j) * (t1335 + t1334)) + (t1345 + t1347) * np.exp((1j) * (t1336 + t1333)) + (-t1346 + t1348) * np.exp((1j) * (t1335 + t1333))) + + if Bindx == 57: + t1361 = np.cos(phi) + t1360 = t1361 ** 2 + t1363 = t1360 ** 2 + t1362 = t1361 * t1360 + t1365 = t1362 ** 2 + t1367 = t1363 ** 2 + t1372 = 8 + 152 * t1360 + 56 * t1363 - 280 * t1365 - 64 * t1367 + t1353 = t1361 * t1367 + t1355 = t1361 * t1365 + t1357 = t1361 * t1363 + t1371 = -9 * t1353 - 188 * t1355 - 182 * t1357 + 55 * t1361 + 196 * t1362 + t1370 = -136 + 680 * t1360 + 136 * t1363 - 1768 * t1365 + 1088 * t1367 + t1369 = -306 * t1353 - 680 * t1355 + 2516 * t1357 + 238 * t1361 - 1768 * t1362 + tfunc[..., c] = (0.19e2 / 0.3072e4) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * ((-t1371 + t1372) * np.exp((-8*1j) * (phi1 - phi2)) + (t1369 + t1370) * np.exp((-4*1j) * (phi1 - 2 * phi2)) + (-t1369 + t1370) * np.exp((4*1j) * (phi1 + 2 * phi2)) + (t1371 + t1372) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 58: + t1385 = np.cos(phi) + t1384 = t1385 ** 2 + t1391 = t1384 ** 2 + t1395 = t1391 ** 2 + t1377 = t1385 * t1395 + t1398 = -1 - t1377 + t1397 = -34 - 34 * t1377 + t1390 = t1385 * t1384 + t1393 = t1390 ** 2 + t1389 = 4 * phi1 + t1388 = 8 * phi1 + t1387 = -9 * phi2 + t1386 = 9 * phi2 + t1381 = t1385 * t1391 + t1379 = t1385 * t1393 + tfunc[..., c] = (0.19e2 / 0.2048e4*1j) * np.sqrt((1 - t1385)) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * ((1 + t1385) ** (-0.1e1 / 0.2e1)) * ((-102 * t1395 + 272 * t1393 - 204 * t1381 - 204 * t1391 + 272 * t1390 - 102 * t1385 - t1397) * np.exp((-1*1j) * (t1389 + t1387)) + (-170 * t1395 - 272 * t1379 + 476 * t1381 + 476 * t1391 - 272 * t1384 - 170 * t1385 + t1397) * np.exp((1j) * (t1389 + t1386)) + (7 * t1395 - 20 * t1379 + 28 * t1393 - 14 * t1381 - 14 * t1391 + 28 * t1390 - 20 * t1384 + 7 * t1385 + t1398) * np.exp((-1*1j) * (t1388 + t1387)) + (9 * t1395 + 36 * t1379 + 84 * t1393 + 126 * t1381 + 126 * t1391 + 84 * t1390 + 36 * t1384 + 9 * t1385 - t1398) * np.exp((1j) * (t1388 + t1386))) + + if Bindx == 59: + t1412 = np.cos(phi) + t1411 = t1412 ** 2 + t1418 = t1411 ** 2 + t1421 = t1418 ** 2 + t1422 = t1412 * t1421 + t1428 = t1412 - t1422 + t1417 = t1412 * t1411 + t1419 = t1417 ** 2 + t1407 = t1412 * t1419 + t1427 = -48 * t1407 + 48 * t1417 + 8 * t1428 + t1426 = 96 * t1407 - 96 * t1417 + 48 * t1428 + t1404 = t1412 * t1422 + t1425 = -1 + t1404 - 27 * t1411 - 42 * t1418 + 42 * t1419 + 27 * t1421 + t1424 = -12 + 12 * t1404 - 36 * t1411 + 168 * t1418 - 168 * t1419 + 36 * t1421 + t1416 = 2 * phi1 + t1415 = 4 * phi1 + t1414 = -5 * phi2 + t1413 = 5 * phi2 + tfunc[..., c] = -(0.7e1 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * ((-26 * t1404 + 130 * t1421 - 260 * t1419 + 260 * t1418 - 130 * t1411 + 26) * np.exp((-10*1j) * phi2) + (t1425 - t1427) * np.exp((-2*1j) * (t1415 + t1413)) + (t1425 + t1427) * np.exp((2*1j) * (t1415 + t1414)) + (t1424 - t1426) * np.exp((-2*1j) * (t1416 + t1413)) + (t1424 + t1426) * np.exp((2*1j) * (t1416 + t1414))) + + if Bindx == 60: + t1443 = np.cos(phi) + t1442 = t1443 ** 2 + t1449 = t1442 ** 2 + t1450 = t1443 * t1449 + t1434 = t1450 ** 2 + t1457 = 5 * t1434 - 84 * t1449 - 126 * t1450 + t1456 = 60 * t1434 + 336 * t1449 + 504 * t1450 + t1453 = t1449 ** 2 + t1448 = t1443 * t1442 + t1451 = t1448 ** 2 + t1447 = 4 * phi1 + t1446 = 8 * phi1 + t1445 = -9 * phi2 + t1444 = 9 * phi2 + t1437 = t1443 * t1451 + t1435 = t1443 * t1453 + tfunc[..., c] = (-0.7e1 / 0.8192e4*1j) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt((1 + t1443)) * (130 * (-t1434 + t1435 + 4 * t1453 - 4 * t1437 - 6 * t1451 + 6 * t1450 + 4 * t1449 - 4 * t1448 - t1442 + t1443) * np.exp((-9*1j) * phi2) + (156 * t1435 - 72 * t1453 - 480 * t1437 - 168 * t1451 - 192 * t1448 - 180 * t1442 + 12 * t1443 + 24 + t1456) * np.exp((-1*1j) * (t1447 + t1444)) + (31 * t1435 + 72 * t1453 + 60 * t1437 - 42 * t1451 + 12 * t1448 + 45 * t1442 + 23 * t1443 + 4 + t1457) * np.exp((-1*1j) * (t1446 + t1444)) + (-276 * t1435 + 360 * t1453 + 192 * t1437 - 840 * t1451 - 480 * t1448 + 108 * t1442 + 60 * t1443 - 24 + t1456) * np.exp((1j) * (t1447 + t1445)) + (-41 * t1435 + 144 * t1453 - 276 * t1437 + 294 * t1451 + 156 * t1448 - 99 * t1442 + 31 * t1443 - 4 + t1457) * np.exp((1j) * (t1446 + t1445))) * ((1 - t1443) ** (-0.1e1 / 0.2e1)) + + if Bindx == 61: + t1472 = np.cos(phi) + t1471 = t1472 ** 2 + t1474 = t1471 ** 2 + t1478 = t1474 ** 2 + t1464 = t1472 * t1478 + t1473 = t1472 * t1471 + t1476 = t1473 ** 2 + t1466 = t1472 * t1476 + t1475 = t1472 * t1474 + t1484 = -608 * t1464 - 1536 * t1466 - 320 * t1472 + 192 * t1473 + 2016 * t1475 + t1483 = -3648 * t1464 + 7680 * t1466 + 384 * t1472 - 384 * t1473 - 4032 * t1475 + t1463 = t1475 ** 2 + t1482 = -59 - 95 * t1463 - 531 * t1471 + 1722 * t1474 + 210 * t1476 - 1503 * t1478 + t1481 = -132 - 1140 * t1463 + 1980 * t1471 - 6888 * t1474 + 7224 * t1476 - 1044 * t1478 + tfunc[..., c] = (0.7e1 / 0.8192e4) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.11e2) * ((2470 * t1463 - 10010 * t1478 + 15340 * t1476 - 10660 * t1474 + 2990 * t1471 - 130) * np.exp((-8*1j) * phi2) + (t1482 + t1484) * np.exp((-8*1j) * (phi1 + phi2)) + (t1481 + t1483) * np.exp((-4*1j) * (phi1 + 2 * phi2)) + (t1481 - t1483) * np.exp((4*1j) * (phi1 - 2 * phi2)) + (t1482 - t1484) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 62: + t1500 = np.cos(phi) + t1499 = t1500 ** 2 + t1506 = t1499 ** 2 + t1505 = t1500 * t1499 + t1508 = t1505 ** 2 + t1510 = t1506 ** 2 + t1507 = t1500 * t1506 + t1512 = t1507 ** 2 + t1517 = 44 + 44 * t1499 - 1208 * t1506 + 1960 * t1508 - 308 * t1510 - 532 * t1512 + t1516 = 8 - 184 * t1499 + 2416 * t1506 - 7504 * t1508 + 8456 * t1510 - 3192 * t1512 + t1490 = t1500 * t1512 + t1492 = t1500 * t1510 + t1494 = t1500 * t1508 + t1515 = 95 * t1490 + 1013 * t1492 - 1514 * t1494 - 175 * t1500 + 763 * t1505 - 182 * t1507 + t1514 = 1140 * t1490 - 516 * t1492 - 4376 * t1494 + 364 * t1500 - 2716 * t1505 + 6104 * t1507 + t1504 = 4 * phi1 + t1503 = 8 * phi1 + t1502 = -7 * phi2 + t1501 = 7 * phi2 + tfunc[..., c] = (-0.21e2 / 0.8192e4*1j) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * ((1 + t1500) ** (-0.1e1 / 0.2e1)) * ((1 - t1500) ** (-0.1e1 / 0.2e1)) * ((-2470 * t1490 + 10270 * t1492 - 16380 * t1494 + 12220 * t1507 - 4030 * t1505 + 390 * t1500) * np.exp((-7*1j) * phi2) + (t1514 - t1516) * np.exp((-1*1j) * (t1504 + t1501)) + (t1515 - t1517) * np.exp((-1*1j) * (t1503 + t1501)) + (t1514 + t1516) * np.exp((1j) * (t1504 + t1502)) + (t1515 + t1517) * np.exp((1j) * (t1503 + t1502))) + + if Bindx == 63: + t1532 = np.cos(phi) + t1531 = t1532 ** 2 + t1538 = t1531 ** 2 + t1542 = t1538 ** 2 + t1524 = t1532 * t1542 + t1537 = t1532 * t1531 + t1540 = t1537 ** 2 + t1526 = t1532 * t1540 + t1539 = t1532 * t1538 + t1548 = -7752 * t1524 + 1360 * t1526 + 1224 * t1532 - 10064 * t1537 + 15232 * t1539 + t1523 = t1539 ** 2 + t1547 = 527 - 1615 * t1523 - 2635 * t1531 - 4522 * t1538 + 19754 * t1540 - 11509 * t1542 + t1546 = -332 - 19380 * t1523 + 9628 * t1531 - 35672 * t1538 + 32088 * t1540 + 13668 * t1542 + t1545 = 46512 * t1524 - 101728 * t1526 + 1872 * t1532 - 20128 * t1537 + 73472 * t1539 + t1536 = 2 * phi1 + t1535 = 4 * phi1 + t1534 = -3 * phi2 + t1533 = 3 * phi2 + tfunc[..., c] = (0.21e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.11e2) * ((41990 * t1523 - 139230 * t1542 + 166140 * t1540 - 82940 * t1538 + 14430 * t1531 - 390) * np.exp((-6*1j) * phi2) + (-t1545 + t1546) * np.exp((-2*1j) * (t1536 + t1533)) + (t1547 + t1548) * np.exp((-2*1j) * (t1535 + t1533)) + (t1545 + t1546) * np.exp((2*1j) * (t1536 + t1534)) + (t1547 - t1548) * np.exp((2*1j) * (t1535 + t1534))) + + if Bindx == 64: + t1564 = np.cos(phi) + t1563 = t1564 ** 2 + t1570 = t1563 ** 2 + t1574 = t1570 ** 2 + t1571 = t1564 * t1570 + t1576 = t1571 ** 2 + t1581 = -68 + 748 * t1563 - 1632 * t1570 + 2244 * t1574 - 1292 * t1576 + t1569 = t1564 * t1563 + t1572 = t1569 ** 2 + t1580 = 40 - 1400 * t1563 + 8640 * t1570 - 20608 * t1572 + 21080 * t1574 - 7752 * t1576 + t1554 = t1564 * t1576 + t1556 = t1564 * t1574 + t1558 = t1564 * t1572 + t1579 = 323 * t1554 + 1037 * t1556 - 3978 * t1558 + 17 * t1564 - 969 * t1569 + 3570 * t1571 + t1578 = 3876 * t1554 - 7140 * t1556 + 1640 * t1558 + 172 * t1564 - 1740 * t1569 + 3192 * t1571 + t1568 = 4 * phi1 + t1567 = 8 * phi1 + t1566 = -5 * phi2 + t1565 = 5 * phi2 + tfunc[..., c] = (-0.21e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.11e2) * ((-8398 * t1554 + 29614 * t1556 - 38844 * t1558 + 22828 * t1571 - 5590 * t1569 + 390 * t1564) * np.exp((-5*1j) * phi2) + (t1579 - t1581) * np.exp((-1*1j) * (t1567 + t1565)) + (t1579 + t1581) * np.exp((1j) * (t1567 + t1566)) + (t1578 - t1580) * np.exp((-1*1j) * (t1568 + t1565)) + (t1578 + t1580) * np.exp((1j) * (t1568 + t1566))) * ((1 + t1564) ** (-0.1e1 / 0.2e1)) * ((1 - t1564) ** (-0.1e1 / 0.2e1)) + + if Bindx == 65: + t1596 = np.cos(phi) + t1595 = t1596 ** 2 + t1599 = t1595 ** 2 + t1603 = t1599 ** 2 + t1588 = t1596 * t1603 + t1598 = t1596 * t1595 + t1601 = t1598 ** 2 + t1590 = t1596 * t1601 + t1600 = t1596 * t1599 + t1609 = -5168 * t1588 + 10880 * t1590 + 544 * t1596 - 544 * t1598 - 5712 * t1600 + t1608 = -31008 * t1588 + 69632 * t1590 - 1216 * t1596 + 15424 * t1598 - 53088 * t1600 + t1587 = t1600 ** 2 + t1607 = 4 + 19380 * t1587 - 156 * t1595 - 2296 * t1599 + 19432 * t1601 - 36108 * t1603 + t1606 = 187 + 1615 * t1587 - 2805 * t1595 + 9758 * t1599 - 10234 * t1601 + 1479 * t1603 + t1597 = 2 * phi1 + tfunc[..., c] = -(0.21e2 / 0.4096e4) * np.sqrt(0.11e2) * ((-41990 * t1587 + 117130 * t1603 - 114140 * t1601 + 44980 * t1599 - 6110 * t1595 + 130) * np.exp((-4*1j) * phi2) + (t1607 - t1608) * np.exp((-4*1j) * (phi1 + phi2)) + (t1606 - t1609) * np.exp((-4*1j) * (t1597 + phi2)) + (t1607 + t1608) * np.exp((4*1j) * (phi1 - phi2)) + (t1606 + t1609) * np.exp((4*1j) * (t1597 - phi2))) + + if Bindx == 66: + t1625 = np.cos(phi) + t1624 = t1625 ** 2 + t1631 = t1624 ** 2 + t1630 = t1625 * t1624 + t1633 = t1630 ** 2 + t1635 = t1631 ** 2 + t1632 = t1625 * t1631 + t1637 = t1632 ** 2 + t1642 = 68 - 1292 * t1624 + 7344 * t1631 - 14960 * t1633 + 12716 * t1635 - 3876 * t1637 + t1641 = 88 - 3784 * t1624 + 26016 * t1631 - 63392 * t1633 + 64328 * t1635 - 23256 * t1637 + t1615 = t1625 * t1637 + t1617 = t1625 * t1635 + t1619 = t1625 * t1633 + t1640 = -1615 * t1615 + 2839 * t1617 + 1938 * t1619 - 765 * t1625 + 4301 * t1630 - 6698 * t1632 + t1639 = -19380 * t1615 + 53652 * t1617 - 54408 * t1619 + 324 * t1625 - 4996 * t1630 + 24808 * t1632 + t1629 = 4 * phi1 + t1628 = 8 * phi1 + t1627 = -3 * phi2 + t1626 = 3 * phi2 + tfunc[..., c] = (0.21e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.11e2) * ((41990 * t1615 - 130390 * t1617 + 148460 * t1619 - 74620 * t1632 + 15470 * t1630 - 910 * t1625) * np.exp((-3*1j) * phi2) + (t1639 + t1641) * np.exp((-1*1j) * (t1629 + t1626)) + (t1640 + t1642) * np.exp((-1*1j) * (t1628 + t1626)) + (t1639 - t1641) * np.exp((1j) * (t1629 + t1627)) + (t1640 - t1642) * np.exp((1j) * (t1628 + t1627))) * ((1 + t1625) ** (-0.1e1 / 0.2e1)) * ((1 - t1625) ** (-0.1e1 / 0.2e1)) + + if Bindx == 67: + t1657 = np.cos(phi) + t1656 = t1657 ** 2 + t1661 = t1656 ** 2 + t1665 = t1661 ** 2 + t1649 = t1657 * t1665 + t1660 = t1657 * t1656 + t1663 = t1660 ** 2 + t1651 = t1657 * t1663 + t1662 = t1657 * t1661 + t1671 = -2584 * t1649 + 8432 * t1651 - 680 * t1657 + 4624 * t1660 - 9792 * t1662 + t1670 = -15504 * t1649 + 35360 * t1651 - 496 * t1657 + 7136 * t1660 - 26496 * t1662 + t1648 = t1662 ** 2 + t1669 = -17 - 1615 * t1648 + 357 * t1656 + 646 * t1661 - 3910 * t1663 + 4539 * t1665 + t1668 = 52 - 19380 * t1648 - 2340 * t1656 + 17128 * t1661 - 45032 * t1663 + 49572 * t1665 + t1659 = 2 * phi1 + t1658 = 4 * phi1 + tfunc[..., c] = (0.21e2 / 0.8192e4) * np.sqrt(0.13e2) * np.sqrt(0.11e2) * ((41990 * t1648 - 103870 * t1665 + 89180 * t1663 - 30940 * t1661 + 3710 * t1656 - 70) * np.exp((-2*1j) * phi2) + (t1668 + t1670) * np.exp((-2*1j) * (t1659 + phi2)) + (t1669 + t1671) * np.exp((-2*1j) * (t1658 + phi2)) + (t1668 - t1670) * np.exp((2*1j) * (t1659 - phi2)) + (t1669 - t1671) * np.exp((2*1j) * (t1658 - phi2))) + + if Bindx == 68: + t1687 = np.cos(phi) + t1686 = t1687 ** 2 + t1691 = t1686 ** 2 + t1690 = t1687 * t1686 + t1693 = t1690 ** 2 + t1695 = t1691 ** 2 + t1692 = t1687 * t1691 + t1697 = t1692 ** 2 + t1702 = 68 - 1564 * t1686 + 5576 * t1691 - 8024 * t1693 + 5236 * t1695 - 1292 * t1697 + t1701 = 24 - 1128 * t1686 + 8304 * t1691 - 21072 * t1693 + 21624 * t1695 - 7752 * t1697 + t1677 = t1687 * t1697 + t1679 = t1687 * t1695 + t1681 = t1687 * t1693 + t1700 = -1615 * t1677 + 6851 * t1679 - 11254 * t1681 + 391 * t1687 - 3179 * t1690 + 8806 * t1692 + t1699 = -19380 * t1677 + 62628 * t1679 - 74856 * t1681 + 564 * t1687 - 8868 * t1690 + 39912 * t1692 + t1689 = 4 * phi1 + t1688 = 8 * phi1 + tfunc[..., c] = (0.7e1 / 0.4096e4*1j) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * ((41990 * t1677 - 121550 * t1679 + 128700 * t1681 - 60060 * t1692 + 11550 * t1690 - 630 * t1687) * np.exp((-1*1j) * phi2) + (t1699 + t1701) * np.exp((-1*1j) * (t1689 + phi2)) + (t1700 + t1702) * np.exp((-1*1j) * (t1688 + phi2)) + (t1699 - t1701) * np.exp((1j) * (t1689 - phi2)) + (t1700 - t1702) * np.exp((1j) * (t1688 - phi2))) * ((1 + t1687) ** (-0.1e1 / 0.2e1)) * ((1 - t1687) ** (-0.1e1 / 0.2e1)) + + if Bindx == 69: + t1710 = np.cos(phi) + t1709 = t1710 ** 2 + t1711 = t1709 ** 2 + t1713 = t1711 ** 2 + t1707 = t1709 * t1711 + t1705 = t1709 * t1713 + tfunc[..., c] = -0.7e1 / 0.4096e4 * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * (-(46189 * t1705) + (109395 * t1713) - (90090 * t1707) + (30030 * t1711) - (3465 * t1709) + 0.63e2 + (42636 * t1705 - 118932 * t1713 + 115896 * t1707 - 45672 * t1711 + 6204 * t1709 - 132) * np.cos((4 * phi1)) + (3553 * t1705 - 14399 * t1713 + 22066 * t1707 - 15334 * t1711 + 4301 * t1709 - 187) * np.cos((8 * phi1))) + + if Bindx == 70: + t1730 = np.cos(phi) + t1729 = t1730 ** 2 + t1734 = t1729 ** 2 + t1733 = t1730 * t1729 + t1736 = t1733 ** 2 + t1738 = t1734 ** 2 + t1735 = t1730 * t1734 + t1740 = t1735 ** 2 + t1745 = 68 - 1564 * t1729 + 5576 * t1734 - 8024 * t1736 + 5236 * t1738 - 1292 * t1740 + t1744 = 24 - 1128 * t1729 + 8304 * t1734 - 21072 * t1736 + 21624 * t1738 - 7752 * t1740 + t1720 = t1730 * t1740 + t1722 = t1730 * t1738 + t1724 = t1730 * t1736 + t1743 = -1615 * t1720 + 6851 * t1722 - 11254 * t1724 + 391 * t1730 - 3179 * t1733 + 8806 * t1735 + t1742 = -19380 * t1720 + 62628 * t1722 - 74856 * t1724 + 564 * t1730 - 8868 * t1733 + 39912 * t1735 + t1732 = 4 * phi1 + t1731 = 8 * phi1 + tfunc[..., c] = (0.7e1 / 0.4096e4*1j) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * ((41990 * t1720 - 121550 * t1722 + 128700 * t1724 - 60060 * t1735 + 11550 * t1733 - 630 * t1730) * np.exp((1j) * phi2) + (t1742 - t1744) * np.exp((-1*1j) * (t1732 - phi2)) + (t1743 - t1745) * np.exp((-1*1j) * (t1731 - phi2)) + (t1742 + t1744) * np.exp((1j) * (t1732 + phi2)) + (t1743 + t1745) * np.exp((1j) * (t1731 + phi2))) * ((1 + t1730) ** (-0.1e1 / 0.2e1)) * ((1 - t1730) ** (-0.1e1 / 0.2e1)) + + if Bindx == 71: + t1760 = np.cos(phi) + t1759 = t1760 ** 2 + t1764 = t1759 ** 2 + t1768 = t1764 ** 2 + t1752 = t1760 * t1768 + t1763 = t1760 * t1759 + t1766 = t1763 ** 2 + t1754 = t1760 * t1766 + t1765 = t1760 * t1764 + t1774 = -2584 * t1752 + 8432 * t1754 - 680 * t1760 + 4624 * t1763 - 9792 * t1765 + t1773 = -15504 * t1752 + 35360 * t1754 - 496 * t1760 + 7136 * t1763 - 26496 * t1765 + t1751 = t1765 ** 2 + t1772 = 17 + 1615 * t1751 - 357 * t1759 - 646 * t1764 + 3910 * t1766 - 4539 * t1768 + t1771 = -52 + 19380 * t1751 + 2340 * t1759 - 17128 * t1764 + 45032 * t1766 - 49572 * t1768 + t1762 = 2 * phi1 + t1761 = 4 * phi1 + tfunc[..., c] = -(0.21e2 / 0.8192e4) * np.sqrt(0.13e2) * np.sqrt(0.11e2) * ((-41990 * t1751 + 103870 * t1768 - 89180 * t1766 + 30940 * t1764 - 3710 * t1759 + 70) * np.exp((2*1j) * phi2) + (t1771 + t1773) * np.exp((-2*1j) * (t1762 - phi2)) + (t1772 + t1774) * np.exp((-2*1j) * (t1761 - phi2)) + (t1771 - t1773) * np.exp((2*1j) * (t1762 + phi2)) + (t1772 - t1774) * np.exp((2*1j) * (t1761 + phi2))) + + if Bindx == 72: + t1790 = np.cos(phi) + t1789 = t1790 ** 2 + t1796 = t1789 ** 2 + t1795 = t1790 * t1789 + t1798 = t1795 ** 2 + t1800 = t1796 ** 2 + t1797 = t1790 * t1796 + t1802 = t1797 ** 2 + t1807 = 68 - 1292 * t1789 + 7344 * t1796 - 14960 * t1798 + 12716 * t1800 - 3876 * t1802 + t1806 = 88 - 3784 * t1789 + 26016 * t1796 - 63392 * t1798 + 64328 * t1800 - 23256 * t1802 + t1780 = t1790 * t1802 + t1782 = t1790 * t1800 + t1784 = t1790 * t1798 + t1805 = 1615 * t1780 - 2839 * t1782 - 1938 * t1784 + 765 * t1790 - 4301 * t1795 + 6698 * t1797 + t1804 = 19380 * t1780 - 53652 * t1782 + 54408 * t1784 - 324 * t1790 + 4996 * t1795 - 24808 * t1797 + t1794 = 4 * phi1 + t1793 = 8 * phi1 + t1792 = -3 * phi2 + t1791 = 3 * phi2 + tfunc[..., c] = (-0.21e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.11e2) * ((-41990 * t1780 + 130390 * t1782 - 148460 * t1784 + 74620 * t1797 - 15470 * t1795 + 910 * t1790) * np.exp((3*1j) * phi2) + (t1804 + t1806) * np.exp((-1*1j) * (t1794 + t1792)) + (t1805 + t1807) * np.exp((-1*1j) * (t1793 + t1792)) + (t1804 - t1806) * np.exp((1j) * (t1794 + t1791)) + (t1805 - t1807) * np.exp((1j) * (t1793 + t1791))) * ((1 + t1790) ** (-0.1e1 / 0.2e1)) * ((1 - t1790) ** (-0.1e1 / 0.2e1)) + + if Bindx == 73: + t1822 = np.cos(phi) + t1821 = t1822 ** 2 + t1825 = t1821 ** 2 + t1829 = t1825 ** 2 + t1814 = t1822 * t1829 + t1824 = t1822 * t1821 + t1827 = t1824 ** 2 + t1816 = t1822 * t1827 + t1826 = t1822 * t1825 + t1835 = -5168 * t1814 + 10880 * t1816 + 544 * t1822 - 544 * t1824 - 5712 * t1826 + t1834 = -31008 * t1814 + 69632 * t1816 - 1216 * t1822 + 15424 * t1824 - 53088 * t1826 + t1813 = t1826 ** 2 + t1833 = 4 + 19380 * t1813 - 156 * t1821 - 2296 * t1825 + 19432 * t1827 - 36108 * t1829 + t1832 = 187 + 1615 * t1813 - 2805 * t1821 + 9758 * t1825 - 10234 * t1827 + 1479 * t1829 + t1823 = 2 * phi1 + tfunc[..., c] = -(0.21e2 / 0.4096e4) * np.sqrt(0.11e2) * ((-41990 * t1813 + 117130 * t1829 - 114140 * t1827 + 44980 * t1825 - 6110 * t1821 + 130) * np.exp((4*1j) * phi2) + (t1833 + t1834) * np.exp((-4*1j) * (phi1 - phi2)) + (t1832 + t1835) * np.exp((-4*1j) * (t1823 - phi2)) + (t1833 - t1834) * np.exp((4*1j) * (phi1 + phi2)) + (t1832 - t1835) * np.exp((4*1j) * (t1823 + phi2))) + + if Bindx == 74: + t1851 = np.cos(phi) + t1850 = t1851 ** 2 + t1857 = t1850 ** 2 + t1861 = t1857 ** 2 + t1858 = t1851 * t1857 + t1863 = t1858 ** 2 + t1868 = -68 + 748 * t1850 - 1632 * t1857 + 2244 * t1861 - 1292 * t1863 + t1856 = t1851 * t1850 + t1859 = t1856 ** 2 + t1867 = 40 - 1400 * t1850 + 8640 * t1857 - 20608 * t1859 + 21080 * t1861 - 7752 * t1863 + t1841 = t1851 * t1863 + t1843 = t1851 * t1861 + t1845 = t1851 * t1859 + t1866 = 323 * t1841 + 1037 * t1843 - 3978 * t1845 + 17 * t1851 - 969 * t1856 + 3570 * t1858 + t1865 = 3876 * t1841 - 7140 * t1843 + 1640 * t1845 + 172 * t1851 - 1740 * t1856 + 3192 * t1858 + t1855 = 4 * phi1 + t1854 = 8 * phi1 + t1853 = -5 * phi2 + t1852 = 5 * phi2 + tfunc[..., c] = (-0.21e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.11e2) * ((1 + t1851) ** (-0.1e1 / 0.2e1)) * ((1 - t1851) ** (-0.1e1 / 0.2e1)) * ((-8398 * t1841 + 29614 * t1843 - 38844 * t1845 + 22828 * t1858 - 5590 * t1856 + 390 * t1851) * np.exp((5*1j) * phi2) + (t1866 + t1868) * np.exp((-1*1j) * (t1854 + t1853)) + (t1866 - t1868) * np.exp((1j) * (t1854 + t1852)) + (t1865 + t1867) * np.exp((-1*1j) * (t1855 + t1853)) + (t1865 - t1867) * np.exp((1j) * (t1855 + t1852))) + + if Bindx == 75: + t1883 = np.cos(phi) + t1882 = t1883 ** 2 + t1889 = t1882 ** 2 + t1893 = t1889 ** 2 + t1875 = t1883 * t1893 + t1888 = t1883 * t1882 + t1891 = t1888 ** 2 + t1877 = t1883 * t1891 + t1890 = t1883 * t1889 + t1899 = -7752 * t1875 + 1360 * t1877 + 1224 * t1883 - 10064 * t1888 + 15232 * t1890 + t1874 = t1890 ** 2 + t1898 = -527 + 1615 * t1874 + 2635 * t1882 + 4522 * t1889 - 19754 * t1891 + 11509 * t1893 + t1897 = 332 + 19380 * t1874 - 9628 * t1882 + 35672 * t1889 - 32088 * t1891 - 13668 * t1893 + t1896 = 46512 * t1875 - 101728 * t1877 + 1872 * t1883 - 20128 * t1888 + 73472 * t1890 + t1887 = 2 * phi1 + t1886 = 4 * phi1 + t1885 = -3 * phi2 + t1884 = 3 * phi2 + tfunc[..., c] = -(0.21e2 / 0.16384e5) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((-41990 * t1874 + 139230 * t1893 - 166140 * t1891 + 82940 * t1889 - 14430 * t1882 + 390) * np.exp((6*1j) * phi2) + (-t1896 + t1897) * np.exp((-2*1j) * (t1887 + t1885)) + (t1898 + t1899) * np.exp((-2*1j) * (t1886 + t1885)) + (t1896 + t1897) * np.exp((2*1j) * (t1887 + t1884)) + (t1898 - t1899) * np.exp((2*1j) * (t1886 + t1884))) + + if Bindx == 76: + t1915 = np.cos(phi) + t1914 = t1915 ** 2 + t1921 = t1914 ** 2 + t1920 = t1915 * t1914 + t1923 = t1920 ** 2 + t1925 = t1921 ** 2 + t1922 = t1915 * t1921 + t1927 = t1922 ** 2 + t1932 = 44 + 44 * t1914 - 1208 * t1921 + 1960 * t1923 - 308 * t1925 - 532 * t1927 + t1931 = 8 - 184 * t1914 + 2416 * t1921 - 7504 * t1923 + 8456 * t1925 - 3192 * t1927 + t1905 = t1915 * t1927 + t1907 = t1915 * t1925 + t1909 = t1915 * t1923 + t1930 = 95 * t1905 + 1013 * t1907 - 1514 * t1909 - 175 * t1915 + 763 * t1920 - 182 * t1922 + t1929 = 1140 * t1905 - 516 * t1907 - 4376 * t1909 + 364 * t1915 - 2716 * t1920 + 6104 * t1922 + t1919 = 4 * phi1 + t1918 = 8 * phi1 + t1917 = -7 * phi2 + t1916 = 7 * phi2 + tfunc[..., c] = (-0.21e2 / 0.8192e4*1j) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * ((1 + t1915) ** (-0.1e1 / 0.2e1)) * ((1 - t1915) ** (-0.1e1 / 0.2e1)) * ((-2470 * t1905 + 10270 * t1907 - 16380 * t1909 + 12220 * t1922 - 4030 * t1920 + 390 * t1915) * np.exp((7*1j) * phi2) + (t1929 + t1931) * np.exp((-1*1j) * (t1919 + t1917)) + (t1930 + t1932) * np.exp((-1*1j) * (t1918 + t1917)) + (t1929 - t1931) * np.exp((1j) * (t1919 + t1916)) + (t1930 - t1932) * np.exp((1j) * (t1918 + t1916))) + + if Bindx == 77: + t1947 = np.cos(phi) + t1946 = t1947 ** 2 + t1949 = t1946 ** 2 + t1953 = t1949 ** 2 + t1939 = t1947 * t1953 + t1948 = t1947 * t1946 + t1951 = t1948 ** 2 + t1941 = t1947 * t1951 + t1950 = t1947 * t1949 + t1959 = -608 * t1939 - 1536 * t1941 - 320 * t1947 + 192 * t1948 + 2016 * t1950 + t1958 = -3648 * t1939 + 7680 * t1941 + 384 * t1947 - 384 * t1948 - 4032 * t1950 + t1938 = t1950 ** 2 + t1957 = 59 + 95 * t1938 + 531 * t1946 - 1722 * t1949 - 210 * t1951 + 1503 * t1953 + t1956 = 132 + 1140 * t1938 - 1980 * t1946 + 6888 * t1949 - 7224 * t1951 + 1044 * t1953 + tfunc[..., c] = -(0.7e1 / 0.8192e4) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.11e2) * ((-2470 * t1938 + 10010 * t1953 - 15340 * t1951 + 10660 * t1949 - 2990 * t1946 + 130) * np.exp((8*1j) * phi2) + (t1957 + t1959) * np.exp((-8*1j) * (phi1 - phi2)) + (t1956 + t1958) * np.exp((-4*1j) * (phi1 - 2 * phi2)) + (t1956 - t1958) * np.exp((4*1j) * (phi1 + 2 * phi2)) + (t1957 - t1959) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 78: + t1974 = np.cos(phi) + t1973 = t1974 ** 2 + t1980 = t1973 ** 2 + t1981 = t1974 * t1980 + t1965 = t1981 ** 2 + t1988 = 5 * t1965 - 84 * t1980 + 126 * t1981 + t1987 = 60 * t1965 + 336 * t1980 - 504 * t1981 + t1984 = t1980 ** 2 + t1979 = t1974 * t1973 + t1982 = t1979 ** 2 + t1978 = 4 * phi1 + t1977 = 8 * phi1 + t1976 = -9 * phi2 + t1975 = 9 * phi2 + t1968 = t1974 * t1982 + t1966 = t1974 * t1984 + tfunc[..., c] = (0.7e1 / 0.8192e4*1j) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt((1 - t1974)) * ((1 + t1974) ** (-0.1e1 / 0.2e1)) * (130 * (-t1965 - t1966 + 4 * t1984 + 4 * t1968 - 6 * t1982 - 6 * t1981 + 4 * t1980 + 4 * t1979 - t1973 - t1974) * np.exp((9*1j) * phi2) + (-156 * t1966 - 72 * t1984 + 480 * t1968 - 168 * t1982 + 192 * t1979 - 180 * t1973 - 12 * t1974 + 24 + t1987) * np.exp((-1*1j) * (t1978 + t1976)) + (-31 * t1966 + 72 * t1984 - 60 * t1968 - 42 * t1982 - 12 * t1979 + 45 * t1973 - 23 * t1974 + 4 + t1988) * np.exp((-1*1j) * (t1977 + t1976)) + (276 * t1966 + 360 * t1984 - 192 * t1968 - 840 * t1982 + 480 * t1979 + 108 * t1973 - 60 * t1974 - 24 + t1987) * np.exp((1j) * (t1978 + t1975)) + (41 * t1966 + 144 * t1984 + 276 * t1968 + 294 * t1982 - 156 * t1979 - 99 * t1973 - 31 * t1974 - 4 + t1988) * np.exp((1j) * (t1977 + t1975))) + + if Bindx == 79: + t2002 = np.cos(phi) + t2001 = t2002 ** 2 + t2008 = t2001 ** 2 + t2011 = t2008 ** 2 + t2012 = t2002 * t2011 + t2018 = t2002 - t2012 + t2007 = t2002 * t2001 + t2009 = t2007 ** 2 + t1997 = t2002 * t2009 + t2017 = -48 * t1997 + 48 * t2007 + 8 * t2018 + t2016 = 96 * t1997 - 96 * t2007 + 48 * t2018 + t1994 = t2002 * t2012 + t2015 = 1 - t1994 + 27 * t2001 + 42 * t2008 - 42 * t2009 - 27 * t2011 + t2014 = 12 - 12 * t1994 + 36 * t2001 - 168 * t2008 + 168 * t2009 - 36 * t2011 + t2006 = 2 * phi1 + t2005 = 4 * phi1 + t2004 = -5 * phi2 + t2003 = 5 * phi2 + tfunc[..., c] = (0.7e1 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * ((26 * t1994 - 130 * t2011 + 260 * t2009 - 260 * t2008 + 130 * t2001 - 26) * np.exp((10*1j) * phi2) + (t2015 - t2017) * np.exp((-2*1j) * (t2005 + t2004)) + (t2015 + t2017) * np.exp((2*1j) * (t2005 + t2003)) + (t2014 - t2016) * np.exp((-2*1j) * (t2006 + t2004)) + (t2014 + t2016) * np.exp((2*1j) * (t2006 + t2003))) + + if Bindx == 80: + t2035 = np.cos(phi) + t2034 = t2035 ** 2 + t2040 = t2034 ** 2 + t2041 = t2035 * t2040 + t2046 = t2041 ** 2 + t2053 = (-t2046 - 1) * t2035 + t2044 = t2040 ** 2 + t2027 = t2035 * t2044 + t2039 = t2035 * t2034 + t2042 = t2039 ** 2 + t2029 = t2035 * t2042 + t2052 = -40 * t2027 + 48 * t2029 - 40 * t2039 + 48 * t2041 + 8 * t2053 + t2051 = 48 * t2027 - 32 * t2029 + 48 * t2039 - 32 * t2041 + 16 * t2053 + t2024 = t2042 ** 2 + t2050 = 4 + 4 * t2024 + 8 * t2034 - 68 * t2040 + 112 * t2042 - 68 * t2044 + 8 * t2046 + t2049 = -1 - t2024 - 26 * t2034 - 15 * t2040 + 84 * t2042 - 15 * t2044 - 26 * t2046 + t2038 = 2 * phi1 + t2037 = -3 * phi2 + t2036 = 3 * phi2 + tfunc[..., c] = -(0.15e2 / 0.671744e6) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * ((-6 * t2024 + 36 * t2046 - 90 * t2044 + 120 * t2042 - 90 * t2040 + 36 * t2034 - 6) * np.exp((-12*1j) * phi2) + (t2049 + t2052) * np.exp((-4*1j) * (t2038 + t2036)) + (t2049 - t2052) * np.exp((4*1j) * (t2038 + t2037)) + (t2050 - t2051) * np.exp((-4*1j) * (phi1 + t2036)) + (t2050 + t2051) * np.exp((4*1j) * (phi1 + t2037))) + + if Bindx == 81: + t2072 = np.cos(phi) + t2071 = t2072 ** 2 + t2077 = t2071 ** 2 + t2081 = t2077 ** 2 + t2094 = t2081 + t2077 + t2078 = t2072 * t2077 + t2083 = t2078 ** 2 + t2093 = t2083 + t2071 + t2062 = t2072 * t2083 + t2092 = -t2062 - t2072 + t2076 = t2072 * t2071 + t2079 = t2076 ** 2 + t2061 = t2079 ** 2 + t2091 = 1025 * t2061 + 67650 * t2071 + 947100 * t2079 + 67650 * t2083 + 507375 * t2094 + 1025 + t2064 = t2072 * t2081 + t2066 = t2072 * t2079 + t2090 = -12300 * t2062 - 12300 * t2072 - 811800 * t2066 - 811800 * t2078 - 225500 * t2064 - 225500 * t2076 + t2089 = 510048 * t2066 + 510048 * t2078 - 425040 * t2064 - 425040 * t2076 + 85008 * t2092 + t2088 = 10626 * t2061 - 892584 * t2079 + 276276 * t2093 + 159390 * t2094 + 10626 + t2087 = 8825652 * t2064 + 8825652 * t2076 - 5883768 * t2066 - 5883768 * t2078 + 2941884 * t2092 + t2086 = 735471 * t2061 + 20593188 * t2079 + 1470942 * t2093 - 12503007 * t2094 + 735471 + t2075 = 2 * phi1 + t2074 = -3 * phi2 + t2073 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.32243712e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * ((-54083120 * t2079 + 2704156 * t2061 + 2704156 + 40562340 * t2081 + 40562340 * t2077 - 16224936 * t2083 - 16224936 * t2071) * np.exp((-12*1j) * phi2) + (t2086 - t2087) * np.exp((-4*1j) * (phi1 + t2073)) + (t2088 - t2089) * np.exp((-4*1j) * (t2075 + t2073)) + (t2086 + t2087) * np.exp((4*1j) * (phi1 + t2074)) + (t2088 + t2089) * np.exp((4*1j) * (t2075 + t2074)) + (-t2090 + t2091) * np.exp((-12*1j) * (phi1 + phi2)) + (t2090 + t2091) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 82: + t2110 = np.cos(phi) + t2109 = t2110 ** 2 + t2116 = t2109 ** 2 + t2117 = t2110 * t2116 + t2122 = t2117 ** 2 + t2100 = t2110 * t2122 + t2125 = 3 * t2100 + t2124 = 1 - t2110 + t2120 = t2116 ** 2 + t2115 = t2110 * t2109 + t2118 = t2115 ** 2 + t2114 = 4 * phi1 + t2113 = 8 * phi1 + t2112 = -11 * phi2 + t2111 = 11 * phi2 + t2104 = t2110 * t2118 + t2102 = t2110 * t2120 + tfunc[..., c] = (0.5e1 / 0.335872e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * ((1 + t2110) ** (0.3e1 / 0.2e1)) * (t2124 ** (-0.1e1 / 0.2e1)) * (18 * (t2100 - 2 * t2122 - 3 * t2102 + 8 * t2120 + 2 * t2104 - 12 * t2118 + 2 * t2117 + 8 * t2116 - 3 * t2115 - 2 * t2109 + t2110) * np.exp((-11*1j) * phi2) + 4 * (-3 * t2100 - 5 * t2122 + 11 * t2102 + 21 * t2120 - 14 * t2104 - 34 * t2118 + 6 * t2117 + 26 * t2116 + t2115 - 9 * t2109 + t2124) * np.exp((-1*1j) * (t2114 + t2111)) + (t2125 + 16 * t2122 + 27 * t2102 - 2 * t2120 - 58 * t2104 - 56 * t2118 + 14 * t2117 + 52 * t2116 + 23 * t2115 - 8 * t2109 - 9 * t2110 - 2) * np.exp((-1*1j) * (t2113 + t2111)) + (-12 * t2100 + 68 * t2122 - 132 * t2102 + 44 * t2120 + 200 * t2104 - 280 * t2118 + 56 * t2117 + 152 * t2116 - 124 * t2115 + 20 * t2109 + 12 * t2110 - 4) * np.exp((1j) * (t2114 + t2112)) + (t2125 - 28 * t2122 + 115 * t2102 - 270 * t2120 + 390 * t2104 - 336 * t2118 + 126 * t2117 + 60 * t2116 - 105 * t2115 + 60 * t2109 - 17 * t2110 + 2) * np.exp((1j) * (t2113 + t2112))) + + if Bindx == 83: + t2144 = np.cos(phi) + t2143 = t2144 ** 2 + t2150 = t2144 * t2143 + t2153 = t2150 ** 2 + t2133 = t2153 ** 2 + t2162 = 1025 * t2133 + t2161 = 10626 * t2133 + t2160 = 735471 * t2133 + t2151 = t2143 ** 2 + t2152 = t2144 * t2151 + t2157 = t2152 ** 2 + t2155 = t2151 ** 2 + t2149 = 4 * phi1 + t2148 = 8 * phi1 + t2147 = 12 * phi1 + t2146 = -11 * phi2 + t2145 = 11 * phi2 + t2138 = t2144 * t2153 + t2136 = t2144 * t2155 + t2134 = t2144 * t2157 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.41e2) * np.sqrt((1 + t2144)) * ((1 - t2144) ** (-0.1e1 / 0.2e1)) * (2704156 * (10 * t2155 - 10 * t2138 - 10 * t2153 + 10 * t2152 - 5 * t2157 + 5 * t2136 + 5 * t2151 - 5 * t2150 + t2133 - t2134 - t2143 + t2144) * np.exp((-11*1j) * phi2) + (-1716099 * t2155 - 7845024 * t2136 + 11767536 * t2138 + 2451570 * t2143 - 6619239 * t2151 - 7845024 * t2152 + 6864396 * t2153 - 1470942 * t2157 + t2160 - 245157 + 1961256 * t2134 + 1961256 * t2150) * np.exp((-1*1j) * (t2149 + t2145)) + (t2162 + 10250 * t2134 + 45100 * t2157 - 45100 * t2143 - 10250 * t2144 - 1025 + 169125 * t2155 - 169125 * t2151 + 135300 * t2138 - 135300 * t2152 + 112750 * t2136 - 112750 * t2150) * np.exp((-1*1j) * (t2147 + t2145)) + (67298 * t2134 + 88550 * t2136 - 403788 * t2138 - 60214 * t2143 - 38962 * t2144 + 53130 * t2150 + 265650 * t2151 + 233772 * t2152 - 148764 * t2153 - 212520 * t2155 + 152306 * t2157 + t2161 - 7084) * np.exp((-1*1j) * (t2148 + t2145)) + (-3432198 * t2134 + 5393454 * t2136 + 4903140 * t2138 - 1961256 * t2143 - 490314 * t2144 + 6374082 * t2150 - 1716099 * t2151 - 12748164 * t2152 + 13728792 * t2153 - 14954577 * t2155 + 3922512 * t2157 + t2160 + 245157) * np.exp((1j) * (t2149 + t2146)) + (-88550 * t2134 - 549010 * t2136 + 191268 * t2138 + 152306 * t2143 - 53130 * t2144 - 159390 * t2150 - 159390 * t2151 + 658812 * t2152 - 743820 * t2153 + 425040 * t2155 + 308154 * t2157 + t2161 + 7084) * np.exp((1j) * (t2148 + t2146)) + (t2162 - 12300 * t2134 + 67650 * t2157 + 947100 * t2153 + 67650 * t2143 - 12300 * t2144 + 1025 - 811800 * t2138 - 811800 * t2152 + 507375 * t2155 + 507375 * t2151 - 225500 * t2136 - 225500 * t2150) * np.exp((1j) * (t2147 + t2146))) + + if Bindx == 84: + t2179 = np.cos(phi) + t2178 = t2179 ** 2 + t2185 = t2178 ** 2 + t2186 = t2179 * t2185 + t2191 = t2186 ** 2 + t2169 = t2179 * t2191 + t2189 = t2185 ** 2 + t2171 = t2179 * t2189 + t2184 = t2179 * t2178 + t2187 = t2184 ** 2 + t2173 = t2179 * t2187 + t2197 = -920 * t2169 + 2680 * t2171 - 2416 * t2173 - 104 * t2179 + 392 * t2184 + 368 * t2186 + t2196 = -460 * t2169 - 1060 * t2171 + 2472 * t2173 + 140 * t2179 - 540 * t2184 - 552 * t2186 + t2168 = t2187 ** 2 + t2195 = -29 + 69 * t2168 - 116 * t2178 + 1335 * t2185 - 1680 * t2187 - 735 * t2189 + 1156 * t2191 + t2194 = 20 - 276 * t2168 - 400 * t2178 + 2020 * t2185 - 3584 * t2187 + 2332 * t2189 - 112 * t2191 + t2183 = 2 * phi1 + t2182 = 4 * phi1 + t2181 = -5 * phi2 + t2180 = 5 * phi2 + tfunc[..., c] = (0.5e1 / 0.335872e6) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((414 * t2168 - 2088 * t2191 + 4230 * t2189 - 4320 * t2187 + 2250 * t2185 - 504 * t2178 + 18) * np.exp((-10*1j) * phi2) + (t2194 + t2197) * np.exp((-2*1j) * (t2183 + t2180)) + (t2195 - t2196) * np.exp((-2*1j) * (t2182 + t2180)) + (t2194 - t2197) * np.exp((2*1j) * (t2183 + t2181)) + (t2195 + t2196) * np.exp((2*1j) * (t2182 + t2181))) + + if Bindx == 85: + t2216 = np.cos(phi) + t2215 = t2216 ** 2 + t2222 = t2216 * t2215 + t2225 = t2222 ** 2 + t2205 = t2225 ** 2 + t2223 = t2215 ** 2 + t2227 = t2223 ** 2 + t2224 = t2216 * t2223 + t2229 = t2224 ** 2 + t2237 = 1025 * t2205 - 45100 * t2215 + 45100 * t2229 - 1025 + 169125 * t2227 - 169125 * t2223 + t2206 = t2216 * t2229 + t2208 = t2216 * t2227 + t2210 = t2216 * t2225 + t2236 = -70840 * t2206 - 163240 * t2208 + 380688 * t2210 + 21560 * t2216 - 83160 * t2222 - 85008 * t2224 + t2235 = -10250 * t2206 + 10250 * t2216 - 135300 * t2210 + 135300 * t2224 - 112750 * t2208 + 112750 * t2222 + t2234 = 10626 * t2205 - 17864 * t2215 + 205590 * t2223 - 258720 * t2225 - 113190 * t2227 + 178024 * t2229 - 4466 + t2233 = -2451570 * t2206 + 7141530 * t2208 - 6438036 * t2210 - 277134 * t2216 + 1044582 * t2222 + 980628 * t2224 + t2232 = 735471 * t2205 + 1065900 * t2215 - 5382795 * t2223 + 9550464 * t2225 - 6214197 * t2227 + 298452 * t2229 - 53295 + t2221 = 2 * phi1 + t2220 = 4 * phi1 + t2219 = 6 * phi1 + t2218 = -5 * phi2 + t2217 = 5 * phi2 + tfunc[..., c] = (0.5e1 / 0.5373952e7) * np.sqrt(0.2e1) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * ((2704156 * t2205 - 13638352 * t2229 + 27629420 * t2227 - 28217280 * t2225 + 14696500 * t2223 - 3292016 * t2215 + 117572) * np.exp((-10*1j) * phi2) + (-t2235 + t2237) * np.exp((-2*1j) * (t2219 + t2217)) + (t2235 + t2237) * np.exp((2*1j) * (t2219 + t2218)) + (t2232 - t2233) * np.exp((-2*1j) * (t2221 + t2217)) + (t2234 - t2236) * np.exp((-2*1j) * (t2220 + t2217)) + (t2232 + t2233) * np.exp((2*1j) * (t2221 + t2218)) + (t2234 + t2236) * np.exp((2*1j) * (t2220 + t2218))) + + if Bindx == 86: + t2254 = np.cos(phi) + t2253 = t2254 ** 2 + t2259 = t2254 * t2253 + t2262 = t2259 ** 2 + t2243 = t2262 ** 2 + t2270 = 253 * t2243 + t2269 = -1012 * t2243 + t2260 = t2253 ** 2 + t2261 = t2254 * t2260 + t2266 = t2261 ** 2 + t2264 = t2260 ** 2 + t2258 = 4 * phi1 + t2257 = 8 * phi1 + t2256 = -9 * phi2 + t2255 = 9 * phi2 + t2248 = t2254 * t2262 + t2246 = t2254 * t2264 + t2244 = t2254 * t2266 + tfunc[..., c] = (0.15e2 / 0.335872e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * np.sqrt(0.41e2) * np.sqrt((1 + t2254)) * ((1 - t2254) ** (-0.1e1 / 0.2e1)) * ((1518 * t2243 - 1518 * t2244 - 6270 * t2266 + 6270 * t2246 + 9900 * t2264 - 9900 * t2248 - 7260 * t2262 + 7260 * t2261 + 2310 * t2260 - 2310 * t2259 - 198 * t2253 + 198 * t2254) * np.exp((-9*1j) * phi2) + (t2269 - 2024 * t2244 + 2816 * t2266 + 7432 * t2246 - 2036 * t2264 - 10384 * t2248 - 672 * t2262 + 6800 * t2261 + 1252 * t2260 - 2056 * t2259 - 352 * t2253 + 232 * t2254 + 4) * np.exp((-1*1j) * (t2258 + t2255)) + (t2270 + 1265 * t2244 + 1837 * t2266 - 865 * t2246 - 4680 * t2264 - 2934 * t2248 + 2730 * t2262 + 3774 * t2261 + 285 * t2260 - 1355 * t2259 - 487 * t2253 + 115 * t2254 + 62) * np.exp((-1*1j) * (t2257 + t2255)) + (t2269 + 4048 * t2244 - 3256 * t2266 - 6992 * t2246 + 12388 * t2264 + 32 * t2248 - 11088 * t2262 + 4960 * t2261 + 3092 * t2260 - 2288 * t2259 - 120 * t2253 + 240 * t2254 - 4) * np.exp((1j) * (t2258 + t2256)) + (t2270 - 1771 * t2244 + 4873 * t2266 - 5845 * t2246 + 300 * t2264 + 7314 * t2248 - 7518 * t2262 + 1014 * t2261 + 3045 * t2260 - 1975 * t2259 + 133 * t2253 + 239 * t2254 - 62) * np.exp((1j) * (t2257 + t2256))) + + if Bindx == 87: + t2289 = np.cos(phi) + t2288 = t2289 ** 2 + t2294 = t2289 * t2288 + t2297 = t2294 ** 2 + t2278 = t2297 ** 2 + t2306 = 1025 * t2278 + t2305 = 10626 * t2278 + t2304 = 735471 * t2278 + t2295 = t2288 ** 2 + t2296 = t2289 * t2295 + t2301 = t2296 ** 2 + t2299 = t2295 ** 2 + t2293 = 4 * phi1 + t2292 = 8 * phi1 + t2291 = -9 * phi2 + t2290 = 9 * phi2 + t2283 = t2289 * t2297 + t2281 = t2289 * t2299 + t2279 = t2289 * t2301 + tfunc[..., c] = (0.5e1 / 0.8060928e7*1j) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.3e1) * np.sqrt((1 + t2289)) * ((1 - t2289) ** (-0.1e1 / 0.2e1)) * ((17635800 * t2299 - 17635800 * t2283 - 12932920 * t2297 + 12932920 * t2296 - 11169340 * t2301 + 11169340 * t2281 + 4115020 * t2295 - 4115020 * t2294 + 2704156 * t2278 - 2704156 * t2279 - 352716 * t2288 + 352716 * t2289) * np.exp((-9*1j) * phi2) + (t2306 - 10250 * t2279 + 45100 * t2301 - 45100 * t2288 + 10250 * t2289 - 1025 + 169125 * t2299 - 169125 * t2295 - 135300 * t2283 + 135300 * t2296 - 112750 * t2281 + 112750 * t2294) * np.exp((3*1j) * (t2293 - 3 * phi2)) + (t2306 + 8200 * t2279 + 26650 * t2301 + 41000 * t2281 + 15375 * t2299 - 49200 * t2283 - 86100 * t2297 - 49200 * t2296 + 15375 * t2295 + 41000 * t2294 + 26650 * t2288 + 8200 * t2289 + 1025) * np.exp((-3*1j) * (t2293 + 3 * phi2)) + (1470942 * t2279 - 5401206 * t2281 + 7546572 * t2283 + 255816 * t2288 - 168606 * t2289 + 1494198 * t2294 - 909891 * t2295 - 4941900 * t2296 + 488376 * t2297 + 1479663 * t2299 - 2046528 * t2301 - 2907 + t2304) * np.exp((-1*1j) * (t2293 + t2290)) + (t2305 + 53130 * t2279 + 77154 * t2301 - 36330 * t2281 - 196560 * t2299 - 123228 * t2283 + 114660 * t2297 + 158508 * t2296 + 11970 * t2295 - 56910 * t2294 - 20454 * t2288 + 4830 * t2289 + 2604) * np.exp((-1*1j) * (t2292 + t2290)) + (-2941884 * t2279 + 5081436 * t2281 - 23256 * t2283 + 87210 * t2288 - 174420 * t2289 + 1662804 * t2294 - 2247111 * t2295 - 3604680 * t2296 + 8058204 * t2297 - 9002979 * t2299 + 2366298 * t2301 + 2907 + t2304) * np.exp((1j) * (t2293 + t2291)) + (t2305 - 74382 * t2279 + 204666 * t2301 - 245490 * t2281 + 12600 * t2299 + 307188 * t2283 - 315756 * t2297 + 42588 * t2296 + 127890 * t2295 - 82950 * t2294 + 5586 * t2288 + 10038 * t2289 - 2604) * np.exp((1j) * (t2292 + t2291))) + + if Bindx == 88: + t2323 = np.cos(phi) + t2322 = t2323 ** 2 + t2325 = t2322 ** 2 + t2326 = t2323 * t2325 + t2331 = t2326 ** 2 + t2313 = t2323 * t2331 + t2329 = t2325 ** 2 + t2315 = t2323 * t2329 + t2324 = t2323 * t2322 + t2327 = t2324 ** 2 + t2317 = t2323 * t2327 + t2337 = -28336 * t2313 - 2800 * t2315 + 84384 * t2317 - 176 * t2323 + 15120 * t2324 - 69216 * t2326 + t2312 = t2327 ** 2 + t2336 = -673 - 5313 * t2312 + 9422 * t2322 - 22575 * t2325 - 20412 * t2327 + 87345 * t2329 - 48818 * t2331 + t2335 = 21252 * t2312 + 9576 * t2322 - 53956 * t2325 + 103600 * t2327 - 61124 * t2329 - 19096 * t2331 - 252 + t2334 = 56672 * t2313 - 161056 * t2315 + 161728 * t2317 - 672 * t2323 + 10976 * t2324 - 67648 * t2326 + tfunc[..., c] = -(0.5e1 / 0.671744e6) * np.sqrt(0.41e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * ((-31878 * t2312 + 135828 * t2331 - 224730 * t2329 + 178200 * t2327 - 66330 * t2325 + 9108 * t2322 - 198) * np.exp((-8*1j) * phi2) + (t2336 + t2337) * np.exp((-8*1j) * (phi1 + phi2)) + (t2334 + t2335) * np.exp((-4*1j) * (phi1 + 2 * phi2)) + (-t2334 + t2335) * np.exp((4*1j) * (phi1 - 2 * phi2)) + (t2336 - t2337) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 89: + t2356 = np.cos(phi) + t2355 = t2356 ** 2 + t2361 = t2355 ** 2 + t2362 = t2356 * t2361 + t2367 = t2362 ** 2 + t2346 = t2356 * t2367 + t2365 = t2361 ** 2 + t2348 = t2356 * t2365 + t2360 = t2356 * t2355 + t2363 = t2360 ** 2 + t2350 = t2356 * t2363 + t2375 = -8200 * t2346 - 41000 * t2348 + 49200 * t2350 - 8200 * t2356 - 41000 * t2360 + 49200 * t2362 + t2345 = t2363 ** 2 + t2374 = 1025 + 1025 * t2345 + 26650 * t2355 + 15375 * t2361 - 86100 * t2363 + 15375 * t2365 + 26650 * t2367 + t2373 = 10626 * t2345 - 18844 * t2355 + 45150 * t2361 + 40824 * t2363 - 174690 * t2365 + 97636 * t2367 + 1346 + t2372 = 56672 * t2346 + 5600 * t2348 - 168768 * t2350 + 352 * t2356 - 30240 * t2360 + 138432 * t2362 + t2371 = -1961256 * t2346 + 5573688 * t2348 - 5596944 * t2350 + 23256 * t2356 - 379848 * t2360 + 2341104 * t2362 + t2370 = 735471 * t2345 + 331398 * t2355 - 1867263 * t2361 + 3585300 * t2363 - 2115327 * t2365 - 660858 * t2367 - 8721 + t2359 = 3 * phi1 + t2358 = -2 * phi2 + t2357 = 2 * phi2 + tfunc[..., c] = (0.5e1 / 0.5373952e7) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * ((2704156 * t2345 - 11522056 * t2367 + 19063460 * t2365 - 15116400 * t2363 + 5626660 * t2361 - 772616 * t2355 + 16796) * np.exp((-8*1j) * phi2) + (t2372 + t2373) * np.exp((-8*1j) * (phi1 + phi2)) + (t2370 - t2371) * np.exp((-4*1j) * (phi1 + t2357)) + (t2374 - t2375) * np.exp((-4*1j) * (t2359 + t2357)) + (t2370 + t2371) * np.exp((4*1j) * (phi1 + t2358)) + (t2374 + t2375) * np.exp((4*1j) * (t2359 + t2358)) + (-t2372 + t2373) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 90: + t2393 = np.cos(phi) + t2392 = t2393 ** 2 + t2399 = t2392 ** 2 + t2398 = t2393 * t2392 + t2401 = t2398 ** 2 + t2403 = t2399 ** 2 + t2400 = t2393 * t2399 + t2405 = t2400 ** 2 + t2407 = t2401 ** 2 + t2412 = 278 - 6116 * t2392 + 34650 * t2399 - 66360 * t2401 + 29610 * t2403 + 32732 * t2405 - 24794 * t2407 + t2381 = t2393 * t2407 + t2383 = t2393 * t2405 + t2385 = t2393 * t2403 + t2387 = t2393 * t2401 + t2411 = -21252 * t2381 + 41272 * t2383 + 7140 * t2385 - 64624 * t2387 + 924 * t2393 - 13384 * t2398 + 49924 * t2400 + t2410 = 5313 * t2381 + 29414 * t2383 - 95405 * t2385 + 82980 * t2387 + 1141 * t2393 - 3178 * t2398 - 20265 * t2400 + t2409 = -3864 * t2392 + 33740 * t2399 - 121296 * t2401 + 205996 * t2403 - 164248 * t2405 + 49588 * t2407 + 84 + t2397 = 4 * phi1 + t2396 = 8 * phi1 + t2395 = -7 * phi2 + t2394 = 7 * phi2 + tfunc[..., c] = (0.5e1 / 0.335872e6*1j) * np.sqrt(0.17e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * np.sqrt(0.41e2) * ((1 + t2393) ** (-0.1e1 / 0.2e1)) * ((1 - t2393) ** (-0.1e1 / 0.2e1)) * ((31878 * t2381 - 141372 * t2383 + 247698 * t2385 - 214632 * t2387 + 93258 * t2400 - 17820 * t2398 + 990 * t2393) * np.exp((-7*1j) * phi2) + (-t2409 + t2411) * np.exp((-1*1j) * (t2397 + t2394)) + (t2410 - t2412) * np.exp((-1*1j) * (t2396 + t2394)) + (t2409 + t2411) * np.exp((1j) * (t2397 + t2395)) + (t2410 + t2412) * np.exp((1j) * (t2396 + t2395))) + + if Bindx == 91: + t2432 = np.cos(phi) + t2431 = t2432 ** 2 + t2439 = t2431 ** 2 + t2438 = t2432 * t2431 + t2441 = t2438 ** 2 + t2443 = t2439 ** 2 + t2440 = t2432 * t2439 + t2445 = t2440 ** 2 + t2447 = t2441 ** 2 + t2454 = -1025 - 18450 * t2431 + 25625 * t2439 + 36900 * t2441 - 64575 * t2443 + 14350 * t2445 + 7175 * t2447 + t2420 = t2432 * t2447 + t2422 = t2432 * t2445 + t2424 = t2432 * t2443 + t2426 = t2432 * t2441 + t2453 = 1025 * t2420 + 18450 * t2422 - 25625 * t2424 - 36900 * t2426 - 7175 * t2432 - 14350 * t2438 + 64575 * t2440 + t2452 = -12232 * t2431 + 69300 * t2439 - 132720 * t2441 + 59220 * t2443 + 65464 * t2445 - 49588 * t2447 + 556 + t2451 = 10626 * t2420 + 58828 * t2422 - 190810 * t2424 + 165960 * t2426 + 2282 * t2432 - 6356 * t2438 - 40530 * t2440 + t2450 = -133722 * t2431 + 1167645 * t2439 - 4197708 * t2441 + 7128933 * t2443 - 5684154 * t2445 + 1716099 * t2447 + 2907 + t2449 = 735471 * t2420 - 1428306 * t2422 - 247095 * t2424 + 2236452 * t2426 - 31977 * t2432 + 463182 * t2438 - 1727727 * t2440 + t2437 = 4 * phi1 + t2436 = 8 * phi1 + t2435 = -7 * phi2 + t2434 = 7 * phi2 + t2433 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * ((1 + t2432) ** (-0.1e1 / 0.2e1)) * ((1 - t2432) ** (-0.1e1 / 0.2e1)) * ((-18206864 * t2426 + 7910916 * t2440 - 1511640 * t2438 + 83980 * t2432 + 2704156 * t2420 - 11992344 * t2422 + 21011796 * t2424) * np.exp((-7*1j) * phi2) + (t2449 + t2450) * np.exp((-1*1j) * (t2437 + t2434)) + (t2451 - t2452) * np.exp((-1*1j) * (t2436 + t2434)) + (t2449 - t2450) * np.exp((1j) * (t2437 + t2435)) + (t2451 + t2452) * np.exp((1j) * (t2436 + t2435)) + (t2453 + t2454) * np.exp((-1*1j) * (t2433 + t2434)) + (t2453 - t2454) * np.exp((1j) * (t2433 + t2435))) + + if Bindx == 92: + t2471 = np.cos(phi) + t2470 = t2471 ** 2 + t2477 = t2470 ** 2 + t2478 = t2471 * t2477 + t2483 = t2478 ** 2 + t2461 = t2471 * t2483 + t2481 = t2477 ** 2 + t2463 = t2471 * t2481 + t2476 = t2471 * t2470 + t2479 = t2476 ** 2 + t2465 = t2471 * t2479 + t2489 = -134596 * t2461 + 217588 * t2463 - 7752 * t2465 - 7676 * t2471 + 63308 * t2476 - 130872 * t2478 + t2460 = t2479 ** 2 + t2488 = 134596 * t2460 + 10192 * t2470 - 68180 * t2477 + 103936 * t2479 + 77140 * t2481 - 257488 * t2483 - 196 + t2487 = -269192 * t2461 + 750120 * t2463 - 768208 * t2465 + 3976 * t2471 - 69160 * t2476 + 352464 * t2478 + t2486 = -33649 * t2460 - 10108 * t2470 + 106533 * t2477 - 338352 * t2479 + 386403 * t2481 - 111188 * t2483 + 361 + t2475 = 2 * phi1 + t2474 = 4 * phi1 + t2473 = -3 * phi2 + t2472 = 3 * phi2 + tfunc[..., c] = -(0.15e2 / 0.335872e6) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * ((-201894 * t2460 + 737352 * t2483 - 1019502 * t2481 + 653664 * t2479 - 189090 * t2477 + 19800 * t2470 - 330) * np.exp((-6*1j) * phi2) + (-t2487 + t2488) * np.exp((-2*1j) * (t2475 + t2472)) + (t2486 + t2489) * np.exp((-2*1j) * (t2474 + t2472)) + (t2487 + t2488) * np.exp((2*1j) * (t2475 + t2473)) + (t2486 - t2489) * np.exp((2*1j) * (t2474 + t2473))) + + if Bindx == 93: + t2508 = np.cos(phi) + t2507 = t2508 ** 2 + t2513 = t2508 * t2507 + t2516 = t2513 ** 2 + t2497 = t2516 ** 2 + t2514 = t2507 ** 2 + t2518 = t2514 ** 2 + t2515 = t2508 * t2514 + t2520 = t2515 ** 2 + t2528 = -1025 + 1025 * t2497 - 12300 * t2507 + 27675 * t2514 - 27675 * t2518 + 12300 * t2520 + t2498 = t2508 * t2520 + t2500 = t2508 * t2518 + t2502 = t2508 * t2516 + t2527 = -6150 * t2498 - 2050 * t2500 + 36900 * t2502 + 6150 * t2508 + 2050 * t2513 - 36900 * t2515 + t2526 = -42504 * t2498 + 68712 * t2500 - 2448 * t2502 - 2424 * t2508 + 19992 * t2513 - 41328 * t2515 + t2525 = 10626 * t2497 + 3192 * t2507 - 33642 * t2514 + 106848 * t2516 - 122022 * t2518 + 35112 * t2520 - 114 + t2524 = -1470942 * t2498 + 4098870 * t2500 - 4197708 * t2502 + 21726 * t2508 - 377910 * t2513 + 1925964 * t2515 + t2523 = 735471 * t2497 + 55692 * t2507 - 372555 * t2514 + 567936 * t2516 + 421515 * t2518 - 1406988 * t2520 - 1071 + t2512 = 2 * phi1 + t2511 = 4 * phi1 + t2510 = -3 * phi2 + t2509 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.16121856e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * ((2704156 * t2497 - 9876048 * t2520 + 13655148 * t2518 - 8755136 * t2516 + 2532660 * t2514 - 265200 * t2507 + 4420) * np.exp((-6*1j) * phi2) + (-t2527 + t2528) * np.exp((-6*1j) * (t2512 + phi2)) + (t2527 + t2528) * np.exp((6*1j) * (t2512 - phi2)) + (t2523 - t2524) * np.exp((-2*1j) * (t2512 + t2509)) + (t2525 - t2526) * np.exp((-2*1j) * (t2511 + t2509)) + (t2523 + t2524) * np.exp((2*1j) * (t2512 + t2510)) + (t2525 + t2526) * np.exp((2*1j) * (t2511 + t2510))) + + if Bindx == 94: + t2546 = np.cos(phi) + t2545 = t2546 ** 2 + t2552 = t2545 ** 2 + t2551 = t2546 * t2545 + t2554 = t2551 ** 2 + t2556 = t2552 ** 2 + t2553 = t2546 * t2552 + t2558 = t2553 ** 2 + t2560 = t2554 ** 2 + t2565 = -11628 * t2545 + 26562 * t2552 + 94392 * t2554 - 367878 * t2556 + 402420 * t2558 - 144210 * t2560 + 342 + t2534 = t2546 * t2560 + t2536 = t2546 * t2558 + t2538 = t2546 * t2556 + t2540 = t2546 * t2554 + t2564 = 43263 * t2534 + 21318 * t2536 - 341487 * t2538 + 506388 * t2540 - 5985 * t2546 + 72390 * t2551 - 295887 * t2553 + t2563 = -173052 * t2534 + 486552 * t2536 - 475380 * t2538 + 174480 * t2540 + 404 * t2546 - 5544 * t2551 - 7460 * t2553 + t2562 = 19720 * t2545 - 189820 * t2552 + 690736 * t2554 - 1173516 * t2556 + 941640 * t2558 - 288420 * t2560 - 340 + t2550 = 4 * phi1 + t2549 = 8 * phi1 + t2548 = -5 * phi2 + t2547 = 5 * phi2 + tfunc[..., c] = (0.5e1 / 0.335872e6*1j) * np.sqrt(0.41e2) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * ((1 + t2546) ** (-0.1e1 / 0.2e1)) * ((1 - t2546) ** (-0.1e1 / 0.2e1)) * ((259578 * t2534 - 1015740 * t2536 + 1546182 * t2538 - 1142856 * t2540 + 415206 * t2553 - 65340 * t2551 + 2970 * t2546) * np.exp((-5*1j) * phi2) + (t2562 + t2563) * np.exp((-1*1j) * (t2550 + t2547)) + (t2564 - t2565) * np.exp((-1*1j) * (t2549 + t2547)) + (-t2562 + t2563) * np.exp((1j) * (t2550 + t2548)) + (t2564 + t2565) * np.exp((1j) * (t2549 + t2548))) + + if Bindx == 95: + t2585 = np.cos(phi) + t2584 = t2585 ** 2 + t2592 = t2584 ** 2 + t2591 = t2585 * t2584 + t2594 = t2591 ** 2 + t2596 = t2592 ** 2 + t2593 = t2585 * t2592 + t2598 = t2593 ** 2 + t2600 = t2594 ** 2 + t2607 = 84 - 2856 * t2584 + 6524 * t2592 + 23184 * t2594 - 90356 * t2596 + 98840 * t2598 - 35420 * t2600 + t2606 = -1025 - 6150 * t2584 + 29725 * t2592 - 36900 * t2594 + 9225 * t2596 + 10250 * t2598 - 5125 * t2600 + t2573 = t2585 * t2600 + t2575 = t2585 * t2598 + t2577 = t2585 * t2596 + t2579 = t2585 * t2594 + t2605 = 1025 * t2573 + 6150 * t2575 - 29725 * t2577 + 36900 * t2579 + 5125 * t2585 - 10250 * t2591 - 9225 * t2593 + t2604 = 10626 * t2573 + 5236 * t2575 - 83874 * t2577 + 124376 * t2579 - 1470 * t2585 + 17780 * t2591 - 72674 * t2593 + t2603 = 735471 * t2573 - 2067846 * t2575 + 2020365 * t2577 - 741540 * t2579 - 1717 * t2585 + 23562 * t2591 + 31705 * t2593 + t2602 = 83810 * t2584 - 806735 * t2592 + 2935628 * t2594 - 4987443 * t2596 + 4001970 * t2598 - 1225785 * t2600 - 1445 + t2590 = 4 * phi1 + t2589 = 8 * phi1 + t2588 = -5 * phi2 + t2587 = 5 * phi2 + t2586 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.23e2) * ((1 + t2585) ** (-0.1e1 / 0.2e1)) * ((1 - t2585) ** (-0.1e1 / 0.2e1)) * ((2704156 * t2573 - 10581480 * t2575 + 16107364 * t2577 - 11905712 * t2579 + 4325412 * t2593 - 680680 * t2591 + 30940 * t2585) * np.exp((-5*1j) * phi2) + (-t2602 + t2603) * np.exp((-1*1j) * (t2590 + t2587)) + (t2604 - t2607) * np.exp((-1*1j) * (t2589 + t2587)) + (t2602 + t2603) * np.exp((1j) * (t2590 + t2588)) + (t2604 + t2607) * np.exp((1j) * (t2589 + t2588)) + (t2605 - t2606) * np.exp((-1*1j) * (t2586 + t2587)) + (t2605 + t2606) * np.exp((1j) * (t2586 + t2588))) + + if Bindx == 96: + t2624 = np.cos(phi) + t2623 = t2624 ** 2 + t2627 = t2623 ** 2 + t2628 = t2624 * t2627 + t2633 = t2628 ** 2 + t2614 = t2624 * t2633 + t2631 = t2627 ** 2 + t2616 = t2624 * t2631 + t2626 = t2624 * t2623 + t2629 = t2626 ** 2 + t2618 = t2624 * t2629 + t2639 = -1961256 * t2614 + 5573688 * t2616 - 5596944 * t2618 + 23256 * t2624 - 379848 * t2626 + 2341104 * t2628 + t2638 = -3922512 * t2614 + 10775280 * t2616 - 10821792 * t2618 + 42416 * t2624 - 857360 * t2626 + 4775776 * t2628 + t2613 = t2629 ** 2 + t2637 = -2941884 * t2613 + 59272 * t2623 - 698500 * t2627 + 3352944 * t2629 - 7538820 * t2631 + 7759752 * t2633 - 956 + t2636 = 735471 * t2613 + 331398 * t2623 - 1867263 * t2627 + 3585300 * t2629 - 2115327 * t2631 - 660858 * t2633 - 8721 + t2625 = 2 * phi1 + tfunc[..., c] = (0.5e1 / 0.671744e6) * np.sqrt(0.41e2) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * ((4412826 * t2613 - 14197788 * t2633 + 17075718 * t2631 - 9411336 * t2629 + 2325510 * t2627 - 207900 * t2623 + 2970) * np.exp((-4*1j) * phi2) + (t2637 + t2638) * np.exp((-4*1j) * (phi1 + phi2)) + (t2636 - t2639) * np.exp((-4*1j) * (t2625 + phi2)) + (t2637 - t2638) * np.exp((4*1j) * (phi1 - phi2)) + (t2636 + t2639) * np.exp((4*1j) * (t2625 - phi2))) + + if Bindx == 97: + t2658 = np.cos(phi) + t2657 = t2658 ** 2 + t2662 = t2657 ** 2 + t2663 = t2658 * t2662 + t2668 = t2663 ** 2 + t2648 = t2658 * t2668 + t2666 = t2662 ** 2 + t2650 = t2658 * t2666 + t2661 = t2658 * t2657 + t2664 = t2661 ** 2 + t2652 = t2658 * t2664 + t2676 = -4100 * t2648 + 12300 * t2650 - 8200 * t2652 - 4100 * t2658 + 12300 * t2661 - 8200 * t2663 + t2675 = -28336 * t2648 + 80528 * t2650 - 80864 * t2652 + 336 * t2658 - 5488 * t2661 + 33824 * t2663 + t2647 = t2664 ** 2 + t2674 = -126 + 10626 * t2647 + 4788 * t2657 - 26978 * t2662 + 51800 * t2664 - 30562 * t2666 - 9548 * t2668 + t2673 = 1025 + 1025 * t2647 + 2050 * t2657 - 17425 * t2662 + 28700 * t2664 - 17425 * t2666 + 2050 * t2668 + t2672 = -980628 * t2648 + 2693820 * t2650 - 2705448 * t2652 + 10604 * t2658 - 214340 * t2661 + 1193944 * t2663 + t2671 = 735471 * t2647 - 14818 * t2657 + 174625 * t2662 - 838236 * t2664 + 1884705 * t2666 - 1939938 * t2668 + 239 + t2660 = 2 * phi1 + t2659 = 3 * phi1 + tfunc[..., c] = (0.5e1 / 0.10747904e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * ((2704156 * t2647 - 8700328 * t2668 + 10463908 * t2666 - 5767216 * t2664 + 1425060 * t2662 - 127400 * t2657 + 1820) * np.exp((-4*1j) * phi2) + (t2671 - t2672) * np.exp((-4*1j) * (phi1 + phi2)) + (t2674 - t2675) * np.exp((-4*1j) * (t2660 + phi2)) + (t2673 - t2676) * np.exp((-4*1j) * (t2659 + phi2)) + (t2671 + t2672) * np.exp((4*1j) * (phi1 - phi2)) + (t2674 + t2675) * np.exp((4*1j) * (t2660 - phi2)) + (t2673 + t2676) * np.exp((4*1j) * (t2659 - phi2))) + + if Bindx == 98: + t2694 = np.cos(phi) + t2693 = t2694 ** 2 + t2699 = t2694 * t2693 + t2702 = t2699 ** 2 + t2708 = t2702 ** 2 + t2682 = t2694 * t2708 + t2700 = t2693 ** 2 + t2701 = t2694 * t2700 + t2706 = t2701 ** 2 + t2684 = t2694 * t2706 + t2704 = t2700 ** 2 + t2686 = t2694 * t2704 + t2688 = t2694 * t2702 + t2713 = 81719 * t2682 - 234498 * t2684 + 179265 * t2686 + 55556 * t2688 - 3553 * t2694 + 44574 * t2699 - 123063 * t2701 + t2712 = 27132 * t2693 - 203490 * t2700 + 617576 * t2702 - 893418 * t2704 + 616284 * t2706 - 163438 * t2708 - 646 + t2711 = 17688 * t2693 - 188100 * t2700 + 731408 * t2702 - 1290708 * t2704 + 1056856 * t2706 - 326876 * t2708 - 268 + t2710 = -326876 * t2682 + 1108536 * t2684 - 1466420 * t2686 + 952272 * t2688 - 2028 * t2694 + 45688 * t2699 - 311172 * t2701 + t2698 = 4 * phi1 + t2697 = 8 * phi1 + t2696 = -3 * phi2 + t2695 = 3 * phi2 + tfunc[..., c] = (0.15e2 / 0.167936e6*1j) * np.sqrt(0.41e2) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * ((1 + t2694) ** (-0.1e1 / 0.2e1)) * ((1 - t2694) ** (-0.1e1 / 0.2e1)) * ((2970 * t2694 + 490314 * t2682 - 1748076 * t2684 + 2408934 * t2686 - 1602216 * t2688 + 521334 * t2701 - 73260 * t2699) * np.exp((-3*1j) * phi2) + (t2710 + t2711) * np.exp((-1*1j) * (t2698 + t2695)) + (-t2712 + t2713) * np.exp((-1*1j) * (t2697 + t2695)) + (t2710 - t2711) * np.exp((1j) * (t2698 + t2696)) + (t2712 + t2713) * np.exp((1j) * (t2697 + t2696))) + + if Bindx == 99: + t2733 = np.cos(phi) + t2732 = t2733 ** 2 + t2739 = t2732 ** 2 + t2738 = t2733 * t2732 + t2741 = t2738 ** 2 + t2743 = t2739 ** 2 + t2740 = t2733 * t2739 + t2745 = t2740 ** 2 + t2747 = t2741 ** 2 + t2754 = 1025 - 2050 * t2732 - 5125 * t2739 + 20500 * t2741 - 25625 * t2743 + 14350 * t2745 - 3075 * t2747 + t2721 = t2733 * t2747 + t2723 = t2733 * t2745 + t2725 = t2733 * t2743 + t2727 = t2733 * t2741 + t2753 = 1025 * t2721 - 2050 * t2723 - 5125 * t2725 + 20500 * t2727 - 3075 * t2733 + 14350 * t2738 - 25625 * t2740 + t2752 = 10626 * t2721 - 30492 * t2723 + 23310 * t2725 + 7224 * t2727 - 462 * t2733 + 5796 * t2738 - 16002 * t2740 + t2751 = 3528 * t2732 - 26460 * t2739 + 80304 * t2741 - 116172 * t2743 + 80136 * t2745 - 21252 * t2747 - 84 + t2750 = 39798 * t2732 - 423225 * t2739 + 1645668 * t2741 - 2904093 * t2743 + 2377926 * t2745 - 735471 * t2747 - 603 + t2749 = 735471 * t2721 - 2494206 * t2723 + 3299445 * t2725 - 2142612 * t2727 + 4563 * t2733 - 102798 * t2738 + 700137 * t2740 + t2737 = 4 * phi1 + t2736 = 8 * phi1 + t2735 = -3 * phi2 + t2734 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.8060928e7*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * ((1 + t2733) ** (-0.1e1 / 0.2e1)) * ((1 - t2733) ** (-0.1e1 / 0.2e1)) * ((2704156 * t2721 - 9640904 * t2723 + 13285636 * t2725 - 8836464 * t2727 + 2875236 * t2740 - 404040 * t2738 + 16380 * t2733) * np.exp((-3*1j) * phi2) + (t2753 - t2754) * np.exp((-3*1j) * (t2737 + phi2)) + (t2749 - t2750) * np.exp((-1*1j) * (t2737 + t2734)) + (-t2751 + t2752) * np.exp((-1*1j) * (t2736 + t2734)) + (t2749 + t2750) * np.exp((1j) * (t2737 + t2735)) + (t2751 + t2752) * np.exp((1j) * (t2736 + t2735)) + (t2753 + t2754) * np.exp((3*1j) * (t2737 - phi2))) + + if Bindx == 100: + t2771 = np.cos(phi) + t2770 = t2771 ** 2 + t2775 = t2770 ** 2 + t2776 = t2771 * t2775 + t2781 = t2776 ** 2 + t2761 = t2771 * t2781 + t2779 = t2775 ** 2 + t2763 = t2771 * t2779 + t2774 = t2771 * t2770 + t2777 = t2774 ** 2 + t2765 = t2771 * t2777 + t2787 = -653752 * t2761 + 1780376 * t2763 - 1741616 * t2765 + 5560 * t2771 - 122520 * t2774 + 731952 * t2776 + t2786 = -326876 * t2761 + 1169260 * t2763 - 1563320 * t2765 + 16796 * t2771 - 239020 * t2774 + 943160 * t2776 + t2760 = t2777 ** 2 + t2785 = 245157 * t2760 - 14212 * t2770 + 143735 * t2775 - 594320 * t2777 + 1057825 * t2779 - 838508 * t2781 + 323 + t2784 = -980628 * t2760 + 42160 * t2770 - 465180 * t2775 + 1888768 * t2777 - 3497444 * t2779 + 3012944 * t2781 - 620 + t2773 = 2 * phi1 + t2772 = 4 * phi1 + tfunc[..., c] = (0.5e1 / 0.335872e6) * np.sqrt(0.41e2) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * ((1470942 * t2760 - 4348872 * t2781 + 4796550 * t2779 - 2423520 * t2777 + 549450 * t2775 - 45144 * t2770 + 594) * np.exp((-2*1j) * phi2) + (t2784 + t2787) * np.exp((-2*1j) * (t2773 + phi2)) + (t2785 - t2786) * np.exp((-2*1j) * (t2772 + phi2)) + (t2784 - t2787) * np.exp((2*1j) * (t2773 - phi2)) + (t2785 + t2786) * np.exp((2*1j) * (t2772 - phi2))) + + if Bindx == 101: + t2806 = np.cos(phi) + t2805 = t2806 ** 2 + t2810 = t2806 * t2805 + t2813 = t2810 ** 2 + t2795 = t2813 ** 2 + t2811 = t2805 ** 2 + t2815 = t2811 ** 2 + t2812 = t2806 * t2811 + t2817 = t2812 ** 2 + t2825 = -1025 + 1025 * t2795 + 4100 * t2805 - 5125 * t2811 + 5125 * t2815 - 4100 * t2817 + t2796 = t2806 * t2817 + t2798 = t2806 * t2815 + t2800 = t2806 * t2813 + t2824 = -14168 * t2796 + 50680 * t2798 - 67760 * t2800 + 728 * t2806 - 10360 * t2810 + 40880 * t2812 + t2823 = -2050 * t2796 + 10250 * t2798 - 20500 * t2800 + 2050 * t2806 - 10250 * t2810 + 20500 * t2812 + t2822 = 14 + 10626 * t2795 - 616 * t2805 + 6230 * t2811 - 25760 * t2813 + 45850 * t2815 - 36344 * t2817 + t2821 = -490314 * t2796 + 1335282 * t2798 - 1306212 * t2800 + 4170 * t2806 - 91890 * t2810 + 548964 * t2812 + t2820 = 735471 * t2795 - 31620 * t2805 + 348885 * t2811 - 1416576 * t2813 + 2623083 * t2815 - 2259708 * t2817 + 465 + t2809 = 2 * phi1 + t2808 = 4 * phi1 + t2807 = 6 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * ((2704156 * t2795 - 7994896 * t2817 + 8817900 * t2815 - 4455360 * t2813 + 1010100 * t2811 - 82992 * t2805 + 1092) * np.exp((-2*1j) * phi2) + (-t2823 + t2825) * np.exp((-2*1j) * (t2807 + phi2)) + (t2823 + t2825) * np.exp((2*1j) * (t2807 - phi2)) + (t2820 - t2821) * np.exp((-2*1j) * (t2809 + phi2)) + (t2822 - t2824) * np.exp((-2*1j) * (t2808 + phi2)) + (t2820 + t2821) * np.exp((2*1j) * (t2809 - phi2)) + (t2822 + t2824) * np.exp((2*1j) * (t2808 - phi2))) + + if Bindx == 102: + t2843 = np.cos(phi) + t2842 = t2843 ** 2 + t2847 = t2842 ** 2 + t2846 = t2843 * t2842 + t2849 = t2846 ** 2 + t2851 = t2847 ** 2 + t2848 = t2843 * t2847 + t2853 = t2848 ** 2 + t2855 = t2849 ** 2 + t2860 = 9800 * t2842 - 109620 * t2847 + 443632 * t2849 - 804916 * t2851 + 669256 * t2853 - 208012 * t2855 - 140 + t2859 = 29716 * t2842 - 216410 * t2847 + 581400 * t2849 - 733210 * t2851 + 443156 * t2853 - 104006 * t2855 - 646 + t2831 = t2843 * t2855 + t2833 = t2843 * t2853 + t2835 = t2843 * t2851 + t2837 = t2843 * t2849 + t2858 = 156009 * t2831 - 709954 * t2833 + 1287155 * t2835 - 1169260 * t2837 + 7429 * t2843 - 115634 * t2846 + 544255 * t2848 + t2857 = -624036 * t2831 + 2297176 * t2833 - 3282972 * t2835 + 2275280 * t2837 - 4900 * t2843 + 114520 * t2846 - 775068 * t2848 + t2845 = 4 * phi1 + t2844 = 8 * phi1 + tfunc[..., c] = (0.5e1 / 0.167936e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((1 + t2843) ** (-0.1e1 / 0.2e1)) * ((1 - t2843) ** (-0.1e1 / 0.2e1)) * ((936054 * t2831 - 3174444 * t2833 + 4157010 * t2835 - 2625480 * t2837 + 810810 * t2848 - 108108 * t2846 + 4158 * t2843) * np.exp((-1*1j) * phi2) + (t2857 + t2860) * np.exp((-1*1j) * (t2845 + phi2)) + (t2858 - t2859) * np.exp((-1*1j) * (t2844 + phi2)) + (t2857 - t2860) * np.exp((1j) * (t2845 - phi2)) + (t2858 + t2859) * np.exp((1j) * (t2844 - phi2))) + + if Bindx == 103: + t2880 = np.cos(phi) + t2879 = t2880 ** 2 + t2885 = t2879 ** 2 + t2884 = t2880 * t2879 + t2887 = t2884 ** 2 + t2889 = t2885 ** 2 + t2886 = t2880 * t2885 + t2891 = t2886 ** 2 + t2893 = t2887 ** 2 + t2900 = -1 + 6 * t2879 - 15 * t2885 + 20 * t2887 - 15 * t2889 + 6 * t2891 - t2893 + t2868 = t2880 * t2893 + t2870 = t2880 * t2891 + t2872 = t2880 * t2889 + t2874 = t2880 * t2887 + t2899 = t2868 + t2880 - 6 * t2870 + 15 * t2872 - 20 * t2874 - 6 * t2884 + 15 * t2886 + t2898 = -44 + 2024 * t2879 - 14740 * t2885 + 39600 * t2887 - 49940 * t2889 + 30184 * t2891 - 7084 * t2893 + t2897 = 10626 * t2868 - 48356 * t2870 + 87670 * t2872 - 79640 * t2874 + 506 * t2880 - 7876 * t2884 + 37070 * t2886 + t2896 = 11550 * t2879 - 129195 * t2885 + 522852 * t2887 - 948651 * t2889 + 788766 * t2891 - 245157 * t2893 - 165 + t2895 = 735471 * t2868 - 2707386 * t2870 + 3869217 * t2872 - 2681580 * t2874 + 5775 * t2880 - 134970 * t2884 + 913473 * t2886 + t2883 = 4 * phi1 + t2882 = 8 * phi1 + t2881 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.2e1) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * ((1 + t2880) ** (-0.1e1 / 0.2e1)) * ((1 - t2880) ** (-0.1e1 / 0.2e1)) * ((2704156 * t2868 - 9170616 * t2870 + 12009140 * t2872 - 7584720 * t2874 + 2342340 * t2886 - 312312 * t2884 + 12012 * t2880) * np.exp((-1*1j) * phi2) + (t2895 - t2896) * np.exp((-1*1j) * (t2883 + phi2)) + (t2897 - t2898) * np.exp((-1*1j) * (t2882 + phi2)) + (t2895 + t2896) * np.exp((1j) * (t2883 - phi2)) + (t2897 + t2898) * np.exp((1j) * (t2882 - phi2)) + 1025 * (t2899 - t2900) * np.exp((-1*1j) * (t2881 + phi2)) + 1025 * (t2899 + t2900) * np.exp((1j) * (t2881 - phi2))) + + if Bindx == 104: + t2909 = np.cos(phi) + t2908 = t2909 ** 2 + t2910 = t2908 ** 2 + t2912 = t2910 ** 2 + t2911 = t2908 * t2910 + t2904 = t2908 * t2912 + t2903 = t2911 ** 2 + tfunc[..., c] = -0.15e2 / 0.167936e6 * np.sqrt(0.11e2) * np.sqrt(0.41e2) * (-(2028117 * t2903) + (5819814 * t2904) - (6235515 * t2912) + (3063060 * t2911) - (675675 * t2910) + (54054 * t2908) - 0.693e3 + (2704156 * t2903 - 8700328 * t2904 + 10463908 * t2912 - 5767216 * t2911 + 1425060 * t2910 - 127400 * t2908 + 1820) * np.cos((4 * phi1)) + (-676039 * t2903 + 2880514 * t2904 - 4765865 * t2912 + 3779100 * t2911 - 1406665 * t2910 + 193154 * t2908 - 4199) * np.cos((8 * phi1))) + + if Bindx == 105: + t2924 = np.cos(phi) + t2923 = t2924 ** 2 + t2925 = t2923 ** 2 + t2927 = t2925 ** 2 + t2926 = t2923 * t2925 + t2919 = t2923 * t2927 + t2918 = t2926 ** 2 + tfunc[..., c] = 0.5e1 / 0.8060928e7 * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * ((1352078 * t2918) - (3879876 * t2919) + (4157010 * t2927) - (2042040 * t2926) + (450450 * t2925) - (36036 * t2923) + 0.462e3 + (735471 * t2918 - 2366298 * t2919 + 2845953 * t2927 - 1568556 * t2926 + 387585 * t2925 - 34650 * t2923 + 495) * np.cos((4 * phi1)) + (10626 * t2918 - 45276 * t2919 + 74910 * t2927 - 59400 * t2926 + 22110 * t2925 - 3036 * t2923 + 66) * np.cos((8 * phi1)) + (1025 * t2918 - 6150 * t2919 + 15375 * t2927 - 20500 * t2926 + 15375 * t2925 - 6150 * t2923 + 1025) * np.cos((12 * phi1))) + + if Bindx == 106: + t2947 = np.cos(phi) + t2946 = t2947 ** 2 + t2951 = t2946 ** 2 + t2950 = t2947 * t2946 + t2953 = t2950 ** 2 + t2955 = t2951 ** 2 + t2952 = t2947 * t2951 + t2957 = t2952 ** 2 + t2959 = t2953 ** 2 + t2964 = 9800 * t2946 - 109620 * t2951 + 443632 * t2953 - 804916 * t2955 + 669256 * t2957 - 208012 * t2959 - 140 + t2963 = 29716 * t2946 - 216410 * t2951 + 581400 * t2953 - 733210 * t2955 + 443156 * t2957 - 104006 * t2959 - 646 + t2935 = t2947 * t2959 + t2937 = t2947 * t2957 + t2939 = t2947 * t2955 + t2941 = t2947 * t2953 + t2962 = 156009 * t2935 - 709954 * t2937 + 1287155 * t2939 - 1169260 * t2941 + 7429 * t2947 - 115634 * t2950 + 544255 * t2952 + t2961 = -624036 * t2935 + 2297176 * t2937 - 3282972 * t2939 + 2275280 * t2941 - 4900 * t2947 + 114520 * t2950 - 775068 * t2952 + t2949 = 4 * phi1 + t2948 = 8 * phi1 + tfunc[..., c] = (0.5e1 / 0.167936e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((1 + t2947) ** (-0.1e1 / 0.2e1)) * ((1 - t2947) ** (-0.1e1 / 0.2e1)) * ((936054 * t2935 - 3174444 * t2937 + 4157010 * t2939 - 2625480 * t2941 + 810810 * t2952 - 108108 * t2950 + 4158 * t2947) * np.exp((1j) * phi2) + (t2961 - t2964) * np.exp((-1*1j) * (t2949 - phi2)) + (t2962 + t2963) * np.exp((-1*1j) * (t2948 - phi2)) + (t2961 + t2964) * np.exp((1j) * (t2949 + phi2)) + (t2962 - t2963) * np.exp((1j) * (t2948 + phi2))) + + if Bindx == 107: + t2984 = np.cos(phi) + t2983 = t2984 ** 2 + t2989 = t2983 ** 2 + t2988 = t2984 * t2983 + t2991 = t2988 ** 2 + t2993 = t2989 ** 2 + t2990 = t2984 * t2989 + t2995 = t2990 ** 2 + t2997 = t2991 ** 2 + t3004 = -1 + 6 * t2983 - 15 * t2989 + 20 * t2991 - 15 * t2993 + 6 * t2995 - t2997 + t2972 = t2984 * t2997 + t2974 = t2984 * t2995 + t2976 = t2984 * t2993 + t2978 = t2984 * t2991 + t3003 = t2972 + t2984 - 6 * t2974 + 15 * t2976 - 20 * t2978 - 6 * t2988 + 15 * t2990 + t3002 = -44 + 2024 * t2983 - 14740 * t2989 + 39600 * t2991 - 49940 * t2993 + 30184 * t2995 - 7084 * t2997 + t3001 = 10626 * t2972 - 48356 * t2974 + 87670 * t2976 - 79640 * t2978 + 506 * t2984 - 7876 * t2988 + 37070 * t2990 + t3000 = 11550 * t2983 - 129195 * t2989 + 522852 * t2991 - 948651 * t2993 + 788766 * t2995 - 245157 * t2997 - 165 + t2999 = 735471 * t2972 - 2707386 * t2974 + 3869217 * t2976 - 2681580 * t2978 + 5775 * t2984 - 134970 * t2988 + 913473 * t2990 + t2987 = 4 * phi1 + t2986 = 8 * phi1 + t2985 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.2e1) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * ((1 + t2984) ** (-0.1e1 / 0.2e1)) * ((1 - t2984) ** (-0.1e1 / 0.2e1)) * ((2704156 * t2972 - 9170616 * t2974 + 12009140 * t2976 - 7584720 * t2978 + 2342340 * t2990 - 312312 * t2988 + 12012 * t2984) * np.exp((1j) * phi2) + (t2999 + t3000) * np.exp((-1*1j) * (t2987 - phi2)) + (t3001 + t3002) * np.exp((-1*1j) * (t2986 - phi2)) + (t2999 - t3000) * np.exp((1j) * (t2987 + phi2)) + (t3001 - t3002) * np.exp((1j) * (t2986 + phi2)) + 1025 * (t3003 + t3004) * np.exp((-1*1j) * (t2985 - phi2)) + 1025 * (t3003 - t3004) * np.exp((1j) * (t2985 + phi2))) + + if Bindx == 108: + t3021 = np.cos(phi) + t3020 = t3021 ** 2 + t3025 = t3020 ** 2 + t3026 = t3021 * t3025 + t3031 = t3026 ** 2 + t3011 = t3021 * t3031 + t3029 = t3025 ** 2 + t3013 = t3021 * t3029 + t3024 = t3021 * t3020 + t3027 = t3024 ** 2 + t3015 = t3021 * t3027 + t3037 = -653752 * t3011 + 1780376 * t3013 - 1741616 * t3015 + 5560 * t3021 - 122520 * t3024 + 731952 * t3026 + t3036 = -326876 * t3011 + 1169260 * t3013 - 1563320 * t3015 + 16796 * t3021 - 239020 * t3024 + 943160 * t3026 + t3010 = t3027 ** 2 + t3035 = 245157 * t3010 - 14212 * t3020 + 143735 * t3025 - 594320 * t3027 + 1057825 * t3029 - 838508 * t3031 + 323 + t3034 = -980628 * t3010 + 42160 * t3020 - 465180 * t3025 + 1888768 * t3027 - 3497444 * t3029 + 3012944 * t3031 - 620 + t3023 = 2 * phi1 + t3022 = 4 * phi1 + tfunc[..., c] = (0.5e1 / 0.335872e6) * np.sqrt(0.41e2) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * ((1470942 * t3010 - 4348872 * t3031 + 4796550 * t3029 - 2423520 * t3027 + 549450 * t3025 - 45144 * t3020 + 594) * np.exp((2*1j) * phi2) + (t3034 - t3037) * np.exp((-2*1j) * (t3023 - phi2)) + (t3035 + t3036) * np.exp((-2*1j) * (t3022 - phi2)) + (t3034 + t3037) * np.exp((2*1j) * (t3023 + phi2)) + (t3035 - t3036) * np.exp((2*1j) * (t3022 + phi2))) + + if Bindx == 109: + t3056 = np.cos(phi) + t3055 = t3056 ** 2 + t3060 = t3056 * t3055 + t3063 = t3060 ** 2 + t3045 = t3063 ** 2 + t3061 = t3055 ** 2 + t3065 = t3061 ** 2 + t3062 = t3056 * t3061 + t3067 = t3062 ** 2 + t3075 = -1025 + 1025 * t3045 + 4100 * t3055 - 5125 * t3061 + 5125 * t3065 - 4100 * t3067 + t3046 = t3056 * t3067 + t3048 = t3056 * t3065 + t3050 = t3056 * t3063 + t3074 = -14168 * t3046 + 50680 * t3048 - 67760 * t3050 + 728 * t3056 - 10360 * t3060 + 40880 * t3062 + t3073 = -2050 * t3046 + 10250 * t3048 - 20500 * t3050 + 2050 * t3056 - 10250 * t3060 + 20500 * t3062 + t3072 = 14 + 10626 * t3045 - 616 * t3055 + 6230 * t3061 - 25760 * t3063 + 45850 * t3065 - 36344 * t3067 + t3071 = -490314 * t3046 + 1335282 * t3048 - 1306212 * t3050 + 4170 * t3056 - 91890 * t3060 + 548964 * t3062 + t3070 = 735471 * t3045 - 31620 * t3055 + 348885 * t3061 - 1416576 * t3063 + 2623083 * t3065 - 2259708 * t3067 + 465 + t3059 = 2 * phi1 + t3058 = 4 * phi1 + t3057 = 6 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * ((2704156 * t3045 - 7994896 * t3067 + 8817900 * t3065 - 4455360 * t3063 + 1010100 * t3061 - 82992 * t3055 + 1092) * np.exp((2*1j) * phi2) + (t3073 + t3075) * np.exp((-2*1j) * (t3057 - phi2)) + (-t3073 + t3075) * np.exp((2*1j) * (t3057 + phi2)) + (t3070 + t3071) * np.exp((-2*1j) * (t3059 - phi2)) + (t3072 + t3074) * np.exp((-2*1j) * (t3058 - phi2)) + (t3070 - t3071) * np.exp((2*1j) * (t3059 + phi2)) + (t3072 - t3074) * np.exp((2*1j) * (t3058 + phi2))) + + if Bindx == 110: + t3093 = np.cos(phi) + t3092 = t3093 ** 2 + t3098 = t3093 * t3092 + t3101 = t3098 ** 2 + t3107 = t3101 ** 2 + t3081 = t3093 * t3107 + t3099 = t3092 ** 2 + t3100 = t3093 * t3099 + t3105 = t3100 ** 2 + t3083 = t3093 * t3105 + t3103 = t3099 ** 2 + t3085 = t3093 * t3103 + t3087 = t3093 * t3101 + t3112 = 81719 * t3081 - 234498 * t3083 + 179265 * t3085 + 55556 * t3087 - 3553 * t3093 + 44574 * t3098 - 123063 * t3100 + t3111 = 27132 * t3092 - 203490 * t3099 + 617576 * t3101 - 893418 * t3103 + 616284 * t3105 - 163438 * t3107 - 646 + t3110 = 17688 * t3092 - 188100 * t3099 + 731408 * t3101 - 1290708 * t3103 + 1056856 * t3105 - 326876 * t3107 - 268 + t3109 = -326876 * t3081 + 1108536 * t3083 - 1466420 * t3085 + 952272 * t3087 - 2028 * t3093 + 45688 * t3098 - 311172 * t3100 + t3097 = 4 * phi1 + t3096 = 8 * phi1 + t3095 = -3 * phi2 + t3094 = 3 * phi2 + tfunc[..., c] = (0.15e2 / 0.167936e6*1j) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * ((1 + t3093) ** (-0.1e1 / 0.2e1)) * ((1 - t3093) ** (-0.1e1 / 0.2e1)) * ((490314 * t3081 - 1748076 * t3083 + 2408934 * t3085 - 1602216 * t3087 + 521334 * t3100 - 73260 * t3098 + 2970 * t3093) * np.exp((3*1j) * phi2) + (t3109 - t3110) * np.exp((-1*1j) * (t3097 + t3095)) + (t3111 + t3112) * np.exp((-1*1j) * (t3096 + t3095)) + (t3109 + t3110) * np.exp((1j) * (t3097 + t3094)) + (-t3111 + t3112) * np.exp((1j) * (t3096 + t3094))) + + if Bindx == 111: + t3132 = np.cos(phi) + t3131 = t3132 ** 2 + t3138 = t3131 ** 2 + t3137 = t3132 * t3131 + t3140 = t3137 ** 2 + t3142 = t3138 ** 2 + t3139 = t3132 * t3138 + t3144 = t3139 ** 2 + t3146 = t3140 ** 2 + t3153 = 1025 - 2050 * t3131 - 5125 * t3138 + 20500 * t3140 - 25625 * t3142 + 14350 * t3144 - 3075 * t3146 + t3120 = t3132 * t3146 + t3122 = t3132 * t3144 + t3124 = t3132 * t3142 + t3126 = t3132 * t3140 + t3152 = 1025 * t3120 - 2050 * t3122 - 5125 * t3124 + 20500 * t3126 - 3075 * t3132 + 14350 * t3137 - 25625 * t3139 + t3151 = 10626 * t3120 - 30492 * t3122 + 23310 * t3124 + 7224 * t3126 - 462 * t3132 + 5796 * t3137 - 16002 * t3139 + t3150 = 3528 * t3131 - 26460 * t3138 + 80304 * t3140 - 116172 * t3142 + 80136 * t3144 - 21252 * t3146 - 84 + t3149 = 39798 * t3131 - 423225 * t3138 + 1645668 * t3140 - 2904093 * t3142 + 2377926 * t3144 - 735471 * t3146 - 603 + t3148 = 735471 * t3120 - 2494206 * t3122 + 3299445 * t3124 - 2142612 * t3126 + 4563 * t3132 - 102798 * t3137 + 700137 * t3139 + t3136 = 4 * phi1 + t3135 = 8 * phi1 + t3134 = -3 * phi2 + t3133 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.8060928e7*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * ((1 + t3132) ** (-0.1e1 / 0.2e1)) * ((1 - t3132) ** (-0.1e1 / 0.2e1)) * ((2704156 * t3120 - 9640904 * t3122 + 13285636 * t3124 - 8836464 * t3126 + 2875236 * t3139 - 404040 * t3137 + 16380 * t3132) * np.exp((3*1j) * phi2) + (t3152 + t3153) * np.exp((-3*1j) * (t3136 - phi2)) + (t3148 + t3149) * np.exp((-1*1j) * (t3136 + t3134)) + (t3150 + t3151) * np.exp((-1*1j) * (t3135 + t3134)) + (t3148 - t3149) * np.exp((1j) * (t3136 + t3133)) + (-t3150 + t3151) * np.exp((1j) * (t3135 + t3133)) + (t3152 - t3153) * np.exp((3*1j) * (t3136 + phi2))) + + if Bindx == 112: + t3170 = np.cos(phi) + t3169 = t3170 ** 2 + t3173 = t3169 ** 2 + t3174 = t3170 * t3173 + t3179 = t3174 ** 2 + t3160 = t3170 * t3179 + t3177 = t3173 ** 2 + t3162 = t3170 * t3177 + t3172 = t3170 * t3169 + t3175 = t3172 ** 2 + t3164 = t3170 * t3175 + t3185 = -1961256 * t3160 + 5573688 * t3162 - 5596944 * t3164 + 23256 * t3170 - 379848 * t3172 + 2341104 * t3174 + t3184 = -3922512 * t3160 + 10775280 * t3162 - 10821792 * t3164 + 42416 * t3170 - 857360 * t3172 + 4775776 * t3174 + t3159 = t3175 ** 2 + t3183 = -2941884 * t3159 + 59272 * t3169 - 698500 * t3173 + 3352944 * t3175 - 7538820 * t3177 + 7759752 * t3179 - 956 + t3182 = 735471 * t3159 + 331398 * t3169 - 1867263 * t3173 + 3585300 * t3175 - 2115327 * t3177 - 660858 * t3179 - 8721 + t3171 = 2 * phi1 + tfunc[..., c] = (0.5e1 / 0.671744e6) * np.sqrt(0.41e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * ((4412826 * t3159 - 14197788 * t3179 + 17075718 * t3177 - 9411336 * t3175 + 2325510 * t3173 - 207900 * t3169 + 2970) * np.exp((4*1j) * phi2) + (t3183 - t3184) * np.exp((-4*1j) * (phi1 - phi2)) + (t3182 + t3185) * np.exp((-4*1j) * (t3171 - phi2)) + (t3183 + t3184) * np.exp((4*1j) * (phi1 + phi2)) + (t3182 - t3185) * np.exp((4*1j) * (t3171 + phi2))) + + if Bindx == 113: + t3204 = np.cos(phi) + t3203 = t3204 ** 2 + t3208 = t3203 ** 2 + t3209 = t3204 * t3208 + t3214 = t3209 ** 2 + t3194 = t3204 * t3214 + t3212 = t3208 ** 2 + t3196 = t3204 * t3212 + t3207 = t3204 * t3203 + t3210 = t3207 ** 2 + t3198 = t3204 * t3210 + t3222 = -4100 * t3194 + 12300 * t3196 - 8200 * t3198 - 4100 * t3204 + 12300 * t3207 - 8200 * t3209 + t3221 = -28336 * t3194 + 80528 * t3196 - 80864 * t3198 + 336 * t3204 - 5488 * t3207 + 33824 * t3209 + t3193 = t3210 ** 2 + t3220 = -126 + 10626 * t3193 + 4788 * t3203 - 26978 * t3208 + 51800 * t3210 - 30562 * t3212 - 9548 * t3214 + t3219 = 1025 + 1025 * t3193 + 2050 * t3203 - 17425 * t3208 + 28700 * t3210 - 17425 * t3212 + 2050 * t3214 + t3218 = -980628 * t3194 + 2693820 * t3196 - 2705448 * t3198 + 10604 * t3204 - 214340 * t3207 + 1193944 * t3209 + t3217 = 735471 * t3193 - 14818 * t3203 + 174625 * t3208 - 838236 * t3210 + 1884705 * t3212 - 1939938 * t3214 + 239 + t3206 = 2 * phi1 + t3205 = 3 * phi1 + tfunc[..., c] = (0.5e1 / 0.10747904e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * ((2704156 * t3193 - 8700328 * t3214 + 10463908 * t3212 - 5767216 * t3210 + 1425060 * t3208 - 127400 * t3203 + 1820) * np.exp((4*1j) * phi2) + (t3217 + t3218) * np.exp((-4*1j) * (phi1 - phi2)) + (t3220 + t3221) * np.exp((-4*1j) * (t3206 - phi2)) + (t3219 + t3222) * np.exp((-4*1j) * (t3205 - phi2)) + (t3217 - t3218) * np.exp((4*1j) * (phi1 + phi2)) + (t3220 - t3221) * np.exp((4*1j) * (t3206 + phi2)) + (t3219 - t3222) * np.exp((4*1j) * (t3205 + phi2))) + + if Bindx == 114: + t3240 = np.cos(phi) + t3239 = t3240 ** 2 + t3246 = t3239 ** 2 + t3245 = t3240 * t3239 + t3248 = t3245 ** 2 + t3250 = t3246 ** 2 + t3247 = t3240 * t3246 + t3252 = t3247 ** 2 + t3254 = t3248 ** 2 + t3259 = -11628 * t3239 + 26562 * t3246 + 94392 * t3248 - 367878 * t3250 + 402420 * t3252 - 144210 * t3254 + 342 + t3228 = t3240 * t3254 + t3230 = t3240 * t3252 + t3232 = t3240 * t3250 + t3234 = t3240 * t3248 + t3258 = -43263 * t3228 - 21318 * t3230 + 341487 * t3232 - 506388 * t3234 + 5985 * t3240 - 72390 * t3245 + 295887 * t3247 + t3257 = 173052 * t3228 - 486552 * t3230 + 475380 * t3232 - 174480 * t3234 - 404 * t3240 + 5544 * t3245 + 7460 * t3247 + t3256 = 19720 * t3239 - 189820 * t3246 + 690736 * t3248 - 1173516 * t3250 + 941640 * t3252 - 288420 * t3254 - 340 + t3244 = 4 * phi1 + t3243 = 8 * phi1 + t3242 = -5 * phi2 + t3241 = 5 * phi2 + tfunc[..., c] = (-0.5e1 / 0.335872e6*1j) * np.sqrt(0.41e2) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * ((-259578 * t3228 + 1015740 * t3230 - 1546182 * t3232 + 1142856 * t3234 - 415206 * t3247 + 65340 * t3245 - 2970 * t3240) * np.exp((5*1j) * phi2) + (t3256 + t3257) * np.exp((-1*1j) * (t3244 + t3242)) + (t3258 - t3259) * np.exp((-1*1j) * (t3243 + t3242)) + (-t3256 + t3257) * np.exp((1j) * (t3244 + t3241)) + (t3258 + t3259) * np.exp((1j) * (t3243 + t3241))) * ((1 + t3240) ** (-0.1e1 / 0.2e1)) * ((1 - t3240) ** (-0.1e1 / 0.2e1)) + + if Bindx == 115: + t3279 = np.cos(phi) + t3278 = t3279 ** 2 + t3286 = t3278 ** 2 + t3285 = t3279 * t3278 + t3288 = t3285 ** 2 + t3290 = t3286 ** 2 + t3287 = t3279 * t3286 + t3292 = t3287 ** 2 + t3294 = t3288 ** 2 + t3301 = 84 - 2856 * t3278 + 6524 * t3286 + 23184 * t3288 - 90356 * t3290 + 98840 * t3292 - 35420 * t3294 + t3300 = -1025 - 6150 * t3278 + 29725 * t3286 - 36900 * t3288 + 9225 * t3290 + 10250 * t3292 - 5125 * t3294 + t3267 = t3279 * t3294 + t3269 = t3279 * t3292 + t3271 = t3279 * t3290 + t3273 = t3279 * t3288 + t3299 = 1025 * t3267 + 6150 * t3269 - 29725 * t3271 + 36900 * t3273 + 5125 * t3279 - 10250 * t3285 - 9225 * t3287 + t3298 = 10626 * t3267 + 5236 * t3269 - 83874 * t3271 + 124376 * t3273 - 1470 * t3279 + 17780 * t3285 - 72674 * t3287 + t3297 = 735471 * t3267 - 2067846 * t3269 + 2020365 * t3271 - 741540 * t3273 - 1717 * t3279 + 23562 * t3285 + 31705 * t3287 + t3296 = 83810 * t3278 - 806735 * t3286 + 2935628 * t3288 - 4987443 * t3290 + 4001970 * t3292 - 1225785 * t3294 - 1445 + t3284 = 4 * phi1 + t3283 = 8 * phi1 + t3282 = -5 * phi2 + t3281 = 5 * phi2 + t3280 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.23e2) * ((1 + t3279) ** (-0.1e1 / 0.2e1)) * ((1 - t3279) ** (-0.1e1 / 0.2e1)) * ((2704156 * t3267 - 10581480 * t3269 + 16107364 * t3271 - 11905712 * t3273 + 4325412 * t3287 - 680680 * t3285 + 30940 * t3279) * np.exp((5*1j) * phi2) + (t3296 + t3297) * np.exp((-1*1j) * (t3284 + t3282)) + (t3298 + t3301) * np.exp((-1*1j) * (t3283 + t3282)) + (-t3296 + t3297) * np.exp((1j) * (t3284 + t3281)) + (t3298 - t3301) * np.exp((1j) * (t3283 + t3281)) + (t3299 + t3300) * np.exp((-1*1j) * (t3280 + t3282)) + (t3299 - t3300) * np.exp((1j) * (t3280 + t3281))) + + if Bindx == 116: + t3318 = np.cos(phi) + t3317 = t3318 ** 2 + t3324 = t3317 ** 2 + t3325 = t3318 * t3324 + t3330 = t3325 ** 2 + t3308 = t3318 * t3330 + t3328 = t3324 ** 2 + t3310 = t3318 * t3328 + t3323 = t3318 * t3317 + t3326 = t3323 ** 2 + t3312 = t3318 * t3326 + t3336 = -134596 * t3308 + 217588 * t3310 - 7752 * t3312 - 7676 * t3318 + 63308 * t3323 - 130872 * t3325 + t3307 = t3326 ** 2 + t3335 = -134596 * t3307 - 10192 * t3317 + 68180 * t3324 - 103936 * t3326 - 77140 * t3328 + 257488 * t3330 + 196 + t3334 = -269192 * t3308 + 750120 * t3310 - 768208 * t3312 + 3976 * t3318 - 69160 * t3323 + 352464 * t3325 + t3333 = 33649 * t3307 + 10108 * t3317 - 106533 * t3324 + 338352 * t3326 - 386403 * t3328 + 111188 * t3330 - 361 + t3322 = 2 * phi1 + t3321 = 4 * phi1 + t3320 = -3 * phi2 + t3319 = 3 * phi2 + tfunc[..., c] = (0.15e2 / 0.335872e6) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.41e2) * ((201894 * t3307 - 737352 * t3330 + 1019502 * t3328 - 653664 * t3326 + 189090 * t3324 - 19800 * t3317 + 330) * np.exp((6*1j) * phi2) + (-t3334 + t3335) * np.exp((-2*1j) * (t3322 + t3320)) + (t3333 + t3336) * np.exp((-2*1j) * (t3321 + t3320)) + (t3334 + t3335) * np.exp((2*1j) * (t3322 + t3319)) + (t3333 - t3336) * np.exp((2*1j) * (t3321 + t3319))) + + if Bindx == 117: + t3355 = np.cos(phi) + t3354 = t3355 ** 2 + t3360 = t3355 * t3354 + t3363 = t3360 ** 2 + t3344 = t3363 ** 2 + t3361 = t3354 ** 2 + t3365 = t3361 ** 2 + t3362 = t3355 * t3361 + t3367 = t3362 ** 2 + t3375 = -1025 + 1025 * t3344 - 12300 * t3354 + 27675 * t3361 - 27675 * t3365 + 12300 * t3367 + t3345 = t3355 * t3367 + t3347 = t3355 * t3365 + t3349 = t3355 * t3363 + t3374 = -6150 * t3345 - 2050 * t3347 + 36900 * t3349 + 6150 * t3355 + 2050 * t3360 - 36900 * t3362 + t3373 = -42504 * t3345 + 68712 * t3347 - 2448 * t3349 - 2424 * t3355 + 19992 * t3360 - 41328 * t3362 + t3372 = 10626 * t3344 + 3192 * t3354 - 33642 * t3361 + 106848 * t3363 - 122022 * t3365 + 35112 * t3367 - 114 + t3371 = -1470942 * t3345 + 4098870 * t3347 - 4197708 * t3349 + 21726 * t3355 - 377910 * t3360 + 1925964 * t3362 + t3370 = 735471 * t3344 + 55692 * t3354 - 372555 * t3361 + 567936 * t3363 + 421515 * t3365 - 1406988 * t3367 - 1071 + t3359 = 2 * phi1 + t3358 = 4 * phi1 + t3357 = -3 * phi2 + t3356 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.16121856e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.7e1) * np.sqrt(0.19e2) * ((2704156 * t3344 - 9876048 * t3367 + 13655148 * t3365 - 8755136 * t3363 + 2532660 * t3361 - 265200 * t3354 + 4420) * np.exp((6*1j) * phi2) + (t3374 + t3375) * np.exp((-6*1j) * (t3359 - phi2)) + (-t3374 + t3375) * np.exp((6*1j) * (t3359 + phi2)) + (t3370 + t3371) * np.exp((-2*1j) * (t3359 + t3357)) + (t3372 + t3373) * np.exp((-2*1j) * (t3358 + t3357)) + (t3370 - t3371) * np.exp((2*1j) * (t3359 + t3356)) + (t3372 - t3373) * np.exp((2*1j) * (t3358 + t3356))) + + if Bindx == 118: + t3393 = np.cos(phi) + t3392 = t3393 ** 2 + t3399 = t3392 ** 2 + t3398 = t3393 * t3392 + t3401 = t3398 ** 2 + t3403 = t3399 ** 2 + t3400 = t3393 * t3399 + t3405 = t3400 ** 2 + t3407 = t3401 ** 2 + t3412 = 278 - 6116 * t3392 + 34650 * t3399 - 66360 * t3401 + 29610 * t3403 + 32732 * t3405 - 24794 * t3407 + t3381 = t3393 * t3407 + t3383 = t3393 * t3405 + t3385 = t3393 * t3403 + t3387 = t3393 * t3401 + t3411 = -21252 * t3381 + 41272 * t3383 + 7140 * t3385 - 64624 * t3387 + 924 * t3393 - 13384 * t3398 + 49924 * t3400 + t3410 = 5313 * t3381 + 29414 * t3383 - 95405 * t3385 + 82980 * t3387 + 1141 * t3393 - 3178 * t3398 - 20265 * t3400 + t3409 = -3864 * t3392 + 33740 * t3399 - 121296 * t3401 + 205996 * t3403 - 164248 * t3405 + 49588 * t3407 + 84 + t3397 = 4 * phi1 + t3396 = 8 * phi1 + t3395 = -7 * phi2 + t3394 = 7 * phi2 + tfunc[..., c] = (0.5e1 / 0.335872e6*1j) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.41e2) * ((1 + t3393) ** (-0.1e1 / 0.2e1)) * ((1 - t3393) ** (-0.1e1 / 0.2e1)) * ((31878 * t3381 - 141372 * t3383 + 247698 * t3385 - 214632 * t3387 + 93258 * t3400 - 17820 * t3398 + 990 * t3393) * np.exp((7*1j) * phi2) + (t3409 + t3411) * np.exp((-1*1j) * (t3397 + t3395)) + (t3410 + t3412) * np.exp((-1*1j) * (t3396 + t3395)) + (-t3409 + t3411) * np.exp((1j) * (t3397 + t3394)) + (t3410 - t3412) * np.exp((1j) * (t3396 + t3394))) + + if Bindx == 119: + t3432 = np.cos(phi) + t3431 = t3432 ** 2 + t3439 = t3431 ** 2 + t3438 = t3432 * t3431 + t3441 = t3438 ** 2 + t3443 = t3439 ** 2 + t3440 = t3432 * t3439 + t3445 = t3440 ** 2 + t3447 = t3441 ** 2 + t3454 = -1025 - 18450 * t3431 + 25625 * t3439 + 36900 * t3441 - 64575 * t3443 + 14350 * t3445 + 7175 * t3447 + t3420 = t3432 * t3447 + t3422 = t3432 * t3445 + t3424 = t3432 * t3443 + t3426 = t3432 * t3441 + t3453 = 1025 * t3420 + 18450 * t3422 - 25625 * t3424 - 36900 * t3426 - 7175 * t3432 - 14350 * t3438 + 64575 * t3440 + t3452 = -12232 * t3431 + 69300 * t3439 - 132720 * t3441 + 59220 * t3443 + 65464 * t3445 - 49588 * t3447 + 556 + t3451 = 10626 * t3420 + 58828 * t3422 - 190810 * t3424 + 165960 * t3426 + 2282 * t3432 - 6356 * t3438 - 40530 * t3440 + t3450 = -133722 * t3431 + 1167645 * t3439 - 4197708 * t3441 + 7128933 * t3443 - 5684154 * t3445 + 1716099 * t3447 + 2907 + t3449 = 735471 * t3420 - 1428306 * t3422 - 247095 * t3424 + 2236452 * t3426 - 31977 * t3432 + 463182 * t3438 - 1727727 * t3440 + t3437 = 4 * phi1 + t3436 = 8 * phi1 + t3435 = -7 * phi2 + t3434 = 7 * phi2 + t3433 = 12 * phi1 + tfunc[..., c] = (0.5e1 / 0.2686976e7*1j) * np.sqrt(0.11e2) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.41e2) * ((1 + t3432) ** (-0.1e1 / 0.2e1)) * ((1 - t3432) ** (-0.1e1 / 0.2e1)) * ((2704156 * t3420 - 11992344 * t3422 + 21011796 * t3424 - 18206864 * t3426 + 7910916 * t3440 - 1511640 * t3438 + 83980 * t3432) * np.exp((7*1j) * phi2) + (t3449 - t3450) * np.exp((-1*1j) * (t3437 + t3435)) + (t3451 + t3452) * np.exp((-1*1j) * (t3436 + t3435)) + (t3449 + t3450) * np.exp((1j) * (t3437 + t3434)) + (t3451 - t3452) * np.exp((1j) * (t3436 + t3434)) + (t3453 - t3454) * np.exp((-1*1j) * (t3433 + t3435)) + (t3453 + t3454) * np.exp((1j) * (t3433 + t3434))) + + if Bindx == 120: + t3471 = np.cos(phi) + t3470 = t3471 ** 2 + t3473 = t3470 ** 2 + t3474 = t3471 * t3473 + t3479 = t3474 ** 2 + t3461 = t3471 * t3479 + t3477 = t3473 ** 2 + t3463 = t3471 * t3477 + t3472 = t3471 * t3470 + t3475 = t3472 ** 2 + t3465 = t3471 * t3475 + t3485 = -28336 * t3461 - 2800 * t3463 + 84384 * t3465 - 176 * t3471 + 15120 * t3472 - 69216 * t3474 + t3460 = t3475 ** 2 + t3484 = -673 - 5313 * t3460 + 9422 * t3470 - 22575 * t3473 - 20412 * t3475 + 87345 * t3477 - 48818 * t3479 + t3483 = 21252 * t3460 + 9576 * t3470 - 53956 * t3473 + 103600 * t3475 - 61124 * t3477 - 19096 * t3479 - 252 + t3482 = 56672 * t3461 - 161056 * t3463 + 161728 * t3465 - 672 * t3471 + 10976 * t3472 - 67648 * t3474 + tfunc[..., c] = -(0.5e1 / 0.671744e6) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.13e2) * ((-31878 * t3460 + 135828 * t3479 - 224730 * t3477 + 178200 * t3475 - 66330 * t3473 + 9108 * t3470 - 198) * np.exp((8*1j) * phi2) + (t3484 - t3485) * np.exp((-8*1j) * (phi1 - phi2)) + (-t3482 + t3483) * np.exp((-4*1j) * (phi1 - 2 * phi2)) + (t3482 + t3483) * np.exp((4*1j) * (phi1 + 2 * phi2)) + (t3484 + t3485) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 121: + t3504 = np.cos(phi) + t3503 = t3504 ** 2 + t3509 = t3503 ** 2 + t3510 = t3504 * t3509 + t3515 = t3510 ** 2 + t3494 = t3504 * t3515 + t3513 = t3509 ** 2 + t3496 = t3504 * t3513 + t3508 = t3504 * t3503 + t3511 = t3508 ** 2 + t3498 = t3504 * t3511 + t3523 = -8200 * t3494 - 41000 * t3496 + 49200 * t3498 - 8200 * t3504 - 41000 * t3508 + 49200 * t3510 + t3493 = t3511 ** 2 + t3522 = 1025 + 1025 * t3493 + 26650 * t3503 + 15375 * t3509 - 86100 * t3511 + 15375 * t3513 + 26650 * t3515 + t3521 = 10626 * t3493 - 18844 * t3503 + 45150 * t3509 + 40824 * t3511 - 174690 * t3513 + 97636 * t3515 + 1346 + t3520 = 56672 * t3494 + 5600 * t3496 - 168768 * t3498 + 352 * t3504 - 30240 * t3508 + 138432 * t3510 + t3519 = -1961256 * t3494 + 5573688 * t3496 - 5596944 * t3498 + 23256 * t3504 - 379848 * t3508 + 2341104 * t3510 + t3518 = 735471 * t3493 + 331398 * t3503 - 1867263 * t3509 + 3585300 * t3511 - 2115327 * t3513 - 660858 * t3515 - 8721 + t3507 = 3 * phi1 + t3506 = -2 * phi2 + t3505 = 2 * phi2 + tfunc[..., c] = (0.5e1 / 0.5373952e7) * np.sqrt(0.41e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.11e2) * ((19063460 * t3513 - 15116400 * t3511 + 5626660 * t3509 - 772616 * t3503 + 2704156 * t3493 - 11522056 * t3515 + 16796) * np.exp((8*1j) * phi2) + (-t3520 + t3521) * np.exp((-8*1j) * (phi1 - phi2)) + (t3518 + t3519) * np.exp((-4*1j) * (phi1 + t3506)) + (t3522 + t3523) * np.exp((-4*1j) * (t3507 + t3506)) + (t3518 - t3519) * np.exp((4*1j) * (phi1 + t3505)) + (t3522 - t3523) * np.exp((4*1j) * (t3507 + t3505)) + (t3520 + t3521) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 122: + t3540 = np.cos(phi) + t3539 = t3540 ** 2 + t3545 = t3540 * t3539 + t3548 = t3545 ** 2 + t3529 = t3548 ** 2 + t3556 = -253 * t3529 + t3555 = 1012 * t3529 + t3546 = t3539 ** 2 + t3547 = t3540 * t3546 + t3552 = t3547 ** 2 + t3550 = t3546 ** 2 + t3544 = 4 * phi1 + t3543 = 8 * phi1 + t3542 = -9 * phi2 + t3541 = 9 * phi2 + t3534 = t3540 * t3548 + t3532 = t3540 * t3550 + t3530 = t3540 * t3552 + tfunc[..., c] = (0.15e2 / 0.335872e6*1j) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.41e2) * np.sqrt((1 - t3540)) * ((-1518 * t3529 - 1518 * t3530 + 6270 * t3552 + 6270 * t3532 - 9900 * t3550 - 9900 * t3534 + 7260 * t3548 + 7260 * t3547 - 2310 * t3546 - 2310 * t3545 + 198 * t3539 + 198 * t3540) * np.exp((9*1j) * phi2) + (t3555 - 2024 * t3530 - 2816 * t3552 + 7432 * t3532 + 2036 * t3550 - 10384 * t3534 + 672 * t3548 + 6800 * t3547 - 1252 * t3546 - 2056 * t3545 + 352 * t3539 + 232 * t3540 - 4) * np.exp((-1*1j) * (t3544 + t3542)) + (t3556 + 1265 * t3530 - 1837 * t3552 - 865 * t3532 + 4680 * t3550 - 2934 * t3534 - 2730 * t3548 + 3774 * t3547 - 285 * t3546 - 1355 * t3545 + 487 * t3539 + 115 * t3540 - 62) * np.exp((-1*1j) * (t3543 + t3542)) + (t3555 + 4048 * t3530 + 3256 * t3552 - 6992 * t3532 - 12388 * t3550 + 32 * t3534 + 11088 * t3548 + 4960 * t3547 - 3092 * t3546 - 2288 * t3545 + 120 * t3539 + 240 * t3540 + 4) * np.exp((1j) * (t3544 + t3541)) + (t3556 - 1771 * t3530 - 4873 * t3552 - 5845 * t3532 - 300 * t3550 + 7314 * t3534 + 7518 * t3548 + 1014 * t3547 - 3045 * t3546 - 1975 * t3545 - 133 * t3539 + 239 * t3540 + 62) * np.exp((1j) * (t3543 + t3541))) * ((1 + t3540) ** (-0.1e1 / 0.2e1)) + + if Bindx == 123: + t3575 = np.cos(phi) + t3574 = t3575 ** 2 + t3580 = t3575 * t3574 + t3583 = t3580 ** 2 + t3564 = t3583 ** 2 + t3592 = 1025 * t3564 + t3591 = 10626 * t3564 + t3590 = 735471 * t3564 + t3581 = t3574 ** 2 + t3582 = t3575 * t3581 + t3587 = t3582 ** 2 + t3585 = t3581 ** 2 + t3579 = 4 * phi1 + t3578 = 8 * phi1 + t3577 = -9 * phi2 + t3576 = 9 * phi2 + t3569 = t3575 * t3583 + t3567 = t3575 * t3585 + t3565 = t3575 * t3587 + tfunc[..., c] = (-0.5e1 / 0.8060928e7*1j) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.3e1) * np.sqrt((1 - t3575)) * ((1 + t3575) ** (-0.1e1 / 0.2e1)) * ((17635800 * t3585 + 17635800 * t3569 - 12932920 * t3583 - 12932920 * t3582 - 11169340 * t3587 - 11169340 * t3567 + 4115020 * t3581 + 4115020 * t3580 + 2704156 * t3564 + 2704156 * t3565 - 352716 * t3574 - 352716 * t3575) * np.exp((9*1j) * phi2) + (t3592 + 10250 * t3565 + 45100 * t3587 - 45100 * t3574 - 10250 * t3575 - 1025 + 169125 * t3585 - 169125 * t3581 + 135300 * t3569 - 135300 * t3582 + 112750 * t3567 - 112750 * t3580) * np.exp((3*1j) * (t3579 + 3 * phi2)) + (t3592 - 8200 * t3565 + 26650 * t3587 - 41000 * t3567 + 15375 * t3585 + 49200 * t3569 - 86100 * t3583 + 49200 * t3582 + 15375 * t3581 - 41000 * t3580 + 26650 * t3574 - 8200 * t3575 + 1025) * np.exp((-3*1j) * (t3579 - 3 * phi2)) + (-1470942 * t3565 + 5401206 * t3567 - 7546572 * t3569 + 255816 * t3574 + 168606 * t3575 - 1494198 * t3580 - 909891 * t3581 + 4941900 * t3582 + 488376 * t3583 + 1479663 * t3585 - 2046528 * t3587 - 2907 + t3590) * np.exp((-1*1j) * (t3579 + t3577)) + (t3591 - 53130 * t3565 + 77154 * t3587 + 36330 * t3567 - 196560 * t3585 + 123228 * t3569 + 114660 * t3583 - 158508 * t3582 + 11970 * t3581 + 56910 * t3580 - 20454 * t3574 - 4830 * t3575 + 2604) * np.exp((-1*1j) * (t3578 + t3577)) + (2941884 * t3565 - 5081436 * t3567 + 23256 * t3569 + 87210 * t3574 + 174420 * t3575 - 1662804 * t3580 - 2247111 * t3581 + 3604680 * t3582 + 8058204 * t3583 - 9002979 * t3585 + 2366298 * t3587 + 2907 + t3590) * np.exp((1j) * (t3579 + t3576)) + (t3591 + 74382 * t3565 + 204666 * t3587 + 245490 * t3567 + 12600 * t3585 - 307188 * t3569 - 315756 * t3583 - 42588 * t3582 + 127890 * t3581 + 82950 * t3580 + 5586 * t3574 - 10038 * t3575 - 2604) * np.exp((1j) * (t3578 + t3576))) + + if Bindx == 124: + t3609 = np.cos(phi) + t3608 = t3609 ** 2 + t3615 = t3608 ** 2 + t3616 = t3609 * t3615 + t3621 = t3616 ** 2 + t3599 = t3609 * t3621 + t3619 = t3615 ** 2 + t3601 = t3609 * t3619 + t3614 = t3609 * t3608 + t3617 = t3614 ** 2 + t3603 = t3609 * t3617 + t3627 = -920 * t3599 + 2680 * t3601 - 2416 * t3603 - 104 * t3609 + 392 * t3614 + 368 * t3616 + t3626 = -460 * t3599 - 1060 * t3601 + 2472 * t3603 + 140 * t3609 - 540 * t3614 - 552 * t3616 + t3598 = t3617 ** 2 + t3625 = 29 - 69 * t3598 + 116 * t3608 - 1335 * t3615 + 1680 * t3617 + 735 * t3619 - 1156 * t3621 + t3624 = -20 + 276 * t3598 + 400 * t3608 - 2020 * t3615 + 3584 * t3617 - 2332 * t3619 + 112 * t3621 + t3613 = 2 * phi1 + t3612 = 4 * phi1 + t3611 = -5 * phi2 + t3610 = 5 * phi2 + tfunc[..., c] = -(0.5e1 / 0.335872e6) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.7e1) * np.sqrt(0.41e2) * ((-414 * t3598 + 2088 * t3621 - 4230 * t3619 + 4320 * t3617 - 2250 * t3615 + 504 * t3608 - 18) * np.exp((10*1j) * phi2) + (t3624 + t3627) * np.exp((-2*1j) * (t3613 + t3611)) + (t3625 - t3626) * np.exp((-2*1j) * (t3612 + t3611)) + (t3624 - t3627) * np.exp((2*1j) * (t3613 + t3610)) + (t3625 + t3626) * np.exp((2*1j) * (t3612 + t3610))) + + if Bindx == 125: + t3646 = np.cos(phi) + t3645 = t3646 ** 2 + t3652 = t3646 * t3645 + t3655 = t3652 ** 2 + t3635 = t3655 ** 2 + t3653 = t3645 ** 2 + t3657 = t3653 ** 2 + t3654 = t3646 * t3653 + t3659 = t3654 ** 2 + t3667 = 1025 * t3635 - 45100 * t3645 + 45100 * t3659 - 1025 + 169125 * t3657 - 169125 * t3653 + t3636 = t3646 * t3659 + t3638 = t3646 * t3657 + t3640 = t3646 * t3655 + t3666 = -70840 * t3636 - 163240 * t3638 + 380688 * t3640 + 21560 * t3646 - 83160 * t3652 - 85008 * t3654 + t3665 = -10250 * t3636 + 10250 * t3646 - 135300 * t3640 + 135300 * t3654 - 112750 * t3638 + 112750 * t3652 + t3664 = 10626 * t3635 - 17864 * t3645 + 205590 * t3653 - 258720 * t3655 - 113190 * t3657 + 178024 * t3659 - 4466 + t3663 = -2451570 * t3636 + 7141530 * t3638 - 6438036 * t3640 - 277134 * t3646 + 1044582 * t3652 + 980628 * t3654 + t3662 = 735471 * t3635 + 1065900 * t3645 - 5382795 * t3653 + 9550464 * t3655 - 6214197 * t3657 + 298452 * t3659 - 53295 + t3651 = 2 * phi1 + t3650 = 4 * phi1 + t3649 = 6 * phi1 + t3648 = -5 * phi2 + t3647 = 5 * phi2 + tfunc[..., c] = (0.5e1 / 0.5373952e7) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.41e2) * ((2704156 * t3635 - 13638352 * t3659 + 27629420 * t3657 - 28217280 * t3655 + 14696500 * t3653 - 3292016 * t3645 + 117572) * np.exp((10*1j) * phi2) + (t3665 + t3667) * np.exp((-2*1j) * (t3649 + t3648)) + (-t3665 + t3667) * np.exp((2*1j) * (t3649 + t3647)) + (t3662 + t3663) * np.exp((-2*1j) * (t3651 + t3648)) + (t3664 + t3666) * np.exp((-2*1j) * (t3650 + t3648)) + (t3662 - t3663) * np.exp((2*1j) * (t3651 + t3647)) + (t3664 - t3666) * np.exp((2*1j) * (t3650 + t3647))) + + if Bindx == 126: + t3683 = np.cos(phi) + t3682 = t3683 ** 2 + t3689 = t3682 ** 2 + t3690 = t3683 * t3689 + t3695 = t3690 ** 2 + t3673 = t3683 * t3695 + t3698 = 3 * t3673 + t3697 = 1 + t3683 + t3693 = t3689 ** 2 + t3688 = t3683 * t3682 + t3691 = t3688 ** 2 + t3687 = 4 * phi1 + t3686 = 8 * phi1 + t3685 = -11 * phi2 + t3684 = 11 * phi2 + t3677 = t3683 * t3691 + t3675 = t3683 * t3693 + tfunc[..., c] = (0.5e1 / 0.335872e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.41e2) * np.sqrt(0.11e2) * ((1 - t3683) ** (0.3e1 / 0.2e1)) * (t3697 ** (-0.1e1 / 0.2e1)) * (18 * (t3673 + 2 * t3695 - 3 * t3675 - 8 * t3693 + 2 * t3677 + 12 * t3691 + 2 * t3690 - 8 * t3689 - 3 * t3688 + 2 * t3682 + t3683) * np.exp((11*1j) * phi2) + 4 * (-3 * t3673 + 5 * t3695 + 11 * t3675 - 21 * t3693 - 14 * t3677 + 34 * t3691 + 6 * t3690 - 26 * t3689 + t3688 + 9 * t3682 - t3697) * np.exp((-1*1j) * (t3687 + t3685)) + (t3698 - 16 * t3695 + 27 * t3675 + 2 * t3693 - 58 * t3677 + 56 * t3691 + 14 * t3690 - 52 * t3689 + 23 * t3688 + 8 * t3682 - 9 * t3683 + 2) * np.exp((-1*1j) * (t3686 + t3685)) + (-12 * t3673 - 68 * t3695 - 132 * t3675 - 44 * t3693 + 200 * t3677 + 280 * t3691 + 56 * t3690 - 152 * t3689 - 124 * t3688 - 20 * t3682 + 12 * t3683 + 4) * np.exp((1j) * (t3687 + t3684)) + (t3698 + 28 * t3695 + 115 * t3675 + 270 * t3693 + 390 * t3677 + 336 * t3691 + 126 * t3690 - 60 * t3689 - 105 * t3688 - 60 * t3682 - 17 * t3683 - 2) * np.exp((1j) * (t3686 + t3684))) + + if Bindx == 127: + t3717 = np.cos(phi) + t3716 = t3717 ** 2 + t3723 = t3717 * t3716 + t3726 = t3723 ** 2 + t3706 = t3726 ** 2 + t3735 = 1025 * t3706 + t3734 = 10626 * t3706 + t3733 = 735471 * t3706 + t3724 = t3716 ** 2 + t3725 = t3717 * t3724 + t3730 = t3725 ** 2 + t3728 = t3724 ** 2 + t3722 = 4 * phi1 + t3721 = 8 * phi1 + t3720 = 12 * phi1 + t3719 = -11 * phi2 + t3718 = 11 * phi2 + t3711 = t3717 * t3726 + t3709 = t3717 * t3728 + t3707 = t3717 * t3730 + tfunc[..., c] = (-0.5e1 / 0.2686976e7*1j) * np.sqrt(0.41e2) * np.sqrt((1 - t3717)) * ((1 + t3717) ** (-0.1e1 / 0.2e1)) * (2704156 * (10 * t3728 + 10 * t3711 - 10 * t3726 - 10 * t3725 - 5 * t3730 - 5 * t3709 + 5 * t3724 + 5 * t3723 + t3706 + t3707 - t3716 - t3717) * np.exp((11*1j) * phi2) + (-1716099 * t3728 + 7845024 * t3709 - 11767536 * t3711 + 2451570 * t3716 - 6619239 * t3724 + 7845024 * t3725 + 6864396 * t3726 - 1470942 * t3730 + t3733 - 245157 - 1961256 * t3707 - 1961256 * t3723) * np.exp((-1*1j) * (t3722 + t3719)) + (t3735 - 10250 * t3707 + 45100 * t3730 - 45100 * t3716 + 10250 * t3717 - 1025 + 169125 * t3728 - 169125 * t3724 - 135300 * t3711 + 135300 * t3725 - 112750 * t3709 + 112750 * t3723) * np.exp((-1*1j) * (t3720 + t3719)) + (-67298 * t3707 - 88550 * t3709 + 403788 * t3711 - 60214 * t3716 + 38962 * t3717 - 53130 * t3723 + 265650 * t3724 - 233772 * t3725 - 148764 * t3726 - 212520 * t3728 + 152306 * t3730 + t3734 - 7084) * np.exp((-1*1j) * (t3721 + t3719)) + (3432198 * t3707 - 5393454 * t3709 - 4903140 * t3711 - 1961256 * t3716 + 490314 * t3717 - 6374082 * t3723 - 1716099 * t3724 + 12748164 * t3725 + 13728792 * t3726 - 14954577 * t3728 + 3922512 * t3730 + t3733 + 245157) * np.exp((1j) * (t3722 + t3718)) + (88550 * t3707 + 549010 * t3709 - 191268 * t3711 + 152306 * t3716 + 53130 * t3717 + 159390 * t3723 - 159390 * t3724 - 658812 * t3725 - 743820 * t3726 + 425040 * t3728 + 308154 * t3730 + t3734 + 7084) * np.exp((1j) * (t3721 + t3718)) + (t3735 + 12300 * t3707 + 67650 * t3730 + 947100 * t3726 + 67650 * t3716 + 12300 * t3717 + 1025 + 811800 * t3711 + 811800 * t3725 + 507375 * t3728 + 507375 * t3724 + 225500 * t3709 + 225500 * t3723) * np.exp((1j) * (t3720 + t3718))) + + if Bindx == 128: + t3752 = np.cos(phi) + t3751 = t3752 ** 2 + t3757 = t3751 ** 2 + t3758 = t3752 * t3757 + t3763 = t3758 ** 2 + t3770 = (-t3763 - 1) * t3752 + t3761 = t3757 ** 2 + t3744 = t3752 * t3761 + t3756 = t3752 * t3751 + t3759 = t3756 ** 2 + t3746 = t3752 * t3759 + t3769 = -40 * t3744 + 48 * t3746 - 40 * t3756 + 48 * t3758 + 8 * t3770 + t3768 = 48 * t3744 - 32 * t3746 + 48 * t3756 - 32 * t3758 + 16 * t3770 + t3741 = t3759 ** 2 + t3767 = -4 - 4 * t3741 - 8 * t3751 + 68 * t3757 - 112 * t3759 + 68 * t3761 - 8 * t3763 + t3766 = 1 + t3741 + 26 * t3751 + 15 * t3757 - 84 * t3759 + 15 * t3761 + 26 * t3763 + t3755 = 2 * phi1 + t3754 = -3 * phi2 + t3753 = 3 * phi2 + tfunc[..., c] = (0.15e2 / 0.671744e6) * np.sqrt(0.11e2) * np.sqrt(0.41e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * ((6 * t3741 - 36 * t3763 + 90 * t3761 - 120 * t3759 + 90 * t3757 - 36 * t3751 + 6) * np.exp((12*1j) * phi2) + (t3766 + t3769) * np.exp((-4*1j) * (t3755 + t3754)) + (t3766 - t3769) * np.exp((4*1j) * (t3755 + t3753)) + (t3767 - t3768) * np.exp((-4*1j) * (phi1 + t3754)) + (t3767 + t3768) * np.exp((4*1j) * (phi1 + t3753))) + + if Bindx == 129: + t3789 = np.cos(phi) + t3788 = t3789 ** 2 + t3794 = t3788 ** 2 + t3798 = t3794 ** 2 + t3811 = t3798 + t3794 + t3795 = t3789 * t3794 + t3800 = t3795 ** 2 + t3810 = t3800 + t3788 + t3779 = t3789 * t3800 + t3809 = -t3779 - t3789 + t3793 = t3789 * t3788 + t3796 = t3793 ** 2 + t3778 = t3796 ** 2 + t3808 = 1025 * t3778 + 67650 * t3788 + 947100 * t3796 + 67650 * t3800 + 507375 * t3811 + 1025 + t3781 = t3789 * t3798 + t3783 = t3789 * t3796 + t3807 = -12300 * t3779 - 12300 * t3789 - 811800 * t3783 - 811800 * t3795 - 225500 * t3781 - 225500 * t3793 + t3806 = 510048 * t3783 + 510048 * t3795 - 425040 * t3781 - 425040 * t3793 + 85008 * t3809 + t3805 = 10626 * t3778 - 892584 * t3796 + 276276 * t3810 + 159390 * t3811 + 10626 + t3804 = 8825652 * t3781 + 8825652 * t3793 - 5883768 * t3783 - 5883768 * t3795 + 2941884 * t3809 + t3803 = 735471 * t3778 + 20593188 * t3796 + 1470942 * t3810 - 12503007 * t3811 + 735471 + t3792 = 2 * phi1 + t3791 = -3 * phi2 + t3790 = 3 * phi2 + tfunc[..., c] = (0.5e1 / 0.32243712e8) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.41e2) * ((-54083120 * t3796 + 2704156 * t3778 + 2704156 + 40562340 * t3798 + 40562340 * t3794 - 16224936 * t3800 - 16224936 * t3788) * np.exp((12*1j) * phi2) + (t3803 + t3804) * np.exp((-4*1j) * (phi1 + t3791)) + (t3805 + t3806) * np.exp((-4*1j) * (t3792 + t3791)) + (t3803 - t3804) * np.exp((4*1j) * (phi1 + t3790)) + (t3805 - t3806) * np.exp((4*1j) * (t3792 + t3790)) + (t3807 + t3808) * np.exp((-12*1j) * (phi1 - phi2)) + (-t3807 + t3808) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 130: + t3830 = np.cos(phi) + t3829 = t3830 ** 2 + t3836 = t3830 * t3829 + t3839 = t3836 ** 2 + t3845 = t3839 ** 2 + t3818 = t3830 * t3845 + t3849 = 1 - t3818 + t3848 = 20 - 20 * t3818 + t3847 = 475 - 475 * t3818 + t3837 = t3829 ** 2 + t3838 = t3830 * t3837 + t3843 = t3838 ** 2 + t3841 = t3837 ** 2 + t3835 = 4 * phi1 + t3834 = 8 * phi1 + t3833 = 12 * phi1 + t3832 = -13 * phi2 + t3831 = 13 * phi2 + t3824 = t3830 * t3839 + t3822 = t3830 * t3841 + t3820 = t3830 * t3843 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt((1 + t3830)) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * ((-11 * t3845 - 54 * t3820 - 154 * t3843 - 275 * t3822 - 297 * t3841 - 132 * t3824 + 132 * t3839 + 297 * t3838 + 275 * t3837 + 154 * t3836 + 54 * t3829 + 11 * t3830 + t3849) * np.exp((-1*1j) * (t3833 + t3831)) + (-13 * t3845 + 78 * t3820 - 286 * t3843 + 715 * t3822 - 1287 * t3841 + 1716 * t3824 - 1716 * t3839 + 1287 * t3838 - 715 * t3837 + 286 * t3836 - 78 * t3829 + 13 * t3830 - t3849) * np.exp((1j) * (t3833 + t3832)) + (1425 * t3845 - 950 * t3820 - 6650 * t3843 - 2375 * t3822 + 11875 * t3841 + 9500 * t3824 - 9500 * t3839 - 11875 * t3838 + 2375 * t3837 + 6650 * t3836 + 950 * t3829 - 1425 * t3830 - t3847) * np.exp((-1*1j) * (t3835 + t3831)) + (140 * t3845 + 360 * t3820 + 280 * t3843 - 500 * t3822 - 1260 * t3841 - 720 * t3824 + 720 * t3839 + 1260 * t3838 + 500 * t3837 - 280 * t3836 - 360 * t3829 - 140 * t3830 - t3848) * np.exp((-1*1j) * (t3834 + t3831)) + (2375 * t3845 - 2850 * t3820 - 4750 * t3843 + 13775 * t3822 - 4275 * t3841 - 17100 * t3824 + 17100 * t3839 + 4275 * t3838 - 13775 * t3837 + 4750 * t3836 + 2850 * t3829 - 2375 * t3830 + t3847) * np.exp((1j) * (t3835 + t3832)) + (180 * t3845 - 680 * t3820 + 1320 * t3843 - 1100 * t3822 - 660 * t3841 + 2640 * t3824 - 2640 * t3839 + 660 * t3838 + 1100 * t3837 - 1320 * t3836 + 680 * t3829 - 180 * t3830 + t3848) * np.exp((1j) * (t3834 + t3832))) * ((1 - t3830) ** (-0.1e1 / 0.2e1)) + + if Bindx == 131: + t3868 = np.cos(phi) + t3867 = t3868 ** 2 + t3873 = t3867 ** 2 + t3872 = t3868 * t3867 + t3875 = t3872 ** 2 + t3877 = t3873 ** 2 + t3874 = t3868 * t3873 + t3879 = t3874 ** 2 + t3881 = t3875 ** 2 + t3888 = 12 + 636 * t3867 + 3080 * t3873 + 792 * t3875 - 4356 * t3877 - 2068 * t3879 - 144 * t3881 + t3887 = -160 - 2080 * t3867 + 8000 * t3873 + 960 * t3875 - 14880 * t3877 + 6240 * t3879 + 1920 * t3881 + t3856 = t3868 * t3881 + t3858 = t3868 * t3879 + t3860 = t3868 * t3877 + t3862 = t3868 * t3875 + t3886 = -13 * t3856 - 714 * t3858 - 3795 * t3860 - 2508 * t3862 + 131 * t3868 + 1782 * t3872 + 3069 * t3874 + t3885 = -260 * t3856 - 5480 * t3858 + 2500 * t3860 + 14160 * t3862 + 1020 * t3868 - 360 * t3872 - 11580 * t3874 + t3884 = -1900 + 20900 * t3867 - 41800 * t3873 - 3800 * t3875 + 81700 * t3877 - 77900 * t3879 + 22800 * t3881 + t3883 = -6175 * t3856 - 4750 * t3858 + 82175 * t3860 - 157700 * t3862 + 1425 * t3868 - 35150 * t3872 + 120175 * t3874 + t3871 = 2 * phi1 + t3870 = -3 * phi2 + t3869 = 3 * phi2 + tfunc[..., c] = -(0.9e1 / 0.131072e6) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((-t3883 + t3884) * np.exp((-4*1j) * (phi1 + t3869)) + (-t3885 + t3887) * np.exp((-4*1j) * (t3871 + t3869)) + (t3883 + t3884) * np.exp((4*1j) * (phi1 + t3870)) + (t3885 + t3887) * np.exp((4*1j) * (t3871 + t3870)) + (t3886 + t3888) * np.exp((-12*1j) * (phi1 + phi2)) + (-t3886 + t3888) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 132: + t3908 = np.cos(phi) + t3907 = t3908 ** 2 + t3914 = t3908 * t3907 + t3917 = t3914 ** 2 + t3923 = t3917 ** 2 + t3896 = t3908 * t3923 + t3915 = t3907 ** 2 + t3916 = t3908 * t3915 + t3921 = t3916 ** 2 + t3898 = t3908 * t3921 + t3919 = t3915 ** 2 + t3900 = t3908 * t3919 + t3918 = t3908 * t3917 + t3931 = 132 * t3896 + 1408 * t3898 + 836 * t3900 + 108 * t3908 + 992 * t3914 - 308 * t3916 - 3168 * t3918 + t3930 = -1760 * t3896 - 2816 * t3898 + 13600 * t3900 - 416 * t3908 + 2560 * t3914 - 1184 * t3916 - 9984 * t3918 + t3895 = t3918 ** 2 + t3929 = -11 - 13 * t3895 - 451 * t3907 - 1023 * t3915 + 2409 * t3917 + 1551 * t3919 - 1881 * t3921 - 581 * t3923 + t3928 = -92 - 260 * t3895 - 92 * t3907 + 4660 * t3915 - 11532 * t3917 + 5100 * t3919 + 6508 * t3921 - 4292 * t3923 + t3927 = -20900 * t3896 + 80256 * t3898 - 109668 * t3900 + 2356 * t3908 - 12768 * t3914 + 6612 * t3916 + 54112 * t3918 + t3926 = 6175 * t3895 - 8303 * t3907 + 51813 * t3915 - 125267 * t3917 + 136363 * t3919 - 58653 * t3921 - 2489 * t3923 + 361 + t3913 = 4 * phi1 + t3912 = 8 * phi1 + t3911 = 12 * phi1 + t3910 = -11 * phi2 + t3909 = 11 * phi2 + tfunc[..., c] = (0.45e2 / 0.131072e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * ((1 + t3908) ** (-0.1e1 / 0.2e1)) * ((1 - t3908) ** (-0.1e1 / 0.2e1)) * ((-t3926 + t3927) * np.exp((-1*1j) * (t3913 + t3909)) + (t3928 + t3930) * np.exp((-1*1j) * (t3912 + t3909)) + (t3926 + t3927) * np.exp((1j) * (t3913 + t3910)) + (-t3928 + t3930) * np.exp((1j) * (t3912 + t3910)) + (-t3929 + t3931) * np.exp((-1*1j) * (t3911 + t3909)) + (t3929 + t3931) * np.exp((1j) * (t3911 + t3910))) + + if Bindx == 133: + t3950 = np.cos(phi) + t3949 = t3950 ** 2 + t3957 = t3949 ** 2 + t3956 = t3950 * t3949 + t3959 = t3956 ** 2 + t3961 = t3957 ** 2 + t3958 = t3950 * t3957 + t3963 = t3958 ** 2 + t3965 = t3959 ** 2 + t3972 = -10 - 310 * t3949 - 220 * t3957 + 1716 * t3959 - 66 * t3961 - 990 * t3963 - 120 * t3965 + t3971 = 48 - 432 * t3949 - 352 * t3957 + 5664 * t3959 - 8208 * t3961 + 1680 * t3963 + 1600 * t3965 + t3938 = t3950 * t3965 + t3940 = t3950 * t3963 + t3942 = t3950 * t3961 + t3944 = t3950 * t3959 + t3970 = -13 * t3938 - 472 * t3940 - 1045 * t3942 + 1320 * t3944 - 87 * t3950 - 528 * t3956 + 825 * t3958 + t3969 = -38 + 1254 * t3949 + 1596 * t3957 - 30932 * t3959 + 71250 * t3961 - 62130 * t3963 + 19000 * t3965 + t3968 = -260 * t3938 - 3456 * t3940 + 5052 * t3942 + 1632 * t3944 - 108 * t3950 + 1568 * t3956 - 4428 * t3958 + t3967 = -6175 * t3938 + 3192 * t3940 + 37449 * t3942 - 70984 * t3944 + 1235 * t3950 - 13680 * t3956 + 48963 * t3958 + t3955 = 2 * phi1 + t3954 = 4 * phi1 + t3953 = 6 * phi1 + t3952 = -5 * phi2 + t3951 = 5 * phi2 + tfunc[..., c] = -(0.45e2 / 0.65536e5) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((-t3967 + t3969) * np.exp((-2*1j) * (t3955 + t3951)) + (-t3968 + t3971) * np.exp((-2*1j) * (t3954 + t3951)) + (t3970 + t3972) * np.exp((-2*1j) * (t3953 + t3951)) + (t3967 + t3969) * np.exp((2*1j) * (t3955 + t3952)) + (t3968 + t3971) * np.exp((2*1j) * (t3954 + t3952)) + (-t3970 + t3972) * np.exp((2*1j) * (t3953 + t3952))) + + if Bindx == 134: + t3992 = np.cos(phi) + t3991 = t3992 ** 2 + t3997 = t3992 * t3991 + t4000 = t3997 ** 2 + t4006 = t4000 ** 2 + t3980 = t3992 * t4006 + t3998 = t3991 ** 2 + t3999 = t3992 * t3998 + t4004 = t3999 ** 2 + t3982 = t3992 * t4004 + t4002 = t3998 ** 2 + t3984 = t3992 * t4002 + t4001 = t3992 * t4000 + t4014 = 2484 * t3980 + 12696 * t3982 - 21252 * t3984 - 1564 * t3992 - 3496 * t3997 + 23276 * t3999 - 12144 * t4001 + t3979 = t4001 ** 2 + t4013 = -207 + 299 * t3979 - 4347 * t3991 + 8349 * t3998 + 17457 * t4000 - 32637 * t4002 + 2783 * t4004 + 8303 * t4006 + t4012 = 5980 * t3979 + 9348 * t3991 - 39708 * t3998 + 32532 * t4000 + 83388 * t4002 - 147444 * t4004 + 56396 * t4006 - 492 + t4011 = -33120 * t3980 + 13248 * t3982 + 124128 * t3984 - 480 * t3992 - 8768 * t3997 + 74336 * t3999 - 169344 * t4001 + t4010 = -393300 * t3980 + 1457832 * t3982 - 2053596 * t3984 - 2052 * t3992 + 46056 * t3997 - 403788 * t3999 + 1348848 * t4001 + t4009 = -142025 * t3979 + 59641 * t3991 - 416271 * t3998 + 1107605 * t4000 - 1258769 * t4002 + 427899 * t4004 + 223307 * t4006 - 1387 + t3996 = 4 * phi1 + t3995 = 8 * phi1 + t3994 = -9 * phi2 + t3993 = 9 * phi2 + tfunc[..., c] = (0.45e2 / 0.131072e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((1 + t3992) ** (-0.1e1 / 0.2e1)) * ((1 - t3992) ** (-0.1e1 / 0.2e1)) * ((t4013 + t4014) * np.exp((-3*1j) * (t3996 + 3 * phi2)) + (t4009 + t4010) * np.exp((-1*1j) * (t3996 + t3993)) + (t4011 - t4012) * np.exp((-1*1j) * (t3995 + t3993)) + (-t4009 + t4010) * np.exp((1j) * (t3996 + t3994)) + (t4011 + t4012) * np.exp((1j) * (t3995 + t3994)) + (-t4013 + t4014) * np.exp((3*1j) * (t3996 - 3 * phi2))) + + if Bindx == 135: + t4033 = np.cos(phi) + t4032 = t4033 ** 2 + t4038 = t4032 ** 2 + t4037 = t4033 * t4032 + t4040 = t4037 ** 2 + t4042 = t4038 ** 2 + t4039 = t4033 * t4038 + t4044 = t4039 ** 2 + t4046 = t4040 ** 2 + t4053 = 26312 * t4032 - 101200 * t4038 - 12144 * t4040 + 188232 * t4042 - 78936 * t4044 - 24288 * t4046 + 2024 + t4021 = t4033 * t4046 + t4023 = t4033 * t4044 + t4025 = t4033 * t4042 + t4027 = t4033 * t4040 + t4052 = 3289 * t4021 + 69322 * t4023 - 31625 * t4025 - 179124 * t4027 - 12903 * t4033 + 4554 * t4037 + 146487 * t4039 + t4051 = -65780 * t4021 - 455400 * t4023 + 1156980 * t4025 - 743856 * t4027 - 13236 * t4033 + 77848 * t4037 + 39348 * t4039 + t4050 = 46656 * t4032 - 346240 * t4038 + 859776 * t4040 - 635328 * t4042 - 242880 * t4044 + 323840 * t4046 - 1728 + t4049 = -193800 * t4032 + 1901520 * t4038 - 7659280 * t4040 + 14215800 * t4042 - 12113640 * t4044 + 3845600 * t4046 + 3800 + t4048 = -1562275 * t4021 + 2451570 * t4023 + 2047155 * t4025 - 6055300 * t4027 + 61085 * t4033 - 977550 * t4037 + 4035315 * t4039 + t4036 = 3 * phi1 + t4035 = -2 * phi2 + t4034 = 2 * phi2 + tfunc[..., c] = -(0.9e1 / 0.65536e5) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((t4050 - t4051) * np.exp((-8*1j) * (phi1 + phi2)) + (-t4048 + t4049) * np.exp((-4*1j) * (phi1 + t4034)) + (-t4052 + t4053) * np.exp((-4*1j) * (t4036 + t4034)) + (t4048 + t4049) * np.exp((4*1j) * (phi1 + t4035)) + (t4052 + t4053) * np.exp((4*1j) * (t4036 + t4035)) + (t4050 + t4051) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 136: + t4073 = np.cos(phi) + t4072 = t4073 ** 2 + t4079 = t4073 * t4072 + t4082 = t4079 ** 2 + t4088 = t4082 ** 2 + t4061 = t4073 * t4088 + t4080 = t4072 ** 2 + t4081 = t4073 * t4080 + t4086 = t4081 ** 2 + t4063 = t4073 * t4086 + t4084 = t4080 ** 2 + t4065 = t4073 * t4084 + t4083 = t4073 * t4082 + t4096 = -21252 * t4061 - 14168 * t4063 + 162932 * t4065 - 9108 * t4073 + 34408 * t4079 + 29348 * t4081 - 182160 * t4083 + t4060 = t4083 ** 2 + t4095 = -3289 * t4060 - 8855 * t4072 + 90321 * t4080 - 143451 * t4082 + 6831 * t4084 + 107019 * t4086 - 46805 * t4088 - 1771 + t4094 = 283360 * t4061 - 566720 * t4063 + 72352 * t4065 - 10144 * t4073 + 117824 * t4079 - 428512 * t4081 + 531840 * t4083 + t4093 = -65780 * t4060 + 980 * t4072 - 100492 * t4080 + 599844 * t4082 - 1269204 * t4084 + 1082620 * t4086 - 247940 * t4088 - 28 + t4092 = 3364900 * t4061 - 12113640 * t4063 + 16989420 * t4065 + 31540 * t4073 - 640680 * t4079 + 4043580 * t4081 - 11675120 * t4083 + t4091 = -1562275 * t4060 + 128915 * t4072 - 1110645 * t4080 + 3057575 * t4082 - 2494795 * t4084 - 1934295 * t4086 + 3917705 * t4088 - 2185 + t4078 = 4 * phi1 + t4077 = 8 * phi1 + t4076 = -7 * phi2 + t4075 = 7 * phi2 + t4074 = 12 * phi1 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * ((-t4091 + t4092) * np.exp((-1*1j) * (t4078 + t4075)) + (-t4093 + t4094) * np.exp((-1*1j) * (t4077 + t4075)) + (t4091 + t4092) * np.exp((1j) * (t4078 + t4076)) + (t4093 + t4094) * np.exp((1j) * (t4077 + t4076)) + (t4095 + t4096) * np.exp((-1*1j) * (t4074 + t4075)) + (-t4095 + t4096) * np.exp((1j) * (t4074 + t4076))) * ((1 + t4073) ** (-0.1e1 / 0.2e1)) * ((1 - t4073) ** (-0.1e1 / 0.2e1)) + + if Bindx == 137: + t4115 = np.cos(phi) + t4114 = t4115 ** 2 + t4120 = t4115 * t4114 + t4123 = t4120 ** 2 + t4129 = t4123 ** 2 + t4103 = t4115 * t4129 + t4121 = t4114 ** 2 + t4122 = t4115 * t4121 + t4127 = t4122 ** 2 + t4105 = t4115 * t4127 + t4125 = t4121 ** 2 + t4107 = t4115 * t4125 + t4109 = t4115 * t4123 + t4136 = -3289 * t4103 - 30360 * t4105 + 91839 * t4107 - 54648 * t4109 - 5819 * t4115 + 36432 * t4120 - 34155 * t4122 + t4135 = 1518 * t4114 + 47564 * t4121 - 118404 * t4123 + 77418 * t4125 + 11638 * t4127 - 18216 * t4129 - 1518 + t4134 = 20336 * t4114 - 99616 * t4121 + 82272 * t4123 + 276816 * t4125 - 522192 * t4127 + 242880 * t4129 - 496 + t4133 = -65780 * t4103 - 129536 * t4105 + 658636 * t4107 - 742176 * t4109 + 4164 * t4115 - 63456 * t4120 + 338148 * t4122 + t4132 = -1562275 * t4103 + 3730232 * t4105 - 2590555 * t4107 + 28120 * t4109 + 7239 * t4115 - 138320 * t4120 + 525559 * t4122 + t4131 = -130910 * t4114 + 1390420 * t4121 - 5531356 * t4123 + 10201670 * t4125 - 8816038 * t4127 + 2884200 * t4129 + 2014 + t4119 = 2 * phi1 + t4118 = 4 * phi1 + t4117 = -3 * phi2 + t4116 = 3 * phi2 + tfunc[..., c] = -(0.45e2 / 0.65536e5) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * ((t4135 + t4136) * np.exp((-6*1j) * (t4119 + phi2)) + (t4131 - t4132) * np.exp((-2*1j) * (t4119 + t4116)) + (-t4133 + t4134) * np.exp((-2*1j) * (t4118 + t4116)) + (t4131 + t4132) * np.exp((2*1j) * (t4119 + t4117)) + (t4133 + t4134) * np.exp((2*1j) * (t4118 + t4117)) + (t4135 - t4136) * np.exp((6*1j) * (t4119 - phi2))) + + if Bindx == 138: + t4156 = np.cos(phi) + t4155 = t4156 ** 2 + t4162 = t4156 * t4155 + t4165 = t4162 ** 2 + t4171 = t4165 ** 2 + t4144 = t4156 * t4171 + t4163 = t4155 ** 2 + t4164 = t4156 * t4163 + t4169 = t4164 ** 2 + t4146 = t4156 * t4169 + t4167 = t4163 ** 2 + t4148 = t4156 * t4167 + t4166 = t4156 * t4165 + t4179 = -15180 * t4144 + 40480 * t4146 - 7084 * t4148 + 3036 * t4156 - 32384 * t4162 + 83996 * t4164 - 72864 * t4166 + t4143 = t4166 ** 2 + t4178 = -3289 * t4143 - 8855 * t4155 - 3795 * t4163 + 75141 * t4165 - 129789 * t4167 + 82731 * t4169 - 13409 * t4171 + 1265 + t4177 = 202400 * t4144 - 647680 * t4146 + 756128 * t4148 - 544 * t4156 + 2816 * t4162 + 63968 * t4164 - 377088 * t4166 + t4176 = -65780 * t4143 + 20116 * t4155 - 160988 * t4163 + 503844 * t4165 - 703236 * t4167 + 375100 * t4169 + 31372 * t4171 - 428 + t4175 = -1562275 * t4143 - 3053 * t4155 + 88895 * t4163 - 870585 * t4165 + 3394065 * t4167 - 6060791 * t4169 + 5013701 * t4171 + 43 + t4174 = 2403500 * t4144 - 8460320 * t4146 + 11611660 * t4148 + 15940 * t4156 - 376960 * t4162 + 2589188 * t4164 - 7783008 * t4166 + t4161 = 4 * phi1 + t4160 = 8 * phi1 + t4159 = -5 * phi2 + t4158 = 5 * phi2 + t4157 = 12 * phi1 + tfunc[..., c] = (-0.45e2 / 0.131072e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((t4174 - t4175) * np.exp((-1*1j) * (t4161 + t4158)) + (-t4176 + t4177) * np.exp((-1*1j) * (t4160 + t4158)) + (t4174 + t4175) * np.exp((1j) * (t4161 + t4159)) + (t4176 + t4177) * np.exp((1j) * (t4160 + t4159)) + (t4178 + t4179) * np.exp((-1*1j) * (t4157 + t4158)) + (-t4178 + t4179) * np.exp((1j) * (t4157 + t4159))) * ((1 + t4156) ** (-0.1e1 / 0.2e1)) * ((1 - t4156) ** (-0.1e1 / 0.2e1)) + + if Bindx == 139: + t4198 = np.cos(phi) + t4197 = t4198 ** 2 + t4202 = t4197 ** 2 + t4201 = t4198 * t4197 + t4204 = t4201 ** 2 + t4206 = t4202 ** 2 + t4203 = t4198 * t4202 + t4208 = t4203 ** 2 + t4210 = t4204 ** 2 + t4217 = 1012 - 11132 * t4197 + 22264 * t4202 + 2024 * t4204 - 43516 * t4206 + 41492 * t4208 - 12144 * t4210 + t4186 = t4198 * t4210 + t4188 = t4198 * t4208 + t4190 = t4198 * t4206 + t4192 = t4198 * t4204 + t4216 = 3289 * t4186 + 2530 * t4188 - 43769 * t4190 + 83996 * t4192 - 759 * t4198 + 18722 * t4201 - 64009 * t4203 + t4215 = 65780 * t4186 - 103224 * t4188 - 86196 * t4190 + 254960 * t4192 - 2572 * t4198 + 41160 * t4201 - 169908 * t4203 + t4214 = -8160 * t4197 + 80064 * t4202 - 322496 * t4204 + 598560 * t4206 - 510048 * t4208 + 161920 * t4210 + 160 + t4213 = -65700 * t4197 + 780360 * t4202 - 3347496 * t4204 + 6460380 * t4206 - 5749172 * t4208 + 1922800 * t4210 + 876 + t4212 = -1562275 * t4186 + 4643562 * t4188 - 5292925 * t4190 + 2917260 * t4192 - 3891 * t4198 + 100650 * t4201 - 804429 * t4203 + t4200 = 2 * phi1 + t4199 = 3 * phi1 + tfunc[..., c] = -(0.45e2 / 0.131072e6) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * ((-t4212 + t4213) * np.exp((-4*1j) * (phi1 + phi2)) + (t4214 + t4215) * np.exp((-4*1j) * (t4200 + phi2)) + (-t4216 + t4217) * np.exp((-4*1j) * (t4199 + phi2)) + (t4212 + t4213) * np.exp((4*1j) * (phi1 - phi2)) + (t4214 - t4215) * np.exp((4*1j) * (t4200 - phi2)) + (t4216 + t4217) * np.exp((4*1j) * (t4199 - phi2))) + + if Bindx == 140: + t4237 = np.cos(phi) + t4236 = t4237 ** 2 + t4242 = t4237 * t4236 + t4245 = t4242 ** 2 + t4251 = t4245 ** 2 + t4225 = t4237 * t4251 + t4243 = t4236 ** 2 + t4244 = t4237 * t4243 + t4249 = t4244 ** 2 + t4227 = t4237 * t4249 + t4247 = t4243 ** 2 + t4229 = t4237 * t4247 + t4246 = t4237 * t4245 + t4259 = -9108 * t4225 + 44528 * t4227 - 86020 * t4229 + 1012 * t4237 + 4048 * t4242 - 35420 * t4244 + 80960 * t4246 + t4224 = t4246 ** 2 + t4258 = -759 - 3289 * t4224 + 11385 * t4236 - 42251 * t4243 + 67045 * t4245 - 46805 * t4247 + 5819 * t4249 + 8855 * t4251 + t4257 = -65780 * t4224 + 4180 * t4236 - 29244 * t4243 + 43172 * t4245 + 71068 * t4247 - 240900 * t4249 + 217580 * t4251 - 76 + t4256 = 121440 * t4225 - 485760 * t4227 + 763104 * t4229 + 2208 * t4237 - 41088 * t4242 + 231456 * t4244 - 591360 * t4246 + t4255 = 1442100 * t4225 - 4999280 * t4227 + 6696740 * t4229 + 7020 * t4237 - 183120 * t4242 + 1359420 * t4244 - 4322880 * t4246 + t4254 = -1562275 * t4224 - 34365 * t4236 + 461655 * t4243 - 2414745 * t4245 + 6209865 * t4247 - 8404935 * t4249 + 5744365 * t4251 + 435 + t4241 = 4 * phi1 + t4240 = 8 * phi1 + t4239 = -3 * phi2 + t4238 = 3 * phi2 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * ((t4258 + t4259) * np.exp((-3*1j) * (t4241 + phi2)) + (-t4254 + t4255) * np.exp((-1*1j) * (t4241 + t4238)) + (t4256 - t4257) * np.exp((-1*1j) * (t4240 + t4238)) + (t4254 + t4255) * np.exp((1j) * (t4241 + t4239)) + (t4256 + t4257) * np.exp((1j) * (t4240 + t4239)) + (-t4258 + t4259) * np.exp((3*1j) * (t4241 - phi2))) * ((1 + t4237) ** (-0.1e1 / 0.2e1)) * ((1 - t4237) ** (-0.1e1 / 0.2e1)) + + if Bindx == 141: + t4278 = np.cos(phi) + t4277 = t4278 ** 2 + t4283 = t4277 ** 2 + t4282 = t4278 * t4277 + t4285 = t4282 ** 2 + t4287 = t4283 ** 2 + t4284 = t4278 * t4283 + t4289 = t4284 ** 2 + t4291 = t4285 ** 2 + t4298 = -46 + 782 * t4277 - 3220 * t4283 + 5980 * t4285 - 5750 * t4287 + 2806 * t4289 - 552 * t4291 + t4266 = t4278 * t4291 + t4268 = t4278 * t4289 + t4270 = t4278 * t4287 + t4272 = t4278 * t4285 + t4297 = -299 * t4266 + 1288 * t4268 - 1955 * t4270 + 920 * t4272 + 207 * t4278 - 736 * t4282 + 575 * t4284 + t4296 = 16 - 912 * t4277 + 8160 * t4283 - 26528 * t4285 + 39504 * t4287 - 27600 * t4289 + 7360 * t4291 + t4295 = -5980 * t4266 + 22080 * t4268 - 31260 * t4270 + 21152 * t4272 - 52 * t4278 + 1056 * t4282 - 6996 * t4284 + t4294 = -2430 * t4277 + 30900 * t4283 - 140220 * t4285 + 282150 * t4287 - 257830 * t4289 + 87400 * t4291 + 30 + t4293 = -142025 * t4266 + 471960 * t4268 - 606385 * t4270 + 376200 * t4272 - 555 * t4278 + 14880 * t4282 - 114075 * t4284 + t4281 = 2 * phi1 + t4280 = 4 * phi1 + t4279 = 6 * phi1 + tfunc[..., c] = -(0.9e1 / 0.32768e5) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.19e2) * np.sqrt(0.17e2) * ((-t4293 + t4294) * np.exp((-2*1j) * (t4281 + phi2)) + (-t4295 + t4296) * np.exp((-2*1j) * (t4280 + phi2)) + (t4297 + t4298) * np.exp((-2*1j) * (t4279 + phi2)) + (t4293 + t4294) * np.exp((2*1j) * (t4281 - phi2)) + (t4295 + t4296) * np.exp((2*1j) * (t4280 - phi2)) + (-t4297 + t4298) * np.exp((2*1j) * (t4279 - phi2))) + + if Bindx == 142: + t4318 = np.cos(phi) + t4317 = t4318 ** 2 + t4322 = t4318 * t4317 + t4325 = t4322 ** 2 + t4331 = t4325 ** 2 + t4306 = t4318 * t4331 + t4323 = t4317 ** 2 + t4324 = t4318 * t4323 + t4329 = t4324 ** 2 + t4308 = t4318 * t4329 + t4327 = t4323 ** 2 + t4310 = t4318 * t4327 + t4326 = t4318 * t4325 + t4339 = -276 * t4306 + 1656 * t4308 - 4140 * t4310 - 276 * t4318 + 1656 * t4322 - 4140 * t4324 + 5520 * t4326 + t4338 = 3680 * t4306 - 16192 * t4308 + 28064 * t4310 + 96 * t4318 - 1856 * t4322 + 10144 * t4324 - 23936 * t4326 + t4305 = t4326 ** 2 + t4337 = 23 - 299 * t4305 - 437 * t4317 + 2139 * t4323 - 4945 * t4325 + 6325 * t4327 - 4623 * t4329 + 1817 * t4331 + t4336 = 12 - 5980 * t4305 - 708 * t4317 + 7036 * t4323 - 27284 * t4325 + 52516 * t4327 - 53836 * t4329 + 28244 * t4331 + t4335 = 43700 * t4306 - 150328 * t4308 + 198284 * t4310 + 180 * t4318 - 4920 * t4322 + 38028 * t4324 - 124944 * t4326 + t4334 = -142025 * t4305 - 3735 * t4317 + 51225 * t4323 - 266187 * t4325 + 664791 * t4327 - 859541 * t4329 + 555427 * t4331 + 45 + t4321 = 4 * phi1 + t4320 = 8 * phi1 + t4319 = 12 * phi1 + tfunc[..., c] = (-0.45e2 / 0.65536e5*1j) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * ((-t4334 + t4335) * np.exp((-1*1j) * (t4321 + phi2)) + (-t4336 + t4338) * np.exp((-1*1j) * (t4320 + phi2)) + (t4334 + t4335) * np.exp((1j) * (t4321 - phi2)) + (t4336 + t4338) * np.exp((1j) * (t4320 - phi2)) + (t4337 + t4339) * np.exp((-1*1j) * (t4319 + phi2)) + (-t4337 + t4339) * np.exp((1j) * (t4319 - phi2))) * ((1 + t4318) ** (-0.1e1 / 0.2e1)) * ((1 - t4318) ** (-0.1e1 / 0.2e1)) + + if Bindx == 143: + t4349 = np.cos(phi) + t4348 = t4349 ** 2 + t4350 = t4348 ** 2 + t4352 = t4350 ** 2 + t4351 = t4348 * t4350 + t4344 = t4348 * t4352 + t4343 = t4351 ** 2 + tfunc[..., c] = (-0.45e2 / 0.32768e5*1j) * t4349 * np.sqrt(0.3e1) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * np.sqrt(0.11e2) * np.sqrt(0.1547e4) * ((-10925 * t4343 + 37582 * t4344 - 49571 * t4352 + 31236 * t4351 - 9507 * t4350 + 1230 * t4348 - 45) * np.sin((4 * phi1)) + (-460 * t4343 + 2024 * t4344 - 3508 * t4352 + 2992 * t4351 - 1268 * t4350 + 232 * t4348 - 12) * np.sin((8 * phi1)) + (23 * t4343 - 138 * t4344 + 345 * t4352 - 460 * t4351 + 345 * t4350 - 138 * t4348 + 23) * np.sin((12 * phi1))) + + if Bindx == 144: + t4374 = np.cos(phi) + t4373 = t4374 ** 2 + t4378 = t4374 * t4373 + t4381 = t4378 ** 2 + t4387 = t4381 ** 2 + t4362 = t4374 * t4387 + t4379 = t4373 ** 2 + t4380 = t4374 * t4379 + t4385 = t4380 ** 2 + t4364 = t4374 * t4385 + t4383 = t4379 ** 2 + t4366 = t4374 * t4383 + t4382 = t4374 * t4381 + t4395 = -276 * t4362 + 1656 * t4364 - 4140 * t4366 - 276 * t4374 + 1656 * t4378 - 4140 * t4380 + 5520 * t4382 + t4394 = 3680 * t4362 - 16192 * t4364 + 28064 * t4366 + 96 * t4374 - 1856 * t4378 + 10144 * t4380 - 23936 * t4382 + t4361 = t4382 ** 2 + t4393 = 23 - 299 * t4361 - 437 * t4373 + 2139 * t4379 - 4945 * t4381 + 6325 * t4383 - 4623 * t4385 + 1817 * t4387 + t4392 = 12 - 5980 * t4361 - 708 * t4373 + 7036 * t4379 - 27284 * t4381 + 52516 * t4383 - 53836 * t4385 + 28244 * t4387 + t4391 = 43700 * t4362 - 150328 * t4364 + 198284 * t4366 + 180 * t4374 - 4920 * t4378 + 38028 * t4380 - 124944 * t4382 + t4390 = -142025 * t4361 - 3735 * t4373 + 51225 * t4379 - 266187 * t4381 + 664791 * t4383 - 859541 * t4385 + 555427 * t4387 + 45 + t4377 = 4 * phi1 + t4376 = 8 * phi1 + t4375 = 12 * phi1 + tfunc[..., c] = (0.45e2 / 0.65536e5*1j) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * ((1 + t4374) ** (-0.1e1 / 0.2e1)) * ((1 - t4374) ** (-0.1e1 / 0.2e1)) * ((t4390 + t4391) * np.exp((-1*1j) * (t4377 - phi2)) + (t4392 + t4394) * np.exp((-1*1j) * (t4376 - phi2)) + (-t4390 + t4391) * np.exp((1j) * (t4377 + phi2)) + (-t4392 + t4394) * np.exp((1j) * (t4376 + phi2)) + (-t4393 + t4395) * np.exp((-1*1j) * (t4375 - phi2)) + (t4393 + t4395) * np.exp((1j) * (t4375 + phi2))) + + if Bindx == 145: + t4414 = np.cos(phi) + t4413 = t4414 ** 2 + t4419 = t4413 ** 2 + t4418 = t4414 * t4413 + t4421 = t4418 ** 2 + t4423 = t4419 ** 2 + t4420 = t4414 * t4419 + t4425 = t4420 ** 2 + t4427 = t4421 ** 2 + t4434 = -46 + 782 * t4413 - 3220 * t4419 + 5980 * t4421 - 5750 * t4423 + 2806 * t4425 - 552 * t4427 + t4402 = t4414 * t4427 + t4404 = t4414 * t4425 + t4406 = t4414 * t4423 + t4408 = t4414 * t4421 + t4433 = -299 * t4402 + 1288 * t4404 - 1955 * t4406 + 920 * t4408 + 207 * t4414 - 736 * t4418 + 575 * t4420 + t4432 = 16 - 912 * t4413 + 8160 * t4419 - 26528 * t4421 + 39504 * t4423 - 27600 * t4425 + 7360 * t4427 + t4431 = -5980 * t4402 + 22080 * t4404 - 31260 * t4406 + 21152 * t4408 - 52 * t4414 + 1056 * t4418 - 6996 * t4420 + t4430 = -2430 * t4413 + 30900 * t4419 - 140220 * t4421 + 282150 * t4423 - 257830 * t4425 + 87400 * t4427 + 30 + t4429 = -142025 * t4402 + 471960 * t4404 - 606385 * t4406 + 376200 * t4408 - 555 * t4414 + 14880 * t4418 - 114075 * t4420 + t4417 = 2 * phi1 + t4416 = 4 * phi1 + t4415 = 6 * phi1 + tfunc[..., c] = (0.9e1 / 0.32768e5) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * np.sqrt(0.11e2) * np.sqrt(0.17e2) * ((t4429 + t4430) * np.exp((-2*1j) * (t4417 - phi2)) + (t4431 + t4432) * np.exp((-2*1j) * (t4416 - phi2)) + (-t4433 + t4434) * np.exp((-2*1j) * (t4415 - phi2)) + (-t4429 + t4430) * np.exp((2*1j) * (t4417 + phi2)) + (-t4431 + t4432) * np.exp((2*1j) * (t4416 + phi2)) + (t4433 + t4434) * np.exp((2*1j) * (t4415 + phi2))) + + if Bindx == 146: + t4454 = np.cos(phi) + t4453 = t4454 ** 2 + t4459 = t4454 * t4453 + t4462 = t4459 ** 2 + t4468 = t4462 ** 2 + t4442 = t4454 * t4468 + t4460 = t4453 ** 2 + t4461 = t4454 * t4460 + t4466 = t4461 ** 2 + t4444 = t4454 * t4466 + t4464 = t4460 ** 2 + t4446 = t4454 * t4464 + t4463 = t4454 * t4462 + t4476 = 9108 * t4442 - 44528 * t4444 + 86020 * t4446 - 1012 * t4454 - 4048 * t4459 + 35420 * t4461 - 80960 * t4463 + t4441 = t4463 ** 2 + t4475 = -759 - 3289 * t4441 + 11385 * t4453 - 42251 * t4460 + 67045 * t4462 - 46805 * t4464 + 5819 * t4466 + 8855 * t4468 + t4474 = -65780 * t4441 + 4180 * t4453 - 29244 * t4460 + 43172 * t4462 + 71068 * t4464 - 240900 * t4466 + 217580 * t4468 - 76 + t4473 = -121440 * t4442 + 485760 * t4444 - 763104 * t4446 - 2208 * t4454 + 41088 * t4459 - 231456 * t4461 + 591360 * t4463 + t4472 = -1442100 * t4442 + 4999280 * t4444 - 6696740 * t4446 - 7020 * t4454 + 183120 * t4459 - 1359420 * t4461 + 4322880 * t4463 + t4471 = -1562275 * t4441 - 34365 * t4453 + 461655 * t4460 - 2414745 * t4462 + 6209865 * t4464 - 8404935 * t4466 + 5744365 * t4468 + 435 + t4458 = 4 * phi1 + t4457 = 8 * phi1 + t4456 = -3 * phi2 + t4455 = 3 * phi2 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.17e2) * np.sqrt(0.19e2) * ((t4475 + t4476) * np.exp((-3*1j) * (t4458 - phi2)) + (-t4471 + t4472) * np.exp((-1*1j) * (t4458 + t4456)) + (t4473 - t4474) * np.exp((-1*1j) * (t4457 + t4456)) + (t4471 + t4472) * np.exp((1j) * (t4458 + t4455)) + (t4473 + t4474) * np.exp((1j) * (t4457 + t4455)) + (-t4475 + t4476) * np.exp((3*1j) * (t4458 + phi2))) * ((1 + t4454) ** (-0.1e1 / 0.2e1)) * ((1 - t4454) ** (-0.1e1 / 0.2e1)) + + if Bindx == 147: + t4495 = np.cos(phi) + t4494 = t4495 ** 2 + t4499 = t4494 ** 2 + t4498 = t4495 * t4494 + t4501 = t4498 ** 2 + t4503 = t4499 ** 2 + t4500 = t4495 * t4499 + t4505 = t4500 ** 2 + t4507 = t4501 ** 2 + t4514 = 1012 - 11132 * t4494 + 22264 * t4499 + 2024 * t4501 - 43516 * t4503 + 41492 * t4505 - 12144 * t4507 + t4483 = t4495 * t4507 + t4485 = t4495 * t4505 + t4487 = t4495 * t4503 + t4489 = t4495 * t4501 + t4513 = -3289 * t4483 - 2530 * t4485 + 43769 * t4487 - 83996 * t4489 + 759 * t4495 - 18722 * t4498 + 64009 * t4500 + t4512 = 65780 * t4483 - 103224 * t4485 - 86196 * t4487 + 254960 * t4489 - 2572 * t4495 + 41160 * t4498 - 169908 * t4500 + t4511 = -8160 * t4494 + 80064 * t4499 - 322496 * t4501 + 598560 * t4503 - 510048 * t4505 + 161920 * t4507 + 160 + t4510 = -65700 * t4494 + 780360 * t4499 - 3347496 * t4501 + 6460380 * t4503 - 5749172 * t4505 + 1922800 * t4507 + 876 + t4509 = -1562275 * t4483 + 4643562 * t4485 - 5292925 * t4487 + 2917260 * t4489 - 3891 * t4495 + 100650 * t4498 - 804429 * t4500 + t4497 = 2 * phi1 + t4496 = 3 * phi1 + tfunc[..., c] = (0.45e2 / 0.131072e6) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * ((t4509 + t4510) * np.exp((-4*1j) * (phi1 - phi2)) + (t4511 - t4512) * np.exp((-4*1j) * (t4497 - phi2)) + (-t4513 + t4514) * np.exp((-4*1j) * (t4496 - phi2)) + (-t4509 + t4510) * np.exp((4*1j) * (phi1 + phi2)) + (t4511 + t4512) * np.exp((4*1j) * (t4497 + phi2)) + (t4513 + t4514) * np.exp((4*1j) * (t4496 + phi2))) + + if Bindx == 148: + t4534 = np.cos(phi) + t4533 = t4534 ** 2 + t4540 = t4534 * t4533 + t4543 = t4540 ** 2 + t4549 = t4543 ** 2 + t4522 = t4534 * t4549 + t4541 = t4533 ** 2 + t4542 = t4534 * t4541 + t4547 = t4542 ** 2 + t4524 = t4534 * t4547 + t4545 = t4541 ** 2 + t4526 = t4534 * t4545 + t4544 = t4534 * t4543 + t4557 = -15180 * t4522 + 40480 * t4524 - 7084 * t4526 + 3036 * t4534 - 32384 * t4540 + 83996 * t4542 - 72864 * t4544 + t4521 = t4544 ** 2 + t4556 = -3289 * t4521 - 8855 * t4533 - 3795 * t4541 + 75141 * t4543 - 129789 * t4545 + 82731 * t4547 - 13409 * t4549 + 1265 + t4555 = 202400 * t4522 - 647680 * t4524 + 756128 * t4526 - 544 * t4534 + 2816 * t4540 + 63968 * t4542 - 377088 * t4544 + t4554 = -65780 * t4521 + 20116 * t4533 - 160988 * t4541 + 503844 * t4543 - 703236 * t4545 + 375100 * t4547 + 31372 * t4549 - 428 + t4553 = -1562275 * t4521 - 3053 * t4533 + 88895 * t4541 - 870585 * t4543 + 3394065 * t4545 - 6060791 * t4547 + 5013701 * t4549 + 43 + t4552 = 2403500 * t4522 - 8460320 * t4524 + 11611660 * t4526 + 15940 * t4534 - 376960 * t4540 + 2589188 * t4542 - 7783008 * t4544 + t4539 = 4 * phi1 + t4538 = 8 * phi1 + t4537 = -5 * phi2 + t4536 = 5 * phi2 + t4535 = 12 * phi1 + tfunc[..., c] = (0.45e2 / 0.131072e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((1 + t4534) ** (-0.1e1 / 0.2e1)) * ((1 - t4534) ** (-0.1e1 / 0.2e1)) * ((t4552 + t4553) * np.exp((-1*1j) * (t4539 + t4537)) + (t4554 + t4555) * np.exp((-1*1j) * (t4538 + t4537)) + (t4552 - t4553) * np.exp((1j) * (t4539 + t4536)) + (-t4554 + t4555) * np.exp((1j) * (t4538 + t4536)) + (-t4556 + t4557) * np.exp((-1*1j) * (t4535 + t4537)) + (t4556 + t4557) * np.exp((1j) * (t4535 + t4536))) + + if Bindx == 149: + t4576 = np.cos(phi) + t4575 = t4576 ** 2 + t4581 = t4576 * t4575 + t4584 = t4581 ** 2 + t4590 = t4584 ** 2 + t4564 = t4576 * t4590 + t4582 = t4575 ** 2 + t4583 = t4576 * t4582 + t4588 = t4583 ** 2 + t4566 = t4576 * t4588 + t4586 = t4582 ** 2 + t4568 = t4576 * t4586 + t4570 = t4576 * t4584 + t4597 = -3289 * t4564 - 30360 * t4566 + 91839 * t4568 - 54648 * t4570 - 5819 * t4576 + 36432 * t4581 - 34155 * t4583 + t4596 = 1518 * t4575 + 47564 * t4582 - 118404 * t4584 + 77418 * t4586 + 11638 * t4588 - 18216 * t4590 - 1518 + t4595 = 20336 * t4575 - 99616 * t4582 + 82272 * t4584 + 276816 * t4586 - 522192 * t4588 + 242880 * t4590 - 496 + t4594 = -65780 * t4564 - 129536 * t4566 + 658636 * t4568 - 742176 * t4570 + 4164 * t4576 - 63456 * t4581 + 338148 * t4583 + t4593 = -1562275 * t4564 + 3730232 * t4566 - 2590555 * t4568 + 28120 * t4570 + 7239 * t4576 - 138320 * t4581 + 525559 * t4583 + t4592 = -130910 * t4575 + 1390420 * t4582 - 5531356 * t4584 + 10201670 * t4586 - 8816038 * t4588 + 2884200 * t4590 + 2014 + t4580 = 2 * phi1 + t4579 = 4 * phi1 + t4578 = -3 * phi2 + t4577 = 3 * phi2 + tfunc[..., c] = (0.45e2 / 0.65536e5) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * ((t4596 - t4597) * np.exp((-6*1j) * (t4580 - phi2)) + (t4592 + t4593) * np.exp((-2*1j) * (t4580 + t4578)) + (t4594 + t4595) * np.exp((-2*1j) * (t4579 + t4578)) + (t4592 - t4593) * np.exp((2*1j) * (t4580 + t4577)) + (-t4594 + t4595) * np.exp((2*1j) * (t4579 + t4577)) + (t4596 + t4597) * np.exp((6*1j) * (t4580 + phi2))) + + if Bindx == 150: + t4617 = np.cos(phi) + t4616 = t4617 ** 2 + t4623 = t4617 * t4616 + t4626 = t4623 ** 2 + t4632 = t4626 ** 2 + t4605 = t4617 * t4632 + t4624 = t4616 ** 2 + t4625 = t4617 * t4624 + t4630 = t4625 ** 2 + t4607 = t4617 * t4630 + t4628 = t4624 ** 2 + t4609 = t4617 * t4628 + t4627 = t4617 * t4626 + t4640 = 21252 * t4605 + 14168 * t4607 - 162932 * t4609 + 9108 * t4617 - 34408 * t4623 - 29348 * t4625 + 182160 * t4627 + t4604 = t4627 ** 2 + t4639 = -3289 * t4604 - 8855 * t4616 + 90321 * t4624 - 143451 * t4626 + 6831 * t4628 + 107019 * t4630 - 46805 * t4632 - 1771 + t4638 = -283360 * t4605 + 566720 * t4607 - 72352 * t4609 + 10144 * t4617 - 117824 * t4623 + 428512 * t4625 - 531840 * t4627 + t4637 = -65780 * t4604 + 980 * t4616 - 100492 * t4624 + 599844 * t4626 - 1269204 * t4628 + 1082620 * t4630 - 247940 * t4632 - 28 + t4636 = -3364900 * t4605 + 12113640 * t4607 - 16989420 * t4609 - 31540 * t4617 + 640680 * t4623 - 4043580 * t4625 + 11675120 * t4627 + t4635 = -1562275 * t4604 + 128915 * t4616 - 1110645 * t4624 + 3057575 * t4626 - 2494795 * t4628 - 1934295 * t4630 + 3917705 * t4632 - 2185 + t4622 = 4 * phi1 + t4621 = 8 * phi1 + t4620 = -7 * phi2 + t4619 = 7 * phi2 + t4618 = 12 * phi1 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * ((-t4635 + t4636) * np.exp((-1*1j) * (t4622 + t4620)) + (-t4637 + t4638) * np.exp((-1*1j) * (t4621 + t4620)) + (t4635 + t4636) * np.exp((1j) * (t4622 + t4619)) + (t4637 + t4638) * np.exp((1j) * (t4621 + t4619)) + (t4639 + t4640) * np.exp((-1*1j) * (t4618 + t4620)) + (-t4639 + t4640) * np.exp((1j) * (t4618 + t4619))) * ((1 + t4617) ** (-0.1e1 / 0.2e1)) * ((1 - t4617) ** (-0.1e1 / 0.2e1)) + + if Bindx == 151: + t4659 = np.cos(phi) + t4658 = t4659 ** 2 + t4664 = t4658 ** 2 + t4663 = t4659 * t4658 + t4666 = t4663 ** 2 + t4668 = t4664 ** 2 + t4665 = t4659 * t4664 + t4670 = t4665 ** 2 + t4672 = t4666 ** 2 + t4679 = 26312 * t4658 - 101200 * t4664 - 12144 * t4666 + 188232 * t4668 - 78936 * t4670 - 24288 * t4672 + 2024 + t4647 = t4659 * t4672 + t4649 = t4659 * t4670 + t4651 = t4659 * t4668 + t4653 = t4659 * t4666 + t4678 = 3289 * t4647 + 69322 * t4649 - 31625 * t4651 - 179124 * t4653 - 12903 * t4659 + 4554 * t4663 + 146487 * t4665 + t4677 = -65780 * t4647 - 455400 * t4649 + 1156980 * t4651 - 743856 * t4653 - 13236 * t4659 + 77848 * t4663 + 39348 * t4665 + t4676 = 46656 * t4658 - 346240 * t4664 + 859776 * t4666 - 635328 * t4668 - 242880 * t4670 + 323840 * t4672 - 1728 + t4675 = -193800 * t4658 + 1901520 * t4664 - 7659280 * t4666 + 14215800 * t4668 - 12113640 * t4670 + 3845600 * t4672 + 3800 + t4674 = -1562275 * t4647 + 2451570 * t4649 + 2047155 * t4651 - 6055300 * t4653 + 61085 * t4659 - 977550 * t4663 + 4035315 * t4665 + t4662 = 3 * phi1 + t4661 = -2 * phi2 + t4660 = 2 * phi2 + tfunc[..., c] = (0.9e1 / 0.65536e5) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((t4676 + t4677) * np.exp((-8*1j) * (phi1 - phi2)) + (t4674 + t4675) * np.exp((-4*1j) * (phi1 + t4661)) + (t4678 + t4679) * np.exp((-4*1j) * (t4662 + t4661)) + (-t4674 + t4675) * np.exp((4*1j) * (phi1 + t4660)) + (-t4678 + t4679) * np.exp((4*1j) * (t4662 + t4660)) + (t4676 - t4677) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 152: + t4699 = np.cos(phi) + t4698 = t4699 ** 2 + t4704 = t4699 * t4698 + t4707 = t4704 ** 2 + t4713 = t4707 ** 2 + t4687 = t4699 * t4713 + t4705 = t4698 ** 2 + t4706 = t4699 * t4705 + t4711 = t4706 ** 2 + t4689 = t4699 * t4711 + t4709 = t4705 ** 2 + t4691 = t4699 * t4709 + t4708 = t4699 * t4707 + t4721 = -2484 * t4687 - 12696 * t4689 + 21252 * t4691 + 1564 * t4699 + 3496 * t4704 - 23276 * t4706 + 12144 * t4708 + t4686 = t4708 ** 2 + t4720 = 207 - 299 * t4686 + 4347 * t4698 - 8349 * t4705 - 17457 * t4707 + 32637 * t4709 - 2783 * t4711 - 8303 * t4713 + t4719 = 5980 * t4686 + 9348 * t4698 - 39708 * t4705 + 32532 * t4707 + 83388 * t4709 - 147444 * t4711 + 56396 * t4713 - 492 + t4718 = 33120 * t4687 - 13248 * t4689 - 124128 * t4691 + 480 * t4699 + 8768 * t4704 - 74336 * t4706 + 169344 * t4708 + t4717 = 393300 * t4687 - 1457832 * t4689 + 2053596 * t4691 + 2052 * t4699 - 46056 * t4704 + 403788 * t4706 - 1348848 * t4708 + t4716 = -142025 * t4686 + 59641 * t4698 - 416271 * t4705 + 1107605 * t4707 - 1258769 * t4709 + 427899 * t4711 + 223307 * t4713 - 1387 + t4703 = 4 * phi1 + t4702 = 8 * phi1 + t4701 = -9 * phi2 + t4700 = 9 * phi2 + tfunc[..., c] = (0.45e2 / 0.131072e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((1 + t4699) ** (-0.1e1 / 0.2e1)) * ((1 - t4699) ** (-0.1e1 / 0.2e1)) * ((-t4720 + t4721) * np.exp((-3*1j) * (t4703 - 3 * phi2)) + (t4716 + t4717) * np.exp((-1*1j) * (t4703 + t4701)) + (t4718 - t4719) * np.exp((-1*1j) * (t4702 + t4701)) + (-t4716 + t4717) * np.exp((1j) * (t4703 + t4700)) + (t4718 + t4719) * np.exp((1j) * (t4702 + t4700)) + (t4720 + t4721) * np.exp((3*1j) * (t4703 + 3 * phi2))) + + if Bindx == 153: + t4740 = np.cos(phi) + t4739 = t4740 ** 2 + t4747 = t4739 ** 2 + t4746 = t4740 * t4739 + t4749 = t4746 ** 2 + t4751 = t4747 ** 2 + t4748 = t4740 * t4747 + t4753 = t4748 ** 2 + t4755 = t4749 ** 2 + t4762 = 10 + 310 * t4739 + 220 * t4747 - 1716 * t4749 + 66 * t4751 + 990 * t4753 + 120 * t4755 + t4761 = -48 + 432 * t4739 + 352 * t4747 - 5664 * t4749 + 8208 * t4751 - 1680 * t4753 - 1600 * t4755 + t4728 = t4740 * t4755 + t4730 = t4740 * t4753 + t4732 = t4740 * t4751 + t4734 = t4740 * t4749 + t4760 = -13 * t4728 - 472 * t4730 - 1045 * t4732 + 1320 * t4734 - 87 * t4740 - 528 * t4746 + 825 * t4748 + t4759 = 38 - 1254 * t4739 - 1596 * t4747 + 30932 * t4749 - 71250 * t4751 + 62130 * t4753 - 19000 * t4755 + t4758 = -260 * t4728 - 3456 * t4730 + 5052 * t4732 + 1632 * t4734 - 108 * t4740 + 1568 * t4746 - 4428 * t4748 + t4757 = -6175 * t4728 + 3192 * t4730 + 37449 * t4732 - 70984 * t4734 + 1235 * t4740 - 13680 * t4746 + 48963 * t4748 + t4745 = 2 * phi1 + t4744 = 4 * phi1 + t4743 = 6 * phi1 + t4742 = -5 * phi2 + t4741 = 5 * phi2 + tfunc[..., c] = -(0.45e2 / 0.65536e5) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((-t4757 + t4759) * np.exp((-2*1j) * (t4745 + t4742)) + (-t4758 + t4761) * np.exp((-2*1j) * (t4744 + t4742)) + (t4760 + t4762) * np.exp((-2*1j) * (t4743 + t4742)) + (t4757 + t4759) * np.exp((2*1j) * (t4745 + t4741)) + (t4758 + t4761) * np.exp((2*1j) * (t4744 + t4741)) + (-t4760 + t4762) * np.exp((2*1j) * (t4743 + t4741))) + + if Bindx == 154: + t4782 = np.cos(phi) + t4781 = t4782 ** 2 + t4788 = t4782 * t4781 + t4791 = t4788 ** 2 + t4797 = t4791 ** 2 + t4770 = t4782 * t4797 + t4789 = t4781 ** 2 + t4790 = t4782 * t4789 + t4795 = t4790 ** 2 + t4772 = t4782 * t4795 + t4793 = t4789 ** 2 + t4774 = t4782 * t4793 + t4792 = t4782 * t4791 + t4805 = 132 * t4770 + 1408 * t4772 + 836 * t4774 + 108 * t4782 + 992 * t4788 - 308 * t4790 - 3168 * t4792 + t4804 = -1760 * t4770 - 2816 * t4772 + 13600 * t4774 - 416 * t4782 + 2560 * t4788 - 1184 * t4790 - 9984 * t4792 + t4769 = t4792 ** 2 + t4803 = -11 - 13 * t4769 - 451 * t4781 - 1023 * t4789 + 2409 * t4791 + 1551 * t4793 - 1881 * t4795 - 581 * t4797 + t4802 = -92 - 260 * t4769 - 92 * t4781 + 4660 * t4789 - 11532 * t4791 + 5100 * t4793 + 6508 * t4795 - 4292 * t4797 + t4801 = -20900 * t4770 + 80256 * t4772 - 109668 * t4774 + 2356 * t4782 - 12768 * t4788 + 6612 * t4790 + 54112 * t4792 + t4800 = 6175 * t4769 - 8303 * t4781 + 51813 * t4789 - 125267 * t4791 + 136363 * t4793 - 58653 * t4795 - 2489 * t4797 + 361 + t4787 = 4 * phi1 + t4786 = 8 * phi1 + t4785 = 12 * phi1 + t4784 = -11 * phi2 + t4783 = 11 * phi2 + tfunc[..., c] = (-0.45e2 / 0.131072e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * ((t4800 + t4801) * np.exp((-1*1j) * (t4787 + t4784)) + (-t4802 + t4804) * np.exp((-1*1j) * (t4786 + t4784)) + (-t4800 + t4801) * np.exp((1j) * (t4787 + t4783)) + (t4802 + t4804) * np.exp((1j) * (t4786 + t4783)) + (t4803 + t4805) * np.exp((-1*1j) * (t4785 + t4784)) + (-t4803 + t4805) * np.exp((1j) * (t4785 + t4783))) * ((1 + t4782) ** (-0.1e1 / 0.2e1)) * ((1 - t4782) ** (-0.1e1 / 0.2e1)) + + if Bindx == 155: + t4824 = np.cos(phi) + t4823 = t4824 ** 2 + t4829 = t4823 ** 2 + t4828 = t4824 * t4823 + t4831 = t4828 ** 2 + t4833 = t4829 ** 2 + t4830 = t4824 * t4829 + t4835 = t4830 ** 2 + t4837 = t4831 ** 2 + t4844 = 12 + 636 * t4823 + 3080 * t4829 + 792 * t4831 - 4356 * t4833 - 2068 * t4835 - 144 * t4837 + t4843 = -160 - 2080 * t4823 + 8000 * t4829 + 960 * t4831 - 14880 * t4833 + 6240 * t4835 + 1920 * t4837 + t4812 = t4824 * t4837 + t4814 = t4824 * t4835 + t4816 = t4824 * t4833 + t4818 = t4824 * t4831 + t4842 = -13 * t4812 - 714 * t4814 - 3795 * t4816 - 2508 * t4818 + 131 * t4824 + 1782 * t4828 + 3069 * t4830 + t4841 = -260 * t4812 - 5480 * t4814 + 2500 * t4816 + 14160 * t4818 + 1020 * t4824 - 360 * t4828 - 11580 * t4830 + t4840 = -1900 + 20900 * t4823 - 41800 * t4829 - 3800 * t4831 + 81700 * t4833 - 77900 * t4835 + 22800 * t4837 + t4839 = -6175 * t4812 - 4750 * t4814 + 82175 * t4816 - 157700 * t4818 + 1425 * t4824 - 35150 * t4828 + 120175 * t4830 + t4827 = 2 * phi1 + t4826 = -3 * phi2 + t4825 = 3 * phi2 + tfunc[..., c] = (0.9e1 / 0.131072e6) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.2e1) * ((t4839 + t4840) * np.exp((-4*1j) * (phi1 + t4826)) + (t4841 + t4843) * np.exp((-4*1j) * (t4827 + t4826)) + (-t4839 + t4840) * np.exp((4*1j) * (phi1 + t4825)) + (-t4841 + t4843) * np.exp((4*1j) * (t4827 + t4825)) + (-t4842 + t4844) * np.exp((-12*1j) * (phi1 - phi2)) + (t4842 + t4844) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 156: + t4863 = np.cos(phi) + t4862 = t4863 ** 2 + t4869 = t4863 * t4862 + t4872 = t4869 ** 2 + t4878 = t4872 ** 2 + t4851 = t4863 * t4878 + t4882 = -1 - t4851 + t4881 = -20 - 20 * t4851 + t4880 = -475 - 475 * t4851 + t4870 = t4862 ** 2 + t4871 = t4863 * t4870 + t4876 = t4871 ** 2 + t4874 = t4870 ** 2 + t4868 = 4 * phi1 + t4867 = 8 * phi1 + t4866 = 12 * phi1 + t4865 = -13 * phi2 + t4864 = 13 * phi2 + t4857 = t4863 * t4872 + t4855 = t4863 * t4874 + t4853 = t4863 * t4876 + tfunc[..., c] = (-0.9e1 / 0.131072e6*1j) * np.sqrt((1 - t4863)) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * ((1 + t4863) ** (-0.1e1 / 0.2e1)) * ((-11 * t4878 + 54 * t4853 - 154 * t4876 + 275 * t4855 - 297 * t4874 + 132 * t4857 + 132 * t4872 - 297 * t4871 + 275 * t4870 - 154 * t4869 + 54 * t4862 - 11 * t4863 - t4882) * np.exp((-1*1j) * (t4866 + t4865)) + (-13 * t4878 - 78 * t4853 - 286 * t4876 - 715 * t4855 - 1287 * t4874 - 1716 * t4857 - 1716 * t4872 - 1287 * t4871 - 715 * t4870 - 286 * t4869 - 78 * t4862 - 13 * t4863 + t4882) * np.exp((1j) * (t4866 + t4864)) + (1425 * t4878 + 950 * t4853 - 6650 * t4876 + 2375 * t4855 + 11875 * t4874 - 9500 * t4857 - 9500 * t4872 + 11875 * t4871 + 2375 * t4870 - 6650 * t4869 + 950 * t4862 + 1425 * t4863 + t4880) * np.exp((-1*1j) * (t4868 + t4865)) + (140 * t4878 - 360 * t4853 + 280 * t4876 + 500 * t4855 - 1260 * t4874 + 720 * t4857 + 720 * t4872 - 1260 * t4871 + 500 * t4870 + 280 * t4869 - 360 * t4862 + 140 * t4863 + t4881) * np.exp((-1*1j) * (t4867 + t4865)) + (2375 * t4878 + 2850 * t4853 - 4750 * t4876 - 13775 * t4855 - 4275 * t4874 + 17100 * t4857 + 17100 * t4872 - 4275 * t4871 - 13775 * t4870 - 4750 * t4869 + 2850 * t4862 + 2375 * t4863 - t4880) * np.exp((1j) * (t4868 + t4864)) + (180 * t4878 + 680 * t4853 + 1320 * t4876 + 1100 * t4855 - 660 * t4874 - 2640 * t4857 - 2640 * t4872 - 660 * t4871 + 1100 * t4870 + 1320 * t4869 + 680 * t4862 + 180 * t4863 - t4881) * np.exp((1j) * (t4867 + t4864))) + + if Bindx == 157: + t4902 = np.cos(phi) + t4901 = t4902 ** 2 + t4909 = t4901 ** 2 + t4910 = t4902 * t4909 + t4914 = t4910 ** 2 + t4893 = t4902 * t4914 + t4908 = t4902 * t4901 + t4927 = -4 * t4893 + 4 * t4908 + t4911 = t4908 ** 2 + t4916 = t4911 ** 2 + t4917 = t4902 * t4916 + t4926 = t4902 - t4917 + t4890 = t4902 * t4917 + t4925 = -1 + t4890 + t4912 = t4909 ** 2 + t4895 = t4902 * t4912 + t4924 = -20 * t4895 + 20 * t4910 + 4 * t4926 - 4 * t4927 + t4923 = -208 * t4893 - 572 * t4895 + 12 * t4902 + 208 * t4908 + 572 * t4910 - 12 * t4917 + t4922 = 2288 * t4895 - 2288 * t4910 + 208 * t4926 + 208 * t4927 + t4921 = t4916 - t4901 + 19 * t4909 - 45 * t4911 + 45 * t4912 - 19 * t4914 + t4925 + t4920 = -65 * t4901 - 429 * t4909 - 429 * t4911 + 429 * t4912 + 429 * t4914 + 65 * t4916 + t4925 + t4919 = -26 + 26 * t4890 - 650 * t4901 + 286 * t4909 + 2574 * t4911 - 2574 * t4912 - 286 * t4914 + 650 * t4916 + t4907 = 2 * phi1 + t4906 = 4 * phi1 + t4905 = 6 * phi1 + t4904 = -7 * phi2 + t4903 = 7 * phi2 + tfunc[..., c] = (0.87e2 / 0.1048576e7) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((-340 * t4890 + 2380 * t4916 - 7140 * t4914 + 11900 * t4912 - 11900 * t4911 + 7140 * t4909 - 2380 * t4901 + 340) * np.exp((-14*1j) * phi2) + (t4920 - t4923) * np.exp((-2*1j) * (t4905 + t4903)) + (t4920 + t4923) * np.exp((2*1j) * (t4905 + t4904)) + (t4919 - t4922) * np.exp((-2*1j) * (t4906 + t4903)) + (t4919 + t4922) * np.exp((2*1j) * (t4906 + t4904)) + 143 * (t4921 - t4924) * np.exp((-2*1j) * (t4907 + t4903)) + 143 * (t4921 + t4924) * np.exp((2*1j) * (t4907 + t4904))) + + if Bindx == 158: + t4948 = np.cos(phi) + t4947 = t4948 ** 2 + t4954 = t4948 * t4947 + t4957 = t4954 ** 2 + t4958 = t4948 * t4957 + t4935 = t4958 ** 2 + t4968 = 7 * t4935 - 1287 * t4957 - 1716 * t4958 + t4967 = 182 * t4935 + 7722 * t4957 + 10296 * t4958 + t4966 = 1001 * t4935 - 19305 * t4957 - 25740 * t4958 + t4963 = t4957 ** 2 + t4955 = t4947 ** 2 + t4956 = t4948 * t4955 + t4961 = t4956 ** 2 + t4959 = t4955 ** 2 + t4953 = 4 * phi1 + t4952 = 8 * phi1 + t4951 = 12 * phi1 + t4950 = -13 * phi2 + t4949 = 13 * phi2 + t4940 = t4948 * t4959 + t4938 = t4948 * t4961 + t4936 = t4948 * t4963 + tfunc[..., c] = (0.87e2 / 0.524288e6*1j) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt((1 + t4948)) * (2380 * (-t4935 + t4936 + 6 * t4963 - 6 * t4938 - 15 * t4961 + 15 * t4940 + 20 * t4959 - 20 * t4958 - 15 * t4957 + 15 * t4956 + 6 * t4955 - 6 * t4954 - t4947 + t4948) * np.exp((-13*1j) * phi2) + (1170 * t4936 + 2548 * t4963 + 676 * t4938 - 6006 * t4961 - 8866 * t4940 - 2002 * t4956 - 5148 * t4955 - 1820 * t4954 + 598 * t4947 + 546 * t4948 + 104 + t4967) * np.exp((-1*1j) * (t4952 + t4949)) + (2717 * t4936 - 2860 * t4963 - 13442 * t4938 - 1001 * t4961 + 26455 * t4940 + 12870 * t4959 + 12155 * t4956 + 12584 * t4955 - 2002 * t4954 - 3575 * t4947 - 143 * t4948 + 286 + t4966) * np.exp((-1*1j) * (t4953 + t4949)) + (-4719 * t4936 + 4576 * t4963 + 11726 * t4938 - 26169 * t4961 + 715 * t4940 + 38610 * t4959 + 26455 * t4956 - 1716 * t4955 - 8866 * t4954 + 3289 * t4947 + 429 * t4948 - 286 + t4966) * np.exp((1j) * (t4953 + t4950)) + (-1534 * t4936 + 5252 * t4963 - 8476 * t4938 + 3146 * t4961 + 11726 * t4940 - 20592 * t4959 - 13442 * t4956 + 6292 * t4955 + 676 * t4954 - 1898 * t4947 + 754 * t4948 - 104 + t4967) * np.exp((1j) * (t4952 + t4950)) + (71 * t4936 + 312 * t4963 + 754 * t4938 + 1001 * t4961 + 429 * t4940 - 858 * t4959 - 143 * t4956 + 572 * t4955 + 546 * t4954 + 247 * t4947 + 59 * t4948 + 6 + t4968) * np.exp((-1*1j) * (t4951 + t4949)) + (-85 * t4936 + 468 * t4963 - 1534 * t4938 + 3289 * t4961 - 4719 * t4940 + 4290 * t4959 + 2717 * t4956 - 2288 * t4955 + 1170 * t4954 - 377 * t4947 + 71 * t4948 - 6 + t4968) * np.exp((1j) * (t4951 + t4950))) * ((1 - t4948) ** (-0.1e1 / 0.2e1)) + + if Bindx == 159: + t4989 = np.cos(phi) + t4988 = t4989 ** 2 + t4993 = t4989 * t4988 + t4996 = t4993 ** 2 + t5002 = t4996 ** 2 + t4977 = t4989 * t5002 + t4994 = t4988 ** 2 + t4995 = t4989 * t4994 + t5000 = t4995 ** 2 + t4979 = t4989 * t5000 + t4998 = t4994 ** 2 + t4981 = t4989 * t4998 + t4997 = t4989 * t4996 + t5010 = -1944 * t4977 - 21840 * t4979 - 19448 * t4981 - 1320 * t4989 - 11024 * t4993 + 10296 * t4995 + 41184 * t4997 + t4976 = t4997 ** 2 + t5009 = 137 + 189 * t4976 + 5343 * t4988 + 9009 * t4994 - 36465 * t4996 - 14157 * t4998 + 31317 * t5000 + 8723 * t5002 + t5008 = 33696 * t4977 + 62400 * t4979 - 260832 * t4981 + 6240 * t4989 - 47424 * t4993 + 41184 * t4995 + 164736 * t4997 + t5007 = 92664 * t4977 - 350064 * t4979 + 463320 * t4981 - 10296 * t4989 + 61776 * t4993 - 51480 * t4995 - 205920 * t4997 + t5006 = 4914 * t4976 - 1482 * t4988 - 74646 * t4994 + 218790 * t4996 - 120978 * t4998 - 112398 * t5000 + 84318 * t5002 + 1482 + t5005 = 27027 * t4976 - 32175 * t4988 + 217503 * t4994 - 546975 * t4996 + 611325 * t4998 - 271557 * t5000 - 6435 * t5002 + 1287 + t4992 = 2 * phi1 + t4991 = -3 * phi2 + t4990 = 3 * phi2 + tfunc[..., c] = (0.29e2 / 0.1572864e7) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((2380 - 64260 * t4976 + 387940 * t5002 - 978180 * t5000 + 1320900 * t4998 - 1011500 * t4996 + 421260 * t4994 - 78540 * t4988) * np.exp((-12*1j) * phi2) + (t5005 + t5007) * np.exp((-4*1j) * (phi1 + t4990)) + (t5006 + t5008) * np.exp((-4*1j) * (t4992 + t4990)) + (t5005 - t5007) * np.exp((4*1j) * (phi1 + t4991)) + (t5006 - t5008) * np.exp((4*1j) * (t4992 + t4991)) + (t5009 - t5010) * np.exp((-12*1j) * (phi1 + phi2)) + (t5009 + t5010) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 160: + t5032 = np.cos(phi) + t5031 = t5032 ** 2 + t5039 = t5031 ** 2 + t5038 = t5032 * t5031 + t5041 = t5038 ** 2 + t5043 = t5039 ** 2 + t5040 = t5032 * t5039 + t5045 = t5040 ** 2 + t5047 = t5041 ** 2 + t5042 = t5032 * t5041 + t5049 = t5042 ** 2 + t5056 = 38 + 1026 * t5031 - 1394 * t5039 - 7062 * t5041 + 10098 * t5043 + 2662 * t5045 - 4774 * t5047 - 594 * t5049 + t5018 = t5032 * t5049 + t5020 = t5032 * t5047 + t5022 = t5032 * t5045 + t5024 = t5032 * t5043 + t5055 = 63 * t5018 + 2351 * t5020 + 4131 * t5022 - 10637 * t5024 - 319 * t5032 - 1199 * t5038 + 6237 * t5040 - 627 * t5042 + t5054 = 232 - 3016 * t5031 + 4104 * t5039 + 28248 * t5041 - 82632 * t5043 + 67496 * t5045 - 4136 * t5047 - 10296 * t5049 + t5053 = 1638 * t5018 + 21606 * t5020 - 51074 * t5022 + 10142 * t5024 - 374 * t5032 + 8426 * t5038 - 36366 * t5040 + 46002 * t5042 + t5052 = 9009 * t5018 - 11583 * t5020 - 57651 * t5022 + 167805 * t5024 + 1551 * t5032 - 20097 * t5038 + 89331 * t5040 - 178365 * t5042 + t5051 = -2442 * t5031 + 4026 * t5039 + 35310 * t5041 - 134970 * t5043 + 188034 * t5045 - 118338 * t5047 + 28314 * t5049 + 66 + t5037 = 4 * phi1 + t5036 = 8 * phi1 + t5035 = 12 * phi1 + t5034 = -11 * phi2 + t5033 = 11 * phi2 + tfunc[..., c] = (0.29e2 / 0.524288e6*1j) * np.sqrt(0.5e1) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * ((-21420 * t5018 + 130900 * t5020 - 335580 * t5022 + 464100 * t5024 - 368900 * t5042 + 164220 * t5040 - 35700 * t5038 + 2380 * t5032) * np.exp((-11*1j) * phi2) + (t5051 + t5052) * np.exp((-1*1j) * (t5037 + t5033)) + (t5053 - t5054) * np.exp((-1*1j) * (t5036 + t5033)) + (-t5051 + t5052) * np.exp((1j) * (t5037 + t5034)) + (t5053 + t5054) * np.exp((1j) * (t5036 + t5034)) + (t5055 - t5056) * np.exp((-1*1j) * (t5035 + t5033)) + (t5055 + t5056) * np.exp((1j) * (t5035 + t5034))) * ((1 + t5032) ** (-0.1e1 / 0.2e1)) * ((1 - t5032) ** (-0.1e1 / 0.2e1)) + + if Bindx == 161: + t5077 = np.cos(phi) + t5076 = t5077 ** 2 + t5083 = t5077 * t5076 + t5086 = t5083 ** 2 + t5092 = t5086 ** 2 + t5065 = t5077 * t5092 + t5084 = t5076 ** 2 + t5085 = t5077 * t5084 + t5090 = t5085 ** 2 + t5067 = t5077 * t5090 + t5088 = t5084 ** 2 + t5069 = t5077 * t5088 + t5087 = t5077 * t5086 + t5100 = 2700 * t5065 + 16400 * t5067 - 15620 * t5069 - 1100 * t5077 - 400 * t5083 + 19140 * t5085 - 21120 * t5087 + t5064 = t5087 ** 2 + t5099 = -155 + 315 * t5064 - 2635 * t5076 + 10065 * t5084 + 7425 * t5086 - 33825 * t5088 + 9295 * t5090 + 9515 * t5092 + t5098 = 8190 * t5064 + 10258 * t5076 - 54582 * t5084 + 73722 * t5086 + 67782 * t5088 - 191114 * t5090 + 86190 * t5092 - 446 + t5097 = 46800 * t5065 + 1600 * t5067 - 196592 * t5069 + 1072 * t5077 + 4032 * t5083 - 76560 * t5085 + 219648 * t5087 + t5096 = -128700 * t5065 + 462000 * t5067 - 620268 * t5069 - 132 * t5077 + 6864 * t5083 - 95700 * t5085 + 375936 * t5087 + t5095 = 45045 * t5064 - 17061 * t5076 + 127743 * t5084 - 361713 * t5086 + 438801 * t5088 - 175263 * t5090 - 57915 * t5092 + 363 + t5082 = 2 * phi1 + t5081 = 4 * phi1 + t5080 = 6 * phi1 + t5079 = -5 * phi2 + t5078 = 5 * phi2 + tfunc[..., c] = (0.29e2 / 0.1048576e7) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((476 + 559300 * t5092 - 1190476 * t5090 + 1311380 * t5088 - 778260 * t5086 + 230860 * t5084 - 26180 * t5076 - 107100 * t5064) * np.exp((-10*1j) * phi2) + (t5095 - t5096) * np.exp((-2*1j) * (t5082 + t5078)) + (t5097 + t5098) * np.exp((-2*1j) * (t5081 + t5078)) + (t5099 + t5100) * np.exp((-2*1j) * (t5080 + t5078)) + (t5095 + t5096) * np.exp((2*1j) * (t5082 + t5079)) + (-t5097 + t5098) * np.exp((2*1j) * (t5081 + t5079)) + (t5099 - t5100) * np.exp((2*1j) * (t5080 + t5079))) + + if Bindx == 162: + t5122 = np.cos(phi) + t5121 = t5122 ** 2 + t5128 = t5121 ** 2 + t5127 = t5122 * t5121 + t5130 = t5127 ** 2 + t5132 = t5128 ** 2 + t5129 = t5122 * t5128 + t5134 = t5129 ** 2 + t5136 = t5130 ** 2 + t5131 = t5122 * t5130 + t5138 = t5131 ** 2 + t5145 = -370 - 2590 * t5121 + 27710 * t5128 - 43230 * t5130 - 23430 * t5132 + 73590 * t5134 - 24390 * t5136 - 7290 * t5138 + t5108 = t5122 * t5138 + t5110 = t5122 * t5136 + t5112 = t5122 * t5134 + t5114 = t5122 * t5132 + t5144 = 945 * t5108 + 21565 * t5110 - 15635 * t5112 - 64295 * t5114 + 2115 * t5122 - 8385 * t5127 - 16665 * t5129 + 80355 * t5131 + t5143 = 24570 * t5108 + 175890 * t5110 - 608670 * t5112 + 587994 * t5114 - 4194 * t5122 + 38646 * t5127 - 74250 * t5129 - 139986 * t5131 + t5142 = 10296 * t5121 - 108984 * t5128 + 409464 * t5130 - 620136 * t5132 + 277992 * t5134 + 158040 * t5136 - 126360 * t5138 - 312 + t5141 = 11286 * t5121 - 134838 * t5128 + 689238 * t5130 - 1716066 * t5132 + 2196018 * t5134 - 1392930 * t5136 + 347490 * t5138 - 198 + t5140 = 135135 * t5108 - 302445 * t5110 - 120285 * t5112 + 896247 * t5114 + 4653 * t5122 - 86031 * t5127 + 446985 * t5129 - 974259 * t5131 + t5126 = 4 * phi1 + t5125 = 8 * phi1 + t5124 = -9 * phi2 + t5123 = 9 * phi2 + tfunc[..., c] = (0.29e2 / 0.786432e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * ((1 + t5122) ** (-0.1e1 / 0.2e1)) * ((1 - t5122) ** (-0.1e1 / 0.2e1)) * ((-321300 * t5108 + 1725500 * t5110 - 3815140 * t5112 + 4438700 * t5114 - 2867900 * t5131 + 987700 * t5129 - 154700 * t5127 + 7140 * t5122) * np.exp((-9*1j) * phi2) + (t5144 - t5145) * np.exp((-3*1j) * (t5126 + 3 * phi2)) + (t5140 + t5141) * np.exp((-1*1j) * (t5126 + t5123)) + (-t5142 + t5143) * np.exp((-1*1j) * (t5125 + t5123)) + (t5140 - t5141) * np.exp((1j) * (t5126 + t5124)) + (t5142 + t5143) * np.exp((1j) * (t5125 + t5124)) + (t5144 + t5145) * np.exp((3*1j) * (t5126 - 3 * phi2))) + + if Bindx == 163: + t5166 = np.cos(phi) + t5165 = t5166 ** 2 + t5170 = t5166 * t5165 + t5173 = t5170 ** 2 + t5179 = t5173 ** 2 + t5154 = t5166 * t5179 + t5171 = t5165 ** 2 + t5172 = t5166 * t5171 + t5177 = t5172 ** 2 + t5156 = t5166 * t5177 + t5175 = t5171 ** 2 + t5158 = t5166 * t5175 + t5174 = t5166 * t5173 + t5187 = 49680 * t5154 + 92000 * t5156 - 384560 * t5158 + 9200 * t5166 - 69920 * t5170 + 60720 * t5172 + 242880 * t5174 + t5153 = t5174 ** 2 + t5186 = -7245 * t5153 + 2185 * t5165 + 110055 * t5171 - 322575 * t5173 + 178365 * t5175 + 165715 * t5177 - 124315 * t5179 - 2185 + t5185 = -188370 * t5153 - 23206 * t5165 - 48378 * t5171 + 1048410 * t5173 - 3069198 * t5175 + 3235870 * t5177 - 959790 * t5179 + 566 + t5184 = -861120 * t5154 + 1251200 * t5156 + 663872 * t5158 + 18880 * t5166 - 263808 * t5170 + 1189056 * t5172 - 2002176 * t5174 + t5183 = -2368080 * t5154 + 8136480 * t5156 - 10771728 * t5158 - 14256 * t5166 + 317856 * t5170 - 2195952 * t5172 + 6895680 * t5174 + t5182 = -1036035 * t5153 + 83655 * t5165 - 796983 * t5171 + 2494239 * t5173 - 2777709 * t5175 - 185955 * t5177 + 2220075 * t5179 - 1287 + t5169 = 3 * phi1 + t5168 = -2 * phi2 + t5167 = 2 * phi2 + tfunc[..., c] = -(0.29e2 / 0.524288e6) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((-2380 + 2463300 * t5153 - 11221700 * t5179 + 20418020 * t5177 - 18723460 * t5175 + 8932140 * t5173 - 2039660 * t5171 + 173740 * t5165) * np.exp((-8*1j) * phi2) + (t5184 + t5185) * np.exp((-8*1j) * (phi1 + phi2)) + (t5182 + t5183) * np.exp((-4*1j) * (phi1 + t5167)) + (t5186 - t5187) * np.exp((-4*1j) * (t5169 + t5167)) + (t5182 - t5183) * np.exp((4*1j) * (phi1 + t5168)) + (t5186 + t5187) * np.exp((4*1j) * (t5169 + t5168)) + (-t5184 + t5185) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 164: + t5209 = np.cos(phi) + t5208 = t5209 ** 2 + t5216 = t5208 ** 2 + t5215 = t5209 * t5208 + t5218 = t5215 ** 2 + t5220 = t5216 ** 2 + t5217 = t5209 * t5216 + t5222 = t5217 ** 2 + t5224 = t5218 ** 2 + t5219 = t5209 * t5218 + t5226 = t5219 ** 2 + t5233 = 230 - 2070 * t5208 - 1610 * t5216 + 31050 * t5218 - 60030 * t5220 + 36110 * t5222 + 2530 * t5224 - 6210 * t5226 + t5195 = t5209 * t5226 + t5197 = t5209 * t5224 + t5199 = t5209 * t5222 + t5201 = t5209 * t5220 + t5232 = 1035 * t5195 + 11615 * t5197 - 36225 * t5199 + 22195 * t5201 - 575 * t5209 + 8165 * t5215 - 25875 * t5217 + 19665 * t5219 + t5231 = -7448 * t5208 + 54936 * t5216 - 136920 * t5218 + 74376 * t5220 + 184184 * t5222 - 276920 * t5224 + 107640 * t5226 + 152 + t5230 = 26910 * t5195 + 62790 * t5197 - 388010 * t5199 + 563486 * t5201 + 650 * t5209 - 13454 * t5215 + 106386 * t5217 - 358758 * t5219 + t5229 = 148005 * t5195 - 444015 * t5197 + 406065 * t5199 - 3267 * t5201 + 783 * t5209 - 17877 * t5215 + 102243 * t5217 - 191937 * t5219 + t5228 = 9198 * t5208 - 114702 * t5216 + 563598 * t5218 - 1373322 * t5220 + 1765434 * t5222 - 1146090 * t5224 + 296010 * t5226 - 126 + t5214 = 4 * phi1 + t5213 = 8 * phi1 + t5212 = -7 * phi2 + t5211 = 7 * phi2 + t5210 = 12 * phi1 + tfunc[..., c] = (0.29e2 / 0.262144e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.19e2) * np.sqrt(0.7e1) * ((-351900 * t5195 + 1681300 * t5197 - 3260940 * t5199 + 3271140 * t5201 - 1784660 * t5219 + 506940 * t5217 - 64260 * t5215 + 2380 * t5209) * np.exp((-7*1j) * phi2) + (t5228 + t5229) * np.exp((-1*1j) * (t5214 + t5211)) + (t5230 + t5231) * np.exp((-1*1j) * (t5213 + t5211)) + (-t5228 + t5229) * np.exp((1j) * (t5214 + t5212)) + (t5230 - t5231) * np.exp((1j) * (t5213 + t5212)) + (t5232 - t5233) * np.exp((-1*1j) * (t5210 + t5211)) + (t5232 + t5233) * np.exp((1j) * (t5210 + t5212))) * ((1 + t5209) ** (-0.1e1 / 0.2e1)) * ((1 - t5209) ** (-0.1e1 / 0.2e1)) + + if Bindx == 165: + t5254 = np.cos(phi) + t5253 = t5254 ** 2 + t5259 = t5254 * t5253 + t5262 = t5259 ** 2 + t5263 = t5254 * t5262 + t5241 = t5263 ** 2 + t5260 = t5253 ** 2 + t5264 = t5260 ** 2 + t5261 = t5254 * t5260 + t5266 = t5261 ** 2 + t5268 = t5262 ** 2 + t5276 = -21735 * t5241 - 50025 * t5253 + 133515 * t5260 + 83835 * t5262 - 580635 * t5264 + 584085 * t5266 - 152375 * t5268 + 3335 + t5242 = t5254 * t5268 + t5244 = t5254 * t5266 + t5246 = t5254 * t5264 + t5275 = -111780 * t5242 + 160080 * t5244 + 272780 * t5246 + 1380 * t5254 - 86480 * t5259 + 426420 * t5261 - 662400 * t5263 + t5274 = -3108105 * t5241 + 22041 * t5253 - 191259 * t5260 + 22581 * t5262 + 3050091 * t5264 - 8527365 * t5266 + 8732295 * t5268 - 279 + t5273 = -1937520 * t5242 + 5056320 * t5244 - 4317744 * t5246 + 10608 * t5254 - 161088 * t5259 + 358704 * t5261 + 990720 * t5263 + t5272 = -5328180 * t5242 + 17669520 * t5244 - 22706244 * t5246 - 22284 * t5254 + 581616 * t5259 - 4355964 * t5261 + 14161536 * t5263 + t5271 = -565110 * t5241 + 102630 * t5253 - 989874 * t5260 + 3787038 * t5262 - 6543198 * t5264 + 4703730 * t5266 - 493350 * t5268 - 1866 + t5258 = 2 * phi1 + t5257 = 4 * phi1 + t5256 = -3 * phi2 + t5255 = 3 * phi2 + tfunc[..., c] = -(0.29e2 / 0.3145728e7) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((-37477860 * t5264 + 15162980 * t5262 - 2905980 * t5260 + 207060 * t5253 + 7389900 * t5241 - 29833300 * t5268 + 47459580 * t5266 - 2380) * np.exp((-6*1j) * phi2) + (t5275 + t5276) * np.exp((-6*1j) * (t5258 + phi2)) + (t5272 + t5274) * np.exp((-2*1j) * (t5258 + t5255)) + (t5271 + t5273) * np.exp((-2*1j) * (t5257 + t5255)) + (-t5272 + t5274) * np.exp((2*1j) * (t5258 + t5256)) + (t5271 - t5273) * np.exp((2*1j) * (t5257 + t5256)) + (-t5275 + t5276) * np.exp((6*1j) * (t5258 - phi2))) + + if Bindx == 166: + t5298 = np.cos(phi) + t5297 = t5298 ** 2 + t5305 = t5297 ** 2 + t5304 = t5298 * t5297 + t5307 = t5304 ** 2 + t5309 = t5305 ** 2 + t5306 = t5298 * t5305 + t5311 = t5306 ** 2 + t5313 = t5307 ** 2 + t5308 = t5298 * t5307 + t5315 = t5308 ** 2 + t5322 = 230 - 4830 * t5297 + 25070 * t5305 - 46230 * t5307 + 22770 * t5309 + 25990 * t5311 - 33350 * t5313 + 10350 * t5315 + t5284 = t5298 * t5315 + t5286 = t5298 * t5313 + t5288 = t5298 * t5311 + t5290 = t5298 * t5309 + t5321 = 2415 * t5284 + 6095 * t5286 - 51405 * t5288 + 95795 * t5290 - 575 * t5298 - 575 * t5304 + 23805 * t5306 - 75555 * t5308 + t5320 = 62790 * t5284 - 80730 * t5286 - 226274 * t5288 + 578334 * t5290 + 1674 * t5298 - 33718 * t5304 + 197170 * t5306 - 499246 * t5308 + t5319 = -1464 * t5297 + 25976 * t5305 - 184920 * t5307 + 578248 * t5309 - 877864 * t5311 + 639400 * t5313 - 179400 * t5315 + 24 + t5318 = 345345 * t5284 - 1233375 * t5286 + 1732797 * t5288 - 1215555 * t5290 - 241 * t5298 + 7695 * t5304 - 85421 * t5306 + 448755 * t5308 + t5317 = 11050 * t5297 - 154330 * t5305 + 817426 * t5307 - 2090310 * t5309 + 2782494 * t5311 - 1859550 * t5313 + 493350 * t5315 - 130 + t5303 = 4 * phi1 + t5302 = 8 * phi1 + t5301 = -5 * phi2 + t5300 = 5 * phi2 + t5299 = 12 * phi1 + tfunc[..., c] = (0.29e2 / 0.524288e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.5e1) * ((2380 * t5298 - 821100 * t5284 + 3558100 * t5286 - 6207516 * t5288 + 5551588 * t5290 - 2676548 * t5308 + 666876 * t5306 - 73780 * t5304) * np.exp((-5*1j) * phi2) + (t5317 + t5318) * np.exp((-1*1j) * (t5303 + t5300)) + (-t5319 + t5320) * np.exp((-1*1j) * (t5302 + t5300)) + (-t5317 + t5318) * np.exp((1j) * (t5303 + t5301)) + (t5319 + t5320) * np.exp((1j) * (t5302 + t5301)) + (t5321 + t5322) * np.exp((-1*1j) * (t5299 + t5300)) + (t5321 - t5322) * np.exp((1j) * (t5299 + t5301))) * ((1 + t5298) ** (-0.1e1 / 0.2e1)) * ((1 - t5298) ** (-0.1e1 / 0.2e1)) + + if Bindx == 167: + t5343 = np.cos(phi) + t5342 = t5343 ** 2 + t5346 = t5343 * t5342 + t5349 = t5346 ** 2 + t5350 = t5343 * t5349 + t5330 = t5350 ** 2 + t5347 = t5342 ** 2 + t5351 = t5347 ** 2 + t5348 = t5343 * t5347 + t5353 = t5348 ** 2 + t5355 = t5349 ** 2 + t5363 = 45885 * t5330 - 54625 * t5342 + 369265 * t5347 - 928625 * t5349 + 1037875 * t5351 - 461035 * t5353 - 10925 * t5355 + 2185 + t5331 = t5343 * t5355 + t5333 = t5343 * t5353 + t5335 = t5343 * t5351 + t5362 = -157320 * t5331 + 594320 * t5333 - 786600 * t5335 + 17480 * t5343 - 104880 * t5346 + 87400 * t5348 + 349600 * t5350 + t5361 = -2726880 * t5331 + 9369280 * t5333 - 12403808 * t5335 - 16416 * t5343 + 366016 * t5346 - 2528672 * t5348 + 7940480 * t5350 + t5360 = -7498920 * t5331 + 24227280 * t5333 - 30111048 * t5335 - 21656 * t5343 + 631184 * t5346 - 5166328 * t5348 + 17935392 * t5350 + t5359 = 1193010 * t5330 - 96330 * t5342 + 917738 * t5347 - 2872154 * t5349 + 3198574 * t5351 + 214130 * t5353 - 2556450 * t5355 + 1482 + t5358 = 6561555 * t5330 + 67729 * t5342 - 1030561 * t5347 + 6149217 * t5349 - 18088323 * t5351 + 27904635 * t5353 - 21559395 * t5355 - 761 + t5345 = 2 * phi1 + t5344 = 3 * phi1 + tfunc[..., c] = (0.29e2 / 0.524288e6) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * ((2380 - 15600900 * t5330 + 57203300 * t5355 - 82164740 * t5353 + 58288580 * t5351 - 21117740 * t5349 + 3619980 * t5347 - 230860 * t5342) * np.exp((-4*1j) * phi2) + (t5358 - t5360) * np.exp((-4*1j) * (phi1 + phi2)) + (t5359 - t5361) * np.exp((-4*1j) * (t5345 + phi2)) + (-t5362 + t5363) * np.exp((-4*1j) * (t5344 + phi2)) + (t5358 + t5360) * np.exp((4*1j) * (phi1 - phi2)) + (t5359 + t5361) * np.exp((4*1j) * (t5345 - phi2)) + (t5362 + t5363) * np.exp((4*1j) * (t5344 - phi2))) + + if Bindx == 168: + t5385 = np.cos(phi) + t5384 = t5385 ** 2 + t5391 = t5384 ** 2 + t5390 = t5385 * t5384 + t5393 = t5390 ** 2 + t5395 = t5391 ** 2 + t5392 = t5385 * t5391 + t5397 = t5392 ** 2 + t5399 = t5393 ** 2 + t5394 = t5385 * t5393 + t5401 = t5394 ** 2 + t5408 = -126730 * t5384 + 1630010 * t5391 - 6402050 * t5393 + 11689750 * t5395 - 11147870 * t5397 + 5414430 * t5399 - 1061910 * t5401 + 4370 + t5407 = -865458 * t5384 + 13063842 * t5391 - 73723914 * t5393 + 197967726 * t5395 - 273162582 * t5397 + 187328790 * t5399 - 50617710 * t5401 + 9306 + t5406 = -975384 * t5384 + 11273688 * t5391 - 50659320 * t5393 + 112284072 * t5395 - 131299272 * t5397 + 77768520 * t5399 - 18406440 * t5401 + 14136 + t5371 = t5385 * t5401 + t5373 = t5385 * t5399 + t5375 = t5385 * t5397 + t5377 = t5385 * t5395 + t5405 = -10737090 * t5371 + 39710190 * t5373 - 54314730 * t5375 + 31494438 * t5377 - 33630 * t5385 + 660402 * t5390 - 2172726 * t5392 - 4606854 * t5394 + t5404 = -412965 * t5371 + 1352515 * t5373 - 404225 * t5375 - 3812825 * t5377 - 163875 * t5385 + 1531685 * t5390 - 4787335 * t5392 + 6697025 * t5394 + t5403 = -59053995 * t5371 + 233403885 * t5373 - 373143375 * t5375 + 308275209 * t5377 + 112563 * t5385 - 3526677 * t5390 + 32902551 * t5392 - 138970161 * t5394 + t5389 = 4 * phi1 + t5388 = 8 * phi1 + t5387 = -3 * phi2 + t5386 = 3 * phi2 + tfunc[..., c] = (-0.29e2 / 0.1572864e7*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * ((-235620 * t5385 + 140408100 * t5371 - 566832700 * t5373 + 918372980 * t5375 - 760555180 * t5377 + 338743020 * t5394 - 77833140 * t5392 + 7932540 * t5390) * np.exp((-3*1j) * phi2) + (t5404 + t5408) * np.exp((-3*1j) * (t5389 + phi2)) + (t5403 + t5407) * np.exp((-1*1j) * (t5389 + t5386)) + (t5405 + t5406) * np.exp((-1*1j) * (t5388 + t5386)) + (t5403 - t5407) * np.exp((1j) * (t5389 + t5387)) + (t5405 - t5406) * np.exp((1j) * (t5388 + t5387)) + (t5404 - t5408) * np.exp((3*1j) * (t5389 - phi2))) * ((1 + t5385) ** (-0.1e1 / 0.2e1)) * ((1 - t5385) ** (-0.1e1 / 0.2e1)) + + if Bindx == 169: + t5429 = np.cos(phi) + t5428 = t5429 ** 2 + t5433 = t5429 * t5428 + t5436 = t5433 ** 2 + t5437 = t5429 * t5436 + t5416 = t5437 ** 2 + t5434 = t5428 ** 2 + t5438 = t5434 ** 2 + t5435 = t5429 * t5434 + t5440 = t5435 ** 2 + t5442 = t5436 ** 2 + t5450 = -137655 * t5416 + 67735 * t5428 - 168245 * t5434 - 98325 * t5436 + 797525 * t5438 - 1090315 * t5440 + 631465 * t5442 - 2185 + t5417 = t5429 * t5442 + t5419 = t5429 * t5440 + t5421 = t5429 * t5438 + t5449 = -235980 * t5417 + 1223600 * t5419 - 2578300 * t5421 - 43700 * t5429 + 454480 * t5433 - 1616900 * t5435 + 2796800 * t5437 + t5448 = -11248380 * t5417 + 35764080 * t5419 - 43320684 * t5421 - 25476 * t5429 + 790416 * t5433 - 6839316 * t5435 + 24879360 * t5437 + t5447 = -4090320 * t5417 + 16081600 * t5419 - 24590864 * t5421 - 44080 * t5429 + 1020224 * t5433 - 6606832 * t5435 + 18230272 * t5437 + t5446 = -3579030 * t5416 - 110618 * t5428 + 1380046 * t5434 - 6900610 * t5436 + 17083394 * t5438 - 22020430 * t5440 + 14145690 * t5442 + 1558 + t5445 = -19684665 * t5416 - 279015 * t5428 + 4317093 * t5434 - 25055547 * t5436 + 69396987 * t5438 - 99000165 * t5440 + 70302375 * t5442 + 2937 + t5432 = 2 * phi1 + t5431 = 4 * phi1 + t5430 = 6 * phi1 + tfunc[..., c] = -(0.29e2 / 0.1048576e7) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * ((-4620 + 217372540 * t5440 - 144749220 * t5438 + 49244580 * t5436 - 7932540 * t5434 + 475860 * t5428 + 46802700 * t5416 - 161209300 * t5442) * np.exp((-2*1j) * phi2) + (t5445 + t5448) * np.exp((-2*1j) * (t5432 + phi2)) + (t5446 + t5447) * np.exp((-2*1j) * (t5431 + phi2)) + (t5449 + t5450) * np.exp((-2*1j) * (t5430 + phi2)) + (t5445 - t5448) * np.exp((2*1j) * (t5432 - phi2)) + (t5446 - t5447) * np.exp((2*1j) * (t5431 - phi2)) + (-t5449 + t5450) * np.exp((2*1j) * (t5430 - phi2))) + + if Bindx == 170: + t5472 = np.cos(phi) + t5471 = t5472 ** 2 + t5477 = t5471 ** 2 + t5476 = t5472 * t5471 + t5479 = t5476 ** 2 + t5481 = t5477 ** 2 + t5478 = t5472 * t5477 + t5483 = t5478 ** 2 + t5485 = t5479 ** 2 + t5480 = t5472 * t5479 + t5487 = t5480 ** 2 + t5494 = 83226 * t5471 - 1305018 * t5477 + 7613034 * t5479 - 21013278 * t5481 + 29620734 * t5483 - 20622030 * t5485 + 5624190 * t5487 - 858 + t5493 = -144210 * t5471 + 773490 * t5477 - 1857250 * t5479 + 2425350 * t5481 - 1796070 * t5483 + 712310 * t5485 - 117990 * t5487 + 4370 + t5492 = -144248 * t5471 + 1693432 * t5477 - 7415928 * t5479 + 15545192 * t5481 - 16952104 * t5483 + 9316840 * t5485 - 2045160 * t5487 + 1976 + t5458 = t5472 * t5487 + t5460 = t5472 * t5485 + t5462 = t5472 * t5483 + t5464 = t5472 * t5481 + t5491 = -137655 * t5458 + 849965 * t5460 - 2209035 * t5462 + 3113625 * t5464 + 24035 * t5472 - 281865 * t5476 + 1186455 * t5478 - 2545525 * t5480 + t5490 = -3579030 * t5458 + 17554290 * t5460 - 35165390 * t5462 + 36735322 * t5464 + 36062 * t5472 - 882778 * t5476 + 6408662 * t5478 - 21107138 * t5480 + t5489 = -19684665 * t5458 + 81550755 * t5460 - 135917925 * t5462 + 116078391 * t5464 + 41613 * t5472 - 1346631 * t5476 + 12724569 * t5478 - 53446107 * t5480 + t5475 = 4 * phi1 + t5474 = 8 * phi1 + t5473 = 12 * phi1 + tfunc[..., c] = (-0.29e2 / 0.262144e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * ((46802700 * t5458 - 182010500 * t5460 + 283936380 * t5462 - 226326100 * t5464 + 96996900 * t5480 - 21441420 * t5478 + 2102100 * t5476 - 60060 * t5472) * np.exp((-1*1j) * phi2) + (t5489 - t5494) * np.exp((-1*1j) * (t5475 + phi2)) + (t5490 + t5492) * np.exp((-1*1j) * (t5474 + phi2)) + (t5489 + t5494) * np.exp((1j) * (t5475 - phi2)) + (t5490 - t5492) * np.exp((1j) * (t5474 - phi2)) + (t5491 + t5493) * np.exp((-1*1j) * (t5473 + phi2)) + (t5491 - t5493) * np.exp((1j) * (t5473 - phi2))) * ((1 + t5472) ** (-0.1e1 / 0.2e1)) * ((1 - t5472) ** (-0.1e1 / 0.2e1)) + + if Bindx == 171: + t5505 = np.cos(phi) + t5504 = t5505 ** 2 + t5506 = t5504 ** 2 + t5507 = t5504 * t5506 + t5510 = t5507 ** 2 + t5508 = t5506 ** 2 + t5500 = t5504 * t5508 + t5498 = t5504 * t5510 + tfunc[..., c] = 0.29e2 / 0.393216e6 * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.17e2) * (-(10029150 * t5498) + (33801950 * t5510) - (44618574 * t5500) + (29099070 * t5508) - (9699690 * t5507) + (1531530 * t5506) - (90090 * t5504) + 0.858e3 + (8436285 * t5498 - 30933045 * t5510 + 44431101 * t5500 - 31519917 * t5508 + 11419551 * t5507 - 1957527 * t5506 + 124839 * t5504 - 1287) * np.cos((4 * phi1)) + (1533870 * t5498 - 6987630 * t5510 + 12714078 * t5500 - 11658894 * t5508 + 5561946 * t5507 - 1270074 * t5506 + 108186 * t5504 - 1482) * np.cos((8 * phi1)) + (58995 * t5498 - 356155 * t5510 + 898035 * t5500 - 1212675 * t5508 + 928625 * t5507 - 386745 * t5506 + 72105 * t5504 - 2185) * np.cos((12 * phi1))) + + if Bindx == 172: + t5533 = np.cos(phi) + t5532 = t5533 ** 2 + t5538 = t5532 ** 2 + t5537 = t5533 * t5532 + t5540 = t5537 ** 2 + t5542 = t5538 ** 2 + t5539 = t5533 * t5538 + t5544 = t5539 ** 2 + t5546 = t5540 ** 2 + t5541 = t5533 * t5540 + t5548 = t5541 ** 2 + t5555 = 83226 * t5532 - 1305018 * t5538 + 7613034 * t5540 - 21013278 * t5542 + 29620734 * t5544 - 20622030 * t5546 + 5624190 * t5548 - 858 + t5554 = -144210 * t5532 + 773490 * t5538 - 1857250 * t5540 + 2425350 * t5542 - 1796070 * t5544 + 712310 * t5546 - 117990 * t5548 + 4370 + t5553 = -144248 * t5532 + 1693432 * t5538 - 7415928 * t5540 + 15545192 * t5542 - 16952104 * t5544 + 9316840 * t5546 - 2045160 * t5548 + 1976 + t5519 = t5533 * t5548 + t5521 = t5533 * t5546 + t5523 = t5533 * t5544 + t5525 = t5533 * t5542 + t5552 = 137655 * t5519 - 849965 * t5521 + 2209035 * t5523 - 3113625 * t5525 - 24035 * t5533 + 281865 * t5537 - 1186455 * t5539 + 2545525 * t5541 + t5551 = 3579030 * t5519 - 17554290 * t5521 + 35165390 * t5523 - 36735322 * t5525 - 36062 * t5533 + 882778 * t5537 - 6408662 * t5539 + 21107138 * t5541 + t5550 = 19684665 * t5519 - 81550755 * t5521 + 135917925 * t5523 - 116078391 * t5525 - 41613 * t5533 + 1346631 * t5537 - 12724569 * t5539 + 53446107 * t5541 + t5536 = 4 * phi1 + t5535 = 8 * phi1 + t5534 = 12 * phi1 + tfunc[..., c] = (0.29e2 / 0.262144e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * ((-46802700 * t5519 + 182010500 * t5521 - 283936380 * t5523 + 226326100 * t5525 - 96996900 * t5541 + 21441420 * t5539 - 2102100 * t5537 + 60060 * t5533) * np.exp((1j) * phi2) + (t5550 - t5555) * np.exp((-1*1j) * (t5536 - phi2)) + (t5551 + t5553) * np.exp((-1*1j) * (t5535 - phi2)) + (t5550 + t5555) * np.exp((1j) * (t5536 + phi2)) + (t5551 - t5553) * np.exp((1j) * (t5535 + phi2)) + (t5552 + t5554) * np.exp((-1*1j) * (t5534 - phi2)) + (t5552 - t5554) * np.exp((1j) * (t5534 + phi2))) * ((1 + t5533) ** (-0.1e1 / 0.2e1)) * ((1 - t5533) ** (-0.1e1 / 0.2e1)) + + if Bindx == 173: + t5576 = np.cos(phi) + t5575 = t5576 ** 2 + t5580 = t5576 * t5575 + t5583 = t5580 ** 2 + t5584 = t5576 * t5583 + t5563 = t5584 ** 2 + t5581 = t5575 ** 2 + t5585 = t5581 ** 2 + t5582 = t5576 * t5581 + t5587 = t5582 ** 2 + t5589 = t5583 ** 2 + t5597 = -137655 * t5563 + 67735 * t5575 - 168245 * t5581 - 98325 * t5583 + 797525 * t5585 - 1090315 * t5587 + 631465 * t5589 - 2185 + t5564 = t5576 * t5589 + t5566 = t5576 * t5587 + t5568 = t5576 * t5585 + t5596 = -235980 * t5564 + 1223600 * t5566 - 2578300 * t5568 - 43700 * t5576 + 454480 * t5580 - 1616900 * t5582 + 2796800 * t5584 + t5595 = -11248380 * t5564 + 35764080 * t5566 - 43320684 * t5568 - 25476 * t5576 + 790416 * t5580 - 6839316 * t5582 + 24879360 * t5584 + t5594 = -4090320 * t5564 + 16081600 * t5566 - 24590864 * t5568 - 44080 * t5576 + 1020224 * t5580 - 6606832 * t5582 + 18230272 * t5584 + t5593 = -3579030 * t5563 - 110618 * t5575 + 1380046 * t5581 - 6900610 * t5583 + 17083394 * t5585 - 22020430 * t5587 + 14145690 * t5589 + 1558 + t5592 = -19684665 * t5563 - 279015 * t5575 + 4317093 * t5581 - 25055547 * t5583 + 69396987 * t5585 - 99000165 * t5587 + 70302375 * t5589 + 2937 + t5579 = 2 * phi1 + t5578 = 4 * phi1 + t5577 = 6 * phi1 + tfunc[..., c] = -(0.29e2 / 0.1048576e7) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * ((-4620 + 46802700 * t5563 - 161209300 * t5589 + 217372540 * t5587 - 144749220 * t5585 + 49244580 * t5583 - 7932540 * t5581 + 475860 * t5575) * np.exp((2*1j) * phi2) + (t5592 - t5595) * np.exp((-2*1j) * (t5579 - phi2)) + (t5593 - t5594) * np.exp((-2*1j) * (t5578 - phi2)) + (-t5596 + t5597) * np.exp((-2*1j) * (t5577 - phi2)) + (t5592 + t5595) * np.exp((2*1j) * (t5579 + phi2)) + (t5593 + t5594) * np.exp((2*1j) * (t5578 + phi2)) + (t5596 + t5597) * np.exp((2*1j) * (t5577 + phi2))) + + if Bindx == 174: + t5619 = np.cos(phi) + t5618 = t5619 ** 2 + t5625 = t5618 ** 2 + t5624 = t5619 * t5618 + t5627 = t5624 ** 2 + t5629 = t5625 ** 2 + t5626 = t5619 * t5625 + t5631 = t5626 ** 2 + t5633 = t5627 ** 2 + t5628 = t5619 * t5627 + t5635 = t5628 ** 2 + t5642 = -126730 * t5618 + 1630010 * t5625 - 6402050 * t5627 + 11689750 * t5629 - 11147870 * t5631 + 5414430 * t5633 - 1061910 * t5635 + 4370 + t5641 = -865458 * t5618 + 13063842 * t5625 - 73723914 * t5627 + 197967726 * t5629 - 273162582 * t5631 + 187328790 * t5633 - 50617710 * t5635 + 9306 + t5640 = -975384 * t5618 + 11273688 * t5625 - 50659320 * t5627 + 112284072 * t5629 - 131299272 * t5631 + 77768520 * t5633 - 18406440 * t5635 + 14136 + t5605 = t5619 * t5635 + t5607 = t5619 * t5633 + t5609 = t5619 * t5631 + t5611 = t5619 * t5629 + t5639 = 10737090 * t5605 - 39710190 * t5607 + 54314730 * t5609 - 31494438 * t5611 + 33630 * t5619 - 660402 * t5624 + 2172726 * t5626 + 4606854 * t5628 + t5638 = 412965 * t5605 - 1352515 * t5607 + 404225 * t5609 + 3812825 * t5611 + 163875 * t5619 - 1531685 * t5624 + 4787335 * t5626 - 6697025 * t5628 + t5637 = 59053995 * t5605 - 233403885 * t5607 + 373143375 * t5609 - 308275209 * t5611 - 112563 * t5619 + 3526677 * t5624 - 32902551 * t5626 + 138970161 * t5628 + t5623 = 4 * phi1 + t5622 = 8 * phi1 + t5621 = -3 * phi2 + t5620 = 3 * phi2 + tfunc[..., c] = (0.29e2 / 0.1572864e7*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * ((760555180 * t5611 - 338743020 * t5628 + 77833140 * t5626 - 7932540 * t5624 + 235620 * t5619 - 140408100 * t5605 + 566832700 * t5607 - 918372980 * t5609) * np.exp((3*1j) * phi2) + (t5638 + t5642) * np.exp((-3*1j) * (t5623 - phi2)) + (t5637 + t5641) * np.exp((-1*1j) * (t5623 + t5621)) + (t5639 + t5640) * np.exp((-1*1j) * (t5622 + t5621)) + (t5637 - t5641) * np.exp((1j) * (t5623 + t5620)) + (t5639 - t5640) * np.exp((1j) * (t5622 + t5620)) + (t5638 - t5642) * np.exp((3*1j) * (t5623 + phi2))) * ((1 + t5619) ** (-0.1e1 / 0.2e1)) * ((1 - t5619) ** (-0.1e1 / 0.2e1)) + + if Bindx == 175: + t5663 = np.cos(phi) + t5662 = t5663 ** 2 + t5666 = t5663 * t5662 + t5669 = t5666 ** 2 + t5670 = t5663 * t5669 + t5650 = t5670 ** 2 + t5667 = t5662 ** 2 + t5671 = t5667 ** 2 + t5668 = t5663 * t5667 + t5673 = t5668 ** 2 + t5675 = t5669 ** 2 + t5683 = 45885 * t5650 - 54625 * t5662 + 369265 * t5667 - 928625 * t5669 + 1037875 * t5671 - 461035 * t5673 - 10925 * t5675 + 2185 + t5651 = t5663 * t5675 + t5653 = t5663 * t5673 + t5655 = t5663 * t5671 + t5682 = -157320 * t5651 + 594320 * t5653 - 786600 * t5655 + 17480 * t5663 - 104880 * t5666 + 87400 * t5668 + 349600 * t5670 + t5681 = -2726880 * t5651 + 9369280 * t5653 - 12403808 * t5655 - 16416 * t5663 + 366016 * t5666 - 2528672 * t5668 + 7940480 * t5670 + t5680 = -7498920 * t5651 + 24227280 * t5653 - 30111048 * t5655 - 21656 * t5663 + 631184 * t5666 - 5166328 * t5668 + 17935392 * t5670 + t5679 = 1193010 * t5650 - 96330 * t5662 + 917738 * t5667 - 2872154 * t5669 + 3198574 * t5671 + 214130 * t5673 - 2556450 * t5675 + 1482 + t5678 = 6561555 * t5650 + 67729 * t5662 - 1030561 * t5667 + 6149217 * t5669 - 18088323 * t5671 + 27904635 * t5673 - 21559395 * t5675 - 761 + t5665 = 2 * phi1 + t5664 = 3 * phi1 + tfunc[..., c] = (0.29e2 / 0.524288e6) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * ((-15600900 * t5650 + 57203300 * t5675 - 82164740 * t5673 + 58288580 * t5671 - 21117740 * t5669 + 3619980 * t5667 - 230860 * t5662 + 2380) * np.exp((4*1j) * phi2) + (t5678 + t5680) * np.exp((-4*1j) * (phi1 - phi2)) + (t5679 + t5681) * np.exp((-4*1j) * (t5665 - phi2)) + (t5682 + t5683) * np.exp((-4*1j) * (t5664 - phi2)) + (t5678 - t5680) * np.exp((4*1j) * (phi1 + phi2)) + (t5679 - t5681) * np.exp((4*1j) * (t5665 + phi2)) + (-t5682 + t5683) * np.exp((4*1j) * (t5664 + phi2))) + + if Bindx == 176: + t5705 = np.cos(phi) + t5704 = t5705 ** 2 + t5712 = t5704 ** 2 + t5711 = t5705 * t5704 + t5714 = t5711 ** 2 + t5716 = t5712 ** 2 + t5713 = t5705 * t5712 + t5718 = t5713 ** 2 + t5720 = t5714 ** 2 + t5715 = t5705 * t5714 + t5722 = t5715 ** 2 + t5729 = -230 + 4830 * t5704 - 25070 * t5712 + 46230 * t5714 - 22770 * t5716 - 25990 * t5718 + 33350 * t5720 - 10350 * t5722 + t5691 = t5705 * t5722 + t5693 = t5705 * t5720 + t5695 = t5705 * t5718 + t5697 = t5705 * t5716 + t5728 = 2415 * t5691 + 6095 * t5693 - 51405 * t5695 + 95795 * t5697 - 575 * t5705 - 575 * t5711 + 23805 * t5713 - 75555 * t5715 + t5727 = 62790 * t5691 - 80730 * t5693 - 226274 * t5695 + 578334 * t5697 + 1674 * t5705 - 33718 * t5711 + 197170 * t5713 - 499246 * t5715 + t5726 = -1464 * t5704 + 25976 * t5712 - 184920 * t5714 + 578248 * t5716 - 877864 * t5718 + 639400 * t5720 - 179400 * t5722 + 24 + t5725 = 345345 * t5691 - 1233375 * t5693 + 1732797 * t5695 - 1215555 * t5697 - 241 * t5705 + 7695 * t5711 - 85421 * t5713 + 448755 * t5715 + t5724 = 11050 * t5704 - 154330 * t5712 + 817426 * t5714 - 2090310 * t5716 + 2782494 * t5718 - 1859550 * t5720 + 493350 * t5722 - 130 + t5710 = 4 * phi1 + t5709 = 8 * phi1 + t5708 = -5 * phi2 + t5707 = 5 * phi2 + t5706 = 12 * phi1 + tfunc[..., c] = (0.29e2 / 0.524288e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((1 + t5705) ** (-0.1e1 / 0.2e1)) * ((1 - t5705) ** (-0.1e1 / 0.2e1)) * ((-821100 * t5691 + 3558100 * t5693 - 6207516 * t5695 + 5551588 * t5697 - 2676548 * t5715 + 666876 * t5713 - 73780 * t5711 + 2380 * t5705) * np.exp((5*1j) * phi2) + (-t5724 + t5725) * np.exp((-1*1j) * (t5710 + t5708)) + (t5726 + t5727) * np.exp((-1*1j) * (t5709 + t5708)) + (t5724 + t5725) * np.exp((1j) * (t5710 + t5707)) + (-t5726 + t5727) * np.exp((1j) * (t5709 + t5707)) + (t5728 + t5729) * np.exp((-1*1j) * (t5706 + t5708)) + (t5728 - t5729) * np.exp((1j) * (t5706 + t5707))) + + if Bindx == 177: + t5750 = np.cos(phi) + t5749 = t5750 ** 2 + t5755 = t5750 * t5749 + t5758 = t5755 ** 2 + t5759 = t5750 * t5758 + t5737 = t5759 ** 2 + t5756 = t5749 ** 2 + t5760 = t5756 ** 2 + t5757 = t5750 * t5756 + t5762 = t5757 ** 2 + t5764 = t5758 ** 2 + t5772 = -21735 * t5737 - 50025 * t5749 + 133515 * t5756 + 83835 * t5758 - 580635 * t5760 + 584085 * t5762 - 152375 * t5764 + 3335 + t5738 = t5750 * t5764 + t5740 = t5750 * t5762 + t5742 = t5750 * t5760 + t5771 = -111780 * t5738 + 160080 * t5740 + 272780 * t5742 + 1380 * t5750 - 86480 * t5755 + 426420 * t5757 - 662400 * t5759 + t5770 = -3108105 * t5737 + 22041 * t5749 - 191259 * t5756 + 22581 * t5758 + 3050091 * t5760 - 8527365 * t5762 + 8732295 * t5764 - 279 + t5769 = -1937520 * t5738 + 5056320 * t5740 - 4317744 * t5742 + 10608 * t5750 - 161088 * t5755 + 358704 * t5757 + 990720 * t5759 + t5768 = -5328180 * t5738 + 17669520 * t5740 - 22706244 * t5742 - 22284 * t5750 + 581616 * t5755 - 4355964 * t5757 + 14161536 * t5759 + t5767 = -565110 * t5737 + 102630 * t5749 - 989874 * t5756 + 3787038 * t5758 - 6543198 * t5760 + 4703730 * t5762 - 493350 * t5764 - 1866 + t5754 = 2 * phi1 + t5753 = 4 * phi1 + t5752 = -3 * phi2 + t5751 = 3 * phi2 + tfunc[..., c] = -(0.29e2 / 0.3145728e7) * np.sqrt(0.3e1) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((7389900 * t5737 - 29833300 * t5764 + 47459580 * t5762 - 37477860 * t5760 + 15162980 * t5758 - 2905980 * t5756 + 207060 * t5749 - 2380) * np.exp((6*1j) * phi2) + (-t5771 + t5772) * np.exp((-6*1j) * (t5754 - phi2)) + (-t5768 + t5770) * np.exp((-2*1j) * (t5754 + t5752)) + (t5767 - t5769) * np.exp((-2*1j) * (t5753 + t5752)) + (t5768 + t5770) * np.exp((2*1j) * (t5754 + t5751)) + (t5767 + t5769) * np.exp((2*1j) * (t5753 + t5751)) + (t5771 + t5772) * np.exp((6*1j) * (t5754 + phi2))) + + if Bindx == 178: + t5794 = np.cos(phi) + t5793 = t5794 ** 2 + t5801 = t5793 ** 2 + t5800 = t5794 * t5793 + t5803 = t5800 ** 2 + t5805 = t5801 ** 2 + t5802 = t5794 * t5801 + t5807 = t5802 ** 2 + t5809 = t5803 ** 2 + t5804 = t5794 * t5803 + t5811 = t5804 ** 2 + t5818 = 230 - 2070 * t5793 - 1610 * t5801 + 31050 * t5803 - 60030 * t5805 + 36110 * t5807 + 2530 * t5809 - 6210 * t5811 + t5780 = t5794 * t5811 + t5782 = t5794 * t5809 + t5784 = t5794 * t5807 + t5786 = t5794 * t5805 + t5817 = 1035 * t5780 + 11615 * t5782 - 36225 * t5784 + 22195 * t5786 - 575 * t5794 + 8165 * t5800 - 25875 * t5802 + 19665 * t5804 + t5816 = -7448 * t5793 + 54936 * t5801 - 136920 * t5803 + 74376 * t5805 + 184184 * t5807 - 276920 * t5809 + 107640 * t5811 + 152 + t5815 = 26910 * t5780 + 62790 * t5782 - 388010 * t5784 + 563486 * t5786 + 650 * t5794 - 13454 * t5800 + 106386 * t5802 - 358758 * t5804 + t5814 = 148005 * t5780 - 444015 * t5782 + 406065 * t5784 - 3267 * t5786 + 783 * t5794 - 17877 * t5800 + 102243 * t5802 - 191937 * t5804 + t5813 = 9198 * t5793 - 114702 * t5801 + 563598 * t5803 - 1373322 * t5805 + 1765434 * t5807 - 1146090 * t5809 + 296010 * t5811 - 126 + t5799 = 4 * phi1 + t5798 = 8 * phi1 + t5797 = -7 * phi2 + t5796 = 7 * phi2 + t5795 = 12 * phi1 + tfunc[..., c] = (0.29e2 / 0.262144e6*1j) * np.sqrt(0.11e2) * np.sqrt(0.13e2) * np.sqrt(0.19e2) * np.sqrt(0.7e1) * ((-351900 * t5780 + 1681300 * t5782 - 3260940 * t5784 + 3271140 * t5786 - 1784660 * t5804 + 506940 * t5802 - 64260 * t5800 + 2380 * t5794) * np.exp((7*1j) * phi2) + (-t5813 + t5814) * np.exp((-1*1j) * (t5799 + t5797)) + (t5815 - t5816) * np.exp((-1*1j) * (t5798 + t5797)) + (t5813 + t5814) * np.exp((1j) * (t5799 + t5796)) + (t5815 + t5816) * np.exp((1j) * (t5798 + t5796)) + (t5817 + t5818) * np.exp((-1*1j) * (t5795 + t5797)) + (t5817 - t5818) * np.exp((1j) * (t5795 + t5796))) * ((1 + t5794) ** (-0.1e1 / 0.2e1)) * ((1 - t5794) ** (-0.1e1 / 0.2e1)) + + if Bindx == 179: + t5839 = np.cos(phi) + t5838 = t5839 ** 2 + t5843 = t5839 * t5838 + t5846 = t5843 ** 2 + t5852 = t5846 ** 2 + t5827 = t5839 * t5852 + t5844 = t5838 ** 2 + t5845 = t5839 * t5844 + t5850 = t5845 ** 2 + t5829 = t5839 * t5850 + t5848 = t5844 ** 2 + t5831 = t5839 * t5848 + t5847 = t5839 * t5846 + t5860 = 49680 * t5827 + 92000 * t5829 - 384560 * t5831 + 9200 * t5839 - 69920 * t5843 + 60720 * t5845 + 242880 * t5847 + t5826 = t5847 ** 2 + t5859 = 7245 * t5826 - 2185 * t5838 - 110055 * t5844 + 322575 * t5846 - 178365 * t5848 - 165715 * t5850 + 124315 * t5852 + 2185 + t5858 = 188370 * t5826 + 23206 * t5838 + 48378 * t5844 - 1048410 * t5846 + 3069198 * t5848 - 3235870 * t5850 + 959790 * t5852 - 566 + t5857 = -861120 * t5827 + 1251200 * t5829 + 663872 * t5831 + 18880 * t5839 - 263808 * t5843 + 1189056 * t5845 - 2002176 * t5847 + t5856 = -2368080 * t5827 + 8136480 * t5829 - 10771728 * t5831 - 14256 * t5839 + 317856 * t5843 - 2195952 * t5845 + 6895680 * t5847 + t5855 = 1036035 * t5826 - 83655 * t5838 + 796983 * t5844 - 2494239 * t5846 + 2777709 * t5848 + 185955 * t5850 - 2220075 * t5852 + 1287 + t5842 = 3 * phi1 + t5841 = -2 * phi2 + t5840 = 2 * phi2 + tfunc[..., c] = (0.29e2 / 0.524288e6) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((2380 - 2463300 * t5826 + 11221700 * t5852 - 20418020 * t5850 + 18723460 * t5848 - 8932140 * t5846 + 2039660 * t5844 - 173740 * t5838) * np.exp((8*1j) * phi2) + (t5857 + t5858) * np.exp((-8*1j) * (phi1 - phi2)) + (t5855 + t5856) * np.exp((-4*1j) * (phi1 + t5841)) + (t5859 - t5860) * np.exp((-4*1j) * (t5842 + t5841)) + (t5855 - t5856) * np.exp((4*1j) * (phi1 + t5840)) + (t5859 + t5860) * np.exp((4*1j) * (t5842 + t5840)) + (-t5857 + t5858) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 180: + t5882 = np.cos(phi) + t5881 = t5882 ** 2 + t5888 = t5881 ** 2 + t5887 = t5882 * t5881 + t5890 = t5887 ** 2 + t5892 = t5888 ** 2 + t5889 = t5882 * t5888 + t5894 = t5889 ** 2 + t5896 = t5890 ** 2 + t5891 = t5882 * t5890 + t5898 = t5891 ** 2 + t5905 = -370 - 2590 * t5881 + 27710 * t5888 - 43230 * t5890 - 23430 * t5892 + 73590 * t5894 - 24390 * t5896 - 7290 * t5898 + t5868 = t5882 * t5898 + t5870 = t5882 * t5896 + t5872 = t5882 * t5894 + t5874 = t5882 * t5892 + t5904 = 945 * t5868 + 21565 * t5870 - 15635 * t5872 - 64295 * t5874 + 2115 * t5882 - 8385 * t5887 - 16665 * t5889 + 80355 * t5891 + t5903 = 24570 * t5868 + 175890 * t5870 - 608670 * t5872 + 587994 * t5874 - 4194 * t5882 + 38646 * t5887 - 74250 * t5889 - 139986 * t5891 + t5902 = 10296 * t5881 - 108984 * t5888 + 409464 * t5890 - 620136 * t5892 + 277992 * t5894 + 158040 * t5896 - 126360 * t5898 - 312 + t5901 = 11286 * t5881 - 134838 * t5888 + 689238 * t5890 - 1716066 * t5892 + 2196018 * t5894 - 1392930 * t5896 + 347490 * t5898 - 198 + t5900 = 135135 * t5868 - 302445 * t5870 - 120285 * t5872 + 896247 * t5874 + 4653 * t5882 - 86031 * t5887 + 446985 * t5889 - 974259 * t5891 + t5886 = 4 * phi1 + t5885 = 8 * phi1 + t5884 = -9 * phi2 + t5883 = 9 * phi2 + tfunc[..., c] = (0.29e2 / 0.786432e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((-321300 * t5868 + 1725500 * t5870 - 3815140 * t5872 + 4438700 * t5874 - 2867900 * t5891 + 987700 * t5889 - 154700 * t5887 + 7140 * t5882) * np.exp((9*1j) * phi2) + (t5904 + t5905) * np.exp((-3*1j) * (t5886 - 3 * phi2)) + (t5900 - t5901) * np.exp((-1*1j) * (t5886 + t5884)) + (t5902 + t5903) * np.exp((-1*1j) * (t5885 + t5884)) + (t5900 + t5901) * np.exp((1j) * (t5886 + t5883)) + (-t5902 + t5903) * np.exp((1j) * (t5885 + t5883)) + (t5904 - t5905) * np.exp((3*1j) * (t5886 + 3 * phi2))) * ((1 + t5882) ** (-0.1e1 / 0.2e1)) * ((1 - t5882) ** (-0.1e1 / 0.2e1)) + + if Bindx == 181: + t5926 = np.cos(phi) + t5925 = t5926 ** 2 + t5932 = t5926 * t5925 + t5935 = t5932 ** 2 + t5941 = t5935 ** 2 + t5914 = t5926 * t5941 + t5933 = t5925 ** 2 + t5934 = t5926 * t5933 + t5939 = t5934 ** 2 + t5916 = t5926 * t5939 + t5937 = t5933 ** 2 + t5918 = t5926 * t5937 + t5936 = t5926 * t5935 + t5949 = -2700 * t5914 - 16400 * t5916 + 15620 * t5918 + 1100 * t5926 + 400 * t5932 - 19140 * t5934 + 21120 * t5936 + t5913 = t5936 ** 2 + t5948 = -155 + 315 * t5913 - 2635 * t5925 + 10065 * t5933 + 7425 * t5935 - 33825 * t5937 + 9295 * t5939 + 9515 * t5941 + t5947 = 8190 * t5913 + 10258 * t5925 - 54582 * t5933 + 73722 * t5935 + 67782 * t5937 - 191114 * t5939 + 86190 * t5941 - 446 + t5946 = 46800 * t5914 + 1600 * t5916 - 196592 * t5918 + 1072 * t5926 + 4032 * t5932 - 76560 * t5934 + 219648 * t5936 + t5945 = -128700 * t5914 + 462000 * t5916 - 620268 * t5918 - 132 * t5926 + 6864 * t5932 - 95700 * t5934 + 375936 * t5936 + t5944 = 45045 * t5913 - 17061 * t5925 + 127743 * t5933 - 361713 * t5935 + 438801 * t5937 - 175263 * t5939 - 57915 * t5941 + 363 + t5931 = 2 * phi1 + t5930 = 4 * phi1 + t5929 = 6 * phi1 + t5928 = -5 * phi2 + t5927 = 5 * phi2 + tfunc[..., c] = (0.29e2 / 0.1048576e7) * np.sqrt(0.13e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * ((476 - 107100 * t5913 + 559300 * t5941 - 1190476 * t5939 + 1311380 * t5937 - 778260 * t5935 + 230860 * t5933 - 26180 * t5925) * np.exp((10*1j) * phi2) + (t5944 + t5945) * np.exp((-2*1j) * (t5931 + t5928)) + (-t5946 + t5947) * np.exp((-2*1j) * (t5930 + t5928)) + (t5948 + t5949) * np.exp((-2*1j) * (t5929 + t5928)) + (t5944 - t5945) * np.exp((2*1j) * (t5931 + t5927)) + (t5946 + t5947) * np.exp((2*1j) * (t5930 + t5927)) + (t5948 - t5949) * np.exp((2*1j) * (t5929 + t5927))) + + if Bindx == 182: + t5971 = np.cos(phi) + t5970 = t5971 ** 2 + t5978 = t5970 ** 2 + t5977 = t5971 * t5970 + t5980 = t5977 ** 2 + t5982 = t5978 ** 2 + t5979 = t5971 * t5978 + t5984 = t5979 ** 2 + t5986 = t5980 ** 2 + t5981 = t5971 * t5980 + t5988 = t5981 ** 2 + t5995 = 38 + 1026 * t5970 - 1394 * t5978 - 7062 * t5980 + 10098 * t5982 + 2662 * t5984 - 4774 * t5986 - 594 * t5988 + t5957 = t5971 * t5988 + t5959 = t5971 * t5986 + t5961 = t5971 * t5984 + t5963 = t5971 * t5982 + t5994 = 63 * t5957 + 2351 * t5959 + 4131 * t5961 - 10637 * t5963 - 319 * t5971 - 1199 * t5977 + 6237 * t5979 - 627 * t5981 + t5993 = 232 - 3016 * t5970 + 4104 * t5978 + 28248 * t5980 - 82632 * t5982 + 67496 * t5984 - 4136 * t5986 - 10296 * t5988 + t5992 = 1638 * t5957 + 21606 * t5959 - 51074 * t5961 + 10142 * t5963 - 374 * t5971 + 8426 * t5977 - 36366 * t5979 + 46002 * t5981 + t5991 = 9009 * t5957 - 11583 * t5959 - 57651 * t5961 + 167805 * t5963 + 1551 * t5971 - 20097 * t5977 + 89331 * t5979 - 178365 * t5981 + t5990 = -2442 * t5970 + 4026 * t5978 + 35310 * t5980 - 134970 * t5982 + 188034 * t5984 - 118338 * t5986 + 28314 * t5988 + 66 + t5976 = 4 * phi1 + t5975 = 8 * phi1 + t5974 = 12 * phi1 + t5973 = -11 * phi2 + t5972 = 11 * phi2 + tfunc[..., c] = (0.29e2 / 0.524288e6*1j) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * ((1 + t5971) ** (-0.1e1 / 0.2e1)) * ((1 - t5971) ** (-0.1e1 / 0.2e1)) * ((-21420 * t5957 + 130900 * t5959 - 335580 * t5961 + 464100 * t5963 - 368900 * t5981 + 164220 * t5979 - 35700 * t5977 + 2380 * t5971) * np.exp((11*1j) * phi2) + (-t5990 + t5991) * np.exp((-1*1j) * (t5976 + t5973)) + (t5992 + t5993) * np.exp((-1*1j) * (t5975 + t5973)) + (t5990 + t5991) * np.exp((1j) * (t5976 + t5972)) + (t5992 - t5993) * np.exp((1j) * (t5975 + t5972)) + (t5994 + t5995) * np.exp((-1*1j) * (t5974 + t5973)) + (t5994 - t5995) * np.exp((1j) * (t5974 + t5972))) + + if Bindx == 183: + t6016 = np.cos(phi) + t6015 = t6016 ** 2 + t6020 = t6016 * t6015 + t6023 = t6020 ** 2 + t6029 = t6023 ** 2 + t6004 = t6016 * t6029 + t6021 = t6015 ** 2 + t6022 = t6016 * t6021 + t6027 = t6022 ** 2 + t6006 = t6016 * t6027 + t6025 = t6021 ** 2 + t6008 = t6016 * t6025 + t6024 = t6016 * t6023 + t6037 = -1944 * t6004 - 21840 * t6006 - 19448 * t6008 - 1320 * t6016 - 11024 * t6020 + 10296 * t6022 + 41184 * t6024 + t6003 = t6024 ** 2 + t6036 = 137 + 189 * t6003 + 5343 * t6015 + 9009 * t6021 - 36465 * t6023 - 14157 * t6025 + 31317 * t6027 + 8723 * t6029 + t6035 = 33696 * t6004 + 62400 * t6006 - 260832 * t6008 + 6240 * t6016 - 47424 * t6020 + 41184 * t6022 + 164736 * t6024 + t6034 = 92664 * t6004 - 350064 * t6006 + 463320 * t6008 - 10296 * t6016 + 61776 * t6020 - 51480 * t6022 - 205920 * t6024 + t6033 = 4914 * t6003 - 1482 * t6015 - 74646 * t6021 + 218790 * t6023 - 120978 * t6025 - 112398 * t6027 + 84318 * t6029 + 1482 + t6032 = 27027 * t6003 - 32175 * t6015 + 217503 * t6021 - 546975 * t6023 + 611325 * t6025 - 271557 * t6027 - 6435 * t6029 + 1287 + t6019 = 2 * phi1 + t6018 = -3 * phi2 + t6017 = 3 * phi2 + tfunc[..., c] = (0.29e2 / 0.1572864e7) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((-64260 * t6003 + 387940 * t6029 - 978180 * t6027 + 1320900 * t6025 - 1011500 * t6023 + 421260 * t6021 - 78540 * t6015 + 2380) * np.exp((12*1j) * phi2) + (t6032 - t6034) * np.exp((-4*1j) * (phi1 + t6018)) + (t6033 - t6035) * np.exp((-4*1j) * (t6019 + t6018)) + (t6032 + t6034) * np.exp((4*1j) * (phi1 + t6017)) + (t6033 + t6035) * np.exp((4*1j) * (t6019 + t6017)) + (t6036 + t6037) * np.exp((-12*1j) * (phi1 - phi2)) + (t6036 - t6037) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 184: + t6058 = np.cos(phi) + t6057 = t6058 ** 2 + t6064 = t6058 * t6057 + t6067 = t6064 ** 2 + t6068 = t6058 * t6067 + t6045 = t6068 ** 2 + t6078 = 7 * t6045 - 1287 * t6067 + 1716 * t6068 + t6077 = 182 * t6045 + 7722 * t6067 - 10296 * t6068 + t6076 = 1001 * t6045 - 19305 * t6067 + 25740 * t6068 + t6073 = t6067 ** 2 + t6065 = t6057 ** 2 + t6066 = t6058 * t6065 + t6071 = t6066 ** 2 + t6069 = t6065 ** 2 + t6063 = 4 * phi1 + t6062 = 8 * phi1 + t6061 = 12 * phi1 + t6060 = -13 * phi2 + t6059 = 13 * phi2 + t6050 = t6058 * t6069 + t6048 = t6058 * t6071 + t6046 = t6058 * t6073 + tfunc[..., c] = (-0.87e2 / 0.524288e6*1j) * np.sqrt(0.23e2) * np.sqrt(0.5e1) * np.sqrt(0.19e2) * np.sqrt(0.2e1) * np.sqrt((1 - t6058)) * ((1 + t6058) ** (-0.1e1 / 0.2e1)) * (2380 * (-t6045 - t6046 + 6 * t6073 + 6 * t6048 - 15 * t6071 - 15 * t6050 + 20 * t6069 + 20 * t6068 - 15 * t6067 - 15 * t6066 + 6 * t6065 + 6 * t6064 - t6057 - t6058) * np.exp((13*1j) * phi2) + (-1170 * t6046 + 2548 * t6073 - 676 * t6048 - 6006 * t6071 + 8866 * t6050 + 2002 * t6066 - 5148 * t6065 + 1820 * t6064 + 598 * t6057 - 546 * t6058 + 104 + t6077) * np.exp((-1*1j) * (t6062 + t6060)) + (-2717 * t6046 - 2860 * t6073 + 13442 * t6048 - 1001 * t6071 - 26455 * t6050 + 12870 * t6069 - 12155 * t6066 + 12584 * t6065 + 2002 * t6064 - 3575 * t6057 + 143 * t6058 + 286 + t6076) * np.exp((-1*1j) * (t6063 + t6060)) + (4719 * t6046 + 4576 * t6073 - 11726 * t6048 - 26169 * t6071 - 715 * t6050 + 38610 * t6069 - 26455 * t6066 - 1716 * t6065 + 8866 * t6064 + 3289 * t6057 - 429 * t6058 - 286 + t6076) * np.exp((1j) * (t6063 + t6059)) + (1534 * t6046 + 5252 * t6073 + 8476 * t6048 + 3146 * t6071 - 11726 * t6050 - 20592 * t6069 + 13442 * t6066 + 6292 * t6065 - 676 * t6064 - 1898 * t6057 - 754 * t6058 - 104 + t6077) * np.exp((1j) * (t6062 + t6059)) + (-71 * t6046 + 312 * t6073 - 754 * t6048 + 1001 * t6071 - 429 * t6050 - 858 * t6069 + 143 * t6066 + 572 * t6065 - 546 * t6064 + 247 * t6057 - 59 * t6058 + 6 + t6078) * np.exp((-1*1j) * (t6061 + t6060)) + (85 * t6046 + 468 * t6073 + 1534 * t6048 + 3289 * t6071 + 4719 * t6050 + 4290 * t6069 - 2717 * t6066 - 2288 * t6065 - 1170 * t6064 - 377 * t6057 - 71 * t6058 - 6 + t6078) * np.exp((1j) * (t6061 + t6059))) + + if Bindx == 185: + t6098 = np.cos(phi) + t6097 = t6098 ** 2 + t6105 = t6097 ** 2 + t6106 = t6098 * t6105 + t6110 = t6106 ** 2 + t6089 = t6098 * t6110 + t6104 = t6098 * t6097 + t6123 = -4 * t6089 + 4 * t6104 + t6107 = t6104 ** 2 + t6112 = t6107 ** 2 + t6113 = t6098 * t6112 + t6122 = t6098 - t6113 + t6086 = t6098 * t6113 + t6121 = 1 - t6086 + t6108 = t6105 ** 2 + t6091 = t6098 * t6108 + t6120 = -20 * t6091 + 20 * t6106 + 4 * t6122 - 4 * t6123 + t6119 = -208 * t6089 - 572 * t6091 + 12 * t6098 + 208 * t6104 + 572 * t6106 - 12 * t6113 + t6118 = 2288 * t6091 - 2288 * t6106 + 208 * t6122 + 208 * t6123 + t6117 = t6097 - 19 * t6105 + 45 * t6107 - 45 * t6108 + 19 * t6110 - t6112 + t6121 + t6116 = 65 * t6097 + 429 * t6105 + 429 * t6107 - 429 * t6108 - 429 * t6110 - 65 * t6112 + t6121 + t6115 = 26 - 26 * t6086 + 650 * t6097 - 286 * t6105 - 2574 * t6107 + 2574 * t6108 + 286 * t6110 - 650 * t6112 + t6103 = 2 * phi1 + t6102 = 4 * phi1 + t6101 = 6 * phi1 + t6100 = -7 * phi2 + t6099 = 7 * phi2 + tfunc[..., c] = -(0.87e2 / 0.1048576e7) * np.sqrt(0.5e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((340 * t6086 - 2380 * t6112 + 7140 * t6110 - 11900 * t6108 + 11900 * t6107 - 7140 * t6105 + 2380 * t6097 - 340) * np.exp((14*1j) * phi2) + (t6116 - t6119) * np.exp((-2*1j) * (t6101 + t6100)) + (t6116 + t6119) * np.exp((2*1j) * (t6101 + t6099)) + (t6115 - t6118) * np.exp((-2*1j) * (t6102 + t6100)) + (t6115 + t6118) * np.exp((2*1j) * (t6102 + t6099)) + 143 * (t6117 - t6120) * np.exp((-2*1j) * (t6103 + t6100)) + 143 * (t6117 + t6120) * np.exp((2*1j) * (t6103 + t6099))) + + if Bindx == 186: + t6142 = np.cos(phi) + t6141 = t6142 ** 2 + t6148 = t6141 ** 2 + t6149 = t6142 * t6148 + t6153 = t6149 ** 2 + t6133 = t6142 * t6153 + t6147 = t6142 * t6141 + t6166 = -4 * t6133 + 4 * t6147 + t6150 = t6147 ** 2 + t6155 = t6150 ** 2 + t6156 = t6142 * t6155 + t6165 = t6156 - t6142 + t6130 = t6142 * t6156 + t6164 = 1 - t6130 + t6151 = t6148 ** 2 + t6135 = t6142 * t6151 + t6163 = 20 * t6135 - 20 * t6149 + 4 * t6165 + 4 * t6166 + t6162 = 208 * t6133 + 572 * t6135 - 12 * t6142 - 208 * t6147 - 572 * t6149 + 12 * t6156 + t6161 = 3168 * t6135 - 3168 * t6149 - 288 * t6165 + 288 * t6166 + t6160 = t6141 - 19 * t6148 + 45 * t6150 - 45 * t6151 + 19 * t6153 - t6155 + t6164 + t6159 = 65 * t6141 + 429 * t6148 + 429 * t6150 - 429 * t6151 - 429 * t6153 - 65 * t6155 + t6164 + t6158 = 36 - 36 * t6130 + 900 * t6141 - 396 * t6148 - 3564 * t6150 + 3564 * t6151 + 396 * t6153 - 900 * t6155 + t6146 = 4 * phi1 + t6145 = 8 * phi1 + t6144 = -15 * phi2 + t6143 = 15 * phi2 + tfunc[..., c] = (0.155e3 / 0.524288e6*1j) * np.sqrt((1 - t6142)) * np.sqrt((1 + t6142)) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * np.sqrt(0.29e2) * ((-t6159 + t6162) * np.exp((-3*1j) * (t6146 + 5 * phi2)) + (t6159 + t6162) * np.exp((3*1j) * (t6146 - 5 * phi2)) + (t6158 + t6161) * np.exp((-1*1j) * (t6145 + t6143)) + (-t6158 + t6161) * np.exp((1j) * (t6145 + t6144)) + 69 * (-t6160 + t6163) * np.exp((-1*1j) * (t6146 + t6143)) + 69 * (t6160 + t6163) * np.exp((1j) * (t6146 + t6144))) + + if Bindx == 187: + t6187 = np.cos(phi) + t6186 = t6187 ** 2 + t6193 = t6187 * t6186 + t6196 = t6193 ** 2 + t6197 = t6187 * t6196 + t6204 = t6197 ** 2 + t6212 = -4 - 56 * t6204 + t6194 = t6186 ** 2 + t6198 = t6194 ** 2 + t6195 = t6187 * t6194 + t6200 = t6195 ** 2 + t6202 = t6196 ** 2 + t6211 = 56 * t6186 - 164 * t6194 + 120 * t6196 + 180 * t6198 - 376 * t6200 + 244 * t6202 + t6212 + t6173 = t6187 * t6204 + t6175 = t6187 * t6202 + t6177 = t6187 * t6200 + t6179 = t6187 * t6198 + t6210 = t6175 - 15 * t6173 + 221 * t6177 - 595 * t6179 - t6187 + 79 * t6193 - 365 * t6195 + 675 * t6197 + t6209 = -200 * t6186 - 676 * t6194 + 1144 * t6196 + 1716 * t6198 - 1144 * t6200 - 780 * t6202 + t6212 + t6208 = -5 * t6173 - 277 * t6175 - 1313 * t6177 + 143 * t6179 - 43 * t6187 - 507 * t6193 - 143 * t6195 + 2145 * t6197 + t6207 = 96 + 960 * t6186 - 6816 * t6194 + 6336 * t6196 + 9504 * t6198 - 14784 * t6200 + 3360 * t6202 + 1344 * t6204 + t6206 = -180 * t6173 - 3732 * t6175 + 5052 * t6177 + 9372 * t6179 - 588 * t6187 + 1428 * t6193 + 6468 * t6195 - 17820 * t6197 + t6192 = 2 * phi1 + t6191 = 4 * phi1 + t6190 = 6 * phi1 + t6189 = -7 * phi2 + t6188 = 7 * phi2 + tfunc[..., c] = (0.31e2 / 0.262144e6) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.29e2) * ((-t6206 + t6207) * np.exp((-2*1j) * (t6191 + t6188)) + (t6208 + t6209) * np.exp((-2*1j) * (t6190 + t6188)) + (t6206 + t6207) * np.exp((2*1j) * (t6191 + t6189)) + (-t6208 + t6209) * np.exp((2*1j) * (t6190 + t6189)) + 23 * (t6210 + t6211) * np.exp((-2*1j) * (t6192 + t6188)) + 23 * (-t6210 + t6211) * np.exp((2*1j) * (t6192 + t6189))) + + if Bindx == 188: + t6233 = np.cos(phi) + t6232 = t6233 ** 2 + t6239 = t6233 * t6232 + t6242 = t6239 ** 2 + t6243 = t6233 * t6242 + t6250 = t6243 ** 2 + t6219 = t6233 * t6250 + t6254 = 91 - 145 * t6219 + t6253 = 391 - 10005 * t6219 + t6252 = 1356 - 5220 * t6219 + t6248 = t6242 ** 2 + t6240 = t6232 ** 2 + t6241 = t6233 * t6240 + t6246 = t6241 ** 2 + t6244 = t6240 ** 2 + t6238 = 4 * phi1 + t6237 = 8 * phi1 + t6236 = 12 * phi1 + t6235 = -13 * phi2 + t6234 = 13 * phi2 + t6225 = t6233 * t6244 + t6223 = t6233 * t6246 + t6221 = t6233 * t6248 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.7e1) * np.sqrt(0.13e2) * np.sqrt((1 + t6233)) * ((-24679 * t6250 + 35627 * t6221 + 128225 * t6248 - 23897 * t6223 - 271331 * t6246 - 64745 * t6225 + 297045 * t6244 + 135585 * t6243 - 175605 * t6242 - 103615 * t6241 + 52739 * t6240 + 35213 * t6239 - 6785 * t6232 - 4163 * t6233 + t6253) * np.exp((-1*1j) * (t6238 + t6234)) + (30972 * t6250 + 56340 * t6221 - 17652 * t6248 - 184044 * t6223 - 170676 * t6246 + 128964 * t6225 + 302940 * t6244 + 79596 * t6243 - 171468 * t6242 - 126852 * t6241 + 17892 * t6240 + 44700 * t6239 + 9348 * t6232 - 3924 * t6233 - t6252) * np.exp((-1*1j) * (t6237 + t6234)) + (-44689 * t6250 + 33741 * t6221 + 130111 * t6248 - 234439 * t6223 - 60789 * t6246 + 396865 * t6225 - 164565 * t6244 - 268065 * t6243 + 228045 * t6242 + 51175 * t6241 - 102051 * t6240 + 14099 * t6239 + 14329 * t6232 - 3381 * t6233 - t6253) * np.exp((1j) * (t6238 + t6235)) + (41412 * t6250 - 128724 * t6221 + 167412 * t6248 + 34284 * t6223 - 389004 * t6246 + 430716 * t6225 + 1188 * t6244 - 383724 * t6243 + 291852 * t6242 + 6468 * t6241 - 115428 * t6240 + 52836 * t6239 + 1212 * t6232 - 6636 * t6233 + t6252) * np.exp((1j) * (t6237 + t6235)) + (-1363 * t6250 - 5369 * t6221 - 10803 * t6248 - 9061 * t6223 + 6721 * t6246 + 24739 * t6225 + 22737 * t6244 + 429 * t6243 - 17017 * t6242 - 14443 * t6241 - 2873 * t6240 + 3081 * t6239 + 2507 * t6232 + 769 * t6233 + t6254) * np.exp((-1*1j) * (t6236 + t6234)) + (-1653 * t6250 + 8385 * t6221 - 24557 * t6248 + 44421 * t6223 - 46761 * t6246 + 15301 * t6225 + 32175 * t6244 - 55341 * t6243 + 38753 * t6242 - 7293 * t6241 - 10023 * t6240 + 9815 * t6239 - 4227 * t6232 + 951 * t6233 - t6254) * np.exp((1j) * (t6236 + t6235))) * ((1 - t6233) ** (-0.1e1 / 0.2e1)) + + if Bindx == 189: + t6275 = np.cos(phi) + t6274 = t6275 ** 2 + t6280 = t6274 ** 2 + t6279 = t6275 * t6274 + t6282 = t6279 ** 2 + t6284 = t6280 ** 2 + t6281 = t6275 * t6280 + t6286 = t6281 ** 2 + t6288 = t6282 ** 2 + t6283 = t6275 * t6282 + t6290 = t6283 ** 2 + t6297 = -11712 * t6274 + 30264 * t6280 + 80080 * t6282 - 175032 * t6284 - 13728 * t6286 + 84968 * t6288 + 9744 * t6290 - 488 + t6261 = t6275 * t6290 + t6263 = t6275 * t6288 + t6265 = t6275 * t6286 + t6267 = t6275 * t6284 + t6296 = 1015 * t6261 + 39669 * t6263 + 86775 * t6265 - 152867 * t6267 - 3945 * t6275 - 9451 * t6279 + 90519 * t6281 - 47619 * t6283 + t6295 = -22080 * t6274 + 60168 * t6280 + 193200 * t6282 - 952200 * t6284 + 1415328 * t6286 - 919080 * t6288 + 224112 * t6290 + 552 + t6294 = -36540 * t6261 - 510804 * t6263 + 1082340 * t6265 - 2772 * t6267 + 3780 * t6275 - 147924 * t6279 + 767844 * t6281 - 1155924 * t6283 + t6293 = 70035 * t6261 - 75831 * t6263 - 499629 * t6265 + 1366545 * t6267 + 10419 * t6275 - 144279 * t6279 + 677235 * t6281 - 1404495 * t6283 + t6292 = -64512 * t6274 + 149184 * t6280 + 443520 * t6282 - 1729728 * t6284 + 1596672 * t6286 - 165312 * t6288 - 233856 * t6290 + 4032 + t6278 = 2 * phi1 + t6277 = -3 * phi2 + t6276 = 3 * phi2 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * ((t6293 + t6295) * np.exp((-4*1j) * (phi1 + t6276)) + (t6292 + t6294) * np.exp((-4*1j) * (t6278 + t6276)) + (-t6293 + t6295) * np.exp((4*1j) * (phi1 + t6277)) + (t6292 - t6294) * np.exp((4*1j) * (t6278 + t6277)) + (t6296 + t6297) * np.exp((-12*1j) * (phi1 + phi2)) + (-t6296 + t6297) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 190: + t6319 = np.cos(phi) + t6318 = t6319 ** 2 + t6326 = t6318 ** 2 + t6330 = t6326 ** 2 + t6304 = t6330 ** 2 + t6325 = t6319 * t6318 + t6328 = t6325 ** 2 + t6327 = t6319 * t6326 + t6332 = t6327 ** 2 + t6334 = t6328 ** 2 + t6329 = t6319 * t6328 + t6336 = t6329 ** 2 + t6344 = 3045 * t6304 + 13068 * t6318 - 106392 * t6326 + 101244 * t6328 + 285714 * t6330 - 444444 * t6332 + 51168 * t6334 + 95508 * t6336 + 1089 + t6305 = t6319 * t6336 + t6307 = t6319 * t6334 + t6309 = t6319 * t6332 + t6311 = t6319 * t6330 + t6343 = 26796 * t6305 + 158532 * t6307 - 262548 * t6309 - 169884 * t6311 + 7212 * t6319 - 19068 * t6325 - 120276 * t6327 + 379236 * t6329 + t6342 = 616308 * t6305 - 2765796 * t6307 + 4907188 * t6309 - 4295940 * t6311 + 1012 * t6319 + 3036 * t6325 - 322828 * t6327 + 1857020 * t6329 + t6341 = -643104 * t6305 + 376992 * t6307 + 3161312 * t6309 - 5615456 * t6311 + 16352 * t6319 - 35168 * t6325 - 712992 * t6327 + 3452064 * t6329 + t6340 = -210105 * t6304 - 72956 * t6318 + 643448 * t6326 - 2285740 * t6328 + 3908390 * t6330 - 3170228 * t6332 + 758816 * t6334 + 426972 * t6336 + 1403 + t6339 = -109620 * t6304 + 112112 * t6318 - 805728 * t6326 + 1955184 * t6328 - 899976 * t6330 - 2748592 * t6332 + 3650752 * t6334 - 1150128 * t6336 - 4004 + t6324 = 4 * phi1 + t6323 = 8 * phi1 + t6322 = 12 * phi1 + t6321 = -11 * phi2 + t6320 = 11 * phi2 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((1 + t6319) ** (-0.1e1 / 0.2e1)) * ((1 - t6319) ** (-0.1e1 / 0.2e1)) * ((-t6340 + t6342) * np.exp((-1*1j) * (t6324 + t6320)) + (t6339 + t6341) * np.exp((-1*1j) * (t6323 + t6320)) + (t6340 + t6342) * np.exp((1j) * (t6324 + t6321)) + (-t6339 + t6341) * np.exp((1j) * (t6323 + t6321)) + (t6343 + t6344) * np.exp((-1*1j) * (t6322 + t6320)) + (t6343 - t6344) * np.exp((1j) * (t6322 + t6321))) + + if Bindx == 191: + t6365 = np.cos(phi) + t6364 = t6365 ** 2 + t6371 = t6365 * t6364 + t6374 = t6371 ** 2 + t6375 = t6365 * t6374 + t6382 = t6375 ** 2 + t6351 = t6365 * t6382 + t6380 = t6374 ** 2 + t6353 = t6365 * t6380 + t6372 = t6364 ** 2 + t6373 = t6365 * t6372 + t6378 = t6373 ** 2 + t6355 = t6365 * t6378 + t6376 = t6372 ** 2 + t6357 = t6365 * t6376 + t6389 = -7917 * t6351 - 200109 * t6353 + 11271 * t6355 + 646503 * t6357 - 10179 * t6365 + 70941 * t6371 + 5577 * t6373 - 516087 * t6375 + t6388 = 4056 * t6364 - 158964 * t6372 + 408408 * t6374 - 46332 * t6376 - 545688 * t6378 + 273156 * t6380 + 63336 * t6382 + 2028 + t6387 = 28520 * t6364 - 394220 * t6372 + 2304232 * t6374 - 6282404 * t6376 + 8545880 * t6378 - 5658276 * t6380 + 1456728 * t6382 - 460 + t6386 = 59584 * t6364 - 895776 * t6372 + 4154304 * t6374 - 7488096 * t6376 + 4459840 * t6378 + 1231776 * t6380 - 1520064 * t6382 - 1568 + t6385 = -285012 * t6351 - 2355444 * t6353 + 6984796 * t6355 - 5422340 * t6357 + 39508 * t6365 - 445452 * t6371 + 1285284 * t6373 + 198660 * t6375 + t6384 = -546273 * t6351 + 1061151 * t6353 + 1026467 * t6355 - 4261693 * t6357 - 16399 * t6365 + 327313 * t6371 - 1815827 * t6373 + 4225261 * t6375 + t6370 = 2 * phi1 + t6369 = 4 * phi1 + t6368 = 6 * phi1 + t6367 = -5 * phi2 + t6366 = 5 * phi2 + tfunc[..., c] = -(0.155e3 / 0.262144e6) * ((-t6384 + t6387) * np.exp((-2*1j) * (t6370 + t6366)) + (t6385 + t6386) * np.exp((-2*1j) * (t6369 + t6366)) + (t6388 - t6389) * np.exp((-2*1j) * (t6368 + t6366)) + (t6384 + t6387) * np.exp((2*1j) * (t6370 + t6367)) + (-t6385 + t6386) * np.exp((2*1j) * (t6369 + t6367)) + (t6388 + t6389) * np.exp((2*1j) * (t6368 + t6367))) * np.sqrt(0.3e1) + + if Bindx == 192: + t6411 = np.cos(phi) + t6410 = t6411 ** 2 + t6417 = t6410 ** 2 + t6421 = t6417 ** 2 + t6396 = t6421 ** 2 + t6416 = t6411 * t6410 + t6419 = t6416 ** 2 + t6418 = t6411 * t6417 + t6423 = t6418 ** 2 + t6425 = t6419 ** 2 + t6420 = t6411 * t6419 + t6427 = t6420 ** 2 + t6435 = -65975 * t6396 - 92040 * t6410 - 337740 * t6417 + 2865720 * t6419 - 5049330 * t6421 + 1870440 * t6423 + 2038660 * t6425 - 1241240 * t6427 + 11505 + t6397 = t6411 * t6427 + t6399 = t6411 * t6425 + t6401 = t6411 * t6423 + t6403 = t6411 * t6421 + t6434 = 475020 * t6397 + 907140 * t6399 - 4685460 * t6401 + 4126980 * t6403 - 36660 * t6411 + 511940 * t6416 - 1536340 * t6418 + 237380 * t6420 + t6433 = 10925460 * t6397 - 46877220 * t6399 + 80974260 * t6401 - 71862948 * t6403 - 42412 * t6411 + 1076124 * t6416 - 8869260 * t6418 + 34675996 * t6420 + t6432 = -11400480 * t6397 + 22407840 * t6399 + 6179040 * t6401 - 45335136 * t6403 - 150304 * t6411 + 2620576 * t6416 - 15916320 * t6418 + 41594784 * t6420 + t6431 = -4552275 * t6396 - 349416 * t6410 + 3919108 * t6417 - 15865768 * t6419 + 28351686 * t6421 - 19359928 * t6423 - 4957420 * t6425 + 12809160 * t6427 + 4853 + t6430 = -2375100 * t6396 + 431424 * t6410 - 1683696 * t6417 - 5115264 * t6419 + 37361016 * t6421 - 71845312 * t6423 + 55815760 * t6425 - 12579840 * t6427 - 8988 + t6415 = 4 * phi1 + t6414 = 8 * phi1 + t6413 = -9 * phi2 + t6412 = 9 * phi2 + tfunc[..., c] = (-0.93e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * ((1 + t6411) ** (-0.1e1 / 0.2e1)) * ((1 - t6411) ** (-0.1e1 / 0.2e1)) * ((t6434 - t6435) * np.exp((-3*1j) * (t6415 + 3 * phi2)) + (-t6431 + t6433) * np.exp((-1*1j) * (t6415 + t6412)) + (t6430 + t6432) * np.exp((-1*1j) * (t6414 + t6412)) + (t6431 + t6433) * np.exp((1j) * (t6415 + t6413)) + (-t6430 + t6432) * np.exp((1j) * (t6414 + t6413)) + (t6434 + t6435) * np.exp((3*1j) * (t6415 - 3 * phi2))) + + if Bindx == 193: + t6456 = np.cos(phi) + t6455 = t6456 ** 2 + t6460 = t6456 * t6455 + t6463 = t6460 ** 2 + t6464 = t6456 * t6463 + t6471 = t6464 ** 2 + t6442 = t6456 * t6471 + t6469 = t6463 ** 2 + t6444 = t6456 * t6469 + t6461 = t6455 ** 2 + t6462 = t6456 * t6461 + t6467 = t6462 ** 2 + t6446 = t6456 * t6467 + t6465 = t6461 ** 2 + t6448 = t6456 * t6465 + t6478 = -28275 * t6442 - 395265 * t6444 + 837525 * t6446 - 2145 * t6448 + 2925 * t6456 - 114465 * t6460 + 594165 * t6462 - 894465 * t6464 + t6477 = 49920 * t6455 - 115440 * t6461 - 343200 * t6463 + 1338480 * t6465 - 1235520 * t6467 + 127920 * t6469 + 180960 * t6471 - 3120 + t6476 = 88320 * t6455 - 1194896 * t6461 + 6331808 * t6463 - 16530928 * t6465 + 22573120 * t6467 - 15428400 * t6469 + 4162080 * t6471 - 1104 + t6475 = 222208 * t6455 - 1954176 * t6461 + 6209280 * t6463 - 6880896 * t6465 - 2168320 * t6467 + 8910720 * t6469 - 4343040 * t6471 - 3968 + t6474 = -1950975 * t6442 + 5162235 * t6444 - 3158935 * t6446 - 2535589 * t6448 - 10143 * t6456 + 255323 * t6460 - 1650871 * t6462 + 3888955 * t6464 + t6473 = -1017900 * t6442 - 3652740 * t6444 + 16225300 * t6446 - 19711076 * t6448 - 4780 * t6456 + 172284 * t6460 - 2288748 * t6462 + 10269468 * t6464 + t6459 = 3 * phi1 + t6458 = -2 * phi2 + t6457 = 2 * phi2 + tfunc[..., c] = -(0.31e2 / 0.131072e6) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * ((t6473 + t6475) * np.exp((-8*1j) * (phi1 + phi2)) + (-t6474 + t6476) * np.exp((-4*1j) * (phi1 + t6457)) + (t6477 - t6478) * np.exp((-4*1j) * (t6459 + t6457)) + (t6474 + t6476) * np.exp((4*1j) * (phi1 + t6458)) + (t6477 + t6478) * np.exp((4*1j) * (t6459 + t6458)) + (-t6473 + t6475) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 194: + t6500 = np.cos(phi) + t6499 = t6500 ** 2 + t6506 = t6500 * t6499 + t6509 = t6506 ** 2 + t6510 = t6500 * t6509 + t6517 = t6510 ** 2 + t6486 = t6500 * t6517 + t6515 = t6509 ** 2 + t6488 = t6500 * t6515 + t6507 = t6499 ** 2 + t6508 = t6500 * t6507 + t6513 = t6508 ** 2 + t6490 = t6500 * t6513 + t6511 = t6507 ** 2 + t6492 = t6500 * t6511 + t6525 = -158340 * t6486 + 205140 * t6488 + 596700 * t6490 - 1467180 * t6492 + 3900 * t6500 + 17940 * t6506 - 339300 * t6508 + 1141140 * t6510 + t6485 = t6511 ** 2 + t6524 = -28275 * t6485 + 42120 * t6499 - 244140 * t6507 + 429000 * t6509 + 115830 * t6511 - 1046760 * t6513 + 982020 * t6515 - 248040 * t6517 - 1755 + t6523 = 3800160 * t6486 - 11662560 * t6488 + 11989600 * t6490 - 3012064 * t6492 + 19040 * t6500 - 376544 * t6506 + 1843296 * t6508 - 2600928 * t6510 + t6522 = -3641820 * t6486 + 15051660 * t6488 - 25166140 * t6490 + 21755148 * t6492 + 9828 * t6500 - 290612 * t6506 + 2562308 * t6508 - 10280372 * t6510 + t6521 = -1950975 * t6485 - 21912 * t6499 + 273188 * t6507 - 1015512 * t6509 + 653518 * t6511 + 3562424 * t6513 - 8210540 * t6515 + 6709560 * t6517 + 249 + t6520 = -1017900 * t6485 - 101632 * t6499 + 1259664 * t6507 - 6578880 * t6509 + 16803864 * t6511 - 21574784 * t6513 + 12368720 * t6515 - 1160640 * t6517 + 1588 + t6505 = 4 * phi1 + t6504 = 8 * phi1 + t6503 = -7 * phi2 + t6502 = 7 * phi2 + t6501 = 12 * phi1 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * ((t6521 + t6522) * np.exp((-1*1j) * (t6505 + t6502)) + (-t6520 + t6523) * np.exp((-1*1j) * (t6504 + t6502)) + (-t6521 + t6522) * np.exp((1j) * (t6505 + t6503)) + (t6520 + t6523) * np.exp((1j) * (t6504 + t6503)) + (t6524 + t6525) * np.exp((-1*1j) * (t6501 + t6502)) + (-t6524 + t6525) * np.exp((1j) * (t6501 + t6503))) * ((1 + t6500) ** (-0.1e1 / 0.2e1)) * ((1 - t6500) ** (-0.1e1 / 0.2e1)) + + if Bindx == 195: + t6546 = np.cos(phi) + t6545 = t6546 ** 2 + t6552 = t6545 ** 2 + t6551 = t6546 * t6545 + t6554 = t6551 ** 2 + t6556 = t6552 ** 2 + t6553 = t6546 * t6552 + t6558 = t6553 ** 2 + t6560 = t6554 ** 2 + t6555 = t6546 * t6554 + t6562 = t6555 ** 2 + t6569 = -7800 * t6545 + 64740 * t6552 - 190840 * t6554 + 212940 * t6556 - 29640 * t6558 - 94900 * t6560 + 45240 * t6562 + 260 + t6532 = t6546 * t6562 + t6534 = t6546 * t6560 + t6536 = t6546 * t6558 + t6538 = t6546 * t6556 + t6568 = 9425 * t6532 + 48945 * t6534 - 234195 * t6536 + 306605 * t6538 - 2145 * t6546 + 14495 * t6551 + 195 * t6553 - 143325 * t6555 + t6567 = 6720 * t6545 - 31584 * t6552 - 245952 * t6554 + 1689312 * t6556 - 3608640 * t6558 + 3276000 * t6560 - 1085760 * t6562 - 96 + t6566 = -650325 * t6532 + 2076555 * t6534 - 2498145 * t6536 + 1383519 * t6538 - 91 * t6546 + 1509 * t6551 + 23089 * t6553 - 336111 * t6555 + t6565 = 15416 * t6545 - 234884 * t6552 + 1344056 * t6554 - 3687084 * t6556 + 5235720 * t6558 - 3713580 * t6560 + 1040520 * t6562 - 164 + t6564 = -339300 * t6532 + 16380 * t6534 + 2222220 * t6536 - 3803604 * t6538 - 5532 * t6546 + 128772 * t6551 - 883596 * t6553 + 2664660 * t6555 + t6550 = 2 * phi1 + t6549 = 4 * phi1 + t6548 = -3 * phi2 + t6547 = 3 * phi2 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * ((t6568 + t6569) * np.exp((-6*1j) * (t6550 + phi2)) + (t6565 - t6566) * np.exp((-2*1j) * (t6550 + t6547)) + (t6564 + t6567) * np.exp((-2*1j) * (t6549 + t6547)) + (t6565 + t6566) * np.exp((2*1j) * (t6550 + t6548)) + (-t6564 + t6567) * np.exp((2*1j) * (t6549 + t6548)) + (-t6568 + t6569) * np.exp((6*1j) * (t6550 - phi2))) + + if Bindx == 196: + t6591 = np.cos(phi) + t6590 = t6591 ** 2 + t6597 = t6591 * t6590 + t6600 = t6597 ** 2 + t6601 = t6591 * t6600 + t6608 = t6601 ** 2 + t6577 = t6591 * t6608 + t6606 = t6600 ** 2 + t6579 = t6591 * t6606 + t6598 = t6590 ** 2 + t6599 = t6591 * t6598 + t6604 = t6599 ** 2 + t6581 = t6591 * t6604 + t6602 = t6598 ** 2 + t6583 = t6591 * t6602 + t6616 = -158340 * t6577 + 585780 * t6579 - 694980 * t6581 + 80340 * t6583 - 8580 * t6591 + 99060 * t6597 - 358020 * t6599 + 454740 * t6601 + t6576 = t6602 ** 2 + t6615 = 39585 * t6576 + 7020 * t6590 - 129480 * t6598 + 659100 * t6600 - 1435590 * t6602 + 1506180 * t6604 - 695760 * t6606 + 49140 * t6608 - 195 + t6614 = 3800160 * t6577 - 14807520 * t6579 + 22965600 * t6581 - 18033120 * t6583 - 6048 * t6591 + 170016 * t6597 - 1645728 * t6599 + 7556640 * t6601 + t6613 = -3641820 * t6577 + 14621100 * t6579 - 23668380 * t6581 + 19688460 * t6583 + 6436 * t6591 - 213780 * t6597 + 2064164 * t6599 - 8856180 * t6601 + t6612 = -2731365 * t6576 + 11300 * t6590 - 208280 * t6598 + 1596596 * t6600 - 6313890 * t6602 + 13835052 * t6604 - 16863600 * t6606 + 10674300 * t6608 - 113 + t6611 = -1425060 * t6576 - 95760 * t6590 + 1165920 * t6598 - 5280912 * t6600 + 11071704 * t6602 - 10525872 * t6604 + 2271360 * t6606 + 2817360 * t6608 + 1260 + t6596 = 4 * phi1 + t6595 = 8 * phi1 + t6594 = -5 * phi2 + t6593 = 5 * phi2 + t6592 = 12 * phi1 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * ((t6612 + t6613) * np.exp((-1*1j) * (t6596 + t6593)) + (-t6611 + t6614) * np.exp((-1*1j) * (t6595 + t6593)) + (-t6612 + t6613) * np.exp((1j) * (t6596 + t6594)) + (t6611 + t6614) * np.exp((1j) * (t6595 + t6594)) + (-t6615 + t6616) * np.exp((-1*1j) * (t6592 + t6593)) + (t6615 + t6616) * np.exp((1j) * (t6592 + t6594))) * ((1 + t6591) ** (-0.1e1 / 0.2e1)) * ((1 - t6591) ** (-0.1e1 / 0.2e1)) + + if Bindx == 197: + t6637 = np.cos(phi) + t6636 = t6637 ** 2 + t6641 = t6636 ** 2 + t6640 = t6637 * t6636 + t6643 = t6640 ** 2 + t6645 = t6641 ** 2 + t6642 = t6637 * t6641 + t6647 = t6642 ** 2 + t6649 = t6643 ** 2 + t6644 = t6637 * t6643 + t6651 = t6644 ** 2 + t6658 = 62400 * t6636 - 170040 * t6641 - 546000 * t6643 + 2691000 * t6645 - 3999840 * t6647 + 2597400 * t6649 - 633360 * t6651 - 1560 + t6623 = t6637 * t6651 + t6625 = t6637 * t6649 + t6627 = t6637 * t6647 + t6629 = t6637 * t6645 + t6657 = -197925 * t6623 + 214305 * t6625 + 1411995 * t6627 - 3861975 * t6629 - 29445 * t6637 + 407745 * t6640 - 1913925 * t6642 + 3969225 * t6644 + t6656 = -155584 * t6636 + 2590456 * t6641 - 15955632 * t6643 + 46384008 * t6645 - 68856480 * t6647 + 50554920 * t6649 - 14567280 * t6651 + 1496 + t6655 = 322560 * t6636 - 4363968 * t6641 + 23124864 * t6643 - 60373824 * t6645 + 82440960 * t6647 - 56347200 * t6649 + 15200640 * t6651 - 4032 + t6654 = -7125300 * t6623 + 18853380 * t6625 - 11536980 * t6627 - 9260412 * t6629 - 37044 * t6637 + 932484 * t6640 - 6029268 * t6642 + 14203140 * t6644 + t6653 = -13656825 * t6623 + 48944805 * t6625 - 70490745 * t6627 + 52191117 * t6629 + 12199 * t6637 - 426811 * t6640 + 4448807 * t6642 - 21026643 * t6644 + t6639 = 2 * phi1 + t6638 = 3 * phi1 + tfunc[..., c] = (0.31e2 / 0.262144e6) * np.sqrt(0.2e1) * np.sqrt(0.23e2) * ((t6653 + t6656) * np.exp((-4*1j) * (phi1 + phi2)) + (-t6654 + t6655) * np.exp((-4*1j) * (t6639 + phi2)) + (t6657 + t6658) * np.exp((-4*1j) * (t6638 + phi2)) + (-t6653 + t6656) * np.exp((4*1j) * (phi1 - phi2)) + (t6654 + t6655) * np.exp((4*1j) * (t6639 - phi2)) + (-t6657 + t6658) * np.exp((4*1j) * (t6638 - phi2))) + + if Bindx == 198: + t6680 = np.cos(phi) + t6679 = t6680 ** 2 + t6685 = t6680 * t6679 + t6688 = t6685 ** 2 + t6689 = t6680 * t6688 + t6696 = t6689 ** 2 + t6666 = t6680 * t6696 + t6694 = t6688 ** 2 + t6668 = t6680 * t6694 + t6686 = t6679 ** 2 + t6687 = t6680 * t6686 + t6692 = t6687 ** 2 + t6670 = t6680 * t6692 + t6690 = t6686 ** 2 + t6672 = t6680 * t6690 + t6704 = 158340 * t6666 - 839540 * t6668 + 1826500 * t6670 - 2081300 * t6672 - 3900 * t6680 + 67340 * t6685 - 436540 * t6687 + 1309100 * t6689 + t6665 = t6690 ** 2 + t6703 = 65975 * t6665 + 37180 * t6679 - 253760 * t6686 + 674700 * t6688 - 809250 * t6690 + 311220 * t6692 + 224120 * t6694 - 249340 * t6696 - 845 + t6702 = -2375100 * t6665 - 7056 * t6679 + 30912 * t6686 + 549360 * t6688 - 4270392 * t6690 + 11685072 * t6692 - 15244320 * t6694 + 9631440 * t6696 + 84 + t6701 = 3641820 * t6666 - 14334060 * t6668 + 22608540 * t6670 - 18182604 * t6672 - 4708 * t6680 + 168212 * t6685 - 1730916 * t6687 + 7833716 * t6689 + t6700 = -3800160 * t6666 + 16904160 * t6668 - 30492000 * t6670 + 28473312 * t6672 + 16800 * t6680 - 471072 * t6685 + 3892896 * t6687 - 14523936 * t6689 + t6699 = -4552275 * t6665 + 34452 * t6679 - 624448 * t6686 + 4404356 * t6688 - 15553494 * t6690 + 30338748 * t6692 - 33260760 * t6694 + 19213740 * t6696 - 319 + t6684 = 4 * phi1 + t6683 = 8 * phi1 + t6682 = -3 * phi2 + t6681 = 3 * phi2 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((1 + t6680) ** (-0.1e1 / 0.2e1)) * ((1 - t6680) ** (-0.1e1 / 0.2e1)) * ((t6703 + t6704) * np.exp((-3*1j) * (t6684 + phi2)) + (-t6699 + t6701) * np.exp((-1*1j) * (t6684 + t6681)) + (t6700 + t6702) * np.exp((-1*1j) * (t6683 + t6681)) + (t6699 + t6701) * np.exp((1j) * (t6684 + t6682)) + (t6700 - t6702) * np.exp((1j) * (t6683 + t6682)) + (-t6703 + t6704) * np.exp((3*1j) * (t6684 - phi2))) + + if Bindx == 199: + t6725 = np.cos(phi) + t6724 = t6725 ** 2 + t6729 = t6725 * t6724 + t6732 = t6729 ** 2 + t6733 = t6725 * t6732 + t6740 = t6733 ** 2 + t6711 = t6725 * t6740 + t6738 = t6732 ** 2 + t6713 = t6725 * t6738 + t6730 = t6724 ** 2 + t6731 = t6725 * t6730 + t6736 = t6731 ** 2 + t6715 = t6725 * t6736 + t6734 = t6730 ** 2 + t6717 = t6725 * t6734 + t6747 = -15225 * t6711 + 73815 * t6713 - 140325 * t6715 + 127275 * t6717 - 375 * t6725 + 4185 * t6729 - 75 * t6731 - 49275 * t6733 + t6746 = 8280 * t6724 - 63060 * t6730 + 197400 * t6732 - 318300 * t6734 + 280680 * t6736 - 129180 * t6738 + 24360 * t6740 - 180 + t6745 = 4840 * t6724 - 84876 * t6730 + 547624 * t6732 - 1656644 * t6734 + 2540120 * t6736 - 1911300 * t6738 + 560280 * t6740 - 44 + t6744 = -19264 * t6724 + 263648 * t6730 - 1332800 * t6732 + 3190432 * t6734 - 3926720 * t6736 + 2409120 * t6738 - 584640 * t6740 + 224 + t6743 = -548100 * t6711 + 2304540 * t6713 - 3894100 * t6715 + 3376268 * t6717 + 1540 * t6725 - 44828 * t6729 + 394324 * t6731 - 1589644 * t6733 + t6742 = -1050525 * t6711 + 4011315 * t6713 - 6142265 * t6715 + 4801687 * t6717 + 1133 * t6725 - 41283 * t6729 + 434313 * t6731 - 2014375 * t6733 + t6728 = 2 * phi1 + t6727 = 4 * phi1 + t6726 = 6 * phi1 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((-t6742 + t6745) * np.exp((-2*1j) * (t6728 + phi2)) + (t6743 + t6744) * np.exp((-2*1j) * (t6727 + phi2)) + (t6746 - t6747) * np.exp((-2*1j) * (t6726 + phi2)) + (t6742 + t6745) * np.exp((2*1j) * (t6728 - phi2)) + (-t6743 + t6744) * np.exp((2*1j) * (t6727 - phi2)) + (t6746 + t6747) * np.exp((2*1j) * (t6726 - phi2))) + + if Bindx == 200: + t6769 = np.cos(phi) + t6768 = t6769 ** 2 + t6773 = t6769 * t6768 + t6776 = t6773 ** 2 + t6777 = t6769 * t6776 + t6784 = t6777 ** 2 + t6755 = t6769 * t6784 + t6782 = t6776 ** 2 + t6757 = t6769 * t6782 + t6774 = t6768 ** 2 + t6775 = t6769 * t6774 + t6780 = t6775 ** 2 + t6759 = t6769 * t6780 + t6778 = t6774 ** 2 + t6761 = t6769 * t6778 + t6792 = -1740 * t6755 + 10620 * t6757 - 27180 * t6759 + 37500 * t6761 + 180 * t6769 - 2820 * t6773 + 13140 * t6775 - 29700 * t6777 + t6754 = t6778 ** 2 + t6791 = -15 - 2175 * t6754 + 720 * t6768 - 6180 * t6774 + 22800 * t6776 - 45450 * t6778 + 53040 * t6780 - 36420 * t6782 + 13680 * t6784 + t6790 = -40020 * t6755 + 155940 * t6757 - 242420 * t6759 + 191268 * t6761 + 44 * t6769 - 1628 * t6773 + 17292 * t6775 - 80476 * t6777 + t6789 = 41760 * t6755 - 197280 * t6757 + 377120 * t6759 - 371104 * t6761 - 224 * t6769 + 6496 * t6773 - 53984 * t6775 + 197216 * t6777 + t6788 = 78300 * t6754 - 2464 * t6768 + 36176 * t6774 - 206304 * t6776 + 590056 * t6778 - 936032 * t6780 + 839120 * t6782 - 398880 * t6784 + 28 + t6787 = -150075 * t6754 + 1232 * t6768 - 22836 * t6774 + 162448 * t6776 - 571186 * t6778 + 1097008 * t6780 - 1173460 * t6782 + 656880 * t6784 - 11 + t6772 = 4 * phi1 + t6771 = 8 * phi1 + t6770 = 12 * phi1 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.323e3) * ((t6787 + t6790) * np.exp((-1*1j) * (t6772 + phi2)) + (t6788 + t6789) * np.exp((-1*1j) * (t6771 + phi2)) + (-t6787 + t6790) * np.exp((1j) * (t6772 - phi2)) + (-t6788 + t6789) * np.exp((1j) * (t6771 - phi2)) + (t6791 + t6792) * np.exp((-1*1j) * (t6770 + phi2)) + (-t6791 + t6792) * np.exp((1j) * (t6770 - phi2))) * ((1 + t6769) ** (-0.1e1 / 0.2e1)) * ((1 - t6769) ** (-0.1e1 / 0.2e1)) + + if Bindx == 201: + t6803 = np.cos(phi) + t6802 = t6803 ** 2 + t6804 = t6802 ** 2 + t6805 = t6802 * t6804 + t6808 = t6805 ** 2 + t6806 = t6804 ** 2 + t6798 = t6802 * t6806 + t6796 = t6802 * t6808 + tfunc[..., c] = (0.93e2 / 0.65536e5*1j) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * t6803 * np.sqrt(0.2e1) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.323e3) * ((10005 * t6796 - 38985 * t6808 + 60605 * t6798 - 47817 * t6806 + 20119 * t6805 - 4323 * t6804 + 407 * t6802 - 11) * np.sin((4 * phi1)) + (-5220 * t6796 + 24660 * t6808 - 47140 * t6798 + 46388 * t6806 - 24652 * t6805 + 6748 * t6804 - 812 * t6802 + 28) * np.sin((8 * phi1)) + (145 * t6796 - 885 * t6808 + 2265 * t6798 - 3125 * t6806 + 2475 * t6805 - 1095 * t6804 + 235 * t6802 - 15) * np.sin((12 * phi1))) + + if Bindx == 202: + t6831 = np.cos(phi) + t6830 = t6831 ** 2 + t6835 = t6831 * t6830 + t6838 = t6835 ** 2 + t6839 = t6831 * t6838 + t6846 = t6839 ** 2 + t6817 = t6831 * t6846 + t6844 = t6838 ** 2 + t6819 = t6831 * t6844 + t6836 = t6830 ** 2 + t6837 = t6831 * t6836 + t6842 = t6837 ** 2 + t6821 = t6831 * t6842 + t6840 = t6836 ** 2 + t6823 = t6831 * t6840 + t6854 = -1740 * t6817 + 10620 * t6819 - 27180 * t6821 + 37500 * t6823 + 180 * t6831 - 2820 * t6835 + 13140 * t6837 - 29700 * t6839 + t6816 = t6840 ** 2 + t6853 = 15 + 2175 * t6816 - 720 * t6830 + 6180 * t6836 - 22800 * t6838 + 45450 * t6840 - 53040 * t6842 + 36420 * t6844 - 13680 * t6846 + t6852 = -40020 * t6817 + 155940 * t6819 - 242420 * t6821 + 191268 * t6823 + 44 * t6831 - 1628 * t6835 + 17292 * t6837 - 80476 * t6839 + t6851 = 41760 * t6817 - 197280 * t6819 + 377120 * t6821 - 371104 * t6823 - 224 * t6831 + 6496 * t6835 - 53984 * t6837 + 197216 * t6839 + t6850 = 78300 * t6816 - 2464 * t6830 + 36176 * t6836 - 206304 * t6838 + 590056 * t6840 - 936032 * t6842 + 839120 * t6844 - 398880 * t6846 + 28 + t6849 = -150075 * t6816 + 1232 * t6830 - 22836 * t6836 + 162448 * t6838 - 571186 * t6840 + 1097008 * t6842 - 1173460 * t6844 + 656880 * t6846 - 11 + t6834 = 4 * phi1 + t6833 = 8 * phi1 + t6832 = 12 * phi1 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.323e3) * ((1 + t6831) ** (-0.1e1 / 0.2e1)) * ((1 - t6831) ** (-0.1e1 / 0.2e1)) * ((-t6849 + t6852) * np.exp((-1*1j) * (t6834 - phi2)) + (-t6850 + t6851) * np.exp((-1*1j) * (t6833 - phi2)) + (t6849 + t6852) * np.exp((1j) * (t6834 + phi2)) + (t6850 + t6851) * np.exp((1j) * (t6833 + phi2)) + (t6853 + t6854) * np.exp((-1*1j) * (t6832 - phi2)) + (-t6853 + t6854) * np.exp((1j) * (t6832 + phi2))) + + if Bindx == 203: + t6875 = np.cos(phi) + t6874 = t6875 ** 2 + t6879 = t6875 * t6874 + t6882 = t6879 ** 2 + t6883 = t6875 * t6882 + t6890 = t6883 ** 2 + t6861 = t6875 * t6890 + t6888 = t6882 ** 2 + t6863 = t6875 * t6888 + t6880 = t6874 ** 2 + t6881 = t6875 * t6880 + t6886 = t6881 ** 2 + t6865 = t6875 * t6886 + t6884 = t6880 ** 2 + t6867 = t6875 * t6884 + t6897 = -15225 * t6861 + 73815 * t6863 - 140325 * t6865 + 127275 * t6867 - 375 * t6875 + 4185 * t6879 - 75 * t6881 - 49275 * t6883 + t6896 = -8280 * t6874 + 63060 * t6880 - 197400 * t6882 + 318300 * t6884 - 280680 * t6886 + 129180 * t6888 - 24360 * t6890 + 180 + t6895 = -4840 * t6874 + 84876 * t6880 - 547624 * t6882 + 1656644 * t6884 - 2540120 * t6886 + 1911300 * t6888 - 560280 * t6890 + 44 + t6894 = 19264 * t6874 - 263648 * t6880 + 1332800 * t6882 - 3190432 * t6884 + 3926720 * t6886 - 2409120 * t6888 + 584640 * t6890 - 224 + t6893 = -548100 * t6861 + 2304540 * t6863 - 3894100 * t6865 + 3376268 * t6867 + 1540 * t6875 - 44828 * t6879 + 394324 * t6881 - 1589644 * t6883 + t6892 = -1050525 * t6861 + 4011315 * t6863 - 6142265 * t6865 + 4801687 * t6867 + 1133 * t6875 - 41283 * t6879 + 434313 * t6881 - 2014375 * t6883 + t6878 = 2 * phi1 + t6877 = 4 * phi1 + t6876 = 6 * phi1 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((-t6892 + t6895) * np.exp((-2*1j) * (t6878 - phi2)) + (t6893 + t6894) * np.exp((-2*1j) * (t6877 - phi2)) + (t6896 - t6897) * np.exp((-2*1j) * (t6876 - phi2)) + (t6892 + t6895) * np.exp((2*1j) * (t6878 + phi2)) + (-t6893 + t6894) * np.exp((2*1j) * (t6877 + phi2)) + (t6896 + t6897) * np.exp((2*1j) * (t6876 + phi2))) + + if Bindx == 204: + t6919 = np.cos(phi) + t6918 = t6919 ** 2 + t6924 = t6919 * t6918 + t6927 = t6924 ** 2 + t6928 = t6919 * t6927 + t6935 = t6928 ** 2 + t6905 = t6919 * t6935 + t6933 = t6927 ** 2 + t6907 = t6919 * t6933 + t6925 = t6918 ** 2 + t6926 = t6919 * t6925 + t6931 = t6926 ** 2 + t6909 = t6919 * t6931 + t6929 = t6925 ** 2 + t6911 = t6919 * t6929 + t6943 = 158340 * t6905 - 839540 * t6907 + 1826500 * t6909 - 2081300 * t6911 - 3900 * t6919 + 67340 * t6924 - 436540 * t6926 + 1309100 * t6928 + t6904 = t6929 ** 2 + t6942 = 65975 * t6904 + 37180 * t6918 - 253760 * t6925 + 674700 * t6927 - 809250 * t6929 + 311220 * t6931 + 224120 * t6933 - 249340 * t6935 - 845 + t6941 = -2375100 * t6904 - 7056 * t6918 + 30912 * t6925 + 549360 * t6927 - 4270392 * t6929 + 11685072 * t6931 - 15244320 * t6933 + 9631440 * t6935 + 84 + t6940 = 3641820 * t6905 - 14334060 * t6907 + 22608540 * t6909 - 18182604 * t6911 - 4708 * t6919 + 168212 * t6924 - 1730916 * t6926 + 7833716 * t6928 + t6939 = -3800160 * t6905 + 16904160 * t6907 - 30492000 * t6909 + 28473312 * t6911 + 16800 * t6919 - 471072 * t6924 + 3892896 * t6926 - 14523936 * t6928 + t6938 = -4552275 * t6904 + 34452 * t6918 - 624448 * t6925 + 4404356 * t6927 - 15553494 * t6929 + 30338748 * t6931 - 33260760 * t6933 + 19213740 * t6935 - 319 + t6923 = 4 * phi1 + t6922 = 8 * phi1 + t6921 = -3 * phi2 + t6920 = 3 * phi2 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((-t6942 + t6943) * np.exp((-3*1j) * (t6923 - phi2)) + (t6938 + t6940) * np.exp((-1*1j) * (t6923 + t6921)) + (t6939 - t6941) * np.exp((-1*1j) * (t6922 + t6921)) + (-t6938 + t6940) * np.exp((1j) * (t6923 + t6920)) + (t6939 + t6941) * np.exp((1j) * (t6922 + t6920)) + (t6942 + t6943) * np.exp((3*1j) * (t6923 + phi2))) * ((1 + t6919) ** (-0.1e1 / 0.2e1)) * ((1 - t6919) ** (-0.1e1 / 0.2e1)) + + if Bindx == 205: + t6964 = np.cos(phi) + t6963 = t6964 ** 2 + t6968 = t6963 ** 2 + t6967 = t6964 * t6963 + t6970 = t6967 ** 2 + t6972 = t6968 ** 2 + t6969 = t6964 * t6968 + t6974 = t6969 ** 2 + t6976 = t6970 ** 2 + t6971 = t6964 * t6970 + t6978 = t6971 ** 2 + t6985 = 62400 * t6963 - 170040 * t6968 - 546000 * t6970 + 2691000 * t6972 - 3999840 * t6974 + 2597400 * t6976 - 633360 * t6978 - 1560 + t6950 = t6964 * t6978 + t6952 = t6964 * t6976 + t6954 = t6964 * t6974 + t6956 = t6964 * t6972 + t6984 = -197925 * t6950 + 214305 * t6952 + 1411995 * t6954 - 3861975 * t6956 - 29445 * t6964 + 407745 * t6967 - 1913925 * t6969 + 3969225 * t6971 + t6983 = -155584 * t6963 + 2590456 * t6968 - 15955632 * t6970 + 46384008 * t6972 - 68856480 * t6974 + 50554920 * t6976 - 14567280 * t6978 + 1496 + t6982 = 322560 * t6963 - 4363968 * t6968 + 23124864 * t6970 - 60373824 * t6972 + 82440960 * t6974 - 56347200 * t6976 + 15200640 * t6978 - 4032 + t6981 = -7125300 * t6950 + 18853380 * t6952 - 11536980 * t6954 - 9260412 * t6956 - 37044 * t6964 + 932484 * t6967 - 6029268 * t6969 + 14203140 * t6971 + t6980 = -13656825 * t6950 + 48944805 * t6952 - 70490745 * t6954 + 52191117 * t6956 + 12199 * t6964 - 426811 * t6967 + 4448807 * t6969 - 21026643 * t6971 + t6966 = 2 * phi1 + t6965 = 3 * phi1 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.2e1) * np.sqrt(0.23e2) * ((-t6980 + t6983) * np.exp((-4*1j) * (phi1 - phi2)) + (t6981 + t6982) * np.exp((-4*1j) * (t6966 - phi2)) + (-t6984 + t6985) * np.exp((-4*1j) * (t6965 - phi2)) + (t6980 + t6983) * np.exp((4*1j) * (phi1 + phi2)) + (-t6981 + t6982) * np.exp((4*1j) * (t6966 + phi2)) + (t6984 + t6985) * np.exp((4*1j) * (t6965 + phi2))) + + if Bindx == 206: + t7007 = np.cos(phi) + t7006 = t7007 ** 2 + t7013 = t7007 * t7006 + t7016 = t7013 ** 2 + t7017 = t7007 * t7016 + t7024 = t7017 ** 2 + t6993 = t7007 * t7024 + t7022 = t7016 ** 2 + t6995 = t7007 * t7022 + t7014 = t7006 ** 2 + t7015 = t7007 * t7014 + t7020 = t7015 ** 2 + t6997 = t7007 * t7020 + t7018 = t7014 ** 2 + t6999 = t7007 * t7018 + t7032 = -158340 * t6993 + 585780 * t6995 - 694980 * t6997 + 80340 * t6999 - 8580 * t7007 + 99060 * t7013 - 358020 * t7015 + 454740 * t7017 + t6992 = t7018 ** 2 + t7031 = 39585 * t6992 + 7020 * t7006 - 129480 * t7014 + 659100 * t7016 - 1435590 * t7018 + 1506180 * t7020 - 695760 * t7022 + 49140 * t7024 - 195 + t7030 = 3800160 * t6993 - 14807520 * t6995 + 22965600 * t6997 - 18033120 * t6999 - 6048 * t7007 + 170016 * t7013 - 1645728 * t7015 + 7556640 * t7017 + t7029 = -3641820 * t6993 + 14621100 * t6995 - 23668380 * t6997 + 19688460 * t6999 + 6436 * t7007 - 213780 * t7013 + 2064164 * t7015 - 8856180 * t7017 + t7028 = -2731365 * t6992 + 11300 * t7006 - 208280 * t7014 + 1596596 * t7016 - 6313890 * t7018 + 13835052 * t7020 - 16863600 * t7022 + 10674300 * t7024 - 113 + t7027 = -1425060 * t6992 - 95760 * t7006 + 1165920 * t7014 - 5280912 * t7016 + 11071704 * t7018 - 10525872 * t7020 + 2271360 * t7022 + 2817360 * t7024 + 1260 + t7012 = 4 * phi1 + t7011 = 8 * phi1 + t7010 = -5 * phi2 + t7009 = 5 * phi2 + t7008 = 12 * phi1 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * ((1 + t7007) ** (-0.1e1 / 0.2e1)) * ((1 - t7007) ** (-0.1e1 / 0.2e1)) * ((-t7028 + t7029) * np.exp((-1*1j) * (t7012 + t7010)) + (t7027 + t7030) * np.exp((-1*1j) * (t7011 + t7010)) + (t7028 + t7029) * np.exp((1j) * (t7012 + t7009)) + (-t7027 + t7030) * np.exp((1j) * (t7011 + t7009)) + (t7031 + t7032) * np.exp((-1*1j) * (t7008 + t7010)) + (-t7031 + t7032) * np.exp((1j) * (t7008 + t7009))) + + if Bindx == 207: + t7053 = np.cos(phi) + t7052 = t7053 ** 2 + t7059 = t7052 ** 2 + t7058 = t7053 * t7052 + t7061 = t7058 ** 2 + t7063 = t7059 ** 2 + t7060 = t7053 * t7059 + t7065 = t7060 ** 2 + t7067 = t7061 ** 2 + t7062 = t7053 * t7061 + t7069 = t7062 ** 2 + t7076 = -7800 * t7052 + 64740 * t7059 - 190840 * t7061 + 212940 * t7063 - 29640 * t7065 - 94900 * t7067 + 45240 * t7069 + 260 + t7039 = t7053 * t7069 + t7041 = t7053 * t7067 + t7043 = t7053 * t7065 + t7045 = t7053 * t7063 + t7075 = 9425 * t7039 + 48945 * t7041 - 234195 * t7043 + 306605 * t7045 - 2145 * t7053 + 14495 * t7058 + 195 * t7060 - 143325 * t7062 + t7074 = 6720 * t7052 - 31584 * t7059 - 245952 * t7061 + 1689312 * t7063 - 3608640 * t7065 + 3276000 * t7067 - 1085760 * t7069 - 96 + t7073 = -650325 * t7039 + 2076555 * t7041 - 2498145 * t7043 + 1383519 * t7045 - 91 * t7053 + 1509 * t7058 + 23089 * t7060 - 336111 * t7062 + t7072 = 15416 * t7052 - 234884 * t7059 + 1344056 * t7061 - 3687084 * t7063 + 5235720 * t7065 - 3713580 * t7067 + 1040520 * t7069 - 164 + t7071 = -339300 * t7039 + 16380 * t7041 + 2222220 * t7043 - 3803604 * t7045 - 5532 * t7053 + 128772 * t7058 - 883596 * t7060 + 2664660 * t7062 + t7057 = 2 * phi1 + t7056 = 4 * phi1 + t7055 = -3 * phi2 + t7054 = 3 * phi2 + tfunc[..., c] = (0.31e2 / 0.262144e6) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.11e2) * ((-t7075 + t7076) * np.exp((-6*1j) * (t7057 - phi2)) + (t7072 + t7073) * np.exp((-2*1j) * (t7057 + t7055)) + (-t7071 + t7074) * np.exp((-2*1j) * (t7056 + t7055)) + (t7072 - t7073) * np.exp((2*1j) * (t7057 + t7054)) + (t7071 + t7074) * np.exp((2*1j) * (t7056 + t7054)) + (t7075 + t7076) * np.exp((6*1j) * (t7057 + phi2))) + + if Bindx == 208: + t7098 = np.cos(phi) + t7097 = t7098 ** 2 + t7104 = t7098 * t7097 + t7107 = t7104 ** 2 + t7108 = t7098 * t7107 + t7115 = t7108 ** 2 + t7084 = t7098 * t7115 + t7113 = t7107 ** 2 + t7086 = t7098 * t7113 + t7105 = t7097 ** 2 + t7106 = t7098 * t7105 + t7111 = t7106 ** 2 + t7088 = t7098 * t7111 + t7109 = t7105 ** 2 + t7090 = t7098 * t7109 + t7123 = -158340 * t7084 + 205140 * t7086 + 596700 * t7088 - 1467180 * t7090 + 3900 * t7098 + 17940 * t7104 - 339300 * t7106 + 1141140 * t7108 + t7083 = t7109 ** 2 + t7122 = -28275 * t7083 + 42120 * t7097 - 244140 * t7105 + 429000 * t7107 + 115830 * t7109 - 1046760 * t7111 + 982020 * t7113 - 248040 * t7115 - 1755 + t7121 = 3800160 * t7084 - 11662560 * t7086 + 11989600 * t7088 - 3012064 * t7090 + 19040 * t7098 - 376544 * t7104 + 1843296 * t7106 - 2600928 * t7108 + t7120 = -3641820 * t7084 + 15051660 * t7086 - 25166140 * t7088 + 21755148 * t7090 + 9828 * t7098 - 290612 * t7104 + 2562308 * t7106 - 10280372 * t7108 + t7119 = -1950975 * t7083 - 21912 * t7097 + 273188 * t7105 - 1015512 * t7107 + 653518 * t7109 + 3562424 * t7111 - 8210540 * t7113 + 6709560 * t7115 + 249 + t7118 = -1017900 * t7083 - 101632 * t7097 + 1259664 * t7105 - 6578880 * t7107 + 16803864 * t7109 - 21574784 * t7111 + 12368720 * t7113 - 1160640 * t7115 + 1588 + t7103 = 4 * phi1 + t7102 = 8 * phi1 + t7101 = -7 * phi2 + t7100 = 7 * phi2 + t7099 = 12 * phi1 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * ((1 + t7098) ** (-0.1e1 / 0.2e1)) * ((1 - t7098) ** (-0.1e1 / 0.2e1)) * ((-t7119 + t7120) * np.exp((-1*1j) * (t7103 + t7101)) + (t7118 + t7121) * np.exp((-1*1j) * (t7102 + t7101)) + (t7119 + t7120) * np.exp((1j) * (t7103 + t7100)) + (-t7118 + t7121) * np.exp((1j) * (t7102 + t7100)) + (-t7122 + t7123) * np.exp((-1*1j) * (t7099 + t7101)) + (t7122 + t7123) * np.exp((1j) * (t7099 + t7100))) + + if Bindx == 209: + t7144 = np.cos(phi) + t7143 = t7144 ** 2 + t7148 = t7144 * t7143 + t7151 = t7148 ** 2 + t7152 = t7144 * t7151 + t7159 = t7152 ** 2 + t7130 = t7144 * t7159 + t7157 = t7151 ** 2 + t7132 = t7144 * t7157 + t7149 = t7143 ** 2 + t7150 = t7144 * t7149 + t7155 = t7150 ** 2 + t7134 = t7144 * t7155 + t7153 = t7149 ** 2 + t7136 = t7144 * t7153 + t7166 = -28275 * t7130 - 395265 * t7132 + 837525 * t7134 - 2145 * t7136 + 2925 * t7144 - 114465 * t7148 + 594165 * t7150 - 894465 * t7152 + t7165 = 49920 * t7143 - 115440 * t7149 - 343200 * t7151 + 1338480 * t7153 - 1235520 * t7155 + 127920 * t7157 + 180960 * t7159 - 3120 + t7164 = 88320 * t7143 - 1194896 * t7149 + 6331808 * t7151 - 16530928 * t7153 + 22573120 * t7155 - 15428400 * t7157 + 4162080 * t7159 - 1104 + t7163 = 222208 * t7143 - 1954176 * t7149 + 6209280 * t7151 - 6880896 * t7153 - 2168320 * t7155 + 8910720 * t7157 - 4343040 * t7159 - 3968 + t7162 = -1950975 * t7130 + 5162235 * t7132 - 3158935 * t7134 - 2535589 * t7136 - 10143 * t7144 + 255323 * t7148 - 1650871 * t7150 + 3888955 * t7152 + t7161 = -1017900 * t7130 - 3652740 * t7132 + 16225300 * t7134 - 19711076 * t7136 - 4780 * t7144 + 172284 * t7148 - 2288748 * t7150 + 10269468 * t7152 + t7147 = 3 * phi1 + t7146 = -2 * phi2 + t7145 = 2 * phi2 + tfunc[..., c] = (0.31e2 / 0.131072e6) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * ((-t7161 + t7163) * np.exp((-8*1j) * (phi1 - phi2)) + (t7162 + t7164) * np.exp((-4*1j) * (phi1 + t7146)) + (t7165 + t7166) * np.exp((-4*1j) * (t7147 + t7146)) + (-t7162 + t7164) * np.exp((4*1j) * (phi1 + t7145)) + (t7165 - t7166) * np.exp((4*1j) * (t7147 + t7145)) + (t7161 + t7163) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 210: + t7188 = np.cos(phi) + t7187 = t7188 ** 2 + t7194 = t7187 ** 2 + t7198 = t7194 ** 2 + t7173 = t7198 ** 2 + t7193 = t7188 * t7187 + t7196 = t7193 ** 2 + t7195 = t7188 * t7194 + t7200 = t7195 ** 2 + t7202 = t7196 ** 2 + t7197 = t7188 * t7196 + t7204 = t7197 ** 2 + t7212 = -65975 * t7173 - 92040 * t7187 - 337740 * t7194 + 2865720 * t7196 - 5049330 * t7198 + 1870440 * t7200 + 2038660 * t7202 - 1241240 * t7204 + 11505 + t7174 = t7188 * t7204 + t7176 = t7188 * t7202 + t7178 = t7188 * t7200 + t7180 = t7188 * t7198 + t7211 = -475020 * t7174 - 907140 * t7176 + 4685460 * t7178 - 4126980 * t7180 + 36660 * t7188 - 511940 * t7193 + 1536340 * t7195 - 237380 * t7197 + t7210 = -10925460 * t7174 + 46877220 * t7176 - 80974260 * t7178 + 71862948 * t7180 + 42412 * t7188 - 1076124 * t7193 + 8869260 * t7195 - 34675996 * t7197 + t7209 = 11400480 * t7174 - 22407840 * t7176 - 6179040 * t7178 + 45335136 * t7180 + 150304 * t7188 - 2620576 * t7193 + 15916320 * t7195 - 41594784 * t7197 + t7208 = -4552275 * t7173 - 349416 * t7187 + 3919108 * t7194 - 15865768 * t7196 + 28351686 * t7198 - 19359928 * t7200 - 4957420 * t7202 + 12809160 * t7204 + 4853 + t7207 = -2375100 * t7173 + 431424 * t7187 - 1683696 * t7194 - 5115264 * t7196 + 37361016 * t7198 - 71845312 * t7200 + 55815760 * t7202 - 12579840 * t7204 - 8988 + t7192 = 4 * phi1 + t7191 = 8 * phi1 + t7190 = -9 * phi2 + t7189 = 9 * phi2 + tfunc[..., c] = (-0.93e2 / 0.524288e6*1j) * np.sqrt(0.2e1) * ((1 + t7188) ** (-0.1e1 / 0.2e1)) * ((1 - t7188) ** (-0.1e1 / 0.2e1)) * ((t7211 - t7212) * np.exp((-3*1j) * (t7192 - 3 * phi2)) + (-t7208 + t7210) * np.exp((-1*1j) * (t7192 + t7190)) + (t7207 + t7209) * np.exp((-1*1j) * (t7191 + t7190)) + (t7208 + t7210) * np.exp((1j) * (t7192 + t7189)) + (-t7207 + t7209) * np.exp((1j) * (t7191 + t7189)) + (t7211 + t7212) * np.exp((3*1j) * (t7192 + 3 * phi2))) + + if Bindx == 211: + t7233 = np.cos(phi) + t7232 = t7233 ** 2 + t7239 = t7233 * t7232 + t7242 = t7239 ** 2 + t7243 = t7233 * t7242 + t7250 = t7243 ** 2 + t7219 = t7233 * t7250 + t7248 = t7242 ** 2 + t7221 = t7233 * t7248 + t7240 = t7232 ** 2 + t7241 = t7233 * t7240 + t7246 = t7241 ** 2 + t7223 = t7233 * t7246 + t7244 = t7240 ** 2 + t7225 = t7233 * t7244 + t7257 = -7917 * t7219 - 200109 * t7221 + 11271 * t7223 + 646503 * t7225 - 10179 * t7233 + 70941 * t7239 + 5577 * t7241 - 516087 * t7243 + t7256 = 4056 * t7232 - 158964 * t7240 + 408408 * t7242 - 46332 * t7244 - 545688 * t7246 + 273156 * t7248 + 63336 * t7250 + 2028 + t7255 = 28520 * t7232 - 394220 * t7240 + 2304232 * t7242 - 6282404 * t7244 + 8545880 * t7246 - 5658276 * t7248 + 1456728 * t7250 - 460 + t7254 = 59584 * t7232 - 895776 * t7240 + 4154304 * t7242 - 7488096 * t7244 + 4459840 * t7246 + 1231776 * t7248 - 1520064 * t7250 - 1568 + t7253 = -285012 * t7219 - 2355444 * t7221 + 6984796 * t7223 - 5422340 * t7225 + 39508 * t7233 - 445452 * t7239 + 1285284 * t7241 + 198660 * t7243 + t7252 = -546273 * t7219 + 1061151 * t7221 + 1026467 * t7223 - 4261693 * t7225 - 16399 * t7233 + 327313 * t7239 - 1815827 * t7241 + 4225261 * t7243 + t7238 = 2 * phi1 + t7237 = 4 * phi1 + t7236 = 6 * phi1 + t7235 = -5 * phi2 + t7234 = 5 * phi2 + tfunc[..., c] = (0.155e3 / 0.262144e6) * ((t7252 + t7255) * np.exp((-2*1j) * (t7238 + t7235)) + (-t7253 + t7254) * np.exp((-2*1j) * (t7237 + t7235)) + (t7256 + t7257) * np.exp((-2*1j) * (t7236 + t7235)) + (-t7252 + t7255) * np.exp((2*1j) * (t7238 + t7234)) + (t7253 + t7254) * np.exp((2*1j) * (t7237 + t7234)) + (t7256 - t7257) * np.exp((2*1j) * (t7236 + t7234))) * np.sqrt(0.3e1) + + if Bindx == 212: + t7279 = np.cos(phi) + t7278 = t7279 ** 2 + t7286 = t7278 ** 2 + t7290 = t7286 ** 2 + t7264 = t7290 ** 2 + t7285 = t7279 * t7278 + t7288 = t7285 ** 2 + t7287 = t7279 * t7286 + t7292 = t7287 ** 2 + t7294 = t7288 ** 2 + t7289 = t7279 * t7288 + t7296 = t7289 ** 2 + t7304 = 3045 * t7264 + 13068 * t7278 - 106392 * t7286 + 101244 * t7288 + 285714 * t7290 - 444444 * t7292 + 51168 * t7294 + 95508 * t7296 + 1089 + t7265 = t7279 * t7296 + t7267 = t7279 * t7294 + t7269 = t7279 * t7292 + t7271 = t7279 * t7290 + t7303 = -26796 * t7265 - 158532 * t7267 + 262548 * t7269 + 169884 * t7271 - 7212 * t7279 + 19068 * t7285 + 120276 * t7287 - 379236 * t7289 + t7302 = -616308 * t7265 + 2765796 * t7267 - 4907188 * t7269 + 4295940 * t7271 - 1012 * t7279 - 3036 * t7285 + 322828 * t7287 - 1857020 * t7289 + t7301 = 643104 * t7265 - 376992 * t7267 - 3161312 * t7269 + 5615456 * t7271 - 16352 * t7279 + 35168 * t7285 + 712992 * t7287 - 3452064 * t7289 + t7300 = -210105 * t7264 - 72956 * t7278 + 643448 * t7286 - 2285740 * t7288 + 3908390 * t7290 - 3170228 * t7292 + 758816 * t7294 + 426972 * t7296 + 1403 + t7299 = -109620 * t7264 + 112112 * t7278 - 805728 * t7286 + 1955184 * t7288 - 899976 * t7290 - 2748592 * t7292 + 3650752 * t7294 - 1150128 * t7296 - 4004 + t7284 = 4 * phi1 + t7283 = 8 * phi1 + t7282 = 12 * phi1 + t7281 = -11 * phi2 + t7280 = 11 * phi2 + tfunc[..., c] = (-0.31e2 / 0.524288e6*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * ((1 + t7279) ** (-0.1e1 / 0.2e1)) * ((1 - t7279) ** (-0.1e1 / 0.2e1)) * ((-t7300 + t7302) * np.exp((-1*1j) * (t7284 + t7281)) + (t7299 + t7301) * np.exp((-1*1j) * (t7283 + t7281)) + (t7300 + t7302) * np.exp((1j) * (t7284 + t7280)) + (-t7299 + t7301) * np.exp((1j) * (t7283 + t7280)) + (t7303 + t7304) * np.exp((-1*1j) * (t7282 + t7281)) + (t7303 - t7304) * np.exp((1j) * (t7282 + t7280))) + + if Bindx == 213: + t7325 = np.cos(phi) + t7324 = t7325 ** 2 + t7330 = t7324 ** 2 + t7329 = t7325 * t7324 + t7332 = t7329 ** 2 + t7334 = t7330 ** 2 + t7331 = t7325 * t7330 + t7336 = t7331 ** 2 + t7338 = t7332 ** 2 + t7333 = t7325 * t7332 + t7340 = t7333 ** 2 + t7347 = 11712 * t7324 - 30264 * t7330 - 80080 * t7332 + 175032 * t7334 + 13728 * t7336 - 84968 * t7338 - 9744 * t7340 + 488 + t7311 = t7325 * t7340 + t7313 = t7325 * t7338 + t7315 = t7325 * t7336 + t7317 = t7325 * t7334 + t7346 = 1015 * t7311 + 39669 * t7313 + 86775 * t7315 - 152867 * t7317 - 3945 * t7325 - 9451 * t7329 + 90519 * t7331 - 47619 * t7333 + t7345 = 22080 * t7324 - 60168 * t7330 - 193200 * t7332 + 952200 * t7334 - 1415328 * t7336 + 919080 * t7338 - 224112 * t7340 - 552 + t7344 = -36540 * t7311 - 510804 * t7313 + 1082340 * t7315 - 2772 * t7317 + 3780 * t7325 - 147924 * t7329 + 767844 * t7331 - 1155924 * t7333 + t7343 = 70035 * t7311 - 75831 * t7313 - 499629 * t7315 + 1366545 * t7317 + 10419 * t7325 - 144279 * t7329 + 677235 * t7331 - 1404495 * t7333 + t7342 = 64512 * t7324 - 149184 * t7330 - 443520 * t7332 + 1729728 * t7334 - 1596672 * t7336 + 165312 * t7338 + 233856 * t7340 - 4032 + t7328 = 2 * phi1 + t7327 = -3 * phi2 + t7326 = 3 * phi2 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * ((t7343 + t7345) * np.exp((-4*1j) * (phi1 + t7327)) + (t7342 + t7344) * np.exp((-4*1j) * (t7328 + t7327)) + (-t7343 + t7345) * np.exp((4*1j) * (phi1 + t7326)) + (t7342 - t7344) * np.exp((4*1j) * (t7328 + t7326)) + (t7346 + t7347) * np.exp((-12*1j) * (phi1 - phi2)) + (-t7346 + t7347) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 214: + t7368 = np.cos(phi) + t7367 = t7368 ** 2 + t7374 = t7368 * t7367 + t7377 = t7374 ** 2 + t7378 = t7368 * t7377 + t7385 = t7378 ** 2 + t7354 = t7368 * t7385 + t7389 = -91 - 145 * t7354 + t7388 = -1356 - 5220 * t7354 + t7387 = -391 - 10005 * t7354 + t7383 = t7377 ** 2 + t7375 = t7367 ** 2 + t7376 = t7368 * t7375 + t7381 = t7376 ** 2 + t7379 = t7375 ** 2 + t7373 = 4 * phi1 + t7372 = 8 * phi1 + t7371 = 12 * phi1 + t7370 = -13 * phi2 + t7369 = 13 * phi2 + t7360 = t7368 * t7379 + t7358 = t7368 * t7381 + t7356 = t7368 * t7383 + tfunc[..., c] = (0.31e2 / 0.524288e6*1j) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.3e1) * np.sqrt((1 - t7368)) * ((1 + t7368) ** (-0.1e1 / 0.2e1)) * ((-24679 * t7385 - 35627 * t7356 + 128225 * t7383 + 23897 * t7358 - 271331 * t7381 + 64745 * t7360 + 297045 * t7379 - 135585 * t7378 - 175605 * t7377 + 103615 * t7376 + 52739 * t7375 - 35213 * t7374 - 6785 * t7367 + 4163 * t7368 - t7387) * np.exp((-1*1j) * (t7373 + t7370)) + (30972 * t7385 - 56340 * t7356 - 17652 * t7383 + 184044 * t7358 - 170676 * t7381 - 128964 * t7360 + 302940 * t7379 - 79596 * t7378 - 171468 * t7377 + 126852 * t7376 + 17892 * t7375 - 44700 * t7374 + 9348 * t7367 + 3924 * t7368 + t7388) * np.exp((-1*1j) * (t7372 + t7370)) + (-44689 * t7385 - 33741 * t7356 + 130111 * t7383 + 234439 * t7358 - 60789 * t7381 - 396865 * t7360 - 164565 * t7379 + 268065 * t7378 + 228045 * t7377 - 51175 * t7376 - 102051 * t7375 - 14099 * t7374 + 14329 * t7367 + 3381 * t7368 + t7387) * np.exp((1j) * (t7373 + t7369)) + (41412 * t7385 + 128724 * t7356 + 167412 * t7383 - 34284 * t7358 - 389004 * t7381 - 430716 * t7360 + 1188 * t7379 + 383724 * t7378 + 291852 * t7377 - 6468 * t7376 - 115428 * t7375 - 52836 * t7374 + 1212 * t7367 + 6636 * t7368 - t7388) * np.exp((1j) * (t7372 + t7369)) + (-1363 * t7385 + 5369 * t7356 - 10803 * t7383 + 9061 * t7358 + 6721 * t7381 - 24739 * t7360 + 22737 * t7379 - 429 * t7378 - 17017 * t7377 + 14443 * t7376 - 2873 * t7375 - 3081 * t7374 + 2507 * t7367 - 769 * t7368 - t7389) * np.exp((-1*1j) * (t7371 + t7370)) + (-1653 * t7385 - 8385 * t7356 - 24557 * t7383 - 44421 * t7358 - 46761 * t7381 - 15301 * t7360 + 32175 * t7379 + 55341 * t7378 + 38753 * t7377 + 7293 * t7376 - 10023 * t7375 - 9815 * t7374 - 4227 * t7367 - 951 * t7368 + t7389) * np.exp((1j) * (t7371 + t7369))) + + if Bindx == 215: + t7410 = np.cos(phi) + t7409 = t7410 ** 2 + t7416 = t7410 * t7409 + t7419 = t7416 ** 2 + t7420 = t7410 * t7419 + t7427 = t7420 ** 2 + t7435 = -4 - 56 * t7427 + t7417 = t7409 ** 2 + t7421 = t7417 ** 2 + t7418 = t7410 * t7417 + t7423 = t7418 ** 2 + t7425 = t7419 ** 2 + t7434 = 56 * t7409 - 164 * t7417 + 120 * t7419 + 180 * t7421 - 376 * t7423 + 244 * t7425 + t7435 + t7396 = t7410 * t7427 + t7398 = t7410 * t7425 + t7400 = t7410 * t7423 + t7402 = t7410 * t7421 + t7433 = t7398 - 15 * t7396 + 221 * t7400 - 595 * t7402 - t7410 + 79 * t7416 - 365 * t7418 + 675 * t7420 + t7432 = -200 * t7409 - 676 * t7417 + 1144 * t7419 + 1716 * t7421 - 1144 * t7423 - 780 * t7425 + t7435 + t7431 = -5 * t7396 - 277 * t7398 - 1313 * t7400 + 143 * t7402 - 43 * t7410 - 507 * t7416 - 143 * t7418 + 2145 * t7420 + t7430 = 96 + 960 * t7409 - 6816 * t7417 + 6336 * t7419 + 9504 * t7421 - 14784 * t7423 + 3360 * t7425 + 1344 * t7427 + t7429 = -180 * t7396 - 3732 * t7398 + 5052 * t7400 + 9372 * t7402 - 588 * t7410 + 1428 * t7416 + 6468 * t7418 - 17820 * t7420 + t7415 = 2 * phi1 + t7414 = 4 * phi1 + t7413 = 6 * phi1 + t7412 = -7 * phi2 + t7411 = 7 * phi2 + tfunc[..., c] = -(0.31e2 / 0.262144e6) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.7e1) * np.sqrt(0.29e2) * ((t7429 + t7430) * np.exp((-2*1j) * (t7414 + t7412)) + (-t7431 + t7432) * np.exp((-2*1j) * (t7413 + t7412)) + (-t7429 + t7430) * np.exp((2*1j) * (t7414 + t7411)) + (t7431 + t7432) * np.exp((2*1j) * (t7413 + t7411)) + 23 * (-t7433 + t7434) * np.exp((-2*1j) * (t7415 + t7412)) + 23 * (t7433 + t7434) * np.exp((2*1j) * (t7415 + t7411))) + + if Bindx == 216: + t7454 = np.cos(phi) + t7453 = t7454 ** 2 + t7460 = t7453 ** 2 + t7461 = t7454 * t7460 + t7465 = t7461 ** 2 + t7445 = t7454 * t7465 + t7459 = t7454 * t7453 + t7478 = -4 * t7445 + 4 * t7459 + t7462 = t7459 ** 2 + t7467 = t7462 ** 2 + t7468 = t7454 * t7467 + t7477 = t7468 - t7454 + t7442 = t7454 * t7468 + t7476 = 1 - t7442 + t7463 = t7460 ** 2 + t7447 = t7454 * t7463 + t7475 = 20 * t7447 - 20 * t7461 + 4 * t7477 + 4 * t7478 + t7474 = 208 * t7445 + 572 * t7447 - 12 * t7454 - 208 * t7459 - 572 * t7461 + 12 * t7468 + t7473 = 3168 * t7447 - 3168 * t7461 - 288 * t7477 + 288 * t7478 + t7472 = t7453 - 19 * t7460 + 45 * t7462 - 45 * t7463 + 19 * t7465 - t7467 + t7476 + t7471 = 65 * t7453 + 429 * t7460 + 429 * t7462 - 429 * t7463 - 429 * t7465 - 65 * t7467 + t7476 + t7470 = 36 - 36 * t7442 + 900 * t7453 - 396 * t7460 - 3564 * t7462 + 3564 * t7463 + 396 * t7465 - 900 * t7467 + t7458 = 4 * phi1 + t7457 = 8 * phi1 + t7456 = -15 * phi2 + t7455 = 15 * phi2 + tfunc[..., c] = (-0.155e3 / 0.524288e6*1j) * np.sqrt((1 - t7454)) * np.sqrt((1 + t7454)) * np.sqrt(0.2e1) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * np.sqrt(0.29e2) * ((t7471 + t7474) * np.exp((-3*1j) * (t7458 - 5 * phi2)) + (-t7471 + t7474) * np.exp((3*1j) * (t7458 + 5 * phi2)) + (-t7470 + t7473) * np.exp((-1*1j) * (t7457 + t7456)) + (t7470 + t7473) * np.exp((1j) * (t7457 + t7455)) + 69 * (t7472 + t7475) * np.exp((-1*1j) * (t7458 + t7456)) + 69 * (-t7472 + t7475) * np.exp((1j) * (t7458 + t7455))) + + if Bindx == 217: + t7501 = np.cos(phi) + t7500 = t7501 ** 2 + t7505 = t7501 * t7500 + t7508 = t7505 ** 2 + t7509 = t7501 * t7508 + t7516 = t7509 ** 2 + t7487 = t7501 * t7516 + t7525 = -t7487 - t7501 + t7506 = t7500 ** 2 + t7510 = t7506 ** 2 + t7486 = t7510 ** 2 + t7514 = t7508 ** 2 + t7524 = 1 + t7486 + 64 * t7500 + 364 * t7506 - 858 * t7510 + 364 * t7514 + 64 * t7516 + t7507 = t7501 * t7506 + t7512 = t7507 ** 2 + t7523 = -49 - 49 * t7486 + 980 * t7506 - 3136 * t7508 + 4410 * t7510 - 3136 * t7512 + 980 * t7514 + t7489 = t7501 * t7514 + t7491 = t7501 * t7512 + t7493 = t7501 * t7510 + t7522 = -12 * t7487 - 196 * t7489 - 364 * t7491 + 572 * t7493 - 12 * t7501 - 196 * t7505 - 364 * t7507 + 572 * t7509 + t7521 = -240 * t7489 + 1200 * t7491 - 880 * t7493 - 240 * t7505 + 1200 * t7507 - 880 * t7509 + 80 * t7525 + t7520 = 980 * t7489 - 1764 * t7491 + 980 * t7493 + 980 * t7505 - 1764 * t7507 + 980 * t7509 + 196 * t7525 + t7519 = 10 + 10 * t7486 + 240 * t7500 - 360 * t7506 - 880 * t7508 + 1980 * t7510 - 880 * t7512 - 360 * t7514 + 240 * t7516 + t7504 = 3 * phi1 + t7503 = -4 * phi2 + t7502 = 4 * phi2 + tfunc[..., c] = -(0.33e2 / 0.59244544e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.899e3) * np.sqrt(0.5e1) * ((76 * t7486 - 608 * t7516 + 2128 * t7514 - 4256 * t7512 + 5320 * t7510 - 4256 * t7508 + 2128 * t7506 - 608 * t7500 + 76) * np.exp((-16*1j) * phi2) + (-t7522 + t7524) * np.exp((-4*1j) * (t7504 + t7502)) + (t7522 + t7524) * np.exp((4*1j) * (t7504 + t7503)) + (t7520 + t7523) * np.exp((-4*1j) * (phi1 + t7502)) + (-t7520 + t7523) * np.exp((4*1j) * (phi1 + t7503)) + (t7519 - t7521) * np.exp((-8*1j) * (phi1 + 2 * phi2)) + (t7519 + t7521) * np.exp((8*1j) * (phi1 - 2 * phi2))) + + if Bindx == 218: + t7550 = np.cos(phi) + t7549 = t7550 ** 2 + t7554 = t7550 * t7549 + t7557 = t7554 ** 2 + t7558 = t7550 * t7557 + t7565 = t7558 ** 2 + t7536 = t7550 * t7565 + t7578 = -t7536 - t7550 + t7563 = t7557 ** 2 + t7538 = t7550 * t7563 + t7582 = -t7538 - t7554 + t7555 = t7549 ** 2 + t7559 = t7555 ** 2 + t7542 = t7550 * t7559 + t7581 = -t7542 - t7558 + t7556 = t7550 * t7555 + t7561 = t7556 ** 2 + t7580 = t7561 + t7557 + t7579 = t7563 + t7555 + t7576 = t7581 + t7582 + t7535 = t7559 ** 2 + t7575 = 248 * t7535 + 15872 * t7549 + 90272 * t7555 - 212784 * t7559 + 90272 * t7563 + 15872 * t7565 + 248 + t7540 = t7550 * t7561 + t7574 = -56058912 * t7540 - 56058912 * t7556 + 6228768 * t7578 + t7573 = 2976 * t7536 + 48608 * t7538 + 90272 * t7540 + 2976 * t7550 + 48608 * t7554 + 90272 * t7556 + 141856 * t7581 + t7572 = 1557192 * t7535 - 140147280 * t7559 + 99660288 * t7580 + 1557192 + t7571 = -63280 * t7538 - 63280 * t7554 - 1292720 * t7542 - 1292720 * t7558 - 493584 * t7540 - 493584 * t7556 + 1808 * t7578 + t7570 = 113 * t7535 + 13560 * t7549 + 1454310 * t7559 + 13560 * t7565 + 205660 * t7579 + 904904 * t7580 + 113 + t7569 = 72540 * t7535 + 1740960 * t7549 + 14362920 * t7559 + 1740960 * t7565 - 2611440 * t7579 - 6383520 * t7580 + 72540 + t7568 = 8704800 * t7540 + 8704800 * t7556 + 6383520 * t7581 + 1740960 * t7582 + 580320 * t7578 + t7553 = 3 * phi1 + t7552 = -4 * phi2 + t7551 = 4 * phi2 + t7532 = np.exp((-4*1j) * (phi1 + t7551)) + t7530 = np.exp((4*1j) * (phi1 + t7552)) + tfunc[..., c] = (0.11e2 / 0.1895825408e10) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.29e2) * np.sqrt(0.113e3) * ((290176740 * t7559 + 4145382 * t7535 + 4145382 - 232141392 * t7561 - 232141392 * t7557 + 116070696 * t7563 + 116070696 * t7555 - 33163056 * t7565 - 33163056 * t7549) * np.exp((-16*1j) * phi2) + ((t7572 - t7574) * t7532) + ((t7572 + t7574) * t7530) + (t7573 + t7575) * np.exp((-4*1j) * (t7553 + t7551)) + (-t7573 + t7575) * np.exp((4*1j) * (t7553 + t7552)) + (-t7568 + t7569) * np.exp((-8*1j) * (phi1 + 2 * phi2)) + (t7568 + t7569) * np.exp((8*1j) * (phi1 - 2 * phi2)) + (t7570 - t7571) * np.exp((-16*1j) * (phi1 + phi2)) + (t7570 + t7571) * np.exp((16*1j) * (phi1 - phi2)) + (31143840 * (t7576 - t7579) * t7532) + (31143840 * (-t7576 - t7579) * t7530)) + + if Bindx == 219: + t7604 = np.cos(phi) + t7603 = t7604 ** 2 + t7609 = t7604 * t7603 + t7612 = t7609 ** 2 + t7613 = t7604 * t7612 + t7620 = t7613 ** 2 + t7590 = t7604 * t7620 + t7625 = 4 * t7590 + t7624 = 40 * t7590 + t7623 = -196 * t7590 + t7622 = 49 - 637 * t7603 - 98 * t7604 + t7618 = t7612 ** 2 + t7610 = t7603 ** 2 + t7611 = t7604 * t7610 + t7616 = t7611 ** 2 + t7614 = t7610 ** 2 + t7608 = 4 * phi1 + t7607 = 8 * phi1 + t7606 = -15 * phi2 + t7605 = 15 * phi2 + t7596 = t7604 * t7614 + t7594 = t7604 * t7616 + t7592 = t7604 * t7618 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.899e3) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.23e2) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * ((1 + t7604) ** (0.3e1 / 0.2e1)) * ((1 - t7604) ** (-0.1e1 / 0.2e1)) * (304 * (t7590 - 2 * t7620 - 5 * t7592 + 12 * t7618 + 9 * t7594 - 30 * t7616 - 5 * t7596 + 40 * t7614 - 5 * t7613 - 30 * t7612 + 9 * t7611 + 12 * t7610 - 5 * t7609 - 2 * t7603 + t7604) * np.exp((-15*1j) * phi2) + (t7625 + 37 * t7620 + 142 * t7592 + 271 * t7618 + 184 * t7594 - 275 * t7616 - 726 * t7596 - 561 * t7614 + 132 * t7613 + 583 * t7612 + 418 * t7611 + 37 * t7610 - 128 * t7609 - 89 * t7603 - 26 * t7604 - 3) * np.exp((-3*1j) * (t7608 + 5 * phi2)) + (t7625 - 53 * t7620 + 322 * t7592 - 1183 * t7618 + 2912 * t7594 - 5005 * t7616 + 6006 * t7596 - 4719 * t7614 + 1716 * t7613 + 1001 * t7612 - 2002 * t7611 + 1547 * t7610 - 728 * t7609 + 217 * t7603 - 38 * t7604 + 3) * np.exp((3*1j) * (t7608 - 5 * phi2)) + (t7623 - 343 * t7620 + 1078 * t7592 + 2107 * t7618 - 2352 * t7594 - 5439 * t7616 + 2450 * t7596 + 7595 * t7614 - 980 * t7613 - 6125 * t7612 - 294 * t7611 + 2793 * t7610 + 392 * t7609 + t7622) * np.exp((-1*1j) * (t7608 + t7605)) + (t7624 + 220 * t7620 + 320 * t7592 - 380 * t7618 - 1480 * t7594 - 740 * t7616 + 1840 * t7596 + 2340 * t7614 - 360 * t7613 - 2060 * t7612 - 800 * t7611 + 620 * t7610 + 520 * t7609 + 20 * t7603 - 80 * t7604 - 20) * np.exp((-1*1j) * (t7607 + t7605)) + (t7623 + 1127 * t7620 - 1862 * t7592 - 1323 * t7618 + 7448 * t7594 - 5537 * t7616 - 7154 * t7596 + 12789 * t7614 - 1764 * t7613 - 8771 * t7612 + 5782 * t7611 + 1127 * t7610 - 2352 * t7609 - t7622) * np.exp((1j) * (t7608 + t7606)) + (t7624 - 380 * t7620 + 1520 * t7592 - 3140 * t7618 + 2840 * t7594 + 1540 * t7616 - 7040 * t7596 + 7260 * t7614 - 1320 * t7613 - 4180 * t7612 + 4400 * t7611 - 1580 * t7610 - 280 * t7609 + 460 * t7603 - 160 * t7604 + 20) * np.exp((1j) * (t7607 + t7606))) + + if Bindx == 220: + t7650 = np.cos(phi) + t7676 = 1 - t7650 + t7649 = t7650 ** 2 + t7657 = t7649 ** 2 + t7661 = t7657 ** 2 + t7635 = t7661 ** 2 + t7675 = 113 * t7635 + t7674 = 248 * t7635 + t7673 = 72540 * t7635 + t7672 = 1557192 * t7635 + t7642 = t7650 * t7661 + t7658 = t7650 * t7657 + t7656 = t7650 * t7649 + t7659 = t7656 ** 2 + t7663 = t7658 ** 2 + t7671 = 186 + 79794 * t7642 - 28210 * t7658 - 62062 * t7659 + 62062 * t7663 + t7665 = t7659 ** 2 + t7638 = t7650 * t7665 + t7660 = t7650 * t7659 + t7667 = t7660 ** 2 + t7670 = 25304370 * t7638 - 5839470 * t7649 + 5839470 * t7667 + 389298 * t7676 + t7655 = 4 * phi1 + t7654 = 8 * phi1 + t7653 = 16 * phi1 + t7652 = -15 * phi2 + t7651 = 15 * phi2 + t7640 = t7650 * t7663 + t7636 = t7650 * t7667 + tfunc[..., c] = (0.11e2 / 0.236978176e9*1j) * np.sqrt(0.113e3) * np.sqrt(0.29e2) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt((1 + t7650)) * t7676 ** (-0.1e1 / 0.2e1) * (4145382 * (-35 * t7663 + 35 * t7642 + 35 * t7661 - 35 * t7660 + 21 * t7665 - 21 * t7640 - 21 * t7659 + 21 * t7658 - 7 * t7667 + 7 * t7638 + 7 * t7657 - 7 * t7656 + t7635 - t7636 - t7649 + t7650) * np.exp((-15*1j) * phi2) + (t7675 + 1582 * t7636 + 10170 * t7667 + 39550 * t7638 - 39550 * t7656 - 10170 * t7649 - 1582 * t7650 - 113 + 226226 * t7663 - 226226 * t7659 + 185094 * t7640 - 185094 * t7658 + 161590 * t7642 - 161590 * t7660 + 102830 * t7665 - 102830 * t7657) * np.exp((-1*1j) * (t7653 + t7651)) + (t7674 + 2542 * t7636 + 11098 * t7667 + 25606 * t7638 + 28210 * t7665 - 5642 * t7640 - 26598 * t7661 + 44330 * t7660 - 5642 * t7657 - 13454 * t7656 - 7130 * t7649 - 1798 * t7650 - t7671) * np.exp((-3*1j) * (t7655 + 5 * phi2)) + (t7674 - 3038 * t7636 + 16678 * t7667 - 53382 * t7638 + 107198 * t7665 - 129766 * t7640 - 186186 * t7661 + 168454 * t7660 + 50778 * t7657 - 31682 * t7656 + 11098 * t7649 - 2170 * t7650 + t7671) * np.exp((3*1j) * (t7655 - 5 * phi2)) + (4282278 * t7636 + 61898382 * t7640 - 79806090 * t7642 + 1946490 * t7656 - 25304370 * t7657 - 19854198 * t7658 + 50998038 * t7659 + 56448210 * t7660 - 52555230 * t7661 + 23747178 * t7663 + 1946490 * t7665 - t7670 + t7672) * np.exp((-1*1j) * (t7655 + t7651)) + (471510 * t7636 - 108810 * t7638 - 4025970 * t7640 + 7580430 * t7642 - 108810 * t7649 - 181350 * t7650 + 979290 * t7656 + 2067390 * t7657 - 326430 * t7658 - 5186610 * t7659 - 4388670 * t7660 + 3590730 * t7661 + 1994850 * t7663 - 3373110 * t7665 + 979290 * t7667 + t7673 - 36270) * np.exp((-1*1j) * (t7654 + t7651)) + (-7396662 * t7636 - 15182622 * t7640 - 44769270 * t7642 + 13625430 * t7656 + 9732450 * t7657 - 54891018 * t7658 + 23747178 * t7659 + 83699070 * t7660 - 87592050 * t7661 + 100828182 * t7663 - 48662250 * t7665 + t7670 + t7672) * np.exp((1j) * (t7655 + t7652)) + (-616590 * t7636 - 2937870 * t7638 + 7943130 * t7640 + 398970 * t7642 + 544050 * t7649 - 253890 * t7650 + 326430 * t7656 - 3373110 * t7657 + 5114070 * t7658 + 398970 * t7659 - 9974250 * t7660 + 10772190 * t7661 - 9974250 * t7663 - 544050 * t7665 + 2067390 * t7667 + t7673 + 36270) * np.exp((1j) * (t7654 + t7652)) + (t7675 - 1808 * t7636 + 13560 * t7667 - 63280 * t7638 + 1454310 * t7661 - 63280 * t7656 + 13560 * t7649 - 1808 * t7650 + 113 - 1292720 * t7642 - 1292720 * t7660 + 904904 * t7663 + 904904 * t7659 - 493584 * t7640 - 493584 * t7658 + 205660 * t7665 + 205660 * t7657) * np.exp((1j) * (t7653 + t7652))) + + if Bindx == 221: + t7699 = np.cos(phi) + t7698 = t7699 ** 2 + t7705 = t7699 * t7698 + t7708 = t7705 ** 2 + t7709 = t7699 * t7708 + t7716 = t7709 ** 2 + t7685 = t7699 * t7716 + t7714 = t7708 ** 2 + t7687 = t7699 * t7714 + t7706 = t7698 ** 2 + t7707 = t7699 * t7706 + t7712 = t7707 ** 2 + t7689 = t7699 * t7712 + t7710 = t7706 ** 2 + t7691 = t7699 * t7710 + t7724 = -651 * t7685 - 7259 * t7687 - 2639 * t7689 + 20449 * t7691 + 315 * t7699 + 1771 * t7705 - 7553 * t7707 - 4433 * t7709 + t7723 = -4340 * t7685 - 5460 * t7687 + 43260 * t7689 - 49060 * t7691 + 500 * t7699 - 6060 * t7705 + 14340 * t7707 + 6820 * t7709 + t7722 = -10633 * t7685 + 49735 * t7687 - 88053 * t7689 + 66395 * t7691 - 1127 * t7699 + 9065 * t7705 - 17787 * t7707 - 7595 * t7709 + t7684 = t7710 ** 2 + t7721 = -34 + 62 * t7684 - 1156 * t7698 + 728 * t7706 + 12012 * t7708 - 12012 * t7710 - 12012 * t7712 + 9464 * t7714 + 2948 * t7716 + t7720 = -140 + 620 * t7684 + 840 * t7698 + 5880 * t7706 - 33880 * t7708 + 47520 * t7710 - 8360 * t7712 - 23160 * t7714 + 10680 * t7716 + t7719 = -3038 * t7684 - 2940 * t7698 + 25480 * t7706 - 83692 * t7708 + 132300 * t7710 - 104468 * t7712 + 33320 * t7714 + 2940 * t7716 + 98 + t7704 = 2 * phi1 + t7703 = 4 * phi1 + t7702 = 6 * phi1 + t7701 = -7 * phi2 + t7700 = 7 * phi2 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.29e2) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((4712 * t7684 - 33136 * t7716 + 100016 * t7714 - 168112 * t7712 + 170240 * t7710 - 104272 * t7708 + 36176 * t7706 - 5776 * t7698 + 152) * np.exp((-14*1j) * phi2) + (t7719 + t7722) * np.exp((-2*1j) * (t7704 + t7700)) + (t7720 - t7723) * np.exp((-2*1j) * (t7703 + t7700)) + (t7721 - t7724) * np.exp((-2*1j) * (t7702 + t7700)) + (t7719 - t7722) * np.exp((2*1j) * (t7704 + t7701)) + (t7720 + t7723) * np.exp((2*1j) * (t7703 + t7701)) + (t7721 + t7724) * np.exp((2*1j) * (t7702 + t7701))) + + if Bindx == 222: + t7749 = np.cos(phi) + t7748 = t7749 ** 2 + t7756 = t7749 * t7748 + t7759 = t7756 ** 2 + t7760 = t7749 * t7759 + t7767 = t7760 ** 2 + t7735 = t7749 * t7767 + t7765 = t7759 ** 2 + t7737 = t7749 * t7765 + t7757 = t7748 ** 2 + t7758 = t7749 * t7757 + t7763 = t7758 ** 2 + t7739 = t7749 * t7763 + t7761 = t7757 ** 2 + t7741 = t7749 * t7761 + t7777 = -2604 * t7735 - 29036 * t7737 - 10556 * t7739 + 81796 * t7741 + 1260 * t7749 + 7084 * t7756 - 30212 * t7758 - 17732 * t7760 + t7734 = t7761 ** 2 + t7776 = -136 + 248 * t7734 - 4624 * t7748 + 2912 * t7757 + 48048 * t7759 - 48048 * t7761 - 48048 * t7763 + 37856 * t7765 + 11792 * t7767 + t7775 = 113 * t7734 - 10170 * t7748 + 10170 * t7767 - 113 + 226226 * t7763 - 226226 * t7759 + 102830 * t7765 - 102830 * t7757 + t7774 = -1582 * t7735 - 39550 * t7737 + 1582 * t7749 + 39550 * t7756 - 185094 * t7739 + 185094 * t7758 - 161590 * t7741 + 161590 * t7760 + t7773 = 72540 * t7734 + 98280 * t7748 + 687960 * t7757 - 3963960 * t7759 + 5559840 * t7761 - 978120 * t7763 - 2709720 * t7765 + 1249560 * t7767 - 16380 + t7772 = -507780 * t7735 - 638820 * t7737 + 5061420 * t7739 - 5740020 * t7741 + 58500 * t7749 - 709020 * t7756 + 1677780 * t7758 + 797940 * t7760 + t7771 = -5450172 * t7735 + 25492740 * t7737 - 45133452 * t7739 + 34032180 * t7741 - 577668 * t7749 + 4646460 * t7756 - 9117108 * t7758 - 3892980 * t7760 + t7770 = 1557192 * t7734 + 1506960 * t7748 - 13060320 * t7757 + 42898128 * t7759 - 67813200 * t7761 + 53547312 * t7763 - 17078880 * t7765 - 1506960 * t7767 - 50232 + t7755 = 2 * phi1 + t7754 = 4 * phi1 + t7753 = 6 * phi1 + t7752 = 8 * phi1 + t7751 = -7 * phi2 + t7750 = 7 * phi2 + tfunc[..., c] = (0.11e2 / 0.473956352e9) * np.sqrt(0.31e2) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.29e2) * np.sqrt(0.113e3) * ((4145382 * t7734 - 29151396 * t7767 + 87989076 * t7765 - 147896532 * t7763 + 149768640 * t7761 - 91733292 * t7759 + 31825836 * t7757 - 5081436 * t7748 + 133722) * np.exp((-14*1j) * phi2) + (-t7774 + t7775) * np.exp((-2*1j) * (t7752 + t7750)) + (t7774 + t7775) * np.exp((2*1j) * (t7752 + t7751)) + (t7770 - t7771) * np.exp((-2*1j) * (t7755 + t7750)) + (-t7772 + t7773) * np.exp((-2*1j) * (t7754 + t7750)) + (t7776 - t7777) * np.exp((-2*1j) * (t7753 + t7750)) + (t7770 + t7771) * np.exp((2*1j) * (t7755 + t7751)) + (t7772 + t7773) * np.exp((2*1j) * (t7754 + t7751)) + (t7776 + t7777) * np.exp((2*1j) * (t7753 + t7751))) + + if Bindx == 223: + t7800 = np.cos(phi) + t7799 = t7800 ** 2 + t7807 = t7799 ** 2 + t7811 = t7807 ** 2 + t7785 = t7811 ** 2 + t7822 = 620 * t7785 + t7821 = 6200 * t7785 + t7820 = -30380 * t7785 + t7806 = t7800 * t7799 + t7809 = t7806 ** 2 + t7810 = t7800 * t7809 + t7817 = t7810 ** 2 + t7815 = t7809 ** 2 + t7808 = t7800 * t7807 + t7813 = t7808 ** 2 + t7805 = 4 * phi1 + t7804 = 8 * phi1 + t7803 = 12 * phi1 + t7802 = -13 * phi2 + t7801 = 13 * phi2 + t7792 = t7800 * t7811 + t7790 = t7800 * t7813 + t7788 = t7800 * t7815 + t7786 = t7800 * t7817 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.29e2) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * np.sqrt((1 + t7800)) * ((1 - t7800) ** (-0.1e1 / 0.2e1)) * ((47120 * t7785 - 47120 * t7786 + 74480 * t7807 - 74480 * t7806 - 4560 * t7799 + 4560 * t7800 - 1010800 * t7813 + 1010800 * t7792 + 798000 * t7811 - 798000 * t7810 + 734160 * t7815 - 734160 * t7790 - 351120 * t7809 + 351120 * t7808 - 287280 * t7817 + 287280 * t7788) * np.exp((-13*1j) * phi2) + (t7820 - 68355 * t7786 + 126175 * t7817 + 370685 * t7788 - 161945 * t7815 - 831775 * t7790 - 19845 * t7813 + 991025 * t7792 + 238875 * t7811 - 667625 * t7810 - 232995 * t7809 + 249655 * t7808 + 94325 * t7807 - 47285 * t7806 - 14455 * t7799 + 3675 * t7800 + 245) * np.exp((-1*1j) * (t7805 + t7801)) + (t7821 + 34100 * t7786 + 50700 * t7817 - 51740 * t7788 - 213380 * t7815 - 109020 * t7790 + 250140 * t7813 + 306900 * t7792 - 61380 * t7811 - 260260 * t7810 - 74140 * t7809 + 89580 * t7808 + 51700 * t7807 - 9140 * t7806 - 10380 * t7799 - 420 * t7800 + 540) * np.exp((-1*1j) * (t7804 + t7801)) + (t7820 + 129115 * t7786 - 71295 * t7817 - 425565 * t7788 + 634305 * t7815 + 359415 * t7790 - 1211035 * t7813 + 239855 * t7792 + 990045 * t7811 - 561295 * t7810 - 339325 * t7809 + 322665 * t7808 + 21315 * t7807 - 68355 * t7806 + 6615 * t7799 + 4165 * t7800 - 245) * np.exp((1j) * (t7805 + t7802)) + (t7821 - 46500 * t7786 + 131300 * t7817 - 130260 * t7788 - 134860 * t7815 + 457260 * t7790 - 316140 * t7813 - 240900 * t7792 + 486420 * t7811 - 164780 * t7810 - 169620 * t7809 + 154180 * t7808 - 12900 * t7807 - 29660 * t7806 + 10140 * t7799 + 660 * t7800 - 540) * np.exp((1j) * (t7804 + t7802)) + (t7822 + 5425 * t7786 + 19155 * t7817 + 31129 * t7788 + 8099 * t7815 - 55419 * t7790 - 89089 * t7813 - 29315 * t7792 + 63063 * t7811 + 77363 * t7810 + 17017 * t7809 - 27573 * t7808 - 22295 * t7807 - 3241 * t7806 + 3189 * t7799 + 1631 * t7800 + 241) * np.exp((-1*1j) * (t7803 + t7801)) + (t7822 - 6665 * t7786 + 31245 * t7817 - 81529 * t7788 + 120757 * t7815 - 73437 * t7790 - 71071 * t7813 + 189475 * t7792 - 155727 * t7811 + 15301 * t7810 + 79079 * t7809 - 68523 * t7808 + 18655 * t7807 + 6881 * t7806 - 6933 * t7799 + 2113 * t7800 - 241) * np.exp((1j) * (t7803 + t7802))) + + if Bindx == 224: + t7847 = np.cos(phi) + t7846 = t7847 ** 2 + t7855 = t7846 ** 2 + t7859 = t7855 ** 2 + t7832 = t7859 ** 2 + t7871 = 565 * t7832 + t7870 = 1240 * t7832 + t7869 = 362700 * t7832 + t7868 = 7785960 * t7832 + t7854 = t7847 * t7846 + t7857 = t7854 ** 2 + t7858 = t7847 * t7857 + t7865 = t7858 ** 2 + t7863 = t7857 ** 2 + t7856 = t7847 * t7855 + t7861 = t7856 ** 2 + t7853 = 4 * phi1 + t7852 = 8 * phi1 + t7851 = 12 * phi1 + t7850 = 16 * phi1 + t7849 = -13 * phi2 + t7848 = 13 * phi2 + t7839 = t7847 * t7859 + t7837 = t7847 * t7861 + t7835 = t7847 * t7863 + t7833 = t7847 * t7865 + tfunc[..., c] = (0.11e2 / 0.236978176e9*1j) * np.sqrt(0.31e2) * np.sqrt(0.113e3) * np.sqrt(0.29e2) * np.sqrt(0.3e1) * np.sqrt((1 + t7847)) * ((1 - t7847) ** (-0.1e1 / 0.2e1)) * ((t7871 + 6780 * t7833 + 36160 * t7865 - 484770 * t7859 + 36160 * t7846 + 6780 * t7847 + 565 - 323180 * t7839 - 323180 * t7858 + 110740 * t7835 + 110740 * t7854 + 205660 * t7863 + 205660 * t7837 + 205660 * t7856 + 205660 * t7855) * np.exp((-1*1j) * (t7850 + t7848)) + (-444625650 * t7861 + 444625650 * t7839 + 351020250 * t7859 - 351020250 * t7858 + 322938630 * t7863 - 322938630 * t7837 - 154448910 * t7857 + 154448910 * t7856 - 126367290 * t7865 + 126367290 * t7835 + 32761890 * t7855 - 32761890 * t7854 + 20726910 * t7832 - 20726910 * t7833 - 2005830 * t7846 + 2005830 * t7847) * np.exp((-13*1j) * phi2) + (t7871 - 7910 * t7833 + 50850 * t7865 - 50850 * t7846 + 7910 * t7847 - 565 + 1131130 * t7861 - 1131130 * t7857 - 925470 * t7837 + 925470 * t7856 - 807950 * t7839 + 807950 * t7858 + 514150 * t7863 - 514150 * t7855 - 197750 * t7835 + 197750 * t7854) * np.exp((1j) * (t7850 + t7849)) + (17518410 * t7833 - 95001270 * t7835 + 213172050 * t7837 - 253985550 * t7839 + 3704610 * t7846 - 941850 * t7847 + 12118470 * t7854 - 24174150 * t7855 - 63983010 * t7856 + 59713290 * t7857 + 171102750 * t7858 - 61220250 * t7859 + 5085990 * t7861 + 41504190 * t7863 - 32336850 * t7865 + t7868 - 62790) * np.exp((-1*1j) * (t7853 + t7848)) + (1994850 * t7833 - 3026790 * t7835 - 6377670 * t7837 + 17953650 * t7839 - 607230 * t7846 - 24570 * t7847 - 534690 * t7854 + 3024450 * t7855 + 5240430 * t7856 - 4337190 * t7857 - 15225210 * t7858 - 3590730 * t7859 + 14633190 * t7861 - 12482730 * t7863 + 2965950 * t7865 + 31590 + t7869) * np.exp((-1*1j) * (t7852 + t7848)) + (-33090330 * t7833 + 109066230 * t7835 - 92112930 * t7837 - 61471410 * t7839 - 1695330 * t7846 - 1067430 * t7847 + 17518410 * t7854 - 5462730 * t7855 - 82694430 * t7856 + 86964150 * t7857 + 143851890 * t7858 - 253734390 * t7859 + 310370970 * t7861 - 162563310 * t7863 + 18271890 * t7865 + t7868 + 62790) * np.exp((1j) * (t7853 + t7849)) + (-2720250 * t7833 - 7620210 * t7835 + 26749710 * t7837 - 14092650 * t7839 + 593190 * t7846 + 38610 * t7847 - 1735110 * t7854 - 754650 * t7855 + 9019530 * t7856 - 9922770 * t7857 - 9639630 * t7858 + 28455570 * t7859 - 18494190 * t7861 - 7889310 * t7863 + 7681050 * t7865 - 31590 + t7869) * np.exp((1j) * (t7852 + t7849)) + (482 + t7870 + 10850 * t7833 + 38310 * t7865 + 62258 * t7835 + 16198 * t7863 - 110838 * t7837 - 178178 * t7861 - 58630 * t7839 + 126126 * t7859 + 154726 * t7858 + 34034 * t7857 - 55146 * t7856 - 44590 * t7855 - 6482 * t7854 + 6378 * t7846 + 3262 * t7847) * np.exp((-1*1j) * (t7851 + t7848)) + (-482 + t7870 - 13330 * t7833 + 62490 * t7865 - 163058 * t7835 + 241514 * t7863 - 146874 * t7837 - 142142 * t7861 + 378950 * t7839 - 311454 * t7859 + 30602 * t7858 + 158158 * t7857 - 137046 * t7856 + 37310 * t7855 + 13762 * t7854 - 13866 * t7846 + 4226 * t7847) * np.exp((1j) * (t7851 + t7849))) + + if Bindx == 225: + t7894 = np.cos(phi) + t7893 = t7894 ** 2 + t7898 = t7894 * t7893 + t7901 = t7898 ** 2 + t7902 = t7894 * t7901 + t7909 = t7902 ** 2 + t7880 = t7894 * t7909 + t7907 = t7901 ** 2 + t7882 = t7894 * t7907 + t7899 = t7893 ** 2 + t7900 = t7894 * t7899 + t7905 = t7900 ** 2 + t7884 = t7894 * t7905 + t7903 = t7899 ** 2 + t7886 = t7894 * t7903 + t7917 = -40455 * t7880 - 269381 * t7882 + 308217 * t7884 + 383955 * t7886 - 7303 * t7894 + 35259 * t7898 + 98553 * t7900 - 512941 * t7902 + t7916 = -269700 * t7880 + 67860 * t7882 + 1468380 * t7884 - 2280300 * t7886 + 6780 * t7894 - 34540 * t7898 - 170020 * t7900 + 1211540 * t7902 + t7915 = -660765 * t7880 + 2905945 * t7882 - 5004125 * t7884 + 4170145 * t7886 - 2205 * t7894 + 20825 * t7898 + 201635 * t7900 - 1631455 * t7902 + t7879 = t7903 ** 2 + t7914 = -4495 * t7879 - 9528 * t7893 + 129220 * t7899 - 216216 * t7901 - 265122 * t7903 + 664664 * t7905 - 151788 * t7907 - 149640 * t7909 - 1191 + t7913 = -44950 * t7879 + 35520 * t7893 - 299400 * t7899 + 855360 * t7901 - 642180 * t7903 - 855360 * t7905 + 1462520 * t7907 - 510400 * t7909 - 1110 + t7912 = 220255 * t7879 + 68600 * t7893 - 639940 * t7899 + 2381400 * t7901 - 4235070 * t7903 + 3592680 * t7905 - 988820 * t7907 - 397880 * t7909 - 1225 + t7897 = 2 * phi1 + t7896 = -3 * phi2 + t7895 = 3 * phi2 + tfunc[..., c] = (0.33e2 / 0.29622272e8) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((-1140 - 341620 * t7879 + 2115840 * t7909 - 5522160 * t7907 + 7831040 * t7905 - 6463800 * t7903 + 3064320 * t7901 - 755440 * t7899 + 72960 * t7893) * np.exp((-12*1j) * phi2) + (t7912 - t7915) * np.exp((-4*1j) * (phi1 + t7895)) + (t7913 + t7916) * np.exp((-4*1j) * (t7897 + t7895)) + (t7912 + t7915) * np.exp((4*1j) * (phi1 + t7896)) + (t7913 - t7916) * np.exp((4*1j) * (t7897 + t7896)) + (t7914 + t7917) * np.exp((-12*1j) * (phi1 + phi2)) + (t7914 - t7917) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 226: + t7942 = np.cos(phi) + t7941 = t7942 ** 2 + t7948 = t7941 ** 2 + t7952 = t7948 ** 2 + t7927 = t7952 ** 2 + t7947 = t7942 * t7941 + t7950 = t7947 ** 2 + t7956 = t7950 ** 2 + t7951 = t7942 * t7950 + t7958 = t7951 ** 2 + t7968 = 16385 * t7927 + 1048640 * t7941 + 5964140 * t7948 - 14058330 * t7952 + 5964140 * t7956 + 1048640 * t7958 + 16385 + t7949 = t7942 * t7948 + t7954 = t7949 ** 2 + t7967 = 35960 * t7927 + 76224 * t7941 - 1033760 * t7948 + 1729728 * t7950 + 2120976 * t7952 - 5317312 * t7954 + 1214304 * t7956 + 1197120 * t7958 + 9528 + t7928 = t7942 * t7958 + t7930 = t7942 * t7956 + t7932 = t7942 * t7954 + t7934 = t7942 * t7952 + t7966 = -323640 * t7928 - 2155048 * t7930 + 2465736 * t7932 + 3071640 * t7934 - 58424 * t7942 + 282072 * t7947 + 788424 * t7949 - 4103528 * t7951 + t7965 = 9372220 * t7934 + 9372220 * t7951 - 5964140 * t7932 - 5964140 * t7949 - 3211460 * t7930 - 3211460 * t7947 - 196620 * t7928 - 196620 * t7942 + t7964 = -63109800 * t7928 + 15879240 * t7930 + 343600920 * t7932 - 533590200 * t7934 + 1586520 * t7942 - 8082360 * t7947 - 39784680 * t7949 + 283500360 * t7951 + t7963 = -677378520 * t7928 + 2979008760 * t7930 - 5129943000 * t7932 + 4274994360 * t7934 - 2260440 * t7942 + 21348600 * t7947 + 206704680 * t7949 - 1672474440 * t7951 + t7962 = 10518300 * t7927 - 8311680 * t7941 + 70059600 * t7948 - 200154240 * t7950 + 150270120 * t7952 + 200154240 * t7954 - 342229680 * t7956 + 119433600 * t7958 + 259740 + t7961 = 225792840 * t7927 + 70324800 * t7941 - 656029920 * t7948 + 2441275200 * t7950 - 4341551760 * t7952 + 3683010240 * t7954 - 1013681760 * t7956 - 407883840 * t7958 - 1255800 + t7946 = 2 * phi1 + t7945 = 4 * phi1 + t7944 = -3 * phi2 + t7943 = 3 * phi2 + tfunc[..., c] = (0.11e2 / 0.473956352e9) * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * ((601080390 * t7927 - 3722820480 * t7958 + 9716240520 * t7956 - 13778714880 * t7954 + 11373056100 * t7952 - 5391671040 * t7950 + 1329196680 * t7948 - 128373120 * t7941 + 2005830) * np.exp((-12*1j) * phi2) + (-t7965 + t7968) * np.exp((-4*1j) * (t7945 + t7943)) + (t7965 + t7968) * np.exp((4*1j) * (t7945 + t7944)) + (t7961 - t7963) * np.exp((-4*1j) * (phi1 + t7943)) + (t7962 - t7964) * np.exp((-4*1j) * (t7946 + t7943)) + (t7961 + t7963) * np.exp((4*1j) * (phi1 + t7944)) + (t7962 + t7964) * np.exp((4*1j) * (t7946 + t7944)) + (-t7966 + t7967) * np.exp((-12*1j) * (phi1 + phi2)) + (t7966 + t7967) * np.exp((12*1j) * (phi1 - phi2))) + + if Bindx == 227: + t7992 = np.cos(phi) + t7991 = t7992 ** 2 + t7999 = t7991 ** 2 + t8003 = t7999 ** 2 + t8011 = t8003 ** 2 + t7976 = t7992 * t8011 + t7998 = t7992 * t7991 + t8001 = t7998 ** 2 + t8002 = t7992 * t8001 + t8009 = t8002 ** 2 + t7978 = t7992 * t8009 + t8007 = t8001 ** 2 + t7980 = t7992 * t8007 + t8000 = t7992 * t7999 + t8005 = t8000 ** 2 + t7982 = t7992 * t8005 + t7984 = t7992 * t8003 + t8018 = 3596 * t7976 + 95236 * t7978 - 48132 * t7980 - 364156 * t7982 + 473044 * t7984 - 2640 * t7992 + 30668 * t7998 - 62348 * t8000 - 125268 * t8002 + t8017 = 2468 * t7991 + 43272 * t7999 - 224588 * t8001 + 286286 * t8003 + 94380 * t8005 - 354640 * t8007 + 123772 * t8009 + 29667 * t8011 - 617 + t8016 = -2640 * t7991 + 77920 * t7999 - 520400 * t8001 + 1390840 * t8003 - 1634160 * t8005 + 605440 * t8007 + 280720 * t8009 - 197780 * t8011 + 60 + t8015 = -5236 * t7991 + 93016 * t7999 - 696388 * t8001 + 2441978 * t8003 - 4485404 * t8005 + 4480784 * t8007 - 2313388 * t8009 + 484561 * t8011 + 77 + t8014 = 35960 * t7976 + 296960 * t7978 - 1191520 * t7980 + 1374720 * t7982 - 398640 * t7984 + 3960 * t7992 - 56320 * t7998 + 244640 * t8000 - 309760 * t8002 + t8013 = -176204 * t7976 + 471772 * t7978 + 186788 * t7980 - 2025380 * t7982 + 2969932 * t7984 + 4592 * t7992 - 102956 * t7998 + 677740 * t8000 - 2006284 * t8002 + t7997 = 4 * phi1 + t7996 = 8 * phi1 + t7995 = 12 * phi1 + t7994 = -11 * phi2 + t7993 = 11 * phi2 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * ((1 + t7992) ** (-0.1e1 / 0.2e1)) * ((1 - t7992) ** (-0.1e1 / 0.2e1)) * ((273296 * t7976 - 1727936 * t7978 + 4632960 * t7980 - 6815680 * t7982 + 5931040 * t7984 - 3053376 * t8002 + 870656 * t8000 - 115520 * t7998 + 4560 * t7992) * np.exp((-11*1j) * phi2) + (t8013 - t8015) * np.exp((-1*1j) * (t7997 + t7993)) + (t8014 - t8016) * np.exp((-1*1j) * (t7996 + t7993)) + (t8013 + t8015) * np.exp((1j) * (t7997 + t7994)) + (t8014 + t8016) * np.exp((1j) * (t7996 + t7994)) + (t8017 + t8018) * np.exp((-1*1j) * (t7995 + t7993)) + (-t8017 + t8018) * np.exp((1j) * (t7995 + t7994))) + + if Bindx == 228: + t8044 = np.cos(phi) + t8043 = t8044 ** 2 + t8052 = t8043 ** 2 + t8051 = t8044 * t8043 + t8054 = t8051 ** 2 + t8056 = t8052 ** 2 + t8053 = t8044 * t8052 + t8058 = t8053 ** 2 + t8060 = t8054 ** 2 + t8055 = t8044 * t8054 + t8062 = t8055 ** 2 + t8064 = t8056 ** 2 + t8073 = 4936 * t8043 + 86544 * t8052 - 449176 * t8054 + 572572 * t8056 + 188760 * t8058 - 709280 * t8060 + 247544 * t8062 + 59334 * t8064 - 1234 + t8028 = t8044 * t8064 + t8030 = t8044 * t8062 + t8032 = t8044 * t8060 + t8034 = t8044 * t8058 + t8036 = t8044 * t8056 + t8072 = 7192 * t8028 + 190472 * t8030 - 96264 * t8032 - 728312 * t8034 + 946088 * t8036 - 5280 * t8044 + 61336 * t8051 - 124696 * t8053 - 250536 * t8055 + t8071 = -170404 * t8043 - 550536 * t8052 + 1192828 * t8054 + 937222 * t8056 - 1874444 * t8058 + 432564 * t8062 + 36047 * t8064 - 3277 + t8070 = 3277 * t8028 + 170404 * t8030 + 550536 * t8032 - 1192828 * t8034 - 937222 * t8036 - 36047 * t8044 - 432564 * t8051 + 1874444 * t8055 + t8069 = 154440 * t8043 - 4558320 * t8052 + 30443400 * t8054 - 81364140 * t8056 + 95598360 * t8058 - 35418240 * t8060 - 16422120 * t8062 + 11570130 * t8064 - 3510 + t8068 = 1341912 * t8043 - 23838672 * t8052 + 178474296 * t8054 - 625844076 * t8056 + 1149544968 * t8058 - 1148360928 * t8060 + 592888296 * t8062 - 124186062 * t8064 - 19734 + t8067 = 2103660 * t8028 + 17372160 * t8030 - 69703920 * t8032 + 80421120 * t8034 - 23320440 * t8036 + 231660 * t8044 - 3294720 * t8051 + 14311440 * t8053 - 18120960 * t8055 + t8066 = 45158568 * t8028 - 120908424 * t8030 - 47871096 * t8032 + 519075960 * t8034 - 761151144 * t8036 - 1176864 * t8044 + 26386152 * t8051 - 173695080 * t8053 + 514181928 * t8055 + t8050 = 4 * phi1 + t8049 = 8 * phi1 + t8048 = 12 * phi1 + t8047 = 16 * phi1 + t8046 = -11 * phi2 + t8045 = 11 * phi2 + tfunc[..., c] = (0.11e2 / 0.236978176e9*1j) * np.sqrt(0.7e1) * np.sqrt(0.31e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((1 + t8044) ** (-0.1e1 / 0.2e1)) * ((1 - t8044) ** (-0.1e1 / 0.2e1)) * ((120216078 * t8028 - 760075848 * t8030 + 2037923280 * t8032 - 2998047240 * t8034 + 2608916220 * t8036 - 1343103768 * t8055 + 382979808 * t8053 - 50814360 * t8051 + 2005830 * t8044) * np.exp((-11*1j) * phi2) + (t8070 + t8071) * np.exp((-1*1j) * (t8047 + t8045)) + (t8070 - t8071) * np.exp((1j) * (t8047 + t8046)) + (t8066 - t8068) * np.exp((-1*1j) * (t8050 + t8045)) + (t8067 + t8069) * np.exp((-1*1j) * (t8049 + t8045)) + (t8066 + t8068) * np.exp((1j) * (t8050 + t8046)) + (t8067 - t8069) * np.exp((1j) * (t8049 + t8046)) + (t8072 + t8073) * np.exp((-1*1j) * (t8048 + t8045)) + (t8072 - t8073) * np.exp((1j) * (t8048 + t8046))) + + if Bindx == 229: + t8096 = np.cos(phi) + t8095 = t8096 ** 2 + t8103 = t8095 ** 2 + t8107 = t8103 ** 2 + t8081 = t8107 ** 2 + t8102 = t8096 * t8095 + t8105 = t8102 ** 2 + t8104 = t8096 * t8103 + t8109 = t8104 ** 2 + t8111 = t8105 ** 2 + t8106 = t8096 * t8105 + t8113 = t8106 ** 2 + t8121 = 16182 * t8081 + 23436 * t8095 - 8424 * t8103 - 447876 * t8105 + 1189188 * t8107 - 787644 * t8109 - 326664 * t8111 + 343476 * t8113 - 1674 + t8082 = t8096 * t8113 + t8084 = t8096 * t8111 + t8086 = t8096 * t8109 + t8088 = t8096 * t8107 + t8120 = -121365 * t8082 - 346869 * t8084 + 1146015 * t8086 - 606177 * t8088 + 3429 * t8096 - 81819 * t8102 + 368433 * t8104 - 361647 * t8106 + t8119 = 161820 * t8081 - 31320 * t8095 + 190680 * t8103 - 88440 * t8105 - 1663200 * t8107 + 4298360 * t8109 - 3902040 * t8111 + 1033560 * t8113 + 580 + t8118 = -792918 * t8081 - 55692 * t8095 + 670152 * t8103 - 2900604 * t8105 + 5610780 * t8107 - 4516932 * t8109 - 10584 * t8111 + 1995084 * t8113 + 714 + t8117 = -809100 * t8082 + 1237140 * t8084 + 1213380 * t8086 - 3611740 * t8088 - 6260 * t8096 + 126060 * t8102 - 902340 * t8104 + 2752860 * t8106 + t8116 = -1982295 * t8082 + 8248905 * t8084 - 13703067 * t8086 + 11560101 * t8088 + 4935 * t8096 - 136185 * t8102 + 1229067 * t8104 - 5221461 * t8106 + t8101 = 2 * phi1 + t8100 = 4 * phi1 + t8099 = 6 * phi1 + t8098 = -5 * phi2 + t8097 = 5 * phi2 + tfunc[..., c] = -(0.11e2 / 0.29622272e8) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((760 - 4828432 * t8105 + 910480 * t8103 - 65360 * t8095 + 1229832 * t8081 - 6744240 * t8113 + 15335280 * t8111 - 18557680 * t8109 + 12719360 * t8107) * np.exp((-10*1j) * phi2) + (t8116 + t8118) * np.exp((-2*1j) * (t8101 + t8097)) + (-t8117 + t8119) * np.exp((-2*1j) * (t8100 + t8097)) + (-t8120 + t8121) * np.exp((-2*1j) * (t8099 + t8097)) + (-t8116 + t8118) * np.exp((2*1j) * (t8101 + t8098)) + (t8117 + t8119) * np.exp((2*1j) * (t8100 + t8098)) + (t8120 + t8121) * np.exp((2*1j) * (t8099 + t8098))) + + if Bindx == 230: + t8146 = np.cos(phi) + t8145 = t8146 ** 2 + t8153 = t8146 * t8145 + t8156 = t8153 ** 2 + t8157 = t8146 * t8156 + t8164 = t8157 ** 2 + t8132 = t8146 * t8164 + t8162 = t8156 ** 2 + t8134 = t8146 * t8162 + t8154 = t8145 ** 2 + t8155 = t8146 * t8154 + t8160 = t8155 ** 2 + t8136 = t8146 * t8160 + t8158 = t8154 ** 2 + t8138 = t8146 * t8158 + t8174 = -53940 * t8132 - 154164 * t8134 + 509340 * t8136 - 269412 * t8138 + 1524 * t8146 - 36364 * t8153 + 163748 * t8155 - 160732 * t8157 + t8131 = t8158 ** 2 + t8173 = 7192 * t8131 + 10416 * t8145 - 3744 * t8154 - 199056 * t8156 + 528528 * t8158 - 350064 * t8160 - 145184 * t8162 + 152656 * t8164 - 744 + t8172 = 3277 * t8131 - 3277 + 937222 * t8156 - 937222 * t8160 - 255606 * t8154 + 255606 * t8162 + 137634 * t8164 - 137634 * t8145 + t8171 = -32770 * t8132 + 32770 * t8146 + 937222 * t8138 - 937222 * t8157 - 294930 * t8134 + 294930 * t8153 + 255606 * t8136 - 255606 * t8155 + t8170 = -10518300 * t8132 + 16082820 * t8134 + 15773940 * t8136 - 46952620 * t8138 - 81380 * t8146 + 1638780 * t8153 - 11730420 * t8155 + 35787180 * t8157 + t8169 = -112896420 * t8132 + 469794780 * t8134 - 780422292 * t8136 + 658375276 * t8138 + 281060 * t8146 - 7756060 * t8153 + 69998292 * t8155 - 297374636 * t8157 + t8168 = 2103660 * t8131 - 407160 * t8145 + 2478840 * t8154 - 1149720 * t8156 - 21621600 * t8158 + 55878680 * t8160 - 50726520 * t8162 + 13436280 * t8164 + 7540 + t8167 = 45158568 * t8131 + 3171792 * t8145 - 38166752 * t8154 + 165196304 * t8156 - 319547280 * t8158 + 257250032 * t8160 + 602784 * t8162 - 113624784 * t8164 - 40664 + t8152 = 2 * phi1 + t8151 = 4 * phi1 + t8150 = 6 * phi1 + t8149 = 8 * phi1 + t8148 = -5 * phi2 + t8147 = 5 * phi2 + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.7e1) * np.sqrt(0.31e2) * ((74290 + 120216078 * t8131 - 659249460 * t8164 + 1499023620 * t8162 - 1814013220 * t8160 + 1243317440 * t8158 - 471979228 * t8156 + 88999420 * t8154 - 6388940 * t8145) * np.exp((-10*1j) * phi2) + (-t8171 + t8172) * np.exp((-2*1j) * (t8149 + t8147)) + (t8171 + t8172) * np.exp((2*1j) * (t8149 + t8148)) + (t8167 - t8169) * np.exp((-2*1j) * (t8152 + t8147)) + (t8168 - t8170) * np.exp((-2*1j) * (t8151 + t8147)) + (t8173 - t8174) * np.exp((-2*1j) * (t8150 + t8147)) + (t8167 + t8169) * np.exp((2*1j) * (t8152 + t8148)) + (t8168 + t8170) * np.exp((2*1j) * (t8151 + t8148)) + (t8173 + t8174) * np.exp((2*1j) * (t8150 + t8148))) + + if Bindx == 231: + t8198 = np.cos(phi) + t8197 = t8198 ** 2 + t8204 = t8197 ** 2 + t8203 = t8198 * t8197 + t8206 = t8203 ** 2 + t8208 = t8204 ** 2 + t8205 = t8198 * t8204 + t8210 = t8205 ** 2 + t8212 = t8206 ** 2 + t8207 = t8198 * t8206 + t8214 = t8207 ** 2 + t8216 = t8208 ** 2 + t8223 = 564408 * t8197 - 3006900 * t8204 + 2683512 * t8206 + 13577850 * t8208 - 33575256 * t8210 + 24655644 * t8212 - 2035800 * t8214 - 2839941 * t8216 - 23517 + t8222 = 697600 * t8197 - 7675920 * t8204 + 33512640 * t8206 - 65998680 * t8208 + 47583360 * t8210 + 21818160 * t8212 - 48859200 * t8214 + 18932940 * t8216 - 10900 + t8182 = t8198 * t8216 + t8184 = t8198 * t8214 + t8186 = t8198 * t8212 + t8188 = t8198 * t8210 + t8190 = t8198 * t8208 + t8221 = 420732 * t8182 + 6528132 * t8184 - 16157700 * t8186 + 1919268 * t8188 + 22007700 * t8190 - 22464 * t8198 - 666900 * t8203 + 6990516 * t8205 - 21019284 * t8207 + t8220 = 685608 * t8197 - 10596348 * t8204 + 66588648 * t8206 - 215248866 * t8208 + 388443384 * t8210 - 394957836 * t8212 + 211478904 * t8214 - 46385703 * t8216 - 7791 + t8219 = 4207320 * t8182 + 15743520 * t8184 - 90417600 * t8186 + 142498720 * t8188 - 101815120 * t8190 + 18360 * t8198 - 130080 * t8203 - 4018560 * t8205 + 33913440 * t8207 + t8218 = -20615868 * t8182 + 68497884 * t8184 - 61159644 * t8186 - 35399364 * t8188 + 99922956 * t8190 + 98784 * t8198 - 2804172 * t8203 + 21869484 * t8205 - 70410060 * t8207 + t8202 = 4 * phi1 + t8201 = 8 * phi1 + t8200 = -9 * phi2 + t8199 = 9 * phi2 + tfunc[..., c] = (-0.11e2 / 0.59244544e8*1j) * np.sqrt(0.17e2) * np.sqrt(0.226e3) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * ((1 + t8198) ** (-0.1e1 / 0.2e1)) * ((1 - t8198) ** (-0.1e1 / 0.2e1)) * ((31975632 * t8182 - 181539072 * t8184 + 431795520 * t8186 - 555176960 * t8188 + 414525280 * t8190 - 179009792 * t8207 + 41717312 * t8205 - 4426240 * t8203 + 138320 * t8198) * np.exp((-9*1j) * phi2) + (t8221 - t8223) * np.exp((-3*1j) * (t8202 + 3 * phi2)) + (t8218 + t8220) * np.exp((-1*1j) * (t8202 + t8199)) + (t8219 + t8222) * np.exp((-1*1j) * (t8201 + t8199)) + (t8218 - t8220) * np.exp((1j) * (t8202 + t8200)) + (t8219 - t8222) * np.exp((1j) * (t8201 + t8200)) + (t8221 + t8223) * np.exp((3*1j) * (t8202 - 3 * phi2))) + + if Bindx == 232: + t8249 = np.cos(phi) + t8248 = t8249 ** 2 + t8256 = t8248 ** 2 + t8255 = t8249 * t8248 + t8258 = t8255 ** 2 + t8260 = t8256 ** 2 + t8257 = t8249 * t8256 + t8262 = t8257 ** 2 + t8264 = t8258 ** 2 + t8259 = t8249 * t8258 + t8266 = t8259 ** 2 + t8268 = t8260 ** 2 + t8277 = -9648 * t8248 + 51400 * t8256 - 45872 * t8258 - 232100 * t8260 + 573936 * t8262 - 421464 * t8264 + 34800 * t8266 + 48546 * t8268 + 402 + t8276 = -104864 * t8248 + 39324 * t8256 + 681616 * t8258 - 937222 * t8260 + 511212 * t8264 - 157296 * t8266 - 29493 * t8268 - 3277 + t8233 = t8249 * t8268 + t8235 = t8249 * t8266 + t8237 = t8249 * t8264 + t8239 = t8249 * t8262 + t8241 = t8249 * t8260 + t8275 = 3277 * t8233 + 104864 * t8235 - 39324 * t8237 - 681616 * t8239 + 937222 * t8241 + 29493 * t8249 + 157296 * t8255 - 511212 * t8257 + t8274 = 7192 * t8233 + 111592 * t8235 - 276200 * t8237 + 32808 * t8239 + 376200 * t8241 - 384 * t8249 - 11400 * t8255 + 119496 * t8257 - 359304 * t8259 + t8273 = 2103660 * t8233 + 7871760 * t8235 - 45208800 * t8237 + 71249360 * t8239 - 50907560 * t8241 + 9180 * t8249 - 65040 * t8255 - 2009280 * t8257 + 16956720 * t8259 + t8272 = -348800 * t8248 + 3837960 * t8256 - 16756320 * t8258 + 32999340 * t8260 - 23791680 * t8262 - 10909080 * t8264 + 24429600 * t8266 - 9466470 * t8268 + 5450 + t8271 = -1501808 * t8248 + 23211048 * t8256 - 145860848 * t8258 + 471497516 * t8260 - 850875984 * t8262 + 865145736 * t8264 - 463239504 * t8266 + 101606778 * t8268 + 17066 + t8270 = 45158568 * t8233 - 150042984 * t8235 + 133968744 * t8237 + 77541464 * t8239 - 218878856 * t8241 - 216384 * t8249 + 6142472 * t8255 - 47904584 * t8257 + 154231560 * t8259 + t8254 = 4 * phi1 + t8253 = 8 * phi1 + t8252 = -9 * phi2 + t8251 = 9 * phi2 + t8250 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * ((1 + t8249) ** (-0.1e1 / 0.2e1)) * ((1 - t8249) ** (-0.1e1 / 0.2e1)) * ((120216078 * t8233 - 682517088 * t8235 + 1623385080 * t8237 - 2087251840 * t8239 + 1558455620 * t8241 - 673007968 * t8259 + 156841048 * t8257 - 16640960 * t8255 + 520030 * t8249) * np.exp((-9*1j) * phi2) + (t8275 - t8276) * np.exp((-1*1j) * (t8250 + t8251)) + (t8275 + t8276) * np.exp((1j) * (t8250 + t8252)) + (t8274 + t8277) * np.exp((-3*1j) * (t8254 + 3 * phi2)) + (t8270 + t8271) * np.exp((-1*1j) * (t8254 + t8251)) + (-t8272 + t8273) * np.exp((-1*1j) * (t8253 + t8251)) + (t8270 - t8271) * np.exp((1j) * (t8254 + t8252)) + (t8272 + t8273) * np.exp((1j) * (t8253 + t8252)) + (t8274 - t8277) * np.exp((3*1j) * (t8254 - 3 * phi2))) + + if Bindx == 233: + t8300 = np.cos(phi) + t8299 = t8300 ** 2 + t8304 = t8300 * t8299 + t8307 = t8304 ** 2 + t8308 = t8300 * t8307 + t8315 = t8308 ** 2 + t8286 = t8300 * t8315 + t8313 = t8307 ** 2 + t8288 = t8300 * t8313 + t8305 = t8299 ** 2 + t8306 = t8300 * t8305 + t8311 = t8306 ** 2 + t8290 = t8300 * t8311 + t8309 = t8305 ** 2 + t8292 = t8300 * t8309 + t8323 = -3155490 * t8286 + 793962 * t8288 + 17180046 * t8290 - 26679510 * t8292 + 79326 * t8300 - 404118 * t8304 - 1989234 * t8306 + 14175018 * t8308 + t8322 = -21036600 * t8286 + 54152280 * t8288 - 38034360 * t8290 - 10769000 * t8292 - 81464 * t8300 + 1854552 * t8304 - 11197368 * t8306 + 25095576 * t8308 + t8321 = -51539670 * t8286 + 204496110 * t8288 - 326265030 * t8290 + 267388590 * t8292 + 91434 * t8300 - 2931474 * t8304 + 27776826 * t8306 - 119016786 * t8308 + t8285 = t8309 ** 2 + t8320 = 525915 * t8285 - 415584 * t8299 + 3502980 * t8305 - 10007712 * t8307 + 7513506 * t8309 + 10007712 * t8311 - 17111484 * t8313 + 5971680 * t8315 + 12987 + t8319 = -25769835 * t8285 - 324576 * t8299 + 4565820 * t8305 - 20975136 * t8307 + 33747966 * t8309 + 7032480 * t8311 - 78083460 * t8313 + 79803360 * t8315 + 3381 + t8318 = 5259150 * t8285 + 247824 * t8299 - 3755640 * t8305 + 24131184 * t8307 - 73520172 * t8309 + 110430320 * t8311 - 74987640 * t8313 + 12214800 * t8315 - 3442 + t8303 = 3 * phi1 + t8302 = -2 * phi2 + t8301 = 2 * phi2 + tfunc[..., c] = -(0.11e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((243168536 * t8309 - 77625184 * t8307 + 12144496 * t8305 - 719264 * t8299 + 39969540 * t8285 - 195979680 * t8315 + 393559920 * t8313 - 414525280 * t8311 + 6916) * np.exp((-8*1j) * phi2) + (t8318 - t8322) * np.exp((-8*1j) * (phi1 + phi2)) + (t8319 + t8321) * np.exp((-4*1j) * (phi1 + t8301)) + (t8320 - t8323) * np.exp((-4*1j) * (t8303 + t8301)) + (t8319 - t8321) * np.exp((4*1j) * (phi1 + t8302)) + (t8320 + t8323) * np.exp((4*1j) * (t8303 + t8302)) + (t8318 + t8322) * np.exp((8*1j) * (phi1 - phi2))) + + if Bindx == 234: + t8348 = np.cos(phi) + t8347 = t8348 ** 2 + t8353 = t8348 * t8347 + t8356 = t8353 ** 2 + t8357 = t8348 * t8356 + t8364 = t8357 ** 2 + t8334 = t8348 * t8364 + t8362 = t8356 ** 2 + t8336 = t8348 * t8362 + t8354 = t8347 ** 2 + t8355 = t8348 * t8354 + t8360 = t8355 ** 2 + t8338 = t8348 * t8360 + t8358 = t8354 ** 2 + t8340 = t8348 * t8358 + t8374 = -215760 * t8334 + 54288 * t8336 + 1174704 * t8338 - 1824240 * t8340 + 5424 * t8348 - 27632 * t8353 - 136016 * t8355 + 969232 * t8357 + t8333 = t8358 ** 2 + t8373 = 35960 * t8333 - 28416 * t8347 + 239520 * t8354 + 513744 * t8358 - 1170016 * t8362 + 408320 * t8364 + 888 + 684288 * t8360 - 684288 * t8356 + t8372 = 16385 * t8333 + 3244230 * t8358 + 16385 - 1441880 * t8356 - 1441880 * t8360 - 589860 * t8362 - 589860 * t8354 + 393240 * t8347 + 393240 * t8364 + t8371 = 1966200 * t8338 + 1966200 * t8355 - 1441880 * t8340 - 1441880 * t8357 - 393240 * t8336 - 393240 * t8353 - 131080 * t8334 - 131080 * t8348 + t8370 = -42073200 * t8334 + 108304560 * t8336 - 76068720 * t8338 - 21538000 * t8340 - 162928 * t8348 + 3709104 * t8353 - 22394736 * t8355 + 50191152 * t8357 + t8369 = 10518300 * t8333 + 495648 * t8347 - 7511280 * t8354 + 48262368 * t8356 - 147040344 * t8358 + 220860640 * t8360 - 149975280 * t8362 + 24429600 * t8364 - 6884 + t8368 = -451585680 * t8334 + 1791775440 * t8336 - 2858703120 * t8338 + 2342833360 * t8340 + 801136 * t8348 - 25685296 * t8353 + 243377904 * t8355 - 1042813744 * t8357 + t8367 = 225792840 * t8333 + 2843904 * t8347 - 40005280 * t8354 + 183782144 * t8356 - 295696464 * t8358 - 61617920 * t8360 + 684159840 * t8362 - 699229440 * t8364 - 29624 + t8352 = 2 * phi1 + t8351 = 3 * phi1 + t8350 = -2 * phi2 + t8349 = 2 * phi2 + tfunc[..., c] = (0.33e2 / 0.947912704e9) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * ((601080390 * t8333 - 2947232880 * t8364 + 5918535720 * t8362 - 6233822480 * t8360 + 3656880676 * t8358 - 1167363344 * t8356 + 182634536 * t8354 - 10816624 * t8347 + 104006) * np.exp((-8*1j) * phi2) + (t8369 - t8370) * np.exp((-8*1j) * (phi1 + phi2)) + (-t8371 + t8372) * np.exp((-8*1j) * (t8352 + phi2)) + (t8367 - t8368) * np.exp((-4*1j) * (phi1 + t8349)) + (t8373 - t8374) * np.exp((-4*1j) * (t8351 + t8349)) + (t8367 + t8368) * np.exp((4*1j) * (phi1 + t8350)) + (t8373 + t8374) * np.exp((4*1j) * (t8351 + t8350)) + (t8369 + t8370) * np.exp((8*1j) * (phi1 - phi2)) + (t8371 + t8372) * np.exp((8*1j) * (t8352 - phi2))) + + if Bindx == 235: + t8398 = np.cos(phi) + t8397 = t8398 ** 2 + t8405 = t8397 ** 2 + t8404 = t8398 * t8397 + t8407 = t8404 ** 2 + t8409 = t8405 ** 2 + t8406 = t8398 * t8405 + t8411 = t8406 ** 2 + t8413 = t8407 ** 2 + t8408 = t8398 * t8407 + t8415 = t8408 ** 2 + t8417 = t8409 ** 2 + t8424 = 170040 * t8397 - 2640924 * t8405 + 13620984 * t8407 - 30161274 * t8409 + 29024424 * t8411 - 6153420 * t8413 - 7536984 * t8415 + 3681405 * t8417 - 4251 + t8423 = 239680 * t8397 - 2574544 * t8405 + 7121408 * t8407 + 6378152 * t8409 - 62152640 * t8411 + 112585200 * t8413 - 86136960 * t8415 + 24542700 * t8417 - 2996 + t8422 = 606424 * t8397 - 10521868 * t8405 + 71006488 * t8407 - 240374498 * t8409 + 450355080 * t8411 - 474730620 * t8413 + 263794440 * t8415 - 60129615 * t8417 - 5831 + t8382 = t8398 * t8417 + t8384 = t8398 * t8415 + t8386 = t8398 * t8413 + t8388 = t8398 * t8411 + t8390 = t8398 * t8409 + t8421 = 7012200 * t8382 + 904800 * t8384 - 68752320 * t8386 + 143517920 * t8388 - 131487664 * t8390 - 65272 * t8398 + 1796512 * t8404 - 15524096 * t8406 + 62597920 * t8408 + t8420 = -34359780 * t8382 + 131897220 * t8384 - 194272260 * t8386 + 131591460 * t8388 - 33328428 * t8390 + 17248 * t8398 - 543508 * t8404 + 3862964 * t8406 - 4864916 * t8408 + t8419 = 701220 * t8382 + 4727580 * t8384 - 23128092 * t8386 + 32868732 * t8388 - 16667508 * t8390 + 104832 * t8398 - 1278732 * t8404 + 4345068 * t8406 - 1673100 * t8408 + t8403 = 4 * phi1 + t8402 = 8 * phi1 + t8401 = -7 * phi2 + t8400 = 7 * phi2 + t8399 = 12 * phi1 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * ((1 + t8398) ** (-0.1e1 / 0.2e1)) * ((1 - t8398) ** (-0.1e1 / 0.2e1)) * ((53292720 * t8382 - 275059200 * t8384 + 589717440 * t8386 - 676898560 * t8388 + 446441632 * t8390 - 168450048 * t8408 + 33971392 * t8406 - 3098368 * t8404 + 82992 * t8398) * np.exp((-7*1j) * phi2) + (t8420 + t8422) * np.exp((-1*1j) * (t8403 + t8400)) + (t8421 + t8423) * np.exp((-1*1j) * (t8402 + t8400)) + (t8420 - t8422) * np.exp((1j) * (t8403 + t8401)) + (t8421 - t8423) * np.exp((1j) * (t8402 + t8401)) + (t8419 + t8424) * np.exp((-1*1j) * (t8399 + t8400)) + (t8419 - t8424) * np.exp((1j) * (t8399 + t8401))) + + if Bindx == 236: + t8450 = np.cos(phi) + t8449 = t8450 ** 2 + t8458 = t8449 ** 2 + t8462 = t8458 ** 2 + t8470 = t8462 ** 2 + t8434 = t8450 * t8470 + t8457 = t8450 * t8449 + t8460 = t8457 ** 2 + t8461 = t8450 * t8460 + t8468 = t8461 ** 2 + t8436 = t8450 * t8468 + t8466 = t8460 ** 2 + t8438 = t8450 * t8466 + t8459 = t8450 * t8458 + t8464 = t8459 ** 2 + t8440 = t8450 * t8464 + t8442 = t8450 * t8462 + t8479 = 35960 * t8434 + 242440 * t8436 - 1186056 * t8438 + 1685576 * t8440 - 854744 * t8442 + 5376 * t8450 - 65576 * t8457 + 222824 * t8459 - 85800 * t8461 + t8478 = -262160 * t8449 + 983100 * t8458 - 524320 * t8460 - 1802350 * t8462 + 2883760 * t8464 - 1376340 * t8466 + 114695 * t8470 - 16385 + t8477 = 16385 * t8434 + 262160 * t8436 - 983100 * t8438 + 524320 * t8440 + 1802350 * t8442 - 114695 * t8450 + 1376340 * t8459 - 2883760 * t8461 + t8476 = 8720 * t8449 - 135432 * t8458 + 698512 * t8460 - 1546732 * t8462 + 1488432 * t8464 - 315560 * t8466 - 386512 * t8468 + 188790 * t8470 - 218 + t8475 = -359520 * t8449 + 3861816 * t8458 - 10682112 * t8460 - 9567228 * t8462 + 93228960 * t8464 - 168877800 * t8466 + 129205440 * t8468 - 36814050 * t8470 + 4494 + t8474 = 10518300 * t8434 + 1357200 * t8436 - 103128480 * t8438 + 215276880 * t8440 - 197231496 * t8442 - 97908 * t8450 + 2694768 * t8457 - 23286144 * t8459 + 93896880 * t8461 + t8473 = -3985072 * t8449 + 69143704 * t8458 - 466614064 * t8460 + 1579603844 * t8462 - 2959476240 * t8464 + 3119658360 * t8466 - 1733506320 * t8468 + 395137470 * t8470 + 38318 + t8472 = 225792840 * t8434 - 866753160 * t8436 + 1276646280 * t8438 - 864743880 * t8440 + 219015384 * t8442 - 113344 * t8450 + 3571624 * t8457 - 25385192 * t8459 + 31969448 * t8461 + t8456 = 4 * phi1 + t8455 = 8 * phi1 + t8454 = -7 * phi2 + t8453 = 7 * phi2 + t8452 = 12 * phi1 + t8451 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * ((1 + t8450) ** (-0.1e1 / 0.2e1)) * ((1 - t8450) ** (-0.1e1 / 0.2e1)) * ((601080390 * t8434 - 3102350400 * t8436 + 6651332280 * t8438 - 7634634720 * t8440 + 5035346484 * t8442 - 1899922176 * t8461 + 383158104 * t8459 - 34946016 * t8457 + 936054 * t8450) * np.exp((-7*1j) * phi2) + (t8477 + t8478) * np.exp((-1*1j) * (t8451 + t8453)) + (t8477 - t8478) * np.exp((1j) * (t8451 + t8454)) + (t8472 + t8473) * np.exp((-1*1j) * (t8456 + t8453)) + (t8474 - t8475) * np.exp((-1*1j) * (t8455 + t8453)) + (t8472 - t8473) * np.exp((1j) * (t8456 + t8454)) + (t8474 + t8475) * np.exp((1j) * (t8455 + t8454)) + (t8476 + t8479) * np.exp((-1*1j) * (t8452 + t8453)) + (-t8476 + t8479) * np.exp((1j) * (t8452 + t8454))) + + if Bindx == 237: + t8502 = np.cos(phi) + t8501 = t8502 ** 2 + t8507 = t8502 * t8501 + t8510 = t8507 ** 2 + t8511 = t8502 * t8510 + t8518 = t8511 ** 2 + t8488 = t8502 * t8518 + t8516 = t8510 ** 2 + t8490 = t8502 * t8516 + t8508 = t8501 ** 2 + t8509 = t8502 * t8508 + t8514 = t8509 ** 2 + t8492 = t8502 * t8514 + t8512 = t8508 ** 2 + t8494 = t8502 * t8512 + t8526 = -241920900 * t8488 + 819409500 * t8490 - 1071825300 * t8492 + 674978700 * t8494 + 12420 * t8502 - 1065820 * t8507 + 26793620 * t8509 - 206382220 * t8511 + t8525 = 36288135 * t8488 - 96898425 * t8490 + 39248235 * t8492 + 111151755 * t8494 + 811785 * t8502 - 12822615 * t8507 + 67315365 * t8509 - 145094235 * t8511 + t8487 = t8512 ** 2 + t8524 = 8064030 * t8487 - 1237860 * t8501 - 466440 * t8508 + 51505740 * t8510 - 193590540 * t8512 + 286340340 * t8514 - 180297000 * t8516 + 29654820 * t8518 + 26910 + t8523 = -592706205 * t8488 + 2262480675 * t8490 - 3468111465 * t8492 + 2719929975 * t8494 + 658413 * t8502 - 23898035 * t8507 + 249456697 * t8509 - 1147810055 * t8511 + t8522 = 80640300 * t8487 + 3837320 * t8501 - 53355400 * t8508 + 279605480 * t8510 - 693462880 * t8512 + 833432600 * t8514 - 383019000 * t8516 - 67633800 * t8518 - 44620 + t8521 = -395137470 * t8487 + 334180 * t8501 - 8606360 * t8508 + 95133108 * t8510 - 503971860 * t8512 + 1370454540 * t8514 - 1960303800 * t8516 + 1402100700 * t8518 - 3038 + t8506 = 2 * phi1 + t8505 = 4 * phi1 + t8504 = -3 * phi2 + t8503 = 3 * phi2 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((612866280 * t8487 - 2728243440 * t8518 + 4937016240 * t8516 - 4649784880 * t8514 + 2421469440 * t8512 - 682443216 * t8510 + 93974608 * t8508 - 4896528 * t8501 + 41496) * np.exp((-6*1j) * phi2) + (t8524 + t8525) * np.exp((-6*1j) * (t8506 + phi2)) + (t8521 + t8523) * np.exp((-2*1j) * (t8506 + t8503)) + (t8522 - t8526) * np.exp((-2*1j) * (t8505 + t8503)) + (t8521 - t8523) * np.exp((2*1j) * (t8506 + t8504)) + (t8522 + t8526) * np.exp((2*1j) * (t8505 + t8504)) + (t8524 - t8525) * np.exp((6*1j) * (t8506 - phi2))) + + if Bindx == 238: + t8551 = np.cos(phi) + t8550 = t8551 ** 2 + t8558 = t8550 ** 2 + t8562 = t8558 ** 2 + t8536 = t8562 ** 2 + t8557 = t8551 * t8550 + t8560 = t8557 ** 2 + t8559 = t8551 * t8558 + t8564 = t8559 ** 2 + t8566 = t8560 ** 2 + t8561 = t8551 * t8560 + t8568 = t8561 ** 2 + t8578 = 35960 * t8536 - 5520 * t8550 - 2080 * t8558 + 229680 * t8560 - 863280 * t8562 + 1276880 * t8564 - 804000 * t8566 + 132240 * t8568 + 120 + t8577 = 16385 * t8536 - 16385 + 1081410 * t8564 - 1081410 * t8560 - 819250 * t8566 + 819250 * t8558 - 163850 * t8550 + 163850 * t8568 + t8537 = t8551 * t8568 + t8539 = t8551 * t8566 + t8541 = t8551 * t8564 + t8543 = t8551 * t8562 + t8576 = -161820 * t8537 + 432100 * t8539 - 175020 * t8541 - 495660 * t8543 - 3620 * t8551 + 57180 * t8557 - 300180 * t8559 + 647020 * t8561 + t8575 = 98310 * t8537 - 98310 * t8551 + 1802350 * t8543 - 1802350 * t8561 - 557090 * t8541 + 557090 * t8559 - 163850 * t8539 + 163850 * t8557 + t8574 = -31554900 * t8537 + 106879500 * t8539 - 139803300 * t8541 + 88040700 * t8543 + 1620 * t8551 - 139020 * t8557 + 3494820 * t8559 - 26919420 * t8561 + t8573 = 10518300 * t8536 + 500520 * t8550 - 6959400 * t8558 + 36470280 * t8560 - 90451680 * t8562 + 108708600 * t8564 - 49959000 * t8566 - 8821800 * t8568 - 5820 + t8572 = -338689260 * t8537 + 1292846100 * t8539 - 1981777980 * t8541 + 1554245700 * t8543 + 376236 * t8551 - 13656020 * t8557 + 142546684 * t8559 - 655891460 * t8561 + t8571 = 225792840 * t8536 - 190960 * t8550 + 4917920 * t8558 - 54361776 * t8560 + 287983920 * t8562 - 783116880 * t8564 + 1120173600 * t8566 - 801200400 * t8568 + 1736 + t8556 = 2 * phi1 + t8555 = 4 * phi1 + t8554 = 8 * phi1 + t8553 = -3 * phi2 + t8552 = 3 * phi2 + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * ((601080390 * t8536 - 2675777220 * t8568 + 4842073620 * t8566 - 4560365940 * t8564 + 2374902720 * t8562 - 669319308 * t8560 + 92167404 * t8558 - 4802364 * t8550 + 40698) * np.exp((-6*1j) * phi2) + (t8575 + t8577) * np.exp((-2*1j) * (t8554 + t8552)) + (-t8575 + t8577) * np.exp((2*1j) * (t8554 + t8553)) + (-t8576 + t8578) * np.exp((-6*1j) * (t8556 + phi2)) + (t8571 - t8572) * np.exp((-2*1j) * (t8556 + t8552)) + (t8573 - t8574) * np.exp((-2*1j) * (t8555 + t8552)) + (t8571 + t8572) * np.exp((2*1j) * (t8556 + t8553)) + (t8573 + t8574) * np.exp((2*1j) * (t8555 + t8553)) + (t8576 + t8578) * np.exp((6*1j) * (t8556 - phi2))) + + if Bindx == 239: + t8602 = np.cos(phi) + t8601 = t8602 ** 2 + t8609 = t8601 ** 2 + t8608 = t8602 * t8601 + t8611 = t8608 ** 2 + t8613 = t8609 ** 2 + t8610 = t8602 * t8609 + t8615 = t8610 ** 2 + t8617 = t8611 ** 2 + t8612 = t8602 * t8611 + t8619 = t8612 ** 2 + t8621 = t8613 ** 2 + t8628 = 3089360 * t8601 - 51854880 * t8609 + 361477200 * t8611 - 1300498200 * t8613 + 2598383600 * t8615 - 2903409600 * t8617 + 1696047600 * t8619 - 403201500 * t8621 - 33580 + t8627 = 6815116 * t8601 - 129812760 * t8609 + 945287420 * t8611 - 3398637550 * t8613 + 6678759780 * t8615 - 7320811680 * t8617 + 4206302100 * t8619 - 987843675 * t8621 - 58751 + t8626 = 5364060 * t8601 - 40580280 * t8609 + 103459980 * t8611 - 51227670 * t8613 - 200048940 * t8615 + 371860320 * t8617 - 249204540 * t8619 + 60480225 * t8621 - 103155 + t8586 = t8602 * t8621 + t8588 = t8602 * t8619 + t8590 = t8602 * t8617 + t8592 = t8602 * t8615 + t8594 = t8602 * t8613 + t8625 = 16128060 * t8586 + 2601300 * t8588 - 227066580 * t8590 + 570366420 * t8592 - 627953820 * t8594 - 645840 * t8602 + 13257660 * t8608 - 101953020 * t8610 + 355265820 * t8612 + t8624 = 161280600 * t8586 - 416208000 * t8588 + 55255200 * t8590 + 850595200 * t8592 - 1149361520 * t8594 - 713000 * t8602 + 21417600 * t8608 - 181530720 * t8610 + 659264640 * t8612 + t8623 = -790274940 * t8586 + 3339548940 * t8588 - 5814981900 * t8590 + 5393033100 * t8592 - 2876351940 * t8594 - 301840 * t8602 + 12062820 * t8608 - 150426276 * t8610 + 887692036 * t8612 + t8607 = 4 * phi1 + t8606 = 8 * phi1 + t8605 = -5 * phi2 + t8604 = 5 * phi2 + t8603 = 12 * phi1 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((1 + t8602) ** (-0.1e1 / 0.2e1)) * ((1 - t8602) ** (-0.1e1 / 0.2e1)) * ((1225732560 * t8586 - 5851884480 * t8588 + 11551063680 * t8590 - 12149159360 * t8592 + 7308947360 * t8594 - 2505552192 * t8612 + 457673216 * t8610 - 37733696 * t8608 + 912912 * t8602) * np.exp((-5*1j) * phi2) + (t8623 + t8627) * np.exp((-1*1j) * (t8607 + t8604)) + (t8624 - t8628) * np.exp((-1*1j) * (t8606 + t8604)) + (t8623 - t8627) * np.exp((1j) * (t8607 + t8605)) + (t8624 + t8628) * np.exp((1j) * (t8606 + t8605)) + (t8625 + t8626) * np.exp((-1*1j) * (t8603 + t8604)) + (t8625 - t8626) * np.exp((1j) * (t8603 + t8605))) + + if Bindx == 240: + t8654 = np.cos(phi) + t8653 = t8654 ** 2 + t8662 = t8653 ** 2 + t8666 = t8662 ** 2 + t8674 = t8666 ** 2 + t8638 = t8654 * t8674 + t8661 = t8654 * t8653 + t8664 = t8661 ** 2 + t8665 = t8654 * t8664 + t8672 = t8665 ** 2 + t8640 = t8654 * t8672 + t8670 = t8664 ** 2 + t8642 = t8654 * t8670 + t8663 = t8654 * t8662 + t8668 = t8663 ** 2 + t8644 = t8654 * t8668 + t8646 = t8654 * t8666 + t8683 = 35960 * t8638 + 5800 * t8640 - 506280 * t8642 + 1271720 * t8644 - 1400120 * t8646 - 1440 * t8654 + 29560 * t8661 - 227320 * t8663 + 792120 * t8665 + t8682 = -11960 * t8653 + 90480 * t8662 - 230680 * t8664 + 114220 * t8666 + 446040 * t8668 - 829120 * t8670 + 555640 * t8672 - 134850 * t8674 + 230 + t8681 = 65540 * t8653 - 655400 * t8662 + 1638500 * t8664 - 1802350 * t8666 + 720940 * t8668 + 262160 * t8670 - 327700 * t8672 + 81925 * t8674 + 16385 + t8680 = 16385 * t8638 + 65540 * t8640 - 655400 * t8642 + 1638500 * t8644 - 1802350 * t8646 + 81925 * t8654 - 327700 * t8661 + 262160 * t8663 + 720940 * t8665 + t8679 = -201480 * t8653 + 3381840 * t8662 - 23574600 * t8664 + 84815100 * t8666 - 169459800 * t8668 + 189352800 * t8670 - 110611800 * t8672 + 26295750 * t8674 + 2190 + t8678 = 10518300 * t8638 - 27144000 * t8640 + 3603600 * t8642 + 55473600 * t8644 - 74958360 * t8646 - 46500 * t8654 + 1396800 * t8661 - 11838960 * t8663 + 42995520 * t8665 + t8677 = 1947176 * t8653 - 37089360 * t8662 + 270082120 * t8664 - 971039300 * t8666 + 1908217080 * t8668 - 2091660480 * t8670 + 1201800600 * t8672 - 282241050 * t8674 - 16786 + t8676 = 225792840 * t8638 - 954156840 * t8640 + 1661423400 * t8642 - 1540866600 * t8644 + 821814840 * t8646 + 86240 * t8654 - 3446520 * t8661 + 42978936 * t8663 - 253626296 * t8665 + t8660 = 4 * phi1 + t8659 = 8 * phi1 + t8658 = -5 * phi2 + t8657 = 5 * phi2 + t8656 = 12 * phi1 + t8655 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.31e2) * ((1 + t8654) ** (-0.1e1 / 0.2e1)) * ((1 - t8654) ** (-0.1e1 / 0.2e1)) * ((601080390 * t8638 - 2869674120 * t8640 + 5664463920 * t8642 - 5957760840 * t8644 + 3584195340 * t8646 - 1228684248 * t8665 + 224435904 * t8663 - 18504024 * t8661 + 447678 * t8654) * np.exp((-5*1j) * phi2) + (t8676 - t8677) * np.exp((-1*1j) * (t8660 + t8657)) + (t8678 + t8679) * np.exp((-1*1j) * (t8659 + t8657)) + (t8676 + t8677) * np.exp((1j) * (t8660 + t8658)) + (t8678 - t8679) * np.exp((1j) * (t8659 + t8658)) + (-t8682 + t8683) * np.exp((-1*1j) * (t8656 + t8657)) + (t8680 + t8681) * np.exp((-1*1j) * (t8655 + t8657)) + (t8682 + t8683) * np.exp((1j) * (t8656 + t8658)) + (t8680 - t8681) * np.exp((1j) * (t8655 + t8658))) + + if Bindx == 241: + t8706 = np.cos(phi) + t8705 = t8706 ** 2 + t8709 = t8706 * t8705 + t8712 = t8709 ** 2 + t8713 = t8706 * t8712 + t8720 = t8713 ** 2 + t8692 = t8706 * t8720 + t8718 = t8712 ** 2 + t8694 = t8706 * t8718 + t8710 = t8705 ** 2 + t8711 = t8706 * t8710 + t8716 = t8711 ** 2 + t8696 = t8706 * t8716 + t8714 = t8710 ** 2 + t8698 = t8706 * t8714 + t8728 = -12096045 * t8692 + 53196585 * t8694 - 91606125 * t8696 + 76339185 * t8698 - 40365 * t8706 + 381225 * t8709 + 3691155 * t8711 - 29865615 * t8713 + t8727 = 80640300 * t8692 - 319959900 * t8694 + 510482700 * t8696 - 418363100 * t8698 - 143060 * t8706 + 4586660 * t8709 - 43460340 * t8711 + 186216740 * t8713 + t8726 = -197568735 * t8692 + 732916275 * t8694 - 1085670495 * t8696 + 816475275 * t8698 + 150689 * t8706 - 5943245 * t8709 + 66777249 * t8711 - 327165685 * t8713 + t8691 = t8714 ** 2 + t8725 = 4032015 * t8691 + 1255800 * t8705 - 11714820 * t8710 + 43594200 * t8712 - 77527710 * t8714 + 65768040 * t8716 - 18101460 * t8718 - 7283640 * t8720 - 22425 + t8724 = 40320150 * t8691 + 507840 * t8705 - 7143800 * t8710 + 32818240 * t8712 - 52802940 * t8714 - 11003200 * t8716 + 122171400 * t8718 - 124862400 * t8720 - 5290 + t8723 = -197568735 * t8691 + 766920 * t8705 - 15373820 * t8710 + 119731304 * t8712 - 466534530 * t8714 + 1002881880 * t8716 - 1208707500 * t8718 + 764782200 * t8720 - 6391 + t8708 = 2 * phi1 + t8707 = 3 * phi1 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((306433140 * t8691 - 1265272320 * t8720 + 2117422320 * t8718 - 1839735040 * t8716 + 882372920 * t8714 - 228836608 * t8712 + 28995824 * t8710 - 1391104 * t8705 + 10868) * np.exp((-4*1j) * phi2) + (t8723 + t8726) * np.exp((-4*1j) * (phi1 + phi2)) + (t8724 + t8727) * np.exp((-4*1j) * (t8708 + phi2)) + (t8725 - t8728) * np.exp((-4*1j) * (t8707 + phi2)) + (t8723 - t8726) * np.exp((4*1j) * (phi1 - phi2)) + (t8724 - t8727) * np.exp((4*1j) * (t8708 - phi2)) + (t8725 + t8728) * np.exp((4*1j) * (t8707 - phi2))) + + if Bindx == 242: + t8753 = np.cos(phi) + t8752 = t8753 ** 2 + t8758 = t8752 ** 2 + t8757 = t8753 * t8752 + t8760 = t8757 ** 2 + t8766 = t8760 ** 2 + t8780 = -t8758 - t8766 + t8741 = t8753 * t8766 + t8762 = t8758 ** 2 + t8745 = t8753 * t8762 + t8761 = t8753 * t8760 + t8779 = -t8741 - t8745 - t8757 - t8761 + t8768 = t8761 ** 2 + t8739 = t8753 * t8768 + t8759 = t8753 * t8758 + t8764 = t8759 ** 2 + t8743 = t8753 * t8764 + t8778 = -65540 * t8739 - 65540 * t8753 - 589860 * t8743 - 589860 * t8759 + t8738 = t8762 ** 2 + t8777 = 16385 * t8738 + 1048640 * t8760 - 1474650 * t8762 + 1048640 * t8764 + 16385 + t8776 = -107880 * t8739 + 474440 * t8741 - 817000 * t8743 + 680840 * t8745 - 360 * t8753 + 3400 * t8757 + 32920 * t8759 - 266360 * t8761 + t8775 = 35960 * t8738 + 11200 * t8752 - 104480 * t8758 + 388800 * t8760 - 691440 * t8762 + 586560 * t8764 - 161440 * t8766 - 64960 * t8768 - 200 + t8774 = -21036600 * t8739 + 83467800 * t8741 - 133169400 * t8743 + 109138200 * t8745 + 37320 * t8753 - 1196520 * t8757 + 11337480 * t8759 - 48578280 * t8761 + t8773 = 10518300 * t8738 + 132480 * t8752 - 1863600 * t8758 + 8561280 * t8760 - 13774680 * t8762 - 2870400 * t8764 + 31870800 * t8766 - 32572800 * t8768 - 1380 + t8772 = -225792840 * t8739 + 837618600 * t8741 - 1240766280 * t8743 + 933114600 * t8745 + 172216 * t8753 - 6792280 * t8757 + 76316856 * t8759 - 373903640 * t8761 + t8771 = 225792840 * t8738 - 876480 * t8752 + 17570080 * t8758 - 136835776 * t8760 + 533182320 * t8762 - 1146150720 * t8764 + 1381380000 * t8766 - 874036800 * t8768 + 7304 + t8756 = 2 * phi1 + t8755 = 3 * phi1 + t8754 = 4 * phi1 + t8733 = np.exp((-4*1j) * (t8754 + phi2)) + t8729 = np.exp((4*1j) * (t8754 - phi2)) + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * ((1730808420 * t8762 - 448871808 * t8760 + 56876424 * t8758 - 2728704 * t8752 + 601080390 * t8738 - 2481880320 * t8768 + 4153405320 * t8766 - 3608711040 * t8764 + 21318) * np.exp((-4*1j) * phi2) + ((t8777 - t8778) * t8733) + ((t8777 + t8778) * t8729) + (t8771 - t8772) * np.exp((-4*1j) * (phi1 + phi2)) + (t8773 - t8774) * np.exp((-4*1j) * (t8756 + phi2)) + (t8775 - t8776) * np.exp((-4*1j) * (t8755 + phi2)) + (t8771 + t8772) * np.exp((4*1j) * (phi1 - phi2)) + (t8773 + t8774) * np.exp((4*1j) * (t8756 - phi2)) + (t8775 + t8776) * np.exp((4*1j) * (t8755 - phi2)) + (327700 * (t8779 + t8780) * t8733) + (327700 * (-t8779 + t8780) * t8729)) + + if Bindx == 243: + t8804 = np.cos(phi) + t8803 = t8804 ** 2 + t8810 = t8803 ** 2 + t8814 = t8810 ** 2 + t8822 = t8814 ** 2 + t8788 = t8804 * t8822 + t8809 = t8804 * t8803 + t8812 = t8809 ** 2 + t8813 = t8804 * t8812 + t8820 = t8813 ** 2 + t8790 = t8804 * t8820 + t8818 = t8812 ** 2 + t8792 = t8804 * t8818 + t8811 = t8804 * t8810 + t8816 = t8811 ** 2 + t8794 = t8804 * t8816 + t8796 = t8804 * t8814 + t8829 = 12406200 * t8788 - 54427200 * t8790 + 96346080 * t8792 - 87753280 * t8794 + 43435408 * t8796 + 1656 * t8804 - 78016 * t8809 + 1440352 * t8811 - 11371200 * t8813 + t8828 = 211140 * t8803 - 2405616 * t8810 + 11775540 * t8812 - 29937030 * t8814 + 42912204 * t8816 - 35121000 * t8818 + 15359676 * t8820 - 2791395 * t8822 - 3519 + t8827 = 1240620 * t8788 - 5242620 * t8790 + 6933948 * t8792 + 595332 * t8794 - 10337580 * t8796 - 47472 * t8804 + 907212 * t8809 - 4784460 * t8811 + 10735020 * t8813 + t8826 = 285200 * t8803 - 4716288 * t8810 + 30110864 * t8812 - 95939624 * t8814 + 168707760 * t8816 - 166759200 * t8818 + 86923440 * t8820 - 18609300 * t8822 - 2852 + t8825 = -238700 * t8803 + 4830672 * t8810 - 37111228 * t8812 + 139707106 * t8814 - 285308100 * t8816 + 322740600 * t8818 - 190215060 * t8820 + 45592785 * t8822 + 1925 + t8824 = -60790380 * t8788 + 272576220 * t8790 - 507352860 * t8792 + 505961820 * t8794 - 290663604 * t8796 - 33264 * t8804 + 1382612 * t8809 - 17053652 * t8811 + 95973108 * t8813 + t8808 = 4 * phi1 + t8807 = 8 * phi1 + t8806 = -3 * phi2 + t8805 = 3 * phi2 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * ((1 + t8804) ** (-0.1e1 / 0.2e1)) * ((1 - t8804) ** (-0.1e1 / 0.2e1)) * ((94287120 * t8788 - 425812800 * t8790 + 793731840 * t8792 - 787159360 * t8794 + 445935776 * t8796 - 143805376 * t8813 + 24692096 * t8811 - 1912768 * t8809 + 43472 * t8804) * np.exp((-3*1j) * phi2) + (t8827 - t8828) * np.exp((-3*1j) * (t8808 + phi2)) + (t8824 - t8825) * np.exp((-1*1j) * (t8808 + t8805)) + (-t8826 + t8829) * np.exp((-1*1j) * (t8807 + t8805)) + (t8824 + t8825) * np.exp((1j) * (t8808 + t8806)) + (t8826 + t8829) * np.exp((1j) * (t8807 + t8806)) + (t8827 + t8828) * np.exp((3*1j) * (t8808 - phi2))) + + if Bindx == 244: + t8855 = np.cos(phi) + t8854 = t8855 ** 2 + t8861 = t8855 * t8854 + t8864 = t8861 ** 2 + t8862 = t8854 ** 2 + t8866 = t8862 ** 2 + t8863 = t8855 * t8862 + t8868 = t8863 ** 2 + t8870 = t8864 ** 2 + t8865 = t8855 * t8864 + t8872 = t8865 ** 2 + t8874 = t8866 ** 2 + t8883 = 65540 * t8854 - 458780 * t8864 + 1146950 * t8866 - 1376340 * t8868 + 917560 * t8870 - 327700 * t8872 + 49155 * t8874 - 16385 + t8839 = t8855 * t8874 + t8841 = t8855 * t8872 + t8845 = t8855 * t8868 + t8847 = t8855 * t8866 + t8882 = 16385 * t8839 - 65540 * t8841 + 458780 * t8845 - 1146950 * t8847 - 49155 * t8855 + 327700 * t8861 - 917560 * t8863 + 1376340 * t8865 + t8881 = -6120 * t8854 + 69728 * t8862 - 341320 * t8864 + 867740 * t8866 - 1243832 * t8868 + 1018000 * t8870 - 445208 * t8872 + 80910 * t8874 + 102 + t8843 = t8855 * t8870 + t8880 = 35960 * t8839 - 151960 * t8841 + 200984 * t8843 + 17256 * t8845 - 299640 * t8847 - 1376 * t8855 + 26296 * t8861 - 138680 * t8863 + 311160 * t8865 + t8879 = 10518300 * t8839 - 46144800 * t8841 + 81684720 * t8843 - 74399520 * t8845 + 36825672 * t8847 + 1404 * t8855 - 66144 * t8861 + 1221168 * t8863 - 9640800 * t8865 + t8878 = 241800 * t8854 - 3998592 * t8862 + 25528776 * t8864 - 81340116 * t8866 + 143034840 * t8868 - 141382800 * t8870 + 73695960 * t8872 - 15777450 * t8874 - 2418 + t8877 = 886600 * t8854 - 17942496 * t8862 + 137841704 * t8864 - 518912108 * t8866 + 1059715800 * t8868 - 1198750800 * t8870 + 706513080 * t8872 - 169344630 * t8874 - 7150 + t8876 = 225792840 * t8839 - 1012425960 * t8841 + 1884453480 * t8843 - 1879286760 * t8845 + 1079607672 * t8847 + 123552 * t8855 - 5135416 * t8861 + 63342136 * t8863 - 356471544 * t8865 + t8860 = 4 * phi1 + t8859 = 8 * phi1 + t8858 = -3 * phi2 + t8857 = 3 * phi2 + t8856 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.7e1) * np.sqrt(0.23e2) * np.sqrt(0.31e2) * ((1 + t8855) ** (-0.1e1 / 0.2e1)) * ((1 - t8855) ** (-0.1e1 / 0.2e1)) * ((601080390 * t8839 - 2714556600 * t8841 + 5060040480 * t8843 - 5018140920 * t8845 + 2842840572 * t8847 - 916759272 * t8865 + 157412112 * t8863 - 12193896 * t8861 + 277134 * t8855) * np.exp((-3*1j) * phi2) + (t8882 + t8883) * np.exp((-1*1j) * (t8856 + t8857)) + (t8882 - t8883) * np.exp((1j) * (t8856 + t8858)) + (t8880 + t8881) * np.exp((-3*1j) * (t8860 + phi2)) + (t8876 - t8877) * np.exp((-1*1j) * (t8860 + t8857)) + (-t8878 + t8879) * np.exp((-1*1j) * (t8859 + t8857)) + (t8876 + t8877) * np.exp((1j) * (t8860 + t8858)) + (t8878 + t8879) * np.exp((1j) * (t8859 + t8858)) + (t8880 - t8881) * np.exp((3*1j) * (t8860 - phi2))) + + if Bindx == 245: + t8906 = np.cos(phi) + t8905 = t8906 ** 2 + t8911 = t8905 ** 2 + t8915 = t8911 ** 2 + t8891 = t8915 ** 2 + t8910 = t8906 * t8905 + t8913 = t8910 ** 2 + t8912 = t8906 * t8911 + t8917 = t8912 ** 2 + t8919 = t8913 ** 2 + t8914 = t8906 * t8913 + t8921 = t8914 ** 2 + t8929 = -620310 * t8891 - 8556 * t8905 - 19320 * t8911 + 840420 * t8913 - 3622500 * t8915 + 6763932 * t8917 - 6495384 * t8919 + 3161580 * t8921 + 138 + t8892 = t8906 * t8921 + t8894 = t8906 * t8919 + t8896 = t8906 * t8917 + t8898 = t8906 * t8915 + t8928 = -930465 * t8892 + 5056527 * t8894 - 11351949 * t8896 + 13478115 * t8898 + 26289 * t8906 - 535647 * t8910 + 3214365 * t8912 - 8957235 * t8914 + t8927 = -15197595 * t8892 + 55397685 * t8894 - 80180415 * t8896 + 58575825 * t8898 + 9163 * t8906 - 378917 * t8910 + 4448367 * t8912 - 22674113 * t8914 + t8926 = -6203100 * t8892 + 26773380 * t8894 - 46405260 * t8896 + 41057300 * t8898 + 14812 * t8906 - 495236 * t8910 + 4700556 * t8912 - 19442452 * t8914 + t8925 = -6203100 * t8891 + 65688 * t8905 - 1127000 * t8911 + 7572152 * t8913 - 25606176 * t8915 + 47886920 * t8917 - 50201640 * t8919 + 27613800 * t8921 - 644 + t8924 = 30395190 * t8891 - 135828 * t8905 + 2798488 * t8911 - 21928676 * t8913 + 84349188 * t8915 - 176285340 * t8917 + 204347640 * t8919 - 123541740 * t8921 + 1078 + t8909 = 2 * phi1 + t8908 = 4 * phi1 + t8907 = 6 * phi1 + tfunc[..., c] = (0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.19e2) * ((-47143560 * t8891 + 185532720 * t8921 - 295866480 * t8919 + 244964720 * t8917 - 111983872 * t8915 + 27691664 * t8913 - 3347344 * t8911 + 153296 * t8905 - 1144) * np.exp((-2*1j) * phi2) + (t8924 - t8927) * np.exp((-2*1j) * (t8909 + phi2)) + (t8925 + t8926) * np.exp((-2*1j) * (t8908 + phi2)) + (t8928 + t8929) * np.exp((-2*1j) * (t8907 + phi2)) + (t8924 + t8927) * np.exp((2*1j) * (t8909 - phi2)) + (t8925 - t8926) * np.exp((2*1j) * (t8908 - phi2)) + (-t8928 + t8929) * np.exp((2*1j) * (t8907 - phi2))) + + if Bindx == 246: + t8954 = np.cos(phi) + t8953 = t8954 ** 2 + t8959 = t8954 * t8953 + t8962 = t8959 ** 2 + t8968 = t8962 ** 2 + t8942 = t8954 * t8968 + t8982 = t8959 - t8942 + t8960 = t8953 ** 2 + t8961 = t8954 * t8960 + t8966 = t8961 ** 2 + t8981 = t8962 + t8968 - t8960 - t8966 + t8964 = t8960 ** 2 + t8939 = t8964 ** 2 + t8963 = t8954 * t8962 + t8970 = t8963 ** 2 + t8980 = -16385 + 16385 * t8939 + 98310 * t8953 - 98310 * t8970 + t8940 = t8954 * t8970 + t8944 = t8954 * t8966 + t8946 = t8954 * t8964 + t8979 = -32770 * t8940 + 32770 * t8954 + 1146950 * t8946 - 1146950 * t8963 - 688170 * t8944 + 688170 * t8961 + t8978 = 35960 * t8939 + 496 * t8953 + 1120 * t8960 - 48720 * t8962 + 210000 * t8964 - 392112 * t8966 + 376544 * t8968 - 183280 * t8970 - 8 + t8977 = 53940 * t8940 - 293132 * t8942 + 658084 * t8944 - 781340 * t8946 - 1524 * t8954 + 31052 * t8959 - 186340 * t8961 + 519260 * t8963 + t8976 = -10518300 * t8940 + 45398340 * t8942 - 78687180 * t8944 + 69618900 * t8946 + 25116 * t8954 - 839748 * t8959 + 7970508 * t8961 - 32967636 * t8963 + t8975 = -112896420 * t8940 + 411525660 * t8942 - 595625940 * t8944 + 435134700 * t8946 + 68068 * t8954 - 2814812 * t8959 + 33045012 * t8961 - 168436268 * t8963 + t8974 = 10518300 * t8939 - 111384 * t8953 + 1911000 * t8960 - 12839736 * t8962 + 43419168 * t8964 - 81199560 * t8966 + 85124520 * t8968 - 46823400 * t8970 + 1092 + t8973 = 225792840 * t8939 - 1009008 * t8953 + 20788768 * t8960 - 162898736 * t8962 + 626593968 * t8964 - 1309548240 * t8966 + 1518011040 * t8968 - 917738640 * t8970 + 8008 + t8958 = 2 * phi1 + t8957 = 4 * phi1 + t8956 = 6 * phi1 + t8955 = 8 * phi1 + t8934 = np.exp((-2*1j) * (t8955 + phi2)) + t8930 = np.exp((2*1j) * (t8955 - phi2)) + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.23e2) * np.sqrt(0.19e2) * ((14586 + 601080390 * t8939 - 2365542180 * t8970 + 3772297620 * t8968 - 3123300180 * t8966 + 1427794368 * t8964 - 353068716 * t8962 + 42678636 * t8960 - 1954524 * t8953) * np.exp((-2*1j) * phi2) + ((-t8979 + t8980) * t8934) + ((t8979 + t8980) * t8930) + (t8973 - t8975) * np.exp((-2*1j) * (t8958 + phi2)) + (t8974 - t8976) * np.exp((-2*1j) * (t8957 + phi2)) + (t8977 + t8978) * np.exp((-2*1j) * (t8956 + phi2)) + (t8973 + t8975) * np.exp((2*1j) * (t8958 - phi2)) + (t8974 + t8976) * np.exp((2*1j) * (t8957 - phi2)) + (-t8977 + t8978) * np.exp((2*1j) * (t8956 - phi2)) + (229390 * (t8981 + t8982) * t8934) + (229390 * (t8981 - t8982) * t8930)) + + if Bindx == 247: + t9006 = np.cos(phi) + t9005 = t9006 ** 2 + t9011 = t9005 ** 2 + t9010 = t9006 * t9005 + t9013 = t9010 ** 2 + t9015 = t9011 ** 2 + t9012 = t9006 * t9011 + t9017 = t9012 ** 2 + t9019 = t9013 ** 2 + t9014 = t9006 * t9013 + t9021 = t9014 ** 2 + t9023 = t9015 ** 2 + t9030 = 596160 * t9005 - 6172740 * t9011 + 25038720 * t9013 - 52816050 * t9015 + 63987840 * t9017 - 45121860 * t9019 + 17288640 * t9021 - 2791395 * t9023 - 9315 + t9029 = 334880 * t9005 - 5654320 * t9011 + 36141280 * t9013 - 113216120 * t9015 + 192997600 * t9017 - 183236400 * t9019 + 91245600 * t9021 - 18609300 * t9023 - 3220 + t9028 = 206976 * t9005 - 4314156 * t9011 + 34047552 * t9013 - 131284230 * t9015 + 273725760 * t9017 - 315041580 * t9019 + 188254080 * t9021 - 45592785 * t9023 - 1617 + t8990 = t9006 * t9023 + t8992 = t9006 * t9021 + t8994 = t9006 * t9019 + t8996 = t9006 * t9017 + t8998 = t9006 * t9015 + t9027 = 3721860 * t8990 - 23891940 * t8992 + 65291940 * t8994 - 98445060 * t8996 + 88533900 * t8998 + 99360 * t9006 - 2156940 * t9010 + 14576940 * t9012 - 47730060 * t9014 + t9026 = 37218600 * t8990 - 196898400 * t8992 + 434534400 * t8994 - 516101600 * t8996 + 354463120 * t8998 + 83720 * t9006 - 2910880 * t9010 + 29933120 * t9012 - 140322080 * t9014 + t9025 = -182371140 * t8990 + 841260420 * t8992 - 1604014020 * t8994 + 1629439140 * t8996 - 946882860 * t8998 - 103488 * t9006 + 4417644 * t9010 - 55385484 * t9012 + 313639788 * t9014 + t9009 = 4 * phi1 + t9008 = 8 * phi1 + t9007 = 12 * phi1 + tfunc[..., c] = (-0.11e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.1921e4) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((282861360 * t8990 - 1240940160 * t8992 + 2246529600 * t8994 - 2163324800 * t8996 + 1189828640 * t8998 - 372468096 * t9014 + 62078016 * t9012 - 4667520 * t9010 + 102960 * t9006) * np.exp((-1*1j) * phi2) + (t9025 + t9028) * np.exp((-1*1j) * (t9009 + phi2)) + (t9026 - t9029) * np.exp((-1*1j) * (t9008 + phi2)) + (t9025 - t9028) * np.exp((1j) * (t9009 - phi2)) + (t9026 + t9029) * np.exp((1j) * (t9008 - phi2)) + (t9027 - t9030) * np.exp((-1*1j) * (t9007 + phi2)) + (t9027 + t9030) * np.exp((1j) * (t9007 - phi2))) * ((1 + t9006) ** (-0.1e1 / 0.2e1)) * ((1 - t9006) ** (-0.1e1 / 0.2e1)) + + if Bindx == 248: + t9056 = np.cos(phi) + t9055 = t9056 ** 2 + t9062 = t9055 ** 2 + t9061 = t9056 * t9055 + t9064 = t9061 ** 2 + t9066 = t9062 ** 2 + t9063 = t9056 * t9062 + t9068 = t9063 ** 2 + t9070 = t9064 ** 2 + t9065 = t9056 * t9064 + t9072 = t9065 ** 2 + t9074 = t9066 ** 2 + t9083 = -5760 * t9055 + 59640 * t9062 - 241920 * t9064 + 510300 * t9066 - 618240 * t9068 + 435960 * t9070 - 167040 * t9072 + 26970 * t9074 + 90 + t9040 = t9056 * t9074 + t9042 = t9056 * t9072 + t9044 = t9056 * t9070 + t9046 = t9056 * t9068 + t9048 = t9056 * t9066 + t9082 = 35960 * t9040 - 230840 * t9042 + 630840 * t9044 - 951160 * t9046 + 855400 * t9048 + 960 * t9056 - 20840 * t9061 + 140840 * t9063 - 461160 * t9065 + t9081 = -131080 * t9055 + 458780 * t9062 - 917560 * t9064 + 1146950 * t9066 - 917560 * t9068 + 458780 * t9070 - 131080 * t9072 + 16385 * t9074 + 16385 + t9080 = 16385 * t9040 - 131080 * t9042 + 458780 * t9044 - 917560 * t9046 + 1146950 * t9048 + 16385 * t9056 - 131080 * t9061 + 458780 * t9063 - 917560 * t9065 + t9079 = -94640 * t9055 + 1597960 * t9062 - 10213840 * t9064 + 31995860 * t9066 - 54542800 * t9068 + 51784200 * t9070 - 25786800 * t9072 + 5259150 * t9074 + 910 + t9078 = 256256 * t9055 - 5341336 * t9062 + 42154112 * t9064 - 162542380 * t9066 + 338898560 * t9068 - 390051480 * t9070 + 233076480 * t9072 - 56448210 * t9074 - 2002 + t9077 = 10518300 * t9040 - 55645200 * t9042 + 122803200 * t9044 - 145854800 * t9046 + 100174360 * t9048 + 23660 * t9056 - 822640 * t9061 + 8459360 * t9063 - 39656240 * t9065 + t9076 = 225792840 * t9040 - 1041560520 * t9042 + 1985922120 * t9044 - 2017400840 * t9046 + 1172331160 * t9048 + 128128 * t9056 - 5469464 * t9061 + 68572504 * t9063 - 388315928 * t9065 + t9060 = 4 * phi1 + t9059 = 8 * phi1 + t9058 = 12 * phi1 + t9057 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * ((1 + t9056) ** (-0.1e1 / 0.2e1)) * ((1 - t9056) ** (-0.1e1 / 0.2e1)) * ((601080390 * t9040 - 2636997840 * t9042 + 4773875400 * t9044 - 4597065200 * t9046 + 2528385860 * t9048 - 791494704 * t9065 + 131915784 * t9063 - 9918480 * t9061 + 218790 * t9056) * np.exp((-1*1j) * phi2) + (t9076 - t9078) * np.exp((-1*1j) * (t9060 + phi2)) + (t9077 + t9079) * np.exp((-1*1j) * (t9059 + phi2)) + (t9076 + t9078) * np.exp((1j) * (t9060 - phi2)) + (t9077 - t9079) * np.exp((1j) * (t9059 - phi2)) + (t9082 + t9083) * np.exp((-1*1j) * (t9058 + phi2)) + (t9080 + t9081) * np.exp((-1*1j) * (t9057 + phi2)) + (t9082 - t9083) * np.exp((1j) * (t9058 - phi2)) + (t9080 - t9081) * np.exp((1j) * (t9057 - phi2))) + + if Bindx == 249: + t9095 = np.cos(phi) + t9094 = t9095 ** 2 + t9096 = t9094 ** 2 + t9097 = t9094 * t9096 + t9100 = t9097 ** 2 + t9098 = t9096 ** 2 + t9090 = t9094 * t9098 + t9088 = t9094 * t9100 + t9087 = t9098 ** 2 + tfunc[..., c] = 0.11e2 / 0.29622272e8 * np.sqrt(0.2e1) * np.sqrt(0.113e3) * np.sqrt(0.19e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * (-(601080390 * t9087) + (2326762800 * t9088) - (3650610600 * t9100) + (2974571600 * t9090) - (1338557220 * t9098) + (325909584 * t9097) - (38798760 * t9096) + (1750320 * t9094) - 0.12870e5 + (775077345 * t9087 - 3200319360 * t9088 + 5355706860 * t9100 - 4653337920 * t9090 + 2231831910 * t9098 - 578808384 * t9097 + 73340652 * t9096 - 3518592 * t9094 + 27489) * np.cos((4 * phi1)) + (-158179050 * t9087 + 775587600 * t9088 - 1557509400 * t9100 + 1640479600 * t9090 - 962337020 * t9098 + 307200880 * t9097 - 48061720 * t9096 + 2846480 * t9094 - 27370) * np.cos((8 * phi1)) + (-15817905 * t9087 + 97968960 * t9088 - 255690540 * t9100 + 362597760 * t9090 - 299290950 * t9098 + 141886080 * t9097 - 34978860 * t9096 + 3378240 * t9094 - 52785) * np.cos((12 * phi1))) + + if Bindx == 250: + t9115 = np.cos(phi) + t9114 = t9115 ** 2 + t9116 = t9114 ** 2 + t9117 = t9114 * t9116 + t9120 = t9117 ** 2 + t9118 = t9116 ** 2 + t9110 = t9114 * t9118 + t9108 = t9114 * t9120 + t9107 = t9118 ** 2 + tfunc[..., c] = 0.33e2 / 0.473956352e9 * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.17e2) * ((300540195 * t9107) - (1163381400 * t9108) + (1825305300 * t9120) - (1487285800 * t9110) + (669278610 * t9118) - (162954792 * t9117) + (19399380 * t9116) - (875160 * t9114) + 0.6435e4 + (225792840 * t9107 - 932305920 * t9108 + 1560205920 * t9120 - 1355594240 * t9110 + 650169520 * t9118 - 168616448 * t9117 + 21365344 * t9116 - 1025024 * t9114 + 8008) * np.cos((4 * phi1)) + (10518300 * t9107 - 51573600 * t9108 + 103568400 * t9120 - 109085600 * t9110 + 63991720 * t9118 - 20427680 * t9117 + 3195920 * t9116 - 189280 * t9114 + 1820) * np.cos((8 * phi1)) + (35960 * t9107 - 222720 * t9108 + 581280 * t9120 - 824320 * t9110 + 680400 * t9118 - 322560 * t9117 + 79520 * t9116 - 7680 * t9114 + 120) * np.cos((12 * phi1)) + (16385 * t9107 + 1146950 * t9118 + 16385 - 917560 * t9110 - 917560 * t9117 + 458780 * t9120 + 458780 * t9116 - 131080 * t9108 - 131080 * t9114) * np.cos((16 * phi1))) + + if Bindx == 251: + t9146 = np.cos(phi) + t9145 = t9146 ** 2 + t9151 = t9145 ** 2 + t9150 = t9146 * t9145 + t9153 = t9150 ** 2 + t9155 = t9151 ** 2 + t9152 = t9146 * t9151 + t9157 = t9152 ** 2 + t9159 = t9153 ** 2 + t9154 = t9146 * t9153 + t9161 = t9154 ** 2 + t9163 = t9155 ** 2 + t9170 = 596160 * t9145 - 6172740 * t9151 + 25038720 * t9153 - 52816050 * t9155 + 63987840 * t9157 - 45121860 * t9159 + 17288640 * t9161 - 2791395 * t9163 - 9315 + t9169 = 334880 * t9145 - 5654320 * t9151 + 36141280 * t9153 - 113216120 * t9155 + 192997600 * t9157 - 183236400 * t9159 + 91245600 * t9161 - 18609300 * t9163 - 3220 + t9168 = 206976 * t9145 - 4314156 * t9151 + 34047552 * t9153 - 131284230 * t9155 + 273725760 * t9157 - 315041580 * t9159 + 188254080 * t9161 - 45592785 * t9163 - 1617 + t9130 = t9146 * t9163 + t9132 = t9146 * t9161 + t9134 = t9146 * t9159 + t9136 = t9146 * t9157 + t9138 = t9146 * t9155 + t9167 = 3721860 * t9130 - 23891940 * t9132 + 65291940 * t9134 - 98445060 * t9136 + 88533900 * t9138 + 99360 * t9146 - 2156940 * t9150 + 14576940 * t9152 - 47730060 * t9154 + t9166 = 37218600 * t9130 - 196898400 * t9132 + 434534400 * t9134 - 516101600 * t9136 + 354463120 * t9138 + 83720 * t9146 - 2910880 * t9150 + 29933120 * t9152 - 140322080 * t9154 + t9165 = -182371140 * t9130 + 841260420 * t9132 - 1604014020 * t9134 + 1629439140 * t9136 - 946882860 * t9138 - 103488 * t9146 + 4417644 * t9150 - 55385484 * t9152 + 313639788 * t9154 + t9149 = 4 * phi1 + t9148 = 8 * phi1 + t9147 = 12 * phi1 + tfunc[..., c] = (-0.11e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.1921e4) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * np.sqrt(0.19e2) * ((282861360 * t9130 - 1240940160 * t9132 + 2246529600 * t9134 - 2163324800 * t9136 + 1189828640 * t9138 - 372468096 * t9154 + 62078016 * t9152 - 4667520 * t9150 + 102960 * t9146) * np.exp((1j) * phi2) + (t9165 - t9168) * np.exp((-1*1j) * (t9149 - phi2)) + (t9166 + t9169) * np.exp((-1*1j) * (t9148 - phi2)) + (t9165 + t9168) * np.exp((1j) * (t9149 + phi2)) + (t9166 - t9169) * np.exp((1j) * (t9148 + phi2)) + (t9167 + t9170) * np.exp((-1*1j) * (t9147 - phi2)) + (t9167 - t9170) * np.exp((1j) * (t9147 + phi2))) * ((1 + t9146) ** (-0.1e1 / 0.2e1)) * ((1 - t9146) ** (-0.1e1 / 0.2e1)) + + if Bindx == 252: + t9196 = np.cos(phi) + t9195 = t9196 ** 2 + t9202 = t9195 ** 2 + t9201 = t9196 * t9195 + t9204 = t9201 ** 2 + t9206 = t9202 ** 2 + t9203 = t9196 * t9202 + t9208 = t9203 ** 2 + t9210 = t9204 ** 2 + t9205 = t9196 * t9204 + t9212 = t9205 ** 2 + t9214 = t9206 ** 2 + t9223 = -5760 * t9195 + 59640 * t9202 - 241920 * t9204 + 510300 * t9206 - 618240 * t9208 + 435960 * t9210 - 167040 * t9212 + 26970 * t9214 + 90 + t9180 = t9196 * t9214 + t9182 = t9196 * t9212 + t9184 = t9196 * t9210 + t9186 = t9196 * t9208 + t9188 = t9196 * t9206 + t9222 = 35960 * t9180 - 230840 * t9182 + 630840 * t9184 - 951160 * t9186 + 855400 * t9188 + 960 * t9196 - 20840 * t9201 + 140840 * t9203 - 461160 * t9205 + t9221 = -131080 * t9195 + 458780 * t9202 - 917560 * t9204 + 1146950 * t9206 - 917560 * t9208 + 458780 * t9210 - 131080 * t9212 + 16385 * t9214 + 16385 + t9220 = 16385 * t9180 - 131080 * t9182 + 458780 * t9184 - 917560 * t9186 + 1146950 * t9188 + 16385 * t9196 - 131080 * t9201 + 458780 * t9203 - 917560 * t9205 + t9219 = -94640 * t9195 + 1597960 * t9202 - 10213840 * t9204 + 31995860 * t9206 - 54542800 * t9208 + 51784200 * t9210 - 25786800 * t9212 + 5259150 * t9214 + 910 + t9218 = -256256 * t9195 + 5341336 * t9202 - 42154112 * t9204 + 162542380 * t9206 - 338898560 * t9208 + 390051480 * t9210 - 233076480 * t9212 + 56448210 * t9214 + 2002 + t9217 = 10518300 * t9180 - 55645200 * t9182 + 122803200 * t9184 - 145854800 * t9186 + 100174360 * t9188 + 23660 * t9196 - 822640 * t9201 + 8459360 * t9203 - 39656240 * t9205 + t9216 = 225792840 * t9180 - 1041560520 * t9182 + 1985922120 * t9184 - 2017400840 * t9186 + 1172331160 * t9188 + 128128 * t9196 - 5469464 * t9201 + 68572504 * t9203 - 388315928 * t9205 + t9200 = 4 * phi1 + t9199 = 8 * phi1 + t9198 = 12 * phi1 + t9197 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * np.sqrt(0.31e2) * ((1 + t9196) ** (-0.1e1 / 0.2e1)) * ((1 - t9196) ** (-0.1e1 / 0.2e1)) * ((218790 * t9196 + 601080390 * t9180 - 2636997840 * t9182 + 4773875400 * t9184 - 4597065200 * t9186 + 2528385860 * t9188 - 791494704 * t9205 + 131915784 * t9203 - 9918480 * t9201) * np.exp((1j) * phi2) + (t9216 - t9218) * np.exp((-1*1j) * (t9200 - phi2)) + (t9217 - t9219) * np.exp((-1*1j) * (t9199 - phi2)) + (t9216 + t9218) * np.exp((1j) * (t9200 + phi2)) + (t9217 + t9219) * np.exp((1j) * (t9199 + phi2)) + (t9222 - t9223) * np.exp((-1*1j) * (t9198 - phi2)) + (t9220 - t9221) * np.exp((-1*1j) * (t9197 - phi2)) + (t9222 + t9223) * np.exp((1j) * (t9198 + phi2)) + (t9220 + t9221) * np.exp((1j) * (t9197 + phi2))) + + if Bindx == 253: + t9246 = np.cos(phi) + t9245 = t9246 ** 2 + t9251 = t9245 ** 2 + t9255 = t9251 ** 2 + t9231 = t9255 ** 2 + t9250 = t9246 * t9245 + t9253 = t9250 ** 2 + t9252 = t9246 * t9251 + t9257 = t9252 ** 2 + t9259 = t9253 ** 2 + t9254 = t9246 * t9253 + t9261 = t9254 ** 2 + t9269 = 620310 * t9231 + 8556 * t9245 + 19320 * t9251 - 840420 * t9253 + 3622500 * t9255 - 6763932 * t9257 + 6495384 * t9259 - 3161580 * t9261 - 138 + t9232 = t9246 * t9261 + t9234 = t9246 * t9259 + t9236 = t9246 * t9257 + t9238 = t9246 * t9255 + t9268 = -930465 * t9232 + 5056527 * t9234 - 11351949 * t9236 + 13478115 * t9238 + 26289 * t9246 - 535647 * t9250 + 3214365 * t9252 - 8957235 * t9254 + t9267 = -15197595 * t9232 + 55397685 * t9234 - 80180415 * t9236 + 58575825 * t9238 + 9163 * t9246 - 378917 * t9250 + 4448367 * t9252 - 22674113 * t9254 + t9266 = -6203100 * t9232 + 26773380 * t9234 - 46405260 * t9236 + 41057300 * t9238 + 14812 * t9246 - 495236 * t9250 + 4700556 * t9252 - 19442452 * t9254 + t9265 = 6203100 * t9231 - 65688 * t9245 + 1127000 * t9251 - 7572152 * t9253 + 25606176 * t9255 - 47886920 * t9257 + 50201640 * t9259 - 27613800 * t9261 + 644 + t9264 = -30395190 * t9231 + 135828 * t9245 - 2798488 * t9251 + 21928676 * t9253 - 84349188 * t9255 + 176285340 * t9257 - 204347640 * t9259 + 123541740 * t9261 - 1078 + t9249 = 2 * phi1 + t9248 = 4 * phi1 + t9247 = 6 * phi1 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * np.sqrt(0.19e2) * ((47143560 * t9231 - 185532720 * t9261 + 295866480 * t9259 - 244964720 * t9257 + 111983872 * t9255 - 27691664 * t9253 + 3347344 * t9251 - 153296 * t9245 + 1144) * np.exp((2*1j) * phi2) + (t9264 - t9267) * np.exp((-2*1j) * (t9249 - phi2)) + (t9265 + t9266) * np.exp((-2*1j) * (t9248 - phi2)) + (t9268 + t9269) * np.exp((-2*1j) * (t9247 - phi2)) + (t9264 + t9267) * np.exp((2*1j) * (t9249 + phi2)) + (t9265 - t9266) * np.exp((2*1j) * (t9248 + phi2)) + (-t9268 + t9269) * np.exp((2*1j) * (t9247 + phi2))) + + if Bindx == 254: + t9294 = np.cos(phi) + t9293 = t9294 ** 2 + t9299 = t9294 * t9293 + t9302 = t9299 ** 2 + t9308 = t9302 ** 2 + t9282 = t9294 * t9308 + t9322 = t9299 - t9282 + t9300 = t9293 ** 2 + t9301 = t9294 * t9300 + t9306 = t9301 ** 2 + t9321 = t9302 + t9308 - t9300 - t9306 + t9304 = t9300 ** 2 + t9279 = t9304 ** 2 + t9303 = t9294 * t9302 + t9310 = t9303 ** 2 + t9320 = -16385 + 16385 * t9279 + 98310 * t9293 - 98310 * t9310 + t9280 = t9294 * t9310 + t9284 = t9294 * t9306 + t9286 = t9294 * t9304 + t9319 = -32770 * t9280 + 32770 * t9294 + 1146950 * t9286 - 1146950 * t9303 - 688170 * t9284 + 688170 * t9301 + t9318 = 35960 * t9279 + 496 * t9293 + 1120 * t9300 - 48720 * t9302 + 210000 * t9304 - 392112 * t9306 + 376544 * t9308 - 183280 * t9310 - 8 + t9317 = 53940 * t9280 - 293132 * t9282 + 658084 * t9284 - 781340 * t9286 - 1524 * t9294 + 31052 * t9299 - 186340 * t9301 + 519260 * t9303 + t9316 = -10518300 * t9280 + 45398340 * t9282 - 78687180 * t9284 + 69618900 * t9286 + 25116 * t9294 - 839748 * t9299 + 7970508 * t9301 - 32967636 * t9303 + t9315 = -112896420 * t9280 + 411525660 * t9282 - 595625940 * t9284 + 435134700 * t9286 + 68068 * t9294 - 2814812 * t9299 + 33045012 * t9301 - 168436268 * t9303 + t9314 = 10518300 * t9279 - 111384 * t9293 + 1911000 * t9300 - 12839736 * t9302 + 43419168 * t9304 - 81199560 * t9306 + 85124520 * t9308 - 46823400 * t9310 + 1092 + t9313 = 225792840 * t9279 - 1009008 * t9293 + 20788768 * t9300 - 162898736 * t9302 + 626593968 * t9304 - 1309548240 * t9306 + 1518011040 * t9308 - 917738640 * t9310 + 8008 + t9298 = 2 * phi1 + t9297 = 4 * phi1 + t9296 = 6 * phi1 + t9295 = 8 * phi1 + t9274 = np.exp((-2*1j) * (t9295 - phi2)) + t9270 = np.exp((2*1j) * (t9295 + phi2)) + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.19e2) * np.sqrt(0.23e2) * ((14586 + 601080390 * t9279 - 2365542180 * t9310 + 3772297620 * t9308 - 3123300180 * t9306 + 1427794368 * t9304 - 353068716 * t9302 + 42678636 * t9300 - 1954524 * t9293) * np.exp((2*1j) * phi2) + ((t9319 + t9320) * t9274) + ((-t9319 + t9320) * t9270) + (t9313 + t9315) * np.exp((-2*1j) * (t9298 - phi2)) + (t9314 + t9316) * np.exp((-2*1j) * (t9297 - phi2)) + (-t9317 + t9318) * np.exp((-2*1j) * (t9296 - phi2)) + (t9313 - t9315) * np.exp((2*1j) * (t9298 + phi2)) + (t9314 - t9316) * np.exp((2*1j) * (t9297 + phi2)) + (t9317 + t9318) * np.exp((2*1j) * (t9296 + phi2)) + (229390 * (t9321 - t9322) * t9274) + (229390 * (t9321 + t9322) * t9270)) + + if Bindx == 255: + t9346 = np.cos(phi) + t9345 = t9346 ** 2 + t9352 = t9345 ** 2 + t9356 = t9352 ** 2 + t9364 = t9356 ** 2 + t9330 = t9346 * t9364 + t9351 = t9346 * t9345 + t9354 = t9351 ** 2 + t9355 = t9346 * t9354 + t9362 = t9355 ** 2 + t9332 = t9346 * t9362 + t9360 = t9354 ** 2 + t9334 = t9346 * t9360 + t9353 = t9346 * t9352 + t9358 = t9353 ** 2 + t9336 = t9346 * t9358 + t9338 = t9346 * t9356 + t9371 = 12406200 * t9330 - 54427200 * t9332 + 96346080 * t9334 - 87753280 * t9336 + 43435408 * t9338 + 1656 * t9346 - 78016 * t9351 + 1440352 * t9353 - 11371200 * t9355 + t9370 = 211140 * t9345 - 2405616 * t9352 + 11775540 * t9354 - 29937030 * t9356 + 42912204 * t9358 - 35121000 * t9360 + 15359676 * t9362 - 2791395 * t9364 - 3519 + t9369 = 1240620 * t9330 - 5242620 * t9332 + 6933948 * t9334 + 595332 * t9336 - 10337580 * t9338 - 47472 * t9346 + 907212 * t9351 - 4784460 * t9353 + 10735020 * t9355 + t9368 = 285200 * t9345 - 4716288 * t9352 + 30110864 * t9354 - 95939624 * t9356 + 168707760 * t9358 - 166759200 * t9360 + 86923440 * t9362 - 18609300 * t9364 - 2852 + t9367 = 238700 * t9345 - 4830672 * t9352 + 37111228 * t9354 - 139707106 * t9356 + 285308100 * t9358 - 322740600 * t9360 + 190215060 * t9362 - 45592785 * t9364 - 1925 + t9366 = -60790380 * t9330 + 272576220 * t9332 - 507352860 * t9334 + 505961820 * t9336 - 290663604 * t9338 - 33264 * t9346 + 1382612 * t9351 - 17053652 * t9353 + 95973108 * t9355 + t9350 = 4 * phi1 + t9349 = 8 * phi1 + t9348 = -3 * phi2 + t9347 = 3 * phi2 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * ((94287120 * t9330 - 425812800 * t9332 + 793731840 * t9334 - 787159360 * t9336 + 445935776 * t9338 - 143805376 * t9355 + 24692096 * t9353 - 1912768 * t9351 + 43472 * t9346) * np.exp((3*1j) * phi2) + (t9369 + t9370) * np.exp((-3*1j) * (t9350 - phi2)) + (t9366 - t9367) * np.exp((-1*1j) * (t9350 + t9348)) + (t9368 + t9371) * np.exp((-1*1j) * (t9349 + t9348)) + (t9366 + t9367) * np.exp((1j) * (t9350 + t9347)) + (-t9368 + t9371) * np.exp((1j) * (t9349 + t9347)) + (t9369 - t9370) * np.exp((3*1j) * (t9350 + phi2))) * ((1 + t9346) ** (-0.1e1 / 0.2e1)) * ((1 - t9346) ** (-0.1e1 / 0.2e1)) + + if Bindx == 256: + t9397 = np.cos(phi) + t9396 = t9397 ** 2 + t9403 = t9397 * t9396 + t9406 = t9403 ** 2 + t9404 = t9396 ** 2 + t9408 = t9404 ** 2 + t9405 = t9397 * t9404 + t9410 = t9405 ** 2 + t9412 = t9406 ** 2 + t9407 = t9397 * t9406 + t9414 = t9407 ** 2 + t9416 = t9408 ** 2 + t9425 = 65540 * t9396 - 458780 * t9406 + 1146950 * t9408 - 1376340 * t9410 + 917560 * t9412 - 327700 * t9414 + 49155 * t9416 - 16385 + t9381 = t9397 * t9416 + t9383 = t9397 * t9414 + t9387 = t9397 * t9410 + t9389 = t9397 * t9408 + t9424 = 16385 * t9381 - 65540 * t9383 + 458780 * t9387 - 1146950 * t9389 - 49155 * t9397 + 327700 * t9403 - 917560 * t9405 + 1376340 * t9407 + t9423 = -6120 * t9396 + 69728 * t9404 - 341320 * t9406 + 867740 * t9408 - 1243832 * t9410 + 1018000 * t9412 - 445208 * t9414 + 80910 * t9416 + 102 + t9385 = t9397 * t9412 + t9422 = 35960 * t9381 - 151960 * t9383 + 200984 * t9385 + 17256 * t9387 - 299640 * t9389 - 1376 * t9397 + 26296 * t9403 - 138680 * t9405 + 311160 * t9407 + t9421 = 10518300 * t9381 - 46144800 * t9383 + 81684720 * t9385 - 74399520 * t9387 + 36825672 * t9389 + 1404 * t9397 - 66144 * t9403 + 1221168 * t9405 - 9640800 * t9407 + t9420 = 241800 * t9396 - 3998592 * t9404 + 25528776 * t9406 - 81340116 * t9408 + 143034840 * t9410 - 141382800 * t9412 + 73695960 * t9414 - 15777450 * t9416 - 2418 + t9419 = -886600 * t9396 + 17942496 * t9404 - 137841704 * t9406 + 518912108 * t9408 - 1059715800 * t9410 + 1198750800 * t9412 - 706513080 * t9414 + 169344630 * t9416 + 7150 + t9418 = 225792840 * t9381 - 1012425960 * t9383 + 1884453480 * t9385 - 1879286760 * t9387 + 1079607672 * t9389 + 123552 * t9397 - 5135416 * t9403 + 63342136 * t9405 - 356471544 * t9407 + t9402 = 4 * phi1 + t9401 = 8 * phi1 + t9400 = -3 * phi2 + t9399 = 3 * phi2 + t9398 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.23e2) * np.sqrt(0.7e1) * ((1 + t9397) ** (-0.1e1 / 0.2e1)) * ((1 - t9397) ** (-0.1e1 / 0.2e1)) * ((601080390 * t9381 - 2714556600 * t9383 + 5060040480 * t9385 - 5018140920 * t9387 + 2842840572 * t9389 - 916759272 * t9407 + 157412112 * t9405 - 12193896 * t9403 + 277134 * t9397) * np.exp((3*1j) * phi2) + (t9424 - t9425) * np.exp((-1*1j) * (t9398 + t9400)) + (t9424 + t9425) * np.exp((1j) * (t9398 + t9399)) + (t9422 - t9423) * np.exp((-3*1j) * (t9402 - phi2)) + (t9418 - t9419) * np.exp((-1*1j) * (t9402 + t9400)) + (t9420 + t9421) * np.exp((-1*1j) * (t9401 + t9400)) + (t9418 + t9419) * np.exp((1j) * (t9402 + t9399)) + (-t9420 + t9421) * np.exp((1j) * (t9401 + t9399)) + (t9422 + t9423) * np.exp((3*1j) * (t9402 + phi2))) + + if Bindx == 257: + t9448 = np.cos(phi) + t9447 = t9448 ** 2 + t9451 = t9448 * t9447 + t9454 = t9451 ** 2 + t9455 = t9448 * t9454 + t9462 = t9455 ** 2 + t9434 = t9448 * t9462 + t9460 = t9454 ** 2 + t9436 = t9448 * t9460 + t9452 = t9447 ** 2 + t9453 = t9448 * t9452 + t9458 = t9453 ** 2 + t9438 = t9448 * t9458 + t9456 = t9452 ** 2 + t9440 = t9448 * t9456 + t9470 = -12096045 * t9434 + 53196585 * t9436 - 91606125 * t9438 + 76339185 * t9440 - 40365 * t9448 + 381225 * t9451 + 3691155 * t9453 - 29865615 * t9455 + t9469 = -80640300 * t9434 + 319959900 * t9436 - 510482700 * t9438 + 418363100 * t9440 + 143060 * t9448 - 4586660 * t9451 + 43460340 * t9453 - 186216740 * t9455 + t9468 = -197568735 * t9434 + 732916275 * t9436 - 1085670495 * t9438 + 816475275 * t9440 + 150689 * t9448 - 5943245 * t9451 + 66777249 * t9453 - 327165685 * t9455 + t9433 = t9456 ** 2 + t9467 = 4032015 * t9433 + 1255800 * t9447 - 11714820 * t9452 + 43594200 * t9454 - 77527710 * t9456 + 65768040 * t9458 - 18101460 * t9460 - 7283640 * t9462 - 22425 + t9466 = 40320150 * t9433 + 507840 * t9447 - 7143800 * t9452 + 32818240 * t9454 - 52802940 * t9456 - 11003200 * t9458 + 122171400 * t9460 - 124862400 * t9462 - 5290 + t9465 = -197568735 * t9433 + 766920 * t9447 - 15373820 * t9452 + 119731304 * t9454 - 466534530 * t9456 + 1002881880 * t9458 - 1208707500 * t9460 + 764782200 * t9462 - 6391 + t9450 = 2 * phi1 + t9449 = 3 * phi1 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.2e1) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((306433140 * t9433 - 1265272320 * t9462 + 2117422320 * t9460 - 1839735040 * t9458 + 882372920 * t9456 - 228836608 * t9454 + 28995824 * t9452 - 1391104 * t9447 + 10868) * np.exp((4*1j) * phi2) + (t9465 - t9468) * np.exp((-4*1j) * (phi1 - phi2)) + (t9466 + t9469) * np.exp((-4*1j) * (t9450 - phi2)) + (t9467 + t9470) * np.exp((-4*1j) * (t9449 - phi2)) + (t9465 + t9468) * np.exp((4*1j) * (phi1 + phi2)) + (t9466 - t9469) * np.exp((4*1j) * (t9450 + phi2)) + (t9467 - t9470) * np.exp((4*1j) * (t9449 + phi2))) + + if Bindx == 258: + t9495 = np.cos(phi) + t9494 = t9495 ** 2 + t9500 = t9494 ** 2 + t9499 = t9495 * t9494 + t9502 = t9499 ** 2 + t9508 = t9502 ** 2 + t9522 = -t9500 - t9508 + t9483 = t9495 * t9508 + t9504 = t9500 ** 2 + t9487 = t9495 * t9504 + t9503 = t9495 * t9502 + t9521 = -t9483 - t9487 - t9499 - t9503 + t9510 = t9503 ** 2 + t9481 = t9495 * t9510 + t9501 = t9495 * t9500 + t9506 = t9501 ** 2 + t9485 = t9495 * t9506 + t9520 = -65540 * t9481 - 65540 * t9495 - 589860 * t9485 - 589860 * t9501 + t9480 = t9504 ** 2 + t9519 = 16385 * t9480 + 1048640 * t9502 - 1474650 * t9504 + 1048640 * t9506 + 16385 + t9518 = -107880 * t9481 + 474440 * t9483 - 817000 * t9485 + 680840 * t9487 - 360 * t9495 + 3400 * t9499 + 32920 * t9501 - 266360 * t9503 + t9517 = 35960 * t9480 + 11200 * t9494 - 104480 * t9500 + 388800 * t9502 - 691440 * t9504 + 586560 * t9506 - 161440 * t9508 - 64960 * t9510 - 200 + t9516 = -21036600 * t9481 + 83467800 * t9483 - 133169400 * t9485 + 109138200 * t9487 + 37320 * t9495 - 1196520 * t9499 + 11337480 * t9501 - 48578280 * t9503 + t9515 = 10518300 * t9480 + 132480 * t9494 - 1863600 * t9500 + 8561280 * t9502 - 13774680 * t9504 - 2870400 * t9506 + 31870800 * t9508 - 32572800 * t9510 - 1380 + t9514 = -225792840 * t9481 + 837618600 * t9483 - 1240766280 * t9485 + 933114600 * t9487 + 172216 * t9495 - 6792280 * t9499 + 76316856 * t9501 - 373903640 * t9503 + t9513 = 225792840 * t9480 - 876480 * t9494 + 17570080 * t9500 - 136835776 * t9502 + 533182320 * t9504 - 1146150720 * t9506 + 1381380000 * t9508 - 874036800 * t9510 + 7304 + t9498 = 2 * phi1 + t9497 = 3 * phi1 + t9496 = 4 * phi1 + t9475 = np.exp((-4*1j) * (t9496 - phi2)) + t9471 = np.exp((4*1j) * (t9496 + phi2)) + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.7e1) * ((601080390 * t9480 - 2481880320 * t9510 + 4153405320 * t9508 - 3608711040 * t9506 + 1730808420 * t9504 - 448871808 * t9502 + 56876424 * t9500 - 2728704 * t9494 + 21318) * np.exp((4*1j) * phi2) + ((t9519 + t9520) * t9475) + ((t9519 - t9520) * t9471) + (t9513 + t9514) * np.exp((-4*1j) * (phi1 - phi2)) + (t9515 + t9516) * np.exp((-4*1j) * (t9498 - phi2)) + (t9517 + t9518) * np.exp((-4*1j) * (t9497 - phi2)) + (t9513 - t9514) * np.exp((4*1j) * (phi1 + phi2)) + (t9515 - t9516) * np.exp((4*1j) * (t9498 + phi2)) + (t9517 - t9518) * np.exp((4*1j) * (t9497 + phi2)) + (327700 * (-t9521 + t9522) * t9475) + (327700 * (t9521 + t9522) * t9471)) + + if Bindx == 259: + t9546 = np.cos(phi) + t9545 = t9546 ** 2 + t9553 = t9545 ** 2 + t9552 = t9546 * t9545 + t9555 = t9552 ** 2 + t9557 = t9553 ** 2 + t9554 = t9546 * t9553 + t9559 = t9554 ** 2 + t9561 = t9555 ** 2 + t9556 = t9546 * t9555 + t9563 = t9556 ** 2 + t9565 = t9557 ** 2 + t9572 = 3089360 * t9545 - 51854880 * t9553 + 361477200 * t9555 - 1300498200 * t9557 + 2598383600 * t9559 - 2903409600 * t9561 + 1696047600 * t9563 - 403201500 * t9565 - 33580 + t9571 = 6815116 * t9545 - 129812760 * t9553 + 945287420 * t9555 - 3398637550 * t9557 + 6678759780 * t9559 - 7320811680 * t9561 + 4206302100 * t9563 - 987843675 * t9565 - 58751 + t9570 = 5364060 * t9545 - 40580280 * t9553 + 103459980 * t9555 - 51227670 * t9557 - 200048940 * t9559 + 371860320 * t9561 - 249204540 * t9563 + 60480225 * t9565 - 103155 + t9530 = t9546 * t9565 + t9532 = t9546 * t9563 + t9534 = t9546 * t9561 + t9536 = t9546 * t9559 + t9538 = t9546 * t9557 + t9569 = 16128060 * t9530 + 2601300 * t9532 - 227066580 * t9534 + 570366420 * t9536 - 627953820 * t9538 - 645840 * t9546 + 13257660 * t9552 - 101953020 * t9554 + 355265820 * t9556 + t9568 = 161280600 * t9530 - 416208000 * t9532 + 55255200 * t9534 + 850595200 * t9536 - 1149361520 * t9538 - 713000 * t9546 + 21417600 * t9552 - 181530720 * t9554 + 659264640 * t9556 + t9567 = -790274940 * t9530 + 3339548940 * t9532 - 5814981900 * t9534 + 5393033100 * t9536 - 2876351940 * t9538 - 301840 * t9546 + 12062820 * t9552 - 150426276 * t9554 + 887692036 * t9556 + t9551 = 4 * phi1 + t9550 = 8 * phi1 + t9549 = -5 * phi2 + t9548 = 5 * phi2 + t9547 = 12 * phi1 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((1225732560 * t9530 - 5851884480 * t9532 + 11551063680 * t9534 - 12149159360 * t9536 + 7308947360 * t9538 - 2505552192 * t9556 + 457673216 * t9554 - 37733696 * t9552 + 912912 * t9546) * np.exp((5*1j) * phi2) + (t9567 - t9571) * np.exp((-1*1j) * (t9551 + t9549)) + (t9568 + t9572) * np.exp((-1*1j) * (t9550 + t9549)) + (t9567 + t9571) * np.exp((1j) * (t9551 + t9548)) + (t9568 - t9572) * np.exp((1j) * (t9550 + t9548)) + (t9569 - t9570) * np.exp((-1*1j) * (t9547 + t9549)) + (t9569 + t9570) * np.exp((1j) * (t9547 + t9548))) * ((1 + t9546) ** (-0.1e1 / 0.2e1)) * ((1 - t9546) ** (-0.1e1 / 0.2e1)) + + if Bindx == 260: + t9598 = np.cos(phi) + t9597 = t9598 ** 2 + t9606 = t9597 ** 2 + t9610 = t9606 ** 2 + t9618 = t9610 ** 2 + t9582 = t9598 * t9618 + t9605 = t9598 * t9597 + t9608 = t9605 ** 2 + t9609 = t9598 * t9608 + t9616 = t9609 ** 2 + t9584 = t9598 * t9616 + t9614 = t9608 ** 2 + t9586 = t9598 * t9614 + t9607 = t9598 * t9606 + t9612 = t9607 ** 2 + t9588 = t9598 * t9612 + t9590 = t9598 * t9610 + t9627 = 35960 * t9582 + 5800 * t9584 - 506280 * t9586 + 1271720 * t9588 - 1400120 * t9590 - 1440 * t9598 + 29560 * t9605 - 227320 * t9607 + 792120 * t9609 + t9626 = -11960 * t9597 + 90480 * t9606 - 230680 * t9608 + 114220 * t9610 + 446040 * t9612 - 829120 * t9614 + 555640 * t9616 - 134850 * t9618 + 230 + t9625 = 65540 * t9597 - 655400 * t9606 + 1638500 * t9608 - 1802350 * t9610 + 720940 * t9612 + 262160 * t9614 - 327700 * t9616 + 81925 * t9618 + 16385 + t9624 = 16385 * t9582 + 65540 * t9584 - 655400 * t9586 + 1638500 * t9588 - 1802350 * t9590 + 81925 * t9598 - 327700 * t9605 + 262160 * t9607 + 720940 * t9609 + t9623 = 201480 * t9597 - 3381840 * t9606 + 23574600 * t9608 - 84815100 * t9610 + 169459800 * t9612 - 189352800 * t9614 + 110611800 * t9616 - 26295750 * t9618 - 2190 + t9622 = 10518300 * t9582 - 27144000 * t9584 + 3603600 * t9586 + 55473600 * t9588 - 74958360 * t9590 - 46500 * t9598 + 1396800 * t9605 - 11838960 * t9607 + 42995520 * t9609 + t9621 = 1947176 * t9597 - 37089360 * t9606 + 270082120 * t9608 - 971039300 * t9610 + 1908217080 * t9612 - 2091660480 * t9614 + 1201800600 * t9616 - 282241050 * t9618 - 16786 + t9620 = 225792840 * t9582 - 954156840 * t9584 + 1661423400 * t9586 - 1540866600 * t9588 + 821814840 * t9590 + 86240 * t9598 - 3446520 * t9605 + 42978936 * t9607 - 253626296 * t9609 + t9604 = 4 * phi1 + t9603 = 8 * phi1 + t9602 = -5 * phi2 + t9601 = 5 * phi2 + t9600 = 12 * phi1 + t9599 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.31e2) * ((1 + t9598) ** (-0.1e1 / 0.2e1)) * ((1 - t9598) ** (-0.1e1 / 0.2e1)) * ((601080390 * t9582 - 2869674120 * t9584 + 5664463920 * t9586 - 5957760840 * t9588 + 3584195340 * t9590 - 1228684248 * t9609 + 224435904 * t9607 - 18504024 * t9605 + 447678 * t9598) * np.exp((5*1j) * phi2) + (t9620 + t9621) * np.exp((-1*1j) * (t9604 + t9602)) + (t9622 + t9623) * np.exp((-1*1j) * (t9603 + t9602)) + (t9620 - t9621) * np.exp((1j) * (t9604 + t9601)) + (t9622 - t9623) * np.exp((1j) * (t9603 + t9601)) + (t9626 + t9627) * np.exp((-1*1j) * (t9600 + t9602)) + (t9624 - t9625) * np.exp((-1*1j) * (t9599 + t9602)) + (-t9626 + t9627) * np.exp((1j) * (t9600 + t9601)) + (t9624 + t9625) * np.exp((1j) * (t9599 + t9601))) + + if Bindx == 261: + t9650 = np.cos(phi) + t9649 = t9650 ** 2 + t9655 = t9650 * t9649 + t9658 = t9655 ** 2 + t9659 = t9650 * t9658 + t9666 = t9659 ** 2 + t9636 = t9650 * t9666 + t9664 = t9658 ** 2 + t9638 = t9650 * t9664 + t9656 = t9649 ** 2 + t9657 = t9650 * t9656 + t9662 = t9657 ** 2 + t9640 = t9650 * t9662 + t9660 = t9656 ** 2 + t9642 = t9650 * t9660 + t9674 = -241920900 * t9636 + 819409500 * t9638 - 1071825300 * t9640 + 674978700 * t9642 + 12420 * t9650 - 1065820 * t9655 + 26793620 * t9657 - 206382220 * t9659 + t9673 = -36288135 * t9636 + 96898425 * t9638 - 39248235 * t9640 - 111151755 * t9642 - 811785 * t9650 + 12822615 * t9655 - 67315365 * t9657 + 145094235 * t9659 + t9635 = t9660 ** 2 + t9672 = 8064030 * t9635 - 1237860 * t9649 - 466440 * t9656 + 51505740 * t9658 - 193590540 * t9660 + 286340340 * t9662 - 180297000 * t9664 + 29654820 * t9666 + 26910 + t9671 = 592706205 * t9636 - 2262480675 * t9638 + 3468111465 * t9640 - 2719929975 * t9642 - 658413 * t9650 + 23898035 * t9655 - 249456697 * t9657 + 1147810055 * t9659 + t9670 = 80640300 * t9635 + 3837320 * t9649 - 53355400 * t9656 + 279605480 * t9658 - 693462880 * t9660 + 833432600 * t9662 - 383019000 * t9664 - 67633800 * t9666 - 44620 + t9669 = -395137470 * t9635 + 334180 * t9649 - 8606360 * t9656 + 95133108 * t9658 - 503971860 * t9660 + 1370454540 * t9662 - 1960303800 * t9664 + 1402100700 * t9666 - 3038 + t9654 = 2 * phi1 + t9653 = 4 * phi1 + t9652 = -3 * phi2 + t9651 = 3 * phi2 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * ((612866280 * t9635 - 2728243440 * t9666 + 4937016240 * t9664 - 4649784880 * t9662 + 2421469440 * t9660 - 682443216 * t9658 + 93974608 * t9656 - 4896528 * t9649 + 41496) * np.exp((6*1j) * phi2) + (t9672 + t9673) * np.exp((-6*1j) * (t9654 - phi2)) + (t9669 + t9671) * np.exp((-2*1j) * (t9654 + t9652)) + (t9670 + t9674) * np.exp((-2*1j) * (t9653 + t9652)) + (t9669 - t9671) * np.exp((2*1j) * (t9654 + t9651)) + (t9670 - t9674) * np.exp((2*1j) * (t9653 + t9651)) + (t9672 - t9673) * np.exp((6*1j) * (t9654 + phi2))) + + if Bindx == 262: + t9699 = np.cos(phi) + t9698 = t9699 ** 2 + t9706 = t9698 ** 2 + t9710 = t9706 ** 2 + t9684 = t9710 ** 2 + t9705 = t9699 * t9698 + t9708 = t9705 ** 2 + t9707 = t9699 * t9706 + t9712 = t9707 ** 2 + t9714 = t9708 ** 2 + t9709 = t9699 * t9708 + t9716 = t9709 ** 2 + t9726 = 35960 * t9684 - 5520 * t9698 - 2080 * t9706 + 229680 * t9708 - 863280 * t9710 + 1276880 * t9712 - 804000 * t9714 + 132240 * t9716 + 120 + t9725 = 16385 * t9684 + 819250 * t9706 - 1081410 * t9708 + 1081410 * t9712 - 819250 * t9714 - 16385 - 163850 * t9698 + 163850 * t9716 + t9685 = t9699 * t9716 + t9687 = t9699 * t9714 + t9689 = t9699 * t9712 + t9691 = t9699 * t9710 + t9724 = -161820 * t9685 + 432100 * t9687 - 175020 * t9689 - 495660 * t9691 - 3620 * t9699 + 57180 * t9705 - 300180 * t9707 + 647020 * t9709 + t9723 = 98310 * t9685 - 557090 * t9689 + 1802350 * t9691 - 98310 * t9699 + 557090 * t9707 - 1802350 * t9709 - 163850 * t9687 + 163850 * t9705 + t9722 = -31554900 * t9685 + 106879500 * t9687 - 139803300 * t9689 + 88040700 * t9691 + 1620 * t9699 - 139020 * t9705 + 3494820 * t9707 - 26919420 * t9709 + t9721 = 10518300 * t9684 + 500520 * t9698 - 6959400 * t9706 + 36470280 * t9708 - 90451680 * t9710 + 108708600 * t9712 - 49959000 * t9714 - 8821800 * t9716 - 5820 + t9720 = -338689260 * t9685 + 1292846100 * t9687 - 1981777980 * t9689 + 1554245700 * t9691 + 376236 * t9699 - 13656020 * t9705 + 142546684 * t9707 - 655891460 * t9709 + t9719 = 225792840 * t9684 - 190960 * t9698 + 4917920 * t9706 - 54361776 * t9708 + 287983920 * t9710 - 783116880 * t9712 + 1120173600 * t9714 - 801200400 * t9716 + 1736 + t9704 = 2 * phi1 + t9703 = 4 * phi1 + t9702 = 8 * phi1 + t9701 = -3 * phi2 + t9700 = 3 * phi2 + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * ((40698 + 601080390 * t9684 - 2675777220 * t9716 + 4842073620 * t9714 - 4560365940 * t9712 + 2374902720 * t9710 - 669319308 * t9708 + 92167404 * t9706 - 4802364 * t9698) * np.exp((6*1j) * phi2) + (-t9723 + t9725) * np.exp((-2*1j) * (t9702 + t9701)) + (t9723 + t9725) * np.exp((2*1j) * (t9702 + t9700)) + (t9724 + t9726) * np.exp((-6*1j) * (t9704 - phi2)) + (t9719 + t9720) * np.exp((-2*1j) * (t9704 + t9701)) + (t9721 + t9722) * np.exp((-2*1j) * (t9703 + t9701)) + (t9719 - t9720) * np.exp((2*1j) * (t9704 + t9700)) + (t9721 - t9722) * np.exp((2*1j) * (t9703 + t9700)) + (-t9724 + t9726) * np.exp((6*1j) * (t9704 + phi2))) + + if Bindx == 263: + t9750 = np.cos(phi) + t9749 = t9750 ** 2 + t9757 = t9749 ** 2 + t9756 = t9750 * t9749 + t9759 = t9756 ** 2 + t9761 = t9757 ** 2 + t9758 = t9750 * t9757 + t9763 = t9758 ** 2 + t9765 = t9759 ** 2 + t9760 = t9750 * t9759 + t9767 = t9760 ** 2 + t9769 = t9761 ** 2 + t9776 = 170040 * t9749 - 2640924 * t9757 + 13620984 * t9759 - 30161274 * t9761 + 29024424 * t9763 - 6153420 * t9765 - 7536984 * t9767 + 3681405 * t9769 - 4251 + t9775 = 239680 * t9749 - 2574544 * t9757 + 7121408 * t9759 + 6378152 * t9761 - 62152640 * t9763 + 112585200 * t9765 - 86136960 * t9767 + 24542700 * t9769 - 2996 + t9774 = 606424 * t9749 - 10521868 * t9757 + 71006488 * t9759 - 240374498 * t9761 + 450355080 * t9763 - 474730620 * t9765 + 263794440 * t9767 - 60129615 * t9769 - 5831 + t9734 = t9750 * t9769 + t9736 = t9750 * t9767 + t9738 = t9750 * t9765 + t9740 = t9750 * t9763 + t9742 = t9750 * t9761 + t9773 = 7012200 * t9734 + 904800 * t9736 - 68752320 * t9738 + 143517920 * t9740 - 131487664 * t9742 - 65272 * t9750 + 1796512 * t9756 - 15524096 * t9758 + 62597920 * t9760 + t9772 = -34359780 * t9734 + 131897220 * t9736 - 194272260 * t9738 + 131591460 * t9740 - 33328428 * t9742 + 17248 * t9750 - 543508 * t9756 + 3862964 * t9758 - 4864916 * t9760 + t9771 = 701220 * t9734 + 4727580 * t9736 - 23128092 * t9738 + 32868732 * t9740 - 16667508 * t9742 + 104832 * t9750 - 1278732 * t9756 + 4345068 * t9758 - 1673100 * t9760 + t9755 = 4 * phi1 + t9754 = 8 * phi1 + t9753 = -7 * phi2 + t9752 = 7 * phi2 + t9751 = 12 * phi1 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * ((53292720 * t9734 - 275059200 * t9736 + 589717440 * t9738 - 676898560 * t9740 + 446441632 * t9742 - 168450048 * t9760 + 33971392 * t9758 - 3098368 * t9756 + 82992 * t9750) * np.exp((7*1j) * phi2) + (t9772 - t9774) * np.exp((-1*1j) * (t9755 + t9753)) + (t9773 - t9775) * np.exp((-1*1j) * (t9754 + t9753)) + (t9772 + t9774) * np.exp((1j) * (t9755 + t9752)) + (t9773 + t9775) * np.exp((1j) * (t9754 + t9752)) + (t9771 - t9776) * np.exp((-1*1j) * (t9751 + t9753)) + (t9771 + t9776) * np.exp((1j) * (t9751 + t9752))) * ((1 + t9750) ** (-0.1e1 / 0.2e1)) * ((1 - t9750) ** (-0.1e1 / 0.2e1)) + + if Bindx == 264: + t9802 = np.cos(phi) + t9801 = t9802 ** 2 + t9810 = t9801 ** 2 + t9814 = t9810 ** 2 + t9822 = t9814 ** 2 + t9786 = t9802 * t9822 + t9809 = t9802 * t9801 + t9812 = t9809 ** 2 + t9813 = t9802 * t9812 + t9820 = t9813 ** 2 + t9788 = t9802 * t9820 + t9818 = t9812 ** 2 + t9790 = t9802 * t9818 + t9811 = t9802 * t9810 + t9816 = t9811 ** 2 + t9792 = t9802 * t9816 + t9794 = t9802 * t9814 + t9831 = 35960 * t9786 + 242440 * t9788 - 1186056 * t9790 + 1685576 * t9792 - 854744 * t9794 + 5376 * t9802 - 65576 * t9809 + 222824 * t9811 - 85800 * t9813 + t9830 = -262160 * t9801 + 983100 * t9810 - 524320 * t9812 - 1802350 * t9814 + 2883760 * t9816 - 1376340 * t9818 + 114695 * t9822 - 16385 + t9829 = 16385 * t9786 + 262160 * t9788 - 983100 * t9790 + 524320 * t9792 + 1802350 * t9794 - 114695 * t9802 + 1376340 * t9811 - 2883760 * t9813 + t9828 = 8720 * t9801 - 135432 * t9810 + 698512 * t9812 - 1546732 * t9814 + 1488432 * t9816 - 315560 * t9818 - 386512 * t9820 + 188790 * t9822 - 218 + t9827 = 359520 * t9801 - 3861816 * t9810 + 10682112 * t9812 + 9567228 * t9814 - 93228960 * t9816 + 168877800 * t9818 - 129205440 * t9820 + 36814050 * t9822 - 4494 + t9826 = 10518300 * t9786 + 1357200 * t9788 - 103128480 * t9790 + 215276880 * t9792 - 197231496 * t9794 - 97908 * t9802 + 2694768 * t9809 - 23286144 * t9811 + 93896880 * t9813 + t9825 = 3985072 * t9801 - 69143704 * t9810 + 466614064 * t9812 - 1579603844 * t9814 + 2959476240 * t9816 - 3119658360 * t9818 + 1733506320 * t9820 - 395137470 * t9822 - 38318 + t9824 = 225792840 * t9786 - 866753160 * t9788 + 1276646280 * t9790 - 864743880 * t9792 + 219015384 * t9794 - 113344 * t9802 + 3571624 * t9809 - 25385192 * t9811 + 31969448 * t9813 + t9808 = 4 * phi1 + t9807 = 8 * phi1 + t9806 = -7 * phi2 + t9805 = 7 * phi2 + t9804 = 12 * phi1 + t9803 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.31e2) * ((1 + t9802) ** (-0.1e1 / 0.2e1)) * ((1 - t9802) ** (-0.1e1 / 0.2e1)) * ((-1899922176 * t9813 + 383158104 * t9811 - 34946016 * t9809 + 936054 * t9802 + 601080390 * t9786 - 3102350400 * t9788 + 6651332280 * t9790 - 7634634720 * t9792 + 5035346484 * t9794) * np.exp((7*1j) * phi2) + (t9829 - t9830) * np.exp((-1*1j) * (t9803 + t9806)) + (t9829 + t9830) * np.exp((1j) * (t9803 + t9805)) + (t9824 + t9825) * np.exp((-1*1j) * (t9808 + t9806)) + (t9826 - t9827) * np.exp((-1*1j) * (t9807 + t9806)) + (t9824 - t9825) * np.exp((1j) * (t9808 + t9805)) + (t9826 + t9827) * np.exp((1j) * (t9807 + t9805)) + (-t9828 + t9831) * np.exp((-1*1j) * (t9804 + t9806)) + (t9828 + t9831) * np.exp((1j) * (t9804 + t9805))) + + if Bindx == 265: + t9854 = np.cos(phi) + t9853 = t9854 ** 2 + t9858 = t9854 * t9853 + t9861 = t9858 ** 2 + t9862 = t9854 * t9861 + t9869 = t9862 ** 2 + t9840 = t9854 * t9869 + t9867 = t9861 ** 2 + t9842 = t9854 * t9867 + t9859 = t9853 ** 2 + t9860 = t9854 * t9859 + t9865 = t9860 ** 2 + t9844 = t9854 * t9865 + t9863 = t9859 ** 2 + t9846 = t9854 * t9863 + t9877 = -3155490 * t9840 + 793962 * t9842 + 17180046 * t9844 - 26679510 * t9846 + 79326 * t9854 - 404118 * t9858 - 1989234 * t9860 + 14175018 * t9862 + t9876 = -21036600 * t9840 + 54152280 * t9842 - 38034360 * t9844 - 10769000 * t9846 - 81464 * t9854 + 1854552 * t9858 - 11197368 * t9860 + 25095576 * t9862 + t9875 = -51539670 * t9840 + 204496110 * t9842 - 326265030 * t9844 + 267388590 * t9846 + 91434 * t9854 - 2931474 * t9858 + 27776826 * t9860 - 119016786 * t9862 + t9839 = t9863 ** 2 + t9874 = 525915 * t9839 - 415584 * t9853 + 3502980 * t9859 - 10007712 * t9861 + 7513506 * t9863 + 10007712 * t9865 - 17111484 * t9867 + 5971680 * t9869 + 12987 + t9873 = -25769835 * t9839 - 324576 * t9853 + 4565820 * t9859 - 20975136 * t9861 + 33747966 * t9863 + 7032480 * t9865 - 78083460 * t9867 + 79803360 * t9869 + 3381 + t9872 = 5259150 * t9839 + 247824 * t9853 - 3755640 * t9859 + 24131184 * t9861 - 73520172 * t9863 + 110430320 * t9865 - 74987640 * t9867 + 12214800 * t9869 - 3442 + t9857 = 3 * phi1 + t9856 = -2 * phi2 + t9855 = 2 * phi2 + tfunc[..., c] = -(0.11e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((39969540 * t9839 - 195979680 * t9869 + 393559920 * t9867 - 414525280 * t9865 + 243168536 * t9863 - 77625184 * t9861 + 12144496 * t9859 - 719264 * t9853 + 6916) * np.exp((8*1j) * phi2) + (t9872 + t9876) * np.exp((-8*1j) * (phi1 - phi2)) + (t9873 - t9875) * np.exp((-4*1j) * (phi1 + t9856)) + (t9874 + t9877) * np.exp((-4*1j) * (t9857 + t9856)) + (t9873 + t9875) * np.exp((4*1j) * (phi1 + t9855)) + (t9874 - t9877) * np.exp((4*1j) * (t9857 + t9855)) + (t9872 - t9876) * np.exp((8*1j) * (phi1 + phi2))) + + if Bindx == 266: + t9902 = np.cos(phi) + t9901 = t9902 ** 2 + t9907 = t9902 * t9901 + t9910 = t9907 ** 2 + t9911 = t9902 * t9910 + t9918 = t9911 ** 2 + t9888 = t9902 * t9918 + t9916 = t9910 ** 2 + t9890 = t9902 * t9916 + t9908 = t9901 ** 2 + t9909 = t9902 * t9908 + t9914 = t9909 ** 2 + t9892 = t9902 * t9914 + t9912 = t9908 ** 2 + t9894 = t9902 * t9912 + t9928 = -215760 * t9888 + 54288 * t9890 + 1174704 * t9892 - 1824240 * t9894 + 5424 * t9902 - 27632 * t9907 - 136016 * t9909 + 969232 * t9911 + t9887 = t9912 ** 2 + t9927 = 35960 * t9887 - 28416 * t9901 + 239520 * t9908 + 513744 * t9912 - 1170016 * t9916 + 408320 * t9918 + 888 + 684288 * t9914 - 684288 * t9910 + t9926 = 16385 * t9887 + 3244230 * t9912 + 16385 - 1441880 * t9910 - 1441880 * t9914 - 589860 * t9916 - 589860 * t9908 + 393240 * t9901 + 393240 * t9918 + t9925 = 1966200 * t9892 + 1966200 * t9909 - 1441880 * t9894 - 1441880 * t9911 - 393240 * t9890 - 393240 * t9907 - 131080 * t9888 - 131080 * t9902 + t9924 = -42073200 * t9888 + 108304560 * t9890 - 76068720 * t9892 - 21538000 * t9894 - 162928 * t9902 + 3709104 * t9907 - 22394736 * t9909 + 50191152 * t9911 + t9923 = 10518300 * t9887 + 495648 * t9901 - 7511280 * t9908 + 48262368 * t9910 - 147040344 * t9912 + 220860640 * t9914 - 149975280 * t9916 + 24429600 * t9918 - 6884 + t9922 = -451585680 * t9888 + 1791775440 * t9890 - 2858703120 * t9892 + 2342833360 * t9894 + 801136 * t9902 - 25685296 * t9907 + 243377904 * t9909 - 1042813744 * t9911 + t9921 = 225792840 * t9887 + 2843904 * t9901 - 40005280 * t9908 + 183782144 * t9910 - 295696464 * t9912 - 61617920 * t9914 + 684159840 * t9916 - 699229440 * t9918 - 29624 + t9906 = 2 * phi1 + t9905 = 3 * phi1 + t9904 = -2 * phi2 + t9903 = 2 * phi2 + tfunc[..., c] = (0.33e2 / 0.947912704e9) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * np.sqrt(0.13e2) * ((104006 + 601080390 * t9887 - 2947232880 * t9918 + 5918535720 * t9916 - 6233822480 * t9914 + 3656880676 * t9912 - 1167363344 * t9910 + 182634536 * t9908 - 10816624 * t9901) * np.exp((8*1j) * phi2) + (t9923 + t9924) * np.exp((-8*1j) * (phi1 - phi2)) + (t9925 + t9926) * np.exp((-8*1j) * (t9906 - phi2)) + (t9921 + t9922) * np.exp((-4*1j) * (phi1 + t9904)) + (t9927 + t9928) * np.exp((-4*1j) * (t9905 + t9904)) + (t9921 - t9922) * np.exp((4*1j) * (phi1 + t9903)) + (t9927 - t9928) * np.exp((4*1j) * (t9905 + t9903)) + (t9923 - t9924) * np.exp((8*1j) * (phi1 + phi2)) + (-t9925 + t9926) * np.exp((8*1j) * (t9906 + phi2))) + + if Bindx == 267: + t9952 = np.cos(phi) + t9951 = t9952 ** 2 + t9958 = t9951 ** 2 + t9957 = t9952 * t9951 + t9960 = t9957 ** 2 + t9962 = t9958 ** 2 + t9959 = t9952 * t9958 + t9964 = t9959 ** 2 + t9966 = t9960 ** 2 + t9961 = t9952 * t9960 + t9968 = t9961 ** 2 + t9970 = t9962 ** 2 + t9977 = 564408 * t9951 - 3006900 * t9958 + 2683512 * t9960 + 13577850 * t9962 - 33575256 * t9964 + 24655644 * t9966 - 2035800 * t9968 - 2839941 * t9970 - 23517 + t9976 = 697600 * t9951 - 7675920 * t9958 + 33512640 * t9960 - 65998680 * t9962 + 47583360 * t9964 + 21818160 * t9966 - 48859200 * t9968 + 18932940 * t9970 - 10900 + t9936 = t9952 * t9970 + t9938 = t9952 * t9968 + t9940 = t9952 * t9966 + t9942 = t9952 * t9964 + t9944 = t9952 * t9962 + t9975 = 420732 * t9936 + 6528132 * t9938 - 16157700 * t9940 + 1919268 * t9942 + 22007700 * t9944 - 22464 * t9952 - 666900 * t9957 + 6990516 * t9959 - 21019284 * t9961 + t9974 = 685608 * t9951 - 10596348 * t9958 + 66588648 * t9960 - 215248866 * t9962 + 388443384 * t9964 - 394957836 * t9966 + 211478904 * t9968 - 46385703 * t9970 - 7791 + t9973 = 4207320 * t9936 + 15743520 * t9938 - 90417600 * t9940 + 142498720 * t9942 - 101815120 * t9944 + 18360 * t9952 - 130080 * t9957 - 4018560 * t9959 + 33913440 * t9961 + t9972 = -20615868 * t9936 + 68497884 * t9938 - 61159644 * t9940 - 35399364 * t9942 + 99922956 * t9944 + 98784 * t9952 - 2804172 * t9957 + 21869484 * t9959 - 70410060 * t9961 + t9956 = 4 * phi1 + t9955 = 8 * phi1 + t9954 = -9 * phi2 + t9953 = 9 * phi2 + tfunc[..., c] = (-0.11e2 / 0.59244544e8*1j) * np.sqrt(0.17e2) * np.sqrt(0.226e3) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * ((1 + t9952) ** (-0.1e1 / 0.2e1)) * ((1 - t9952) ** (-0.1e1 / 0.2e1)) * ((31975632 * t9936 - 181539072 * t9938 + 431795520 * t9940 - 555176960 * t9942 + 414525280 * t9944 - 179009792 * t9961 + 41717312 * t9959 - 4426240 * t9957 + 138320 * t9952) * np.exp((9*1j) * phi2) + (t9975 + t9977) * np.exp((-3*1j) * (t9956 - 3 * phi2)) + (t9972 - t9974) * np.exp((-1*1j) * (t9956 + t9954)) + (t9973 - t9976) * np.exp((-1*1j) * (t9955 + t9954)) + (t9972 + t9974) * np.exp((1j) * (t9956 + t9953)) + (t9973 + t9976) * np.exp((1j) * (t9955 + t9953)) + (t9975 - t9977) * np.exp((3*1j) * (t9956 + 3 * phi2))) + + if Bindx == 268: + t10003 = np.cos(phi) + t10002 = t10003 ** 2 + t10010 = t10002 ** 2 + t10009 = t10003 * t10002 + t10012 = t10009 ** 2 + t10014 = t10010 ** 2 + t10011 = t10003 * t10010 + t10016 = t10011 ** 2 + t10018 = t10012 ** 2 + t10013 = t10003 * t10012 + t10020 = t10013 ** 2 + t10022 = t10014 ** 2 + t10031 = -9648 * t10002 + 51400 * t10010 - 45872 * t10012 - 232100 * t10014 + 573936 * t10016 - 421464 * t10018 + 34800 * t10020 + 48546 * t10022 + 402 + t10030 = -104864 * t10002 + 39324 * t10010 + 681616 * t10012 - 937222 * t10014 + 511212 * t10018 - 157296 * t10020 - 29493 * t10022 - 3277 + t9987 = t10003 * t10022 + t9989 = t10003 * t10020 + t9991 = t10003 * t10018 + t9993 = t10003 * t10016 + t9995 = t10003 * t10014 + t10029 = 29493 * t10003 + 157296 * t10009 - 511212 * t10011 + 3277 * t9987 + 104864 * t9989 - 39324 * t9991 - 681616 * t9993 + 937222 * t9995 + t10028 = -384 * t10003 - 11400 * t10009 + 119496 * t10011 - 359304 * t10013 + 7192 * t9987 + 111592 * t9989 - 276200 * t9991 + 32808 * t9993 + 376200 * t9995 + t10027 = 9180 * t10003 - 65040 * t10009 - 2009280 * t10011 + 16956720 * t10013 + 2103660 * t9987 + 7871760 * t9989 - 45208800 * t9991 + 71249360 * t9993 - 50907560 * t9995 + t10026 = -348800 * t10002 + 3837960 * t10010 - 16756320 * t10012 + 32999340 * t10014 - 23791680 * t10016 - 10909080 * t10018 + 24429600 * t10020 - 9466470 * t10022 + 5450 + t10025 = 1501808 * t10002 - 23211048 * t10010 + 145860848 * t10012 - 471497516 * t10014 + 850875984 * t10016 - 865145736 * t10018 + 463239504 * t10020 - 101606778 * t10022 - 17066 + t10024 = -216384 * t10003 + 6142472 * t10009 - 47904584 * t10011 + 154231560 * t10013 + 45158568 * t9987 - 150042984 * t9989 + 133968744 * t9991 + 77541464 * t9993 - 218878856 * t9995 + t10008 = 4 * phi1 + t10007 = 8 * phi1 + t10006 = -9 * phi2 + t10005 = 9 * phi2 + t10004 = 16 * phi1 + tfunc[..., c] = (0.33e2 / 0.236978176e9*1j) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.31e2) * ((1 + t10003) ** (-0.1e1 / 0.2e1)) * ((1 - t10003) ** (-0.1e1 / 0.2e1)) * ((520030 * t10003 + 120216078 * t9987 - 682517088 * t9989 + 1623385080 * t9991 - 2087251840 * t9993 + 1558455620 * t9995 - 673007968 * t10013 + 156841048 * t10011 - 16640960 * t10009) * np.exp((9*1j) * phi2) + (t10029 + t10030) * np.exp((-1*1j) * (t10004 + t10006)) + (t10029 - t10030) * np.exp((1j) * (t10004 + t10005)) + (t10028 - t10031) * np.exp((-3*1j) * (t10008 - 3 * phi2)) + (t10024 + t10025) * np.exp((-1*1j) * (t10008 + t10006)) + (t10026 + t10027) * np.exp((-1*1j) * (t10007 + t10006)) + (t10024 - t10025) * np.exp((1j) * (t10008 + t10005)) + (-t10026 + t10027) * np.exp((1j) * (t10007 + t10005)) + (t10028 + t10031) * np.exp((3*1j) * (t10008 + 3 * phi2))) + + if Bindx == 269: + t10054 = np.cos(phi) + t10053 = t10054 ** 2 + t10061 = t10053 ** 2 + t10065 = t10061 ** 2 + t10039 = t10065 ** 2 + t10060 = t10054 * t10053 + t10063 = t10060 ** 2 + t10062 = t10054 * t10061 + t10067 = t10062 ** 2 + t10069 = t10063 ** 2 + t10064 = t10054 * t10063 + t10071 = t10064 ** 2 + t10079 = 16182 * t10039 + 23436 * t10053 - 8424 * t10061 - 447876 * t10063 + 1189188 * t10065 - 787644 * t10067 - 326664 * t10069 + 343476 * t10071 - 1674 + t10040 = t10054 * t10071 + t10042 = t10054 * t10069 + t10044 = t10054 * t10067 + t10046 = t10054 * t10065 + t10078 = -121365 * t10040 - 346869 * t10042 + 1146015 * t10044 - 606177 * t10046 + 3429 * t10054 - 81819 * t10060 + 368433 * t10062 - 361647 * t10064 + t10077 = 161820 * t10039 - 31320 * t10053 + 190680 * t10061 - 88440 * t10063 - 1663200 * t10065 + 4298360 * t10067 - 3902040 * t10069 + 1033560 * t10071 + 580 + t10076 = -792918 * t10039 - 55692 * t10053 + 670152 * t10061 - 2900604 * t10063 + 5610780 * t10065 - 4516932 * t10067 - 10584 * t10069 + 1995084 * t10071 + 714 + t10075 = -809100 * t10040 + 1237140 * t10042 + 1213380 * t10044 - 3611740 * t10046 - 6260 * t10054 + 126060 * t10060 - 902340 * t10062 + 2752860 * t10064 + t10074 = -1982295 * t10040 + 8248905 * t10042 - 13703067 * t10044 + 11560101 * t10046 + 4935 * t10054 - 136185 * t10060 + 1229067 * t10062 - 5221461 * t10064 + t10059 = 2 * phi1 + t10058 = 4 * phi1 + t10057 = 6 * phi1 + t10056 = -5 * phi2 + t10055 = 5 * phi2 + tfunc[..., c] = -(0.11e2 / 0.29622272e8) * np.sqrt(0.7e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * ((760 + 1229832 * t10039 - 6744240 * t10071 + 15335280 * t10069 - 18557680 * t10067 + 12719360 * t10065 - 4828432 * t10063 + 910480 * t10061 - 65360 * t10053) * np.exp((10*1j) * phi2) + (-t10074 + t10076) * np.exp((-2*1j) * (t10059 + t10056)) + (t10075 + t10077) * np.exp((-2*1j) * (t10058 + t10056)) + (t10078 + t10079) * np.exp((-2*1j) * (t10057 + t10056)) + (t10074 + t10076) * np.exp((2*1j) * (t10059 + t10055)) + (-t10075 + t10077) * np.exp((2*1j) * (t10058 + t10055)) + (-t10078 + t10079) * np.exp((2*1j) * (t10057 + t10055))) + + if Bindx == 270: + t10104 = np.cos(phi) + t10103 = t10104 ** 2 + t10111 = t10104 * t10103 + t10114 = t10111 ** 2 + t10115 = t10104 * t10114 + t10122 = t10115 ** 2 + t10090 = t10104 * t10122 + t10120 = t10114 ** 2 + t10092 = t10104 * t10120 + t10112 = t10103 ** 2 + t10113 = t10104 * t10112 + t10118 = t10113 ** 2 + t10094 = t10104 * t10118 + t10116 = t10112 ** 2 + t10096 = t10104 * t10116 + t10132 = -53940 * t10090 - 154164 * t10092 + 509340 * t10094 - 269412 * t10096 + 1524 * t10104 - 36364 * t10111 + 163748 * t10113 - 160732 * t10115 + t10089 = t10116 ** 2 + t10131 = 7192 * t10089 + 10416 * t10103 - 3744 * t10112 - 199056 * t10114 + 528528 * t10116 - 350064 * t10118 - 145184 * t10120 + 152656 * t10122 - 744 + t10130 = 3277 * t10089 - 3277 + 937222 * t10114 - 937222 * t10118 - 255606 * t10112 + 255606 * t10120 + 137634 * t10122 - 137634 * t10103 + t10129 = -32770 * t10090 + 32770 * t10104 + 937222 * t10096 - 937222 * t10115 - 294930 * t10092 + 294930 * t10111 + 255606 * t10094 - 255606 * t10113 + t10128 = -10518300 * t10090 + 16082820 * t10092 + 15773940 * t10094 - 46952620 * t10096 - 81380 * t10104 + 1638780 * t10111 - 11730420 * t10113 + 35787180 * t10115 + t10127 = 112896420 * t10090 - 469794780 * t10092 + 780422292 * t10094 - 658375276 * t10096 - 281060 * t10104 + 7756060 * t10111 - 69998292 * t10113 + 297374636 * t10115 + t10126 = 2103660 * t10089 - 407160 * t10103 + 2478840 * t10112 - 1149720 * t10114 - 21621600 * t10116 + 55878680 * t10118 - 50726520 * t10120 + 13436280 * t10122 + 7540 + t10125 = 45158568 * t10089 + 3171792 * t10103 - 38166752 * t10112 + 165196304 * t10114 - 319547280 * t10116 + 257250032 * t10118 + 602784 * t10120 - 113624784 * t10122 - 40664 + t10110 = 2 * phi1 + t10109 = 4 * phi1 + t10108 = 6 * phi1 + t10107 = 8 * phi1 + t10106 = -5 * phi2 + t10105 = 5 * phi2 + tfunc[..., c] = (0.33e2 / 0.473956352e9) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.7e1) * np.sqrt(0.31e2) * ((120216078 * t10089 - 659249460 * t10122 + 1499023620 * t10120 - 1814013220 * t10118 + 1243317440 * t10116 - 471979228 * t10114 + 88999420 * t10112 - 6388940 * t10103 + 74290) * np.exp((10*1j) * phi2) + (t10129 + t10130) * np.exp((-2*1j) * (t10107 + t10106)) + (-t10129 + t10130) * np.exp((2*1j) * (t10107 + t10105)) + (t10125 - t10127) * np.exp((-2*1j) * (t10110 + t10106)) + (t10126 + t10128) * np.exp((-2*1j) * (t10109 + t10106)) + (t10131 + t10132) * np.exp((-2*1j) * (t10108 + t10106)) + (t10125 + t10127) * np.exp((2*1j) * (t10110 + t10105)) + (t10126 - t10128) * np.exp((2*1j) * (t10109 + t10105)) + (t10131 - t10132) * np.exp((2*1j) * (t10108 + t10105))) + + if Bindx == 271: + t10156 = np.cos(phi) + t10155 = t10156 ** 2 + t10163 = t10155 ** 2 + t10167 = t10163 ** 2 + t10175 = t10167 ** 2 + t10140 = t10156 * t10175 + t10162 = t10156 * t10155 + t10165 = t10162 ** 2 + t10166 = t10156 * t10165 + t10173 = t10166 ** 2 + t10142 = t10156 * t10173 + t10171 = t10165 ** 2 + t10144 = t10156 * t10171 + t10164 = t10156 * t10163 + t10169 = t10164 ** 2 + t10146 = t10156 * t10169 + t10148 = t10156 * t10167 + t10182 = 3596 * t10140 + 95236 * t10142 - 48132 * t10144 - 364156 * t10146 + 473044 * t10148 - 2640 * t10156 + 30668 * t10162 - 62348 * t10164 - 125268 * t10166 + t10181 = 2468 * t10155 + 43272 * t10163 - 224588 * t10165 + 286286 * t10167 + 94380 * t10169 - 354640 * t10171 + 123772 * t10173 + 29667 * t10175 - 617 + t10180 = -2640 * t10155 + 77920 * t10163 - 520400 * t10165 + 1390840 * t10167 - 1634160 * t10169 + 605440 * t10171 + 280720 * t10173 - 197780 * t10175 + 60 + t10179 = -5236 * t10155 + 93016 * t10163 - 696388 * t10165 + 2441978 * t10167 - 4485404 * t10169 + 4480784 * t10171 - 2313388 * t10173 + 484561 * t10175 + 77 + t10178 = 35960 * t10140 + 296960 * t10142 - 1191520 * t10144 + 1374720 * t10146 - 398640 * t10148 + 3960 * t10156 - 56320 * t10162 + 244640 * t10164 - 309760 * t10166 + t10177 = -176204 * t10140 + 471772 * t10142 + 186788 * t10144 - 2025380 * t10146 + 2969932 * t10148 + 4592 * t10156 - 102956 * t10162 + 677740 * t10164 - 2006284 * t10166 + t10161 = 4 * phi1 + t10160 = 8 * phi1 + t10159 = 12 * phi1 + t10158 = -11 * phi2 + t10157 = 11 * phi2 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.5e1) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.7e1) * np.sqrt(0.2e1) * ((1 + t10156) ** (-0.1e1 / 0.2e1)) * ((1 - t10156) ** (-0.1e1 / 0.2e1)) * ((273296 * t10140 - 1727936 * t10142 + 4632960 * t10144 - 6815680 * t10146 + 5931040 * t10148 - 3053376 * t10166 + 870656 * t10164 - 115520 * t10162 + 4560 * t10156) * np.exp((11*1j) * phi2) + (t10177 + t10179) * np.exp((-1*1j) * (t10161 + t10158)) + (t10178 + t10180) * np.exp((-1*1j) * (t10160 + t10158)) + (t10177 - t10179) * np.exp((1j) * (t10161 + t10157)) + (t10178 - t10180) * np.exp((1j) * (t10160 + t10157)) + (-t10181 + t10182) * np.exp((-1*1j) * (t10159 + t10158)) + (t10181 + t10182) * np.exp((1j) * (t10159 + t10157))) + + if Bindx == 272: + t10208 = np.cos(phi) + t10207 = t10208 ** 2 + t10216 = t10207 ** 2 + t10215 = t10208 * t10207 + t10218 = t10215 ** 2 + t10220 = t10216 ** 2 + t10217 = t10208 * t10216 + t10222 = t10217 ** 2 + t10224 = t10218 ** 2 + t10219 = t10208 * t10218 + t10226 = t10219 ** 2 + t10228 = t10220 ** 2 + t10237 = 4936 * t10207 + 86544 * t10216 - 449176 * t10218 + 572572 * t10220 + 188760 * t10222 - 709280 * t10224 + 247544 * t10226 + 59334 * t10228 - 1234 + t10192 = t10208 * t10228 + t10194 = t10208 * t10226 + t10196 = t10208 * t10224 + t10198 = t10208 * t10222 + t10200 = t10208 * t10220 + t10236 = 7192 * t10192 + 190472 * t10194 - 96264 * t10196 - 728312 * t10198 + 946088 * t10200 - 5280 * t10208 + 61336 * t10215 - 124696 * t10217 - 250536 * t10219 + t10235 = -170404 * t10207 - 550536 * t10216 + 1192828 * t10218 + 937222 * t10220 - 1874444 * t10222 + 432564 * t10226 + 36047 * t10228 - 3277 + t10234 = 3277 * t10192 + 170404 * t10194 + 550536 * t10196 - 1192828 * t10198 - 937222 * t10200 - 36047 * t10208 - 432564 * t10215 + 1874444 * t10219 + t10233 = 154440 * t10207 - 4558320 * t10216 + 30443400 * t10218 - 81364140 * t10220 + 95598360 * t10222 - 35418240 * t10224 - 16422120 * t10226 + 11570130 * t10228 - 3510 + t10232 = 1341912 * t10207 - 23838672 * t10216 + 178474296 * t10218 - 625844076 * t10220 + 1149544968 * t10222 - 1148360928 * t10224 + 592888296 * t10226 - 124186062 * t10228 - 19734 + t10231 = 2103660 * t10192 + 17372160 * t10194 - 69703920 * t10196 + 80421120 * t10198 - 23320440 * t10200 + 231660 * t10208 - 3294720 * t10215 + 14311440 * t10217 - 18120960 * t10219 + t10230 = 45158568 * t10192 - 120908424 * t10194 - 47871096 * t10196 + 519075960 * t10198 - 761151144 * t10200 - 1176864 * t10208 + 26386152 * t10215 - 173695080 * t10217 + 514181928 * t10219 + t10214 = 4 * phi1 + t10213 = 8 * phi1 + t10212 = 12 * phi1 + t10211 = 16 * phi1 + t10210 = -11 * phi2 + t10209 = 11 * phi2 + tfunc[..., c] = (0.11e2 / 0.236978176e9*1j) * np.sqrt(0.7e1) * np.sqrt(0.31e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * ((1 + t10208) ** (-0.1e1 / 0.2e1)) * ((1 - t10208) ** (-0.1e1 / 0.2e1)) * ((120216078 * t10192 - 760075848 * t10194 + 2037923280 * t10196 - 2998047240 * t10198 + 2608916220 * t10200 - 1343103768 * t10219 + 382979808 * t10217 - 50814360 * t10215 + 2005830 * t10208) * np.exp((11*1j) * phi2) + (t10234 - t10235) * np.exp((-1*1j) * (t10211 + t10210)) + (t10234 + t10235) * np.exp((1j) * (t10211 + t10209)) + (t10230 + t10232) * np.exp((-1*1j) * (t10214 + t10210)) + (t10231 - t10233) * np.exp((-1*1j) * (t10213 + t10210)) + (t10230 - t10232) * np.exp((1j) * (t10214 + t10209)) + (t10231 + t10233) * np.exp((1j) * (t10213 + t10209)) + (t10236 - t10237) * np.exp((-1*1j) * (t10212 + t10210)) + (t10236 + t10237) * np.exp((1j) * (t10212 + t10209))) + + if Bindx == 273: + t10260 = np.cos(phi) + t10259 = t10260 ** 2 + t10264 = t10260 * t10259 + t10267 = t10264 ** 2 + t10268 = t10260 * t10267 + t10275 = t10268 ** 2 + t10246 = t10260 * t10275 + t10273 = t10267 ** 2 + t10248 = t10260 * t10273 + t10265 = t10259 ** 2 + t10266 = t10260 * t10265 + t10271 = t10266 ** 2 + t10250 = t10260 * t10271 + t10269 = t10265 ** 2 + t10252 = t10260 * t10269 + t10283 = -40455 * t10246 - 269381 * t10248 + 308217 * t10250 + 383955 * t10252 - 7303 * t10260 + 35259 * t10264 + 98553 * t10266 - 512941 * t10268 + t10282 = -269700 * t10246 + 67860 * t10248 + 1468380 * t10250 - 2280300 * t10252 + 6780 * t10260 - 34540 * t10264 - 170020 * t10266 + 1211540 * t10268 + t10281 = -660765 * t10246 + 2905945 * t10248 - 5004125 * t10250 + 4170145 * t10252 - 2205 * t10260 + 20825 * t10264 + 201635 * t10266 - 1631455 * t10268 + t10245 = t10269 ** 2 + t10280 = 4495 * t10245 + 9528 * t10259 - 129220 * t10265 + 216216 * t10267 + 265122 * t10269 - 664664 * t10271 + 151788 * t10273 + 149640 * t10275 + 1191 + t10279 = 44950 * t10245 - 35520 * t10259 + 299400 * t10265 - 855360 * t10267 + 642180 * t10269 + 855360 * t10271 - 1462520 * t10273 + 510400 * t10275 + 1110 + t10278 = -220255 * t10245 - 68600 * t10259 + 639940 * t10265 - 2381400 * t10267 + 4235070 * t10269 - 3592680 * t10271 + 988820 * t10273 + 397880 * t10275 + 1225 + t10263 = 2 * phi1 + t10262 = -3 * phi2 + t10261 = 3 * phi2 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.2e1) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.3e1) * ((1140 + 755440 * t10265 - 72960 * t10259 + 341620 * t10245 - 2115840 * t10275 + 5522160 * t10273 - 7831040 * t10271 + 6463800 * t10269 - 3064320 * t10267) * np.exp((12*1j) * phi2) + (t10278 - t10281) * np.exp((-4*1j) * (phi1 + t10262)) + (t10279 + t10282) * np.exp((-4*1j) * (t10263 + t10262)) + (t10278 + t10281) * np.exp((4*1j) * (phi1 + t10261)) + (t10279 - t10282) * np.exp((4*1j) * (t10263 + t10261)) + (t10280 + t10283) * np.exp((-12*1j) * (phi1 - phi2)) + (t10280 - t10283) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 274: + t10308 = np.cos(phi) + t10307 = t10308 ** 2 + t10314 = t10307 ** 2 + t10318 = t10314 ** 2 + t10293 = t10318 ** 2 + t10313 = t10308 * t10307 + t10316 = t10313 ** 2 + t10322 = t10316 ** 2 + t10317 = t10308 * t10316 + t10324 = t10317 ** 2 + t10334 = 16385 * t10293 + 1048640 * t10307 + 5964140 * t10314 - 14058330 * t10318 + 5964140 * t10322 + 1048640 * t10324 + 16385 + t10315 = t10308 * t10314 + t10320 = t10315 ** 2 + t10333 = 35960 * t10293 + 76224 * t10307 - 1033760 * t10314 + 1729728 * t10316 + 2120976 * t10318 - 5317312 * t10320 + 1214304 * t10322 + 1197120 * t10324 + 9528 + t10294 = t10308 * t10324 + t10296 = t10308 * t10322 + t10298 = t10308 * t10320 + t10300 = t10308 * t10318 + t10332 = -323640 * t10294 - 2155048 * t10296 + 2465736 * t10298 + 3071640 * t10300 - 58424 * t10308 + 282072 * t10313 + 788424 * t10315 - 4103528 * t10317 + t10331 = 9372220 * t10300 + 9372220 * t10317 - 5964140 * t10298 - 5964140 * t10315 - 3211460 * t10296 - 3211460 * t10313 - 196620 * t10294 - 196620 * t10308 + t10330 = -63109800 * t10294 + 15879240 * t10296 + 343600920 * t10298 - 533590200 * t10300 + 1586520 * t10308 - 8082360 * t10313 - 39784680 * t10315 + 283500360 * t10317 + t10329 = 677378520 * t10294 - 2979008760 * t10296 + 5129943000 * t10298 - 4274994360 * t10300 + 2260440 * t10308 - 21348600 * t10313 - 206704680 * t10315 + 1672474440 * t10317 + t10328 = 10518300 * t10293 - 8311680 * t10307 + 70059600 * t10314 - 200154240 * t10316 + 150270120 * t10318 + 200154240 * t10320 - 342229680 * t10322 + 119433600 * t10324 + 259740 + t10327 = 225792840 * t10293 + 70324800 * t10307 - 656029920 * t10314 + 2441275200 * t10316 - 4341551760 * t10318 + 3683010240 * t10320 - 1013681760 * t10322 - 407883840 * t10324 - 1255800 + t10312 = 2 * phi1 + t10311 = 4 * phi1 + t10310 = -3 * phi2 + t10309 = 3 * phi2 + tfunc[..., c] = (0.11e2 / 0.473956352e9) * np.sqrt(0.3e1) * np.sqrt(0.113e3) * np.sqrt(0.31e2) * ((601080390 * t10293 - 3722820480 * t10324 + 9716240520 * t10322 - 13778714880 * t10320 + 11373056100 * t10318 - 5391671040 * t10316 + 1329196680 * t10314 - 128373120 * t10307 + 2005830) * np.exp((12*1j) * phi2) + (t10331 + t10334) * np.exp((-4*1j) * (t10311 + t10310)) + (-t10331 + t10334) * np.exp((4*1j) * (t10311 + t10309)) + (t10327 - t10329) * np.exp((-4*1j) * (phi1 + t10310)) + (t10328 + t10330) * np.exp((-4*1j) * (t10312 + t10310)) + (t10327 + t10329) * np.exp((4*1j) * (phi1 + t10309)) + (t10328 - t10330) * np.exp((4*1j) * (t10312 + t10309)) + (t10332 + t10333) * np.exp((-12*1j) * (phi1 - phi2)) + (-t10332 + t10333) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 275: + t10357 = np.cos(phi) + t10356 = t10357 ** 2 + t10364 = t10356 ** 2 + t10368 = t10364 ** 2 + t10342 = t10368 ** 2 + t10379 = 620 * t10342 + t10378 = 6200 * t10342 + t10377 = -30380 * t10342 + t10363 = t10357 * t10356 + t10366 = t10363 ** 2 + t10367 = t10357 * t10366 + t10374 = t10367 ** 2 + t10372 = t10366 ** 2 + t10365 = t10357 * t10364 + t10370 = t10365 ** 2 + t10362 = 4 * phi1 + t10361 = 8 * phi1 + t10360 = 12 * phi1 + t10359 = -13 * phi2 + t10358 = 13 * phi2 + t10349 = t10357 * t10368 + t10347 = t10357 * t10370 + t10345 = t10357 * t10372 + t10343 = t10357 * t10374 + tfunc[..., c] = (0.33e2 / 0.59244544e8*1j) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.23e2) * np.sqrt(0.29e2) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * np.sqrt((1 - t10357)) * ((1 + t10357) ** (-0.1e1 / 0.2e1)) * ((47120 * t10342 + 47120 * t10343 + 74480 * t10364 + 74480 * t10363 - 4560 * t10356 - 4560 * t10357 - 1010800 * t10370 - 1010800 * t10349 + 798000 * t10368 + 798000 * t10367 + 734160 * t10372 + 734160 * t10347 - 351120 * t10366 - 351120 * t10365 - 287280 * t10374 - 287280 * t10345) * np.exp((13*1j) * phi2) + (t10377 + 68355 * t10343 + 126175 * t10374 - 370685 * t10345 - 161945 * t10372 + 831775 * t10347 - 19845 * t10370 - 991025 * t10349 + 238875 * t10368 + 667625 * t10367 - 232995 * t10366 - 249655 * t10365 + 94325 * t10364 + 47285 * t10363 - 14455 * t10356 - 3675 * t10357 + 245) * np.exp((-1*1j) * (t10362 + t10359)) + (t10378 - 34100 * t10343 + 50700 * t10374 + 51740 * t10345 - 213380 * t10372 + 109020 * t10347 + 250140 * t10370 - 306900 * t10349 - 61380 * t10368 + 260260 * t10367 - 74140 * t10366 - 89580 * t10365 + 51700 * t10364 + 9140 * t10363 - 10380 * t10356 + 420 * t10357 + 540) * np.exp((-1*1j) * (t10361 + t10359)) + (t10377 - 129115 * t10343 - 71295 * t10374 + 425565 * t10345 + 634305 * t10372 - 359415 * t10347 - 1211035 * t10370 - 239855 * t10349 + 990045 * t10368 + 561295 * t10367 - 339325 * t10366 - 322665 * t10365 + 21315 * t10364 + 68355 * t10363 + 6615 * t10356 - 4165 * t10357 - 245) * np.exp((1j) * (t10362 + t10358)) + (t10378 + 46500 * t10343 + 131300 * t10374 + 130260 * t10345 - 134860 * t10372 - 457260 * t10347 - 316140 * t10370 + 240900 * t10349 + 486420 * t10368 + 164780 * t10367 - 169620 * t10366 - 154180 * t10365 - 12900 * t10364 + 29660 * t10363 + 10140 * t10356 - 660 * t10357 - 540) * np.exp((1j) * (t10361 + t10358)) + (t10379 - 5425 * t10343 + 19155 * t10374 - 31129 * t10345 + 8099 * t10372 + 55419 * t10347 - 89089 * t10370 + 29315 * t10349 + 63063 * t10368 - 77363 * t10367 + 17017 * t10366 + 27573 * t10365 - 22295 * t10364 + 3241 * t10363 + 3189 * t10356 - 1631 * t10357 + 241) * np.exp((-1*1j) * (t10360 + t10359)) + (t10379 + 6665 * t10343 + 31245 * t10374 + 81529 * t10345 + 120757 * t10372 + 73437 * t10347 - 71071 * t10370 - 189475 * t10349 - 155727 * t10368 - 15301 * t10367 + 79079 * t10366 + 68523 * t10365 + 18655 * t10364 - 6881 * t10363 - 6933 * t10356 - 2113 * t10357 - 241) * np.exp((1j) * (t10360 + t10358))) + + if Bindx == 276: + t10404 = np.cos(phi) + t10403 = t10404 ** 2 + t10412 = t10403 ** 2 + t10416 = t10412 ** 2 + t10389 = t10416 ** 2 + t10428 = 565 * t10389 + t10427 = 1240 * t10389 + t10426 = 362700 * t10389 + t10425 = 7785960 * t10389 + t10411 = t10404 * t10403 + t10414 = t10411 ** 2 + t10415 = t10404 * t10414 + t10422 = t10415 ** 2 + t10420 = t10414 ** 2 + t10413 = t10404 * t10412 + t10418 = t10413 ** 2 + t10410 = 4 * phi1 + t10409 = 8 * phi1 + t10408 = 12 * phi1 + t10407 = 16 * phi1 + t10406 = -13 * phi2 + t10405 = 13 * phi2 + t10396 = t10404 * t10416 + t10394 = t10404 * t10418 + t10392 = t10404 * t10420 + t10390 = t10404 * t10422 + tfunc[..., c] = (-0.11e2 / 0.236978176e9*1j) * np.sqrt(0.31e2) * np.sqrt(0.113e3) * np.sqrt(0.29e2) * np.sqrt(0.3e1) * np.sqrt((1 - t10404)) * ((1 + t10404) ** (-0.1e1 / 0.2e1)) * ((t10428 - 6780 * t10390 + 36160 * t10422 - 484770 * t10416 + 36160 * t10403 - 6780 * t10404 + 565 + 323180 * t10396 + 323180 * t10415 - 110740 * t10392 - 110740 * t10411 + 205660 * t10420 - 205660 * t10394 - 205660 * t10413 + 205660 * t10412) * np.exp((-1*1j) * (t10407 + t10406)) + (-444625650 * t10418 - 444625650 * t10396 + 351020250 * t10416 + 351020250 * t10415 + 322938630 * t10420 + 322938630 * t10394 - 154448910 * t10414 - 154448910 * t10413 - 126367290 * t10422 - 126367290 * t10392 + 32761890 * t10412 + 32761890 * t10411 + 20726910 * t10389 + 20726910 * t10390 - 2005830 * t10403 - 2005830 * t10404) * np.exp((13*1j) * phi2) + (t10428 + 7910 * t10390 + 50850 * t10422 - 50850 * t10403 - 7910 * t10404 - 565 + 1131130 * t10418 - 1131130 * t10414 + 925470 * t10394 - 925470 * t10413 + 807950 * t10396 - 807950 * t10415 + 514150 * t10420 - 514150 * t10412 + 197750 * t10392 - 197750 * t10411) * np.exp((1j) * (t10407 + t10405)) + (-17518410 * t10390 + 95001270 * t10392 - 213172050 * t10394 + 253985550 * t10396 + 3704610 * t10403 + 941850 * t10404 - 12118470 * t10411 - 24174150 * t10412 + 63983010 * t10413 + 59713290 * t10414 - 171102750 * t10415 - 61220250 * t10416 + 5085990 * t10418 + 41504190 * t10420 - 32336850 * t10422 + t10425 - 62790) * np.exp((-1*1j) * (t10410 + t10406)) + (-1994850 * t10390 + 3026790 * t10392 + 6377670 * t10394 - 17953650 * t10396 - 607230 * t10403 + 24570 * t10404 + 534690 * t10411 + 3024450 * t10412 - 5240430 * t10413 - 4337190 * t10414 + 15225210 * t10415 - 3590730 * t10416 + 14633190 * t10418 - 12482730 * t10420 + 2965950 * t10422 + 31590 + t10426) * np.exp((-1*1j) * (t10409 + t10406)) + (33090330 * t10390 - 109066230 * t10392 + 92112930 * t10394 + 61471410 * t10396 - 1695330 * t10403 + 1067430 * t10404 - 17518410 * t10411 - 5462730 * t10412 + 82694430 * t10413 + 86964150 * t10414 - 143851890 * t10415 - 253734390 * t10416 + 310370970 * t10418 - 162563310 * t10420 + 18271890 * t10422 + t10425 + 62790) * np.exp((1j) * (t10410 + t10405)) + (2720250 * t10390 + 7620210 * t10392 - 26749710 * t10394 + 14092650 * t10396 + 593190 * t10403 - 38610 * t10404 + 1735110 * t10411 - 754650 * t10412 - 9019530 * t10413 - 9922770 * t10414 + 9639630 * t10415 + 28455570 * t10416 - 18494190 * t10418 - 7889310 * t10420 + 7681050 * t10422 - 31590 + t10426) * np.exp((1j) * (t10409 + t10405)) + (t10427 - 10850 * t10390 + 38310 * t10422 - 62258 * t10392 + 16198 * t10420 + 110838 * t10394 - 178178 * t10418 + 58630 * t10396 + 126126 * t10416 - 154726 * t10415 + 34034 * t10414 + 55146 * t10413 - 44590 * t10412 + 6482 * t10411 + 6378 * t10403 - 3262 * t10404 + 482) * np.exp((-1*1j) * (t10408 + t10406)) + (-482 + t10427 + 13330 * t10390 + 62490 * t10422 + 163058 * t10392 + 241514 * t10420 + 146874 * t10394 - 142142 * t10418 - 378950 * t10396 - 311454 * t10416 - 30602 * t10415 + 158158 * t10414 + 137046 * t10413 + 37310 * t10412 - 13762 * t10411 - 13866 * t10403 - 4226 * t10404) * np.exp((1j) * (t10408 + t10405))) + + if Bindx == 277: + t10451 = np.cos(phi) + t10450 = t10451 ** 2 + t10457 = t10451 * t10450 + t10460 = t10457 ** 2 + t10461 = t10451 * t10460 + t10468 = t10461 ** 2 + t10437 = t10451 * t10468 + t10466 = t10460 ** 2 + t10439 = t10451 * t10466 + t10458 = t10450 ** 2 + t10459 = t10451 * t10458 + t10464 = t10459 ** 2 + t10441 = t10451 * t10464 + t10462 = t10458 ** 2 + t10443 = t10451 * t10462 + t10476 = -651 * t10437 - 7259 * t10439 - 2639 * t10441 + 20449 * t10443 + 315 * t10451 + 1771 * t10457 - 7553 * t10459 - 4433 * t10461 + t10475 = -4340 * t10437 - 5460 * t10439 + 43260 * t10441 - 49060 * t10443 + 500 * t10451 - 6060 * t10457 + 14340 * t10459 + 6820 * t10461 + t10474 = -10633 * t10437 + 49735 * t10439 - 88053 * t10441 + 66395 * t10443 - 1127 * t10451 + 9065 * t10457 - 17787 * t10459 - 7595 * t10461 + t10436 = t10462 ** 2 + t10473 = -34 + 62 * t10436 - 1156 * t10450 + 728 * t10458 + 12012 * t10460 - 12012 * t10462 - 12012 * t10464 + 9464 * t10466 + 2948 * t10468 + t10472 = -140 + 620 * t10436 + 840 * t10450 + 5880 * t10458 - 33880 * t10460 + 47520 * t10462 - 8360 * t10464 - 23160 * t10466 + 10680 * t10468 + t10471 = -3038 * t10436 - 2940 * t10450 + 25480 * t10458 - 83692 * t10460 + 132300 * t10462 - 104468 * t10464 + 33320 * t10466 + 2940 * t10468 + 98 + t10456 = 2 * phi1 + t10455 = 4 * phi1 + t10454 = 6 * phi1 + t10453 = -7 * phi2 + t10452 = 7 * phi2 + tfunc[..., c] = -(0.33e2 / 0.29622272e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.5e1) * np.sqrt(0.23e2) * np.sqrt(0.3e1) * np.sqrt(0.13e2) * np.sqrt(0.29e2) * ((4712 * t10436 - 33136 * t10468 + 100016 * t10466 - 168112 * t10464 + 170240 * t10462 - 104272 * t10460 + 36176 * t10458 - 5776 * t10450 + 152) * np.exp((14*1j) * phi2) + (t10471 - t10474) * np.exp((-2*1j) * (t10456 + t10453)) + (t10472 + t10475) * np.exp((-2*1j) * (t10455 + t10453)) + (t10473 + t10476) * np.exp((-2*1j) * (t10454 + t10453)) + (t10471 + t10474) * np.exp((2*1j) * (t10456 + t10452)) + (t10472 - t10475) * np.exp((2*1j) * (t10455 + t10452)) + (t10473 - t10476) * np.exp((2*1j) * (t10454 + t10452))) + + if Bindx == 278: + t10501 = np.cos(phi) + t10500 = t10501 ** 2 + t10508 = t10501 * t10500 + t10511 = t10508 ** 2 + t10512 = t10501 * t10511 + t10519 = t10512 ** 2 + t10487 = t10501 * t10519 + t10517 = t10511 ** 2 + t10489 = t10501 * t10517 + t10509 = t10500 ** 2 + t10510 = t10501 * t10509 + t10515 = t10510 ** 2 + t10491 = t10501 * t10515 + t10513 = t10509 ** 2 + t10493 = t10501 * t10513 + t10529 = -2604 * t10487 - 29036 * t10489 - 10556 * t10491 + 81796 * t10493 + 1260 * t10501 + 7084 * t10508 - 30212 * t10510 - 17732 * t10512 + t10486 = t10513 ** 2 + t10528 = -136 + 248 * t10486 - 4624 * t10500 + 2912 * t10509 + 48048 * t10511 - 48048 * t10513 - 48048 * t10515 + 37856 * t10517 + 11792 * t10519 + t10527 = 113 * t10486 - 10170 * t10500 + 10170 * t10519 - 113 + 226226 * t10515 - 226226 * t10511 + 102830 * t10517 - 102830 * t10509 + t10526 = -1582 * t10487 - 39550 * t10489 + 1582 * t10501 + 39550 * t10508 - 185094 * t10491 + 185094 * t10510 - 161590 * t10493 + 161590 * t10512 + t10525 = 72540 * t10486 + 98280 * t10500 + 687960 * t10509 - 3963960 * t10511 + 5559840 * t10513 - 978120 * t10515 - 2709720 * t10517 + 1249560 * t10519 - 16380 + t10524 = -507780 * t10487 - 638820 * t10489 + 5061420 * t10491 - 5740020 * t10493 + 58500 * t10501 - 709020 * t10508 + 1677780 * t10510 + 797940 * t10512 + t10523 = -5450172 * t10487 + 25492740 * t10489 - 45133452 * t10491 + 34032180 * t10493 - 577668 * t10501 + 4646460 * t10508 - 9117108 * t10510 - 3892980 * t10512 + t10522 = 1557192 * t10486 + 1506960 * t10500 - 13060320 * t10509 + 42898128 * t10511 - 67813200 * t10513 + 53547312 * t10515 - 17078880 * t10517 - 1506960 * t10519 - 50232 + t10507 = 2 * phi1 + t10506 = 4 * phi1 + t10505 = 6 * phi1 + t10504 = 8 * phi1 + t10503 = -7 * phi2 + t10502 = 7 * phi2 + tfunc[..., c] = (0.11e2 / 0.473956352e9) * np.sqrt(0.31e2) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.29e2) * np.sqrt(0.113e3) * ((133722 - 5081436 * t10500 + 4145382 * t10486 - 29151396 * t10519 + 87989076 * t10517 - 147896532 * t10515 + 149768640 * t10513 - 91733292 * t10511 + 31825836 * t10509) * np.exp((14*1j) * phi2) + (t10526 + t10527) * np.exp((-2*1j) * (t10504 + t10503)) + (-t10526 + t10527) * np.exp((2*1j) * (t10504 + t10502)) + (t10522 + t10523) * np.exp((-2*1j) * (t10507 + t10503)) + (t10524 + t10525) * np.exp((-2*1j) * (t10506 + t10503)) + (t10528 + t10529) * np.exp((-2*1j) * (t10505 + t10503)) + (t10522 - t10523) * np.exp((2*1j) * (t10507 + t10502)) + (-t10524 + t10525) * np.exp((2*1j) * (t10506 + t10502)) + (t10528 - t10529) * np.exp((2*1j) * (t10505 + t10502))) + + if Bindx == 279: + t10551 = np.cos(phi) + t10550 = t10551 ** 2 + t10556 = t10551 * t10550 + t10559 = t10556 ** 2 + t10560 = t10551 * t10559 + t10567 = t10560 ** 2 + t10537 = t10551 * t10567 + t10572 = 4 * t10537 + t10571 = 40 * t10537 + t10570 = -196 * t10537 + t10569 = 49 - 637 * t10550 + 98 * t10551 + t10565 = t10559 ** 2 + t10557 = t10550 ** 2 + t10558 = t10551 * t10557 + t10563 = t10558 ** 2 + t10561 = t10557 ** 2 + t10555 = 4 * phi1 + t10554 = 8 * phi1 + t10553 = -15 * phi2 + t10552 = 15 * phi2 + t10543 = t10551 * t10561 + t10541 = t10551 * t10563 + t10539 = t10551 * t10565 + tfunc[..., c] = (-0.33e2 / 0.59244544e8*1j) * np.sqrt(0.3e1) * np.sqrt(0.899e3) * np.sqrt(0.23e2) * np.sqrt(0.5e1) * np.sqrt(0.13e2) * np.sqrt(0.113e3) * np.sqrt(0.17e2) * np.sqrt(0.2e1) * ((1 - t10551) ** (0.3e1 / 0.2e1)) * ((1 + t10551) ** (-0.1e1 / 0.2e1)) * (304 * (t10537 + 2 * t10567 - 5 * t10539 - 12 * t10565 + 9 * t10541 + 30 * t10563 - 5 * t10543 - 40 * t10561 - 5 * t10560 + 30 * t10559 + 9 * t10558 - 12 * t10557 - 5 * t10556 + 2 * t10550 + t10551) * np.exp((15*1j) * phi2) + (t10572 - 37 * t10567 + 142 * t10539 - 271 * t10565 + 184 * t10541 + 275 * t10563 - 726 * t10543 + 561 * t10561 + 132 * t10560 - 583 * t10559 + 418 * t10558 - 37 * t10557 - 128 * t10556 + 89 * t10550 - 26 * t10551 + 3) * np.exp((-3*1j) * (t10555 - 5 * phi2)) + (t10572 + 53 * t10567 + 322 * t10539 + 1183 * t10565 + 2912 * t10541 + 5005 * t10563 + 6006 * t10543 + 4719 * t10561 + 1716 * t10560 - 1001 * t10559 - 2002 * t10558 - 1547 * t10557 - 728 * t10556 - 217 * t10550 - 38 * t10551 - 3) * np.exp((3*1j) * (t10555 + 5 * phi2)) + (t10570 + 343 * t10567 + 1078 * t10539 - 2107 * t10565 - 2352 * t10541 + 5439 * t10563 + 2450 * t10543 - 7595 * t10561 - 980 * t10560 + 6125 * t10559 - 294 * t10558 - 2793 * t10557 + 392 * t10556 - t10569) * np.exp((-1*1j) * (t10555 + t10553)) + (t10571 - 220 * t10567 + 320 * t10539 + 380 * t10565 - 1480 * t10541 + 740 * t10563 + 1840 * t10543 - 2340 * t10561 - 360 * t10560 + 2060 * t10559 - 800 * t10558 - 620 * t10557 + 520 * t10556 - 20 * t10550 - 80 * t10551 + 20) * np.exp((-1*1j) * (t10554 + t10553)) + (t10570 - 1127 * t10567 - 1862 * t10539 + 1323 * t10565 + 7448 * t10541 + 5537 * t10563 - 7154 * t10543 - 12789 * t10561 - 1764 * t10560 + 8771 * t10559 + 5782 * t10558 - 1127 * t10557 - 2352 * t10556 + t10569) * np.exp((1j) * (t10555 + t10552)) + (t10571 + 380 * t10567 + 1520 * t10539 + 3140 * t10565 + 2840 * t10541 - 1540 * t10563 - 7040 * t10543 - 7260 * t10561 - 1320 * t10560 + 4180 * t10559 + 4400 * t10558 + 1580 * t10557 - 280 * t10556 - 460 * t10550 - 160 * t10551 - 20) * np.exp((1j) * (t10554 + t10552))) + + if Bindx == 280: + t10597 = np.cos(phi) + t10623 = 1 + t10597 + t10596 = t10597 ** 2 + t10604 = t10596 ** 2 + t10608 = t10604 ** 2 + t10582 = t10608 ** 2 + t10622 = 113 * t10582 + t10621 = 248 * t10582 + t10620 = 72540 * t10582 + t10619 = 1557192 * t10582 + t10589 = t10597 * t10608 + t10605 = t10597 * t10604 + t10603 = t10597 * t10596 + t10606 = t10603 ** 2 + t10610 = t10605 ** 2 + t10618 = -186 + 79794 * t10589 - 28210 * t10605 + 62062 * t10606 - 62062 * t10610 + t10612 = t10606 ** 2 + t10585 = t10597 * t10612 + t10607 = t10597 * t10606 + t10614 = t10607 ** 2 + t10617 = -25304370 * t10585 - 5839470 * t10596 + 5839470 * t10614 + 389298 * t10623 + t10602 = 4 * phi1 + t10601 = 8 * phi1 + t10600 = 16 * phi1 + t10599 = -15 * phi2 + t10598 = 15 * phi2 + t10587 = t10597 * t10610 + t10583 = t10597 * t10614 + tfunc[..., c] = (-0.11e2 / 0.236978176e9*1j) * np.sqrt(0.113e3) * np.sqrt(0.29e2) * np.sqrt(0.5e1) * np.sqrt(0.3e1) * np.sqrt((1 - t10597)) * t10623 ** (-0.1e1 / 0.2e1) * (4145382 * (-35 * t10610 - 35 * t10589 + 35 * t10608 + 35 * t10607 + 21 * t10612 + 21 * t10587 - 21 * t10606 - 21 * t10605 - 7 * t10614 - 7 * t10585 + 7 * t10604 + 7 * t10603 + t10582 + t10583 - t10596 - t10597) * np.exp((15*1j) * phi2) + (t10622 - 1582 * t10583 + 10170 * t10614 - 39550 * t10585 + 39550 * t10603 - 10170 * t10596 + 1582 * t10597 - 113 + 226226 * t10610 - 226226 * t10606 - 185094 * t10587 + 185094 * t10605 - 161590 * t10589 + 161590 * t10607 + 102830 * t10612 - 102830 * t10604) * np.exp((-1*1j) * (t10600 + t10599)) + (t10621 - 2542 * t10583 + 11098 * t10614 - 25606 * t10585 + 28210 * t10612 + 5642 * t10587 - 26598 * t10608 - 44330 * t10607 - 5642 * t10604 + 13454 * t10603 - 7130 * t10596 + 1798 * t10597 + t10618) * np.exp((-3*1j) * (t10602 - 5 * phi2)) + (t10621 + 3038 * t10583 + 16678 * t10614 + 53382 * t10585 + 107198 * t10612 + 129766 * t10587 - 186186 * t10608 - 168454 * t10607 + 50778 * t10604 + 31682 * t10603 + 11098 * t10596 + 2170 * t10597 - t10618) * np.exp((3*1j) * (t10602 + 5 * phi2)) + (-4282278 * t10583 - 61898382 * t10587 + 79806090 * t10589 - 1946490 * t10603 - 25304370 * t10604 + 19854198 * t10605 + 50998038 * t10606 - 56448210 * t10607 - 52555230 * t10608 + 23747178 * t10610 + 1946490 * t10612 - t10617 + t10619) * np.exp((-1*1j) * (t10602 + t10599)) + (-471510 * t10583 + 108810 * t10585 + 4025970 * t10587 - 7580430 * t10589 - 108810 * t10596 + 181350 * t10597 - 979290 * t10603 + 2067390 * t10604 + 326430 * t10605 - 5186610 * t10606 + 4388670 * t10607 + 3590730 * t10608 + 1994850 * t10610 - 3373110 * t10612 + 979290 * t10614 + t10620 - 36270) * np.exp((-1*1j) * (t10601 + t10599)) + (7396662 * t10583 + 15182622 * t10587 + 44769270 * t10589 - 13625430 * t10603 + 9732450 * t10604 + 54891018 * t10605 + 23747178 * t10606 - 83699070 * t10607 - 87592050 * t10608 + 100828182 * t10610 - 48662250 * t10612 + t10617 + t10619) * np.exp((1j) * (t10602 + t10598)) + (616590 * t10583 + 2937870 * t10585 - 7943130 * t10587 - 398970 * t10589 + 544050 * t10596 + 253890 * t10597 - 326430 * t10603 - 3373110 * t10604 - 5114070 * t10605 + 398970 * t10606 + 9974250 * t10607 + 10772190 * t10608 - 9974250 * t10610 - 544050 * t10612 + 2067390 * t10614 + t10620 + 36270) * np.exp((1j) * (t10601 + t10598)) + (t10622 + 1808 * t10583 + 13560 * t10614 + 63280 * t10585 + 1454310 * t10608 + 63280 * t10603 + 13560 * t10596 + 1808 * t10597 + 113 + 1292720 * t10589 + 1292720 * t10607 + 904904 * t10610 + 904904 * t10606 + 493584 * t10587 + 493584 * t10605 + 205660 * t10612 + 205660 * t10604) * np.exp((1j) * (t10600 + t10598))) + + if Bindx == 281: + t10646 = np.cos(phi) + t10645 = t10646 ** 2 + t10650 = t10646 * t10645 + t10653 = t10650 ** 2 + t10654 = t10646 * t10653 + t10661 = t10654 ** 2 + t10632 = t10646 * t10661 + t10670 = -t10632 - t10646 + t10651 = t10645 ** 2 + t10655 = t10651 ** 2 + t10631 = t10655 ** 2 + t10659 = t10653 ** 2 + t10669 = 1 + t10631 + 64 * t10645 + 364 * t10651 - 858 * t10655 + 364 * t10659 + 64 * t10661 + t10652 = t10646 * t10651 + t10657 = t10652 ** 2 + t10668 = -49 - 49 * t10631 + 980 * t10651 - 3136 * t10653 + 4410 * t10655 - 3136 * t10657 + 980 * t10659 + t10634 = t10646 * t10659 + t10636 = t10646 * t10657 + t10638 = t10646 * t10655 + t10667 = -12 * t10632 - 196 * t10634 - 364 * t10636 + 572 * t10638 - 12 * t10646 - 196 * t10650 - 364 * t10652 + 572 * t10654 + t10666 = -240 * t10634 + 1200 * t10636 - 880 * t10638 - 240 * t10650 + 1200 * t10652 - 880 * t10654 + 80 * t10670 + t10665 = 980 * t10634 - 1764 * t10636 + 980 * t10638 + 980 * t10650 - 1764 * t10652 + 980 * t10654 + 196 * t10670 + t10664 = 10 + 10 * t10631 + 240 * t10645 - 360 * t10651 - 880 * t10653 + 1980 * t10655 - 880 * t10657 - 360 * t10659 + 240 * t10661 + t10649 = 3 * phi1 + t10648 = -4 * phi2 + t10647 = 4 * phi2 + tfunc[..., c] = -(0.33e2 / 0.59244544e8) * np.sqrt(0.17e2) * np.sqrt(0.113e3) * np.sqrt(0.23e2) * np.sqrt(0.13e2) * np.sqrt(0.5e1) * np.sqrt(0.899e3) * np.sqrt(0.3e1) * ((76 * t10631 - 608 * t10661 + 2128 * t10659 - 4256 * t10657 + 5320 * t10655 - 4256 * t10653 + 2128 * t10651 - 608 * t10645 + 76) * np.exp((16*1j) * phi2) + (t10667 + t10669) * np.exp((-4*1j) * (t10649 + t10648)) + (-t10667 + t10669) * np.exp((4*1j) * (t10649 + t10647)) + (-t10665 + t10668) * np.exp((-4*1j) * (phi1 + t10648)) + (t10665 + t10668) * np.exp((4*1j) * (phi1 + t10647)) + (t10664 + t10666) * np.exp((-8*1j) * (phi1 - 2 * phi2)) + (t10664 - t10666) * np.exp((8*1j) * (phi1 + 2 * phi2))) + + if Bindx == 282: + t10695 = np.cos(phi) + t10694 = t10695 ** 2 + t10699 = t10695 * t10694 + t10702 = t10699 ** 2 + t10703 = t10695 * t10702 + t10710 = t10703 ** 2 + t10681 = t10695 * t10710 + t10723 = -t10681 - t10695 + t10708 = t10702 ** 2 + t10683 = t10695 * t10708 + t10727 = -t10683 - t10699 + t10700 = t10694 ** 2 + t10704 = t10700 ** 2 + t10687 = t10695 * t10704 + t10726 = -t10687 - t10703 + t10701 = t10695 * t10700 + t10706 = t10701 ** 2 + t10725 = t10706 + t10702 + t10724 = t10708 + t10700 + t10721 = t10726 + t10727 + t10680 = t10704 ** 2 + t10720 = 248 * t10680 + 15872 * t10694 + 90272 * t10700 - 212784 * t10704 + 90272 * t10708 + 15872 * t10710 + 248 + t10685 = t10695 * t10706 + t10719 = -56058912 * t10685 - 56058912 * t10701 + 6228768 * t10723 + t10718 = 2976 * t10681 + 48608 * t10683 + 90272 * t10685 + 2976 * t10695 + 48608 * t10699 + 90272 * t10701 + 141856 * t10726 + t10717 = 1557192 * t10680 - 140147280 * t10704 + 99660288 * t10725 + 1557192 + t10716 = -63280 * t10683 - 63280 * t10699 - 1292720 * t10687 - 1292720 * t10703 - 493584 * t10685 - 493584 * t10701 + 1808 * t10723 + t10715 = 113 * t10680 + 13560 * t10694 + 1454310 * t10704 + 13560 * t10710 + 205660 * t10724 + 904904 * t10725 + 113 + t10714 = 72540 * t10680 + 1740960 * t10694 + 14362920 * t10704 + 1740960 * t10710 - 2611440 * t10724 - 6383520 * t10725 + 72540 + t10713 = 8704800 * t10685 + 8704800 * t10701 + 6383520 * t10726 + 1740960 * t10727 + 580320 * t10723 + t10698 = 3 * phi1 + t10697 = -4 * phi2 + t10696 = 4 * phi2 + t10677 = np.exp((-4*1j) * (phi1 + t10697)) + t10675 = np.exp((4*1j) * (phi1 + t10696)) + tfunc[..., c] = (0.11e2 / 0.1895825408e10) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt(0.5e1) * np.sqrt(0.29e2) * np.sqrt(0.113e3) * ((290176740 * t10704 + 4145382 * t10680 + 4145382 - 232141392 * t10706 - 232141392 * t10702 + 116070696 * t10708 + 116070696 * t10700 - 33163056 * t10710 - 33163056 * t10694) * np.exp((16*1j) * phi2) + ((t10717 + t10719) * t10677) + ((t10717 - t10719) * t10675) + (-t10718 + t10720) * np.exp((-4*1j) * (t10698 + t10697)) + (t10718 + t10720) * np.exp((4*1j) * (t10698 + t10696)) + (t10713 + t10714) * np.exp((-8*1j) * (phi1 - 2 * phi2)) + (-t10713 + t10714) * np.exp((8*1j) * (phi1 + 2 * phi2)) + (t10715 + t10716) * np.exp((-16*1j) * (phi1 - phi2)) + (t10715 - t10716) * np.exp((16*1j) * (phi1 + phi2)) + (31143840 * (-t10721 - t10724) * t10677) + (31143840 * (t10721 - t10724) * t10675)) + + c += 1 + + return tfunc + + +if __name__ == '__main__': + X = np.zeros([2, 3]) + phi1 = np.array([0.1, 0.2]) + X[:, 0] = phi1 + phi = np.array([0.0, 0.4]) + X[:, 1] = phi + phi2 = np.array([0.3, 0.6]) + X[:, 2] = phi2 + + indxvec = gsh_basis_info() + + lte2 = indxvec[:, 0] <= 2 + + Bvec = np.arange(indxvec.shape[0])[lte2] diff --git a/pymks/bases/gsh_functions/gsh_hex_tri_L0_16.py b/pymks/bases/gsh_functions/gsh_hex_tri_L0_16.py new file mode 100644 index 00000000..e63b4582 --- /dev/null +++ b/pymks/bases/gsh_functions/gsh_hex_tri_L0_16.py @@ -0,0 +1,7402 @@ +import numpy as np + + +def gsh_basis_info(): + + indxvec = np.array([[0, 0, 1], + [2, -2, 1], + [2, -1, 1], + [2, 0, 1], + [2, 1, 1], + [2, 2, 1], + [4, -4, 1], + [4, -3, 1], + [4, -2, 1], + [4, -1, 1], + [4, 0, 1], + [4, 1, 1], + [4, 2, 1], + [4, 3, 1], + [4, 4, 1], + [6, -6, 1], + [6, -6, 2], + [6, -5, 1], + [6, -5, 2], + [6, -4, 1], + [6, -4, 2], + [6, -3, 1], + [6, -3, 2], + [6, -2, 1], + [6, -2, 2], + [6, -1, 1], + [6, -1, 2], + [6, 0, 1], + [6, 0, 2], + [6, 1, 1], + [6, 1, 2], + [6, 2, 1], + [6, 2, 2], + [6, 3, 1], + [6, 3, 2], + [6, 4, 1], + [6, 4, 2], + [6, 5, 1], + [6, 5, 2], + [6, 6, 1], + [6, 6, 2], + [7, -7, 1], + [7, -6, 1], + [7, -5, 1], + [7, -4, 1], + [7, -3, 1], + [7, -2, 1], + [7, -1, 1], + [7, 0, 1], + [7, 1, 1], + [7, 2, 1], + [7, 3, 1], + [7, 4, 1], + [7, 5, 1], + [7, 6, 1], + [7, 7, 1], + [8, -8, 1], + [8, -8, 2], + [8, -7, 1], + [8, -7, 2], + [8, -6, 1], + [8, -6, 2], + [8, -5, 1], + [8, -5, 2], + [8, -4, 1], + [8, -4, 2], + [8, -3, 1], + [8, -3, 2], + [8, -2, 1], + [8, -2, 2], + [8, -1, 1], + [8, -1, 2], + [8, 0, 1], + [8, 0, 2], + [8, 1, 1], + [8, 1, 2], + [8, 2, 1], + [8, 2, 2], + [8, 3, 1], + [8, 3, 2], + [8, 4, 1], + [8, 4, 2], + [8, 5, 1], + [8, 5, 2], + [8, 6, 1], + [8, 6, 2], + [8, 7, 1], + [8, 7, 2], + [8, 8, 1], + [8, 8, 2], + [9, -9, 1], + [9, -8, 1], + [9, -7, 1], + [9, -6, 1], + [9, -5, 1], + [9, -4, 1], + [9, -3, 1], + [9, -2, 1], + [9, -1, 1], + [9, 0, 1], + [9, 1, 1], + [9, 2, 1], + [9, 3, 1], + [9, 4, 1], + [9, 5, 1], + [9, 6, 1], + [9, 7, 1], + [9, 8, 1], + [9, 9, 1], + [10, -10, 1], + [10, -10, 2], + [10, -9, 1], + [10, -9, 2], + [10, -8, 1], + [10, -8, 2], + [10, -7, 1], + [10, -7, 2], + [10, -6, 1], + [10, -6, 2], + [10, -5, 1], + [10, -5, 2], + [10, -4, 1], + [10, -4, 2], + [10, -3, 1], + [10, -3, 2], + [10, -2, 1], + [10, -2, 2], + [10, -1, 1], + [10, -1, 2], + [10, 0, 1], + [10, 0, 2], + [10, 1, 1], + [10, 1, 2], + [10, 2, 1], + [10, 2, 2], + [10, 3, 1], + [10, 3, 2], + [10, 4, 1], + [10, 4, 2], + [10, 5, 1], + [10, 5, 2], + [10, 6, 1], + [10, 6, 2], + [10, 7, 1], + [10, 7, 2], + [10, 8, 1], + [10, 8, 2], + [10, 9, 1], + [10, 9, 2], + [10, 10, 1], + [10, 10, 2], + [11, -11, 1], + [11, -10, 1], + [11, -9, 1], + [11, -8, 1], + [11, -7, 1], + [11, -6, 1], + [11, -5, 1], + [11, -4, 1], + [11, -3, 1], + [11, -2, 1], + [11, -1, 1], + [11, 0, 1], + [11, 1, 1], + [11, 2, 1], + [11, 3, 1], + [11, 4, 1], + [11, 5, 1], + [11, 6, 1], + [11, 7, 1], + [11, 8, 1], + [11, 9, 1], + [11, 10, 1], + [11, 11, 1], + [12, -12, 1], + [12, -12, 2], + [12, -12, 3], + [12, -11, 1], + [12, -11, 2], + [12, -11, 3], + [12, -10, 1], + [12, -10, 2], + [12, -10, 3], + [12, -9, 1], + [12, -9, 2], + [12, -9, 3], + [12, -8, 1], + [12, -8, 2], + [12, -8, 3], + [12, -7, 1], + [12, -7, 2], + [12, -7, 3], + [12, -6, 1], + [12, -6, 2], + [12, -6, 3], + [12, -5, 1], + [12, -5, 2], + [12, -5, 3], + [12, -4, 1], + [12, -4, 2], + [12, -4, 3], + [12, -3, 1], + [12, -3, 2], + [12, -3, 3], + [12, -2, 1], + [12, -2, 2], + [12, -2, 3], + [12, -1, 1], + [12, -1, 2], + [12, -1, 3], + [12, 0, 1], + [12, 0, 2], + [12, 0, 3], + [12, 1, 1], + [12, 1, 2], + [12, 1, 3], + [12, 2, 1], + [12, 2, 2], + [12, 2, 3], + [12, 3, 1], + [12, 3, 2], + [12, 3, 3], + [12, 4, 1], + [12, 4, 2], + [12, 4, 3], + [12, 5, 1], + [12, 5, 2], + [12, 5, 3], + [12, 6, 1], + [12, 6, 2], + [12, 6, 3], + [12, 7, 1], + [12, 7, 2], + [12, 7, 3], + [12, 8, 1], + [12, 8, 2], + [12, 8, 3], + [12, 9, 1], + [12, 9, 2], + [12, 9, 3], + [12, 10, 1], + [12, 10, 2], + [12, 10, 3], + [12, 11, 1], + [12, 11, 2], + [12, 11, 3], + [12, 12, 1], + [12, 12, 2], + [12, 12, 3], + [13, -13, 1], + [13, -13, 2], + [13, -12, 1], + [13, -12, 2], + [13, -11, 1], + [13, -11, 2], + [13, -10, 1], + [13, -10, 2], + [13, -9, 1], + [13, -9, 2], + [13, -8, 1], + [13, -8, 2], + [13, -7, 1], + [13, -7, 2], + [13, -6, 1], + [13, -6, 2], + [13, -5, 1], + [13, -5, 2], + [13, -4, 1], + [13, -4, 2], + [13, -3, 1], + [13, -3, 2], + [13, -2, 1], + [13, -2, 2], + [13, -1, 1], + [13, -1, 2], + [13, 0, 1], + [13, 0, 2], + [13, 1, 1], + [13, 1, 2], + [13, 2, 1], + [13, 2, 2], + [13, 3, 1], + [13, 3, 2], + [13, 4, 1], + [13, 4, 2], + [13, 5, 1], + [13, 5, 2], + [13, 6, 1], + [13, 6, 2], + [13, 7, 1], + [13, 7, 2], + [13, 8, 1], + [13, 8, 2], + [13, 9, 1], + [13, 9, 2], + [13, 10, 1], + [13, 10, 2], + [13, 11, 1], + [13, 11, 2], + [13, 12, 1], + [13, 12, 2], + [13, 13, 1], + [13, 13, 2], + [14, -14, 1], + [14, -14, 2], + [14, -14, 3], + [14, -13, 1], + [14, -13, 2], + [14, -13, 3], + [14, -12, 1], + [14, -12, 2], + [14, -12, 3], + [14, -11, 1], + [14, -11, 2], + [14, -11, 3], + [14, -10, 1], + [14, -10, 2], + [14, -10, 3], + [14, -9, 1], + [14, -9, 2], + [14, -9, 3], + [14, -8, 1], + [14, -8, 2], + [14, -8, 3], + [14, -7, 1], + [14, -7, 2], + [14, -7, 3], + [14, -6, 1], + [14, -6, 2], + [14, -6, 3], + [14, -5, 1], + [14, -5, 2], + [14, -5, 3], + [14, -4, 1], + [14, -4, 2], + [14, -4, 3], + [14, -3, 1], + [14, -3, 2], + [14, -3, 3], + [14, -2, 1], + [14, -2, 2], + [14, -2, 3], + [14, -1, 1], + [14, -1, 2], + [14, -1, 3], + [14, 0, 1], + [14, 0, 2], + [14, 0, 3], + [14, 1, 1], + [14, 1, 2], + [14, 1, 3], + [14, 2, 1], + [14, 2, 2], + [14, 2, 3], + [14, 3, 1], + [14, 3, 2], + [14, 3, 3], + [14, 4, 1], + [14, 4, 2], + [14, 4, 3], + [14, 5, 1], + [14, 5, 2], + [14, 5, 3], + [14, 6, 1], + [14, 6, 2], + [14, 6, 3], + [14, 7, 1], + [14, 7, 2], + [14, 7, 3], + [14, 8, 1], + [14, 8, 2], + [14, 8, 3], + [14, 9, 1], + [14, 9, 2], + [14, 9, 3], + [14, 10, 1], + [14, 10, 2], + [14, 10, 3], + [14, 11, 1], + [14, 11, 2], + [14, 11, 3], + [14, 12, 1], + [14, 12, 2], + [14, 12, 3], + [14, 13, 1], + [14, 13, 2], + [14, 13, 3], + [14, 14, 1], + [14, 14, 2], + [14, 14, 3], + [15, -15, 1], + [15, -15, 2], + [15, -14, 1], + [15, -14, 2], + [15, -13, 1], + [15, -13, 2], + [15, -12, 1], + [15, -12, 2], + [15, -11, 1], + [15, -11, 2], + [15, -10, 1], + [15, -10, 2], + [15, -9, 1], + [15, -9, 2], + [15, -8, 1], + [15, -8, 2], + [15, -7, 1], + [15, -7, 2], + [15, -6, 1], + [15, -6, 2], + [15, -5, 1], + [15, -5, 2], + [15, -4, 1], + [15, -4, 2], + [15, -3, 1], + [15, -3, 2], + [15, -2, 1], + [15, -2, 2], + [15, -1, 1], + [15, -1, 2], + [15, 0, 1], + [15, 0, 2], + [15, 1, 1], + [15, 1, 2], + [15, 2, 1], + [15, 2, 2], + [15, 3, 1], + [15, 3, 2], + [15, 4, 1], + [15, 4, 2], + [15, 5, 1], + [15, 5, 2], + [15, 6, 1], + [15, 6, 2], + [15, 7, 1], + [15, 7, 2], + [15, 8, 1], + [15, 8, 2], + [15, 9, 1], + [15, 9, 2], + [15, 10, 1], + [15, 10, 2], + [15, 11, 1], + [15, 11, 2], + [15, 12, 1], + [15, 12, 2], + [15, 13, 1], + [15, 13, 2], + [15, 14, 1], + [15, 14, 2], + [15, 15, 1], + [15, 15, 2], + [16, -16, 1], + [16, -16, 2], + [16, -16, 3], + [16, -15, 1], + [16, -15, 2], + [16, -15, 3], + [16, -14, 1], + [16, -14, 2], + [16, -14, 3], + [16, -13, 1], + [16, -13, 2], + [16, -13, 3], + [16, -12, 1], + [16, -12, 2], + [16, -12, 3], + [16, -11, 1], + [16, -11, 2], + [16, -11, 3], + [16, -10, 1], + [16, -10, 2], + [16, -10, 3], + [16, -9, 1], + [16, -9, 2], + [16, -9, 3], + [16, -8, 1], + [16, -8, 2], + [16, -8, 3], + [16, -7, 1], + [16, -7, 2], + [16, -7, 3], + [16, -6, 1], + [16, -6, 2], + [16, -6, 3], + [16, -5, 1], + [16, -5, 2], + [16, -5, 3], + [16, -4, 1], + [16, -4, 2], + [16, -4, 3], + [16, -3, 1], + [16, -3, 2], + [16, -3, 3], + [16, -2, 1], + [16, -2, 2], + [16, -2, 3], + [16, -1, 1], + [16, -1, 2], + [16, -1, 3], + [16, 0, 1], + [16, 0, 2], + [16, 0, 3], + [16, 1, 1], + [16, 1, 2], + [16, 1, 3], + [16, 2, 1], + [16, 2, 2], + [16, 2, 3], + [16, 3, 1], + [16, 3, 2], + [16, 3, 3], + [16, 4, 1], + [16, 4, 2], + [16, 4, 3], + [16, 5, 1], + [16, 5, 2], + [16, 5, 3], + [16, 6, 1], + [16, 6, 2], + [16, 6, 3], + [16, 7, 1], + [16, 7, 2], + [16, 7, 3], + [16, 8, 1], + [16, 8, 2], + [16, 8, 3], + [16, 9, 1], + [16, 9, 2], + [16, 9, 3], + [16, 10, 1], + [16, 10, 2], + [16, 10, 3], + [16, 11, 1], + [16, 11, 2], + [16, 11, 3], + [16, 12, 1], + [16, 12, 2], + [16, 12, 3], + [16, 13, 1], + [16, 13, 2], + [16, 13, 3], + [16, 14, 1], + [16, 14, 2], + [16, 14, 3], + [16, 15, 1], + [16, 15, 2], + [16, 15, 3], + [16, 16, 1], + [16, 16, 2], + [16, 16, 3]]) + + return indxvec + + +def gsh_eval(X, Bvec): + + phi1 = X[..., 0] + phi = X[..., 1] + phi2 = X[..., 2] + + zvec = np.abs(phi) < 1e-8 + zvec = zvec.astype(int) + randvec = np.round(np.random.rand(zvec.size)).reshape(zvec.shape) + randvecopp = np.ones(zvec.shape) - randvec + phi += (1e-7)*zvec*(randvec - randvecopp) + + final_shape = np.hstack([phi1.shape, len(Bvec)]) + tfunc = np.zeros(final_shape, dtype='complex128') + + c = 0 + for Bindx in Bvec: + + if Bindx == 0: + tfunc[..., c] = 1 + + if Bindx == 1: + t913 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.6e1) * t913 ** 2 + + if Bindx == 2: + t914 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.6e1) * np.sqrt((1 + t914)) * t914 * np.sqrt((1 - t914)) + + if Bindx == 3: + t915 = np.cos(phi) + tfunc[..., c] = 0.15e2 / 0.2e1 * t915 ** 2 - 0.5e1 / 0.2e1 + + if Bindx == 4: + t916 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.6e1) * np.sqrt((1 - t916)) * np.sqrt((1 + t916)) * t916 + + if Bindx == 5: + t917 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((2*1j) * phi1) * np.sqrt(0.6e1) * t917 ** 2 + + if Bindx == 6: + t920 = np.sin(phi) + t918 = t920 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.70e2) * t918 ** 2 + + if Bindx == 7: + t921 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * t921 * (1 + (-2 + t921) * t921) * ((1 + t921) ** (0.3e1 / 0.2e1)) * np.sqrt(0.35e2) * np.exp((-3*1j) * phi1) * ((1 - t921) ** (-0.1e1 / 0.2e1)) + + if Bindx == 8: + t923 = np.cos(phi) + t922 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.10e2) * t922 ** 2 * (7 * t923 ** 2 - 1) + + if Bindx == 9: + t924 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.5e1) * t924 * np.sqrt((1 + t924)) * np.sqrt((1 - t924)) * (7 * t924 ** 2 - 3) + + if Bindx == 10: + t929 = np.cos(phi) + t930 = t929 ** 2 + tfunc[..., c] = 0.27e2 / 0.8e1 + (-0.135e3 / 0.4e1 + 0.315e3 / 0.8e1 * t930) * t930 + + if Bindx == 11: + t932 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.5e1) * np.sqrt((1 - t932)) * np.sqrt((1 + t932)) * t932 * (7 * t932 ** 2 - 3) + + if Bindx == 12: + t934 = np.cos(phi) + t933 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((2*1j) * phi1) * np.sqrt(0.10e2) * t933 ** 2 * (7 * t934 ** 2 - 1) + + if Bindx == 13: + t935 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.35e2) * ((1 - t935) ** (0.3e1 / 0.2e1)) * ((1 + t935) ** (0.3e1 / 0.2e1)) * t935 + + if Bindx == 14: + t938 = np.sin(phi) + t936 = t938 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((4*1j) * phi1) * np.sqrt(0.70e2) * t936 ** 2 + + if Bindx == 15: + t942 = np.sin(phi) + t939 = t942 ** 2 + t940 = t942 * t939 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.231e3) * t940 ** 2 + + if Bindx == 16: + t950 = np.cos(phi) + t957 = -6 * t950 + t949 = t950 ** 2 + t951 = t950 * t949 + t952 = t949 ** 2 + t956 = t952 * t957 - 20 * t951 + t957 + t955 = t951 ** 2 + 15 * t949 + 15 * t952 + 1 + tfunc[..., c] = (0.13e2 / 0.128e3) * np.sqrt(0.2e1) * ((t955 + t956) * np.exp((-6*1j) * (phi1 - phi2)) + (t955 - t956) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 17: + t958 = np.cos(phi) + t959 = t958 ** 2 + tfunc[..., c] = (0.39e2 / 0.16e2*1j) * t958 * (-3 * t959 - 1 + (t959 + 3) * t958) * ((1 + t958) ** (0.5e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((-5*1j) * phi1) * ((1 - t958) ** (-0.1e1 / 0.2e1)) + + if Bindx == 18: + t967 = np.cos(phi) + t966 = t967 ** 2 + t970 = t966 ** 2 + t973 = -1 - 10 * t966 - 5 * t970 + t972 = (10 * t966 + t970 + 5) * t967 + t968 = 5 * phi1 + tfunc[..., c] = (-0.13e2 / 0.64e2*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt((1 - t967)) * np.sqrt((1 + t967)) * ((t972 + t973) * np.exp((-1*1j) * (t968 - 6 * phi2)) + (t972 - t973) * np.exp((-1*1j) * (t968 + 6 * phi2))) + + if Bindx == 19: + t977 = np.sin(phi) + t975 = t977 ** 2 + t974 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.14e2) * t975 ** 2 * (11 * t974 ** 2 - 1) + + if Bindx == 20: + t984 = np.cos(phi) + t983 = t984 ** 2 + t986 = t983 ** 2 + t987 = t984 * t986 + t990 = 4 * t984 - 4 * t987 + t989 = t984 * t987 - 5 * t983 + 5 * t986 - 1 + t985 = 2 * phi1 + tfunc[..., c] = (0.13e2 / 0.64e2) * ((t989 + t990) * np.exp((-2*1j) * (t985 - 3 * phi2)) + (t989 - t990) * np.exp((-2*1j) * (t985 + 3 * phi2))) * np.sqrt(0.33e2) + + if Bindx == 21: + t991 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * (11 * t991 ** 2 - 3) * t991 * ((1 + t991) ** (0.3e1 / 0.2e1)) * np.sqrt(0.105e3) * np.exp((-3*1j) * phi1) * ((1 - t991) ** (0.3e1 / 0.2e1)) + + if Bindx == 22: + t998 = np.cos(phi) + t997 = t998 ** 2 + t1004 = 2 * t997 + t1000 = t997 ** 2 + t1003 = 1 - 3 * t1000 + t1004 + t1002 = (t1000 + t1004 - 3) * t998 + tfunc[..., c] = (-0.13e2 / 0.64e2*1j) * np.sqrt(0.55e2) * np.sqrt(0.2e1) * np.sqrt((1 - t998)) * np.sqrt((1 + t998)) * ((t1002 + t1003) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t1002 - t1003) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 23: + t1006 = np.cos(phi) + t1007 = t1006 ** 2 + t1005 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.105e3) * t1005 ** 2 * (1 + (-18 + 33 * t1007) * t1007) + + if Bindx == 24: + t1016 = np.cos(phi) + t1015 = t1016 ** 2 + t1017 = t1016 * t1015 + t1018 = t1015 ** 2 + t1022 = -2 * t1016 * t1018 - 2 * t1016 + 4 * t1017 + t1021 = t1017 ** 2 - t1015 - t1018 + 1 + tfunc[..., c] = (0.39e2 / 0.128e3) * np.sqrt(0.55e2) * np.sqrt(0.2e1) * ((t1021 + t1022) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t1021 - t1022) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 25: + t1023 = np.cos(phi) + t1024 = t1023 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.42e2) * np.sqrt((1 + t1023)) * t1023 * np.sqrt((1 - t1023)) * (5 + (-30 + 33 * t1024) * t1024) + + if Bindx == 26: + t1036 = np.cos(phi) + t1035 = t1036 ** 2 + t1042 = 1 + (-2 + t1035) * t1035 + t1040 = t1042 * t1036 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.sqrt(0.11e2) * np.sqrt((1 - t1036)) * np.sqrt((1 + t1036)) * ((t1040 - t1042) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t1040 + t1042) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 27: + t1043 = np.cos(phi) + t1044 = t1043 ** 2 + t1045 = t1044 ** 2 + tfunc[..., c] = -0.4095e4 / 0.16e2 * t1045 - 0.65e2 / 0.16e2 + (0.3003e4 / 0.16e2 * t1045 + 0.1365e4 / 0.16e2) * t1044 + + if Bindx == 28: + t1047 = np.cos(phi) + t1048 = t1047 ** 2 + t1049 = t1048 ** 2 + tfunc[..., c] = 0.13e2 / 0.32e2 * np.sqrt(0.231e3) * np.sqrt(0.2e1) * np.cos((6 * phi2)) * (-3 * t1049 - 1 + (t1049 + 3) * t1048) + + if Bindx == 29: + t1051 = np.cos(phi) + t1052 = t1051 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.42e2) * np.sqrt((1 - t1051)) * np.sqrt((1 + t1051)) * t1051 * (5 + (-30 + 33 * t1052) * t1052) + + if Bindx == 30: + t1060 = np.cos(phi) + t1059 = t1060 ** 2 + t1066 = 1 + (-2 + t1059) * t1059 + t1064 = t1066 * t1060 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.sqrt(0.11e2) * np.sqrt((1 - t1060)) * np.sqrt((1 + t1060)) * ((t1064 - t1066) * np.exp((1j) * (phi1 - 6 * phi2)) + (t1064 + t1066) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 31: + t1068 = np.cos(phi) + t1069 = t1068 ** 2 + t1067 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((2*1j) * phi1) * np.sqrt(0.105e3) * t1067 ** 2 * (1 + (-18 + 33 * t1069) * t1069) + + if Bindx == 32: + t1078 = np.cos(phi) + t1077 = t1078 ** 2 + t1079 = t1078 * t1077 + t1080 = t1077 ** 2 + t1084 = -2 * t1078 * t1080 - 2 * t1078 + 4 * t1079 + t1083 = t1079 ** 2 - t1077 - t1080 + 1 + tfunc[..., c] = (0.39e2 / 0.128e3) * np.sqrt(0.55e2) * np.sqrt(0.2e1) * ((t1083 + t1084) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t1083 - t1084) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 33: + t1085 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.105e3) * ((1 - t1085) ** (0.3e1 / 0.2e1)) * ((1 + t1085) ** (0.3e1 / 0.2e1)) * t1085 * (11 * t1085 ** 2 - 3) + + if Bindx == 34: + t1092 = np.cos(phi) + t1091 = t1092 ** 2 + t1098 = 2 * t1091 + t1094 = t1091 ** 2 + t1097 = 1 + t1098 - 3 * t1094 + t1096 = (t1094 + t1098 - 3) * t1092 + tfunc[..., c] = (-0.13e2 / 0.64e2*1j) * np.sqrt(0.55e2) * np.sqrt(0.2e1) * np.sqrt((1 - t1092)) * np.sqrt((1 + t1092)) * ((t1096 + t1097) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t1096 - t1097) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 35: + t1102 = np.sin(phi) + t1100 = t1102 ** 2 + t1099 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((4*1j) * phi1) * np.sqrt(0.14e2) * t1100 ** 2 * (11 * t1099 ** 2 - 1) + + if Bindx == 36: + t1109 = np.cos(phi) + t1108 = t1109 ** 2 + t1111 = t1108 ** 2 + t1112 = t1109 * t1111 + t1115 = 4 * t1109 - 4 * t1112 + t1114 = t1109 * t1112 - 5 * t1108 + 5 * t1111 - 1 + t1110 = 2 * phi1 + tfunc[..., c] = (0.13e2 / 0.64e2) * ((t1114 + t1115) * np.exp((2*1j) * (t1110 - 3 * phi2)) + (t1114 - t1115) * np.exp((2*1j) * (t1110 + 3 * phi2))) * np.sqrt(0.33e2) + + if Bindx == 37: + t1116 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.16e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.77e2) * ((1 - t1116) ** (0.5e1 / 0.2e1)) * ((1 + t1116) ** (0.5e1 / 0.2e1)) * t1116 + + if Bindx == 38: + t1123 = np.cos(phi) + t1122 = t1123 ** 2 + t1126 = t1122 ** 2 + t1129 = -1 - 10 * t1122 - 5 * t1126 + t1128 = (10 * t1122 + t1126 + 5) * t1123 + t1124 = 5 * phi1 + tfunc[..., c] = (-0.13e2 / 0.64e2*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * np.sqrt((1 - t1123)) * np.sqrt((1 + t1123)) * ((t1128 + t1129) * np.exp((1j) * (t1124 - 6 * phi2)) + (t1128 - t1129) * np.exp((1j) * (t1124 + 6 * phi2))) + + if Bindx == 39: + t1133 = np.sin(phi) + t1130 = t1133 ** 2 + t1131 = t1133 * t1130 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((6*1j) * phi1) * np.sqrt(0.231e3) * t1131 ** 2 + + if Bindx == 40: + t1141 = np.cos(phi) + t1148 = -6 * t1141 + t1140 = t1141 ** 2 + t1142 = t1141 * t1140 + t1143 = t1140 ** 2 + t1147 = t1143 * t1148 - 20 * t1142 + t1148 + t1146 = t1142 ** 2 + 15 * t1140 + 15 * t1143 + 1 + tfunc[..., c] = (0.13e2 / 0.128e3) * np.sqrt(0.2e1) * ((t1146 + t1147) * np.exp((6*1j) * (phi1 - phi2)) + (t1146 - t1147) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 41: + t1157 = np.cos(phi) + t1156 = t1157 ** 2 + t1159 = t1157 * t1156 + t1162 = t1159 ** 2 + t1164 = -t1157 * t1162 + 1 + t1160 = t1156 ** 2 + t1158 = 7 * phi1 + t1153 = t1157 * t1160 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.sqrt(0.7e1) * np.sqrt((1 + t1157)) * ((7 * t1162 - 21 * t1153 + 35 * t1160 - 35 * t1159 + 21 * t1156 - 7 * t1157 + t1164) * np.exp((-1*1j) * (t1158 - 6 * phi2)) + (5 * t1162 + 9 * t1153 + 5 * t1160 - 5 * t1159 - 9 * t1156 - 5 * t1157 - t1164) * np.exp((-1*1j) * (t1158 + 6 * phi2))) * ((1 - t1157) ** (-0.1e1 / 0.2e1)) + + if Bindx == 42: + t1173 = np.cos(phi) + t1172 = t1173 ** 2 + t1175 = t1172 ** 2 + t1174 = t1173 * t1172 + t1177 = t1174 ** 2 + t1180 = -6 - 48 * t1172 + 50 * t1175 + 36 * t1177 + t1179 = 15 * t1174 + (-69 * t1175 - 7 * t1177 + 29) * t1173 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.sqrt(0.2e1) * ((t1179 + t1180) * np.exp((-6*1j) * (phi1 - phi2)) + (-t1179 + t1180) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 43: + t1190 = np.cos(phi) + t1189 = t1190 ** 2 + t1192 = t1190 * t1189 + t1193 = t1189 ** 2 + t1195 = t1192 ** 2 + t1199 = 38 * t1192 + (10 * t1193 - 30 * t1195 - 18) * t1190 + t1198 = -10 * t1189 - 38 * t1195 - 5 + (60 - 7 * t1193) * t1193 + t1191 = 5 * phi1 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.sqrt(0.13e2) * ((-t1198 + t1199) * np.exp((-1*1j) * (t1191 - 6 * phi2)) + (t1198 + t1199) * np.exp((-1*1j) * (t1191 + 6 * phi2))) * ((1 + t1190) ** (-0.1e1 / 0.2e1)) * ((1 - t1190) ** (-0.1e1 / 0.2e1)) + + if Bindx == 44: + t1208 = np.cos(phi) + t1207 = t1208 ** 2 + t1211 = t1207 ** 2 + t1210 = t1208 * t1207 + t1213 = t1210 ** 2 + t1216 = 4 - 8 * t1207 - 20 * t1211 + 24 * t1213 + t1215 = 35 * t1210 + (-19 * t1211 - 7 * t1213 - 9) * t1208 + t1209 = 2 * phi1 + tfunc[..., c] = -(0.15e2 / 0.64e2) * ((t1215 + t1216) * np.exp((-2*1j) * (t1209 - 3 * phi2)) + (-t1215 + t1216) * np.exp((-2*1j) * (t1209 + 3 * phi2))) * np.sqrt(0.13e2) + + if Bindx == 45: + t1226 = np.cos(phi) + t1225 = t1226 ** 2 + t1227 = t1226 * t1225 + t1228 = t1225 ** 2 + t1230 = t1227 ** 2 + t1234 = -22 * t1227 + (38 * t1228 - 18 * t1230 + 2) * t1226 + t1233 = -18 * t1225 + 2 * t1230 + 3 + (20 - 7 * t1228) * t1228 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.sqrt(0.143e3) * ((-t1233 + t1234) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t1233 + t1234) * np.exp((-3*1j) * (phi1 + 2 * phi2))) * ((1 + t1226) ** (-0.1e1 / 0.2e1)) * ((1 - t1226) ** (-0.1e1 / 0.2e1)) + + if Bindx == 46: + t1243 = np.cos(phi) + t1242 = t1243 ** 2 + t1245 = t1242 ** 2 + t1244 = t1243 * t1242 + t1247 = t1244 ** 2 + t1250 = 2 - 16 * t1242 + 26 * t1245 - 12 * t1247 + t1249 = -t1244 + (11 * t1245 - 7 * t1247 - 3) * t1243 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.sqrt(0.143e3) * np.sqrt(0.2e1) * ((-t1249 + t1250) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t1249 + t1250) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 47: + t1260 = np.cos(phi) + t1259 = t1260 ** 2 + t1261 = t1260 * t1259 + t1262 = t1259 ** 2 + t1264 = t1261 ** 2 + t1268 = 18 * t1261 + 6 * (-3 * t1262 + t1264 - 1) * t1260 + t1267 = 10 * t1259 + 22 * t1264 - 1 + (-24 - 7 * t1262) * t1262 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((t1267 + t1268) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (-t1267 + t1268) * np.exp((-1*1j) * (phi1 + 6 * phi2))) * ((1 + t1260) ** (-0.1e1 / 0.2e1)) * ((1 - t1260) ** (-0.1e1 / 0.2e1)) + + if Bindx == 48: + t1272 = np.sin(phi) + t1269 = t1272 ** 2 + t1270 = t1272 * t1269 + tfunc[..., c] = (-0.15e2 / 0.32e2*1j) * np.sqrt(0.3003e4) * np.sqrt(0.2e1) * np.cos(phi) * np.sin((6 * phi2)) * t1270 ** 2 + + if Bindx == 49: + t1282 = np.cos(phi) + t1281 = t1282 ** 2 + t1283 = t1282 * t1281 + t1284 = t1281 ** 2 + t1286 = t1283 ** 2 + t1290 = -18 * t1283 + 6 * (3 * t1284 - t1286 + 1) * t1282 + t1289 = 10 * t1281 + 22 * t1286 - 1 + (-24 - 7 * t1284) * t1284 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((1 + t1282) ** (-0.1e1 / 0.2e1)) * ((1 - t1282) ** (-0.1e1 / 0.2e1)) * ((-t1289 + t1290) * np.exp((1j) * (phi1 - 6 * phi2)) + (t1289 + t1290) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 50: + t1299 = np.cos(phi) + t1298 = t1299 ** 2 + t1301 = t1298 ** 2 + t1300 = t1299 * t1298 + t1303 = t1300 ** 2 + t1306 = 2 - 16 * t1298 + 26 * t1301 - 12 * t1303 + t1305 = -t1300 + (11 * t1301 - 7 * t1303 - 3) * t1299 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.sqrt(0.143e3) * np.sqrt(0.2e1) * ((-t1305 + t1306) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t1305 + t1306) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 51: + t1316 = np.cos(phi) + t1315 = t1316 ** 2 + t1317 = t1316 * t1315 + t1318 = t1315 ** 2 + t1320 = t1317 ** 2 + t1324 = -22 * t1317 + (38 * t1318 - 18 * t1320 + 2) * t1316 + t1323 = -18 * t1315 + 2 * t1320 + 3 + (20 - 7 * t1318) * t1318 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.sqrt(0.143e3) * ((1 + t1316) ** (-0.1e1 / 0.2e1)) * ((1 - t1316) ** (-0.1e1 / 0.2e1)) * ((-t1323 + t1324) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t1323 + t1324) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 52: + t1333 = np.cos(phi) + t1332 = t1333 ** 2 + t1336 = t1332 ** 2 + t1335 = t1333 * t1332 + t1338 = t1335 ** 2 + t1341 = -4 + 8 * t1332 + 20 * t1336 - 24 * t1338 + t1340 = 35 * t1335 + (-19 * t1336 - 7 * t1338 - 9) * t1333 + t1334 = 2 * phi1 + tfunc[..., c] = -(0.15e2 / 0.64e2) * ((-t1340 + t1341) * np.exp((2*1j) * (t1334 - 3 * phi2)) + (t1340 + t1341) * np.exp((2*1j) * (t1334 + 3 * phi2))) * np.sqrt(0.13e2) + + if Bindx == 53: + t1351 = np.cos(phi) + t1350 = t1351 ** 2 + t1353 = t1351 * t1350 + t1354 = t1350 ** 2 + t1356 = t1353 ** 2 + t1360 = 38 * t1353 + (10 * t1354 - 30 * t1356 - 18) * t1351 + t1359 = -10 * t1350 - 38 * t1356 - 5 + (60 - 7 * t1354) * t1354 + t1352 = 5 * phi1 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.sqrt(0.13e2) * ((1 - t1351) ** (-0.1e1 / 0.2e1)) * ((1 + t1351) ** (-0.1e1 / 0.2e1)) * ((-t1359 + t1360) * np.exp((1j) * (t1352 - 6 * phi2)) + (t1359 + t1360) * np.exp((1j) * (t1352 + 6 * phi2))) + + if Bindx == 54: + t1369 = np.cos(phi) + t1368 = t1369 ** 2 + t1371 = t1368 ** 2 + t1370 = t1369 * t1368 + t1373 = t1370 ** 2 + t1376 = 6 + 48 * t1368 - 50 * t1371 - 36 * t1373 + t1375 = 15 * t1370 + (-69 * t1371 - 7 * t1373 + 29) * t1369 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.sqrt(0.2e1) * ((-t1375 + t1376) * np.exp((6*1j) * (phi1 - phi2)) + (t1375 + t1376) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 55: + t1385 = np.cos(phi) + t1384 = t1385 ** 2 + t1387 = t1385 * t1384 + t1390 = t1387 ** 2 + t1392 = -t1385 * t1390 - 1 + t1388 = t1384 ** 2 + t1386 = 7 * phi1 + t1381 = t1385 * t1388 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.sqrt(0.7e1) * np.sqrt((1 - t1385)) * ((1 + t1385) ** (-0.1e1 / 0.2e1)) * ((-5 * t1390 + 9 * t1381 - 5 * t1388 - 5 * t1387 + 9 * t1384 - 5 * t1385 - t1392) * np.exp((1j) * (t1386 - 6 * phi2)) + (-7 * t1390 - 21 * t1381 - 35 * t1388 - 35 * t1387 - 21 * t1384 - 7 * t1385 + t1392) * np.exp((1j) * (t1386 + 6 * phi2))) + + if Bindx == 56: + t1396 = np.sin(phi) + t1393 = t1396 ** 2 + t1394 = t1393 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.1430e4) * t1394 ** 2 + + if Bindx == 57: + t1405 = np.cos(phi) + t1404 = t1405 ** 2 + t1414 = -14 * t1404 + t1407 = t1405 * t1404 + t1409 = t1407 ** 2 + t1410 = t1405 * t1409 + t1413 = t1405 * t1410 + 14 * t1409 + t1414 - 1 + t1412 = 6 * t1405 - 6 * t1410 + (t1414 + 14) * t1407 + t1406 = 4 * phi1 + tfunc[..., c] = (0.17e2 / 0.128e3) * ((t1412 + t1413) * np.exp((-2*1j) * (t1406 - 3 * phi2)) + (-t1412 + t1413) * np.exp((-2*1j) * (t1406 + 3 * phi2))) * np.sqrt(0.15e2) + + if Bindx == 58: + t1415 = np.cos(phi) + t1419 = -4 * t1415 + t1416 = t1415 ** 2 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * t1415 * (t1419 + 1 + (t1419 + 6 + t1416) * t1416) * ((1 + t1415) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((-7*1j) * phi1) * ((1 - t1415) ** (-0.1e1 / 0.2e1)) + + if Bindx == 59: + t1429 = np.cos(phi) + t1428 = t1429 ** 2 + t1432 = t1428 ** 2 + t1438 = 4 * t1432 ** 2 + t1431 = t1429 * t1428 + t1437 = -3 + 21 * t1431 + 35 * t1432 + t1434 = t1431 ** 2 + t1430 = 7 * phi1 + t1425 = t1429 * t1432 + t1423 = t1429 * t1434 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt((1 + t1429)) * np.sqrt(0.15e2) * ((1 - t1429) ** (-0.1e1 / 0.2e1)) * ((t1438 - 25 * t1423 + 63 * t1434 - 77 * t1425 - 35 * t1428 + 17 * t1429 + t1437) * np.exp((-1*1j) * (t1430 - 6 * phi2)) + (t1438 + 17 * t1423 + 21 * t1434 - 7 * t1425 + 7 * t1428 + 11 * t1429 - t1437) * np.exp((-1*1j) * (t1430 + 6 * phi2))) + + if Bindx == 60: + t1443 = np.sin(phi) + t1440 = t1443 ** 2 + t1441 = t1443 * t1440 + t1439 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.429e3) * t1441 ** 2 * (15 * t1439 ** 2 - 1) + + if Bindx == 61: + t1452 = np.cos(phi) + t1453 = t1452 ** 2 + t1455 = t1453 ** 2 + t1454 = t1452 * t1453 + t1457 = t1454 ** 2 + t1461 = 196 * t1457 + 16 + (-210 + 30 * t1455) * t1455 + t1460 = 175 * t1454 + (-21 * t1455 - 135 * t1457 - 51) * t1452 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.sqrt(0.2e1) * ((t1460 + t1461) * np.exp((-6*1j) * (phi1 - phi2)) + (-t1460 + t1461) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 62: + t1462 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * (5 * t1462 ** 2 - 1) * t1462 * ((1 + t1462) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((-5*1j) * phi1) * ((1 - t1462) ** (0.5e1 / 0.2e1)) + + if Bindx == 63: + t1473 = np.cos(phi) + t1472 = t1473 ** 2 + t1476 = t1472 ** 2 + t1475 = t1473 * t1472 + t1478 = t1475 ** 2 + t1480 = t1476 ** 2 + t1483 = 7 - 42 * t1472 + 20 * t1476 + 90 * t1478 - 75 * t1480 + t1482 = 90 * t1475 + (-162 * t1476 + 62 * t1478 + 20 * t1480 - 10) * t1473 + t1474 = 5 * phi1 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.21e2) * ((1 + t1473) ** (-0.1e1 / 0.2e1)) * ((1 - t1473) ** (-0.1e1 / 0.2e1)) * ((t1482 + t1483) * np.exp((-1*1j) * (t1474 - 6 * phi2)) + (t1482 - t1483) * np.exp((-1*1j) * (t1474 + 6 * phi2))) + + if Bindx == 64: + t1489 = np.sin(phi) + t1487 = t1489 ** 2 + t1484 = np.cos(phi) + t1485 = t1484 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.154e3) * t1487 ** 2 * (1 + (-26 + 65 * t1485) * t1485) + + if Bindx == 65: + t1499 = np.cos(phi) + t1498 = t1499 ** 2 + t1501 = t1499 * t1498 + t1502 = t1498 ** 2 + t1504 = t1501 ** 2 + t1508 = -5 * t1501 + (21 * t1502 - 15 * t1504 - 1) * t1499 + t1507 = 10 * t1498 + 6 * t1504 - 1 + (-20 + 5 * t1502) * t1502 + t1500 = 2 * phi1 + tfunc[..., c] = (0.17e2 / 0.64e2) * ((t1507 + t1508) * np.exp((-2*1j) * (t1500 - 3 * phi2)) + (t1507 - t1508) * np.exp((-2*1j) * (t1500 + 3 * phi2))) * np.sqrt(0.273e3) + + if Bindx == 66: + t1509 = np.cos(phi) + t1510 = t1509 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (3 + (-26 + 39 * t1510) * t1510) * t1509 * ((1 + t1509) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2310e4) * np.exp((-3*1j) * phi1) * ((1 - t1509) ** (0.3e1 / 0.2e1)) + + if Bindx == 67: + t1522 = np.cos(phi) + t1521 = t1522 ** 2 + t1524 = t1521 ** 2 + t1523 = t1522 * t1521 + t1526 = t1523 ** 2 + t1528 = t1524 ** 2 + t1531 = -1 + 14 * t1521 - 52 * t1524 + 66 * t1526 - 27 * t1528 + t1530 = 22 * t1523 - 14 * (t1524 + t1526) * t1522 + (12 * t1528 - 6) * t1522 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.455e3) * ((1 + t1522) ** (-0.1e1 / 0.2e1)) * ((1 - t1522) ** (-0.1e1 / 0.2e1)) * ((t1530 + t1531) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t1530 - t1531) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 68: + t1534 = np.cos(phi) + t1535 = t1534 ** 2 + t1536 = t1535 ** 2 + t1533 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.35e2) * t1533 ** 2 * (-143 * t1536 - 1 + (143 * t1536 + 33) * t1535) + + if Bindx == 69: + t1546 = np.cos(phi) + t1545 = t1546 ** 2 + t1547 = t1546 * t1545 + t1548 = t1545 ** 2 + t1550 = t1547 ** 2 + t1553 = 2 * t1547 + 2 * (-2 * t1548 + t1550) * t1546 + t1552 = 1 - 5 * t1545 + 7 * t1548 - 3 * t1550 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.sqrt(0.15015e5) * t1546 * np.sqrt(0.2e1) * ((t1552 + t1553) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (-t1552 + t1553) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 70: + t1554 = np.cos(phi) + t1555 = t1554 ** 2 + t1556 = t1554 * t1555 + t1561 = -1001 * t1555 ** 2 + 715 * t1556 ** 2 - 35 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 + t1554)) * t1554 * (t1561 * t1554 - 385 * t1555 + 385 * t1556 - t1561) * ((1 - t1554) ** (-0.1e1 / 0.2e1)) + + if Bindx == 71: + t1572 = np.cos(phi) + t1571 = t1572 ** 2 + t1574 = t1571 ** 2 + t1573 = t1572 * t1571 + t1576 = t1573 ** 2 + t1578 = t1574 ** 2 + t1581 = -1 + 18 * t1571 - 48 * t1574 + 46 * t1576 - 15 * t1578 + t1580 = -38 * t1573 + (78 * t1574 - 66 * t1576 + 20 * t1578 + 6) * t1572 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((1 + t1572) ** (-0.1e1 / 0.2e1)) * ((1 - t1572) ** (-0.1e1 / 0.2e1)) * ((t1580 + t1581) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t1580 - t1581) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 72: + t1582 = np.cos(phi) + t1583 = t1582 ** 2 + t1584 = t1583 ** 2 + tfunc[..., c] = -0.5355e4 / 0.32e2 * t1583 + 0.595e3 / 0.128e3 + (-0.51051e5 / 0.32e2 * t1583 + 0.58905e5 / 0.64e2 + 0.109395e6 / 0.128e3 * t1584) * t1584 + + if Bindx == 73: + t1587 = np.cos(phi) + t1588 = t1587 ** 2 + t1589 = t1588 ** 2 + tfunc[..., c] = 0.17e2 / 0.64e2 * np.sqrt(0.429e3) * np.sqrt(0.2e1) * (-18 * t1588 + 1 + (-46 * t1588 + 48 + 15 * t1589) * t1589) * np.cos((6 * phi2)) + + if Bindx == 74: + t1592 = np.cos(phi) + t1593 = t1592 ** 2 + t1594 = t1593 ** 2 + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 - t1592)) * np.sqrt((1 + t1592)) * t1592 * (-1001 * t1594 - 35 + (715 * t1594 + 385) * t1593) + + if Bindx == 75: + t1606 = np.cos(phi) + t1605 = t1606 ** 2 + t1608 = t1605 ** 2 + t1607 = t1606 * t1605 + t1610 = t1607 ** 2 + t1612 = t1608 ** 2 + t1615 = -1 + 18 * t1605 - 48 * t1608 + 46 * t1610 - 15 * t1612 + t1614 = -38 * t1607 + (78 * t1608 - 66 * t1610 + 20 * t1612 + 6) * t1606 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((1 + t1606) ** (-0.1e1 / 0.2e1)) * ((1 - t1606) ** (-0.1e1 / 0.2e1)) * ((t1614 + t1615) * np.exp((1j) * (phi1 - 6 * phi2)) + (t1614 - t1615) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 76: + t1617 = np.cos(phi) + t1618 = t1617 ** 2 + t1619 = t1618 ** 2 + t1616 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((2*1j) * phi1) * np.sqrt(0.35e2) * t1616 ** 2 * (-143 * t1619 - 1 + (143 * t1619 + 33) * t1618) + + if Bindx == 77: + t1629 = np.cos(phi) + t1628 = t1629 ** 2 + t1630 = t1629 * t1628 + t1631 = t1628 ** 2 + t1633 = t1630 ** 2 + t1636 = 2 * t1630 + 2 * (-2 * t1631 + t1633) * t1629 + t1635 = 1 - 5 * t1628 + 7 * t1631 - 3 * t1633 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.sqrt(0.15015e5) * t1629 * np.sqrt(0.2e1) * ((t1635 + t1636) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (-t1635 + t1636) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 78: + t1637 = np.cos(phi) + t1638 = t1637 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.2310e4) * ((1 - t1637) ** (0.3e1 / 0.2e1)) * ((1 + t1637) ** (0.3e1 / 0.2e1)) * t1637 * (3 + (-26 + 39 * t1638) * t1638) + + if Bindx == 79: + t1650 = np.cos(phi) + t1649 = t1650 ** 2 + t1652 = t1649 ** 2 + t1651 = t1650 * t1649 + t1654 = t1651 ** 2 + t1656 = t1652 ** 2 + t1659 = -1 + 14 * t1649 - 52 * t1652 + 66 * t1654 - 27 * t1656 + t1658 = 22 * t1651 - 14 * (t1652 + t1654) * t1650 + (12 * t1656 - 6) * t1650 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.455e3) * ((1 + t1650) ** (-0.1e1 / 0.2e1)) * ((1 - t1650) ** (-0.1e1 / 0.2e1)) * ((t1658 + t1659) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t1658 - t1659) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 80: + t1666 = np.sin(phi) + t1664 = t1666 ** 2 + t1661 = np.cos(phi) + t1662 = t1661 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((4*1j) * phi1) * np.sqrt(0.154e3) * t1664 ** 2 * (1 + (-26 + 65 * t1662) * t1662) + + if Bindx == 81: + t1676 = np.cos(phi) + t1675 = t1676 ** 2 + t1678 = t1676 * t1675 + t1679 = t1675 ** 2 + t1681 = t1678 ** 2 + t1685 = -5 * t1678 + (21 * t1679 - 15 * t1681 - 1) * t1676 + t1684 = 10 * t1675 + 6 * t1681 - 1 + (-20 + 5 * t1679) * t1679 + t1677 = 2 * phi1 + tfunc[..., c] = (0.17e2 / 0.64e2) * ((t1684 + t1685) * np.exp((2*1j) * (t1677 - 3 * phi2)) + (t1684 - t1685) * np.exp((2*1j) * (t1677 + 3 * phi2))) * np.sqrt(0.273e3) + + if Bindx == 82: + t1686 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.2002e4) * ((1 - t1686) ** (0.5e1 / 0.2e1)) * ((1 + t1686) ** (0.5e1 / 0.2e1)) * t1686 * (5 * t1686 ** 2 - 1) + + if Bindx == 83: + t1697 = np.cos(phi) + t1696 = t1697 ** 2 + t1700 = t1696 ** 2 + t1699 = t1697 * t1696 + t1702 = t1699 ** 2 + t1704 = t1700 ** 2 + t1707 = 7 - 42 * t1696 + 20 * t1700 + 90 * t1702 - 75 * t1704 + t1706 = 90 * t1699 + (-162 * t1700 + 62 * t1702 + 20 * t1704 - 10) * t1697 + t1698 = 5 * phi1 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.sqrt(0.21e2) * ((1 + t1697) ** (-0.1e1 / 0.2e1)) * ((1 - t1697) ** (-0.1e1 / 0.2e1)) * ((t1706 + t1707) * np.exp((1j) * (t1698 - 6 * phi2)) + (t1706 - t1707) * np.exp((1j) * (t1698 + 6 * phi2))) + + if Bindx == 84: + t1712 = np.sin(phi) + t1709 = t1712 ** 2 + t1710 = t1712 * t1709 + t1708 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((6*1j) * phi1) * np.sqrt(0.429e3) * t1710 ** 2 * (15 * t1708 ** 2 - 1) + + if Bindx == 85: + t1721 = np.cos(phi) + t1722 = t1721 ** 2 + t1724 = t1722 ** 2 + t1723 = t1721 * t1722 + t1726 = t1723 ** 2 + t1730 = 196 * t1726 + 16 + (-210 + 30 * t1724) * t1724 + t1729 = 175 * t1723 + (-21 * t1724 - 135 * t1726 - 51) * t1721 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.sqrt(0.2e1) * ((t1729 + t1730) * np.exp((6*1j) * (phi1 - phi2)) + (-t1729 + t1730) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 86: + t1731 = np.cos(phi) + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.1430e4) * ((1 - t1731) ** (0.7e1 / 0.2e1)) * ((1 + t1731) ** (0.7e1 / 0.2e1)) * t1731 + + if Bindx == 87: + t1741 = np.cos(phi) + t1740 = t1741 ** 2 + t1744 = t1740 ** 2 + t1750 = 4 * t1744 ** 2 + t1743 = t1741 * t1740 + t1749 = 3 + 21 * t1743 - 35 * t1744 + t1746 = t1743 ** 2 + t1742 = 7 * phi1 + t1737 = t1741 * t1744 + t1735 = t1741 * t1746 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.sqrt((1 - t1741)) * np.sqrt(0.15e2) * ((1 + t1741) ** (-0.1e1 / 0.2e1)) * ((t1750 - 17 * t1735 + 21 * t1746 + 7 * t1737 + 7 * t1740 - 11 * t1741 + t1749) * np.exp((1j) * (t1742 - 6 * phi2)) + (t1750 + 25 * t1735 + 63 * t1746 + 77 * t1737 - 35 * t1740 - 17 * t1741 - t1749) * np.exp((1j) * (t1742 + 6 * phi2))) + + if Bindx == 88: + t1754 = np.sin(phi) + t1751 = t1754 ** 2 + t1752 = t1751 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((8*1j) * phi1) * np.sqrt(0.1430e4) * t1752 ** 2 + + if Bindx == 89: + t1763 = np.cos(phi) + t1762 = t1763 ** 2 + t1772 = -14 * t1762 + t1765 = t1763 * t1762 + t1767 = t1765 ** 2 + t1768 = t1763 * t1767 + t1771 = t1763 * t1768 + 14 * t1767 + t1772 - 1 + t1770 = 6 * t1763 - 6 * t1768 + (t1772 + 14) * t1765 + t1764 = 4 * phi1 + tfunc[..., c] = (0.17e2 / 0.128e3) * ((t1770 + t1771) * np.exp((2*1j) * (t1764 - 3 * phi2)) + (-t1770 + t1771) * np.exp((2*1j) * (t1764 + 3 * phi2))) * np.sqrt(0.15e2) + + if Bindx == 90: + t1781 = np.cos(phi) + t1780 = t1781 ** 2 + t1790 = 14 * t1780 + t1783 = t1781 * t1780 + t1785 = t1783 ** 2 + t1786 = t1781 * t1785 + t1789 = -t1781 * t1786 - 14 * t1785 + t1790 + 1 + t1788 = -6 * t1781 + 6 * t1786 + (t1790 - 14) * t1783 + t1782 = 3 * phi1 + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.51e2) * np.sqrt((1 - t1781)) * np.sqrt((1 + t1781)) * ((t1788 + t1789) * np.exp((-3*1j) * (t1782 - 2 * phi2)) + (t1788 - t1789) * np.exp((-3*1j) * (t1782 + 2 * phi2))) + + if Bindx == 91: + t1801 = np.cos(phi) + t1800 = t1801 ** 2 + t1804 = t1800 ** 2 + t1803 = t1801 * t1800 + t1806 = t1803 ** 2 + t1808 = t1804 ** 2 + t1811 = 2 + 10 * t1800 - 42 * t1804 + 14 * t1806 + 16 * t1808 + t1810 = 14 * t1803 + (28 * t1804 - 30 * t1806 - 3 * t1808 - 9) * t1801 + t1802 = 4 * phi1 + tfunc[..., c] = -(0.19e2 / 0.128e3) * ((t1810 + t1811) * np.exp((-2*1j) * (t1802 - 3 * phi2)) + (-t1810 + t1811) * np.exp((-2*1j) * (t1802 + 3 * phi2))) * np.sqrt(0.51e2) + + if Bindx == 92: + t1822 = np.cos(phi) + t1821 = t1822 ** 2 + t1825 = t1821 ** 2 + t1829 = t1825 ** 2 + t1831 = -51 * t1822 * t1829 + 21 + t1824 = t1822 * t1821 + t1827 = t1824 ** 2 + t1823 = 7 * phi1 + t1818 = t1822 * t1825 + t1816 = t1822 * t1827 + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.sqrt(0.2e1) * np.sqrt((1 + t1822)) * np.sqrt(0.3e1) * ((1 - t1822) ** (-0.1e1 / 0.2e1)) * ((289 * t1829 - 616 * t1816 + 504 * t1827 + 154 * t1818 - 574 * t1825 + 336 * t1824 + 16 * t1821 - 79 * t1822 + t1831) * np.exp((-1*1j) * (t1823 - 6 * phi2)) + (187 * t1829 + 140 * t1816 - 252 * t1827 - 406 * t1818 - 14 * t1825 + 252 * t1824 + 100 * t1821 - 37 * t1822 - t1831) * np.exp((-1*1j) * (t1823 + 6 * phi2))) + + if Bindx == 93: + t1842 = np.cos(phi) + t1841 = t1842 ** 2 + t1844 = t1841 ** 2 + t1843 = t1842 * t1841 + t1846 = t1843 ** 2 + t1848 = t1844 ** 2 + t1851 = -23 + 207 * t1841 - 273 * t1844 - 287 * t1846 + 408 * t1848 + t1850 = -308 * t1843 + (798 * t1844 - 432 * t1846 - 102 * t1848 + 12) * t1842 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.sqrt(0.2e1) * ((t1850 + t1851) * np.exp((-6*1j) * (phi1 - phi2)) + (-t1850 + t1851) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 94: + t1863 = np.cos(phi) + t1862 = t1863 ** 2 + t1865 = t1863 * t1862 + t1866 = t1862 ** 2 + t1867 = t1863 * t1866 + t1868 = t1865 ** 2 + t1870 = t1866 ** 2 + t1874 = -30 * t1865 - 126 * t1867 + (310 * t1868 - 170 * t1870 + 16) * t1863 + t1873 = -51 * t1867 ** 2 + 75 * t1862 - 280 * t1866 + 336 * t1868 - 75 * t1870 - 5 + t1864 = 5 * phi1 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.15e2) * ((-t1873 + t1874) * np.exp((-1*1j) * (t1864 - 6 * phi2)) + (t1873 + t1874) * np.exp((-1*1j) * (t1864 + 6 * phi2))) * ((1 + t1863) ** (-0.1e1 / 0.2e1)) * ((1 - t1863) ** (-0.1e1 / 0.2e1)) + + if Bindx == 95: + t1885 = np.cos(phi) + t1884 = t1885 ** 2 + t1888 = t1884 ** 2 + t1887 = t1885 * t1884 + t1890 = t1887 ** 2 + t1892 = t1888 ** 2 + t1895 = 1 - 19 * t1884 + 131 * t1888 - 249 * t1890 + 136 * t1892 + t1894 = -106 * t1887 + (144 * t1888 - 6 * t1890 - 51 * t1892 + 19) * t1885 + t1886 = 2 * phi1 + tfunc[..., c] = -(0.19e2 / 0.64e2) * ((t1894 + t1895) * np.exp((-2*1j) * (t1886 - 3 * phi2)) + (-t1894 + t1895) * np.exp((-2*1j) * (t1886 + 3 * phi2))) * np.sqrt(0.21e2) + + if Bindx == 96: + t1907 = np.cos(phi) + t1906 = t1907 ** 2 + t1908 = t1907 * t1906 + t1909 = t1906 ** 2 + t1910 = t1907 * t1909 + t1911 = t1908 ** 2 + t1913 = t1909 ** 2 + t1917 = 98 * t1908 - 262 * t1910 + (278 * t1911 - 102 * t1913 - 12) * t1907 + t1916 = -51 * t1910 ** 2 + 23 * t1906 - 52 * t1909 - 12 * t1911 + 93 * t1913 - 1 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.sqrt(0.91e2) * np.sqrt(0.2e1) * ((-t1916 + t1917) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t1916 + t1917) * np.exp((-3*1j) * (phi1 + 2 * phi2))) * ((1 + t1907) ** (-0.1e1 / 0.2e1)) * ((1 - t1907) ** (-0.1e1 / 0.2e1)) + + if Bindx == 97: + t1928 = np.cos(phi) + t1927 = t1928 ** 2 + t1930 = t1927 ** 2 + t1929 = t1928 * t1927 + t1932 = t1929 ** 2 + t1934 = t1930 ** 2 + t1937 = -3 + 75 * t1927 - 277 * t1930 + 341 * t1932 - 136 * t1934 + t1936 = 44 * t1929 + (-178 * t1930 + 240 * t1932 - 102 * t1934 - 4) * t1928 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.sqrt(0.2e1) * np.sqrt(0.39e2) * ((-t1936 + t1937) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t1936 + t1937) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 98: + t1949 = np.cos(phi) + t1948 = t1949 ** 2 + t1950 = t1949 * t1948 + t1951 = t1948 ** 2 + t1952 = t1949 * t1951 + t1953 = t1950 ** 2 + t1955 = t1951 ** 2 + t1959 = -52 * t1950 + 120 * t1952 + (-108 * t1953 + 34 * t1955 + 6) * t1949 + t1958 = -51 * t1952 ** 2 - 27 * t1948 + 126 * t1951 - 226 * t1953 + 177 * t1955 + 1 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((1 + t1949) ** (-0.1e1 / 0.2e1)) * ((1 - t1949) ** (-0.1e1 / 0.2e1)) * ((t1958 + t1959) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (-t1958 + t1959) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 99: + t1964 = np.sin(phi) + t1961 = t1964 ** 2 + t1962 = t1964 * t1961 + t1960 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * t1960 * t1962 ** 2 * (17 * t1960 ** 2 - 3) * np.sin((6 * phi2)) * np.sqrt(0.2145e4) * np.sqrt(0.2e1) + + if Bindx == 100: + t1976 = np.cos(phi) + t1975 = t1976 ** 2 + t1977 = t1976 * t1975 + t1978 = t1975 ** 2 + t1979 = t1976 * t1978 + t1980 = t1977 ** 2 + t1982 = t1978 ** 2 + t1986 = 52 * t1977 - 120 * t1979 + (108 * t1980 - 34 * t1982 - 6) * t1976 + t1985 = -51 * t1979 ** 2 - 27 * t1975 + 126 * t1978 - 226 * t1980 + 177 * t1982 + 1 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.sqrt(0.429e3) * ((1 + t1976) ** (-0.1e1 / 0.2e1)) * ((1 - t1976) ** (-0.1e1 / 0.2e1)) * ((-t1985 + t1986) * np.exp((1j) * (phi1 - 6 * phi2)) + (t1985 + t1986) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 101: + t1997 = np.cos(phi) + t1996 = t1997 ** 2 + t1999 = t1996 ** 2 + t1998 = t1997 * t1996 + t2001 = t1998 ** 2 + t2003 = t1999 ** 2 + t2006 = -3 + 75 * t1996 - 277 * t1999 + 341 * t2001 - 136 * t2003 + t2005 = 44 * t1998 + (-178 * t1999 + 240 * t2001 - 102 * t2003 - 4) * t1997 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.sqrt(0.2e1) * np.sqrt(0.39e2) * ((-t2005 + t2006) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t2005 + t2006) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 102: + t2018 = np.cos(phi) + t2017 = t2018 ** 2 + t2019 = t2018 * t2017 + t2020 = t2017 ** 2 + t2021 = t2018 * t2020 + t2022 = t2019 ** 2 + t2024 = t2020 ** 2 + t2028 = 98 * t2019 - 262 * t2021 + (278 * t2022 - 102 * t2024 - 12) * t2018 + t2027 = -51 * t2021 ** 2 + 23 * t2017 - 52 * t2020 - 12 * t2022 + 93 * t2024 - 1 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.sqrt(0.91e2) * np.sqrt(0.2e1) * ((1 + t2018) ** (-0.1e1 / 0.2e1)) * ((1 - t2018) ** (-0.1e1 / 0.2e1)) * ((-t2027 + t2028) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t2027 + t2028) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 103: + t2039 = np.cos(phi) + t2038 = t2039 ** 2 + t2042 = t2038 ** 2 + t2041 = t2039 * t2038 + t2044 = t2041 ** 2 + t2046 = t2042 ** 2 + t2049 = -1 + 19 * t2038 - 131 * t2042 + 249 * t2044 - 136 * t2046 + t2048 = -106 * t2041 + (144 * t2042 - 6 * t2044 - 51 * t2046 + 19) * t2039 + t2040 = 2 * phi1 + tfunc[..., c] = -(0.19e2 / 0.64e2) * ((-t2048 + t2049) * np.exp((2*1j) * (t2040 - 3 * phi2)) + (t2048 + t2049) * np.exp((2*1j) * (t2040 + 3 * phi2))) * np.sqrt(0.21e2) + + if Bindx == 104: + t2061 = np.cos(phi) + t2060 = t2061 ** 2 + t2063 = t2061 * t2060 + t2064 = t2060 ** 2 + t2065 = t2061 * t2064 + t2066 = t2063 ** 2 + t2068 = t2064 ** 2 + t2072 = -30 * t2063 - 126 * t2065 + (310 * t2066 - 170 * t2068 + 16) * t2061 + t2071 = -51 * t2065 ** 2 + 75 * t2060 - 280 * t2064 + 336 * t2066 - 75 * t2068 - 5 + t2062 = 5 * phi1 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.15e2) * ((1 + t2061) ** (-0.1e1 / 0.2e1)) * ((1 - t2061) ** (-0.1e1 / 0.2e1)) * ((-t2071 + t2072) * np.exp((1j) * (t2062 - 6 * phi2)) + (t2071 + t2072) * np.exp((1j) * (t2062 + 6 * phi2))) + + if Bindx == 105: + t2083 = np.cos(phi) + t2082 = t2083 ** 2 + t2085 = t2082 ** 2 + t2084 = t2083 * t2082 + t2087 = t2084 ** 2 + t2089 = t2085 ** 2 + t2092 = 23 - 207 * t2082 + 273 * t2085 + 287 * t2087 - 408 * t2089 + t2091 = -308 * t2084 + (798 * t2085 - 432 * t2087 - 102 * t2089 + 12) * t2083 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.sqrt(0.2e1) * ((-t2091 + t2092) * np.exp((6*1j) * (phi1 - phi2)) + (t2091 + t2092) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 106: + t2103 = np.cos(phi) + t2102 = t2103 ** 2 + t2106 = t2102 ** 2 + t2110 = t2106 ** 2 + t2112 = -51 * t2103 * t2110 - 21 + t2105 = t2103 * t2102 + t2108 = t2105 ** 2 + t2104 = 7 * phi1 + t2099 = t2103 * t2106 + t2097 = t2103 * t2108 + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.sqrt(0.2e1) * np.sqrt((1 - t2103)) * np.sqrt(0.3e1) * ((1 + t2103) ** (-0.1e1 / 0.2e1)) * ((-187 * t2110 + 140 * t2097 + 252 * t2108 - 406 * t2099 + 14 * t2106 + 252 * t2105 - 100 * t2102 - 37 * t2103 - t2112) * np.exp((1j) * (t2104 - 6 * phi2)) + (-289 * t2110 - 616 * t2097 - 504 * t2108 + 154 * t2099 + 574 * t2106 + 336 * t2105 - 16 * t2102 - 79 * t2103 + t2112) * np.exp((1j) * (t2104 + 6 * phi2))) + + if Bindx == 107: + t2123 = np.cos(phi) + t2122 = t2123 ** 2 + t2126 = t2122 ** 2 + t2125 = t2123 * t2122 + t2128 = t2125 ** 2 + t2130 = t2126 ** 2 + t2133 = 2 + 10 * t2122 - 42 * t2126 + 14 * t2128 + 16 * t2130 + t2132 = 14 * t2125 + (28 * t2126 - 30 * t2128 - 3 * t2130 - 9) * t2123 + t2124 = 4 * phi1 + tfunc[..., c] = (0.19e2 / 0.128e3) * ((t2132 + t2133) * np.exp((2*1j) * (t2124 - 3 * phi2)) + (-t2132 + t2133) * np.exp((2*1j) * (t2124 + 3 * phi2))) * np.sqrt(0.51e2) + + if Bindx == 108: + t2142 = np.cos(phi) + t2141 = t2142 ** 2 + t2144 = t2142 * t2141 + t2146 = t2144 ** 2 + t2147 = t2142 * t2146 + t2150 = -t2142 * t2147 + 14 * t2141 - 14 * t2146 + 1 + t2149 = 6 * t2142 - 6 * t2147 + (-14 * t2141 + 14) * t2144 + t2143 = 3 * phi1 + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.51e2) * np.sqrt((1 - t2142)) * np.sqrt((1 + t2142)) * ((t2149 - t2150) * np.exp((3*1j) * (t2143 - 2 * phi2)) + (t2149 + t2150) * np.exp((3*1j) * (t2143 + 2 * phi2))) + + if Bindx == 109: + t2155 = np.sin(phi) + t2151 = t2155 ** 2 + t2153 = t2155 * t2151 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-10*1j) * phi1) * np.sqrt(0.46189e5) * t2153 ** 2 + + if Bindx == 110: + t2167 = np.cos(phi) + t2179 = -6 * t2167 + t2166 = t2167 ** 2 + t2169 = t2167 * t2166 + t2170 = t2166 ** 2 + t2171 = t2167 * t2170 + t2172 = t2169 ** 2 + t2174 = t2170 ** 2 + t2178 = -8 * t2167 * t2172 + t2174 * t2179 - 8 * t2169 + 28 * t2171 + t2179 + t2177 = t2171 ** 2 + 13 * t2166 - 14 * t2170 - 14 * t2172 + 13 * t2174 + 1 + t2168 = 5 * phi1 + tfunc[..., c] = (0.21e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.4845e4) * ((t2177 + t2178) * np.exp((-2*1j) * (t2168 - 3 * phi2)) + (t2177 - t2178) * np.exp((-2*1j) * (t2168 + 3 * phi2))) + + if Bindx == 111: + t2180 = np.cos(phi) + t2181 = t2180 ** 2 + t2183 = t2181 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * t2180 * (-10 * t2181 - 5 * t2183 - 1 + (10 * t2181 + t2183 + 5) * t2180) * ((1 + t2180) ** (0.9e1 / 0.2e1)) * np.sqrt(0.230945e6) * np.exp((-9*1j) * phi1) * ((1 - t2180) ** (-0.1e1 / 0.2e1)) + + if Bindx == 112: + t2195 = np.cos(phi) + t2194 = t2195 ** 2 + t2198 = t2194 ** 2 + t2202 = t2198 ** 2 + t2204 = 5 * t2195 * t2202 + t2197 = t2195 * t2194 + t2200 = t2197 ** 2 + t2196 = 3 * phi1 + t2191 = t2195 * t2198 + t2189 = t2195 * t2200 + tfunc[..., c] = (0.21e2 / 0.1024e4*1j) * np.sqrt(0.2e1) * ((1 + t2195) ** (0.3e1 / 0.2e1)) * np.sqrt(0.969e3) * ((1 - t2195) ** (-0.1e1 / 0.2e1)) * ((t2204 - 37 * t2202 + 116 * t2189 - 196 * t2200 + 182 * t2191 - 70 * t2198 - 28 * t2197 + 44 * t2194 - 19 * t2195 + 3) * np.exp((-3*1j) * (t2196 - 2 * phi2)) + (t2204 + 17 * t2202 + 8 * t2189 - 32 * t2200 - 38 * t2191 + 10 * t2198 + 32 * t2197 + 8 * t2194 - 7 * t2195 - 3) * np.exp((-3*1j) * (t2196 + 2 * phi2))) + + if Bindx == 113: + t2209 = np.sin(phi) + t2206 = t2209 ** 2 + t2207 = t2206 ** 2 + t2205 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.24310e5) * t2207 ** 2 * (19 * t2205 ** 2 - 1) + + if Bindx == 114: + t2221 = np.cos(phi) + t2220 = t2221 ** 2 + t2223 = t2221 * t2220 + t2224 = t2220 ** 2 + t2225 = t2221 * t2224 + t2226 = t2223 ** 2 + t2228 = t2224 ** 2 + t2232 = -592 * t2223 + 896 * t2225 + (80 * t2226 - 456 * t2228 + 72) * t2221 + t2231 = 95 * t2225 ** 2 + 155 * t2220 + 266 * t2224 - 1162 * t2226 + 677 * t2228 - 31 + t2222 = 4 * phi1 + tfunc[..., c] = (0.21e2 / 0.1024e4) * ((t2231 + t2232) * np.exp((-2*1j) * (t2222 - 3 * phi2)) + (t2231 - t2232) * np.exp((-2*1j) * (t2222 + 3 * phi2))) * np.sqrt(0.51e2) + + if Bindx == 115: + t2233 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (19 * t2233 ** 2 - 3) * t2233 * ((1 + t2233) ** (0.7e1 / 0.2e1)) * np.sqrt(0.36465e5) * np.exp((-7*1j) * phi1) * ((1 - t2233) ** (0.7e1 / 0.2e1)) + + if Bindx == 116: + t2245 = np.cos(phi) + t2244 = t2245 ** 2 + t2248 = t2244 ** 2 + t2249 = t2245 * t2248 + t2255 = 285 * t2249 ** 2 + t2252 = t2248 ** 2 + t2247 = t2245 * t2244 + t2250 = t2247 ** 2 + t2246 = 7 * phi1 + t2239 = t2245 * t2250 + t2237 = t2245 * t2252 + tfunc[..., c] = (0.21e2 / 0.1024e4*1j) * np.sqrt(0.2e1) * np.sqrt((1 + t2245)) * np.sqrt(0.17e2) * ((1 - t2245) ** (-0.1e1 / 0.2e1)) * ((t2255 - 1482 * t2237 + 2673 * t2252 - 1112 * t2239 - 2422 * t2250 + 3108 * t2249 - 518 * t2248 - 1048 * t2247 + 537 * t2244 + 22 * t2245 - 43) * np.exp((-1*1j) * (t2246 - 6 * phi2)) + (t2255 + 912 * t2237 + 279 * t2252 - 1840 * t2239 - 1694 * t2250 + 1008 * t2249 + 1582 * t2248 - 16 * t2247 - 495 * t2244 - 64 * t2245 + 43) * np.exp((-1*1j) * (t2246 + 6 * phi2))) + + if Bindx == 117: + t2262 = np.sin(phi) + t2259 = t2262 ** 2 + t2260 = t2262 * t2259 + t2256 = np.cos(phi) + t2257 = t2256 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-6*1j) * phi1) * np.sqrt(0.2145e4) * t2260 ** 2 * (3 + (-102 + 323 * t2257) * t2257) + + if Bindx == 118: + t2274 = np.cos(phi) + t2273 = t2274 ** 2 + t2275 = t2274 * t2273 + t2276 = t2273 ** 2 + t2277 = t2274 * t2276 + t2278 = t2275 ** 2 + t2280 = t2276 ** 2 + t2284 = -6168 * t2275 - 1932 * t2277 + (23528 * t2278 - 17442 * t2280 + 1502) * t2274 + t2283 = 4845 * t2277 ** 2 - 4503 * t2273 + 22442 * t2276 - 34902 * t2278 + 12393 * t2280 + 237 + tfunc[..., c] = (0.21e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t2283 + t2284) * np.exp((-6*1j) * (phi1 - phi2)) + (t2283 - t2284) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 119: + t2285 = np.cos(phi) + t2286 = t2285 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (15 + (-170 + 323 * t2286) * t2286) * t2285 * ((1 + t2285) ** (0.5e1 / 0.2e1)) * np.sqrt(0.429e3) * np.exp((-5*1j) * phi1) * ((1 - t2285) ** (0.5e1 / 0.2e1)) + + if Bindx == 120: + t2300 = np.cos(phi) + t2299 = t2300 ** 2 + t2303 = t2299 ** 2 + t2302 = t2300 * t2299 + t2305 = t2302 ** 2 + t2307 = t2303 ** 2 + t2304 = t2300 * t2303 + t2309 = t2304 ** 2 + t2312 = -1 + 25 * t2299 + 1110 * t2303 - 4942 * t2305 + 6715 * t2307 - 2907 * t2309 + t2311 = -2035 * t2302 + 5278 * t2304 + (-4710 * t2305 + 255 * t2307 + 969 * t2309 + 243) * t2300 + t2301 = 5 * phi1 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * ((1 + t2300) ** (-0.1e1 / 0.2e1)) * ((1 - t2300) ** (-0.1e1 / 0.2e1)) * ((t2311 + t2312) * np.exp((-1*1j) * (t2301 - 6 * phi2)) + (t2311 - t2312) * np.exp((-1*1j) * (t2301 + 6 * phi2))) + + if Bindx == 121: + t2319 = np.sin(phi) + t2317 = t2319 ** 2 + t2313 = np.cos(phi) + t2314 = t2313 ** 2 + t2315 = t2314 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.4290e4) * t2317 ** 2 * (-255 * t2315 - 1 + (323 * t2315 + 45) * t2314) + + if Bindx == 122: + t2331 = np.cos(phi) + t2330 = t2331 ** 2 + t2333 = t2331 * t2330 + t2334 = t2330 ** 2 + t2335 = t2331 * t2334 + t2336 = t2333 ** 2 + t2338 = t2334 ** 2 + t2342 = 0.13209e5 / 0.64e2 * t2333 - 0.6027e4 / 0.8e1 * t2335 + (0.66759e5 / 0.64e2 * t2336 - 0.61047e5 / 0.128e3 * t2338 - 0.2457e4 / 0.128e3) * t2331 + t2341 = 0.101745e6 / 0.512e3 * t2335 ** 2 - 0.50547e5 / 0.512e3 * t2330 + 0.93639e5 / 0.256e3 * t2334 - 0.84231e5 / 0.256e3 * t2336 - 0.71757e5 / 0.512e3 * t2338 + 0.1743e4 / 0.512e3 + t2332 = 2 * phi1 + tfunc[..., c] = (t2341 + t2342) * np.exp((-2*1j) * (t2332 - 3 * phi2)) + (t2341 - t2342) * np.exp((-2*1j) * (t2332 + 3 * phi2)) + + if Bindx == 123: + t2343 = np.cos(phi) + t2344 = t2343 ** 2 + t2345 = t2344 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (-357 * t2345 - 7 + (323 * t2345 + 105) * t2344) * t2343 * ((1 + t2343) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2145e4) * np.exp((-3*1j) * phi1) * ((1 - t2343) ** (0.3e1 / 0.2e1)) + + if Bindx == 124: + t2359 = np.cos(phi) + t2358 = t2359 ** 2 + t2361 = t2358 ** 2 + t2360 = t2359 * t2358 + t2363 = t2360 ** 2 + t2365 = t2361 ** 2 + t2362 = t2359 * t2361 + t2367 = t2362 ** 2 + t2370 = 69 - 2277 * t2358 + 12898 * t2361 - 27962 * t2363 + 25993 * t2365 - 8721 * t2367 + t2369 = -1423 * t2360 + 662 * t2362 + (7122 * t2363 - 11373 * t2365 + 4845 * t2367 + 167) * t2359 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.2e1) * ((1 + t2359) ** (-0.1e1 / 0.2e1)) * ((1 - t2359) ** (-0.1e1 / 0.2e1)) * ((t2369 + t2370) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t2369 - t2370) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 125: + t2372 = np.cos(phi) + t2373 = t2372 ** 2 + t2374 = t2373 ** 2 + t2371 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * phi1) * np.sqrt(0.330e3) * t2371 ** 2 * (-364 * t2373 + 7 + (-6188 * t2373 + 2730 + 4199 * t2374) * t2374) + + if Bindx == 126: + t2388 = np.cos(phi) + t2387 = t2388 ** 2 + t2389 = t2388 * t2387 + t2390 = t2387 ** 2 + t2391 = t2388 * t2390 + t2392 = t2389 ** 2 + t2394 = t2390 ** 2 + t2398 = 4888 * t2389 - 14276 * t2391 + (15640 * t2392 - 5814 * t2394 - 438) * t2388 + t2397 = 4845 * t2391 ** 2 + 665 * t2387 - 4486 * t2390 + 11898 * t2392 - 12903 * t2394 - 19 + tfunc[..., c] = (0.21e2 / 0.1024e4) * ((t2397 + t2398) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t2397 - t2398) * np.exp((-2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.13e2) + + if Bindx == 127: + t2399 = np.cos(phi) + t2400 = t2399 ** 2 + t2401 = t2399 * t2400 + t2402 = t2400 ** 2 + t2408 = -7956 * t2401 ** 2 + 63 + (4914 + 4199 * t2402) * t2402 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.110e3) * np.sqrt((1 + t2399)) * t2399 * (t2408 * t2399 + 1092 * t2400 - 1092 * t2401 - t2408) * ((1 - t2399) ** (-0.1e1 / 0.2e1)) + + if Bindx == 128: + t2421 = np.cos(phi) + t2420 = t2421 ** 2 + t2423 = t2420 ** 2 + t2422 = t2421 * t2420 + t2425 = t2422 ** 2 + t2427 = t2423 ** 2 + t2424 = t2421 * t2423 + t2429 = t2424 ** 2 + t2432 = 9 - 333 * t2420 + 1914 * t2423 - 3834 * t2425 + 3213 * t2427 - 969 * t2429 + t2431 = 1387 * t2422 - 5110 * t2424 + (8118 * t2425 - 5899 * t2427 + 1615 * t2429 - 111) * t2421 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.39e2) * ((1 + t2421) ** (-0.1e1 / 0.2e1)) * ((1 - t2421) ** (-0.1e1 / 0.2e1)) * ((t2431 + t2432) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t2431 - t2432) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 129: + t2433 = np.cos(phi) + t2434 = t2433 ** 2 + t2435 = t2434 ** 2 + t2437 = t2435 ** 2 + tfunc[..., c] = -0.2297295e7 / 0.256e3 * t2437 - 0.315315e6 / 0.128e3 * t2435 - 0.1323e4 / 0.256e3 + (0.969969e6 / 0.256e3 * t2437 + 0.945945e6 / 0.128e3 * t2435 + 0.72765e5 / 0.256e3) * t2434 + + if Bindx == 130: + t2439 = np.cos(phi) + t2440 = t2439 ** 2 + t2441 = t2440 ** 2 + t2443 = t2441 ** 2 + tfunc[..., c] = 0.21e2 / 0.512e3 * np.sqrt(0.2145e4) * np.sqrt(0.2e1) * (-638 * t2441 - 1071 * t2443 - 3 + (1278 * t2441 + 323 * t2443 + 111) * t2440) * np.cos((6 * phi2)) + + if Bindx == 131: + t2445 = np.cos(phi) + t2446 = t2445 ** 2 + t2447 = t2446 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.110e3) * np.sqrt((1 - t2445)) * np.sqrt((1 + t2445)) * t2445 * (-1092 * t2446 + 63 + (-7956 * t2446 + 4914 + 4199 * t2447) * t2447) + + if Bindx == 132: + t2462 = np.cos(phi) + t2461 = t2462 ** 2 + t2464 = t2461 ** 2 + t2463 = t2462 * t2461 + t2466 = t2463 ** 2 + t2468 = t2464 ** 2 + t2465 = t2462 * t2464 + t2470 = t2465 ** 2 + t2473 = 9 - 333 * t2461 + 1914 * t2464 - 3834 * t2466 + 3213 * t2468 - 969 * t2470 + t2472 = 1387 * t2463 - 5110 * t2465 + (8118 * t2466 - 5899 * t2468 + 1615 * t2470 - 111) * t2462 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.39e2) * ((1 + t2462) ** (-0.1e1 / 0.2e1)) * ((1 - t2462) ** (-0.1e1 / 0.2e1)) * ((t2472 + t2473) * np.exp((1j) * (phi1 - 6 * phi2)) + (t2472 - t2473) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 133: + t2475 = np.cos(phi) + t2476 = t2475 ** 2 + t2477 = t2476 ** 2 + t2474 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * phi1) * np.sqrt(0.330e3) * t2474 ** 2 * (-364 * t2476 + 7 + (-6188 * t2476 + 2730 + 4199 * t2477) * t2477) + + if Bindx == 134: + t2491 = np.cos(phi) + t2490 = t2491 ** 2 + t2492 = t2491 * t2490 + t2493 = t2490 ** 2 + t2494 = t2491 * t2493 + t2495 = t2492 ** 2 + t2497 = t2493 ** 2 + t2501 = 4888 * t2492 - 14276 * t2494 + (15640 * t2495 - 5814 * t2497 - 438) * t2491 + t2500 = 4845 * t2494 ** 2 + 665 * t2490 - 4486 * t2493 + 11898 * t2495 - 12903 * t2497 - 19 + tfunc[..., c] = (0.21e2 / 0.1024e4) * ((t2500 + t2501) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t2500 - t2501) * np.exp((2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.13e2) + + if Bindx == 135: + t2502 = np.cos(phi) + t2503 = t2502 ** 2 + t2504 = t2503 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.2145e4) * ((1 - t2502) ** (0.3e1 / 0.2e1)) * ((1 + t2502) ** (0.3e1 / 0.2e1)) * t2502 * (-357 * t2504 - 7 + (323 * t2504 + 105) * t2503) + + if Bindx == 136: + t2518 = np.cos(phi) + t2517 = t2518 ** 2 + t2520 = t2517 ** 2 + t2519 = t2518 * t2517 + t2522 = t2519 ** 2 + t2524 = t2520 ** 2 + t2521 = t2518 * t2520 + t2526 = t2521 ** 2 + t2529 = 69 - 2277 * t2517 + 12898 * t2520 - 27962 * t2522 + 25993 * t2524 - 8721 * t2526 + t2528 = -1423 * t2519 + 662 * t2521 + (7122 * t2522 - 11373 * t2524 + 4845 * t2526 + 167) * t2518 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.2e1) * ((1 + t2518) ** (-0.1e1 / 0.2e1)) * ((1 - t2518) ** (-0.1e1 / 0.2e1)) * ((t2528 + t2529) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t2528 - t2529) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 137: + t2536 = np.sin(phi) + t2534 = t2536 ** 2 + t2530 = np.cos(phi) + t2531 = t2530 ** 2 + t2532 = t2531 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * phi1) * np.sqrt(0.4290e4) * t2534 ** 2 * (-255 * t2532 - 1 + (323 * t2532 + 45) * t2531) + + if Bindx == 138: + t2548 = np.cos(phi) + t2547 = t2548 ** 2 + t2550 = t2548 * t2547 + t2551 = t2547 ** 2 + t2552 = t2548 * t2551 + t2553 = t2550 ** 2 + t2555 = t2551 ** 2 + t2559 = 0.13209e5 / 0.64e2 * t2550 - 0.6027e4 / 0.8e1 * t2552 + (0.66759e5 / 0.64e2 * t2553 - 0.61047e5 / 0.128e3 * t2555 - 0.2457e4 / 0.128e3) * t2548 + t2558 = 0.101745e6 / 0.512e3 * t2552 ** 2 - 0.50547e5 / 0.512e3 * t2547 + 0.93639e5 / 0.256e3 * t2551 - 0.84231e5 / 0.256e3 * t2553 - 0.71757e5 / 0.512e3 * t2555 + 0.1743e4 / 0.512e3 + t2549 = 2 * phi1 + tfunc[..., c] = (t2558 + t2559) * np.exp((2*1j) * (t2549 - 3 * phi2)) + (t2558 - t2559) * np.exp((2*1j) * (t2549 + 3 * phi2)) + + if Bindx == 139: + t2560 = np.cos(phi) + t2561 = t2560 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.429e3) * ((1 - t2560) ** (0.5e1 / 0.2e1)) * ((1 + t2560) ** (0.5e1 / 0.2e1)) * t2560 * (15 + (-170 + 323 * t2561) * t2561) + + if Bindx == 140: + t2575 = np.cos(phi) + t2574 = t2575 ** 2 + t2578 = t2574 ** 2 + t2577 = t2575 * t2574 + t2580 = t2577 ** 2 + t2582 = t2578 ** 2 + t2579 = t2575 * t2578 + t2584 = t2579 ** 2 + t2587 = -1 + 25 * t2574 + 1110 * t2578 - 4942 * t2580 + 6715 * t2582 - 2907 * t2584 + t2586 = -2035 * t2577 + 5278 * t2579 + (-4710 * t2580 + 255 * t2582 + 969 * t2584 + 243) * t2575 + t2576 = 5 * phi1 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * ((1 + t2575) ** (-0.1e1 / 0.2e1)) * ((1 - t2575) ** (-0.1e1 / 0.2e1)) * ((t2586 + t2587) * np.exp((1j) * (t2576 - 6 * phi2)) + (t2586 - t2587) * np.exp((1j) * (t2576 + 6 * phi2))) + + if Bindx == 141: + t2594 = np.sin(phi) + t2591 = t2594 ** 2 + t2592 = t2594 * t2591 + t2588 = np.cos(phi) + t2589 = t2588 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((6*1j) * phi1) * np.sqrt(0.2145e4) * t2592 ** 2 * (3 + (-102 + 323 * t2589) * t2589) + + if Bindx == 142: + t2606 = np.cos(phi) + t2605 = t2606 ** 2 + t2607 = t2606 * t2605 + t2608 = t2605 ** 2 + t2609 = t2606 * t2608 + t2610 = t2607 ** 2 + t2612 = t2608 ** 2 + t2616 = -6168 * t2607 - 1932 * t2609 + (23528 * t2610 - 17442 * t2612 + 1502) * t2606 + t2615 = 4845 * t2609 ** 2 - 4503 * t2605 + 22442 * t2608 - 34902 * t2610 + 12393 * t2612 + 237 + tfunc[..., c] = (0.21e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t2615 + t2616) * np.exp((6*1j) * (phi1 - phi2)) + (t2615 - t2616) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 143: + t2617 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.36465e5) * ((1 - t2617) ** (0.7e1 / 0.2e1)) * ((1 + t2617) ** (0.7e1 / 0.2e1)) * t2617 * (19 * t2617 ** 2 - 3) + + if Bindx == 144: + t2629 = np.cos(phi) + t2628 = t2629 ** 2 + t2632 = t2628 ** 2 + t2633 = t2629 * t2632 + t2639 = 285 * t2633 ** 2 + t2636 = t2632 ** 2 + t2631 = t2629 * t2628 + t2634 = t2631 ** 2 + t2630 = 7 * phi1 + t2623 = t2629 * t2634 + t2621 = t2629 * t2636 + tfunc[..., c] = (-0.21e2 / 0.1024e4*1j) * np.sqrt(0.2e1) * np.sqrt((1 - t2629)) * np.sqrt(0.17e2) * ((1 + t2629) ** (-0.1e1 / 0.2e1)) * ((t2639 - 912 * t2621 + 279 * t2636 + 1840 * t2623 - 1694 * t2634 - 1008 * t2633 + 1582 * t2632 + 16 * t2631 - 495 * t2628 + 64 * t2629 + 43) * np.exp((1j) * (t2630 - 6 * phi2)) + (t2639 + 1482 * t2621 + 2673 * t2636 + 1112 * t2623 - 2422 * t2634 - 3108 * t2633 - 518 * t2632 + 1048 * t2631 + 537 * t2628 - 22 * t2629 - 43) * np.exp((1j) * (t2630 + 6 * phi2))) + + if Bindx == 145: + t2644 = np.sin(phi) + t2641 = t2644 ** 2 + t2642 = t2641 ** 2 + t2640 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((8*1j) * phi1) * np.sqrt(0.24310e5) * t2642 ** 2 * (19 * t2640 ** 2 - 1) + + if Bindx == 146: + t2656 = np.cos(phi) + t2655 = t2656 ** 2 + t2658 = t2656 * t2655 + t2659 = t2655 ** 2 + t2660 = t2656 * t2659 + t2661 = t2658 ** 2 + t2663 = t2659 ** 2 + t2667 = -592 * t2658 + 896 * t2660 + (80 * t2661 - 456 * t2663 + 72) * t2656 + t2666 = 95 * t2660 ** 2 + 155 * t2655 + 266 * t2659 - 1162 * t2661 + 677 * t2663 - 31 + t2657 = 4 * phi1 + tfunc[..., c] = (0.21e2 / 0.1024e4) * ((t2666 + t2667) * np.exp((2*1j) * (t2657 - 3 * phi2)) + (t2666 - t2667) * np.exp((2*1j) * (t2657 + 3 * phi2))) * np.sqrt(0.51e2) + + if Bindx == 147: + t2668 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.230945e6) * ((1 - t2668) ** (0.9e1 / 0.2e1)) * ((1 + t2668) ** (0.9e1 / 0.2e1)) * t2668 + + if Bindx == 148: + t2679 = np.cos(phi) + t2678 = t2679 ** 2 + t2682 = t2678 ** 2 + t2686 = t2682 ** 2 + t2688 = 5 * t2679 * t2686 + t2681 = t2679 * t2678 + t2684 = t2681 ** 2 + t2680 = 3 * phi1 + t2675 = t2679 * t2682 + t2673 = t2679 * t2684 + tfunc[..., c] = (0.21e2 / 0.1024e4*1j) * np.sqrt(0.2e1) * ((1 - t2679) ** (0.3e1 / 0.2e1)) * np.sqrt(0.969e3) * ((1 + t2679) ** (-0.1e1 / 0.2e1)) * ((t2688 - 17 * t2686 + 8 * t2673 + 32 * t2684 - 38 * t2675 - 10 * t2682 + 32 * t2681 - 8 * t2678 - 7 * t2679 + 3) * np.exp((3*1j) * (t2680 - 2 * phi2)) + (t2688 + 37 * t2686 + 116 * t2673 + 196 * t2684 + 182 * t2675 + 70 * t2682 - 28 * t2681 - 44 * t2678 - 19 * t2679 - 3) * np.exp((3*1j) * (t2680 + 2 * phi2))) + + if Bindx == 149: + t2693 = np.sin(phi) + t2689 = t2693 ** 2 + t2691 = t2693 * t2689 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((10*1j) * phi1) * np.sqrt(0.46189e5) * t2691 ** 2 + + if Bindx == 150: + t2705 = np.cos(phi) + t2717 = -6 * t2705 + t2704 = t2705 ** 2 + t2707 = t2705 * t2704 + t2708 = t2704 ** 2 + t2709 = t2705 * t2708 + t2710 = t2707 ** 2 + t2712 = t2708 ** 2 + t2716 = -8 * t2705 * t2710 + t2712 * t2717 - 8 * t2707 + 28 * t2709 + t2717 + t2715 = t2709 ** 2 + 13 * t2704 - 14 * t2708 - 14 * t2710 + 13 * t2712 + 1 + t2706 = 5 * phi1 + tfunc[..., c] = (0.21e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.4845e4) * ((t2715 + t2716) * np.exp((2*1j) * (t2706 - 3 * phi2)) + (t2715 - t2716) * np.exp((2*1j) * (t2706 + 3 * phi2))) + + if Bindx == 151: + t2729 = np.cos(phi) + t2741 = -6 * t2729 + t2728 = t2729 ** 2 + t2731 = t2729 * t2728 + t2732 = t2728 ** 2 + t2733 = t2729 * t2732 + t2734 = t2731 ** 2 + t2736 = t2732 ** 2 + t2740 = -8 * t2729 * t2734 + t2736 * t2741 - 8 * t2731 + 28 * t2733 + t2741 + t2739 = -t2733 ** 2 - 13 * t2728 + 14 * t2732 + 14 * t2734 - 13 * t2736 - 1 + t2730 = 11 * phi1 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.sqrt(0.1463e4) * np.sqrt((1 - t2729)) * np.sqrt((1 + t2729)) * ((-t2739 + t2740) * np.exp((-1*1j) * (t2730 - 6 * phi2)) + (t2739 + t2740) * np.exp((-1*1j) * (t2730 + 6 * phi2))) + + if Bindx == 152: + t2754 = np.cos(phi) + t2753 = t2754 ** 2 + t2757 = t2753 ** 2 + t2756 = t2754 * t2753 + t2759 = t2756 ** 2 + t2761 = t2757 ** 2 + t2758 = t2754 * t2757 + t2763 = t2758 ** 2 + t2766 = -6 - 12 * t2753 + 172 * t2757 - 224 * t2759 + 10 * t2761 + 60 * t2763 + t2765 = -95 * t2756 - 14 * t2758 + (202 * t2759 - 107 * t2761 - 11 * t2763 + 25) * t2754 + t2755 = 5 * phi1 + tfunc[..., c] = -(0.69e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.133e3) * ((t2765 + t2766) * np.exp((-2*1j) * (t2755 - 3 * phi2)) + (-t2765 + t2766) * np.exp((-2*1j) * (t2755 + 3 * phi2))) + + if Bindx == 153: + t2778 = np.cos(phi) + t2777 = t2778 ** 2 + t2781 = t2777 ** 2 + t2782 = t2778 * t2781 + t2788 = -231 * t2782 ** 2 - 61 + t2785 = t2781 ** 2 + t2780 = t2778 * t2777 + t2783 = t2780 ** 2 + t2779 = 3 * phi1 + t2772 = t2778 * t2783 + t2770 = t2778 * t2785 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.sqrt(0.57e2) * ((1 + t2778) ** (0.3e1 / 0.2e1)) * ((-1596 * t2770 + 4513 * t2785 - 6368 * t2772 + 3766 * t2783 + 1288 * t2782 - 3374 * t2781 + 1792 * t2780 - 77 * t2777 - 236 * t2778 - t2788) * np.exp((-3*1j) * (t2779 - 2 * phi2)) + (-672 * t2770 + 23 * t2785 + 1688 * t2772 + 1058 * t2783 - 1352 * t2782 - 1322 * t2781 + 328 * t2780 + 533 * t2777 + 8 * t2778 + t2788) * np.exp((-3*1j) * (t2779 + 2 * phi2))) * ((1 - t2778) ** (-0.1e1 / 0.2e1)) + + if Bindx == 154: + t2801 = np.cos(phi) + t2800 = t2801 ** 2 + t2804 = t2800 ** 2 + t2803 = t2801 * t2800 + t2806 = t2803 ** 2 + t2808 = t2804 ** 2 + t2805 = t2801 * t2804 + t2810 = t2805 ** 2 + t2813 = -8 + 128 * t2800 - 448 * t2804 + 336 * t2806 + 328 * t2808 - 336 * t2810 + t2812 = 63 * t2803 - 574 * t2805 + (958 * t2806 - 383 * t2808 - 77 * t2810 + 13) * t2801 + t2802 = 4 * phi1 + tfunc[..., c] = (0.69e2 / 0.1024e4) * ((-t2812 + t2813) * np.exp((-2*1j) * (t2802 - 3 * phi2)) + (t2812 + t2813) * np.exp((-2*1j) * (t2802 + 3 * phi2))) * np.sqrt(0.95e2) + + if Bindx == 155: + t2826 = np.cos(phi) + t2825 = t2826 ** 2 + t2829 = t2825 ** 2 + t2830 = t2826 * t2829 + t2835 = t2830 ** 2 + t2837 = -1463 * t2826 * t2835 + 35 + t2833 = t2829 ** 2 + t2828 = t2826 * t2825 + t2831 = t2828 ** 2 + t2827 = 7 * phi1 + t2820 = t2826 * t2831 + t2818 = t2826 * t2833 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.sqrt((1 + t2826)) * np.sqrt(0.5e1) * ((1 - t2826) ** (-0.1e1 / 0.2e1)) * ((7049 * t2835 - 10697 * t2818 - 209 * t2833 + 16058 * t2820 - 12390 * t2831 - 3794 * t2830 + 7742 * t2829 - 1491 * t2828 - 1203 * t2825 + 363 * t2826 + t2837) * np.exp((-1*1j) * (t2827 - 6 * phi2)) + (4123 * t2835 - 475 * t2818 - 10431 * t2833 - 5418 * t2820 + 9086 * t2831 + 7098 * t2830 - 3150 * t2829 - 3101 * t2828 + 407 * t2825 + 433 * t2826 - t2837) * np.exp((-1*1j) * (t2827 + 6 * phi2))) + + if Bindx == 156: + t2850 = np.cos(phi) + t2849 = t2850 ** 2 + t2852 = t2849 ** 2 + t2851 = t2850 * t2849 + t2854 = t2851 ** 2 + t2856 = t2852 ** 2 + t2853 = t2850 * t2852 + t2858 = t2853 ** 2 + t2861 = 94 - 2820 * t2849 + 420 * t2852 + 40096 * t2854 - 80370 * t2856 + 43092 * t2858 + t2860 = 21805 * t2851 - 69846 * t2853 + (79650 * t2854 - 16815 * t2856 - 13167 * t2858 - 2139) * t2850 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t2860 + t2861) * np.exp((-6*1j) * (phi1 - phi2)) + (-t2860 + t2861) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 157: + t2875 = np.cos(phi) + t2874 = t2875 ** 2 + t2877 = t2875 * t2874 + t2878 = t2874 ** 2 + t2879 = t2875 * t2878 + t2880 = t2877 ** 2 + t2882 = t2878 ** 2 + t2884 = t2879 ** 2 + t2888 = -2090 * t2877 + 12852 * t2879 + (-31412 * t2880 + 32490 * t2882 - 11970 * t2884 + 130) * t2875 + t2887 = -1908 * t2874 + 10745 * t2878 + 13815 * t2882 + 2964 * t2884 + 53 + (-21280 - 4389 * t2880) * t2880 + t2876 = 5 * phi1 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.sqrt(0.51e2) * ((-t2887 + t2888) * np.exp((-1*1j) * (t2876 - 6 * phi2)) + (t2887 + t2888) * np.exp((-1*1j) * (t2876 + 6 * phi2))) * ((1 + t2875) ** (-0.1e1 / 0.2e1)) * ((1 - t2875) ** (-0.1e1 / 0.2e1)) + + if Bindx == 158: + t2901 = np.cos(phi) + t2900 = t2901 ** 2 + t2904 = t2900 ** 2 + t2903 = t2901 * t2900 + t2906 = t2903 ** 2 + t2908 = t2904 ** 2 + t2905 = t2901 * t2904 + t2910 = t2905 ** 2 + t2913 = 4 - 160 * t2900 + 1120 * t2904 - 3016 * t2906 + 3420 * t2908 - 1368 * t2910 + t2912 = 345 * t2903 - 874 * t2905 + (330 * t2906 + 855 * t2908 - 627 * t2910 - 29) * t2901 + t2902 = 2 * phi1 + tfunc[..., c] = (0.23e2 / 0.512e3) * ((-t2912 + t2913) * np.exp((-2*1j) * (t2902 - 3 * phi2)) + (t2912 + t2913) * np.exp((-2*1j) * (t2902 + 3 * phi2))) * np.sqrt(0.357e3) + + if Bindx == 159: + t2927 = np.cos(phi) + t2926 = t2927 ** 2 + t2928 = t2927 * t2926 + t2929 = t2926 ** 2 + t2930 = t2927 * t2929 + t2931 = t2928 ** 2 + t2933 = t2929 ** 2 + t2935 = t2930 ** 2 + t2939 = -1502 * t2928 + 6460 * t2930 + (-11900 * t2931 + 9918 * t2933 - 3078 * t2935 + 102) * t2927 + t2938 = -44 * t2926 + 13 * t2929 - 4917 * t2933 + 5244 * t2935 + 1 + (1584 - 1881 * t2931) * t2931 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.sqrt(0.595e3) * np.sqrt(0.2e1) * ((-t2938 + t2939) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t2938 + t2939) * np.exp((-3*1j) * (phi1 + 2 * phi2))) * ((1 + t2927) ** (-0.1e1 / 0.2e1)) * ((1 - t2927) ** (-0.1e1 / 0.2e1)) + + if Bindx == 160: + t2952 = np.cos(phi) + t2951 = t2952 ** 2 + t2954 = t2951 ** 2 + t2953 = t2952 * t2951 + t2956 = t2953 ** 2 + t2958 = t2954 ** 2 + t2955 = t2952 * t2954 + t2960 = t2955 ** 2 + t2963 = 6 - 276 * t2951 + 1940 * t2954 - 4672 * t2956 + 4598 * t2958 - 1596 * t2960 + t2962 = -459 * t2953 + 2218 * t2955 + (-4638 * t2956 + 4313 * t2958 - 1463 * t2960 + 29) * t2952 + tfunc[..., c] = (0.69e2 / 0.1024e4) * ((-t2962 + t2963) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t2962 + t2963) * np.exp((-2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.85e2) + + if Bindx == 161: + t2977 = np.cos(phi) + t2976 = t2977 ** 2 + t2978 = t2977 * t2976 + t2979 = t2976 ** 2 + t2980 = t2977 * t2979 + t2981 = t2978 ** 2 + t2983 = t2979 ** 2 + t2985 = t2980 ** 2 + t2989 = 470 * t2978 - 2028 * t2980 + (3564 * t2981 - 2774 * t2983 + 798 * t2985 - 30) * t2977 + t2988 = 240 * t2976 - 1925 * t2979 - 8319 * t2983 + 5624 * t2985 - 5 + (5848 - 1463 * t2981) * t2981 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.sqrt(0.221e3) * np.sqrt(0.2e1) * ((1 + t2977) ** (-0.1e1 / 0.2e1)) * ((1 - t2977) ** (-0.1e1 / 0.2e1)) * ((t2988 + t2989) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (-t2988 + t2989) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 162: + t2996 = np.sin(phi) + t2993 = t2996 ** 2 + t2994 = t2996 * t2993 + t2990 = np.cos(phi) + t2991 = t2990 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * t2990 * t2994 ** 2 * (15 + (-190 + 399 * t2991) * t2991) * np.sin((6 * phi2)) * np.sqrt(0.7293e4) * np.sqrt(0.2e1) + + if Bindx == 163: + t3010 = np.cos(phi) + t3009 = t3010 ** 2 + t3011 = t3010 * t3009 + t3012 = t3009 ** 2 + t3013 = t3010 * t3012 + t3014 = t3011 ** 2 + t3016 = t3012 ** 2 + t3018 = t3013 ** 2 + t3022 = -470 * t3011 + 2028 * t3013 + (-3564 * t3014 + 2774 * t3016 - 798 * t3018 + 30) * t3010 + t3021 = 240 * t3009 - 1925 * t3012 - 8319 * t3016 + 5624 * t3018 - 5 + (5848 - 1463 * t3014) * t3014 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.sqrt(0.221e3) * np.sqrt(0.2e1) * ((1 + t3010) ** (-0.1e1 / 0.2e1)) * ((1 - t3010) ** (-0.1e1 / 0.2e1)) * ((-t3021 + t3022) * np.exp((1j) * (phi1 - 6 * phi2)) + (t3021 + t3022) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 164: + t3035 = np.cos(phi) + t3034 = t3035 ** 2 + t3037 = t3034 ** 2 + t3036 = t3035 * t3034 + t3039 = t3036 ** 2 + t3041 = t3037 ** 2 + t3038 = t3035 * t3037 + t3043 = t3038 ** 2 + t3046 = 6 - 276 * t3034 + 1940 * t3037 - 4672 * t3039 + 4598 * t3041 - 1596 * t3043 + t3045 = -459 * t3036 + 2218 * t3038 + (-4638 * t3039 + 4313 * t3041 - 1463 * t3043 + 29) * t3035 + tfunc[..., c] = -(0.69e2 / 0.1024e4) * ((-t3045 + t3046) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t3045 + t3046) * np.exp((2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.85e2) + + if Bindx == 165: + t3060 = np.cos(phi) + t3059 = t3060 ** 2 + t3061 = t3060 * t3059 + t3062 = t3059 ** 2 + t3063 = t3060 * t3062 + t3064 = t3061 ** 2 + t3066 = t3062 ** 2 + t3068 = t3063 ** 2 + t3072 = -1502 * t3061 + 6460 * t3063 + (-11900 * t3064 + 9918 * t3066 - 3078 * t3068 + 102) * t3060 + t3071 = -44 * t3059 + 13 * t3062 - 4917 * t3066 + 5244 * t3068 + 1 + (1584 - 1881 * t3064) * t3064 + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.sqrt(0.595e3) * np.sqrt(0.2e1) * ((1 + t3060) ** (-0.1e1 / 0.2e1)) * ((1 - t3060) ** (-0.1e1 / 0.2e1)) * ((-t3071 + t3072) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t3071 + t3072) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 166: + t3085 = np.cos(phi) + t3084 = t3085 ** 2 + t3088 = t3084 ** 2 + t3087 = t3085 * t3084 + t3090 = t3087 ** 2 + t3092 = t3088 ** 2 + t3089 = t3085 * t3088 + t3094 = t3089 ** 2 + t3097 = -4 + 160 * t3084 - 1120 * t3088 + 3016 * t3090 - 3420 * t3092 + 1368 * t3094 + t3096 = 345 * t3087 - 874 * t3089 + (330 * t3090 + 855 * t3092 - 627 * t3094 - 29) * t3085 + t3086 = 2 * phi1 + tfunc[..., c] = (0.23e2 / 0.512e3) * ((t3096 + t3097) * np.exp((2*1j) * (t3086 - 3 * phi2)) + (-t3096 + t3097) * np.exp((2*1j) * (t3086 + 3 * phi2))) * np.sqrt(0.357e3) + + if Bindx == 167: + t3111 = np.cos(phi) + t3110 = t3111 ** 2 + t3113 = t3111 * t3110 + t3114 = t3110 ** 2 + t3115 = t3111 * t3114 + t3116 = t3113 ** 2 + t3118 = t3114 ** 2 + t3120 = t3115 ** 2 + t3124 = -2090 * t3113 + 12852 * t3115 + (-31412 * t3116 + 32490 * t3118 - 11970 * t3120 + 130) * t3111 + t3123 = -1908 * t3110 + 10745 * t3114 + 13815 * t3118 + 2964 * t3120 + 53 + (-21280 - 4389 * t3116) * t3116 + t3112 = 5 * phi1 + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.sqrt(0.51e2) * ((-t3123 + t3124) * np.exp((1j) * (t3112 - 6 * phi2)) + (t3123 + t3124) * np.exp((1j) * (t3112 + 6 * phi2))) * ((1 + t3111) ** (-0.1e1 / 0.2e1)) * ((1 - t3111) ** (-0.1e1 / 0.2e1)) + + if Bindx == 168: + t3137 = np.cos(phi) + t3136 = t3137 ** 2 + t3139 = t3136 ** 2 + t3138 = t3137 * t3136 + t3141 = t3138 ** 2 + t3143 = t3139 ** 2 + t3140 = t3137 * t3139 + t3145 = t3140 ** 2 + t3148 = -94 + 2820 * t3136 - 420 * t3139 - 40096 * t3141 + 80370 * t3143 - 43092 * t3145 + t3147 = 21805 * t3138 - 69846 * t3140 + (79650 * t3141 - 16815 * t3143 - 13167 * t3145 - 2139) * t3137 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.sqrt(0.2e1) * ((-t3147 + t3148) * np.exp((6*1j) * (phi1 - phi2)) + (t3147 + t3148) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 169: + t3161 = np.cos(phi) + t3160 = t3161 ** 2 + t3164 = t3160 ** 2 + t3165 = t3161 * t3164 + t3170 = t3165 ** 2 + t3172 = -1463 * t3161 * t3170 - 35 + t3168 = t3164 ** 2 + t3163 = t3161 * t3160 + t3166 = t3163 ** 2 + t3162 = 7 * phi1 + t3155 = t3161 * t3166 + t3153 = t3161 * t3168 + tfunc[..., c] = (0.69e2 / 0.2048e4*1j) * np.sqrt((1 - t3161)) * np.sqrt(0.5e1) * ((1 + t3161) ** (-0.1e1 / 0.2e1)) * ((-4123 * t3170 - 475 * t3153 + 10431 * t3168 - 5418 * t3155 - 9086 * t3166 + 7098 * t3165 + 3150 * t3164 - 3101 * t3163 - 407 * t3160 + 433 * t3161 - t3172) * np.exp((1j) * (t3162 - 6 * phi2)) + (-7049 * t3170 - 10697 * t3153 + 209 * t3168 + 16058 * t3155 + 12390 * t3166 - 3794 * t3165 - 7742 * t3164 - 1491 * t3163 + 1203 * t3160 + 363 * t3161 + t3172) * np.exp((1j) * (t3162 + 6 * phi2))) + + if Bindx == 170: + t3185 = np.cos(phi) + t3184 = t3185 ** 2 + t3188 = t3184 ** 2 + t3187 = t3185 * t3184 + t3190 = t3187 ** 2 + t3192 = t3188 ** 2 + t3189 = t3185 * t3188 + t3194 = t3189 ** 2 + t3197 = -8 + 128 * t3184 - 448 * t3188 + 336 * t3190 + 328 * t3192 - 336 * t3194 + t3196 = 63 * t3187 - 574 * t3189 + (958 * t3190 - 383 * t3192 - 77 * t3194 + 13) * t3185 + t3186 = 4 * phi1 + tfunc[..., c] = -(0.69e2 / 0.1024e4) * ((-t3196 + t3197) * np.exp((2*1j) * (t3186 - 3 * phi2)) + (t3196 + t3197) * np.exp((2*1j) * (t3186 + 3 * phi2))) * np.sqrt(0.95e2) + + if Bindx == 171: + t3209 = np.cos(phi) + t3208 = t3209 ** 2 + t3212 = t3208 ** 2 + t3213 = t3209 * t3212 + t3219 = -231 * t3213 ** 2 - 61 + t3216 = t3212 ** 2 + t3211 = t3209 * t3208 + t3214 = t3211 ** 2 + t3210 = 3 * phi1 + t3203 = t3209 * t3214 + t3201 = t3209 * t3216 + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * ((1 - t3209) ** (0.3e1 / 0.2e1)) * np.sqrt(0.57e2) * ((1 + t3209) ** (-0.1e1 / 0.2e1)) * ((-672 * t3201 - 23 * t3216 + 1688 * t3203 - 1058 * t3214 - 1352 * t3213 + 1322 * t3212 + 328 * t3211 - 533 * t3208 + 8 * t3209 - t3219) * np.exp((3*1j) * (t3210 - 2 * phi2)) + (-1596 * t3201 - 4513 * t3216 - 6368 * t3203 - 3766 * t3214 + 1288 * t3213 + 3374 * t3212 + 1792 * t3211 + 77 * t3208 - 236 * t3209 + t3219) * np.exp((3*1j) * (t3210 + 2 * phi2))) + + if Bindx == 172: + t3232 = np.cos(phi) + t3231 = t3232 ** 2 + t3235 = t3231 ** 2 + t3234 = t3232 * t3231 + t3237 = t3234 ** 2 + t3239 = t3235 ** 2 + t3236 = t3232 * t3235 + t3241 = t3236 ** 2 + t3244 = 6 + 12 * t3231 - 172 * t3235 + 224 * t3237 - 10 * t3239 - 60 * t3241 + t3243 = -95 * t3234 - 14 * t3236 + (202 * t3237 - 107 * t3239 - 11 * t3241 + 25) * t3232 + t3233 = 5 * phi1 + tfunc[..., c] = -(0.69e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.133e3) * ((-t3243 + t3244) * np.exp((2*1j) * (t3233 - 3 * phi2)) + (t3243 + t3244) * np.exp((2*1j) * (t3233 + 3 * phi2))) + + if Bindx == 173: + t3256 = np.cos(phi) + t3268 = 6 * t3256 + t3255 = t3256 ** 2 + t3258 = t3256 * t3255 + t3259 = t3255 ** 2 + t3260 = t3256 * t3259 + t3261 = t3258 ** 2 + t3263 = t3259 ** 2 + t3267 = 8 * t3256 * t3261 + t3263 * t3268 + 8 * t3258 - 28 * t3260 + t3268 + t3266 = -t3260 ** 2 - 13 * t3255 + 14 * t3259 + 14 * t3261 - 13 * t3263 - 1 + t3257 = 11 * phi1 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.sqrt(0.1463e4) * np.sqrt((1 - t3256)) * np.sqrt((1 + t3256)) * ((t3266 + t3267) * np.exp((1j) * (t3257 - 6 * phi2)) + (-t3266 + t3267) * np.exp((1j) * (t3257 + 6 * phi2))) + + if Bindx == 174: + t3273 = np.sin(phi) + t3269 = t3273 ** 2 + t3270 = t3273 * t3269 + t3271 = t3270 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-12*1j) * phi1) * np.sqrt(0.676039e6) * t3271 ** 2 + + if Bindx == 175: + t3286 = np.cos(phi) + t3285 = t3286 ** 2 + t3289 = t3285 ** 2 + t3292 = t3289 ** 2 + t3290 = t3286 * t3289 + t3294 = t3290 ** 2 + t3295 = t3286 * t3294 + t3298 = t3286 * t3295 - 12 * t3285 + 27 * t3289 - 27 * t3292 + 12 * t3294 - 1 + t3297 = -6 * t3295 + 2 * (18 * t3285 - 18) * t3290 + 2 * (t3285 - t3292 + 3) * t3286 + t3287 = 2 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.33649e5) * ((t3297 + t3298) * np.exp((-6*1j) * (t3287 - phi2)) + (-t3297 + t3298) * np.exp((-6*1j) * (t3287 + phi2))) + + if Bindx == 176: + t3312 = np.cos(phi) + t3325 = -12 * t3312 + t3311 = t3312 ** 2 + t3313 = t3312 * t3311 + t3314 = t3311 ** 2 + t3315 = t3312 * t3314 + t3316 = t3313 ** 2 + t3318 = t3314 ** 2 + t3320 = t3315 ** 2 + t3324 = t3320 * t3325 - 220 * t3313 - 792 * t3315 + t3325 + (-792 * t3316 - 220 * t3318) * t3312 + t3323 = 66 * t3311 + 495 * t3314 + 495 * t3318 + 66 * t3320 + 1 + (924 + t3316) * t3316 + tfunc[..., c] = (0.25e2 / 0.8192e4) * np.sqrt(0.2e1) * ((t3323 + t3324) * np.exp((-12*1j) * (phi1 - phi2)) + (t3323 - t3324) * np.exp((-12*1j) * (phi1 + phi2))) + + if Bindx == 177: + t3326 = np.cos(phi) + t3332 = -6 * t3326 + t3327 = t3326 ** 2 + t3328 = t3326 * t3327 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * t3326 * (t3332 + 1 + (-20 + t3328) * t3328 + (15 + (t3332 + 15) * t3327) * t3327) * ((1 + t3326) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4056234e7) * np.exp((-11*1j) * phi1) * ((1 - t3326) ** (-0.1e1 / 0.2e1)) + + if Bindx == 178: + t3344 = np.cos(phi) + t3343 = t3344 ** 2 + t3347 = t3343 ** 2 + t3348 = t3344 * t3347 + t3355 = 2 * t3348 ** 2 + t3354 = 1 + t3344 + t3351 = t3347 ** 2 + t3346 = t3344 * t3343 + t3349 = t3346 ** 2 + t3345 = 11 * phi1 + t3338 = t3344 * t3349 + t3336 = t3344 * t3351 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (t3354 ** (0.5e1 / 0.2e1)) * np.sqrt(0.100947e6) * ((1 - t3344) ** (-0.1e1 / 0.2e1)) * ((t3355 - 17 * t3336 + 63 * t3351 - 132 * t3338 + 168 * t3349 - 126 * t3348 + 42 * t3347 + 12 * t3346 - 18 * t3343 + 7 * t3344 - 1) * np.exp((-1*1j) * (t3345 - 6 * phi2)) + (t3355 + 5 * t3336 - 3 * t3351 - 16 * t3338 - 4 * t3349 + 18 * t3348 + 10 * t3347 - 8 * t3346 - 6 * t3343 + t3354) * np.exp((-1*1j) * (t3345 + 6 * phi2))) + + if Bindx == 179: + t3368 = np.cos(phi) + t3367 = t3368 ** 2 + t3371 = t3367 ** 2 + t3370 = t3368 * t3367 + t3373 = t3370 ** 2 + t3375 = t3371 ** 2 + t3372 = t3368 * t3371 + t3377 = t3372 ** 2 + t3380 = -1 - 55 * t3367 - 330 * t3371 - 462 * t3373 - 165 * t3375 - 11 * t3377 + t3379 = 165 * t3370 + 462 * t3372 + (330 * t3373 + 55 * t3375 + t3377 + 11) * t3368 + t3369 = 11 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.3e1) * np.sqrt((1 - t3368)) * np.sqrt((1 + t3368)) * ((t3379 + t3380) * np.exp((-1*1j) * (t3369 - 12 * phi2)) + (t3379 - t3380) * np.exp((-1*1j) * (t3369 + 12 * phi2))) + + if Bindx == 180: + t3386 = np.sin(phi) + t3382 = t3386 ** 2 + t3384 = t3386 * t3382 ** 2 + t3381 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-10*1j) * phi1) * np.sqrt(0.88179e5) * t3384 ** 2 * (23 * t3381 ** 2 - 1) + + if Bindx == 181: + t3400 = np.cos(phi) + t3399 = t3400 ** 2 + t3402 = t3400 * t3399 + t3403 = t3399 ** 2 + t3404 = t3400 * t3403 + t3405 = t3402 ** 2 + t3407 = t3403 ** 2 + t3409 = t3404 ** 2 + t3413 = 121 * t3402 - 366 * t3404 + (282 * t3405 + 85 * t3407 - 115 * t3409 - 7) * t3400 + t3412 = -50 * t3399 + 45 * t3403 - 441 * t3407 + 166 * t3409 + 5 + (252 + 23 * t3405) * t3405 + t3401 = 5 * phi1 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.4389e4) * ((t3412 + t3413) * np.exp((-2*1j) * (t3401 - 3 * phi2)) + (t3412 - t3413) * np.exp((-2*1j) * (t3401 + 3 * phi2))) + + if Bindx == 182: + t3426 = np.cos(phi) + t3425 = t3426 ** 2 + t3429 = t3425 ** 2 + t3432 = t3429 ** 2 + t3430 = t3426 * t3429 + t3434 = t3430 ** 2 + t3435 = t3426 * t3434 + t3438 = t3426 * t3435 - 44 * t3425 - 165 * t3429 + 165 * t3432 + 44 * t3434 - 1 + t3437 = -10 * t3435 + (-132 * t3425 + 132) * t3430 + (110 * t3425 - 110 * t3432 + 10) * t3426 + t3427 = 5 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.69e2) * ((t3437 + t3438) * np.exp((-2*1j) * (t3427 - 6 * phi2)) + (-t3437 + t3438) * np.exp((-2*1j) * (t3427 + 6 * phi2))) + + if Bindx == 183: + t3439 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (23 * t3439 ** 2 - 3) * t3439 * ((1 + t3439) ** (0.9e1 / 0.2e1)) * np.sqrt(0.646646e6) * np.exp((-9*1j) * phi1) * ((1 - t3439) ** (0.9e1 / 0.2e1)) + + if Bindx == 184: + t3452 = np.cos(phi) + t3451 = t3452 ** 2 + t3455 = t3451 ** 2 + t3456 = t3452 * t3455 + t3461 = t3456 ** 2 + t3463 = 506 * t3452 * t3461 + t3459 = t3455 ** 2 + t3454 = t3452 * t3451 + t3457 = t3454 ** 2 + t3453 = 3 * phi1 + t3446 = t3452 * t3457 + t3444 = t3452 * t3459 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * ((1 + t3452) ** (0.3e1 / 0.2e1)) * np.sqrt(0.133e3) * ((1 - t3452) ** (-0.1e1 / 0.2e1)) * ((t3463 - 3289 * t3461 + 8426 * t3444 - 9687 * t3459 + 1860 * t3446 + 7350 * t3457 - 7308 * t3456 + 1314 * t3455 + 1602 * t3454 - 845 * t3451 + 34 * t3452 + 37) * np.exp((-3*1j) * (t3453 - 2 * phi2)) + (t3463 + 1265 * t3461 - 682 * t3444 - 3777 * t3459 - 852 * t3446 + 4098 * t3457 + 1908 * t3456 - 1962 * t3455 - 1062 * t3454 + 413 * t3451 + 182 * t3452 - 37) * np.exp((-3*1j) * (t3453 + 2 * phi2))) + + if Bindx == 185: + t3476 = np.cos(phi) + t3475 = t3476 ** 2 + t3479 = t3475 ** 2 + t3478 = t3476 * t3475 + t3481 = t3478 ** 2 + t3483 = t3479 ** 2 + t3480 = t3476 * t3479 + t3485 = t3480 ** 2 + t3488 = 1 + 35 * t3475 + 90 * t3479 - 42 * t3481 - 75 * t3483 - 9 * t3485 + t3487 = -75 * t3478 - 42 * t3480 + (90 * t3481 + 35 * t3483 + t3485 - 9) * t3476 + t3477 = 3 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.253e3) * np.sqrt((1 - t3476)) * np.sqrt((1 + t3476)) * ((t3487 + t3488) * np.exp((-3*1j) * (t3477 - 4 * phi2)) + (t3487 - t3488) * np.exp((-3*1j) * (t3477 + 4 * phi2))) + + if Bindx == 186: + t3495 = np.sin(phi) + t3492 = t3495 ** 2 + t3493 = t3492 ** 2 + t3489 = np.cos(phi) + t3490 = t3489 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-8*1j) * phi1) * np.sqrt(0.277134e6) * t3493 ** 2 * (1 + (-42 + 161 * t3490) * t3490) + + if Bindx == 187: + t3509 = np.cos(phi) + t3508 = t3509 ** 2 + t3511 = t3509 * t3508 + t3512 = t3508 ** 2 + t3513 = t3509 * t3512 + t3514 = t3511 ** 2 + t3516 = t3512 ** 2 + t3518 = t3513 ** 2 + t3522 = 3332 * t3511 - 6888 * t3513 + (-408 * t3514 + 11452 * t3516 - 7084 * t3518 - 404) * t3509 + t3521 = 532 * t3508 - 5607 * t3512 - 20337 * t3516 + 5852 * t3518 - 19 + (17808 + 1771 * t3514) * t3514 + t3510 = 4 * phi1 + tfunc[..., c] = (0.25e2 / 0.2048e4) * ((t3521 + t3522) * np.exp((-2*1j) * (t3510 - 3 * phi2)) + (t3521 - t3522) * np.exp((-2*1j) * (t3510 + 3 * phi2))) * np.sqrt(0.57e2) + + if Bindx == 188: + t3536 = np.cos(phi) + t3535 = t3536 ** 2 + t3538 = t3536 * t3535 + t3539 = t3535 ** 2 + t3540 = t3536 * t3539 + t3541 = t3538 ** 2 + t3543 = t3539 ** 2 + t3545 = t3540 ** 2 + t3549 = -40 * t3538 + 48 * t3540 + 8 * (6 * t3541 - 5 * t3543 - t3545 - 1) * t3536 + t3548 = 26 * t3535 + 15 * t3539 + 15 * t3543 + 26 * t3545 + 1 + (-84 + t3541) * t3541 + t3537 = 2 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * ((t3548 + t3549) * np.exp((-4*1j) * (t3537 - 3 * phi2)) + (t3548 - t3549) * np.exp((-4*1j) * (t3537 + 3 * phi2))) * np.sqrt(0.5313e4) + + if Bindx == 189: + t3550 = np.cos(phi) + t3551 = t3550 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (5 + (-70 + 161 * t3551) * t3551) * t3550 * ((1 + t3550) ** (0.7e1 / 0.2e1)) * np.sqrt(0.277134e6) * np.exp((-7*1j) * phi1) * ((1 - t3550) ** (0.7e1 / 0.2e1)) + + if Bindx == 190: + t3566 = np.cos(phi) + t3565 = t3566 ** 2 + t3568 = t3566 * t3565 + t3571 = t3568 ** 2 + t3578 = 3542 * t3571 ** 2 + t3569 = t3565 ** 2 + t3570 = t3566 * t3569 + t3575 = t3570 ** 2 + t3573 = t3569 ** 2 + t3567 = 7 * phi1 + t3560 = t3566 * t3571 + t3558 = t3566 * t3573 + t3556 = t3566 * t3575 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.sqrt((1 + t3566)) * np.sqrt(0.57e2) * ((1 - t3566) ** (-0.1e1 / 0.2e1)) * ((t3578 - 15939 * t3556 + 20097 * t3575 + 10675 * t3558 - 42195 * t3573 + 19074 * t3560 + 21882 * t3571 - 19866 * t3570 - 1680 * t3569 + 5425 * t3568 - 651 * t3565 - 393 * t3566 + 29) * np.exp((-1*1j) * (t3567 - 6 * phi2)) + (t3578 + 8855 * t3556 - 4697 * t3575 - 26075 * t3558 - 5445 * t3573 + 28566 * t3560 + 12390 * t3571 - 14406 * t3570 - 7140 * t3569 + 3395 * t3568 + 1379 * t3565 - 335 * t3566 - 29) * np.exp((-1*1j) * (t3567 + 6 * phi2))) + + if Bindx == 191: + t3591 = np.cos(phi) + t3590 = t3591 ** 2 + t3594 = t3590 ** 2 + t3593 = t3591 * t3590 + t3596 = t3593 ** 2 + t3598 = t3594 ** 2 + t3595 = t3591 * t3594 + t3600 = t3595 ** 2 + t3603 = -1 - 19 * t3590 + 6 * t3594 + 42 * t3596 - 21 * t3598 - 7 * t3600 + t3602 = 21 * t3593 - 42 * t3595 + (-6 * t3596 + 19 * t3598 + t3600 + 7) * t3591 + t3592 = 7 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.5313e4) * np.sqrt((1 - t3591)) * np.sqrt((1 + t3591)) * ((t3602 + t3603) * np.exp((-1*1j) * (t3592 - 12 * phi2)) + (t3602 - t3603) * np.exp((-1*1j) * (t3592 + 12 * phi2))) + + if Bindx == 192: + t3611 = np.sin(phi) + t3608 = t3611 ** 2 + t3609 = t3611 * t3608 + t3604 = np.cos(phi) + t3605 = t3604 ** 2 + t3606 = t3605 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-6*1j) * phi1) * np.sqrt(0.2431e4) * t3609 ** 2 * (-1995 * t3606 - 5 + (3059 * t3606 + 285) * t3605) + + if Bindx == 193: + t3625 = np.cos(phi) + t3624 = t3625 ** 2 + t3627 = t3624 ** 2 + t3626 = t3625 * t3624 + t3629 = t3626 ** 2 + t3631 = t3627 ** 2 + t3628 = t3625 * t3627 + t3633 = t3628 ** 2 + t3637 = 11298 * t3624 - 75285 * t3627 - 158175 * t3631 + 8778 * t3633 - 269 + (180516 + 33649 * t3629) * t3629 + t3636 = -2695 * t3626 + 49266 * t3628 + (-178182 * t3629 + 232085 * t3631 - 100947 * t3633 - 39) * t3625 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t3636 + t3637) * np.exp((-6*1j) * (phi1 - phi2)) + (-t3636 + t3637) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 194: + t3650 = np.cos(phi) + t3649 = t3650 ** 2 + t3652 = t3649 ** 2 + t3655 = t3652 ** 2 + t3653 = t3650 * t3652 + t3657 = t3653 ** 2 + t3658 = t3650 * t3657 + t3661 = t3650 * t3658 - 12 * t3649 + 27 * t3652 - 27 * t3655 + 12 * t3657 - 1 + t3660 = -6 * t3658 + 2 * (18 * t3649 - 18) * t3653 + 2 * (t3649 - t3655 + 3) * t3650 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.33649e5) * ((t3660 + t3661) * np.exp((-6*1j) * (phi1 - 2 * phi2)) + (-t3660 + t3661) * np.exp((-6*1j) * (phi1 + 2 * phi2))) + + if Bindx == 195: + t3662 = np.cos(phi) + t3663 = t3662 ** 2 + t3664 = t3663 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * (-399 * t3664 - 5 + (437 * t3664 + 95) * t3663) * t3662 * ((1 + t3662) ** (0.5e1 / 0.2e1)) * np.sqrt(0.34034e5) * np.exp((-5*1j) * phi1) * ((1 - t3662) ** (0.5e1 / 0.2e1)) + + if Bindx == 196: + t3680 = np.cos(phi) + t3679 = t3680 ** 2 + t3683 = t3679 ** 2 + t3682 = t3680 * t3679 + t3685 = t3682 ** 2 + t3687 = t3683 ** 2 + t3684 = t3680 * t3683 + t3689 = t3684 ** 2 + t3691 = t3685 ** 2 + t3694 = -25 + 1200 * t3679 - 11465 * t3683 + 45592 * t3685 - 84987 * t3687 + 73720 * t3689 - 24035 * t3691 + t3693 = 5958 * t3682 - 24450 * t3684 + (37500 * t3685 - 14440 * t3687 - 13794 * t3689 + 9614 * t3691 - 388) * t3680 + t3681 = 5 * phi1 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.sqrt(0.7e1) * ((1 + t3680) ** (-0.1e1 / 0.2e1)) * ((1 - t3680) ** (-0.1e1 / 0.2e1)) * ((t3693 + t3694) * np.exp((-1*1j) * (t3681 - 6 * phi2)) + (t3693 - t3694) * np.exp((-1*1j) * (t3681 + 6 * phi2))) + + if Bindx == 197: + t3707 = np.cos(phi) + t3706 = t3707 ** 2 + t3710 = t3706 ** 2 + t3709 = t3707 * t3706 + t3712 = t3709 ** 2 + t3714 = t3710 ** 2 + t3711 = t3707 * t3710 + t3716 = t3711 ** 2 + t3719 = 1 + 7 * t3706 - 22 * t3710 + 14 * t3712 + 5 * t3714 - 5 * t3716 + t3718 = 5 * t3709 + 14 * t3711 + (-22 * t3712 + 7 * t3714 + t3716 - 5) * t3707 + t3708 = 5 * phi1 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.sqrt(0.4807e4) * np.sqrt((1 - t3707)) * np.sqrt((1 + t3707)) * ((t3718 + t3719) * np.exp((-1*1j) * (t3708 - 12 * phi2)) + (t3718 - t3719) * np.exp((-1*1j) * (t3708 + 12 * phi2))) + + if Bindx == 198: + t3727 = np.sin(phi) + t3725 = t3727 ** 2 + t3720 = np.cos(phi) + t3721 = t3720 ** 2 + t3722 = t3721 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-4*1j) * phi1) * np.sqrt(0.1001e4) * t3725 ** 2 * (-340 * t3721 + 5 + (-9044 * t3721 + 3230 + 7429 * t3722) * t3722) + + if Bindx == 199: + t3741 = np.cos(phi) + t3740 = t3741 ** 2 + t3743 = t3741 * t3740 + t3744 = t3740 ** 2 + t3745 = t3741 * t3744 + t3746 = t3743 ** 2 + t3748 = t3744 ** 2 + t3750 = t3745 ** 2 + t3754 = -2470 * t3743 + 12588 * t3745 + (-27436 * t3746 + 26790 * t3748 - 9614 * t3750 + 142) * t3741 + t3753 = 364 * t3740 - 2435 * t3744 + 2755 * t3748 - 9196 * t3750 - 7 + (3712 + 4807 * t3746) * t3746 + t3742 = 2 * phi1 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.119e3) * ((t3753 + t3754) * np.exp((-2*1j) * (t3742 - 3 * phi2)) + (t3753 - t3754) * np.exp((-2*1j) * (t3742 + 3 * phi2))) + + if Bindx == 200: + t3768 = np.cos(phi) + t3767 = t3768 ** 2 + t3769 = t3768 * t3767 + t3770 = t3767 ** 2 + t3771 = t3768 * t3770 + t3772 = t3769 ** 2 + t3774 = t3770 ** 2 + t3776 = t3771 ** 2 + t3780 = 12 * t3769 - 8 * t3771 + 4 * (-2 * t3772 + 3 * t3774 - t3776 - 1) * t3768 + t3779 = 2 * t3767 - 17 * t3770 - 17 * t3774 + 2 * t3776 + 1 + (28 + t3772) * t3772 + tfunc[..., c] = (0.75e2 / 0.8192e4) * np.sqrt(0.2e1) * np.sqrt(0.81719e5) * ((t3779 + t3780) * np.exp((-4*1j) * (phi1 - 3 * phi2)) + (t3779 - t3780) * np.exp((-4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 201: + t3781 = np.cos(phi) + t3782 = t3781 ** 2 + t3783 = t3782 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * (-1020 * t3782 + 45 + (-11628 * t3782 + 5814 + 7429 * t3783) * t3783) * t3781 * ((1 + t3781) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((-3*1j) * phi1) * ((1 - t3781) ** (0.3e1 / 0.2e1)) + + if Bindx == 202: + t3800 = np.cos(phi) + t3799 = t3800 ** 2 + t3802 = t3799 ** 2 + t3801 = t3800 * t3799 + t3804 = t3801 ** 2 + t3806 = t3802 ** 2 + t3803 = t3800 * t3802 + t3808 = t3803 ** 2 + t3810 = t3804 ** 2 + t3813 = -23 + 1288 * t3799 - 11855 * t3802 + 40952 * t3804 - 65797 * t3806 + 49856 * t3808 - 14421 * t3810 + t3812 = -362 * t3801 + 4222 * t3803 + (-18692 * t3804 + 35720 * t3806 - 30514 * t3808 + 9614 * t3810 + 12) * t3800 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.sqrt(0.119e3) * np.sqrt(0.2e1) * ((1 + t3800) ** (-0.1e1 / 0.2e1)) * ((1 - t3800) ** (-0.1e1 / 0.2e1)) * ((t3812 + t3813) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t3812 - t3813) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 203: + t3826 = np.cos(phi) + t3825 = t3826 ** 2 + t3828 = t3825 ** 2 + t3827 = t3826 * t3825 + t3830 = t3827 ** 2 + t3832 = t3828 ** 2 + t3829 = t3826 * t3828 + t3834 = t3829 ** 2 + t3837 = -1 + t3825 + 6 * t3828 - 14 * t3830 + 11 * t3832 - 3 * t3834 + t3836 = -11 * t3827 + 14 * t3829 + (-6 * t3830 - t3832 + t3834 + 3) * t3826 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.81719e5) * np.sqrt((1 - t3826)) * np.sqrt((1 + t3826)) * ((t3836 + t3837) * np.exp((-3*1j) * (phi1 - 4 * phi2)) + (t3836 - t3837) * np.exp((-3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 204: + t3839 = np.cos(phi) + t3840 = t3839 ** 2 + t3841 = t3840 ** 2 + t3843 = t3841 ** 2 + t3838 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * phi1) * np.sqrt(0.6006e4) * t3838 ** 2 * (-2550 * t3841 - 14535 * t3843 - 3 + (9690 * t3841 + 7429 * t3843 + 225) * t3840) + + if Bindx == 205: + t3858 = np.cos(phi) + t3857 = t3858 ** 2 + t3859 = t3858 * t3857 + t3860 = t3857 ** 2 + t3861 = t3858 * t3860 + t3862 = t3859 ** 2 + t3864 = t3860 ** 2 + t3866 = t3861 ** 2 + t3870 = -1595 * t3859 + 8122 * t3861 + (-16606 * t3862 + 14801 * t3864 - 4807 * t3866 + 85) * t3858 + t3869 = -290 * t3857 + 2845 * t3860 + 18791 * t3864 - 15466 * t3866 + 5 + (-10692 + 4807 * t3862) * t3862 + tfunc[..., c] = (0.25e2 / 0.1024e4) * ((t3869 + t3870) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t3869 - t3870) * np.exp((-2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.357e3) + + if Bindx == 206: + t3883 = np.cos(phi) + t3882 = t3883 ** 2 + t3885 = t3882 ** 2 + t3895 = 5 * t3885 ** 2 + t3886 = t3883 * t3885 + t3890 = t3886 ** 2 + t3891 = t3883 * t3890 + t3894 = t3883 * t3891 + 4 * t3882 - 5 * t3885 - 4 * t3890 + t3895 - 1 + t3893 = 2 * t3883 * t3895 + 2 * t3883 + 20 * t3886 - 2 * t3891 + 2 * (-5 * t3883 - 10 * t3886) * t3882 + tfunc[..., c] = (0.25e2 / 0.2048e4) * ((t3893 + t3894) * np.exp((-2*1j) * (phi1 - 6 * phi2)) + (-t3893 + t3894) * np.exp((-2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.245157e6) + + if Bindx == 207: + t3896 = np.cos(phi) + t3897 = t3896 ** 2 + t3898 = t3896 * t3897 + t3899 = t3897 ** 2 + t3900 = t3896 * t3899 + t3907 = 106590 * t3898 ** 2 - 124355 * t3899 ** 2 + 52003 * t3900 ** 2 - 231 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.39e2) * np.sqrt((1 + t3896)) * t3896 * (t3907 * t3896 - 5775 * t3897 + 5775 * t3898 + 39270 * t3899 - 39270 * t3900 - t3907) * ((1 - t3896) ** (-0.1e1 / 0.2e1)) + + if Bindx == 208: + t3922 = np.cos(phi) + t3921 = t3922 ** 2 + t3924 = t3921 ** 2 + t3923 = t3922 * t3921 + t3926 = t3923 ** 2 + t3928 = t3924 ** 2 + t3925 = t3922 * t3924 + t3930 = t3925 ** 2 + t3932 = t3926 ** 2 + t3935 = -5 + 300 * t3921 - 2865 * t3924 + 9904 * t3926 - 15447 * t3928 + 11172 * t3930 - 3059 * t3932 + t3934 = -2010 * t3923 + 11814 * t3925 + (-30500 * t3926 + 39216 * t3928 - 24738 * t3930 + 6118 * t3932 + 100) * t3922 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.sqrt(0.561e3) * np.sqrt(0.2e1) * ((1 + t3922) ** (-0.1e1 / 0.2e1)) * ((1 - t3922) ** (-0.1e1 / 0.2e1)) * ((t3934 + t3935) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t3934 - t3935) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 209: + t3948 = np.cos(phi) + t3947 = t3948 ** 2 + t3949 = t3948 * t3947 + t3950 = t3947 ** 2 + t3951 = t3948 * t3950 + t3960 = 10 * t3949 ** 2 - 5 * t3950 ** 2 + t3951 ** 2 - 1 + t3959 = -5 * t3947 + 10 * t3950 - t3960 + t3958 = t3960 * t3948 + 5 * t3949 - 10 * t3951 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.156009e6) * np.sqrt((1 - t3948)) * np.sqrt((1 + t3948)) * ((t3958 + t3959) * np.exp((-1*1j) * (phi1 - 12 * phi2)) + (t3958 - t3959) * np.exp((-1*1j) * (phi1 + 12 * phi2))) + + if Bindx == 210: + t3961 = np.cos(phi) + t3962 = t3961 ** 2 + t3963 = t3962 ** 2 + t3965 = t3963 ** 2 + t3964 = t3962 * t3963 + tfunc[..., c] = 0.51962625e8 / 0.1024e4 * t3965 + 0.5630625e7 / 0.1024e4 * t3963 + 0.5775e4 / 0.1024e4 + (-0.6381375e7 / 0.256e3 + 0.16900975e8 / 0.1024e4 * t3964) * t3964 + (-0.24249225e8 / 0.512e3 * t3965 - 0.225225e6 / 0.512e3) * t3962 + + if Bindx == 211: + t3968 = np.cos(phi) + t3969 = t3968 ** 2 + t3970 = t3969 ** 2 + t3972 = t3970 ** 2 + t3971 = t3969 * t3970 + tfunc[..., c] = 0.25e2 / 0.1024e4 * np.sqrt(0.2431e4) * np.sqrt(0.2e1) * (2865 * t3970 + 15447 * t3972 + 5 + (-9904 + 3059 * t3971) * t3971 + (-11172 * t3972 - 300) * t3969) * np.cos((6 * phi2)) + + if Bindx == 212: + t3975 = np.cos(phi) + t3976 = t3975 ** 2 + t3982 = -6 * t3976 + t3977 = t3976 ** 2 + t3978 = t3976 * t3977 + tfunc[..., c] = 0.25e2 / 0.2048e4 * np.sqrt(0.676039e6) * np.sqrt(0.2e1) * np.cos((12 * phi2)) * (t3982 + 1 + (-20 + t3978) * t3978 + (15 + (t3982 + 15) * t3977) * t3977) + + if Bindx == 213: + t3983 = np.cos(phi) + t3984 = t3983 ** 2 + t3985 = t3984 ** 2 + t3987 = t3985 ** 2 + tfunc[..., c] = (-0.25e2 / 0.512e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.39e2) * np.sqrt((1 - t3983)) * np.sqrt((1 + t3983)) * t3983 * (-39270 * t3985 - 124355 * t3987 - 231 + (106590 * t3985 + 52003 * t3987 + 5775) * t3984) + + if Bindx == 214: + t4003 = np.cos(phi) + t4002 = t4003 ** 2 + t4005 = t4002 ** 2 + t4004 = t4003 * t4002 + t4007 = t4004 ** 2 + t4009 = t4005 ** 2 + t4006 = t4003 * t4005 + t4011 = t4006 ** 2 + t4013 = t4007 ** 2 + t4016 = -5 + 300 * t4002 - 2865 * t4005 + 9904 * t4007 - 15447 * t4009 + 11172 * t4011 - 3059 * t4013 + t4015 = -2010 * t4004 + 11814 * t4006 + (-30500 * t4007 + 39216 * t4009 - 24738 * t4011 + 6118 * t4013 + 100) * t4003 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.sqrt(0.561e3) * np.sqrt(0.2e1) * ((1 + t4003) ** (-0.1e1 / 0.2e1)) * ((1 - t4003) ** (-0.1e1 / 0.2e1)) * ((t4015 + t4016) * np.exp((1j) * (phi1 - 6 * phi2)) + (t4015 - t4016) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 215: + t4029 = np.cos(phi) + t4028 = t4029 ** 2 + t4030 = t4029 * t4028 + t4031 = t4028 ** 2 + t4032 = t4029 * t4031 + t4041 = 10 * t4030 ** 2 - 5 * t4031 ** 2 + t4032 ** 2 - 1 + t4040 = -5 * t4028 + 10 * t4031 - t4041 + t4039 = t4041 * t4029 + 5 * t4030 - 10 * t4032 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.156009e6) * np.sqrt((1 - t4029)) * np.sqrt((1 + t4029)) * ((t4039 + t4040) * np.exp((1j) * (phi1 - 12 * phi2)) + (t4039 - t4040) * np.exp((1j) * (phi1 + 12 * phi2))) + + if Bindx == 216: + t4043 = np.cos(phi) + t4044 = t4043 ** 2 + t4045 = t4044 ** 2 + t4047 = t4045 ** 2 + t4042 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * phi1) * np.sqrt(0.6006e4) * t4042 ** 2 * (-2550 * t4045 - 14535 * t4047 - 3 + (9690 * t4045 + 7429 * t4047 + 225) * t4044) + + if Bindx == 217: + t4062 = np.cos(phi) + t4061 = t4062 ** 2 + t4063 = t4062 * t4061 + t4064 = t4061 ** 2 + t4065 = t4062 * t4064 + t4066 = t4063 ** 2 + t4068 = t4064 ** 2 + t4070 = t4065 ** 2 + t4074 = -1595 * t4063 + 8122 * t4065 + (-16606 * t4066 + 14801 * t4068 - 4807 * t4070 + 85) * t4062 + t4073 = -290 * t4061 + 2845 * t4064 + 18791 * t4068 - 15466 * t4070 + 5 + (-10692 + 4807 * t4066) * t4066 + tfunc[..., c] = (0.25e2 / 0.1024e4) * ((t4073 + t4074) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t4073 - t4074) * np.exp((2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.357e3) + + if Bindx == 218: + t4087 = np.cos(phi) + t4086 = t4087 ** 2 + t4089 = t4086 ** 2 + t4099 = 5 * t4089 ** 2 + t4090 = t4087 * t4089 + t4094 = t4090 ** 2 + t4095 = t4087 * t4094 + t4098 = t4087 * t4095 + 4 * t4086 - 5 * t4089 - 4 * t4094 + t4099 - 1 + t4097 = 2 * t4087 * t4099 + 2 * t4087 + 20 * t4090 - 2 * t4095 + 2 * (-5 * t4087 - 10 * t4090) * t4086 + tfunc[..., c] = (0.25e2 / 0.2048e4) * ((t4097 + t4098) * np.exp((2*1j) * (phi1 - 6 * phi2)) + (-t4097 + t4098) * np.exp((2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.245157e6) + + if Bindx == 219: + t4100 = np.cos(phi) + t4101 = t4100 ** 2 + t4102 = t4101 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.1001e4) * ((1 - t4100) ** (0.3e1 / 0.2e1)) * ((1 + t4100) ** (0.3e1 / 0.2e1)) * t4100 * (-1020 * t4101 + 45 + (-11628 * t4101 + 5814 + 7429 * t4102) * t4102) + + if Bindx == 220: + t4119 = np.cos(phi) + t4118 = t4119 ** 2 + t4121 = t4118 ** 2 + t4120 = t4119 * t4118 + t4123 = t4120 ** 2 + t4125 = t4121 ** 2 + t4122 = t4119 * t4121 + t4127 = t4122 ** 2 + t4129 = t4123 ** 2 + t4132 = -23 + 1288 * t4118 - 11855 * t4121 + 40952 * t4123 - 65797 * t4125 + 49856 * t4127 - 14421 * t4129 + t4131 = -362 * t4120 + 4222 * t4122 + (-18692 * t4123 + 35720 * t4125 - 30514 * t4127 + 9614 * t4129 + 12) * t4119 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.sqrt(0.119e3) * np.sqrt(0.2e1) * ((1 + t4119) ** (-0.1e1 / 0.2e1)) * ((1 - t4119) ** (-0.1e1 / 0.2e1)) * ((t4131 + t4132) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t4131 - t4132) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 221: + t4145 = np.cos(phi) + t4144 = t4145 ** 2 + t4147 = t4144 ** 2 + t4146 = t4145 * t4144 + t4149 = t4146 ** 2 + t4151 = t4147 ** 2 + t4148 = t4145 * t4147 + t4153 = t4148 ** 2 + t4156 = -1 + t4144 + 6 * t4147 - 14 * t4149 + 11 * t4151 - 3 * t4153 + t4155 = -11 * t4146 + 14 * t4148 + (-6 * t4149 - t4151 + t4153 + 3) * t4145 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.81719e5) * np.sqrt((1 - t4145)) * np.sqrt((1 + t4145)) * ((t4155 + t4156) * np.exp((3*1j) * (phi1 - 4 * phi2)) + (t4155 - t4156) * np.exp((3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 222: + t4164 = np.sin(phi) + t4162 = t4164 ** 2 + t4157 = np.cos(phi) + t4158 = t4157 ** 2 + t4159 = t4158 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((4*1j) * phi1) * np.sqrt(0.1001e4) * t4162 ** 2 * (-340 * t4158 + 5 + (-9044 * t4158 + 3230 + 7429 * t4159) * t4159) + + if Bindx == 223: + t4178 = np.cos(phi) + t4177 = t4178 ** 2 + t4180 = t4178 * t4177 + t4181 = t4177 ** 2 + t4182 = t4178 * t4181 + t4183 = t4180 ** 2 + t4185 = t4181 ** 2 + t4187 = t4182 ** 2 + t4191 = -2470 * t4180 + 12588 * t4182 + (-27436 * t4183 + 26790 * t4185 - 9614 * t4187 + 142) * t4178 + t4190 = 364 * t4177 - 2435 * t4181 + 2755 * t4185 - 9196 * t4187 - 7 + (3712 + 4807 * t4183) * t4183 + t4179 = 2 * phi1 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.119e3) * ((t4190 + t4191) * np.exp((2*1j) * (t4179 - 3 * phi2)) + (t4190 - t4191) * np.exp((2*1j) * (t4179 + 3 * phi2))) + + if Bindx == 224: + t4205 = np.cos(phi) + t4204 = t4205 ** 2 + t4206 = t4205 * t4204 + t4207 = t4204 ** 2 + t4208 = t4205 * t4207 + t4209 = t4206 ** 2 + t4211 = t4207 ** 2 + t4213 = t4208 ** 2 + t4217 = 12 * t4206 - 8 * t4208 + 4 * (-2 * t4209 + 3 * t4211 - t4213 - 1) * t4205 + t4216 = 2 * t4204 - 17 * t4207 - 17 * t4211 + 2 * t4213 + 1 + (28 + t4209) * t4209 + tfunc[..., c] = (0.75e2 / 0.8192e4) * np.sqrt(0.2e1) * np.sqrt(0.81719e5) * ((t4216 + t4217) * np.exp((4*1j) * (phi1 - 3 * phi2)) + (t4216 - t4217) * np.exp((4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 225: + t4218 = np.cos(phi) + t4219 = t4218 ** 2 + t4220 = t4219 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.34034e5) * ((1 - t4218) ** (0.5e1 / 0.2e1)) * ((1 + t4218) ** (0.5e1 / 0.2e1)) * t4218 * (-399 * t4220 - 5 + (437 * t4220 + 95) * t4219) + + if Bindx == 226: + t4236 = np.cos(phi) + t4235 = t4236 ** 2 + t4239 = t4235 ** 2 + t4238 = t4236 * t4235 + t4241 = t4238 ** 2 + t4243 = t4239 ** 2 + t4240 = t4236 * t4239 + t4245 = t4240 ** 2 + t4247 = t4241 ** 2 + t4250 = -25 + 1200 * t4235 - 11465 * t4239 + 45592 * t4241 - 84987 * t4243 + 73720 * t4245 - 24035 * t4247 + t4249 = 5958 * t4238 - 24450 * t4240 + (37500 * t4241 - 14440 * t4243 - 13794 * t4245 + 9614 * t4247 - 388) * t4236 + t4237 = 5 * phi1 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.sqrt(0.7e1) * ((1 + t4236) ** (-0.1e1 / 0.2e1)) * ((1 - t4236) ** (-0.1e1 / 0.2e1)) * ((t4249 + t4250) * np.exp((1j) * (t4237 - 6 * phi2)) + (t4249 - t4250) * np.exp((1j) * (t4237 + 6 * phi2))) + + if Bindx == 227: + t4263 = np.cos(phi) + t4262 = t4263 ** 2 + t4266 = t4262 ** 2 + t4265 = t4263 * t4262 + t4268 = t4265 ** 2 + t4270 = t4266 ** 2 + t4267 = t4263 * t4266 + t4272 = t4267 ** 2 + t4275 = 1 + 7 * t4262 - 22 * t4266 + 14 * t4268 + 5 * t4270 - 5 * t4272 + t4274 = 5 * t4265 + 14 * t4267 + (-22 * t4268 + 7 * t4270 + t4272 - 5) * t4263 + t4264 = 5 * phi1 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.sqrt(0.4807e4) * np.sqrt((1 - t4263)) * np.sqrt((1 + t4263)) * ((t4274 + t4275) * np.exp((1j) * (t4264 - 12 * phi2)) + (t4274 - t4275) * np.exp((1j) * (t4264 + 12 * phi2))) + + if Bindx == 228: + t4283 = np.sin(phi) + t4280 = t4283 ** 2 + t4281 = t4283 * t4280 + t4276 = np.cos(phi) + t4277 = t4276 ** 2 + t4278 = t4277 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((6*1j) * phi1) * np.sqrt(0.2431e4) * t4281 ** 2 * (-1995 * t4278 - 5 + (3059 * t4278 + 285) * t4277) + + if Bindx == 229: + t4297 = np.cos(phi) + t4296 = t4297 ** 2 + t4299 = t4296 ** 2 + t4298 = t4297 * t4296 + t4301 = t4298 ** 2 + t4303 = t4299 ** 2 + t4300 = t4297 * t4299 + t4305 = t4300 ** 2 + t4309 = 11298 * t4296 - 75285 * t4299 - 158175 * t4303 + 8778 * t4305 - 269 + (180516 + 33649 * t4301) * t4301 + t4308 = -2695 * t4298 + 49266 * t4300 + (-178182 * t4301 + 232085 * t4303 - 100947 * t4305 - 39) * t4297 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t4308 + t4309) * np.exp((6*1j) * (phi1 - phi2)) + (-t4308 + t4309) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 230: + t4322 = np.cos(phi) + t4321 = t4322 ** 2 + t4324 = t4321 ** 2 + t4327 = t4324 ** 2 + t4325 = t4322 * t4324 + t4329 = t4325 ** 2 + t4330 = t4322 * t4329 + t4333 = t4322 * t4330 - 12 * t4321 + 27 * t4324 - 27 * t4327 + 12 * t4329 - 1 + t4332 = -6 * t4330 + 2 * (18 * t4321 - 18) * t4325 + 2 * (t4321 - t4327 + 3) * t4322 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.33649e5) * ((t4332 + t4333) * np.exp((6*1j) * (phi1 - 2 * phi2)) + (-t4332 + t4333) * np.exp((6*1j) * (phi1 + 2 * phi2))) + + if Bindx == 231: + t4334 = np.cos(phi) + t4335 = t4334 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.277134e6) * ((1 - t4334) ** (0.7e1 / 0.2e1)) * ((1 + t4334) ** (0.7e1 / 0.2e1)) * t4334 * (5 + (-70 + 161 * t4335) * t4335) + + if Bindx == 232: + t4350 = np.cos(phi) + t4349 = t4350 ** 2 + t4352 = t4350 * t4349 + t4355 = t4352 ** 2 + t4362 = 3542 * t4355 ** 2 + t4353 = t4349 ** 2 + t4354 = t4350 * t4353 + t4359 = t4354 ** 2 + t4357 = t4353 ** 2 + t4351 = 7 * phi1 + t4344 = t4350 * t4355 + t4342 = t4350 * t4357 + t4340 = t4350 * t4359 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt((1 - t4350)) * np.sqrt(0.57e2) * ((1 + t4350) ** (-0.1e1 / 0.2e1)) * ((t4362 - 8855 * t4340 - 4697 * t4359 + 26075 * t4342 - 5445 * t4357 - 28566 * t4344 + 12390 * t4355 + 14406 * t4354 - 7140 * t4353 - 3395 * t4352 + 1379 * t4349 + 335 * t4350 - 29) * np.exp((1j) * (t4351 - 6 * phi2)) + (t4362 + 15939 * t4340 + 20097 * t4359 - 10675 * t4342 - 42195 * t4357 - 19074 * t4344 + 21882 * t4355 + 19866 * t4354 - 1680 * t4353 - 5425 * t4352 - 651 * t4349 + 393 * t4350 + 29) * np.exp((1j) * (t4351 + 6 * phi2))) + + if Bindx == 233: + t4375 = np.cos(phi) + t4374 = t4375 ** 2 + t4378 = t4374 ** 2 + t4377 = t4375 * t4374 + t4380 = t4377 ** 2 + t4382 = t4378 ** 2 + t4379 = t4375 * t4378 + t4384 = t4379 ** 2 + t4387 = -1 - 19 * t4374 + 6 * t4378 + 42 * t4380 - 21 * t4382 - 7 * t4384 + t4386 = 21 * t4377 - 42 * t4379 + (-6 * t4380 + 19 * t4382 + t4384 + 7) * t4375 + t4376 = 7 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.5313e4) * np.sqrt((1 - t4375)) * np.sqrt((1 + t4375)) * ((t4386 + t4387) * np.exp((1j) * (t4376 - 12 * phi2)) + (t4386 - t4387) * np.exp((1j) * (t4376 + 12 * phi2))) + + if Bindx == 234: + t4394 = np.sin(phi) + t4391 = t4394 ** 2 + t4392 = t4391 ** 2 + t4388 = np.cos(phi) + t4389 = t4388 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((8*1j) * phi1) * np.sqrt(0.277134e6) * t4392 ** 2 * (1 + (-42 + 161 * t4389) * t4389) + + if Bindx == 235: + t4408 = np.cos(phi) + t4407 = t4408 ** 2 + t4410 = t4408 * t4407 + t4411 = t4407 ** 2 + t4412 = t4408 * t4411 + t4413 = t4410 ** 2 + t4415 = t4411 ** 2 + t4417 = t4412 ** 2 + t4421 = 3332 * t4410 - 6888 * t4412 + (-408 * t4413 + 11452 * t4415 - 7084 * t4417 - 404) * t4408 + t4420 = 532 * t4407 - 5607 * t4411 - 20337 * t4415 + 5852 * t4417 - 19 + (17808 + 1771 * t4413) * t4413 + t4409 = 4 * phi1 + tfunc[..., c] = (0.25e2 / 0.2048e4) * ((t4420 + t4421) * np.exp((2*1j) * (t4409 - 3 * phi2)) + (t4420 - t4421) * np.exp((2*1j) * (t4409 + 3 * phi2))) * np.sqrt(0.57e2) + + if Bindx == 236: + t4435 = np.cos(phi) + t4434 = t4435 ** 2 + t4437 = t4435 * t4434 + t4438 = t4434 ** 2 + t4439 = t4435 * t4438 + t4440 = t4437 ** 2 + t4442 = t4438 ** 2 + t4444 = t4439 ** 2 + t4448 = -40 * t4437 + 48 * t4439 + 8 * (6 * t4440 - 5 * t4442 - t4444 - 1) * t4435 + t4447 = 26 * t4434 + 15 * t4438 + 15 * t4442 + 26 * t4444 + 1 + (-84 + t4440) * t4440 + t4436 = 2 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * ((t4447 + t4448) * np.exp((4*1j) * (t4436 - 3 * phi2)) + (t4447 - t4448) * np.exp((4*1j) * (t4436 + 3 * phi2))) * np.sqrt(0.5313e4) + + if Bindx == 237: + t4449 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.646646e6) * ((1 - t4449) ** (0.9e1 / 0.2e1)) * ((1 + t4449) ** (0.9e1 / 0.2e1)) * t4449 * (23 * t4449 ** 2 - 3) + + if Bindx == 238: + t4462 = np.cos(phi) + t4461 = t4462 ** 2 + t4465 = t4461 ** 2 + t4466 = t4462 * t4465 + t4471 = t4466 ** 2 + t4473 = 506 * t4462 * t4471 + t4469 = t4465 ** 2 + t4464 = t4462 * t4461 + t4467 = t4464 ** 2 + t4463 = 3 * phi1 + t4456 = t4462 * t4467 + t4454 = t4462 * t4469 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * ((1 - t4462) ** (0.3e1 / 0.2e1)) * np.sqrt(0.133e3) * ((1 + t4462) ** (-0.1e1 / 0.2e1)) * ((t4473 - 1265 * t4471 - 682 * t4454 + 3777 * t4469 - 852 * t4456 - 4098 * t4467 + 1908 * t4466 + 1962 * t4465 - 1062 * t4464 - 413 * t4461 + 182 * t4462 + 37) * np.exp((3*1j) * (t4463 - 2 * phi2)) + (t4473 + 3289 * t4471 + 8426 * t4454 + 9687 * t4469 + 1860 * t4456 - 7350 * t4467 - 7308 * t4466 - 1314 * t4465 + 1602 * t4464 + 845 * t4461 + 34 * t4462 - 37) * np.exp((3*1j) * (t4463 + 2 * phi2))) + + if Bindx == 239: + t4486 = np.cos(phi) + t4485 = t4486 ** 2 + t4489 = t4485 ** 2 + t4488 = t4486 * t4485 + t4491 = t4488 ** 2 + t4493 = t4489 ** 2 + t4490 = t4486 * t4489 + t4495 = t4490 ** 2 + t4498 = 1 + 35 * t4485 + 90 * t4489 - 42 * t4491 - 75 * t4493 - 9 * t4495 + t4497 = -75 * t4488 - 42 * t4490 + (90 * t4491 + 35 * t4493 + t4495 - 9) * t4486 + t4487 = 3 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.253e3) * np.sqrt((1 - t4486)) * np.sqrt((1 + t4486)) * ((t4497 + t4498) * np.exp((3*1j) * (t4487 - 4 * phi2)) + (t4497 - t4498) * np.exp((3*1j) * (t4487 + 4 * phi2))) + + if Bindx == 240: + t4504 = np.sin(phi) + t4500 = t4504 ** 2 + t4502 = t4504 * t4500 ** 2 + t4499 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((10*1j) * phi1) * np.sqrt(0.88179e5) * t4502 ** 2 * (23 * t4499 ** 2 - 1) + + if Bindx == 241: + t4518 = np.cos(phi) + t4517 = t4518 ** 2 + t4520 = t4518 * t4517 + t4521 = t4517 ** 2 + t4522 = t4518 * t4521 + t4523 = t4520 ** 2 + t4525 = t4521 ** 2 + t4527 = t4522 ** 2 + t4531 = 121 * t4520 - 366 * t4522 + (282 * t4523 + 85 * t4525 - 115 * t4527 - 7) * t4518 + t4530 = -50 * t4517 + 45 * t4521 - 441 * t4525 + 166 * t4527 + 5 + (252 + 23 * t4523) * t4523 + t4519 = 5 * phi1 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.4389e4) * ((t4530 + t4531) * np.exp((2*1j) * (t4519 - 3 * phi2)) + (t4530 - t4531) * np.exp((2*1j) * (t4519 + 3 * phi2))) + + if Bindx == 242: + t4544 = np.cos(phi) + t4543 = t4544 ** 2 + t4547 = t4543 ** 2 + t4550 = t4547 ** 2 + t4548 = t4544 * t4547 + t4552 = t4548 ** 2 + t4553 = t4544 * t4552 + t4556 = t4544 * t4553 - 44 * t4543 - 165 * t4547 + 165 * t4550 + 44 * t4552 - 1 + t4555 = -10 * t4553 + (-132 * t4543 + 132) * t4548 + (110 * t4543 - 110 * t4550 + 10) * t4544 + t4545 = 5 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.69e2) * ((t4555 + t4556) * np.exp((2*1j) * (t4545 - 6 * phi2)) + (-t4555 + t4556) * np.exp((2*1j) * (t4545 + 6 * phi2))) + + if Bindx == 243: + t4557 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.4056234e7) * ((1 - t4557) ** (0.11e2 / 0.2e1)) * ((1 + t4557) ** (0.11e2 / 0.2e1)) * t4557 + + if Bindx == 244: + t4569 = np.cos(phi) + t4568 = t4569 ** 2 + t4572 = t4568 ** 2 + t4573 = t4569 * t4572 + t4580 = 2 * t4573 ** 2 + t4579 = 1 - t4569 + t4576 = t4572 ** 2 + t4571 = t4569 * t4568 + t4574 = t4571 ** 2 + t4570 = 11 * phi1 + t4563 = t4569 * t4574 + t4561 = t4569 * t4576 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (t4579 ** (0.5e1 / 0.2e1)) * np.sqrt(0.100947e6) * ((1 + t4569) ** (-0.1e1 / 0.2e1)) * ((t4580 - 5 * t4561 - 3 * t4576 + 16 * t4563 - 4 * t4574 - 18 * t4573 + 10 * t4572 + 8 * t4571 - 6 * t4568 + t4579) * np.exp((1j) * (t4570 - 6 * phi2)) + (t4580 + 17 * t4561 + 63 * t4576 + 132 * t4563 + 168 * t4574 + 126 * t4573 + 42 * t4572 - 12 * t4571 - 18 * t4568 - 7 * t4569 - 1) * np.exp((1j) * (t4570 + 6 * phi2))) + + if Bindx == 245: + t4593 = np.cos(phi) + t4592 = t4593 ** 2 + t4596 = t4592 ** 2 + t4595 = t4593 * t4592 + t4598 = t4595 ** 2 + t4600 = t4596 ** 2 + t4597 = t4593 * t4596 + t4602 = t4597 ** 2 + t4605 = -1 - 55 * t4592 - 330 * t4596 - 462 * t4598 - 165 * t4600 - 11 * t4602 + t4604 = 165 * t4595 + 462 * t4597 + (330 * t4598 + 55 * t4600 + t4602 + 11) * t4593 + t4594 = 11 * phi1 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.sqrt(0.3e1) * np.sqrt((1 - t4593)) * np.sqrt((1 + t4593)) * ((t4604 + t4605) * np.exp((1j) * (t4594 - 12 * phi2)) + (t4604 - t4605) * np.exp((1j) * (t4594 + 12 * phi2))) + + if Bindx == 246: + t4610 = np.sin(phi) + t4606 = t4610 ** 2 + t4607 = t4610 * t4606 + t4608 = t4607 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((12*1j) * phi1) * np.sqrt(0.676039e6) * t4608 ** 2 + + if Bindx == 247: + t4623 = np.cos(phi) + t4622 = t4623 ** 2 + t4626 = t4622 ** 2 + t4629 = t4626 ** 2 + t4627 = t4623 * t4626 + t4631 = t4627 ** 2 + t4632 = t4623 * t4631 + t4635 = t4623 * t4632 - 12 * t4622 + 27 * t4626 - 27 * t4629 + 12 * t4631 - 1 + t4634 = -6 * t4632 + 2 * (18 * t4622 - 18) * t4627 + 2 * (t4622 - t4629 + 3) * t4623 + t4624 = 2 * phi1 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.33649e5) * ((t4634 + t4635) * np.exp((6*1j) * (t4624 - phi2)) + (-t4634 + t4635) * np.exp((6*1j) * (t4624 + phi2))) + + if Bindx == 248: + t4649 = np.cos(phi) + t4662 = -12 * t4649 + t4648 = t4649 ** 2 + t4650 = t4649 * t4648 + t4651 = t4648 ** 2 + t4652 = t4649 * t4651 + t4653 = t4650 ** 2 + t4655 = t4651 ** 2 + t4657 = t4652 ** 2 + t4661 = t4657 * t4662 - 220 * t4650 - 792 * t4652 + t4662 + (-792 * t4653 - 220 * t4655) * t4649 + t4660 = 66 * t4648 + 495 * t4651 + 495 * t4655 + 66 * t4657 + 1 + (924 + t4653) * t4653 + tfunc[..., c] = (0.25e2 / 0.8192e4) * np.sqrt(0.2e1) * ((t4660 + t4661) * np.exp((12*1j) * (phi1 - phi2)) + (t4660 - t4661) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 249: + t4675 = np.cos(phi) + t4674 = t4675 ** 2 + t4678 = t4674 ** 2 + t4681 = t4678 ** 2 + t4679 = t4675 * t4678 + t4683 = t4679 ** 2 + t4684 = t4675 * t4683 + t4687 = -t4675 * t4684 + 12 * t4674 - 27 * t4678 + 27 * t4681 - 12 * t4683 + 1 + t4686 = 6 * t4684 + 2 * (-18 * t4674 + 18) * t4679 + 2 * (-t4674 + t4681 - 3) * t4675 + t4676 = 13 * phi1 + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.sqrt(0.3289e4) * np.sqrt((1 - t4675)) * np.sqrt((1 + t4675)) * ((t4686 + t4687) * np.exp((-1*1j) * (t4676 - 6 * phi2)) + (t4686 - t4687) * np.exp((-1*1j) * (t4676 + 6 * phi2))) + + if Bindx == 250: + t4702 = np.cos(phi) + t4701 = t4702 ** 2 + t4704 = t4702 * t4701 + t4707 = t4704 ** 2 + t4713 = t4707 ** 2 + t4715 = -t4702 * t4713 + 1 + t4705 = t4701 ** 2 + t4706 = t4702 * t4705 + t4711 = t4706 ** 2 + t4709 = t4705 ** 2 + t4703 = 13 * phi1 + t4696 = t4702 * t4707 + t4694 = t4702 * t4709 + t4692 = t4702 * t4711 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.sqrt(0.13e2) * np.sqrt((1 + t4702)) * ((13 * t4713 - 78 * t4692 + 286 * t4711 - 715 * t4694 + 1287 * t4709 - 1716 * t4696 + 1716 * t4707 - 1287 * t4706 + 715 * t4705 - 286 * t4704 + 78 * t4701 - 13 * t4702 + t4715) * np.exp((-1*1j) * (t4703 - 12 * phi2)) + (11 * t4713 + 54 * t4692 + 154 * t4711 + 275 * t4694 + 297 * t4709 + 132 * t4696 - 132 * t4707 - 297 * t4706 - 275 * t4705 - 154 * t4704 - 54 * t4701 - 11 * t4702 - t4715) * np.exp((-1*1j) * (t4703 + 12 * phi2))) * ((1 - t4702) ** (-0.1e1 / 0.2e1)) + + if Bindx == 251: + t4730 = np.cos(phi) + t4729 = t4730 ** 2 + t4733 = t4729 ** 2 + t4732 = t4730 * t4729 + t4735 = t4732 ** 2 + t4737 = t4733 ** 2 + t4734 = t4730 * t4733 + t4739 = t4734 ** 2 + t4741 = t4735 ** 2 + t4744 = -6 + 6 * t4729 + 188 * t4733 - 468 * t4735 + 306 * t4737 + 46 * t4739 - 72 * t4741 + t4743 = 144 * t4732 - 135 * t4734 + (-216 * t4735 + 363 * t4737 - 120 * t4739 - 13 * t4741 - 23) * t4730 + t4731 = 2 * phi1 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((-t4743 + t4744) * np.exp((-6*1j) * (t4731 - phi2)) + (t4743 + t4744) * np.exp((-6*1j) * (t4731 + phi2))) + + if Bindx == 252: + t4759 = np.cos(phi) + t4758 = t4759 ** 2 + t4761 = t4758 ** 2 + t4760 = t4759 * t4758 + t4763 = t4760 ** 2 + t4765 = t4761 ** 2 + t4762 = t4759 * t4761 + t4767 = t4762 ** 2 + t4769 = t4763 ** 2 + t4772 = 12 + 636 * t4758 + 3080 * t4761 + 792 * t4763 - 4356 * t4765 - 2068 * t4767 - 144 * t4769 + t4771 = 1782 * t4760 + 3069 * t4762 + (-2508 * t4763 - 3795 * t4765 - 714 * t4767 - 13 * t4769 + 131) * t4759 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.sqrt(0.2e1) * ((-t4771 + t4772) * np.exp((-12*1j) * (phi1 - phi2)) + (t4771 + t4772) * np.exp((-12*1j) * (phi1 + phi2))) + + if Bindx == 253: + t4785 = np.cos(phi) + t4784 = t4785 ** 2 + t4788 = t4784 ** 2 + t4789 = t4785 * t4788 + t4794 = t4789 ** 2 + t4796 = -325 * t4785 * t4794 + 59 + t4792 = t4788 ** 2 + t4787 = t4785 * t4784 + t4790 = t4787 ** 2 + t4786 = 11 * phi1 + t4779 = t4785 * t4790 + t4777 = t4785 * t4792 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * ((1 + t4785) ** (0.5e1 / 0.2e1)) * np.sqrt(0.253e3) * ((1 - t4785) ** (-0.1e1 / 0.2e1)) * ((2625 * t4794 - 9059 * t4777 + 17031 * t4792 - 17874 * t4779 + 8106 * t4790 + 3066 * t4789 - 6066 * t4788 + 2919 * t4787 - 251 * t4784 - 231 * t4785 + t4796) * np.exp((-1*1j) * (t4786 - 6 * phi2)) + (675 * t4794 - 841 * t4777 - 2423 * t4792 + 450 * t4779 + 3278 * t4790 + 446 * t4789 - 2046 * t4788 - 503 * t4787 + 575 * t4784 + 123 * t4785 - t4796) * np.exp((-1*1j) * (t4786 + 6 * phi2))) + + if Bindx == 254: + t4812 = np.cos(phi) + t4811 = t4812 ** 2 + t4814 = t4812 * t4811 + t4815 = t4811 ** 2 + t4816 = t4812 * t4815 + t4817 = t4814 ** 2 + t4818 = t4812 * t4817 + t4819 = t4815 ** 2 + t4821 = t4816 ** 2 + t4823 = t4817 ** 2 + t4827 = 992 * t4814 - 308 * t4816 - 3168 * t4818 + (836 * t4819 + 1408 * t4821 + 132 * t4823 + 108) * t4812 + t4826 = -13 * t4818 ** 2 - 451 * t4811 - 1023 * t4815 + 2409 * t4817 + 1551 * t4819 - 1881 * t4821 - 581 * t4823 - 11 + t4813 = 11 * phi1 + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * ((1 + t4812) ** (-0.1e1 / 0.2e1)) * ((1 - t4812) ** (-0.1e1 / 0.2e1)) * ((t4826 + t4827) * np.exp((-1*1j) * (t4813 - 12 * phi2)) + (-t4826 + t4827) * np.exp((-1*1j) * (t4813 + 12 * phi2))) + + if Bindx == 255: + t4842 = np.cos(phi) + t4841 = t4842 ** 2 + t4845 = t4841 ** 2 + t4844 = t4842 * t4841 + t4847 = t4844 ** 2 + t4849 = t4845 ** 2 + t4846 = t4842 * t4845 + t4851 = t4846 ** 2 + t4853 = t4847 ** 2 + t4856 = -17 + 391 * t4841 - 2246 * t4845 + 4182 * t4847 - 1605 * t4849 - 2205 * t4851 + 1500 * t4853 + t4855 = 210 * t4844 + 1377 * t4846 + (-5436 * t4847 + 5951 * t4849 - 1702 * t4851 - 325 * t4853 - 75) * t4842 + t4843 = 5 * phi1 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((t4855 + t4856) * np.exp((-2*1j) * (t4843 - 3 * phi2)) + (-t4855 + t4856) * np.exp((-2*1j) * (t4843 + 3 * phi2))) + + if Bindx == 256: + t4871 = np.cos(phi) + t4870 = t4871 ** 2 + t4874 = t4870 ** 2 + t4873 = t4871 * t4870 + t4876 = t4873 ** 2 + t4878 = t4874 ** 2 + t4875 = t4871 * t4874 + t4880 = t4875 ** 2 + t4882 = t4876 ** 2 + t4885 = 10 + 310 * t4870 + 220 * t4874 - 1716 * t4876 + 66 * t4878 + 990 * t4880 + 120 * t4882 + t4884 = -528 * t4873 + 825 * t4875 + (1320 * t4876 - 1045 * t4878 - 472 * t4880 - 13 * t4882 - 87) * t4871 + t4872 = 5 * phi1 + tfunc[..., c] = -(0.135e3 / 0.4096e4) * ((t4884 + t4885) * np.exp((-2*1j) * (t4872 - 6 * phi2)) + (-t4884 + t4885) * np.exp((-2*1j) * (t4872 + 6 * phi2))) * np.sqrt(0.2e1) + + if Bindx == 257: + t4899 = np.cos(phi) + t4898 = t4899 ** 2 + t4901 = t4899 * t4898 + t4904 = t4901 ** 2 + t4911 = -7475 * t4904 ** 2 - 27 + t4902 = t4898 ** 2 + t4903 = t4899 * t4902 + t4908 = t4903 ** 2 + t4906 = t4902 ** 2 + t4900 = 3 * phi1 + t4893 = t4899 * t4904 + t4891 = t4899 * t4906 + t4889 = t4899 * t4908 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * ((1 + t4899) ** (0.3e1 / 0.2e1)) * np.sqrt(0.11e2) * ((1 - t4899) ** (-0.1e1 / 0.2e1)) * ((46000 * t4889 - 107042 * t4908 + 95772 * t4891 + 34059 * t4906 - 135024 * t4893 + 80388 * t4904 + 21384 * t4903 - 39357 * t4902 + 9056 * t4901 + 3614 * t4898 - 1348 * t4899 + t4911) * np.exp((-3*1j) * (t4900 - 2 * phi2)) + (16100 * t4889 - 17158 * t4908 - 54096 * t4891 + 6789 * t4906 + 69384 * t4893 + 10044 * t4904 - 42336 * t4903 - 9171 * t4902 + 12404 * t4901 + 1994 * t4898 - 1456 * t4899 - t4911) * np.exp((-3*1j) * (t4900 + 2 * phi2))) + + if Bindx == 258: + t4927 = np.cos(phi) + t4926 = t4927 ** 2 + t4929 = t4927 * t4926 + t4930 = t4926 ** 2 + t4931 = t4927 * t4930 + t4932 = t4929 ** 2 + t4933 = t4927 * t4932 + t4934 = t4930 ** 2 + t4936 = t4931 ** 2 + t4938 = t4932 ** 2 + t4942 = 152 * t4929 - 1012 * t4931 + 528 * t4933 + (924 * t4934 - 552 * t4936 - 108 * t4938 + 68) * t4927 + t4941 = -13 * t4933 ** 2 + 189 * t4926 - 363 * t4930 - 759 * t4932 + 1419 * t4934 - 121 * t4936 - 361 * t4938 + 9 + t4928 = 3 * phi1 + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * ((-t4941 + t4942) * np.exp((-3*1j) * (t4928 - 4 * phi2)) + (t4941 + t4942) * np.exp((-3*1j) * (t4928 + 4 * phi2))) * ((1 - t4927) ** (-0.1e1 / 0.2e1)) * ((1 + t4927) ** (-0.1e1 / 0.2e1)) + + if Bindx == 259: + t4957 = np.cos(phi) + t4956 = t4957 ** 2 + t4960 = t4956 ** 2 + t4959 = t4957 * t4956 + t4962 = t4959 ** 2 + t4964 = t4960 ** 2 + t4961 = t4957 * t4960 + t4966 = t4961 ** 2 + t4968 = t4962 ** 2 + t4971 = 5084 * t4956 - 24904 * t4960 + 20568 * t4962 + 69204 * t4964 - 130548 * t4966 + 60720 * t4968 - 124 + t4970 = 15864 * t4959 - 84537 * t4961 + (185544 * t4962 - 164659 * t4964 + 32384 * t4966 + 16445 * t4968 - 1041) * t4957 + t4958 = 4 * phi1 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * ((-t4970 + t4971) * np.exp((-2*1j) * (t4958 - 3 * phi2)) + (t4970 + t4971) * np.exp((-2*1j) * (t4958 + 3 * phi2))) * np.sqrt(0.5e1) + + if Bindx == 260: + t4986 = np.cos(phi) + t4985 = t4986 ** 2 + t4989 = t4985 ** 2 + t4988 = t4986 * t4985 + t4991 = t4988 ** 2 + t4993 = t4989 ** 2 + t4990 = t4986 * t4989 + t4995 = t4990 ** 2 + t4997 = t4991 ** 2 + t5000 = -8 - 104 * t4985 + 400 * t4989 + 48 * t4991 - 744 * t4993 + 312 * t4995 + 96 * t4997 + t4999 = -18 * t4988 - 579 * t4990 + (708 * t4991 + 125 * t4993 - 274 * t4995 - 13 * t4997 + 51) * t4986 + t4987 = 2 * phi1 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * ((t4999 + t5000) * np.exp((-4*1j) * (t4987 - 3 * phi2)) + (-t4999 + t5000) * np.exp((-4*1j) * (t4987 + 3 * phi2))) * np.sqrt(0.1265e4) + + if Bindx == 261: + t5015 = np.cos(phi) + t5014 = t5015 ** 2 + t5017 = t5015 * t5014 + t5020 = t5017 ** 2 + t5026 = t5020 ** 2 + t5028 = -16445 * t5015 * t5026 - 85 + t5018 = t5014 ** 2 + t5019 = t5015 * t5018 + t5024 = t5019 ** 2 + t5022 = t5018 ** 2 + t5016 = 7 * phi1 + t5009 = t5015 * t5020 + t5007 = t5015 * t5022 + t5005 = t5015 * t5024 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt((1 + t5015)) * np.sqrt(0.35e2) * ((1 - t5015) ** (-0.1e1 / 0.2e1)) * ((69575 * t5026 - 71346 * t5005 - 84502 * t5024 + 197461 * t5007 - 35523 * t5022 - 147804 * t5009 + 82140 * t5020 + 37917 * t5019 - 33871 * t5018 - 1682 * t5017 + 4314 * t5014 - 149 * t5015 + t5028) * np.exp((-1*1j) * (t5016 - 6 * phi2)) + (36685 * t5026 - 34914 * t5005 - 120934 * t5024 + 7975 * t5007 + 153963 * t5022 + 29364 * t5009 - 95028 * t5020 - 25029 * t5019 + 29075 * t5018 + 6478 * t5017 - 3846 * t5014 - 319 * t5015 - t5028) * np.exp((-1*1j) * (t5016 + 6 * phi2))) + + if Bindx == 262: + t5044 = np.cos(phi) + t5043 = t5044 ** 2 + t5046 = t5044 * t5043 + t5047 = t5043 ** 2 + t5048 = t5044 * t5047 + t5049 = t5046 ** 2 + t5050 = t5044 * t5049 + t5051 = t5047 ** 2 + t5053 = t5048 ** 2 + t5055 = t5049 ** 2 + t5059 = 136 * t5046 + 116 * t5048 - 720 * t5050 + (644 * t5051 - 56 * t5053 - 84 * t5055 - 36) * t5044 + t5058 = -13 * t5050 ** 2 - 35 * t5043 + 357 * t5047 - 567 * t5049 + 27 * t5051 + 423 * t5053 - 185 * t5055 - 7 + t5045 = 7 * phi1 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.sqrt(0.8855e4) * np.sqrt(0.2e1) * ((-t5058 + t5059) * np.exp((-1*1j) * (t5045 - 12 * phi2)) + (t5058 + t5059) * np.exp((-1*1j) * (t5045 + 12 * phi2))) * ((1 + t5044) ** (-0.1e1 / 0.2e1)) * ((1 - t5044) ** (-0.1e1 / 0.2e1)) + + if Bindx == 263: + t5074 = np.cos(phi) + t5073 = t5074 ** 2 + t5075 = t5074 * t5073 + t5076 = t5073 ** 2 + t5077 = t5074 * t5076 + t5078 = t5075 ** 2 + t5080 = t5076 ** 2 + t5082 = t5077 ** 2 + t5084 = t5078 ** 2 + t5087 = 45270 * t5075 - 217629 * t5077 + (410700 * t5078 - 270435 * t5080 - 47058 * t5082 + 82225 * t5084 - 2561) * t5074 + t5086 = -3795 * t5073 + 48670 * t5076 - 257454 * t5078 + 595305 * t5080 - 609983 * t5082 + 227700 * t5084 + 69 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t5086 - t5087) * np.exp((-6*1j) * (phi1 - phi2)) + (t5086 + t5087) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 264: + t5102 = np.cos(phi) + t5101 = t5102 ** 2 + t5104 = t5101 ** 2 + t5103 = t5102 * t5101 + t5106 = t5103 ** 2 + t5108 = t5104 ** 2 + t5105 = t5102 * t5104 + t5110 = t5105 ** 2 + t5112 = t5106 ** 2 + t5115 = 6 - 6 * t5101 - 188 * t5104 + 468 * t5106 - 306 * t5108 - 46 * t5110 + 72 * t5112 + t5114 = 144 * t5103 - 135 * t5105 + (-216 * t5106 + 363 * t5108 - 120 * t5110 - 13 * t5112 - 23) * t5102 + tfunc[..., c] = -(0.135e3 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((t5114 + t5115) * np.exp((-6*1j) * (phi1 - 2 * phi2)) + (-t5114 + t5115) * np.exp((-6*1j) * (phi1 + 2 * phi2))) + + if Bindx == 265: + t5131 = np.cos(phi) + t5130 = t5131 ** 2 + t5134 = t5130 ** 2 + t5133 = t5131 * t5130 + t5136 = t5133 ** 2 + t5137 = t5131 * t5136 + t5138 = t5134 ** 2 + t5135 = t5131 * t5134 + t5140 = t5135 ** 2 + t5142 = t5136 ** 2 + t5146 = 82225 * t5137 ** 2 - 6893 * t5130 + 63345 * t5134 - 196005 * t5136 + 221115 * t5138 + 6369 * t5140 - 170269 * t5142 + 113 + t5145 = 26140 * t5133 - 175642 * t5135 + 542952 * t5137 + (-845090 * t5138 + 642620 * t5140 - 189750 * t5142 - 1230) * t5131 + t5132 = 5 * phi1 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.sqrt(0.19e2) * ((t5145 + t5146) * np.exp((-1*1j) * (t5132 - 6 * phi2)) + (t5145 - t5146) * np.exp((-1*1j) * (t5132 + 6 * phi2))) * ((1 + t5131) ** (-0.1e1 / 0.2e1)) * ((1 - t5131) ** (-0.1e1 / 0.2e1)) + + if Bindx == 266: + t5162 = np.cos(phi) + t5161 = t5162 ** 2 + t5164 = t5162 * t5161 + t5165 = t5161 ** 2 + t5166 = t5162 * t5165 + t5167 = t5164 ** 2 + t5168 = t5162 * t5167 + t5169 = t5165 ** 2 + t5171 = t5166 ** 2 + t5173 = t5167 ** 2 + t5177 = 128 * t5164 - 332 * t5166 + 288 * t5168 + (28 * t5169 - 160 * t5171 + 60 * t5173 - 12) * t5162 + t5176 = -13 * t5168 ** 2 - 35 * t5161 - 15 * t5165 + 297 * t5167 - 513 * t5169 + 327 * t5171 - 53 * t5173 + 5 + t5163 = 5 * phi1 + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.sqrt(0.4807e4) * ((1 + t5162) ** (-0.1e1 / 0.2e1)) * ((1 - t5162) ** (-0.1e1 / 0.2e1)) * ((t5176 + t5177) * np.exp((-1*1j) * (t5163 - 12 * phi2)) + (-t5176 + t5177) * np.exp((-1*1j) * (t5163 + 12 * phi2))) + + if Bindx == 267: + t5192 = np.cos(phi) + t5191 = t5192 ** 2 + t5194 = t5192 * t5191 + t5195 = t5191 ** 2 + t5196 = t5192 * t5195 + t5197 = t5194 ** 2 + t5199 = t5195 ** 2 + t5201 = t5196 ** 2 + t5203 = t5197 ** 2 + t5206 = 7280 * t5194 - 27661 * t5196 + (-1480 * t5197 + 136345 * t5199 - 196328 * t5201 + 82225 * t5203 - 381) * t5192 + t5205 = -6890 * t5191 + 73180 * t5195 - 291124 * t5197 + 536930 * t5199 - 464002 * t5201 + 151800 * t5203 + 106 + t5193 = 2 * phi1 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((t5205 - t5206) * np.exp((-2*1j) * (t5193 - 3 * phi2)) + (t5205 + t5206) * np.exp((-2*1j) * (t5193 + 3 * phi2))) + + if Bindx == 268: + t5221 = np.cos(phi) + t5220 = t5221 ** 2 + t5223 = t5220 ** 2 + t5222 = t5221 * t5220 + t5225 = t5222 ** 2 + t5227 = t5223 ** 2 + t5224 = t5221 * t5223 + t5229 = t5224 ** 2 + t5231 = t5225 ** 2 + t5234 = -4 + 44 * t5220 - 88 * t5223 - 8 * t5225 + 172 * t5227 - 164 * t5229 + 48 * t5231 + t5233 = -74 * t5222 + 253 * t5224 + (-332 * t5225 + 173 * t5227 - 10 * t5229 - 13 * t5231 + 3) * t5221 + tfunc[..., c] = -(0.135e3 / 0.8192e4) * np.sqrt(0.2e1) * np.sqrt(0.4807e4) * ((t5233 + t5234) * np.exp((-4*1j) * (phi1 - 3 * phi2)) + (-t5233 + t5234) * np.exp((-4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 269: + t5250 = np.cos(phi) + t5249 = t5250 ** 2 + t5252 = t5249 ** 2 + t5251 = t5250 * t5249 + t5254 = t5251 ** 2 + t5255 = t5250 * t5254 + t5256 = t5252 ** 2 + t5253 = t5250 * t5252 + t5258 = t5253 ** 2 + t5260 = t5254 ** 2 + t5264 = -16445 * t5255 ** 2 - 207 * t5249 + 2939 * t5252 - 17743 * t5254 + 52529 * t5256 - 79013 * t5258 + 57937 * t5260 + 3 + t5263 = 4444 * t5251 - 29390 * t5253 + 84680 * t5255 + (-120766 * t5256 + 83996 * t5258 - 22770 * t5260 - 194) * t5250 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.sqrt(0.1615e4) * ((t5263 - t5264) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t5263 + t5264) * np.exp((-3*1j) * (phi1 + 2 * phi2))) * ((1 + t5250) ** (-0.1e1 / 0.2e1)) * ((1 - t5250) ** (-0.1e1 / 0.2e1)) + + if Bindx == 270: + t5280 = np.cos(phi) + t5279 = t5280 ** 2 + t5281 = t5280 * t5279 + t5282 = t5279 ** 2 + t5283 = t5280 * t5282 + t5284 = t5281 ** 2 + t5285 = t5280 * t5284 + t5286 = t5282 ** 2 + t5288 = t5283 ** 2 + t5290 = t5284 ** 2 + t5294 = 16 * t5281 - 140 * t5283 + 320 * t5285 + (-340 * t5286 + 176 * t5288 - 36 * t5290 + 4) * t5280 + t5293 = -13 * t5285 ** 2 + 45 * t5279 - 167 * t5282 + 265 * t5284 - 185 * t5286 + 23 * t5288 + 35 * t5290 - 3 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.sqrt(0.408595e6) * ((-t5293 + t5294) * np.exp((-3*1j) * (phi1 - 4 * phi2)) + (t5293 + t5294) * np.exp((-3*1j) * (phi1 + 4 * phi2))) * ((1 + t5280) ** (-0.1e1 / 0.2e1)) * ((1 - t5280) ** (-0.1e1 / 0.2e1)) + + if Bindx == 271: + t5309 = np.cos(phi) + t5308 = t5309 ** 2 + t5311 = t5308 ** 2 + t5310 = t5309 * t5308 + t5313 = t5310 ** 2 + t5315 = t5311 ** 2 + t5312 = t5309 * t5311 + t5317 = t5312 ** 2 + t5319 = t5313 ** 2 + t5322 = -1 + 71 * t5308 - 790 * t5311 + 3142 * t5313 - 5573 * t5315 + 4531 * t5317 - 1380 * t5319 + t5321 = 214 * t5310 - 1493 * t5312 + (4588 * t5313 - 7003 * t5315 + 5198 * t5317 - 1495 * t5319 - 9) * t5309 + tfunc[..., c] = (0.27e2 / 0.1024e4) * ((-t5321 + t5322) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t5321 + t5322) * np.exp((-2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.17765e5) + + if Bindx == 272: + t5337 = np.cos(phi) + t5336 = t5337 ** 2 + t5339 = t5336 ** 2 + t5338 = t5337 * t5336 + t5341 = t5338 ** 2 + t5343 = t5339 ** 2 + t5340 = t5337 * t5339 + t5345 = t5340 ** 2 + t5347 = t5341 ** 2 + t5350 = 2 - 34 * t5336 + 140 * t5339 - 260 * t5341 + 250 * t5343 - 122 * t5345 + 24 * t5347 + t5349 = -32 * t5338 + 25 * t5340 + (40 * t5341 - 85 * t5343 + 56 * t5345 - 13 * t5347 + 9) * t5337 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * ((t5349 + t5350) * np.exp((-2*1j) * (phi1 - 6 * phi2)) + (-t5349 + t5350) * np.exp((-2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.37145e5) + + if Bindx == 273: + t5366 = np.cos(phi) + t5365 = t5366 ** 2 + t5367 = t5366 * t5365 + t5368 = t5365 ** 2 + t5369 = t5366 * t5368 + t5370 = t5367 ** 2 + t5371 = t5366 * t5370 + t5372 = t5368 ** 2 + t5374 = t5369 ** 2 + t5376 = t5370 ** 2 + t5380 = -720 * t5367 + 4878 * t5369 - 14064 * t5371 + (19674 * t5372 - 13248 * t5374 + 3450 * t5376 + 30) * t5366 + t5379 = -7475 * t5371 ** 2 - 365 * t5365 + 4425 * t5368 - 20473 * t5370 + 45919 * t5372 - 53799 * t5374 + 31763 * t5376 + 5 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.sqrt(0.3553e4) * ((1 + t5366) ** (-0.1e1 / 0.2e1)) * ((1 - t5366) ** (-0.1e1 / 0.2e1)) * ((t5379 + t5380) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (-t5379 + t5380) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 274: + t5396 = np.cos(phi) + t5395 = t5396 ** 2 + t5397 = t5396 * t5395 + t5398 = t5395 ** 2 + t5399 = t5396 * t5398 + t5400 = t5397 ** 2 + t5401 = t5396 * t5400 + t5402 = t5398 ** 2 + t5404 = t5399 ** 2 + t5406 = t5400 ** 2 + t5410 = 72 * t5397 - 180 * t5399 + 240 * t5401 + 12 * (-15 * t5402 + 6 * t5404 - t5406 - 1) * t5396 + t5409 = -13 * t5401 ** 2 - 19 * t5395 + 93 * t5398 - 215 * t5400 + 275 * t5402 - 201 * t5404 + 79 * t5406 + 1 + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.sqrt(0.7429e4) * ((-t5409 + t5410) * np.exp((-1*1j) * (phi1 - 12 * phi2)) + (t5409 + t5410) * np.exp((-1*1j) * (phi1 + 12 * phi2))) * ((1 + t5396) ** (-0.1e1 / 0.2e1)) * ((1 - t5396) ** (-0.1e1 / 0.2e1)) + + if Bindx == 275: + t5418 = np.sin(phi) + t5415 = t5418 ** 2 + t5416 = t5418 * t5415 + t5411 = np.cos(phi) + t5412 = t5411 ** 2 + t5413 = t5412 ** 2 + tfunc[..., c] = (-0.27e2 / 0.1024e4*1j) * t5411 * t5416 ** 2 * (-483 * t5413 - 5 + (575 * t5413 + 105) * t5412) * np.sin((6 * phi2)) * np.sqrt(0.323323e6) * np.sqrt(0.2e1) + + if Bindx == 276: + t5423 = np.sin(phi) + t5419 = t5423 ** 2 + t5420 = t5423 * t5419 + t5421 = t5420 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4*1j) * np.sqrt(0.676039e6) * np.sqrt(0.2e1) * np.cos(phi) * np.sin((12 * phi2)) * t5421 ** 2 + + if Bindx == 277: + t5439 = np.cos(phi) + t5438 = t5439 ** 2 + t5440 = t5439 * t5438 + t5441 = t5438 ** 2 + t5442 = t5439 * t5441 + t5443 = t5440 ** 2 + t5444 = t5439 * t5443 + t5445 = t5441 ** 2 + t5447 = t5442 ** 2 + t5449 = t5443 ** 2 + t5453 = 720 * t5440 - 4878 * t5442 + 14064 * t5444 + (-19674 * t5445 + 13248 * t5447 - 3450 * t5449 - 30) * t5439 + t5452 = -7475 * t5444 ** 2 - 365 * t5438 + 4425 * t5441 - 20473 * t5443 + 45919 * t5445 - 53799 * t5447 + 31763 * t5449 + 5 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.sqrt(0.3553e4) * ((1 + t5439) ** (-0.1e1 / 0.2e1)) * ((1 - t5439) ** (-0.1e1 / 0.2e1)) * ((-t5452 + t5453) * np.exp((1j) * (phi1 - 6 * phi2)) + (t5452 + t5453) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 278: + t5469 = np.cos(phi) + t5468 = t5469 ** 2 + t5470 = t5469 * t5468 + t5471 = t5468 ** 2 + t5472 = t5469 * t5471 + t5473 = t5470 ** 2 + t5474 = t5469 * t5473 + t5475 = t5471 ** 2 + t5477 = t5472 ** 2 + t5479 = t5473 ** 2 + t5483 = 72 * t5470 - 180 * t5472 + 240 * t5474 + 12 * (-15 * t5475 + 6 * t5477 - t5479 - 1) * t5469 + t5482 = -13 * t5474 ** 2 - 19 * t5468 + 93 * t5471 - 215 * t5473 + 275 * t5475 - 201 * t5477 + 79 * t5479 + 1 + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.sqrt(0.7429e4) * ((1 + t5469) ** (-0.1e1 / 0.2e1)) * ((1 - t5469) ** (-0.1e1 / 0.2e1)) * ((-t5482 + t5483) * np.exp((1j) * (phi1 - 12 * phi2)) + (t5482 + t5483) * np.exp((1j) * (phi1 + 12 * phi2))) + + if Bindx == 279: + t5498 = np.cos(phi) + t5497 = t5498 ** 2 + t5500 = t5497 ** 2 + t5499 = t5498 * t5497 + t5502 = t5499 ** 2 + t5504 = t5500 ** 2 + t5501 = t5498 * t5500 + t5506 = t5501 ** 2 + t5508 = t5502 ** 2 + t5511 = -1 + 71 * t5497 - 790 * t5500 + 3142 * t5502 - 5573 * t5504 + 4531 * t5506 - 1380 * t5508 + t5510 = 214 * t5499 - 1493 * t5501 + (4588 * t5502 - 7003 * t5504 + 5198 * t5506 - 1495 * t5508 - 9) * t5498 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * ((-t5510 + t5511) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t5510 + t5511) * np.exp((2*1j) * (phi1 + 3 * phi2))) * np.sqrt(0.17765e5) + + if Bindx == 280: + t5526 = np.cos(phi) + t5525 = t5526 ** 2 + t5528 = t5525 ** 2 + t5527 = t5526 * t5525 + t5530 = t5527 ** 2 + t5532 = t5528 ** 2 + t5529 = t5526 * t5528 + t5534 = t5529 ** 2 + t5536 = t5530 ** 2 + t5539 = -2 + 34 * t5525 - 140 * t5528 + 260 * t5530 - 250 * t5532 + 122 * t5534 - 24 * t5536 + t5538 = -32 * t5527 + 25 * t5529 + (40 * t5530 - 85 * t5532 + 56 * t5534 - 13 * t5536 + 9) * t5526 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * ((-t5538 + t5539) * np.exp((2*1j) * (phi1 - 6 * phi2)) + (t5538 + t5539) * np.exp((2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.37145e5) + + if Bindx == 281: + t5555 = np.cos(phi) + t5554 = t5555 ** 2 + t5557 = t5554 ** 2 + t5556 = t5555 * t5554 + t5559 = t5556 ** 2 + t5560 = t5555 * t5559 + t5561 = t5557 ** 2 + t5558 = t5555 * t5557 + t5563 = t5558 ** 2 + t5565 = t5559 ** 2 + t5569 = -16445 * t5560 ** 2 - 207 * t5554 + 2939 * t5557 - 17743 * t5559 + 52529 * t5561 - 79013 * t5563 + 57937 * t5565 + 3 + t5568 = 4444 * t5556 - 29390 * t5558 + 84680 * t5560 + (-120766 * t5561 + 83996 * t5563 - 22770 * t5565 - 194) * t5555 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.sqrt(0.1615e4) * ((1 + t5555) ** (-0.1e1 / 0.2e1)) * ((1 - t5555) ** (-0.1e1 / 0.2e1)) * ((t5568 - t5569) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t5568 + t5569) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 282: + t5585 = np.cos(phi) + t5584 = t5585 ** 2 + t5586 = t5585 * t5584 + t5587 = t5584 ** 2 + t5588 = t5585 * t5587 + t5589 = t5586 ** 2 + t5590 = t5585 * t5589 + t5591 = t5587 ** 2 + t5593 = t5588 ** 2 + t5595 = t5589 ** 2 + t5599 = 16 * t5586 - 140 * t5588 + 320 * t5590 + (-340 * t5591 + 176 * t5593 - 36 * t5595 + 4) * t5585 + t5598 = -13 * t5590 ** 2 + 45 * t5584 - 167 * t5587 + 265 * t5589 - 185 * t5591 + 23 * t5593 + 35 * t5595 - 3 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.sqrt(0.408595e6) * ((1 + t5585) ** (-0.1e1 / 0.2e1)) * ((1 - t5585) ** (-0.1e1 / 0.2e1)) * ((-t5598 + t5599) * np.exp((3*1j) * (phi1 - 4 * phi2)) + (t5598 + t5599) * np.exp((3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 283: + t5614 = np.cos(phi) + t5613 = t5614 ** 2 + t5616 = t5614 * t5613 + t5617 = t5613 ** 2 + t5618 = t5614 * t5617 + t5619 = t5616 ** 2 + t5621 = t5617 ** 2 + t5623 = t5618 ** 2 + t5625 = t5619 ** 2 + t5628 = 7280 * t5616 - 27661 * t5618 + (-1480 * t5619 + 136345 * t5621 - 196328 * t5623 + 82225 * t5625 - 381) * t5614 + t5627 = 6890 * t5613 - 73180 * t5617 + 291124 * t5619 - 536930 * t5621 + 464002 * t5623 - 151800 * t5625 - 106 + t5615 = 2 * phi1 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.19e2) * ((t5627 + t5628) * np.exp((2*1j) * (t5615 - 3 * phi2)) + (t5627 - t5628) * np.exp((2*1j) * (t5615 + 3 * phi2))) + + if Bindx == 284: + t5643 = np.cos(phi) + t5642 = t5643 ** 2 + t5645 = t5642 ** 2 + t5644 = t5643 * t5642 + t5647 = t5644 ** 2 + t5649 = t5645 ** 2 + t5646 = t5643 * t5645 + t5651 = t5646 ** 2 + t5653 = t5647 ** 2 + t5656 = 4 - 44 * t5642 + 88 * t5645 + 8 * t5647 - 172 * t5649 + 164 * t5651 - 48 * t5653 + t5655 = -74 * t5644 + 253 * t5646 + (-332 * t5647 + 173 * t5649 - 10 * t5651 - 13 * t5653 + 3) * t5643 + tfunc[..., c] = -(0.135e3 / 0.8192e4) * np.sqrt(0.2e1) * np.sqrt(0.4807e4) * ((-t5655 + t5656) * np.exp((4*1j) * (phi1 - 3 * phi2)) + (t5655 + t5656) * np.exp((4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 285: + t5672 = np.cos(phi) + t5671 = t5672 ** 2 + t5675 = t5671 ** 2 + t5674 = t5672 * t5671 + t5677 = t5674 ** 2 + t5678 = t5672 * t5677 + t5679 = t5675 ** 2 + t5676 = t5672 * t5675 + t5681 = t5676 ** 2 + t5683 = t5677 ** 2 + t5687 = 82225 * t5678 ** 2 - 6893 * t5671 + 63345 * t5675 - 196005 * t5677 + 221115 * t5679 + 6369 * t5681 - 170269 * t5683 + 113 + t5686 = 26140 * t5674 - 175642 * t5676 + 542952 * t5678 + (-845090 * t5679 + 642620 * t5681 - 189750 * t5683 - 1230) * t5672 + t5673 = 5 * phi1 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.sqrt(0.19e2) * ((1 + t5672) ** (-0.1e1 / 0.2e1)) * ((1 - t5672) ** (-0.1e1 / 0.2e1)) * ((t5686 + t5687) * np.exp((1j) * (t5673 - 6 * phi2)) + (t5686 - t5687) * np.exp((1j) * (t5673 + 6 * phi2))) + + if Bindx == 286: + t5703 = np.cos(phi) + t5702 = t5703 ** 2 + t5705 = t5703 * t5702 + t5706 = t5702 ** 2 + t5707 = t5703 * t5706 + t5708 = t5705 ** 2 + t5709 = t5703 * t5708 + t5710 = t5706 ** 2 + t5712 = t5707 ** 2 + t5714 = t5708 ** 2 + t5718 = 128 * t5705 - 332 * t5707 + 288 * t5709 + (28 * t5710 - 160 * t5712 + 60 * t5714 - 12) * t5703 + t5717 = -13 * t5709 ** 2 - 35 * t5702 - 15 * t5706 + 297 * t5708 - 513 * t5710 + 327 * t5712 - 53 * t5714 + 5 + t5704 = 5 * phi1 + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.sqrt(0.4807e4) * ((t5717 + t5718) * np.exp((1j) * (t5704 - 12 * phi2)) + (-t5717 + t5718) * np.exp((1j) * (t5704 + 12 * phi2))) * ((1 + t5703) ** (-0.1e1 / 0.2e1)) * ((1 - t5703) ** (-0.1e1 / 0.2e1)) + + if Bindx == 287: + t5733 = np.cos(phi) + t5732 = t5733 ** 2 + t5734 = t5733 * t5732 + t5735 = t5732 ** 2 + t5736 = t5733 * t5735 + t5737 = t5734 ** 2 + t5739 = t5735 ** 2 + t5741 = t5736 ** 2 + t5743 = t5737 ** 2 + t5746 = 45270 * t5734 - 217629 * t5736 + (410700 * t5737 - 270435 * t5739 - 47058 * t5741 + 82225 * t5743 - 2561) * t5733 + t5745 = 3795 * t5732 - 48670 * t5735 + 257454 * t5737 - 595305 * t5739 + 609983 * t5741 - 227700 * t5743 - 69 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.sqrt(0.2e1) * ((t5745 + t5746) * np.exp((6*1j) * (phi1 - phi2)) + (t5745 - t5746) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 288: + t5761 = np.cos(phi) + t5760 = t5761 ** 2 + t5763 = t5760 ** 2 + t5762 = t5761 * t5760 + t5765 = t5762 ** 2 + t5767 = t5763 ** 2 + t5764 = t5761 * t5763 + t5769 = t5764 ** 2 + t5771 = t5765 ** 2 + t5774 = -6 + 6 * t5760 + 188 * t5763 - 468 * t5765 + 306 * t5767 + 46 * t5769 - 72 * t5771 + t5773 = 144 * t5762 - 135 * t5764 + (-216 * t5765 + 363 * t5767 - 120 * t5769 - 13 * t5771 - 23) * t5761 + tfunc[..., c] = -(0.135e3 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((-t5773 + t5774) * np.exp((6*1j) * (phi1 - 2 * phi2)) + (t5773 + t5774) * np.exp((6*1j) * (phi1 + 2 * phi2))) + + if Bindx == 289: + t5789 = np.cos(phi) + t5788 = t5789 ** 2 + t5791 = t5789 * t5788 + t5794 = t5791 ** 2 + t5800 = t5794 ** 2 + t5802 = -16445 * t5789 * t5800 + 85 + t5792 = t5788 ** 2 + t5793 = t5789 * t5792 + t5798 = t5793 ** 2 + t5796 = t5792 ** 2 + t5790 = 7 * phi1 + t5783 = t5789 * t5794 + t5781 = t5789 * t5796 + t5779 = t5789 * t5798 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt((1 - t5789)) * np.sqrt(0.35e2) * ((36685 * t5800 + 34914 * t5779 - 120934 * t5798 - 7975 * t5781 + 153963 * t5796 - 29364 * t5783 - 95028 * t5794 + 25029 * t5793 + 29075 * t5792 - 6478 * t5791 - 3846 * t5788 + 319 * t5789 + t5802) * np.exp((1j) * (t5790 - 6 * phi2)) + (69575 * t5800 + 71346 * t5779 - 84502 * t5798 - 197461 * t5781 - 35523 * t5796 + 147804 * t5783 + 82140 * t5794 - 37917 * t5793 - 33871 * t5792 + 1682 * t5791 + 4314 * t5788 + 149 * t5789 - t5802) * np.exp((1j) * (t5790 + 6 * phi2))) * ((1 + t5789) ** (-0.1e1 / 0.2e1)) + + if Bindx == 290: + t5818 = np.cos(phi) + t5817 = t5818 ** 2 + t5820 = t5818 * t5817 + t5821 = t5817 ** 2 + t5822 = t5818 * t5821 + t5823 = t5820 ** 2 + t5824 = t5818 * t5823 + t5825 = t5821 ** 2 + t5827 = t5822 ** 2 + t5829 = t5823 ** 2 + t5833 = -136 * t5820 - 116 * t5822 + 720 * t5824 + (-644 * t5825 + 56 * t5827 + 84 * t5829 + 36) * t5818 + t5832 = -13 * t5824 ** 2 - 35 * t5817 + 357 * t5821 - 567 * t5823 + 27 * t5825 + 423 * t5827 - 185 * t5829 - 7 + t5819 = 7 * phi1 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.sqrt(0.8855e4) * np.sqrt(0.2e1) * ((t5832 + t5833) * np.exp((1j) * (t5819 - 12 * phi2)) + (-t5832 + t5833) * np.exp((1j) * (t5819 + 12 * phi2))) * ((1 + t5818) ** (-0.1e1 / 0.2e1)) * ((1 - t5818) ** (-0.1e1 / 0.2e1)) + + if Bindx == 291: + t5848 = np.cos(phi) + t5847 = t5848 ** 2 + t5851 = t5847 ** 2 + t5850 = t5848 * t5847 + t5853 = t5850 ** 2 + t5855 = t5851 ** 2 + t5852 = t5848 * t5851 + t5857 = t5852 ** 2 + t5859 = t5853 ** 2 + t5862 = 5084 * t5847 - 24904 * t5851 + 20568 * t5853 + 69204 * t5855 - 130548 * t5857 + 60720 * t5859 - 124 + t5861 = 15864 * t5850 - 84537 * t5852 + (185544 * t5853 - 164659 * t5855 + 32384 * t5857 + 16445 * t5859 - 1041) * t5848 + t5849 = 4 * phi1 + tfunc[..., c] = (0.27e2 / 0.2048e4) * ((-t5861 + t5862) * np.exp((2*1j) * (t5849 - 3 * phi2)) + (t5861 + t5862) * np.exp((2*1j) * (t5849 + 3 * phi2))) * np.sqrt(0.5e1) + + if Bindx == 292: + t5877 = np.cos(phi) + t5876 = t5877 ** 2 + t5880 = t5876 ** 2 + t5879 = t5877 * t5876 + t5882 = t5879 ** 2 + t5884 = t5880 ** 2 + t5881 = t5877 * t5880 + t5886 = t5881 ** 2 + t5888 = t5882 ** 2 + t5891 = 8 + 104 * t5876 - 400 * t5880 - 48 * t5882 + 744 * t5884 - 312 * t5886 - 96 * t5888 + t5890 = -18 * t5879 - 579 * t5881 + (708 * t5882 + 125 * t5884 - 274 * t5886 - 13 * t5888 + 51) * t5877 + t5878 = 2 * phi1 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * ((-t5890 + t5891) * np.exp((4*1j) * (t5878 - 3 * phi2)) + (t5890 + t5891) * np.exp((4*1j) * (t5878 + 3 * phi2))) * np.sqrt(0.1265e4) + + if Bindx == 293: + t5905 = np.cos(phi) + t5904 = t5905 ** 2 + t5907 = t5905 * t5904 + t5910 = t5907 ** 2 + t5917 = -7475 * t5910 ** 2 - 27 + t5908 = t5904 ** 2 + t5909 = t5905 * t5908 + t5914 = t5909 ** 2 + t5912 = t5908 ** 2 + t5906 = 3 * phi1 + t5899 = t5905 * t5910 + t5897 = t5905 * t5912 + t5895 = t5905 * t5914 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.sqrt(0.2e1) * np.sqrt(0.11e2) * ((1 - t5905) ** (0.3e1 / 0.2e1)) * ((16100 * t5895 + 17158 * t5914 - 54096 * t5897 - 6789 * t5912 + 69384 * t5899 - 10044 * t5910 - 42336 * t5909 + 9171 * t5908 + 12404 * t5907 - 1994 * t5904 - 1456 * t5905 + t5917) * np.exp((3*1j) * (t5906 - 2 * phi2)) + (46000 * t5895 + 107042 * t5914 + 95772 * t5897 - 34059 * t5912 - 135024 * t5899 - 80388 * t5910 + 21384 * t5909 + 39357 * t5908 + 9056 * t5907 - 3614 * t5904 - 1348 * t5905 - t5917) * np.exp((3*1j) * (t5906 + 2 * phi2))) * ((1 + t5905) ** (-0.1e1 / 0.2e1)) + + if Bindx == 294: + t5933 = np.cos(phi) + t5932 = t5933 ** 2 + t5935 = t5933 * t5932 + t5936 = t5932 ** 2 + t5937 = t5933 * t5936 + t5938 = t5935 ** 2 + t5939 = t5933 * t5938 + t5940 = t5936 ** 2 + t5942 = t5937 ** 2 + t5944 = t5938 ** 2 + t5948 = 152 * t5935 - 1012 * t5937 + 528 * t5939 + (924 * t5940 - 552 * t5942 - 108 * t5944 + 68) * t5933 + t5947 = -13 * t5939 ** 2 + 189 * t5932 - 363 * t5936 - 759 * t5938 + 1419 * t5940 - 121 * t5942 - 361 * t5944 + 9 + t5934 = 3 * phi1 + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.sqrt(0.23e2) * np.sqrt(0.2e1) * ((1 + t5933) ** (-0.1e1 / 0.2e1)) * ((1 - t5933) ** (-0.1e1 / 0.2e1)) * ((-t5947 + t5948) * np.exp((3*1j) * (t5934 - 4 * phi2)) + (t5947 + t5948) * np.exp((3*1j) * (t5934 + 4 * phi2))) + + if Bindx == 295: + t5963 = np.cos(phi) + t5962 = t5963 ** 2 + t5966 = t5962 ** 2 + t5965 = t5963 * t5962 + t5968 = t5965 ** 2 + t5970 = t5966 ** 2 + t5967 = t5963 * t5966 + t5972 = t5967 ** 2 + t5974 = t5968 ** 2 + t5977 = 17 - 391 * t5962 + 2246 * t5966 - 4182 * t5968 + 1605 * t5970 + 2205 * t5972 - 1500 * t5974 + t5976 = 210 * t5965 + 1377 * t5967 + (-5436 * t5968 + 5951 * t5970 - 1702 * t5972 - 325 * t5974 - 75) * t5963 + t5964 = 5 * phi1 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((-t5976 + t5977) * np.exp((2*1j) * (t5964 - 3 * phi2)) + (t5976 + t5977) * np.exp((2*1j) * (t5964 + 3 * phi2))) + + if Bindx == 296: + t5992 = np.cos(phi) + t5991 = t5992 ** 2 + t5995 = t5991 ** 2 + t5994 = t5992 * t5991 + t5997 = t5994 ** 2 + t5999 = t5995 ** 2 + t5996 = t5992 * t5995 + t6001 = t5996 ** 2 + t6003 = t5997 ** 2 + t6006 = 10 + 310 * t5991 + 220 * t5995 - 1716 * t5997 + 66 * t5999 + 990 * t6001 + 120 * t6003 + t6005 = -528 * t5994 + 825 * t5996 + (1320 * t5997 - 1045 * t5999 - 472 * t6001 - 13 * t6003 - 87) * t5992 + t5993 = 5 * phi1 + tfunc[..., c] = (0.135e3 / 0.4096e4) * ((t6005 + t6006) * np.exp((2*1j) * (t5993 - 6 * phi2)) + (-t6005 + t6006) * np.exp((2*1j) * (t5993 + 6 * phi2))) * np.sqrt(0.2e1) + + if Bindx == 297: + t6019 = np.cos(phi) + t6018 = t6019 ** 2 + t6022 = t6018 ** 2 + t6023 = t6019 * t6022 + t6028 = t6023 ** 2 + t6030 = -325 * t6019 * t6028 - 59 + t6026 = t6022 ** 2 + t6021 = t6019 * t6018 + t6024 = t6021 ** 2 + t6020 = 11 * phi1 + t6013 = t6019 * t6024 + t6011 = t6019 * t6026 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * ((1 - t6019) ** (0.5e1 / 0.2e1)) * np.sqrt(0.253e3) * ((1 + t6019) ** (-0.1e1 / 0.2e1)) * ((-675 * t6028 - 841 * t6011 + 2423 * t6026 + 450 * t6013 - 3278 * t6024 + 446 * t6023 + 2046 * t6022 - 503 * t6021 - 575 * t6018 + 123 * t6019 - t6030) * np.exp((1j) * (t6020 - 6 * phi2)) + (-2625 * t6028 - 9059 * t6011 - 17031 * t6026 - 17874 * t6013 - 8106 * t6024 + 3066 * t6023 + 6066 * t6022 + 2919 * t6021 + 251 * t6018 - 231 * t6019 + t6030) * np.exp((1j) * (t6020 + 6 * phi2))) + + if Bindx == 298: + t6046 = np.cos(phi) + t6045 = t6046 ** 2 + t6048 = t6046 * t6045 + t6049 = t6045 ** 2 + t6050 = t6046 * t6049 + t6051 = t6048 ** 2 + t6052 = t6046 * t6051 + t6053 = t6049 ** 2 + t6055 = t6050 ** 2 + t6057 = t6051 ** 2 + t6061 = 992 * t6048 - 308 * t6050 - 3168 * t6052 + (836 * t6053 + 1408 * t6055 + 132 * t6057 + 108) * t6046 + t6060 = -13 * t6052 ** 2 - 451 * t6045 - 1023 * t6049 + 2409 * t6051 + 1551 * t6053 - 1881 * t6055 - 581 * t6057 - 11 + t6047 = 11 * phi1 + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * ((t6060 + t6061) * np.exp((1j) * (t6047 - 12 * phi2)) + (-t6060 + t6061) * np.exp((1j) * (t6047 + 12 * phi2))) * ((1 + t6046) ** (-0.1e1 / 0.2e1)) * ((1 - t6046) ** (-0.1e1 / 0.2e1)) + + if Bindx == 299: + t6076 = np.cos(phi) + t6075 = t6076 ** 2 + t6079 = t6075 ** 2 + t6078 = t6076 * t6075 + t6081 = t6078 ** 2 + t6083 = t6079 ** 2 + t6080 = t6076 * t6079 + t6085 = t6080 ** 2 + t6087 = t6081 ** 2 + t6090 = -6 + 6 * t6075 + 188 * t6079 - 468 * t6081 + 306 * t6083 + 46 * t6085 - 72 * t6087 + t6089 = 144 * t6078 - 135 * t6080 + (-216 * t6081 + 363 * t6083 - 120 * t6085 - 13 * t6087 - 23) * t6076 + t6077 = 2 * phi1 + tfunc[..., c] = -(0.135e3 / 0.4096e4) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((-t6089 + t6090) * np.exp((6*1j) * (t6077 - phi2)) + (t6089 + t6090) * np.exp((6*1j) * (t6077 + phi2))) + + if Bindx == 300: + t6105 = np.cos(phi) + t6104 = t6105 ** 2 + t6107 = t6104 ** 2 + t6106 = t6105 * t6104 + t6109 = t6106 ** 2 + t6111 = t6107 ** 2 + t6108 = t6105 * t6107 + t6113 = t6108 ** 2 + t6115 = t6109 ** 2 + t6118 = 12 + 636 * t6104 + 3080 * t6107 + 792 * t6109 - 4356 * t6111 - 2068 * t6113 - 144 * t6115 + t6117 = 1782 * t6106 + 3069 * t6108 + (-2508 * t6109 - 3795 * t6111 - 714 * t6113 - 13 * t6115 + 131) * t6105 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.sqrt(0.2e1) * ((-t6117 + t6118) * np.exp((12*1j) * (phi1 - phi2)) + (t6117 + t6118) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 301: + t6131 = np.cos(phi) + t6130 = t6131 ** 2 + t6134 = t6130 ** 2 + t6137 = t6134 ** 2 + t6135 = t6131 * t6134 + t6139 = t6135 ** 2 + t6140 = t6131 * t6139 + t6143 = -t6131 * t6140 + 12 * t6130 - 27 * t6134 + 27 * t6137 - 12 * t6139 + 1 + t6142 = 6 * t6140 + 2 * (-18 * t6130 + 18) * t6135 + 2 * (-t6130 + t6137 - 3) * t6131 + t6132 = 13 * phi1 + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.sqrt(0.3289e4) * np.sqrt((1 - t6131)) * np.sqrt((1 + t6131)) * ((t6142 + t6143) * np.exp((1j) * (t6132 - 6 * phi2)) + (t6142 - t6143) * np.exp((1j) * (t6132 + 6 * phi2))) + + if Bindx == 302: + t6158 = np.cos(phi) + t6157 = t6158 ** 2 + t6160 = t6158 * t6157 + t6163 = t6160 ** 2 + t6169 = t6163 ** 2 + t6171 = -t6158 * t6169 - 1 + t6161 = t6157 ** 2 + t6162 = t6158 * t6161 + t6167 = t6162 ** 2 + t6165 = t6161 ** 2 + t6159 = 13 * phi1 + t6152 = t6158 * t6163 + t6150 = t6158 * t6165 + t6148 = t6158 * t6167 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.sqrt(0.13e2) * np.sqrt((1 - t6158)) * ((11 * t6169 - 54 * t6148 + 154 * t6167 - 275 * t6150 + 297 * t6165 - 132 * t6152 - 132 * t6163 + 297 * t6162 - 275 * t6161 + 154 * t6160 - 54 * t6157 + 11 * t6158 + t6171) * np.exp((1j) * (t6159 - 12 * phi2)) + (13 * t6169 + 78 * t6148 + 286 * t6167 + 715 * t6150 + 1287 * t6165 + 1716 * t6152 + 1716 * t6163 + 1287 * t6162 + 715 * t6161 + 286 * t6160 + 78 * t6157 + 13 * t6158 - t6171) * np.exp((1j) * (t6159 + 12 * phi2))) * ((1 + t6158) ** (-0.1e1 / 0.2e1)) + + if Bindx == 303: + t6177 = np.sin(phi) + t6172 = t6177 ** 2 + t6173 = t6177 * t6172 + t6175 = t6177 * t6173 ** 2 + tfunc[..., c] = -(0.435e3 / 0.8192e4) * np.exp((-14*1j) * phi1) * np.sqrt(0.44574e5) * t6175 ** 2 + + if Bindx == 304: + t6193 = np.cos(phi) + t6209 = -6 * t6193 + t6192 = t6193 ** 2 + t6195 = t6193 * t6192 + t6196 = t6192 ** 2 + t6197 = t6193 * t6196 + t6198 = t6195 ** 2 + t6199 = t6193 * t6198 + t6200 = t6196 ** 2 + t6202 = t6197 ** 2 + t6204 = t6198 ** 2 + t6208 = t6204 * t6209 + 4 * t6195 + 38 * t6197 - 72 * t6199 + t6209 + (38 * t6200 + 4 * t6202) * t6193 + t6207 = t6199 ** 2 + 11 * t6192 - 39 * t6196 + 27 * t6198 + 27 * t6200 - 39 * t6202 + 11 * t6204 + 1 + t6194 = 7 * phi1 + tfunc[..., c] = (0.87e2 / 0.32768e5) * np.sqrt(0.345345e6) * np.sqrt(0.2e1) * ((t6207 + t6208) * np.exp((-2*1j) * (t6194 - 3 * phi2)) + (t6207 - t6208) * np.exp((-2*1j) * (t6194 + 3 * phi2))) + + if Bindx == 305: + t6224 = np.cos(phi) + t6223 = t6224 ** 2 + t6226 = t6224 * t6223 + t6227 = t6223 ** 2 + t6228 = t6224 * t6227 + t6230 = t6227 ** 2 + t6232 = t6228 ** 2 + t6229 = t6226 ** 2 + t6234 = t6229 ** 2 + t6235 = t6224 * t6234 + t6238 = 208 * t6226 + 572 * t6228 - 12 * t6235 + (-572 * t6230 - 208 * t6232 + 12) * t6224 + t6237 = t6224 * t6235 - 65 * t6223 - 429 * t6227 - 429 * t6229 + 429 * t6230 + 429 * t6232 + 65 * t6234 - 1 + t6225 = 7 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * ((t6237 + t6238) * np.exp((-2*1j) * (t6225 - 6 * phi2)) + (t6237 - t6238) * np.exp((-2*1j) * (t6225 + 6 * phi2))) * np.sqrt(0.21e2) + + if Bindx == 306: + t6239 = np.cos(phi) + t6240 = t6239 ** 2 + t6241 = t6239 * t6240 + t6244 = t6241 ** 2 + t6242 = t6240 ** 2 + tfunc[..., c] = (0.435e3 / 0.4096e4*1j) * t6239 * (-21 * t6240 + 35 * t6241 - 35 * t6242 - 7 * t6244 - 1 + (21 * t6242 + t6244 + 7) * t6239) * ((1 + t6239) ** (0.13e2 / 0.2e1)) * np.sqrt(0.312018e6) * np.exp((-13*1j) * phi1) * ((1 - t6239) ** (-0.1e1 / 0.2e1)) + + if Bindx == 307: + t6258 = np.cos(phi) + t6257 = t6258 ** 2 + t6261 = t6257 ** 2 + t6262 = t6258 * t6261 + t6267 = t6262 ** 2 + t6269 = 7 * t6258 * t6267 + t6265 = t6261 ** 2 + t6260 = t6258 * t6257 + t6263 = t6260 ** 2 + t6259 = 13 * phi1 + t6252 = t6258 * t6263 + t6250 = t6258 * t6265 + tfunc[..., c] = (0.87e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * ((1 + t6258) ** (0.7e1 / 0.2e1)) * np.sqrt(0.49335e5) * ((1 - t6258) ** (-0.1e1 / 0.2e1)) * ((t6269 - 67 * t6267 + 285 * t6250 - 705 * t6265 + 1110 * t6252 - 1134 * t6263 + 714 * t6262 - 210 * t6261 - 45 * t6260 + 65 * t6257 - 23 * t6258 + 3) * np.exp((-1*1j) * (t6259 - 6 * phi2)) + (t6269 + 11 * t6267 - 27 * t6250 - 47 * t6265 + 38 * t6252 + 78 * t6263 - 22 * t6262 - 62 * t6261 + 3 * t6260 + 23 * t6257 + t6258 - 3) * np.exp((-1*1j) * (t6259 + 6 * phi2))) + + if Bindx == 308: + t6285 = np.cos(phi) + t6284 = t6285 ** 2 + t6287 = t6285 * t6284 + t6290 = t6287 ** 2 + t6291 = t6285 * t6290 + t6299 = -1287 * t6290 + (-1716 + 7 * t6291) * t6291 + t6296 = t6290 ** 2 + t6288 = t6284 ** 2 + t6289 = t6285 * t6288 + t6294 = t6289 ** 2 + t6292 = t6288 ** 2 + t6286 = 13 * phi1 + t6277 = t6285 * t6292 + t6275 = t6285 * t6294 + t6273 = t6285 * t6296 + tfunc[..., c] = (0.87e2 / 0.8192e4*1j) * np.sqrt((1 + t6285)) * np.sqrt(0.3e1) * ((1 - t6285) ** (-0.1e1 / 0.2e1)) * ((-85 * t6273 + 468 * t6296 - 1534 * t6275 + 3289 * t6294 - 4719 * t6277 + 4290 * t6292 + 2717 * t6289 - 2288 * t6288 + 1170 * t6287 - 377 * t6284 + 71 * t6285 - 6 + t6299) * np.exp((-1*1j) * (t6286 - 12 * phi2)) + (71 * t6273 + 312 * t6296 + 754 * t6275 + 1001 * t6294 + 429 * t6277 - 858 * t6292 - 143 * t6289 + 572 * t6288 + 546 * t6287 + 247 * t6284 + 59 * t6285 + 6 + t6299) * np.exp((-1*1j) * (t6286 + 12 * phi2))) + + if Bindx == 309: + t6305 = np.sin(phi) + t6301 = t6305 ** 2 + t6302 = t6305 * t6301 + t6303 = t6302 ** 2 + t6300 = np.cos(phi) + tfunc[..., c] = (0.145e3 / 0.4096e4) * np.exp((-12*1j) * phi1) * np.sqrt(0.52003e5) * t6303 ** 2 * (27 * t6300 ** 2 - 1) + + if Bindx == 310: + t6321 = np.cos(phi) + t6320 = t6321 ** 2 + t6323 = t6321 * t6320 + t6324 = t6320 ** 2 + t6325 = t6321 * t6324 + t6326 = t6323 ** 2 + t6327 = t6321 * t6326 + t6328 = t6324 ** 2 + t6330 = t6325 ** 2 + t6332 = t6326 ** 2 + t6336 = -752 * t6323 + 3708 * t6325 - 5760 * t6327 + (2372 * t6328 + 1392 * t6330 - 972 * t6332 + 12) * t6321 + t6335 = 189 * t6327 ** 2 + 435 * t6320 - 1161 * t6324 - 729 * t6326 + 5049 * t6328 - 5079 * t6330 + 1325 * t6332 - 29 + t6322 = 2 * phi1 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t6335 + t6336) * np.exp((-6*1j) * (t6322 - phi2)) + (t6335 - t6336) * np.exp((-6*1j) * (t6322 + phi2))) * np.sqrt(0.16445e5) + + if Bindx == 311: + t6352 = np.cos(phi) + t6351 = t6352 ** 2 + t6353 = t6352 * t6351 + t6354 = t6351 ** 2 + t6355 = t6352 * t6354 + t6356 = t6353 ** 2 + t6357 = t6352 * t6356 + t6358 = t6354 ** 2 + t6360 = t6355 ** 2 + t6362 = t6356 ** 2 + t6366 = -11024 * t6353 + 10296 * t6355 + 41184 * t6357 + (-19448 * t6358 - 21840 * t6360 - 1944 * t6362 - 1320) * t6352 + t6365 = 189 * t6357 ** 2 + 5343 * t6351 + 9009 * t6354 - 36465 * t6356 - 14157 * t6358 + 31317 * t6360 + 8723 * t6362 + 137 + tfunc[..., c] = (0.29e2 / 0.16384e5) * np.sqrt(0.2e1) * ((t6365 + t6366) * np.exp((-12*1j) * (phi1 - phi2)) + (t6365 - t6366) * np.exp((-12*1j) * (phi1 + phi2))) + + if Bindx == 312: + t6367 = np.cos(phi) + tfunc[..., c] = (0.145e3 / 0.4096e4*1j) * (9 * t6367 ** 2 - 1) * t6367 * ((1 + t6367) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4056234e7) * np.exp((-11*1j) * phi1) * ((1 - t6367) ** (0.11e2 / 0.2e1)) + + if Bindx == 313: + t6381 = np.cos(phi) + t6380 = t6381 ** 2 + t6383 = t6381 * t6380 + t6386 = t6383 ** 2 + t6393 = 819 * t6386 ** 2 + t6384 = t6380 ** 2 + t6385 = t6381 * t6384 + t6390 = t6385 ** 2 + t6388 = t6384 ** 2 + t6382 = 11 * phi1 + t6375 = t6381 * t6386 + t6373 = t6381 * t6388 + t6371 = t6381 * t6390 + tfunc[..., c] = (0.29e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * ((1 + t6381) ** (0.5e1 / 0.2e1)) * np.sqrt(0.3795e4) * ((1 - t6381) ** (-0.1e1 / 0.2e1)) * ((t6393 - 6318 * t6371 + 20384 * t6390 - 34250 * t6373 + 28035 * t6388 - 1068 * t6375 - 18984 * t6386 + 15372 * t6385 - 2775 * t6384 - 2310 * t6383 + 1224 * t6380 - 98 * t6381 - 31) * np.exp((-1*1j) * (t6382 - 6 * phi2)) + (t6393 + 1404 * t6371 - 2782 * t6390 - 5452 * t6373 + 3417 * t6388 + 8216 * t6375 - 1780 * t6386 - 5976 * t6385 + 373 * t6384 + 2092 * t6383 - 78 * t6380 - 284 * t6381 + 31) * np.exp((-1*1j) * (t6382 + 6 * phi2))) + + if Bindx == 314: + t6410 = np.cos(phi) + t6409 = t6410 ** 2 + t6413 = t6409 ** 2 + t6412 = t6410 * t6409 + t6415 = t6412 ** 2 + t6417 = t6413 ** 2 + t6414 = t6410 * t6413 + t6419 = t6414 ** 2 + t6421 = t6415 ** 2 + t6416 = t6410 * t6415 + t6423 = t6416 ** 2 + t6426 = 38 + 1026 * t6409 - 1394 * t6413 - 7062 * t6415 + 10098 * t6417 + 2662 * t6419 - 4774 * t6421 - 594 * t6423 + t6425 = -1199 * t6412 + 6237 * t6414 - 627 * t6416 + (-10637 * t6417 + 4131 * t6419 + 2351 * t6421 + 63 * t6423 - 319) * t6410 + t6411 = 11 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.39e2) * ((1 + t6410) ** (-0.1e1 / 0.2e1)) * ((1 - t6410) ** (-0.1e1 / 0.2e1)) * ((t6425 + t6426) * np.exp((-1*1j) * (t6411 - 12 * phi2)) + (t6425 - t6426) * np.exp((-1*1j) * (t6411 + 12 * phi2))) + + if Bindx == 315: + t6434 = np.sin(phi) + t6430 = t6434 ** 2 + t6432 = t6434 * t6430 ** 2 + t6427 = np.cos(phi) + t6428 = t6427 ** 2 + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((-10*1j) * phi1) * np.sqrt(0.4056234e7) * t6432 ** 2 * (1 + (-50 + 225 * t6428) * t6428) + + if Bindx == 316: + t6450 = np.cos(phi) + t6449 = t6450 ** 2 + t6452 = t6450 * t6449 + t6453 = t6449 ** 2 + t6454 = t6450 * t6453 + t6455 = t6452 ** 2 + t6456 = t6450 * t6455 + t6457 = t6453 ** 2 + t6459 = t6454 ** 2 + t6461 = t6455 ** 2 + t6465 = -7532 * t6452 + 27822 * t6454 - 34152 * t6456 + (-5714 * t6457 + 36500 * t6459 - 17550 * t6461 + 626) * t6450 + t6464 = 4095 * t6456 ** 2 + 37 * t6449 + 6039 * t6453 - 38331 * t6455 + 81477 * t6457 - 68201 * t6459 + 14885 * t6461 - 1 + t6451 = 5 * phi1 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.3795e4) * np.sqrt(0.2e1) * ((t6464 + t6465) * np.exp((-2*1j) * (t6451 - 3 * phi2)) + (t6464 - t6465) * np.exp((-2*1j) * (t6451 + 3 * phi2))) + + if Bindx == 317: + t6481 = np.cos(phi) + t6480 = t6481 ** 2 + t6483 = t6481 * t6480 + t6484 = t6480 ** 2 + t6485 = t6481 * t6484 + t6486 = t6483 ** 2 + t6487 = t6481 * t6486 + t6488 = t6484 ** 2 + t6490 = t6485 ** 2 + t6492 = t6486 ** 2 + t6496 = 80 * t6483 - 3828 * t6485 + 4224 * t6487 + (3124 * t6488 - 3280 * t6490 - 540 * t6492 + 220) * t6481 + t6495 = 63 * t6487 ** 2 - 527 * t6480 + 2013 * t6484 + 1485 * t6486 - 6765 * t6488 + 1859 * t6490 + 1903 * t6492 - 31 + t6482 = 5 * phi1 + tfunc[..., c] = (0.145e3 / 0.16384e5) * ((t6495 + t6496) * np.exp((-2*1j) * (t6482 - 6 * phi2)) + (t6495 - t6496) * np.exp((-2*1j) * (t6482 + 6 * phi2))) * np.sqrt(0.39e2) + + if Bindx == 318: + t6497 = np.cos(phi) + t6498 = t6497 ** 2 + tfunc[..., c] = (-0.29e2 / 0.2048e4*1j) * (3 + (-50 + 135 * t6498) * t6498) * t6497 * ((1 + t6497) ** (0.9e1 / 0.2e1)) * np.sqrt(0.3380195e7) * np.exp((-9*1j) * phi1) * ((1 - t6497) ** (0.9e1 / 0.2e1)) + + if Bindx == 319: + t6514 = np.cos(phi) + t6513 = t6514 ** 2 + t6516 = t6514 * t6513 + t6519 = t6516 ** 2 + t6525 = t6519 ** 2 + t6527 = 12285 * t6514 * t6525 + t6517 = t6513 ** 2 + t6518 = t6514 * t6517 + t6523 = t6518 ** 2 + t6521 = t6517 ** 2 + t6515 = 3 * phi1 + t6508 = t6514 * t6519 + t6506 = t6514 * t6521 + t6504 = t6514 * t6523 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * ((1 + t6514) ** (0.3e1 / 0.2e1)) * np.sqrt(0.253e3) * ((1 - t6514) ** (-0.1e1 / 0.2e1)) * ((t6527 - 71955 * t6525 + 152230 * t6504 - 97010 * t6523 - 123665 * t6506 + 229431 * t6521 - 70908 * t6508 - 92652 * t6519 + 72459 * t6518 + 1035 * t6517 - 14346 * t6516 + 2558 * t6513 + 617 * t6514 - 79) * np.exp((-3*1j) * (t6515 - 2 * phi2)) + (t6527 + 22815 * t6525 - 37310 * t6504 - 83690 * t6523 + 39235 * t6506 + 120129 * t6521 - 15204 * t6508 - 84684 * t6519 + 819 * t6518 + 29745 * t6517 - 126 * t6516 - 4394 * t6513 + 301 * t6514 + 79) * np.exp((-3*1j) * (t6515 + 2 * phi2))) + + if Bindx == 320: + t6544 = np.cos(phi) + t6543 = t6544 ** 2 + t6547 = t6543 ** 2 + t6546 = t6544 * t6543 + t6549 = t6546 ** 2 + t6551 = t6547 ** 2 + t6548 = t6544 * t6547 + t6553 = t6548 ** 2 + t6555 = t6549 ** 2 + t6550 = t6544 * t6549 + t6557 = t6550 ** 2 + t6560 = -74 - 518 * t6543 + 5542 * t6547 - 8646 * t6549 - 4686 * t6551 + 14718 * t6553 - 4878 * t6555 - 1458 * t6557 + t6559 = -1677 * t6546 - 3333 * t6548 + 16071 * t6550 + (-12859 * t6551 - 3127 * t6553 + 4313 * t6555 + 189 * t6557 + 423) * t6544 + t6545 = 3 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((1 + t6544) ** (-0.1e1 / 0.2e1)) * ((1 - t6544) ** (-0.1e1 / 0.2e1)) * ((t6559 + t6560) * np.exp((-3*1j) * (t6545 - 4 * phi2)) + (t6559 - t6560) * np.exp((-3*1j) * (t6545 + 4 * phi2))) + + if Bindx == 321: + t6568 = np.sin(phi) + t6565 = t6568 ** 2 + t6566 = t6565 ** 2 + t6561 = np.cos(phi) + t6562 = t6561 ** 2 + t6563 = t6562 ** 2 + tfunc[..., c] = (0.29e2 / 0.4096e4) * np.exp((-8*1j) * phi1) * np.sqrt(0.881790e6) * t6566 ** 2 * (-575 * t6563 - 1 + (1035 * t6563 + 69) * t6562) + + if Bindx == 322: + t6584 = np.cos(phi) + t6583 = t6584 ** 2 + t6586 = t6584 * t6583 + t6587 = t6583 ** 2 + t6588 = t6584 * t6587 + t6589 = t6586 ** 2 + t6590 = t6584 * t6589 + t6591 = t6587 ** 2 + t6593 = t6588 ** 2 + t6595 = t6589 ** 2 + t6599 = -26848 * t6586 + 59784 * t6588 + 165120 * t6590 + (-719624 * t6591 + 842720 * t6593 - 322920 * t6595 + 1768) * t6584 + t6598 = 94185 * t6590 ** 2 - 17105 * t6583 + 164979 * t6587 - 631173 * t6589 + 1090533 * t6591 - 783955 * t6593 + 82225 * t6595 + 311 + t6585 = 4 * phi1 + tfunc[..., c] = (0.29e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.33e2) * ((t6598 + t6599) * np.exp((-2*1j) * (t6585 - 3 * phi2)) + (t6598 - t6599) * np.exp((-2*1j) * (t6585 + 3 * phi2))) + + if Bindx == 323: + t6615 = np.cos(phi) + t6614 = t6615 ** 2 + t6617 = t6615 * t6614 + t6618 = t6614 ** 2 + t6619 = t6615 * t6618 + t6620 = t6617 ** 2 + t6621 = t6615 * t6620 + t6622 = t6618 ** 2 + t6624 = t6619 ** 2 + t6626 = t6620 ** 2 + t6630 = 608 * t6617 - 528 * t6619 - 2112 * t6621 + (3344 * t6622 - 800 * t6624 - 432 * t6626 - 80) * t6615 + t6629 = 63 * t6621 ** 2 - 19 * t6614 - 957 * t6618 + 2805 * t6620 - 1551 * t6622 - 1441 * t6624 + 1081 * t6626 + 19 + t6616 = 2 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4) * ((t6629 + t6630) * np.exp((-4*1j) * (t6616 - 3 * phi2)) + (t6629 - t6630) * np.exp((-4*1j) * (t6616 + 3 * phi2))) * np.sqrt(0.4485e4) + + if Bindx == 324: + t6631 = np.cos(phi) + t6632 = t6631 ** 2 + t6633 = t6632 ** 2 + tfunc[..., c] = (0.29e2 / 0.2048e4*1j) * (-805 * t6633 - 7 + (1035 * t6633 + 161) * t6632) * t6631 * ((1 + t6631) ** (0.7e1 / 0.2e1)) * np.sqrt(0.692835e6) * np.exp((-7*1j) * phi1) * ((1 - t6631) ** (0.7e1 / 0.2e1)) + + if Bindx == 325: + t6650 = np.cos(phi) + t6649 = t6650 ** 2 + t6652 = t6650 * t6649 + t6655 = t6652 ** 2 + t6656 = t6650 * t6655 + t6664 = 148005 * t6656 ** 2 + t6661 = t6655 ** 2 + t6653 = t6649 ** 2 + t6654 = t6650 * t6653 + t6659 = t6654 ** 2 + t6657 = t6653 ** 2 + t6651 = 7 * phi1 + t6642 = t6650 * t6657 + t6640 = t6650 * t6659 + t6638 = t6650 * t6661 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt((1 + t6650)) * np.sqrt(0.21e2) * ((1 - t6650) ** (-0.1e1 / 0.2e1)) * ((-592020 * t6638 + 1001880 * t6640 - 152812 * t6642 - 3813 * t6649 + 3372 * t6650 - 67048 * t6652 + 95307 * t6653 + 342228 * t6654 - 639777 * t6655 - 543792 * t6656 + 1650693 * t6657 - 1719135 * t6659 + 476905 * t6661 + 7 + t6664) * np.exp((-1*1j) * (t6651 - 6 * phi2)) + (296010 * t6638 - 1067660 * t6640 + 1521542 * t6642 - 2945 * t6649 + 3386 * t6650 - 67916 * t6652 + 39657 * t6653 + 397878 * t6654 - 100329 * t6655 - 1083240 * t6656 - 23661 * t6657 + 350405 * t6659 - 411125 * t6661 - 7 + t6664) * np.exp((-1*1j) * (t6651 + 6 * phi2))) + + if Bindx == 326: + t6681 = np.cos(phi) + t6680 = t6681 ** 2 + t6684 = t6680 ** 2 + t6683 = t6681 * t6680 + t6686 = t6683 ** 2 + t6688 = t6684 ** 2 + t6685 = t6681 * t6684 + t6690 = t6685 ** 2 + t6692 = t6686 ** 2 + t6687 = t6681 * t6686 + t6694 = t6687 ** 2 + t6697 = 2 - 18 * t6680 - 14 * t6684 + 270 * t6686 - 522 * t6688 + 314 * t6690 + 22 * t6692 - 54 * t6694 + t6696 = 71 * t6683 - 225 * t6685 + 171 * t6687 + (193 * t6688 - 315 * t6690 + 101 * t6692 + 9 * t6694 - 5) * t6681 + t6682 = 7 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.345345e6) * np.sqrt(0.2e1) * ((1 + t6681) ** (-0.1e1 / 0.2e1)) * ((1 - t6681) ** (-0.1e1 / 0.2e1)) * ((t6696 + t6697) * np.exp((-1*1j) * (t6682 - 12 * phi2)) + (t6696 - t6697) * np.exp((-1*1j) * (t6682 + 12 * phi2))) + + if Bindx == 327: + t6706 = np.sin(phi) + t6703 = t6706 ** 2 + t6704 = t6706 * t6703 + t6698 = np.cos(phi) + t6699 = t6698 ** 2 + t6700 = t6699 ** 2 + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((-6*1j) * phi1) * np.sqrt(0.3233230e7) * t6704 ** 2 * (-84 * t6699 + 1 + (-3220 * t6699 + 966 + 3105 * t6700) * t6700) + + if Bindx == 328: + t6722 = np.cos(phi) + t6721 = t6722 ** 2 + t6723 = t6722 * t6721 + t6724 = t6721 ** 2 + t6725 = t6722 * t6724 + t6726 = t6723 ** 2 + t6727 = t6722 * t6726 + t6728 = t6724 ** 2 + t6730 = t6725 ** 2 + t6732 = t6726 ** 2 + t6736 = 520532 * t6723 - 4172562 * t6725 + 15354648 * t6727 + (-27848722 * t6728 + 24151380 * t6730 - 7992270 * t6732 - 21198) * t6722 + t6735 = 3108105 * t6727 ** 2 - 230253 * t6721 + 2425617 * t6724 - 8886957 * t6726 + 13338963 * t6728 - 5787375 * t6730 - 3963245 * t6732 + 3337 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.2e1) * ((t6735 + t6736) * np.exp((-6*1j) * (phi1 - phi2)) + (t6735 - t6736) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 329: + t6752 = np.cos(phi) + t6751 = t6752 ** 2 + t6753 = t6752 * t6751 + t6754 = t6751 ** 2 + t6755 = t6752 * t6754 + t6756 = t6753 ** 2 + t6757 = t6752 * t6756 + t6758 = t6754 ** 2 + t6760 = t6755 ** 2 + t6762 = t6756 ** 2 + t6766 = -752 * t6753 + 3708 * t6755 - 5760 * t6757 + (2372 * t6758 + 1392 * t6760 - 972 * t6762 + 12) * t6752 + t6765 = 189 * t6757 ** 2 + 435 * t6751 - 1161 * t6754 - 729 * t6756 + 5049 * t6758 - 5079 * t6760 + 1325 * t6762 - 29 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t6765 + t6766) * np.exp((-6*1j) * (phi1 - 2 * phi2)) + (t6765 - t6766) * np.exp((-6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.16445e5) + + if Bindx == 330: + t6767 = np.cos(phi) + t6768 = t6767 ** 2 + t6769 = t6768 ** 2 + tfunc[..., c] = (-0.87e2 / 0.4096e4*1j) * (-140 * t6768 + 5 + (-2300 * t6768 + 966 + 1725 * t6769) * t6769) * t6767 * ((1 + t6767) ** (0.5e1 / 0.2e1)) * np.sqrt(0.646646e6) * np.exp((-5*1j) * phi1) * ((1 - t6767) ** (0.5e1 / 0.2e1)) + + if Bindx == 331: + t6788 = np.cos(phi) + t6787 = t6788 ** 2 + t6791 = t6787 ** 2 + t6790 = t6788 * t6787 + t6793 = t6790 ** 2 + t6795 = t6791 ** 2 + t6792 = t6788 * t6791 + t6797 = t6792 ** 2 + t6799 = t6793 ** 2 + t6794 = t6788 * t6793 + t6801 = t6794 ** 2 + t6804 = 17175 * t6787 - 222785 * t6791 + 1148347 * t6793 - 2946015 * t6795 + 3989557 * t6797 - 2726075 * t6799 + 740025 * t6801 - 229 + t6803 = -46785 * t6790 + 291903 * t6792 - 673485 * t6794 + (430705 * t6795 + 554829 * t6797 - 904475 * t6799 + 345345 * t6801 + 1963) * t6788 + t6789 = 5 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * ((1 + t6788) ** (-0.1e1 / 0.2e1)) * ((1 - t6788) ** (-0.1e1 / 0.2e1)) * ((t6803 - t6804) * np.exp((-1*1j) * (t6789 - 6 * phi2)) + (t6803 + t6804) * np.exp((-1*1j) * (t6789 + 6 * phi2))) + + if Bindx == 332: + t6821 = np.cos(phi) + t6820 = t6821 ** 2 + t6824 = t6820 ** 2 + t6823 = t6821 * t6820 + t6826 = t6823 ** 2 + t6828 = t6824 ** 2 + t6825 = t6821 * t6824 + t6830 = t6825 ** 2 + t6832 = t6826 ** 2 + t6827 = t6821 * t6826 + t6834 = t6827 ** 2 + t6837 = -2 + 42 * t6820 - 218 * t6824 + 402 * t6826 - 198 * t6828 - 226 * t6830 + 290 * t6832 - 90 * t6834 + t6836 = -5 * t6823 + 207 * t6825 - 657 * t6827 + (833 * t6828 - 447 * t6830 + 53 * t6832 + 21 * t6834 - 5) * t6821 + t6822 = 5 * phi1 + tfunc[..., c] = (0.435e3 / 0.8192e4*1j) * np.sqrt(0.3289e4) * ((1 + t6821) ** (-0.1e1 / 0.2e1)) * ((1 - t6821) ** (-0.1e1 / 0.2e1)) * ((t6836 + t6837) * np.exp((-1*1j) * (t6822 - 12 * phi2)) + (t6836 - t6837) * np.exp((-1*1j) * (t6822 + 12 * phi2))) + + if Bindx == 333: + t6846 = np.sin(phi) + t6844 = t6846 ** 2 + t6838 = np.cos(phi) + t6839 = t6838 ** 2 + t6840 = t6839 ** 2 + t6842 = t6840 ** 2 + tfunc[..., c] = (0.87e2 / 0.4096e4) * np.exp((-4*1j) * phi1) * np.sqrt(0.85085e5) * t6844 ** 2 * (-1330 * t6840 - 10925 * t6842 - 1 + (6118 * t6840 + 6555 * t6842 + 95) * t6839) + + if Bindx == 334: + t6862 = np.cos(phi) + t6861 = t6862 ** 2 + t6865 = t6861 ** 2 + t6864 = t6862 * t6861 + t6867 = t6864 ** 2 + t6868 = t6862 * t6867 + t6869 = t6865 ** 2 + t6866 = t6862 * t6865 + t6871 = t6866 ** 2 + t6873 = t6867 ** 2 + t6877 = 345345 * t6868 ** 2 - 2449 * t6861 + 21251 * t6865 - 2509 * t6867 - 338899 * t6869 + 947485 * t6871 - 970255 * t6873 + 31 + t6876 = 64624 * t6864 - 483996 * t6866 + 1573504 * t6868 + (-2522916 * t6869 + 1963280 * t6871 - 592020 * t6873 - 2476) * t6862 + t6863 = 2 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * ((t6876 + t6877) * np.exp((-2*1j) * (t6863 - 3 * phi2)) + (-t6876 + t6877) * np.exp((-2*1j) * (t6863 + 3 * phi2))) * np.sqrt(0.19e2) + + if Bindx == 335: + t6893 = np.cos(phi) + t6892 = t6893 ** 2 + t6894 = t6893 * t6892 + t6895 = t6892 ** 2 + t6896 = t6893 * t6895 + t6897 = t6894 ** 2 + t6898 = t6893 * t6897 + t6899 = t6895 ** 2 + t6901 = t6896 ** 2 + t6903 = t6897 ** 2 + t6907 = -48 * t6894 + 40 * t6896 + 160 * t6898 + (-360 * t6899 + 272 * t6901 - 72 * t6903 + 8) * t6893 + t6906 = 21 * t6898 ** 2 - 25 * t6892 + 169 * t6895 - 425 * t6897 + 475 * t6899 - 211 * t6901 - 5 * t6903 + 1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * np.sqrt(0.312455e6) * np.sqrt(0.2e1) * ((t6906 + t6907) * np.exp((-4*1j) * (phi1 - 3 * phi2)) + (t6906 - t6907) * np.exp((-4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 336: + t6908 = np.cos(phi) + t6909 = t6908 ** 2 + t6910 = t6909 ** 2 + t6912 = t6910 ** 2 + tfunc[..., c] = (0.29e2 / 0.4096e4*1j) * (-26334 * t6910 - 120175 * t6912 - 99 + (86526 * t6910 + 58995 * t6912 + 3135) * t6909) * t6908 * ((1 + t6908) ** (0.3e1 / 0.2e1)) * np.sqrt(0.15470e5) * np.exp((-3*1j) * phi1) * ((1 - t6908) ** (0.3e1 / 0.2e1)) + + if Bindx == 337: + t6930 = np.cos(phi) + t6929 = t6930 ** 2 + t6932 = t6929 ** 2 + t6931 = t6930 * t6929 + t6934 = t6931 ** 2 + t6936 = t6932 ** 2 + t6933 = t6930 * t6932 + t6938 = t6933 ** 2 + t6940 = t6934 ** 2 + t6935 = t6930 * t6934 + t6942 = t6935 ** 2 + t6945 = 10043 * t6929 - 136357 * t6932 + 699343 * t6934 - 1726427 * t6936 + 2214049 * t6938 - 1423815 * t6940 + 363285 * t6942 - 121 + t6944 = 13093 * t6931 - 123587 * t6933 + 551233 * t6935 + (-1303597 * t6936 + 1667615 * t6938 - 1086865 * t6940 + 282555 * t6942 - 447) * t6930 + tfunc[..., c] = (0.29e2 / 0.16384e5*1j) * np.sqrt(0.209e3) * np.sqrt(0.2e1) * ((1 + t6930) ** (-0.1e1 / 0.2e1)) * ((1 - t6930) ** (-0.1e1 / 0.2e1)) * ((t6944 - t6945) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t6944 + t6945) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 338: + t6962 = np.cos(phi) + t6961 = t6962 ** 2 + t6964 = t6961 ** 2 + t6963 = t6962 * t6961 + t6966 = t6963 ** 2 + t6968 = t6964 ** 2 + t6965 = t6962 * t6964 + t6970 = t6965 ** 2 + t6972 = t6966 ** 2 + t6967 = t6962 * t6966 + t6974 = t6967 ** 2 + t6977 = 2 - 58 * t6961 + 746 * t6964 - 2930 * t6966 + 5350 * t6968 - 5102 * t6970 + 2478 * t6972 - 486 * t6974 + t6976 = -701 * t6963 + 2191 * t6965 - 3065 * t6967 + (1745 * t6968 + 185 * t6970 - 619 * t6972 + 189 * t6974 + 75) * t6962 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.28405e5) * ((1 + t6962) ** (-0.1e1 / 0.2e1)) * ((1 - t6962) ** (-0.1e1 / 0.2e1)) * ((t6976 + t6977) * np.exp((-3*1j) * (phi1 - 4 * phi2)) + (t6976 - t6977) * np.exp((-3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 339: + t6979 = np.cos(phi) + t6980 = t6979 ** 2 + t6981 = t6980 ** 2 + t6983 = t6981 ** 2 + t6982 = t6980 * t6981 + t6978 = np.sin(phi) + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((-2*1j) * phi1) * np.sqrt(0.2730e4) * t6978 ** 2 * (53295 * t6981 + 735471 * t6983 + 33 + (-298452 + 334305 * t6982) * t6982 + (-817190 * t6983 - 3366) * t6980) + + if Bindx == 340: + t7001 = np.cos(phi) + t7000 = t7001 ** 2 + t7002 = t7001 * t7000 + t7003 = t7000 ** 2 + t7004 = t7001 * t7003 + t7005 = t7002 ** 2 + t7006 = t7001 * t7005 + t7007 = t7003 ** 2 + t7009 = t7004 ** 2 + t7011 = t7005 ** 2 + t7015 = -9596 * t7002 + 74182 * t7004 - 241480 * t7006 + (377798 * t7007 - 281980 * t7009 + 80730 * t7011 + 346) * t7001 + t7014 = 94185 * t7006 ** 2 + 1955 * t7000 - 27487 * t7003 + 147411 * t7005 - 383021 * t7007 + 518305 * t7009 - 351325 * t7011 - 23 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.10659e5) * np.sqrt(0.2e1) * ((t7014 - t7015) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t7014 + t7015) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 341: + t7031 = np.cos(phi) + t7030 = t7031 ** 2 + t7033 = t7030 ** 2 + t7032 = t7031 * t7030 + t7035 = t7032 ** 2 + t7036 = t7031 * t7035 + t7037 = t7033 ** 2 + t7034 = t7031 * t7033 + t7039 = t7034 ** 2 + t7041 = t7035 ** 2 + t7045 = 63 * t7036 ** 2 - 31 * t7030 + 77 * t7033 + 45 * t7035 - 365 * t7037 + 499 * t7039 - 289 * t7041 + 1 + t7044 = 208 * t7032 - 740 * t7034 + 1280 * t7036 + (-1180 * t7037 + 560 * t7039 - 108 * t7041 - 20) * t7031 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t7044 + t7045) * np.exp((-2*1j) * (phi1 - 6 * phi2)) + (-t7044 + t7045) * np.exp((-2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.1448655e7) + + if Bindx == 342: + t7046 = np.cos(phi) + t7047 = t7046 ** 2 + t7049 = t7047 ** 2 + t7050 = t7046 * t7049 + t7048 = t7046 * t7047 + t7051 = t7048 ** 2 + t7059 = -1062347 * t7049 ** 2 + 965770 * t7050 ** 2 - 429 + (554268 - 334305 * t7051) * t7051 + tfunc[..., c] = (0.29e2 / 0.2048e4*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.210e3) * np.sqrt((1 + t7046)) * t7046 * (-t7059 * t7046 + 14586 * t7047 - 14586 * t7048 - 138567 * t7049 + 138567 * t7050 + t7059) * ((1 - t7046) ** (-0.1e1 / 0.2e1)) + + if Bindx == 343: + t7076 = np.cos(phi) + t7075 = t7076 ** 2 + t7078 = t7075 ** 2 + t7077 = t7076 * t7075 + t7080 = t7077 ** 2 + t7082 = t7078 ** 2 + t7079 = t7076 * t7078 + t7084 = t7079 ** 2 + t7086 = t7080 ** 2 + t7081 = t7076 * t7080 + t7088 = t7081 ** 2 + t7091 = 1 - 87 * t7075 + 1221 * t7078 - 6371 * t7080 + 15747 * t7082 - 19941 * t7084 + 12535 * t7086 - 3105 * t7088 + t7090 = 843 * t7077 - 7185 * t7079 + 27367 * t7081 + (-54231 * t7082 + 58305 * t7084 - 32315 * t7086 + 7245 * t7088 - 29) * t7076 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.138567e6) * np.sqrt(0.2e1) * ((1 + t7076) ** (-0.1e1 / 0.2e1)) * ((1 - t7076) ** (-0.1e1 / 0.2e1)) * ((t7090 + t7091) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t7090 - t7091) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 344: + t7108 = np.cos(phi) + t7107 = t7108 ** 2 + t7110 = t7107 ** 2 + t7109 = t7108 * t7107 + t7112 = t7109 ** 2 + t7114 = t7110 ** 2 + t7111 = t7108 * t7110 + t7116 = t7111 ** 2 + t7118 = t7112 ** 2 + t7113 = t7108 * t7112 + t7120 = t7113 ** 2 + t7123 = 2 - 66 * t7107 + 354 * t7110 - 850 * t7112 + 1110 * t7114 - 822 * t7116 + 326 * t7118 - 54 * t7120 + t7122 = 129 * t7109 - 543 * t7111 + 1165 * t7113 + (-1425 * t7114 + 1011 * t7116 - 389 * t7118 + 63 * t7120 - 11) * t7108 + tfunc[..., c] = (0.29e2 / 0.4096e4*1j) * np.sqrt(0.111435e6) * ((1 + t7108) ** (-0.1e1 / 0.2e1)) * ((1 - t7108) ** (-0.1e1 / 0.2e1)) * ((t7122 + t7123) * np.exp((-1*1j) * (phi1 - 12 * phi2)) + (t7122 - t7123) * np.exp((-1*1j) * (phi1 + 12 * phi2))) + + if Bindx == 345: + t7124 = np.cos(phi) + t7125 = t7124 ** 2 + t7126 = t7125 ** 2 + t7127 = t7125 * t7126 + t7130 = t7127 ** 2 + t7128 = t7126 ** 2 + tfunc[..., c] = -0.490128275e9 / 0.2048e4 * t7130 - 0.421936515e9 / 0.2048e4 * t7128 + 0.140645505e9 / 0.2048e4 * t7127 - 0.22207185e8 / 0.2048e4 * t7126 - 0.12441e5 / 0.2048e4 + (0.145422675e9 / 0.2048e4 * t7130 + 0.646969323e9 / 0.2048e4 * t7128 + 0.1306305e7 / 0.2048e4) * t7125 + + if Bindx == 346: + t7132 = np.cos(phi) + t7133 = t7132 ** 2 + t7134 = t7133 ** 2 + t7135 = t7133 * t7134 + t7138 = t7135 ** 2 + t7136 = t7134 ** 2 + tfunc[..., c] = 0.29e2 / 0.4096e4 * np.sqrt(0.1616615e7) * (-1221 * t7134 + 6371 * t7135 - 15747 * t7136 - 12535 * t7138 - 1 + (19941 * t7136 + 3105 * t7138 + 87) * t7133) * np.cos((6 * phi2)) + + if Bindx == 347: + t7140 = np.cos(phi) + t7141 = t7140 ** 2 + t7142 = t7141 ** 2 + t7143 = t7141 * t7142 + t7146 = t7143 ** 2 + t7144 = t7142 ** 2 + tfunc[..., c] = 0.145e3 / 0.4096e4 * np.sqrt(0.52003e5) * np.sqrt(0.2e1) * (-177 * t7142 + 425 * t7143 - 555 * t7144 - 163 * t7146 - 1 + (411 * t7144 + 27 * t7146 + 33) * t7141) * np.cos((12 * phi2)) + + if Bindx == 348: + t7148 = np.cos(phi) + t7149 = t7148 ** 2 + t7150 = t7149 ** 2 + t7152 = t7150 ** 2 + t7151 = t7149 * t7150 + tfunc[..., c] = (-0.29e2 / 0.2048e4*1j) * np.exp((1j) * phi1) * np.sqrt(0.210e3) * np.sqrt((1 - t7148)) * np.sqrt((1 + t7148)) * t7148 * (138567 * t7150 + 1062347 * t7152 + 429 + (-554268 + 334305 * t7151) * t7151 + (-965770 * t7152 - 14586) * t7149) + + if Bindx == 349: + t7171 = np.cos(phi) + t7170 = t7171 ** 2 + t7173 = t7170 ** 2 + t7172 = t7171 * t7170 + t7175 = t7172 ** 2 + t7177 = t7173 ** 2 + t7174 = t7171 * t7173 + t7179 = t7174 ** 2 + t7181 = t7175 ** 2 + t7176 = t7171 * t7175 + t7183 = t7176 ** 2 + t7186 = 1 - 87 * t7170 + 1221 * t7173 - 6371 * t7175 + 15747 * t7177 - 19941 * t7179 + 12535 * t7181 - 3105 * t7183 + t7185 = 843 * t7172 - 7185 * t7174 + 27367 * t7176 + (-54231 * t7177 + 58305 * t7179 - 32315 * t7181 + 7245 * t7183 - 29) * t7171 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.138567e6) * np.sqrt(0.2e1) * ((1 + t7171) ** (-0.1e1 / 0.2e1)) * ((1 - t7171) ** (-0.1e1 / 0.2e1)) * ((t7185 + t7186) * np.exp((1j) * (phi1 - 6 * phi2)) + (t7185 - t7186) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 350: + t7203 = np.cos(phi) + t7202 = t7203 ** 2 + t7205 = t7202 ** 2 + t7204 = t7203 * t7202 + t7207 = t7204 ** 2 + t7209 = t7205 ** 2 + t7206 = t7203 * t7205 + t7211 = t7206 ** 2 + t7213 = t7207 ** 2 + t7208 = t7203 * t7207 + t7215 = t7208 ** 2 + t7218 = 2 - 66 * t7202 + 354 * t7205 - 850 * t7207 + 1110 * t7209 - 822 * t7211 + 326 * t7213 - 54 * t7215 + t7217 = 129 * t7204 - 543 * t7206 + 1165 * t7208 + (-1425 * t7209 + 1011 * t7211 - 389 * t7213 + 63 * t7215 - 11) * t7203 + tfunc[..., c] = (0.29e2 / 0.4096e4*1j) * np.sqrt(0.111435e6) * ((1 + t7203) ** (-0.1e1 / 0.2e1)) * ((1 - t7203) ** (-0.1e1 / 0.2e1)) * ((t7217 + t7218) * np.exp((1j) * (phi1 - 12 * phi2)) + (t7217 - t7218) * np.exp((1j) * (phi1 + 12 * phi2))) + + if Bindx == 351: + t7220 = np.cos(phi) + t7221 = t7220 ** 2 + t7222 = t7221 ** 2 + t7224 = t7222 ** 2 + t7223 = t7221 * t7222 + t7219 = np.sin(phi) + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((2*1j) * phi1) * np.sqrt(0.2730e4) * t7219 ** 2 * (53295 * t7222 + 735471 * t7224 + 33 + (-298452 + 334305 * t7223) * t7223 + (-817190 * t7224 - 3366) * t7221) + + if Bindx == 352: + t7242 = np.cos(phi) + t7241 = t7242 ** 2 + t7243 = t7242 * t7241 + t7244 = t7241 ** 2 + t7245 = t7242 * t7244 + t7246 = t7243 ** 2 + t7247 = t7242 * t7246 + t7248 = t7244 ** 2 + t7250 = t7245 ** 2 + t7252 = t7246 ** 2 + t7256 = -9596 * t7243 + 74182 * t7245 - 241480 * t7247 + (377798 * t7248 - 281980 * t7250 + 80730 * t7252 + 346) * t7242 + t7255 = 94185 * t7247 ** 2 + 1955 * t7241 - 27487 * t7244 + 147411 * t7246 - 383021 * t7248 + 518305 * t7250 - 351325 * t7252 - 23 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.10659e5) * np.sqrt(0.2e1) * ((t7255 - t7256) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t7255 + t7256) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 353: + t7272 = np.cos(phi) + t7271 = t7272 ** 2 + t7274 = t7271 ** 2 + t7273 = t7272 * t7271 + t7276 = t7273 ** 2 + t7277 = t7272 * t7276 + t7278 = t7274 ** 2 + t7275 = t7272 * t7274 + t7280 = t7275 ** 2 + t7282 = t7276 ** 2 + t7286 = 63 * t7277 ** 2 - 31 * t7271 + 77 * t7274 + 45 * t7276 - 365 * t7278 + 499 * t7280 - 289 * t7282 + 1 + t7285 = 208 * t7273 - 740 * t7275 + 1280 * t7277 + (-1180 * t7278 + 560 * t7280 - 108 * t7282 - 20) * t7272 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t7285 + t7286) * np.exp((2*1j) * (phi1 - 6 * phi2)) + (-t7285 + t7286) * np.exp((2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.1448655e7) + + if Bindx == 354: + t7287 = np.cos(phi) + t7288 = t7287 ** 2 + t7289 = t7288 ** 2 + t7291 = t7289 ** 2 + tfunc[..., c] = (0.29e2 / 0.4096e4*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.15470e5) * ((1 - t7287) ** (0.3e1 / 0.2e1)) * ((1 + t7287) ** (0.3e1 / 0.2e1)) * t7287 * (-26334 * t7289 - 120175 * t7291 - 99 + (86526 * t7289 + 58995 * t7291 + 3135) * t7288) + + if Bindx == 355: + t7309 = np.cos(phi) + t7308 = t7309 ** 2 + t7311 = t7308 ** 2 + t7310 = t7309 * t7308 + t7313 = t7310 ** 2 + t7315 = t7311 ** 2 + t7312 = t7309 * t7311 + t7317 = t7312 ** 2 + t7319 = t7313 ** 2 + t7314 = t7309 * t7313 + t7321 = t7314 ** 2 + t7324 = 10043 * t7308 - 136357 * t7311 + 699343 * t7313 - 1726427 * t7315 + 2214049 * t7317 - 1423815 * t7319 + 363285 * t7321 - 121 + t7323 = 13093 * t7310 - 123587 * t7312 + 551233 * t7314 + (-1303597 * t7315 + 1667615 * t7317 - 1086865 * t7319 + 282555 * t7321 - 447) * t7309 + tfunc[..., c] = (0.29e2 / 0.16384e5*1j) * np.sqrt(0.209e3) * np.sqrt(0.2e1) * ((1 + t7309) ** (-0.1e1 / 0.2e1)) * ((1 - t7309) ** (-0.1e1 / 0.2e1)) * ((t7323 - t7324) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t7323 + t7324) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 356: + t7341 = np.cos(phi) + t7340 = t7341 ** 2 + t7343 = t7340 ** 2 + t7342 = t7341 * t7340 + t7345 = t7342 ** 2 + t7347 = t7343 ** 2 + t7344 = t7341 * t7343 + t7349 = t7344 ** 2 + t7351 = t7345 ** 2 + t7346 = t7341 * t7345 + t7353 = t7346 ** 2 + t7356 = 2 - 58 * t7340 + 746 * t7343 - 2930 * t7345 + 5350 * t7347 - 5102 * t7349 + 2478 * t7351 - 486 * t7353 + t7355 = -701 * t7342 + 2191 * t7344 - 3065 * t7346 + (1745 * t7347 + 185 * t7349 - 619 * t7351 + 189 * t7353 + 75) * t7341 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.28405e5) * ((1 + t7341) ** (-0.1e1 / 0.2e1)) * ((1 - t7341) ** (-0.1e1 / 0.2e1)) * ((t7355 + t7356) * np.exp((3*1j) * (phi1 - 4 * phi2)) + (t7355 - t7356) * np.exp((3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 357: + t7365 = np.sin(phi) + t7363 = t7365 ** 2 + t7357 = np.cos(phi) + t7358 = t7357 ** 2 + t7359 = t7358 ** 2 + t7361 = t7359 ** 2 + tfunc[..., c] = (0.87e2 / 0.4096e4) * np.exp((4*1j) * phi1) * np.sqrt(0.85085e5) * t7363 ** 2 * (-1330 * t7359 - 10925 * t7361 - 1 + (6118 * t7359 + 6555 * t7361 + 95) * t7358) + + if Bindx == 358: + t7381 = np.cos(phi) + t7380 = t7381 ** 2 + t7384 = t7380 ** 2 + t7383 = t7381 * t7380 + t7386 = t7383 ** 2 + t7387 = t7381 * t7386 + t7388 = t7384 ** 2 + t7385 = t7381 * t7384 + t7390 = t7385 ** 2 + t7392 = t7386 ** 2 + t7396 = 345345 * t7387 ** 2 - 2449 * t7380 + 21251 * t7384 - 2509 * t7386 - 338899 * t7388 + 947485 * t7390 - 970255 * t7392 + 31 + t7395 = 64624 * t7383 - 483996 * t7385 + 1573504 * t7387 + (-2522916 * t7388 + 1963280 * t7390 - 592020 * t7392 - 2476) * t7381 + t7382 = 2 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * ((t7395 + t7396) * np.exp((2*1j) * (t7382 - 3 * phi2)) + (-t7395 + t7396) * np.exp((2*1j) * (t7382 + 3 * phi2))) * np.sqrt(0.19e2) + + if Bindx == 359: + t7412 = np.cos(phi) + t7411 = t7412 ** 2 + t7413 = t7412 * t7411 + t7414 = t7411 ** 2 + t7415 = t7412 * t7414 + t7416 = t7413 ** 2 + t7417 = t7412 * t7416 + t7418 = t7414 ** 2 + t7420 = t7415 ** 2 + t7422 = t7416 ** 2 + t7426 = -48 * t7413 + 40 * t7415 + 160 * t7417 + (-360 * t7418 + 272 * t7420 - 72 * t7422 + 8) * t7412 + t7425 = 21 * t7417 ** 2 - 25 * t7411 + 169 * t7414 - 425 * t7416 + 475 * t7418 - 211 * t7420 - 5 * t7422 + 1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * np.sqrt(0.312455e6) * np.sqrt(0.2e1) * ((t7425 + t7426) * np.exp((4*1j) * (phi1 - 3 * phi2)) + (t7425 - t7426) * np.exp((4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 360: + t7427 = np.cos(phi) + t7428 = t7427 ** 2 + t7429 = t7428 ** 2 + tfunc[..., c] = (-0.87e2 / 0.4096e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.646646e6) * ((1 - t7427) ** (0.5e1 / 0.2e1)) * ((1 + t7427) ** (0.5e1 / 0.2e1)) * t7427 * (-140 * t7428 + 5 + (-2300 * t7428 + 966 + 1725 * t7429) * t7429) + + if Bindx == 361: + t7448 = np.cos(phi) + t7447 = t7448 ** 2 + t7451 = t7447 ** 2 + t7450 = t7448 * t7447 + t7453 = t7450 ** 2 + t7455 = t7451 ** 2 + t7452 = t7448 * t7451 + t7457 = t7452 ** 2 + t7459 = t7453 ** 2 + t7454 = t7448 * t7453 + t7461 = t7454 ** 2 + t7464 = 17175 * t7447 - 222785 * t7451 + 1148347 * t7453 - 2946015 * t7455 + 3989557 * t7457 - 2726075 * t7459 + 740025 * t7461 - 229 + t7463 = -46785 * t7450 + 291903 * t7452 - 673485 * t7454 + (430705 * t7455 + 554829 * t7457 - 904475 * t7459 + 345345 * t7461 + 1963) * t7448 + t7449 = 5 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.5e1) * ((1 + t7448) ** (-0.1e1 / 0.2e1)) * ((1 - t7448) ** (-0.1e1 / 0.2e1)) * ((t7463 - t7464) * np.exp((1j) * (t7449 - 6 * phi2)) + (t7463 + t7464) * np.exp((1j) * (t7449 + 6 * phi2))) + + if Bindx == 362: + t7481 = np.cos(phi) + t7480 = t7481 ** 2 + t7484 = t7480 ** 2 + t7483 = t7481 * t7480 + t7486 = t7483 ** 2 + t7488 = t7484 ** 2 + t7485 = t7481 * t7484 + t7490 = t7485 ** 2 + t7492 = t7486 ** 2 + t7487 = t7481 * t7486 + t7494 = t7487 ** 2 + t7497 = -2 + 42 * t7480 - 218 * t7484 + 402 * t7486 - 198 * t7488 - 226 * t7490 + 290 * t7492 - 90 * t7494 + t7496 = -5 * t7483 + 207 * t7485 - 657 * t7487 + (833 * t7488 - 447 * t7490 + 53 * t7492 + 21 * t7494 - 5) * t7481 + t7482 = 5 * phi1 + tfunc[..., c] = (0.435e3 / 0.8192e4*1j) * np.sqrt(0.3289e4) * ((1 + t7481) ** (-0.1e1 / 0.2e1)) * ((1 - t7481) ** (-0.1e1 / 0.2e1)) * ((t7496 + t7497) * np.exp((1j) * (t7482 - 12 * phi2)) + (t7496 - t7497) * np.exp((1j) * (t7482 + 12 * phi2))) + + if Bindx == 363: + t7506 = np.sin(phi) + t7503 = t7506 ** 2 + t7504 = t7506 * t7503 + t7498 = np.cos(phi) + t7499 = t7498 ** 2 + t7500 = t7499 ** 2 + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((6*1j) * phi1) * np.sqrt(0.3233230e7) * t7504 ** 2 * (-84 * t7499 + 1 + (-3220 * t7499 + 966 + 3105 * t7500) * t7500) + + if Bindx == 364: + t7522 = np.cos(phi) + t7521 = t7522 ** 2 + t7523 = t7522 * t7521 + t7524 = t7521 ** 2 + t7525 = t7522 * t7524 + t7526 = t7523 ** 2 + t7527 = t7522 * t7526 + t7528 = t7524 ** 2 + t7530 = t7525 ** 2 + t7532 = t7526 ** 2 + t7536 = 520532 * t7523 - 4172562 * t7525 + 15354648 * t7527 + (-27848722 * t7528 + 24151380 * t7530 - 7992270 * t7532 - 21198) * t7522 + t7535 = 3108105 * t7527 ** 2 - 230253 * t7521 + 2425617 * t7524 - 8886957 * t7526 + 13338963 * t7528 - 5787375 * t7530 - 3963245 * t7532 + 3337 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.2e1) * ((t7535 + t7536) * np.exp((6*1j) * (phi1 - phi2)) + (t7535 - t7536) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 365: + t7552 = np.cos(phi) + t7551 = t7552 ** 2 + t7553 = t7552 * t7551 + t7554 = t7551 ** 2 + t7555 = t7552 * t7554 + t7556 = t7553 ** 2 + t7557 = t7552 * t7556 + t7558 = t7554 ** 2 + t7560 = t7555 ** 2 + t7562 = t7556 ** 2 + t7566 = -752 * t7553 + 3708 * t7555 - 5760 * t7557 + (2372 * t7558 + 1392 * t7560 - 972 * t7562 + 12) * t7552 + t7565 = 189 * t7557 ** 2 + 435 * t7551 - 1161 * t7554 - 729 * t7556 + 5049 * t7558 - 5079 * t7560 + 1325 * t7562 - 29 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t7565 + t7566) * np.exp((6*1j) * (phi1 - 2 * phi2)) + (t7565 - t7566) * np.exp((6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.16445e5) + + if Bindx == 366: + t7567 = np.cos(phi) + t7568 = t7567 ** 2 + t7569 = t7568 ** 2 + tfunc[..., c] = (0.29e2 / 0.2048e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.692835e6) * ((1 - t7567) ** (0.7e1 / 0.2e1)) * ((1 + t7567) ** (0.7e1 / 0.2e1)) * t7567 * (-805 * t7569 - 7 + (1035 * t7569 + 161) * t7568) + + if Bindx == 367: + t7586 = np.cos(phi) + t7585 = t7586 ** 2 + t7588 = t7586 * t7585 + t7591 = t7588 ** 2 + t7592 = t7586 * t7591 + t7600 = 148005 * t7592 ** 2 + t7597 = t7591 ** 2 + t7589 = t7585 ** 2 + t7590 = t7586 * t7589 + t7595 = t7590 ** 2 + t7593 = t7589 ** 2 + t7587 = 7 * phi1 + t7578 = t7586 * t7593 + t7576 = t7586 * t7595 + t7574 = t7586 * t7597 + tfunc[..., c] = (-0.29e2 / 0.8192e4*1j) * np.sqrt((1 - t7586)) * np.sqrt(0.21e2) * ((1 + t7586) ** (-0.1e1 / 0.2e1)) * ((-296010 * t7574 + 1067660 * t7576 - 1521542 * t7578 - 2945 * t7585 - 3386 * t7586 + 67916 * t7588 + 39657 * t7589 - 397878 * t7590 - 100329 * t7591 + 1083240 * t7592 - 23661 * t7593 + 350405 * t7595 - 411125 * t7597 - 7 + t7600) * np.exp((1j) * (t7587 - 6 * phi2)) + (592020 * t7574 - 1001880 * t7576 + 152812 * t7578 - 3813 * t7585 - 3372 * t7586 + 67048 * t7588 + 95307 * t7589 - 342228 * t7590 - 639777 * t7591 + 543792 * t7592 + 1650693 * t7593 - 1719135 * t7595 + 476905 * t7597 + 7 + t7600) * np.exp((1j) * (t7587 + 6 * phi2))) + + if Bindx == 368: + t7617 = np.cos(phi) + t7616 = t7617 ** 2 + t7620 = t7616 ** 2 + t7619 = t7617 * t7616 + t7622 = t7619 ** 2 + t7624 = t7620 ** 2 + t7621 = t7617 * t7620 + t7626 = t7621 ** 2 + t7628 = t7622 ** 2 + t7623 = t7617 * t7622 + t7630 = t7623 ** 2 + t7633 = 2 - 18 * t7616 - 14 * t7620 + 270 * t7622 - 522 * t7624 + 314 * t7626 + 22 * t7628 - 54 * t7630 + t7632 = 71 * t7619 - 225 * t7621 + 171 * t7623 + (193 * t7624 - 315 * t7626 + 101 * t7628 + 9 * t7630 - 5) * t7617 + t7618 = 7 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.345345e6) * np.sqrt(0.2e1) * ((1 + t7617) ** (-0.1e1 / 0.2e1)) * ((1 - t7617) ** (-0.1e1 / 0.2e1)) * ((t7632 + t7633) * np.exp((1j) * (t7618 - 12 * phi2)) + (t7632 - t7633) * np.exp((1j) * (t7618 + 12 * phi2))) + + if Bindx == 369: + t7641 = np.sin(phi) + t7638 = t7641 ** 2 + t7639 = t7638 ** 2 + t7634 = np.cos(phi) + t7635 = t7634 ** 2 + t7636 = t7635 ** 2 + tfunc[..., c] = (0.29e2 / 0.4096e4) * np.exp((8*1j) * phi1) * np.sqrt(0.881790e6) * t7639 ** 2 * (-575 * t7636 - 1 + (1035 * t7636 + 69) * t7635) + + if Bindx == 370: + t7657 = np.cos(phi) + t7656 = t7657 ** 2 + t7659 = t7657 * t7656 + t7660 = t7656 ** 2 + t7661 = t7657 * t7660 + t7662 = t7659 ** 2 + t7663 = t7657 * t7662 + t7664 = t7660 ** 2 + t7666 = t7661 ** 2 + t7668 = t7662 ** 2 + t7672 = -26848 * t7659 + 59784 * t7661 + 165120 * t7663 + (-719624 * t7664 + 842720 * t7666 - 322920 * t7668 + 1768) * t7657 + t7671 = 94185 * t7663 ** 2 - 17105 * t7656 + 164979 * t7660 - 631173 * t7662 + 1090533 * t7664 - 783955 * t7666 + 82225 * t7668 + 311 + t7658 = 4 * phi1 + tfunc[..., c] = (0.29e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.33e2) * ((t7671 + t7672) * np.exp((2*1j) * (t7658 - 3 * phi2)) + (t7671 - t7672) * np.exp((2*1j) * (t7658 + 3 * phi2))) + + if Bindx == 371: + t7688 = np.cos(phi) + t7687 = t7688 ** 2 + t7690 = t7688 * t7687 + t7691 = t7687 ** 2 + t7692 = t7688 * t7691 + t7693 = t7690 ** 2 + t7694 = t7688 * t7693 + t7695 = t7691 ** 2 + t7697 = t7692 ** 2 + t7699 = t7693 ** 2 + t7703 = 608 * t7690 - 528 * t7692 - 2112 * t7694 + (3344 * t7695 - 800 * t7697 - 432 * t7699 - 80) * t7688 + t7702 = 63 * t7694 ** 2 - 19 * t7687 - 957 * t7691 + 2805 * t7693 - 1551 * t7695 - 1441 * t7697 + 1081 * t7699 + 19 + t7689 = 2 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4) * ((t7702 + t7703) * np.exp((4*1j) * (t7689 - 3 * phi2)) + (t7702 - t7703) * np.exp((4*1j) * (t7689 + 3 * phi2))) * np.sqrt(0.4485e4) + + if Bindx == 372: + t7704 = np.cos(phi) + t7705 = t7704 ** 2 + tfunc[..., c] = (-0.29e2 / 0.2048e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.3380195e7) * ((1 - t7704) ** (0.9e1 / 0.2e1)) * ((1 + t7704) ** (0.9e1 / 0.2e1)) * t7704 * (3 + (-50 + 135 * t7705) * t7705) + + if Bindx == 373: + t7721 = np.cos(phi) + t7720 = t7721 ** 2 + t7723 = t7721 * t7720 + t7726 = t7723 ** 2 + t7732 = t7726 ** 2 + t7734 = 12285 * t7721 * t7732 + t7724 = t7720 ** 2 + t7725 = t7721 * t7724 + t7730 = t7725 ** 2 + t7728 = t7724 ** 2 + t7722 = 3 * phi1 + t7715 = t7721 * t7726 + t7713 = t7721 * t7728 + t7711 = t7721 * t7730 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * ((1 - t7721) ** (0.3e1 / 0.2e1)) * np.sqrt(0.253e3) * ((1 + t7721) ** (-0.1e1 / 0.2e1)) * ((t7734 - 22815 * t7732 - 37310 * t7711 + 83690 * t7730 + 39235 * t7713 - 120129 * t7728 - 15204 * t7715 + 84684 * t7726 + 819 * t7725 - 29745 * t7724 - 126 * t7723 + 4394 * t7720 + 301 * t7721 - 79) * np.exp((3*1j) * (t7722 - 2 * phi2)) + (t7734 + 71955 * t7732 + 152230 * t7711 + 97010 * t7730 - 123665 * t7713 - 229431 * t7728 - 70908 * t7715 + 92652 * t7726 + 72459 * t7725 - 1035 * t7724 - 14346 * t7723 - 2558 * t7720 + 617 * t7721 + 79) * np.exp((3*1j) * (t7722 + 2 * phi2))) + + if Bindx == 374: + t7751 = np.cos(phi) + t7750 = t7751 ** 2 + t7754 = t7750 ** 2 + t7753 = t7751 * t7750 + t7756 = t7753 ** 2 + t7758 = t7754 ** 2 + t7755 = t7751 * t7754 + t7760 = t7755 ** 2 + t7762 = t7756 ** 2 + t7757 = t7751 * t7756 + t7764 = t7757 ** 2 + t7767 = -74 - 518 * t7750 + 5542 * t7754 - 8646 * t7756 - 4686 * t7758 + 14718 * t7760 - 4878 * t7762 - 1458 * t7764 + t7766 = -1677 * t7753 - 3333 * t7755 + 16071 * t7757 + (-12859 * t7758 - 3127 * t7760 + 4313 * t7762 + 189 * t7764 + 423) * t7751 + t7752 = 3 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((1 + t7751) ** (-0.1e1 / 0.2e1)) * ((1 - t7751) ** (-0.1e1 / 0.2e1)) * ((t7766 + t7767) * np.exp((3*1j) * (t7752 - 4 * phi2)) + (t7766 - t7767) * np.exp((3*1j) * (t7752 + 4 * phi2))) + + if Bindx == 375: + t7775 = np.sin(phi) + t7771 = t7775 ** 2 + t7773 = t7775 * t7771 ** 2 + t7768 = np.cos(phi) + t7769 = t7768 ** 2 + tfunc[..., c] = -(0.29e2 / 0.8192e4) * np.exp((10*1j) * phi1) * np.sqrt(0.4056234e7) * t7773 ** 2 * (1 + (-50 + 225 * t7769) * t7769) + + if Bindx == 376: + t7791 = np.cos(phi) + t7790 = t7791 ** 2 + t7793 = t7791 * t7790 + t7794 = t7790 ** 2 + t7795 = t7791 * t7794 + t7796 = t7793 ** 2 + t7797 = t7791 * t7796 + t7798 = t7794 ** 2 + t7800 = t7795 ** 2 + t7802 = t7796 ** 2 + t7806 = -7532 * t7793 + 27822 * t7795 - 34152 * t7797 + (-5714 * t7798 + 36500 * t7800 - 17550 * t7802 + 626) * t7791 + t7805 = 4095 * t7797 ** 2 + 37 * t7790 + 6039 * t7794 - 38331 * t7796 + 81477 * t7798 - 68201 * t7800 + 14885 * t7802 - 1 + t7792 = 5 * phi1 + tfunc[..., c] = (0.29e2 / 0.32768e5) * np.sqrt(0.3795e4) * np.sqrt(0.2e1) * ((t7805 + t7806) * np.exp((2*1j) * (t7792 - 3 * phi2)) + (t7805 - t7806) * np.exp((2*1j) * (t7792 + 3 * phi2))) + + if Bindx == 377: + t7822 = np.cos(phi) + t7821 = t7822 ** 2 + t7824 = t7822 * t7821 + t7825 = t7821 ** 2 + t7826 = t7822 * t7825 + t7827 = t7824 ** 2 + t7828 = t7822 * t7827 + t7829 = t7825 ** 2 + t7831 = t7826 ** 2 + t7833 = t7827 ** 2 + t7837 = 80 * t7824 - 3828 * t7826 + 4224 * t7828 + (3124 * t7829 - 3280 * t7831 - 540 * t7833 + 220) * t7822 + t7836 = 63 * t7828 ** 2 - 527 * t7821 + 2013 * t7825 + 1485 * t7827 - 6765 * t7829 + 1859 * t7831 + 1903 * t7833 - 31 + t7823 = 5 * phi1 + tfunc[..., c] = (0.145e3 / 0.16384e5) * ((t7836 + t7837) * np.exp((2*1j) * (t7823 - 6 * phi2)) + (t7836 - t7837) * np.exp((2*1j) * (t7823 + 6 * phi2))) * np.sqrt(0.39e2) + + if Bindx == 378: + t7838 = np.cos(phi) + tfunc[..., c] = (0.145e3 / 0.4096e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.4056234e7) * ((1 - t7838) ** (0.11e2 / 0.2e1)) * ((1 + t7838) ** (0.11e2 / 0.2e1)) * t7838 * (9 * t7838 ** 2 - 1) + + if Bindx == 379: + t7852 = np.cos(phi) + t7851 = t7852 ** 2 + t7854 = t7852 * t7851 + t7857 = t7854 ** 2 + t7864 = 819 * t7857 ** 2 + t7855 = t7851 ** 2 + t7856 = t7852 * t7855 + t7861 = t7856 ** 2 + t7859 = t7855 ** 2 + t7853 = 11 * phi1 + t7846 = t7852 * t7857 + t7844 = t7852 * t7859 + t7842 = t7852 * t7861 + tfunc[..., c] = (-0.29e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * ((1 - t7852) ** (0.5e1 / 0.2e1)) * np.sqrt(0.3795e4) * ((1 + t7852) ** (-0.1e1 / 0.2e1)) * ((t7864 - 1404 * t7842 - 2782 * t7861 + 5452 * t7844 + 3417 * t7859 - 8216 * t7846 - 1780 * t7857 + 5976 * t7856 + 373 * t7855 - 2092 * t7854 - 78 * t7851 + 284 * t7852 + 31) * np.exp((1j) * (t7853 - 6 * phi2)) + (t7864 + 6318 * t7842 + 20384 * t7861 + 34250 * t7844 + 28035 * t7859 + 1068 * t7846 - 18984 * t7857 - 15372 * t7856 - 2775 * t7855 + 2310 * t7854 + 1224 * t7851 + 98 * t7852 - 31) * np.exp((1j) * (t7853 + 6 * phi2))) + + if Bindx == 380: + t7881 = np.cos(phi) + t7880 = t7881 ** 2 + t7884 = t7880 ** 2 + t7883 = t7881 * t7880 + t7886 = t7883 ** 2 + t7888 = t7884 ** 2 + t7885 = t7881 * t7884 + t7890 = t7885 ** 2 + t7892 = t7886 ** 2 + t7887 = t7881 * t7886 + t7894 = t7887 ** 2 + t7897 = 38 + 1026 * t7880 - 1394 * t7884 - 7062 * t7886 + 10098 * t7888 + 2662 * t7890 - 4774 * t7892 - 594 * t7894 + t7896 = -1199 * t7883 + 6237 * t7885 - 627 * t7887 + (-10637 * t7888 + 4131 * t7890 + 2351 * t7892 + 63 * t7894 - 319) * t7881 + t7882 = 11 * phi1 + tfunc[..., c] = (0.29e2 / 0.8192e4*1j) * np.sqrt(0.39e2) * ((1 + t7881) ** (-0.1e1 / 0.2e1)) * ((1 - t7881) ** (-0.1e1 / 0.2e1)) * ((t7896 + t7897) * np.exp((1j) * (t7882 - 12 * phi2)) + (t7896 - t7897) * np.exp((1j) * (t7882 + 12 * phi2))) + + if Bindx == 381: + t7903 = np.sin(phi) + t7899 = t7903 ** 2 + t7900 = t7903 * t7899 + t7901 = t7900 ** 2 + t7898 = np.cos(phi) + tfunc[..., c] = (0.145e3 / 0.4096e4) * np.exp((12*1j) * phi1) * np.sqrt(0.52003e5) * t7901 ** 2 * (27 * t7898 ** 2 - 1) + + if Bindx == 382: + t7919 = np.cos(phi) + t7918 = t7919 ** 2 + t7921 = t7919 * t7918 + t7922 = t7918 ** 2 + t7923 = t7919 * t7922 + t7924 = t7921 ** 2 + t7925 = t7919 * t7924 + t7926 = t7922 ** 2 + t7928 = t7923 ** 2 + t7930 = t7924 ** 2 + t7934 = -752 * t7921 + 3708 * t7923 - 5760 * t7925 + (2372 * t7926 + 1392 * t7928 - 972 * t7930 + 12) * t7919 + t7933 = 189 * t7925 ** 2 + 435 * t7918 - 1161 * t7922 - 729 * t7924 + 5049 * t7926 - 5079 * t7928 + 1325 * t7930 - 29 + t7920 = 2 * phi1 + tfunc[..., c] = (0.29e2 / 0.16384e5) * ((t7933 + t7934) * np.exp((6*1j) * (t7920 - phi2)) + (t7933 - t7934) * np.exp((6*1j) * (t7920 + phi2))) * np.sqrt(0.16445e5) + + if Bindx == 383: + t7950 = np.cos(phi) + t7949 = t7950 ** 2 + t7951 = t7950 * t7949 + t7952 = t7949 ** 2 + t7953 = t7950 * t7952 + t7954 = t7951 ** 2 + t7955 = t7950 * t7954 + t7956 = t7952 ** 2 + t7958 = t7953 ** 2 + t7960 = t7954 ** 2 + t7964 = -11024 * t7951 + 10296 * t7953 + 41184 * t7955 + (-19448 * t7956 - 21840 * t7958 - 1944 * t7960 - 1320) * t7950 + t7963 = 189 * t7955 ** 2 + 5343 * t7949 + 9009 * t7952 - 36465 * t7954 - 14157 * t7956 + 31317 * t7958 + 8723 * t7960 + 137 + tfunc[..., c] = (0.29e2 / 0.16384e5) * np.sqrt(0.2e1) * ((t7963 + t7964) * np.exp((12*1j) * (phi1 - phi2)) + (t7963 - t7964) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 384: + t7965 = np.cos(phi) + tfunc[..., c] = (-0.435e3 / 0.4096e4*1j) * np.exp((13*1j) * phi1) * np.sqrt(0.312018e6) * ((1 - t7965) ** (0.13e2 / 0.2e1)) * ((1 + t7965) ** (0.13e2 / 0.2e1)) * t7965 + + if Bindx == 385: + t7978 = np.cos(phi) + t7977 = t7978 ** 2 + t7981 = t7977 ** 2 + t7982 = t7978 * t7981 + t7987 = t7982 ** 2 + t7989 = 7 * t7978 * t7987 + t7985 = t7981 ** 2 + t7980 = t7978 * t7977 + t7983 = t7980 ** 2 + t7979 = 13 * phi1 + t7972 = t7978 * t7983 + t7970 = t7978 * t7985 + tfunc[..., c] = (0.87e2 / 0.16384e5*1j) * np.sqrt(0.2e1) * ((1 - t7978) ** (0.7e1 / 0.2e1)) * np.sqrt(0.49335e5) * ((1 + t7978) ** (-0.1e1 / 0.2e1)) * ((t7989 - 11 * t7987 - 27 * t7970 + 47 * t7985 + 38 * t7972 - 78 * t7983 - 22 * t7982 + 62 * t7981 + 3 * t7980 - 23 * t7977 + t7978 + 3) * np.exp((1j) * (t7979 - 6 * phi2)) + (t7989 + 67 * t7987 + 285 * t7970 + 705 * t7985 + 1110 * t7972 + 1134 * t7983 + 714 * t7982 + 210 * t7981 - 45 * t7980 - 65 * t7977 - 23 * t7978 - 3) * np.exp((1j) * (t7979 + 6 * phi2))) + + if Bindx == 386: + t8005 = np.cos(phi) + t8004 = t8005 ** 2 + t8007 = t8005 * t8004 + t8010 = t8007 ** 2 + t8011 = t8005 * t8010 + t8019 = -1287 * t8010 + (1716 + 7 * t8011) * t8011 + t8016 = t8010 ** 2 + t8008 = t8004 ** 2 + t8009 = t8005 * t8008 + t8014 = t8009 ** 2 + t8012 = t8008 ** 2 + t8006 = 13 * phi1 + t7997 = t8005 * t8012 + t7995 = t8005 * t8014 + t7993 = t8005 * t8016 + tfunc[..., c] = (-0.87e2 / 0.8192e4*1j) * np.sqrt((1 - t8005)) * np.sqrt(0.3e1) * ((1 + t8005) ** (-0.1e1 / 0.2e1)) * ((-71 * t7993 + 312 * t8016 - 754 * t7995 + 1001 * t8014 - 429 * t7997 - 858 * t8012 + 143 * t8009 + 572 * t8008 - 546 * t8007 + 247 * t8004 - 59 * t8005 + 6 + t8019) * np.exp((1j) * (t8006 - 12 * phi2)) + (85 * t7993 + 468 * t8016 + 1534 * t7995 + 3289 * t8014 + 4719 * t7997 + 4290 * t8012 - 2717 * t8009 - 2288 * t8008 - 1170 * t8007 - 377 * t8004 - 71 * t8005 - 6 + t8019) * np.exp((1j) * (t8006 + 12 * phi2))) + + if Bindx == 387: + t8025 = np.sin(phi) + t8020 = t8025 ** 2 + t8021 = t8025 * t8020 + t8023 = t8025 * t8021 ** 2 + tfunc[..., c] = -(0.435e3 / 0.8192e4) * np.exp((14*1j) * phi1) * np.sqrt(0.44574e5) * t8023 ** 2 + + if Bindx == 388: + t8041 = np.cos(phi) + t8057 = -6 * t8041 + t8040 = t8041 ** 2 + t8043 = t8041 * t8040 + t8044 = t8040 ** 2 + t8045 = t8041 * t8044 + t8046 = t8043 ** 2 + t8047 = t8041 * t8046 + t8048 = t8044 ** 2 + t8050 = t8045 ** 2 + t8052 = t8046 ** 2 + t8056 = t8052 * t8057 + 4 * t8043 + 38 * t8045 - 72 * t8047 + t8057 + (38 * t8048 + 4 * t8050) * t8041 + t8055 = t8047 ** 2 + 11 * t8040 - 39 * t8044 + 27 * t8046 + 27 * t8048 - 39 * t8050 + 11 * t8052 + 1 + t8042 = 7 * phi1 + tfunc[..., c] = (0.87e2 / 0.32768e5) * np.sqrt(0.345345e6) * np.sqrt(0.2e1) * ((t8055 + t8056) * np.exp((2*1j) * (t8042 - 3 * phi2)) + (t8055 - t8056) * np.exp((2*1j) * (t8042 + 3 * phi2))) + + if Bindx == 389: + t8072 = np.cos(phi) + t8071 = t8072 ** 2 + t8074 = t8072 * t8071 + t8075 = t8071 ** 2 + t8076 = t8072 * t8075 + t8078 = t8075 ** 2 + t8080 = t8076 ** 2 + t8077 = t8074 ** 2 + t8082 = t8077 ** 2 + t8083 = t8072 * t8082 + t8086 = 208 * t8074 + 572 * t8076 - 12 * t8083 + (-572 * t8078 - 208 * t8080 + 12) * t8072 + t8085 = t8072 * t8083 - 65 * t8071 - 429 * t8075 - 429 * t8077 + 429 * t8078 + 429 * t8080 + 65 * t8082 - 1 + t8073 = 7 * phi1 + tfunc[..., c] = (0.87e2 / 0.16384e5) * ((t8085 + t8086) * np.exp((2*1j) * (t8073 - 6 * phi2)) + (t8085 - t8086) * np.exp((2*1j) * (t8073 + 6 * phi2))) * np.sqrt(0.21e2) + + if Bindx == 390: + t8102 = np.cos(phi) + t8118 = 6 * t8102 + t8101 = t8102 ** 2 + t8104 = t8102 * t8101 + t8105 = t8101 ** 2 + t8106 = t8102 * t8105 + t8107 = t8104 ** 2 + t8108 = t8102 * t8107 + t8109 = t8105 ** 2 + t8111 = t8106 ** 2 + t8113 = t8107 ** 2 + t8117 = t8113 * t8118 - 4 * t8104 - 38 * t8106 + 72 * t8108 + t8118 + (-38 * t8109 - 4 * t8111) * t8102 + t8116 = -t8108 ** 2 - 11 * t8101 + 39 * t8105 - 27 * t8107 - 27 * t8109 + 39 * t8111 - 11 * t8113 - 1 + t8103 = 5 * phi1 + tfunc[..., c] = (0.155e3 / 0.32768e5*1j) * np.sqrt(0.286143e6) * np.sqrt((1 - t8102)) * np.sqrt((1 + t8102)) * ((t8116 + t8117) * np.exp((-3*1j) * (t8103 - 2 * phi2)) + (-t8116 + t8117) * np.exp((-3*1j) * (t8103 + 2 * phi2))) + + if Bindx == 391: + t8133 = np.cos(phi) + t8132 = t8133 ** 2 + t8135 = t8133 * t8132 + t8136 = t8132 ** 2 + t8137 = t8133 * t8136 + t8139 = t8136 ** 2 + t8141 = t8137 ** 2 + t8138 = t8135 ** 2 + t8143 = t8138 ** 2 + t8144 = t8133 * t8143 + t8147 = 208 * t8135 + 572 * t8137 - 12 * t8144 + (-572 * t8139 - 208 * t8141 + 12) * t8133 + t8146 = -t8133 * t8144 + 65 * t8132 + 429 * t8136 + 429 * t8138 - 429 * t8139 - 429 * t8141 - 65 * t8143 + 1 + t8134 = 5 * phi1 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.1015e4) * np.sqrt((1 - t8133)) * np.sqrt((1 + t8133)) * ((-t8146 + t8147) * np.exp((-3*1j) * (t8134 - 4 * phi2)) + (t8146 + t8147) * np.exp((-3*1j) * (t8134 + 4 * phi2))) + + if Bindx == 392: + t8164 = np.cos(phi) + t8163 = t8164 ** 2 + t8167 = t8163 ** 2 + t8166 = t8164 * t8163 + t8169 = t8166 ** 2 + t8171 = t8167 ** 2 + t8168 = t8164 * t8167 + t8173 = t8168 ** 2 + t8175 = t8169 ** 2 + t8170 = t8164 * t8169 + t8177 = t8170 ** 2 + t8180 = 2 - 8 * t8163 - 58 * t8167 + 244 * t8169 - 306 * t8171 + 112 * t8173 + 42 * t8175 - 28 * t8177 + t8179 = -63 * t8166 + 119 * t8168 + 9 * t8170 + (-211 * t8171 + 187 * t8173 - 43 * t8175 - 5 * t8177 + 7) * t8164 + t8165 = 7 * phi1 + tfunc[..., c] = (0.93e2 / 0.32768e5) * np.sqrt(0.476905e6) * np.sqrt(0.2e1) * ((-t8179 + t8180) * np.exp((-2*1j) * (t8165 - 3 * phi2)) + (t8179 + t8180) * np.exp((-2*1j) * (t8165 + 3 * phi2))) + + if Bindx == 393: + t8197 = np.cos(phi) + t8196 = t8197 ** 2 + t8200 = t8196 ** 2 + t8199 = t8197 * t8196 + t8202 = t8199 ** 2 + t8204 = t8200 ** 2 + t8201 = t8197 * t8200 + t8206 = t8201 ** 2 + t8208 = t8202 ** 2 + t8203 = t8197 * t8202 + t8210 = t8203 ** 2 + t8213 = 4 + 200 * t8196 + 676 * t8200 - 1144 * t8202 - 1716 * t8204 + 1144 * t8206 + 780 * t8208 + 56 * t8210 + t8212 = -507 * t8199 - 143 * t8201 + 2145 * t8203 + (143 * t8204 - 1313 * t8206 - 277 * t8208 - 5 * t8210 - 43) * t8197 + t8198 = 7 * phi1 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((t8212 + t8213) * np.exp((-2*1j) * (t8198 - 6 * phi2)) + (-t8212 + t8213) * np.exp((-2*1j) * (t8198 + 6 * phi2))) * np.sqrt(0.609e3) + + if Bindx == 394: + t8227 = np.cos(phi) + t8226 = t8227 ** 2 + t8229 = t8227 * t8226 + t8232 = t8229 ** 2 + t8239 = -145 * t8232 ** 2 - 19 + t8230 = t8226 ** 2 + t8231 = t8227 * t8230 + t8236 = t8231 ** 2 + t8234 = t8230 ** 2 + t8228 = 13 * phi1 + t8221 = t8227 * t8232 + t8219 = t8227 * t8234 + t8217 = t8227 * t8236 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * ((1 + t8227) ** (0.7e1 / 0.2e1)) * np.sqrt(0.16445e5) * ((-1334 * t8217 + 5384 * t8236 - 12370 * t8219 + 17385 * t8234 - 14460 * t8221 + 5208 * t8232 + 2172 * t8231 - 3405 * t8230 + 1490 * t8229 - 160 * t8226 - 74 * t8227 - t8239) * np.exp((-1*1j) * (t8228 - 6 * phi2)) + (-174 * t8217 + 648 * t8236 + 774 * t8219 - 1161 * t8234 - 1356 * t8221 + 1064 * t8232 + 1164 * t8231 - 531 * t8230 - 486 * t8229 + 144 * t8226 + 78 * t8227 + t8239) * np.exp((-1*1j) * (t8228 + 6 * phi2))) * ((1 - t8227) ** (-0.1e1 / 0.2e1)) + + if Bindx == 395: + t8256 = np.cos(phi) + t8255 = t8256 ** 2 + t8258 = t8256 * t8255 + t8261 = t8258 ** 2 + t8262 = t8256 * t8261 + t8269 = t8262 ** 2 + t8271 = -145 * t8256 * t8269 + 91 + t8267 = t8261 ** 2 + t8259 = t8255 ** 2 + t8260 = t8256 * t8259 + t8265 = t8260 ** 2 + t8263 = t8259 ** 2 + t8257 = 13 * phi1 + t8248 = t8256 * t8263 + t8246 = t8256 * t8265 + t8244 = t8256 * t8267 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt((1 + t8256)) * np.sqrt(0.21e2) * ((1 - t8256) ** (-0.1e1 / 0.2e1)) * ((1653 * t8269 - 8385 * t8244 + 24557 * t8267 - 44421 * t8246 + 46761 * t8265 - 15301 * t8248 - 32175 * t8263 + 55341 * t8262 - 38753 * t8261 + 7293 * t8260 + 10023 * t8259 - 9815 * t8258 + 4227 * t8255 - 951 * t8256 + t8271) * np.exp((-1*1j) * (t8257 - 12 * phi2)) + (1363 * t8269 + 5369 * t8244 + 10803 * t8267 + 9061 * t8246 - 6721 * t8265 - 24739 * t8248 - 22737 * t8263 - 429 * t8262 + 17017 * t8261 + 14443 * t8260 + 2873 * t8259 - 3081 * t8258 - 2507 * t8255 - 769 * t8256 - t8271) * np.exp((-1*1j) * (t8257 + 12 * phi2))) + + if Bindx == 396: + t8288 = np.cos(phi) + t8287 = t8288 ** 2 + t8291 = t8287 ** 2 + t8290 = t8288 * t8287 + t8293 = t8290 ** 2 + t8295 = t8291 ** 2 + t8292 = t8288 * t8291 + t8297 = t8292 ** 2 + t8299 = t8293 ** 2 + t8294 = t8288 * t8293 + t8301 = t8294 ** 2 + t8304 = 4 - 120 * t8287 + 996 * t8291 - 2936 * t8293 + 3276 * t8295 - 456 * t8297 - 1460 * t8299 + 696 * t8301 + t8303 = -223 * t8290 - 3 * t8292 + 2205 * t8294 + (-4717 * t8295 + 3603 * t8297 - 753 * t8299 - 145 * t8301 + 33) * t8288 + t8289 = 2 * phi1 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((t8303 + t8304) * np.exp((-6*1j) * (t8289 - phi2)) + (-t8303 + t8304) * np.exp((-6*1j) * (t8289 + phi2))) * np.sqrt(0.345345e6) + + if Bindx == 397: + t8321 = np.cos(phi) + t8320 = t8321 ** 2 + t8323 = t8320 ** 2 + t8322 = t8321 * t8320 + t8325 = t8322 ** 2 + t8327 = t8323 ** 2 + t8324 = t8321 * t8323 + t8329 = t8324 ** 2 + t8331 = t8325 ** 2 + t8326 = t8321 * t8325 + t8333 = t8326 ** 2 + t8336 = -11712 * t8320 + 30264 * t8323 + 80080 * t8325 - 175032 * t8327 - 13728 * t8329 + 84968 * t8331 + 9744 * t8333 - 488 + t8335 = -9451 * t8322 + 90519 * t8324 - 47619 * t8326 + (-152867 * t8327 + 86775 * t8329 + 39669 * t8331 + 1015 * t8333 - 3945) * t8321 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * np.sqrt(0.2e1) * ((-t8335 + t8336) * np.exp((-12*1j) * (phi1 - phi2)) + (t8335 + t8336) * np.exp((-12*1j) * (phi1 + phi2))) + + if Bindx == 398: + t8351 = np.cos(phi) + t8350 = t8351 ** 2 + t8353 = t8351 * t8350 + t8356 = t8353 ** 2 + t8362 = t8356 ** 2 + t8364 = -435 * t8351 * t8362 - 1 + t8354 = t8350 ** 2 + t8355 = t8351 * t8354 + t8360 = t8355 ** 2 + t8358 = t8354 ** 2 + t8352 = 11 * phi1 + t8345 = t8351 * t8356 + t8343 = t8351 * t8358 + t8341 = t8351 * t8360 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * ((1 + t8351) ** (0.5e1 / 0.2e1)) * np.sqrt(0.115115e6) * ((1 - t8351) ** (-0.1e1 / 0.2e1)) * ((3219 * t8362 - 9738 * t8341 + 14514 * t8360 - 8225 * t8343 - 5895 * t8358 + 12132 * t8345 - 5844 * t8356 - 1413 * t8355 + 2325 * t8354 - 570 * t8353 - 126 * t8350 + 57 * t8351 + t8364) * np.exp((-1*1j) * (t8352 - 6 * phi2)) + (609 * t8362 - 1746 * t8341 - 2502 * t8360 + 2813 * t8343 + 4047 * t8358 - 2364 * t8345 - 3220 * t8356 + 1149 * t8355 + 1263 * t8354 - 338 * t8353 - 198 * t8350 + 51 * t8351 - t8364) * np.exp((-1*1j) * (t8352 + 6 * phi2))) + + if Bindx == 399: + t8382 = np.cos(phi) + t8381 = t8382 ** 2 + t8384 = t8382 * t8381 + t8385 = t8381 ** 2 + t8386 = t8382 * t8385 + t8387 = t8384 ** 2 + t8388 = t8382 * t8387 + t8389 = t8385 ** 2 + t8391 = t8386 ** 2 + t8393 = t8387 ** 2 + t8395 = t8388 ** 2 + t8399 = -6356 * t8384 - 40092 * t8386 + 126412 * t8388 + (-56628 * t8389 - 87516 * t8391 + 52844 * t8393 + 8932 * t8395 + 2404) * t8382 + t8398 = 4356 * t8381 - 35464 * t8385 + 33748 * t8387 - 148148 * t8391 + 17056 * t8393 + 31836 * t8395 + 363 + (95238 + 1015 * t8389) * t8389 + t8383 = 11 * phi1 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * ((1 - t8382) ** (-0.1e1 / 0.2e1)) * ((1 + t8382) ** (-0.1e1 / 0.2e1)) * ((-t8398 + t8399) * np.exp((-1*1j) * (t8383 - 12 * phi2)) + (t8398 + t8399) * np.exp((-1*1j) * (t8383 + 12 * phi2))) + + if Bindx == 400: + t8416 = np.cos(phi) + t8415 = t8416 ** 2 + t8419 = t8415 ** 2 + t8418 = t8416 * t8415 + t8421 = t8418 ** 2 + t8423 = t8419 ** 2 + t8420 = t8416 * t8419 + t8425 = t8420 ** 2 + t8427 = t8421 ** 2 + t8422 = t8416 * t8421 + t8429 = t8422 ** 2 + t8432 = -6 + 312 * t8415 - 2386 * t8419 + 6116 * t8421 - 3562 * t8423 - 7728 * t8425 + 11778 * t8427 - 4524 * t8429 + t8431 = 511 * t8418 - 4343 * t8420 + 15175 * t8422 + (-24125 * t8423 + 16549 * t8425 - 2613 * t8427 - 1131 * t8429 - 23) * t8416 + t8417 = 5 * phi1 + tfunc[..., c] = (0.465e3 / 0.32768e5) * np.sqrt(0.1771e4) * np.sqrt(0.2e1) * ((-t8431 + t8432) * np.exp((-2*1j) * (t8417 - 3 * phi2)) + (t8431 + t8432) * np.exp((-2*1j) * (t8417 + 3 * phi2))) + + if Bindx == 401: + t8449 = np.cos(phi) + t8448 = t8449 ** 2 + t8452 = t8448 ** 2 + t8451 = t8449 * t8448 + t8454 = t8451 ** 2 + t8456 = t8452 ** 2 + t8453 = t8449 * t8452 + t8458 = t8453 ** 2 + t8460 = t8454 ** 2 + t8455 = t8449 * t8454 + t8462 = t8455 ** 2 + t8465 = -52 - 104 * t8448 + 4076 * t8452 - 10472 * t8454 + 1188 * t8456 + 13992 * t8458 - 7004 * t8460 - 1624 * t8462 + t8464 = 1819 * t8451 + 143 * t8453 - 13233 * t8455 + (16577 * t8456 + 289 * t8458 - 5131 * t8460 - 203 * t8462 - 261) * t8449 + t8450 = 5 * phi1 + tfunc[..., c] = (0.93e2 / 0.16384e5) * ((-t8464 + t8465) * np.exp((-2*1j) * (t8450 - 6 * phi2)) + (t8464 + t8465) * np.exp((-2*1j) * (t8450 + 6 * phi2))) * np.sqrt(0.195e3) + + if Bindx == 402: + t8481 = np.cos(phi) + t8480 = t8481 ** 2 + t8483 = t8481 * t8480 + t8486 = t8483 ** 2 + t8487 = t8481 * t8486 + t8495 = -28275 * t8487 ** 2 + 59 + t8492 = t8486 ** 2 + t8484 = t8480 ** 2 + t8485 = t8481 * t8484 + t8490 = t8485 ** 2 + t8488 = t8484 ** 2 + t8482 = 3 * phi1 + t8473 = t8481 * t8488 + t8471 = t8481 * t8490 + t8469 = t8481 * t8492 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.5313e4) * ((1 + t8481) ** (0.3e1 / 0.2e1)) * ((158340 * t8469 - 304395 * t8492 + 112320 * t8471 + 390585 * t8490 - 498300 * t8473 + 11969 * t8488 + 316448 * t8487 - 148393 * t8486 - 54964 * t8485 + 50255 * t8484 - 1504 * t8483 - 4573 * t8480 + 428 * t8481 + t8495) * np.exp((-3*1j) * (t8482 - 2 * phi2)) + (45240 * t8469 - 102765 * t8492 - 177840 * t8471 + 147615 * t8490 + 277800 * t8473 - 109169 * t8488 - 217376 * t8487 + 47449 * t8486 + 87176 * t8485 - 13735 * t8484 - 15664 * t8483 + 2389 * t8480 + 664 * t8481 - t8495) * np.exp((-3*1j) * (t8482 + 2 * phi2))) * ((1 - t8481) ** (-0.1e1 / 0.2e1)) + + if Bindx == 403: + t8513 = np.cos(phi) + t8512 = t8513 ** 2 + t8515 = t8513 * t8512 + t8516 = t8512 ** 2 + t8517 = t8513 * t8516 + t8518 = t8515 ** 2 + t8519 = t8513 * t8518 + t8520 = t8516 ** 2 + t8522 = t8517 ** 2 + t8524 = t8518 ** 2 + t8526 = t8519 ** 2 + t8530 = -7876 * t8515 + 23636 * t8517 - 3652 * t8519 + (-63492 * t8520 + 72084 * t8522 - 13956 * t8524 - 7308 * t8526 + 564) * t8513 + t8529 = -1416 * t8512 - 5196 * t8516 + 44088 * t8518 + 28776 * t8522 + 31364 * t8524 - 19096 * t8526 + 177 + (-77682 - 1015 * t8520) * t8520 + t8514 = 3 * phi1 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((-t8529 + t8530) * np.exp((-3*1j) * (t8514 - 4 * phi2)) + (t8529 + t8530) * np.exp((-3*1j) * (t8514 + 4 * phi2))) * ((1 - t8513) ** (-0.1e1 / 0.2e1)) * ((1 + t8513) ** (-0.1e1 / 0.2e1)) + + if Bindx == 404: + t8547 = np.cos(phi) + t8546 = t8547 ** 2 + t8550 = t8546 ** 2 + t8549 = t8547 * t8546 + t8552 = t8549 ** 2 + t8554 = t8550 ** 2 + t8551 = t8547 * t8550 + t8556 = t8551 ** 2 + t8558 = t8552 ** 2 + t8553 = t8547 * t8552 + t8560 = t8553 ** 2 + t8563 = -560 * t8546 + 2632 * t8550 + 20496 * t8552 - 140776 * t8554 + 300720 * t8556 - 273000 * t8558 + 90480 * t8560 + 8 + t8562 = -10731 * t8549 + 73633 * t8551 - 222055 * t8553 + (316967 * t8554 - 185185 * t8556 - 1365 * t8558 + 28275 * t8560 + 461) * t8547 + t8548 = 4 * phi1 + tfunc[..., c] = -(0.93e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((-t8562 + t8563) * np.exp((-2*1j) * (t8548 - 3 * phi2)) + (t8562 + t8563) * np.exp((-2*1j) * (t8548 + 3 * phi2))) + + if Bindx == 405: + t8580 = np.cos(phi) + t8579 = t8580 ** 2 + t8583 = t8579 ** 2 + t8582 = t8580 * t8579 + t8585 = t8582 ** 2 + t8587 = t8583 ** 2 + t8584 = t8580 * t8583 + t8589 = t8584 ** 2 + t8591 = t8585 ** 2 + t8586 = t8580 * t8585 + t8593 = t8586 ** 2 + t8596 = 16 - 256 * t8579 + 592 * t8583 + 1760 * t8585 - 6864 * t8587 + 6336 * t8589 - 656 * t8591 - 928 * t8593 + t8595 = -587 * t8582 + 3047 * t8584 - 4587 * t8586 + (-11 * t8587 + 4295 * t8589 - 2027 * t8591 - 145 * t8593 + 15) * t8580 + t8581 = 2 * phi1 + tfunc[..., c] = (0.93e2 / 0.8192e4) * ((-t8595 + t8596) * np.exp((-4*1j) * (t8581 - 3 * phi2)) + (t8595 + t8596) * np.exp((-4*1j) * (t8581 + 3 * phi2))) * np.sqrt(0.1365e4) + + if Bindx == 406: + t8613 = np.cos(phi) + t8612 = t8613 ** 2 + t8615 = t8613 * t8612 + t8618 = t8615 ** 2 + t8619 = t8613 * t8618 + t8626 = t8619 ** 2 + t8628 = -650325 * t8613 * t8626 + 509 + t8624 = t8618 ** 2 + t8616 = t8612 ** 2 + t8617 = t8613 * t8616 + t8622 = t8617 ** 2 + t8620 = t8616 ** 2 + t8614 = 7 * phi1 + t8605 = t8613 * t8620 + t8603 = t8613 * t8622 + t8601 = t8613 * t8624 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt((1 + t8613)) * np.sqrt(0.11e2) * ((1 - t8613) ** (-0.1e1 / 0.2e1)) * ((-1475565 * t8601 + 7105735 * t8603 - 8315489 * t8605 - 37611 * t8612 - 2091 * t8613 + 85869 * t8615 + 416129 * t8616 - 949319 * t8617 - 1439319 * t8618 + 4184801 * t8619 + 1154071 * t8620 + 2694335 * t8622 - 5242965 * t8624 + 2471235 * t8626 + t8628) * np.exp((-1*1j) * (t8614 - 6 * phi2)) + (-2166255 * t8601 + 2689505 * t8603 - 1489411 * t8605 + 40775 * t8612 - 1073 * t8613 + 7483 * t8615 - 509481 * t8616 - 23709 * t8617 + 2412347 * t8618 + 333135 * t8619 - 5672007 * t8620 + 7110565 * t8622 - 4552275 * t8624 + 1170585 * t8626 - t8628) * np.exp((-1*1j) * (t8614 + 6 * phi2))) + + if Bindx == 407: + t8646 = np.cos(phi) + t8645 = t8646 ** 2 + t8648 = t8646 * t8645 + t8649 = t8645 ** 2 + t8650 = t8646 * t8649 + t8651 = t8648 ** 2 + t8652 = t8646 * t8651 + t8653 = t8649 ** 2 + t8655 = t8650 ** 2 + t8657 = t8651 ** 2 + t8659 = t8652 ** 2 + t8663 = 92 * t8648 - 1740 * t8650 + 5852 * t8652 + (-7524 * t8653 + 3060 * t8655 + 1052 * t8657 - 812 * t8659 + 20) * t8646 + t8662 = 216 * t8645 - 1252 * t8649 + 2200 * t8651 - 5368 * t8655 + 5036 * t8657 - 1272 * t8659 - 9 + (594 - 145 * t8653) * t8653 + t8647 = 7 * phi1 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * np.sqrt(0.31395e5) * np.sqrt(0.2e1) * ((-t8662 + t8663) * np.exp((-1*1j) * (t8647 - 12 * phi2)) + (t8662 + t8663) * np.exp((-1*1j) * (t8647 + 12 * phi2))) * ((1 - t8646) ** (-0.1e1 / 0.2e1)) * ((1 + t8646) ** (-0.1e1 / 0.2e1)) + + if Bindx == 408: + t8680 = np.cos(phi) + t8679 = t8680 ** 2 + t8682 = t8679 ** 2 + t8681 = t8680 * t8679 + t8684 = t8681 ** 2 + t8686 = t8682 ** 2 + t8683 = t8680 * t8682 + t8688 = t8683 ** 2 + t8690 = t8684 ** 2 + t8685 = t8680 * t8684 + t8692 = t8685 ** 2 + t8695 = 194712 * t8679 - 2861418 * t8682 + 16846228 * t8684 - 49242402 * t8686 + 75232080 * t8688 - 57327270 * t8690 + 17168580 * t8692 - 2318 + t8694 = 981547 * t8681 - 7094787 * t8683 + 20356083 * t8685 + (-23637537 * t8686 + 3108105 * t8688 + 13468455 * t8690 - 7153575 * t8692 - 36483) * t8680 + tfunc[..., c] = -(0.31e2 / 0.32768e5) * np.sqrt(0.2e1) * ((t8694 + t8695) * np.exp((-6*1j) * (phi1 - phi2)) + (-t8694 + t8695) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 409: + t8712 = np.cos(phi) + t8711 = t8712 ** 2 + t8714 = t8711 ** 2 + t8713 = t8712 * t8711 + t8716 = t8713 ** 2 + t8718 = t8714 ** 2 + t8715 = t8712 * t8714 + t8720 = t8715 ** 2 + t8722 = t8716 ** 2 + t8717 = t8712 * t8716 + t8724 = t8717 ** 2 + t8727 = 4 - 120 * t8711 + 996 * t8714 - 2936 * t8716 + 3276 * t8718 - 456 * t8720 - 1460 * t8722 + 696 * t8724 + t8726 = -223 * t8713 - 3 * t8715 + 2205 * t8717 + (-4717 * t8718 + 3603 * t8720 - 753 * t8722 - 145 * t8724 + 33) * t8712 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((t8726 + t8727) * np.exp((-6*1j) * (phi1 - 2 * phi2)) + (-t8726 + t8727) * np.exp((-6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.345345e6) + + if Bindx == 410: + t8745 = np.cos(phi) + t8744 = t8745 ** 2 + t8747 = t8745 * t8744 + t8748 = t8744 ** 2 + t8749 = t8745 * t8748 + t8750 = t8747 ** 2 + t8751 = t8745 * t8750 + t8752 = t8748 ** 2 + t8754 = t8749 ** 2 + t8756 = t8750 ** 2 + t8758 = t8751 ** 2 + t8762 = -179930 * t8747 + 1640574 * t8749 - 6840290 * t8751 + (15081330 * t8752 - 18193230 * t8754 + 11347050 * t8756 - 2861430 * t8758 + 5926) * t8745 + t8761 = -19350 * t8744 + 258470 * t8748 - 1137694 * t8750 + 596574 * t8754 - 4440150 * t8756 + 4440150 * t8758 + 215 + (1732500 - 1430715 * t8752) * t8752 + t8746 = 5 * phi1 + tfunc[..., c] = (0.31e2 / 0.32768e5*1j) * np.sqrt(0.105e3) * ((-t8761 + t8762) * np.exp((-1*1j) * (t8746 - 6 * phi2)) + (t8761 + t8762) * np.exp((-1*1j) * (t8746 + 6 * phi2))) * ((1 - t8745) ** (-0.1e1 / 0.2e1)) * ((1 + t8745) ** (-0.1e1 / 0.2e1)) + + if Bindx == 411: + t8780 = np.cos(phi) + t8779 = t8780 ** 2 + t8782 = t8780 * t8779 + t8783 = t8779 ** 2 + t8784 = t8780 * t8783 + t8785 = t8782 ** 2 + t8786 = t8780 * t8785 + t8787 = t8783 ** 2 + t8789 = t8784 ** 2 + t8791 = t8785 ** 2 + t8793 = t8786 ** 2 + t8797 = -508 * t8782 + 1836 * t8784 - 2332 * t8786 + (-412 * t8787 + 3564 * t8789 - 3004 * t8791 + 812 * t8793 + 44) * t8780 + t8796 = -36 * t8779 + 664 * t8783 - 3380 * t8785 - 7724 * t8789 + 3568 * t8791 - 252 * t8793 + 1 + (7362 - 203 * t8787) * t8787 + t8781 = 5 * phi1 + tfunc[..., c] = (-0.465e3 / 0.32768e5*1j) * np.sqrt(0.3289e4) * np.sqrt(0.2e1) * ((1 + t8780) ** (-0.1e1 / 0.2e1)) * ((1 - t8780) ** (-0.1e1 / 0.2e1)) * ((t8796 + t8797) * np.exp((-1*1j) * (t8781 - 12 * phi2)) + (-t8796 + t8797) * np.exp((-1*1j) * (t8781 + 12 * phi2))) + + if Bindx == 412: + t8814 = np.cos(phi) + t8813 = t8814 ** 2 + t8816 = t8814 * t8813 + t8817 = t8813 ** 2 + t8818 = t8814 * t8817 + t8819 = t8816 ** 2 + t8820 = t8814 * t8819 + t8821 = t8817 ** 2 + t8823 = t8818 ** 2 + t8825 = t8819 ** 2 + t8827 = t8820 ** 2 + t8830 = 1509 * t8816 + 23089 * t8818 - 336111 * t8820 + (1383519 * t8821 - 2498145 * t8823 + 2076555 * t8825 - 650325 * t8827 - 91) * t8814 + t8829 = 15416 * t8813 - 234884 * t8817 + 1344056 * t8819 - 3687084 * t8821 + 5235720 * t8823 - 3713580 * t8825 + 1040520 * t8827 - 164 + t8815 = 2 * phi1 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((t8829 + t8830) * np.exp((-2*1j) * (t8815 - 3 * phi2)) + (t8829 - t8830) * np.exp((-2*1j) * (t8815 + 3 * phi2))) * np.sqrt(0.231e3) + + if Bindx == 413: + t8847 = np.cos(phi) + t8846 = t8847 ** 2 + t8849 = t8846 ** 2 + t8848 = t8847 * t8846 + t8851 = t8848 ** 2 + t8853 = t8849 ** 2 + t8850 = t8847 * t8849 + t8855 = t8850 ** 2 + t8857 = t8851 ** 2 + t8852 = t8847 * t8851 + t8859 = t8852 ** 2 + t8862 = 8 - 320 * t8846 + 872 * t8849 + 2800 * t8851 - 13800 * t8853 + 20512 * t8855 - 13320 * t8857 + 3248 * t8859 + t8861 = 2091 * t8848 - 9815 * t8850 + 20355 * t8852 + (-19805 * t8853 + 7241 * t8855 + 1099 * t8857 - 1015 * t8859 - 151) * t8847 + tfunc[..., c] = -(0.93e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.1495e4) * ((t8861 + t8862) * np.exp((-4*1j) * (phi1 - 3 * phi2)) + (-t8861 + t8862) * np.exp((-4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 414: + t8880 = np.cos(phi) + t8879 = t8880 ** 2 + t8881 = t8880 * t8879 + t8882 = t8879 ** 2 + t8883 = t8880 * t8882 + t8884 = t8881 ** 2 + t8885 = t8880 * t8884 + t8886 = t8882 ** 2 + t8888 = t8883 ** 2 + t8890 = t8884 ** 2 + t8892 = t8885 ** 2 + t8896 = -54794 * t8881 + 516046 * t8883 - 2153714 * t8885 + (4647426 * t8886 - 5414430 * t8888 + 3238170 * t8890 - 780390 * t8892 + 1686) * t8880 + t8895 = 4606 * t8879 - 79694 * t8882 + 559206 * t8884 + 4046298 * t8888 - 4565730 * t8890 + 2699970 * t8892 - 47 + (-2014284 - 650325 * t8886) * t8886 + tfunc[..., c] = (0.31e2 / 0.32768e5*1j) * np.sqrt(0.1463e4) * ((-t8895 + t8896) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t8895 + t8896) * np.exp((-3*1j) * (phi1 + 2 * phi2))) * ((1 + t8880) ** (-0.1e1 / 0.2e1)) * ((1 - t8880) ** (-0.1e1 / 0.2e1)) + + if Bindx == 415: + t8914 = np.cos(phi) + t8913 = t8914 ** 2 + t8915 = t8914 * t8913 + t8916 = t8913 ** 2 + t8917 = t8914 * t8916 + t8918 = t8915 ** 2 + t8919 = t8914 * t8918 + t8920 = t8916 ** 2 + t8922 = t8917 ** 2 + t8924 = t8918 ** 2 + t8926 = t8919 ** 2 + t8930 = -1036 * t8915 + 6716 * t8917 - 20140 * t8919 + (32020 * t8920 - 28100 * t8922 + 12916 * t8924 - 2436 * t8926 + 60) * t8914 + t8929 = -572 * t8913 + 3904 * t8916 - 10380 * t8918 - 4788 * t8922 - 3448 * t8924 + 3836 * t8926 + 13 + (12450 - 1015 * t8920) * t8920 + tfunc[..., c] = (0.31e2 / 0.32768e5*1j) * np.sqrt(0.85215e5) * np.sqrt(0.2e1) * ((-t8929 + t8930) * np.exp((-3*1j) * (phi1 - 4 * phi2)) + (t8929 + t8930) * np.exp((-3*1j) * (phi1 + 4 * phi2))) * ((1 + t8914) ** (-0.1e1 / 0.2e1)) * ((1 - t8914) ** (-0.1e1 / 0.2e1)) + + if Bindx == 416: + t8947 = np.cos(phi) + t8946 = t8947 ** 2 + t8949 = t8946 ** 2 + t8948 = t8947 * t8946 + t8951 = t8948 ** 2 + t8953 = t8949 ** 2 + t8950 = t8947 * t8949 + t8955 = t8950 ** 2 + t8957 = t8951 ** 2 + t8952 = t8947 * t8951 + t8959 = t8952 ** 2 + t8962 = -600 * t8946 + 9554 * t8949 - 55972 * t8951 + 153962 * t8953 - 215280 * t8955 + 148350 * t8957 - 40020 * t8959 + 6 + t8961 = 2763 * t8948 - 26851 * t8950 + 116355 * t8952 + (-261809 * t8953 + 318665 * t8955 - 199065 * t8957 + 50025 * t8959 - 83) * t8947 + tfunc[..., c] = (0.93e2 / 0.32768e5) * np.sqrt(0.19019e5) * np.sqrt(0.2e1) * ((t8961 + t8962) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (-t8961 + t8962) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 417: + t8979 = np.cos(phi) + t8978 = t8979 ** 2 + t8980 = t8979 * t8978 + t8981 = t8978 ** 2 + t8982 = t8979 * t8981 + t8983 = t8980 ** 2 + t8984 = t8979 * t8983 + t8985 = t8981 ** 2 + t8987 = t8982 ** 2 + t8989 = t8983 ** 2 + t8991 = t8984 ** 2 + t8994 = 279 * t8980 - 5 * t8982 - 3285 * t8984 + (8485 * t8985 - 9355 * t8987 + 4921 * t8989 - 1015 * t8991 - 25) * t8979 + t8993 = -12 + 552 * t8978 - 4204 * t8981 + 13160 * t8983 - 21220 * t8985 + 18712 * t8987 - 8612 * t8989 + 1624 * t8991 + tfunc[..., c] = -(0.93e2 / 0.16384e5) * ((t8993 + t8994) * np.exp((-2*1j) * (phi1 - 6 * phi2)) + (t8993 - t8994) * np.exp((-2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.6555e4) + + if Bindx == 418: + t9012 = np.cos(phi) + t9011 = t9012 ** 2 + t9013 = t9012 * t9011 + t9014 = t9011 ** 2 + t9015 = t9012 * t9014 + t9016 = t9013 ** 2 + t9017 = t9012 * t9016 + t9018 = t9014 ** 2 + t9020 = t9015 ** 2 + t9022 = t9016 ** 2 + t9024 = t9017 ** 2 + t9028 = 1414 * t9013 - 13650 * t9015 + 57726 * t9017 + (-124798 * t9018 + 144210 * t9020 - 84870 * t9022 + 20010 * t9024 - 42) * t9012 + t9027 = 714 * t9011 - 12082 * t9014 + 78722 * t9016 + 451582 * t9020 - 448270 * t9022 + 233910 * t9024 - 7 + (-254544 - 50025 * t9018) * t9018 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt(0.46189e5) * ((1 + t9012) ** (-0.1e1 / 0.2e1)) * ((1 - t9012) ** (-0.1e1 / 0.2e1)) * ((t9027 + t9028) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (-t9027 + t9028) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 419: + t9046 = np.cos(phi) + t9045 = t9046 ** 2 + t9047 = t9046 * t9045 + t9048 = t9045 ** 2 + t9049 = t9046 * t9048 + t9050 = t9047 ** 2 + t9051 = t9046 * t9050 + t9052 = t9048 ** 2 + t9054 = t9049 ** 2 + t9056 = t9050 ** 2 + t9058 = t9051 ** 2 + t9062 = -188 * t9047 + 876 * t9049 - 1980 * t9051 + (2500 * t9052 - 1812 * t9054 + 708 * t9056 - 116 * t9058 + 12) * t9046 + t9061 = 48 * t9045 - 412 * t9048 + 1520 * t9050 + 3536 * t9054 - 2428 * t9056 + 912 * t9058 - 1 + (-3030 - 145 * t9052) * t9052 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * np.sqrt(0.780045e6) * np.sqrt(0.2e1) * ((-t9061 + t9062) * np.exp((-1*1j) * (phi1 - 12 * phi2)) + (t9061 + t9062) * np.exp((-1*1j) * (phi1 + 12 * phi2))) * ((1 + t9046) ** (-0.1e1 / 0.2e1)) * ((1 - t9046) ** (-0.1e1 / 0.2e1)) + + if Bindx == 420: + t9071 = np.sin(phi) + t9068 = t9071 ** 2 + t9069 = t9071 * t9068 + t9063 = np.cos(phi) + t9064 = t9063 ** 2 + t9065 = t9064 ** 2 + tfunc[..., c] = (-0.31e2 / 0.4096e4*1j) * t9063 * t9069 ** 2 * (-644 * t9064 + 21 + (-12420 * t9064 + 4830 + 10005 * t9065) * t9065) * np.sin((6 * phi2)) * np.sqrt(0.692835e6) + + if Bindx == 421: + t9077 = np.sin(phi) + t9073 = t9077 ** 2 + t9074 = t9077 * t9073 + t9075 = t9074 ** 2 + t9072 = np.cos(phi) + tfunc[..., c] = (0.465e3 / 0.4096e4*1j) * t9072 * t9075 ** 2 * (29 * t9072 ** 2 - 3) * np.sin((12 * phi2)) * np.sqrt(0.52003e5) * np.sqrt(0.2e1) + + if Bindx == 422: + t9095 = np.cos(phi) + t9094 = t9095 ** 2 + t9096 = t9095 * t9094 + t9097 = t9094 ** 2 + t9098 = t9095 * t9097 + t9099 = t9096 ** 2 + t9100 = t9095 * t9099 + t9101 = t9097 ** 2 + t9103 = t9098 ** 2 + t9105 = t9099 ** 2 + t9107 = t9100 ** 2 + t9111 = -1414 * t9096 + 13650 * t9098 - 57726 * t9100 + (124798 * t9101 - 144210 * t9103 + 84870 * t9105 - 20010 * t9107 + 42) * t9095 + t9110 = 714 * t9094 - 12082 * t9097 + 78722 * t9099 + 451582 * t9103 - 448270 * t9105 + 233910 * t9107 - 7 + (-254544 - 50025 * t9101) * t9101 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt(0.46189e5) * ((1 + t9095) ** (-0.1e1 / 0.2e1)) * ((1 - t9095) ** (-0.1e1 / 0.2e1)) * ((-t9110 + t9111) * np.exp((1j) * (phi1 - 6 * phi2)) + (t9110 + t9111) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 423: + t9129 = np.cos(phi) + t9128 = t9129 ** 2 + t9130 = t9129 * t9128 + t9131 = t9128 ** 2 + t9132 = t9129 * t9131 + t9133 = t9130 ** 2 + t9134 = t9129 * t9133 + t9135 = t9131 ** 2 + t9137 = t9132 ** 2 + t9139 = t9133 ** 2 + t9141 = t9134 ** 2 + t9145 = 188 * t9130 - 876 * t9132 + 1980 * t9134 + (-2500 * t9135 + 1812 * t9137 - 708 * t9139 + 116 * t9141 - 12) * t9129 + t9144 = 48 * t9128 - 412 * t9131 + 1520 * t9133 + 3536 * t9137 - 2428 * t9139 + 912 * t9141 - 1 + (-3030 - 145 * t9135) * t9135 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * np.sqrt(0.780045e6) * np.sqrt(0.2e1) * ((t9144 + t9145) * np.exp((1j) * (phi1 - 12 * phi2)) + (-t9144 + t9145) * np.exp((1j) * (phi1 + 12 * phi2))) * ((1 + t9129) ** (-0.1e1 / 0.2e1)) * ((1 - t9129) ** (-0.1e1 / 0.2e1)) + + if Bindx == 424: + t9162 = np.cos(phi) + t9161 = t9162 ** 2 + t9164 = t9161 ** 2 + t9163 = t9162 * t9161 + t9166 = t9163 ** 2 + t9168 = t9164 ** 2 + t9165 = t9162 * t9164 + t9170 = t9165 ** 2 + t9172 = t9166 ** 2 + t9167 = t9162 * t9166 + t9174 = t9167 ** 2 + t9177 = -600 * t9161 + 9554 * t9164 - 55972 * t9166 + 153962 * t9168 - 215280 * t9170 + 148350 * t9172 - 40020 * t9174 + 6 + t9176 = 2763 * t9163 - 26851 * t9165 + 116355 * t9167 + (-261809 * t9168 + 318665 * t9170 - 199065 * t9172 + 50025 * t9174 - 83) * t9162 + tfunc[..., c] = -(0.93e2 / 0.32768e5) * np.sqrt(0.19019e5) * np.sqrt(0.2e1) * ((t9176 + t9177) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (-t9176 + t9177) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 425: + t9194 = np.cos(phi) + t9193 = t9194 ** 2 + t9195 = t9194 * t9193 + t9196 = t9193 ** 2 + t9197 = t9194 * t9196 + t9198 = t9195 ** 2 + t9199 = t9194 * t9198 + t9200 = t9196 ** 2 + t9202 = t9197 ** 2 + t9204 = t9198 ** 2 + t9206 = t9199 ** 2 + t9209 = 279 * t9195 - 5 * t9197 - 3285 * t9199 + (8485 * t9200 - 9355 * t9202 + 4921 * t9204 - 1015 * t9206 - 25) * t9194 + t9208 = -12 + 552 * t9193 - 4204 * t9196 + 13160 * t9198 - 21220 * t9200 + 18712 * t9202 - 8612 * t9204 + 1624 * t9206 + tfunc[..., c] = (0.93e2 / 0.16384e5) * ((t9208 + t9209) * np.exp((2*1j) * (phi1 - 6 * phi2)) + (t9208 - t9209) * np.exp((2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.6555e4) + + if Bindx == 426: + t9227 = np.cos(phi) + t9226 = t9227 ** 2 + t9228 = t9227 * t9226 + t9229 = t9226 ** 2 + t9230 = t9227 * t9229 + t9231 = t9228 ** 2 + t9232 = t9227 * t9231 + t9233 = t9229 ** 2 + t9235 = t9230 ** 2 + t9237 = t9231 ** 2 + t9239 = t9232 ** 2 + t9243 = -54794 * t9228 + 516046 * t9230 - 2153714 * t9232 + (4647426 * t9233 - 5414430 * t9235 + 3238170 * t9237 - 780390 * t9239 + 1686) * t9227 + t9242 = 4606 * t9226 - 79694 * t9229 + 559206 * t9231 + 4046298 * t9235 - 4565730 * t9237 + 2699970 * t9239 - 47 + (-2014284 - 650325 * t9233) * t9233 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.1463e4) * ((1 + t9227) ** (-0.1e1 / 0.2e1)) * ((1 - t9227) ** (-0.1e1 / 0.2e1)) * ((-t9242 + t9243) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t9242 + t9243) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 427: + t9261 = np.cos(phi) + t9260 = t9261 ** 2 + t9262 = t9261 * t9260 + t9263 = t9260 ** 2 + t9264 = t9261 * t9263 + t9265 = t9262 ** 2 + t9266 = t9261 * t9265 + t9267 = t9263 ** 2 + t9269 = t9264 ** 2 + t9271 = t9265 ** 2 + t9273 = t9266 ** 2 + t9277 = -1036 * t9262 + 6716 * t9264 - 20140 * t9266 + (32020 * t9267 - 28100 * t9269 + 12916 * t9271 - 2436 * t9273 + 60) * t9261 + t9276 = -572 * t9260 + 3904 * t9263 - 10380 * t9265 - 4788 * t9269 - 3448 * t9271 + 3836 * t9273 + 13 + (12450 - 1015 * t9267) * t9267 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.85215e5) * np.sqrt(0.2e1) * ((1 + t9261) ** (-0.1e1 / 0.2e1)) * ((1 - t9261) ** (-0.1e1 / 0.2e1)) * ((-t9276 + t9277) * np.exp((3*1j) * (phi1 - 4 * phi2)) + (t9276 + t9277) * np.exp((3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 428: + t9294 = np.cos(phi) + t9293 = t9294 ** 2 + t9296 = t9294 * t9293 + t9297 = t9293 ** 2 + t9298 = t9294 * t9297 + t9299 = t9296 ** 2 + t9300 = t9294 * t9299 + t9301 = t9297 ** 2 + t9303 = t9298 ** 2 + t9305 = t9299 ** 2 + t9307 = t9300 ** 2 + t9310 = 1509 * t9296 + 23089 * t9298 - 336111 * t9300 + (1383519 * t9301 - 2498145 * t9303 + 2076555 * t9305 - 650325 * t9307 - 91) * t9294 + t9309 = -15416 * t9293 + 234884 * t9297 - 1344056 * t9299 + 3687084 * t9301 - 5235720 * t9303 + 3713580 * t9305 - 1040520 * t9307 + 164 + t9295 = 2 * phi1 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((t9309 - t9310) * np.exp((2*1j) * (t9295 - 3 * phi2)) + (t9309 + t9310) * np.exp((2*1j) * (t9295 + 3 * phi2))) * np.sqrt(0.231e3) + + if Bindx == 429: + t9327 = np.cos(phi) + t9326 = t9327 ** 2 + t9329 = t9326 ** 2 + t9328 = t9327 * t9326 + t9331 = t9328 ** 2 + t9333 = t9329 ** 2 + t9330 = t9327 * t9329 + t9335 = t9330 ** 2 + t9337 = t9331 ** 2 + t9332 = t9327 * t9331 + t9339 = t9332 ** 2 + t9342 = 8 - 320 * t9326 + 872 * t9329 + 2800 * t9331 - 13800 * t9333 + 20512 * t9335 - 13320 * t9337 + 3248 * t9339 + t9341 = 2091 * t9328 - 9815 * t9330 + 20355 * t9332 + (-19805 * t9333 + 7241 * t9335 + 1099 * t9337 - 1015 * t9339 - 151) * t9327 + tfunc[..., c] = (0.93e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.1495e4) * ((t9341 + t9342) * np.exp((4*1j) * (phi1 - 3 * phi2)) + (-t9341 + t9342) * np.exp((4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 430: + t9360 = np.cos(phi) + t9359 = t9360 ** 2 + t9362 = t9360 * t9359 + t9363 = t9359 ** 2 + t9364 = t9360 * t9363 + t9365 = t9362 ** 2 + t9366 = t9360 * t9365 + t9367 = t9363 ** 2 + t9369 = t9364 ** 2 + t9371 = t9365 ** 2 + t9373 = t9366 ** 2 + t9377 = -179930 * t9362 + 1640574 * t9364 - 6840290 * t9366 + (15081330 * t9367 - 18193230 * t9369 + 11347050 * t9371 - 2861430 * t9373 + 5926) * t9360 + t9376 = -19350 * t9359 + 258470 * t9363 - 1137694 * t9365 + 596574 * t9369 - 4440150 * t9371 + 4440150 * t9373 + 215 + (1732500 - 1430715 * t9367) * t9367 + t9361 = 5 * phi1 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.105e3) * ((1 + t9360) ** (-0.1e1 / 0.2e1)) * ((1 - t9360) ** (-0.1e1 / 0.2e1)) * ((-t9376 + t9377) * np.exp((1j) * (t9361 - 6 * phi2)) + (t9376 + t9377) * np.exp((1j) * (t9361 + 6 * phi2))) + + if Bindx == 431: + t9395 = np.cos(phi) + t9394 = t9395 ** 2 + t9397 = t9395 * t9394 + t9398 = t9394 ** 2 + t9399 = t9395 * t9398 + t9400 = t9397 ** 2 + t9401 = t9395 * t9400 + t9402 = t9398 ** 2 + t9404 = t9399 ** 2 + t9406 = t9400 ** 2 + t9408 = t9401 ** 2 + t9412 = 508 * t9397 - 1836 * t9399 + 2332 * t9401 + (412 * t9402 - 3564 * t9404 + 3004 * t9406 - 812 * t9408 - 44) * t9395 + t9411 = -36 * t9394 + 664 * t9398 - 3380 * t9400 - 7724 * t9404 + 3568 * t9406 - 252 * t9408 + 1 + (7362 - 203 * t9402) * t9402 + t9396 = 5 * phi1 + tfunc[..., c] = (-0.465e3 / 0.32768e5*1j) * np.sqrt(0.3289e4) * np.sqrt(0.2e1) * ((-t9411 + t9412) * np.exp((1j) * (t9396 - 12 * phi2)) + (t9411 + t9412) * np.exp((1j) * (t9396 + 12 * phi2))) * ((1 + t9395) ** (-0.1e1 / 0.2e1)) * ((1 - t9395) ** (-0.1e1 / 0.2e1)) + + if Bindx == 432: + t9429 = np.cos(phi) + t9428 = t9429 ** 2 + t9431 = t9428 ** 2 + t9430 = t9429 * t9428 + t9433 = t9430 ** 2 + t9435 = t9431 ** 2 + t9432 = t9429 * t9431 + t9437 = t9432 ** 2 + t9439 = t9433 ** 2 + t9434 = t9429 * t9433 + t9441 = t9434 ** 2 + t9444 = -194712 * t9428 + 2861418 * t9431 - 16846228 * t9433 + 49242402 * t9435 - 75232080 * t9437 + 57327270 * t9439 - 17168580 * t9441 + 2318 + t9443 = 981547 * t9430 - 7094787 * t9432 + 20356083 * t9434 + (-23637537 * t9435 + 3108105 * t9437 + 13468455 * t9439 - 7153575 * t9441 - 36483) * t9429 + tfunc[..., c] = -(0.31e2 / 0.32768e5) * np.sqrt(0.2e1) * ((-t9443 + t9444) * np.exp((6*1j) * (phi1 - phi2)) + (t9443 + t9444) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 433: + t9461 = np.cos(phi) + t9460 = t9461 ** 2 + t9463 = t9460 ** 2 + t9462 = t9461 * t9460 + t9465 = t9462 ** 2 + t9467 = t9463 ** 2 + t9464 = t9461 * t9463 + t9469 = t9464 ** 2 + t9471 = t9465 ** 2 + t9466 = t9461 * t9465 + t9473 = t9466 ** 2 + t9476 = -4 + 120 * t9460 - 996 * t9463 + 2936 * t9465 - 3276 * t9467 + 456 * t9469 + 1460 * t9471 - 696 * t9473 + t9475 = -223 * t9462 - 3 * t9464 + 2205 * t9466 + (-4717 * t9467 + 3603 * t9469 - 753 * t9471 - 145 * t9473 + 33) * t9461 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((-t9475 + t9476) * np.exp((6*1j) * (phi1 - 2 * phi2)) + (t9475 + t9476) * np.exp((6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.345345e6) + + if Bindx == 434: + t9493 = np.cos(phi) + t9492 = t9493 ** 2 + t9495 = t9493 * t9492 + t9498 = t9495 ** 2 + t9499 = t9493 * t9498 + t9506 = t9499 ** 2 + t9508 = -650325 * t9493 * t9506 - 509 + t9504 = t9498 ** 2 + t9496 = t9492 ** 2 + t9497 = t9493 * t9496 + t9502 = t9497 ** 2 + t9500 = t9496 ** 2 + t9494 = 7 * phi1 + t9485 = t9493 * t9500 + t9483 = t9493 * t9502 + t9481 = t9493 * t9504 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt((1 - t9493)) * np.sqrt(0.11e2) * ((2166255 * t9481 - 2689505 * t9483 + 1489411 * t9485 + 40775 * t9492 + 1073 * t9493 - 7483 * t9495 - 509481 * t9496 + 23709 * t9497 + 2412347 * t9498 - 333135 * t9499 - 5672007 * t9500 + 7110565 * t9502 - 4552275 * t9504 + 1170585 * t9506 + t9508) * np.exp((1j) * (t9494 - 6 * phi2)) + (1475565 * t9481 - 7105735 * t9483 + 8315489 * t9485 - 37611 * t9492 + 2091 * t9493 - 85869 * t9495 + 416129 * t9496 + 949319 * t9497 - 1439319 * t9498 - 4184801 * t9499 + 1154071 * t9500 + 2694335 * t9502 - 5242965 * t9504 + 2471235 * t9506 - t9508) * np.exp((1j) * (t9494 + 6 * phi2))) * ((1 + t9493) ** (-0.1e1 / 0.2e1)) + + if Bindx == 435: + t9526 = np.cos(phi) + t9525 = t9526 ** 2 + t9528 = t9526 * t9525 + t9529 = t9525 ** 2 + t9530 = t9526 * t9529 + t9531 = t9528 ** 2 + t9532 = t9526 * t9531 + t9533 = t9529 ** 2 + t9535 = t9530 ** 2 + t9537 = t9531 ** 2 + t9539 = t9532 ** 2 + t9543 = 92 * t9528 - 1740 * t9530 + 5852 * t9532 + (-7524 * t9533 + 3060 * t9535 + 1052 * t9537 - 812 * t9539 + 20) * t9526 + t9542 = 216 * t9525 - 1252 * t9529 + 2200 * t9531 - 5368 * t9535 + 5036 * t9537 - 1272 * t9539 - 9 + (594 - 145 * t9533) * t9533 + t9527 = 7 * phi1 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt(0.31395e5) * np.sqrt(0.2e1) * ((1 + t9526) ** (-0.1e1 / 0.2e1)) * ((1 - t9526) ** (-0.1e1 / 0.2e1)) * ((-t9542 + t9543) * np.exp((1j) * (t9527 - 12 * phi2)) + (t9542 + t9543) * np.exp((1j) * (t9527 + 12 * phi2))) + + if Bindx == 436: + t9560 = np.cos(phi) + t9559 = t9560 ** 2 + t9563 = t9559 ** 2 + t9562 = t9560 * t9559 + t9565 = t9562 ** 2 + t9567 = t9563 ** 2 + t9564 = t9560 * t9563 + t9569 = t9564 ** 2 + t9571 = t9565 ** 2 + t9566 = t9560 * t9565 + t9573 = t9566 ** 2 + t9576 = 560 * t9559 - 2632 * t9563 - 20496 * t9565 + 140776 * t9567 - 300720 * t9569 + 273000 * t9571 - 90480 * t9573 - 8 + t9575 = -10731 * t9562 + 73633 * t9564 - 222055 * t9566 + (316967 * t9567 - 185185 * t9569 - 1365 * t9571 + 28275 * t9573 + 461) * t9560 + t9561 = 4 * phi1 + tfunc[..., c] = -(0.93e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.253e3) * ((t9575 + t9576) * np.exp((2*1j) * (t9561 - 3 * phi2)) + (-t9575 + t9576) * np.exp((2*1j) * (t9561 + 3 * phi2))) + + if Bindx == 437: + t9593 = np.cos(phi) + t9592 = t9593 ** 2 + t9596 = t9592 ** 2 + t9595 = t9593 * t9592 + t9598 = t9595 ** 2 + t9600 = t9596 ** 2 + t9597 = t9593 * t9596 + t9602 = t9597 ** 2 + t9604 = t9598 ** 2 + t9599 = t9593 * t9598 + t9606 = t9599 ** 2 + t9609 = 16 - 256 * t9592 + 592 * t9596 + 1760 * t9598 - 6864 * t9600 + 6336 * t9602 - 656 * t9604 - 928 * t9606 + t9608 = -587 * t9595 + 3047 * t9597 - 4587 * t9599 + (-11 * t9600 + 4295 * t9602 - 2027 * t9604 - 145 * t9606 + 15) * t9593 + t9594 = 2 * phi1 + tfunc[..., c] = -(0.93e2 / 0.8192e4) * ((-t9608 + t9609) * np.exp((4*1j) * (t9594 - 3 * phi2)) + (t9608 + t9609) * np.exp((4*1j) * (t9594 + 3 * phi2))) * np.sqrt(0.1365e4) + + if Bindx == 438: + t9625 = np.cos(phi) + t9624 = t9625 ** 2 + t9627 = t9625 * t9624 + t9630 = t9627 ** 2 + t9631 = t9625 * t9630 + t9639 = -28275 * t9631 ** 2 + 59 + t9636 = t9630 ** 2 + t9628 = t9624 ** 2 + t9629 = t9625 * t9628 + t9634 = t9629 ** 2 + t9632 = t9628 ** 2 + t9626 = 3 * phi1 + t9617 = t9625 * t9632 + t9615 = t9625 * t9634 + t9613 = t9625 * t9636 + tfunc[..., c] = (0.31e2 / 0.32768e5*1j) * ((1 - t9625) ** (0.3e1 / 0.2e1)) * np.sqrt(0.5313e4) * ((45240 * t9613 + 102765 * t9636 - 177840 * t9615 - 147615 * t9634 + 277800 * t9617 + 109169 * t9632 - 217376 * t9631 - 47449 * t9630 + 87176 * t9629 + 13735 * t9628 - 15664 * t9627 - 2389 * t9624 + 664 * t9625 + t9639) * np.exp((3*1j) * (t9626 - 2 * phi2)) + (158340 * t9613 + 304395 * t9636 + 112320 * t9615 - 390585 * t9634 - 498300 * t9617 - 11969 * t9632 + 316448 * t9631 + 148393 * t9630 - 54964 * t9629 - 50255 * t9628 - 1504 * t9627 + 4573 * t9624 + 428 * t9625 - t9639) * np.exp((3*1j) * (t9626 + 2 * phi2))) * ((1 + t9625) ** (-0.1e1 / 0.2e1)) + + if Bindx == 439: + t9657 = np.cos(phi) + t9656 = t9657 ** 2 + t9659 = t9657 * t9656 + t9660 = t9656 ** 2 + t9661 = t9657 * t9660 + t9662 = t9659 ** 2 + t9663 = t9657 * t9662 + t9664 = t9660 ** 2 + t9666 = t9661 ** 2 + t9668 = t9662 ** 2 + t9670 = t9663 ** 2 + t9674 = -7876 * t9659 + 23636 * t9661 - 3652 * t9663 + (-63492 * t9664 + 72084 * t9666 - 13956 * t9668 - 7308 * t9670 + 564) * t9657 + t9673 = -1416 * t9656 - 5196 * t9660 + 44088 * t9662 + 28776 * t9666 + 31364 * t9668 - 19096 * t9670 + 177 + (-77682 - 1015 * t9664) * t9664 + t9658 = 3 * phi1 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((1 + t9657) ** (-0.1e1 / 0.2e1)) * ((1 - t9657) ** (-0.1e1 / 0.2e1)) * ((-t9673 + t9674) * np.exp((3*1j) * (t9658 - 4 * phi2)) + (t9673 + t9674) * np.exp((3*1j) * (t9658 + 4 * phi2))) + + if Bindx == 440: + t9691 = np.cos(phi) + t9690 = t9691 ** 2 + t9694 = t9690 ** 2 + t9693 = t9691 * t9690 + t9696 = t9693 ** 2 + t9698 = t9694 ** 2 + t9695 = t9691 * t9694 + t9700 = t9695 ** 2 + t9702 = t9696 ** 2 + t9697 = t9691 * t9696 + t9704 = t9697 ** 2 + t9707 = -6 + 312 * t9690 - 2386 * t9694 + 6116 * t9696 - 3562 * t9698 - 7728 * t9700 + 11778 * t9702 - 4524 * t9704 + t9706 = 511 * t9693 - 4343 * t9695 + 15175 * t9697 + (-24125 * t9698 + 16549 * t9700 - 2613 * t9702 - 1131 * t9704 - 23) * t9691 + t9692 = 5 * phi1 + tfunc[..., c] = -(0.465e3 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.1771e4) * ((-t9706 + t9707) * np.exp((2*1j) * (t9692 - 3 * phi2)) + (t9706 + t9707) * np.exp((2*1j) * (t9692 + 3 * phi2))) + + if Bindx == 441: + t9724 = np.cos(phi) + t9723 = t9724 ** 2 + t9727 = t9723 ** 2 + t9726 = t9724 * t9723 + t9729 = t9726 ** 2 + t9731 = t9727 ** 2 + t9728 = t9724 * t9727 + t9733 = t9728 ** 2 + t9735 = t9729 ** 2 + t9730 = t9724 * t9729 + t9737 = t9730 ** 2 + t9740 = 52 + 104 * t9723 - 4076 * t9727 + 10472 * t9729 - 1188 * t9731 - 13992 * t9733 + 7004 * t9735 + 1624 * t9737 + t9739 = 1819 * t9726 + 143 * t9728 - 13233 * t9730 + (16577 * t9731 + 289 * t9733 - 5131 * t9735 - 203 * t9737 - 261) * t9724 + t9725 = 5 * phi1 + tfunc[..., c] = (0.93e2 / 0.16384e5) * ((t9739 + t9740) * np.exp((2*1j) * (t9725 - 6 * phi2)) + (-t9739 + t9740) * np.exp((2*1j) * (t9725 + 6 * phi2))) * np.sqrt(0.195e3) + + if Bindx == 442: + t9755 = np.cos(phi) + t9754 = t9755 ** 2 + t9757 = t9755 * t9754 + t9760 = t9757 ** 2 + t9766 = t9760 ** 2 + t9768 = -435 * t9755 * t9766 + 1 + t9758 = t9754 ** 2 + t9759 = t9755 * t9758 + t9764 = t9759 ** 2 + t9762 = t9758 ** 2 + t9756 = 11 * phi1 + t9749 = t9755 * t9760 + t9747 = t9755 * t9762 + t9745 = t9755 * t9764 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * ((1 - t9755) ** (0.5e1 / 0.2e1)) * np.sqrt(0.115115e6) * ((1 + t9755) ** (-0.1e1 / 0.2e1)) * ((-609 * t9766 - 1746 * t9745 + 2502 * t9764 + 2813 * t9747 - 4047 * t9762 - 2364 * t9749 + 3220 * t9760 + 1149 * t9759 - 1263 * t9758 - 338 * t9757 + 198 * t9754 + 51 * t9755 - t9768) * np.exp((1j) * (t9756 - 6 * phi2)) + (-3219 * t9766 - 9738 * t9745 - 14514 * t9764 - 8225 * t9747 + 5895 * t9762 + 12132 * t9749 + 5844 * t9760 - 1413 * t9759 - 2325 * t9758 - 570 * t9757 + 126 * t9754 + 57 * t9755 + t9768) * np.exp((1j) * (t9756 + 6 * phi2))) + + if Bindx == 443: + t9786 = np.cos(phi) + t9785 = t9786 ** 2 + t9788 = t9786 * t9785 + t9789 = t9785 ** 2 + t9790 = t9786 * t9789 + t9791 = t9788 ** 2 + t9792 = t9786 * t9791 + t9793 = t9789 ** 2 + t9795 = t9790 ** 2 + t9797 = t9791 ** 2 + t9799 = t9792 ** 2 + t9803 = -6356 * t9788 - 40092 * t9790 + 126412 * t9792 + (-56628 * t9793 - 87516 * t9795 + 52844 * t9797 + 8932 * t9799 + 2404) * t9786 + t9802 = 4356 * t9785 - 35464 * t9789 + 33748 * t9791 - 148148 * t9795 + 17056 * t9797 + 31836 * t9799 + 363 + (95238 + 1015 * t9793) * t9793 + t9787 = 11 * phi1 + tfunc[..., c] = (0.93e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.3e1) * ((-t9802 + t9803) * np.exp((1j) * (t9787 - 12 * phi2)) + (t9802 + t9803) * np.exp((1j) * (t9787 + 12 * phi2))) * ((1 + t9786) ** (-0.1e1 / 0.2e1)) * ((1 - t9786) ** (-0.1e1 / 0.2e1)) + + if Bindx == 444: + t9820 = np.cos(phi) + t9819 = t9820 ** 2 + t9823 = t9819 ** 2 + t9822 = t9820 * t9819 + t9825 = t9822 ** 2 + t9827 = t9823 ** 2 + t9824 = t9820 * t9823 + t9829 = t9824 ** 2 + t9831 = t9825 ** 2 + t9826 = t9820 * t9825 + t9833 = t9826 ** 2 + t9836 = 4 - 120 * t9819 + 996 * t9823 - 2936 * t9825 + 3276 * t9827 - 456 * t9829 - 1460 * t9831 + 696 * t9833 + t9835 = -223 * t9822 - 3 * t9824 + 2205 * t9826 + (-4717 * t9827 + 3603 * t9829 - 753 * t9831 - 145 * t9833 + 33) * t9820 + t9821 = 2 * phi1 + tfunc[..., c] = (0.31e2 / 0.16384e5) * ((t9835 + t9836) * np.exp((6*1j) * (t9821 - phi2)) + (-t9835 + t9836) * np.exp((6*1j) * (t9821 + phi2))) * np.sqrt(0.345345e6) + + if Bindx == 445: + t9853 = np.cos(phi) + t9852 = t9853 ** 2 + t9855 = t9852 ** 2 + t9854 = t9853 * t9852 + t9857 = t9854 ** 2 + t9859 = t9855 ** 2 + t9856 = t9853 * t9855 + t9861 = t9856 ** 2 + t9863 = t9857 ** 2 + t9858 = t9853 * t9857 + t9865 = t9858 ** 2 + t9868 = 11712 * t9852 - 30264 * t9855 - 80080 * t9857 + 175032 * t9859 + 13728 * t9861 - 84968 * t9863 - 9744 * t9865 + 488 + t9867 = -9451 * t9854 + 90519 * t9856 - 47619 * t9858 + (-152867 * t9859 + 86775 * t9861 + 39669 * t9863 + 1015 * t9865 - 3945) * t9853 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * np.sqrt(0.2e1) * ((t9867 + t9868) * np.exp((12*1j) * (phi1 - phi2)) + (-t9867 + t9868) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 446: + t9882 = np.cos(phi) + t9881 = t9882 ** 2 + t9884 = t9882 * t9881 + t9887 = t9884 ** 2 + t9894 = -145 * t9887 ** 2 - 19 + t9885 = t9881 ** 2 + t9886 = t9882 * t9885 + t9891 = t9886 ** 2 + t9889 = t9885 ** 2 + t9883 = 13 * phi1 + t9876 = t9882 * t9887 + t9874 = t9882 * t9889 + t9872 = t9882 * t9891 + tfunc[..., c] = (-0.93e2 / 0.32768e5*1j) * ((1 - t9882) ** (0.7e1 / 0.2e1)) * np.sqrt(0.16445e5) * ((1 + t9882) ** (-0.1e1 / 0.2e1)) * ((-174 * t9872 - 648 * t9891 + 774 * t9874 + 1161 * t9889 - 1356 * t9876 - 1064 * t9887 + 1164 * t9886 + 531 * t9885 - 486 * t9884 - 144 * t9881 + 78 * t9882 - t9894) * np.exp((1j) * (t9883 - 6 * phi2)) + (-1334 * t9872 - 5384 * t9891 - 12370 * t9874 - 17385 * t9889 - 14460 * t9876 - 5208 * t9887 + 2172 * t9886 + 3405 * t9885 + 1490 * t9884 + 160 * t9881 - 74 * t9882 + t9894) * np.exp((1j) * (t9883 + 6 * phi2))) + + if Bindx == 447: + t9911 = np.cos(phi) + t9910 = t9911 ** 2 + t9913 = t9911 * t9910 + t9916 = t9913 ** 2 + t9917 = t9911 * t9916 + t9924 = t9917 ** 2 + t9926 = -145 * t9911 * t9924 - 91 + t9922 = t9916 ** 2 + t9914 = t9910 ** 2 + t9915 = t9911 * t9914 + t9920 = t9915 ** 2 + t9918 = t9914 ** 2 + t9912 = 13 * phi1 + t9903 = t9911 * t9918 + t9901 = t9911 * t9920 + t9899 = t9911 * t9922 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt((1 - t9911)) * np.sqrt(0.21e2) * ((1363 * t9924 - 5369 * t9899 + 10803 * t9922 - 9061 * t9901 - 6721 * t9920 + 24739 * t9903 - 22737 * t9918 + 429 * t9917 + 17017 * t9916 - 14443 * t9915 + 2873 * t9914 + 3081 * t9913 - 2507 * t9910 + 769 * t9911 + t9926) * np.exp((1j) * (t9912 - 12 * phi2)) + (1653 * t9924 + 8385 * t9899 + 24557 * t9922 + 44421 * t9901 + 46761 * t9920 + 15301 * t9903 - 32175 * t9918 - 55341 * t9917 - 38753 * t9916 - 7293 * t9915 + 10023 * t9914 + 9815 * t9913 + 4227 * t9910 + 951 * t9911 - t9926) * np.exp((1j) * (t9912 + 12 * phi2))) * ((1 + t9911) ** (-0.1e1 / 0.2e1)) + + if Bindx == 448: + t9943 = np.cos(phi) + t9942 = t9943 ** 2 + t9946 = t9942 ** 2 + t9945 = t9943 * t9942 + t9948 = t9945 ** 2 + t9950 = t9946 ** 2 + t9947 = t9943 * t9946 + t9952 = t9947 ** 2 + t9954 = t9948 ** 2 + t9949 = t9943 * t9948 + t9956 = t9949 ** 2 + t9959 = 2 - 8 * t9942 - 58 * t9946 + 244 * t9948 - 306 * t9950 + 112 * t9952 + 42 * t9954 - 28 * t9956 + t9958 = -63 * t9945 + 119 * t9947 + 9 * t9949 + (-211 * t9950 + 187 * t9952 - 43 * t9954 - 5 * t9956 + 7) * t9943 + t9944 = 7 * phi1 + tfunc[..., c] = -(0.93e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.476905e6) * ((-t9958 + t9959) * np.exp((2*1j) * (t9944 - 3 * phi2)) + (t9958 + t9959) * np.exp((2*1j) * (t9944 + 3 * phi2))) + + if Bindx == 449: + t9976 = np.cos(phi) + t9975 = t9976 ** 2 + t9979 = t9975 ** 2 + t9978 = t9976 * t9975 + t9981 = t9978 ** 2 + t9983 = t9979 ** 2 + t9980 = t9976 * t9979 + t9985 = t9980 ** 2 + t9987 = t9981 ** 2 + t9982 = t9976 * t9981 + t9989 = t9982 ** 2 + t9992 = -4 - 200 * t9975 - 676 * t9979 + 1144 * t9981 + 1716 * t9983 - 1144 * t9985 - 780 * t9987 - 56 * t9989 + t9991 = -507 * t9978 - 143 * t9980 + 2145 * t9982 + (143 * t9983 - 1313 * t9985 - 277 * t9987 - 5 * t9989 - 43) * t9976 + t9977 = 7 * phi1 + tfunc[..., c] = -(0.31e2 / 0.16384e5) * ((-t9991 + t9992) * np.exp((2*1j) * (t9977 - 6 * phi2)) + (t9991 + t9992) * np.exp((2*1j) * (t9977 + 6 * phi2))) * np.sqrt(0.609e3) + + if Bindx == 450: + t10008 = np.cos(phi) + t10024 = 6 * t10008 + t10007 = t10008 ** 2 + t10010 = t10008 * t10007 + t10011 = t10007 ** 2 + t10012 = t10008 * t10011 + t10013 = t10010 ** 2 + t10014 = t10008 * t10013 + t10015 = t10011 ** 2 + t10017 = t10012 ** 2 + t10019 = t10013 ** 2 + t10023 = t10019 * t10024 - 4 * t10010 - 38 * t10012 + 72 * t10014 + t10024 + (-38 * t10015 - 4 * t10017) * t10008 + t10022 = -t10014 ** 2 - 11 * t10007 + 39 * t10011 - 27 * t10013 - 27 * t10015 + 39 * t10017 - 11 * t10019 - 1 + t10009 = 5 * phi1 + tfunc[..., c] = (-0.155e3 / 0.32768e5*1j) * np.sqrt(0.286143e6) * np.sqrt((1 - t10008)) * np.sqrt((1 + t10008)) * ((t10022 + t10023) * np.exp((3*1j) * (t10009 - 2 * phi2)) + (-t10022 + t10023) * np.exp((3*1j) * (t10009 + 2 * phi2))) + + if Bindx == 451: + t10039 = np.cos(phi) + t10038 = t10039 ** 2 + t10041 = t10039 * t10038 + t10042 = t10038 ** 2 + t10043 = t10039 * t10042 + t10045 = t10042 ** 2 + t10047 = t10043 ** 2 + t10044 = t10041 ** 2 + t10049 = t10044 ** 2 + t10050 = t10039 * t10049 + t10053 = -208 * t10041 - 572 * t10043 + 12 * t10050 + (572 * t10045 + 208 * t10047 - 12) * t10039 + t10052 = -t10039 * t10050 + 65 * t10038 + 429 * t10042 + 429 * t10044 - 429 * t10045 - 429 * t10047 - 65 * t10049 + 1 + t10040 = 5 * phi1 + tfunc[..., c] = (-0.31e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.1015e4) * np.sqrt((1 - t10039)) * np.sqrt((1 + t10039)) * ((t10052 + t10053) * np.exp((3*1j) * (t10040 - 4 * phi2)) + (-t10052 + t10053) * np.exp((3*1j) * (t10040 + 4 * phi2))) + + if Bindx == 452: + t10058 = np.sin(phi) + t10054 = t10058 ** 2 + t10055 = t10054 ** 2 + t10056 = t10055 ** 2 + tfunc[..., c] = (0.99e2 / 0.65536e5) * np.exp((-16*1j) * phi1) * np.sqrt(0.66786710e8) * t10056 ** 2 + + if Bindx == 453: + t10075 = np.cos(phi) + t10074 = t10075 ** 2 + t10078 = t10074 ** 2 + t10077 = t10075 * t10074 + t10080 = t10077 ** 2 + t10079 = t10075 * t10078 + t10083 = t10079 ** 2 + t10085 = t10080 ** 2 + t10081 = t10075 * t10080 + t10087 = t10081 ** 2 + t10088 = t10075 * t10087 + t10091 = t10075 * t10088 - 10 * t10074 + 50 * t10078 - 66 * t10080 + 66 * t10083 - 50 * t10085 + 10 * t10087 - 1 + t10090 = -10 * t10077 - 34 * t10079 - 6 * t10088 + (-110 * t10074 + 110) * t10081 + (34 * t10083 + 10 * t10085 + 6) * t10075 + t10076 = 8 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.4032015e7) * ((t10090 + t10091) * np.exp((-2*1j) * (t10076 - 3 * phi2)) + (-t10090 + t10091) * np.exp((-2*1j) * (t10076 + 3 * phi2))) + + if Bindx == 454: + t10107 = np.cos(phi) + t10124 = -12 * t10107 + t10106 = t10107 ** 2 + t10110 = t10106 ** 2 + t10113 = t10110 ** 2 + t10114 = t10107 * t10113 + t10115 = t10106 * t10114 + t10116 = t10107 * t10115 + t10111 = t10107 * t10110 + t10112 = t10106 * t10111 + t10118 = t10112 ** 2 + t10122 = 64 * t10106 + 364 * t10110 + 364 * t10116 + 64 * t10118 + 1 + (-858 + t10113) * t10113 + t10121 = t10118 * t10124 - 364 * t10111 + 572 * t10112 + 572 * t10114 - 364 * t10115 + t10124 - 196 * (t10106 + t10116) * t10107 + t10108 = 4 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * ((t10121 + t10122) * np.exp((-4*1j) * (t10108 - 3 * phi2)) + (-t10121 + t10122) * np.exp((-4*1j) * (t10108 + 3 * phi2))) * np.sqrt(0.4495e4) + + if Bindx == 455: + t10125 = np.cos(phi) + t10133 = -8 * t10125 + t10126 = t10125 ** 2 + t10128 = t10126 ** 2 + t10127 = t10125 * t10126 + tfunc[..., c] = (0.99e2 / 0.8192e4*1j) * t10125 * (28 * t10126 + t10133 + 1 + (-56 * t10125 + 70 + t10128) * t10128 + (-56 + (t10133 + 28) * t10127) * t10127) * ((1 + t10125) ** (0.15e2 / 0.2e1)) * np.sqrt(0.33393355e8) * np.exp((-15*1j) * phi1) * ((1 - t10125) ** (-0.1e1 / 0.2e1)) + + if Bindx == 456: + t10147 = np.cos(phi) + t10146 = t10147 ** 2 + t10149 = t10147 * t10146 + t10152 = t10149 ** 2 + t10159 = 8 * t10152 ** 2 + t10150 = t10146 ** 2 + t10151 = t10147 * t10150 + t10156 = t10151 ** 2 + t10154 = t10150 ** 2 + t10148 = 5 * phi1 + t10141 = t10147 * t10152 + t10139 = t10147 * t10154 + t10137 = t10147 * t10156 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 + t10147) ** (0.9e1 / 0.2e1)) * np.sqrt(0.4032015e7) * ((1 - t10147) ** (-0.1e1 / 0.2e1)) * ((t10159 - 85 * t10137 + 407 * t10156 - 1155 * t10139 + 2145 * t10154 - 2706 * t10141 + 2310 * t10152 - 1254 * t10151 + 330 * t10150 + 55 * t10149 - 77 * t10146 + 25 * t10147 - 3) * np.exp((-3*1j) * (t10148 - 2 * phi2)) + (t10159 + 5 * t10137 - 43 * t10156 - 25 * t10139 + 95 * t10154 + 50 * t10141 - 110 * t10152 - 50 * t10151 + 70 * t10150 + 25 * t10149 - 23 * t10146 - 5 * t10147 + 3) * np.exp((-3*1j) * (t10148 + 2 * phi2))) + + if Bindx == 457: + t10176 = np.cos(phi) + t10175 = t10176 ** 2 + t10178 = t10176 * t10175 + t10181 = t10178 ** 2 + t10182 = t10176 * t10181 + t10189 = t10182 ** 2 + t10191 = 4 * t10176 * t10189 + t10187 = t10181 ** 2 + t10179 = t10175 ** 2 + t10180 = t10176 * t10179 + t10185 = t10180 ** 2 + t10183 = t10179 ** 2 + t10177 = 5 * phi1 + t10168 = t10176 * t10183 + t10166 = t10176 * t10185 + t10164 = t10176 * t10187 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * ((1 + t10176) ** (0.3e1 / 0.2e1)) * np.sqrt(0.4495e4) * ((1 - t10176) ** (-0.1e1 / 0.2e1)) * ((t10191 - 53 * t10189 + 322 * t10164 - 1183 * t10187 + 2912 * t10166 - 5005 * t10185 + 6006 * t10168 - 4719 * t10183 + 1716 * t10182 + 1001 * t10181 - 2002 * t10180 + 1547 * t10179 - 728 * t10178 + 217 * t10175 - 38 * t10176 + 3) * np.exp((-3*1j) * (t10177 - 4 * phi2)) + (t10191 + 37 * t10189 + 142 * t10164 + 271 * t10187 + 184 * t10166 - 275 * t10185 - 726 * t10168 - 561 * t10183 + 132 * t10182 + 583 * t10181 + 418 * t10180 + 37 * t10179 - 128 * t10178 - 89 * t10175 - 26 * t10176 - 3) * np.exp((-3*1j) * (t10177 + 4 * phi2))) + + if Bindx == 458: + t10198 = np.sin(phi) + t10193 = t10198 ** 2 + t10194 = t10198 * t10193 + t10196 = t10198 * t10194 ** 2 + t10192 = np.cos(phi) + tfunc[..., c] = -(0.99e2 / 0.16384e5) * np.exp((-14*1j) * phi1) * np.sqrt(0.2154410e7) * t10196 ** 2 * (31 * t10192 ** 2 - 1) + + if Bindx == 459: + t10216 = np.cos(phi) + t10215 = t10216 ** 2 + t10218 = t10216 * t10215 + t10219 = t10215 ** 2 + t10220 = t10216 * t10219 + t10221 = t10218 ** 2 + t10222 = t10216 * t10221 + t10223 = t10219 ** 2 + t10225 = t10220 ** 2 + t10227 = t10221 ** 2 + t10229 = t10222 ** 2 + t10233 = 335 * t10218 - 2599 * t10220 + 6215 * t10222 + (-5885 * t10223 + 1141 * t10225 + 1435 * t10227 - 651 * t10229 + 9) * t10216 + t10232 = -280 * t10215 + 1190 * t10219 - 924 * t10221 + 6336 * t10225 - 4310 * t10227 + 820 * t10229 + 14 + (-2970 + 124 * t10223) * t10223 + t10217 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.130065e6) * ((t10232 + t10233) * np.exp((-2*1j) * (t10217 - 3 * phi2)) + (t10232 - t10233) * np.exp((-2*1j) * (t10217 + 3 * phi2))) + + if Bindx == 460: + t10251 = np.cos(phi) + t10250 = t10251 ** 2 + t10253 = t10251 * t10250 + t10254 = t10250 ** 2 + t10255 = t10251 * t10254 + t10256 = t10253 ** 2 + t10257 = t10251 * t10256 + t10258 = t10254 ** 2 + t10260 = t10255 ** 2 + t10262 = t10256 ** 2 + t10264 = t10257 ** 2 + t10268 = 1771 * t10253 - 7553 * t10255 - 4433 * t10257 + (20449 * t10258 - 2639 * t10260 - 7259 * t10262 - 651 * t10264 + 315) * t10251 + t10267 = -1156 * t10250 + 728 * t10254 + 12012 * t10256 - 12012 * t10260 + 9464 * t10262 + 2948 * t10264 - 34 + (-12012 + 62 * t10258) * t10258 + t10252 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t10267 + t10268) * np.exp((-2*1j) * (t10252 - 6 * phi2)) + (t10267 - t10268) * np.exp((-2*1j) * (t10252 + 6 * phi2))) * np.sqrt(0.145e3) + + if Bindx == 461: + t10269 = np.cos(phi) + tfunc[..., c] = (-0.495e3 / 0.8192e4*1j) * (31 * t10269 ** 2 - 3) * t10269 * ((1 + t10269) ** (0.13e2 / 0.2e1)) * np.sqrt(0.215441e6) * np.exp((-13*1j) * phi1) * ((1 - t10269) ** (0.13e2 / 0.2e1)) + + if Bindx == 462: + t10284 = np.cos(phi) + t10283 = t10284 ** 2 + t10286 = t10284 * t10283 + t10289 = t10286 ** 2 + t10295 = t10289 ** 2 + t10297 = 248 * t10284 * t10295 + t10287 = t10283 ** 2 + t10288 = t10284 * t10287 + t10293 = t10288 ** 2 + t10291 = t10287 ** 2 + t10285 = 13 * phi1 + t10278 = t10284 * t10289 + t10276 = t10284 * t10291 + t10274 = t10284 * t10293 + tfunc[..., c] = (0.165e3 / 0.32768e5*1j) * ((1 + t10284) ** (0.7e1 / 0.2e1)) * np.sqrt(0.26013e5) * ((1 - t10284) ** (-0.1e1 / 0.2e1)) * ((t10297 - 2201 * t10295 + 8454 * t10274 - 18040 * t10293 + 22330 * t10276 - 13761 * t10291 - 1188 * t10278 + 8712 * t10289 - 5940 * t10288 + 1045 * t10287 + 638 * t10286 - 336 * t10283 + 34 * t10284 + 5) * np.exp((-1*1j) * (t10285 - 6 * phi2)) + (t10297 + 217 * t10295 - 1218 * t10274 - 984 * t10293 + 2466 * t10276 + 1761 * t10291 - 2644 * t10278 - 1544 * t10289 + 1596 * t10288 + 651 * t10287 - 522 * t10286 - 96 * t10283 + 74 * t10284 - 5) * np.exp((-1*1j) * (t10285 + 6 * phi2))) + + if Bindx == 463: + t10315 = np.cos(phi) + t10314 = t10315 ** 2 + t10318 = t10314 ** 2 + t10322 = t10318 ** 2 + t10331 = 620 * t10322 ** 2 + t10317 = t10315 * t10314 + t10320 = t10317 ** 2 + t10321 = t10315 * t10320 + t10328 = t10321 ** 2 + t10326 = t10320 ** 2 + t10319 = t10315 * t10318 + t10324 = t10319 ** 2 + t10316 = 13 * phi1 + t10307 = t10315 * t10322 + t10305 = t10315 * t10324 + t10303 = t10315 * t10326 + t10301 = t10315 * t10328 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt((1 + t10315)) * np.sqrt(0.29e2) * ((1 - t10315) ** (-0.1e1 / 0.2e1)) * ((t10331 - 6665 * t10301 + 31245 * t10328 - 81529 * t10303 + 120757 * t10326 - 73437 * t10305 - 71071 * t10324 + 189475 * t10307 - 155727 * t10322 + 15301 * t10321 + 79079 * t10320 - 68523 * t10319 + 18655 * t10318 + 6881 * t10317 - 6933 * t10314 + 2113 * t10315 - 241) * np.exp((-1*1j) * (t10316 - 12 * phi2)) + (t10331 + 5425 * t10301 + 19155 * t10328 + 31129 * t10303 + 8099 * t10326 - 55419 * t10305 - 89089 * t10324 - 29315 * t10307 + 63063 * t10322 + 77363 * t10321 + 17017 * t10320 - 27573 * t10319 - 22295 * t10318 - 3241 * t10317 + 3189 * t10314 + 1631 * t10315 + 241) * np.exp((-1*1j) * (t10316 + 12 * phi2))) + + if Bindx == 464: + t10339 = np.sin(phi) + t10335 = t10339 ** 2 + t10336 = t10339 * t10335 + t10337 = t10336 ** 2 + t10332 = np.cos(phi) + t10333 = t10332 ** 2 + tfunc[..., c] = (0.495e3 / 0.16384e5) * np.exp((-12*1j) * phi1) * np.sqrt(0.7429e4) * t10337 ** 2 * (3 + (-174 + 899 * t10333) * t10333) + + if Bindx == 465: + t10357 = np.cos(phi) + t10356 = t10357 ** 2 + t10359 = t10357 * t10356 + t10360 = t10356 ** 2 + t10361 = t10357 * t10360 + t10362 = t10359 ** 2 + t10363 = t10357 * t10362 + t10364 = t10360 ** 2 + t10366 = t10361 ** 2 + t10368 = t10362 ** 2 + t10370 = t10363 ** 2 + t10374 = 2859 * t10359 - 15009 * t10361 + 32351 * t10363 + (-24783 * t10364 - 8751 * t10366 + 21605 * t10368 - 8091 * t10370 - 181) * t10357 + t10373 = -276 * t10356 - 104 * t10360 + 11484 * t10362 + 63844 * t10366 - 40200 * t10368 + 6612 * t10370 + 6 + (-43164 + 1798 * t10364) * t10364 + t10358 = 2 * phi1 + tfunc[..., c] = (0.165e3 / 0.16384e5) * ((t10373 + t10374) * np.exp((-6*1j) * (t10358 - phi2)) + (t10373 - t10374) * np.exp((-6*1j) * (t10358 + phi2))) * np.sqrt(0.897e3) + + if Bindx == 466: + t10392 = np.cos(phi) + t10391 = t10392 ** 2 + t10393 = t10392 * t10391 + t10394 = t10391 ** 2 + t10395 = t10392 * t10394 + t10396 = t10393 ** 2 + t10397 = t10392 * t10396 + t10398 = t10394 ** 2 + t10400 = t10395 ** 2 + t10402 = t10396 ** 2 + t10404 = t10397 ** 2 + t10408 = 35259 * t10393 + 98553 * t10395 - 512941 * t10397 + (383955 * t10398 + 308217 * t10400 - 269381 * t10402 - 40455 * t10404 - 7303) * t10392 + t10407 = 9528 * t10391 - 129220 * t10394 + 216216 * t10396 - 664664 * t10400 + 151788 * t10402 + 149640 * t10404 + 1191 + (265122 + 4495 * t10398) * t10398 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.sqrt(0.2e1) * ((t10407 + t10408) * np.exp((-12*1j) * (phi1 - phi2)) + (t10407 - t10408) * np.exp((-12*1j) * (phi1 + phi2))) + + if Bindx == 467: + t10409 = np.cos(phi) + t10410 = t10409 ** 2 + tfunc[..., c] = (0.99e2 / 0.8192e4*1j) * (15 + (-290 + 899 * t10410) * t10410) * t10409 * ((1 + t10409) ** (0.11e2 / 0.2e1)) * np.sqrt(0.260015e6) * np.exp((-11*1j) * phi1) * ((1 - t10409) ** (0.11e2 / 0.2e1)) + + if Bindx == 468: + t10427 = np.cos(phi) + t10426 = t10427 ** 2 + t10429 = t10427 * t10426 + t10432 = t10429 ** 2 + t10433 = t10427 * t10432 + t10441 = 7192 * t10433 ** 2 + t10438 = t10432 ** 2 + t10430 = t10426 ** 2 + t10431 = t10427 * t10430 + t10436 = t10431 ** 2 + t10434 = t10430 ** 2 + t10428 = 11 * phi1 + t10419 = t10427 * t10434 + t10417 = t10427 * t10436 + t10415 = t10427 * t10438 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 + t10427) ** (0.5e1 / 0.2e1)) * np.sqrt(0.31395e5) * ((1 - t10427) ** (-0.1e1 / 0.2e1)) * ((t10441 - 51243 * t10415 + 145667 * t10438 - 190298 * t10417 + 52602 * t10436 + 164087 * t10419 - 197703 * t10434 + 42372 * t10433 + 66132 * t10432 - 45133 * t10431 + 1221 * t10430 + 6646 * t10429 - 1414 * t10426 - 159 * t10427 + 31) * np.exp((-1*1j) * (t10428 - 6 * phi2)) + (t10441 + 8091 * t10415 - 32335 * t10438 - 34626 * t10417 + 60258 * t10436 + 58773 * t10419 - 60401 * t10434 - 49500 * t10433 + 35156 * t10432 + 20757 * t10431 - 11649 * t10430 - 3522 * t10429 + 1810 * t10426 + 27 * t10427 - 31) * np.exp((-1*1j) * (t10428 + 6 * phi2))) + + if Bindx == 469: + t10460 = np.cos(phi) + t10459 = t10460 ** 2 + t10462 = t10460 * t10459 + t10463 = t10459 ** 2 + t10464 = t10460 * t10463 + t10465 = t10462 ** 2 + t10466 = t10460 * t10465 + t10467 = t10463 ** 2 + t10469 = t10464 ** 2 + t10471 = t10465 ** 2 + t10473 = t10466 ** 2 + t10475 = t10467 ** 2 + t10478 = 30668 * t10462 - 62348 * t10464 - 125268 * t10466 + (473044 * t10467 - 364156 * t10469 - 48132 * t10471 + 95236 * t10473 + 3596 * t10475 - 2640) * t10460 + t10477 = 2468 * t10459 + 43272 * t10463 - 224588 * t10465 + 286286 * t10467 + 94380 * t10469 - 354640 * t10471 + 123772 * t10473 + 29667 * t10475 - 617 + t10461 = 11 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.35e2) * ((1 + t10460) ** (-0.1e1 / 0.2e1)) * ((1 - t10460) ** (-0.1e1 / 0.2e1)) * ((-t10477 + t10478) * np.exp((-1*1j) * (t10461 - 12 * phi2)) + (t10477 + t10478) * np.exp((-1*1j) * (t10461 + 12 * phi2))) + + if Bindx == 470: + t10487 = np.sin(phi) + t10483 = t10487 ** 2 + t10485 = t10487 * t10483 ** 2 + t10479 = np.cos(phi) + t10480 = t10479 ** 2 + t10481 = t10480 ** 2 + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((-10*1j) * phi1) * np.sqrt(0.520030e6) * t10485 ** 2 * (-3915 * t10481 - 5 + (8091 * t10481 + 405) * t10480) + + if Bindx == 471: + t10505 = np.cos(phi) + t10504 = t10505 ** 2 + t10507 = t10505 * t10504 + t10508 = t10504 ** 2 + t10509 = t10505 * t10508 + t10510 = t10507 ** 2 + t10511 = t10505 * t10510 + t10512 = t10508 ** 2 + t10514 = t10509 ** 2 + t10516 = t10510 ** 2 + t10518 = t10511 ** 2 + t10522 = -4075 * t10507 + 20963 * t10509 - 32659 * t10511 + (-25311 * t10512 + 124407 * t10514 - 123975 * t10516 + 40455 * t10518 + 195) * t10505 + t10521 = 952 * t10504 - 12422 * t10508 + 67804 * t10510 + 232672 * t10514 - 134346 * t10516 + 12876 * t10518 - 14 + (-178310 + 10788 * t10512) * t10512 + t10506 = 5 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.31395e5) * ((t10521 - t10522) * np.exp((-2*1j) * (t10506 - 3 * phi2)) + (t10521 + t10522) * np.exp((-2*1j) * (t10506 + 3 * phi2))) + + if Bindx == 472: + t10540 = np.cos(phi) + t10539 = t10540 ** 2 + t10542 = t10540 * t10539 + t10543 = t10539 ** 2 + t10544 = t10540 * t10543 + t10545 = t10542 ** 2 + t10546 = t10540 * t10545 + t10547 = t10543 ** 2 + t10549 = t10544 ** 2 + t10551 = t10545 ** 2 + t10553 = t10546 ** 2 + t10557 = 9091 * t10542 - 40937 * t10544 + 40183 * t10546 + (67353 * t10547 - 127335 * t10549 + 38541 * t10551 + 13485 * t10553 - 381) * t10540 + t10556 = 2604 * t10539 - 936 * t10543 - 49764 * t10545 - 87516 * t10549 - 36296 * t10551 + 38164 * t10553 - 186 + (132132 + 1798 * t10547) * t10547 + t10541 = 5 * phi1 + tfunc[..., c] = (0.99e2 / 0.16384e5) * ((t10556 - t10557) * np.exp((-2*1j) * (t10541 - 6 * phi2)) + (t10556 + t10557) * np.exp((-2*1j) * (t10541 + 6 * phi2))) * np.sqrt(0.35e2) + + if Bindx == 473: + t10558 = np.cos(phi) + t10559 = t10558 ** 2 + t10560 = t10559 ** 2 + tfunc[..., c] = (-0.33e2 / 0.8192e4*1j) * (-5481 * t10560 - 35 + (8091 * t10560 + 945) * t10559) * t10558 * ((1 + t10558) ** (0.9e1 / 0.2e1)) * np.sqrt(0.482885e6) * np.exp((-9*1j) * phi1) * ((1 - t10558) ** (0.9e1 / 0.2e1)) + + if Bindx == 474: + t10578 = np.cos(phi) + t10577 = t10578 ** 2 + t10580 = t10578 * t10577 + t10583 = t10580 ** 2 + t10584 = t10578 * t10583 + t10591 = t10584 ** 2 + t10593 = 280488 * t10578 * t10591 + t10589 = t10583 ** 2 + t10581 = t10577 ** 2 + t10582 = t10578 * t10581 + t10587 = t10582 ** 2 + t10585 = t10581 ** 2 + t10579 = 3 * phi1 + t10570 = t10578 * t10585 + t10568 = t10578 * t10587 + t10566 = t10578 * t10589 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 + t10578) ** (0.3e1 / 0.2e1)) * np.sqrt(0.345e3) * ((1 - t10578) ** (-0.1e1 / 0.2e1)) * ((2628444 * t10566 - 4592952 * t10568 + 1373372 * t10570 - 1897 * t10577 - 3436 * t10578 + 92792 * t10580 - 100429 * t10581 - 598444 * t10582 + 1161083 * t10583 + 983576 * t10584 - 3870691 * t10585 + 4369365 * t10587 - 213759 * t10589 - 1507623 * t10591 + 111 + t10593) * np.exp((-3*1j) * (t10579 - 2 * phi2)) + (-1158144 * t10566 + 1975896 * t10568 - 1823536 * t10570 + 14753 * t10577 - 2992 * t10578 + 59048 * t10580 - 216107 * t10581 - 333344 * t10582 + 1019029 * t10583 + 1002584 * t10584 - 2281741 * t10585 + 2683395 * t10587 - 1604889 * t10589 + 385671 * t10591 - 111 + t10593) * np.exp((-3*1j) * (t10579 + 2 * phi2))) + + if Bindx == 475: + t10612 = np.cos(phi) + t10611 = t10612 ** 2 + t10615 = t10611 ** 2 + t10614 = t10612 * t10611 + t10617 = t10614 ** 2 + t10619 = t10615 ** 2 + t10616 = t10612 * t10615 + t10621 = t10616 ** 2 + t10623 = t10617 ** 2 + t10618 = t10612 * t10617 + t10625 = t10618 ** 2 + t10627 = t10619 ** 2 + t10630 = -4824 * t10611 + 25700 * t10615 - 22936 * t10617 - 116050 * t10619 + 286968 * t10621 - 210732 * t10623 + 17400 * t10625 + 24273 * t10627 + 201 + t10629 = -5700 * t10614 + 59748 * t10616 - 179652 * t10618 + (188100 * t10619 + 16404 * t10621 - 138100 * t10623 + 55796 * t10625 + 3596 * t10627 - 192) * t10612 + t10613 = 3 * phi1 + tfunc[..., c] = (0.99e2 / 0.32768e5*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((1 + t10612) ** (-0.1e1 / 0.2e1)) * ((1 - t10612) ** (-0.1e1 / 0.2e1)) * ((t10629 - t10630) * np.exp((-3*1j) * (t10613 - 4 * phi2)) + (t10629 + t10630) * np.exp((-3*1j) * (t10613 + 4 * phi2))) + + if Bindx == 476: + t10639 = np.sin(phi) + t10636 = t10639 ** 2 + t10637 = t10636 ** 2 + t10631 = np.cos(phi) + t10632 = t10631 ** 2 + t10633 = t10632 ** 2 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.exp((-8*1j) * phi1) * np.sqrt(0.965770e6) * t10637 ** 2 * (-700 * t10632 + 7 + (-36540 * t10632 + 9450 + 40455 * t10633) * t10633) + + if Bindx == 477: + t10657 = np.cos(phi) + t10656 = t10657 ** 2 + t10659 = t10657 * t10656 + t10660 = t10656 ** 2 + t10661 = t10657 * t10660 + t10662 = t10659 ** 2 + t10663 = t10657 * t10662 + t10664 = t10660 ** 2 + t10666 = t10661 ** 2 + t10668 = t10662 ** 2 + t10670 = t10663 ** 2 + t10674 = -2317 * t10659 + 58247 * t10661 - 448657 * t10663 + (1467345 * t10664 - 2330055 * t10666 + 1781325 * t10668 - 525915 * t10670 + 27) * t10657 + t10673 = 8342 * t10656 - 115990 * t10660 + 607838 * t10662 + 1811810 * t10666 - 832650 * t10668 - 147030 * t10670 - 97 + (-1507528 + 175305 * t10664) * t10664 + t10658 = 4 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.345e3) * ((t10673 + t10674) * np.exp((-2*1j) * (t10658 - 3 * phi2)) + (t10673 - t10674) * np.exp((-2*1j) * (t10658 + 3 * phi2))) + + if Bindx == 478: + t10692 = np.cos(phi) + t10691 = t10692 ** 2 + t10695 = t10691 ** 2 + t10694 = t10692 * t10691 + t10697 = t10694 ** 2 + t10699 = t10695 ** 2 + t10696 = t10692 * t10695 + t10701 = t10696 ** 2 + t10703 = t10697 ** 2 + t10698 = t10692 * t10697 + t10705 = t10698 ** 2 + t10709 = -3552 * t10691 + 29940 * t10695 - 85536 * t10697 + 85536 * t10701 - 146252 * t10703 + 51040 * t10705 + 111 + (64218 + 4495 * t10699) * t10699 + t10708 = 3454 * t10694 + 17002 * t10696 - 121154 * t10698 + (228030 * t10699 - 146838 * t10701 - 6786 * t10703 + 26970 * t10705 - 678) * t10692 + t10693 = 2 * phi1 + tfunc[..., c] = (0.99e2 / 0.16384e5) * ((-t10708 + t10709) * np.exp((-4*1j) * (t10693 - 3 * phi2)) + (t10708 + t10709) * np.exp((-4*1j) * (t10693 + 3 * phi2))) * np.sqrt(0.65e2) + + if Bindx == 479: + t10710 = np.cos(phi) + t10711 = t10710 ** 2 + t10712 = t10711 ** 2 + tfunc[..., c] = (0.33e2 / 0.8192e4*1j) * (-700 * t10711 + 21 + (-15660 * t10711 + 5670 + 13485 * t10712) * t10712) * t10710 * ((1 + t10710) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1448655e7) * np.exp((-7*1j) * phi1) * ((1 - t10710) ** (0.7e1 / 0.2e1)) + + if Bindx == 480: + t10732 = np.cos(phi) + t10731 = t10732 ** 2 + t10735 = t10731 ** 2 + t10739 = t10735 ** 2 + t10748 = 1402440 * t10739 ** 2 + t10734 = t10732 * t10731 + t10737 = t10734 ** 2 + t10738 = t10732 * t10737 + t10745 = t10738 ** 2 + t10743 = t10737 ** 2 + t10736 = t10732 * t10735 + t10741 = t10736 ** 2 + t10733 = 7 * phi1 + t10724 = t10732 * t10739 + t10722 = t10732 * t10741 + t10720 = t10732 * t10743 + t10718 = t10732 * t10745 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt((1 + t10732)) * np.sqrt(0.115e3) * ((1 - t10732) ** (-0.1e1 / 0.2e1)) * ((-5083845 * t10718 + 12785955 * t10720 - 10053225 * t10722 + 887535 * t10724 + 24645 * t10731 - 5563 * t10732 + 153181 * t10734 - 490735 * t10735 - 1072407 * t10736 + 3587353 * t10737 + 2371985 * t10738 - 12032361 * t10739 + 19594575 * t10741 - 14088165 * t10743 + 2018835 * t10745 - 203 + t10748) * np.exp((-1*1j) * (t10733 - 6 * phi2)) + (2278965 * t10718 - 9460815 * t10720 + 15982785 * t10722 - 14040675 * t10724 - 13113 * t10731 - 5969 * t10732 + 190939 * t10734 + 146615 * t10735 - 1709757 * t10736 - 805189 * t10737 + 6764527 * t10738 + 2895849 * t10739 - 6441435 * t10741 + 8158605 * t10743 - 5343975 * t10745 + 203 + t10748) * np.exp((-1*1j) * (t10733 + 6 * phi2))) + + if Bindx == 481: + t10767 = np.cos(phi) + t10766 = t10767 ** 2 + t10770 = t10766 ** 2 + t10769 = t10767 * t10766 + t10772 = t10769 ** 2 + t10774 = t10770 ** 2 + t10771 = t10767 * t10770 + t10776 = t10771 ** 2 + t10778 = t10772 ** 2 + t10773 = t10767 * t10772 + t10780 = t10773 ** 2 + t10782 = t10774 ** 2 + t10785 = -4360 * t10766 + 67716 * t10770 - 349256 * t10772 + 773366 * t10774 - 744216 * t10776 + 157780 * t10778 + 193256 * t10780 - 94395 * t10782 + 109 + t10784 = -32788 * t10769 + 111412 * t10771 - 42900 * t10773 + (-427372 * t10774 + 842788 * t10776 - 593028 * t10778 + 121220 * t10780 + 17980 * t10782 + 2688) * t10767 + t10768 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.195e3) * np.sqrt(0.2e1) * ((1 + t10767) ** (-0.1e1 / 0.2e1)) * ((1 - t10767) ** (-0.1e1 / 0.2e1)) * ((t10784 + t10785) * np.exp((-1*1j) * (t10768 - 12 * phi2)) + (t10784 - t10785) * np.exp((-1*1j) * (t10768 + 12 * phi2))) + + if Bindx == 482: + t10795 = np.sin(phi) + t10792 = t10795 ** 2 + t10793 = t10795 * t10792 + t10786 = np.cos(phi) + t10787 = t10786 ** 2 + t10788 = t10787 ** 2 + t10790 = t10788 ** 2 + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((-6*1j) * phi1) * np.sqrt(0.25194e5) * t10793 ** 2 * (-40250 * t10788 - 450225 * t10790 - 21 + (217350 * t10788 + 310155 * t10790 + 2415) * t10787) + + if Bindx == 483: + t10813 = np.cos(phi) + t10812 = t10813 ** 2 + t10814 = t10813 * t10812 + t10815 = t10812 ** 2 + t10816 = t10813 * t10815 + t10817 = t10814 ** 2 + t10818 = t10813 * t10817 + t10819 = t10815 ** 2 + t10821 = t10816 ** 2 + t10823 = t10817 ** 2 + t10825 = t10818 ** 2 + t10829 = -1247925 * t10814 + 12674109 * t10816 - 58935085 * t10818 + (144532575 * t10819 - 192796695 * t10821 + 132015975 * t10823 - 36288135 * t10825 + 36989) * t10813 + t10828 = 227400 * t10812 - 3468010 * t10815 + 18442116 * t10817 + 33153120 * t10821 + 16011450 * t10823 - 39019500 * t10825 - 2274 + (-41464170 + 16128060 * t10819) * t10819 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * ((t10828 + t10829) * np.exp((-6*1j) * (phi1 - phi2)) + (t10828 - t10829) * np.exp((-6*1j) * (phi1 + phi2))) + + if Bindx == 484: + t10847 = np.cos(phi) + t10846 = t10847 ** 2 + t10848 = t10847 * t10846 + t10849 = t10846 ** 2 + t10850 = t10847 * t10849 + t10851 = t10848 ** 2 + t10852 = t10847 * t10851 + t10853 = t10849 ** 2 + t10855 = t10850 ** 2 + t10857 = t10851 ** 2 + t10859 = t10852 ** 2 + t10863 = 2859 * t10848 - 15009 * t10850 + 32351 * t10852 + (-24783 * t10853 - 8751 * t10855 + 21605 * t10857 - 8091 * t10859 - 181) * t10847 + t10862 = -276 * t10846 - 104 * t10849 + 11484 * t10851 + 63844 * t10855 - 40200 * t10857 + 6612 * t10859 + 6 + (-43164 + 1798 * t10853) * t10853 + tfunc[..., c] = (0.165e3 / 0.16384e5) * ((t10862 + t10863) * np.exp((-6*1j) * (phi1 - 2 * phi2)) + (t10862 - t10863) * np.exp((-6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.897e3) + + if Bindx == 485: + t10864 = np.cos(phi) + t10865 = t10864 ** 2 + t10866 = t10865 ** 2 + t10868 = t10866 ** 2 + tfunc[..., c] = (-0.33e2 / 0.8192e4*1j) * (-88550 * t10866 - 550275 * t10868 - 231 + (341550 * t10866 + 310155 * t10868 + 8855) * t10865) * t10864 * ((1 + t10864) ** (0.5e1 / 0.2e1)) * np.sqrt(0.12597e5) * np.exp((-5*1j) * phi1) * ((1 - t10864) ** (0.5e1 / 0.2e1)) + + if Bindx == 486: + t10888 = np.cos(phi) + t10887 = t10888 ** 2 + t10891 = t10887 ** 2 + t10890 = t10888 * t10887 + t10893 = t10890 ** 2 + t10895 = t10891 ** 2 + t10892 = t10888 * t10891 + t10897 = t10892 ** 2 + t10899 = t10893 ** 2 + t10894 = t10888 * t10893 + t10901 = t10894 ** 2 + t10903 = t10895 ** 2 + t10906 = 487918 * t10887 - 8673630 * t10891 + 60237590 * t10893 - 210521300 * t10895 + 407970090 * t10897 - 445243890 * t10899 + 256228050 * t10901 - 60480225 * t10903 - 4603 + t10905 = 745790 * t10890 - 6298922 * t10892 + 18128022 * t10894 + (-4075830 * t10895 - 74406150 * t10897 + 147870450 * t10899 - 114197070 * t10901 + 32256120 * t10903 - 22410) * t10888 + t10889 = 5 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 + t10888) ** (-0.1e1 / 0.2e1)) * ((1 - t10888) ** (-0.1e1 / 0.2e1)) * ((t10905 + t10906) * np.exp((-1*1j) * (t10889 - 6 * phi2)) + (t10905 - t10906) * np.exp((-1*1j) * (t10889 + 6 * phi2))) + + if Bindx == 487: + t10925 = np.cos(phi) + t10924 = t10925 ** 2 + t10928 = t10924 ** 2 + t10927 = t10925 * t10924 + t10930 = t10927 ** 2 + t10932 = t10928 ** 2 + t10929 = t10925 * t10928 + t10934 = t10929 ** 2 + t10936 = t10930 ** 2 + t10931 = t10925 * t10930 + t10938 = t10931 ** 2 + t10940 = t10932 ** 2 + t10943 = 23 - 1196 * t10924 + 9048 * t10928 - 23068 * t10930 + 11422 * t10932 + 44604 * t10934 - 82912 * t10936 + 55564 * t10938 - 13485 * t10940 + t10942 = 2956 * t10927 - 22732 * t10929 + 79212 * t10931 + (-140012 * t10932 + 127172 * t10934 - 50628 * t10936 + 580 * t10938 + 3596 * t10940 - 144) * t10925 + t10926 = 5 * phi1 + tfunc[..., c] = (0.165e3 / 0.32768e5*1j) * np.sqrt(0.897e3) * np.sqrt(0.2e1) * ((1 + t10925) ** (-0.1e1 / 0.2e1)) * ((1 - t10925) ** (-0.1e1 / 0.2e1)) * ((t10942 + t10943) * np.exp((-1*1j) * (t10926 - 12 * phi2)) + (t10942 - t10943) * np.exp((-1*1j) * (t10926 + 12 * phi2))) + + if Bindx == 488: + t10953 = np.sin(phi) + t10951 = t10953 ** 2 + t10944 = np.cos(phi) + t10945 = t10944 ** 2 + t10946 = t10945 ** 2 + t10948 = t10946 ** 2 + t10947 = t10945 * t10946 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.exp((-4*1j) * phi1) * np.sqrt(0.88179e5) * t10951 ** 2 * (26565 * t10946 + 512325 * t10948 + 11 + (-177100 + 310155 * t10947) * t10947 + (-660330 * t10948 - 1386) * t10945) + + if Bindx == 489: + t10971 = np.cos(phi) + t10970 = t10971 ** 2 + t10973 = t10971 * t10970 + t10974 = t10970 ** 2 + t10975 = t10971 * t10974 + t10976 = t10973 ** 2 + t10977 = t10971 * t10976 + t10978 = t10974 ** 2 + t10980 = t10975 ** 2 + t10982 = t10976 ** 2 + t10984 = t10977 ** 2 + t10988 = -487715 * t10973 + 5090953 * t10975 - 23424695 * t10977 + (55508775 * t10978 - 70777785 * t10980 + 46173075 * t10982 - 12096045 * t10984 + 13437) * t10971 + t10987 = -6820 * t10970 + 175640 * t10974 - 1941492 * t10976 - 27968460 * t10980 + 40006200 * t10982 - 28614300 * t10984 + 62 + (10285140 + 8064030 * t10978) * t10978 + t10972 = 2 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t10987 + t10988) * np.exp((-2*1j) * (t10972 - 3 * phi2)) + (t10987 - t10988) * np.exp((-2*1j) * (t10972 + 3 * phi2))) * np.sqrt(0.7e1) + + if Bindx == 490: + t11006 = np.cos(phi) + t11005 = t11006 ** 2 + t11007 = t11006 * t11005 + t11008 = t11005 ** 2 + t11009 = t11006 * t11008 + t11010 = t11007 ** 2 + t11011 = t11006 * t11010 + t11012 = t11008 ** 2 + t11014 = t11009 ** 2 + t11016 = t11010 ** 2 + t11018 = t11011 ** 2 + t11022 = 85 * t11007 + 823 * t11009 - 6659 * t11011 + (17021 * t11012 - 20425 * t11014 + 11861 * t11016 - 2697 * t11018 - 9) * t11006 + t11021 = 280 * t11005 - 2612 * t11008 + 9720 * t11010 + 14664 * t11014 - 4036 * t11016 - 1624 * t11018 - 5 + (-17286 + 899 * t11012) * t11012 + tfunc[..., c] = (0.165e3 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.6279e4) * ((t11021 + t11022) * np.exp((-4*1j) * (phi1 - 3 * phi2)) + (t11021 - t11022) * np.exp((-4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 491: + t11023 = np.cos(phi) + t11024 = t11023 ** 2 + t11025 = t11024 ** 2 + t11027 = t11025 ** 2 + t11026 = t11024 * t11025 + tfunc[..., c] = (0.33e2 / 0.8192e4*1j) * (69069 * t11025 + 740025 * t11027 + 143 + (-328900 + 310155 * t11026) * t11026 + (-780390 * t11027 - 6006) * t11024) * t11023 * ((1 + t11023) ** (0.3e1 / 0.2e1)) * np.sqrt(0.33915e5) * np.exp((-3*1j) * phi1) * ((1 - t11023) ** (0.3e1 / 0.2e1)) + + if Bindx == 492: + t11048 = np.cos(phi) + t11047 = t11048 ** 2 + t11050 = t11047 ** 2 + t11049 = t11048 * t11047 + t11052 = t11049 ** 2 + t11054 = t11050 ** 2 + t11051 = t11048 * t11050 + t11056 = t11051 ** 2 + t11058 = t11052 ** 2 + t11053 = t11048 * t11052 + t11060 = t11053 ** 2 + t11062 = t11054 ** 2 + t11065 = 22914 * t11047 - 427922 * t11050 + 3048346 * t11052 - 10700612 * t11054 + 20497830 * t11056 - 21875070 * t11058 + 12226110 * t11060 - 2791395 * t11062 - 201 + t11064 = -56458 * t11049 + 670766 * t11051 - 3726834 * t11053 + (11325522 * t11054 - 19943070 * t11056 + 20272890 * t11058 - 11025510 * t11060 + 2481240 * t11062 + 1454) * t11048 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.455e3) * ((1 + t11048) ** (-0.1e1 / 0.2e1)) * ((1 - t11048) ** (-0.1e1 / 0.2e1)) * ((t11064 + t11065) * np.exp((-3*1j) * (phi1 - 2 * phi2)) + (t11064 - t11065) * np.exp((-3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 493: + t11084 = np.cos(phi) + t11083 = t11084 ** 2 + t11085 = t11084 * t11083 + t11086 = t11083 ** 2 + t11087 = t11084 * t11086 + t11088 = t11085 ** 2 + t11089 = t11084 * t11088 + t11090 = t11086 ** 2 + t11092 = t11087 ** 2 + t11094 = t11088 ** 2 + t11096 = t11089 ** 2 + t11098 = t11090 ** 2 + t11101 = 13148 * t11085 - 69340 * t11087 + 155580 * t11089 + (-149820 * t11090 + 8628 * t11092 + 100492 * t11094 - 75980 * t11096 + 17980 * t11098 - 688) * t11084 + t11100 = -3060 * t11083 + 34864 * t11086 - 170660 * t11088 + 433870 * t11090 - 621916 * t11092 + 509000 * t11094 - 222604 * t11096 + 40455 * t11098 + 51 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2415e4) * np.sqrt(0.2e1) * ((1 + t11084) ** (-0.1e1 / 0.2e1)) * ((1 - t11084) ** (-0.1e1 / 0.2e1)) * ((-t11100 + t11101) * np.exp((-3*1j) * (phi1 - 4 * phi2)) + (t11100 + t11101) * np.exp((-3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 494: + t11103 = np.cos(phi) + t11104 = t11103 ** 2 + t11105 = t11104 ** 2 + t11106 = t11104 * t11105 + t11109 = t11106 ** 2 + t11107 = t11105 ** 2 + t11102 = np.sin(phi) + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((-2*1j) * phi1) * np.sqrt(0.510e3) * t11102 ** 2 * (-399399 * t11105 + 3062059 * t11106 - 10935925 * t11107 - 17298645 * t11109 - 143 + (19684665 * t11107 + 5892945 * t11109 + 19019) * t11104) + + if Bindx == 495: + t11128 = np.cos(phi) + t11127 = t11128 ** 2 + t11129 = t11128 * t11127 + t11130 = t11127 ** 2 + t11131 = t11128 * t11130 + t11132 = t11129 ** 2 + t11133 = t11128 * t11132 + t11134 = t11130 ** 2 + t11136 = t11131 ** 2 + t11138 = t11132 ** 2 + t11140 = t11133 ** 2 + t11144 = -37555 * t11129 + 405419 * t11131 - 1900651 * t11133 + (4521225 * t11134 - 5710785 * t11136 + 3651825 * t11138 - 930465 * t11140 + 987) * t11128 + t11143 = -8120 * t11127 + 155134 * t11130 - 1136268 * t11132 - 8169600 * t11136 + 9042450 * t11138 - 5242620 * t11140 + 70 + (4118334 + 1240620 * t11134) * t11134 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.1235e4) * np.sqrt(0.2e1) * ((t11143 + t11144) * np.exp((-2*1j) * (phi1 - 3 * phi2)) + (t11143 - t11144) * np.exp((-2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 496: + t11162 = np.cos(phi) + t11161 = t11162 ** 2 + t11164 = t11161 ** 2 + t11163 = t11162 * t11161 + t11166 = t11163 ** 2 + t11168 = t11164 ** 2 + t11165 = t11162 * t11164 + t11170 = t11165 ** 2 + t11172 = t11166 ** 2 + t11167 = t11162 * t11166 + t11174 = t11167 ** 2 + t11178 = 124 * t11161 + 280 * t11164 - 12180 * t11166 - 98028 * t11170 + 94136 * t11172 - 45820 * t11174 - 2 + (52500 + 8990 * t11168) * t11168 + t11177 = -7763 * t11163 + 46585 * t11165 - 129815 * t11167 + (195335 * t11168 - 164521 * t11170 + 73283 * t11172 - 13485 * t11174 + 381) * t11162 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t11177 + t11178) * np.exp((-2*1j) * (phi1 - 6 * phi2)) + (-t11177 + t11178) * np.exp((-2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.6555e4) + + if Bindx == 497: + t11179 = np.cos(phi) + t11195 = 1 - t11179 + t11180 = t11179 ** 2 + t11181 = t11179 * t11180 + t11184 = t11181 ** 2 + t11185 = t11179 * t11184 + t11182 = t11180 ** 2 + t11183 = t11179 * t11182 + tfunc[..., c] = (0.33e2 / 0.8192e4*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.17e2) * np.sqrt((1 + t11179)) * t11179 * (t11195 * (54679625 * t11182 ** 2 - 80528175 * t11183 ** 2 + 59879925 * t11184 ** 2 - 17678835 * t11185 ** 2 + 6435) - 19684665 * t11184 + 19684665 * t11185 + 3594591 * t11182 - 3594591 * t11183 - 285285 * t11180 + 285285 * t11181) * (t11195 ** (-0.1e1 / 0.2e1)) + + if Bindx == 498: + t11214 = np.cos(phi) + t11213 = t11214 ** 2 + t11216 = t11213 ** 2 + t11215 = t11214 * t11213 + t11218 = t11215 ** 2 + t11220 = t11216 ** 2 + t11217 = t11214 * t11216 + t11222 = t11217 ** 2 + t11224 = t11218 ** 2 + t11219 = t11214 * t11218 + t11226 = t11219 ** 2 + t11228 = t11220 ** 2 + t11231 = 7434 * t11213 - 142674 * t11216 + 1036098 * t11218 - 3676320 * t11220 + 7059390 * t11222 - 7495470 * t11224 + 4142070 * t11226 - 930465 * t11228 - 63 + t11230 = -97594 * t11215 + 1131214 * t11217 - 5937858 * t11219 + (16667410 * t11220 - 26756590 * t11222 + 24655770 * t11224 - 12146070 * t11226 + 2481240 * t11228 + 2478) * t11214 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.741e3) * ((1 + t11214) ** (-0.1e1 / 0.2e1)) * ((1 - t11214) ** (-0.1e1 / 0.2e1)) * ((t11230 + t11231) * np.exp((-1*1j) * (phi1 - 6 * phi2)) + (t11230 - t11231) * np.exp((-1*1j) * (phi1 + 6 * phi2))) + + if Bindx == 499: + t11250 = np.cos(phi) + t11249 = t11250 ** 2 + t11252 = t11249 ** 2 + t11251 = t11250 * t11249 + t11254 = t11251 ** 2 + t11256 = t11252 ** 2 + t11253 = t11250 * t11252 + t11258 = t11253 ** 2 + t11260 = t11254 ** 2 + t11255 = t11250 * t11254 + t11262 = t11255 ** 2 + t11264 = t11256 ** 2 + t11267 = -9 + 576 * t11249 - 5964 * t11252 + 24192 * t11254 - 51030 * t11256 + 61824 * t11258 - 43596 * t11260 + 16704 * t11262 - 2697 * t11264 + t11266 = -2084 * t11251 + 14084 * t11253 - 46116 * t11255 + (85540 * t11256 - 95116 * t11258 + 63084 * t11260 - 23084 * t11262 + 3596 * t11264 + 96) * t11250 + tfunc[..., c] = (0.495e3 / 0.32768e5*1j) * np.sqrt(0.437e3) * np.sqrt(0.2e1) * ((1 + t11250) ** (-0.1e1 / 0.2e1)) * ((1 - t11250) ** (-0.1e1 / 0.2e1)) * ((t11266 + t11267) * np.exp((-1*1j) * (phi1 - 12 * phi2)) + (t11266 - t11267) * np.exp((-1*1j) * (phi1 + 12 * phi2))) + + if Bindx == 500: + t11268 = np.cos(phi) + t11269 = t11268 ** 2 + t11270 = t11269 ** 2 + t11271 = t11269 * t11270 + t11274 = t11271 ** 2 + t11272 = t11270 ** 2 + tfunc[..., c] = 0.15058768725e11 / 0.8192e4 * t11274 - 0.672188517e9 / 0.4096e4 * t11271 + 0.160044885e9 / 0.8192e4 * t11270 + 0.212355e6 / 0.32768e5 + (0.11043097065e11 / 0.16384e5 + 0.9917826435e10 / 0.32768e5 * t11272) * t11272 + (-0.4798948275e10 / 0.4096e4 * t11274 - 0.6135053925e10 / 0.4096e4 * t11272 - 0.3610035e7 / 0.4096e4) * t11269 + + if Bindx == 501: + t11277 = np.cos(phi) + t11278 = t11277 ** 2 + t11279 = t11278 ** 2 + t11280 = t11278 * t11279 + t11283 = t11280 ** 2 + t11281 = t11279 ** 2 + tfunc[..., c] = 0.33e2 / 0.8192e4 * np.sqrt(0.12597e5) * (47558 * t11279 - 345366 * t11280 + 2498490 * t11283 + 21 + (1225440 + 310155 * t11281) * t11281 + (-2353130 * t11281 - 1380690 * t11283 - 2478) * t11278) * np.cos((6 * phi2)) + + if Bindx == 502: + t11286 = np.cos(phi) + t11287 = t11286 ** 2 + t11288 = t11287 ** 2 + t11289 = t11287 * t11288 + t11292 = t11289 ** 2 + t11290 = t11288 ** 2 + tfunc[..., c] = 0.495e3 / 0.16384e5 * np.sqrt(0.7429e4) * np.sqrt(0.2e1) * (1988 * t11288 - 8064 * t11289 + 14532 * t11292 + 3 + (17010 + 899 * t11290) * t11290 + (-20608 * t11290 - 5568 * t11292 - 192) * t11287) * np.cos((12 * phi2)) + + if Bindx == 503: + t11295 = np.cos(phi) + t11296 = t11295 ** 2 + t11297 = t11296 ** 2 + t11298 = t11296 * t11297 + t11301 = t11298 ** 2 + t11299 = t11297 ** 2 + tfunc[..., c] = (-0.33e2 / 0.8192e4*1j) * np.exp((1j) * phi1) * np.sqrt(0.17e2) * np.sqrt((1 - t11295)) * np.sqrt((1 + t11295)) * t11295 * (-3594591 * t11297 + 19684665 * t11298 - 54679625 * t11299 - 59879925 * t11301 - 6435 + (80528175 * t11299 + 17678835 * t11301 + 285285) * t11296) + + if Bindx == 504: + t11321 = np.cos(phi) + t11320 = t11321 ** 2 + t11323 = t11320 ** 2 + t11322 = t11321 * t11320 + t11325 = t11322 ** 2 + t11327 = t11323 ** 2 + t11324 = t11321 * t11323 + t11329 = t11324 ** 2 + t11331 = t11325 ** 2 + t11326 = t11321 * t11325 + t11333 = t11326 ** 2 + t11335 = t11327 ** 2 + t11338 = 7434 * t11320 - 142674 * t11323 + 1036098 * t11325 - 3676320 * t11327 + 7059390 * t11329 - 7495470 * t11331 + 4142070 * t11333 - 930465 * t11335 - 63 + t11337 = -97594 * t11322 + 1131214 * t11324 - 5937858 * t11326 + (16667410 * t11327 - 26756590 * t11329 + 24655770 * t11331 - 12146070 * t11333 + 2481240 * t11335 + 2478) * t11321 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.741e3) * ((1 + t11321) ** (-0.1e1 / 0.2e1)) * ((1 - t11321) ** (-0.1e1 / 0.2e1)) * ((t11337 + t11338) * np.exp((1j) * (phi1 - 6 * phi2)) + (t11337 - t11338) * np.exp((1j) * (phi1 + 6 * phi2))) + + if Bindx == 505: + t11357 = np.cos(phi) + t11356 = t11357 ** 2 + t11359 = t11356 ** 2 + t11358 = t11357 * t11356 + t11361 = t11358 ** 2 + t11363 = t11359 ** 2 + t11360 = t11357 * t11359 + t11365 = t11360 ** 2 + t11367 = t11361 ** 2 + t11362 = t11357 * t11361 + t11369 = t11362 ** 2 + t11371 = t11363 ** 2 + t11374 = -9 + 576 * t11356 - 5964 * t11359 + 24192 * t11361 - 51030 * t11363 + 61824 * t11365 - 43596 * t11367 + 16704 * t11369 - 2697 * t11371 + t11373 = -2084 * t11358 + 14084 * t11360 - 46116 * t11362 + (85540 * t11363 - 95116 * t11365 + 63084 * t11367 - 23084 * t11369 + 3596 * t11371 + 96) * t11357 + tfunc[..., c] = (0.495e3 / 0.32768e5*1j) * np.sqrt(0.437e3) * np.sqrt(0.2e1) * ((1 + t11357) ** (-0.1e1 / 0.2e1)) * ((1 - t11357) ** (-0.1e1 / 0.2e1)) * ((t11373 + t11374) * np.exp((1j) * (phi1 - 12 * phi2)) + (t11373 - t11374) * np.exp((1j) * (phi1 + 12 * phi2))) + + if Bindx == 506: + t11376 = np.cos(phi) + t11377 = t11376 ** 2 + t11378 = t11377 ** 2 + t11379 = t11377 * t11378 + t11382 = t11379 ** 2 + t11380 = t11378 ** 2 + t11375 = np.sin(phi) + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((2*1j) * phi1) * np.sqrt(0.510e3) * t11375 ** 2 * (-399399 * t11378 + 3062059 * t11379 - 10935925 * t11380 - 17298645 * t11382 - 143 + (19684665 * t11380 + 5892945 * t11382 + 19019) * t11377) + + if Bindx == 507: + t11401 = np.cos(phi) + t11400 = t11401 ** 2 + t11402 = t11401 * t11400 + t11403 = t11400 ** 2 + t11404 = t11401 * t11403 + t11405 = t11402 ** 2 + t11406 = t11401 * t11405 + t11407 = t11403 ** 2 + t11409 = t11404 ** 2 + t11411 = t11405 ** 2 + t11413 = t11406 ** 2 + t11417 = -37555 * t11402 + 405419 * t11404 - 1900651 * t11406 + (4521225 * t11407 - 5710785 * t11409 + 3651825 * t11411 - 930465 * t11413 + 987) * t11401 + t11416 = -8120 * t11400 + 155134 * t11403 - 1136268 * t11405 - 8169600 * t11409 + 9042450 * t11411 - 5242620 * t11413 + 70 + (4118334 + 1240620 * t11407) * t11407 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.1235e4) * np.sqrt(0.2e1) * ((t11416 + t11417) * np.exp((2*1j) * (phi1 - 3 * phi2)) + (t11416 - t11417) * np.exp((2*1j) * (phi1 + 3 * phi2))) + + if Bindx == 508: + t11435 = np.cos(phi) + t11434 = t11435 ** 2 + t11437 = t11434 ** 2 + t11436 = t11435 * t11434 + t11439 = t11436 ** 2 + t11441 = t11437 ** 2 + t11438 = t11435 * t11437 + t11443 = t11438 ** 2 + t11445 = t11439 ** 2 + t11440 = t11435 * t11439 + t11447 = t11440 ** 2 + t11451 = 124 * t11434 + 280 * t11437 - 12180 * t11439 - 98028 * t11443 + 94136 * t11445 - 45820 * t11447 - 2 + (52500 + 8990 * t11441) * t11441 + t11450 = -7763 * t11436 + 46585 * t11438 - 129815 * t11440 + (195335 * t11441 - 164521 * t11443 + 73283 * t11445 - 13485 * t11447 + 381) * t11435 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t11450 + t11451) * np.exp((2*1j) * (phi1 - 6 * phi2)) + (-t11450 + t11451) * np.exp((2*1j) * (phi1 + 6 * phi2))) * np.sqrt(0.6555e4) + + if Bindx == 509: + t11452 = np.cos(phi) + t11453 = t11452 ** 2 + t11454 = t11453 ** 2 + t11456 = t11454 ** 2 + t11455 = t11453 * t11454 + tfunc[..., c] = (0.33e2 / 0.8192e4*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.33915e5) * ((1 - t11452) ** (0.3e1 / 0.2e1)) * ((1 + t11452) ** (0.3e1 / 0.2e1)) * t11452 * (69069 * t11454 + 740025 * t11456 + 143 + (-328900 + 310155 * t11455) * t11455 + (-780390 * t11456 - 6006) * t11453) + + if Bindx == 510: + t11477 = np.cos(phi) + t11476 = t11477 ** 2 + t11479 = t11476 ** 2 + t11478 = t11477 * t11476 + t11481 = t11478 ** 2 + t11483 = t11479 ** 2 + t11480 = t11477 * t11479 + t11485 = t11480 ** 2 + t11487 = t11481 ** 2 + t11482 = t11477 * t11481 + t11489 = t11482 ** 2 + t11491 = t11483 ** 2 + t11494 = 22914 * t11476 - 427922 * t11479 + 3048346 * t11481 - 10700612 * t11483 + 20497830 * t11485 - 21875070 * t11487 + 12226110 * t11489 - 2791395 * t11491 - 201 + t11493 = -56458 * t11478 + 670766 * t11480 - 3726834 * t11482 + (11325522 * t11483 - 19943070 * t11485 + 20272890 * t11487 - 11025510 * t11489 + 2481240 * t11491 + 1454) * t11477 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.455e3) * ((1 + t11477) ** (-0.1e1 / 0.2e1)) * ((1 - t11477) ** (-0.1e1 / 0.2e1)) * ((t11493 + t11494) * np.exp((3*1j) * (phi1 - 2 * phi2)) + (t11493 - t11494) * np.exp((3*1j) * (phi1 + 2 * phi2))) + + if Bindx == 511: + t11513 = np.cos(phi) + t11512 = t11513 ** 2 + t11514 = t11513 * t11512 + t11515 = t11512 ** 2 + t11516 = t11513 * t11515 + t11517 = t11514 ** 2 + t11518 = t11513 * t11517 + t11519 = t11515 ** 2 + t11521 = t11516 ** 2 + t11523 = t11517 ** 2 + t11525 = t11518 ** 2 + t11527 = t11519 ** 2 + t11530 = 13148 * t11514 - 69340 * t11516 + 155580 * t11518 + (-149820 * t11519 + 8628 * t11521 + 100492 * t11523 - 75980 * t11525 + 17980 * t11527 - 688) * t11513 + t11529 = -3060 * t11512 + 34864 * t11515 - 170660 * t11517 + 433870 * t11519 - 621916 * t11521 + 509000 * t11523 - 222604 * t11525 + 40455 * t11527 + 51 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2415e4) * np.sqrt(0.2e1) * ((1 + t11513) ** (-0.1e1 / 0.2e1)) * ((1 - t11513) ** (-0.1e1 / 0.2e1)) * ((-t11529 + t11530) * np.exp((3*1j) * (phi1 - 4 * phi2)) + (t11529 + t11530) * np.exp((3*1j) * (phi1 + 4 * phi2))) + + if Bindx == 512: + t11540 = np.sin(phi) + t11538 = t11540 ** 2 + t11531 = np.cos(phi) + t11532 = t11531 ** 2 + t11533 = t11532 ** 2 + t11535 = t11533 ** 2 + t11534 = t11532 * t11533 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.exp((4*1j) * phi1) * np.sqrt(0.88179e5) * t11538 ** 2 * (26565 * t11533 + 512325 * t11535 + 11 + (-177100 + 310155 * t11534) * t11534 + (-660330 * t11535 - 1386) * t11532) + + if Bindx == 513: + t11558 = np.cos(phi) + t11557 = t11558 ** 2 + t11560 = t11558 * t11557 + t11561 = t11557 ** 2 + t11562 = t11558 * t11561 + t11563 = t11560 ** 2 + t11564 = t11558 * t11563 + t11565 = t11561 ** 2 + t11567 = t11562 ** 2 + t11569 = t11563 ** 2 + t11571 = t11564 ** 2 + t11575 = -487715 * t11560 + 5090953 * t11562 - 23424695 * t11564 + (55508775 * t11565 - 70777785 * t11567 + 46173075 * t11569 - 12096045 * t11571 + 13437) * t11558 + t11574 = -6820 * t11557 + 175640 * t11561 - 1941492 * t11563 - 27968460 * t11567 + 40006200 * t11569 - 28614300 * t11571 + 62 + (10285140 + 8064030 * t11565) * t11565 + t11559 = 2 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t11574 + t11575) * np.exp((2*1j) * (t11559 - 3 * phi2)) + (t11574 - t11575) * np.exp((2*1j) * (t11559 + 3 * phi2))) * np.sqrt(0.7e1) + + if Bindx == 514: + t11593 = np.cos(phi) + t11592 = t11593 ** 2 + t11594 = t11593 * t11592 + t11595 = t11592 ** 2 + t11596 = t11593 * t11595 + t11597 = t11594 ** 2 + t11598 = t11593 * t11597 + t11599 = t11595 ** 2 + t11601 = t11596 ** 2 + t11603 = t11597 ** 2 + t11605 = t11598 ** 2 + t11609 = 85 * t11594 + 823 * t11596 - 6659 * t11598 + (17021 * t11599 - 20425 * t11601 + 11861 * t11603 - 2697 * t11605 - 9) * t11593 + t11608 = 280 * t11592 - 2612 * t11595 + 9720 * t11597 + 14664 * t11601 - 4036 * t11603 - 1624 * t11605 - 5 + (-17286 + 899 * t11599) * t11599 + tfunc[..., c] = (0.165e3 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.6279e4) * ((t11608 + t11609) * np.exp((4*1j) * (phi1 - 3 * phi2)) + (t11608 - t11609) * np.exp((4*1j) * (phi1 + 3 * phi2))) + + if Bindx == 515: + t11610 = np.cos(phi) + t11611 = t11610 ** 2 + t11612 = t11611 ** 2 + t11614 = t11612 ** 2 + tfunc[..., c] = (-0.33e2 / 0.8192e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.12597e5) * ((1 - t11610) ** (0.5e1 / 0.2e1)) * ((1 + t11610) ** (0.5e1 / 0.2e1)) * t11610 * (-88550 * t11612 - 550275 * t11614 - 231 + (341550 * t11612 + 310155 * t11614 + 8855) * t11611) + + if Bindx == 516: + t11634 = np.cos(phi) + t11633 = t11634 ** 2 + t11637 = t11633 ** 2 + t11636 = t11634 * t11633 + t11639 = t11636 ** 2 + t11641 = t11637 ** 2 + t11638 = t11634 * t11637 + t11643 = t11638 ** 2 + t11645 = t11639 ** 2 + t11640 = t11634 * t11639 + t11647 = t11640 ** 2 + t11649 = t11641 ** 2 + t11652 = 487918 * t11633 - 8673630 * t11637 + 60237590 * t11639 - 210521300 * t11641 + 407970090 * t11643 - 445243890 * t11645 + 256228050 * t11647 - 60480225 * t11649 - 4603 + t11651 = 745790 * t11636 - 6298922 * t11638 + 18128022 * t11640 + (-4075830 * t11641 - 74406150 * t11643 + 147870450 * t11645 - 114197070 * t11647 + 32256120 * t11649 - 22410) * t11634 + t11635 = 5 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 + t11634) ** (-0.1e1 / 0.2e1)) * ((1 - t11634) ** (-0.1e1 / 0.2e1)) * ((t11651 + t11652) * np.exp((1j) * (t11635 - 6 * phi2)) + (t11651 - t11652) * np.exp((1j) * (t11635 + 6 * phi2))) + + if Bindx == 517: + t11671 = np.cos(phi) + t11670 = t11671 ** 2 + t11674 = t11670 ** 2 + t11673 = t11671 * t11670 + t11676 = t11673 ** 2 + t11678 = t11674 ** 2 + t11675 = t11671 * t11674 + t11680 = t11675 ** 2 + t11682 = t11676 ** 2 + t11677 = t11671 * t11676 + t11684 = t11677 ** 2 + t11686 = t11678 ** 2 + t11689 = 23 - 1196 * t11670 + 9048 * t11674 - 23068 * t11676 + 11422 * t11678 + 44604 * t11680 - 82912 * t11682 + 55564 * t11684 - 13485 * t11686 + t11688 = 2956 * t11673 - 22732 * t11675 + 79212 * t11677 + (-140012 * t11678 + 127172 * t11680 - 50628 * t11682 + 580 * t11684 + 3596 * t11686 - 144) * t11671 + t11672 = 5 * phi1 + tfunc[..., c] = (0.165e3 / 0.32768e5*1j) * np.sqrt(0.897e3) * np.sqrt(0.2e1) * ((1 + t11671) ** (-0.1e1 / 0.2e1)) * ((1 - t11671) ** (-0.1e1 / 0.2e1)) * ((t11688 + t11689) * np.exp((1j) * (t11672 - 12 * phi2)) + (t11688 - t11689) * np.exp((1j) * (t11672 + 12 * phi2))) + + if Bindx == 518: + t11699 = np.sin(phi) + t11696 = t11699 ** 2 + t11697 = t11699 * t11696 + t11690 = np.cos(phi) + t11691 = t11690 ** 2 + t11692 = t11691 ** 2 + t11694 = t11692 ** 2 + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((6*1j) * phi1) * np.sqrt(0.25194e5) * t11697 ** 2 * (-40250 * t11692 - 450225 * t11694 - 21 + (217350 * t11692 + 310155 * t11694 + 2415) * t11691) + + if Bindx == 519: + t11717 = np.cos(phi) + t11716 = t11717 ** 2 + t11718 = t11717 * t11716 + t11719 = t11716 ** 2 + t11720 = t11717 * t11719 + t11721 = t11718 ** 2 + t11722 = t11717 * t11721 + t11723 = t11719 ** 2 + t11725 = t11720 ** 2 + t11727 = t11721 ** 2 + t11729 = t11722 ** 2 + t11733 = -1247925 * t11718 + 12674109 * t11720 - 58935085 * t11722 + (144532575 * t11723 - 192796695 * t11725 + 132015975 * t11727 - 36288135 * t11729 + 36989) * t11717 + t11732 = 227400 * t11716 - 3468010 * t11719 + 18442116 * t11721 + 33153120 * t11725 + 16011450 * t11727 - 39019500 * t11729 - 2274 + (-41464170 + 16128060 * t11723) * t11723 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * ((t11732 + t11733) * np.exp((6*1j) * (phi1 - phi2)) + (t11732 - t11733) * np.exp((6*1j) * (phi1 + phi2))) + + if Bindx == 520: + t11751 = np.cos(phi) + t11750 = t11751 ** 2 + t11752 = t11751 * t11750 + t11753 = t11750 ** 2 + t11754 = t11751 * t11753 + t11755 = t11752 ** 2 + t11756 = t11751 * t11755 + t11757 = t11753 ** 2 + t11759 = t11754 ** 2 + t11761 = t11755 ** 2 + t11763 = t11756 ** 2 + t11767 = 2859 * t11752 - 15009 * t11754 + 32351 * t11756 + (-24783 * t11757 - 8751 * t11759 + 21605 * t11761 - 8091 * t11763 - 181) * t11751 + t11766 = -276 * t11750 - 104 * t11753 + 11484 * t11755 + 63844 * t11759 - 40200 * t11761 + 6612 * t11763 + 6 + (-43164 + 1798 * t11757) * t11757 + tfunc[..., c] = (0.165e3 / 0.16384e5) * ((t11766 + t11767) * np.exp((6*1j) * (phi1 - 2 * phi2)) + (t11766 - t11767) * np.exp((6*1j) * (phi1 + 2 * phi2))) * np.sqrt(0.897e3) + + if Bindx == 521: + t11768 = np.cos(phi) + t11769 = t11768 ** 2 + t11770 = t11769 ** 2 + tfunc[..., c] = (0.33e2 / 0.8192e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.1448655e7) * ((1 - t11768) ** (0.7e1 / 0.2e1)) * ((1 + t11768) ** (0.7e1 / 0.2e1)) * t11768 * (-700 * t11769 + 21 + (-15660 * t11769 + 5670 + 13485 * t11770) * t11770) + + if Bindx == 522: + t11790 = np.cos(phi) + t11789 = t11790 ** 2 + t11793 = t11789 ** 2 + t11797 = t11793 ** 2 + t11806 = 1402440 * t11797 ** 2 + t11792 = t11790 * t11789 + t11795 = t11792 ** 2 + t11796 = t11790 * t11795 + t11803 = t11796 ** 2 + t11801 = t11795 ** 2 + t11794 = t11790 * t11793 + t11799 = t11794 ** 2 + t11791 = 7 * phi1 + t11782 = t11790 * t11797 + t11780 = t11790 * t11799 + t11778 = t11790 * t11801 + t11776 = t11790 * t11803 + tfunc[..., c] = (-0.33e2 / 0.32768e5*1j) * np.sqrt((1 - t11790)) * np.sqrt(0.115e3) * ((1 + t11790) ** (-0.1e1 / 0.2e1)) * ((-2278965 * t11776 + 9460815 * t11778 - 15982785 * t11780 + 14040675 * t11782 - 13113 * t11789 + 5969 * t11790 - 190939 * t11792 + 146615 * t11793 + 1709757 * t11794 - 805189 * t11795 - 6764527 * t11796 + 2895849 * t11797 - 6441435 * t11799 + 8158605 * t11801 - 5343975 * t11803 + 203 + t11806) * np.exp((1j) * (t11791 - 6 * phi2)) + (5083845 * t11776 - 12785955 * t11778 + 10053225 * t11780 - 887535 * t11782 + 24645 * t11789 + 5563 * t11790 - 153181 * t11792 - 490735 * t11793 + 1072407 * t11794 + 3587353 * t11795 - 2371985 * t11796 - 12032361 * t11797 + 19594575 * t11799 - 14088165 * t11801 + 2018835 * t11803 - 203 + t11806) * np.exp((1j) * (t11791 + 6 * phi2))) + + if Bindx == 523: + t11825 = np.cos(phi) + t11824 = t11825 ** 2 + t11828 = t11824 ** 2 + t11827 = t11825 * t11824 + t11830 = t11827 ** 2 + t11832 = t11828 ** 2 + t11829 = t11825 * t11828 + t11834 = t11829 ** 2 + t11836 = t11830 ** 2 + t11831 = t11825 * t11830 + t11838 = t11831 ** 2 + t11840 = t11832 ** 2 + t11843 = -4360 * t11824 + 67716 * t11828 - 349256 * t11830 + 773366 * t11832 - 744216 * t11834 + 157780 * t11836 + 193256 * t11838 - 94395 * t11840 + 109 + t11842 = -32788 * t11827 + 111412 * t11829 - 42900 * t11831 + (-427372 * t11832 + 842788 * t11834 - 593028 * t11836 + 121220 * t11838 + 17980 * t11840 + 2688) * t11825 + t11826 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.195e3) * np.sqrt(0.2e1) * ((1 + t11825) ** (-0.1e1 / 0.2e1)) * ((1 - t11825) ** (-0.1e1 / 0.2e1)) * ((t11842 + t11843) * np.exp((1j) * (t11826 - 12 * phi2)) + (t11842 - t11843) * np.exp((1j) * (t11826 + 12 * phi2))) + + if Bindx == 524: + t11852 = np.sin(phi) + t11849 = t11852 ** 2 + t11850 = t11849 ** 2 + t11844 = np.cos(phi) + t11845 = t11844 ** 2 + t11846 = t11845 ** 2 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.exp((8*1j) * phi1) * np.sqrt(0.965770e6) * t11850 ** 2 * (-700 * t11845 + 7 + (-36540 * t11845 + 9450 + 40455 * t11846) * t11846) + + if Bindx == 525: + t11870 = np.cos(phi) + t11869 = t11870 ** 2 + t11872 = t11870 * t11869 + t11873 = t11869 ** 2 + t11874 = t11870 * t11873 + t11875 = t11872 ** 2 + t11876 = t11870 * t11875 + t11877 = t11873 ** 2 + t11879 = t11874 ** 2 + t11881 = t11875 ** 2 + t11883 = t11876 ** 2 + t11887 = -2317 * t11872 + 58247 * t11874 - 448657 * t11876 + (1467345 * t11877 - 2330055 * t11879 + 1781325 * t11881 - 525915 * t11883 + 27) * t11870 + t11886 = 8342 * t11869 - 115990 * t11873 + 607838 * t11875 + 1811810 * t11879 - 832650 * t11881 - 147030 * t11883 - 97 + (-1507528 + 175305 * t11877) * t11877 + t11871 = 4 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.sqrt(0.2e1) * np.sqrt(0.345e3) * ((t11886 + t11887) * np.exp((2*1j) * (t11871 - 3 * phi2)) + (t11886 - t11887) * np.exp((2*1j) * (t11871 + 3 * phi2))) + + if Bindx == 526: + t11905 = np.cos(phi) + t11904 = t11905 ** 2 + t11908 = t11904 ** 2 + t11907 = t11905 * t11904 + t11910 = t11907 ** 2 + t11912 = t11908 ** 2 + t11909 = t11905 * t11908 + t11914 = t11909 ** 2 + t11916 = t11910 ** 2 + t11911 = t11905 * t11910 + t11918 = t11911 ** 2 + t11922 = -3552 * t11904 + 29940 * t11908 - 85536 * t11910 + 85536 * t11914 - 146252 * t11916 + 51040 * t11918 + 111 + (64218 + 4495 * t11912) * t11912 + t11921 = 3454 * t11907 + 17002 * t11909 - 121154 * t11911 + (228030 * t11912 - 146838 * t11914 - 6786 * t11916 + 26970 * t11918 - 678) * t11905 + t11906 = 2 * phi1 + tfunc[..., c] = (0.99e2 / 0.16384e5) * ((-t11921 + t11922) * np.exp((4*1j) * (t11906 - 3 * phi2)) + (t11921 + t11922) * np.exp((4*1j) * (t11906 + 3 * phi2))) * np.sqrt(0.65e2) + + if Bindx == 527: + t11923 = np.cos(phi) + t11924 = t11923 ** 2 + t11925 = t11924 ** 2 + tfunc[..., c] = (-0.33e2 / 0.8192e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.482885e6) * ((1 - t11923) ** (0.9e1 / 0.2e1)) * ((1 + t11923) ** (0.9e1 / 0.2e1)) * t11923 * (-5481 * t11925 - 35 + (8091 * t11925 + 945) * t11924) + + if Bindx == 528: + t11943 = np.cos(phi) + t11942 = t11943 ** 2 + t11945 = t11943 * t11942 + t11948 = t11945 ** 2 + t11949 = t11943 * t11948 + t11956 = t11949 ** 2 + t11958 = 280488 * t11943 * t11956 + t11954 = t11948 ** 2 + t11946 = t11942 ** 2 + t11947 = t11943 * t11946 + t11952 = t11947 ** 2 + t11950 = t11946 ** 2 + t11944 = 3 * phi1 + t11935 = t11943 * t11950 + t11933 = t11943 * t11952 + t11931 = t11943 * t11954 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * ((1 - t11943) ** (0.3e1 / 0.2e1)) * np.sqrt(0.345e3) * ((1 + t11943) ** (-0.1e1 / 0.2e1)) * ((-1158144 * t11931 + 1975896 * t11933 - 1823536 * t11935 - 14753 * t11942 - 2992 * t11943 + 59048 * t11945 + 216107 * t11946 - 333344 * t11947 - 1019029 * t11948 + 1002584 * t11949 + 2281741 * t11950 - 2683395 * t11952 + 1604889 * t11954 - 385671 * t11956 + 111 + t11958) * np.exp((3*1j) * (t11944 - 2 * phi2)) + (2628444 * t11931 - 4592952 * t11933 + 1373372 * t11935 + 1897 * t11942 - 3436 * t11943 + 92792 * t11945 + 100429 * t11946 - 598444 * t11947 - 1161083 * t11948 + 983576 * t11949 + 3870691 * t11950 - 4369365 * t11952 + 213759 * t11954 + 1507623 * t11956 - 111 + t11958) * np.exp((3*1j) * (t11944 + 2 * phi2))) + + if Bindx == 529: + t11977 = np.cos(phi) + t11976 = t11977 ** 2 + t11980 = t11976 ** 2 + t11979 = t11977 * t11976 + t11982 = t11979 ** 2 + t11984 = t11980 ** 2 + t11981 = t11977 * t11980 + t11986 = t11981 ** 2 + t11988 = t11982 ** 2 + t11983 = t11977 * t11982 + t11990 = t11983 ** 2 + t11992 = t11984 ** 2 + t11995 = -4824 * t11976 + 25700 * t11980 - 22936 * t11982 - 116050 * t11984 + 286968 * t11986 - 210732 * t11988 + 17400 * t11990 + 24273 * t11992 + 201 + t11994 = -5700 * t11979 + 59748 * t11981 - 179652 * t11983 + (188100 * t11984 + 16404 * t11986 - 138100 * t11988 + 55796 * t11990 + 3596 * t11992 - 192) * t11977 + t11978 = 3 * phi1 + tfunc[..., c] = (0.99e2 / 0.32768e5*1j) * np.sqrt(0.65e2) * np.sqrt(0.2e1) * ((1 + t11977) ** (-0.1e1 / 0.2e1)) * ((1 - t11977) ** (-0.1e1 / 0.2e1)) * ((t11994 - t11995) * np.exp((3*1j) * (t11978 - 4 * phi2)) + (t11994 + t11995) * np.exp((3*1j) * (t11978 + 4 * phi2))) + + if Bindx == 530: + t12004 = np.sin(phi) + t12000 = t12004 ** 2 + t12002 = t12004 * t12000 ** 2 + t11996 = np.cos(phi) + t11997 = t11996 ** 2 + t11998 = t11997 ** 2 + tfunc[..., c] = -(0.33e2 / 0.16384e5) * np.exp((10*1j) * phi1) * np.sqrt(0.520030e6) * t12002 ** 2 * (-3915 * t11998 - 5 + (8091 * t11998 + 405) * t11997) + + if Bindx == 531: + t12022 = np.cos(phi) + t12021 = t12022 ** 2 + t12024 = t12022 * t12021 + t12025 = t12021 ** 2 + t12026 = t12022 * t12025 + t12027 = t12024 ** 2 + t12028 = t12022 * t12027 + t12029 = t12025 ** 2 + t12031 = t12026 ** 2 + t12033 = t12027 ** 2 + t12035 = t12028 ** 2 + t12039 = -4075 * t12024 + 20963 * t12026 - 32659 * t12028 + (-25311 * t12029 + 124407 * t12031 - 123975 * t12033 + 40455 * t12035 + 195) * t12022 + t12038 = 952 * t12021 - 12422 * t12025 + 67804 * t12027 + 232672 * t12031 - 134346 * t12033 + 12876 * t12035 - 14 + (-178310 + 10788 * t12029) * t12029 + t12023 = 5 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.31395e5) * ((t12038 - t12039) * np.exp((2*1j) * (t12023 - 3 * phi2)) + (t12038 + t12039) * np.exp((2*1j) * (t12023 + 3 * phi2))) + + if Bindx == 532: + t12057 = np.cos(phi) + t12056 = t12057 ** 2 + t12059 = t12057 * t12056 + t12060 = t12056 ** 2 + t12061 = t12057 * t12060 + t12062 = t12059 ** 2 + t12063 = t12057 * t12062 + t12064 = t12060 ** 2 + t12066 = t12061 ** 2 + t12068 = t12062 ** 2 + t12070 = t12063 ** 2 + t12074 = 9091 * t12059 - 40937 * t12061 + 40183 * t12063 + (67353 * t12064 - 127335 * t12066 + 38541 * t12068 + 13485 * t12070 - 381) * t12057 + t12073 = 2604 * t12056 - 936 * t12060 - 49764 * t12062 - 87516 * t12066 - 36296 * t12068 + 38164 * t12070 - 186 + (132132 + 1798 * t12064) * t12064 + t12058 = 5 * phi1 + tfunc[..., c] = (0.99e2 / 0.16384e5) * ((t12073 - t12074) * np.exp((2*1j) * (t12058 - 6 * phi2)) + (t12073 + t12074) * np.exp((2*1j) * (t12058 + 6 * phi2))) * np.sqrt(0.35e2) + + if Bindx == 533: + t12075 = np.cos(phi) + t12076 = t12075 ** 2 + tfunc[..., c] = (0.99e2 / 0.8192e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.260015e6) * ((1 - t12075) ** (0.11e2 / 0.2e1)) * ((1 + t12075) ** (0.11e2 / 0.2e1)) * t12075 * (15 + (-290 + 899 * t12076) * t12076) + + if Bindx == 534: + t12093 = np.cos(phi) + t12092 = t12093 ** 2 + t12095 = t12093 * t12092 + t12098 = t12095 ** 2 + t12099 = t12093 * t12098 + t12107 = 7192 * t12099 ** 2 + t12104 = t12098 ** 2 + t12096 = t12092 ** 2 + t12097 = t12093 * t12096 + t12102 = t12097 ** 2 + t12100 = t12096 ** 2 + t12094 = 11 * phi1 + t12085 = t12093 * t12100 + t12083 = t12093 * t12102 + t12081 = t12093 * t12104 + tfunc[..., c] = (-0.33e2 / 0.32768e5*1j) * ((1 - t12093) ** (0.5e1 / 0.2e1)) * np.sqrt(0.31395e5) * ((1 + t12093) ** (-0.1e1 / 0.2e1)) * ((t12107 - 8091 * t12081 - 32335 * t12104 + 34626 * t12083 + 60258 * t12102 - 58773 * t12085 - 60401 * t12100 + 49500 * t12099 + 35156 * t12098 - 20757 * t12097 - 11649 * t12096 + 3522 * t12095 + 1810 * t12092 - 27 * t12093 - 31) * np.exp((1j) * (t12094 - 6 * phi2)) + (t12107 + 51243 * t12081 + 145667 * t12104 + 190298 * t12083 + 52602 * t12102 - 164087 * t12085 - 197703 * t12100 - 42372 * t12099 + 66132 * t12098 + 45133 * t12097 + 1221 * t12096 - 6646 * t12095 - 1414 * t12092 + 159 * t12093 + 31) * np.exp((1j) * (t12094 + 6 * phi2))) + + if Bindx == 535: + t12126 = np.cos(phi) + t12125 = t12126 ** 2 + t12128 = t12126 * t12125 + t12129 = t12125 ** 2 + t12130 = t12126 * t12129 + t12131 = t12128 ** 2 + t12132 = t12126 * t12131 + t12133 = t12129 ** 2 + t12135 = t12130 ** 2 + t12137 = t12131 ** 2 + t12139 = t12132 ** 2 + t12141 = t12133 ** 2 + t12144 = 30668 * t12128 - 62348 * t12130 - 125268 * t12132 + (473044 * t12133 - 364156 * t12135 - 48132 * t12137 + 95236 * t12139 + 3596 * t12141 - 2640) * t12126 + t12143 = 2468 * t12125 + 43272 * t12129 - 224588 * t12131 + 286286 * t12133 + 94380 * t12135 - 354640 * t12137 + 123772 * t12139 + 29667 * t12141 - 617 + t12127 = 11 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt(0.35e2) * ((1 + t12126) ** (-0.1e1 / 0.2e1)) * ((1 - t12126) ** (-0.1e1 / 0.2e1)) * ((-t12143 + t12144) * np.exp((1j) * (t12127 - 12 * phi2)) + (t12143 + t12144) * np.exp((1j) * (t12127 + 12 * phi2))) + + if Bindx == 536: + t12152 = np.sin(phi) + t12148 = t12152 ** 2 + t12149 = t12152 * t12148 + t12150 = t12149 ** 2 + t12145 = np.cos(phi) + t12146 = t12145 ** 2 + tfunc[..., c] = (0.495e3 / 0.16384e5) * np.exp((12*1j) * phi1) * np.sqrt(0.7429e4) * t12150 ** 2 * (3 + (-174 + 899 * t12146) * t12146) + + if Bindx == 537: + t12170 = np.cos(phi) + t12169 = t12170 ** 2 + t12172 = t12170 * t12169 + t12173 = t12169 ** 2 + t12174 = t12170 * t12173 + t12175 = t12172 ** 2 + t12176 = t12170 * t12175 + t12177 = t12173 ** 2 + t12179 = t12174 ** 2 + t12181 = t12175 ** 2 + t12183 = t12176 ** 2 + t12187 = 2859 * t12172 - 15009 * t12174 + 32351 * t12176 + (-24783 * t12177 - 8751 * t12179 + 21605 * t12181 - 8091 * t12183 - 181) * t12170 + t12186 = -276 * t12169 - 104 * t12173 + 11484 * t12175 + 63844 * t12179 - 40200 * t12181 + 6612 * t12183 + 6 + (-43164 + 1798 * t12177) * t12177 + t12171 = 2 * phi1 + tfunc[..., c] = (0.165e3 / 0.16384e5) * ((t12186 + t12187) * np.exp((6*1j) * (t12171 - phi2)) + (t12186 - t12187) * np.exp((6*1j) * (t12171 + phi2))) * np.sqrt(0.897e3) + + if Bindx == 538: + t12205 = np.cos(phi) + t12204 = t12205 ** 2 + t12206 = t12205 * t12204 + t12207 = t12204 ** 2 + t12208 = t12205 * t12207 + t12209 = t12206 ** 2 + t12210 = t12205 * t12209 + t12211 = t12207 ** 2 + t12213 = t12208 ** 2 + t12215 = t12209 ** 2 + t12217 = t12210 ** 2 + t12221 = 35259 * t12206 + 98553 * t12208 - 512941 * t12210 + (383955 * t12211 + 308217 * t12213 - 269381 * t12215 - 40455 * t12217 - 7303) * t12205 + t12220 = 9528 * t12204 - 129220 * t12207 + 216216 * t12209 - 664664 * t12213 + 151788 * t12215 + 149640 * t12217 + 1191 + (265122 + 4495 * t12211) * t12211 + tfunc[..., c] = (0.33e2 / 0.16384e5) * np.sqrt(0.2e1) * ((t12220 + t12221) * np.exp((12*1j) * (phi1 - phi2)) + (t12220 - t12221) * np.exp((12*1j) * (phi1 + phi2))) + + if Bindx == 539: + t12222 = np.cos(phi) + tfunc[..., c] = (-0.495e3 / 0.8192e4*1j) * np.exp((13*1j) * phi1) * np.sqrt(0.215441e6) * ((1 - t12222) ** (0.13e2 / 0.2e1)) * ((1 + t12222) ** (0.13e2 / 0.2e1)) * t12222 * (31 * t12222 ** 2 - 3) + + if Bindx == 540: + t12237 = np.cos(phi) + t12236 = t12237 ** 2 + t12239 = t12237 * t12236 + t12242 = t12239 ** 2 + t12248 = t12242 ** 2 + t12250 = 248 * t12237 * t12248 + t12240 = t12236 ** 2 + t12241 = t12237 * t12240 + t12246 = t12241 ** 2 + t12244 = t12240 ** 2 + t12238 = 13 * phi1 + t12231 = t12237 * t12242 + t12229 = t12237 * t12244 + t12227 = t12237 * t12246 + tfunc[..., c] = (0.165e3 / 0.32768e5*1j) * ((1 - t12237) ** (0.7e1 / 0.2e1)) * np.sqrt(0.26013e5) * ((1 + t12237) ** (-0.1e1 / 0.2e1)) * ((t12250 - 217 * t12248 - 1218 * t12227 + 984 * t12246 + 2466 * t12229 - 1761 * t12244 - 2644 * t12231 + 1544 * t12242 + 1596 * t12241 - 651 * t12240 - 522 * t12239 + 96 * t12236 + 74 * t12237 + 5) * np.exp((1j) * (t12238 - 6 * phi2)) + (t12250 + 2201 * t12248 + 8454 * t12227 + 18040 * t12246 + 22330 * t12229 + 13761 * t12244 - 1188 * t12231 - 8712 * t12242 - 5940 * t12241 - 1045 * t12240 + 638 * t12239 + 336 * t12236 + 34 * t12237 - 5) * np.exp((1j) * (t12238 + 6 * phi2))) + + if Bindx == 541: + t12268 = np.cos(phi) + t12267 = t12268 ** 2 + t12271 = t12267 ** 2 + t12275 = t12271 ** 2 + t12284 = 620 * t12275 ** 2 + t12270 = t12268 * t12267 + t12273 = t12270 ** 2 + t12274 = t12268 * t12273 + t12281 = t12274 ** 2 + t12279 = t12273 ** 2 + t12272 = t12268 * t12271 + t12277 = t12272 ** 2 + t12269 = 13 * phi1 + t12260 = t12268 * t12275 + t12258 = t12268 * t12277 + t12256 = t12268 * t12279 + t12254 = t12268 * t12281 + tfunc[..., c] = (-0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * np.sqrt((1 - t12268)) * np.sqrt(0.29e2) * ((1 + t12268) ** (-0.1e1 / 0.2e1)) * ((t12284 - 5425 * t12254 + 19155 * t12281 - 31129 * t12256 + 8099 * t12279 + 55419 * t12258 - 89089 * t12277 + 29315 * t12260 + 63063 * t12275 - 77363 * t12274 + 17017 * t12273 + 27573 * t12272 - 22295 * t12271 + 3241 * t12270 + 3189 * t12267 - 1631 * t12268 + 241) * np.exp((1j) * (t12269 - 12 * phi2)) + (t12284 + 6665 * t12254 + 31245 * t12281 + 81529 * t12256 + 120757 * t12279 + 73437 * t12258 - 71071 * t12277 - 189475 * t12260 - 155727 * t12275 - 15301 * t12274 + 79079 * t12273 + 68523 * t12272 + 18655 * t12271 - 6881 * t12270 - 6933 * t12267 - 2113 * t12268 - 241) * np.exp((1j) * (t12269 + 12 * phi2))) + + if Bindx == 542: + t12291 = np.sin(phi) + t12286 = t12291 ** 2 + t12287 = t12291 * t12286 + t12289 = t12291 * t12287 ** 2 + t12285 = np.cos(phi) + tfunc[..., c] = -(0.99e2 / 0.16384e5) * np.exp((14*1j) * phi1) * np.sqrt(0.2154410e7) * t12289 ** 2 * (31 * t12285 ** 2 - 1) + + if Bindx == 543: + t12309 = np.cos(phi) + t12308 = t12309 ** 2 + t12311 = t12309 * t12308 + t12312 = t12308 ** 2 + t12313 = t12309 * t12312 + t12314 = t12311 ** 2 + t12315 = t12309 * t12314 + t12316 = t12312 ** 2 + t12318 = t12313 ** 2 + t12320 = t12314 ** 2 + t12322 = t12315 ** 2 + t12326 = 335 * t12311 - 2599 * t12313 + 6215 * t12315 + (-5885 * t12316 + 1141 * t12318 + 1435 * t12320 - 651 * t12322 + 9) * t12309 + t12325 = -280 * t12308 + 1190 * t12312 - 924 * t12314 + 6336 * t12318 - 4310 * t12320 + 820 * t12322 + 14 + (-2970 + 124 * t12316) * t12316 + t12310 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.130065e6) * ((t12325 + t12326) * np.exp((2*1j) * (t12310 - 3 * phi2)) + (t12325 - t12326) * np.exp((2*1j) * (t12310 + 3 * phi2))) + + if Bindx == 544: + t12344 = np.cos(phi) + t12343 = t12344 ** 2 + t12346 = t12344 * t12343 + t12347 = t12343 ** 2 + t12348 = t12344 * t12347 + t12349 = t12346 ** 2 + t12350 = t12344 * t12349 + t12351 = t12347 ** 2 + t12353 = t12348 ** 2 + t12355 = t12349 ** 2 + t12357 = t12350 ** 2 + t12361 = 1771 * t12346 - 7553 * t12348 - 4433 * t12350 + (20449 * t12351 - 2639 * t12353 - 7259 * t12355 - 651 * t12357 + 315) * t12344 + t12360 = -1156 * t12343 + 728 * t12347 + 12012 * t12349 - 12012 * t12353 + 9464 * t12355 + 2948 * t12357 - 34 + (-12012 + 62 * t12351) * t12351 + t12345 = 7 * phi1 + tfunc[..., c] = (0.33e2 / 0.16384e5) * ((t12360 + t12361) * np.exp((2*1j) * (t12345 - 6 * phi2)) + (t12360 - t12361) * np.exp((2*1j) * (t12345 + 6 * phi2))) * np.sqrt(0.145e3) + + if Bindx == 545: + t12362 = np.cos(phi) + tfunc[..., c] = (0.99e2 / 0.8192e4*1j) * np.exp((15*1j) * phi1) * np.sqrt(0.33393355e8) * ((1 - t12362) ** (0.15e2 / 0.2e1)) * ((1 + t12362) ** (0.15e2 / 0.2e1)) * t12362 + + if Bindx == 546: + t12376 = np.cos(phi) + t12375 = t12376 ** 2 + t12378 = t12376 * t12375 + t12381 = t12378 ** 2 + t12388 = 8 * t12381 ** 2 + t12379 = t12375 ** 2 + t12380 = t12376 * t12379 + t12385 = t12380 ** 2 + t12383 = t12379 ** 2 + t12377 = 5 * phi1 + t12370 = t12376 * t12381 + t12368 = t12376 * t12383 + t12366 = t12376 * t12385 + tfunc[..., c] = (-0.33e2 / 0.32768e5*1j) * ((1 - t12376) ** (0.9e1 / 0.2e1)) * np.sqrt(0.4032015e7) * ((1 + t12376) ** (-0.1e1 / 0.2e1)) * ((t12388 - 5 * t12366 - 43 * t12385 + 25 * t12368 + 95 * t12383 - 50 * t12370 - 110 * t12381 + 50 * t12380 + 70 * t12379 - 25 * t12378 - 23 * t12375 + 5 * t12376 + 3) * np.exp((3*1j) * (t12377 - 2 * phi2)) + (t12388 + 85 * t12366 + 407 * t12385 + 1155 * t12368 + 2145 * t12383 + 2706 * t12370 + 2310 * t12381 + 1254 * t12380 + 330 * t12379 - 55 * t12378 - 77 * t12375 - 25 * t12376 - 3) * np.exp((3*1j) * (t12377 + 2 * phi2))) + + if Bindx == 547: + t12405 = np.cos(phi) + t12404 = t12405 ** 2 + t12407 = t12405 * t12404 + t12410 = t12407 ** 2 + t12411 = t12405 * t12410 + t12418 = t12411 ** 2 + t12420 = 4 * t12405 * t12418 + t12416 = t12410 ** 2 + t12408 = t12404 ** 2 + t12409 = t12405 * t12408 + t12414 = t12409 ** 2 + t12412 = t12408 ** 2 + t12406 = 5 * phi1 + t12397 = t12405 * t12412 + t12395 = t12405 * t12414 + t12393 = t12405 * t12416 + tfunc[..., c] = (0.33e2 / 0.32768e5*1j) * np.sqrt(0.2e1) * ((1 - t12405) ** (0.3e1 / 0.2e1)) * np.sqrt(0.4495e4) * ((1 + t12405) ** (-0.1e1 / 0.2e1)) * ((t12420 - 37 * t12418 + 142 * t12393 - 271 * t12416 + 184 * t12395 + 275 * t12414 - 726 * t12397 + 561 * t12412 + 132 * t12411 - 583 * t12410 + 418 * t12409 - 37 * t12408 - 128 * t12407 + 89 * t12404 - 26 * t12405 + 3) * np.exp((3*1j) * (t12406 - 4 * phi2)) + (t12420 + 53 * t12418 + 322 * t12393 + 1183 * t12416 + 2912 * t12395 + 5005 * t12414 + 6006 * t12397 + 4719 * t12412 + 1716 * t12411 - 1001 * t12410 - 2002 * t12409 - 1547 * t12408 - 728 * t12407 - 217 * t12404 - 38 * t12405 - 3) * np.exp((3*1j) * (t12406 + 4 * phi2))) + + if Bindx == 548: + t12425 = np.sin(phi) + t12421 = t12425 ** 2 + t12422 = t12421 ** 2 + t12423 = t12422 ** 2 + tfunc[..., c] = (0.99e2 / 0.65536e5) * np.exp((16*1j) * phi1) * np.sqrt(0.66786710e8) * t12423 ** 2 + + if Bindx == 549: + t12442 = np.cos(phi) + t12441 = t12442 ** 2 + t12445 = t12441 ** 2 + t12444 = t12442 * t12441 + t12447 = t12444 ** 2 + t12446 = t12442 * t12445 + t12450 = t12446 ** 2 + t12452 = t12447 ** 2 + t12448 = t12442 * t12447 + t12454 = t12448 ** 2 + t12455 = t12442 * t12454 + t12458 = t12442 * t12455 - 10 * t12441 + 50 * t12445 - 66 * t12447 + 66 * t12450 - 50 * t12452 + 10 * t12454 - 1 + t12457 = -10 * t12444 - 34 * t12446 - 6 * t12455 + (-110 * t12441 + 110) * t12448 + (34 * t12450 + 10 * t12452 + 6) * t12442 + t12443 = 8 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * np.sqrt(0.2e1) * np.sqrt(0.4032015e7) * ((t12457 + t12458) * np.exp((2*1j) * (t12443 - 3 * phi2)) + (-t12457 + t12458) * np.exp((2*1j) * (t12443 + 3 * phi2))) + + if Bindx == 550: + t12474 = np.cos(phi) + t12491 = -12 * t12474 + t12473 = t12474 ** 2 + t12477 = t12473 ** 2 + t12480 = t12477 ** 2 + t12481 = t12474 * t12480 + t12482 = t12473 * t12481 + t12483 = t12474 * t12482 + t12478 = t12474 * t12477 + t12479 = t12473 * t12478 + t12485 = t12479 ** 2 + t12489 = 64 * t12473 + 364 * t12477 + 364 * t12483 + 64 * t12485 + 1 + (-858 + t12480) * t12480 + t12488 = t12485 * t12491 - 364 * t12478 + 572 * t12479 + 572 * t12481 - 364 * t12482 + t12491 - 196 * (t12473 + t12483) * t12474 + t12475 = 4 * phi1 + tfunc[..., c] = (0.33e2 / 0.32768e5) * ((t12488 + t12489) * np.exp((4*1j) * (t12475 - 3 * phi2)) + (-t12488 + t12489) * np.exp((4*1j) * (t12475 + 3 * phi2))) * np.sqrt(0.4495e4) + + c += 1 + + return tfunc + + +if __name__ == '__main__': + X = np.zeros([2, 3]) + phi1 = np.array([0.1, 0.2]) + X[:, 0] = phi1 + phi = np.array([0.0, 0.4]) + X[:, 1] = phi + phi2 = np.array([0.3, 0.6]) + X[:, 2] = phi2 + + indxvec = gsh_basis_info() + + lte2 = indxvec[:, 0] <= 2 + + Bvec = np.arange(indxvec.shape[0])[lte2] + + out_tvalues = gsh_eval(X, Bvec) + diff --git a/pymks/bases/gsh_functions/gsh_tri_tri_L0_13.py b/pymks/bases/gsh_functions/gsh_tri_tri_L0_13.py new file mode 100644 index 00000000..8c0053cc --- /dev/null +++ b/pymks/bases/gsh_functions/gsh_tri_tri_L0_13.py @@ -0,0 +1,28647 @@ +import numpy as np + + +def gsh_basis_info(): + + indxvec = np.array([[0, 0, 0], + [1, -1, -1], + [1, -1, 0], + [1, -1, 1], + [1, 0, -1], + [1, 0, 0], + [1, 0, 1], + [1, 1, -1], + [1, 1, 0], + [1, 1, 1], + [2, -2, -2], + [2, -2, -1], + [2, -2, 0], + [2, -2, 1], + [2, -2, 2], + [2, -1, -2], + [2, -1, -1], + [2, -1, 0], + [2, -1, 1], + [2, -1, 2], + [2, 0, -2], + [2, 0, -1], + [2, 0, 0], + [2, 0, 1], + [2, 0, 2], + [2, 1, -2], + [2, 1, -1], + [2, 1, 0], + [2, 1, 1], + [2, 1, 2], + [2, 2, -2], + [2, 2, -1], + [2, 2, 0], + [2, 2, 1], + [2, 2, 2], + [3, -3, -3], + [3, -3, -2], + [3, -3, -1], + [3, -3, 0], + [3, -3, 1], + [3, -3, 2], + [3, -3, 3], + [3, -2, -3], + [3, -2, -2], + [3, -2, -1], + [3, -2, 0], + [3, -2, 1], + [3, -2, 2], + [3, -2, 3], + [3, -1, -3], + [3, -1, -2], + [3, -1, -1], + [3, -1, 0], + [3, -1, 1], + [3, -1, 2], + [3, -1, 3], + [3, 0, -3], + [3, 0, -2], + [3, 0, -1], + [3, 0, 0], + [3, 0, 1], + [3, 0, 2], + [3, 0, 3], + [3, 1, -3], + [3, 1, -2], + [3, 1, -1], + [3, 1, 0], + [3, 1, 1], + [3, 1, 2], + [3, 1, 3], + [3, 2, -3], + [3, 2, -2], + [3, 2, -1], + [3, 2, 0], + [3, 2, 1], + [3, 2, 2], + [3, 2, 3], + [3, 3, -3], + [3, 3, -2], + [3, 3, -1], + [3, 3, 0], + [3, 3, 1], + [3, 3, 2], + [3, 3, 3], + [4, -4, -4], + [4, -4, -3], + [4, -4, -2], + [4, -4, -1], + [4, -4, 0], + [4, -4, 1], + [4, -4, 2], + [4, -4, 3], + [4, -4, 4], + [4, -3, -4], + [4, -3, -3], + [4, -3, -2], + [4, -3, -1], + [4, -3, 0], + [4, -3, 1], + [4, -3, 2], + [4, -3, 3], + [4, -3, 4], + [4, -2, -4], + [4, -2, -3], + [4, -2, -2], + [4, -2, -1], + [4, -2, 0], + [4, -2, 1], + [4, -2, 2], + [4, -2, 3], + [4, -2, 4], + [4, -1, -4], + [4, -1, -3], + [4, -1, -2], + [4, -1, -1], + [4, -1, 0], + [4, -1, 1], + [4, -1, 2], + [4, -1, 3], + [4, -1, 4], + [4, 0, -4], + [4, 0, -3], + [4, 0, -2], + [4, 0, -1], + [4, 0, 0], + [4, 0, 1], + [4, 0, 2], + [4, 0, 3], + [4, 0, 4], + [4, 1, -4], + [4, 1, -3], + [4, 1, -2], + [4, 1, -1], + [4, 1, 0], + [4, 1, 1], + [4, 1, 2], + [4, 1, 3], + [4, 1, 4], + [4, 2, -4], + [4, 2, -3], + [4, 2, -2], + [4, 2, -1], + [4, 2, 0], + [4, 2, 1], + [4, 2, 2], + [4, 2, 3], + [4, 2, 4], + [4, 3, -4], + [4, 3, -3], + [4, 3, -2], + [4, 3, -1], + [4, 3, 0], + [4, 3, 1], + [4, 3, 2], + [4, 3, 3], + [4, 3, 4], + [4, 4, -4], + [4, 4, -3], + [4, 4, -2], + [4, 4, -1], + [4, 4, 0], + [4, 4, 1], + [4, 4, 2], + [4, 4, 3], + [4, 4, 4], + [5, -5, -5], + [5, -5, -4], + [5, -5, -3], + [5, -5, -2], + [5, -5, -1], + [5, -5, 0], + [5, -5, 1], + [5, -5, 2], + [5, -5, 3], + [5, -5, 4], + [5, -5, 5], + [5, -4, -5], + [5, -4, -4], + [5, -4, -3], + [5, -4, -2], + [5, -4, -1], + [5, -4, 0], + [5, -4, 1], + [5, -4, 2], + [5, -4, 3], + [5, -4, 4], + [5, -4, 5], + [5, -3, -5], + [5, -3, -4], + [5, -3, -3], + [5, -3, -2], + [5, -3, -1], + [5, -3, 0], + [5, -3, 1], + [5, -3, 2], + [5, -3, 3], + [5, -3, 4], + [5, -3, 5], + [5, -2, -5], + [5, -2, -4], + [5, -2, -3], + [5, -2, -2], + [5, -2, -1], + [5, -2, 0], + [5, -2, 1], + [5, -2, 2], + [5, -2, 3], + [5, -2, 4], + [5, -2, 5], + [5, -1, -5], + [5, -1, -4], + [5, -1, -3], + [5, -1, -2], + [5, -1, -1], + [5, -1, 0], + [5, -1, 1], + [5, -1, 2], + [5, -1, 3], + [5, -1, 4], + [5, -1, 5], + [5, 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[13, -2, 7], + [13, -2, 8], + [13, -2, 9], + [13, -2, 10], + [13, -2, 11], + [13, -2, 12], + [13, -2, 13], + [13, -1, -13], + [13, -1, -12], + [13, -1, -11], + [13, -1, -10], + [13, -1, -9], + [13, -1, -8], + [13, -1, -7], + [13, -1, -6], + [13, -1, -5], + [13, -1, -4], + [13, -1, -3], + [13, -1, -2], + [13, -1, -1], + [13, -1, 0], + [13, -1, 1], + [13, -1, 2], + [13, -1, 3], + [13, -1, 4], + [13, -1, 5], + [13, -1, 6], + [13, -1, 7], + [13, -1, 8], + [13, -1, 9], + [13, -1, 10], + [13, -1, 11], + [13, -1, 12], + [13, -1, 13], + [13, 0, -13], + [13, 0, -12], + [13, 0, -11], + [13, 0, -10], + [13, 0, -9], + [13, 0, -8], + [13, 0, -7], + [13, 0, -6], + [13, 0, -5], + [13, 0, -4], + [13, 0, -3], + [13, 0, -2], + [13, 0, -1], + [13, 0, 0], + [13, 0, 1], + [13, 0, 2], + [13, 0, 3], + [13, 0, 4], + [13, 0, 5], + [13, 0, 6], + [13, 0, 7], + [13, 0, 8], + [13, 0, 9], + [13, 0, 10], + [13, 0, 11], + [13, 0, 12], + [13, 0, 13], + [13, 1, -13], + [13, 1, -12], + [13, 1, -11], + [13, 1, -10], + [13, 1, -9], + [13, 1, -8], + [13, 1, -7], + [13, 1, -6], + [13, 1, -5], + [13, 1, -4], + [13, 1, -3], + [13, 1, -2], + [13, 1, -1], + [13, 1, 0], + [13, 1, 1], + [13, 1, 2], + [13, 1, 3], + [13, 1, 4], + [13, 1, 5], + [13, 1, 6], + [13, 1, 7], + [13, 1, 8], + [13, 1, 9], + [13, 1, 10], + [13, 1, 11], + [13, 1, 12], + [13, 1, 13], + [13, 2, -13], + [13, 2, -12], + [13, 2, -11], + [13, 2, -10], + [13, 2, -9], + [13, 2, -8], + [13, 2, -7], + [13, 2, -6], + [13, 2, -5], + [13, 2, -4], + [13, 2, -3], + [13, 2, -2], + [13, 2, -1], + [13, 2, 0], + [13, 2, 1], + [13, 2, 2], + [13, 2, 3], + [13, 2, 4], + [13, 2, 5], + [13, 2, 6], + [13, 2, 7], + [13, 2, 8], + [13, 2, 9], + [13, 2, 10], + [13, 2, 11], + [13, 2, 12], + [13, 2, 13], + [13, 3, -13], + [13, 3, -12], + [13, 3, -11], + [13, 3, -10], + [13, 3, -9], + [13, 3, -8], + [13, 3, -7], + [13, 3, -6], + [13, 3, -5], + [13, 3, -4], + [13, 3, -3], + [13, 3, -2], + [13, 3, -1], + [13, 3, 0], + [13, 3, 1], + [13, 3, 2], + [13, 3, 3], + [13, 3, 4], + [13, 3, 5], + [13, 3, 6], + [13, 3, 7], + [13, 3, 8], + [13, 3, 9], + [13, 3, 10], + [13, 3, 11], + [13, 3, 12], + [13, 3, 13], + [13, 4, -13], + [13, 4, -12], + [13, 4, -11], + [13, 4, -10], + [13, 4, -9], + [13, 4, -8], + [13, 4, -7], + [13, 4, -6], + [13, 4, -5], + [13, 4, -4], + [13, 4, -3], + [13, 4, -2], + [13, 4, -1], + [13, 4, 0], + [13, 4, 1], + [13, 4, 2], + [13, 4, 3], + [13, 4, 4], + [13, 4, 5], + [13, 4, 6], + [13, 4, 7], + [13, 4, 8], + [13, 4, 9], + [13, 4, 10], + [13, 4, 11], + [13, 4, 12], + [13, 4, 13], + [13, 5, -13], + [13, 5, -12], + [13, 5, -11], + [13, 5, -10], + [13, 5, -9], + [13, 5, -8], + [13, 5, -7], + [13, 5, -6], + [13, 5, -5], + [13, 5, -4], + [13, 5, -3], + [13, 5, -2], + [13, 5, -1], + [13, 5, 0], + [13, 5, 1], + [13, 5, 2], + [13, 5, 3], + [13, 5, 4], + [13, 5, 5], + [13, 5, 6], + [13, 5, 7], + [13, 5, 8], + [13, 5, 9], + [13, 5, 10], + [13, 5, 11], + [13, 5, 12], + [13, 5, 13], + [13, 6, -13], + [13, 6, -12], + [13, 6, -11], + [13, 6, -10], + [13, 6, -9], + [13, 6, -8], + [13, 6, -7], + [13, 6, -6], + [13, 6, -5], + [13, 6, -4], + [13, 6, -3], + [13, 6, -2], + [13, 6, -1], + [13, 6, 0], + [13, 6, 1], + [13, 6, 2], + [13, 6, 3], + [13, 6, 4], + [13, 6, 5], + [13, 6, 6], + [13, 6, 7], + [13, 6, 8], + [13, 6, 9], + [13, 6, 10], + [13, 6, 11], + [13, 6, 12], + [13, 6, 13], + [13, 7, -13], + [13, 7, -12], + [13, 7, -11], + [13, 7, -10], + [13, 7, -9], + [13, 7, -8], + [13, 7, -7], + [13, 7, -6], + [13, 7, -5], + [13, 7, -4], + [13, 7, -3], + [13, 7, -2], + [13, 7, -1], + [13, 7, 0], + [13, 7, 1], + [13, 7, 2], + [13, 7, 3], + [13, 7, 4], + [13, 7, 5], + [13, 7, 6], + [13, 7, 7], + [13, 7, 8], + [13, 7, 9], + [13, 7, 10], + [13, 7, 11], + [13, 7, 12], + [13, 7, 13], + [13, 8, -13], + [13, 8, -12], + [13, 8, -11], + [13, 8, -10], + [13, 8, -9], + [13, 8, -8], + [13, 8, -7], + [13, 8, -6], + [13, 8, -5], + [13, 8, -4], + [13, 8, -3], + [13, 8, -2], + [13, 8, -1], + [13, 8, 0], + [13, 8, 1], + [13, 8, 2], + [13, 8, 3], + [13, 8, 4], + [13, 8, 5], + [13, 8, 6], + [13, 8, 7], + [13, 8, 8], + [13, 8, 9], + [13, 8, 10], + [13, 8, 11], + [13, 8, 12], + [13, 8, 13], + [13, 9, -13], + [13, 9, -12], + [13, 9, -11], + [13, 9, -10], + [13, 9, -9], + [13, 9, -8], + [13, 9, -7], + [13, 9, -6], + [13, 9, -5], + [13, 9, -4], + [13, 9, -3], + [13, 9, -2], + [13, 9, -1], + [13, 9, 0], + [13, 9, 1], + [13, 9, 2], + [13, 9, 3], + [13, 9, 4], + [13, 9, 5], + [13, 9, 6], + [13, 9, 7], + [13, 9, 8], + [13, 9, 9], + [13, 9, 10], + [13, 9, 11], + [13, 9, 12], + [13, 9, 13], + [13, 10, -13], + [13, 10, -12], + [13, 10, -11], + [13, 10, -10], + [13, 10, -9], + [13, 10, -8], + [13, 10, -7], + [13, 10, -6], + [13, 10, -5], + [13, 10, -4], + [13, 10, -3], + [13, 10, -2], + [13, 10, -1], + [13, 10, 0], + [13, 10, 1], + [13, 10, 2], + [13, 10, 3], + [13, 10, 4], + [13, 10, 5], + [13, 10, 6], + [13, 10, 7], + [13, 10, 8], + [13, 10, 9], + [13, 10, 10], + [13, 10, 11], + [13, 10, 12], + [13, 10, 13], + [13, 11, -13], + [13, 11, -12], + [13, 11, -11], + [13, 11, -10], + [13, 11, -9], + [13, 11, -8], + [13, 11, -7], + [13, 11, -6], + [13, 11, -5], + [13, 11, -4], + [13, 11, -3], + [13, 11, -2], + [13, 11, -1], + [13, 11, 0], + [13, 11, 1], + [13, 11, 2], + [13, 11, 3], + [13, 11, 4], + [13, 11, 5], + [13, 11, 6], + [13, 11, 7], + [13, 11, 8], + [13, 11, 9], + [13, 11, 10], + [13, 11, 11], + [13, 11, 12], + [13, 11, 13], + [13, 12, -13], + [13, 12, -12], + [13, 12, -11], + [13, 12, -10], + [13, 12, -9], + [13, 12, -8], + [13, 12, -7], + [13, 12, -6], + [13, 12, -5], + [13, 12, -4], + [13, 12, -3], + [13, 12, -2], + [13, 12, -1], + [13, 12, 0], + [13, 12, 1], + [13, 12, 2], + [13, 12, 3], + [13, 12, 4], + [13, 12, 5], + [13, 12, 6], + [13, 12, 7], + [13, 12, 8], + [13, 12, 9], + [13, 12, 10], + [13, 12, 11], + [13, 12, 12], + [13, 12, 13], + [13, 13, -13], + [13, 13, -12], + [13, 13, -11], + [13, 13, -10], + [13, 13, -9], + [13, 13, -8], + [13, 13, -7], + [13, 13, -6], + [13, 13, -5], + [13, 13, -4], + [13, 13, -3], + [13, 13, -2], + [13, 13, -1], + [13, 13, 0], + [13, 13, 1], + [13, 13, 2], + [13, 13, 3], + [13, 13, 4], + [13, 13, 5], + [13, 13, 6], + [13, 13, 7], + [13, 13, 8], + [13, 13, 9], + [13, 13, 10], + [13, 13, 11], + [13, 13, 12], + [13, 13, 13]]) + + return indxvec + + +def gsh_eval(X, Bvec): + + phi1 = X[..., 0] + phi = X[..., 1] + phi2 = X[..., 2] + + zvec = np.abs(phi) < 1e-8 + zvec = zvec.astype(int) + randvec = np.round(np.random.rand(zvec.size)).reshape(zvec.shape) + randvecopp = np.ones(zvec.shape) - randvec + phi += (1e-7)*zvec*(randvec - randvecopp) + + final_shape = np.hstack([phi1.shape, len(Bvec)]) + tfunc = np.zeros(final_shape, dtype='complex128') + + c = 0 + for Bindx in Bvec: + + if Bindx == 0: + tfunc[..., c] = 1 + + if Bindx == 1: + tfunc[..., c] = (0.3e1 / 0.2e1) * (0.1e1 + np.cos(phi)) * np.exp((-1*1j) * (phi1 + phi2)) + + if Bindx == 2: + t3442 = np.cos(phi) + tfunc[..., c] = (-0.3e1 / 0.2e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 + t3442)) * np.sqrt((1 - t3442)) + + if Bindx == 3: + tfunc[..., c] = (0.3e1 / 0.2e1) * (-0.1e1 + np.cos(phi)) * np.exp((-1*1j) * (phi1 - phi2)) + + if Bindx == 4: + t3443 = np.cos(phi) + tfunc[..., c] = (-0.3e1 / 0.2e1*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.2e1) * np.sqrt((1 - t3443)) * np.sqrt((1 + t3443)) + + if Bindx == 5: + tfunc[..., c] = 0.3e1 * np.cos(phi) + + if Bindx == 6: + t3444 = np.cos(phi) + tfunc[..., c] = (-0.3e1 / 0.2e1*1j) * np.exp((1j) * phi2) * np.sqrt(0.2e1) * np.sqrt((1 - t3444)) * np.sqrt((1 + t3444)) + + if Bindx == 7: + tfunc[..., c] = (0.3e1 / 0.2e1) * (-0.1e1 + np.cos(phi)) * np.exp((1j) * (phi1 - phi2)) + + if Bindx == 8: + t3445 = np.cos(phi) + tfunc[..., c] = (-0.3e1 / 0.2e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 - t3445)) * np.sqrt((1 + t3445)) + + if Bindx == 9: + tfunc[..., c] = (0.3e1 / 0.2e1) * (0.1e1 + np.cos(phi)) * np.exp((1j) * (phi1 + phi2)) + + if Bindx == 10: + t3446 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.4e1) * np.exp((-2*1j) * (phi1 + phi2)) * (1 + (2 + t3446) * t3446) + + if Bindx == 11: + t3447 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * ((1 + t3447) ** (0.3e1 / 0.2e1)) * np.sqrt((1 - t3447)) + + if Bindx == 12: + t3448 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.6e1) * t3448 ** 2 + + if Bindx == 13: + t3449 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t3449) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3449)) + + if Bindx == 14: + t3450 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.4e1) * np.exp((-2*1j) * (phi1 - phi2)) * (1 + (-2 + t3450) * t3450) + + if Bindx == 15: + t3451 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt((1 - t3451)) * ((1 + t3451) ** (0.3e1 / 0.2e1)) + + if Bindx == 16: + t3452 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1) * np.exp((-1*1j) * (phi1 + phi2)) * (2 * t3452 ** 2 + t3452 - 1) + + if Bindx == 17: + t3453 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.6e1) * np.sqrt((1 + t3453)) * t3453 * np.sqrt((1 - t3453)) + + if Bindx == 18: + t3454 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1) * np.exp((-1*1j) * (phi1 - phi2)) * (2 * t3454 ** 2 - t3454 - 1) + + if Bindx == 19: + t3455 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t3455) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3455)) + + if Bindx == 20: + t3456 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((-2*1j) * phi2) * np.sqrt(0.6e1) * t3456 ** 2 + + if Bindx == 21: + t3457 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.6e1) * np.sqrt((1 - t3457)) * np.sqrt((1 + t3457)) * t3457 + + if Bindx == 22: + t3458 = np.cos(phi) + tfunc[..., c] = 0.15e2 / 0.2e1 * t3458 ** 2 - 0.5e1 / 0.2e1 + + if Bindx == 23: + t3459 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((1j) * phi2) * np.sqrt(0.6e1) * np.sqrt((1 + t3459)) * t3459 * np.sqrt((1 - t3459)) + + if Bindx == 24: + t3460 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((2*1j) * phi2) * np.sqrt(0.6e1) * t3460 ** 2 + + if Bindx == 25: + t3461 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * ((1 - t3461) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3461)) + + if Bindx == 26: + t3462 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1) * np.exp((1j) * (phi1 - phi2)) * (2 * t3462 ** 2 - t3462 - 1) + + if Bindx == 27: + t3463 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.6e1) * np.sqrt((1 - t3463)) * np.sqrt((1 + t3463)) * t3463 + + if Bindx == 28: + t3464 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1) * np.exp((1j) * (phi1 + phi2)) * (2 * t3464 ** 2 + t3464 - 1) + + if Bindx == 29: + t3465 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt((1 - t3465)) * ((1 + t3465) ** (0.3e1 / 0.2e1)) + + if Bindx == 30: + t3466 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.4e1) * np.exp((2*1j) * (phi1 - phi2)) * (1 + (-2 + t3466) * t3466) + + if Bindx == 31: + t3467 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.2e1*1j) * np.exp((1j) * (2 * phi1 - phi2)) * ((1 - t3467) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3467)) + + if Bindx == 32: + t3468 = np.sin(phi) + tfunc[..., c] = -(0.5e1 / 0.4e1) * np.exp((2*1j) * phi1) * np.sqrt(0.6e1) * t3468 ** 2 + + if Bindx == 33: + t3469 = np.cos(phi) + tfunc[..., c] = (-0.5e1 / 0.2e1*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt((1 - t3469)) * ((1 + t3469) ** (0.3e1 / 0.2e1)) + + if Bindx == 34: + t3470 = np.cos(phi) + tfunc[..., c] = (0.5e1 / 0.4e1) * np.exp((2*1j) * (phi1 + phi2)) * (1 + (2 + t3470) * t3470) + + if Bindx == 35: + t3471 = np.cos(phi) + t3472 = t3471 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((-3*1j) * (phi1 + phi2)) * (3 * t3472 + 1 + (t3472 + 3) * t3471) + + if Bindx == 36: + t3474 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * ((1 + t3474) ** (0.5e1 / 0.2e1)) * np.sqrt((1 - t3474)) + + if Bindx == 37: + t3475 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.15e2) * t3475 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 38: + t3476 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((-3*1j) * phi1) * np.sqrt(0.5e1) * ((1 - t3476) ** (0.3e1 / 0.2e1)) * ((1 + t3476) ** (0.3e1 / 0.2e1)) + + if Bindx == 39: + t3477 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.15e2) * t3477 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 40: + t3478 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.6e1) * ((1 - t3478) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3478)) + + if Bindx == 41: + t3479 = np.cos(phi) + t3480 = t3479 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((-3*1j) * (phi1 - phi2)) * (-3 * t3480 - 1 + (t3480 + 3) * t3479) + + if Bindx == 42: + t3482 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t3482)) * ((1 + t3482) ** (0.5e1 / 0.2e1)) + + if Bindx == 43: + t3483 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1) * np.exp((-2*1j) * (phi1 + phi2)) * (-t3483 - 2 + (3 * t3483 + 4) * t3483 ** 2) + + if Bindx == 44: + t3486 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.10e2) * ((1 + t3486) ** (0.3e1 / 0.2e1)) * (1 + (-4 + 3 * t3486) * t3486) * ((1 - t3486) ** (-0.1e1 / 0.2e1)) + + if Bindx == 45: + t3487 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.4e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.30e2) * t3487 ** 2 * np.cos(phi) + + if Bindx == 46: + t3488 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * (1 + 3 * t3488) * np.sqrt((1 + t3488)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t3488) ** (0.3e1 / 0.2e1)) + + if Bindx == 47: + t3489 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1) * np.exp((-2*1j) * (phi1 - phi2)) * (-t3489 + 2 + (3 * t3489 - 4) * t3489 ** 2) + + if Bindx == 48: + t3492 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.6e1) * ((1 - t3492) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3492)) + + if Bindx == 49: + t3493 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.15e2) * t3493 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 50: + t3494 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t3494)) * ((1 + t3494) ** (0.3e1 / 0.2e1)) * (-1 + 3 * t3494) + + if Bindx == 51: + t3495 = np.cos(phi) + t3496 = t3495 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((-1*1j) * (phi1 + phi2)) * (5 * t3496 - 1 + (15 * t3496 - 11) * t3495) + + if Bindx == 52: + t3498 = np.cos(phi) + t3501 = 1 - t3498 + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.3e1) * np.sqrt((1 + t3498)) * (t3501 + (5 * t3498 - 5) * t3498 ** 2) * (t3501 ** (-0.1e1 / 0.2e1)) + + if Bindx == 53: + t3502 = np.cos(phi) + t3503 = t3502 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((-1*1j) * (phi1 - phi2)) * (-5 * t3503 + 1 + (15 * t3503 - 11) * t3502) + + if Bindx == 54: + t3505 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * (1 + 3 * t3505) * np.sqrt((1 + t3505)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t3505) ** (0.3e1 / 0.2e1)) + + if Bindx == 55: + t3506 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.15e2) * t3506 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 56: + t3507 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.5e1) * ((1 - t3507) ** (0.3e1 / 0.2e1)) * ((1 + t3507) ** (0.3e1 / 0.2e1)) + + if Bindx == 57: + t3508 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.4e1) * np.exp((-2*1j) * phi2) * np.sqrt(0.30e2) * t3508 ** 2 * np.cos(phi) + + if Bindx == 58: + t3509 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.4e1*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.3e1) * np.sqrt((1 - t3509)) * np.sqrt((1 + t3509)) * (5 * t3509 ** 2 - 1) + + if Bindx == 59: + t3510 = np.cos(phi) + tfunc[..., c] = 0.7e1 / 0.2e1 * t3510 * (5 * t3510 ** 2 - 3) + + if Bindx == 60: + t3511 = np.cos(phi) + t3514 = 1 - t3511 + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((1j) * phi2) * np.sqrt(0.3e1) * np.sqrt((1 + t3511)) * (t3514 + (5 * t3511 - 5) * t3511 ** 2) * (t3514 ** (-0.1e1 / 0.2e1)) + + if Bindx == 61: + t3515 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.4e1) * np.exp((2*1j) * phi2) * np.sqrt(0.30e2) * t3515 ** 2 * np.cos(phi) + + if Bindx == 62: + t3516 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((3*1j) * phi2) * np.sqrt(0.5e1) * ((1 - t3516) ** (0.3e1 / 0.2e1)) * ((1 + t3516) ** (0.3e1 / 0.2e1)) + + if Bindx == 63: + t3517 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.15e2) * t3517 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 64: + t3518 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.10e2) * ((1 - t3518) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3518)) * (1 + 3 * t3518) + + if Bindx == 65: + t3519 = np.cos(phi) + t3520 = t3519 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((1j) * (phi1 - phi2)) * (-5 * t3520 + 1 + (15 * t3520 - 11) * t3519) + + if Bindx == 66: + t3522 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.4e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.3e1) * np.sqrt((1 - t3522)) * np.sqrt((1 + t3522)) * (5 * t3522 ** 2 - 1) + + if Bindx == 67: + t3523 = np.cos(phi) + t3524 = t3523 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((1j) * (phi1 + phi2)) * (5 * t3524 - 1 + (15 * t3524 - 11) * t3523) + + if Bindx == 68: + t3526 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.10e2) * ((1 + t3526) ** (0.3e1 / 0.2e1)) * (1 + (-4 + 3 * t3526) * t3526) * ((1 - t3526) ** (-0.1e1 / 0.2e1)) + + if Bindx == 69: + t3527 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.15e2) * t3527 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 70: + t3528 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.6e1) * ((1 - t3528) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3528)) + + if Bindx == 71: + t3529 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1) * np.exp((2*1j) * (phi1 - phi2)) * (-t3529 + 2 + (3 * t3529 - 4) * t3529 ** 2) + + if Bindx == 72: + t3532 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.10e2) * ((1 - t3532) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3532)) * (1 + 3 * t3532) + + if Bindx == 73: + t3533 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.4e1) * np.exp((2*1j) * phi1) * np.sqrt(0.30e2) * t3533 ** 2 * np.cos(phi) + + if Bindx == 74: + t3534 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t3534)) * ((1 + t3534) ** (0.3e1 / 0.2e1)) * (-1 + 3 * t3534) + + if Bindx == 75: + t3535 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1) * np.exp((2*1j) * (phi1 + phi2)) * (-t3535 - 2 + (3 * t3535 + 4) * t3535 ** 2) + + if Bindx == 76: + t3538 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t3538)) * ((1 + t3538) ** (0.5e1 / 0.2e1)) + + if Bindx == 77: + t3539 = np.cos(phi) + t3540 = t3539 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((3*1j) * (phi1 - phi2)) * (-3 * t3540 - 1 + (t3540 + 3) * t3539) + + if Bindx == 78: + t3542 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.6e1) * ((1 - t3542) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3542)) + + if Bindx == 79: + t3543 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.15e2) * t3543 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 80: + t3544 = np.cos(phi) + tfunc[..., c] = (0.7e1 / 0.4e1*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.5e1) * ((1 - t3544) ** (0.3e1 / 0.2e1)) * ((1 + t3544) ** (0.3e1 / 0.2e1)) + + if Bindx == 81: + t3545 = np.sin(phi) + tfunc[..., c] = -(0.7e1 / 0.8e1) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.15e2) * t3545 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 82: + t3546 = np.cos(phi) + tfunc[..., c] = (-0.7e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t3546)) * ((1 + t3546) ** (0.5e1 / 0.2e1)) + + if Bindx == 83: + t3547 = np.cos(phi) + t3548 = t3547 ** 2 + tfunc[..., c] = (0.7e1 / 0.8e1) * np.exp((3*1j) * (phi1 + phi2)) * (3 * t3548 + 1 + (t3548 + 3) * t3547) + + if Bindx == 84: + t3550 = np.cos(phi) + t3554 = 4 * t3550 + t3551 = t3550 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((-4*1j) * (phi1 + phi2)) * (t3554 + 1 + (t3554 + 6 + t3551) * t3551) + + if Bindx == 85: + t3555 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * ((1 + t3555) ** (0.7e1 / 0.2e1)) * np.sqrt((1 - t3555)) + + if Bindx == 86: + t3556 = np.cos(phi) + t3559 = 1 + t3556 + t3557 = t3559 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.7e1) * (-1 + t3556) * t3559 * t3557 + + if Bindx == 87: + t3560 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (4 * phi1 + phi2)) * np.sqrt(0.14e2) * ((1 - t3560) ** (0.3e1 / 0.2e1)) * ((1 + t3560) ** (0.5e1 / 0.2e1)) + + if Bindx == 88: + t3563 = np.sin(phi) + t3561 = t3563 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.70e2) * t3561 ** 2 + + if Bindx == 89: + t3564 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (4 * phi1 - phi2)) * np.sqrt(0.14e2) * ((1 - t3564) ** (0.5e1 / 0.2e1)) * ((1 + t3564) ** (0.3e1 / 0.2e1)) + + if Bindx == 90: + t3565 = np.cos(phi) + t3568 = -1 + t3565 + t3566 = t3568 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.7e1) * t3568 * t3566 * (1 + t3565) + + if Bindx == 91: + t3569 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * ((1 - t3569) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t3569)) + + if Bindx == 92: + t3570 = np.cos(phi) + t3574 = -4 * t3570 + t3571 = t3570 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((-4*1j) * (phi1 - phi2)) * (t3574 + 1 + (t3574 + 6 + t3571) * t3571) + + if Bindx == 93: + t3575 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3575)) * ((1 + t3575) ** (0.7e1 / 0.2e1)) + + if Bindx == 94: + t3576 = np.cos(phi) + t3577 = t3576 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-3*1j) * (phi1 + phi2)) * (-5 * t3576 - 3 + (9 * t3576 + 3 + 4 * t3577) * t3577) + + if Bindx == 95: + t3580 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.14e2) * ((1 + t3580) ** (0.5e1 / 0.2e1)) * (1 + (-3 + 2 * t3580) * t3580) * ((1 - t3580) ** (-0.1e1 / 0.2e1)) + + if Bindx == 96: + t3582 = np.cos(phi) + t3581 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.7e1) * t3581 ** 2 * (-1 + (3 + 4 * t3582) * t3582) + + if Bindx == 97: + t3583 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * t3583 * (1 + (-2 + t3583) * t3583) * ((1 + t3583) ** (0.3e1 / 0.2e1)) * np.sqrt(0.35e2) * np.exp((-3*1j) * phi1) * ((1 - t3583) ** (-0.1e1 / 0.2e1)) + + if Bindx == 98: + t3585 = np.cos(phi) + t3584 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.7e1) * t3584 ** 2 * (-1 + (-3 + 4 * t3585) * t3585) + + if Bindx == 99: + t3586 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * (1 + 2 * t3586) * np.sqrt((1 + t3586)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t3586) ** (0.5e1 / 0.2e1)) + + if Bindx == 100: + t3587 = np.cos(phi) + t3588 = t3587 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-3*1j) * (phi1 - phi2)) * (5 * t3587 - 3 + (-9 * t3587 + 3 + 4 * t3588) * t3588) + + if Bindx == 101: + t3591 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.2e1) * ((1 - t3591) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t3591)) + + if Bindx == 102: + t3592 = np.cos(phi) + t3595 = 1 + t3592 + t3593 = t3595 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.7e1) * (-1 + t3592) * t3595 * t3593 + + if Bindx == 103: + t3596 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t3596)) * ((1 + t3596) ** (0.5e1 / 0.2e1)) * (2 * t3596 - 1) + + if Bindx == 104: + t3597 = np.cos(phi) + t3598 = t3597 ** 2 + tfunc[..., c] = (0.9e1 / 0.4e1) * np.exp((-2*1j) * (phi1 + phi2)) * (-5 * t3597 + 1 + (7 * t3597 - 6 + 7 * t3598) * t3598) + + if Bindx == 105: + t3601 = np.cos(phi) + t3602 = t3601 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.2e1) * ((1 + t3601) ** (0.3e1 / 0.2e1)) * (-21 * t3602 + 1 + (14 * t3602 + 6) * t3601) * ((1 - t3601) ** (-0.1e1 / 0.2e1)) + + if Bindx == 106: + t3605 = np.cos(phi) + t3604 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.10e2) * t3604 ** 2 * (7 * t3605 ** 2 - 1) + + if Bindx == 107: + t3606 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * (-1 + (7 + 14 * t3606) * t3606) * np.sqrt((1 + t3606)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t3606) ** (0.3e1 / 0.2e1)) + + if Bindx == 108: + t3607 = np.cos(phi) + t3608 = t3607 ** 2 + tfunc[..., c] = (0.9e1 / 0.4e1) * np.exp((-2*1j) * (phi1 - phi2)) * (5 * t3607 + 1 + (-7 * t3607 - 6 + 7 * t3608) * t3608) + + if Bindx == 109: + t3611 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * (1 + 2 * t3611) * np.sqrt((1 + t3611)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t3611) ** (0.5e1 / 0.2e1)) + + if Bindx == 110: + t3612 = np.cos(phi) + t3615 = -1 + t3612 + t3613 = t3615 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.7e1) * t3615 * t3613 * (1 + t3612) + + if Bindx == 111: + t3616 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t3616) ** (0.3e1 / 0.2e1)) * ((1 + t3616) ** (0.5e1 / 0.2e1)) + + if Bindx == 112: + t3618 = np.cos(phi) + t3617 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.7e1) * t3617 ** 2 * (-1 + (3 + 4 * t3618) * t3618) + + if Bindx == 113: + t3619 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3619)) * ((1 + t3619) ** (0.3e1 / 0.2e1)) * (-1 + (-7 + 14 * t3619) * t3619) + + if Bindx == 114: + t3620 = np.cos(phi) + t3621 = t3620 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-1*1j) * (phi1 + phi2)) * (-3 * t3620 + 3 + (7 * t3620 - 27 + 28 * t3621) * t3621) + + if Bindx == 115: + t3624 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.5e1) * np.sqrt((1 + t3624)) * t3624 * np.sqrt((1 - t3624)) * (7 * t3624 ** 2 - 3) + + if Bindx == 116: + t3629 = np.cos(phi) + t3630 = t3629 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((-1*1j) * (phi1 - phi2)) * (3 * t3629 + 3 + (-7 * t3629 - 27 + 28 * t3630) * t3630) + + if Bindx == 117: + t3633 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * (-1 + (7 + 14 * t3633) * t3633) * np.sqrt((1 + t3633)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t3633) ** (0.3e1 / 0.2e1)) + + if Bindx == 118: + t3635 = np.cos(phi) + t3634 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.7e1) * t3634 ** 2 * (-1 + (-3 + 4 * t3635) * t3635) + + if Bindx == 119: + t3636 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t3636) ** (0.5e1 / 0.2e1)) * ((1 + t3636) ** (0.3e1 / 0.2e1)) + + if Bindx == 120: + t3639 = np.sin(phi) + t3637 = t3639 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((-4*1j) * phi2) * np.sqrt(0.70e2) * t3637 ** 2 + + if Bindx == 121: + t3640 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.35e2) * ((1 - t3640) ** (0.3e1 / 0.2e1)) * ((1 + t3640) ** (0.3e1 / 0.2e1)) * t3640 + + if Bindx == 122: + t3642 = np.cos(phi) + t3641 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((-2*1j) * phi2) * np.sqrt(0.10e2) * t3641 ** 2 * (7 * t3642 ** 2 - 1) + + if Bindx == 123: + t3643 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.5e1) * np.sqrt((1 - t3643)) * np.sqrt((1 + t3643)) * t3643 * (7 * t3643 ** 2 - 3) + + if Bindx == 124: + t3644 = np.cos(phi) + t3645 = t3644 ** 2 + tfunc[..., c] = 0.27e2 / 0.8e1 + (-0.135e3 / 0.4e1 + 0.315e3 / 0.8e1 * t3645) * t3645 + + if Bindx == 125: + t3647 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((1j) * phi2) * np.sqrt(0.5e1) * np.sqrt((1 + t3647)) * t3647 * np.sqrt((1 - t3647)) * (7 * t3647 ** 2 - 3) + + if Bindx == 126: + t3653 = np.cos(phi) + t3652 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((2*1j) * phi2) * np.sqrt(0.10e2) * t3652 ** 2 * (7 * t3653 ** 2 - 1) + + if Bindx == 127: + t3654 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * t3654 * ((1 + t3654) ** (0.3e1 / 0.2e1)) * (1 + (-2 + t3654) * t3654) * np.sqrt(0.35e2) * np.exp((3*1j) * phi2) * ((1 - t3654) ** (-0.1e1 / 0.2e1)) + + if Bindx == 128: + t3657 = np.sin(phi) + t3655 = t3657 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((4*1j) * phi2) * np.sqrt(0.70e2) * t3655 ** 2 + + if Bindx == 129: + t3658 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t3658) ** (0.5e1 / 0.2e1)) * ((1 + t3658) ** (0.3e1 / 0.2e1)) + + if Bindx == 130: + t3660 = np.cos(phi) + t3659 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.7e1) * t3659 ** 2 * (-1 + (-3 + 4 * t3660) * t3660) + + if Bindx == 131: + t3661 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.2e1) * ((1 - t3661) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3661)) * (-1 + (7 + 14 * t3661) * t3661) + + if Bindx == 132: + t3662 = np.cos(phi) + t3663 = t3662 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((1j) * (phi1 - phi2)) * (3 * t3662 + 3 + (-7 * t3662 - 27 + 28 * t3663) * t3663) + + if Bindx == 133: + t3666 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.4e1*1j) * np.exp((1j) * phi1) * np.sqrt(0.5e1) * np.sqrt((1 - t3666)) * np.sqrt((1 + t3666)) * t3666 * (7 * t3666 ** 2 - 3) + + if Bindx == 134: + t3667 = np.cos(phi) + t3668 = t3667 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((1j) * (phi1 + phi2)) * (-3 * t3667 + 3 + (7 * t3667 - 27 + 28 * t3668) * t3668) + + if Bindx == 135: + t3671 = np.cos(phi) + t3672 = t3671 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.2e1) * ((1 + t3671) ** (0.3e1 / 0.2e1)) * (-21 * t3672 + 1 + (14 * t3672 + 6) * t3671) * ((1 - t3671) ** (-0.1e1 / 0.2e1)) + + if Bindx == 136: + t3675 = np.cos(phi) + t3674 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.7e1) * t3674 ** 2 * (-1 + (3 + 4 * t3675) * t3675) + + if Bindx == 137: + t3676 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (phi1 + 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t3676) ** (0.3e1 / 0.2e1)) * ((1 + t3676) ** (0.5e1 / 0.2e1)) + + if Bindx == 138: + t3677 = np.cos(phi) + t3680 = -1 + t3677 + t3678 = t3680 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.7e1) * t3680 * t3678 * (1 + t3677) + + if Bindx == 139: + t3681 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.14e2) * ((1 - t3681) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3681)) * (1 + 2 * t3681) + + if Bindx == 140: + t3682 = np.cos(phi) + t3683 = t3682 ** 2 + tfunc[..., c] = (0.9e1 / 0.4e1) * np.exp((2*1j) * (phi1 - phi2)) * (5 * t3682 + 1 + (-7 * t3682 - 6 + 7 * t3683) * t3683) + + if Bindx == 141: + t3686 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.2e1) * ((1 - t3686) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3686)) * (-1 + (7 + 14 * t3686) * t3686) + + if Bindx == 142: + t3688 = np.cos(phi) + t3687 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((2*1j) * phi1) * np.sqrt(0.10e2) * t3687 ** 2 * (7 * t3688 ** 2 - 1) + + if Bindx == 143: + t3689 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3689)) * ((1 + t3689) ** (0.3e1 / 0.2e1)) * (-1 + (-7 + 14 * t3689) * t3689) + + if Bindx == 144: + t3690 = np.cos(phi) + t3691 = t3690 ** 2 + tfunc[..., c] = (0.9e1 / 0.4e1) * np.exp((2*1j) * (phi1 + phi2)) * (-5 * t3690 + 1 + (7 * t3690 - 6 + 7 * t3691) * t3691) + + if Bindx == 145: + t3694 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.14e2) * ((1 + t3694) ** (0.5e1 / 0.2e1)) * (1 + (-3 + 2 * t3694) * t3694) * ((1 - t3694) ** (-0.1e1 / 0.2e1)) + + if Bindx == 146: + t3695 = np.cos(phi) + t3698 = 1 + t3695 + t3696 = t3698 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.7e1) * (-1 + t3695) * t3698 * t3696 + + if Bindx == 147: + t3699 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.2e1) * ((1 - t3699) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t3699)) + + if Bindx == 148: + t3700 = np.cos(phi) + t3701 = t3700 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((3*1j) * (phi1 - phi2)) * (5 * t3700 - 3 + (-9 * t3700 + 3 + 4 * t3701) * t3701) + + if Bindx == 149: + t3704 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.14e2) * ((1 - t3704) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3704)) * (1 + 2 * t3704) + + if Bindx == 150: + t3706 = np.cos(phi) + t3705 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.7e1) * t3705 ** 2 * (-1 + (-3 + 4 * t3706) * t3706) + + if Bindx == 151: + t3707 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.4e1*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.35e2) * ((1 - t3707) ** (0.3e1 / 0.2e1)) * ((1 + t3707) ** (0.3e1 / 0.2e1)) * t3707 + + if Bindx == 152: + t3709 = np.cos(phi) + t3708 = np.sin(phi) + tfunc[..., c] = -(0.9e1 / 0.8e1) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.7e1) * t3708 ** 2 * (-1 + (3 + 4 * t3709) * t3709) + + if Bindx == 153: + t3710 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t3710)) * ((1 + t3710) ** (0.5e1 / 0.2e1)) * (2 * t3710 - 1) + + if Bindx == 154: + t3711 = np.cos(phi) + t3712 = t3711 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((3*1j) * (phi1 + phi2)) * (-5 * t3711 - 3 + (9 * t3711 + 3 + 4 * t3712) * t3712) + + if Bindx == 155: + t3715 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3715)) * ((1 + t3715) ** (0.7e1 / 0.2e1)) + + if Bindx == 156: + t3716 = np.cos(phi) + t3720 = -4 * t3716 + t3717 = t3716 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((4*1j) * (phi1 - phi2)) * (t3720 + 1 + (t3720 + 6 + t3717) * t3717) + + if Bindx == 157: + t3721 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * ((1 - t3721) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t3721)) + + if Bindx == 158: + t3722 = np.cos(phi) + t3725 = -1 + t3722 + t3723 = t3725 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.7e1) * t3725 * t3723 * (1 + t3722) + + if Bindx == 159: + t3726 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.14e2) * ((1 - t3726) ** (0.5e1 / 0.2e1)) * ((1 + t3726) ** (0.3e1 / 0.2e1)) + + if Bindx == 160: + t3729 = np.sin(phi) + t3727 = t3729 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((4*1j) * phi1) * np.sqrt(0.70e2) * t3727 ** 2 + + if Bindx == 161: + t3730 = np.cos(phi) + tfunc[..., c] = (0.9e1 / 0.8e1*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.14e2) * ((1 - t3730) ** (0.3e1 / 0.2e1)) * ((1 + t3730) ** (0.5e1 / 0.2e1)) + + if Bindx == 162: + t3731 = np.cos(phi) + t3734 = 1 + t3731 + t3732 = t3734 ** 2 + tfunc[..., c] = (0.9e1 / 0.8e1) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.7e1) * (-1 + t3731) * t3734 * t3732 + + if Bindx == 163: + t3735 = np.cos(phi) + tfunc[..., c] = (-0.9e1 / 0.8e1*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3735)) * ((1 + t3735) ** (0.7e1 / 0.2e1)) + + if Bindx == 164: + t3736 = np.cos(phi) + t3740 = 4 * t3736 + t3737 = t3736 ** 2 + tfunc[..., c] = (0.9e1 / 0.16e2) * np.exp((4*1j) * (phi1 + phi2)) * (t3740 + 1 + (t3740 + 6 + t3737) * t3737) + + if Bindx == 165: + t3741 = np.cos(phi) + t3742 = t3741 ** 2 + t3746 = 10 * t3742 + t3744 = t3742 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-5*1j) * (phi1 + phi2)) * (5 * t3744 + t3746 + 1 + (t3744 + t3746 + 5) * t3741) + + if Bindx == 166: + t3747 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * ((1 + t3747) ** (0.9e1 / 0.2e1)) * np.sqrt((1 - t3747)) + + if Bindx == 167: + t3748 = np.cos(phi) + t3751 = 1 + t3748 + t3749 = t3751 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.5e1) * (-1 + t3748) * t3749 ** 2 + + if Bindx == 168: + t3752 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.30e2) * ((1 - t3752) ** (0.3e1 / 0.2e1)) * ((1 + t3752) ** (0.7e1 / 0.2e1)) + + if Bindx == 169: + t3755 = np.sin(phi) + t3753 = t3755 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.210e3) * t3753 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 170: + t3756 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.16e2*1j) * np.exp((-5*1j) * phi1) * np.sqrt(0.7e1) * ((1 - t3756) ** (0.5e1 / 0.2e1)) * ((1 + t3756) ** (0.5e1 / 0.2e1)) + + if Bindx == 171: + t3759 = np.sin(phi) + t3757 = t3759 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.210e3) * t3757 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 172: + t3760 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.30e2) * ((1 - t3760) ** (0.7e1 / 0.2e1)) * ((1 + t3760) ** (0.3e1 / 0.2e1)) + + if Bindx == 173: + t3761 = np.cos(phi) + t3764 = -1 + t3761 + t3762 = t3764 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.5e1) * t3762 ** 2 * (1 + t3761) + + if Bindx == 174: + t3765 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.10e2) * ((1 - t3765) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t3765)) + + if Bindx == 175: + t3766 = np.cos(phi) + t3767 = t3766 ** 2 + t3769 = t3767 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-5*1j) * (phi1 - phi2)) * (-10 * t3767 - 5 * t3769 - 1 + (10 * t3767 + t3769 + 5) * t3766) + + if Bindx == 176: + t3771 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t3771)) * ((1 + t3771) ** (0.9e1 / 0.2e1)) + + if Bindx == 177: + t3772 = np.cos(phi) + t3773 = t3772 ** 2 + t3775 = t3773 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((-4*1j) * (phi1 + phi2)) * (-4 * t3773 + 16 * t3775 - 4 + (14 * t3773 + 5 * t3775 - 11) * t3772) + + if Bindx == 178: + t3777 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * ((1 + t3777) ** (0.7e1 / 0.2e1)) * (3 + (-8 + 5 * t3777) * t3777) * ((1 - t3777) ** (-0.1e1 / 0.2e1)) + + if Bindx == 179: + t3779 = np.cos(phi) + t3778 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.3e1) * t3778 ** 2 * (t3779 - 2 + (5 * t3779 + 8) * t3779 ** 2) + + if Bindx == 180: + t3782 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * (-1 + 5 * t3782) * ((1 + t3782) ** (0.5e1 / 0.2e1)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t3782) ** (0.3e1 / 0.2e1)) + + if Bindx == 181: + t3785 = np.sin(phi) + t3783 = t3785 ** 2 + tfunc[..., c] = (0.33e2 / 0.16e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.70e2) * t3783 ** 2 * np.cos(phi) + + if Bindx == 182: + t3786 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * (1 + 5 * t3786) * ((1 + t3786) ** (0.3e1 / 0.2e1)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t3786) ** (0.5e1 / 0.2e1)) + + if Bindx == 183: + t3788 = np.cos(phi) + t3787 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.3e1) * t3787 ** 2 * (t3788 + 2 + (5 * t3788 - 8) * t3788 ** 2) + + if Bindx == 184: + t3791 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * (3 + 5 * t3791) * np.sqrt((1 + t3791)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t3791) ** (0.7e1 / 0.2e1)) + + if Bindx == 185: + t3792 = np.cos(phi) + t3793 = t3792 ** 2 + t3795 = t3793 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((-4*1j) * (phi1 - phi2)) * (4 * t3793 - 16 * t3795 + 4 + (14 * t3793 + 5 * t3795 - 11) * t3792) + + if Bindx == 186: + t3797 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.10e2) * ((1 - t3797) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t3797)) + + if Bindx == 187: + t3798 = np.cos(phi) + t3801 = 1 + t3798 + t3799 = t3801 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * (-1 + t3798) * t3799 ** 2 + + if Bindx == 188: + t3802 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.32e2*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t3802)) * ((1 + t3802) ** (0.7e1 / 0.2e1)) * (-3 + 5 * t3802) + + if Bindx == 189: + t3803 = np.cos(phi) + t3804 = t3803 ** 2 + t3806 = t3804 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-3*1j) * (phi1 + phi2)) * (-78 * t3804 + 81 * t3806 + 13 + (-14 * t3804 + 45 * t3806 - 15) * t3803) + + if Bindx == 190: + t3808 = np.cos(phi) + t3809 = t3808 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * ((1 + t3808) ** (0.5e1 / 0.2e1)) * (-27 * t3809 - 1 + (15 * t3809 + 13) * t3808) * ((1 - t3808) ** (-0.1e1 / 0.2e1)) + + if Bindx == 191: + t3812 = np.cos(phi) + t3813 = t3812 ** 2 + t3811 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.42e2) * t3811 ** 2 * (9 * t3813 - 1 + (15 * t3813 - 7) * t3812) + + if Bindx == 192: + t3815 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * (9 * t3815 ** 2 - 1) * ((1 + t3815) ** (0.3e1 / 0.2e1)) * np.sqrt(0.35e2) * np.exp((-3*1j) * phi1) * ((1 - t3815) ** (0.3e1 / 0.2e1)) + + if Bindx == 193: + t3817 = np.cos(phi) + t3818 = t3817 ** 2 + t3816 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.42e2) * t3816 ** 2 * (-9 * t3818 + 1 + (15 * t3818 - 7) * t3817) + + if Bindx == 194: + t3820 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * (1 + (12 + 15 * t3820) * t3820) * np.sqrt((1 + t3820)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t3820) ** (0.5e1 / 0.2e1)) + + if Bindx == 195: + t3821 = np.cos(phi) + t3822 = t3821 ** 2 + t3824 = t3822 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-3*1j) * (phi1 - phi2)) * (78 * t3822 - 81 * t3824 - 13 + (-14 * t3822 + 45 * t3824 - 15) * t3821) + + if Bindx == 196: + t3826 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * (3 + 5 * t3826) * np.sqrt((1 + t3826)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t3826) ** (0.7e1 / 0.2e1)) + + if Bindx == 197: + t3827 = np.cos(phi) + t3830 = -1 + t3827 + t3828 = t3830 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t3828 ** 2 * (1 + t3827) + + if Bindx == 198: + t3831 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.30e2) * ((1 - t3831) ** (0.3e1 / 0.2e1)) * ((1 + t3831) ** (0.7e1 / 0.2e1)) + + if Bindx == 199: + t3833 = np.cos(phi) + t3832 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.3e1) * t3832 ** 2 * (t3833 - 2 + (5 * t3833 + 8) * t3833 ** 2) + + if Bindx == 200: + t3836 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t3836)) * ((1 + t3836) ** (0.5e1 / 0.2e1)) * (1 + (-12 + 15 * t3836) * t3836) + + if Bindx == 201: + t3837 = np.cos(phi) + t3838 = t3837 ** 2 + t3840 = t3838 ** 2 + tfunc[..., c] = (0.11e2 / 0.4e1) * np.exp((-2*1j) * (phi1 + phi2)) * (-11 * t3838 + 12 * t3840 + 1 + (-18 * t3838 + 15 * t3840 + 5) * t3837) + + if Bindx == 202: + t3842 = np.cos(phi) + t3843 = t3842 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.7e1) * ((1 + t3842) ** (0.3e1 / 0.2e1)) * (4 * t3842 - 1 + (-24 * t3842 + 6 + 15 * t3843) * t3843) * ((1 - t3842) ** (-0.1e1 / 0.2e1)) + + if Bindx == 203: + t3847 = np.cos(phi) + t3846 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * phi1) * np.sqrt(0.210e3) * t3846 ** 2 * t3847 * (3 * t3847 ** 2 - 1) + + if Bindx == 204: + t3848 = np.cos(phi) + t3849 = t3848 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * (9 * t3849 - 1 + (15 * t3849 - 3) * t3848) * np.sqrt((1 + t3848)) * np.sqrt(0.7e1) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t3848) ** (0.3e1 / 0.2e1)) + + if Bindx == 205: + t3851 = np.cos(phi) + t3852 = t3851 ** 2 + t3854 = t3852 ** 2 + tfunc[..., c] = (0.11e2 / 0.4e1) * np.exp((-2*1j) * (phi1 - phi2)) * (11 * t3852 - 12 * t3854 - 1 + (-18 * t3852 + 15 * t3854 + 5) * t3851) + + if Bindx == 206: + t3856 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * (1 + (12 + 15 * t3856) * t3856) * np.sqrt((1 + t3856)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t3856) ** (0.5e1 / 0.2e1)) + + if Bindx == 207: + t3858 = np.cos(phi) + t3857 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.3e1) * t3857 ** 2 * (t3858 + 2 + (5 * t3858 - 8) * t3858 ** 2) + + if Bindx == 208: + t3861 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.30e2) * ((1 - t3861) ** (0.7e1 / 0.2e1)) * ((1 + t3861) ** (0.3e1 / 0.2e1)) + + if Bindx == 209: + t3864 = np.sin(phi) + t3862 = t3864 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.210e3) * t3862 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 210: + t3865 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.21e2) * ((1 - t3865) ** (0.3e1 / 0.2e1)) * ((1 + t3865) ** (0.5e1 / 0.2e1)) * (-1 + 5 * t3865) + + if Bindx == 211: + t3867 = np.cos(phi) + t3868 = t3867 ** 2 + t3866 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.42e2) * t3866 ** 2 * (9 * t3868 - 1 + (15 * t3868 - 7) * t3867) + + if Bindx == 212: + t3870 = np.cos(phi) + t3871 = t3870 ** 2 + tfunc[..., c] = (-0.11e2 / 0.8e1*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.7e1) * np.sqrt((1 - t3870)) * ((1 + t3870) ** (0.3e1 / 0.2e1)) * (-9 * t3871 + 1 + (15 * t3871 - 3) * t3870) + + if Bindx == 213: + t3873 = np.cos(phi) + t3874 = t3873 ** 2 + t3876 = t3874 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((-1*1j) * (phi1 + phi2)) * (-14 * t3874 + 21 * t3876 + 1 + (-126 * t3874 + 105 * t3876 + 29) * t3873) + + if Bindx == 214: + t3878 = np.cos(phi) + t3879 = t3878 ** 2 + t3884 = (-14 + 21 * t3879) * t3879 + t3883 = 1 - t3878 + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.30e2) * np.sqrt((1 + t3878)) * (t3884 * t3878 - t3883 - t3884) * (t3883 ** (-0.1e1 / 0.2e1)) + + if Bindx == 215: + t3885 = np.cos(phi) + t3886 = t3885 ** 2 + t3888 = t3886 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((-1*1j) * (phi1 - phi2)) * (14 * t3886 - 21 * t3888 - 1 + (-126 * t3886 + 105 * t3888 + 29) * t3885) + + if Bindx == 216: + t3890 = np.cos(phi) + t3891 = t3890 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * (9 * t3891 - 1 + (15 * t3891 - 3) * t3890) * np.sqrt((1 + t3890)) * np.sqrt(0.7e1) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t3890) ** (0.3e1 / 0.2e1)) + + if Bindx == 217: + t3894 = np.cos(phi) + t3895 = t3894 ** 2 + t3893 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.42e2) * t3893 ** 2 * (-9 * t3895 + 1 + (15 * t3895 - 7) * t3894) + + if Bindx == 218: + t3897 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * (1 + 5 * t3897) * ((1 + t3897) ** (0.3e1 / 0.2e1)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t3897) ** (0.5e1 / 0.2e1)) + + if Bindx == 219: + t3900 = np.sin(phi) + t3898 = t3900 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.210e3) * t3898 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 220: + t3901 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.16e2*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.7e1) * ((1 - t3901) ** (0.5e1 / 0.2e1)) * ((1 + t3901) ** (0.5e1 / 0.2e1)) + + if Bindx == 221: + t3904 = np.sin(phi) + t3902 = t3904 ** 2 + tfunc[..., c] = (0.33e2 / 0.16e2) * np.exp((-4*1j) * phi2) * np.sqrt(0.70e2) * t3902 ** 2 * np.cos(phi) + + if Bindx == 222: + t3905 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.35e2) * ((1 - t3905) ** (0.3e1 / 0.2e1)) * ((1 + t3905) ** (0.3e1 / 0.2e1)) * (9 * t3905 ** 2 - 1) + + if Bindx == 223: + t3907 = np.cos(phi) + t3906 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((-2*1j) * phi2) * np.sqrt(0.210e3) * t3906 ** 2 * t3907 * (3 * t3907 ** 2 - 1) + + if Bindx == 224: + t3908 = np.cos(phi) + t3909 = t3908 ** 2 + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.30e2) * np.sqrt((1 - t3908)) * np.sqrt((1 + t3908)) * (1 + (-14 + 21 * t3909) * t3909) + + if Bindx == 225: + t3911 = np.cos(phi) + t3912 = t3911 ** 2 + tfunc[..., c] = 0.11e2 / 0.8e1 * t3911 * (15 + (-70 + 63 * t3912) * t3912) + + if Bindx == 226: + t3914 = np.cos(phi) + t3915 = t3914 ** 2 + t3920 = (-14 + 21 * t3915) * t3915 + t3919 = 1 - t3914 + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * phi2) * np.sqrt(0.30e2) * np.sqrt((1 + t3914)) * (t3920 * t3914 - t3919 - t3920) * (t3919 ** (-0.1e1 / 0.2e1)) + + if Bindx == 227: + t3922 = np.cos(phi) + t3921 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * phi2) * np.sqrt(0.210e3) * t3921 ** 2 * t3922 * (3 * t3922 ** 2 - 1) + + if Bindx == 228: + t3923 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * (9 * t3923 ** 2 - 1) * ((1 + t3923) ** (0.3e1 / 0.2e1)) * np.sqrt(0.35e2) * np.exp((3*1j) * phi2) * ((1 - t3923) ** (0.3e1 / 0.2e1)) + + if Bindx == 229: + t3926 = np.sin(phi) + t3924 = t3926 ** 2 + tfunc[..., c] = (0.33e2 / 0.16e2) * np.exp((4*1j) * phi2) * np.sqrt(0.70e2) * t3924 ** 2 * np.cos(phi) + + if Bindx == 230: + t3927 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.16e2*1j) * np.exp((5*1j) * phi2) * np.sqrt(0.7e1) * ((1 - t3927) ** (0.5e1 / 0.2e1)) * ((1 + t3927) ** (0.5e1 / 0.2e1)) + + if Bindx == 231: + t3930 = np.sin(phi) + t3928 = t3930 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.210e3) * t3928 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 232: + t3931 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.21e2) * ((1 - t3931) ** (0.5e1 / 0.2e1)) * ((1 + t3931) ** (0.3e1 / 0.2e1)) * (1 + 5 * t3931) + + if Bindx == 233: + t3933 = np.cos(phi) + t3934 = t3933 ** 2 + t3932 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.42e2) * t3932 ** 2 * (-9 * t3934 + 1 + (15 * t3934 - 7) * t3933) + + if Bindx == 234: + t3936 = np.cos(phi) + t3937 = t3936 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.7e1) * ((1 - t3936) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3936)) * (9 * t3937 - 1 + (15 * t3937 - 3) * t3936) + + if Bindx == 235: + t3939 = np.cos(phi) + t3940 = t3939 ** 2 + t3942 = t3940 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((1j) * (phi1 - phi2)) * (14 * t3940 - 21 * t3942 - 1 + (-126 * t3940 + 105 * t3942 + 29) * t3939) + + if Bindx == 236: + t3944 = np.cos(phi) + t3945 = t3944 ** 2 + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.30e2) * np.sqrt((1 - t3944)) * np.sqrt((1 + t3944)) * (1 + (-14 + 21 * t3945) * t3945) + + if Bindx == 237: + t3947 = np.cos(phi) + t3948 = t3947 ** 2 + t3950 = t3948 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((1j) * (phi1 + phi2)) * (-14 * t3948 + 21 * t3950 + 1 + (-126 * t3948 + 105 * t3950 + 29) * t3947) + + if Bindx == 238: + t3952 = np.cos(phi) + t3953 = t3952 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.7e1) * ((1 + t3952) ** (0.3e1 / 0.2e1)) * (4 * t3952 - 1 + (-24 * t3952 + 6 + 15 * t3953) * t3953) * ((1 - t3952) ** (-0.1e1 / 0.2e1)) + + if Bindx == 239: + t3957 = np.cos(phi) + t3958 = t3957 ** 2 + t3956 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.42e2) * t3956 ** 2 * (9 * t3958 - 1 + (15 * t3958 - 7) * t3957) + + if Bindx == 240: + t3960 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * (-1 + 5 * t3960) * ((1 + t3960) ** (0.5e1 / 0.2e1)) * np.sqrt(0.21e2) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t3960) ** (0.3e1 / 0.2e1)) + + if Bindx == 241: + t3963 = np.sin(phi) + t3961 = t3963 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.210e3) * t3961 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 242: + t3964 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.30e2) * ((1 - t3964) ** (0.7e1 / 0.2e1)) * ((1 + t3964) ** (0.3e1 / 0.2e1)) + + if Bindx == 243: + t3966 = np.cos(phi) + t3965 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.3e1) * t3965 ** 2 * (t3966 + 2 + (5 * t3966 - 8) * t3966 ** 2) + + if Bindx == 244: + t3969 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.6e1) * ((1 - t3969) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t3969)) * (1 + (12 + 15 * t3969) * t3969) + + if Bindx == 245: + t3970 = np.cos(phi) + t3971 = t3970 ** 2 + t3973 = t3971 ** 2 + tfunc[..., c] = (0.11e2 / 0.4e1) * np.exp((2*1j) * (phi1 - phi2)) * (11 * t3971 - 12 * t3973 - 1 + (-18 * t3971 + 15 * t3973 + 5) * t3970) + + if Bindx == 246: + t3975 = np.cos(phi) + t3976 = t3975 ** 2 + tfunc[..., c] = (0.11e2 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.7e1) * ((1 - t3975) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t3975)) * (9 * t3976 - 1 + (15 * t3976 - 3) * t3975) + + if Bindx == 247: + t3979 = np.cos(phi) + t3978 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * phi1) * np.sqrt(0.210e3) * t3978 ** 2 * t3979 * (3 * t3979 ** 2 - 1) + + if Bindx == 248: + t3980 = np.cos(phi) + t3981 = t3980 ** 2 + tfunc[..., c] = (-0.11e2 / 0.8e1*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.7e1) * np.sqrt((1 - t3980)) * ((1 + t3980) ** (0.3e1 / 0.2e1)) * (-9 * t3981 + 1 + (15 * t3981 - 3) * t3980) + + if Bindx == 249: + t3983 = np.cos(phi) + t3984 = t3983 ** 2 + t3986 = t3984 ** 2 + tfunc[..., c] = (0.11e2 / 0.4e1) * np.exp((2*1j) * (phi1 + phi2)) * (-11 * t3984 + 12 * t3986 + 1 + (-18 * t3984 + 15 * t3986 + 5) * t3983) + + if Bindx == 250: + t3988 = np.cos(phi) + t3989 = t3988 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * ((1 + t3988) ** (0.5e1 / 0.2e1)) * (-27 * t3989 - 1 + (15 * t3989 + 13) * t3988) * ((1 - t3988) ** (-0.1e1 / 0.2e1)) + + if Bindx == 251: + t3992 = np.cos(phi) + t3991 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.3e1) * t3991 ** 2 * (t3992 - 2 + (5 * t3992 + 8) * t3992 ** 2) + + if Bindx == 252: + t3995 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.30e2) * ((1 - t3995) ** (0.3e1 / 0.2e1)) * ((1 + t3995) ** (0.7e1 / 0.2e1)) + + if Bindx == 253: + t3996 = np.cos(phi) + t3999 = -1 + t3996 + t3997 = t3999 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t3997 ** 2 * (1 + t3996) + + if Bindx == 254: + t4000 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.2e1) * ((1 - t4000) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t4000)) * (3 + 5 * t4000) + + if Bindx == 255: + t4001 = np.cos(phi) + t4002 = t4001 ** 2 + t4004 = t4002 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((3*1j) * (phi1 - phi2)) * (78 * t4002 - 81 * t4004 - 13 + (-14 * t4002 + 45 * t4004 - 15) * t4001) + + if Bindx == 256: + t4006 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.6e1) * ((1 - t4006) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t4006)) * (1 + (12 + 15 * t4006) * t4006) + + if Bindx == 257: + t4008 = np.cos(phi) + t4009 = t4008 ** 2 + t4007 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.42e2) * t4007 ** 2 * (-9 * t4009 + 1 + (15 * t4009 - 7) * t4008) + + if Bindx == 258: + t4011 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.35e2) * ((1 - t4011) ** (0.3e1 / 0.2e1)) * ((1 + t4011) ** (0.3e1 / 0.2e1)) * (9 * t4011 ** 2 - 1) + + if Bindx == 259: + t4013 = np.cos(phi) + t4014 = t4013 ** 2 + t4012 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.32e2) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.42e2) * t4012 ** 2 * (9 * t4014 - 1 + (15 * t4014 - 7) * t4013) + + if Bindx == 260: + t4016 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t4016)) * ((1 + t4016) ** (0.5e1 / 0.2e1)) * (1 + (-12 + 15 * t4016) * t4016) + + if Bindx == 261: + t4017 = np.cos(phi) + t4018 = t4017 ** 2 + t4020 = t4018 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((3*1j) * (phi1 + phi2)) * (-78 * t4018 + 81 * t4020 + 13 + (-14 * t4018 + 45 * t4020 - 15) * t4017) + + if Bindx == 262: + t4022 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * ((1 + t4022) ** (0.7e1 / 0.2e1)) * (3 + (-8 + 5 * t4022) * t4022) * ((1 - t4022) ** (-0.1e1 / 0.2e1)) + + if Bindx == 263: + t4023 = np.cos(phi) + t4026 = 1 + t4023 + t4024 = t4026 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * (-1 + t4023) * t4024 ** 2 + + if Bindx == 264: + t4027 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.10e2) * ((1 - t4027) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t4027)) + + if Bindx == 265: + t4028 = np.cos(phi) + t4029 = t4028 ** 2 + t4031 = t4029 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((4*1j) * (phi1 - phi2)) * (4 * t4029 - 16 * t4031 + 4 + (14 * t4029 + 5 * t4031 - 11) * t4028) + + if Bindx == 266: + t4033 = np.cos(phi) + tfunc[..., c] = (0.33e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * ((1 - t4033) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t4033)) * (3 + 5 * t4033) + + if Bindx == 267: + t4035 = np.cos(phi) + t4034 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.3e1) * t4034 ** 2 * (t4035 + 2 + (5 * t4035 - 8) * t4035 ** 2) + + if Bindx == 268: + t4038 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.16e2*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.21e2) * ((1 - t4038) ** (0.5e1 / 0.2e1)) * ((1 + t4038) ** (0.3e1 / 0.2e1)) * (1 + 5 * t4038) + + if Bindx == 269: + t4041 = np.sin(phi) + t4039 = t4041 ** 2 + tfunc[..., c] = (0.33e2 / 0.16e2) * np.exp((4*1j) * phi1) * np.sqrt(0.70e2) * t4039 ** 2 * np.cos(phi) + + if Bindx == 270: + t4042 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.21e2) * ((1 - t4042) ** (0.3e1 / 0.2e1)) * ((1 + t4042) ** (0.5e1 / 0.2e1)) * (-1 + 5 * t4042) + + if Bindx == 271: + t4044 = np.cos(phi) + t4043 = np.sin(phi) + tfunc[..., c] = -(0.11e2 / 0.8e1) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.3e1) * t4043 ** 2 * (t4044 - 2 + (5 * t4044 + 8) * t4044 ** 2) + + if Bindx == 272: + t4047 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t4047)) * ((1 + t4047) ** (0.7e1 / 0.2e1)) * (-3 + 5 * t4047) + + if Bindx == 273: + t4048 = np.cos(phi) + t4049 = t4048 ** 2 + t4051 = t4049 ** 2 + tfunc[..., c] = (0.11e2 / 0.16e2) * np.exp((4*1j) * (phi1 + phi2)) * (-4 * t4049 + 16 * t4051 - 4 + (14 * t4049 + 5 * t4051 - 11) * t4048) + + if Bindx == 274: + t4053 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t4053)) * ((1 + t4053) ** (0.9e1 / 0.2e1)) + + if Bindx == 275: + t4054 = np.cos(phi) + t4055 = t4054 ** 2 + t4057 = t4055 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((5*1j) * (phi1 - phi2)) * (-10 * t4055 - 5 * t4057 - 1 + (10 * t4055 + t4057 + 5) * t4054) + + if Bindx == 276: + t4059 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.10e2) * ((1 - t4059) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t4059)) + + if Bindx == 277: + t4060 = np.cos(phi) + t4063 = -1 + t4060 + t4061 = t4063 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.5e1) * t4061 ** 2 * (1 + t4060) + + if Bindx == 278: + t4064 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.30e2) * ((1 - t4064) ** (0.7e1 / 0.2e1)) * ((1 + t4064) ** (0.3e1 / 0.2e1)) + + if Bindx == 279: + t4067 = np.sin(phi) + t4065 = t4067 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.210e3) * t4065 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 280: + t4068 = np.cos(phi) + tfunc[..., c] = (-0.33e2 / 0.16e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.7e1) * ((1 - t4068) ** (0.5e1 / 0.2e1)) * ((1 + t4068) ** (0.5e1 / 0.2e1)) + + if Bindx == 281: + t4071 = np.sin(phi) + t4069 = t4071 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.210e3) * t4069 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 282: + t4072 = np.cos(phi) + tfunc[..., c] = (0.11e2 / 0.16e2*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.30e2) * ((1 - t4072) ** (0.3e1 / 0.2e1)) * ((1 + t4072) ** (0.7e1 / 0.2e1)) + + if Bindx == 283: + t4073 = np.cos(phi) + t4076 = 1 + t4073 + t4074 = t4076 ** 2 + tfunc[..., c] = (0.33e2 / 0.32e2) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.5e1) * (-1 + t4073) * t4074 ** 2 + + if Bindx == 284: + t4077 = np.cos(phi) + tfunc[..., c] = (-0.11e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t4077)) * ((1 + t4077) ** (0.9e1 / 0.2e1)) + + if Bindx == 285: + t4078 = np.cos(phi) + t4079 = t4078 ** 2 + t4083 = 10 * t4079 + t4081 = t4079 ** 2 + tfunc[..., c] = (0.11e2 / 0.32e2) * np.exp((5*1j) * (phi1 + phi2)) * (5 * t4081 + t4083 + 1 + (t4081 + t4083 + 5) * t4078) + + if Bindx == 286: + t4084 = np.cos(phi) + t4090 = 6 * t4084 + t4085 = t4084 ** 2 + t4086 = t4084 * t4085 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-6*1j) * (phi1 + phi2)) * (t4090 + 1 + (20 + t4086) * t4086 + (15 + (t4090 + 15) * t4085) * t4085) + + if Bindx == 287: + t4091 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.3e1) * ((1 + t4091) ** (0.11e2 / 0.2e1)) * np.sqrt((1 - t4091)) + + if Bindx == 288: + t4092 = np.cos(phi) + t4096 = 1 + t4092 + t4093 = t4096 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.66e2) * (-1 + t4092) * t4096 * t4093 ** 2 + + if Bindx == 289: + t4097 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.55e2) * ((1 - t4097) ** (0.3e1 / 0.2e1)) * ((1 + t4097) ** (0.9e1 / 0.2e1)) + + if Bindx == 290: + t4099 = np.cos(phi) + t4102 = 1 + t4099 + t4100 = t4102 ** 2 + t4098 = -1 + t4099 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.55e2) * t4098 ** 2 * t4100 ** 2 + + if Bindx == 291: + t4103 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (6 * phi1 + phi2)) * np.sqrt(0.22e2) * ((1 - t4103) ** (0.5e1 / 0.2e1)) * ((1 + t4103) ** (0.7e1 / 0.2e1)) + + if Bindx == 292: + t4107 = np.sin(phi) + t4104 = t4107 ** 2 + t4105 = t4107 * t4104 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.231e3) * t4105 ** 2 + + if Bindx == 293: + t4108 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (6 * phi1 - phi2)) * np.sqrt(0.22e2) * ((1 - t4108) ** (0.7e1 / 0.2e1)) * ((1 + t4108) ** (0.5e1 / 0.2e1)) + + if Bindx == 294: + t4110 = np.cos(phi) + t4113 = -1 + t4110 + t4111 = t4113 ** 2 + t4109 = 1 + t4110 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.55e2) * t4111 ** 2 * t4109 ** 2 + + if Bindx == 295: + t4114 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.55e2) * ((1 - t4114) ** (0.9e1 / 0.2e1)) * ((1 + t4114) ** (0.3e1 / 0.2e1)) + + if Bindx == 296: + t4115 = np.cos(phi) + t4119 = -1 + t4115 + t4116 = t4119 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.66e2) * t4119 * t4116 ** 2 * (1 + t4115) + + if Bindx == 297: + t4120 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.3e1) * ((1 - t4120) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t4120)) + + if Bindx == 298: + t4121 = np.cos(phi) + t4127 = -6 * t4121 + t4122 = t4121 ** 2 + t4123 = t4121 * t4122 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-6*1j) * (phi1 - phi2)) * (t4127 + 1 + (-20 + t4123) * t4123 + (15 + (t4127 + 15) * t4122) * t4122) + + if Bindx == 299: + t4128 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t4128)) * ((1 + t4128) ** (0.11e2 / 0.2e1)) + + if Bindx == 300: + t4129 = np.cos(phi) + t4130 = t4129 ** 2 + t4132 = t4130 ** 2 + t4131 = t4129 * t4130 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-5*1j) * (phi1 + phi2)) * (-20 * t4130 + 35 * t4132 - 5 + (10 + 6 * t4131) * t4131 + (25 * t4132 - 19) * t4129) + + if Bindx == 301: + t4135 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.22e2) * ((1 + t4135) ** (0.9e1 / 0.2e1)) * (2 + (-5 + 3 * t4135) * t4135) * ((1 - t4135) ** (-0.1e1 / 0.2e1)) + + if Bindx == 302: + t4137 = np.cos(phi) + t4138 = t4137 ** 2 + t4136 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.165e3) * t4136 ** 2 * (-t4137 - 1 + (5 * t4137 + 3 + 2 * t4138) * t4138) + + if Bindx == 303: + t4141 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (-1 + 3 * t4141) * ((1 + t4141) ** (0.7e1 / 0.2e1)) * np.sqrt(0.165e3) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t4141) ** (0.3e1 / 0.2e1)) + + if Bindx == 304: + t4145 = np.sin(phi) + t4143 = t4145 ** 2 + t4142 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.66e2) * t4143 ** 2 * (-1 + (5 + 6 * t4142) * t4142) + + if Bindx == 305: + t4146 = np.cos(phi) + t4147 = t4146 ** 2 + tfunc[..., c] = (0.39e2 / 0.16e2*1j) * t4146 * (-3 * t4147 - 1 + (t4147 + 3) * t4146) * ((1 + t4146) ** (0.5e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((-5*1j) * phi1) * ((1 - t4146) ** (-0.1e1 / 0.2e1)) + + if Bindx == 306: + t4152 = np.sin(phi) + t4150 = t4152 ** 2 + t4149 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.66e2) * t4150 ** 2 * (-1 + (-5 + 6 * t4149) * t4149) + + if Bindx == 307: + t4153 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (1 + 3 * t4153) * ((1 + t4153) ** (0.3e1 / 0.2e1)) * np.sqrt(0.165e3) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t4153) ** (0.7e1 / 0.2e1)) + + if Bindx == 308: + t4155 = np.cos(phi) + t4156 = t4155 ** 2 + t4154 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.165e3) * t4154 ** 2 * (t4155 - 1 + (-5 * t4155 + 3 + 2 * t4156) * t4156) + + if Bindx == 309: + t4159 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * (2 + 3 * t4159) * np.sqrt((1 + t4159)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t4159) ** (0.9e1 / 0.2e1)) + + if Bindx == 310: + t4160 = np.cos(phi) + t4161 = t4160 ** 2 + t4163 = t4161 ** 2 + t4162 = t4160 * t4161 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-5*1j) * (phi1 - phi2)) * (-20 * t4161 + 35 * t4163 - 5 + (-10 + 6 * t4162) * t4162 + (-25 * t4163 + 19) * t4160) + + if Bindx == 311: + t4166 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.3e1) * ((1 - t4166) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t4166)) + + if Bindx == 312: + t4167 = np.cos(phi) + t4171 = 1 + t4167 + t4168 = t4171 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.66e2) * (-1 + t4167) * t4171 * t4168 ** 2 + + if Bindx == 313: + t4172 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t4172)) * ((1 + t4172) ** (0.9e1 / 0.2e1)) * (-2 + 3 * t4172) + + if Bindx == 314: + t4173 = np.cos(phi) + t4174 = t4173 ** 2 + t4176 = t4174 ** 2 + t4175 = t4173 * t4174 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-4*1j) * (phi1 + phi2)) * (-65 * t4174 + 35 * t4176 + 13 + (-80 + 33 * t4175) * t4175 + (88 * t4176 + 8) * t4173) + + if Bindx == 315: + t4179 = np.cos(phi) + t4180 = t4179 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * ((1 + t4179) ** (0.7e1 / 0.2e1)) * (-22 * t4180 - 2 + (11 * t4180 + 13) * t4179) * ((1 - t4179) ** (-0.1e1 / 0.2e1)) + + if Bindx == 316: + t4183 = np.cos(phi) + t4184 = t4183 ** 2 + t4182 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.30e2) * t4182 ** 2 * (-20 * t4183 + 1 + (44 * t4183 - 10 + 33 * t4184) * t4184) + + if Bindx == 317: + t4187 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * (-2 + (-11 + 33 * t4187) * t4187) * ((1 + t4187) ** (0.5e1 / 0.2e1)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t4187) ** (0.3e1 / 0.2e1)) + + if Bindx == 318: + t4191 = np.sin(phi) + t4189 = t4191 ** 2 + t4188 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.14e2) * t4189 ** 2 * (11 * t4188 ** 2 - 1) + + if Bindx == 319: + t4192 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * (-2 + (11 + 33 * t4192) * t4192) * ((1 + t4192) ** (0.3e1 / 0.2e1)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t4192) ** (0.5e1 / 0.2e1)) + + if Bindx == 320: + t4194 = np.cos(phi) + t4195 = t4194 ** 2 + t4193 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.30e2) * t4193 ** 2 * (20 * t4194 + 1 + (-44 * t4194 - 10 + 33 * t4195) * t4195) + + if Bindx == 321: + t4198 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (2 + (11 + 11 * t4198) * t4198) * np.sqrt((1 + t4198)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t4198) ** (0.7e1 / 0.2e1)) + + if Bindx == 322: + t4199 = np.cos(phi) + t4200 = t4199 ** 2 + t4202 = t4200 ** 2 + t4201 = t4199 * t4200 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-4*1j) * (phi1 - phi2)) * (-65 * t4200 + 35 * t4202 + 13 + (80 + 33 * t4201) * t4201 + (-88 * t4202 - 8) * t4199) + + if Bindx == 323: + t4205 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * (2 + 3 * t4205) * np.sqrt((1 + t4205)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t4205) ** (0.9e1 / 0.2e1)) + + if Bindx == 324: + t4206 = np.cos(phi) + t4210 = -1 + t4206 + t4207 = t4210 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.66e2) * t4210 * t4207 ** 2 * (1 + t4206) + + if Bindx == 325: + t4211 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.55e2) * ((1 - t4211) ** (0.3e1 / 0.2e1)) * ((1 + t4211) ** (0.9e1 / 0.2e1)) + + if Bindx == 326: + t4212 = np.cos(phi) + t4215 = 1 + t4212 + t4213 = t4215 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.165e3) * (-1 + t4212) * t4213 ** 2 * (2 * t4212 - 1) + + if Bindx == 327: + t4216 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t4216)) * ((1 + t4216) ** (0.7e1 / 0.2e1)) * (2 + (-11 + 11 * t4216) * t4216) + + if Bindx == 328: + t4217 = np.cos(phi) + t4218 = t4217 ** 2 + t4220 = t4218 ** 2 + t4219 = t4217 * t4218 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-3*1j) * (phi1 + phi2)) * (12 * t4218 - 105 * t4220 - 1 + (-206 + 110 * t4219) * t4219 + (165 * t4220 + 57) * t4217) + + if Bindx == 329: + t4223 = np.cos(phi) + t4224 = t4223 ** 2 + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * ((1 + t4223) ** (0.5e1 / 0.2e1)) * (-2 * t4223 - 3 + (-110 * t4223 + 60 + 55 * t4224) * t4224) * ((1 - t4223) ** (-0.1e1 / 0.2e1)) + + if Bindx == 330: + t4228 = np.cos(phi) + t4229 = t4228 ** 2 + t4227 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.10e2) * t4227 ** 2 * (-3 * t4228 + 1 + (11 * t4228 - 15 + 22 * t4229) * t4229) + + if Bindx == 331: + t4232 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * (11 * t4232 ** 2 - 3) * t4232 * ((1 + t4232) ** (0.3e1 / 0.2e1)) * np.sqrt(0.105e3) * np.exp((-3*1j) * phi1) * ((1 - t4232) ** (0.3e1 / 0.2e1)) + + if Bindx == 332: + t4234 = np.cos(phi) + t4235 = t4234 ** 2 + t4233 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.10e2) * t4233 ** 2 * (3 * t4234 + 1 + (-11 * t4234 - 15 + 22 * t4235) * t4235) + + if Bindx == 333: + t4238 = np.cos(phi) + t4241 = 55 * t4238 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * (t4241 - 3 + (t4241 + 5) * t4238) * np.sqrt((1 + t4238)) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t4238) ** (0.5e1 / 0.2e1)) + + if Bindx == 334: + t4242 = np.cos(phi) + t4243 = t4242 ** 2 + t4245 = t4243 ** 2 + t4244 = t4242 * t4243 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-3*1j) * (phi1 - phi2)) * (12 * t4243 - 105 * t4245 - 1 + (206 + 110 * t4244) * t4244 + (-165 * t4245 - 57) * t4242) + + if Bindx == 335: + t4248 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (2 + (11 + 11 * t4248) * t4248) * np.sqrt((1 + t4248)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t4248) ** (0.7e1 / 0.2e1)) + + if Bindx == 336: + t4250 = np.cos(phi) + t4251 = t4250 ** 2 + t4249 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.165e3) * t4249 ** 2 * (t4250 - 1 + (-5 * t4250 + 3 + 2 * t4251) * t4251) + + if Bindx == 337: + t4254 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.55e2) * ((1 - t4254) ** (0.9e1 / 0.2e1)) * ((1 + t4254) ** (0.3e1 / 0.2e1)) + + if Bindx == 338: + t4256 = np.cos(phi) + t4259 = 1 + t4256 + t4257 = t4259 ** 2 + t4255 = -1 + t4256 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.55e2) * t4255 ** 2 * t4257 ** 2 + + if Bindx == 339: + t4260 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.165e3) * ((1 - t4260) ** (0.3e1 / 0.2e1)) * ((1 + t4260) ** (0.7e1 / 0.2e1)) * (-1 + 3 * t4260) + + if Bindx == 340: + t4262 = np.cos(phi) + t4263 = t4262 ** 2 + t4261 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.30e2) * t4261 ** 2 * (-20 * t4262 + 1 + (44 * t4262 - 10 + 33 * t4263) * t4263) + + if Bindx == 341: + t4266 = np.cos(phi) + t4267 = t4266 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt((1 - t4266)) * ((1 + t4266) ** (0.5e1 / 0.2e1)) * (-55 * t4267 + 3 + (55 * t4267 + 5) * t4266) + + if Bindx == 342: + t4269 = np.cos(phi) + t4270 = t4269 ** 2 + t4272 = t4270 ** 2 + t4271 = t4269 * t4270 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + phi2)) * (289 * t4270 - 735 * t4272 - 17 + (-372 + 495 * t4271) * t4271 + (330 * t4272 + 74) * t4269) + + if Bindx == 343: + t4275 = np.cos(phi) + t4276 = t4275 ** 2 + t4278 = t4276 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.10e2) * ((1 + t4275) ** (0.3e1 / 0.2e1)) * (54 * t4276 - 165 * t4278 - 1 + (30 * t4276 + 99 * t4278 - 17) * t4275) * ((1 - t4275) ** (-0.1e1 / 0.2e1)) + + if Bindx == 344: + t4281 = np.cos(phi) + t4282 = t4281 ** 2 + t4280 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.105e3) * t4280 ** 2 * (1 + (-18 + 33 * t4282) * t4282) + + if Bindx == 345: + t4284 = np.cos(phi) + t4285 = t4284 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (-18 * t4284 + 1 + (66 * t4284 - 36 + 99 * t4285) * t4285) * np.sqrt((1 + t4284)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t4284) ** (0.3e1 / 0.2e1)) + + if Bindx == 346: + t4288 = np.cos(phi) + t4289 = t4288 ** 2 + t4291 = t4289 ** 2 + t4290 = t4288 * t4289 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - phi2)) * (289 * t4289 - 735 * t4291 - 17 + (372 + 495 * t4290) * t4290 + (-330 * t4291 - 74) * t4288) + + if Bindx == 347: + t4294 = np.cos(phi) + t4297 = 55 * t4294 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * (t4297 - 3 + (t4297 + 5) * t4294) * np.sqrt((1 + t4294)) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t4294) ** (0.5e1 / 0.2e1)) + + if Bindx == 348: + t4299 = np.cos(phi) + t4300 = t4299 ** 2 + t4298 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.30e2) * t4298 ** 2 * (20 * t4299 + 1 + (-44 * t4299 - 10 + 33 * t4300) * t4300) + + if Bindx == 349: + t4303 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (1 + 3 * t4303) * ((1 + t4303) ** (0.3e1 / 0.2e1)) * np.sqrt(0.165e3) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t4303) ** (0.7e1 / 0.2e1)) + + if Bindx == 350: + t4305 = np.cos(phi) + t4308 = -1 + t4305 + t4306 = t4308 ** 2 + t4304 = 1 + t4305 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.55e2) * t4306 ** 2 * t4304 ** 2 + + if Bindx == 351: + t4309 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.22e2) * ((1 - t4309) ** (0.5e1 / 0.2e1)) * ((1 + t4309) ** (0.7e1 / 0.2e1)) + + if Bindx == 352: + t4313 = np.sin(phi) + t4311 = t4313 ** 2 + t4310 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.66e2) * t4311 ** 2 * (-1 + (5 + 6 * t4310) * t4310) + + if Bindx == 353: + t4314 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.3e1) * ((1 - t4314) ** (0.3e1 / 0.2e1)) * ((1 + t4314) ** (0.5e1 / 0.2e1)) * (-2 + (-11 + 33 * t4314) * t4314) + + if Bindx == 354: + t4316 = np.cos(phi) + t4317 = t4316 ** 2 + t4315 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.10e2) * t4315 ** 2 * (-3 * t4316 + 1 + (11 * t4316 - 15 + 22 * t4317) * t4317) + + if Bindx == 355: + t4320 = np.cos(phi) + t4321 = t4320 ** 2 + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t4320)) * ((1 + t4320) ** (0.3e1 / 0.2e1)) * (18 * t4320 + 1 + (-66 * t4320 - 36 + 99 * t4321) * t4321) + + if Bindx == 356: + t4324 = np.cos(phi) + t4325 = t4324 ** 2 + t4327 = t4325 ** 2 + t4326 = t4324 * t4325 + tfunc[..., c] = (0.13e2 / 0.16e2) * np.exp((-1*1j) * (phi1 + phi2)) * (100 * t4325 - 285 * t4327 - 5 + (-30 + 198 * t4326) * t4326 + (33 * t4327 + 5) * t4324) + + if Bindx == 357: + t4330 = np.cos(phi) + t4331 = t4330 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.42e2) * np.sqrt((1 + t4330)) * t4330 * np.sqrt((1 - t4330)) * (5 + (-30 + 33 * t4331) * t4331) + + if Bindx == 358: + t4337 = np.cos(phi) + t4338 = t4337 ** 2 + t4340 = t4338 ** 2 + t4339 = t4337 * t4338 + tfunc[..., c] = (0.13e2 / 0.16e2) * np.exp((-1*1j) * (phi1 - phi2)) * (100 * t4338 - 285 * t4340 - 5 + (30 + 198 * t4339) * t4339 + (-33 * t4340 - 5) * t4337) + + if Bindx == 359: + t4343 = np.cos(phi) + t4344 = t4343 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (-18 * t4343 + 1 + (66 * t4343 - 36 + 99 * t4344) * t4344) * np.sqrt((1 + t4343)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t4343) ** (0.3e1 / 0.2e1)) + + if Bindx == 360: + t4348 = np.cos(phi) + t4349 = t4348 ** 2 + t4347 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.10e2) * t4347 ** 2 * (3 * t4348 + 1 + (-11 * t4348 - 15 + 22 * t4349) * t4349) + + if Bindx == 361: + t4352 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * (-2 + (11 + 33 * t4352) * t4352) * ((1 + t4352) ** (0.3e1 / 0.2e1)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t4352) ** (0.5e1 / 0.2e1)) + + if Bindx == 362: + t4356 = np.sin(phi) + t4354 = t4356 ** 2 + t4353 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.66e2) * t4354 ** 2 * (-1 + (-5 + 6 * t4353) * t4353) + + if Bindx == 363: + t4357 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * np.sqrt(0.22e2) * ((1 - t4357) ** (0.7e1 / 0.2e1)) * ((1 + t4357) ** (0.5e1 / 0.2e1)) + + if Bindx == 364: + t4361 = np.sin(phi) + t4358 = t4361 ** 2 + t4359 = t4361 * t4358 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-6*1j) * phi2) * np.sqrt(0.231e3) * t4359 ** 2 + + if Bindx == 365: + t4362 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.16e2*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.77e2) * ((1 - t4362) ** (0.5e1 / 0.2e1)) * ((1 + t4362) ** (0.5e1 / 0.2e1)) * t4362 + + if Bindx == 366: + t4366 = np.sin(phi) + t4364 = t4366 ** 2 + t4363 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((-4*1j) * phi2) * np.sqrt(0.14e2) * t4364 ** 2 * (11 * t4363 ** 2 - 1) + + if Bindx == 367: + t4367 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.105e3) * ((1 - t4367) ** (0.3e1 / 0.2e1)) * ((1 + t4367) ** (0.3e1 / 0.2e1)) * t4367 * (11 * t4367 ** 2 - 3) + + if Bindx == 368: + t4369 = np.cos(phi) + t4370 = t4369 ** 2 + t4368 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((-2*1j) * phi2) * np.sqrt(0.105e3) * t4368 ** 2 * (1 + (-18 + 33 * t4370) * t4370) + + if Bindx == 369: + t4372 = np.cos(phi) + t4373 = t4372 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.42e2) * np.sqrt((1 - t4372)) * np.sqrt((1 + t4372)) * t4372 * (5 + (-30 + 33 * t4373) * t4373) + + if Bindx == 370: + t4375 = np.cos(phi) + t4376 = t4375 ** 2 + t4377 = t4376 ** 2 + tfunc[..., c] = -0.4095e4 / 0.16e2 * t4377 - 0.65e2 / 0.16e2 + (0.3003e4 / 0.16e2 * t4377 + 0.1365e4 / 0.16e2) * t4376 + + if Bindx == 371: + t4379 = np.cos(phi) + t4380 = t4379 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((1j) * phi2) * np.sqrt(0.42e2) * np.sqrt((1 + t4379)) * t4379 * np.sqrt((1 - t4379)) * (5 + (-30 + 33 * t4380) * t4380) + + if Bindx == 372: + t4387 = np.cos(phi) + t4388 = t4387 ** 2 + t4386 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((2*1j) * phi2) * np.sqrt(0.105e3) * t4386 ** 2 * (1 + (-18 + 33 * t4388) * t4388) + + if Bindx == 373: + t4390 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * (11 * t4390 ** 2 - 3) * t4390 * ((1 + t4390) ** (0.3e1 / 0.2e1)) * np.sqrt(0.105e3) * np.exp((3*1j) * phi2) * ((1 - t4390) ** (0.3e1 / 0.2e1)) + + if Bindx == 374: + t4394 = np.sin(phi) + t4392 = t4394 ** 2 + t4391 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((4*1j) * phi2) * np.sqrt(0.14e2) * t4392 ** 2 * (11 * t4391 ** 2 - 1) + + if Bindx == 375: + t4395 = np.cos(phi) + t4396 = t4395 ** 2 + tfunc[..., c] = (0.39e2 / 0.16e2*1j) * t4395 * ((1 + t4395) ** (0.5e1 / 0.2e1)) * (-3 * t4396 - 1 + (t4396 + 3) * t4395) * np.sqrt(0.77e2) * np.exp((5*1j) * phi2) * ((1 - t4395) ** (-0.1e1 / 0.2e1)) + + if Bindx == 376: + t4401 = np.sin(phi) + t4398 = t4401 ** 2 + t4399 = t4401 * t4398 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((6*1j) * phi2) * np.sqrt(0.231e3) * t4399 ** 2 + + if Bindx == 377: + t4402 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.22e2) * ((1 - t4402) ** (0.7e1 / 0.2e1)) * ((1 + t4402) ** (0.5e1 / 0.2e1)) + + if Bindx == 378: + t4406 = np.sin(phi) + t4404 = t4406 ** 2 + t4403 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.66e2) * t4404 ** 2 * (-1 + (-5 + 6 * t4403) * t4403) + + if Bindx == 379: + t4407 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.3e1) * ((1 - t4407) ** (0.5e1 / 0.2e1)) * ((1 + t4407) ** (0.3e1 / 0.2e1)) * (-2 + (11 + 33 * t4407) * t4407) + + if Bindx == 380: + t4409 = np.cos(phi) + t4410 = t4409 ** 2 + t4408 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.10e2) * t4408 ** 2 * (3 * t4409 + 1 + (-11 * t4409 - 15 + 22 * t4410) * t4410) + + if Bindx == 381: + t4413 = np.cos(phi) + t4414 = t4413 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.10e2) * ((1 - t4413) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t4413)) * (-18 * t4413 + 1 + (66 * t4413 - 36 + 99 * t4414) * t4414) + + if Bindx == 382: + t4417 = np.cos(phi) + t4418 = t4417 ** 2 + t4420 = t4418 ** 2 + t4419 = t4417 * t4418 + tfunc[..., c] = (0.13e2 / 0.16e2) * np.exp((1j) * (phi1 - phi2)) * (100 * t4418 - 285 * t4420 - 5 + (30 + 198 * t4419) * t4419 + (-33 * t4420 - 5) * t4417) + + if Bindx == 383: + t4423 = np.cos(phi) + t4424 = t4423 ** 2 + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.42e2) * np.sqrt((1 - t4423)) * np.sqrt((1 + t4423)) * t4423 * (5 + (-30 + 33 * t4424) * t4424) + + if Bindx == 384: + t4426 = np.cos(phi) + t4427 = t4426 ** 2 + t4429 = t4427 ** 2 + t4428 = t4426 * t4427 + tfunc[..., c] = (0.13e2 / 0.16e2) * np.exp((1j) * (phi1 + phi2)) * (100 * t4427 - 285 * t4429 - 5 + (-30 + 198 * t4428) * t4428 + (33 * t4429 + 5) * t4426) + + if Bindx == 385: + t4432 = np.cos(phi) + t4433 = t4432 ** 2 + t4435 = t4433 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.10e2) * ((1 + t4432) ** (0.3e1 / 0.2e1)) * (54 * t4433 - 165 * t4435 - 1 + (30 * t4433 + 99 * t4435 - 17) * t4432) * ((1 - t4432) ** (-0.1e1 / 0.2e1)) + + if Bindx == 386: + t4438 = np.cos(phi) + t4439 = t4438 ** 2 + t4437 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.10e2) * t4437 ** 2 * (-3 * t4438 + 1 + (11 * t4438 - 15 + 22 * t4439) * t4439) + + if Bindx == 387: + t4442 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * (-2 + (-11 + 33 * t4442) * t4442) * ((1 + t4442) ** (0.5e1 / 0.2e1)) * np.sqrt(0.3e1) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t4442) ** (0.3e1 / 0.2e1)) + + if Bindx == 388: + t4446 = np.sin(phi) + t4444 = t4446 ** 2 + t4443 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.66e2) * t4444 ** 2 * (-1 + (5 + 6 * t4443) * t4443) + + if Bindx == 389: + t4447 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((1j) * (phi1 + 6 * phi2)) * np.sqrt(0.22e2) * ((1 - t4447) ** (0.5e1 / 0.2e1)) * ((1 + t4447) ** (0.7e1 / 0.2e1)) + + if Bindx == 390: + t4449 = np.cos(phi) + t4452 = -1 + t4449 + t4450 = t4452 ** 2 + t4448 = 1 + t4449 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.55e2) * t4450 ** 2 * t4448 ** 2 + + if Bindx == 391: + t4453 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.165e3) * ((1 - t4453) ** (0.7e1 / 0.2e1)) * ((1 + t4453) ** (0.3e1 / 0.2e1)) * (1 + 3 * t4453) + + if Bindx == 392: + t4455 = np.cos(phi) + t4456 = t4455 ** 2 + t4454 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.30e2) * t4454 ** 2 * (20 * t4455 + 1 + (-44 * t4455 - 10 + 33 * t4456) * t4456) + + if Bindx == 393: + t4459 = np.cos(phi) + t4462 = 55 * t4459 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * ((1 - t4459) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t4459)) * (t4462 - 3 + (t4462 + 5) * t4459) + + if Bindx == 394: + t4463 = np.cos(phi) + t4464 = t4463 ** 2 + t4466 = t4464 ** 2 + t4465 = t4463 * t4464 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (phi1 - phi2)) * (289 * t4464 - 735 * t4466 - 17 + (372 + 495 * t4465) * t4465 + (-330 * t4466 - 74) * t4463) + + if Bindx == 395: + t4469 = np.cos(phi) + t4470 = t4469 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.10e2) * ((1 - t4469) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t4469)) * (-18 * t4469 + 1 + (66 * t4469 - 36 + 99 * t4470) * t4470) + + if Bindx == 396: + t4474 = np.cos(phi) + t4475 = t4474 ** 2 + t4473 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((2*1j) * phi1) * np.sqrt(0.105e3) * t4473 ** 2 * (1 + (-18 + 33 * t4475) * t4475) + + if Bindx == 397: + t4477 = np.cos(phi) + t4478 = t4477 ** 2 + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t4477)) * ((1 + t4477) ** (0.3e1 / 0.2e1)) * (18 * t4477 + 1 + (-66 * t4477 - 36 + 99 * t4478) * t4478) + + if Bindx == 398: + t4481 = np.cos(phi) + t4482 = t4481 ** 2 + t4484 = t4482 ** 2 + t4483 = t4481 * t4482 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (phi1 + phi2)) * (289 * t4482 - 735 * t4484 - 17 + (-372 + 495 * t4483) * t4483 + (330 * t4484 + 74) * t4481) + + if Bindx == 399: + t4487 = np.cos(phi) + t4488 = t4487 ** 2 + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * ((1 + t4487) ** (0.5e1 / 0.2e1)) * (-2 * t4487 - 3 + (-110 * t4487 + 60 + 55 * t4488) * t4488) * ((1 - t4487) ** (-0.1e1 / 0.2e1)) + + if Bindx == 400: + t4492 = np.cos(phi) + t4493 = t4492 ** 2 + t4491 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.30e2) * t4491 ** 2 * (-20 * t4492 + 1 + (44 * t4492 - 10 + 33 * t4493) * t4493) + + if Bindx == 401: + t4496 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * (-1 + 3 * t4496) * ((1 + t4496) ** (0.7e1 / 0.2e1)) * np.sqrt(0.165e3) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t4496) ** (0.3e1 / 0.2e1)) + + if Bindx == 402: + t4498 = np.cos(phi) + t4501 = 1 + t4498 + t4499 = t4501 ** 2 + t4497 = -1 + t4498 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.55e2) * t4497 ** 2 * t4499 ** 2 + + if Bindx == 403: + t4502 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.55e2) * ((1 - t4502) ** (0.9e1 / 0.2e1)) * ((1 + t4502) ** (0.3e1 / 0.2e1)) + + if Bindx == 404: + t4503 = np.cos(phi) + t4506 = -1 + t4503 + t4504 = t4506 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.165e3) * t4504 ** 2 * (1 + t4503) * (1 + 2 * t4503) + + if Bindx == 405: + t4507 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.30e2) * ((1 - t4507) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t4507)) * (2 + (11 + 11 * t4507) * t4507) + + if Bindx == 406: + t4508 = np.cos(phi) + t4509 = t4508 ** 2 + t4511 = t4509 ** 2 + t4510 = t4508 * t4509 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((3*1j) * (phi1 - phi2)) * (12 * t4509 - 105 * t4511 - 1 + (206 + 110 * t4510) * t4510 + (-165 * t4511 - 57) * t4508) + + if Bindx == 407: + t4514 = np.cos(phi) + t4517 = 55 * t4514 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * ((1 - t4514) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t4514)) * (t4517 - 3 + (t4517 + 5) * t4514) + + if Bindx == 408: + t4519 = np.cos(phi) + t4520 = t4519 ** 2 + t4518 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.10e2) * t4518 ** 2 * (3 * t4519 + 1 + (-11 * t4519 - 15 + 22 * t4520) * t4520) + + if Bindx == 409: + t4523 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.105e3) * ((1 - t4523) ** (0.3e1 / 0.2e1)) * ((1 + t4523) ** (0.3e1 / 0.2e1)) * t4523 * (11 * t4523 ** 2 - 3) + + if Bindx == 410: + t4525 = np.cos(phi) + t4526 = t4525 ** 2 + t4524 = np.sin(phi) + tfunc[..., c] = -(0.39e2 / 0.32e2) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.10e2) * t4524 ** 2 * (-3 * t4525 + 1 + (11 * t4525 - 15 + 22 * t4526) * t4526) + + if Bindx == 411: + t4529 = np.cos(phi) + t4530 = t4529 ** 2 + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt((1 - t4529)) * ((1 + t4529) ** (0.5e1 / 0.2e1)) * (-55 * t4530 + 3 + (55 * t4530 + 5) * t4529) + + if Bindx == 412: + t4532 = np.cos(phi) + t4533 = t4532 ** 2 + t4535 = t4533 ** 2 + t4534 = t4532 * t4533 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((3*1j) * (phi1 + phi2)) * (12 * t4533 - 105 * t4535 - 1 + (-206 + 110 * t4534) * t4534 + (165 * t4535 + 57) * t4532) + + if Bindx == 413: + t4538 = np.cos(phi) + t4539 = t4538 ** 2 + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * ((1 + t4538) ** (0.7e1 / 0.2e1)) * (-22 * t4539 - 2 + (11 * t4539 + 13) * t4538) * ((1 - t4538) ** (-0.1e1 / 0.2e1)) + + if Bindx == 414: + t4542 = np.cos(phi) + t4543 = t4542 ** 2 + t4541 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.165e3) * t4541 ** 2 * (-t4542 - 1 + (5 * t4542 + 3 + 2 * t4543) * t4543) + + if Bindx == 415: + t4546 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.55e2) * ((1 - t4546) ** (0.3e1 / 0.2e1)) * ((1 + t4546) ** (0.9e1 / 0.2e1)) + + if Bindx == 416: + t4547 = np.cos(phi) + t4551 = -1 + t4547 + t4548 = t4551 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.66e2) * t4551 * t4548 ** 2 * (1 + t4547) + + if Bindx == 417: + t4552 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.22e2) * ((1 - t4552) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t4552)) * (2 + 3 * t4552) + + if Bindx == 418: + t4553 = np.cos(phi) + t4554 = t4553 ** 2 + t4556 = t4554 ** 2 + t4555 = t4553 * t4554 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((4*1j) * (phi1 - phi2)) * (-65 * t4554 + 35 * t4556 + 13 + (80 + 33 * t4555) * t4555 + (-88 * t4556 - 8) * t4553) + + if Bindx == 419: + t4559 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.30e2) * ((1 - t4559) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t4559)) * (2 + (11 + 11 * t4559) * t4559) + + if Bindx == 420: + t4561 = np.cos(phi) + t4562 = t4561 ** 2 + t4560 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.30e2) * t4560 ** 2 * (20 * t4561 + 1 + (-44 * t4561 - 10 + 33 * t4562) * t4562) + + if Bindx == 421: + t4565 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.16e2*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.3e1) * ((1 - t4565) ** (0.5e1 / 0.2e1)) * ((1 + t4565) ** (0.3e1 / 0.2e1)) * (-2 + (11 + 33 * t4565) * t4565) + + if Bindx == 422: + t4569 = np.sin(phi) + t4567 = t4569 ** 2 + t4566 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2) * np.exp((4*1j) * phi1) * np.sqrt(0.14e2) * t4567 ** 2 * (11 * t4566 ** 2 - 1) + + if Bindx == 423: + t4570 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.16e2*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.3e1) * ((1 - t4570) ** (0.3e1 / 0.2e1)) * ((1 + t4570) ** (0.5e1 / 0.2e1)) * (-2 + (-11 + 33 * t4570) * t4570) + + if Bindx == 424: + t4572 = np.cos(phi) + t4573 = t4572 ** 2 + t4571 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.30e2) * t4571 ** 2 * (-20 * t4572 + 1 + (44 * t4572 - 10 + 33 * t4573) * t4573) + + if Bindx == 425: + t4576 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t4576)) * ((1 + t4576) ** (0.7e1 / 0.2e1)) * (2 + (-11 + 11 * t4576) * t4576) + + if Bindx == 426: + t4577 = np.cos(phi) + t4578 = t4577 ** 2 + t4580 = t4578 ** 2 + t4579 = t4577 * t4578 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((4*1j) * (phi1 + phi2)) * (-65 * t4578 + 35 * t4580 + 13 + (-80 + 33 * t4579) * t4579 + (88 * t4580 + 8) * t4577) + + if Bindx == 427: + t4583 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.22e2) * ((1 + t4583) ** (0.9e1 / 0.2e1)) * (2 + (-5 + 3 * t4583) * t4583) * ((1 - t4583) ** (-0.1e1 / 0.2e1)) + + if Bindx == 428: + t4584 = np.cos(phi) + t4588 = 1 + t4584 + t4585 = t4588 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.66e2) * (-1 + t4584) * t4588 * t4585 ** 2 + + if Bindx == 429: + t4589 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.3e1) * ((1 - t4589) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t4589)) + + if Bindx == 430: + t4590 = np.cos(phi) + t4591 = t4590 ** 2 + t4593 = t4591 ** 2 + t4592 = t4590 * t4591 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((5*1j) * (phi1 - phi2)) * (-20 * t4591 + 35 * t4593 - 5 + (-10 + 6 * t4592) * t4592 + (-25 * t4593 + 19) * t4590) + + if Bindx == 431: + t4596 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.22e2) * ((1 - t4596) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t4596)) * (2 + 3 * t4596) + + if Bindx == 432: + t4598 = np.cos(phi) + t4599 = t4598 ** 2 + t4597 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.165e3) * t4597 ** 2 * (t4598 - 1 + (-5 * t4598 + 3 + 2 * t4599) * t4599) + + if Bindx == 433: + t4602 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.165e3) * ((1 - t4602) ** (0.7e1 / 0.2e1)) * ((1 + t4602) ** (0.3e1 / 0.2e1)) * (1 + 3 * t4602) + + if Bindx == 434: + t4606 = np.sin(phi) + t4604 = t4606 ** 2 + t4603 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.66e2) * t4604 ** 2 * (-1 + (-5 + 6 * t4603) * t4603) + + if Bindx == 435: + t4607 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.16e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.77e2) * ((1 - t4607) ** (0.5e1 / 0.2e1)) * ((1 + t4607) ** (0.5e1 / 0.2e1)) * t4607 + + if Bindx == 436: + t4611 = np.sin(phi) + t4609 = t4611 ** 2 + t4608 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.66e2) * t4609 ** 2 * (-1 + (5 + 6 * t4608) * t4608) + + if Bindx == 437: + t4612 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.165e3) * ((1 - t4612) ** (0.3e1 / 0.2e1)) * ((1 + t4612) ** (0.7e1 / 0.2e1)) * (-1 + 3 * t4612) + + if Bindx == 438: + t4614 = np.cos(phi) + t4615 = t4614 ** 2 + t4613 = np.sin(phi) + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.165e3) * t4613 ** 2 * (-t4614 - 1 + (5 * t4614 + 3 + 2 * t4615) * t4615) + + if Bindx == 439: + t4618 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t4618)) * ((1 + t4618) ** (0.9e1 / 0.2e1)) * (-2 + 3 * t4618) + + if Bindx == 440: + t4619 = np.cos(phi) + t4620 = t4619 ** 2 + t4622 = t4620 ** 2 + t4621 = t4619 * t4620 + tfunc[..., c] = (0.13e2 / 0.32e2) * np.exp((5*1j) * (phi1 + phi2)) * (-20 * t4620 + 35 * t4622 - 5 + (10 + 6 * t4621) * t4621 + (25 * t4622 - 19) * t4619) + + if Bindx == 441: + t4625 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t4625)) * ((1 + t4625) ** (0.11e2 / 0.2e1)) + + if Bindx == 442: + t4626 = np.cos(phi) + t4632 = -6 * t4626 + t4627 = t4626 ** 2 + t4628 = t4626 * t4627 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((6*1j) * (phi1 - phi2)) * (t4632 + 1 + (-20 + t4628) * t4628 + (15 + (t4632 + 15) * t4627) * t4627) + + if Bindx == 443: + t4633 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.3e1) * ((1 - t4633) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t4633)) + + if Bindx == 444: + t4634 = np.cos(phi) + t4638 = -1 + t4634 + t4635 = t4638 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.66e2) * t4638 * t4635 ** 2 * (1 + t4634) + + if Bindx == 445: + t4639 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.55e2) * ((1 - t4639) ** (0.9e1 / 0.2e1)) * ((1 + t4639) ** (0.3e1 / 0.2e1)) + + if Bindx == 446: + t4641 = np.cos(phi) + t4644 = -1 + t4641 + t4642 = t4644 ** 2 + t4640 = 1 + t4641 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.55e2) * t4642 ** 2 * t4640 ** 2 + + if Bindx == 447: + t4645 = np.cos(phi) + tfunc[..., c] = (0.39e2 / 0.32e2*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.22e2) * ((1 - t4645) ** (0.7e1 / 0.2e1)) * ((1 + t4645) ** (0.5e1 / 0.2e1)) + + if Bindx == 448: + t4649 = np.sin(phi) + t4646 = t4649 ** 2 + t4647 = t4649 * t4646 + tfunc[..., c] = -(0.13e2 / 0.32e2) * np.exp((6*1j) * phi1) * np.sqrt(0.231e3) * t4647 ** 2 + + if Bindx == 449: + t4650 = np.cos(phi) + tfunc[..., c] = (-0.39e2 / 0.32e2*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.22e2) * ((1 - t4650) ** (0.5e1 / 0.2e1)) * ((1 + t4650) ** (0.7e1 / 0.2e1)) + + if Bindx == 450: + t4652 = np.cos(phi) + t4655 = 1 + t4652 + t4653 = t4655 ** 2 + t4651 = -1 + t4652 + tfunc[..., c] = (0.39e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.55e2) * t4651 ** 2 * t4653 ** 2 + + if Bindx == 451: + t4656 = np.cos(phi) + tfunc[..., c] = (0.13e2 / 0.32e2*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.55e2) * ((1 - t4656) ** (0.3e1 / 0.2e1)) * ((1 + t4656) ** (0.9e1 / 0.2e1)) + + if Bindx == 452: + t4657 = np.cos(phi) + t4661 = 1 + t4657 + t4658 = t4661 ** 2 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.66e2) * (-1 + t4657) * t4661 * t4658 ** 2 + + if Bindx == 453: + t4662 = np.cos(phi) + tfunc[..., c] = (-0.13e2 / 0.32e2*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t4662)) * ((1 + t4662) ** (0.11e2 / 0.2e1)) + + if Bindx == 454: + t4663 = np.cos(phi) + t4669 = 6 * t4663 + t4664 = t4663 ** 2 + t4665 = t4663 * t4664 + tfunc[..., c] = (0.13e2 / 0.64e2) * np.exp((6*1j) * (phi1 + phi2)) * (t4669 + 1 + (20 + t4665) * t4665 + (15 + (t4669 + 15) * t4664) * t4664) + + if Bindx == 455: + t4670 = np.cos(phi) + t4671 = t4670 ** 2 + t4672 = t4670 * t4671 + t4675 = t4672 ** 2 + t4673 = t4671 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-7*1j) * (phi1 + phi2)) * (21 * t4671 + 35 * t4672 + 35 * t4673 + 7 * t4675 + 1 + (21 * t4673 + t4675 + 7) * t4670) + + if Bindx == 456: + t4677 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.14e2) * ((1 + t4677) ** (0.13e2 / 0.2e1)) * np.sqrt((1 - t4677)) + + if Bindx == 457: + t4678 = np.cos(phi) + t4682 = 1 + t4678 + t4679 = t4682 ** 2 + t4680 = t4682 * t4679 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.91e2) * (-1 + t4678) * t4680 ** 2 + + if Bindx == 458: + t4683 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.91e2) * ((1 - t4683) ** (0.3e1 / 0.2e1)) * ((1 + t4683) ** (0.11e2 / 0.2e1)) + + if Bindx == 459: + t4685 = np.cos(phi) + t4689 = 1 + t4685 + t4686 = t4689 ** 2 + t4684 = -1 + t4685 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.1001e4) * t4684 ** 2 * t4689 * t4686 ** 2 + + if Bindx == 460: + t4690 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t4690) ** (0.5e1 / 0.2e1)) * ((1 + t4690) ** (0.9e1 / 0.2e1)) + + if Bindx == 461: + t4694 = np.sin(phi) + t4691 = t4694 ** 2 + t4692 = t4694 * t4691 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.3003e4) * t4692 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 462: + t4695 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-7*1j) * phi1) * np.sqrt(0.858e3) * ((1 - t4695) ** (0.7e1 / 0.2e1)) * ((1 + t4695) ** (0.7e1 / 0.2e1)) + + if Bindx == 463: + t4699 = np.sin(phi) + t4696 = t4699 ** 2 + t4697 = t4699 * t4696 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.3003e4) * t4697 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 464: + t4700 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t4700) ** (0.9e1 / 0.2e1)) * ((1 + t4700) ** (0.5e1 / 0.2e1)) + + if Bindx == 465: + t4702 = np.cos(phi) + t4706 = -1 + t4702 + t4703 = t4706 ** 2 + t4701 = 1 + t4702 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.1001e4) * t4706 * t4703 ** 2 * t4701 ** 2 + + if Bindx == 466: + t4707 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.91e2) * ((1 - t4707) ** (0.11e2 / 0.2e1)) * ((1 + t4707) ** (0.3e1 / 0.2e1)) + + if Bindx == 467: + t4708 = np.cos(phi) + t4712 = -1 + t4708 + t4709 = t4712 ** 2 + t4710 = t4712 * t4709 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.91e2) * t4710 ** 2 * (1 + t4708) + + if Bindx == 468: + t4713 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.14e2) * ((1 - t4713) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t4713)) + + if Bindx == 469: + t4714 = np.cos(phi) + t4715 = t4714 ** 2 + t4716 = t4714 * t4715 + t4719 = t4716 ** 2 + t4717 = t4715 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-7*1j) * (phi1 - phi2)) * (-21 * t4715 + 35 * t4716 - 35 * t4717 - 7 * t4719 - 1 + (21 * t4717 + t4719 + 7) * t4714) + + if Bindx == 470: + t4721 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t4721)) * ((1 + t4721) ** (0.13e2 / 0.2e1)) + + if Bindx == 471: + t4722 = np.cos(phi) + t4723 = t4722 ** 2 + t4724 = t4722 * t4723 + t4727 = t4724 ** 2 + t4725 = t4723 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-6*1j) * (phi1 + phi2)) * (-48 * t4723 - 15 * t4724 + 50 * t4725 + 36 * t4727 - 6 + (69 * t4725 + 7 * t4727 - 29) * t4722) + + if Bindx == 472: + t4729 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.26e2) * ((1 + t4729) ** (0.11e2 / 0.2e1)) * (5 + (-12 + 7 * t4729) * t4729) * ((1 - t4729) ** (-0.1e1 / 0.2e1)) + + if Bindx == 473: + t4731 = np.cos(phi) + t4732 = t4731 ** 2 + t4734 = t4732 ** 2 + t4730 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.26e2) * t4730 ** 2 * (4 * t4732 + 24 * t4734 - 4 + (26 * t4732 + 7 * t4734 - 9) * t4731) + + if Bindx == 474: + t4736 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (-3 + 7 * t4736) * ((1 + t4736) ** (0.9e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t4736) ** (0.3e1 / 0.2e1)) + + if Bindx == 475: + t4742 = np.sin(phi) + t4740 = t4742 ** 2 + t4737 = np.cos(phi) + t4738 = t4737 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.143e3) * t4740 ** 2 * (12 * t4738 - 2 + (7 * t4738 + 3) * t4737) + + if Bindx == 476: + t4743 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (-1 + 7 * t4743) * ((1 + t4743) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t4743) ** (0.5e1 / 0.2e1)) + + if Bindx == 477: + t4747 = np.sin(phi) + t4744 = t4747 ** 2 + t4745 = t4747 * t4744 + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.3003e4) * t4745 ** 2 * np.cos(phi) + + if Bindx == 478: + t4748 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + 7 * t4748) * ((1 + t4748) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t4748) ** (0.7e1 / 0.2e1)) + + if Bindx == 479: + t4754 = np.sin(phi) + t4752 = t4754 ** 2 + t4749 = np.cos(phi) + t4750 = t4749 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.143e3) * t4752 ** 2 * (-12 * t4750 + 2 + (7 * t4750 + 3) * t4749) + + if Bindx == 480: + t4755 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (3 + 7 * t4755) * ((1 + t4755) ** (0.3e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t4755) ** (0.9e1 / 0.2e1)) + + if Bindx == 481: + t4757 = np.cos(phi) + t4758 = t4757 ** 2 + t4760 = t4758 ** 2 + t4756 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.26e2) * t4756 ** 2 * (-4 * t4758 - 24 * t4760 + 4 + (26 * t4758 + 7 * t4760 - 9) * t4757) + + if Bindx == 482: + t4762 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (5 + 7 * t4762) * np.sqrt((1 + t4762)) * np.sqrt(0.26e2) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t4762) ** (0.11e2 / 0.2e1)) + + if Bindx == 483: + t4763 = np.cos(phi) + t4764 = t4763 ** 2 + t4765 = t4763 * t4764 + t4768 = t4765 ** 2 + t4766 = t4764 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-6*1j) * (phi1 - phi2)) * (48 * t4764 - 15 * t4765 - 50 * t4766 - 36 * t4768 + 6 + (69 * t4766 + 7 * t4768 - 29) * t4763) + + if Bindx == 484: + t4770 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.14e2) * ((1 - t4770) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t4770)) + + if Bindx == 485: + t4771 = np.cos(phi) + t4775 = 1 + t4771 + t4772 = t4775 ** 2 + t4773 = t4775 * t4772 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.91e2) * (-1 + t4771) * t4773 ** 2 + + if Bindx == 486: + t4776 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t4776)) * ((1 + t4776) ** (0.11e2 / 0.2e1)) * (-5 + 7 * t4776) + + if Bindx == 487: + t4777 = np.cos(phi) + t4778 = t4777 ** 2 + t4779 = t4777 * t4778 + t4782 = t4779 ** 2 + t4780 = t4778 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-5*1j) * (phi1 + phi2)) * (-129 * t4778 - 415 * t4779 - 175 * t4780 + 325 * t4782 + 43 + (303 * t4780 + 91 * t4782 + 85) * t4777) + + if Bindx == 488: + t4784 = np.cos(phi) + t4785 = t4784 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * ((1 + t4784) ** (0.9e1 / 0.2e1)) * (-195 * t4785 - 25 + (91 * t4785 + 129) * t4784) * ((1 - t4784) ** (-0.1e1 / 0.2e1)) + + if Bindx == 489: + t4788 = np.cos(phi) + t4789 = t4788 ** 2 + t4791 = t4789 ** 2 + t4787 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * t4787 ** 2 * (-110 * t4789 + 195 * t4791 + 11 + (50 * t4789 + 91 * t4791 - 45) * t4788) + + if Bindx == 490: + t4793 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + (-52 + 91 * t4793) * t4793) * ((1 + t4793) ** (0.7e1 / 0.2e1)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t4793) ** (0.3e1 / 0.2e1)) + + if Bindx == 491: + t4799 = np.sin(phi) + t4797 = t4799 ** 2 + t4794 = np.cos(phi) + t4795 = t4794 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.33e2) * t4797 ** 2 * (65 * t4795 - 5 + (91 * t4795 - 31) * t4794) + + if Bindx == 492: + t4800 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (13 * t4800 ** 2 - 1) * ((1 + t4800) ** (0.5e1 / 0.2e1)) * np.sqrt(0.462e3) * np.exp((-5*1j) * phi1) * ((1 - t4800) ** (0.5e1 / 0.2e1)) + + if Bindx == 493: + t4806 = np.sin(phi) + t4804 = t4806 ** 2 + t4801 = np.cos(phi) + t4802 = t4801 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.33e2) * t4804 ** 2 * (-65 * t4802 + 5 + (91 * t4802 - 31) * t4801) + + if Bindx == 494: + t4807 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + (52 + 91 * t4807) * t4807) * ((1 + t4807) ** (0.3e1 / 0.2e1)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t4807) ** (0.7e1 / 0.2e1)) + + if Bindx == 495: + t4809 = np.cos(phi) + t4810 = t4809 ** 2 + t4812 = t4810 ** 2 + t4808 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.11e2) * t4808 ** 2 * (110 * t4810 - 195 * t4812 - 11 + (50 * t4810 + 91 * t4812 - 45) * t4809) + + if Bindx == 496: + t4814 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (25 + (104 + 91 * t4814) * t4814) * np.sqrt((1 + t4814)) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t4814) ** (0.9e1 / 0.2e1)) + + if Bindx == 497: + t4815 = np.cos(phi) + t4816 = t4815 ** 2 + t4817 = t4815 * t4816 + t4820 = t4817 ** 2 + t4818 = t4816 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-5*1j) * (phi1 - phi2)) * (129 * t4816 - 415 * t4817 + 175 * t4818 - 325 * t4820 - 43 + (303 * t4818 + 91 * t4820 + 85) * t4815) + + if Bindx == 498: + t4822 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (5 + 7 * t4822) * np.sqrt((1 + t4822)) * np.sqrt(0.26e2) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t4822) ** (0.11e2 / 0.2e1)) + + if Bindx == 499: + t4823 = np.cos(phi) + t4827 = -1 + t4823 + t4824 = t4827 ** 2 + t4825 = t4827 * t4824 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.91e2) * t4825 ** 2 * (1 + t4823) + + if Bindx == 500: + t4828 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.91e2) * ((1 - t4828) ** (0.3e1 / 0.2e1)) * ((1 + t4828) ** (0.11e2 / 0.2e1)) + + if Bindx == 501: + t4829 = np.cos(phi) + t4833 = 1 + t4829 + t4830 = t4833 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.26e2) * (-1 + t4829) * t4833 * t4830 ** 2 * (-4 + 7 * t4829) + + if Bindx == 502: + t4834 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt((1 - t4834)) * ((1 + t4834) ** (0.9e1 / 0.2e1)) * (25 + (-104 + 91 * t4834) * t4834) + + if Bindx == 503: + t4835 = np.cos(phi) + t4836 = t4835 ** 2 + t4837 = t4835 * t4836 + t4840 = t4837 ** 2 + t4838 = t4836 ** 2 + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((-4*1j) * (phi1 + phi2)) * (96 * t4836 - 115 * t4837 - 280 * t4838 + 208 * t4840 - 8 + (-3 * t4838 + 91 * t4840 + 43) * t4835) + + if Bindx == 504: + t4842 = np.cos(phi) + t4843 = t4842 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * ((1 + t4842) ** (0.7e1 / 0.2e1)) * (-32 * t4842 - 1 + (-208 * t4842 + 150 + 91 * t4843) * t4843) * ((1 - t4842) ** (-0.1e1 / 0.2e1)) + + if Bindx == 505: + t4847 = np.cos(phi) + t4848 = t4847 ** 2 + t4850 = t4848 ** 2 + t4846 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.22e2) * t4846 ** 2 * (-68 * t4848 + 104 * t4850 + 4 + (-62 * t4848 + 91 * t4850 + 11) * t4847) + + if Bindx == 506: + t4852 = np.cos(phi) + t4853 = t4852 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (-39 * t4853 + 3 + (91 * t4853 - 15) * t4852) * ((1 + t4852) ** (0.5e1 / 0.2e1)) * np.sqrt(0.33e2) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t4852) ** (0.3e1 / 0.2e1)) + + if Bindx == 507: + t4858 = np.sin(phi) + t4856 = t4858 ** 2 + t4855 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((-4*1j) * phi1) * np.sqrt(0.462e3) * t4856 ** 2 * t4855 * (13 * t4855 ** 2 - 3) + + if Bindx == 508: + t4859 = np.cos(phi) + t4860 = t4859 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (39 * t4860 - 3 + (91 * t4860 - 15) * t4859) * ((1 + t4859) ** (0.3e1 / 0.2e1)) * np.sqrt(0.33e2) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t4859) ** (0.5e1 / 0.2e1)) + + if Bindx == 509: + t4863 = np.cos(phi) + t4864 = t4863 ** 2 + t4866 = t4864 ** 2 + t4862 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.22e2) * t4862 ** 2 * (68 * t4864 - 104 * t4866 - 4 + (-62 * t4864 + 91 * t4866 + 11) * t4863) + + if Bindx == 510: + t4868 = np.cos(phi) + t4869 = t4868 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (117 * t4869 - 1 + (91 * t4869 + 33) * t4868) * np.sqrt((1 + t4868)) * np.sqrt(0.11e2) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t4868) ** (0.7e1 / 0.2e1)) + + if Bindx == 511: + t4871 = np.cos(phi) + t4872 = t4871 ** 2 + t4873 = t4871 * t4872 + t4876 = t4873 ** 2 + t4874 = t4872 ** 2 + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((-4*1j) * (phi1 - phi2)) * (-96 * t4872 - 115 * t4873 + 280 * t4874 - 208 * t4876 + 8 + (-3 * t4874 + 91 * t4876 + 43) * t4871) + + if Bindx == 512: + t4878 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (25 + (104 + 91 * t4878) * t4878) * np.sqrt((1 + t4878)) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t4878) ** (0.9e1 / 0.2e1)) + + if Bindx == 513: + t4880 = np.cos(phi) + t4881 = t4880 ** 2 + t4883 = t4881 ** 2 + t4879 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.26e2) * t4879 ** 2 * (-4 * t4881 - 24 * t4883 + 4 + (26 * t4881 + 7 * t4883 - 9) * t4880) + + if Bindx == 514: + t4885 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.91e2) * ((1 - t4885) ** (0.11e2 / 0.2e1)) * ((1 + t4885) ** (0.3e1 / 0.2e1)) + + if Bindx == 515: + t4887 = np.cos(phi) + t4891 = 1 + t4887 + t4888 = t4891 ** 2 + t4886 = -1 + t4887 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.1001e4) * t4886 ** 2 * t4891 * t4888 ** 2 + + if Bindx == 516: + t4892 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.286e3) * ((1 - t4892) ** (0.3e1 / 0.2e1)) * ((1 + t4892) ** (0.9e1 / 0.2e1)) * (-3 + 7 * t4892) + + if Bindx == 517: + t4894 = np.cos(phi) + t4895 = t4894 ** 2 + t4897 = t4895 ** 2 + t4893 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.11e2) * t4893 ** 2 * (-110 * t4895 + 195 * t4897 + 11 + (50 * t4895 + 91 * t4897 - 45) * t4894) + + if Bindx == 518: + t4899 = np.cos(phi) + t4900 = t4899 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.11e2) * np.sqrt((1 - t4899)) * ((1 + t4899) ** (0.7e1 / 0.2e1)) * (-117 * t4900 + 1 + (91 * t4900 + 33) * t4899) + + if Bindx == 519: + t4902 = np.cos(phi) + t4903 = t4902 ** 2 + t4904 = t4902 * t4903 + t4907 = t4904 ** 2 + t4905 = t4903 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-3*1j) * (phi1 + phi2)) * (741 * t4903 + 555 * t4904 - 1925 * t4905 + 1287 * t4907 - 39 + (-1419 * t4905 + 1001 * t4907 - 73) * t4902) + + if Bindx == 520: + t4909 = np.cos(phi) + t4910 = t4909 ** 2 + t4912 = t4910 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2e1) * ((1 + t4909) ** (0.5e1 / 0.2e1)) * (110 * t4910 - 2145 * t4912 + 19 + (1210 * t4910 + 1001 * t4912 - 195) * t4909) * ((1 - t4909) ** (-0.1e1 / 0.2e1)) + + if Bindx == 521: + t4915 = np.cos(phi) + t4916 = t4915 ** 2 + t4918 = t4916 ** 2 + t4914 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.3e1) * t4914 ** 2 * (-198 * t4916 + 429 * t4918 + 9 + (-902 * t4916 + 1001 * t4918 + 141) * t4915) + + if Bindx == 522: + t4920 = np.cos(phi) + t4921 = t4920 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (3 + (-66 + 143 * t4921) * t4921) * ((1 + t4920) ** (0.3e1 / 0.2e1)) * np.sqrt(0.42e2) * np.exp((-3*1j) * phi1) * ((1 - t4920) ** (0.3e1 / 0.2e1)) + + if Bindx == 523: + t4924 = np.cos(phi) + t4925 = t4924 ** 2 + t4927 = t4925 ** 2 + t4923 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.3e1) * t4923 ** 2 * (198 * t4925 - 429 * t4927 - 9 + (-902 * t4925 + 1001 * t4927 + 141) * t4924) + + if Bindx == 524: + t4929 = np.cos(phi) + t4930 = t4929 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (-176 * t4929 - 19 + (1144 * t4929 + 66 + 1001 * t4930) * t4930) * np.sqrt((1 + t4929)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t4929) ** (0.5e1 / 0.2e1)) + + if Bindx == 525: + t4933 = np.cos(phi) + t4934 = t4933 ** 2 + t4935 = t4933 * t4934 + t4938 = t4935 ** 2 + t4936 = t4934 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-3*1j) * (phi1 - phi2)) * (-741 * t4934 + 555 * t4935 + 1925 * t4936 - 1287 * t4938 + 39 + (-1419 * t4936 + 1001 * t4938 - 73) * t4933) + + if Bindx == 526: + t4940 = np.cos(phi) + t4941 = t4940 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (117 * t4941 - 1 + (91 * t4941 + 33) * t4940) * np.sqrt((1 + t4940)) * np.sqrt(0.11e2) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t4940) ** (0.7e1 / 0.2e1)) + + if Bindx == 527: + t4944 = np.cos(phi) + t4945 = t4944 ** 2 + t4947 = t4945 ** 2 + t4943 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.11e2) * t4943 ** 2 * (110 * t4945 - 195 * t4947 - 11 + (50 * t4945 + 91 * t4947 - 45) * t4944) + + if Bindx == 528: + t4949 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (3 + 7 * t4949) * ((1 + t4949) ** (0.3e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t4949) ** (0.9e1 / 0.2e1)) + + if Bindx == 529: + t4951 = np.cos(phi) + t4955 = -1 + t4951 + t4952 = t4955 ** 2 + t4950 = 1 + t4951 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.1001e4) * t4955 * t4952 ** 2 * t4950 ** 2 + + if Bindx == 530: + t4956 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t4956) ** (0.5e1 / 0.2e1)) * ((1 + t4956) ** (0.9e1 / 0.2e1)) + + if Bindx == 531: + t4962 = np.sin(phi) + t4960 = t4962 ** 2 + t4957 = np.cos(phi) + t4958 = t4957 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.143e3) * t4960 ** 2 * (12 * t4958 - 2 + (7 * t4958 + 3) * t4957) + + if Bindx == 532: + t4963 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.22e2) * ((1 - t4963) ** (0.3e1 / 0.2e1)) * ((1 + t4963) ** (0.7e1 / 0.2e1)) * (1 + (-52 + 91 * t4963) * t4963) + + if Bindx == 533: + t4965 = np.cos(phi) + t4966 = t4965 ** 2 + t4968 = t4966 ** 2 + t4964 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.22e2) * t4964 ** 2 * (-68 * t4966 + 104 * t4968 + 4 + (-62 * t4966 + 91 * t4968 + 11) * t4965) + + if Bindx == 534: + t4970 = np.cos(phi) + t4971 = t4970 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t4970)) * ((1 + t4970) ** (0.5e1 / 0.2e1)) * (176 * t4970 - 19 + (-1144 * t4970 + 66 + 1001 * t4971) * t4971) + + if Bindx == 535: + t4974 = np.cos(phi) + t4975 = t4974 ** 2 + t4976 = t4974 * t4975 + t4979 = t4976 ** 2 + t4977 = t4975 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + phi2)) * (240 * t4975 + 895 * t4976 - 770 * t4977 + 572 * t4979 - 10 + (-1749 * t4977 + 1001 * t4979 - 115) * t4974) + + if Bindx == 536: + t4981 = np.cos(phi) + t4982 = t4981 ** 2 + t4984 = t4982 ** 2 + t4983 = t4981 * t4982 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.6e1) * ((1 + t4981) ** (0.3e1 / 0.2e1)) * (-285 * t4982 + 165 * t4984 + 15 + (880 + 1001 * t4983) * t4983 + (-1716 * t4984 - 60) * t4981) * ((1 - t4981) ** (-0.1e1 / 0.2e1)) + + if Bindx == 537: + t4988 = np.cos(phi) + t4989 = t4988 ** 2 + t4987 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.21e2) * t4987 ** 2 * t4988 * (15 + (-110 + 143 * t4989) * t4989) + + if Bindx == 538: + t4991 = np.cos(phi) + t4992 = t4991 ** 2 + t4994 = t4992 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (-330 * t4992 + 715 * t4994 + 15 + (-550 * t4992 + 1001 * t4994 + 45) * t4991) * np.sqrt((1 + t4991)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t4991) ** (0.3e1 / 0.2e1)) + + if Bindx == 539: + t4996 = np.cos(phi) + t4997 = t4996 ** 2 + t4998 = t4996 * t4997 + t5001 = t4998 ** 2 + t4999 = t4997 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - phi2)) * (-240 * t4997 + 895 * t4998 + 770 * t4999 - 572 * t5001 + 10 + (-1749 * t4999 + 1001 * t5001 - 115) * t4996) + + if Bindx == 540: + t5003 = np.cos(phi) + t5004 = t5003 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (-176 * t5003 - 19 + (1144 * t5003 + 66 + 1001 * t5004) * t5004) * np.sqrt((1 + t5003)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t5003) ** (0.5e1 / 0.2e1)) + + if Bindx == 541: + t5008 = np.cos(phi) + t5009 = t5008 ** 2 + t5011 = t5009 ** 2 + t5007 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.22e2) * t5007 ** 2 * (68 * t5009 - 104 * t5011 - 4 + (-62 * t5009 + 91 * t5011 + 11) * t5008) + + if Bindx == 542: + t5013 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + (52 + 91 * t5013) * t5013) * ((1 + t5013) ** (0.3e1 / 0.2e1)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t5013) ** (0.7e1 / 0.2e1)) + + if Bindx == 543: + t5019 = np.sin(phi) + t5017 = t5019 ** 2 + t5014 = np.cos(phi) + t5015 = t5014 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.143e3) * t5017 ** 2 * (-12 * t5015 + 2 + (7 * t5015 + 3) * t5014) + + if Bindx == 544: + t5020 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t5020) ** (0.9e1 / 0.2e1)) * ((1 + t5020) ** (0.5e1 / 0.2e1)) + + if Bindx == 545: + t5024 = np.sin(phi) + t5021 = t5024 ** 2 + t5022 = t5024 * t5021 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.3003e4) * t5022 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 546: + t5025 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t5025) ** (0.5e1 / 0.2e1)) * ((1 + t5025) ** (0.7e1 / 0.2e1)) * (-1 + 7 * t5025) + + if Bindx == 547: + t5031 = np.sin(phi) + t5029 = t5031 ** 2 + t5026 = np.cos(phi) + t5027 = t5026 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.33e2) * t5029 ** 2 * (65 * t5027 - 5 + (91 * t5027 - 31) * t5026) + + if Bindx == 548: + t5032 = np.cos(phi) + t5033 = t5032 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.33e2) * ((1 - t5032) ** (0.3e1 / 0.2e1)) * ((1 + t5032) ** (0.5e1 / 0.2e1)) * (-39 * t5033 + 3 + (91 * t5033 - 15) * t5032) + + if Bindx == 549: + t5036 = np.cos(phi) + t5037 = t5036 ** 2 + t5039 = t5037 ** 2 + t5035 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.3e1) * t5035 ** 2 * (-198 * t5037 + 429 * t5039 + 9 + (-902 * t5037 + 1001 * t5039 + 141) * t5036) + + if Bindx == 550: + t5041 = np.cos(phi) + t5042 = t5041 ** 2 + t5044 = t5042 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t5041)) * ((1 + t5041) ** (0.3e1 / 0.2e1)) * (330 * t5042 - 715 * t5044 - 15 + (-550 * t5042 + 1001 * t5044 + 45) * t5041) + + if Bindx == 551: + t5046 = np.cos(phi) + t5047 = t5046 ** 2 + t5048 = t5046 * t5047 + t5051 = t5048 ** 2 + t5049 = t5047 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + phi2)) * (135 * t5047 + 2385 * t5048 - 495 * t5049 + 429 * t5051 - 5 + (-5049 * t5049 + 3003 * t5051 - 275) * t5046) + + if Bindx == 552: + t5053 = np.cos(phi) + t5054 = t5053 ** 2 + t5055 = t5053 * t5054 + t5060 = -495 * t5054 ** 2 + 429 * t5055 ** 2 - 5 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.14e2) * np.sqrt((1 + t5053)) * (t5060 * t5053 - 135 * t5054 + 135 * t5055 - t5060) * ((1 - t5053) ** (-0.1e1 / 0.2e1)) + + if Bindx == 553: + t5061 = np.cos(phi) + t5062 = t5061 ** 2 + t5063 = t5061 * t5062 + t5066 = t5063 ** 2 + t5064 = t5062 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - phi2)) * (-135 * t5062 + 2385 * t5063 + 495 * t5064 - 429 * t5066 + 5 + (-5049 * t5064 + 3003 * t5066 - 275) * t5061) + + if Bindx == 554: + t5068 = np.cos(phi) + t5069 = t5068 ** 2 + t5071 = t5069 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (-330 * t5069 + 715 * t5071 + 15 + (-550 * t5069 + 1001 * t5071 + 45) * t5068) * np.sqrt((1 + t5068)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t5068) ** (0.3e1 / 0.2e1)) + + if Bindx == 555: + t5074 = np.cos(phi) + t5075 = t5074 ** 2 + t5077 = t5075 ** 2 + t5073 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.3e1) * t5073 ** 2 * (198 * t5075 - 429 * t5077 - 9 + (-902 * t5075 + 1001 * t5077 + 141) * t5074) + + if Bindx == 556: + t5079 = np.cos(phi) + t5080 = t5079 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (39 * t5080 - 3 + (91 * t5080 - 15) * t5079) * ((1 + t5079) ** (0.3e1 / 0.2e1)) * np.sqrt(0.33e2) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t5079) ** (0.5e1 / 0.2e1)) + + if Bindx == 557: + t5087 = np.sin(phi) + t5085 = t5087 ** 2 + t5082 = np.cos(phi) + t5083 = t5082 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.33e2) * t5085 ** 2 * (-65 * t5083 + 5 + (91 * t5083 - 31) * t5082) + + if Bindx == 558: + t5088 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + 7 * t5088) * ((1 + t5088) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t5088) ** (0.7e1 / 0.2e1)) + + if Bindx == 559: + t5092 = np.sin(phi) + t5089 = t5092 ** 2 + t5090 = t5092 * t5089 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.3003e4) * t5090 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 560: + t5093 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.858e3) * ((1 - t5093) ** (0.7e1 / 0.2e1)) * ((1 + t5093) ** (0.7e1 / 0.2e1)) + + if Bindx == 561: + t5097 = np.sin(phi) + t5094 = t5097 ** 2 + t5095 = t5097 * t5094 + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((-6*1j) * phi2) * np.sqrt(0.3003e4) * t5095 ** 2 * np.cos(phi) + + if Bindx == 562: + t5098 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.462e3) * ((1 - t5098) ** (0.5e1 / 0.2e1)) * ((1 + t5098) ** (0.5e1 / 0.2e1)) * (13 * t5098 ** 2 - 1) + + if Bindx == 563: + t5102 = np.sin(phi) + t5100 = t5102 ** 2 + t5099 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((-4*1j) * phi2) * np.sqrt(0.462e3) * t5100 ** 2 * t5099 * (13 * t5099 ** 2 - 3) + + if Bindx == 564: + t5103 = np.cos(phi) + t5104 = t5103 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.42e2) * ((1 - t5103) ** (0.3e1 / 0.2e1)) * ((1 + t5103) ** (0.3e1 / 0.2e1)) * (3 + (-66 + 143 * t5104) * t5104) + + if Bindx == 565: + t5107 = np.cos(phi) + t5108 = t5107 ** 2 + t5106 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((-2*1j) * phi2) * np.sqrt(0.21e2) * t5106 ** 2 * t5107 * (15 + (-110 + 143 * t5108) * t5108) + + if Bindx == 566: + t5110 = np.cos(phi) + t5111 = t5110 ** 2 + t5112 = t5111 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.14e2) * np.sqrt((1 - t5110)) * np.sqrt((1 + t5110)) * (-495 * t5112 - 5 + (429 * t5112 + 135) * t5111) + + if Bindx == 567: + t5114 = np.cos(phi) + t5115 = t5114 ** 2 + t5116 = t5115 ** 2 + tfunc[..., c] = 0.15e2 / 0.16e2 * t5114 * (-693 * t5116 - 35 + (429 * t5116 + 315) * t5115) + + if Bindx == 568: + t5118 = np.cos(phi) + t5119 = t5118 ** 2 + t5120 = t5118 * t5119 + t5125 = -495 * t5119 ** 2 + 429 * t5120 ** 2 - 5 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * phi2) * np.sqrt(0.14e2) * np.sqrt((1 + t5118)) * (t5125 * t5118 - 135 * t5119 + 135 * t5120 - t5125) * ((1 - t5118) ** (-0.1e1 / 0.2e1)) + + if Bindx == 569: + t5127 = np.cos(phi) + t5128 = t5127 ** 2 + t5126 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((2*1j) * phi2) * np.sqrt(0.21e2) * t5126 ** 2 * t5127 * (15 + (-110 + 143 * t5128) * t5128) + + if Bindx == 570: + t5130 = np.cos(phi) + t5131 = t5130 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (3 + (-66 + 143 * t5131) * t5131) * ((1 + t5130) ** (0.3e1 / 0.2e1)) * np.sqrt(0.42e2) * np.exp((3*1j) * phi2) * ((1 - t5130) ** (0.3e1 / 0.2e1)) + + if Bindx == 571: + t5136 = np.sin(phi) + t5134 = t5136 ** 2 + t5133 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((4*1j) * phi2) * np.sqrt(0.462e3) * t5134 ** 2 * t5133 * (13 * t5133 ** 2 - 3) + + if Bindx == 572: + t5137 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * (13 * t5137 ** 2 - 1) * ((1 + t5137) ** (0.5e1 / 0.2e1)) * np.sqrt(0.462e3) * np.exp((5*1j) * phi2) * ((1 - t5137) ** (0.5e1 / 0.2e1)) + + if Bindx == 573: + t5141 = np.sin(phi) + t5138 = t5141 ** 2 + t5139 = t5141 * t5138 + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((6*1j) * phi2) * np.sqrt(0.3003e4) * t5139 ** 2 * np.cos(phi) + + if Bindx == 574: + t5142 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((7*1j) * phi2) * np.sqrt(0.858e3) * ((1 - t5142) ** (0.7e1 / 0.2e1)) * ((1 + t5142) ** (0.7e1 / 0.2e1)) + + if Bindx == 575: + t5146 = np.sin(phi) + t5143 = t5146 ** 2 + t5144 = t5146 * t5143 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.3003e4) * t5144 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 576: + t5147 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t5147) ** (0.7e1 / 0.2e1)) * ((1 + t5147) ** (0.5e1 / 0.2e1)) * (1 + 7 * t5147) + + if Bindx == 577: + t5153 = np.sin(phi) + t5151 = t5153 ** 2 + t5148 = np.cos(phi) + t5149 = t5148 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.33e2) * t5151 ** 2 * (-65 * t5149 + 5 + (91 * t5149 - 31) * t5148) + + if Bindx == 578: + t5154 = np.cos(phi) + t5155 = t5154 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.33e2) * ((1 - t5154) ** (0.5e1 / 0.2e1)) * ((1 + t5154) ** (0.3e1 / 0.2e1)) * (39 * t5155 - 3 + (91 * t5155 - 15) * t5154) + + if Bindx == 579: + t5158 = np.cos(phi) + t5159 = t5158 ** 2 + t5161 = t5159 ** 2 + t5157 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.3e1) * t5157 ** 2 * (198 * t5159 - 429 * t5161 - 9 + (-902 * t5159 + 1001 * t5161 + 141) * t5158) + + if Bindx == 580: + t5163 = np.cos(phi) + t5164 = t5163 ** 2 + t5166 = t5164 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.6e1) * ((1 - t5163) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t5163)) * (-330 * t5164 + 715 * t5166 + 15 + (-550 * t5164 + 1001 * t5166 + 45) * t5163) + + if Bindx == 581: + t5168 = np.cos(phi) + t5169 = t5168 ** 2 + t5170 = t5168 * t5169 + t5173 = t5170 ** 2 + t5171 = t5169 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (phi1 - phi2)) * (-135 * t5169 + 2385 * t5170 + 495 * t5171 - 429 * t5173 + 5 + (-5049 * t5171 + 3003 * t5173 - 275) * t5168) + + if Bindx == 582: + t5175 = np.cos(phi) + t5176 = t5175 ** 2 + t5177 = t5176 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.14e2) * np.sqrt((1 - t5175)) * np.sqrt((1 + t5175)) * (-495 * t5177 - 5 + (429 * t5177 + 135) * t5176) + + if Bindx == 583: + t5179 = np.cos(phi) + t5180 = t5179 ** 2 + t5181 = t5179 * t5180 + t5184 = t5181 ** 2 + t5182 = t5180 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (phi1 + phi2)) * (135 * t5180 + 2385 * t5181 - 495 * t5182 + 429 * t5184 - 5 + (-5049 * t5182 + 3003 * t5184 - 275) * t5179) + + if Bindx == 584: + t5186 = np.cos(phi) + t5187 = t5186 ** 2 + t5189 = t5187 ** 2 + t5188 = t5186 * t5187 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.6e1) * ((1 + t5186) ** (0.3e1 / 0.2e1)) * (-285 * t5187 + 165 * t5189 + 15 + (880 + 1001 * t5188) * t5188 + (-1716 * t5189 - 60) * t5186) * ((1 - t5186) ** (-0.1e1 / 0.2e1)) + + if Bindx == 585: + t5193 = np.cos(phi) + t5194 = t5193 ** 2 + t5196 = t5194 ** 2 + t5192 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.3e1) * t5192 ** 2 * (-198 * t5194 + 429 * t5196 + 9 + (-902 * t5194 + 1001 * t5196 + 141) * t5193) + + if Bindx == 586: + t5198 = np.cos(phi) + t5199 = t5198 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * (-39 * t5199 + 3 + (91 * t5199 - 15) * t5198) * ((1 + t5198) ** (0.5e1 / 0.2e1)) * np.sqrt(0.33e2) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t5198) ** (0.3e1 / 0.2e1)) + + if Bindx == 587: + t5206 = np.sin(phi) + t5204 = t5206 ** 2 + t5201 = np.cos(phi) + t5202 = t5201 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.33e2) * t5204 ** 2 * (65 * t5202 - 5 + (91 * t5202 - 31) * t5201) + + if Bindx == 588: + t5207 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * (-1 + 7 * t5207) * ((1 + t5207) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t5207) ** (0.5e1 / 0.2e1)) + + if Bindx == 589: + t5211 = np.sin(phi) + t5208 = t5211 ** 2 + t5209 = t5211 * t5208 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.3003e4) * t5209 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 590: + t5212 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t5212) ** (0.9e1 / 0.2e1)) * ((1 + t5212) ** (0.5e1 / 0.2e1)) + + if Bindx == 591: + t5218 = np.sin(phi) + t5216 = t5218 ** 2 + t5213 = np.cos(phi) + t5214 = t5213 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.143e3) * t5216 ** 2 * (-12 * t5214 + 2 + (7 * t5214 + 3) * t5213) + + if Bindx == 592: + t5219 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.22e2) * ((1 - t5219) ** (0.7e1 / 0.2e1)) * ((1 + t5219) ** (0.3e1 / 0.2e1)) * (1 + (52 + 91 * t5219) * t5219) + + if Bindx == 593: + t5221 = np.cos(phi) + t5222 = t5221 ** 2 + t5224 = t5222 ** 2 + t5220 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.22e2) * t5220 ** 2 * (68 * t5222 - 104 * t5224 - 4 + (-62 * t5222 + 91 * t5224 + 11) * t5221) + + if Bindx == 594: + t5226 = np.cos(phi) + t5227 = t5226 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * ((1 - t5226) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t5226)) * (-176 * t5226 - 19 + (1144 * t5226 + 66 + 1001 * t5227) * t5227) + + if Bindx == 595: + t5230 = np.cos(phi) + t5231 = t5230 ** 2 + t5232 = t5230 * t5231 + t5235 = t5232 ** 2 + t5233 = t5231 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 - phi2)) * (-240 * t5231 + 895 * t5232 + 770 * t5233 - 572 * t5235 + 10 + (-1749 * t5233 + 1001 * t5235 - 115) * t5230) + + if Bindx == 596: + t5237 = np.cos(phi) + t5238 = t5237 ** 2 + t5240 = t5238 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.6e1) * ((1 - t5237) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t5237)) * (-330 * t5238 + 715 * t5240 + 15 + (-550 * t5238 + 1001 * t5240 + 45) * t5237) + + if Bindx == 597: + t5243 = np.cos(phi) + t5244 = t5243 ** 2 + t5242 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((2*1j) * phi1) * np.sqrt(0.21e2) * t5242 ** 2 * t5243 * (15 + (-110 + 143 * t5244) * t5244) + + if Bindx == 598: + t5246 = np.cos(phi) + t5247 = t5246 ** 2 + t5249 = t5247 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t5246)) * ((1 + t5246) ** (0.3e1 / 0.2e1)) * (330 * t5247 - 715 * t5249 - 15 + (-550 * t5247 + 1001 * t5249 + 45) * t5246) + + if Bindx == 599: + t5251 = np.cos(phi) + t5252 = t5251 ** 2 + t5253 = t5251 * t5252 + t5256 = t5253 ** 2 + t5254 = t5252 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 + phi2)) * (240 * t5252 + 895 * t5253 - 770 * t5254 + 572 * t5256 - 10 + (-1749 * t5254 + 1001 * t5256 - 115) * t5251) + + if Bindx == 600: + t5258 = np.cos(phi) + t5259 = t5258 ** 2 + t5261 = t5259 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * ((1 + t5258) ** (0.5e1 / 0.2e1)) * (110 * t5259 - 2145 * t5261 + 19 + (1210 * t5259 + 1001 * t5261 - 195) * t5258) * ((1 - t5258) ** (-0.1e1 / 0.2e1)) + + if Bindx == 601: + t5264 = np.cos(phi) + t5265 = t5264 ** 2 + t5267 = t5265 ** 2 + t5263 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.22e2) * t5263 ** 2 * (-68 * t5265 + 104 * t5267 + 4 + (-62 * t5265 + 91 * t5267 + 11) * t5264) + + if Bindx == 602: + t5269 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (1 + (-52 + 91 * t5269) * t5269) * ((1 + t5269) ** (0.7e1 / 0.2e1)) * np.sqrt(0.22e2) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t5269) ** (0.3e1 / 0.2e1)) + + if Bindx == 603: + t5275 = np.sin(phi) + t5273 = t5275 ** 2 + t5270 = np.cos(phi) + t5271 = t5270 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.143e3) * t5273 ** 2 * (12 * t5271 - 2 + (7 * t5271 + 3) * t5270) + + if Bindx == 604: + t5276 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t5276) ** (0.5e1 / 0.2e1)) * ((1 + t5276) ** (0.9e1 / 0.2e1)) + + if Bindx == 605: + t5278 = np.cos(phi) + t5282 = -1 + t5278 + t5279 = t5282 ** 2 + t5277 = 1 + t5278 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.1001e4) * t5282 * t5279 ** 2 * t5277 ** 2 + + if Bindx == 606: + t5283 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.286e3) * ((1 - t5283) ** (0.9e1 / 0.2e1)) * ((1 + t5283) ** (0.3e1 / 0.2e1)) * (3 + 7 * t5283) + + if Bindx == 607: + t5285 = np.cos(phi) + t5286 = t5285 ** 2 + t5288 = t5286 ** 2 + t5284 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.11e2) * t5284 ** 2 * (110 * t5286 - 195 * t5288 - 11 + (50 * t5286 + 91 * t5288 - 45) * t5285) + + if Bindx == 608: + t5290 = np.cos(phi) + t5291 = t5290 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.11e2) * ((1 - t5290) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t5290)) * (117 * t5291 - 1 + (91 * t5291 + 33) * t5290) + + if Bindx == 609: + t5293 = np.cos(phi) + t5294 = t5293 ** 2 + t5295 = t5293 * t5294 + t5298 = t5295 ** 2 + t5296 = t5294 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((3*1j) * (phi1 - phi2)) * (-741 * t5294 + 555 * t5295 + 1925 * t5296 - 1287 * t5298 + 39 + (-1419 * t5296 + 1001 * t5298 - 73) * t5293) + + if Bindx == 610: + t5300 = np.cos(phi) + t5301 = t5300 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.2e1) * ((1 - t5300) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t5300)) * (-176 * t5300 - 19 + (1144 * t5300 + 66 + 1001 * t5301) * t5301) + + if Bindx == 611: + t5305 = np.cos(phi) + t5306 = t5305 ** 2 + t5308 = t5306 ** 2 + t5304 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.3e1) * t5304 ** 2 * (198 * t5306 - 429 * t5308 - 9 + (-902 * t5306 + 1001 * t5308 + 141) * t5305) + + if Bindx == 612: + t5310 = np.cos(phi) + t5311 = t5310 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.42e2) * ((1 - t5310) ** (0.3e1 / 0.2e1)) * ((1 + t5310) ** (0.3e1 / 0.2e1)) * (3 + (-66 + 143 * t5311) * t5311) + + if Bindx == 613: + t5314 = np.cos(phi) + t5315 = t5314 ** 2 + t5317 = t5315 ** 2 + t5313 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.3e1) * t5313 ** 2 * (-198 * t5315 + 429 * t5317 + 9 + (-902 * t5315 + 1001 * t5317 + 141) * t5314) + + if Bindx == 614: + t5319 = np.cos(phi) + t5320 = t5319 ** 2 + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t5319)) * ((1 + t5319) ** (0.5e1 / 0.2e1)) * (176 * t5319 - 19 + (-1144 * t5319 + 66 + 1001 * t5320) * t5320) + + if Bindx == 615: + t5323 = np.cos(phi) + t5324 = t5323 ** 2 + t5325 = t5323 * t5324 + t5328 = t5325 ** 2 + t5326 = t5324 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((3*1j) * (phi1 + phi2)) * (741 * t5324 + 555 * t5325 - 1925 * t5326 + 1287 * t5328 - 39 + (-1419 * t5326 + 1001 * t5328 - 73) * t5323) + + if Bindx == 616: + t5330 = np.cos(phi) + t5331 = t5330 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.11e2) * ((1 + t5330) ** (0.7e1 / 0.2e1)) * (-32 * t5330 - 1 + (-208 * t5330 + 150 + 91 * t5331) * t5331) * ((1 - t5330) ** (-0.1e1 / 0.2e1)) + + if Bindx == 617: + t5335 = np.cos(phi) + t5336 = t5335 ** 2 + t5338 = t5336 ** 2 + t5334 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.11e2) * t5334 ** 2 * (-110 * t5336 + 195 * t5338 + 11 + (50 * t5336 + 91 * t5338 - 45) * t5335) + + if Bindx == 618: + t5340 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * (-3 + 7 * t5340) * ((1 + t5340) ** (0.9e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t5340) ** (0.3e1 / 0.2e1)) + + if Bindx == 619: + t5342 = np.cos(phi) + t5346 = 1 + t5342 + t5343 = t5346 ** 2 + t5341 = -1 + t5342 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.1001e4) * t5341 ** 2 * t5346 * t5343 ** 2 + + if Bindx == 620: + t5347 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.91e2) * ((1 - t5347) ** (0.11e2 / 0.2e1)) * ((1 + t5347) ** (0.3e1 / 0.2e1)) + + if Bindx == 621: + t5348 = np.cos(phi) + t5352 = -1 + t5348 + t5349 = t5352 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.26e2) * t5352 * t5349 ** 2 * (1 + t5348) * (4 + 7 * t5348) + + if Bindx == 622: + t5353 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * ((1 - t5353) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t5353)) * (25 + (104 + 91 * t5353) * t5353) + + if Bindx == 623: + t5354 = np.cos(phi) + t5355 = t5354 ** 2 + t5356 = t5354 * t5355 + t5359 = t5356 ** 2 + t5357 = t5355 ** 2 + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((4*1j) * (phi1 - phi2)) * (-96 * t5355 - 115 * t5356 + 280 * t5357 - 208 * t5359 + 8 + (-3 * t5357 + 91 * t5359 + 43) * t5354) + + if Bindx == 624: + t5361 = np.cos(phi) + t5362 = t5361 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.11e2) * ((1 - t5361) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t5361)) * (117 * t5362 - 1 + (91 * t5362 + 33) * t5361) + + if Bindx == 625: + t5365 = np.cos(phi) + t5366 = t5365 ** 2 + t5368 = t5366 ** 2 + t5364 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.22e2) * t5364 ** 2 * (68 * t5366 - 104 * t5368 - 4 + (-62 * t5366 + 91 * t5368 + 11) * t5365) + + if Bindx == 626: + t5370 = np.cos(phi) + t5371 = t5370 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.33e2) * ((1 - t5370) ** (0.5e1 / 0.2e1)) * ((1 + t5370) ** (0.3e1 / 0.2e1)) * (39 * t5371 - 3 + (91 * t5371 - 15) * t5370) + + if Bindx == 627: + t5376 = np.sin(phi) + t5374 = t5376 ** 2 + t5373 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((4*1j) * phi1) * np.sqrt(0.462e3) * t5374 ** 2 * t5373 * (13 * t5373 ** 2 - 3) + + if Bindx == 628: + t5377 = np.cos(phi) + t5378 = t5377 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.33e2) * ((1 - t5377) ** (0.3e1 / 0.2e1)) * ((1 + t5377) ** (0.5e1 / 0.2e1)) * (-39 * t5378 + 3 + (91 * t5378 - 15) * t5377) + + if Bindx == 629: + t5381 = np.cos(phi) + t5382 = t5381 ** 2 + t5384 = t5382 ** 2 + t5380 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.22e2) * t5380 ** 2 * (-68 * t5382 + 104 * t5384 + 4 + (-62 * t5382 + 91 * t5384 + 11) * t5381) + + if Bindx == 630: + t5386 = np.cos(phi) + t5387 = t5386 ** 2 + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * np.sqrt((1 - t5386)) * ((1 + t5386) ** (0.7e1 / 0.2e1)) * (-117 * t5387 + 1 + (91 * t5387 + 33) * t5386) + + if Bindx == 631: + t5389 = np.cos(phi) + t5390 = t5389 ** 2 + t5391 = t5389 * t5390 + t5394 = t5391 ** 2 + t5392 = t5390 ** 2 + tfunc[..., c] = (0.15e2 / 0.32e2) * np.exp((4*1j) * (phi1 + phi2)) * (96 * t5390 - 115 * t5391 - 280 * t5392 + 208 * t5394 - 8 + (-3 * t5392 + 91 * t5394 + 43) * t5389) + + if Bindx == 632: + t5396 = np.cos(phi) + t5397 = t5396 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * ((1 + t5396) ** (0.9e1 / 0.2e1)) * (-195 * t5397 - 25 + (91 * t5397 + 129) * t5396) * ((1 - t5396) ** (-0.1e1 / 0.2e1)) + + if Bindx == 633: + t5400 = np.cos(phi) + t5401 = t5400 ** 2 + t5403 = t5401 ** 2 + t5399 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.26e2) * t5399 ** 2 * (4 * t5401 + 24 * t5403 - 4 + (26 * t5401 + 7 * t5403 - 9) * t5400) + + if Bindx == 634: + t5405 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.91e2) * ((1 - t5405) ** (0.3e1 / 0.2e1)) * ((1 + t5405) ** (0.11e2 / 0.2e1)) + + if Bindx == 635: + t5406 = np.cos(phi) + t5410 = -1 + t5406 + t5407 = t5410 ** 2 + t5408 = t5410 * t5407 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.91e2) * t5408 ** 2 * (1 + t5406) + + if Bindx == 636: + t5411 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.26e2) * ((1 - t5411) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t5411)) * (5 + 7 * t5411) + + if Bindx == 637: + t5412 = np.cos(phi) + t5413 = t5412 ** 2 + t5414 = t5412 * t5413 + t5417 = t5414 ** 2 + t5415 = t5413 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((5*1j) * (phi1 - phi2)) * (129 * t5413 - 415 * t5414 + 175 * t5415 - 325 * t5417 - 43 + (303 * t5415 + 91 * t5417 + 85) * t5412) + + if Bindx == 638: + t5419 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * ((1 - t5419) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t5419)) * (25 + (104 + 91 * t5419) * t5419) + + if Bindx == 639: + t5421 = np.cos(phi) + t5422 = t5421 ** 2 + t5424 = t5422 ** 2 + t5420 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.11e2) * t5420 ** 2 * (110 * t5422 - 195 * t5424 - 11 + (50 * t5422 + 91 * t5424 - 45) * t5421) + + if Bindx == 640: + t5426 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.22e2) * ((1 - t5426) ** (0.7e1 / 0.2e1)) * ((1 + t5426) ** (0.3e1 / 0.2e1)) * (1 + (52 + 91 * t5426) * t5426) + + if Bindx == 641: + t5432 = np.sin(phi) + t5430 = t5432 ** 2 + t5427 = np.cos(phi) + t5428 = t5427 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.33e2) * t5430 ** 2 * (-65 * t5428 + 5 + (91 * t5428 - 31) * t5427) + + if Bindx == 642: + t5433 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.462e3) * ((1 - t5433) ** (0.5e1 / 0.2e1)) * ((1 + t5433) ** (0.5e1 / 0.2e1)) * (13 * t5433 ** 2 - 1) + + if Bindx == 643: + t5439 = np.sin(phi) + t5437 = t5439 ** 2 + t5434 = np.cos(phi) + t5435 = t5434 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.33e2) * t5437 ** 2 * (65 * t5435 - 5 + (91 * t5435 - 31) * t5434) + + if Bindx == 644: + t5440 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.22e2) * ((1 - t5440) ** (0.3e1 / 0.2e1)) * ((1 + t5440) ** (0.7e1 / 0.2e1)) * (1 + (-52 + 91 * t5440) * t5440) + + if Bindx == 645: + t5442 = np.cos(phi) + t5443 = t5442 ** 2 + t5445 = t5443 ** 2 + t5441 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * t5441 ** 2 * (-110 * t5443 + 195 * t5445 + 11 + (50 * t5443 + 91 * t5445 - 45) * t5442) + + if Bindx == 646: + t5447 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt((1 - t5447)) * ((1 + t5447) ** (0.9e1 / 0.2e1)) * (25 + (-104 + 91 * t5447) * t5447) + + if Bindx == 647: + t5448 = np.cos(phi) + t5449 = t5448 ** 2 + t5450 = t5448 * t5449 + t5453 = t5450 ** 2 + t5451 = t5449 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((5*1j) * (phi1 + phi2)) * (-129 * t5449 - 415 * t5450 - 175 * t5451 + 325 * t5453 + 43 + (303 * t5451 + 91 * t5453 + 85) * t5448) + + if Bindx == 648: + t5455 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.26e2) * ((1 + t5455) ** (0.11e2 / 0.2e1)) * (5 + (-12 + 7 * t5455) * t5455) * ((1 - t5455) ** (-0.1e1 / 0.2e1)) + + if Bindx == 649: + t5456 = np.cos(phi) + t5460 = 1 + t5456 + t5457 = t5460 ** 2 + t5458 = t5460 * t5457 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.91e2) * (-1 + t5456) * t5458 ** 2 + + if Bindx == 650: + t5461 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.14e2) * ((1 - t5461) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t5461)) + + if Bindx == 651: + t5462 = np.cos(phi) + t5463 = t5462 ** 2 + t5464 = t5462 * t5463 + t5467 = t5464 ** 2 + t5465 = t5463 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((6*1j) * (phi1 - phi2)) * (48 * t5463 - 15 * t5464 - 50 * t5465 - 36 * t5467 + 6 + (69 * t5465 + 7 * t5467 - 29) * t5462) + + if Bindx == 652: + t5469 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.26e2) * ((1 - t5469) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t5469)) * (5 + 7 * t5469) + + if Bindx == 653: + t5471 = np.cos(phi) + t5472 = t5471 ** 2 + t5474 = t5472 ** 2 + t5470 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.26e2) * t5470 ** 2 * (-4 * t5472 - 24 * t5474 + 4 + (26 * t5472 + 7 * t5474 - 9) * t5471) + + if Bindx == 654: + t5476 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.286e3) * ((1 - t5476) ** (0.9e1 / 0.2e1)) * ((1 + t5476) ** (0.3e1 / 0.2e1)) * (3 + 7 * t5476) + + if Bindx == 655: + t5482 = np.sin(phi) + t5480 = t5482 ** 2 + t5477 = np.cos(phi) + t5478 = t5477 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.143e3) * t5480 ** 2 * (-12 * t5478 + 2 + (7 * t5478 + 3) * t5477) + + if Bindx == 656: + t5483 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.858e3) * ((1 - t5483) ** (0.7e1 / 0.2e1)) * ((1 + t5483) ** (0.5e1 / 0.2e1)) * (1 + 7 * t5483) + + if Bindx == 657: + t5487 = np.sin(phi) + t5484 = t5487 ** 2 + t5485 = t5487 * t5484 + tfunc[..., c] = -(0.15e2 / 0.32e2) * np.exp((6*1j) * phi1) * np.sqrt(0.3003e4) * t5485 ** 2 * np.cos(phi) + + if Bindx == 658: + t5488 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.858e3) * ((1 - t5488) ** (0.5e1 / 0.2e1)) * ((1 + t5488) ** (0.7e1 / 0.2e1)) * (-1 + 7 * t5488) + + if Bindx == 659: + t5494 = np.sin(phi) + t5492 = t5494 ** 2 + t5489 = np.cos(phi) + t5490 = t5489 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.143e3) * t5492 ** 2 * (12 * t5490 - 2 + (7 * t5490 + 3) * t5489) + + if Bindx == 660: + t5495 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.286e3) * ((1 - t5495) ** (0.3e1 / 0.2e1)) * ((1 + t5495) ** (0.9e1 / 0.2e1)) * (-3 + 7 * t5495) + + if Bindx == 661: + t5497 = np.cos(phi) + t5498 = t5497 ** 2 + t5500 = t5498 ** 2 + t5496 = np.sin(phi) + tfunc[..., c] = -(0.15e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.26e2) * t5496 ** 2 * (4 * t5498 + 24 * t5500 - 4 + (26 * t5498 + 7 * t5500 - 9) * t5497) + + if Bindx == 662: + t5502 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t5502)) * ((1 + t5502) ** (0.11e2 / 0.2e1)) * (-5 + 7 * t5502) + + if Bindx == 663: + t5503 = np.cos(phi) + t5504 = t5503 ** 2 + t5505 = t5503 * t5504 + t5508 = t5505 ** 2 + t5506 = t5504 ** 2 + tfunc[..., c] = (0.15e2 / 0.64e2) * np.exp((6*1j) * (phi1 + phi2)) * (-48 * t5504 - 15 * t5505 + 50 * t5506 + 36 * t5508 - 6 + (69 * t5506 + 7 * t5508 - 29) * t5503) + + if Bindx == 664: + t5510 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t5510)) * ((1 + t5510) ** (0.13e2 / 0.2e1)) + + if Bindx == 665: + t5511 = np.cos(phi) + t5512 = t5511 ** 2 + t5513 = t5511 * t5512 + t5516 = t5513 ** 2 + t5514 = t5512 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((7*1j) * (phi1 - phi2)) * (-21 * t5512 + 35 * t5513 - 35 * t5514 - 7 * t5516 - 1 + (21 * t5514 + t5516 + 7) * t5511) + + if Bindx == 666: + t5518 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.14e2) * ((1 - t5518) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t5518)) + + if Bindx == 667: + t5519 = np.cos(phi) + t5523 = -1 + t5519 + t5520 = t5523 ** 2 + t5521 = t5523 * t5520 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.91e2) * t5521 ** 2 * (1 + t5519) + + if Bindx == 668: + t5524 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.91e2) * ((1 - t5524) ** (0.11e2 / 0.2e1)) * ((1 + t5524) ** (0.3e1 / 0.2e1)) + + if Bindx == 669: + t5526 = np.cos(phi) + t5530 = -1 + t5526 + t5527 = t5530 ** 2 + t5525 = 1 + t5526 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.1001e4) * t5530 * t5527 ** 2 * t5525 ** 2 + + if Bindx == 670: + t5531 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t5531) ** (0.9e1 / 0.2e1)) * ((1 + t5531) ** (0.5e1 / 0.2e1)) + + if Bindx == 671: + t5535 = np.sin(phi) + t5532 = t5535 ** 2 + t5533 = t5535 * t5532 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.3003e4) * t5533 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 672: + t5536 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.858e3) * ((1 - t5536) ** (0.7e1 / 0.2e1)) * ((1 + t5536) ** (0.7e1 / 0.2e1)) + + if Bindx == 673: + t5540 = np.sin(phi) + t5537 = t5540 ** 2 + t5538 = t5540 * t5537 + tfunc[..., c] = -(0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.3003e4) * t5538 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 674: + t5541 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t5541) ** (0.5e1 / 0.2e1)) * ((1 + t5541) ** (0.9e1 / 0.2e1)) + + if Bindx == 675: + t5543 = np.cos(phi) + t5547 = 1 + t5543 + t5544 = t5547 ** 2 + t5542 = -1 + t5543 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.1001e4) * t5542 ** 2 * t5547 * t5544 ** 2 + + if Bindx == 676: + t5548 = np.cos(phi) + tfunc[..., c] = (0.15e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.91e2) * ((1 - t5548) ** (0.3e1 / 0.2e1)) * ((1 + t5548) ** (0.11e2 / 0.2e1)) + + if Bindx == 677: + t5549 = np.cos(phi) + t5553 = 1 + t5549 + t5550 = t5553 ** 2 + t5551 = t5553 * t5550 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.91e2) * (-1 + t5549) * t5551 ** 2 + + if Bindx == 678: + t5554 = np.cos(phi) + tfunc[..., c] = (-0.15e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t5554)) * ((1 + t5554) ** (0.13e2 / 0.2e1)) + + if Bindx == 679: + t5555 = np.cos(phi) + t5556 = t5555 ** 2 + t5557 = t5555 * t5556 + t5560 = t5557 ** 2 + t5558 = t5556 ** 2 + tfunc[..., c] = (0.15e2 / 0.128e3) * np.exp((7*1j) * (phi1 + phi2)) * (21 * t5556 + 35 * t5557 + 35 * t5558 + 7 * t5560 + 1 + (21 * t5558 + t5560 + 7) * t5555) + + if Bindx == 680: + t5562 = np.cos(phi) + t5570 = 8 * t5562 + t5563 = t5562 ** 2 + t5565 = t5563 ** 2 + t5564 = t5562 * t5563 + tfunc[..., c] = (0.17e2 / 0.256e3) * np.exp((-8*1j) * (phi1 + phi2)) * (28 * t5563 + t5570 + 1 + (56 * t5562 + 70 + t5565) * t5565 + (56 + (t5570 + 28) * t5564) * t5564) + + if Bindx == 681: + t5571 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * ((1 + t5571) ** (0.15e2 / 0.2e1)) * np.sqrt((1 - t5571)) + + if Bindx == 682: + t5572 = np.cos(phi) + t5577 = 1 + t5572 + t5573 = t5577 ** 2 + t5574 = t5577 * t5573 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * (-1 + t5572) * t5577 * t5574 ** 2 + + if Bindx == 683: + t5578 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.35e2) * ((1 - t5578) ** (0.3e1 / 0.2e1)) * ((1 + t5578) ** (0.13e2 / 0.2e1)) + + if Bindx == 684: + t5580 = np.cos(phi) + t5584 = 1 + t5580 + t5581 = t5584 ** 2 + t5582 = t5584 * t5581 + t5579 = -1 + t5580 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.455e3) * t5579 ** 2 * t5582 ** 2 + + if Bindx == 685: + t5585 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * ((1 - t5585) ** (0.5e1 / 0.2e1)) * ((1 + t5585) ** (0.11e2 / 0.2e1)) + + if Bindx == 686: + t5586 = np.cos(phi) + t5593 = -1 + t5586 + t5592 = 1 + t5586 + t5589 = t5592 ** 2 + t5587 = t5593 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.2002e4) * t5593 * t5587 * t5592 * t5589 ** 2 + + if Bindx == 687: + t5594 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 + phi2)) * np.sqrt(0.715e3) * ((1 - t5594) ** (0.7e1 / 0.2e1)) * ((1 + t5594) ** (0.9e1 / 0.2e1)) + + if Bindx == 688: + t5598 = np.sin(phi) + t5595 = t5598 ** 2 + t5596 = t5595 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.1430e4) * t5596 ** 2 + + if Bindx == 689: + t5599 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 - phi2)) * np.sqrt(0.715e3) * ((1 - t5599) ** (0.9e1 / 0.2e1)) * ((1 + t5599) ** (0.7e1 / 0.2e1)) + + if Bindx == 690: + t5600 = np.cos(phi) + t5607 = -1 + t5600 + t5606 = 1 + t5600 + t5604 = t5606 ** 2 + t5601 = t5607 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.2002e4) * t5607 * t5601 ** 2 * t5606 * t5604 + + if Bindx == 691: + t5608 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * ((1 - t5608) ** (0.11e2 / 0.2e1)) * ((1 + t5608) ** (0.5e1 / 0.2e1)) + + if Bindx == 692: + t5610 = np.cos(phi) + t5614 = -1 + t5610 + t5611 = t5614 ** 2 + t5612 = t5614 * t5611 + t5609 = 1 + t5610 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.455e3) * t5612 ** 2 * t5609 ** 2 + + if Bindx == 693: + t5615 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.35e2) * ((1 - t5615) ** (0.13e2 / 0.2e1)) * ((1 + t5615) ** (0.3e1 / 0.2e1)) + + if Bindx == 694: + t5616 = np.cos(phi) + t5621 = -1 + t5616 + t5617 = t5621 ** 2 + t5618 = t5621 * t5617 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.30e2) * t5621 * t5618 ** 2 * (1 + t5616) + + if Bindx == 695: + t5622 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t5622) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t5622)) + + if Bindx == 696: + t5623 = np.cos(phi) + t5631 = -8 * t5623 + t5624 = t5623 ** 2 + t5626 = t5624 ** 2 + t5625 = t5623 * t5624 + tfunc[..., c] = (0.17e2 / 0.256e3) * np.exp((-8*1j) * (phi1 - phi2)) * (28 * t5624 + t5631 + 1 + (-56 * t5623 + 70 + t5626) * t5626 + (-56 + (t5631 + 28) * t5625) * t5625) + + if Bindx == 697: + t5632 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt((1 - t5632)) * ((1 + t5632) ** (0.15e2 / 0.2e1)) + + if Bindx == 698: + t5633 = np.cos(phi) + t5634 = t5633 ** 2 + t5635 = t5633 * t5634 + t5638 = t5635 ** 2 + t5636 = t5634 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-7*1j) * (phi1 + phi2)) * (-91 * t5634 - 77 * t5635 + 119 * t5638 - 7 + (35 + 8 * t5636) * t5636 + (133 * t5636 + 49 * t5638 - 41) * t5633) + + if Bindx == 699: + t5641 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.30e2) * ((1 + t5641) ** (0.13e2 / 0.2e1)) * (3 + (-7 + 4 * t5641) * t5641) * ((1 - t5641) ** (-0.1e1 / 0.2e1)) + + if Bindx == 700: + t5643 = np.cos(phi) + t5644 = t5643 ** 2 + t5646 = t5644 ** 2 + t5645 = t5643 * t5644 + t5642 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.35e2) * t5642 ** 2 * (-10 * t5644 + 55 * t5646 - 5 + (30 + 8 * t5645) * t5645 + (35 * t5646 - 17) * t5643) + + if Bindx == 701: + t5649 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (2 * t5649 - 1) * ((1 + t5649) ** (0.11e2 / 0.2e1)) * np.sqrt(0.455e3) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t5649) ** (0.3e1 / 0.2e1)) + + if Bindx == 702: + t5656 = np.sin(phi) + t5654 = t5656 ** 2 + t5650 = np.cos(phi) + t5651 = t5650 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * t5654 ** 2 * (-t5650 - 3 + (21 * t5650 + 15 + 8 * t5651) * t5651) + + if Bindx == 703: + t5657 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-1 + 4 * t5657) * ((1 + t5657) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t5657) ** (0.5e1 / 0.2e1)) + + if Bindx == 704: + t5662 = np.sin(phi) + t5659 = t5662 ** 2 + t5660 = t5662 * t5659 + t5658 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.715e3) * t5660 ** 2 * (-1 + (7 + 8 * t5658) * t5658) + + if Bindx == 705: + t5663 = np.cos(phi) + t5667 = -4 * t5663 + t5664 = t5663 ** 2 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * t5663 * (t5667 + 1 + (t5667 + 6 + t5664) * t5664) * ((1 + t5663) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((-7*1j) * phi1) * ((1 - t5663) ** (-0.1e1 / 0.2e1)) + + if Bindx == 706: + t5672 = np.sin(phi) + t5669 = t5672 ** 2 + t5670 = t5672 * t5669 + t5668 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.715e3) * t5670 ** 2 * (-1 + (-7 + 8 * t5668) * t5668) + + if Bindx == 707: + t5673 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (1 + 4 * t5673) * ((1 + t5673) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t5673) ** (0.9e1 / 0.2e1)) + + if Bindx == 708: + t5680 = np.sin(phi) + t5678 = t5680 ** 2 + t5674 = np.cos(phi) + t5675 = t5674 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * t5678 ** 2 * (t5674 - 3 + (-21 * t5674 + 15 + 8 * t5675) * t5675) + + if Bindx == 709: + t5681 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (1 + 2 * t5681) * ((1 + t5681) ** (0.3e1 / 0.2e1)) * np.sqrt(0.455e3) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t5681) ** (0.11e2 / 0.2e1)) + + if Bindx == 710: + t5683 = np.cos(phi) + t5684 = t5683 ** 2 + t5686 = t5684 ** 2 + t5685 = t5683 * t5684 + t5682 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.35e2) * t5682 ** 2 * (-10 * t5684 + 55 * t5686 - 5 + (-30 + 8 * t5685) * t5685 + (-35 * t5686 + 17) * t5683) + + if Bindx == 711: + t5689 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (3 + 4 * t5689) * np.sqrt((1 + t5689)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t5689) ** (0.13e2 / 0.2e1)) + + if Bindx == 712: + t5690 = np.cos(phi) + t5691 = t5690 ** 2 + t5692 = t5690 * t5691 + t5695 = t5692 ** 2 + t5693 = t5691 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-7*1j) * (phi1 - phi2)) * (-91 * t5691 + 77 * t5692 + 119 * t5695 - 7 + (35 + 8 * t5693) * t5693 + (-133 * t5693 - 49 * t5695 + 41) * t5690) + + if Bindx == 713: + t5698 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t5698) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t5698)) + + if Bindx == 714: + t5699 = np.cos(phi) + t5704 = 1 + t5699 + t5700 = t5704 ** 2 + t5701 = t5704 * t5700 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * (-1 + t5699) * t5704 * t5701 ** 2 + + if Bindx == 715: + t5705 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t5705)) * ((1 + t5705) ** (0.13e2 / 0.2e1)) * (-3 + 4 * t5705) + + if Bindx == 716: + t5706 = np.cos(phi) + t5707 = t5706 ** 2 + t5708 = t5706 * t5707 + t5711 = t5708 ** 2 + t5709 = t5707 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-6*1j) * (phi1 + phi2)) * (-175 * t5708 + 196 * t5711 + 16 + (-210 + 30 * t5709) * t5709 + (21 * t5709 + 135 * t5711 + 51) * t5706) + + if Bindx == 717: + t5714 = np.cos(phi) + t5715 = t5714 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.42e2) * ((1 + t5714) ** (0.11e2 / 0.2e1)) * (-45 * t5715 - 7 + (20 * t5715 + 32) * t5714) * ((1 - t5714) ** (-0.1e1 / 0.2e1)) + + if Bindx == 718: + t5718 = np.cos(phi) + t5719 = t5718 ** 2 + t5720 = t5718 * t5719 + t5717 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.546e3) * t5717 ** 2 * (-t5718 + 1 + (-6 + 5 * t5720) * t5720 + (-9 + (15 * t5718 + 11) * t5719) * t5719) + + if Bindx == 719: + t5724 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (1 + (-9 + 12 * t5724) * t5724) * ((1 + t5724) ** (0.9e1 / 0.2e1)) * np.sqrt(0.910e3) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t5724) ** (0.3e1 / 0.2e1)) + + if Bindx == 720: + t5730 = np.sin(phi) + t5728 = t5730 ** 2 + t5725 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.15015e5) * t5728 ** 2 * t5725 * (-1 + (2 * t5725 + 3) * t5725 ** 2) + + if Bindx == 721: + t5731 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-1 + (-5 + 20 * t5731) * t5731) * ((1 + t5731) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t5731) ** (0.5e1 / 0.2e1)) + + if Bindx == 722: + t5736 = np.sin(phi) + t5733 = t5736 ** 2 + t5734 = t5736 * t5733 + t5732 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.429e3) * t5734 ** 2 * (15 * t5732 ** 2 - 1) + + if Bindx == 723: + t5737 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (-1 + (5 + 20 * t5737) * t5737) * ((1 + t5737) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t5737) ** (0.7e1 / 0.2e1)) + + if Bindx == 724: + t5743 = np.sin(phi) + t5741 = t5743 ** 2 + t5738 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.15015e5) * t5741 ** 2 * t5738 * (1 + (2 * t5738 - 3) * t5738 ** 2) + + if Bindx == 725: + t5744 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (1 + (9 + 12 * t5744) * t5744) * ((1 + t5744) ** (0.3e1 / 0.2e1)) * np.sqrt(0.910e3) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t5744) ** (0.9e1 / 0.2e1)) + + if Bindx == 726: + t5746 = np.cos(phi) + t5747 = t5746 ** 2 + t5748 = t5746 * t5747 + t5745 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.546e3) * t5745 ** 2 * (t5746 + 1 + (6 + 5 * t5748) * t5748 + (-9 + (-15 * t5746 + 11) * t5747) * t5747) + + if Bindx == 727: + t5752 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (7 + (25 + 20 * t5752) * t5752) * np.sqrt((1 + t5752)) * np.sqrt(0.42e2) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t5752) ** (0.11e2 / 0.2e1)) + + if Bindx == 728: + t5753 = np.cos(phi) + t5754 = t5753 ** 2 + t5755 = t5753 * t5754 + t5758 = t5755 ** 2 + t5756 = t5754 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-6*1j) * (phi1 - phi2)) * (175 * t5755 + 196 * t5758 + 16 + (-210 + 30 * t5756) * t5756 + (-21 * t5756 - 135 * t5758 - 51) * t5753) + + if Bindx == 729: + t5761 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (3 + 4 * t5761) * np.sqrt((1 + t5761)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t5761) ** (0.13e2 / 0.2e1)) + + if Bindx == 730: + t5762 = np.cos(phi) + t5767 = -1 + t5762 + t5763 = t5767 ** 2 + t5764 = t5767 * t5763 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.30e2) * t5767 * t5764 ** 2 * (1 + t5762) + + if Bindx == 731: + t5768 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.35e2) * ((1 - t5768) ** (0.3e1 / 0.2e1)) * ((1 + t5768) ** (0.13e2 / 0.2e1)) + + if Bindx == 732: + t5769 = np.cos(phi) + t5773 = 1 + t5769 + t5770 = t5773 ** 2 + t5771 = t5773 * t5770 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.35e2) * (-1 + t5769) * t5771 ** 2 * (-5 + 8 * t5769) + + if Bindx == 733: + t5774 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.42e2) * np.sqrt((1 - t5774)) * ((1 + t5774) ** (0.11e2 / 0.2e1)) * (7 + (-25 + 20 * t5774) * t5774) + + if Bindx == 734: + t5775 = np.cos(phi) + t5776 = t5775 ** 2 + t5777 = t5775 * t5776 + t5780 = t5777 ** 2 + t5778 = t5776 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-5*1j) * (phi1 + phi2)) * (495 * t5776 + 145 * t5777 + 469 * t5780 - 45 + (-1135 + 280 * t5778) * t5778 + (-1025 * t5778 + 875 * t5780 + 69) * t5775) + + if Bindx == 735: + t5783 = np.cos(phi) + t5784 = t5783 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.13e2) * ((1 + t5783) ** (0.9e1 / 0.2e1)) * (-45 * t5783 + 3 + (-175 * t5783 + 147 + 70 * t5784) * t5784) * ((1 - t5783) ** (-0.1e1 / 0.2e1)) + + if Bindx == 736: + t5788 = np.cos(phi) + t5789 = t5788 ** 2 + t5791 = t5789 ** 2 + t5790 = t5788 * t5789 + t5787 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.195e3) * t5787 ** 2 * (-18 * t5789 - 7 * t5791 + 1 + (-90 + 56 * t5790) * t5790 + (105 * t5791 + 17) * t5788) + + if Bindx == 737: + t5794 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (1 + (28 * t5794 - 21) * t5794 ** 2) * ((1 + t5794) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t5794) ** (0.3e1 / 0.2e1)) + + if Bindx == 738: + t5803 = np.sin(phi) + t5801 = t5803 ** 2 + t5797 = np.cos(phi) + t5798 = t5797 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.1001e4) * t5801 ** 2 * (-5 * t5797 + 1 + (25 * t5797 - 21 + 40 * t5798) * t5798) + + if Bindx == 739: + t5804 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * (5 * t5804 ** 2 - 1) * t5804 * ((1 + t5804) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((-5*1j) * phi1) * ((1 - t5804) ** (0.5e1 / 0.2e1)) + + if Bindx == 740: + t5811 = np.sin(phi) + t5809 = t5811 ** 2 + t5805 = np.cos(phi) + t5806 = t5805 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.1001e4) * t5809 ** 2 * (5 * t5805 + 1 + (-25 * t5805 - 21 + 40 * t5806) * t5806) + + if Bindx == 741: + t5812 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (-1 + (28 * t5812 + 21) * t5812 ** 2) * ((1 + t5812) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t5812) ** (0.7e1 / 0.2e1)) + + if Bindx == 742: + t5816 = np.cos(phi) + t5817 = t5816 ** 2 + t5819 = t5817 ** 2 + t5818 = t5816 * t5817 + t5815 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.195e3) * t5815 ** 2 * (-18 * t5817 - 7 * t5819 + 1 + (90 + 56 * t5818) * t5818 + (-105 * t5819 - 17) * t5816) + + if Bindx == 743: + t5822 = np.cos(phi) + t5823 = t5822 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * (105 * t5823 + 3 + (70 * t5823 + 42) * t5822) * np.sqrt((1 + t5822)) * np.sqrt(0.13e2) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t5822) ** (0.9e1 / 0.2e1)) + + if Bindx == 744: + t5825 = np.cos(phi) + t5826 = t5825 ** 2 + t5827 = t5825 * t5826 + t5830 = t5827 ** 2 + t5828 = t5826 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-5*1j) * (phi1 - phi2)) * (495 * t5826 - 145 * t5827 + 469 * t5830 - 45 + (-1135 + 280 * t5828) * t5828 + (1025 * t5828 - 875 * t5830 - 69) * t5825) + + if Bindx == 745: + t5833 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (7 + (25 + 20 * t5833) * t5833) * np.sqrt((1 + t5833)) * np.sqrt(0.42e2) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t5833) ** (0.11e2 / 0.2e1)) + + if Bindx == 746: + t5835 = np.cos(phi) + t5836 = t5835 ** 2 + t5838 = t5836 ** 2 + t5837 = t5835 * t5836 + t5834 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.35e2) * t5834 ** 2 * (-10 * t5836 + 55 * t5838 - 5 + (-30 + 8 * t5837) * t5837 + (-35 * t5838 + 17) * t5835) + + if Bindx == 747: + t5841 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.35e2) * ((1 - t5841) ** (0.13e2 / 0.2e1)) * ((1 + t5841) ** (0.3e1 / 0.2e1)) + + if Bindx == 748: + t5843 = np.cos(phi) + t5847 = 1 + t5843 + t5844 = t5847 ** 2 + t5845 = t5847 * t5844 + t5842 = -1 + t5843 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.455e3) * t5842 ** 2 * t5845 ** 2 + + if Bindx == 749: + t5848 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.455e3) * ((1 - t5848) ** (0.3e1 / 0.2e1)) * ((1 + t5848) ** (0.11e2 / 0.2e1)) * (2 * t5848 - 1) + + if Bindx == 750: + t5850 = np.cos(phi) + t5851 = t5850 ** 2 + t5852 = t5850 * t5851 + t5849 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.546e3) * t5849 ** 2 * (-t5850 + 1 + (-6 + 5 * t5852) * t5852 + (-9 + (15 * t5850 + 11) * t5851) * t5851) + + if Bindx == 751: + t5856 = np.cos(phi) + t5857 = t5856 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.13e2) * np.sqrt((1 - t5856)) * ((1 + t5856) ** (0.9e1 / 0.2e1)) * (-105 * t5857 - 3 + (70 * t5857 + 42) * t5856) + + if Bindx == 752: + t5859 = np.cos(phi) + t5860 = t5859 ** 2 + t5861 = t5859 * t5860 + t5864 = t5861 ** 2 + t5862 = t5860 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-4*1j) * (phi1 + phi2)) * (180 * t5860 + 770 * t5861 - 364 * t5864 - 9 + (-230 + 455 * t5862) * t5862 + (-1534 * t5862 + 910 * t5864 - 114) * t5859) + + if Bindx == 753: + t5867 = np.cos(phi) + t5868 = t5867 ** 2 + t5870 = t5868 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.15e2) * ((1 + t5867) ** (0.7e1 / 0.2e1)) * (-78 * t5868 - 455 * t5870 + 5 + (364 * t5868 + 182 * t5870 - 18) * t5867) * ((1 - t5867) ** (-0.1e1 / 0.2e1)) + + if Bindx == 754: + t5873 = np.cos(phi) + t5874 = t5873 ** 2 + t5876 = t5874 ** 2 + t5875 = t5873 * t5874 + t5872 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.110e3) * t5872 ** 2 * (25 * t5874 - 91 * t5876 - 1 + (-78 + 91 * t5875) * t5875 + (91 * t5876 + 11) * t5873) + + if Bindx == 755: + t5879 = np.cos(phi) + t5880 = t5879 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (13 * t5879 + 1 + (-65 * t5879 - 39 + 130 * t5880) * t5880) * ((1 + t5879) ** (0.5e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t5879) ** (0.3e1 / 0.2e1)) + + if Bindx == 756: + t5888 = np.sin(phi) + t5886 = t5888 ** 2 + t5883 = np.cos(phi) + t5884 = t5883 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.154e3) * t5886 ** 2 * (1 + (-26 + 65 * t5884) * t5884) + + if Bindx == 757: + t5889 = np.cos(phi) + t5890 = t5889 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * (-13 * t5889 + 1 + (65 * t5889 - 39 + 130 * t5890) * t5890) * ((1 + t5889) ** (0.3e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t5889) ** (0.5e1 / 0.2e1)) + + if Bindx == 758: + t5894 = np.cos(phi) + t5895 = t5894 ** 2 + t5900 = -91 * t5895 ** 2 + t5896 = t5894 * t5895 + t5893 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.110e3) * t5893 ** 2 * (25 * t5895 + t5900 - 1 + (78 + 91 * t5896) * t5896 + (t5900 - 11) * t5894) + + if Bindx == 759: + t5901 = np.cos(phi) + t5902 = t5901 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (-13 * t5901 - 5 + (273 * t5901 + 91 + 182 * t5902) * t5902) * np.sqrt((1 + t5901)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t5901) ** (0.7e1 / 0.2e1)) + + if Bindx == 760: + t5905 = np.cos(phi) + t5906 = t5905 ** 2 + t5907 = t5905 * t5906 + t5910 = t5907 ** 2 + t5908 = t5906 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-4*1j) * (phi1 - phi2)) * (180 * t5906 - 770 * t5907 - 364 * t5910 - 9 + (-230 + 455 * t5908) * t5908 + (1534 * t5908 - 910 * t5910 + 114) * t5905) + + if Bindx == 761: + t5913 = np.cos(phi) + t5914 = t5913 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * (105 * t5914 + 3 + (70 * t5914 + 42) * t5913) * np.sqrt((1 + t5913)) * np.sqrt(0.13e2) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t5913) ** (0.9e1 / 0.2e1)) + + if Bindx == 762: + t5917 = np.cos(phi) + t5918 = t5917 ** 2 + t5919 = t5917 * t5918 + t5916 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.546e3) * t5916 ** 2 * (t5917 + 1 + (6 + 5 * t5919) * t5919 + (-9 + (-15 * t5917 + 11) * t5918) * t5918) + + if Bindx == 763: + t5923 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (1 + 2 * t5923) * ((1 + t5923) ** (0.3e1 / 0.2e1)) * np.sqrt(0.455e3) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t5923) ** (0.11e2 / 0.2e1)) + + if Bindx == 764: + t5925 = np.cos(phi) + t5929 = -1 + t5925 + t5926 = t5929 ** 2 + t5927 = t5929 * t5926 + t5924 = 1 + t5925 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.455e3) * t5927 ** 2 * t5924 ** 2 + + if Bindx == 765: + t5930 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.273e3) * ((1 - t5930) ** (0.5e1 / 0.2e1)) * ((1 + t5930) ** (0.11e2 / 0.2e1)) + + if Bindx == 766: + t5932 = np.cos(phi) + t5936 = 1 + t5932 + t5933 = t5936 ** 2 + t5931 = -1 + t5932 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.273e3) * t5931 ** 2 * t5936 * t5933 ** 2 * (-3 + 8 * t5932) + + if Bindx == 767: + t5937 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.910e3) * ((1 - t5937) ** (0.3e1 / 0.2e1)) * ((1 + t5937) ** (0.9e1 / 0.2e1)) * (1 + (-9 + 12 * t5937) * t5937) + + if Bindx == 768: + t5939 = np.cos(phi) + t5940 = t5939 ** 2 + t5942 = t5940 ** 2 + t5941 = t5939 * t5940 + t5938 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.195e3) * t5938 ** 2 * (-18 * t5940 - 7 * t5942 + 1 + (-90 + 56 * t5941) * t5941 + (105 * t5942 + 17) * t5939) + + if Bindx == 769: + t5945 = np.cos(phi) + t5946 = t5945 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t5945)) * ((1 + t5945) ** (0.7e1 / 0.2e1)) * (13 * t5945 - 5 + (-273 * t5945 + 91 + 182 * t5946) * t5946) + + if Bindx == 770: + t5949 = np.cos(phi) + t5950 = t5949 ** 2 + t5951 = t5949 * t5950 + t5954 = t5951 ** 2 + t5952 = t5950 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-3*1j) * (phi1 + phi2)) * (-459 * t5950 + 2107 * t5951 - 3913 * t5954 + 17 + (2235 + 2184 * t5952) * t5952 + (-4251 * t5952 + 2457 * t5954 - 249) * t5949) + + if Bindx == 771: + t5957 = np.cos(phi) + t5958 = t5957 ** 2 + t5960 = t5958 ** 2 + t5959 = t5957 * t5958 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.66e2) * ((1 + t5957) ** (0.5e1 / 0.2e1)) * (-150 * t5958 + 455 * t5960 + 3 + (130 + 364 * t5959) * t5959 + (-819 * t5960 + 17) * t5957) * ((1 - t5957) ** (-0.1e1 / 0.2e1)) + + if Bindx == 772: + t5964 = np.cos(phi) + t5965 = t5964 ** 2 + t5967 = t5965 ** 2 + t5966 = t5964 * t5965 + t5963 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.1155e4) * t5963 ** 2 * (30 * t5965 - 117 * t5967 - 1 + (-26 + 104 * t5966) * t5966 + (39 * t5967 + 3) * t5964) + + if Bindx == 773: + t5970 = np.cos(phi) + t5971 = t5970 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (3 + (-26 + 39 * t5971) * t5971) * t5970 * ((1 + t5970) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2310e4) * np.exp((-3*1j) * phi1) * ((1 - t5970) ** (0.3e1 / 0.2e1)) + + if Bindx == 774: + t5974 = np.cos(phi) + t5975 = t5974 ** 2 + t5977 = t5975 ** 2 + t5976 = t5974 * t5975 + t5973 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.1155e4) * t5973 ** 2 * (30 * t5975 - 117 * t5977 - 1 + (26 + 104 * t5976) * t5976 + (-39 * t5977 - 3) * t5974) + + if Bindx == 775: + t5980 = np.cos(phi) + t5981 = t5980 ** 2 + t5982 = t5981 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-130 * t5981 + 455 * t5982 + 3 + (364 * t5982 - 20) * t5980) * np.sqrt((1 + t5980)) * np.sqrt(0.66e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t5980) ** (0.5e1 / 0.2e1)) + + if Bindx == 776: + t5984 = np.cos(phi) + t5985 = t5984 ** 2 + t5986 = t5984 * t5985 + t5989 = t5986 ** 2 + t5987 = t5985 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-3*1j) * (phi1 - phi2)) * (-459 * t5985 - 2107 * t5986 - 3913 * t5989 + 17 + (2235 + 2184 * t5987) * t5987 + (4251 * t5987 - 2457 * t5989 + 249) * t5984) + + if Bindx == 777: + t5992 = np.cos(phi) + t5993 = t5992 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (-13 * t5992 - 5 + (273 * t5992 + 91 + 182 * t5993) * t5993) * np.sqrt((1 + t5992)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t5992) ** (0.7e1 / 0.2e1)) + + if Bindx == 778: + t5997 = np.cos(phi) + t5998 = t5997 ** 2 + t6000 = t5998 ** 2 + t5999 = t5997 * t5998 + t5996 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.195e3) * t5996 ** 2 * (-18 * t5998 - 7 * t6000 + 1 + (90 + 56 * t5999) * t5999 + (-105 * t6000 - 17) * t5997) + + if Bindx == 779: + t6003 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (1 + (9 + 12 * t6003) * t6003) * ((1 + t6003) ** (0.3e1 / 0.2e1)) * np.sqrt(0.910e3) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t6003) ** (0.9e1 / 0.2e1)) + + if Bindx == 780: + t6010 = np.sin(phi) + t6008 = t6010 ** 2 + t6004 = np.cos(phi) + t6005 = t6004 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.273e3) * t6008 ** 2 * (t6004 - 3 + (-21 * t6004 + 15 + 8 * t6005) * t6005) + + if Bindx == 781: + t6011 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.273e3) * ((1 - t6011) ** (0.11e2 / 0.2e1)) * ((1 + t6011) ** (0.5e1 / 0.2e1)) + + if Bindx == 782: + t6012 = np.cos(phi) + t6019 = -1 + t6012 + t6018 = 1 + t6012 + t6015 = t6018 ** 2 + t6013 = t6019 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.2002e4) * t6019 * t6013 * t6018 * t6015 ** 2 + + if Bindx == 783: + t6020 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t6020) ** (0.5e1 / 0.2e1)) * ((1 + t6020) ** (0.9e1 / 0.2e1)) * (-1 + 4 * t6020) + + if Bindx == 784: + t6026 = np.sin(phi) + t6024 = t6026 ** 2 + t6021 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.15015e5) * t6024 ** 2 * t6021 * (-1 + (2 * t6021 + 3) * t6021 ** 2) + + if Bindx == 785: + t6027 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.1430e4) * ((1 - t6027) ** (0.3e1 / 0.2e1)) * ((1 + t6027) ** (0.7e1 / 0.2e1)) * (1 + (28 * t6027 - 21) * t6027 ** 2) + + if Bindx == 786: + t6031 = np.cos(phi) + t6032 = t6031 ** 2 + t6034 = t6032 ** 2 + t6033 = t6031 * t6032 + t6030 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.110e3) * t6030 ** 2 * (25 * t6032 - 91 * t6034 - 1 + (-78 + 91 * t6033) * t6033 + (91 * t6034 + 11) * t6031) + + if Bindx == 787: + t6037 = np.cos(phi) + t6038 = t6037 ** 2 + t6039 = t6038 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.66e2) * np.sqrt((1 - t6037)) * ((1 + t6037) ** (0.5e1 / 0.2e1)) * (130 * t6038 - 455 * t6039 - 3 + (364 * t6039 - 20) * t6037) + + if Bindx == 788: + t6041 = np.cos(phi) + t6042 = t6041 ** 2 + t6043 = t6041 * t6042 + t6046 = t6043 ** 2 + t6044 = t6042 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + phi2)) * (-512 * t6042 + 671 * t6043 - 4004 * t6046 + 16 + (2530 + 2002 * t6044) * t6044 + (-1573 * t6044 + 1001 * t6046 - 67) * t6041) + + if Bindx == 789: + t6049 = np.cos(phi) + t6050 = t6049 ** 2 + t6051 = t6049 * t6050 + t6053 = t6051 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.70e2) * ((1 + t6049) ** (0.3e1 / 0.2e1)) * (-220 * t6051 - 1001 * t6053 + 1 + (-99 + 715 * t6050) * t6050 + (572 * t6053 + 32) * t6049) * ((1 - t6049) ** (-0.1e1 / 0.2e1)) + + if Bindx == 790: + t6056 = np.cos(phi) + t6057 = t6056 ** 2 + t6058 = t6057 ** 2 + t6055 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.35e2) * t6055 ** 2 * (-143 * t6058 - 1 + (143 * t6058 + 33) * t6057) + + if Bindx == 791: + t6060 = np.cos(phi) + t6061 = t6060 ** 2 + t6063 = t6061 ** 2 + t6062 = t6060 * t6061 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (66 * t6061 - 429 * t6063 - 1 + (-286 + 572 * t6062) * t6062 + (429 * t6063 + 33) * t6060) * np.sqrt((1 + t6060)) * np.sqrt(0.70e2) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t6060) ** (0.3e1 / 0.2e1)) + + if Bindx == 792: + t6066 = np.cos(phi) + t6067 = t6066 ** 2 + t6068 = t6066 * t6067 + t6071 = t6068 ** 2 + t6069 = t6067 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - phi2)) * (-512 * t6067 - 671 * t6068 - 4004 * t6071 + 16 + (2530 + 2002 * t6069) * t6069 + (1573 * t6069 - 1001 * t6071 + 67) * t6066) + + if Bindx == 793: + t6074 = np.cos(phi) + t6075 = t6074 ** 2 + t6076 = t6075 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-130 * t6075 + 455 * t6076 + 3 + (364 * t6076 - 20) * t6074) * np.sqrt((1 + t6074)) * np.sqrt(0.66e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t6074) ** (0.5e1 / 0.2e1)) + + if Bindx == 794: + t6079 = np.cos(phi) + t6080 = t6079 ** 2 + t6085 = -91 * t6080 ** 2 + t6081 = t6079 * t6080 + t6078 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.110e3) * t6078 ** 2 * (25 * t6080 + t6085 - 1 + (78 + 91 * t6081) * t6081 + (t6085 - 11) * t6079) + + if Bindx == 795: + t6086 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (-1 + (28 * t6086 + 21) * t6086 ** 2) * ((1 + t6086) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t6086) ** (0.7e1 / 0.2e1)) + + if Bindx == 796: + t6094 = np.sin(phi) + t6092 = t6094 ** 2 + t6089 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.15015e5) * t6092 ** 2 * t6089 * (1 + (2 * t6089 - 3) * t6089 ** 2) + + if Bindx == 797: + t6095 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (1 + 4 * t6095) * ((1 + t6095) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t6095) ** (0.9e1 / 0.2e1)) + + if Bindx == 798: + t6096 = np.cos(phi) + t6103 = -1 + t6096 + t6102 = 1 + t6096 + t6100 = t6102 ** 2 + t6097 = t6103 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.2002e4) * t6103 * t6097 ** 2 * t6102 * t6100 + + if Bindx == 799: + t6104 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.715e3) * ((1 - t6104) ** (0.7e1 / 0.2e1)) * ((1 + t6104) ** (0.9e1 / 0.2e1)) + + if Bindx == 800: + t6109 = np.sin(phi) + t6106 = t6109 ** 2 + t6107 = t6109 * t6106 + t6105 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.715e3) * t6107 ** 2 * (-1 + (7 + 8 * t6105) * t6105) + + if Bindx == 801: + t6110 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t6110) ** (0.5e1 / 0.2e1)) * ((1 + t6110) ** (0.7e1 / 0.2e1)) * (-1 + (-5 + 20 * t6110) * t6110) + + if Bindx == 802: + t6117 = np.sin(phi) + t6115 = t6117 ** 2 + t6111 = np.cos(phi) + t6112 = t6111 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.1001e4) * t6115 ** 2 * (-5 * t6111 + 1 + (25 * t6111 - 21 + 40 * t6112) * t6112) + + if Bindx == 803: + t6118 = np.cos(phi) + t6119 = t6118 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.77e2) * ((1 - t6118) ** (0.3e1 / 0.2e1)) * ((1 + t6118) ** (0.5e1 / 0.2e1)) * (13 * t6118 + 1 + (-65 * t6118 - 39 + 130 * t6119) * t6119) + + if Bindx == 804: + t6123 = np.cos(phi) + t6124 = t6123 ** 2 + t6126 = t6124 ** 2 + t6125 = t6123 * t6124 + t6122 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.1155e4) * t6122 ** 2 * (30 * t6124 - 117 * t6126 - 1 + (-26 + 104 * t6125) * t6125 + (39 * t6126 + 3) * t6123) + + if Bindx == 805: + t6129 = np.cos(phi) + t6130 = t6129 ** 2 + t6135 = -429 * t6130 ** 2 + t6131 = t6129 * t6130 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.70e2) * np.sqrt((1 - t6129)) * ((1 + t6129) ** (0.3e1 / 0.2e1)) * (66 * t6130 + t6135 - 1 + (286 + 572 * t6131) * t6131 + (t6135 - 33) * t6129) + + if Bindx == 806: + t6136 = np.cos(phi) + t6137 = t6136 ** 2 + t6138 = t6136 * t6137 + t6141 = t6138 ** 2 + t6139 = t6137 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 + phi2)) * (-1225 * t6137 + 385 * t6138 - 11011 * t6141 + 35 + (6545 + 5720 * t6139) * t6139 + (-1001 * t6139 + 715 * t6141 - 35) * t6136) + + if Bindx == 807: + t6144 = np.cos(phi) + t6145 = t6144 ** 2 + t6146 = t6144 * t6145 + t6151 = -1001 * t6145 ** 2 + 715 * t6146 ** 2 - 35 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 + t6144)) * t6144 * (t6151 * t6144 - 385 * t6145 + 385 * t6146 - t6151) * ((1 - t6144) ** (-0.1e1 / 0.2e1)) + + if Bindx == 808: + t6152 = np.cos(phi) + t6153 = t6152 ** 2 + t6154 = t6152 * t6153 + t6157 = t6154 ** 2 + t6155 = t6153 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - phi2)) * (-1225 * t6153 - 385 * t6154 - 11011 * t6157 + 35 + (6545 + 5720 * t6155) * t6155 + (1001 * t6155 - 715 * t6157 + 35) * t6152) + + if Bindx == 809: + t6160 = np.cos(phi) + t6161 = t6160 ** 2 + t6163 = t6161 ** 2 + t6162 = t6160 * t6161 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (66 * t6161 - 429 * t6163 - 1 + (-286 + 572 * t6162) * t6162 + (429 * t6163 + 33) * t6160) * np.sqrt((1 + t6160)) * np.sqrt(0.70e2) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t6160) ** (0.3e1 / 0.2e1)) + + if Bindx == 810: + t6167 = np.cos(phi) + t6168 = t6167 ** 2 + t6170 = t6168 ** 2 + t6169 = t6167 * t6168 + t6166 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.1155e4) * t6166 ** 2 * (30 * t6168 - 117 * t6170 - 1 + (26 + 104 * t6169) * t6169 + (-39 * t6170 - 3) * t6167) + + if Bindx == 811: + t6173 = np.cos(phi) + t6174 = t6173 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * (-13 * t6173 + 1 + (65 * t6173 - 39 + 130 * t6174) * t6174) * ((1 + t6173) ** (0.3e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t6173) ** (0.5e1 / 0.2e1)) + + if Bindx == 812: + t6183 = np.sin(phi) + t6181 = t6183 ** 2 + t6177 = np.cos(phi) + t6178 = t6177 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.1001e4) * t6181 ** 2 * (5 * t6177 + 1 + (-25 * t6177 - 21 + 40 * t6178) * t6178) + + if Bindx == 813: + t6184 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (-1 + (5 + 20 * t6184) * t6184) * ((1 + t6184) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t6184) ** (0.7e1 / 0.2e1)) + + if Bindx == 814: + t6189 = np.sin(phi) + t6186 = t6189 ** 2 + t6187 = t6189 * t6186 + t6185 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.715e3) * t6187 ** 2 * (-1 + (-7 + 8 * t6185) * t6185) + + if Bindx == 815: + t6190 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * np.sqrt(0.715e3) * ((1 - t6190) ** (0.9e1 / 0.2e1)) * ((1 + t6190) ** (0.7e1 / 0.2e1)) + + if Bindx == 816: + t6194 = np.sin(phi) + t6191 = t6194 ** 2 + t6192 = t6191 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((-8*1j) * phi2) * np.sqrt(0.1430e4) * t6192 ** 2 + + if Bindx == 817: + t6195 = np.cos(phi) + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.1430e4) * ((1 - t6195) ** (0.7e1 / 0.2e1)) * ((1 + t6195) ** (0.7e1 / 0.2e1)) * t6195 + + if Bindx == 818: + t6200 = np.sin(phi) + t6197 = t6200 ** 2 + t6198 = t6200 * t6197 + t6196 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((-6*1j) * phi2) * np.sqrt(0.429e3) * t6198 ** 2 * (15 * t6196 ** 2 - 1) + + if Bindx == 819: + t6201 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.2002e4) * ((1 - t6201) ** (0.5e1 / 0.2e1)) * ((1 + t6201) ** (0.5e1 / 0.2e1)) * t6201 * (5 * t6201 ** 2 - 1) + + if Bindx == 820: + t6207 = np.sin(phi) + t6205 = t6207 ** 2 + t6202 = np.cos(phi) + t6203 = t6202 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((-4*1j) * phi2) * np.sqrt(0.154e3) * t6205 ** 2 * (1 + (-26 + 65 * t6203) * t6203) + + if Bindx == 821: + t6208 = np.cos(phi) + t6209 = t6208 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.2310e4) * ((1 - t6208) ** (0.3e1 / 0.2e1)) * ((1 + t6208) ** (0.3e1 / 0.2e1)) * t6208 * (3 + (-26 + 39 * t6209) * t6209) + + if Bindx == 822: + t6212 = np.cos(phi) + t6213 = t6212 ** 2 + t6214 = t6213 ** 2 + t6211 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((-2*1j) * phi2) * np.sqrt(0.35e2) * t6211 ** 2 * (-143 * t6214 - 1 + (143 * t6214 + 33) * t6213) + + if Bindx == 823: + t6216 = np.cos(phi) + t6217 = t6216 ** 2 + t6218 = t6217 ** 2 + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.2e1) * np.sqrt((1 - t6216)) * np.sqrt((1 + t6216)) * t6216 * (-1001 * t6218 - 35 + (715 * t6218 + 385) * t6217) + + if Bindx == 824: + t6220 = np.cos(phi) + t6221 = t6220 ** 2 + t6222 = t6221 ** 2 + tfunc[..., c] = -0.5355e4 / 0.32e2 * t6221 + 0.595e3 / 0.128e3 + (-0.51051e5 / 0.32e2 * t6221 + 0.58905e5 / 0.64e2 + 0.109395e6 / 0.128e3 * t6222) * t6222 + + if Bindx == 825: + t6225 = np.cos(phi) + t6226 = t6225 ** 2 + t6227 = t6225 * t6226 + t6232 = -1001 * t6226 ** 2 + 715 * t6227 ** 2 - 35 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((1j) * phi2) * np.sqrt(0.2e1) * np.sqrt((1 + t6225)) * t6225 * (t6232 * t6225 - 385 * t6226 + 385 * t6227 - t6232) * ((1 - t6225) ** (-0.1e1 / 0.2e1)) + + if Bindx == 826: + t6234 = np.cos(phi) + t6235 = t6234 ** 2 + t6236 = t6235 ** 2 + t6233 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((2*1j) * phi2) * np.sqrt(0.35e2) * t6233 ** 2 * (-143 * t6236 - 1 + (143 * t6236 + 33) * t6235) + + if Bindx == 827: + t6238 = np.cos(phi) + t6239 = t6238 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (3 + (-26 + 39 * t6239) * t6239) * t6238 * ((1 + t6238) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2310e4) * np.exp((3*1j) * phi2) * ((1 - t6238) ** (0.3e1 / 0.2e1)) + + if Bindx == 828: + t6246 = np.sin(phi) + t6244 = t6246 ** 2 + t6241 = np.cos(phi) + t6242 = t6241 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((4*1j) * phi2) * np.sqrt(0.154e3) * t6244 ** 2 * (1 + (-26 + 65 * t6242) * t6242) + + if Bindx == 829: + t6247 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * (5 * t6247 ** 2 - 1) * t6247 * ((1 + t6247) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((5*1j) * phi2) * ((1 - t6247) ** (0.5e1 / 0.2e1)) + + if Bindx == 830: + t6252 = np.sin(phi) + t6249 = t6252 ** 2 + t6250 = t6252 * t6249 + t6248 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((6*1j) * phi2) * np.sqrt(0.429e3) * t6250 ** 2 * (15 * t6248 ** 2 - 1) + + if Bindx == 831: + t6253 = np.cos(phi) + t6257 = -4 * t6253 + t6254 = t6253 ** 2 + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * t6253 * (t6257 + 1 + (t6257 + 6 + t6254) * t6254) * ((1 + t6253) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((7*1j) * phi2) * ((1 - t6253) ** (-0.1e1 / 0.2e1)) + + if Bindx == 832: + t6261 = np.sin(phi) + t6258 = t6261 ** 2 + t6259 = t6258 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((8*1j) * phi2) * np.sqrt(0.1430e4) * t6259 ** 2 + + if Bindx == 833: + t6262 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.715e3) * ((1 - t6262) ** (0.9e1 / 0.2e1)) * ((1 + t6262) ** (0.7e1 / 0.2e1)) + + if Bindx == 834: + t6267 = np.sin(phi) + t6264 = t6267 ** 2 + t6265 = t6267 * t6264 + t6263 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.715e3) * t6265 ** 2 * (-1 + (-7 + 8 * t6263) * t6263) + + if Bindx == 835: + t6268 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t6268) ** (0.7e1 / 0.2e1)) * ((1 + t6268) ** (0.5e1 / 0.2e1)) * (-1 + (5 + 20 * t6268) * t6268) + + if Bindx == 836: + t6275 = np.sin(phi) + t6273 = t6275 ** 2 + t6269 = np.cos(phi) + t6270 = t6269 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.1001e4) * t6273 ** 2 * (5 * t6269 + 1 + (-25 * t6269 - 21 + 40 * t6270) * t6270) + + if Bindx == 837: + t6276 = np.cos(phi) + t6277 = t6276 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.77e2) * ((1 - t6276) ** (0.5e1 / 0.2e1)) * ((1 + t6276) ** (0.3e1 / 0.2e1)) * (-13 * t6276 + 1 + (65 * t6276 - 39 + 130 * t6277) * t6277) + + if Bindx == 838: + t6281 = np.cos(phi) + t6282 = t6281 ** 2 + t6284 = t6282 ** 2 + t6283 = t6281 * t6282 + t6280 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.1155e4) * t6280 ** 2 * (30 * t6282 - 117 * t6284 - 1 + (26 + 104 * t6283) * t6283 + (-39 * t6284 - 3) * t6281) + + if Bindx == 839: + t6287 = np.cos(phi) + t6288 = t6287 ** 2 + t6290 = t6288 ** 2 + t6289 = t6287 * t6288 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.70e2) * ((1 - t6287) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t6287)) * (66 * t6288 - 429 * t6290 - 1 + (-286 + 572 * t6289) * t6289 + (429 * t6290 + 33) * t6287) + + if Bindx == 840: + t6293 = np.cos(phi) + t6294 = t6293 ** 2 + t6295 = t6293 * t6294 + t6298 = t6295 ** 2 + t6296 = t6294 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (phi1 - phi2)) * (-1225 * t6294 - 385 * t6295 - 11011 * t6298 + 35 + (6545 + 5720 * t6296) * t6296 + (1001 * t6296 - 715 * t6298 + 35) * t6293) + + if Bindx == 841: + t6301 = np.cos(phi) + t6302 = t6301 ** 2 + t6303 = t6302 ** 2 + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((1j) * phi1) * np.sqrt(0.2e1) * np.sqrt((1 - t6301)) * np.sqrt((1 + t6301)) * t6301 * (-1001 * t6303 - 35 + (715 * t6303 + 385) * t6302) + + if Bindx == 842: + t6305 = np.cos(phi) + t6306 = t6305 ** 2 + t6307 = t6305 * t6306 + t6310 = t6307 ** 2 + t6308 = t6306 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (phi1 + phi2)) * (-1225 * t6306 + 385 * t6307 - 11011 * t6310 + 35 + (6545 + 5720 * t6308) * t6308 + (-1001 * t6308 + 715 * t6310 - 35) * t6305) + + if Bindx == 843: + t6313 = np.cos(phi) + t6314 = t6313 ** 2 + t6315 = t6313 * t6314 + t6317 = t6315 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.70e2) * ((1 + t6313) ** (0.3e1 / 0.2e1)) * (-220 * t6315 - 1001 * t6317 + 1 + (-99 + 715 * t6314) * t6314 + (572 * t6317 + 32) * t6313) * ((1 - t6313) ** (-0.1e1 / 0.2e1)) + + if Bindx == 844: + t6320 = np.cos(phi) + t6321 = t6320 ** 2 + t6323 = t6321 ** 2 + t6322 = t6320 * t6321 + t6319 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.1155e4) * t6319 ** 2 * (30 * t6321 - 117 * t6323 - 1 + (-26 + 104 * t6322) * t6322 + (39 * t6323 + 3) * t6320) + + if Bindx == 845: + t6326 = np.cos(phi) + t6327 = t6326 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (13 * t6326 + 1 + (-65 * t6326 - 39 + 130 * t6327) * t6327) * ((1 + t6326) ** (0.5e1 / 0.2e1)) * np.sqrt(0.77e2) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t6326) ** (0.3e1 / 0.2e1)) + + if Bindx == 846: + t6336 = np.sin(phi) + t6334 = t6336 ** 2 + t6330 = np.cos(phi) + t6331 = t6330 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.1001e4) * t6334 ** 2 * (-5 * t6330 + 1 + (25 * t6330 - 21 + 40 * t6331) * t6331) + + if Bindx == 847: + t6337 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-1 + (-5 + 20 * t6337) * t6337) * ((1 + t6337) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t6337) ** (0.5e1 / 0.2e1)) + + if Bindx == 848: + t6342 = np.sin(phi) + t6339 = t6342 ** 2 + t6340 = t6342 * t6339 + t6338 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.715e3) * t6340 ** 2 * (-1 + (7 + 8 * t6338) * t6338) + + if Bindx == 849: + t6343 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (phi1 + 8 * phi2)) * np.sqrt(0.715e3) * ((1 - t6343) ** (0.7e1 / 0.2e1)) * ((1 + t6343) ** (0.9e1 / 0.2e1)) + + if Bindx == 850: + t6344 = np.cos(phi) + t6351 = -1 + t6344 + t6350 = 1 + t6344 + t6348 = t6350 ** 2 + t6345 = t6351 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.2002e4) * t6351 * t6345 ** 2 * t6350 * t6348 + + if Bindx == 851: + t6352 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.2002e4) * ((1 - t6352) ** (0.9e1 / 0.2e1)) * ((1 + t6352) ** (0.5e1 / 0.2e1)) * (1 + 4 * t6352) + + if Bindx == 852: + t6358 = np.sin(phi) + t6356 = t6358 ** 2 + t6353 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.15015e5) * t6356 ** 2 * t6353 * (1 + (2 * t6353 - 3) * t6353 ** 2) + + if Bindx == 853: + t6359 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.1430e4) * ((1 - t6359) ** (0.7e1 / 0.2e1)) * ((1 + t6359) ** (0.3e1 / 0.2e1)) * (-1 + (28 * t6359 + 21) * t6359 ** 2) + + if Bindx == 854: + t6363 = np.cos(phi) + t6364 = t6363 ** 2 + t6369 = -91 * t6364 ** 2 + t6365 = t6363 * t6364 + t6362 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.110e3) * t6362 ** 2 * (25 * t6364 + t6369 - 1 + (78 + 91 * t6365) * t6365 + (t6369 - 11) * t6363) + + if Bindx == 855: + t6370 = np.cos(phi) + t6371 = t6370 ** 2 + t6372 = t6371 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.66e2) * ((1 - t6370) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t6370)) * (-130 * t6371 + 455 * t6372 + 3 + (364 * t6372 - 20) * t6370) + + if Bindx == 856: + t6374 = np.cos(phi) + t6375 = t6374 ** 2 + t6376 = t6374 * t6375 + t6379 = t6376 ** 2 + t6377 = t6375 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 - phi2)) * (-512 * t6375 - 671 * t6376 - 4004 * t6379 + 16 + (2530 + 2002 * t6377) * t6377 + (1573 * t6377 - 1001 * t6379 + 67) * t6374) + + if Bindx == 857: + t6382 = np.cos(phi) + t6383 = t6382 ** 2 + t6385 = t6383 ** 2 + t6384 = t6382 * t6383 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.70e2) * ((1 - t6382) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t6382)) * (66 * t6383 - 429 * t6385 - 1 + (-286 + 572 * t6384) * t6384 + (429 * t6385 + 33) * t6382) + + if Bindx == 858: + t6389 = np.cos(phi) + t6390 = t6389 ** 2 + t6391 = t6390 ** 2 + t6388 = np.sin(phi) + tfunc[..., c] = -(0.51e2 / 0.64e2) * np.exp((2*1j) * phi1) * np.sqrt(0.35e2) * t6388 ** 2 * (-143 * t6391 - 1 + (143 * t6391 + 33) * t6390) + + if Bindx == 859: + t6393 = np.cos(phi) + t6394 = t6393 ** 2 + t6399 = -429 * t6394 ** 2 + t6395 = t6393 * t6394 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.70e2) * np.sqrt((1 - t6393)) * ((1 + t6393) ** (0.3e1 / 0.2e1)) * (66 * t6394 + t6399 - 1 + (286 + 572 * t6395) * t6395 + (t6399 - 33) * t6393) + + if Bindx == 860: + t6400 = np.cos(phi) + t6401 = t6400 ** 2 + t6402 = t6400 * t6401 + t6405 = t6402 ** 2 + t6403 = t6401 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 + phi2)) * (-512 * t6401 + 671 * t6402 - 4004 * t6405 + 16 + (2530 + 2002 * t6403) * t6403 + (-1573 * t6403 + 1001 * t6405 - 67) * t6400) + + if Bindx == 861: + t6408 = np.cos(phi) + t6409 = t6408 ** 2 + t6411 = t6409 ** 2 + t6410 = t6408 * t6409 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.66e2) * ((1 + t6408) ** (0.5e1 / 0.2e1)) * (-150 * t6409 + 455 * t6411 + 3 + (130 + 364 * t6410) * t6410 + (-819 * t6411 + 17) * t6408) * ((1 - t6408) ** (-0.1e1 / 0.2e1)) + + if Bindx == 862: + t6415 = np.cos(phi) + t6416 = t6415 ** 2 + t6418 = t6416 ** 2 + t6417 = t6415 * t6416 + t6414 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.110e3) * t6414 ** 2 * (25 * t6416 - 91 * t6418 - 1 + (-78 + 91 * t6417) * t6417 + (91 * t6418 + 11) * t6415) + + if Bindx == 863: + t6421 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (1 + (28 * t6421 - 21) * t6421 ** 2) * ((1 + t6421) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1430e4) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t6421) ** (0.3e1 / 0.2e1)) + + if Bindx == 864: + t6429 = np.sin(phi) + t6427 = t6429 ** 2 + t6424 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.15015e5) * t6427 ** 2 * t6424 * (-1 + (2 * t6424 + 3) * t6424 ** 2) + + if Bindx == 865: + t6430 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * (-1 + 4 * t6430) * ((1 + t6430) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2002e4) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t6430) ** (0.5e1 / 0.2e1)) + + if Bindx == 866: + t6431 = np.cos(phi) + t6438 = -1 + t6431 + t6437 = 1 + t6431 + t6434 = t6437 ** 2 + t6432 = t6438 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.2002e4) * t6438 * t6432 * t6437 * t6434 ** 2 + + if Bindx == 867: + t6439 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.273e3) * ((1 - t6439) ** (0.11e2 / 0.2e1)) * ((1 + t6439) ** (0.5e1 / 0.2e1)) + + if Bindx == 868: + t6441 = np.cos(phi) + t6445 = -1 + t6441 + t6442 = t6445 ** 2 + t6440 = 1 + t6441 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.273e3) * t6445 * t6442 ** 2 * t6440 ** 2 * (3 + 8 * t6441) + + if Bindx == 869: + t6446 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.910e3) * ((1 - t6446) ** (0.9e1 / 0.2e1)) * ((1 + t6446) ** (0.3e1 / 0.2e1)) * (1 + (9 + 12 * t6446) * t6446) + + if Bindx == 870: + t6448 = np.cos(phi) + t6449 = t6448 ** 2 + t6451 = t6449 ** 2 + t6450 = t6448 * t6449 + t6447 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.195e3) * t6447 ** 2 * (-18 * t6449 - 7 * t6451 + 1 + (90 + 56 * t6450) * t6450 + (-105 * t6451 - 17) * t6448) + + if Bindx == 871: + t6454 = np.cos(phi) + t6455 = t6454 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.15e2) * ((1 - t6454) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t6454)) * (-13 * t6454 - 5 + (273 * t6454 + 91 + 182 * t6455) * t6455) + + if Bindx == 872: + t6458 = np.cos(phi) + t6459 = t6458 ** 2 + t6460 = t6458 * t6459 + t6463 = t6460 ** 2 + t6461 = t6459 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((3*1j) * (phi1 - phi2)) * (-459 * t6459 - 2107 * t6460 - 3913 * t6463 + 17 + (2235 + 2184 * t6461) * t6461 + (4251 * t6461 - 2457 * t6463 + 249) * t6458) + + if Bindx == 873: + t6466 = np.cos(phi) + t6467 = t6466 ** 2 + t6468 = t6467 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.66e2) * ((1 - t6466) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t6466)) * (-130 * t6467 + 455 * t6468 + 3 + (364 * t6468 - 20) * t6466) + + if Bindx == 874: + t6471 = np.cos(phi) + t6472 = t6471 ** 2 + t6474 = t6472 ** 2 + t6473 = t6471 * t6472 + t6470 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.1155e4) * t6470 ** 2 * (30 * t6472 - 117 * t6474 - 1 + (26 + 104 * t6473) * t6473 + (-39 * t6474 - 3) * t6471) + + if Bindx == 875: + t6477 = np.cos(phi) + t6478 = t6477 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.2310e4) * ((1 - t6477) ** (0.3e1 / 0.2e1)) * ((1 + t6477) ** (0.3e1 / 0.2e1)) * t6477 * (3 + (-26 + 39 * t6478) * t6478) + + if Bindx == 876: + t6481 = np.cos(phi) + t6482 = t6481 ** 2 + t6484 = t6482 ** 2 + t6483 = t6481 * t6482 + t6480 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.1155e4) * t6480 ** 2 * (30 * t6482 - 117 * t6484 - 1 + (-26 + 104 * t6483) * t6483 + (39 * t6484 + 3) * t6481) + + if Bindx == 877: + t6487 = np.cos(phi) + t6488 = t6487 ** 2 + t6489 = t6488 ** 2 + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.66e2) * np.sqrt((1 - t6487)) * ((1 + t6487) ** (0.5e1 / 0.2e1)) * (130 * t6488 - 455 * t6489 - 3 + (364 * t6489 - 20) * t6487) + + if Bindx == 878: + t6491 = np.cos(phi) + t6492 = t6491 ** 2 + t6493 = t6491 * t6492 + t6496 = t6493 ** 2 + t6494 = t6492 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((3*1j) * (phi1 + phi2)) * (-459 * t6492 + 2107 * t6493 - 3913 * t6496 + 17 + (2235 + 2184 * t6494) * t6494 + (-4251 * t6494 + 2457 * t6496 - 249) * t6491) + + if Bindx == 879: + t6499 = np.cos(phi) + t6500 = t6499 ** 2 + t6502 = t6500 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.15e2) * ((1 + t6499) ** (0.7e1 / 0.2e1)) * (-78 * t6500 - 455 * t6502 + 5 + (364 * t6500 + 182 * t6502 - 18) * t6499) * ((1 - t6499) ** (-0.1e1 / 0.2e1)) + + if Bindx == 880: + t6505 = np.cos(phi) + t6506 = t6505 ** 2 + t6508 = t6506 ** 2 + t6507 = t6505 * t6506 + t6504 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.195e3) * t6504 ** 2 * (-18 * t6506 - 7 * t6508 + 1 + (-90 + 56 * t6507) * t6507 + (105 * t6508 + 17) * t6505) + + if Bindx == 881: + t6511 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * (1 + (-9 + 12 * t6511) * t6511) * ((1 + t6511) ** (0.9e1 / 0.2e1)) * np.sqrt(0.910e3) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t6511) ** (0.3e1 / 0.2e1)) + + if Bindx == 882: + t6518 = np.sin(phi) + t6516 = t6518 ** 2 + t6512 = np.cos(phi) + t6513 = t6512 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.273e3) * t6516 ** 2 * (-t6512 - 3 + (21 * t6512 + 15 + 8 * t6513) * t6513) + + if Bindx == 883: + t6519 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.273e3) * ((1 - t6519) ** (0.5e1 / 0.2e1)) * ((1 + t6519) ** (0.11e2 / 0.2e1)) + + if Bindx == 884: + t6521 = np.cos(phi) + t6525 = -1 + t6521 + t6522 = t6525 ** 2 + t6523 = t6525 * t6522 + t6520 = 1 + t6521 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.455e3) * t6523 ** 2 * t6520 ** 2 + + if Bindx == 885: + t6526 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.455e3) * ((1 - t6526) ** (0.11e2 / 0.2e1)) * ((1 + t6526) ** (0.3e1 / 0.2e1)) * (1 + 2 * t6526) + + if Bindx == 886: + t6528 = np.cos(phi) + t6529 = t6528 ** 2 + t6530 = t6528 * t6529 + t6527 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.546e3) * t6527 ** 2 * (t6528 + 1 + (6 + 5 * t6530) * t6530 + (-9 + (-15 * t6528 + 11) * t6529) * t6529) + + if Bindx == 887: + t6534 = np.cos(phi) + t6535 = t6534 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.13e2) * ((1 - t6534) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t6534)) * (105 * t6535 + 3 + (70 * t6535 + 42) * t6534) + + if Bindx == 888: + t6537 = np.cos(phi) + t6538 = t6537 ** 2 + t6539 = t6537 * t6538 + t6542 = t6539 ** 2 + t6540 = t6538 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((4*1j) * (phi1 - phi2)) * (180 * t6538 - 770 * t6539 - 364 * t6542 - 9 + (-230 + 455 * t6540) * t6540 + (1534 * t6540 - 910 * t6542 + 114) * t6537) + + if Bindx == 889: + t6545 = np.cos(phi) + t6546 = t6545 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.15e2) * ((1 - t6545) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t6545)) * (-13 * t6545 - 5 + (273 * t6545 + 91 + 182 * t6546) * t6546) + + if Bindx == 890: + t6550 = np.cos(phi) + t6551 = t6550 ** 2 + t6556 = -91 * t6551 ** 2 + t6552 = t6550 * t6551 + t6549 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.110e3) * t6549 ** 2 * (25 * t6551 + t6556 - 1 + (78 + 91 * t6552) * t6552 + (t6556 - 11) * t6550) + + if Bindx == 891: + t6557 = np.cos(phi) + t6558 = t6557 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.77e2) * ((1 - t6557) ** (0.5e1 / 0.2e1)) * ((1 + t6557) ** (0.3e1 / 0.2e1)) * (-13 * t6557 + 1 + (65 * t6557 - 39 + 130 * t6558) * t6558) + + if Bindx == 892: + t6566 = np.sin(phi) + t6564 = t6566 ** 2 + t6561 = np.cos(phi) + t6562 = t6561 ** 2 + tfunc[..., c] = (0.51e2 / 0.128e3) * np.exp((4*1j) * phi1) * np.sqrt(0.154e3) * t6564 ** 2 * (1 + (-26 + 65 * t6562) * t6562) + + if Bindx == 893: + t6567 = np.cos(phi) + t6568 = t6567 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.77e2) * ((1 - t6567) ** (0.3e1 / 0.2e1)) * ((1 + t6567) ** (0.5e1 / 0.2e1)) * (13 * t6567 + 1 + (-65 * t6567 - 39 + 130 * t6568) * t6568) + + if Bindx == 894: + t6572 = np.cos(phi) + t6573 = t6572 ** 2 + t6575 = t6573 ** 2 + t6574 = t6572 * t6573 + t6571 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.110e3) * t6571 ** 2 * (25 * t6573 - 91 * t6575 - 1 + (-78 + 91 * t6574) * t6574 + (91 * t6575 + 11) * t6572) + + if Bindx == 895: + t6578 = np.cos(phi) + t6579 = t6578 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t6578)) * ((1 + t6578) ** (0.7e1 / 0.2e1)) * (13 * t6578 - 5 + (-273 * t6578 + 91 + 182 * t6579) * t6579) + + if Bindx == 896: + t6582 = np.cos(phi) + t6583 = t6582 ** 2 + t6584 = t6582 * t6583 + t6587 = t6584 ** 2 + t6585 = t6583 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((4*1j) * (phi1 + phi2)) * (180 * t6583 + 770 * t6584 - 364 * t6587 - 9 + (-230 + 455 * t6585) * t6585 + (-1534 * t6585 + 910 * t6587 - 114) * t6582) + + if Bindx == 897: + t6590 = np.cos(phi) + t6591 = t6590 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.13e2) * ((1 + t6590) ** (0.9e1 / 0.2e1)) * (-45 * t6590 + 3 + (-175 * t6590 + 147 + 70 * t6591) * t6591) * ((1 - t6590) ** (-0.1e1 / 0.2e1)) + + if Bindx == 898: + t6595 = np.cos(phi) + t6596 = t6595 ** 2 + t6597 = t6595 * t6596 + t6594 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.546e3) * t6594 ** 2 * (-t6595 + 1 + (-6 + 5 * t6597) * t6597 + (-9 + (15 * t6595 + 11) * t6596) * t6596) + + if Bindx == 899: + t6601 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * (2 * t6601 - 1) * ((1 + t6601) ** (0.11e2 / 0.2e1)) * np.sqrt(0.455e3) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t6601) ** (0.3e1 / 0.2e1)) + + if Bindx == 900: + t6603 = np.cos(phi) + t6607 = 1 + t6603 + t6604 = t6607 ** 2 + t6605 = t6607 * t6604 + t6602 = -1 + t6603 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.455e3) * t6602 ** 2 * t6605 ** 2 + + if Bindx == 901: + t6608 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.35e2) * ((1 - t6608) ** (0.13e2 / 0.2e1)) * ((1 + t6608) ** (0.3e1 / 0.2e1)) + + if Bindx == 902: + t6609 = np.cos(phi) + t6613 = -1 + t6609 + t6610 = t6613 ** 2 + t6611 = t6613 * t6610 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.35e2) * t6611 ** 2 * (1 + t6609) * (5 + 8 * t6609) + + if Bindx == 903: + t6614 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.42e2) * ((1 - t6614) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t6614)) * (7 + (25 + 20 * t6614) * t6614) + + if Bindx == 904: + t6615 = np.cos(phi) + t6616 = t6615 ** 2 + t6617 = t6615 * t6616 + t6620 = t6617 ** 2 + t6618 = t6616 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((5*1j) * (phi1 - phi2)) * (495 * t6616 - 145 * t6617 + 469 * t6620 - 45 + (-1135 + 280 * t6618) * t6618 + (1025 * t6618 - 875 * t6620 - 69) * t6615) + + if Bindx == 905: + t6623 = np.cos(phi) + t6624 = t6623 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.13e2) * ((1 - t6623) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t6623)) * (105 * t6624 + 3 + (70 * t6624 + 42) * t6623) + + if Bindx == 906: + t6627 = np.cos(phi) + t6628 = t6627 ** 2 + t6630 = t6628 ** 2 + t6629 = t6627 * t6628 + t6626 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.195e3) * t6626 ** 2 * (-18 * t6628 - 7 * t6630 + 1 + (90 + 56 * t6629) * t6629 + (-105 * t6630 - 17) * t6627) + + if Bindx == 907: + t6633 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.1430e4) * ((1 - t6633) ** (0.7e1 / 0.2e1)) * ((1 + t6633) ** (0.3e1 / 0.2e1)) * (-1 + (28 * t6633 + 21) * t6633 ** 2) + + if Bindx == 908: + t6642 = np.sin(phi) + t6640 = t6642 ** 2 + t6636 = np.cos(phi) + t6637 = t6636 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.1001e4) * t6640 ** 2 * (5 * t6636 + 1 + (-25 * t6636 - 21 + 40 * t6637) * t6637) + + if Bindx == 909: + t6643 = np.cos(phi) + tfunc[..., c] = (-0.51e2 / 0.64e2*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.2002e4) * ((1 - t6643) ** (0.5e1 / 0.2e1)) * ((1 + t6643) ** (0.5e1 / 0.2e1)) * t6643 * (5 * t6643 ** 2 - 1) + + if Bindx == 910: + t6650 = np.sin(phi) + t6648 = t6650 ** 2 + t6644 = np.cos(phi) + t6645 = t6644 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.1001e4) * t6648 ** 2 * (-5 * t6644 + 1 + (25 * t6644 - 21 + 40 * t6645) * t6645) + + if Bindx == 911: + t6651 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.1430e4) * ((1 - t6651) ** (0.3e1 / 0.2e1)) * ((1 + t6651) ** (0.7e1 / 0.2e1)) * (1 + (28 * t6651 - 21) * t6651 ** 2) + + if Bindx == 912: + t6655 = np.cos(phi) + t6656 = t6655 ** 2 + t6658 = t6656 ** 2 + t6657 = t6655 * t6656 + t6654 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.195e3) * t6654 ** 2 * (-18 * t6656 - 7 * t6658 + 1 + (-90 + 56 * t6657) * t6657 + (105 * t6658 + 17) * t6655) + + if Bindx == 913: + t6661 = np.cos(phi) + t6662 = t6661 ** 2 + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.13e2) * np.sqrt((1 - t6661)) * ((1 + t6661) ** (0.9e1 / 0.2e1)) * (-105 * t6662 - 3 + (70 * t6662 + 42) * t6661) + + if Bindx == 914: + t6664 = np.cos(phi) + t6665 = t6664 ** 2 + t6666 = t6664 * t6665 + t6669 = t6666 ** 2 + t6667 = t6665 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((5*1j) * (phi1 + phi2)) * (495 * t6665 + 145 * t6666 + 469 * t6669 - 45 + (-1135 + 280 * t6667) * t6667 + (-1025 * t6667 + 875 * t6669 + 69) * t6664) + + if Bindx == 915: + t6672 = np.cos(phi) + t6673 = t6672 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.42e2) * ((1 + t6672) ** (0.11e2 / 0.2e1)) * (-45 * t6673 - 7 + (20 * t6673 + 32) * t6672) * ((1 - t6672) ** (-0.1e1 / 0.2e1)) + + if Bindx == 916: + t6676 = np.cos(phi) + t6677 = t6676 ** 2 + t6679 = t6677 ** 2 + t6678 = t6676 * t6677 + t6675 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.35e2) * t6675 ** 2 * (-10 * t6677 + 55 * t6679 - 5 + (30 + 8 * t6678) * t6678 + (35 * t6679 - 17) * t6676) + + if Bindx == 917: + t6682 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.35e2) * ((1 - t6682) ** (0.3e1 / 0.2e1)) * ((1 + t6682) ** (0.13e2 / 0.2e1)) + + if Bindx == 918: + t6683 = np.cos(phi) + t6688 = -1 + t6683 + t6684 = t6688 ** 2 + t6685 = t6688 * t6684 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.30e2) * t6688 * t6685 ** 2 * (1 + t6683) + + if Bindx == 919: + t6689 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.30e2) * ((1 - t6689) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t6689)) * (3 + 4 * t6689) + + if Bindx == 920: + t6690 = np.cos(phi) + t6691 = t6690 ** 2 + t6692 = t6690 * t6691 + t6695 = t6692 ** 2 + t6693 = t6691 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((6*1j) * (phi1 - phi2)) * (175 * t6692 + 196 * t6695 + 16 + (-210 + 30 * t6693) * t6693 + (-21 * t6693 - 135 * t6695 - 51) * t6690) + + if Bindx == 921: + t6698 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.42e2) * ((1 - t6698) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t6698)) * (7 + (25 + 20 * t6698) * t6698) + + if Bindx == 922: + t6700 = np.cos(phi) + t6701 = t6700 ** 2 + t6702 = t6700 * t6701 + t6699 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.546e3) * t6699 ** 2 * (t6700 + 1 + (6 + 5 * t6702) * t6702 + (-9 + (-15 * t6700 + 11) * t6701) * t6701) + + if Bindx == 923: + t6706 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.910e3) * ((1 - t6706) ** (0.9e1 / 0.2e1)) * ((1 + t6706) ** (0.3e1 / 0.2e1)) * (1 + (9 + 12 * t6706) * t6706) + + if Bindx == 924: + t6712 = np.sin(phi) + t6710 = t6712 ** 2 + t6707 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.15015e5) * t6710 ** 2 * t6707 * (1 + (2 * t6707 - 3) * t6707 ** 2) + + if Bindx == 925: + t6713 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.858e3) * ((1 - t6713) ** (0.7e1 / 0.2e1)) * ((1 + t6713) ** (0.5e1 / 0.2e1)) * (-1 + (5 + 20 * t6713) * t6713) + + if Bindx == 926: + t6718 = np.sin(phi) + t6715 = t6718 ** 2 + t6716 = t6718 * t6715 + t6714 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((6*1j) * phi1) * np.sqrt(0.429e3) * t6716 ** 2 * (15 * t6714 ** 2 - 1) + + if Bindx == 927: + t6719 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.858e3) * ((1 - t6719) ** (0.5e1 / 0.2e1)) * ((1 + t6719) ** (0.7e1 / 0.2e1)) * (-1 + (-5 + 20 * t6719) * t6719) + + if Bindx == 928: + t6725 = np.sin(phi) + t6723 = t6725 ** 2 + t6720 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.15015e5) * t6723 ** 2 * t6720 * (-1 + (2 * t6720 + 3) * t6720 ** 2) + + if Bindx == 929: + t6726 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.910e3) * ((1 - t6726) ** (0.3e1 / 0.2e1)) * ((1 + t6726) ** (0.9e1 / 0.2e1)) * (1 + (-9 + 12 * t6726) * t6726) + + if Bindx == 930: + t6728 = np.cos(phi) + t6729 = t6728 ** 2 + t6730 = t6728 * t6729 + t6727 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.546e3) * t6727 ** 2 * (-t6728 + 1 + (-6 + 5 * t6730) * t6730 + (-9 + (15 * t6728 + 11) * t6729) * t6729) + + if Bindx == 931: + t6734 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.42e2) * np.sqrt((1 - t6734)) * ((1 + t6734) ** (0.11e2 / 0.2e1)) * (7 + (-25 + 20 * t6734) * t6734) + + if Bindx == 932: + t6735 = np.cos(phi) + t6736 = t6735 ** 2 + t6737 = t6735 * t6736 + t6740 = t6737 ** 2 + t6738 = t6736 ** 2 + tfunc[..., c] = (0.17e2 / 0.64e2) * np.exp((6*1j) * (phi1 + phi2)) * (-175 * t6737 + 196 * t6740 + 16 + (-210 + 30 * t6738) * t6738 + (21 * t6738 + 135 * t6740 + 51) * t6735) + + if Bindx == 933: + t6743 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.30e2) * ((1 + t6743) ** (0.13e2 / 0.2e1)) * (3 + (-7 + 4 * t6743) * t6743) * ((1 - t6743) ** (-0.1e1 / 0.2e1)) + + if Bindx == 934: + t6744 = np.cos(phi) + t6749 = 1 + t6744 + t6745 = t6749 ** 2 + t6746 = t6749 * t6745 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * (-1 + t6744) * t6749 * t6746 ** 2 + + if Bindx == 935: + t6750 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * ((1 - t6750) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t6750)) + + if Bindx == 936: + t6751 = np.cos(phi) + t6752 = t6751 ** 2 + t6753 = t6751 * t6752 + t6756 = t6753 ** 2 + t6754 = t6752 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((7*1j) * (phi1 - phi2)) * (-91 * t6752 + 77 * t6753 + 119 * t6756 - 7 + (35 + 8 * t6754) * t6754 + (-133 * t6754 - 49 * t6756 + 41) * t6751) + + if Bindx == 937: + t6759 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.30e2) * ((1 - t6759) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t6759)) * (3 + 4 * t6759) + + if Bindx == 938: + t6761 = np.cos(phi) + t6762 = t6761 ** 2 + t6764 = t6762 ** 2 + t6763 = t6761 * t6762 + t6760 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.35e2) * t6760 ** 2 * (-10 * t6762 + 55 * t6764 - 5 + (-30 + 8 * t6763) * t6763 + (-35 * t6764 + 17) * t6761) + + if Bindx == 939: + t6767 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.455e3) * ((1 - t6767) ** (0.11e2 / 0.2e1)) * ((1 + t6767) ** (0.3e1 / 0.2e1)) * (1 + 2 * t6767) + + if Bindx == 940: + t6774 = np.sin(phi) + t6772 = t6774 ** 2 + t6768 = np.cos(phi) + t6769 = t6768 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * t6772 ** 2 * (t6768 - 3 + (-21 * t6768 + 15 + 8 * t6769) * t6769) + + if Bindx == 941: + t6775 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t6775) ** (0.9e1 / 0.2e1)) * ((1 + t6775) ** (0.5e1 / 0.2e1)) * (1 + 4 * t6775) + + if Bindx == 942: + t6780 = np.sin(phi) + t6777 = t6780 ** 2 + t6778 = t6780 * t6777 + t6776 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.715e3) * t6778 ** 2 * (-1 + (-7 + 8 * t6776) * t6776) + + if Bindx == 943: + t6781 = np.cos(phi) + tfunc[..., c] = (0.51e2 / 0.64e2*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.1430e4) * ((1 - t6781) ** (0.7e1 / 0.2e1)) * ((1 + t6781) ** (0.7e1 / 0.2e1)) * t6781 + + if Bindx == 944: + t6786 = np.sin(phi) + t6783 = t6786 ** 2 + t6784 = t6786 * t6783 + t6782 = np.cos(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.715e3) * t6784 ** 2 * (-1 + (7 + 8 * t6782) * t6782) + + if Bindx == 945: + t6787 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.2002e4) * ((1 - t6787) ** (0.5e1 / 0.2e1)) * ((1 + t6787) ** (0.9e1 / 0.2e1)) * (-1 + 4 * t6787) + + if Bindx == 946: + t6794 = np.sin(phi) + t6792 = t6794 ** 2 + t6788 = np.cos(phi) + t6789 = t6788 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * t6792 ** 2 * (-t6788 - 3 + (21 * t6788 + 15 + 8 * t6789) * t6789) + + if Bindx == 947: + t6795 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.455e3) * ((1 - t6795) ** (0.3e1 / 0.2e1)) * ((1 + t6795) ** (0.11e2 / 0.2e1)) * (2 * t6795 - 1) + + if Bindx == 948: + t6797 = np.cos(phi) + t6798 = t6797 ** 2 + t6800 = t6798 ** 2 + t6799 = t6797 * t6798 + t6796 = np.sin(phi) + tfunc[..., c] = -(0.17e2 / 0.128e3) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.35e2) * t6796 ** 2 * (-10 * t6798 + 55 * t6800 - 5 + (30 + 8 * t6799) * t6799 + (35 * t6800 - 17) * t6797) + + if Bindx == 949: + t6803 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t6803)) * ((1 + t6803) ** (0.13e2 / 0.2e1)) * (-3 + 4 * t6803) + + if Bindx == 950: + t6804 = np.cos(phi) + t6805 = t6804 ** 2 + t6806 = t6804 * t6805 + t6809 = t6806 ** 2 + t6807 = t6805 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((7*1j) * (phi1 + phi2)) * (-91 * t6805 - 77 * t6806 + 119 * t6809 - 7 + (35 + 8 * t6807) * t6807 + (133 * t6807 + 49 * t6809 - 41) * t6804) + + if Bindx == 951: + t6812 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * np.sqrt((1 - t6812)) * ((1 + t6812) ** (0.15e2 / 0.2e1)) + + if Bindx == 952: + t6813 = np.cos(phi) + t6821 = -8 * t6813 + t6814 = t6813 ** 2 + t6816 = t6814 ** 2 + t6815 = t6813 * t6814 + tfunc[..., c] = (0.17e2 / 0.256e3) * np.exp((8*1j) * (phi1 - phi2)) * (28 * t6814 + t6821 + 1 + (-56 * t6813 + 70 + t6816) * t6816 + (-56 + (t6821 + 28) * t6815) * t6815) + + if Bindx == 953: + t6822 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * ((1 - t6822) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t6822)) + + if Bindx == 954: + t6823 = np.cos(phi) + t6828 = -1 + t6823 + t6824 = t6828 ** 2 + t6825 = t6828 * t6824 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.30e2) * t6828 * t6825 ** 2 * (1 + t6823) + + if Bindx == 955: + t6829 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.35e2) * ((1 - t6829) ** (0.13e2 / 0.2e1)) * ((1 + t6829) ** (0.3e1 / 0.2e1)) + + if Bindx == 956: + t6831 = np.cos(phi) + t6835 = -1 + t6831 + t6832 = t6835 ** 2 + t6833 = t6835 * t6832 + t6830 = 1 + t6831 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.455e3) * t6833 ** 2 * t6830 ** 2 + + if Bindx == 957: + t6836 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * ((1 - t6836) ** (0.11e2 / 0.2e1)) * ((1 + t6836) ** (0.5e1 / 0.2e1)) + + if Bindx == 958: + t6837 = np.cos(phi) + t6844 = -1 + t6837 + t6843 = 1 + t6837 + t6841 = t6843 ** 2 + t6838 = t6844 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.2002e4) * t6844 * t6838 ** 2 * t6843 * t6841 + + if Bindx == 959: + t6845 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.715e3) * ((1 - t6845) ** (0.9e1 / 0.2e1)) * ((1 + t6845) ** (0.7e1 / 0.2e1)) + + if Bindx == 960: + t6849 = np.sin(phi) + t6846 = t6849 ** 2 + t6847 = t6846 ** 2 + tfunc[..., c] = (0.51e2 / 0.256e3) * np.exp((8*1j) * phi1) * np.sqrt(0.1430e4) * t6847 ** 2 + + if Bindx == 961: + t6850 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.715e3) * ((1 - t6850) ** (0.7e1 / 0.2e1)) * ((1 + t6850) ** (0.9e1 / 0.2e1)) + + if Bindx == 962: + t6851 = np.cos(phi) + t6858 = -1 + t6851 + t6857 = 1 + t6851 + t6854 = t6857 ** 2 + t6852 = t6858 ** 2 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.2002e4) * t6858 * t6852 * t6857 * t6854 ** 2 + + if Bindx == 963: + t6859 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * ((1 - t6859) ** (0.5e1 / 0.2e1)) * ((1 + t6859) ** (0.11e2 / 0.2e1)) + + if Bindx == 964: + t6861 = np.cos(phi) + t6865 = 1 + t6861 + t6862 = t6865 ** 2 + t6863 = t6865 * t6862 + t6860 = -1 + t6861 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.455e3) * t6860 ** 2 * t6863 ** 2 + + if Bindx == 965: + t6866 = np.cos(phi) + tfunc[..., c] = (0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.35e2) * ((1 - t6866) ** (0.3e1 / 0.2e1)) * ((1 + t6866) ** (0.13e2 / 0.2e1)) + + if Bindx == 966: + t6867 = np.cos(phi) + t6872 = 1 + t6867 + t6868 = t6872 ** 2 + t6869 = t6872 * t6868 + tfunc[..., c] = (0.17e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * (-1 + t6867) * t6872 * t6869 ** 2 + + if Bindx == 967: + t6873 = np.cos(phi) + tfunc[..., c] = (-0.17e2 / 0.64e2*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt((1 - t6873)) * ((1 + t6873) ** (0.15e2 / 0.2e1)) + + if Bindx == 968: + t6874 = np.cos(phi) + t6882 = 8 * t6874 + t6875 = t6874 ** 2 + t6877 = t6875 ** 2 + t6876 = t6874 * t6875 + tfunc[..., c] = (0.17e2 / 0.256e3) * np.exp((8*1j) * (phi1 + phi2)) * (28 * t6875 + t6882 + 1 + (56 * t6874 + 70 + t6877) * t6877 + (56 + (t6882 + 28) * t6876) * t6876) + + if Bindx == 969: + t6883 = np.cos(phi) + t6884 = t6883 ** 2 + t6886 = t6884 ** 2 + t6892 = 126 * t6886 + t6890 = t6886 ** 2 + t6885 = t6883 * t6884 + t6888 = t6885 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((-9*1j) * (phi1 + phi2)) * (36 * t6884 + 84 * t6885 + 84 * t6888 + 9 * t6890 + t6892 + 1 + (36 * t6888 + t6890 + t6892 + 9) * t6883) + + if Bindx == 970: + t6893 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((-1*1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.2e1) * ((1 + t6893) ** (0.17e2 / 0.2e1)) * np.sqrt((1 - t6893)) + + if Bindx == 971: + t6894 = np.cos(phi) + t6898 = 1 + t6894 + t6895 = t6898 ** 2 + t6896 = t6895 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * (-1 + t6894) * t6896 ** 2 + + if Bindx == 972: + t6899 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t6899) ** (0.3e1 / 0.2e1)) * ((1 + t6899) ** (0.15e2 / 0.2e1)) + + if Bindx == 973: + t6901 = np.cos(phi) + t6906 = 1 + t6901 + t6902 = t6906 ** 2 + t6903 = t6906 * t6902 + t6900 = -1 + t6901 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-1*1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t6900 ** 2 * t6906 * t6903 ** 2 + + if Bindx == 974: + t6907 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.238e3) * ((1 - t6907) ** (0.5e1 / 0.2e1)) * ((1 + t6907) ** (0.13e2 / 0.2e1)) + + if Bindx == 975: + t6908 = np.cos(phi) + t6915 = -1 + t6908 + t6914 = 1 + t6908 + t6911 = t6914 ** 2 + t6912 = t6914 * t6911 + t6909 = t6915 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.4641e4) * t6915 * t6909 * t6912 ** 2 + + if Bindx == 976: + t6916 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((-1*1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.221e3) * ((1 - t6916) ** (0.7e1 / 0.2e1)) * ((1 + t6916) ** (0.11e2 / 0.2e1)) + + if Bindx == 977: + t6920 = np.sin(phi) + t6917 = t6920 ** 2 + t6918 = t6917 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 + phi2)) * np.sqrt(0.4862e4) * t6918 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 978: + t6921 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((-9*1j) * phi1) * np.sqrt(0.12155e5) * ((1 - t6921) ** (0.9e1 / 0.2e1)) * ((1 + t6921) ** (0.9e1 / 0.2e1)) + + if Bindx == 979: + t6925 = np.sin(phi) + t6922 = t6925 ** 2 + t6923 = t6922 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 - phi2)) * np.sqrt(0.4862e4) * t6923 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 980: + t6926 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((-1*1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.221e3) * ((1 - t6926) ** (0.11e2 / 0.2e1)) * ((1 + t6926) ** (0.7e1 / 0.2e1)) + + if Bindx == 981: + t6927 = np.cos(phi) + t6934 = -1 + t6927 + t6933 = 1 + t6927 + t6931 = t6933 ** 2 + t6928 = t6934 ** 2 + t6929 = t6934 * t6928 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.4641e4) * t6929 ** 2 * t6933 * t6931 + + if Bindx == 982: + t6935 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.238e3) * ((1 - t6935) ** (0.13e2 / 0.2e1)) * ((1 + t6935) ** (0.5e1 / 0.2e1)) + + if Bindx == 983: + t6937 = np.cos(phi) + t6942 = -1 + t6937 + t6938 = t6942 ** 2 + t6939 = t6942 * t6938 + t6936 = 1 + t6937 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-1*1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t6942 * t6939 ** 2 * t6936 ** 2 + + if Bindx == 984: + t6943 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t6943) ** (0.15e2 / 0.2e1)) * ((1 + t6943) ** (0.3e1 / 0.2e1)) + + if Bindx == 985: + t6944 = np.cos(phi) + t6948 = -1 + t6944 + t6945 = t6948 ** 2 + t6946 = t6945 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * t6946 ** 2 * (1 + t6944) + + if Bindx == 986: + t6949 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((-1*1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.2e1) * ((1 - t6949) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t6949)) + + if Bindx == 987: + t6950 = np.cos(phi) + t6951 = t6950 ** 2 + t6953 = t6951 ** 2 + t6957 = t6953 ** 2 + t6952 = t6950 * t6951 + t6955 = t6952 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((-9*1j) * (phi1 - phi2)) * (-36 * t6951 + 84 * t6952 - 126 * t6953 - 84 * t6955 - 9 * t6957 - 1 + (126 * t6953 + 36 * t6955 + t6957 + 9) * t6950) + + if Bindx == 988: + t6959 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((-1*1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t6959)) * ((1 + t6959) ** (0.17e2 / 0.2e1)) + + if Bindx == 989: + t6960 = np.cos(phi) + t6961 = t6960 ** 2 + t6963 = t6961 ** 2 + t6967 = t6963 ** 2 + t6962 = t6960 * t6961 + t6965 = t6962 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-8*1j) * (phi1 + phi2)) * (-152 * t6961 - 196 * t6962 - 56 * t6963 + 280 * t6965 + 64 * t6967 - 8 + (182 * t6963 + 188 * t6965 + 9 * t6967 - 55) * t6960) + + if Bindx == 990: + t6969 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.34e2) * ((1 + t6969) ** (0.15e2 / 0.2e1)) * (7 + (-16 + 9 * t6969) * t6969) * ((1 - t6969) ** (-0.1e1 / 0.2e1)) + + if Bindx == 991: + t6970 = np.cos(phi) + t6975 = 1 + t6970 + t6971 = t6975 ** 2 + t6972 = t6975 * t6971 + tfunc[..., c] = (0.19e2 / 0.128e3) * (-2 + 3 * t6970) * (-1 + t6970) * t6975 * t6972 ** 2 * np.sqrt(0.102e3) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) + + if Bindx == 992: + t6976 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (-5 + 9 * t6976) * ((1 + t6976) ** (0.13e2 / 0.2e1)) * np.sqrt(0.170e3) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * ((1 - t6976) ** (0.3e1 / 0.2e1)) + + if Bindx == 993: + t6984 = np.sin(phi) + t6982 = t6984 ** 2 + t6977 = np.cos(phi) + t6978 = t6977 ** 2 + t6980 = t6978 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.119e3) * t6982 ** 2 * (12 * t6978 + 32 * t6980 - 4 + (38 * t6978 + 9 * t6980 - 7) * t6977) + + if Bindx == 994: + t6985 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (-1 + 3 * t6985) * ((1 + t6985) ** (0.11e2 / 0.2e1)) * np.sqrt(0.9282e4) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * ((1 - t6985) ** (0.5e1 / 0.2e1)) + + if Bindx == 995: + t6992 = np.sin(phi) + t6989 = t6992 ** 2 + t6990 = t6992 * t6989 + t6986 = np.cos(phi) + t6987 = t6986 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.442e3) * t6990 ** 2 * (16 * t6987 - 2 + (9 * t6987 + 5) * t6986) + + if Bindx == 996: + t6993 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (-1 + 9 * t6993) * ((1 + t6993) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2431e4) * np.exp((-1*1j) * (8 * phi1 + phi2)) * ((1 - t6993) ** (0.7e1 / 0.2e1)) + + if Bindx == 997: + t6997 = np.sin(phi) + t6994 = t6997 ** 2 + t6995 = t6994 ** 2 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.24310e5) * t6995 ** 2 * np.cos(phi) + + if Bindx == 998: + t6998 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (9 * t6998 + 1) * ((1 + t6998) ** (0.7e1 / 0.2e1)) * np.sqrt(0.2431e4) * np.exp((-1*1j) * (8 * phi1 - phi2)) * ((1 - t6998) ** (0.9e1 / 0.2e1)) + + if Bindx == 999: + t7005 = np.sin(phi) + t7002 = t7005 ** 2 + t7003 = t7005 * t7002 + t6999 = np.cos(phi) + t7000 = t6999 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.442e3) * t7003 ** 2 * (-16 * t7000 + 2 + (9 * t7000 + 5) * t6999) + + if Bindx == 1000: + t7006 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (1 + 3 * t7006) * ((1 + t7006) ** (0.5e1 / 0.2e1)) * np.sqrt(0.9282e4) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * ((1 - t7006) ** (0.11e2 / 0.2e1)) + + if Bindx == 1001: + t7014 = np.sin(phi) + t7012 = t7014 ** 2 + t7007 = np.cos(phi) + t7008 = t7007 ** 2 + t7010 = t7008 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.119e3) * t7012 ** 2 * (-12 * t7008 - 32 * t7010 + 4 + (38 * t7008 + 9 * t7010 - 7) * t7007) + + if Bindx == 1002: + t7015 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (5 + 9 * t7015) * ((1 + t7015) ** (0.3e1 / 0.2e1)) * np.sqrt(0.170e3) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * ((1 - t7015) ** (0.13e2 / 0.2e1)) + + if Bindx == 1003: + t7016 = np.cos(phi) + t7021 = -1 + t7016 + t7017 = t7021 ** 2 + t7018 = t7021 * t7017 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.102e3) * (1 + t7016) * t7021 * t7018 ** 2 * (2 + 3 * t7016) + + if Bindx == 1004: + t7022 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * (7 + 9 * t7022) * np.sqrt((1 + t7022)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t7022) ** (0.15e2 / 0.2e1)) + + if Bindx == 1005: + t7023 = np.cos(phi) + t7024 = t7023 ** 2 + t7026 = t7024 ** 2 + t7030 = t7026 ** 2 + t7025 = t7023 * t7024 + t7028 = t7025 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-8*1j) * (phi1 - phi2)) * (152 * t7024 - 196 * t7025 + 56 * t7026 - 280 * t7028 - 64 * t7030 + 8 + (182 * t7026 + 188 * t7028 + 9 * t7030 - 55) * t7023) + + if Bindx == 1006: + t7032 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((-1*1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.2e1) * ((1 - t7032) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t7032)) + + if Bindx == 1007: + t7033 = np.cos(phi) + t7037 = 1 + t7033 + t7034 = t7037 ** 2 + t7035 = t7034 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.17e2) * (-1 + t7033) * t7035 ** 2 + + if Bindx == 1008: + t7038 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.512e3*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.34e2) * np.sqrt((1 - t7038)) * ((1 + t7038) ** (0.15e2 / 0.2e1)) * (-7 + 9 * t7038) + + if Bindx == 1009: + t7039 = np.cos(phi) + t7040 = t7039 ** 2 + t7042 = t7040 ** 2 + t7046 = t7042 ** 2 + t7041 = t7039 * t7040 + t7044 = t7041 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((-7*1j) * (phi1 + phi2)) * (356 * t7040 - 812 * t7041 - 2002 * t7042 + 980 * t7044 + 833 * t7046 + 89 + (-1106 * t7042 + 1636 * t7044 + 153 * t7046 + 385) * t7039) + + if Bindx == 1010: + t7048 = np.cos(phi) + t7049 = t7048 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.3e1) * ((1 + t7048) ** (0.13e2 / 0.2e1)) * (-119 * t7049 - 21 + (51 * t7049 + 89) * t7048) * ((1 - t7048) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1011: + t7052 = np.cos(phi) + t7053 = t7052 ** 2 + t7054 = t7052 * t7053 + t7057 = t7054 ** 2 + t7055 = t7053 ** 2 + t7051 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * t7051 ** 2 * (-287 * t7053 - 525 * t7054 + 35 * t7055 + 595 * t7057 + 41 + (721 * t7055 + 153 * t7057 + 35) * t7052) + + if Bindx == 1012: + t7059 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (23 + (-136 + 153 * t7059) * t7059) * ((1 + t7059) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t7059) ** (0.3e1 / 0.2e1)) + + if Bindx == 1013: + t7067 = np.sin(phi) + t7065 = t7067 ** 2 + t7060 = np.cos(phi) + t7061 = t7060 ** 2 + t7063 = t7061 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * t7065 ** 2 * (-42 * t7061 + 119 * t7063 + 3 + (54 * t7061 + 51 * t7063 - 25) * t7060) + + if Bindx == 1014: + t7068 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-1 + (-68 + 153 * t7068) * t7068) * ((1 + t7068) ** (0.9e1 / 0.2e1)) * np.sqrt(0.13e2) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t7068) ** (0.5e1 / 0.2e1)) + + if Bindx == 1015: + t7075 = np.sin(phi) + t7072 = t7075 ** 2 + t7073 = t7075 * t7072 + t7069 = np.cos(phi) + t7070 = t7069 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.286e3) * t7073 ** 2 * (119 * t7070 - 7 + (153 * t7070 - 41) * t7069) + + if Bindx == 1016: + t7076 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * (17 * t7076 ** 2 - 1) * ((1 + t7076) ** (0.7e1 / 0.2e1)) * np.sqrt(0.715e3) * np.exp((-7*1j) * phi1) * ((1 - t7076) ** (0.7e1 / 0.2e1)) + + if Bindx == 1017: + t7083 = np.sin(phi) + t7080 = t7083 ** 2 + t7081 = t7083 * t7080 + t7077 = np.cos(phi) + t7078 = t7077 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.286e3) * t7081 ** 2 * (-119 * t7078 + 7 + (153 * t7078 - 41) * t7077) + + if Bindx == 1018: + t7084 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-1 + (68 + 153 * t7084) * t7084) * ((1 + t7084) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13e2) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t7084) ** (0.9e1 / 0.2e1)) + + if Bindx == 1019: + t7092 = np.sin(phi) + t7090 = t7092 ** 2 + t7085 = np.cos(phi) + t7086 = t7085 ** 2 + t7088 = t7086 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * t7090 ** 2 * (42 * t7086 - 119 * t7088 - 3 + (54 * t7086 + 51 * t7088 - 25) * t7085) + + if Bindx == 1020: + t7093 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (23 + (136 + 153 * t7093) * t7093) * ((1 + t7093) ** (0.3e1 / 0.2e1)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t7093) ** (0.11e2 / 0.2e1)) + + if Bindx == 1021: + t7095 = np.cos(phi) + t7096 = t7095 ** 2 + t7097 = t7095 * t7096 + t7100 = t7097 ** 2 + t7098 = t7096 ** 2 + t7094 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t7094 ** 2 * (287 * t7096 - 525 * t7097 - 35 * t7098 - 595 * t7100 - 41 + (721 * t7098 + 153 * t7100 + 35) * t7095) + + if Bindx == 1022: + t7102 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (21 + (68 + 51 * t7102) * t7102) * np.sqrt((1 + t7102)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t7102) ** (0.13e2 / 0.2e1)) + + if Bindx == 1023: + t7103 = np.cos(phi) + t7104 = t7103 ** 2 + t7106 = t7104 ** 2 + t7110 = t7106 ** 2 + t7105 = t7103 * t7104 + t7108 = t7105 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((-7*1j) * (phi1 - phi2)) * (-356 * t7104 - 812 * t7105 + 2002 * t7106 - 980 * t7108 - 833 * t7110 - 89 + (-1106 * t7106 + 1636 * t7108 + 153 * t7110 + 385) * t7103) + + if Bindx == 1024: + t7112 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * (7 + 9 * t7112) * np.sqrt((1 + t7112)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t7112) ** (0.15e2 / 0.2e1)) + + if Bindx == 1025: + t7113 = np.cos(phi) + t7117 = -1 + t7113 + t7114 = t7117 ** 2 + t7115 = t7114 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.17e2) * t7115 ** 2 * (1 + t7113) + + if Bindx == 1026: + t7118 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.51e2) * ((1 - t7118) ** (0.3e1 / 0.2e1)) * ((1 + t7118) ** (0.15e2 / 0.2e1)) + + if Bindx == 1027: + t7119 = np.cos(phi) + t7124 = 1 + t7119 + t7120 = t7124 ** 2 + t7121 = t7124 * t7120 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.102e3) * (-1 + t7119) * t7124 * t7121 ** 2 * (-2 + 3 * t7119) + + if Bindx == 1028: + t7125 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t7125)) * ((1 + t7125) ** (0.13e2 / 0.2e1)) * (21 + (-68 + 51 * t7125) * t7125) + + if Bindx == 1029: + t7126 = np.cos(phi) + t7127 = t7126 ** 2 + t7129 = t7127 ** 2 + t7133 = t7129 ** 2 + t7128 = t7126 * t7127 + t7131 = t7128 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-6*1j) * (phi1 + phi2)) * (207 * t7127 + 308 * t7128 - 273 * t7129 - 287 * t7131 + 408 * t7133 - 23 + (-798 * t7129 + 432 * t7131 + 102 * t7133 - 12) * t7126) + + if Bindx == 1030: + t7135 = np.cos(phi) + t7136 = t7135 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.15e2) * ((1 + t7135) ** (0.11e2 / 0.2e1)) * (-46 * t7135 + 5 + (-136 * t7135 + 126 + 51 * t7136) * t7136) * ((1 - t7135) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1031: + t7140 = np.cos(phi) + t7141 = t7140 ** 2 + t7142 = t7140 * t7141 + t7145 = t7142 ** 2 + t7143 = t7141 ** 2 + t7139 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.42e2) * t7139 ** 2 * (18 * t7141 - 87 * t7142 - 113 * t7143 + 136 * t7145 - 1 + (57 * t7143 + 51 * t7145 + 19) * t7140) + + if Bindx == 1032: + t7147 = np.cos(phi) + t7148 = t7147 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (-51 * t7148 + 1 + (51 * t7148 + 9) * t7147) * ((1 + t7147) ** (0.9e1 / 0.2e1)) * np.sqrt(0.91e2) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t7147) ** (0.3e1 / 0.2e1)) + + if Bindx == 1033: + t7157 = np.sin(phi) + t7155 = t7157 ** 2 + t7150 = np.cos(phi) + t7151 = t7150 ** 2 + t7153 = t7151 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.39e2) * t7155 ** 2 * (-69 * t7151 + 136 * t7153 + 3 + (-36 * t7151 + 102 * t7153 + 4) * t7150) + + if Bindx == 1034: + t7158 = np.cos(phi) + t7159 = t7158 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-17 * t7159 + 1 + (51 * t7159 - 7) * t7158) * ((1 + t7158) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t7158) ** (0.5e1 / 0.2e1)) + + if Bindx == 1035: + t7165 = np.sin(phi) + t7162 = t7165 ** 2 + t7163 = t7165 * t7162 + t7161 = np.cos(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-6*1j) * phi1) * np.sqrt(0.2145e4) * t7163 ** 2 * t7161 * (17 * t7161 ** 2 - 3) + + if Bindx == 1036: + t7166 = np.cos(phi) + t7167 = t7166 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (17 * t7167 - 1 + (51 * t7167 - 7) * t7166) * ((1 + t7166) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t7166) ** (0.7e1 / 0.2e1)) + + if Bindx == 1037: + t7176 = np.sin(phi) + t7174 = t7176 ** 2 + t7169 = np.cos(phi) + t7170 = t7169 ** 2 + t7172 = t7170 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.39e2) * t7174 ** 2 * (69 * t7170 - 136 * t7172 - 3 + (-36 * t7170 + 102 * t7172 + 4) * t7169) + + if Bindx == 1038: + t7177 = np.cos(phi) + t7180 = 51 * t7177 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * (t7180 - 1 + (t7180 + 9) * t7177) * ((1 + t7177) ** (0.3e1 / 0.2e1)) * np.sqrt(0.91e2) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t7177) ** (0.9e1 / 0.2e1)) + + if Bindx == 1039: + t7182 = np.cos(phi) + t7183 = t7182 ** 2 + t7184 = t7182 * t7183 + t7187 = t7184 ** 2 + t7185 = t7183 ** 2 + t7181 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.42e2) * t7181 ** 2 * (-18 * t7183 - 87 * t7184 + 113 * t7185 - 136 * t7187 + 1 + (57 * t7185 + 51 * t7187 + 19) * t7182) + + if Bindx == 1040: + t7189 = np.cos(phi) + t7190 = t7189 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (85 * t7190 + 5 + (51 * t7190 + 41) * t7189) * np.sqrt((1 + t7189)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t7189) ** (0.11e2 / 0.2e1)) + + if Bindx == 1041: + t7192 = np.cos(phi) + t7193 = t7192 ** 2 + t7195 = t7193 ** 2 + t7199 = t7195 ** 2 + t7194 = t7192 * t7193 + t7197 = t7194 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-6*1j) * (phi1 - phi2)) * (-207 * t7193 + 308 * t7194 + 273 * t7195 + 287 * t7197 - 408 * t7199 + 23 + (-798 * t7195 + 432 * t7197 + 102 * t7199 - 12) * t7192) + + if Bindx == 1042: + t7201 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (21 + (68 + 51 * t7201) * t7201) * np.sqrt((1 + t7201)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t7201) ** (0.13e2 / 0.2e1)) + + if Bindx == 1043: + t7203 = np.cos(phi) + t7204 = t7203 ** 2 + t7205 = t7203 * t7204 + t7208 = t7205 ** 2 + t7206 = t7204 ** 2 + t7202 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.102e3) * t7202 ** 2 * (12 * t7204 + 5 * t7205 - 30 * t7206 - 16 * t7208 + 2 + (33 * t7206 + 3 * t7208 - 9) * t7203) + + if Bindx == 1044: + t7210 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.51e2) * ((1 - t7210) ** (0.15e2 / 0.2e1)) * ((1 + t7210) ** (0.3e1 / 0.2e1)) + + if Bindx == 1045: + t7212 = np.cos(phi) + t7217 = 1 + t7212 + t7213 = t7217 ** 2 + t7214 = t7217 * t7213 + t7211 = -1 + t7212 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.85e2) * t7211 ** 2 * t7217 * t7214 ** 2 + + if Bindx == 1046: + t7218 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.170e3) * ((1 - t7218) ** (0.3e1 / 0.2e1)) * ((1 + t7218) ** (0.13e2 / 0.2e1)) * (-5 + 9 * t7218) + + if Bindx == 1047: + t7220 = np.cos(phi) + t7221 = t7220 ** 2 + t7222 = t7220 * t7221 + t7225 = t7222 ** 2 + t7223 = t7221 ** 2 + t7219 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.5e1) * t7219 ** 2 * (-287 * t7221 - 525 * t7222 + 35 * t7223 + 595 * t7225 + 41 + (721 * t7223 + 153 * t7225 + 35) * t7220) + + if Bindx == 1048: + t7227 = np.cos(phi) + t7228 = t7227 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t7227)) * ((1 + t7227) ** (0.11e2 / 0.2e1)) * (-85 * t7228 - 5 + (51 * t7228 + 41) * t7227) + + if Bindx == 1049: + t7230 = np.cos(phi) + t7231 = t7230 ** 2 + t7233 = t7231 ** 2 + t7237 = t7233 ** 2 + t7232 = t7230 * t7231 + t7235 = t7232 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-5*1j) * (phi1 + phi2)) * (-180 * t7231 + 1540 * t7232 + 1610 * t7233 - 3500 * t7235 + 2125 * t7237 + 9 + (-2366 * t7233 + 380 * t7235 + 765 * t7237 - 255) * t7230) + + if Bindx == 1050: + t7239 = np.cos(phi) + t7240 = t7239 ** 2 + t7242 = t7240 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.70e2) * ((1 + t7239) ** (0.9e1 / 0.2e1)) * (-150 * t7240 - 425 * t7242 + 3 + (410 * t7240 + 153 * t7242 + 9) * t7239) * ((1 - t7239) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1051: + t7245 = np.cos(phi) + t7246 = t7245 ** 2 + t7247 = t7245 * t7246 + t7250 = t7247 ** 2 + t7248 = t7246 ** 2 + t7244 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.1365e4) * t7244 ** 2 * (27 * t7246 - 3 * t7247 - 95 * t7248 + 85 * t7250 - 1 + (-33 * t7248 + 51 * t7250 + 1) * t7245) + + if Bindx == 1052: + t7252 = np.cos(phi) + t7253 = t7252 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (18 * t7252 - 1 + (-136 * t7252 - 6 + 153 * t7253) * t7253) * ((1 + t7252) ** (0.7e1 / 0.2e1)) * np.sqrt(0.65e2) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t7252) ** (0.3e1 / 0.2e1)) + + if Bindx == 1053: + t7263 = np.sin(phi) + t7261 = t7263 ** 2 + t7256 = np.cos(phi) + t7257 = t7256 ** 2 + t7259 = t7257 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.1430e4) * t7261 ** 2 * (-30 * t7257 + 85 * t7259 + 1 + (-110 * t7257 + 153 * t7259 + 13) * t7256) + + if Bindx == 1054: + t7264 = np.cos(phi) + t7265 = t7264 ** 2 + tfunc[..., c] = (-0.57e2 / 0.128e3*1j) * (1 + (-30 + 85 * t7265) * t7265) * ((1 + t7264) ** (0.5e1 / 0.2e1)) * np.sqrt(0.143e3) * np.exp((-5*1j) * phi1) * ((1 - t7264) ** (0.5e1 / 0.2e1)) + + if Bindx == 1055: + t7274 = np.sin(phi) + t7272 = t7274 ** 2 + t7267 = np.cos(phi) + t7268 = t7267 ** 2 + t7270 = t7268 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.1430e4) * t7272 ** 2 * (30 * t7268 - 85 * t7270 - 1 + (-110 * t7268 + 153 * t7270 + 13) * t7267) + + if Bindx == 1056: + t7275 = np.cos(phi) + t7276 = t7275 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (-18 * t7275 - 1 + (136 * t7275 - 6 + 153 * t7276) * t7276) * ((1 + t7275) ** (0.3e1 / 0.2e1)) * np.sqrt(0.65e2) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t7275) ** (0.7e1 / 0.2e1)) + + if Bindx == 1057: + t7280 = np.cos(phi) + t7281 = t7280 ** 2 + t7282 = t7280 * t7281 + t7285 = t7282 ** 2 + t7283 = t7281 ** 2 + t7279 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.1365e4) * t7279 ** 2 * (-27 * t7281 - 3 * t7282 + 95 * t7283 - 85 * t7285 + 1 + (-33 * t7283 + 51 * t7285 + 1) * t7280) + + if Bindx == 1058: + t7287 = np.cos(phi) + t7288 = t7287 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (12 * t7287 - 3 + (272 * t7287 + 138 + 153 * t7288) * t7288) * np.sqrt((1 + t7287)) * np.sqrt(0.70e2) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t7287) ** (0.9e1 / 0.2e1)) + + if Bindx == 1059: + t7291 = np.cos(phi) + t7292 = t7291 ** 2 + t7294 = t7292 ** 2 + t7298 = t7294 ** 2 + t7293 = t7291 * t7292 + t7296 = t7293 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-5*1j) * (phi1 - phi2)) * (180 * t7292 + 1540 * t7293 - 1610 * t7294 + 3500 * t7296 - 2125 * t7298 - 9 + (-2366 * t7294 + 380 * t7296 + 765 * t7298 - 255) * t7291) + + if Bindx == 1060: + t7300 = np.cos(phi) + t7301 = t7300 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (85 * t7301 + 5 + (51 * t7301 + 41) * t7300) * np.sqrt((1 + t7300)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t7300) ** (0.11e2 / 0.2e1)) + + if Bindx == 1061: + t7304 = np.cos(phi) + t7305 = t7304 ** 2 + t7306 = t7304 * t7305 + t7309 = t7306 ** 2 + t7307 = t7305 ** 2 + t7303 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.5e1) * t7303 ** 2 * (287 * t7305 - 525 * t7306 - 35 * t7307 - 595 * t7309 - 41 + (721 * t7307 + 153 * t7309 + 35) * t7304) + + if Bindx == 1062: + t7311 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (5 + 9 * t7311) * ((1 + t7311) ** (0.3e1 / 0.2e1)) * np.sqrt(0.170e3) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * ((1 - t7311) ** (0.13e2 / 0.2e1)) + + if Bindx == 1063: + t7313 = np.cos(phi) + t7318 = -1 + t7313 + t7314 = t7318 ** 2 + t7315 = t7318 * t7314 + t7312 = 1 + t7313 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.85e2) * t7318 * t7315 ** 2 * t7312 ** 2 + + if Bindx == 1064: + t7319 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.238e3) * ((1 - t7319) ** (0.5e1 / 0.2e1)) * ((1 + t7319) ** (0.13e2 / 0.2e1)) + + if Bindx == 1065: + t7321 = np.cos(phi) + t7325 = 1 + t7321 + t7322 = t7325 ** 2 + t7323 = t7325 * t7322 + t7320 = -1 + t7321 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.119e3) * t7320 ** 2 * t7323 ** 2 * (-4 + 9 * t7321) + + if Bindx == 1066: + t7326 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.14e2) * ((1 - t7326) ** (0.3e1 / 0.2e1)) * ((1 + t7326) ** (0.11e2 / 0.2e1)) * (23 + (-136 + 153 * t7326) * t7326) + + if Bindx == 1067: + t7328 = np.cos(phi) + t7329 = t7328 ** 2 + t7330 = t7328 * t7329 + t7333 = t7330 ** 2 + t7331 = t7329 ** 2 + t7327 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.42e2) * t7327 ** 2 * (18 * t7329 - 87 * t7330 - 113 * t7331 + 136 * t7333 - 1 + (57 * t7331 + 51 * t7333 + 19) * t7328) + + if Bindx == 1068: + t7335 = np.cos(phi) + t7336 = t7335 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.70e2) * np.sqrt((1 - t7335)) * ((1 + t7335) ** (0.9e1 / 0.2e1)) * (-12 * t7335 - 3 + (-272 * t7335 + 138 + 153 * t7336) * t7336) + + if Bindx == 1069: + t7339 = np.cos(phi) + t7340 = t7339 ** 2 + t7342 = t7340 ** 2 + t7346 = t7342 ** 2 + t7341 = t7339 * t7340 + t7344 = t7341 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-4*1j) * (phi1 + phi2)) * (-522 * t7340 + 212 * t7341 + 2398 * t7342 - 3766 * t7344 + 1904 * t7346 + 18 + (266 * t7342 - 1484 * t7344 + 1071 * t7346 - 33) * t7339) + + if Bindx == 1070: + t7348 = np.cos(phi) + t7349 = t7348 ** 2 + t7351 = t7349 ** 2 + t7350 = t7348 * t7349 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.78e2) * ((1 + t7348) ** (0.7e1 / 0.2e1)) * (-105 * t7349 + 805 * t7351 - 1 + (-140 + 357 * t7350) * t7350 + (-952 * t7351 + 36) * t7348) * ((1 - t7348) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1071: + t7355 = np.cos(phi) + t7356 = t7355 ** 2 + t7357 = t7355 * t7356 + t7360 = t7357 ** 2 + t7358 = t7356 ** 2 + t7354 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.182e3) * t7354 ** 2 * (34 * t7356 + 75 * t7357 - 145 * t7358 + 136 * t7360 - 1 + (-197 * t7358 + 153 * t7360 - 7) * t7355) + + if Bindx == 1072: + t7362 = np.cos(phi) + t7363 = t7362 ** 2 + t7365 = t7363 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (30 * t7363 - 85 * t7365 - 1 + (-70 * t7363 + 153 * t7365 + 5) * t7362) * ((1 + t7362) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t7362) ** (0.3e1 / 0.2e1)) + + if Bindx == 1073: + t7372 = np.sin(phi) + t7370 = t7372 ** 2 + t7367 = np.cos(phi) + t7368 = t7367 ** 2 + tfunc[..., c] = (0.57e2 / 0.128e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.10010e5) * t7370 ** 2 * t7367 * (1 + (-10 + 17 * t7368) * t7368) + + if Bindx == 1074: + t7373 = np.cos(phi) + t7374 = t7373 ** 2 + t7376 = t7374 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-30 * t7374 + 85 * t7376 + 1 + (-70 * t7374 + 153 * t7376 + 5) * t7373) * ((1 + t7373) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t7373) ** (0.5e1 / 0.2e1)) + + if Bindx == 1075: + t7379 = np.cos(phi) + t7380 = t7379 ** 2 + t7381 = t7379 * t7380 + t7384 = t7381 ** 2 + t7382 = t7380 ** 2 + t7378 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.182e3) * t7378 ** 2 * (-34 * t7380 + 75 * t7381 + 145 * t7382 - 136 * t7384 + 1 + (-197 * t7382 + 153 * t7384 - 7) * t7379) + + if Bindx == 1076: + t7386 = np.cos(phi) + t7387 = t7386 ** 2 + t7389 = t7387 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (-70 * t7387 + 595 * t7389 - 1 + (210 * t7387 + 357 * t7389 - 35) * t7386) * np.sqrt((1 + t7386)) * np.sqrt(0.78e2) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t7386) ** (0.7e1 / 0.2e1)) + + if Bindx == 1077: + t7391 = np.cos(phi) + t7392 = t7391 ** 2 + t7394 = t7392 ** 2 + t7398 = t7394 ** 2 + t7393 = t7391 * t7392 + t7396 = t7393 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-4*1j) * (phi1 - phi2)) * (522 * t7392 + 212 * t7393 - 2398 * t7394 + 3766 * t7396 - 1904 * t7398 - 18 + (266 * t7394 - 1484 * t7396 + 1071 * t7398 - 33) * t7391) + + if Bindx == 1078: + t7400 = np.cos(phi) + t7401 = t7400 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (12 * t7400 - 3 + (272 * t7400 + 138 + 153 * t7401) * t7401) * np.sqrt((1 + t7400)) * np.sqrt(0.70e2) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t7400) ** (0.9e1 / 0.2e1)) + + if Bindx == 1079: + t7405 = np.cos(phi) + t7406 = t7405 ** 2 + t7407 = t7405 * t7406 + t7410 = t7407 ** 2 + t7408 = t7406 ** 2 + t7404 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.42e2) * t7404 ** 2 * (-18 * t7406 - 87 * t7407 + 113 * t7408 - 136 * t7410 + 1 + (57 * t7408 + 51 * t7410 + 19) * t7405) + + if Bindx == 1080: + t7412 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (23 + (136 + 153 * t7412) * t7412) * ((1 + t7412) ** (0.3e1 / 0.2e1)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t7412) ** (0.11e2 / 0.2e1)) + + if Bindx == 1081: + t7420 = np.sin(phi) + t7418 = t7420 ** 2 + t7413 = np.cos(phi) + t7414 = t7413 ** 2 + t7416 = t7414 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.119e3) * t7418 ** 2 * (-12 * t7414 - 32 * t7416 + 4 + (38 * t7414 + 9 * t7416 - 7) * t7413) + + if Bindx == 1082: + t7421 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.238e3) * ((1 - t7421) ** (0.13e2 / 0.2e1)) * ((1 + t7421) ** (0.5e1 / 0.2e1)) + + if Bindx == 1083: + t7422 = np.cos(phi) + t7429 = -1 + t7422 + t7428 = 1 + t7422 + t7425 = t7428 ** 2 + t7426 = t7428 * t7425 + t7423 = t7429 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.4641e4) * t7429 * t7423 * t7426 ** 2 + + if Bindx == 1084: + t7430 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.9282e4) * ((1 - t7430) ** (0.5e1 / 0.2e1)) * ((1 + t7430) ** (0.11e2 / 0.2e1)) * (-1 + 3 * t7430) + + if Bindx == 1085: + t7438 = np.sin(phi) + t7436 = t7438 ** 2 + t7431 = np.cos(phi) + t7432 = t7431 ** 2 + t7434 = t7432 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.273e3) * t7436 ** 2 * (-42 * t7432 + 119 * t7434 + 3 + (54 * t7432 + 51 * t7434 - 25) * t7431) + + if Bindx == 1086: + t7439 = np.cos(phi) + t7440 = t7439 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.91e2) * ((1 - t7439) ** (0.3e1 / 0.2e1)) * ((1 + t7439) ** (0.9e1 / 0.2e1)) * (-51 * t7440 + 1 + (51 * t7440 + 9) * t7439) + + if Bindx == 1087: + t7443 = np.cos(phi) + t7444 = t7443 ** 2 + t7445 = t7443 * t7444 + t7448 = t7445 ** 2 + t7446 = t7444 ** 2 + t7442 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.1365e4) * t7442 ** 2 * (27 * t7444 - 3 * t7445 - 95 * t7446 + 85 * t7448 - 1 + (-33 * t7446 + 51 * t7448 + 1) * t7443) + + if Bindx == 1088: + t7450 = np.cos(phi) + t7451 = t7450 ** 2 + t7453 = t7451 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.78e2) * np.sqrt((1 - t7450)) * ((1 + t7450) ** (0.7e1 / 0.2e1)) * (70 * t7451 - 595 * t7453 + 1 + (210 * t7451 + 357 * t7453 - 35) * t7450) + + if Bindx == 1089: + t7455 = np.cos(phi) + t7456 = t7455 ** 2 + t7458 = t7456 ** 2 + t7464 = 4641 * t7458 ** 2 + t7457 = t7455 * t7456 + t7460 = t7457 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-3*1j) * (phi1 + phi2)) * (-1044 * t7456 - 2012 * t7457 + 5538 * t7458 - 9100 * t7460 + t7464 + 29 + (7098 * t7458 - 9828 * t7460 + t7464 + 165) * t7455) + + if Bindx == 1090: + t7465 = np.cos(phi) + t7466 = t7465 ** 2 + t7467 = t7465 * t7466 + t7470 = t7467 ** 2 + t7468 = t7466 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.21e2) * ((1 + t7465) ** (0.5e1 / 0.2e1)) * (39 * t7466 - 455 * t7467 + 455 * t7468 - 1547 * t7470 - 3 + (819 * t7468 + 663 * t7470 + 29) * t7465) * ((1 - t7465) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1091: + t7473 = np.cos(phi) + t7474 = t7473 ** 2 + t7475 = t7473 * t7474 + t7478 = t7475 ** 2 + t7476 = t7474 ** 2 + t7472 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.462e3) * t7472 ** 2 * (39 * t7474 + 325 * t7475 - 195 * t7476 + 221 * t7478 - 1 + (-897 * t7476 + 663 * t7478 - 27) * t7473) + + if Bindx == 1092: + t7480 = np.cos(phi) + t7481 = t7480 ** 2 + t7482 = t7481 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (-195 * t7482 - 1 + (221 * t7482 + 39) * t7481) * ((1 + t7480) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1155e4) * np.exp((-3*1j) * phi1) * ((1 - t7480) ** (0.3e1 / 0.2e1)) + + if Bindx == 1093: + t7485 = np.cos(phi) + t7486 = t7485 ** 2 + t7487 = t7485 * t7486 + t7490 = t7487 ** 2 + t7488 = t7486 ** 2 + t7484 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.462e3) * t7484 ** 2 * (-39 * t7486 + 325 * t7487 + 195 * t7488 - 221 * t7490 + 1 + (-897 * t7488 + 663 * t7490 - 27) * t7485) + + if Bindx == 1094: + t7492 = np.cos(phi) + t7493 = t7492 ** 2 + t7495 = t7493 ** 2 + t7494 = t7492 * t7493 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * (-65 * t7493 - 65 * t7495 + 3 + (-390 + 663 * t7494) * t7494 + (884 * t7495 + 26) * t7492) * np.sqrt((1 + t7492)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t7492) ** (0.5e1 / 0.2e1)) + + if Bindx == 1095: + t7498 = np.cos(phi) + t7499 = t7498 ** 2 + t7501 = t7499 ** 2 + t7505 = t7501 ** 2 + t7500 = t7498 * t7499 + t7503 = t7500 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((-3*1j) * (phi1 - phi2)) * (1044 * t7499 - 2012 * t7500 - 5538 * t7501 + 9100 * t7503 - 4641 * t7505 - 29 + (7098 * t7501 - 9828 * t7503 + 4641 * t7505 + 165) * t7498) + + if Bindx == 1096: + t7507 = np.cos(phi) + t7508 = t7507 ** 2 + t7510 = t7508 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (-70 * t7508 + 595 * t7510 - 1 + (210 * t7508 + 357 * t7510 - 35) * t7507) * np.sqrt((1 + t7507)) * np.sqrt(0.78e2) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t7507) ** (0.7e1 / 0.2e1)) + + if Bindx == 1097: + t7513 = np.cos(phi) + t7514 = t7513 ** 2 + t7515 = t7513 * t7514 + t7518 = t7515 ** 2 + t7516 = t7514 ** 2 + t7512 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.1365e4) * t7512 ** 2 * (-27 * t7514 - 3 * t7515 + 95 * t7516 - 85 * t7518 + 1 + (-33 * t7516 + 51 * t7518 + 1) * t7513) + + if Bindx == 1098: + t7520 = np.cos(phi) + t7523 = 51 * t7520 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * (t7523 - 1 + (t7523 + 9) * t7520) * ((1 + t7520) ** (0.3e1 / 0.2e1)) * np.sqrt(0.91e2) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t7520) ** (0.9e1 / 0.2e1)) + + if Bindx == 1099: + t7531 = np.sin(phi) + t7529 = t7531 ** 2 + t7524 = np.cos(phi) + t7525 = t7524 ** 2 + t7527 = t7525 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.273e3) * t7529 ** 2 * (42 * t7525 - 119 * t7527 - 3 + (54 * t7525 + 51 * t7527 - 25) * t7524) + + if Bindx == 1100: + t7532 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (1 + 3 * t7532) * ((1 + t7532) ** (0.5e1 / 0.2e1)) * np.sqrt(0.9282e4) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * ((1 - t7532) ** (0.11e2 / 0.2e1)) + + if Bindx == 1101: + t7533 = np.cos(phi) + t7540 = -1 + t7533 + t7539 = 1 + t7533 + t7537 = t7539 ** 2 + t7534 = t7540 ** 2 + t7535 = t7540 * t7534 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.4641e4) * t7535 ** 2 * t7539 * t7537 + + if Bindx == 1102: + t7541 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.221e3) * ((1 - t7541) ** (0.7e1 / 0.2e1)) * ((1 + t7541) ** (0.11e2 / 0.2e1)) + + if Bindx == 1103: + t7548 = np.sin(phi) + t7545 = t7548 ** 2 + t7546 = t7548 * t7545 + t7542 = np.cos(phi) + t7543 = t7542 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.442e3) * t7546 ** 2 * (16 * t7543 - 2 + (9 * t7543 + 5) * t7542) + + if Bindx == 1104: + t7549 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.13e2) * ((1 - t7549) ** (0.5e1 / 0.2e1)) * ((1 + t7549) ** (0.9e1 / 0.2e1)) * (-1 + (-68 + 153 * t7549) * t7549) + + if Bindx == 1105: + t7557 = np.sin(phi) + t7555 = t7557 ** 2 + t7550 = np.cos(phi) + t7551 = t7550 ** 2 + t7553 = t7551 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.39e2) * t7555 ** 2 * (-69 * t7551 + 136 * t7553 + 3 + (-36 * t7551 + 102 * t7553 + 4) * t7550) + + if Bindx == 1106: + t7558 = np.cos(phi) + t7559 = t7558 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.65e2) * ((1 - t7558) ** (0.3e1 / 0.2e1)) * ((1 + t7558) ** (0.7e1 / 0.2e1)) * (18 * t7558 - 1 + (-136 * t7558 - 6 + 153 * t7559) * t7559) + + if Bindx == 1107: + t7563 = np.cos(phi) + t7564 = t7563 ** 2 + t7565 = t7563 * t7564 + t7568 = t7565 ** 2 + t7566 = t7564 ** 2 + t7562 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.182e3) * t7562 ** 2 * (34 * t7564 + 75 * t7565 - 145 * t7566 + 136 * t7568 - 1 + (-197 * t7566 + 153 * t7568 - 7) * t7563) + + if Bindx == 1108: + t7570 = np.cos(phi) + t7571 = t7570 ** 2 + t7573 = t7571 ** 2 + t7572 = t7570 * t7571 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.21e2) * np.sqrt((1 - t7570)) * ((1 + t7570) ** (0.5e1 / 0.2e1)) * (-65 * t7571 - 65 * t7573 + 3 + (390 + 663 * t7572) * t7572 + (-884 * t7573 - 26) * t7570) + + if Bindx == 1109: + t7576 = np.cos(phi) + t7577 = t7576 ** 2 + t7579 = t7577 ** 2 + t7583 = t7579 ** 2 + t7578 = t7576 * t7577 + t7581 = t7578 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 + phi2)) * (-287 * t7577 - 1876 * t7578 + 1729 * t7579 - 3185 * t7581 + 1768 * t7583 + 7 + (6734 * t7579 - 8944 * t7581 + 3978 * t7583 + 140) * t7576) + + if Bindx == 1110: + t7585 = np.cos(phi) + t7586 = t7585 ** 2 + t7587 = t7585 * t7586 + t7590 = t7587 ** 2 + t7588 = t7586 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.22e2) * ((1 + t7585) ** (0.3e1 / 0.2e1)) * (252 * t7586 - 728 * t7587 - 364 * t7590 - 7 + (-910 + 1989 * t7588) * t7588 + (3276 * t7588 - 3536 * t7590 + 28) * t7585) * ((1 - t7585) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1111: + t7594 = np.cos(phi) + t7595 = t7594 ** 2 + t7596 = t7595 ** 2 + t7593 = np.sin(phi) + tfunc[..., c] = -(0.57e2 / 0.64e2) * np.exp((-2*1j) * phi1) * np.sqrt(0.55e2) * t7593 ** 2 * t7594 * (-273 * t7596 - 7 + (221 * t7596 + 91) * t7595) + + if Bindx == 1112: + t7598 = np.cos(phi) + t7599 = t7598 ** 2 + t7600 = t7598 * t7599 + t7603 = t7600 ** 2 + t7601 = t7599 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (273 * t7599 + 455 * t7600 - 1365 * t7601 + 1547 * t7603 - 7 + (-1911 * t7601 + 1989 * t7603 - 21) * t7598) * np.sqrt((1 + t7598)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t7598) ** (0.3e1 / 0.2e1)) + + if Bindx == 1113: + t7605 = np.cos(phi) + t7606 = t7605 ** 2 + t7608 = t7606 ** 2 + t7612 = t7608 ** 2 + t7607 = t7605 * t7606 + t7610 = t7607 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - phi2)) * (287 * t7606 - 1876 * t7607 - 1729 * t7608 + 3185 * t7610 - 1768 * t7612 - 7 + (6734 * t7608 - 8944 * t7610 + 3978 * t7612 + 140) * t7605) + + if Bindx == 1114: + t7614 = np.cos(phi) + t7615 = t7614 ** 2 + t7617 = t7615 ** 2 + t7616 = t7614 * t7615 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * (-65 * t7615 - 65 * t7617 + 3 + (-390 + 663 * t7616) * t7616 + (884 * t7617 + 26) * t7614) * np.sqrt((1 + t7614)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t7614) ** (0.5e1 / 0.2e1)) + + if Bindx == 1115: + t7621 = np.cos(phi) + t7622 = t7621 ** 2 + t7623 = t7621 * t7622 + t7626 = t7623 ** 2 + t7624 = t7622 ** 2 + t7620 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.182e3) * t7620 ** 2 * (-34 * t7622 + 75 * t7623 + 145 * t7624 - 136 * t7626 + 1 + (-197 * t7624 + 153 * t7626 - 7) * t7621) + + if Bindx == 1116: + t7628 = np.cos(phi) + t7629 = t7628 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (-18 * t7628 - 1 + (136 * t7628 - 6 + 153 * t7629) * t7629) * ((1 + t7628) ** (0.3e1 / 0.2e1)) * np.sqrt(0.65e2) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t7628) ** (0.7e1 / 0.2e1)) + + if Bindx == 1117: + t7639 = np.sin(phi) + t7637 = t7639 ** 2 + t7632 = np.cos(phi) + t7633 = t7632 ** 2 + t7635 = t7633 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.39e2) * t7637 ** 2 * (69 * t7633 - 136 * t7635 - 3 + (-36 * t7633 + 102 * t7635 + 4) * t7632) + + if Bindx == 1118: + t7640 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-1 + (68 + 153 * t7640) * t7640) * ((1 + t7640) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13e2) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t7640) ** (0.9e1 / 0.2e1)) + + if Bindx == 1119: + t7647 = np.sin(phi) + t7644 = t7647 ** 2 + t7645 = t7647 * t7644 + t7641 = np.cos(phi) + t7642 = t7641 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.442e3) * t7645 ** 2 * (-16 * t7642 + 2 + (9 * t7642 + 5) * t7641) + + if Bindx == 1120: + t7648 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((-1*1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.221e3) * ((1 - t7648) ** (0.11e2 / 0.2e1)) * ((1 + t7648) ** (0.7e1 / 0.2e1)) + + if Bindx == 1121: + t7652 = np.sin(phi) + t7649 = t7652 ** 2 + t7650 = t7649 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (phi1 + 9 * phi2)) * np.sqrt(0.4862e4) * t7650 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 1122: + t7653 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.2431e4) * ((1 - t7653) ** (0.7e1 / 0.2e1)) * ((1 + t7653) ** (0.9e1 / 0.2e1)) * (-1 + 9 * t7653) + + if Bindx == 1123: + t7660 = np.sin(phi) + t7657 = t7660 ** 2 + t7658 = t7660 * t7657 + t7654 = np.cos(phi) + t7655 = t7654 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.286e3) * t7658 ** 2 * (119 * t7655 - 7 + (153 * t7655 - 41) * t7654) + + if Bindx == 1124: + t7661 = np.cos(phi) + t7662 = t7661 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t7661) ** (0.5e1 / 0.2e1)) * ((1 + t7661) ** (0.7e1 / 0.2e1)) * (-17 * t7662 + 1 + (51 * t7662 - 7) * t7661) + + if Bindx == 1125: + t7671 = np.sin(phi) + t7669 = t7671 ** 2 + t7664 = np.cos(phi) + t7665 = t7664 ** 2 + t7667 = t7665 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.1430e4) * t7669 ** 2 * (-30 * t7665 + 85 * t7667 + 1 + (-110 * t7665 + 153 * t7667 + 13) * t7664) + + if Bindx == 1126: + t7672 = np.cos(phi) + t7673 = t7672 ** 2 + t7675 = t7673 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.1001e4) * ((1 - t7672) ** (0.3e1 / 0.2e1)) * ((1 + t7672) ** (0.5e1 / 0.2e1)) * (30 * t7673 - 85 * t7675 - 1 + (-70 * t7673 + 153 * t7675 + 5) * t7672) + + if Bindx == 1127: + t7678 = np.cos(phi) + t7679 = t7678 ** 2 + t7680 = t7678 * t7679 + t7683 = t7680 ** 2 + t7681 = t7679 ** 2 + t7677 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.462e3) * t7677 ** 2 * (39 * t7679 + 325 * t7680 - 195 * t7681 + 221 * t7683 - 1 + (-897 * t7681 + 663 * t7683 - 27) * t7678) + + if Bindx == 1128: + t7685 = np.cos(phi) + t7686 = t7685 ** 2 + t7687 = t7685 * t7686 + t7690 = t7687 ** 2 + t7688 = t7686 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t7685)) * ((1 + t7685) ** (0.3e1 / 0.2e1)) * (-273 * t7686 + 455 * t7687 + 1365 * t7688 - 1547 * t7690 + 7 + (-1911 * t7688 + 1989 * t7690 - 21) * t7685) + + if Bindx == 1129: + t7692 = np.cos(phi) + t7693 = t7692 ** 2 + t7695 = t7693 ** 2 + t7699 = t7695 ** 2 + t7694 = t7692 * t7693 + t7697 = t7694 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + phi2)) * (-308 * t7693 - 8932 * t7694 + 2002 * t7695 - 4004 * t7697 + 2431 * t7699 + 7 + (34034 * t7695 - 47476 * t7697 + 21879 * t7699 + 623) * t7692) + + if Bindx == 1130: + t7701 = np.cos(phi) + t7702 = t7701 ** 2 + t7703 = t7701 * t7702 + t7704 = t7702 ** 2 + t7710 = -4004 * t7703 ** 2 + 7 + (2002 + 2431 * t7704) * t7704 + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.10e2) * np.sqrt((1 + t7701)) * (t7710 * t7701 + 308 * t7702 - 308 * t7703 - t7710) * ((1 - t7701) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1131: + t7711 = np.cos(phi) + t7712 = t7711 ** 2 + t7714 = t7712 ** 2 + t7718 = t7714 ** 2 + t7713 = t7711 * t7712 + t7716 = t7713 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - phi2)) * (308 * t7712 - 8932 * t7713 - 2002 * t7714 + 4004 * t7716 - 2431 * t7718 - 7 + (34034 * t7714 - 47476 * t7716 + 21879 * t7718 + 623) * t7711) + + if Bindx == 1132: + t7720 = np.cos(phi) + t7721 = t7720 ** 2 + t7722 = t7720 * t7721 + t7725 = t7722 ** 2 + t7723 = t7721 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (273 * t7721 + 455 * t7722 - 1365 * t7723 + 1547 * t7725 - 7 + (-1911 * t7723 + 1989 * t7725 - 21) * t7720) * np.sqrt((1 + t7720)) * np.sqrt(0.22e2) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t7720) ** (0.3e1 / 0.2e1)) + + if Bindx == 1133: + t7728 = np.cos(phi) + t7729 = t7728 ** 2 + t7730 = t7728 * t7729 + t7733 = t7730 ** 2 + t7731 = t7729 ** 2 + t7727 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.462e3) * t7727 ** 2 * (-39 * t7729 + 325 * t7730 + 195 * t7731 - 221 * t7733 + 1 + (-897 * t7731 + 663 * t7733 - 27) * t7728) + + if Bindx == 1134: + t7735 = np.cos(phi) + t7736 = t7735 ** 2 + t7738 = t7736 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-30 * t7736 + 85 * t7738 + 1 + (-70 * t7736 + 153 * t7738 + 5) * t7735) * ((1 + t7735) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t7735) ** (0.5e1 / 0.2e1)) + + if Bindx == 1135: + t7747 = np.sin(phi) + t7745 = t7747 ** 2 + t7740 = np.cos(phi) + t7741 = t7740 ** 2 + t7743 = t7741 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.1430e4) * t7745 ** 2 * (30 * t7741 - 85 * t7743 - 1 + (-110 * t7741 + 153 * t7743 + 13) * t7740) + + if Bindx == 1136: + t7748 = np.cos(phi) + t7749 = t7748 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (17 * t7749 - 1 + (51 * t7749 - 7) * t7748) * ((1 + t7748) ** (0.5e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t7748) ** (0.7e1 / 0.2e1)) + + if Bindx == 1137: + t7757 = np.sin(phi) + t7754 = t7757 ** 2 + t7755 = t7757 * t7754 + t7751 = np.cos(phi) + t7752 = t7751 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.286e3) * t7755 ** 2 * (-119 * t7752 + 7 + (153 * t7752 - 41) * t7751) + + if Bindx == 1138: + t7758 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (9 * t7758 + 1) * ((1 + t7758) ** (0.7e1 / 0.2e1)) * np.sqrt(0.2431e4) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * ((1 - t7758) ** (0.9e1 / 0.2e1)) + + if Bindx == 1139: + t7762 = np.sin(phi) + t7759 = t7762 ** 2 + t7760 = t7759 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((-1*1j) * (phi1 - 9 * phi2)) * np.sqrt(0.4862e4) * t7760 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 1140: + t7763 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((-9*1j) * phi2) * np.sqrt(0.12155e5) * ((1 - t7763) ** (0.9e1 / 0.2e1)) * ((1 + t7763) ** (0.9e1 / 0.2e1)) + + if Bindx == 1141: + t7767 = np.sin(phi) + t7764 = t7767 ** 2 + t7765 = t7764 ** 2 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((-8*1j) * phi2) * np.sqrt(0.24310e5) * t7765 ** 2 * np.cos(phi) + + if Bindx == 1142: + t7768 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.715e3) * ((1 - t7768) ** (0.7e1 / 0.2e1)) * ((1 + t7768) ** (0.7e1 / 0.2e1)) * (17 * t7768 ** 2 - 1) + + if Bindx == 1143: + t7773 = np.sin(phi) + t7770 = t7773 ** 2 + t7771 = t7773 * t7770 + t7769 = np.cos(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((-6*1j) * phi2) * np.sqrt(0.2145e4) * t7771 ** 2 * t7769 * (17 * t7769 ** 2 - 3) + + if Bindx == 1144: + t7774 = np.cos(phi) + t7775 = t7774 ** 2 + tfunc[..., c] = (-0.57e2 / 0.128e3*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.143e3) * ((1 - t7774) ** (0.5e1 / 0.2e1)) * ((1 + t7774) ** (0.5e1 / 0.2e1)) * (1 + (-30 + 85 * t7775) * t7775) + + if Bindx == 1145: + t7782 = np.sin(phi) + t7780 = t7782 ** 2 + t7777 = np.cos(phi) + t7778 = t7777 ** 2 + tfunc[..., c] = (0.57e2 / 0.128e3) * np.exp((-4*1j) * phi2) * np.sqrt(0.10010e5) * t7780 ** 2 * t7777 * (1 + (-10 + 17 * t7778) * t7778) + + if Bindx == 1146: + t7783 = np.cos(phi) + t7784 = t7783 ** 2 + t7785 = t7784 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.1155e4) * ((1 - t7783) ** (0.3e1 / 0.2e1)) * ((1 + t7783) ** (0.3e1 / 0.2e1)) * (-195 * t7785 - 1 + (221 * t7785 + 39) * t7784) + + if Bindx == 1147: + t7788 = np.cos(phi) + t7789 = t7788 ** 2 + t7790 = t7789 ** 2 + t7787 = np.sin(phi) + tfunc[..., c] = -(0.57e2 / 0.64e2) * np.exp((-2*1j) * phi2) * np.sqrt(0.55e2) * t7787 ** 2 * t7788 * (-273 * t7790 - 7 + (221 * t7790 + 91) * t7789) + + if Bindx == 1148: + t7792 = np.cos(phi) + t7793 = t7792 ** 2 + t7794 = t7793 ** 2 + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.10e2) * np.sqrt((1 - t7792)) * np.sqrt((1 + t7792)) * (-308 * t7793 + 7 + (-4004 * t7793 + 2002 + 2431 * t7794) * t7794) + + if Bindx == 1149: + t7797 = np.cos(phi) + t7798 = t7797 ** 2 + t7799 = t7798 ** 2 + tfunc[..., c] = 0.19e2 / 0.128e3 * t7797 * (-4620 * t7798 + 315 + (-25740 * t7798 + 18018 + 12155 * t7799) * t7799) + + if Bindx == 1150: + t7802 = np.cos(phi) + t7803 = t7802 ** 2 + t7804 = t7802 * t7803 + t7805 = t7803 ** 2 + t7811 = -4004 * t7804 ** 2 + 7 + (2002 + 2431 * t7805) * t7805 + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * np.exp((1j) * phi2) * np.sqrt(0.10e2) * np.sqrt((1 + t7802)) * (t7811 * t7802 + 308 * t7803 - 308 * t7804 - t7811) * ((1 - t7802) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1151: + t7813 = np.cos(phi) + t7814 = t7813 ** 2 + t7815 = t7814 ** 2 + t7812 = np.sin(phi) + tfunc[..., c] = -(0.57e2 / 0.64e2) * np.exp((2*1j) * phi2) * np.sqrt(0.55e2) * t7812 ** 2 * t7813 * (-273 * t7815 - 7 + (221 * t7815 + 91) * t7814) + + if Bindx == 1152: + t7817 = np.cos(phi) + t7818 = t7817 ** 2 + t7819 = t7818 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (-195 * t7819 - 1 + (221 * t7819 + 39) * t7818) * ((1 + t7817) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1155e4) * np.exp((3*1j) * phi2) * ((1 - t7817) ** (0.3e1 / 0.2e1)) + + if Bindx == 1153: + t7826 = np.sin(phi) + t7824 = t7826 ** 2 + t7821 = np.cos(phi) + t7822 = t7821 ** 2 + tfunc[..., c] = (0.57e2 / 0.128e3) * np.exp((4*1j) * phi2) * np.sqrt(0.10010e5) * t7824 ** 2 * t7821 * (1 + (-10 + 17 * t7822) * t7822) + + if Bindx == 1154: + t7827 = np.cos(phi) + t7828 = t7827 ** 2 + tfunc[..., c] = (-0.57e2 / 0.128e3*1j) * (1 + (-30 + 85 * t7828) * t7828) * ((1 + t7827) ** (0.5e1 / 0.2e1)) * np.sqrt(0.143e3) * np.exp((5*1j) * phi2) * ((1 - t7827) ** (0.5e1 / 0.2e1)) + + if Bindx == 1155: + t7834 = np.sin(phi) + t7831 = t7834 ** 2 + t7832 = t7834 * t7831 + t7830 = np.cos(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((6*1j) * phi2) * np.sqrt(0.2145e4) * t7832 ** 2 * t7830 * (17 * t7830 ** 2 - 3) + + if Bindx == 1156: + t7835 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * (17 * t7835 ** 2 - 1) * ((1 + t7835) ** (0.7e1 / 0.2e1)) * np.sqrt(0.715e3) * np.exp((7*1j) * phi2) * ((1 - t7835) ** (0.7e1 / 0.2e1)) + + if Bindx == 1157: + t7839 = np.sin(phi) + t7836 = t7839 ** 2 + t7837 = t7836 ** 2 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((8*1j) * phi2) * np.sqrt(0.24310e5) * t7837 ** 2 * np.cos(phi) + + if Bindx == 1158: + t7840 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((9*1j) * phi2) * np.sqrt(0.12155e5) * ((1 - t7840) ** (0.9e1 / 0.2e1)) * ((1 + t7840) ** (0.9e1 / 0.2e1)) + + if Bindx == 1159: + t7844 = np.sin(phi) + t7841 = t7844 ** 2 + t7842 = t7841 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (phi1 - 9 * phi2)) * np.sqrt(0.4862e4) * t7842 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 1160: + t7845 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.2431e4) * ((1 - t7845) ** (0.9e1 / 0.2e1)) * ((1 + t7845) ** (0.7e1 / 0.2e1)) * (9 * t7845 + 1) + + if Bindx == 1161: + t7852 = np.sin(phi) + t7849 = t7852 ** 2 + t7850 = t7852 * t7849 + t7846 = np.cos(phi) + t7847 = t7846 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.286e3) * t7850 ** 2 * (-119 * t7847 + 7 + (153 * t7847 - 41) * t7846) + + if Bindx == 1162: + t7853 = np.cos(phi) + t7854 = t7853 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.858e3) * ((1 - t7853) ** (0.7e1 / 0.2e1)) * ((1 + t7853) ** (0.5e1 / 0.2e1)) * (17 * t7854 - 1 + (51 * t7854 - 7) * t7853) + + if Bindx == 1163: + t7863 = np.sin(phi) + t7861 = t7863 ** 2 + t7856 = np.cos(phi) + t7857 = t7856 ** 2 + t7859 = t7857 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.1430e4) * t7861 ** 2 * (30 * t7857 - 85 * t7859 - 1 + (-110 * t7857 + 153 * t7859 + 13) * t7856) + + if Bindx == 1164: + t7864 = np.cos(phi) + t7865 = t7864 ** 2 + t7867 = t7865 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.1001e4) * ((1 - t7864) ** (0.5e1 / 0.2e1)) * ((1 + t7864) ** (0.3e1 / 0.2e1)) * (-30 * t7865 + 85 * t7867 + 1 + (-70 * t7865 + 153 * t7867 + 5) * t7864) + + if Bindx == 1165: + t7870 = np.cos(phi) + t7871 = t7870 ** 2 + t7872 = t7870 * t7871 + t7875 = t7872 ** 2 + t7873 = t7871 ** 2 + t7869 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.462e3) * t7869 ** 2 * (-39 * t7871 + 325 * t7872 + 195 * t7873 - 221 * t7875 + 1 + (-897 * t7873 + 663 * t7875 - 27) * t7870) + + if Bindx == 1166: + t7877 = np.cos(phi) + t7878 = t7877 ** 2 + t7879 = t7877 * t7878 + t7882 = t7879 ** 2 + t7880 = t7878 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.22e2) * ((1 - t7877) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t7877)) * (273 * t7878 + 455 * t7879 - 1365 * t7880 + 1547 * t7882 - 7 + (-1911 * t7880 + 1989 * t7882 - 21) * t7877) + + if Bindx == 1167: + t7884 = np.cos(phi) + t7885 = t7884 ** 2 + t7887 = t7885 ** 2 + t7891 = t7887 ** 2 + t7886 = t7884 * t7885 + t7889 = t7886 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (phi1 - phi2)) * (308 * t7885 - 8932 * t7886 - 2002 * t7887 + 4004 * t7889 - 2431 * t7891 - 7 + (34034 * t7887 - 47476 * t7889 + 21879 * t7891 + 623) * t7884) + + if Bindx == 1168: + t7893 = np.cos(phi) + t7894 = t7893 ** 2 + t7895 = t7894 ** 2 + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.10e2) * np.sqrt((1 - t7893)) * np.sqrt((1 + t7893)) * (-308 * t7894 + 7 + (-4004 * t7894 + 2002 + 2431 * t7895) * t7895) + + if Bindx == 1169: + t7898 = np.cos(phi) + t7899 = t7898 ** 2 + t7901 = t7899 ** 2 + t7905 = t7901 ** 2 + t7900 = t7898 * t7899 + t7903 = t7900 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (phi1 + phi2)) * (-308 * t7899 - 8932 * t7900 + 2002 * t7901 - 4004 * t7903 + 2431 * t7905 + 7 + (34034 * t7901 - 47476 * t7903 + 21879 * t7905 + 623) * t7898) + + if Bindx == 1170: + t7907 = np.cos(phi) + t7908 = t7907 ** 2 + t7909 = t7907 * t7908 + t7912 = t7909 ** 2 + t7910 = t7908 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.22e2) * ((1 + t7907) ** (0.3e1 / 0.2e1)) * (252 * t7908 - 728 * t7909 - 364 * t7912 - 7 + (-910 + 1989 * t7910) * t7910 + (3276 * t7910 - 3536 * t7912 + 28) * t7907) * ((1 - t7907) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1171: + t7916 = np.cos(phi) + t7917 = t7916 ** 2 + t7918 = t7916 * t7917 + t7921 = t7918 ** 2 + t7919 = t7917 ** 2 + t7915 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.462e3) * t7915 ** 2 * (39 * t7917 + 325 * t7918 - 195 * t7919 + 221 * t7921 - 1 + (-897 * t7919 + 663 * t7921 - 27) * t7916) + + if Bindx == 1172: + t7923 = np.cos(phi) + t7924 = t7923 ** 2 + t7926 = t7924 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * (30 * t7924 - 85 * t7926 - 1 + (-70 * t7924 + 153 * t7926 + 5) * t7923) * ((1 + t7923) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t7923) ** (0.3e1 / 0.2e1)) + + if Bindx == 1173: + t7935 = np.sin(phi) + t7933 = t7935 ** 2 + t7928 = np.cos(phi) + t7929 = t7928 ** 2 + t7931 = t7929 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.1430e4) * t7933 ** 2 * (-30 * t7929 + 85 * t7931 + 1 + (-110 * t7929 + 153 * t7931 + 13) * t7928) + + if Bindx == 1174: + t7936 = np.cos(phi) + t7937 = t7936 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-17 * t7937 + 1 + (51 * t7937 - 7) * t7936) * ((1 + t7936) ** (0.7e1 / 0.2e1)) * np.sqrt(0.858e3) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t7936) ** (0.5e1 / 0.2e1)) + + if Bindx == 1175: + t7945 = np.sin(phi) + t7942 = t7945 ** 2 + t7943 = t7945 * t7942 + t7939 = np.cos(phi) + t7940 = t7939 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.286e3) * t7943 ** 2 * (119 * t7940 - 7 + (153 * t7940 - 41) * t7939) + + if Bindx == 1176: + t7946 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (-1 + 9 * t7946) * ((1 + t7946) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2431e4) * np.exp((1j) * (phi1 + 8 * phi2)) * ((1 - t7946) ** (0.7e1 / 0.2e1)) + + if Bindx == 1177: + t7950 = np.sin(phi) + t7947 = t7950 ** 2 + t7948 = t7947 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (phi1 + 9 * phi2)) * np.sqrt(0.4862e4) * t7948 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 1178: + t7951 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.221e3) * ((1 - t7951) ** (0.11e2 / 0.2e1)) * ((1 + t7951) ** (0.7e1 / 0.2e1)) + + if Bindx == 1179: + t7958 = np.sin(phi) + t7955 = t7958 ** 2 + t7956 = t7958 * t7955 + t7952 = np.cos(phi) + t7953 = t7952 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.442e3) * t7956 ** 2 * (-16 * t7953 + 2 + (9 * t7953 + 5) * t7952) + + if Bindx == 1180: + t7959 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.13e2) * ((1 - t7959) ** (0.9e1 / 0.2e1)) * ((1 + t7959) ** (0.5e1 / 0.2e1)) * (-1 + (68 + 153 * t7959) * t7959) + + if Bindx == 1181: + t7967 = np.sin(phi) + t7965 = t7967 ** 2 + t7960 = np.cos(phi) + t7961 = t7960 ** 2 + t7963 = t7961 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.39e2) * t7965 ** 2 * (69 * t7961 - 136 * t7963 - 3 + (-36 * t7961 + 102 * t7963 + 4) * t7960) + + if Bindx == 1182: + t7968 = np.cos(phi) + t7969 = t7968 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.65e2) * ((1 - t7968) ** (0.7e1 / 0.2e1)) * ((1 + t7968) ** (0.3e1 / 0.2e1)) * (-18 * t7968 - 1 + (136 * t7968 - 6 + 153 * t7969) * t7969) + + if Bindx == 1183: + t7973 = np.cos(phi) + t7974 = t7973 ** 2 + t7975 = t7973 * t7974 + t7978 = t7975 ** 2 + t7976 = t7974 ** 2 + t7972 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.182e3) * t7972 ** 2 * (-34 * t7974 + 75 * t7975 + 145 * t7976 - 136 * t7978 + 1 + (-197 * t7976 + 153 * t7978 - 7) * t7973) + + if Bindx == 1184: + t7980 = np.cos(phi) + t7981 = t7980 ** 2 + t7983 = t7981 ** 2 + t7982 = t7980 * t7981 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.21e2) * ((1 - t7980) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t7980)) * (-65 * t7981 - 65 * t7983 + 3 + (-390 + 663 * t7982) * t7982 + (884 * t7983 + 26) * t7980) + + if Bindx == 1185: + t7986 = np.cos(phi) + t7987 = t7986 ** 2 + t7989 = t7987 ** 2 + t7993 = t7989 ** 2 + t7988 = t7986 * t7987 + t7991 = t7988 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 - phi2)) * (287 * t7987 - 1876 * t7988 - 1729 * t7989 + 3185 * t7991 - 1768 * t7993 - 7 + (6734 * t7989 - 8944 * t7991 + 3978 * t7993 + 140) * t7986) + + if Bindx == 1186: + t7995 = np.cos(phi) + t7996 = t7995 ** 2 + t7997 = t7995 * t7996 + t8000 = t7997 ** 2 + t7998 = t7996 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.22e2) * ((1 - t7995) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t7995)) * (273 * t7996 + 455 * t7997 - 1365 * t7998 + 1547 * t8000 - 7 + (-1911 * t7998 + 1989 * t8000 - 21) * t7995) + + if Bindx == 1187: + t8003 = np.cos(phi) + t8004 = t8003 ** 2 + t8005 = t8004 ** 2 + t8002 = np.sin(phi) + tfunc[..., c] = -(0.57e2 / 0.64e2) * np.exp((2*1j) * phi1) * np.sqrt(0.55e2) * t8002 ** 2 * t8003 * (-273 * t8005 - 7 + (221 * t8005 + 91) * t8004) + + if Bindx == 1188: + t8007 = np.cos(phi) + t8008 = t8007 ** 2 + t8009 = t8007 * t8008 + t8012 = t8009 ** 2 + t8010 = t8008 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t8007)) * ((1 + t8007) ** (0.3e1 / 0.2e1)) * (-273 * t8008 + 455 * t8009 + 1365 * t8010 - 1547 * t8012 + 7 + (-1911 * t8010 + 1989 * t8012 - 21) * t8007) + + if Bindx == 1189: + t8014 = np.cos(phi) + t8015 = t8014 ** 2 + t8017 = t8015 ** 2 + t8021 = t8017 ** 2 + t8016 = t8014 * t8015 + t8019 = t8016 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 + phi2)) * (-287 * t8015 - 1876 * t8016 + 1729 * t8017 - 3185 * t8019 + 1768 * t8021 + 7 + (6734 * t8017 - 8944 * t8019 + 3978 * t8021 + 140) * t8014) + + if Bindx == 1190: + t8023 = np.cos(phi) + t8024 = t8023 ** 2 + t8025 = t8023 * t8024 + t8028 = t8025 ** 2 + t8026 = t8024 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.21e2) * ((1 + t8023) ** (0.5e1 / 0.2e1)) * (39 * t8024 - 455 * t8025 + 455 * t8026 - 1547 * t8028 - 3 + (819 * t8026 + 663 * t8028 + 29) * t8023) * ((1 - t8023) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1191: + t8031 = np.cos(phi) + t8032 = t8031 ** 2 + t8033 = t8031 * t8032 + t8036 = t8033 ** 2 + t8034 = t8032 ** 2 + t8030 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.182e3) * t8030 ** 2 * (34 * t8032 + 75 * t8033 - 145 * t8034 + 136 * t8036 - 1 + (-197 * t8034 + 153 * t8036 - 7) * t8031) + + if Bindx == 1192: + t8038 = np.cos(phi) + t8039 = t8038 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (18 * t8038 - 1 + (-136 * t8038 - 6 + 153 * t8039) * t8039) * ((1 + t8038) ** (0.7e1 / 0.2e1)) * np.sqrt(0.65e2) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t8038) ** (0.3e1 / 0.2e1)) + + if Bindx == 1193: + t8049 = np.sin(phi) + t8047 = t8049 ** 2 + t8042 = np.cos(phi) + t8043 = t8042 ** 2 + t8045 = t8043 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.39e2) * t8047 ** 2 * (-69 * t8043 + 136 * t8045 + 3 + (-36 * t8043 + 102 * t8045 + 4) * t8042) + + if Bindx == 1194: + t8050 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * (-1 + (-68 + 153 * t8050) * t8050) * ((1 + t8050) ** (0.9e1 / 0.2e1)) * np.sqrt(0.13e2) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t8050) ** (0.5e1 / 0.2e1)) + + if Bindx == 1195: + t8057 = np.sin(phi) + t8054 = t8057 ** 2 + t8055 = t8057 * t8054 + t8051 = np.cos(phi) + t8052 = t8051 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.442e3) * t8055 ** 2 * (16 * t8052 - 2 + (9 * t8052 + 5) * t8051) + + if Bindx == 1196: + t8058 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.221e3) * ((1 - t8058) ** (0.7e1 / 0.2e1)) * ((1 + t8058) ** (0.11e2 / 0.2e1)) + + if Bindx == 1197: + t8059 = np.cos(phi) + t8066 = -1 + t8059 + t8065 = 1 + t8059 + t8063 = t8065 ** 2 + t8060 = t8066 ** 2 + t8061 = t8066 * t8060 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.4641e4) * t8061 ** 2 * t8065 * t8063 + + if Bindx == 1198: + t8067 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.9282e4) * ((1 - t8067) ** (0.11e2 / 0.2e1)) * ((1 + t8067) ** (0.5e1 / 0.2e1)) * (1 + 3 * t8067) + + if Bindx == 1199: + t8075 = np.sin(phi) + t8073 = t8075 ** 2 + t8068 = np.cos(phi) + t8069 = t8068 ** 2 + t8071 = t8069 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.273e3) * t8073 ** 2 * (42 * t8069 - 119 * t8071 - 3 + (54 * t8069 + 51 * t8071 - 25) * t8068) + + if Bindx == 1200: + t8076 = np.cos(phi) + t8079 = 51 * t8076 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.91e2) * ((1 - t8076) ** (0.9e1 / 0.2e1)) * ((1 + t8076) ** (0.3e1 / 0.2e1)) * (t8079 - 1 + (t8079 + 9) * t8076) + + if Bindx == 1201: + t8081 = np.cos(phi) + t8082 = t8081 ** 2 + t8083 = t8081 * t8082 + t8086 = t8083 ** 2 + t8084 = t8082 ** 2 + t8080 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.1365e4) * t8080 ** 2 * (-27 * t8082 - 3 * t8083 + 95 * t8084 - 85 * t8086 + 1 + (-33 * t8084 + 51 * t8086 + 1) * t8081) + + if Bindx == 1202: + t8088 = np.cos(phi) + t8089 = t8088 ** 2 + t8091 = t8089 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.78e2) * ((1 - t8088) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t8088)) * (-70 * t8089 + 595 * t8091 - 1 + (210 * t8089 + 357 * t8091 - 35) * t8088) + + if Bindx == 1203: + t8093 = np.cos(phi) + t8094 = t8093 ** 2 + t8096 = t8094 ** 2 + t8100 = t8096 ** 2 + t8095 = t8093 * t8094 + t8098 = t8095 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((3*1j) * (phi1 - phi2)) * (1044 * t8094 - 2012 * t8095 - 5538 * t8096 + 9100 * t8098 - 4641 * t8100 - 29 + (7098 * t8096 - 9828 * t8098 + 4641 * t8100 + 165) * t8093) + + if Bindx == 1204: + t8102 = np.cos(phi) + t8103 = t8102 ** 2 + t8105 = t8103 ** 2 + t8104 = t8102 * t8103 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.21e2) * ((1 - t8102) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t8102)) * (-65 * t8103 - 65 * t8105 + 3 + (-390 + 663 * t8104) * t8104 + (884 * t8105 + 26) * t8102) + + if Bindx == 1205: + t8109 = np.cos(phi) + t8110 = t8109 ** 2 + t8111 = t8109 * t8110 + t8114 = t8111 ** 2 + t8112 = t8110 ** 2 + t8108 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.462e3) * t8108 ** 2 * (-39 * t8110 + 325 * t8111 + 195 * t8112 - 221 * t8114 + 1 + (-897 * t8112 + 663 * t8114 - 27) * t8109) + + if Bindx == 1206: + t8116 = np.cos(phi) + t8117 = t8116 ** 2 + t8118 = t8117 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.1155e4) * ((1 - t8116) ** (0.3e1 / 0.2e1)) * ((1 + t8116) ** (0.3e1 / 0.2e1)) * (-195 * t8118 - 1 + (221 * t8118 + 39) * t8117) + + if Bindx == 1207: + t8121 = np.cos(phi) + t8122 = t8121 ** 2 + t8123 = t8121 * t8122 + t8126 = t8123 ** 2 + t8124 = t8122 ** 2 + t8120 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.462e3) * t8120 ** 2 * (39 * t8122 + 325 * t8123 - 195 * t8124 + 221 * t8126 - 1 + (-897 * t8124 + 663 * t8126 - 27) * t8121) + + if Bindx == 1208: + t8128 = np.cos(phi) + t8129 = t8128 ** 2 + t8131 = t8129 ** 2 + t8130 = t8128 * t8129 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.21e2) * np.sqrt((1 - t8128)) * ((1 + t8128) ** (0.5e1 / 0.2e1)) * (-65 * t8129 - 65 * t8131 + 3 + (390 + 663 * t8130) * t8130 + (-884 * t8131 - 26) * t8128) + + if Bindx == 1209: + t8134 = np.cos(phi) + t8135 = t8134 ** 2 + t8137 = t8135 ** 2 + t8143 = 4641 * t8137 ** 2 + t8136 = t8134 * t8135 + t8139 = t8136 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((3*1j) * (phi1 + phi2)) * (-1044 * t8135 - 2012 * t8136 + 5538 * t8137 - 9100 * t8139 + t8143 + 29 + (7098 * t8137 - 9828 * t8139 + t8143 + 165) * t8134) + + if Bindx == 1210: + t8144 = np.cos(phi) + t8145 = t8144 ** 2 + t8147 = t8145 ** 2 + t8146 = t8144 * t8145 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.78e2) * ((1 + t8144) ** (0.7e1 / 0.2e1)) * (-105 * t8145 + 805 * t8147 - 1 + (-140 + 357 * t8146) * t8146 + (-952 * t8147 + 36) * t8144) * ((1 - t8144) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1211: + t8151 = np.cos(phi) + t8152 = t8151 ** 2 + t8153 = t8151 * t8152 + t8156 = t8153 ** 2 + t8154 = t8152 ** 2 + t8150 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.1365e4) * t8150 ** 2 * (27 * t8152 - 3 * t8153 - 95 * t8154 + 85 * t8156 - 1 + (-33 * t8154 + 51 * t8156 + 1) * t8151) + + if Bindx == 1212: + t8158 = np.cos(phi) + t8159 = t8158 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * (-51 * t8159 + 1 + (51 * t8159 + 9) * t8158) * ((1 + t8158) ** (0.9e1 / 0.2e1)) * np.sqrt(0.91e2) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t8158) ** (0.3e1 / 0.2e1)) + + if Bindx == 1213: + t8168 = np.sin(phi) + t8166 = t8168 ** 2 + t8161 = np.cos(phi) + t8162 = t8161 ** 2 + t8164 = t8162 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.273e3) * t8166 ** 2 * (-42 * t8162 + 119 * t8164 + 3 + (54 * t8162 + 51 * t8164 - 25) * t8161) + + if Bindx == 1214: + t8169 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * (-1 + 3 * t8169) * ((1 + t8169) ** (0.11e2 / 0.2e1)) * np.sqrt(0.9282e4) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * ((1 - t8169) ** (0.5e1 / 0.2e1)) + + if Bindx == 1215: + t8170 = np.cos(phi) + t8177 = -1 + t8170 + t8176 = 1 + t8170 + t8173 = t8176 ** 2 + t8174 = t8176 * t8173 + t8171 = t8177 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.4641e4) * t8177 * t8171 * t8174 ** 2 + + if Bindx == 1216: + t8178 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.238e3) * ((1 - t8178) ** (0.13e2 / 0.2e1)) * ((1 + t8178) ** (0.5e1 / 0.2e1)) + + if Bindx == 1217: + t8180 = np.cos(phi) + t8184 = -1 + t8180 + t8181 = t8184 ** 2 + t8182 = t8184 * t8181 + t8179 = 1 + t8180 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.119e3) * t8182 ** 2 * t8179 ** 2 * (4 + 9 * t8180) + + if Bindx == 1218: + t8185 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.14e2) * ((1 - t8185) ** (0.11e2 / 0.2e1)) * ((1 + t8185) ** (0.3e1 / 0.2e1)) * (23 + (136 + 153 * t8185) * t8185) + + if Bindx == 1219: + t8187 = np.cos(phi) + t8188 = t8187 ** 2 + t8189 = t8187 * t8188 + t8192 = t8189 ** 2 + t8190 = t8188 ** 2 + t8186 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.42e2) * t8186 ** 2 * (-18 * t8188 - 87 * t8189 + 113 * t8190 - 136 * t8192 + 1 + (57 * t8190 + 51 * t8192 + 19) * t8187) + + if Bindx == 1220: + t8194 = np.cos(phi) + t8195 = t8194 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.70e2) * ((1 - t8194) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t8194)) * (12 * t8194 - 3 + (272 * t8194 + 138 + 153 * t8195) * t8195) + + if Bindx == 1221: + t8198 = np.cos(phi) + t8199 = t8198 ** 2 + t8201 = t8199 ** 2 + t8205 = t8201 ** 2 + t8200 = t8198 * t8199 + t8203 = t8200 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((4*1j) * (phi1 - phi2)) * (522 * t8199 + 212 * t8200 - 2398 * t8201 + 3766 * t8203 - 1904 * t8205 - 18 + (266 * t8201 - 1484 * t8203 + 1071 * t8205 - 33) * t8198) + + if Bindx == 1222: + t8207 = np.cos(phi) + t8208 = t8207 ** 2 + t8210 = t8208 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.78e2) * ((1 - t8207) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t8207)) * (-70 * t8208 + 595 * t8210 - 1 + (210 * t8208 + 357 * t8210 - 35) * t8207) + + if Bindx == 1223: + t8213 = np.cos(phi) + t8214 = t8213 ** 2 + t8215 = t8213 * t8214 + t8218 = t8215 ** 2 + t8216 = t8214 ** 2 + t8212 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.182e3) * t8212 ** 2 * (-34 * t8214 + 75 * t8215 + 145 * t8216 - 136 * t8218 + 1 + (-197 * t8216 + 153 * t8218 - 7) * t8213) + + if Bindx == 1224: + t8220 = np.cos(phi) + t8221 = t8220 ** 2 + t8223 = t8221 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.1001e4) * ((1 - t8220) ** (0.5e1 / 0.2e1)) * ((1 + t8220) ** (0.3e1 / 0.2e1)) * (-30 * t8221 + 85 * t8223 + 1 + (-70 * t8221 + 153 * t8223 + 5) * t8220) + + if Bindx == 1225: + t8230 = np.sin(phi) + t8228 = t8230 ** 2 + t8225 = np.cos(phi) + t8226 = t8225 ** 2 + tfunc[..., c] = (0.57e2 / 0.128e3) * np.exp((4*1j) * phi1) * np.sqrt(0.10010e5) * t8228 ** 2 * t8225 * (1 + (-10 + 17 * t8226) * t8226) + + if Bindx == 1226: + t8231 = np.cos(phi) + t8232 = t8231 ** 2 + t8234 = t8232 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.1001e4) * ((1 - t8231) ** (0.3e1 / 0.2e1)) * ((1 + t8231) ** (0.5e1 / 0.2e1)) * (30 * t8232 - 85 * t8234 - 1 + (-70 * t8232 + 153 * t8234 + 5) * t8231) + + if Bindx == 1227: + t8237 = np.cos(phi) + t8238 = t8237 ** 2 + t8239 = t8237 * t8238 + t8242 = t8239 ** 2 + t8240 = t8238 ** 2 + t8236 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.182e3) * t8236 ** 2 * (34 * t8238 + 75 * t8239 - 145 * t8240 + 136 * t8242 - 1 + (-197 * t8240 + 153 * t8242 - 7) * t8237) + + if Bindx == 1228: + t8244 = np.cos(phi) + t8245 = t8244 ** 2 + t8247 = t8245 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.78e2) * np.sqrt((1 - t8244)) * ((1 + t8244) ** (0.7e1 / 0.2e1)) * (70 * t8245 - 595 * t8247 + 1 + (210 * t8245 + 357 * t8247 - 35) * t8244) + + if Bindx == 1229: + t8249 = np.cos(phi) + t8250 = t8249 ** 2 + t8252 = t8250 ** 2 + t8256 = t8252 ** 2 + t8251 = t8249 * t8250 + t8254 = t8251 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((4*1j) * (phi1 + phi2)) * (-522 * t8250 + 212 * t8251 + 2398 * t8252 - 3766 * t8254 + 1904 * t8256 + 18 + (266 * t8252 - 1484 * t8254 + 1071 * t8256 - 33) * t8249) + + if Bindx == 1230: + t8258 = np.cos(phi) + t8259 = t8258 ** 2 + t8261 = t8259 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.70e2) * ((1 + t8258) ** (0.9e1 / 0.2e1)) * (-150 * t8259 - 425 * t8261 + 3 + (410 * t8259 + 153 * t8261 + 9) * t8258) * ((1 - t8258) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1231: + t8264 = np.cos(phi) + t8265 = t8264 ** 2 + t8266 = t8264 * t8265 + t8269 = t8266 ** 2 + t8267 = t8265 ** 2 + t8263 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.42e2) * t8263 ** 2 * (18 * t8265 - 87 * t8266 - 113 * t8267 + 136 * t8269 - 1 + (57 * t8267 + 51 * t8269 + 19) * t8264) + + if Bindx == 1232: + t8271 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (23 + (-136 + 153 * t8271) * t8271) * ((1 + t8271) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14e2) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t8271) ** (0.3e1 / 0.2e1)) + + if Bindx == 1233: + t8279 = np.sin(phi) + t8277 = t8279 ** 2 + t8272 = np.cos(phi) + t8273 = t8272 ** 2 + t8275 = t8273 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.119e3) * t8277 ** 2 * (12 * t8273 + 32 * t8275 - 4 + (38 * t8273 + 9 * t8275 - 7) * t8272) + + if Bindx == 1234: + t8280 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.238e3) * ((1 - t8280) ** (0.5e1 / 0.2e1)) * ((1 + t8280) ** (0.13e2 / 0.2e1)) + + if Bindx == 1235: + t8282 = np.cos(phi) + t8287 = -1 + t8282 + t8283 = t8287 ** 2 + t8284 = t8287 * t8283 + t8281 = 1 + t8282 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.85e2) * t8287 * t8284 ** 2 * t8281 ** 2 + + if Bindx == 1236: + t8288 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.170e3) * ((1 - t8288) ** (0.13e2 / 0.2e1)) * ((1 + t8288) ** (0.3e1 / 0.2e1)) * (5 + 9 * t8288) + + if Bindx == 1237: + t8290 = np.cos(phi) + t8291 = t8290 ** 2 + t8292 = t8290 * t8291 + t8295 = t8292 ** 2 + t8293 = t8291 ** 2 + t8289 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.5e1) * t8289 ** 2 * (287 * t8291 - 525 * t8292 - 35 * t8293 - 595 * t8295 - 41 + (721 * t8293 + 153 * t8295 + 35) * t8290) + + if Bindx == 1238: + t8297 = np.cos(phi) + t8298 = t8297 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.15e2) * ((1 - t8297) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t8297)) * (85 * t8298 + 5 + (51 * t8298 + 41) * t8297) + + if Bindx == 1239: + t8300 = np.cos(phi) + t8301 = t8300 ** 2 + t8303 = t8301 ** 2 + t8307 = t8303 ** 2 + t8302 = t8300 * t8301 + t8305 = t8302 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((5*1j) * (phi1 - phi2)) * (180 * t8301 + 1540 * t8302 - 1610 * t8303 + 3500 * t8305 - 2125 * t8307 - 9 + (-2366 * t8303 + 380 * t8305 + 765 * t8307 - 255) * t8300) + + if Bindx == 1240: + t8309 = np.cos(phi) + t8310 = t8309 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.70e2) * ((1 - t8309) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t8309)) * (12 * t8309 - 3 + (272 * t8309 + 138 + 153 * t8310) * t8310) + + if Bindx == 1241: + t8314 = np.cos(phi) + t8315 = t8314 ** 2 + t8316 = t8314 * t8315 + t8319 = t8316 ** 2 + t8317 = t8315 ** 2 + t8313 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.1365e4) * t8313 ** 2 * (-27 * t8315 - 3 * t8316 + 95 * t8317 - 85 * t8319 + 1 + (-33 * t8317 + 51 * t8319 + 1) * t8314) + + if Bindx == 1242: + t8321 = np.cos(phi) + t8322 = t8321 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.65e2) * ((1 - t8321) ** (0.7e1 / 0.2e1)) * ((1 + t8321) ** (0.3e1 / 0.2e1)) * (-18 * t8321 - 1 + (136 * t8321 - 6 + 153 * t8322) * t8322) + + if Bindx == 1243: + t8332 = np.sin(phi) + t8330 = t8332 ** 2 + t8325 = np.cos(phi) + t8326 = t8325 ** 2 + t8328 = t8326 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.1430e4) * t8330 ** 2 * (30 * t8326 - 85 * t8328 - 1 + (-110 * t8326 + 153 * t8328 + 13) * t8325) + + if Bindx == 1244: + t8333 = np.cos(phi) + t8334 = t8333 ** 2 + tfunc[..., c] = (-0.57e2 / 0.128e3*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.143e3) * ((1 - t8333) ** (0.5e1 / 0.2e1)) * ((1 + t8333) ** (0.5e1 / 0.2e1)) * (1 + (-30 + 85 * t8334) * t8334) + + if Bindx == 1245: + t8343 = np.sin(phi) + t8341 = t8343 ** 2 + t8336 = np.cos(phi) + t8337 = t8336 ** 2 + t8339 = t8337 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.1430e4) * t8341 ** 2 * (-30 * t8337 + 85 * t8339 + 1 + (-110 * t8337 + 153 * t8339 + 13) * t8336) + + if Bindx == 1246: + t8344 = np.cos(phi) + t8345 = t8344 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.65e2) * ((1 - t8344) ** (0.3e1 / 0.2e1)) * ((1 + t8344) ** (0.7e1 / 0.2e1)) * (18 * t8344 - 1 + (-136 * t8344 - 6 + 153 * t8345) * t8345) + + if Bindx == 1247: + t8349 = np.cos(phi) + t8350 = t8349 ** 2 + t8351 = t8349 * t8350 + t8354 = t8351 ** 2 + t8352 = t8350 ** 2 + t8348 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.1365e4) * t8348 ** 2 * (27 * t8350 - 3 * t8351 - 95 * t8352 + 85 * t8354 - 1 + (-33 * t8352 + 51 * t8354 + 1) * t8349) + + if Bindx == 1248: + t8356 = np.cos(phi) + t8357 = t8356 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.70e2) * np.sqrt((1 - t8356)) * ((1 + t8356) ** (0.9e1 / 0.2e1)) * (-12 * t8356 - 3 + (-272 * t8356 + 138 + 153 * t8357) * t8357) + + if Bindx == 1249: + t8360 = np.cos(phi) + t8361 = t8360 ** 2 + t8363 = t8361 ** 2 + t8367 = t8363 ** 2 + t8362 = t8360 * t8361 + t8365 = t8362 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((5*1j) * (phi1 + phi2)) * (-180 * t8361 + 1540 * t8362 + 1610 * t8363 - 3500 * t8365 + 2125 * t8367 + 9 + (-2366 * t8363 + 380 * t8365 + 765 * t8367 - 255) * t8360) + + if Bindx == 1250: + t8369 = np.cos(phi) + t8370 = t8369 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.15e2) * ((1 + t8369) ** (0.11e2 / 0.2e1)) * (-46 * t8369 + 5 + (-136 * t8369 + 126 + 51 * t8370) * t8370) * ((1 - t8369) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1251: + t8374 = np.cos(phi) + t8375 = t8374 ** 2 + t8376 = t8374 * t8375 + t8379 = t8376 ** 2 + t8377 = t8375 ** 2 + t8373 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.5e1) * t8373 ** 2 * (-287 * t8375 - 525 * t8376 + 35 * t8377 + 595 * t8379 + 41 + (721 * t8377 + 153 * t8379 + 35) * t8374) + + if Bindx == 1252: + t8381 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * (-5 + 9 * t8381) * ((1 + t8381) ** (0.13e2 / 0.2e1)) * np.sqrt(0.170e3) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * ((1 - t8381) ** (0.3e1 / 0.2e1)) + + if Bindx == 1253: + t8383 = np.cos(phi) + t8388 = 1 + t8383 + t8384 = t8388 ** 2 + t8385 = t8388 * t8384 + t8382 = -1 + t8383 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.85e2) * t8382 ** 2 * t8388 * t8385 ** 2 + + if Bindx == 1254: + t8389 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.51e2) * ((1 - t8389) ** (0.15e2 / 0.2e1)) * ((1 + t8389) ** (0.3e1 / 0.2e1)) + + if Bindx == 1255: + t8390 = np.cos(phi) + t8395 = -1 + t8390 + t8391 = t8395 ** 2 + t8392 = t8395 * t8391 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.102e3) * t8395 * t8392 ** 2 * (1 + t8390) * (2 + 3 * t8390) + + if Bindx == 1256: + t8396 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.3e1) * ((1 - t8396) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t8396)) * (21 + (68 + 51 * t8396) * t8396) + + if Bindx == 1257: + t8397 = np.cos(phi) + t8398 = t8397 ** 2 + t8400 = t8398 ** 2 + t8404 = t8400 ** 2 + t8399 = t8397 * t8398 + t8402 = t8399 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((6*1j) * (phi1 - phi2)) * (-207 * t8398 + 308 * t8399 + 273 * t8400 + 287 * t8402 - 408 * t8404 + 23 + (-798 * t8400 + 432 * t8402 + 102 * t8404 - 12) * t8397) + + if Bindx == 1258: + t8406 = np.cos(phi) + t8407 = t8406 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.15e2) * ((1 - t8406) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t8406)) * (85 * t8407 + 5 + (51 * t8407 + 41) * t8406) + + if Bindx == 1259: + t8410 = np.cos(phi) + t8411 = t8410 ** 2 + t8412 = t8410 * t8411 + t8415 = t8412 ** 2 + t8413 = t8411 ** 2 + t8409 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.42e2) * t8409 ** 2 * (-18 * t8411 - 87 * t8412 + 113 * t8413 - 136 * t8415 + 1 + (57 * t8413 + 51 * t8415 + 19) * t8410) + + if Bindx == 1260: + t8417 = np.cos(phi) + t8420 = 51 * t8417 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.91e2) * ((1 - t8417) ** (0.9e1 / 0.2e1)) * ((1 + t8417) ** (0.3e1 / 0.2e1)) * (t8420 - 1 + (t8420 + 9) * t8417) + + if Bindx == 1261: + t8428 = np.sin(phi) + t8426 = t8428 ** 2 + t8421 = np.cos(phi) + t8422 = t8421 ** 2 + t8424 = t8422 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.39e2) * t8426 ** 2 * (69 * t8422 - 136 * t8424 - 3 + (-36 * t8422 + 102 * t8424 + 4) * t8421) + + if Bindx == 1262: + t8429 = np.cos(phi) + t8430 = t8429 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.858e3) * ((1 - t8429) ** (0.7e1 / 0.2e1)) * ((1 + t8429) ** (0.5e1 / 0.2e1)) * (17 * t8430 - 1 + (51 * t8430 - 7) * t8429) + + if Bindx == 1263: + t8436 = np.sin(phi) + t8433 = t8436 ** 2 + t8434 = t8436 * t8433 + t8432 = np.cos(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((6*1j) * phi1) * np.sqrt(0.2145e4) * t8434 ** 2 * t8432 * (17 * t8432 ** 2 - 3) + + if Bindx == 1264: + t8437 = np.cos(phi) + t8438 = t8437 ** 2 + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.858e3) * ((1 - t8437) ** (0.5e1 / 0.2e1)) * ((1 + t8437) ** (0.7e1 / 0.2e1)) * (-17 * t8438 + 1 + (51 * t8438 - 7) * t8437) + + if Bindx == 1265: + t8447 = np.sin(phi) + t8445 = t8447 ** 2 + t8440 = np.cos(phi) + t8441 = t8440 ** 2 + t8443 = t8441 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.39e2) * t8445 ** 2 * (-69 * t8441 + 136 * t8443 + 3 + (-36 * t8441 + 102 * t8443 + 4) * t8440) + + if Bindx == 1266: + t8448 = np.cos(phi) + t8449 = t8448 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.91e2) * ((1 - t8448) ** (0.3e1 / 0.2e1)) * ((1 + t8448) ** (0.9e1 / 0.2e1)) * (-51 * t8449 + 1 + (51 * t8449 + 9) * t8448) + + if Bindx == 1267: + t8452 = np.cos(phi) + t8453 = t8452 ** 2 + t8454 = t8452 * t8453 + t8457 = t8454 ** 2 + t8455 = t8453 ** 2 + t8451 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.64e2) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.42e2) * t8451 ** 2 * (18 * t8453 - 87 * t8454 - 113 * t8455 + 136 * t8457 - 1 + (57 * t8455 + 51 * t8457 + 19) * t8452) + + if Bindx == 1268: + t8459 = np.cos(phi) + t8460 = t8459 ** 2 + tfunc[..., c] = (-0.19e2 / 0.64e2*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t8459)) * ((1 + t8459) ** (0.11e2 / 0.2e1)) * (-85 * t8460 - 5 + (51 * t8460 + 41) * t8459) + + if Bindx == 1269: + t8462 = np.cos(phi) + t8463 = t8462 ** 2 + t8465 = t8463 ** 2 + t8469 = t8465 ** 2 + t8464 = t8462 * t8463 + t8467 = t8464 ** 2 + tfunc[..., c] = (0.19e2 / 0.64e2) * np.exp((6*1j) * (phi1 + phi2)) * (207 * t8463 + 308 * t8464 - 273 * t8465 - 287 * t8467 + 408 * t8469 - 23 + (-798 * t8465 + 432 * t8467 + 102 * t8469 - 12) * t8462) + + if Bindx == 1270: + t8471 = np.cos(phi) + t8472 = t8471 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.3e1) * ((1 + t8471) ** (0.13e2 / 0.2e1)) * (-119 * t8472 - 21 + (51 * t8472 + 89) * t8471) * ((1 - t8471) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1271: + t8475 = np.cos(phi) + t8476 = t8475 ** 2 + t8477 = t8475 * t8476 + t8480 = t8477 ** 2 + t8478 = t8476 ** 2 + t8474 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.102e3) * t8474 ** 2 * (-12 * t8476 + 5 * t8477 + 30 * t8478 + 16 * t8480 - 2 + (33 * t8478 + 3 * t8480 - 9) * t8475) + + if Bindx == 1272: + t8482 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.51e2) * ((1 - t8482) ** (0.3e1 / 0.2e1)) * ((1 + t8482) ** (0.15e2 / 0.2e1)) + + if Bindx == 1273: + t8483 = np.cos(phi) + t8487 = -1 + t8483 + t8484 = t8487 ** 2 + t8485 = t8484 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.17e2) * t8485 ** 2 * (1 + t8483) + + if Bindx == 1274: + t8488 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * np.sqrt(0.34e2) * ((1 - t8488) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t8488)) * (7 + 9 * t8488) + + if Bindx == 1275: + t8489 = np.cos(phi) + t8490 = t8489 ** 2 + t8492 = t8490 ** 2 + t8496 = t8492 ** 2 + t8491 = t8489 * t8490 + t8494 = t8491 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((7*1j) * (phi1 - phi2)) * (-356 * t8490 - 812 * t8491 + 2002 * t8492 - 980 * t8494 - 833 * t8496 - 89 + (-1106 * t8492 + 1636 * t8494 + 153 * t8496 + 385) * t8489) + + if Bindx == 1276: + t8498 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.3e1) * ((1 - t8498) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t8498)) * (21 + (68 + 51 * t8498) * t8498) + + if Bindx == 1277: + t8500 = np.cos(phi) + t8501 = t8500 ** 2 + t8502 = t8500 * t8501 + t8505 = t8502 ** 2 + t8503 = t8501 ** 2 + t8499 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t8499 ** 2 * (287 * t8501 - 525 * t8502 - 35 * t8503 - 595 * t8505 - 41 + (721 * t8503 + 153 * t8505 + 35) * t8500) + + if Bindx == 1278: + t8507 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t8507) ** (0.11e2 / 0.2e1)) * ((1 + t8507) ** (0.3e1 / 0.2e1)) * (23 + (136 + 153 * t8507) * t8507) + + if Bindx == 1279: + t8515 = np.sin(phi) + t8513 = t8515 ** 2 + t8508 = np.cos(phi) + t8509 = t8508 ** 2 + t8511 = t8509 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.273e3) * t8513 ** 2 * (42 * t8509 - 119 * t8511 - 3 + (54 * t8509 + 51 * t8511 - 25) * t8508) + + if Bindx == 1280: + t8516 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.13e2) * ((1 - t8516) ** (0.9e1 / 0.2e1)) * ((1 + t8516) ** (0.5e1 / 0.2e1)) * (-1 + (68 + 153 * t8516) * t8516) + + if Bindx == 1281: + t8523 = np.sin(phi) + t8520 = t8523 ** 2 + t8521 = t8523 * t8520 + t8517 = np.cos(phi) + t8518 = t8517 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.286e3) * t8521 ** 2 * (-119 * t8518 + 7 + (153 * t8518 - 41) * t8517) + + if Bindx == 1282: + t8524 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.256e3*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.715e3) * ((1 - t8524) ** (0.7e1 / 0.2e1)) * ((1 + t8524) ** (0.7e1 / 0.2e1)) * (17 * t8524 ** 2 - 1) + + if Bindx == 1283: + t8531 = np.sin(phi) + t8528 = t8531 ** 2 + t8529 = t8531 * t8528 + t8525 = np.cos(phi) + t8526 = t8525 ** 2 + tfunc[..., c] = -(0.19e2 / 0.512e3) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.286e3) * t8529 ** 2 * (119 * t8526 - 7 + (153 * t8526 - 41) * t8525) + + if Bindx == 1284: + t8532 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.13e2) * ((1 - t8532) ** (0.5e1 / 0.2e1)) * ((1 + t8532) ** (0.9e1 / 0.2e1)) * (-1 + (-68 + 153 * t8532) * t8532) + + if Bindx == 1285: + t8540 = np.sin(phi) + t8538 = t8540 ** 2 + t8533 = np.cos(phi) + t8534 = t8533 ** 2 + t8536 = t8534 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.273e3) * t8538 ** 2 * (-42 * t8534 + 119 * t8536 + 3 + (54 * t8534 + 51 * t8536 - 25) * t8533) + + if Bindx == 1286: + t8541 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.14e2) * ((1 - t8541) ** (0.3e1 / 0.2e1)) * ((1 + t8541) ** (0.11e2 / 0.2e1)) * (23 + (-136 + 153 * t8541) * t8541) + + if Bindx == 1287: + t8543 = np.cos(phi) + t8544 = t8543 ** 2 + t8545 = t8543 * t8544 + t8548 = t8545 ** 2 + t8546 = t8544 ** 2 + t8542 = np.sin(phi) + tfunc[..., c] = -(0.19e2 / 0.256e3) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * t8542 ** 2 * (-287 * t8544 - 525 * t8545 + 35 * t8546 + 595 * t8548 + 41 + (721 * t8546 + 153 * t8548 + 35) * t8543) + + if Bindx == 1288: + t8550 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.128e3*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t8550)) * ((1 + t8550) ** (0.13e2 / 0.2e1)) * (21 + (-68 + 51 * t8550) * t8550) + + if Bindx == 1289: + t8551 = np.cos(phi) + t8552 = t8551 ** 2 + t8554 = t8552 ** 2 + t8558 = t8554 ** 2 + t8553 = t8551 * t8552 + t8556 = t8553 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((7*1j) * (phi1 + phi2)) * (356 * t8552 - 812 * t8553 - 2002 * t8554 + 980 * t8556 + 833 * t8558 + 89 + (-1106 * t8554 + 1636 * t8556 + 153 * t8558 + 385) * t8551) + + if Bindx == 1290: + t8560 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.34e2) * ((1 + t8560) ** (0.15e2 / 0.2e1)) * (7 + (-16 + 9 * t8560) * t8560) * ((1 - t8560) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1291: + t8561 = np.cos(phi) + t8565 = 1 + t8561 + t8562 = t8565 ** 2 + t8563 = t8562 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.17e2) * (-1 + t8561) * t8563 ** 2 + + if Bindx == 1292: + t8566 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.2e1) * ((1 - t8566) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t8566)) + + if Bindx == 1293: + t8567 = np.cos(phi) + t8568 = t8567 ** 2 + t8570 = t8568 ** 2 + t8574 = t8570 ** 2 + t8569 = t8567 * t8568 + t8572 = t8569 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((8*1j) * (phi1 - phi2)) * (152 * t8568 - 196 * t8569 + 56 * t8570 - 280 * t8572 - 64 * t8574 + 8 + (182 * t8570 + 188 * t8572 + 9 * t8574 - 55) * t8567) + + if Bindx == 1294: + t8576 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * np.sqrt(0.34e2) * ((1 - t8576) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t8576)) * (7 + 9 * t8576) + + if Bindx == 1295: + t8577 = np.cos(phi) + t8582 = -1 + t8577 + t8578 = t8582 ** 2 + t8579 = t8582 * t8578 + tfunc[..., c] = (0.19e2 / 0.128e3) * (2 + 3 * t8577) * (1 + t8577) * t8582 * t8579 ** 2 * np.sqrt(0.102e3) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) + + if Bindx == 1296: + t8583 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.170e3) * ((1 - t8583) ** (0.13e2 / 0.2e1)) * ((1 + t8583) ** (0.3e1 / 0.2e1)) * (5 + 9 * t8583) + + if Bindx == 1297: + t8591 = np.sin(phi) + t8589 = t8591 ** 2 + t8584 = np.cos(phi) + t8585 = t8584 ** 2 + t8587 = t8585 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.119e3) * t8589 ** 2 * (-12 * t8585 - 32 * t8587 + 4 + (38 * t8585 + 9 * t8587 - 7) * t8584) + + if Bindx == 1298: + t8592 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.9282e4) * ((1 - t8592) ** (0.11e2 / 0.2e1)) * ((1 + t8592) ** (0.5e1 / 0.2e1)) * (1 + 3 * t8592) + + if Bindx == 1299: + t8599 = np.sin(phi) + t8596 = t8599 ** 2 + t8597 = t8599 * t8596 + t8593 = np.cos(phi) + t8594 = t8593 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.442e3) * t8597 ** 2 * (-16 * t8594 + 2 + (9 * t8594 + 5) * t8593) + + if Bindx == 1300: + t8600 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.2431e4) * ((1 - t8600) ** (0.9e1 / 0.2e1)) * ((1 + t8600) ** (0.7e1 / 0.2e1)) * (9 * t8600 + 1) + + if Bindx == 1301: + t8604 = np.sin(phi) + t8601 = t8604 ** 2 + t8602 = t8601 ** 2 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((8*1j) * phi1) * np.sqrt(0.24310e5) * t8602 ** 2 * np.cos(phi) + + if Bindx == 1302: + t8605 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.2431e4) * ((1 - t8605) ** (0.7e1 / 0.2e1)) * ((1 + t8605) ** (0.9e1 / 0.2e1)) * (-1 + 9 * t8605) + + if Bindx == 1303: + t8612 = np.sin(phi) + t8609 = t8612 ** 2 + t8610 = t8612 * t8609 + t8606 = np.cos(phi) + t8607 = t8606 ** 2 + tfunc[..., c] = -(0.19e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.442e3) * t8610 ** 2 * (16 * t8607 - 2 + (9 * t8607 + 5) * t8606) + + if Bindx == 1304: + t8613 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.9282e4) * ((1 - t8613) ** (0.5e1 / 0.2e1)) * ((1 + t8613) ** (0.11e2 / 0.2e1)) * (-1 + 3 * t8613) + + if Bindx == 1305: + t8621 = np.sin(phi) + t8619 = t8621 ** 2 + t8614 = np.cos(phi) + t8615 = t8614 ** 2 + t8617 = t8615 ** 2 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.119e3) * t8619 ** 2 * (12 * t8615 + 32 * t8617 - 4 + (38 * t8615 + 9 * t8617 - 7) * t8614) + + if Bindx == 1306: + t8622 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.170e3) * ((1 - t8622) ** (0.3e1 / 0.2e1)) * ((1 + t8622) ** (0.13e2 / 0.2e1)) * (-5 + 9 * t8622) + + if Bindx == 1307: + t8623 = np.cos(phi) + t8628 = 1 + t8623 + t8624 = t8628 ** 2 + t8625 = t8628 * t8624 + tfunc[..., c] = (0.19e2 / 0.128e3) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.102e3) * (-1 + t8623) * t8628 * t8625 ** 2 * (-2 + 3 * t8623) + + if Bindx == 1308: + t8629 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.34e2) * np.sqrt((1 - t8629)) * ((1 + t8629) ** (0.15e2 / 0.2e1)) * (-7 + 9 * t8629) + + if Bindx == 1309: + t8630 = np.cos(phi) + t8631 = t8630 ** 2 + t8633 = t8631 ** 2 + t8637 = t8633 ** 2 + t8632 = t8630 * t8631 + t8635 = t8632 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((8*1j) * (phi1 + phi2)) * (-152 * t8631 - 196 * t8632 - 56 * t8633 + 280 * t8635 + 64 * t8637 - 8 + (182 * t8633 + 188 * t8635 + 9 * t8637 - 55) * t8630) + + if Bindx == 1310: + t8639 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t8639)) * ((1 + t8639) ** (0.17e2 / 0.2e1)) + + if Bindx == 1311: + t8640 = np.cos(phi) + t8641 = t8640 ** 2 + t8643 = t8641 ** 2 + t8647 = t8643 ** 2 + t8642 = t8640 * t8641 + t8645 = t8642 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((9*1j) * (phi1 - phi2)) * (-36 * t8641 + 84 * t8642 - 126 * t8643 - 84 * t8645 - 9 * t8647 - 1 + (126 * t8643 + 36 * t8645 + t8647 + 9) * t8640) + + if Bindx == 1312: + t8649 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.2e1) * ((1 - t8649) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t8649)) + + if Bindx == 1313: + t8650 = np.cos(phi) + t8654 = -1 + t8650 + t8651 = t8654 ** 2 + t8652 = t8651 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * t8652 ** 2 * (1 + t8650) + + if Bindx == 1314: + t8655 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t8655) ** (0.15e2 / 0.2e1)) * ((1 + t8655) ** (0.3e1 / 0.2e1)) + + if Bindx == 1315: + t8657 = np.cos(phi) + t8662 = -1 + t8657 + t8658 = t8662 ** 2 + t8659 = t8662 * t8658 + t8656 = 1 + t8657 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t8662 * t8659 ** 2 * t8656 ** 2 + + if Bindx == 1316: + t8663 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.238e3) * ((1 - t8663) ** (0.13e2 / 0.2e1)) * ((1 + t8663) ** (0.5e1 / 0.2e1)) + + if Bindx == 1317: + t8664 = np.cos(phi) + t8671 = -1 + t8664 + t8670 = 1 + t8664 + t8668 = t8670 ** 2 + t8665 = t8671 ** 2 + t8666 = t8671 * t8665 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.4641e4) * t8666 ** 2 * t8670 * t8668 + + if Bindx == 1318: + t8672 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.221e3) * ((1 - t8672) ** (0.11e2 / 0.2e1)) * ((1 + t8672) ** (0.7e1 / 0.2e1)) + + if Bindx == 1319: + t8676 = np.sin(phi) + t8673 = t8676 ** 2 + t8674 = t8673 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (9 * phi1 - phi2)) * np.sqrt(0.4862e4) * t8674 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 1320: + t8677 = np.cos(phi) + tfunc[..., c] = (-0.19e2 / 0.256e3*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.12155e5) * ((1 - t8677) ** (0.9e1 / 0.2e1)) * ((1 + t8677) ** (0.9e1 / 0.2e1)) + + if Bindx == 1321: + t8681 = np.sin(phi) + t8678 = t8681 ** 2 + t8679 = t8678 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (9 * phi1 + phi2)) * np.sqrt(0.4862e4) * t8679 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 1322: + t8682 = np.cos(phi) + tfunc[..., c] = (0.57e2 / 0.128e3*1j) * np.exp((1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.221e3) * ((1 - t8682) ** (0.7e1 / 0.2e1)) * ((1 + t8682) ** (0.11e2 / 0.2e1)) + + if Bindx == 1323: + t8683 = np.cos(phi) + t8690 = -1 + t8683 + t8689 = 1 + t8683 + t8686 = t8689 ** 2 + t8687 = t8689 * t8686 + t8684 = t8690 ** 2 + tfunc[..., c] = (0.19e2 / 0.256e3) * np.exp((3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.4641e4) * t8690 * t8684 * t8687 ** 2 + + if Bindx == 1324: + t8691 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.256e3*1j) * np.exp((1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.238e3) * ((1 - t8691) ** (0.5e1 / 0.2e1)) * ((1 + t8691) ** (0.13e2 / 0.2e1)) + + if Bindx == 1325: + t8693 = np.cos(phi) + t8698 = 1 + t8693 + t8694 = t8698 ** 2 + t8695 = t8698 * t8694 + t8692 = -1 + t8693 + tfunc[..., c] = (0.57e2 / 0.256e3) * np.exp((1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t8692 ** 2 * t8698 * t8695 ** 2 + + if Bindx == 1326: + t8699 = np.cos(phi) + tfunc[..., c] = (0.19e2 / 0.128e3*1j) * np.exp((3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t8699) ** (0.3e1 / 0.2e1)) * ((1 + t8699) ** (0.15e2 / 0.2e1)) + + if Bindx == 1327: + t8700 = np.cos(phi) + t8704 = 1 + t8700 + t8701 = t8704 ** 2 + t8702 = t8701 ** 2 + tfunc[..., c] = (0.57e2 / 0.512e3) * np.exp((1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * (-1 + t8700) * t8702 ** 2 + + if Bindx == 1328: + t8705 = np.cos(phi) + tfunc[..., c] = (-0.57e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t8705)) * ((1 + t8705) ** (0.17e2 / 0.2e1)) + + if Bindx == 1329: + t8706 = np.cos(phi) + t8707 = t8706 ** 2 + t8709 = t8707 ** 2 + t8715 = 126 * t8709 + t8713 = t8709 ** 2 + t8708 = t8706 * t8707 + t8711 = t8708 ** 2 + tfunc[..., c] = (0.19e2 / 0.512e3) * np.exp((9*1j) * (phi1 + phi2)) * (36 * t8707 + 84 * t8708 + 84 * t8711 + 9 * t8713 + t8715 + 1 + (36 * t8711 + t8713 + t8715 + 9) * t8706) + + if Bindx == 1330: + t8716 = np.cos(phi) + t8726 = 10 * t8716 + t8717 = t8716 ** 2 + t8719 = t8717 ** 2 + t8720 = t8716 * t8719 + t8718 = t8716 * t8717 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 + phi2)) * (45 * t8717 + t8726 + 1 + (252 + t8720) * t8720 + (210 + (t8726 + 45) * t8719) * t8719 + (120 + (120 * t8716 + 210) * t8718) * t8718) + + if Bindx == 1331: + t8727 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.5e1) * ((1 + t8727) ** (0.19e2 / 0.2e1)) * np.sqrt((1 - t8727)) + + if Bindx == 1332: + t8728 = np.cos(phi) + t8733 = 1 + t8728 + t8729 = t8733 ** 2 + t8730 = t8729 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.190e3) * (-1 + t8728) * t8733 * t8730 ** 2 + + if Bindx == 1333: + t8734 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 + 7 * phi2)) * np.sqrt(0.285e3) * ((1 - t8734) ** (0.3e1 / 0.2e1)) * ((1 + t8734) ** (0.17e2 / 0.2e1)) + + if Bindx == 1334: + t8736 = np.cos(phi) + t8740 = 1 + t8736 + t8737 = t8740 ** 2 + t8738 = t8737 ** 2 + t8735 = -1 + t8736 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.4845e4) * t8735 ** 2 * t8738 ** 2 + + if Bindx == 1335: + t8741 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-5*1j) * (2 * phi1 + phi2)) * np.sqrt(0.969e3) * ((1 - t8741) ** (0.5e1 / 0.2e1)) * ((1 + t8741) ** (0.15e2 / 0.2e1)) + + if Bindx == 1336: + t8742 = np.cos(phi) + t8750 = -1 + t8742 + t8749 = 1 + t8742 + t8745 = t8749 ** 2 + t8746 = t8749 * t8745 + t8743 = t8750 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.9690e4) * t8750 * t8743 * t8749 * t8746 ** 2 + + if Bindx == 1337: + t8751 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (10 * phi1 + 3 * phi2)) * np.sqrt(0.4845e4) * ((1 - t8751) ** (0.7e1 / 0.2e1)) * ((1 + t8751) ** (0.13e2 / 0.2e1)) + + if Bindx == 1338: + t8752 = np.cos(phi) + t8759 = -1 + t8752 + t8758 = 1 + t8752 + t8755 = t8758 ** 2 + t8756 = t8758 * t8755 + t8753 = t8759 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.125970e6) * t8753 ** 2 * t8756 ** 2 + + if Bindx == 1339: + t8760 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 + phi2)) * np.sqrt(0.41990e5) * ((1 - t8760) ** (0.9e1 / 0.2e1)) * ((1 + t8760) ** (0.11e2 / 0.2e1)) + + if Bindx == 1340: + t8765 = np.sin(phi) + t8761 = t8765 ** 2 + t8763 = t8765 * t8761 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-10*1j) * phi1) * np.sqrt(0.46189e5) * t8763 ** 2 + + if Bindx == 1341: + t8766 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 - phi2)) * np.sqrt(0.41990e5) * ((1 - t8766) ** (0.11e2 / 0.2e1)) * ((1 + t8766) ** (0.9e1 / 0.2e1)) + + if Bindx == 1342: + t8767 = np.cos(phi) + t8774 = -1 + t8767 + t8773 = 1 + t8767 + t8771 = t8773 ** 2 + t8768 = t8774 ** 2 + t8769 = t8774 * t8768 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.125970e6) * t8769 ** 2 * t8771 ** 2 + + if Bindx == 1343: + t8775 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (10 * phi1 - 3 * phi2)) * np.sqrt(0.4845e4) * ((1 - t8775) ** (0.13e2 / 0.2e1)) * ((1 + t8775) ** (0.7e1 / 0.2e1)) + + if Bindx == 1344: + t8776 = np.cos(phi) + t8784 = -1 + t8776 + t8783 = 1 + t8776 + t8781 = t8783 ** 2 + t8777 = t8784 ** 2 + t8778 = t8784 * t8777 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.9690e4) * t8784 * t8778 ** 2 * t8783 * t8781 + + if Bindx == 1345: + t8785 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-5*1j) * (2 * phi1 - phi2)) * np.sqrt(0.969e3) * ((1 - t8785) ** (0.15e2 / 0.2e1)) * ((1 + t8785) ** (0.5e1 / 0.2e1)) + + if Bindx == 1346: + t8787 = np.cos(phi) + t8791 = -1 + t8787 + t8788 = t8791 ** 2 + t8789 = t8788 ** 2 + t8786 = 1 + t8787 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.4845e4) * t8789 ** 2 * t8786 ** 2 + + if Bindx == 1347: + t8792 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 - 7 * phi2)) * np.sqrt(0.285e3) * ((1 - t8792) ** (0.17e2 / 0.2e1)) * ((1 + t8792) ** (0.3e1 / 0.2e1)) + + if Bindx == 1348: + t8793 = np.cos(phi) + t8798 = -1 + t8793 + t8794 = t8798 ** 2 + t8795 = t8794 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.190e3) * t8798 * t8795 ** 2 * (1 + t8793) + + if Bindx == 1349: + t8799 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (10 * phi1 - 9 * phi2)) * np.sqrt(0.5e1) * ((1 - t8799) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t8799)) + + if Bindx == 1350: + t8800 = np.cos(phi) + t8810 = -10 * t8800 + t8801 = t8800 ** 2 + t8803 = t8801 ** 2 + t8804 = t8800 * t8803 + t8802 = t8800 * t8801 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 - phi2)) * (45 * t8801 + t8810 + 1 + (-252 + t8804) * t8804 + (210 + (t8810 + 45) * t8803) * t8803 + (-120 + (-120 * t8800 + 210) * t8802) * t8802) + + if Bindx == 1351: + t8811 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t8811)) * ((1 + t8811) ** (0.19e2 / 0.2e1)) + + if Bindx == 1352: + t8812 = np.cos(phi) + t8813 = t8812 ** 2 + t8815 = t8813 ** 2 + t8819 = t8815 ** 2 + t8814 = t8812 * t8813 + t8817 = t8814 ** 2 + t8816 = t8812 * t8815 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-9*1j) * (phi1 + phi2)) * (-234 * t8813 - 396 * t8814 - 294 * t8815 + 504 * t8817 + 279 * t8819 - 9 + (126 + 10 * t8816) * t8816 + (516 * t8817 + 81 * t8819 - 71) * t8812) + + if Bindx == 1353: + t8822 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.38e2) * ((1 + t8822) ** (0.17e2 / 0.2e1)) * (4 + (-9 + 5 * t8822) * t8822) * ((1 - t8822) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1354: + t8823 = np.cos(phi) + t8827 = 1 + t8823 + t8824 = t8827 ** 2 + t8825 = t8824 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * (-7 + 10 * t8823) * (-1 + t8823) * t8825 ** 2 * np.sqrt(0.57e2) * np.exp((-1*1j) * (9 * phi1 + 7 * phi2)) + + if Bindx == 1355: + t8828 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-3 + 5 * t8828) * ((1 + t8828) ** (0.15e2 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-3*1j) * (3 * phi1 + 2 * phi2)) * ((1 - t8828) ** (0.3e1 / 0.2e1)) + + if Bindx == 1356: + t8837 = np.sin(phi) + t8835 = t8837 ** 2 + t8829 = np.cos(phi) + t8830 = t8829 ** 2 + t8832 = t8830 ** 2 + t8831 = t8829 * t8830 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.4845e4) * t8835 ** 2 * (15 * t8832 - 1 + (10 + 2 * t8831) * t8831 + (9 * t8832 - 3) * t8829) + + if Bindx == 1357: + t8838 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-2 + 5 * t8838) * ((1 + t8838) ** (0.13e2 / 0.2e1)) * np.sqrt(0.1938e4) * np.exp((-1*1j) * (9 * phi1 + 4 * phi2)) * ((1 - t8838) ** (0.5e1 / 0.2e1)) + + if Bindx == 1358: + t8846 = np.sin(phi) + t8843 = t8846 ** 2 + t8844 = t8846 * t8843 + t8839 = np.cos(phi) + t8840 = t8839 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.969e3) * t8844 ** 2 * (t8839 - 3 + (27 * t8839 + 21 + 10 * t8840) * t8840) + + if Bindx == 1359: + t8847 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-1 + 5 * t8847) * ((1 + t8847) ** (0.11e2 / 0.2e1)) * np.sqrt(0.25194e5) * np.exp((-1*1j) * (9 * phi1 + 2 * phi2)) * ((1 - t8847) ** (0.7e1 / 0.2e1)) + + if Bindx == 1360: + t8852 = np.sin(phi) + t8849 = t8852 ** 2 + t8850 = t8849 ** 2 + t8848 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 + phi2)) * np.sqrt(0.8398e4) * t8850 ** 2 * (-1 + (9 + 10 * t8848) * t8848) + + if Bindx == 1361: + t8853 = np.cos(phi) + t8854 = t8853 ** 2 + t8856 = t8854 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * t8853 * (-10 * t8854 - 5 * t8856 - 1 + (10 * t8854 + t8856 + 5) * t8853) * ((1 + t8853) ** (0.9e1 / 0.2e1)) * np.sqrt(0.230945e6) * np.exp((-9*1j) * phi1) * ((1 - t8853) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1362: + t8862 = np.sin(phi) + t8859 = t8862 ** 2 + t8860 = t8859 ** 2 + t8858 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 - phi2)) * np.sqrt(0.8398e4) * t8860 ** 2 * (-1 + (-9 + 10 * t8858) * t8858) + + if Bindx == 1363: + t8863 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (1 + 5 * t8863) * ((1 + t8863) ** (0.7e1 / 0.2e1)) * np.sqrt(0.25194e5) * np.exp((-1*1j) * (9 * phi1 - 2 * phi2)) * ((1 - t8863) ** (0.11e2 / 0.2e1)) + + if Bindx == 1364: + t8871 = np.sin(phi) + t8868 = t8871 ** 2 + t8869 = t8871 * t8868 + t8864 = np.cos(phi) + t8865 = t8864 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.969e3) * t8869 ** 2 * (-t8864 - 3 + (-27 * t8864 + 21 + 10 * t8865) * t8865) + + if Bindx == 1365: + t8872 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (2 + 5 * t8872) * ((1 + t8872) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1938e4) * np.exp((-1*1j) * (9 * phi1 - 4 * phi2)) * ((1 - t8872) ** (0.13e2 / 0.2e1)) + + if Bindx == 1366: + t8881 = np.sin(phi) + t8879 = t8881 ** 2 + t8873 = np.cos(phi) + t8874 = t8873 ** 2 + t8876 = t8874 ** 2 + t8875 = t8873 * t8874 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.4845e4) * t8879 ** 2 * (15 * t8876 - 1 + (-10 + 2 * t8875) * t8875 + (-9 * t8876 + 3) * t8873) + + if Bindx == 1367: + t8882 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (3 + 5 * t8882) * ((1 + t8882) ** (0.3e1 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-3*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t8882) ** (0.15e2 / 0.2e1)) + + if Bindx == 1368: + t8883 = np.cos(phi) + t8887 = -1 + t8883 + t8884 = t8887 ** 2 + t8885 = t8884 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.57e2) * (1 + t8883) * t8885 ** 2 * (7 + 10 * t8883) + + if Bindx == 1369: + t8888 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (4 + 5 * t8888) * np.sqrt((1 + t8888)) * np.sqrt(0.38e2) * np.exp((-1*1j) * (9 * phi1 - 8 * phi2)) * ((1 - t8888) ** (0.17e2 / 0.2e1)) + + if Bindx == 1370: + t8889 = np.cos(phi) + t8890 = t8889 ** 2 + t8892 = t8890 ** 2 + t8896 = t8892 ** 2 + t8891 = t8889 * t8890 + t8894 = t8891 ** 2 + t8893 = t8889 * t8892 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-9*1j) * (phi1 - phi2)) * (-234 * t8890 + 396 * t8891 - 294 * t8892 + 504 * t8894 + 279 * t8896 - 9 + (-126 + 10 * t8893) * t8893 + (-516 * t8894 - 81 * t8896 + 71) * t8889) + + if Bindx == 1371: + t8899 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (9 * phi1 - 10 * phi2)) * np.sqrt(0.5e1) * ((1 - t8899) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t8899)) + + if Bindx == 1372: + t8900 = np.cos(phi) + t8905 = 1 + t8900 + t8901 = t8905 ** 2 + t8902 = t8901 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.190e3) * (-1 + t8900) * t8905 * t8902 ** 2 + + if Bindx == 1373: + t8906 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.38e2) * np.sqrt((1 - t8906)) * ((1 + t8906) ** (0.17e2 / 0.2e1)) * (-4 + 5 * t8906) + + if Bindx == 1374: + t8907 = np.cos(phi) + t8908 = t8907 ** 2 + t8910 = t8908 ** 2 + t8914 = t8910 ** 2 + t8909 = t8907 * t8908 + t8912 = t8909 ** 2 + t8911 = t8907 * t8910 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-8*1j) * (phi1 + phi2)) * (531 * t8908 - 192 * t8909 - 1722 * t8910 - 210 * t8912 + 1503 * t8914 + 59 + (-2016 + 95 * t8911) * t8911 + (1536 * t8912 + 608 * t8914 + 320) * t8907) + + if Bindx == 1375: + t8917 = np.cos(phi) + t8918 = t8917 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.6e1) * ((1 + t8917) ** (0.15e2 / 0.2e1)) * (-228 * t8918 - 44 + (95 * t8918 + 177) * t8917) * ((1 - t8917) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1376: + t8921 = np.cos(phi) + t8922 = t8921 ** 2 + t8923 = t8921 * t8922 + t8926 = t8923 ** 2 + t8924 = t8922 ** 2 + t8920 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.102e3) * t8920 ** 2 * (-124 * t8922 - 520 * t8923 + 772 * t8926 + 31 + (-390 + 95 * t8924) * t8924 + (376 * t8924 + 456 * t8926 + 72) * t8921) + + if Bindx == 1377: + t8929 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (4 + (-19 + 19 * t8929) * t8929) * ((1 + t8929) ** (0.13e2 / 0.2e1)) * np.sqrt(0.510e3) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * ((1 - t8929) ** (0.3e1 / 0.2e1)) + + if Bindx == 1378: + t8938 = np.sin(phi) + t8936 = t8938 ** 2 + t8930 = np.cos(phi) + t8931 = t8930 ** 2 + t8933 = t8931 ** 2 + t8932 = t8930 * t8931 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.51e2) * t8936 ** 2 * (-143 * t8931 + 277 * t8933 + 11 + (-32 + 95 * t8932) * t8932 + (304 * t8933 - 32) * t8930) + + if Bindx == 1379: + t8939 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (4 + (-57 + 95 * t8939) * t8939) * ((1 + t8939) ** (0.11e2 / 0.2e1)) * np.sqrt(0.102e3) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * ((1 - t8939) ** (0.5e1 / 0.2e1)) + + if Bindx == 1380: + t8947 = np.sin(phi) + t8944 = t8947 ** 2 + t8945 = t8947 * t8944 + t8940 = np.cos(phi) + t8941 = t8940 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.663e3) * t8945 ** 2 * (-40 * t8940 - 1 + (152 * t8940 + 18 + 95 * t8941) * t8941) + + if Bindx == 1381: + t8948 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-4 + (-19 + 95 * t8948) * t8948) * ((1 + t8948) ** (0.9e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (8 * phi1 + phi2)) * ((1 - t8948) ** (0.7e1 / 0.2e1)) + + if Bindx == 1382: + t8953 = np.sin(phi) + t8950 = t8953 ** 2 + t8951 = t8950 ** 2 + t8949 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.24310e5) * t8951 ** 2 * (19 * t8949 ** 2 - 1) + + if Bindx == 1383: + t8954 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-4 + (19 + 95 * t8954) * t8954) * ((1 + t8954) ** (0.7e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (8 * phi1 - phi2)) * ((1 - t8954) ** (0.9e1 / 0.2e1)) + + if Bindx == 1384: + t8962 = np.sin(phi) + t8959 = t8962 ** 2 + t8960 = t8962 * t8959 + t8955 = np.cos(phi) + t8956 = t8955 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.663e3) * t8960 ** 2 * (40 * t8955 - 1 + (-152 * t8955 + 18 + 95 * t8956) * t8956) + + if Bindx == 1385: + t8963 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (4 + (57 + 95 * t8963) * t8963) * ((1 + t8963) ** (0.5e1 / 0.2e1)) * np.sqrt(0.102e3) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * ((1 - t8963) ** (0.11e2 / 0.2e1)) + + if Bindx == 1386: + t8972 = np.sin(phi) + t8970 = t8972 ** 2 + t8964 = np.cos(phi) + t8965 = t8964 ** 2 + t8967 = t8965 ** 2 + t8966 = t8964 * t8965 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.51e2) * t8970 ** 2 * (-143 * t8965 + 277 * t8967 + 11 + (32 + 95 * t8966) * t8966 + (-304 * t8967 + 32) * t8964) + + if Bindx == 1387: + t8973 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (4 + (19 + 19 * t8973) * t8973) * ((1 + t8973) ** (0.3e1 / 0.2e1)) * np.sqrt(0.510e3) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * ((1 - t8973) ** (0.13e2 / 0.2e1)) + + if Bindx == 1388: + t8975 = np.cos(phi) + t8976 = t8975 ** 2 + t8977 = t8975 * t8976 + t8980 = t8977 ** 2 + t8978 = t8976 ** 2 + t8974 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.102e3) * t8974 ** 2 * (-124 * t8976 + 520 * t8977 + 772 * t8980 + 31 + (-390 + 95 * t8978) * t8978 + (-376 * t8978 - 456 * t8980 - 72) * t8975) + + if Bindx == 1389: + t8983 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (44 + (133 + 95 * t8983) * t8983) * np.sqrt((1 + t8983)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t8983) ** (0.15e2 / 0.2e1)) + + if Bindx == 1390: + t8984 = np.cos(phi) + t8985 = t8984 ** 2 + t8987 = t8985 ** 2 + t8991 = t8987 ** 2 + t8986 = t8984 * t8985 + t8989 = t8986 ** 2 + t8988 = t8984 * t8987 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-8*1j) * (phi1 - phi2)) * (531 * t8985 + 192 * t8986 - 1722 * t8987 - 210 * t8989 + 1503 * t8991 + 59 + (2016 + 95 * t8988) * t8988 + (-1536 * t8989 - 608 * t8991 - 320) * t8984) + + if Bindx == 1391: + t8994 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (4 + 5 * t8994) * np.sqrt((1 + t8994)) * np.sqrt(0.38e2) * np.exp((-1*1j) * (8 * phi1 - 9 * phi2)) * ((1 - t8994) ** (0.17e2 / 0.2e1)) + + if Bindx == 1392: + t8995 = np.cos(phi) + t9000 = -1 + t8995 + t8996 = t9000 ** 2 + t8997 = t8996 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.190e3) * t9000 * t8997 ** 2 * (1 + t8995) + + if Bindx == 1393: + t9001 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (7 * phi1 + 10 * phi2)) * np.sqrt(0.285e3) * ((1 - t9001) ** (0.3e1 / 0.2e1)) * ((1 + t9001) ** (0.17e2 / 0.2e1)) + + if Bindx == 1394: + t9002 = np.cos(phi) + t9006 = 1 + t9002 + t9003 = t9006 ** 2 + t9004 = t9003 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.57e2) * (-1 + t9002) * t9004 ** 2 * (-7 + 10 * t9002) + + if Bindx == 1395: + t9007 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t9007)) * ((1 + t9007) ** (0.15e2 / 0.2e1)) * (44 + (-133 + 95 * t9007) * t9007) + + if Bindx == 1396: + t9008 = np.cos(phi) + t9009 = t9008 ** 2 + t9011 = t9009 ** 2 + t9015 = t9011 ** 2 + t9010 = t9008 * t9009 + t9013 = t9010 ** 2 + t9012 = t9008 * t9011 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-7*1j) * (phi1 + phi2)) * (966 * t9009 + 3188 * t9010 + 938 * t9011 - 6440 * t9013 + 4383 * t9015 - 161 + (-5586 + 570 * t9012) * t9012 + (196 * t9013 + 2793 * t9015 - 335) * t9008) + + if Bindx == 1397: + t9018 = np.cos(phi) + t9019 = t9018 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.17e2) * ((1 + t9018) ** (0.13e2 / 0.2e1)) * (-322 * t9018 + 43 + (-798 * t9018 + 792 + 285 * t9019) * t9019) * ((1 - t9018) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1398: + t9023 = np.cos(phi) + t9024 = t9023 ** 2 + t9025 = t9023 * t9024 + t9028 = t9025 ** 2 + t9026 = t9024 ** 2 + t9022 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t9022 ** 2 * (119 * t9024 - 91 * t9025 + 357 * t9028 - 7 + (-455 + 114 * t9026) * t9026 + (-217 * t9026 + 399 * t9028 + 37) * t9023) + + if Bindx == 1399: + t9031 = np.cos(phi) + t9032 = t9031 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-342 * t9032 - 2 + (285 * t9032 + 99) * t9031) * ((1 + t9031) ** (0.11e2 / 0.2e1)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t9031) ** (0.3e1 / 0.2e1)) + + if Bindx == 1400: + t9042 = np.sin(phi) + t9040 = t9042 ** 2 + t9034 = np.cos(phi) + t9035 = t9034 ** 2 + t9037 = t9035 ** 2 + t9036 = t9034 * t9035 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.17e2) * t9040 ** 2 * (-264 * t9035 + 243 * t9037 + 11 + (-742 + 570 * t9036) * t9036 + (1197 * t9037 + 105) * t9034) + + if Bindx == 1401: + t9043 = np.cos(phi) + t9044 = t9043 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (-171 * t9044 + 7 + (285 * t9044 - 9) * t9043) * ((1 + t9043) ** (0.9e1 / 0.2e1)) * np.sqrt(0.442e3) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t9043) ** (0.5e1 / 0.2e1)) + + if Bindx == 1402: + t9053 = np.sin(phi) + t9050 = t9053 ** 2 + t9051 = t9053 * t9050 + t9046 = np.cos(phi) + t9047 = t9046 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.1326e4) * t9051 ** 2 * (-21 * t9046 + 3 + (133 * t9046 - 81 + 190 * t9047) * t9047) + + if Bindx == 1403: + t9054 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (19 * t9054 ** 2 - 3) * t9054 * ((1 + t9054) ** (0.7e1 / 0.2e1)) * np.sqrt(0.36465e5) * np.exp((-7*1j) * phi1) * ((1 - t9054) ** (0.7e1 / 0.2e1)) + + if Bindx == 1404: + t9062 = np.sin(phi) + t9059 = t9062 ** 2 + t9060 = t9062 * t9059 + t9055 = np.cos(phi) + t9056 = t9055 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.1326e4) * t9060 ** 2 * (21 * t9055 + 3 + (-133 * t9055 - 81 + 190 * t9056) * t9056) + + if Bindx == 1405: + t9063 = np.cos(phi) + t9064 = t9063 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (171 * t9064 - 7 + (285 * t9064 - 9) * t9063) * ((1 + t9063) ** (0.5e1 / 0.2e1)) * np.sqrt(0.442e3) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t9063) ** (0.9e1 / 0.2e1)) + + if Bindx == 1406: + t9074 = np.sin(phi) + t9072 = t9074 ** 2 + t9066 = np.cos(phi) + t9067 = t9066 ** 2 + t9069 = t9067 ** 2 + t9068 = t9066 * t9067 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.17e2) * t9072 ** 2 * (-264 * t9067 + 243 * t9069 + 11 + (742 + 570 * t9068) * t9068 + (-1197 * t9069 - 105) * t9066) + + if Bindx == 1407: + t9075 = np.cos(phi) + t9076 = t9075 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (342 * t9076 + 2 + (285 * t9076 + 99) * t9075) * ((1 + t9075) ** (0.3e1 / 0.2e1)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t9075) ** (0.11e2 / 0.2e1)) + + if Bindx == 1408: + t9079 = np.cos(phi) + t9080 = t9079 ** 2 + t9081 = t9079 * t9080 + t9084 = t9081 ** 2 + t9082 = t9080 ** 2 + t9078 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t9078 ** 2 * (119 * t9080 + 91 * t9081 + 357 * t9084 - 7 + (-455 + 114 * t9082) * t9082 + (217 * t9082 - 399 * t9084 - 37) * t9079) + + if Bindx == 1409: + t9087 = np.cos(phi) + t9088 = t9087 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (513 * t9088 + 43 + (285 * t9088 + 279) * t9087) * np.sqrt((1 + t9087)) * np.sqrt(0.17e2) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t9087) ** (0.13e2 / 0.2e1)) + + if Bindx == 1410: + t9090 = np.cos(phi) + t9091 = t9090 ** 2 + t9093 = t9091 ** 2 + t9097 = t9093 ** 2 + t9092 = t9090 * t9091 + t9095 = t9092 ** 2 + t9094 = t9090 * t9093 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-7*1j) * (phi1 - phi2)) * (966 * t9091 - 3188 * t9092 + 938 * t9093 - 6440 * t9095 + 4383 * t9097 - 161 + (5586 + 570 * t9094) * t9094 + (-196 * t9095 - 2793 * t9097 + 335) * t9090) + + if Bindx == 1411: + t9100 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (44 + (133 + 95 * t9100) * t9100) * np.sqrt((1 + t9100)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t9100) ** (0.15e2 / 0.2e1)) + + if Bindx == 1412: + t9101 = np.cos(phi) + t9105 = -1 + t9101 + t9102 = t9105 ** 2 + t9103 = t9102 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.57e2) * (1 + t9101) * t9103 ** 2 * (7 + 10 * t9101) + + if Bindx == 1413: + t9106 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (7 * phi1 - 10 * phi2)) * np.sqrt(0.285e3) * ((1 - t9106) ** (0.17e2 / 0.2e1)) * ((1 + t9106) ** (0.3e1 / 0.2e1)) + + if Bindx == 1414: + t9108 = np.cos(phi) + t9112 = 1 + t9108 + t9109 = t9112 ** 2 + t9110 = t9109 ** 2 + t9107 = -1 + t9108 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.4845e4) * t9107 ** 2 * t9110 ** 2 + + if Bindx == 1415: + t9113 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.969e3) * ((1 - t9113) ** (0.3e1 / 0.2e1)) * ((1 + t9113) ** (0.15e2 / 0.2e1)) * (-3 + 5 * t9113) + + if Bindx == 1416: + t9115 = np.cos(phi) + t9116 = t9115 ** 2 + t9117 = t9115 * t9116 + t9120 = t9117 ** 2 + t9118 = t9116 ** 2 + t9114 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.102e3) * t9114 ** 2 * (-124 * t9116 - 520 * t9117 + 772 * t9120 + 31 + (-390 + 95 * t9118) * t9118 + (376 * t9118 + 456 * t9120 + 72) * t9115) + + if Bindx == 1417: + t9123 = np.cos(phi) + t9124 = t9123 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * np.sqrt((1 - t9123)) * ((1 + t9123) ** (0.13e2 / 0.2e1)) * (-513 * t9124 - 43 + (285 * t9124 + 279) * t9123) + + if Bindx == 1418: + t9126 = np.cos(phi) + t9127 = t9126 ** 2 + t9129 = t9127 ** 2 + t9133 = t9129 ** 2 + t9128 = t9126 * t9127 + t9131 = t9128 ** 2 + t9130 = t9126 * t9129 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 + phi2)) * (-4503 * t9127 + 6168 * t9128 + 22442 * t9129 - 34902 * t9131 + 12393 * t9133 + 237 + (1932 + 4845 * t9130) * t9130 + (-23528 * t9131 + 17442 * t9133 - 1502) * t9126) + + if Bindx == 1419: + t9136 = np.cos(phi) + t9137 = t9136 ** 2 + t9139 = t9137 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * ((1 + t9136) ** (0.11e2 / 0.2e1)) * (-1462 * t9137 - 2907 * t9139 + 1 + (3162 * t9137 + 969 * t9139 + 237) * t9136) * ((1 - t9136) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1420: + t9142 = np.cos(phi) + t9143 = t9142 ** 2 + t9144 = t9142 * t9143 + t9147 = t9144 ** 2 + t9145 = t9143 ** 2 + t9141 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2e1) * t9141 ** 2 * (2324 * t9143 + 4564 * t9144 + 1428 * t9147 - 83 + (-6594 + 4845 * t9145) * t9145 + (-13804 * t9145 + 11628 * t9147 - 468) * t9142) + + if Bindx == 1421: + t9150 = np.cos(phi) + t9151 = t9150 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (374 * t9150 - 69 + (-5814 * t9150 + 1224 + 4845 * t9151) * t9151) * ((1 + t9150) ** (0.9e1 / 0.2e1)) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t9150) ** (0.3e1 / 0.2e1)) + + if Bindx == 1422: + t9162 = np.sin(phi) + t9160 = t9162 ** 2 + t9154 = np.cos(phi) + t9155 = t9154 ** 2 + t9157 = t9155 ** 2 + t9156 = t9154 * t9155 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.26e2) * t9160 ** 2 * (627 * t9155 - 3213 * t9157 - 19 + (-4012 + 4845 * t9156) * t9156 + (5814 * t9157 + 438) * t9154) + + if Bindx == 1423: + t9163 = np.cos(phi) + t9164 = t9163 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (102 * t9163 + 9 + (-646 * t9163 - 408 + 1615 * t9164) * t9164) * ((1 + t9163) ** (0.7e1 / 0.2e1)) * np.sqrt(0.78e2) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t9163) ** (0.5e1 / 0.2e1)) + + if Bindx == 1424: + t9173 = np.sin(phi) + t9170 = t9173 ** 2 + t9171 = t9173 * t9170 + t9167 = np.cos(phi) + t9168 = t9167 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-6*1j) * phi1) * np.sqrt(0.2145e4) * t9171 ** 2 * (3 + (-102 + 323 * t9168) * t9168) + + if Bindx == 1425: + t9174 = np.cos(phi) + t9175 = t9174 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-102 * t9174 + 9 + (646 * t9174 - 408 + 1615 * t9175) * t9175) * ((1 + t9174) ** (0.5e1 / 0.2e1)) * np.sqrt(0.78e2) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t9174) ** (0.7e1 / 0.2e1)) + + if Bindx == 1426: + t9186 = np.sin(phi) + t9184 = t9186 ** 2 + t9178 = np.cos(phi) + t9179 = t9178 ** 2 + t9181 = t9179 ** 2 + t9180 = t9178 * t9179 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.26e2) * t9184 ** 2 * (627 * t9179 - 3213 * t9181 - 19 + (4012 + 4845 * t9180) * t9180 + (-5814 * t9181 - 438) * t9178) + + if Bindx == 1427: + t9187 = np.cos(phi) + t9188 = t9187 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-374 * t9187 - 69 + (5814 * t9187 + 1224 + 4845 * t9188) * t9188) * ((1 + t9187) ** (0.3e1 / 0.2e1)) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t9187) ** (0.9e1 / 0.2e1)) + + if Bindx == 1428: + t9192 = np.cos(phi) + t9193 = t9192 ** 2 + t9194 = t9192 * t9193 + t9197 = t9194 ** 2 + t9195 = t9193 ** 2 + t9191 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.2e1) * t9191 ** 2 * (2324 * t9193 - 4564 * t9194 + 1428 * t9197 - 83 + (-6594 + 4845 * t9195) * t9195 + (13804 * t9195 - 11628 * t9197 + 468) * t9192) + + if Bindx == 1429: + t9200 = np.cos(phi) + t9201 = t9200 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (238 * t9200 - 1 + (1938 * t9200 + 1224 + 969 * t9201) * t9201) * np.sqrt((1 + t9200)) * np.sqrt(0.5e1) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t9200) ** (0.11e2 / 0.2e1)) + + if Bindx == 1430: + t9204 = np.cos(phi) + t9205 = t9204 ** 2 + t9207 = t9205 ** 2 + t9211 = t9207 ** 2 + t9206 = t9204 * t9205 + t9209 = t9206 ** 2 + t9208 = t9204 * t9207 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 - phi2)) * (-4503 * t9205 - 6168 * t9206 + 22442 * t9207 - 34902 * t9209 + 12393 * t9211 + 237 + (-1932 + 4845 * t9208) * t9208 + (23528 * t9209 - 17442 * t9211 + 1502) * t9204) + + if Bindx == 1431: + t9214 = np.cos(phi) + t9215 = t9214 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (513 * t9215 + 43 + (285 * t9215 + 279) * t9214) * np.sqrt((1 + t9214)) * np.sqrt(0.17e2) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t9214) ** (0.13e2 / 0.2e1)) + + if Bindx == 1432: + t9218 = np.cos(phi) + t9219 = t9218 ** 2 + t9220 = t9218 * t9219 + t9223 = t9220 ** 2 + t9221 = t9219 ** 2 + t9217 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.102e3) * t9217 ** 2 * (-124 * t9219 + 520 * t9220 + 772 * t9223 + 31 + (-390 + 95 * t9221) * t9221 + (-376 * t9221 - 456 * t9223 - 72) * t9218) + + if Bindx == 1433: + t9226 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (3 + 5 * t9226) * ((1 + t9226) ** (0.3e1 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-3*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t9226) ** (0.15e2 / 0.2e1)) + + if Bindx == 1434: + t9228 = np.cos(phi) + t9232 = -1 + t9228 + t9229 = t9232 ** 2 + t9230 = t9229 ** 2 + t9227 = 1 + t9228 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.4845e4) * t9230 ** 2 * t9227 ** 2 + + if Bindx == 1435: + t9233 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-5*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t9233) ** (0.5e1 / 0.2e1)) * ((1 + t9233) ** (0.15e2 / 0.2e1)) + + if Bindx == 1436: + t9235 = np.cos(phi) + t9240 = 1 + t9235 + t9236 = t9240 ** 2 + t9237 = t9240 * t9236 + t9234 = -1 + t9235 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.4845e4) * t9234 ** 2 * t9240 * t9237 ** 2 * (2 * t9235 - 1) + + if Bindx == 1437: + t9241 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.510e3) * ((1 - t9241) ** (0.3e1 / 0.2e1)) * ((1 + t9241) ** (0.13e2 / 0.2e1)) * (4 + (-19 + 19 * t9241) * t9241) + + if Bindx == 1438: + t9243 = np.cos(phi) + t9244 = t9243 ** 2 + t9245 = t9243 * t9244 + t9248 = t9245 ** 2 + t9246 = t9244 ** 2 + t9242 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.85e2) * t9242 ** 2 * (119 * t9244 - 91 * t9245 + 357 * t9248 - 7 + (-455 + 114 * t9246) * t9246 + (-217 * t9246 + 399 * t9248 + 37) * t9243) + + if Bindx == 1439: + t9251 = np.cos(phi) + t9252 = t9251 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t9251)) * ((1 + t9251) ** (0.11e2 / 0.2e1)) * (-238 * t9251 - 1 + (-1938 * t9251 + 1224 + 969 * t9252) * t9252) + + if Bindx == 1440: + t9255 = np.cos(phi) + t9256 = t9255 ** 2 + t9258 = t9256 ** 2 + t9262 = t9258 ** 2 + t9257 = t9255 * t9256 + t9260 = t9257 ** 2 + t9259 = t9255 * t9258 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-5*1j) * (phi1 + phi2)) * (-930 * t9256 - 1660 * t9257 + 3710 * t9258 - 3920 * t9260 - 765 * t9262 + 31 + (6594 + 1938 * t9259) * t9259 + (-9860 * t9260 + 4845 * t9262 + 145) * t9255) + + if Bindx == 1441: + t9265 = np.cos(phi) + t9266 = t9265 ** 2 + t9268 = t9266 ** 2 + t9267 = t9265 * t9266 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * ((1 + t9265) ** (0.9e1 / 0.2e1)) * (-15 * t9266 + 3060 * t9268 - 10 + (-1190 + 969 * t9267) * t9267 + (-2907 * t9268 + 93) * t9265) * ((1 - t9265) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1442: + t9272 = np.cos(phi) + t9273 = t9272 ** 2 + t9274 = t9272 * t9273 + t9277 = t9274 ** 2 + t9275 = t9273 ** 2 + t9271 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.5e1) * t9271 ** 2 * (-37 * t9273 + 1525 * t9274 - 2091 * t9277 + 1 + (525 + 1938 * t9275) * t9275 + (-3961 * t9275 + 2907 * t9277 - 135) * t9272) + + if Bindx == 1443: + t9280 = np.cos(phi) + t9281 = t9280 ** 2 + t9283 = t9281 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (238 * t9281 - 969 * t9283 - 5 + (-102 * t9281 + 969 * t9283 - 19) * t9280) * ((1 + t9280) ** (0.7e1 / 0.2e1)) * np.sqrt(0.130e3) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t9280) ** (0.3e1 / 0.2e1)) + + if Bindx == 1444: + t9293 = np.sin(phi) + t9291 = t9293 ** 2 + t9285 = np.cos(phi) + t9286 = t9285 ** 2 + t9288 = t9286 ** 2 + t9287 = t9285 * t9286 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.390e3) * t9291 ** 2 * (120 * t9286 - 595 * t9288 - 3 + (-170 + 646 * t9287) * t9287 + (323 * t9288 + 15) * t9285) + + if Bindx == 1445: + t9294 = np.cos(phi) + t9295 = t9294 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (15 + (-170 + 323 * t9295) * t9295) * t9294 * ((1 + t9294) ** (0.5e1 / 0.2e1)) * np.sqrt(0.429e3) * np.exp((-5*1j) * phi1) * ((1 - t9294) ** (0.5e1 / 0.2e1)) + + if Bindx == 1446: + t9305 = np.sin(phi) + t9303 = t9305 ** 2 + t9297 = np.cos(phi) + t9298 = t9297 ** 2 + t9300 = t9298 ** 2 + t9299 = t9297 * t9298 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.390e3) * t9303 ** 2 * (120 * t9298 - 595 * t9300 - 3 + (170 + 646 * t9299) * t9299 + (-323 * t9300 - 15) * t9297) + + if Bindx == 1447: + t9306 = np.cos(phi) + t9307 = t9306 ** 2 + t9311 = 969 * t9307 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-238 * t9307 + t9311 + 5 + (-102 * t9307 + t9311 - 19) * t9306) * ((1 + t9306) ** (0.3e1 / 0.2e1)) * np.sqrt(0.130e3) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t9306) ** (0.7e1 / 0.2e1)) + + if Bindx == 1448: + t9313 = np.cos(phi) + t9314 = t9313 ** 2 + t9315 = t9313 * t9314 + t9318 = t9315 ** 2 + t9316 = t9314 ** 2 + t9312 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.5e1) * t9312 ** 2 * (-37 * t9314 - 1525 * t9315 - 2091 * t9318 + 1 + (525 + 1938 * t9316) * t9316 + (3961 * t9316 - 2907 * t9318 + 135) * t9313) + + if Bindx == 1449: + t9321 = np.cos(phi) + t9322 = t9321 ** 2 + t9324 = t9322 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (68 * t9322 + 1938 * t9324 - 10 + (1122 * t9322 + 969 * t9324 - 83) * t9321) * np.sqrt((1 + t9321)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t9321) ** (0.9e1 / 0.2e1)) + + if Bindx == 1450: + t9326 = np.cos(phi) + t9327 = t9326 ** 2 + t9329 = t9327 ** 2 + t9333 = t9329 ** 2 + t9328 = t9326 * t9327 + t9331 = t9328 ** 2 + t9330 = t9326 * t9329 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-5*1j) * (phi1 - phi2)) * (-930 * t9327 + 1660 * t9328 + 3710 * t9329 - 3920 * t9331 - 765 * t9333 + 31 + (-6594 + 1938 * t9330) * t9330 + (9860 * t9331 - 4845 * t9333 - 145) * t9326) + + if Bindx == 1451: + t9336 = np.cos(phi) + t9337 = t9336 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (238 * t9336 - 1 + (1938 * t9336 + 1224 + 969 * t9337) * t9337) * np.sqrt((1 + t9336)) * np.sqrt(0.5e1) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t9336) ** (0.11e2 / 0.2e1)) + + if Bindx == 1452: + t9341 = np.cos(phi) + t9342 = t9341 ** 2 + t9343 = t9341 * t9342 + t9346 = t9343 ** 2 + t9344 = t9342 ** 2 + t9340 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.85e2) * t9340 ** 2 * (119 * t9342 + 91 * t9343 + 357 * t9346 - 7 + (-455 + 114 * t9344) * t9344 + (217 * t9344 - 399 * t9346 - 37) * t9341) + + if Bindx == 1453: + t9349 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (4 + (19 + 19 * t9349) * t9349) * ((1 + t9349) ** (0.3e1 / 0.2e1)) * np.sqrt(0.510e3) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * ((1 - t9349) ** (0.13e2 / 0.2e1)) + + if Bindx == 1454: + t9358 = np.sin(phi) + t9356 = t9358 ** 2 + t9350 = np.cos(phi) + t9351 = t9350 ** 2 + t9353 = t9351 ** 2 + t9352 = t9350 * t9351 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.4845e4) * t9356 ** 2 * (15 * t9353 - 1 + (-10 + 2 * t9352) * t9352 + (-9 * t9353 + 3) * t9350) + + if Bindx == 1455: + t9359 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-5*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t9359) ** (0.15e2 / 0.2e1)) * ((1 + t9359) ** (0.5e1 / 0.2e1)) + + if Bindx == 1456: + t9360 = np.cos(phi) + t9368 = -1 + t9360 + t9367 = 1 + t9360 + t9363 = t9367 ** 2 + t9364 = t9367 * t9363 + t9361 = t9368 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.9690e4) * t9368 * t9361 * t9367 * t9364 ** 2 + + if Bindx == 1457: + t9369 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.1938e4) * ((1 - t9369) ** (0.5e1 / 0.2e1)) * ((1 + t9369) ** (0.13e2 / 0.2e1)) * (-2 + 5 * t9369) + + if Bindx == 1458: + t9378 = np.sin(phi) + t9376 = t9378 ** 2 + t9370 = np.cos(phi) + t9371 = t9370 ** 2 + t9373 = t9371 ** 2 + t9372 = t9370 * t9371 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.51e2) * t9376 ** 2 * (-143 * t9371 + 277 * t9373 + 11 + (-32 + 95 * t9372) * t9372 + (304 * t9373 - 32) * t9370) + + if Bindx == 1459: + t9379 = np.cos(phi) + t9380 = t9379 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.34e2) * ((1 - t9379) ** (0.3e1 / 0.2e1)) * ((1 + t9379) ** (0.11e2 / 0.2e1)) * (-342 * t9380 - 2 + (285 * t9380 + 99) * t9379) + + if Bindx == 1460: + t9383 = np.cos(phi) + t9384 = t9383 ** 2 + t9385 = t9383 * t9384 + t9388 = t9385 ** 2 + t9386 = t9384 ** 2 + t9382 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * t9382 ** 2 * (2324 * t9384 + 4564 * t9385 + 1428 * t9388 - 83 + (-6594 + 4845 * t9386) * t9386 + (-13804 * t9386 + 11628 * t9388 - 468) * t9383) + + if Bindx == 1461: + t9391 = np.cos(phi) + t9392 = t9391 ** 2 + t9394 = t9392 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t9391)) * ((1 + t9391) ** (0.9e1 / 0.2e1)) * (-68 * t9392 - 1938 * t9394 + 10 + (1122 * t9392 + 969 * t9394 - 83) * t9391) + + if Bindx == 1462: + t9396 = np.cos(phi) + t9397 = t9396 ** 2 + t9399 = t9397 ** 2 + t9403 = t9399 ** 2 + t9398 = t9396 * t9397 + t9401 = t9398 ** 2 + t9400 = t9396 * t9399 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-4*1j) * (phi1 + phi2)) * (-39 * t9397 - 3856 * t9398 - 574 * t9399 + 4858 * t9401 - 9027 * t9403 + 1 + (13272 + 4845 * t9400) * t9400 + (-17408 * t9401 + 7752 * t9403 + 304) * t9396) + + if Bindx == 1463: + t9406 = np.cos(phi) + t9407 = t9406 ** 2 + t9408 = t9406 * t9407 + t9411 = t9408 ** 2 + t9409 = t9407 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * ((1 + t9406) ** (0.7e1 / 0.2e1)) * (1050 * t9407 - 2905 * t9408 - 1190 * t9409 - 13566 * t9411 - 22 + (11781 * t9409 + 4845 * t9411 + 7) * t9406) * ((1 - t9406) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1464: + t9414 = np.cos(phi) + t9415 = t9414 ** 2 + t9416 = t9414 * t9415 + t9419 = t9416 ** 2 + t9417 = t9415 ** 2 + t9413 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.13e2) * t9413 ** 2 * (-572 * t9415 + 1660 * t9416 - 7548 * t9419 + 13 + (3710 + 4845 * t9417) * t9417 + (-4964 * t9417 + 3876 * t9419 - 124) * t9414) + + if Bindx == 1465: + t9422 = np.cos(phi) + t9423 = t9422 ** 2 + t9425 = t9423 ** 2 + t9424 = t9422 * t9423 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (135 * t9423 - 1020 * t9425 - 2 + (510 + 1615 * t9424) * t9424 + (-969 * t9425 - 45) * t9422) * ((1 + t9422) ** (0.5e1 / 0.2e1)) * np.sqrt(0.39e2) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t9422) ** (0.3e1 / 0.2e1)) + + if Bindx == 1466: + t9434 = np.sin(phi) + t9432 = t9434 ** 2 + t9428 = np.cos(phi) + t9429 = t9428 ** 2 + t9430 = t9429 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.4290e4) * t9432 ** 2 * (-255 * t9430 - 1 + (323 * t9430 + 45) * t9429) + + if Bindx == 1467: + t9435 = np.cos(phi) + t9436 = t9435 ** 2 + t9438 = t9436 ** 2 + t9437 = t9435 * t9436 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (135 * t9436 - 1020 * t9438 - 2 + (-510 + 1615 * t9437) * t9437 + (969 * t9438 + 45) * t9435) * ((1 + t9435) ** (0.3e1 / 0.2e1)) * np.sqrt(0.39e2) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t9435) ** (0.5e1 / 0.2e1)) + + if Bindx == 1468: + t9442 = np.cos(phi) + t9443 = t9442 ** 2 + t9444 = t9442 * t9443 + t9447 = t9444 ** 2 + t9445 = t9443 ** 2 + t9441 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.13e2) * t9441 ** 2 * (-572 * t9443 - 1660 * t9444 - 7548 * t9447 + 13 + (3710 + 4845 * t9445) * t9445 + (4964 * t9445 - 3876 * t9447 + 124) * t9442) + + if Bindx == 1469: + t9450 = np.cos(phi) + t9451 = t9450 ** 2 + t9453 = t9451 ** 2 + t9452 = t9450 * t9451 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (-1035 * t9451 + 3060 * t9453 + 22 + (-1870 + 4845 * t9452) * t9452 + (8721 * t9453 - 15) * t9450) * np.sqrt((1 + t9450)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t9450) ** (0.7e1 / 0.2e1)) + + if Bindx == 1470: + t9456 = np.cos(phi) + t9457 = t9456 ** 2 + t9459 = t9457 ** 2 + t9463 = t9459 ** 2 + t9458 = t9456 * t9457 + t9461 = t9458 ** 2 + t9460 = t9456 * t9459 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-4*1j) * (phi1 - phi2)) * (-39 * t9457 + 3856 * t9458 - 574 * t9459 + 4858 * t9461 - 9027 * t9463 + 1 + (-13272 + 4845 * t9460) * t9460 + (17408 * t9461 - 7752 * t9463 - 304) * t9456) + + if Bindx == 1471: + t9466 = np.cos(phi) + t9467 = t9466 ** 2 + t9469 = t9467 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (68 * t9467 + 1938 * t9469 - 10 + (1122 * t9467 + 969 * t9469 - 83) * t9466) * np.sqrt((1 + t9466)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t9466) ** (0.9e1 / 0.2e1)) + + if Bindx == 1472: + t9472 = np.cos(phi) + t9473 = t9472 ** 2 + t9474 = t9472 * t9473 + t9477 = t9474 ** 2 + t9475 = t9473 ** 2 + t9471 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * t9471 ** 2 * (2324 * t9473 - 4564 * t9474 + 1428 * t9477 - 83 + (-6594 + 4845 * t9475) * t9475 + (13804 * t9475 - 11628 * t9477 + 468) * t9472) + + if Bindx == 1473: + t9480 = np.cos(phi) + t9481 = t9480 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (342 * t9481 + 2 + (285 * t9481 + 99) * t9480) * ((1 + t9480) ** (0.3e1 / 0.2e1)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t9480) ** (0.11e2 / 0.2e1)) + + if Bindx == 1474: + t9491 = np.sin(phi) + t9489 = t9491 ** 2 + t9483 = np.cos(phi) + t9484 = t9483 ** 2 + t9486 = t9484 ** 2 + t9485 = t9483 * t9484 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.51e2) * t9489 ** 2 * (-143 * t9484 + 277 * t9486 + 11 + (32 + 95 * t9485) * t9485 + (-304 * t9486 + 32) * t9483) + + if Bindx == 1475: + t9492 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (2 + 5 * t9492) * ((1 + t9492) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1938e4) * np.exp((-1*1j) * (4 * phi1 - 9 * phi2)) * ((1 - t9492) ** (0.13e2 / 0.2e1)) + + if Bindx == 1476: + t9493 = np.cos(phi) + t9501 = -1 + t9493 + t9500 = 1 + t9493 + t9498 = t9500 ** 2 + t9494 = t9501 ** 2 + t9495 = t9501 * t9494 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.9690e4) * t9501 * t9495 ** 2 * t9500 * t9498 + + if Bindx == 1477: + t9502 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (3 * phi1 + 10 * phi2)) * np.sqrt(0.4845e4) * ((1 - t9502) ** (0.7e1 / 0.2e1)) * ((1 + t9502) ** (0.13e2 / 0.2e1)) + + if Bindx == 1478: + t9503 = np.cos(phi) + t9510 = -1 + t9503 + t9509 = 1 + t9503 + t9506 = t9509 ** 2 + t9507 = t9509 * t9506 + t9504 = t9510 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.969e3) * t9510 * t9504 * t9507 ** 2 * (-3 + 10 * t9503) + + if Bindx == 1479: + t9511 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.102e3) * ((1 - t9511) ** (0.5e1 / 0.2e1)) * ((1 + t9511) ** (0.11e2 / 0.2e1)) * (4 + (-57 + 95 * t9511) * t9511) + + if Bindx == 1480: + t9520 = np.sin(phi) + t9518 = t9520 ** 2 + t9512 = np.cos(phi) + t9513 = t9512 ** 2 + t9515 = t9513 ** 2 + t9514 = t9512 * t9513 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * t9518 ** 2 * (-264 * t9513 + 243 * t9515 + 11 + (-742 + 570 * t9514) * t9514 + (1197 * t9515 + 105) * t9512) + + if Bindx == 1481: + t9521 = np.cos(phi) + t9522 = t9521 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * ((1 - t9521) ** (0.3e1 / 0.2e1)) * ((1 + t9521) ** (0.9e1 / 0.2e1)) * (374 * t9521 - 69 + (-5814 * t9521 + 1224 + 4845 * t9522) * t9522) + + if Bindx == 1482: + t9526 = np.cos(phi) + t9527 = t9526 ** 2 + t9528 = t9526 * t9527 + t9531 = t9528 ** 2 + t9529 = t9527 ** 2 + t9525 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * t9525 ** 2 * (-37 * t9527 + 1525 * t9528 - 2091 * t9531 + 1 + (525 + 1938 * t9529) * t9529 + (-3961 * t9529 + 2907 * t9531 - 135) * t9526) + + if Bindx == 1483: + t9534 = np.cos(phi) + t9535 = t9534 ** 2 + t9537 = t9535 ** 2 + t9536 = t9534 * t9535 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t9534)) * ((1 + t9534) ** (0.7e1 / 0.2e1)) * (-1035 * t9535 + 3060 * t9537 + 22 + (1870 + 4845 * t9536) * t9536 + (-8721 * t9537 + 15) * t9534) + + if Bindx == 1484: + t9540 = np.cos(phi) + t9541 = t9540 ** 2 + t9543 = t9541 ** 2 + t9547 = t9543 ** 2 + t9542 = t9540 * t9541 + t9545 = t9542 ** 2 + t9544 = t9540 * t9543 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-3*1j) * (phi1 + phi2)) * (966 * t9541 - 3612 * t9542 - 7322 * t9543 + 20160 * t9545 - 23409 * t9547 - 21 + (13818 + 9690 * t9544) * t9544 + (-19108 * t9545 + 8721 * t9547 + 245) * t9540) + + if Bindx == 1485: + t9550 = np.cos(phi) + t9551 = t9550 ** 2 + t9552 = t9550 * t9551 + t9555 = t9552 ** 2 + t9553 = t9551 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.26e2) * ((1 + t9550) ** (0.5e1 / 0.2e1)) * (616 * t9551 + 140 * t9552 + 5712 * t9555 - 7 + (-4830 + 4845 * t9553) * t9553 + (5236 * t9553 - 11628 * t9555 - 84) * t9550) * ((1 - t9550) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1486: + t9559 = np.cos(phi) + t9560 = t9559 ** 2 + t9561 = t9559 * t9560 + t9564 = t9561 ** 2 + t9562 = t9560 ** 2 + t9558 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.78e2) * t9558 ** 2 * (-343 * t9560 + 315 * t9561 - 5117 * t9564 + 7 + (2415 + 3230 * t9562) * t9562 + (-1071 * t9562 + 969 * t9564 - 21) * t9559) + + if Bindx == 1487: + t9567 = np.cos(phi) + t9568 = t9567 ** 2 + t9569 = t9568 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (-357 * t9569 - 7 + (323 * t9569 + 105) * t9568) * t9567 * ((1 + t9567) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2145e4) * np.exp((-3*1j) * phi1) * ((1 - t9567) ** (0.3e1 / 0.2e1)) + + if Bindx == 1488: + t9572 = np.cos(phi) + t9573 = t9572 ** 2 + t9574 = t9572 * t9573 + t9577 = t9574 ** 2 + t9575 = t9573 ** 2 + t9571 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.78e2) * t9571 ** 2 * (-343 * t9573 - 315 * t9574 - 5117 * t9577 + 7 + (2415 + 3230 * t9575) * t9575 + (1071 * t9575 - 969 * t9577 + 21) * t9572) + + if Bindx == 1489: + t9580 = np.cos(phi) + t9581 = t9580 ** 2 + t9582 = t9580 * t9581 + t9585 = t9582 ** 2 + t9583 = t9581 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (525 * t9581 - 665 * t9582 - 4165 * t9583 + 6783 * t9585 - 7 + (-1071 * t9583 + 4845 * t9585 + 91) * t9580) * np.sqrt((1 + t9580)) * np.sqrt(0.26e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t9580) ** (0.5e1 / 0.2e1)) + + if Bindx == 1490: + t9587 = np.cos(phi) + t9588 = t9587 ** 2 + t9590 = t9588 ** 2 + t9594 = t9590 ** 2 + t9589 = t9587 * t9588 + t9592 = t9589 ** 2 + t9591 = t9587 * t9590 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((-3*1j) * (phi1 - phi2)) * (966 * t9588 + 3612 * t9589 - 7322 * t9590 + 20160 * t9592 - 23409 * t9594 - 21 + (-13818 + 9690 * t9591) * t9591 + (19108 * t9592 - 8721 * t9594 - 245) * t9587) + + if Bindx == 1491: + t9597 = np.cos(phi) + t9598 = t9597 ** 2 + t9600 = t9598 ** 2 + t9599 = t9597 * t9598 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (-1035 * t9598 + 3060 * t9600 + 22 + (-1870 + 4845 * t9599) * t9599 + (8721 * t9600 - 15) * t9597) * np.sqrt((1 + t9597)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t9597) ** (0.7e1 / 0.2e1)) + + if Bindx == 1492: + t9604 = np.cos(phi) + t9605 = t9604 ** 2 + t9606 = t9604 * t9605 + t9609 = t9606 ** 2 + t9607 = t9605 ** 2 + t9603 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t9603 ** 2 * (-37 * t9605 - 1525 * t9606 - 2091 * t9609 + 1 + (525 + 1938 * t9607) * t9607 + (3961 * t9607 - 2907 * t9609 + 135) * t9604) + + if Bindx == 1493: + t9612 = np.cos(phi) + t9613 = t9612 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-374 * t9612 - 69 + (5814 * t9612 + 1224 + 4845 * t9613) * t9613) * ((1 + t9612) ** (0.3e1 / 0.2e1)) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t9612) ** (0.9e1 / 0.2e1)) + + if Bindx == 1494: + t9624 = np.sin(phi) + t9622 = t9624 ** 2 + t9616 = np.cos(phi) + t9617 = t9616 ** 2 + t9619 = t9617 ** 2 + t9618 = t9616 * t9617 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * t9622 ** 2 * (-264 * t9617 + 243 * t9619 + 11 + (742 + 570 * t9618) * t9618 + (-1197 * t9619 - 105) * t9616) + + if Bindx == 1495: + t9625 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (4 + (57 + 95 * t9625) * t9625) * ((1 + t9625) ** (0.5e1 / 0.2e1)) * np.sqrt(0.102e3) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * ((1 - t9625) ** (0.11e2 / 0.2e1)) + + if Bindx == 1496: + t9633 = np.sin(phi) + t9630 = t9633 ** 2 + t9631 = t9633 * t9630 + t9626 = np.cos(phi) + t9627 = t9626 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.969e3) * t9631 ** 2 * (-t9626 - 3 + (-27 * t9626 + 21 + 10 * t9627) * t9627) + + if Bindx == 1497: + t9634 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (3 * phi1 - 10 * phi2)) * np.sqrt(0.4845e4) * ((1 - t9634) ** (0.13e2 / 0.2e1)) * ((1 + t9634) ** (0.7e1 / 0.2e1)) + + if Bindx == 1498: + t9635 = np.cos(phi) + t9642 = -1 + t9635 + t9641 = 1 + t9635 + t9638 = t9641 ** 2 + t9639 = t9641 * t9638 + t9636 = t9642 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.125970e6) * t9636 ** 2 * t9639 ** 2 + + if Bindx == 1499: + t9643 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.25194e5) * ((1 - t9643) ** (0.7e1 / 0.2e1)) * ((1 + t9643) ** (0.11e2 / 0.2e1)) * (-1 + 5 * t9643) + + if Bindx == 1500: + t9651 = np.sin(phi) + t9648 = t9651 ** 2 + t9649 = t9651 * t9648 + t9644 = np.cos(phi) + t9645 = t9644 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.663e3) * t9649 ** 2 * (-40 * t9644 - 1 + (152 * t9644 + 18 + 95 * t9645) * t9645) + + if Bindx == 1501: + t9652 = np.cos(phi) + t9653 = t9652 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.442e3) * ((1 - t9652) ** (0.5e1 / 0.2e1)) * ((1 + t9652) ** (0.9e1 / 0.2e1)) * (-171 * t9653 + 7 + (285 * t9653 - 9) * t9652) + + if Bindx == 1502: + t9663 = np.sin(phi) + t9661 = t9663 ** 2 + t9655 = np.cos(phi) + t9656 = t9655 ** 2 + t9658 = t9656 ** 2 + t9657 = t9655 * t9656 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.26e2) * t9661 ** 2 * (627 * t9656 - 3213 * t9658 - 19 + (-4012 + 4845 * t9657) * t9657 + (5814 * t9658 + 438) * t9655) + + if Bindx == 1503: + t9664 = np.cos(phi) + t9665 = t9664 ** 2 + t9667 = t9665 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.130e3) * ((1 - t9664) ** (0.3e1 / 0.2e1)) * ((1 + t9664) ** (0.7e1 / 0.2e1)) * (238 * t9665 - 969 * t9667 - 5 + (-102 * t9665 + 969 * t9667 - 19) * t9664) + + if Bindx == 1504: + t9670 = np.cos(phi) + t9671 = t9670 ** 2 + t9672 = t9670 * t9671 + t9675 = t9672 ** 2 + t9673 = t9671 ** 2 + t9669 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.13e2) * t9669 ** 2 * (-572 * t9671 + 1660 * t9672 - 7548 * t9675 + 13 + (3710 + 4845 * t9673) * t9673 + (-4964 * t9673 + 3876 * t9675 - 124) * t9670) + + if Bindx == 1505: + t9678 = np.cos(phi) + t9679 = t9678 ** 2 + t9680 = t9678 * t9679 + t9683 = t9680 ** 2 + t9681 = t9679 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t9678)) * ((1 + t9678) ** (0.5e1 / 0.2e1)) * (-525 * t9679 - 665 * t9680 + 4165 * t9681 - 6783 * t9683 + 7 + (-1071 * t9681 + 4845 * t9683 + 91) * t9678) + + if Bindx == 1506: + t9685 = np.cos(phi) + t9686 = t9685 ** 2 + t9688 = t9686 ** 2 + t9692 = t9688 ** 2 + t9687 = t9685 * t9686 + t9690 = t9687 ** 2 + t9689 = t9685 * t9688 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + phi2)) * (6069 * t9686 - 8008 * t9687 - 48958 * t9688 + 137410 * t9690 - 157131 * t9692 - 119 + (33852 + 62985 * t9689) * t9689 + (-51272 * t9690 + 25194 * t9692 + 490) * t9685) + + if Bindx == 1507: + t9695 = np.cos(phi) + t9696 = t9695 ** 2 + t9698 = t9696 ** 2 + t9702 = t9698 ** 2 + t9697 = t9695 * t9696 + t9700 = t9697 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.3e1) * ((1 + t9695) ** (0.3e1 / 0.2e1)) * (1092 * t9696 + 4732 * t9697 - 13650 * t9698 + 43316 * t9700 - 37791 * t9702 - 7 + (-10374 * t9698 - 7956 * t9700 + 20995 * t9702 - 357) * t9695) * ((1 - t9695) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1508: + t9705 = np.cos(phi) + t9706 = t9705 ** 2 + t9707 = t9706 ** 2 + t9704 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * phi1) * np.sqrt(0.330e3) * t9704 ** 2 * (-364 * t9706 + 7 + (-6188 * t9706 + 2730 + 4199 * t9707) * t9707) + + if Bindx == 1509: + t9710 = np.cos(phi) + t9711 = t9710 ** 2 + t9712 = t9710 * t9711 + t9715 = t9712 ** 2 + t9713 = t9711 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-728 * t9711 + 5460 * t9712 - 24752 * t9715 + 7 + (8190 + 20995 * t9713) * t9713 + (-18564 * t9713 + 16796 * t9715 - 364) * t9710) * np.sqrt((1 + t9710)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t9710) ** (0.3e1 / 0.2e1)) + + if Bindx == 1510: + t9718 = np.cos(phi) + t9719 = t9718 ** 2 + t9721 = t9719 ** 2 + t9725 = t9721 ** 2 + t9720 = t9718 * t9719 + t9723 = t9720 ** 2 + t9722 = t9718 * t9721 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - phi2)) * (6069 * t9719 + 8008 * t9720 - 48958 * t9721 + 137410 * t9723 - 157131 * t9725 - 119 + (-33852 + 62985 * t9722) * t9722 + (51272 * t9723 - 25194 * t9725 - 490) * t9718) + + if Bindx == 1511: + t9728 = np.cos(phi) + t9729 = t9728 ** 2 + t9730 = t9728 * t9729 + t9733 = t9730 ** 2 + t9731 = t9729 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (525 * t9729 - 665 * t9730 - 4165 * t9731 + 6783 * t9733 - 7 + (-1071 * t9731 + 4845 * t9733 + 91) * t9728) * np.sqrt((1 + t9728)) * np.sqrt(0.26e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t9728) ** (0.5e1 / 0.2e1)) + + if Bindx == 1512: + t9736 = np.cos(phi) + t9737 = t9736 ** 2 + t9738 = t9736 * t9737 + t9741 = t9738 ** 2 + t9739 = t9737 ** 2 + t9735 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.13e2) * t9735 ** 2 * (-572 * t9737 - 1660 * t9738 - 7548 * t9741 + 13 + (3710 + 4845 * t9739) * t9739 + (4964 * t9739 - 3876 * t9741 + 124) * t9736) + + if Bindx == 1513: + t9744 = np.cos(phi) + t9745 = t9744 ** 2 + t9749 = 969 * t9745 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-238 * t9745 + t9749 + 5 + (-102 * t9745 + t9749 - 19) * t9744) * ((1 + t9744) ** (0.3e1 / 0.2e1)) * np.sqrt(0.130e3) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t9744) ** (0.7e1 / 0.2e1)) + + if Bindx == 1514: + t9758 = np.sin(phi) + t9756 = t9758 ** 2 + t9750 = np.cos(phi) + t9751 = t9750 ** 2 + t9753 = t9751 ** 2 + t9752 = t9750 * t9751 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.26e2) * t9756 ** 2 * (627 * t9751 - 3213 * t9753 - 19 + (4012 + 4845 * t9752) * t9752 + (-5814 * t9753 - 438) * t9750) + + if Bindx == 1515: + t9759 = np.cos(phi) + t9760 = t9759 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (171 * t9760 - 7 + (285 * t9760 - 9) * t9759) * ((1 + t9759) ** (0.5e1 / 0.2e1)) * np.sqrt(0.442e3) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t9759) ** (0.9e1 / 0.2e1)) + + if Bindx == 1516: + t9769 = np.sin(phi) + t9766 = t9769 ** 2 + t9767 = t9769 * t9766 + t9762 = np.cos(phi) + t9763 = t9762 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.663e3) * t9767 ** 2 * (40 * t9762 - 1 + (-152 * t9762 + 18 + 95 * t9763) * t9763) + + if Bindx == 1517: + t9770 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (1 + 5 * t9770) * ((1 + t9770) ** (0.7e1 / 0.2e1)) * np.sqrt(0.25194e5) * np.exp((-1*1j) * (2 * phi1 - 9 * phi2)) * ((1 - t9770) ** (0.11e2 / 0.2e1)) + + if Bindx == 1518: + t9771 = np.cos(phi) + t9778 = -1 + t9771 + t9777 = 1 + t9771 + t9775 = t9777 ** 2 + t9772 = t9778 ** 2 + t9773 = t9778 * t9772 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.125970e6) * t9773 ** 2 * t9775 ** 2 + + if Bindx == 1519: + t9779 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (phi1 + 10 * phi2)) * np.sqrt(0.41990e5) * ((1 - t9779) ** (0.9e1 / 0.2e1)) * ((1 + t9779) ** (0.11e2 / 0.2e1)) + + if Bindx == 1520: + t9784 = np.sin(phi) + t9781 = t9784 ** 2 + t9782 = t9781 ** 2 + t9780 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (phi1 + 9 * phi2)) * np.sqrt(0.8398e4) * t9782 ** 2 * (-1 + (9 + 10 * t9780) * t9780) + + if Bindx == 1521: + t9785 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.221e3) * ((1 - t9785) ** (0.7e1 / 0.2e1)) * ((1 + t9785) ** (0.9e1 / 0.2e1)) * (-4 + (-19 + 95 * t9785) * t9785) + + if Bindx == 1522: + t9793 = np.sin(phi) + t9790 = t9793 ** 2 + t9791 = t9793 * t9790 + t9786 = np.cos(phi) + t9787 = t9786 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.1326e4) * t9791 ** 2 * (-21 * t9786 + 3 + (133 * t9786 - 81 + 190 * t9787) * t9787) + + if Bindx == 1523: + t9794 = np.cos(phi) + t9795 = t9794 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.78e2) * ((1 - t9794) ** (0.5e1 / 0.2e1)) * ((1 + t9794) ** (0.7e1 / 0.2e1)) * (102 * t9794 + 9 + (-646 * t9794 - 408 + 1615 * t9795) * t9795) + + if Bindx == 1524: + t9806 = np.sin(phi) + t9804 = t9806 ** 2 + t9798 = np.cos(phi) + t9799 = t9798 ** 2 + t9801 = t9799 ** 2 + t9800 = t9798 * t9799 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.390e3) * t9804 ** 2 * (120 * t9799 - 595 * t9801 - 3 + (-170 + 646 * t9800) * t9800 + (323 * t9801 + 15) * t9798) + + if Bindx == 1525: + t9807 = np.cos(phi) + t9808 = t9807 ** 2 + t9810 = t9808 ** 2 + t9809 = t9807 * t9808 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.39e2) * ((1 - t9807) ** (0.3e1 / 0.2e1)) * ((1 + t9807) ** (0.5e1 / 0.2e1)) * (135 * t9808 - 1020 * t9810 - 2 + (510 + 1615 * t9809) * t9809 + (-969 * t9810 - 45) * t9807) + + if Bindx == 1526: + t9814 = np.cos(phi) + t9815 = t9814 ** 2 + t9816 = t9814 * t9815 + t9819 = t9816 ** 2 + t9817 = t9815 ** 2 + t9813 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.78e2) * t9813 ** 2 * (-343 * t9815 + 315 * t9816 - 5117 * t9819 + 7 + (2415 + 3230 * t9817) * t9817 + (-1071 * t9817 + 969 * t9819 - 21) * t9814) + + if Bindx == 1527: + t9822 = np.cos(phi) + t9823 = t9822 ** 2 + t9824 = t9822 * t9823 + t9827 = t9824 ** 2 + t9825 = t9823 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t9822)) * ((1 + t9822) ** (0.3e1 / 0.2e1)) * (-728 * t9823 - 5460 * t9824 - 24752 * t9827 + 7 + (8190 + 20995 * t9825) * t9825 + (18564 * t9825 - 16796 * t9827 + 364) * t9822) + + if Bindx == 1528: + t9830 = np.cos(phi) + t9831 = t9830 ** 2 + t9833 = t9831 ** 2 + t9837 = t9833 ** 2 + t9832 = t9830 * t9831 + t9835 = t9832 ** 2 + t9834 = t9830 * t9833 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 + phi2)) * (3402 * t9831 - 1092 * t9832 - 28938 * t9833 + 85176 * t9835 - 101439 * t9837 - 63 + (4914 + 41990 * t9834) * t9834 + (-7956 * t9835 + 4199 * t9837 + 63) * t9830) + + if Bindx == 1529: + t9840 = np.cos(phi) + t9841 = t9840 ** 2 + t9842 = t9840 * t9841 + t9843 = t9841 ** 2 + t9849 = -7956 * t9842 ** 2 + 63 + (4914 + 4199 * t9843) * t9843 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.110e3) * t9840 * np.sqrt((1 + t9840)) * (t9849 * t9840 + 1092 * t9841 - 1092 * t9842 - t9849) * ((1 - t9840) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1530: + t9850 = np.cos(phi) + t9851 = t9850 ** 2 + t9853 = t9851 ** 2 + t9857 = t9853 ** 2 + t9852 = t9850 * t9851 + t9855 = t9852 ** 2 + t9854 = t9850 * t9853 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - phi2)) * (3402 * t9851 + 1092 * t9852 - 28938 * t9853 + 85176 * t9855 - 101439 * t9857 - 63 + (-4914 + 41990 * t9854) * t9854 + (7956 * t9855 - 4199 * t9857 - 63) * t9850) + + if Bindx == 1531: + t9860 = np.cos(phi) + t9861 = t9860 ** 2 + t9862 = t9860 * t9861 + t9865 = t9862 ** 2 + t9863 = t9861 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-728 * t9861 + 5460 * t9862 - 24752 * t9865 + 7 + (8190 + 20995 * t9863) * t9863 + (-18564 * t9863 + 16796 * t9865 - 364) * t9860) * np.sqrt((1 + t9860)) * np.sqrt(0.3e1) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t9860) ** (0.3e1 / 0.2e1)) + + if Bindx == 1532: + t9869 = np.cos(phi) + t9870 = t9869 ** 2 + t9871 = t9869 * t9870 + t9874 = t9871 ** 2 + t9872 = t9870 ** 2 + t9868 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.78e2) * t9868 ** 2 * (-343 * t9870 - 315 * t9871 - 5117 * t9874 + 7 + (2415 + 3230 * t9872) * t9872 + (1071 * t9872 - 969 * t9874 + 21) * t9869) + + if Bindx == 1533: + t9877 = np.cos(phi) + t9878 = t9877 ** 2 + t9880 = t9878 ** 2 + t9879 = t9877 * t9878 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (135 * t9878 - 1020 * t9880 - 2 + (-510 + 1615 * t9879) * t9879 + (969 * t9880 + 45) * t9877) * ((1 + t9877) ** (0.3e1 / 0.2e1)) * np.sqrt(0.39e2) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t9877) ** (0.5e1 / 0.2e1)) + + if Bindx == 1534: + t9891 = np.sin(phi) + t9889 = t9891 ** 2 + t9883 = np.cos(phi) + t9884 = t9883 ** 2 + t9886 = t9884 ** 2 + t9885 = t9883 * t9884 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.390e3) * t9889 ** 2 * (120 * t9884 - 595 * t9886 - 3 + (170 + 646 * t9885) * t9885 + (-323 * t9886 - 15) * t9883) + + if Bindx == 1535: + t9892 = np.cos(phi) + t9893 = t9892 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-102 * t9892 + 9 + (646 * t9892 - 408 + 1615 * t9893) * t9893) * ((1 + t9892) ** (0.5e1 / 0.2e1)) * np.sqrt(0.78e2) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t9892) ** (0.7e1 / 0.2e1)) + + if Bindx == 1536: + t9903 = np.sin(phi) + t9900 = t9903 ** 2 + t9901 = t9903 * t9900 + t9896 = np.cos(phi) + t9897 = t9896 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.1326e4) * t9901 ** 2 * (21 * t9896 + 3 + (-133 * t9896 - 81 + 190 * t9897) * t9897) + + if Bindx == 1537: + t9904 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-4 + (19 + 95 * t9904) * t9904) * ((1 + t9904) ** (0.7e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * ((1 - t9904) ** (0.9e1 / 0.2e1)) + + if Bindx == 1538: + t9909 = np.sin(phi) + t9906 = t9909 ** 2 + t9907 = t9906 ** 2 + t9905 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-1*1j) * (phi1 - 9 * phi2)) * np.sqrt(0.8398e4) * t9907 ** 2 * (-1 + (-9 + 10 * t9905) * t9905) + + if Bindx == 1539: + t9910 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((-1*1j) * (phi1 - 10 * phi2)) * np.sqrt(0.41990e5) * ((1 - t9910) ** (0.11e2 / 0.2e1)) * ((1 + t9910) ** (0.9e1 / 0.2e1)) + + if Bindx == 1540: + t9915 = np.sin(phi) + t9911 = t9915 ** 2 + t9913 = t9915 * t9911 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-10*1j) * phi2) * np.sqrt(0.46189e5) * t9913 ** 2 + + if Bindx == 1541: + t9916 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-9*1j) * phi2) * np.sqrt(0.230945e6) * ((1 - t9916) ** (0.9e1 / 0.2e1)) * ((1 + t9916) ** (0.9e1 / 0.2e1)) * t9916 + + if Bindx == 1542: + t9921 = np.sin(phi) + t9918 = t9921 ** 2 + t9919 = t9918 ** 2 + t9917 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((-8*1j) * phi2) * np.sqrt(0.24310e5) * t9919 ** 2 * (19 * t9917 ** 2 - 1) + + if Bindx == 1543: + t9922 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.36465e5) * ((1 - t9922) ** (0.7e1 / 0.2e1)) * ((1 + t9922) ** (0.7e1 / 0.2e1)) * t9922 * (19 * t9922 ** 2 - 3) + + if Bindx == 1544: + t9929 = np.sin(phi) + t9926 = t9929 ** 2 + t9927 = t9929 * t9926 + t9923 = np.cos(phi) + t9924 = t9923 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-6*1j) * phi2) * np.sqrt(0.2145e4) * t9927 ** 2 * (3 + (-102 + 323 * t9924) * t9924) + + if Bindx == 1545: + t9930 = np.cos(phi) + t9931 = t9930 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.429e3) * ((1 - t9930) ** (0.5e1 / 0.2e1)) * ((1 + t9930) ** (0.5e1 / 0.2e1)) * t9930 * (15 + (-170 + 323 * t9931) * t9931) + + if Bindx == 1546: + t9939 = np.sin(phi) + t9937 = t9939 ** 2 + t9933 = np.cos(phi) + t9934 = t9933 ** 2 + t9935 = t9934 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((-4*1j) * phi2) * np.sqrt(0.4290e4) * t9937 ** 2 * (-255 * t9935 - 1 + (323 * t9935 + 45) * t9934) + + if Bindx == 1547: + t9940 = np.cos(phi) + t9941 = t9940 ** 2 + t9942 = t9941 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.2145e4) * ((1 - t9940) ** (0.3e1 / 0.2e1)) * ((1 + t9940) ** (0.3e1 / 0.2e1)) * t9940 * (-357 * t9942 - 7 + (323 * t9942 + 105) * t9941) + + if Bindx == 1548: + t9945 = np.cos(phi) + t9946 = t9945 ** 2 + t9947 = t9946 ** 2 + t9944 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((-2*1j) * phi2) * np.sqrt(0.330e3) * t9944 ** 2 * (-364 * t9946 + 7 + (-6188 * t9946 + 2730 + 4199 * t9947) * t9947) + + if Bindx == 1549: + t9950 = np.cos(phi) + t9951 = t9950 ** 2 + t9952 = t9951 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.110e3) * np.sqrt((1 - t9950)) * np.sqrt((1 + t9950)) * t9950 * (-1092 * t9951 + 63 + (-7956 * t9951 + 4914 + 4199 * t9952) * t9952) + + if Bindx == 1550: + t9955 = np.cos(phi) + t9956 = t9955 ** 2 + t9957 = t9956 ** 2 + t9959 = t9957 ** 2 + tfunc[..., c] = -0.2297295e7 / 0.256e3 * t9959 - 0.315315e6 / 0.128e3 * t9957 - 0.1323e4 / 0.256e3 + (0.969969e6 / 0.256e3 * t9959 + 0.945945e6 / 0.128e3 * t9957 + 0.72765e5 / 0.256e3) * t9956 + + if Bindx == 1551: + t9961 = np.cos(phi) + t9962 = t9961 ** 2 + t9963 = t9961 * t9962 + t9964 = t9962 ** 2 + t9970 = -7956 * t9963 ** 2 + 63 + (4914 + 4199 * t9964) * t9964 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * phi2) * np.sqrt(0.110e3) * t9961 * np.sqrt((1 + t9961)) * (t9970 * t9961 + 1092 * t9962 - 1092 * t9963 - t9970) * ((1 - t9961) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1552: + t9972 = np.cos(phi) + t9973 = t9972 ** 2 + t9974 = t9973 ** 2 + t9971 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * phi2) * np.sqrt(0.330e3) * t9971 ** 2 * (-364 * t9973 + 7 + (-6188 * t9973 + 2730 + 4199 * t9974) * t9974) + + if Bindx == 1553: + t9977 = np.cos(phi) + t9978 = t9977 ** 2 + t9979 = t9978 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (-357 * t9979 - 7 + (323 * t9979 + 105) * t9978) * t9977 * ((1 + t9977) ** (0.3e1 / 0.2e1)) * np.sqrt(0.2145e4) * np.exp((3*1j) * phi2) * ((1 - t9977) ** (0.3e1 / 0.2e1)) + + if Bindx == 1554: + t9987 = np.sin(phi) + t9985 = t9987 ** 2 + t9981 = np.cos(phi) + t9982 = t9981 ** 2 + t9983 = t9982 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * phi2) * np.sqrt(0.4290e4) * t9985 ** 2 * (-255 * t9983 - 1 + (323 * t9983 + 45) * t9982) + + if Bindx == 1555: + t9988 = np.cos(phi) + t9989 = t9988 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * (15 + (-170 + 323 * t9989) * t9989) * t9988 * ((1 + t9988) ** (0.5e1 / 0.2e1)) * np.sqrt(0.429e3) * np.exp((5*1j) * phi2) * ((1 - t9988) ** (0.5e1 / 0.2e1)) + + if Bindx == 1556: + t9997 = np.sin(phi) + t9994 = t9997 ** 2 + t9995 = t9997 * t9994 + t9991 = np.cos(phi) + t9992 = t9991 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((6*1j) * phi2) * np.sqrt(0.2145e4) * t9995 ** 2 * (3 + (-102 + 323 * t9992) * t9992) + + if Bindx == 1557: + t9998 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (19 * t9998 ** 2 - 3) * t9998 * ((1 + t9998) ** (0.7e1 / 0.2e1)) * np.sqrt(0.36465e5) * np.exp((7*1j) * phi2) * ((1 - t9998) ** (0.7e1 / 0.2e1)) + + if Bindx == 1558: + t10003 = np.sin(phi) + t10000 = t10003 ** 2 + t10001 = t10000 ** 2 + t9999 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((8*1j) * phi2) * np.sqrt(0.24310e5) * t10001 ** 2 * (19 * t9999 ** 2 - 1) + + if Bindx == 1559: + t10004 = np.cos(phi) + t10005 = t10004 ** 2 + t10007 = t10005 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * t10004 * ((1 + t10004) ** (0.9e1 / 0.2e1)) * (-10 * t10005 - 5 * t10007 - 1 + (10 * t10005 + t10007 + 5) * t10004) * np.sqrt(0.230945e6) * np.exp((9*1j) * phi2) * ((1 - t10004) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1560: + t10013 = np.sin(phi) + t10009 = t10013 ** 2 + t10011 = t10013 * t10009 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((10*1j) * phi2) * np.sqrt(0.46189e5) * t10011 ** 2 + + if Bindx == 1561: + t10014 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (phi1 - 10 * phi2)) * np.sqrt(0.41990e5) * ((1 - t10014) ** (0.11e2 / 0.2e1)) * ((1 + t10014) ** (0.9e1 / 0.2e1)) + + if Bindx == 1562: + t10019 = np.sin(phi) + t10016 = t10019 ** 2 + t10017 = t10016 ** 2 + t10015 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (phi1 - 9 * phi2)) * np.sqrt(0.8398e4) * t10017 ** 2 * (-1 + (-9 + 10 * t10015) * t10015) + + if Bindx == 1563: + t10020 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.221e3) * ((1 - t10020) ** (0.9e1 / 0.2e1)) * ((1 + t10020) ** (0.7e1 / 0.2e1)) * (-4 + (19 + 95 * t10020) * t10020) + + if Bindx == 1564: + t10028 = np.sin(phi) + t10025 = t10028 ** 2 + t10026 = t10028 * t10025 + t10021 = np.cos(phi) + t10022 = t10021 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.1326e4) * t10026 ** 2 * (21 * t10021 + 3 + (-133 * t10021 - 81 + 190 * t10022) * t10022) + + if Bindx == 1565: + t10029 = np.cos(phi) + t10030 = t10029 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.78e2) * ((1 - t10029) ** (0.7e1 / 0.2e1)) * ((1 + t10029) ** (0.5e1 / 0.2e1)) * (-102 * t10029 + 9 + (646 * t10029 - 408 + 1615 * t10030) * t10030) + + if Bindx == 1566: + t10041 = np.sin(phi) + t10039 = t10041 ** 2 + t10033 = np.cos(phi) + t10034 = t10033 ** 2 + t10036 = t10034 ** 2 + t10035 = t10033 * t10034 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.390e3) * t10039 ** 2 * (120 * t10034 - 595 * t10036 - 3 + (170 + 646 * t10035) * t10035 + (-323 * t10036 - 15) * t10033) + + if Bindx == 1567: + t10042 = np.cos(phi) + t10043 = t10042 ** 2 + t10045 = t10043 ** 2 + t10044 = t10042 * t10043 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.39e2) * ((1 - t10042) ** (0.5e1 / 0.2e1)) * ((1 + t10042) ** (0.3e1 / 0.2e1)) * (135 * t10043 - 1020 * t10045 - 2 + (-510 + 1615 * t10044) * t10044 + (969 * t10045 + 45) * t10042) + + if Bindx == 1568: + t10049 = np.cos(phi) + t10050 = t10049 ** 2 + t10051 = t10049 * t10050 + t10054 = t10051 ** 2 + t10052 = t10050 ** 2 + t10048 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.78e2) * t10048 ** 2 * (-343 * t10050 - 315 * t10051 - 5117 * t10054 + 7 + (2415 + 3230 * t10052) * t10052 + (1071 * t10052 - 969 * t10054 + 21) * t10049) + + if Bindx == 1569: + t10057 = np.cos(phi) + t10058 = t10057 ** 2 + t10059 = t10057 * t10058 + t10062 = t10059 ** 2 + t10060 = t10058 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.3e1) * ((1 - t10057) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t10057)) * (-728 * t10058 + 5460 * t10059 - 24752 * t10062 + 7 + (8190 + 20995 * t10060) * t10060 + (-18564 * t10060 + 16796 * t10062 - 364) * t10057) + + if Bindx == 1570: + t10065 = np.cos(phi) + t10066 = t10065 ** 2 + t10068 = t10066 ** 2 + t10072 = t10068 ** 2 + t10067 = t10065 * t10066 + t10070 = t10067 ** 2 + t10069 = t10065 * t10068 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (phi1 - phi2)) * (3402 * t10066 + 1092 * t10067 - 28938 * t10068 + 85176 * t10070 - 101439 * t10072 - 63 + (-4914 + 41990 * t10069) * t10069 + (7956 * t10070 - 4199 * t10072 - 63) * t10065) + + if Bindx == 1571: + t10075 = np.cos(phi) + t10076 = t10075 ** 2 + t10077 = t10076 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.110e3) * np.sqrt((1 - t10075)) * np.sqrt((1 + t10075)) * t10075 * (-1092 * t10076 + 63 + (-7956 * t10076 + 4914 + 4199 * t10077) * t10077) + + if Bindx == 1572: + t10080 = np.cos(phi) + t10081 = t10080 ** 2 + t10083 = t10081 ** 2 + t10087 = t10083 ** 2 + t10082 = t10080 * t10081 + t10085 = t10082 ** 2 + t10084 = t10080 * t10083 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (phi1 + phi2)) * (3402 * t10081 - 1092 * t10082 - 28938 * t10083 + 85176 * t10085 - 101439 * t10087 - 63 + (4914 + 41990 * t10084) * t10084 + (-7956 * t10085 + 4199 * t10087 + 63) * t10080) + + if Bindx == 1573: + t10090 = np.cos(phi) + t10091 = t10090 ** 2 + t10093 = t10091 ** 2 + t10097 = t10093 ** 2 + t10092 = t10090 * t10091 + t10095 = t10092 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.3e1) * ((1 + t10090) ** (0.3e1 / 0.2e1)) * (1092 * t10091 + 4732 * t10092 - 13650 * t10093 + 43316 * t10095 - 37791 * t10097 - 7 + (-10374 * t10093 - 7956 * t10095 + 20995 * t10097 - 357) * t10090) * ((1 - t10090) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1574: + t10100 = np.cos(phi) + t10101 = t10100 ** 2 + t10102 = t10100 * t10101 + t10105 = t10102 ** 2 + t10103 = t10101 ** 2 + t10099 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.78e2) * t10099 ** 2 * (-343 * t10101 + 315 * t10102 - 5117 * t10105 + 7 + (2415 + 3230 * t10103) * t10103 + (-1071 * t10103 + 969 * t10105 - 21) * t10100) + + if Bindx == 1575: + t10108 = np.cos(phi) + t10109 = t10108 ** 2 + t10111 = t10109 ** 2 + t10110 = t10108 * t10109 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * (135 * t10109 - 1020 * t10111 - 2 + (510 + 1615 * t10110) * t10110 + (-969 * t10111 - 45) * t10108) * ((1 + t10108) ** (0.5e1 / 0.2e1)) * np.sqrt(0.39e2) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t10108) ** (0.3e1 / 0.2e1)) + + if Bindx == 1576: + t10122 = np.sin(phi) + t10120 = t10122 ** 2 + t10114 = np.cos(phi) + t10115 = t10114 ** 2 + t10117 = t10115 ** 2 + t10116 = t10114 * t10115 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.390e3) * t10120 ** 2 * (120 * t10115 - 595 * t10117 - 3 + (-170 + 646 * t10116) * t10116 + (323 * t10117 + 15) * t10114) + + if Bindx == 1577: + t10123 = np.cos(phi) + t10124 = t10123 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (102 * t10123 + 9 + (-646 * t10123 - 408 + 1615 * t10124) * t10124) * ((1 + t10123) ** (0.7e1 / 0.2e1)) * np.sqrt(0.78e2) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t10123) ** (0.5e1 / 0.2e1)) + + if Bindx == 1578: + t10134 = np.sin(phi) + t10131 = t10134 ** 2 + t10132 = t10134 * t10131 + t10127 = np.cos(phi) + t10128 = t10127 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.1326e4) * t10132 ** 2 * (-21 * t10127 + 3 + (133 * t10127 - 81 + 190 * t10128) * t10128) + + if Bindx == 1579: + t10135 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-4 + (-19 + 95 * t10135) * t10135) * ((1 + t10135) ** (0.9e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((1j) * (phi1 + 8 * phi2)) * ((1 - t10135) ** (0.7e1 / 0.2e1)) + + if Bindx == 1580: + t10140 = np.sin(phi) + t10137 = t10140 ** 2 + t10138 = t10137 ** 2 + t10136 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (phi1 + 9 * phi2)) * np.sqrt(0.8398e4) * t10138 ** 2 * (-1 + (9 + 10 * t10136) * t10136) + + if Bindx == 1581: + t10141 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (phi1 + 10 * phi2)) * np.sqrt(0.41990e5) * ((1 - t10141) ** (0.9e1 / 0.2e1)) * ((1 + t10141) ** (0.11e2 / 0.2e1)) + + if Bindx == 1582: + t10142 = np.cos(phi) + t10149 = -1 + t10142 + t10148 = 1 + t10142 + t10146 = t10148 ** 2 + t10143 = t10149 ** 2 + t10144 = t10149 * t10143 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.125970e6) * t10144 ** 2 * t10146 ** 2 + + if Bindx == 1583: + t10150 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.25194e5) * ((1 - t10150) ** (0.11e2 / 0.2e1)) * ((1 + t10150) ** (0.7e1 / 0.2e1)) * (1 + 5 * t10150) + + if Bindx == 1584: + t10158 = np.sin(phi) + t10155 = t10158 ** 2 + t10156 = t10158 * t10155 + t10151 = np.cos(phi) + t10152 = t10151 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.663e3) * t10156 ** 2 * (40 * t10151 - 1 + (-152 * t10151 + 18 + 95 * t10152) * t10152) + + if Bindx == 1585: + t10159 = np.cos(phi) + t10160 = t10159 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.442e3) * ((1 - t10159) ** (0.9e1 / 0.2e1)) * ((1 + t10159) ** (0.5e1 / 0.2e1)) * (171 * t10160 - 7 + (285 * t10160 - 9) * t10159) + + if Bindx == 1586: + t10170 = np.sin(phi) + t10168 = t10170 ** 2 + t10162 = np.cos(phi) + t10163 = t10162 ** 2 + t10165 = t10163 ** 2 + t10164 = t10162 * t10163 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.26e2) * t10168 ** 2 * (627 * t10163 - 3213 * t10165 - 19 + (4012 + 4845 * t10164) * t10164 + (-5814 * t10165 - 438) * t10162) + + if Bindx == 1587: + t10171 = np.cos(phi) + t10172 = t10171 ** 2 + t10176 = 969 * t10172 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.130e3) * ((1 - t10171) ** (0.7e1 / 0.2e1)) * ((1 + t10171) ** (0.3e1 / 0.2e1)) * (-238 * t10172 + t10176 + 5 + (-102 * t10172 + t10176 - 19) * t10171) + + if Bindx == 1588: + t10178 = np.cos(phi) + t10179 = t10178 ** 2 + t10180 = t10178 * t10179 + t10183 = t10180 ** 2 + t10181 = t10179 ** 2 + t10177 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.13e2) * t10177 ** 2 * (-572 * t10179 - 1660 * t10180 - 7548 * t10183 + 13 + (3710 + 4845 * t10181) * t10181 + (4964 * t10181 - 3876 * t10183 + 124) * t10178) + + if Bindx == 1589: + t10186 = np.cos(phi) + t10187 = t10186 ** 2 + t10188 = t10186 * t10187 + t10191 = t10188 ** 2 + t10189 = t10187 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.26e2) * ((1 - t10186) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t10186)) * (525 * t10187 - 665 * t10188 - 4165 * t10189 + 6783 * t10191 - 7 + (-1071 * t10189 + 4845 * t10191 + 91) * t10186) + + if Bindx == 1590: + t10193 = np.cos(phi) + t10194 = t10193 ** 2 + t10196 = t10194 ** 2 + t10200 = t10196 ** 2 + t10195 = t10193 * t10194 + t10198 = t10195 ** 2 + t10197 = t10193 * t10196 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (phi1 - phi2)) * (6069 * t10194 + 8008 * t10195 - 48958 * t10196 + 137410 * t10198 - 157131 * t10200 - 119 + (-33852 + 62985 * t10197) * t10197 + (51272 * t10198 - 25194 * t10200 - 490) * t10193) + + if Bindx == 1591: + t10203 = np.cos(phi) + t10204 = t10203 ** 2 + t10205 = t10203 * t10204 + t10208 = t10205 ** 2 + t10206 = t10204 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.3e1) * ((1 - t10203) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t10203)) * (-728 * t10204 + 5460 * t10205 - 24752 * t10208 + 7 + (8190 + 20995 * t10206) * t10206 + (-18564 * t10206 + 16796 * t10208 - 364) * t10203) + + if Bindx == 1592: + t10212 = np.cos(phi) + t10213 = t10212 ** 2 + t10214 = t10213 ** 2 + t10211 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * phi1) * np.sqrt(0.330e3) * t10211 ** 2 * (-364 * t10213 + 7 + (-6188 * t10213 + 2730 + 4199 * t10214) * t10214) + + if Bindx == 1593: + t10217 = np.cos(phi) + t10218 = t10217 ** 2 + t10219 = t10217 * t10218 + t10222 = t10219 ** 2 + t10220 = t10218 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.3e1) * np.sqrt((1 - t10217)) * ((1 + t10217) ** (0.3e1 / 0.2e1)) * (-728 * t10218 - 5460 * t10219 - 24752 * t10222 + 7 + (8190 + 20995 * t10220) * t10220 + (18564 * t10220 - 16796 * t10222 + 364) * t10217) + + if Bindx == 1594: + t10225 = np.cos(phi) + t10226 = t10225 ** 2 + t10228 = t10226 ** 2 + t10232 = t10228 ** 2 + t10227 = t10225 * t10226 + t10230 = t10227 ** 2 + t10229 = t10225 * t10228 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (phi1 + phi2)) * (6069 * t10226 - 8008 * t10227 - 48958 * t10228 + 137410 * t10230 - 157131 * t10232 - 119 + (33852 + 62985 * t10229) * t10229 + (-51272 * t10230 + 25194 * t10232 + 490) * t10225) + + if Bindx == 1595: + t10235 = np.cos(phi) + t10236 = t10235 ** 2 + t10237 = t10235 * t10236 + t10240 = t10237 ** 2 + t10238 = t10236 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.26e2) * ((1 + t10235) ** (0.5e1 / 0.2e1)) * (616 * t10236 + 140 * t10237 + 5712 * t10240 - 7 + (-4830 + 4845 * t10238) * t10238 + (5236 * t10238 - 11628 * t10240 - 84) * t10235) * ((1 - t10235) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1596: + t10244 = np.cos(phi) + t10245 = t10244 ** 2 + t10246 = t10244 * t10245 + t10249 = t10246 ** 2 + t10247 = t10245 ** 2 + t10243 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.13e2) * t10243 ** 2 * (-572 * t10245 + 1660 * t10246 - 7548 * t10249 + 13 + (3710 + 4845 * t10247) * t10247 + (-4964 * t10247 + 3876 * t10249 - 124) * t10244) + + if Bindx == 1597: + t10252 = np.cos(phi) + t10253 = t10252 ** 2 + t10255 = t10253 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (238 * t10253 - 969 * t10255 - 5 + (-102 * t10253 + 969 * t10255 - 19) * t10252) * ((1 + t10252) ** (0.7e1 / 0.2e1)) * np.sqrt(0.130e3) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t10252) ** (0.3e1 / 0.2e1)) + + if Bindx == 1598: + t10265 = np.sin(phi) + t10263 = t10265 ** 2 + t10257 = np.cos(phi) + t10258 = t10257 ** 2 + t10260 = t10258 ** 2 + t10259 = t10257 * t10258 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.26e2) * t10263 ** 2 * (627 * t10258 - 3213 * t10260 - 19 + (-4012 + 4845 * t10259) * t10259 + (5814 * t10260 + 438) * t10257) + + if Bindx == 1599: + t10266 = np.cos(phi) + t10267 = t10266 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * (-171 * t10267 + 7 + (285 * t10267 - 9) * t10266) * ((1 + t10266) ** (0.9e1 / 0.2e1)) * np.sqrt(0.442e3) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t10266) ** (0.5e1 / 0.2e1)) + + if Bindx == 1600: + t10276 = np.sin(phi) + t10273 = t10276 ** 2 + t10274 = t10276 * t10273 + t10269 = np.cos(phi) + t10270 = t10269 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.663e3) * t10274 ** 2 * (-40 * t10269 - 1 + (152 * t10269 + 18 + 95 * t10270) * t10270) + + if Bindx == 1601: + t10277 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-1 + 5 * t10277) * ((1 + t10277) ** (0.11e2 / 0.2e1)) * np.sqrt(0.25194e5) * np.exp((1j) * (2 * phi1 + 9 * phi2)) * ((1 - t10277) ** (0.7e1 / 0.2e1)) + + if Bindx == 1602: + t10278 = np.cos(phi) + t10285 = -1 + t10278 + t10284 = 1 + t10278 + t10281 = t10284 ** 2 + t10282 = t10284 * t10281 + t10279 = t10285 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.125970e6) * t10279 ** 2 * t10282 ** 2 + + if Bindx == 1603: + t10286 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 - 10 * phi2)) * np.sqrt(0.4845e4) * ((1 - t10286) ** (0.13e2 / 0.2e1)) * ((1 + t10286) ** (0.7e1 / 0.2e1)) + + if Bindx == 1604: + t10287 = np.cos(phi) + t10294 = -1 + t10287 + t10293 = 1 + t10287 + t10291 = t10293 ** 2 + t10288 = t10294 ** 2 + t10289 = t10294 * t10288 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.969e3) * t10289 ** 2 * t10293 * t10291 * (3 + 10 * t10287) + + if Bindx == 1605: + t10295 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.102e3) * ((1 - t10295) ** (0.11e2 / 0.2e1)) * ((1 + t10295) ** (0.5e1 / 0.2e1)) * (4 + (57 + 95 * t10295) * t10295) + + if Bindx == 1606: + t10304 = np.sin(phi) + t10302 = t10304 ** 2 + t10296 = np.cos(phi) + t10297 = t10296 ** 2 + t10299 = t10297 ** 2 + t10298 = t10296 * t10297 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * t10302 ** 2 * (-264 * t10297 + 243 * t10299 + 11 + (742 + 570 * t10298) * t10298 + (-1197 * t10299 - 105) * t10296) + + if Bindx == 1607: + t10305 = np.cos(phi) + t10306 = t10305 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * ((1 - t10305) ** (0.9e1 / 0.2e1)) * ((1 + t10305) ** (0.3e1 / 0.2e1)) * (-374 * t10305 - 69 + (5814 * t10305 + 1224 + 4845 * t10306) * t10306) + + if Bindx == 1608: + t10310 = np.cos(phi) + t10311 = t10310 ** 2 + t10312 = t10310 * t10311 + t10315 = t10312 ** 2 + t10313 = t10311 ** 2 + t10309 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * t10309 ** 2 * (-37 * t10311 - 1525 * t10312 - 2091 * t10315 + 1 + (525 + 1938 * t10313) * t10313 + (3961 * t10313 - 2907 * t10315 + 135) * t10310) + + if Bindx == 1609: + t10318 = np.cos(phi) + t10319 = t10318 ** 2 + t10321 = t10319 ** 2 + t10320 = t10318 * t10319 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.2e1) * ((1 - t10318) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t10318)) * (-1035 * t10319 + 3060 * t10321 + 22 + (-1870 + 4845 * t10320) * t10320 + (8721 * t10321 - 15) * t10318) + + if Bindx == 1610: + t10324 = np.cos(phi) + t10325 = t10324 ** 2 + t10327 = t10325 ** 2 + t10331 = t10327 ** 2 + t10326 = t10324 * t10325 + t10329 = t10326 ** 2 + t10328 = t10324 * t10327 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((3*1j) * (phi1 - phi2)) * (966 * t10325 + 3612 * t10326 - 7322 * t10327 + 20160 * t10329 - 23409 * t10331 - 21 + (-13818 + 9690 * t10328) * t10328 + (19108 * t10329 - 8721 * t10331 - 245) * t10324) + + if Bindx == 1611: + t10334 = np.cos(phi) + t10335 = t10334 ** 2 + t10336 = t10334 * t10335 + t10339 = t10336 ** 2 + t10337 = t10335 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.26e2) * ((1 - t10334) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t10334)) * (525 * t10335 - 665 * t10336 - 4165 * t10337 + 6783 * t10339 - 7 + (-1071 * t10337 + 4845 * t10339 + 91) * t10334) + + if Bindx == 1612: + t10342 = np.cos(phi) + t10343 = t10342 ** 2 + t10344 = t10342 * t10343 + t10347 = t10344 ** 2 + t10345 = t10343 ** 2 + t10341 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.78e2) * t10341 ** 2 * (-343 * t10343 - 315 * t10344 - 5117 * t10347 + 7 + (2415 + 3230 * t10345) * t10345 + (1071 * t10345 - 969 * t10347 + 21) * t10342) + + if Bindx == 1613: + t10350 = np.cos(phi) + t10351 = t10350 ** 2 + t10352 = t10351 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.2145e4) * ((1 - t10350) ** (0.3e1 / 0.2e1)) * ((1 + t10350) ** (0.3e1 / 0.2e1)) * t10350 * (-357 * t10352 - 7 + (323 * t10352 + 105) * t10351) + + if Bindx == 1614: + t10355 = np.cos(phi) + t10356 = t10355 ** 2 + t10357 = t10355 * t10356 + t10360 = t10357 ** 2 + t10358 = t10356 ** 2 + t10354 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.78e2) * t10354 ** 2 * (-343 * t10356 + 315 * t10357 - 5117 * t10360 + 7 + (2415 + 3230 * t10358) * t10358 + (-1071 * t10358 + 969 * t10360 - 21) * t10355) + + if Bindx == 1615: + t10363 = np.cos(phi) + t10364 = t10363 ** 2 + t10365 = t10363 * t10364 + t10368 = t10365 ** 2 + t10366 = t10364 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t10363)) * ((1 + t10363) ** (0.5e1 / 0.2e1)) * (-525 * t10364 - 665 * t10365 + 4165 * t10366 - 6783 * t10368 + 7 + (-1071 * t10366 + 4845 * t10368 + 91) * t10363) + + if Bindx == 1616: + t10370 = np.cos(phi) + t10371 = t10370 ** 2 + t10373 = t10371 ** 2 + t10377 = t10373 ** 2 + t10372 = t10370 * t10371 + t10375 = t10372 ** 2 + t10374 = t10370 * t10373 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((3*1j) * (phi1 + phi2)) * (966 * t10371 - 3612 * t10372 - 7322 * t10373 + 20160 * t10375 - 23409 * t10377 - 21 + (13818 + 9690 * t10374) * t10374 + (-19108 * t10375 + 8721 * t10377 + 245) * t10370) + + if Bindx == 1617: + t10380 = np.cos(phi) + t10381 = t10380 ** 2 + t10382 = t10380 * t10381 + t10385 = t10382 ** 2 + t10383 = t10381 ** 2 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * ((1 + t10380) ** (0.7e1 / 0.2e1)) * (1050 * t10381 - 2905 * t10382 - 1190 * t10383 - 13566 * t10385 - 22 + (11781 * t10383 + 4845 * t10385 + 7) * t10380) * ((1 - t10380) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1618: + t10388 = np.cos(phi) + t10389 = t10388 ** 2 + t10390 = t10388 * t10389 + t10393 = t10390 ** 2 + t10391 = t10389 ** 2 + t10387 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * t10387 ** 2 * (-37 * t10389 + 1525 * t10390 - 2091 * t10393 + 1 + (525 + 1938 * t10391) * t10391 + (-3961 * t10391 + 2907 * t10393 - 135) * t10388) + + if Bindx == 1619: + t10396 = np.cos(phi) + t10397 = t10396 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (374 * t10396 - 69 + (-5814 * t10396 + 1224 + 4845 * t10397) * t10397) * ((1 + t10396) ** (0.9e1 / 0.2e1)) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t10396) ** (0.3e1 / 0.2e1)) + + if Bindx == 1620: + t10408 = np.sin(phi) + t10406 = t10408 ** 2 + t10400 = np.cos(phi) + t10401 = t10400 ** 2 + t10403 = t10401 ** 2 + t10402 = t10400 * t10401 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * t10406 ** 2 * (-264 * t10401 + 243 * t10403 + 11 + (-742 + 570 * t10402) * t10402 + (1197 * t10403 + 105) * t10400) + + if Bindx == 1621: + t10409 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (4 + (-57 + 95 * t10409) * t10409) * ((1 + t10409) ** (0.11e2 / 0.2e1)) * np.sqrt(0.102e3) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * ((1 - t10409) ** (0.5e1 / 0.2e1)) + + if Bindx == 1622: + t10417 = np.sin(phi) + t10414 = t10417 ** 2 + t10415 = t10417 * t10414 + t10410 = np.cos(phi) + t10411 = t10410 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.969e3) * t10415 ** 2 * (t10410 - 3 + (27 * t10410 + 21 + 10 * t10411) * t10411) + + if Bindx == 1623: + t10418 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (3 * phi1 + 10 * phi2)) * np.sqrt(0.4845e4) * ((1 - t10418) ** (0.7e1 / 0.2e1)) * ((1 + t10418) ** (0.13e2 / 0.2e1)) + + if Bindx == 1624: + t10419 = np.cos(phi) + t10427 = -1 + t10419 + t10426 = 1 + t10419 + t10424 = t10426 ** 2 + t10420 = t10427 ** 2 + t10421 = t10427 * t10420 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.9690e4) * t10427 * t10421 ** 2 * t10426 * t10424 + + if Bindx == 1625: + t10428 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.1938e4) * ((1 - t10428) ** (0.13e2 / 0.2e1)) * ((1 + t10428) ** (0.5e1 / 0.2e1)) * (2 + 5 * t10428) + + if Bindx == 1626: + t10437 = np.sin(phi) + t10435 = t10437 ** 2 + t10429 = np.cos(phi) + t10430 = t10429 ** 2 + t10432 = t10430 ** 2 + t10431 = t10429 * t10430 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.51e2) * t10435 ** 2 * (-143 * t10430 + 277 * t10432 + 11 + (32 + 95 * t10431) * t10431 + (-304 * t10432 + 32) * t10429) + + if Bindx == 1627: + t10438 = np.cos(phi) + t10439 = t10438 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.34e2) * ((1 - t10438) ** (0.11e2 / 0.2e1)) * ((1 + t10438) ** (0.3e1 / 0.2e1)) * (342 * t10439 + 2 + (285 * t10439 + 99) * t10438) + + if Bindx == 1628: + t10442 = np.cos(phi) + t10443 = t10442 ** 2 + t10444 = t10442 * t10443 + t10447 = t10444 ** 2 + t10445 = t10443 ** 2 + t10441 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * t10441 ** 2 * (2324 * t10443 - 4564 * t10444 + 1428 * t10447 - 83 + (-6594 + 4845 * t10445) * t10445 + (13804 * t10445 - 11628 * t10447 + 468) * t10442) + + if Bindx == 1629: + t10450 = np.cos(phi) + t10451 = t10450 ** 2 + t10453 = t10451 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.10e2) * ((1 - t10450) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t10450)) * (68 * t10451 + 1938 * t10453 - 10 + (1122 * t10451 + 969 * t10453 - 83) * t10450) + + if Bindx == 1630: + t10455 = np.cos(phi) + t10456 = t10455 ** 2 + t10458 = t10456 ** 2 + t10462 = t10458 ** 2 + t10457 = t10455 * t10456 + t10460 = t10457 ** 2 + t10459 = t10455 * t10458 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((4*1j) * (phi1 - phi2)) * (-39 * t10456 + 3856 * t10457 - 574 * t10458 + 4858 * t10460 - 9027 * t10462 + 1 + (-13272 + 4845 * t10459) * t10459 + (17408 * t10460 - 7752 * t10462 - 304) * t10455) + + if Bindx == 1631: + t10465 = np.cos(phi) + t10466 = t10465 ** 2 + t10468 = t10466 ** 2 + t10467 = t10465 * t10466 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.2e1) * ((1 - t10465) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t10465)) * (-1035 * t10466 + 3060 * t10468 + 22 + (-1870 + 4845 * t10467) * t10467 + (8721 * t10468 - 15) * t10465) + + if Bindx == 1632: + t10472 = np.cos(phi) + t10473 = t10472 ** 2 + t10474 = t10472 * t10473 + t10477 = t10474 ** 2 + t10475 = t10473 ** 2 + t10471 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.13e2) * t10471 ** 2 * (-572 * t10473 - 1660 * t10474 - 7548 * t10477 + 13 + (3710 + 4845 * t10475) * t10475 + (4964 * t10475 - 3876 * t10477 + 124) * t10472) + + if Bindx == 1633: + t10480 = np.cos(phi) + t10481 = t10480 ** 2 + t10483 = t10481 ** 2 + t10482 = t10480 * t10481 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.39e2) * ((1 - t10480) ** (0.5e1 / 0.2e1)) * ((1 + t10480) ** (0.3e1 / 0.2e1)) * (135 * t10481 - 1020 * t10483 - 2 + (-510 + 1615 * t10482) * t10482 + (969 * t10483 + 45) * t10480) + + if Bindx == 1634: + t10492 = np.sin(phi) + t10490 = t10492 ** 2 + t10486 = np.cos(phi) + t10487 = t10486 ** 2 + t10488 = t10487 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * phi1) * np.sqrt(0.4290e4) * t10490 ** 2 * (-255 * t10488 - 1 + (323 * t10488 + 45) * t10487) + + if Bindx == 1635: + t10493 = np.cos(phi) + t10494 = t10493 ** 2 + t10496 = t10494 ** 2 + t10495 = t10493 * t10494 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.39e2) * ((1 - t10493) ** (0.3e1 / 0.2e1)) * ((1 + t10493) ** (0.5e1 / 0.2e1)) * (135 * t10494 - 1020 * t10496 - 2 + (510 + 1615 * t10495) * t10495 + (-969 * t10496 - 45) * t10493) + + if Bindx == 1636: + t10500 = np.cos(phi) + t10501 = t10500 ** 2 + t10502 = t10500 * t10501 + t10505 = t10502 ** 2 + t10503 = t10501 ** 2 + t10499 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.13e2) * t10499 ** 2 * (-572 * t10501 + 1660 * t10502 - 7548 * t10505 + 13 + (3710 + 4845 * t10503) * t10503 + (-4964 * t10503 + 3876 * t10505 - 124) * t10500) + + if Bindx == 1637: + t10508 = np.cos(phi) + t10509 = t10508 ** 2 + t10511 = t10509 ** 2 + t10510 = t10508 * t10509 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t10508)) * ((1 + t10508) ** (0.7e1 / 0.2e1)) * (-1035 * t10509 + 3060 * t10511 + 22 + (1870 + 4845 * t10510) * t10510 + (-8721 * t10511 + 15) * t10508) + + if Bindx == 1638: + t10514 = np.cos(phi) + t10515 = t10514 ** 2 + t10517 = t10515 ** 2 + t10521 = t10517 ** 2 + t10516 = t10514 * t10515 + t10519 = t10516 ** 2 + t10518 = t10514 * t10517 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((4*1j) * (phi1 + phi2)) * (-39 * t10515 - 3856 * t10516 - 574 * t10517 + 4858 * t10519 - 9027 * t10521 + 1 + (13272 + 4845 * t10518) * t10518 + (-17408 * t10519 + 7752 * t10521 + 304) * t10514) + + if Bindx == 1639: + t10524 = np.cos(phi) + t10525 = t10524 ** 2 + t10527 = t10525 ** 2 + t10526 = t10524 * t10525 + tfunc[..., c] = (0.21e2 / 0.128e3*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.10e2) * ((1 + t10524) ** (0.9e1 / 0.2e1)) * (-15 * t10525 + 3060 * t10527 - 10 + (-1190 + 969 * t10526) * t10526 + (-2907 * t10527 + 93) * t10524) * ((1 - t10524) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1640: + t10531 = np.cos(phi) + t10532 = t10531 ** 2 + t10533 = t10531 * t10532 + t10536 = t10533 ** 2 + t10534 = t10532 ** 2 + t10530 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2e1) * t10530 ** 2 * (2324 * t10532 + 4564 * t10533 + 1428 * t10536 - 83 + (-6594 + 4845 * t10534) * t10534 + (-13804 * t10534 + 11628 * t10536 - 468) * t10531) + + if Bindx == 1641: + t10539 = np.cos(phi) + t10540 = t10539 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (-342 * t10540 - 2 + (285 * t10540 + 99) * t10539) * ((1 + t10539) ** (0.11e2 / 0.2e1)) * np.sqrt(0.34e2) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t10539) ** (0.3e1 / 0.2e1)) + + if Bindx == 1642: + t10550 = np.sin(phi) + t10548 = t10550 ** 2 + t10542 = np.cos(phi) + t10543 = t10542 ** 2 + t10545 = t10543 ** 2 + t10544 = t10542 * t10543 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.51e2) * t10548 ** 2 * (-143 * t10543 + 277 * t10545 + 11 + (-32 + 95 * t10544) * t10544 + (304 * t10545 - 32) * t10542) + + if Bindx == 1643: + t10551 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * (-2 + 5 * t10551) * ((1 + t10551) ** (0.13e2 / 0.2e1)) * np.sqrt(0.1938e4) * np.exp((1j) * (4 * phi1 + 9 * phi2)) * ((1 - t10551) ** (0.5e1 / 0.2e1)) + + if Bindx == 1644: + t10552 = np.cos(phi) + t10560 = -1 + t10552 + t10559 = 1 + t10552 + t10555 = t10559 ** 2 + t10556 = t10559 * t10555 + t10553 = t10560 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.9690e4) * t10560 * t10553 * t10559 * t10556 ** 2 + + if Bindx == 1645: + t10561 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((5*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t10561) ** (0.15e2 / 0.2e1)) * ((1 + t10561) ** (0.5e1 / 0.2e1)) + + if Bindx == 1646: + t10563 = np.cos(phi) + t10568 = -1 + t10563 + t10564 = t10568 ** 2 + t10565 = t10568 * t10564 + t10562 = 1 + t10563 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.4845e4) * t10568 * t10565 ** 2 * t10562 ** 2 * (1 + 2 * t10563) + + if Bindx == 1647: + t10569 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.510e3) * ((1 - t10569) ** (0.13e2 / 0.2e1)) * ((1 + t10569) ** (0.3e1 / 0.2e1)) * (4 + (19 + 19 * t10569) * t10569) + + if Bindx == 1648: + t10571 = np.cos(phi) + t10572 = t10571 ** 2 + t10573 = t10571 * t10572 + t10576 = t10573 ** 2 + t10574 = t10572 ** 2 + t10570 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.85e2) * t10570 ** 2 * (119 * t10572 + 91 * t10573 + 357 * t10576 - 7 + (-455 + 114 * t10574) * t10574 + (217 * t10574 - 399 * t10576 - 37) * t10571) + + if Bindx == 1649: + t10579 = np.cos(phi) + t10580 = t10579 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.5e1) * ((1 - t10579) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t10579)) * (238 * t10579 - 1 + (1938 * t10579 + 1224 + 969 * t10580) * t10580) + + if Bindx == 1650: + t10583 = np.cos(phi) + t10584 = t10583 ** 2 + t10586 = t10584 ** 2 + t10590 = t10586 ** 2 + t10585 = t10583 * t10584 + t10588 = t10585 ** 2 + t10587 = t10583 * t10586 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((5*1j) * (phi1 - phi2)) * (-930 * t10584 + 1660 * t10585 + 3710 * t10586 - 3920 * t10588 - 765 * t10590 + 31 + (-6594 + 1938 * t10587) * t10587 + (9860 * t10588 - 4845 * t10590 - 145) * t10583) + + if Bindx == 1651: + t10593 = np.cos(phi) + t10594 = t10593 ** 2 + t10596 = t10594 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.10e2) * ((1 - t10593) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t10593)) * (68 * t10594 + 1938 * t10596 - 10 + (1122 * t10594 + 969 * t10596 - 83) * t10593) + + if Bindx == 1652: + t10599 = np.cos(phi) + t10600 = t10599 ** 2 + t10601 = t10599 * t10600 + t10604 = t10601 ** 2 + t10602 = t10600 ** 2 + t10598 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.5e1) * t10598 ** 2 * (-37 * t10600 - 1525 * t10601 - 2091 * t10604 + 1 + (525 + 1938 * t10602) * t10602 + (3961 * t10602 - 2907 * t10604 + 135) * t10599) + + if Bindx == 1653: + t10607 = np.cos(phi) + t10608 = t10607 ** 2 + t10612 = 969 * t10608 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.130e3) * ((1 - t10607) ** (0.7e1 / 0.2e1)) * ((1 + t10607) ** (0.3e1 / 0.2e1)) * (-238 * t10608 + t10612 + 5 + (-102 * t10608 + t10612 - 19) * t10607) + + if Bindx == 1654: + t10621 = np.sin(phi) + t10619 = t10621 ** 2 + t10613 = np.cos(phi) + t10614 = t10613 ** 2 + t10616 = t10614 ** 2 + t10615 = t10613 * t10614 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.390e3) * t10619 ** 2 * (120 * t10614 - 595 * t10616 - 3 + (170 + 646 * t10615) * t10615 + (-323 * t10616 - 15) * t10613) + + if Bindx == 1655: + t10622 = np.cos(phi) + t10623 = t10622 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.429e3) * ((1 - t10622) ** (0.5e1 / 0.2e1)) * ((1 + t10622) ** (0.5e1 / 0.2e1)) * t10622 * (15 + (-170 + 323 * t10623) * t10623) + + if Bindx == 1656: + t10633 = np.sin(phi) + t10631 = t10633 ** 2 + t10625 = np.cos(phi) + t10626 = t10625 ** 2 + t10628 = t10626 ** 2 + t10627 = t10625 * t10626 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.390e3) * t10631 ** 2 * (120 * t10626 - 595 * t10628 - 3 + (-170 + 646 * t10627) * t10627 + (323 * t10628 + 15) * t10625) + + if Bindx == 1657: + t10634 = np.cos(phi) + t10635 = t10634 ** 2 + t10637 = t10635 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.130e3) * ((1 - t10634) ** (0.3e1 / 0.2e1)) * ((1 + t10634) ** (0.7e1 / 0.2e1)) * (238 * t10635 - 969 * t10637 - 5 + (-102 * t10635 + 969 * t10637 - 19) * t10634) + + if Bindx == 1658: + t10640 = np.cos(phi) + t10641 = t10640 ** 2 + t10642 = t10640 * t10641 + t10645 = t10642 ** 2 + t10643 = t10641 ** 2 + t10639 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.128e3) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.5e1) * t10639 ** 2 * (-37 * t10641 + 1525 * t10642 - 2091 * t10645 + 1 + (525 + 1938 * t10643) * t10643 + (-3961 * t10643 + 2907 * t10645 - 135) * t10640) + + if Bindx == 1659: + t10648 = np.cos(phi) + t10649 = t10648 ** 2 + t10651 = t10649 ** 2 + tfunc[..., c] = (-0.21e2 / 0.128e3*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t10648)) * ((1 + t10648) ** (0.9e1 / 0.2e1)) * (-68 * t10649 - 1938 * t10651 + 10 + (1122 * t10649 + 969 * t10651 - 83) * t10648) + + if Bindx == 1660: + t10653 = np.cos(phi) + t10654 = t10653 ** 2 + t10656 = t10654 ** 2 + t10660 = t10656 ** 2 + t10655 = t10653 * t10654 + t10658 = t10655 ** 2 + t10657 = t10653 * t10656 + tfunc[..., c] = (0.21e2 / 0.128e3) * np.exp((5*1j) * (phi1 + phi2)) * (-930 * t10654 - 1660 * t10655 + 3710 * t10656 - 3920 * t10658 - 765 * t10660 + 31 + (6594 + 1938 * t10657) * t10657 + (-9860 * t10658 + 4845 * t10660 + 145) * t10653) + + if Bindx == 1661: + t10663 = np.cos(phi) + t10664 = t10663 ** 2 + t10666 = t10664 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.5e1) * ((1 + t10663) ** (0.11e2 / 0.2e1)) * (-1462 * t10664 - 2907 * t10666 + 1 + (3162 * t10664 + 969 * t10666 + 237) * t10663) * ((1 - t10663) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1662: + t10669 = np.cos(phi) + t10670 = t10669 ** 2 + t10671 = t10669 * t10670 + t10674 = t10671 ** 2 + t10672 = t10670 ** 2 + t10668 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.85e2) * t10668 ** 2 * (119 * t10670 - 91 * t10671 + 357 * t10674 - 7 + (-455 + 114 * t10672) * t10672 + (-217 * t10672 + 399 * t10674 + 37) * t10669) + + if Bindx == 1663: + t10677 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * (4 + (-19 + 19 * t10677) * t10677) * ((1 + t10677) ** (0.13e2 / 0.2e1)) * np.sqrt(0.510e3) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * ((1 - t10677) ** (0.3e1 / 0.2e1)) + + if Bindx == 1664: + t10686 = np.sin(phi) + t10684 = t10686 ** 2 + t10678 = np.cos(phi) + t10679 = t10678 ** 2 + t10681 = t10679 ** 2 + t10680 = t10678 * t10679 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.4845e4) * t10684 ** 2 * (15 * t10681 - 1 + (10 + 2 * t10680) * t10680 + (9 * t10681 - 3) * t10678) + + if Bindx == 1665: + t10687 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((5*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t10687) ** (0.5e1 / 0.2e1)) * ((1 + t10687) ** (0.15e2 / 0.2e1)) + + if Bindx == 1666: + t10689 = np.cos(phi) + t10693 = -1 + t10689 + t10690 = t10693 ** 2 + t10691 = t10690 ** 2 + t10688 = 1 + t10689 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.4845e4) * t10691 ** 2 * t10688 ** 2 + + if Bindx == 1667: + t10694 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.969e3) * ((1 - t10694) ** (0.15e2 / 0.2e1)) * ((1 + t10694) ** (0.3e1 / 0.2e1)) * (3 + 5 * t10694) + + if Bindx == 1668: + t10696 = np.cos(phi) + t10697 = t10696 ** 2 + t10698 = t10696 * t10697 + t10701 = t10698 ** 2 + t10699 = t10697 ** 2 + t10695 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.102e3) * t10695 ** 2 * (-124 * t10697 + 520 * t10698 + 772 * t10701 + 31 + (-390 + 95 * t10699) * t10699 + (-376 * t10699 - 456 * t10701 - 72) * t10696) + + if Bindx == 1669: + t10704 = np.cos(phi) + t10705 = t10704 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * ((1 - t10704) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t10704)) * (513 * t10705 + 43 + (285 * t10705 + 279) * t10704) + + if Bindx == 1670: + t10707 = np.cos(phi) + t10708 = t10707 ** 2 + t10710 = t10708 ** 2 + t10714 = t10710 ** 2 + t10709 = t10707 * t10708 + t10712 = t10709 ** 2 + t10711 = t10707 * t10710 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((6*1j) * (phi1 - phi2)) * (-4503 * t10708 - 6168 * t10709 + 22442 * t10710 - 34902 * t10712 + 12393 * t10714 + 237 + (-1932 + 4845 * t10711) * t10711 + (23528 * t10712 - 17442 * t10714 + 1502) * t10707) + + if Bindx == 1671: + t10717 = np.cos(phi) + t10718 = t10717 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.5e1) * ((1 - t10717) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t10717)) * (238 * t10717 - 1 + (1938 * t10717 + 1224 + 969 * t10718) * t10718) + + if Bindx == 1672: + t10722 = np.cos(phi) + t10723 = t10722 ** 2 + t10724 = t10722 * t10723 + t10727 = t10724 ** 2 + t10725 = t10723 ** 2 + t10721 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.2e1) * t10721 ** 2 * (2324 * t10723 - 4564 * t10724 + 1428 * t10727 - 83 + (-6594 + 4845 * t10725) * t10725 + (13804 * t10725 - 11628 * t10727 + 468) * t10722) + + if Bindx == 1673: + t10730 = np.cos(phi) + t10731 = t10730 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * ((1 - t10730) ** (0.9e1 / 0.2e1)) * ((1 + t10730) ** (0.3e1 / 0.2e1)) * (-374 * t10730 - 69 + (5814 * t10730 + 1224 + 4845 * t10731) * t10731) + + if Bindx == 1674: + t10742 = np.sin(phi) + t10740 = t10742 ** 2 + t10734 = np.cos(phi) + t10735 = t10734 ** 2 + t10737 = t10735 ** 2 + t10736 = t10734 * t10735 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.26e2) * t10740 ** 2 * (627 * t10735 - 3213 * t10737 - 19 + (4012 + 4845 * t10736) * t10736 + (-5814 * t10737 - 438) * t10734) + + if Bindx == 1675: + t10743 = np.cos(phi) + t10744 = t10743 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.78e2) * ((1 - t10743) ** (0.7e1 / 0.2e1)) * ((1 + t10743) ** (0.5e1 / 0.2e1)) * (-102 * t10743 + 9 + (646 * t10743 - 408 + 1615 * t10744) * t10744) + + if Bindx == 1676: + t10753 = np.sin(phi) + t10750 = t10753 ** 2 + t10751 = t10753 * t10750 + t10747 = np.cos(phi) + t10748 = t10747 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((6*1j) * phi1) * np.sqrt(0.2145e4) * t10751 ** 2 * (3 + (-102 + 323 * t10748) * t10748) + + if Bindx == 1677: + t10754 = np.cos(phi) + t10755 = t10754 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.78e2) * ((1 - t10754) ** (0.5e1 / 0.2e1)) * ((1 + t10754) ** (0.7e1 / 0.2e1)) * (102 * t10754 + 9 + (-646 * t10754 - 408 + 1615 * t10755) * t10755) + + if Bindx == 1678: + t10766 = np.sin(phi) + t10764 = t10766 ** 2 + t10758 = np.cos(phi) + t10759 = t10758 ** 2 + t10761 = t10759 ** 2 + t10760 = t10758 * t10759 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.26e2) * t10764 ** 2 * (627 * t10759 - 3213 * t10761 - 19 + (-4012 + 4845 * t10760) * t10760 + (5814 * t10761 + 438) * t10758) + + if Bindx == 1679: + t10767 = np.cos(phi) + t10768 = t10767 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * ((1 - t10767) ** (0.3e1 / 0.2e1)) * ((1 + t10767) ** (0.9e1 / 0.2e1)) * (374 * t10767 - 69 + (-5814 * t10767 + 1224 + 4845 * t10768) * t10768) + + if Bindx == 1680: + t10772 = np.cos(phi) + t10773 = t10772 ** 2 + t10774 = t10772 * t10773 + t10777 = t10774 ** 2 + t10775 = t10773 ** 2 + t10771 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2e1) * t10771 ** 2 * (2324 * t10773 + 4564 * t10774 + 1428 * t10777 - 83 + (-6594 + 4845 * t10775) * t10775 + (-13804 * t10775 + 11628 * t10777 - 468) * t10772) + + if Bindx == 1681: + t10780 = np.cos(phi) + t10781 = t10780 ** 2 + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t10780)) * ((1 + t10780) ** (0.11e2 / 0.2e1)) * (-238 * t10780 - 1 + (-1938 * t10780 + 1224 + 969 * t10781) * t10781) + + if Bindx == 1682: + t10784 = np.cos(phi) + t10785 = t10784 ** 2 + t10787 = t10785 ** 2 + t10791 = t10787 ** 2 + t10786 = t10784 * t10785 + t10789 = t10786 ** 2 + t10788 = t10784 * t10787 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((6*1j) * (phi1 + phi2)) * (-4503 * t10785 + 6168 * t10786 + 22442 * t10787 - 34902 * t10789 + 12393 * t10791 + 237 + (1932 + 4845 * t10788) * t10788 + (-23528 * t10789 + 17442 * t10791 - 1502) * t10784) + + if Bindx == 1683: + t10794 = np.cos(phi) + t10795 = t10794 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * ((1 + t10794) ** (0.13e2 / 0.2e1)) * (-322 * t10794 + 43 + (-798 * t10794 + 792 + 285 * t10795) * t10795) * ((1 - t10794) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1684: + t10799 = np.cos(phi) + t10800 = t10799 ** 2 + t10801 = t10799 * t10800 + t10804 = t10801 ** 2 + t10802 = t10800 ** 2 + t10798 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.102e3) * t10798 ** 2 * (-124 * t10800 - 520 * t10801 + 772 * t10804 + 31 + (-390 + 95 * t10802) * t10802 + (376 * t10802 + 456 * t10804 + 72) * t10799) + + if Bindx == 1685: + t10807 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * (-3 + 5 * t10807) * ((1 + t10807) ** (0.15e2 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((3*1j) * (2 * phi1 + 3 * phi2)) * ((1 - t10807) ** (0.3e1 / 0.2e1)) + + if Bindx == 1686: + t10809 = np.cos(phi) + t10813 = 1 + t10809 + t10810 = t10813 ** 2 + t10811 = t10810 ** 2 + t10808 = -1 + t10809 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.4845e4) * t10808 ** 2 * t10811 ** 2 + + if Bindx == 1687: + t10814 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 10 * phi2)) * np.sqrt(0.285e3) * ((1 - t10814) ** (0.17e2 / 0.2e1)) * ((1 + t10814) ** (0.3e1 / 0.2e1)) + + if Bindx == 1688: + t10815 = np.cos(phi) + t10819 = -1 + t10815 + t10816 = t10819 ** 2 + t10817 = t10816 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.57e2) * t10817 ** 2 * (1 + t10815) * (7 + 10 * t10815) + + if Bindx == 1689: + t10820 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * np.sqrt(0.6e1) * ((1 - t10820) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t10820)) * (44 + (133 + 95 * t10820) * t10820) + + if Bindx == 1690: + t10821 = np.cos(phi) + t10822 = t10821 ** 2 + t10824 = t10822 ** 2 + t10828 = t10824 ** 2 + t10823 = t10821 * t10822 + t10826 = t10823 ** 2 + t10825 = t10821 * t10824 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((7*1j) * (phi1 - phi2)) * (966 * t10822 - 3188 * t10823 + 938 * t10824 - 6440 * t10826 + 4383 * t10828 - 161 + (5586 + 570 * t10825) * t10825 + (-196 * t10826 - 2793 * t10828 + 335) * t10821) + + if Bindx == 1691: + t10831 = np.cos(phi) + t10832 = t10831 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.17e2) * ((1 - t10831) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t10831)) * (513 * t10832 + 43 + (285 * t10832 + 279) * t10831) + + if Bindx == 1692: + t10835 = np.cos(phi) + t10836 = t10835 ** 2 + t10837 = t10835 * t10836 + t10840 = t10837 ** 2 + t10838 = t10836 ** 2 + t10834 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t10834 ** 2 * (119 * t10836 + 91 * t10837 + 357 * t10840 - 7 + (-455 + 114 * t10838) * t10838 + (217 * t10838 - 399 * t10840 - 37) * t10835) + + if Bindx == 1693: + t10843 = np.cos(phi) + t10844 = t10843 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.34e2) * ((1 - t10843) ** (0.11e2 / 0.2e1)) * ((1 + t10843) ** (0.3e1 / 0.2e1)) * (342 * t10844 + 2 + (285 * t10844 + 99) * t10843) + + if Bindx == 1694: + t10854 = np.sin(phi) + t10852 = t10854 ** 2 + t10846 = np.cos(phi) + t10847 = t10846 ** 2 + t10849 = t10847 ** 2 + t10848 = t10846 * t10847 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.17e2) * t10852 ** 2 * (-264 * t10847 + 243 * t10849 + 11 + (742 + 570 * t10848) * t10848 + (-1197 * t10849 - 105) * t10846) + + if Bindx == 1695: + t10855 = np.cos(phi) + t10856 = t10855 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.442e3) * ((1 - t10855) ** (0.9e1 / 0.2e1)) * ((1 + t10855) ** (0.5e1 / 0.2e1)) * (171 * t10856 - 7 + (285 * t10856 - 9) * t10855) + + if Bindx == 1696: + t10865 = np.sin(phi) + t10862 = t10865 ** 2 + t10863 = t10865 * t10862 + t10858 = np.cos(phi) + t10859 = t10858 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.1326e4) * t10863 ** 2 * (21 * t10858 + 3 + (-133 * t10858 - 81 + 190 * t10859) * t10859) + + if Bindx == 1697: + t10866 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.36465e5) * ((1 - t10866) ** (0.7e1 / 0.2e1)) * ((1 + t10866) ** (0.7e1 / 0.2e1)) * t10866 * (19 * t10866 ** 2 - 3) + + if Bindx == 1698: + t10874 = np.sin(phi) + t10871 = t10874 ** 2 + t10872 = t10874 * t10871 + t10867 = np.cos(phi) + t10868 = t10867 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.1326e4) * t10872 ** 2 * (-21 * t10867 + 3 + (133 * t10867 - 81 + 190 * t10868) * t10868) + + if Bindx == 1699: + t10875 = np.cos(phi) + t10876 = t10875 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.442e3) * ((1 - t10875) ** (0.5e1 / 0.2e1)) * ((1 + t10875) ** (0.9e1 / 0.2e1)) * (-171 * t10876 + 7 + (285 * t10876 - 9) * t10875) + + if Bindx == 1700: + t10886 = np.sin(phi) + t10884 = t10886 ** 2 + t10878 = np.cos(phi) + t10879 = t10878 ** 2 + t10881 = t10879 ** 2 + t10880 = t10878 * t10879 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.17e2) * t10884 ** 2 * (-264 * t10879 + 243 * t10881 + 11 + (-742 + 570 * t10880) * t10880 + (1197 * t10881 + 105) * t10878) + + if Bindx == 1701: + t10887 = np.cos(phi) + t10888 = t10887 ** 2 + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.34e2) * ((1 - t10887) ** (0.3e1 / 0.2e1)) * ((1 + t10887) ** (0.11e2 / 0.2e1)) * (-342 * t10888 - 2 + (285 * t10888 + 99) * t10887) + + if Bindx == 1702: + t10891 = np.cos(phi) + t10892 = t10891 ** 2 + t10893 = t10891 * t10892 + t10896 = t10893 ** 2 + t10894 = t10892 ** 2 + t10890 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t10890 ** 2 * (119 * t10892 - 91 * t10893 + 357 * t10896 - 7 + (-455 + 114 * t10894) * t10894 + (-217 * t10894 + 399 * t10896 + 37) * t10891) + + if Bindx == 1703: + t10899 = np.cos(phi) + t10900 = t10899 ** 2 + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.17e2) * np.sqrt((1 - t10899)) * ((1 + t10899) ** (0.13e2 / 0.2e1)) * (-513 * t10900 - 43 + (285 * t10900 + 279) * t10899) + + if Bindx == 1704: + t10902 = np.cos(phi) + t10903 = t10902 ** 2 + t10905 = t10903 ** 2 + t10909 = t10905 ** 2 + t10904 = t10902 * t10903 + t10907 = t10904 ** 2 + t10906 = t10902 * t10905 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((7*1j) * (phi1 + phi2)) * (966 * t10903 + 3188 * t10904 + 938 * t10905 - 6440 * t10907 + 4383 * t10909 - 161 + (-5586 + 570 * t10906) * t10906 + (196 * t10907 + 2793 * t10909 - 335) * t10902) + + if Bindx == 1705: + t10912 = np.cos(phi) + t10913 = t10912 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.6e1) * ((1 + t10912) ** (0.15e2 / 0.2e1)) * (-228 * t10913 - 44 + (95 * t10913 + 177) * t10912) * ((1 - t10912) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1706: + t10915 = np.cos(phi) + t10919 = 1 + t10915 + t10916 = t10919 ** 2 + t10917 = t10916 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.57e2) * (-1 + t10915) * t10917 ** 2 * (-7 + 10 * t10915) + + if Bindx == 1707: + t10920 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 10 * phi2)) * np.sqrt(0.285e3) * ((1 - t10920) ** (0.3e1 / 0.2e1)) * ((1 + t10920) ** (0.17e2 / 0.2e1)) + + if Bindx == 1708: + t10921 = np.cos(phi) + t10926 = -1 + t10921 + t10922 = t10926 ** 2 + t10923 = t10922 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.190e3) * t10926 * t10923 ** 2 * (1 + t10921) + + if Bindx == 1709: + t10927 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.38e2) * ((1 - t10927) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t10927)) * (4 + 5 * t10927) + + if Bindx == 1710: + t10928 = np.cos(phi) + t10929 = t10928 ** 2 + t10931 = t10929 ** 2 + t10935 = t10931 ** 2 + t10930 = t10928 * t10929 + t10933 = t10930 ** 2 + t10932 = t10928 * t10931 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((8*1j) * (phi1 - phi2)) * (531 * t10929 + 192 * t10930 - 1722 * t10931 - 210 * t10933 + 1503 * t10935 + 59 + (2016 + 95 * t10932) * t10932 + (-1536 * t10933 - 608 * t10935 - 320) * t10928) + + if Bindx == 1711: + t10938 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * np.sqrt(0.6e1) * ((1 - t10938) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t10938)) * (44 + (133 + 95 * t10938) * t10938) + + if Bindx == 1712: + t10940 = np.cos(phi) + t10941 = t10940 ** 2 + t10942 = t10940 * t10941 + t10945 = t10942 ** 2 + t10943 = t10941 ** 2 + t10939 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.102e3) * t10939 ** 2 * (-124 * t10941 + 520 * t10942 + 772 * t10945 + 31 + (-390 + 95 * t10943) * t10943 + (-376 * t10943 - 456 * t10945 - 72) * t10940) + + if Bindx == 1713: + t10948 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.510e3) * ((1 - t10948) ** (0.13e2 / 0.2e1)) * ((1 + t10948) ** (0.3e1 / 0.2e1)) * (4 + (19 + 19 * t10948) * t10948) + + if Bindx == 1714: + t10957 = np.sin(phi) + t10955 = t10957 ** 2 + t10949 = np.cos(phi) + t10950 = t10949 ** 2 + t10952 = t10950 ** 2 + t10951 = t10949 * t10950 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.51e2) * t10955 ** 2 * (-143 * t10950 + 277 * t10952 + 11 + (32 + 95 * t10951) * t10951 + (-304 * t10952 + 32) * t10949) + + if Bindx == 1715: + t10958 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.102e3) * ((1 - t10958) ** (0.11e2 / 0.2e1)) * ((1 + t10958) ** (0.5e1 / 0.2e1)) * (4 + (57 + 95 * t10958) * t10958) + + if Bindx == 1716: + t10966 = np.sin(phi) + t10963 = t10966 ** 2 + t10964 = t10966 * t10963 + t10959 = np.cos(phi) + t10960 = t10959 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.663e3) * t10964 ** 2 * (40 * t10959 - 1 + (-152 * t10959 + 18 + 95 * t10960) * t10960) + + if Bindx == 1717: + t10967 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.221e3) * ((1 - t10967) ** (0.9e1 / 0.2e1)) * ((1 + t10967) ** (0.7e1 / 0.2e1)) * (-4 + (19 + 95 * t10967) * t10967) + + if Bindx == 1718: + t10972 = np.sin(phi) + t10969 = t10972 ** 2 + t10970 = t10969 ** 2 + t10968 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((8*1j) * phi1) * np.sqrt(0.24310e5) * t10970 ** 2 * (19 * t10968 ** 2 - 1) + + if Bindx == 1719: + t10973 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.221e3) * ((1 - t10973) ** (0.7e1 / 0.2e1)) * ((1 + t10973) ** (0.9e1 / 0.2e1)) * (-4 + (-19 + 95 * t10973) * t10973) + + if Bindx == 1720: + t10981 = np.sin(phi) + t10978 = t10981 ** 2 + t10979 = t10981 * t10978 + t10974 = np.cos(phi) + t10975 = t10974 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.663e3) * t10979 ** 2 * (-40 * t10974 - 1 + (152 * t10974 + 18 + 95 * t10975) * t10975) + + if Bindx == 1721: + t10982 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.102e3) * ((1 - t10982) ** (0.5e1 / 0.2e1)) * ((1 + t10982) ** (0.11e2 / 0.2e1)) * (4 + (-57 + 95 * t10982) * t10982) + + if Bindx == 1722: + t10991 = np.sin(phi) + t10989 = t10991 ** 2 + t10983 = np.cos(phi) + t10984 = t10983 ** 2 + t10986 = t10984 ** 2 + t10985 = t10983 * t10984 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.51e2) * t10989 ** 2 * (-143 * t10984 + 277 * t10986 + 11 + (-32 + 95 * t10985) * t10985 + (304 * t10986 - 32) * t10983) + + if Bindx == 1723: + t10992 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.510e3) * ((1 - t10992) ** (0.3e1 / 0.2e1)) * ((1 + t10992) ** (0.13e2 / 0.2e1)) * (4 + (-19 + 19 * t10992) * t10992) + + if Bindx == 1724: + t10994 = np.cos(phi) + t10995 = t10994 ** 2 + t10996 = t10994 * t10995 + t10999 = t10996 ** 2 + t10997 = t10995 ** 2 + t10993 = np.sin(phi) + tfunc[..., c] = -(0.21e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.102e3) * t10993 ** 2 * (-124 * t10995 - 520 * t10996 + 772 * t10999 + 31 + (-390 + 95 * t10997) * t10997 + (376 * t10997 + 456 * t10999 + 72) * t10994) + + if Bindx == 1725: + t11002 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t11002)) * ((1 + t11002) ** (0.15e2 / 0.2e1)) * (44 + (-133 + 95 * t11002) * t11002) + + if Bindx == 1726: + t11003 = np.cos(phi) + t11004 = t11003 ** 2 + t11006 = t11004 ** 2 + t11010 = t11006 ** 2 + t11005 = t11003 * t11004 + t11008 = t11005 ** 2 + t11007 = t11003 * t11006 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((8*1j) * (phi1 + phi2)) * (531 * t11004 - 192 * t11005 - 1722 * t11006 - 210 * t11008 + 1503 * t11010 + 59 + (-2016 + 95 * t11007) * t11007 + (1536 * t11008 + 608 * t11010 + 320) * t11003) + + if Bindx == 1727: + t11013 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.38e2) * ((1 + t11013) ** (0.17e2 / 0.2e1)) * (4 + (-9 + 5 * t11013) * t11013) * ((1 - t11013) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1728: + t11014 = np.cos(phi) + t11019 = 1 + t11014 + t11015 = t11019 ** 2 + t11016 = t11015 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.190e3) * (-1 + t11014) * t11019 * t11016 ** 2 + + if Bindx == 1729: + t11020 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 - 10 * phi2)) * np.sqrt(0.5e1) * ((1 - t11020) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t11020)) + + if Bindx == 1730: + t11021 = np.cos(phi) + t11022 = t11021 ** 2 + t11024 = t11022 ** 2 + t11028 = t11024 ** 2 + t11023 = t11021 * t11022 + t11026 = t11023 ** 2 + t11025 = t11021 * t11024 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((9*1j) * (phi1 - phi2)) * (-234 * t11022 + 396 * t11023 - 294 * t11024 + 504 * t11026 + 279 * t11028 - 9 + (-126 + 10 * t11025) * t11025 + (-516 * t11026 - 81 * t11028 + 71) * t11021) + + if Bindx == 1731: + t11031 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.38e2) * ((1 - t11031) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t11031)) * (4 + 5 * t11031) + + if Bindx == 1732: + t11032 = np.cos(phi) + t11036 = -1 + t11032 + t11033 = t11036 ** 2 + t11034 = t11033 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * (7 + 10 * t11032) * (1 + t11032) * t11034 ** 2 * np.sqrt(0.57e2) * np.exp((1j) * (9 * phi1 - 7 * phi2)) + + if Bindx == 1733: + t11037 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t11037) ** (0.15e2 / 0.2e1)) * ((1 + t11037) ** (0.3e1 / 0.2e1)) * (3 + 5 * t11037) + + if Bindx == 1734: + t11046 = np.sin(phi) + t11044 = t11046 ** 2 + t11038 = np.cos(phi) + t11039 = t11038 ** 2 + t11041 = t11039 ** 2 + t11040 = t11038 * t11039 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.4845e4) * t11044 ** 2 * (15 * t11041 - 1 + (-10 + 2 * t11040) * t11040 + (-9 * t11041 + 3) * t11038) + + if Bindx == 1735: + t11047 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.1938e4) * ((1 - t11047) ** (0.13e2 / 0.2e1)) * ((1 + t11047) ** (0.5e1 / 0.2e1)) * (2 + 5 * t11047) + + if Bindx == 1736: + t11055 = np.sin(phi) + t11052 = t11055 ** 2 + t11053 = t11055 * t11052 + t11048 = np.cos(phi) + t11049 = t11048 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.969e3) * t11053 ** 2 * (-t11048 - 3 + (-27 * t11048 + 21 + 10 * t11049) * t11049) + + if Bindx == 1737: + t11056 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.25194e5) * ((1 - t11056) ** (0.11e2 / 0.2e1)) * ((1 + t11056) ** (0.7e1 / 0.2e1)) * (1 + 5 * t11056) + + if Bindx == 1738: + t11061 = np.sin(phi) + t11058 = t11061 ** 2 + t11059 = t11058 ** 2 + t11057 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (9 * phi1 - phi2)) * np.sqrt(0.8398e4) * t11059 ** 2 * (-1 + (-9 + 10 * t11057) * t11057) + + if Bindx == 1739: + t11062 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.230945e6) * ((1 - t11062) ** (0.9e1 / 0.2e1)) * ((1 + t11062) ** (0.9e1 / 0.2e1)) * t11062 + + if Bindx == 1740: + t11067 = np.sin(phi) + t11064 = t11067 ** 2 + t11065 = t11064 ** 2 + t11063 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (9 * phi1 + phi2)) * np.sqrt(0.8398e4) * t11065 ** 2 * (-1 + (9 + 10 * t11063) * t11063) + + if Bindx == 1741: + t11068 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.25194e5) * ((1 - t11068) ** (0.7e1 / 0.2e1)) * ((1 + t11068) ** (0.11e2 / 0.2e1)) * (-1 + 5 * t11068) + + if Bindx == 1742: + t11076 = np.sin(phi) + t11073 = t11076 ** 2 + t11074 = t11076 * t11073 + t11069 = np.cos(phi) + t11070 = t11069 ** 2 + tfunc[..., c] = -(0.21e2 / 0.256e3) * np.exp((3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.969e3) * t11074 ** 2 * (t11069 - 3 + (27 * t11069 + 21 + 10 * t11070) * t11070) + + if Bindx == 1743: + t11077 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.1938e4) * ((1 - t11077) ** (0.5e1 / 0.2e1)) * ((1 + t11077) ** (0.13e2 / 0.2e1)) * (-2 + 5 * t11077) + + if Bindx == 1744: + t11086 = np.sin(phi) + t11084 = t11086 ** 2 + t11078 = np.cos(phi) + t11079 = t11078 ** 2 + t11081 = t11079 ** 2 + t11080 = t11078 * t11079 + tfunc[..., c] = (0.21e2 / 0.256e3) * np.exp((1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.4845e4) * t11084 ** 2 * (15 * t11081 - 1 + (10 + 2 * t11080) * t11080 + (9 * t11081 - 3) * t11078) + + if Bindx == 1745: + t11087 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t11087) ** (0.3e1 / 0.2e1)) * ((1 + t11087) ** (0.15e2 / 0.2e1)) * (-3 + 5 * t11087) + + if Bindx == 1746: + t11088 = np.cos(phi) + t11092 = 1 + t11088 + t11089 = t11092 ** 2 + t11090 = t11089 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.57e2) * (-1 + t11088) * t11090 ** 2 * (-7 + 10 * t11088) + + if Bindx == 1747: + t11093 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.38e2) * np.sqrt((1 - t11093)) * ((1 + t11093) ** (0.17e2 / 0.2e1)) * (-4 + 5 * t11093) + + if Bindx == 1748: + t11094 = np.cos(phi) + t11095 = t11094 ** 2 + t11097 = t11095 ** 2 + t11101 = t11097 ** 2 + t11096 = t11094 * t11095 + t11099 = t11096 ** 2 + t11098 = t11094 * t11097 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((9*1j) * (phi1 + phi2)) * (-234 * t11095 - 396 * t11096 - 294 * t11097 + 504 * t11099 + 279 * t11101 - 9 + (126 + 10 * t11098) * t11098 + (516 * t11099 + 81 * t11101 - 71) * t11094) + + if Bindx == 1749: + t11104 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t11104)) * ((1 + t11104) ** (0.19e2 / 0.2e1)) + + if Bindx == 1750: + t11105 = np.cos(phi) + t11115 = -10 * t11105 + t11106 = t11105 ** 2 + t11108 = t11106 ** 2 + t11109 = t11105 * t11108 + t11107 = t11105 * t11106 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((10*1j) * (phi1 - phi2)) * (45 * t11106 + t11115 + 1 + (-252 + t11109) * t11109 + (210 + (t11115 + 45) * t11108) * t11108 + (-120 + (-120 * t11105 + 210) * t11107) * t11107) + + if Bindx == 1751: + t11116 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 - 9 * phi2)) * np.sqrt(0.5e1) * ((1 - t11116) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t11116)) + + if Bindx == 1752: + t11117 = np.cos(phi) + t11122 = -1 + t11117 + t11118 = t11122 ** 2 + t11119 = t11118 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.190e3) * t11122 * t11119 ** 2 * (1 + t11117) + + if Bindx == 1753: + t11123 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 - 7 * phi2)) * np.sqrt(0.285e3) * ((1 - t11123) ** (0.17e2 / 0.2e1)) * ((1 + t11123) ** (0.3e1 / 0.2e1)) + + if Bindx == 1754: + t11125 = np.cos(phi) + t11129 = -1 + t11125 + t11126 = t11129 ** 2 + t11127 = t11126 ** 2 + t11124 = 1 + t11125 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.4845e4) * t11127 ** 2 * t11124 ** 2 + + if Bindx == 1755: + t11130 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((5*1j) * (2 * phi1 - phi2)) * np.sqrt(0.969e3) * ((1 - t11130) ** (0.15e2 / 0.2e1)) * ((1 + t11130) ** (0.5e1 / 0.2e1)) + + if Bindx == 1756: + t11131 = np.cos(phi) + t11139 = -1 + t11131 + t11138 = 1 + t11131 + t11136 = t11138 ** 2 + t11132 = t11139 ** 2 + t11133 = t11139 * t11132 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.9690e4) * t11139 * t11133 ** 2 * t11138 * t11136 + + if Bindx == 1757: + t11140 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((1j) * (10 * phi1 - 3 * phi2)) * np.sqrt(0.4845e4) * ((1 - t11140) ** (0.13e2 / 0.2e1)) * ((1 + t11140) ** (0.7e1 / 0.2e1)) + + if Bindx == 1758: + t11141 = np.cos(phi) + t11148 = -1 + t11141 + t11147 = 1 + t11141 + t11145 = t11147 ** 2 + t11142 = t11148 ** 2 + t11143 = t11148 * t11142 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.125970e6) * t11143 ** 2 * t11145 ** 2 + + if Bindx == 1759: + t11149 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 - phi2)) * np.sqrt(0.41990e5) * ((1 - t11149) ** (0.11e2 / 0.2e1)) * ((1 + t11149) ** (0.9e1 / 0.2e1)) + + if Bindx == 1760: + t11154 = np.sin(phi) + t11150 = t11154 ** 2 + t11152 = t11154 * t11150 ** 2 + tfunc[..., c] = -(0.21e2 / 0.512e3) * np.exp((10*1j) * phi1) * np.sqrt(0.46189e5) * t11152 ** 2 + + if Bindx == 1761: + t11155 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 + phi2)) * np.sqrt(0.41990e5) * ((1 - t11155) ** (0.9e1 / 0.2e1)) * ((1 + t11155) ** (0.11e2 / 0.2e1)) + + if Bindx == 1762: + t11156 = np.cos(phi) + t11163 = -1 + t11156 + t11162 = 1 + t11156 + t11159 = t11162 ** 2 + t11160 = t11162 * t11159 + t11157 = t11163 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.125970e6) * t11157 ** 2 * t11160 ** 2 + + if Bindx == 1763: + t11164 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.256e3*1j) * np.exp((1j) * (10 * phi1 + 3 * phi2)) * np.sqrt(0.4845e4) * ((1 - t11164) ** (0.7e1 / 0.2e1)) * ((1 + t11164) ** (0.13e2 / 0.2e1)) + + if Bindx == 1764: + t11165 = np.cos(phi) + t11173 = -1 + t11165 + t11172 = 1 + t11165 + t11168 = t11172 ** 2 + t11169 = t11172 * t11168 + t11166 = t11173 ** 2 + tfunc[..., c] = (0.21e2 / 0.512e3) * np.exp((2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.9690e4) * t11173 * t11166 * t11172 * t11169 ** 2 + + if Bindx == 1765: + t11174 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.256e3*1j) * np.exp((5*1j) * (2 * phi1 + phi2)) * np.sqrt(0.969e3) * ((1 - t11174) ** (0.5e1 / 0.2e1)) * ((1 + t11174) ** (0.15e2 / 0.2e1)) + + if Bindx == 1766: + t11176 = np.cos(phi) + t11180 = 1 + t11176 + t11177 = t11180 ** 2 + t11178 = t11177 ** 2 + t11175 = -1 + t11176 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.4845e4) * t11175 ** 2 * t11178 ** 2 + + if Bindx == 1767: + t11181 = np.cos(phi) + tfunc[..., c] = (0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 + 7 * phi2)) * np.sqrt(0.285e3) * ((1 - t11181) ** (0.3e1 / 0.2e1)) * ((1 + t11181) ** (0.17e2 / 0.2e1)) + + if Bindx == 1768: + t11182 = np.cos(phi) + t11187 = 1 + t11182 + t11183 = t11187 ** 2 + t11184 = t11183 ** 2 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.190e3) * (-1 + t11182) * t11187 * t11184 ** 2 + + if Bindx == 1769: + t11188 = np.cos(phi) + tfunc[..., c] = (-0.21e2 / 0.512e3*1j) * np.exp((1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t11188)) * ((1 + t11188) ** (0.19e2 / 0.2e1)) + + if Bindx == 1770: + t11189 = np.cos(phi) + t11199 = 10 * t11189 + t11190 = t11189 ** 2 + t11192 = t11190 ** 2 + t11193 = t11189 * t11192 + t11191 = t11189 * t11190 + tfunc[..., c] = (0.21e2 / 0.1024e4) * np.exp((10*1j) * (phi1 + phi2)) * (45 * t11190 + t11199 + 1 + (252 + t11193) * t11193 + (210 + (t11199 + 45) * t11192) * t11192 + (120 + (120 * t11189 + 210) * t11191) * t11191) + + if Bindx == 1771: + t11200 = np.cos(phi) + t11201 = t11200 ** 2 + t11203 = t11201 ** 2 + t11204 = t11200 * t11203 + t11209 = t11204 ** 2 + t11207 = t11203 ** 2 + t11202 = t11200 * t11201 + t11205 = t11202 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-11*1j) * (phi1 + phi2)) * (55 * t11201 + 165 * t11202 + 330 * t11203 + 462 * t11204 + 462 * t11205 + 165 * t11207 + 11 * t11209 + 1 + (330 * t11205 + 55 * t11207 + t11209 + 11) * t11200) + + if Bindx == 1772: + t11211 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.22e2) * ((1 + t11211) ** (0.21e2 / 0.2e1)) * np.sqrt((1 - t11211)) + + if Bindx == 1773: + t11212 = np.cos(phi) + t11217 = 1 + t11212 + t11213 = t11217 ** 2 + t11215 = t11217 * t11213 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 9 * phi2)) * np.sqrt(0.231e3) * (-1 + t11212) * t11215 ** 2 + + if Bindx == 1774: + t11218 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (11 * phi1 + 8 * phi2)) * np.sqrt(0.385e3) * ((1 - t11218) ** (0.3e1 / 0.2e1)) * ((1 + t11218) ** (0.19e2 / 0.2e1)) + + if Bindx == 1775: + t11220 = np.cos(phi) + t11225 = 1 + t11220 + t11221 = t11225 ** 2 + t11222 = t11221 ** 2 + t11219 = -1 + t11220 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.7315e4) * t11219 ** 2 * t11225 * t11222 ** 2 + + if Bindx == 1776: + t11226 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 + 6 * phi2)) * np.sqrt(0.2926e4) * ((1 - t11226) ** (0.5e1 / 0.2e1)) * ((1 + t11226) ** (0.17e2 / 0.2e1)) + + if Bindx == 1777: + t11227 = np.cos(phi) + t11234 = -1 + t11227 + t11233 = 1 + t11227 + t11230 = t11233 ** 2 + t11231 = t11230 ** 2 + t11228 = t11234 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.74613e5) * t11234 * t11228 * t11231 ** 2 + + if Bindx == 1778: + t11235 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (11 * phi1 + 4 * phi2)) * np.sqrt(0.10659e5) * ((1 - t11235) ** (0.7e1 / 0.2e1)) * ((1 + t11235) ** (0.15e2 / 0.2e1)) + + if Bindx == 1779: + t11236 = np.cos(phi) + t11244 = -1 + t11236 + t11243 = 1 + t11236 + t11239 = t11243 ** 2 + t11240 = t11243 * t11239 + t11237 = t11244 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t11237 ** 2 * t11243 * t11240 ** 2 + + if Bindx == 1780: + t11245 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (11 * phi1 + 2 * phi2)) * np.sqrt(0.124355e6) * ((1 - t11245) ** (0.9e1 / 0.2e1)) * ((1 + t11245) ** (0.13e2 / 0.2e1)) + + if Bindx == 1781: + t11250 = np.sin(phi) + t11246 = t11250 ** 2 + t11248 = t11250 * t11246 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + phi2)) * np.sqrt(0.646646e6) * t11248 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 1782: + t11251 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-11*1j) * phi1) * np.sqrt(0.176358e6) * ((1 - t11251) ** (0.11e2 / 0.2e1)) * ((1 + t11251) ** (0.11e2 / 0.2e1)) + + if Bindx == 1783: + t11256 = np.sin(phi) + t11252 = t11256 ** 2 + t11254 = t11256 * t11252 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - phi2)) * np.sqrt(0.646646e6) * t11254 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 1784: + t11257 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (11 * phi1 - 2 * phi2)) * np.sqrt(0.124355e6) * ((1 - t11257) ** (0.13e2 / 0.2e1)) * ((1 + t11257) ** (0.9e1 / 0.2e1)) + + if Bindx == 1785: + t11258 = np.cos(phi) + t11266 = -1 + t11258 + t11265 = 1 + t11258 + t11263 = t11265 ** 2 + t11259 = t11266 ** 2 + t11260 = t11266 * t11259 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t11266 * t11260 ** 2 * t11263 ** 2 + + if Bindx == 1786: + t11267 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (11 * phi1 - 4 * phi2)) * np.sqrt(0.10659e5) * ((1 - t11267) ** (0.15e2 / 0.2e1)) * ((1 + t11267) ** (0.7e1 / 0.2e1)) + + if Bindx == 1787: + t11268 = np.cos(phi) + t11275 = -1 + t11268 + t11274 = 1 + t11268 + t11272 = t11274 ** 2 + t11269 = t11275 ** 2 + t11270 = t11269 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.74613e5) * t11270 ** 2 * t11274 * t11272 + + if Bindx == 1788: + t11276 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 - 6 * phi2)) * np.sqrt(0.2926e4) * ((1 - t11276) ** (0.17e2 / 0.2e1)) * ((1 + t11276) ** (0.5e1 / 0.2e1)) + + if Bindx == 1789: + t11278 = np.cos(phi) + t11283 = -1 + t11278 + t11279 = t11283 ** 2 + t11280 = t11279 ** 2 + t11277 = 1 + t11278 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.7315e4) * t11283 * t11280 ** 2 * t11277 ** 2 + + if Bindx == 1790: + t11284 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (11 * phi1 - 8 * phi2)) * np.sqrt(0.385e3) * ((1 - t11284) ** (0.19e2 / 0.2e1)) * ((1 + t11284) ** (0.3e1 / 0.2e1)) + + if Bindx == 1791: + t11285 = np.cos(phi) + t11290 = -1 + t11285 + t11286 = t11290 ** 2 + t11288 = t11290 * t11286 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 9 * phi2)) * np.sqrt(0.231e3) * t11288 ** 2 * (1 + t11285) + + if Bindx == 1792: + t11291 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 - 10 * phi2)) * np.sqrt(0.22e2) * ((1 - t11291) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t11291)) + + if Bindx == 1793: + t11292 = np.cos(phi) + t11293 = t11292 ** 2 + t11295 = t11293 ** 2 + t11296 = t11292 * t11295 + t11301 = t11296 ** 2 + t11299 = t11295 ** 2 + t11294 = t11292 * t11293 + t11297 = t11294 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-11*1j) * (phi1 - phi2)) * (-55 * t11293 + 165 * t11294 - 330 * t11295 + 462 * t11296 - 462 * t11297 - 165 * t11299 - 11 * t11301 - 1 + (330 * t11297 + 55 * t11299 + t11301 + 11) * t11292) + + if Bindx == 1794: + t11303 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t11303)) * ((1 + t11303) ** (0.21e2 / 0.2e1)) + + if Bindx == 1795: + t11304 = np.cos(phi) + t11305 = t11304 ** 2 + t11307 = t11305 ** 2 + t11308 = t11304 * t11307 + t11313 = t11308 ** 2 + t11311 = t11307 ** 2 + t11306 = t11304 * t11305 + t11309 = t11306 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 + phi2)) * (-340 * t11305 - 705 * t11306 - 780 * t11307 - 210 * t11308 + 672 * t11309 + 870 * t11311 + 100 * t11313 - 10 + (1110 * t11309 + 395 * t11311 + 11 * t11313 - 89) * t11304) + + if Bindx == 1796: + t11315 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.42e2) * ((1 + t11315) ** (0.19e2 / 0.2e1)) * (9 + (-20 + 11 * t11315) * t11315) * ((1 - t11315) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1797: + t11316 = np.cos(phi) + t11321 = 1 + t11316 + t11317 = t11321 ** 2 + t11318 = t11317 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * (-8 + 11 * t11316) * (-1 + t11316) * t11321 * t11318 ** 2 * np.sqrt(0.70e2) * np.exp((-2*1j) * (5 * phi1 + 4 * phi2)) + + if Bindx == 1798: + t11322 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (-7 + 11 * t11322) * ((1 + t11322) ** (0.17e2 / 0.2e1)) * np.sqrt(0.1330e4) * np.exp((-1*1j) * (10 * phi1 + 7 * phi2)) * ((1 - t11322) ** (0.3e1 / 0.2e1)) + + if Bindx == 1799: + t11324 = np.cos(phi) + t11328 = 1 + t11324 + t11325 = t11328 ** 2 + t11326 = t11325 ** 2 + t11323 = -1 + t11324 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.133e3) * t11326 ** 2 * t11323 ** 2 * (-6 + 11 * t11324) + + if Bindx == 1800: + t11329 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * (-5 + 11 * t11329) * ((1 + t11329) ** (0.15e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-5*1j) * (2 * phi1 + phi2)) * ((1 - t11329) ** (0.5e1 / 0.2e1)) + + if Bindx == 1801: + t11338 = np.sin(phi) + t11335 = t11338 ** 2 + t11336 = t11338 * t11335 + t11330 = np.cos(phi) + t11331 = t11330 ** 2 + t11333 = t11331 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.1938e4) * t11336 ** 2 * (20 * t11331 + 40 * t11333 - 4 + (50 * t11331 + 11 * t11333 - 5) * t11330) + + if Bindx == 1802: + t11339 = np.cos(phi) + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (-3 + 11 * t11339) * ((1 + t11339) ** (0.13e2 / 0.2e1)) * np.sqrt(0.1615e4) * np.exp((-1*1j) * (10 * phi1 + 3 * phi2)) * ((1 - t11339) ** (0.7e1 / 0.2e1)) + + if Bindx == 1803: + t11346 = np.sin(phi) + t11343 = t11346 ** 2 + t11344 = t11343 ** 2 + t11340 = np.cos(phi) + t11341 = t11340 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.22610e5) * t11344 ** 2 * (20 * t11341 - 2 + (11 * t11341 + 7) * t11340) + + if Bindx == 1804: + t11347 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-1 + 11 * t11347) * ((1 + t11347) ** (0.11e2 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((-1*1j) * (10 * phi1 + phi2)) * ((1 - t11347) ** (0.9e1 / 0.2e1)) + + if Bindx == 1805: + t11352 = np.sin(phi) + t11348 = t11352 ** 2 + t11350 = t11352 * t11348 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-10*1j) * phi1) * np.sqrt(0.969969e6) * t11350 ** 2 * np.cos(phi) + + if Bindx == 1806: + t11353 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (1 + 11 * t11353) * ((1 + t11353) ** (0.9e1 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((-1*1j) * (10 * phi1 - phi2)) * ((1 - t11353) ** (0.11e2 / 0.2e1)) + + if Bindx == 1807: + t11360 = np.sin(phi) + t11357 = t11360 ** 2 + t11358 = t11357 ** 2 + t11354 = np.cos(phi) + t11355 = t11354 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.22610e5) * t11358 ** 2 * (-20 * t11355 + 2 + (11 * t11355 + 7) * t11354) + + if Bindx == 1808: + t11361 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (3 + 11 * t11361) * ((1 + t11361) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1615e4) * np.exp((-1*1j) * (10 * phi1 - 3 * phi2)) * ((1 - t11361) ** (0.13e2 / 0.2e1)) + + if Bindx == 1809: + t11370 = np.sin(phi) + t11367 = t11370 ** 2 + t11368 = t11370 * t11367 + t11362 = np.cos(phi) + t11363 = t11362 ** 2 + t11365 = t11363 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.1938e4) * t11368 ** 2 * (-20 * t11363 - 40 * t11365 + 4 + (50 * t11363 + 11 * t11365 - 5) * t11362) + + if Bindx == 1810: + t11371 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (5 + 11 * t11371) * ((1 + t11371) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-5*1j) * (2 * phi1 - phi2)) * ((1 - t11371) ** (0.15e2 / 0.2e1)) + + if Bindx == 1811: + t11373 = np.cos(phi) + t11377 = -1 + t11373 + t11374 = t11377 ** 2 + t11375 = t11374 ** 2 + t11372 = 1 + t11373 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.133e3) * t11372 ** 2 * t11375 ** 2 * (6 + 11 * t11373) + + if Bindx == 1812: + t11378 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * (7 + 11 * t11378) * ((1 + t11378) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1330e4) * np.exp((-1*1j) * (10 * phi1 - 7 * phi2)) * ((1 - t11378) ** (0.17e2 / 0.2e1)) + + if Bindx == 1813: + t11379 = np.cos(phi) + t11384 = -1 + t11379 + t11380 = t11384 ** 2 + t11381 = t11380 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.70e2) * (1 + t11379) * t11384 * t11381 ** 2 * (8 + 11 * t11379) + + if Bindx == 1814: + t11385 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (9 + 11 * t11385) * np.sqrt((1 + t11385)) * np.sqrt(0.42e2) * np.exp((-1*1j) * (10 * phi1 - 9 * phi2)) * ((1 - t11385) ** (0.19e2 / 0.2e1)) + + if Bindx == 1815: + t11386 = np.cos(phi) + t11387 = t11386 ** 2 + t11389 = t11387 ** 2 + t11390 = t11386 * t11389 + t11395 = t11390 ** 2 + t11393 = t11389 ** 2 + t11388 = t11386 * t11387 + t11391 = t11388 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 - phi2)) * (340 * t11387 - 705 * t11388 + 780 * t11389 - 210 * t11390 - 672 * t11391 - 870 * t11393 - 100 * t11395 + 10 + (1110 * t11391 + 395 * t11393 + 11 * t11395 - 89) * t11386) + + if Bindx == 1816: + t11397 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 - 11 * phi2)) * np.sqrt(0.22e2) * ((1 - t11397) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t11397)) + + if Bindx == 1817: + t11398 = np.cos(phi) + t11403 = 1 + t11398 + t11399 = t11403 ** 2 + t11401 = t11403 * t11399 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.231e3) * (-1 + t11398) * t11401 ** 2 + + if Bindx == 1818: + t11404 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.42e2) * np.sqrt((1 - t11404)) * ((1 + t11404) ** (0.19e2 / 0.2e1)) * (-9 + 11 * t11404) + + if Bindx == 1819: + t11405 = np.cos(phi) + t11406 = t11405 ** 2 + t11408 = t11406 ** 2 + t11409 = t11405 * t11408 + t11414 = t11409 ** 2 + t11412 = t11408 ** 2 + t11407 = t11405 * t11406 + t11410 = t11407 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-9*1j) * (phi1 + phi2)) * (2265 * t11406 + 1155 * t11407 - 4410 * t11408 - 9198 * t11409 - 5838 * t11410 + 7155 * t11412 + 1701 * t11414 + 151 + (2790 * t11410 + 5065 * t11412 + 231 * t11414 + 981) * t11405) + + if Bindx == 1820: + t11416 = np.cos(phi) + t11417 = t11416 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.15e2) * ((1 + t11416) ** (0.17e2 / 0.2e1)) * (-189 * t11417 - 39 + (77 * t11417 + 151) * t11416) * ((1 - t11416) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1821: + t11420 = np.cos(phi) + t11421 = t11420 ** 2 + t11423 = t11421 ** 2 + t11427 = t11423 ** 2 + t11422 = t11420 * t11421 + t11425 = t11422 ** 2 + t11419 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.285e3) * t11419 ** 2 * (-504 * t11422 - 798 * t11423 + 840 * t11425 + 441 * t11427 + 29 + (-126 * t11423 + 960 * t11425 + 77 * t11427 + 105) * t11420) + + if Bindx == 1822: + t11429 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (61 + (-252 + 231 * t11429) * t11429) * ((1 + t11429) ** (0.15e2 / 0.2e1)) * np.sqrt(0.114e3) * np.exp((-3*1j) * (3 * phi1 + 2 * phi2)) * ((1 - t11429) ** (0.3e1 / 0.2e1)) + + if Bindx == 1823: + t11439 = np.sin(phi) + t11437 = t11439 ** 2 + t11430 = np.cos(phi) + t11431 = t11430 ** 2 + t11432 = t11430 * t11431 + t11435 = t11432 ** 2 + t11433 = t11431 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.323e3) * t11437 ** 2 * (-143 * t11431 - 185 * t11432 + 135 * t11433 + 315 * t11435 + 13 + (433 * t11433 + 77 * t11435 - 5) * t11430) + + if Bindx == 1824: + t11440 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * (1 + (-8 + 11 * t11440) * t11440) * ((1 + t11440) ** (0.13e2 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (9 * phi1 + 4 * phi2)) * ((1 - t11440) ** (0.5e1 / 0.2e1)) + + if Bindx == 1825: + t11449 = np.sin(phi) + t11446 = t11449 ** 2 + t11447 = t11449 * t11446 + t11441 = np.cos(phi) + t11442 = t11441 ** 2 + t11444 = t11442 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.67830e5) * t11447 ** 2 * (-18 * t11442 + 81 * t11444 + 1 + (46 * t11442 + 33 * t11444 - 15) * t11441) + + if Bindx == 1826: + t11450 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1 + (-28 + 77 * t11450) * t11450) * ((1 + t11450) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (9 * phi1 + 2 * phi2)) * ((1 - t11450) ** (0.7e1 / 0.2e1)) + + if Bindx == 1827: + t11457 = np.sin(phi) + t11454 = t11457 ** 2 + t11455 = t11454 ** 2 + t11451 = np.cos(phi) + t11452 = t11451 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + phi2)) * np.sqrt(0.25194e5) * t11455 ** 2 * (63 * t11452 - 3 + (77 * t11452 - 17) * t11451) + + if Bindx == 1828: + t11458 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (21 * t11458 ** 2 - 1) * ((1 + t11458) ** (0.9e1 / 0.2e1)) * np.sqrt(0.92378e5) * np.exp((-9*1j) * phi1) * ((1 - t11458) ** (0.9e1 / 0.2e1)) + + if Bindx == 1829: + t11465 = np.sin(phi) + t11462 = t11465 ** 2 + t11463 = t11462 ** 2 + t11459 = np.cos(phi) + t11460 = t11459 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - phi2)) * np.sqrt(0.25194e5) * t11463 ** 2 * (-63 * t11460 + 3 + (77 * t11460 - 17) * t11459) + + if Bindx == 1830: + t11466 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1 + (28 + 77 * t11466) * t11466) * ((1 + t11466) ** (0.7e1 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (9 * phi1 - 2 * phi2)) * ((1 - t11466) ** (0.11e2 / 0.2e1)) + + if Bindx == 1831: + t11475 = np.sin(phi) + t11472 = t11475 ** 2 + t11473 = t11475 * t11472 + t11467 = np.cos(phi) + t11468 = t11467 ** 2 + t11470 = t11468 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.67830e5) * t11473 ** 2 * (18 * t11468 - 81 * t11470 - 1 + (46 * t11468 + 33 * t11470 - 15) * t11467) + + if Bindx == 1832: + t11476 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * (1 + (8 + 11 * t11476) * t11476) * ((1 + t11476) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (9 * phi1 - 4 * phi2)) * ((1 - t11476) ** (0.13e2 / 0.2e1)) + + if Bindx == 1833: + t11486 = np.sin(phi) + t11484 = t11486 ** 2 + t11477 = np.cos(phi) + t11478 = t11477 ** 2 + t11479 = t11477 * t11478 + t11482 = t11479 ** 2 + t11480 = t11478 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.323e3) * t11484 ** 2 * (143 * t11478 - 185 * t11479 - 135 * t11480 - 315 * t11482 - 13 + (433 * t11480 + 77 * t11482 - 5) * t11477) + + if Bindx == 1834: + t11487 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (61 + (252 + 231 * t11487) * t11487) * ((1 + t11487) ** (0.3e1 / 0.2e1)) * np.sqrt(0.114e3) * np.exp((-3*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t11487) ** (0.15e2 / 0.2e1)) + + if Bindx == 1835: + t11489 = np.cos(phi) + t11490 = t11489 ** 2 + t11492 = t11490 ** 2 + t11496 = t11492 ** 2 + t11491 = t11489 * t11490 + t11494 = t11491 ** 2 + t11488 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.285e3) * t11488 ** 2 * (-504 * t11491 + 798 * t11492 - 840 * t11494 - 441 * t11496 - 29 + (-126 * t11492 + 960 * t11494 + 77 * t11496 + 105) * t11489) + + if Bindx == 1836: + t11498 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (39 + (112 + 77 * t11498) * t11498) * np.sqrt((1 + t11498)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (9 * phi1 - 8 * phi2)) * ((1 - t11498) ** (0.17e2 / 0.2e1)) + + if Bindx == 1837: + t11499 = np.cos(phi) + t11500 = t11499 ** 2 + t11502 = t11500 ** 2 + t11503 = t11499 * t11502 + t11508 = t11503 ** 2 + t11506 = t11502 ** 2 + t11501 = t11499 * t11500 + t11504 = t11501 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-9*1j) * (phi1 - phi2)) * (-2265 * t11500 + 1155 * t11501 + 4410 * t11502 - 9198 * t11503 + 5838 * t11504 - 7155 * t11506 - 1701 * t11508 - 151 + (2790 * t11504 + 5065 * t11506 + 231 * t11508 + 981) * t11499) + + if Bindx == 1838: + t11510 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (9 + 11 * t11510) * np.sqrt((1 + t11510)) * np.sqrt(0.42e2) * np.exp((-1*1j) * (9 * phi1 - 10 * phi2)) * ((1 - t11510) ** (0.19e2 / 0.2e1)) + + if Bindx == 1839: + t11511 = np.cos(phi) + t11516 = -1 + t11511 + t11512 = t11516 ** 2 + t11514 = t11516 * t11512 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.231e3) * t11514 ** 2 * (1 + t11511) + + if Bindx == 1840: + t11517 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 11 * phi2)) * np.sqrt(0.385e3) * ((1 - t11517) ** (0.3e1 / 0.2e1)) * ((1 + t11517) ** (0.19e2 / 0.2e1)) + + if Bindx == 1841: + t11518 = np.cos(phi) + t11523 = 1 + t11518 + t11519 = t11523 ** 2 + t11520 = t11519 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.70e2) * (-1 + t11518) * t11523 * t11520 ** 2 * (-8 + 11 * t11518) + + if Bindx == 1842: + t11524 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t11524)) * ((1 + t11524) ** (0.17e2 / 0.2e1)) * (39 + (-112 + 77 * t11524) * t11524) + + if Bindx == 1843: + t11525 = np.cos(phi) + t11526 = t11525 ** 2 + t11528 = t11526 ** 2 + t11529 = t11525 * t11528 + t11534 = t11529 ** 2 + t11532 = t11528 ** 2 + t11527 = t11525 * t11526 + t11530 = t11527 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-8*1j) * (phi1 + phi2)) * (256 * t11526 + 2877 * t11527 + 3360 * t11528 - 2478 * t11529 - 8064 * t11530 + 2592 * t11532 + 2240 * t11534 - 128 + (-4734 * t11530 + 4645 * t11532 + 385 * t11534 - 439) * t11525) + + if Bindx == 1844: + t11536 = np.cos(phi) + t11537 = t11536 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.19e2) * ((1 + t11536) ** (0.15e2 / 0.2e1)) * (-512 * t11536 + 77 + (-1120 * t11536 + 1170 + 385 * t11537) * t11537) * ((1 - t11536) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1845: + t11541 = np.cos(phi) + t11542 = t11541 ** 2 + t11544 = t11542 ** 2 + t11548 = t11544 ** 2 + t11543 = t11541 * t11542 + t11546 = t11543 ** 2 + t11540 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.190e3) * t11540 ** 2 * (120 * t11542 + 76 * t11543 - 328 * t11544 + 8 * t11546 + 336 * t11548 - 8 + (-498 * t11544 + 460 * t11546 + 77 * t11548 + 13) * t11541) + + if Bindx == 1846: + t11550 = np.cos(phi) + t11551 = t11550 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-105 * t11551 - 3 + (77 * t11551 + 39) * t11550) * ((1 + t11550) ** (0.13e2 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * ((1 - t11550) ** (0.3e1 / 0.2e1)) + + if Bindx == 1847: + t11561 = np.sin(phi) + t11559 = t11561 ** 2 + t11553 = np.cos(phi) + t11554 = t11553 ** 2 + t11555 = t11553 * t11554 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.33915e5) * t11559 ** 2 * t11553 * (3 + (-16 + 11 * t11555) * t11555 + (-19 + (32 * t11553 + 21) * t11554) * t11554) + + if Bindx == 1848: + t11562 = np.cos(phi) + t11563 = t11562 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (-45 * t11563 + 1 + (55 * t11563 + 5) * t11562) * ((1 + t11562) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4522e4) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * ((1 - t11562) ** (0.5e1 / 0.2e1)) + + if Bindx == 1849: + t11573 = np.sin(phi) + t11570 = t11573 ** 2 + t11571 = t11573 * t11570 + t11565 = np.cos(phi) + t11566 = t11565 ** 2 + t11568 = t11566 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.323e3) * t11571 ** 2 * (-232 * t11566 + 560 * t11568 + 8 + (-50 * t11566 + 385 * t11568 + 1) * t11565) + + if Bindx == 1850: + t11574 = np.cos(phi) + t11575 = t11574 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-21 * t11575 + 1 + (77 * t11575 - 9) * t11574) * ((1 + t11574) ** (0.9e1 / 0.2e1)) * np.sqrt(0.41990e5) * np.exp((-1*1j) * (8 * phi1 + phi2)) * ((1 - t11574) ** (0.7e1 / 0.2e1)) + + if Bindx == 1851: + t11581 = np.sin(phi) + t11578 = t11581 ** 2 + t11579 = t11578 ** 2 + t11577 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-8*1j) * phi1) * np.sqrt(0.1385670e7) * t11579 ** 2 * t11577 * (7 * t11577 ** 2 - 1) + + if Bindx == 1852: + t11582 = np.cos(phi) + t11583 = t11582 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (21 * t11583 - 1 + (77 * t11583 - 9) * t11582) * ((1 + t11582) ** (0.7e1 / 0.2e1)) * np.sqrt(0.41990e5) * np.exp((-1*1j) * (8 * phi1 - phi2)) * ((1 - t11582) ** (0.9e1 / 0.2e1)) + + if Bindx == 1853: + t11593 = np.sin(phi) + t11590 = t11593 ** 2 + t11591 = t11593 * t11590 + t11585 = np.cos(phi) + t11586 = t11585 ** 2 + t11588 = t11586 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.323e3) * t11591 ** 2 * (232 * t11586 - 560 * t11588 - 8 + (-50 * t11586 + 385 * t11588 + 1) * t11585) + + if Bindx == 1854: + t11594 = np.cos(phi) + t11595 = t11594 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (45 * t11595 - 1 + (55 * t11595 + 5) * t11594) * ((1 + t11594) ** (0.5e1 / 0.2e1)) * np.sqrt(0.4522e4) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * ((1 - t11594) ** (0.11e2 / 0.2e1)) + + if Bindx == 1855: + t11605 = np.sin(phi) + t11603 = t11605 ** 2 + t11597 = np.cos(phi) + t11598 = t11597 ** 2 + t11599 = t11597 * t11598 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.33915e5) * t11603 ** 2 * t11597 * (3 + (16 + 11 * t11599) * t11599 + (-19 + (-32 * t11597 + 21) * t11598) * t11598) + + if Bindx == 1856: + t11606 = np.cos(phi) + t11607 = t11606 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (105 * t11607 + 3 + (77 * t11607 + 39) * t11606) * ((1 + t11606) ** (0.3e1 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * ((1 - t11606) ** (0.13e2 / 0.2e1)) + + if Bindx == 1857: + t11610 = np.cos(phi) + t11611 = t11610 ** 2 + t11613 = t11611 ** 2 + t11617 = t11613 ** 2 + t11612 = t11610 * t11611 + t11615 = t11612 ** 2 + t11609 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.190e3) * t11609 ** 2 * (-120 * t11611 + 76 * t11612 + 328 * t11613 - 8 * t11615 - 336 * t11617 + 8 + (-498 * t11613 + 460 * t11615 + 77 * t11617 + 13) * t11610) + + if Bindx == 1858: + t11619 = np.cos(phi) + t11620 = t11619 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (735 * t11620 + 77 + (385 * t11620 + 435) * t11619) * np.sqrt((1 + t11619)) * np.sqrt(0.19e2) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t11619) ** (0.15e2 / 0.2e1)) + + if Bindx == 1859: + t11622 = np.cos(phi) + t11623 = t11622 ** 2 + t11625 = t11623 ** 2 + t11626 = t11622 * t11625 + t11631 = t11626 ** 2 + t11629 = t11625 ** 2 + t11624 = t11622 * t11623 + t11627 = t11624 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-8*1j) * (phi1 - phi2)) * (-256 * t11623 + 2877 * t11624 - 3360 * t11625 - 2478 * t11626 + 8064 * t11627 - 2592 * t11629 - 2240 * t11631 + 128 + (-4734 * t11627 + 4645 * t11629 + 385 * t11631 - 439) * t11622) + + if Bindx == 1860: + t11633 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (39 + (112 + 77 * t11633) * t11633) * np.sqrt((1 + t11633)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (8 * phi1 - 9 * phi2)) * ((1 - t11633) ** (0.17e2 / 0.2e1)) + + if Bindx == 1861: + t11634 = np.cos(phi) + t11639 = -1 + t11634 + t11635 = t11639 ** 2 + t11636 = t11635 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.70e2) * t11639 * t11636 ** 2 * (1 + t11634) * (8 + 11 * t11634) + + if Bindx == 1862: + t11640 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 - 11 * phi2)) * np.sqrt(0.385e3) * ((1 - t11640) ** (0.19e2 / 0.2e1)) * ((1 + t11640) ** (0.3e1 / 0.2e1)) + + if Bindx == 1863: + t11642 = np.cos(phi) + t11647 = 1 + t11642 + t11643 = t11647 ** 2 + t11644 = t11643 ** 2 + t11641 = -1 + t11642 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.7315e4) * t11641 ** 2 * t11647 * t11644 ** 2 + + if Bindx == 1864: + t11648 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 10 * phi2)) * np.sqrt(0.1330e4) * ((1 - t11648) ** (0.3e1 / 0.2e1)) * ((1 + t11648) ** (0.17e2 / 0.2e1)) * (-7 + 11 * t11648) + + if Bindx == 1865: + t11650 = np.cos(phi) + t11651 = t11650 ** 2 + t11653 = t11651 ** 2 + t11657 = t11653 ** 2 + t11652 = t11650 * t11651 + t11655 = t11652 ** 2 + t11649 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.285e3) * t11649 ** 2 * (-504 * t11652 - 798 * t11653 + 840 * t11655 + 441 * t11657 + 29 + (-126 * t11653 + 960 * t11655 + 77 * t11657 + 105) * t11650) + + if Bindx == 1866: + t11659 = np.cos(phi) + t11660 = t11659 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.19e2) * np.sqrt((1 - t11659)) * ((1 + t11659) ** (0.15e2 / 0.2e1)) * (-735 * t11660 - 77 + (385 * t11660 + 435) * t11659) + + if Bindx == 1867: + t11662 = np.cos(phi) + t11663 = t11662 ** 2 + t11665 = t11663 ** 2 + t11666 = t11662 * t11665 + t11671 = t11666 ** 2 + t11669 = t11665 ** 2 + t11664 = t11662 * t11663 + t11667 = t11664 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-7*1j) * (phi1 + phi2)) * (-10931 * t11663 - 3297 * t11664 + 41790 * t11665 + 47418 * t11666 - 37926 * t11667 - 25137 * t11669 + 32585 * t11671 + 643 + (-88866 * t11667 + 39805 * t11669 + 7315 * t11671 - 1351) * t11662) + + if Bindx == 1868: + t11673 = np.cos(phi) + t11674 = t11673 ** 2 + t11676 = t11674 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.10e2) * ((1 + t11673) ** (0.13e2 / 0.2e1)) * (-2926 * t11674 - 4655 * t11676 - 35 + (5510 * t11674 + 1463 * t11676 + 643) * t11673) * ((1 - t11673) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1869: + t11679 = np.cos(phi) + t11680 = t11679 ** 2 + t11682 = t11680 ** 2 + t11686 = t11682 ** 2 + t11681 = t11679 * t11680 + t11684 = t11681 ** 2 + t11678 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.255e3) * t11678 ** 2 * (252 * t11680 + 2380 * t11681 + 658 * t11682 - 4788 * t11684 + 4655 * t11686 - 9 + (-5614 * t11682 + 2812 * t11684 + 1463 * t11686 - 273) * t11679) + + if Bindx == 1870: + t11688 = np.cos(phi) + t11689 = t11688 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-3 + (-304 * t11688 + 114 + 209 * t11689) * t11689) * ((1 + t11688) ** (0.11e2 / 0.2e1)) * np.sqrt(0.1785e4) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t11688) ** (0.3e1 / 0.2e1)) + + if Bindx == 1871: + t11701 = np.sin(phi) + t11699 = t11701 ** 2 + t11692 = np.cos(phi) + t11693 = t11692 ** 2 + t11694 = t11692 * t11693 + t11697 = t11694 ** 2 + t11695 = t11693 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.238e3) * t11699 ** 2 * (385 * t11693 - 353 * t11694 - 1729 * t11695 + 1995 * t11697 - 11 + (-95 * t11695 + 1045 * t11697 + 43) * t11692) + + if Bindx == 1872: + t11702 = np.cos(phi) + t11703 = t11702 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (608 * t11702 - 17 + (-5320 * t11702 - 570 + 7315 * t11703) * t11703) * ((1 + t11702) ** (0.9e1 / 0.2e1)) * np.sqrt(0.17e2) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t11702) ** (0.5e1 / 0.2e1)) + + if Bindx == 1873: + t11714 = np.sin(phi) + t11711 = t11714 ** 2 + t11712 = t11714 * t11711 + t11706 = np.cos(phi) + t11707 = t11706 ** 2 + t11709 = t11707 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.2210e4) * t11712 ** 2 * (-266 * t11707 + 931 * t11709 + 7 + (-874 * t11707 + 1463 * t11709 + 83) * t11706) + + if Bindx == 1874: + t11715 = np.cos(phi) + t11716 = t11715 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (1 + (-38 + 133 * t11716) * t11716) * ((1 + t11715) ** (0.7e1 / 0.2e1)) * np.sqrt(0.72930e5) * np.exp((-7*1j) * phi1) * ((1 - t11715) ** (0.7e1 / 0.2e1)) + + if Bindx == 1875: + t11726 = np.sin(phi) + t11723 = t11726 ** 2 + t11724 = t11726 * t11723 + t11718 = np.cos(phi) + t11719 = t11718 ** 2 + t11721 = t11719 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.2210e4) * t11724 ** 2 * (266 * t11719 - 931 * t11721 - 7 + (-874 * t11719 + 1463 * t11721 + 83) * t11718) + + if Bindx == 1876: + t11727 = np.cos(phi) + t11728 = t11727 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-608 * t11727 - 17 + (5320 * t11727 - 570 + 7315 * t11728) * t11728) * ((1 + t11727) ** (0.5e1 / 0.2e1)) * np.sqrt(0.17e2) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t11727) ** (0.9e1 / 0.2e1)) + + if Bindx == 1877: + t11740 = np.sin(phi) + t11738 = t11740 ** 2 + t11731 = np.cos(phi) + t11732 = t11731 ** 2 + t11733 = t11731 * t11732 + t11736 = t11733 ** 2 + t11734 = t11732 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.238e3) * t11738 ** 2 * (-385 * t11732 - 353 * t11733 + 1729 * t11734 - 1995 * t11736 + 11 + (-95 * t11734 + 1045 * t11736 + 43) * t11731) + + if Bindx == 1878: + t11741 = np.cos(phi) + t11742 = t11741 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-3 + (304 * t11741 + 114 + 209 * t11742) * t11742) * ((1 + t11741) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1785e4) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t11741) ** (0.11e2 / 0.2e1)) + + if Bindx == 1879: + t11746 = np.cos(phi) + t11747 = t11746 ** 2 + t11749 = t11747 ** 2 + t11753 = t11749 ** 2 + t11748 = t11746 * t11747 + t11751 = t11748 ** 2 + t11745 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.255e3) * t11745 ** 2 * (-252 * t11747 + 2380 * t11748 - 658 * t11749 + 4788 * t11751 - 4655 * t11753 + 9 + (-5614 * t11749 + 2812 * t11751 + 1463 * t11753 - 273) * t11746) + + if Bindx == 1880: + t11755 = np.cos(phi) + t11756 = t11755 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * (608 * t11755 + 35 + (3192 * t11755 + 2318 + 1463 * t11756) * t11756) * np.sqrt((1 + t11755)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t11755) ** (0.13e2 / 0.2e1)) + + if Bindx == 1881: + t11759 = np.cos(phi) + t11760 = t11759 ** 2 + t11762 = t11760 ** 2 + t11763 = t11759 * t11762 + t11768 = t11763 ** 2 + t11766 = t11762 ** 2 + t11761 = t11759 * t11760 + t11764 = t11761 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-7*1j) * (phi1 - phi2)) * (10931 * t11760 - 3297 * t11761 - 41790 * t11762 + 47418 * t11763 + 37926 * t11764 + 25137 * t11766 - 32585 * t11768 - 643 + (-88866 * t11764 + 39805 * t11766 + 7315 * t11768 - 1351) * t11759) + + if Bindx == 1882: + t11770 = np.cos(phi) + t11771 = t11770 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (735 * t11771 + 77 + (385 * t11771 + 435) * t11770) * np.sqrt((1 + t11770)) * np.sqrt(0.19e2) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t11770) ** (0.15e2 / 0.2e1)) + + if Bindx == 1883: + t11774 = np.cos(phi) + t11775 = t11774 ** 2 + t11777 = t11775 ** 2 + t11781 = t11777 ** 2 + t11776 = t11774 * t11775 + t11779 = t11776 ** 2 + t11773 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.285e3) * t11773 ** 2 * (-504 * t11776 + 798 * t11777 - 840 * t11779 - 441 * t11781 - 29 + (-126 * t11777 + 960 * t11779 + 77 * t11781 + 105) * t11774) + + if Bindx == 1884: + t11783 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * (7 + 11 * t11783) * ((1 + t11783) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1330e4) * np.exp((-1*1j) * (7 * phi1 - 10 * phi2)) * ((1 - t11783) ** (0.17e2 / 0.2e1)) + + if Bindx == 1885: + t11785 = np.cos(phi) + t11790 = -1 + t11785 + t11786 = t11790 ** 2 + t11787 = t11786 ** 2 + t11784 = 1 + t11785 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.7315e4) * t11790 * t11787 ** 2 * t11784 ** 2 + + if Bindx == 1886: + t11791 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 11 * phi2)) * np.sqrt(0.2926e4) * ((1 - t11791) ** (0.5e1 / 0.2e1)) * ((1 + t11791) ** (0.17e2 / 0.2e1)) + + if Bindx == 1887: + t11793 = np.cos(phi) + t11797 = 1 + t11793 + t11794 = t11797 ** 2 + t11795 = t11794 ** 2 + t11792 = -1 + t11793 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.133e3) * t11792 ** 2 * t11795 ** 2 * (-6 + 11 * t11793) + + if Bindx == 1888: + t11798 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((-3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.114e3) * ((1 - t11798) ** (0.3e1 / 0.2e1)) * ((1 + t11798) ** (0.15e2 / 0.2e1)) * (61 + (-252 + 231 * t11798) * t11798) + + if Bindx == 1889: + t11800 = np.cos(phi) + t11801 = t11800 ** 2 + t11803 = t11801 ** 2 + t11807 = t11803 ** 2 + t11802 = t11800 * t11801 + t11805 = t11802 ** 2 + t11799 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.190e3) * t11799 ** 2 * (120 * t11801 + 76 * t11802 - 328 * t11803 + 8 * t11805 + 336 * t11807 - 8 + (-498 * t11803 + 460 * t11805 + 77 * t11807 + 13) * t11800) + + if Bindx == 1890: + t11809 = np.cos(phi) + t11810 = t11809 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t11809)) * ((1 + t11809) ** (0.13e2 / 0.2e1)) * (-608 * t11809 + 35 + (-3192 * t11809 + 2318 + 1463 * t11810) * t11810) + + if Bindx == 1891: + t11813 = np.cos(phi) + t11814 = t11813 ** 2 + t11816 = t11814 ** 2 + t11817 = t11813 * t11816 + t11822 = t11817 ** 2 + t11820 = t11816 ** 2 + t11815 = t11813 * t11814 + t11818 = t11815 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 + phi2)) * (-2820 * t11814 - 21805 * t11815 + 420 * t11816 + 69846 * t11817 + 40096 * t11818 - 80370 * t11820 + 43092 * t11822 + 94 + (-79650 * t11818 + 16815 * t11820 + 13167 * t11822 + 2139) * t11813) + + if Bindx == 1892: + t11824 = np.cos(phi) + t11825 = t11824 ** 2 + t11827 = t11825 ** 2 + t11826 = t11824 * t11825 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.102e3) * ((1 + t11824) ** (0.11e2 / 0.2e1)) * (1575 * t11825 + 17385 * t11827 - 53 + (-9120 + 4389 * t11826) * t11826 + (-14364 * t11827 + 188) * t11824) * ((1 - t11824) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1893: + t11831 = np.cos(phi) + t11832 = t11831 ** 2 + t11834 = t11832 ** 2 + t11838 = t11834 ** 2 + t11833 = t11831 * t11832 + t11836 = t11833 ** 2 + t11830 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.714e3) * t11830 ** 2 * (-156 * t11832 + 316 * t11833 + 964 * t11834 - 2052 * t11836 + 1368 * t11838 + 4 + (-558 * t11834 - 228 * t11836 + 627 * t11838 - 29) * t11831) + + if Bindx == 1894: + t11840 = np.cos(phi) + t11841 = t11840 ** 2 + t11843 = t11841 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (342 * t11841 - 2565 * t11843 - 1 + (570 * t11841 + 1881 * t11843 - 99) * t11840) * ((1 + t11840) ** (0.9e1 / 0.2e1)) * np.sqrt(0.595e3) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t11840) ** (0.3e1 / 0.2e1)) + + if Bindx == 1895: + t11854 = np.sin(phi) + t11852 = t11854 ** 2 + t11845 = np.cos(phi) + t11846 = t11845 ** 2 + t11847 = t11845 * t11846 + t11850 = t11847 ** 2 + t11848 = t11846 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.170e3) * t11852 ** 2 * (264 * t11846 + 401 * t11847 - 1406 * t11848 + 1596 * t11850 - 6 + (-1387 * t11848 + 1463 * t11850 - 29) * t11845) + + if Bindx == 1896: + t11855 = np.cos(phi) + t11856 = t11855 ** 2 + t11858 = t11856 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (190 * t11856 - 665 * t11858 - 5 + (-570 * t11856 + 1463 * t11858 + 35) * t11855) * ((1 + t11855) ** (0.7e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t11855) ** (0.5e1 / 0.2e1)) + + if Bindx == 1897: + t11866 = np.sin(phi) + t11863 = t11866 ** 2 + t11864 = t11866 * t11863 + t11860 = np.cos(phi) + t11861 = t11860 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-6*1j) * phi1) * np.sqrt(0.7293e4) * t11864 ** 2 * t11860 * (15 + (-190 + 399 * t11861) * t11861) + + if Bindx == 1898: + t11867 = np.cos(phi) + t11868 = t11867 ** 2 + t11870 = t11868 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (-190 * t11868 + 665 * t11870 + 5 + (-570 * t11868 + 1463 * t11870 + 35) * t11867) * ((1 + t11867) ** (0.5e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t11867) ** (0.7e1 / 0.2e1)) + + if Bindx == 1899: + t11881 = np.sin(phi) + t11879 = t11881 ** 2 + t11872 = np.cos(phi) + t11873 = t11872 ** 2 + t11874 = t11872 * t11873 + t11877 = t11874 ** 2 + t11875 = t11873 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.170e3) * t11879 ** 2 * (-264 * t11873 + 401 * t11874 + 1406 * t11875 - 1596 * t11877 + 6 + (-1387 * t11875 + 1463 * t11877 - 29) * t11872) + + if Bindx == 1900: + t11882 = np.cos(phi) + t11883 = t11882 ** 2 + t11885 = t11883 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-342 * t11883 + 2565 * t11885 + 1 + (570 * t11883 + 1881 * t11885 - 99) * t11882) * ((1 + t11882) ** (0.3e1 / 0.2e1)) * np.sqrt(0.595e3) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t11882) ** (0.9e1 / 0.2e1)) + + if Bindx == 1901: + t11888 = np.cos(phi) + t11889 = t11888 ** 2 + t11891 = t11889 ** 2 + t11895 = t11891 ** 2 + t11890 = t11888 * t11889 + t11893 = t11890 ** 2 + t11887 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.714e3) * t11887 ** 2 * (156 * t11889 + 316 * t11890 - 964 * t11891 + 2052 * t11893 - 1368 * t11895 - 4 + (-558 * t11891 - 228 * t11893 + 627 * t11895 - 29) * t11888) + + if Bindx == 1902: + t11897 = np.cos(phi) + t11898 = t11897 ** 2 + t11900 = t11898 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (1710 * t11898 + 9975 * t11900 - 53 + (7410 * t11898 + 4389 * t11900 - 135) * t11897) * np.sqrt((1 + t11897)) * np.sqrt(0.102e3) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t11897) ** (0.11e2 / 0.2e1)) + + if Bindx == 1903: + t11902 = np.cos(phi) + t11903 = t11902 ** 2 + t11905 = t11903 ** 2 + t11906 = t11902 * t11905 + t11911 = t11906 ** 2 + t11909 = t11905 ** 2 + t11904 = t11902 * t11903 + t11907 = t11904 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 - phi2)) * (2820 * t11903 - 21805 * t11904 - 420 * t11905 + 69846 * t11906 - 40096 * t11907 + 80370 * t11909 - 43092 * t11911 - 94 + (-79650 * t11907 + 16815 * t11909 + 13167 * t11911 + 2139) * t11902) + + if Bindx == 1904: + t11913 = np.cos(phi) + t11914 = t11913 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * (608 * t11913 + 35 + (3192 * t11913 + 2318 + 1463 * t11914) * t11914) * np.sqrt((1 + t11913)) * np.sqrt(0.10e2) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t11913) ** (0.13e2 / 0.2e1)) + + if Bindx == 1905: + t11918 = np.cos(phi) + t11919 = t11918 ** 2 + t11921 = t11919 ** 2 + t11925 = t11921 ** 2 + t11920 = t11918 * t11919 + t11923 = t11920 ** 2 + t11917 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.190e3) * t11917 ** 2 * (-120 * t11919 + 76 * t11920 + 328 * t11921 - 8 * t11923 - 336 * t11925 + 8 + (-498 * t11921 + 460 * t11923 + 77 * t11925 + 13) * t11918) + + if Bindx == 1906: + t11927 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (61 + (252 + 231 * t11927) * t11927) * ((1 + t11927) ** (0.3e1 / 0.2e1)) * np.sqrt(0.114e3) * np.exp((-3*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t11927) ** (0.15e2 / 0.2e1)) + + if Bindx == 1907: + t11929 = np.cos(phi) + t11933 = -1 + t11929 + t11930 = t11933 ** 2 + t11931 = t11930 ** 2 + t11928 = 1 + t11929 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.133e3) * t11931 ** 2 * t11928 ** 2 * (6 + 11 * t11929) + + if Bindx == 1908: + t11934 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 - 11 * phi2)) * np.sqrt(0.2926e4) * ((1 - t11934) ** (0.17e2 / 0.2e1)) * ((1 + t11934) ** (0.5e1 / 0.2e1)) + + if Bindx == 1909: + t11935 = np.cos(phi) + t11942 = -1 + t11935 + t11941 = 1 + t11935 + t11938 = t11941 ** 2 + t11939 = t11938 ** 2 + t11936 = t11942 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.74613e5) * t11942 * t11936 * t11939 ** 2 + + if Bindx == 1910: + t11943 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-5*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.13566e5) * ((1 - t11943) ** (0.5e1 / 0.2e1)) * ((1 + t11943) ** (0.15e2 / 0.2e1)) * (-5 + 11 * t11943) + + if Bindx == 1911: + t11953 = np.sin(phi) + t11951 = t11953 ** 2 + t11944 = np.cos(phi) + t11945 = t11944 ** 2 + t11946 = t11944 * t11945 + t11949 = t11946 ** 2 + t11947 = t11945 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.323e3) * t11951 ** 2 * (-143 * t11945 - 185 * t11946 + 135 * t11947 + 315 * t11949 + 13 + (433 * t11947 + 77 * t11949 - 5) * t11944) + + if Bindx == 1912: + t11954 = np.cos(phi) + t11955 = t11954 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.4845e4) * ((1 - t11954) ** (0.3e1 / 0.2e1)) * ((1 + t11954) ** (0.13e2 / 0.2e1)) * (-105 * t11955 - 3 + (77 * t11955 + 39) * t11954) + + if Bindx == 1913: + t11958 = np.cos(phi) + t11959 = t11958 ** 2 + t11961 = t11959 ** 2 + t11965 = t11961 ** 2 + t11960 = t11958 * t11959 + t11963 = t11960 ** 2 + t11957 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.255e3) * t11957 ** 2 * (252 * t11959 + 2380 * t11960 + 658 * t11961 - 4788 * t11963 + 4655 * t11965 - 9 + (-5614 * t11961 + 2812 * t11963 + 1463 * t11965 - 273) * t11958) + + if Bindx == 1914: + t11967 = np.cos(phi) + t11968 = t11967 ** 2 + t11970 = t11968 ** 2 + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.102e3) * np.sqrt((1 - t11967)) * ((1 + t11967) ** (0.11e2 / 0.2e1)) * (-1710 * t11968 - 9975 * t11970 + 53 + (7410 * t11968 + 4389 * t11970 - 135) * t11967) + + if Bindx == 1915: + t11972 = np.cos(phi) + t11973 = t11972 ** 2 + t11975 = t11973 ** 2 + t11976 = t11972 * t11975 + t11981 = t11976 ** 2 + t11979 = t11975 ** 2 + t11974 = t11972 * t11973 + t11977 = t11974 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-5*1j) * (phi1 + phi2)) * (16195 * t11973 - 42455 * t11974 - 118510 * t11975 + 117670 * t11976 + 336294 * t11977 - 402135 * t11979 + 169575 * t11981 - 395 + (-69870 * t11977 - 82365 * t11979 + 74613 * t11981 + 3431) * t11972) + + if Bindx == 1916: + t11983 = np.cos(phi) + t11984 = t11983 ** 2 + t11985 = t11983 * t11984 + t11988 = t11985 ** 2 + t11986 = t11984 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.7e1) * ((1 + t11983) ** (0.9e1 / 0.2e1)) * (2703 * t11984 - 2295 * t11985 - 14535 * t11986 - 33915 * t11988 - 13 + (37791 * t11986 + 10659 * t11988 - 395) * t11983) * ((1 - t11983) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1917: + t11991 = np.cos(phi) + t11992 = t11991 ** 2 + t11994 = t11992 ** 2 + t11998 = t11994 ** 2 + t11993 = t11991 * t11992 + t11996 = t11993 ** 2 + t11990 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.210e3) * t11990 ** 2 * (-1680 * t11992 - 1240 * t11993 + 11390 * t11994 - 23256 * t11996 + 14535 * t11998 + 35 + (7038 * t11994 - 15504 * t11996 + 10659 * t11998 + 71) * t11991) + + if Bindx == 1918: + t12000 = np.cos(phi) + t12001 = t12000 ** 2 + t12003 = t12001 ** 2 + t12002 = t12000 * t12001 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-867 * t12001 - 4845 * t12003 + 41 + (10336 + 24871 * t12002) * t12002 + (-27132 * t12003 - 612) * t12000) * ((1 + t12000) ** (0.7e1 / 0.2e1)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t12000) ** (0.3e1 / 0.2e1)) + + if Bindx == 1919: + t12015 = np.sin(phi) + t12013 = t12015 ** 2 + t12006 = np.cos(phi) + t12007 = t12006 ** 2 + t12008 = t12006 * t12007 + t12011 = t12008 ** 2 + t12009 = t12007 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.78e2) * t12013 ** 2 * (1275 * t12007 + 8245 * t12008 - 8075 * t12009 + 11305 * t12011 - 25 + (-28101 * t12009 + 24871 * t12011 - 535) * t12006) + + if Bindx == 1920: + t12016 = np.cos(phi) + t12017 = t12016 ** 2 + t12018 = t12017 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (-1615 * t12018 - 5 + (2261 * t12018 + 255) * t12017) * ((1 + t12016) ** (0.5e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((-5*1j) * phi1) * ((1 - t12016) ** (0.5e1 / 0.2e1)) + + if Bindx == 1921: + t12029 = np.sin(phi) + t12027 = t12029 ** 2 + t12020 = np.cos(phi) + t12021 = t12020 ** 2 + t12022 = t12020 * t12021 + t12025 = t12022 ** 2 + t12023 = t12021 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.78e2) * t12027 ** 2 * (-1275 * t12021 + 8245 * t12022 + 8075 * t12023 - 11305 * t12025 + 25 + (-28101 * t12023 + 24871 * t12025 - 535) * t12020) + + if Bindx == 1922: + t12030 = np.cos(phi) + t12031 = t12030 ** 2 + t12033 = t12031 ** 2 + t12032 = t12030 * t12031 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-867 * t12031 - 4845 * t12033 + 41 + (-10336 + 24871 * t12032) * t12032 + (27132 * t12033 + 612) * t12030) * ((1 + t12030) ** (0.3e1 / 0.2e1)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t12030) ** (0.7e1 / 0.2e1)) + + if Bindx == 1923: + t12037 = np.cos(phi) + t12038 = t12037 ** 2 + t12040 = t12038 ** 2 + t12044 = t12040 ** 2 + t12039 = t12037 * t12038 + t12042 = t12039 ** 2 + t12036 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.210e3) * t12036 ** 2 * (1680 * t12038 - 1240 * t12039 - 11390 * t12040 + 23256 * t12042 - 14535 * t12044 - 35 + (7038 * t12040 - 15504 * t12042 + 10659 * t12044 + 71) * t12037) + + if Bindx == 1924: + t12046 = np.cos(phi) + t12047 = t12046 ** 2 + t12048 = t12047 ** 2 + t12049 = t12046 * t12048 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * (-2295 * t12047 + 14535 * t12048 + 23256 * t12049 + 13 + (10659 * t12049 - 408) * t12046) * np.sqrt((1 + t12046)) * np.sqrt(0.7e1) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t12046) ** (0.9e1 / 0.2e1)) + + if Bindx == 1925: + t12051 = np.cos(phi) + t12052 = t12051 ** 2 + t12054 = t12052 ** 2 + t12055 = t12051 * t12054 + t12060 = t12055 ** 2 + t12058 = t12054 ** 2 + t12053 = t12051 * t12052 + t12056 = t12053 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-5*1j) * (phi1 - phi2)) * (-16195 * t12052 - 42455 * t12053 + 118510 * t12054 + 117670 * t12055 - 336294 * t12056 + 402135 * t12058 - 169575 * t12060 + 395 + (-69870 * t12056 - 82365 * t12058 + 74613 * t12060 + 3431) * t12051) + + if Bindx == 1926: + t12062 = np.cos(phi) + t12063 = t12062 ** 2 + t12065 = t12063 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (1710 * t12063 + 9975 * t12065 - 53 + (7410 * t12063 + 4389 * t12065 - 135) * t12062) * np.sqrt((1 + t12062)) * np.sqrt(0.102e3) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t12062) ** (0.11e2 / 0.2e1)) + + if Bindx == 1927: + t12068 = np.cos(phi) + t12069 = t12068 ** 2 + t12071 = t12069 ** 2 + t12075 = t12071 ** 2 + t12070 = t12068 * t12069 + t12073 = t12070 ** 2 + t12067 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.255e3) * t12067 ** 2 * (-252 * t12069 + 2380 * t12070 - 658 * t12071 + 4788 * t12073 - 4655 * t12075 + 9 + (-5614 * t12071 + 2812 * t12073 + 1463 * t12075 - 273) * t12068) + + if Bindx == 1928: + t12077 = np.cos(phi) + t12078 = t12077 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (105 * t12078 + 3 + (77 * t12078 + 39) * t12077) * ((1 + t12077) ** (0.3e1 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * ((1 - t12077) ** (0.13e2 / 0.2e1)) + + if Bindx == 1929: + t12089 = np.sin(phi) + t12087 = t12089 ** 2 + t12080 = np.cos(phi) + t12081 = t12080 ** 2 + t12082 = t12080 * t12081 + t12085 = t12082 ** 2 + t12083 = t12081 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.323e3) * t12087 ** 2 * (143 * t12081 - 185 * t12082 - 135 * t12083 - 315 * t12085 - 13 + (433 * t12083 + 77 * t12085 - 5) * t12080) + + if Bindx == 1930: + t12090 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (5 + 11 * t12090) * ((1 + t12090) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-5*1j) * (phi1 - 2 * phi2)) * ((1 - t12090) ** (0.15e2 / 0.2e1)) + + if Bindx == 1931: + t12091 = np.cos(phi) + t12098 = -1 + t12091 + t12097 = 1 + t12091 + t12095 = t12097 ** 2 + t12092 = t12098 ** 2 + t12093 = t12092 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.74613e5) * t12093 ** 2 * t12097 * t12095 + + if Bindx == 1932: + t12099 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 + 11 * phi2)) * np.sqrt(0.10659e5) * ((1 - t12099) ** (0.7e1 / 0.2e1)) * ((1 + t12099) ** (0.15e2 / 0.2e1)) + + if Bindx == 1933: + t12100 = np.cos(phi) + t12108 = -1 + t12100 + t12107 = 1 + t12100 + t12103 = t12107 ** 2 + t12104 = t12107 * t12103 + t12101 = t12108 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.1938e4) * t12108 * t12101 * t12107 * t12104 ** 2 * (-4 + 11 * t12100) + + if Bindx == 1934: + t12109 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.2261e4) * ((1 - t12109) ** (0.5e1 / 0.2e1)) * ((1 + t12109) ** (0.13e2 / 0.2e1)) * (1 + (-8 + 11 * t12109) * t12109) + + if Bindx == 1935: + t12118 = np.sin(phi) + t12116 = t12118 ** 2 + t12110 = np.cos(phi) + t12111 = t12110 ** 2 + t12112 = t12110 * t12111 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.33915e5) * t12116 ** 2 * t12110 * (3 + (-16 + 11 * t12112) * t12112 + (-19 + (32 * t12110 + 21) * t12111) * t12111) + + if Bindx == 1936: + t12119 = np.cos(phi) + t12120 = t12119 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.1785e4) * ((1 - t12119) ** (0.3e1 / 0.2e1)) * ((1 + t12119) ** (0.11e2 / 0.2e1)) * (-3 + (-304 * t12119 + 114 + 209 * t12120) * t12120) + + if Bindx == 1937: + t12124 = np.cos(phi) + t12125 = t12124 ** 2 + t12127 = t12125 ** 2 + t12131 = t12127 ** 2 + t12126 = t12124 * t12125 + t12129 = t12126 ** 2 + t12123 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.714e3) * t12123 ** 2 * (-156 * t12125 + 316 * t12126 + 964 * t12127 - 2052 * t12129 + 1368 * t12131 + 4 + (-558 * t12127 - 228 * t12129 + 627 * t12131 - 29) * t12124) + + if Bindx == 1938: + t12133 = np.cos(phi) + t12134 = t12133 ** 2 + t12135 = t12134 ** 2 + t12136 = t12133 * t12135 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.7e1) * np.sqrt((1 - t12133)) * ((1 + t12133) ** (0.9e1 / 0.2e1)) * (-2295 * t12134 + 14535 * t12135 - 23256 * t12136 + 13 + (10659 * t12136 + 408) * t12133) + + if Bindx == 1939: + t12138 = np.cos(phi) + t12139 = t12138 ** 2 + t12141 = t12139 ** 2 + t12142 = t12138 * t12141 + t12147 = t12142 ** 2 + t12145 = t12141 ** 2 + t12140 = t12138 * t12139 + t12143 = t12140 ** 2 + tfunc[..., c] = (0.23e2 / 0.128e3) * np.exp((-4*1j) * (phi1 + phi2)) * (1600 * t12139 + 775 * t12140 - 12520 * t12141 - 5978 * t12142 + 34272 * t12143 - 38760 * t12145 + 15504 * t12147 - 32 + (18870 * t12143 - 24225 * t12145 + 10659 * t12147 - 37) * t12138) + + if Bindx == 1940: + t12149 = np.cos(phi) + t12150 = t12149 ** 2 + t12151 = t12149 * t12150 + t12153 = t12151 ** 2 + t12152 = t12150 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * ((1 + t12149) ** (0.7e1 / 0.2e1)) * (364 * t12150 + 3808 * t12151 + 27132 * t12153 + 11 + (-10710 + 10659 * t12152) * t12152 + (-31008 * t12153 - 256) * t12149) * ((1 - t12149) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1941: + t12157 = np.cos(phi) + t12158 = t12157 ** 2 + t12160 = t12158 ** 2 + t12164 = t12160 ** 2 + t12159 = t12157 * t12158 + t12162 = t12159 ** 2 + t12156 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.105e3) * t12156 ** 2 * (-220 * t12158 - 860 * t12159 + 1700 * t12160 - 3876 * t12162 + 2584 * t12164 + 4 + (3910 * t12160 - 6460 * t12162 + 3553 * t12164 + 49) * t12157) + + if Bindx == 1942: + t12166 = np.cos(phi) + t12167 = t12166 ** 2 + t12168 = t12166 * t12167 + t12171 = t12168 ** 2 + t12169 = t12167 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-255 * t12167 + 595 * t12168 + 1615 * t12169 - 2261 * t12171 + 5 + (-2907 * t12169 + 3553 * t12171 - 25) * t12166) * ((1 + t12166) ** (0.5e1 / 0.2e1)) * np.sqrt(0.546e3) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t12166) ** (0.3e1 / 0.2e1)) + + if Bindx == 1943: + t12179 = np.sin(phi) + t12177 = t12179 ** 2 + t12173 = np.cos(phi) + t12174 = t12173 ** 2 + t12175 = t12174 ** 2 + tfunc[..., c] = (0.69e2 / 0.256e3) * np.exp((-4*1j) * phi1) * np.sqrt(0.2002e4) * t12177 ** 2 * t12173 * (-323 * t12175 - 5 + (323 * t12175 + 85) * t12174) + + if Bindx == 1944: + t12180 = np.cos(phi) + t12181 = t12180 ** 2 + t12182 = t12180 * t12181 + t12185 = t12182 ** 2 + t12183 = t12181 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * (255 * t12181 + 595 * t12182 - 1615 * t12183 + 2261 * t12185 - 5 + (-2907 * t12183 + 3553 * t12185 - 25) * t12180) * ((1 + t12180) ** (0.3e1 / 0.2e1)) * np.sqrt(0.546e3) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t12180) ** (0.5e1 / 0.2e1)) + + if Bindx == 1945: + t12188 = np.cos(phi) + t12189 = t12188 ** 2 + t12191 = t12189 ** 2 + t12195 = t12191 ** 2 + t12190 = t12188 * t12189 + t12193 = t12190 ** 2 + t12187 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.105e3) * t12187 ** 2 * (220 * t12189 - 860 * t12190 - 1700 * t12191 + 3876 * t12193 - 2584 * t12195 - 4 + (3910 * t12191 - 6460 * t12193 + 3553 * t12195 + 49) * t12188) + + if Bindx == 1946: + t12197 = np.cos(phi) + t12198 = t12197 ** 2 + t12199 = t12197 * t12198 + t12202 = t12199 ** 2 + t12200 = t12198 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (119 * t12198 - 3927 * t12199 - 6783 * t12200 + 20349 * t12202 + 11 + (6783 * t12200 + 10659 * t12202 + 245) * t12197) * np.sqrt((1 + t12197)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t12197) ** (0.7e1 / 0.2e1)) + + if Bindx == 1947: + t12204 = np.cos(phi) + t12205 = t12204 ** 2 + t12207 = t12205 ** 2 + t12208 = t12204 * t12207 + t12213 = t12208 ** 2 + t12211 = t12207 ** 2 + t12206 = t12204 * t12205 + t12209 = t12206 ** 2 + tfunc[..., c] = (0.23e2 / 0.128e3) * np.exp((-4*1j) * (phi1 - phi2)) * (-1600 * t12205 + 775 * t12206 + 12520 * t12207 - 5978 * t12208 - 34272 * t12209 + 38760 * t12211 - 15504 * t12213 + 32 + (18870 * t12209 - 24225 * t12211 + 10659 * t12213 - 37) * t12204) + + if Bindx == 1948: + t12215 = np.cos(phi) + t12216 = t12215 ** 2 + t12217 = t12216 ** 2 + t12218 = t12215 * t12217 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * (-2295 * t12216 + 14535 * t12217 + 23256 * t12218 + 13 + (10659 * t12218 - 408) * t12215) * np.sqrt((1 + t12215)) * np.sqrt(0.7e1) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t12215) ** (0.9e1 / 0.2e1)) + + if Bindx == 1949: + t12221 = np.cos(phi) + t12222 = t12221 ** 2 + t12224 = t12222 ** 2 + t12228 = t12224 ** 2 + t12223 = t12221 * t12222 + t12226 = t12223 ** 2 + t12220 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.714e3) * t12220 ** 2 * (156 * t12222 + 316 * t12223 - 964 * t12224 + 2052 * t12226 - 1368 * t12228 - 4 + (-558 * t12224 - 228 * t12226 + 627 * t12228 - 29) * t12221) + + if Bindx == 1950: + t12230 = np.cos(phi) + t12231 = t12230 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-3 + (304 * t12230 + 114 + 209 * t12231) * t12231) * ((1 + t12230) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1785e4) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t12230) ** (0.11e2 / 0.2e1)) + + if Bindx == 1951: + t12242 = np.sin(phi) + t12240 = t12242 ** 2 + t12234 = np.cos(phi) + t12235 = t12234 ** 2 + t12236 = t12234 * t12235 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.33915e5) * t12240 ** 2 * t12234 * (3 + (16 + 11 * t12236) * t12236 + (-19 + (-32 * t12234 + 21) * t12235) * t12235) + + if Bindx == 1952: + t12243 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * (1 + (8 + 11 * t12243) * t12243) * ((1 + t12243) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (4 * phi1 - 9 * phi2)) * ((1 - t12243) ** (0.13e2 / 0.2e1)) + + if Bindx == 1953: + t12252 = np.sin(phi) + t12249 = t12252 ** 2 + t12250 = t12252 * t12249 + t12244 = np.cos(phi) + t12245 = t12244 ** 2 + t12247 = t12245 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.1938e4) * t12250 ** 2 * (-20 * t12245 - 40 * t12247 + 4 + (50 * t12245 + 11 * t12247 - 5) * t12244) + + if Bindx == 1954: + t12253 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (4 * phi1 - 11 * phi2)) * np.sqrt(0.10659e5) * ((1 - t12253) ** (0.15e2 / 0.2e1)) * ((1 + t12253) ** (0.7e1 / 0.2e1)) + + if Bindx == 1955: + t12254 = np.cos(phi) + t12262 = -1 + t12254 + t12261 = 1 + t12254 + t12257 = t12261 ** 2 + t12258 = t12261 * t12257 + t12255 = t12262 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.35530e5) * t12255 ** 2 * t12261 * t12258 ** 2 + + if Bindx == 1956: + t12263 = np.cos(phi) + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 10 * phi2)) * np.sqrt(0.1615e4) * ((1 - t12263) ** (0.7e1 / 0.2e1)) * ((1 + t12263) ** (0.13e2 / 0.2e1)) * (-3 + 11 * t12263) + + if Bindx == 1957: + t12272 = np.sin(phi) + t12269 = t12272 ** 2 + t12270 = t12272 * t12269 + t12264 = np.cos(phi) + t12265 = t12264 ** 2 + t12267 = t12265 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.67830e5) * t12270 ** 2 * (-18 * t12265 + 81 * t12267 + 1 + (46 * t12265 + 33 * t12267 - 15) * t12264) + + if Bindx == 1958: + t12273 = np.cos(phi) + t12274 = t12273 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.4522e4) * ((1 - t12273) ** (0.5e1 / 0.2e1)) * ((1 + t12273) ** (0.11e2 / 0.2e1)) * (-45 * t12274 + 1 + (55 * t12274 + 5) * t12273) + + if Bindx == 1959: + t12285 = np.sin(phi) + t12283 = t12285 ** 2 + t12276 = np.cos(phi) + t12277 = t12276 ** 2 + t12278 = t12276 * t12277 + t12281 = t12278 ** 2 + t12279 = t12277 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.238e3) * t12283 ** 2 * (385 * t12277 - 353 * t12278 - 1729 * t12279 + 1995 * t12281 - 11 + (-95 * t12279 + 1045 * t12281 + 43) * t12276) + + if Bindx == 1960: + t12286 = np.cos(phi) + t12287 = t12286 ** 2 + t12289 = t12287 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.595e3) * ((1 - t12286) ** (0.3e1 / 0.2e1)) * ((1 + t12286) ** (0.9e1 / 0.2e1)) * (342 * t12287 - 2565 * t12289 - 1 + (570 * t12287 + 1881 * t12289 - 99) * t12286) + + if Bindx == 1961: + t12292 = np.cos(phi) + t12293 = t12292 ** 2 + t12295 = t12293 ** 2 + t12299 = t12295 ** 2 + t12294 = t12292 * t12293 + t12297 = t12294 ** 2 + t12291 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.210e3) * t12291 ** 2 * (-1680 * t12293 - 1240 * t12294 + 11390 * t12295 - 23256 * t12297 + 14535 * t12299 + 35 + (7038 * t12295 - 15504 * t12297 + 10659 * t12299 + 71) * t12292) + + if Bindx == 1962: + t12301 = np.cos(phi) + t12302 = t12301 ** 2 + t12308 = 6783 * t12302 ** 2 + t12303 = t12301 * t12302 + t12306 = t12303 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t12301)) * ((1 + t12301) ** (0.7e1 / 0.2e1)) * (-119 * t12302 - 3927 * t12303 - 20349 * t12306 + t12308 - 11 + (10659 * t12306 + t12308 + 245) * t12301) + + if Bindx == 1963: + t12309 = np.cos(phi) + t12310 = t12309 ** 2 + t12312 = t12310 ** 2 + t12313 = t12309 * t12312 + t12318 = t12313 ** 2 + t12316 = t12312 ** 2 + t12311 = t12309 * t12310 + t12314 = t12311 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-3*1j) * (phi1 + phi2)) * (10203 * t12310 + 35201 * t12311 - 89310 * t12312 - 190218 * t12313 + 265846 * t12314 - 316863 * t12316 + 130815 * t12318 - 179 + (428706 * t12314 - 431205 * t12316 + 159885 * t12318 - 1857) * t12309) + + if Bindx == 1964: + t12320 = np.cos(phi) + t12321 = t12320 ** 2 + t12323 = t12321 ** 2 + t12327 = t12323 ** 2 + t12322 = t12320 * t12321 + t12325 = t12322 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.14e2) * ((1 + t12320) ** (0.5e1 / 0.2e1)) * (-660 * t12321 + 4900 * t12322 - 1190 * t12323 + 27132 * t12325 - 43605 * t12327 + 19 + (-23562 * t12323 + 19380 * t12325 + 17765 * t12327 - 179) * t12320) * ((1 - t12320) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1965: + t12330 = np.cos(phi) + t12331 = t12330 ** 2 + t12333 = t12331 ** 2 + t12337 = t12333 ** 2 + t12332 = t12330 * t12331 + t12335 = t12332 ** 2 + t12329 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.455e3) * t12329 ** 2 * (-60 * t12331 - 780 * t12332 + 510 * t12333 - 1292 * t12335 + 969 * t12337 + 1 + (3774 * t12333 - 6460 * t12335 + 3553 * t12337 + 41) * t12330) + + if Bindx == 1966: + t12339 = np.cos(phi) + t12340 = t12339 ** 2 + t12341 = t12340 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-60 * t12340 + 1 + (-1292 * t12340 + 510 + 969 * t12341) * t12341) * ((1 + t12339) ** (0.3e1 / 0.2e1)) * np.sqrt(0.15015e5) * np.exp((-3*1j) * phi1) * ((1 - t12339) ** (0.3e1 / 0.2e1)) + + if Bindx == 1967: + t12345 = np.cos(phi) + t12346 = t12345 ** 2 + t12348 = t12346 ** 2 + t12352 = t12348 ** 2 + t12347 = t12345 * t12346 + t12350 = t12347 ** 2 + t12344 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.455e3) * t12344 ** 2 * (60 * t12346 - 780 * t12347 - 510 * t12348 + 1292 * t12350 - 969 * t12352 - 1 + (3774 * t12348 - 6460 * t12350 + 3553 * t12352 + 41) * t12345) + + if Bindx == 1968: + t12354 = np.cos(phi) + t12355 = t12354 ** 2 + t12356 = t12354 * t12355 + t12359 = t12356 ** 2 + t12357 = t12355 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (820 * t12355 + 4080 * t12356 - 6460 * t12359 - 19 + (-2890 + 17765 * t12357) * t12357 + (-20672 * t12357 + 25840 * t12359 - 160) * t12354) * np.sqrt((1 + t12354)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t12354) ** (0.5e1 / 0.2e1)) + + if Bindx == 1969: + t12362 = np.cos(phi) + t12363 = t12362 ** 2 + t12365 = t12363 ** 2 + t12366 = t12362 * t12365 + t12371 = t12366 ** 2 + t12369 = t12365 ** 2 + t12364 = t12362 * t12363 + t12367 = t12364 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-3*1j) * (phi1 - phi2)) * (-10203 * t12363 + 35201 * t12364 + 89310 * t12365 - 190218 * t12366 - 265846 * t12367 + 316863 * t12369 - 130815 * t12371 + 179 + (428706 * t12367 - 431205 * t12369 + 159885 * t12371 - 1857) * t12362) + + if Bindx == 1970: + t12373 = np.cos(phi) + t12374 = t12373 ** 2 + t12375 = t12373 * t12374 + t12378 = t12375 ** 2 + t12376 = t12374 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (119 * t12374 - 3927 * t12375 - 6783 * t12376 + 20349 * t12378 + 11 + (6783 * t12376 + 10659 * t12378 + 245) * t12373) * np.sqrt((1 + t12373)) * np.sqrt(0.30e2) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t12373) ** (0.7e1 / 0.2e1)) + + if Bindx == 1971: + t12381 = np.cos(phi) + t12382 = t12381 ** 2 + t12384 = t12382 ** 2 + t12388 = t12384 ** 2 + t12383 = t12381 * t12382 + t12386 = t12383 ** 2 + t12380 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.210e3) * t12380 ** 2 * (1680 * t12382 - 1240 * t12383 - 11390 * t12384 + 23256 * t12386 - 14535 * t12388 - 35 + (7038 * t12384 - 15504 * t12386 + 10659 * t12388 + 71) * t12381) + + if Bindx == 1972: + t12390 = np.cos(phi) + t12391 = t12390 ** 2 + t12393 = t12391 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-342 * t12391 + 2565 * t12393 + 1 + (570 * t12391 + 1881 * t12393 - 99) * t12390) * ((1 + t12390) ** (0.3e1 / 0.2e1)) * np.sqrt(0.595e3) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t12390) ** (0.9e1 / 0.2e1)) + + if Bindx == 1973: + t12404 = np.sin(phi) + t12402 = t12404 ** 2 + t12395 = np.cos(phi) + t12396 = t12395 ** 2 + t12397 = t12395 * t12396 + t12400 = t12397 ** 2 + t12398 = t12396 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.238e3) * t12402 ** 2 * (-385 * t12396 - 353 * t12397 + 1729 * t12398 - 1995 * t12400 + 11 + (-95 * t12398 + 1045 * t12400 + 43) * t12395) + + if Bindx == 1974: + t12405 = np.cos(phi) + t12406 = t12405 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (45 * t12406 - 1 + (55 * t12406 + 5) * t12405) * ((1 + t12405) ** (0.5e1 / 0.2e1)) * np.sqrt(0.4522e4) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * ((1 - t12405) ** (0.11e2 / 0.2e1)) + + if Bindx == 1975: + t12416 = np.sin(phi) + t12413 = t12416 ** 2 + t12414 = t12416 * t12413 + t12408 = np.cos(phi) + t12409 = t12408 ** 2 + t12411 = t12409 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.67830e5) * t12414 ** 2 * (18 * t12409 - 81 * t12411 - 1 + (46 * t12409 + 33 * t12411 - 15) * t12408) + + if Bindx == 1976: + t12417 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (3 + 11 * t12417) * ((1 + t12417) ** (0.7e1 / 0.2e1)) * np.sqrt(0.1615e4) * np.exp((-1*1j) * (3 * phi1 - 10 * phi2)) * ((1 - t12417) ** (0.13e2 / 0.2e1)) + + if Bindx == 1977: + t12418 = np.cos(phi) + t12426 = -1 + t12418 + t12425 = 1 + t12418 + t12423 = t12425 ** 2 + t12419 = t12426 ** 2 + t12420 = t12426 * t12419 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.35530e5) * t12426 * t12420 ** 2 * t12423 ** 2 + + if Bindx == 1978: + t12427 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 11 * phi2)) * np.sqrt(0.124355e6) * ((1 - t12427) ** (0.9e1 / 0.2e1)) * ((1 + t12427) ** (0.13e2 / 0.2e1)) + + if Bindx == 1979: + t12434 = np.sin(phi) + t12431 = t12434 ** 2 + t12432 = t12431 ** 2 + t12428 = np.cos(phi) + t12429 = t12428 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.22610e5) * t12432 ** 2 * (20 * t12429 - 2 + (11 * t12429 + 7) * t12428) + + if Bindx == 1980: + t12435 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.4845e4) * ((1 - t12435) ** (0.7e1 / 0.2e1)) * ((1 + t12435) ** (0.11e2 / 0.2e1)) * (-1 + (-28 + 77 * t12435) * t12435) + + if Bindx == 1981: + t12444 = np.sin(phi) + t12441 = t12444 ** 2 + t12442 = t12444 * t12441 + t12436 = np.cos(phi) + t12437 = t12436 ** 2 + t12439 = t12437 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.323e3) * t12442 ** 2 * (-232 * t12437 + 560 * t12439 + 8 + (-50 * t12437 + 385 * t12439 + 1) * t12436) + + if Bindx == 1982: + t12445 = np.cos(phi) + t12446 = t12445 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.17e2) * ((1 - t12445) ** (0.5e1 / 0.2e1)) * ((1 + t12445) ** (0.9e1 / 0.2e1)) * (608 * t12445 - 17 + (-5320 * t12445 - 570 + 7315 * t12446) * t12446) + + if Bindx == 1983: + t12458 = np.sin(phi) + t12456 = t12458 ** 2 + t12449 = np.cos(phi) + t12450 = t12449 ** 2 + t12451 = t12449 * t12450 + t12454 = t12451 ** 2 + t12452 = t12450 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.170e3) * t12456 ** 2 * (264 * t12450 + 401 * t12451 - 1406 * t12452 + 1596 * t12454 - 6 + (-1387 * t12452 + 1463 * t12454 - 29) * t12449) + + if Bindx == 1984: + t12459 = np.cos(phi) + t12460 = t12459 ** 2 + t12462 = t12460 ** 2 + t12461 = t12459 * t12460 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.15e2) * ((1 - t12459) ** (0.3e1 / 0.2e1)) * ((1 + t12459) ** (0.7e1 / 0.2e1)) * (-867 * t12460 - 4845 * t12462 + 41 + (10336 + 24871 * t12461) * t12461 + (-27132 * t12462 - 612) * t12459) + + if Bindx == 1985: + t12466 = np.cos(phi) + t12467 = t12466 ** 2 + t12469 = t12467 ** 2 + t12473 = t12469 ** 2 + t12468 = t12466 * t12467 + t12471 = t12468 ** 2 + t12465 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.105e3) * t12465 ** 2 * (-220 * t12467 - 860 * t12468 + 1700 * t12469 - 3876 * t12471 + 2584 * t12473 + 4 + (3910 * t12469 - 6460 * t12471 + 3553 * t12473 + 49) * t12466) + + if Bindx == 1986: + t12475 = np.cos(phi) + t12476 = t12475 ** 2 + t12477 = t12475 * t12476 + t12480 = t12477 ** 2 + t12478 = t12476 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t12475)) * ((1 + t12475) ** (0.5e1 / 0.2e1)) * (820 * t12476 - 4080 * t12477 - 6460 * t12480 - 19 + (-2890 + 17765 * t12478) * t12478 + (20672 * t12478 - 25840 * t12480 + 160) * t12475) + + if Bindx == 1987: + t12483 = np.cos(phi) + t12484 = t12483 ** 2 + t12486 = t12484 ** 2 + t12487 = t12483 * t12486 + t12492 = t12487 ** 2 + t12490 = t12486 ** 2 + t12485 = t12483 * t12484 + t12488 = t12485 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + phi2)) * (2604 * t12484 + 26103 * t12485 - 24780 * t12486 - 147714 * t12487 + 79968 * t12488 - 102714 * t12490 + 45220 * t12492 - 42 + (339558 * t12488 - 340765 * t12490 + 124355 * t12492 - 1281) * t12483) + + if Bindx == 1988: + t12494 = np.cos(phi) + t12495 = t12494 ** 2 + t12497 = t12495 ** 2 + t12501 = t12497 ** 2 + t12496 = t12494 * t12495 + t12499 = t12496 ** 2 + t12498 = t12494 * t12497 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.130e3) * ((1 + t12494) ** (0.3e1 / 0.2e1)) * (-1197 * t12495 + 3360 * t12496 + 8610 * t12497 - 12138 * t12499 - 14535 * t12501 + 21 + (-25704 + 24871 * t12498) * t12498 + (62016 * t12499 - 45220 * t12501 - 84) * t12494) * ((1 - t12494) ** (-0.1e1 / 0.2e1)) + + if Bindx == 1989: + t12505 = np.cos(phi) + t12506 = t12505 ** 2 + t12507 = t12506 ** 2 + t12504 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * phi1) * np.sqrt(0.4290e4) * t12504 ** 2 * t12505 * (-420 * t12506 + 21 + (-3876 * t12506 + 2142 + 2261 * t12507) * t12507) + + if Bindx == 1990: + t12510 = np.cos(phi) + t12511 = t12510 ** 2 + t12513 = t12511 ** 2 + t12517 = t12513 ** 2 + t12512 = t12510 * t12511 + t12515 = t12512 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1260 * t12511 - 2100 * t12512 + 10710 * t12513 - 27132 * t12515 + 20349 * t12517 + 21 + (14994 * t12513 - 34884 * t12515 + 24871 * t12517 + 63) * t12510) * np.sqrt((1 + t12510)) * np.sqrt(0.130e3) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t12510) ** (0.3e1 / 0.2e1)) + + if Bindx == 1991: + t12519 = np.cos(phi) + t12520 = t12519 ** 2 + t12522 = t12520 ** 2 + t12523 = t12519 * t12522 + t12528 = t12523 ** 2 + t12526 = t12522 ** 2 + t12521 = t12519 * t12520 + t12524 = t12521 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - phi2)) * (-2604 * t12520 + 26103 * t12521 + 24780 * t12522 - 147714 * t12523 - 79968 * t12524 + 102714 * t12526 - 45220 * t12528 + 42 + (339558 * t12524 - 340765 * t12526 + 124355 * t12528 - 1281) * t12519) + + if Bindx == 1992: + t12530 = np.cos(phi) + t12531 = t12530 ** 2 + t12532 = t12530 * t12531 + t12535 = t12532 ** 2 + t12533 = t12531 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (820 * t12531 + 4080 * t12532 - 6460 * t12535 - 19 + (-2890 + 17765 * t12533) * t12533 + (-20672 * t12533 + 25840 * t12535 - 160) * t12530) * np.sqrt((1 + t12530)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t12530) ** (0.5e1 / 0.2e1)) + + if Bindx == 1993: + t12539 = np.cos(phi) + t12540 = t12539 ** 2 + t12542 = t12540 ** 2 + t12546 = t12542 ** 2 + t12541 = t12539 * t12540 + t12544 = t12541 ** 2 + t12538 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.105e3) * t12538 ** 2 * (220 * t12540 - 860 * t12541 - 1700 * t12542 + 3876 * t12544 - 2584 * t12546 - 4 + (3910 * t12542 - 6460 * t12544 + 3553 * t12546 + 49) * t12539) + + if Bindx == 1994: + t12548 = np.cos(phi) + t12549 = t12548 ** 2 + t12551 = t12549 ** 2 + t12550 = t12548 * t12549 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-867 * t12549 - 4845 * t12551 + 41 + (-10336 + 24871 * t12550) * t12550 + (27132 * t12551 + 612) * t12548) * ((1 + t12548) ** (0.3e1 / 0.2e1)) * np.sqrt(0.15e2) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t12548) ** (0.7e1 / 0.2e1)) + + if Bindx == 1995: + t12563 = np.sin(phi) + t12561 = t12563 ** 2 + t12554 = np.cos(phi) + t12555 = t12554 ** 2 + t12556 = t12554 * t12555 + t12559 = t12556 ** 2 + t12557 = t12555 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.170e3) * t12561 ** 2 * (-264 * t12555 + 401 * t12556 + 1406 * t12557 - 1596 * t12559 + 6 + (-1387 * t12557 + 1463 * t12559 - 29) * t12554) + + if Bindx == 1996: + t12564 = np.cos(phi) + t12565 = t12564 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-608 * t12564 - 17 + (5320 * t12564 - 570 + 7315 * t12565) * t12565) * ((1 + t12564) ** (0.5e1 / 0.2e1)) * np.sqrt(0.17e2) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t12564) ** (0.9e1 / 0.2e1)) + + if Bindx == 1997: + t12576 = np.sin(phi) + t12573 = t12576 ** 2 + t12574 = t12576 * t12573 + t12568 = np.cos(phi) + t12569 = t12568 ** 2 + t12571 = t12569 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.323e3) * t12574 ** 2 * (232 * t12569 - 560 * t12571 - 8 + (-50 * t12569 + 385 * t12571 + 1) * t12568) + + if Bindx == 1998: + t12577 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1 + (28 + 77 * t12577) * t12577) * ((1 + t12577) ** (0.7e1 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((-1*1j) * (2 * phi1 - 9 * phi2)) * ((1 - t12577) ** (0.11e2 / 0.2e1)) + + if Bindx == 1999: + t12584 = np.sin(phi) + t12581 = t12584 ** 2 + t12582 = t12581 ** 2 + t12578 = np.cos(phi) + t12579 = t12578 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.22610e5) * t12582 ** 2 * (-20 * t12579 + 2 + (11 * t12579 + 7) * t12578) + + if Bindx == 2000: + t12585 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 - 11 * phi2)) * np.sqrt(0.124355e6) * ((1 - t12585) ** (0.13e2 / 0.2e1)) * ((1 + t12585) ** (0.9e1 / 0.2e1)) + + if Bindx == 2001: + t12590 = np.sin(phi) + t12586 = t12590 ** 2 + t12588 = t12590 * t12586 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 11 * phi2)) * np.sqrt(0.646646e6) * t12588 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 2002: + t12591 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 10 * phi2)) * np.sqrt(0.29393e5) * ((1 - t12591) ** (0.9e1 / 0.2e1)) * ((1 + t12591) ** (0.11e2 / 0.2e1)) * (-1 + 11 * t12591) + + if Bindx == 2003: + t12598 = np.sin(phi) + t12595 = t12598 ** 2 + t12596 = t12595 ** 2 + t12592 = np.cos(phi) + t12593 = t12592 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 9 * phi2)) * np.sqrt(0.25194e5) * t12596 ** 2 * (63 * t12593 - 3 + (77 * t12593 - 17) * t12592) + + if Bindx == 2004: + t12599 = np.cos(phi) + t12600 = t12599 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.41990e5) * ((1 - t12599) ** (0.7e1 / 0.2e1)) * ((1 + t12599) ** (0.9e1 / 0.2e1)) * (-21 * t12600 + 1 + (77 * t12600 - 9) * t12599) + + if Bindx == 2005: + t12610 = np.sin(phi) + t12607 = t12610 ** 2 + t12608 = t12610 * t12607 + t12602 = np.cos(phi) + t12603 = t12602 ** 2 + t12605 = t12603 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.2210e4) * t12608 ** 2 * (-266 * t12603 + 931 * t12605 + 7 + (-874 * t12603 + 1463 * t12605 + 83) * t12602) + + if Bindx == 2006: + t12611 = np.cos(phi) + t12612 = t12611 ** 2 + t12614 = t12612 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.221e3) * ((1 - t12611) ** (0.5e1 / 0.2e1)) * ((1 + t12611) ** (0.7e1 / 0.2e1)) * (190 * t12612 - 665 * t12614 - 5 + (-570 * t12612 + 1463 * t12614 + 35) * t12611) + + if Bindx == 2007: + t12625 = np.sin(phi) + t12623 = t12625 ** 2 + t12616 = np.cos(phi) + t12617 = t12616 ** 2 + t12618 = t12616 * t12617 + t12621 = t12618 ** 2 + t12619 = t12617 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.78e2) * t12623 ** 2 * (1275 * t12617 + 8245 * t12618 - 8075 * t12619 + 11305 * t12621 - 25 + (-28101 * t12619 + 24871 * t12621 - 535) * t12616) + + if Bindx == 2008: + t12626 = np.cos(phi) + t12627 = t12626 ** 2 + t12628 = t12626 * t12627 + t12631 = t12628 ** 2 + t12629 = t12627 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.546e3) * ((1 - t12626) ** (0.3e1 / 0.2e1)) * ((1 + t12626) ** (0.5e1 / 0.2e1)) * (-255 * t12627 + 595 * t12628 + 1615 * t12629 - 2261 * t12631 + 5 + (-2907 * t12629 + 3553 * t12631 - 25) * t12626) + + if Bindx == 2009: + t12634 = np.cos(phi) + t12635 = t12634 ** 2 + t12637 = t12635 ** 2 + t12641 = t12637 ** 2 + t12636 = t12634 * t12635 + t12639 = t12636 ** 2 + t12633 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.455e3) * t12633 ** 2 * (-60 * t12635 - 780 * t12636 + 510 * t12637 - 1292 * t12639 + 969 * t12641 + 1 + (3774 * t12637 - 6460 * t12639 + 3553 * t12641 + 41) * t12634) + + if Bindx == 2010: + t12643 = np.cos(phi) + t12644 = t12643 ** 2 + t12646 = t12644 ** 2 + t12650 = t12646 ** 2 + t12645 = t12643 * t12644 + t12648 = t12645 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.130e3) * np.sqrt((1 - t12643)) * ((1 + t12643) ** (0.3e1 / 0.2e1)) * (1260 * t12644 - 2100 * t12645 - 10710 * t12646 + 27132 * t12648 - 20349 * t12650 - 21 + (14994 * t12646 - 34884 * t12648 + 24871 * t12650 + 63) * t12643) + + if Bindx == 2011: + t12652 = np.cos(phi) + t12653 = t12652 ** 2 + t12655 = t12653 ** 2 + t12656 = t12652 * t12655 + t12661 = t12656 ** 2 + t12659 = t12655 ** 2 + t12654 = t12652 * t12653 + t12657 = t12654 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 + phi2)) * (1365 * t12653 + 58695 * t12654 - 13650 * t12655 - 346710 * t12656 + 46410 * t12657 - 62985 * t12659 + 29393 * t12661 - 21 + (828750 * t12657 - 860795 * t12659 + 323323 * t12661 - 2751) * t12652) + + if Bindx == 2012: + t12663 = np.cos(phi) + t12664 = t12663 ** 2 + t12665 = t12663 * t12664 + t12666 = t12664 ** 2 + t12667 = t12663 * t12666 + t12674 = 46410 * t12665 ** 2 - 62985 * t12666 ** 2 + 29393 * t12667 ** 2 - 21 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.33e2) * np.sqrt((1 + t12663)) * (t12674 * t12663 - 1365 * t12664 + 1365 * t12665 + 13650 * t12666 - 13650 * t12667 - t12674) * ((1 - t12663) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2013: + t12675 = np.cos(phi) + t12676 = t12675 ** 2 + t12678 = t12676 ** 2 + t12679 = t12675 * t12678 + t12684 = t12679 ** 2 + t12682 = t12678 ** 2 + t12677 = t12675 * t12676 + t12680 = t12677 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 - phi2)) * (-1365 * t12676 + 58695 * t12677 + 13650 * t12678 - 346710 * t12679 - 46410 * t12680 + 62985 * t12682 - 29393 * t12684 + 21 + (828750 * t12680 - 860795 * t12682 + 323323 * t12684 - 2751) * t12675) + + if Bindx == 2014: + t12686 = np.cos(phi) + t12687 = t12686 ** 2 + t12689 = t12687 ** 2 + t12693 = t12689 ** 2 + t12688 = t12686 * t12687 + t12691 = t12688 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1260 * t12687 - 2100 * t12688 + 10710 * t12689 - 27132 * t12691 + 20349 * t12693 + 21 + (14994 * t12689 - 34884 * t12691 + 24871 * t12693 + 63) * t12686) * np.sqrt((1 + t12686)) * np.sqrt(0.130e3) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t12686) ** (0.3e1 / 0.2e1)) + + if Bindx == 2015: + t12696 = np.cos(phi) + t12697 = t12696 ** 2 + t12699 = t12697 ** 2 + t12703 = t12699 ** 2 + t12698 = t12696 * t12697 + t12701 = t12698 ** 2 + t12695 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.455e3) * t12695 ** 2 * (60 * t12697 - 780 * t12698 - 510 * t12699 + 1292 * t12701 - 969 * t12703 - 1 + (3774 * t12699 - 6460 * t12701 + 3553 * t12703 + 41) * t12696) + + if Bindx == 2016: + t12705 = np.cos(phi) + t12706 = t12705 ** 2 + t12707 = t12705 * t12706 + t12710 = t12707 ** 2 + t12708 = t12706 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * (255 * t12706 + 595 * t12707 - 1615 * t12708 + 2261 * t12710 - 5 + (-2907 * t12708 + 3553 * t12710 - 25) * t12705) * ((1 + t12705) ** (0.3e1 / 0.2e1)) * np.sqrt(0.546e3) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t12705) ** (0.5e1 / 0.2e1)) + + if Bindx == 2017: + t12721 = np.sin(phi) + t12719 = t12721 ** 2 + t12712 = np.cos(phi) + t12713 = t12712 ** 2 + t12714 = t12712 * t12713 + t12717 = t12714 ** 2 + t12715 = t12713 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.78e2) * t12719 ** 2 * (-1275 * t12713 + 8245 * t12714 + 8075 * t12715 - 11305 * t12717 + 25 + (-28101 * t12715 + 24871 * t12717 - 535) * t12712) + + if Bindx == 2018: + t12722 = np.cos(phi) + t12723 = t12722 ** 2 + t12725 = t12723 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (-190 * t12723 + 665 * t12725 + 5 + (-570 * t12723 + 1463 * t12725 + 35) * t12722) * ((1 + t12722) ** (0.5e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t12722) ** (0.7e1 / 0.2e1)) + + if Bindx == 2019: + t12735 = np.sin(phi) + t12732 = t12735 ** 2 + t12733 = t12735 * t12732 + t12727 = np.cos(phi) + t12728 = t12727 ** 2 + t12730 = t12728 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.2210e4) * t12733 ** 2 * (266 * t12728 - 931 * t12730 - 7 + (-874 * t12728 + 1463 * t12730 + 83) * t12727) + + if Bindx == 2020: + t12736 = np.cos(phi) + t12737 = t12736 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (21 * t12737 - 1 + (77 * t12737 - 9) * t12736) * ((1 + t12736) ** (0.7e1 / 0.2e1)) * np.sqrt(0.41990e5) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * ((1 - t12736) ** (0.9e1 / 0.2e1)) + + if Bindx == 2021: + t12745 = np.sin(phi) + t12742 = t12745 ** 2 + t12743 = t12742 ** 2 + t12739 = np.cos(phi) + t12740 = t12739 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 9 * phi2)) * np.sqrt(0.25194e5) * t12743 ** 2 * (-63 * t12740 + 3 + (77 * t12740 - 17) * t12739) + + if Bindx == 2022: + t12746 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (1 + 11 * t12746) * ((1 + t12746) ** (0.9e1 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((-1*1j) * (phi1 - 10 * phi2)) * ((1 - t12746) ** (0.11e2 / 0.2e1)) + + if Bindx == 2023: + t12751 = np.sin(phi) + t12747 = t12751 ** 2 + t12749 = t12751 * t12747 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 11 * phi2)) * np.sqrt(0.646646e6) * t12749 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 2024: + t12752 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-11*1j) * phi2) * np.sqrt(0.176358e6) * ((1 - t12752) ** (0.11e2 / 0.2e1)) * ((1 + t12752) ** (0.11e2 / 0.2e1)) + + if Bindx == 2025: + t12757 = np.sin(phi) + t12753 = t12757 ** 2 + t12755 = t12757 * t12753 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-10*1j) * phi2) * np.sqrt(0.969969e6) * t12755 ** 2 * np.cos(phi) + + if Bindx == 2026: + t12758 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((-9*1j) * phi2) * np.sqrt(0.92378e5) * ((1 - t12758) ** (0.9e1 / 0.2e1)) * ((1 + t12758) ** (0.9e1 / 0.2e1)) * (21 * t12758 ** 2 - 1) + + if Bindx == 2027: + t12763 = np.sin(phi) + t12760 = t12763 ** 2 + t12761 = t12760 ** 2 + t12759 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((-8*1j) * phi2) * np.sqrt(0.1385670e7) * t12761 ** 2 * t12759 * (7 * t12759 ** 2 - 1) + + if Bindx == 2028: + t12764 = np.cos(phi) + t12765 = t12764 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.72930e5) * ((1 - t12764) ** (0.7e1 / 0.2e1)) * ((1 + t12764) ** (0.7e1 / 0.2e1)) * (1 + (-38 + 133 * t12765) * t12765) + + if Bindx == 2029: + t12773 = np.sin(phi) + t12770 = t12773 ** 2 + t12771 = t12773 * t12770 + t12767 = np.cos(phi) + t12768 = t12767 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-6*1j) * phi2) * np.sqrt(0.7293e4) * t12771 ** 2 * t12767 * (15 + (-190 + 399 * t12768) * t12768) + + if Bindx == 2030: + t12774 = np.cos(phi) + t12775 = t12774 ** 2 + t12776 = t12775 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.286e3) * ((1 - t12774) ** (0.5e1 / 0.2e1)) * ((1 + t12774) ** (0.5e1 / 0.2e1)) * (-1615 * t12776 - 5 + (2261 * t12776 + 255) * t12775) + + if Bindx == 2031: + t12784 = np.sin(phi) + t12782 = t12784 ** 2 + t12778 = np.cos(phi) + t12779 = t12778 ** 2 + t12780 = t12779 ** 2 + tfunc[..., c] = (0.69e2 / 0.256e3) * np.exp((-4*1j) * phi2) * np.sqrt(0.2002e4) * t12782 ** 2 * t12778 * (-323 * t12780 - 5 + (323 * t12780 + 85) * t12779) + + if Bindx == 2032: + t12785 = np.cos(phi) + t12786 = t12785 ** 2 + t12787 = t12786 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.15015e5) * ((1 - t12785) ** (0.3e1 / 0.2e1)) * ((1 + t12785) ** (0.3e1 / 0.2e1)) * (-60 * t12786 + 1 + (-1292 * t12786 + 510 + 969 * t12787) * t12787) + + if Bindx == 2033: + t12791 = np.cos(phi) + t12792 = t12791 ** 2 + t12793 = t12792 ** 2 + t12790 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((-2*1j) * phi2) * np.sqrt(0.4290e4) * t12790 ** 2 * t12791 * (-420 * t12792 + 21 + (-3876 * t12792 + 2142 + 2261 * t12793) * t12793) + + if Bindx == 2034: + t12796 = np.cos(phi) + t12797 = t12796 ** 2 + t12798 = t12797 ** 2 + t12800 = t12798 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.33e2) * np.sqrt((1 - t12796)) * np.sqrt((1 + t12796)) * (-13650 * t12798 - 62985 * t12800 - 21 + (46410 * t12798 + 29393 * t12800 + 1365) * t12797) + + if Bindx == 2035: + t12802 = np.cos(phi) + t12803 = t12802 ** 2 + t12804 = t12803 ** 2 + t12806 = t12804 ** 2 + tfunc[..., c] = 0.23e2 / 0.256e3 * t12802 * (-90090 * t12804 - 230945 * t12806 - 693 + (218790 * t12804 + 88179 * t12806 + 15015) * t12803) + + if Bindx == 2036: + t12808 = np.cos(phi) + t12809 = t12808 ** 2 + t12810 = t12808 * t12809 + t12811 = t12809 ** 2 + t12812 = t12808 * t12811 + t12819 = 46410 * t12810 ** 2 - 62985 * t12811 ** 2 + 29393 * t12812 ** 2 - 21 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * phi2) * np.sqrt(0.33e2) * np.sqrt((1 + t12808)) * (t12819 * t12808 - 1365 * t12809 + 1365 * t12810 + 13650 * t12811 - 13650 * t12812 - t12819) * ((1 - t12808) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2037: + t12821 = np.cos(phi) + t12822 = t12821 ** 2 + t12823 = t12822 ** 2 + t12820 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * phi2) * np.sqrt(0.4290e4) * t12820 ** 2 * t12821 * (-420 * t12822 + 21 + (-3876 * t12822 + 2142 + 2261 * t12823) * t12823) + + if Bindx == 2038: + t12826 = np.cos(phi) + t12827 = t12826 ** 2 + t12828 = t12827 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-60 * t12827 + 1 + (-1292 * t12827 + 510 + 969 * t12828) * t12828) * ((1 + t12826) ** (0.3e1 / 0.2e1)) * np.sqrt(0.15015e5) * np.exp((3*1j) * phi2) * ((1 - t12826) ** (0.3e1 / 0.2e1)) + + if Bindx == 2039: + t12837 = np.sin(phi) + t12835 = t12837 ** 2 + t12831 = np.cos(phi) + t12832 = t12831 ** 2 + t12833 = t12832 ** 2 + tfunc[..., c] = (0.69e2 / 0.256e3) * np.exp((4*1j) * phi2) * np.sqrt(0.2002e4) * t12835 ** 2 * t12831 * (-323 * t12833 - 5 + (323 * t12833 + 85) * t12832) + + if Bindx == 2040: + t12838 = np.cos(phi) + t12839 = t12838 ** 2 + t12840 = t12839 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (-1615 * t12840 - 5 + (2261 * t12840 + 255) * t12839) * ((1 + t12838) ** (0.5e1 / 0.2e1)) * np.sqrt(0.286e3) * np.exp((5*1j) * phi2) * ((1 - t12838) ** (0.5e1 / 0.2e1)) + + if Bindx == 2041: + t12848 = np.sin(phi) + t12845 = t12848 ** 2 + t12846 = t12848 * t12845 + t12842 = np.cos(phi) + t12843 = t12842 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((6*1j) * phi2) * np.sqrt(0.7293e4) * t12846 ** 2 * t12842 * (15 + (-190 + 399 * t12843) * t12843) + + if Bindx == 2042: + t12849 = np.cos(phi) + t12850 = t12849 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (1 + (-38 + 133 * t12850) * t12850) * ((1 + t12849) ** (0.7e1 / 0.2e1)) * np.sqrt(0.72930e5) * np.exp((7*1j) * phi2) * ((1 - t12849) ** (0.7e1 / 0.2e1)) + + if Bindx == 2043: + t12856 = np.sin(phi) + t12853 = t12856 ** 2 + t12854 = t12853 ** 2 + t12852 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((8*1j) * phi2) * np.sqrt(0.1385670e7) * t12854 ** 2 * t12852 * (7 * t12852 ** 2 - 1) + + if Bindx == 2044: + t12857 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (21 * t12857 ** 2 - 1) * ((1 + t12857) ** (0.9e1 / 0.2e1)) * np.sqrt(0.92378e5) * np.exp((9*1j) * phi2) * ((1 - t12857) ** (0.9e1 / 0.2e1)) + + if Bindx == 2045: + t12862 = np.sin(phi) + t12858 = t12862 ** 2 + t12860 = t12862 * t12858 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((10*1j) * phi2) * np.sqrt(0.969969e6) * t12860 ** 2 * np.cos(phi) + + if Bindx == 2046: + t12863 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((11*1j) * phi2) * np.sqrt(0.176358e6) * ((1 - t12863) ** (0.11e2 / 0.2e1)) * ((1 + t12863) ** (0.11e2 / 0.2e1)) + + if Bindx == 2047: + t12868 = np.sin(phi) + t12864 = t12868 ** 2 + t12866 = t12868 * t12864 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 - 11 * phi2)) * np.sqrt(0.646646e6) * t12866 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 2048: + t12869 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 10 * phi2)) * np.sqrt(0.29393e5) * ((1 - t12869) ** (0.11e2 / 0.2e1)) * ((1 + t12869) ** (0.9e1 / 0.2e1)) * (1 + 11 * t12869) + + if Bindx == 2049: + t12876 = np.sin(phi) + t12873 = t12876 ** 2 + t12874 = t12873 ** 2 + t12870 = np.cos(phi) + t12871 = t12870 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 - 9 * phi2)) * np.sqrt(0.25194e5) * t12874 ** 2 * (-63 * t12871 + 3 + (77 * t12871 - 17) * t12870) + + if Bindx == 2050: + t12877 = np.cos(phi) + t12878 = t12877 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.41990e5) * ((1 - t12877) ** (0.9e1 / 0.2e1)) * ((1 + t12877) ** (0.7e1 / 0.2e1)) * (21 * t12878 - 1 + (77 * t12878 - 9) * t12877) + + if Bindx == 2051: + t12888 = np.sin(phi) + t12885 = t12888 ** 2 + t12886 = t12888 * t12885 + t12880 = np.cos(phi) + t12881 = t12880 ** 2 + t12883 = t12881 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.2210e4) * t12886 ** 2 * (266 * t12881 - 931 * t12883 - 7 + (-874 * t12881 + 1463 * t12883 + 83) * t12880) + + if Bindx == 2052: + t12889 = np.cos(phi) + t12890 = t12889 ** 2 + t12892 = t12890 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.221e3) * ((1 - t12889) ** (0.7e1 / 0.2e1)) * ((1 + t12889) ** (0.5e1 / 0.2e1)) * (-190 * t12890 + 665 * t12892 + 5 + (-570 * t12890 + 1463 * t12892 + 35) * t12889) + + if Bindx == 2053: + t12903 = np.sin(phi) + t12901 = t12903 ** 2 + t12894 = np.cos(phi) + t12895 = t12894 ** 2 + t12896 = t12894 * t12895 + t12899 = t12896 ** 2 + t12897 = t12895 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.78e2) * t12901 ** 2 * (-1275 * t12895 + 8245 * t12896 + 8075 * t12897 - 11305 * t12899 + 25 + (-28101 * t12897 + 24871 * t12899 - 535) * t12894) + + if Bindx == 2054: + t12904 = np.cos(phi) + t12905 = t12904 ** 2 + t12906 = t12904 * t12905 + t12909 = t12906 ** 2 + t12907 = t12905 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.546e3) * ((1 - t12904) ** (0.5e1 / 0.2e1)) * ((1 + t12904) ** (0.3e1 / 0.2e1)) * (255 * t12905 + 595 * t12906 - 1615 * t12907 + 2261 * t12909 - 5 + (-2907 * t12907 + 3553 * t12909 - 25) * t12904) + + if Bindx == 2055: + t12912 = np.cos(phi) + t12913 = t12912 ** 2 + t12915 = t12913 ** 2 + t12919 = t12915 ** 2 + t12914 = t12912 * t12913 + t12917 = t12914 ** 2 + t12911 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.455e3) * t12911 ** 2 * (60 * t12913 - 780 * t12914 - 510 * t12915 + 1292 * t12917 - 969 * t12919 - 1 + (3774 * t12915 - 6460 * t12917 + 3553 * t12919 + 41) * t12912) + + if Bindx == 2056: + t12921 = np.cos(phi) + t12922 = t12921 ** 2 + t12924 = t12922 ** 2 + t12928 = t12924 ** 2 + t12923 = t12921 * t12922 + t12926 = t12923 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.130e3) * ((1 - t12921) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t12921)) * (-1260 * t12922 - 2100 * t12923 + 10710 * t12924 - 27132 * t12926 + 20349 * t12928 + 21 + (14994 * t12924 - 34884 * t12926 + 24871 * t12928 + 63) * t12921) + + if Bindx == 2057: + t12930 = np.cos(phi) + t12931 = t12930 ** 2 + t12933 = t12931 ** 2 + t12934 = t12930 * t12933 + t12939 = t12934 ** 2 + t12937 = t12933 ** 2 + t12932 = t12930 * t12931 + t12935 = t12932 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((1j) * (phi1 - phi2)) * (-1365 * t12931 + 58695 * t12932 + 13650 * t12933 - 346710 * t12934 - 46410 * t12935 + 62985 * t12937 - 29393 * t12939 + 21 + (828750 * t12935 - 860795 * t12937 + 323323 * t12939 - 2751) * t12930) + + if Bindx == 2058: + t12941 = np.cos(phi) + t12942 = t12941 ** 2 + t12943 = t12942 ** 2 + t12945 = t12943 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.33e2) * np.sqrt((1 - t12941)) * np.sqrt((1 + t12941)) * (-13650 * t12943 - 62985 * t12945 - 21 + (46410 * t12943 + 29393 * t12945 + 1365) * t12942) + + if Bindx == 2059: + t12947 = np.cos(phi) + t12948 = t12947 ** 2 + t12950 = t12948 ** 2 + t12951 = t12947 * t12950 + t12956 = t12951 ** 2 + t12954 = t12950 ** 2 + t12949 = t12947 * t12948 + t12952 = t12949 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((1j) * (phi1 + phi2)) * (1365 * t12948 + 58695 * t12949 - 13650 * t12950 - 346710 * t12951 + 46410 * t12952 - 62985 * t12954 + 29393 * t12956 - 21 + (828750 * t12952 - 860795 * t12954 + 323323 * t12956 - 2751) * t12947) + + if Bindx == 2060: + t12958 = np.cos(phi) + t12959 = t12958 ** 2 + t12961 = t12959 ** 2 + t12965 = t12961 ** 2 + t12960 = t12958 * t12959 + t12963 = t12960 ** 2 + t12962 = t12958 * t12961 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.130e3) * ((1 + t12958) ** (0.3e1 / 0.2e1)) * (-1197 * t12959 + 3360 * t12960 + 8610 * t12961 - 12138 * t12963 - 14535 * t12965 + 21 + (-25704 + 24871 * t12962) * t12962 + (62016 * t12963 - 45220 * t12965 - 84) * t12958) * ((1 - t12958) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2061: + t12969 = np.cos(phi) + t12970 = t12969 ** 2 + t12972 = t12970 ** 2 + t12976 = t12972 ** 2 + t12971 = t12969 * t12970 + t12974 = t12971 ** 2 + t12968 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.455e3) * t12968 ** 2 * (-60 * t12970 - 780 * t12971 + 510 * t12972 - 1292 * t12974 + 969 * t12976 + 1 + (3774 * t12972 - 6460 * t12974 + 3553 * t12976 + 41) * t12969) + + if Bindx == 2062: + t12978 = np.cos(phi) + t12979 = t12978 ** 2 + t12980 = t12978 * t12979 + t12983 = t12980 ** 2 + t12981 = t12979 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-255 * t12979 + 595 * t12980 + 1615 * t12981 - 2261 * t12983 + 5 + (-2907 * t12981 + 3553 * t12983 - 25) * t12978) * ((1 + t12978) ** (0.5e1 / 0.2e1)) * np.sqrt(0.546e3) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t12978) ** (0.3e1 / 0.2e1)) + + if Bindx == 2063: + t12994 = np.sin(phi) + t12992 = t12994 ** 2 + t12985 = np.cos(phi) + t12986 = t12985 ** 2 + t12987 = t12985 * t12986 + t12990 = t12987 ** 2 + t12988 = t12986 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.78e2) * t12992 ** 2 * (1275 * t12986 + 8245 * t12987 - 8075 * t12988 + 11305 * t12990 - 25 + (-28101 * t12988 + 24871 * t12990 - 535) * t12985) + + if Bindx == 2064: + t12995 = np.cos(phi) + t12996 = t12995 ** 2 + t12998 = t12996 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (190 * t12996 - 665 * t12998 - 5 + (-570 * t12996 + 1463 * t12998 + 35) * t12995) * ((1 + t12995) ** (0.7e1 / 0.2e1)) * np.sqrt(0.221e3) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t12995) ** (0.5e1 / 0.2e1)) + + if Bindx == 2065: + t13008 = np.sin(phi) + t13005 = t13008 ** 2 + t13006 = t13008 * t13005 + t13000 = np.cos(phi) + t13001 = t13000 ** 2 + t13003 = t13001 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.2210e4) * t13006 ** 2 * (-266 * t13001 + 931 * t13003 + 7 + (-874 * t13001 + 1463 * t13003 + 83) * t13000) + + if Bindx == 2066: + t13009 = np.cos(phi) + t13010 = t13009 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-21 * t13010 + 1 + (77 * t13010 - 9) * t13009) * ((1 + t13009) ** (0.9e1 / 0.2e1)) * np.sqrt(0.41990e5) * np.exp((1j) * (phi1 + 8 * phi2)) * ((1 - t13009) ** (0.7e1 / 0.2e1)) + + if Bindx == 2067: + t13018 = np.sin(phi) + t13015 = t13018 ** 2 + t13016 = t13015 ** 2 + t13012 = np.cos(phi) + t13013 = t13012 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 + 9 * phi2)) * np.sqrt(0.25194e5) * t13016 ** 2 * (63 * t13013 - 3 + (77 * t13013 - 17) * t13012) + + if Bindx == 2068: + t13019 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (-1 + 11 * t13019) * ((1 + t13019) ** (0.11e2 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((1j) * (phi1 + 10 * phi2)) * ((1 - t13019) ** (0.9e1 / 0.2e1)) + + if Bindx == 2069: + t13024 = np.sin(phi) + t13020 = t13024 ** 2 + t13022 = t13024 * t13020 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (phi1 + 11 * phi2)) * np.sqrt(0.646646e6) * t13022 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 2070: + t13025 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 11 * phi2)) * np.sqrt(0.124355e6) * ((1 - t13025) ** (0.13e2 / 0.2e1)) * ((1 + t13025) ** (0.9e1 / 0.2e1)) + + if Bindx == 2071: + t13032 = np.sin(phi) + t13029 = t13032 ** 2 + t13030 = t13029 ** 2 + t13026 = np.cos(phi) + t13027 = t13026 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.22610e5) * t13030 ** 2 * (-20 * t13027 + 2 + (11 * t13027 + 7) * t13026) + + if Bindx == 2072: + t13033 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.4845e4) * ((1 - t13033) ** (0.11e2 / 0.2e1)) * ((1 + t13033) ** (0.7e1 / 0.2e1)) * (-1 + (28 + 77 * t13033) * t13033) + + if Bindx == 2073: + t13042 = np.sin(phi) + t13039 = t13042 ** 2 + t13040 = t13042 * t13039 + t13034 = np.cos(phi) + t13035 = t13034 ** 2 + t13037 = t13035 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.323e3) * t13040 ** 2 * (232 * t13035 - 560 * t13037 - 8 + (-50 * t13035 + 385 * t13037 + 1) * t13034) + + if Bindx == 2074: + t13043 = np.cos(phi) + t13044 = t13043 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.17e2) * ((1 - t13043) ** (0.9e1 / 0.2e1)) * ((1 + t13043) ** (0.5e1 / 0.2e1)) * (-608 * t13043 - 17 + (5320 * t13043 - 570 + 7315 * t13044) * t13044) + + if Bindx == 2075: + t13056 = np.sin(phi) + t13054 = t13056 ** 2 + t13047 = np.cos(phi) + t13048 = t13047 ** 2 + t13049 = t13047 * t13048 + t13052 = t13049 ** 2 + t13050 = t13048 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.170e3) * t13054 ** 2 * (-264 * t13048 + 401 * t13049 + 1406 * t13050 - 1596 * t13052 + 6 + (-1387 * t13050 + 1463 * t13052 - 29) * t13047) + + if Bindx == 2076: + t13057 = np.cos(phi) + t13058 = t13057 ** 2 + t13060 = t13058 ** 2 + t13059 = t13057 * t13058 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.15e2) * ((1 - t13057) ** (0.7e1 / 0.2e1)) * ((1 + t13057) ** (0.3e1 / 0.2e1)) * (-867 * t13058 - 4845 * t13060 + 41 + (-10336 + 24871 * t13059) * t13059 + (27132 * t13060 + 612) * t13057) + + if Bindx == 2077: + t13064 = np.cos(phi) + t13065 = t13064 ** 2 + t13067 = t13065 ** 2 + t13071 = t13067 ** 2 + t13066 = t13064 * t13065 + t13069 = t13066 ** 2 + t13063 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.105e3) * t13063 ** 2 * (220 * t13065 - 860 * t13066 - 1700 * t13067 + 3876 * t13069 - 2584 * t13071 - 4 + (3910 * t13067 - 6460 * t13069 + 3553 * t13071 + 49) * t13064) + + if Bindx == 2078: + t13073 = np.cos(phi) + t13074 = t13073 ** 2 + t13075 = t13073 * t13074 + t13078 = t13075 ** 2 + t13076 = t13074 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.14e2) * ((1 - t13073) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t13073)) * (820 * t13074 + 4080 * t13075 - 6460 * t13078 - 19 + (-2890 + 17765 * t13076) * t13076 + (-20672 * t13076 + 25840 * t13078 - 160) * t13073) + + if Bindx == 2079: + t13081 = np.cos(phi) + t13082 = t13081 ** 2 + t13084 = t13082 ** 2 + t13085 = t13081 * t13084 + t13090 = t13085 ** 2 + t13088 = t13084 ** 2 + t13083 = t13081 * t13082 + t13086 = t13083 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((2*1j) * (phi1 - phi2)) * (-2604 * t13082 + 26103 * t13083 + 24780 * t13084 - 147714 * t13085 - 79968 * t13086 + 102714 * t13088 - 45220 * t13090 + 42 + (339558 * t13086 - 340765 * t13088 + 124355 * t13090 - 1281) * t13081) + + if Bindx == 2080: + t13092 = np.cos(phi) + t13093 = t13092 ** 2 + t13095 = t13093 ** 2 + t13099 = t13095 ** 2 + t13094 = t13092 * t13093 + t13097 = t13094 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.130e3) * ((1 - t13092) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t13092)) * (-1260 * t13093 - 2100 * t13094 + 10710 * t13095 - 27132 * t13097 + 20349 * t13099 + 21 + (14994 * t13095 - 34884 * t13097 + 24871 * t13099 + 63) * t13092) + + if Bindx == 2081: + t13102 = np.cos(phi) + t13103 = t13102 ** 2 + t13104 = t13103 ** 2 + t13101 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * phi1) * np.sqrt(0.4290e4) * t13101 ** 2 * t13102 * (-420 * t13103 + 21 + (-3876 * t13103 + 2142 + 2261 * t13104) * t13104) + + if Bindx == 2082: + t13107 = np.cos(phi) + t13108 = t13107 ** 2 + t13110 = t13108 ** 2 + t13114 = t13110 ** 2 + t13109 = t13107 * t13108 + t13112 = t13109 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.130e3) * np.sqrt((1 - t13107)) * ((1 + t13107) ** (0.3e1 / 0.2e1)) * (1260 * t13108 - 2100 * t13109 - 10710 * t13110 + 27132 * t13112 - 20349 * t13114 - 21 + (14994 * t13110 - 34884 * t13112 + 24871 * t13114 + 63) * t13107) + + if Bindx == 2083: + t13116 = np.cos(phi) + t13117 = t13116 ** 2 + t13119 = t13117 ** 2 + t13120 = t13116 * t13119 + t13125 = t13120 ** 2 + t13123 = t13119 ** 2 + t13118 = t13116 * t13117 + t13121 = t13118 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((2*1j) * (phi1 + phi2)) * (2604 * t13117 + 26103 * t13118 - 24780 * t13119 - 147714 * t13120 + 79968 * t13121 - 102714 * t13123 + 45220 * t13125 - 42 + (339558 * t13121 - 340765 * t13123 + 124355 * t13125 - 1281) * t13116) + + if Bindx == 2084: + t13127 = np.cos(phi) + t13128 = t13127 ** 2 + t13130 = t13128 ** 2 + t13134 = t13130 ** 2 + t13129 = t13127 * t13128 + t13132 = t13129 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.14e2) * ((1 + t13127) ** (0.5e1 / 0.2e1)) * (-660 * t13128 + 4900 * t13129 - 1190 * t13130 + 27132 * t13132 - 43605 * t13134 + 19 + (-23562 * t13130 + 19380 * t13132 + 17765 * t13134 - 179) * t13127) * ((1 - t13127) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2085: + t13137 = np.cos(phi) + t13138 = t13137 ** 2 + t13140 = t13138 ** 2 + t13144 = t13140 ** 2 + t13139 = t13137 * t13138 + t13142 = t13139 ** 2 + t13136 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.105e3) * t13136 ** 2 * (-220 * t13138 - 860 * t13139 + 1700 * t13140 - 3876 * t13142 + 2584 * t13144 + 4 + (3910 * t13140 - 6460 * t13142 + 3553 * t13144 + 49) * t13137) + + if Bindx == 2086: + t13146 = np.cos(phi) + t13147 = t13146 ** 2 + t13149 = t13147 ** 2 + t13148 = t13146 * t13147 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-867 * t13147 - 4845 * t13149 + 41 + (10336 + 24871 * t13148) * t13148 + (-27132 * t13149 - 612) * t13146) * ((1 + t13146) ** (0.7e1 / 0.2e1)) * np.sqrt(0.15e2) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t13146) ** (0.3e1 / 0.2e1)) + + if Bindx == 2087: + t13161 = np.sin(phi) + t13159 = t13161 ** 2 + t13152 = np.cos(phi) + t13153 = t13152 ** 2 + t13154 = t13152 * t13153 + t13157 = t13154 ** 2 + t13155 = t13153 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.170e3) * t13159 ** 2 * (264 * t13153 + 401 * t13154 - 1406 * t13155 + 1596 * t13157 - 6 + (-1387 * t13155 + 1463 * t13157 - 29) * t13152) + + if Bindx == 2088: + t13162 = np.cos(phi) + t13163 = t13162 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * (608 * t13162 - 17 + (-5320 * t13162 - 570 + 7315 * t13163) * t13163) * ((1 + t13162) ** (0.9e1 / 0.2e1)) * np.sqrt(0.17e2) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t13162) ** (0.5e1 / 0.2e1)) + + if Bindx == 2089: + t13174 = np.sin(phi) + t13171 = t13174 ** 2 + t13172 = t13174 * t13171 + t13166 = np.cos(phi) + t13167 = t13166 ** 2 + t13169 = t13167 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.323e3) * t13172 ** 2 * (-232 * t13167 + 560 * t13169 + 8 + (-50 * t13167 + 385 * t13169 + 1) * t13166) + + if Bindx == 2090: + t13175 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-1 + (-28 + 77 * t13175) * t13175) * ((1 + t13175) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((1j) * (2 * phi1 + 9 * phi2)) * ((1 - t13175) ** (0.7e1 / 0.2e1)) + + if Bindx == 2091: + t13182 = np.sin(phi) + t13179 = t13182 ** 2 + t13180 = t13179 ** 2 + t13176 = np.cos(phi) + t13177 = t13176 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.22610e5) * t13180 ** 2 * (20 * t13177 - 2 + (11 * t13177 + 7) * t13176) + + if Bindx == 2092: + t13183 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + 11 * phi2)) * np.sqrt(0.124355e6) * ((1 - t13183) ** (0.9e1 / 0.2e1)) * ((1 + t13183) ** (0.13e2 / 0.2e1)) + + if Bindx == 2093: + t13184 = np.cos(phi) + t13192 = -1 + t13184 + t13191 = 1 + t13184 + t13189 = t13191 ** 2 + t13185 = t13192 ** 2 + t13186 = t13192 * t13185 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.35530e5) * t13192 * t13186 ** 2 * t13189 ** 2 + + if Bindx == 2094: + t13193 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 10 * phi2)) * np.sqrt(0.1615e4) * ((1 - t13193) ** (0.13e2 / 0.2e1)) * ((1 + t13193) ** (0.7e1 / 0.2e1)) * (3 + 11 * t13193) + + if Bindx == 2095: + t13202 = np.sin(phi) + t13199 = t13202 ** 2 + t13200 = t13202 * t13199 + t13194 = np.cos(phi) + t13195 = t13194 ** 2 + t13197 = t13195 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.67830e5) * t13200 ** 2 * (18 * t13195 - 81 * t13197 - 1 + (46 * t13195 + 33 * t13197 - 15) * t13194) + + if Bindx == 2096: + t13203 = np.cos(phi) + t13204 = t13203 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.4522e4) * ((1 - t13203) ** (0.11e2 / 0.2e1)) * ((1 + t13203) ** (0.5e1 / 0.2e1)) * (45 * t13204 - 1 + (55 * t13204 + 5) * t13203) + + if Bindx == 2097: + t13215 = np.sin(phi) + t13213 = t13215 ** 2 + t13206 = np.cos(phi) + t13207 = t13206 ** 2 + t13208 = t13206 * t13207 + t13211 = t13208 ** 2 + t13209 = t13207 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.238e3) * t13213 ** 2 * (-385 * t13207 - 353 * t13208 + 1729 * t13209 - 1995 * t13211 + 11 + (-95 * t13209 + 1045 * t13211 + 43) * t13206) + + if Bindx == 2098: + t13216 = np.cos(phi) + t13217 = t13216 ** 2 + t13219 = t13217 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.595e3) * ((1 - t13216) ** (0.9e1 / 0.2e1)) * ((1 + t13216) ** (0.3e1 / 0.2e1)) * (-342 * t13217 + 2565 * t13219 + 1 + (570 * t13217 + 1881 * t13219 - 99) * t13216) + + if Bindx == 2099: + t13222 = np.cos(phi) + t13223 = t13222 ** 2 + t13225 = t13223 ** 2 + t13229 = t13225 ** 2 + t13224 = t13222 * t13223 + t13227 = t13224 ** 2 + t13221 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.210e3) * t13221 ** 2 * (1680 * t13223 - 1240 * t13224 - 11390 * t13225 + 23256 * t13227 - 14535 * t13229 - 35 + (7038 * t13225 - 15504 * t13227 + 10659 * t13229 + 71) * t13222) + + if Bindx == 2100: + t13231 = np.cos(phi) + t13232 = t13231 ** 2 + t13233 = t13231 * t13232 + t13236 = t13233 ** 2 + t13234 = t13232 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.30e2) * ((1 - t13231) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t13231)) * (119 * t13232 - 3927 * t13233 - 6783 * t13234 + 20349 * t13236 + 11 + (6783 * t13234 + 10659 * t13236 + 245) * t13231) + + if Bindx == 2101: + t13238 = np.cos(phi) + t13239 = t13238 ** 2 + t13241 = t13239 ** 2 + t13242 = t13238 * t13241 + t13247 = t13242 ** 2 + t13245 = t13241 ** 2 + t13240 = t13238 * t13239 + t13243 = t13240 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((3*1j) * (phi1 - phi2)) * (-10203 * t13239 + 35201 * t13240 + 89310 * t13241 - 190218 * t13242 - 265846 * t13243 + 316863 * t13245 - 130815 * t13247 + 179 + (428706 * t13243 - 431205 * t13245 + 159885 * t13247 - 1857) * t13238) + + if Bindx == 2102: + t13249 = np.cos(phi) + t13250 = t13249 ** 2 + t13251 = t13249 * t13250 + t13254 = t13251 ** 2 + t13252 = t13250 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.14e2) * ((1 - t13249) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t13249)) * (820 * t13250 + 4080 * t13251 - 6460 * t13254 - 19 + (-2890 + 17765 * t13252) * t13252 + (-20672 * t13252 + 25840 * t13254 - 160) * t13249) + + if Bindx == 2103: + t13258 = np.cos(phi) + t13259 = t13258 ** 2 + t13261 = t13259 ** 2 + t13265 = t13261 ** 2 + t13260 = t13258 * t13259 + t13263 = t13260 ** 2 + t13257 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.455e3) * t13257 ** 2 * (60 * t13259 - 780 * t13260 - 510 * t13261 + 1292 * t13263 - 969 * t13265 - 1 + (3774 * t13261 - 6460 * t13263 + 3553 * t13265 + 41) * t13258) + + if Bindx == 2104: + t13267 = np.cos(phi) + t13268 = t13267 ** 2 + t13269 = t13268 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.15015e5) * ((1 - t13267) ** (0.3e1 / 0.2e1)) * ((1 + t13267) ** (0.3e1 / 0.2e1)) * (-60 * t13268 + 1 + (-1292 * t13268 + 510 + 969 * t13269) * t13269) + + if Bindx == 2105: + t13273 = np.cos(phi) + t13274 = t13273 ** 2 + t13276 = t13274 ** 2 + t13280 = t13276 ** 2 + t13275 = t13273 * t13274 + t13278 = t13275 ** 2 + t13272 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.455e3) * t13272 ** 2 * (-60 * t13274 - 780 * t13275 + 510 * t13276 - 1292 * t13278 + 969 * t13280 + 1 + (3774 * t13276 - 6460 * t13278 + 3553 * t13280 + 41) * t13273) + + if Bindx == 2106: + t13282 = np.cos(phi) + t13283 = t13282 ** 2 + t13284 = t13282 * t13283 + t13287 = t13284 ** 2 + t13285 = t13283 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t13282)) * ((1 + t13282) ** (0.5e1 / 0.2e1)) * (820 * t13283 - 4080 * t13284 - 6460 * t13287 - 19 + (-2890 + 17765 * t13285) * t13285 + (20672 * t13285 - 25840 * t13287 + 160) * t13282) + + if Bindx == 2107: + t13290 = np.cos(phi) + t13291 = t13290 ** 2 + t13293 = t13291 ** 2 + t13294 = t13290 * t13293 + t13299 = t13294 ** 2 + t13297 = t13293 ** 2 + t13292 = t13290 * t13291 + t13295 = t13292 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((3*1j) * (phi1 + phi2)) * (10203 * t13291 + 35201 * t13292 - 89310 * t13293 - 190218 * t13294 + 265846 * t13295 - 316863 * t13297 + 130815 * t13299 - 179 + (428706 * t13295 - 431205 * t13297 + 159885 * t13299 - 1857) * t13290) + + if Bindx == 2108: + t13301 = np.cos(phi) + t13302 = t13301 ** 2 + t13303 = t13301 * t13302 + t13305 = t13303 ** 2 + t13304 = t13302 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.30e2) * ((1 + t13301) ** (0.7e1 / 0.2e1)) * (364 * t13302 + 3808 * t13303 + 27132 * t13305 + 11 + (-10710 + 10659 * t13304) * t13304 + (-31008 * t13305 - 256) * t13301) * ((1 - t13301) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2109: + t13309 = np.cos(phi) + t13310 = t13309 ** 2 + t13312 = t13310 ** 2 + t13316 = t13312 ** 2 + t13311 = t13309 * t13310 + t13314 = t13311 ** 2 + t13308 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.210e3) * t13308 ** 2 * (-1680 * t13310 - 1240 * t13311 + 11390 * t13312 - 23256 * t13314 + 14535 * t13316 + 35 + (7038 * t13312 - 15504 * t13314 + 10659 * t13316 + 71) * t13309) + + if Bindx == 2110: + t13318 = np.cos(phi) + t13319 = t13318 ** 2 + t13321 = t13319 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (342 * t13319 - 2565 * t13321 - 1 + (570 * t13319 + 1881 * t13321 - 99) * t13318) * ((1 + t13318) ** (0.9e1 / 0.2e1)) * np.sqrt(0.595e3) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t13318) ** (0.3e1 / 0.2e1)) + + if Bindx == 2111: + t13332 = np.sin(phi) + t13330 = t13332 ** 2 + t13323 = np.cos(phi) + t13324 = t13323 ** 2 + t13325 = t13323 * t13324 + t13328 = t13325 ** 2 + t13326 = t13324 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.238e3) * t13330 ** 2 * (385 * t13324 - 353 * t13325 - 1729 * t13326 + 1995 * t13328 - 11 + (-95 * t13326 + 1045 * t13328 + 43) * t13323) + + if Bindx == 2112: + t13333 = np.cos(phi) + t13334 = t13333 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * (-45 * t13334 + 1 + (55 * t13334 + 5) * t13333) * ((1 + t13333) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4522e4) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * ((1 - t13333) ** (0.5e1 / 0.2e1)) + + if Bindx == 2113: + t13344 = np.sin(phi) + t13341 = t13344 ** 2 + t13342 = t13344 * t13341 + t13336 = np.cos(phi) + t13337 = t13336 ** 2 + t13339 = t13337 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.67830e5) * t13342 ** 2 * (-18 * t13337 + 81 * t13339 + 1 + (46 * t13337 + 33 * t13339 - 15) * t13336) + + if Bindx == 2114: + t13345 = np.cos(phi) + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * (-3 + 11 * t13345) * ((1 + t13345) ** (0.13e2 / 0.2e1)) * np.sqrt(0.1615e4) * np.exp((1j) * (3 * phi1 + 10 * phi2)) * ((1 - t13345) ** (0.7e1 / 0.2e1)) + + if Bindx == 2115: + t13346 = np.cos(phi) + t13354 = -1 + t13346 + t13353 = 1 + t13346 + t13349 = t13353 ** 2 + t13350 = t13353 * t13349 + t13347 = t13354 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.35530e5) * t13347 ** 2 * t13353 * t13350 ** 2 + + if Bindx == 2116: + t13355 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - 11 * phi2)) * np.sqrt(0.10659e5) * ((1 - t13355) ** (0.15e2 / 0.2e1)) * ((1 + t13355) ** (0.7e1 / 0.2e1)) + + if Bindx == 2117: + t13356 = np.cos(phi) + t13364 = -1 + t13356 + t13363 = 1 + t13356 + t13361 = t13363 ** 2 + t13357 = t13364 ** 2 + t13358 = t13364 * t13357 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.1938e4) * t13364 * t13358 ** 2 * t13363 * t13361 * (4 + 11 * t13356) + + if Bindx == 2118: + t13365 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.2261e4) * ((1 - t13365) ** (0.13e2 / 0.2e1)) * ((1 + t13365) ** (0.5e1 / 0.2e1)) * (1 + (8 + 11 * t13365) * t13365) + + if Bindx == 2119: + t13374 = np.sin(phi) + t13372 = t13374 ** 2 + t13366 = np.cos(phi) + t13367 = t13366 ** 2 + t13368 = t13366 * t13367 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.33915e5) * t13372 ** 2 * t13366 * (3 + (16 + 11 * t13368) * t13368 + (-19 + (-32 * t13366 + 21) * t13367) * t13367) + + if Bindx == 2120: + t13375 = np.cos(phi) + t13376 = t13375 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.1785e4) * ((1 - t13375) ** (0.11e2 / 0.2e1)) * ((1 + t13375) ** (0.3e1 / 0.2e1)) * (-3 + (304 * t13375 + 114 + 209 * t13376) * t13376) + + if Bindx == 2121: + t13380 = np.cos(phi) + t13381 = t13380 ** 2 + t13383 = t13381 ** 2 + t13387 = t13383 ** 2 + t13382 = t13380 * t13381 + t13385 = t13382 ** 2 + t13379 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.714e3) * t13379 ** 2 * (156 * t13381 + 316 * t13382 - 964 * t13383 + 2052 * t13385 - 1368 * t13387 - 4 + (-558 * t13383 - 228 * t13385 + 627 * t13387 - 29) * t13380) + + if Bindx == 2122: + t13389 = np.cos(phi) + t13390 = t13389 ** 2 + t13391 = t13390 ** 2 + t13392 = t13389 * t13391 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.7e1) * ((1 - t13389) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t13389)) * (-2295 * t13390 + 14535 * t13391 + 23256 * t13392 + 13 + (10659 * t13392 - 408) * t13389) + + if Bindx == 2123: + t13394 = np.cos(phi) + t13395 = t13394 ** 2 + t13397 = t13395 ** 2 + t13398 = t13394 * t13397 + t13403 = t13398 ** 2 + t13401 = t13397 ** 2 + t13396 = t13394 * t13395 + t13399 = t13396 ** 2 + tfunc[..., c] = (0.23e2 / 0.128e3) * np.exp((4*1j) * (phi1 - phi2)) * (-1600 * t13395 + 775 * t13396 + 12520 * t13397 - 5978 * t13398 - 34272 * t13399 + 38760 * t13401 - 15504 * t13403 + 32 + (18870 * t13399 - 24225 * t13401 + 10659 * t13403 - 37) * t13394) + + if Bindx == 2124: + t13405 = np.cos(phi) + t13406 = t13405 ** 2 + t13407 = t13405 * t13406 + t13410 = t13407 ** 2 + t13408 = t13406 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.30e2) * ((1 - t13405) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t13405)) * (119 * t13406 - 3927 * t13407 - 6783 * t13408 + 20349 * t13410 + 11 + (6783 * t13408 + 10659 * t13410 + 245) * t13405) + + if Bindx == 2125: + t13413 = np.cos(phi) + t13414 = t13413 ** 2 + t13416 = t13414 ** 2 + t13420 = t13416 ** 2 + t13415 = t13413 * t13414 + t13418 = t13415 ** 2 + t13412 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.105e3) * t13412 ** 2 * (220 * t13414 - 860 * t13415 - 1700 * t13416 + 3876 * t13418 - 2584 * t13420 - 4 + (3910 * t13416 - 6460 * t13418 + 3553 * t13420 + 49) * t13413) + + if Bindx == 2126: + t13422 = np.cos(phi) + t13423 = t13422 ** 2 + t13424 = t13422 * t13423 + t13427 = t13424 ** 2 + t13425 = t13423 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.546e3) * ((1 - t13422) ** (0.5e1 / 0.2e1)) * ((1 + t13422) ** (0.3e1 / 0.2e1)) * (255 * t13423 + 595 * t13424 - 1615 * t13425 + 2261 * t13427 - 5 + (-2907 * t13425 + 3553 * t13427 - 25) * t13422) + + if Bindx == 2127: + t13435 = np.sin(phi) + t13433 = t13435 ** 2 + t13429 = np.cos(phi) + t13430 = t13429 ** 2 + t13431 = t13430 ** 2 + tfunc[..., c] = (0.69e2 / 0.256e3) * np.exp((4*1j) * phi1) * np.sqrt(0.2002e4) * t13433 ** 2 * t13429 * (-323 * t13431 - 5 + (323 * t13431 + 85) * t13430) + + if Bindx == 2128: + t13436 = np.cos(phi) + t13437 = t13436 ** 2 + t13438 = t13436 * t13437 + t13441 = t13438 ** 2 + t13439 = t13437 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.546e3) * ((1 - t13436) ** (0.3e1 / 0.2e1)) * ((1 + t13436) ** (0.5e1 / 0.2e1)) * (-255 * t13437 + 595 * t13438 + 1615 * t13439 - 2261 * t13441 + 5 + (-2907 * t13439 + 3553 * t13441 - 25) * t13436) + + if Bindx == 2129: + t13444 = np.cos(phi) + t13445 = t13444 ** 2 + t13447 = t13445 ** 2 + t13451 = t13447 ** 2 + t13446 = t13444 * t13445 + t13449 = t13446 ** 2 + t13443 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.256e3) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.105e3) * t13443 ** 2 * (-220 * t13445 - 860 * t13446 + 1700 * t13447 - 3876 * t13449 + 2584 * t13451 + 4 + (3910 * t13447 - 6460 * t13449 + 3553 * t13451 + 49) * t13444) + + if Bindx == 2130: + t13453 = np.cos(phi) + t13454 = t13453 ** 2 + t13460 = 6783 * t13454 ** 2 + t13455 = t13453 * t13454 + t13458 = t13455 ** 2 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.30e2) * np.sqrt((1 - t13453)) * ((1 + t13453) ** (0.7e1 / 0.2e1)) * (-119 * t13454 - 3927 * t13455 - 20349 * t13458 + t13460 - 11 + (10659 * t13458 + t13460 + 245) * t13453) + + if Bindx == 2131: + t13461 = np.cos(phi) + t13462 = t13461 ** 2 + t13464 = t13462 ** 2 + t13465 = t13461 * t13464 + t13470 = t13465 ** 2 + t13468 = t13464 ** 2 + t13463 = t13461 * t13462 + t13466 = t13463 ** 2 + tfunc[..., c] = (0.23e2 / 0.128e3) * np.exp((4*1j) * (phi1 + phi2)) * (1600 * t13462 + 775 * t13463 - 12520 * t13464 - 5978 * t13465 + 34272 * t13466 - 38760 * t13468 + 15504 * t13470 - 32 + (18870 * t13466 - 24225 * t13468 + 10659 * t13470 - 37) * t13461) + + if Bindx == 2132: + t13472 = np.cos(phi) + t13473 = t13472 ** 2 + t13474 = t13472 * t13473 + t13477 = t13474 ** 2 + t13475 = t13473 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.7e1) * ((1 + t13472) ** (0.9e1 / 0.2e1)) * (2703 * t13473 - 2295 * t13474 - 14535 * t13475 - 33915 * t13477 - 13 + (37791 * t13475 + 10659 * t13477 - 395) * t13472) * ((1 - t13472) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2133: + t13480 = np.cos(phi) + t13481 = t13480 ** 2 + t13483 = t13481 ** 2 + t13487 = t13483 ** 2 + t13482 = t13480 * t13481 + t13485 = t13482 ** 2 + t13479 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.714e3) * t13479 ** 2 * (-156 * t13481 + 316 * t13482 + 964 * t13483 - 2052 * t13485 + 1368 * t13487 + 4 + (-558 * t13483 - 228 * t13485 + 627 * t13487 - 29) * t13480) + + if Bindx == 2134: + t13489 = np.cos(phi) + t13490 = t13489 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * (-3 + (-304 * t13489 + 114 + 209 * t13490) * t13490) * ((1 + t13489) ** (0.11e2 / 0.2e1)) * np.sqrt(0.1785e4) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t13489) ** (0.3e1 / 0.2e1)) + + if Bindx == 2135: + t13501 = np.sin(phi) + t13499 = t13501 ** 2 + t13493 = np.cos(phi) + t13494 = t13493 ** 2 + t13495 = t13493 * t13494 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.33915e5) * t13499 ** 2 * t13493 * (3 + (-16 + 11 * t13495) * t13495 + (-19 + (32 * t13493 + 21) * t13494) * t13494) + + if Bindx == 2136: + t13502 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * (1 + (-8 + 11 * t13502) * t13502) * ((1 + t13502) ** (0.13e2 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((1j) * (4 * phi1 + 9 * phi2)) * ((1 - t13502) ** (0.5e1 / 0.2e1)) + + if Bindx == 2137: + t13511 = np.sin(phi) + t13508 = t13511 ** 2 + t13509 = t13511 * t13508 + t13503 = np.cos(phi) + t13504 = t13503 ** 2 + t13506 = t13504 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.1938e4) * t13509 ** 2 * (20 * t13504 + 40 * t13506 - 4 + (50 * t13504 + 11 * t13506 - 5) * t13503) + + if Bindx == 2138: + t13512 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (4 * phi1 + 11 * phi2)) * np.sqrt(0.10659e5) * ((1 - t13512) ** (0.7e1 / 0.2e1)) * ((1 + t13512) ** (0.15e2 / 0.2e1)) + + if Bindx == 2139: + t13513 = np.cos(phi) + t13520 = -1 + t13513 + t13519 = 1 + t13513 + t13517 = t13519 ** 2 + t13514 = t13520 ** 2 + t13515 = t13514 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.74613e5) * t13515 ** 2 * t13519 * t13517 + + if Bindx == 2140: + t13521 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((5*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.13566e5) * ((1 - t13521) ** (0.15e2 / 0.2e1)) * ((1 + t13521) ** (0.5e1 / 0.2e1)) * (5 + 11 * t13521) + + if Bindx == 2141: + t13531 = np.sin(phi) + t13529 = t13531 ** 2 + t13522 = np.cos(phi) + t13523 = t13522 ** 2 + t13524 = t13522 * t13523 + t13527 = t13524 ** 2 + t13525 = t13523 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.323e3) * t13529 ** 2 * (143 * t13523 - 185 * t13524 - 135 * t13525 - 315 * t13527 - 13 + (433 * t13525 + 77 * t13527 - 5) * t13522) + + if Bindx == 2142: + t13532 = np.cos(phi) + t13533 = t13532 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.4845e4) * ((1 - t13532) ** (0.13e2 / 0.2e1)) * ((1 + t13532) ** (0.3e1 / 0.2e1)) * (105 * t13533 + 3 + (77 * t13533 + 39) * t13532) + + if Bindx == 2143: + t13536 = np.cos(phi) + t13537 = t13536 ** 2 + t13539 = t13537 ** 2 + t13543 = t13539 ** 2 + t13538 = t13536 * t13537 + t13541 = t13538 ** 2 + t13535 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.255e3) * t13535 ** 2 * (-252 * t13537 + 2380 * t13538 - 658 * t13539 + 4788 * t13541 - 4655 * t13543 + 9 + (-5614 * t13539 + 2812 * t13541 + 1463 * t13543 - 273) * t13536) + + if Bindx == 2144: + t13545 = np.cos(phi) + t13546 = t13545 ** 2 + t13548 = t13546 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.102e3) * ((1 - t13545) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t13545)) * (1710 * t13546 + 9975 * t13548 - 53 + (7410 * t13546 + 4389 * t13548 - 135) * t13545) + + if Bindx == 2145: + t13550 = np.cos(phi) + t13551 = t13550 ** 2 + t13553 = t13551 ** 2 + t13554 = t13550 * t13553 + t13559 = t13554 ** 2 + t13557 = t13553 ** 2 + t13552 = t13550 * t13551 + t13555 = t13552 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((5*1j) * (phi1 - phi2)) * (-16195 * t13551 - 42455 * t13552 + 118510 * t13553 + 117670 * t13554 - 336294 * t13555 + 402135 * t13557 - 169575 * t13559 + 395 + (-69870 * t13555 - 82365 * t13557 + 74613 * t13559 + 3431) * t13550) + + if Bindx == 2146: + t13561 = np.cos(phi) + t13562 = t13561 ** 2 + t13563 = t13562 ** 2 + t13564 = t13561 * t13563 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.7e1) * ((1 - t13561) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t13561)) * (-2295 * t13562 + 14535 * t13563 + 23256 * t13564 + 13 + (10659 * t13564 - 408) * t13561) + + if Bindx == 2147: + t13567 = np.cos(phi) + t13568 = t13567 ** 2 + t13570 = t13568 ** 2 + t13574 = t13570 ** 2 + t13569 = t13567 * t13568 + t13572 = t13569 ** 2 + t13566 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.210e3) * t13566 ** 2 * (1680 * t13568 - 1240 * t13569 - 11390 * t13570 + 23256 * t13572 - 14535 * t13574 - 35 + (7038 * t13570 - 15504 * t13572 + 10659 * t13574 + 71) * t13567) + + if Bindx == 2148: + t13576 = np.cos(phi) + t13577 = t13576 ** 2 + t13579 = t13577 ** 2 + t13578 = t13576 * t13577 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.15e2) * ((1 - t13576) ** (0.7e1 / 0.2e1)) * ((1 + t13576) ** (0.3e1 / 0.2e1)) * (-867 * t13577 - 4845 * t13579 + 41 + (-10336 + 24871 * t13578) * t13578 + (27132 * t13579 + 612) * t13576) + + if Bindx == 2149: + t13591 = np.sin(phi) + t13589 = t13591 ** 2 + t13582 = np.cos(phi) + t13583 = t13582 ** 2 + t13584 = t13582 * t13583 + t13587 = t13584 ** 2 + t13585 = t13583 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.78e2) * t13589 ** 2 * (-1275 * t13583 + 8245 * t13584 + 8075 * t13585 - 11305 * t13587 + 25 + (-28101 * t13585 + 24871 * t13587 - 535) * t13582) + + if Bindx == 2150: + t13592 = np.cos(phi) + t13593 = t13592 ** 2 + t13594 = t13593 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.286e3) * ((1 - t13592) ** (0.5e1 / 0.2e1)) * ((1 + t13592) ** (0.5e1 / 0.2e1)) * (-1615 * t13594 - 5 + (2261 * t13594 + 255) * t13593) + + if Bindx == 2151: + t13605 = np.sin(phi) + t13603 = t13605 ** 2 + t13596 = np.cos(phi) + t13597 = t13596 ** 2 + t13598 = t13596 * t13597 + t13601 = t13598 ** 2 + t13599 = t13597 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.78e2) * t13603 ** 2 * (1275 * t13597 + 8245 * t13598 - 8075 * t13599 + 11305 * t13601 - 25 + (-28101 * t13599 + 24871 * t13601 - 535) * t13596) + + if Bindx == 2152: + t13606 = np.cos(phi) + t13607 = t13606 ** 2 + t13609 = t13607 ** 2 + t13608 = t13606 * t13607 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.15e2) * ((1 - t13606) ** (0.3e1 / 0.2e1)) * ((1 + t13606) ** (0.7e1 / 0.2e1)) * (-867 * t13607 - 4845 * t13609 + 41 + (10336 + 24871 * t13608) * t13608 + (-27132 * t13609 - 612) * t13606) + + if Bindx == 2153: + t13613 = np.cos(phi) + t13614 = t13613 ** 2 + t13616 = t13614 ** 2 + t13620 = t13616 ** 2 + t13615 = t13613 * t13614 + t13618 = t13615 ** 2 + t13612 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.210e3) * t13612 ** 2 * (-1680 * t13614 - 1240 * t13615 + 11390 * t13616 - 23256 * t13618 + 14535 * t13620 + 35 + (7038 * t13616 - 15504 * t13618 + 10659 * t13620 + 71) * t13613) + + if Bindx == 2154: + t13622 = np.cos(phi) + t13623 = t13622 ** 2 + t13624 = t13623 ** 2 + t13625 = t13622 * t13624 + tfunc[..., c] = (-0.23e2 / 0.512e3*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.7e1) * np.sqrt((1 - t13622)) * ((1 + t13622) ** (0.9e1 / 0.2e1)) * (-2295 * t13623 + 14535 * t13624 - 23256 * t13625 + 13 + (10659 * t13625 + 408) * t13622) + + if Bindx == 2155: + t13627 = np.cos(phi) + t13628 = t13627 ** 2 + t13630 = t13628 ** 2 + t13631 = t13627 * t13630 + t13636 = t13631 ** 2 + t13634 = t13630 ** 2 + t13629 = t13627 * t13628 + t13632 = t13629 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((5*1j) * (phi1 + phi2)) * (16195 * t13628 - 42455 * t13629 - 118510 * t13630 + 117670 * t13631 + 336294 * t13632 - 402135 * t13634 + 169575 * t13636 - 395 + (-69870 * t13632 - 82365 * t13634 + 74613 * t13636 + 3431) * t13627) + + if Bindx == 2156: + t13638 = np.cos(phi) + t13639 = t13638 ** 2 + t13641 = t13639 ** 2 + t13640 = t13638 * t13639 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.102e3) * ((1 + t13638) ** (0.11e2 / 0.2e1)) * (1575 * t13639 + 17385 * t13641 - 53 + (-9120 + 4389 * t13640) * t13640 + (-14364 * t13641 + 188) * t13638) * ((1 - t13638) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2157: + t13645 = np.cos(phi) + t13646 = t13645 ** 2 + t13648 = t13646 ** 2 + t13652 = t13648 ** 2 + t13647 = t13645 * t13646 + t13650 = t13647 ** 2 + t13644 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.255e3) * t13644 ** 2 * (252 * t13646 + 2380 * t13647 + 658 * t13648 - 4788 * t13650 + 4655 * t13652 - 9 + (-5614 * t13648 + 2812 * t13650 + 1463 * t13652 - 273) * t13645) + + if Bindx == 2158: + t13654 = np.cos(phi) + t13655 = t13654 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * (-105 * t13655 - 3 + (77 * t13655 + 39) * t13654) * ((1 + t13654) ** (0.13e2 / 0.2e1)) * np.sqrt(0.4845e4) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * ((1 - t13654) ** (0.3e1 / 0.2e1)) + + if Bindx == 2159: + t13666 = np.sin(phi) + t13664 = t13666 ** 2 + t13657 = np.cos(phi) + t13658 = t13657 ** 2 + t13659 = t13657 * t13658 + t13662 = t13659 ** 2 + t13660 = t13658 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.323e3) * t13664 ** 2 * (-143 * t13658 - 185 * t13659 + 135 * t13660 + 315 * t13662 + 13 + (433 * t13660 + 77 * t13662 - 5) * t13657) + + if Bindx == 2160: + t13667 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * (-5 + 11 * t13667) * ((1 + t13667) ** (0.15e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((5*1j) * (phi1 + 2 * phi2)) * ((1 - t13667) ** (0.5e1 / 0.2e1)) + + if Bindx == 2161: + t13668 = np.cos(phi) + t13675 = -1 + t13668 + t13674 = 1 + t13668 + t13671 = t13674 ** 2 + t13672 = t13671 ** 2 + t13669 = t13675 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.74613e5) * t13675 * t13669 * t13672 ** 2 + + if Bindx == 2162: + t13676 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 11 * phi2)) * np.sqrt(0.2926e4) * ((1 - t13676) ** (0.17e2 / 0.2e1)) * ((1 + t13676) ** (0.5e1 / 0.2e1)) + + if Bindx == 2163: + t13678 = np.cos(phi) + t13682 = -1 + t13678 + t13679 = t13682 ** 2 + t13680 = t13679 ** 2 + t13677 = 1 + t13678 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.133e3) * t13680 ** 2 * t13677 ** 2 * (6 + 11 * t13678) + + if Bindx == 2164: + t13683 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.114e3) * ((1 - t13683) ** (0.15e2 / 0.2e1)) * ((1 + t13683) ** (0.3e1 / 0.2e1)) * (61 + (252 + 231 * t13683) * t13683) + + if Bindx == 2165: + t13685 = np.cos(phi) + t13686 = t13685 ** 2 + t13688 = t13686 ** 2 + t13692 = t13688 ** 2 + t13687 = t13685 * t13686 + t13690 = t13687 ** 2 + t13684 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.190e3) * t13684 ** 2 * (-120 * t13686 + 76 * t13687 + 328 * t13688 - 8 * t13690 - 336 * t13692 + 8 + (-498 * t13688 + 460 * t13690 + 77 * t13692 + 13) * t13685) + + if Bindx == 2166: + t13694 = np.cos(phi) + t13695 = t13694 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.10e2) * ((1 - t13694) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t13694)) * (608 * t13694 + 35 + (3192 * t13694 + 2318 + 1463 * t13695) * t13695) + + if Bindx == 2167: + t13698 = np.cos(phi) + t13699 = t13698 ** 2 + t13701 = t13699 ** 2 + t13702 = t13698 * t13701 + t13707 = t13702 ** 2 + t13705 = t13701 ** 2 + t13700 = t13698 * t13699 + t13703 = t13700 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((6*1j) * (phi1 - phi2)) * (2820 * t13699 - 21805 * t13700 - 420 * t13701 + 69846 * t13702 - 40096 * t13703 + 80370 * t13705 - 43092 * t13707 - 94 + (-79650 * t13703 + 16815 * t13705 + 13167 * t13707 + 2139) * t13698) + + if Bindx == 2168: + t13709 = np.cos(phi) + t13710 = t13709 ** 2 + t13712 = t13710 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.102e3) * ((1 - t13709) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t13709)) * (1710 * t13710 + 9975 * t13712 - 53 + (7410 * t13710 + 4389 * t13712 - 135) * t13709) + + if Bindx == 2169: + t13715 = np.cos(phi) + t13716 = t13715 ** 2 + t13718 = t13716 ** 2 + t13722 = t13718 ** 2 + t13717 = t13715 * t13716 + t13720 = t13717 ** 2 + t13714 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.714e3) * t13714 ** 2 * (156 * t13716 + 316 * t13717 - 964 * t13718 + 2052 * t13720 - 1368 * t13722 - 4 + (-558 * t13718 - 228 * t13720 + 627 * t13722 - 29) * t13715) + + if Bindx == 2170: + t13724 = np.cos(phi) + t13725 = t13724 ** 2 + t13727 = t13725 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.595e3) * ((1 - t13724) ** (0.9e1 / 0.2e1)) * ((1 + t13724) ** (0.3e1 / 0.2e1)) * (-342 * t13725 + 2565 * t13727 + 1 + (570 * t13725 + 1881 * t13727 - 99) * t13724) + + if Bindx == 2171: + t13738 = np.sin(phi) + t13736 = t13738 ** 2 + t13729 = np.cos(phi) + t13730 = t13729 ** 2 + t13731 = t13729 * t13730 + t13734 = t13731 ** 2 + t13732 = t13730 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.170e3) * t13736 ** 2 * (-264 * t13730 + 401 * t13731 + 1406 * t13732 - 1596 * t13734 + 6 + (-1387 * t13732 + 1463 * t13734 - 29) * t13729) + + if Bindx == 2172: + t13739 = np.cos(phi) + t13740 = t13739 ** 2 + t13742 = t13740 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.221e3) * ((1 - t13739) ** (0.7e1 / 0.2e1)) * ((1 + t13739) ** (0.5e1 / 0.2e1)) * (-190 * t13740 + 665 * t13742 + 5 + (-570 * t13740 + 1463 * t13742 + 35) * t13739) + + if Bindx == 2173: + t13750 = np.sin(phi) + t13747 = t13750 ** 2 + t13748 = t13750 * t13747 + t13744 = np.cos(phi) + t13745 = t13744 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((6*1j) * phi1) * np.sqrt(0.7293e4) * t13748 ** 2 * t13744 * (15 + (-190 + 399 * t13745) * t13745) + + if Bindx == 2174: + t13751 = np.cos(phi) + t13752 = t13751 ** 2 + t13754 = t13752 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.221e3) * ((1 - t13751) ** (0.5e1 / 0.2e1)) * ((1 + t13751) ** (0.7e1 / 0.2e1)) * (190 * t13752 - 665 * t13754 - 5 + (-570 * t13752 + 1463 * t13754 + 35) * t13751) + + if Bindx == 2175: + t13765 = np.sin(phi) + t13763 = t13765 ** 2 + t13756 = np.cos(phi) + t13757 = t13756 ** 2 + t13758 = t13756 * t13757 + t13761 = t13758 ** 2 + t13759 = t13757 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.170e3) * t13763 ** 2 * (264 * t13757 + 401 * t13758 - 1406 * t13759 + 1596 * t13761 - 6 + (-1387 * t13759 + 1463 * t13761 - 29) * t13756) + + if Bindx == 2176: + t13766 = np.cos(phi) + t13767 = t13766 ** 2 + t13769 = t13767 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.595e3) * ((1 - t13766) ** (0.3e1 / 0.2e1)) * ((1 + t13766) ** (0.9e1 / 0.2e1)) * (342 * t13767 - 2565 * t13769 - 1 + (570 * t13767 + 1881 * t13769 - 99) * t13766) + + if Bindx == 2177: + t13772 = np.cos(phi) + t13773 = t13772 ** 2 + t13775 = t13773 ** 2 + t13779 = t13775 ** 2 + t13774 = t13772 * t13773 + t13777 = t13774 ** 2 + t13771 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.714e3) * t13771 ** 2 * (-156 * t13773 + 316 * t13774 + 964 * t13775 - 2052 * t13777 + 1368 * t13779 + 4 + (-558 * t13775 - 228 * t13777 + 627 * t13779 - 29) * t13772) + + if Bindx == 2178: + t13781 = np.cos(phi) + t13782 = t13781 ** 2 + t13784 = t13782 ** 2 + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.102e3) * np.sqrt((1 - t13781)) * ((1 + t13781) ** (0.11e2 / 0.2e1)) * (-1710 * t13782 - 9975 * t13784 + 53 + (7410 * t13782 + 4389 * t13784 - 135) * t13781) + + if Bindx == 2179: + t13786 = np.cos(phi) + t13787 = t13786 ** 2 + t13789 = t13787 ** 2 + t13790 = t13786 * t13789 + t13795 = t13790 ** 2 + t13793 = t13789 ** 2 + t13788 = t13786 * t13787 + t13791 = t13788 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((6*1j) * (phi1 + phi2)) * (-2820 * t13787 - 21805 * t13788 + 420 * t13789 + 69846 * t13790 + 40096 * t13791 - 80370 * t13793 + 43092 * t13795 + 94 + (-79650 * t13791 + 16815 * t13793 + 13167 * t13795 + 2139) * t13786) + + if Bindx == 2180: + t13797 = np.cos(phi) + t13798 = t13797 ** 2 + t13800 = t13798 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.10e2) * ((1 + t13797) ** (0.13e2 / 0.2e1)) * (-2926 * t13798 - 4655 * t13800 - 35 + (5510 * t13798 + 1463 * t13800 + 643) * t13797) * ((1 - t13797) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2181: + t13803 = np.cos(phi) + t13804 = t13803 ** 2 + t13806 = t13804 ** 2 + t13810 = t13806 ** 2 + t13805 = t13803 * t13804 + t13808 = t13805 ** 2 + t13802 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.190e3) * t13802 ** 2 * (120 * t13804 + 76 * t13805 - 328 * t13806 + 8 * t13808 + 336 * t13810 - 8 + (-498 * t13806 + 460 * t13808 + 77 * t13810 + 13) * t13803) + + if Bindx == 2182: + t13812 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (61 + (-252 + 231 * t13812) * t13812) * ((1 + t13812) ** (0.15e2 / 0.2e1)) * np.sqrt(0.114e3) * np.exp((3*1j) * (2 * phi1 + 3 * phi2)) * ((1 - t13812) ** (0.3e1 / 0.2e1)) + + if Bindx == 2183: + t13814 = np.cos(phi) + t13818 = 1 + t13814 + t13815 = t13818 ** 2 + t13816 = t13815 ** 2 + t13813 = -1 + t13814 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.133e3) * t13813 ** 2 * t13816 ** 2 * (-6 + 11 * t13814) + + if Bindx == 2184: + t13819 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 11 * phi2)) * np.sqrt(0.2926e4) * ((1 - t13819) ** (0.5e1 / 0.2e1)) * ((1 + t13819) ** (0.17e2 / 0.2e1)) + + if Bindx == 2185: + t13821 = np.cos(phi) + t13826 = -1 + t13821 + t13822 = t13826 ** 2 + t13823 = t13822 ** 2 + t13820 = 1 + t13821 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.7315e4) * t13826 * t13823 ** 2 * t13820 ** 2 + + if Bindx == 2186: + t13827 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 10 * phi2)) * np.sqrt(0.1330e4) * ((1 - t13827) ** (0.17e2 / 0.2e1)) * ((1 + t13827) ** (0.3e1 / 0.2e1)) * (7 + 11 * t13827) + + if Bindx == 2187: + t13829 = np.cos(phi) + t13830 = t13829 ** 2 + t13832 = t13830 ** 2 + t13836 = t13832 ** 2 + t13831 = t13829 * t13830 + t13834 = t13831 ** 2 + t13828 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.285e3) * t13828 ** 2 * (-504 * t13831 + 798 * t13832 - 840 * t13834 - 441 * t13836 - 29 + (-126 * t13832 + 960 * t13834 + 77 * t13836 + 105) * t13829) + + if Bindx == 2188: + t13838 = np.cos(phi) + t13839 = t13838 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * np.sqrt(0.19e2) * ((1 - t13838) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t13838)) * (735 * t13839 + 77 + (385 * t13839 + 435) * t13838) + + if Bindx == 2189: + t13841 = np.cos(phi) + t13842 = t13841 ** 2 + t13844 = t13842 ** 2 + t13845 = t13841 * t13844 + t13850 = t13845 ** 2 + t13848 = t13844 ** 2 + t13843 = t13841 * t13842 + t13846 = t13843 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((7*1j) * (phi1 - phi2)) * (10931 * t13842 - 3297 * t13843 - 41790 * t13844 + 47418 * t13845 + 37926 * t13846 + 25137 * t13848 - 32585 * t13850 - 643 + (-88866 * t13846 + 39805 * t13848 + 7315 * t13850 - 1351) * t13841) + + if Bindx == 2190: + t13852 = np.cos(phi) + t13853 = t13852 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.10e2) * ((1 - t13852) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t13852)) * (608 * t13852 + 35 + (3192 * t13852 + 2318 + 1463 * t13853) * t13853) + + if Bindx == 2191: + t13857 = np.cos(phi) + t13858 = t13857 ** 2 + t13860 = t13858 ** 2 + t13864 = t13860 ** 2 + t13859 = t13857 * t13858 + t13862 = t13859 ** 2 + t13856 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.255e3) * t13856 ** 2 * (-252 * t13858 + 2380 * t13859 - 658 * t13860 + 4788 * t13862 - 4655 * t13864 + 9 + (-5614 * t13860 + 2812 * t13862 + 1463 * t13864 - 273) * t13857) + + if Bindx == 2192: + t13866 = np.cos(phi) + t13867 = t13866 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.1785e4) * ((1 - t13866) ** (0.11e2 / 0.2e1)) * ((1 + t13866) ** (0.3e1 / 0.2e1)) * (-3 + (304 * t13866 + 114 + 209 * t13867) * t13867) + + if Bindx == 2193: + t13879 = np.sin(phi) + t13877 = t13879 ** 2 + t13870 = np.cos(phi) + t13871 = t13870 ** 2 + t13872 = t13870 * t13871 + t13875 = t13872 ** 2 + t13873 = t13871 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.238e3) * t13877 ** 2 * (-385 * t13871 - 353 * t13872 + 1729 * t13873 - 1995 * t13875 + 11 + (-95 * t13873 + 1045 * t13875 + 43) * t13870) + + if Bindx == 2194: + t13880 = np.cos(phi) + t13881 = t13880 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.17e2) * ((1 - t13880) ** (0.9e1 / 0.2e1)) * ((1 + t13880) ** (0.5e1 / 0.2e1)) * (-608 * t13880 - 17 + (5320 * t13880 - 570 + 7315 * t13881) * t13881) + + if Bindx == 2195: + t13892 = np.sin(phi) + t13889 = t13892 ** 2 + t13890 = t13892 * t13889 + t13884 = np.cos(phi) + t13885 = t13884 ** 2 + t13887 = t13885 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.2210e4) * t13890 ** 2 * (266 * t13885 - 931 * t13887 - 7 + (-874 * t13885 + 1463 * t13887 + 83) * t13884) + + if Bindx == 2196: + t13893 = np.cos(phi) + t13894 = t13893 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.72930e5) * ((1 - t13893) ** (0.7e1 / 0.2e1)) * ((1 + t13893) ** (0.7e1 / 0.2e1)) * (1 + (-38 + 133 * t13894) * t13894) + + if Bindx == 2197: + t13904 = np.sin(phi) + t13901 = t13904 ** 2 + t13902 = t13904 * t13901 + t13896 = np.cos(phi) + t13897 = t13896 ** 2 + t13899 = t13897 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.2210e4) * t13902 ** 2 * (-266 * t13897 + 931 * t13899 + 7 + (-874 * t13897 + 1463 * t13899 + 83) * t13896) + + if Bindx == 2198: + t13905 = np.cos(phi) + t13906 = t13905 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.17e2) * ((1 - t13905) ** (0.5e1 / 0.2e1)) * ((1 + t13905) ** (0.9e1 / 0.2e1)) * (608 * t13905 - 17 + (-5320 * t13905 - 570 + 7315 * t13906) * t13906) + + if Bindx == 2199: + t13918 = np.sin(phi) + t13916 = t13918 ** 2 + t13909 = np.cos(phi) + t13910 = t13909 ** 2 + t13911 = t13909 * t13910 + t13914 = t13911 ** 2 + t13912 = t13910 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.238e3) * t13916 ** 2 * (385 * t13910 - 353 * t13911 - 1729 * t13912 + 1995 * t13914 - 11 + (-95 * t13912 + 1045 * t13914 + 43) * t13909) + + if Bindx == 2200: + t13919 = np.cos(phi) + t13920 = t13919 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.1785e4) * ((1 - t13919) ** (0.3e1 / 0.2e1)) * ((1 + t13919) ** (0.11e2 / 0.2e1)) * (-3 + (-304 * t13919 + 114 + 209 * t13920) * t13920) + + if Bindx == 2201: + t13924 = np.cos(phi) + t13925 = t13924 ** 2 + t13927 = t13925 ** 2 + t13931 = t13927 ** 2 + t13926 = t13924 * t13925 + t13929 = t13926 ** 2 + t13923 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.255e3) * t13923 ** 2 * (252 * t13925 + 2380 * t13926 + 658 * t13927 - 4788 * t13929 + 4655 * t13931 - 9 + (-5614 * t13927 + 2812 * t13929 + 1463 * t13931 - 273) * t13924) + + if Bindx == 2202: + t13933 = np.cos(phi) + t13934 = t13933 ** 2 + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.10e2) * np.sqrt((1 - t13933)) * ((1 + t13933) ** (0.13e2 / 0.2e1)) * (-608 * t13933 + 35 + (-3192 * t13933 + 2318 + 1463 * t13934) * t13934) + + if Bindx == 2203: + t13937 = np.cos(phi) + t13938 = t13937 ** 2 + t13940 = t13938 ** 2 + t13941 = t13937 * t13940 + t13946 = t13941 ** 2 + t13944 = t13940 ** 2 + t13939 = t13937 * t13938 + t13942 = t13939 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((7*1j) * (phi1 + phi2)) * (-10931 * t13938 - 3297 * t13939 + 41790 * t13940 + 47418 * t13941 - 37926 * t13942 - 25137 * t13944 + 32585 * t13946 + 643 + (-88866 * t13942 + 39805 * t13944 + 7315 * t13946 - 1351) * t13937) + + if Bindx == 2204: + t13948 = np.cos(phi) + t13949 = t13948 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.19e2) * ((1 + t13948) ** (0.15e2 / 0.2e1)) * (-512 * t13948 + 77 + (-1120 * t13948 + 1170 + 385 * t13949) * t13949) * ((1 - t13948) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2205: + t13953 = np.cos(phi) + t13954 = t13953 ** 2 + t13956 = t13954 ** 2 + t13960 = t13956 ** 2 + t13955 = t13953 * t13954 + t13958 = t13955 ** 2 + t13952 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.285e3) * t13952 ** 2 * (-504 * t13955 - 798 * t13956 + 840 * t13958 + 441 * t13960 + 29 + (-126 * t13956 + 960 * t13958 + 77 * t13960 + 105) * t13953) + + if Bindx == 2206: + t13962 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * (-7 + 11 * t13962) * ((1 + t13962) ** (0.17e2 / 0.2e1)) * np.sqrt(0.1330e4) * np.exp((1j) * (7 * phi1 + 10 * phi2)) * ((1 - t13962) ** (0.3e1 / 0.2e1)) + + if Bindx == 2207: + t13964 = np.cos(phi) + t13969 = 1 + t13964 + t13965 = t13969 ** 2 + t13966 = t13965 ** 2 + t13963 = -1 + t13964 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.7315e4) * t13963 ** 2 * t13969 * t13966 ** 2 + + if Bindx == 2208: + t13970 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 11 * phi2)) * np.sqrt(0.385e3) * ((1 - t13970) ** (0.19e2 / 0.2e1)) * ((1 + t13970) ** (0.3e1 / 0.2e1)) + + if Bindx == 2209: + t13971 = np.cos(phi) + t13976 = -1 + t13971 + t13972 = t13976 ** 2 + t13973 = t13972 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.70e2) * t13976 * t13973 ** 2 * (1 + t13971) * (8 + 11 * t13971) + + if Bindx == 2210: + t13977 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.15e2) * ((1 - t13977) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t13977)) * (39 + (112 + 77 * t13977) * t13977) + + if Bindx == 2211: + t13978 = np.cos(phi) + t13979 = t13978 ** 2 + t13981 = t13979 ** 2 + t13982 = t13978 * t13981 + t13987 = t13982 ** 2 + t13985 = t13981 ** 2 + t13980 = t13978 * t13979 + t13983 = t13980 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((8*1j) * (phi1 - phi2)) * (-256 * t13979 + 2877 * t13980 - 3360 * t13981 - 2478 * t13982 + 8064 * t13983 - 2592 * t13985 - 2240 * t13987 + 128 + (-4734 * t13983 + 4645 * t13985 + 385 * t13987 - 439) * t13978) + + if Bindx == 2212: + t13989 = np.cos(phi) + t13990 = t13989 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * np.sqrt(0.19e2) * ((1 - t13989) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t13989)) * (735 * t13990 + 77 + (385 * t13990 + 435) * t13989) + + if Bindx == 2213: + t13993 = np.cos(phi) + t13994 = t13993 ** 2 + t13996 = t13994 ** 2 + t14000 = t13996 ** 2 + t13995 = t13993 * t13994 + t13998 = t13995 ** 2 + t13992 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.190e3) * t13992 ** 2 * (-120 * t13994 + 76 * t13995 + 328 * t13996 - 8 * t13998 - 336 * t14000 + 8 + (-498 * t13996 + 460 * t13998 + 77 * t14000 + 13) * t13993) + + if Bindx == 2214: + t14002 = np.cos(phi) + t14003 = t14002 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.4845e4) * ((1 - t14002) ** (0.13e2 / 0.2e1)) * ((1 + t14002) ** (0.3e1 / 0.2e1)) * (105 * t14003 + 3 + (77 * t14003 + 39) * t14002) + + if Bindx == 2215: + t14013 = np.sin(phi) + t14011 = t14013 ** 2 + t14005 = np.cos(phi) + t14006 = t14005 ** 2 + t14007 = t14005 * t14006 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.33915e5) * t14011 ** 2 * t14005 * (3 + (16 + 11 * t14007) * t14007 + (-19 + (-32 * t14005 + 21) * t14006) * t14006) + + if Bindx == 2216: + t14014 = np.cos(phi) + t14015 = t14014 ** 2 + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.4522e4) * ((1 - t14014) ** (0.11e2 / 0.2e1)) * ((1 + t14014) ** (0.5e1 / 0.2e1)) * (45 * t14015 - 1 + (55 * t14015 + 5) * t14014) + + if Bindx == 2217: + t14025 = np.sin(phi) + t14022 = t14025 ** 2 + t14023 = t14025 * t14022 + t14017 = np.cos(phi) + t14018 = t14017 ** 2 + t14020 = t14018 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.323e3) * t14023 ** 2 * (232 * t14018 - 560 * t14020 - 8 + (-50 * t14018 + 385 * t14020 + 1) * t14017) + + if Bindx == 2218: + t14026 = np.cos(phi) + t14027 = t14026 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.41990e5) * ((1 - t14026) ** (0.9e1 / 0.2e1)) * ((1 + t14026) ** (0.7e1 / 0.2e1)) * (21 * t14027 - 1 + (77 * t14027 - 9) * t14026) + + if Bindx == 2219: + t14033 = np.sin(phi) + t14030 = t14033 ** 2 + t14031 = t14030 ** 2 + t14029 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((8*1j) * phi1) * np.sqrt(0.1385670e7) * t14031 ** 2 * t14029 * (7 * t14029 ** 2 - 1) + + if Bindx == 2220: + t14034 = np.cos(phi) + t14035 = t14034 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.41990e5) * ((1 - t14034) ** (0.7e1 / 0.2e1)) * ((1 + t14034) ** (0.9e1 / 0.2e1)) * (-21 * t14035 + 1 + (77 * t14035 - 9) * t14034) + + if Bindx == 2221: + t14045 = np.sin(phi) + t14042 = t14045 ** 2 + t14043 = t14045 * t14042 + t14037 = np.cos(phi) + t14038 = t14037 ** 2 + t14040 = t14038 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.323e3) * t14043 ** 2 * (-232 * t14038 + 560 * t14040 + 8 + (-50 * t14038 + 385 * t14040 + 1) * t14037) + + if Bindx == 2222: + t14046 = np.cos(phi) + t14047 = t14046 ** 2 + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.4522e4) * ((1 - t14046) ** (0.5e1 / 0.2e1)) * ((1 + t14046) ** (0.11e2 / 0.2e1)) * (-45 * t14047 + 1 + (55 * t14047 + 5) * t14046) + + if Bindx == 2223: + t14057 = np.sin(phi) + t14055 = t14057 ** 2 + t14049 = np.cos(phi) + t14050 = t14049 ** 2 + t14051 = t14049 * t14050 + tfunc[..., c] = (0.23e2 / 0.256e3) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.33915e5) * t14055 ** 2 * t14049 * (3 + (-16 + 11 * t14051) * t14051 + (-19 + (32 * t14049 + 21) * t14050) * t14050) + + if Bindx == 2224: + t14058 = np.cos(phi) + t14059 = t14058 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.4845e4) * ((1 - t14058) ** (0.3e1 / 0.2e1)) * ((1 + t14058) ** (0.13e2 / 0.2e1)) * (-105 * t14059 - 3 + (77 * t14059 + 39) * t14058) + + if Bindx == 2225: + t14062 = np.cos(phi) + t14063 = t14062 ** 2 + t14065 = t14063 ** 2 + t14069 = t14065 ** 2 + t14064 = t14062 * t14063 + t14067 = t14064 ** 2 + t14061 = np.sin(phi) + tfunc[..., c] = -(0.69e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.190e3) * t14061 ** 2 * (120 * t14063 + 76 * t14064 - 328 * t14065 + 8 * t14067 + 336 * t14069 - 8 + (-498 * t14065 + 460 * t14067 + 77 * t14069 + 13) * t14062) + + if Bindx == 2226: + t14071 = np.cos(phi) + t14072 = t14071 ** 2 + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.19e2) * np.sqrt((1 - t14071)) * ((1 + t14071) ** (0.15e2 / 0.2e1)) * (-735 * t14072 - 77 + (385 * t14072 + 435) * t14071) + + if Bindx == 2227: + t14074 = np.cos(phi) + t14075 = t14074 ** 2 + t14077 = t14075 ** 2 + t14078 = t14074 * t14077 + t14083 = t14078 ** 2 + t14081 = t14077 ** 2 + t14076 = t14074 * t14075 + t14079 = t14076 ** 2 + tfunc[..., c] = (0.23e2 / 0.512e3) * np.exp((8*1j) * (phi1 + phi2)) * (256 * t14075 + 2877 * t14076 + 3360 * t14077 - 2478 * t14078 - 8064 * t14079 + 2592 * t14081 + 2240 * t14083 - 128 + (-4734 * t14079 + 4645 * t14081 + 385 * t14083 - 439) * t14074) + + if Bindx == 2228: + t14085 = np.cos(phi) + t14086 = t14085 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.15e2) * ((1 + t14085) ** (0.17e2 / 0.2e1)) * (-189 * t14086 - 39 + (77 * t14086 + 151) * t14085) * ((1 - t14085) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2229: + t14088 = np.cos(phi) + t14093 = 1 + t14088 + t14089 = t14093 ** 2 + t14090 = t14089 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.70e2) * (-1 + t14088) * t14093 * t14090 ** 2 * (-8 + 11 * t14088) + + if Bindx == 2230: + t14094 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 11 * phi2)) * np.sqrt(0.385e3) * ((1 - t14094) ** (0.3e1 / 0.2e1)) * ((1 + t14094) ** (0.19e2 / 0.2e1)) + + if Bindx == 2231: + t14095 = np.cos(phi) + t14100 = -1 + t14095 + t14096 = t14100 ** 2 + t14098 = t14100 * t14096 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.231e3) * t14098 ** 2 * (1 + t14095) + + if Bindx == 2232: + t14101 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 - 10 * phi2)) * np.sqrt(0.42e2) * ((1 - t14101) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t14101)) * (9 + 11 * t14101) + + if Bindx == 2233: + t14102 = np.cos(phi) + t14103 = t14102 ** 2 + t14105 = t14103 ** 2 + t14106 = t14102 * t14105 + t14111 = t14106 ** 2 + t14109 = t14105 ** 2 + t14104 = t14102 * t14103 + t14107 = t14104 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((9*1j) * (phi1 - phi2)) * (-2265 * t14103 + 1155 * t14104 + 4410 * t14105 - 9198 * t14106 + 5838 * t14107 - 7155 * t14109 - 1701 * t14111 - 151 + (2790 * t14107 + 5065 * t14109 + 231 * t14111 + 981) * t14102) + + if Bindx == 2234: + t14113 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.15e2) * ((1 - t14113) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t14113)) * (39 + (112 + 77 * t14113) * t14113) + + if Bindx == 2235: + t14115 = np.cos(phi) + t14116 = t14115 ** 2 + t14118 = t14116 ** 2 + t14122 = t14118 ** 2 + t14117 = t14115 * t14116 + t14120 = t14117 ** 2 + t14114 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.285e3) * t14114 ** 2 * (-504 * t14117 + 798 * t14118 - 840 * t14120 - 441 * t14122 - 29 + (-126 * t14118 + 960 * t14120 + 77 * t14122 + 105) * t14115) + + if Bindx == 2236: + t14124 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.114e3) * ((1 - t14124) ** (0.15e2 / 0.2e1)) * ((1 + t14124) ** (0.3e1 / 0.2e1)) * (61 + (252 + 231 * t14124) * t14124) + + if Bindx == 2237: + t14134 = np.sin(phi) + t14132 = t14134 ** 2 + t14125 = np.cos(phi) + t14126 = t14125 ** 2 + t14127 = t14125 * t14126 + t14130 = t14127 ** 2 + t14128 = t14126 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.323e3) * t14132 ** 2 * (143 * t14126 - 185 * t14127 - 135 * t14128 - 315 * t14130 - 13 + (433 * t14128 + 77 * t14130 - 5) * t14125) + + if Bindx == 2238: + t14135 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.2261e4) * ((1 - t14135) ** (0.13e2 / 0.2e1)) * ((1 + t14135) ** (0.5e1 / 0.2e1)) * (1 + (8 + 11 * t14135) * t14135) + + if Bindx == 2239: + t14144 = np.sin(phi) + t14141 = t14144 ** 2 + t14142 = t14144 * t14141 + t14136 = np.cos(phi) + t14137 = t14136 ** 2 + t14139 = t14137 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.67830e5) * t14142 ** 2 * (18 * t14137 - 81 * t14139 - 1 + (46 * t14137 + 33 * t14139 - 15) * t14136) + + if Bindx == 2240: + t14145 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.4845e4) * ((1 - t14145) ** (0.11e2 / 0.2e1)) * ((1 + t14145) ** (0.7e1 / 0.2e1)) * (-1 + (28 + 77 * t14145) * t14145) + + if Bindx == 2241: + t14152 = np.sin(phi) + t14149 = t14152 ** 2 + t14150 = t14149 ** 2 + t14146 = np.cos(phi) + t14147 = t14146 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - phi2)) * np.sqrt(0.25194e5) * t14150 ** 2 * (-63 * t14147 + 3 + (77 * t14147 - 17) * t14146) + + if Bindx == 2242: + t14153 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.92378e5) * ((1 - t14153) ** (0.9e1 / 0.2e1)) * ((1 + t14153) ** (0.9e1 / 0.2e1)) * (21 * t14153 ** 2 - 1) + + if Bindx == 2243: + t14160 = np.sin(phi) + t14157 = t14160 ** 2 + t14158 = t14157 ** 2 + t14154 = np.cos(phi) + t14155 = t14154 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + phi2)) * np.sqrt(0.25194e5) * t14158 ** 2 * (63 * t14155 - 3 + (77 * t14155 - 17) * t14154) + + if Bindx == 2244: + t14161 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.4845e4) * ((1 - t14161) ** (0.7e1 / 0.2e1)) * ((1 + t14161) ** (0.11e2 / 0.2e1)) * (-1 + (-28 + 77 * t14161) * t14161) + + if Bindx == 2245: + t14170 = np.sin(phi) + t14167 = t14170 ** 2 + t14168 = t14170 * t14167 + t14162 = np.cos(phi) + t14163 = t14162 ** 2 + t14165 = t14163 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.67830e5) * t14168 ** 2 * (-18 * t14163 + 81 * t14165 + 1 + (46 * t14163 + 33 * t14165 - 15) * t14162) + + if Bindx == 2246: + t14171 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.512e3*1j) * np.exp((1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.2261e4) * ((1 - t14171) ** (0.5e1 / 0.2e1)) * ((1 + t14171) ** (0.13e2 / 0.2e1)) * (1 + (-8 + 11 * t14171) * t14171) + + if Bindx == 2247: + t14181 = np.sin(phi) + t14179 = t14181 ** 2 + t14172 = np.cos(phi) + t14173 = t14172 ** 2 + t14174 = t14172 * t14173 + t14177 = t14174 ** 2 + t14175 = t14173 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.323e3) * t14179 ** 2 * (-143 * t14173 - 185 * t14174 + 135 * t14175 + 315 * t14177 + 13 + (433 * t14175 + 77 * t14177 - 5) * t14172) + + if Bindx == 2248: + t14182 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.114e3) * ((1 - t14182) ** (0.3e1 / 0.2e1)) * ((1 + t14182) ** (0.15e2 / 0.2e1)) * (61 + (-252 + 231 * t14182) * t14182) + + if Bindx == 2249: + t14184 = np.cos(phi) + t14185 = t14184 ** 2 + t14187 = t14185 ** 2 + t14191 = t14187 ** 2 + t14186 = t14184 * t14185 + t14189 = t14186 ** 2 + t14183 = np.sin(phi) + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.285e3) * t14183 ** 2 * (-504 * t14186 - 798 * t14187 + 840 * t14189 + 441 * t14191 + 29 + (-126 * t14187 + 960 * t14189 + 77 * t14191 + 105) * t14184) + + if Bindx == 2250: + t14193 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.15e2) * np.sqrt((1 - t14193)) * ((1 + t14193) ** (0.17e2 / 0.2e1)) * (39 + (-112 + 77 * t14193) * t14193) + + if Bindx == 2251: + t14194 = np.cos(phi) + t14195 = t14194 ** 2 + t14197 = t14195 ** 2 + t14198 = t14194 * t14197 + t14203 = t14198 ** 2 + t14201 = t14197 ** 2 + t14196 = t14194 * t14195 + t14199 = t14196 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((9*1j) * (phi1 + phi2)) * (2265 * t14195 + 1155 * t14196 - 4410 * t14197 - 9198 * t14198 - 5838 * t14199 + 7155 * t14201 + 1701 * t14203 + 151 + (2790 * t14199 + 5065 * t14201 + 231 * t14203 + 981) * t14194) + + if Bindx == 2252: + t14205 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.42e2) * ((1 + t14205) ** (0.19e2 / 0.2e1)) * (9 + (-20 + 11 * t14205) * t14205) * ((1 - t14205) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2253: + t14206 = np.cos(phi) + t14211 = 1 + t14206 + t14207 = t14211 ** 2 + t14209 = t14211 * t14207 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.231e3) * (-1 + t14206) * t14209 ** 2 + + if Bindx == 2254: + t14212 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 11 * phi2)) * np.sqrt(0.22e2) * ((1 - t14212) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t14212)) + + if Bindx == 2255: + t14213 = np.cos(phi) + t14214 = t14213 ** 2 + t14216 = t14214 ** 2 + t14217 = t14213 * t14216 + t14222 = t14217 ** 2 + t14220 = t14216 ** 2 + t14215 = t14213 * t14214 + t14218 = t14215 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((10*1j) * (phi1 - phi2)) * (340 * t14214 - 705 * t14215 + 780 * t14216 - 210 * t14217 - 672 * t14218 - 870 * t14220 - 100 * t14222 + 10 + (1110 * t14218 + 395 * t14220 + 11 * t14222 - 89) * t14213) + + if Bindx == 2256: + t14224 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 9 * phi2)) * np.sqrt(0.42e2) * ((1 - t14224) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t14224)) * (9 + 11 * t14224) + + if Bindx == 2257: + t14225 = np.cos(phi) + t14230 = -1 + t14225 + t14226 = t14230 ** 2 + t14227 = t14226 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * (8 + 11 * t14225) * (1 + t14225) * t14230 * t14227 ** 2 * np.sqrt(0.70e2) * np.exp((2*1j) * (5 * phi1 - 4 * phi2)) + + if Bindx == 2258: + t14231 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 7 * phi2)) * np.sqrt(0.1330e4) * ((1 - t14231) ** (0.17e2 / 0.2e1)) * ((1 + t14231) ** (0.3e1 / 0.2e1)) * (7 + 11 * t14231) + + if Bindx == 2259: + t14233 = np.cos(phi) + t14237 = -1 + t14233 + t14234 = t14237 ** 2 + t14235 = t14234 ** 2 + t14232 = 1 + t14233 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.133e3) * t14235 ** 2 * t14232 ** 2 * (6 + 11 * t14233) + + if Bindx == 2260: + t14238 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((5*1j) * (2 * phi1 - phi2)) * np.sqrt(0.13566e5) * ((1 - t14238) ** (0.15e2 / 0.2e1)) * ((1 + t14238) ** (0.5e1 / 0.2e1)) * (5 + 11 * t14238) + + if Bindx == 2261: + t14247 = np.sin(phi) + t14244 = t14247 ** 2 + t14245 = t14247 * t14244 + t14239 = np.cos(phi) + t14240 = t14239 ** 2 + t14242 = t14240 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.1938e4) * t14245 ** 2 * (-20 * t14240 - 40 * t14242 + 4 + (50 * t14240 + 11 * t14242 - 5) * t14239) + + if Bindx == 2262: + t14248 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 - 3 * phi2)) * np.sqrt(0.1615e4) * ((1 - t14248) ** (0.13e2 / 0.2e1)) * ((1 + t14248) ** (0.7e1 / 0.2e1)) * (3 + 11 * t14248) + + if Bindx == 2263: + t14255 = np.sin(phi) + t14252 = t14255 ** 2 + t14253 = t14252 ** 2 + t14249 = np.cos(phi) + t14250 = t14249 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.22610e5) * t14253 ** 2 * (-20 * t14250 + 2 + (11 * t14250 + 7) * t14249) + + if Bindx == 2264: + t14256 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 - phi2)) * np.sqrt(0.29393e5) * ((1 - t14256) ** (0.11e2 / 0.2e1)) * ((1 + t14256) ** (0.9e1 / 0.2e1)) * (1 + 11 * t14256) + + if Bindx == 2265: + t14261 = np.sin(phi) + t14257 = t14261 ** 2 + t14259 = t14261 * t14257 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((10*1j) * phi1) * np.sqrt(0.969969e6) * t14259 ** 2 * np.cos(phi) + + if Bindx == 2266: + t14262 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 + phi2)) * np.sqrt(0.29393e5) * ((1 - t14262) ** (0.9e1 / 0.2e1)) * ((1 + t14262) ** (0.11e2 / 0.2e1)) * (-1 + 11 * t14262) + + if Bindx == 2267: + t14269 = np.sin(phi) + t14266 = t14269 ** 2 + t14267 = t14266 ** 2 + t14263 = np.cos(phi) + t14264 = t14263 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.22610e5) * t14267 ** 2 * (20 * t14264 - 2 + (11 * t14264 + 7) * t14263) + + if Bindx == 2268: + t14270 = np.cos(phi) + tfunc[..., c] = (0.69e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 + 3 * phi2)) * np.sqrt(0.1615e4) * ((1 - t14270) ** (0.7e1 / 0.2e1)) * ((1 + t14270) ** (0.13e2 / 0.2e1)) * (-3 + 11 * t14270) + + if Bindx == 2269: + t14279 = np.sin(phi) + t14276 = t14279 ** 2 + t14277 = t14279 * t14276 + t14271 = np.cos(phi) + t14272 = t14271 ** 2 + t14274 = t14272 ** 2 + tfunc[..., c] = -(0.23e2 / 0.512e3) * np.exp((2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.1938e4) * t14277 ** 2 * (20 * t14272 + 40 * t14274 - 4 + (50 * t14272 + 11 * t14274 - 5) * t14271) + + if Bindx == 2270: + t14280 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((5*1j) * (2 * phi1 + phi2)) * np.sqrt(0.13566e5) * ((1 - t14280) ** (0.5e1 / 0.2e1)) * ((1 + t14280) ** (0.15e2 / 0.2e1)) * (-5 + 11 * t14280) + + if Bindx == 2271: + t14282 = np.cos(phi) + t14286 = 1 + t14282 + t14283 = t14286 ** 2 + t14284 = t14283 ** 2 + t14281 = -1 + t14282 + tfunc[..., c] = (0.69e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.133e3) * t14281 ** 2 * t14284 ** 2 * (-6 + 11 * t14282) + + if Bindx == 2272: + t14287 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 7 * phi2)) * np.sqrt(0.1330e4) * ((1 - t14287) ** (0.3e1 / 0.2e1)) * ((1 + t14287) ** (0.17e2 / 0.2e1)) * (-7 + 11 * t14287) + + if Bindx == 2273: + t14288 = np.cos(phi) + t14293 = 1 + t14288 + t14289 = t14293 ** 2 + t14290 = t14289 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.70e2) * (-1 + t14288) * t14293 * t14290 ** 2 * (-8 + 11 * t14288) + + if Bindx == 2274: + t14294 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.42e2) * np.sqrt((1 - t14294)) * ((1 + t14294) ** (0.19e2 / 0.2e1)) * (-9 + 11 * t14294) + + if Bindx == 2275: + t14295 = np.cos(phi) + t14296 = t14295 ** 2 + t14298 = t14296 ** 2 + t14299 = t14295 * t14298 + t14304 = t14299 ** 2 + t14302 = t14298 ** 2 + t14297 = t14295 * t14296 + t14300 = t14297 ** 2 + tfunc[..., c] = (0.23e2 / 0.1024e4) * np.exp((10*1j) * (phi1 + phi2)) * (-340 * t14296 - 705 * t14297 - 780 * t14298 - 210 * t14299 + 672 * t14300 + 870 * t14302 + 100 * t14304 - 10 + (1110 * t14300 + 395 * t14302 + 11 * t14304 - 89) * t14295) + + if Bindx == 2276: + t14306 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t14306)) * ((1 + t14306) ** (0.21e2 / 0.2e1)) + + if Bindx == 2277: + t14307 = np.cos(phi) + t14308 = t14307 ** 2 + t14310 = t14308 ** 2 + t14311 = t14307 * t14310 + t14316 = t14311 ** 2 + t14314 = t14310 ** 2 + t14309 = t14307 * t14308 + t14312 = t14309 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((11*1j) * (phi1 - phi2)) * (-55 * t14308 + 165 * t14309 - 330 * t14310 + 462 * t14311 - 462 * t14312 - 165 * t14314 - 11 * t14316 - 1 + (330 * t14312 + 55 * t14314 + t14316 + 11) * t14307) + + if Bindx == 2278: + t14318 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 10 * phi2)) * np.sqrt(0.22e2) * ((1 - t14318) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t14318)) + + if Bindx == 2279: + t14319 = np.cos(phi) + t14324 = -1 + t14319 + t14320 = t14324 ** 2 + t14322 = t14324 * t14320 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 9 * phi2)) * np.sqrt(0.231e3) * t14322 ** 2 * (1 + t14319) + + if Bindx == 2280: + t14325 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 - 8 * phi2)) * np.sqrt(0.385e3) * ((1 - t14325) ** (0.19e2 / 0.2e1)) * ((1 + t14325) ** (0.3e1 / 0.2e1)) + + if Bindx == 2281: + t14327 = np.cos(phi) + t14332 = -1 + t14327 + t14328 = t14332 ** 2 + t14329 = t14328 ** 2 + t14326 = 1 + t14327 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.7315e4) * t14332 * t14329 ** 2 * t14326 ** 2 + + if Bindx == 2282: + t14333 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 6 * phi2)) * np.sqrt(0.2926e4) * ((1 - t14333) ** (0.17e2 / 0.2e1)) * ((1 + t14333) ** (0.5e1 / 0.2e1)) + + if Bindx == 2283: + t14334 = np.cos(phi) + t14341 = -1 + t14334 + t14340 = 1 + t14334 + t14338 = t14340 ** 2 + t14335 = t14341 ** 2 + t14336 = t14335 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.74613e5) * t14336 ** 2 * t14340 * t14338 + + if Bindx == 2284: + t14342 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (11 * phi1 - 4 * phi2)) * np.sqrt(0.10659e5) * ((1 - t14342) ** (0.15e2 / 0.2e1)) * ((1 + t14342) ** (0.7e1 / 0.2e1)) + + if Bindx == 2285: + t14343 = np.cos(phi) + t14351 = -1 + t14343 + t14350 = 1 + t14343 + t14348 = t14350 ** 2 + t14344 = t14351 ** 2 + t14345 = t14351 * t14344 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t14351 * t14345 ** 2 * t14348 ** 2 + + if Bindx == 2286: + t14352 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 - 2 * phi2)) * np.sqrt(0.124355e6) * ((1 - t14352) ** (0.13e2 / 0.2e1)) * ((1 + t14352) ** (0.9e1 / 0.2e1)) + + if Bindx == 2287: + t14357 = np.sin(phi) + t14353 = t14357 ** 2 + t14355 = t14357 * t14353 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - phi2)) * np.sqrt(0.646646e6) * t14355 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 2288: + t14358 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.176358e6) * ((1 - t14358) ** (0.11e2 / 0.2e1)) * ((1 + t14358) ** (0.11e2 / 0.2e1)) + + if Bindx == 2289: + t14363 = np.sin(phi) + t14359 = t14363 ** 2 + t14361 = t14363 * t14359 ** 2 + tfunc[..., c] = -(0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + phi2)) * np.sqrt(0.646646e6) * t14361 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 2290: + t14364 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 + 2 * phi2)) * np.sqrt(0.124355e6) * ((1 - t14364) ** (0.9e1 / 0.2e1)) * ((1 + t14364) ** (0.13e2 / 0.2e1)) + + if Bindx == 2291: + t14365 = np.cos(phi) + t14373 = -1 + t14365 + t14372 = 1 + t14365 + t14368 = t14372 ** 2 + t14369 = t14372 * t14368 + t14366 = t14373 ** 2 + tfunc[..., c] = (0.69e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t14366 ** 2 * t14372 * t14369 ** 2 + + if Bindx == 2292: + t14374 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.512e3*1j) * np.exp((1j) * (11 * phi1 + 4 * phi2)) * np.sqrt(0.10659e5) * ((1 - t14374) ** (0.7e1 / 0.2e1)) * ((1 + t14374) ** (0.15e2 / 0.2e1)) + + if Bindx == 2293: + t14375 = np.cos(phi) + t14382 = -1 + t14375 + t14381 = 1 + t14375 + t14378 = t14381 ** 2 + t14379 = t14378 ** 2 + t14376 = t14382 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.74613e5) * t14382 * t14376 * t14379 ** 2 + + if Bindx == 2294: + t14383 = np.cos(phi) + tfunc[..., c] = (-0.69e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 6 * phi2)) * np.sqrt(0.2926e4) * ((1 - t14383) ** (0.5e1 / 0.2e1)) * ((1 + t14383) ** (0.17e2 / 0.2e1)) + + if Bindx == 2295: + t14385 = np.cos(phi) + t14390 = 1 + t14385 + t14386 = t14390 ** 2 + t14387 = t14386 ** 2 + t14384 = -1 + t14385 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.7315e4) * t14384 ** 2 * t14390 * t14387 ** 2 + + if Bindx == 2296: + t14391 = np.cos(phi) + tfunc[..., c] = (0.23e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 + 8 * phi2)) * np.sqrt(0.385e3) * ((1 - t14391) ** (0.3e1 / 0.2e1)) * ((1 + t14391) ** (0.19e2 / 0.2e1)) + + if Bindx == 2297: + t14392 = np.cos(phi) + t14397 = 1 + t14392 + t14393 = t14397 ** 2 + t14395 = t14397 * t14393 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 9 * phi2)) * np.sqrt(0.231e3) * (-1 + t14392) * t14395 ** 2 + + if Bindx == 2298: + t14398 = np.cos(phi) + tfunc[..., c] = (-0.23e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.22e2) * np.sqrt((1 - t14398)) * ((1 + t14398) ** (0.21e2 / 0.2e1)) + + if Bindx == 2299: + t14399 = np.cos(phi) + t14400 = t14399 ** 2 + t14402 = t14400 ** 2 + t14403 = t14399 * t14402 + t14408 = t14403 ** 2 + t14406 = t14402 ** 2 + t14401 = t14399 * t14400 + t14404 = t14401 ** 2 + tfunc[..., c] = (0.23e2 / 0.2048e4) * np.exp((11*1j) * (phi1 + phi2)) * (55 * t14400 + 165 * t14401 + 330 * t14402 + 462 * t14403 + 462 * t14404 + 165 * t14406 + 11 * t14408 + 1 + (330 * t14404 + 55 * t14406 + t14408 + 11) * t14399) + + if Bindx == 2300: + t14410 = np.cos(phi) + t14422 = 12 * t14410 + t14411 = t14410 ** 2 + t14412 = t14410 * t14411 + t14415 = t14412 ** 2 + t14413 = t14411 ** 2 + t14414 = t14410 * t14413 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-12*1j) * (phi1 + phi2)) * (66 * t14411 + 220 * t14412 + t14422 + 1 + (792 * t14410 + 924 + t14415) * t14415 + (792 + (t14422 + 66) * t14414) * t14414 + (495 + (220 * t14410 + 495) * t14413) * t14413) + + if Bindx == 2301: + t14423 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 + 11 * phi2)) * np.sqrt(0.6e1) * ((1 + t14423) ** (0.23e2 / 0.2e1)) * np.sqrt((1 - t14423)) + + if Bindx == 2302: + t14424 = np.cos(phi) + t14430 = 1 + t14424 + t14425 = t14430 ** 2 + t14427 = t14430 * t14425 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.69e2) * (-1 + t14424) * t14430 * t14427 ** 2 + + if Bindx == 2303: + t14431 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-3*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.506e3) * ((1 - t14431) ** (0.3e1 / 0.2e1)) * ((1 + t14431) ** (0.21e2 / 0.2e1)) + + if Bindx == 2304: + t14433 = np.cos(phi) + t14438 = 1 + t14433 + t14434 = t14438 ** 2 + t14436 = t14438 * t14434 ** 2 + t14432 = -1 + t14433 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.10626e5) * t14432 ** 2 * t14436 ** 2 + + if Bindx == 2305: + t14439 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 + 7 * phi2)) * np.sqrt(0.10626e5) * ((1 - t14439) ** (0.5e1 / 0.2e1)) * ((1 + t14439) ** (0.19e2 / 0.2e1)) + + if Bindx == 2306: + t14440 = np.cos(phi) + t14448 = -1 + t14440 + t14447 = 1 + t14440 + t14443 = t14447 ** 2 + t14444 = t14443 ** 2 + t14441 = t14448 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-6*1j) * (2 * phi1 + phi2)) * np.sqrt(0.33649e5) * t14448 * t14441 * t14447 * t14444 ** 2 + + if Bindx == 2307: + t14449 = np.cos(phi) + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 + 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t14449) ** (0.7e1 / 0.2e1)) * ((1 + t14449) ** (0.17e2 / 0.2e1)) + + if Bindx == 2308: + t14450 = np.cos(phi) + t14457 = -1 + t14450 + t14456 = 1 + t14450 + t14453 = t14456 ** 2 + t14454 = t14453 ** 2 + t14451 = t14457 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 + phi2)) * np.sqrt(0.81719e5) * t14451 ** 2 * t14454 ** 2 + + if Bindx == 2309: + t14458 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-3*1j) * (4 * phi1 + phi2)) * np.sqrt(0.81719e5) * ((1 - t14458) ** (0.9e1 / 0.2e1)) * ((1 + t14458) ** (0.15e2 / 0.2e1)) + + if Bindx == 2310: + t14459 = np.cos(phi) + t14468 = -1 + t14459 + t14467 = 1 + t14459 + t14463 = t14467 ** 2 + t14464 = t14467 * t14463 + t14460 = t14468 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 + phi2)) * np.sqrt(0.490314e6) * t14468 * t14460 ** 2 * t14467 * t14464 ** 2 + + if Bindx == 2311: + t14469 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (12 * phi1 + phi2)) * np.sqrt(0.156009e6) * ((1 - t14469) ** (0.11e2 / 0.2e1)) * ((1 + t14469) ** (0.13e2 / 0.2e1)) + + if Bindx == 2312: + t14474 = np.sin(phi) + t14470 = t14474 ** 2 + t14471 = t14474 * t14470 + t14472 = t14471 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-12*1j) * phi1) * np.sqrt(0.676039e6) * t14472 ** 2 + + if Bindx == 2313: + t14475 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (12 * phi1 - phi2)) * np.sqrt(0.156009e6) * ((1 - t14475) ** (0.13e2 / 0.2e1)) * ((1 + t14475) ** (0.11e2 / 0.2e1)) + + if Bindx == 2314: + t14476 = np.cos(phi) + t14485 = -1 + t14476 + t14484 = 1 + t14476 + t14481 = t14484 ** 2 + t14477 = t14485 ** 2 + t14478 = t14485 * t14477 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 - phi2)) * np.sqrt(0.490314e6) * t14485 * t14478 ** 2 * t14484 * t14481 ** 2 + + if Bindx == 2315: + t14486 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-3*1j) * (4 * phi1 - phi2)) * np.sqrt(0.81719e5) * ((1 - t14486) ** (0.15e2 / 0.2e1)) * ((1 + t14486) ** (0.9e1 / 0.2e1)) + + if Bindx == 2316: + t14487 = np.cos(phi) + t14494 = -1 + t14487 + t14493 = 1 + t14487 + t14491 = t14493 ** 2 + t14488 = t14494 ** 2 + t14489 = t14488 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 - phi2)) * np.sqrt(0.81719e5) * t14489 ** 2 * t14491 ** 2 + + if Bindx == 2317: + t14495 = np.cos(phi) + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 - 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t14495) ** (0.17e2 / 0.2e1)) * ((1 + t14495) ** (0.7e1 / 0.2e1)) + + if Bindx == 2318: + t14496 = np.cos(phi) + t14504 = -1 + t14496 + t14503 = 1 + t14496 + t14501 = t14503 ** 2 + t14497 = t14504 ** 2 + t14498 = t14497 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-6*1j) * (2 * phi1 - phi2)) * np.sqrt(0.33649e5) * t14504 * t14498 ** 2 * t14503 * t14501 + + if Bindx == 2319: + t14505 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 - 7 * phi2)) * np.sqrt(0.10626e5) * ((1 - t14505) ** (0.19e2 / 0.2e1)) * ((1 + t14505) ** (0.5e1 / 0.2e1)) + + if Bindx == 2320: + t14507 = np.cos(phi) + t14512 = -1 + t14507 + t14508 = t14512 ** 2 + t14510 = t14512 * t14508 ** 2 + t14506 = 1 + t14507 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.10626e5) * t14510 ** 2 * t14506 ** 2 + + if Bindx == 2321: + t14513 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-3*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.506e3) * ((1 - t14513) ** (0.21e2 / 0.2e1)) * ((1 + t14513) ** (0.3e1 / 0.2e1)) + + if Bindx == 2322: + t14514 = np.cos(phi) + t14520 = -1 + t14514 + t14515 = t14520 ** 2 + t14517 = t14520 * t14515 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.69e2) * t14520 * t14517 ** 2 * (1 + t14514) + + if Bindx == 2323: + t14521 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (12 * phi1 - 11 * phi2)) * np.sqrt(0.6e1) * ((1 - t14521) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t14521)) + + if Bindx == 2324: + t14522 = np.cos(phi) + t14534 = -12 * t14522 + t14523 = t14522 ** 2 + t14524 = t14522 * t14523 + t14527 = t14524 ** 2 + t14525 = t14523 ** 2 + t14526 = t14522 * t14525 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-12*1j) * (phi1 - phi2)) * (66 * t14523 - 220 * t14524 + t14534 + 1 + (-792 * t14522 + 924 + t14527) * t14527 + (-792 + (t14534 + 66) * t14526) * t14526 + (495 + (-220 * t14522 + 495) * t14525) * t14525) + + if Bindx == 2325: + t14535 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 + 12 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t14535)) * ((1 + t14535) ** (0.23e2 / 0.2e1)) + + if Bindx == 2326: + t14536 = np.cos(phi) + t14537 = t14536 ** 2 + t14539 = t14537 ** 2 + t14540 = t14536 * t14539 + t14545 = t14540 ** 2 + t14543 = t14539 ** 2 + t14538 = t14536 * t14537 + t14541 = t14538 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-11*1j) * (phi1 + phi2)) * (-473 * t14537 - 1155 * t14538 - 1650 * t14539 - 1122 * t14540 + 2145 * t14543 + 539 * t14545 - 11 + (462 + 12 * t14541) * t14541 + (1914 * t14541 + 1375 * t14543 + 121 * t14545 - 109) * t14536) + + if Bindx == 2327: + t14548 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.46e2) * ((1 + t14548) ** (0.21e2 / 0.2e1)) * (5 + (-11 + 6 * t14548) * t14548) * ((1 - t14548) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2328: + t14549 = np.cos(phi) + t14554 = 1 + t14549 + t14550 = t14554 ** 2 + t14552 = t14554 * t14550 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * (-3 + 4 * t14549) * (-1 + t14549) * t14552 ** 2 * np.sqrt(0.759e3) * np.exp((-1*1j) * (11 * phi1 + 9 * phi2)) + + if Bindx == 2329: + t14555 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-2 + 3 * t14555) * ((1 + t14555) ** (0.19e2 / 0.2e1)) * np.sqrt(0.1771e4) * np.exp((-1*1j) * (11 * phi1 + 8 * phi2)) * ((1 - t14555) ** (0.3e1 / 0.2e1)) + + if Bindx == 2330: + t14557 = np.cos(phi) + t14562 = 1 + t14557 + t14558 = t14562 ** 2 + t14559 = t14558 ** 2 + t14556 = -1 + t14557 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.1771e4) * t14562 * t14559 ** 2 * t14556 ** 2 * (-7 + 12 * t14557) + + if Bindx == 2331: + t14563 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (2 * t14563 - 1) * ((1 + t14563) ** (0.17e2 / 0.2e1)) * np.sqrt(0.201894e6) * np.exp((-1*1j) * (11 * phi1 + 6 * phi2)) * ((1 - t14563) ** (0.5e1 / 0.2e1)) + + if Bindx == 2332: + t14573 = np.sin(phi) + t14570 = t14573 ** 2 + t14571 = t14573 * t14570 + t14564 = np.cos(phi) + t14565 = t14564 ** 2 + t14567 = t14565 ** 2 + t14566 = t14564 * t14565 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.14421e5) * t14571 ** 2 * (10 * t14565 + 95 * t14567 - 5 + (70 + 12 * t14566) * t14566 + (55 * t14567 - 13) * t14564) + + if Bindx == 2333: + t14574 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-1 + 3 * t14574) * ((1 + t14574) ** (0.15e2 / 0.2e1)) * np.sqrt(0.490314e6) * np.exp((-1*1j) * (11 * phi1 + 4 * phi2)) * ((1 - t14574) ** (0.7e1 / 0.2e1)) + + if Bindx == 2334: + t14582 = np.sin(phi) + t14579 = t14582 ** 2 + t14580 = t14579 ** 2 + t14575 = np.cos(phi) + t14576 = t14575 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.490314e6) * t14580 ** 2 * (t14575 - 1 + (11 * t14575 + 9 + 4 * t14576) * t14576) + + if Bindx == 2335: + t14583 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-1 + 6 * t14583) * ((1 + t14583) ** (0.13e2 / 0.2e1)) * np.sqrt(0.81719e5) * np.exp((-1*1j) * (11 * phi1 + 2 * phi2)) * ((1 - t14583) ** (0.9e1 / 0.2e1)) + + if Bindx == 2336: + t14589 = np.sin(phi) + t14585 = t14589 ** 2 + t14587 = t14589 * t14585 ** 2 + t14584 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 + phi2)) * np.sqrt(0.104006e6) * t14587 ** 2 * (-1 + (11 + 12 * t14584) * t14584) + + if Bindx == 2337: + t14590 = np.cos(phi) + t14596 = -6 * t14590 + t14591 = t14590 ** 2 + t14592 = t14590 * t14591 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * t14590 * (t14596 + 1 + (-20 + t14592) * t14592 + (15 + (t14596 + 15) * t14591) * t14591) * ((1 + t14590) ** (0.11e2 / 0.2e1)) * np.sqrt(0.4056234e7) * np.exp((-11*1j) * phi1) * ((1 - t14590) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2338: + t14602 = np.sin(phi) + t14598 = t14602 ** 2 + t14600 = t14602 * t14598 ** 2 + t14597 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - phi2)) * np.sqrt(0.104006e6) * t14600 ** 2 * (-1 + (-11 + 12 * t14597) * t14597) + + if Bindx == 2339: + t14603 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (1 + 6 * t14603) * ((1 + t14603) ** (0.9e1 / 0.2e1)) * np.sqrt(0.81719e5) * np.exp((-1*1j) * (11 * phi1 - 2 * phi2)) * ((1 - t14603) ** (0.13e2 / 0.2e1)) + + if Bindx == 2340: + t14611 = np.sin(phi) + t14608 = t14611 ** 2 + t14609 = t14608 ** 2 + t14604 = np.cos(phi) + t14605 = t14604 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.490314e6) * t14609 ** 2 * (-t14604 - 1 + (-11 * t14604 + 9 + 4 * t14605) * t14605) + + if Bindx == 2341: + t14612 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (1 + 3 * t14612) * ((1 + t14612) ** (0.7e1 / 0.2e1)) * np.sqrt(0.490314e6) * np.exp((-1*1j) * (11 * phi1 - 4 * phi2)) * ((1 - t14612) ** (0.15e2 / 0.2e1)) + + if Bindx == 2342: + t14622 = np.sin(phi) + t14619 = t14622 ** 2 + t14620 = t14622 * t14619 + t14613 = np.cos(phi) + t14614 = t14613 ** 2 + t14616 = t14614 ** 2 + t14615 = t14613 * t14614 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.14421e5) * t14620 ** 2 * (10 * t14614 + 95 * t14616 - 5 + (-70 + 12 * t14615) * t14615 + (-55 * t14616 + 13) * t14613) + + if Bindx == 2343: + t14623 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (1 + 2 * t14623) * ((1 + t14623) ** (0.5e1 / 0.2e1)) * np.sqrt(0.201894e6) * np.exp((-1*1j) * (11 * phi1 - 6 * phi2)) * ((1 - t14623) ** (0.17e2 / 0.2e1)) + + if Bindx == 2344: + t14625 = np.cos(phi) + t14630 = -1 + t14625 + t14626 = t14630 ** 2 + t14627 = t14626 ** 2 + t14624 = 1 + t14625 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.1771e4) * t14624 ** 2 * t14630 * t14627 ** 2 * (7 + 12 * t14625) + + if Bindx == 2345: + t14631 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (2 + 3 * t14631) * ((1 + t14631) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1771e4) * np.exp((-1*1j) * (11 * phi1 - 8 * phi2)) * ((1 - t14631) ** (0.19e2 / 0.2e1)) + + if Bindx == 2346: + t14632 = np.cos(phi) + t14637 = -1 + t14632 + t14633 = t14637 ** 2 + t14635 = t14637 * t14633 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (11 * phi1 - 9 * phi2)) * np.sqrt(0.759e3) * (1 + t14632) * t14635 ** 2 * (3 + 4 * t14632) + + if Bindx == 2347: + t14638 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (5 + 6 * t14638) * np.sqrt((1 + t14638)) * np.sqrt(0.46e2) * np.exp((-1*1j) * (11 * phi1 - 10 * phi2)) * ((1 - t14638) ** (0.21e2 / 0.2e1)) + + if Bindx == 2348: + t14639 = np.cos(phi) + t14640 = t14639 ** 2 + t14642 = t14640 ** 2 + t14643 = t14639 * t14642 + t14648 = t14643 ** 2 + t14646 = t14642 ** 2 + t14641 = t14639 * t14640 + t14644 = t14641 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-11*1j) * (phi1 - phi2)) * (-473 * t14640 + 1155 * t14641 - 1650 * t14642 + 1122 * t14643 + 2145 * t14646 + 539 * t14648 - 11 + (462 + 12 * t14644) * t14644 + (-1914 * t14644 - 1375 * t14646 - 121 * t14648 + 109) * t14639) + + if Bindx == 2349: + t14651 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (11 * phi1 - 12 * phi2)) * np.sqrt(0.6e1) * ((1 - t14651) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t14651)) + + if Bindx == 2350: + t14652 = np.cos(phi) + t14658 = 1 + t14652 + t14653 = t14658 ** 2 + t14655 = t14658 * t14653 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.69e2) * (-1 + t14652) * t14658 * t14655 ** 2 + + if Bindx == 2351: + t14659 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.46e2) * np.sqrt((1 - t14659)) * ((1 + t14659) ** (0.21e2 / 0.2e1)) * (-5 + 6 * t14659) + + if Bindx == 2352: + t14660 = np.cos(phi) + t14661 = t14660 ** 2 + t14663 = t14661 ** 2 + t14664 = t14660 * t14663 + t14669 = t14664 ** 2 + t14667 = t14663 ** 2 + t14662 = t14660 * t14661 + t14665 = t14662 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 + phi2)) * (1034 * t14661 + 1155 * t14662 - 825 * t14663 - 4026 * t14664 + 2805 * t14667 + 2002 * t14669 + 47 + (-4620 + 69 * t14665) * t14665 + (-1122 * t14665 + 3575 * t14667 + 575 * t14669 + 355) * t14660) + + if Bindx == 2353: + t14672 = np.cos(phi) + t14673 = t14672 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.66e2) * ((1 + t14672) ** (0.19e2 / 0.2e1)) * (-115 * t14673 - 25 + (46 * t14673 + 94) * t14672) * ((1 - t14672) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2354: + t14676 = np.cos(phi) + t14677 = t14676 ** 2 + t14679 = t14677 ** 2 + t14683 = t14679 ** 2 + t14678 = t14676 * t14677 + t14681 = t14678 ** 2 + t14680 = t14676 * t14679 + t14675 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.154e3) * t14675 ** 2 * (145 * t14677 - 400 * t14678 - 1190 * t14679 + 490 * t14681 + 1225 * t14683 + 29 + (-952 + 69 * t14680) * t14680 + (1520 * t14681 + 460 * t14683 + 140) * t14676) + + if Bindx == 2355: + t14686 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (43 + (-161 + 138 * t14686) * t14686) * ((1 + t14686) ** (0.17e2 / 0.2e1)) * np.sqrt(0.154e3) * np.exp((-1*1j) * (10 * phi1 + 7 * phi2)) * ((1 - t14686) ** (0.3e1 / 0.2e1)) + + if Bindx == 2356: + t14697 = np.sin(phi) + t14695 = t14697 ** 2 + t14687 = np.cos(phi) + t14688 = t14687 ** 2 + t14689 = t14687 * t14688 + t14692 = t14689 ** 2 + t14690 = t14688 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.4389e4) * t14695 ** 2 * (-40 * t14688 - 107 * t14689 + 212 * t14692 + 5 + (-40 + 23 * t14690) * t14690 + (145 * t14690 + 115 * t14692 + 7) * t14687) + + if Bindx == 2357: + t14698 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (19 + (-115 + 138 * t14698) * t14698) * ((1 + t14698) ** (0.15e2 / 0.2e1)) * np.sqrt(0.1254e4) * np.exp((-5*1j) * (2 * phi1 + phi2)) * ((1 - t14698) ** (0.5e1 / 0.2e1)) + + if Bindx == 2358: + t14708 = np.sin(phi) + t14705 = t14708 ** 2 + t14706 = t14708 * t14705 + t14699 = np.cos(phi) + t14700 = t14699 ** 2 + t14702 = t14700 ** 2 + t14701 = t14699 * t14700 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.10659e5) * t14706 ** 2 * (-85 * t14700 + 235 * t14702 + 5 + (20 + 69 * t14701) * t14701 + (230 * t14702 - 26) * t14699) + + if Bindx == 2359: + t14709 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (1 + (-23 + 46 * t14709) * t14709) * ((1 + t14709) ** (0.13e2 / 0.2e1)) * np.sqrt(0.10659e5) * np.exp((-1*1j) * (10 * phi1 + 3 * phi2)) * ((1 - t14709) ** (0.7e1 / 0.2e1)) + + if Bindx == 2360: + t14717 = np.sin(phi) + t14714 = t14717 ** 2 + t14715 = t14714 ** 2 + t14710 = np.cos(phi) + t14711 = t14710 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.7106e4) * t14715 ** 2 * (-25 * t14710 - 1 + (115 * t14710 + 22 + 69 * t14711) * t14711) + + if Bindx == 2361: + t14718 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-5 + (-23 + 138 * t14718) * t14718) * ((1 + t14718) ** (0.11e2 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (10 * phi1 + phi2)) * ((1 - t14718) ** (0.9e1 / 0.2e1)) + + if Bindx == 2362: + t14724 = np.sin(phi) + t14720 = t14724 ** 2 + t14722 = t14724 * t14720 ** 2 + t14719 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-10*1j) * phi1) * np.sqrt(0.88179e5) * t14722 ** 2 * (23 * t14719 ** 2 - 1) + + if Bindx == 2363: + t14725 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-5 + (23 + 138 * t14725) * t14725) * ((1 + t14725) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (10 * phi1 - phi2)) * ((1 - t14725) ** (0.11e2 / 0.2e1)) + + if Bindx == 2364: + t14733 = np.sin(phi) + t14730 = t14733 ** 2 + t14731 = t14730 ** 2 + t14726 = np.cos(phi) + t14727 = t14726 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.7106e4) * t14731 ** 2 * (25 * t14726 - 1 + (-115 * t14726 + 22 + 69 * t14727) * t14727) + + if Bindx == 2365: + t14734 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (1 + (23 + 46 * t14734) * t14734) * ((1 + t14734) ** (0.7e1 / 0.2e1)) * np.sqrt(0.10659e5) * np.exp((-1*1j) * (10 * phi1 - 3 * phi2)) * ((1 - t14734) ** (0.13e2 / 0.2e1)) + + if Bindx == 2366: + t14744 = np.sin(phi) + t14741 = t14744 ** 2 + t14742 = t14744 * t14741 + t14735 = np.cos(phi) + t14736 = t14735 ** 2 + t14738 = t14736 ** 2 + t14737 = t14735 * t14736 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.10659e5) * t14742 ** 2 * (-85 * t14736 + 235 * t14738 + 5 + (-20 + 69 * t14737) * t14737 + (-230 * t14738 + 26) * t14735) + + if Bindx == 2367: + t14745 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (19 + (115 + 138 * t14745) * t14745) * ((1 + t14745) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1254e4) * np.exp((-5*1j) * (2 * phi1 - phi2)) * ((1 - t14745) ** (0.15e2 / 0.2e1)) + + if Bindx == 2368: + t14756 = np.sin(phi) + t14754 = t14756 ** 2 + t14746 = np.cos(phi) + t14747 = t14746 ** 2 + t14748 = t14746 * t14747 + t14751 = t14748 ** 2 + t14749 = t14747 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.4389e4) * t14754 ** 2 * (-40 * t14747 + 107 * t14748 + 212 * t14751 + 5 + (-40 + 23 * t14749) * t14749 + (-145 * t14749 - 115 * t14751 - 7) * t14746) + + if Bindx == 2369: + t14757 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (43 + (161 + 138 * t14757) * t14757) * ((1 + t14757) ** (0.3e1 / 0.2e1)) * np.sqrt(0.154e3) * np.exp((-1*1j) * (10 * phi1 - 7 * phi2)) * ((1 - t14757) ** (0.17e2 / 0.2e1)) + + if Bindx == 2370: + t14758 = np.cos(phi) + t14763 = -1 + t14758 + t14759 = t14763 ** 2 + t14760 = t14759 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.154e3) * (1 + t14758) * t14763 * t14760 ** 2 * (29 + (92 + 69 * t14758) * t14758) + + if Bindx == 2371: + t14764 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (25 + (69 + 46 * t14764) * t14764) * np.sqrt((1 + t14764)) * np.sqrt(0.66e2) * np.exp((-1*1j) * (10 * phi1 - 9 * phi2)) * ((1 - t14764) ** (0.19e2 / 0.2e1)) + + if Bindx == 2372: + t14765 = np.cos(phi) + t14766 = t14765 ** 2 + t14768 = t14766 ** 2 + t14769 = t14765 * t14768 + t14774 = t14769 ** 2 + t14772 = t14768 ** 2 + t14767 = t14765 * t14766 + t14770 = t14767 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 - phi2)) * (1034 * t14766 - 1155 * t14767 - 825 * t14768 + 4026 * t14769 + 2805 * t14772 + 2002 * t14774 + 47 + (-4620 + 69 * t14770) * t14770 + (1122 * t14770 - 3575 * t14772 - 575 * t14774 - 355) * t14765) + + if Bindx == 2373: + t14777 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (5 + 6 * t14777) * np.sqrt((1 + t14777)) * np.sqrt(0.46e2) * np.exp((-1*1j) * (10 * phi1 - 11 * phi2)) * ((1 - t14777) ** (0.21e2 / 0.2e1)) + + if Bindx == 2374: + t14778 = np.cos(phi) + t14784 = -1 + t14778 + t14779 = t14784 ** 2 + t14781 = t14784 * t14779 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.69e2) * t14784 * t14781 ** 2 * (1 + t14778) + + if Bindx == 2375: + t14785 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-3*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.506e3) * ((1 - t14785) ** (0.3e1 / 0.2e1)) * ((1 + t14785) ** (0.21e2 / 0.2e1)) + + if Bindx == 2376: + t14786 = np.cos(phi) + t14791 = 1 + t14786 + t14787 = t14791 ** 2 + t14789 = t14791 * t14787 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.759e3) * (-1 + t14786) * t14789 ** 2 * (-3 + 4 * t14786) + + if Bindx == 2377: + t14792 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.66e2) * np.sqrt((1 - t14792)) * ((1 + t14792) ** (0.19e2 / 0.2e1)) * (25 + (-69 + 46 * t14792) * t14792) + + if Bindx == 2378: + t14793 = np.cos(phi) + t14794 = t14793 ** 2 + t14796 = t14794 ** 2 + t14797 = t14793 * t14796 + t14802 = t14797 ** 2 + t14800 = t14796 ** 2 + t14795 = t14793 * t14794 + t14798 = t14795 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-9*1j) * (phi1 + phi2)) * (-1143 * t14794 + 7915 * t14795 + 17730 * t14796 + 5058 * t14797 - 7785 * t14800 + 17589 * t14802 - 381 + (-25998 + 1012 * t14798) * t14798 + (-34506 * t14798 + 17505 * t14800 + 6831 * t14802 - 1779) * t14793) + + if Bindx == 2379: + t14805 = np.cos(phi) + t14806 = t14805 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.21e2) * ((1 + t14805) ** (0.17e2 / 0.2e1)) * (-381 * t14805 + 62 + (-759 * t14805 + 825 + 253 * t14806) * t14806) * ((1 - t14805) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2380: + t14810 = np.cos(phi) + t14811 = t14810 ** 2 + t14813 = t14811 ** 2 + t14817 = t14813 ** 2 + t14812 = t14810 * t14811 + t14815 = t14812 ** 2 + t14814 = t14810 * t14813 + t14809 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.21e2) * t14809 ** 2 * (1764 * t14811 + 3336 * t14812 - 2142 * t14813 - 7728 * t14815 + 9801 * t14817 - 147 + (-10710 + 1012 * t14814) * t14814 + (4704 * t14815 + 5313 * t14817 - 83) * t14810) + + if Bindx == 2381: + t14820 = np.cos(phi) + t14821 = t14820 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-759 * t14821 - 37 + (506 * t14821 + 330) * t14820) * ((1 + t14820) ** (0.15e2 / 0.2e1)) * np.sqrt(0.266e3) * np.exp((-3*1j) * (3 * phi1 + 2 * phi2)) * ((1 - t14820) ** (0.3e1 / 0.2e1)) + + if Bindx == 2382: + t14833 = np.sin(phi) + t14831 = t14833 ** 2 + t14823 = np.cos(phi) + t14824 = t14823 ** 2 + t14825 = t14823 * t14824 + t14828 = t14825 ** 2 + t14826 = t14824 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.19e2) * t14831 ** 2 * (575 * t14824 - 1383 * t14825 + 4213 * t14828 - 25 + (-3535 + 1012 * t14826) * t14826 + (-465 * t14826 + 3795 * t14828 + 293) * t14823) + + if Bindx == 2383: + t14834 = np.cos(phi) + t14835 = t14834 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * (-253 * t14835 + 1 + (253 * t14835 + 55) * t14834) * ((1 + t14834) ** (0.13e2 / 0.2e1)) * np.sqrt(0.646e3) * np.exp((-1*1j) * (9 * phi1 + 4 * phi2)) * ((1 - t14834) ** (0.5e1 / 0.2e1)) + + if Bindx == 2384: + t14846 = np.sin(phi) + t14843 = t14846 ** 2 + t14844 = t14846 * t14843 + t14837 = np.cos(phi) + t14838 = t14837 ** 2 + t14840 = t14838 ** 2 + t14839 = t14837 * t14838 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.646e3) * t14844 ** 2 * (-510 * t14838 + 825 * t14840 + 17 + (-1050 + 1012 * t14839) * t14839 + (2277 * t14840 + 117) * t14837) + + if Bindx == 2385: + t14847 = np.cos(phi) + t14848 = t14847 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-253 * t14848 + 9 + (506 * t14848 - 22) * t14847) * ((1 + t14847) ** (0.11e2 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-1*1j) * (9 * phi1 + 2 * phi2)) * ((1 - t14847) ** (0.7e1 / 0.2e1)) + + if Bindx == 2386: + t14857 = np.sin(phi) + t14854 = t14857 ** 2 + t14855 = t14854 ** 2 + t14850 = np.cos(phi) + t14851 = t14850 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 + phi2)) * np.sqrt(0.149226e6) * t14855 ** 2 * (-9 * t14850 + 1 + (69 * t14850 - 33 + 92 * t14851) * t14851) + + if Bindx == 2387: + t14858 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (23 * t14858 ** 2 - 3) * t14858 * ((1 + t14858) ** (0.9e1 / 0.2e1)) * np.sqrt(0.646646e6) * np.exp((-9*1j) * phi1) * ((1 - t14858) ** (0.9e1 / 0.2e1)) + + if Bindx == 2388: + t14866 = np.sin(phi) + t14863 = t14866 ** 2 + t14864 = t14863 ** 2 + t14859 = np.cos(phi) + t14860 = t14859 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - phi2)) * np.sqrt(0.149226e6) * t14864 ** 2 * (9 * t14859 + 1 + (-69 * t14859 - 33 + 92 * t14860) * t14860) + + if Bindx == 2389: + t14867 = np.cos(phi) + t14868 = t14867 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (253 * t14868 - 9 + (506 * t14868 - 22) * t14867) * ((1 + t14867) ** (0.7e1 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-1*1j) * (9 * phi1 - 2 * phi2)) * ((1 - t14867) ** (0.11e2 / 0.2e1)) + + if Bindx == 2390: + t14879 = np.sin(phi) + t14876 = t14879 ** 2 + t14877 = t14879 * t14876 + t14870 = np.cos(phi) + t14871 = t14870 ** 2 + t14873 = t14871 ** 2 + t14872 = t14870 * t14871 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.646e3) * t14877 ** 2 * (-510 * t14871 + 825 * t14873 + 17 + (1050 + 1012 * t14872) * t14872 + (-2277 * t14873 - 117) * t14870) + + if Bindx == 2391: + t14880 = np.cos(phi) + t14883 = 253 * t14880 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * (t14883 - 1 + (t14883 + 55) * t14880) * ((1 + t14880) ** (0.5e1 / 0.2e1)) * np.sqrt(0.646e3) * np.exp((-1*1j) * (9 * phi1 - 4 * phi2)) * ((1 - t14880) ** (0.13e2 / 0.2e1)) + + if Bindx == 2392: + t14894 = np.sin(phi) + t14892 = t14894 ** 2 + t14884 = np.cos(phi) + t14885 = t14884 ** 2 + t14886 = t14884 * t14885 + t14889 = t14886 ** 2 + t14887 = t14885 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.19e2) * t14892 ** 2 * (575 * t14885 + 1383 * t14886 + 4213 * t14889 - 25 + (-3535 + 1012 * t14887) * t14887 + (465 * t14887 - 3795 * t14889 - 293) * t14884) + + if Bindx == 2393: + t14895 = np.cos(phi) + t14896 = t14895 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (759 * t14896 + 37 + (506 * t14896 + 330) * t14895) * ((1 + t14895) ** (0.3e1 / 0.2e1)) * np.sqrt(0.266e3) * np.exp((-3*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t14895) ** (0.15e2 / 0.2e1)) + + if Bindx == 2394: + t14899 = np.cos(phi) + t14900 = t14899 ** 2 + t14902 = t14900 ** 2 + t14906 = t14902 ** 2 + t14901 = t14899 * t14900 + t14904 = t14901 ** 2 + t14903 = t14899 * t14902 + t14898 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.21e2) * t14898 ** 2 * (1764 * t14900 - 3336 * t14901 - 2142 * t14902 - 7728 * t14904 + 9801 * t14906 - 147 + (10710 + 1012 * t14903) * t14903 + (-4704 * t14904 - 5313 * t14906 + 83) * t14899) + + if Bindx == 2395: + t14909 = np.cos(phi) + t14910 = t14909 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (506 * t14910 + 62 + (253 * t14910 + 319) * t14909) * np.sqrt((1 + t14909)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (9 * phi1 - 8 * phi2)) * ((1 - t14909) ** (0.17e2 / 0.2e1)) + + if Bindx == 2396: + t14912 = np.cos(phi) + t14913 = t14912 ** 2 + t14915 = t14913 ** 2 + t14916 = t14912 * t14915 + t14921 = t14916 ** 2 + t14919 = t14915 ** 2 + t14914 = t14912 * t14913 + t14917 = t14914 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-9*1j) * (phi1 - phi2)) * (-1143 * t14913 - 7915 * t14914 + 17730 * t14915 - 5058 * t14916 - 7785 * t14919 + 17589 * t14921 - 381 + (-25998 + 1012 * t14917) * t14917 + (34506 * t14917 - 17505 * t14919 - 6831 * t14921 + 1779) * t14912) + + if Bindx == 2397: + t14924 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (25 + (69 + 46 * t14924) * t14924) * np.sqrt((1 + t14924)) * np.sqrt(0.66e2) * np.exp((-1*1j) * (9 * phi1 - 10 * phi2)) * ((1 - t14924) ** (0.19e2 / 0.2e1)) + + if Bindx == 2398: + t14925 = np.cos(phi) + t14930 = -1 + t14925 + t14926 = t14930 ** 2 + t14928 = t14930 * t14926 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.759e3) * (1 + t14925) * t14928 ** 2 * (3 + 4 * t14925) + + if Bindx == 2399: + t14931 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-3*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.506e3) * ((1 - t14931) ** (0.21e2 / 0.2e1)) * ((1 + t14931) ** (0.3e1 / 0.2e1)) + + if Bindx == 2400: + t14933 = np.cos(phi) + t14938 = 1 + t14933 + t14934 = t14938 ** 2 + t14936 = t14938 * t14934 ** 2 + t14932 = -1 + t14933 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.10626e5) * t14932 ** 2 * t14936 ** 2 + + if Bindx == 2401: + t14939 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 11 * phi2)) * np.sqrt(0.1771e4) * ((1 - t14939) ** (0.3e1 / 0.2e1)) * ((1 + t14939) ** (0.19e2 / 0.2e1)) * (-2 + 3 * t14939) + + if Bindx == 2402: + t14940 = np.cos(phi) + t14945 = 1 + t14940 + t14941 = t14945 ** 2 + t14942 = t14941 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.154e3) * (-1 + t14940) * t14945 * t14942 ** 2 * (29 + (-92 + 69 * t14940) * t14940) + + if Bindx == 2403: + t14946 = np.cos(phi) + t14947 = t14946 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.21e2) * np.sqrt((1 - t14946)) * ((1 + t14946) ** (0.17e2 / 0.2e1)) * (-506 * t14947 - 62 + (253 * t14947 + 319) * t14946) + + if Bindx == 2404: + t14949 = np.cos(phi) + t14950 = t14949 ** 2 + t14952 = t14950 ** 2 + t14953 = t14949 * t14952 + t14958 = t14953 ** 2 + t14956 = t14952 ** 2 + t14951 = t14949 * t14950 + t14954 = t14951 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-8*1j) * (phi1 + phi2)) * (-9422 * t14950 - 15120 * t14951 + 22575 * t14952 + 69216 * t14953 - 87345 * t14956 + 48818 * t14958 + 673 + (20412 + 5313 * t14954) * t14954 + (-84384 * t14954 + 2800 * t14956 + 28336 * t14958 + 176) * t14949) + + if Bindx == 2405: + t14961 = np.cos(phi) + t14962 = t14961 ** 2 + t14964 = t14962 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * ((1 + t14961) ** (0.15e2 / 0.2e1)) * (-13020 * t14962 - 17710 * t14964 - 278 + (22330 * t14962 + 5313 * t14964 + 3365) * t14961) * ((1 - t14961) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2406: + t14967 = np.cos(phi) + t14968 = t14967 ** 2 + t14970 = t14968 ** 2 + t14974 = t14970 ** 2 + t14969 = t14967 * t14968 + t14972 = t14969 ** 2 + t14971 = t14967 * t14970 + t14966 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.114e3) * t14966 ** 2 * (-513 * t14968 + 2928 * t14969 + 5094 * t14970 - 12714 * t14972 + 7623 * t14974 + 19 + (-3960 + 1771 * t14971) * t14971 + (-4368 * t14972 + 7084 * t14974 - 404) * t14967) + + if Bindx == 2407: + t14977 = np.cos(phi) + t14978 = t14977 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-75 * t14977 - 6 + (-1265 * t14977 + 627 + 759 * t14978) * t14978) * ((1 + t14977) ** (0.13e2 / 0.2e1)) * np.sqrt(0.399e3) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * ((1 - t14977) ** (0.3e1 / 0.2e1)) + + if Bindx == 2408: + t14991 = np.sin(phi) + t14989 = t14991 ** 2 + t14981 = np.cos(phi) + t14982 = t14981 ** 2 + t14983 = t14981 * t14982 + t14986 = t14983 ** 2 + t14984 = t14982 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.13566e5) * t14989 ** 2 * (324 * t14982 + 344 * t14983 + 836 * t14986 - 9 + (-1270 + 759 * t14984) * t14984 + (-1704 * t14984 + 2024 * t14986 - 24) * t14981) + + if Bindx == 2409: + t14992 = np.cos(phi) + t14993 = t14992 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (17 * t14992 - 2 + (-253 * t14992 + 33 + 253 * t14993) * t14993) * ((1 + t14992) ** (0.11e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * ((1 - t14992) ** (0.5e1 / 0.2e1)) + + if Bindx == 2410: + t15005 = np.sin(phi) + t15002 = t15005 ** 2 + t15003 = t15005 * t15002 + t14996 = np.cos(phi) + t14997 = t14996 ** 2 + t14999 = t14997 ** 2 + t14998 = t14996 * t14997 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.2261e4) * t15003 ** 2 * (41 * t14997 - 319 * t14999 - 1 + (-584 + 759 * t14998) * t14998 + (1012 * t14999 + 52) * t14996) + + if Bindx == 2411: + t15006 = np.cos(phi) + t15007 = t15006 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (21 * t15006 + 2 + (-161 * t15006 - 105 + 483 * t15007) * t15007) * ((1 + t15006) ** (0.9e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (8 * phi1 + phi2)) * ((1 - t15006) ** (0.7e1 / 0.2e1)) + + if Bindx == 2412: + t15016 = np.sin(phi) + t15013 = t15016 ** 2 + t15014 = t15013 ** 2 + t15010 = np.cos(phi) + t15011 = t15010 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-8*1j) * phi1) * np.sqrt(0.277134e6) * t15014 ** 2 * (1 + (-42 + 161 * t15011) * t15011) + + if Bindx == 2413: + t15017 = np.cos(phi) + t15018 = t15017 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-21 * t15017 + 2 + (161 * t15017 - 105 + 483 * t15018) * t15018) * ((1 + t15017) ** (0.7e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (8 * phi1 - phi2)) * ((1 - t15017) ** (0.9e1 / 0.2e1)) + + if Bindx == 2414: + t15030 = np.sin(phi) + t15027 = t15030 ** 2 + t15028 = t15030 * t15027 + t15021 = np.cos(phi) + t15022 = t15021 ** 2 + t15024 = t15022 ** 2 + t15023 = t15021 * t15022 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.2261e4) * t15028 ** 2 * (41 * t15022 - 319 * t15024 - 1 + (584 + 759 * t15023) * t15023 + (-1012 * t15024 - 52) * t15021) + + if Bindx == 2415: + t15031 = np.cos(phi) + t15032 = t15031 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-17 * t15031 - 2 + (253 * t15031 + 33 + 253 * t15032) * t15032) * ((1 + t15031) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * ((1 - t15031) ** (0.11e2 / 0.2e1)) + + if Bindx == 2416: + t15045 = np.sin(phi) + t15043 = t15045 ** 2 + t15035 = np.cos(phi) + t15036 = t15035 ** 2 + t15037 = t15035 * t15036 + t15040 = t15037 ** 2 + t15038 = t15036 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.13566e5) * t15043 ** 2 * (324 * t15036 - 344 * t15037 + 836 * t15040 - 9 + (-1270 + 759 * t15038) * t15038 + (1704 * t15038 - 2024 * t15040 + 24) * t15035) + + if Bindx == 2417: + t15046 = np.cos(phi) + t15047 = t15046 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (75 * t15046 - 6 + (1265 * t15046 + 627 + 759 * t15047) * t15047) * ((1 + t15046) ** (0.3e1 / 0.2e1)) * np.sqrt(0.399e3) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * ((1 - t15046) ** (0.13e2 / 0.2e1)) + + if Bindx == 2418: + t15051 = np.cos(phi) + t15052 = t15051 ** 2 + t15054 = t15052 ** 2 + t15058 = t15054 ** 2 + t15053 = t15051 * t15052 + t15056 = t15053 ** 2 + t15055 = t15051 * t15054 + t15050 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.114e3) * t15050 ** 2 * (-513 * t15052 - 2928 * t15053 + 5094 * t15054 - 12714 * t15056 + 7623 * t15058 + 19 + (3960 + 1771 * t15055) * t15055 + (4368 * t15056 - 7084 * t15058 + 404) * t15051) + + if Bindx == 2419: + t15061 = np.cos(phi) + t15062 = t15061 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (3087 * t15061 + 278 + (12397 * t15061 + 9933 + 5313 * t15062) * t15062) * np.sqrt((1 + t15061)) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t15061) ** (0.15e2 / 0.2e1)) + + if Bindx == 2420: + t15065 = np.cos(phi) + t15066 = t15065 ** 2 + t15068 = t15066 ** 2 + t15069 = t15065 * t15068 + t15074 = t15069 ** 2 + t15072 = t15068 ** 2 + t15067 = t15065 * t15066 + t15070 = t15067 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-8*1j) * (phi1 - phi2)) * (-9422 * t15066 + 15120 * t15067 + 22575 * t15068 - 69216 * t15069 - 87345 * t15072 + 48818 * t15074 + 673 + (20412 + 5313 * t15070) * t15070 + (84384 * t15070 - 2800 * t15072 - 28336 * t15074 - 176) * t15065) + + if Bindx == 2421: + t15077 = np.cos(phi) + t15078 = t15077 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (506 * t15078 + 62 + (253 * t15078 + 319) * t15077) * np.sqrt((1 + t15077)) * np.sqrt(0.21e2) * np.exp((-1*1j) * (8 * phi1 - 9 * phi2)) * ((1 - t15077) ** (0.17e2 / 0.2e1)) + + if Bindx == 2422: + t15080 = np.cos(phi) + t15085 = -1 + t15080 + t15081 = t15085 ** 2 + t15082 = t15081 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.154e3) * (1 + t15080) * t15085 * t15082 ** 2 * (29 + (92 + 69 * t15080) * t15080) + + if Bindx == 2423: + t15086 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (2 + 3 * t15086) * ((1 + t15086) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1771e4) * np.exp((-1*1j) * (8 * phi1 - 11 * phi2)) * ((1 - t15086) ** (0.19e2 / 0.2e1)) + + if Bindx == 2424: + t15088 = np.cos(phi) + t15093 = -1 + t15088 + t15089 = t15093 ** 2 + t15091 = t15093 * t15089 ** 2 + t15087 = 1 + t15088 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.10626e5) * t15091 ** 2 * t15087 ** 2 + + if Bindx == 2425: + t15094 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 12 * phi2)) * np.sqrt(0.10626e5) * ((1 - t15094) ** (0.5e1 / 0.2e1)) * ((1 + t15094) ** (0.19e2 / 0.2e1)) + + if Bindx == 2426: + t15096 = np.cos(phi) + t15101 = 1 + t15096 + t15097 = t15101 ** 2 + t15098 = t15097 ** 2 + t15095 = -1 + t15096 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.1771e4) * t15095 ** 2 * t15101 * t15098 ** 2 * (-7 + 12 * t15096) + + if Bindx == 2427: + t15102 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 10 * phi2)) * np.sqrt(0.154e3) * ((1 - t15102) ** (0.3e1 / 0.2e1)) * ((1 + t15102) ** (0.17e2 / 0.2e1)) * (43 + (-161 + 138 * t15102) * t15102) + + if Bindx == 2428: + t15104 = np.cos(phi) + t15105 = t15104 ** 2 + t15107 = t15105 ** 2 + t15111 = t15107 ** 2 + t15106 = t15104 * t15105 + t15109 = t15106 ** 2 + t15108 = t15104 * t15107 + t15103 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.21e2) * t15103 ** 2 * (1764 * t15105 + 3336 * t15106 - 2142 * t15107 - 7728 * t15109 + 9801 * t15111 - 147 + (-10710 + 1012 * t15108) * t15108 + (4704 * t15109 + 5313 * t15111 - 83) * t15104) + + if Bindx == 2429: + t15114 = np.cos(phi) + t15115 = t15114 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt((1 - t15114)) * ((1 + t15114) ** (0.15e2 / 0.2e1)) * (-3087 * t15114 + 278 + (-12397 * t15114 + 9933 + 5313 * t15115) * t15115) + + if Bindx == 2430: + t15118 = np.cos(phi) + t15119 = t15118 ** 2 + t15121 = t15119 ** 2 + t15122 = t15118 * t15121 + t15127 = t15122 ** 2 + t15125 = t15121 ** 2 + t15120 = t15118 * t15119 + t15123 = t15120 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-7*1j) * (phi1 + phi2)) * (4669 * t15119 - 38745 * t15120 - 57750 * t15121 + 88746 * t15122 - 245565 * t15125 + 78617 * t15127 - 161 + (199962 + 21252 * t15123) * t15123 + (-14994 * t15123 - 125195 * t15125 + 86779 * t15127 + 4433) * t15118) + + if Bindx == 2431: + t15130 = np.cos(phi) + t15131 = t15130 ** 2 + t15133 = t15131 ** 2 + t15132 = t15130 * t15131 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.114e3) * ((1 + t15130) ** (0.13e2 / 0.2e1)) * (2780 * t15131 + 16555 * t15133 - 29 + (-10290 + 3542 * t15132) * t15132 + (-12397 * t15133 - 161) * t15130) * ((1 - t15130) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2432: + t15137 = np.cos(phi) + t15138 = t15137 ** 2 + t15140 = t15138 ** 2 + t15144 = t15140 ** 2 + t15139 = t15137 * t15138 + t15142 = t15139 ** 2 + t15141 = t15137 * t15140 + t15136 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.399e3) * t15136 ** 2 * (-1080 * t15138 - 500 * t15139 + 6010 * t15140 - 10140 * t15142 + 2915 * t15144 + 27 + (5138 + 3036 * t15141) * t15141 + (-12740 * t15142 + 8855 * t15144 + 15) * t15137) + + if Bindx == 2433: + t15147 = np.cos(phi) + t15148 = t15147 ** 2 + t15150 = t15148 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (30 * t15148 - 1265 * t15150 + 3 + (550 * t15148 + 759 * t15150 - 45) * t15147) * ((1 + t15147) ** (0.11e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t15147) ** (0.3e1 / 0.2e1)) + + if Bindx == 2434: + t15162 = np.sin(phi) + t15160 = t15162 ** 2 + t15152 = np.cos(phi) + t15153 = t15152 ** 2 + t15154 = t15152 * t15153 + t15157 = t15154 ** 2 + t15155 = t15153 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.13566e5) * t15160 ** 2 * (47 * t15153 + 609 * t15154 - 539 * t15157 - 1 + (-135 + 1012 * t15155) * t15155 + (-1953 * t15155 + 1771 * t15157 - 43) * t15152) + + if Bindx == 2435: + t15163 = np.cos(phi) + t15164 = t15163 ** 2 + t15166 = t15164 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (270 * t15164 - 1265 * t15166 - 5 + (-220 * t15164 + 1518 * t15166 - 10) * t15163) * ((1 + t15163) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t15163) ** (0.5e1 / 0.2e1)) + + if Bindx == 2436: + t15177 = np.sin(phi) + t15174 = t15177 ** 2 + t15175 = t15177 * t15174 + t15168 = np.cos(phi) + t15169 = t15168 ** 2 + t15171 = t15169 ** 2 + t15170 = t15168 * t15169 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.7106e4) * t15175 ** 2 * (250 * t15169 - 1505 * t15171 - 5 + (-490 + 1932 * t15170) * t15170 + (1127 * t15171 + 35) * t15168) + + if Bindx == 2437: + t15178 = np.cos(phi) + t15179 = t15178 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (5 + (-70 + 161 * t15179) * t15179) * t15178 * ((1 + t15178) ** (0.7e1 / 0.2e1)) * np.sqrt(0.277134e6) * np.exp((-7*1j) * phi1) * ((1 - t15178) ** (0.7e1 / 0.2e1)) + + if Bindx == 2438: + t15190 = np.sin(phi) + t15187 = t15190 ** 2 + t15188 = t15190 * t15187 + t15181 = np.cos(phi) + t15182 = t15181 ** 2 + t15184 = t15182 ** 2 + t15183 = t15181 * t15182 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.7106e4) * t15188 ** 2 * (250 * t15182 - 1505 * t15184 - 5 + (490 + 1932 * t15183) * t15183 + (-1127 * t15184 - 35) * t15181) + + if Bindx == 2439: + t15191 = np.cos(phi) + t15192 = t15191 ** 2 + t15194 = t15192 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-270 * t15192 + 1265 * t15194 + 5 + (-220 * t15192 + 1518 * t15194 - 10) * t15191) * ((1 + t15191) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t15191) ** (0.9e1 / 0.2e1)) + + if Bindx == 2440: + t15206 = np.sin(phi) + t15204 = t15206 ** 2 + t15196 = np.cos(phi) + t15197 = t15196 ** 2 + t15198 = t15196 * t15197 + t15201 = t15198 ** 2 + t15199 = t15197 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.13566e5) * t15204 ** 2 * (47 * t15197 - 609 * t15198 - 539 * t15201 - 1 + (-135 + 1012 * t15199) * t15199 + (1953 * t15199 - 1771 * t15201 + 43) * t15196) + + if Bindx == 2441: + t15207 = np.cos(phi) + t15208 = t15207 ** 2 + t15210 = t15208 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-30 * t15208 + 1265 * t15210 - 3 + (550 * t15208 + 759 * t15210 - 45) * t15207) * ((1 + t15207) ** (0.3e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t15207) ** (0.11e2 / 0.2e1)) + + if Bindx == 2442: + t15213 = np.cos(phi) + t15214 = t15213 ** 2 + t15216 = t15214 ** 2 + t15220 = t15216 ** 2 + t15215 = t15213 * t15214 + t15218 = t15215 ** 2 + t15217 = t15213 * t15216 + t15212 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.399e3) * t15212 ** 2 * (-1080 * t15214 + 500 * t15215 + 6010 * t15216 - 10140 * t15218 + 2915 * t15220 + 27 + (-5138 + 3036 * t15217) * t15217 + (12740 * t15218 - 8855 * t15220 - 15) * t15213) + + if Bindx == 2443: + t15223 = np.cos(phi) + t15224 = t15223 ** 2 + t15226 = t15224 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (2590 * t15224 + 8855 * t15226 - 29 + (7700 * t15224 + 3542 * t15226 + 190) * t15223) * np.sqrt((1 + t15223)) * np.sqrt(0.114e3) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t15223) ** (0.13e2 / 0.2e1)) + + if Bindx == 2444: + t15228 = np.cos(phi) + t15229 = t15228 ** 2 + t15231 = t15229 ** 2 + t15232 = t15228 * t15231 + t15237 = t15232 ** 2 + t15235 = t15231 ** 2 + t15230 = t15228 * t15229 + t15233 = t15230 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-7*1j) * (phi1 - phi2)) * (4669 * t15229 + 38745 * t15230 - 57750 * t15231 - 88746 * t15232 - 245565 * t15235 + 78617 * t15237 - 161 + (199962 + 21252 * t15233) * t15233 + (14994 * t15233 + 125195 * t15235 - 86779 * t15237 - 4433) * t15228) + + if Bindx == 2445: + t15240 = np.cos(phi) + t15241 = t15240 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (3087 * t15240 + 278 + (12397 * t15240 + 9933 + 5313 * t15241) * t15241) * np.sqrt((1 + t15240)) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t15240) ** (0.15e2 / 0.2e1)) + + if Bindx == 2446: + t15245 = np.cos(phi) + t15246 = t15245 ** 2 + t15248 = t15246 ** 2 + t15252 = t15248 ** 2 + t15247 = t15245 * t15246 + t15250 = t15247 ** 2 + t15249 = t15245 * t15248 + t15244 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.21e2) * t15244 ** 2 * (1764 * t15246 - 3336 * t15247 - 2142 * t15248 - 7728 * t15250 + 9801 * t15252 - 147 + (10710 + 1012 * t15249) * t15249 + (-4704 * t15250 - 5313 * t15252 + 83) * t15245) + + if Bindx == 2447: + t15255 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (43 + (161 + 138 * t15255) * t15255) * ((1 + t15255) ** (0.3e1 / 0.2e1)) * np.sqrt(0.154e3) * np.exp((-1*1j) * (7 * phi1 - 10 * phi2)) * ((1 - t15255) ** (0.17e2 / 0.2e1)) + + if Bindx == 2448: + t15257 = np.cos(phi) + t15262 = -1 + t15257 + t15258 = t15262 ** 2 + t15259 = t15258 ** 2 + t15256 = 1 + t15257 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.1771e4) * t15256 ** 2 * t15262 * t15259 ** 2 * (7 + 12 * t15257) + + if Bindx == 2449: + t15263 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 - 12 * phi2)) * np.sqrt(0.10626e5) * ((1 - t15263) ** (0.19e2 / 0.2e1)) * ((1 + t15263) ** (0.5e1 / 0.2e1)) + + if Bindx == 2450: + t15264 = np.cos(phi) + t15272 = -1 + t15264 + t15271 = 1 + t15264 + t15267 = t15271 ** 2 + t15268 = t15267 ** 2 + t15265 = t15272 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-6*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.33649e5) * t15272 * t15265 * t15271 * t15268 ** 2 + + if Bindx == 2451: + t15273 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 11 * phi2)) * np.sqrt(0.201894e6) * ((1 - t15273) ** (0.5e1 / 0.2e1)) * ((1 + t15273) ** (0.17e2 / 0.2e1)) * (2 * t15273 - 1) + + if Bindx == 2452: + t15284 = np.sin(phi) + t15282 = t15284 ** 2 + t15274 = np.cos(phi) + t15275 = t15274 ** 2 + t15276 = t15274 * t15275 + t15279 = t15276 ** 2 + t15277 = t15275 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.4389e4) * t15282 ** 2 * (-40 * t15275 - 107 * t15276 + 212 * t15279 + 5 + (-40 + 23 * t15277) * t15277 + (145 * t15277 + 115 * t15279 + 7) * t15274) + + if Bindx == 2453: + t15285 = np.cos(phi) + t15286 = t15285 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.266e3) * ((1 - t15285) ** (0.3e1 / 0.2e1)) * ((1 + t15285) ** (0.15e2 / 0.2e1)) * (-759 * t15286 - 37 + (506 * t15286 + 330) * t15285) + + if Bindx == 2454: + t15289 = np.cos(phi) + t15290 = t15289 ** 2 + t15292 = t15290 ** 2 + t15296 = t15292 ** 2 + t15291 = t15289 * t15290 + t15294 = t15291 ** 2 + t15293 = t15289 * t15292 + t15288 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.114e3) * t15288 ** 2 * (-513 * t15290 + 2928 * t15291 + 5094 * t15292 - 12714 * t15294 + 7623 * t15296 + 19 + (-3960 + 1771 * t15293) * t15293 + (-4368 * t15294 + 7084 * t15296 - 404) * t15289) + + if Bindx == 2455: + t15299 = np.cos(phi) + t15300 = t15299 ** 2 + t15302 = t15300 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.114e3) * np.sqrt((1 - t15299)) * ((1 + t15299) ** (0.13e2 / 0.2e1)) * (-2590 * t15300 - 8855 * t15302 + 29 + (7700 * t15300 + 3542 * t15302 + 190) * t15299) + + if Bindx == 2456: + t15304 = np.cos(phi) + t15305 = t15304 ** 2 + t15307 = t15305 ** 2 + t15308 = t15304 * t15307 + t15313 = t15308 ** 2 + t15311 = t15307 ** 2 + t15306 = t15304 * t15305 + t15309 = t15306 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 + phi2)) * (11298 * t15305 + 2695 * t15306 - 75285 * t15307 - 49266 * t15308 - 158175 * t15311 + 8778 * t15313 - 269 + (180516 + 33649 * t15309) * t15309 + (178182 * t15309 - 232085 * t15311 + 100947 * t15313 + 39) * t15304) + + if Bindx == 2457: + t15316 = np.cos(phi) + t15317 = t15316 ** 2 + t15318 = t15316 * t15317 + t15321 = t15318 ** 2 + t15319 = t15317 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.14e2) * ((1 + t15316) ** (0.11e2 / 0.2e1)) * (1653 * t15317 + 3610 * t15318 - 24605 * t15319 - 33649 * t15321 + 25 + (43890 * t15319 + 9614 * t15321 - 538) * t15316) * ((1 - t15316) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2458: + t15324 = np.cos(phi) + t15325 = t15324 ** 2 + t15327 = t15325 ** 2 + t15331 = t15327 ** 2 + t15326 = t15324 * t15325 + t15329 = t15326 ** 2 + t15328 = t15324 * t15327 + t15323 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.119e3) * t15323 ** 2 * (-357 * t15325 - 2328 * t15326 + 2078 * t15327 - 1634 * t15329 - 4389 * t15331 + 7 + (10260 + 4807 * t15328) * t15328 + (-17176 * t15329 + 9614 * t15331 + 142) * t15324) + + if Bindx == 2459: + t15334 = np.cos(phi) + t15335 = t15334 ** 2 + t15337 = t15335 ** 2 + t15336 = t15334 * t15335 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-1140 * t15335 + 3135 * t15337 + 23 + (3230 + 9614 * t15336) * t15336 + (-14421 * t15337 - 57) * t15334) * ((1 + t15334) ** (0.9e1 / 0.2e1)) * np.sqrt(0.119e3) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t15334) ** (0.3e1 / 0.2e1)) + + if Bindx == 2460: + t15350 = np.sin(phi) + t15348 = t15350 ** 2 + t15340 = np.cos(phi) + t15341 = t15340 ** 2 + t15342 = t15340 * t15341 + t15345 = t15342 ** 2 + t15343 = t15341 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.714e3) * t15348 ** 2 * (-280 * t15341 + 1425 * t15342 - 5852 * t15345 + 5 + (2280 + 4807 * t15343) * t15343 + (-5187 * t15343 + 4807 * t15345 - 85) * t15340) + + if Bindx == 2461: + t15351 = np.cos(phi) + t15352 = t15351 ** 2 + t15354 = t15352 ** 2 + t15353 = t15351 * t15352 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (380 * t15352 - 3325 * t15354 - 5 + (1330 + 6118 * t15353) * t15353 + (-3059 * t15354 - 95) * t15351) * ((1 + t15351) ** (0.7e1 / 0.2e1)) * np.sqrt(0.561e3) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t15351) ** (0.5e1 / 0.2e1)) + + if Bindx == 2462: + t15364 = np.sin(phi) + t15361 = t15364 ** 2 + t15362 = t15364 * t15361 + t15357 = np.cos(phi) + t15358 = t15357 ** 2 + t15359 = t15358 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-6*1j) * phi1) * np.sqrt(0.2431e4) * t15362 ** 2 * (-1995 * t15359 - 5 + (3059 * t15359 + 285) * t15358) + + if Bindx == 2463: + t15365 = np.cos(phi) + t15366 = t15365 ** 2 + t15368 = t15366 ** 2 + t15367 = t15365 * t15366 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (380 * t15366 - 3325 * t15368 - 5 + (-1330 + 6118 * t15367) * t15367 + (3059 * t15368 + 95) * t15365) * ((1 + t15365) ** (0.5e1 / 0.2e1)) * np.sqrt(0.561e3) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t15365) ** (0.7e1 / 0.2e1)) + + if Bindx == 2464: + t15381 = np.sin(phi) + t15379 = t15381 ** 2 + t15371 = np.cos(phi) + t15372 = t15371 ** 2 + t15373 = t15371 * t15372 + t15376 = t15373 ** 2 + t15374 = t15372 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.714e3) * t15379 ** 2 * (-280 * t15372 - 1425 * t15373 - 5852 * t15376 + 5 + (2280 + 4807 * t15374) * t15374 + (5187 * t15374 - 4807 * t15376 + 85) * t15371) + + if Bindx == 2465: + t15382 = np.cos(phi) + t15383 = t15382 ** 2 + t15385 = t15383 ** 2 + t15384 = t15382 * t15383 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-1140 * t15383 + 3135 * t15385 + 23 + (-3230 + 9614 * t15384) * t15384 + (14421 * t15385 + 57) * t15382) * ((1 + t15382) ** (0.3e1 / 0.2e1)) * np.sqrt(0.119e3) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t15382) ** (0.9e1 / 0.2e1)) + + if Bindx == 2466: + t15389 = np.cos(phi) + t15390 = t15389 ** 2 + t15392 = t15390 ** 2 + t15396 = t15392 ** 2 + t15391 = t15389 * t15390 + t15394 = t15391 ** 2 + t15393 = t15389 * t15392 + t15388 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.119e3) * t15388 ** 2 * (-357 * t15390 + 2328 * t15391 + 2078 * t15392 - 1634 * t15394 - 4389 * t15396 + 7 + (-10260 + 4807 * t15393) * t15393 + (17176 * t15394 - 9614 * t15396 - 142) * t15389) + + if Bindx == 2467: + t15399 = np.cos(phi) + t15400 = t15399 ** 2 + t15402 = t15400 ** 2 + t15401 = t15399 * t15400 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * (-1140 * t15400 + 19855 * t15402 - 25 + (4750 + 9614 * t15401) * t15401 + (24035 * t15402 - 513) * t15399) * np.sqrt((1 + t15399)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t15399) ** (0.11e2 / 0.2e1)) + + if Bindx == 2468: + t15405 = np.cos(phi) + t15406 = t15405 ** 2 + t15408 = t15406 ** 2 + t15409 = t15405 * t15408 + t15414 = t15409 ** 2 + t15412 = t15408 ** 2 + t15407 = t15405 * t15406 + t15410 = t15407 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 - phi2)) * (11298 * t15406 - 2695 * t15407 - 75285 * t15408 + 49266 * t15409 - 158175 * t15412 + 8778 * t15414 - 269 + (180516 + 33649 * t15410) * t15410 + (-178182 * t15410 + 232085 * t15412 - 100947 * t15414 - 39) * t15405) + + if Bindx == 2469: + t15417 = np.cos(phi) + t15418 = t15417 ** 2 + t15420 = t15418 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (2590 * t15418 + 8855 * t15420 - 29 + (7700 * t15418 + 3542 * t15420 + 190) * t15417) * np.sqrt((1 + t15417)) * np.sqrt(0.114e3) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t15417) ** (0.13e2 / 0.2e1)) + + if Bindx == 2470: + t15423 = np.cos(phi) + t15424 = t15423 ** 2 + t15426 = t15424 ** 2 + t15430 = t15426 ** 2 + t15425 = t15423 * t15424 + t15428 = t15425 ** 2 + t15427 = t15423 * t15426 + t15422 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.114e3) * t15422 ** 2 * (-513 * t15424 - 2928 * t15425 + 5094 * t15426 - 12714 * t15428 + 7623 * t15430 + 19 + (3960 + 1771 * t15427) * t15427 + (4368 * t15428 - 7084 * t15430 + 404) * t15423) + + if Bindx == 2471: + t15433 = np.cos(phi) + t15434 = t15433 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (759 * t15434 + 37 + (506 * t15434 + 330) * t15433) * ((1 + t15433) ** (0.3e1 / 0.2e1)) * np.sqrt(0.266e3) * np.exp((-3*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t15433) ** (0.15e2 / 0.2e1)) + + if Bindx == 2472: + t15446 = np.sin(phi) + t15444 = t15446 ** 2 + t15436 = np.cos(phi) + t15437 = t15436 ** 2 + t15438 = t15436 * t15437 + t15441 = t15438 ** 2 + t15439 = t15437 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.4389e4) * t15444 ** 2 * (-40 * t15437 + 107 * t15438 + 212 * t15441 + 5 + (-40 + 23 * t15439) * t15439 + (-145 * t15439 - 115 * t15441 - 7) * t15436) + + if Bindx == 2473: + t15447 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (1 + 2 * t15447) * ((1 + t15447) ** (0.5e1 / 0.2e1)) * np.sqrt(0.201894e6) * np.exp((-1*1j) * (6 * phi1 - 11 * phi2)) * ((1 - t15447) ** (0.17e2 / 0.2e1)) + + if Bindx == 2474: + t15448 = np.cos(phi) + t15456 = -1 + t15448 + t15455 = 1 + t15448 + t15453 = t15455 ** 2 + t15449 = t15456 ** 2 + t15450 = t15449 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-6*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.33649e5) * t15456 * t15450 ** 2 * t15455 * t15453 + + if Bindx == 2475: + t15457 = np.cos(phi) + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (5 * phi1 + 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t15457) ** (0.7e1 / 0.2e1)) * ((1 + t15457) ** (0.17e2 / 0.2e1)) + + if Bindx == 2476: + t15458 = np.cos(phi) + t15465 = -1 + t15458 + t15464 = 1 + t15458 + t15461 = t15464 ** 2 + t15462 = t15461 ** 2 + t15459 = t15465 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.14421e5) * t15465 * t15459 * t15462 ** 2 * (-5 + 12 * t15458) + + if Bindx == 2477: + t15466 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-5*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.1254e4) * ((1 - t15466) ** (0.5e1 / 0.2e1)) * ((1 + t15466) ** (0.15e2 / 0.2e1)) * (19 + (-115 + 138 * t15466) * t15466) + + if Bindx == 2478: + t15477 = np.sin(phi) + t15475 = t15477 ** 2 + t15467 = np.cos(phi) + t15468 = t15467 ** 2 + t15469 = t15467 * t15468 + t15472 = t15469 ** 2 + t15470 = t15468 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.19e2) * t15475 ** 2 * (575 * t15468 - 1383 * t15469 + 4213 * t15472 - 25 + (-3535 + 1012 * t15470) * t15470 + (-465 * t15470 + 3795 * t15472 + 293) * t15467) + + if Bindx == 2479: + t15478 = np.cos(phi) + t15479 = t15478 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.399e3) * ((1 - t15478) ** (0.3e1 / 0.2e1)) * ((1 + t15478) ** (0.13e2 / 0.2e1)) * (-75 * t15478 - 6 + (-1265 * t15478 + 627 + 759 * t15479) * t15479) + + if Bindx == 2480: + t15483 = np.cos(phi) + t15484 = t15483 ** 2 + t15486 = t15484 ** 2 + t15490 = t15486 ** 2 + t15485 = t15483 * t15484 + t15488 = t15485 ** 2 + t15487 = t15483 * t15486 + t15482 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.399e3) * t15482 ** 2 * (-1080 * t15484 - 500 * t15485 + 6010 * t15486 - 10140 * t15488 + 2915 * t15490 + 27 + (5138 + 3036 * t15487) * t15487 + (-12740 * t15488 + 8855 * t15490 + 15) * t15483) + + if Bindx == 2481: + t15493 = np.cos(phi) + t15494 = t15493 ** 2 + t15496 = t15494 ** 2 + t15495 = t15493 * t15494 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t15493)) * ((1 + t15493) ** (0.11e2 / 0.2e1)) * (-1140 * t15494 + 19855 * t15496 - 25 + (-4750 + 9614 * t15495) * t15495 + (-24035 * t15496 + 513) * t15493) + + if Bindx == 2482: + t15499 = np.cos(phi) + t15500 = t15499 ** 2 + t15502 = t15500 ** 2 + t15503 = t15499 * t15502 + t15508 = t15503 ** 2 + t15506 = t15502 ** 2 + t15501 = t15499 * t15500 + t15504 = t15501 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-5*1j) * (phi1 + phi2)) * (13939 * t15500 + 79585 * t15501 - 98650 * t15502 - 419402 * t15503 + 25365 * t15506 - 292809 * t15508 - 263 + (180390 + 173052 * t15504) * t15504 + (949506 * t15504 - 964725 * t15506 + 360525 * t15508 - 4465) * t15499) + + if Bindx == 2483: + t15511 = np.cos(phi) + t15512 = t15511 ** 2 + t15513 = t15511 * t15512 + t15516 = t15513 ** 2 + t15514 = t15512 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.34e2) * ((1 + t15511) ** (0.9e1 / 0.2e1)) * (-3150 * t15512 + 21546 * t15513 + 166782 * t15516 + 85 + (-23940 + 43263 * t15514) * t15514 + (-59850 * t15514 - 144210 * t15516 - 526) * t15511) * ((1 - t15511) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2484: + t15520 = np.cos(phi) + t15521 = t15520 ** 2 + t15523 = t15521 ** 2 + t15527 = t15523 ** 2 + t15522 = t15520 * t15521 + t15525 = t15522 ** 2 + t15524 = t15520 * t15523 + t15519 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.34e2) * t15519 ** 2 * (540 * t15521 - 5560 * t15522 - 5530 * t15523 + 21280 * t15525 - 34485 * t15527 - 9 + (26334 + 19228 * t15524) * t15524 + (-44080 * t15525 + 24035 * t15527 + 295) * t15520) + + if Bindx == 2485: + t15530 = np.cos(phi) + t15531 = t15530 ** 2 + t15532 = t15530 * t15531 + t15535 = t15532 ** 2 + t15533 = t15531 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-1995 * t15531 - 1330 * t15532 + 17955 * t15533 - 33649 * t15535 + 25 + (-8778 * t15533 + 28842 * t15535 + 210) * t15530) * ((1 + t15530) ** (0.7e1 / 0.2e1)) * np.sqrt(0.51e2) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t15530) ** (0.3e1 / 0.2e1)) + + if Bindx == 2486: + t15547 = np.sin(phi) + t15545 = t15547 ** 2 + t15537 = np.cos(phi) + t15538 = t15537 ** 2 + t15539 = t15537 * t15538 + t15542 = t15539 ** 2 + t15540 = t15538 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.7854e4) * t15545 ** 2 * (-315 * t15538 + 475 * t15539 - 7049 * t15542 + 5 + (2755 + 5244 * t15540) * t15540 + (-1995 * t15540 + 2185 * t15542 - 25) * t15537) + + if Bindx == 2487: + t15548 = np.cos(phi) + t15549 = t15548 ** 2 + t15550 = t15549 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * (-399 * t15550 - 5 + (437 * t15550 + 95) * t15549) * t15548 * ((1 + t15548) ** (0.5e1 / 0.2e1)) * np.sqrt(0.34034e5) * np.exp((-5*1j) * phi1) * ((1 - t15548) ** (0.5e1 / 0.2e1)) + + if Bindx == 2488: + t15562 = np.sin(phi) + t15560 = t15562 ** 2 + t15552 = np.cos(phi) + t15553 = t15552 ** 2 + t15554 = t15552 * t15553 + t15557 = t15554 ** 2 + t15555 = t15553 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.7854e4) * t15560 ** 2 * (-315 * t15553 - 475 * t15554 - 7049 * t15557 + 5 + (2755 + 5244 * t15555) * t15555 + (1995 * t15555 - 2185 * t15557 + 25) * t15552) + + if Bindx == 2489: + t15563 = np.cos(phi) + t15564 = t15563 ** 2 + t15565 = t15563 * t15564 + t15568 = t15565 ** 2 + t15566 = t15564 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (1995 * t15564 - 1330 * t15565 - 17955 * t15566 + 33649 * t15568 - 25 + (-8778 * t15566 + 28842 * t15568 + 210) * t15563) * ((1 + t15563) ** (0.3e1 / 0.2e1)) * np.sqrt(0.51e2) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t15563) ** (0.7e1 / 0.2e1)) + + if Bindx == 2490: + t15571 = np.cos(phi) + t15572 = t15571 ** 2 + t15574 = t15572 ** 2 + t15578 = t15574 ** 2 + t15573 = t15571 * t15572 + t15576 = t15573 ** 2 + t15575 = t15571 * t15574 + t15570 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.34e2) * t15570 ** 2 * (540 * t15572 + 5560 * t15573 - 5530 * t15574 + 21280 * t15576 - 34485 * t15578 - 9 + (-26334 + 19228 * t15575) * t15575 + (44080 * t15576 - 24035 * t15578 - 295) * t15571) + + if Bindx == 2491: + t15581 = np.cos(phi) + t15582 = t15581 ** 2 + t15583 = t15581 * t15582 + t15586 = t15583 ** 2 + t15584 = t15582 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (-3591 * t15582 - 17955 * t15583 - 5985 * t15584 + 100947 * t15586 + 85 + (65835 * t15584 + 43263 * t15586 + 441) * t15581) * np.sqrt((1 + t15581)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t15581) ** (0.9e1 / 0.2e1)) + + if Bindx == 2492: + t15588 = np.cos(phi) + t15589 = t15588 ** 2 + t15591 = t15589 ** 2 + t15592 = t15588 * t15591 + t15597 = t15592 ** 2 + t15595 = t15591 ** 2 + t15590 = t15588 * t15589 + t15593 = t15590 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-5*1j) * (phi1 - phi2)) * (13939 * t15589 - 79585 * t15590 - 98650 * t15591 + 419402 * t15592 + 25365 * t15595 - 292809 * t15597 - 263 + (180390 + 173052 * t15593) * t15593 + (-949506 * t15593 + 964725 * t15595 - 360525 * t15597 + 4465) * t15588) + + if Bindx == 2493: + t15600 = np.cos(phi) + t15601 = t15600 ** 2 + t15603 = t15601 ** 2 + t15602 = t15600 * t15601 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * (-1140 * t15601 + 19855 * t15603 - 25 + (4750 + 9614 * t15602) * t15602 + (24035 * t15603 - 513) * t15600) * np.sqrt((1 + t15600)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t15600) ** (0.11e2 / 0.2e1)) + + if Bindx == 2494: + t15607 = np.cos(phi) + t15608 = t15607 ** 2 + t15610 = t15608 ** 2 + t15614 = t15610 ** 2 + t15609 = t15607 * t15608 + t15612 = t15609 ** 2 + t15611 = t15607 * t15610 + t15606 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.399e3) * t15606 ** 2 * (-1080 * t15608 + 500 * t15609 + 6010 * t15610 - 10140 * t15612 + 2915 * t15614 + 27 + (-5138 + 3036 * t15611) * t15611 + (12740 * t15612 - 8855 * t15614 - 15) * t15607) + + if Bindx == 2495: + t15617 = np.cos(phi) + t15618 = t15617 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (75 * t15617 - 6 + (1265 * t15617 + 627 + 759 * t15618) * t15618) * ((1 + t15617) ** (0.3e1 / 0.2e1)) * np.sqrt(0.399e3) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * ((1 - t15617) ** (0.13e2 / 0.2e1)) + + if Bindx == 2496: + t15631 = np.sin(phi) + t15629 = t15631 ** 2 + t15621 = np.cos(phi) + t15622 = t15621 ** 2 + t15623 = t15621 * t15622 + t15626 = t15623 ** 2 + t15624 = t15622 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.19e2) * t15629 ** 2 * (575 * t15622 + 1383 * t15623 + 4213 * t15626 - 25 + (-3535 + 1012 * t15624) * t15624 + (465 * t15624 - 3795 * t15626 - 293) * t15621) + + if Bindx == 2497: + t15632 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (19 + (115 + 138 * t15632) * t15632) * ((1 + t15632) ** (0.5e1 / 0.2e1)) * np.sqrt(0.1254e4) * np.exp((-5*1j) * (phi1 - 2 * phi2)) * ((1 - t15632) ** (0.15e2 / 0.2e1)) + + if Bindx == 2498: + t15642 = np.sin(phi) + t15639 = t15642 ** 2 + t15640 = t15642 * t15639 + t15633 = np.cos(phi) + t15634 = t15633 ** 2 + t15636 = t15634 ** 2 + t15635 = t15633 * t15634 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.14421e5) * t15640 ** 2 * (10 * t15634 + 95 * t15636 - 5 + (-70 + 12 * t15635) * t15635 + (-55 * t15636 + 13) * t15633) + + if Bindx == 2499: + t15643 = np.cos(phi) + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (5 * phi1 - 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t15643) ** (0.17e2 / 0.2e1)) * ((1 + t15643) ** (0.7e1 / 0.2e1)) + + if Bindx == 2500: + t15644 = np.cos(phi) + t15651 = -1 + t15644 + t15650 = 1 + t15644 + t15647 = t15650 ** 2 + t15648 = t15647 ** 2 + t15645 = t15651 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.81719e5) * t15645 ** 2 * t15648 ** 2 + + if Bindx == 2501: + t15652 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (4 * phi1 + 11 * phi2)) * np.sqrt(0.490314e6) * ((1 - t15652) ** (0.7e1 / 0.2e1)) * ((1 + t15652) ** (0.15e2 / 0.2e1)) * (-1 + 3 * t15652) + + if Bindx == 2502: + t15662 = np.sin(phi) + t15659 = t15662 ** 2 + t15660 = t15662 * t15659 + t15653 = np.cos(phi) + t15654 = t15653 ** 2 + t15656 = t15654 ** 2 + t15655 = t15653 * t15654 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.10659e5) * t15660 ** 2 * (-85 * t15654 + 235 * t15656 + 5 + (20 + 69 * t15655) * t15655 + (230 * t15656 - 26) * t15653) + + if Bindx == 2503: + t15663 = np.cos(phi) + t15664 = t15663 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((-1*1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.646e3) * ((1 - t15663) ** (0.5e1 / 0.2e1)) * ((1 + t15663) ** (0.13e2 / 0.2e1)) * (-253 * t15664 + 1 + (253 * t15664 + 55) * t15663) + + if Bindx == 2504: + t15676 = np.sin(phi) + t15674 = t15676 ** 2 + t15666 = np.cos(phi) + t15667 = t15666 ** 2 + t15668 = t15666 * t15667 + t15671 = t15668 ** 2 + t15669 = t15667 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.13566e5) * t15674 ** 2 * (324 * t15667 + 344 * t15668 + 836 * t15671 - 9 + (-1270 + 759 * t15669) * t15669 + (-1704 * t15669 + 2024 * t15671 - 24) * t15666) + + if Bindx == 2505: + t15677 = np.cos(phi) + t15678 = t15677 ** 2 + t15680 = t15678 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.13566e5) * ((1 - t15677) ** (0.3e1 / 0.2e1)) * ((1 + t15677) ** (0.11e2 / 0.2e1)) * (30 * t15678 - 1265 * t15680 + 3 + (550 * t15678 + 759 * t15680 - 45) * t15677) + + if Bindx == 2506: + t15683 = np.cos(phi) + t15684 = t15683 ** 2 + t15686 = t15684 ** 2 + t15690 = t15686 ** 2 + t15685 = t15683 * t15684 + t15688 = t15685 ** 2 + t15687 = t15683 * t15686 + t15682 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.119e3) * t15682 ** 2 * (-357 * t15684 - 2328 * t15685 + 2078 * t15686 - 1634 * t15688 - 4389 * t15690 + 7 + (10260 + 4807 * t15687) * t15687 + (-17176 * t15688 + 9614 * t15690 + 142) * t15683) + + if Bindx == 2507: + t15693 = np.cos(phi) + t15694 = t15693 ** 2 + t15695 = t15693 * t15694 + t15698 = t15695 ** 2 + t15696 = t15694 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.34e2) * np.sqrt((1 - t15693)) * ((1 + t15693) ** (0.9e1 / 0.2e1)) * (3591 * t15694 - 17955 * t15695 + 5985 * t15696 - 100947 * t15698 - 85 + (65835 * t15696 + 43263 * t15698 + 441) * t15693) + + if Bindx == 2508: + t15700 = np.cos(phi) + t15701 = t15700 ** 2 + t15703 = t15701 ** 2 + t15704 = t15700 * t15703 + t15709 = t15704 ** 2 + t15707 = t15703 ** 2 + t15702 = t15700 * t15701 + t15705 = t15702 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 + phi2)) * (-14818 * t15701 + 214340 * t15702 + 174625 * t15703 - 1193944 * t15704 + 1884705 * t15707 - 1939938 * t15709 + 239 + (-838236 + 735471 * t15705) * t15705 + (2705448 * t15705 - 2693820 * t15707 + 980628 * t15709 - 10604) * t15700) + + if Bindx == 2509: + t15712 = np.cos(phi) + t15713 = t15712 ** 2 + t15715 = t15713 ** 2 + t15719 = t15715 ** 2 + t15714 = t15712 * t15713 + t15717 = t15714 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * ((1 + t15712) ** (0.7e1 / 0.2e1)) * (-5780 * t15713 + 9996 * t15714 + 40698 * t15715 + 27132 * t15717 - 245157 * t15719 + 67 + (-122094 * t15715 + 213180 * t15717 + 81719 * t15719 + 239) * t15712) * ((1 - t15712) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2510: + t15722 = np.cos(phi) + t15723 = t15722 ** 2 + t15725 = t15723 ** 2 + t15729 = t15725 ** 2 + t15724 = t15722 * t15723 + t15727 = t15724 ** 2 + t15726 = t15722 * t15725 + t15721 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.6e1) * t15721 ** 2 * (10385 * t15723 - 29240 * t15724 - 105910 * t15725 + 366282 * t15727 - 508079 * t15729 - 155 + (153748 + 245157 * t15726) * t15726 + (-281656 * t15727 + 163438 * t15729 + 1390) * t15722) + + if Bindx == 2511: + t15732 = np.cos(phi) + t15733 = t15732 ** 2 + t15734 = t15732 * t15733 + t15737 = t15734 ** 2 + t15735 = t15733 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-510 * t15733 - 3230 * t15734 - 22610 * t15737 + 5 + (6460 + 22287 * t15735) * t15735 + (13566 * t15735 - 14858 * t15737 + 170) * t15732) * ((1 + t15732) ** (0.5e1 / 0.2e1)) * np.sqrt(0.231e3) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t15732) ** (0.3e1 / 0.2e1)) + + if Bindx == 2512: + t15747 = np.sin(phi) + t15745 = t15747 ** 2 + t15740 = np.cos(phi) + t15741 = t15740 ** 2 + t15742 = t15741 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-4*1j) * phi1) * np.sqrt(0.1001e4) * t15745 ** 2 * (-340 * t15741 + 5 + (-9044 * t15741 + 3230 + 7429 * t15742) * t15742) + + if Bindx == 2513: + t15748 = np.cos(phi) + t15749 = t15748 ** 2 + t15750 = t15748 * t15749 + t15753 = t15750 ** 2 + t15751 = t15749 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-510 * t15749 + 3230 * t15750 - 22610 * t15753 + 5 + (6460 + 22287 * t15751) * t15751 + (-13566 * t15751 + 14858 * t15753 - 170) * t15748) * ((1 + t15748) ** (0.3e1 / 0.2e1)) * np.sqrt(0.231e3) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t15748) ** (0.5e1 / 0.2e1)) + + if Bindx == 2514: + t15757 = np.cos(phi) + t15758 = t15757 ** 2 + t15760 = t15758 ** 2 + t15764 = t15760 ** 2 + t15759 = t15757 * t15758 + t15762 = t15759 ** 2 + t15761 = t15757 * t15760 + t15756 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.6e1) * t15756 ** 2 * (10385 * t15758 + 29240 * t15759 - 105910 * t15760 + 366282 * t15762 - 508079 * t15764 - 155 + (-153748 + 245157 * t15761) * t15761 + (281656 * t15762 - 163438 * t15764 - 1390) * t15757) + + if Bindx == 2515: + t15767 = np.cos(phi) + t15768 = t15767 ** 2 + t15769 = t15767 * t15768 + t15772 = t15769 ** 2 + t15770 = t15768 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * (5474 * t15768 + 4522 * t15769 + 49742 * t15772 - 67 + (-45220 + 81719 * t15770) * t15770 + (-76874 * t15770 + 163438 * t15772 + 306) * t15767) * np.sqrt((1 + t15767)) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t15767) ** (0.7e1 / 0.2e1)) + + if Bindx == 2516: + t15775 = np.cos(phi) + t15776 = t15775 ** 2 + t15778 = t15776 ** 2 + t15779 = t15775 * t15778 + t15784 = t15779 ** 2 + t15782 = t15778 ** 2 + t15777 = t15775 * t15776 + t15780 = t15777 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 - phi2)) * (-14818 * t15776 - 214340 * t15777 + 174625 * t15778 + 1193944 * t15779 + 1884705 * t15782 - 1939938 * t15784 + 239 + (-838236 + 735471 * t15780) * t15780 + (-2705448 * t15780 + 2693820 * t15782 - 980628 * t15784 + 10604) * t15775) + + if Bindx == 2517: + t15787 = np.cos(phi) + t15788 = t15787 ** 2 + t15789 = t15787 * t15788 + t15792 = t15789 ** 2 + t15790 = t15788 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (-3591 * t15788 - 17955 * t15789 - 5985 * t15790 + 100947 * t15792 + 85 + (65835 * t15790 + 43263 * t15792 + 441) * t15787) * np.sqrt((1 + t15787)) * np.sqrt(0.34e2) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t15787) ** (0.9e1 / 0.2e1)) + + if Bindx == 2518: + t15795 = np.cos(phi) + t15796 = t15795 ** 2 + t15798 = t15796 ** 2 + t15802 = t15798 ** 2 + t15797 = t15795 * t15796 + t15800 = t15797 ** 2 + t15799 = t15795 * t15798 + t15794 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.119e3) * t15794 ** 2 * (-357 * t15796 + 2328 * t15797 + 2078 * t15798 - 1634 * t15800 - 4389 * t15802 + 7 + (-10260 + 4807 * t15799) * t15799 + (17176 * t15800 - 9614 * t15802 - 142) * t15795) + + if Bindx == 2519: + t15805 = np.cos(phi) + t15806 = t15805 ** 2 + t15808 = t15806 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-30 * t15806 + 1265 * t15808 - 3 + (550 * t15806 + 759 * t15808 - 45) * t15805) * ((1 + t15805) ** (0.3e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t15805) ** (0.11e2 / 0.2e1)) + + if Bindx == 2520: + t15820 = np.sin(phi) + t15818 = t15820 ** 2 + t15810 = np.cos(phi) + t15811 = t15810 ** 2 + t15812 = t15810 * t15811 + t15815 = t15812 ** 2 + t15813 = t15811 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.13566e5) * t15818 ** 2 * (324 * t15811 - 344 * t15812 + 836 * t15815 - 9 + (-1270 + 759 * t15813) * t15813 + (1704 * t15813 - 2024 * t15815 + 24) * t15810) + + if Bindx == 2521: + t15821 = np.cos(phi) + t15824 = 253 * t15821 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * (t15824 - 1 + (t15824 + 55) * t15821) * ((1 + t15821) ** (0.5e1 / 0.2e1)) * np.sqrt(0.646e3) * np.exp((-1*1j) * (4 * phi1 - 9 * phi2)) * ((1 - t15821) ** (0.13e2 / 0.2e1)) + + if Bindx == 2522: + t15834 = np.sin(phi) + t15831 = t15834 ** 2 + t15832 = t15834 * t15831 + t15825 = np.cos(phi) + t15826 = t15825 ** 2 + t15828 = t15826 ** 2 + t15827 = t15825 * t15826 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.10659e5) * t15832 ** 2 * (-85 * t15826 + 235 * t15828 + 5 + (-20 + 69 * t15827) * t15827 + (-230 * t15828 + 26) * t15825) + + if Bindx == 2523: + t15835 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (1 + 3 * t15835) * ((1 + t15835) ** (0.7e1 / 0.2e1)) * np.sqrt(0.490314e6) * np.exp((-1*1j) * (4 * phi1 - 11 * phi2)) * ((1 - t15835) ** (0.15e2 / 0.2e1)) + + if Bindx == 2524: + t15836 = np.cos(phi) + t15843 = -1 + t15836 + t15842 = 1 + t15836 + t15840 = t15842 ** 2 + t15837 = t15843 ** 2 + t15838 = t15837 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.81719e5) * t15838 ** 2 * t15840 ** 2 + + if Bindx == 2525: + t15844 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-3*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.81719e5) * ((1 - t15844) ** (0.9e1 / 0.2e1)) * ((1 + t15844) ** (0.15e2 / 0.2e1)) + + if Bindx == 2526: + t15852 = np.sin(phi) + t15849 = t15852 ** 2 + t15850 = t15849 ** 2 + t15845 = np.cos(phi) + t15846 = t15845 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.490314e6) * t15850 ** 2 * (t15845 - 1 + (11 * t15845 + 9 + 4 * t15846) * t15846) + + if Bindx == 2527: + t15853 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 10 * phi2)) * np.sqrt(0.10659e5) * ((1 - t15853) ** (0.7e1 / 0.2e1)) * ((1 + t15853) ** (0.13e2 / 0.2e1)) * (1 + (-23 + 46 * t15853) * t15853) + + if Bindx == 2528: + t15863 = np.sin(phi) + t15860 = t15863 ** 2 + t15861 = t15863 * t15860 + t15854 = np.cos(phi) + t15855 = t15854 ** 2 + t15857 = t15855 ** 2 + t15856 = t15854 * t15855 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.646e3) * t15861 ** 2 * (-510 * t15855 + 825 * t15857 + 17 + (-1050 + 1012 * t15856) * t15856 + (2277 * t15857 + 117) * t15854) + + if Bindx == 2529: + t15864 = np.cos(phi) + t15865 = t15864 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.13566e5) * ((1 - t15864) ** (0.5e1 / 0.2e1)) * ((1 + t15864) ** (0.11e2 / 0.2e1)) * (17 * t15864 - 2 + (-253 * t15864 + 33 + 253 * t15865) * t15865) + + if Bindx == 2530: + t15878 = np.sin(phi) + t15876 = t15878 ** 2 + t15868 = np.cos(phi) + t15869 = t15868 ** 2 + t15870 = t15868 * t15869 + t15873 = t15870 ** 2 + t15871 = t15869 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.13566e5) * t15876 ** 2 * (47 * t15869 + 609 * t15870 - 539 * t15873 - 1 + (-135 + 1012 * t15871) * t15871 + (-1953 * t15871 + 1771 * t15873 - 43) * t15868) + + if Bindx == 2531: + t15879 = np.cos(phi) + t15880 = t15879 ** 2 + t15882 = t15880 ** 2 + t15881 = t15879 * t15880 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.119e3) * ((1 - t15879) ** (0.3e1 / 0.2e1)) * ((1 + t15879) ** (0.9e1 / 0.2e1)) * (-1140 * t15880 + 3135 * t15882 + 23 + (3230 + 9614 * t15881) * t15881 + (-14421 * t15882 - 57) * t15879) + + if Bindx == 2532: + t15886 = np.cos(phi) + t15887 = t15886 ** 2 + t15889 = t15887 ** 2 + t15893 = t15889 ** 2 + t15888 = t15886 * t15887 + t15891 = t15888 ** 2 + t15890 = t15886 * t15889 + t15885 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.34e2) * t15885 ** 2 * (540 * t15887 - 5560 * t15888 - 5530 * t15889 + 21280 * t15891 - 34485 * t15893 - 9 + (26334 + 19228 * t15890) * t15890 + (-44080 * t15891 + 24035 * t15893 + 295) * t15886) + + if Bindx == 2533: + t15896 = np.cos(phi) + t15897 = t15896 ** 2 + t15898 = t15896 * t15897 + t15901 = t15898 ** 2 + t15899 = t15897 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt((1 - t15896)) * ((1 + t15896) ** (0.7e1 / 0.2e1)) * (5474 * t15897 - 4522 * t15898 + 49742 * t15901 - 67 + (-45220 + 81719 * t15899) * t15899 + (76874 * t15899 - 163438 * t15901 - 306) * t15896) + + if Bindx == 2534: + t15904 = np.cos(phi) + t15905 = t15904 ** 2 + t15907 = t15905 ** 2 + t15908 = t15904 * t15907 + t15913 = t15908 ** 2 + t15911 = t15907 ** 2 + t15906 = t15904 * t15905 + t15909 = t15906 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-3*1j) * (phi1 + phi2)) * (-12213 * t15905 + 42105 * t15906 + 137430 * t15907 - 256122 * t15908 + 1090125 * t15911 - 969969 * t15913 + 177 + (-571914 + 326876 * t15909) * t15909 + (622098 * t15909 - 650845 * t15911 + 245157 * t15913 - 1881) * t15904) + + if Bindx == 2535: + t15916 = np.cos(phi) + t15917 = t15916 ** 2 + t15919 = t15917 ** 2 + t15923 = t15919 ** 2 + t15918 = t15916 * t15917 + t15921 = t15918 ** 2 + t15920 = t15916 * t15919 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * ((1 + t15916) ** (0.5e1 / 0.2e1)) * (-6030 * t15917 - 9180 * t15918 + 82110 * t15919 - 271320 * t15921 + 159885 * t15923 + 45 + (-40698 + 163438 * t15920) * t15920 + (329460 * t15921 - 408595 * t15923 + 885) * t15916) * ((1 - t15916) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2536: + t15927 = np.cos(phi) + t15928 = t15927 ** 2 + t15930 = t15928 ** 2 + t15934 = t15930 ** 2 + t15929 = t15927 * t15928 + t15932 = t15929 ** 2 + t15931 = t15927 * t15930 + t15926 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.231e3) * t15926 ** 2 * (1080 * t15928 - 1020 * t15929 - 11730 * t15930 + 42636 * t15932 - 61047 * t15934 - 15 + (5814 + 29716 * t15931) * t15931 + (-11628 * t15932 + 7429 * t15934 + 45) * t15927) + + if Bindx == 2537: + t15937 = np.cos(phi) + t15938 = t15937 ** 2 + t15939 = t15938 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * (-1020 * t15938 + 45 + (-11628 * t15938 + 5814 + 7429 * t15939) * t15939) * t15937 * ((1 + t15937) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((-3*1j) * phi1) * ((1 - t15937) ** (0.3e1 / 0.2e1)) + + if Bindx == 2538: + t15943 = np.cos(phi) + t15944 = t15943 ** 2 + t15946 = t15944 ** 2 + t15950 = t15946 ** 2 + t15945 = t15943 * t15944 + t15948 = t15945 ** 2 + t15947 = t15943 * t15946 + t15942 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.231e3) * t15942 ** 2 * (1080 * t15944 + 1020 * t15945 - 11730 * t15946 + 42636 * t15948 - 61047 * t15950 - 15 + (-5814 + 29716 * t15947) * t15947 + (11628 * t15948 - 7429 * t15950 - 45) * t15943) + + if Bindx == 2539: + t15953 = np.cos(phi) + t15954 = t15953 ** 2 + t15956 = t15954 ** 2 + t15960 = t15956 ** 2 + t15955 = t15953 * t15954 + t15958 = t15955 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-5100 * t15954 + 14280 * t15955 + 67830 * t15956 - 244188 * t15958 + 245157 * t15960 + 45 + (-27132 * t15956 - 85272 * t15958 + 163438 * t15960 - 930) * t15953) * np.sqrt((1 + t15953)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t15953) ** (0.5e1 / 0.2e1)) + + if Bindx == 2540: + t15962 = np.cos(phi) + t15963 = t15962 ** 2 + t15965 = t15963 ** 2 + t15966 = t15962 * t15965 + t15971 = t15966 ** 2 + t15969 = t15965 ** 2 + t15964 = t15962 * t15963 + t15967 = t15964 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-3*1j) * (phi1 - phi2)) * (-12213 * t15963 - 42105 * t15964 + 137430 * t15965 + 256122 * t15966 + 1090125 * t15969 - 969969 * t15971 + 177 + (-571914 + 326876 * t15967) * t15967 + (-622098 * t15967 + 650845 * t15969 - 245157 * t15971 + 1881) * t15962) + + if Bindx == 2541: + t15974 = np.cos(phi) + t15975 = t15974 ** 2 + t15976 = t15974 * t15975 + t15979 = t15976 ** 2 + t15977 = t15975 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * (5474 * t15975 + 4522 * t15976 + 49742 * t15979 - 67 + (-45220 + 81719 * t15977) * t15977 + (-76874 * t15977 + 163438 * t15979 + 306) * t15974) * np.sqrt((1 + t15974)) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t15974) ** (0.7e1 / 0.2e1)) + + if Bindx == 2542: + t15983 = np.cos(phi) + t15984 = t15983 ** 2 + t15986 = t15984 ** 2 + t15990 = t15986 ** 2 + t15985 = t15983 * t15984 + t15988 = t15985 ** 2 + t15987 = t15983 * t15986 + t15982 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.34e2) * t15982 ** 2 * (540 * t15984 + 5560 * t15985 - 5530 * t15986 + 21280 * t15988 - 34485 * t15990 - 9 + (-26334 + 19228 * t15987) * t15987 + (44080 * t15988 - 24035 * t15990 - 295) * t15983) + + if Bindx == 2543: + t15993 = np.cos(phi) + t15994 = t15993 ** 2 + t15996 = t15994 ** 2 + t15995 = t15993 * t15994 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-1140 * t15994 + 3135 * t15996 + 23 + (-3230 + 9614 * t15995) * t15995 + (14421 * t15996 + 57) * t15993) * ((1 + t15993) ** (0.3e1 / 0.2e1)) * np.sqrt(0.119e3) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t15993) ** (0.9e1 / 0.2e1)) + + if Bindx == 2544: + t16009 = np.sin(phi) + t16007 = t16009 ** 2 + t15999 = np.cos(phi) + t16000 = t15999 ** 2 + t16001 = t15999 * t16000 + t16004 = t16001 ** 2 + t16002 = t16000 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.13566e5) * t16007 ** 2 * (47 * t16000 - 609 * t16001 - 539 * t16004 - 1 + (-135 + 1012 * t16002) * t16002 + (1953 * t16002 - 1771 * t16004 + 43) * t15999) + + if Bindx == 2545: + t16010 = np.cos(phi) + t16011 = t16010 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-17 * t16010 - 2 + (253 * t16010 + 33 + 253 * t16011) * t16011) * ((1 + t16010) ** (0.5e1 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * ((1 - t16010) ** (0.11e2 / 0.2e1)) + + if Bindx == 2546: + t16023 = np.sin(phi) + t16020 = t16023 ** 2 + t16021 = t16023 * t16020 + t16014 = np.cos(phi) + t16015 = t16014 ** 2 + t16017 = t16015 ** 2 + t16016 = t16014 * t16015 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.646e3) * t16021 ** 2 * (-510 * t16015 + 825 * t16017 + 17 + (1050 + 1012 * t16016) * t16016 + (-2277 * t16017 - 117) * t16014) + + if Bindx == 2547: + t16024 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (1 + (23 + 46 * t16024) * t16024) * ((1 + t16024) ** (0.7e1 / 0.2e1)) * np.sqrt(0.10659e5) * np.exp((-1*1j) * (3 * phi1 - 10 * phi2)) * ((1 - t16024) ** (0.13e2 / 0.2e1)) + + if Bindx == 2548: + t16032 = np.sin(phi) + t16029 = t16032 ** 2 + t16030 = t16029 ** 2 + t16025 = np.cos(phi) + t16026 = t16025 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.490314e6) * t16030 ** 2 * (-t16025 - 1 + (-11 * t16025 + 9 + 4 * t16026) * t16026) + + if Bindx == 2549: + t16033 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-3*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.81719e5) * ((1 - t16033) ** (0.15e2 / 0.2e1)) * ((1 + t16033) ** (0.9e1 / 0.2e1)) + + if Bindx == 2550: + t16034 = np.cos(phi) + t16043 = -1 + t16034 + t16042 = 1 + t16034 + t16038 = t16042 ** 2 + t16039 = t16042 * t16038 + t16035 = t16043 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.490314e6) * t16043 * t16035 ** 2 * t16042 * t16039 ** 2 + + if Bindx == 2551: + t16044 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 11 * phi2)) * np.sqrt(0.81719e5) * ((1 - t16044) ** (0.9e1 / 0.2e1)) * ((1 + t16044) ** (0.13e2 / 0.2e1)) * (-1 + 6 * t16044) + + if Bindx == 2552: + t16052 = np.sin(phi) + t16049 = t16052 ** 2 + t16050 = t16049 ** 2 + t16045 = np.cos(phi) + t16046 = t16045 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.7106e4) * t16050 ** 2 * (-25 * t16045 - 1 + (115 * t16045 + 22 + 69 * t16046) * t16046) + + if Bindx == 2553: + t16053 = np.cos(phi) + t16054 = t16053 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.969e3) * ((1 - t16053) ** (0.7e1 / 0.2e1)) * ((1 + t16053) ** (0.11e2 / 0.2e1)) * (-253 * t16054 + 9 + (506 * t16054 - 22) * t16053) + + if Bindx == 2554: + t16065 = np.sin(phi) + t16062 = t16065 ** 2 + t16063 = t16065 * t16062 + t16056 = np.cos(phi) + t16057 = t16056 ** 2 + t16059 = t16057 ** 2 + t16058 = t16056 * t16057 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.2261e4) * t16063 ** 2 * (41 * t16057 - 319 * t16059 - 1 + (-584 + 759 * t16058) * t16058 + (1012 * t16059 + 52) * t16056) + + if Bindx == 2555: + t16066 = np.cos(phi) + t16067 = t16066 ** 2 + t16069 = t16067 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.2261e4) * ((1 - t16066) ** (0.5e1 / 0.2e1)) * ((1 + t16066) ** (0.9e1 / 0.2e1)) * (270 * t16067 - 1265 * t16069 - 5 + (-220 * t16067 + 1518 * t16069 - 10) * t16066) + + if Bindx == 2556: + t16081 = np.sin(phi) + t16079 = t16081 ** 2 + t16071 = np.cos(phi) + t16072 = t16071 ** 2 + t16073 = t16071 * t16072 + t16076 = t16073 ** 2 + t16074 = t16072 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.714e3) * t16079 ** 2 * (-280 * t16072 + 1425 * t16073 - 5852 * t16076 + 5 + (2280 + 4807 * t16074) * t16074 + (-5187 * t16074 + 4807 * t16076 - 85) * t16071) + + if Bindx == 2557: + t16082 = np.cos(phi) + t16083 = t16082 ** 2 + t16084 = t16082 * t16083 + t16087 = t16084 ** 2 + t16085 = t16083 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.51e2) * ((1 - t16082) ** (0.3e1 / 0.2e1)) * ((1 + t16082) ** (0.7e1 / 0.2e1)) * (-1995 * t16083 - 1330 * t16084 + 17955 * t16085 - 33649 * t16087 + 25 + (-8778 * t16085 + 28842 * t16087 + 210) * t16082) + + if Bindx == 2558: + t16090 = np.cos(phi) + t16091 = t16090 ** 2 + t16093 = t16091 ** 2 + t16097 = t16093 ** 2 + t16092 = t16090 * t16091 + t16095 = t16092 ** 2 + t16094 = t16090 * t16093 + t16089 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.6e1) * t16089 ** 2 * (10385 * t16091 - 29240 * t16092 - 105910 * t16093 + 366282 * t16095 - 508079 * t16097 - 155 + (153748 + 245157 * t16094) * t16094 + (-281656 * t16095 + 163438 * t16097 + 1390) * t16090) + + if Bindx == 2559: + t16100 = np.cos(phi) + t16101 = t16100 ** 2 + t16103 = t16101 ** 2 + t16107 = t16103 ** 2 + t16102 = t16100 * t16101 + t16105 = t16102 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t16100)) * ((1 + t16100) ** (0.5e1 / 0.2e1)) * (5100 * t16101 + 14280 * t16102 - 67830 * t16103 + 244188 * t16105 - 245157 * t16107 - 45 + (-27132 * t16103 - 85272 * t16105 + 163438 * t16107 - 930) * t16100) + + if Bindx == 2560: + t16109 = np.cos(phi) + t16110 = t16109 ** 2 + t16112 = t16110 ** 2 + t16113 = t16109 * t16112 + t16118 = t16113 ** 2 + t16116 = t16112 ** 2 + t16111 = t16109 * t16110 + t16114 = t16111 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + phi2)) * (-8214 * t16110 + 10875 * t16111 + 97575 * t16112 - 70890 * t16113 + 818805 * t16116 - 731918 * t16118 + 111 + (-421260 + 245157 * t16114) * t16114 + (184110 * t16114 - 205105 * t16116 + 81719 * t16118 - 453) * t16109) + + if Bindx == 2561: + t16121 = np.cos(phi) + t16122 = t16121 ** 2 + t16124 = t16122 ** 2 + t16125 = t16121 * t16124 + t16130 = t16125 ** 2 + t16128 = t16124 ** 2 + t16123 = t16121 * t16122 + t16126 = t16123 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.154e3) * ((1 + t16121) ** (0.3e1 / 0.2e1)) * (-675 * t16122 - 4650 * t16123 + 12750 * t16124 + 21420 * t16125 - 67830 * t16126 + 130815 * t16128 - 81719 * t16130 + 3 + (-19380 * t16126 - 35530 * t16128 + 44574 * t16130 + 222) * t16121) * ((1 - t16121) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2562: + t16133 = np.cos(phi) + t16134 = t16133 ** 2 + t16135 = t16134 ** 2 + t16137 = t16135 ** 2 + t16132 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * phi1) * np.sqrt(0.6006e4) * t16132 ** 2 * (-2550 * t16135 - 14535 * t16137 - 3 + (9690 * t16135 + 7429 * t16137 + 225) * t16134) + + if Bindx == 2563: + t16139 = np.cos(phi) + t16140 = t16139 ** 2 + t16142 = t16140 ** 2 + t16146 = t16142 ** 2 + t16141 = t16139 * t16140 + t16144 = t16141 ** 2 + t16143 = t16139 * t16142 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (450 * t16140 - 5100 * t16141 - 7650 * t16142 + 38760 * t16144 - 72675 * t16146 - 3 + (29070 + 44574 * t16143) * t16143 + (-58140 * t16144 + 37145 * t16146 + 225) * t16139) * np.sqrt((1 + t16139)) * np.sqrt(0.154e3) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t16139) ** (0.3e1 / 0.2e1)) + + if Bindx == 2564: + t16149 = np.cos(phi) + t16150 = t16149 ** 2 + t16152 = t16150 ** 2 + t16153 = t16149 * t16152 + t16158 = t16153 ** 2 + t16156 = t16152 ** 2 + t16151 = t16149 * t16150 + t16154 = t16151 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - phi2)) * (-8214 * t16150 - 10875 * t16151 + 97575 * t16152 + 70890 * t16153 + 818805 * t16156 - 731918 * t16158 + 111 + (-421260 + 245157 * t16154) * t16154 + (-184110 * t16154 + 205105 * t16156 - 81719 * t16158 + 453) * t16149) + + if Bindx == 2565: + t16161 = np.cos(phi) + t16162 = t16161 ** 2 + t16164 = t16162 ** 2 + t16168 = t16164 ** 2 + t16163 = t16161 * t16162 + t16166 = t16163 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-5100 * t16162 + 14280 * t16163 + 67830 * t16164 - 244188 * t16166 + 245157 * t16168 + 45 + (-27132 * t16164 - 85272 * t16166 + 163438 * t16168 - 930) * t16161) * np.sqrt((1 + t16161)) * np.sqrt(0.6e1) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t16161) ** (0.5e1 / 0.2e1)) + + if Bindx == 2566: + t16171 = np.cos(phi) + t16172 = t16171 ** 2 + t16174 = t16172 ** 2 + t16178 = t16174 ** 2 + t16173 = t16171 * t16172 + t16176 = t16173 ** 2 + t16175 = t16171 * t16174 + t16170 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.6e1) * t16170 ** 2 * (10385 * t16172 + 29240 * t16173 - 105910 * t16174 + 366282 * t16176 - 508079 * t16178 - 155 + (-153748 + 245157 * t16175) * t16175 + (281656 * t16176 - 163438 * t16178 - 1390) * t16171) + + if Bindx == 2567: + t16181 = np.cos(phi) + t16182 = t16181 ** 2 + t16183 = t16181 * t16182 + t16186 = t16183 ** 2 + t16184 = t16182 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (1995 * t16182 - 1330 * t16183 - 17955 * t16184 + 33649 * t16186 - 25 + (-8778 * t16184 + 28842 * t16186 + 210) * t16181) * ((1 + t16181) ** (0.3e1 / 0.2e1)) * np.sqrt(0.51e2) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t16181) ** (0.7e1 / 0.2e1)) + + if Bindx == 2568: + t16198 = np.sin(phi) + t16196 = t16198 ** 2 + t16188 = np.cos(phi) + t16189 = t16188 ** 2 + t16190 = t16188 * t16189 + t16193 = t16190 ** 2 + t16191 = t16189 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.714e3) * t16196 ** 2 * (-280 * t16189 - 1425 * t16190 - 5852 * t16193 + 5 + (2280 + 4807 * t16191) * t16191 + (5187 * t16191 - 4807 * t16193 + 85) * t16188) + + if Bindx == 2569: + t16199 = np.cos(phi) + t16200 = t16199 ** 2 + t16202 = t16200 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-270 * t16200 + 1265 * t16202 + 5 + (-220 * t16200 + 1518 * t16202 - 10) * t16199) * ((1 + t16199) ** (0.5e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t16199) ** (0.9e1 / 0.2e1)) + + if Bindx == 2570: + t16213 = np.sin(phi) + t16210 = t16213 ** 2 + t16211 = t16213 * t16210 + t16204 = np.cos(phi) + t16205 = t16204 ** 2 + t16207 = t16205 ** 2 + t16206 = t16204 * t16205 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.2261e4) * t16211 ** 2 * (41 * t16205 - 319 * t16207 - 1 + (584 + 759 * t16206) * t16206 + (-1012 * t16207 - 52) * t16204) + + if Bindx == 2571: + t16214 = np.cos(phi) + t16215 = t16214 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (253 * t16215 - 9 + (506 * t16215 - 22) * t16214) * ((1 + t16214) ** (0.7e1 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((-1*1j) * (2 * phi1 - 9 * phi2)) * ((1 - t16214) ** (0.11e2 / 0.2e1)) + + if Bindx == 2572: + t16224 = np.sin(phi) + t16221 = t16224 ** 2 + t16222 = t16221 ** 2 + t16217 = np.cos(phi) + t16218 = t16217 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.7106e4) * t16222 ** 2 * (25 * t16217 - 1 + (-115 * t16217 + 22 + 69 * t16218) * t16218) + + if Bindx == 2573: + t16225 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (1 + 6 * t16225) * ((1 + t16225) ** (0.9e1 / 0.2e1)) * np.sqrt(0.81719e5) * np.exp((-1*1j) * (2 * phi1 - 11 * phi2)) * ((1 - t16225) ** (0.13e2 / 0.2e1)) + + if Bindx == 2574: + t16226 = np.cos(phi) + t16235 = -1 + t16226 + t16234 = 1 + t16226 + t16231 = t16234 ** 2 + t16227 = t16235 ** 2 + t16228 = t16235 * t16227 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 - 6 * phi2)) * np.sqrt(0.490314e6) * t16235 * t16228 ** 2 * t16234 * t16231 ** 2 + + if Bindx == 2575: + t16236 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 12 * phi2)) * np.sqrt(0.156009e6) * ((1 - t16236) ** (0.11e2 / 0.2e1)) * ((1 + t16236) ** (0.13e2 / 0.2e1)) + + if Bindx == 2576: + t16242 = np.sin(phi) + t16238 = t16242 ** 2 + t16240 = t16242 * t16238 ** 2 + t16237 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 11 * phi2)) * np.sqrt(0.104006e6) * t16240 ** 2 * (-1 + (11 + 12 * t16237) * t16237) + + if Bindx == 2577: + t16243 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 10 * phi2)) * np.sqrt(0.2261e4) * ((1 - t16243) ** (0.9e1 / 0.2e1)) * ((1 + t16243) ** (0.11e2 / 0.2e1)) * (-5 + (-23 + 138 * t16243) * t16243) + + if Bindx == 2578: + t16251 = np.sin(phi) + t16248 = t16251 ** 2 + t16249 = t16248 ** 2 + t16244 = np.cos(phi) + t16245 = t16244 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 9 * phi2)) * np.sqrt(0.149226e6) * t16249 ** 2 * (-9 * t16244 + 1 + (69 * t16244 - 33 + 92 * t16245) * t16245) + + if Bindx == 2579: + t16252 = np.cos(phi) + t16253 = t16252 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.7106e4) * ((1 - t16252) ** (0.7e1 / 0.2e1)) * ((1 + t16252) ** (0.9e1 / 0.2e1)) * (21 * t16252 + 2 + (-161 * t16252 - 105 + 483 * t16253) * t16253) + + if Bindx == 2580: + t16265 = np.sin(phi) + t16262 = t16265 ** 2 + t16263 = t16265 * t16262 + t16256 = np.cos(phi) + t16257 = t16256 ** 2 + t16259 = t16257 ** 2 + t16258 = t16256 * t16257 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.7106e4) * t16263 ** 2 * (250 * t16257 - 1505 * t16259 - 5 + (-490 + 1932 * t16258) * t16258 + (1127 * t16259 + 35) * t16256) + + if Bindx == 2581: + t16266 = np.cos(phi) + t16267 = t16266 ** 2 + t16269 = t16267 ** 2 + t16268 = t16266 * t16267 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.561e3) * ((1 - t16266) ** (0.5e1 / 0.2e1)) * ((1 + t16266) ** (0.7e1 / 0.2e1)) * (380 * t16267 - 3325 * t16269 - 5 + (1330 + 6118 * t16268) * t16268 + (-3059 * t16269 - 95) * t16266) + + if Bindx == 2582: + t16282 = np.sin(phi) + t16280 = t16282 ** 2 + t16272 = np.cos(phi) + t16273 = t16272 ** 2 + t16274 = t16272 * t16273 + t16277 = t16274 ** 2 + t16275 = t16273 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.7854e4) * t16280 ** 2 * (-315 * t16273 + 475 * t16274 - 7049 * t16277 + 5 + (2755 + 5244 * t16275) * t16275 + (-1995 * t16275 + 2185 * t16277 - 25) * t16272) + + if Bindx == 2583: + t16283 = np.cos(phi) + t16284 = t16283 ** 2 + t16285 = t16283 * t16284 + t16288 = t16285 ** 2 + t16286 = t16284 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.231e3) * ((1 - t16283) ** (0.3e1 / 0.2e1)) * ((1 + t16283) ** (0.5e1 / 0.2e1)) * (-510 * t16284 - 3230 * t16285 - 22610 * t16288 + 5 + (6460 + 22287 * t16286) * t16286 + (13566 * t16286 - 14858 * t16288 + 170) * t16283) + + if Bindx == 2584: + t16292 = np.cos(phi) + t16293 = t16292 ** 2 + t16295 = t16293 ** 2 + t16299 = t16295 ** 2 + t16294 = t16292 * t16293 + t16297 = t16294 ** 2 + t16296 = t16292 * t16295 + t16291 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.231e3) * t16291 ** 2 * (1080 * t16293 - 1020 * t16294 - 11730 * t16295 + 42636 * t16297 - 61047 * t16299 - 15 + (5814 + 29716 * t16296) * t16296 + (-11628 * t16297 + 7429 * t16299 + 45) * t16292) + + if Bindx == 2585: + t16302 = np.cos(phi) + t16303 = t16302 ** 2 + t16305 = t16303 ** 2 + t16309 = t16305 ** 2 + t16304 = t16302 * t16303 + t16307 = t16304 ** 2 + t16306 = t16302 * t16305 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.154e3) * np.sqrt((1 - t16302)) * ((1 + t16302) ** (0.3e1 / 0.2e1)) * (450 * t16303 + 5100 * t16304 - 7650 * t16305 + 38760 * t16307 - 72675 * t16309 - 3 + (-29070 + 44574 * t16306) * t16306 + (58140 * t16307 - 37145 * t16309 - 225) * t16302) + + if Bindx == 2586: + t16312 = np.cos(phi) + t16313 = t16312 ** 2 + t16315 = t16313 ** 2 + t16316 = t16312 * t16315 + t16321 = t16316 ** 2 + t16319 = t16315 ** 2 + t16314 = t16312 * t16313 + t16317 = t16314 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 + phi2)) * (-17787 * t16313 + 5775 * t16314 + 219450 * t16315 - 39270 * t16316 + 1971915 * t16319 - 1815583 * t16321 + 231 + (-981750 + 624036 * t16317) * t16317 + (106590 * t16317 - 124355 * t16319 + 52003 * t16321 - 231) * t16312) + + if Bindx == 2587: + t16324 = np.cos(phi) + t16325 = t16324 ** 2 + t16326 = t16324 * t16325 + t16327 = t16325 ** 2 + t16328 = t16324 * t16327 + t16335 = 106590 * t16326 ** 2 - 124355 * t16327 ** 2 + 52003 * t16328 ** 2 - 231 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.39e2) * np.sqrt((1 + t16324)) * t16324 * (t16324 * t16335 - 5775 * t16325 + 5775 * t16326 + 39270 * t16327 - 39270 * t16328 - t16335) * ((1 - t16324) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2588: + t16336 = np.cos(phi) + t16337 = t16336 ** 2 + t16339 = t16337 ** 2 + t16340 = t16336 * t16339 + t16345 = t16340 ** 2 + t16343 = t16339 ** 2 + t16338 = t16336 * t16337 + t16341 = t16338 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 - phi2)) * (-17787 * t16337 - 5775 * t16338 + 219450 * t16339 + 39270 * t16340 + 1971915 * t16343 - 1815583 * t16345 + 231 + (-981750 + 624036 * t16341) * t16341 + (-106590 * t16341 + 124355 * t16343 - 52003 * t16345 + 231) * t16336) + + if Bindx == 2589: + t16348 = np.cos(phi) + t16349 = t16348 ** 2 + t16351 = t16349 ** 2 + t16355 = t16351 ** 2 + t16350 = t16348 * t16349 + t16353 = t16350 ** 2 + t16352 = t16348 * t16351 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (450 * t16349 - 5100 * t16350 - 7650 * t16351 + 38760 * t16353 - 72675 * t16355 - 3 + (29070 + 44574 * t16352) * t16352 + (-58140 * t16353 + 37145 * t16355 + 225) * t16348) * np.sqrt((1 + t16348)) * np.sqrt(0.154e3) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t16348) ** (0.3e1 / 0.2e1)) + + if Bindx == 2590: + t16359 = np.cos(phi) + t16360 = t16359 ** 2 + t16362 = t16360 ** 2 + t16366 = t16362 ** 2 + t16361 = t16359 * t16360 + t16364 = t16361 ** 2 + t16363 = t16359 * t16362 + t16358 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.231e3) * t16358 ** 2 * (1080 * t16360 + 1020 * t16361 - 11730 * t16362 + 42636 * t16364 - 61047 * t16366 - 15 + (-5814 + 29716 * t16363) * t16363 + (11628 * t16364 - 7429 * t16366 - 45) * t16359) + + if Bindx == 2591: + t16369 = np.cos(phi) + t16370 = t16369 ** 2 + t16371 = t16369 * t16370 + t16374 = t16371 ** 2 + t16372 = t16370 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-510 * t16370 + 3230 * t16371 - 22610 * t16374 + 5 + (6460 + 22287 * t16372) * t16372 + (-13566 * t16372 + 14858 * t16374 - 170) * t16369) * ((1 + t16369) ** (0.3e1 / 0.2e1)) * np.sqrt(0.231e3) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t16369) ** (0.5e1 / 0.2e1)) + + if Bindx == 2592: + t16387 = np.sin(phi) + t16385 = t16387 ** 2 + t16377 = np.cos(phi) + t16378 = t16377 ** 2 + t16379 = t16377 * t16378 + t16382 = t16379 ** 2 + t16380 = t16378 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.7854e4) * t16385 ** 2 * (-315 * t16378 - 475 * t16379 - 7049 * t16382 + 5 + (2755 + 5244 * t16380) * t16380 + (1995 * t16380 - 2185 * t16382 + 25) * t16377) + + if Bindx == 2593: + t16388 = np.cos(phi) + t16389 = t16388 ** 2 + t16391 = t16389 ** 2 + t16390 = t16388 * t16389 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (380 * t16389 - 3325 * t16391 - 5 + (-1330 + 6118 * t16390) * t16390 + (3059 * t16391 + 95) * t16388) * ((1 + t16388) ** (0.5e1 / 0.2e1)) * np.sqrt(0.561e3) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t16388) ** (0.7e1 / 0.2e1)) + + if Bindx == 2594: + t16403 = np.sin(phi) + t16400 = t16403 ** 2 + t16401 = t16403 * t16400 + t16394 = np.cos(phi) + t16395 = t16394 ** 2 + t16397 = t16395 ** 2 + t16396 = t16394 * t16395 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.7106e4) * t16401 ** 2 * (250 * t16395 - 1505 * t16397 - 5 + (490 + 1932 * t16396) * t16396 + (-1127 * t16397 - 35) * t16394) + + if Bindx == 2595: + t16404 = np.cos(phi) + t16405 = t16404 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-21 * t16404 + 2 + (161 * t16404 - 105 + 483 * t16405) * t16405) * ((1 + t16404) ** (0.7e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * ((1 - t16404) ** (0.9e1 / 0.2e1)) + + if Bindx == 2596: + t16415 = np.sin(phi) + t16412 = t16415 ** 2 + t16413 = t16412 ** 2 + t16408 = np.cos(phi) + t16409 = t16408 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 9 * phi2)) * np.sqrt(0.149226e6) * t16413 ** 2 * (9 * t16408 + 1 + (-69 * t16408 - 33 + 92 * t16409) * t16409) + + if Bindx == 2597: + t16416 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-5 + (23 + 138 * t16416) * t16416) * ((1 + t16416) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((-1*1j) * (phi1 - 10 * phi2)) * ((1 - t16416) ** (0.11e2 / 0.2e1)) + + if Bindx == 2598: + t16422 = np.sin(phi) + t16418 = t16422 ** 2 + t16420 = t16422 * t16418 ** 2 + t16417 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - 11 * phi2)) * np.sqrt(0.104006e6) * t16420 ** 2 * (-1 + (-11 + 12 * t16417) * t16417) + + if Bindx == 2599: + t16423 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 - 12 * phi2)) * np.sqrt(0.156009e6) * ((1 - t16423) ** (0.13e2 / 0.2e1)) * ((1 + t16423) ** (0.11e2 / 0.2e1)) + + if Bindx == 2600: + t16428 = np.sin(phi) + t16424 = t16428 ** 2 + t16425 = t16428 * t16424 + t16426 = t16425 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-12*1j) * phi2) * np.sqrt(0.676039e6) * t16426 ** 2 + + if Bindx == 2601: + t16429 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-11*1j) * phi2) * np.sqrt(0.4056234e7) * ((1 - t16429) ** (0.11e2 / 0.2e1)) * ((1 + t16429) ** (0.11e2 / 0.2e1)) * t16429 + + if Bindx == 2602: + t16435 = np.sin(phi) + t16431 = t16435 ** 2 + t16433 = t16435 * t16431 ** 2 + t16430 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-10*1j) * phi2) * np.sqrt(0.88179e5) * t16433 ** 2 * (23 * t16430 ** 2 - 1) + + if Bindx == 2603: + t16436 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((-9*1j) * phi2) * np.sqrt(0.646646e6) * ((1 - t16436) ** (0.9e1 / 0.2e1)) * ((1 + t16436) ** (0.9e1 / 0.2e1)) * t16436 * (23 * t16436 ** 2 - 3) + + if Bindx == 2604: + t16443 = np.sin(phi) + t16440 = t16443 ** 2 + t16441 = t16440 ** 2 + t16437 = np.cos(phi) + t16438 = t16437 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((-8*1j) * phi2) * np.sqrt(0.277134e6) * t16441 ** 2 * (1 + (-42 + 161 * t16438) * t16438) + + if Bindx == 2605: + t16444 = np.cos(phi) + t16445 = t16444 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.277134e6) * ((1 - t16444) ** (0.7e1 / 0.2e1)) * ((1 + t16444) ** (0.7e1 / 0.2e1)) * t16444 * (5 + (-70 + 161 * t16445) * t16445) + + if Bindx == 2606: + t16454 = np.sin(phi) + t16451 = t16454 ** 2 + t16452 = t16454 * t16451 + t16447 = np.cos(phi) + t16448 = t16447 ** 2 + t16449 = t16448 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-6*1j) * phi2) * np.sqrt(0.2431e4) * t16452 ** 2 * (-1995 * t16449 - 5 + (3059 * t16449 + 285) * t16448) + + if Bindx == 2607: + t16455 = np.cos(phi) + t16456 = t16455 ** 2 + t16457 = t16456 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.34034e5) * ((1 - t16455) ** (0.5e1 / 0.2e1)) * ((1 + t16455) ** (0.5e1 / 0.2e1)) * t16455 * (-399 * t16457 - 5 + (437 * t16457 + 95) * t16456) + + if Bindx == 2608: + t16466 = np.sin(phi) + t16464 = t16466 ** 2 + t16459 = np.cos(phi) + t16460 = t16459 ** 2 + t16461 = t16460 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((-4*1j) * phi2) * np.sqrt(0.1001e4) * t16464 ** 2 * (-340 * t16460 + 5 + (-9044 * t16460 + 3230 + 7429 * t16461) * t16461) + + if Bindx == 2609: + t16467 = np.cos(phi) + t16468 = t16467 ** 2 + t16469 = t16468 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.1001e4) * ((1 - t16467) ** (0.3e1 / 0.2e1)) * ((1 + t16467) ** (0.3e1 / 0.2e1)) * t16467 * (-1020 * t16468 + 45 + (-11628 * t16468 + 5814 + 7429 * t16469) * t16469) + + if Bindx == 2610: + t16473 = np.cos(phi) + t16474 = t16473 ** 2 + t16475 = t16474 ** 2 + t16477 = t16475 ** 2 + t16472 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((-2*1j) * phi2) * np.sqrt(0.6006e4) * t16472 ** 2 * (-2550 * t16475 - 14535 * t16477 - 3 + (9690 * t16475 + 7429 * t16477 + 225) * t16474) + + if Bindx == 2611: + t16479 = np.cos(phi) + t16480 = t16479 ** 2 + t16481 = t16480 ** 2 + t16483 = t16481 ** 2 + tfunc[..., c] = (-0.25e2 / 0.512e3*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.39e2) * np.sqrt((1 - t16479)) * np.sqrt((1 + t16479)) * t16479 * (-39270 * t16481 - 124355 * t16483 - 231 + (106590 * t16481 + 52003 * t16483 + 5775) * t16480) + + if Bindx == 2612: + t16485 = np.cos(phi) + t16486 = t16485 ** 2 + t16487 = t16486 ** 2 + t16489 = t16487 ** 2 + t16488 = t16486 * t16487 + tfunc[..., c] = 0.51962625e8 / 0.1024e4 * t16489 + 0.5630625e7 / 0.1024e4 * t16487 + 0.5775e4 / 0.1024e4 + (-0.6381375e7 / 0.256e3 + 0.16900975e8 / 0.1024e4 * t16488) * t16488 + (-0.24249225e8 / 0.512e3 * t16489 - 0.225225e6 / 0.512e3) * t16486 + + if Bindx == 2613: + t16492 = np.cos(phi) + t16493 = t16492 ** 2 + t16494 = t16492 * t16493 + t16495 = t16493 ** 2 + t16496 = t16492 * t16495 + t16503 = 106590 * t16494 ** 2 - 124355 * t16495 ** 2 + 52003 * t16496 ** 2 - 231 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((1j) * phi2) * np.sqrt(0.39e2) * np.sqrt((1 + t16492)) * t16492 * (t16492 * t16503 - 5775 * t16493 + 5775 * t16494 + 39270 * t16495 - 39270 * t16496 - t16503) * ((1 - t16492) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2614: + t16505 = np.cos(phi) + t16506 = t16505 ** 2 + t16507 = t16506 ** 2 + t16509 = t16507 ** 2 + t16504 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * phi2) * np.sqrt(0.6006e4) * t16504 ** 2 * (-2550 * t16507 - 14535 * t16509 - 3 + (9690 * t16507 + 7429 * t16509 + 225) * t16506) + + if Bindx == 2615: + t16511 = np.cos(phi) + t16512 = t16511 ** 2 + t16513 = t16512 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * (-1020 * t16512 + 45 + (-11628 * t16512 + 5814 + 7429 * t16513) * t16513) * t16511 * ((1 + t16511) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1001e4) * np.exp((3*1j) * phi2) * ((1 - t16511) ** (0.3e1 / 0.2e1)) + + if Bindx == 2616: + t16523 = np.sin(phi) + t16521 = t16523 ** 2 + t16516 = np.cos(phi) + t16517 = t16516 ** 2 + t16518 = t16517 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((4*1j) * phi2) * np.sqrt(0.1001e4) * t16521 ** 2 * (-340 * t16517 + 5 + (-9044 * t16517 + 3230 + 7429 * t16518) * t16518) + + if Bindx == 2617: + t16524 = np.cos(phi) + t16525 = t16524 ** 2 + t16526 = t16525 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * (-399 * t16526 - 5 + (437 * t16526 + 95) * t16525) * t16524 * ((1 + t16524) ** (0.5e1 / 0.2e1)) * np.sqrt(0.34034e5) * np.exp((5*1j) * phi2) * ((1 - t16524) ** (0.5e1 / 0.2e1)) + + if Bindx == 2618: + t16535 = np.sin(phi) + t16532 = t16535 ** 2 + t16533 = t16535 * t16532 + t16528 = np.cos(phi) + t16529 = t16528 ** 2 + t16530 = t16529 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((6*1j) * phi2) * np.sqrt(0.2431e4) * t16533 ** 2 * (-1995 * t16530 - 5 + (3059 * t16530 + 285) * t16529) + + if Bindx == 2619: + t16536 = np.cos(phi) + t16537 = t16536 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (5 + (-70 + 161 * t16537) * t16537) * t16536 * ((1 + t16536) ** (0.7e1 / 0.2e1)) * np.sqrt(0.277134e6) * np.exp((7*1j) * phi2) * ((1 - t16536) ** (0.7e1 / 0.2e1)) + + if Bindx == 2620: + t16545 = np.sin(phi) + t16542 = t16545 ** 2 + t16543 = t16542 ** 2 + t16539 = np.cos(phi) + t16540 = t16539 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((8*1j) * phi2) * np.sqrt(0.277134e6) * t16543 ** 2 * (1 + (-42 + 161 * t16540) * t16540) + + if Bindx == 2621: + t16546 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (23 * t16546 ** 2 - 3) * t16546 * ((1 + t16546) ** (0.9e1 / 0.2e1)) * np.sqrt(0.646646e6) * np.exp((9*1j) * phi2) * ((1 - t16546) ** (0.9e1 / 0.2e1)) + + if Bindx == 2622: + t16552 = np.sin(phi) + t16548 = t16552 ** 2 + t16550 = t16552 * t16548 ** 2 + t16547 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((10*1j) * phi2) * np.sqrt(0.88179e5) * t16550 ** 2 * (23 * t16547 ** 2 - 1) + + if Bindx == 2623: + t16553 = np.cos(phi) + t16559 = -6 * t16553 + t16554 = t16553 ** 2 + t16555 = t16553 * t16554 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * t16553 * ((1 + t16553) ** (0.11e2 / 0.2e1)) * (t16559 + 1 + (-20 + t16555) * t16555 + (15 + (t16559 + 15) * t16554) * t16554) * np.sqrt(0.4056234e7) * np.exp((11*1j) * phi2) * ((1 - t16553) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2624: + t16564 = np.sin(phi) + t16560 = t16564 ** 2 + t16561 = t16564 * t16560 + t16562 = t16561 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((12*1j) * phi2) * np.sqrt(0.676039e6) * t16562 ** 2 + + if Bindx == 2625: + t16565 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 12 * phi2)) * np.sqrt(0.156009e6) * ((1 - t16565) ** (0.13e2 / 0.2e1)) * ((1 + t16565) ** (0.11e2 / 0.2e1)) + + if Bindx == 2626: + t16571 = np.sin(phi) + t16567 = t16571 ** 2 + t16569 = t16571 * t16567 ** 2 + t16566 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 - 11 * phi2)) * np.sqrt(0.104006e6) * t16569 ** 2 * (-1 + (-11 + 12 * t16566) * t16566) + + if Bindx == 2627: + t16572 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 10 * phi2)) * np.sqrt(0.2261e4) * ((1 - t16572) ** (0.11e2 / 0.2e1)) * ((1 + t16572) ** (0.9e1 / 0.2e1)) * (-5 + (23 + 138 * t16572) * t16572) + + if Bindx == 2628: + t16580 = np.sin(phi) + t16577 = t16580 ** 2 + t16578 = t16577 ** 2 + t16573 = np.cos(phi) + t16574 = t16573 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 - 9 * phi2)) * np.sqrt(0.149226e6) * t16578 ** 2 * (9 * t16573 + 1 + (-69 * t16573 - 33 + 92 * t16574) * t16574) + + if Bindx == 2629: + t16581 = np.cos(phi) + t16582 = t16581 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.7106e4) * ((1 - t16581) ** (0.9e1 / 0.2e1)) * ((1 + t16581) ** (0.7e1 / 0.2e1)) * (-21 * t16581 + 2 + (161 * t16581 - 105 + 483 * t16582) * t16582) + + if Bindx == 2630: + t16594 = np.sin(phi) + t16591 = t16594 ** 2 + t16592 = t16594 * t16591 + t16585 = np.cos(phi) + t16586 = t16585 ** 2 + t16588 = t16586 ** 2 + t16587 = t16585 * t16586 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.7106e4) * t16592 ** 2 * (250 * t16586 - 1505 * t16588 - 5 + (490 + 1932 * t16587) * t16587 + (-1127 * t16588 - 35) * t16585) + + if Bindx == 2631: + t16595 = np.cos(phi) + t16596 = t16595 ** 2 + t16598 = t16596 ** 2 + t16597 = t16595 * t16596 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.561e3) * ((1 - t16595) ** (0.7e1 / 0.2e1)) * ((1 + t16595) ** (0.5e1 / 0.2e1)) * (380 * t16596 - 3325 * t16598 - 5 + (-1330 + 6118 * t16597) * t16597 + (3059 * t16598 + 95) * t16595) + + if Bindx == 2632: + t16611 = np.sin(phi) + t16609 = t16611 ** 2 + t16601 = np.cos(phi) + t16602 = t16601 ** 2 + t16603 = t16601 * t16602 + t16606 = t16603 ** 2 + t16604 = t16602 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.7854e4) * t16609 ** 2 * (-315 * t16602 - 475 * t16603 - 7049 * t16606 + 5 + (2755 + 5244 * t16604) * t16604 + (1995 * t16604 - 2185 * t16606 + 25) * t16601) + + if Bindx == 2633: + t16612 = np.cos(phi) + t16613 = t16612 ** 2 + t16614 = t16612 * t16613 + t16617 = t16614 ** 2 + t16615 = t16613 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.231e3) * ((1 - t16612) ** (0.5e1 / 0.2e1)) * ((1 + t16612) ** (0.3e1 / 0.2e1)) * (-510 * t16613 + 3230 * t16614 - 22610 * t16617 + 5 + (6460 + 22287 * t16615) * t16615 + (-13566 * t16615 + 14858 * t16617 - 170) * t16612) + + if Bindx == 2634: + t16621 = np.cos(phi) + t16622 = t16621 ** 2 + t16624 = t16622 ** 2 + t16628 = t16624 ** 2 + t16623 = t16621 * t16622 + t16626 = t16623 ** 2 + t16625 = t16621 * t16624 + t16620 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.231e3) * t16620 ** 2 * (1080 * t16622 + 1020 * t16623 - 11730 * t16624 + 42636 * t16626 - 61047 * t16628 - 15 + (-5814 + 29716 * t16625) * t16625 + (11628 * t16626 - 7429 * t16628 - 45) * t16621) + + if Bindx == 2635: + t16631 = np.cos(phi) + t16632 = t16631 ** 2 + t16634 = t16632 ** 2 + t16638 = t16634 ** 2 + t16633 = t16631 * t16632 + t16636 = t16633 ** 2 + t16635 = t16631 * t16634 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.154e3) * ((1 - t16631) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t16631)) * (450 * t16632 - 5100 * t16633 - 7650 * t16634 + 38760 * t16636 - 72675 * t16638 - 3 + (29070 + 44574 * t16635) * t16635 + (-58140 * t16636 + 37145 * t16638 + 225) * t16631) + + if Bindx == 2636: + t16641 = np.cos(phi) + t16642 = t16641 ** 2 + t16644 = t16642 ** 2 + t16645 = t16641 * t16644 + t16650 = t16645 ** 2 + t16648 = t16644 ** 2 + t16643 = t16641 * t16642 + t16646 = t16643 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((1j) * (phi1 - phi2)) * (-17787 * t16642 - 5775 * t16643 + 219450 * t16644 + 39270 * t16645 + 1971915 * t16648 - 1815583 * t16650 + 231 + (-981750 + 624036 * t16646) * t16646 + (-106590 * t16646 + 124355 * t16648 - 52003 * t16650 + 231) * t16641) + + if Bindx == 2637: + t16653 = np.cos(phi) + t16654 = t16653 ** 2 + t16655 = t16654 ** 2 + t16657 = t16655 ** 2 + tfunc[..., c] = (-0.25e2 / 0.512e3*1j) * np.exp((1j) * phi1) * np.sqrt(0.39e2) * np.sqrt((1 - t16653)) * np.sqrt((1 + t16653)) * t16653 * (-39270 * t16655 - 124355 * t16657 - 231 + (106590 * t16655 + 52003 * t16657 + 5775) * t16654) + + if Bindx == 2638: + t16659 = np.cos(phi) + t16660 = t16659 ** 2 + t16662 = t16660 ** 2 + t16663 = t16659 * t16662 + t16668 = t16663 ** 2 + t16666 = t16662 ** 2 + t16661 = t16659 * t16660 + t16664 = t16661 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((1j) * (phi1 + phi2)) * (-17787 * t16660 + 5775 * t16661 + 219450 * t16662 - 39270 * t16663 + 1971915 * t16666 - 1815583 * t16668 + 231 + (-981750 + 624036 * t16664) * t16664 + (106590 * t16664 - 124355 * t16666 + 52003 * t16668 - 231) * t16659) + + if Bindx == 2639: + t16671 = np.cos(phi) + t16672 = t16671 ** 2 + t16674 = t16672 ** 2 + t16675 = t16671 * t16674 + t16680 = t16675 ** 2 + t16678 = t16674 ** 2 + t16673 = t16671 * t16672 + t16676 = t16673 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.154e3) * ((1 + t16671) ** (0.3e1 / 0.2e1)) * (-675 * t16672 - 4650 * t16673 + 12750 * t16674 + 21420 * t16675 - 67830 * t16676 + 130815 * t16678 - 81719 * t16680 + 3 + (-19380 * t16676 - 35530 * t16678 + 44574 * t16680 + 222) * t16671) * ((1 - t16671) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2640: + t16683 = np.cos(phi) + t16684 = t16683 ** 2 + t16686 = t16684 ** 2 + t16690 = t16686 ** 2 + t16685 = t16683 * t16684 + t16688 = t16685 ** 2 + t16687 = t16683 * t16686 + t16682 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.231e3) * t16682 ** 2 * (1080 * t16684 - 1020 * t16685 - 11730 * t16686 + 42636 * t16688 - 61047 * t16690 - 15 + (5814 + 29716 * t16687) * t16687 + (-11628 * t16688 + 7429 * t16690 + 45) * t16683) + + if Bindx == 2641: + t16693 = np.cos(phi) + t16694 = t16693 ** 2 + t16695 = t16693 * t16694 + t16698 = t16695 ** 2 + t16696 = t16694 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-510 * t16694 - 3230 * t16695 - 22610 * t16698 + 5 + (6460 + 22287 * t16696) * t16696 + (13566 * t16696 - 14858 * t16698 + 170) * t16693) * ((1 + t16693) ** (0.5e1 / 0.2e1)) * np.sqrt(0.231e3) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t16693) ** (0.3e1 / 0.2e1)) + + if Bindx == 2642: + t16711 = np.sin(phi) + t16709 = t16711 ** 2 + t16701 = np.cos(phi) + t16702 = t16701 ** 2 + t16703 = t16701 * t16702 + t16706 = t16703 ** 2 + t16704 = t16702 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.7854e4) * t16709 ** 2 * (-315 * t16702 + 475 * t16703 - 7049 * t16706 + 5 + (2755 + 5244 * t16704) * t16704 + (-1995 * t16704 + 2185 * t16706 - 25) * t16701) + + if Bindx == 2643: + t16712 = np.cos(phi) + t16713 = t16712 ** 2 + t16715 = t16713 ** 2 + t16714 = t16712 * t16713 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (380 * t16713 - 3325 * t16715 - 5 + (1330 + 6118 * t16714) * t16714 + (-3059 * t16715 - 95) * t16712) * ((1 + t16712) ** (0.7e1 / 0.2e1)) * np.sqrt(0.561e3) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t16712) ** (0.5e1 / 0.2e1)) + + if Bindx == 2644: + t16727 = np.sin(phi) + t16724 = t16727 ** 2 + t16725 = t16727 * t16724 + t16718 = np.cos(phi) + t16719 = t16718 ** 2 + t16721 = t16719 ** 2 + t16720 = t16718 * t16719 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.7106e4) * t16725 ** 2 * (250 * t16719 - 1505 * t16721 - 5 + (-490 + 1932 * t16720) * t16720 + (1127 * t16721 + 35) * t16718) + + if Bindx == 2645: + t16728 = np.cos(phi) + t16729 = t16728 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (21 * t16728 + 2 + (-161 * t16728 - 105 + 483 * t16729) * t16729) * ((1 + t16728) ** (0.9e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((1j) * (phi1 + 8 * phi2)) * ((1 - t16728) ** (0.7e1 / 0.2e1)) + + if Bindx == 2646: + t16739 = np.sin(phi) + t16736 = t16739 ** 2 + t16737 = t16736 ** 2 + t16732 = np.cos(phi) + t16733 = t16732 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 + 9 * phi2)) * np.sqrt(0.149226e6) * t16737 ** 2 * (-9 * t16732 + 1 + (69 * t16732 - 33 + 92 * t16733) * t16733) + + if Bindx == 2647: + t16740 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-5 + (-23 + 138 * t16740) * t16740) * ((1 + t16740) ** (0.11e2 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((1j) * (phi1 + 10 * phi2)) * ((1 - t16740) ** (0.9e1 / 0.2e1)) + + if Bindx == 2648: + t16746 = np.sin(phi) + t16742 = t16746 ** 2 + t16744 = t16746 * t16742 ** 2 + t16741 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (phi1 + 11 * phi2)) * np.sqrt(0.104006e6) * t16744 ** 2 * (-1 + (11 + 12 * t16741) * t16741) + + if Bindx == 2649: + t16747 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 + 12 * phi2)) * np.sqrt(0.156009e6) * ((1 - t16747) ** (0.11e2 / 0.2e1)) * ((1 + t16747) ** (0.13e2 / 0.2e1)) + + if Bindx == 2650: + t16748 = np.cos(phi) + t16757 = -1 + t16748 + t16756 = 1 + t16748 + t16753 = t16756 ** 2 + t16749 = t16757 ** 2 + t16750 = t16757 * t16749 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (phi1 - 6 * phi2)) * np.sqrt(0.490314e6) * t16757 * t16750 ** 2 * t16756 * t16753 ** 2 + + if Bindx == 2651: + t16758 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 11 * phi2)) * np.sqrt(0.81719e5) * ((1 - t16758) ** (0.13e2 / 0.2e1)) * ((1 + t16758) ** (0.9e1 / 0.2e1)) * (1 + 6 * t16758) + + if Bindx == 2652: + t16766 = np.sin(phi) + t16763 = t16766 ** 2 + t16764 = t16763 ** 2 + t16759 = np.cos(phi) + t16760 = t16759 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.7106e4) * t16764 ** 2 * (25 * t16759 - 1 + (-115 * t16759 + 22 + 69 * t16760) * t16760) + + if Bindx == 2653: + t16767 = np.cos(phi) + t16768 = t16767 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.969e3) * ((1 - t16767) ** (0.11e2 / 0.2e1)) * ((1 + t16767) ** (0.7e1 / 0.2e1)) * (253 * t16768 - 9 + (506 * t16768 - 22) * t16767) + + if Bindx == 2654: + t16779 = np.sin(phi) + t16776 = t16779 ** 2 + t16777 = t16779 * t16776 + t16770 = np.cos(phi) + t16771 = t16770 ** 2 + t16773 = t16771 ** 2 + t16772 = t16770 * t16771 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.2261e4) * t16777 ** 2 * (41 * t16771 - 319 * t16773 - 1 + (584 + 759 * t16772) * t16772 + (-1012 * t16773 - 52) * t16770) + + if Bindx == 2655: + t16780 = np.cos(phi) + t16781 = t16780 ** 2 + t16783 = t16781 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.2261e4) * ((1 - t16780) ** (0.9e1 / 0.2e1)) * ((1 + t16780) ** (0.5e1 / 0.2e1)) * (-270 * t16781 + 1265 * t16783 + 5 + (-220 * t16781 + 1518 * t16783 - 10) * t16780) + + if Bindx == 2656: + t16795 = np.sin(phi) + t16793 = t16795 ** 2 + t16785 = np.cos(phi) + t16786 = t16785 ** 2 + t16787 = t16785 * t16786 + t16790 = t16787 ** 2 + t16788 = t16786 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.714e3) * t16793 ** 2 * (-280 * t16786 - 1425 * t16787 - 5852 * t16790 + 5 + (2280 + 4807 * t16788) * t16788 + (5187 * t16788 - 4807 * t16790 + 85) * t16785) + + if Bindx == 2657: + t16796 = np.cos(phi) + t16797 = t16796 ** 2 + t16798 = t16796 * t16797 + t16801 = t16798 ** 2 + t16799 = t16797 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.51e2) * ((1 - t16796) ** (0.7e1 / 0.2e1)) * ((1 + t16796) ** (0.3e1 / 0.2e1)) * (1995 * t16797 - 1330 * t16798 - 17955 * t16799 + 33649 * t16801 - 25 + (-8778 * t16799 + 28842 * t16801 + 210) * t16796) + + if Bindx == 2658: + t16804 = np.cos(phi) + t16805 = t16804 ** 2 + t16807 = t16805 ** 2 + t16811 = t16807 ** 2 + t16806 = t16804 * t16805 + t16809 = t16806 ** 2 + t16808 = t16804 * t16807 + t16803 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.6e1) * t16803 ** 2 * (10385 * t16805 + 29240 * t16806 - 105910 * t16807 + 366282 * t16809 - 508079 * t16811 - 155 + (-153748 + 245157 * t16808) * t16808 + (281656 * t16809 - 163438 * t16811 - 1390) * t16804) + + if Bindx == 2659: + t16814 = np.cos(phi) + t16815 = t16814 ** 2 + t16817 = t16815 ** 2 + t16821 = t16817 ** 2 + t16816 = t16814 * t16815 + t16819 = t16816 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.6e1) * ((1 - t16814) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t16814)) * (-5100 * t16815 + 14280 * t16816 + 67830 * t16817 - 244188 * t16819 + 245157 * t16821 + 45 + (-27132 * t16817 - 85272 * t16819 + 163438 * t16821 - 930) * t16814) + + if Bindx == 2660: + t16823 = np.cos(phi) + t16824 = t16823 ** 2 + t16826 = t16824 ** 2 + t16827 = t16823 * t16826 + t16832 = t16827 ** 2 + t16830 = t16826 ** 2 + t16825 = t16823 * t16824 + t16828 = t16825 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3) * np.exp((2*1j) * (phi1 - phi2)) * (-8214 * t16824 - 10875 * t16825 + 97575 * t16826 + 70890 * t16827 + 818805 * t16830 - 731918 * t16832 + 111 + (-421260 + 245157 * t16828) * t16828 + (-184110 * t16828 + 205105 * t16830 - 81719 * t16832 + 453) * t16823) + + if Bindx == 2661: + t16835 = np.cos(phi) + t16836 = t16835 ** 2 + t16838 = t16836 ** 2 + t16842 = t16838 ** 2 + t16837 = t16835 * t16836 + t16840 = t16837 ** 2 + t16839 = t16835 * t16838 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.154e3) * ((1 - t16835) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t16835)) * (450 * t16836 - 5100 * t16837 - 7650 * t16838 + 38760 * t16840 - 72675 * t16842 - 3 + (29070 + 44574 * t16839) * t16839 + (-58140 * t16840 + 37145 * t16842 + 225) * t16835) + + if Bindx == 2662: + t16846 = np.cos(phi) + t16847 = t16846 ** 2 + t16848 = t16847 ** 2 + t16850 = t16848 ** 2 + t16845 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * phi1) * np.sqrt(0.6006e4) * t16845 ** 2 * (-2550 * t16848 - 14535 * t16850 - 3 + (9690 * t16848 + 7429 * t16850 + 225) * t16847) + + if Bindx == 2663: + t16852 = np.cos(phi) + t16853 = t16852 ** 2 + t16855 = t16853 ** 2 + t16859 = t16855 ** 2 + t16854 = t16852 * t16853 + t16857 = t16854 ** 2 + t16856 = t16852 * t16855 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.154e3) * np.sqrt((1 - t16852)) * ((1 + t16852) ** (0.3e1 / 0.2e1)) * (450 * t16853 + 5100 * t16854 - 7650 * t16855 + 38760 * t16857 - 72675 * t16859 - 3 + (-29070 + 44574 * t16856) * t16856 + (58140 * t16857 - 37145 * t16859 - 225) * t16852) + + if Bindx == 2664: + t16862 = np.cos(phi) + t16863 = t16862 ** 2 + t16865 = t16863 ** 2 + t16866 = t16862 * t16865 + t16871 = t16866 ** 2 + t16869 = t16865 ** 2 + t16864 = t16862 * t16863 + t16867 = t16864 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3) * np.exp((2*1j) * (phi1 + phi2)) * (-8214 * t16863 + 10875 * t16864 + 97575 * t16865 - 70890 * t16866 + 818805 * t16869 - 731918 * t16871 + 111 + (-421260 + 245157 * t16867) * t16867 + (184110 * t16867 - 205105 * t16869 + 81719 * t16871 - 453) * t16862) + + if Bindx == 2665: + t16874 = np.cos(phi) + t16875 = t16874 ** 2 + t16877 = t16875 ** 2 + t16881 = t16877 ** 2 + t16876 = t16874 * t16875 + t16879 = t16876 ** 2 + t16878 = t16874 * t16877 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.6e1) * ((1 + t16874) ** (0.5e1 / 0.2e1)) * (-6030 * t16875 - 9180 * t16876 + 82110 * t16877 - 271320 * t16879 + 159885 * t16881 + 45 + (-40698 + 163438 * t16878) * t16878 + (329460 * t16879 - 408595 * t16881 + 885) * t16874) * ((1 - t16874) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2666: + t16885 = np.cos(phi) + t16886 = t16885 ** 2 + t16888 = t16886 ** 2 + t16892 = t16888 ** 2 + t16887 = t16885 * t16886 + t16890 = t16887 ** 2 + t16889 = t16885 * t16888 + t16884 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.6e1) * t16884 ** 2 * (10385 * t16886 - 29240 * t16887 - 105910 * t16888 + 366282 * t16890 - 508079 * t16892 - 155 + (153748 + 245157 * t16889) * t16889 + (-281656 * t16890 + 163438 * t16892 + 1390) * t16885) + + if Bindx == 2667: + t16895 = np.cos(phi) + t16896 = t16895 ** 2 + t16897 = t16895 * t16896 + t16900 = t16897 ** 2 + t16898 = t16896 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-1995 * t16896 - 1330 * t16897 + 17955 * t16898 - 33649 * t16900 + 25 + (-8778 * t16898 + 28842 * t16900 + 210) * t16895) * ((1 + t16895) ** (0.7e1 / 0.2e1)) * np.sqrt(0.51e2) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t16895) ** (0.3e1 / 0.2e1)) + + if Bindx == 2668: + t16912 = np.sin(phi) + t16910 = t16912 ** 2 + t16902 = np.cos(phi) + t16903 = t16902 ** 2 + t16904 = t16902 * t16903 + t16907 = t16904 ** 2 + t16905 = t16903 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.714e3) * t16910 ** 2 * (-280 * t16903 + 1425 * t16904 - 5852 * t16907 + 5 + (2280 + 4807 * t16905) * t16905 + (-5187 * t16905 + 4807 * t16907 - 85) * t16902) + + if Bindx == 2669: + t16913 = np.cos(phi) + t16914 = t16913 ** 2 + t16916 = t16914 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (270 * t16914 - 1265 * t16916 - 5 + (-220 * t16914 + 1518 * t16916 - 10) * t16913) * ((1 + t16913) ** (0.9e1 / 0.2e1)) * np.sqrt(0.2261e4) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t16913) ** (0.5e1 / 0.2e1)) + + if Bindx == 2670: + t16927 = np.sin(phi) + t16924 = t16927 ** 2 + t16925 = t16927 * t16924 + t16918 = np.cos(phi) + t16919 = t16918 ** 2 + t16921 = t16919 ** 2 + t16920 = t16918 * t16919 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.2261e4) * t16925 ** 2 * (41 * t16919 - 319 * t16921 - 1 + (-584 + 759 * t16920) * t16920 + (1012 * t16921 + 52) * t16918) + + if Bindx == 2671: + t16928 = np.cos(phi) + t16929 = t16928 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-253 * t16929 + 9 + (506 * t16929 - 22) * t16928) * ((1 + t16928) ** (0.11e2 / 0.2e1)) * np.sqrt(0.969e3) * np.exp((1j) * (2 * phi1 + 9 * phi2)) * ((1 - t16928) ** (0.7e1 / 0.2e1)) + + if Bindx == 2672: + t16938 = np.sin(phi) + t16935 = t16938 ** 2 + t16936 = t16935 ** 2 + t16931 = np.cos(phi) + t16932 = t16931 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.7106e4) * t16936 ** 2 * (-25 * t16931 - 1 + (115 * t16931 + 22 + 69 * t16932) * t16932) + + if Bindx == 2673: + t16939 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (-1 + 6 * t16939) * ((1 + t16939) ** (0.13e2 / 0.2e1)) * np.sqrt(0.81719e5) * np.exp((1j) * (2 * phi1 + 11 * phi2)) * ((1 - t16939) ** (0.9e1 / 0.2e1)) + + if Bindx == 2674: + t16940 = np.cos(phi) + t16949 = -1 + t16940 + t16948 = 1 + t16940 + t16944 = t16948 ** 2 + t16945 = t16948 * t16944 + t16941 = t16949 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.490314e6) * t16949 * t16941 ** 2 * t16948 * t16945 ** 2 + + if Bindx == 2675: + t16950 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.81719e5) * ((1 - t16950) ** (0.15e2 / 0.2e1)) * ((1 + t16950) ** (0.9e1 / 0.2e1)) + + if Bindx == 2676: + t16958 = np.sin(phi) + t16955 = t16958 ** 2 + t16956 = t16955 ** 2 + t16951 = np.cos(phi) + t16952 = t16951 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.490314e6) * t16956 ** 2 * (-t16951 - 1 + (-11 * t16951 + 9 + 4 * t16952) * t16952) + + if Bindx == 2677: + t16959 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 10 * phi2)) * np.sqrt(0.10659e5) * ((1 - t16959) ** (0.13e2 / 0.2e1)) * ((1 + t16959) ** (0.7e1 / 0.2e1)) * (1 + (23 + 46 * t16959) * t16959) + + if Bindx == 2678: + t16969 = np.sin(phi) + t16966 = t16969 ** 2 + t16967 = t16969 * t16966 + t16960 = np.cos(phi) + t16961 = t16960 ** 2 + t16963 = t16961 ** 2 + t16962 = t16960 * t16961 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.646e3) * t16967 ** 2 * (-510 * t16961 + 825 * t16963 + 17 + (1050 + 1012 * t16962) * t16962 + (-2277 * t16963 - 117) * t16960) + + if Bindx == 2679: + t16970 = np.cos(phi) + t16971 = t16970 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.13566e5) * ((1 - t16970) ** (0.11e2 / 0.2e1)) * ((1 + t16970) ** (0.5e1 / 0.2e1)) * (-17 * t16970 - 2 + (253 * t16970 + 33 + 253 * t16971) * t16971) + + if Bindx == 2680: + t16984 = np.sin(phi) + t16982 = t16984 ** 2 + t16974 = np.cos(phi) + t16975 = t16974 ** 2 + t16976 = t16974 * t16975 + t16979 = t16976 ** 2 + t16977 = t16975 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.13566e5) * t16982 ** 2 * (47 * t16975 - 609 * t16976 - 539 * t16979 - 1 + (-135 + 1012 * t16977) * t16977 + (1953 * t16977 - 1771 * t16979 + 43) * t16974) + + if Bindx == 2681: + t16985 = np.cos(phi) + t16986 = t16985 ** 2 + t16988 = t16986 ** 2 + t16987 = t16985 * t16986 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.119e3) * ((1 - t16985) ** (0.9e1 / 0.2e1)) * ((1 + t16985) ** (0.3e1 / 0.2e1)) * (-1140 * t16986 + 3135 * t16988 + 23 + (-3230 + 9614 * t16987) * t16987 + (14421 * t16988 + 57) * t16985) + + if Bindx == 2682: + t16992 = np.cos(phi) + t16993 = t16992 ** 2 + t16995 = t16993 ** 2 + t16999 = t16995 ** 2 + t16994 = t16992 * t16993 + t16997 = t16994 ** 2 + t16996 = t16992 * t16995 + t16991 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.34e2) * t16991 ** 2 * (540 * t16993 + 5560 * t16994 - 5530 * t16995 + 21280 * t16997 - 34485 * t16999 - 9 + (-26334 + 19228 * t16996) * t16996 + (44080 * t16997 - 24035 * t16999 - 295) * t16992) + + if Bindx == 2683: + t17002 = np.cos(phi) + t17003 = t17002 ** 2 + t17004 = t17002 * t17003 + t17007 = t17004 ** 2 + t17005 = t17003 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * ((1 - t17002) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t17002)) * (5474 * t17003 + 4522 * t17004 + 49742 * t17007 - 67 + (-45220 + 81719 * t17005) * t17005 + (-76874 * t17005 + 163438 * t17007 + 306) * t17002) + + if Bindx == 2684: + t17010 = np.cos(phi) + t17011 = t17010 ** 2 + t17013 = t17011 ** 2 + t17014 = t17010 * t17013 + t17019 = t17014 ** 2 + t17017 = t17013 ** 2 + t17012 = t17010 * t17011 + t17015 = t17012 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((3*1j) * (phi1 - phi2)) * (-12213 * t17011 - 42105 * t17012 + 137430 * t17013 + 256122 * t17014 + 1090125 * t17017 - 969969 * t17019 + 177 + (-571914 + 326876 * t17015) * t17015 + (-622098 * t17015 + 650845 * t17017 - 245157 * t17019 + 1881) * t17010) + + if Bindx == 2685: + t17022 = np.cos(phi) + t17023 = t17022 ** 2 + t17025 = t17023 ** 2 + t17029 = t17025 ** 2 + t17024 = t17022 * t17023 + t17027 = t17024 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.6e1) * ((1 - t17022) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t17022)) * (-5100 * t17023 + 14280 * t17024 + 67830 * t17025 - 244188 * t17027 + 245157 * t17029 + 45 + (-27132 * t17025 - 85272 * t17027 + 163438 * t17029 - 930) * t17022) + + if Bindx == 2686: + t17032 = np.cos(phi) + t17033 = t17032 ** 2 + t17035 = t17033 ** 2 + t17039 = t17035 ** 2 + t17034 = t17032 * t17033 + t17037 = t17034 ** 2 + t17036 = t17032 * t17035 + t17031 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.231e3) * t17031 ** 2 * (1080 * t17033 + 1020 * t17034 - 11730 * t17035 + 42636 * t17037 - 61047 * t17039 - 15 + (-5814 + 29716 * t17036) * t17036 + (11628 * t17037 - 7429 * t17039 - 45) * t17032) + + if Bindx == 2687: + t17042 = np.cos(phi) + t17043 = t17042 ** 2 + t17044 = t17043 ** 2 + tfunc[..., c] = (0.25e2 / 0.512e3*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.1001e4) * ((1 - t17042) ** (0.3e1 / 0.2e1)) * ((1 + t17042) ** (0.3e1 / 0.2e1)) * t17042 * (-1020 * t17043 + 45 + (-11628 * t17043 + 5814 + 7429 * t17044) * t17044) + + if Bindx == 2688: + t17048 = np.cos(phi) + t17049 = t17048 ** 2 + t17051 = t17049 ** 2 + t17055 = t17051 ** 2 + t17050 = t17048 * t17049 + t17053 = t17050 ** 2 + t17052 = t17048 * t17051 + t17047 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.231e3) * t17047 ** 2 * (1080 * t17049 - 1020 * t17050 - 11730 * t17051 + 42636 * t17053 - 61047 * t17055 - 15 + (5814 + 29716 * t17052) * t17052 + (-11628 * t17053 + 7429 * t17055 + 45) * t17048) + + if Bindx == 2689: + t17058 = np.cos(phi) + t17059 = t17058 ** 2 + t17061 = t17059 ** 2 + t17065 = t17061 ** 2 + t17060 = t17058 * t17059 + t17063 = t17060 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t17058)) * ((1 + t17058) ** (0.5e1 / 0.2e1)) * (5100 * t17059 + 14280 * t17060 - 67830 * t17061 + 244188 * t17063 - 245157 * t17065 - 45 + (-27132 * t17061 - 85272 * t17063 + 163438 * t17065 - 930) * t17058) + + if Bindx == 2690: + t17067 = np.cos(phi) + t17068 = t17067 ** 2 + t17070 = t17068 ** 2 + t17071 = t17067 * t17070 + t17076 = t17071 ** 2 + t17074 = t17070 ** 2 + t17069 = t17067 * t17068 + t17072 = t17069 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((3*1j) * (phi1 + phi2)) * (-12213 * t17068 + 42105 * t17069 + 137430 * t17070 - 256122 * t17071 + 1090125 * t17074 - 969969 * t17076 + 177 + (-571914 + 326876 * t17072) * t17072 + (622098 * t17072 - 650845 * t17074 + 245157 * t17076 - 1881) * t17067) + + if Bindx == 2691: + t17079 = np.cos(phi) + t17080 = t17079 ** 2 + t17082 = t17080 ** 2 + t17086 = t17082 ** 2 + t17081 = t17079 * t17080 + t17084 = t17081 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * ((1 + t17079) ** (0.7e1 / 0.2e1)) * (-5780 * t17080 + 9996 * t17081 + 40698 * t17082 + 27132 * t17084 - 245157 * t17086 + 67 + (-122094 * t17082 + 213180 * t17084 + 81719 * t17086 + 239) * t17079) * ((1 - t17079) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2692: + t17089 = np.cos(phi) + t17090 = t17089 ** 2 + t17092 = t17090 ** 2 + t17096 = t17092 ** 2 + t17091 = t17089 * t17090 + t17094 = t17091 ** 2 + t17093 = t17089 * t17092 + t17088 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.34e2) * t17088 ** 2 * (540 * t17090 - 5560 * t17091 - 5530 * t17092 + 21280 * t17094 - 34485 * t17096 - 9 + (26334 + 19228 * t17093) * t17093 + (-44080 * t17094 + 24035 * t17096 + 295) * t17089) + + if Bindx == 2693: + t17099 = np.cos(phi) + t17100 = t17099 ** 2 + t17102 = t17100 ** 2 + t17101 = t17099 * t17100 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-1140 * t17100 + 3135 * t17102 + 23 + (3230 + 9614 * t17101) * t17101 + (-14421 * t17102 - 57) * t17099) * ((1 + t17099) ** (0.9e1 / 0.2e1)) * np.sqrt(0.119e3) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t17099) ** (0.3e1 / 0.2e1)) + + if Bindx == 2694: + t17115 = np.sin(phi) + t17113 = t17115 ** 2 + t17105 = np.cos(phi) + t17106 = t17105 ** 2 + t17107 = t17105 * t17106 + t17110 = t17107 ** 2 + t17108 = t17106 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.13566e5) * t17113 ** 2 * (47 * t17106 + 609 * t17107 - 539 * t17110 - 1 + (-135 + 1012 * t17108) * t17108 + (-1953 * t17108 + 1771 * t17110 - 43) * t17105) + + if Bindx == 2695: + t17116 = np.cos(phi) + t17117 = t17116 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * (17 * t17116 - 2 + (-253 * t17116 + 33 + 253 * t17117) * t17117) * ((1 + t17116) ** (0.11e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * ((1 - t17116) ** (0.5e1 / 0.2e1)) + + if Bindx == 2696: + t17129 = np.sin(phi) + t17126 = t17129 ** 2 + t17127 = t17129 * t17126 + t17120 = np.cos(phi) + t17121 = t17120 ** 2 + t17123 = t17121 ** 2 + t17122 = t17120 * t17121 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.646e3) * t17127 ** 2 * (-510 * t17121 + 825 * t17123 + 17 + (-1050 + 1012 * t17122) * t17122 + (2277 * t17123 + 117) * t17120) + + if Bindx == 2697: + t17130 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (1 + (-23 + 46 * t17130) * t17130) * ((1 + t17130) ** (0.13e2 / 0.2e1)) * np.sqrt(0.10659e5) * np.exp((1j) * (3 * phi1 + 10 * phi2)) * ((1 - t17130) ** (0.7e1 / 0.2e1)) + + if Bindx == 2698: + t17138 = np.sin(phi) + t17135 = t17138 ** 2 + t17136 = t17135 ** 2 + t17131 = np.cos(phi) + t17132 = t17131 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.490314e6) * t17136 ** 2 * (t17131 - 1 + (11 * t17131 + 9 + 4 * t17132) * t17132) + + if Bindx == 2699: + t17139 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.81719e5) * ((1 - t17139) ** (0.9e1 / 0.2e1)) * ((1 + t17139) ** (0.15e2 / 0.2e1)) + + if Bindx == 2700: + t17140 = np.cos(phi) + t17147 = -1 + t17140 + t17146 = 1 + t17140 + t17144 = t17146 ** 2 + t17141 = t17147 ** 2 + t17142 = t17141 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((4*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.81719e5) * t17142 ** 2 * t17144 ** 2 + + if Bindx == 2701: + t17148 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (4 * phi1 - 11 * phi2)) * np.sqrt(0.490314e6) * ((1 - t17148) ** (0.15e2 / 0.2e1)) * ((1 + t17148) ** (0.7e1 / 0.2e1)) * (1 + 3 * t17148) + + if Bindx == 2702: + t17158 = np.sin(phi) + t17155 = t17158 ** 2 + t17156 = t17158 * t17155 + t17149 = np.cos(phi) + t17150 = t17149 ** 2 + t17152 = t17150 ** 2 + t17151 = t17149 * t17150 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.10659e5) * t17156 ** 2 * (-85 * t17150 + 235 * t17152 + 5 + (-20 + 69 * t17151) * t17151 + (-230 * t17152 + 26) * t17149) + + if Bindx == 2703: + t17159 = np.cos(phi) + t17162 = 253 * t17159 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.646e3) * ((1 - t17159) ** (0.13e2 / 0.2e1)) * ((1 + t17159) ** (0.5e1 / 0.2e1)) * (t17162 - 1 + (t17162 + 55) * t17159) + + if Bindx == 2704: + t17173 = np.sin(phi) + t17171 = t17173 ** 2 + t17163 = np.cos(phi) + t17164 = t17163 ** 2 + t17165 = t17163 * t17164 + t17168 = t17165 ** 2 + t17166 = t17164 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.13566e5) * t17171 ** 2 * (324 * t17164 - 344 * t17165 + 836 * t17168 - 9 + (-1270 + 759 * t17166) * t17166 + (1704 * t17166 - 2024 * t17168 + 24) * t17163) + + if Bindx == 2705: + t17174 = np.cos(phi) + t17175 = t17174 ** 2 + t17177 = t17175 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.13566e5) * ((1 - t17174) ** (0.11e2 / 0.2e1)) * ((1 + t17174) ** (0.3e1 / 0.2e1)) * (-30 * t17175 + 1265 * t17177 - 3 + (550 * t17175 + 759 * t17177 - 45) * t17174) + + if Bindx == 2706: + t17180 = np.cos(phi) + t17181 = t17180 ** 2 + t17183 = t17181 ** 2 + t17187 = t17183 ** 2 + t17182 = t17180 * t17181 + t17185 = t17182 ** 2 + t17184 = t17180 * t17183 + t17179 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.119e3) * t17179 ** 2 * (-357 * t17181 + 2328 * t17182 + 2078 * t17183 - 1634 * t17185 - 4389 * t17187 + 7 + (-10260 + 4807 * t17184) * t17184 + (17176 * t17185 - 9614 * t17187 - 142) * t17180) + + if Bindx == 2707: + t17190 = np.cos(phi) + t17191 = t17190 ** 2 + t17192 = t17190 * t17191 + t17195 = t17192 ** 2 + t17193 = t17191 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.34e2) * ((1 - t17190) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t17190)) * (-3591 * t17191 - 17955 * t17192 - 5985 * t17193 + 100947 * t17195 + 85 + (65835 * t17193 + 43263 * t17195 + 441) * t17190) + + if Bindx == 2708: + t17197 = np.cos(phi) + t17198 = t17197 ** 2 + t17200 = t17198 ** 2 + t17201 = t17197 * t17200 + t17206 = t17201 ** 2 + t17204 = t17200 ** 2 + t17199 = t17197 * t17198 + t17202 = t17199 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (phi1 - phi2)) * (-14818 * t17198 - 214340 * t17199 + 174625 * t17200 + 1193944 * t17201 + 1884705 * t17204 - 1939938 * t17206 + 239 + (-838236 + 735471 * t17202) * t17202 + (-2705448 * t17202 + 2693820 * t17204 - 980628 * t17206 + 10604) * t17197) + + if Bindx == 2709: + t17209 = np.cos(phi) + t17210 = t17209 ** 2 + t17211 = t17209 * t17210 + t17214 = t17211 ** 2 + t17212 = t17210 ** 2 + tfunc[..., c] = (0.75e2 / 0.1024e4*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * ((1 - t17209) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t17209)) * (5474 * t17210 + 4522 * t17211 + 49742 * t17214 - 67 + (-45220 + 81719 * t17212) * t17212 + (-76874 * t17212 + 163438 * t17214 + 306) * t17209) + + if Bindx == 2710: + t17218 = np.cos(phi) + t17219 = t17218 ** 2 + t17221 = t17219 ** 2 + t17225 = t17221 ** 2 + t17220 = t17218 * t17219 + t17223 = t17220 ** 2 + t17222 = t17218 * t17221 + t17217 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.6e1) * t17217 ** 2 * (10385 * t17219 + 29240 * t17220 - 105910 * t17221 + 366282 * t17223 - 508079 * t17225 - 155 + (-153748 + 245157 * t17222) * t17222 + (281656 * t17223 - 163438 * t17225 - 1390) * t17218) + + if Bindx == 2711: + t17228 = np.cos(phi) + t17229 = t17228 ** 2 + t17230 = t17228 * t17229 + t17233 = t17230 ** 2 + t17231 = t17229 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.231e3) * ((1 - t17228) ** (0.5e1 / 0.2e1)) * ((1 + t17228) ** (0.3e1 / 0.2e1)) * (-510 * t17229 + 3230 * t17230 - 22610 * t17233 + 5 + (6460 + 22287 * t17231) * t17231 + (-13566 * t17231 + 14858 * t17233 - 170) * t17228) + + if Bindx == 2712: + t17243 = np.sin(phi) + t17241 = t17243 ** 2 + t17236 = np.cos(phi) + t17237 = t17236 ** 2 + t17238 = t17237 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((4*1j) * phi1) * np.sqrt(0.1001e4) * t17241 ** 2 * (-340 * t17237 + 5 + (-9044 * t17237 + 3230 + 7429 * t17238) * t17238) + + if Bindx == 2713: + t17244 = np.cos(phi) + t17245 = t17244 ** 2 + t17246 = t17244 * t17245 + t17249 = t17246 ** 2 + t17247 = t17245 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.231e3) * ((1 - t17244) ** (0.3e1 / 0.2e1)) * ((1 + t17244) ** (0.5e1 / 0.2e1)) * (-510 * t17245 - 3230 * t17246 - 22610 * t17249 + 5 + (6460 + 22287 * t17247) * t17247 + (13566 * t17247 - 14858 * t17249 + 170) * t17244) + + if Bindx == 2714: + t17253 = np.cos(phi) + t17254 = t17253 ** 2 + t17256 = t17254 ** 2 + t17260 = t17256 ** 2 + t17255 = t17253 * t17254 + t17258 = t17255 ** 2 + t17257 = t17253 * t17256 + t17252 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.6e1) * t17252 ** 2 * (10385 * t17254 - 29240 * t17255 - 105910 * t17256 + 366282 * t17258 - 508079 * t17260 - 155 + (153748 + 245157 * t17257) * t17257 + (-281656 * t17258 + 163438 * t17260 + 1390) * t17253) + + if Bindx == 2715: + t17263 = np.cos(phi) + t17264 = t17263 ** 2 + t17265 = t17263 * t17264 + t17268 = t17265 ** 2 + t17266 = t17264 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt((1 - t17263)) * ((1 + t17263) ** (0.7e1 / 0.2e1)) * (5474 * t17264 - 4522 * t17265 + 49742 * t17268 - 67 + (-45220 + 81719 * t17266) * t17266 + (76874 * t17266 - 163438 * t17268 - 306) * t17263) + + if Bindx == 2716: + t17271 = np.cos(phi) + t17272 = t17271 ** 2 + t17274 = t17272 ** 2 + t17275 = t17271 * t17274 + t17280 = t17275 ** 2 + t17278 = t17274 ** 2 + t17273 = t17271 * t17272 + t17276 = t17273 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (phi1 + phi2)) * (-14818 * t17272 + 214340 * t17273 + 174625 * t17274 - 1193944 * t17275 + 1884705 * t17278 - 1939938 * t17280 + 239 + (-838236 + 735471 * t17276) * t17276 + (2705448 * t17276 - 2693820 * t17278 + 980628 * t17280 - 10604) * t17271) + + if Bindx == 2717: + t17283 = np.cos(phi) + t17284 = t17283 ** 2 + t17285 = t17283 * t17284 + t17288 = t17285 ** 2 + t17286 = t17284 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.34e2) * ((1 + t17283) ** (0.9e1 / 0.2e1)) * (-3150 * t17284 + 21546 * t17285 + 166782 * t17288 + 85 + (-23940 + 43263 * t17286) * t17286 + (-59850 * t17286 - 144210 * t17288 - 526) * t17283) * ((1 - t17283) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2718: + t17292 = np.cos(phi) + t17293 = t17292 ** 2 + t17295 = t17293 ** 2 + t17299 = t17295 ** 2 + t17294 = t17292 * t17293 + t17297 = t17294 ** 2 + t17296 = t17292 * t17295 + t17291 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.119e3) * t17291 ** 2 * (-357 * t17293 - 2328 * t17294 + 2078 * t17295 - 1634 * t17297 - 4389 * t17299 + 7 + (10260 + 4807 * t17296) * t17296 + (-17176 * t17297 + 9614 * t17299 + 142) * t17292) + + if Bindx == 2719: + t17302 = np.cos(phi) + t17303 = t17302 ** 2 + t17305 = t17303 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (30 * t17303 - 1265 * t17305 + 3 + (550 * t17303 + 759 * t17305 - 45) * t17302) * ((1 + t17302) ** (0.11e2 / 0.2e1)) * np.sqrt(0.13566e5) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t17302) ** (0.3e1 / 0.2e1)) + + if Bindx == 2720: + t17317 = np.sin(phi) + t17315 = t17317 ** 2 + t17307 = np.cos(phi) + t17308 = t17307 ** 2 + t17309 = t17307 * t17308 + t17312 = t17309 ** 2 + t17310 = t17308 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.13566e5) * t17315 ** 2 * (324 * t17308 + 344 * t17309 + 836 * t17312 - 9 + (-1270 + 759 * t17310) * t17310 + (-1704 * t17310 + 2024 * t17312 - 24) * t17307) + + if Bindx == 2721: + t17318 = np.cos(phi) + t17319 = t17318 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * (-253 * t17319 + 1 + (253 * t17319 + 55) * t17318) * ((1 + t17318) ** (0.13e2 / 0.2e1)) * np.sqrt(0.646e3) * np.exp((1j) * (4 * phi1 + 9 * phi2)) * ((1 - t17318) ** (0.5e1 / 0.2e1)) + + if Bindx == 2722: + t17330 = np.sin(phi) + t17327 = t17330 ** 2 + t17328 = t17330 * t17327 + t17321 = np.cos(phi) + t17322 = t17321 ** 2 + t17324 = t17322 ** 2 + t17323 = t17321 * t17322 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.10659e5) * t17328 ** 2 * (-85 * t17322 + 235 * t17324 + 5 + (20 + 69 * t17323) * t17323 + (230 * t17324 - 26) * t17321) + + if Bindx == 2723: + t17331 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-1 + 3 * t17331) * ((1 + t17331) ** (0.15e2 / 0.2e1)) * np.sqrt(0.490314e6) * np.exp((1j) * (4 * phi1 + 11 * phi2)) * ((1 - t17331) ** (0.7e1 / 0.2e1)) + + if Bindx == 2724: + t17332 = np.cos(phi) + t17339 = -1 + t17332 + t17338 = 1 + t17332 + t17335 = t17338 ** 2 + t17336 = t17335 ** 2 + t17333 = t17339 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((4*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.81719e5) * t17333 ** 2 * t17336 ** 2 + + if Bindx == 2725: + t17340 = np.cos(phi) + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 - 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t17340) ** (0.17e2 / 0.2e1)) * ((1 + t17340) ** (0.7e1 / 0.2e1)) + + if Bindx == 2726: + t17341 = np.cos(phi) + t17348 = -1 + t17341 + t17347 = 1 + t17341 + t17345 = t17347 ** 2 + t17342 = t17348 ** 2 + t17343 = t17342 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.14421e5) * t17343 ** 2 * t17347 * t17345 * (5 + 12 * t17341) + + if Bindx == 2727: + t17349 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((5*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.1254e4) * ((1 - t17349) ** (0.15e2 / 0.2e1)) * ((1 + t17349) ** (0.5e1 / 0.2e1)) * (19 + (115 + 138 * t17349) * t17349) + + if Bindx == 2728: + t17360 = np.sin(phi) + t17358 = t17360 ** 2 + t17350 = np.cos(phi) + t17351 = t17350 ** 2 + t17352 = t17350 * t17351 + t17355 = t17352 ** 2 + t17353 = t17351 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.19e2) * t17358 ** 2 * (575 * t17351 + 1383 * t17352 + 4213 * t17355 - 25 + (-3535 + 1012 * t17353) * t17353 + (465 * t17353 - 3795 * t17355 - 293) * t17350) + + if Bindx == 2729: + t17361 = np.cos(phi) + t17362 = t17361 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.399e3) * ((1 - t17361) ** (0.13e2 / 0.2e1)) * ((1 + t17361) ** (0.3e1 / 0.2e1)) * (75 * t17361 - 6 + (1265 * t17361 + 627 + 759 * t17362) * t17362) + + if Bindx == 2730: + t17366 = np.cos(phi) + t17367 = t17366 ** 2 + t17369 = t17367 ** 2 + t17373 = t17369 ** 2 + t17368 = t17366 * t17367 + t17371 = t17368 ** 2 + t17370 = t17366 * t17369 + t17365 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.399e3) * t17365 ** 2 * (-1080 * t17367 + 500 * t17368 + 6010 * t17369 - 10140 * t17371 + 2915 * t17373 + 27 + (-5138 + 3036 * t17370) * t17370 + (12740 * t17371 - 8855 * t17373 - 15) * t17366) + + if Bindx == 2731: + t17376 = np.cos(phi) + t17377 = t17376 ** 2 + t17379 = t17377 ** 2 + t17378 = t17376 * t17377 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.14e2) * ((1 - t17376) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t17376)) * (-1140 * t17377 + 19855 * t17379 - 25 + (4750 + 9614 * t17378) * t17378 + (24035 * t17379 - 513) * t17376) + + if Bindx == 2732: + t17382 = np.cos(phi) + t17383 = t17382 ** 2 + t17385 = t17383 ** 2 + t17386 = t17382 * t17385 + t17391 = t17386 ** 2 + t17389 = t17385 ** 2 + t17384 = t17382 * t17383 + t17387 = t17384 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((5*1j) * (phi1 - phi2)) * (13939 * t17383 - 79585 * t17384 - 98650 * t17385 + 419402 * t17386 + 25365 * t17389 - 292809 * t17391 - 263 + (180390 + 173052 * t17387) * t17387 + (-949506 * t17387 + 964725 * t17389 - 360525 * t17391 + 4465) * t17382) + + if Bindx == 2733: + t17394 = np.cos(phi) + t17395 = t17394 ** 2 + t17396 = t17394 * t17395 + t17399 = t17396 ** 2 + t17397 = t17395 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.34e2) * ((1 - t17394) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t17394)) * (-3591 * t17395 - 17955 * t17396 - 5985 * t17397 + 100947 * t17399 + 85 + (65835 * t17397 + 43263 * t17399 + 441) * t17394) + + if Bindx == 2734: + t17402 = np.cos(phi) + t17403 = t17402 ** 2 + t17405 = t17403 ** 2 + t17409 = t17405 ** 2 + t17404 = t17402 * t17403 + t17407 = t17404 ** 2 + t17406 = t17402 * t17405 + t17401 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.34e2) * t17401 ** 2 * (540 * t17403 + 5560 * t17404 - 5530 * t17405 + 21280 * t17407 - 34485 * t17409 - 9 + (-26334 + 19228 * t17406) * t17406 + (44080 * t17407 - 24035 * t17409 - 295) * t17402) + + if Bindx == 2735: + t17412 = np.cos(phi) + t17413 = t17412 ** 2 + t17414 = t17412 * t17413 + t17417 = t17414 ** 2 + t17415 = t17413 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t17412) ** (0.7e1 / 0.2e1)) * ((1 + t17412) ** (0.3e1 / 0.2e1)) * (1995 * t17413 - 1330 * t17414 - 17955 * t17415 + 33649 * t17417 - 25 + (-8778 * t17415 + 28842 * t17417 + 210) * t17412) + + if Bindx == 2736: + t17429 = np.sin(phi) + t17427 = t17429 ** 2 + t17419 = np.cos(phi) + t17420 = t17419 ** 2 + t17421 = t17419 * t17420 + t17424 = t17421 ** 2 + t17422 = t17420 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.7854e4) * t17427 ** 2 * (-315 * t17420 - 475 * t17421 - 7049 * t17424 + 5 + (2755 + 5244 * t17422) * t17422 + (1995 * t17422 - 2185 * t17424 + 25) * t17419) + + if Bindx == 2737: + t17430 = np.cos(phi) + t17431 = t17430 ** 2 + t17432 = t17431 ** 2 + tfunc[..., c] = (-0.75e2 / 0.1024e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.34034e5) * ((1 - t17430) ** (0.5e1 / 0.2e1)) * ((1 + t17430) ** (0.5e1 / 0.2e1)) * t17430 * (-399 * t17432 - 5 + (437 * t17432 + 95) * t17431) + + if Bindx == 2738: + t17444 = np.sin(phi) + t17442 = t17444 ** 2 + t17434 = np.cos(phi) + t17435 = t17434 ** 2 + t17436 = t17434 * t17435 + t17439 = t17436 ** 2 + t17437 = t17435 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.7854e4) * t17442 ** 2 * (-315 * t17435 + 475 * t17436 - 7049 * t17439 + 5 + (2755 + 5244 * t17437) * t17437 + (-1995 * t17437 + 2185 * t17439 - 25) * t17434) + + if Bindx == 2739: + t17445 = np.cos(phi) + t17446 = t17445 ** 2 + t17447 = t17445 * t17446 + t17450 = t17447 ** 2 + t17448 = t17446 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.51e2) * ((1 - t17445) ** (0.3e1 / 0.2e1)) * ((1 + t17445) ** (0.7e1 / 0.2e1)) * (-1995 * t17446 - 1330 * t17447 + 17955 * t17448 - 33649 * t17450 + 25 + (-8778 * t17448 + 28842 * t17450 + 210) * t17445) + + if Bindx == 2740: + t17453 = np.cos(phi) + t17454 = t17453 ** 2 + t17456 = t17454 ** 2 + t17460 = t17456 ** 2 + t17455 = t17453 * t17454 + t17458 = t17455 ** 2 + t17457 = t17453 * t17456 + t17452 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.34e2) * t17452 ** 2 * (540 * t17454 - 5560 * t17455 - 5530 * t17456 + 21280 * t17458 - 34485 * t17460 - 9 + (26334 + 19228 * t17457) * t17457 + (-44080 * t17458 + 24035 * t17460 + 295) * t17453) + + if Bindx == 2741: + t17463 = np.cos(phi) + t17464 = t17463 ** 2 + t17465 = t17463 * t17464 + t17468 = t17465 ** 2 + t17466 = t17464 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.34e2) * np.sqrt((1 - t17463)) * ((1 + t17463) ** (0.9e1 / 0.2e1)) * (3591 * t17464 - 17955 * t17465 + 5985 * t17466 - 100947 * t17468 - 85 + (65835 * t17466 + 43263 * t17468 + 441) * t17463) + + if Bindx == 2742: + t17470 = np.cos(phi) + t17471 = t17470 ** 2 + t17473 = t17471 ** 2 + t17474 = t17470 * t17473 + t17479 = t17474 ** 2 + t17477 = t17473 ** 2 + t17472 = t17470 * t17471 + t17475 = t17472 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((5*1j) * (phi1 + phi2)) * (13939 * t17471 + 79585 * t17472 - 98650 * t17473 - 419402 * t17474 + 25365 * t17477 - 292809 * t17479 - 263 + (180390 + 173052 * t17475) * t17475 + (949506 * t17475 - 964725 * t17477 + 360525 * t17479 - 4465) * t17470) + + if Bindx == 2743: + t17482 = np.cos(phi) + t17483 = t17482 ** 2 + t17484 = t17482 * t17483 + t17487 = t17484 ** 2 + t17485 = t17483 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.14e2) * ((1 + t17482) ** (0.11e2 / 0.2e1)) * (1653 * t17483 + 3610 * t17484 - 24605 * t17485 - 33649 * t17487 + 25 + (43890 * t17485 + 9614 * t17487 - 538) * t17482) * ((1 - t17482) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2744: + t17490 = np.cos(phi) + t17491 = t17490 ** 2 + t17493 = t17491 ** 2 + t17497 = t17493 ** 2 + t17492 = t17490 * t17491 + t17495 = t17492 ** 2 + t17494 = t17490 * t17493 + t17489 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.399e3) * t17489 ** 2 * (-1080 * t17491 - 500 * t17492 + 6010 * t17493 - 10140 * t17495 + 2915 * t17497 + 27 + (5138 + 3036 * t17494) * t17494 + (-12740 * t17495 + 8855 * t17497 + 15) * t17490) + + if Bindx == 2745: + t17500 = np.cos(phi) + t17501 = t17500 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-75 * t17500 - 6 + (-1265 * t17500 + 627 + 759 * t17501) * t17501) * ((1 + t17500) ** (0.13e2 / 0.2e1)) * np.sqrt(0.399e3) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * ((1 - t17500) ** (0.3e1 / 0.2e1)) + + if Bindx == 2746: + t17514 = np.sin(phi) + t17512 = t17514 ** 2 + t17504 = np.cos(phi) + t17505 = t17504 ** 2 + t17506 = t17504 * t17505 + t17509 = t17506 ** 2 + t17507 = t17505 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.19e2) * t17512 ** 2 * (575 * t17505 - 1383 * t17506 + 4213 * t17509 - 25 + (-3535 + 1012 * t17507) * t17507 + (-465 * t17507 + 3795 * t17509 + 293) * t17504) + + if Bindx == 2747: + t17515 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (19 + (-115 + 138 * t17515) * t17515) * ((1 + t17515) ** (0.15e2 / 0.2e1)) * np.sqrt(0.1254e4) * np.exp((5*1j) * (phi1 + 2 * phi2)) * ((1 - t17515) ** (0.5e1 / 0.2e1)) + + if Bindx == 2748: + t17525 = np.sin(phi) + t17522 = t17525 ** 2 + t17523 = t17525 * t17522 + t17516 = np.cos(phi) + t17517 = t17516 ** 2 + t17519 = t17517 ** 2 + t17518 = t17516 * t17517 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.14421e5) * t17523 ** 2 * (10 * t17517 + 95 * t17519 - 5 + (70 + 12 * t17518) * t17518 + (55 * t17519 - 13) * t17516) + + if Bindx == 2749: + t17526 = np.cos(phi) + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 + 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t17526) ** (0.7e1 / 0.2e1)) * ((1 + t17526) ** (0.17e2 / 0.2e1)) + + if Bindx == 2750: + t17527 = np.cos(phi) + t17535 = -1 + t17527 + t17534 = 1 + t17527 + t17532 = t17534 ** 2 + t17528 = t17535 ** 2 + t17529 = t17528 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((6*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.33649e5) * t17535 * t17529 ** 2 * t17534 * t17532 + + if Bindx == 2751: + t17536 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 11 * phi2)) * np.sqrt(0.201894e6) * ((1 - t17536) ** (0.17e2 / 0.2e1)) * ((1 + t17536) ** (0.5e1 / 0.2e1)) * (1 + 2 * t17536) + + if Bindx == 2752: + t17547 = np.sin(phi) + t17545 = t17547 ** 2 + t17537 = np.cos(phi) + t17538 = t17537 ** 2 + t17539 = t17537 * t17538 + t17542 = t17539 ** 2 + t17540 = t17538 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.4389e4) * t17545 ** 2 * (-40 * t17538 + 107 * t17539 + 212 * t17542 + 5 + (-40 + 23 * t17540) * t17540 + (-145 * t17540 - 115 * t17542 - 7) * t17537) + + if Bindx == 2753: + t17548 = np.cos(phi) + t17549 = t17548 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.266e3) * ((1 - t17548) ** (0.15e2 / 0.2e1)) * ((1 + t17548) ** (0.3e1 / 0.2e1)) * (759 * t17549 + 37 + (506 * t17549 + 330) * t17548) + + if Bindx == 2754: + t17552 = np.cos(phi) + t17553 = t17552 ** 2 + t17555 = t17553 ** 2 + t17559 = t17555 ** 2 + t17554 = t17552 * t17553 + t17557 = t17554 ** 2 + t17556 = t17552 * t17555 + t17551 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.114e3) * t17551 ** 2 * (-513 * t17553 - 2928 * t17554 + 5094 * t17555 - 12714 * t17557 + 7623 * t17559 + 19 + (3960 + 1771 * t17556) * t17556 + (4368 * t17557 - 7084 * t17559 + 404) * t17552) + + if Bindx == 2755: + t17562 = np.cos(phi) + t17563 = t17562 ** 2 + t17565 = t17563 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.114e3) * ((1 - t17562) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t17562)) * (2590 * t17563 + 8855 * t17565 - 29 + (7700 * t17563 + 3542 * t17565 + 190) * t17562) + + if Bindx == 2756: + t17567 = np.cos(phi) + t17568 = t17567 ** 2 + t17570 = t17568 ** 2 + t17571 = t17567 * t17570 + t17576 = t17571 ** 2 + t17574 = t17570 ** 2 + t17569 = t17567 * t17568 + t17572 = t17569 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((6*1j) * (phi1 - phi2)) * (11298 * t17568 - 2695 * t17569 - 75285 * t17570 + 49266 * t17571 - 158175 * t17574 + 8778 * t17576 - 269 + (180516 + 33649 * t17572) * t17572 + (-178182 * t17572 + 232085 * t17574 - 100947 * t17576 - 39) * t17567) + + if Bindx == 2757: + t17579 = np.cos(phi) + t17580 = t17579 ** 2 + t17582 = t17580 ** 2 + t17581 = t17579 * t17580 + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.14e2) * ((1 - t17579) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t17579)) * (-1140 * t17580 + 19855 * t17582 - 25 + (4750 + 9614 * t17581) * t17581 + (24035 * t17582 - 513) * t17579) + + if Bindx == 2758: + t17586 = np.cos(phi) + t17587 = t17586 ** 2 + t17589 = t17587 ** 2 + t17593 = t17589 ** 2 + t17588 = t17586 * t17587 + t17591 = t17588 ** 2 + t17590 = t17586 * t17589 + t17585 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.119e3) * t17585 ** 2 * (-357 * t17587 + 2328 * t17588 + 2078 * t17589 - 1634 * t17591 - 4389 * t17593 + 7 + (-10260 + 4807 * t17590) * t17590 + (17176 * t17591 - 9614 * t17593 - 142) * t17586) + + if Bindx == 2759: + t17596 = np.cos(phi) + t17597 = t17596 ** 2 + t17599 = t17597 ** 2 + t17598 = t17596 * t17597 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.119e3) * ((1 - t17596) ** (0.9e1 / 0.2e1)) * ((1 + t17596) ** (0.3e1 / 0.2e1)) * (-1140 * t17597 + 3135 * t17599 + 23 + (-3230 + 9614 * t17598) * t17598 + (14421 * t17599 + 57) * t17596) + + if Bindx == 2760: + t17612 = np.sin(phi) + t17610 = t17612 ** 2 + t17602 = np.cos(phi) + t17603 = t17602 ** 2 + t17604 = t17602 * t17603 + t17607 = t17604 ** 2 + t17605 = t17603 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.714e3) * t17610 ** 2 * (-280 * t17603 - 1425 * t17604 - 5852 * t17607 + 5 + (2280 + 4807 * t17605) * t17605 + (5187 * t17605 - 4807 * t17607 + 85) * t17602) + + if Bindx == 2761: + t17613 = np.cos(phi) + t17614 = t17613 ** 2 + t17616 = t17614 ** 2 + t17615 = t17613 * t17614 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.561e3) * ((1 - t17613) ** (0.7e1 / 0.2e1)) * ((1 + t17613) ** (0.5e1 / 0.2e1)) * (380 * t17614 - 3325 * t17616 - 5 + (-1330 + 6118 * t17615) * t17615 + (3059 * t17616 + 95) * t17613) + + if Bindx == 2762: + t17626 = np.sin(phi) + t17623 = t17626 ** 2 + t17624 = t17626 * t17623 + t17619 = np.cos(phi) + t17620 = t17619 ** 2 + t17621 = t17620 ** 2 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((6*1j) * phi1) * np.sqrt(0.2431e4) * t17624 ** 2 * (-1995 * t17621 - 5 + (3059 * t17621 + 285) * t17620) + + if Bindx == 2763: + t17627 = np.cos(phi) + t17628 = t17627 ** 2 + t17630 = t17628 ** 2 + t17629 = t17627 * t17628 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.561e3) * ((1 - t17627) ** (0.5e1 / 0.2e1)) * ((1 + t17627) ** (0.7e1 / 0.2e1)) * (380 * t17628 - 3325 * t17630 - 5 + (1330 + 6118 * t17629) * t17629 + (-3059 * t17630 - 95) * t17627) + + if Bindx == 2764: + t17643 = np.sin(phi) + t17641 = t17643 ** 2 + t17633 = np.cos(phi) + t17634 = t17633 ** 2 + t17635 = t17633 * t17634 + t17638 = t17635 ** 2 + t17636 = t17634 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.714e3) * t17641 ** 2 * (-280 * t17634 + 1425 * t17635 - 5852 * t17638 + 5 + (2280 + 4807 * t17636) * t17636 + (-5187 * t17636 + 4807 * t17638 - 85) * t17633) + + if Bindx == 2765: + t17644 = np.cos(phi) + t17645 = t17644 ** 2 + t17647 = t17645 ** 2 + t17646 = t17644 * t17645 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.119e3) * ((1 - t17644) ** (0.3e1 / 0.2e1)) * ((1 + t17644) ** (0.9e1 / 0.2e1)) * (-1140 * t17645 + 3135 * t17647 + 23 + (3230 + 9614 * t17646) * t17646 + (-14421 * t17647 - 57) * t17644) + + if Bindx == 2766: + t17651 = np.cos(phi) + t17652 = t17651 ** 2 + t17654 = t17652 ** 2 + t17658 = t17654 ** 2 + t17653 = t17651 * t17652 + t17656 = t17653 ** 2 + t17655 = t17651 * t17654 + t17650 = np.sin(phi) + tfunc[..., c] = -(0.75e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.119e3) * t17650 ** 2 * (-357 * t17652 - 2328 * t17653 + 2078 * t17654 - 1634 * t17656 - 4389 * t17658 + 7 + (10260 + 4807 * t17655) * t17655 + (-17176 * t17656 + 9614 * t17658 + 142) * t17651) + + if Bindx == 2767: + t17661 = np.cos(phi) + t17662 = t17661 ** 2 + t17664 = t17662 ** 2 + t17663 = t17661 * t17662 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t17661)) * ((1 + t17661) ** (0.11e2 / 0.2e1)) * (-1140 * t17662 + 19855 * t17664 - 25 + (-4750 + 9614 * t17663) * t17663 + (-24035 * t17664 + 513) * t17661) + + if Bindx == 2768: + t17667 = np.cos(phi) + t17668 = t17667 ** 2 + t17670 = t17668 ** 2 + t17671 = t17667 * t17670 + t17676 = t17671 ** 2 + t17674 = t17670 ** 2 + t17669 = t17667 * t17668 + t17672 = t17669 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((6*1j) * (phi1 + phi2)) * (11298 * t17668 + 2695 * t17669 - 75285 * t17670 - 49266 * t17671 - 158175 * t17674 + 8778 * t17676 - 269 + (180516 + 33649 * t17672) * t17672 + (178182 * t17672 - 232085 * t17674 + 100947 * t17676 + 39) * t17667) + + if Bindx == 2769: + t17679 = np.cos(phi) + t17680 = t17679 ** 2 + t17682 = t17680 ** 2 + t17681 = t17679 * t17680 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.114e3) * ((1 + t17679) ** (0.13e2 / 0.2e1)) * (2780 * t17680 + 16555 * t17682 - 29 + (-10290 + 3542 * t17681) * t17681 + (-12397 * t17682 - 161) * t17679) * ((1 - t17679) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2770: + t17686 = np.cos(phi) + t17687 = t17686 ** 2 + t17689 = t17687 ** 2 + t17693 = t17689 ** 2 + t17688 = t17686 * t17687 + t17691 = t17688 ** 2 + t17690 = t17686 * t17689 + t17685 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.114e3) * t17685 ** 2 * (-513 * t17687 + 2928 * t17688 + 5094 * t17689 - 12714 * t17691 + 7623 * t17693 + 19 + (-3960 + 1771 * t17690) * t17690 + (-4368 * t17691 + 7084 * t17693 - 404) * t17686) + + if Bindx == 2771: + t17696 = np.cos(phi) + t17697 = t17696 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (-759 * t17697 - 37 + (506 * t17697 + 330) * t17696) * ((1 + t17696) ** (0.15e2 / 0.2e1)) * np.sqrt(0.266e3) * np.exp((3*1j) * (2 * phi1 + 3 * phi2)) * ((1 - t17696) ** (0.3e1 / 0.2e1)) + + if Bindx == 2772: + t17709 = np.sin(phi) + t17707 = t17709 ** 2 + t17699 = np.cos(phi) + t17700 = t17699 ** 2 + t17701 = t17699 * t17700 + t17704 = t17701 ** 2 + t17702 = t17700 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.4389e4) * t17707 ** 2 * (-40 * t17700 - 107 * t17701 + 212 * t17704 + 5 + (-40 + 23 * t17702) * t17702 + (145 * t17702 + 115 * t17704 + 7) * t17699) + + if Bindx == 2773: + t17710 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * (2 * t17710 - 1) * ((1 + t17710) ** (0.17e2 / 0.2e1)) * np.sqrt(0.201894e6) * np.exp((1j) * (6 * phi1 + 11 * phi2)) * ((1 - t17710) ** (0.5e1 / 0.2e1)) + + if Bindx == 2774: + t17711 = np.cos(phi) + t17719 = -1 + t17711 + t17718 = 1 + t17711 + t17714 = t17718 ** 2 + t17715 = t17714 ** 2 + t17712 = t17719 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((6*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.33649e5) * t17719 * t17712 * t17718 * t17715 ** 2 + + if Bindx == 2775: + t17720 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 12 * phi2)) * np.sqrt(0.10626e5) * ((1 - t17720) ** (0.19e2 / 0.2e1)) * ((1 + t17720) ** (0.5e1 / 0.2e1)) + + if Bindx == 2776: + t17722 = np.cos(phi) + t17727 = -1 + t17722 + t17723 = t17727 ** 2 + t17724 = t17723 ** 2 + t17721 = 1 + t17722 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.1771e4) * t17727 * t17724 ** 2 * t17721 ** 2 * (7 + 12 * t17722) + + if Bindx == 2777: + t17728 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 10 * phi2)) * np.sqrt(0.154e3) * ((1 - t17728) ** (0.17e2 / 0.2e1)) * ((1 + t17728) ** (0.3e1 / 0.2e1)) * (43 + (161 + 138 * t17728) * t17728) + + if Bindx == 2778: + t17730 = np.cos(phi) + t17731 = t17730 ** 2 + t17733 = t17731 ** 2 + t17737 = t17733 ** 2 + t17732 = t17730 * t17731 + t17735 = t17732 ** 2 + t17734 = t17730 * t17733 + t17729 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.21e2) * t17729 ** 2 * (1764 * t17731 - 3336 * t17732 - 2142 * t17733 - 7728 * t17735 + 9801 * t17737 - 147 + (10710 + 1012 * t17734) * t17734 + (-4704 * t17735 - 5313 * t17737 + 83) * t17730) + + if Bindx == 2779: + t17740 = np.cos(phi) + t17741 = t17740 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * ((1 - t17740) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t17740)) * (3087 * t17740 + 278 + (12397 * t17740 + 9933 + 5313 * t17741) * t17741) + + if Bindx == 2780: + t17744 = np.cos(phi) + t17745 = t17744 ** 2 + t17747 = t17745 ** 2 + t17748 = t17744 * t17747 + t17753 = t17748 ** 2 + t17751 = t17747 ** 2 + t17746 = t17744 * t17745 + t17749 = t17746 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((7*1j) * (phi1 - phi2)) * (4669 * t17745 + 38745 * t17746 - 57750 * t17747 - 88746 * t17748 - 245565 * t17751 + 78617 * t17753 - 161 + (199962 + 21252 * t17749) * t17749 + (14994 * t17749 + 125195 * t17751 - 86779 * t17753 - 4433) * t17744) + + if Bindx == 2781: + t17756 = np.cos(phi) + t17757 = t17756 ** 2 + t17759 = t17757 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.114e3) * ((1 - t17756) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t17756)) * (2590 * t17757 + 8855 * t17759 - 29 + (7700 * t17757 + 3542 * t17759 + 190) * t17756) + + if Bindx == 2782: + t17762 = np.cos(phi) + t17763 = t17762 ** 2 + t17765 = t17763 ** 2 + t17769 = t17765 ** 2 + t17764 = t17762 * t17763 + t17767 = t17764 ** 2 + t17766 = t17762 * t17765 + t17761 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.399e3) * t17761 ** 2 * (-1080 * t17763 + 500 * t17764 + 6010 * t17765 - 10140 * t17767 + 2915 * t17769 + 27 + (-5138 + 3036 * t17766) * t17766 + (12740 * t17767 - 8855 * t17769 - 15) * t17762) + + if Bindx == 2783: + t17772 = np.cos(phi) + t17773 = t17772 ** 2 + t17775 = t17773 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.13566e5) * ((1 - t17772) ** (0.11e2 / 0.2e1)) * ((1 + t17772) ** (0.3e1 / 0.2e1)) * (-30 * t17773 + 1265 * t17775 - 3 + (550 * t17773 + 759 * t17775 - 45) * t17772) + + if Bindx == 2784: + t17787 = np.sin(phi) + t17785 = t17787 ** 2 + t17777 = np.cos(phi) + t17778 = t17777 ** 2 + t17779 = t17777 * t17778 + t17782 = t17779 ** 2 + t17780 = t17778 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.13566e5) * t17785 ** 2 * (47 * t17778 - 609 * t17779 - 539 * t17782 - 1 + (-135 + 1012 * t17780) * t17780 + (1953 * t17780 - 1771 * t17782 + 43) * t17777) + + if Bindx == 2785: + t17788 = np.cos(phi) + t17789 = t17788 ** 2 + t17791 = t17789 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.2261e4) * ((1 - t17788) ** (0.9e1 / 0.2e1)) * ((1 + t17788) ** (0.5e1 / 0.2e1)) * (-270 * t17789 + 1265 * t17791 + 5 + (-220 * t17789 + 1518 * t17791 - 10) * t17788) + + if Bindx == 2786: + t17802 = np.sin(phi) + t17799 = t17802 ** 2 + t17800 = t17802 * t17799 + t17793 = np.cos(phi) + t17794 = t17793 ** 2 + t17796 = t17794 ** 2 + t17795 = t17793 * t17794 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.7106e4) * t17800 ** 2 * (250 * t17794 - 1505 * t17796 - 5 + (490 + 1932 * t17795) * t17795 + (-1127 * t17796 - 35) * t17793) + + if Bindx == 2787: + t17803 = np.cos(phi) + t17804 = t17803 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.277134e6) * ((1 - t17803) ** (0.7e1 / 0.2e1)) * ((1 + t17803) ** (0.7e1 / 0.2e1)) * t17803 * (5 + (-70 + 161 * t17804) * t17804) + + if Bindx == 2788: + t17815 = np.sin(phi) + t17812 = t17815 ** 2 + t17813 = t17815 * t17812 + t17806 = np.cos(phi) + t17807 = t17806 ** 2 + t17809 = t17807 ** 2 + t17808 = t17806 * t17807 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.7106e4) * t17813 ** 2 * (250 * t17807 - 1505 * t17809 - 5 + (-490 + 1932 * t17808) * t17808 + (1127 * t17809 + 35) * t17806) + + if Bindx == 2789: + t17816 = np.cos(phi) + t17817 = t17816 ** 2 + t17819 = t17817 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.2261e4) * ((1 - t17816) ** (0.5e1 / 0.2e1)) * ((1 + t17816) ** (0.9e1 / 0.2e1)) * (270 * t17817 - 1265 * t17819 - 5 + (-220 * t17817 + 1518 * t17819 - 10) * t17816) + + if Bindx == 2790: + t17831 = np.sin(phi) + t17829 = t17831 ** 2 + t17821 = np.cos(phi) + t17822 = t17821 ** 2 + t17823 = t17821 * t17822 + t17826 = t17823 ** 2 + t17824 = t17822 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.13566e5) * t17829 ** 2 * (47 * t17822 + 609 * t17823 - 539 * t17826 - 1 + (-135 + 1012 * t17824) * t17824 + (-1953 * t17824 + 1771 * t17826 - 43) * t17821) + + if Bindx == 2791: + t17832 = np.cos(phi) + t17833 = t17832 ** 2 + t17835 = t17833 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.13566e5) * ((1 - t17832) ** (0.3e1 / 0.2e1)) * ((1 + t17832) ** (0.11e2 / 0.2e1)) * (30 * t17833 - 1265 * t17835 + 3 + (550 * t17833 + 759 * t17835 - 45) * t17832) + + if Bindx == 2792: + t17838 = np.cos(phi) + t17839 = t17838 ** 2 + t17841 = t17839 ** 2 + t17845 = t17841 ** 2 + t17840 = t17838 * t17839 + t17843 = t17840 ** 2 + t17842 = t17838 * t17841 + t17837 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.399e3) * t17837 ** 2 * (-1080 * t17839 - 500 * t17840 + 6010 * t17841 - 10140 * t17843 + 2915 * t17845 + 27 + (5138 + 3036 * t17842) * t17842 + (-12740 * t17843 + 8855 * t17845 + 15) * t17838) + + if Bindx == 2793: + t17848 = np.cos(phi) + t17849 = t17848 ** 2 + t17851 = t17849 ** 2 + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.114e3) * np.sqrt((1 - t17848)) * ((1 + t17848) ** (0.13e2 / 0.2e1)) * (-2590 * t17849 - 8855 * t17851 + 29 + (7700 * t17849 + 3542 * t17851 + 190) * t17848) + + if Bindx == 2794: + t17853 = np.cos(phi) + t17854 = t17853 ** 2 + t17856 = t17854 ** 2 + t17857 = t17853 * t17856 + t17862 = t17857 ** 2 + t17860 = t17856 ** 2 + t17855 = t17853 * t17854 + t17858 = t17855 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((7*1j) * (phi1 + phi2)) * (4669 * t17854 - 38745 * t17855 - 57750 * t17856 + 88746 * t17857 - 245565 * t17860 + 78617 * t17862 - 161 + (199962 + 21252 * t17858) * t17858 + (-14994 * t17858 - 125195 * t17860 + 86779 * t17862 + 4433) * t17853) + + if Bindx == 2795: + t17865 = np.cos(phi) + t17866 = t17865 ** 2 + t17868 = t17866 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * ((1 + t17865) ** (0.15e2 / 0.2e1)) * (-13020 * t17866 - 17710 * t17868 - 278 + (22330 * t17866 + 5313 * t17868 + 3365) * t17865) * ((1 - t17865) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2796: + t17871 = np.cos(phi) + t17872 = t17871 ** 2 + t17874 = t17872 ** 2 + t17878 = t17874 ** 2 + t17873 = t17871 * t17872 + t17876 = t17873 ** 2 + t17875 = t17871 * t17874 + t17870 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.21e2) * t17870 ** 2 * (1764 * t17872 + 3336 * t17873 - 2142 * t17874 - 7728 * t17876 + 9801 * t17878 - 147 + (-10710 + 1012 * t17875) * t17875 + (4704 * t17876 + 5313 * t17878 - 83) * t17871) + + if Bindx == 2797: + t17881 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * (43 + (-161 + 138 * t17881) * t17881) * ((1 + t17881) ** (0.17e2 / 0.2e1)) * np.sqrt(0.154e3) * np.exp((1j) * (7 * phi1 + 10 * phi2)) * ((1 - t17881) ** (0.3e1 / 0.2e1)) + + if Bindx == 2798: + t17883 = np.cos(phi) + t17888 = 1 + t17883 + t17884 = t17888 ** 2 + t17885 = t17884 ** 2 + t17882 = -1 + t17883 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.1771e4) * t17882 ** 2 * t17888 * t17885 ** 2 * (-7 + 12 * t17883) + + if Bindx == 2799: + t17889 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 12 * phi2)) * np.sqrt(0.10626e5) * ((1 - t17889) ** (0.5e1 / 0.2e1)) * ((1 + t17889) ** (0.19e2 / 0.2e1)) + + if Bindx == 2800: + t17891 = np.cos(phi) + t17896 = -1 + t17891 + t17892 = t17896 ** 2 + t17894 = t17896 * t17892 ** 2 + t17890 = 1 + t17891 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.10626e5) * t17894 ** 2 * t17890 ** 2 + + if Bindx == 2801: + t17897 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 11 * phi2)) * np.sqrt(0.1771e4) * ((1 - t17897) ** (0.19e2 / 0.2e1)) * ((1 + t17897) ** (0.3e1 / 0.2e1)) * (2 + 3 * t17897) + + if Bindx == 2802: + t17898 = np.cos(phi) + t17903 = -1 + t17898 + t17899 = t17903 ** 2 + t17900 = t17899 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.154e3) * t17903 * t17900 ** 2 * (1 + t17898) * (29 + (92 + 69 * t17898) * t17898) + + if Bindx == 2803: + t17904 = np.cos(phi) + t17905 = t17904 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.21e2) * ((1 - t17904) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t17904)) * (506 * t17905 + 62 + (253 * t17905 + 319) * t17904) + + if Bindx == 2804: + t17907 = np.cos(phi) + t17908 = t17907 ** 2 + t17910 = t17908 ** 2 + t17911 = t17907 * t17910 + t17916 = t17911 ** 2 + t17914 = t17910 ** 2 + t17909 = t17907 * t17908 + t17912 = t17909 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((8*1j) * (phi1 - phi2)) * (-9422 * t17908 + 15120 * t17909 + 22575 * t17910 - 69216 * t17911 - 87345 * t17914 + 48818 * t17916 + 673 + (20412 + 5313 * t17912) * t17912 + (84384 * t17912 - 2800 * t17914 - 28336 * t17916 - 176) * t17907) + + if Bindx == 2805: + t17919 = np.cos(phi) + t17920 = t17919 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * ((1 - t17919) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t17919)) * (3087 * t17919 + 278 + (12397 * t17919 + 9933 + 5313 * t17920) * t17920) + + if Bindx == 2806: + t17924 = np.cos(phi) + t17925 = t17924 ** 2 + t17927 = t17925 ** 2 + t17931 = t17927 ** 2 + t17926 = t17924 * t17925 + t17929 = t17926 ** 2 + t17928 = t17924 * t17927 + t17923 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.114e3) * t17923 ** 2 * (-513 * t17925 - 2928 * t17926 + 5094 * t17927 - 12714 * t17929 + 7623 * t17931 + 19 + (3960 + 1771 * t17928) * t17928 + (4368 * t17929 - 7084 * t17931 + 404) * t17924) + + if Bindx == 2807: + t17934 = np.cos(phi) + t17935 = t17934 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.399e3) * ((1 - t17934) ** (0.13e2 / 0.2e1)) * ((1 + t17934) ** (0.3e1 / 0.2e1)) * (75 * t17934 - 6 + (1265 * t17934 + 627 + 759 * t17935) * t17935) + + if Bindx == 2808: + t17948 = np.sin(phi) + t17946 = t17948 ** 2 + t17938 = np.cos(phi) + t17939 = t17938 ** 2 + t17940 = t17938 * t17939 + t17943 = t17940 ** 2 + t17941 = t17939 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.13566e5) * t17946 ** 2 * (324 * t17939 - 344 * t17940 + 836 * t17943 - 9 + (-1270 + 759 * t17941) * t17941 + (1704 * t17941 - 2024 * t17943 + 24) * t17938) + + if Bindx == 2809: + t17949 = np.cos(phi) + t17950 = t17949 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.13566e5) * ((1 - t17949) ** (0.11e2 / 0.2e1)) * ((1 + t17949) ** (0.5e1 / 0.2e1)) * (-17 * t17949 - 2 + (253 * t17949 + 33 + 253 * t17950) * t17950) + + if Bindx == 2810: + t17962 = np.sin(phi) + t17959 = t17962 ** 2 + t17960 = t17962 * t17959 + t17953 = np.cos(phi) + t17954 = t17953 ** 2 + t17956 = t17954 ** 2 + t17955 = t17953 * t17954 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.2261e4) * t17960 ** 2 * (41 * t17954 - 319 * t17956 - 1 + (584 + 759 * t17955) * t17955 + (-1012 * t17956 - 52) * t17953) + + if Bindx == 2811: + t17963 = np.cos(phi) + t17964 = t17963 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.7106e4) * ((1 - t17963) ** (0.9e1 / 0.2e1)) * ((1 + t17963) ** (0.7e1 / 0.2e1)) * (-21 * t17963 + 2 + (161 * t17963 - 105 + 483 * t17964) * t17964) + + if Bindx == 2812: + t17973 = np.sin(phi) + t17970 = t17973 ** 2 + t17971 = t17970 ** 2 + t17967 = np.cos(phi) + t17968 = t17967 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((8*1j) * phi1) * np.sqrt(0.277134e6) * t17971 ** 2 * (1 + (-42 + 161 * t17968) * t17968) + + if Bindx == 2813: + t17974 = np.cos(phi) + t17975 = t17974 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.7106e4) * ((1 - t17974) ** (0.7e1 / 0.2e1)) * ((1 + t17974) ** (0.9e1 / 0.2e1)) * (21 * t17974 + 2 + (-161 * t17974 - 105 + 483 * t17975) * t17975) + + if Bindx == 2814: + t17987 = np.sin(phi) + t17984 = t17987 ** 2 + t17985 = t17987 * t17984 + t17978 = np.cos(phi) + t17979 = t17978 ** 2 + t17981 = t17979 ** 2 + t17980 = t17978 * t17979 + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.2261e4) * t17985 ** 2 * (41 * t17979 - 319 * t17981 - 1 + (-584 + 759 * t17980) * t17980 + (1012 * t17981 + 52) * t17978) + + if Bindx == 2815: + t17988 = np.cos(phi) + t17989 = t17988 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.13566e5) * ((1 - t17988) ** (0.5e1 / 0.2e1)) * ((1 + t17988) ** (0.11e2 / 0.2e1)) * (17 * t17988 - 2 + (-253 * t17988 + 33 + 253 * t17989) * t17989) + + if Bindx == 2816: + t18002 = np.sin(phi) + t18000 = t18002 ** 2 + t17992 = np.cos(phi) + t17993 = t17992 ** 2 + t17994 = t17992 * t17993 + t17997 = t17994 ** 2 + t17995 = t17993 ** 2 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.13566e5) * t18000 ** 2 * (324 * t17993 + 344 * t17994 + 836 * t17997 - 9 + (-1270 + 759 * t17995) * t17995 + (-1704 * t17995 + 2024 * t17997 - 24) * t17992) + + if Bindx == 2817: + t18003 = np.cos(phi) + t18004 = t18003 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.399e3) * ((1 - t18003) ** (0.3e1 / 0.2e1)) * ((1 + t18003) ** (0.13e2 / 0.2e1)) * (-75 * t18003 - 6 + (-1265 * t18003 + 627 + 759 * t18004) * t18004) + + if Bindx == 2818: + t18008 = np.cos(phi) + t18009 = t18008 ** 2 + t18011 = t18009 ** 2 + t18015 = t18011 ** 2 + t18010 = t18008 * t18009 + t18013 = t18010 ** 2 + t18012 = t18008 * t18011 + t18007 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.114e3) * t18007 ** 2 * (-513 * t18009 + 2928 * t18010 + 5094 * t18011 - 12714 * t18013 + 7623 * t18015 + 19 + (-3960 + 1771 * t18012) * t18012 + (-4368 * t18013 + 7084 * t18015 - 404) * t18008) + + if Bindx == 2819: + t18018 = np.cos(phi) + t18019 = t18018 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt((1 - t18018)) * ((1 + t18018) ** (0.15e2 / 0.2e1)) * (-3087 * t18018 + 278 + (-12397 * t18018 + 9933 + 5313 * t18019) * t18019) + + if Bindx == 2820: + t18022 = np.cos(phi) + t18023 = t18022 ** 2 + t18025 = t18023 ** 2 + t18026 = t18022 * t18025 + t18031 = t18026 ** 2 + t18029 = t18025 ** 2 + t18024 = t18022 * t18023 + t18027 = t18024 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((8*1j) * (phi1 + phi2)) * (-9422 * t18023 - 15120 * t18024 + 22575 * t18025 + 69216 * t18026 - 87345 * t18029 + 48818 * t18031 + 673 + (20412 + 5313 * t18027) * t18027 + (-84384 * t18027 + 2800 * t18029 + 28336 * t18031 + 176) * t18022) + + if Bindx == 2821: + t18034 = np.cos(phi) + t18035 = t18034 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.21e2) * ((1 + t18034) ** (0.17e2 / 0.2e1)) * (-381 * t18034 + 62 + (-759 * t18034 + 825 + 253 * t18035) * t18035) * ((1 - t18034) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2822: + t18038 = np.cos(phi) + t18043 = 1 + t18038 + t18039 = t18043 ** 2 + t18040 = t18039 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.154e3) * (-1 + t18038) * t18043 * t18040 ** 2 * (29 + (-92 + 69 * t18038) * t18038) + + if Bindx == 2823: + t18044 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * (-2 + 3 * t18044) * ((1 + t18044) ** (0.19e2 / 0.2e1)) * np.sqrt(0.1771e4) * np.exp((1j) * (8 * phi1 + 11 * phi2)) * ((1 - t18044) ** (0.3e1 / 0.2e1)) + + if Bindx == 2824: + t18046 = np.cos(phi) + t18051 = 1 + t18046 + t18047 = t18051 ** 2 + t18049 = t18051 * t18047 ** 2 + t18045 = -1 + t18046 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.10626e5) * t18045 ** 2 * t18049 ** 2 + + if Bindx == 2825: + t18052 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.506e3) * ((1 - t18052) ** (0.21e2 / 0.2e1)) * ((1 + t18052) ** (0.3e1 / 0.2e1)) + + if Bindx == 2826: + t18053 = np.cos(phi) + t18058 = -1 + t18053 + t18054 = t18058 ** 2 + t18056 = t18058 * t18054 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.759e3) * t18056 ** 2 * (1 + t18053) * (3 + 4 * t18053) + + if Bindx == 2827: + t18059 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 - 10 * phi2)) * np.sqrt(0.66e2) * ((1 - t18059) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t18059)) * (25 + (69 + 46 * t18059) * t18059) + + if Bindx == 2828: + t18060 = np.cos(phi) + t18061 = t18060 ** 2 + t18063 = t18061 ** 2 + t18064 = t18060 * t18063 + t18069 = t18064 ** 2 + t18067 = t18063 ** 2 + t18062 = t18060 * t18061 + t18065 = t18062 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((9*1j) * (phi1 - phi2)) * (-1143 * t18061 - 7915 * t18062 + 17730 * t18063 - 5058 * t18064 - 7785 * t18067 + 17589 * t18069 - 381 + (-25998 + 1012 * t18065) * t18065 + (34506 * t18065 - 17505 * t18067 - 6831 * t18069 + 1779) * t18060) + + if Bindx == 2829: + t18072 = np.cos(phi) + t18073 = t18072 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.21e2) * ((1 - t18072) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t18072)) * (506 * t18073 + 62 + (253 * t18073 + 319) * t18072) + + if Bindx == 2830: + t18076 = np.cos(phi) + t18077 = t18076 ** 2 + t18079 = t18077 ** 2 + t18083 = t18079 ** 2 + t18078 = t18076 * t18077 + t18081 = t18078 ** 2 + t18080 = t18076 * t18079 + t18075 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.21e2) * t18075 ** 2 * (1764 * t18077 - 3336 * t18078 - 2142 * t18079 - 7728 * t18081 + 9801 * t18083 - 147 + (10710 + 1012 * t18080) * t18080 + (-4704 * t18081 - 5313 * t18083 + 83) * t18076) + + if Bindx == 2831: + t18086 = np.cos(phi) + t18087 = t18086 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.266e3) * ((1 - t18086) ** (0.15e2 / 0.2e1)) * ((1 + t18086) ** (0.3e1 / 0.2e1)) * (759 * t18087 + 37 + (506 * t18087 + 330) * t18086) + + if Bindx == 2832: + t18099 = np.sin(phi) + t18097 = t18099 ** 2 + t18089 = np.cos(phi) + t18090 = t18089 ** 2 + t18091 = t18089 * t18090 + t18094 = t18091 ** 2 + t18092 = t18090 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.19e2) * t18097 ** 2 * (575 * t18090 + 1383 * t18091 + 4213 * t18094 - 25 + (-3535 + 1012 * t18092) * t18092 + (465 * t18092 - 3795 * t18094 - 293) * t18089) + + if Bindx == 2833: + t18100 = np.cos(phi) + t18103 = 253 * t18100 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.646e3) * ((1 - t18100) ** (0.13e2 / 0.2e1)) * ((1 + t18100) ** (0.5e1 / 0.2e1)) * (t18103 - 1 + (t18103 + 55) * t18100) + + if Bindx == 2834: + t18113 = np.sin(phi) + t18110 = t18113 ** 2 + t18111 = t18113 * t18110 + t18104 = np.cos(phi) + t18105 = t18104 ** 2 + t18107 = t18105 ** 2 + t18106 = t18104 * t18105 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.646e3) * t18111 ** 2 * (-510 * t18105 + 825 * t18107 + 17 + (1050 + 1012 * t18106) * t18106 + (-2277 * t18107 - 117) * t18104) + + if Bindx == 2835: + t18114 = np.cos(phi) + t18115 = t18114 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t18114) ** (0.11e2 / 0.2e1)) * ((1 + t18114) ** (0.7e1 / 0.2e1)) * (253 * t18115 - 9 + (506 * t18115 - 22) * t18114) + + if Bindx == 2836: + t18124 = np.sin(phi) + t18121 = t18124 ** 2 + t18122 = t18121 ** 2 + t18117 = np.cos(phi) + t18118 = t18117 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 - phi2)) * np.sqrt(0.149226e6) * t18122 ** 2 * (9 * t18117 + 1 + (-69 * t18117 - 33 + 92 * t18118) * t18118) + + if Bindx == 2837: + t18125 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.646646e6) * ((1 - t18125) ** (0.9e1 / 0.2e1)) * ((1 + t18125) ** (0.9e1 / 0.2e1)) * t18125 * (23 * t18125 ** 2 - 3) + + if Bindx == 2838: + t18133 = np.sin(phi) + t18130 = t18133 ** 2 + t18131 = t18130 ** 2 + t18126 = np.cos(phi) + t18127 = t18126 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + phi2)) * np.sqrt(0.149226e6) * t18131 ** 2 * (-9 * t18126 + 1 + (69 * t18126 - 33 + 92 * t18127) * t18127) + + if Bindx == 2839: + t18134 = np.cos(phi) + t18135 = t18134 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.969e3) * ((1 - t18134) ** (0.7e1 / 0.2e1)) * ((1 + t18134) ** (0.11e2 / 0.2e1)) * (-253 * t18135 + 9 + (506 * t18135 - 22) * t18134) + + if Bindx == 2840: + t18146 = np.sin(phi) + t18143 = t18146 ** 2 + t18144 = t18146 * t18143 + t18137 = np.cos(phi) + t18138 = t18137 ** 2 + t18140 = t18138 ** 2 + t18139 = t18137 * t18138 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.646e3) * t18144 ** 2 * (-510 * t18138 + 825 * t18140 + 17 + (-1050 + 1012 * t18139) * t18139 + (2277 * t18140 + 117) * t18137) + + if Bindx == 2841: + t18147 = np.cos(phi) + t18148 = t18147 ** 2 + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.646e3) * ((1 - t18147) ** (0.5e1 / 0.2e1)) * ((1 + t18147) ** (0.13e2 / 0.2e1)) * (-253 * t18148 + 1 + (253 * t18148 + 55) * t18147) + + if Bindx == 2842: + t18160 = np.sin(phi) + t18158 = t18160 ** 2 + t18150 = np.cos(phi) + t18151 = t18150 ** 2 + t18152 = t18150 * t18151 + t18155 = t18152 ** 2 + t18153 = t18151 ** 2 + tfunc[..., c] = (0.75e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.19e2) * t18158 ** 2 * (575 * t18151 - 1383 * t18152 + 4213 * t18155 - 25 + (-3535 + 1012 * t18153) * t18153 + (-465 * t18153 + 3795 * t18155 + 293) * t18150) + + if Bindx == 2843: + t18161 = np.cos(phi) + t18162 = t18161 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.266e3) * ((1 - t18161) ** (0.3e1 / 0.2e1)) * ((1 + t18161) ** (0.15e2 / 0.2e1)) * (-759 * t18162 - 37 + (506 * t18162 + 330) * t18161) + + if Bindx == 2844: + t18165 = np.cos(phi) + t18166 = t18165 ** 2 + t18168 = t18166 ** 2 + t18172 = t18168 ** 2 + t18167 = t18165 * t18166 + t18170 = t18167 ** 2 + t18169 = t18165 * t18168 + t18164 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.21e2) * t18164 ** 2 * (1764 * t18166 + 3336 * t18167 - 2142 * t18168 - 7728 * t18170 + 9801 * t18172 - 147 + (-10710 + 1012 * t18169) * t18169 + (4704 * t18170 + 5313 * t18172 - 83) * t18165) + + if Bindx == 2845: + t18175 = np.cos(phi) + t18176 = t18175 ** 2 + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.21e2) * np.sqrt((1 - t18175)) * ((1 + t18175) ** (0.17e2 / 0.2e1)) * (-506 * t18176 - 62 + (253 * t18176 + 319) * t18175) + + if Bindx == 2846: + t18178 = np.cos(phi) + t18179 = t18178 ** 2 + t18181 = t18179 ** 2 + t18182 = t18178 * t18181 + t18187 = t18182 ** 2 + t18185 = t18181 ** 2 + t18180 = t18178 * t18179 + t18183 = t18180 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((9*1j) * (phi1 + phi2)) * (-1143 * t18179 + 7915 * t18180 + 17730 * t18181 + 5058 * t18182 - 7785 * t18185 + 17589 * t18187 - 381 + (-25998 + 1012 * t18183) * t18183 + (-34506 * t18183 + 17505 * t18185 + 6831 * t18187 - 1779) * t18178) + + if Bindx == 2847: + t18190 = np.cos(phi) + t18191 = t18190 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.66e2) * ((1 + t18190) ** (0.19e2 / 0.2e1)) * (-115 * t18191 - 25 + (46 * t18191 + 94) * t18190) * ((1 - t18190) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2848: + t18193 = np.cos(phi) + t18198 = 1 + t18193 + t18194 = t18198 ** 2 + t18196 = t18198 * t18194 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.759e3) * (-1 + t18193) * t18196 ** 2 * (-3 + 4 * t18193) + + if Bindx == 2849: + t18199 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.506e3) * ((1 - t18199) ** (0.3e1 / 0.2e1)) * ((1 + t18199) ** (0.21e2 / 0.2e1)) + + if Bindx == 2850: + t18200 = np.cos(phi) + t18206 = -1 + t18200 + t18201 = t18206 ** 2 + t18203 = t18206 * t18201 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.69e2) * t18206 * t18203 ** 2 * (1 + t18200) + + if Bindx == 2851: + t18207 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 11 * phi2)) * np.sqrt(0.46e2) * ((1 - t18207) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t18207)) * (5 + 6 * t18207) + + if Bindx == 2852: + t18208 = np.cos(phi) + t18209 = t18208 ** 2 + t18211 = t18209 ** 2 + t18212 = t18208 * t18211 + t18217 = t18212 ** 2 + t18215 = t18211 ** 2 + t18210 = t18208 * t18209 + t18213 = t18210 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((10*1j) * (phi1 - phi2)) * (1034 * t18209 - 1155 * t18210 - 825 * t18211 + 4026 * t18212 + 2805 * t18215 + 2002 * t18217 + 47 + (-4620 + 69 * t18213) * t18213 + (1122 * t18213 - 3575 * t18215 - 575 * t18217 - 355) * t18208) + + if Bindx == 2853: + t18220 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 9 * phi2)) * np.sqrt(0.66e2) * ((1 - t18220) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t18220)) * (25 + (69 + 46 * t18220) * t18220) + + if Bindx == 2854: + t18222 = np.cos(phi) + t18223 = t18222 ** 2 + t18225 = t18223 ** 2 + t18229 = t18225 ** 2 + t18224 = t18222 * t18223 + t18227 = t18224 ** 2 + t18226 = t18222 * t18225 + t18221 = np.sin(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.154e3) * t18221 ** 2 * (145 * t18223 + 400 * t18224 - 1190 * t18225 + 490 * t18227 + 1225 * t18229 + 29 + (952 + 69 * t18226) * t18226 + (-1520 * t18227 - 460 * t18229 - 140) * t18222) + + if Bindx == 2855: + t18232 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 7 * phi2)) * np.sqrt(0.154e3) * ((1 - t18232) ** (0.17e2 / 0.2e1)) * ((1 + t18232) ** (0.3e1 / 0.2e1)) * (43 + (161 + 138 * t18232) * t18232) + + if Bindx == 2856: + t18243 = np.sin(phi) + t18241 = t18243 ** 2 + t18233 = np.cos(phi) + t18234 = t18233 ** 2 + t18235 = t18233 * t18234 + t18238 = t18235 ** 2 + t18236 = t18234 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.4389e4) * t18241 ** 2 * (-40 * t18234 + 107 * t18235 + 212 * t18238 + 5 + (-40 + 23 * t18236) * t18236 + (-145 * t18236 - 115 * t18238 - 7) * t18233) + + if Bindx == 2857: + t18244 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((5*1j) * (2 * phi1 - phi2)) * np.sqrt(0.1254e4) * ((1 - t18244) ** (0.15e2 / 0.2e1)) * ((1 + t18244) ** (0.5e1 / 0.2e1)) * (19 + (115 + 138 * t18244) * t18244) + + if Bindx == 2858: + t18254 = np.sin(phi) + t18251 = t18254 ** 2 + t18252 = t18254 * t18251 + t18245 = np.cos(phi) + t18246 = t18245 ** 2 + t18248 = t18246 ** 2 + t18247 = t18245 * t18246 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.10659e5) * t18252 ** 2 * (-85 * t18246 + 235 * t18248 + 5 + (-20 + 69 * t18247) * t18247 + (-230 * t18248 + 26) * t18245) + + if Bindx == 2859: + t18255 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 - 3 * phi2)) * np.sqrt(0.10659e5) * ((1 - t18255) ** (0.13e2 / 0.2e1)) * ((1 + t18255) ** (0.7e1 / 0.2e1)) * (1 + (23 + 46 * t18255) * t18255) + + if Bindx == 2860: + t18263 = np.sin(phi) + t18260 = t18263 ** 2 + t18261 = t18260 ** 2 + t18256 = np.cos(phi) + t18257 = t18256 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.7106e4) * t18261 ** 2 * (25 * t18256 - 1 + (-115 * t18256 + 22 + 69 * t18257) * t18257) + + if Bindx == 2861: + t18264 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 - phi2)) * np.sqrt(0.2261e4) * ((1 - t18264) ** (0.11e2 / 0.2e1)) * ((1 + t18264) ** (0.9e1 / 0.2e1)) * (-5 + (23 + 138 * t18264) * t18264) + + if Bindx == 2862: + t18270 = np.sin(phi) + t18266 = t18270 ** 2 + t18268 = t18270 * t18266 ** 2 + t18265 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.1024e4) * np.exp((10*1j) * phi1) * np.sqrt(0.88179e5) * t18268 ** 2 * (23 * t18265 ** 2 - 1) + + if Bindx == 2863: + t18271 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 + phi2)) * np.sqrt(0.2261e4) * ((1 - t18271) ** (0.9e1 / 0.2e1)) * ((1 + t18271) ** (0.11e2 / 0.2e1)) * (-5 + (-23 + 138 * t18271) * t18271) + + if Bindx == 2864: + t18279 = np.sin(phi) + t18276 = t18279 ** 2 + t18277 = t18276 ** 2 + t18272 = np.cos(phi) + t18273 = t18272 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.7106e4) * t18277 ** 2 * (-25 * t18272 - 1 + (115 * t18272 + 22 + 69 * t18273) * t18273) + + if Bindx == 2865: + t18280 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (10 * phi1 + 3 * phi2)) * np.sqrt(0.10659e5) * ((1 - t18280) ** (0.7e1 / 0.2e1)) * ((1 + t18280) ** (0.13e2 / 0.2e1)) * (1 + (-23 + 46 * t18280) * t18280) + + if Bindx == 2866: + t18290 = np.sin(phi) + t18287 = t18290 ** 2 + t18288 = t18290 * t18287 + t18281 = np.cos(phi) + t18282 = t18281 ** 2 + t18284 = t18282 ** 2 + t18283 = t18281 * t18282 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.10659e5) * t18288 ** 2 * (-85 * t18282 + 235 * t18284 + 5 + (20 + 69 * t18283) * t18283 + (230 * t18284 - 26) * t18281) + + if Bindx == 2867: + t18291 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((5*1j) * (2 * phi1 + phi2)) * np.sqrt(0.1254e4) * ((1 - t18291) ** (0.5e1 / 0.2e1)) * ((1 + t18291) ** (0.15e2 / 0.2e1)) * (19 + (-115 + 138 * t18291) * t18291) + + if Bindx == 2868: + t18302 = np.sin(phi) + t18300 = t18302 ** 2 + t18292 = np.cos(phi) + t18293 = t18292 ** 2 + t18294 = t18292 * t18293 + t18297 = t18294 ** 2 + t18295 = t18293 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.4389e4) * t18300 ** 2 * (-40 * t18293 - 107 * t18294 + 212 * t18297 + 5 + (-40 + 23 * t18295) * t18295 + (145 * t18295 + 115 * t18297 + 7) * t18292) + + if Bindx == 2869: + t18303 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 7 * phi2)) * np.sqrt(0.154e3) * ((1 - t18303) ** (0.3e1 / 0.2e1)) * ((1 + t18303) ** (0.17e2 / 0.2e1)) * (43 + (-161 + 138 * t18303) * t18303) + + if Bindx == 2870: + t18304 = np.cos(phi) + t18309 = 1 + t18304 + t18305 = t18309 ** 2 + t18306 = t18305 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.154e3) * (-1 + t18304) * t18309 * t18306 ** 2 * (29 + (-92 + 69 * t18304) * t18304) + + if Bindx == 2871: + t18310 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.66e2) * np.sqrt((1 - t18310)) * ((1 + t18310) ** (0.19e2 / 0.2e1)) * (25 + (-69 + 46 * t18310) * t18310) + + if Bindx == 2872: + t18311 = np.cos(phi) + t18312 = t18311 ** 2 + t18314 = t18312 ** 2 + t18315 = t18311 * t18314 + t18320 = t18315 ** 2 + t18318 = t18314 ** 2 + t18313 = t18311 * t18312 + t18316 = t18313 ** 2 + tfunc[..., c] = (0.25e2 / 0.1024e4) * np.exp((10*1j) * (phi1 + phi2)) * (1034 * t18312 + 1155 * t18313 - 825 * t18314 - 4026 * t18315 + 2805 * t18318 + 2002 * t18320 + 47 + (-4620 + 69 * t18316) * t18316 + (-1122 * t18316 + 3575 * t18318 + 575 * t18320 + 355) * t18311) + + if Bindx == 2873: + t18323 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.46e2) * ((1 + t18323) ** (0.21e2 / 0.2e1)) * (5 + (-11 + 6 * t18323) * t18323) * ((1 - t18323) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2874: + t18324 = np.cos(phi) + t18330 = 1 + t18324 + t18325 = t18330 ** 2 + t18327 = t18330 * t18325 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.69e2) * (-1 + t18324) * t18330 * t18327 ** 2 + + if Bindx == 2875: + t18331 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 12 * phi2)) * np.sqrt(0.6e1) * ((1 - t18331) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t18331)) + + if Bindx == 2876: + t18332 = np.cos(phi) + t18333 = t18332 ** 2 + t18335 = t18333 ** 2 + t18336 = t18332 * t18335 + t18341 = t18336 ** 2 + t18339 = t18335 ** 2 + t18334 = t18332 * t18333 + t18337 = t18334 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((11*1j) * (phi1 - phi2)) * (-473 * t18333 + 1155 * t18334 - 1650 * t18335 + 1122 * t18336 + 2145 * t18339 + 539 * t18341 - 11 + (462 + 12 * t18337) * t18337 + (-1914 * t18337 - 1375 * t18339 - 121 * t18341 + 109) * t18332) + + if Bindx == 2877: + t18344 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 10 * phi2)) * np.sqrt(0.46e2) * ((1 - t18344) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t18344)) * (5 + 6 * t18344) + + if Bindx == 2878: + t18345 = np.cos(phi) + t18350 = -1 + t18345 + t18346 = t18350 ** 2 + t18348 = t18350 * t18346 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * (3 + 4 * t18345) * (1 + t18345) * t18348 ** 2 * np.sqrt(0.759e3) * np.exp((1j) * (11 * phi1 - 9 * phi2)) + + if Bindx == 2879: + t18351 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 - 8 * phi2)) * np.sqrt(0.1771e4) * ((1 - t18351) ** (0.19e2 / 0.2e1)) * ((1 + t18351) ** (0.3e1 / 0.2e1)) * (2 + 3 * t18351) + + if Bindx == 2880: + t18353 = np.cos(phi) + t18358 = -1 + t18353 + t18354 = t18358 ** 2 + t18355 = t18354 ** 2 + t18352 = 1 + t18353 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.1771e4) * t18358 * t18355 ** 2 * t18352 ** 2 * (7 + 12 * t18353) + + if Bindx == 2881: + t18359 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 6 * phi2)) * np.sqrt(0.201894e6) * ((1 - t18359) ** (0.17e2 / 0.2e1)) * ((1 + t18359) ** (0.5e1 / 0.2e1)) * (1 + 2 * t18359) + + if Bindx == 2882: + t18369 = np.sin(phi) + t18366 = t18369 ** 2 + t18367 = t18369 * t18366 + t18360 = np.cos(phi) + t18361 = t18360 ** 2 + t18363 = t18361 ** 2 + t18362 = t18360 * t18361 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.14421e5) * t18367 ** 2 * (10 * t18361 + 95 * t18363 - 5 + (-70 + 12 * t18362) * t18362 + (-55 * t18363 + 13) * t18360) + + if Bindx == 2883: + t18370 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 4 * phi2)) * np.sqrt(0.490314e6) * ((1 - t18370) ** (0.15e2 / 0.2e1)) * ((1 + t18370) ** (0.7e1 / 0.2e1)) * (1 + 3 * t18370) + + if Bindx == 2884: + t18378 = np.sin(phi) + t18375 = t18378 ** 2 + t18376 = t18375 ** 2 + t18371 = np.cos(phi) + t18372 = t18371 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.490314e6) * t18376 ** 2 * (-t18371 - 1 + (-11 * t18371 + 9 + 4 * t18372) * t18372) + + if Bindx == 2885: + t18379 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 - 2 * phi2)) * np.sqrt(0.81719e5) * ((1 - t18379) ** (0.13e2 / 0.2e1)) * ((1 + t18379) ** (0.9e1 / 0.2e1)) * (1 + 6 * t18379) + + if Bindx == 2886: + t18385 = np.sin(phi) + t18381 = t18385 ** 2 + t18383 = t18385 * t18381 ** 2 + t18380 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 - phi2)) * np.sqrt(0.104006e6) * t18383 ** 2 * (-1 + (-11 + 12 * t18380) * t18380) + + if Bindx == 2887: + t18386 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.4056234e7) * ((1 - t18386) ** (0.11e2 / 0.2e1)) * ((1 + t18386) ** (0.11e2 / 0.2e1)) * t18386 + + if Bindx == 2888: + t18392 = np.sin(phi) + t18388 = t18392 ** 2 + t18390 = t18392 * t18388 ** 2 + t18387 = np.cos(phi) + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + phi2)) * np.sqrt(0.104006e6) * t18390 ** 2 * (-1 + (11 + 12 * t18387) * t18387) + + if Bindx == 2889: + t18393 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 + 2 * phi2)) * np.sqrt(0.81719e5) * ((1 - t18393) ** (0.9e1 / 0.2e1)) * ((1 + t18393) ** (0.13e2 / 0.2e1)) * (-1 + 6 * t18393) + + if Bindx == 2890: + t18401 = np.sin(phi) + t18398 = t18401 ** 2 + t18399 = t18398 ** 2 + t18394 = np.cos(phi) + t18395 = t18394 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.490314e6) * t18399 ** 2 * (t18394 - 1 + (11 * t18394 + 9 + 4 * t18395) * t18395) + + if Bindx == 2891: + t18402 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 4 * phi2)) * np.sqrt(0.490314e6) * ((1 - t18402) ** (0.7e1 / 0.2e1)) * ((1 + t18402) ** (0.15e2 / 0.2e1)) * (-1 + 3 * t18402) + + if Bindx == 2892: + t18412 = np.sin(phi) + t18409 = t18412 ** 2 + t18410 = t18412 * t18409 + t18403 = np.cos(phi) + t18404 = t18403 ** 2 + t18406 = t18404 ** 2 + t18405 = t18403 * t18404 + tfunc[..., c] = -(0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.14421e5) * t18410 ** 2 * (10 * t18404 + 95 * t18406 - 5 + (70 + 12 * t18405) * t18405 + (55 * t18406 - 13) * t18403) + + if Bindx == 2893: + t18413 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 6 * phi2)) * np.sqrt(0.201894e6) * ((1 - t18413) ** (0.5e1 / 0.2e1)) * ((1 + t18413) ** (0.17e2 / 0.2e1)) * (2 * t18413 - 1) + + if Bindx == 2894: + t18415 = np.cos(phi) + t18420 = 1 + t18415 + t18416 = t18420 ** 2 + t18417 = t18416 ** 2 + t18414 = -1 + t18415 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.1771e4) * t18414 ** 2 * t18420 * t18417 ** 2 * (-7 + 12 * t18415) + + if Bindx == 2895: + t18421 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (11 * phi1 + 8 * phi2)) * np.sqrt(0.1771e4) * ((1 - t18421) ** (0.3e1 / 0.2e1)) * ((1 + t18421) ** (0.19e2 / 0.2e1)) * (-2 + 3 * t18421) + + if Bindx == 2896: + t18422 = np.cos(phi) + t18427 = 1 + t18422 + t18423 = t18427 ** 2 + t18425 = t18427 * t18423 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((1j) * (11 * phi1 + 9 * phi2)) * np.sqrt(0.759e3) * (-1 + t18422) * t18425 ** 2 * (-3 + 4 * t18422) + + if Bindx == 2897: + t18428 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.46e2) * np.sqrt((1 - t18428)) * ((1 + t18428) ** (0.21e2 / 0.2e1)) * (-5 + 6 * t18428) + + if Bindx == 2898: + t18429 = np.cos(phi) + t18430 = t18429 ** 2 + t18432 = t18430 ** 2 + t18433 = t18429 * t18432 + t18438 = t18433 ** 2 + t18436 = t18432 ** 2 + t18431 = t18429 * t18430 + t18434 = t18431 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((11*1j) * (phi1 + phi2)) * (-473 * t18430 - 1155 * t18431 - 1650 * t18432 - 1122 * t18433 + 2145 * t18436 + 539 * t18438 - 11 + (462 + 12 * t18434) * t18434 + (1914 * t18434 + 1375 * t18436 + 121 * t18438 - 109) * t18429) + + if Bindx == 2899: + t18441 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 12 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t18441)) * ((1 + t18441) ** (0.23e2 / 0.2e1)) + + if Bindx == 2900: + t18442 = np.cos(phi) + t18454 = -12 * t18442 + t18443 = t18442 ** 2 + t18444 = t18442 * t18443 + t18447 = t18444 ** 2 + t18445 = t18443 ** 2 + t18446 = t18442 * t18445 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((12*1j) * (phi1 - phi2)) * (66 * t18443 - 220 * t18444 + t18454 + 1 + (-792 * t18442 + 924 + t18447) * t18447 + (-792 + (t18454 + 66) * t18446) * t18446 + (495 + (-220 * t18442 + 495) * t18445) * t18445) + + if Bindx == 2901: + t18455 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 - 11 * phi2)) * np.sqrt(0.6e1) * ((1 - t18455) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t18455)) + + if Bindx == 2902: + t18456 = np.cos(phi) + t18462 = -1 + t18456 + t18457 = t18462 ** 2 + t18459 = t18462 * t18457 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.69e2) * t18462 * t18459 ** 2 * (1 + t18456) + + if Bindx == 2903: + t18463 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.506e3) * ((1 - t18463) ** (0.21e2 / 0.2e1)) * ((1 + t18463) ** (0.3e1 / 0.2e1)) + + if Bindx == 2904: + t18465 = np.cos(phi) + t18470 = -1 + t18465 + t18466 = t18470 ** 2 + t18468 = t18470 * t18466 ** 2 + t18464 = 1 + t18465 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.10626e5) * t18468 ** 2 * t18464 ** 2 + + if Bindx == 2905: + t18471 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 - 7 * phi2)) * np.sqrt(0.10626e5) * ((1 - t18471) ** (0.19e2 / 0.2e1)) * ((1 + t18471) ** (0.5e1 / 0.2e1)) + + if Bindx == 2906: + t18472 = np.cos(phi) + t18480 = -1 + t18472 + t18479 = 1 + t18472 + t18477 = t18479 ** 2 + t18473 = t18480 ** 2 + t18474 = t18473 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((6*1j) * (2 * phi1 - phi2)) * np.sqrt(0.33649e5) * t18480 * t18474 ** 2 * t18479 * t18477 + + if Bindx == 2907: + t18481 = np.cos(phi) + tfunc[..., c] = (-0.75e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 - 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t18481) ** (0.17e2 / 0.2e1)) * ((1 + t18481) ** (0.7e1 / 0.2e1)) + + if Bindx == 2908: + t18482 = np.cos(phi) + t18489 = -1 + t18482 + t18488 = 1 + t18482 + t18486 = t18488 ** 2 + t18483 = t18489 ** 2 + t18484 = t18483 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 - phi2)) * np.sqrt(0.81719e5) * t18484 ** 2 * t18486 ** 2 + + if Bindx == 2909: + t18490 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (4 * phi1 - phi2)) * np.sqrt(0.81719e5) * ((1 - t18490) ** (0.15e2 / 0.2e1)) * ((1 + t18490) ** (0.9e1 / 0.2e1)) + + if Bindx == 2910: + t18491 = np.cos(phi) + t18500 = -1 + t18491 + t18499 = 1 + t18491 + t18496 = t18499 ** 2 + t18492 = t18500 ** 2 + t18493 = t18500 * t18492 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 - phi2)) * np.sqrt(0.490314e6) * t18500 * t18493 ** 2 * t18499 * t18496 ** 2 + + if Bindx == 2911: + t18501 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((1j) * (12 * phi1 - phi2)) * np.sqrt(0.156009e6) * ((1 - t18501) ** (0.13e2 / 0.2e1)) * ((1 + t18501) ** (0.11e2 / 0.2e1)) + + if Bindx == 2912: + t18506 = np.sin(phi) + t18502 = t18506 ** 2 + t18503 = t18506 * t18502 + t18504 = t18503 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((12*1j) * phi1) * np.sqrt(0.676039e6) * t18504 ** 2 + + if Bindx == 2913: + t18507 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.1024e4*1j) * np.exp((1j) * (12 * phi1 + phi2)) * np.sqrt(0.156009e6) * ((1 - t18507) ** (0.11e2 / 0.2e1)) * ((1 + t18507) ** (0.13e2 / 0.2e1)) + + if Bindx == 2914: + t18508 = np.cos(phi) + t18517 = -1 + t18508 + t18516 = 1 + t18508 + t18512 = t18516 ** 2 + t18513 = t18516 * t18512 + t18509 = t18517 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 + phi2)) * np.sqrt(0.490314e6) * t18517 * t18509 ** 2 * t18516 * t18513 ** 2 + + if Bindx == 2915: + t18518 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.1024e4*1j) * np.exp((3*1j) * (4 * phi1 + phi2)) * np.sqrt(0.81719e5) * ((1 - t18518) ** (0.9e1 / 0.2e1)) * ((1 + t18518) ** (0.15e2 / 0.2e1)) + + if Bindx == 2916: + t18519 = np.cos(phi) + t18526 = -1 + t18519 + t18525 = 1 + t18519 + t18522 = t18525 ** 2 + t18523 = t18522 ** 2 + t18520 = t18526 ** 2 + tfunc[..., c] = (0.75e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 + phi2)) * np.sqrt(0.81719e5) * t18520 ** 2 * t18523 ** 2 + + if Bindx == 2917: + t18527 = np.cos(phi) + tfunc[..., c] = (0.75e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 + 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t18527) ** (0.7e1 / 0.2e1)) * ((1 + t18527) ** (0.17e2 / 0.2e1)) + + if Bindx == 2918: + t18528 = np.cos(phi) + t18536 = -1 + t18528 + t18535 = 1 + t18528 + t18531 = t18535 ** 2 + t18532 = t18531 ** 2 + t18529 = t18536 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((6*1j) * (2 * phi1 + phi2)) * np.sqrt(0.33649e5) * t18536 * t18529 * t18535 * t18532 ** 2 + + if Bindx == 2919: + t18537 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 + 7 * phi2)) * np.sqrt(0.10626e5) * ((1 - t18537) ** (0.5e1 / 0.2e1)) * ((1 + t18537) ** (0.19e2 / 0.2e1)) + + if Bindx == 2920: + t18539 = np.cos(phi) + t18544 = 1 + t18539 + t18540 = t18544 ** 2 + t18542 = t18544 * t18540 ** 2 + t18538 = -1 + t18539 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.10626e5) * t18538 ** 2 * t18542 ** 2 + + if Bindx == 2921: + t18545 = np.cos(phi) + tfunc[..., c] = (0.25e2 / 0.2048e4*1j) * np.exp((3*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.506e3) * ((1 - t18545) ** (0.3e1 / 0.2e1)) * ((1 + t18545) ** (0.21e2 / 0.2e1)) + + if Bindx == 2922: + t18546 = np.cos(phi) + t18552 = 1 + t18546 + t18547 = t18552 ** 2 + t18549 = t18552 * t18547 ** 2 + tfunc[..., c] = (0.25e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.69e2) * (-1 + t18546) * t18552 * t18549 ** 2 + + if Bindx == 2923: + t18553 = np.cos(phi) + tfunc[..., c] = (-0.25e2 / 0.2048e4*1j) * np.exp((1j) * (12 * phi1 + 11 * phi2)) * np.sqrt(0.6e1) * np.sqrt((1 - t18553)) * ((1 + t18553) ** (0.23e2 / 0.2e1)) + + if Bindx == 2924: + t18554 = np.cos(phi) + t18566 = 12 * t18554 + t18555 = t18554 ** 2 + t18556 = t18554 * t18555 + t18559 = t18556 ** 2 + t18557 = t18555 ** 2 + t18558 = t18554 * t18557 + tfunc[..., c] = (0.25e2 / 0.4096e4) * np.exp((12*1j) * (phi1 + phi2)) * (66 * t18555 + 220 * t18556 + t18566 + 1 + (792 * t18554 + 924 + t18559) * t18559 + (792 + (t18566 + 66) * t18558) * t18558 + (495 + (220 * t18554 + 495) * t18557) * t18557) + + if Bindx == 2925: + t18567 = np.cos(phi) + t18568 = t18567 ** 2 + t18569 = t18567 * t18568 + t18572 = t18569 ** 2 + t18580 = 1716 * t18572 + t18578 = t18572 ** 2 + t18570 = t18568 ** 2 + t18571 = t18567 * t18570 + t18576 = t18571 ** 2 + t18574 = t18570 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-13*1j) * (phi1 + phi2)) * (78 * t18568 + 286 * t18569 + 715 * t18570 + 1287 * t18571 + 1287 * t18574 + 286 * t18576 + 13 * t18578 + t18580 + 1 + (715 * t18574 + 78 * t18576 + t18578 + t18580 + 13) * t18567) + + if Bindx == 2926: + t18581 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (13 * phi1 + 12 * phi2)) * np.sqrt(0.26e2) * ((1 + t18581) ** (0.25e2 / 0.2e1)) * np.sqrt((1 - t18581)) + + if Bindx == 2927: + t18582 = np.cos(phi) + t18587 = 1 + t18582 + t18583 = t18587 ** 2 + t18584 = t18587 * t18583 + t18585 = t18584 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 + 11 * phi2)) * np.sqrt(0.13e2) * (-1 + t18582) * t18585 ** 2 + + if Bindx == 2928: + t18588 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 + 10 * phi2)) * np.sqrt(0.26e2) * ((1 - t18588) ** (0.3e1 / 0.2e1)) * ((1 + t18588) ** (0.23e2 / 0.2e1)) + + if Bindx == 2929: + t18590 = np.cos(phi) + t18596 = 1 + t18590 + t18591 = t18596 ** 2 + t18593 = t18596 * t18591 ** 2 + t18589 = -1 + t18590 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 + 9 * phi2)) * np.sqrt(0.598e3) * t18589 ** 2 * t18596 * t18593 ** 2 + + if Bindx == 2930: + t18597 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 + 8 * phi2)) * np.sqrt(0.16445e5) * ((1 - t18597) ** (0.5e1 / 0.2e1)) * ((1 + t18597) ** (0.21e2 / 0.2e1)) + + if Bindx == 2931: + t18598 = np.cos(phi) + t18606 = -1 + t18598 + t18605 = 1 + t18598 + t18601 = t18605 ** 2 + t18603 = t18605 * t18601 ** 2 + t18599 = t18606 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 + 7 * phi2)) * np.sqrt(0.230230e6) * t18606 * t18599 * t18603 ** 2 + + if Bindx == 2932: + t18607 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 + 6 * phi2)) * np.sqrt(0.6578e4) * ((1 - t18607) ** (0.7e1 / 0.2e1)) * ((1 + t18607) ** (0.19e2 / 0.2e1)) + + if Bindx == 2933: + t18608 = np.cos(phi) + t18616 = -1 + t18608 + t18615 = 1 + t18608 + t18611 = t18615 ** 2 + t18612 = t18611 ** 2 + t18609 = t18616 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 + 5 * phi2)) * np.sqrt(0.62491e5) * t18609 ** 2 * t18615 * t18612 ** 2 + + if Bindx == 2934: + t18617 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (13 * phi1 + 4 * phi2)) * np.sqrt(0.124982e6) * ((1 - t18617) ** (0.9e1 / 0.2e1)) * ((1 + t18617) ** (0.17e2 / 0.2e1)) + + if Bindx == 2935: + t18618 = np.cos(phi) + t18626 = -1 + t18618 + t18625 = 1 + t18618 + t18622 = t18625 ** 2 + t18623 = t18622 ** 2 + t18619 = t18626 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 + 3 * phi2)) * np.sqrt(0.5311735e7) * t18626 * t18619 ** 2 * t18623 ** 2 + + if Bindx == 2936: + t18627 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (13 * phi1 + 2 * phi2)) * np.sqrt(0.482885e6) * ((1 - t18627) ** (0.11e2 / 0.2e1)) * ((1 + t18627) ** (0.15e2 / 0.2e1)) + + if Bindx == 2937: + t18632 = np.sin(phi) + t18628 = t18632 ** 2 + t18629 = t18632 * t18628 + t18630 = t18629 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-1*1j) * (13 * phi1 + phi2)) * np.sqrt(0.96577e5) * t18630 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 2938: + t18633 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((-13*1j) * phi1) * np.sqrt(0.104006e6) * ((1 - t18633) ** (0.13e2 / 0.2e1)) * ((1 + t18633) ** (0.13e2 / 0.2e1)) + + if Bindx == 2939: + t18638 = np.sin(phi) + t18634 = t18638 ** 2 + t18635 = t18638 * t18634 + t18636 = t18635 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-1*1j) * (13 * phi1 - phi2)) * np.sqrt(0.96577e5) * t18636 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 2940: + t18639 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (13 * phi1 - 2 * phi2)) * np.sqrt(0.482885e6) * ((1 - t18639) ** (0.15e2 / 0.2e1)) * ((1 + t18639) ** (0.11e2 / 0.2e1)) + + if Bindx == 2941: + t18640 = np.cos(phi) + t18648 = -1 + t18640 + t18647 = 1 + t18640 + t18644 = t18647 ** 2 + t18641 = t18648 ** 2 + t18642 = t18641 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 - 3 * phi2)) * np.sqrt(0.5311735e7) * t18642 ** 2 * t18647 * t18644 ** 2 + + if Bindx == 2942: + t18649 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (13 * phi1 - 4 * phi2)) * np.sqrt(0.124982e6) * ((1 - t18649) ** (0.17e2 / 0.2e1)) * ((1 + t18649) ** (0.9e1 / 0.2e1)) + + if Bindx == 2943: + t18650 = np.cos(phi) + t18658 = -1 + t18650 + t18657 = 1 + t18650 + t18655 = t18657 ** 2 + t18651 = t18658 ** 2 + t18652 = t18651 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 - 5 * phi2)) * np.sqrt(0.62491e5) * t18658 * t18652 ** 2 * t18655 ** 2 + + if Bindx == 2944: + t18659 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 - 6 * phi2)) * np.sqrt(0.6578e4) * ((1 - t18659) ** (0.19e2 / 0.2e1)) * ((1 + t18659) ** (0.7e1 / 0.2e1)) + + if Bindx == 2945: + t18660 = np.cos(phi) + t18668 = -1 + t18660 + t18667 = 1 + t18660 + t18665 = t18667 ** 2 + t18661 = t18668 ** 2 + t18663 = t18668 * t18661 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 - 7 * phi2)) * np.sqrt(0.230230e6) * t18663 ** 2 * t18667 * t18665 + + if Bindx == 2946: + t18669 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 - 8 * phi2)) * np.sqrt(0.16445e5) * ((1 - t18669) ** (0.21e2 / 0.2e1)) * ((1 + t18669) ** (0.5e1 / 0.2e1)) + + if Bindx == 2947: + t18671 = np.cos(phi) + t18677 = -1 + t18671 + t18672 = t18677 ** 2 + t18674 = t18677 * t18672 ** 2 + t18670 = 1 + t18671 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 - 9 * phi2)) * np.sqrt(0.598e3) * t18677 * t18674 ** 2 * t18670 ** 2 + + if Bindx == 2948: + t18678 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (13 * phi1 - 10 * phi2)) * np.sqrt(0.26e2) * ((1 - t18678) ** (0.23e2 / 0.2e1)) * ((1 + t18678) ** (0.3e1 / 0.2e1)) + + if Bindx == 2949: + t18679 = np.cos(phi) + t18684 = -1 + t18679 + t18680 = t18684 ** 2 + t18681 = t18684 * t18680 + t18682 = t18681 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (13 * phi1 - 11 * phi2)) * np.sqrt(0.13e2) * t18682 ** 2 * (1 + t18679) + + if Bindx == 2950: + t18685 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (13 * phi1 - 12 * phi2)) * np.sqrt(0.26e2) * ((1 - t18685) ** (0.25e2 / 0.2e1)) * np.sqrt((1 + t18685)) + + if Bindx == 2951: + t18686 = np.cos(phi) + t18687 = t18686 ** 2 + t18688 = t18686 * t18687 + t18691 = t18688 ** 2 + t18697 = t18691 ** 2 + t18689 = t18687 ** 2 + t18690 = t18686 * t18689 + t18695 = t18690 ** 2 + t18693 = t18689 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-13*1j) * (phi1 - phi2)) * (-78 * t18687 + 286 * t18688 - 715 * t18689 + 1287 * t18690 - 1716 * t18691 - 1287 * t18693 - 286 * t18695 - 13 * t18697 - 1 + (1716 * t18691 + 715 * t18693 + 78 * t18695 + t18697 + 13) * t18686) + + if Bindx == 2952: + t18699 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (12 * phi1 + 13 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t18699)) * ((1 + t18699) ** (0.25e2 / 0.2e1)) + + if Bindx == 2953: + t18700 = np.cos(phi) + t18701 = t18700 ** 2 + t18702 = t18700 * t18701 + t18705 = t18702 ** 2 + t18711 = t18705 ** 2 + t18703 = t18701 ** 2 + t18704 = t18700 * t18703 + t18709 = t18704 ** 2 + t18707 = t18703 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-12*1j) * (phi1 + phi2)) * (-636 * t18701 - 1782 * t18702 - 3080 * t18703 - 3069 * t18704 - 792 * t18705 + 4356 * t18707 + 2068 * t18709 + 144 * t18711 - 12 + (2508 * t18705 + 3795 * t18707 + 714 * t18709 + 13 * t18711 - 131) * t18700) + + if Bindx == 2954: + t18713 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (12 * phi1 + 11 * phi2)) * np.sqrt(0.2e1) * ((1 + t18713) ** (0.23e2 / 0.2e1)) * (11 + (-24 + 13 * t18713) * t18713) * ((1 - t18713) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2955: + t18714 = np.cos(phi) + t18720 = 1 + t18714 + t18715 = t18720 ** 2 + t18717 = t18720 * t18715 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * (-10 + 13 * t18714) * (-1 + t18714) * t18720 * t18717 ** 2 * np.exp((-2*1j) * (6 * phi1 + 5 * phi2)) + + if Bindx == 2956: + t18721 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * (-9 + 13 * t18721) * ((1 + t18721) ** (0.21e2 / 0.2e1)) * np.sqrt(0.23e2) * np.exp((-3*1j) * (4 * phi1 + 3 * phi2)) * ((1 - t18721) ** (0.3e1 / 0.2e1)) + + if Bindx == 2957: + t18723 = np.cos(phi) + t18728 = 1 + t18723 + t18724 = t18728 ** 2 + t18726 = t18728 * t18724 ** 2 + t18722 = -1 + t18723 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2530e4) * t18726 ** 2 * t18722 ** 2 * (-8 + 13 * t18723) + + if Bindx == 2958: + t18729 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-7 + 13 * t18729) * ((1 + t18729) ** (0.19e2 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (12 * phi1 + 7 * phi2)) * ((1 - t18729) ** (0.5e1 / 0.2e1)) + + if Bindx == 2959: + t18740 = np.sin(phi) + t18737 = t18740 ** 2 + t18738 = t18740 * t18737 + t18730 = np.cos(phi) + t18731 = t18730 ** 2 + t18732 = t18730 * t18731 + t18735 = t18732 ** 2 + t18733 = t18731 ** 2 + tfunc[..., c] = -(0.135e3 / 0.2048e4) * np.exp((-6*1j) * (2 * phi1 + phi2)) * np.sqrt(0.253e3) * t18738 ** 2 * (-12 * t18731 + 75 * t18732 + 170 * t18733 + 72 * t18735 - 6 + (159 * t18733 + 13 * t18735 - 23) * t18730) + + if Bindx == 2960: + t18741 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * (-5 + 13 * t18741) * ((1 + t18741) ** (0.17e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (12 * phi1 + 5 * phi2)) * ((1 - t18741) ** (0.7e1 / 0.2e1)) + + if Bindx == 2961: + t18750 = np.sin(phi) + t18747 = t18750 ** 2 + t18748 = t18747 ** 2 + t18742 = np.cos(phi) + t18743 = t18742 ** 2 + t18745 = t18743 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 + phi2)) * np.sqrt(0.4807e4) * t18748 ** 2 * (28 * t18743 + 48 * t18745 - 4 + (62 * t18743 + 13 * t18745 - 3) * t18742) + + if Bindx == 2962: + t18751 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * (-3 + 13 * t18751) * ((1 + t18751) ** (0.15e2 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-3*1j) * (4 * phi1 + phi2)) * ((1 - t18751) ** (0.9e1 / 0.2e1)) + + if Bindx == 2963: + t18759 = np.sin(phi) + t18755 = t18759 ** 2 + t18757 = t18759 * t18755 ** 2 + t18752 = np.cos(phi) + t18753 = t18752 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 + phi2)) * np.sqrt(0.74290e5) * t18757 ** 2 * (24 * t18753 - 2 + (13 * t18753 + 9) * t18752) + + if Bindx == 2964: + t18760 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * (-1 + 13 * t18760) * ((1 + t18760) ** (0.13e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (12 * phi1 + phi2)) * ((1 - t18760) ** (0.11e2 / 0.2e1)) + + if Bindx == 2965: + t18765 = np.sin(phi) + t18761 = t18765 ** 2 + t18762 = t18765 * t18761 + t18763 = t18762 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-12*1j) * phi1) * np.sqrt(0.676039e6) * t18763 ** 2 * np.cos(phi) + + if Bindx == 2966: + t18766 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * (1 + 13 * t18766) * ((1 + t18766) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (12 * phi1 - phi2)) * ((1 - t18766) ** (0.13e2 / 0.2e1)) + + if Bindx == 2967: + t18774 = np.sin(phi) + t18770 = t18774 ** 2 + t18772 = t18774 * t18770 ** 2 + t18767 = np.cos(phi) + t18768 = t18767 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 - phi2)) * np.sqrt(0.74290e5) * t18772 ** 2 * (-24 * t18768 + 2 + (13 * t18768 + 9) * t18767) + + if Bindx == 2968: + t18775 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (3 + 13 * t18775) * ((1 + t18775) ** (0.9e1 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-3*1j) * (4 * phi1 - phi2)) * ((1 - t18775) ** (0.15e2 / 0.2e1)) + + if Bindx == 2969: + t18784 = np.sin(phi) + t18781 = t18784 ** 2 + t18782 = t18781 ** 2 + t18776 = np.cos(phi) + t18777 = t18776 ** 2 + t18779 = t18777 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 - phi2)) * np.sqrt(0.4807e4) * t18782 ** 2 * (-28 * t18777 - 48 * t18779 + 4 + (62 * t18777 + 13 * t18779 - 3) * t18776) + + if Bindx == 2970: + t18785 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * (5 + 13 * t18785) * ((1 + t18785) ** (0.7e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (12 * phi1 - 5 * phi2)) * ((1 - t18785) ** (0.17e2 / 0.2e1)) + + if Bindx == 2971: + t18786 = np.cos(phi) + t18794 = -1 + t18786 + t18793 = 1 + t18786 + t18791 = t18793 ** 2 + t18787 = t18794 ** 2 + t18788 = t18787 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-6*1j) * (2 * phi1 - phi2)) * np.sqrt(0.253e3) * t18793 * t18791 * t18794 * t18788 ** 2 * (6 + 13 * t18786) + + if Bindx == 2972: + t18795 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (7 + 13 * t18795) * ((1 + t18795) ** (0.5e1 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (12 * phi1 - 7 * phi2)) * ((1 - t18795) ** (0.19e2 / 0.2e1)) + + if Bindx == 2973: + t18797 = np.cos(phi) + t18802 = -1 + t18797 + t18798 = t18802 ** 2 + t18800 = t18802 * t18798 ** 2 + t18796 = 1 + t18797 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.2530e4) * t18796 ** 2 * t18800 ** 2 * (8 + 13 * t18797) + + if Bindx == 2974: + t18803 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * (9 + 13 * t18803) * ((1 + t18803) ** (0.3e1 / 0.2e1)) * np.sqrt(0.23e2) * np.exp((-3*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t18803) ** (0.21e2 / 0.2e1)) + + if Bindx == 2975: + t18804 = np.cos(phi) + t18810 = -1 + t18804 + t18805 = t18810 ** 2 + t18807 = t18810 * t18805 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-2*1j) * (6 * phi1 - 5 * phi2)) * (1 + t18804) * t18810 * t18807 ** 2 * (10 + 13 * t18804) + + if Bindx == 2976: + t18811 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * (11 + 13 * t18811) * np.sqrt((1 + t18811)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (12 * phi1 - 11 * phi2)) * ((1 - t18811) ** (0.23e2 / 0.2e1)) + + if Bindx == 2977: + t18812 = np.cos(phi) + t18813 = t18812 ** 2 + t18814 = t18812 * t18813 + t18817 = t18814 ** 2 + t18823 = t18817 ** 2 + t18815 = t18813 ** 2 + t18816 = t18812 * t18815 + t18821 = t18816 ** 2 + t18819 = t18815 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-12*1j) * (phi1 - phi2)) * (636 * t18813 - 1782 * t18814 + 3080 * t18815 - 3069 * t18816 + 792 * t18817 - 4356 * t18819 - 2068 * t18821 - 144 * t18823 + 12 + (2508 * t18817 + 3795 * t18819 + 714 * t18821 + 13 * t18823 - 131) * t18812) + + if Bindx == 2978: + t18825 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (12 * phi1 - 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t18825) ** (0.25e2 / 0.2e1)) * np.sqrt((1 + t18825)) + + if Bindx == 2979: + t18826 = np.cos(phi) + t18831 = 1 + t18826 + t18827 = t18831 ** 2 + t18828 = t18831 * t18827 + t18829 = t18828 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 + 13 * phi2)) * np.sqrt(0.13e2) * (-1 + t18826) * t18829 ** 2 + + if Bindx == 2980: + t18832 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (11 * phi1 + 12 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t18832)) * ((1 + t18832) ** (0.23e2 / 0.2e1)) * (-11 + 13 * t18832) + + if Bindx == 2981: + t18833 = np.cos(phi) + t18834 = t18833 ** 2 + t18835 = t18833 * t18834 + t18838 = t18835 ** 2 + t18844 = t18838 ** 2 + t18836 = t18834 ** 2 + t18837 = t18833 * t18836 + t18842 = t18837 ** 2 + t18840 = t18836 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-11*1j) * (phi1 + phi2)) * (6870 * t18834 + 11110 * t18835 + 2695 * t18836 - 22077 * t18837 - 41052 * t18838 + 6435 * t18840 + 25894 * t18842 + 3025 * t18844 + 229 + (-28380 * t18838 + 29095 * t18840 + 12054 * t18842 + 325 * t18844 + 1969) * t18833) + + if Bindx == 2982: + t18846 = np.cos(phi) + t18847 = t18846 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.2e1) * ((1 + t18846) ** (0.21e2 / 0.2e1)) * (-825 * t18847 - 187 + (325 * t18847 + 687) * t18846) * ((1 - t18846) ** (-0.1e1 / 0.2e1)) + + if Bindx == 2983: + t18849 = np.cos(phi) + t18854 = 1 + t18849 + t18850 = t18854 ** 2 + t18852 = t18854 * t18850 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * (149 + (-450 + 325 * t18849) * t18849) * (-1 + t18849) * t18852 ** 2 * np.sqrt(0.46e2) * np.exp((-1*1j) * (11 * phi1 + 9 * phi2)) + + if Bindx == 2984: + t18855 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (23 + (-80 + 65 * t18855) * t18855) * ((1 + t18855) ** (0.19e2 / 0.2e1)) * np.sqrt(0.1265e4) * np.exp((-1*1j) * (11 * phi1 + 8 * phi2)) * ((1 - t18855) ** (0.3e1 / 0.2e1)) + + if Bindx == 2985: + t18867 = np.sin(phi) + t18865 = t18867 ** 2 + t18856 = np.cos(phi) + t18857 = t18856 ** 2 + t18859 = t18857 ** 2 + t18863 = t18859 ** 2 + t18858 = t18856 * t18857 + t18861 = t18858 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.17710e5) * t18865 ** 2 * (-68 * t18857 - 420 * t18858 - 490 * t18859 + 924 * t18861 + 385 * t18863 + 17 + (182 * t18859 + 892 * t18861 + 65 * t18863 + 49) * t18856) + + if Bindx == 2986: + t18868 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (59 + (-300 + 325 * t18868) * t18868) * ((1 + t18868) ** (0.17e2 / 0.2e1)) * np.sqrt(0.506e3) * np.exp((-1*1j) * (11 * phi1 + 6 * phi2)) * ((1 - t18868) ** (0.5e1 / 0.2e1)) + + if Bindx == 2987: + t18879 = np.sin(phi) + t18876 = t18879 ** 2 + t18877 = t18879 * t18876 + t18869 = np.cos(phi) + t18870 = t18869 ** 2 + t18871 = t18869 * t18870 + t18874 = t18871 ** 2 + t18872 = t18870 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.4807e4) * t18877 ** 2 * (-555 * t18870 - 505 * t18871 + 935 * t18872 + 1375 * t18874 + 37 + (2037 * t18872 + 325 * t18874 - 65) * t18869) + + if Bindx == 2988: + t18880 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (19 + (-200 + 325 * t18880) * t18880) * ((1 + t18880) ** (0.15e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (11 * phi1 + 4 * phi2)) * ((1 - t18880) ** (0.7e1 / 0.2e1)) + + if Bindx == 2989: + t18889 = np.sin(phi) + t18886 = t18889 ** 2 + t18887 = t18886 ** 2 + t18881 = np.cos(phi) + t18882 = t18881 ** 2 + t18884 = t18882 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.408595e6) * t18887 ** 2 * (-22 * t18882 + 165 * t18884 + 1 + (106 * t18882 + 65 * t18884 - 27) * t18881) + + if Bindx == 2990: + t18890 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-1 + (-20 + 65 * t18890) * t18890) * ((1 + t18890) ** (0.13e2 / 0.2e1)) * np.sqrt(0.37145e5) * np.exp((-1*1j) * (11 * phi1 + 2 * phi2)) * ((1 - t18890) ** (0.9e1 / 0.2e1)) + + if Bindx == 2991: + t18898 = np.sin(phi) + t18894 = t18898 ** 2 + t18896 = t18898 * t18894 ** 2 + t18891 = np.cos(phi) + t18892 = t18891 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (11 * phi1 + phi2)) * np.sqrt(0.7429e4) * t18896 ** 2 * (275 * t18892 - 11 + (325 * t18892 - 61) * t18891) + + if Bindx == 2992: + t18899 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (25 * t18899 ** 2 - 1) * ((1 + t18899) ** (0.11e2 / 0.2e1)) * np.sqrt(0.1352078e7) * np.exp((-11*1j) * phi1) * ((1 - t18899) ** (0.11e2 / 0.2e1)) + + if Bindx == 2993: + t18907 = np.sin(phi) + t18903 = t18907 ** 2 + t18905 = t18907 * t18903 ** 2 + t18900 = np.cos(phi) + t18901 = t18900 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (11 * phi1 - phi2)) * np.sqrt(0.7429e4) * t18905 ** 2 * (-275 * t18901 + 11 + (325 * t18901 - 61) * t18900) + + if Bindx == 2994: + t18908 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-1 + (20 + 65 * t18908) * t18908) * ((1 + t18908) ** (0.9e1 / 0.2e1)) * np.sqrt(0.37145e5) * np.exp((-1*1j) * (11 * phi1 - 2 * phi2)) * ((1 - t18908) ** (0.13e2 / 0.2e1)) + + if Bindx == 2995: + t18917 = np.sin(phi) + t18914 = t18917 ** 2 + t18915 = t18914 ** 2 + t18909 = np.cos(phi) + t18910 = t18909 ** 2 + t18912 = t18910 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.408595e6) * t18915 ** 2 * (22 * t18910 - 165 * t18912 - 1 + (106 * t18910 + 65 * t18912 - 27) * t18909) + + if Bindx == 2996: + t18918 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (19 + (200 + 325 * t18918) * t18918) * ((1 + t18918) ** (0.7e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (11 * phi1 - 4 * phi2)) * ((1 - t18918) ** (0.15e2 / 0.2e1)) + + if Bindx == 2997: + t18929 = np.sin(phi) + t18926 = t18929 ** 2 + t18927 = t18929 * t18926 + t18919 = np.cos(phi) + t18920 = t18919 ** 2 + t18921 = t18919 * t18920 + t18924 = t18921 ** 2 + t18922 = t18920 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.4807e4) * t18927 ** 2 * (555 * t18920 - 505 * t18921 - 935 * t18922 - 1375 * t18924 - 37 + (2037 * t18922 + 325 * t18924 - 65) * t18919) + + if Bindx == 2998: + t18930 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (59 + (300 + 325 * t18930) * t18930) * ((1 + t18930) ** (0.5e1 / 0.2e1)) * np.sqrt(0.506e3) * np.exp((-1*1j) * (11 * phi1 - 6 * phi2)) * ((1 - t18930) ** (0.17e2 / 0.2e1)) + + if Bindx == 2999: + t18932 = np.cos(phi) + t18937 = -1 + t18932 + t18933 = t18937 ** 2 + t18934 = t18933 ** 2 + t18931 = 1 + t18932 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.17710e5) * t18931 ** 2 * t18937 * t18934 ** 2 * (17 + (70 + 65 * t18932) * t18932) + + if Bindx == 3000: + t18938 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (23 + (80 + 65 * t18938) * t18938) * ((1 + t18938) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1265e4) * np.exp((-1*1j) * (11 * phi1 - 8 * phi2)) * ((1 - t18938) ** (0.19e2 / 0.2e1)) + + if Bindx == 3001: + t18939 = np.cos(phi) + t18944 = -1 + t18939 + t18940 = t18944 ** 2 + t18942 = t18944 * t18940 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 - 9 * phi2)) * np.sqrt(0.46e2) * (1 + t18939) * t18942 ** 2 * (149 + (450 + 325 * t18939) * t18939) + + if Bindx == 3002: + t18945 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (187 + (500 + 325 * t18945) * t18945) * np.sqrt((1 + t18945)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (11 * phi1 - 10 * phi2)) * ((1 - t18945) ** (0.21e2 / 0.2e1)) + + if Bindx == 3003: + t18946 = np.cos(phi) + t18947 = t18946 ** 2 + t18948 = t18946 * t18947 + t18951 = t18948 ** 2 + t18957 = t18951 ** 2 + t18949 = t18947 ** 2 + t18950 = t18946 * t18949 + t18955 = t18950 ** 2 + t18953 = t18949 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-11*1j) * (phi1 - phi2)) * (-6870 * t18947 + 11110 * t18948 - 2695 * t18949 - 22077 * t18950 + 41052 * t18951 - 6435 * t18953 - 25894 * t18955 - 3025 * t18957 - 229 + (-28380 * t18951 + 29095 * t18953 + 12054 * t18955 + 325 * t18957 + 1969) * t18946) + + if Bindx == 3004: + t18959 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * (11 + 13 * t18959) * np.sqrt((1 + t18959)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (11 * phi1 - 12 * phi2)) * ((1 - t18959) ** (0.23e2 / 0.2e1)) + + if Bindx == 3005: + t18960 = np.cos(phi) + t18965 = -1 + t18960 + t18961 = t18965 ** 2 + t18962 = t18965 * t18961 + t18963 = t18962 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (11 * phi1 - 13 * phi2)) * np.sqrt(0.13e2) * t18963 ** 2 * (1 + t18960) + + if Bindx == 3006: + t18966 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (10 * phi1 + 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t18966) ** (0.3e1 / 0.2e1)) * ((1 + t18966) ** (0.23e2 / 0.2e1)) + + if Bindx == 3007: + t18967 = np.cos(phi) + t18973 = 1 + t18967 + t18968 = t18973 ** 2 + t18970 = t18973 * t18968 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 6 * phi2)) * (-1 + t18967) * t18973 * t18970 ** 2 * (-10 + 13 * t18967) + + if Bindx == 3008: + t18974 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t18974)) * ((1 + t18974) ** (0.21e2 / 0.2e1)) * (187 + (-500 + 325 * t18974) * t18974) + + if Bindx == 3009: + t18975 = np.cos(phi) + t18976 = t18975 ** 2 + t18977 = t18975 * t18976 + t18980 = t18977 ** 2 + t18986 = t18980 ** 2 + t18978 = t18976 ** 2 + t18979 = t18975 * t18978 + t18984 = t18979 ** 2 + t18982 = t18978 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 + phi2)) * (-1215 * t18976 + 1870 * t18977 + 8470 * t18978 + 8415 * t18979 - 5478 * t18980 - 14355 * t18982 + 10725 * t18984 + 2500 * t18986 - 135 + (-19140 * t18980 + 2145 * t18982 + 7686 * t18984 + 325 * t18986 - 789) * t18975) + + if Bindx == 3010: + t18988 = np.cos(phi) + t18989 = t18988 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.23e2) * ((1 + t18988) ** (0.19e2 / 0.2e1)) * (-540 * t18988 + 93 + (-1000 * t18988 + 1122 + 325 * t18989) * t18989) * ((1 - t18988) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3011: + t18993 = np.cos(phi) + t18994 = t18993 ** 2 + t18996 = t18994 ** 2 + t18997 = t18993 * t18996 + t19002 = t18997 ** 2 + t19000 = t18996 ** 2 + t18995 = t18993 * t18994 + t18998 = t18995 ** 2 + t18992 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.2530e4) * t18992 ** 2 * (96 * t18994 + 365 * t18995 + 184 * t18996 - 742 * t18997 - 1232 * t18998 + 820 * t19000 + 400 * t19002 - 12 + (-334 * t18998 + 929 * t19000 + 65 * t19002 - 27) * t18993) + + if Bindx == 3012: + t19004 = np.cos(phi) + t19005 = t19004 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-105 * t19005 - 7 + (65 * t19005 + 51) * t19004) * ((1 + t19004) ** (0.17e2 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (10 * phi1 + 7 * phi2)) * ((1 - t19004) ** (0.3e1 / 0.2e1)) + + if Bindx == 3013: + t19018 = np.sin(phi) + t19016 = t19018 ** 2 + t19007 = np.cos(phi) + t19008 = t19007 ** 2 + t19010 = t19008 ** 2 + t19014 = t19010 ** 2 + t19009 = t19007 * t19008 + t19012 = t19009 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.253e3) * t19016 ** 2 * (357 * t19008 - 60 * t19009 - 1515 * t19010 + 795 * t19012 + 1500 * t19014 - 17 + (-1572 * t19010 + 2352 * t19012 + 325 * t19014 + 75) * t19007) + + if Bindx == 3014: + t19019 = np.cos(phi) + t19020 = t19019 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-375 * t19020 - 5 + (325 * t19020 + 111) * t19019) * ((1 + t19019) ** (0.15e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-5*1j) * (2 * phi1 + phi2)) * ((1 - t19019) ** (0.5e1 / 0.2e1)) + + if Bindx == 3015: + t19032 = np.sin(phi) + t19029 = t19032 ** 2 + t19030 = t19032 * t19029 + t19022 = np.cos(phi) + t19023 = t19022 ** 2 + t19024 = t19022 * t19023 + t19027 = t19024 ** 2 + t19025 = t19023 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.4807e4) * t19030 ** 2 * (-60 * t19023 - 525 * t19024 - 270 * t19025 + 1000 * t19027 + 2 + (807 * t19025 + 325 * t19027 + 65) * t19022) + + if Bindx == 3016: + t19033 = np.cos(phi) + t19034 = t19033 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-45 * t19034 + 1 + (65 * t19034 + 3) * t19033) * ((1 + t19033) ** (0.13e2 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-1*1j) * (10 * phi1 + 3 * phi2)) * ((1 - t19033) ** (0.7e1 / 0.2e1)) + + if Bindx == 3017: + t19044 = np.sin(phi) + t19041 = t19044 ** 2 + t19042 = t19041 ** 2 + t19036 = np.cos(phi) + t19037 = t19036 ** 2 + t19039 = t19037 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.74290e5) * t19042 ** 2 * (-35 * t19037 + 100 * t19039 + 1 + (2 * t19037 + 65 * t19039 - 1) * t19036) + + if Bindx == 3018: + t19045 = np.cos(phi) + t19046 = t19045 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-75 * t19046 + 3 + (325 * t19046 - 33) * t19045) * ((1 + t19045) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (10 * phi1 + phi2)) * ((1 - t19045) ** (0.9e1 / 0.2e1)) + + if Bindx == 3019: + t19053 = np.sin(phi) + t19049 = t19053 ** 2 + t19051 = t19053 * t19049 ** 2 + t19048 = np.cos(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-10*1j) * phi1) * np.sqrt(0.676039e6) * t19051 ** 2 * t19048 * (25 * t19048 ** 2 - 3) + + if Bindx == 3020: + t19054 = np.cos(phi) + t19055 = t19054 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (75 * t19055 - 3 + (325 * t19055 - 33) * t19054) * ((1 + t19054) ** (0.9e1 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (10 * phi1 - phi2)) * ((1 - t19054) ** (0.11e2 / 0.2e1)) + + if Bindx == 3021: + t19065 = np.sin(phi) + t19062 = t19065 ** 2 + t19063 = t19062 ** 2 + t19057 = np.cos(phi) + t19058 = t19057 ** 2 + t19060 = t19058 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.74290e5) * t19063 ** 2 * (35 * t19058 - 100 * t19060 - 1 + (2 * t19058 + 65 * t19060 - 1) * t19057) + + if Bindx == 3022: + t19066 = np.cos(phi) + t19067 = t19066 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (45 * t19067 - 1 + (65 * t19067 + 3) * t19066) * ((1 + t19066) ** (0.7e1 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-1*1j) * (10 * phi1 - 3 * phi2)) * ((1 - t19066) ** (0.13e2 / 0.2e1)) + + if Bindx == 3023: + t19079 = np.sin(phi) + t19076 = t19079 ** 2 + t19077 = t19079 * t19076 + t19069 = np.cos(phi) + t19070 = t19069 ** 2 + t19071 = t19069 * t19070 + t19074 = t19071 ** 2 + t19072 = t19070 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.4807e4) * t19077 ** 2 * (60 * t19070 - 525 * t19071 + 270 * t19072 - 1000 * t19074 - 2 + (807 * t19072 + 325 * t19074 + 65) * t19069) + + if Bindx == 3024: + t19080 = np.cos(phi) + t19081 = t19080 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (375 * t19081 + 5 + (325 * t19081 + 111) * t19080) * ((1 + t19080) ** (0.5e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-5*1j) * (2 * phi1 - phi2)) * ((1 - t19080) ** (0.15e2 / 0.2e1)) + + if Bindx == 3025: + t19094 = np.sin(phi) + t19092 = t19094 ** 2 + t19083 = np.cos(phi) + t19084 = t19083 ** 2 + t19086 = t19084 ** 2 + t19090 = t19086 ** 2 + t19085 = t19083 * t19084 + t19088 = t19085 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.253e3) * t19092 ** 2 * (-357 * t19084 - 60 * t19085 + 1515 * t19086 - 795 * t19088 - 1500 * t19090 + 17 + (-1572 * t19086 + 2352 * t19088 + 325 * t19090 + 75) * t19083) + + if Bindx == 3026: + t19095 = np.cos(phi) + t19096 = t19095 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (105 * t19096 + 7 + (65 * t19096 + 51) * t19095) * ((1 + t19095) ** (0.3e1 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (10 * phi1 - 7 * phi2)) * ((1 - t19095) ** (0.17e2 / 0.2e1)) + + if Bindx == 3027: + t19099 = np.cos(phi) + t19100 = t19099 ** 2 + t19102 = t19100 ** 2 + t19103 = t19099 * t19102 + t19108 = t19103 ** 2 + t19106 = t19102 ** 2 + t19101 = t19099 * t19100 + t19104 = t19101 ** 2 + t19098 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.2530e4) * t19098 ** 2 * (-96 * t19100 + 365 * t19101 - 184 * t19102 - 742 * t19103 + 1232 * t19104 - 820 * t19106 - 400 * t19108 + 12 + (-334 * t19104 + 929 * t19106 + 65 * t19108 - 27) * t19099) + + if Bindx == 3028: + t19110 = np.cos(phi) + t19111 = t19110 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (675 * t19111 + 93 + (325 * t19111 + 447) * t19110) * np.sqrt((1 + t19110)) * np.sqrt(0.23e2) * np.exp((-1*1j) * (10 * phi1 - 9 * phi2)) * ((1 - t19110) ** (0.19e2 / 0.2e1)) + + if Bindx == 3029: + t19113 = np.cos(phi) + t19114 = t19113 ** 2 + t19115 = t19113 * t19114 + t19118 = t19115 ** 2 + t19124 = t19118 ** 2 + t19116 = t19114 ** 2 + t19117 = t19113 * t19116 + t19122 = t19117 ** 2 + t19120 = t19116 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-10*1j) * (phi1 - phi2)) * (1215 * t19114 + 1870 * t19115 - 8470 * t19116 + 8415 * t19117 + 5478 * t19118 + 14355 * t19120 - 10725 * t19122 - 2500 * t19124 + 135 + (-19140 * t19118 + 2145 * t19120 + 7686 * t19122 + 325 * t19124 - 789) * t19113) + + if Bindx == 3030: + t19126 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (187 + (500 + 325 * t19126) * t19126) * np.sqrt((1 + t19126)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (10 * phi1 - 11 * phi2)) * ((1 - t19126) ** (0.21e2 / 0.2e1)) + + if Bindx == 3031: + t19127 = np.cos(phi) + t19133 = -1 + t19127 + t19128 = t19133 ** 2 + t19130 = t19133 * t19128 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-2*1j) * (5 * phi1 - 6 * phi2)) * (1 + t19127) * t19133 * t19130 ** 2 * (10 + 13 * t19127) + + if Bindx == 3032: + t19134 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (10 * phi1 - 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t19134) ** (0.23e2 / 0.2e1)) * ((1 + t19134) ** (0.3e1 / 0.2e1)) + + if Bindx == 3033: + t19136 = np.cos(phi) + t19142 = 1 + t19136 + t19137 = t19142 ** 2 + t19139 = t19142 * t19137 ** 2 + t19135 = -1 + t19136 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 + 13 * phi2)) * np.sqrt(0.598e3) * t19135 ** 2 * t19142 * t19139 ** 2 + + if Bindx == 3034: + t19143 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-3*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.23e2) * ((1 - t19143) ** (0.3e1 / 0.2e1)) * ((1 + t19143) ** (0.21e2 / 0.2e1)) * (-9 + 13 * t19143) + + if Bindx == 3035: + t19144 = np.cos(phi) + t19149 = 1 + t19144 + t19145 = t19149 ** 2 + t19147 = t19149 * t19145 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.46e2) * (-1 + t19144) * t19147 ** 2 * (149 + (-450 + 325 * t19144) * t19144) + + if Bindx == 3036: + t19150 = np.cos(phi) + t19151 = t19150 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.23e2) * np.sqrt((1 - t19150)) * ((1 + t19150) ** (0.19e2 / 0.2e1)) * (-675 * t19151 - 93 + (325 * t19151 + 447) * t19150) + + if Bindx == 3037: + t19153 = np.cos(phi) + t19154 = t19153 ** 2 + t19155 = t19153 * t19154 + t19158 = t19155 ** 2 + t19164 = t19158 ** 2 + t19156 = t19154 ** 2 + t19157 = t19153 * t19156 + t19162 = t19157 ** 2 + t19160 = t19156 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-9*1j) * (phi1 + phi2)) * (-12270 * t19154 - 40590 * t19155 - 2695 * t19156 + 125829 * t19157 + 146124 * t19158 - 236115 * t19160 + 59202 * t19162 + 46575 * t19164 + 1227 + (-64020 * t19158 - 132495 * t19160 + 103362 * t19162 + 7475 * t19164 + 2487) * t19153) + + if Bindx == 3038: + t19166 = np.cos(phi) + t19167 = t19166 ** 2 + t19169 = t19167 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.110e3) * ((1 + t19166) ** (0.17e2 / 0.2e1)) * (-4278 * t19167 - 5175 * t19169 - 123 + (6854 * t19167 + 1495 * t19169 + 1227) * t19166) * ((1 - t19166) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3039: + t19172 = np.cos(phi) + t19173 = t19172 ** 2 + t19175 = t19173 ** 2 + t19176 = t19172 * t19175 + t19181 = t19176 ** 2 + t19179 = t19175 ** 2 + t19174 = t19172 * t19173 + t19177 = t19174 ** 2 + t19171 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.385e3) * t19171 ** 2 * (-1175 * t19173 + 1323 * t19174 + 7326 * t19175 + 3402 * t19176 - 12390 * t19177 + 483 * t19179 + 7245 * t19181 + 47 + (-15570 * t19177 + 11201 * t19179 + 1495 * t19181 - 315) * t19172) + + if Bindx == 3040: + t19183 = np.cos(phi) + t19184 = t19183 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-1564 * t19183 + 27 + (-13800 * t19183 + 8142 + 7475 * t19184) * t19184) * ((1 + t19183) ** (0.15e2 / 0.2e1)) * np.sqrt(0.11e2) * np.exp((-3*1j) * (3 * phi1 + 2 * phi2)) * ((1 - t19183) ** (0.3e1 / 0.2e1)) + + if Bindx == 3041: + t19198 = np.sin(phi) + t19196 = t19198 ** 2 + t19187 = np.cos(phi) + t19188 = t19187 ** 2 + t19190 = t19188 ** 2 + t19194 = t19190 ** 2 + t19189 = t19187 * t19188 + t19192 = t19189 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.418e3) * t19196 ** 2 * (2196 * t19188 + 8820 * t19189 - 3870 * t19190 - 15180 * t19192 + 25875 * t19194 - 61 + (-28926 * t19190 + 22356 * t19192 + 7475 * t19194 - 765) * t19187) + + if Bindx == 3042: + t19199 = np.cos(phi) + t19200 = t19199 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (184 * t19199 - 73 + (-9200 * t19199 + 2622 + 7475 * t19200) * t19200) * ((1 + t19199) ** (0.13e2 / 0.2e1)) * np.sqrt(0.209e3) * np.exp((-1*1j) * (9 * phi1 + 4 * phi2)) * ((1 - t19199) ** (0.5e1 / 0.2e1)) + + if Bindx == 3043: + t19213 = np.sin(phi) + t19210 = t19213 ** 2 + t19211 = t19213 * t19210 + t19203 = np.cos(phi) + t19204 = t19203 ** 2 + t19205 = t19203 * t19204 + t19208 = t19205 ** 2 + t19206 = t19204 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.35530e5) * t19211 ** 2 * (387 * t19204 - 699 * t19205 - 2139 * t19206 + 3105 * t19208 - 9 + (483 * t19206 + 1495 * t19208 + 65) * t19203) + + if Bindx == 3044: + t19214 = np.cos(phi) + t19215 = t19214 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (92 * t19214 - 1 + (-920 * t19214 - 138 + 1495 * t19215) * t19215) * ((1 + t19214) ** (0.11e2 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-1*1j) * (9 * phi1 + 2 * phi2)) * ((1 - t19214) ** (0.7e1 / 0.2e1)) + + if Bindx == 3045: + t19226 = np.sin(phi) + t19223 = t19226 ** 2 + t19224 = t19223 ** 2 + t19218 = np.cos(phi) + t19219 = t19218 ** 2 + t19221 = t19219 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (9 * phi1 + phi2)) * np.sqrt(0.646e3) * t19224 ** 2 * (-1242 * t19219 + 5175 * t19221 + 27 + (-3818 * t19219 + 7475 * t19221 + 303) * t19218) + + if Bindx == 3046: + t19227 = np.cos(phi) + t19228 = t19227 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (3 + (-138 + 575 * t19228) * t19228) * ((1 + t19227) ** (0.9e1 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((-9*1j) * phi1) * ((1 - t19227) ** (0.9e1 / 0.2e1)) + + if Bindx == 3047: + t19238 = np.sin(phi) + t19235 = t19238 ** 2 + t19236 = t19235 ** 2 + t19230 = np.cos(phi) + t19231 = t19230 ** 2 + t19233 = t19231 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (9 * phi1 - phi2)) * np.sqrt(0.646e3) * t19236 ** 2 * (1242 * t19231 - 5175 * t19233 - 27 + (-3818 * t19231 + 7475 * t19233 + 303) * t19230) + + if Bindx == 3048: + t19239 = np.cos(phi) + t19240 = t19239 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-92 * t19239 - 1 + (920 * t19239 - 138 + 1495 * t19240) * t19240) * ((1 + t19239) ** (0.7e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-1*1j) * (9 * phi1 - 2 * phi2)) * ((1 - t19239) ** (0.11e2 / 0.2e1)) + + if Bindx == 3049: + t19253 = np.sin(phi) + t19250 = t19253 ** 2 + t19251 = t19253 * t19250 + t19243 = np.cos(phi) + t19244 = t19243 ** 2 + t19245 = t19243 * t19244 + t19248 = t19245 ** 2 + t19246 = t19244 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.35530e5) * t19251 ** 2 * (-387 * t19244 - 699 * t19245 + 2139 * t19246 - 3105 * t19248 + 9 + (483 * t19246 + 1495 * t19248 + 65) * t19243) + + if Bindx == 3050: + t19254 = np.cos(phi) + t19255 = t19254 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-184 * t19254 - 73 + (9200 * t19254 + 2622 + 7475 * t19255) * t19255) * ((1 + t19254) ** (0.5e1 / 0.2e1)) * np.sqrt(0.209e3) * np.exp((-1*1j) * (9 * phi1 - 4 * phi2)) * ((1 - t19254) ** (0.13e2 / 0.2e1)) + + if Bindx == 3051: + t19269 = np.sin(phi) + t19267 = t19269 ** 2 + t19258 = np.cos(phi) + t19259 = t19258 ** 2 + t19261 = t19259 ** 2 + t19265 = t19261 ** 2 + t19260 = t19258 * t19259 + t19263 = t19260 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.418e3) * t19267 ** 2 * (-2196 * t19259 + 8820 * t19260 + 3870 * t19261 + 15180 * t19263 - 25875 * t19265 + 61 + (-28926 * t19261 + 22356 * t19263 + 7475 * t19265 - 765) * t19258) + + if Bindx == 3052: + t19270 = np.cos(phi) + t19271 = t19270 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (1564 * t19270 + 27 + (13800 * t19270 + 8142 + 7475 * t19271) * t19271) * ((1 + t19270) ** (0.3e1 / 0.2e1)) * np.sqrt(0.11e2) * np.exp((-3*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t19270) ** (0.15e2 / 0.2e1)) + + if Bindx == 3053: + t19275 = np.cos(phi) + t19276 = t19275 ** 2 + t19278 = t19276 ** 2 + t19279 = t19275 * t19278 + t19284 = t19279 ** 2 + t19282 = t19278 ** 2 + t19277 = t19275 * t19276 + t19280 = t19277 ** 2 + t19274 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.385e3) * t19274 ** 2 * (1175 * t19276 + 1323 * t19277 - 7326 * t19278 + 3402 * t19279 + 12390 * t19280 - 483 * t19282 - 7245 * t19284 - 47 + (-15570 * t19280 + 11201 * t19282 + 1495 * t19284 - 315) * t19275) + + if Bindx == 3054: + t19286 = np.cos(phi) + t19287 = t19286 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (1104 * t19286 + 123 + (3680 * t19286 + 3174 + 1495 * t19287) * t19287) * np.sqrt((1 + t19286)) * np.sqrt(0.110e3) * np.exp((-1*1j) * (9 * phi1 - 8 * phi2)) * ((1 - t19286) ** (0.17e2 / 0.2e1)) + + if Bindx == 3055: + t19290 = np.cos(phi) + t19291 = t19290 ** 2 + t19292 = t19290 * t19291 + t19295 = t19292 ** 2 + t19301 = t19295 ** 2 + t19293 = t19291 ** 2 + t19294 = t19290 * t19293 + t19299 = t19294 ** 2 + t19297 = t19293 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-9*1j) * (phi1 - phi2)) * (12270 * t19291 - 40590 * t19292 + 2695 * t19293 + 125829 * t19294 - 146124 * t19295 + 236115 * t19297 - 59202 * t19299 - 46575 * t19301 - 1227 + (-64020 * t19295 - 132495 * t19297 + 103362 * t19299 + 7475 * t19301 + 2487) * t19290) + + if Bindx == 3056: + t19303 = np.cos(phi) + t19304 = t19303 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (675 * t19304 + 93 + (325 * t19304 + 447) * t19303) * np.sqrt((1 + t19303)) * np.sqrt(0.23e2) * np.exp((-1*1j) * (9 * phi1 - 10 * phi2)) * ((1 - t19303) ** (0.19e2 / 0.2e1)) + + if Bindx == 3057: + t19306 = np.cos(phi) + t19311 = -1 + t19306 + t19307 = t19311 ** 2 + t19309 = t19311 * t19307 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.46e2) * (1 + t19306) * t19309 ** 2 * (149 + (450 + 325 * t19306) * t19306) + + if Bindx == 3058: + t19312 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * (9 + 13 * t19312) * ((1 + t19312) ** (0.3e1 / 0.2e1)) * np.sqrt(0.23e2) * np.exp((-3*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t19312) ** (0.21e2 / 0.2e1)) + + if Bindx == 3059: + t19314 = np.cos(phi) + t19320 = -1 + t19314 + t19315 = t19320 ** 2 + t19317 = t19320 * t19315 ** 2 + t19313 = 1 + t19314 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (9 * phi1 - 13 * phi2)) * np.sqrt(0.598e3) * t19320 * t19317 ** 2 * t19313 ** 2 + + if Bindx == 3060: + t19321 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (8 * phi1 + 13 * phi2)) * np.sqrt(0.16445e5) * ((1 - t19321) ** (0.5e1 / 0.2e1)) * ((1 + t19321) ** (0.21e2 / 0.2e1)) + + if Bindx == 3061: + t19323 = np.cos(phi) + t19328 = 1 + t19323 + t19324 = t19328 ** 2 + t19326 = t19328 * t19324 ** 2 + t19322 = -1 + t19323 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2530e4) * t19322 ** 2 * t19326 ** 2 * (-8 + 13 * t19323) + + if Bindx == 3062: + t19329 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (8 * phi1 + 11 * phi2)) * np.sqrt(0.1265e4) * ((1 - t19329) ** (0.3e1 / 0.2e1)) * ((1 + t19329) ** (0.19e2 / 0.2e1)) * (23 + (-80 + 65 * t19329) * t19329) + + if Bindx == 3063: + t19331 = np.cos(phi) + t19332 = t19331 ** 2 + t19334 = t19332 ** 2 + t19335 = t19331 * t19334 + t19340 = t19335 ** 2 + t19338 = t19334 ** 2 + t19333 = t19331 * t19332 + t19336 = t19333 ** 2 + t19330 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.2530e4) * t19330 ** 2 * (96 * t19332 + 365 * t19333 + 184 * t19334 - 742 * t19335 - 1232 * t19336 + 820 * t19338 + 400 * t19340 - 12 + (-334 * t19336 + 929 * t19338 + 65 * t19340 - 27) * t19331) + + if Bindx == 3064: + t19342 = np.cos(phi) + t19343 = t19342 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.110e3) * np.sqrt((1 - t19342)) * ((1 + t19342) ** (0.17e2 / 0.2e1)) * (-1104 * t19342 + 123 + (-3680 * t19342 + 3174 + 1495 * t19343) * t19343) + + if Bindx == 3065: + t19346 = np.cos(phi) + t19347 = t19346 ** 2 + t19348 = t19346 * t19347 + t19351 = t19348 ** 2 + t19357 = t19351 ** 2 + t19349 = t19347 ** 2 + t19350 = t19346 * t19349 + t19355 = t19350 ** 2 + t19353 = t19349 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-8*1j) * (phi1 + phi2)) * (11664 * t19347 - 19462 * t19348 - 86560 * t19349 - 9837 * t19350 + 214944 * t19351 - 158832 * t19353 - 60720 * t19355 + 80960 * t19357 - 432 + (185964 * t19351 - 289245 * t19353 + 113850 * t19355 + 16445 * t19357 + 3309) * t19346) + + if Bindx == 3066: + t19359 = np.cos(phi) + t19360 = t19359 ** 2 + t19362 = t19360 ** 2 + t19361 = t19359 * t19360 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.14e2) * ((1 + t19359) ** (0.15e2 / 0.2e1)) * (20295 * t19360 + 87285 * t19362 + 7 + (-60720 + 16445 * t19361) * t19361 + (-60720 * t19362 - 2592) * t19359) * ((1 - t19359) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3067: + t19366 = np.cos(phi) + t19367 = t19366 ** 2 + t19369 = t19367 ** 2 + t19370 = t19366 * t19369 + t19375 = t19370 ** 2 + t19373 = t19369 ** 2 + t19368 = t19366 * t19367 + t19371 = t19368 ** 2 + t19365 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.10e2) * t19365 ** 2 * (-4960 * t19367 - 14823 * t19368 + 19944 * t19369 + 69714 * t19370 - 624 * t19371 - 69828 * t19373 + 60720 * t19375 + 124 + (-115830 * t19371 + 48829 * t19373 + 16445 * t19375 + 1041) * t19366) + + if Bindx == 3068: + t19377 = np.cos(phi) + t19378 = t19377 ** 2 + t19380 = t19378 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2530 * t19378 - 31625 * t19380 + 107 + (18722 * t19378 + 16445 * t19380 - 671) * t19377) * ((1 + t19377) ** (0.13e2 / 0.2e1)) * np.sqrt(0.95e2) * np.exp((-1*1j) * (8 * phi1 + 5 * phi2)) * ((1 - t19377) ** (0.3e1 / 0.2e1)) + + if Bindx == 3069: + t19393 = np.sin(phi) + t19391 = t19393 ** 2 + t19382 = np.cos(phi) + t19383 = t19382 ** 2 + t19385 = t19383 ** 2 + t19389 = t19385 ** 2 + t19384 = t19382 * t19383 + t19387 = t19384 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.190e3) * t19391 ** 2 * (-1960 * t19383 + 9004 * t19384 + 16056 * t19385 - 46552 * t19387 + 40480 * t19389 + 40 + (-23826 * t19385 + 7084 * t19387 + 16445 * t19389 - 643) * t19382) + + if Bindx == 3070: + t19394 = np.cos(phi) + t19395 = t19394 ** 2 + t19399 = 2530 * t19395 + t19397 = t19395 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-18975 * t19397 + t19399 - 19 + (16445 * t19397 + t19399 - 495) * t19394) * ((1 + t19394) ** (0.11e2 / 0.2e1)) * np.sqrt(0.323e3) * np.exp((-1*1j) * (8 * phi1 + 3 * phi2)) * ((1 - t19394) ** (0.5e1 / 0.2e1)) + + if Bindx == 3071: + t19410 = np.sin(phi) + t19407 = t19410 ** 2 + t19408 = t19410 * t19407 + t19400 = np.cos(phi) + t19401 = t19400 ** 2 + t19402 = t19400 * t19401 + t19405 = t19402 ** 2 + t19403 = t19401 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.3553e4) * t19408 ** 2 * (216 * t19401 + 225 * t19402 - 1380 * t19403 + 1840 * t19405 - 4 + (-1035 * t19403 + 1495 * t19405 - 13) * t19400) + + if Bindx == 3072: + t19411 = np.cos(phi) + t19412 = t19411 ** 2 + t19414 = t19412 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (138 * t19412 - 575 * t19414 - 3 + (-506 * t19412 + 1495 * t19414 + 27) * t19411) * ((1 + t19411) ** (0.9e1 / 0.2e1)) * np.sqrt(0.17765e5) * np.exp((-1*1j) * (8 * phi1 + phi2)) * ((1 - t19411) ** (0.7e1 / 0.2e1)) + + if Bindx == 3073: + t19422 = np.sin(phi) + t19419 = t19422 ** 2 + t19420 = t19419 ** 2 + t19416 = np.cos(phi) + t19417 = t19416 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-8*1j) * phi1) * np.sqrt(0.3233230e7) * t19420 ** 2 * t19416 * (3 + (-46 + 115 * t19417) * t19417) + + if Bindx == 3074: + t19423 = np.cos(phi) + t19424 = t19423 ** 2 + t19426 = t19424 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-138 * t19424 + 575 * t19426 + 3 + (-506 * t19424 + 1495 * t19426 + 27) * t19423) * ((1 + t19423) ** (0.7e1 / 0.2e1)) * np.sqrt(0.17765e5) * np.exp((-1*1j) * (8 * phi1 - phi2)) * ((1 - t19423) ** (0.9e1 / 0.2e1)) + + if Bindx == 3075: + t19438 = np.sin(phi) + t19435 = t19438 ** 2 + t19436 = t19438 * t19435 + t19428 = np.cos(phi) + t19429 = t19428 ** 2 + t19430 = t19428 * t19429 + t19433 = t19430 ** 2 + t19431 = t19429 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.3553e4) * t19436 ** 2 * (-216 * t19429 + 225 * t19430 + 1380 * t19431 - 1840 * t19433 + 4 + (-1035 * t19431 + 1495 * t19433 - 13) * t19428) + + if Bindx == 3076: + t19439 = np.cos(phi) + t19440 = t19439 ** 2 + t19442 = t19440 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2530 * t19440 + 18975 * t19442 + 19 + (2530 * t19440 + 16445 * t19442 - 495) * t19439) * ((1 + t19439) ** (0.5e1 / 0.2e1)) * np.sqrt(0.323e3) * np.exp((-1*1j) * (8 * phi1 - 3 * phi2)) * ((1 - t19439) ** (0.11e2 / 0.2e1)) + + if Bindx == 3077: + t19455 = np.sin(phi) + t19453 = t19455 ** 2 + t19444 = np.cos(phi) + t19445 = t19444 ** 2 + t19447 = t19445 ** 2 + t19451 = t19447 ** 2 + t19446 = t19444 * t19445 + t19449 = t19446 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.190e3) * t19453 ** 2 * (1960 * t19445 + 9004 * t19446 - 16056 * t19447 + 46552 * t19449 - 40480 * t19451 - 40 + (-23826 * t19447 + 7084 * t19449 + 16445 * t19451 - 643) * t19444) + + if Bindx == 3078: + t19456 = np.cos(phi) + t19457 = t19456 ** 2 + t19459 = t19457 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (2530 * t19457 + 31625 * t19459 - 107 + (18722 * t19457 + 16445 * t19459 - 671) * t19456) * ((1 + t19456) ** (0.3e1 / 0.2e1)) * np.sqrt(0.95e2) * np.exp((-1*1j) * (8 * phi1 - 5 * phi2)) * ((1 - t19456) ** (0.13e2 / 0.2e1)) + + if Bindx == 3079: + t19462 = np.cos(phi) + t19463 = t19462 ** 2 + t19465 = t19463 ** 2 + t19466 = t19462 * t19465 + t19471 = t19466 ** 2 + t19469 = t19465 ** 2 + t19464 = t19462 * t19463 + t19467 = t19464 ** 2 + t19461 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.10e2) * t19461 ** 2 * (4960 * t19463 - 14823 * t19464 - 19944 * t19465 + 69714 * t19466 + 624 * t19467 + 69828 * t19469 - 60720 * t19471 - 124 + (-115830 * t19467 + 48829 * t19469 + 16445 * t19471 + 1041) * t19462) + + if Bindx == 3080: + t19473 = np.cos(phi) + t19474 = t19473 ** 2 + t19476 = t19474 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (17710 * t19474 + 44275 * t19476 + 7 + (43010 * t19474 + 16445 * t19476 + 2585) * t19473) * np.sqrt((1 + t19473)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (8 * phi1 - 7 * phi2)) * ((1 - t19473) ** (0.15e2 / 0.2e1)) + + if Bindx == 3081: + t19478 = np.cos(phi) + t19479 = t19478 ** 2 + t19480 = t19478 * t19479 + t19483 = t19480 ** 2 + t19489 = t19483 ** 2 + t19481 = t19479 ** 2 + t19482 = t19478 * t19481 + t19487 = t19482 ** 2 + t19485 = t19481 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-8*1j) * (phi1 - phi2)) * (-11664 * t19479 - 19462 * t19480 + 86560 * t19481 - 9837 * t19482 - 214944 * t19483 + 158832 * t19485 + 60720 * t19487 - 80960 * t19489 + 432 + (185964 * t19483 - 289245 * t19485 + 113850 * t19487 + 16445 * t19489 + 3309) * t19478) + + if Bindx == 3082: + t19491 = np.cos(phi) + t19492 = t19491 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (1104 * t19491 + 123 + (3680 * t19491 + 3174 + 1495 * t19492) * t19492) * np.sqrt((1 + t19491)) * np.sqrt(0.110e3) * np.exp((-1*1j) * (8 * phi1 - 9 * phi2)) * ((1 - t19491) ** (0.17e2 / 0.2e1)) + + if Bindx == 3083: + t19496 = np.cos(phi) + t19497 = t19496 ** 2 + t19499 = t19497 ** 2 + t19500 = t19496 * t19499 + t19505 = t19500 ** 2 + t19503 = t19499 ** 2 + t19498 = t19496 * t19497 + t19501 = t19498 ** 2 + t19495 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.2530e4) * t19495 ** 2 * (-96 * t19497 + 365 * t19498 - 184 * t19499 - 742 * t19500 + 1232 * t19501 - 820 * t19503 - 400 * t19505 + 12 + (-334 * t19501 + 929 * t19503 + 65 * t19505 - 27) * t19496) + + if Bindx == 3084: + t19507 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (23 + (80 + 65 * t19507) * t19507) * ((1 + t19507) ** (0.3e1 / 0.2e1)) * np.sqrt(0.1265e4) * np.exp((-1*1j) * (8 * phi1 - 11 * phi2)) * ((1 - t19507) ** (0.19e2 / 0.2e1)) + + if Bindx == 3085: + t19509 = np.cos(phi) + t19514 = -1 + t19509 + t19510 = t19514 ** 2 + t19512 = t19514 * t19510 ** 2 + t19508 = 1 + t19509 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.2530e4) * t19512 ** 2 * t19508 ** 2 * (8 + 13 * t19509) + + if Bindx == 3086: + t19515 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (8 * phi1 - 13 * phi2)) * np.sqrt(0.16445e5) * ((1 - t19515) ** (0.21e2 / 0.2e1)) * ((1 + t19515) ** (0.5e1 / 0.2e1)) + + if Bindx == 3087: + t19516 = np.cos(phi) + t19524 = -1 + t19516 + t19523 = 1 + t19516 + t19519 = t19523 ** 2 + t19521 = t19523 * t19519 ** 2 + t19517 = t19524 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 + 13 * phi2)) * np.sqrt(0.230230e6) * t19524 * t19517 * t19521 ** 2 + + if Bindx == 3088: + t19525 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (7 * phi1 + 12 * phi2)) * np.sqrt(0.8855e4) * ((1 - t19525) ** (0.5e1 / 0.2e1)) * ((1 + t19525) ** (0.19e2 / 0.2e1)) * (-7 + 13 * t19525) + + if Bindx == 3089: + t19537 = np.sin(phi) + t19535 = t19537 ** 2 + t19526 = np.cos(phi) + t19527 = t19526 ** 2 + t19529 = t19527 ** 2 + t19533 = t19529 ** 2 + t19528 = t19526 * t19527 + t19531 = t19528 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.17710e5) * t19535 ** 2 * (-68 * t19527 - 420 * t19528 - 490 * t19529 + 924 * t19531 + 385 * t19533 + 17 + (182 * t19529 + 892 * t19531 + 65 * t19533 + 49) * t19526) + + if Bindx == 3090: + t19538 = np.cos(phi) + t19539 = t19538 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 10 * phi2)) * np.sqrt(0.8855e4) * ((1 - t19538) ** (0.3e1 / 0.2e1)) * ((1 + t19538) ** (0.17e2 / 0.2e1)) * (-105 * t19539 - 7 + (65 * t19539 + 51) * t19538) + + if Bindx == 3091: + t19542 = np.cos(phi) + t19543 = t19542 ** 2 + t19545 = t19543 ** 2 + t19546 = t19542 * t19545 + t19551 = t19546 ** 2 + t19549 = t19545 ** 2 + t19544 = t19542 * t19543 + t19547 = t19544 ** 2 + t19541 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.385e3) * t19541 ** 2 * (-1175 * t19543 + 1323 * t19544 + 7326 * t19545 + 3402 * t19546 - 12390 * t19547 + 483 * t19549 + 7245 * t19551 + 47 + (-15570 * t19547 + 11201 * t19549 + 1495 * t19551 - 315) * t19542) + + if Bindx == 3092: + t19553 = np.cos(phi) + t19554 = t19553 ** 2 + t19556 = t19554 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t19553)) * ((1 + t19553) ** (0.15e2 / 0.2e1)) * (-17710 * t19554 - 44275 * t19556 - 7 + (43010 * t19554 + 16445 * t19556 + 2585) * t19553) + + if Bindx == 3093: + t19558 = np.cos(phi) + t19559 = t19558 ** 2 + t19560 = t19558 * t19559 + t19563 = t19560 ** 2 + t19569 = t19563 ** 2 + t19561 = t19559 ** 2 + t19562 = t19558 * t19561 + t19567 = t19562 ** 2 + t19565 = t19561 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-7*1j) * (phi1 + phi2)) * (34818 * t19559 + 96866 * t19560 - 183295 * t19561 - 545139 * t19562 + 205548 * t19563 + 379701 * t19565 - 867790 * t19567 + 433895 * t19569 - 829 + (1261068 * t19563 - 1185415 * t19565 + 265650 * t19567 + 115115 * t19569 - 6097) * t19558) + + if Bindx == 3094: + t19571 = np.cos(phi) + t19572 = t19571 ** 2 + t19573 = t19571 * t19572 + t19576 = t19573 ** 2 + t19574 = t19572 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.35e2) * ((1 + t19571) ** (0.13e2 / 0.2e1)) * (-147 * t19572 + 18095 * t19573 - 61985 * t19574 - 61985 * t19576 + 85 + (90321 * t19574 + 16445 * t19576 - 829) * t19571) * ((1 - t19571) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3095: + t19579 = np.cos(phi) + t19580 = t19579 ** 2 + t19582 = t19580 ** 2 + t19583 = t19579 * t19582 + t19588 = t19583 ** 2 + t19586 = t19582 ** 2 + t19581 = t19579 * t19580 + t19584 = t19581 ** 2 + t19578 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.1330e4) * t19578 ** 2 * (1007 * t19580 - 8895 * t19581 - 11022 * t19582 + 34926 * t19583 + 47502 * t19584 - 79695 * t19586 + 44275 * t19588 - 19 + (-43758 * t19584 + 2783 * t19586 + 16445 * t19588 + 547) * t19579) + + if Bindx == 3096: + t19590 = np.cos(phi) + t19591 = t19590 ** 2 + t19593 = t19591 ** 2 + t19592 = t19590 * t19591 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2409 * t19591 + 14421 * t19593 + 23 + (2024 + 16445 * t19592) * t19592 + (-30360 * t19593 + 240) * t19590) * ((1 + t19590) ** (0.11e2 / 0.2e1)) * np.sqrt(0.665e3) * np.exp((-1*1j) * (7 * phi1 + 4 * phi2)) * ((1 - t19590) ** (0.3e1 / 0.2e1)) + + if Bindx == 3097: + t19607 = np.sin(phi) + t19605 = t19607 ** 2 + t19596 = np.cos(phi) + t19597 = t19596 ** 2 + t19599 = t19597 ** 2 + t19603 = t19599 ** 2 + t19598 = t19596 * t19597 + t19601 = t19598 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.4522e4) * t19605 ** 2 * (-1740 * t19597 + 292 * t19598 + 14406 * t19599 - 35420 * t19601 + 26565 * t19603 + 29 + (2310 * t19599 - 15180 * t19601 + 16445 * t19603 - 27) * t19596) + + if Bindx == 3098: + t19608 = np.cos(phi) + t19609 = t19608 ** 2 + t19611 = t19609 ** 2 + t19610 = t19608 * t19609 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-15 * t19609 - 345 * t19611 + 1 + (460 + 1495 * t19610) * t19610 + (-1380 * t19611 - 24) * t19608) * ((1 + t19608) ** (0.9e1 / 0.2e1)) * np.sqrt(0.49742e5) * np.exp((-1*1j) * (7 * phi1 + 2 * phi2)) * ((1 - t19608) ** (0.5e1 / 0.2e1)) + + if Bindx == 3099: + t19624 = np.sin(phi) + t19621 = t19624 ** 2 + t19622 = t19624 * t19621 + t19614 = np.cos(phi) + t19615 = t19614 ** 2 + t19616 = t19614 * t19615 + t19619 = t19616 ** 2 + t19617 = t19615 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (7 * phi1 + phi2)) * np.sqrt(0.248710e6) * t19622 ** 2 * (63 * t19615 + 357 * t19616 - 483 * t19617 + 805 * t19619 - 1 + (-1449 * t19617 + 1495 * t19619 - 19) * t19614) + + if Bindx == 3100: + t19625 = np.cos(phi) + t19626 = t19625 ** 2 + t19627 = t19626 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-483 * t19627 - 1 + (805 * t19627 + 63) * t19626) * ((1 + t19625) ** (0.7e1 / 0.2e1)) * np.sqrt(0.230945e6) * np.exp((-7*1j) * phi1) * ((1 - t19625) ** (0.7e1 / 0.2e1)) + + if Bindx == 3101: + t19639 = np.sin(phi) + t19636 = t19639 ** 2 + t19637 = t19639 * t19636 + t19629 = np.cos(phi) + t19630 = t19629 ** 2 + t19631 = t19629 * t19630 + t19634 = t19631 ** 2 + t19632 = t19630 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (7 * phi1 - phi2)) * np.sqrt(0.248710e6) * t19637 ** 2 * (-63 * t19630 + 357 * t19631 + 483 * t19632 - 805 * t19634 + 1 + (-1449 * t19632 + 1495 * t19634 - 19) * t19629) + + if Bindx == 3102: + t19640 = np.cos(phi) + t19641 = t19640 ** 2 + t19643 = t19641 ** 2 + t19642 = t19640 * t19641 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-15 * t19641 - 345 * t19643 + 1 + (-460 + 1495 * t19642) * t19642 + (1380 * t19643 + 24) * t19640) * ((1 + t19640) ** (0.5e1 / 0.2e1)) * np.sqrt(0.49742e5) * np.exp((-1*1j) * (7 * phi1 - 2 * phi2)) * ((1 - t19640) ** (0.9e1 / 0.2e1)) + + if Bindx == 3103: + t19657 = np.sin(phi) + t19655 = t19657 ** 2 + t19646 = np.cos(phi) + t19647 = t19646 ** 2 + t19649 = t19647 ** 2 + t19653 = t19649 ** 2 + t19648 = t19646 * t19647 + t19651 = t19648 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.4522e4) * t19655 ** 2 * (1740 * t19647 + 292 * t19648 - 14406 * t19649 + 35420 * t19651 - 26565 * t19653 - 29 + (2310 * t19649 - 15180 * t19651 + 16445 * t19653 - 27) * t19646) + + if Bindx == 3104: + t19658 = np.cos(phi) + t19659 = t19658 ** 2 + t19661 = t19659 ** 2 + t19660 = t19658 * t19659 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2409 * t19659 + 14421 * t19661 + 23 + (-2024 + 16445 * t19660) * t19660 + (30360 * t19661 - 240) * t19658) * ((1 + t19658) ** (0.3e1 / 0.2e1)) * np.sqrt(0.665e3) * np.exp((-1*1j) * (7 * phi1 - 4 * phi2)) * ((1 - t19658) ** (0.11e2 / 0.2e1)) + + if Bindx == 3105: + t19665 = np.cos(phi) + t19666 = t19665 ** 2 + t19668 = t19666 ** 2 + t19669 = t19665 * t19668 + t19674 = t19669 ** 2 + t19672 = t19668 ** 2 + t19667 = t19665 * t19666 + t19670 = t19667 ** 2 + t19664 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.1330e4) * t19664 ** 2 * (-1007 * t19666 - 8895 * t19667 + 11022 * t19668 + 34926 * t19669 - 47502 * t19670 + 79695 * t19672 - 44275 * t19674 + 19 + (-43758 * t19670 + 2783 * t19672 + 16445 * t19674 + 547) * t19665) + + if Bindx == 3106: + t19676 = np.cos(phi) + t19677 = t19676 ** 2 + t19679 = t19677 ** 2 + t19678 = t19676 * t19677 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (891 * t19677 + 44781 * t19679 - 85 + (17204 + 16445 * t19678) * t19678 + (45540 * t19679 - 744) * t19676) * np.sqrt((1 + t19676)) * np.sqrt(0.35e2) * np.exp((-1*1j) * (7 * phi1 - 6 * phi2)) * ((1 - t19676) ** (0.13e2 / 0.2e1)) + + if Bindx == 3107: + t19682 = np.cos(phi) + t19683 = t19682 ** 2 + t19684 = t19682 * t19683 + t19687 = t19684 ** 2 + t19693 = t19687 ** 2 + t19685 = t19683 ** 2 + t19686 = t19682 * t19685 + t19691 = t19686 ** 2 + t19689 = t19685 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-7*1j) * (phi1 - phi2)) * (-34818 * t19683 + 96866 * t19684 + 183295 * t19685 - 545139 * t19686 - 205548 * t19687 - 379701 * t19689 + 867790 * t19691 - 433895 * t19693 + 829 + (1261068 * t19687 - 1185415 * t19689 + 265650 * t19691 + 115115 * t19693 - 6097) * t19682) + + if Bindx == 3108: + t19695 = np.cos(phi) + t19696 = t19695 ** 2 + t19698 = t19696 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (17710 * t19696 + 44275 * t19698 + 7 + (43010 * t19696 + 16445 * t19698 + 2585) * t19695) * np.sqrt((1 + t19695)) * np.sqrt(0.14e2) * np.exp((-1*1j) * (7 * phi1 - 8 * phi2)) * ((1 - t19695) ** (0.15e2 / 0.2e1)) + + if Bindx == 3109: + t19701 = np.cos(phi) + t19702 = t19701 ** 2 + t19704 = t19702 ** 2 + t19705 = t19701 * t19704 + t19710 = t19705 ** 2 + t19708 = t19704 ** 2 + t19703 = t19701 * t19702 + t19706 = t19703 ** 2 + t19700 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.385e3) * t19700 ** 2 * (1175 * t19702 + 1323 * t19703 - 7326 * t19704 + 3402 * t19705 + 12390 * t19706 - 483 * t19708 - 7245 * t19710 - 47 + (-15570 * t19706 + 11201 * t19708 + 1495 * t19710 - 315) * t19701) + + if Bindx == 3110: + t19712 = np.cos(phi) + t19713 = t19712 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (105 * t19713 + 7 + (65 * t19713 + 51) * t19712) * ((1 + t19712) ** (0.3e1 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (7 * phi1 - 10 * phi2)) * ((1 - t19712) ** (0.17e2 / 0.2e1)) + + if Bindx == 3111: + t19726 = np.sin(phi) + t19724 = t19726 ** 2 + t19715 = np.cos(phi) + t19716 = t19715 ** 2 + t19718 = t19716 ** 2 + t19722 = t19718 ** 2 + t19717 = t19715 * t19716 + t19720 = t19717 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.17710e5) * t19724 ** 2 * (68 * t19716 - 420 * t19717 + 490 * t19718 - 924 * t19720 - 385 * t19722 - 17 + (182 * t19718 + 892 * t19720 + 65 * t19722 + 49) * t19715) + + if Bindx == 3112: + t19727 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (7 + 13 * t19727) * ((1 + t19727) ** (0.5e1 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((-1*1j) * (7 * phi1 - 12 * phi2)) * ((1 - t19727) ** (0.19e2 / 0.2e1)) + + if Bindx == 3113: + t19728 = np.cos(phi) + t19736 = -1 + t19728 + t19735 = 1 + t19728 + t19733 = t19735 ** 2 + t19729 = t19736 ** 2 + t19731 = t19736 * t19729 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (7 * phi1 - 13 * phi2)) * np.sqrt(0.230230e6) * t19731 ** 2 * t19735 * t19733 + + if Bindx == 3114: + t19737 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (6 * phi1 + 13 * phi2)) * np.sqrt(0.6578e4) * ((1 - t19737) ** (0.7e1 / 0.2e1)) * ((1 + t19737) ** (0.19e2 / 0.2e1)) + + if Bindx == 3115: + t19738 = np.cos(phi) + t19746 = -1 + t19738 + t19745 = 1 + t19738 + t19741 = t19745 ** 2 + t19742 = t19741 ** 2 + t19739 = t19746 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-6*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.253e3) * t19746 * t19739 * t19745 * t19742 ** 2 * (-6 + 13 * t19738) + + if Bindx == 3116: + t19747 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (6 * phi1 + 11 * phi2)) * np.sqrt(0.506e3) * ((1 - t19747) ** (0.5e1 / 0.2e1)) * ((1 + t19747) ** (0.17e2 / 0.2e1)) * (59 + (-300 + 325 * t19747) * t19747) + + if Bindx == 3117: + t19759 = np.sin(phi) + t19757 = t19759 ** 2 + t19748 = np.cos(phi) + t19749 = t19748 ** 2 + t19751 = t19749 ** 2 + t19755 = t19751 ** 2 + t19750 = t19748 * t19749 + t19753 = t19750 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.253e3) * t19757 ** 2 * (357 * t19749 - 60 * t19750 - 1515 * t19751 + 795 * t19753 + 1500 * t19755 - 17 + (-1572 * t19751 + 2352 * t19753 + 325 * t19755 + 75) * t19748) + + if Bindx == 3118: + t19760 = np.cos(phi) + t19761 = t19760 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-3*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * ((1 - t19760) ** (0.3e1 / 0.2e1)) * ((1 + t19760) ** (0.15e2 / 0.2e1)) * (-1564 * t19760 + 27 + (-13800 * t19760 + 8142 + 7475 * t19761) * t19761) + + if Bindx == 3119: + t19765 = np.cos(phi) + t19766 = t19765 ** 2 + t19768 = t19766 ** 2 + t19769 = t19765 * t19768 + t19774 = t19769 ** 2 + t19772 = t19768 ** 2 + t19767 = t19765 * t19766 + t19770 = t19767 ** 2 + t19764 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * t19764 ** 2 * (-4960 * t19766 - 14823 * t19767 + 19944 * t19768 + 69714 * t19769 - 624 * t19770 - 69828 * t19772 + 60720 * t19774 + 124 + (-115830 * t19770 + 48829 * t19772 + 16445 * t19774 + 1041) * t19765) + + if Bindx == 3120: + t19776 = np.cos(phi) + t19777 = t19776 ** 2 + t19779 = t19777 ** 2 + t19778 = t19776 * t19777 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.35e2) * np.sqrt((1 - t19776)) * ((1 + t19776) ** (0.13e2 / 0.2e1)) * (891 * t19777 + 44781 * t19779 - 85 + (-17204 + 16445 * t19778) * t19778 + (-45540 * t19779 + 744) * t19776) + + if Bindx == 3121: + t19782 = np.cos(phi) + t19783 = t19782 ** 2 + t19784 = t19782 * t19783 + t19787 = t19784 ** 2 + t19793 = t19787 ** 2 + t19785 = t19783 ** 2 + t19786 = t19782 * t19785 + t19791 = t19786 ** 2 + t19789 = t19785 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 + phi2)) * (-3795 * t19783 + 45270 * t19784 + 48670 * t19785 - 217629 * t19786 - 257454 * t19787 + 595305 * t19789 - 609983 * t19791 + 227700 * t19793 + 69 + (410700 * t19787 - 270435 * t19789 - 47058 * t19791 + 82225 * t19793 - 2561) * t19782) + + if Bindx == 3122: + t19795 = np.cos(phi) + t19796 = t19795 ** 2 + t19797 = t19795 * t19796 + t19800 = t19797 ** 2 + t19798 = t19796 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.38e2) * ((1 + t19795) ** (0.11e2 / 0.2e1)) * (-11900 * t19796 + 34720 * t19797 + 417956 * t19800 + 113 + (20790 + 82225 * t19798) * t19798 + (-240856 * t19798 - 303600 * t19800 + 552) * t19795) * ((1 - t19795) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3123: + t19804 = np.cos(phi) + t19805 = t19804 ** 2 + t19807 = t19805 ** 2 + t19808 = t19804 * t19807 + t19813 = t19808 ** 2 + t19811 = t19807 ** 2 + t19806 = t19804 * t19805 + t19809 = t19806 ** 2 + t19803 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.19e2) * t19803 ** 2 * (6784 * t19805 - 6899 * t19806 - 66396 * t19807 + 20762 * t19808 + 224728 * t19809 - 312202 * t19811 + 151800 * t19813 - 106 + (22242 * t19809 - 114103 * t19811 + 82225 * t19813 + 381) * t19804) + + if Bindx == 3124: + t19815 = np.cos(phi) + t19816 = t19815 ** 2 + t19817 = t19815 * t19816 + t19820 = t19817 ** 2 + t19818 = t19816 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-399 * t19816 - 3465 * t19817 + 8855 * t19818 - 26565 * t19820 - 3 + (5313 * t19818 + 16445 * t19820 + 203) * t19815) * ((1 + t19815) ** (0.9e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-3*1j) * (2 * phi1 + phi2)) * ((1 - t19815) ** (0.3e1 / 0.2e1)) + + if Bindx == 3125: + t19833 = np.sin(phi) + t19831 = t19833 ** 2 + t19822 = np.cos(phi) + t19823 = t19822 ** 2 + t19825 = t19823 ** 2 + t19829 = t19825 ** 2 + t19824 = t19822 * t19823 + t19827 = t19824 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.35530e5) * t19831 ** 2 * (-69 * t19823 - 196 * t19824 + 651 * t19825 - 1771 * t19827 + 1380 * t19829 + 1 + (1092 * t19825 - 2208 * t19827 + 1495 * t19829 + 9) * t19822) + + if Bindx == 3126: + t19834 = np.cos(phi) + t19835 = t19834 ** 2 + t19836 = t19834 * t19835 + t19839 = t19836 ** 2 + t19837 = t19835 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-315 * t19835 + 945 * t19836 + 2415 * t19837 - 4025 * t19839 + 5 + (-5313 * t19837 + 7475 * t19839 - 35) * t19834) * ((1 + t19834) ** (0.7e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (6 * phi1 + phi2)) * ((1 - t19834) ** (0.5e1 / 0.2e1)) + + if Bindx == 3127: + t19848 = np.sin(phi) + t19845 = t19848 ** 2 + t19846 = t19848 * t19845 + t19841 = np.cos(phi) + t19842 = t19841 ** 2 + t19843 = t19842 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-6*1j) * phi1) * np.sqrt(0.323323e6) * t19846 ** 2 * t19841 * (-483 * t19843 - 5 + (575 * t19843 + 105) * t19842) + + if Bindx == 3128: + t19849 = np.cos(phi) + t19850 = t19849 ** 2 + t19851 = t19849 * t19850 + t19854 = t19851 ** 2 + t19852 = t19850 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (315 * t19850 + 945 * t19851 - 2415 * t19852 + 4025 * t19854 - 5 + (-5313 * t19852 + 7475 * t19854 - 35) * t19849) * ((1 + t19849) ** (0.5e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (6 * phi1 - phi2)) * ((1 - t19849) ** (0.7e1 / 0.2e1)) + + if Bindx == 3129: + t19867 = np.sin(phi) + t19865 = t19867 ** 2 + t19856 = np.cos(phi) + t19857 = t19856 ** 2 + t19859 = t19857 ** 2 + t19863 = t19859 ** 2 + t19858 = t19856 * t19857 + t19861 = t19858 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.35530e5) * t19865 ** 2 * (69 * t19857 - 196 * t19858 - 651 * t19859 + 1771 * t19861 - 1380 * t19863 - 1 + (1092 * t19859 - 2208 * t19861 + 1495 * t19863 + 9) * t19856) + + if Bindx == 3130: + t19868 = np.cos(phi) + t19869 = t19868 ** 2 + t19870 = t19868 * t19869 + t19873 = t19870 ** 2 + t19871 = t19869 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (399 * t19869 - 3465 * t19870 - 8855 * t19871 + 26565 * t19873 + 3 + (5313 * t19871 + 16445 * t19873 + 203) * t19868) * ((1 + t19868) ** (0.3e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-3*1j) * (2 * phi1 - phi2)) * ((1 - t19868) ** (0.9e1 / 0.2e1)) + + if Bindx == 3131: + t19876 = np.cos(phi) + t19877 = t19876 ** 2 + t19879 = t19877 ** 2 + t19880 = t19876 * t19879 + t19885 = t19880 ** 2 + t19883 = t19879 ** 2 + t19878 = t19876 * t19877 + t19881 = t19878 ** 2 + t19875 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.19e2) * t19875 ** 2 * (-6784 * t19877 - 6899 * t19878 + 66396 * t19879 + 20762 * t19880 - 224728 * t19881 + 312202 * t19883 - 151800 * t19885 + 106 + (22242 * t19881 - 114103 * t19883 + 82225 * t19885 + 381) * t19876) + + if Bindx == 3132: + t19887 = np.cos(phi) + t19888 = t19887 ** 2 + t19889 = t19887 * t19888 + t19892 = t19889 ** 2 + t19890 = t19888 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-11235 * t19888 - 23485 * t19889 + 44275 * t19890 + 221375 * t19892 + 113 + (196581 * t19890 + 82225 * t19892 - 665) * t19887) * np.sqrt((1 + t19887)) * np.sqrt(0.38e2) * np.exp((-1*1j) * (6 * phi1 - 5 * phi2)) * ((1 - t19887) ** (0.11e2 / 0.2e1)) + + if Bindx == 3133: + t19894 = np.cos(phi) + t19895 = t19894 ** 2 + t19896 = t19894 * t19895 + t19899 = t19896 ** 2 + t19905 = t19899 ** 2 + t19897 = t19895 ** 2 + t19898 = t19894 * t19897 + t19903 = t19898 ** 2 + t19901 = t19897 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-6*1j) * (phi1 - phi2)) * (3795 * t19895 + 45270 * t19896 - 48670 * t19897 - 217629 * t19898 + 257454 * t19899 - 595305 * t19901 + 609983 * t19903 - 227700 * t19905 - 69 + (410700 * t19899 - 270435 * t19901 - 47058 * t19903 + 82225 * t19905 - 2561) * t19894) + + if Bindx == 3134: + t19907 = np.cos(phi) + t19908 = t19907 ** 2 + t19910 = t19908 ** 2 + t19909 = t19907 * t19908 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (891 * t19908 + 44781 * t19910 - 85 + (17204 + 16445 * t19909) * t19909 + (45540 * t19910 - 744) * t19907) * np.sqrt((1 + t19907)) * np.sqrt(0.35e2) * np.exp((-1*1j) * (6 * phi1 - 7 * phi2)) * ((1 - t19907) ** (0.13e2 / 0.2e1)) + + if Bindx == 3135: + t19914 = np.cos(phi) + t19915 = t19914 ** 2 + t19917 = t19915 ** 2 + t19918 = t19914 * t19917 + t19923 = t19918 ** 2 + t19921 = t19917 ** 2 + t19916 = t19914 * t19915 + t19919 = t19916 ** 2 + t19913 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.10e2) * t19913 ** 2 * (4960 * t19915 - 14823 * t19916 - 19944 * t19917 + 69714 * t19918 + 624 * t19919 + 69828 * t19921 - 60720 * t19923 - 124 + (-115830 * t19919 + 48829 * t19921 + 16445 * t19923 + 1041) * t19914) + + if Bindx == 3136: + t19925 = np.cos(phi) + t19926 = t19925 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (1564 * t19925 + 27 + (13800 * t19925 + 8142 + 7475 * t19926) * t19926) * ((1 + t19925) ** (0.3e1 / 0.2e1)) * np.sqrt(0.11e2) * np.exp((-3*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t19925) ** (0.15e2 / 0.2e1)) + + if Bindx == 3137: + t19940 = np.sin(phi) + t19938 = t19940 ** 2 + t19929 = np.cos(phi) + t19930 = t19929 ** 2 + t19932 = t19930 ** 2 + t19936 = t19932 ** 2 + t19931 = t19929 * t19930 + t19934 = t19931 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.253e3) * t19938 ** 2 * (-357 * t19930 - 60 * t19931 + 1515 * t19932 - 795 * t19934 - 1500 * t19936 + 17 + (-1572 * t19932 + 2352 * t19934 + 325 * t19936 + 75) * t19929) + + if Bindx == 3138: + t19941 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (59 + (300 + 325 * t19941) * t19941) * ((1 + t19941) ** (0.5e1 / 0.2e1)) * np.sqrt(0.506e3) * np.exp((-1*1j) * (6 * phi1 - 11 * phi2)) * ((1 - t19941) ** (0.17e2 / 0.2e1)) + + if Bindx == 3139: + t19942 = np.cos(phi) + t19950 = -1 + t19942 + t19949 = 1 + t19942 + t19947 = t19949 ** 2 + t19943 = t19950 ** 2 + t19944 = t19943 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-6*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.253e3) * t19950 * t19944 ** 2 * t19949 * t19947 * (6 + 13 * t19942) + + if Bindx == 3140: + t19951 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (6 * phi1 - 13 * phi2)) * np.sqrt(0.6578e4) * ((1 - t19951) ** (0.19e2 / 0.2e1)) * ((1 + t19951) ** (0.7e1 / 0.2e1)) + + if Bindx == 3141: + t19952 = np.cos(phi) + t19960 = -1 + t19952 + t19959 = 1 + t19952 + t19955 = t19959 ** 2 + t19956 = t19955 ** 2 + t19953 = t19960 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 + 13 * phi2)) * np.sqrt(0.62491e5) * t19953 ** 2 * t19959 * t19956 ** 2 + + if Bindx == 3142: + t19961 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (5 * phi1 + 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t19961) ** (0.7e1 / 0.2e1)) * ((1 + t19961) ** (0.17e2 / 0.2e1)) * (-5 + 13 * t19961) + + if Bindx == 3143: + t19972 = np.sin(phi) + t19969 = t19972 ** 2 + t19970 = t19972 * t19969 + t19962 = np.cos(phi) + t19963 = t19962 ** 2 + t19964 = t19962 * t19963 + t19967 = t19964 ** 2 + t19965 = t19963 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.4807e4) * t19970 ** 2 * (-555 * t19963 - 505 * t19964 + 935 * t19965 + 1375 * t19967 + 37 + (2037 * t19965 + 325 * t19967 - 65) * t19962) + + if Bindx == 3144: + t19973 = np.cos(phi) + t19974 = t19973 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-5*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.9614e4) * ((1 - t19973) ** (0.5e1 / 0.2e1)) * ((1 + t19973) ** (0.15e2 / 0.2e1)) * (-375 * t19974 - 5 + (325 * t19974 + 111) * t19973) + + if Bindx == 3145: + t19987 = np.sin(phi) + t19985 = t19987 ** 2 + t19976 = np.cos(phi) + t19977 = t19976 ** 2 + t19979 = t19977 ** 2 + t19983 = t19979 ** 2 + t19978 = t19976 * t19977 + t19981 = t19978 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.418e3) * t19985 ** 2 * (2196 * t19977 + 8820 * t19978 - 3870 * t19979 - 15180 * t19981 + 25875 * t19983 - 61 + (-28926 * t19979 + 22356 * t19981 + 7475 * t19983 - 765) * t19976) + + if Bindx == 3146: + t19988 = np.cos(phi) + t19989 = t19988 ** 2 + t19991 = t19989 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (5 * phi1 + 8 * phi2)) * np.sqrt(0.95e2) * ((1 - t19988) ** (0.3e1 / 0.2e1)) * ((1 + t19988) ** (0.13e2 / 0.2e1)) * (-2530 * t19989 - 31625 * t19991 + 107 + (18722 * t19989 + 16445 * t19991 - 671) * t19988) + + if Bindx == 3147: + t19994 = np.cos(phi) + t19995 = t19994 ** 2 + t19997 = t19995 ** 2 + t19998 = t19994 * t19997 + t20003 = t19998 ** 2 + t20001 = t19997 ** 2 + t19996 = t19994 * t19995 + t19999 = t19996 ** 2 + t19993 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.1330e4) * t19993 ** 2 * (1007 * t19995 - 8895 * t19996 - 11022 * t19997 + 34926 * t19998 + 47502 * t19999 - 79695 * t20001 + 44275 * t20003 - 19 + (-43758 * t19999 + 2783 * t20001 + 16445 * t20003 + 547) * t19994) + + if Bindx == 3148: + t20005 = np.cos(phi) + t20006 = t20005 ** 2 + t20007 = t20005 * t20006 + t20010 = t20007 ** 2 + t20008 = t20006 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.38e2) * np.sqrt((1 - t20005)) * ((1 + t20005) ** (0.11e2 / 0.2e1)) * (11235 * t20006 - 23485 * t20007 - 44275 * t20008 - 221375 * t20010 - 113 + (196581 * t20008 + 82225 * t20010 - 665) * t20005) + + if Bindx == 3149: + t20012 = np.cos(phi) + t20013 = t20012 ** 2 + t20014 = t20012 * t20013 + t20017 = t20014 ** 2 + t20023 = t20017 ** 2 + t20015 = t20013 ** 2 + t20016 = t20012 * t20015 + t20021 = t20016 ** 2 + t20019 = t20015 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-5*1j) * (phi1 + phi2)) * (-118470 * t20013 + 162730 * t20014 + 1285585 * t20015 - 705195 * t20016 - 5251524 * t20017 + 9975285 * t20019 - 8892950 * t20021 + 3004375 * t20023 + 1795 + (505020 * t20017 + 1919665 * t20019 - 3432198 * t20021 + 1562275 * t20023 - 8201) * t20012) + + if Bindx == 3150: + t20025 = np.cos(phi) + t20026 = t20025 ** 2 + t20028 = t20026 ** 2 + t20032 = t20028 ** 2 + t20027 = t20025 * t20026 + t20030 = t20027 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * ((1 + t20025) ** (0.9e1 / 0.2e1)) * (-77292 * t20026 - 151620 * t20027 + 1280790 * t20028 - 2018940 * t20030 - 5407875 * t20032 - 43 + (-1606374 * t20028 + 6402924 * t20030 + 1562275 * t20032 + 16155) * t20025) * ((1 - t20025) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3151: + t20035 = np.cos(phi) + t20036 = t20035 ** 2 + t20038 = t20036 ** 2 + t20039 = t20035 * t20038 + t20044 = t20039 ** 2 + t20042 = t20038 ** 2 + t20037 = t20035 * t20036 + t20040 = t20037 ** 2 + t20034 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.85e2) * t20034 ** 2 * (13505 * t20036 + 24515 * t20037 - 146490 * t20038 - 171038 * t20039 + 526946 * t20040 - 745085 * t20042 + 360525 * t20044 - 185 + (502854 * t20040 - 658559 * t20042 + 312455 * t20044 - 1011) * t20035) + + if Bindx == 3152: + t20046 = np.cos(phi) + t20047 = t20046 ** 2 + t20048 = t20046 * t20047 + t20051 = t20048 ** 2 + t20049 = t20047 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (532 * t20047 - 4256 * t20048 - 12236 * t20051 - 11 + (-1330 + 28405 * t20049) * t20049 + (24472 * t20049 - 34960 * t20051 + 152) * t20046) * ((1 + t20046) ** (0.7e1 / 0.2e1)) * np.sqrt(0.935e3) * np.exp((-1*1j) * (5 * phi1 + 2 * phi2)) * ((1 - t20046) ** (0.3e1 / 0.2e1)) + + if Bindx == 3153: + t20065 = np.sin(phi) + t20063 = t20065 ** 2 + t20054 = np.cos(phi) + t20055 = t20054 ** 2 + t20057 = t20055 ** 2 + t20061 = t20057 ** 2 + t20056 = t20054 * t20055 + t20059 = t20056 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (5 * phi1 + phi2)) * np.sqrt(0.187e3) * t20063 ** 2 * (-1900 * t20055 - 18620 * t20056 + 19950 * t20057 - 61180 * t20059 + 54625 * t20061 + 25 + (109326 * t20057 - 221996 * t20059 + 142025 * t20061 + 785) * t20054) + + if Bindx == 3154: + t20066 = np.cos(phi) + t20067 = t20066 ** 2 + t20068 = t20067 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-380 * t20067 + 5 + (-12236 * t20067 + 3990 + 10925 * t20068) * t20068) * ((1 + t20066) ** (0.5e1 / 0.2e1)) * np.sqrt(0.34034e5) * np.exp((-5*1j) * phi1) * ((1 - t20066) ** (0.5e1 / 0.2e1)) + + if Bindx == 3155: + t20082 = np.sin(phi) + t20080 = t20082 ** 2 + t20071 = np.cos(phi) + t20072 = t20071 ** 2 + t20074 = t20072 ** 2 + t20078 = t20074 ** 2 + t20073 = t20071 * t20072 + t20076 = t20073 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (5 * phi1 - phi2)) * np.sqrt(0.187e3) * t20080 ** 2 * (1900 * t20072 - 18620 * t20073 - 19950 * t20074 + 61180 * t20076 - 54625 * t20078 - 25 + (109326 * t20074 - 221996 * t20076 + 142025 * t20078 + 785) * t20071) + + if Bindx == 3156: + t20083 = np.cos(phi) + t20084 = t20083 ** 2 + t20085 = t20083 * t20084 + t20088 = t20085 ** 2 + t20086 = t20084 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (532 * t20084 + 4256 * t20085 - 12236 * t20088 - 11 + (-1330 + 28405 * t20086) * t20086 + (-24472 * t20086 + 34960 * t20088 - 152) * t20083) * ((1 + t20083) ** (0.3e1 / 0.2e1)) * np.sqrt(0.935e3) * np.exp((-1*1j) * (5 * phi1 - 2 * phi2)) * ((1 - t20083) ** (0.7e1 / 0.2e1)) + + if Bindx == 3157: + t20092 = np.cos(phi) + t20093 = t20092 ** 2 + t20095 = t20093 ** 2 + t20096 = t20092 * t20095 + t20101 = t20096 ** 2 + t20099 = t20095 ** 2 + t20094 = t20092 * t20093 + t20097 = t20094 ** 2 + t20091 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.85e2) * t20091 ** 2 * (-13505 * t20093 + 24515 * t20094 + 146490 * t20095 - 171038 * t20096 - 526946 * t20097 + 745085 * t20099 - 360525 * t20101 + 185 + (502854 * t20097 - 658559 * t20099 + 312455 * t20101 - 1011) * t20092) + + if Bindx == 3158: + t20103 = np.cos(phi) + t20104 = t20103 ** 2 + t20105 = t20103 * t20104 + t20108 = t20105 ** 2 + t20106 = t20104 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * (61180 * t20104 - 212800 * t20105 + 2557324 * t20108 + 43 + (-1067990 + 1562275 * t20106) * t20106 + (-538384 * t20106 + 3845600 * t20108 + 16112) * t20103) * np.sqrt((1 + t20103)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (5 * phi1 - 4 * phi2)) * ((1 - t20103) ** (0.9e1 / 0.2e1)) + + if Bindx == 3159: + t20111 = np.cos(phi) + t20112 = t20111 ** 2 + t20113 = t20111 * t20112 + t20116 = t20113 ** 2 + t20122 = t20116 ** 2 + t20114 = t20112 ** 2 + t20115 = t20111 * t20114 + t20120 = t20115 ** 2 + t20118 = t20114 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-5*1j) * (phi1 - phi2)) * (118470 * t20112 + 162730 * t20113 - 1285585 * t20114 - 705195 * t20115 + 5251524 * t20116 - 9975285 * t20118 + 8892950 * t20120 - 3004375 * t20122 - 1795 + (505020 * t20116 + 1919665 * t20118 - 3432198 * t20120 + 1562275 * t20122 - 8201) * t20111) + + if Bindx == 3160: + t20124 = np.cos(phi) + t20125 = t20124 ** 2 + t20126 = t20124 * t20125 + t20129 = t20126 ** 2 + t20127 = t20125 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-11235 * t20125 - 23485 * t20126 + 44275 * t20127 + 221375 * t20129 + 113 + (196581 * t20127 + 82225 * t20129 - 665) * t20124) * np.sqrt((1 + t20124)) * np.sqrt(0.38e2) * np.exp((-1*1j) * (5 * phi1 - 6 * phi2)) * ((1 - t20124) ** (0.11e2 / 0.2e1)) + + if Bindx == 3161: + t20132 = np.cos(phi) + t20133 = t20132 ** 2 + t20135 = t20133 ** 2 + t20136 = t20132 * t20135 + t20141 = t20136 ** 2 + t20139 = t20135 ** 2 + t20134 = t20132 * t20133 + t20137 = t20134 ** 2 + t20131 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.1330e4) * t20131 ** 2 * (-1007 * t20133 - 8895 * t20134 + 11022 * t20135 + 34926 * t20136 - 47502 * t20137 + 79695 * t20139 - 44275 * t20141 + 19 + (-43758 * t20137 + 2783 * t20139 + 16445 * t20141 + 547) * t20132) + + if Bindx == 3162: + t20143 = np.cos(phi) + t20144 = t20143 ** 2 + t20146 = t20144 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (2530 * t20144 + 31625 * t20146 - 107 + (18722 * t20144 + 16445 * t20146 - 671) * t20143) * ((1 + t20143) ** (0.3e1 / 0.2e1)) * np.sqrt(0.95e2) * np.exp((-1*1j) * (5 * phi1 - 8 * phi2)) * ((1 - t20143) ** (0.13e2 / 0.2e1)) + + if Bindx == 3163: + t20159 = np.sin(phi) + t20157 = t20159 ** 2 + t20148 = np.cos(phi) + t20149 = t20148 ** 2 + t20151 = t20149 ** 2 + t20155 = t20151 ** 2 + t20150 = t20148 * t20149 + t20153 = t20150 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.418e3) * t20157 ** 2 * (-2196 * t20149 + 8820 * t20150 + 3870 * t20151 + 15180 * t20153 - 25875 * t20155 + 61 + (-28926 * t20151 + 22356 * t20153 + 7475 * t20155 - 765) * t20148) + + if Bindx == 3164: + t20160 = np.cos(phi) + t20161 = t20160 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (375 * t20161 + 5 + (325 * t20161 + 111) * t20160) * ((1 + t20160) ** (0.5e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-5*1j) * (phi1 - 2 * phi2)) * ((1 - t20160) ** (0.15e2 / 0.2e1)) + + if Bindx == 3165: + t20173 = np.sin(phi) + t20170 = t20173 ** 2 + t20171 = t20173 * t20170 + t20163 = np.cos(phi) + t20164 = t20163 ** 2 + t20165 = t20163 * t20164 + t20168 = t20165 ** 2 + t20166 = t20164 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.4807e4) * t20171 ** 2 * (555 * t20164 - 505 * t20165 - 935 * t20166 - 1375 * t20168 - 37 + (2037 * t20166 + 325 * t20168 - 65) * t20163) + + if Bindx == 3166: + t20174 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * (5 + 13 * t20174) * ((1 + t20174) ** (0.7e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (5 * phi1 - 12 * phi2)) * ((1 - t20174) ** (0.17e2 / 0.2e1)) + + if Bindx == 3167: + t20175 = np.cos(phi) + t20183 = -1 + t20175 + t20182 = 1 + t20175 + t20180 = t20182 ** 2 + t20176 = t20183 ** 2 + t20177 = t20176 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((-1*1j) * (5 * phi1 - 13 * phi2)) * np.sqrt(0.62491e5) * t20183 * t20177 ** 2 * t20180 ** 2 + + if Bindx == 3168: + t20184 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (4 * phi1 + 13 * phi2)) * np.sqrt(0.124982e6) * ((1 - t20184) ** (0.9e1 / 0.2e1)) * ((1 + t20184) ** (0.17e2 / 0.2e1)) + + if Bindx == 3169: + t20185 = np.cos(phi) + t20192 = -1 + t20185 + t20191 = 1 + t20185 + t20188 = t20191 ** 2 + t20189 = t20188 ** 2 + t20186 = t20192 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-4*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.4807e4) * t20186 ** 2 * t20189 ** 2 * (-4 + 13 * t20185) + + if Bindx == 3170: + t20193 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (4 * phi1 + 11 * phi2)) * np.sqrt(0.9614e4) * ((1 - t20193) ** (0.7e1 / 0.2e1)) * ((1 + t20193) ** (0.15e2 / 0.2e1)) * (19 + (-200 + 325 * t20193) * t20193) + + if Bindx == 3171: + t20204 = np.sin(phi) + t20201 = t20204 ** 2 + t20202 = t20204 * t20201 + t20194 = np.cos(phi) + t20195 = t20194 ** 2 + t20196 = t20194 * t20195 + t20199 = t20196 ** 2 + t20197 = t20195 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.4807e4) * t20202 ** 2 * (-60 * t20195 - 525 * t20196 - 270 * t20197 + 1000 * t20199 + 2 + (807 * t20197 + 325 * t20199 + 65) * t20194) + + if Bindx == 3172: + t20205 = np.cos(phi) + t20206 = t20205 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (4 * phi1 + 9 * phi2)) * np.sqrt(0.209e3) * ((1 - t20205) ** (0.5e1 / 0.2e1)) * ((1 + t20205) ** (0.13e2 / 0.2e1)) * (184 * t20205 - 73 + (-9200 * t20205 + 2622 + 7475 * t20206) * t20206) + + if Bindx == 3173: + t20220 = np.sin(phi) + t20218 = t20220 ** 2 + t20209 = np.cos(phi) + t20210 = t20209 ** 2 + t20212 = t20210 ** 2 + t20216 = t20212 ** 2 + t20211 = t20209 * t20210 + t20214 = t20211 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.190e3) * t20218 ** 2 * (-1960 * t20210 + 9004 * t20211 + 16056 * t20212 - 46552 * t20214 + 40480 * t20216 + 40 + (-23826 * t20212 + 7084 * t20214 + 16445 * t20216 - 643) * t20209) + + if Bindx == 3174: + t20221 = np.cos(phi) + t20222 = t20221 ** 2 + t20224 = t20222 ** 2 + t20223 = t20221 * t20222 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (4 * phi1 + 7 * phi2)) * np.sqrt(0.665e3) * ((1 - t20221) ** (0.3e1 / 0.2e1)) * ((1 + t20221) ** (0.11e2 / 0.2e1)) * (-2409 * t20222 + 14421 * t20224 + 23 + (2024 + 16445 * t20223) * t20223 + (-30360 * t20224 + 240) * t20221) + + if Bindx == 3175: + t20228 = np.cos(phi) + t20229 = t20228 ** 2 + t20231 = t20229 ** 2 + t20232 = t20228 * t20231 + t20237 = t20232 ** 2 + t20235 = t20231 ** 2 + t20230 = t20228 * t20229 + t20233 = t20230 ** 2 + t20227 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.19e2) * t20227 ** 2 * (6784 * t20229 - 6899 * t20230 - 66396 * t20231 + 20762 * t20232 + 224728 * t20233 - 312202 * t20235 + 151800 * t20237 - 106 + (22242 * t20233 - 114103 * t20235 + 82225 * t20237 + 381) * t20228) + + if Bindx == 3176: + t20239 = np.cos(phi) + t20240 = t20239 ** 2 + t20241 = t20239 * t20240 + t20244 = t20241 ** 2 + t20242 = t20240 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t20239)) * ((1 + t20239) ** (0.9e1 / 0.2e1)) * (61180 * t20240 + 212800 * t20241 + 2557324 * t20244 + 43 + (-1067990 + 1562275 * t20242) * t20242 + (538384 * t20242 - 3845600 * t20244 - 16112) * t20239) + + if Bindx == 3177: + t20247 = np.cos(phi) + t20248 = t20247 ** 2 + t20249 = t20247 * t20248 + t20252 = t20249 ** 2 + t20258 = t20252 ** 2 + t20250 = t20248 ** 2 + t20251 = t20247 * t20250 + t20256 = t20251 ** 2 + t20254 = t20250 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 + phi2)) * (-65700 * t20248 - 100650 * t20249 + 780360 * t20250 + 804429 * t20251 - 3347496 * t20252 + 6460380 * t20254 - 5749172 * t20256 + 1922800 * t20258 + 876 + (-2917260 * t20252 + 5292925 * t20254 - 4643562 * t20256 + 1562275 * t20258 + 3891) * t20247) + + if Bindx == 3178: + t20260 = np.cos(phi) + t20261 = t20260 ** 2 + t20263 = t20261 ** 2 + t20267 = t20263 ** 2 + t20262 = t20260 * t20261 + t20265 = t20262 ** 2 + t20264 = t20260 * t20263 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.170e3) * ((1 + t20260) ** (0.7e1 / 0.2e1)) * (387 * t20261 - 48336 * t20262 + 91770 * t20263 - 640794 * t20265 + 821997 * t20267 - 87 + (191520 + 312455 * t20264) * t20264 + (230736 * t20265 - 961400 * t20267 + 1752) * t20260) * ((1 - t20260) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3179: + t20271 = np.cos(phi) + t20272 = t20271 ** 2 + t20274 = t20272 ** 2 + t20275 = t20271 * t20274 + t20280 = t20275 ** 2 + t20278 = t20274 ** 2 + t20273 = t20271 * t20272 + t20276 = t20273 ** 2 + t20270 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.1870e4) * t20270 ** 2 * (480 * t20272 + 2865 * t20273 - 5700 * t20274 - 19950 * t20275 + 22344 * t20276 - 34086 * t20278 + 17480 * t20280 - 6 + (55290 * t20276 - 65987 * t20278 + 28405 * t20280 - 111) * t20271) + + if Bindx == 3180: + t20282 = np.cos(phi) + t20283 = t20282 ** 2 + t20285 = t20283 ** 2 + t20289 = t20285 ** 2 + t20284 = t20282 * t20283 + t20287 = t20284 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (3420 * t20283 - 7980 * t20284 - 35910 * t20285 + 110124 * t20287 - 98325 * t20289 - 45 + (64638 * t20285 - 173052 * t20287 + 142025 * t20289 + 225) * t20282) * ((1 + t20282) ** (0.5e1 / 0.2e1)) * np.sqrt(0.374e3) * np.exp((-1*1j) * (4 * phi1 + phi2)) * ((1 - t20282) ** (0.3e1 / 0.2e1)) + + if Bindx == 3181: + t20298 = np.sin(phi) + t20296 = t20298 ** 2 + t20291 = np.cos(phi) + t20292 = t20291 ** 2 + t20293 = t20292 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-4*1j) * phi1) * np.sqrt(0.17017e5) * t20296 ** 2 * t20291 * (-1140 * t20292 + 45 + (-15732 * t20292 + 7182 + 10925 * t20293) * t20293) + + if Bindx == 3182: + t20299 = np.cos(phi) + t20300 = t20299 ** 2 + t20302 = t20300 ** 2 + t20306 = t20302 ** 2 + t20301 = t20299 * t20300 + t20304 = t20301 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-3420 * t20300 - 7980 * t20301 + 35910 * t20302 - 110124 * t20304 + 98325 * t20306 + 45 + (64638 * t20302 - 173052 * t20304 + 142025 * t20306 + 225) * t20299) * ((1 + t20299) ** (0.3e1 / 0.2e1)) * np.sqrt(0.374e3) * np.exp((-1*1j) * (4 * phi1 - phi2)) * ((1 - t20299) ** (0.5e1 / 0.2e1)) + + if Bindx == 3183: + t20309 = np.cos(phi) + t20310 = t20309 ** 2 + t20312 = t20310 ** 2 + t20313 = t20309 * t20312 + t20318 = t20313 ** 2 + t20316 = t20312 ** 2 + t20311 = t20309 * t20310 + t20314 = t20311 ** 2 + t20308 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.1870e4) * t20308 ** 2 * (-480 * t20310 + 2865 * t20311 + 5700 * t20312 - 19950 * t20313 - 22344 * t20314 + 34086 * t20316 - 17480 * t20318 + 6 + (55290 * t20314 - 65987 * t20316 + 28405 * t20318 - 111) * t20309) + + if Bindx == 3184: + t20320 = np.cos(phi) + t20321 = t20320 ** 2 + t20323 = t20321 ** 2 + t20327 = t20323 ** 2 + t20322 = t20320 * t20321 + t20325 = t20322 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (2052 * t20321 + 46284 * t20322 + 45486 * t20323 - 403788 * t20325 + 648945 * t20327 - 87 + (-237006 * t20323 + 173052 * t20325 + 312455 * t20327 - 1665) * t20320) * np.sqrt((1 + t20320)) * np.sqrt(0.170e3) * np.exp((-1*1j) * (4 * phi1 - 3 * phi2)) * ((1 - t20320) ** (0.7e1 / 0.2e1)) + + if Bindx == 3185: + t20329 = np.cos(phi) + t20330 = t20329 ** 2 + t20331 = t20329 * t20330 + t20334 = t20331 ** 2 + t20340 = t20334 ** 2 + t20332 = t20330 ** 2 + t20333 = t20329 * t20332 + t20338 = t20333 ** 2 + t20336 = t20332 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 - phi2)) * (65700 * t20330 - 100650 * t20331 - 780360 * t20332 + 804429 * t20333 + 3347496 * t20334 - 6460380 * t20336 + 5749172 * t20338 - 1922800 * t20340 - 876 + (-2917260 * t20334 + 5292925 * t20336 - 4643562 * t20338 + 1562275 * t20340 + 3891) * t20329) + + if Bindx == 3186: + t20342 = np.cos(phi) + t20343 = t20342 ** 2 + t20344 = t20342 * t20343 + t20347 = t20344 ** 2 + t20345 = t20343 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * (61180 * t20343 - 212800 * t20344 + 2557324 * t20347 + 43 + (-1067990 + 1562275 * t20345) * t20345 + (-538384 * t20345 + 3845600 * t20347 + 16112) * t20342) * np.sqrt((1 + t20342)) * np.sqrt(0.2e1) * np.exp((-1*1j) * (4 * phi1 - 5 * phi2)) * ((1 - t20342) ** (0.9e1 / 0.2e1)) + + if Bindx == 3187: + t20351 = np.cos(phi) + t20352 = t20351 ** 2 + t20354 = t20352 ** 2 + t20355 = t20351 * t20354 + t20360 = t20355 ** 2 + t20358 = t20354 ** 2 + t20353 = t20351 * t20352 + t20356 = t20353 ** 2 + t20350 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.19e2) * t20350 ** 2 * (-6784 * t20352 - 6899 * t20353 + 66396 * t20354 + 20762 * t20355 - 224728 * t20356 + 312202 * t20358 - 151800 * t20360 + 106 + (22242 * t20356 - 114103 * t20358 + 82225 * t20360 + 381) * t20351) + + if Bindx == 3188: + t20362 = np.cos(phi) + t20363 = t20362 ** 2 + t20365 = t20363 ** 2 + t20364 = t20362 * t20363 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2409 * t20363 + 14421 * t20365 + 23 + (-2024 + 16445 * t20364) * t20364 + (30360 * t20365 - 240) * t20362) * ((1 + t20362) ** (0.3e1 / 0.2e1)) * np.sqrt(0.665e3) * np.exp((-1*1j) * (4 * phi1 - 7 * phi2)) * ((1 - t20362) ** (0.11e2 / 0.2e1)) + + if Bindx == 3189: + t20379 = np.sin(phi) + t20377 = t20379 ** 2 + t20368 = np.cos(phi) + t20369 = t20368 ** 2 + t20371 = t20369 ** 2 + t20375 = t20371 ** 2 + t20370 = t20368 * t20369 + t20373 = t20370 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.190e3) * t20377 ** 2 * (1960 * t20369 + 9004 * t20370 - 16056 * t20371 + 46552 * t20373 - 40480 * t20375 - 40 + (-23826 * t20371 + 7084 * t20373 + 16445 * t20375 - 643) * t20368) + + if Bindx == 3190: + t20380 = np.cos(phi) + t20381 = t20380 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-184 * t20380 - 73 + (9200 * t20380 + 2622 + 7475 * t20381) * t20381) * ((1 + t20380) ** (0.5e1 / 0.2e1)) * np.sqrt(0.209e3) * np.exp((-1*1j) * (4 * phi1 - 9 * phi2)) * ((1 - t20380) ** (0.13e2 / 0.2e1)) + + if Bindx == 3191: + t20394 = np.sin(phi) + t20391 = t20394 ** 2 + t20392 = t20394 * t20391 + t20384 = np.cos(phi) + t20385 = t20384 ** 2 + t20386 = t20384 * t20385 + t20389 = t20386 ** 2 + t20387 = t20385 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.4807e4) * t20392 ** 2 * (60 * t20385 - 525 * t20386 + 270 * t20387 - 1000 * t20389 - 2 + (807 * t20387 + 325 * t20389 + 65) * t20384) + + if Bindx == 3192: + t20395 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (19 + (200 + 325 * t20395) * t20395) * ((1 + t20395) ** (0.7e1 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((-1*1j) * (4 * phi1 - 11 * phi2)) * ((1 - t20395) ** (0.15e2 / 0.2e1)) + + if Bindx == 3193: + t20404 = np.sin(phi) + t20401 = t20404 ** 2 + t20402 = t20401 ** 2 + t20396 = np.cos(phi) + t20397 = t20396 ** 2 + t20399 = t20397 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-4*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.4807e4) * t20402 ** 2 * (-28 * t20397 - 48 * t20399 + 4 + (62 * t20397 + 13 * t20399 - 3) * t20396) + + if Bindx == 3194: + t20405 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((-1*1j) * (4 * phi1 - 13 * phi2)) * np.sqrt(0.124982e6) * ((1 - t20405) ** (0.17e2 / 0.2e1)) * ((1 + t20405) ** (0.9e1 / 0.2e1)) + + if Bindx == 3195: + t20406 = np.cos(phi) + t20414 = -1 + t20406 + t20413 = 1 + t20406 + t20410 = t20413 ** 2 + t20411 = t20410 ** 2 + t20407 = t20414 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 + 13 * phi2)) * np.sqrt(0.5311735e7) * t20414 * t20407 ** 2 * t20411 ** 2 + + if Bindx == 3196: + t20415 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-3*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.817190e6) * ((1 - t20415) ** (0.9e1 / 0.2e1)) * ((1 + t20415) ** (0.15e2 / 0.2e1)) * (-3 + 13 * t20415) + + if Bindx == 3197: + t20424 = np.sin(phi) + t20421 = t20424 ** 2 + t20422 = t20421 ** 2 + t20416 = np.cos(phi) + t20417 = t20416 ** 2 + t20419 = t20417 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.408595e6) * t20422 ** 2 * (-22 * t20417 + 165 * t20419 + 1 + (106 * t20417 + 65 * t20419 - 27) * t20416) + + if Bindx == 3198: + t20425 = np.cos(phi) + t20426 = t20425 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (3 * phi1 + 10 * phi2)) * np.sqrt(0.817190e6) * ((1 - t20425) ** (0.7e1 / 0.2e1)) * ((1 + t20425) ** (0.13e2 / 0.2e1)) * (-45 * t20426 + 1 + (65 * t20426 + 3) * t20425) + + if Bindx == 3199: + t20438 = np.sin(phi) + t20435 = t20438 ** 2 + t20436 = t20438 * t20435 + t20428 = np.cos(phi) + t20429 = t20428 ** 2 + t20430 = t20428 * t20429 + t20433 = t20430 ** 2 + t20431 = t20429 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t20436 ** 2 * (387 * t20429 - 699 * t20430 - 2139 * t20431 + 3105 * t20433 - 9 + (483 * t20431 + 1495 * t20433 + 65) * t20428) + + if Bindx == 3200: + t20439 = np.cos(phi) + t20440 = t20439 ** 2 + t20444 = 2530 * t20440 + t20442 = t20440 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (3 * phi1 + 8 * phi2)) * np.sqrt(0.323e3) * ((1 - t20439) ** (0.5e1 / 0.2e1)) * ((1 + t20439) ** (0.11e2 / 0.2e1)) * (-18975 * t20442 + t20444 - 19 + (16445 * t20442 + t20444 - 495) * t20439) + + if Bindx == 3201: + t20456 = np.sin(phi) + t20454 = t20456 ** 2 + t20445 = np.cos(phi) + t20446 = t20445 ** 2 + t20448 = t20446 ** 2 + t20452 = t20448 ** 2 + t20447 = t20445 * t20446 + t20450 = t20447 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.4522e4) * t20454 ** 2 * (-1740 * t20446 + 292 * t20447 + 14406 * t20448 - 35420 * t20450 + 26565 * t20452 + 29 + (2310 * t20448 - 15180 * t20450 + 16445 * t20452 - 27) * t20445) + + if Bindx == 3202: + t20457 = np.cos(phi) + t20458 = t20457 ** 2 + t20459 = t20457 * t20458 + t20462 = t20459 ** 2 + t20460 = t20458 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-3*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.3230e4) * ((1 - t20457) ** (0.3e1 / 0.2e1)) * ((1 + t20457) ** (0.9e1 / 0.2e1)) * (-399 * t20458 - 3465 * t20459 + 8855 * t20460 - 26565 * t20462 - 3 + (5313 * t20460 + 16445 * t20462 + 203) * t20457) + + if Bindx == 3203: + t20465 = np.cos(phi) + t20466 = t20465 ** 2 + t20468 = t20466 ** 2 + t20469 = t20465 * t20468 + t20474 = t20469 ** 2 + t20472 = t20468 ** 2 + t20467 = t20465 * t20466 + t20470 = t20467 ** 2 + t20464 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t20464 ** 2 * (13505 * t20466 + 24515 * t20467 - 146490 * t20468 - 171038 * t20469 + 526946 * t20470 - 745085 * t20472 + 360525 * t20474 - 185 + (502854 * t20470 - 658559 * t20472 + 312455 * t20474 - 1011) * t20465) + + if Bindx == 3204: + t20476 = np.cos(phi) + t20477 = t20476 ** 2 + t20479 = t20477 ** 2 + t20483 = t20479 ** 2 + t20478 = t20476 * t20477 + t20481 = t20478 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((-1*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.170e3) * np.sqrt((1 - t20476)) * ((1 + t20476) ** (0.7e1 / 0.2e1)) * (-2052 * t20477 + 46284 * t20478 - 45486 * t20479 + 403788 * t20481 - 648945 * t20483 + 87 + (-237006 * t20479 + 173052 * t20481 + 312455 * t20483 - 1665) * t20476) + + if Bindx == 3205: + t20485 = np.cos(phi) + t20486 = t20485 ** 2 + t20487 = t20485 * t20486 + t20490 = t20487 ** 2 + t20496 = t20490 ** 2 + t20488 = t20486 ** 2 + t20489 = t20485 * t20488 + t20494 = t20489 ** 2 + t20492 = t20488 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-3*1j) * (phi1 + phi2)) * (-93726 * t20486 - 495342 * t20487 + 1208445 * t20488 + 3864321 * t20489 - 5569812 * t20490 + 11404161 * t20492 - 10623470 * t20494 + 3677355 * t20496 + 1143 + (-13011732 * t20490 + 21477885 * t20492 - 17160990 * t20494 + 5311735 * t20496 + 18219) * t20485) + + if Bindx == 3206: + t20498 = np.cos(phi) + t20499 = t20498 ** 2 + t20501 = t20499 ** 2 + t20502 = t20498 * t20501 + t20507 = t20502 ** 2 + t20505 = t20501 ** 2 + t20500 = t20498 * t20499 + t20503 = t20500 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.11e2) * ((1 + t20498) ** (0.5e1 / 0.2e1)) * (7395 * t20499 - 47175 * t20500 - 29070 * t20501 + 393414 * t20502 - 257754 * t20503 + 1225785 * t20505 - 1225785 * t20507 - 123 + (-959310 * t20503 + 408595 * t20505 + 482885 * t20507 + 1143) * t20498) * ((1 - t20498) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3207: + t20510 = np.cos(phi) + t20511 = t20510 ** 2 + t20513 = t20511 ** 2 + t20514 = t20510 * t20513 + t20519 = t20514 ** 2 + t20517 = t20513 ** 2 + t20512 = t20510 * t20511 + t20515 = t20512 ** 2 + t20509 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (3 * phi1 + phi2)) * np.sqrt(0.55e2) * t20509 ** 2 * (2295 * t20511 + 42585 * t20512 - 29070 * t20513 - 312018 * t20514 + 122094 * t20515 - 200583 * t20517 + 111435 * t20519 - 27 + (901170 * t20515 - 1106921 * t20517 + 482885 * t20519 - 1557) * t20510) + + if Bindx == 3208: + t20521 = np.cos(phi) + t20522 = t20521 ** 2 + t20523 = t20522 ** 2 + t20525 = t20523 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-9690 * t20523 - 66861 * t20525 - 9 + (40698 * t20523 + 37145 * t20525 + 765) * t20522) * ((1 + t20521) ** (0.3e1 / 0.2e1)) * np.sqrt(0.10010e5) * np.exp((-3*1j) * phi1) * ((1 - t20521) ** (0.3e1 / 0.2e1)) + + if Bindx == 3209: + t20528 = np.cos(phi) + t20529 = t20528 ** 2 + t20531 = t20529 ** 2 + t20532 = t20528 * t20531 + t20537 = t20532 ** 2 + t20535 = t20531 ** 2 + t20530 = t20528 * t20529 + t20533 = t20530 ** 2 + t20527 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (3 * phi1 - phi2)) * np.sqrt(0.55e2) * t20527 ** 2 * (-2295 * t20529 + 42585 * t20530 + 29070 * t20531 - 312018 * t20532 - 122094 * t20533 + 200583 * t20535 - 111435 * t20537 + 27 + (901170 * t20533 - 1106921 * t20535 + 482885 * t20537 - 1557) * t20528) + + if Bindx == 3210: + t20539 = np.cos(phi) + t20540 = t20539 ** 2 + t20542 = t20540 ** 2 + t20546 = t20542 ** 2 + t20541 = t20539 * t20540 + t20544 = t20541 ** 2 + t20543 = t20539 * t20542 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-8415 * t20540 - 38760 * t20541 + 67830 * t20542 - 67830 * t20544 - 334305 * t20546 + 123 + (325584 + 482885 * t20543) * t20543 + (-891480 * t20544 + 742900 * t20546 + 1020) * t20539) * np.sqrt((1 + t20539)) * np.sqrt(0.11e2) * np.exp((-1*1j) * (3 * phi1 - 2 * phi2)) * ((1 - t20539) ** (0.5e1 / 0.2e1)) + + if Bindx == 3211: + t20549 = np.cos(phi) + t20550 = t20549 ** 2 + t20551 = t20549 * t20550 + t20554 = t20551 ** 2 + t20560 = t20554 ** 2 + t20552 = t20550 ** 2 + t20553 = t20549 * t20552 + t20558 = t20553 ** 2 + t20556 = t20552 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-3*1j) * (phi1 - phi2)) * (93726 * t20550 - 495342 * t20551 - 1208445 * t20552 + 3864321 * t20553 + 5569812 * t20554 - 11404161 * t20556 + 10623470 * t20558 - 3677355 * t20560 - 1143 + (-13011732 * t20554 + 21477885 * t20556 - 17160990 * t20558 + 5311735 * t20560 + 18219) * t20549) + + if Bindx == 3212: + t20562 = np.cos(phi) + t20563 = t20562 ** 2 + t20565 = t20563 ** 2 + t20569 = t20565 ** 2 + t20564 = t20562 * t20563 + t20567 = t20564 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (2052 * t20563 + 46284 * t20564 + 45486 * t20565 - 403788 * t20567 + 648945 * t20569 - 87 + (-237006 * t20565 + 173052 * t20567 + 312455 * t20569 - 1665) * t20562) * np.sqrt((1 + t20562)) * np.sqrt(0.170e3) * np.exp((-1*1j) * (3 * phi1 - 4 * phi2)) * ((1 - t20562) ** (0.7e1 / 0.2e1)) + + if Bindx == 3213: + t20572 = np.cos(phi) + t20573 = t20572 ** 2 + t20575 = t20573 ** 2 + t20576 = t20572 * t20575 + t20581 = t20576 ** 2 + t20579 = t20575 ** 2 + t20574 = t20572 * t20573 + t20577 = t20574 ** 2 + t20571 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t20571 ** 2 * (-13505 * t20573 + 24515 * t20574 + 146490 * t20575 - 171038 * t20576 - 526946 * t20577 + 745085 * t20579 - 360525 * t20581 + 185 + (502854 * t20577 - 658559 * t20579 + 312455 * t20581 - 1011) * t20572) + + if Bindx == 3214: + t20583 = np.cos(phi) + t20584 = t20583 ** 2 + t20585 = t20583 * t20584 + t20588 = t20585 ** 2 + t20586 = t20584 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (399 * t20584 - 3465 * t20585 - 8855 * t20586 + 26565 * t20588 + 3 + (5313 * t20586 + 16445 * t20588 + 203) * t20583) * ((1 + t20583) ** (0.3e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-3*1j) * (phi1 - 2 * phi2)) * ((1 - t20583) ** (0.9e1 / 0.2e1)) + + if Bindx == 3215: + t20601 = np.sin(phi) + t20599 = t20601 ** 2 + t20590 = np.cos(phi) + t20591 = t20590 ** 2 + t20593 = t20591 ** 2 + t20597 = t20593 ** 2 + t20592 = t20590 * t20591 + t20595 = t20592 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.4522e4) * t20599 ** 2 * (1740 * t20591 + 292 * t20592 - 14406 * t20593 + 35420 * t20595 - 26565 * t20597 - 29 + (2310 * t20593 - 15180 * t20595 + 16445 * t20597 - 27) * t20590) + + if Bindx == 3216: + t20602 = np.cos(phi) + t20603 = t20602 ** 2 + t20605 = t20603 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2530 * t20603 + 18975 * t20605 + 19 + (2530 * t20603 + 16445 * t20605 - 495) * t20602) * ((1 + t20602) ** (0.5e1 / 0.2e1)) * np.sqrt(0.323e3) * np.exp((-1*1j) * (3 * phi1 - 8 * phi2)) * ((1 - t20602) ** (0.11e2 / 0.2e1)) + + if Bindx == 3217: + t20617 = np.sin(phi) + t20614 = t20617 ** 2 + t20615 = t20617 * t20614 + t20607 = np.cos(phi) + t20608 = t20607 ** 2 + t20609 = t20607 * t20608 + t20612 = t20609 ** 2 + t20610 = t20608 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((-3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t20615 ** 2 * (-387 * t20608 - 699 * t20609 + 2139 * t20610 - 3105 * t20612 + 9 + (483 * t20610 + 1495 * t20612 + 65) * t20607) + + if Bindx == 3218: + t20618 = np.cos(phi) + t20619 = t20618 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (45 * t20619 - 1 + (65 * t20619 + 3) * t20618) * ((1 + t20618) ** (0.7e1 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-1*1j) * (3 * phi1 - 10 * phi2)) * ((1 - t20618) ** (0.13e2 / 0.2e1)) + + if Bindx == 3219: + t20629 = np.sin(phi) + t20626 = t20629 ** 2 + t20627 = t20626 ** 2 + t20621 = np.cos(phi) + t20622 = t20621 ** 2 + t20624 = t20622 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.408595e6) * t20627 ** 2 * (22 * t20622 - 165 * t20624 - 1 + (106 * t20622 + 65 * t20624 - 27) * t20621) + + if Bindx == 3220: + t20630 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (3 + 13 * t20630) * ((1 + t20630) ** (0.9e1 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((-3*1j) * (phi1 - 4 * phi2)) * ((1 - t20630) ** (0.15e2 / 0.2e1)) + + if Bindx == 3221: + t20631 = np.cos(phi) + t20639 = -1 + t20631 + t20638 = 1 + t20631 + t20635 = t20638 ** 2 + t20632 = t20639 ** 2 + t20633 = t20632 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((-1*1j) * (3 * phi1 - 13 * phi2)) * np.sqrt(0.5311735e7) * t20633 ** 2 * t20638 * t20635 ** 2 + + if Bindx == 3222: + t20640 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 13 * phi2)) * np.sqrt(0.482885e6) * ((1 - t20640) ** (0.11e2 / 0.2e1)) * ((1 + t20640) ** (0.15e2 / 0.2e1)) + + if Bindx == 3223: + t20648 = np.sin(phi) + t20644 = t20648 ** 2 + t20646 = t20648 * t20644 ** 2 + t20641 = np.cos(phi) + t20642 = t20641 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.74290e5) * t20646 ** 2 * (24 * t20642 - 2 + (13 * t20642 + 9) * t20641) + + if Bindx == 3224: + t20649 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 11 * phi2)) * np.sqrt(0.37145e5) * ((1 - t20649) ** (0.9e1 / 0.2e1)) * ((1 + t20649) ** (0.13e2 / 0.2e1)) * (-1 + (-20 + 65 * t20649) * t20649) + + if Bindx == 3225: + t20658 = np.sin(phi) + t20655 = t20658 ** 2 + t20656 = t20655 ** 2 + t20650 = np.cos(phi) + t20651 = t20650 ** 2 + t20653 = t20651 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.74290e5) * t20656 ** 2 * (-35 * t20651 + 100 * t20653 + 1 + (2 * t20651 + 65 * t20653 - 1) * t20650) + + if Bindx == 3226: + t20659 = np.cos(phi) + t20660 = t20659 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 9 * phi2)) * np.sqrt(0.3230e4) * ((1 - t20659) ** (0.7e1 / 0.2e1)) * ((1 + t20659) ** (0.11e2 / 0.2e1)) * (92 * t20659 - 1 + (-920 * t20659 - 138 + 1495 * t20660) * t20660) + + if Bindx == 3227: + t20673 = np.sin(phi) + t20670 = t20673 ** 2 + t20671 = t20673 * t20670 + t20663 = np.cos(phi) + t20664 = t20663 ** 2 + t20665 = t20663 * t20664 + t20668 = t20665 ** 2 + t20666 = t20664 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.3553e4) * t20671 ** 2 * (216 * t20664 + 225 * t20665 - 1380 * t20666 + 1840 * t20668 - 4 + (-1035 * t20666 + 1495 * t20668 - 13) * t20663) + + if Bindx == 3228: + t20674 = np.cos(phi) + t20675 = t20674 ** 2 + t20677 = t20675 ** 2 + t20676 = t20674 * t20675 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 7 * phi2)) * np.sqrt(0.49742e5) * ((1 - t20674) ** (0.5e1 / 0.2e1)) * ((1 + t20674) ** (0.9e1 / 0.2e1)) * (-15 * t20675 - 345 * t20677 + 1 + (460 + 1495 * t20676) * t20676 + (-1380 * t20677 - 24) * t20674) + + if Bindx == 3229: + t20691 = np.sin(phi) + t20689 = t20691 ** 2 + t20680 = np.cos(phi) + t20681 = t20680 ** 2 + t20683 = t20681 ** 2 + t20687 = t20683 ** 2 + t20682 = t20680 * t20681 + t20685 = t20682 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t20689 ** 2 * (-69 * t20681 - 196 * t20682 + 651 * t20683 - 1771 * t20685 + 1380 * t20687 + 1 + (1092 * t20683 - 2208 * t20685 + 1495 * t20687 + 9) * t20680) + + if Bindx == 3230: + t20692 = np.cos(phi) + t20693 = t20692 ** 2 + t20694 = t20692 * t20693 + t20697 = t20694 ** 2 + t20695 = t20693 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.935e3) * ((1 - t20692) ** (0.3e1 / 0.2e1)) * ((1 + t20692) ** (0.7e1 / 0.2e1)) * (532 * t20693 - 4256 * t20694 - 12236 * t20697 - 11 + (-1330 + 28405 * t20695) * t20695 + (24472 * t20695 - 34960 * t20697 + 152) * t20692) + + if Bindx == 3231: + t20701 = np.cos(phi) + t20702 = t20701 ** 2 + t20704 = t20702 ** 2 + t20705 = t20701 * t20704 + t20710 = t20705 ** 2 + t20708 = t20704 ** 2 + t20703 = t20701 * t20702 + t20706 = t20703 ** 2 + t20700 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.1870e4) * t20700 ** 2 * (480 * t20702 + 2865 * t20703 - 5700 * t20704 - 19950 * t20705 + 22344 * t20706 - 34086 * t20708 + 17480 * t20710 - 6 + (55290 * t20706 - 65987 * t20708 + 28405 * t20710 - 111) * t20701) + + if Bindx == 3232: + t20712 = np.cos(phi) + t20713 = t20712 ** 2 + t20715 = t20713 ** 2 + t20719 = t20715 ** 2 + t20714 = t20712 * t20713 + t20717 = t20714 ** 2 + t20716 = t20712 * t20715 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * np.sqrt((1 - t20712)) * ((1 + t20712) ** (0.5e1 / 0.2e1)) * (-8415 * t20713 + 38760 * t20714 + 67830 * t20715 - 67830 * t20717 - 334305 * t20719 + 123 + (-325584 + 482885 * t20716) * t20716 + (891480 * t20717 - 742900 * t20719 - 1020) * t20712) + + if Bindx == 3233: + t20722 = np.cos(phi) + t20723 = t20722 ** 2 + t20724 = t20722 * t20723 + t20727 = t20724 ** 2 + t20733 = t20727 ** 2 + t20725 = t20723 ** 2 + t20726 = t20722 * t20725 + t20731 = t20726 ** 2 + t20729 = t20725 ** 2 + tfunc[..., c] = (0.27e2 / 0.512e3) * np.exp((-2*1j) * (phi1 + phi2)) * (-2871 * t20723 - 40722 * t20724 + 39270 * t20725 + 331551 * t20726 - 191862 * t20727 + 415701 * t20729 - 408595 * t20731 + 148580 * t20733 + 33 + (-1151172 * t20727 + 1936385 * t20729 - 1560090 * t20731 + 482885 * t20733 + 1419) * t20722) + + if Bindx == 3234: + t20735 = np.cos(phi) + t20736 = t20735 ** 2 + t20738 = t20736 ** 2 + t20739 = t20735 * t20738 + t20744 = t20739 ** 2 + t20742 = t20738 ** 2 + t20737 = t20735 * t20736 + t20740 = t20737 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * np.exp((-1*1j) * (2 * phi1 + phi2)) * np.sqrt(0.5e1) * ((1 + t20735) ** (0.3e1 / 0.2e1)) * (8118 * t20736 - 22440 * t20737 - 92565 * t20738 + 255816 * t20739 - 159885 * t20742 - 490314 * t20744 - 99 + (298452 + 482885 * t20740) * t20740 + (-1023264 * t20740 + 1634380 * t20742 - 891480 * t20744 + 396) * t20735) * ((1 - t20735) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3235: + t20748 = np.cos(phi) + t20749 = t20748 ** 2 + t20750 = t20749 ** 2 + t20752 = t20750 ** 2 + t20747 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * phi1) * np.sqrt(0.910e3) * t20747 ** 2 * t20748 * (-21318 * t20750 - 81719 * t20752 - 99 + (63954 * t20750 + 37145 * t20752 + 2805) * t20749) + + if Bindx == 3236: + t20754 = np.cos(phi) + t20755 = t20754 ** 2 + t20757 = t20755 ** 2 + t20758 = t20754 * t20757 + t20763 = t20758 ** 2 + t20761 = t20757 ** 2 + t20756 = t20754 * t20755 + t20759 = t20756 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * (8415 * t20755 + 14025 * t20756 - 106590 * t20757 - 149226 * t20758 + 447678 * t20759 - 735471 * t20761 + 408595 * t20763 - 99 + (575586 * t20759 - 898909 * t20761 + 482885 * t20763 - 297) * t20754) * np.sqrt((1 + t20754)) * np.sqrt(0.5e1) * np.exp((-1*1j) * (2 * phi1 - phi2)) * ((1 - t20754) ** (0.3e1 / 0.2e1)) + + if Bindx == 3237: + t20765 = np.cos(phi) + t20766 = t20765 ** 2 + t20767 = t20765 * t20766 + t20770 = t20767 ** 2 + t20776 = t20770 ** 2 + t20768 = t20766 ** 2 + t20769 = t20765 * t20768 + t20774 = t20769 ** 2 + t20772 = t20768 ** 2 + tfunc[..., c] = (0.27e2 / 0.512e3) * np.exp((-2*1j) * (phi1 - phi2)) * (2871 * t20766 - 40722 * t20767 - 39270 * t20768 + 331551 * t20769 + 191862 * t20770 - 415701 * t20772 + 408595 * t20774 - 148580 * t20776 - 33 + (-1151172 * t20770 + 1936385 * t20772 - 1560090 * t20774 + 482885 * t20776 + 1419) * t20765) + + if Bindx == 3238: + t20778 = np.cos(phi) + t20779 = t20778 ** 2 + t20781 = t20779 ** 2 + t20785 = t20781 ** 2 + t20780 = t20778 * t20779 + t20783 = t20780 ** 2 + t20782 = t20778 * t20781 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-8415 * t20779 - 38760 * t20780 + 67830 * t20781 - 67830 * t20783 - 334305 * t20785 + 123 + (325584 + 482885 * t20782) * t20782 + (-891480 * t20783 + 742900 * t20785 + 1020) * t20778) * np.sqrt((1 + t20778)) * np.sqrt(0.11e2) * np.exp((-1*1j) * (2 * phi1 - 3 * phi2)) * ((1 - t20778) ** (0.5e1 / 0.2e1)) + + if Bindx == 3239: + t20789 = np.cos(phi) + t20790 = t20789 ** 2 + t20792 = t20790 ** 2 + t20793 = t20789 * t20792 + t20798 = t20793 ** 2 + t20796 = t20792 ** 2 + t20791 = t20789 * t20790 + t20794 = t20791 ** 2 + t20788 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.1870e4) * t20788 ** 2 * (-480 * t20790 + 2865 * t20791 + 5700 * t20792 - 19950 * t20793 - 22344 * t20794 + 34086 * t20796 - 17480 * t20798 + 6 + (55290 * t20794 - 65987 * t20796 + 28405 * t20798 - 111) * t20789) + + if Bindx == 3240: + t20800 = np.cos(phi) + t20801 = t20800 ** 2 + t20802 = t20800 * t20801 + t20805 = t20802 ** 2 + t20803 = t20801 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (532 * t20801 + 4256 * t20802 - 12236 * t20805 - 11 + (-1330 + 28405 * t20803) * t20803 + (-24472 * t20803 + 34960 * t20805 - 152) * t20800) * ((1 + t20800) ** (0.3e1 / 0.2e1)) * np.sqrt(0.935e3) * np.exp((-1*1j) * (2 * phi1 - 5 * phi2)) * ((1 - t20800) ** (0.7e1 / 0.2e1)) + + if Bindx == 3241: + t20819 = np.sin(phi) + t20817 = t20819 ** 2 + t20808 = np.cos(phi) + t20809 = t20808 ** 2 + t20811 = t20809 ** 2 + t20815 = t20811 ** 2 + t20810 = t20808 * t20809 + t20813 = t20810 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t20817 ** 2 * (69 * t20809 - 196 * t20810 - 651 * t20811 + 1771 * t20813 - 1380 * t20815 - 1 + (1092 * t20811 - 2208 * t20813 + 1495 * t20815 + 9) * t20808) + + if Bindx == 3242: + t20820 = np.cos(phi) + t20821 = t20820 ** 2 + t20823 = t20821 ** 2 + t20822 = t20820 * t20821 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-15 * t20821 - 345 * t20823 + 1 + (-460 + 1495 * t20822) * t20822 + (1380 * t20823 + 24) * t20820) * ((1 + t20820) ** (0.5e1 / 0.2e1)) * np.sqrt(0.49742e5) * np.exp((-1*1j) * (2 * phi1 - 7 * phi2)) * ((1 - t20820) ** (0.9e1 / 0.2e1)) + + if Bindx == 3243: + t20836 = np.sin(phi) + t20833 = t20836 ** 2 + t20834 = t20836 * t20833 + t20826 = np.cos(phi) + t20827 = t20826 ** 2 + t20828 = t20826 * t20827 + t20831 = t20828 ** 2 + t20829 = t20827 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.3553e4) * t20834 ** 2 * (-216 * t20827 + 225 * t20828 + 1380 * t20829 - 1840 * t20831 + 4 + (-1035 * t20829 + 1495 * t20831 - 13) * t20826) + + if Bindx == 3244: + t20837 = np.cos(phi) + t20838 = t20837 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-92 * t20837 - 1 + (920 * t20837 - 138 + 1495 * t20838) * t20838) * ((1 + t20837) ** (0.7e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((-1*1j) * (2 * phi1 - 9 * phi2)) * ((1 - t20837) ** (0.11e2 / 0.2e1)) + + if Bindx == 3245: + t20849 = np.sin(phi) + t20846 = t20849 ** 2 + t20847 = t20846 ** 2 + t20841 = np.cos(phi) + t20842 = t20841 ** 2 + t20844 = t20842 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((-2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.74290e5) * t20847 ** 2 * (35 * t20842 - 100 * t20844 - 1 + (2 * t20842 + 65 * t20844 - 1) * t20841) + + if Bindx == 3246: + t20850 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-1 + (20 + 65 * t20850) * t20850) * ((1 + t20850) ** (0.9e1 / 0.2e1)) * np.sqrt(0.37145e5) * np.exp((-1*1j) * (2 * phi1 - 11 * phi2)) * ((1 - t20850) ** (0.13e2 / 0.2e1)) + + if Bindx == 3247: + t20858 = np.sin(phi) + t20854 = t20858 ** 2 + t20856 = t20858 * t20854 ** 2 + t20851 = np.cos(phi) + t20852 = t20851 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((-2*1j) * (phi1 - 6 * phi2)) * np.sqrt(0.74290e5) * t20856 ** 2 * (-24 * t20852 + 2 + (13 * t20852 + 9) * t20851) + + if Bindx == 3248: + t20859 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (2 * phi1 - 13 * phi2)) * np.sqrt(0.482885e6) * ((1 - t20859) ** (0.15e2 / 0.2e1)) * ((1 + t20859) ** (0.11e2 / 0.2e1)) + + if Bindx == 3249: + t20864 = np.sin(phi) + t20860 = t20864 ** 2 + t20861 = t20864 * t20860 + t20862 = t20861 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 13 * phi2)) * np.sqrt(0.96577e5) * t20862 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 3250: + t20865 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((-1*1j) * (phi1 + 12 * phi2)) * np.sqrt(0.14858e5) * ((1 - t20865) ** (0.11e2 / 0.2e1)) * ((1 + t20865) ** (0.13e2 / 0.2e1)) * (-1 + 13 * t20865) + + if Bindx == 3251: + t20873 = np.sin(phi) + t20869 = t20873 ** 2 + t20871 = t20873 * t20869 ** 2 + t20866 = np.cos(phi) + t20867 = t20866 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 11 * phi2)) * np.sqrt(0.7429e4) * t20871 ** 2 * (275 * t20867 - 11 + (325 * t20867 - 61) * t20866) + + if Bindx == 3252: + t20874 = np.cos(phi) + t20875 = t20874 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (phi1 + 10 * phi2)) * np.sqrt(0.14858e5) * ((1 - t20874) ** (0.9e1 / 0.2e1)) * ((1 + t20874) ** (0.11e2 / 0.2e1)) * (-75 * t20875 + 3 + (325 * t20875 - 33) * t20874) + + if Bindx == 3253: + t20885 = np.sin(phi) + t20882 = t20885 ** 2 + t20883 = t20882 ** 2 + t20877 = np.cos(phi) + t20878 = t20877 ** 2 + t20880 = t20878 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 9 * phi2)) * np.sqrt(0.646e3) * t20883 ** 2 * (-1242 * t20878 + 5175 * t20880 + 27 + (-3818 * t20878 + 7475 * t20880 + 303) * t20877) + + if Bindx == 3254: + t20886 = np.cos(phi) + t20887 = t20886 ** 2 + t20889 = t20887 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (phi1 + 8 * phi2)) * np.sqrt(0.17765e5) * ((1 - t20886) ** (0.7e1 / 0.2e1)) * ((1 + t20886) ** (0.9e1 / 0.2e1)) * (138 * t20887 - 575 * t20889 - 3 + (-506 * t20887 + 1495 * t20889 + 27) * t20886) + + if Bindx == 3255: + t20901 = np.sin(phi) + t20898 = t20901 ** 2 + t20899 = t20901 * t20898 + t20891 = np.cos(phi) + t20892 = t20891 ** 2 + t20893 = t20891 * t20892 + t20896 = t20893 ** 2 + t20894 = t20892 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 7 * phi2)) * np.sqrt(0.248710e6) * t20899 ** 2 * (63 * t20892 + 357 * t20893 - 483 * t20894 + 805 * t20896 - 1 + (-1449 * t20894 + 1495 * t20896 - 19) * t20891) + + if Bindx == 3256: + t20902 = np.cos(phi) + t20903 = t20902 ** 2 + t20904 = t20902 * t20903 + t20907 = t20904 ** 2 + t20905 = t20903 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.7106e4) * ((1 - t20902) ** (0.5e1 / 0.2e1)) * ((1 + t20902) ** (0.7e1 / 0.2e1)) * (-315 * t20903 + 945 * t20904 + 2415 * t20905 - 4025 * t20907 + 5 + (-5313 * t20905 + 7475 * t20907 - 35) * t20902) + + if Bindx == 3257: + t20920 = np.sin(phi) + t20918 = t20920 ** 2 + t20909 = np.cos(phi) + t20910 = t20909 ** 2 + t20912 = t20910 ** 2 + t20916 = t20912 ** 2 + t20911 = t20909 * t20910 + t20914 = t20911 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.187e3) * t20918 ** 2 * (-1900 * t20910 - 18620 * t20911 + 19950 * t20912 - 61180 * t20914 + 54625 * t20916 + 25 + (109326 * t20912 - 221996 * t20914 + 142025 * t20916 + 785) * t20909) + + if Bindx == 3258: + t20921 = np.cos(phi) + t20922 = t20921 ** 2 + t20924 = t20922 ** 2 + t20928 = t20924 ** 2 + t20923 = t20921 * t20922 + t20926 = t20923 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-1*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.374e3) * ((1 - t20921) ** (0.3e1 / 0.2e1)) * ((1 + t20921) ** (0.5e1 / 0.2e1)) * (3420 * t20922 - 7980 * t20923 - 35910 * t20924 + 110124 * t20926 - 98325 * t20928 - 45 + (64638 * t20924 - 173052 * t20926 + 142025 * t20928 + 225) * t20921) + + if Bindx == 3259: + t20931 = np.cos(phi) + t20932 = t20931 ** 2 + t20934 = t20932 ** 2 + t20935 = t20931 * t20934 + t20940 = t20935 ** 2 + t20938 = t20934 ** 2 + t20933 = t20931 * t20932 + t20936 = t20933 ** 2 + t20930 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.55e2) * t20930 ** 2 * (2295 * t20932 + 42585 * t20933 - 29070 * t20934 - 312018 * t20935 + 122094 * t20936 - 200583 * t20938 + 111435 * t20940 - 27 + (901170 * t20936 - 1106921 * t20938 + 482885 * t20940 - 1557) * t20931) + + if Bindx == 3260: + t20942 = np.cos(phi) + t20943 = t20942 ** 2 + t20945 = t20943 ** 2 + t20946 = t20942 * t20945 + t20951 = t20946 ** 2 + t20949 = t20945 ** 2 + t20944 = t20942 * t20943 + t20947 = t20944 ** 2 + tfunc[..., c] = (-0.27e2 / 0.1024e4*1j) * np.exp((-1*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t20942)) * ((1 + t20942) ** (0.3e1 / 0.2e1)) * (-8415 * t20943 + 14025 * t20944 + 106590 * t20945 - 149226 * t20946 - 447678 * t20947 + 735471 * t20949 - 408595 * t20951 + 99 + (575586 * t20947 - 898909 * t20949 + 482885 * t20951 - 297) * t20942) + + if Bindx == 3261: + t20953 = np.cos(phi) + t20954 = t20953 ** 2 + t20955 = t20953 * t20954 + t20958 = t20955 ** 2 + t20964 = t20958 ** 2 + t20956 = t20954 ** 2 + t20957 = t20953 * t20956 + t20962 = t20957 ** 2 + t20960 = t20956 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 + phi2)) * (-2970 * t20954 - 177210 * t20955 + 42075 * t20956 + 1489455 * t20957 - 213180 * t20958 + 479655 * t20960 - 490314 * t20962 + 185725 * t20964 + 33 + (-5329500 * t20958 + 9220035 * t20960 - 7622154 * t20962 + 2414425 * t20964 + 5973) * t20953) + + if Bindx == 3262: + t20966 = np.cos(phi) + t20967 = t20966 ** 2 + t20969 = t20967 ** 2 + t20970 = t20966 * t20969 + t20968 = t20966 * t20967 + t20971 = t20968 ** 2 + t20979 = -479655 * t20969 ** 2 + 490314 * t20970 ** 2 - 33 + (213180 - 185725 * t20971) * t20971 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * phi1) * np.sqrt(0.182e3) * np.sqrt((1 + t20966)) * (-t20966 * t20979 + 2970 * t20967 - 2970 * t20968 - 42075 * t20969 + 42075 * t20970 + t20979) * ((1 - t20966) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3263: + t20980 = np.cos(phi) + t20981 = t20980 ** 2 + t20982 = t20980 * t20981 + t20985 = t20982 ** 2 + t20991 = t20985 ** 2 + t20983 = t20981 ** 2 + t20984 = t20980 * t20983 + t20989 = t20984 ** 2 + t20987 = t20983 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-1*1j) * (phi1 - phi2)) * (2970 * t20981 - 177210 * t20982 - 42075 * t20983 + 1489455 * t20984 + 213180 * t20985 - 479655 * t20987 + 490314 * t20989 - 185725 * t20991 - 33 + (-5329500 * t20985 + 9220035 * t20987 - 7622154 * t20989 + 2414425 * t20991 + 5973) * t20980) + + if Bindx == 3264: + t20993 = np.cos(phi) + t20994 = t20993 ** 2 + t20996 = t20994 ** 2 + t20997 = t20993 * t20996 + t21002 = t20997 ** 2 + t21000 = t20996 ** 2 + t20995 = t20993 * t20994 + t20998 = t20995 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * (8415 * t20994 + 14025 * t20995 - 106590 * t20996 - 149226 * t20997 + 447678 * t20998 - 735471 * t21000 + 408595 * t21002 - 99 + (575586 * t20998 - 898909 * t21000 + 482885 * t21002 - 297) * t20993) * np.sqrt((1 + t20993)) * np.sqrt(0.5e1) * np.exp((-1*1j) * (phi1 - 2 * phi2)) * ((1 - t20993) ** (0.3e1 / 0.2e1)) + + if Bindx == 3265: + t21005 = np.cos(phi) + t21006 = t21005 ** 2 + t21008 = t21006 ** 2 + t21009 = t21005 * t21008 + t21014 = t21009 ** 2 + t21012 = t21008 ** 2 + t21007 = t21005 * t21006 + t21010 = t21007 ** 2 + t21004 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.55e2) * t21004 ** 2 * (-2295 * t21006 + 42585 * t21007 + 29070 * t21008 - 312018 * t21009 - 122094 * t21010 + 200583 * t21012 - 111435 * t21014 + 27 + (901170 * t21010 - 1106921 * t21012 + 482885 * t21014 - 1557) * t21005) + + if Bindx == 3266: + t21016 = np.cos(phi) + t21017 = t21016 ** 2 + t21019 = t21017 ** 2 + t21023 = t21019 ** 2 + t21018 = t21016 * t21017 + t21021 = t21018 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-3420 * t21017 - 7980 * t21018 + 35910 * t21019 - 110124 * t21021 + 98325 * t21023 + 45 + (64638 * t21019 - 173052 * t21021 + 142025 * t21023 + 225) * t21016) * ((1 + t21016) ** (0.3e1 / 0.2e1)) * np.sqrt(0.374e3) * np.exp((-1*1j) * (phi1 - 4 * phi2)) * ((1 - t21016) ** (0.5e1 / 0.2e1)) + + if Bindx == 3267: + t21036 = np.sin(phi) + t21034 = t21036 ** 2 + t21025 = np.cos(phi) + t21026 = t21025 ** 2 + t21028 = t21026 ** 2 + t21032 = t21028 ** 2 + t21027 = t21025 * t21026 + t21030 = t21027 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.187e3) * t21034 ** 2 * (1900 * t21026 - 18620 * t21027 - 19950 * t21028 + 61180 * t21030 - 54625 * t21032 - 25 + (109326 * t21028 - 221996 * t21030 + 142025 * t21032 + 785) * t21025) + + if Bindx == 3268: + t21037 = np.cos(phi) + t21038 = t21037 ** 2 + t21039 = t21037 * t21038 + t21042 = t21039 ** 2 + t21040 = t21038 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (315 * t21038 + 945 * t21039 - 2415 * t21040 + 4025 * t21042 - 5 + (-5313 * t21040 + 7475 * t21042 - 35) * t21037) * ((1 + t21037) ** (0.5e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((-1*1j) * (phi1 - 6 * phi2)) * ((1 - t21037) ** (0.7e1 / 0.2e1)) + + if Bindx == 3269: + t21054 = np.sin(phi) + t21051 = t21054 ** 2 + t21052 = t21054 * t21051 + t21044 = np.cos(phi) + t21045 = t21044 ** 2 + t21046 = t21044 * t21045 + t21049 = t21046 ** 2 + t21047 = t21045 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 7 * phi2)) * np.sqrt(0.248710e6) * t21052 ** 2 * (-63 * t21045 + 357 * t21046 + 483 * t21047 - 805 * t21049 + 1 + (-1449 * t21047 + 1495 * t21049 - 19) * t21044) + + if Bindx == 3270: + t21055 = np.cos(phi) + t21056 = t21055 ** 2 + t21058 = t21056 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-138 * t21056 + 575 * t21058 + 3 + (-506 * t21056 + 1495 * t21058 + 27) * t21055) * ((1 + t21055) ** (0.7e1 / 0.2e1)) * np.sqrt(0.17765e5) * np.exp((-1*1j) * (phi1 - 8 * phi2)) * ((1 - t21055) ** (0.9e1 / 0.2e1)) + + if Bindx == 3271: + t21068 = np.sin(phi) + t21065 = t21068 ** 2 + t21066 = t21065 ** 2 + t21060 = np.cos(phi) + t21061 = t21060 ** 2 + t21063 = t21061 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 9 * phi2)) * np.sqrt(0.646e3) * t21066 ** 2 * (1242 * t21061 - 5175 * t21063 - 27 + (-3818 * t21061 + 7475 * t21063 + 303) * t21060) + + if Bindx == 3272: + t21069 = np.cos(phi) + t21070 = t21069 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (75 * t21070 - 3 + (325 * t21070 - 33) * t21069) * ((1 + t21069) ** (0.9e1 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (phi1 - 10 * phi2)) * ((1 - t21069) ** (0.11e2 / 0.2e1)) + + if Bindx == 3273: + t21079 = np.sin(phi) + t21075 = t21079 ** 2 + t21077 = t21079 * t21075 ** 2 + t21072 = np.cos(phi) + t21073 = t21072 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 11 * phi2)) * np.sqrt(0.7429e4) * t21077 ** 2 * (-275 * t21073 + 11 + (325 * t21073 - 61) * t21072) + + if Bindx == 3274: + t21080 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * (1 + 13 * t21080) * ((1 + t21080) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((-1*1j) * (phi1 - 12 * phi2)) * ((1 - t21080) ** (0.13e2 / 0.2e1)) + + if Bindx == 3275: + t21085 = np.sin(phi) + t21081 = t21085 ** 2 + t21082 = t21085 * t21081 + t21083 = t21082 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((-1*1j) * (phi1 - 13 * phi2)) * np.sqrt(0.96577e5) * t21083 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 3276: + t21086 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((-13*1j) * phi2) * np.sqrt(0.104006e6) * ((1 - t21086) ** (0.13e2 / 0.2e1)) * ((1 + t21086) ** (0.13e2 / 0.2e1)) + + if Bindx == 3277: + t21091 = np.sin(phi) + t21087 = t21091 ** 2 + t21088 = t21091 * t21087 + t21089 = t21088 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((-12*1j) * phi2) * np.sqrt(0.676039e6) * t21089 ** 2 * np.cos(phi) + + if Bindx == 3278: + t21092 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-11*1j) * phi2) * np.sqrt(0.1352078e7) * ((1 - t21092) ** (0.11e2 / 0.2e1)) * ((1 + t21092) ** (0.11e2 / 0.2e1)) * (25 * t21092 ** 2 - 1) + + if Bindx == 3279: + t21098 = np.sin(phi) + t21094 = t21098 ** 2 + t21096 = t21098 * t21094 ** 2 + t21093 = np.cos(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-10*1j) * phi2) * np.sqrt(0.676039e6) * t21096 ** 2 * t21093 * (25 * t21093 ** 2 - 3) + + if Bindx == 3280: + t21099 = np.cos(phi) + t21100 = t21099 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-9*1j) * phi2) * np.sqrt(0.29393e5) * ((1 - t21099) ** (0.9e1 / 0.2e1)) * ((1 + t21099) ** (0.9e1 / 0.2e1)) * (3 + (-138 + 575 * t21100) * t21100) + + if Bindx == 3281: + t21108 = np.sin(phi) + t21105 = t21108 ** 2 + t21106 = t21105 ** 2 + t21102 = np.cos(phi) + t21103 = t21102 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-8*1j) * phi2) * np.sqrt(0.3233230e7) * t21106 ** 2 * t21102 * (3 + (-46 + 115 * t21103) * t21103) + + if Bindx == 3282: + t21109 = np.cos(phi) + t21110 = t21109 ** 2 + t21111 = t21110 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((-7*1j) * phi2) * np.sqrt(0.230945e6) * ((1 - t21109) ** (0.7e1 / 0.2e1)) * ((1 + t21109) ** (0.7e1 / 0.2e1)) * (-483 * t21111 - 1 + (805 * t21111 + 63) * t21110) + + if Bindx == 3283: + t21120 = np.sin(phi) + t21117 = t21120 ** 2 + t21118 = t21120 * t21117 + t21113 = np.cos(phi) + t21114 = t21113 ** 2 + t21115 = t21114 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-6*1j) * phi2) * np.sqrt(0.323323e6) * t21118 ** 2 * t21113 * (-483 * t21115 - 5 + (575 * t21115 + 105) * t21114) + + if Bindx == 3284: + t21121 = np.cos(phi) + t21122 = t21121 ** 2 + t21123 = t21122 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((-5*1j) * phi2) * np.sqrt(0.34034e5) * ((1 - t21121) ** (0.5e1 / 0.2e1)) * ((1 + t21121) ** (0.5e1 / 0.2e1)) * (-380 * t21122 + 5 + (-12236 * t21122 + 3990 + 10925 * t21123) * t21123) + + if Bindx == 3285: + t21133 = np.sin(phi) + t21131 = t21133 ** 2 + t21126 = np.cos(phi) + t21127 = t21126 ** 2 + t21128 = t21127 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((-4*1j) * phi2) * np.sqrt(0.17017e5) * t21131 ** 2 * t21126 * (-1140 * t21127 + 45 + (-15732 * t21127 + 7182 + 10925 * t21128) * t21128) + + if Bindx == 3286: + t21134 = np.cos(phi) + t21135 = t21134 ** 2 + t21136 = t21135 ** 2 + t21138 = t21136 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((-3*1j) * phi2) * np.sqrt(0.10010e5) * ((1 - t21134) ** (0.3e1 / 0.2e1)) * ((1 + t21134) ** (0.3e1 / 0.2e1)) * (-9690 * t21136 - 66861 * t21138 - 9 + (40698 * t21136 + 37145 * t21138 + 765) * t21135) + + if Bindx == 3287: + t21141 = np.cos(phi) + t21142 = t21141 ** 2 + t21143 = t21142 ** 2 + t21145 = t21143 ** 2 + t21140 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((-2*1j) * phi2) * np.sqrt(0.910e3) * t21140 ** 2 * t21141 * (-21318 * t21143 - 81719 * t21145 - 99 + (63954 * t21143 + 37145 * t21145 + 2805) * t21142) + + if Bindx == 3288: + t21147 = np.cos(phi) + t21148 = t21147 ** 2 + t21149 = t21148 ** 2 + t21151 = t21149 ** 2 + t21150 = t21148 * t21149 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((-1*1j) * phi2) * np.sqrt(0.182e3) * np.sqrt((1 - t21147)) * np.sqrt((1 + t21147)) * (42075 * t21149 + 479655 * t21151 + 33 + (-213180 + 185725 * t21150) * t21150 + (-490314 * t21151 - 2970) * t21148) + + if Bindx == 3289: + t21154 = np.cos(phi) + t21155 = t21154 ** 2 + t21156 = t21155 ** 2 + t21158 = t21156 ** 2 + t21157 = t21155 * t21156 + tfunc[..., c] = 0.27e2 / 0.1024e4 * t21154 * (765765 * t21156 + 4849845 * t21158 + 3003 + (-2771340 + 1300075 * t21157) * t21157 + (-4056234 * t21158 - 90090) * t21155) + + if Bindx == 3290: + t21161 = np.cos(phi) + t21162 = t21161 ** 2 + t21164 = t21162 ** 2 + t21165 = t21161 * t21164 + t21163 = t21161 * t21162 + t21166 = t21163 ** 2 + t21174 = -479655 * t21164 ** 2 + 490314 * t21165 ** 2 - 33 + (213180 - 185725 * t21166) * t21166 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * phi2) * np.sqrt(0.182e3) * np.sqrt((1 + t21161)) * (-t21161 * t21174 + 2970 * t21162 - 2970 * t21163 - 42075 * t21164 + 42075 * t21165 + t21174) * ((1 - t21161) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3291: + t21176 = np.cos(phi) + t21177 = t21176 ** 2 + t21178 = t21177 ** 2 + t21180 = t21178 ** 2 + t21175 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * phi2) * np.sqrt(0.910e3) * t21175 ** 2 * t21176 * (-21318 * t21178 - 81719 * t21180 - 99 + (63954 * t21178 + 37145 * t21180 + 2805) * t21177) + + if Bindx == 3292: + t21182 = np.cos(phi) + t21183 = t21182 ** 2 + t21184 = t21183 ** 2 + t21186 = t21184 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-9690 * t21184 - 66861 * t21186 - 9 + (40698 * t21184 + 37145 * t21186 + 765) * t21183) * ((1 + t21182) ** (0.3e1 / 0.2e1)) * np.sqrt(0.10010e5) * np.exp((3*1j) * phi2) * ((1 - t21182) ** (0.3e1 / 0.2e1)) + + if Bindx == 3293: + t21195 = np.sin(phi) + t21193 = t21195 ** 2 + t21188 = np.cos(phi) + t21189 = t21188 ** 2 + t21190 = t21189 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((4*1j) * phi2) * np.sqrt(0.17017e5) * t21193 ** 2 * t21188 * (-1140 * t21189 + 45 + (-15732 * t21189 + 7182 + 10925 * t21190) * t21190) + + if Bindx == 3294: + t21196 = np.cos(phi) + t21197 = t21196 ** 2 + t21198 = t21197 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-380 * t21197 + 5 + (-12236 * t21197 + 3990 + 10925 * t21198) * t21198) * ((1 + t21196) ** (0.5e1 / 0.2e1)) * np.sqrt(0.34034e5) * np.exp((5*1j) * phi2) * ((1 - t21196) ** (0.5e1 / 0.2e1)) + + if Bindx == 3295: + t21208 = np.sin(phi) + t21205 = t21208 ** 2 + t21206 = t21208 * t21205 + t21201 = np.cos(phi) + t21202 = t21201 ** 2 + t21203 = t21202 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((6*1j) * phi2) * np.sqrt(0.323323e6) * t21206 ** 2 * t21201 * (-483 * t21203 - 5 + (575 * t21203 + 105) * t21202) + + if Bindx == 3296: + t21209 = np.cos(phi) + t21210 = t21209 ** 2 + t21211 = t21210 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-483 * t21211 - 1 + (805 * t21211 + 63) * t21210) * ((1 + t21209) ** (0.7e1 / 0.2e1)) * np.sqrt(0.230945e6) * np.exp((7*1j) * phi2) * ((1 - t21209) ** (0.7e1 / 0.2e1)) + + if Bindx == 3297: + t21219 = np.sin(phi) + t21216 = t21219 ** 2 + t21217 = t21216 ** 2 + t21213 = np.cos(phi) + t21214 = t21213 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((8*1j) * phi2) * np.sqrt(0.3233230e7) * t21217 ** 2 * t21213 * (3 + (-46 + 115 * t21214) * t21214) + + if Bindx == 3298: + t21220 = np.cos(phi) + t21221 = t21220 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (3 + (-138 + 575 * t21221) * t21221) * ((1 + t21220) ** (0.9e1 / 0.2e1)) * np.sqrt(0.29393e5) * np.exp((9*1j) * phi2) * ((1 - t21220) ** (0.9e1 / 0.2e1)) + + if Bindx == 3299: + t21228 = np.sin(phi) + t21224 = t21228 ** 2 + t21226 = t21228 * t21224 ** 2 + t21223 = np.cos(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((10*1j) * phi2) * np.sqrt(0.676039e6) * t21226 ** 2 * t21223 * (25 * t21223 ** 2 - 3) + + if Bindx == 3300: + t21229 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (25 * t21229 ** 2 - 1) * ((1 + t21229) ** (0.11e2 / 0.2e1)) * np.sqrt(0.1352078e7) * np.exp((11*1j) * phi2) * ((1 - t21229) ** (0.11e2 / 0.2e1)) + + if Bindx == 3301: + t21234 = np.sin(phi) + t21230 = t21234 ** 2 + t21231 = t21234 * t21230 + t21232 = t21231 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((12*1j) * phi2) * np.sqrt(0.676039e6) * t21232 ** 2 * np.cos(phi) + + if Bindx == 3302: + t21235 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((13*1j) * phi2) * np.sqrt(0.104006e6) * ((1 - t21235) ** (0.13e2 / 0.2e1)) * ((1 + t21235) ** (0.13e2 / 0.2e1)) + + if Bindx == 3303: + t21240 = np.sin(phi) + t21236 = t21240 ** 2 + t21237 = t21240 * t21236 + t21238 = t21237 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((1j) * (phi1 - 13 * phi2)) * np.sqrt(0.96577e5) * t21238 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 3304: + t21241 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((1j) * (phi1 - 12 * phi2)) * np.sqrt(0.14858e5) * ((1 - t21241) ** (0.13e2 / 0.2e1)) * ((1 + t21241) ** (0.11e2 / 0.2e1)) * (1 + 13 * t21241) + + if Bindx == 3305: + t21249 = np.sin(phi) + t21245 = t21249 ** 2 + t21247 = t21249 * t21245 ** 2 + t21242 = np.cos(phi) + t21243 = t21242 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 - 11 * phi2)) * np.sqrt(0.7429e4) * t21247 ** 2 * (-275 * t21243 + 11 + (325 * t21243 - 61) * t21242) + + if Bindx == 3306: + t21250 = np.cos(phi) + t21251 = t21250 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (phi1 - 10 * phi2)) * np.sqrt(0.14858e5) * ((1 - t21250) ** (0.11e2 / 0.2e1)) * ((1 + t21250) ** (0.9e1 / 0.2e1)) * (75 * t21251 - 3 + (325 * t21251 - 33) * t21250) + + if Bindx == 3307: + t21261 = np.sin(phi) + t21258 = t21261 ** 2 + t21259 = t21258 ** 2 + t21253 = np.cos(phi) + t21254 = t21253 ** 2 + t21256 = t21254 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 - 9 * phi2)) * np.sqrt(0.646e3) * t21259 ** 2 * (1242 * t21254 - 5175 * t21256 - 27 + (-3818 * t21254 + 7475 * t21256 + 303) * t21253) + + if Bindx == 3308: + t21262 = np.cos(phi) + t21263 = t21262 ** 2 + t21265 = t21263 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (phi1 - 8 * phi2)) * np.sqrt(0.17765e5) * ((1 - t21262) ** (0.9e1 / 0.2e1)) * ((1 + t21262) ** (0.7e1 / 0.2e1)) * (-138 * t21263 + 575 * t21265 + 3 + (-506 * t21263 + 1495 * t21265 + 27) * t21262) + + if Bindx == 3309: + t21277 = np.sin(phi) + t21274 = t21277 ** 2 + t21275 = t21277 * t21274 + t21267 = np.cos(phi) + t21268 = t21267 ** 2 + t21269 = t21267 * t21268 + t21272 = t21269 ** 2 + t21270 = t21268 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 - 7 * phi2)) * np.sqrt(0.248710e6) * t21275 ** 2 * (-63 * t21268 + 357 * t21269 + 483 * t21270 - 805 * t21272 + 1 + (-1449 * t21270 + 1495 * t21272 - 19) * t21267) + + if Bindx == 3310: + t21278 = np.cos(phi) + t21279 = t21278 ** 2 + t21280 = t21278 * t21279 + t21283 = t21280 ** 2 + t21281 = t21279 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (phi1 - 6 * phi2)) * np.sqrt(0.7106e4) * ((1 - t21278) ** (0.7e1 / 0.2e1)) * ((1 + t21278) ** (0.5e1 / 0.2e1)) * (315 * t21279 + 945 * t21280 - 2415 * t21281 + 4025 * t21283 - 5 + (-5313 * t21281 + 7475 * t21283 - 35) * t21278) + + if Bindx == 3311: + t21296 = np.sin(phi) + t21294 = t21296 ** 2 + t21285 = np.cos(phi) + t21286 = t21285 ** 2 + t21288 = t21286 ** 2 + t21292 = t21288 ** 2 + t21287 = t21285 * t21286 + t21290 = t21287 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 - 5 * phi2)) * np.sqrt(0.187e3) * t21294 ** 2 * (1900 * t21286 - 18620 * t21287 - 19950 * t21288 + 61180 * t21290 - 54625 * t21292 - 25 + (109326 * t21288 - 221996 * t21290 + 142025 * t21292 + 785) * t21285) + + if Bindx == 3312: + t21297 = np.cos(phi) + t21298 = t21297 ** 2 + t21300 = t21298 ** 2 + t21304 = t21300 ** 2 + t21299 = t21297 * t21298 + t21302 = t21299 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (phi1 - 4 * phi2)) * np.sqrt(0.374e3) * ((1 - t21297) ** (0.5e1 / 0.2e1)) * ((1 + t21297) ** (0.3e1 / 0.2e1)) * (-3420 * t21298 - 7980 * t21299 + 35910 * t21300 - 110124 * t21302 + 98325 * t21304 + 45 + (64638 * t21300 - 173052 * t21302 + 142025 * t21304 + 225) * t21297) + + if Bindx == 3313: + t21307 = np.cos(phi) + t21308 = t21307 ** 2 + t21310 = t21308 ** 2 + t21311 = t21307 * t21310 + t21316 = t21311 ** 2 + t21314 = t21310 ** 2 + t21309 = t21307 * t21308 + t21312 = t21309 ** 2 + t21306 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 - 3 * phi2)) * np.sqrt(0.55e2) * t21306 ** 2 * (-2295 * t21308 + 42585 * t21309 + 29070 * t21310 - 312018 * t21311 - 122094 * t21312 + 200583 * t21314 - 111435 * t21316 + 27 + (901170 * t21312 - 1106921 * t21314 + 482885 * t21316 - 1557) * t21307) + + if Bindx == 3314: + t21318 = np.cos(phi) + t21319 = t21318 ** 2 + t21321 = t21319 ** 2 + t21322 = t21318 * t21321 + t21327 = t21322 ** 2 + t21325 = t21321 ** 2 + t21320 = t21318 * t21319 + t21323 = t21320 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 - 2 * phi2)) * np.sqrt(0.5e1) * ((1 - t21318) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t21318)) * (8415 * t21319 + 14025 * t21320 - 106590 * t21321 - 149226 * t21322 + 447678 * t21323 - 735471 * t21325 + 408595 * t21327 - 99 + (575586 * t21323 - 898909 * t21325 + 482885 * t21327 - 297) * t21318) + + if Bindx == 3315: + t21329 = np.cos(phi) + t21330 = t21329 ** 2 + t21331 = t21329 * t21330 + t21334 = t21331 ** 2 + t21340 = t21334 ** 2 + t21332 = t21330 ** 2 + t21333 = t21329 * t21332 + t21338 = t21333 ** 2 + t21336 = t21332 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((1j) * (phi1 - phi2)) * (2970 * t21330 - 177210 * t21331 - 42075 * t21332 + 1489455 * t21333 + 213180 * t21334 - 479655 * t21336 + 490314 * t21338 - 185725 * t21340 - 33 + (-5329500 * t21334 + 9220035 * t21336 - 7622154 * t21338 + 2414425 * t21340 + 5973) * t21329) + + if Bindx == 3316: + t21342 = np.cos(phi) + t21343 = t21342 ** 2 + t21344 = t21343 ** 2 + t21346 = t21344 ** 2 + t21345 = t21343 * t21344 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * phi1) * np.sqrt(0.182e3) * np.sqrt((1 - t21342)) * np.sqrt((1 + t21342)) * (42075 * t21344 + 479655 * t21346 + 33 + (-213180 + 185725 * t21345) * t21345 + (-490314 * t21346 - 2970) * t21343) + + if Bindx == 3317: + t21349 = np.cos(phi) + t21350 = t21349 ** 2 + t21351 = t21349 * t21350 + t21354 = t21351 ** 2 + t21360 = t21354 ** 2 + t21352 = t21350 ** 2 + t21353 = t21349 * t21352 + t21358 = t21353 ** 2 + t21356 = t21352 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((1j) * (phi1 + phi2)) * (-2970 * t21350 - 177210 * t21351 + 42075 * t21352 + 1489455 * t21353 - 213180 * t21354 + 479655 * t21356 - 490314 * t21358 + 185725 * t21360 + 33 + (-5329500 * t21354 + 9220035 * t21356 - 7622154 * t21358 + 2414425 * t21360 + 5973) * t21349) + + if Bindx == 3318: + t21362 = np.cos(phi) + t21363 = t21362 ** 2 + t21365 = t21363 ** 2 + t21366 = t21362 * t21365 + t21371 = t21366 ** 2 + t21369 = t21365 ** 2 + t21364 = t21362 * t21363 + t21367 = t21364 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * np.exp((1j) * (phi1 + 2 * phi2)) * np.sqrt(0.5e1) * ((1 + t21362) ** (0.3e1 / 0.2e1)) * (8118 * t21363 - 22440 * t21364 - 92565 * t21365 + 255816 * t21366 - 159885 * t21369 - 490314 * t21371 - 99 + (298452 + 482885 * t21367) * t21367 + (-1023264 * t21367 + 1634380 * t21369 - 891480 * t21371 + 396) * t21362) * ((1 - t21362) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3319: + t21375 = np.cos(phi) + t21376 = t21375 ** 2 + t21378 = t21376 ** 2 + t21379 = t21375 * t21378 + t21384 = t21379 ** 2 + t21382 = t21378 ** 2 + t21377 = t21375 * t21376 + t21380 = t21377 ** 2 + t21374 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 + 3 * phi2)) * np.sqrt(0.55e2) * t21374 ** 2 * (2295 * t21376 + 42585 * t21377 - 29070 * t21378 - 312018 * t21379 + 122094 * t21380 - 200583 * t21382 + 111435 * t21384 - 27 + (901170 * t21380 - 1106921 * t21382 + 482885 * t21384 - 1557) * t21375) + + if Bindx == 3320: + t21386 = np.cos(phi) + t21387 = t21386 ** 2 + t21389 = t21387 ** 2 + t21393 = t21389 ** 2 + t21388 = t21386 * t21387 + t21391 = t21388 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (3420 * t21387 - 7980 * t21388 - 35910 * t21389 + 110124 * t21391 - 98325 * t21393 - 45 + (64638 * t21389 - 173052 * t21391 + 142025 * t21393 + 225) * t21386) * ((1 + t21386) ** (0.5e1 / 0.2e1)) * np.sqrt(0.374e3) * np.exp((1j) * (phi1 + 4 * phi2)) * ((1 - t21386) ** (0.3e1 / 0.2e1)) + + if Bindx == 3321: + t21406 = np.sin(phi) + t21404 = t21406 ** 2 + t21395 = np.cos(phi) + t21396 = t21395 ** 2 + t21398 = t21396 ** 2 + t21402 = t21398 ** 2 + t21397 = t21395 * t21396 + t21400 = t21397 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 + 5 * phi2)) * np.sqrt(0.187e3) * t21404 ** 2 * (-1900 * t21396 - 18620 * t21397 + 19950 * t21398 - 61180 * t21400 + 54625 * t21402 + 25 + (109326 * t21398 - 221996 * t21400 + 142025 * t21402 + 785) * t21395) + + if Bindx == 3322: + t21407 = np.cos(phi) + t21408 = t21407 ** 2 + t21409 = t21407 * t21408 + t21412 = t21409 ** 2 + t21410 = t21408 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-315 * t21408 + 945 * t21409 + 2415 * t21410 - 4025 * t21412 + 5 + (-5313 * t21410 + 7475 * t21412 - 35) * t21407) * ((1 + t21407) ** (0.7e1 / 0.2e1)) * np.sqrt(0.7106e4) * np.exp((1j) * (phi1 + 6 * phi2)) * ((1 - t21407) ** (0.5e1 / 0.2e1)) + + if Bindx == 3323: + t21424 = np.sin(phi) + t21421 = t21424 ** 2 + t21422 = t21424 * t21421 + t21414 = np.cos(phi) + t21415 = t21414 ** 2 + t21416 = t21414 * t21415 + t21419 = t21416 ** 2 + t21417 = t21415 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 + 7 * phi2)) * np.sqrt(0.248710e6) * t21422 ** 2 * (63 * t21415 + 357 * t21416 - 483 * t21417 + 805 * t21419 - 1 + (-1449 * t21417 + 1495 * t21419 - 19) * t21414) + + if Bindx == 3324: + t21425 = np.cos(phi) + t21426 = t21425 ** 2 + t21428 = t21426 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (138 * t21426 - 575 * t21428 - 3 + (-506 * t21426 + 1495 * t21428 + 27) * t21425) * ((1 + t21425) ** (0.9e1 / 0.2e1)) * np.sqrt(0.17765e5) * np.exp((1j) * (phi1 + 8 * phi2)) * ((1 - t21425) ** (0.7e1 / 0.2e1)) + + if Bindx == 3325: + t21438 = np.sin(phi) + t21435 = t21438 ** 2 + t21436 = t21435 ** 2 + t21430 = np.cos(phi) + t21431 = t21430 ** 2 + t21433 = t21431 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 + 9 * phi2)) * np.sqrt(0.646e3) * t21436 ** 2 * (-1242 * t21431 + 5175 * t21433 + 27 + (-3818 * t21431 + 7475 * t21433 + 303) * t21430) + + if Bindx == 3326: + t21439 = np.cos(phi) + t21440 = t21439 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-75 * t21440 + 3 + (325 * t21440 - 33) * t21439) * ((1 + t21439) ** (0.11e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((1j) * (phi1 + 10 * phi2)) * ((1 - t21439) ** (0.9e1 / 0.2e1)) + + if Bindx == 3327: + t21449 = np.sin(phi) + t21445 = t21449 ** 2 + t21447 = t21449 * t21445 ** 2 + t21442 = np.cos(phi) + t21443 = t21442 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (phi1 + 11 * phi2)) * np.sqrt(0.7429e4) * t21447 ** 2 * (275 * t21443 - 11 + (325 * t21443 - 61) * t21442) + + if Bindx == 3328: + t21450 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * (-1 + 13 * t21450) * ((1 + t21450) ** (0.13e2 / 0.2e1)) * np.sqrt(0.14858e5) * np.exp((1j) * (phi1 + 12 * phi2)) * ((1 - t21450) ** (0.11e2 / 0.2e1)) + + if Bindx == 3329: + t21455 = np.sin(phi) + t21451 = t21455 ** 2 + t21452 = t21455 * t21451 + t21453 = t21452 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((1j) * (phi1 + 13 * phi2)) * np.sqrt(0.96577e5) * t21453 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 3330: + t21456 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 13 * phi2)) * np.sqrt(0.482885e6) * ((1 - t21456) ** (0.15e2 / 0.2e1)) * ((1 + t21456) ** (0.11e2 / 0.2e1)) + + if Bindx == 3331: + t21464 = np.sin(phi) + t21460 = t21464 ** 2 + t21462 = t21464 * t21460 ** 2 + t21457 = np.cos(phi) + t21458 = t21457 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (phi1 - 6 * phi2)) * np.sqrt(0.74290e5) * t21462 ** 2 * (-24 * t21458 + 2 + (13 * t21458 + 9) * t21457) + + if Bindx == 3332: + t21465 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 11 * phi2)) * np.sqrt(0.37145e5) * ((1 - t21465) ** (0.13e2 / 0.2e1)) * ((1 + t21465) ** (0.9e1 / 0.2e1)) * (-1 + (20 + 65 * t21465) * t21465) + + if Bindx == 3333: + t21474 = np.sin(phi) + t21471 = t21474 ** 2 + t21472 = t21471 ** 2 + t21466 = np.cos(phi) + t21467 = t21466 ** 2 + t21469 = t21467 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 5 * phi2)) * np.sqrt(0.74290e5) * t21472 ** 2 * (35 * t21467 - 100 * t21469 - 1 + (2 * t21467 + 65 * t21469 - 1) * t21466) + + if Bindx == 3334: + t21475 = np.cos(phi) + t21476 = t21475 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 9 * phi2)) * np.sqrt(0.3230e4) * ((1 - t21475) ** (0.11e2 / 0.2e1)) * ((1 + t21475) ** (0.7e1 / 0.2e1)) * (-92 * t21475 - 1 + (920 * t21475 - 138 + 1495 * t21476) * t21476) + + if Bindx == 3335: + t21489 = np.sin(phi) + t21486 = t21489 ** 2 + t21487 = t21489 * t21486 + t21479 = np.cos(phi) + t21480 = t21479 ** 2 + t21481 = t21479 * t21480 + t21484 = t21481 ** 2 + t21482 = t21480 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.3553e4) * t21487 ** 2 * (-216 * t21480 + 225 * t21481 + 1380 * t21482 - 1840 * t21484 + 4 + (-1035 * t21482 + 1495 * t21484 - 13) * t21479) + + if Bindx == 3336: + t21490 = np.cos(phi) + t21491 = t21490 ** 2 + t21493 = t21491 ** 2 + t21492 = t21490 * t21491 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 7 * phi2)) * np.sqrt(0.49742e5) * ((1 - t21490) ** (0.9e1 / 0.2e1)) * ((1 + t21490) ** (0.5e1 / 0.2e1)) * (-15 * t21491 - 345 * t21493 + 1 + (-460 + 1495 * t21492) * t21492 + (1380 * t21493 + 24) * t21490) + + if Bindx == 3337: + t21507 = np.sin(phi) + t21505 = t21507 ** 2 + t21496 = np.cos(phi) + t21497 = t21496 ** 2 + t21499 = t21497 ** 2 + t21503 = t21499 ** 2 + t21498 = t21496 * t21497 + t21501 = t21498 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t21505 ** 2 * (69 * t21497 - 196 * t21498 - 651 * t21499 + 1771 * t21501 - 1380 * t21503 - 1 + (1092 * t21499 - 2208 * t21501 + 1495 * t21503 + 9) * t21496) + + if Bindx == 3338: + t21508 = np.cos(phi) + t21509 = t21508 ** 2 + t21510 = t21508 * t21509 + t21513 = t21510 ** 2 + t21511 = t21509 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.935e3) * ((1 - t21508) ** (0.7e1 / 0.2e1)) * ((1 + t21508) ** (0.3e1 / 0.2e1)) * (532 * t21509 + 4256 * t21510 - 12236 * t21513 - 11 + (-1330 + 28405 * t21511) * t21511 + (-24472 * t21511 + 34960 * t21513 - 152) * t21508) + + if Bindx == 3339: + t21517 = np.cos(phi) + t21518 = t21517 ** 2 + t21520 = t21518 ** 2 + t21521 = t21517 * t21520 + t21526 = t21521 ** 2 + t21524 = t21520 ** 2 + t21519 = t21517 * t21518 + t21522 = t21519 ** 2 + t21516 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.1870e4) * t21516 ** 2 * (-480 * t21518 + 2865 * t21519 + 5700 * t21520 - 19950 * t21521 - 22344 * t21522 + 34086 * t21524 - 17480 * t21526 + 6 + (55290 * t21522 - 65987 * t21524 + 28405 * t21526 - 111) * t21517) + + if Bindx == 3340: + t21528 = np.cos(phi) + t21529 = t21528 ** 2 + t21531 = t21529 ** 2 + t21535 = t21531 ** 2 + t21530 = t21528 * t21529 + t21533 = t21530 ** 2 + t21532 = t21528 * t21531 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.11e2) * ((1 - t21528) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t21528)) * (-8415 * t21529 - 38760 * t21530 + 67830 * t21531 - 67830 * t21533 - 334305 * t21535 + 123 + (325584 + 482885 * t21532) * t21532 + (-891480 * t21533 + 742900 * t21535 + 1020) * t21528) + + if Bindx == 3341: + t21538 = np.cos(phi) + t21539 = t21538 ** 2 + t21540 = t21538 * t21539 + t21543 = t21540 ** 2 + t21549 = t21543 ** 2 + t21541 = t21539 ** 2 + t21542 = t21538 * t21541 + t21547 = t21542 ** 2 + t21545 = t21541 ** 2 + tfunc[..., c] = (0.27e2 / 0.512e3) * np.exp((2*1j) * (phi1 - phi2)) * (2871 * t21539 - 40722 * t21540 - 39270 * t21541 + 331551 * t21542 + 191862 * t21543 - 415701 * t21545 + 408595 * t21547 - 148580 * t21549 - 33 + (-1151172 * t21543 + 1936385 * t21545 - 1560090 * t21547 + 482885 * t21549 + 1419) * t21538) + + if Bindx == 3342: + t21551 = np.cos(phi) + t21552 = t21551 ** 2 + t21554 = t21552 ** 2 + t21555 = t21551 * t21554 + t21560 = t21555 ** 2 + t21558 = t21554 ** 2 + t21553 = t21551 * t21552 + t21556 = t21553 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 - phi2)) * np.sqrt(0.5e1) * ((1 - t21551) ** (0.3e1 / 0.2e1)) * np.sqrt((1 + t21551)) * (8415 * t21552 + 14025 * t21553 - 106590 * t21554 - 149226 * t21555 + 447678 * t21556 - 735471 * t21558 + 408595 * t21560 - 99 + (575586 * t21556 - 898909 * t21558 + 482885 * t21560 - 297) * t21551) + + if Bindx == 3343: + t21563 = np.cos(phi) + t21564 = t21563 ** 2 + t21565 = t21564 ** 2 + t21567 = t21565 ** 2 + t21562 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * phi1) * np.sqrt(0.910e3) * t21562 ** 2 * t21563 * (-21318 * t21565 - 81719 * t21567 - 99 + (63954 * t21565 + 37145 * t21567 + 2805) * t21564) + + if Bindx == 3344: + t21569 = np.cos(phi) + t21570 = t21569 ** 2 + t21572 = t21570 ** 2 + t21573 = t21569 * t21572 + t21578 = t21573 ** 2 + t21576 = t21572 ** 2 + t21571 = t21569 * t21570 + t21574 = t21571 ** 2 + tfunc[..., c] = (-0.27e2 / 0.1024e4*1j) * np.exp((1j) * (2 * phi1 + phi2)) * np.sqrt(0.5e1) * np.sqrt((1 - t21569)) * ((1 + t21569) ** (0.3e1 / 0.2e1)) * (-8415 * t21570 + 14025 * t21571 + 106590 * t21572 - 149226 * t21573 - 447678 * t21574 + 735471 * t21576 - 408595 * t21578 + 99 + (575586 * t21574 - 898909 * t21576 + 482885 * t21578 - 297) * t21569) + + if Bindx == 3345: + t21580 = np.cos(phi) + t21581 = t21580 ** 2 + t21582 = t21580 * t21581 + t21585 = t21582 ** 2 + t21591 = t21585 ** 2 + t21583 = t21581 ** 2 + t21584 = t21580 * t21583 + t21589 = t21584 ** 2 + t21587 = t21583 ** 2 + tfunc[..., c] = (0.27e2 / 0.512e3) * np.exp((2*1j) * (phi1 + phi2)) * (-2871 * t21581 - 40722 * t21582 + 39270 * t21583 + 331551 * t21584 - 191862 * t21585 + 415701 * t21587 - 408595 * t21589 + 148580 * t21591 + 33 + (-1151172 * t21585 + 1936385 * t21587 - 1560090 * t21589 + 482885 * t21591 + 1419) * t21580) + + if Bindx == 3346: + t21593 = np.cos(phi) + t21594 = t21593 ** 2 + t21596 = t21594 ** 2 + t21597 = t21593 * t21596 + t21602 = t21597 ** 2 + t21600 = t21596 ** 2 + t21595 = t21593 * t21594 + t21598 = t21595 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.11e2) * ((1 + t21593) ** (0.5e1 / 0.2e1)) * (7395 * t21594 - 47175 * t21595 - 29070 * t21596 + 393414 * t21597 - 257754 * t21598 + 1225785 * t21600 - 1225785 * t21602 - 123 + (-959310 * t21598 + 408595 * t21600 + 482885 * t21602 + 1143) * t21593) * ((1 - t21593) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3347: + t21605 = np.cos(phi) + t21606 = t21605 ** 2 + t21608 = t21606 ** 2 + t21609 = t21605 * t21608 + t21614 = t21609 ** 2 + t21612 = t21608 ** 2 + t21607 = t21605 * t21606 + t21610 = t21607 ** 2 + t21604 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.1870e4) * t21604 ** 2 * (480 * t21606 + 2865 * t21607 - 5700 * t21608 - 19950 * t21609 + 22344 * t21610 - 34086 * t21612 + 17480 * t21614 - 6 + (55290 * t21610 - 65987 * t21612 + 28405 * t21614 - 111) * t21605) + + if Bindx == 3348: + t21616 = np.cos(phi) + t21617 = t21616 ** 2 + t21618 = t21616 * t21617 + t21621 = t21618 ** 2 + t21619 = t21617 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (532 * t21617 - 4256 * t21618 - 12236 * t21621 - 11 + (-1330 + 28405 * t21619) * t21619 + (24472 * t21619 - 34960 * t21621 + 152) * t21616) * ((1 + t21616) ** (0.7e1 / 0.2e1)) * np.sqrt(0.935e3) * np.exp((1j) * (2 * phi1 + 5 * phi2)) * ((1 - t21616) ** (0.3e1 / 0.2e1)) + + if Bindx == 3349: + t21635 = np.sin(phi) + t21633 = t21635 ** 2 + t21624 = np.cos(phi) + t21625 = t21624 ** 2 + t21627 = t21625 ** 2 + t21631 = t21627 ** 2 + t21626 = t21624 * t21625 + t21629 = t21626 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t21633 ** 2 * (-69 * t21625 - 196 * t21626 + 651 * t21627 - 1771 * t21629 + 1380 * t21631 + 1 + (1092 * t21627 - 2208 * t21629 + 1495 * t21631 + 9) * t21624) + + if Bindx == 3350: + t21636 = np.cos(phi) + t21637 = t21636 ** 2 + t21639 = t21637 ** 2 + t21638 = t21636 * t21637 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-15 * t21637 - 345 * t21639 + 1 + (460 + 1495 * t21638) * t21638 + (-1380 * t21639 - 24) * t21636) * ((1 + t21636) ** (0.9e1 / 0.2e1)) * np.sqrt(0.49742e5) * np.exp((1j) * (2 * phi1 + 7 * phi2)) * ((1 - t21636) ** (0.5e1 / 0.2e1)) + + if Bindx == 3351: + t21652 = np.sin(phi) + t21649 = t21652 ** 2 + t21650 = t21652 * t21649 + t21642 = np.cos(phi) + t21643 = t21642 ** 2 + t21644 = t21642 * t21643 + t21647 = t21644 ** 2 + t21645 = t21643 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 4 * phi2)) * np.sqrt(0.3553e4) * t21650 ** 2 * (216 * t21643 + 225 * t21644 - 1380 * t21645 + 1840 * t21647 - 4 + (-1035 * t21645 + 1495 * t21647 - 13) * t21642) + + if Bindx == 3352: + t21653 = np.cos(phi) + t21654 = t21653 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (92 * t21653 - 1 + (-920 * t21653 - 138 + 1495 * t21654) * t21654) * ((1 + t21653) ** (0.11e2 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((1j) * (2 * phi1 + 9 * phi2)) * ((1 - t21653) ** (0.7e1 / 0.2e1)) + + if Bindx == 3353: + t21665 = np.sin(phi) + t21662 = t21665 ** 2 + t21663 = t21662 ** 2 + t21657 = np.cos(phi) + t21658 = t21657 ** 2 + t21660 = t21658 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (phi1 + 5 * phi2)) * np.sqrt(0.74290e5) * t21663 ** 2 * (-35 * t21658 + 100 * t21660 + 1 + (2 * t21658 + 65 * t21660 - 1) * t21657) + + if Bindx == 3354: + t21666 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * (-1 + (-20 + 65 * t21666) * t21666) * ((1 + t21666) ** (0.13e2 / 0.2e1)) * np.sqrt(0.37145e5) * np.exp((1j) * (2 * phi1 + 11 * phi2)) * ((1 - t21666) ** (0.9e1 / 0.2e1)) + + if Bindx == 3355: + t21674 = np.sin(phi) + t21670 = t21674 ** 2 + t21672 = t21674 * t21670 ** 2 + t21667 = np.cos(phi) + t21668 = t21667 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (phi1 + 6 * phi2)) * np.sqrt(0.74290e5) * t21672 ** 2 * (24 * t21668 - 2 + (13 * t21668 + 9) * t21667) + + if Bindx == 3356: + t21675 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (2 * phi1 + 13 * phi2)) * np.sqrt(0.482885e6) * ((1 - t21675) ** (0.11e2 / 0.2e1)) * ((1 + t21675) ** (0.15e2 / 0.2e1)) + + if Bindx == 3357: + t21676 = np.cos(phi) + t21684 = -1 + t21676 + t21683 = 1 + t21676 + t21680 = t21683 ** 2 + t21677 = t21684 ** 2 + t21678 = t21677 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 - 13 * phi2)) * np.sqrt(0.5311735e7) * t21678 ** 2 * t21683 * t21680 ** 2 + + if Bindx == 3358: + t21685 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((3*1j) * (phi1 - 4 * phi2)) * np.sqrt(0.817190e6) * ((1 - t21685) ** (0.15e2 / 0.2e1)) * ((1 + t21685) ** (0.9e1 / 0.2e1)) * (3 + 13 * t21685) + + if Bindx == 3359: + t21694 = np.sin(phi) + t21691 = t21694 ** 2 + t21692 = t21691 ** 2 + t21686 = np.cos(phi) + t21687 = t21686 ** 2 + t21689 = t21687 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 - 11 * phi2)) * np.sqrt(0.408595e6) * t21692 ** 2 * (22 * t21687 - 165 * t21689 - 1 + (106 * t21687 + 65 * t21689 - 27) * t21686) + + if Bindx == 3360: + t21695 = np.cos(phi) + t21696 = t21695 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (3 * phi1 - 10 * phi2)) * np.sqrt(0.817190e6) * ((1 - t21695) ** (0.13e2 / 0.2e1)) * ((1 + t21695) ** (0.7e1 / 0.2e1)) * (45 * t21696 - 1 + (65 * t21696 + 3) * t21695) + + if Bindx == 3361: + t21708 = np.sin(phi) + t21705 = t21708 ** 2 + t21706 = t21708 * t21705 + t21698 = np.cos(phi) + t21699 = t21698 ** 2 + t21700 = t21698 * t21699 + t21703 = t21700 ** 2 + t21701 = t21699 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((3*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.35530e5) * t21706 ** 2 * (-387 * t21699 - 699 * t21700 + 2139 * t21701 - 3105 * t21703 + 9 + (483 * t21701 + 1495 * t21703 + 65) * t21698) + + if Bindx == 3362: + t21709 = np.cos(phi) + t21710 = t21709 ** 2 + t21712 = t21710 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (3 * phi1 - 8 * phi2)) * np.sqrt(0.323e3) * ((1 - t21709) ** (0.11e2 / 0.2e1)) * ((1 + t21709) ** (0.5e1 / 0.2e1)) * (-2530 * t21710 + 18975 * t21712 + 19 + (2530 * t21710 + 16445 * t21712 - 495) * t21709) + + if Bindx == 3363: + t21725 = np.sin(phi) + t21723 = t21725 ** 2 + t21714 = np.cos(phi) + t21715 = t21714 ** 2 + t21717 = t21715 ** 2 + t21721 = t21717 ** 2 + t21716 = t21714 * t21715 + t21719 = t21716 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 - 7 * phi2)) * np.sqrt(0.4522e4) * t21723 ** 2 * (1740 * t21715 + 292 * t21716 - 14406 * t21717 + 35420 * t21719 - 26565 * t21721 - 29 + (2310 * t21717 - 15180 * t21719 + 16445 * t21721 - 27) * t21714) + + if Bindx == 3364: + t21726 = np.cos(phi) + t21727 = t21726 ** 2 + t21728 = t21726 * t21727 + t21731 = t21728 ** 2 + t21729 = t21727 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((3*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.3230e4) * ((1 - t21726) ** (0.9e1 / 0.2e1)) * ((1 + t21726) ** (0.3e1 / 0.2e1)) * (399 * t21727 - 3465 * t21728 - 8855 * t21729 + 26565 * t21731 + 3 + (5313 * t21729 + 16445 * t21731 + 203) * t21726) + + if Bindx == 3365: + t21734 = np.cos(phi) + t21735 = t21734 ** 2 + t21737 = t21735 ** 2 + t21738 = t21734 * t21737 + t21743 = t21738 ** 2 + t21741 = t21737 ** 2 + t21736 = t21734 * t21735 + t21739 = t21736 ** 2 + t21733 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.85e2) * t21733 ** 2 * (-13505 * t21735 + 24515 * t21736 + 146490 * t21737 - 171038 * t21738 - 526946 * t21739 + 745085 * t21741 - 360525 * t21743 + 185 + (502854 * t21739 - 658559 * t21741 + 312455 * t21743 - 1011) * t21734) + + if Bindx == 3366: + t21745 = np.cos(phi) + t21746 = t21745 ** 2 + t21748 = t21746 ** 2 + t21752 = t21748 ** 2 + t21747 = t21745 * t21746 + t21750 = t21747 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.170e3) * ((1 - t21745) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t21745)) * (2052 * t21746 + 46284 * t21747 + 45486 * t21748 - 403788 * t21750 + 648945 * t21752 - 87 + (-237006 * t21748 + 173052 * t21750 + 312455 * t21752 - 1665) * t21745) + + if Bindx == 3367: + t21754 = np.cos(phi) + t21755 = t21754 ** 2 + t21756 = t21754 * t21755 + t21759 = t21756 ** 2 + t21765 = t21759 ** 2 + t21757 = t21755 ** 2 + t21758 = t21754 * t21757 + t21763 = t21758 ** 2 + t21761 = t21757 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((3*1j) * (phi1 - phi2)) * (93726 * t21755 - 495342 * t21756 - 1208445 * t21757 + 3864321 * t21758 + 5569812 * t21759 - 11404161 * t21761 + 10623470 * t21763 - 3677355 * t21765 - 1143 + (-13011732 * t21759 + 21477885 * t21761 - 17160990 * t21763 + 5311735 * t21765 + 18219) * t21754) + + if Bindx == 3368: + t21767 = np.cos(phi) + t21768 = t21767 ** 2 + t21770 = t21768 ** 2 + t21774 = t21770 ** 2 + t21769 = t21767 * t21768 + t21772 = t21769 ** 2 + t21771 = t21767 * t21770 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.11e2) * ((1 - t21767) ** (0.5e1 / 0.2e1)) * np.sqrt((1 + t21767)) * (-8415 * t21768 - 38760 * t21769 + 67830 * t21770 - 67830 * t21772 - 334305 * t21774 + 123 + (325584 + 482885 * t21771) * t21771 + (-891480 * t21772 + 742900 * t21774 + 1020) * t21767) + + if Bindx == 3369: + t21778 = np.cos(phi) + t21779 = t21778 ** 2 + t21781 = t21779 ** 2 + t21782 = t21778 * t21781 + t21787 = t21782 ** 2 + t21785 = t21781 ** 2 + t21780 = t21778 * t21779 + t21783 = t21780 ** 2 + t21777 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (3 * phi1 - phi2)) * np.sqrt(0.55e2) * t21777 ** 2 * (-2295 * t21779 + 42585 * t21780 + 29070 * t21781 - 312018 * t21782 - 122094 * t21783 + 200583 * t21785 - 111435 * t21787 + 27 + (901170 * t21783 - 1106921 * t21785 + 482885 * t21787 - 1557) * t21778) + + if Bindx == 3370: + t21789 = np.cos(phi) + t21790 = t21789 ** 2 + t21791 = t21790 ** 2 + t21793 = t21791 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((3*1j) * phi1) * np.sqrt(0.10010e5) * ((1 - t21789) ** (0.3e1 / 0.2e1)) * ((1 + t21789) ** (0.3e1 / 0.2e1)) * (-9690 * t21791 - 66861 * t21793 - 9 + (40698 * t21791 + 37145 * t21793 + 765) * t21790) + + if Bindx == 3371: + t21796 = np.cos(phi) + t21797 = t21796 ** 2 + t21799 = t21797 ** 2 + t21800 = t21796 * t21799 + t21805 = t21800 ** 2 + t21803 = t21799 ** 2 + t21798 = t21796 * t21797 + t21801 = t21798 ** 2 + t21795 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (3 * phi1 + phi2)) * np.sqrt(0.55e2) * t21795 ** 2 * (2295 * t21797 + 42585 * t21798 - 29070 * t21799 - 312018 * t21800 + 122094 * t21801 - 200583 * t21803 + 111435 * t21805 - 27 + (901170 * t21801 - 1106921 * t21803 + 482885 * t21805 - 1557) * t21796) + + if Bindx == 3372: + t21807 = np.cos(phi) + t21808 = t21807 ** 2 + t21810 = t21808 ** 2 + t21814 = t21810 ** 2 + t21809 = t21807 * t21808 + t21812 = t21809 ** 2 + t21811 = t21807 * t21810 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.11e2) * np.sqrt((1 - t21807)) * ((1 + t21807) ** (0.5e1 / 0.2e1)) * (-8415 * t21808 + 38760 * t21809 + 67830 * t21810 - 67830 * t21812 - 334305 * t21814 + 123 + (-325584 + 482885 * t21811) * t21811 + (891480 * t21812 - 742900 * t21814 - 1020) * t21807) + + if Bindx == 3373: + t21817 = np.cos(phi) + t21818 = t21817 ** 2 + t21819 = t21817 * t21818 + t21822 = t21819 ** 2 + t21828 = t21822 ** 2 + t21820 = t21818 ** 2 + t21821 = t21817 * t21820 + t21826 = t21821 ** 2 + t21824 = t21820 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((3*1j) * (phi1 + phi2)) * (-93726 * t21818 - 495342 * t21819 + 1208445 * t21820 + 3864321 * t21821 - 5569812 * t21822 + 11404161 * t21824 - 10623470 * t21826 + 3677355 * t21828 + 1143 + (-13011732 * t21822 + 21477885 * t21824 - 17160990 * t21826 + 5311735 * t21828 + 18219) * t21817) + + if Bindx == 3374: + t21830 = np.cos(phi) + t21831 = t21830 ** 2 + t21833 = t21831 ** 2 + t21837 = t21833 ** 2 + t21832 = t21830 * t21831 + t21835 = t21832 ** 2 + t21834 = t21830 * t21833 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.170e3) * ((1 + t21830) ** (0.7e1 / 0.2e1)) * (387 * t21831 - 48336 * t21832 + 91770 * t21833 - 640794 * t21835 + 821997 * t21837 - 87 + (191520 + 312455 * t21834) * t21834 + (230736 * t21835 - 961400 * t21837 + 1752) * t21830) * ((1 - t21830) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3375: + t21841 = np.cos(phi) + t21842 = t21841 ** 2 + t21844 = t21842 ** 2 + t21845 = t21841 * t21844 + t21850 = t21845 ** 2 + t21848 = t21844 ** 2 + t21843 = t21841 * t21842 + t21846 = t21843 ** 2 + t21840 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.85e2) * t21840 ** 2 * (13505 * t21842 + 24515 * t21843 - 146490 * t21844 - 171038 * t21845 + 526946 * t21846 - 745085 * t21848 + 360525 * t21850 - 185 + (502854 * t21846 - 658559 * t21848 + 312455 * t21850 - 1011) * t21841) + + if Bindx == 3376: + t21852 = np.cos(phi) + t21853 = t21852 ** 2 + t21854 = t21852 * t21853 + t21857 = t21854 ** 2 + t21855 = t21853 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-399 * t21853 - 3465 * t21854 + 8855 * t21855 - 26565 * t21857 - 3 + (5313 * t21855 + 16445 * t21857 + 203) * t21852) * ((1 + t21852) ** (0.9e1 / 0.2e1)) * np.sqrt(0.3230e4) * np.exp((3*1j) * (phi1 + 2 * phi2)) * ((1 - t21852) ** (0.3e1 / 0.2e1)) + + if Bindx == 3377: + t21870 = np.sin(phi) + t21868 = t21870 ** 2 + t21859 = np.cos(phi) + t21860 = t21859 ** 2 + t21862 = t21860 ** 2 + t21866 = t21862 ** 2 + t21861 = t21859 * t21860 + t21864 = t21861 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 + 7 * phi2)) * np.sqrt(0.4522e4) * t21868 ** 2 * (-1740 * t21860 + 292 * t21861 + 14406 * t21862 - 35420 * t21864 + 26565 * t21866 + 29 + (2310 * t21862 - 15180 * t21864 + 16445 * t21866 - 27) * t21859) + + if Bindx == 3378: + t21871 = np.cos(phi) + t21872 = t21871 ** 2 + t21876 = 2530 * t21872 + t21874 = t21872 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-18975 * t21874 + t21876 - 19 + (16445 * t21874 + t21876 - 495) * t21871) * ((1 + t21871) ** (0.11e2 / 0.2e1)) * np.sqrt(0.323e3) * np.exp((1j) * (3 * phi1 + 8 * phi2)) * ((1 - t21871) ** (0.5e1 / 0.2e1)) + + if Bindx == 3379: + t21887 = np.sin(phi) + t21884 = t21887 ** 2 + t21885 = t21887 * t21884 + t21877 = np.cos(phi) + t21878 = t21877 ** 2 + t21879 = t21877 * t21878 + t21882 = t21879 ** 2 + t21880 = t21878 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((3*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.35530e5) * t21885 ** 2 * (387 * t21878 - 699 * t21879 - 2139 * t21880 + 3105 * t21882 - 9 + (483 * t21880 + 1495 * t21882 + 65) * t21877) + + if Bindx == 3380: + t21888 = np.cos(phi) + t21889 = t21888 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-45 * t21889 + 1 + (65 * t21889 + 3) * t21888) * ((1 + t21888) ** (0.13e2 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((1j) * (3 * phi1 + 10 * phi2)) * ((1 - t21888) ** (0.7e1 / 0.2e1)) + + if Bindx == 3381: + t21899 = np.sin(phi) + t21896 = t21899 ** 2 + t21897 = t21896 ** 2 + t21891 = np.cos(phi) + t21892 = t21891 ** 2 + t21894 = t21892 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 + 11 * phi2)) * np.sqrt(0.408595e6) * t21897 ** 2 * (-22 * t21892 + 165 * t21894 + 1 + (106 * t21892 + 65 * t21894 - 27) * t21891) + + if Bindx == 3382: + t21900 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * (-3 + 13 * t21900) * ((1 + t21900) ** (0.15e2 / 0.2e1)) * np.sqrt(0.817190e6) * np.exp((3*1j) * (phi1 + 4 * phi2)) * ((1 - t21900) ** (0.9e1 / 0.2e1)) + + if Bindx == 3383: + t21901 = np.cos(phi) + t21909 = -1 + t21901 + t21908 = 1 + t21901 + t21905 = t21908 ** 2 + t21906 = t21905 ** 2 + t21902 = t21909 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (3 * phi1 + 13 * phi2)) * np.sqrt(0.5311735e7) * t21909 * t21902 ** 2 * t21906 ** 2 + + if Bindx == 3384: + t21910 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 - 13 * phi2)) * np.sqrt(0.124982e6) * ((1 - t21910) ** (0.17e2 / 0.2e1)) * ((1 + t21910) ** (0.9e1 / 0.2e1)) + + if Bindx == 3385: + t21911 = np.cos(phi) + t21918 = -1 + t21911 + t21917 = 1 + t21911 + t21915 = t21917 ** 2 + t21912 = t21918 ** 2 + t21913 = t21912 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((4*1j) * (phi1 - 3 * phi2)) * np.sqrt(0.4807e4) * t21913 ** 2 * t21915 ** 2 * (4 + 13 * t21911) + + if Bindx == 3386: + t21919 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 - 11 * phi2)) * np.sqrt(0.9614e4) * ((1 - t21919) ** (0.15e2 / 0.2e1)) * ((1 + t21919) ** (0.7e1 / 0.2e1)) * (19 + (200 + 325 * t21919) * t21919) + + if Bindx == 3387: + t21930 = np.sin(phi) + t21927 = t21930 ** 2 + t21928 = t21930 * t21927 + t21920 = np.cos(phi) + t21921 = t21920 ** 2 + t21922 = t21920 * t21921 + t21925 = t21922 ** 2 + t21923 = t21921 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - 5 * phi2)) * np.sqrt(0.4807e4) * t21928 ** 2 * (60 * t21921 - 525 * t21922 + 270 * t21923 - 1000 * t21925 - 2 + (807 * t21923 + 325 * t21925 + 65) * t21920) + + if Bindx == 3388: + t21931 = np.cos(phi) + t21932 = t21931 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (4 * phi1 - 9 * phi2)) * np.sqrt(0.209e3) * ((1 - t21931) ** (0.13e2 / 0.2e1)) * ((1 + t21931) ** (0.5e1 / 0.2e1)) * (-184 * t21931 - 73 + (9200 * t21931 + 2622 + 7475 * t21932) * t21932) + + if Bindx == 3389: + t21946 = np.sin(phi) + t21944 = t21946 ** 2 + t21935 = np.cos(phi) + t21936 = t21935 ** 2 + t21938 = t21936 ** 2 + t21942 = t21938 ** 2 + t21937 = t21935 * t21936 + t21940 = t21937 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.190e3) * t21944 ** 2 * (1960 * t21936 + 9004 * t21937 - 16056 * t21938 + 46552 * t21940 - 40480 * t21942 - 40 + (-23826 * t21938 + 7084 * t21940 + 16445 * t21942 - 643) * t21935) + + if Bindx == 3390: + t21947 = np.cos(phi) + t21948 = t21947 ** 2 + t21950 = t21948 ** 2 + t21949 = t21947 * t21948 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (4 * phi1 - 7 * phi2)) * np.sqrt(0.665e3) * ((1 - t21947) ** (0.11e2 / 0.2e1)) * ((1 + t21947) ** (0.3e1 / 0.2e1)) * (-2409 * t21948 + 14421 * t21950 + 23 + (-2024 + 16445 * t21949) * t21949 + (30360 * t21950 - 240) * t21947) + + if Bindx == 3391: + t21954 = np.cos(phi) + t21955 = t21954 ** 2 + t21957 = t21955 ** 2 + t21958 = t21954 * t21957 + t21963 = t21958 ** 2 + t21961 = t21957 ** 2 + t21956 = t21954 * t21955 + t21959 = t21956 ** 2 + t21953 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.19e2) * t21953 ** 2 * (-6784 * t21955 - 6899 * t21956 + 66396 * t21957 + 20762 * t21958 - 224728 * t21959 + 312202 * t21961 - 151800 * t21963 + 106 + (22242 * t21959 - 114103 * t21961 + 82225 * t21963 + 381) * t21954) + + if Bindx == 3392: + t21965 = np.cos(phi) + t21966 = t21965 ** 2 + t21967 = t21965 * t21966 + t21970 = t21967 ** 2 + t21968 = t21966 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.2e1) * ((1 - t21965) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t21965)) * (61180 * t21966 - 212800 * t21967 + 2557324 * t21970 + 43 + (-1067990 + 1562275 * t21968) * t21968 + (-538384 * t21968 + 3845600 * t21970 + 16112) * t21965) + + if Bindx == 3393: + t21973 = np.cos(phi) + t21974 = t21973 ** 2 + t21975 = t21973 * t21974 + t21978 = t21975 ** 2 + t21984 = t21978 ** 2 + t21976 = t21974 ** 2 + t21977 = t21973 * t21976 + t21982 = t21977 ** 2 + t21980 = t21976 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (phi1 - phi2)) * (65700 * t21974 - 100650 * t21975 - 780360 * t21976 + 804429 * t21977 + 3347496 * t21978 - 6460380 * t21980 + 5749172 * t21982 - 1922800 * t21984 - 876 + (-2917260 * t21978 + 5292925 * t21980 - 4643562 * t21982 + 1562275 * t21984 + 3891) * t21973) + + if Bindx == 3394: + t21986 = np.cos(phi) + t21987 = t21986 ** 2 + t21989 = t21987 ** 2 + t21993 = t21989 ** 2 + t21988 = t21986 * t21987 + t21991 = t21988 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.170e3) * ((1 - t21986) ** (0.7e1 / 0.2e1)) * np.sqrt((1 + t21986)) * (2052 * t21987 + 46284 * t21988 + 45486 * t21989 - 403788 * t21991 + 648945 * t21993 - 87 + (-237006 * t21989 + 173052 * t21991 + 312455 * t21993 - 1665) * t21986) + + if Bindx == 3395: + t21996 = np.cos(phi) + t21997 = t21996 ** 2 + t21999 = t21997 ** 2 + t22000 = t21996 * t21999 + t22005 = t22000 ** 2 + t22003 = t21999 ** 2 + t21998 = t21996 * t21997 + t22001 = t21998 ** 2 + t21995 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 - phi2)) * np.sqrt(0.1870e4) * t21995 ** 2 * (-480 * t21997 + 2865 * t21998 + 5700 * t21999 - 19950 * t22000 - 22344 * t22001 + 34086 * t22003 - 17480 * t22005 + 6 + (55290 * t22001 - 65987 * t22003 + 28405 * t22005 - 111) * t21996) + + if Bindx == 3396: + t22007 = np.cos(phi) + t22008 = t22007 ** 2 + t22010 = t22008 ** 2 + t22014 = t22010 ** 2 + t22009 = t22007 * t22008 + t22012 = t22009 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (4 * phi1 - phi2)) * np.sqrt(0.374e3) * ((1 - t22007) ** (0.5e1 / 0.2e1)) * ((1 + t22007) ** (0.3e1 / 0.2e1)) * (-3420 * t22008 - 7980 * t22009 + 35910 * t22010 - 110124 * t22012 + 98325 * t22014 + 45 + (64638 * t22010 - 173052 * t22012 + 142025 * t22014 + 225) * t22007) + + if Bindx == 3397: + t22023 = np.sin(phi) + t22021 = t22023 ** 2 + t22016 = np.cos(phi) + t22017 = t22016 ** 2 + t22018 = t22017 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((4*1j) * phi1) * np.sqrt(0.17017e5) * t22021 ** 2 * t22016 * (-1140 * t22017 + 45 + (-15732 * t22017 + 7182 + 10925 * t22018) * t22018) + + if Bindx == 3398: + t22024 = np.cos(phi) + t22025 = t22024 ** 2 + t22027 = t22025 ** 2 + t22031 = t22027 ** 2 + t22026 = t22024 * t22025 + t22029 = t22026 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (4 * phi1 + phi2)) * np.sqrt(0.374e3) * ((1 - t22024) ** (0.3e1 / 0.2e1)) * ((1 + t22024) ** (0.5e1 / 0.2e1)) * (3420 * t22025 - 7980 * t22026 - 35910 * t22027 + 110124 * t22029 - 98325 * t22031 - 45 + (64638 * t22027 - 173052 * t22029 + 142025 * t22031 + 225) * t22024) + + if Bindx == 3399: + t22034 = np.cos(phi) + t22035 = t22034 ** 2 + t22037 = t22035 ** 2 + t22038 = t22034 * t22037 + t22043 = t22038 ** 2 + t22041 = t22037 ** 2 + t22036 = t22034 * t22035 + t22039 = t22036 ** 2 + t22033 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + phi2)) * np.sqrt(0.1870e4) * t22033 ** 2 * (480 * t22035 + 2865 * t22036 - 5700 * t22037 - 19950 * t22038 + 22344 * t22039 - 34086 * t22041 + 17480 * t22043 - 6 + (55290 * t22039 - 65987 * t22041 + 28405 * t22043 - 111) * t22034) + + if Bindx == 3400: + t22045 = np.cos(phi) + t22046 = t22045 ** 2 + t22048 = t22046 ** 2 + t22052 = t22048 ** 2 + t22047 = t22045 * t22046 + t22050 = t22047 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.170e3) * np.sqrt((1 - t22045)) * ((1 + t22045) ** (0.7e1 / 0.2e1)) * (-2052 * t22046 + 46284 * t22047 - 45486 * t22048 + 403788 * t22050 - 648945 * t22052 + 87 + (-237006 * t22048 + 173052 * t22050 + 312455 * t22052 - 1665) * t22045) + + if Bindx == 3401: + t22054 = np.cos(phi) + t22055 = t22054 ** 2 + t22056 = t22054 * t22055 + t22059 = t22056 ** 2 + t22065 = t22059 ** 2 + t22057 = t22055 ** 2 + t22058 = t22054 * t22057 + t22063 = t22058 ** 2 + t22061 = t22057 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (phi1 + phi2)) * (-65700 * t22055 - 100650 * t22056 + 780360 * t22057 + 804429 * t22058 - 3347496 * t22059 + 6460380 * t22061 - 5749172 * t22063 + 1922800 * t22065 + 876 + (-2917260 * t22059 + 5292925 * t22061 - 4643562 * t22063 + 1562275 * t22065 + 3891) * t22054) + + if Bindx == 3402: + t22067 = np.cos(phi) + t22068 = t22067 ** 2 + t22070 = t22068 ** 2 + t22074 = t22070 ** 2 + t22069 = t22067 * t22068 + t22072 = t22069 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.2e1) * ((1 + t22067) ** (0.9e1 / 0.2e1)) * (-77292 * t22068 - 151620 * t22069 + 1280790 * t22070 - 2018940 * t22072 - 5407875 * t22074 - 43 + (-1606374 * t22070 + 6402924 * t22072 + 1562275 * t22074 + 16155) * t22067) * ((1 - t22067) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3403: + t22077 = np.cos(phi) + t22078 = t22077 ** 2 + t22080 = t22078 ** 2 + t22081 = t22077 * t22080 + t22086 = t22081 ** 2 + t22084 = t22080 ** 2 + t22079 = t22077 * t22078 + t22082 = t22079 ** 2 + t22076 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.19e2) * t22076 ** 2 * (6784 * t22078 - 6899 * t22079 - 66396 * t22080 + 20762 * t22081 + 224728 * t22082 - 312202 * t22084 + 151800 * t22086 - 106 + (22242 * t22082 - 114103 * t22084 + 82225 * t22086 + 381) * t22077) + + if Bindx == 3404: + t22088 = np.cos(phi) + t22089 = t22088 ** 2 + t22091 = t22089 ** 2 + t22090 = t22088 * t22089 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2409 * t22089 + 14421 * t22091 + 23 + (2024 + 16445 * t22090) * t22090 + (-30360 * t22091 + 240) * t22088) * ((1 + t22088) ** (0.11e2 / 0.2e1)) * np.sqrt(0.665e3) * np.exp((1j) * (4 * phi1 + 7 * phi2)) * ((1 - t22088) ** (0.3e1 / 0.2e1)) + + if Bindx == 3405: + t22105 = np.sin(phi) + t22103 = t22105 ** 2 + t22094 = np.cos(phi) + t22095 = t22094 ** 2 + t22097 = t22095 ** 2 + t22101 = t22097 ** 2 + t22096 = t22094 * t22095 + t22099 = t22096 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.190e3) * t22103 ** 2 * (-1960 * t22095 + 9004 * t22096 + 16056 * t22097 - 46552 * t22099 + 40480 * t22101 + 40 + (-23826 * t22097 + 7084 * t22099 + 16445 * t22101 - 643) * t22094) + + if Bindx == 3406: + t22106 = np.cos(phi) + t22107 = t22106 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (184 * t22106 - 73 + (-9200 * t22106 + 2622 + 7475 * t22107) * t22107) * ((1 + t22106) ** (0.13e2 / 0.2e1)) * np.sqrt(0.209e3) * np.exp((1j) * (4 * phi1 + 9 * phi2)) * ((1 - t22106) ** (0.5e1 / 0.2e1)) + + if Bindx == 3407: + t22120 = np.sin(phi) + t22117 = t22120 ** 2 + t22118 = t22120 * t22117 + t22110 = np.cos(phi) + t22111 = t22110 ** 2 + t22112 = t22110 * t22111 + t22115 = t22112 ** 2 + t22113 = t22111 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (2 * phi1 + 5 * phi2)) * np.sqrt(0.4807e4) * t22118 ** 2 * (-60 * t22111 - 525 * t22112 - 270 * t22113 + 1000 * t22115 + 2 + (807 * t22113 + 325 * t22115 + 65) * t22110) + + if Bindx == 3408: + t22121 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * (19 + (-200 + 325 * t22121) * t22121) * ((1 + t22121) ** (0.15e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((1j) * (4 * phi1 + 11 * phi2)) * ((1 - t22121) ** (0.7e1 / 0.2e1)) + + if Bindx == 3409: + t22130 = np.sin(phi) + t22127 = t22130 ** 2 + t22128 = t22127 ** 2 + t22122 = np.cos(phi) + t22123 = t22122 ** 2 + t22125 = t22123 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((4*1j) * (phi1 + 3 * phi2)) * np.sqrt(0.4807e4) * t22128 ** 2 * (28 * t22123 + 48 * t22125 - 4 + (62 * t22123 + 13 * t22125 - 3) * t22122) + + if Bindx == 3410: + t22131 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (4 * phi1 + 13 * phi2)) * np.sqrt(0.124982e6) * ((1 - t22131) ** (0.9e1 / 0.2e1)) * ((1 + t22131) ** (0.17e2 / 0.2e1)) + + if Bindx == 3411: + t22132 = np.cos(phi) + t22140 = -1 + t22132 + t22139 = 1 + t22132 + t22137 = t22139 ** 2 + t22133 = t22140 ** 2 + t22134 = t22133 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (5 * phi1 - 13 * phi2)) * np.sqrt(0.62491e5) * t22140 * t22134 ** 2 * t22137 ** 2 + + if Bindx == 3412: + t22141 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (5 * phi1 - 12 * phi2)) * np.sqrt(0.9614e4) * ((1 - t22141) ** (0.17e2 / 0.2e1)) * ((1 + t22141) ** (0.7e1 / 0.2e1)) * (5 + 13 * t22141) + + if Bindx == 3413: + t22152 = np.sin(phi) + t22149 = t22152 ** 2 + t22150 = t22152 * t22149 + t22142 = np.cos(phi) + t22143 = t22142 ** 2 + t22144 = t22142 * t22143 + t22147 = t22144 ** 2 + t22145 = t22143 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 - 11 * phi2)) * np.sqrt(0.4807e4) * t22150 ** 2 * (555 * t22143 - 505 * t22144 - 935 * t22145 - 1375 * t22147 - 37 + (2037 * t22145 + 325 * t22147 - 65) * t22142) + + if Bindx == 3414: + t22153 = np.cos(phi) + t22154 = t22153 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((5*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.9614e4) * ((1 - t22153) ** (0.15e2 / 0.2e1)) * ((1 + t22153) ** (0.5e1 / 0.2e1)) * (375 * t22154 + 5 + (325 * t22154 + 111) * t22153) + + if Bindx == 3415: + t22167 = np.sin(phi) + t22165 = t22167 ** 2 + t22156 = np.cos(phi) + t22157 = t22156 ** 2 + t22159 = t22157 ** 2 + t22163 = t22159 ** 2 + t22158 = t22156 * t22157 + t22161 = t22158 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 - 9 * phi2)) * np.sqrt(0.418e3) * t22165 ** 2 * (-2196 * t22157 + 8820 * t22158 + 3870 * t22159 + 15180 * t22161 - 25875 * t22163 + 61 + (-28926 * t22159 + 22356 * t22161 + 7475 * t22163 - 765) * t22156) + + if Bindx == 3416: + t22168 = np.cos(phi) + t22169 = t22168 ** 2 + t22171 = t22169 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (5 * phi1 - 8 * phi2)) * np.sqrt(0.95e2) * ((1 - t22168) ** (0.13e2 / 0.2e1)) * ((1 + t22168) ** (0.3e1 / 0.2e1)) * (2530 * t22169 + 31625 * t22171 - 107 + (18722 * t22169 + 16445 * t22171 - 671) * t22168) + + if Bindx == 3417: + t22174 = np.cos(phi) + t22175 = t22174 ** 2 + t22177 = t22175 ** 2 + t22178 = t22174 * t22177 + t22183 = t22178 ** 2 + t22181 = t22177 ** 2 + t22176 = t22174 * t22175 + t22179 = t22176 ** 2 + t22173 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 - 7 * phi2)) * np.sqrt(0.1330e4) * t22173 ** 2 * (-1007 * t22175 - 8895 * t22176 + 11022 * t22177 + 34926 * t22178 - 47502 * t22179 + 79695 * t22181 - 44275 * t22183 + 19 + (-43758 * t22179 + 2783 * t22181 + 16445 * t22183 + 547) * t22174) + + if Bindx == 3418: + t22185 = np.cos(phi) + t22186 = t22185 ** 2 + t22187 = t22185 * t22186 + t22190 = t22187 ** 2 + t22188 = t22186 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (5 * phi1 - 6 * phi2)) * np.sqrt(0.38e2) * ((1 - t22185) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t22185)) * (-11235 * t22186 - 23485 * t22187 + 44275 * t22188 + 221375 * t22190 + 113 + (196581 * t22188 + 82225 * t22190 - 665) * t22185) + + if Bindx == 3419: + t22192 = np.cos(phi) + t22193 = t22192 ** 2 + t22194 = t22192 * t22193 + t22197 = t22194 ** 2 + t22203 = t22197 ** 2 + t22195 = t22193 ** 2 + t22196 = t22192 * t22195 + t22201 = t22196 ** 2 + t22199 = t22195 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((5*1j) * (phi1 - phi2)) * (118470 * t22193 + 162730 * t22194 - 1285585 * t22195 - 705195 * t22196 + 5251524 * t22197 - 9975285 * t22199 + 8892950 * t22201 - 3004375 * t22203 - 1795 + (505020 * t22197 + 1919665 * t22199 - 3432198 * t22201 + 1562275 * t22203 - 8201) * t22192) + + if Bindx == 3420: + t22205 = np.cos(phi) + t22206 = t22205 ** 2 + t22207 = t22205 * t22206 + t22210 = t22207 ** 2 + t22208 = t22206 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.2e1) * ((1 - t22205) ** (0.9e1 / 0.2e1)) * np.sqrt((1 + t22205)) * (61180 * t22206 - 212800 * t22207 + 2557324 * t22210 + 43 + (-1067990 + 1562275 * t22208) * t22208 + (-538384 * t22208 + 3845600 * t22210 + 16112) * t22205) + + if Bindx == 3421: + t22214 = np.cos(phi) + t22215 = t22214 ** 2 + t22217 = t22215 ** 2 + t22218 = t22214 * t22217 + t22223 = t22218 ** 2 + t22221 = t22217 ** 2 + t22216 = t22214 * t22215 + t22219 = t22216 ** 2 + t22213 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.85e2) * t22213 ** 2 * (-13505 * t22215 + 24515 * t22216 + 146490 * t22217 - 171038 * t22218 - 526946 * t22219 + 745085 * t22221 - 360525 * t22223 + 185 + (502854 * t22219 - 658559 * t22221 + 312455 * t22223 - 1011) * t22214) + + if Bindx == 3422: + t22225 = np.cos(phi) + t22226 = t22225 ** 2 + t22227 = t22225 * t22226 + t22230 = t22227 ** 2 + t22228 = t22226 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.935e3) * ((1 - t22225) ** (0.7e1 / 0.2e1)) * ((1 + t22225) ** (0.3e1 / 0.2e1)) * (532 * t22226 + 4256 * t22227 - 12236 * t22230 - 11 + (-1330 + 28405 * t22228) * t22228 + (-24472 * t22228 + 34960 * t22230 - 152) * t22225) + + if Bindx == 3423: + t22244 = np.sin(phi) + t22242 = t22244 ** 2 + t22233 = np.cos(phi) + t22234 = t22233 ** 2 + t22236 = t22234 ** 2 + t22240 = t22236 ** 2 + t22235 = t22233 * t22234 + t22238 = t22235 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (5 * phi1 - phi2)) * np.sqrt(0.187e3) * t22242 ** 2 * (1900 * t22234 - 18620 * t22235 - 19950 * t22236 + 61180 * t22238 - 54625 * t22240 - 25 + (109326 * t22236 - 221996 * t22238 + 142025 * t22240 + 785) * t22233) + + if Bindx == 3424: + t22245 = np.cos(phi) + t22246 = t22245 ** 2 + t22247 = t22246 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((5*1j) * phi1) * np.sqrt(0.34034e5) * ((1 - t22245) ** (0.5e1 / 0.2e1)) * ((1 + t22245) ** (0.5e1 / 0.2e1)) * (-380 * t22246 + 5 + (-12236 * t22246 + 3990 + 10925 * t22247) * t22247) + + if Bindx == 3425: + t22261 = np.sin(phi) + t22259 = t22261 ** 2 + t22250 = np.cos(phi) + t22251 = t22250 ** 2 + t22253 = t22251 ** 2 + t22257 = t22253 ** 2 + t22252 = t22250 * t22251 + t22255 = t22252 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (5 * phi1 + phi2)) * np.sqrt(0.187e3) * t22259 ** 2 * (-1900 * t22251 - 18620 * t22252 + 19950 * t22253 - 61180 * t22255 + 54625 * t22257 + 25 + (109326 * t22253 - 221996 * t22255 + 142025 * t22257 + 785) * t22250) + + if Bindx == 3426: + t22262 = np.cos(phi) + t22263 = t22262 ** 2 + t22264 = t22262 * t22263 + t22267 = t22264 ** 2 + t22265 = t22263 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.935e3) * ((1 - t22262) ** (0.3e1 / 0.2e1)) * ((1 + t22262) ** (0.7e1 / 0.2e1)) * (532 * t22263 - 4256 * t22264 - 12236 * t22267 - 11 + (-1330 + 28405 * t22265) * t22265 + (24472 * t22265 - 34960 * t22267 + 152) * t22262) + + if Bindx == 3427: + t22271 = np.cos(phi) + t22272 = t22271 ** 2 + t22274 = t22272 ** 2 + t22275 = t22271 * t22274 + t22280 = t22275 ** 2 + t22278 = t22274 ** 2 + t22273 = t22271 * t22272 + t22276 = t22273 ** 2 + t22270 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.85e2) * t22270 ** 2 * (13505 * t22272 + 24515 * t22273 - 146490 * t22274 - 171038 * t22275 + 526946 * t22276 - 745085 * t22278 + 360525 * t22280 - 185 + (502854 * t22276 - 658559 * t22278 + 312455 * t22280 - 1011) * t22271) + + if Bindx == 3428: + t22282 = np.cos(phi) + t22283 = t22282 ** 2 + t22284 = t22282 * t22283 + t22287 = t22284 ** 2 + t22285 = t22283 ** 2 + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t22282)) * ((1 + t22282) ** (0.9e1 / 0.2e1)) * (61180 * t22283 + 212800 * t22284 + 2557324 * t22287 + 43 + (-1067990 + 1562275 * t22285) * t22285 + (538384 * t22285 - 3845600 * t22287 - 16112) * t22282) + + if Bindx == 3429: + t22290 = np.cos(phi) + t22291 = t22290 ** 2 + t22292 = t22290 * t22291 + t22295 = t22292 ** 2 + t22301 = t22295 ** 2 + t22293 = t22291 ** 2 + t22294 = t22290 * t22293 + t22299 = t22294 ** 2 + t22297 = t22293 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((5*1j) * (phi1 + phi2)) * (-118470 * t22291 + 162730 * t22292 + 1285585 * t22293 - 705195 * t22294 - 5251524 * t22295 + 9975285 * t22297 - 8892950 * t22299 + 3004375 * t22301 + 1795 + (505020 * t22295 + 1919665 * t22297 - 3432198 * t22299 + 1562275 * t22301 - 8201) * t22290) + + if Bindx == 3430: + t22303 = np.cos(phi) + t22304 = t22303 ** 2 + t22305 = t22303 * t22304 + t22308 = t22305 ** 2 + t22306 = t22304 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (5 * phi1 + 6 * phi2)) * np.sqrt(0.38e2) * ((1 + t22303) ** (0.11e2 / 0.2e1)) * (-11900 * t22304 + 34720 * t22305 + 417956 * t22308 + 113 + (20790 + 82225 * t22306) * t22306 + (-240856 * t22306 - 303600 * t22308 + 552) * t22303) * ((1 - t22303) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3431: + t22312 = np.cos(phi) + t22313 = t22312 ** 2 + t22315 = t22313 ** 2 + t22316 = t22312 * t22315 + t22321 = t22316 ** 2 + t22319 = t22315 ** 2 + t22314 = t22312 * t22313 + t22317 = t22314 ** 2 + t22311 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 + 7 * phi2)) * np.sqrt(0.1330e4) * t22311 ** 2 * (1007 * t22313 - 8895 * t22314 - 11022 * t22315 + 34926 * t22316 + 47502 * t22317 - 79695 * t22319 + 44275 * t22321 - 19 + (-43758 * t22317 + 2783 * t22319 + 16445 * t22321 + 547) * t22312) + + if Bindx == 3432: + t22323 = np.cos(phi) + t22324 = t22323 ** 2 + t22326 = t22324 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (-2530 * t22324 - 31625 * t22326 + 107 + (18722 * t22324 + 16445 * t22326 - 671) * t22323) * ((1 + t22323) ** (0.13e2 / 0.2e1)) * np.sqrt(0.95e2) * np.exp((1j) * (5 * phi1 + 8 * phi2)) * ((1 - t22323) ** (0.3e1 / 0.2e1)) + + if Bindx == 3433: + t22339 = np.sin(phi) + t22337 = t22339 ** 2 + t22328 = np.cos(phi) + t22329 = t22328 ** 2 + t22331 = t22329 ** 2 + t22335 = t22331 ** 2 + t22330 = t22328 * t22329 + t22333 = t22330 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 + 9 * phi2)) * np.sqrt(0.418e3) * t22337 ** 2 * (2196 * t22329 + 8820 * t22330 - 3870 * t22331 - 15180 * t22333 + 25875 * t22335 - 61 + (-28926 * t22331 + 22356 * t22333 + 7475 * t22335 - 765) * t22328) + + if Bindx == 3434: + t22340 = np.cos(phi) + t22341 = t22340 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-375 * t22341 - 5 + (325 * t22341 + 111) * t22340) * ((1 + t22340) ** (0.15e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((5*1j) * (phi1 + 2 * phi2)) * ((1 - t22340) ** (0.5e1 / 0.2e1)) + + if Bindx == 3435: + t22353 = np.sin(phi) + t22350 = t22353 ** 2 + t22351 = t22353 * t22350 + t22343 = np.cos(phi) + t22344 = t22343 ** 2 + t22345 = t22343 * t22344 + t22348 = t22345 ** 2 + t22346 = t22344 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (5 * phi1 + 11 * phi2)) * np.sqrt(0.4807e4) * t22351 ** 2 * (-555 * t22344 - 505 * t22345 + 935 * t22346 + 1375 * t22348 + 37 + (2037 * t22346 + 325 * t22348 - 65) * t22343) + + if Bindx == 3436: + t22354 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * (-5 + 13 * t22354) * ((1 + t22354) ** (0.17e2 / 0.2e1)) * np.sqrt(0.9614e4) * np.exp((1j) * (5 * phi1 + 12 * phi2)) * ((1 - t22354) ** (0.7e1 / 0.2e1)) + + if Bindx == 3437: + t22355 = np.cos(phi) + t22363 = -1 + t22355 + t22362 = 1 + t22355 + t22358 = t22362 ** 2 + t22359 = t22358 ** 2 + t22356 = t22363 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (5 * phi1 + 13 * phi2)) * np.sqrt(0.62491e5) * t22356 ** 2 * t22362 * t22359 ** 2 + + if Bindx == 3438: + t22364 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (6 * phi1 - 13 * phi2)) * np.sqrt(0.6578e4) * ((1 - t22364) ** (0.19e2 / 0.2e1)) * ((1 + t22364) ** (0.7e1 / 0.2e1)) + + if Bindx == 3439: + t22365 = np.cos(phi) + t22373 = -1 + t22365 + t22372 = 1 + t22365 + t22370 = t22372 ** 2 + t22366 = t22373 ** 2 + t22367 = t22366 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((6*1j) * (phi1 - 2 * phi2)) * np.sqrt(0.253e3) * t22373 * t22367 ** 2 * t22372 * t22370 * (6 + 13 * t22365) + + if Bindx == 3440: + t22374 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (6 * phi1 - 11 * phi2)) * np.sqrt(0.506e3) * ((1 - t22374) ** (0.17e2 / 0.2e1)) * ((1 + t22374) ** (0.5e1 / 0.2e1)) * (59 + (300 + 325 * t22374) * t22374) + + if Bindx == 3441: + t22386 = np.sin(phi) + t22384 = t22386 ** 2 + t22375 = np.cos(phi) + t22376 = t22375 ** 2 + t22378 = t22376 ** 2 + t22382 = t22378 ** 2 + t22377 = t22375 * t22376 + t22380 = t22377 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - 5 * phi2)) * np.sqrt(0.253e3) * t22384 ** 2 * (-357 * t22376 - 60 * t22377 + 1515 * t22378 - 795 * t22380 - 1500 * t22382 + 17 + (-1572 * t22378 + 2352 * t22380 + 325 * t22382 + 75) * t22375) + + if Bindx == 3442: + t22387 = np.cos(phi) + t22388 = t22387 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((3*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.11e2) * ((1 - t22387) ** (0.15e2 / 0.2e1)) * ((1 + t22387) ** (0.3e1 / 0.2e1)) * (1564 * t22387 + 27 + (13800 * t22387 + 8142 + 7475 * t22388) * t22388) + + if Bindx == 3443: + t22392 = np.cos(phi) + t22393 = t22392 ** 2 + t22395 = t22393 ** 2 + t22396 = t22392 * t22395 + t22401 = t22396 ** 2 + t22399 = t22395 ** 2 + t22394 = t22392 * t22393 + t22397 = t22394 ** 2 + t22391 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.10e2) * t22391 ** 2 * (4960 * t22393 - 14823 * t22394 - 19944 * t22395 + 69714 * t22396 + 624 * t22397 + 69828 * t22399 - 60720 * t22401 - 124 + (-115830 * t22397 + 48829 * t22399 + 16445 * t22401 + 1041) * t22392) + + if Bindx == 3444: + t22403 = np.cos(phi) + t22404 = t22403 ** 2 + t22406 = t22404 ** 2 + t22405 = t22403 * t22404 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - 7 * phi2)) * np.sqrt(0.35e2) * ((1 - t22403) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t22403)) * (891 * t22404 + 44781 * t22406 - 85 + (17204 + 16445 * t22405) * t22405 + (45540 * t22406 - 744) * t22403) + + if Bindx == 3445: + t22409 = np.cos(phi) + t22410 = t22409 ** 2 + t22411 = t22409 * t22410 + t22414 = t22411 ** 2 + t22420 = t22414 ** 2 + t22412 = t22410 ** 2 + t22413 = t22409 * t22412 + t22418 = t22413 ** 2 + t22416 = t22412 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((6*1j) * (phi1 - phi2)) * (3795 * t22410 + 45270 * t22411 - 48670 * t22412 - 217629 * t22413 + 257454 * t22414 - 595305 * t22416 + 609983 * t22418 - 227700 * t22420 - 69 + (410700 * t22414 - 270435 * t22416 - 47058 * t22418 + 82225 * t22420 - 2561) * t22409) + + if Bindx == 3446: + t22422 = np.cos(phi) + t22423 = t22422 ** 2 + t22424 = t22422 * t22423 + t22427 = t22424 ** 2 + t22425 = t22423 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (6 * phi1 - 5 * phi2)) * np.sqrt(0.38e2) * ((1 - t22422) ** (0.11e2 / 0.2e1)) * np.sqrt((1 + t22422)) * (-11235 * t22423 - 23485 * t22424 + 44275 * t22425 + 221375 * t22427 + 113 + (196581 * t22425 + 82225 * t22427 - 665) * t22422) + + if Bindx == 3447: + t22430 = np.cos(phi) + t22431 = t22430 ** 2 + t22433 = t22431 ** 2 + t22434 = t22430 * t22433 + t22439 = t22434 ** 2 + t22437 = t22433 ** 2 + t22432 = t22430 * t22431 + t22435 = t22432 ** 2 + t22429 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.19e2) * t22429 ** 2 * (-6784 * t22431 - 6899 * t22432 + 66396 * t22433 + 20762 * t22434 - 224728 * t22435 + 312202 * t22437 - 151800 * t22439 + 106 + (22242 * t22435 - 114103 * t22437 + 82225 * t22439 + 381) * t22430) + + if Bindx == 3448: + t22441 = np.cos(phi) + t22442 = t22441 ** 2 + t22443 = t22441 * t22442 + t22446 = t22443 ** 2 + t22444 = t22442 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((3*1j) * (2 * phi1 - phi2)) * np.sqrt(0.3230e4) * ((1 - t22441) ** (0.9e1 / 0.2e1)) * ((1 + t22441) ** (0.3e1 / 0.2e1)) * (399 * t22442 - 3465 * t22443 - 8855 * t22444 + 26565 * t22446 + 3 + (5313 * t22444 + 16445 * t22446 + 203) * t22441) + + if Bindx == 3449: + t22459 = np.sin(phi) + t22457 = t22459 ** 2 + t22448 = np.cos(phi) + t22449 = t22448 ** 2 + t22451 = t22449 ** 2 + t22455 = t22451 ** 2 + t22450 = t22448 * t22449 + t22453 = t22450 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 - phi2)) * np.sqrt(0.35530e5) * t22457 ** 2 * (69 * t22449 - 196 * t22450 - 651 * t22451 + 1771 * t22453 - 1380 * t22455 - 1 + (1092 * t22451 - 2208 * t22453 + 1495 * t22455 + 9) * t22448) + + if Bindx == 3450: + t22460 = np.cos(phi) + t22461 = t22460 ** 2 + t22462 = t22460 * t22461 + t22465 = t22462 ** 2 + t22463 = t22461 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 - phi2)) * np.sqrt(0.7106e4) * ((1 - t22460) ** (0.7e1 / 0.2e1)) * ((1 + t22460) ** (0.5e1 / 0.2e1)) * (315 * t22461 + 945 * t22462 - 2415 * t22463 + 4025 * t22465 - 5 + (-5313 * t22463 + 7475 * t22465 - 35) * t22460) + + if Bindx == 3451: + t22474 = np.sin(phi) + t22471 = t22474 ** 2 + t22472 = t22474 * t22471 + t22467 = np.cos(phi) + t22468 = t22467 ** 2 + t22469 = t22468 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((6*1j) * phi1) * np.sqrt(0.323323e6) * t22472 ** 2 * t22467 * (-483 * t22469 - 5 + (575 * t22469 + 105) * t22468) + + if Bindx == 3452: + t22475 = np.cos(phi) + t22476 = t22475 ** 2 + t22477 = t22475 * t22476 + t22480 = t22477 ** 2 + t22478 = t22476 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + phi2)) * np.sqrt(0.7106e4) * ((1 - t22475) ** (0.5e1 / 0.2e1)) * ((1 + t22475) ** (0.7e1 / 0.2e1)) * (-315 * t22476 + 945 * t22477 + 2415 * t22478 - 4025 * t22480 + 5 + (-5313 * t22478 + 7475 * t22480 - 35) * t22475) + + if Bindx == 3453: + t22493 = np.sin(phi) + t22491 = t22493 ** 2 + t22482 = np.cos(phi) + t22483 = t22482 ** 2 + t22485 = t22483 ** 2 + t22489 = t22485 ** 2 + t22484 = t22482 * t22483 + t22487 = t22484 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + phi2)) * np.sqrt(0.35530e5) * t22491 ** 2 * (-69 * t22483 - 196 * t22484 + 651 * t22485 - 1771 * t22487 + 1380 * t22489 + 1 + (1092 * t22485 - 2208 * t22487 + 1495 * t22489 + 9) * t22482) + + if Bindx == 3454: + t22494 = np.cos(phi) + t22495 = t22494 ** 2 + t22496 = t22494 * t22495 + t22499 = t22496 ** 2 + t22497 = t22495 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((3*1j) * (2 * phi1 + phi2)) * np.sqrt(0.3230e4) * ((1 - t22494) ** (0.3e1 / 0.2e1)) * ((1 + t22494) ** (0.9e1 / 0.2e1)) * (-399 * t22495 - 3465 * t22496 + 8855 * t22497 - 26565 * t22499 - 3 + (5313 * t22497 + 16445 * t22499 + 203) * t22494) + + if Bindx == 3455: + t22502 = np.cos(phi) + t22503 = t22502 ** 2 + t22505 = t22503 ** 2 + t22506 = t22502 * t22505 + t22511 = t22506 ** 2 + t22509 = t22505 ** 2 + t22504 = t22502 * t22503 + t22507 = t22504 ** 2 + t22501 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.19e2) * t22501 ** 2 * (6784 * t22503 - 6899 * t22504 - 66396 * t22505 + 20762 * t22506 + 224728 * t22507 - 312202 * t22509 + 151800 * t22511 - 106 + (22242 * t22507 - 114103 * t22509 + 82225 * t22511 + 381) * t22502) + + if Bindx == 3456: + t22513 = np.cos(phi) + t22514 = t22513 ** 2 + t22515 = t22513 * t22514 + t22518 = t22515 ** 2 + t22516 = t22514 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (6 * phi1 + 5 * phi2)) * np.sqrt(0.38e2) * np.sqrt((1 - t22513)) * ((1 + t22513) ** (0.11e2 / 0.2e1)) * (11235 * t22514 - 23485 * t22515 - 44275 * t22516 - 221375 * t22518 - 113 + (196581 * t22516 + 82225 * t22518 - 665) * t22513) + + if Bindx == 3457: + t22520 = np.cos(phi) + t22521 = t22520 ** 2 + t22522 = t22520 * t22521 + t22525 = t22522 ** 2 + t22531 = t22525 ** 2 + t22523 = t22521 ** 2 + t22524 = t22520 * t22523 + t22529 = t22524 ** 2 + t22527 = t22523 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((6*1j) * (phi1 + phi2)) * (-3795 * t22521 + 45270 * t22522 + 48670 * t22523 - 217629 * t22524 - 257454 * t22525 + 595305 * t22527 - 609983 * t22529 + 227700 * t22531 + 69 + (410700 * t22525 - 270435 * t22527 - 47058 * t22529 + 82225 * t22531 - 2561) * t22520) + + if Bindx == 3458: + t22533 = np.cos(phi) + t22534 = t22533 ** 2 + t22535 = t22533 * t22534 + t22538 = t22535 ** 2 + t22536 = t22534 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (6 * phi1 + 7 * phi2)) * np.sqrt(0.35e2) * ((1 + t22533) ** (0.13e2 / 0.2e1)) * (-147 * t22534 + 18095 * t22535 - 61985 * t22536 - 61985 * t22538 + 85 + (90321 * t22536 + 16445 * t22538 - 829) * t22533) * ((1 - t22533) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3459: + t22541 = np.cos(phi) + t22542 = t22541 ** 2 + t22544 = t22542 ** 2 + t22545 = t22541 * t22544 + t22550 = t22545 ** 2 + t22548 = t22544 ** 2 + t22543 = t22541 * t22542 + t22546 = t22543 ** 2 + t22540 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (3 * phi1 + 4 * phi2)) * np.sqrt(0.10e2) * t22540 ** 2 * (-4960 * t22542 - 14823 * t22543 + 19944 * t22544 + 69714 * t22545 - 624 * t22546 - 69828 * t22548 + 60720 * t22550 + 124 + (-115830 * t22546 + 48829 * t22548 + 16445 * t22550 + 1041) * t22541) + + if Bindx == 3460: + t22552 = np.cos(phi) + t22553 = t22552 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-1564 * t22552 + 27 + (-13800 * t22552 + 8142 + 7475 * t22553) * t22553) * ((1 + t22552) ** (0.15e2 / 0.2e1)) * np.sqrt(0.11e2) * np.exp((3*1j) * (2 * phi1 + 3 * phi2)) * ((1 - t22552) ** (0.3e1 / 0.2e1)) + + if Bindx == 3461: + t22567 = np.sin(phi) + t22565 = t22567 ** 2 + t22556 = np.cos(phi) + t22557 = t22556 ** 2 + t22559 = t22557 ** 2 + t22563 = t22559 ** 2 + t22558 = t22556 * t22557 + t22561 = t22558 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (3 * phi1 + 5 * phi2)) * np.sqrt(0.253e3) * t22565 ** 2 * (357 * t22557 - 60 * t22558 - 1515 * t22559 + 795 * t22561 + 1500 * t22563 - 17 + (-1572 * t22559 + 2352 * t22561 + 325 * t22563 + 75) * t22556) + + if Bindx == 3462: + t22568 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (59 + (-300 + 325 * t22568) * t22568) * ((1 + t22568) ** (0.17e2 / 0.2e1)) * np.sqrt(0.506e3) * np.exp((1j) * (6 * phi1 + 11 * phi2)) * ((1 - t22568) ** (0.5e1 / 0.2e1)) + + if Bindx == 3463: + t22569 = np.cos(phi) + t22577 = -1 + t22569 + t22576 = 1 + t22569 + t22572 = t22576 ** 2 + t22573 = t22572 ** 2 + t22570 = t22577 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((6*1j) * (phi1 + 2 * phi2)) * np.sqrt(0.253e3) * t22577 * t22570 * t22576 * t22573 ** 2 * (-6 + 13 * t22569) + + if Bindx == 3464: + t22578 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (6 * phi1 + 13 * phi2)) * np.sqrt(0.6578e4) * ((1 - t22578) ** (0.7e1 / 0.2e1)) * ((1 + t22578) ** (0.19e2 / 0.2e1)) + + if Bindx == 3465: + t22579 = np.cos(phi) + t22587 = -1 + t22579 + t22586 = 1 + t22579 + t22584 = t22586 ** 2 + t22580 = t22587 ** 2 + t22582 = t22587 * t22580 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 - 13 * phi2)) * np.sqrt(0.230230e6) * t22582 ** 2 * t22586 * t22584 + + if Bindx == 3466: + t22588 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (7 * phi1 - 12 * phi2)) * np.sqrt(0.8855e4) * ((1 - t22588) ** (0.19e2 / 0.2e1)) * ((1 + t22588) ** (0.5e1 / 0.2e1)) * (7 + 13 * t22588) + + if Bindx == 3467: + t22600 = np.sin(phi) + t22598 = t22600 ** 2 + t22589 = np.cos(phi) + t22590 = t22589 ** 2 + t22592 = t22590 ** 2 + t22596 = t22592 ** 2 + t22591 = t22589 * t22590 + t22594 = t22591 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 - 11 * phi2)) * np.sqrt(0.17710e5) * t22598 ** 2 * (68 * t22590 - 420 * t22591 + 490 * t22592 - 924 * t22594 - 385 * t22596 - 17 + (182 * t22592 + 892 * t22594 + 65 * t22596 + 49) * t22589) + + if Bindx == 3468: + t22601 = np.cos(phi) + t22602 = t22601 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 10 * phi2)) * np.sqrt(0.8855e4) * ((1 - t22601) ** (0.17e2 / 0.2e1)) * ((1 + t22601) ** (0.3e1 / 0.2e1)) * (105 * t22602 + 7 + (65 * t22602 + 51) * t22601) + + if Bindx == 3469: + t22605 = np.cos(phi) + t22606 = t22605 ** 2 + t22608 = t22606 ** 2 + t22609 = t22605 * t22608 + t22614 = t22609 ** 2 + t22612 = t22608 ** 2 + t22607 = t22605 * t22606 + t22610 = t22607 ** 2 + t22604 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (7 * phi1 - 9 * phi2)) * np.sqrt(0.385e3) * t22604 ** 2 * (1175 * t22606 + 1323 * t22607 - 7326 * t22608 + 3402 * t22609 + 12390 * t22610 - 483 * t22612 - 7245 * t22614 - 47 + (-15570 * t22610 + 11201 * t22612 + 1495 * t22614 - 315) * t22605) + + if Bindx == 3470: + t22616 = np.cos(phi) + t22617 = t22616 ** 2 + t22619 = t22617 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (7 * phi1 - 8 * phi2)) * np.sqrt(0.14e2) * ((1 - t22616) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t22616)) * (17710 * t22617 + 44275 * t22619 + 7 + (43010 * t22617 + 16445 * t22619 + 2585) * t22616) + + if Bindx == 3471: + t22621 = np.cos(phi) + t22622 = t22621 ** 2 + t22623 = t22621 * t22622 + t22626 = t22623 ** 2 + t22632 = t22626 ** 2 + t22624 = t22622 ** 2 + t22625 = t22621 * t22624 + t22630 = t22625 ** 2 + t22628 = t22624 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((7*1j) * (phi1 - phi2)) * (-34818 * t22622 + 96866 * t22623 + 183295 * t22624 - 545139 * t22625 - 205548 * t22626 - 379701 * t22628 + 867790 * t22630 - 433895 * t22632 + 829 + (1261068 * t22626 - 1185415 * t22628 + 265650 * t22630 + 115115 * t22632 - 6097) * t22621) + + if Bindx == 3472: + t22634 = np.cos(phi) + t22635 = t22634 ** 2 + t22637 = t22635 ** 2 + t22636 = t22634 * t22635 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 6 * phi2)) * np.sqrt(0.35e2) * ((1 - t22634) ** (0.13e2 / 0.2e1)) * np.sqrt((1 + t22634)) * (891 * t22635 + 44781 * t22637 - 85 + (17204 + 16445 * t22636) * t22636 + (45540 * t22637 - 744) * t22634) + + if Bindx == 3473: + t22641 = np.cos(phi) + t22642 = t22641 ** 2 + t22644 = t22642 ** 2 + t22645 = t22641 * t22644 + t22650 = t22645 ** 2 + t22648 = t22644 ** 2 + t22643 = t22641 * t22642 + t22646 = t22643 ** 2 + t22640 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 - 5 * phi2)) * np.sqrt(0.1330e4) * t22640 ** 2 * (-1007 * t22642 - 8895 * t22643 + 11022 * t22644 + 34926 * t22645 - 47502 * t22646 + 79695 * t22648 - 44275 * t22650 + 19 + (-43758 * t22646 + 2783 * t22648 + 16445 * t22650 + 547) * t22641) + + if Bindx == 3474: + t22652 = np.cos(phi) + t22653 = t22652 ** 2 + t22655 = t22653 ** 2 + t22654 = t22652 * t22653 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (7 * phi1 - 4 * phi2)) * np.sqrt(0.665e3) * ((1 - t22652) ** (0.11e2 / 0.2e1)) * ((1 + t22652) ** (0.3e1 / 0.2e1)) * (-2409 * t22653 + 14421 * t22655 + 23 + (-2024 + 16445 * t22654) * t22654 + (30360 * t22655 - 240) * t22652) + + if Bindx == 3475: + t22669 = np.sin(phi) + t22667 = t22669 ** 2 + t22658 = np.cos(phi) + t22659 = t22658 ** 2 + t22661 = t22659 ** 2 + t22665 = t22661 ** 2 + t22660 = t22658 * t22659 + t22663 = t22660 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 - 3 * phi2)) * np.sqrt(0.4522e4) * t22667 ** 2 * (1740 * t22659 + 292 * t22660 - 14406 * t22661 + 35420 * t22663 - 26565 * t22665 - 29 + (2310 * t22661 - 15180 * t22663 + 16445 * t22665 - 27) * t22658) + + if Bindx == 3476: + t22670 = np.cos(phi) + t22671 = t22670 ** 2 + t22673 = t22671 ** 2 + t22672 = t22670 * t22671 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 - 2 * phi2)) * np.sqrt(0.49742e5) * ((1 - t22670) ** (0.9e1 / 0.2e1)) * ((1 + t22670) ** (0.5e1 / 0.2e1)) * (-15 * t22671 - 345 * t22673 + 1 + (-460 + 1495 * t22672) * t22672 + (1380 * t22673 + 24) * t22670) + + if Bindx == 3477: + t22686 = np.sin(phi) + t22683 = t22686 ** 2 + t22684 = t22686 * t22683 + t22676 = np.cos(phi) + t22677 = t22676 ** 2 + t22678 = t22676 * t22677 + t22681 = t22678 ** 2 + t22679 = t22677 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (7 * phi1 - phi2)) * np.sqrt(0.248710e6) * t22684 ** 2 * (-63 * t22677 + 357 * t22678 + 483 * t22679 - 805 * t22681 + 1 + (-1449 * t22679 + 1495 * t22681 - 19) * t22676) + + if Bindx == 3478: + t22687 = np.cos(phi) + t22688 = t22687 ** 2 + t22689 = t22688 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((7*1j) * phi1) * np.sqrt(0.230945e6) * ((1 - t22687) ** (0.7e1 / 0.2e1)) * ((1 + t22687) ** (0.7e1 / 0.2e1)) * (-483 * t22689 - 1 + (805 * t22689 + 63) * t22688) + + if Bindx == 3479: + t22701 = np.sin(phi) + t22698 = t22701 ** 2 + t22699 = t22701 * t22698 + t22691 = np.cos(phi) + t22692 = t22691 ** 2 + t22693 = t22691 * t22692 + t22696 = t22693 ** 2 + t22694 = t22692 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (7 * phi1 + phi2)) * np.sqrt(0.248710e6) * t22699 ** 2 * (63 * t22692 + 357 * t22693 - 483 * t22694 + 805 * t22696 - 1 + (-1449 * t22694 + 1495 * t22696 - 19) * t22691) + + if Bindx == 3480: + t22702 = np.cos(phi) + t22703 = t22702 ** 2 + t22705 = t22703 ** 2 + t22704 = t22702 * t22703 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 2 * phi2)) * np.sqrt(0.49742e5) * ((1 - t22702) ** (0.5e1 / 0.2e1)) * ((1 + t22702) ** (0.9e1 / 0.2e1)) * (-15 * t22703 - 345 * t22705 + 1 + (460 + 1495 * t22704) * t22704 + (-1380 * t22705 - 24) * t22702) + + if Bindx == 3481: + t22719 = np.sin(phi) + t22717 = t22719 ** 2 + t22708 = np.cos(phi) + t22709 = t22708 ** 2 + t22711 = t22709 ** 2 + t22715 = t22711 ** 2 + t22710 = t22708 * t22709 + t22713 = t22710 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 + 3 * phi2)) * np.sqrt(0.4522e4) * t22717 ** 2 * (-1740 * t22709 + 292 * t22710 + 14406 * t22711 - 35420 * t22713 + 26565 * t22715 + 29 + (2310 * t22711 - 15180 * t22713 + 16445 * t22715 - 27) * t22708) + + if Bindx == 3482: + t22720 = np.cos(phi) + t22721 = t22720 ** 2 + t22723 = t22721 ** 2 + t22722 = t22720 * t22721 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (7 * phi1 + 4 * phi2)) * np.sqrt(0.665e3) * ((1 - t22720) ** (0.3e1 / 0.2e1)) * ((1 + t22720) ** (0.11e2 / 0.2e1)) * (-2409 * t22721 + 14421 * t22723 + 23 + (2024 + 16445 * t22722) * t22722 + (-30360 * t22723 + 240) * t22720) + + if Bindx == 3483: + t22727 = np.cos(phi) + t22728 = t22727 ** 2 + t22730 = t22728 ** 2 + t22731 = t22727 * t22730 + t22736 = t22731 ** 2 + t22734 = t22730 ** 2 + t22729 = t22727 * t22728 + t22732 = t22729 ** 2 + t22726 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 + 5 * phi2)) * np.sqrt(0.1330e4) * t22726 ** 2 * (1007 * t22728 - 8895 * t22729 - 11022 * t22730 + 34926 * t22731 + 47502 * t22732 - 79695 * t22734 + 44275 * t22736 - 19 + (-43758 * t22732 + 2783 * t22734 + 16445 * t22736 + 547) * t22727) + + if Bindx == 3484: + t22738 = np.cos(phi) + t22739 = t22738 ** 2 + t22741 = t22739 ** 2 + t22740 = t22738 * t22739 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (7 * phi1 + 6 * phi2)) * np.sqrt(0.35e2) * np.sqrt((1 - t22738)) * ((1 + t22738) ** (0.13e2 / 0.2e1)) * (891 * t22739 + 44781 * t22741 - 85 + (-17204 + 16445 * t22740) * t22740 + (-45540 * t22741 + 744) * t22738) + + if Bindx == 3485: + t22744 = np.cos(phi) + t22745 = t22744 ** 2 + t22746 = t22744 * t22745 + t22749 = t22746 ** 2 + t22755 = t22749 ** 2 + t22747 = t22745 ** 2 + t22748 = t22744 * t22747 + t22753 = t22748 ** 2 + t22751 = t22747 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((7*1j) * (phi1 + phi2)) * (34818 * t22745 + 96866 * t22746 - 183295 * t22747 - 545139 * t22748 + 205548 * t22749 + 379701 * t22751 - 867790 * t22753 + 433895 * t22755 - 829 + (1261068 * t22749 - 1185415 * t22751 + 265650 * t22753 + 115115 * t22755 - 6097) * t22744) + + if Bindx == 3486: + t22757 = np.cos(phi) + t22758 = t22757 ** 2 + t22760 = t22758 ** 2 + t22759 = t22757 * t22758 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (7 * phi1 + 8 * phi2)) * np.sqrt(0.14e2) * ((1 + t22757) ** (0.15e2 / 0.2e1)) * (20295 * t22758 + 87285 * t22760 + 7 + (-60720 + 16445 * t22759) * t22759 + (-60720 * t22760 - 2592) * t22757) * ((1 - t22757) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3487: + t22764 = np.cos(phi) + t22765 = t22764 ** 2 + t22767 = t22765 ** 2 + t22768 = t22764 * t22767 + t22773 = t22768 ** 2 + t22771 = t22767 ** 2 + t22766 = t22764 * t22765 + t22769 = t22766 ** 2 + t22763 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (7 * phi1 + 9 * phi2)) * np.sqrt(0.385e3) * t22763 ** 2 * (-1175 * t22765 + 1323 * t22766 + 7326 * t22767 + 3402 * t22768 - 12390 * t22769 + 483 * t22771 + 7245 * t22773 + 47 + (-15570 * t22769 + 11201 * t22771 + 1495 * t22773 - 315) * t22764) + + if Bindx == 3488: + t22775 = np.cos(phi) + t22776 = t22775 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * (-105 * t22776 - 7 + (65 * t22776 + 51) * t22775) * ((1 + t22775) ** (0.17e2 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((1j) * (7 * phi1 + 10 * phi2)) * ((1 - t22775) ** (0.3e1 / 0.2e1)) + + if Bindx == 3489: + t22789 = np.sin(phi) + t22787 = t22789 ** 2 + t22778 = np.cos(phi) + t22779 = t22778 ** 2 + t22781 = t22779 ** 2 + t22785 = t22781 ** 2 + t22780 = t22778 * t22779 + t22783 = t22780 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 + 11 * phi2)) * np.sqrt(0.17710e5) * t22787 ** 2 * (-68 * t22779 - 420 * t22780 - 490 * t22781 + 924 * t22783 + 385 * t22785 + 17 + (182 * t22781 + 892 * t22783 + 65 * t22785 + 49) * t22778) + + if Bindx == 3490: + t22790 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * (-7 + 13 * t22790) * ((1 + t22790) ** (0.19e2 / 0.2e1)) * np.sqrt(0.8855e4) * np.exp((1j) * (7 * phi1 + 12 * phi2)) * ((1 - t22790) ** (0.5e1 / 0.2e1)) + + if Bindx == 3491: + t22791 = np.cos(phi) + t22799 = -1 + t22791 + t22798 = 1 + t22791 + t22794 = t22798 ** 2 + t22796 = t22798 * t22794 ** 2 + t22792 = t22799 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (7 * phi1 + 13 * phi2)) * np.sqrt(0.230230e6) * t22799 * t22792 * t22796 ** 2 + + if Bindx == 3492: + t22800 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 13 * phi2)) * np.sqrt(0.16445e5) * ((1 - t22800) ** (0.21e2 / 0.2e1)) * ((1 + t22800) ** (0.5e1 / 0.2e1)) + + if Bindx == 3493: + t22802 = np.cos(phi) + t22807 = -1 + t22802 + t22803 = t22807 ** 2 + t22805 = t22807 * t22803 ** 2 + t22801 = 1 + t22802 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 - 3 * phi2)) * np.sqrt(0.2530e4) * t22805 ** 2 * t22801 ** 2 * (8 + 13 * t22802) + + if Bindx == 3494: + t22808 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 11 * phi2)) * np.sqrt(0.1265e4) * ((1 - t22808) ** (0.19e2 / 0.2e1)) * ((1 + t22808) ** (0.3e1 / 0.2e1)) * (23 + (80 + 65 * t22808) * t22808) + + if Bindx == 3495: + t22810 = np.cos(phi) + t22811 = t22810 ** 2 + t22813 = t22811 ** 2 + t22814 = t22810 * t22813 + t22819 = t22814 ** 2 + t22817 = t22813 ** 2 + t22812 = t22810 * t22811 + t22815 = t22812 ** 2 + t22809 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 - 5 * phi2)) * np.sqrt(0.2530e4) * t22809 ** 2 * (-96 * t22811 + 365 * t22812 - 184 * t22813 - 742 * t22814 + 1232 * t22815 - 820 * t22817 - 400 * t22819 + 12 + (-334 * t22815 + 929 * t22817 + 65 * t22819 - 27) * t22810) + + if Bindx == 3496: + t22821 = np.cos(phi) + t22822 = t22821 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 9 * phi2)) * np.sqrt(0.110e3) * ((1 - t22821) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t22821)) * (1104 * t22821 + 123 + (3680 * t22821 + 3174 + 1495 * t22822) * t22822) + + if Bindx == 3497: + t22825 = np.cos(phi) + t22826 = t22825 ** 2 + t22827 = t22825 * t22826 + t22830 = t22827 ** 2 + t22836 = t22830 ** 2 + t22828 = t22826 ** 2 + t22829 = t22825 * t22828 + t22834 = t22829 ** 2 + t22832 = t22828 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((8*1j) * (phi1 - phi2)) * (-11664 * t22826 - 19462 * t22827 + 86560 * t22828 - 9837 * t22829 - 214944 * t22830 + 158832 * t22832 + 60720 * t22834 - 80960 * t22836 + 432 + (185964 * t22830 - 289245 * t22832 + 113850 * t22834 + 16445 * t22836 + 3309) * t22825) + + if Bindx == 3498: + t22838 = np.cos(phi) + t22839 = t22838 ** 2 + t22841 = t22839 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 7 * phi2)) * np.sqrt(0.14e2) * ((1 - t22838) ** (0.15e2 / 0.2e1)) * np.sqrt((1 + t22838)) * (17710 * t22839 + 44275 * t22841 + 7 + (43010 * t22839 + 16445 * t22841 + 2585) * t22838) + + if Bindx == 3499: + t22844 = np.cos(phi) + t22845 = t22844 ** 2 + t22847 = t22845 ** 2 + t22848 = t22844 * t22847 + t22853 = t22848 ** 2 + t22851 = t22847 ** 2 + t22846 = t22844 * t22845 + t22849 = t22846 ** 2 + t22843 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.10e2) * t22843 ** 2 * (4960 * t22845 - 14823 * t22846 - 19944 * t22847 + 69714 * t22848 + 624 * t22849 + 69828 * t22851 - 60720 * t22853 - 124 + (-115830 * t22849 + 48829 * t22851 + 16445 * t22853 + 1041) * t22844) + + if Bindx == 3500: + t22855 = np.cos(phi) + t22856 = t22855 ** 2 + t22858 = t22856 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 5 * phi2)) * np.sqrt(0.95e2) * ((1 - t22855) ** (0.13e2 / 0.2e1)) * ((1 + t22855) ** (0.3e1 / 0.2e1)) * (2530 * t22856 + 31625 * t22858 - 107 + (18722 * t22856 + 16445 * t22858 - 671) * t22855) + + if Bindx == 3501: + t22871 = np.sin(phi) + t22869 = t22871 ** 2 + t22860 = np.cos(phi) + t22861 = t22860 ** 2 + t22863 = t22861 ** 2 + t22867 = t22863 ** 2 + t22862 = t22860 * t22861 + t22865 = t22862 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 - phi2)) * np.sqrt(0.190e3) * t22869 ** 2 * (1960 * t22861 + 9004 * t22862 - 16056 * t22863 + 46552 * t22865 - 40480 * t22867 - 40 + (-23826 * t22863 + 7084 * t22865 + 16445 * t22867 - 643) * t22860) + + if Bindx == 3502: + t22872 = np.cos(phi) + t22873 = t22872 ** 2 + t22875 = t22873 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 - 3 * phi2)) * np.sqrt(0.323e3) * ((1 - t22872) ** (0.11e2 / 0.2e1)) * ((1 + t22872) ** (0.5e1 / 0.2e1)) * (-2530 * t22873 + 18975 * t22875 + 19 + (2530 * t22873 + 16445 * t22875 - 495) * t22872) + + if Bindx == 3503: + t22887 = np.sin(phi) + t22884 = t22887 ** 2 + t22885 = t22887 * t22884 + t22877 = np.cos(phi) + t22878 = t22877 ** 2 + t22879 = t22877 * t22878 + t22882 = t22879 ** 2 + t22880 = t22878 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 - phi2)) * np.sqrt(0.3553e4) * t22885 ** 2 * (-216 * t22878 + 225 * t22879 + 1380 * t22880 - 1840 * t22882 + 4 + (-1035 * t22880 + 1495 * t22882 - 13) * t22877) + + if Bindx == 3504: + t22888 = np.cos(phi) + t22889 = t22888 ** 2 + t22891 = t22889 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (8 * phi1 - phi2)) * np.sqrt(0.17765e5) * ((1 - t22888) ** (0.9e1 / 0.2e1)) * ((1 + t22888) ** (0.7e1 / 0.2e1)) * (-138 * t22889 + 575 * t22891 + 3 + (-506 * t22889 + 1495 * t22891 + 27) * t22888) + + if Bindx == 3505: + t22899 = np.sin(phi) + t22896 = t22899 ** 2 + t22897 = t22896 ** 2 + t22893 = np.cos(phi) + t22894 = t22893 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((8*1j) * phi1) * np.sqrt(0.3233230e7) * t22897 ** 2 * t22893 * (3 + (-46 + 115 * t22894) * t22894) + + if Bindx == 3506: + t22900 = np.cos(phi) + t22901 = t22900 ** 2 + t22903 = t22901 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (8 * phi1 + phi2)) * np.sqrt(0.17765e5) * ((1 - t22900) ** (0.7e1 / 0.2e1)) * ((1 + t22900) ** (0.9e1 / 0.2e1)) * (138 * t22901 - 575 * t22903 - 3 + (-506 * t22901 + 1495 * t22903 + 27) * t22900) + + if Bindx == 3507: + t22915 = np.sin(phi) + t22912 = t22915 ** 2 + t22913 = t22915 * t22912 + t22905 = np.cos(phi) + t22906 = t22905 ** 2 + t22907 = t22905 * t22906 + t22910 = t22907 ** 2 + t22908 = t22906 ** 2 + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((2*1j) * (4 * phi1 + phi2)) * np.sqrt(0.3553e4) * t22913 ** 2 * (216 * t22906 + 225 * t22907 - 1380 * t22908 + 1840 * t22910 - 4 + (-1035 * t22908 + 1495 * t22910 - 13) * t22905) + + if Bindx == 3508: + t22916 = np.cos(phi) + t22917 = t22916 ** 2 + t22921 = 2530 * t22917 + t22919 = t22917 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 + 3 * phi2)) * np.sqrt(0.323e3) * ((1 - t22916) ** (0.5e1 / 0.2e1)) * ((1 + t22916) ** (0.11e2 / 0.2e1)) * (-18975 * t22919 + t22921 - 19 + (16445 * t22919 + t22921 - 495) * t22916) + + if Bindx == 3509: + t22933 = np.sin(phi) + t22931 = t22933 ** 2 + t22922 = np.cos(phi) + t22923 = t22922 ** 2 + t22925 = t22923 ** 2 + t22929 = t22925 ** 2 + t22924 = t22922 * t22923 + t22927 = t22924 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 + phi2)) * np.sqrt(0.190e3) * t22931 ** 2 * (-1960 * t22923 + 9004 * t22924 + 16056 * t22925 - 46552 * t22927 + 40480 * t22929 + 40 + (-23826 * t22925 + 7084 * t22927 + 16445 * t22929 - 643) * t22922) + + if Bindx == 3510: + t22934 = np.cos(phi) + t22935 = t22934 ** 2 + t22937 = t22935 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 + 5 * phi2)) * np.sqrt(0.95e2) * ((1 - t22934) ** (0.3e1 / 0.2e1)) * ((1 + t22934) ** (0.13e2 / 0.2e1)) * (-2530 * t22935 - 31625 * t22937 + 107 + (18722 * t22935 + 16445 * t22937 - 671) * t22934) + + if Bindx == 3511: + t22940 = np.cos(phi) + t22941 = t22940 ** 2 + t22943 = t22941 ** 2 + t22944 = t22940 * t22943 + t22949 = t22944 ** 2 + t22947 = t22943 ** 2 + t22942 = t22940 * t22941 + t22945 = t22942 ** 2 + t22939 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.10e2) * t22939 ** 2 * (-4960 * t22941 - 14823 * t22942 + 19944 * t22943 + 69714 * t22944 - 624 * t22945 - 69828 * t22947 + 60720 * t22949 + 124 + (-115830 * t22945 + 48829 * t22947 + 16445 * t22949 + 1041) * t22940) + + if Bindx == 3512: + t22951 = np.cos(phi) + t22952 = t22951 ** 2 + t22954 = t22952 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 + 7 * phi2)) * np.sqrt(0.14e2) * np.sqrt((1 - t22951)) * ((1 + t22951) ** (0.15e2 / 0.2e1)) * (-17710 * t22952 - 44275 * t22954 - 7 + (43010 * t22952 + 16445 * t22954 + 2585) * t22951) + + if Bindx == 3513: + t22956 = np.cos(phi) + t22957 = t22956 ** 2 + t22958 = t22956 * t22957 + t22961 = t22958 ** 2 + t22967 = t22961 ** 2 + t22959 = t22957 ** 2 + t22960 = t22956 * t22959 + t22965 = t22960 ** 2 + t22963 = t22959 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4) * np.exp((8*1j) * (phi1 + phi2)) * (11664 * t22957 - 19462 * t22958 - 86560 * t22959 - 9837 * t22960 + 214944 * t22961 - 158832 * t22963 - 60720 * t22965 + 80960 * t22967 - 432 + (185964 * t22961 - 289245 * t22963 + 113850 * t22965 + 16445 * t22967 + 3309) * t22956) + + if Bindx == 3514: + t22969 = np.cos(phi) + t22970 = t22969 ** 2 + t22972 = t22970 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 + 9 * phi2)) * np.sqrt(0.110e3) * ((1 + t22969) ** (0.17e2 / 0.2e1)) * (-4278 * t22970 - 5175 * t22972 - 123 + (6854 * t22970 + 1495 * t22972 + 1227) * t22969) * ((1 - t22969) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3515: + t22975 = np.cos(phi) + t22976 = t22975 ** 2 + t22978 = t22976 ** 2 + t22979 = t22975 * t22978 + t22984 = t22979 ** 2 + t22982 = t22978 ** 2 + t22977 = t22975 * t22976 + t22980 = t22977 ** 2 + t22974 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (4 * phi1 + 5 * phi2)) * np.sqrt(0.2530e4) * t22974 ** 2 * (96 * t22976 + 365 * t22977 + 184 * t22978 - 742 * t22979 - 1232 * t22980 + 820 * t22982 + 400 * t22984 - 12 + (-334 * t22980 + 929 * t22982 + 65 * t22984 - 27) * t22975) + + if Bindx == 3516: + t22986 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * (23 + (-80 + 65 * t22986) * t22986) * ((1 + t22986) ** (0.19e2 / 0.2e1)) * np.sqrt(0.1265e4) * np.exp((1j) * (8 * phi1 + 11 * phi2)) * ((1 - t22986) ** (0.3e1 / 0.2e1)) + + if Bindx == 3517: + t22988 = np.cos(phi) + t22993 = 1 + t22988 + t22989 = t22993 ** 2 + t22991 = t22993 * t22989 ** 2 + t22987 = -1 + t22988 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (2 * phi1 + 3 * phi2)) * np.sqrt(0.2530e4) * t22987 ** 2 * t22991 ** 2 * (-8 + 13 * t22988) + + if Bindx == 3518: + t22994 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (8 * phi1 + 13 * phi2)) * np.sqrt(0.16445e5) * ((1 - t22994) ** (0.5e1 / 0.2e1)) * ((1 + t22994) ** (0.21e2 / 0.2e1)) + + if Bindx == 3519: + t22996 = np.cos(phi) + t23002 = -1 + t22996 + t22997 = t23002 ** 2 + t22999 = t23002 * t22997 ** 2 + t22995 = 1 + t22996 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (9 * phi1 - 13 * phi2)) * np.sqrt(0.598e3) * t23002 * t22999 ** 2 * t22995 ** 2 + + if Bindx == 3520: + t23003 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((3*1j) * (3 * phi1 - 4 * phi2)) * np.sqrt(0.23e2) * ((1 - t23003) ** (0.21e2 / 0.2e1)) * ((1 + t23003) ** (0.3e1 / 0.2e1)) * (9 + 13 * t23003) + + if Bindx == 3521: + t23004 = np.cos(phi) + t23009 = -1 + t23004 + t23005 = t23009 ** 2 + t23007 = t23009 * t23005 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (9 * phi1 - 11 * phi2)) * np.sqrt(0.46e2) * t23007 ** 2 * (1 + t23004) * (149 + (450 + 325 * t23004) * t23004) + + if Bindx == 3522: + t23010 = np.cos(phi) + t23011 = t23010 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 - 10 * phi2)) * np.sqrt(0.23e2) * ((1 - t23010) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t23010)) * (675 * t23011 + 93 + (325 * t23011 + 447) * t23010) + + if Bindx == 3523: + t23013 = np.cos(phi) + t23014 = t23013 ** 2 + t23015 = t23013 * t23014 + t23018 = t23015 ** 2 + t23024 = t23018 ** 2 + t23016 = t23014 ** 2 + t23017 = t23013 * t23016 + t23022 = t23017 ** 2 + t23020 = t23016 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((9*1j) * (phi1 - phi2)) * (12270 * t23014 - 40590 * t23015 + 2695 * t23016 + 125829 * t23017 - 146124 * t23018 + 236115 * t23020 - 59202 * t23022 - 46575 * t23024 - 1227 + (-64020 * t23018 - 132495 * t23020 + 103362 * t23022 + 7475 * t23024 + 2487) * t23013) + + if Bindx == 3524: + t23026 = np.cos(phi) + t23027 = t23026 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (9 * phi1 - 8 * phi2)) * np.sqrt(0.110e3) * ((1 - t23026) ** (0.17e2 / 0.2e1)) * np.sqrt((1 + t23026)) * (1104 * t23026 + 123 + (3680 * t23026 + 3174 + 1495 * t23027) * t23027) + + if Bindx == 3525: + t23031 = np.cos(phi) + t23032 = t23031 ** 2 + t23034 = t23032 ** 2 + t23035 = t23031 * t23034 + t23040 = t23035 ** 2 + t23038 = t23034 ** 2 + t23033 = t23031 * t23032 + t23036 = t23033 ** 2 + t23030 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (9 * phi1 - 7 * phi2)) * np.sqrt(0.385e3) * t23030 ** 2 * (1175 * t23032 + 1323 * t23033 - 7326 * t23034 + 3402 * t23035 + 12390 * t23036 - 483 * t23038 - 7245 * t23040 - 47 + (-15570 * t23036 + 11201 * t23038 + 1495 * t23040 - 315) * t23031) + + if Bindx == 3526: + t23042 = np.cos(phi) + t23043 = t23042 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.11e2) * ((1 - t23042) ** (0.15e2 / 0.2e1)) * ((1 + t23042) ** (0.3e1 / 0.2e1)) * (1564 * t23042 + 27 + (13800 * t23042 + 8142 + 7475 * t23043) * t23043) + + if Bindx == 3527: + t23057 = np.sin(phi) + t23055 = t23057 ** 2 + t23046 = np.cos(phi) + t23047 = t23046 ** 2 + t23049 = t23047 ** 2 + t23053 = t23049 ** 2 + t23048 = t23046 * t23047 + t23051 = t23048 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (9 * phi1 - 5 * phi2)) * np.sqrt(0.418e3) * t23055 ** 2 * (-2196 * t23047 + 8820 * t23048 + 3870 * t23049 + 15180 * t23051 - 25875 * t23053 + 61 + (-28926 * t23049 + 22356 * t23051 + 7475 * t23053 - 765) * t23046) + + if Bindx == 3528: + t23058 = np.cos(phi) + t23059 = t23058 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (9 * phi1 - 4 * phi2)) * np.sqrt(0.209e3) * ((1 - t23058) ** (0.13e2 / 0.2e1)) * ((1 + t23058) ** (0.5e1 / 0.2e1)) * (-184 * t23058 - 73 + (9200 * t23058 + 2622 + 7475 * t23059) * t23059) + + if Bindx == 3529: + t23072 = np.sin(phi) + t23069 = t23072 ** 2 + t23070 = t23072 * t23069 + t23062 = np.cos(phi) + t23063 = t23062 ** 2 + t23064 = t23062 * t23063 + t23067 = t23064 ** 2 + t23065 = t23063 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((3*1j) * (3 * phi1 - phi2)) * np.sqrt(0.35530e5) * t23070 ** 2 * (-387 * t23063 - 699 * t23064 + 2139 * t23065 - 3105 * t23067 + 9 + (483 * t23065 + 1495 * t23067 + 65) * t23062) + + if Bindx == 3530: + t23073 = np.cos(phi) + t23074 = t23073 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 - 2 * phi2)) * np.sqrt(0.3230e4) * ((1 - t23073) ** (0.11e2 / 0.2e1)) * ((1 + t23073) ** (0.7e1 / 0.2e1)) * (-92 * t23073 - 1 + (920 * t23073 - 138 + 1495 * t23074) * t23074) + + if Bindx == 3531: + t23085 = np.sin(phi) + t23082 = t23085 ** 2 + t23083 = t23082 ** 2 + t23077 = np.cos(phi) + t23078 = t23077 ** 2 + t23080 = t23078 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (9 * phi1 - phi2)) * np.sqrt(0.646e3) * t23083 ** 2 * (1242 * t23078 - 5175 * t23080 - 27 + (-3818 * t23078 + 7475 * t23080 + 303) * t23077) + + if Bindx == 3532: + t23086 = np.cos(phi) + t23087 = t23086 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((9*1j) * phi1) * np.sqrt(0.29393e5) * ((1 - t23086) ** (0.9e1 / 0.2e1)) * ((1 + t23086) ** (0.9e1 / 0.2e1)) * (3 + (-138 + 575 * t23087) * t23087) + + if Bindx == 3533: + t23097 = np.sin(phi) + t23094 = t23097 ** 2 + t23095 = t23094 ** 2 + t23089 = np.cos(phi) + t23090 = t23089 ** 2 + t23092 = t23090 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((1j) * (9 * phi1 + phi2)) * np.sqrt(0.646e3) * t23095 ** 2 * (-1242 * t23090 + 5175 * t23092 + 27 + (-3818 * t23090 + 7475 * t23092 + 303) * t23089) + + if Bindx == 3534: + t23098 = np.cos(phi) + t23099 = t23098 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 + 2 * phi2)) * np.sqrt(0.3230e4) * ((1 - t23098) ** (0.7e1 / 0.2e1)) * ((1 + t23098) ** (0.11e2 / 0.2e1)) * (92 * t23098 - 1 + (-920 * t23098 - 138 + 1495 * t23099) * t23099) + + if Bindx == 3535: + t23112 = np.sin(phi) + t23109 = t23112 ** 2 + t23110 = t23112 * t23109 + t23102 = np.cos(phi) + t23103 = t23102 ** 2 + t23104 = t23102 * t23103 + t23107 = t23104 ** 2 + t23105 = t23103 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((3*1j) * (3 * phi1 + phi2)) * np.sqrt(0.35530e5) * t23110 ** 2 * (387 * t23103 - 699 * t23104 - 2139 * t23105 + 3105 * t23107 - 9 + (483 * t23105 + 1495 * t23107 + 65) * t23102) + + if Bindx == 3536: + t23113 = np.cos(phi) + t23114 = t23113 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (9 * phi1 + 4 * phi2)) * np.sqrt(0.209e3) * ((1 - t23113) ** (0.5e1 / 0.2e1)) * ((1 + t23113) ** (0.13e2 / 0.2e1)) * (184 * t23113 - 73 + (-9200 * t23113 + 2622 + 7475 * t23114) * t23114) + + if Bindx == 3537: + t23128 = np.sin(phi) + t23126 = t23128 ** 2 + t23117 = np.cos(phi) + t23118 = t23117 ** 2 + t23120 = t23118 ** 2 + t23124 = t23120 ** 2 + t23119 = t23117 * t23118 + t23122 = t23119 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (9 * phi1 + 5 * phi2)) * np.sqrt(0.418e3) * t23126 ** 2 * (2196 * t23118 + 8820 * t23119 - 3870 * t23120 - 15180 * t23122 + 25875 * t23124 - 61 + (-28926 * t23120 + 22356 * t23122 + 7475 * t23124 - 765) * t23117) + + if Bindx == 3538: + t23129 = np.cos(phi) + t23130 = t23129 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((3*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.11e2) * ((1 - t23129) ** (0.3e1 / 0.2e1)) * ((1 + t23129) ** (0.15e2 / 0.2e1)) * (-1564 * t23129 + 27 + (-13800 * t23129 + 8142 + 7475 * t23130) * t23130) + + if Bindx == 3539: + t23134 = np.cos(phi) + t23135 = t23134 ** 2 + t23137 = t23135 ** 2 + t23138 = t23134 * t23137 + t23143 = t23138 ** 2 + t23141 = t23137 ** 2 + t23136 = t23134 * t23135 + t23139 = t23136 ** 2 + t23133 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (9 * phi1 + 7 * phi2)) * np.sqrt(0.385e3) * t23133 ** 2 * (-1175 * t23135 + 1323 * t23136 + 7326 * t23137 + 3402 * t23138 - 12390 * t23139 + 483 * t23141 + 7245 * t23143 + 47 + (-15570 * t23139 + 11201 * t23141 + 1495 * t23143 - 315) * t23134) + + if Bindx == 3540: + t23145 = np.cos(phi) + t23146 = t23145 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (9 * phi1 + 8 * phi2)) * np.sqrt(0.110e3) * np.sqrt((1 - t23145)) * ((1 + t23145) ** (0.17e2 / 0.2e1)) * (-1104 * t23145 + 123 + (-3680 * t23145 + 3174 + 1495 * t23146) * t23146) + + if Bindx == 3541: + t23149 = np.cos(phi) + t23150 = t23149 ** 2 + t23151 = t23149 * t23150 + t23154 = t23151 ** 2 + t23160 = t23154 ** 2 + t23152 = t23150 ** 2 + t23153 = t23149 * t23152 + t23158 = t23153 ** 2 + t23156 = t23152 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((9*1j) * (phi1 + phi2)) * (-12270 * t23150 - 40590 * t23151 - 2695 * t23152 + 125829 * t23153 + 146124 * t23154 - 236115 * t23156 + 59202 * t23158 + 46575 * t23160 + 1227 + (-64020 * t23154 - 132495 * t23156 + 103362 * t23158 + 7475 * t23160 + 2487) * t23149) + + if Bindx == 3542: + t23162 = np.cos(phi) + t23163 = t23162 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (9 * phi1 + 10 * phi2)) * np.sqrt(0.23e2) * ((1 + t23162) ** (0.19e2 / 0.2e1)) * (-540 * t23162 + 93 + (-1000 * t23162 + 1122 + 325 * t23163) * t23163) * ((1 - t23162) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3543: + t23166 = np.cos(phi) + t23171 = 1 + t23166 + t23167 = t23171 ** 2 + t23169 = t23171 * t23167 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (9 * phi1 + 11 * phi2)) * np.sqrt(0.46e2) * (-1 + t23166) * t23169 ** 2 * (149 + (-450 + 325 * t23166) * t23166) + + if Bindx == 3544: + t23172 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * (-9 + 13 * t23172) * ((1 + t23172) ** (0.21e2 / 0.2e1)) * np.sqrt(0.23e2) * np.exp((3*1j) * (3 * phi1 + 4 * phi2)) * ((1 - t23172) ** (0.3e1 / 0.2e1)) + + if Bindx == 3545: + t23174 = np.cos(phi) + t23180 = 1 + t23174 + t23175 = t23180 ** 2 + t23177 = t23180 * t23175 ** 2 + t23173 = -1 + t23174 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (9 * phi1 + 13 * phi2)) * np.sqrt(0.598e3) * t23173 ** 2 * t23180 * t23177 ** 2 + + if Bindx == 3546: + t23181 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 - 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t23181) ** (0.23e2 / 0.2e1)) * ((1 + t23181) ** (0.3e1 / 0.2e1)) + + if Bindx == 3547: + t23182 = np.cos(phi) + t23188 = -1 + t23182 + t23183 = t23188 ** 2 + t23185 = t23188 * t23183 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 6 * phi2)) * t23188 * t23185 ** 2 * (1 + t23182) * (10 + 13 * t23182) + + if Bindx == 3548: + t23189 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 - 11 * phi2)) * np.sqrt(0.2e1) * ((1 - t23189) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t23189)) * (187 + (500 + 325 * t23189) * t23189) + + if Bindx == 3549: + t23190 = np.cos(phi) + t23191 = t23190 ** 2 + t23192 = t23190 * t23191 + t23195 = t23192 ** 2 + t23201 = t23195 ** 2 + t23193 = t23191 ** 2 + t23194 = t23190 * t23193 + t23199 = t23194 ** 2 + t23197 = t23193 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((10*1j) * (phi1 - phi2)) * (1215 * t23191 + 1870 * t23192 - 8470 * t23193 + 8415 * t23194 + 5478 * t23195 + 14355 * t23197 - 10725 * t23199 - 2500 * t23201 + 135 + (-19140 * t23195 + 2145 * t23197 + 7686 * t23199 + 325 * t23201 - 789) * t23190) + + if Bindx == 3550: + t23203 = np.cos(phi) + t23204 = t23203 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 9 * phi2)) * np.sqrt(0.23e2) * ((1 - t23203) ** (0.19e2 / 0.2e1)) * np.sqrt((1 + t23203)) * (675 * t23204 + 93 + (325 * t23204 + 447) * t23203) + + if Bindx == 3551: + t23207 = np.cos(phi) + t23208 = t23207 ** 2 + t23210 = t23208 ** 2 + t23211 = t23207 * t23210 + t23216 = t23211 ** 2 + t23214 = t23210 ** 2 + t23209 = t23207 * t23208 + t23212 = t23209 ** 2 + t23206 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 4 * phi2)) * np.sqrt(0.2530e4) * t23206 ** 2 * (-96 * t23208 + 365 * t23209 - 184 * t23210 - 742 * t23211 + 1232 * t23212 - 820 * t23214 - 400 * t23216 + 12 + (-334 * t23212 + 929 * t23214 + 65 * t23216 - 27) * t23207) + + if Bindx == 3552: + t23218 = np.cos(phi) + t23219 = t23218 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - 7 * phi2)) * np.sqrt(0.8855e4) * ((1 - t23218) ** (0.17e2 / 0.2e1)) * ((1 + t23218) ** (0.3e1 / 0.2e1)) * (105 * t23219 + 7 + (65 * t23219 + 51) * t23218) + + if Bindx == 3553: + t23232 = np.sin(phi) + t23230 = t23232 ** 2 + t23221 = np.cos(phi) + t23222 = t23221 ** 2 + t23224 = t23222 ** 2 + t23228 = t23224 ** 2 + t23223 = t23221 * t23222 + t23226 = t23223 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - 3 * phi2)) * np.sqrt(0.253e3) * t23230 ** 2 * (-357 * t23222 - 60 * t23223 + 1515 * t23224 - 795 * t23226 - 1500 * t23228 + 17 + (-1572 * t23224 + 2352 * t23226 + 325 * t23228 + 75) * t23221) + + if Bindx == 3554: + t23233 = np.cos(phi) + t23234 = t23233 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((5*1j) * (2 * phi1 - phi2)) * np.sqrt(0.9614e4) * ((1 - t23233) ** (0.15e2 / 0.2e1)) * ((1 + t23233) ** (0.5e1 / 0.2e1)) * (375 * t23234 + 5 + (325 * t23234 + 111) * t23233) + + if Bindx == 3555: + t23246 = np.sin(phi) + t23243 = t23246 ** 2 + t23244 = t23246 * t23243 + t23236 = np.cos(phi) + t23237 = t23236 ** 2 + t23238 = t23236 * t23237 + t23241 = t23238 ** 2 + t23239 = t23237 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 - 2 * phi2)) * np.sqrt(0.4807e4) * t23244 ** 2 * (60 * t23237 - 525 * t23238 + 270 * t23239 - 1000 * t23241 - 2 + (807 * t23239 + 325 * t23241 + 65) * t23236) + + if Bindx == 3556: + t23247 = np.cos(phi) + t23248 = t23247 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 - 3 * phi2)) * np.sqrt(0.817190e6) * ((1 - t23247) ** (0.13e2 / 0.2e1)) * ((1 + t23247) ** (0.7e1 / 0.2e1)) * (45 * t23248 - 1 + (65 * t23248 + 3) * t23247) + + if Bindx == 3557: + t23258 = np.sin(phi) + t23255 = t23258 ** 2 + t23256 = t23255 ** 2 + t23250 = np.cos(phi) + t23251 = t23250 ** 2 + t23253 = t23251 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 - phi2)) * np.sqrt(0.74290e5) * t23256 ** 2 * (35 * t23251 - 100 * t23253 - 1 + (2 * t23251 + 65 * t23253 - 1) * t23250) + + if Bindx == 3558: + t23259 = np.cos(phi) + t23260 = t23259 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 - phi2)) * np.sqrt(0.14858e5) * ((1 - t23259) ** (0.11e2 / 0.2e1)) * ((1 + t23259) ** (0.9e1 / 0.2e1)) * (75 * t23260 - 3 + (325 * t23260 - 33) * t23259) + + if Bindx == 3559: + t23267 = np.sin(phi) + t23263 = t23267 ** 2 + t23265 = t23267 * t23263 ** 2 + t23262 = np.cos(phi) + tfunc[..., c] = -(0.27e2 / 0.1024e4) * np.exp((10*1j) * phi1) * np.sqrt(0.676039e6) * t23265 ** 2 * t23262 * (25 * t23262 ** 2 - 3) + + if Bindx == 3560: + t23268 = np.cos(phi) + t23269 = t23268 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + phi2)) * np.sqrt(0.14858e5) * ((1 - t23268) ** (0.9e1 / 0.2e1)) * ((1 + t23268) ** (0.11e2 / 0.2e1)) * (-75 * t23269 + 3 + (325 * t23269 - 33) * t23268) + + if Bindx == 3561: + t23279 = np.sin(phi) + t23276 = t23279 ** 2 + t23277 = t23276 ** 2 + t23271 = np.cos(phi) + t23272 = t23271 ** 2 + t23274 = t23272 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + phi2)) * np.sqrt(0.74290e5) * t23277 ** 2 * (-35 * t23272 + 100 * t23274 + 1 + (2 * t23272 + 65 * t23274 - 1) * t23271) + + if Bindx == 3562: + t23280 = np.cos(phi) + t23281 = t23280 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 + 3 * phi2)) * np.sqrt(0.817190e6) * ((1 - t23280) ** (0.7e1 / 0.2e1)) * ((1 + t23280) ** (0.13e2 / 0.2e1)) * (-45 * t23281 + 1 + (65 * t23281 + 3) * t23280) + + if Bindx == 3563: + t23293 = np.sin(phi) + t23290 = t23293 ** 2 + t23291 = t23293 * t23290 + t23283 = np.cos(phi) + t23284 = t23283 ** 2 + t23285 = t23283 * t23284 + t23288 = t23285 ** 2 + t23286 = t23284 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 2 * phi2)) * np.sqrt(0.4807e4) * t23291 ** 2 * (-60 * t23284 - 525 * t23285 - 270 * t23286 + 1000 * t23288 + 2 + (807 * t23286 + 325 * t23288 + 65) * t23283) + + if Bindx == 3564: + t23294 = np.cos(phi) + t23295 = t23294 ** 2 + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((5*1j) * (2 * phi1 + phi2)) * np.sqrt(0.9614e4) * ((1 - t23294) ** (0.5e1 / 0.2e1)) * ((1 + t23294) ** (0.15e2 / 0.2e1)) * (-375 * t23295 - 5 + (325 * t23295 + 111) * t23294) + + if Bindx == 3565: + t23308 = np.sin(phi) + t23306 = t23308 ** 2 + t23297 = np.cos(phi) + t23298 = t23297 ** 2 + t23300 = t23298 ** 2 + t23304 = t23300 ** 2 + t23299 = t23297 * t23298 + t23302 = t23299 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((2*1j) * (5 * phi1 + 3 * phi2)) * np.sqrt(0.253e3) * t23306 ** 2 * (357 * t23298 - 60 * t23299 - 1515 * t23300 + 795 * t23302 + 1500 * t23304 - 17 + (-1572 * t23300 + 2352 * t23302 + 325 * t23304 + 75) * t23297) + + if Bindx == 3566: + t23309 = np.cos(phi) + t23310 = t23309 ** 2 + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 7 * phi2)) * np.sqrt(0.8855e4) * ((1 - t23309) ** (0.3e1 / 0.2e1)) * ((1 + t23309) ** (0.17e2 / 0.2e1)) * (-105 * t23310 - 7 + (65 * t23310 + 51) * t23309) + + if Bindx == 3567: + t23313 = np.cos(phi) + t23314 = t23313 ** 2 + t23316 = t23314 ** 2 + t23317 = t23313 * t23316 + t23322 = t23317 ** 2 + t23320 = t23316 ** 2 + t23315 = t23313 * t23314 + t23318 = t23315 ** 2 + t23312 = np.sin(phi) + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 4 * phi2)) * np.sqrt(0.2530e4) * t23312 ** 2 * (96 * t23314 + 365 * t23315 + 184 * t23316 - 742 * t23317 - 1232 * t23318 + 820 * t23320 + 400 * t23322 - 12 + (-334 * t23318 + 929 * t23320 + 65 * t23322 - 27) * t23313) + + if Bindx == 3568: + t23324 = np.cos(phi) + t23325 = t23324 ** 2 + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (10 * phi1 + 9 * phi2)) * np.sqrt(0.23e2) * np.sqrt((1 - t23324)) * ((1 + t23324) ** (0.19e2 / 0.2e1)) * (-675 * t23325 - 93 + (325 * t23325 + 447) * t23324) + + if Bindx == 3569: + t23327 = np.cos(phi) + t23328 = t23327 ** 2 + t23329 = t23327 * t23328 + t23332 = t23329 ** 2 + t23338 = t23332 ** 2 + t23330 = t23328 ** 2 + t23331 = t23327 * t23330 + t23336 = t23331 ** 2 + t23334 = t23330 ** 2 + tfunc[..., c] = (0.27e2 / 0.1024e4) * np.exp((10*1j) * (phi1 + phi2)) * (-1215 * t23328 + 1870 * t23329 + 8470 * t23330 + 8415 * t23331 - 5478 * t23332 - 14355 * t23334 + 10725 * t23336 + 2500 * t23338 - 135 + (-19140 * t23332 + 2145 * t23334 + 7686 * t23336 + 325 * t23338 - 789) * t23327) + + if Bindx == 3570: + t23340 = np.cos(phi) + t23341 = t23340 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 + 11 * phi2)) * np.sqrt(0.2e1) * ((1 + t23340) ** (0.21e2 / 0.2e1)) * (-825 * t23341 - 187 + (325 * t23341 + 687) * t23340) * ((1 - t23340) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3571: + t23343 = np.cos(phi) + t23349 = 1 + t23343 + t23344 = t23349 ** 2 + t23346 = t23349 * t23344 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((2*1j) * (5 * phi1 + 6 * phi2)) * (-1 + t23343) * t23349 * t23346 ** 2 * (-10 + 13 * t23343) + + if Bindx == 3572: + t23350 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (10 * phi1 + 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t23350) ** (0.3e1 / 0.2e1)) * ((1 + t23350) ** (0.23e2 / 0.2e1)) + + if Bindx == 3573: + t23351 = np.cos(phi) + t23356 = -1 + t23351 + t23352 = t23356 ** 2 + t23353 = t23356 * t23352 + t23354 = t23353 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (11 * phi1 - 13 * phi2)) * np.sqrt(0.13e2) * t23354 ** 2 * (1 + t23351) + + if Bindx == 3574: + t23357 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((1j) * (11 * phi1 - 12 * phi2)) * np.sqrt(0.2e1) * ((1 - t23357) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t23357)) * (11 + 13 * t23357) + + if Bindx == 3575: + t23358 = np.cos(phi) + t23359 = t23358 ** 2 + t23360 = t23358 * t23359 + t23363 = t23360 ** 2 + t23369 = t23363 ** 2 + t23361 = t23359 ** 2 + t23362 = t23358 * t23361 + t23367 = t23362 ** 2 + t23365 = t23361 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((11*1j) * (phi1 - phi2)) * (-6870 * t23359 + 11110 * t23360 - 2695 * t23361 - 22077 * t23362 + 41052 * t23363 - 6435 * t23365 - 25894 * t23367 - 3025 * t23369 - 229 + (-28380 * t23363 + 29095 * t23365 + 12054 * t23367 + 325 * t23369 + 1969) * t23358) + + if Bindx == 3576: + t23371 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 - 10 * phi2)) * np.sqrt(0.2e1) * ((1 - t23371) ** (0.21e2 / 0.2e1)) * np.sqrt((1 + t23371)) * (187 + (500 + 325 * t23371) * t23371) + + if Bindx == 3577: + t23372 = np.cos(phi) + t23377 = -1 + t23372 + t23373 = t23377 ** 2 + t23375 = t23377 * t23373 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * (149 + (450 + 325 * t23372) * t23372) * (1 + t23372) * t23375 ** 2 * np.sqrt(0.46e2) * np.exp((1j) * (11 * phi1 - 9 * phi2)) + + if Bindx == 3578: + t23378 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 - 8 * phi2)) * np.sqrt(0.1265e4) * ((1 - t23378) ** (0.19e2 / 0.2e1)) * ((1 + t23378) ** (0.3e1 / 0.2e1)) * (23 + (80 + 65 * t23378) * t23378) + + if Bindx == 3579: + t23390 = np.sin(phi) + t23388 = t23390 ** 2 + t23379 = np.cos(phi) + t23380 = t23379 ** 2 + t23382 = t23380 ** 2 + t23386 = t23382 ** 2 + t23381 = t23379 * t23380 + t23384 = t23381 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 - 7 * phi2)) * np.sqrt(0.17710e5) * t23388 ** 2 * (68 * t23380 - 420 * t23381 + 490 * t23382 - 924 * t23384 - 385 * t23386 - 17 + (182 * t23382 + 892 * t23384 + 65 * t23386 + 49) * t23379) + + if Bindx == 3580: + t23391 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 - 6 * phi2)) * np.sqrt(0.506e3) * ((1 - t23391) ** (0.17e2 / 0.2e1)) * ((1 + t23391) ** (0.5e1 / 0.2e1)) * (59 + (300 + 325 * t23391) * t23391) + + if Bindx == 3581: + t23402 = np.sin(phi) + t23399 = t23402 ** 2 + t23400 = t23402 * t23399 + t23392 = np.cos(phi) + t23393 = t23392 ** 2 + t23394 = t23392 * t23393 + t23397 = t23394 ** 2 + t23395 = t23393 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 - 5 * phi2)) * np.sqrt(0.4807e4) * t23400 ** 2 * (555 * t23393 - 505 * t23394 - 935 * t23395 - 1375 * t23397 - 37 + (2037 * t23395 + 325 * t23397 - 65) * t23392) + + if Bindx == 3582: + t23403 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (11 * phi1 - 4 * phi2)) * np.sqrt(0.9614e4) * ((1 - t23403) ** (0.15e2 / 0.2e1)) * ((1 + t23403) ** (0.7e1 / 0.2e1)) * (19 + (200 + 325 * t23403) * t23403) + + if Bindx == 3583: + t23412 = np.sin(phi) + t23409 = t23412 ** 2 + t23410 = t23409 ** 2 + t23404 = np.cos(phi) + t23405 = t23404 ** 2 + t23407 = t23405 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 - 3 * phi2)) * np.sqrt(0.408595e6) * t23410 ** 2 * (22 * t23405 - 165 * t23407 - 1 + (106 * t23405 + 65 * t23407 - 27) * t23404) + + if Bindx == 3584: + t23413 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 - 2 * phi2)) * np.sqrt(0.37145e5) * ((1 - t23413) ** (0.13e2 / 0.2e1)) * ((1 + t23413) ** (0.9e1 / 0.2e1)) * (-1 + (20 + 65 * t23413) * t23413) + + if Bindx == 3585: + t23421 = np.sin(phi) + t23417 = t23421 ** 2 + t23419 = t23421 * t23417 ** 2 + t23414 = np.cos(phi) + t23415 = t23414 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (11 * phi1 - phi2)) * np.sqrt(0.7429e4) * t23419 ** 2 * (-275 * t23415 + 11 + (325 * t23415 - 61) * t23414) + + if Bindx == 3586: + t23422 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((11*1j) * phi1) * np.sqrt(0.1352078e7) * ((1 - t23422) ** (0.11e2 / 0.2e1)) * ((1 + t23422) ** (0.11e2 / 0.2e1)) * (25 * t23422 ** 2 - 1) + + if Bindx == 3587: + t23430 = np.sin(phi) + t23426 = t23430 ** 2 + t23428 = t23430 * t23426 ** 2 + t23423 = np.cos(phi) + t23424 = t23423 ** 2 + tfunc[..., c] = -(0.27e2 / 0.4096e4) * np.exp((1j) * (11 * phi1 + phi2)) * np.sqrt(0.7429e4) * t23428 ** 2 * (275 * t23424 - 11 + (325 * t23424 - 61) * t23423) + + if Bindx == 3588: + t23431 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.2048e4*1j) * np.exp((1j) * (11 * phi1 + 2 * phi2)) * np.sqrt(0.37145e5) * ((1 - t23431) ** (0.9e1 / 0.2e1)) * ((1 + t23431) ** (0.13e2 / 0.2e1)) * (-1 + (-20 + 65 * t23431) * t23431) + + if Bindx == 3589: + t23440 = np.sin(phi) + t23437 = t23440 ** 2 + t23438 = t23437 ** 2 + t23432 = np.cos(phi) + t23433 = t23432 ** 2 + t23435 = t23433 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 + 3 * phi2)) * np.sqrt(0.408595e6) * t23438 ** 2 * (-22 * t23433 + 165 * t23435 + 1 + (106 * t23433 + 65 * t23435 - 27) * t23432) + + if Bindx == 3590: + t23441 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((1j) * (11 * phi1 + 4 * phi2)) * np.sqrt(0.9614e4) * ((1 - t23441) ** (0.7e1 / 0.2e1)) * ((1 + t23441) ** (0.15e2 / 0.2e1)) * (19 + (-200 + 325 * t23441) * t23441) + + if Bindx == 3591: + t23452 = np.sin(phi) + t23449 = t23452 ** 2 + t23450 = t23452 * t23449 + t23442 = np.cos(phi) + t23443 = t23442 ** 2 + t23444 = t23442 * t23443 + t23447 = t23444 ** 2 + t23445 = t23443 ** 2 + tfunc[..., c] = -(0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 + 5 * phi2)) * np.sqrt(0.4807e4) * t23450 ** 2 * (-555 * t23443 - 505 * t23444 + 935 * t23445 + 1375 * t23447 + 37 + (2037 * t23445 + 325 * t23447 - 65) * t23442) + + if Bindx == 3592: + t23453 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 + 6 * phi2)) * np.sqrt(0.506e3) * ((1 - t23453) ** (0.5e1 / 0.2e1)) * ((1 + t23453) ** (0.17e2 / 0.2e1)) * (59 + (-300 + 325 * t23453) * t23453) + + if Bindx == 3593: + t23455 = np.cos(phi) + t23460 = 1 + t23455 + t23456 = t23460 ** 2 + t23457 = t23456 ** 2 + t23454 = -1 + t23455 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 + 7 * phi2)) * np.sqrt(0.17710e5) * t23454 ** 2 * t23460 * t23457 ** 2 * (17 + (-70 + 65 * t23455) * t23455) + + if Bindx == 3594: + t23461 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 + 8 * phi2)) * np.sqrt(0.1265e4) * ((1 - t23461) ** (0.3e1 / 0.2e1)) * ((1 + t23461) ** (0.19e2 / 0.2e1)) * (23 + (-80 + 65 * t23461) * t23461) + + if Bindx == 3595: + t23462 = np.cos(phi) + t23467 = 1 + t23462 + t23463 = t23467 ** 2 + t23465 = t23467 * t23463 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (11 * phi1 + 9 * phi2)) * np.sqrt(0.46e2) * (-1 + t23462) * t23465 ** 2 * (149 + (-450 + 325 * t23462) * t23462) + + if Bindx == 3596: + t23468 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (11 * phi1 + 10 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t23468)) * ((1 + t23468) ** (0.21e2 / 0.2e1)) * (187 + (-500 + 325 * t23468) * t23468) + + if Bindx == 3597: + t23469 = np.cos(phi) + t23470 = t23469 ** 2 + t23471 = t23469 * t23470 + t23474 = t23471 ** 2 + t23480 = t23474 ** 2 + t23472 = t23470 ** 2 + t23473 = t23469 * t23472 + t23478 = t23473 ** 2 + t23476 = t23472 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((11*1j) * (phi1 + phi2)) * (6870 * t23470 + 11110 * t23471 + 2695 * t23472 - 22077 * t23473 - 41052 * t23474 + 6435 * t23476 + 25894 * t23478 + 3025 * t23480 + 229 + (-28380 * t23474 + 29095 * t23476 + 12054 * t23478 + 325 * t23480 + 1969) * t23469) + + if Bindx == 3598: + t23482 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((1j) * (11 * phi1 + 12 * phi2)) * np.sqrt(0.2e1) * ((1 + t23482) ** (0.23e2 / 0.2e1)) * (11 + (-24 + 13 * t23482) * t23482) * ((1 - t23482) ** (-0.1e1 / 0.2e1)) + + if Bindx == 3599: + t23483 = np.cos(phi) + t23488 = 1 + t23483 + t23484 = t23488 ** 2 + t23485 = t23488 * t23484 + t23486 = t23485 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (11 * phi1 + 13 * phi2)) * np.sqrt(0.13e2) * (-1 + t23483) * t23486 ** 2 + + if Bindx == 3600: + t23489 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 - 13 * phi2)) * np.sqrt(0.26e2) * ((1 - t23489) ** (0.25e2 / 0.2e1)) * np.sqrt((1 + t23489)) + + if Bindx == 3601: + t23490 = np.cos(phi) + t23491 = t23490 ** 2 + t23492 = t23490 * t23491 + t23495 = t23492 ** 2 + t23501 = t23495 ** 2 + t23493 = t23491 ** 2 + t23494 = t23490 * t23493 + t23499 = t23494 ** 2 + t23497 = t23493 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((12*1j) * (phi1 - phi2)) * (636 * t23491 - 1782 * t23492 + 3080 * t23493 - 3069 * t23494 + 792 * t23495 - 4356 * t23497 - 2068 * t23499 - 144 * t23501 + 12 + (2508 * t23495 + 3795 * t23497 + 714 * t23499 + 13 * t23501 - 131) * t23490) + + if Bindx == 3602: + t23503 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 - 11 * phi2)) * np.sqrt(0.2e1) * ((1 - t23503) ** (0.23e2 / 0.2e1)) * np.sqrt((1 + t23503)) * (11 + 13 * t23503) + + if Bindx == 3603: + t23504 = np.cos(phi) + t23510 = -1 + t23504 + t23505 = t23510 ** 2 + t23507 = t23510 * t23505 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * (10 + 13 * t23504) * (1 + t23504) * t23510 * t23507 ** 2 * np.exp((2*1j) * (6 * phi1 - 5 * phi2)) + + if Bindx == 3604: + t23511 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((3*1j) * (4 * phi1 - 3 * phi2)) * np.sqrt(0.23e2) * ((1 - t23511) ** (0.21e2 / 0.2e1)) * ((1 + t23511) ** (0.3e1 / 0.2e1)) * (9 + 13 * t23511) + + if Bindx == 3605: + t23513 = np.cos(phi) + t23518 = -1 + t23513 + t23514 = t23518 ** 2 + t23516 = t23518 * t23514 ** 2 + t23512 = 1 + t23513 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 - 2 * phi2)) * np.sqrt(0.2530e4) * t23516 ** 2 * t23512 ** 2 * (8 + 13 * t23513) + + if Bindx == 3606: + t23519 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.4096e4*1j) * np.exp((1j) * (12 * phi1 - 7 * phi2)) * np.sqrt(0.8855e4) * ((1 - t23519) ** (0.19e2 / 0.2e1)) * ((1 + t23519) ** (0.5e1 / 0.2e1)) * (7 + 13 * t23519) + + if Bindx == 3607: + t23530 = np.sin(phi) + t23527 = t23530 ** 2 + t23528 = t23530 * t23527 + t23520 = np.cos(phi) + t23521 = t23520 ** 2 + t23522 = t23520 * t23521 + t23525 = t23522 ** 2 + t23523 = t23521 ** 2 + tfunc[..., c] = -(0.135e3 / 0.2048e4) * np.exp((6*1j) * (2 * phi1 - phi2)) * np.sqrt(0.253e3) * t23528 ** 2 * (12 * t23521 + 75 * t23522 - 170 * t23523 - 72 * t23525 + 6 + (159 * t23523 + 13 * t23525 - 23) * t23520) + + if Bindx == 3608: + t23531 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 - 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t23531) ** (0.17e2 / 0.2e1)) * ((1 + t23531) ** (0.7e1 / 0.2e1)) * (5 + 13 * t23531) + + if Bindx == 3609: + t23540 = np.sin(phi) + t23537 = t23540 ** 2 + t23538 = t23537 ** 2 + t23532 = np.cos(phi) + t23533 = t23532 ** 2 + t23535 = t23533 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 - phi2)) * np.sqrt(0.4807e4) * t23538 ** 2 * (-28 * t23533 - 48 * t23535 + 4 + (62 * t23533 + 13 * t23535 - 3) * t23532) + + if Bindx == 3610: + t23541 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.8192e4*1j) * np.exp((3*1j) * (4 * phi1 - phi2)) * np.sqrt(0.817190e6) * ((1 - t23541) ** (0.15e2 / 0.2e1)) * ((1 + t23541) ** (0.9e1 / 0.2e1)) * (3 + 13 * t23541) + + if Bindx == 3611: + t23549 = np.sin(phi) + t23545 = t23549 ** 2 + t23547 = t23549 * t23545 ** 2 + t23542 = np.cos(phi) + t23543 = t23542 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 - phi2)) * np.sqrt(0.74290e5) * t23547 ** 2 * (-24 * t23543 + 2 + (13 * t23543 + 9) * t23542) + + if Bindx == 3612: + t23550 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((1j) * (12 * phi1 - phi2)) * np.sqrt(0.14858e5) * ((1 - t23550) ** (0.13e2 / 0.2e1)) * ((1 + t23550) ** (0.11e2 / 0.2e1)) * (1 + 13 * t23550) + + if Bindx == 3613: + t23555 = np.sin(phi) + t23551 = t23555 ** 2 + t23552 = t23555 * t23551 + t23553 = t23552 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((12*1j) * phi1) * np.sqrt(0.676039e6) * t23553 ** 2 * np.cos(phi) + + if Bindx == 3614: + t23556 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (12 * phi1 + phi2)) * np.sqrt(0.14858e5) * ((1 - t23556) ** (0.11e2 / 0.2e1)) * ((1 + t23556) ** (0.13e2 / 0.2e1)) * (-1 + 13 * t23556) + + if Bindx == 3615: + t23564 = np.sin(phi) + t23560 = t23564 ** 2 + t23562 = t23564 * t23560 ** 2 + t23557 = np.cos(phi) + t23558 = t23557 ** 2 + tfunc[..., c] = -(0.27e2 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 + phi2)) * np.sqrt(0.74290e5) * t23562 ** 2 * (24 * t23558 - 2 + (13 * t23558 + 9) * t23557) + + if Bindx == 3616: + t23565 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((3*1j) * (4 * phi1 + phi2)) * np.sqrt(0.817190e6) * ((1 - t23565) ** (0.9e1 / 0.2e1)) * ((1 + t23565) ** (0.15e2 / 0.2e1)) * (-3 + 13 * t23565) + + if Bindx == 3617: + t23574 = np.sin(phi) + t23571 = t23574 ** 2 + t23572 = t23571 ** 2 + t23566 = np.cos(phi) + t23567 = t23566 ** 2 + t23569 = t23567 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 + phi2)) * np.sqrt(0.4807e4) * t23572 ** 2 * (28 * t23567 + 48 * t23569 - 4 + (62 * t23567 + 13 * t23569 - 3) * t23566) + + if Bindx == 3618: + t23575 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 + 5 * phi2)) * np.sqrt(0.9614e4) * ((1 - t23575) ** (0.7e1 / 0.2e1)) * ((1 + t23575) ** (0.17e2 / 0.2e1)) * (-5 + 13 * t23575) + + if Bindx == 3619: + t23576 = np.cos(phi) + t23584 = -1 + t23576 + t23583 = 1 + t23576 + t23579 = t23583 ** 2 + t23580 = t23579 ** 2 + t23577 = t23584 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((6*1j) * (2 * phi1 + phi2)) * np.sqrt(0.253e3) * t23584 * t23577 * t23583 * t23580 ** 2 * (-6 + 13 * t23576) + + if Bindx == 3620: + t23585 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (12 * phi1 + 7 * phi2)) * np.sqrt(0.8855e4) * ((1 - t23585) ** (0.5e1 / 0.2e1)) * ((1 + t23585) ** (0.19e2 / 0.2e1)) * (-7 + 13 * t23585) + + if Bindx == 3621: + t23587 = np.cos(phi) + t23592 = 1 + t23587 + t23588 = t23592 ** 2 + t23590 = t23592 * t23588 ** 2 + t23586 = -1 + t23587 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((4*1j) * (3 * phi1 + 2 * phi2)) * np.sqrt(0.2530e4) * t23586 ** 2 * t23590 ** 2 * (-8 + 13 * t23587) + + if Bindx == 3622: + t23593 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((3*1j) * (4 * phi1 + 3 * phi2)) * np.sqrt(0.23e2) * ((1 - t23593) ** (0.3e1 / 0.2e1)) * ((1 + t23593) ** (0.21e2 / 0.2e1)) * (-9 + 13 * t23593) + + if Bindx == 3623: + t23594 = np.cos(phi) + t23600 = 1 + t23594 + t23595 = t23600 ** 2 + t23597 = t23600 * t23595 ** 2 + tfunc[..., c] = (0.135e3 / 0.2048e4) * np.exp((2*1j) * (6 * phi1 + 5 * phi2)) * (-1 + t23594) * t23600 * t23597 ** 2 * (-10 + 13 * t23594) + + if Bindx == 3624: + t23601 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 + 11 * phi2)) * np.sqrt(0.2e1) * np.sqrt((1 - t23601)) * ((1 + t23601) ** (0.23e2 / 0.2e1)) * (-11 + 13 * t23601) + + if Bindx == 3625: + t23602 = np.cos(phi) + t23603 = t23602 ** 2 + t23604 = t23602 * t23603 + t23607 = t23604 ** 2 + t23613 = t23607 ** 2 + t23605 = t23603 ** 2 + t23606 = t23602 * t23605 + t23611 = t23606 ** 2 + t23609 = t23605 ** 2 + tfunc[..., c] = (0.27e2 / 0.4096e4) * np.exp((12*1j) * (phi1 + phi2)) * (-636 * t23603 - 1782 * t23604 - 3080 * t23605 - 3069 * t23606 - 792 * t23607 + 4356 * t23609 + 2068 * t23611 + 144 * t23613 - 12 + (2508 * t23607 + 3795 * t23609 + 714 * t23611 + 13 * t23613 - 131) * t23602) + + if Bindx == 3626: + t23615 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (12 * phi1 + 13 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t23615)) * ((1 + t23615) ** (0.25e2 / 0.2e1)) + + if Bindx == 3627: + t23616 = np.cos(phi) + t23617 = t23616 ** 2 + t23618 = t23616 * t23617 + t23621 = t23618 ** 2 + t23627 = t23621 ** 2 + t23619 = t23617 ** 2 + t23620 = t23616 * t23619 + t23625 = t23620 ** 2 + t23623 = t23619 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((13*1j) * (phi1 - phi2)) * (-78 * t23617 + 286 * t23618 - 715 * t23619 + 1287 * t23620 - 1716 * t23621 - 1287 * t23623 - 286 * t23625 - 13 * t23627 - 1 + (1716 * t23621 + 715 * t23623 + 78 * t23625 + t23627 + 13) * t23616) + + if Bindx == 3628: + t23629 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (13 * phi1 - 12 * phi2)) * np.sqrt(0.26e2) * ((1 - t23629) ** (0.25e2 / 0.2e1)) * np.sqrt((1 + t23629)) + + if Bindx == 3629: + t23630 = np.cos(phi) + t23635 = -1 + t23630 + t23631 = t23635 ** 2 + t23632 = t23635 * t23631 + t23633 = t23632 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 - 11 * phi2)) * np.sqrt(0.13e2) * t23633 ** 2 * (1 + t23630) + + if Bindx == 3630: + t23636 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 - 10 * phi2)) * np.sqrt(0.26e2) * ((1 - t23636) ** (0.23e2 / 0.2e1)) * ((1 + t23636) ** (0.3e1 / 0.2e1)) + + if Bindx == 3631: + t23638 = np.cos(phi) + t23644 = -1 + t23638 + t23639 = t23644 ** 2 + t23641 = t23644 * t23639 ** 2 + t23637 = 1 + t23638 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 - 9 * phi2)) * np.sqrt(0.598e3) * t23644 * t23641 ** 2 * t23637 ** 2 + + if Bindx == 3632: + t23645 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 - 8 * phi2)) * np.sqrt(0.16445e5) * ((1 - t23645) ** (0.21e2 / 0.2e1)) * ((1 + t23645) ** (0.5e1 / 0.2e1)) + + if Bindx == 3633: + t23646 = np.cos(phi) + t23654 = -1 + t23646 + t23653 = 1 + t23646 + t23651 = t23653 ** 2 + t23647 = t23654 ** 2 + t23649 = t23654 * t23647 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (13 * phi1 - 7 * phi2)) * np.sqrt(0.230230e6) * t23649 ** 2 * t23653 * t23651 + + if Bindx == 3634: + t23655 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 - 6 * phi2)) * np.sqrt(0.6578e4) * ((1 - t23655) ** (0.19e2 / 0.2e1)) * ((1 + t23655) ** (0.7e1 / 0.2e1)) + + if Bindx == 3635: + t23656 = np.cos(phi) + t23664 = -1 + t23656 + t23663 = 1 + t23656 + t23661 = t23663 ** 2 + t23657 = t23664 ** 2 + t23658 = t23657 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 - 5 * phi2)) * np.sqrt(0.62491e5) * t23664 * t23658 ** 2 * t23661 ** 2 + + if Bindx == 3636: + t23665 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (13 * phi1 - 4 * phi2)) * np.sqrt(0.124982e6) * ((1 - t23665) ** (0.17e2 / 0.2e1)) * ((1 + t23665) ** (0.9e1 / 0.2e1)) + + if Bindx == 3637: + t23666 = np.cos(phi) + t23674 = -1 + t23666 + t23673 = 1 + t23666 + t23670 = t23673 ** 2 + t23667 = t23674 ** 2 + t23668 = t23667 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (13 * phi1 - 3 * phi2)) * np.sqrt(0.5311735e7) * t23668 ** 2 * t23673 * t23670 ** 2 + + if Bindx == 3638: + t23675 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (13 * phi1 - 2 * phi2)) * np.sqrt(0.482885e6) * ((1 - t23675) ** (0.15e2 / 0.2e1)) * ((1 + t23675) ** (0.11e2 / 0.2e1)) + + if Bindx == 3639: + t23680 = np.sin(phi) + t23676 = t23680 ** 2 + t23677 = t23680 * t23676 + t23678 = t23677 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((1j) * (13 * phi1 - phi2)) * np.sqrt(0.96577e5) * t23678 ** 2 * (-0.1e1 + np.cos(phi)) + + if Bindx == 3640: + t23681 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.4096e4*1j) * np.exp((13*1j) * phi1) * np.sqrt(0.104006e6) * ((1 - t23681) ** (0.13e2 / 0.2e1)) * ((1 + t23681) ** (0.13e2 / 0.2e1)) + + if Bindx == 3641: + t23686 = np.sin(phi) + t23682 = t23686 ** 2 + t23683 = t23686 * t23682 + t23684 = t23683 ** 2 + tfunc[..., c] = (0.135e3 / 0.4096e4) * np.exp((1j) * (13 * phi1 + phi2)) * np.sqrt(0.96577e5) * t23684 ** 2 * (0.1e1 + np.cos(phi)) + + if Bindx == 3642: + t23687 = np.cos(phi) + tfunc[..., c] = (0.27e2 / 0.2048e4*1j) * np.exp((1j) * (13 * phi1 + 2 * phi2)) * np.sqrt(0.482885e6) * ((1 - t23687) ** (0.11e2 / 0.2e1)) * ((1 + t23687) ** (0.15e2 / 0.2e1)) + + if Bindx == 3643: + t23688 = np.cos(phi) + t23696 = -1 + t23688 + t23695 = 1 + t23688 + t23692 = t23695 ** 2 + t23693 = t23692 ** 2 + t23689 = t23696 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (13 * phi1 + 3 * phi2)) * np.sqrt(0.5311735e7) * t23696 * t23689 ** 2 * t23693 ** 2 + + if Bindx == 3644: + t23697 = np.cos(phi) + tfunc[..., c] = (-0.135e3 / 0.8192e4*1j) * np.exp((1j) * (13 * phi1 + 4 * phi2)) * np.sqrt(0.124982e6) * ((1 - t23697) ** (0.9e1 / 0.2e1)) * ((1 + t23697) ** (0.17e2 / 0.2e1)) + + if Bindx == 3645: + t23698 = np.cos(phi) + t23706 = -1 + t23698 + t23705 = 1 + t23698 + t23701 = t23705 ** 2 + t23702 = t23701 ** 2 + t23699 = t23706 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 + 5 * phi2)) * np.sqrt(0.62491e5) * t23699 ** 2 * t23705 * t23702 ** 2 + + if Bindx == 3646: + t23707 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 + 6 * phi2)) * np.sqrt(0.6578e4) * ((1 - t23707) ** (0.7e1 / 0.2e1)) * ((1 + t23707) ** (0.19e2 / 0.2e1)) + + if Bindx == 3647: + t23708 = np.cos(phi) + t23716 = -1 + t23708 + t23715 = 1 + t23708 + t23711 = t23715 ** 2 + t23713 = t23715 * t23711 ** 2 + t23709 = t23716 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((1j) * (13 * phi1 + 7 * phi2)) * np.sqrt(0.230230e6) * t23716 * t23709 * t23713 ** 2 + + if Bindx == 3648: + t23717 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 + 8 * phi2)) * np.sqrt(0.16445e5) * ((1 - t23717) ** (0.5e1 / 0.2e1)) * ((1 + t23717) ** (0.21e2 / 0.2e1)) + + if Bindx == 3649: + t23719 = np.cos(phi) + t23725 = 1 + t23719 + t23720 = t23725 ** 2 + t23722 = t23725 * t23720 ** 2 + t23718 = -1 + t23719 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 + 9 * phi2)) * np.sqrt(0.598e3) * t23718 ** 2 * t23725 * t23722 ** 2 + + if Bindx == 3650: + t23726 = np.cos(phi) + tfunc[..., c] = (0.135e3 / 0.4096e4*1j) * np.exp((1j) * (13 * phi1 + 10 * phi2)) * np.sqrt(0.26e2) * ((1 - t23726) ** (0.3e1 / 0.2e1)) * ((1 + t23726) ** (0.23e2 / 0.2e1)) + + if Bindx == 3651: + t23727 = np.cos(phi) + t23732 = 1 + t23727 + t23728 = t23732 ** 2 + t23729 = t23732 * t23728 + t23730 = t23729 ** 2 + tfunc[..., c] = (0.135e3 / 0.8192e4) * np.exp((1j) * (13 * phi1 + 11 * phi2)) * np.sqrt(0.13e2) * (-1 + t23727) * t23730 ** 2 + + if Bindx == 3652: + t23733 = np.cos(phi) + tfunc[..., c] = (-0.27e2 / 0.8192e4*1j) * np.exp((1j) * (13 * phi1 + 12 * phi2)) * np.sqrt(0.26e2) * np.sqrt((1 - t23733)) * ((1 + t23733) ** (0.25e2 / 0.2e1)) + + if Bindx == 3653: + t23734 = np.cos(phi) + t23735 = t23734 ** 2 + t23736 = t23734 * t23735 + t23739 = t23736 ** 2 + t23747 = 1716 * t23739 + t23745 = t23739 ** 2 + t23737 = t23735 ** 2 + t23738 = t23734 * t23737 + t23743 = t23738 ** 2 + t23741 = t23737 ** 2 + tfunc[..., c] = (0.27e2 / 0.8192e4) * np.exp((13*1j) * (phi1 + phi2)) * (78 * t23735 + 286 * t23736 + 715 * t23737 + 1287 * t23738 + 1287 * t23741 + 286 * t23743 + 13 * t23745 + t23747 + 1 + (715 * t23741 + 78 * t23743 + t23745 + t23747 + 13) * t23734) + + c += 1 + + return tfunc + + +if __name__ == '__main__': + X = np.zeros([2, 3]) + phi1 = np.array([0.1,0.2]) + X[:, 0] = phi1 + phi = np.array([0.0, 0.4]) + X[:, 1] = phi + phi2 = np.array([0.3, 0.6]) + X[:, 2] = phi2 + + indxvec = gsh_basis_info() + + lte2 = indxvec[:, 0] <= 2 + + Bvec = np.arange(indxvec.shape[0])[lte2] + + out_tvalues = gsh_eval(X, Bvec) + diff --git a/pymks/bases/imag_ffts.py b/pymks/bases/imag_ffts.py new file mode 100644 index 00000000..f0a577b0 --- /dev/null +++ b/pymks/bases/imag_ffts.py @@ -0,0 +1,49 @@ +from .abstract import _AbstractMicrostructureBasis +import numpy as np +try: + import pyfftw.builders as fftmodule +except: + import numpy.fft as fftmodule + + +class _ImagFFTBasis(_AbstractMicrostructureBasis): + """This class is used to make the bases that create complex valued + microstructure functions use the standard FFT/iFFT algorithms and selects + the appropriate fft module depending on whether or not pyfftw is installed. + """ + + def _fftn(self, X): + """Standard FFT algorithm + + Args: + X: NDarray (n_samples, N_x, ...) + + Returns: + Fourier transform of X + """ + if self._pyfftw: + + return fftmodule.fftn(np.ascontiguousarray(X), + axes=self._axes, threads=self._n_jobs, + planner_effort='FFTW_ESTIMATE', + overwrite_input=True, avoid_copy=True)() + else: + return fftmodule.fftn(X, axes=self._axes) + + def _ifftn(self, X): + """Standard iFFT algorithm + + Args: + X: NDarray (n_samples, N_x, ...) + + Returns: + Inverse Fourier transform of X + """ + if self._pyfftw: + return fftmodule.ifftn(np.ascontiguousarray(X), + axes=self._axes, threads=self._n_jobs, + planner_effort='FFTW_ESTIMATE', + overwrite_input=True, + avoid_copy=True)() + else: + return fftmodule.ifftn(X, axes=self._axes) diff --git a/pymks/bases/legendre.py b/pymks/bases/legendre.py index e71e25f8..48499889 100644 --- a/pymks/bases/legendre.py +++ b/pymks/bases/legendre.py @@ -1,8 +1,8 @@ import numpy as np -from .abstract import _AbstractMicrostructureBasis +from .real_ffts import _RealFFTBasis -class LegendreBasis(_AbstractMicrostructureBasis): +class LegendreBasis(_RealFFTBasis): r""" Discretize a continuous field into `deg` local states using a @@ -10,7 +10,7 @@ class LegendreBasis(_AbstractMicrostructureBasis): .. math:: - \frac{1}{\Delta} \int_s m(h, x) dx = + \frac{1}{\Delta x} \int_s m(h, x) dx = \sum_0^{L-1} m[l, s] P_l(h) where the :math:`P_l` are Legendre polynomials and the local state space @@ -36,7 +36,7 @@ class LegendreBasis(_AbstractMicrostructureBasis): If the microstructure local state values fall outside of the specified domain they will no longer be mapped into the orthogonal domain of the - legendre polynomais. + legendre polynomials. >>> n_states = 2 >>> X = np.array([-1, 1]) @@ -48,14 +48,6 @@ class LegendreBasis(_AbstractMicrostructureBasis): """ - def _get_basis_slice(self, ijk, s0): - """ - Helper method used to calibrate influence coefficients from in - mks_localization_model to account for redundancies from linearly - dependent local states. - """ - return s0 - def discretize(self, X): """ Discretize `X`. @@ -78,9 +70,10 @@ def discretize(self, X): """ self.check(X) + self._select_axes(X) leg = np.polynomial.legendre X_scaled = (2. * X - self.domain[0] - self.domain[1]) /\ (self.domain[1] - self.domain[0]) - norm = (2. * np.arange(self.n_states) + 1) / 2. - X_Legendre = (leg.legval(X_scaled, np.eye(self.n_states) * norm)) + norm = (2. * np.array(self.n_states) + 1) / 2. + X_Legendre = (leg.legval(X_scaled, np.eye(len(self.n_states)) * norm)) return np.rollaxis(X_Legendre, 0, len(X_Legendre.shape)) diff --git a/pymks/bases/primitive.py b/pymks/bases/primitive.py index b3319026..17d71f43 100644 --- a/pymks/bases/primitive.py +++ b/pymks/bases/primitive.py @@ -1,8 +1,8 @@ import numpy as np -from .abstract import _AbstractMicrostructureBasis +from .real_ffts import _RealFFTBasis -class PrimitiveBasis(_AbstractMicrostructureBasis): +class PrimitiveBasis(_RealFFTBasis): r""" Discretize the microstructure function into `n_states` local states such @@ -24,9 +24,9 @@ class PrimitiveBasis(_AbstractMicrostructureBasis): A microstructure function discretized with this basis is subject to the following constraint - ..math:: + .. math:: - \sum_{l=0}^L m[l, s] = 1 + \sum_{l=0}^L m[l, s] = 1 which is equivalent of saying that every location is filled with some configuration of local states. @@ -109,7 +109,7 @@ class PrimitiveBasis(_AbstractMicrostructureBasis): """ - def _get_basis_slice(self, ijk, s0): + def _select_slice(self, ijk, s0): """ Helper method used to calibrate influence coefficients from in mks_localization_model to account for redundancies from linearly @@ -133,5 +133,7 @@ def discretize(self, X): Float valued field of local states between 0 and 1. """ self.check(X) - H = np.linspace(self.domain[0], self.domain[1], self.n_states) - return np.maximum(1 - (abs(X[..., None] - H)) / (H[1] - H[0]), 0) + self._select_axes(X) + H = np.linspace(self.domain[0], self.domain[1], max(self.n_states) + 1) + X_ = np.maximum(1 - (abs(X[..., None] - H)) / (H[1] - H[0]), 0) + return X_[..., list(self.n_states)] diff --git a/pymks/bases/real_ffts.py b/pymks/bases/real_ffts.py new file mode 100644 index 00000000..8890bdcf --- /dev/null +++ b/pymks/bases/real_ffts.py @@ -0,0 +1,49 @@ +from .abstract import _AbstractMicrostructureBasis +import numpy as np +try: + import pyfftw.builders as fftmodule +except: + import numpy.fft as fftmodule + + +class _RealFFTBasis(_AbstractMicrostructureBasis): + """This class is used to make the bases that create real valued + microstructure functions use the real rFFT/irFFT algorithms and selects + the appropriate fft module depending on whether or not pyfftw is installed. + """ + + def _fftn(self, X): + """Real rFFT algorithm + + Args: + X: NDarray (n_samples, N_x, ...) + + Returns: + Fourier transform of X + """ + if self._pyfftw: + return fftmodule.rfftn(np.ascontiguousarray(X), + axes=self._axes, threads=self._n_jobs, + planner_effort='FFTW_ESTIMATE', + overwrite_input=True, avoid_copy=True)() + else: + return fftmodule.rfftn(X, axes=self._axes) + + def _ifftn(self, X): + """Real irFFT algorithm + + Args: + X: NDarray (n_samples, N_x, ...) + + Returns: + Inverse Fourier transform of X + """ + if self._pyfftw: + return fftmodule.irfftn(np.ascontiguousarray(X), + s=self._axes_shape, axes=self._axes, + threads=self._n_jobs, + planner_effort='FFTW_ESTIMATE', + avoid_copy=True)().real + else: + return fftmodule.irfftn(X, axes=self._axes, + s=self._axes_shape).real diff --git a/pymks/datasets/__init__.py b/pymks/datasets/__init__.py index 4014fd32..c2f94292 100644 --- a/pymks/datasets/__init__.py +++ b/pymks/datasets/__init__.py @@ -6,7 +6,7 @@ __all__ = ['make_delta_microstructures', 'make_elastic_FE_strain_delta', 'make_elastic_FE_strain_random', 'make_cahn_hilliard', 'make_microstructure', 'make_checkerboard_microstructure', - 'make_elastic_FE_stress_random'] + 'make_elastic_stress_random'] def make_elastic_FE_strain_delta(elastic_modulus=(100, 150), @@ -114,6 +114,7 @@ def make_elastic_FE_strain_random(n_samples=1, elastic_modulus=(100, 150), microstructure with dimensions of `(5, 5)`. Args: + n_samples (int, optional): number of samples elastic_modulus (list, optional): elastic moduli for the phases poissons_ratio (list, optional): Poisson's ratios for the phases size (tuple, optional): size of the microstructure @@ -187,7 +188,8 @@ def make_cahn_hilliard(n_samples=1, size=(21, 21), dx=0.25, width=1., def make_microstructure(n_samples=10, size=(101, 101), n_phases=2, - grain_size=(33, 14), seed=10): + grain_size=None, seed=10, volume_fraction=None, + percent_variance=None): """ Constructs microstructures for an arbitrary number of phases given the size of the domain, and relative grain size. @@ -198,6 +200,10 @@ def make_microstructure(n_samples=10, size=(101, 101), n_phases=2, n_phases (int, optional): number of phases grain_size (tuple, optional): effective dimensions of grains seed (int, optional): seed for random number microstructureGenerator + volume_fraction(tuple, optional): specify the volume fraction of each + phase + percent_variance(int, optional): varies the volume fraction of the + microstructure up to this percentage Returns: microstructures for the system of shape (n_samples, n_x, ...) @@ -216,9 +222,14 @@ def make_microstructure(n_samples=10, size=(101, 101), n_phases=2, >>> assert(np.allclose(X, Xtest)) """ + if grain_size is None: + grain_size = np.array(size) / 3. + if np.sum(np.array(grain_size) > np.array(size)): + raise RuntimeError('grain_size must be smaller than size') MS = MicrostructureGenerator(n_samples=n_samples, size=size, n_phases=n_phases, grain_size=grain_size, - seed=seed) + seed=seed, volume_fraction=volume_fraction, + percent_variance=percent_variance) return MS.generate() @@ -252,13 +263,15 @@ def make_checkerboard_microstructure(square_size, n_squares): X = np.ones((2 * square_size, 2 * square_size), dtype=int) X[:square_size, :square_size] = 0 X[square_size:, square_size:] = 0 - return np.tile(X, ((n_squares + 1) / 2, (n_squares + 1) / 2))[None, :L, :L] + return np.tile(X, (int((n_squares + 1) / 2), + int((n_squares + 1) / 2)))[None, :L, :L] -def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150), +def make_elastic_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150), poissons_ratio=(0.3, 0.3), size=(21, 21), macro_strain=0.01, grain_size=[(3, 3), (9, 9)], - seed=10): + seed=10, volume_fraction=None, + percent_variance=None): """ Generates microstructures and their macroscopic stress values for an applied macroscopic strain. @@ -274,6 +287,12 @@ def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150) sample. grain_size (tuple, optional): effective dimensions of grains seed (int, optional): seed for random number generator + volume_fraction(tuple, optional): specify the volume fraction of + each phase + percent_variance(int, optional): Only used if volume_fraction is + specified. Randomly varies the volume fraction of the + microstructure. + Returns: array of microstructures with dimensions (n_samples, n_x, ...) and @@ -281,11 +300,11 @@ def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150) Example - >>> X, y = make_elastic_FE_stress_random(n_samples=1, elastic_modulus=(1, 1), + >>> X, y = make_elastic_stress_random(n_samples=1, elastic_modulus=(1, 1), ... poissons_ratio=(1, 1), ... grain_size=(3, 3), macro_strain=1.0) >>> assert np.allclose(y, np.ones(y.shape)) - >>> X, y = make_elastic_FE_stress_random(n_samples=1, grain_size=(1, 1), + >>> X, y = make_elastic_stress_random(n_samples=1, grain_size=(1, 1), ... elastic_modulus=(100, 200), ... size=(2, 2), poissons_ratio=(1, 3), ... macro_strain=1., seed=3) @@ -293,7 +312,7 @@ def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150) ... [0, 1]]]) >>> assert np.allclose(X, X_result) >>> assert float(np.round(y, decimals=5)[0]) == 228.74696 - >>> X, y = make_elastic_FE_stress_random(n_samples=1, grain_size=(1, 1, 1), + >>> X, y = make_elastic_stress_random(n_samples=1, grain_size=(1, 1, 1), ... elastic_modulus=(100, 200), ... poissons_ratio=(1, 3), seed=3, ... macro_strain=1., size=(2, 2, 2)) @@ -309,10 +328,19 @@ def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150) grain_size = (grain_size,) if not isinstance(n_samples, (list, tuple, np.ndarray)): n_samples = (n_samples,) + if volume_fraction is None: + volume_fraction = (None,) * len(n_samples) + vf_0 = volume_fraction[0] + if not isinstance(vf_0, (list, tuple, np.ndarray)) and vf_0 is not None: + volume_fraction = (volume_fraction,) if not isinstance(size, (list, tuple, np.ndarray)) or len(size) > 3: raise RuntimeError('size must have length of 2 or 3') [RuntimeError('dimensions of size and grain_size are not the same.') for grains in grain_size if len(size) != len(grains)] + if vf_0 is not None: + [RuntimeError('dimensions of size and grain_size are not the same.') + for volume_frac in volume_fraction + if len(elastic_modulus) != len(volume_frac)] if len(elastic_modulus) != len(poissons_ratio): raise RuntimeError('length of elastic_modulus and poissons_ratio are \ not the same.') @@ -325,11 +353,13 @@ def make_elastic_FE_stress_random(n_samples=[10, 10], elastic_modulus=(100, 150) model.fit(X_cal, y_cal) X = np.concatenate([make_microstructure(n_samples=sample, size=size, n_phases=n_states, - grain_size=gs, seed=seed) for gs, - sample in zip(grain_size, n_samples)]) + grain_size=gs, seed=seed, + volume_fraction=vf, + percent_variance=percent_variance) + for vf, gs, sample in zip(volume_fraction, + grain_size, n_samples)]) X_ = basis.discretize(X) index = tuple([None for i in range(len(size) + 1)]) + (slice(None),) modulus = np.sum(X_ * np.array(elastic_modulus)[index], axis=-1) y_stress = model.predict(X) * modulus - return X, np.average(y_stress.reshape(np.sum(n_samples), y_stress[0].size), - axis=1) + return X, np.average(y_stress.reshape(len(y_stress), -1), axis=1) diff --git a/pymks/datasets/base_microstructure_generator.py b/pymks/datasets/base_microstructure_generator.py index 684dbfa4..35dcbb0f 100644 --- a/pymks/datasets/base_microstructure_generator.py +++ b/pymks/datasets/base_microstructure_generator.py @@ -1,23 +1,33 @@ import numpy as np +from ..bases.imag_ffts import _ImagFFTBasis +# from ..bases.real_ffts import _RealFFTBasis -class BaseMicrostructureGenerator(object): +class _BaseMicrostructureGenerator(_ImagFFTBasis): def __init__(self, n_samples=1, size=(21, 21), - n_phases=2, grain_size=None, seed=3): + n_phases=2, grain_size=None, seed=3, volume_fraction=None, + percent_variance=None): """ Instantiate a MicrostructureGenerator. Args: - n_samples: number of samples to be generated - size: size of samples - n_phases: number of phases in microstructures - grain_size: size of the grain_size in the microstructure - seed: seed for random number generator + n_samples (int): number of samples to be generated + size (tuple): size of samples + n_phases (int): number of phases in microstructures + grain_size (tuple): size of the grain_size in the microstructure + seed (int): seed for random number generator + volume_fraction (tuple): the percent volume fraction for each phase. + percent_variance (tuple): the percent variance for each value of + volume_fraction. For example volume_fraction=(0.5, 0.5) and + percent_variance=0.01 would be a 50 +/- 1 percent volume + fractions for both values. Returns: n_samples number of a periodic random microstructure with size equal to size and with n_phases number of phases. """ + self._axes = np.arange(len(size)) + 1 + self._axes_shape = size self.n_samples = n_samples self.size = size self.n_phases = n_phases @@ -25,6 +35,23 @@ def __init__(self, n_samples=1, size=(21, 21), if self.grain_size is None: self.grain_size = np.array(size) / 2 np.random.seed(seed) + self.volume_fraction = volume_fraction + if self.volume_fraction is not None: + if len(self.volume_fraction) != self.n_phases: + raise RuntimeError(('n_phases and lenth of volume_fraction' + + ' must be the same')) + cum_frac = np.cumsum(volume_fraction) + if not np.allclose(cum_frac[-1], 1): + raise RuntimeError("volume fractions do not add up to 1") + if percent_variance is None: + percent_variance = 0. + self.percent_variance = percent_variance + min_frac = cum_frac[0] - percent_variance + max_frac = cum_frac[-2] + percent_variance + if max_frac > 1 or min_frac < 0: + raise RuntimeError(('percent_variance cannot extend' + + 'volume_fraction values beyond 0 or 1')) + super(_BaseMicrostructureGenerator, self).__init__() def generate(self): raise NotImplementedError diff --git a/pymks/datasets/cahn_hilliard_simulation.py b/pymks/datasets/cahn_hilliard_simulation.py index 24abba2a..952702c9 100644 --- a/pymks/datasets/cahn_hilliard_simulation.py +++ b/pymks/datasets/cahn_hilliard_simulation.py @@ -1,9 +1,8 @@ import numpy as np -from ..filter import _import_pyfftw -_import_pyfftw() +from ..bases.imag_ffts import _ImagFFTBasis -class CahnHilliardSimulation(object): +class CahnHilliardSimulation(_ImagFFTBasis): r""" Solve the `Cahn-Hilliard equation `__ for @@ -76,6 +75,7 @@ def __init__(self, dx=0.25, gamma=1., dt=0.001): self.dx = dx self.dt = dt self.gamma = gamma + super(CahnHilliardSimulation, self).__init__() def run(self, X): r""" @@ -106,9 +106,9 @@ def run(self, X): i_ = np.indices(X.shape[1:]) ksq = np.sum(k[i_] ** 2, axis=0)[None] - axes = np.arange(len(X.shape) - 1) + 1 - FX = np.fft.fftn(X, axes=axes) - FX3 = np.fft.fftn(X ** 3, axes=axes) + self._axes = np.arange(len(X.shape) - 1) + 1 + FX = self._fftn(X) + FX3 = self._fftn(X ** 3) a1 = 3. a2 = 0. @@ -117,4 +117,4 @@ def run(self, X): dt = self.dt Fy = (FX * (1 + dt * explicit) - ksq * dt * FX3) / (1 - dt * implicit) - self.response = np.fft.ifftn(Fy, axes=axes).real + self.response = self._ifftn(Fy).real diff --git a/pymks/datasets/microstructure_generator.py b/pymks/datasets/microstructure_generator.py index 830be8a4..67b7d31b 100644 --- a/pymks/datasets/microstructure_generator.py +++ b/pymks/datasets/microstructure_generator.py @@ -1,12 +1,10 @@ import numpy as np from ..filter import Filter from scipy.ndimage.fourier import fourier_gaussian -from .base_microstructure_generator import BaseMicrostructureGenerator -from ..filter import _import_pyfftw -_import_pyfftw() +from .base_microstructure_generator import _BaseMicrostructureGenerator -class MicrostructureGenerator(BaseMicrostructureGenerator): +class MicrostructureGenerator(_BaseMicrostructureGenerator): """ Generates n_samples number of a periodic random microstructures with domain size equal to size and with n_phases number of @@ -36,12 +34,13 @@ def generate(self): if len(self.size) != len(self.grain_size): raise RuntimeError("Dimensions of size and grain_size are" " not equal.") + X = np.random.random((self.n_samples,) + self.size) gaussian = fourier_gaussian(np.ones(self.grain_size), np.ones(len(self.size))) - filter_ = Filter(np.fft.fftn(gaussian)[None, ..., None]) + filter_ = Filter(self._fftn(gaussian[None, ..., None]), self) filter_.resize(self.size) - X_blur = filter_.convolve(X[..., None]) + X_blur = filter_.convolve(X[..., None]).real return self._assign_phases(X_blur).astype(int) def _assign_phases(self, X_blur): @@ -53,8 +52,23 @@ def _assign_phases(self, X_blur): Returns: microstructure with assigned phases """ - epsilon = 1e-5 - X0, X1 = np.min(X_blur), np.max(X_blur) - Xphases = float(self.n_phases) * (X_blur - X0) / (X1 - X0) * \ - (1. - epsilon) + epsilon - return np.floor(Xphases) + if self.volume_fraction is None: + epsilon = 1e-5 + X0, X1 = np.min(X_blur), np.max(X_blur) + Xphases = float(self.n_phases) * ((X_blur - X0) / (X1 - X0) * + (1. - epsilon) + epsilon) + X_phases = np.floor(Xphases - epsilon) + else: + v_cum = np.cumsum(self.volume_fraction[:-1]) + X_sort = np.sort(X_blur.reshape((X_blur.shape[0], -1)), axis=1) + seg_shape = (len(X_sort), len(v_cum)) + per_diff = (2 * np.random.random(seg_shape) - + 1) * np.array(self.percent_variance) + if -np.sum(per_diff) < self.percent_variance: + per_diff -= np.sum(per_diff) / len(self.volume_fraction) + seg_ind = np.floor((v_cum + per_diff) * X_sort.shape[1]) + seg_values = np.concatenate([x[list(i)][None] + for i, x in zip(seg_ind, X_sort)]) + X_bool = X_blur[..., None] > seg_values[:, None, None, :] + X_phases = np.sum(X_bool, axis=-1) + return X_phases diff --git a/pymks/datasets/spherical_microstructure_generator.py b/pymks/datasets/spherical_microstructure_generator.py index 106d340a..cd7ee233 100644 --- a/pymks/datasets/spherical_microstructure_generator.py +++ b/pymks/datasets/spherical_microstructure_generator.py @@ -1,8 +1,8 @@ import numpy as np -from .base_microstructure_generator import BaseMicrostructureGenerator +from .base_microstructure_generator import _BaseMicrostructureGenerator -class SphericalMicrostructureGenerator(BaseMicrostructureGenerator): +class SphericalMicrostructureGenerator(_BaseMicrostructureGenerator): """ Generates n_samples number of a periodic random spherical diff --git a/pymks/filter.py b/pymks/filter.py index bed4d208..77870ba5 100644 --- a/pymks/filter.py +++ b/pymks/filter.py @@ -1,44 +1,41 @@ import numpy as np -def _import_pyfftw(): - try: - import pyfftw - np.fft = pyfftw.interfaces.numpy_fft - pyfftw.interfaces.cache.enable() - except: - pass - -_import_pyfftw() - - class Filter(object): - """ Wrapper class for convolution with a kernel and resizing of a kernel """ - def __init__(self, Fkernel): + def __init__(self, Fkernel, basis): """ Instantiate a Filter. Args: - Fkernel: an array representing a convolution kernel + Fkernel: an array representing a convolution kernel + basis: an instance of a bases class. + + """ - self.axes = np.arange(len(Fkernel.shape) - 2) + 1 - self.Fkernel = Fkernel + self.basis = basis + self._Fkernel = Fkernel - def _frequency_2_real(self): + def _frequency_2_real(self, copy=False): """ Converts the kernel from frequency space to real space with the origin shifted to the center. + Args: + copy (boolean): indicates if _Fkernel should be copied. This is + used to make sure the influence cofficients remained unchanged. + Returns: - an array in real space + an array in real space """ - return np.real_if_close(np.fft.fftshift(np.fft.ifftn(self.Fkernel, - axes=self.axes), - axes=self.axes)) + Fkernel = self._Fkernel + if copy: + Fkernel = self._Fkernel.copy() + return np.fft.fftshift(self.basis._ifftn(Fkernel), + axes=self.basis._axes) def _real_2_frequency(self, kernel): """ @@ -50,8 +47,8 @@ def _real_2_frequency(self, kernel): Returns: an array in frequency space """ - return np.fft.fftn(np.fft.ifftshift(kernel, axes=self.axes), - axes=self.axes) + return self.basis._fftn(np.fft.ifftshift(kernel, + axes=self.basis._axes)) def convolve(self, X): """ @@ -63,11 +60,11 @@ def convolve(self, X): Returns: convolution of X with the kernel """ - if X.shape[1:] != self.Fkernel.shape[1:]: + FX = self.basis._fftn(X) + if FX.shape[1:] != self._Fkernel.shape[1:]: raise RuntimeError("Dimensions of X are incorrect.") - FX = np.fft.fftn(X, axes=self.axes) - Fy = self._sum(FX * self.Fkernel) - return np.fft.ifftn(Fy, axes=self.axes).real + Fy = self._sum(FX * self._Fkernel) + return self.basis._ifftn(Fy) def _sum(self, Fy): return np.sum(Fy, axis=-1) @@ -79,13 +76,29 @@ def resize(self, size): Args: size: tuple with the shape of the new kernel """ - if len(size) != len(self.Fkernel.shape[1:-1]): + if len(size) != len(self._Fkernel.shape[1:-1]): raise RuntimeError("length of resize shape is incorrect.") - if not np.all(size >= self.Fkernel.shape[1:-1]): + if not np.all(size >= self._Fkernel.shape[1:-1]): raise RuntimeError("resize shape is too small.") - kernel = self._frequency_2_real() - size = kernel.shape[:1] + size + kernel.shape[-1:] + kernel_pad = self._zero_pad(kernel, size) + self._Fkernel = self._real_2_frequency(kernel_pad) + self.basis._axes_shape = kernel_pad.shape[1:-1] + + def _zero_pad(self, kernel, size): + """ + Zero pads a real space array with zeros and does a Fourier transform + + Args: + kernel: real space array + size: The size of the zero padded array. + + Returns: + Fourier transform of a zero padded kernel + + """ + if len(size) != kernel.ndim: + size = kernel.shape[:1] + tuple(size) + kernel.shape[-1:] padsize = np.array(size) - np.array(kernel.shape) paddown = padsize // 2 padup = padsize - paddown @@ -93,9 +106,7 @@ def resize(self, size): paddown[..., None]), axis=1) pads = tuple([tuple(p) for p in padarray]) kernel_pad = np.pad(kernel, pads, 'constant', constant_values=0) - Fkernel_pad = self._real_2_frequency(kernel_pad) - - self.Fkernel = Fkernel_pad + return kernel_pad class Correlation(Filter): @@ -110,7 +121,7 @@ class Correlation(Filter): >>> from pymks.bases import DiscreteIndicatorBasis >>> basis = DiscreteIndicatorBasis(n_states=n_states) >>> X_ = basis.discretize(X) - >>> filter_ = Correlation(X_) + >>> filter_ = Correlation(X_, basis) >>> X_auto = filter_.convolve(X_) >>> X_test = np.array([[[[3., 0. ], ... [6., 3.], @@ -133,10 +144,20 @@ class Correlation(Filter): Autocorrelations for microstructure X_ """ - def __init__(self, kernel, Fkernel_shape=None): - axes = np.arange(len(kernel.shape) - 2) + 1 - Fkernel = np.conjugate(np.fft.fftn(kernel, axes=axes, s=Fkernel_shape)) - super(Correlation, self).__init__(Fkernel) + def __init__(self, kernel, basis): + """ + Instantiate a Correlation. + + Args: + kernel: an array representing a convolution kernel + basis: an instance of a bases class. + + """ + self.basis = basis + if self.basis._axes_shape > kernel.shape[1:-1]: + kernel = self._zero_pad(kernel, self.basis._axes_shape) + Fkernel = self.basis._fftn(kernel) + super(Correlation, self).__init__(np.conjugate(Fkernel), basis) def convolve(self, X): """ @@ -148,13 +169,12 @@ def convolve(self, X): Returns: correlation of X with the kernel """ - Fkernel_shape = np.array(self.Fkernel.shape)[self.axes] - FX = np.fft.fftn(X, axes=self.axes, s=Fkernel_shape) - Fy = self._sum(FX * self.Fkernel) - correlation = np.real_if_close( - np.fft.ifftn(Fy, axes=self.axes)) - return np.real_if_close(np.fft.fftshift(correlation, axes=self.axes), - tol=1e7) + if X.shape[1:-1] < self.basis._axes_shape: + X = self._zero_pad(X, self.basis._axes_shape) + FX = self.basis._fftn(X) + Fy = self._sum(FX * self._Fkernel) + correlation = self.basis._ifftn(Fy) + return np.fft.fftshift(correlation, axes=self.basis._axes) def _sum(self, Fy): return Fy diff --git a/pymks/mks_homogenization_model.py b/pymks/mks_homogenization_model.py index 6da05ef6..f05964ce 100644 --- a/pymks/mks_homogenization_model.py +++ b/pymks/mks_homogenization_model.py @@ -1,13 +1,11 @@ -from pymks.stats import correlate -from sklearn.base import BaseEstimator -from sklearn.decomposition import RandomizedPCA +from .mks_structure_analysis import MKSStructureAnalysis from sklearn.linear_model import LinearRegression from sklearn.preprocessing import PolynomialFeatures from sklearn.pipeline import Pipeline import numpy as np -class MKSHomogenizationModel(BaseEstimator): +class MKSHomogenizationModel(MKSStructureAnalysis): """ The `MKSHomogenizationModel` takes in microstructures and a their @@ -28,10 +26,15 @@ class MKSHomogenizationModel(BaseEstimator): correlations used to fit the model. reduced_predict_data: Low dimensionality representation of spatial correlations predicted by the model. + periodic_axes: axes that are periodic. (0, 2) would indicate that + axes x and z are periodic in a 3D microstrucure. + coef_: Array of values that are the coefficients. + intercept_: Value that are the intercept - Below is an examlpe of using MKSHomogenizationModel to predict (or + Below is an example of using MKSHomogenizationModel to predict (or classify) the type of microstructure using PCA and Logistic Regression. + >>> import numpy as np >>> n_states = 3 >>> domain = [-1, 1] @@ -61,21 +64,26 @@ class MKSHomogenizationModel(BaseEstimator): """ def __init__(self, basis=None, dimension_reducer=None, n_components=None, - property_linker=None, degree=1, correlations=None, - compute_correlations=True): + property_linker=None, degree=1, periodic_axes=None, + correlations=None, compute_correlations=True, n_jobs=1, + store_correlations=False, mean_center=True): """ Create an instance of a `MKSHomogenizationModel`. Args: basis (class, optional): an instance of a bases class. dimension_reducer (class, optional): an instance of a - dimensionality reduction class with a fit_transform method. + dimensionality reduction class with a fit_transform method. The + default class is RandomizedPCA. property_linker (class, optional): an instance for a machine learning class with fit and predict methods. n_components (int, optional): number of components kept by the dimension_reducer degree (int, optional): degree of the polynomial used by property_linker. + periodic_axes (list, optional): axes that are periodic. (0, 2) + would indicate that axes x and z are periodic in a 3D + microstrucure. correlations (list, optional): list of spatial correlations to compute, default is the autocorrelation with the first local state and all of its cross correlations. For example if basis @@ -84,32 +92,40 @@ def __init__(self, basis=None, dimension_reducer=None, n_components=None, correlations will not be calculated as part of the fit and predict methods. The spatial correlations can be passed as `X` to both methods, default is True. + n_jobs (int, optional): number of parallel jobs to run. only used + if pyfftw is install. + store_correlations (boolean, optional): indicate if spatial + correlations should be stored + mean_center (boolean, optional): If true the data will be mean + centered before dimensionality reduction is computed. """ - self.basis = basis - self.dimension_reducer = dimension_reducer - if self.dimension_reducer is None: - self.dimension_reducer = RandomizedPCA() - if n_components is None: - n_components = self.dimension_reducer.n_components - if n_components is None: - n_components = 2 if property_linker is None: property_linker = LinearRegression() - if correlations is None and basis is not None: - if compute_correlations is True: - correlations = [(0, l) for l in range(basis.n_states)] self._linker = Pipeline([('poly', PolynomialFeatures(degree=degree)), ('connector', property_linker)]) - self._check_methods self.degree = degree - self.n_components = n_components self.property_linker = property_linker - self.correlations = correlations - self._fit = False + if not callable(getattr(self.property_linker, "fit", None)): + raise RuntimeError( + "property_linker does not have fit() method.") + if not callable(getattr(self.property_linker, "predict", None)): + raise RuntimeError( + "property_linker does not have predict() method.") self.compute_correlations = compute_correlations self.reduced_fit_data = None self.reduced_predict_data = None + if self.compute_correlations: + if basis is None: + raise RuntimeError(('a basis is need to compute spatial ') + + ('correlations')) + super(MKSHomogenizationModel, + self).__init__(store_correlations=store_correlations, + dimension_reducer=dimension_reducer, + correlations=correlations, n_jobs=n_jobs, + n_components=n_components, basis=basis, + mean_center=mean_center, + periodic_axes=periodic_axes) @property def n_components(self): @@ -133,6 +149,26 @@ def degree(self, value): self._degree = value self._linker.set_params(poly__degree=value) + @property + def coef_(self): + return self._linker.named_steps['connector'].coef_ + + @coef_.setter + def coef_(self, coef): + """Setter for the coefficients for property_linker. + """ + self._linker.named_steps['connector'].coef_ = coef + + @property + def intercept_(self): + return self._linker.named_steps['connector'].intercept_ + + @intercept_.setter + def intercept_(self, intercept): + """Setter for the intercept for property_linker. + """ + self._linker.named_steps['connector'].intercept_ = intercept + @property def property_linker(self): return self._property_linker @@ -144,27 +180,7 @@ def property_linker(self, prop_linker): self._property_linker = prop_linker self._linker.set_params(connector=prop_linker) - def _check_methods(self): - """ - Helper function to make check that the dimensionality reduction and - property linking methods have the appropriate methods. - """ - if not callable(getattr(self.dimension_reducer, - "fit_transform", None)): - raise RuntimeError( - "dimension_reducer does not have fit_transform() method.") - if not callable(getattr(self.dimension_reducer, "transform", None)): - raise RuntimeError( - "dimension_reducer does not have transform() method.") - if not callable(getattr(self.linker, "fit", None)): - raise RuntimeError( - "property_linker does not have fit() method.") - if not callable(getattr(self.linker, "predict", None)): - raise RuntimeError( - "property_linker does not have predict() method.") - - def fit(self, X, y, reduce_labels=None, - periodic_axes=None, confidence_index=None, size=None): + def fit(self, X, y, reduce_labels=None, confidence_index=None, size=None): """ Fits data by calculating 2-point statistics from X, preforming dimension reduction using dimension_reducer, and fitting the reduced @@ -177,14 +193,15 @@ def fit(self, X, y, reduce_labels=None, y (1D array): The material property associated with `X`. reducer_labels (1D array, optional): label for X used during the fit_transform method for the `dimension_reducer`. - periodic_axes (list, optional): axes that are periodic. (0, 2) - would indicate that axes x and z are periodic in a 3D - microstrucure. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. Example + Let's first start with using the microstructure and effective + properties. + + >>> import numpy as np >>> from sklearn.decomposition import PCA >>> from sklearn.linear_model import LinearRegression >>> from pymks.bases import PrimitiveBasis @@ -202,9 +219,8 @@ def fit(self, X, y, reduce_labels=None, >>> X = np.random.randint(2, size=(3, 15)) >>> y = np.array([1, 2, 3]) >>> model.fit(X, y) - >>> X_ = prim_basis.discretize(X) - >>> X_stats = correlate(X_) - >>> X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size)) + >>> X_stats = correlate(X, prim_basis) + >>> X_reshaped = X_stats.reshape((X_stats.shape[0], -1)) >>> X_pca = reducer.fit_transform(X_reshaped - np.mean(X_reshaped, ... axis=1)[:, None]) >>> assert np.allclose(model.reduced_fit_data, X_pca) @@ -227,8 +243,7 @@ def fit(self, X, y, reduce_labels=None, >>> np.random.seed(99) >>> X = np.random.randint(2, size=(3, 15)) >>> y = np.array([1, 2, 3]) - >>> X_ = prim_basis.discretize(X) - >>> X_stats = correlate(X_, correlations=correlations) + >>> X_stats = correlate(X, prim_basis, correlations=correlations) >>> model.fit(X_stats, y) >>> X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size)) >>> X_pca = reducer.fit_transform(X_reshaped - np.mean(X_reshaped, @@ -237,30 +252,21 @@ def fit(self, X, y, reduce_labels=None, """ - if self.compute_correlations is True: - if periodic_axes is None: - periodic_axes = [] + if self.compute_correlations: if size is not None: - new_shape = (X.shape[0],) + size - X = X.reshape(new_shape) - X = self._correlate(X, periodic_axes, confidence_index) + X = self.basis._reshape_feature(X, size) + X = self._compute_stats(X, confidence_index) X_reshape = self._reduce_shape(X) - X_reduced = self.dimension_reducer.fit_transform(X_reshape, - reduce_labels) + X_reduced = self._fit_transform(X_reshape, reduce_labels) self._linker.fit(X_reduced, y) - self.reduced_fit_data = X_reduced - self._fit = True - def predict(self, X, periodic_axes=None, confidence_index=None): + def predict(self, X, confidence_index=None): """Predicts macroscopic property for the microstructures `X`. Args: X (ND array): The microstructure, an `(n_samples, n_x, ...)` shaped array where `n_samples` is the number of samples and `n_x` is the spatial discretization. - periodic_axes (list, optional): axes that are periodic. (0, 2) - would indicate that axes x and z are periodic in a 3D - microstrucure. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. @@ -269,10 +275,11 @@ def predict(self, X, periodic_axes=None, confidence_index=None): Example + >>> import numpy as np >>> from sklearn.manifold import LocallyLinearEmbedding >>> from sklearn.linear_model import BayesianRidge >>> from pymks.bases import PrimitiveBasis - >>> np.random.seed(99) + >>> np.random.seed(1) >>> X = np.random.randint(2, size=(50, 100)) >>> y = np.random.random(50) >>> reducer = LocallyLinearEmbedding() @@ -292,99 +299,31 @@ def predict(self, X, periodic_axes=None, confidence_index=None): >>> from pymks.stats import correlate >>> model.compute_correlations = False - >>> X_ = prim_basis.discretize(X_test) - >>> X_corr = correlate(X_, correlations=[(0, 0), (0, 1)]) - >>> y_pred_stats = model.predict(X_corr) - >>> assert y_pred_stats == y_pred + >>> X_corr = correlate(X, prim_basis, correlations=[(0, 0)]) + >>> model.fit(X_corr, y) + >>> X_corr_test = correlate(X_test, prim_basis, + ... correlations=[(0, 0)]) + >>> y_pred_stats = model.predict(X_corr_test) + >>> assert np.allclose(y_pred_stats, y_pred, atol=1e-3) """ - if not self._fit: + if not hasattr(self._linker.get_params()['connector'], "coef_"): raise RuntimeError('fit() method must be run before predict().') + _size = self.basis._axes_shape + if self.periodic_axes is None or len(self.periodic_axes) != len(_size): + _axes = range(len(_size)) + if self.periodic_axes is not None: + [_axes.remove(a) for a in self.periodic_axes] + _size = np.ones(len(_size)) * _size + _size[_axes] *= .5 + X = self.basis._reshape_feature(X, tuple(_size)) if self.compute_correlations is True: - if periodic_axes is None: - periodic_axes = [] - X = self._correlate(X, periodic_axes, confidence_index) - X_reshape = self._reduce_shape(X) - X_reduced = self.dimension_reducer.transform(X_reshape) + X = self._compute_stats(X, confidence_index) + X_reduced = self._transform(X) self.reduced_predict_data = X_reduced return self._linker.predict(X_reduced) - def _correlate(self, X, periodic_axes, confidence_index): - """ - Helper function used to calculated 2-point statistics from `X` and - reshape them appropriately for fit and predict methods. - - Args: - X (ND array): The microstructure, an `(n_samples, n_x, ...)` - shaped array where `n_samples` is the number of samples and - `n_x` is the spatial discretization.. - periodic_axes (list, optional): axes that are periodic. (0, 2) - would indicate that axes x and z are periodic in a 3D - microstrucure. - confidence_index (ND array, optional): array with same shape as X - used to assign a confidence value for each data point. - - Returns: - Spatial correlations for each sample formated with dimensions - (n_samples, n_features). - - Example - - >>> from sklearn.manifold import Isomap - >>> from sklearn.linear_model import ARDRegression - >>> from pymks.bases import PrimitiveBasis - >>> reducer = Isomap() - >>> linker = ARDRegression() - >>> prim_basis = PrimitiveBasis(2, [0, 1]) - >>> model = MKSHomogenizationModel(prim_basis, reducer, linker) - >>> X = np.array([[0, 1], - ... [1, 0]]) - >>> X_stats = model._correlate(X, [], None) - >>> X_test = np.array([[[ 0, 0], - ... [0.5, 0]], - ... [[0, 1,], - ... [0.5, 0]]]) - >>> assert np.allclose(X_test, X_stats) - """ - if self.basis is None: - raise AttributeError('basis must be specified') - X_ = self.basis.discretize(X) - X_stats = correlate(X_, periodic_axes=periodic_axes, - confidence_index=confidence_index, - correlations=self.correlations) - return X_stats - - def _reduce_shape(self, X_stats): - """ - Helper function used to reshape 2-point statistics appropriately for - fit and predict methods. - - Args: - `X_stats`: The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - Where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local - states. - - Returns: - Spatial correlations for each sample formated with dimensions - (n_samples, n_features). - - Example - >>> X_stats = np.zeros((2, 2, 2, 2)) - >>> X_stats[1] = 3. - >>> X_stats[..., 1] = 1. - >>> X_results = np.array([[-.5, .5, -.5, .5, -.5, .5, -.5, 0.5], - ... [1., -1., 1., -1., 1., -1., 1., -1.]]) - >>> from pymks import PrimitiveBasis - >>> prim_basis = PrimitiveBasis(2) - >>> model = MKSHomogenizationModel(prim_basis) - >>> assert np.allclose(X_results, model._reduce_shape(X_stats)) - """ - X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size)) - return X_reshaped - np.mean(X_reshaped, axis=1)[:, None] - - def score(self, X, y, periodic_axes=None, confidence_index=None): + def score(self, X, y, confidence_index=None): """ The score function for the MKSHomogenizationModel. It formats the data and uses the score method from the property_linker. @@ -394,9 +333,6 @@ def score(self, X, y, periodic_axes=None, confidence_index=None): shaped array where `n_samples` is the number of samples and `n_x` is the spatial discretization. y (1D array): The material property associated with `X`. - periodic_axes (list, optional): axes that are periodic. (0, 2) - would indicate that axes x and z are periodic in a 3D - microstrucure. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. @@ -404,12 +340,11 @@ def score(self, X, y, periodic_axes=None, confidence_index=None): Score for MKSHomogenizationModel from the selected property_linker. """ - if periodic_axes is None: - periodic_axes = [] if not callable(getattr(self._linker, "score", None)): raise RuntimeError( "property_linker does not have score() method.") - X_corr = self._correlate(X, periodic_axes, confidence_index) - X_reshaped = self._reduce_shape(X_corr) - X_reduced = self.dimension_reducer.transform(X_reshaped) + X = self.basis._reshape_feature(X, self.basis._axes_shape) + if self.compute_correlations: + X = self._compute_stats(X, confidence_index) + X_reduced = self._transform(X) return self._linker.score(X_reduced, y) diff --git a/pymks/mks_localization_model.py b/pymks/mks_localization_model.py index 0c20446d..c75b06b0 100644 --- a/pymks/mks_localization_model.py +++ b/pymks/mks_localization_model.py @@ -1,9 +1,7 @@ import numpy as np -from sklearn.linear_model import LinearRegression from .filter import Filter -from .filter import _import_pyfftw from scipy.linalg import lstsq -_import_pyfftw() +from sklearn.linear_model import LinearRegression class MKSLocalizationModel(LinearRegression): @@ -17,7 +15,7 @@ class MKSLocalizationModel(LinearRegression): basis: Basis function used to discretize the microstucture. n_states: Interger value for number of local states, if a basis is specified, n_states indicates the order of the polynomial. - coef: Array of values that are the influence coefficients. + coef_: Array of values that are the influence coefficients. >>> n_states = 2 >>> n_spaces = 81 @@ -32,21 +30,21 @@ class MKSLocalizationModel(LinearRegression): Use the filter function to construct some coefficients. - >>> coeff = np.linspace(1, 0, n_states)[None,:] * filter(np.linspace(0, 20, + >>> coef_ = np.linspace(1, 0, n_states)[None,:] * filter(np.linspace(0, 20, ... n_spaces))[:,None] - >>> Fcoeff = np.fft.fft(coeff, axis=0) + >>> Fcoef_ = np.fft.fft(coef_, axis=0) Make some test samples. >>> np.random.seed(2) >>> X = np.random.random((n_samples, n_spaces)) - Construct a response with the `Fcoeff`. + Construct a response with the `Fcoef_`. >>> H = np.linspace(0, 1, n_states) >>> X_ = np.maximum(1 - abs(X[:,:,None] - H) / (H[1] - H[0]), 0) >>> FX = np.fft.fft(X_, axis=1) - >>> Fy = np.sum(Fcoeff[None] * FX, axis=-1) + >>> Fy = np.sum(Fcoef_[None] * FX, axis=-1) >>> y = np.fft.ifft(Fy, axis=1).real Use the `MKSLocalizationModel` to reconstruct the coefficients @@ -58,16 +56,21 @@ class MKSLocalizationModel(LinearRegression): Check the result - >>> assert np.allclose(np.fft.fftshift(coeff, axes=(0,)), model.coeff) + >>> assert np.allclose(np.fft.fftshift(coef_, axes=(0,)), model.coef_) """ - def __init__(self, basis, n_states=None): + def __init__(self, basis, n_states=None, n_jobs=1, lstsq_rcond=None): """ Instantiate a MKSLocalizationModel. Args: basis (class): an instance of a bases class. n_states (int, optional): number of local states + n_jobs (int, optional): number of parallel jobs to run. only used + if pyfftw is install. + lstsq_rcond (float, optional): rcond argument to scipy.linalg.lstsq + function. Defaults to 4 orders of magnitude above machine + epsilon. """ self.basis = basis @@ -75,6 +78,10 @@ def __init__(self, basis, n_states=None): if n_states is None: self.n_states = basis.n_states self.domain = basis.domain + self.basis._n_jobs = n_jobs + self.lstsq_rcond = lstsq_rcond + if self.lstsq_rcond is None: + self.lstsq_rcond = np.finfo(float).eps*1e4 def fit(self, X, y, size=None): """ @@ -97,36 +104,40 @@ def fit(self, X, y, size=None): >>> prim_basis = PrimitiveBasis(2, [0, 1]) >>> model = MKSLocalizationModel(basis=prim_basis) >>> model.fit(X, y) - >>> assert np.allclose(model._filter.Fkernel, [[[ 0.5, 0.5], - ... [ -2, 0]], - ... [[-0.5, 0 ], - ... [ -1, 0 ]]]) + >>> assert np.allclose(model._filter._Fkernel, [[[ 0.5, 0.5], + ... [ -2, 0]], + ... [[-0.5, 0 ], + ... [ -1, 0 ]]]) """ self.basis = self.basis.__class__(self.n_states, self.domain) if size is not None: - y = self._reshape_feature(y, size) - X = self._reshape_feature(X, size) - if not len(y.shape) > 1: - raise RuntimeError("The shape of y is incorrect.") - if y.shape != X.shape: - raise RuntimeError("X and y must be the same shape.") + y = self.basis._reshape_localization_data(y, size) + X = self.basis._reshape_feature(X, size) + self.basis._check_shape(X.shape, y.shape) X_ = self.basis.discretize(X) - axes = np.arange(X_.ndim)[1:-1] - FX = np.fft.fftn(X_, axes=axes) - Fy = np.fft.fftn(y, axes=axes) + FX = self.basis._fftn(X_) + Fy = self.basis._fftn(y) Fkernel = np.zeros(FX.shape[1:], dtype=np.complex) s0 = (slice(None),) - for ijk in np.ndindex(X_.shape[1:-1]): - s1 = self.basis._get_basis_slice(ijk, s0) - Fkernel[ijk + s1] = lstsq(FX[s0 + ijk + s1], Fy[s0 + ijk])[0] - self._filter = Filter(Fkernel[None]) + for ijk in np.ndindex(FX.shape[1:-1]): + s1 = self.basis._select_slice(ijk, s0) + Fkernel[ijk + s1] = lstsq(FX[s0 + ijk + s1], Fy[s0 + ijk], + self.lstsq_rcond)[0] + self._filter = Filter(Fkernel[None], self.basis) @property - def coeff(self): + def coef_(self): """Returns the coefficients in real space with origin shifted to the center. """ - return self._filter._frequency_2_real()[0] + return self._filter._frequency_2_real(copy=True)[0] + + @coef_.setter + def coef_(self, kernel): + """Setter for influence coefficients. + """ + self._filter._Fkernel = self._filter._real_2_frequency(kernel[None]) + self.basis._axes_shape = kernel.shape[:-1] def predict(self, X): """Predicts a new response from the microstructure function `X` with @@ -161,10 +172,10 @@ def predict(self, X): if not hasattr(self, '_filter'): raise AttributeError("fit() method must be run before predict().") - y_pred_shape = X.shape - X = self._reshape_feature(X, self._filter.Fkernel.shape[1:-1]) + _pred_shape = self.basis._pred_shape(X) + X = self.basis._reshape_feature(X, self.basis._axes_shape) X_ = self.basis.discretize(X) - return self._filter.convolve(X_).reshape(y_pred_shape) + return self._filter.convolve(X_).reshape(_pred_shape).real def resize_coeff(self, size): """Scale the size of the coefficients and pad with zeros. @@ -181,18 +192,21 @@ def resize_coeff(self, size): coefficients. >>> from pymks.bases import PrimitiveBasis - >>> prim_basis = PrimitiveBasis(n_states=2) + >>> prim_basis = PrimitiveBasis(n_states=1) + >>> prim_basis._axes = np.array([1, 2]) + >>> prim_basis._axes_shape = (5, 4) >>> model = MKSLocalizationModel(prim_basis) - >>> coeff = np.arange(20).reshape((5, 4, 1)) - >>> coeff = np.concatenate((coeff , np.ones_like(coeff)), axis=2) - >>> coeff = np.fft.ifftshift(coeff, axes=(0, 1)) - >>> model._filter = Filter(np.fft.fftn(coeff, axes=(0, 1))[None]) + >>> coef_ = np.arange(20).reshape((1, 5, 4, 1)) + >>> coef_ = np.concatenate((coef_, np.ones_like(coef_)), axis=-1) + >>> coef_ = np.fft.ifftshift(coef_, axes=(1, 2)) + >>> model._filter = Filter(np.fft.rfftn(coef_, axes=(1, 2)), + ... prim_basis) The coefficients can be reshaped by passing the new shape that coefficients should have. >>> model.resize_coeff((10, 7)) - >>> assert np.allclose(model.coeff[:,:,0], + >>> assert np.allclose(model.coef_[... ,0], ... [[0, 0, 0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0, 0, 0], ... [0, 0, 0, 0, 0, 0, 0], @@ -242,19 +256,3 @@ def _test(self): >>> assert np.allclose(FX, FXtest) """ pass - - def _reshape_feature(self, X, size): - """ - Helper function used to check the shape of the microstructure, - and change to appropriate shape. - - Args: - X: The microstructure, an `(n_samples, n_x, ...)` shaped array - where `n_samples` is the number of samples and `n_x` is thes - patial discretization. - - Returns: - microstructure with shape (n_samples, size) - """ - new_shape = (X.shape[0],) + size - return X.reshape(new_shape) diff --git a/pymks/mks_structure_analysis.py b/pymks/mks_structure_analysis.py new file mode 100644 index 00000000..5d2717db --- /dev/null +++ b/pymks/mks_structure_analysis.py @@ -0,0 +1,298 @@ +import numpy as np +from pymks.stats import correlate +from sklearn.base import BaseEstimator +from sklearn.decomposition import RandomizedPCA + + +class MKSStructureAnalysis(BaseEstimator): + """ + `MKSStructureAnalysis` computes the 2-point statistics for a set of + microstructures and does dimensionality reduction. It can be used to + evaluate the selection of spatial correlations and look at clustering of + 2-point statistics. + + Attributes: + n_components: Number of components used by `dimension_reducer`. + dimension_reducer: Instance of a dimensionality reduction class. + correlations: spatial correlations to be computed + basis: instance of a basis class + reduced_fit_data: Low dimensionality representation of spatial + correlations used to fit the components. + reduced_transformed_data: Reduced of spatial correlations. + periodic_axes: axes that are periodic. (0, 2) would indicate that + axes x and z are periodic in a 3D microstrucure. + transformed_correlations: spatial correlations transform into the Low + dimensional space. + + + + Below is an example of using MKSStructureAnalysis using FastICA. + + >>> from pymks.datasets import make_microstructure + >>> from pymks.bases import PrimitiveBasis + >>> from sklearn.decomposition import FastICA + + >>> leg_basis = PrimitiveBasis(n_states=2, domain=[0, 1]) + >>> reducer = FastICA(n_components=3) + >>> analyzer = MKSStructureAnalysis(basis=leg_basis, mean_center=False, + ... dimension_reducer=reducer) + + >>> X = make_microstructure(n_samples=4, size=(13, 13), grain_size=(3, 3)) + >>> print(analyzer.fit_transform(X)) # doctest: +ELLIPSIS + [[ 0.5 -0.5 -0.5] + [ 0.5 0.5 0.5] + [-0.5 -0.5 0.5] + [-0.5 0.5 -0.5]] + + """ + + def __init__(self, basis, correlations=None, dimension_reducer=None, + n_components=None, periodic_axes=None, + store_correlations=False, n_jobs=1, mean_center=True): + """ + Create an instance of a `MKSStructureAnalysis`. + + Args: + basis: an instance of a bases class. + dimension_reducer (class, optional): an instance of a + dimensionality reduction class with a fit_transform method. The + default class is RandomizedPCA. + n_components (int, optional): number of components kept by the + dimension_reducer + correlations (list, optional): list of spatial correlations to + compute, default is the autocorrelation with the first local + state and all of its cross correlations. For example if basis + has n_states=3, correlation would be [(0, 0), (0, 1), (0, 2)] + periodic_axes (list, optional): axes that are periodic. (0, 2) + would indicate that axes x and z are periodic in a 3D + microstrucure. + store_correlations (boolean, optional): If true the computed + 2-point statistics will be saved as an attributes + fit_correlations and transform_correlations. + n_jobs (int, optional): number of parallel jobs to run. only used + if pyfftw is install. + mean_center (boolean, optional): If true the data will be mean + centered before dimensionality reduction is computed. + """ + self.basis = basis + self.correlations = correlations + self.dimension_reducer = dimension_reducer + self.store_correlations = store_correlations + self.mean_center = mean_center + self.periodic_axes = periodic_axes + if basis is not None: + self.basis._n_jobs = n_jobs + if self.dimension_reducer is None: + self.dimension_reducer = RandomizedPCA(copy=False) + if n_components is None: + n_components = self.dimension_reducer.n_components + if n_components is None: + n_components = 5 + self.n_components = n_components + if self.correlations is None and basis is not None: + self.correlations = [(0, l) for l in self.basis.n_states] + if not callable(getattr(self.dimension_reducer, + "fit_transform", None)): + raise RuntimeError( + "dimension_reducer does not have fit_transform() method.") + if not callable(getattr(self.dimension_reducer, "transform", None)): + raise RuntimeError( + "dimension_reducer does not have transform() method.") + + @property + def n_components(self): + return self._n_components + + @n_components.setter + def n_components(self, value): + """Setter for the number of components used by the dimension_reducer + """ + self._n_components = value + self.dimension_reducer.n_components = value + + @property + def components_(self): + stats_shape = ((self.n_components,) + self._components_shape) + return self.dimension_reducer.components_.reshape(stats_shape) + + @components_.setter + def components_(self, components): + """Setter for the components used by the dimension_reducer + """ + self.dimension_reducer.components_ = components.reshape( + self._n_components, -1) + + def fit(self, X, reducer_labels=None, confidence_index=None): + """Fits data by using the 2-point statistics for X to fits the + components used in dimensionality reduction. + + Args: + X (ND array): The microstructures or spatial correlations, a + `(n_samples, n_x, ...)` shaped array where `n_samples` is the + number of samples and `n_x` is the spatial discretization. + reducer_labels (1D array, optional): label for X used during the + fit_transform method for the `dimension_reducer`. + confidence_index (ND array, optional): array with same shape as X + used to assign a confidence value for each data point. + + Example + + >>> from pymks.datasets import make_delta_microstructures + >>> from pymks import PrimitiveBasis + >>> n_states = 2 + >>> analyzer = MKSStructureAnalysis(basis=PrimitiveBasis(n_states), + ... n_components=1) + >>> np.random.seed(5) + >>> size = (2, 3, 3) + >>> X = np.random.randint(2, size=size) + >>> analyzer.fit(X) + >>> print(analyzer.dimension_reducer.components_.reshape(size)[0]) + ... # doctest: +ELLIPSIS + [[ 0.02886463 0.02886463 0.02886463] + [ 0.02886463 -0.43874233 0.49647159] + [ 0.02886463 0.02886463 -0.17896069]] + """ + X_stats = self._compute_stats(X, confidence_index) + self._fit_transform(X_stats, reducer_labels) + + def fit_transform(self, X, confidence_index=None): + """Fits data by using the 2-point statistics for X to fits the + components used in dimensionality reduction and returns the reduction + of the 2-point statistics for X. + + Args: + X (ND array): The microstructures or spatial correlations, a + `(n_samples, n_x, ...)` shaped array where `n_samples` is the + number of samples and `n_x` is the spatial discretization. + reducer_labels (1D array, optional): label for X used during the + fit_transform method for the `dimension_reducer`.. + confidence_index (ND array, optional): array with same shape as X + used to assign a confidence value for each data point. + + Returns: + Reduction of the 2-point statistics of X used to fit the components. + + Example + + >>> from pymks.datasets import make_delta_microstructures + >>> from pymks import PrimitiveBasis + >>> n_states = 2 + >>> analyzer = MKSStructureAnalysis(basis=PrimitiveBasis(n_states), + ... n_components=1) + >>> np.random.seed(5) + >>> size = (2, 3, 3) + >>> X = np.random.randint(2, size=size) + >>> + >>> print(analyzer.fit_transform(X)) # doctest: +ELLIPSIS + [[ 0.26731852] + [-0.26731852]] + """ + X_stats = self._compute_stats(X, confidence_index) + return self._fit_transform(X_stats, None) + + def transform(self, X, confidence_index=None): + """Computes the 2-point statistics for X and applies dimensionality + reduction. + + Args: + X (ND array): The microstructures or spatial correlations, a + `(n_samples, n_x, ...)` shaped array where `n_samples` is the + number of samples and `n_x` is the spatial discretization. + confidence_index (ND array, optional): array with same shape as X + used to assign a confidence value for each data point. + + Returns: + Reduction of the 2-point statistics of X. + + Example + + >>> from pymks.datasets import make_delta_microstructures + >>> from pymks import PrimitiveBasis + >>> n_states = 2 + >>> analyzer = MKSStructureAnalysis(basis=PrimitiveBasis(n_states), + ... n_components=1) + >>> np.random.seed(5) + >>> size = (2, 3, 3) + >>> X = np.random.randint(2, size=size) + >>> print(analyzer.fit_transform(X)) # doctest: +ELLIPSIS + [[ 0.26731852] + [-0.26731852]] + >>> print(analyzer.transform(X)) # doctest: +ELLIPSIS + [[ 0.26731852] + [-0.26731852]] + """ + X_stats = self._compute_stats(X, confidence_index) + return self._transform(X_stats) + + def _transform(self, X): + """Reshapes and reduces X""" + self._store_correlations(X) + X_reshaped = self._reduce_shape(X) + self.transform_data = self.dimension_reducer.transform(X_reshaped) + return self.transform_data + + def _fit_transform(self, X, y): + """Reshapes X and uses it to compute the components""" + if self.store_correlations: + self.fit_correlations = X + X_reshaped = self._reduce_shape(X) + self.reduced_fit_data = self.dimension_reducer.fit_transform( + X_reshaped, y) + self._components_shape = X.shape[1:-2] + (X.shape[-1] * X.shape[-2],) + return self.reduced_fit_data + + def _store_correlations(self, X): + """store stats""" + if self.store_correlations: + if hasattr(self, 'transform_correlations'): + self.transform_correlations = np.concatenate( + (self.transform_correlations, X)) + else: + self.transform_correlations = X + + def _compute_stats(self, X, confidence_index): + """ + Helper function used to calculated 2-point statistics from `X` and + reshape them appropriately for fit and predict methods. + + Args: + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization.. + confidence_index (ND array, optional): array with same shape as X + used to assign a confidence value for each data point. + + Returns: + Spatial correlations for each sample formated with dimensions + (n_samples, n_features). + + Example + """ + if self.basis is None: + raise AttributeError('basis must be specified') + X_stats = correlate(X, self.basis, periodic_axes=self.periodic_axes, + confidence_index=confidence_index, + correlations=self.correlations) + return X_stats + + def _reduce_shape(self, X_stats): + """ + Helper function used to reshape 2-point statistics appropriately for + fit and predict methods. + + Args: + `X_stats`: The discretized microstructure function, an + `(n_samples, n_x, ..., n_states)` shaped array + Where `n_samples` is the number of samples, `n_x` is the + spatial discretization, and n_states is the number of local + states. + + Returns: + Spatial correlations for each sample formated with dimensions + (n_samples, n_features). + + """ + X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size)) + if self.mean_center: + X_reshaped -= np.mean(X_reshaped, axis=1)[:, None] + return X_reshaped diff --git a/pymks/stats.py b/pymks/stats.py index aee5080e..e2f5ba83 100644 --- a/pymks/stats.py +++ b/pymks/stats.py @@ -7,18 +7,20 @@ """ -def autocorrelate(X_, periodic_axes=[], confidence_index=None, +def autocorrelate(X, basis, periodic_axes=[], n_jobs=1, confidence_index=None, autocorrelations=None): """ Computes the autocorrelation from a microstructure function. Args: - X_ (ND array): The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local states. + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization. + basis (class): an instance of a bases class periodic_axes (list, optional): axes that are periodic. (0, 2) would indicate that axes x and z are periodic in a 3D microstrucure. + n_jobs (int, optional): number of parallel jobs to run. only used if + pyfftw is install. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. autocorrelations (list, optional): list of spatial autocorrelatiions to @@ -27,7 +29,7 @@ def autocorrelate(X_, periodic_axes=[], confidence_index=None, all autocorrelations are computed. Returns: - Autocorrelations for microstructure function `X_`. + Autocorrelations for a microstructure. Non-periodic example @@ -37,8 +39,7 @@ def autocorrelate(X_, periodic_axes=[], confidence_index=None, ... [0, 0, 0]]]) >>> from pymks.bases import PrimitiveBasis >>> prim_basis = PrimitiveBasis(n_states=n_states) - >>> X_ = prim_basis.discretize(X) - >>> X_auto = autocorrelate(X_, periodic_axes=(0, 1)) + >>> X_auto = autocorrelate(X, prim_basis, periodic_axes=(0, 1)) >>> X_test = np.array([[[0., 0., 0.], ... [0., 1./9, 0.], ... [0., 0., 0.]]]) @@ -47,65 +48,25 @@ def autocorrelate(X_, periodic_axes=[], confidence_index=None, if periodic_axes is None: periodic_axes = [] if autocorrelations is None: - correlations = _auto_correlations(X_.shape[-1]) - X_ = _mask_X_(X_, confidence_index) - s = _Fkernel_shape(X_, periodic_axes) - auto = _correlate(X_, s, correlations) - return auto / _normalize(X_, s, confidence_index) + correlations = _auto_correlations(basis.n_states) + return _compute_stats(X, basis, correlations, confidence_index, + periodic_axes, n_jobs) -def _correlate(X_, s, correlations): - """ - Helper function used to calculate the unnormalized correlation counts. - - Args: - X_ (ND array): The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local states. - s (tuple): shape of the Fkernel used for the convolution - - Returns: - correlation counts for a given microstructure function - - Example - - >>> from pymks.datasets import make_microstructure - >>> from pymks.bases import PrimitiveBasis - >>> X = make_microstructure(n_samples=2, n_phases=3, - ... size=(2, 2), grain_size=(2, 2), seed=99) - >>> prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2]) - >>> X_ = prim_basis.discretize(X) - >>> correlations = [(l, l) for l in range(3)] - >>> X_corr = _correlate(X_, X_.shape[1:-1], correlations=correlations) - >>> X_result = np.array([[[[0, 0, 0], - ... [0, 0, 2]], - ... [[0, 0, 0], - ... [1, 1, 2]]], - ... [[[0, 0, 0], - ... [0, 0, 0]], - ... [[2, 0, 2], - ... [2, 0, 2]]]]) - >>> assert np.allclose(X_result, X_corr) - """ - - l_0, l_1 = [l[0] for l in correlations], [l[1] for l in correlations] - corr = Correlation(X_[..., l_0], Fkernel_shape=s).convolve(X_[..., l_1]) - return _truncate(corr, X_.shape[:-1]) - - -def crosscorrelate(X_, periodic_axes=None, confidence_index=None, - crosscorrelations=None): +def crosscorrelate(X, basis, periodic_axes=None, n_jobs=1, + confidence_index=None, crosscorrelations=None): """ Computes the crosscorrelations from a microstructure function. Args: - X_ (ND array): The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local states. + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization.s. + basis (class): an instance of a bases class periodic_axes (list, optional): axes that are periodic. (0, 2) would indicate that axes x and z are periodic in a 3D microstrucure. + n_jobs (int, optional): number of parallel jobs to run. only used if + pyfftw is install. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. crosscorrelations (list, optional): list of cross-correlatiions to @@ -114,7 +75,7 @@ def crosscorrelate(X_, periodic_axes=None, confidence_index=None, If no list is passed, all cross-correlations are computed. Returns: - Crosscorelations for microstructure function `X_`. + Crosscorelations for a microstructure. Examples @@ -126,8 +87,7 @@ def crosscorrelate(X_, periodic_axes=None, confidence_index=None, ... [0, 1, 0]]]) >>> from pymks.bases import PrimitiveBasis >>> prim_basis = PrimitiveBasis(n_states=n_states) - >>> X_ = prim_basis.discretize(X) - >>> X_cross = crosscorrelate(X_, periodic_axes=[0, 1]) + >>> X_cross = crosscorrelate(X, prim_basis, periodic_axes=[0, 1]) >>> X_test = np.array([[[[1/3.], [0.], [1/3.]], ... [[1/3.], [0.], [1/3.]], ... [[1/3.], [0.], [1/3.]]]]) @@ -137,46 +97,46 @@ def crosscorrelate(X_, periodic_axes=None, confidence_index=None, >>> n_states = 3 >>> prim_basis = PrimitiveBasis(n_states=n_states) - >>> X_ = prim_basis.discretize(X) - >>> assert(crosscorrelate(X_, periodic_axes=[0, 1]).shape == (1, 3, 3, 3)) + >>> assert(crosscorrelate(X, prim_basis, + ... periodic_axes=[0, 1]).shape == (1, 3, 3, 3)) Test for 4 states >>> n_states = 4 >>> prim_basis = PrimitiveBasis(n_states=n_states) - >>> X_ = prim_basis.discretize(X) - >>> assert(crosscorrelate(X_, periodic_axes=[0, 1]).shape == (1, 3, 3, 6)) + >>> assert(crosscorrelate(X, prim_basis, + ... periodic_axes=[0, 1]).shape == (1, 3, 3, 6)) Test for 5 states >>> n_states = 5 >>> prim_basis = PrimitiveBasis(n_states=n_states) - >>> X_ = prim_basis.discretize(X) - >>> assert(crosscorrelate(X_, periodic_axes=[0, 1]).shape == (1, 3, 3, 10)) + >>> assert(crosscorrelate(X, prim_basis, + ... periodic_axes=[0, 1]).shape == (1, 3, 3, 10)) """ if periodic_axes is None: periodic_axes = [] if crosscorrelations is None: - correlations = _cross_correlations(X_.shape[-1]) - X_ = _mask_X_(X_, confidence_index) - s = _Fkernel_shape(X_, periodic_axes) - cross = _correlate(X_, s, correlations) - return cross / _normalize(X_, s, confidence_index) + correlations = _cross_correlations(basis.n_states) + return _compute_stats(X, basis, correlations, confidence_index, + periodic_axes, n_jobs) -def correlate(X_, periodic_axes=None, +def correlate(X, basis, periodic_axes=None, n_jobs=1, confidence_index=None, correlations=None): """ Computes the autocorrelations and crosscorrelations from a microstructure function. Args: - X_ (ND array): The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local states. + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization. + basis (class): an instance of a bases class periodic_axes (list, optional): axes that are periodic. (0, 2) would indicate that axes x and z are periodic in a 3D microstrucure. + n_jobs (int, optional): number of parallel jobs to run. only used if + pyfftw is install. confidence_index (ND array, optional): array with same shape as X used to assign a confidence value for each data point. correlations (list, optional): list of spatial correlatiions to @@ -186,8 +146,7 @@ def correlate(X_, periodic_axes=None, spatial correlations are computed. Returns: - Autocorrelations and crosscorrelations for microstructure funciton - `X_`. + Autocorrelations and crosscorrelations for a microstructure. Example @@ -196,8 +155,7 @@ def correlate(X_, periodic_axes=None, >>> >>> np.random.seed(0) >>> X = np.random.randint(2, size=(1, 3)) - >>> X_ = prim_basis.discretize(X) - >>> X_corr = correlate(X_) + >>> X_corr = correlate(X, prim_basis) >>> X_result = np.array([[0, 0.5, 0], ... [1 / 3., 2 / 3., 0], ... [0, 0.5, 0.5]]) @@ -206,12 +164,75 @@ def correlate(X_, periodic_axes=None, if periodic_axes is None: periodic_axes = [] if correlations is None: - L = X_.shape[-1] + L = basis.n_states correlations = _auto_correlations(L) + _cross_correlations(L) + return _compute_stats(X, basis, correlations, confidence_index, + periodic_axes, n_jobs) + + +def _compute_stats(X, basis, correlations, confidence_index, + periodic_axes, n_jobs): + """Helper function to compute statistics + + Args: + X (ND array): The microstructure, an `(n_samples, n_x, ...)` + shaped array where `n_samples` is the number of samples and + `n_x` is the spatial discretization. + basis: an instance of a bases class + correlations: list of spatial correlatiions to be computed. + confidence_index: array with same shape as X used to assign a + confidence value for each data point. + periodic_axes: axes that are periodic. (0, 2) would indicate that axes + and z are periodic in a 3D microstrucure. + n_jobs (int, optional): number of parallel jobs to run. + """ + X_ = basis.discretize(X) X_ = _mask_X_(X_, confidence_index) - s = _Fkernel_shape(X_, periodic_axes) - corr = _correlate(X_, s, correlations) - return corr / _normalize(X_, s, confidence_index) + basis._n_jobs = n_jobs + _Fkernel_shape(X.shape, basis, periodic_axes) + _norm = _normalize(X.shape, basis, confidence_index) + return _correlate(X_, basis, correlations) / _norm + + +def _correlate(X_, basis, correlations): + """ + Helper function used to calculate the unnormalized correlation counts. + + Args: + X_ (ND array): The discretized microstructure function, an + `(n_samples, n_x, ..., n_states)` shaped array + where `n_samples` is the number of samples, `n_x` is thes + patial discretization, and n_states is the number of local states. + basis (class): an instance of a bases class. + correlations (list): list of correlations to compute. `[(0, 0), + (1, 1)]` + + Returns: + correlation counts for a given microstructure function + + Example + + >>> from pymks.datasets import make_microstructure + >>> from pymks.bases import PrimitiveBasis + >>> X = make_microstructure(n_samples=2, n_phases=3, + ... size=(2, 2), grain_size=(2, 2), seed=99) + >>> prim_basis = PrimitiveBasis(n_states=3, domain=[0, 2]) + >>> X_ = prim_basis.discretize(X) + >>> correlations = [(l, l) for l in range(3)] + >>> X_corr = _correlate(X_, prim_basis, correlations=correlations) + >>> X_result = np.array([[[[0, 0, 0], + ... [0, 0, 2]], + ... [[0, 0, 0], + ... [1, 1, 2]]], + ... [[[0, 0, 0], + ... [0, 0, 0]], + ... [[2, 0, 2], + ... [2, 0, 2]]]]) + >>> assert np.allclose(X_result, X_corr) + """ + l_0, l_1 = [l[0] for l in correlations], [l[1] for l in correlations] + corr = Correlation(X_[..., l_0], basis).convolve(X_[..., l_1]) + return _truncate(corr, X_.shape[:-1]) def _auto_correlations(n_states): @@ -223,10 +244,10 @@ def _auto_correlations(n_states): Returns: list of tuples for autocorrelations - >>> l = _auto_correlations(3) + >>> l = _auto_correlations(np.arange(3)) >>> assert l == [(0, 0), (1, 1), (2, 2)] """ - local_states = range(n_states) + local_states = n_states return [(l, l) for l in local_states] @@ -239,15 +260,15 @@ def _cross_correlations(n_states): Returns: list of tuples for crosscorrelations - >>> l = _cross_correlations(3) + >>> l = _cross_correlations(np.arange(3)) >>> assert l == [(0, 1), (0, 2), (1, 2)] """ - l = range(n_states) + l = n_states cross_corr = [[(l[i], l[j]) for j in l[1:][i:]] for i in l[:-1]] return [item for sublist in cross_corr for item in sublist] -def _normalize(X_, s, confidence_index): +def _normalize(X_shape, basis, confidence_index): """ Returns the normalization for the statistics @@ -258,59 +279,52 @@ def _normalize(X_, s, confidence_index): `(n_samples, n_x, ..., n_states)` shaped array where `n_samples` is the number of samples, `n_x` is thes patial discretization, and n_states is the number of local states. - _Fkernel_shape : the shape of the kernel is Fourier space (array) - confidence_index: array with same shape as X used to assign a - confidence value for each data point. + basis (class): an instance of a bases class + confidence_index (ND array, optional): array with same shape as X used + to assign a confidence value for each data point. Returns: Normalization - Example - - >>> Nx = Ny = 5 - >>> X_ = np.zeros((1, Nx, Ny, 1)) - >>> _Fkernel_shape = np.array((2 * Nx, Ny)) - >>> norm = _normalize(X_, _Fkernel_shape , None) - >>> assert norm.shape == (1, Nx, Ny, 1) - >>> assert np.allclose(norm[0, Nx / 2, Ny / 2, 0], 25) """ - if (s == X_.shape[1:-1]).all() and confidence_index is None: - return float(np.prod(X_.shape[1:-1])) + if basis._axes_shape == X_shape[1:] and confidence_index is None: + return float(np.prod(X_shape[1:])) else: mask = confidence_index if mask is None: - mask = np.ones(X_.shape[1:-1])[None] - corr = Correlation(mask[..., None], Fkernel_shape=s) - return _truncate(corr.convolve(mask[..., None]), X_.shape[:-1]) + mask = np.ones(X_shape[1:])[None] + corr = Correlation(mask[..., None], basis) + return _truncate(corr.convolve(mask[..., None]), X_shape) -def _Fkernel_shape(X_, periodic_axes): +def _Fkernel_shape(X_shape, basis, periodic_axes): """ - Returns the shape of the kernel in Fourier space with non-periodic padding. + Assigns the shape of the kernel in Fourier space with non-periodic padding + to the basis. Args: - `X_`: The discretized microstructure function, an - `(n_samples, n_x, ..., n_states)` shaped array - where `n_samples` is the number of samples, `n_x` is thes - patial discretization, and n_states is the number of local states. + `X_shape`: The shape of discretized microstructure function, + `(n_samples, n_x, ..., n_states)` where `n_samples` is the number + of samples, `n_x` is the spatial discretization, and n_states is + the number of local states. + basis: an instance of a bases class periodic_axes: the axes of the array that are periodic - Returns: - shape of the new Fkernel array - Example >>> Nx = Ny = 5 >>> X_ = np.zeros((1, Nx, Ny, 1)) >>> periodic_axes = [1] - >>> assert (_Fkernel_shape(X_, - ... periodic_axes=periodic_axes) == [8, 5]).all() + >>> from pymks import PrimitiveBasis + >>> p_basis = PrimitiveBasis(2) + >>> p_basis._axes = np.array([1, 2]) + >>> _Fkernel_shape(X_.shape, p_basis, periodic_axes=periodic_axes) + >>> assert p_basis._axes_shape == (10, 5) """ - axes = np.arange(len(X_.shape) - 2) + 1 - a = np.ones(len(axes), dtype=float) * 1.75 + a = np.ones(len(basis._axes), dtype=float) * 2 a[list(periodic_axes)] = 1 - return (np.array(X_.shape)[axes] * a).astype(int) + basis._axes_shape = tuple((np.array(X_shape)[basis._axes] * a).astype(int)) def _truncate(a, shape): @@ -327,17 +341,17 @@ def _truncate(a, shape): Example - >>> print _truncate(np.arange(10).reshape(1, 10, 1), (1, 5))[0, ..., 0] + >>> print(_truncate(np.arange(10).reshape(1, 10, 1), (1, 5))[0, ..., 0]) [3 4 5 6 7] - >>> print _truncate(np.arange(9).reshape(1, 9, 1), (1, 5))[0, ..., 0] + >>> print(_truncate(np.arange(9).reshape(1, 9, 1), (1, 5))[0, ..., 0]) [2 3 4 5 6] - >>> print _truncate(np.arange(10).reshape((1, 10, 1)), (1, 4))[0, ..., 0] + >>> print(_truncate(np.arange(10).reshape((1, 10, 1)), (1, 4))[0, ..., 0]) [3 4 5 6] - >>> print _truncate(np.arange(9).reshape((1, 9, 1)), (1, 4))[0, ..., 0] + >>> print(_truncate(np.arange(9).reshape((1, 9, 1)), (1, 4))[0, ..., 0]) [2 3 4 5] >>> a = np.arange(5 * 4).reshape((1, 5, 4, 1)) - >>> print _truncate(a, shape=(1, 3, 2))[0, ..., 0] + >>> print(_truncate(a, shape=(1, 3, 2))[0, ..., 0]) [[ 5 6] [ 9 10] [13 14]] diff --git a/pymks/tests/test_basis_functions.py b/pymks/tests/test_basis_functions.py new file mode 100644 index 00000000..c38463ae --- /dev/null +++ b/pymks/tests/test_basis_functions.py @@ -0,0 +1,60 @@ +import numpy as np +from pymks.bases import GSHBasis + + +def test_gsh_no_symmetry(): + """this test checks that a particular gsh basis function with no + symmetry is being evaluated properly""" + + X = np.array([[0.1, 0.2, 0.3], + [6.5, 2.3, 3.4]]) + gsh_basis = GSHBasis(n_states=[1]) + + assert(np.allclose(np.squeeze(gsh_basis.discretize(X)), q_no_symm(X))) + + +def test_gsh_hex(): + """this test checks that a particular gsh basis function for hexagonal + symmetry is being evaluated properly""" + + X = np.array([[0.1, 0.2, 0.3], + [6.5, 2.3, 3.4]]) + gsh_basis = GSHBasis(n_states=[1], domain='hexagonal') + + assert(np.allclose(np.squeeze(gsh_basis.discretize(X)), q_hex(X))) + + +def test_symmetry_check_hex(): + """this test is designed to check that the hexagonal gsh functions + for two symmetrically equivalent orientations output the same gsh + coefficients""" + + X1 = np.array([[30, 70, 45]])*np.pi/180. + X2 = np.array([[30+180, 180-70, 2*60-45]])*np.pi/180. + gsh_basis = GSHBasis(n_states=np.arange(0, 100, 5), domain='hexagonal') + + assert(np.allclose(gsh_basis.discretize(X1), gsh_basis.discretize(X2))) + + +def q_hex(x): + phi1 = x[:, 0] + phi = x[:, 1] + t913 = np.sin(phi) + x_GSH = -((0.5e1 / 0.4e1) * np.exp((-2*1j) * phi1) * + np.sqrt(0.6e1) * t913 ** 2) + return x_GSH + + +def q_no_symm(x): + phi1 = x[:, 0] + phi = x[:, 1] + phi2 = x[:, 2] + x_GSH = ((0.3e1 / 0.2e1) * (0.1e1 + np.cos(phi)) * + np.exp((-1*1j) * (phi1 + phi2))) + return x_GSH + + +if __name__ == '__main__': + test_gsh_no_symmetry() + test_gsh_hex() + test_symmetry_check_hex() diff --git a/pymks/tests/test_filter.py b/pymks/tests/test_filter.py new file mode 100644 index 00000000..47f43c37 --- /dev/null +++ b/pymks/tests/test_filter.py @@ -0,0 +1,19 @@ +import numpy as np + + +def test_frequency_2_real_and_back(): + from pymks.filter import Filter + from pymks import PrimitiveBasis + + X = np.zeros((1, 3, 2, 2)) + X[0, 0, 0] = np.arange(1, 3) * 9 + p_basis = PrimitiveBasis(2) + p_basis._axes = (1, 2) + p_basis._axes_shape = (3, 3) + X_result = np.ones((1, 3, 3, 2)) * np.arange(1, 3)[None, None, None, :] + filter_ = Filter(X, p_basis) + assert np.allclose(filter_._frequency_2_real(copy=True), X_result) + assert np.allclose(filter_._real_2_frequency(X_result), X) + +if __name__ == '__main__': + test_frequency_2_real_and_back() diff --git a/pymks/tests/test_homogenization.py b/pymks/tests/test_homogenization.py index 0987d59e..07e144da 100644 --- a/pymks/tests/test_homogenization.py +++ b/pymks/tests/test_homogenization.py @@ -12,8 +12,8 @@ def test_n_componets_from_reducer(): def test_n_components_with_reducer(): from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis - from sklearn.manifold import SpectralEmbedding - reducer = SpectralEmbedding(n_components=7) + from sklearn.manifold import Isomap + reducer = Isomap(n_components=7) dbasis = DiscreteIndicatorBasis(n_states=3, domain=[0, 2]) model = MKSHomogenizationModel(dimension_reducer=reducer, basis=dbasis, n_components=9) @@ -40,15 +40,11 @@ def test_stress(): model.fit(X, y) test_sample_size = 1 n_samples = [test_sample_size] * len(grain_size) - X_new, y_new = make_elastic_stress_random(n_samples=n_samples, - size=size, grain_size=grain_size, - elastic_modulus=elastic_modulus, - poissons_ratio=poissons_ratio, - macro_strain=macro_strain, - seed=8) + X_new, y_new = make_elastic_stress_random( + n_samples=n_samples, size=size, grain_size=grain_size, + elastic_modulus=elastic_modulus, poissons_ratio=poissons_ratio, + macro_strain=macro_strain, seed=8) y_result = model.predict(X_new) - print np.round(y_result, decimals=2) - print np.round(y_new, decimals=2) assert np.allclose(np.round(y_new, decimals=2), np.round(y_result, decimals=2)) @@ -80,7 +76,7 @@ def test_default_n_components(): from pymks import MKSHomogenizationModel, DiscreteIndicatorBasis dbasis = DiscreteIndicatorBasis(n_states=2) model = MKSHomogenizationModel(basis=dbasis) - assert model.n_components == 2 + assert model.n_components == 5 def test_default_property_linker(): @@ -94,7 +90,7 @@ def test_default_property_linker(): def test_default_dimension_reducer(): from sklearn.decomposition import RandomizedPCA from pymks import MKSHomogenizationModel - model = MKSHomogenizationModel() + model = MKSHomogenizationModel(compute_correlations=False) assert isinstance(model.dimension_reducer, RandomizedPCA) @@ -117,5 +113,31 @@ def test_set_correlations(): assert model_prim.correlations == test_correlations +def test_coef_setter(): + from pymks import MKSHomogenizationModel + from pymks import PrimitiveBasis + p_basis = PrimitiveBasis(2) + model = MKSHomogenizationModel(basis=p_basis) + X = np.random.randint(2, size=(50, 10, 10)) + y = np.random.randint(2, size=(50,)) + model.fit(X, y) + coefs = model.coef_ + model.coef_ = coefs * 2 + assert np.allclose(model.coef_, coefs * 2) + + +def test_intercept_setter(): + from pymks import MKSHomogenizationModel + from pymks import PrimitiveBasis + p_basis = PrimitiveBasis(2) + model = MKSHomogenizationModel(basis=p_basis) + X = np.random.randint(2, size=(50, 10, 10)) + y = np.random.randint(2, size=(50,)) + model.fit(X, y) + intercept = model.intercept_ + model.intercept_ = intercept * 2 + assert np.allclose(model.intercept_, intercept * 2) + + if __name__ == '__main__': - test_default_dimension_reducer() + test_stress() diff --git a/pymks/tests/test_localization.py b/pymks/tests/test_localization.py index def7ca9c..86064afe 100644 --- a/pymks/tests/test_localization.py +++ b/pymks/tests/test_localization.py @@ -56,13 +56,12 @@ def test_resize_coeff(): X_delta, y_delta = get_delta_data(nx, ny) X_big_delta, y_big_delta = get_delta_data(resize * nx, resize * ny) basis = DiscreteIndicatorBasis(n_states=2) - model = MKSRegressionModel(basis) big_model = MKSRegressionModel(basis) model.fit(X_delta, y_delta) big_model.fit(X_big_delta, y_big_delta) model.resize_coeff((resize * nx, resize * ny)) - assert np.allclose(model.coeff, big_model.coeff, + assert np.allclose(model.coef_, big_model.coef_, rtol=1e-2, atol=2.1e-3) @@ -96,3 +95,46 @@ def test_multiphase_FE_strain(): assert np.allclose(strain_pred[0, i:-i], strain[0, i:-i], rtol=1e-2, atol=6.1e-3) + + +def test_coeff_stablity_with_irfftn(): + from pymks import MKSRegressionModel + from pymks.bases import DiscreteIndicatorBasis + + nx, ny = 21, 21 + resize = 3 + X_delta, y_delta = get_delta_data(nx, ny) + X_test, y_test = get_random_data(nx, ny) + X_big_test, y_big_test = get_random_data(resize * nx, resize * ny) + basis = DiscreteIndicatorBasis(n_states=2) + + model = MKSRegressionModel(basis) + model.fit(X_delta, y_delta) + y_pred = model.predict(X_test) + assert np.allclose(y_pred, y_test, rtol=1e-2, atol=6.1e-3) + model.resize_coeff((resize * nx, resize * ny)) + for i in range(4): + model.coef_ + y_big_pred = model.predict(X_big_test) + assert np.allclose(y_big_pred, y_big_test, rtol=1e-2, atol=6.1e-2) + + +def test_setting_kernel(): + from pymks.datasets import make_elastic_FE_strain_delta + from pymks import MKSLocalizationModel + from pymks import PrimitiveBasis + elastic_modulus = (100, 130) + poissons_ratio = (0.3, 0.3) + X_delta, y = make_elastic_FE_strain_delta(size=(21, 21), + elastic_modulus=elastic_modulus, + poissons_ratio=poissons_ratio) + p_basis = PrimitiveBasis(2) + model = MKSLocalizationModel(basis=p_basis) + model.fit(X_delta, y) + coefs = model.coef_ + model.resize_coeff((30, 30)) + model.coef_ = coefs + assert np.allclose(model.predict(X_delta), y, atol=1e-4) + +if __name__ == '__main__': + test_resize_coeff() diff --git a/pymks/tests/test_microstructure_generator.py b/pymks/tests/test_microstructure_generator.py new file mode 100644 index 00000000..07046480 --- /dev/null +++ b/pymks/tests/test_microstructure_generator.py @@ -0,0 +1,44 @@ +from nose.tools import raises +import numpy as np +from pymks.datasets import make_microstructure + + +@raises(RuntimeError) +def test_size_and_grain_size_failure(): + make_microstructure(n_samples=1, size=(7, 7), grain_size=(8, 1)) + + +@raises(RuntimeError) +def test_volume_fraction_failure(): + make_microstructure(n_samples=1, volume_fraction=(0.3, 0.6)) + + +@raises(RuntimeError) +def test_volume_fraction_with_n_phases_failure(): + make_microstructure(n_samples=1, size=(7, 7), n_phases=3, + volume_fraction=(0.5, 0.5)) + + +@raises(RuntimeError) +def test_percent_variance_exceeds_limit_failure(): + make_microstructure(n_samples=1, size=(7, 7), n_phases=3, + volume_fraction=(0.3, 0.3, 0.4), percent_variance=0.5) + + +def test_volume_fraction(): + X = make_microstructure(n_samples=1, n_phases=3, + volume_fraction=(0.3, 0.2, 0.5)) + assert np.allclose(np.sum(X == 1) / float(X.size), 0.2, rtol=1e-4) + assert np.allclose(np.sum(X == 2) / float(X.size), 0.5, atol=1e-4) + + +def test_percent_variance(): + X = make_microstructure(n_samples=1, n_phases=3, + volume_fraction=(0.3, 0.2, 0.5), + percent_variance=.2) + assert np.allclose(np.sum(X == 1) / float(X.size), 0.09, atol=1e-2) + assert np.allclose(np.sum(X == 2) / float(X.size), 0.57, atol=1e-2) + +if __name__ == '__main__': + test_volume_fraction() + test_percent_variance() diff --git a/pymks/tests/test_stats.py b/pymks/tests/test_stats.py index 3dddf835..d6eacef4 100644 --- a/pymks/tests/test_stats.py +++ b/pymks/tests/test_stats.py @@ -13,8 +13,7 @@ def test_nonperiodic_autocorrelation(): [0, 0, 0, 0], [0, 0, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_auto = autocorrelate(X_) + X_auto = autocorrelate(X, basis) X_result = np.array([[[0, 0, 0, 0], [1. / 8, 1. / 12, 3. / 16, 1. / 12], @@ -37,8 +36,7 @@ def test_periodic_autocorrelation(): [0, 0, 0, 0], [0, 0, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_auto = autocorrelate(X_, periodic_axes=(0, 1)) + X_auto = autocorrelate(X, basis, periodic_axes=(0, 1)) X_result = np.array([[[0, 0, 0, 0], [0.1, 0.1, 0.15, 0.1], @@ -61,8 +59,7 @@ def test_nonperiodic_crosscorrelation(): [0, 0, 0, 0], [0, 0, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_cross = crosscorrelate(X_) + X_cross = crosscorrelate(X, basis) X_result = np.array([[[1 / 3., 4 / 9., 0.5, 4 / 9., ], [1 / 8., 0.25, 3 / 16., 0.25], @@ -74,7 +71,7 @@ def test_nonperiodic_crosscorrelation(): def test_periodic_crosscorrelation(): ''' - test nonperiodic crosscorrelation + test periodic crosscorrelation ''' from pymks import DiscreteIndicatorBasis from pymks.stats import crosscorrelate @@ -84,8 +81,7 @@ def test_periodic_crosscorrelation(): [0, 0, 0, 0], [0, 0, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_cross = crosscorrelate(X_, periodic_axes=(0, 1)) + X_cross = crosscorrelate(X, basis, periodic_axes=(0, 1)) X_result = np.array([[[0.3, 0.3, 0.3, 0.3], [0.2, 0.2, 0.15, 0.2], @@ -114,8 +110,7 @@ def test_nonperiodic_correlate(): [0, 0, 0, 0], [0, 1, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_corr = correlate(X_) + X_corr = correlate(X, basis) X_result = [[2 / 3., 4 / 9., 0.75, 4 / 9.], [5 / 8., 0.5, 0.75, 0.5], [0.6, 7 / 15., 0.8, 7 / 15.], @@ -126,7 +121,7 @@ def test_nonperiodic_correlate(): def test_periodic_correlate(): ''' - test corrleate for non-periodic microstructures + test corrleate for periodic microstructures ''' from pymks import DiscreteIndicatorBasis from pymks.stats import correlate @@ -142,8 +137,7 @@ def test_periodic_correlate(): [0, 0, 0, 0], [0, 1, 0, 0]]]) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) - X_corr = correlate(X_, periodic_axes=(0, 1)) + X_corr = correlate(X, basis, periodic_axes=(0, 1)) X_result = [[0.6, 0.6, 0.75, 0.6], [0.6, 0.6, 0.75, 0.6], [0.6, 0.6, 0.8, 0.6], @@ -162,10 +156,9 @@ def test_periodic_mask(): X = make_checkerboard_microstructure(1, 3) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) mask = np.ones((X.shape)) mask[0, 0, 0] = 0 - X_auto_periodic_mask = autocorrelate(X_, periodic_axes=[0, 1], + X_auto_periodic_mask = autocorrelate(X, basis, periodic_axes=[0, 1], confidence_index=mask) X_result_0 = np.array([[[1 / 7., 1 / 7., 3 / 7.], [1 / 7., 0.5, 1 / 7.], @@ -175,7 +168,7 @@ def test_periodic_mask(): [2 / 7., 1 / 7., 2 / 7.]]]) X_result = np.concatenate((X_result_0[..., None], X_result_1[..., None]), axis=-1) - assert np.allclose(X_auto_periodic_mask, np.concatenate(X_result)) + assert np.allclose(X_auto_periodic_mask, X_result) def test_nonperiodic_mask(): @@ -188,10 +181,9 @@ def test_nonperiodic_mask(): X = make_checkerboard_microstructure(1, 3) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) mask = np.ones((X.shape)) mask[0, 0, 0] = 0 - X_auto_nonperiodic_mask = autocorrelate(X_, confidence_index=mask) + X_auto_nonperiodic_mask = autocorrelate(X, basis, confidence_index=mask) X_result_0 = np.array([[[1 / 3., 0, 0.5], [0, 0.5, 0.], [0.5, 0, 1 / 3.]]]) @@ -210,10 +202,9 @@ def test_mixperdic_mask(): X = make_checkerboard_microstructure(1, 3) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) mask = np.ones((X.shape)) mask[0, 0, 0] = 0 - X_auto_mixperiodic_mask = autocorrelate(X_, periodic_axes=[0], + X_auto_mixperiodic_mask = autocorrelate(X, basis, periodic_axes=[0], confidence_index=mask) X_result_0 = np.array([[[1 / 5., 1 / 7., 2 / 5.], [0, 0.5, 0], @@ -234,10 +225,9 @@ def test_mask_two_samples(): X = make_microstructure(n_samples=2, n_phases=2, size=(3, 3), grain_size=(2, 2), seed=99) basis = DiscreteIndicatorBasis(n_states=2) - X_ = basis.discretize(X) mask = np.ones(X.shape) mask[:, 0, 0] = 0. - X_corr = correlate(X_, confidence_index=mask) + X_corr = correlate(X, basis, confidence_index=mask) X_result = np.array([[[[1 / 3., 1 / 3., 1 / 3.], [1 / 5., 1 / 5., 1 / 5.], [1 / 4., 1 / 4., 0]], @@ -256,13 +246,68 @@ def test_mask_two_samples(): [[0., 0., 1 / 2.], [2 / 5., 3 / 5., 0.], [0., 0., 2 / 3.]]]]) - print np.round(X_corr, decimals=4) - print X_result assert np.allclose(X_corr, X_result) +def test_normalization_rfftn(): + """Test normalization with rfftn + """ + from pymks import PrimitiveBasis + from pymks.stats import _normalize + prim_basis = PrimitiveBasis() + Nx = Ny = 5 + X_ = np.zeros((1, Nx, Ny, 1)) + prim_basis._axes = np.arange(X_.ndim - 2) + 1 + prim_basis._axes_shape = (2 * Nx, 2 * Ny) + norm = _normalize(X_.shape[:-1], prim_basis, None) + assert norm.shape == (1, Nx, Ny, 1) + assert np.allclose(norm[0, Nx / 2, Ny / 2, 0], 25) + + +def test_normalization_fftn(): + """Test normalization with fftn + """ + from pymks.bases import FourierBasis + from pymks.stats import _normalize + f_basis = FourierBasis() + Nx = Ny = 5 + X_ = np.zeros((1, Nx, Ny, 1)) + f_basis._axes = np.arange(X_.ndim - 2) + 1 + f_basis._axes_shape = (2 * Nx, 2 * Ny) + norm = _normalize(X_.shape[:-1], f_basis, None) + assert norm.shape == (1, Nx, Ny, 1) + assert np.allclose(norm[0, Nx / 2, Ny / 2, 0], 25) + + +def test_gsh_basis_normalization(): + from pymks.bases import GSHBasis + from pymks.stats import _normalize + gsh_basis = GSHBasis() + Nx = Ny = 5 + X_ = np.zeros((1, Nx, Ny, 1)) + gsh_basis._axes = np.arange(X_.ndim - 2) + 1 + gsh_basis._axes_shape = (2 * Nx, 2 * Ny) + norm = _normalize(X_.shape[:-1], gsh_basis, None) + assert norm.shape == (1, Nx, Ny, 1) + assert np.allclose(norm[0, Nx / 2, Ny / 2, 0], 25) + + +def test_stats_in_parallel(): + import time + from pymks.bases import PrimitiveBasis + from pymks.stats import correlate + from pymks.datasets import make_microstructure + X = make_microstructure(n_samples=5, n_phases=3) + p_basis = PrimitiveBasis(5) + t = [] + for i in range(1, 4): + t_start = time.time() + correlate(X, p_basis, n_jobs=i) + t.append(time.time() - t_start) + if p_basis._pyfftw: + assert t == sorted(t, reverse=True) + else: + pass + if __name__ == '__main__': - test_periodic_crosscorrelation() - test_nonperiodic_crosscorrelation() - test_periodic_autocorrelation() - test_nonperiodic_autocorrelation() + test_normalization_fftn() diff --git a/pymks/tests/test_structure_analysis.py b/pymks/tests/test_structure_analysis.py new file mode 100644 index 00000000..795194f3 --- /dev/null +++ b/pymks/tests/test_structure_analysis.py @@ -0,0 +1,90 @@ +import numpy as np + + +def test_n_componets_from_reducer(): + from pymks import MKSStructureAnalysis + from pymks import DiscreteIndicatorBasis + from sklearn.manifold import LocallyLinearEmbedding + reducer = LocallyLinearEmbedding(n_components=7) + dbasis = DiscreteIndicatorBasis(n_states=3, domain=[0, 2]) + model = MKSStructureAnalysis(dimension_reducer=reducer, basis=dbasis) + assert model.n_components == 7 + + +def test_n_components_with_reducer(): + from pymks import MKSStructureAnalysis + from pymks import DiscreteIndicatorBasis + from sklearn.manifold import Isomap + reducer = Isomap(n_components=7) + dbasis = DiscreteIndicatorBasis(n_states=3, domain=[0, 2]) + model = MKSStructureAnalysis(dimension_reducer=reducer, basis=dbasis, + n_components=9) + assert model.n_components == 9 + + +def test_n_components_change(): + from pymks import MKSStructureAnalysis + from pymks import DiscreteIndicatorBasis + dbasis = DiscreteIndicatorBasis(n_states=2) + model = MKSStructureAnalysis(basis=dbasis) + model.n_components = 27 + assert model.n_components == 27 + + +def test_default_n_components(): + from pymks import MKSStructureAnalysis + from pymks import DiscreteIndicatorBasis + dbasis = DiscreteIndicatorBasis(n_states=2) + model = MKSStructureAnalysis(basis=dbasis) + assert model.n_components == 5 + + +def test_default_dimension_reducer(): + from sklearn.decomposition import RandomizedPCA + from pymks import MKSStructureAnalysis + from pymks import PrimitiveBasis + model = MKSStructureAnalysis(basis=PrimitiveBasis()) + assert isinstance(model.dimension_reducer, RandomizedPCA) + + +def test_default_correlations(): + from pymks import PrimitiveBasis + from pymks import MKSStructureAnalysis + prim_basis = PrimitiveBasis(6) + model_prim = MKSStructureAnalysis(basis=prim_basis) + assert model_prim.correlations == [(0, 0), (0, 1), (0, 2), + (0, 3), (0, 4), (0, 5)] + + +def test_set_correlations(): + from pymks import PrimitiveBasis + from pymks import MKSStructureAnalysis + test_correlations = [(0, 0), (0, 2), (0, 4)] + prim_basis = PrimitiveBasis(6) + model_prim = MKSStructureAnalysis(basis=prim_basis, + correlations=test_correlations) + assert model_prim.correlations == test_correlations + + +def test_reshape_X(): + from pymks import MKSStructureAnalysis + from pymks import PrimitiveBasis + anaylzer = MKSStructureAnalysis(basis=PrimitiveBasis()) + X = np.arange(18).reshape(2, 3, 3) + X_test = np.concatenate((np.arange(-4, 5)[None], np.arange(-4, 5)[None])) + assert np.allclose(anaylzer._reduce_shape(X), X_test) + + +def test_set_components(): + from pymks import MKSStructureAnalysis + from pymks import PrimitiveBasis + p_basis = PrimitiveBasis(2) + model = MKSStructureAnalysis(basis=p_basis) + X = np.random.randint(2, size=(50, 10, 10)) + model.fit(X) + components = model.components_ + model.components_ = components * 2 + assert np.allclose(model.components_, components * 2) + +if __name__ == '__main__': + test_set_correlations() diff --git a/pymks/tools.py b/pymks/tools.py index 15113b34..2318f4e9 100644 --- a/pymks/tools.py +++ b/pymks/tools.py @@ -32,9 +32,10 @@ def _get_response_cmap(): Returns: dictionary with colors and localizations on color bar. """ - HighRGB = np.array([26, 152, 80]) / 255. - MediumRGB = np.array([255, 255, 191]) / 255. - LowRGB = np.array([0, 0, 0]) / 255. + HighRGB = np.array([179, 255, 204]) / 255. + MediumRGB = np.array([28, 137, 63]) / 255. + LowRGB = np.array([11, 53, 24]) / 255. + cdict = _set_cdict(HighRGB, MediumRGB, LowRGB) return colors.LinearSegmentedColormap('coeff_cmap', cdict, 256) @@ -46,6 +47,7 @@ def _get_microstructure_cmap(): Returns: dictionary with colors and microstructure on color bar. """ + HighRGB = np.array([229, 229, 229]) / 255. MediumRGB = np.array([114.5, 114.5, 114.5]) / 255. LowRGB = np.array([0, 0, 0]) / 255. @@ -60,9 +62,9 @@ def _get_diff_cmap(): Returns: dictionary with colors and localizations on color bar. """ - HighRGB = np.array([118, 42, 131]) / 255. - MediumRGB = np.array([255, 255, 191]) / 255. - LowRGB = np.array([0, 0, 0]) / 255. + HighRGB = np.array([255, 207, 181]) / 255. + MediumRGB = np.array([238, 86, 52]) / 255. + LowRGB = np.array([99, 35, 21]) / 255. cdict = _set_cdict(HighRGB, MediumRGB, LowRGB) return colors.LinearSegmentedColormap('diff_cmap', cdict, 256) @@ -74,11 +76,11 @@ def _grid_matrix_cmap(): Returns: dictionary with colors and localizations on color bar. """ - HighRGB = np.array([255, 255, 255]) / 255. - MediumRGB = np.array([150, 150, 150]) / 255. + HighRGB = np.array([229, 229, 229]) / 255. + MediumRGB = np.array([114.5, 114.5, 114.5]) / 255. LowRGB = np.array([0, 0, 0]) / 255. cdict = _set_cdict(HighRGB, MediumRGB, LowRGB) - return colors.LinearSegmentedColormap('diff_cmap', cdict, 256) + return colors.LinearSegmentedColormap('grid_cmap', cdict, 256) def _set_cdict(HighRGB, MediumRGB, LowRGB): @@ -114,9 +116,9 @@ def _get_coeff_cmap(): Returns """ - HighRGB = np.array([244, 109, 67]) / 255. - MediumRGB = np.array([255, 255, 191]) / 255. - LowRGB = np.array([0, 0, 0]) / 255. + HighRGB = np.array([205, 0, 29]) / 255. + MediumRGB = np.array([240, 240, 240]) / 255. + LowRGB = np.array([17, 55, 126]) / 255. cdict = _set_cdict(HighRGB, MediumRGB, LowRGB) return colors.LinearSegmentedColormap('coeff_cmap', cdict, 256) @@ -131,14 +133,14 @@ def _get_color_list(n_sets): Returns: list of colors for n_sets """ - color_list = ['#1a9850', '#f46d43', '#762a83', '#41b6c4', - '#ffff33', '#a50026', '#dd3497', '#ffffff', - '#36454f', '#081d58', '#d9ef8b', '#fee08b'] + color_list = ['#1a9850', '#f46d43', '#1f78b4', '#e31a1c', + '#6a3d9a', '#b2df8a', '#fdbf6f', '#a6cee3', + '#fb9a99', '#cab2d6', '#ffff99', '#b15928'] return color_list[:n_sets] -def draw_coeff(coeff, fontsize=15): +def draw_coeff(coeff, fontsize=15, figsize=None): """ Visualize influence coefficients. @@ -153,7 +155,7 @@ def draw_coeff(coeff, fontsize=15): titles = [r'Influence Coefficients $l = %s$' % ii for ii in np.arange(n_coeff)] _draw_fields(np.rollaxis(coeff, -1, 0), coeff_cmap, - fontsize=fontsize, titles=titles) + fontsize=fontsize, titles=titles, figsize=figsize) def draw_microstructure_strain(microstructure, strain): @@ -168,12 +170,12 @@ def draw_microstructure_strain(microstructure, strain): cmap = _get_response_cmap() fig = plt.figure(figsize=(8, 4)) ax0 = plt.subplot(1, 2, 1) - ax0.imshow(microstructure.swapaxes(0, 1), cmap=_get_microstructure_cmap(), + ax0.imshow(microstructure, cmap=_get_microstructure_cmap(), interpolation='none') ax0.set_xticks(()) ax0.set_yticks(()) ax1 = plt.subplot(1, 2, 2) - im1 = ax1.imshow(strain.swapaxes(0, 1), cmap=cmap, interpolation='none') + im1 = ax1.imshow(strain, cmap=cmap, interpolation='none') ax1.set_xticks(()) ax1.set_yticks(()) ax1.set_title(r'$\mathbf{\varepsilon_{xx}}$', fontsize=25) @@ -190,8 +192,8 @@ def draw_microstructures(*microstructures): Draw microstructures Args: - microstructures (3D array): numpy array with dimensions (n_samples, x, - y) + microstructures (3D array): numpy array with dimensions + (n_samples, x, y) """ cmap = _get_microstructure_cmap() titles = [' ' for s in np.arange(microstructures[0].shape[0])] @@ -270,7 +272,7 @@ def draw_differences(differences, labels=None, fontsize=15): _draw_fields(differences, cmap, fontsize, labels) -def _draw_fields(fields, field_cmap, fontsize, titles): +def _draw_fields(fields, field_cmap, fontsize, titles, figsize=None): """ Helper function used to draw fields. @@ -290,17 +292,21 @@ def _draw_fields(fields, field_cmap, fontsize, titles): raise RuntimeError( "number of plots does not match number of labels.") plt.close('all') - fig, axs = plt.subplots(1, n_fields, figsize=(n_fields * 4, 4)) + if figsize is None: + figsize = (1, n_fields) + fig, axs = plt.subplots(figsize[0], figsize[1], + figsize=(figsize[1] * 4, figsize[0] * 4)) + if n_fields > 1: for field, ax, title in zip(fields, axs.flat, titles): - im = ax.imshow(field.swapaxes(0, 1), + im = ax.imshow(field, cmap=field_cmap, interpolation='none', vmin=vmin, vmax=vmax) ax.set_xticks(()) ax.set_yticks(()) ax.set_title(title, fontsize=fontsize) else: - im = axs.imshow(fields[0].swapaxes(0, 1), cmap=field_cmap, + im = axs.imshow(fields[0], cmap=field_cmap, interpolation='none', vmin=vmin, vmax=vmax) axs.set_xticks(()) axs.set_yticks(()) @@ -409,7 +415,7 @@ def draw_gridscores_matrix(grid_scores, params, score_label=None, ax.set_xlabel(x_label, fontsize=14) ax.set_ylabel(y_label, fontsize=14) ax.grid(False) - im = ax.imshow(np.swapaxes(matrix, 0, 1), + im = ax.imshow(matrix, cmap=X_cmap, interpolation='none') ax.set_title(title, fontsize=22) divider = make_axes_locatable(ax) @@ -438,18 +444,24 @@ def draw_component_variance(variance): plt.show() -def draw_components(datasets, labels, title=None, component_labels=None): +def draw_components_scatter(datasets, labels, title=None, + component_labels=None, view_angles=None, + legend_outside=False, fig_size=None): """ Visualize low dimensional representations of microstructures. Args: datasets (list, 2D arrays): low dimensional data with dimensions - [n_samplles, n_componts]. The length of n_components must be 2 or + [n_samples, n_components]. The length of n_components must be 2 or 3. - labels (list, str): list of labes for each of each array datasets + labels (list, str): list of lables for each of each array datasets title: main title for plot component_labels: labels for components - + view_angles (int,int): the elevation and azimuth angles of the axes + to rotate the axes. + legend_outside: specify to move legend box outside the main plot + domain + figsize: (width, height) figure size in inches """ plt.close('all') if title is None: @@ -463,14 +475,54 @@ def draw_components(datasets, labels, title=None, component_labels=None): raise RuntimeError('number of components and component_labels must' ' have the same length') if n_components[-1] == 2: - _draw_components_2D(datasets, labels, title, component_labels[:2]) + _draw_components_2D(datasets, labels, title, component_labels[:2], + legend_outside, fig_size) elif n_components[-1] == 3: - _draw_components_3D(datasets, labels, title, component_labels) + _draw_components_3D(datasets, labels, title, component_labels, + view_angles, legend_outside, fig_size) else: raise RuntimeError("n_components must be 2 or 3.") -def _draw_components_2D(X, labels, title, component_labels): +def draw_evolution(datasets, labels, title=None, component_labels=None, + view_angles=None, legend_outside=False, fig_size=None): + """ + Visualize low dimensional representations of microstructures. + + Args: + datasets (list, 2D arrays): low dimensional data with dimensions + [n_samples, n_components]. The length of n_components must be 2 or + 3. + labels (list, str): list of lables for each of each array datasets + title: main title for plot + component_labels: labels for components + view_angles (int,int): the elevation and azimuth angles of the axes + to rotate the axes. + legend_outside: specify to move legend box outside the main plot + domain + figsize: (width, height) figure size in inches + """ + plt.close('all') + if title is None: + title = 'Low Dimensional Representation' + n_components = np.array(datasets[0][-1].shape) + if component_labels is None: + component_labels = range(1, n_components + 1) + if len(datasets) != len(labels): + raise RuntimeError('datasets and labels must have the same length') + if n_components != len(component_labels): + raise RuntimeError('number of components and component_labels must' + ' have the same length') + if n_components[-1] == 2: + _draw_components_evolution(datasets, labels, + title, component_labels[:2], + legend_outside, fig_size) + else: + raise RuntimeError("time and one component must be paired") + + +def _draw_components_2D(X, labels, title, component_labels, + legend_outside, fig_size): """ Helper function to plot 2 components. @@ -480,7 +532,10 @@ def _draw_components_2D(X, labels, title, component_labels): """ n_sets = len(X) color_list = _get_color_list(n_sets) - fig = plt.figure() + if fig_size is not None: + fig = plt.figure(figsize=(fig_size[0], fig_size[1])) + else: + fig = plt.figure() ax = fig.add_subplot(111) ax.set_xlabel('Component ' + str(component_labels[0]), fontsize=15) ax.set_ylabel('Component ' + str(component_labels[1]), fontsize=15) @@ -493,12 +548,53 @@ def _draw_components_2D(X, labels, title, component_labels): ax.set_ylim([y_min - y_epsilon, y_max + y_epsilon]) for label, pts, color in zip(labels, X, color_list): ax.plot(pts[:, 0], pts[:, 1], 'o', color=color, label=label) - plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=15) + lg = plt.legend(loc=1, borderaxespad=0., fontsize=15) + if legend_outside is not None: + lg = plt.legend(bbox_to_anchor=(1.05, 1.0), loc=2, + borderaxespad=0., fontsize=15) + lg.draggable() + plt.title(title, fontsize=20) + plt.show() + + +def _draw_components_evolution(X, labels, title, component_labels, + legend_outside, fig_size): + """ + Helper function to plot 2 components. + + Args: + X: Arrays with low dimensional data + labels: labels for each of the low dimensional arrays + """ + n_sets = len(X) + color_list = _get_color_list(n_sets) + if fig_size is not None: + fig = plt.figure(figsize=(fig_size[0], fig_size[1])) + else: + fig = plt.figure() + ax = fig.add_subplot(111) + ax.set_xlabel('Time', fontsize=15) + ax.set_ylabel('Components ', fontsize=15) + X_array = np.concatenate(X) + x_min, x_max = [np.min(X_array[:, 0]), np.max(X_array[:, 0])] + y_min, y_max = [np.min(X_array[:, 1]), np.max(X_array[:, 1])] + x_epsilon = (x_max - x_min) * 0.05 + y_epsilon = (y_max - y_min) * 0.05 + ax.set_xlim([x_min - x_epsilon, x_max + x_epsilon]) + ax.set_ylim([y_min - y_epsilon, y_max + y_epsilon]) + for label, pts, color in zip(labels, X, color_list): + ax.plot(pts[:, 0], pts[:, 1], 'o', color=color, label=label) + lg = plt.legend(loc=1, borderaxespad=0., fontsize=15) + if legend_outside is not None: + lg = plt.legend(bbox_to_anchor=(1.05, 1.0), loc=2, + borderaxespad=0., fontsize=15) + lg.draggable() plt.title(title, fontsize=20) plt.show() -def _draw_components_3D(X, labels, title, component_labels): +def _draw_components_3D(X, labels, title, component_labels, view_angles, + legend_outside, fig_size): """ Helper function to plot 2 components. @@ -508,11 +604,14 @@ def _draw_components_3D(X, labels, title, component_labels): """ n_sets = len(X) color_list = _get_color_list(n_sets) - fig = plt.figure() + if fig_size is not None: + fig = plt.figure(figsize=(fig_size[0], fig_size[1])) + else: + fig = plt.figure() ax = fig.add_subplot(111, projection='3d') - ax.set_xlabel('Component ' + str(component_labels[0]), fontsize=10) - ax.set_ylabel('Component ' + str(component_labels[1]), fontsize=10) - ax.set_zlabel('Component ' + str(component_labels[2]), fontsize=10) + ax.set_xlabel('Component ' + str(component_labels[0]), fontsize=12) + ax.set_ylabel('Component ' + str(component_labels[1]), fontsize=12) + ax.set_zlabel('Component ' + str(component_labels[2]), fontsize=12) X_array = np.concatenate(X) x_min, x_max = [np.min(X_array[:, 0]), np.max(X_array[:, 0])] y_min, y_max = [np.min(X_array[:, 1]), np.max(X_array[:, 1])] @@ -526,7 +625,12 @@ def _draw_components_3D(X, labels, title, component_labels): for label, pts, color in zip(labels, X, color_list): ax.plot(pts[:, 0], pts[:, 1], pts[:, 2], 'o', color=color, label=label) plt.title(title, fontsize=15) - plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., fontsize=15) + if view_angles is not None: + ax.view_init(view_angles[0], view_angles[1]) + lg = plt.legend(loc=1, borderaxespad=0., fontsize=15) + if legend_outside: + lg = plt.legend(bbox_to_anchor=(1.05, 1.0), loc=2, + borderaxespad=0., fontsize=15) plt.show() @@ -558,6 +662,20 @@ def draw_goodness_of_fit(fit_data, pred_data, labels): plt.show() +def draw_components(X_comp, fontsize=15, figsize=None): + """ + Visualize spatial correlations. + + Args: + X_corr (ND array): correlations + correlations (list, optional): correlation labels + """ + cmap = _get_coeff_cmap() + titles = [r'Component $%s$' % (ii + 1) for ii + in np.arange(X_comp.shape[0])] + _draw_fields(X_comp, cmap, fontsize, titles, figsize=figsize) + + def draw_correlations(X_corr, correlations=None): """ Visualize spatial correlations. @@ -568,7 +686,7 @@ def draw_correlations(X_corr, correlations=None): """ if correlations is None: n_cross = X_corr.shape[-1] - L = (np.sqrt(1 + 8 * n_cross) - 1).astype(int) / 2 + L = range((np.sqrt(1 + 8 * n_cross) - 1).astype(int) / 2) correlations = _auto_correlations(L) + _cross_correlations(L) _draw_stats(X_corr, correlations=correlations) @@ -613,8 +731,6 @@ def _draw_stats(X_, correlations=None): plt.close('all') X_cmap = _get_coeff_cmap() n_plots = len(correlations) - vmin = np.min(X_) - vmax = np.max(X_) x_loc, x_labels = _get_ticks_params(X_.shape[0]) y_loc, y_labels = _get_ticks_params(X_.shape[1]) fig, axs = plt.subplots(1, n_plots, figsize=(n_plots * 5, 5)) @@ -626,8 +742,7 @@ def _draw_stats(X_, correlations=None): ax.set_xticklabels(x_labels, fontsize=12) ax.set_yticks(y_loc) ax.set_yticklabels(y_labels, fontsize=12) - im = ax.imshow(np.swapaxes(img, 0, 1), cmap=X_cmap, - interpolation='none', vmin=vmin, vmax=vmax) + im = ax.imshow(img, cmap=X_cmap, interpolation='none') ax.set_title(r"Correlation $l = {0}$, $l' = {1}$".format(label[0], label[1]), fontsize=15) @@ -668,9 +783,10 @@ def _get_ticks_params(l): """ segments = np.roll(np.arange(4, 7, dtype=int), 1, 0) m = segments[np.argmin(l % segments)] - n = max((l + 1) / m, 1) - tick_loc = range(0, l + n, n) - tick_labels = range(- (l - 1) / 2, (l + 1) / 2 + n, n) + n = int(max((l + 1) / m, 1)) + tick_loc = list(range(0, l + n, n)) + tick_labels = list(range(int(round(- (l - 1) / 2)), + int(round(int((l + 1) / 2 + n))), n)) return tick_loc, tick_labels diff --git a/setup.cfg b/setup.cfg index bca643a8..9e9f1600 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,2 +1,4 @@ -[nosetests] -with-doctest = true \ No newline at end of file +[nosetests] +with-doctest = true +[metadata] +description-file = README.md \ No newline at end of file diff --git a/setup.py b/setup.py index 91c90cc0..ea0afb23 100644 --- a/setup.py +++ b/setup.py @@ -41,7 +41,7 @@ def getVersion(version, release=True): return version + '-dev.' + _git_version setup(name='pymks', - version=getVersion('0.2.3', release=True), + version=getVersion('0.3.0', release=True), description='Materials Knowledge Systems in Python (PyMKS)', author='David Brough, Daniel Wheeler', author_email='david.brough.0416@gmail.com',