From b4841fad84946eb655edc3fae4a11278b8acde23 Mon Sep 17 00:00:00 2001 From: Daniel Frank Date: Wed, 10 May 2023 15:33:15 +0200 Subject: [PATCH] add linear system in state space representation --- README.md | 19 +- config/msd.json | 15 +- scripts/create_train_split.py | 2 +- scripts/notebooks/input_sequence.ipynb | 8 +- scripts/notebooks/l2_stability.ipynb | 4 +- scripts/notebooks/linearization.ipynb | 43 ++- scripts/run_datagen.py | 138 ---------- src/statesim/configuration.py | 37 ++- src/statesim/generate/input.py | 16 +- tests/smoke_test.py | 364 +++---------------------- tests/utils.py | 34 +++ 11 files changed, 143 insertions(+), 537 deletions(-) delete mode 100644 scripts/run_datagen.py diff --git a/README.md b/README.md index 47736fc..9060f78 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,5 @@ # Numeric Simulator for state space models -Simulating ordinary differential equations that have a state space representation with external input. For the integration of continuous systems `scipy.integrate.solve_ivp` is used. Discrete systems are iterated with a fixed step size. +It simulates ordinary differential equations with a state space representation and external input. `scipy.integrate.solve_ivp` integrates continuous systems. Discrete systems are iterated with a fixed step size. State space description, given an initial condition $x(0)$ and a fixed time horizon $T$, for discrete system with a step size $\eta$ @@ -37,13 +37,13 @@ $$\dot{x}(t) = f(x(t), u(t)),\qquad y(t) = g(x(t), u(t))$$ ``` ## Simulation -The continuous simulator will evaluate the function depending on the method chosen, the input sequence is always discrete with a constant distance of `step_size`. The output sequence has the same step size as the input sequence, therefore the result of the continuous integrator is resampled (or evaluated) to the `step_size`. +The continuous simulator will evaluate the function depending on the method chosen. The input sequence is always discrete with a constant distance of `step_size`. The output sequence has the same step size as the input sequence. Therefore the result of the continuous integrator is resampled (or evaluated) to the `step_size`. ## Systems -The systems that can be simulated with `statesim` are described by nonlinear differential equations. Each system has a nonlinear symbolic expression that can be used for simulation and is considered to be the ground truth data. From the symbolic nonlinear expressions, linearizations can be derived and evaluated at an equilibrium point with `SymPy`. +The systems that can be simulated with `statesim` are described by nonlinear differential equations. Each system has a nonlinear symbolic expression that can be used for simulation and is considered the ground truth data. From the nonlinear symbolic expressions, linearizations can be derived and evaluated at an equilibrium point with `SymPy`. Currently, the following systems are implemented -- **CartPole**: Zero input, the initial state is around the upright position of the pole (Barto AG, Sutton RS, Anderson CW. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE transactions on systems, man, and cybernetics. 1983 Sep(5):834-46.) +- **CartPole**: Zero input, the initial state is around the upright position of the pole (Barto AG, Sutton RS, Anderson CW. Neuronlike adaptive elements that can solve difficult learning control problems. IEEE Transactions on systems, man, and cybernetics. 1983 Sep(5):834-46.) ![Cartpole](/img/state_cartpole.png) - **Coupled mass spring damper system**: states of 4 coupled masses @@ -52,15 +52,19 @@ Currently, the following systems are implemented - **Inverted pendulum with torque input**: Zero input, the initial state is around the upright position of the pole ![Pendulum](/img/state_pend.png) - ## Input sequences Random input sequences can be generated to excite the system. Currently, the following types of input generation are implemented: - **Random Static**: Static inputs that jump to another static value after a random time from a given interval. ![input sequence](/img/input.png) + +## Generate data for a continuous linear system +To generate a dataset for a continuous linear system, you can use the script `run_datagen_linear.py`. This will use the matrices defined in `config/linear.json`. The following output is generated for a double integrator with Gaussian measurement noise. The input sequence is a random static sequence in the range $u \in [-1, 1]$ +![output double integrator](/img/output_linear.png) + ## Example -In `scripts/notebooks` some examples of how to use `statesim` are shown in *jupyter notebooks*. To generate `.csv` files from the simulations the script `scripts/run_cartpole_datagen.py` shows this for the cartpole example. The configuration can also be external as a `.json` file with the fields: +In `scripts/notebooks`, some examples of how to use `statesim` are shown in *jupyter notebooks*. To generate `.csv` files from the simulations, the script `scripts/run_cartpole_datagen.py` offers this for the cart pole example. The configuration can also be external as a `.json` file with the fields: ```json { "result_directory": "str, Directory where the .csv files will be stored, must exist", @@ -69,8 +73,9 @@ In `scripts/notebooks` some examples of how to use `statesim` are shown in *jupy "K": "int, Number of samples", "T": "float, Simulation end time start is always 0, e.g. [0, T]", "step_size": "float, step between two measurements", - "input": "InputConfig, configuration for generating random input sequences", + "input": "Optional: InputGeneratorConfig, configuration for generating random input sequences, if not defined there will be no input", "system": "SystemConfig, specific configuration for the system to be simulated", "simulator": "SimulatorConfig, configuration for the simulator, requires an initial state" + "measurement_noise": "Optional: NoiseConfig, configuration for measurement noise, if not defined there will be no measurement noise" } ``` \ No newline at end of file diff --git a/config/msd.json b/config/msd.json index 8f83042..036adee 100644 --- a/config/msd.json +++ b/config/msd.json @@ -3,20 +3,21 @@ "base_name": "initial_state-0", "seed": 2023, "K": 200, - "T": 300, - "step_size": 0.05, - "input": { + "T": 1000, + "step_size": 0.5, + "input_generator": { + "type": "random_static_input", "u_max": 0.2, "u_min": -0.2, - "interval_min": 20, - "interval_max": 100 + "interval_min": 10, + "interval_max": 40 }, "system": { "name": "CoupledMsd", "nx": 8, "ny": 1, "nu": 1, - "C": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0], + "C": [[0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]], "xbar": [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0], "ubar": [0.0], "N": 4, @@ -28,6 +29,6 @@ "measurement_noise": { "type": "gaussian", "mean": 0.0, - "std": 0.02 + "std": 0.03 } } \ No newline at end of file diff --git a/scripts/create_train_split.py b/scripts/create_train_split.py index 2637f91..64192bf 100644 --- a/scripts/create_train_split.py +++ b/scripts/create_train_split.py @@ -10,7 +10,7 @@ config = SplitConfig.parse_obj( { - 'raw_data_directory': '/Users/jack/pendulum/initial_state_0_K-100_T-20/raw', + 'raw_data_directory': '/Users/jack/mass-spring-damper/initial_state-0_K-200_T-1000/raw', 'train_split': 0.6, 'validation_split': 0.1, 'seed': 2023, diff --git a/scripts/notebooks/input_sequence.ipynb b/scripts/notebooks/input_sequence.ipynb index 11f9339..726e917 100644 --- a/scripts/notebooks/input_sequence.ipynb +++ b/scripts/notebooks/input_sequence.ipynb @@ -16,7 +16,7 @@ "metadata": {}, "outputs": [], "source": [ - "from statesim.generate.input import generate_random_static_input\n", + "from statesim.generate.input import random_static_input\n", "import numpy as np\n", "from statesim.simulator import ContinuousSimulator\n", "from statesim.model.statespace import Nonlinear\n", @@ -39,7 +39,7 @@ "T = 20\n", "eta = 0.01\n", "N = int(T / eta)\n", - "us = generate_random_static_input(\n", + "us = random_static_input(\n", " N=N, nu=1, amplitude_range=(-10.0, 10.0), frequency_range=(50, 100)\n", ")\n", "us = [np.array([[u]]) for u in np.zeros(N)]" @@ -118,7 +118,7 @@ } ], "source": [ - "us = generate_random_static_input(\n", + "us = random_static_input(\n", " N=N, nu=1, amplitude_range=(-1, 1), frequency_range=(50, 100)\n", ")\n", "# us = [np.array([[u]]) for u in np.zeros(N)]\n", @@ -192,7 +192,7 @@ "T = 200\n", "eta = 0.05\n", "N = int(T / eta)\n", - "us = generate_random_static_input(\n", + "us = random_static_input(\n", " N=N, nu=1, amplitude_range=(-2, 2), frequency_range=(50, 150)\n", ")\n", "# us = [np.array([[u]]) for u in np.zeros(N)]\n", diff --git a/scripts/notebooks/l2_stability.ipynb b/scripts/notebooks/l2_stability.ipynb index 805127e..9c7d0ec 100644 --- a/scripts/notebooks/l2_stability.ipynb +++ b/scripts/notebooks/l2_stability.ipynb @@ -21,7 +21,7 @@ "import cvxpy as cp\n", "import numpy as np\n", "from statesim.system.cartpole import CartPole\n", - "from statesim.generate.input import generate_random_static_input\n", + "from statesim.generate.input import random_static_input\n", "from statesim.system.inverted_pendulum import InvertedPendulum\n", "from statesim.system.coupled_msd import CoupledMsd\n", "from statesim.model.statespace import Lure\n", @@ -691,7 +691,7 @@ " step_size=step_size,\n", ")\n", "u = [np.array([[u]]) for u in np.zeros(shape=(N, 1))]\n", - "u = generate_random_static_input(\n", + "u = random_static_input(\n", " N=N, nu=1, amplitude_range=(-10.0, 10.0), frequency_range=(50, 150)\n", ")\n", "x0 = np.zeros(shape=(2 * nx, 1))\n", diff --git a/scripts/notebooks/linearization.ipynb b/scripts/notebooks/linearization.ipynb index 992d5d3..65132f3 100644 --- a/scripts/notebooks/linearization.ipynb +++ b/scripts/notebooks/linearization.ipynb @@ -2,18 +2,9 @@ "cells": [ { "cell_type": "code", - "execution_count": 3, + "execution_count": 14, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The autoreload extension is already loaded. To reload it, use:\n", - " %reload_ext autoreload\n" - ] - } - ], + "outputs": [], "source": [ "# import necessary functions\n", "%load_ext autoreload\n", @@ -22,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -31,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -46,11 +37,11 @@ " plot_inputs,\n", " plot_comparison,\n", ")\n", - "from statesim.generate.input import generate_random_static_input\n", + "from statesim.generate.input import random_static_input\n", "from statesim.noise import NoiseGeneration\n", "import numpy as np\n", "\n", - "step_size = 0.01" + "step_size = 0.5" ] }, { @@ -215,15 +206,15 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ - "T_end = 10\n", + "T_end = 100\n", "N = int(T_end / step_size)\n", "us = [np.array([[u]]) for u in np.zeros(N)]\n", - "us = generate_random_static_input(\n", - " N=N, nu=1, amplitude_range=(-0.2, 0.2), frequency_range=(20, 100)\n", + "us = random_static_input(\n", + " N=N, nu=1, amplitude_range=(-0.2, 0.2), frequency_range=(10, 40)\n", ")\n", "\n", "noise_config = NoiseGeneration('gaussian', 0.0, 0.01)\n", @@ -254,24 +245,24 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b66250a1f895486eb508153101ccee9f", + "model_id": "ca1de05089214b3f84f2ec766a850c8d", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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qHT16lIiIiPLiD6B169b4+Phw9OjR8gIwMjKyvPj7+zkqq02bNhft5hEaGsrBgwerdM6qkAJQCCFEvZRXlsfb29/mj/g/AGjg0YAH2j3ADU1uwKg3Vvg8Xk5ejGw8kpGNR5KQm8APR35g/on5xKTEsOn0Ju5scyfTO0zHSV97d9VwNeo58tpwza59PYzGi++doihYrdZqP0d1nLMqpAAUQghR7yTkJvDo2kdJzEtEp+i4o9UdPNzhYdyMVdv3trF3Y17u+TJ3tL6D93e9z4aUDXxz6Bs2nd7EW33eooVfCzt9BtVLUZTr6oatqVq1akVycjLJycnlrYBHjhwhJyeH1q1ba5yuesksYCGEEPXKptObmLxkMol5iYS6h/L9yO95uuvTVS7+/qqJdxM+HfwpHw/8GD8XP05kn2DSkknMOToHVVXtdh1xfYYMGULbtm2ZPHkye/bsYceOHUyZMoX+/fvTpUvdGbNZEVIACiGEqDeWxC/h4TUPk2/Kp1NQJ34a/RPtA9s77HoDGw7k97G/MzBiIGarmbd2vMUrW16hzFLmsGuKK1MUhYULF+Lr60u/fv0YMmQITZo04ZdfftE6WrVTVDv/KvL222/z/PPP89hjj/Hhhx9W6D15eXl4e3uTm5uLl5eXPeNcxGQysXTpUkaNGnVJX7yomeSe1S5yv2qX+na/lsQv4YVNL2BVrYyNGsuMnjOua6xfVaiqyneHv+M/e/6DVbXSLrAdHw/8GH/Xii8WXJ33q6SkhISEBBo3boyLi4tDr1VXWa1W8vLy8PLyQqerXHvb1e5DVWsnu7YA7ty5k1mzZl0yA0cIIYTQ0tL4peXF38RmE3m99+vVVvyBreVpWvQ0Phv8GZ5Onhw4d4Bpy6eRWpBabRmE+Cu7FYAFBQVMnjyZL774Al9fX3udVgghhKiS7We28+KmF8uLv5d7voxO0WYEVO8GvZk9ajah7qEk5iVy57I7OZl9UpMson6z21fAww8/zOjRoxkyZIi9TimEEEJUSXxuPE/EPIFZNTOy8UhNi78LGns35vuR3xPlHUV6UTp3rbiL2OxYTTOJ+scuc7p//vln9uzZw86dOyt0fGlpKaWlpeV/z8vLA2zjGxy5L96Fc2u59564PnLPahe5X7VLXb9f2SXZPLz6YfLL8mkf0J6Xu72MxWzBgjZbmv2Vv5M/Xwz5gkfWPcLhrMPcu+JevhjyBU28m1zxPdV5v0wmE6qqYrVaNV2rrja7MMXiwr9jZVitVlRVxWQyXbSINFT9/0GVJ4EkJyfTpUsXVq1aVT72b8CAAXTo0OGKk0BmzJjBq6++esnzc+bMwc3NftPwhRBC1E9W1cp3hd8RZ47DV+fLgx4P4q5zr9zJVCsuphyczXmo6FAVHSaDOyUGH7iOPWovp9hazDeF35BqScVD8eBej3sJ0AdU6Zz2YDAYCAkJISIiAien2ruAdW1XVlZGcnIyaWlpmM0X70tdVFTE7bffXulJIFUuABcsWMCECRMuqkwtFguKoqDT6SgtLb2kar1cC2BERAQZGRkOnwW8atUqhg4dWi9mvNUFcs9qF7lftUtdvl9fHf6KT/d/iovehR+G/0CUT1TF31yYgZIQgy5hA0ryVshNRrGaLzlMNbqDXxOsDbqgNh6AGtkHXLyvO2tOaQ4PrHmAEzknCHEL4bth3xHoFnjJcdV5v0pKSkhOTiYyMlJmAVeSqqrk5+fj6emJUslfFEpKSkhMTCQiIuKys4ADAgIqXQBWuQt48ODBl+xld9ddd9GyZUueffbZS4o/AGdnZ5ydnS953mg0Vss3oeq6jrAfuWe1i9yv2qWu3a+96Xv5/MDnALzQ/QVaBra89ptUFRI2wK6v4NgS+HvBp+jBPRBQwWKCklwUUyGcPYj+7EHY8w3oDNB8BHSaAk2HgK5iW5gFGgP5cviXTFk2hVN5p3hk/SN8O+JbPJ08L3t8ddyvvzbkVHYJk/ruQrfvhX/HytDpdCiKctl7XtX/A1UuAD09PYmOjr7oOXd3d/z9/S95XgghhHCk3NJcntnwDBbVwugmoxnfdPy135S4CVa8CGf2/flcSFtoMgAa94egVuAZenFBZy6D7EQ4dwwSN0LcWsg8CccW2/54R0CfJ6DjnWC4dheqn4sfnw/5nDuW3kFsdixPrHuCz4Z8Vqv3DxY1W+3f2E8IIYQ47987/01aYRoNPRvyUo+Xrt71lpsCy561FWwARndofyt0uQdCrtGAYXCCwOa2P63H2p5LPwp7foD9P0FuMix5Ejb9BwY8D+1vg2u0AoV7hjNzyEymLZ/G9rTtvL7tdV7r9Vqluw+FuBqHtOvGxMRUeBcQIYQQwh42n97MwriFKCi82edN3I1XmfRxeD7M7GUr/hSdreh7bD/c8J9rF39XEtQKRvwLnjwKI98Fj2BbIbjwH/DNSDh7+JqnaOXfig8GfIBO0bHg5AK+P/J95bLUUwMGDODxxx8HIDIyUmqRq5COfSGEELVeoamQV7faVpeY3GoyHYI6XP5AUwksnA5zp0FJLjToDA9tgRs+AI9LJ15UitEFuj9gKyiHvGprWUzeBp/3hbVv2sYQXkXvBr15usvTAHyw+wM2pmy0T656ZufOndx///1ax6ixpAAUQghR632852POFJ6hgUcDHun4yOUPKsqCH8bD3h8ABfr+E+5eYWu5cwSjK/R5HKbvgFZjQbXAhnfh6xGQGXfVt05uNZmJzSZiVa08s+EZkvKSHJOxDgsMDJSl5a5CCkAhhBC12uHMw/x07CcAXun5Cm7Gy/zQz4qHL4dA0lZw9oY758Pgl6A69gP2Dodbf4CbvrYtE3N6F8zqB0cWXvEtiqLwYvcX6RTUiQJTAU/GPEmJucTxWeuQv3cBK4rCl19+yYQJE3Bzc6NZs2YsWrRIu4AakwJQCCFEraWqKu/seAcVlVGNR9EzrOelB2WctLW6ZcWBd0O4ZyVEDaz+sNETbd3NjfpAWQH8OgXWvA7Wy+9MYtQb+Xf/f+Pn4sfx7OO8u/vdag78F6oKZYXa/KnacsUXefXVV7nllls4cOAAo0aNYvLkyWRlZdnt/LWJzAIWQghRay1NWMre9L24Glx5ovMTlx6QFQ/fjYGCsxAcDXfMA8+Q6g96gXc4TFkIq1+BrZ/Axvfg7CFb66DTpZNWgtyCeKffO9y/8n4WxC1AcVUYxajqz20qgn+FVf91AV5Ivey/TWVMmzaN2267DYB//etffPzxx+zYsYMRI0bY5fy1ibQACiGEqJWKTEV8sPsDAO5tey8h7n8r7HKS4buxkJ8KgS1thZeWxd8FegMMfxNu/AIMLhC7HL69AQozLnt4j9AePNzhYQAWFy+W8YBVcGHLWrCtWezl5UV6erqGibQjLYBCCCFqpa8OfUV6UToNPBowtc3Ui18syYPZN9uWYfFvClMWgbv2e+xepN0t4BsJc26F1D3w1VC443fwa3zJofe1u49tZ7ax6+wuXtzyIj+M/gGjrhp3bzG62VritHC5MZ2VPdXfds9QFKV8x476RloAhRBC1DoZxRn8cOQHAJ7q8hTO+r9sL2q1wG93w7mj4BFyvuUvWKOk1xDRzTYm0aehrbv6m1G2MYt/o1N0vNbjNVwUFw5nHWbW/lnVm1NRbN2wWvyRhbAdQgpAIYQQtc7/DvyPYnMx7QLaMaThkItfXPkSnFwFBle47SfbuLuaLKAZ3LMKAlvZuqu/HQ3nYi85LMQ9hHGu4wD44uAX7EvfV81BRV0iBaAQQohaJSU/hbmxcwF4tNOjF2+Vtv8X2Pap7fGEmdCgkwYJK8EzBKb+AUFtoCDtikVgW6e2jI4cjVW18vKWlym1lGoQVtQFMgZQCCFErTJz/0zMVjM9QnvQPbT7ny9knITF52cC93sG2kzQJmBleQTaisDvx8HZg/DDBLhnxSUtmE93eZrtZ7eTkJvArP2zeLTToxoFrnliYmLKHycmJl70mnqZ5WRycnIcG6gGkxZAIYQQtUZcThyL4xcD8Finx/58wVRi297NVAiRfWHAc9oErCp3f5iyAPybQV6KrQgszLzoEC8nL17o/gIA3xz6huNZxzUIKmo7KQCFEELUGrMOzMKqWhkUMYjogOg/X1j5f7ZWMzd/2/IqOr12IavKPcC2U4lXA8iIhdk3QVnRRYcMbTSUIQ2HYFbNvLzlZcxWs0ZhRW0lBaAQQohaISkviRWJKwB4sP2Df74QtxZ2fmF7PGEWeIVqkM7OfCJsRaCrn22JmAUPgnrxciUvdH8BTydPjmQeKR8TKURFSQEohBCiVvjq0FdYVSt9G/SllX8r25Ol+bDo/Bi4rvdBs6HaBbS3wBYwaTbojHBkIbr171z8slsgj3a0fe6f7P2E7JJsLVKKWkoKQCGEEDVeWmEai+IWAXB/u/v/fGHVK7bFnn0awZAZ2oRzpEa9YMxHAOg3v0+DrC0XvXxz85tp4duCvLI8Pt77sRYJRS0lBaAQQoga75tD32C2mukW0o0OQR1sTyZshF1f2R6P/S84e2iWz6E6TobetgkvHZK+hvQj5S/pdXqe7/48APNi53E447AmEUXtIwWgEEKIGi2rJIt5J+YBti3RADCXwh/nZwF3vgua9NcoXTUZPANrk4EY1DIMv99t6/o+r3NwZ0Y1HoWKyls73rrscidC/J0UgEIIIWq0X47/QqmllDb+begecn7dv62fQlYcuAfB0Fe1DVgddDosY2dSbPRFyTxpK37/Uug91eUpXA2u7D+3nzVJazQMKmoLKQCFEELUWKWWUn4+9jMAU9tMte36kXsaNvzbdsCw18HFW8OE1cg9gJ2R01F1Bjg0D3Z/U/5SkFsQd7a+E4CP9nyEyWrSKqWoJaQAFEIIUWMtjV9KVkkWwW7BDGl0fs/flf8HpiKI6AHtbtU2YDXL9miGdeD/2f6y4kXIjCt/7a42d+Hr7EtiXiLzT8zXKKG2BgwYwOOPP17+98jISD788EO7X+fv51UUhQULFtj9Oo4kBaAQQogaSVVVfjj6AwC3t7odo85om/hx+HdQdDDq3/DXfYDrCWv3f9h2OzEVwfwHwGJbBNrDyYMH2j8AwGf7PqPIVHS109QLO3fu5P7777/2gVV05swZRo4c6fDr2JMUgEIIIWqkbWe2cSL7BK4GVyY2mwhWq631D6DzNAhtp2k+zSg6GD8TnL0gZSds/k/5S7c0v4Vwj3AySzL5/sj3GoasGQIDA3Fzc3P4dUJCQnB2dnb4dexJCkAhhBA10o9HfwRgfNPxeDt721r+zuwDJw8Y8IK24bTmE2FrAQWIeRvSDgJg1Bt5pOMjAHx/5Hvyy/KvdIZ64XJdtV9++SUTJkzAzc2NZs2asWjRoqueIz09nTFjxuDq6krjxo2ZPXv2Jcf8tQu4rKyM6dOn06BBA0JCQmjcuDFvvfVW+bE5OTk88MADBAcH4+LiQnR0NIsXL7bL53s9DNV+RSGEEOIaUvJT2JiyEYDJrSaDuQzWvm57sdej4BGoYboaot2tcPQPOLbYNiv4nlWg0zM8cjifH/ichNwE5hydU94tXBWqqlJsLrZD6OvnanC1Tf6xk1dffZV3332Xf//73/z3v/9l8uTJnDp1Cj8/v8seP23aNFJTU1m3bh1Go5FHH32U9PT0K57/448/ZtGiRfz888/4+vqSk5PD6dOnAbBarYwcOZL8/Hx+/PFHoqKiOHLkCHp99e9dLQWgEEKIGmdu7FxUVHqG9qSRVyPY9jlkJ9qWfen5sNbxagZFsbUCJmyA07th55fQ/QH0Oj0PtnuQZzc+y/dHvmdyq8l4OFVtkexiczHd53S3U/Drs/327bgZ7deNO23aNG677TYA/vWvf/Hxxx+zY8cORowYccmxsbGxLFu2jB07dtC1a1cAvvrqK1q1anXF8yclJdGsWTP69OlDfn4+0dHR6HS2DtfVq1ezY8cOjh49SvPmzQFo0qSJ3T636yFdwEIIIWqUMktZ+SzWW1vealv0eMO7thcHPFd3d/yoDK8wGPKK7fGa1yA3BYDhkcNp7N2YvLI85hybo2HAmqdduz/Hjrq7u+Pl5XXFFr2jR49iMBjo3Llz+XMtW7bEx8fniuefNm0a+/bto1WrVjz77LOsXLmy/LV9+/YRHh5eXvxpSVoAhRBC1CgrT60kuzSbYLdg+of3h80fQVEm+EVBpylax6t5Ot8NB36F5O2w7FmYNBu9Ts8D7R7guY3P8d3h77i95e1VagV0Nbiy/fbtdgx9fde2J6PReNHfFUXBarXa7fydOnUiISGBJUuWsHz5ciZNmsSQIUP47bffcHW17+dSFdICKIQQokb59fivAExsPhGDqRi2/Nf2woDnQG+8yjvrKZ0OxnwEOoNtPOBJ204gIyJHEOkVSV5ZHr/F/lalSyiKgpvRTZM/9hz/d71atmyJ2Wxm9+7d5c8dP36cnJycq77Py8uLW2+9lY8++oiffvqJefPmkZWVRbt27UhJSSE2NtbBya9NCkAhhBA1Rmx2LHvT96JX9LalX7bPguJs8G8K0RO1jldzBbWCbucneyx/Hiwm9Do9d0ffDcAPR37AZJHdQa5XixYtGDFiBA888ADbt29n9+7d3HvvvVdtyfvggw/46aefOHbsGCdPnuS3334jJCQEHx8f+vfvT79+/Zg4cSKrVq0iISGBZcuWsXz58mr8rGykABRCCFFjzD0+F4BBDQcRpHOBrZ/YXuj/LOiqf6ZkrdL/GXALgIzjsOMLAEY3GU2QaxDpxeksSViiccDa6ZtvviEsLIz+/ftz4403cv/99xMUFHTF4z09PXn33Xfp1q0bgwYN4tSpUyxdurR8Isi8efPo2rUrt912G61bt+aZZ57BYrFU16dTTsYACiGEqBFKLaXlRcpNzW6CHf+T1r/r4eoDg1+yLQkT8za0uwUn9wDuaH0HH+z+gG8PfcvYqLHolLrb9hMTE3PR3xMTEy/6u6qql7znWt25ISEhl6zTd+edd17xvPfddx/33XcfVquVvLw8vLy8yos/AD8/P77++uurXrM61N3/BUIIIWqVNafWkF+WT6h7KN39o2Hrp7YXpPWv4jreCSHtoDQX1v0LgJua34SH0YO43LjytRWFkAJQCCFEjTD/pG3pl3FNx6Hf/zMUZ4FvJLS5UdtgtYlODyPO7zqx+1vIjMPTyZObW9wMwNeHtG95EjWDFIBCCCE0d7rgNNvP2JYZGdf4Bth6fuZvz+mgl9FK1yWyDzQbBqqlfPeUO1rdgUFnYE/6Hg5nHtY4oKgJpAAUQgihuYUnF6Ki0j20O+GndkBOkm1CQ8c7tI5WOw1+BVDg8HxI3UuQWxDDI4cDMOeoLAwtpAAUQgihMatqZcHJBQBMiBpvW/gZoPuDYKw5C+fWKiHR0O4W2+PVMwCY3HIyAMsSlpFZnKlRMFFTSAEohBBCUzvSdnCm8AyeRk8Gm3Vw9iAY3aHrPVpHq90GvgA6I8THQHwMbQPb0i6gHSarqcILQ19u1qyoPvbcoeTvZGCFEEIITf0R9wcAIxqPwOX8+nV0mgJufhqmqgN8I6HLXbbldGLegcb9ua3VbRzYeIBfj//K3W3vxqi7/M4qRqMRRVE4d+4cgYGBmu7GUVtZrVbKysooKSm5aBmYilBVlbKyMs6dO4dOp8PJycnu+aQAFEIIoZliczGrT60G4Aa/dhD3PqBAjwe1DVZX9HnCNhs4aQskbmR4o+G8v+t90ovTWXNqDSMaj7js2/R6PeHh4aSkpFyylp6oGFVVKS4uxtXVtdIFtJubGw0bNrzuArIipAAUQgihmZjkGIrMRTTwaEDH2PW2J1uMsrVeiarzCoNOU2HnFxDzNsa7lnJL81v4bP9n/HTspysWgAAeHh40a9YMk0m2kKsMk8nEhg0b6NevH0bj9e9hrdfrMRgMDmt9lQJQCCGEZi50/46OGIyy6gPbk9L6Z199noA938GpzZCwkYnNJzLrwCz2pO8hLieOKJ+oK75Vr9ej18si3JWh1+sxm824uLhUqgB0NJkEIoQQQhOZxZlsSd0CwA2FxWAqgqA2ENlX42R1jHcD25hKgJi3CXILon94f4AKTwYRdY8UgEIIITSxPHE5FtVCtH8bGu+ba3uy+wMgEw7sr88TthnBpzZB8o7ynUEWxi2kxFyicTihBSkAhRBCaGJx3GIAbvCIgtwkcPX9c+06YV/e4dD+VtvjTR/SK6wXYe5h5Jfls/LUSm2zCU1IASiEEKLaJeUlcSjzEHpFz4jk81uTdbxTFn52pF6PAQocX4Iu4wQTm08EYO7xudrmEpqQAlAIIUS1W564HIDuAe3wj4uxPdl5mmZ56oXA5tBytO3x5o+Z0HQCBsXAvnP7OJF9QttsotpJASiEEKLaXSgAR5h0gApRg8D/yrNRhZ30ftz28cAvBJrK6B9hmwzy+4nftcskNCEFoBBCiGoVlxPHiewTGHQGBsVutD3Z5W5tQ9UXEV2hUW+wmmDbZ0xoOgGAJfFLMFlkvb/6RApAIYQQ1epC619vzyZ4F2aAZyg0v/KCxMLOej9m+7jne3oHtCPANYDs0mw2pGzQNpeoVlIACiGEqDaqqrI84Xz3b06m7clOU0Bf8xbKrbOaDgX/plCah+HAr4xpMgaABXELtM0lqpUUgEIIIarN8ezjJOYl4qxzYuCpfaDo/lykWFQPnQ66n99tZdtMxkeNBWBjykYyijM0DCaqkxSAQgghqs2yhGUA9HMKxF1VIWqwbY06Ub3a3wYu3pCdQJP0E7QLaIdFtbAkfonWyUQ1kQJQCCFEtVBVlVWnVgEwLP2U7clOd2qYqB5z9oBOU22Pt33GuKbjAFhwcgGqqmoYTFQXKQCFEEJUi+PZx0nOT8ZZMdAv6wy4+UPzkVrHqr+63Q+KHhI2MNKtEc56Z07mnORI1hGtk4lqIAWgEEKIanGh9a+34oabqkK7SWBw0jhVPeYTAa1sE0A8985mYMRA4M8t+kTdJgWgEEKIarH61GoAhl7o/u14h4ZpBADd7rN9PDiXG8IHALZxmmarWbtMolpIASiEEMLh4nLiiM+Nx4CO/gUF0KAzBLfWOpZo1BsCW4GpiF7pifg4+5BZksn2M9u1TiYcTApAIYQQDneh+7enWcFTVaHDZI0TCQAUBbreA4Bx1zeMiLQtyP1H/B9aphLVoMoF4MyZM2nXrh1eXl54eXnRs2dPli1bZo9sQggh6ojy7t/sdNA7QfSNGicS5dpPAicPyDzBGJcGAKxNWkuRqUjjYMKRqlwAhoeH8/bbb7N792527drFoEGDGDduHIcPH7ZHPiGEELVcUl4Sx7OPo0dhYFExtBgJrr5axxIXOHvaikCg7bFVNPRsSLG5mDVJazQOJhypygXgmDFjGDVqFM2aNaN58+a8+eabeHh4sG3bNnvkE0IIUcutTVoLQJcyMz5Wq232r6hZut4LgHJ8GTc06A8gi0LXcQZ7nsxisTB37lwKCwvp2bPnFY8rLS2ltLS0/O95eXkAmEwmTCaTPSNd5MK5HXkNYV9yz2oXuV+1S3XdrwstSYPyclHd/DFH9gf5P3LdHHq/fJuij+iBLnkbw/Py+AzYdmYb6fnp+LpIa21lOPrrq6rnVVQ7LPl98OBBevbsSUlJCR4eHsyZM4dRo0Zd8fgZM2bw6quvXvL8nDlzcHNzq2ocIYQQNUShtZC3895GRWVl0mmK/QZxMFx2/6iJIjI30inpCwqdgpgQ0Zgz1jOMcx1HV+euWkcTl1FUVMTtt99Obm4uXl5e1/1+uxSAZWVlJCUlkZuby2+//caXX37J+vXrad368lP8L9cCGBERQUZGRqU+iYoymUysWrWKoUOHYjQaHXYdYT9yz2oXuV+1S3Xcr0Xxi5ixbQYty0zMPX0G810rUcM6OeRadZ3D71dZIYaPo1FK8/mi/8N8nPQH3YK78fngz+1/rXrA0fcrLy+PgICASheAdukCdnJyomnTpgB07tyZnTt38tFHHzFr1qzLHu/s7Iyzs/MlzxuNxmr5oVFd1xH2I/esdpH7Vbs48n5tTN0IwMDCIvBvhqFhN9vSI6LSHHa/jD4QfRPs/oYRGUl8DOxK30WuOZcA1wD7X6+ecNT9quo5HbIOoNVqvaiFTwghRP1TYi5hS+oWAAYUFUG7W6T4q+k6TQEg4thKov1aYlWtrDkls4HroioXgM8//zwbNmwgMTGRgwcP8vzzzxMTE8PkybLIpxBC1Gc70nZQbC4m2GymVZkJ2sjafzVeWEcIjgZLKcP1/gCsOLVC41DCEapcAKanpzNlyhRatGjB4MGD2blzJytWrGDo0KH2yCeEEKKWWpe8DoABRcUooe0hoKnGicQ1KUp5K+CwpAMA7Erbxbmic1qmEg5Q5TGAX331lT1yCCGEqEOsqpWY5BgA2+LPnSZqmkdch7Y3w8qXCEs7TLsOAzmQG8eqU6u4vdXtWicTdiR7AQshhLC7wxmHySjOwN1qpWtxCbSZoHUkUVFuftBqDAAjTHoAViRKN3BdIwWgEEIIu7vQ/du7qBiniO7g01DjROK6dLKt1Tg0ficAe9L3cLbwrJaJhJ1JASiEEMLu/jr+j2jp/q11IvuBTyNCinPp5B4OwMpTKzUOJexJCkAhhBB2lZyfzMmck+hVlX4lpdB6vNaRxPXS6cpbAYflFwDSDVzXSAEohBDCri5M/uhUUop3RC/wDNY0j6ikDpNB0TEs5RAKCvvP7edMwRmtUwk7kQJQCCGEXV00+1e6f2svrzBoOpRAi5XOTrY1AaUbuO6QAlAIIYTd5JbmsvvsLgAGlJRBq7EaJxJV0sG29MuIrHRAuoHrEikAhRBC2M3G0xuxqFaalpUR0bAfuPtrHUlURfMR4OzN4MxUdCgczDhISn6K1qmEHUgBKIQQwm5ikmIA6f6tM4wu0GY8AVYrXfReAKxJkr2B6wIpAIUQQtiFyWpiy+mNAPQrsUDLURonEnbR7lYABmXaJoCsTVqrZRphJ1IACiGEsIv96fvJNxfhY7HQtmF/cPHWOpKwh4Y9wbshg/OyAdibvpeM4gyNQ4mqkgJQCCGEXWw6vQmAXsUl6NtK92+dodNBu1sIsViIxgUVtXymt6i9pAAUQghhFxtPrQKgb4kZmg3XOI2wq/aTABicbdsObnXSai3TCDuQAlAIIUSVpRWmEZufhKKq9A7tDs4eWkcS9hTQDMI6MaiwEIDtZ7aTX5avcShRFVIACiGEqLLNpzcD0La0DN/WEzROIxyi/SSamMw0seoxW81sTNmodSJRBVIACiGEqLKNCcsB6FNSals7TtQ90RNBZ2BwXhYgy8HUdlIACiGEqBKTxcTWs7sB6OfTCtz8NE4kHMI9AJoOYXBhMWBb9LvEXKJxKFFZUgAKIYSokr3peylSTfhZLLRqdZPWcYQjtbuV1mVlhFhUis3FbDuzTetEopKkABRCCFElG+OXAtCnqARdqzEapxEO1WIkirMXgwoKAOkGrs2kABRCCFElG5PWAdDXPRw8gzVOIxzK6AqtxzKkqAiAmOQYzFaztplEpUgBKIQQotJSC1KJK8tCp6r0bDZe6ziiOrSbRMeSUnwsVnJKc9hzdo/WiUQlSAEohBCi0jbF22b/digtxbuN7P5RLzTqjcGrAQPOtwJKN3DtJAWgEEKIStt4chEAffQ+4NtI2zCieuh00GZC+WzgNUlrUFVV41DiekkBKIQQolLKLGVsz48HoG+joRqnEdWq7U30LCnG1WrlbNFZDmce1jqRuE5SAAohhKiUXckbKEYl0GymRfupWscR1Sm0A86+UfQt+rMVUNQuUgAKIYSolI1Hfgagj9UZJaiFxmlEtVIUaHsTg88XgDHJMZrGEddPCkAhhBCVsiljHwB9Q7prG0RoI3oivYuL0asqJ3NOkpyfrHUicR2kABRCCHHdkrNOkKiWYlBVerSfpnUcoYXAFngHRtO5pBSA9cnrNQ4krocUgEIIIa7b1oPfA9DerOAZ0UPjNEIzbScyQLqBayUpAIUQQly3zac3AtDLp7ltPJion6L/LAB3n91FXlmexoFERUkBKIQQ4rqYzCXsKM0AoHezcRqnEZryaUhEaGeiysowqxY2n96sdSJRQVIACiGEuC4Hj86jQKfgY7HSsvUtWscRWou+qbwVcF3yOo3DiIqSAlAIIcR12RK7AICeTgHojS7ahhHaazOeAcW2iSCbktdjspo0DiQqQgpAIYQQ12Vr7nEAeob30TiJqBE8gmgb2gM/i4V8cxF7zu7ROpGoACkAhRBCVFjumX0c0lkB6NV2mrZhRI2hb3cz/WQ2cK0iBaAQQogK23bgO6yKQlOMBPs30zqOqCla3sCAElvX77rElaiqqnEgcS1SAAohhKiwLWe2AtDTt7XGSUSN4upDzwZ9cbKqnC5OJy4nTutE4hqkABRCCFEhamEmWyy5APRucaPGaURN4xY9ke4lJQDEyGzgGk8KQCGEEBWScOgn0gwGnFToFDVS6ziipmkxkgGlFgBi4pdqHEZcixSAQgghKmRL3GIAOrsE42pw1TiNqHGc3Okf2hOAA7knySjO0DiQuBopAIUQQlybuZTN+QkA9Go4QNssosYKbjuJNqWlqMDG5A1axxFXIQWgEEKIayqLX8cuJwMAvVrcrHEaUWM1G0r/C93AJxZom0VclRSAQgghrmnP4Z8p0ekIVJxo5tdc6ziipjK6MjC4OwBbMw5QYi7ROJC4EikAhRBCXJ2qsuXsTgB6BrRDURSNA4marEW7OwkxmynGwo7UbVrHEVcgBaAQQoirO7OfLTozAL2ajdM4jKjplKaDy2cDrzv2q8ZpxJVIASiEEOKqMo78znFnJwB6RvTTOI2o8QxODAjsDMCGtB1YVavGgcTlSAEohBDiqrYmrACglWswfi5+GqcRtUHX9lNxs1pJV0s5eu6g1nHEZUgBKIQQ4spyU9hSdg6AXg0HaxxG1BZOUYPpXWZr+Vt36AeN04jLkQJQCCHEFVmPLWWriwsAvSOHaJxG1Bp6IwP92wEQk7pJ4zDicqQAFEIIcUWxxxeQadDjqhjoENhB6ziiFunbbio6VeW4pZAzuae0jiP+RgpAIYQQl1eSx5aswwB0C+yAUW/UOJCoTXyaDqeDSQUgZv9XGqcRfycFoBBCiMuLW8MWl/Ozf6X7V1wvnZ6Bvq0AiElap3EY8XcGrQMIURfkleWxM20neaV5qKjoFT3tA9vTyKuRLJoraq2iY4vZ4+IMQO+w3hqnEbVR/zaTeX/Hy+w0Z1NYlIm7m7/WkcR5UgAKUUkmq4n5J+azNGEp+9L3YVEtlxzTwKMBAyMGck/bewhwDdAgpRCVZDGzKykGk58bYc7+NPJqpHUiUQs1bjGORltf4pReYcu+Lxja6zmtI4nzpAtYiOukqiprk9Zy48IbeX3b6+w+uxuLaqGJdxP6hfdjQPgAOgd3xqAzcLrgND8e/ZEb5t/AN4e+ocxSpnV8ISomaStb9ed3/2g4QFqyReXodPT3jAIgJmG5xmHEX0kLoBDXodBUyEubX2LVqVUA+Ln4cXf03QxuOJhwz/CLji0yFbHtzDb+d+B/HM48zAe7P2Bx/GI+HfwpIe4hWsQXouJil7PZ1RWAXg2k+1dU3oCWN/P9nnfYWHoOS0k+ehdPrSMJpAVQiAo7lXeKyUsms+rUKgw6A/e2vZclE5Ywtc3US4o/ADejG4MaDmLO6Dm80fsN/Fz8iM2O5fYlt3M086gGn4EQFaSqnIldQoKTER0K3UO7a51I1GIdWt+CpxWy9ToO7JPZwDWFFIBCVMDe9L3ctvg24nLjCHQN5NsR3/JYp8fwcPK45nt1io5xTcfx8+ifaerTlHPF55i6fCpbU7dWQ3IhKiHjBFtK0wFo698GLycvjQOJ2syod6KvewQAMSf/0DiNuEAKQCGuYV/6Ph5c9SD5pnzaB7bnlxt+oX1g++s+T6hHKN+P/J4eoT0oNhfz+LrHOZZ1zAGJhaii2GVscbXt/tErvK/GYURdMKDpWADWl6RCab7GaQTYoQB866236Nq1K56engQFBTF+/HiOHz9uj2xCaO7AuQM8uPpBisxFdA/pzhfDviDQLbDS5/N08uSzwZ/RLaQbReYiHl79MGmFaXZMLETVWY4vY9uFAjCsl8ZpRF3Qu81tGFSIMxpIPjBH6zgCOxSA69ev5+GHH2bbtm2sWrUKk8nEsGHDKCwstEc+ITQTnxvPg6sepNBUSNeQrvx38H9xNbhW+bxGvZH/DPwPUd5RpBen8481/6DQJF8vooYoyuLw2b3k6fV4GtyJDojWOpGoA7ycvenkEgRAzLHfNE4jwA6zgJcvv3ha97fffktQUBC7d++mX79+VT29EJrILslm+prp5d2+nwz65OLiL+8MxK+DuHWQcwosZWA1g28kNOwJjXpDaHu4wtIZXk5efDbkMyYvncyJ7BO8veNtXu/9evV8ckJczYmVbHG17f7Ro0EvDDpZLELYR//GI9hx7HvWF57izuIccPXROlK9Zvev7NzcXAD8/PyueExpaSmlpaXlf8/LywPAZDJhMpnsHanchXM78hrCvrS4Z2WWMh5b+xjJ+ck0cG/AB30/wIgRk8mEkroX3YZ30MWtvvyb0w7CUdsgZ2toR6x9n0ZtOvSyhWCgcyBv936b+1bfx4KTC+gd0pvBDQc78lNzOPkaq10ud7/0x5aWj//rFtxN7mUNUtu/vno3u5F/H/ue3S5OZB+Yi0enaVpHcihH36+qnldRVVW1UxasVitjx44lJyeHTZs2XfG4GTNm8Oqrr17y/Jw5c3Bzc7NXHCEqZX7RfHaX7cYZZ+73vJ9gfTAuZZm0S/mR0NzdAKgo5Lg1Jt0zmly3RlgVIyoK3sVJ+BXGEpB/FINqW/Q5260Jexo9QIFL6GWvt7J4JRtKN+CquPKI5yN46WTGpdCGYjXT59DDDIoIwKIoPOX5FL56X61jiTpkZtYbnNaV8GKeG64NX9A6Tq1WVFTE7bffTm5uLl5e1/9zw64F4EMPPcSyZcvYtGkT4eGXrot2weVaACMiIsjIyKjUJ1FRJpOJVatWMXToUIxGo8OuI+ynuu/Z4vjFvLztZRQUPh7wMb3DeqPELkO/+FGU4mxURYcafROWPv8EvyZXPlHhOXTbPkW3+2sUUxGq0R3LqPdRo2+65FCTxcTUlVM5ln2MHiE9+HTgp7V21wX5Gqtd/n6/lPgY1i+8k8eDA2nk2ZD5YxZoHVH8RV34+vpoywy+S1zE6IIiXr9jC7hdubewtnP0/crLyyMgIKDSBaDduoCnT5/O4sWL2bBhw1WLPwBnZ2ecnZ0ved5oNFbLf+rquo6wn+q4Z3E5cby16y0AHmr/EAMi+sLK/4Ntn9kOCOuIMv5zlKCW15495RMGI96E3o/AvHtREjdiWPggnNkLI94G3Z9nMBqNvNP/HW754xa2pW1jdcpqRjUZ5ZhPsprI11jtUn6/4laVd//2btBH7mENVZu/vga1nMh3iYvY6OqMcmIJhi53ax3J4Rx1v6p6zirPAlZVlenTpzN//nzWrl1L48aNq3pKIapdkamIp2KeothcTI/QHtzfeir8dvefxV/P6XD3SghqeX0n9gyBKQuh3zOAAjtmwZInwGq96LAm3k24r+19ALy/632ZFSyqn6qixi77c/s3Wf5FOED7wPb46JzJ0+vZe/gnrePUa1UuAB9++GF+/PFH5syZg6enJ2lpaaSlpVFcXGyPfEJUi3d3vlu+y8fbPV5G/8tkOLIA9E5w83cw/E0wOFXu5Do9DHoRbvwfKDrY/S0sfeqSInBa9DQiPCNIL07n8/2fV/lzEuK6pB8lqSCV00YDBp2BriFdtU4k6iC9Tk/f879crM89AQXpGieqv6pcAM6cOZPc3FwGDBhAaGho+Z9ffvnFHvmEcLi1SWuZd2IeCgpv93oN/98fgri1YHSD23+BNuPtc6F2t8D4zwEFdn0Nq1+56GVnvTPPdXsOgB+P/Eh8Trx9ritERRxfyubz3b+dgjrhZpQJecIx+keNBmC9qwscWahxmvrLLl3Al/szbdo0O8QTwrEyijOYsWUGANNaT6Hb5lmQuBGcPG1dt1GD7HvB9rfC+PPdyls+hgNzL3q5X3g/BoQPwKyaeXfXu/a9thBXE7ucLW7S/Sscr3dYbwzoSHQyknhIGou0InsBi3pLVVVe2vwS2aXZtPBtwfSzp+HoIlu376TZENHNMRfucDv0edL2eNEjcObARS8/0/UZDIqBzac3s/vsbsdkEOKvCs9hStnFDhfb5LzeDXprHEjUZR5OHnQN7ADA+pzjkHta20D1lBSAot6af3I+m05vwknnxNse0Tjt+hpQbGP1mvR37MUH/R80HQrmYvh5MhRllb8U4RXBjc1uBODjPR9jx5WahLgs5eQq9ro4UazT4e/iT3Pf5lpHEnVc/8bDAFjn5mIbby2qnRSAol46W3iW93a+B8D0hiNouvEj2wsj3oI2ExwfQKeHiV/Y1hLMTYKl/7zo5fvb3Y+z3pk96XvYnLrZ8XlEvaaLXV4+/q9XWC90ivxoEI41IGIAAPtcnMk9JHsDa0G+ykW9o6oqb2x7g3xTPtE+zbhz22xQrdDhDuj+YPUFcfWFiV+BoodD8+Dw/PKXgt2DmdRiEiCtgMKxdNYylIQYtlxY/qWBjP8TjtfAowFNvSKxKAobc45B9imtI9U7UgCKemdZwjJiUmIw6Ay8diYVQ3E2hHWC0e9fds9eh2rQCfo+ZXu8+MmLlkS4p+09uBncOJp1lLVJa6s3l6g3AvKPkmkp4ZizbZmjnqE9NU4k6osBjYYAsN7N9aJfgEX1kAJQ1CuZxZm8tcO228cDzhE0SzsK7oFw6w9gdNEmVL+nIaQtFGfB4ifgfGufr4svk1tNBuDLg19KK6BwiJC8vWx1s/3fb+XXCn9Xf40Tifqif7htrPVmV1dMh+dpnKb+kQJQ1Ctv73ibnNIcmrsGc8+R9dgmfXwB3lffvtChDE4wYRbojHBsMcQuL3/pjtZ34KJ34VDmIXak7dAuo6ibVJWQ3H3lu3/I7F9RndoGtMXP2Zd8vY492cchM07rSPWKFICi3liTtIblicvRKzpeSzqJEaDPExA1UOtoENwGek23PV7+HJhKAPBz8SufEfzVwa+0SifqqrMHcTZl/Tn+T9b/E9VIr9PTL8LWChjj5gqHftc4Uf0iBaCoF3JLc3lj2xsATDO70qYgGyK6w8AXNE72F33/CZ5hkJ0IW/5b/vTUNlPRK3q2ntnK4czD2uUTdY7uxAqOORnJ1utwM7jR4fzabEJUlwHhAwBbAahKN3C1kgJQ1Av/2f0fMooziDR68VDyUXD2holfgt6odbQ/OXvAsNdtjze+DzlJAIR5hDGq8SgAvj74tVbpRB2kxC4vb/3rFtoNY036ehD1Qs+wnhh1RlKMRuKzT0D6Ma0j1RtSAIo6b/+5/cw7YfvN8tXkeJxVYNS/waehtsEuJ3oiNOpjWyB61cvlT98VfRcAq06tIjkvWat0oi7JO4MubX/5+n+9w2T8n6h+bkY3uoXadl2KcXOFw9INXF2kABR1msVq4c1tbwIwzqSnU3ERtBoL7W7RONkVKAqMfBtQbMsipO4DoJlvM/o06IOKyk/Hf9I0oqgjYpdTqCjsdZECUGjrQjfw+gvjAGXFg2ohBaCo0345/gtHs47iqRh5IvUUuAfBDR9W/3p/1yOkLbS92fZ4zWvlT9/e8nYAFpxYQJGpSItkoi6JXc4OVxcsCoR7hBPhFaF1IlFPXVgOZp+zM1nZcZB2UONE9YMUgKLOyijO4JO9nwDwWEY6/lYrjP0Y3GvBOmcDXwCdAeLWQOImwLZERyOvRuSb8vkj7g+NA4parawI4mPKu39l8WehpVCPUFr6tURVFDZKN3C1kQJQ1Fn/2f0f8k35tLbouCkv39aq1mKk1rEqxq8xdJ5me7z6VVBVdIqO21reBsCcY3NkYWhRefExYC5hs7sHAL1CZfkXoa0LrYDSDVx9pAAUddLus7tZFLcIBfi/tFT0bv4w4m2tY12ffk+DwRVSdpQvDj0uahxuBjfic+PZdmabxgFFrRW7jCSDgRS9gg4dXYK7aJ1I1HMDIgYAsNnVhbKcU5C6R9tA9YAUgKLOMVlN5Wv+TcwvpG1ZGYx8F9wDNE52nTxDoPsDtscb/g2qioeTB+OajgNsrYBCXDerFY4vL+/+bahviLvRXeNQor5r7d+aANcAinQ6drm6yKLQ1UAKQFHnzDk6h5M5J/FRFR7LyobmI2zLq9RGPafbWgFP77Z12wGTWk4CYEPKBtIK0zQMJ2ql1L1QmM6W892/zYzNNA4kBOgUXXk38Fo3V9sqCFarxqnqNikARZ2SUZzBzP0zAXgiIwMfoweM/qBmz/q9Go9A6DzV9njDewA08W5C5+DOWFUr80/O1zCcqJVil2ECdpxvAWxmkAJQ1AyDGg4CYJ27O2readvwF+EwUgCKOuWTvZ9QaCokuszE+IJCGPoaeDfQOlbV9HoUdEY4tQmSbOP+bmp+EwDzT8zHYrVomU7UNseXs8/FmSKs+Dr7EqIP0TqREAB0D+2Oq8GVdL2OI05O0g3sYFIAijrjWNYxfj9h+4bxbEYmusi+0GmqxqnswLsBdLDN/r3QCji00VC8nLw4U3iGLalbNAwnapWcJDh7kM3nt3/rEdoDnSI/BkTN4Kx3pk+DPsD5buAjC0B+wXUY+coXdYKqqry7811UVEYUFNLBooMxH4GujvwX7/04KDo4uQrSDuKsd2Zs1FgAfov9TdtsovY4thSALd62tTBl/T9R0wyMGAjAWg8PKDhbvg6qsL868tNR1Hdrk9eyM20nTqrKE9k50Pef4B+ldSz78Y+C1uNtj7d+BsDEZraJLetT1nOu6JxGwUStcmwxGTodRxUTAD1DpAAUNUu/8H7oFT0njXqSDAY4+KvWkeosKQBFrVdmKeP9Xe8DMDU3jzCvSOj9qLahHKHndNvHg3MhP42mvk3pGNQRi2phwckFmkYTtUBRFpzawtbzkz9a+rXE37UW7Ioj6hVvZ2+6hNjWpVzn5gpHFoGpWONUdZMUgKLW++nYTyTnJxNgtnBPTh6Mfg8MzlrHsr/wzhDRA6wm2PEFADc2uxGAhXELZWcQcXWxK0C1sMUvFIBeYbL7h6iZBkXYZgOv9fKB0rzyhfCFfUkBKGq1rJIsPt//OQCPZufg3noCRA3SOJUD9XzY9nHXV1BWxNBGQ3E1uHIq7xT7z+3XNpuo2Y4txgpscbJ92+8d1rtCb1NVlQ2x5/hgVSz7k3MueS2zoJRdiVks3HeaPUnZlJhk0L6omgvLwew1KmTqdHBgrsaJ6iaD1gGEqIrP9n1GgamAVqVljCvTwfB/aR3JsVqOBt9IyE6E/T/h3vUehjYayqK4RSyMW0iHoA4aBxQ1UlkRnFzDUScjWdZSXA2udAzqCJdZZ/dMbjH7knIoKrOQVVjG3N3JxJ4tAODjNScY2z6MMe3DWB+bzsrDZ0nPL73o/QadQstQT9qH+9j+RPjQNMgDva6WrsUpql2Iewit/VtzJPMI691cufHEStsQBjc/raPVKVIAilrrRPYJ5h63/Wb4dFY2uoEzwCtU21COptNDj3/Asmdg20zocjfjosaxKG4RyxOW82zXZ3ExuGidUtQ08TFgLmZjcARgm/1r1BsxWU0XHfb7nhRenH+I4r+14rk76enUyJdNJzNYtD+VRftTy19TFAjzdiXMx4X4c4VkFpZx6HQeh07nMXt7EgBuTnqiG3jTPtyb9hE+dGzoSwMfV8d+zqJWGxgxkCOZR1jnF8yNBfG2nUG63qN1rDpFCkBRa/1n93+wYmVIYRFdvZtBt/u1jlQ9OtwOa16HzBMQH0OXJv0Jcw8jtTCVdcnrGNl4pNYJRU1zbDEAG718wZxDv/B+F71cYrLw8h9H+WlHMgBNgzwI9XbBzUlP10g/bukagZeLkUOnc/n3iuPEnSugb7MAhrcJoUcTf1yMesDWJZySXcyBlFz2p+SwPzmHg6dzKSqzsCMhix0JWeXXnNqzEc+PalX+XiH+alDDQXy671O2GFSKFAW3A79KAWhnUgCKWmln2k42nt6IQVV5LCsHpswBfT357+zsCe0nwc4vYOeX6KIGMiZqDLMOzGLhyYVSAIqLWcxwfBlZOh0HzbkA5YvtqqrK3kyFf3+8mZScEhQFHhvcjEcGNbtsl210A2++u7vbFS+lKAoRfm5E+Lkxup2tNd5iVYk7V8D+5JzzRWEuB0/n8t3WU2xPyOK/t3WkWbCnAz5xUZs182lGuEc4KQUpbHF1ZUjyNshKAL/GWkerM2QSiKh1VFXlg/PLvkzMLyCy7SRo2F3jVNWs6722j8eXQk4y46LGAbD1zFbOFp7VMJiocZK3QXEWm739UVFp6deSYPdgzuQWc/tXO/k2Vk9KTgkhXi58f3c3Hh/S3K7j9fQ6hebBntzcJYI3xrflj0f68O1dXQnwcOJYWj6j/7uJD1Yep7hMJo+IPymKUj4ZZG1wpO3Jg7LovT1JAShqnRWnVnAo8zBuVisPFllh8AytI1W/oJYQ2RdUK+z+hgivCDoGdcSqWlmeKEsmiL84tgSAjQG28X99G/QlOauImz/fyq5TOTjpVB4dGMXaf/anb7PAaok0oEUQSx/rS//mgZSZrXy89iRDPljP6iPyy4v404UCcL3ejAngwC8gy13ZjRSAolYxWUx8vPtDAKbl5hHQ9xnwqJ4fWjVOt/tsH3d/B+ZSRjUeBcCyhGUahhI1iqrCscWYgU2qbSZvlEcXbpm1lZTsYhr5ufFcewuPDIrCzal6h1AEebrw7V1dmTm5Ew18XDmdU8y93+/i9cVHKDNfZnqyqHc6BHbA19mXPEsJe9w9beOez+zTOladIQWgqFXmxs4lueA0/mYLU/VBlZr4MX9vCq8sPMSyg2fILbp4FuSh07k8+MNu+r27jkd/2suvu5I597dlLmqMFqPBMwyKMuDIQoZFDkOv6DmceZhTeae0TidqgrOHICeJ/W6e5FtKMODOUz9kcya3hGZBHsy5tyv+Gk4aVxSFkW1DWf1kf+7pYxvb9dWmBG6ZtZXDqbnaBRM1gl6nZ0DEAADWhbWwPXlAtoazl3oyal7UBQVlBcza9ykA/8jJxW3sx2BwqvD7VVXlw9Un+GjNCQC+23oKnQKN/N0J8XJBRWVb/J+zFJOyili0PxVng46pvSJ5qH8Uvu4Vv57D6Q3Q5S5Y9ybs+ga/drfQI7QHm1M3syxhGQ+2f1DrhEJr57t/f3INBwopzm1GiQk6NvThyyld8HKuGW0Ark56XrqhNd0b+/HPufvZl5zD6I830bupPw/2j6q2rmlR8wxqOIj5J+ezRlfGs4By8DcY+nr9mfTnQDXjq1+ICvjm0DdkleURWWZiQkgfaDbkqsfnFJUxa30cM2PiWH4ojZcWHiov/oa3CaZpkAdWFRIyCtkan8m2+Cx0Ckzo2ICvpnbhkUFNaRPmRanZyv82xNPv3XW8vvgIR1LzquPTrZiOd4Cig6QtcC62fAbwsoRlsjVcPWexqpzbOQ+AzecLvf4R/Vj8SB9+f6gX/h41b7vEYW1CWPJoX8a0D0OvU9h8MpM7v9rBywsPSbdwPdUjtAeuBlfSynI47BkAhemQEKN1rDpBSmhRK5wrPscPh78B4LHcAow3XnnHD6tV5dddyby74jhZhWUXvaYo8NrYNtzZMxKAs3klJGQUcia3mJwiEwNaBNE4wB2Awa2CeXJoc2KOn+PdFcc5eiaPrzYl8NWmBNqEefHi6Fb0igpwzCdcUV5h0Gw4xC6DPd8xeODzvLb1NeJz44nNjqWFXwtt8wnN/LxyE5MLY0nVGShwzkeHjndG3oyPi7fW0a4qws+N/97WkWdHtOCLDfF8t/UU3289xeHUPD6b3IlgL1novD5xMbjQL7wfKxJXsDKiNdFHNsD+n6Hp1RsAxLVJC6CoFf63fybFVhPtS0oZ3O5u8I+67HEp2UXcPGsrz/1+kKzCMpoFeTCuQxjtw71pFuTBf2/rWF78AQR7udCjiT8TOoZzV+/G5cXfBYqiMLBlEEse6cNXU7swMjoEJ72Ow6l53P7Fdv5vwUEKSs2O/NSvrfM028d9c/DQGcsX+V2asFS7TEJTucUmTm+z7ZKzNKA5AO0C2+Hj4qNhqusT7uvGq+Oi+WpqFzxdDOw+lc2Af8fwysJDnMos1DqeqEZDGw0FYDVFqABHF0OJjBGtKmkBFDVehiWDBfELAXiyWIfS/+nLHrfm6Fme/HU/ucUmPJwNPDG0OVN6NsKor/rvOTqdwuBWwQxuFUx2YRnvrTzO7O1J/LgtidVH0nl4YBS3dI3A2aDBrgZNh9gmg+SnwrHFjGw8ktVJq1mesJzHOz2OosgerPXNV5sSGGjdCjo4EBQAhQX0De+rdaxKGdwqmD+m9+Gxn/eyP8W2gPT3207ROyqAMe1DGdEmFG83o9YxhQP1bdAXF70LycXpHA9qRsv0E7at4S788isqRVoARY0XU7QCCyr9iorpNOAV204Yf2GyWHlr6VHu+W4XucUm2kf4sOyxvtzTp7Fdir+/83V34s0JbZlzb3fCfV1JyyvhpYWH6f9uDAv3nbb79a5Jb7CNBQTY/S39wvvhZnAjtTCV/ef2V38eoanswjIWb9pNV10sZcC2kjSAS7Z/q00iA9xZ8HBvZt/bnYEtAlFV2HQyg2fnHaTX22vYmZh17ZOIWsvN6EbvBr0BWBXeyvbk3tkaJqobpAAUNVpsdiz7LEcBeMQQAu1uvej11JxiJv1vG7M2xANwV+9I5j7Qkwg/N4dn69U0gNVP9ue1cW0I9XYhLa+Ex37ex39WxVb/BIxOdwIKJGzAJS+1fAFV6Qauf2ZtiKePeSsAuyLaU2wpIcg1iBa+tXs8qKIo9G4awDd3dWPD0wN5engLogLdKSyz8MQv+7QfiiEc6kI38EpTJqqih5QdkHFC41S1mxSAokb7dOe7AIwsKKTl0HdA9+d/2Z2JWYz+eCO7T2Xj6WLg8zs68cqYNjgZqu+/tYtRz5SekcQ8PYAH+9vGJX605gRPzd1fvbMWfRpClK3oY99P5bOBVySuwGyVH4z1xaYTGXy7JYHR+u0AbAwIB6BveN86NRSgob8bDw9syoKHexPu60pKdjFvLD6idSzhQP3D+2PUGUksSCYu6vxwhn3SClgVUgCKGmtv+l42ZuxBr6o85NsRInuXvxZzPJ07v9pOdpGJ6AZeLHmkLyOiQzXL6mzQ89zIlrx1Y1v0OoXf95zm5s+3kJxVVH0hOtxu+7j/Z3qGdMfH2Yeskix2pO2ovgxCE6qqMmt9HFO+3o6nKYuuuuMAbDRlArYxVHWRp4uR925uj6LAzzuTWXNUtpKrqzycPOgV1guAVRf2Bt7/M1hlD+nKkgJQ1EiqqvLR5lcBGJ9fRPjgN8tfW3LgDPd9v4sSk5WBLQKZ+0AvGvo7vsu3Im7r1pCvp3XF29XI/pRcRn28kWUHz1TPxVuOBmcvyE3CmLyjvMtEtoar21RV5ZnfDvDWsmNYVXi+8Ul0qJxq0J5ThakYdAZ6hPXQOqbD9Gjizz29bbuIPPbzPj5YeZzsvy3/JOqGC9/TVhUlgasv5J+BuHUap6q9pAAUNdKW05vYnReHk1VlpL4d+Nm6VxfuO80jP+3BZFG5oV0os+7sgquTBjNvr6J/80CWPtaXzo18yS8x89DsPWw8cc7xFza6Qpvxtsf7/+wGXnNqDWUW+YFYV324+gRzd6dg0Cm8Pj6aCc47AdhwfuuszsGdcTe6X+0Utd4/h7egcyNfCkrNfLz2JL3fWcuHq2MpNUvrUF0yIGIABsXAiZyTJLYabXtSuoErTQpAUeP8tfVvUlEZmcE3A7bi74lf9mFV4ZYu4Xw0qWO1jve7Hg18XPn5/h5M7GQbg/XC/IMUlVXDWLwOk20fDy+gs08LgtyCyDfls/H0RsdfW1S7hftOl+9u8+aEaO6MdkM5tRmAdaptrbz+4f01y1ddXIx65j7Qk88md6J1qBdFZRY+XH2C0R9vYvcpmSFcV3g7e9M9tDsAq/2DbU8eWwLF2Rqmqr1q5k9PUa+tivuDoyVncbNamRZ9DyaDB0sOppUXf7d2ieDtG9uh19XsQe1GvY5Xx7UhzNuF5KxiPlxdDTPWIrqDXxMwFaI7toQRkSMAWJ6w3PHXFtVqX3IOT/92AIAH+jXh1q4N4dhiUK3khLZjT5ZtUsSFGeF1nU6nMKptKEse7cN/b+tIgIcTJ9MLuOnzrXy5MV7reMJOymcDZx2C4GiwlMKheRqnqp2kABQ1itlq5pPt7wAwtUyPT/fpxOXB0/MOlhd/b93YFl0NL/4u8HA28Pr4aAC+3BjPwRQHr16vKND+Ntvj/XMYHjkcgA0pGygxlzj22qLaqKpavj/u0NbBPDOipe2FIwsA2NCwLRbVQgvfFjTwaKBdUA0oisKY9mGsfrI/N3ZqgKrCG0uO8tHqE7I/dh0wsOFAdIqOo1lHSWkzxvakrAlYKVIAihrlj0M/kGDOw8diYUqvF0nKNfPVcT0mi8rI6JBaVfxdMLhVMGPah2FV4Ylf912yP7HdXVgrMWEjbQ0+hLiHUGQuYkvqFsdeV1SblUfOciAlFzcnffnMcwozIcHW1b9OZwJsPyzrKx83J96/uT1PDbVthfef1bG8teyYFIG1nJ+LH12DuwKw2sMTdAZI3QPpRzVOVvtIAShqjDJLGTP3fQrAvYov1qYTuP/HvRSaFaLDvPjglg61rvi74OUbWhPk6czJ9AImf7ndsbMUfRtBZF9ARTn4C0Ma2jZNX31qteOuKaqNxary/krbMi93925MgIez7YXjS0C1UBISzeYM2w4wgyLqR/fvlSiKwiODm/HSDa0B+N+GeF5ccAirVYrA2mxII9v3tFVntkBz2zAXmQxy/aQAFDXG3J3/4YxaSpDZzMSB/2b6T/uIO1eIt5PK55M71LjZvtcj0NOZOff1IMDDmaNn8rjjq+3kFDmwCLzQDbzvJ4Y0HAxATHIMJovJcdcU1eKP/anEni3Ay8XAff2a/PnC4QUAbI/sQrG5mFD3UFr6tdQmZA1zT5/GvDOxLYoCc7Yn8eSv+zBbqnGhdmFXgxsORkHhQMYB0lqNsj25/xeQ72/XRQpAUSMUlRXyv2NzAHjQvRnvHfBmQ+w5XI067mthIdjLReOEVdc0yIOf7utOgIcTh1PzeGXRYcddrPVYMLpBVhwdysz4u/iTb8pne9p2x11TOJzJYuU/q2MBeKB/FN6uRtsLRVmQsB6AtU62b+sDIgbUqd0/qurWrg35eFJHDDqFBftSmfrNDk5lFmodS1RCoFsgHYM6ArBCVwbugVCYDidWaZysdpECUNQIP258hSzFSkOTGTXwab7dkgjAeze1JcJD22z21CzYky+n2sav/LE/lYQMB/0AcvaE1uMA0O//icHnWwGlG7h2m7/nNKcyiwjwcOKu3pF/vnB8GVjNWIJaE3NuD1B/Zv9ejzHtw/j8js44G3RsPpnJ0P9s4KPVJ6p320ZhFyMa27p+l51aAe0n2Z7c+4OGiWofKQCF5nKLMvg2eQUAE5w68fyafACeGdGCYa2DtYzmEB0ifBjUMgirCjNjTjruQhe6gQ/NZ2h4PwDWJK2RvYFrKZPFyifrbP9fHuwfhZuT4c8Xz8/+PRDVm6ySLDydPOkc3FmDlDXfkNbBLHusL32aBlBmtrWoPvHLPpkcUssMazQMvaLncOZhTjW3jQkkdgXkp2kbrBaRAlBo7us1T5GvQFOThQ+PjMNiVbmtWwQP9Y/SOprDPDywKQC/7zlNSraD9guO7AveEVCaS5ecs/g4+5BTmsPus7sdcz3hUAv3pZKUZWv9m9y90Z8vFGVB3FoA1rnZhkr0C++HUWfUImat0CTQgx/u6cZHkzpg1CssOXiGz2LitI4lroO/q3/5otDLco9BeDdQLbb9gUWFSAEoNHUu6wRzMm0FiV96Jwpw5+7ejXlzfNs6PX6pcyNfejf1x2xVmbXeQYvU6nTlS8IYDsxlYIRtSZBVp2ScTG1jtlj59Hzr3319m1w8IerwfLCaUUPasjZjH0D5vRZXpigK4zo04NWxtnU631t5nLXHzmqcSlyPC9tdLktYhtrxDtuTe38Aac2tECkAhaZmrXmSEkWhRYlKTN7NPDW0OS/d0KrWLvdyPS60Av6yK5nUnGLHXKTdLbaPcWsZEtIDgLVJa7GqMuapNll84AwJGYX4uhm5o0eji188OBeAhBZDOZV3CqPOSJ8GfTRIWTvd3r0ht3dviKrCgz/uYdynm3ns573M250iy8XUcIMbDsZJ50R8bjyxDdqC0R0yT0LSNq2j1QpSAArNJCdvZV5hAgBq+mBGtW/II4Ob1emWv7/q2cSfrpG+lJmtPDR7DyUmB2xcH9gCQtqB1UyPzBQ8jZ6cKz7H/nP77X8t4RDFZZby/X7v7dsEd+e/jP3LSYKkrYDCWg9PALqHdsfd6K5B0tprxpg25WMC9yfnsHBfKk/N3c/tX24j0VETtUSVeTp50je8LwBLT6+HNhNsL8hkkAqxSwG4YcMGxowZQ1hYGIqisGDBAnucVtRxM9c/h1lRaFVk4JhpBP83upXWkaqVoii8d3N7fNyM7E/O4fnfDzpmIHrbmwFwOryA/hH9AViZuNL+1xEO8dLCQyRkFBLo6cyUnn9v/fvN9jGyD+vSbUMppPv3+jkZdHx/dzeWP96Xz+/ozCODmuJq1LMtPovhH27gm80JMkmkhrrQDbw8Yfmf3cCH50Npvoapage7FICFhYW0b9+eTz/91B6nE/VA7JHfWGzOBCAjfTxPDG1RJ9b6u16N/N357PZO6HUK8/eeZtYGB4wHjJ4IKHBqM0MCbGtnrUlaIz/QaoFfdyXz2+4UdAp8PKkjni5/m9hxvvs3veVIDmQcAKQArCydTqFliBcjokN4algLVjzej95N/Sk1W3n1jyPc9e1OMgpKtY4p/qZ/eH/cDG6kFqay38UF/JuBqQgO/a51tBrPLgXgyJEjeeONN5gwYYI9TifqOlXlv9vfQlUUWua7Y/AawNRekVqn0kyvpgHMGGPbqurd5cc4nJpr3wt4N4BGvQDonZGMq8GVM4VnOJzpwIWoRZUdT8vn5YWHAHhiSHN6RvlffEDaIUg/AnonYjxsi2W2C2hHoFtgdUetkxr6u/HjPd15dWwbnAw6Yo6fY8SHGzmQkqN1NPEXLgaX8jUvlyYug79OBhFXJWMARbXbt+1DYnRl6FSV+PTbeX1cNEZ9/f6veGfPSEa3DcWqwovzHbBXadubAHA5PJ++DWxjZmQ2cM320oJDlJis9GseWD5h6CLnW/9oNoy1aVsBGNhQWv/sSVEUpvaK5I/pfWgR7ElGQSl3fLldisAa5kI38IrEFZjb3gKKHlJ2snPnForLHDC2uo4wXPsQ+ystLaW09M+m9Ly8PABMJhMmk+P28rtwbkdeQ1ydairm48NfgVEhKi+QiFZ96BThdcV7Up/u2fMjmhETm86+5Bx+3JbAbV0j7HfyZqMx6J5GSTvIoO7TWMlKViWu4uG2D9t10k19ul+OtCMxix2JWRj1Cm+MbYXFYsby159jqhXDwbkoQHaLUWw/9B4AfUP7Xte/vdyvimni78LP93Xlvh/2sOtUDnd8uZ23JrTBqNdRUGqmc0MfwnxcHZ5D7telSs1WDMXN8DB6k1WSxdbcONpFDMI7aRV7F37CfUsLuLlzA27vFkG4r+Pv0V85+n5V9byKaueBQIqiMH/+fMaPH3/FY2bMmMGrr756yfNz5szBzc3NnnFEDVOU/gP/cjqOUVWxJDzBP6MD8HLSOlXNsf6Mwu+Jelz1Ki90sNj136Z73AeE5O1jX8gN3OV6BDNmpntOJ0QfYr+LCLuYeUTHsVwdvYKt3Nrk0iV7/AuO0efEvzDpXPlXi3v5rWQhQbogHvV6VIO09UeJBWYd1ROff/EvTQZFZWCYytAGVpz1V3izqBKrCkkFEJenkGdSKDRDZolCUgGYVQXnkPk4+W4noKwzzRObMMv4PhmqFz1LP8GEAQWVaF+VvqEqzb1U6sJiE0VFRdx+++3k5ubi5eV13e/XpAXw+eef58knnyz/e15eHhEREQwbNqxSn0RFmUwmVq1axdChQzEaZZX86qbmn+XOec8BBkKzGzNhcH8m/X1W49/Ut3s2zGLl2KztHDmTzx5LBO+Oamu3cyuHi2HBA7QvPUyvpr3ZcHoDlkgLo9qOsts16tv9coT9Kbkc27odvU7hjcn9iPC99Jdi3VLbns766Amc9cyCVJjQZsJ130u5X9dv2DAz/7fwCPuSc/BxM2KxwrG0fFadVtif58rTw5oxtl2oQ9YyrY/363haPl9tTmTt8XPkFl9+G0s/dyMFBR3Adzvn9IdJso4lR+dHgDWLXwdk815ySzbHZXIwW+FgNjQNdOeOHg0Z3z704mWV7MzR9+tC72llaVIAOjs74+zsfMnzRqOxWv5TV9d1xMVWrnuaI04GXKxgUu5nWu8mGCo49q++3DOjEf51YzvGf7qZ+fvOMLV3EzpE+Njn5K3HwJInULITGOI1lQ2nNxBzOoZ/dPqHfc7/F/XlfjnC5xsSARjfoQFNgrwvPcBcBkcXAVAYPZZt218EYETjEZX+N5f7VXG+RiOfTv5zn2VVVVl15CxvLDlKUlYRT887xOwdKbw4uhVdGvk6ZF3Tunq/zBYr+5JzyCwsI7fYxKojZ1l15M/dWTxdDPSOCqCRvxs+bk4EeTrTqZEvkf5uZBX244YFv1FAJjf2LcbbMAW2fEjHzKXMvm8aJ9ML+H5rIvN2p3DyXCEz/jjK+6tO8MEtHRjq4D3nHXW/qnpOuxSABQUFnDz556b2CQkJ7Nu3Dz8/Pxo2bGiPS4haznzmAP/N3gtGIx5Z7Xnn5j4VLv7qmw4RPtzYqQG/7znNa38cZt5DvezzQ8TJHVqOhoNz6X8uCZ2i42jWUVILUgnzCKv6+UWVHUnNY/XRsygK/GPgFfbCPrkaSnLAI4QYXRkmq4km3k1o6nuZiSLC4RRFYVibEPq3COTrTYl8svYE+5JzuPnzrTTwcWVEdAhdGvkS5uNKhJ8bfu4y5iWzoJQ9STmUma10ifQlyNOZNUfTeXv5MU6mF1x0rKLAqLahTOsVSccInyv+3PD3cOHGFqP5/sj3qO57Udo8CFs+hJOrIC+VpkFhvDYumqeHt+C33Sl8v/UUCRmFPPrTXuY+2JPoBpf5ZauOs0sBuGvXLgYO/HP22YXu3alTp/Ltt9/a4xKiNlNVFq54jESjETeLQq+mj9Gxoa/WqWq0Z0e0ZPmhNPYk5bBofyrjOjSwz4nb3gwH5+J3dAkd2vRkT/oe1iWvY3KryfY5v6g0VVV5a9lRAEa3DSUq0OPyBx781fYxeiIrT9m6godFDquOiOIqnA16HhoQxcRODXh/ZSyL9qdyOqeYrzYl8NWmhPLjbuvWkBljW+NsqD+DBVVV5XBqHov2p7L6yFni/7a7SoCHc/kai14uBpoGeeDtaiTCz40pPSNpGnSFr4W/Gd3EVgCuS1pHXs+X8WrY07ZTzr450O+fAHi6GLmrd2Pu7NGIu7/bxYbYc9z3/S4WTu9NkGf9WovWLk0wAwYMQFXVS/5I8ScASo8v4XNLGgDuBQN5fkRHjRPVfMFeLvxjgK0F6O1lx+y3lEGTgeDqCwVnGexhG3+5Nmmtfc4tqmTd8XQ2nsjASa/jmeEtL39QSR4cXwZAQesb2Jy6GYBhjaQArCmCvFx456Z27HlpKJ/f0ZmJncLp2NCHkPML3f+0I4lbZ20jLbdE46TVY0dCFiM+3MgN/93E/zbElxd/zYI8aBPmhU6BjIJSnAw6HuwfxabnBvH7P3rzzV3deG1cdIWLP4BWfq1o6tOUMmuZbbejjnfaXtj7I1gvnkxl0Ov4720daRLozpncEu7/fjel5vq1ZIwmYwBFPWIx8f2aF0jzMOBhMvLskH/i4cBBt3XJvX2b8NOO5POtCPFMH9Ss6ic1OEHr8bD7GwaeS+HfwO6zu8kpycHHxafq5xeVYrJYeWOJrfXvrt6RNPS/wmoIRxeBuQT8mxFjysJkNdHYuzFNfaT7t6ZxddIzIjqEEdF/zrJfH3uOR3/ay77kHIZ8sJ7mwR409HOjXbgPY9qHEeh56dj42spiVfnv2hN8vOYEVtW23d6QVkGMbR9GzyYBeLvZxq/llZg4dDqXqECPKu8GpSgKY6PG8sHuD1gUt4ibBs+EZc9AdgIkbYHIPhcd7+1q5KupXRn/6Wb2Jefw6dqTPDmsRZUy1Cbyk1g41NHl/+EHVzOgp7XnbQxvbce17eo4F6OeZ0a04LGf9zFrfTyTuzfC1x7jh9reDLu/IeL4apq37kJszgnWp6xnXNNxVT+3qJTZ204Rf64Qf3cnHh50lWJu72zbxw63sSJxBWBr/XPERANhf/2bB/LH9D7c/8MujqXlsycphz1JOSzYl8qbS4/Sr1kAd/RoxMAWQQ6ZRWwviRmF/LormYyC0vJF/FOyi0nMLCQjvxRno617O6uwDIAbOzXglRvalBd9f+XlYqRXVIDdso1uMpoP93zI3vS9JJVk0jD6RtjzPez54ZICEKBxgDtv39iWh2bv4bOYOEZEh9I6zHGrkdQkUgAKh0lITmH5iVlk+7rhZ/Xgs4mPax2p1hnTLozP18dz9EweM9fH8cKoVlU/acOe4NUA8k4z2L0hsTknWJu0VgpAjWQVlvHhmhMAPDG0OV5/3+/3gsw4WyuGoiO31Wg2LbsN+HMXBFE7NPR3Y/EjfTh6Jp/k7CISMgpZdeQs+5JzWHf8HOuOn6NpkAf3923CuI5h5WMFrVaVvUk5FFTDGtCpOcXEHD9HXontYhdWC1ZR2Z2Yzdrj6VxtBeHC80NW3J30vDEhmgkdwx0duVyQWxA9Q3uyOXUzi+IWMb3jFFsBeGQhjHoXXC6d7DGybSgj2oSw/HAaz847wPx/9KoXkxSlABQOoaoqW396ll8CbU36T/V+EWdD3Vu2wNF0OoVnhrfgrm938u2WRO7qHUmodxVXs9fpoM0E2PoJgzLTmAlsSd1CsbkYV0P1rpQvbPs/5xSZaBniyaSr7f6yb47tY9QgVmUdwmw109y3OVE+V5gtLGosg15H23Bv2obbipGHBzYl7lwBv+5MZs72JE6mF/DMvAO8t/I4d/VujKeLga83JxB/rhBXvZ7S4CSm9GqMQa+j1GxBQcHJULWCJf5cAcsPp7HiUBr7U669H/mAFoF0jfTDZLFitaqE+rgS6e9OiLcLJouVEpOFRn7ul231c7SxUWPZnLqZxfGL+Uf7h9AFtICM47YisNOUy77ntfFt2BKXwcHTuXyxMYGHBtT9ryspAIVDbNy2jSTnzRTqPGnmGsYNzey32HB9M6BFIN0i/diRmMVHq0/w9sR2VT9p9ETY+gktTq4nrEU0qYVpbE3dWr6puqgeu09l8/POZABeHx995VYHqwX2/2R73GEyyxKWANL6V5dEBXrw/KhWPDyoKT/vSOLrTYmk5ZXwzvJj5cfoFCi2KLy25Bg/bk9GBZKyijDqFSZ3b8QD/ZsAsHBvKutjz+HmpCfU24UGvq60CvWidagXucUmtsRlsvtUNvklZsxWK6eziznxl+VXFAU6N/QlMsD9z+fOf/T3cOaWLuE0udIs9RpgUMNBeBg9OF1wmt3pe+ja/lZY8xrs/+WKBWCQpwsv3dCap387wH9WxdIzyt9+a7DWUFIACrszW6xkxbzIr6G2bxBP9n4JnVL3m9MdRVEUnhnRgps+38qvu5J5aEAUjfzdr/3GqwnrCL6NUbITGOTeiB8L01iTtEYKwGpktlj5vwWHALi5czhdI/2ufHB8DOSdBhcf0iO6snP3DEAKwLrIy8XI/f2imNarMYv2p/LdlkRKzRZu69aQce2CeWvOalaccb5oKRWLVeWrTQn8sO0UFquKxXr9O7wadAo9o/wZER3C0NbBtXpJFBeDC8MjhzPvxDwWxS2ia9v7bQXgqU2QkwQ+l1+f+KbO4aw+epYVh8/y8Ow9/PFInzq9bqMUgMLuNq6cz1avREyKO139oukd1lvrSLVel0g/+jcPZH3sOb7YGM8b46u4RZyi2FoBN77HoKx0fgTWp6zHbDVj0Mm3herww7ZTHD2Th7erkedGXmHZlwv2/mj72PZmVpyOQUWlQ2AHGnjYaX1IUeM4GXTc1Dmcmzr/OX7OZDLRJ0TlqVt6sy0xl0BPZ5oGeXAsLZ+PVseyJykHgI4NfRjXPgy9TuFMbgmJmYUcPZNPQkYhTnodHRv60DPKn2AvF4x6HR7OBno28deku9ZRxkSNYd6JeaxMXMnz3Z7HLbIvJG6EA7+Wrwn4d4qi8O+b2xN7djMJGYU8/ss+vpnWFX0NnpBTFfKdXthVSWkZJfteY2mYrYXq6V4vywxFO3mwfxTrY88xd1cKjw9pToBHFZeMOF8Adozfik/TFuSU5rI3fS9dQ7raJ7C4IqtVZdb6eACeHt4C/6vdy6IsOGbr8qXjZJbueReQ1r/6zN/DmfEd/yz+g71c6NcsgMOpebg56a/YPVtYakavU3Ax1v1FqDsFdSLcI5yUghTWJq/lhna3ni8Af4G+T9l+Cb4MLxcjM+/oxPhPN7Mh9hwzY07aZwmuGkj65YRdbZn3Mb/5FgEwMnwIrfztMGtVANCjiR/tw70pNVv5dnNi1U8Y3BoCW2GwlNHf3dYlsiZpTdXPK65pW0ImaXkleLkYLmrhuaz9P4OlFELakuzhz6HMQ+gUnez+IS6iKArRDbyvOjbP3dlQL4o/+HNNQIBFJxdB63FgcIGMWEjde9X3tgzx4s3zvSwfrTnB8bR8h+fVghSAwm7OpZ+lOOlTdri6YETH492f1jpSnaIoCg/2t81M+35rIoWl5qqftO1EAAbnZAC2XUHUq63vIOxi4d5UwLbH6VV/IKsq7P7W9rjzXfwRvxiAHqE9CHC139ppQtRFN0TdAMC2M9tIsxTZ9kIHWyvgNdzYqQFDWwdjsqg889t+zBbrNd9T20gBKOzm8M/P8aW/bcDs5FaTCfMI0zhR3TOsTQiNA9zJKzHz046kqp+wzY0A9EzchavehTOFZziWdewabxJVUWKysPTgGYCLuvEuK2mrbfkKoxtq9E0silsEUN6yIYS4sgjPCDoFdUJFZXH8Ymg3yfbCoXlgufov0Iqi8Mb4aDxdDOxPyb1oP+e6QgpAYRfH9m8no2wlJ52c8NC5cm/7B7SOVCfpdQr397Mt9fDFxnhKTFXcu9I/CsI64mK10MvNVoysTZa9gR1p3bF08kvNhHm70O1qM3/hz9a/6InsyTvJ6YLTuBvdZba2EBV0YYH7P+L+QG0yAFz9oPCcbUbwNQR7ufDS6NYAfLAqlqTMIkdGrXZSAIoqs1qsFCx5ipm+tu1zHuz0MN7Ol662Luzjxk4NaODjytm8Un7YeqrqJ4w+3w2cmw3IOEBHm7/3NABjOzS4+nZfRVlweIHtcZe7+CPuD8C29Zss2C1ExQxrNAwXvQvxufEczomF1udbzw/Nq9D7b+4STvfGfpSarfy+N8WBSaufFICiyvat+JY9rqdINxgIcQnitpa3aR2pTnM26Hl0sG2/2Jnr4yio6ljANhMA6Jd8EL2i40T2CZLzkqsaU1xGbpGJmOPnABjf8RpDJP4y+aMkqHX53r9josY4OqYQdYaHk0d5i/nCkwvLf+Hl6B9gLrvm+xVFYWIn20StNUfTHZZTC1IAiioxF+fjtvsNvvaxtf490fUpnPR1d+HMmmJip3AaB7iTVVjG11Udm+IdDg174m210MUlBJBuYEdZdCCVMouVliGetAy5yobzVivs+sr2uPM01qXEUGAqoIFHAzoHd66esELUERfGzC5LXEZZeFfwCIbibNsC6xUwsGUQigIHT+eSllviwKTVSwpAUSXH573KT74qhTodLX1aMqLxCK0j1QsGvY4nhjYH4IsN8eQUXfs32as6/1vxwDzbHqBrk6QAtDeTxcr/NsQBcEuXq+z5CxC/DjJPgrMXtJvEwriFANzQ5AbZVUeI69QjtAdBrkHkluayMXULtB5ve6GC3cCBns7l28KtOXbWMSE1IN9JRKWVnj2BOelH5nvYFn1+seeL8sOpGt3QNpSWIZ7kl5r5cmMVWwFbjwdFx+AzsQDsTd9LZnFm1UOKcgv3pZKcVUyAhxO3dbv8VlTldvzP9rHD7aSZC9iauhWQ2b9CVIZep2d0lG0JmIVxf+kGPrYETBVr0RvSKhioW93A8tNaVI6qkvbr47zv74mqKIxoNIoOQR20TlWv6HQKjw+xtQJ+tzWR/BJT5U/mEQiN+xNisdDayR8VlfUp6+2UVFisKp+tOwnAvX2b4Op0lbX/shIg1jbej673seDkAqyqlc7BnWnodY3CUQhxWWOb2H552piykayAKPAKh7J8OLmqQu8f3CoIgM0nMyguq+LqCzWEFICiUs5s/YUjJXvY6+KCUXHiqa5PaB2pXhrWOpimQR7kl5j5cVsV1wU8/1vxoALbqvfSDWw/iw+kEp9RiI+bkTt6NLr6wTu/BFSIGozVvwnzT8wHYGKziY4PKkQd1dS3Ka39W2NWzSw7tQLajLe9cGGm/TW0CPYk3NeVUrOVTSczHJazOkkBKK5bYW4WltXP8b6fDwD3t7ufEPcQbUPVUzrdn7uDfLUpoWrrAra6AXRGBqfblpbZmrqVQlOhPWLWa1aryqfnW//u6d0YD+erbMFeVgh7f7A97nY/21K3kVqYiqeTJ0MbDa2GtELUXeVbw8Ut+nMc4ImVYC695nsVRflLN3DdGAcoBaC4LqqqsuubJ1nkZeWswUCwawjToqdqHateG9chjAY+rmQUlDJ3dxXWqXL1haZDiDKZaGjwoMxaxubTm+0XtJ5aeugMsWcL8HQ2MKVX5NUPPvALlOSCbyQ0G8q8E7ZB6jc0uQEXg4vDswpRl41qPAqDzsCRzCOcdPcGjxAozYOEDRV6/4Vu4NVH07Faa/+WmVIAiuvyx9I/aJr3B197ewLwTLen5QeTxox6Hff1bQzA/zbEVW3PyrY3oQCDCm0tf7IcTNVYrCr/WWWbWHNP38Z4uxqvfLDVCls/tT3u9gBZZbnl//7S/StE1fm6+NKvQT8AFsUvtvV6ABxdVKH3d2/sj5eLgYyCUrbG1/5JclIAigpbfzSVptv/jw/9vSnV6egS3EW6pWqIW7s2xM/dieSsYhYfOFP5EzUfAQZXBmfazrEheQMmSxUml9RzC/edJu6cbezf3X0aX/3gEyvOL/3iDZ3uZNHJRZitZqL9o2nh16J6AgtRx13oBl4cvxhLi1G2J48tBeu1h884GXSM7WBbwP23qvS21BBSAIoKOZmez46f/0WRWxrLPdzRoeO5bs+hKFfZykpUG1cnPXed716cGROHqlaye8LZA1qMoG1pGf46Z/JN+ew8u9N+QesRk8XKR2tOAHB/vyZ4uVyl9Q9gyye2j52nYnVyZ27sXAAmNpfWPyHspV94P3ycfThXfI5tzgZw8YGiDEjaVqH339TZtobnskNnyKvKygs1gBSA4prySky88M1S7udX3vD3BWw/lKRVomaZ0jMSdyc9x8/ms/ZYFdaqip6IHhhQVAzIbODKmrc7hVOZRQR4ODHtWmP/Tu+xbU6vM0D3B9mSuoWk/CQ8jZ6MajyqWvIKUR8Y9UZGNh4JwMKEJXChFfDoHxV6f/twb5oGeVBisrK0Kr0tNYAUgOKa3vzjCA8UzORXH2finJzwc/HlsU6PaR1L/I33X5YY+awqrYBNh4KzF4NybEsdrEteh1WtwrjCeqjUbOG/a20zfx/sH4Wb01Vm/gJsPd/6Fz0RvBvw87GfARjXdBxuRjdHRhWi3hkXNQ6w/XKb32yI7clji6EC3zMVReGmzra9gWt7N7AUgOKq1seeo3jvrzRzPsjnvt4APN31GbydvTVOJi7nnj6NcTLo2H0qmx0JWZU7idEFWo+lR3EJbuhJL0rncMZh+wat437ZmczpnGKCvZyvve5fduKfa5H1nE5KfgobUmyzEm9tcatDcwpRH7X2b00T7yaUWkpZqS8DoxvkJsOZfRV6/40dG6BTYNepbBIyau9SWVIAiivKKzHx3m8xvGr8htcDfClTFHqG9mR049FaRxNXEOTlUv7b6WcxcZU/UbtJOAF9i893A8ts4AorMVn45Hzr3/SBTXExXmXXD4DNH4FqgSYDIbQdvx7/FRWVXmG9iPSOdHxgIeoZRVEYEzUGgGVJayBqkO2FCzvwXEOQlwv9mwcC8NvuZIdkrA5SAIoremvJEZ4o/oTNHirbXF1x1jvzUo+XZOJHDfdAvyboFFvr7aHTuZU7SaPe4B3BoPw8QMYBXo8ft50iPb+UBj6u3NI14uoH56fB3h9tj/v9kxJzCb+f/B2ASS0mOTipEPXXiMgRAOw8u5OMxn1tT8Yur/D7J57/RXvpwTS7Z6suUgCKyzqZXoB1zw90Nh7g3fMTPx5o9wARXtf4gSY018jfnRva2ZYqmLm+kq2AOh20vZm+RcUYUIjPjSchN8GOKeumwlIzM8+3vD46uCnOhmu0/m35L1jKIKIHNOrNsoRl5JbmEuYeRr/wftWQWIj6KdwznLYBbbGqVlY5ny+FUvdCfsV2+ejfPBCjXiEho7DWdgNLASgu66eVm/g/w4/8x8+HbL2eKO8oprWZpnUsUUEPDbBtD7fs4JnKf3NqPwlPVaV7scwGrqhvtySSWVhGpL8bN3YKv/rBRVmw6xvb437/RAW+P/I9ALe1vA297hrFoxCiSi60Ai4/sxnCOtqePLGyQu/1dDHSNdIPgHVVWXVBQ1IAiksknstncOxrxLpYmefpAcDLPV/GqL/GOmaixmgV6sWglkFYVZhV2VbAwBYQ2oFBhUWAjAO8lrwSE//bEA/AY0OaYdRf49vrtplgKoSQdtB0CJtTN3My5yTuRndZ+0+IajAschgAe9L3kNbkfIv7iYqNAwQY1NK2Ndy641IAijpi3+//ppP+CDMC/AHbNlSdgjtpnEpcr3+cbwWctyeFtNySyp2k/SQGFtkKwAPnDnCu6Jy94tU5X21MILfYRNMgD8a2b3D1g4uyYPvntsd9nwJF4bvD3wFwY7Mb8XTydHBaIUSIewidgmw/21a6nd/SNG4dmEsr9P4BLWwF4Pb4LApLzQ7J6EhSAIqLnIk/xPDUmXzi602ik4EA1wCe6PyE1rFEJXSJ9KNbpB8mi8o3mys5fi/6JgKtCu1KbN8Q1yWvs2PCuiO7sIyvN9n+jZ8c2hy97hoTpbZ+YtuEPjgaWo3leNZxtp3Zhk7RMbnV5GpILIQAGB45HIAV2YfAIxjKCuDU5gq9NyrQnQg/V8osVrbE1b69gaUAFH8yl2H69R6Ouih87+UFwIyeM2TNv1rswv6zi/anYrVWYmFoj0BoOphB51sBZRzg5f1vYzz5pWZahXoxok3I1Q8uzIRt51v/BjwPOl352L+hjYbSwOMarYdCCLsZFjkMnaLjQMZBUppcmA1csXGAiqIw6HwrYJV2X9KIFICiXOrvzxNQepwXAgNQFZjQdAL9I/prHUtUwYAWgXg4GziTW8Le5OzKnaTdrQw6vy3c9rTt5Jfl2zFh7ZdRUMq3mxMBeGpoc3TXav3b/KFt7F9oe2g5mrTCNJYmLAVgauupjg0rhLhIgGsAXYK7ALDGJ8D25HUsBzPg/DjAmOPpld99SSNSAAoATEeXEXbkS97z8+G0UU+IewhPd31a61iiilyMeoa2DgZgcWX3rWw5msY6N5qUmTBbzWxM2WjHhLXf5zFxFJsstI/wYXCroKsfnH8WdnxhezzwRVAUvj38LWarmS7BXWgb2NbxgYUQFxnU0LYQ9Nri06AzQnYCZMVX6L09m/jjYtRxJreE42dr1y/HUgAKyD2Nad6DrHZzZa6XJwoKr/d+XQai1xGj24YCsPTgmcp1AxtdofXY8m7g1Umr7RmvVjubV8IP204Btta/ay6SvvE9MBdDgy7QbBiZxZnMi50HwH3t7nN0XCHEZQxuOBiAvRkHyIjobHsyrmLjnV2MenpF2VoOVx2u2BqCNYUUgPWcpayEzG8mkavm838Btq1t7oq+ix6hPTROJuylb/MAPJ0NnM0rZXdSZbuBJzHs/HIwG1M2UGQqsmPC2uvTdScpNVvpGulL32YBVz84Mw52fW17PPhlUBR+OPIDJZYSov2j6Rna0/GBhRCXCHEPobV/a1RUYgIb2p6Mq/h45xHRtnG/i/an1qpuYCkA67GtcZkse28a3jkH+GdgEIV6iA6IZnrH6VpHE3bkbNAztI2tG3hJZbuBG/WmpWsIDU0mSiylbEjZYMeEtVNKdhE/7UgC4KlhLa7d+rfmNbCaoelQaNKf3NJcfj7+MwD3t7tftlgUQkMXWgHXcP6X24QNYKnY0i4jokNwMug4kV7AsbTa0w0sBWA9deh0Lou+eZsbypbxka8PB1yNuBvcebfvuxh1suBzXXNDuz+7gS2V6QbW6VDa/X979x0W1Zk9cPx7pw9dughYsKGiKCiW1LVmTWLZFI1JXJM1RUw0pmx62axrTDHV2PYXk6wpJptoLCliiSVRRMRYUGwoCiIqvQ8z9/fHKAlrAwSGcc7neXie8d77zhzmwHB871vuqO4F/OlI7RdLvVp9sPYgFqvKgPZ+9G3nd+mLj2+D1KWAAoNeBuCLfV9QYimhQ4sOMtlKCAc7VwAm5u2l2K2FfZmmzORatfUy6bmxk/0O2rLfshotxoYmBaALKrdYWfDZ57ys/YgENzMf+5xd8mXAy7LX71XqmvYBeJp05BRV8MvB0/V7kuhxDJXbwACcKCjjv8nHAZg2uNOlL1ZVSHjR/jj6LgjuRmFlYfXSLxOjJqJR5KNYCEdq592ONl5tsNgsbAzrbj9Yh9vAI6Ltyzct2+E8t4HlU8cFzVu6mhdLppNpgOeD7LcGx3cZX70vorj6GHQaRve0f0DN+GFf/XoB/SLoFBJHa4uFClsl64+vb+Aoncd/Nh+lyqbSt50vMa1bXPrifSvtC8vqTHDjs/b2qf+hqLKICO8IhrQe0gQRCyEuRVGU6tnAa8wG+8E6FIB/6hyIh1FHZn4Z2+s71rqJSQHoYjbtOsDwXVPRaYt5pGUopdjoHdybqTFTHR2aaGRTBnXEy6Rj74nC6rFrdaX0+uvvt4HTa79W1tWkrNLK52ffv/sGtL30xZZyWPWc/XG/ePAOJb88n/+k/geA+J7xaDXaxgxXCFFL524DbyzOwAKQuQ3K8mvV1qTXMuTsWOvvdjjHbWApAF3I8ZwzmL+5h3BNFo8EhZChtRHsHswb172BTqNzdHiikfm6G5g2uCMAb61KI7+0su5PEnkLQy32gmXj8Q2UWEoaMkSn8G3KcfJLLYT7ujEwMujSF2/5EPKOgEcwXDMNgIV7FlJiKSHSN7L6D44QwvG6+XfDz+RHqbWM5KAIUG32ySC1dGuPEMA+2a7KamusMBuMFIAuoqysnKwFd9KLvTwXEEiKSYu73p3ZA2fjZ77MAHZx1bi7b2s6BnmQV2rh7YT9dX8CvYmOkbfRptJCpVrlclvD2Wxq9Z6/f+3f5tJ7/haegA1v2h8PfgWMHpwuO83nez8HID46Xsb+CdGMaBQN17S6BoCNfqH2g3W4DTygvT9+7gbOlFTyixPsDSyfPi5AtVaROucu+liSeLuFLz96mNAqWmZdP4uOLTo6OjzRhHRaDS/d0hWARYkZZBeU1/k5lJjx3HT2NvDKA0sbMrxmb+PB0xw6VYKHUcftsaGXvnjNK/Yt31rFQtQdAMzfOZ9yaznd/btzXeh1TRCxEKIurg217we8UT17d6MOPYB6rYa/39SZ+ffE0Ledb2OE16CkALzaWavYO+9eYgrXMNfbm4U+HgA83/d5+rfq7+DghCMMaO9P7zYtsNpU/pt8rO5PENyN4e5tANhyMokzZc3/f7oNwWZTeWe1vdf0jtgwPE2XWC7p6K/w2xf2xzfNBI2GjMIMvk77GoApvabIun9CNEP9QvqhVbSkl+dwTK+H3ENQWPsxfXfEhjGkazBGXfMf2ysF4NXMauHwvLF0yVnJx15ezPb1BuCxmMe4reNtDg5OONKY3vbV7hdvO1av7eFaxz5It4oKrKguMxlkUeJRUjLy8TDqmHjdJSZ/WC2w8nH74173Qqh9o/l3t79LlVrFta2upU/LPk0QsRCirrwMXvQM7AnAxqAI+8H0q3P/cykAr1aVpRyfdxvtclbxby9v3vLzAWBS9CTu63afY2MTDvfnqJZ4mnQcyy3jl0P1WBew22iGV9gLx5Vnx7RdzU4UlPH6j2kAPDWsEy29zRe/OHEu5KSC2RcGvQLAzlM7WXV0FQqKzLgXopmrvg3sYb9jxpGrc+cjKQCvRkUnyf1wCCE5PzOzhS/v+tl7/iZGTeSh7g85ODjRHJgNWkadXRfwy631uA2sNzOs/Ug0qsrO4gyOFdbjOZyEqqq8+N0eiiuq6BXuw91xrS9+ccFxWDfD/njwP8DNF1VVmZU8C4AR7UfIuFshmrlrW9kLwKTKXMoURXoAhZM4uYeyOTfgXrCLJ/2DWHR2zN/jMY/zaK9HZdyRqHZnb/uuL6tSszlTXFHn9v5944krt7f7fs+iBo2tOflpz0kSUk+i1yrMGN0dzcVm/qoq/PB3+8SPsL4QPQ6ANRlrSD6ZjFFrJD46vgkjF0LUR3uf9rR0b0mFWkWS2Qz5RyG/fmunNmdSAF4FThVV8G7CflZ8+gaWeTeSX3GSMcGhrPI0olW0TL9mOn/t9ldHhymama4h3nQP9cZiVfl2e2bdn6BFG4a728fCrTj0ndNsf1QXZZVWXl2RCsAD17WjU7DnxS9O/Q72rQCNDm6eBRoN5VXlvJH0BgDju44n2D24KcIWQlwBRVGqewE3+J/dHvUq7AWUAtDJ5ZVUct/8tYRteIybD/+TrUaFUa1COWjS4GP04cNBH3JrxK2ODlM0U2P72CeD/N+mdMoqrXVuP6j3I5hsNo5YS9iZtaWhw3O42esOkplfRisfM5Nv7HDxC8vy4Psn7Y+vmQZB9qV2Fu5ZSFZJFsHuwfwt6m9NELEQoiEMaDUAgC2Gs7N5j2xyYDSNQwpAJ1ZSUcWH895nXuEkBut+4SV/Px4KDqREqxLp24XFNy+mf4gs9SIublTPVoS2MJNdWM6CjYfr3N6943AGV9l3kVma9E4DR+dY6adLmL/B/p68cHMXzIZLLOuw6nkoyQH/jnDdEwBkFWfxf7v+D4DHYx/HrLvExBEhRLPSO7g3WkXLUWsxWTotHNloH+ZxFZEC0EkVZe5nzzsjeKbwH+z0KGdUWBjferoDcFfnu/jPnz8lxCPEwVGK5s6k1/L0TZ0BmPPzIU4W1nFhaI2GUWeXFPoxbw9lFUUNHaJDqKrKy8v2UGm1cX3HAIZ2vcSWb4fWQsoiQIFb3wedEYA3t71JhbWC3sG9Gdp6aNMELoRoEJ4GT7r5dwMg0ewOBcfs2zpeRaQAdDJqYRbpnzyIaUFfVDWJsS2DeTLQn5NaCPMMY+HQhTwT9wxGrdHRoQonMTyqJb3CfSizWHlrVVqd28f0e5xWVhvFGoV1SW81QoRNb/OhM6zffwqDVsPLt3a9+OSpsjxYenZiR5+JEN4XgMQTiSQcTUCjaHi6z9My+UoIJ9S3pf33ebNvS/uBq+w2sBSATsBqU0nftYm0D8dSNiuK3TnLuDvEn7+1DCLVZMBN58ak6El8c+s3xAbHOjpc4WQUReH5m7sA8HXycfZkFdSpvcbgxkg/+8Kpy9K/vypuk7yz5gAAY/qE0dbf/eIXfv8UFGWBX/vqNf+qbFW8tvU1AO7sdKcs+yKEkzpXACbqbNgAMq6ucc46RwcgzmezqWw6eJo1idsJzfyBuLK1VJiz+d7djYTwIAq09rFIRq2R0R1G82D3B/Ez+zk4auHMeoW3YHj3lqzceYL5Gw7z7piedWo/YsBzfLjidrZqq7i1KAkY3jiBNoHNh86wNT0Xg1bDwzdEXPzCPUtg11egaGDUPDC4AbA4bTEH8w/iY/SRZV+EcGI9Anpg1pnJrSrjgEFPp4xfHR1Sg2qwHsDZs2fTpk0bTCYTcXFxbN26taGe2mXYygpY+/1ivpgxgdKv/kS3nAns9VrOQ62t3NcyiP96eVKg1eJnCuDRno+ScFsCz8Y9K8WfaBAPXWcvdlbuPFHnsYAt/SOJM/gDkFbs3FvDvbvm7H6/vUMvvuNHUTasmGZ/fO3j1du95ZbnMnvHbAAe6fkI3kbvRo9XCNE49Fo9sUH23+0tJjPkHoaikw6OquE0SA/g4sWLmTZtGnPnziUuLo533nmHoUOHkpaWRmBgYEO8xFVBVVX2nyxGg40IUxGa3EOUnEjhwIlk9p3Zy+GK0xw06EkNMVCi0QC/F3aeeg8GtR7MTW1vondwb3Qa6bwVDSsq1JvY1i3YdjSPzxIzmDa4brcuR3e/jy3Jb/CToZRHMjajj7iukSJtPImHz7DlcC56rcKkG9pf+CJVhWWPQlkuBHeH656qPvV28tsUVRYR6RvJXzr8pYmiFkI0lr4t+7IxcyObffwYX1gIx7ZAlxGODqtBNEgVMWvWLCZOnMiECRMAmDt3LitXruSjjz7i6aefboiXaBB5pZUUW+re7vCpYiqqbHQK8qyxC4Cqqhce3G0poyIvi1MnjlJw5gi5+Uc4fvoQJ4qOUqbkUaovJ1uv4bhOR5b+bApMgOn3RWbNGj1Rft2IbdWP/iH96erXVYo+0ejG92/DtqN5fJ6YQfyNERh1l1j65H8MjByD7/Z3yNHBxk3/YEjE6kaMtOFVWW289uM+AO6IDSPE5yK9f9s/hQM/gdYIo+eDzgDYJ34sPbgUBYVn455Fq6n9eyeEaJ76htjHASZrVSoBw9HNUgCeU1lZSXJyMs8880z1MY1Gw6BBg9i8efOVPn2DWrRqHtvSt7BpzmICPA34uOnR60CnUdBrFXQaBY2iUmG1Um6xkl9ayYmCMorKK9GgYtTa8DYqoFZhsViw2SxotRYUrQVVY8FCFRbFSrEGzmi15Go1lGnO3mU3nv1CA7jViMutSo+hwhe9tj1jet/I9W2iae/TXv6AiCY3rFswQV5GThZW8P2uE4zqGVrrtgatgZHtbuWjQ9/wVdlRhmQkQnhcI0bbsN5be5CUjHw8jTrib7xI719uOvz0rP3xwBcgMBKA8qpy/rH5HwDc0ekOogOjmyBiIURj6+DTAT+TH2fKz/CbyUjvjOZV11yJKy4AT58+jdVqJSio5jpZQUFB7Nu374JtKioqqKj4fe/RwsJCACwWCxZLPbroaik9byW7gnJ+P1CbYU6eZ79qRceF3lKdCp42A0F6HyJ8wmjVIgJF14aSUl88ta0wajzxNuu5qWsQBp29YLRZbdisttq+8FXr3M9DY/5ciJru6h3G22sO8tGmdIZ3DazTEiYjOt/Lxwe/IdFs4uC6V2h913eNGGnDSUzP5YO19pm/r9waSYC77vyfOasF7TcT0VQWYwvvhzVmIpy95sMdH5JRlEGAOYBJUZOc5udVfr+ci+TLMXoH9ebHoz+SZDIRm72TquJcMF6+MGjsfF3p8zrknuKMGTN45ZVXzju+atUq3NzcLtCiYUTo/SmvyMeqgtWmYFNBBVRVgbOPFex/7DQoaBUwaMCktS+VUaVqqbRpUBQNWo0GnaIFDKiqGVQziuKGopgx6VvgZ/TFX++Bh9YdI8bf/4jagDPnIqoCjlbHt7oe27G6ioSEBEeH4DL8LaBTtOzKLGTmZz/S3bduy7p00bZjt+0wXxekcs1X75Dr0byXQSmxwOs7tdhUhbgAG9rjKXx/POW867pkLqZDThIWrRs/e9xG6Y8/AZBtzeaTok8AGKwMZuNq59szVH6/nIvkq2kZKs4O83DzYFJ+AUlL5nDKq1ut2zdWvkpLS6+o/RUXgP7+/mi1Wk6erDkz5uTJkwQHX3jj82eeeYZp06ZV/7uwsJCwsDCGDBmCl5fXlYZ0URbLYBISEhg8eDB6vf688xVVNkorq/A06tBpZYnE5sBisVwyZ6JxHHM7wNwN6azIciP+tv54mmr33lssFg58f4DdJYdZ5unOI6XfY7x9CjTjhZCf/O8u8itP0M7fjXkP9sXdeP7HonJoDbqUlfbHI2dzQ+dbALDarExImIANG38K+xNPXPtEk8Z+peT3y7lIvhwjsiCSZSuXsceooxKIa2nFdv2fL9uusfN17u5pfV1xAWgwGIiJiWHNmjWMHDkSAJvNxpo1a5g8efIF2xiNRozG83eq0Ov1TfJDfbHX0evBwyw7aDRHTfWzIeymDu7ET6k5pJ8u4a3Vh5g+KqrWbSN0EYS5h3CsJIsVhfsZs385dGueM2J/PXSapb+dQFFg1p098fG4wMSPwixYNsn+uPdEdFGjq099tfcrdp/Zjafek+f6Pue0P6Py++VcJF9Nq4NfB3xNvuSW57LLaCTm+Fa0dXj/GytfV/qcDdLNNW3aNBYsWMAnn3zC3r17efjhhykpKameFSyEcC4mvZZ/nS36PkvMYGt6bq3bahQNYzvfDcAn3p5YV78MljruMdwEKqqsPL90NwB3x7UmOszn/IuqKuGr8VB6BoKjYMg/q0+dKD7Bu9vfBWBqzFQC3WTJKyGuRoqiEBMUA0CyyQjHt4HV+cdhNkgBeOedd/Lmm2/y4osvEh0dzY4dO/jxxx/PmxgihHAe/SL8GNM7DIAXlu7GZqv9WMARESPwMXhzXK9ndeUp2DqvscKst/nrD3P4VAn+HkaeGNrpwhf99Awc3womb7jjU9CbAPsSUP9M/CdlVWX0CuzFbR1va8LIhRBN7dyC0NvcPaCqDLJ3OTiiK9dgA90mT57M0aNHqaioIDExkbg451n+QQhxYc/cFImHUUfaySLWpeVcvsFZZp2ZMZFjAfjYxwt1w5v23TOaicz8Mt5fdxCAF26OxNt8gVspKZ9B0r8BBUb/G3zbVZ/67tB3bDi+Ab1Gz0v9XkKjyJhhIa5mscH2AnCHUY8F4HiSQ+NpCPKpJYS4KG83PeP6hgMw5+dDdWo7tvNYjFoju41GtikV8MNTl2/UROatP0RllY24tr7c2iPk/AuOJcGKx+yPb3gaOg6pPpVVnMXMrTMBiI+Op51Pu/PbCyGuKu192uNt9KYMlVSjAY45/3a3UgAKIS7p/gFtMWg1bDuaR9KR2o8F9DX5MrL9SAAWentD6new7/tGirL2corK+TLpGABTBnY4f53DvCPwxRiwVkCnP9fY6s2m2njhlxcothQTHRDNX7v+tekCF0I4jEbREBP4x3GAUgAKIa5ygV4m/hLTCoC5dewFHN9lPBpFw0Y3E3sMelj5OJRf2dIFV+r/NqZTWWWjZ7gP/SL8ap4sL4DP74TS0/ZJH6MXgOb3j8kv9n3B1uytmHVmpl8zXXbrEcKFnJsIss1kgvwMKDp5mRbNmxSAQojLeuC6CBQF1uzLYV927Qu4MK8whrcdDsD7gS2hKAtWv9RYYV5Wfmkli7bYF19/5E/ta/b+VVXA4nvg1D7wbAljF4PRo/p0Wm4as7bNAmBazDTCvcKbNHYhhGOdGweYYjZjBacfBygFoBDistr6u/Pnbi0B+HBd3XoBH+7xMDpFxy962G40wraPIHVZY4R5WQt/OUJJpZUuLb24sdMflm2xVsE390P6etC7w9gvwbtV9elSSylPbniSSlsl14dez52d7nRA9EIIR+rUohPueneKFTho0Dv9bWApAIUQtTLpxggAlu/M4mBOca3bhXmFMbLDSADea9MFFeC7yfaxdk1o04HTzFlvL17jb/xD75/NBsunwN7loDXA2M8hJLpG29eTXie9IJ0AcwCvDni1TvsjCyGuDlqNlu7+3QHYYTTaJ4s5MSkAhRC10jXEm0GRQagqfHh2CZXaerD7gxg0BpIrz7A5rDtUFMB/77MvtNwEko7kMvHTbVRW2RjSJYibup3dptJmg++fgB2LQNHAbQuh3Q012v6Q/gPfHPgGBYUZ186ghalFk8QshGh+ogOjAdhhMkJWilMvCC0FoBCi1qYM7ADA0h2ZpJ8uqXW7YPdg7uh0BwBv+/liNXlDZjKsnAZq7ReYro9dxwuYsDCJMouV6zsG8P5dPdFoFLBZYdkjsO3/AAVGzIbIm2u0PZh3kJd+tY9Z/FvU34hrKeubCuHKogOiAdhhMjn9gtBSAAohai0q1Js/dQ7EpsLsOvYCTuw+EU+DJ/sKDvPf/hPsPW4p/4H1MxspWigst/DwZ8kUV1TRt50v8+6JwajT2nselzz4e8/fqHkQfVeNtsWVxTz282OUVZXRt2Vf4qPjGy1OIYRziAqIQkHhuE7Laa3GqSeCSAEohKiTR8/2Ai5JySQ1q/Yzgn1NvkyOngzAe5mryRvyqv3EzzNg+6cNHqeqqjy/ZDfH88oIbWFm/r2xmPRaKM2FRaNh19eg0dlv+/aoOanj3Hp/RwqPEOwezMzrZsqSL0IIPA2etG/RHoDfjEanXhBaCkAhRJ1Eh/kwpEsQVpvKg4u2kVdS+3F8d3S6g44tOlJYWci76mm49gn7ieVTIPmTBo3zm+2ZLPstC61G4b2xPfEy6eH0Qfj3IDiyEQye9tm+XUee13b2jtmszliNXqNn1vWz8DX5NmhsQgjnVX0b2Gi0D2VxUlIACiHq7PXbuhPu68ax3DLiP99OldVWq3Y6jY7n4p4D4NsD37Iz6laI+SuoNlj+KKx/o0HGBB4+VcyL3+0G4LFBHegV3gJ+Wwzzb4DcQ+AdDvf/BB0Gn9d22aFlzN85H4AX+71IVEDUFccjhLh61JgIkpduv6vghKQAFELUmY+bgQX3xuJm0PLroTP86/t9tW7bK6gXt7S7BRWV5355ntKh/4JrH7efXPdPWDYZKms/weR/VVbZmPLlDkorrfRt58vDff3hm4mw5AGoLILwfjBxDQR1Pa/ttuxtNSZ9nNvKTgghzjnXA7jHaKQSIGu7I8OpNykAhRD10inYk1l3RAPw0S/pfJN8vNZtn+r9FIHmQI4UHmHW9rdh4Itw0+uAAimLYM4AyEisV1xvrkpjV2YBvmYNcyN3of0gBnZ9BYoWbngWxq8Aj8Dz2qXlpvHo2kepslUxuPVgHun5SL1eXwhxdQvzDMPX5ItFgVSjATJTHB1SvUgBKISot2HdgqsnhTyzZBe/HS+oVTsfkw+vXmOfBLI4bTEbjm+AuAfh3qXg1cp+W2XhMFgabx+3V0sb9p/i4w1pjNJsZL3XS/iseRJKz4B/R5jwA9zwd9Dqzmt3pOAIDyQ8QJGliJ6BPZl+zXQ0inw8CiHOpyhKdS/gb0aj9AAKIVzT1IEdGNwliMoqG/Ff7KCwlnNC+of05+7IuwF44ZcXOFly0r4I88O/Qo+x9nGBOxbB7N72PXp3fgXFpy78ZJWl7N24hCOfT+VX4yO8bZiDZ0EamLxh2Ez7c4ZfeA2/rOIsJiZMJLc8l86+nflg4AeYdeZ6vBNCCFdxbhxgiunsRJBGXs+0MZz/X2EhhKgDjUZh1h09GPXhrxzMKebTAxrusNXuw3BKrykkZidyIO8Aj6x9hI+HfYyb2QdGzYXY+2Hjm7D/R9i7zP4F9gkcHgFg9oWKIig+iTX/GJFqFZEACqieLVF6/w1i7wO3i8/gPVJwhIkJE8kuyaaNVxvmDpqLl8Hrit8TIcTVrXuAfUu4XUYj5GRCYVaN/cOdgfQACiGumKdJz/x7YnAzaDlQqOGjX4/Wqp1JZ+K9G9/D1+TL3ty9/H3j37HarPaTYb3hrsXw0Ca45jEIPjsbtyDD/j/ugwlwbAvkpaNVqziu+rPZ+89UjP4EZeouuO6JSxZ/ablpjP9xPNkl2bT1bsuCIQvwM/td6VshhHABkb6RaBQNOTotJ7Vap7wNLAWgEKJBtAvw4LmbOgEwa/UB9mTVbjxgqGco7974LgaNgZ+P/czMpJmof7ydEhwFg162F4JPHIT7E2DM53Dr+6zu9jq3VbzIgPJ3+e76H+k79XOM3UeCVn/J1/wl8xcm/DiB3PJcIn0j+XjYxwS7B9fvGxdCuBw3vRvtfewLQu82GiBTCkAhhAu7PaYVUS1sWKwqU77cQVF57TZKjw6MZvo10wH4Yt8XvPTrS1TZqs6/0CMAwvpA5+EsVQbyt22hbFM7c+9N1xL/pw4oinLJ11FVlYW7FzJpzSSKLEX0CuzFv4f+WxZ6FkLUWZS//a7ELqPBKReElgJQCNFgFEVhTISNAA8DB3OKGf7eJlIy8mrVdljbYfyj/z/QKBqWHFzCk+ufpNJ6/owSm03l41/SeeLr3wC4b0BbHriu3WWfP688j8fXP86s5FnYVBujO4xmwZAFMuZPCFEv5wrA3UYjZO0AW+0WxG8upAAUQjQoDz3MHdeTVj5mMnJLuW3uZmavO1jztu5FjOowilnXz0Kv0bM6YzV3rbyLA3kHqs8fzCni9nmbeXl5KlU2lRHRITw/PPKyPX+rjqxi5HcjSTiagE7R8Wzcs7zc72UMWsMVf79CCNfUzb8bYL8FbKsogNzDDo6obmQWsBCiwXUP9eb7Kdfy/NLdLP8tizd+SmNfdhFv3NYdk157ybYDWw9kzqA5PLn+SdLy0rhzxZ2ManM/WUdj+GnPGWwquBu0PP3nSMb1CUejuXjxl5KTwuwds0k8YV9Uur1Pe14d8Gr1B7cQQtRXhE8EZp2Zkqoy0vU6IrK2g397R4dVa9IDKIRoFN5mPe+Niea10VHoNArLf8ti7IItZOaXXbZtXMs4vr7lGyK94rDYLHx1eC4bKh5D57uO6yPdWTXteu7p2/qCxV9xZTHLDy1n4qqJ3PvDvSSeSESn6Hig+wMsvnmxFH9CiAah0+iI9I0Ezi4H42TjAKUHUAjRaBRFYUyfcFr7ufPQomRSMvIZ8NpaYlq34NoO/uSVVHLkTCkAce186dfOj9ySSn45eIaEvdkcyx2JzjsUU8AaNPp8jIE/kcIqpmzoSExQDAFuAZh1ZmyqjaOFR0kvSGdHzg4qbfaxgzpFx4j2I5jYfSKtPJxrjS4hRPMX5R/F9pzt7DYaGOlkM4GlABRCNLp+EX4smdSfZ77dRWJ6LslH80g+WnNyyPr95+/y4eNm4J6YsYyNe4Lk0+v4NPVT9uXuIy0vjbS8tIu+XhuvNgxtM5SR7UcS6hna4N+PEEIAdAuw31HYZTRA9k6wWi67DFVzIQWgEKJJtAvwYPGD/cguKOenPdnsOJZPkJeJNn5ulFms/HLwDElHcvE26xnQ3o/+Ef4MigzCbLCPGQzxvoVbIm7hVOkpknOS2XlqJ0WVRZRXlWNTbYR7hdPWuy2RvpG092l/2YkhQghxpc7NBN5vMFBhLceYkwotezg4qtqRAlAI0aSCvU2M79+G8f9zfMKAtrVqH+AWwLA2wxjWZljDByeEEHUQ4h6Cr8mX3PJc9hkM9Mjc7jQFoEwCEUIIIYSoB0VR/rAgtNGptoSTAlAIIYQQop66+nUFINVggMwUB0dTe1IACiGEEELUUxe/LgCkGvWQkwqVpQ6OqHakABRCCCGEqKdzBWC6Xk8pNvtsYCcgBaAQQgghRD0FuAXgb/bHpijsN+jBSdYDlAJQCCGEEOIKnOsF3GM0OM1EECkAhRBCCCGuQPU4QIPBabaEkwJQCCGEEOIKdPE9NxHEALmHoSzvMi0cTwpAIYQQQogrcK4H8LDeQJmiwMlUB0d0eVIACiGEEEJcgUC3QPxMftgUSLtvGbQZ4OiQLksKQCGEEEKIK6Aoyu/jAIuPOzia2pECUAghhBDiClUXgGea/+1fkAJQCCGEEOKKnSsA9+budXAktSMFoBBCCCHEFTpXAB7KP0R5VbmDo7k8KQCFEEIIIa5QkFsQviZfrKqV/Xn7HR3OZekcHYAQQgghhLNTFIXHYx/HXe9OG+82jg7nsqQAFEIIIYRoALdG3Fr92GKxODCSy5NbwEIIIYQQLkYKQCGEEEIIFyMFoBBCCCGEi5ECUAghhBDCxUgBKIQQQgjhYqQAFEIIIYRwMVIACiGEEEK4GCkAhRBCCCFcjBSAQgghhBAuRgpAIYQQQggXIwWgEEIIIYSLkQJQCCGEEMLFSAEohBBCCOFidI4OAEBVVQAKCwsb9XUsFgulpaUUFhai1+sb9bVEw5CcORfJl3ORfDkXyZdzaex8nauZztVQddUsCsCioiIAwsLCHByJEEIIIYTzKCoqwtvbu87tFLW+pWMDstlsZGVl4enpiaIojfY6hYWFhIWFcezYMby8vBrtdUTDkZw5F8mXc5F8ORfJl3Np7HypqkpRUREhISFoNHUf0dcsegA1Gg2hoaFN9npeXl7yy+NkJGfORfLlXCRfzkXy5VwaM1/16fk7RyaBCCGEEEK4GCkAhRBCCCFcjEsVgEajkZdeegmj0ejoUEQtSc6ci+TLuUi+nIvky7k093w1i0kgQgghhBCi6bhUD6AQQgghhJACUAghhBDC5UgBKIQQQgjhYqQAFEIIIYRwMS5VAM6ePZs2bdpgMpmIi4tj69atjg5JADNmzKB37954enoSGBjIyJEjSUtLq3FNeXk58fHx+Pn54eHhwV/+8hdOnjzpoIjFH7322msoisLUqVOrj0m+mpfMzEzuvvtu/Pz8MJvNREVFsW3bturzqqry4osv0rJlS8xmM4MGDeLAgQMOjNh1Wa1WXnjhBdq2bYvZbCYiIoJXX321xn6vki/H2bBhA7fccgshISEoisLSpUtrnK9NbnJzcxk3bhxeXl74+Phw//33U1xc3ITfhZ3LFICLFy9m2rRpvPTSS2zfvp0ePXowdOhQcnJyHB2ay1u/fj3x8fFs2bKFhIQELBYLQ4YMoaSkpPqaxx57jOXLl/P111+zfv16srKyGD16tAOjFgBJSUnMmzeP7t271zgu+Wo+8vLyGDBgAHq9nh9++IHU1FTeeustWrRoUX3N66+/znvvvcfcuXNJTEzE3d2doUOHUl5e7sDIXdPMmTOZM2cOH3zwAXv37mXmzJm8/vrrvP/++9XXSL4cp6SkhB49ejB79uwLnq9NbsaNG8eePXtISEhgxYoVbNiwgQceeKCpvoXfqS6iT58+anx8fPW/rVarGhISos6YMcOBUYkLycnJUQF1/fr1qqqqan5+vqrX69Wvv/66+pq9e/eqgLp582ZHhenyioqK1A4dOqgJCQnq9ddfr06ZMkVVVclXc/P3v/9dveaaay563mazqcHBweobb7xRfSw/P181Go3qF1980RQhij8YPny4et9999U4Nnr0aHXcuHGqqkq+mhNAXbJkSfW/a5Ob1NRUFVCTkpKqr/nhhx9URVHUzMzMJotdVVXVJXoAKysrSU5OZtCgQdXHNBoNgwYNYvPmzQ6MTFxIQUEBAL6+vgAkJydjsVhq5K9z586Eh4dL/hwoPj6e4cOH18gLSL6am2XLlhEbG8vtt99OYGAgPXv2ZMGCBdXn09PTyc7OrpEvb29v4uLiJF8O0L9/f9asWcP+/fsB+O2339i0aRM33XQTIPlqzmqTm82bN+Pj40NsbGz1NYMGDUKj0ZCYmNik8eqa9NUc5PTp01itVoKCgmocDwoKYt++fQ6KSlyIzWZj6tSpDBgwgG7dugGQnZ2NwWDAx8enxrVBQUFkZ2c7IErx5Zdfsn37dpKSks47J/lqXg4fPsycOXOYNm0azz77LElJSTz66KMYDAbGjx9fnZMLfT5Kvpre008/TWFhIZ07d0ar1WK1Wpk+fTrjxo0DkHw1Y7XJTXZ2NoGBgTXO63Q6fH19mzx/LlEACucRHx/P7t272bRpk6NDERdx7NgxpkyZQkJCAiaTydHhiMuw2WzExsbyr3/9C4CePXuye/du5s6dy/jx4x0cnfhfX331FZ999hmff/45Xbt2ZceOHUydOpWQkBDJl2hQLnEL2N/fH61We94sxJMnTxIcHOygqMT/mjx5MitWrGDdunWEhoZWHw8ODqayspL8/Pwa10v+HCM5OZmcnBx69eqFTqdDp9Oxfv163nvvPXQ6HUFBQZKvZqRly5Z06dKlxrHIyEgyMjIAqnMin4/Nw5NPPsnTTz/NmDFjiIqK4p577uGxxx5jxowZgOSrOatNboKDg8+bfFpVVUVubm6T588lCkCDwUBMTAxr1qypPmaz2VizZg39+vVzYGQC7NPmJ0+ezJIlS1i7di1t27atcT4mJga9Xl8jf2lpaWRkZEj+HGDgwIHs2rWLHTt2VH/FxsYybty46seSr+ZjwIAB5y2rtH//flq3bg1A27ZtCQ4OrpGvwsJCEhMTJV8OUFpaikZT80+zVqvFZrMBkq/mrDa56devH/n5+SQnJ1dfs3btWmw2G3FxcU0bcJNOOXGgL7/8UjUajerHH3+spqamqg888IDq4+OjZmdnOzo0l/fwww+r3t7e6s8//6yeOHGi+qu0tLT6moceekgNDw9X165dq27btk3t16+f2q9fPwdGLf7oj7OAVVXy1Zxs3bpV1el06vTp09UDBw6on332merm5qYuWrSo+prXXntN9fHxUb/77jt1586d6ogRI9S2bduqZWVlDozcNY0fP15t1aqVumLFCjU9PV399ttvVX9/f/Wpp56qvkby5ThFRUVqSkqKmpKSogLqrFmz1JSUFPXo0aOqqtYuN8OGDVN79uypJiYmqps2bVI7dOigjh07tsm/F5cpAFVVVd9//301PDxcNRgMap8+fdQtW7Y4OiSh2qfSX+hr4cKF1deUlZWpkyZNUlu0aKG6ubmpo0aNUk+cOOG4oEUN/1sASr6al+XLl6vdunVTjUaj2rlzZ3X+/Pk1zttsNvWFF15Qg4KCVKPRqA4cOFBNS0tzULSurbCwUJ0yZYoaHh6umkwmtV27dupzzz2nVlRUVF8j+XKcdevWXfDv1fjx41VVrV1uzpw5o44dO1b18PBQvby81AkTJqhFRUVN/r0oqvqH5cWFEEIIIcRVzyXGAAohhBBCiN9JASiEEEII4WKkABRCCCGEcDFSAAohhBBCuBgpAIUQQgghXIwUgEIIIYQQLkYKQCGEEEIIFyMFoBBCCCGEi5ECUAghhBDCxUgBKIQQQgjhYqQAFEIIIYRwMVIACiGEEEK4GCkAhRBCCCFcjBSAQgghhBAuRgpAIYQQQggXIwWgEEIIIYSLkQJQCCGEEMLF/D9ColkGMbjCpwAAAABJRU5ErkJggg==", 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", + "image/png": 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\n", " " ], diff --git a/scripts/run_datagen.py b/scripts/run_datagen.py deleted file mode 100644 index 5b492c5..0000000 --- a/scripts/run_datagen.py +++ /dev/null @@ -1,138 +0,0 @@ -from statesim.simulator import ContinuousSimulator -from statesim.io import ( - write_measurement_csv, -) -from statesim.generate.input import generate_random_static_input -from statesim.system.cartpole import CartPole -from statesim.system.coupled_msd import CoupledMsd -from statesim.system.inverted_pendulum import InvertedPendulum -from statesim.model.statespace import Nonlinear -from statesim.configuration import ( - GenerateConfig, - CartPoleConfig, - CoupledMsdConfig, - PendulumConfig, -) -from statesim.analysis.plot_simulation_results import plot_outputs - -import os -import argparse -import pathlib -import time -import numpy as np -from numpy.typing import NDArray -import matplotlib.pyplot as plt - - -def main(config_file: pathlib.Path): - config = GenerateConfig.parse_file(config_file) - np.random.seed = config.seed - if isinstance(config.system, CartPoleConfig): - sys = CartPole( - g=config.system.g, - m_c=config.system.m_c, - m_p=config.system.m_p, - length=config.system.length, - mu_c=config.system.mu_c, - mu_p=config.system.mu_p, - ) - elif isinstance(config.system, CoupledMsdConfig): - sys = CoupledMsd( - N=config.system.N, - c=config.system.c, - k=config.system.k, - m=config.system.m, - ) - elif isinstance(config.system, PendulumConfig): - sys = InvertedPendulum( - g=config.system.g, - m_p=config.system.m_p, - length=config.system.length, - mu_p=config.system.mu_p, - ) - else: - raise NotImplementedError - A_sym, B_sym = sys.get_linearization() - A, B = sys.evaluate_linearization( - A_sym=A_sym, - B_sym=B_sym, - x_bar=np.array(config.system.xbar, dtype=np.float64).reshape( - (config.system.nx, 1) - ), - u_bar=np.array(config.system.ubar, dtype=np.float64).reshape( - (config.system.nu, 1) - ), - ) - config.system.A = (A * config.step_size + np.eye(A.shape[0])).tolist() - config.system.B = (B * config.step_size).tolist() - - sim = ContinuousSimulator(T=config.T, step_size=config.step_size) - model = Nonlinear( - f=sys.state_dynamics, - g=sys.output_function, - nx=config.system.nx, - nu=config.system.nu, - ny=config.system.ny, - ) - - # assert os.path.isdir(os.path.expanduser(config.result_directory)) - result_directory_path = os.path.join( - os.path.expanduser(config.result_directory), - f'{config.base_name}_K-{config.K}_T-{int(config.T)}', - 'raw', - ) - os.makedirs(result_directory_path, exist_ok=True) - - print('Write configuration file') - with open( - os.path.join(result_directory_path, 'config.json'), mode='w' - ) as f: - f.write(config.json()) - - N = int(config.T / config.step_size) - for sample in range(config.K): - fullfilename = os.path.join( - result_directory_path, - f'{sample:04d}_nonlinear_simulation_T_{int(config.T)}.csv', - ) - us = generate_random_static_input( - N=N, - nu=config.system.nu, - amplitude_range=(config.input.u_min, config.input.u_max), - frequency_range=[ - config.input.interval_min, - config.input.interval_max, - ], - ) - result = sim.simulate( - model=model, - initial_state=np.array(config.simulator.initial_state).reshape( - config.system.nx, 1 - ), - input=us, - noise_config=config.measurement_noise, - ) - if sample == 1: - plot_outputs(result) - plt.show() - - print(f'{sample}: write csv file: {fullfilename}') - write_measurement_csv( - filepath=fullfilename, - simulation_data=result, - ) - - -if __name__ == "__main__": - parser = argparse.ArgumentParser( - description='Simulate data for dynamical systems' - ) - parser.add_argument( - 'system', type=str, help='system name: msd, cartpole, pendulum' - ) - - args = parser.parse_args() - config_file_path = ( - pathlib.Path.cwd().joinpath('config').joinpath(f'{args.system}.json') - ) - main(config_file=config_file_path) diff --git a/src/statesim/configuration.py b/src/statesim/configuration.py index 3c13dd1..278b0b9 100644 --- a/src/statesim/configuration.py +++ b/src/statesim/configuration.py @@ -2,6 +2,9 @@ from typing import Optional, List, Dict, Union, Literal +CSV_FILE_NAME = 'simulation' + + class SplitConfig(BaseModel): raw_data_directory: str train_split: float @@ -11,18 +14,30 @@ class SplitConfig(BaseModel): split_filenames: Optional[Dict] -class InputConfig(BaseModel): +class InputGeneratorConfig(BaseModel): + type: str u_min: float u_max: float interval_min: float interval_max: float -class SystemConfig(BaseModel): +class LinearSystemConfig(BaseModel): + name: str + A: List[List[float]] + B: List[List[float]] + C: List[List[float]] + D: List[List[float]] + nx: Optional[int] + ny: Optional[int] + nu: Optional[int] + + +class NonlinearSystemConfig(BaseModel): name: str - A: Optional[List] - B: Optional[List] - C: List[float] + A: Optional[List[List[float]]] + B: Optional[List[List[float]]] + C: List[List[float]] xbar: List[float] ubar: List[float] nx: int @@ -30,7 +45,7 @@ class SystemConfig(BaseModel): nu: int -class CartPoleConfig(SystemConfig): +class CartPoleConfig(NonlinearSystemConfig): g: float m_c: float m_p: float @@ -39,14 +54,14 @@ class CartPoleConfig(SystemConfig): mu_p: float -class PendulumConfig(SystemConfig): +class PendulumConfig(NonlinearSystemConfig): g: float m_p: float length: float mu_p: float -class CoupledMsdConfig(SystemConfig): +class CoupledMsdConfig(NonlinearSystemConfig): N: int k: List[float] c: List[float] @@ -71,7 +86,9 @@ class GenerateConfig(BaseModel): K: int T: float step_size: float - input: InputConfig - system: Union[CartPoleConfig, PendulumConfig, CoupledMsdConfig] + input_generator: Optional[InputGeneratorConfig] + system: Union[ + LinearSystemConfig, CartPoleConfig, PendulumConfig, CoupledMsdConfig + ] simulator: Optional[SimulatorConfig] measurement_noise: Optional[NoiseConfig] diff --git a/src/statesim/generate/input.py b/src/statesim/generate/input.py index 88f041c..49acfe2 100644 --- a/src/statesim/generate/input.py +++ b/src/statesim/generate/input.py @@ -1,13 +1,13 @@ from numpy.typing import NDArray import numpy as np -from typing import List, Tuple +from typing import List +from ..configuration import InputGeneratorConfig -def generate_random_static_input( +def random_static_input( N: int, nu: int, - amplitude_range: Tuple[float, float], - frequency_range: Tuple[int, int], + config: InputGeneratorConfig, ) -> List[NDArray[np.float64]]: """ Generate a random static input sequence of length N, @@ -19,7 +19,7 @@ def generate_random_static_input( or the end of the sequence """ - assert frequency_range[1] < N + assert config.interval_max < N us = np.zeros(shape=(nu, N)) for element in range(nu): k_start = 0 @@ -27,12 +27,10 @@ def generate_random_static_input( while k_end < N: k_end = k_start + int( np.random.uniform( - low=frequency_range[0], high=frequency_range[1] + low=config.interval_min, high=config.interval_max ) ) - amplitude = np.random.uniform( - low=amplitude_range[0], high=amplitude_range[1] - ) + amplitude = np.random.uniform(low=config.u_min, high=config.u_max) us[element, k_start:k_end] = amplitude k_start = k_end return [np.array(u).reshape(nu, 1) for u in us.T] diff --git a/tests/smoke_test.py b/tests/smoke_test.py index d2092a4..50d7030 100644 --- a/tests/smoke_test.py +++ b/tests/smoke_test.py @@ -15,179 +15,20 @@ read_measurement_csv, write_measurement_csv, ) -from statesim.generate.input import generate_random_static_input -from typing import List, Dict +from statesim.configuration import InputGeneratorConfig +from statesim.utils import ( + get_callable_from_method_string, + get_callable_from_input_config, + run_simulation_write_csv_files, +) +from statesim.generate.input import random_static_input +from typing import List, Dict, Callable import utils import numpy as np import sympy as sym import os import math - - -def test_linear_continuous_simulator() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - A, B, C, D = utils.get_stable_linear_matrices() - nx = utils.get_state_size() - nu = utils.get_input_size() - ny = utils.get_output_size() - model = Linear(A=A, B=B, C=C, D=D) - - sim = ContinuousSimulator(T=float(len(u))) - measurement_noises = (NoiseGeneration('gaussian', 0.0, 0.01), None) - for measurement_noise in measurement_noises: - result = sim.simulate( - model=model, - initial_state=x0, - input=u, - noise_config=measurement_noise, - ) - - assert isinstance(result.ys, List) - assert isinstance(result.t, np.ndarray) - assert isinstance(result.ys[0], np.ndarray) - # output - assert len(result.ys) == len(u) - assert result.ys[0].shape == (ny, 1) - # state - assert len(result.xs) == len(u) - assert result.xs[0].shape == (nx, 1) - # input - assert len(result.us) == len(u) - assert result.us[0].shape == (nu, 1) - - -def test_linear_continuous_simulator_step_size() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - A, B, C, D = utils.get_stable_linear_matrices() - step_size = 0.02 - model = Linear(A=A, B=B, C=C, D=D) - sim = ContinuousSimulator(T=float(len(u) * step_size), step_size=step_size) - result = sim.simulate(model=model, initial_state=x0, input=u) - - assert (result.t[2] - result.t[1]) - step_size < 1e-5 - - -def test_linear_model() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - A, B, C, D = utils.get_stable_linear_matrices() - model = Linear(A=A, B=B, C=C, D=D) - xdot = model.state_dynamics(x0, u[0]) - - assert isinstance(xdot, np.ndarray) - assert xdot.shape == (2, 1) - - -def test_nonlinear_continuous_simulator() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - nx = utils.get_state_size() - nu = utils.get_input_size() - ny = utils.get_output_size() - model = Nonlinear( - f=utils.get_nonlinear_state_function(), - g=utils.get_nonlinear_output_function(), - nu=nu, - nx=nx, - ny=ny, - ) - sim = ContinuousSimulator(T=float(len(u))) - result = sim.simulate(model=model, initial_state=x0, input=u) - - assert isinstance(result.ys, List) - assert isinstance(result.t, np.ndarray) - assert isinstance(result.ys[0], np.ndarray) - # output - assert len(result.ys) == len(u) - assert result.ys[0].shape == (ny, 1) - # state - assert len(result.xs) == len(u) - assert result.xs[0].shape == (nx, 1) - # input - assert len(result.us) == len(u) - assert result.us[0].shape == (nu, 1) - - -def test_nonlinear_continuous_simulator_step_size() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - nx = utils.get_state_size() - nu = utils.get_input_size() - ny = utils.get_output_size() - step_size = 0.02 - model = Nonlinear( - f=utils.get_nonlinear_state_function(), - g=utils.get_nonlinear_output_function(), - nu=nu, - nx=nx, - ny=ny, - ) - sim = ContinuousSimulator(T=float(len(u) * step_size), step_size=step_size) - result = sim.simulate(model=model, initial_state=x0, input=u) - - assert (result.t[2] - result.t[1]) - step_size < 1e-5 - - -def test_nonlinear_model() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - nx = utils.get_state_size() - nu = utils.get_input_size() - ny = utils.get_output_size() - model = Nonlinear( - f=utils.get_nonlinear_state_function(), - g=utils.get_nonlinear_output_function(), - nu=nu, - nx=nx, - ny=ny, - ) - xdot = model.state_dynamics(u=u[0], x=x0) - - assert isinstance(xdot, np.ndarray) - assert xdot.shape == (2, 1) - - -def test_linear_discrete_simulator() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - A, B, C, D = utils.get_stable_linear_matrices() - nx = utils.get_state_size() - nu = utils.get_input_size() - ny = utils.get_output_size() - model = Linear(A=A, B=B, C=C, D=D) - step_size = 0.02 - - sim = DiscreteSimulator(T=float(len(u) * step_size), step_size=step_size) - measurement_noises = (NoiseGeneration('gaussian', 0.0, 0.01), None) - for measurement_noise in measurement_noises: - result = sim.simulate( - model=model, - initial_state=x0, - input=u, - noise_config=measurement_noise, - ) - - assert isinstance(result.ys, List) - assert isinstance(result.t, np.ndarray) - assert isinstance(result.ys[0], np.ndarray) - # output - assert len(result.ys) == len(u) - assert result.ys[0].shape == (ny, 1) - # state - assert len(result.xs) == len(u) - assert result.xs[0].shape == (nx, 1) - # input - assert len(result.us) == len(u) - assert result.us[0].shape == (nu, 1) - - -def test_plot_simulation_results() -> None: - types = ['xs', 'us', 'ys'] - results = utils.get_simulation_results() - for type in types: - plot_comparison(results=results, type=type) +import pathlib def test_plot_states() -> None: @@ -205,59 +46,6 @@ def test_plot_outputs() -> None: plot_outputs(result=results[0]) -def test_cartpole_state_dynamics() -> None: - system = CartPole() - x0 = utils.get_initial_state_cartpole() - x1 = system.state_dynamics(x=x0, u=np.array([[0]])) - assert x1.shape == (4, 1) - - -def test_cartpole_linearization() -> None: - system = CartPole() - A, B = system.get_linearization() - - assert isinstance(A, sym.matrices.dense.MutableDenseMatrix) - assert isinstance(B, sym.matrices.dense.MutableDenseMatrix) - - -def test_cartpole_linearization_evaluation() -> None: - system = CartPole() - A_sym, B_sym = system.get_linearization() - x_bar = utils.get_linearization_point_cartpole() - A, B = system.evaluate_linearization( - A_sym=A_sym, B_sym=B_sym, x_bar=x_bar, u_bar=np.array([[0]]) - ) - - assert isinstance(A, np.ndarray) - assert A.shape == (4, 4) - assert isinstance(B, np.ndarray) - assert B.shape == (4, 1) - - -def test_read_measurement_csv() -> None: - filepath = os.path.join( - utils.get_directory(), - 'data/2023_03_09-01_25_33_cartpole_linear_continous.csv', - ) - measurement = read_measurement_csv(filepath=filepath) - - # y - assert measurement.ys[0].shape == (2, 1) - # u - assert measurement.us[0].shape == (1, 1) - - -def test_generate_static_random_input() -> None: - us = generate_random_static_input( - N=20, nu=2, amplitude_range=(-4.0, 4.0), frequency_range=(1, 4) - ) - us_numpy = np.array(us) - assert us[0].shape == (2, 1) - assert len(us) == 20 - # check if elements are non zero - assert np.squeeze(np.where(us_numpy == 0)).size == 0 - - def test_write_measurement_csv() -> None: data = utils.get_measurement_data() filepath = os.path.join(utils.get_tmp_directory(), 'measurement.csv') @@ -274,30 +62,11 @@ def test_get_peak_gain() -> None: ana.get_peak_gain() -def test_get_h_inf_norm() -> None: - ana = SystemAnalysisContinuous(utils.get_unstable_linear_matrices()) - - h_inf = ana.get_h_inf_norm() - print(h_inf) - assert math.isinf(h_inf) - - ana = SystemAnalysisContinuous(utils.get_stable_linear_matrices()) - h_inf = ana.get_h_inf_norm() - assert not math.isinf(h_inf) - - def test_get_real_eigenvalues() -> None: ana = SystemAnalysisContinuous(utils.get_stable_linear_matrices()) ana.get_real_eigenvalues() -def test_is_stable() -> None: - ana = SystemAnalysisContinuous(utils.get_stable_linear_matrices()) - assert ana.is_stable() - ana = SystemAnalysisContinuous(utils.get_unstable_linear_matrices()) - assert not ana.is_stable() - - def test_analysis() -> None: ana = SystemAnalysisContinuous(utils.get_stable_linear_matrices()) ana.analysis() @@ -305,99 +74,28 @@ def test_analysis() -> None: ana.analysis() -def test_inverted_pendulum_state_dynamics() -> None: - system = InvertedPendulum() - x0 = utils.get_initial_state_inverted_pendulum() - x1 = system.state_dynamics(x=x0, u=np.array([[0]])) - assert x1.shape == (2, 1) - - -def test_inverted_pendulum_linearization_evaluation() -> None: - system = InvertedPendulum() - A_sym, B_sym = system.get_linearization() - x_bar = utils.get_linearization_point_inverted_pendulum() - A, B = system.evaluate_linearization( - A_sym=A_sym, B_sym=B_sym, x_bar=x_bar, u_bar=np.array([[0]]) +def test_run_simulation_write_csv_files(tmp_path: pathlib.Path) -> None: + generate_config = utils.get_generate_config(str(tmp_path)) + model = Linear( + A=np.array(generate_config.system.A), + B=np.array(generate_config.system.B), + C=np.array(generate_config.system.C), + D=np.array(generate_config.system.D), ) - - assert isinstance(A, np.ndarray) - assert A.shape == (2, 2) - assert isinstance(B, np.ndarray) - assert B.shape == (2, 1) - - -def test_coupled_msd_dynamics() -> None: - system = CoupledMsd() - x0 = utils.get_initial_state_msd() - x1 = system.state_dynamics(x=x0, u=np.array([[0]])) - y1 = system.output_function(x=x1, u=np.array([[0]])) - assert x1.shape == (8, 1) - assert y1.shape == (1, 1) - - -def test_coupled_msd_multiple_carts() -> None: - for N in range(2, 6): - N = 2 - system = CoupledMsd( - N=N, - k=np.array(range(1, N + 1)), - m=np.array(range(1, N + 1)), - c=np.array(range(1, N + 1)), - ) - x0 = np.zeros(shape=(N * 2, 1)) - x1 = system.state_dynamics(x=x0, u=np.array([[0]])) - assert x1.shape == (N * 2, 1) - - -def test_coupled_msd_linearization() -> None: - system = CoupledMsd() - A, B = system.get_linearization() - assert A.shape == (8, 8) - assert B.shape == (8, 1) - - -def test_coupled_msd_evaluate_linearization() -> None: - system = CoupledMsd() - A_sym, B_sym = system.get_linearization() - A, B = system.evaluate_linearization( - A_sym=A_sym, - B_sym=B_sym, - x_bar=np.zeros(shape=(8, 1)), - u_bar=np.array([[0]]), + generate_config.system.nu = len(generate_config.system.B[0]) + generate_config.system.nx = len(generate_config.system.A) + generate_config.system.ny = len(generate_config.system.C) + sim = ContinuousSimulator( + T=generate_config.T, + step_size=generate_config.step_size, ) - assert A.shape == (8, 8) - assert B.shape == (8, 1) - - -def test_lure() -> None: - u = utils.get_input() - x0 = utils.get_initial_state() - A, B1, C1, D11 = utils.get_stable_linear_matrices() - C2 = np.random.normal(size=(4, 2)) - B2 = np.random.normal(size=(2, 4)) - D21 = np.random.normal(size=(4, 1)) - D12 = np.random.normal(size=(1, 4)) - model = Lure( - A=A, - B1=B1, - B2=B2, - C1=C1, - C2=C2, - D11=D11, - D12=D12, - D21=D21, - Delta=np.tanh, + input_generator = get_callable_from_input_config( + generate_config.input_generator + ) + run_simulation_write_csv_files( + config=generate_config, + model=model, + sim=sim, + result_directory_path=tmp_path, + input_generator=input_generator, ) - x1 = model.state_dynamics(x0, u[0]) - y = model.output_layer(xs=[x0, x1], us=[u[0], u[1]]) - - assert x1.shape == (2, 1) - assert y[1].shape == (1, 1) - - -def test_get_noise() -> None: - noise_config = NoiseGeneration(type='gaussian', mean=0.0, std=0.01) - noise_list = get_noise(size=3, lenght=10, config=noise_config) - - assert noise_list[0].shape == (3, 1) - assert len(noise_list) == 10 diff --git a/tests/utils.py b/tests/utils.py index 0ad2f8e..b581631 100644 --- a/tests/utils.py +++ b/tests/utils.py @@ -3,6 +3,13 @@ import numpy as np from statesim.simulator import SimulationData from statesim.io import SimulationData +from statesim.configuration import ( + GenerateConfig, + InputGeneratorConfig, + LinearSystemConfig, + SimulatorConfig, + NoiseConfig, +) import os DIRNAME = os.path.dirname(__file__) @@ -18,6 +25,33 @@ D_un = np.array([[0]]) +def get_generate_config(result_directory: str) -> GenerateConfig: + return GenerateConfig( + result_directory=result_directory, + base_name='test', + seed=2023, + K=10, + T=5.0, + step_size=0.01, + input_generator=InputGeneratorConfig( + type='random_static_input', + u_min=-1.0, + u_max=1.0, + interval_min=10, + interval_max=20, + ), + system=LinearSystemConfig( + name='testSys', + A=A.tolist(), + B=B.tolist(), + C=C.tolist(), + D=D.tolist(), + ), + simulator=SimulatorConfig(initial_state=[1.0, 0.0]), + measurement_noise=NoiseConfig(type='gaussian', mean=0.0, std=0.01), + ) + + def get_tmp_directory() -> str: return os.path.join(DIRNAME, '_tmp')