diff --git a/examples/triaxiality/Elliptical_lenses_MCMC.ipynb b/examples/triaxiality/Elliptical_lenses_MCMC.ipynb
new file mode 100644
index 000000000..971dfb170
--- /dev/null
+++ b/examples/triaxiality/Elliptical_lenses_MCMC.ipynb
@@ -0,0 +1,2973 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "8bb95644-7071-4fbb-b970-1049e67fc4d3",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import clmm\n",
+    "from clmm import Cosmology\n",
+    "import numpy as np\n",
+    "import math\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "import sys  #\n",
+    "import h5py #\n",
+    "import matplotlib.pyplot as plt  #\n",
+    "import numpy as np  #T\n",
+    "import random  #\n",
+    "import pandas  #\n",
+    "from scipy import integrate, signal   #T\n",
+    "from scipy.stats import binned_statistic  #T\n",
+    "from scipy.stats import norm   #T\n",
+    "from scipy.special import erf   #T\n",
+    "import astropy   \n",
+    "import astropy.constants as const\n",
+    "import astropy.units as units\n",
+    "from astropy.io import fits\n",
+    "from astropy.table import Table\n",
+    "from astropy.cosmology import FlatLambdaCDM\n",
+    "from astropy.cosmology import FlatwCDM\n",
+    "from IPython.display import clear_output\n",
+    "import os\n",
+    "import corner\n",
+    "import emcee\n",
+    "from multiprocessing import Pool\n",
+    "from matplotlib.pyplot import cm\n",
+    "import time as pytime\n",
+    "\n",
+    "cosmo = Cosmology(H0=71.0, Omega_dm0=0.2248, Omega_b0=0.2648 - 0.2248, Omega_k0=0.0)\n",
+    "cosmo_astropy = FlatwCDM(H0=71, Om0=0.2648, Ob0=0.04479, w0=-1.000000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "id": "66d97ac4-3836-4a11-a7c2-ec92af62c317",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import InterpolatedUnivariateSpline\n",
+    "def compute_delta_sigma_4theta(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200):\n",
+    "    \n",
+    "    ### DEFINING INTEGRALS:\n",
+    "    r_arr = np.linspace(0.01, 3*np.max(r), sample_N)\n",
+    "    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr\n",
+    "    f = InterpolatedUnivariateSpline(r_arr, (r_arr**3)*sigma_0_arr*eta_0_arr, k=3)  # k=3 order of spline\n",
+    "    integral_vec = np.vectorize(f.integral)\n",
+    "    ###\n",
+    "    \n",
+    "    ### ACTUAL COMPUTATION:\n",
+    "    I_1 = (3/(r**4)) * integral_vec(0, r)\n",
+    "    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    #eta_0 = np.gradient(np.log(sigma_0),r)\n",
+    "    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)\n",
+    "    eta_0 = eta_0_interpolation_func(r) \n",
+    "    \n",
+    "    \n",
+    "    return np.array((ell/4.0)*(2*I_1 - sigma_0*eta_0))\n",
+    "\n",
+    "def compute_delta_sigma_const(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000 ,delta_mdef=200):\n",
+    "\n",
+    "    ### DEFINING INTEGRALS:\n",
+    "    r_arr = np.linspace(0.01, 3*np.max(r), sample_N)\n",
+    "    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr\n",
+    "    f = InterpolatedUnivariateSpline(r_arr, sigma_0_arr*eta_0_arr/r_arr, k=3)  # k=3 order of spline\n",
+    "    integral_vec = np.vectorize(f.integral)\n",
+    "    ###\n",
+    "    \n",
+    "    ### ACTUAL COMPUTATION:\n",
+    "    I_2 = integral_vec(r, np.inf)\n",
+    "    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    #eta_0 = np.gradient(np.log(sigma_0), r)*r\n",
+    "    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)\n",
+    "    eta_0 = eta_0_interpolation_func(r) \n",
+    "    \n",
+    "    return np.array((ell/4.0)*(2*I_2 - sigma_0*eta_0))\n",
+    "\n",
+    "\n",
+    "def compute_delta_sigma_excess(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200):\n",
+    "    r_arr = np.linspace(0.01, 3*np.max(r), sample_N)\n",
+    "    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr  \n",
+    "    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)\n",
+    "    eta_0 = eta_0_interpolation_func(r) \n",
+    "    \n",
+    "    \n",
+    "    \n",
+    "    ## e^2 shenanigans:\n",
+    "    d_eta_0_arr_by_d_r = np.gradient(eta_0_arr,r_arr)*r_arr\n",
+    "    d_eta_0_arr_by_d_r_interpolation_func = InterpolatedUnivariateSpline(r_arr, d_eta_0_arr_by_d_r)\n",
+    "    d_eta_0_by_d_r = d_eta_0_arr_by_d_r_interpolation_func(r)\n",
+    "    \n",
+    "    correction_factor_arr = ((ell)**2*((1/8)*eta_0_arr + (1/16)*d_eta_0_arr_by_d_r + (1/16)*eta_0_arr**2))\n",
+    "    integrand = InterpolatedUnivariateSpline(r_arr, r_arr*sigma_0_arr*correction_factor_arr)\n",
+    "    \n",
+    "    integral_vec = np.vectorize(integrand.integral)\n",
+    "    ##\n",
+    "    \n",
+    "    ### ACTUAL COMPUTATION:\n",
+    "    I = (2/r**2)*integral_vec(0, r)\n",
+    "    \n",
+    "    #q=np.sqrt(2/(1+ell) - 1)\n",
+    "    \n",
+    "    s = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    \n",
+    "    correction_factor = ((ell)**2*((1/8)*eta_0 + (1/16)*d_eta_0_by_d_r + (1/16)*eta_0**2))\n",
+    "    ds_ell_square_correction = I - s*correction_factor\n",
+    "\n",
+    "    \n",
+    "    return clmm.compute_excess_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True) + ds_ell_square_correction"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "id": "99e863b1-eb3d-49bb-898e-733c55d8e507",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## With emcee:\n",
+    "def log_likelihood_monopole(ds_mono,ds_mono_err, r_mono,params):\n",
+    "    \n",
+    "    sigma_c = 3.77988e+15\n",
+    "    z_cl=0.47\n",
+    "    z_gal=0.8\n",
+    "    mdelta,cdelta,ell = params  # km/s\n",
+    "    \n",
+    "    ds_data = ds_mono\n",
+    "    \n",
+    "    #model:\n",
+    "    ds_model = compute_delta_sigma_excess(ell, r_mono, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)#/sigma_c\n",
+    "\n",
+    "    # Gaussian likelihood:\n",
+    "    #sigma = np.full(len(r_mono), ds_model*0.01) \n",
+    "    sigma = ds_mono_err\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*(sigma**2)))\n",
+    "    chi2 = ((ds_data - ds_model)**2 / (sigma)**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "\n",
+    "\n",
+    "    return term1+term2\n",
+    "\n",
+    "def log_likelihood_quadrupole_4theta(ds_quad_4theta,ds_quad_4theta_err, r_quad,params):\n",
+    "    sigma_c = 3.77988e+15\n",
+    "    z_cl=0.47\n",
+    "    z_gal=0.8\n",
+    "    mdelta,cdelta,ell = params  # km/s\n",
+    "    \n",
+    "    ds_data = ds_quad_4theta\n",
+    "    \n",
+    "    #model:\n",
+    "    ds_model = compute_delta_sigma_4theta(ell, r_quad, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)#/sigma_c\n",
+    "    \n",
+    "    # Gaussian likelihood:\n",
+    "    #sigma = np.full(len(r_quad), ds_model*0.05) \n",
+    "    sigma = ds_quad_4theta_err\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*sigma**2))\n",
+    "    chi2 = ((ds_data - ds_model)**2 / sigma**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "\n",
+    "    return term1+term2\n",
+    "\n",
+    "def log_likelihood_quadrupole_const(ds_quad_const,ds_quad_const_err, r_quad,params):\n",
+    "    sigma_c = 3.77988e+15\n",
+    "    z_cl=0.47\n",
+    "    z_gal=0.8\n",
+    "    mdelta,cdelta,ell = params  # km/s\n",
+    "    \n",
+    "    ds_data = ds_quad_const\n",
+    "    \n",
+    "    #model:\n",
+    "    ds_model = compute_delta_sigma_const(ell, r_quad, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)#/sigma_c\n",
+    "    \n",
+    "    # Gaussian likelihood:\n",
+    "    #sigma = np.full(len(r_quad), ds_model*0.05) \n",
+    "    sigma = ds_quad_const_err\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*sigma**2))\n",
+    "    chi2 = ((ds_data - ds_model)**2 / sigma**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "\n",
+    "    return term1+term2\n",
+    "\n",
+    "def priors(params, limits):\n",
+    "    \n",
+    "    a,b,c = params\n",
+    "    if (a < limits[0][0] or a > limits[0][1]):        #mdelta\n",
+    "        return -np.inf\n",
+    "    elif (b < limits[1][0] or b > limits[1][1]):      #cdelta\n",
+    "        return -np.inf\n",
+    "    elif (c < limits[2][0] or c > limits[2][1]):      #ell\n",
+    "        return -np.inf\n",
+    "    #elif (d < limits[3][0] or d > limits[3][1]):      #theta_e\n",
+    "    #    return -np.inf\n",
+    "\n",
+    "    else:\n",
+    "        return 0.0\n",
+    "    \n",
+    "\n",
+    "def log_posterior(params,gamma1, gamma2, x_arcsec, y_arcsec, sigma_c = 3.77988e+15):\n",
+    "    \n",
+    "    mdelta,cdelta,ell = params    \n",
+    "    \n",
+    "    #rotation:\n",
+    "    #theta_e = theta_e*np.pi/180.0 # in radians\n",
+    "    #gamma1,gamma2,x_arcsec,y_arcsec = halo_rotation(gamma1,gamma2,x_arcsec,y_arcsec,theta_e)\n",
+    "    \n",
+    "    #Prepare the data vector -> estimate delta sigma expansion terms\n",
+    "    global run_estimator\n",
+    "    global ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad\n",
+    "    if run_estimator == True:\n",
+    "        run_estimator = False\n",
+    "        ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, sigma_c=3.77988e+15)\n",
+    "    \n",
+    "\n",
+    "    #Evaluate the prior:\n",
+    "    prior=priors(params, limits)\n",
+    "    \n",
+    "    #Avoid likelihood evaluation when prior is -infinity:\n",
+    "    if prior == -np.inf:\n",
+    "        lnP = -np.inf\n",
+    "    else:\n",
+    "        \n",
+    "        lnP = prior + log_likelihood_monopole(ds_mono,ds_mono_err, r_mono,params) + log_likelihood_quadrupole_const(ds_quad_const,ds_quad_const_err, r_quad,params) + log_likelihood_quadrupole_4theta(ds_quad_4theta, ds_quad_4theta_err, r_quad,params)\n",
+    "        #log_likelihood_monopole(ds_mono,ds_mono_err, r_mono,params) +\n",
+    "    \n",
+    "    # safeguard against NaNs:\n",
+    "    if np.isnan(lnP):\n",
+    "        return -np.inf\n",
+    "    else:\n",
+    "        return lnP\n",
+    "    \n",
+    "#---------------------------------------------DIRECT---------------------------------------------------#\n",
+    "\n",
+    "def log_likelihood_direct(gammat, r_mpc, sigma_c,params):\n",
+    "    \n",
+    "    #sigma_c = 3.77988e+15\n",
+    "    #z_cl=0.47\n",
+    "    #z_gal=0.8\n",
+    "    mdelta,cdelta,ell = params  # km/s\n",
+    "    \n",
+    "    gt_data = gammat\n",
+    "    \n",
+    "    \n",
+    "    gt_model = clmm.compute_excess_surface_density(r_mpc, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='critical', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)/sigma_c\n",
+    "    # Gaussian likelihood:\n",
+    "    #sigma = np.full(len(r_quad), ds_model*0.05) \n",
+    "    gt_sigma=0.001\n",
+    "    sigma = np.full(len(gt_data), gt_sigma)\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*sigma**2))\n",
+    "    chi2 = ((gt_data - gt_model)**2 / sigma**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "\n",
+    "    return term1+term2\n",
+    "    \n",
+    "def log_posterior_direct(params,gamma1, gamma2, x_arcsec, y_arcsec, sigma_c = 3.77988e+15):\n",
+    "    \n",
+    "    mdelta,cdelta,ell = params\n",
+    "\n",
+    "    #rotation:\n",
+    "    #theta_e = theta_e*np.pi/180.0 # in radians\n",
+    "    #gamma1,gamma2,x_arcsec,y_arcsec = halo_rotation(gamma1,gamma2,x_arcsec,y_arcsec,theta_e)\n",
+    "\n",
+    "    #Evaluate the prior:\n",
+    "    prior=priors(params, limits)\n",
+    "\n",
+    "    \n",
+    "    #Avoid likelihood evaluation when prior is -infinity:\n",
+    "    if prior == -np.inf:\n",
+    "        lnP = -np.inf\n",
+    "    else:\n",
+    "        q = np.sqrt((1-ell)/(1+ell))\n",
+    "        theta = np.arctan2(q*y_arcsec, x_arcsec)\n",
+    "        sigma_c = cosmo.eval_sigma_crit(0.47,0.8)\n",
+    "        gammat = -1*gamma1*np.cos(2*theta) - gamma2*np.sin(2*theta)\n",
+    "        r = np.sqrt((x_arcsec)**2*q + (y_arcsec)**2/q)#*np.sqrt(q)\n",
+    "        r_mpc = r*cosmo.eval_da(z_cl) * np.pi/180.0 * 1/3600#0.00588784182755241#\n",
+    "        lnP = prior + log_likelihood_direct(gammat, r_mpc, sigma_c,params)\n",
+    "\n",
+    "    # safeguard against NaNs:\n",
+    "    if np.isnan(lnP):\n",
+    "        return -np.inf\n",
+    "    else:\n",
+    "        return lnP\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "93909edd-e1c0-4bf0-ba5e-a5e3fbb9f527",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def emcee_wrapper(gamma1, gamma2, x_arcsec, y_arcsec, z_cl, params_key, limits, true_value,\n",
+    "                 ndim, nwalkers, nsteps=1000, nburn=350, filename=None):  \n",
+    "    \n",
+    "    starting_guess_t=[]\n",
+    "    for i in np.arange(len(params_key)):\n",
+    "        guess=[]\n",
+    "        for j in np.arange(nwalkers):\n",
+    "            guess.append((limits[i][1]-limits[i][0])*np.random.random()+limits[i][0])\n",
+    "        starting_guess_t.append(guess)\n",
+    "    starting_guesses=np.column_stack(starting_guess_t)\n",
+    "    print('Starting guesses for ',nwalkers,' chains:')\n",
+    "    print(starting_guesses)\n",
+    "            \n",
+    "    start=pytime.time()\n",
+    "    \n",
+    "    global run_estimator \n",
+    "    global ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad\n",
+    "    run_estimator = True\n",
+    "    if filename!=None:\n",
+    "        ## STORING THE CHAINS TO FILE\n",
+    "        backend = emcee.backends.HDFBackend(filename)\n",
+    "        backend.reset(nwalkers, ndim) ## Comment this line out to append steps to a previous run stored in the file\n",
+    "        with Pool() as pool:\n",
+    "            sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[gamma1, gamma2, x_arcsec, y_arcsec],\n",
+    "                                             vectorize=False, backend=backend, pool=pool)\n",
+    "            pos, prob, state = sampler.run_mcmc(starting_guesses, nsteps, progress=True)\n",
+    "            \n",
+    "    if filename==None:   \n",
+    "        ## RETURNING THE CHAINS WITHOUT STORING TO FILE\n",
+    "        with Pool() as pool:\n",
+    "            sampler = emcee.EnsembleSampler(nwalkers, ndim, log_posterior, args=[gamma1, gamma2, x_arcsec, y_arcsec],\n",
+    "                                             vectorize=False, pool=pool)\n",
+    "            pos, prob, state = sampler.run_mcmc(starting_guesses, nsteps, progress=True)\n",
+    "\n",
+    "    end=pytime.time()\n",
+    "    print(\"Time taken:{0:.1f} seconds\".format(end-start))\n",
+    "            \n",
+    "    post = np.concatenate(sampler.chain[:, nburn: , :])\n",
+    "        \n",
+    "    #Corner plot:\n",
+    "\n",
+    "    fig = plt.figure(figsize=[10,10])\n",
+    "    figure = corner.corner(post,labels=params_key, quantiles=[0.16, 0.5, 0.84], \n",
+    "                           show_titles=True, title_fmt='.2e', fig=fig)\n",
+    "    figure.patch.set_facecolor('white')\n",
+    "    # Extract the axes\n",
+    "    axes = np.array(figure.axes).reshape((ndim, ndim))\n",
+    "    # Loop over the histograms\n",
+    "    for yi in range(ndim):\n",
+    "        for xi in range(yi):\n",
+    "            ax = axes[yi, xi]\n",
+    "            ax.axvline(true_value[xi], color=\"r\")\n",
+    "            ax.axhline(true_value[yi], color=\"r\")\n",
+    "            ax.plot(true_value[xi], true_value[yi], \"sr\")\n",
+    "            #if yi == 1:\n",
+    "            #    ax.set_xlim([2000,2200])\n",
+    "    \n",
+    "    if f!=None:\n",
+    "        plt.savefig(filename[:-3]+\"_corner_plot.png\",facecolor=\"white\", dpi=\"figure\", format=\"png\")       \n",
+    "    #Chain plot:\n",
+    "\n",
+    "    color=iter(cm.rainbow(np.linspace(0,1,nwalkers)))\n",
+    "    # set up a plot with two windows, one for each parameter:\n",
+    "    plt.figure(figsize=(10,5))\n",
+    "    #plt.patch.set_facecolor('white')\n",
+    "    for i in range(len(params_key)):\n",
+    "        plt.subplot(100*len(params_key)+10+i+1)\n",
+    "        plt.ylabel(params_key[i], fontsize=10)\n",
+    "    for j in range(nwalkers):\n",
+    "        c=next(color)\n",
+    "        for i in range(len(params_key)):\n",
+    "            plt.subplot(100*len(params_key)+10+i+1)\n",
+    "            plt.plot(sampler.chain[j,nburn:,i], ',', color=c)\n",
+    "            \n",
+    "    if f!= None:\n",
+    "        plt.savefig(filename[:-3]+\"_chain_plot.png\",facecolor=\"white\", dpi=\"figure\", format=\"png\")\n",
+    "    #A_fit = np.quantile((post[:,0].flatten()), 0.5)\n",
+    "    #A_std_fit = np.std(post[:,0].flatten())\n",
+    "    #A.append(A_fit)\n",
+    "    #A_err.append(A_std_fit)\n",
+    "    #print(A)\n",
+    "    #return np.array(A), np.array(A_err), z_final\n",
+    "    mean=[]\n",
+    "    err_minus=[]\n",
+    "    err_plus=[]\n",
+    "    for i in range(len(params_key)):\n",
+    "        mean.append(np.quantile((post[:,i].flatten()), 0.5))\n",
+    "        err_minus.append(np.quantile((post[:,i].flatten()), 0.16))\n",
+    "        err_plus.append(np.quantile((post[:,i].flatten()), 0.84))\n",
+    "    mean = np.array(mean)\n",
+    "    err_plus = np.array(err_plus) - mean\n",
+    "    err_minus = mean - np.array(err_minus)\n",
+    "\n",
+    "    return post, mean, err_plus, err_minus"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "c2fe4ab7-3ced-4cbf-99d4-5ce466eaa29c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def Delta_Sigma_const(w, gamma1, Sigma_crit) :\n",
+    "\t## TODO: sum over clusters\n",
+    "\treturn w * Sigma_crit * gamma1 / w\n",
+    "\n",
+    "def Delta_Sigma_4theta(w1, w2, gamma1, gamma2, theta, Sigma_crit) :\n",
+    "\t## TODO: sum over clusters\n",
+    "\treturn Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+    "\n",
+    "def Delta_Sigma_4theta_cross(w1, w2, gamma1, gamma2, theta, Sigma_crit) :\n",
+    "\t## TODO: sum over clusters\n",
+    "\treturn Sigma_crit * (w1*gamma1/np.cos(4*theta) - w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+    "\n",
+    "def Delta_Sigma_const_cross(w, gamma2, Sigma_crit) :\n",
+    "\t## TODO: sum over clusters\n",
+    "\treturn w*Sigma_crit*gamma2 / w\n",
+    "\n",
+    "def weights(Sigma_crit, theta, Sigma_shape=0.0001, Sigma_meas=0) :\n",
+    "\t## EQUATION 35, 32, 33\n",
+    "\tw  = 1 / (Sigma_crit**2 * (Sigma_shape**2 + Sigma_meas**2))\n",
+    "\tw1 = np.cos(4*theta)**2 * w\n",
+    "\tw2 = np.sin(4*theta)**2 * w\n",
+    "\treturn w, w1, w2\n",
+    "\n",
+    "def make_radial_bins(x, y, Nbins=10) :\n",
+    "\tr = np.sqrt(x**2 + y**2)\n",
+    "\t#r_bins = np.linspace(np.min(r), np.max(r), Nbins+1)\n",
+    "\trbin_edges = np.logspace(np.log10(0.4), np.log10(2.5), Nbins+1)\n",
+    "\tinds = np.digitize(r, rbin_edges, right=True) - 1\n",
+    "\trbin_mean = np.array([np.mean(r[inds==i]) for i in range(Nbins)])\n",
+    "\treturn r, rbin_edges, rbin_mean, inds\n",
+    "\n",
+    "def make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, sigma_c=3.77988e+15, method=1):\n",
+    "    sigma_c = cosmo.eval_sigma_crit(0.47,0.8)\n",
+    "    r = np.sqrt((x_arcsec**2 + y_arcsec**2))\n",
+    "    theta = np.arctan2(y_arcsec, x_arcsec)\n",
+    "    #theta = theta - np.pi/2\n",
+    "    r_mpc = r*cosmo.eval_da(z_cl) * np.pi/180.0 * 1/3600#0.00588784182755241#\n",
+    "    \n",
+    "    w, w1, w2 = weights(sigma_c, theta)\n",
+    "    DS4theta = Delta_Sigma_4theta(w1, w2, gamma1, gamma2, theta, sigma_c)\n",
+    "    DSconst = Delta_Sigma_const(w, gamma1, sigma_c)\n",
+    "    \n",
+    "    bins=15\n",
+    "    if method == 1 :\n",
+    "        ## METHOD 1 ##\n",
+    "        \n",
+    "        r_min = 0.3 #Normally 0.4\n",
+    "        r_max = 2.5\n",
+    "        bin_edges = np.logspace(np.log10(r_min), np.log10(r_max), bins)\n",
+    "        N_i = []\n",
+    "        for i in np.arange(bins-1):\n",
+    "            N_i.append(len(r_mpc[(r_mpc > bin_edges[i]) & (r_mpc < bin_edges[i+1])]))\n",
+    "        N_i=np.array(N_i)\n",
+    "\n",
+    "\n",
+    "        result = binned_statistic(r_mpc, gamma1, statistic='mean', bins=bin_edges)\n",
+    "        gamma1_i = result.statistic\n",
+    "        res = binned_statistic(r_mpc, gamma2, statistic='mean', bins=bin_edges)\n",
+    "        gamma2_i = res.statistic\n",
+    "        res = binned_statistic(r_mpc, DS4theta, statistic='mean', bins=bin_edges)\n",
+    "        DS4theta_i_err = binned_statistic(r_mpc, DS4theta, statistic='std', bins=bin_edges).statistic/np.sqrt(N_i)\n",
+    "        DS4theta_i = res.statistic\n",
+    "        res = binned_statistic(r_mpc, DSconst, statistic='mean', bins=bin_edges)\n",
+    "        DSconst_i = res.statistic\n",
+    "        DSconst_i_err = binned_statistic(r_mpc, DSconst, statistic='std', bins=bin_edges).statistic/np.sqrt(N_i)\n",
+    "        #print(bin_edges)\n",
+    "        r_i = bin_edges #+ (bin_edges[1]-bin_edges[0])/2\n",
+    "        #print(r_i)\n",
+    "        \n",
+    "        r_min = 0.3 #Normally 0.4\n",
+    "        r_max = 2.5\n",
+    "        bins_mono=15 #Need more bins always since, if r bin is too thick then averaging doesnt work for shear\n",
+    "        bin_edges = np.logspace(np.log10(r_min), np.log10(r_max), bins_mono)\n",
+    "        #print(bin_edges)\n",
+    "        N_i = []\n",
+    "        for i in np.arange(bins_mono-1):\n",
+    "            N_i.append(len(r_mpc[(r_mpc > bin_edges[i]) & (r_mpc < bin_edges[i+1])]))\n",
+    "        N_i=np.array(N_i)\n",
+    "        \n",
+    "        r_mono = bin_edges #+ (bin_edges[1]-bin_edges[0])/2\n",
+    "        res = binned_statistic(r_mpc, gamma1*np.cos(2*theta), statistic='mean', bins=bin_edges)\n",
+    "        gammat_mono = -2*res.statistic\n",
+    "        ds_mono_err = 2*binned_statistic(r_mpc, gamma1*np.cos(2*theta), statistic='std', bins=bin_edges).statistic/np.sqrt(N_i)*sigma_c\n",
+    "        ds_mono = gammat_mono*sigma_c\n",
+    "        \n",
+    "    elif method == 2 :\n",
+    "        ## METHOD 2 ## -- DEPRECATED AS WE WANT DIFFERENT NUMBER OF BINS FOR MONOPOLE AND QUADRUPOLE\n",
+    "\n",
+    "        Nbins = bins-1\n",
+    "        arcsec_to_Mpc = 0.00588784182755241\n",
+    "        r, rbin_edges, r_bins, r_inds = make_radial_bins(x_arcsec*arcsec_to_Mpc, y_arcsec*arcsec_to_Mpc, Nbins)\n",
+    "\n",
+    "        ds_mono=[]\n",
+    "        ds_mono_err=[]\n",
+    "        delta_sigmas_4theta = []\n",
+    "        delta_sigmas_const = []\n",
+    "        delta_sigmas_4theta_cross = []\n",
+    "        delta_sigmas_const_cross = []\n",
+    "        delta_sigmas_4theta_err = []\n",
+    "        delta_sigmas_const_err = []\n",
+    "        delta_sigmas_4theta_cross_err = []\n",
+    "        delta_sigmas_const_cross_err = []\n",
+    "        for i in range(Nbins):\n",
+    "            select = (r_inds == i)\n",
+    "            theta_i = theta[select]\n",
+    "            gamma1_i = gamma1[select]\n",
+    "            gamma2_i = gamma2[select]\n",
+    "            w_i, w1_i, w2_i = weights(sigma_c, theta_i)\n",
+    "            DS4thetai = Delta_Sigma_4theta(w1_i, w2_i, gamma1_i, gamma2_i, theta_i, sigma_c)\n",
+    "            DSconsti = Delta_Sigma_const(w_i, gamma1_i, sigma_c)\n",
+    "            DS4thetacrossi = Delta_Sigma_4theta_cross(w1_i, w2_i, gamma1_i, gamma2_i, theta_i, sigma_c)\n",
+    "            DSconstcrossi = Delta_Sigma_const_cross(w_i, gamma2_i, sigma_c)\n",
+    "            DSmonoi = gamma1_i*np.cos(2*theta_i)*sigma_c\n",
+    "\n",
+    "            ds_mono.append(-2*np.mean(DSmonoi))\n",
+    "            ds_mono_err.append(2*np.std(DSmonoi)/np.sqrt(len(DSmonoi)))\n",
+    "            delta_sigmas_4theta.append(np.mean(DS4thetai))\n",
+    "            delta_sigmas_4theta_err.append(np.std(DS4thetai)/np.sqrt(len(DS4thetai)))\n",
+    "            delta_sigmas_const.append(np.mean(DSconsti))\n",
+    "            delta_sigmas_const_err.append(np.std(DSconsti)/np.sqrt(len(DSconsti)))\n",
+    "            #delta_sigmas_4theta_cross.append([np.mean(DS4thetacrossi), np.std(DS4thetacrossi)/np.sqrt(len(DS4thetacrossi))])\n",
+    "            #delta_sigmas_const_cross.append([np.mean(DSconstcrossi), np.std(DSconstcrossi)/np.sqrt(len(DSconstcrossi))])\n",
+    "\n",
+    "        r_i=np.array(rbin_edges + (rbin_edges[1]-rbin_edges[0]))\n",
+    "        r_mono=r_i\n",
+    "        ds_mono = np.array(ds_mono)\n",
+    "        ds_mono_err = np.array(ds_mono_err)\n",
+    "        DS4theta_i = np.array(delta_sigmas_4theta)\n",
+    "        DS4theta_i_err = np.array(delta_sigmas_4theta_err)\n",
+    "        DSconst_i = np.array(delta_sigmas_const)\n",
+    "        DSconst_i_err = np.array(delta_sigmas_const_err)\n",
+    "        \n",
+    "    # SAFEGUARD AGAINST BINS WITH NANs and 0.0s\n",
+    "    \n",
+    "    ind = np.invert(np.isnan(ds_mono) | np.isnan(ds_mono_err))\n",
+    "    ds_mono = ds_mono[ind]\n",
+    "    ds_mono_err = ds_mono_err[ind]\n",
+    "    r_mono = np.sqrt(r_mono[:-1]*r_mono[1:])     [ind]\n",
+    "    ind = (ds_mono!= 0.0) & (ds_mono_err!= 0.0)\n",
+    "    ds_mono = ds_mono[ind]\n",
+    "    ds_mono_err = ds_mono_err[ind] \n",
+    "    r_mono = r_mono[ind] \n",
+    "    \n",
+    "    ind = np.invert(np.isnan(DS4theta_i) | np.isnan(DS4theta_i_err) | np.isnan(DSconst_i) | np.isnan(DSconst_i_err))\n",
+    "    DS4theta_i = DS4theta_i[ind]\n",
+    "    DS4theta_i_err = DS4theta_i_err[ind]\n",
+    "    DSconst_i = DSconst_i[ind] \n",
+    "    DSconst_i_err = DSconst_i_err[ind]\n",
+    "    r_i = np.sqrt(r_i[:-1]*r_i[1:])[ind]\n",
+    "    \n",
+    "    return ds_mono,np.abs(ds_mono_err),r_mono,DS4theta_i,np.abs(DS4theta_i_err),DSconst_i,np.abs(DSconst_i_err),r_i\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "32c3a72f-de07-493d-876d-ccfc7d68a385",
+   "metadata": {},
+   "source": [
+    "Use rotation as given by equations 33~36 in man_glafic, ooguri et al. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "22007c39-88e8-4d2e-aa41-4ed9ba022cda",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "Writing the equations A.19, A.20 from Ooguri et al. (Manual for glafic):\n",
+    "\n",
+    "$ \\tilde{x} = x\\cos(\\theta_{e}) + y\\sin(\\theta_{e}) $\\\n",
+    "$ \\tilde{y} = -x\\sin(\\theta_{e}) + y\\cos(\\theta_{e}) $\n",
+    "\n",
+    "And, equations A.33 through A.35:\n",
+    "\n",
+    "$ \\phi_{xx} = \\phi_{\\tilde{x}\\tilde{x}} \\cos^{2}(\\theta_{e}) - 2 \\phi_{\\tilde{x}\\tilde{y}}\\sin(\\theta_{e})\\cos(\\theta_{e}) + \\phi_{\\tilde{y}\\tilde{y}} \\sin^{2}(\\theta_{e}) $\\\n",
+    "$ \\phi_{yy} = \\phi_{\\tilde{x}\\tilde{x}} \\sin^{2}(\\theta_{e}) + 2 \\phi_{\\tilde{x}\\tilde{y}}\\sin(\\theta_{e})\\cos(\\theta_{e}) + \\phi_{\\tilde{y}\\tilde{y}} \\cos^{2}(\\theta_{e}) $\\\n",
+    "$ \\phi_{xy} = \\phi_{\\tilde{x}\\tilde{x}}\\sin(\\theta_{e})\\cos(\\theta_{e}) + \\phi_{\\tilde{x}\\tilde{y}}(\\cos^{2}(\\theta_{e}) - \\sin^{2}(\\theta_{e})) - \\phi_{\\tilde{y}\\tilde{y}}\\sin(\\theta_{e})\\cos(\\theta_{e}) $\n",
+    "\n",
+    "Original co-ordinates are x,y,$\\gamma_{1}$,$\\gamma_{2}$, while the major axis co-ordinates will be rotated by $\\theta_{e}$ to give $\\tilde{x}$,$\\tilde{y}$,$\\tilde\\gamma_{1}$,$\\tilde\\gamma_{2}$ \n",
+    "\n",
+    "And, $\\gamma_{1} = \\dfrac{\\phi_{xx} - \\phi_{yy}}{2}$ ; $\\gamma_{2} = \\phi_{xy}$\n",
+    "\n",
+    "Then:\\\n",
+    "$\\gamma_{1} = \\tilde\\gamma_{1}(\\cos^{2}(\\theta_{e}) - \\sin^{2}(\\theta_{e})) - 2\\tilde\\gamma_{2}\\sin(\\theta_{e})\\cos(\\theta_{e})$\\\n",
+    "$\\gamma_{2} = 2\\tilde\\gamma_{1}\\sin(\\theta_{e})\\cos(\\theta_{e}) + \\tilde\\gamma_{2}(\\cos^{2}(\\theta_{e}) - \\sin^{2}(\\theta_{e}))$\n",
+    "\n",
+    "$\\tilde\\gamma_{1} = \\gamma_{1}(\\cos^{2}(\\theta_{e}) - \\sin^{2}(\\theta_{e})) + 2\\gamma_{2}\\sin(\\theta_{e})\\cos(\\theta_{e})$\\\n",
+    "$\\tilde\\gamma_{2} = - 2\\gamma_{1}\\sin(\\theta_{e})\\cos(\\theta_{e}) + \\gamma_{2}(\\cos^{2}(\\theta_{e}) - \\sin^{2}(\\theta_{e}))$\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "9c4d15e7-ca6a-4891-8a9b-cc0d8e2c05b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "def halo_rotation(gamma1,gamma2,x,y, theta_e):\n",
+    "    '''\n",
+    "    theta_e (radians) is the absolute rotation of the halo, assuming input halo is aligned along the x-axis\n",
+    "    '''\n",
+    "    x_tilde = x*np.cos(theta_e) + y*np.sin(theta_e)\n",
+    "    y_tilde = -1*x*np.sin(theta_e) + y*np.cos(theta_e)\n",
+    "    \n",
+    "    #gamma1_tilde = gamma1*(np.cos(theta_e)**2 - np.sin(theta_e)**2) + 2*gamma2*np.sin(theta_e)*np.cos(theta_e)\n",
+    "    #gamma2_tilde = -2*gamma1*np.sin(theta_e)*np.cos(theta_e) + gamma2*(np.cos(theta_e)**2 - np.sin(theta_e)**2)\n",
+    "    \n",
+    "    '''\n",
+    "    Using complex notation\n",
+    "    '''\n",
+    "    \n",
+    "    gamma = gamma1 + gamma2*1j\n",
+    "    gamma_tilde = gamma*(np.e**(-2*theta_e*1j))\n",
+    "    gamma1_tilde = np.real(gamma_tilde)\n",
+    "    gamma2_tilde = np.imag(gamma_tilde)\n",
+    "    \n",
+    "    \n",
+    "    return gamma1_tilde,gamma2_tilde,x_tilde,y_tilde"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "d5e9c4ab-aba8-42f2-95bc-cb66c1fa580f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2.0 -0.9999999999999996 0.7071067811865476 -0.7071067811865475\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Test Halo rotation function:\n",
+    "\n",
+    "gamma1 = 1.0\n",
+    "gamma2 = 2.0\n",
+    "x=1.0\n",
+    "y=0.0\n",
+    "\n",
+    "theta_e = np.pi/4\n",
+    "gamma1_tilde,gamma2_tilde,x_tilde,y_tilde = halo_rotation(gamma1,gamma2,x,y, theta_e)\n",
+    "print(gamma1_tilde,gamma2_tilde,x_tilde,y_tilde)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "ccbe6cc9-ae57-48d3-a0fe-a8d42d4da713",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "dir_loc = \"Elliptical_lenses_data/catalog_horizontal_rescale_q1.0/\"\n",
+    "\n",
+    "gamma1 = np.load(dir_loc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = np.load(dir_loc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = np.load(dir_loc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = np.load(dir_loc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, sigma_c=3.77988e+15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "a431f10a-1045-4144-b5fc-f9f73179be97",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[0.4        0.51970529 0.67523397 0.87730666 1.13985228 1.48096815\n",
+      " 1.92416745 2.5       ]\n",
+      "[0.51970529 0.63941058 0.79493926 0.99701195 1.25955757 1.60067344\n",
+      " 2.04387274 2.61970529]\n",
+      "[0.4        0.40747333 0.41508628 0.42284146 0.43074154 0.43878922\n",
+      " 0.44698726 0.45533846 0.46384569 0.47251187 0.48133995 0.49033298\n",
+      " 0.49949402 0.50882623 0.51833279 0.52801696 0.53788207 0.54793149\n",
+      " 0.55816866 0.5685971  0.57922038 0.59004213 0.60106608 0.61229598\n",
+      " 0.6237357  0.63538915 0.64726032 0.65935329 0.67167219 0.68422126\n",
+      " 0.69700478 0.71002713 0.72329279 0.7368063  0.75057228 0.76459546\n",
+      " 0.77888064 0.79343271 0.80825666 0.82335757 0.83874062 0.85441107\n",
+      " 0.8703743  0.88663578 0.90320107 0.92007586 0.93726592 0.95477716\n",
+      " 0.97261556 0.99078724 1.00929843 1.02815546 1.04736482 1.06693306\n",
+      " 1.0868669  1.10717318 1.12785884 1.14893098 1.17039682 1.19226371\n",
+      " 1.21453915 1.23723076 1.26034633 1.28389378 1.30788116 1.33231672\n",
+      " 1.35720881 1.38256597 1.40839688 1.4347104  1.46151554 1.48882149\n",
+      " 1.51663761 1.54497343 1.57383865 1.60324317 1.63319706 1.6637106\n",
+      " 1.69479422 1.72645859 1.75871456 1.79157317 1.8250457  1.8591436\n",
+      " 1.89387856 1.92926248 1.9653075  2.00202596 2.03943043 2.07753375\n",
+      " 2.11634896 2.15588937 2.19616853 2.23720023 2.27899855 2.32157779\n",
+      " 2.36495255 2.4091377  2.45414838 2.5       ]\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f59866f1d50>"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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ff0xaAQEB7Nu3D4CPP/440y1vatasCaiv78KFC03uf1b0P1MVK1Y0vKnJiqOjI3Xr1uWnn35i2LBhBAcH069fP6Ofvz7PLSdfn7THZSZtQdcbN25w+vRpVq5cyfPPP58uCNN/Xq6urkDuv+f5GWSKvJNATBRJFStWNNy/cuWKFXtiOh8fH0Ct7MrpCE2JEiUMSf1ZTbNZeg/ItGJjYw0rQ1u0aJHun7k+n6179+7MmTOHunXrGvqvl9dK3Za8xvbt2zMNjNOuglu0aJHRANpUZcuWNQQza9asMflYfU6Ws7MzzZs3T/eaPpfIlJ8FfRtvb+8Mr+W0RtiiRYuyrZ+Xndu3bxsCDn2tv5yYMWMGHh4enD17lpkzZ2baJjdfHxsbG6Ojczllb29P27ZtAdi8eTORkZEmHaf/ntvY2OTqayOsRwIxUSSlDUb07y4LutatWwNqheGaNWtydKyDgwP169cHVGkNYzIrmWEps2fPNkyXPLyB8vXr1wHjq8I0Tcuyr/r8u6wCk7xeo6AYPXo0oN5Q/Pzzz9m237lzp2Ez9KeffjrDz79+1e6ePXuyPI+maYbpV/0xegkJCYbCsTNmzCAyMtLo7caNG9jZ2XH9+nW2bNmS/SechU8++cQwpW/KptwP8/T0NGwt9Nlnn2W6VZL+cz127Fi2b4j0eZ3169fPMLKVF6+88gqgps5NKcp74cIFwxuPJ5980vCmThQOEoiJIum3334z3G/SpIkVe2K6Jk2aGIKGd999N900V2YeTvbXb/uycuVKzp07l6F9cHAw8+bNM1Nvs7Zy5UreeecdQNXy0m/loufh4QGkTjM9bN68eVy+fNno+fU5f2FhYUbb5PUaBcWIESMMq2DHjh3LwYMHjba9cuWKoVq7k5NTplXW9T8ngYGBWZZgWbZsmWGE7+G9ENevX09wcDA2NjYMHDiQYsWKGb2VK1eOzp07A3mrtD9z5kzmzJkDQI8ePXI96jN27FiKFStGREQEX375ZYbX9V+f6OhoZs2aZfQ8e/bsMdT7ymyvyLzo3bu34fP75JNP2LBhg9G29+7d45lnniE2NhYXFxc++ugjs/ZFWJ4EYqJICQoKYvLkyYZiqy1atKBly5ZW7pVpdDod8+bNw9HRkWvXrtG8eXNWrVqVLg/l5s2b/Prrr3Tt2pW33nor3fGvvPIK5cuXJy4ujh49erB161bDiNHBgwfp0qVLnqeGshIUFMTq1avp1asX/fv3JyEhgXLlyrFhw4YMuTz6shF//fUXH330kWHkISwsjE8//ZTXXnsty4TjunXrArBx40ajU0h5vUZB4ezszKpVq/Dy8iIiIoIOHTowZcoUrl69amgTHBzMrFmzaNq0KdeuXUOn0/Hdd98ZtvNJ65lnnjFMVw4fPpyvvvoqXdAfHBzMV199Zaiz1bx58wyBhj6gatOmDWXKlMn2c9Afv3bt2hyVVrh27RpLliyhTZs2hny+evXqZRlAZqdEiRKGMi6zZs3KkBDfsmVLnn76aQAmT57MO++8YxhdBZX7t2DBAnr37o2mafj5+Rm2XjIXnU7H0qVL8fPzIyEhgSeffJJx48YREBCQrh+LFy/G39+fEydOYGtry/z58zMtXZNWSEhIlres3twIC7Fs4X4hzCvtlhsPb23j4eGRbjubevXqaTdv3sxwDlO3ODIm7dYlD0u7xZExWV1f0zRt8+bNWsmSJQ3XsLW11UqWLKm5uLik+/xGjBiR4dhDhw6l207JxcXFsMWRm5ub2bY4cnd3N3zdvb29NQcHh3R9s7W11QYPHqzdu3cv03PFx8drbdu2NbTX6XSap6enYfuhXr16aZMnTzb6vTh//rzm5ORk2NKldOnSWsWKFbWKFStq169fN8s1CpqLFy9qTZo0Sfd1dnFx0dzd3dM9V6xYMe2XX37J8ly3bt3SmjdvnmH7p4fP1bx58wzb5dy4cUOztbXVAO3bb781qe/37t0zbE00c+ZMw/Npf5c8PT0NP1MlS5Y0tNffHB0dtXHjxmlRUVFGr2Pq9kS3b982/PxMnDgxw+sRERFar169MnxdPT090211Vr16de3s2bOZXiOn2zdlJjg4WOvevXu6fjg5OWXYMq1MmTLaX3/9ZfQ8af9uZndr0KBBpsc9/Pc2s9uePXty9Xk+6mRETBRaD69Ei46OxsfHh+7du/Pjjz9y+PBhQ82uwqRr165cvHiRadOm0aZNGzw8PAgLC8PGxobatWvz4osvsm7dOr799tsMxzZp0oSTJ08yYsQIypUrR2JiIh4eHgwdOpSjR49muWF0TkRERKQr5Oju7k6tWrUYMGAAM2bM4Nq1a/z8889GE5jt7e3ZvHkzH3zwAdWrV8fe3h5N02jWrBlz585l3bp1GRLr06pWrRrbtm2jT58+eHt7c+/ePa5evcrVq1cNZTzyeo2CpkqVKhw8eJA1a9bw3HPP4efnh06nIyIiwtCmfv36nD17NsNU8MPKlCnD7t27+e233+jbt69hJDU+Pp7y5cvTt29ffvvtN3bv3p0h32jRokUkJSVhY2NjGDnKTokSJbKdngwNDTX8TEVFReHp6Un9+vUZPHgwc+fO5fbt28ycORMXFxeTrpkVHx8fXnjhBQDmzJmTYdGGm5sb69ev588//2TgwIFUrlyZ5ORkoqKiKF26NN27d2fevHmcOHEiy5p/eeXt7c2mTZv4999/GTFiBDVq1MDBwYGYmBjKlSvHY489xpw5c7h48aJhBNhSsivoeufOHeLj4y3ah6JKp2lSpU0IIQqz6dOnG6aqf/zxR8OepkKIgk8CMSGEKALGjx/PN998g42NDUuWLGHgwIHW7pIQwgQSiAkhRBGgaRrPPvssy5cvx87Ojt9//92wr6kQouCSQEwIIYQQwkokWV8IIYQQwkokEBNCCCGEsBIJxIQQQgghrCT7reuF1SQnJ3Pr1i3c3NzQ6XTW7o4QQgghTKBpGpGRkZQtW9awN64xEogVYLdu3cLX19fa3RBCCCFELly/fp3y5ctn2UYCsQLMzc0NUN9I/SbHQgghhCjYIiIi8PX1Nfwfz4oEYgWYfjrS3d1dAjEhhBCikDElrUiS9YUQQgghrEQCMSGEEEIIK5FATAghhBDCSiRHTAghhDCj5ORk4uPjrd0NYUH29vbY2tqa5VwSiAkhhBBmEh8fz5UrV0hOTrZ2V4SFFS9eHB8fnzzX+ZRATAghhDADTdO4ffs2tra2+Pr6ZlvIUxROmqYRHR1NcHAwAGXKlMnT+SQQE0IIIcwgMTGR6OhoypYti4uLi7W7IyzI2dkZgODgYEqVKpWnaUoJ14UQQggzSEpKAsDBwcHKPRH5QR9sJyQk5Ok8EogJIYQQZiR7Az8azPV9lkBMCCGEEMJKJBATQgghhLASCcSEEEKIR9iwYcPQ6XS8/PLLGV4bPXo0Op2OYcOGZWj/8K1Hjx5s374909fS3hYtWmRoFxYWluGalSpVYubMmeme27t3Lz179sTT0xMnJyfq1avHV199ZcjL00t7HVdXV6pVq8awYcM4cuRItl+HH374gQ4dOuDu7m60b5YggZgQwjSJ0aBJbSQhiiJfX1+WLVtGTEyM4bnY2FiWLl1KhQoVMrTv0aMHt2/fTndbunQprVq1Svdc//79M7QdMGBAjvq2Zs0a2rdvT/ny5dm2bRtnz55l3LhxfPLJJwwcOBBN09K1X7hwIbdv3+b06dPMmTOHBw8e0Lx5c37++ecsrxMdHU2PHj145513ctS/vJLyFUKI7N3cAHuehdKdoP1aa/dGCGFm/v7+XL58mdWrV/Pcc88BsHr1anx9ffHz88vQ3tHRER8fn0zPlfZ5Z2dn4uLijLbNTlRUFCNHjqRPnz788MMPhudHjBhB6dKl6dOnDytWrEgX3OkLrYIaXevWrRtDhw5lzJgx9O7dG09Pz0yvNX78eAC2b9+eq77mloyICSGydnkx7HwCEh/ArT8h4YG1eyREoaBpEBVlndtDg0QmGT58OAsXLjQ8/umnn3jhhRfM+BXJuc2bN3Pv3j0mTpyY4bXevXtTvXp1li5dmu15JkyYQGRkJFu2bLFEN/NERsSEEMYFfAnH3kh9rCXBvQPg09l6fRKikIiOhmLFrHPtBw/A1TVnxwwePJhJkyYRGBiITqdjz549LFu2LNMRog0bNlDsoU/urbfe4r333svRNcuXL5/huejoaMP98+fPA1CrVq1Mj69Zs6ahTVZq1qwJQGBgYI76lx8kEBNCZKRpcPxtCJiuHtf8H8TchKvL4O4eCcSEKIK8vLzo1asXixcvRtM0evXqhZeXV6ZtO3bsyNy5c9M9V6JEiRxfc9euXbi5uaV7rkOHDhnaPZwHlvZ5U+p56Y8viDXeJBATQqSXnAgHX4LLKVMUDadD7Tfg/JyUQGy3dfsnRCHh4qJGpqx17dx44YUXGDNmDABz5swx2s7V1ZWqVavm7iJpVK5cmeLFi6d7zs4uNTSpXr06AAEBAbRq1SrD8WfPnqV27drZXicgIMBwvYJGAjEhRKrEGNgzEG6uA50NNJsPVYar17xbq48h+yE5CWxyv7eaEI8CnS7n04PW1qNHD+Lj4wHo3r27lXsD3bp1o0SJEnz11VcZArF169Zx4cIFPvroo2zPM3PmTNzd3enSpYulupprEogJIZT4MNjRB+7uAhtHaLMCyvdJfd2jHti5QWIkhJ8Cz4bW6qkQwkJsbW0No0dZbWQdFxdHUFBQuufs7OyMTmXmlqurK99//z0DBw7kpZdeYsyYMbi7u7N161beeOMN+vXrR//+/dMdExYWRlBQEHFxcZw/f57vv/+eP/74g59//jnD6FtaQUFBBAUFcfHiRQBOnTqFm5sbFSpUyNW0q6kkEBNCQMxt2NYDwk6CvTu0Xw+l2qVvY2MLXi0haLPKE5NATIgiyd3dPds2mzZtokyZMumeq1GjBmfPnjV7f/r168e2bdv49NNPadeuHTExMVStWpV3332X8ePHZ8j7Gj5cjeI7OTlRrlw52rRpw8GDB/H398/yOvPmzWPq1KmGx+3aqb+BCxcuTFfQ1tx0mrEMOGF1EREReHh4EB4ebtIvhhC5EnkJtnWDB5fBqTR0/Bs8G2Te9tSHcOoDqPgstP4tf/spRAEXGxvLlStXqFy5Mk5OTtbujrCwrL7fOfn/LSNiQjzK7h+D7Y9B7B0oVgU6bYZiGYs3GujzxO7uyZ/+CSFEEScFXYV4VN3ZDls7qCDMsyF03Z11EAZQsjnobCH6GkRdz4dOCiFE0SaBmBCPoutrVE5YQgSUag+dt4OzCVuQ2BeD4inTljIqJoQQeSaBmBCPmovzYXc/SI6D8k9Ax03g4GH68d5t1McQCcSEECKvJBAT4lGhaXB6GhwcCVoyVHkR2qwE2xwmFUuemBBCmI0EYkI8CrRkOPo/OPGOelx7EjT7EWxysV5HH4iFnYCESPP1UQghHkESiAlR1CUnwL6hcO5r9dh/BjT8VJX9zg2XcuBaUQV39w6Yr59CCPEIkkBMiKIsMQp29IXAX0FnBy1/gZoT8n5er5RRsWDZd1IIIfJCAjEhiqq4+/BvV7j9F9g6Q7u1UPl585zbsO+k5IkJIUReSEFXIYqi6BuwrTuEnwH74tDhT/Bule1hJjOsnNwPyYm5yzUTQgghI2JCFDkR52BzaxWEOZeFrrvMG4QBeNRRe1ImPoCwU+Y9txAiXw0bNgydTsfLL7+c4bXRo0ej0+nS7bWob//wrUePHmzfvj3T19LeFi1aZGgXFhaW4ZqVKlVi5syZ6Z7bu3cvPXv2xNPTEycnJ+rVq8dXX31FUlJSunZpr+Pq6kq1atUYNmwYR44cyfJrcP/+fV577TVq1KiBi4sLFSpUYOzYsYSHh5v8dcwtCcSEKEruHYYtbVTle7fq0G0vFK9r/uvoNwAHKWMhRBHg6+vLsmXLiImJMTwXGxvL0qVLqVChQob2PXr04Pbt2+luS5cupVWrVume69+/f4a2AwYMyFHf1qxZQ/v27Slfvjzbtm3j7NmzjBs3jk8++YSBAwfy8JbZCxcu5Pbt25w+fZo5c+bw4MEDmjdvzs8//2z0Grdu3eLWrVt8+eWXnDp1ikWLFrFp0yZefPHFHPU1N2Q+QYiiIugf2PmkGqUq0QQ6bAQnb8tdz6s13P4b7u6GGmMsdx0hhMX5+/tz+fJlVq9ezXPPPQfA6tWr8fX1xc8v49Znjo6O+PhkvhtH2uednZ2Ji4sz2jY7UVFRjBw5kj59+vDDDz8Ynh8xYgSlS5emT58+rFixIl1wV7x4ccP1KlWqRLdu3Rg6dChjxoyhd+/eeHp6ZrhO3bp1+f333w2Pq1SpwieffMLzzz9PYmIidnaWC5dkREyIouDqCtjeUwVhpTtD538tG4SBJOwLYaKoKOO32FjT26YZrMqybW4NHz6chQsXGh7/9NNPvPDCC7k/oRls3ryZe/fuMXHixAyv9e7dm+rVq7N06dJszzNhwgQiIyPZsmWLydcODw/H3d3dokEYSCAmROF3YS7sGajqhVV4RiXm27tZ/rpe+g3Ab0DUNctfT4hCqlgx47enn07ftlQp420feyx920qVMm+XW4MHD2b37t0EBgZy9epV9uzZw/PPZ77SesOGDRQrVizd7aOPPsrxNcuXL5/hPNeupf49OX/+PAC1atXK9PiaNWsa2mSlZs2aAAQGBprUr3v37vHRRx8xatQok9rnxSMRiH333XdUrlwZJycnGjduzK5du7Jsv2PHDho3boyTkxN+fn7MmzcvQ5vff/+d2rVr4+joSO3atVmzZk2616dMmZIhQTG3Q7NCZErT4NRUODQa0KDqy9BqKdg65s/17VzBs5G6L3liQhR6Xl5e9OrVi8WLF7Nw4UJ69eqFl5dXpm07duzI8ePH091effXVHF9z165dGc5TtmzZDO0ezgNL+7zOhOLU+uNNaRsREUGvXr2oXbs2H3zwQbbt86rI54gtX76c8ePH891339G6dWu+//57HnvsMc6cOZNpAuKVK1fo2bMnI0eO5Ndff2XPnj2MHj0ab29vnk5567Jv3z4GDBjARx99xJNPPsmaNWvo378/u3fvpnnz5oZz1alTh3/++cfw2NbW1vKfsHg0aMlweCxcmKMe1/0A6n2Q+2r5ueXdGu4fVoFYpWfz99pCFBIPHhh/7eF/C8HBxtvaPDR0YuLgTo688MILjBmjcj7nzJljtJ2rqytVq1bN8/UqV65M8eLF0z2XdiqwevXqAAQEBNCqVcbV32fPnqV27drZXicgIMBwvaxERkbSo0cPihUrxpo1a7C3t8/23HlV5EfEZsyYwYsvvsiIESOoVasWM2fOxNfXl7lz52baft68eVSoUIGZM2dSq1YtRowYwQsvvMCXX35paDNz5ky6du3KpEmTqFmzJpMmTaJz584Zltva2dnh4+NjuHl7WzhnRzwakuJhz6CUIEwHjb+F+lPyPwgDyRMTwgSursZvTk6mt3V2Nq1tXvTo0YP4+Hji4+Pp3r173k5mBt26daNEiRJ89dVXGV5bt24dFy5c4Nlns38TOHPmTNzd3enSpYvRNhEREXTr1g0HBwfWrVuH08PfHAsp0oFYfHw8R44coVu3bume79atG3v37s30mH379mVo3717dw4fPkxCQkKWbR4+54ULFyhbtiyVK1dm4MCBXL58Ocv+xsXFERERke4mRDoJD2DH43BtOdjYQ6vfrLtiUb/VUdhJSJCfVyEKO1tbWwICAggICMhyFicuLo6goKB0t5CQELP3x9XVle+//561a9fy0ksvcfLkSQIDA1mwYAHDhg2jX79+9O/fP90xYWFhBAUFcfXqVbZs2UK/fv347bffmDt3bobRN73IyEi6detGVFQUCxYsICIiwvB5PVyrzNyK9NRkSEgISUlJlC5dOt3zpUuXJigoKNNjgoKCMm2fmJhISEgIZcqUMdom7Tn1NUuqV6/OnTt3+Pjjj2nVqhWnT5+mZMmSmV572rRpTJ06NTefqngUxIaolZH3D6n8rLaroUy37I+zJJey4FoJogJVlX1r90cIkWfu7u7Zttm0aRNlypRJ91yNGjU4e/as2fvTr18/tm3bxqeffkq7du2IiYmhatWqvPvuu4wfPz5D3tfw4cMBcHJyoly5crRp04aDBw/i7+9v9BpHjhzhwIEDABmmXK9cuUKlSpXM+0mlUaQDMb2Hv0nZJfdl1v7h57M752NplrfUq1ePli1bUqVKFRYvXszrr7+e6XUnTZqU7rWIiAh8fX2N9lM8QqKuwbZuqmq+Y0lovxG8mlm7V4p3GxWI3d0jgZgQhdCiRYuyfP2PP/7I0D67Y7I7d4cOHYwm4Ge2srFt27b89ddf2V7P2Dmzk1V/LK1IB2JeXl7Y2tpmGP0KDg7OMKKl5+Pjk2l7Ozs7w0iWsTbGzglqeLVevXpcuHDBaBtHR0ccHfNpxZsoPMLPwL/dIOYmuPhCx83gUdPavUrl3RoCf5WVk0IIkQtFOkfMwcGBxo0bZyjgtmXLlkxXXwC0bNkyQ/vNmzfTpEkTw+oJY22MnRPUfHpAQECGoVwhshSyH7a0VUGYey21ZVFBCsIgNWH/XsoG4EIIIUxWpAMxgNdff5358+fz008/ERAQwIQJE7h27Zphc9NJkyYxZMgQQ/uXX36Zq1ev8vrrrxMQEMBPP/3EggUL0lX1HTduHJs3b+bzzz/n7NmzfP755/zzzz+MHz/e0GbixIns2LGDK1eucODAAfr160dERARDhw7Nt89dFHK3NsHWzhB/H0o2V5t3u5S3dq8y8qgD9h6QGAVhJ6zdGyGEKFSK9NQkwIABA7h37x4ffvght2/fpm7dumzcuJGKFSsCcPv27XRVfCtXrszGjRuZMGECc+bMoWzZssyaNctQQwygVatWLFu2jMmTJ/Pee+9RpUoVli9fnq6G2I0bN3j22WcJCQnB29ubFi1asH//fsN1hchS4G+wbyhoiVCmO7T9XSXoF0Q6G7UB+O1NanqyRGNr90gIIQoNnWat7DSRrYiICDw8PAz7XYlHxLlZcGScul9xELRYCLYO1u1Tdv77GE6+BxX6Q5vl1u6NEFYRGxtrWGHn/HDRL1HkxMTEEBgYaNi5J62c/P8u8lOTQhQamgYnJqcGYdXHQqtfCn4QBmrlJKgRMXlvJx5R+rpb8fHxVu6JyA/R0dEAea6+X+SnJoUoFJKT4PBouPiDetzgE6g9yTrV8nOjZDPQ2alFBdHXwFWm4MWjx87ODhcXF+7evYu9vT02D+9JJIoETdOIjo4mODiY4sWL53n7QgnEhLC2pFjY+xxcX63yrZrOhaovWbtXOWPnojYAv39IjYpJICYeQTqdjjJlynDlyhWuXr1q7e4ICytevDg+Pj55Po8EYkJYU0IE7HwC7mwDGwe1ZVGFp7M9rEDybp0SiO2GSoOs3RshrMLBwYFq1arJ9GQRZ29vn+eRMD0JxISwlpg7sP0xCD0Gdm7Qfi2U7mjtXuWed2s4N1MKu4pHno2NTb5tGC0KPwnEhLCGB1dUtfwHF8HRGzpughLG90ErFPSFXcNOQXw4OHhYtz9CCFEISCahEPkt9CRsbqWCMNdK0HVP4Q/CAJzLQDE/QFM7AgghhMiWBGJC5Kfg3fBPO4gNguL1VBDmXs3avTIfr5RRsRCZnhRCCFNIICZEfrmxHrZ1hYRwNY3XZQe4lLV2r8xLPz0peWJCCGESCcSEyA+XF8OuJ1WpirKPQ8fN4OBp7V6Znz4QC9kPyQnW7YsQQhQCEogJYWkBX8L+YaAlQeWh0G61qrtVFHnUBvvikBQNobIBuBBCZEcCMSEsRdPg2Jtw7A31uNZEtW+kTd62wyjQdDbg3Urdl+lJIYTIlgRiQlhCciIceAECvlCPG06HRl8Uni2L8sJbEvaFEMJUUkdMCHNLjIE9A+HmOtDZQrMfocpwa/cq/3ilSdjXtEcj+BRCiFySETEhzCk+DLZ1V0GYrRO0Xf1oBWEAJZumbAB+C6ICrd0bIYQo0CQQE8JcYm7DP+3h7i6w94COf0P5PtbuVf6zc0ktUCt5YkIIkSUJxIQwh8iLsLk1hJ0EJx9VI6xUO2v3ynq8pJ6YEEKYQgIxIfLq/jHY0hqirkCxKtBtD3g2sHavrKtUG/VREvaFECJLEogJkRd3tqvpyNhg8Gyotiwq5mftXlmffkQs7D+VNyeEECJTEogJkVs31sK2HpAYCaU6QOft4Fzayp0qIJxLq9FB2QBcCCGyJIGYELkRHwZ7BkFyHJR/Ejr+BQ4e1u5VwWLYd3K3dfshhBAFmARiQuTG1eVqGx+POtBmhSpVIdKTDcCFECJbEogJkRuXF6mPfi+AjdRFzpR3SsL+vQOyAbgQQhghgZgQORUeAPf2q6r5lZ6zdm8KLvea4OAJSTEQetzavRFCiAJJAjEhcurKYgBuaj35el5pYmOt3J+CSmcDXrIBuBBCZEUCMSFyIjkJrvwCwLhZw3n9dWjUCPbts3K/CirJExNCiCxJICZETgRthphbxFGSdYd7AXD2LLRuDePHQ1SUdbtX4KRdOalp1u2LEEIUQBKICZETKUn6+24/R0KSA889B8OGqRjjm2+gbl345x+r9rBgKdEUbOwhNkjtPCCEECIdCcSEMFV8KNz4A4Bv1g8HoF8/WLgQNm2CChUgMBC6doUXX4SwMKv1tOCwcwbPxuq+TE8KIUQGEogJYarApZAcT7xrA/7Y0RAbG+jQQb3UvTv89x+MGaMe//QT1K4Na9darbcFh+SJCSGEURKICWGqlGnJ4xHDAGjSBIoXT33ZzQ2+/RZ27YLq1eH2bXjiCRgwAIKD87uzBYgEYkIIYZQEYkKYIuw03D8EOjsWb1e1w7p0ybxpmzZw4gS8/TbY2sKKFWp0bMmSRzRfXV/CIvw/Nb0rhBDCQAIxIUxxZREAWrnHWfOXN2A8EANwcoJp0+DgQWjQAO7dg+efh9694fr1fOhvQeJcGopVVffvSp0PIYRISwIxIbKTnGioHXbdfhi3b4OzM7Rsmf2h/v5w6BB8/DE4OMCff0KdOjBvHiQnW7jfBUmplO2OQmR6Uggh0pJATIjs3P4bYu+AozfrDvUE1PSjk4n7fNvbw7vvwvHjKniLjIRXXoFOneDCBct1u0DxkjwxIYTIjARiQmTn8kL1sdLzbP7HHsh6WtKYWrVUIv/MmeDiAjt2QP368OWXkJhovu4WSPqE/XsHZQNwIYRIQwIxIbISdw9urgMgseIwtm9XT+cmEAOVvD9unCp10bkzxMbCG29Aq1Zw6pR5ulwgudcAhxJqA/D7x6zdGyGEKDAkEBMiK4G/qREcT38OXahPZCSUKAENG+bttJUrw5YtsGABeHioPLLGjWHKFIiPN0fHC5h0G4Dvtm5fhBCiAJFATIispNQOw2+YYeuiTp3Axgy/OTodvPACnDkDfftCQgJMnaoS/A8ezPv5Cxz99KQk7AshhIEEYkIYE3oSQo+qvRIrDTIEYoZpyYgLapotj8XBypaFNWtg+XLw9obTp1VS/8SJEB2dt0+hQPFOWTl5d88jWlBNCCEykkBMCGP0o2Hl+hCVWJJ9KSWwunSBhPOL0f6sBZv84e/mnPv7V3r2iGfiRPj5Zzh2TOV/mUqng/791ejY88+r0hZffaWS+fV5aYVeySZg46BWoD64bO3eCCFEgSCBmBCZSU6AwF/Vfb9h7Nqlpg4rVQI/P/j4h/bExTugYQf3D1Hj3mAW9K6I65WpvDk2CH9/KFZMVdQfOFBV2jeFlxf88gts2ADly8OlS9CxI4waBeHhFvts84etE5SQDcCFECItCcQeQefPqz0R//rL2j0pwG79BXF3wak0lOnBoR3XebnzXDp3VrW/Pv2mEjUnnmG3902o/zGJ9mUp4xnE1KencO3bitT1u0FSEgQEqCnHuLjUUy9eDK1bq1pi330Hu3dnDLJ69VJTlC+/rB7/8IMqBPvnn/n3JbAIw76TkrAvhBAAdtbugMh/q1apAqP9+8Njj1m7NwWUoXbYc3D+W16v8T6u9R6wVVeHSZPakZgIdZpVom1XgHexq/0mXF8N52bhYGPPyYvluXVLlaS4e/YAdWr5A6oG2YEDsHevuqVVoYKaivz2WzXy5u4Oc+eqTcNHjFCjY48/DoMGwTffqNGzQserNfClJOwLIUQKnaZJ1mxBFRERgYeHB+Hh4bi7u5vtvDt2QIcOUK6c2vdQpzPbqYuG2LuwpixoieBeCyICANhzvhUJjX6g4xN1sLGBkyfVKFUGCQ/AvljKuYLhD19w9IJqr0DVUVy66c3Bg+r4U6fUx7T7T967p0pkgAqY//xTTXHevKkKwmoalCwJs2erIK1Qff9ig2F1aXX/6XvgWMK6/RFCCAvIyf9vGRF7BDVtqgqL3rypAoAKFazdowLm0gIVhAFEBBBPcV6dP51D917EZbOazX/hBSNBGKQGYQAR51Qh05hbcPI9+O9jqlR6lirdx/Lss40MzUJDVZHX8+dTgzBQ9cVOnMiYY3bvHjz7rMon++EHFVQnJ5unrIZFOZUCt+oQeR5C9kG5XtbukRBCWJWMiBVglhoRAxWMHT4MS5eqZHKRQtNghauqAA9Q6Xle//Urvp5biscfV0n0Li4qT6xsWRPPmRQP11bCuW/g/qHU573bQLPvwaO20UOvXlV7VOpHzk6dUsFa2g3DPTzUNkkbN6pgrl49NcWp/+jnV8ACtP0vqKnf2pOg4afW7o0QQpidjIiJbLVqpQKxvXslEEsn7ERqENbmdzTfp1g9SD28e1d9/N//chCEAdg6QOXn1C1kP5ybpQKze4fA0Tu1naZlmGesWFHd+vZNfS4mRi0C2L9fJf4fPAgjR6pNyGNjVZC4enVqexcXaN4c/v03/TmcnXPwOZiTd2sViEmemBBCSCD2qGrdGmbNgj2P+v/C5AQI+ArcqkKFfqm1w3yfhgpPcfmSGpWyt4dNm1SA88wzebieVwt1a/Ql3NsPTmkCse09waU8VH8NPOsbPYWzs6q+7++vylrMnAnvvaeCK0dH6NEDihdXo2OnT6uisA8epD9H06ZqOjTtyFm9elCzpjqHRXml2QA8KV4FqkII8YiSqckCzJJTkzdugK+vyhULC1M1rx45d/fAwVEQflqVqej5H/xZC+JCoMNGKPsYP/yggp127dQiB4sJD4A/00xRluoANcZCuT5gY5vt4RcvqlExffHXFi3UPpY1aqjXIiOhSRP1Wny8+n4nJGQ8j50d9O6dfkTtzh0oVcqMiwI0DVZ7qw3Vu+1TgakQQhQhOfn/XZAyR0Q+Kl9eBWJJSSoh/JESdx8OvARb2qggzNELGk6H4J0qCHMuAz5dAQzbGrVvb+E+udeErruhwjOgs4Xg7bDrKVhfBc58ofqchapVYetW+P57cHNT05aNGsG0aSpHTB+EATg4qGT/vXtV+1dfhbZt1ShaYqKa4tRLSlIblHt6qmD0tddg4UK1eCCzQM4kOl3qqJgUdhVCPOJkRKwAs+SIGKjcsOXL4eOPVZmEIk/TIHAJHH1dFWsF8HsBGk0Hx5Kwow/cXA+134KGn5GcrFYwhoeDjw8cPQplyuRDP6Ouw4W5cOkHNWoE0PJXlWNmghs3VCFYffHX+vXhp5+gceOsj9M0dWxCggreAAIDoVo1FaA9zMEBRo+Gr79OPT421sTcszPT4fhbUP5JaLc6+/ZCCFGIyIiYMEnrlEGJRyZPLPQo7BusgjD3WtBlJ7RYoIKwmDtwa6NqV3kooFYr6iveV6+ugrF84eqrVhP2vQ7NF4BPNzVSpnftd7ixHpKTMj28fHlYvx6WLFH1xk6eVMn6b7+t8siM0enUKKk+CANVWDYqSo2A/fKLWqjQsaNaqRkfrz7q3bqlRuPq1YOhQ1XR2Z07ISIik4vpK+yHyAbgQohHm4yIFWCWHhE7ckRNWRUvrqaqClSJA3N5eCXiwVdUoFNzYvok8YAZcOx/ULI5dN8PwIQJKhEe1MrEpk3zr9tGJSep6cqoq1DMD6qPAb/h4FA80+bBwTBuHCxbph5Xq6Zyx9q2zVs3NA2uXFHTmPoVpBs3qq2ZMlOtGkyeDEOGpByfGItulQckx0PvC2qxhBBCFBEyIiZMUr++Km0QFgZnz1q7NxZwZxtsaqKCFr1mc6HOO+mDME1L3dLIb7jhqZ9/Vk/5+xeQIAxUaY0K/cG+ODy4rKZZ/ygPh16F8IzfxFKlVK24tWvVtOqFCyrX69VXVQJ/bul0auQsbRmPxx5TU5vr18PUqarkhq+veu3ChfSB/s49TlQcd5UnZqzhw/cj2bBBjajJ20JhSVFRqg7f3r1wP03aZUyMejMaHZ2+Rp8Q+UFGxAowS4+IgZpm2r4dfvxR7WdYJMTehWMT4UpKJFV5KLRcZLz9/SMqYLNxhKeCwKE469al1u7atAm6d7d4r3MmMUrlu52bpRYc6DWYBnXezvSQsDB44w2YP189rlBBJev36GHZrt69C8eOQYMGUDpld6Ovv4bXX8/YtnRptchgyhQ1nSqEKZKT1c+Zh0fqYpN//1XT6Tdvpt70qQagijPrR3AXL4Zhw1Jfs7dXuY5OTur23XepbXfsgM8+U8+nbaO/36+f+hkG9cZkx470r6c9pmzZ1Ol9/X/iQrVlmTBKCroKk7VurQKxPXuKQCCmJauRrWNvQvx9QKf2d2zwSdbHGWqHPQkOxUlKgvHj1VMuLtCtmwX7nFt2rlD1JagyUo38nZ8FN9ZB6Q6pbWLvgq0j2Ks/AsWLq4B74EBV6uLKFTWKNWSICozSbq1kTt7eGb+GL74I/uX3cGzdSo7e7MjR4L4EBKhSGZs2wQcfpLZdtkz1u1Gj1Ppp1aqp0ivi0XLqlPr5SBtc3bwJt2+rRSb//AOdO6u2ly7BokUZz+HqqkaKixdPfS42Nn2bhAR10+c3pl2scv266oMxtWunBmJHjsDzzxtvO2+eKo8DKnDs0iVjYKf/OHGi2tYM1Ajzhx+mD+rStm3TJnUUPyIC9u3LGATqH7u5pV8pLfKfBGKPuFat1Me9e63bjzwLPwMHX4a7u9Tj4g3U9kFe2QyrJMVB4G/qfsq0ZHh46j/5Xr0K+DtUnQ58Oqlb1HVVEFbv1BS48ov6vKqPAfdqgPpHdeqUytn65hs1BbtpE8yZo97N5wd3d2jfuwbtE74BvoGn7xGdVIKTJ9Xq1Ppp6tnu3q3+SaXdGcDVFRo2VP/wJk3K4U4HokC5fVsFLA8HV/rbqlXQqZNqe+AAvPlm5ufR6SAkJPVxy5ZqRXi5culv7u4Zf6dHjVJvDmJjU28xMan3q6ZJYWzVSgV4aV/X34+JUUWR9UqUUMHVw230H9PWb9QvpNGfMywsfR/TTqXevAm//mr8a/rZZ6mB2IULWY96T54MH32U2rZVq4yBnf7Wv3/qG/bQUJWC8HDAqP9Yq1Zq2ZzERLX4ydjIYJHMT84BCcQecS1SammeP6/+iHl5Wbc/uRb4mwrCbF2g/keqGKqNCT/eN9er0TOX8lBavZUuUUKtNrx40XjyeYHk6pt6X0tWlesTI9Vo2flvoWxP9XXx6Yqrq46vv4YBA9QG5gEBaseAp56C2bPzqUyHkxe411Abo4fsxaXc47Rokfozqffaa2oU7NgxFaQdP65yffbsUbf3309t+9136nV/fxWk1a9vxa2cHmFxcSpYuHEj8+Dqiy9S3wRu3Jj1aPyNG6n369eH555LH1iVL68++viogsR6deuqm6ns7FRglF1xaz+/9CuLs9K2LWzZYlrbbt0gKCjzAC82VgU2afvw5ZcZ2+jv16mT2tbeXr1pyeyccXHpR8Oio9MHsw9LWwLn/n31Rs6YV19NDcTu3cs6z/bFF1NTJqKj1XUyCwadndUszujRqq2mwaefZj4q6OSkfi4aNEi9ztWraucQfRsHh4LxRlsCsUdciRLqFzwgQA1f9+5t7R7lQGKUmqIDqPOuKsZa5x1wrWD6OfTTkpWHGCrYh4WlFrnVT3MUOjob6H4Agv5ReWS3/ky9uddUX6fKg2nRQgU4H3+s3kWvXq1Gnr7+WpWgsPgfKa/WKhC7uxvKPZ5pkxo11E0vKUm9cTh6VL2D906zS9TatbB5c+pjW1v1862f0nzlFfXHV+SOpql/qmmDKn2w9eqrqVNyv/6adXB18WJqIFalivrePDxypb9Vrpx6XLNmWY8EFWYODqk5lNmpUEGVkjFF/frqdzwzycnpFyfUqKG2RTMWDNZOs/mHu7sajc5spO/hYDAxUS3cSXuutNO99vap96Ojs148ptOlBmLx8WpEz5gnnoA1a1IfV62a/ro6ncqb/fxz4+fIDxKICVq3VoHYnj2FJBCLuQ1HxqtVg932qwDKzhmazcv5eW7/pe5XHkpwsBpqb9xY/XGqUUO92y60dDZQppu6RVyA87NVDl3EWXVL4eiopib69VPvTI8cgeHD1WrL779XtcQsxrs1XP4pRxX29cFV2hECvbFj1ffv6FF1u3tX7bn5338qSBs7NrXtzJnqD7k+96xkybx/OoVZXJxauZo2yOrVKzUIXrFC5RPGxWV+fNu2qYFYuXKpIxKZ3dKOenbooH7mRP6zsUk/LejklD7Yyoq3txqNMkW5cnDtWvrnEhNTA7e0gZi7u8pbziwQjI1VNR31NE3luxoLHNNOKSclqeukDcQ0Lf0oqrXkuAsrV65kxYoVeHl58fLLL9MgzbhfSEgIzZo14/Lly2btpLCsVq3UsHCBzxNLToKL38OJSZAQoQKNkH1Qqk3uznflVzWF59UK3KszdZKa2tLnG3XpYr6uW517NWjyDTT4CC4vVgsT9G79DRe+o0GNcezf15EZX+t4/301slS3rhopGz3aQnkc+sKu9w6pfD3bvO043qtX6nSypqnAQh+UJSSkH+GbPVsldOtVqJA6ctasWQFcKZtLmqamkfTBVb16qW8wtmxRIwI3b2Y+JVWiRGog5uGRGoR5e2cMrtJOAXXrpkY2CsK0jyiYjE0FOziYvqWckxP88INpbW1t1c+kpqk3YPpgrUAsVNByYMmSJZqNjY3Ws2dPrVWrVpqjo6O2ePFiw+tBQUGajY1NTk6ZL+bMmaNVqlRJc3R01Pz9/bWdO3dm2X779u2av7+/5ujoqFWuXFmbO3duhjarVq3SatWqpTk4OGi1atXSVq9enefrPiw8PFwDtPDw8Bwdl1Nnz2oaaJqTk6bFxVn0Url3/5imbWqmaUtQt7+aatq9o7k/X3Kypm2orc514Uft7FlNs7VVX4cKFdTHNWvM1fkC7t/HUr+uG+po2oXvtfNnorS2bdXXATStdWv1c2J2ycmatspLXTt4rwUuYPyyn32mac88o2lVq6Z+nvpb48bp28+apWmrVmna5cvq2IIiLk7TAgM1LTQ09bmDBzVt4EBNa9tW0/z81O912s9t4cLUtn//nf41R0dNq1xZ09q00bQBAzRty5bUtg8eaNqVK5oWG5tPn5wQhVhO/n/nKBBr3Lix9sUXXxgeL1q0SHNyctJ+/vlnTdMKZiC2bNkyzd7eXvvxxx+1M2fOaOPGjdNcXV21q1evZtr+8uXLmouLizZu3DjtzJkz2o8//qjZ29trq1atMrTZu3evZmtrq3366adaQECA9umnn2p2dnba/v37c33dzORXIJacrGklS6o/xAcOWPRSOZcYq2lH/qdpv9mqf9Yr3DXt3GxNS0rM23nvHlDnW+asaXFh2hNPqM+/Sxf10cYm/T+3Ii0sQNMOjta05a6pAdlKTy35yJva4u8CtWLFUv9J//STBa6/o6+65pkvsm1qKWFhmrZ9u6Z9/bWmDR6saVOnpr4WG6tp9vapwUrx4prWqZOm/e9/mrZkiaZduGD+/iQna1pimh/x8+c17cMPNW3UKE17/HFNa9RI00qVSu1T2u/Lw8GV/ublpWkNGmja0qWpbe/d07Q//9S048c17e7dghVkClGYWSwQc3Nz0y5dupTuuT/++ENzcnLSVqxYUSADsWbNmmkvv/xyuudq1qypvf3225m2f/PNN7WaNWume27UqFFaixYtDI/79++v9ejRI12b7t27awMHDsz1dTOTX4GYpmla797qj/WMGRa/VM4kJaaOhO16RtOibprnvAdfUefc85y2c6f63G1tNe3TT9X9Zs3Mc5lCJS5U0wJmaNoflVMDso2NtMBATeveXX1dXF01LTrazNc9PV1da8cTZj6xeYSEaNqIEZrm758+INPf+vdPbZuUpEacjh/XtPj4rM97546mrVypaTNnatobb2jaoEGa1r69GqFzdta0BQtS227enHlwBZrm4KBp33yT2vbWLU378ksVcO3cqWmXLmlaTIw5vyJCiOzk5P93jnLEnJycCA0NTfdc3759WbhwIUOHDuWbrNayWkF8fDxHjhzh7bfTVxrv1q0be40kRO3bt49uD1Wf7N69OwsWLCAhIQF7e3v27dvHhAkTMrSZmbIxYW6uCxAXF0dcmkzYiEx3SzaT0JNwbSU4eoGTN4M6eXH9pDfnjnlBkjfYWnHiPOq62ojbzkUl4jf7EWJuQtnHzHP+pFgIXAqAVnk4b6SkS40YAWfOqPtFKj/MVA7FoeYEqD5WbYB+7huo9CwVK8Jff0HdGpGMbv02t9bqqFIlk+OrvAAl/NX9+0fh0k/Gr1V5cGqNN+eUWhm3NqmtmngosahifyjVTt2POK9WgRrj+wT4pHzzHgRCwJfG25btCeV6qvsxt+G/zAv/lgR+fL8L+D5BfDycOxmC7Zkp3L2rFgNUqwakrLKNCIe/vm/L8P0DcHCA5v6RvNdnEvHxquRGo0ZQLSWBOO4G/DGzBUv2qIqfDnZxfPXc/6CSer1eXOp5m9vCwsmNuGLzosrJKqvRzPY1ihVTOS46UtuWAf7Xp44qZqx3ZAIkJ2T+dXCrBjXHpT4+/jYkPMi8rWtFqP1G6uMT70F8aOZtnX2gbpolbf99DDFBmbd1LAn1p6Y+PjMdoq5l3ta+GDT8LPXx2ZkQeTHztjYO0HhG6uPz36mag8Y0nZ16/9ICuG9kuSGA/4zUrdIu/6zKxRjTcBrYu6n7V5dD8C7jbet/CI4p1ZWvr4Ggrcbb1n039ffn5p9w6y/jbWu/ob5/ALe3wI21xtvWGGeoO0jwTri6wnjbaq9A8ZQlknf3qR0/jMnt34iw/+BCFoux8vI3IuwElO9rvH0+yFEgVrduXXbv3k3jtMVEgIEDBxIZGclo/ZrSAiIkJISkpCRKP7QmuHTp0gQFZf4HISgoKNP2iYmJhISEUKZMGaNt9OfMzXUBpk2bxtSpU42+blb3D8Ppjw0PB5aCgfoVMMuBVkuh0kD1OOQAXPgOHL3ByVt9dPRKfexcTq1azKvkRPULdOp9VYBU/8fWs766mcuNtZAQBi6+rNrVkQMHVIHQDz5IrZPzSAZieja2UL63uqXsu6LTwccjfuHJ8t9BMnAhk+NKt0/9Ixt5ES7MMX6Nkk1T/8japWTrJseqn7OHedRO/SMbczPr87pWTP0jGxucdVsn79RALO5+1m3tXMD3CRwcoF7NSDg/R0U8+nprKV+P4sCznTT+DhhAeDicPRND19fSnFdLbesLPNsxiviyz1OuHFQqn8iYMg/1IaWtOzCs+zPQ5sXU8yydA0ZiIMr2TB+IXfxe7VOamVId0gdilxaoUjCZKdE0fSB25WeINhIwedROH4gF/gYRAZm3da2UPhC7tlL9jcqMo3f6QOzGGhUsZMbWJX0gdnND6krpzKQNxG5tguurjLdtNB1ICcTubE3dUi0z9T5IDcSCd8CFucbb1n4jNRAL2Zv1z2X10amB2L2DWbf1G5oaiIUey7pthWdSA7GwU1m3LdszNRCLOJt129z+jYi6mnXbvPyNCNlXuAKxIUOGsHVr5tH5yJEjiYyM5NtvvzVLx8xJ99DSHU3TMjyXXfuHnzflnDm97qRJk3g9zQZ8ERER+Pr6Gm2fJ+41oNqr6g9u3F2SY+5y59pdvIqFYG+XmPqHANRehln9kWn1G1RK2Xvjznb478OUYM3bMOJmuF+8virk+bCQg3BoFIQeT3l8QK2StLHAPjaG2mFD6exrw//+pwrZhoaqKt/OzqoqtyDdsrcGzUoxdfb72Nup7VYy1ONyT7Pu3aM21H0fozwbpG/r4gvR16FcH/BsmL5tiSap910rZX1e71ap913KZt22VJqlWU7e2Zy3dep9h+JZtn2ifVNCP1ZbSJ044sKuu+8btpIpXRqKe6S27dW2Ib30v+JJ9nA6iz541En/OKv+ulVL/7jOO8ZHxFwrpX9cayIkRmfe1qVc+sc1xkFCeOZtHb3TP642GuLuZt7WoXj6x1VGqH/wmdHXDtSrPFQFk5mxsU//uNIg9Q/eFBX6qZ9NY3Rp/n2W75vx65iWnUvq/bI9M35t0rJP8wNSuosKJo1xTPO3tFT7rH8mnNNsP+HVKuu2+oANVPCd5c9amhoRng2zbpvbvxFu1bJum5e/EaWtXyyySG/6HR8fj4uLCytXruTJJ1OX648bN47jx4+zY8eODMe0a9eORo0apZtmXbNmDf379yc6Ohp7e3sqVKjAhAkT0k1Pfv3118ycOZOrV6/m6rqZyY9Nv9Nq1gwOHdJYsSScZwY4p5YSCD2p3kXG3lV/SONCUu6rII62q6FMV9X24nw4ONL4RdIGbbc2weHXwMEz5d2vpu43nK6GsHUWqJcQfRPWVlBlK3pfBLfUObZZs2DcOOjaNX1RUKFomqrhc/Ei/PZb6r53ZnH8bTjzOVR5EZrPN+OJhRAi/+Xk/3eR3uHJwcGBxo0bs+WhPSa2bNlCq1atMj2mZcuWGdpv3ryZJk2aYJ9Sdc5YG/05c3PdgqB1awAdO/YWT1/PybM+1H4L/L+Elouhw5/Q4yD0vQz9I1OHeUHtedhqCfjPhDqToeoo8H1KDRu710r/jjr6Bjy4CPcPARpUGgyPn4WqIywThIHae1FLJsGzA1qx9IlO//yjPj7S05JZ0OnUlkgAy5eb+eReKSNOOSjsKoQQRYI5VgdER0drcQW0AJW+jMSCBQu0M2fOaOPHj9dcXV21wMBATdM07e2339YGDx5saK8vXzFhwgTtzJkz2oIFCzKUr9izZ49ma2urffbZZ1pAQID22WefGS1fYey6psjPVZOapmkrVqhVWI0a5cvlNC0mWNPu7NS0a79r2r0jlr9ecrKmra+haUvQ3hh5QmvfXtOOHVMvJSRompub+vyP5ENXCqtTp1JX6pm1vEdsSOpKzZi7ZjyxEELkP4uVrzBmypQpmoODg7Zs2TJznM7s5syZo1WsWFFzcHDQ/P39tR07dhheGzp0qNa+fft07bdv3641atRIc3Bw0CpVqpRpQdeVK1dqNWrU0Ozt7bWaNWtqv//+e46ua4r8DsSuX0+toRUZmS+XzF/BezVtCVrgtzU0R8dkDVQNJU3TtL171edeooQqQSCMq11bfa0WLTLzidfXVIHY9bVmPrEQQuSvnPz/NkuOWJMmTQgMDOTGjRs4Gdkv4MSJE9y/f5/mzZvj4pJF8qEwyO8cMYCKFdWeYP/8U4g3vDbm4Ci4+APPL97Dks2t6NgRtm5VU24ffQTvv6/2W1y50todLdg+/FCtMH3sMdi40YwnPjBCrdir9SY0svIuvEIIkQf5niN26dIlGjVqZDQI0+vSpQvTp083xyWFhbROSdUp8PtO5lRiDFxdxpEr/izZrPL0vvgidVGg5IeZTp8ntmUL3LtnxhN7p+wZGiJ5YkKIR4dZArGEhAS8vbNYjgs0aNCAqlWrsmnTJnNcUliIfi1BkQvEbqxBi4/gjeWqvMpzz6XWDIuKgn371H0JxLJXowY0bAiJibB6tRlPrE/Yv3dYbQAuhBCPALMEYpUqVeLMmSyqFaeoWbMmV65cMcclhYXoA7F9+yA52bp9MavLi/jrxGNsO9UKR0f4JE0R9V27ICEBKlUCPz+r9bBQscjqSbeqqr5SchzcP2LGEwshRMFllkCsW7dunDp1ip07jVQ3TqHT6QgPN1L8TxQI9eurKvPh4anb/RR6Udch6B9+2v4CAGPHqlw4Pf20ZOfO6eqXiiz0768+btsGd+6Y6aQ6XWrh1Lu7zXRSIYQo2MwSiI0fPx5nZ2cGDhzIoUOHMm2j338xuylMYV12dtA8ZVeJIjM9eeVnQGPZR3P58Ud45530L0t+WM75+akCwMnJsCqLXWByzFvqiQkhHi1mCcQqVKjAokWLuHfvHm3btuW1117j+PHjhteDg4MZMWIEt27dolOnTua4pLCgIpUnpmmGLY3sqg1hxAgoXjz15eBgOHFC3ZcfzZyxyPSkIWF/r2GfSyGEKMrMVr68X79+/Pnnn5QsWZI5c+bQuHFjnJyc8PT0pEyZMvz6668UK1aMyZMnZ38yYXFJScYDLX0gtqcoDEqE7GXfMS/itBJq77iHbNumPjZoAKVK5XPfCrlnnlEfd++GmzfNdFJPf7B1UttnRZ4300mFEKLgMus+Ml26dOHixYvMmDGD1q1b4+joSHh4OI6OjvTs2ZPdu3dTrVq17E8kLComBnr2hPbtIbNtL1u0UB8vXlQjRoVZ0KHf6TptCzXfOMuNINcMr8u0ZO75+qpyJ5pmxtprtg5qk2GQ6UkhxCPB7Bv6OTs7M378eHbu3El4eDhxcXFER0ezYcMG6tWrZ+7LiVxwcgIvL1V+4JlnVAHXtDw9oU4ddV9f1qFQSozigxl1iYorRukyjpQrl/5lTVO1sKAIFq/NJwMHqo/LlpnxpJInJoR4hJg1EEtMTOTixYvs3buXc+fOkZiYaNgoWxQcOh38+KOqBXX3Ljz1lBolS6so5Imd+Xcr8/8dCsCXM90yrIi8fBmuXgV7e2jb1godLAL69QMbGzhwAAIDzXRSWTkphHiEmC0Qmz59OqVLl6ZGjRq0bduW2rVrU7JkSQYOHMjhw4fNdRlhJi4usGYNlCwJR47AqFHpc6OLQp7YWx94k6zZ8kSns7Rpm7Euxdat6mPLllCsWD53rojw8VFT3AArVpjppF4pP3yR5yH2rplOKoQQBZNZArGPP/6YSZMmERoaSu3atenTpw+dOnXCzs6OFStW0Lx5c8aMGUNCQoI5LifMpFIl9c/T1hZ++QVmzUp9TR+IHT4McYWwyPn2v4LYsL8ltjaJfPalW6ZtJD/MPMy+etKxBHjUVvdDCvGQrBBCmMAsgdj8+fPR6XSsXLmSU6dOsWbNGrZs2UJISAh//vknjRs3Zu7cufTt25fkIlWuvfDr1Am+/FLdf/99uH9f3a9WTeWRxcXBsWPW619uJCfDxInq52zU439So1G5TNvoR8QkPyxvnn5aBfNHj8KFC2Y6qZfkiQkhHg1mCcRu375Nu3btePrpp9M9r9PpeOyxx9i/fz/PPfccf//9Nz/88IM5LinMaNw4eOst2LkTSpRQz+l0hTdPLCI8mTIuAbg5RfDB5PhM2xw/roJONzdo2jR/+1fUeHmljiqabVRMEvaFEI8IswRiXl5eWVbMt7Gx4ccff8TLy4sff/zRHJcUZqTTwWefqVpaaRXWPLHiCbtZP6ELZ2Y0o1SjXpm20U9LduigkvVF3ph9elIfiN0/DEmxZjqpEEIUPGYJxNq2bcvOnTuzzAFzdHSkXbt2BAQEmOOSwoL27YMJE1QSO6gRsUJV5PzyQgDK+7cFO5dMm+inJSU/zDyefFIFtP/9B6dPm+GExaqAUylIjod7sthHCFF0mSUQmzx5MhEREYwZMybLdmFhYXh4eJjjksJC7t5VwcnMmSogs7eHoCAzliawoPv3Ydxr8QSdTKlS6zcs03axsbBrl7ov+WHmUbw49Oih7ptlVEynS80TCylkQ7JCCJEDZgnEXn75ZWrWrMn8+fPp1q1bpht///vvv+zYsYMnnnjCHJcUFuLtraYpQW2OXaWKul8Y8sQ++QRmzXbgqS9/BbdqqWUQHrJvn6qb5uMDtWvncyeLsLTTk2YZQdXvOyl5YkKIIszOHCfZm+a/9D///MPWrVvx9fXF398fd3d3rly5wp49e+jTpw9fffWVOS4pLGjMGLUCbtGi1JGwPXvguees2ausXbkCs2er++8/+aEaDXu4gmuKtGUrjDQRudCnj9q14fx5tZF6w4Z5PKE+T0y/Abh8s4QQRZBZArHbt29z9OhRjh07ZrhduXKFaw/tnXPixAmGDBmCv78/jRo1wt/fn9KlS5ujCyKnbqyH0h3APmONLZ0O5s5VuT76wU39VF5B9c47EB8PXepuoXv9zVDJ+KIQyQ+zDDc36NULfv9dbXmU50DMs1HKBuD3IOIceNQ0RzeFEKJA0WmaZdKwIyIiOH78eLrgLCAggMTERHXhlHe3Pj4+3Lx50xJdKPQiIiLw8PAgPDwcd3d385345Afw34dQaTC0+tlosxs3oFEjCAlRj8PCoCCm+B06BM2agU6ncfTjRjRsUQo6bc60bViY2k0gORmuX4fy5fO3r0XdypXQv78qFnz5shkGsf5pD8E7odmPUHWEOboohBAWl5P/32YZEcuMu7s77dq1o127dobn4uPjOXXqlCEwO3r0KKdOnbJUF4QxZbrB6Y8h8Bco0x0qZz7nWL48rF4N+m/hnj3Qs2c+9tMEmgYTJ6r7gzuspmGlE+D3m9H227erIKxGDQnCLKFXL3B1VVPa+gA5T7xaq0AsZI8EYkKIIilXyfpr1qzh8uXLOT7OwcGBxo0bM2LECObMmcO+ffuIjIzMTRdEXni3hrofqPuHXoEHxr+XbdumrobLZA2G1W3YoArROjkl8fGT48HeA8o/YbS9bGtkWS4u0Lu3um+W1ZOSsC+EKOJyFYg9/fTTVKtWDQ8PD9q2bctrr73G/PnzOXz4MLGxOSu+qJMEXOuo8y54t4XESNjzLCQbrwH3+OPqo76emH4bpIKgdWtV8+zNQevxLXkDKg4EO2ej7SU/zPIGDlQfly9Xo4954p1SzC7yAsQG5/FkQghR8OQqR8zGxgadTkfaQ/UBlY2NDdWrV6dBgwY0bNjQ8FGS8nPOYjlielHXYGMDSAiD2pOg4aeZNjt+XOWKubmpqckzZ1RQVqyY+buUKwmRsNoHkqKh2z7wapFpsxs3wNcXbGzg3j1V+0qYX1wclCoFERFqkUebNnk84Z91Ifw0tF0Dvk+Yo4tCCGFRFs8Rc3NzIzIykoYNGzJ48GAcHR05ceIEx48f5/Tp0wQEBBAQEMDyNHMT3t7eNGzYkIYNG/KZvlCVsC7XCtB8PuzuB2c+A58u4NMpQ7O6dVXQFRmpRpRCQmD4cFixwnoVBRIT1UbTOh1wbaUKwtxrQMnmRo/Rj4Y1aSJBmCU5OsITT8DPP6tRsTwHYt6tVSAWskcCMSFEkZOrqcnLly8zZswYTp8+zcSJE9m1axdvvfUWBw4cIDIykrNnz7J8+XImTZpEz549KVeuHMHBwWzevJkvvvjC3J+DyIsKT0OVkYAG+56H2JAMTezsoHlKfDNsmKq2v2oVfP55vvY0nQ8/VFXxjx/HsKURfsOzjAwlPyz/6Iu7rlwJSUl5PJm+wn7w7jyeSAghCp48la+4fPkykyZNYtWqVdjb2/Pyyy/z/vvvU6JEiQxtQ0NDOXHiBCdOnGDcuHF56vSjwuJTk3qJUbCpCUSchXK9od3aDAHN++/DRx/B4MFqhGPUKNXkzz/hsccs17XM3LoF1apBdDSs/Pk2/WzLgs4G+l4Dl3KZHqNpULas2q7p33+hY8f87fOjJj4eypRR+YRbt0KnjAOtpou8BOurgo099AvPMgdQCCEKgpz8/87TFkd+fn4sX76cAwcO0LJlS2bNmoWfnx/Tpk3LkLTv6elJhw4dJAgriOxcofUysHGAm+vhwncZmrROGZTYuxdeekkFYpoGgwbBhQv5293331dBWMuW8HS9lL76dDMahAEEBKggzNk5dTNzYTkODvDUU+p+nldPFvMDJx+1oOS+bAAuhChazLLXZJMmTdi2bRvr16/H19eXd999l6pVq/LTTz9hoXqxwtw8G0DD6er+0f9BWPr6bs2bqxGwS5fgzh2YNUsFZ2Fh8MwzZlgdZ6L//oOFKTORX05PQhe4WD3wG57lcfppyTZt1DY8wvL005O//w4JxhflZk+nS93uSMpYCCGKGLMEYnq9evXi5MmTzJ8/H51Ox8iRI6lfvz4bN24052WEpdQYC2V7QnKcKmmRGGN4qXhxqFNH3d+7V414rFqlVlPOmqVWIuaHt95SQd9TT0Grqtsg+jrYF4fyfbI8TvLD8l+HDmr15L17ajo4TyQQE0IUUWb/96nT6Rg6dChr166lWbNmnD59muHDsx6tEAWETgctFoJTabVK7dj/0r3cqpX6qN/j3ccHjhxJrbxvaf/+Cxs3qsUDn30GXF6kXqj0rNqT0IjERFVRHyQQy092dtCvn7q/bFkeT6ZP2A/ZA1o+Db8KIUQ+yFMglpCQwH///ceKFSuYMmUK/fv3p27duri6utK0aVMOHDgAgK2trVk6K/KBUylo+Yu6f2EuXP/D8FLaPDG9tDn9Z87Ali2W69oPP6iPL78M1SqGw/Xf1RPZTEseOqRKb5QoYYaNqEWO6Kcn16xR9cVyrUQjsHWG+FC1qEQIIYqIXNURe+qppzhz5gyXL18mKSkpXR5YhQoV6NatG/Xq1TPcatSoYbYOi3xQpivUegMCvoADL0LJJuBS3jAidviw+qfq6Jh6yNGj0L69mqI8eFDt5Whuv/6qVjs+9RRwbQUkxYJHbSjRJMvj9NOSnTrl3xSqUNq0UatVb92CzZtTtz/KMRt7VSMueLuanvSobc5uCiGE1eQqEPvjjz/Q6XT4+/vTvHnzdEGXm5ubufsorKH+x3Bnm1qltvd56LSVKlVs8faGu3fVlKQ+MANV9LVRI1VJvW9fOHAAPDzM2yU7O7VaE4Bji9THysOyrSor+WHWY2MD/fvDzJlqejLXgRioPDF9IFZ1pJl6KIQQ1pWn8YHjx4+zdetWtm3bxo4dO9i1axc3btwwV9+ENdk6QKvfVGmL4B1w5jN0uox5YnoODqp4Z/nycO6cqjdmrpWU+hE4g4hzELIXdLZQ+fksj42Kgn371H0JxKxDPz25bh3ExGTdNkuSsC+EKIJyFYiNHj2a1q1b4+rqyrlz51ixYgWTJ0+md+/eVKxYkZIlS9KpUycmTJjAokWLOHbsGPHx8ebuu7A092rQZI66f+oDuLsv0zwxvdKlYfVqNWW5fr2qfp9X9+6pAKpWLQgMTHnyckrJijI9wLlMlsfv2qVKJ1SqBH5+ee+PyLnmzaFiRXjwQC22yDWvloAOHlyEmDvm6p4QQlhVrqYmZ8+ebbh/5coVQ8V8/X6TgYGBbN++ne3btxs2A7e1taVGjRo0aNCAX3/91Ty9F5ZXeQjc/huuLoW9g2jb4jjgwd69qqDrw7OCTZvCvHlqL8qpU9V0Zd++ub/8Rx9BeLgKpCpUAJKT4MrP6kW/Ydken3Za0lr7Yj7qdDo1PfnFF6q469NP5/JEDsXBow6E/5ey7+RT5uymEEJYRZ62ODImMjIyXWB24sQJTp8+TUxMDDqdjqQ8bz73aMi3LY6yEx8OfzWEqECSyj+Lc5clJCTouHgRqlTJ/JCxY+Hbb6FPH1i7NneXvXRJjYQlJKhE765dgVt/w/Ye4FACnrwFto5ZnqNhQzhxApYuhYEDc9cPkXdHj0Ljxmpng+BgtYl8rhx8GS5+DzVfB/+vzNpHIYQwl5z8/87ViFh23NzcaNOmDW3atDE8l5yczPnz5zlx4oQlLiksycEDWi+FLW2wvbGU957rzvuLhrJ3r/FA7Kuv1H6QL7+c+8tOmqSCsO7dU4IwgCuL1MdKg7INwoKDVRAGedzrUORZo0ZQtSpcvKimrZ99Npcn8m6jAjHJExNCFBG5yhGrVq0aEydOZPv27SaPbtnY2FCzZk0G6DN3ReHi1QLqTQXgzU6vUs3nfKZ5Ynr29vDaa+qjXk7GXvfvV8n/Oh1MT9l5ifhQuL5G3c+mdhikVnNv0EBVeBfWo9OlJu3nae9JfcJ+6NF0Oz8IIURhletVkzNmzKBz5854e3vz/PPPs3z5csLDw83ZN1HQ1H4bSnXA0TaK314dxMH9pi3ASEiAMWPgvfdMu4ymwcSJ6v6wYVC/fsoLV5er7ZeK1wPPRtmeZ+tW9VFWSxYM+kDsr79U3l+uuFZSCzSSE+D+IXN1TQghrCZXgdiFCxcICAhg2rRp1KlTh+XLlzNo0CBKlSpF586d+eabb7h06ZK5+yqszcYWWv1Ksl0Jmvgd4dna75r0D3XLFpgzBz75RK2qzE5kpKpB5uz80MpL/ZZGJtQO07TUKv+dO2d/TWF5detC7doQHw9//JHLk+h0qdsd3d1trq4JIYTVmCVZ/969e/z555+sW7eOLVu2EBkZiU6no0aNGvTp04fevXvTqlUrwwpKYZoCk6z/sBtrYecTABxx/5vGj3fL9pDXX4evvwZXVzXtWLdu9pcJDFSrJQEID4A/a4PODp68qbZiysKlSyonyd4e7t/PQ3K4MKsPP4QPPoDHHstDKYuzM+HoBLVBfYc/zdk9IYQwi5z8/zbLhi8lS5ZkyJAhrFq1ipCQEP766y9eeeUVoqOjmT59Ou3ataNUqVIMGzaM33//nQcPHpjjssJayvfln6uvAFDj/hCIDc72kOnTVcJ8VBQ88QSEhmZ/GUMQBqmjYWV7ZhuEQWrZipYtJQgrSPTTk1u2qBpxuWIo7LpXNgAXQhR6Zt95z97enu7duzN79mwCAwM5fvw4U6ZMwc/Pj19++YVnnnkGLy8vfvzxR3NfWuSjy8W/4r/rdShmdwf2D882E9/OTiVpV6yoRqsGDYKH13lERKiRs6Cghw5OToTAlI3ITagdBpIfVlDVqKEWTyQmqo3Ac8WzIdi6QEKYGikVQohCzOJbINevX5/33nuPAwcOcPPmTb7//nu6desmif2FXPNWzjw7ZymxCY5wayOcm5XtMV5eKjfI2Rk2bYLJk9O/Pn26mr58/PGH4rrbmyHmNjh6Qdle2V4nOTk1EJP8sIJHX89t2bJcnsDGHryaq/shUsZCCFG4WTwQS8vHx4eRI0eybt06JuqXxYlCqW5duBpWj/8tSSmqefxNCD2e7XENG8KCBeDurqYN9W7ehBkz1P3Jkx/KxTfUDntO7YGZjePHVV6Ym5uq9C8Klv791cdt2+BObncq8pJ9J4UQRUO+BmKi6LC1hRYt4Lsto7mS0AeS42HPQEiMyvbYZ59VhT379El97r331IbQbdo8tCVS3H21OABMnpbU54d16JC+jpkoGPz8VICcnAy//57Lk3jLykkhRN6Zf2+hnMu3QOznn3+ma9eu1KpViy5duvDLL7/k16WFhbRqBaBj+o4F4FwWIs7BkfEmHevtnXp/wwZYuFDd//LLh0bDri5VQZ5nQ3UzgeSHFXz66clcF3c1bAB+GWIeTioUQojsHTsG/v5w+LB1+5EvgdiQIUOYNm0aQ4YM4eeff2bo0KFMmzaNoUOH5sflhYWoQAz+3u4FLX8BdHBpPlxbZfI5du+G3r3V/f79oXnzhxqkrR1mgthY2LVL3ZdArOB65hn1cdcuNS2dYw4eqrAvyPSkECJHEhPh00/V/5vjx+GNN6zbH4sHYt999x0nT57k4MGDDB48mKZNmzJ48GAOHDjAvn37mDdvnqW7ICykeXM1enXlCtzWOqnK+wAHRkLUNZPO4eYGLi7g5KR+MdIJ+w/uH1a1wyoNMul8+/apKc4yZdSG4aJg8vWF1q3VtMDKlbk8ibfkiQkhcubiRWjXDt59V+368uSTsGKFdftk8UDsm2++4fPPP8fNzS3d825ubsyYMYOZM2daugvCQjw8oF7KoMS+fUD9qVCyuSorsPc5VXYiGw0awMmTaog4wwbi+tGwcr3ByfvhQzOlzw/r3Dnb4vvCyvK896Q+YV9WTgohsqFp8P336n/Ovn1qwdjixSpP1du0fy8WY9FALCYmhosXL9K+fXsAunTpQpU0/207d+7MhQsXSEhIsGQ3hAXppyf37kWVFWj9G9i5qSTq05+YdI4qVaBmzYeeTE6AwF/VfROT9CE1EJNpyYLvmWfAxkbttBAYmIsT6EfE7h+FxGhzdk0IUYTcvg29esHLL0N0NHTsqAYAhgwpGG/YLRqI2dnZARAXFweAv78/rfT/uYH4eLVptK2trSW7ISxI/+3cox+UKOYHTeeq+/99CMG5XNV2axPE3lFV9Ms+ZtIhYWGpSZdSP6zg8/GBlPdouZsacK2oFoloiXDvoFn7JoQoGlauVOWW/voLHB1VmaR//lHFxQsKiwZi9vb2+Pv78+efaj+46dOnp1stuWrVKvz9/bGxkSoahZU+EDtyRCXKA1D5Oag0WG0/s3cQxJuwn9HDDLXDnlcjbSbYvl2VRKhRA8qXz/klRf7L0/SkTid5YkKITIWFwfPPq0Vg9+9Do0bq/9SECWokviCxeHcmT57MG2+8QeBDcw+XL1/m7bff5r333rN0F4QF+flB6dIq6fHIkTQvNJ0DxapA9HU48FLOirXEhsDN9ep+ZdNX1sq0ZOHz9NOqJt3Ro3DhQi5O4N1GfZRATAiR4p9/VP7ykiUq6Jo8WaVA1Klj7Z5lzuKBWN++fRk3bhyNGzfmrbfeYv78+bz99ts0a9aMN998kz5pq3qKQkeneyhPTM/eDVovVSser6+CSwtMP+nV31SOmKc/eNY3+TAJxAofL6/U71euRsX0I2Ih+2QDcCEecdHRMHYsdO0KN25A1aoqbeajj8Ah+01ZrCZfBujefPNNtmzZQmJiIlu2bCEhIYHNmzfzhrWLdwizyJAnpleyKTT4WN0/Mg7Cz5p2Qv1qSb/hJvfhxg04d069++nQweTDRAGQp+nJ4g3AzjVlA/Az5uyWEKIQOXQIGjeGb79Vj195RdUIa9HCqt0yiV1+Xcjf3x9/f//8upzIR2lHxDTtoVUotd6A21vgzlbY+yx02w+2jsZPFnoCQo+BjQNUetbkPuir6TdpAsWL5/hTEFb0xBMwahT89x+cOQO1a+fgYBs7VTLlzr9qpW7xupbqphCiAEpIUDUoP/oIkpJUDcmffoIePazdM9PlakRszZo1XL582dx9EYVU48Zq2PfuXbh06aEXdTbQ8mdw9FKbgh9/O+uTGWqH9QHHkib3QaYlCy9PT+jeXd3P0/Sk5IkJ8Ug5d04Vhp4yRQVh/fvDqVOFKwiDXAZiTz/9NNWqVcPDw4O2bdvy2muvMX/+fA4fPkysYemceFQ4OqqRKHgoT0zPpSy0SNlM8txMuPVX5idKis9V7TBNk0CssNPvPblsWS424fWSQEyIR0lyMsyerVZCHjqkZkF++029kStp+vt3JT7UpOLjlpSnHLHIyEj27NnDnDlzGDVqFM2bN8fNzY06deowaNAgpk+fzt9//82dO3fM1d8cCQ0NZfDgwXh4eODh4cHgwYMJCwvL8hhN05gyZQply5bF2dmZDh06cPr06XRt4uLieO211/Dy8sLV1ZU+ffpw48aNdG0qVaqETqdLd3v77WxGgwoxo3lieuUeh+qvqfv7hma+UfPtvyAuBJx8oEx3k68dEABBQeDsDC1b5qzfomDo00dtc3X+PJw4kcODvVuqkdeoKxBz2yL9E0IUDDduqBH0115T29l17apGwZ41PZNFiQ+HU1NhbSUI/M0SXTVZrgIxNzc3NE2jYcOGfPXVV8yePZsRI0bQpEkTHB0dCQgIYNmyZUyaNImePXtStmxZfHx86NGjR74GI4MGDeL48eNs2rSJTZs2cfz4cQYPHpzlMdOnT2fGjBnMnj2bQ4cO4ePjQ9euXYmMjDS0GT9+PGvWrGHZsmXs3r2bBw8e8Pjjj5OUlJTuXB9++CG3b9823CZPnmyRz7MgyHTl5MMaTYfi9SHuLuwbknGV2+WUUbPKg1Xuj4n0o2Ft2qh/5qLwcXODnj3V/RxPT9q7g4dsAC5EUaZpatSrXj31N9/ZWSXmb9qUw7qRCQ/g9DRYVxlOTYGECLix2lLdNo2WCyEhIdprr72mOTg4aDY2NtrAgQO1S5cuaZqmacnJydq5c+e0FStWaO+++672+OOPa76+vppOp9N0Op1mY2OTm0vm2JkzZzRA279/v+G5ffv2aYB29uzZTI9JTk7WfHx8tM8++8zwXGxsrObh4aHNmzdP0zRNCwsL0+zt7bVly5YZ2ty8eVOzsbHRNm3aZHiuYsWK2tdff52nzyE8PFwDtPDw8DydJz8EBWkaaJpOp2mhoVk0DDutacucNW0Jmnbmi9TnY+5o2m926vnQ/3J07d691bU//zxXXRcFxIoV6vtYubKmJSfn8OCDo9XPzuHxFumbEMJ67t3TtP791d8H0LSmTTXNyL9x4xKiNe3Ml5q2ylv9rViCpq2vpWlXV2hacpLZ+5yT/9+5CsT0Ll26pPXv31+zsbHRHB0dtXHjxmn37t3LtO39+/e1bdu2aTNnzszLJU22YMECzcPDI8PzHh4e2k8//ZTpMZcuXdIA7ejRo+me79OnjzZkyBBN0zRt69atGqDdv38/XZv69etr77//vuFxxYoVNR8fH61EiRJagwYNtI8//liLi4vL0edQmAIxTdO0KlXUL0maeDRz5+epX4Kl9pp277B6LuBr9dxfTXN0zfh4TXNzU9c9ciRX3RYFRFSUprm6qu/lgQM5PPjKkpSfnyYW6ZsQwjr++kvTypRRfxdsbTVt6lRNS0jIwQkSYzXt7CxN+90nNQBbW1XTLv+iaUmJFut3Tv5/5ylHzM/Pj+XLl3PgwAFatmzJrFmz8PPzY9q0aRmS9j09PenQoQPjxo3LyyVNFhQURKlSpTI8X6pUKYKCMslPSjkGoHTp0umeL126tOG1oKAgHBwc8PT0NNoGYNy4cSxbtoxt27YxZswYZs6cyejRo7Psc1xcHBEREeluhUm2eWJ6VV8C36dU0dY9z6qhYv20ZBXTa4eBStSMjIQSJaBhwxx3WRQgLi7Qu7e6n+PpSf3KydBjkBhl1n4JIfJfVBSMHg2PPaY27a5ZU1XHf/99sDMlcyUpHi58D+urwpGxEBuk9qdtvgAeD4DKz4NNwdjn2iwFXZs0acK2bdtYv349vr6+vPvuu1StWpWffvoJLcdLoLI2ZcqUDEnwD98Op+z8rMtkW3VN0zJ9Pq2HXzflmIfbTJgwgfbt21O/fn1GjBjBvHnzWLBgAffu3TN6jmnTphkWFnh4eODr65vlNQsak/LEQBUaa/YjuJSHyAuwoxeEnVS1wyoOzNE19fXDOncuePuHiZzTF3ddsUKtjDKZSwX186QlyQbgQhRy+/erN9Zz56rHY8eqbdD0q/OzlJwIlxbChhpw6GWIvgHO5aDpXHj8PFR5IUc5yPnBrP+6evXqxcmTJ5k/fz46nY6RI0dSv359Nm7caLZrjBkzhoCAgCxvdevWxcfHJ9PVmnfv3s0w4qXn4+MDkGHELDg42HCMj48P8fHxhIaGGm2TmRYp5X0vXrxotM2kSZMIDw833K5fv260bUHUOmVQ4sABSMxuNbBjCWj5K6CD4J3qufJPgINnVkdloE/U79w5R4eJAqpHD3B3Vyujsg3o09LppIyFEIVcfLzaF7J1a7h4USXhb9kC33yjkvOzlJwEV5bAn7XhwAsQFQhOpaHxN9DnIlR7GWwL6D5HlpgbTUxM1I4cOaK1aNFC0+l0WqlSpSxxmSzpk/UPpEk22b9/v0nJ+p+nyfqOi4vLNFl/+fLlhja3bt3KkKz/sPXr12uAdvXqVZM/h8KWI5aYqGnu7mou/9gxEw86Pjl13v7mXzm6XmSkptnbq+tdvJjj7ooCasgQ9T0dMyaHB56dpX6O/u1hkX4JISzn9GlNa9QoNSH/+eezWfill5ykEu431E79X7LKSy0GS4iydLeNysn/7zyNzyUkJHDu3DnOnDmT7nbx4kUSEhIM05K2tvk/D1urVi169OjByJEj+f777wF46aWXePzxx6lRo4ahXc2aNZk2bRpPPvkkOp2O8ePH8+mnn1KtWjWqVavGp59+iouLC4MGDQLAw8ODF198kf/973+ULFmSEiVKMHHiROrVq0eXlGqi+/btY//+/XTs2BEPDw8OHTrEhAkT6NOnDxUqVMj3r0V+sbVV+3pt3qzyxEzK2ar3ATy4DMnx4JOzaqy7dqntLSpVAj+/3PRYFEQDBsDPP8OqVTBzpvq5MknaDcCTkwpM/ocQwrjkZDXiNWkSxMWpfN/vv4d+/bI5UNPg5jo4+QGEpRQfdPCEWhNVzUp7N4v33VxyFYg99dRTnDlzhsuXL5OUlJQuD6xChQp069aNevXqGW5pA5/8tGTJEsaOHUu3bt0A6NOnD7Nnz07X5ty5c4SHhxsev/nmm8TExDB69GhCQ0Np3rw5mzdvxs0t9Zv69ddfY2dnR//+/YmJiaFz584sWrTIEHA6OjqyfPlypk6dSlxcHBUrVmTkyJG8+eab+fBZW1erVioQ27sXXn3VhANs7KD1klxdS58f1qXLQ/tbikKtSxe17VFQEOzcCR07mnhg8fopG4CHQ/hp8Kxv0X4KIfLm6lUYNgy2b1ePH3sMFixQ+0UapWlwexOcfB/uq3xw7N2hxgSoOQEcPCzca/PTaVrOs+ltbGzQ6XT4+/vTvHnzdEFX2oBF5E1ERAQeHh6Eh4fj7u5u7e6Y5J9/VKXjSpXgyhXLXqthQ1WFfenS1C1yRNEwciTMnw8vvaTeHZtsaxe1wXzT76DaKxbrnxAi9zRNjXqPHQsREWrF9IwZ6vc9yzfVQf/CyfcgJCWB1M4Vqo9Vo2COJfKl76bKyf/vPE1NHj9+nAcPHhASEsL9+/cJCwujfv36lM9RmVtRlDRrplYvBgbCrVtQtqxlrhMcnLoVTqdOlrmGsJ4BA1Qg9vvvak85e3sTD/RuowKxu3skEBOiALp7F0aNgjVr1OOWLVVQVrVqFgcF71IjYMHb1WNbJ6j2KtR+C5y8Ld1li8tVIDZ69GhOnjzJyZMnOXfuHOfOnWPlypWG14sXL06DBg3S3erUqYODQwFdsSDMxt1dbUFx4oSansx2nj+X/v1XfWzQADIpFycKuQ4dwNtb/dH+91+1t5xJvGXlpBAF1YYNMGIE3Lmj3lxNnQpvvJFFXbCQA2oELGiLemzjAFVHQZ1J4JzV/GXhkqtALG2e1ZUrVzhx4oThdvz4cQIDA9m+fTvbt2831NaytbWlRo0aNGjQgF9//dU8vRcFUqtWlg/E0uaHiaLHzk797Mydq4q7mhyIebVI2QA8EKJvgks5S3ZTCGGCyEh4/XU1yg1Qpw78+msWC7ruH1UjYLf+VI91dlDlRajzLrgWrvqapshVjlh2IiMj0wVmJ06c4PTp08TExKDT6TJsji0yVxhzxACWLIHnn4fmzVVhPnPTNKhcWSV6btyoEjxF0bNzJ7RvDx4e6h20o6OJB/7VCEKPQ+vlULG/JbsohMjG7t0wZIjKGdbpVED28cfg5JRJ47BTahXkjZR5S50tVB4Cdd+DYpXztd95lW85Ysa4ubnRpk0b2rRpY3guOTmZ8+fPc0Kf2COKLH2F/aNHISbGhEJ8OXT5sgrC7O2hbVvznlsUHG3aqBzDW7fUSlz99kfZ8mqtArG7eyQQE8JK4uLUdkRffKHePFesCIsXqzdXGYSfhVNT4NoKQAN0UGkQ1P0A3Kvlb8etIN82hbGxsaFmzZoM0O9hIoqsSpXAx0fV+ErZbcqs9NX0W7aEYsXMf35RMNjYwDPPqPs52nvSO+UNYIjkiQlhDSdPqoVb06erIGzYMPVchiAs8hLsHQIb68C15YAGFZ6BXv9Bq18fiSAM8jEQE48OnS4H+07mgj4Qk/ywok9flmTtWjW6ahLDBuDH1YbyQoh8kZSkgq+mTVXg5e2tVkcuXKgWchlEXYUDI9R+kIG/gJYM5fvCY8ehzQrwqG2tT8EqJBATFqHfd9LcgVhycuqKSQnEir7mzdWUxoMHKh/QJK6+4OIrG4ALkY+uXFGrnd96S+0Z2acP/PcfPPFEmkbRN+HQaFhfDS4tUL+jZR6D7oeg3R/g2cA6nbcyCcSERaQdETPncpDjx+H+fXBzU++6RNGm00H/lDSvnE1PShkLIfKDpqlq+PXrq8T8YsXU4z/+SFNaKCYIjoyHdVXgwlxIToDSnaHrXui4EUo2seJnYH0SiAmLaNRIrXILCYELF8x3Xv20ZIcOWdSeEUWKPq10wwY1MmYSL30gttsifRJCqNXMffuq2mAPHqjFUydPwgsvpFTIjw2BY2/COj849w0kx4F3W+i8HTr/A94trfwZFAwSiAmLcHSEJilvcsw5PSn5YY8ef39VdTsmRgVjJnl4A3AhhFmtWQN168L69eDgoFZHbtumSgsRHwonJsO6yhDwBSTFQMnm0HEzdNkBpTNbOvnokkBMWIy588RiY9XQN0gg9ijR6VJHxZYtM/Gg4vXAzg0SIyH8P4v1TYhHTXi4WgX51FNqxqNBA7U6fuJEsE2OgFMfwtrKcPoTSHwAno2g/Qbotg/KdM1mM8lHkwRiwmL0eWJ7zJSms2+fGhUpUwZq1TLPOUXhoA/E/vpL/SPIlo2dqrIPkicmhJls365ywRYvVuVl3n4bDhyAerWi4PRnKgA79QEkhINHXWi7GnocgXK9JADLglmzbKKjozl8+DC3b98mLi7OaLshQ4aY87KigGqZMv1/5gyEhoKnZ97Op5+W7NxZfqcfNXXrquA7IECVsjDpT4h3a7VH3d09UH20xfsoRFEVGwvvvANff60e+/mpjbpbN49RyfdnPoO4u+pF95pQb4qqB6aTsR5TmC0Qe//99/n666+Jjo422kbTNHQ6nQRij4hSpVRuz8WLaqujvG5FJPlhjy6dTtUU++ADNT1pciAGkrAvRB4cO6a2rDtzRj0eORJmfBFHsTvzYf0nEHNbvVCsCtT7ACoOAhtb63W4EDJLIDZ9+nQ+/vhj7OzsePzxx6levTrFpOS5QOWJXbyo8sTyEoiFhaVW6e/c2SxdE4XMgAEqENuyBe7dg5IlszmgZHP1jjz6GkTfAJfy+dJPIYqCxET4/HOYMkXdL10afpqfQM+aC2HbxxB9XTV0qQD13ld7QtrYW7XPhZVZArEff/wRZ2dndu3ahb+/vzlOKYqIVq1UPkFe88S2b1fFXGvUgPLy//SRVKOGSgw+cUKt2BoxIpsD7N2geAMIPZay76RsryaEKS5ehMGD1UwGwDP9Elkw+VfcAj+Eg1fUk87loO674Pci2DpYr7NFgFkmcK9fv0779u0lCBMZ6BP2DxxQ76pyS6YlBaQm7Ztc3FW/76Qk7AuRLU2DefPUG579+6G4RxI7f/mN5UPq4HZ6OERdAafS4D8T+lyEaq9IEGYGZgnEfHx8zHEaUQTVrg0eHhAdrQr95ZYEYgJSA7F//1XFJLMlFfaFMMmtW9CzJ7zyCsTEJPPBC79zZ1ED2to8hy7yPDiWhIbToc8lqDkObJ2s3eUiwyyB2MCBAzl48CChoaHmOJ0oQmxsUldP5rae2I0bcO6cOleHDmbrmiiE/PzU1lbJyfD77yYcoA/Ewo5DQqQluyZEobViBdSrB5s2aTzdfB1BCxszpXM/HKJPg31xqP8x9LkCtd8AO1drd7fIMUsgNmXKFGrXrs1TTz3FpUuXzHFKUYTktZ7Y1q3qY5MmULy4WbokCrEcTU+6lFfJxFoy3Dtg0X4JUdiEhsJzz8GAARpNy2/iv6+as2psX0rZH1cFkeu+B32vqFwwezdrd7fIMkuyfs+ePUlOTmbPnj3UrFmTSpUqUb58eXSZFHvS6XRs1f9nFY+EtBuA54ZMS4q0+vdXVbx37YKbN6FcuWwO8G4NV6+p6Ukf+SESAtTf1WHDoJrHNnZ/8B6tq6e8U7Z1gRpjodZENR0pLM4sgdj27dsN95OSkrh06ZLRkbHMgjNRtDVrpqYVr11T04w5WfWoaRKIifR8fVVZlD17YOVKGD8+mwO828DVpZInJgQqX/ftt+Ho37v5eej7dKqzTb1g6wTVRkPtt8CplHU7+YgxSyB25coVc5xGFFFubmoVzrFjapuiZ54x/dgzZyAoCJydU3PNhBgwQAViy5ebEojpNwDfrzYAl2KT4hF16BBMf+sgI5q9x6wPNgOg2Tigq/oS1J4ELmWt3MNHk1kCsYoVK5rjNKIIa9VKBWJ79uQsENPPYrdtC06ySEek6NcPxo1TS+yvXoUs/wR51AV7d0iIgPBT4Nkwv7opRIGQkAALvjxG+fvvs3LEBgCSscOmynB0dSeDawUr9/DRJhtBiXyR2zyxtPtLCqFXpkzqCtoVK7JpbGMLJWUDcPFounzsNDs/7cfLFf15vNEGkjUb4soNw6bPOWj+gwRhBUCuRsT8/PzQ6XT8888/VK5cGT8/P5OP1el0srLyEaQPxI4dUzkKLi7ZH5OQoCrqg+SHiYwGDIBt29Tek2+8kU1j79YQtFntO1n91XzpnxDWlBx2jgurp1LNYRl+1TSSk3Vcs3mWSr0/wNG9urW7J9LIVSAWGBgIQEJCQrrHQhhTsSKULauKBh4+DO3aZX/MoUMQGQklSkDDhhbvoihknn4aXn0Vjh5VW7JUrZpFYynsKh4VkZeIOvAhTkG/UsMpGYCdgf2o0W8KlarXsXLnRGZyNTWZnJxMcnIy1atXT/fY1Jt49Oh0Oa8nps8P69xZrboUIi0vr9Qp62xripVsDjpbtVFx1HWL902IfBd1De3ASJLX1cQ1+GdsbZL583hvlkceo+2klZSWIKzAkn9vIt/kNE9M8sNEdgYOVB+XLcumoX2x1CR9GRUTRUn0LTg0Bm1dNXSX5mOjS+SvEz0YvuwA1UasY8CohkjVqIJNAjGRb9IGYpqWddsHD1SpC5D8MGHcE0+AvT38958qdZIlL30ZCwnERBEQcweOvA7rq8CFOei0eLb+14n2H+/mcLG/+HF1M6pLKlihIIGYyDeNGqkSFPfvw/nzWbfdtUsl61eqpPYXFCIznp7Qvbu6n+30pCFPbLdF+ySERcXdg2NvwTo/OPc1JMWy+1xrOn7yL2PWbOWrn1vz3ntgZ5biVCI/SCAm8o2Dg9qwGbLPE0tbTV+G1UVW0u49meVIq2ED8JOyAbgofOLD4OT7sLYSBEyHpGhO3GhK98820fbDXTTs1pGjR9WevKJwkUBM5CtT88TSJuoLkZU+fdRI67lzcOJEFg1dyoFrJbUBeMj+/OqeEHmTEAn/fQxrK8N/H0HiA27FNKTvjHU0fOsAAWHd2bpVx9dfqx1IROEjgZjIV6YEYsHBqf9QO3WyfJ9E4ebuDj17qvumT09Knpgo4BKj4Mx0WFcZTr4HCWHEOtVh4tpVlB95hHVHejN4sI6TJ+XvZGEngZjIV/pALCBA5Ypl5t9/1ccGDaCU7D0rTJDj6UlJ2BcFVWIMnP1a5YAdfwvi7qG5VefPsN8o8dwJvlrxNCVK2LBqFfz8MxQvbu0Oi7ySQEzkKy8vDCt59KsiH5Y2P0wIU/TqBa6ucOWKKhhslFfaDcAT86VvQpgkKQ7Ofwfrq8LR1yE2GIr5EVJtEV1mnObxV58lJtaWnj3h1ClV0FgUDRKIiXyX1fSkpkkgJnLO1RV691b3s6wp5lFHbQCe+EAl7QthbckJcPFHWF8dDr8KMbfAxRet6Q8svn8Wv85D+Xe7Ha6u8MMPsGGD2mtVFB25CsReeOEFfvrppxwf9/nnn9NJJrMfeVkFYpcvw9WrqjZU27b52y9RuOmnJ1esAKMbeNjYgldLdV/yxIQ1JSfC5cWwoSYcfAmir4FzGWgym7stL/D0WyMZNtyeyEj1N/PECRg5UlaRF0W5CsQWLVrE7t05r8Vz9uxZduzYkZtLiiKkdcrs0MGDqlZYWvrRsJYt1SiHEKbq0UMl7t+4YXzaGwDvNuqjBGLCGrRkCFwKG+vC/mHw4DI4lQL/GdD7EuvPvUrd+o6sWaPekE6bBjt3QpUq1u64sBSzT03eN5aBLUSKmjVVgml0dMZyAzItKXLLyUlV2odsVk9Kwr6wBk2D66thYwPYOwgizoFDCWj4GfS5TGS5CYx42Zk+fdTK8bp11ZvVt98GW1trd15YklkDsS+//JJy5crx0UcfyebewigbGzXiBemnJ5OTU1dMSiAmckM/PblyJSQlGWlUslnKBuA3IOpavvVNPKI0DW6sh02NYdfTEP4f2HtAvQ+h7xWo/Ra79rlSvz4sWKCmHidOhEOHoGFDa3de5AezBGJJSUmMHDmSt956i7i4OKZMmUK7du0IDAw0x+lFEZRZntjx46qkhZtbagV+IXKiSxe17VFQkJrOyZSdK3g2UvdluyNhKZoGt/6GzS1gZx8IPQZ2xaDOZBWA1XuPuGR33nwT2reHwECoWBG2b4cvvlAjvOLRkOdA7P79+3Tt2pUFCxZQtWpV1q1bR7169di7dy8NGjRg8eLF5uinKGL0eWJpAzH9tGSHDrJPmsgdBwd46il136TpSckTE5ZwZzv80w6294B7B8HWBWq9CX2uQIOPwMGTEyfUG84vvlAx2wsvwMmT0K6dtTsv8lueArHz58/TokULtm/fTqdOnThw4ACPP/44hw4d4vXXX+fBgwe88MILPPPMM4SGhpqrz6IIaNpU5T1cv65uIPlhwjwGDlQff/8942IQAwnEhCXc3QtbO8PWjmq01cYRaoyHPpeh0efg5EVSEnz+ufobeOoUeHvDH3+oaUl3d2t/AsIach2IHTt2jJYtW3Lx4kVGjRrF33//TfGUEr/29vZ8+eWXbN68mTJlyrB69Wrq1avHiSw3ghOPkmLFVOV8UKNisbGwa5d6LIGYyIsOHdQ/t5CQ1JzDDPSFXcNPQUJEfnVNFFX3DsG2x2BLa7jzL9jYQ7XR0OcSNP4anEsDqjxP+/YqAT8hAfr2hf/+Ux/FoyvXgdjJkyeJiIhg5syZzJ07F9tMlnV07tyZU6dO8eSTT3Lr1i0JxEQ6afPE9u1TwViZMlCrlnX7JQo3Ozvo10/dNzo96VIWXCvLBuAib0KPw44+8HczuL1JLQKpMgJ6X4Cmc9RG86ipxx9/hPr1Yc8elQe7cCGsWSPbuIk8BGJubm5s2LCBsWPHZtnO09OTVatWMX/+fFxcXHJ7OVEEpc0T009Ldu4sBQtF3ulXT65ZA3FxRhrJ9KTIrbDTsOsZ+KsR3FwPOhuoPAQePwfNfwTXioamQUHQpw+89BJERakcsJMnYdgw+VsnlFwFYsnJyYSFhdG9e3eTj3nhhReIjIwkyeiacvGo0Y+IHTsG69ap+zItKcyhTRsoWxbCwmDzZiONDIGYrJwUJoo4D3ueg4314PoqQAcVB0LP09ByMbilr7q6erWqB7Zhg1pI8uWXsG0bVKpkld6LAkr2mhRW4+sL5cqpek///aee69zZun0SRYOtLTzzjLpvdHpSH4jdOyAbgIusPbgM+4fDn7Xg6m+ABr5PQc+T0HopeNRM1zw8HIYOVRtz37un8mGPHIH//U/VURQirXz7kThz5gzvvPMOFStWzL6xeCTodKmjYgA1akD58tbrjyha9NOTa9dCTEwmDTzqqMKaiVEQJvmrIhNR1+DAS7C+BlxepHIKyz4OPY5C29+heN0Mh/z7L9SrBz//rIKuSZNUhfy6GZsKAYBFqzXduXOH3377jV9//ZXjx49b8lKikGrdWlVBB5mWFObVogVUqADXrsFff6XWFzPQ2YBXK7j9l8oTK9HYKv0UBVDMbTj9KVz8AZLj1XM+3aD+h+DVPPNDYuCdd2DmTPW4ShUVjKV9sylEZsw+IhYdHc2SJUvo0aMH5cuXZ+LEiRw7dgx3d3c8PDzMfTlRyKX9IyWBmDAnnS51VGzZMiONJGFfpBUbDEf/B+v84PxsFYSV6gBddkKnv40GYUePQpMmqUHYqFFqpxAJwoQpzBKIaZrG5s2bGTJkCKVLl2bIkCFs3rwZOzs7nnzySVatWsWdO3dooC8cJUSKhg3VdGSJEqr+kxDmpA/ENmyABw8yaZA2YV/T8q1fooCJuwfHJ6kA7OwMSIpVo6WdtkKXbVCqbaaHJSbCxx9D8+Zw5gz4+MCff8K8eapWohCmyNPU5PHjx/nll19YunQpd+7cQdM0bGxs6NixI4MGDaJfv364S6lgkQV7e5U/kZgIKfWAhTAbf381RXTpkgrG9FX3DUo2A50dxNyCqKtQrJI1uimsJT4Mzn6tbomR6rkSTaD+R1Cme5b1JS5cgCFDYH9KGbp+/WDuXPDysny3RdGSq0Ds888/55dffiEgIAAt5V2kv78/gwYN4tlnn6VMmTJm7aQo2uTHRViKTqeCr08+UasnMwRidi5qA/D7h9T0pARij4aESDg3CwK+hIQw9VzxBioHrFzvLAMwTVMB1xtvQHQ0eHjA7Nnw3HNSF0zkTq4CsUmTJqHT6fDx8WHEiBEMGjSIGjVqmLtvQgiRZwMGqEBs40ZVViBDqqp3GxWIheyBys9ZpY8iH11fAwdHqulIAI/aUG+qKkehyzpb59YttTn333+rx507qwr5vr4W7rMo0nKdI6ZpGnfu3GHHjh3s2rWLsLAwM3ZLCCHMo25dtW1WfLwqZZGBJOw/OgJ/g939VBDmVg1aLYHHTkKFftkGYcuXq5+lv/8GJyf45htVLFiCMJFXuQrE9u/fz+jRoylRogQ7d+5k1KhR+Pj4GBLz44zuKSKEEPkr7erJTIu76gOxsFMQH55v/RL57PJi2DdY1QLzGw69zkClQWCTcZ/ktEJDYdAgNa0dGgqNG6tVkmPHSnFWYR65+jFq1qwZs2fP5tatW/zxxx889dRT2NjYsHbtWgYMGECpUqUYPnw4W7ZsMeSQCSGEtegDsc2b4f79h1509oFifoAGIfvyu2siP1ycryrja8lQ9SVoPh9sss/M2bJFFWddulTt1vD++7BvnxphFcJc8hTP29nZ0adPH1auXElQUBDff/89rVu35sGDByxevJgePXpQtmxZJkyYwKFDh8zVZyGEyJGaNdU2M4mJav+/DLxkerLIujBX5YShQbVXoem8bKcho6NhzBjo1g1u3oTq1WHvXpg6Va30FsKczDaw6u7uzsiRI9m5cyeXL1/mww8/pHr16ty5c4dZs2bRokULdu7caa7LCSFEjmQ5PVmqjfoYIoFYkXJuFhware7XGA9Nvs12aePBg9CoEcyZox6PGQPHjkGzZpbtqnh0WWSGu2LFikyePJmAgAAOHDjA6NGj8fLyyvdpytDQUAYPHoyHhwceHh4MHjw420UFmqYxZcoUypYti7OzMx06dOD06dPp2vzwww906NABd3d3dDpdpufMzbWFEJajD8T+/ReCgx96UT8iFnIAkhPytV/CQgK+giPj1P1ab4L/jCyDsIQE+OADVQ3//HkoV04l5n/7Lbi45FOfxSPJ4qmGTZs25dtvv+XWrVusXbuWfv36WfqSBoMGDeL48eNs2rSJTZs2cfz4cQYPHpzlMdOnT2fGjBnMnj2bQ4cO4ePjQ9euXYmMjDS0iY6OpkePHrzzzjtmvbYQwnL8/KBpU0hOht9/f+hFj1pgXxySoiFUNgAv9E5Pg2MT1f06k6HhZ1kGYWfPqgDsww8hKUkl5586paYmhbA4rYg6c+aMBmj79+83PLdv3z4N0M6ePZvpMcnJyZqPj4/22WefGZ6LjY3VPDw8tHnz5mVov23bNg3QQkND83ztzISHh2uAFh4ebvIxQgjjvvxS00DT2rXL5MVtPTVtCZoWMDPf+yXMJDlZ005OUd/HJWjayQ+zbJ6UpGkzZ2qak5P6ufD01LRly/Kpr6JIy8n/7yK7+Hbfvn14eHjQvHnqJq0tWrTAw8ODvXv3ZnrMlStXCAoKoluat0GOjo60b9/e6DHmurYQwvL691cfd+1SxTnTSbvvpCh8NA1OvgenpqjHDaZBvfeMNr9+XY14jR8PsbHQvTv891/qFLYQ+aXIBmJBQUGUKlUqw/OlSpUiKCjI6DEApUuXTvd86dKljR5jrmsDxMXFERERke4mhDAfX181BaVpsHLlQy8a8sT2yAbghY2mwfG34PQn6nGjL6HO20ab/vqrKkuxdavK//ruO/jrLyhbNh/7LESKQheITZkyBZ1Ol+Xt8OHDAOgyyQnQNC3T59N6+HVTjsnuHKacZ9q0aYbkfg8PD3ylZLMQZqffb3LZsodeKNkUbOwh5jZEBeZ3t0RuaRocnQABX6jHjWdBrf9l2jQqCgYPVrfwcGjeHI4fh1dekX0ihfUUukBszJgxBAQEZHmrW7cuPj4+3LlzJ8Pxd+/ezTDipefj4wOQYdQqODjY6DHGzpPTa4PawzM8PNxwu379usnXFEKYpl8/9U93/364ejXNC3Yu4Omv7ks9scJBS4bDY+DcN+px07lQ47VMm54/Dy1awJIlqjjrRx/B7t1QrVo+9leITBS6QMzLy4uaNWtmeXNycqJly5aEh4dz8OBBw7EHDhwgPDycVq1aZXruypUr4+Pjw5YtWwzPxcfHs2PHDqPHZCY31waVj+bu7p7uJsT/27vzuKzK/P/jr5sdDe4UUNzKZdzKyVRyV9yXTGvMyUaznJ86mVmaU31t+YVN27RY/ZwWp75GTbmlSTmOGqaCimKKMJqauWamuKACiuzX748DJIoKCBxueD8fDx6P+z73OdyfC70Pb851neuSslWvHoSGWo+//PKSF7XupOswufD9RNj7AeCATnOg+cQid12yBEJCrDFgwcGwdi08/zx4XHtyfZFy53JBrLhat27NoEGDmDBhArGxscTGxjJhwgTuuusuWrZsWbBfq1atiIiIAKzuxKlTp/Lqq68SERHBDz/8wNixY6lRowajRo0qOCYxMZGEhAT27dsHwI4dO0hISOB03topxX1vEbFHfvfkZZO7Bl00Tkwqr9wc2DwO9n9szZLf+VNo9n8u2y07G556Cu69F1JToWdPa3LWHj0qvmSRKyrvWzjtlJSUZEaPHm38/PyMn5+fGT169GVTTQAmPDy84Hlubq4JCwszwcHBxtvb2/Ts2dPs2LGj0DFhYWEGuOzr4u9TnPe+Fk1fIVI+Tp40xt3dmrJg796LXkhLzJv6wGFMxhm7ypOryckyZsMo699pnrsxB+cWuduxY9Y0JdYgMmOefNKYzMwKrlWqrZL8/nYYo9uDKquUlBScTifJycnqphQpYwMHWouAv/wyPPfcRS8s/R2c2w+9lkP9wbbVJ0XIzYKND8DhL8HhAd3mwU1/vGy3DRusqUqOHQM/PwgPt66KiVSUkvz+rrJdkyIiV3PFtSeD8tad1DixyiUnE2Lut0KYmyd0X3RZCDMG3nkHevWyQtitt8KWLQphUrkpiIlItfSHP4Cnp7WUza5dF72gAfuVT04GbBgBvywBNy/oEQGN7im0S2qqFa6nTfttmaLNm0HDcqWyUxATkWqpVi2rexIuuSqWH8SStAB4pZB9AdbdA7/+G9x9oOdSaDCk0C67dkHHjtYkvZ6e1kLdX3wBNWvaU7JISSiIiUi1dXH3ZMFoWf9W4FULci7A6XjbahMgOw3WDYNjK8HdF0KXQf2BhXZZsMAKYT/+CA0aQHQ0TJ6sCVrFdSiIiUi1NWwYeHvDnj2wfXveRocbBObN96dpLOyTdQ6ihkDid+BRE3qtgOC+BS9nZsKUKfCnP1kz5vfta01N0aWLjTWLlIKCmIhUW/7+MCSvl6tw96QG7NsqKxWiBsOJKPDwg97fQt3Qgpd//RV694ZZs6znzz4L334LQUH2lCtyPRTERKRay++eXLDgou7Jiwfsa4afipWZDGsGwMkN4OmEPqt++/fAmhW/fXvYuBGcTvjmG3jlFWvZIhFXpCAmItXakCFQowYcPAhbt+ZtrB1iTZGQngjnD9paX7WSeQbW9IOkWGucXt/VENgJsPLw669Dv35w4gS0bQtxcVb3sogrUxATkWqtZk0YOtR6XNA96eELtTpYj9U9WTHST8HqPnB6K3gHQN+1UNv6N0hOtqYbmT4dcnNh7FjYtAmaNbO3ZJGyoCAmItXexWtP5ubmbSzontxgS03VSvoJWNMHziSATx3oGwW12gLWTRQhIVYXpJcXfPQRfPIJ+PraWbBI2VEQE5Fqb9Aga+D+kSPWlRZAE7tWlAuJsLo3nN0BPsFWCLuxDQCffw6dO8O+fXDzzRATAxMmaGoKqVoUxESk2vPxgbvvth4XdE/mB7HkndbYJSl7ab/Cd6GQvAt8G0C/aHC2JiMDHnkEHnwQLlywgnJcnHVlTKSqURATEeG37slFi6wlcvCpA37NrY0nN13xOCml84etEJb6E9S4yQph/i04fBh69IDZs60rXzNmwH/+AwEBdhcsUj4UxEREsO7Gq1ULEhNh3bq8jflXxTSxa9k6d8gKYef2Q80mVgjza0ZkpDU1xZYtULs2LF8OYWHgpt9UUoXpv7eICNZA8OHDrccF3ZOBGrBf5lL3w3c94fwhuKEZ9Ismt0ZjXnrJ6oJMSoIOHayuyEGD7C5WpPwpiImI5Mmf3PWrryA7m4sWAP8ecjJtq6vKSPnJCmFpv4B/S+gXzemMRgwdCi+8YM0V9pe/wIYN0Lix3cWKVAwFMRGRPL17W8vknDoFa9ZghQWv2pCTDme0APh1Sd5tdUdeOArOW6BvFNt+bECHDlYXpI8PhIfDP/9pPRapLhTERETyeHjAiBHW4wULsBYA1zQW1+/sDiuEpSfCjbdB3yjmzAuma1c4dAiaNrWmDRk71uY6RWygICYicpH87smICMjMRAP2r9eZBGuesIyTUKsdF7quYdyjQYwfDxkZ1qoGcXFw++12FypiDwUxEZGLdO8O9erB2bMQGclFA/a1AHiJJW21li3KSILad3Co2Wq69Qngk0+sOyFffRW+/hpuvNHuQkXsoyAmInIRd3e47z7r8cKFQEAIuHlB+nFrugUpnlObrQW8M89AYBdWZq2iXadaxMdb4/AiI+GZZzQ1hYg+AiIil8jvnvz6a7iQ6VOw+LTGiRXTyRhY0x+ykjGB3Xkp5lsGD3Ny9qy1ZNG2bdC3r91FilQOCmIiIpfo3BluugnOnYMVK4Cg7tYLCmLXdjwa1g6E7FQya/Xm7ndX8sLf/AB47DGIjoaGDW2uUaQSURATEbmEw3FJ96QG7BdP4mqIGgzZ50n27U+bycv494qa1KgBc+fCrFnWxLki8hsFMRGRIuSvPblsGZyv0dV6krwLMk7bV1RldnQlRN8FORf4OWswDR9Yyt4DNWjRAjZvhlGj7C5QpHJSEBMRKUL79tCsGaSlwb8jg8CvhfXCKS0Afplfl8G6uyEnnW3Hh9JifATn0nwYPtxaN7JNG7sLFKm8FMRERIrgcPw2aL9Q96TWnSzslwhYPxxyM/luz3A6P7WYHOPNW2/B4sXg7293gSKVm4KYiMgV5HdPrlgBaTU1w/5lDi+CDfdBbhZfbR3J4FcWEBDkxZo18Ne/WmFWRK5OQUxE5AratIHWra0Z4CPj8+6cPL1FC4ADHJqHibkfTDafb3iAkf/vCzp38WTbNujZ0+7iRFyHgpiIyBVc3D358cIW4B2YtwD4NnsLs9uBzzAbx+AwuYRHj2Xs7E+ZMtWDNWusVQlEpPgUxEREriI/iEVGOsh05t09WZ27J/f9Lyb2zzjI5aM1E5gyfw4Lv3Rn5kzw9LS7OBHXoyAmInIVrVpB27aQnQ0Jv1bvAfvmpw/h+wk4MLwX+SizNs1myxY3RoywuzIR16UgJiJyDflXxeZFVt8FwNP/OwvH1kkAvLNiKjEZ/yB2sxstW9pcmIiLUxATEbmG/CD2z8UhGDdvyDgJqfvsLaoCHY+aic/OKQC8+Z+n8ej4NvPmObjhBpsLE6kCFMRERK6haVMICYH0TG8SM0OsjdVkuaPtC16j7tEnAXj3u+fpNunvPPaYQ1NTiJQRBTERkWLIn1Ns7Q/VYz6xrEzDijde5LbcZwEIj3uRUX9/ia7dlMBEypKCmIhIMRQsAr6m6gexo78a5k3/vwxuOAOAlYmvMub1F6hTx966RKoiD7sLEBFxBY0aQdeuELM9bwqLlN2QkQTeAfYWVsai1hp2fDGdx/q+AcAPXm8xaNpfba5KpOrSFTERkWIaORKSzgXy85m8WwVPbrS3oDJkDLz5puG/4dMKQtjJm2bRZoRCmEh5UhATESmmP/7Rmm1/VULeckdVZMB+cjLcOzyXGrsmM2XQuwBktv2QoO6P2VuYSDWgICYiUkz16kFoKMT8VHXGie3YAR075jIoYCKP9v8AYxyYjv+L160T7S5NpFpQEBMRKYGRIy8KYklbICfD3oKuwxdfQJfOOUzvNY6/9PkYgxuOLp/i+N04u0sTqTYUxERESuDee+HAyeacSA6C3Aw47XoLgGdkwKRJMPahbGY/9BB/Dv0U43DH0fVzaPqg3eWJVCsKYiIiJRAUBH37Oti4N38BcNdad/LwYejZEz7+KIsvJj3AA93nYhweOLrNh8aj7C5PpNpREBMRKaFC3ZMuNGB/1Spo3x7i4zJZMu1+7u+yENw8cXRfBDf90e7yRKolBTERkRL6wx8gdr9152R24sZKvwB4bi688goMHAipyRmsChvB0NuXgJsX9IiARvfYXaJItaUJXUVESqhWLQhs0Z70TG98OAmpe8G/hd1lFenMGRgzBv7zH/DxvMCWmcNpE7AS3H2gx9dQf6DdJYpUa7oiJiJSCvf+0ZstB+4AwFTSaSzi46FDByuE1fJPY98nw/JCmC+ELlMIE6kEFMREREph2DCIPWCNEzu9p/IN2P/kE+jSBQ4ehFtbnuPnz4bQwO078KgJvVZAcF+7SxQRFMRERErF3x+ynFYQyz1eea6IpafDhAkwbpw1TcWIu1OJf2cwfmlR4OEHvb+FuqF2lykieTRGTESklG7p2RVyIMh7Dyb9FA6fQFvrOXgQRoyAbdvAzQ3eeDmZae0H4UiKBU+nFcICO9lao4gUpitiIiKl1P+uAHYfbQ3Avk32LgC+fLk1HmzbNggMhNUrzvDXdv2sEOZVC/quVggTqYQUxERESqlmTTiaZXVPHom3p3syJwdeeAGGDLHukOzYERI2J9Erpw+c3greAdB3DdTuYEt9InJ1CmIiItfhxt9ZQeyG9Bhycyv2vU+dgjvvhJdesp5PmgTrIk/Q4MfecCYBfOpA3yiodXvFFiYixaYgJiJyHdr0toLYbQ22sHljeoW97/ffW7PkR0aCry98/jm8/1Yi3ht6w9kd4BNshbAb21RYTSJScgpiIiLXwTvgd6RkBOHtmUns8rhyfz9jYPZs6NEDfvkFmjeHzZvhgeG/wnehkLwLfBtAv2hwti73ekTk+iiIiYhcD4eDtJrWckfnDsWQk1N+b5WWBmPHwiOPQGamtdTSli3w+6aHrRCW+hPUuMkKYZV0pn8RKUxBTETkOgXektc9WS+G9evL5z327rUmaP3Xv/KmpngDvvoKnO6HrBB2bj/UbGyFML9m5VOEiJQ5BTERkevkEWwFsW4tYli4sOwXAP/mGwgJge3boW5dWL0annoKHOf2WyHs/CG4oRn0Wwc3NC7z9xeR8qMgJiJyvWq1JwcfAv2SSFi3h+zssvm22dkwfTrccw+kpEC3btY8Yb16ASk/WSEs7TD4t7SuhNVsVDZvLCIVRkFMROR6uXvhFmgtAH5LUAxr1lz/tzx+HAYMgNdft55PnQpr10L9+kDybiuEXfgVnLdYd0fWaHD9byoiFU5BTESkDDjqWAP2re7J6/teGzdaU1OsXWtNGrtwIbzzDnh6Yk1N8V0opCfCjbdZIcw3+HrLFxGbVOkgdubMGcaMGYPT6cTpdDJmzBjOnj171WOMMcyYMYP69evj6+tLr1692LlzZ6F9PvroI3r16oW/vz8Oh6PI79m4cWMcDkehr+nTp5dh60SkUgn6bZzYkiXWXY0lZQzMmgWhoXD0KLRubd0Ved99eTucSYDVvSHjJNRqZ82Y7xNUZk0QkYpXpYPYqFGjSEhIYOXKlaxcuZKEhATGjBlz1WPeeOMN3n77bd577z22bNlCcHAw/fv3JzU1tWCftLQ0Bg0axLPPPnvV7/W3v/2NY8eOFXw9//zzZdIuEamEArsA0LL+T3jknCQysmSHnzsHo0bBlCnW2LCRI61JW1vnTwV2Og5W94GMJKh9h7V2pHdA2bZBRCqch90FlJfdu3ezcuVKYmNj6dTJWuj2448/pkuXLuzZs4eWLVtedowxhnfffZfnnnuO4cOHA/DZZ59Rt25d5s2bx8MPPwzA1KlTAYiKirpqDX5+fgQHq8tApFrwrm2N10reRdcWG1m48G7uuqt4h/74IwwfDrt3g4cHvPUWPP44OBx5O5zaDGsHQlayFfh6rQAvZ7k1RUQqTpW9IrZp0yacTmdBCAPo3LkzTqeTjRs3FnnMwYMHSUxMZMCAAQXbvL29CQ0NveIxV/P6668TEBDA7bffziuvvEJmafoqRMR1BFrdk91bbOCbb+DChWsfsmgR3HGHFcLq14eoKOuqWEEIOxkDa/pbISyoO/T+ViFMpAqpskEsMTGROnXqXLa9Tp06JCYmXvEYgLp16xbaXrdu3SsecyVTpkxhwYIFrF27lsmTJ/Puu+8yadKkqx6TkZFBSkpKoS8RcSF548T6/D6G1FRYseLKu2ZlwbRp1vivc+esKSm2bbOmqChwPNq6EpadCnV7Q++V4OlXrk0QkYrlckFsxowZlw2Cv/Rr69atADgK/qT8jTGmyO0Xu/T14hxzqSeeeILQ0FBuu+02xo8fz+zZs5kzZw5JSUlXPOa1114ruLHA6XTSqJHmBBJxKUHWnZNtG8Xh7Zl+xbsnjx2DPn2sOyEB/ud/YNUqa7LWAomrIWowZJ+H4P4Qugw8apZv/SJS4VxujNjkyZO5//77r7pP48aN2b59O8ePH7/stZMnT152xStf/niuxMRE6tWrV7D9xIkTVzymuDp37gzAvn37CAgoeoDtM888w7Rp0wqep6SkKIyJuJIbmoJPXTzSjxPSZCvLlnXn/HlrCop80dHWQPzjx8HfHz77zJqwtZCj38L6eyAnHeoNhp5LwN2nAhsiIhXF5YJYYGAggYGB19yvS5cuJCcn8/3339OxY0cANm/eTHJyMl27di3ymCZNmhAcHMyqVato164dAJmZmURHR/N6/qyKpRQfHw9QKOBdytvbG29v7+t6HxGxkcNhdU/+soRhXWKI+aw7y5ZZwcsYmDnTmik/JwfatIElS6B580u+x6/LYP29kJsJDYZC90XgrvOCSFXlcl2TxdW6dWsGDRrEhAkTiI2NJTY2lgkTJnDXXXcVumOyVatWREREAFaX5NSpU3n11VeJiIjghx9+YOzYsdSoUYNRo0YVHJOYmEhCQgL79u0DYMeOHSQkJHD69GnAulHgnXfeISEhgYMHD/Lll1/y8MMPM2zYMG666aYK/CmISIXLG7A/tHMMAAsWWMsTjRhhrQ+ZkwMPPACxsUWEsF++hvXDrRDWaDh0X6wQJlLVmSosKSnJjB492vj5+Rk/Pz8zevRoc+bMmUL7ACY8PLzgeW5urgkLCzPBwcHG29vb9OzZ0+zYsaPQMWFhYQa47Cv/+8TFxZlOnToZp9NpfHx8TMuWLU1YWJg5f/58iepPTk42gElOTi5N80XEDidjjZmLyVpQ2zgcOcbb25gWLYwBYzw9jfngA2Nyc4s47ucvjZnnYcxcjFk/0piczAovXUTKRkl+fzuMMca+GChXk5KSgtPpJDk5GX9/f7vLEZHiyMmExU7ISWfIh7tYvsGakbVRI1i8GPJGShR2aB5sGgMmFxqPhs6fgpvLjRwRkTwl+f1dZbsmRURs4e4FAdb8hY//yeqe7N/fmpqiyBB24F+/hbCmY6HzZwphItWIgpiISFnLm09sQLsYfvwRVq6EIu8x2j8HYsdaIazZBOg0B9zcK7RUEbGXgpiISFnLG7DvOBVDy5bgVtSZdu9s2DweMND8Ueg4Gxw6JYtUN/rUi4iUtSBrAXBS98KFy+czZM8s2PKI9bjlVAj5h0KYSDWlT76ISFnzqgXOW63Hpy5Zp3b3TIibYj1u/TS0f/uihSVFpLpREBMRKQ95yx1xMua3bTtfg/gnrce3Pg+3/10hTKSaUxATESkPeQP2C4LYjr/Bf5+1Hv/+RWj7kkKYiLjeEkciIi4hP4idiYP4p2H3m9bztq/Crc/YV5eIVCq6IiYiUh5qNgGfYMjN+i2EtXtLIUxEClEQExEpD/kLgOfrMAta/9W+ekSkUlIQExEpL80mQM2boeNH0PIxu6sRkUpIY8RERMpL/YFw9yG7qxCRSkxXxERERERsoiAmIiIiYhMFMRERERGbKIiJiIiI2ERBTERERMQmCmIiIiIiNlEQExEREbGJgpiIiIiITRTERERERGyiICYiIiJiEwUxEREREZsoiImIiIjYREFMRERExCYKYiIiIiI28bC7ALkyYwwAKSkpNlciIiIixZX/ezv/9/jVKIhVYqmpqQA0atTI5kpERESkpFJTU3E6nVfdx2GKE9fEFrm5uRw9ehQ/Pz8cDofd5RRbSkoKjRo14pdffsHf39/ucspddWqv2lo1qa1VU3VqK1Su9hpjSE1NpX79+ri5XX0UmK6IVWJubm40bNjQ7jJKzd/f3/YPQ0WqTu1VW6smtbVqqk5thcrT3mtdCcunwfoiIiIiNlEQExEREbGJgpiUOW9vb8LCwvD29ra7lApRndqrtlZNamvVVJ3aCq7bXg3WFxEREbGJroiJiIiI2ERBTERERMQmCmIiIiIiNlEQExEREbGJgpiUygcffECTJk3w8fGhQ4cOrF+//qr7Z2Rk8Nxzz3HzzTfj7e1Ns2bN+OSTTyqo2utT0rbOnTuXtm3bUqNGDerVq8ef//xnkpKSKqja0lu3bh1Dhw6lfv36OBwOvv7662seEx0dTYcOHfDx8aFp06bMnj27/AstAyVt65IlS+jfvz9BQUH4+/vTpUsXvv3224optgyU5t82X0xMDB4eHtx+++3lVl9ZKk1bXfX8VJq2uuL56bXXXuOOO+7Az8+POnXqcM8997Bnz55rHucq5ycFMSmxhQsXMnXqVJ577jni4+Pp0aMHgwcP5vDhw1c85r777mP16tXMmTOHPXv2MH/+fFq1alWBVZdOSdu6YcMGHnzwQcaNG8fOnTtZtGgRW7ZsYfz48RVcecmdP3+etm3b8t577xVr/4MHD3LnnXfSo0cP4uPjefbZZ3n88cf56quvyrnS61fStq5bt47+/fuzfPly4uLi6N27N0OHDiU+Pr6cKy0bJW1vvuTkZB588EH69u1bTpWVvdK01VXPTyVtq6uen6Kjo3n00UeJjY1l1apVZGdnM2DAAM6fP3/FY1zq/GRESqhjx45m4sSJhba1atXKTJ8+vcj9V6xYYZxOp0lKSqqI8spUSdv65ptvmqZNmxbaNmvWLNOwYcNyq7E8ACYiIuKq+zz99NOmVatWhbY9/PDDpnPnzuVYWdkrTluLcsstt5gXX3yx7AsqZyVp78iRI83zzz9vwsLCTNu2bcu1rvJQnLa68vnpYsVpa1U5P504ccIAJjo6+or7uNL5SVfEpEQyMzOJi4tjwIABhbYPGDCAjRs3FnnM0qVLCQkJ4Y033qBBgwa0aNGCJ598kgsXLlREyaVWmrZ27dqVI0eOsHz5cowxHD9+nMWLFzNkyJCKKLlCbdq06bKfzcCBA9m6dStZWVk2VVUxcnNzSU1NpXbt2naXUm7Cw8PZv38/YWFhdpdSrlz1/FQaVeX8lJycDHDVz58rnZ+06LeUyKlTp8jJyaFu3bqFttetW5fExMQijzlw4AAbNmzAx8eHiIgITp06xaRJkzh9+nSlHodRmrZ27dqVuXPnMnLkSNLT08nOzmbYsGH84x//qIiSK1RiYmKRP5vs7GxOnTpFvXr1bKqs/M2cOZPz589z33332V1Kudi7dy/Tp09n/fr1eHhU7V8Trnp+Ko2qcH4yxjBt2jS6d+9OmzZtrrifK52fdEVMSsXhcBR6boy5bFu+3NxcHA4Hc+fOpWPHjtx55528/fbbfPrppy7xV2dJ2rpr1y4ef/xxXnjhBeLi4li5ciUHDx5k4sSJFVFqhSvqZ1PU9qpk/vz5zJgxg4ULF1KnTh27yylzOTk5jBo1ihdffJEWLVrYXU65c/XzU0lUhfPT5MmT2b59O/Pnz7/mvq5yfqraf+pImQsMDMTd3f2yK0InTpy47K+PfPXq1aNBgwY4nc6Cba1bt8YYw5EjR2jevHm51lxapWnra6+9Rrdu3XjqqacAuO2226hZsyY9evTg5ZdfrlR/hV2v4ODgIn82Hh4eBAQE2FRV+Vq4cCHjxo1j0aJF9OvXz+5yykVqaipbt24lPj6eyZMnA1ZYMcbg4eFBZGQkffr0sbnKsuOq56fScPXz02OPPcbSpUtZt24dDRs2vOq+rnR+0hUxKREvLy86dOjAqlWrCm1ftWoVXbt2LfKYbt26cfToUc6dO1ew7aeffsLNze2aHyY7laataWlpuLkV/li5u7sDv/01VlV06dLlsp9NZGQkISEheHp62lRV+Zk/fz5jx45l3rx5LjempiT8/f3ZsWMHCQkJBV8TJ06kZcuWJCQk0KlTJ7tLLFOuen4qDVc9PxljmDx5MkuWLGHNmjU0adLkmse41PnJllsExKUtWLDAeHp6mjlz5phdu3aZqVOnmpo1a5pDhw4ZY4yZPn26GTNmTMH+qamppmHDhmbEiBFm586dJjo62jRv3tyMHz/eriYUW0nbGh4ebjw8PMwHH3xg9u/fbzZs2GBCQkJMx44d7WpCsaWmppr4+HgTHx9vAPP222+b+Ph48/PPPxtjLm/rgQMHTI0aNcwTTzxhdu3aZebMmWM8PT3N4sWL7WpCsZW0rfPmzTMeHh7m/fffN8eOHSv4Onv2rF1NKJGStvdSrnTXZEnb6srnp5K21VXPT4888ohxOp0mKiqq0OcvLS2tYB9XPj8piEmpvP/+++bmm282Xl5epn379oVuI37ooYdMaGhoof13795t+vXrZ3x9fU3Dhg3NtGnTCn2IKrOStnXWrFnmlltuMb6+vqZevXpm9OjR5siRIxVcdcmtXbvWAJd9PfTQQ8aYotsaFRVl2rVrZ7y8vEzjxo3Nhx9+WPGFl0JJ2xoaGnrV/Su70vzbXsyVglhp2uqq56fStNUVz09FtREw4eHhBfu48vnJYUwlvh4pIiIiUoVpjJiIiIiITRTERERERGyiICYiIiJiEwUxEREREZsoiImIiIjYREFMRERExCYKYiIiIiI2URATERERsYmCmIiIiIhNFMREREREbKIgJiIiImITBTERERERmyiIiYhUoF69euFwOJgxYwZZWVnMnDmTkJAQbrzxRhwOB1FRUXaXKCIVyMPuAkREqqP09HR69erFxo0b8fDwwM/Pz+6SRMQGCmIiIjZ4//33AQgPD2fkyJH4+vqSlJSEw+GwuTIRqUgKYiIiNjh37hxLly5l6NChBdsCAgJsrEhE7KAxYiIiNrj11lsLhTARqZ4UxEREbNCtWze7SxCRSkBBTETEBnXq1LG7BBGpBBTERERs4O7ubncJIlIJKIiJiIiI2ERBTERERMQmCmIiIiIiNlEQExEREbGJgpiIiIiITRTERERERGziMMYYu4sQERERqY50RUxERETEJgpiIiIiIjZREBMRERGxiYKYiIiIiE0UxERERERsoiAmIiIiYhMFMRERERGbKIiJiIiI2ERBTERERMQmCmIiIiIiNlEQExEREbGJgpiIiIiITRTERERERGyiICYiIiJik/8P5p4CwSZyfZoAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=1)\n",
+    "plt.errorbar(r_quad, ds_quad_4theta, ls='solid', color='blue', label='METHOD 1')\n",
+    "plt.errorbar(r_quad, ds_quad_const, ls='solid', color='orange')\n",
+    "\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=2)\n",
+    "plt.errorbar(r_quad, ds_quad_4theta, ls='dashed', color='blue', label='METHOD 2')\n",
+    "plt.errorbar(r_quad, ds_quad_const, ls='dashed', color='orange')\n",
+    "#plt.xlim(0.2,0.5)\n",
+    "plt.xlabel('r', fontsize=18)\n",
+    "plt.title('Binned Data -- QUADRUPOLE',fontsize=18)\n",
+    "plt.ylabel('$\\Delta\\Sigma$ in $M_{\\odot}/Mpc^2$', fontsize=15)\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 127,
+   "id": "262dd5d4-bd95-4f4b-b997-14568e925140",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7f712dba21d0>"
+      ]
+     },
+     "execution_count": 127,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=[10,10])\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=1)\n",
+    "plt.errorbar(r_mono, ds_mono, yerr=ds_mono_err, ls='solid', color='black', label='METHOD 1')\n",
+    "\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=2)\n",
+    "#plt.errorbar(r_mono, ds_mono, yerr=ds_mono_err, ls='dashed', color='slategray', label='METHOD 2')\n",
+    "\n",
+    "#plt.xlim(0.2,0.5)\n",
+    "\n",
+    "plt.xlabel('r', fontsize=18)\n",
+    "plt.title('Binned Data -- MONOPOLE',fontsize=18)\n",
+    "plt.ylabel('$\\Delta\\Sigma$ in $M_{\\odot}/Mpc^2$', fontsize=15)\n",
+    "plt.yscale('log')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "3c57c186-bfe4-4574-b378-7d14c42cb7ed",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "3783554794899798.5\n",
+      "0.005885059939355488\n"
+     ]
+    }
+   ],
+   "source": [
+    "#3.77988e+15\n",
+    "print(cosmo.eval_sigma_crit(0.47,0.8))\n",
+    "print(cosmo.eval_da(0.47) * np.pi/180.0 * 1/3600)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "d06b2eff-bdb4-499e-9ff1-b65c5fb26535",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "199526231496888.28\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(10**14.3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "bbffefde-fea3-4f57-bef4-e90f471c64e1",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting guesses for  10  chains:\n",
+      "[[2.74857938e+14 1.58345008e+01 9.68834297e-01]\n",
+      " [3.57879494e+14 1.05778984e+01 7.91187063e-01]\n",
+      " [3.01778925e+14 1.13608912e+01 4.56918421e-01]\n",
+      " [2.72896708e+14 1.85119328e+01 7.72745832e-01]\n",
+      " [2.12403745e+14 1.42072116e+00 1.17179854e-01]\n",
+      " [3.23301162e+14 1.74258599e+00 6.33557819e-01]\n",
+      " [2.19356018e+14 4.04367949e-01 1.42005419e-01]\n",
+      " [4.45994727e+14 1.66523969e+01 9.35227761e-01]\n",
+      " [4.81867717e+14 1.55631350e+01 5.16677654e-01]\n",
+      " [1.92337318e+14 1.74002430e+01 4.10573854e-01]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 27%|██▋       | 2728/10000 [02:16<05:43, 21.18it/s]"
+     ]
+    }
+   ],
+   "source": [
+    "q_true=0.5\n",
+    "dir_loc_mcmc = \"Elliptical_lenses_data/catalog_horizontal_rescale_q\"+str(q_true)+\"/\"\n",
+    "f=dir_loc_mcmc + \"MCMC\"\n",
+    "\n",
+    "gamma1 = np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "#MCMC parameters:\n",
+    "params_key=[\"M200c\",\"c200c\",\"e\"]#,r\"$\\theta_{e}$ in degrees\"]\n",
+    "ndim = len(params_key)\n",
+    "nwalkers = ndim*2+4\n",
+    "nsteps = 10000\n",
+    "nburns = 4000\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "limits=[[1.0E12,5.0E14],  # mdelta\n",
+    "       [0.0,20.0],     # cdelta\n",
+    "       [0.0001,0.99]]   # e\n",
+    "       #[0.0, 45.0]]    # theta_e #in degrees\n",
+    "z_cl=0.47\n",
+    "z_gal=0.8\n",
+    "#q_true=0.6 #default = 2/3 for the files in the same directory as this notebook\n",
+    "ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "#ell_true= 1*(1-q_true)/(1+q_true)\n",
+    "theta_e_true = 0.0\n",
+    "true_value=[2.0E14, 3.89055, ell_true]#,theta_e_true]\n",
+    "\n",
+    "'''\n",
+    "Axis ratio q:  0.6666666666666667\n",
+    "c:  3.890554441834905\n",
+    "r200_Mpc: 1.026773208537997\n",
+    "ARCSEC_TO_MPC:  0.00588784182755241\n",
+    "BOX_WIDTH_ARCSEC*ARCSEC_TO_MPC:  5.88784182755241\n",
+    "SIGMA_C=3.77988e+15\n",
+    "arcsec_per_pix:  10.0\n",
+    "'''\n",
+    "# PREP DATA:\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, \n",
+    "                                                                                                                      y_arcsec, z_cl=0.47, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, \n",
+    "                                                                                                                      method=1)\n",
+    "\n",
+    "\n",
+    "# CALL EMCEE\n",
+    "post, mean, err_plus, err_minus = emcee_wrapper(gamma1, gamma2, x_arcsec, y_arcsec,z_cl=0.47,\n",
+    "                                                params_key=params_key,limits=limits,true_value=true_value, ndim=ndim,nwalkers=nwalkers,\n",
+    "                                                nsteps=nsteps, nburn=nburns, filename=f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "5ad1fcfc-fa23-4e2b-84e8-b5145635de0f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rho_c: 220540221400.42233 solMass / Mpc3\n",
+      "R200c 1.026773208537997\n",
+      "M200c: 199999999999999.97 solMass / Mpc3\n"
+     ]
+    }
+   ],
+   "source": [
+    "rho_c=cosmo_astropy.critical_density(0.47).to('M_sun/Mpc**3')\n",
+    "R200c=1.026773208537997\n",
+    "print('Rho_c:',rho_c)\n",
+    "print('R200c',R200c)\n",
+    "print('M200c:',4/3*np.pi*R200c**3*200*rho_c)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "875dc515-4e4a-44e6-bd4a-69c62ba68aab",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.6\n",
+      "Bias in ell: 1.3468952539690826\n"
+     ]
+    }
+   ],
+   "source": [
+    "ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "print(ell_true)\n",
+    "bias = np.abs((ell_true -  mean[2]))/ell_true*100\n",
+    "print(\"Bias in ell:\", bias)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "id": "50145a52-52a9-42e1-ad7a-4eaf67191098",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.3333333333333333\n"
+     ]
+    }
+   ],
+   "source": [
+    "ell_true= 1*(1-q_true)/(1+q_true)\n",
+    "print(ell_true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "6482afa1-296f-4c28-9fe5-fa8882ea57f5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0.1049723756906077\n"
+     ]
+    }
+   ],
+   "source": [
+    "q_true=0.9 #default = 2/3 for the files in the same directory as this notebook\n",
+    "ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "print(ell_true)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "18f842d5-05dc-4712-bac8-5bd53e7641b0",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "ell_fit =  0.22216404817544547\n",
+      "14\n",
+      "[-5.90533343e+12 -4.42001910e+12 -3.25414776e+12 -2.35538449e+12\n",
+      " -1.67455287e+12 -1.16739142e+12 -7.95566482e+11 -5.27009384e+11\n",
+      " -3.35719515e+11 -2.01203779e+11 -1.07716386e+11 -4.34330313e+10\n",
+      "  3.45498888e+08  2.99032186e+10]\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 500x1300 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mdelta_fit = mean[0]#2E14#\n",
+    "cdelta_fit = mean[1]#3.89#\n",
+    "ell_fit = mean[2]#ell_true\n",
+    "print(\"ell_fit = \",ell_fit)\n",
+    "#theta_e_fit = mean[3] #in degrees\n",
+    "cosmo = Cosmology(H0=71.0, Omega_dm0=0.2248, Omega_b0=0.2648 - 0.2248, Omega_k0=0.0)\n",
+    "\n",
+    "gamma1 = np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "#theta_e_fit = theta_e_fit*np.pi/180.0\n",
+    "#gamma1,gamma2,x_arcsec,y_arcsec = halo_rotation(gamma1,gamma2,x_arcsec,y_arcsec,theta_e_fit)\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.47, sigma_c=3.77988e+15)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=[5,13])\n",
+    "plt.subplot(311)\n",
+    "plt.errorbar(r_mono, ds_mono, yerr=np.abs(ds_mono_err), label='data', color='orange')\n",
+    "print(len(ds_mono))\n",
+    "ds_model = compute_delta_sigma_excess(ell_fit, r_mono, mdelta_fit, cdelta_fit, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "ds_model_without_e_square = clmm.compute_excess_surface_density(r_mono, mdelta_fit, cdelta_fit, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "plt.plot(r_mono, ds_model, label='model[FIT]', color='darkblue', lw=2)\n",
+    "\n",
+    "\n",
+    "ds_model = compute_delta_sigma_excess((0.0), r_mono, 2.0E14, 3.89, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "#plt.plot(r_mono, ds_model, label='model[TRUTH]', color='gray', lw=2)\n",
+    "#plt.plot(r_mono, (ds_mono-ds_model)/ds_model, label='(data - model) / model', color='darkblue', ls='dashed')\n",
+    "#plt.yscale('log')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.title(r'$\\Delta\\Sigma^{mono}$')\n",
+    "plt.legend()\n",
+    "#plt.xlim([1.75,2.25])\n",
+    "\n",
+    "\n",
+    "plt.subplot(312)\n",
+    "ds_model = compute_delta_sigma_const(ell_fit, r_quad, mdelta_fit, cdelta_fit, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "plt.plot(r_quad, ds_model, label='model[FIT]', color='darkblue')\n",
+    "print(ds_model)\n",
+    "plt.errorbar(r_quad, ds_quad_const,  yerr=ds_quad_const_err, label='data', color='orange')\n",
+    "plt.yscale('linear')\n",
+    "plt.xscale('linear')\n",
+    "plt.title(r'$\\Delta\\Sigma^{quad}_{const}$')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(313)\n",
+    "ds_model = compute_delta_sigma_4theta(ell_fit, r_quad, mdelta_fit, cdelta_fit, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "plt.plot(r_quad, ds_model, label='model[FIT]', color='darkblue')\n",
+    "\n",
+    "plt.errorbar(r_quad, ds_quad_4theta,  yerr=ds_quad_4theta_err, label='data', color='orange')\n",
+    "plt.yscale('linear')\n",
+    "plt.xscale('linear')\n",
+    "plt.title(r'$\\Delta\\Sigma^{quad}_{4\\Theta}$')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.legend()\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "927550c3-a2b3-4d8e-9850-e7403c013b16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-426913.86121114873\n",
+      "-93004.61581753094\n",
+      "-2079487.5609684037\n",
+      "-14879507.916491512\n"
+     ]
+    }
+   ],
+   "source": [
+    "for p in [5E13,7.38E13, 2E14, 5E14]:\n",
+    "    params=( p, 3.89, 0.1049)\n",
+    "    print(log_likelihood_monopole(ds_mono,ds_mono_err, r_mono,params)+log_likelihood_quadrupole_const(ds_quad_const,ds_quad_const_err, r_quad,params) + log_likelihood_quadrupole_4theta(ds_quad_4theta, ds_quad_4theta_err, r_quad,params))\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "id": "9556994e-ca41-4a12-a19d-69e964d62d16",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "-275831.3166667561\n",
+      "[1.88327184e+27 6.88331544e+27 4.90832004e+26 9.65222429e+26\n",
+      " 2.85818093e+26 2.20807959e+27 8.52351988e+25 5.52277072e+27\n",
+      " 1.81374810e+25 3.07262700e+27 6.69441213e+26 8.40732122e+26\n",
+      " 3.58278353e+26 4.04905898e+27 4.75888014e+26 9.00256734e+26\n",
+      " 9.42304956e+25 6.29216713e+25 6.09243160e+23 2.66659670e+25\n",
+      " 9.60348573e+26 3.75799734e+26 3.20553105e+24 1.42379498e+26\n",
+      " 6.39459888e+26 7.49610438e+25 6.46454624e+24 4.25158241e+26\n",
+      " 2.78664327e+26 1.29660720e+26 4.14342260e+26 7.70506099e+25\n",
+      " 2.06988920e+25 1.45913811e+26 4.61896936e+25 1.36694159e+26\n",
+      " 2.00535820e+26 1.48209349e+25 1.28369803e+25 9.08396268e+24\n",
+      " 1.94052468e+25 7.00243732e+24 2.36964082e+26 1.10380578e+26\n",
+      " 4.97743992e+25 9.47472564e+25 9.09417164e+24 5.70086524e+25\n",
+      " 4.05516247e+25 8.08775222e+25 9.84763618e+25 2.48277069e+25\n",
+      " 5.66288351e+24 1.25021191e+24 9.71279680e+24 2.98696258e+23\n",
+      " 2.08850185e+22 7.95774262e+23 6.77373291e+24 1.29894725e+24\n",
+      " 3.83496497e+24 9.76705186e+24 3.28647201e+22 9.48214076e+24\n",
+      " 3.50407560e+24 7.02602243e+24 2.48852102e+24 1.21792218e+22\n",
+      " 3.67535679e+24 4.38523997e+23 4.48765275e+24 9.02543179e+23\n",
+      " 6.13446194e+23 6.95961834e+24 1.55097544e+24 4.24032247e+22\n",
+      " 3.91765402e+23 6.63756449e+22]\n",
+      "[1.75637038e+24 3.22587010e+22 7.50672830e+25 1.45755000e+26\n",
+      " 2.41297583e+26 1.71308949e+24 5.71418204e+25 2.19892649e+22\n",
+      " 1.16519706e+26 5.96984402e+24 1.23626023e+22 1.04861942e+26\n",
+      " 3.49506833e+25 2.31295157e+24 6.05991974e+25 6.43526287e+23\n",
+      " 2.76371619e+25 1.25133843e+26 5.48635040e+25 4.51273919e+25\n",
+      " 4.71443607e+25 4.49900886e+25 7.78341206e+25 2.56778935e+25\n",
+      " 2.89882860e+22 4.15151230e+25 3.15504160e+25 3.22435371e+25\n",
+      " 4.13097060e+25 2.32658114e+25 2.58539614e+25 4.46798451e+25\n",
+      " 2.38135889e+25 1.73623759e+25 4.42579665e+25 1.62972375e+25\n",
+      " 1.78794142e+25 1.94168598e+25 1.99699866e+25 1.46282192e+25\n",
+      " 1.53014842e+25 1.21460204e+25 1.11268684e+25 1.04469731e+25\n",
+      " 8.46903358e+24 1.37890824e+25 1.13951205e+25 7.51963313e+24\n",
+      " 8.18464233e+24 6.89875265e+24 5.73433644e+24 6.28381534e+24\n",
+      " 6.27222374e+24 5.60080391e+24 5.32036703e+24 4.41856655e+24\n",
+      " 3.99743881e+24 4.51213857e+24 3.62521646e+24 3.57427466e+24\n",
+      " 3.76207424e+24 3.12010742e+24 2.03392311e+24 3.24223551e+24\n",
+      " 2.34230463e+24 1.59433029e+24 1.99254805e+24 1.94865974e+24\n",
+      " 1.72675302e+24 1.34994947e+24 1.71778978e+24 1.09996620e+24\n",
+      " 1.27322538e+24 9.17869441e+23 1.14021558e+24 8.88609650e+23\n",
+      " 8.89175059e+23 7.12719878e+23]\n",
+      "[1.07225211e+03 2.13378568e+05 6.53856094e+00 6.62222515e+00\n",
+      " 1.18450458e+00 1.28894585e+03 1.49164304e+00 2.51157587e+05\n",
+      " 1.55660202e-01 5.14691336e+02 5.41505093e+04 8.01751433e+00\n",
+      " 1.02509685e+01 1.75060258e+03 7.85304153e+00 1.39894322e+03\n",
+      " 3.40955761e+00 5.02834965e-01 1.11047074e-02 5.90904237e-01\n",
+      " 2.03703807e+01 8.35294495e+00 4.11841366e-02 5.54482780e+00\n",
+      " 2.20592513e+04 1.80563222e+00 2.04895753e-01 1.31858437e+01\n",
+      " 6.74573494e+00 5.57301517e+00 1.60262581e+01 1.72450486e+00\n",
+      " 8.69205059e-01 8.40402326e+00 1.04364699e+00 8.38756625e+00\n",
+      " 1.12160174e+01 7.63302355e-01 6.42813665e-01 6.20988964e-01\n",
+      " 1.26819376e+00 5.76521124e-01 2.12965655e+01 1.05657951e+01\n",
+      " 5.87722303e+00 6.87117921e+00 7.98075954e-01 7.58130767e+00\n",
+      " 4.95459949e+00 1.17234993e+01 1.71731050e+01 3.95105610e+00\n",
+      " 9.02851006e-01 2.23220083e-01 1.82558774e+00 6.76002623e-02\n",
+      " 5.22459991e-03 1.76362993e-01 1.86850440e+00 3.63415622e-01\n",
+      " 1.01937514e+00 3.13035756e+00 1.61582903e-02 2.92456878e+00\n",
+      " 1.49599483e+00 4.40688011e+00 1.24891393e+00 6.25005052e-03\n",
+      " 2.12847857e+00 3.24844748e-01 2.61245748e+00 8.20519015e-01\n",
+      " 4.81804873e-01 7.58236196e+00 1.36024755e+00 4.77186184e-02\n",
+      " 4.40594232e-01 9.31300598e-02]\n",
+      "[0.6        0.61093735 0.62207408 0.64496027 0.65671719 0.66868844\n",
+      " 0.68087791 0.69328957 0.70592749 0.71879579 0.73189866 0.74524038\n",
+      " 0.75882531 0.77265787 0.78674259 0.80108406 0.81568695 0.83055604\n",
+      " 0.84569618 0.86111231 0.87680945 0.89279274 0.90906739 0.9256387\n",
+      " 0.9425121  0.95969307 0.97718724 0.99500031 1.01313809 1.0316065\n",
+      " 1.05041157 1.06955944 1.08905635 1.10890867 1.12912288 1.14970557\n",
+      " 1.17066346 1.19200338 1.21373232 1.23585735 1.25838569 1.2813247\n",
+      " 1.30468186 1.3284648  1.35268128 1.3773392  1.4024466  1.42801169\n",
+      " 1.4540428  1.48054842 1.50753722 1.53501799 1.56299971 1.59149151\n",
+      " 1.62050268 1.65004269 1.68012118 1.71074798 1.74193306 1.77368662\n",
+      " 1.80601901 1.83894078 1.87246268 1.90659565 1.94135083 1.97673956\n",
+      " 2.01277338 2.04946407 2.08682358 2.12486412 2.16359809 2.20303815\n",
+      " 2.24319715 2.28408821 2.32572467 2.36812011 2.41128838 2.45524356]\n"
+     ]
+    }
+   ],
+   "source": [
+    "params =( 2E14, 3.89, 0.1049)\n",
+    "#params=( 7.38E13, 3.89, 0.1049)\n",
+    "mdelta,cdelta,ell = params\n",
+    "\n",
+    "ds_data = ds_mono\n",
+    "    \n",
+    "#model:\n",
+    "ds_model = compute_delta_sigma_excess(ell, r_mono, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                         sample_N=1000, delta_mdef=200)#/sigma_c\n",
+    "\n",
+    "# Gaussian likelihood:\n",
+    "#sigma = np.full(len(r_mono), ds_model*0.01) \n",
+    "sigma = ds_mono_err\n",
+    "term1 = -0.5*np.sum(np.log(2*np.pi*(sigma**2)))\n",
+    "chi2 = ((ds_data - ds_model)**2 / (sigma)**2)\n",
+    "term2 = -0.5*np.sum(chi2)\n",
+    "print(term1+term2)\n",
+    "print((ds_data - ds_model)**2)\n",
+    "print((sigma)**2)\n",
+    "print((ds_data - ds_model)**2/(sigma)**2)\n",
+    "print(r_mono)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 97,
+   "id": "b6efcff8-3f6c-4f93-887b-67934b208afe",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[2.44654308e+13 2.58312881e+12 1.03538965e+12 2.44046137e+11\n",
+      " 2.31176932e+10 3.27496505e+12 2.41178674e+12 1.29810388e+13\n",
+      " 2.58461934e+11 2.36663994e+13 2.22298539e+12 1.15061746e+13\n",
+      " 9.05523253e+12 2.48902000e+12 1.30759723e+13 1.59902309e+13\n",
+      " 8.66413775e+12 1.09536828e+13 1.09940388e+13 7.08799995e+11\n",
+      " 1.00246768e+13 2.44332643e+12 8.29327922e+12 2.37388799e+11\n",
+      " 7.80649715e+12 7.78454863e+12 7.75438018e+12 9.80355413e+12\n",
+      " 7.11912561e+12 7.72944727e+12 8.99235486e+12 6.71862041e+12\n",
+      " 5.27699851e+12 6.59061023e+12 6.02951252e+12 6.91083276e+12\n",
+      " 5.15906298e+12 5.40841222e+12 4.05761317e+12 4.63399163e+12\n",
+      " 4.16681844e+12 3.98762045e+12 4.38976964e+12 3.78215224e+12\n",
+      " 4.01585090e+12 3.50904032e+12 3.46422657e+12 3.23424265e+12\n",
+      " 3.29071193e+12 3.01158093e+12 2.71692139e+12 2.56018829e+12\n",
+      " 2.44639741e+12 2.44335126e+12 2.20698974e+12 1.91314933e+12\n",
+      " 2.37908773e+12 1.82544534e+12 1.95842025e+12 1.72248922e+12\n",
+      " 1.55837650e+12 1.55026404e+12 1.55691608e+12 1.37202326e+12\n",
+      " 1.43951911e+12 1.21746750e+12 1.16684318e+12 1.17974935e+12\n",
+      " 1.12631757e+12 9.85868031e+11 9.69034820e+11 9.01504675e+11\n",
+      " 8.51728924e+11 8.35576712e+11 7.85164646e+11]\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(sigma)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "a6d51160-819e-4be2-baa9-33be28ad1458",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([3. , 1.5, 2. ])"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "np.array([6,3,2])/np.array([2,2,1])"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "724fa5d4-058f-4c5a-90a9-9c1b970dc34a",
+   "metadata": {},
+   "source": [
+    "<font size=5> MASS BIAS vs q </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "1327f32f-ad1a-4e1d-9cd4-148abf7c7539",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Run no.:  0  q= 0.1\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:38<00:00, 37.75it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:159.6 seconds\n",
+      "Run no.:  1  q= 0.2\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:51<00:00, 34.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:172.6 seconds\n",
+      "Run no.:  2  q= 0.3\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:38<00:00, 37.80it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:159.4 seconds\n",
+      "Run no.:  3  q= 0.4\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:34<00:00, 38.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:155.5 seconds\n",
+      "Run no.:  4  q= 0.5\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:38<00:00, 37.96it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:158.7 seconds\n",
+      "Run no.:  5  q= 0.6\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:33<00:00, 39.16it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:153.9 seconds\n",
+      "Run no.:  6  q= 0.7\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:38<00:00, 37.86it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:159.2 seconds\n",
+      "Run no.:  8  q= 0.9\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 6000/6000 [02:36<00:00, 38.23it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:157.7 seconds\n",
+      "Run no.:  9  q= 1.0\n",
+      "Starting guesses for  12  chains:\n",
+      "[[2.74857938e+14 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57879494e+14 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01778925e+14 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72896708e+14 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.12403745e+14 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.23301162e+14 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.19356018e+14 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45994727e+14 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81867717e+14 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.92337318e+14 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.96070794e+14 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64918565e+14 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      " 23%|██▎       | 1380/6000 [00:34<01:57, 39.30it/s]IOPub message rate exceeded.\n",
+      "The Jupyter server will temporarily stop sending output\n",
+      "to the client in order to avoid crashing it.\n",
+      "To change this limit, set the config variable\n",
+      "`--ServerApp.iopub_msg_rate_limit`.\n",
+      "\n",
+      "Current values:\n",
+      "ServerApp.iopub_msg_rate_limit=1000.0 (msgs/sec)\n",
+      "ServerApp.rate_limit_window=3.0 (secs)\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "dir_loc_partial = \"Elliptical_lenses_data/catalog_horizontal_rescale_q\"\n",
+    "\n",
+    "mdelta_fit=[]\n",
+    "mdelta_merr_fit=[]\n",
+    "mdelta_perr_fit=[]\n",
+    "\n",
+    "cdelta_fit=[]\n",
+    "cdelta_merr_fit=[]\n",
+    "cdelta_perr_fit=[]\n",
+    "\n",
+    "ell_fit=[]\n",
+    "ell_merr_fit=[]\n",
+    "ell_perr_fit=[]\n",
+    "\n",
+    "theta_e_fit=[]\n",
+    "theta_e_merr_fit=[]\n",
+    "theta_e_perr_fit=[]\n",
+    "for i,q in enumerate([0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]):\n",
+    "    print(\"Run no.: \",i,\" q=\",q)\n",
+    "    \n",
+    "    dir_loc = dir_loc_partial+str(q)+'/'\n",
+    "    gamma1 = np.load(dir_loc+\"gamma1.npy\").flatten()\n",
+    "    gamma2 = np.load(dir_loc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "    x_arcsec = np.load(dir_loc+\"x_arcsec.npy\").flatten()\n",
+    "    y_arcsec = np.load(dir_loc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "    #MCMC parameters:\n",
+    "    params_key=[\"M200m\",\"c200m\",\"e\",r\"$\\theta_{e}$ in degrees\"]\n",
+    "    ndim = len(params_key)\n",
+    "    nwalkers = ndim*2+4\n",
+    "    nsteps = 6000\n",
+    "    nburns = 3000\n",
+    "    np.random.seed(0)\n",
+    "\n",
+    "    limits=[[1.0E12,5.0E14],  # mdelta\n",
+    "           [0.0,20.0],     # cdelta\n",
+    "           [0.0001,0.99],   # e\n",
+    "           [0.0, 45.0]]    # theta_e #in degrees\n",
+    "    z_cl=0.47\n",
+    "    z_gal=0.8\n",
+    "    q_true=0.1 #default = 2/3 for the files in the same directory as this notebook\n",
+    "    ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "    theta_e_true = 0.0\n",
+    "    true_value=[2.5E14, 3.89055, ell_true,theta_e_true]\n",
+    "\n",
+    "    '''\n",
+    "    Axis ratio q:  0.6666666666666667\n",
+    "    c:  3.890554441834905\n",
+    "    r200_Mpc: 1.026773208537997\n",
+    "    ARCSEC_TO_MPC:  0.00588784182755241\n",
+    "    BOX_WIDTH_ARCSEC*ARCSEC_TO_MPC:  5.88784182755241\n",
+    "    SIGMA_C=3.77988e+15\n",
+    "    arcsec_per_pix:  10.0\n",
+    "    '''\n",
+    "    # PREP DATA:\n",
+    "    ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, \n",
+    "                                                                                                                          z_cl=0.47, sigma_c=3.77988e+15, \n",
+    "                                                                                                                          method=1)\n",
+    "\n",
+    "\n",
+    "    # CALL EMCEE\n",
+    "    post, mean, err_plus, err_minus = emcee_wrapper(gamma1, gamma2, x_arcsec, y_arcsec,z_cl=0.47,\n",
+    "                                                    params_key=params_key,limits=limits,true_value=true_value, ndim=ndim,nwalkers=nwalkers,nsteps=nsteps, \n",
+    "                                                    nburn=nburns, filename=f)\n",
+    "    \n",
+    "    mdelta_fit.append(mean[0])#2.5E14#\n",
+    "    mdelta_merr_fit.append(err_minus[0])\n",
+    "    mdelta_perr_fit.append(err_plus[0])\n",
+    "    \n",
+    "    cdelta_fit.append(mean[1])#3.89#\n",
+    "    cdelta_merr_fit.append(err_minus[1])\n",
+    "    cdelta_perr_fit.append(err_plus[1])\n",
+    "    \n",
+    "    ell_fit.append(mean[2])#ell_true#\n",
+    "    ell_merr_fit.append(err_minus[2])\n",
+    "    ell_perr_fit.append(err_plus[2])\n",
+    "    \n",
+    "    theta_e_fit.append(mean[3]) #in degrees\n",
+    "    theta_e_merr_fit.append(err_minus[3])\n",
+    "    theta_e_perr_fit.append(err_plus[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "0bf58a67-8e04-436c-ab75-85577b2faf40",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, 'C200m')"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q_arr = [0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n",
+    "plt.figure(figsize=[10,5])\n",
+    "plt.subplot(121)\n",
+    "plt.errorbar(q_arr, mdelta_fit, yerr=mdelta_perr_fit)\n",
+    "plt.fill_between(q_arr, np.array(mdelta_fit)+np.array(mdelta_perr_fit), np.array(mdelta_fit)-np.array(mdelta_perr_fit), color='blue', alpha=0.2)\n",
+    "plt.title('Fit Mass vs q')\n",
+    "plt.xlabel('q')\n",
+    "plt.ylabel('M200m')\n",
+    "plt.yscale('log')\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.errorbar(q_arr, cdelta_fit, yerr=cdelta_perr_fit, color='orange')\n",
+    "plt.fill_between(q_arr, np.array(cdelta_fit)+np.array(cdelta_perr_fit), np.array(cdelta_fit)-np.array(cdelta_perr_fit), color='orange', alpha=0.2)\n",
+    "plt.title('Fit Concentration vs q')\n",
+    "plt.xlabel('q')\n",
+    "plt.ylabel('C200m')\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "4bd55efd-f36e-4cd1-97c0-d50284bf6c1a",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<font size=6> Plotting Shears and their ARBITRARY ROTATION: </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1cf4d3dd-2962-4698-95f3-945d03de25e4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "\n",
+    "dir_loc_arr = [\"Elliptical_lenses_data/axis_ratio_9_10/\",\"Elliptical_lenses_data/axis_ratio_1/\",\"Elliptical_lenses_data/horizontal_rescale/\", \"transform\"]\n",
+    "n=len(dir_loc_arr)\n",
+    "size=5#int(5/(n-2))\n",
+    "plt.figure(figsize=[n*size,size])\n",
+    "for i,dir_loc in enumerate(dir_loc_arr):\n",
+    "    if dir_loc == \"transform\":\n",
+    "        plt.subplot(int(10*n)+100+i+1)\n",
+    "        \n",
+    "        theta_e = np.pi/4-np.pi/16 # in radians\n",
+    "        gamma1,gamma2,x_arcsec,y_arcsec = halo_rotation(gamma1,gamma2,x_arcsec,y_arcsec,theta_e)\n",
+    "        q = plt.quiver(x_arcsec, y_arcsec, gamma1, gamma2, headwidth=0)\n",
+    "        plt.xlim([-100,100])\n",
+    "        plt.ylim([-100,100])\n",
+    "        plt.title(\"Horizontal_rescale + \\n theta_e = \"+str(theta_e*180.0/np.pi))\n",
+    "    \n",
+    "    else: \n",
+    "        \n",
+    "        plt.subplot(int(10*n)+100+i+1)\n",
+    "        gamma1 = np.load(dir_loc+\"gamma1.npy\").flatten()\n",
+    "        gamma2 = np.load(dir_loc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "        x_arcsec = np.load(dir_loc+\"x_arcsec.npy\").flatten()\n",
+    "        y_arcsec = np.load(dir_loc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "\n",
+    "        X = np.arange(-10, 10, 1)\n",
+    "        Y = np.arange(-10, 10, 1)\n",
+    "        U, V = np.meshgrid(X, Y)\n",
+    "\n",
+    "        #plt.figure(figsize=[7,7])\n",
+    "        #fig, ax = plt.subplots()\n",
+    "\n",
+    "        q = plt.quiver(x_arcsec, y_arcsec, gamma1, gamma2, headwidth=0)\n",
+    "        #ax.quiverkey(q, X=0.3, Y=1.1, U=10,\n",
+    "        #             label='Quiver key, length = 10', labelpos='E')\n",
+    "\n",
+    "\n",
+    "        plt.xlim([-100,100])\n",
+    "        plt.ylim([-100,100])\n",
+    "        plt.title(dir_loc)\n",
+    "#plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c5fd84bf-b670-4495-a2e7-d72eb8fdc8ba",
+   "metadata": {},
+   "source": [
+    "<font size=6> TESTING LIKELIHOODS -- [BEWARE] </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "08625898-201a-4919-9417-5af0269505df",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[1.0e+13 2.0e+13 3.0e+13 4.0e+13 5.0e+13 6.0e+13 7.0e+13 8.0e+13 9.0e+13\n",
+      " 1.0e+14 1.1e+14 1.2e+14 1.3e+14 1.4e+14 1.5e+14 1.6e+14 1.7e+14 1.8e+14\n",
+      " 1.9e+14 2.0e+14 2.1e+14 2.2e+14 2.3e+14 2.4e+14 2.5e+14 2.6e+14 2.7e+14\n",
+      " 2.8e+14 2.9e+14 3.0e+14 3.1e+14 3.2e+14 3.3e+14 3.4e+14 3.5e+14 3.6e+14\n",
+      " 3.7e+14 3.8e+14 3.9e+14 4.0e+14 4.1e+14 4.2e+14 4.3e+14 4.4e+14 4.5e+14\n",
+      " 4.6e+14 4.7e+14 4.8e+14 4.9e+14 5.0e+14 5.1e+14 5.2e+14 5.3e+14 5.4e+14\n",
+      " 5.5e+14 5.6e+14 5.7e+14 5.8e+14 5.9e+14 6.0e+14 6.1e+14 6.2e+14 6.3e+14\n",
+      " 6.4e+14 6.5e+14 6.6e+14 6.7e+14 6.8e+14 6.9e+14 7.0e+14 7.1e+14 7.2e+14\n",
+      " 7.3e+14 7.4e+14 7.5e+14 7.6e+14 7.7e+14 7.8e+14 7.9e+14 8.0e+14 8.1e+14\n",
+      " 8.2e+14 8.3e+14 8.4e+14 8.5e+14 8.6e+14 8.7e+14 8.8e+14 8.9e+14 9.0e+14\n",
+      " 9.1e+14 9.2e+14 9.3e+14 9.4e+14 9.5e+14 9.6e+14 9.7e+14 9.8e+14 9.9e+14\n",
+      " 1.0e+15]\n",
+      "[-58494.627138733995, -50157.95457360413, -43172.4036943896, -37128.13730970341, -31822.593260328373, -27132.371152183798, -22973.471564914194, -19284.574623792498, -16018.700205673753, -13138.555138860243, -10613.72093294838, -8418.848176615835, -6532.441145466495, -4936.007947357664, -3613.447343733638, -2550.594555813008, -1734.8772292700748, -1155.049774544227, -800.9847613994174, -663.5066856450464, -734.2577636354574, -1005.5883173743372, -1470.466306036423, -2122.401953794235, -2955.3844167891193, -3963.828150985255, -5142.527170907046, -6486.615782724676, -7991.5346718671735, -9653.001451658476, -11466.984953914203, -13429.68267820704, -15537.500923138336, -17787.037207399077, -20175.064655809423, -22698.518079723934, -25354.481525066367, -28140.177097009684, -31052.954899627304, -34090.283953002705, -37249.74397031622, -40529.01789411434, -43925.88510493852, -47438.21522723947, -51063.962467430036, -54801.16042734334, -58647.91734354018, -62602.41170902707, -66662.8882392104, -70827.65414843295, -75095.07570734738, -79463.57505476507, -83931.627240562, -88497.75747878394, -93160.53859233385, -97918.58863258733, -102770.56865900403, -107715.1806653219, -112751.16564026, -117877.30175184221, -123092.40264550109, -128395.31584706031, -133784.9212625208, -139260.12976731878, -144819.8818783857, -150463.14650293582, -156188.91975843807, -161996.22385870258, -167884.10606145486, -173851.63767314368, -179897.91310709107, -186022.0489914041, -192223.18332335728, -198500.47466721144, -204853.10139267778, -211280.26095144608, -217781.1691893909, -224355.0596922541, -231001.1831627581, -237718.8068272606, -244507.21387019128, -251365.7028946406, -258293.58740758235, -265290.19532832375, -272354.86851885874, -279486.96233491204, -286685.84519651945, -293950.898177083, -301281.5146099041, -308677.0997112541, -316137.07021912216, -323660.85404680885, -331247.8899506076, -338897.62721085036, -346609.5253256469, -354383.0537166751, -362217.691446437, -370112.9269464108, -378068.257755573, -386083.1902687991]\n",
+      "[4.29011449e+14 3.82211609e+14 3.38169880e+14 2.97180628e+14\n",
+      " 2.59439925e+14 2.25047948e+14 1.94016425e+14 1.66279833e+14\n",
+      " 1.41708921e+14 1.20125205e+14 1.01315275e+14 8.50440252e+13\n",
+      " 7.10662189e+13 5.91360676e+13] [1.18149947e+14 9.06761412e+13 9.02986391e+13 7.52074786e+13\n",
+      " 7.31329890e+13 6.19986360e+13 5.35556406e+13 4.35013778e+13\n",
+      " 4.00531629e+13 3.31156639e+13 2.84050659e+13 2.37383893e+13\n",
+      " 1.99862375e+13 1.67306737e+13]\n",
+      "[-4.83174369e+28 -4.24964644e+28 -3.07200761e+28 -2.46360395e+28\n",
+      " -1.73551372e+28 -1.32925390e+28 -9.86461596e+27 -7.53727455e+27\n",
+      " -5.16694661e+27 -3.78533015e+27 -2.65794930e+27 -1.87919050e+27\n",
+      " -1.30458225e+27 -8.99108716e+26]\n"
+     ]
+    },
+    {
+     "data": {
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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "mdelta=1.34E13\n",
+    "cdelta = 12\n",
+    "ell = 0.0\n",
+    "l=[]\n",
+    "\n",
+    "def log_likelihood_monopole_temp(params, ds_mono,ds_mono_err, r_mono):\n",
+    "    sigma_c = 3.77988e+15\n",
+    "    z_cl=0.47\n",
+    "    z_gal=1.0\n",
+    "    mdelta = params  # km/s\n",
+    "    ell=0.0\n",
+    "    cdelta=12.0\n",
+    "    ds_data = ds_mono\n",
+    "    \n",
+    "    #model:\n",
+    "    func = np.vectorize(compute_delta_sigma_excess)\n",
+    "    ds_model = func(ell, r_mono, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)#/sigma_c\n",
+    "    \n",
+    "    # Gaussian likelihood:\n",
+    "    #sigma = np.full(len(r_mono), ds_model*0.01) \n",
+    "    sigma = ds_mono*0.01#np.abs(ds_mono_err)\n",
+    "    term1 = -0.5*np.sum(np.log(2*np.pi*(sigma**2)))\n",
+    "    chi2 = ((ds_data - ds_model)**2 / (sigma)**2)\n",
+    "    term2 = -0.5*np.sum(chi2)\n",
+    "\n",
+    "    return (term2)\n",
+    "m_arr=np.linspace(10**13,10**15,10**2)\n",
+    "for mdelta in m_arr:\n",
+    "    params=mdelta\n",
+    "    l.append(log_likelihood_monopole_temp(params, ds_mono,ds_mono_err, r_mono))\n",
+    "    \n",
+    "plt.figure(figsize=[10,5])\n",
+    "plt.subplot(121)\n",
+    "plt.plot(m_arr,l)\n",
+    "plt.axvline(m_arr[np.argmax(l)], color='red', label='m_fit = '+str(m_arr[np.argmax(l)]))\n",
+    "plt.xscale('log')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "plt.errorbar(r_mono, ds_mono, yerr=np.abs(ds_mono*0.1))\n",
+    "\n",
+    "mdelta=m_arr[np.argmax(l)]\n",
+    "print(m_arr)\n",
+    "cdelta = 12\n",
+    "ell = 0.0\n",
+    "\n",
+    "ds_model = compute_delta_sigma_excess(ell, r_mono, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)\n",
+    "plt.plot(r_mono, ds_model, label='model')\n",
+    "plt.yscale('log')\n",
+    "plt.legend()\n",
+    "\n",
+    "print(l)\n",
+    "mdelta=1E15\n",
+    "ds_model = compute_delta_sigma_excess(ell, r_mono, mdelta, cdelta, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=1000, delta_mdef=200)\n",
+    "print(ds_model, ds_mono)\n",
+    "print(-0.5*(ds_model - ds_mono)**2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 161,
+   "id": "7b3f7ca6-3f6d-4ce3-81cc-ab030eafab3b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  message: Optimization terminated successfully.\n",
+      "  success: True\n",
+      "   status: 0\n",
+      "      fun: -1751919.354392614\n",
+      "        x: [ 1.400e+15]\n",
+      "      nit: 0\n",
+      "      jac: [-2.129e-09]\n",
+      " hess_inv: [[1]]\n",
+      "     nfev: 2\n",
+      "     njev: 1\n"
+     ]
+    }
+   ],
+   "source": [
+    "from scipy.optimize import minimize\n",
+    "res = minimize(log_likelihood_monopole_temp, [14.0E14], args=(ds_mono,ds_mono_err, r_mono), method=None, jac=None, hess=None, hessp=None, bounds=None, constraints=(), \n",
+    "                        tol=None, callback=None, options=None)\n",
+    "\n",
+    "print(res)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 185,
+   "id": "b43d440d-43fc-4431-9878-cd72a9b5e1ca",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array([0.02464215, 0.00029106])"
+      ]
+     },
+     "execution_count": 185,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mdelta=[0.0001]\n",
+    "r=[0.1,1.0]\n",
+    "func = np.vectorize(clmm.compute_excess_surface_density)\n",
+    "func(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "92d37d78-565f-4074-a171-272d84c8cef1",
+   "metadata": {},
+   "source": [
+    "<font size=6> GENERATE NFW MOCK CATALOGS </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "e7524267-19fa-49f9-8786-9eb54d7a5cc6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "from astropy.cosmology import FlatwCDM\n",
+    "cosmo = FlatwCDM(H0=70, Om0=0.3, w0=-1.0)\n",
+    "import astropy.constants as const\n",
+    "import astropy.units as units\n",
+    "from scipy import integrate, signal\n",
+    "from astropy.io import fits\n",
+    "\n",
+    "def sigma_nfw(r,R_cluster,c,M_cluster,z_cluster,cosmo_t):\n",
+    "    rs_cluster = R_cluster/c\n",
+    "    x=r/rs_cluster\n",
+    "    \n",
+    "    rs_cluster_t=rs_cluster*(cosmo_t.angular_diameter_distance(z_cluster)/cosmo.angular_diameter_distance(z_cluster)).value\n",
+    "    k=M_cluster/(4.0*np.pi*rs_cluster_t**2*(np.log(1.0+c)-c/(1.0+c))) # k = rho_c*delta_c*r_s in M_solar/Mpc^2\n",
+    "    if x < 1:\n",
+    "        value=2.0*k*(1.0-2.0*np.arctanh(np.sqrt((1.0-x)/(1.0+x)))/np.sqrt(1.0-x**2))/(x**2-1.0)\n",
+    "    if x == 1:\n",
+    "        value=2.0*k/3.0\n",
+    "    if x > 1:\n",
+    "        value=2.0*k*(1.0-2.0*np.arctan(np.sqrt((x-1)/(1+x)))/np.sqrt(x**2-1.0))/(x**2-1.0)\n",
+    "    \n",
+    "    #value is in M_solar / MPc^2 units\n",
+    "    return value\n",
+    "\n",
+    "def sigma_c(z,z_cluster,cosmo_t):\n",
+    "    value=(const.c**2*cosmo_t.angular_diameter_distance(z)/cosmo_t.angular_diameter_distance(z_cluster)/cosmo_t.angular_diameter_distance_z1z2(z_cluster,z)/(4*np.pi*const.G)).to('Msun/(Mpc**2)').value\n",
+    "    #value in M_solar/Mpc**2 units\n",
+    "    return value\n",
+    "\n",
+    "def kappa_nfw(r,R_cluster,c,M_cluster,z_cluster,cosmo_t):\n",
+    "    return sigma_nfw(r,R_cluster,c,M_cluster,z_cluster,cosmo_t)/sigma_c(z,z_cluster,cosmo_t)\n",
+    "\n",
+    "def g_lt(r,rs_cluster): # arbitrary functions for ease of calculation. Not to be confused with red. shear g\n",
+    "    x=r/rs_cluster\n",
+    "    term1=8.0*np.arctanh(np.sqrt((1.0-x)/(1.0+x)))/(x**2*np.sqrt(1.0-x**2))\n",
+    "    term2=4.0*np.log(x/2.0)/x**2\n",
+    "    term3=2.0/(x**2-1.0)\n",
+    "    term4=4.0*np.arctanh(np.sqrt((1.0-x)/(1.0+x)))/((x**2-1.0)*np.sqrt(1.0-x**2))\n",
+    "    \n",
+    "    return term1+term2-term3+term4\n",
+    "\n",
+    "def g_gt(r,rs_cluster):\n",
+    "    x=r/rs_cluster\n",
+    "    term1=8.0*np.arctan(np.sqrt((x-1.0)/(1.0+x)))/(x**2*np.sqrt(x**2-1.0))\n",
+    "    term2=4.0*np.log(x/2.0)/x**2\n",
+    "    term3=2.0/(x**2-1.0)\n",
+    "    term4=4.0*np.arctan(np.sqrt((x-1.0)/(1.0+x)))/((x**2-1.0)**1.5)\n",
+    "    \n",
+    "    return term1+term2-term3+term4\n",
+    "\n",
+    "def gamma_nfw(r,z,R_cluster,c,M_cluster,z_cluster,cosmo_t):\n",
+    "    rs_cluster = R_cluster/c\n",
+    "    x=r/rs_cluster\n",
+    "    rs_cluster_t=rs_cluster*(cosmo_t.angular_diameter_distance(z_cluster)/cosmo.angular_diameter_distance(z_cluster)).value\n",
+    "    k=M_cluster/(4.0*np.pi*rs_cluster_t**2*(np.log(1.0+c)-c/(1.0+c))) # k = rho_c*delta_c*r_s in M_solar/Mpc^2 units\n",
+    "    if  x < 1:\n",
+    "        value=k*g_lt(r,rs_cluster)/sigma_c(z,z_cluster,cosmo_t)\n",
+    "    if x == 1:\n",
+    "        value=k*(10.0/3.0+4.0*np.log(0.5))/sigma_c(z,z_cluster,cosmo_t)\n",
+    "    if x > 1:\n",
+    "        value=k*g_gt(r,rs_cluster)/sigma_c(z,z_cluster,cosmo_t)\n",
+    "    \n",
+    "    return value\n",
+    "\n",
+    "def gamma_inf(r,z,R_cluster,c,M_cluster,z_cluster,cosmo_t):\n",
+    "    rs_cluster = R_cluster/c\n",
+    "    x=r/rs_cluster\n",
+    "    rs_cluster_t=rs_cluster*(cosmo_t.angular_diameter_distance(z_cluster)/cosmo.angular_diameter_distance(z_cluster)).value\n",
+    "    k=M_cluster/(4.0*np.pi*rs_cluster_t**2*(np.log(1.0+c)-c/(1.0+c))) # k = rho_c*delta_c*r_s in M_solar/Mpc^2 units\n",
+    "    if  x < 1:\n",
+    "        value=k*g_lt(r,rs_cluster)\n",
+    "    if x == 1:\n",
+    "        value=k*(10.0/3.0+4.0*np.log(0.5))\n",
+    "    if x > 1:\n",
+    "        value=k*g_gt(r,rs_cluster)\n",
+    "    \n",
+    "    return value\n",
+    "\n",
+    "def g_nfw(r,z,R_cluster,c,M_cluster,z_cluster,cosmo_t): # Function for reduced shear from gamma and kappa\n",
+    "    val=[]\n",
+    "    for i in range(len(r)):\n",
+    "        #val.append(gamma_nfw(r[i],z[i],rs_cluster,M_cluster,z_cluster,cosmo_t)/(1-kappa_nfw(r[i],z[i],rs_cluster,M_cluster,z_cluster,cosmo_t)))\n",
+    "        val.append(gamma_nfw(r[i],z[i],R_cluster,c,M_cluster,z_cluster,cosmo_t))\n",
+    "    return np.array(val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "991af09d-50b7-481b-ad56-a233ca9ca31f",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "#defining the Probability distribution function for galaxy redshift, 0<=z<=2.5\n",
+    "def gal_z_pdf(z):\n",
+    "    a_pdf=0.612\n",
+    "    b_pdf=8.125\n",
+    "    c_pdf=0.620\n",
+    "    A_pdf=1.555\n",
+    "    \n",
+    "    return A_pdf*(z**a_pdf+z**(a_pdf*b_pdf))/(z**b_pdf+c_pdf)\n",
+    "\n",
+    "def draw_redshift_of_galaxy(N=10**3, z1z2_delta=0.3, z_min=0, z_max=2.5):\n",
+    "    \n",
+    "    print(\"Passed arguments: N=\",N,\" z1z2_delta=\",z1z2_delta,\" z_min=\",z_min,\" z_max=\",z_max)\n",
+    "    #Discretization of redshift range for CDF calculations:\n",
+    "    N_cdf=1000\n",
+    "    del_z=(z_max-z_min)/N_cdf\n",
+    "    z=np.linspace(z_min+del_z,z_max,N_cdf)\n",
+    "\n",
+    "    #Cumulative Distribution function:\n",
+    "    cdf=[]\n",
+    "    z_cdf=[]\n",
+    "    i=0\n",
+    "    for z_i in z:\n",
+    "        zz=np.linspace(z_min,z_i, int(((z_i-z_min)/del_z)*10**2))\n",
+    "        y=gal_z_pdf(zz)\n",
+    "        val=integrate.simps(y, zz)\n",
+    "        cdf.append(val)\n",
+    "        z_cdf.append(z_i+del_z/2)\n",
+    "        i=i+1\n",
+    "    cdf=np.array(cdf/np.max(cdf))\n",
+    "    z_cdf=np.array(z_cdf)\n",
+    "\n",
+    "    #redshift drawing:\n",
+    "    z1_drawn=[]\n",
+    "    i=0\n",
+    "\n",
+    "    while i < N :\n",
+    "        cdf1_r=random.uniform(min(cdf),max(cdf))\n",
+    "        k=np.max(np.where(cdf < cdf1_r))\n",
+    "        l=np.min(np.where(cdf > cdf1_r))\n",
+    "        \n",
+    "        ## linear interpol. between two points on CDF:\n",
+    "        z1_r=(cdf1_r-cdf[k])*(z_cdf[l]-z_cdf[k])/(cdf[l]-cdf[k])+z_cdf[k]\n",
+    "        if z1_r > z_cluster and z1_r < z_max-z1z2_delta:\n",
+    "            z1_drawn.append(z1_r)\n",
+    "            i=i+1\n",
+    "                \n",
+    "    return z1_drawn\n",
+    "\n",
+    "def draw_redshift_pairs(N=10**3, z1z2_delta=0.3, z_min=0, z_max=2.5, want_pairs=True):\n",
+    "    \n",
+    "    print(\"Passed arguments: N=\",N,\" z1z2_delta=\",z1z2_delta,\" z_min=\",z_min,\" z_max=\",z_max)\n",
+    "    print(\"Want_pairs is set to: \",want_pairs)\n",
+    "    #Discretization of redshift range for CDF calculations:\n",
+    "    N_cdf=1000\n",
+    "    del_z=(z_max-z_min)/N_cdf\n",
+    "    z=np.linspace(z_min+del_z,z_max,N_cdf)\n",
+    "\n",
+    "    #Cumulative Distribution function:\n",
+    "    cdf=[]\n",
+    "    z_cdf=[]\n",
+    "    i=0\n",
+    "    for z_i in z:\n",
+    "        zz=np.linspace(z_min,z_i, int(((z_i-z_min)/del_z)*10**2))\n",
+    "        y=gal_z_pdf(zz)\n",
+    "        val=integrate.simps(y, zz)\n",
+    "        cdf.append(val)\n",
+    "        z_cdf.append(z_i+del_z/2)\n",
+    "        i=i+1\n",
+    "    cdf=np.array(cdf/np.max(cdf))\n",
+    "    z_cdf=np.array(z_cdf)\n",
+    "\n",
+    "    #redshift drawing:\n",
+    "    if want_pairs == True:\n",
+    "        z1_drawn=[]\n",
+    "        z2_drawn=[]\n",
+    "        i=0\n",
+    "        while i < N :\n",
+    "            cdf1_r=random.uniform(min(cdf),max(cdf))\n",
+    "            k=np.max(np.where(cdf < cdf1_r))\n",
+    "            l=np.min(np.where(cdf > cdf1_r))\n",
+    "            find_z2=True\n",
+    "            ## linear interpol. between two points on CDF:\n",
+    "            z1_r=(cdf1_r-cdf[k])*(z_cdf[l]-z_cdf[k])/(cdf[l]-cdf[k])+z_cdf[k]  \n",
+    "            if z1_r > z_cluster and z1_r < z_max-z1z2_delta:\n",
+    "                while i < N and find_z2==True:\n",
+    "                    cdf2_r=random.uniform(min(cdf),max(cdf))\n",
+    "                    k=np.max(np.where(cdf < cdf2_r))\n",
+    "                    l=np.min(np.where(cdf > cdf2_r))\n",
+    "                    ## linear interpol. between two points on CDF\n",
+    "                    z2_r=(cdf2_r-cdf[k])*(z_cdf[l]-z_cdf[k])/(cdf[l]-cdf[k])+z_cdf[k]  \n",
+    "                    if z2_r > z1_r+0.3 and z2_r < z_max:\n",
+    "                        z1_drawn.append(z1_r)\n",
+    "                        z2_drawn.append(z2_r)\n",
+    "                        find_z2=False\n",
+    "                        i=i+1\n",
+    "    if want_pairs == False:\n",
+    "        ### For the case when we want to generate N*2 galaxies not behind each other using the same code\n",
+    "        ### See the daw_position_pairs function definition for more details.\n",
+    "        z1_drawn=[]\n",
+    "        z2_drawn=[]\n",
+    "        i=0\n",
+    "        while i < N :\n",
+    "            cdf1_r=random.uniform(min(cdf),max(cdf))\n",
+    "            k=np.max(np.where(cdf < cdf1_r))\n",
+    "            l=np.min(np.where(cdf > cdf1_r))\n",
+    "            ## linear interpol. between two points on CDF:\n",
+    "            z1_r=(cdf1_r-cdf[k])*(z_cdf[l]-z_cdf[k])/(cdf[l]-cdf[k])+z_cdf[k]  \n",
+    "            if z1_r > z_cluster and z1_r < z_max-z1z2_delta: \n",
+    "                z1_drawn.append(z1_r)\n",
+    "                i=i+1\n",
+    "        i=0\n",
+    "        while i < N :\n",
+    "            cdf2_r=random.uniform(min(cdf),max(cdf))\n",
+    "            k=np.max(np.where(cdf < cdf1_r))\n",
+    "            l=np.min(np.where(cdf > cdf1_r))\n",
+    "            ## linear interpol. between two points on CDF:\n",
+    "            z2_r=(cdf2_r-cdf[k])*(z_cdf[l]-z_cdf[k])/(cdf[l]-cdf[k])+z_cdf[k]  \n",
+    "            if z2_r > z_cluster and z1_r < z_max-z1z2_delta: \n",
+    "                z2_drawn.append(z2_r)\n",
+    "                i=i+1\n",
+    "\n",
+    "    z1_gal=np.array(z1_drawn)\n",
+    "    z2_gal=np.array(z2_drawn)\n",
+    "    print(\"Generated Redshift Pairs successfully ...\")\n",
+    "    return z1_gal, z2_gal\n",
+    "\n",
+    "def draw_position_pairs(N=10**3, r_min=0.5, r_max=5.0, want_pairs=True):\n",
+    "    #def draw_position_pairs(*args, **kwargs):\n",
+    "    #Draw (x,y) Mpc for each pair of galaxies\n",
+    "    # Small box size: [-r_min/np.sqrt(2),r_min/np.sqrt(2)] \n",
+    "    # Large box size: [-r_max,r_max]\n",
+    "    \n",
+    "    print(\"Passed arguments: N=\",N,\" r_min=\",r_min,\" r_max=\",r_max)\n",
+    "    print(\"Want_pairs is set to: \",want_pairs)\n",
+    "    x1=np.array([])\n",
+    "    y1=np.array([])\n",
+    "    while (len(x1) < N):\n",
+    "        x_r,y_r = np.random.uniform(r_min/np.sqrt(2),r_max,size=(2,N))*np.random.choice([-1,1], (2,N))\n",
+    "        ind = np.where((np.sqrt(x_r**2+y_r**2) > r_min) & (np.sqrt(x_r**2+y_r**2) < r_max))[0]\n",
+    "        x1 = np.append(x1,x_r[ind])\n",
+    "        y1 = np.append(y1,y_r[ind])\n",
+    "    x1=x1[:N]\n",
+    "    y1=y1[:N]\n",
+    "    \n",
+    "    ## Placing second galaxy EXACTLY behind the first one:\n",
+    "    x2=x1\n",
+    "    y2=y1\n",
+    "    if want_pairs == False:\n",
+    "        ### When we want to generate just N*2 galaxies instead of N galaxy pairs. All that\n",
+    "        ### is needed, is to generate a random position of the second galaxy. The \"paired\" galaxy are \n",
+    "        ### completely disjoint as redshifts will also be drawn independently.\n",
+    "        \n",
+    "        x2=np.array([])\n",
+    "        y2=np.array([])\n",
+    "        while (len(x2) < N):\n",
+    "            x_r,y_r = np.random.uniform(r_min/np.sqrt(2),r_max,size=(2,N))*np.random.choice([-1,1], (2,N))\n",
+    "            ind = np.where((np.sqrt(x_r**2+y_r**2) > r_min) & (np.sqrt(x_r**2+y_r**2) < r_max))[0]\n",
+    "            x2 = np.append(x2,x_r[ind])\n",
+    "            y2 = np.append(y2,y_r[ind])\n",
+    "        x2=x2[:N]\n",
+    "        y2=y2[:N]\n",
+    "        \n",
+    "    print(\"Generated Position Pairs successfully ...\")\n",
+    "    return x1,y1,x2,y2\n",
+    "\n",
+    "def draw_shape_pairs(N=10**3, g_sigma=0.24):\n",
+    "    #def draw_shape_pairs(*args, **kwargs):\n",
+    "    ## e1_1, e1_2: refers to galaxy 1 (e1,e2)\n",
+    "    ## e2_1, e2_2: refers to galaxy 2 (e1,e2)\n",
+    "    \n",
+    "    print(\"Passed arguments: N=\",N,\" g_sigma=\",g_sigma)\n",
+    "    e1_1_arr=np.array([])\n",
+    "    e1_2_arr=np.array([])\n",
+    "    e2_1_arr=np.array([])\n",
+    "    e2_2_arr=np.array([])\n",
+    "    i=0\n",
+    "    while i < N:\n",
+    "        e1_1,e1_2,e2_1,e2_2=np.random.normal(loc=0.0,scale=g_sigma, size=4)\n",
+    "        if e1_1 > -1 and e1_1 < 1 and e1_2 > -1 and e1_2 < 1 and e2_1 > -1 and e2_1 < 1 and e2_2 > -1 and e2_2<1:\n",
+    "            e1_1_arr = np.append(e1_1_arr,e1_1)\n",
+    "            e1_2_arr = np.append(e1_2_arr,e1_2)\n",
+    "            e2_1_arr = np.append(e2_1_arr,e2_1)\n",
+    "            e2_2_arr = np.append(e2_2_arr,e2_2)\n",
+    "            i=i+1\n",
+    "    print(\"Generated\",N,\" Intrinsic shapes successfully ...\")\n",
+    "    return e1_1_arr,e1_2_arr,e2_1_arr,e2_2_arr\n",
+    "\n",
+    "#def generate_shear_catalog(N=10**3, z1z2_delta=0.3, z_min=0, z_max=2.5, \n",
+    "#                            r_min=0.5, r_max=5.0, g_sigma=0.24, *args, **kwargs):\n",
+    "def generate_shear_catalog(R_cluster,c,M_cluster, z_cluster, fid_cosmology, file=None, want_pairs=True,\n",
+    "                           *args, **kwargs): \n",
+    "    \n",
+    "    print(\"Passed arguments: sigma_v_cluster=\",sigma_v_cluster,\" z_cluster=\",z_cluster,\n",
+    "          \"fid_cosmology=\",fid_cosmology)\n",
+    "    #Draw redshifts:\n",
+    "    z1,z2 = draw_redshift_pairs(N, z1z2_delta, z_min, z_max, want_pairs=want_pairs)\n",
+    "    x1,y1,x2,y2 = draw_position_pairs(N, r_min, r_max, want_pairs=want_pairs)\n",
+    "    e1_1, e1_2, e2_1, e2_2 = draw_shape_pairs(N, g_sigma)\n",
+    "    \n",
+    "    r1 = np.sqrt(x1**2+y1**2)\n",
+    "    r2 = np.sqrt(x2**2+y2**2)\n",
+    "    phi_1 = np.arctan2(x1,y1) # in radians\n",
+    "    phi_2 = np.arctan2(x2,y2) # in radians\n",
+    "    \n",
+    "    #Draw tangential reduced shears:\n",
+    "    #r,z,rs_cluster,M_cluster,z_cluster,cosmo_t\n",
+    "    gt_1 = g_nfw(r1,z1,R_cluster,c,M_cluster,z_cluster, fid_cosmology)\n",
+    "    gt_2 = g_nfw(r2,z2,R_cluster,c,M_cluster,z_cluster, fid_cosmology)\n",
+    "    \n",
+    "    ## γt = −[γ1 cos(2ϕ) + γ2 sin(2ϕ)],\n",
+    "    ## SOURCE: Taylor 2007, \"Probing dark energy with the shear-ratio geometric test\"\n",
+    "    \n",
+    "    et_int_1 = -1*(e1_1*np.cos(2*phi_1) +e1_2*np.sin(2*phi_1))\n",
+    "    et_int_2 = -1*(e2_1*np.cos(2*phi_2) +e2_2*np.sin(2*phi_2))\n",
+    "    et_1 = gt_1 #+ et_int_1\n",
+    "    et_2 = gt_2 #+ et_int_2\n",
+    "    \n",
+    "    if file == None:\n",
+    "        dictionary = {\n",
+    "            \"z1\":z1,\n",
+    "            \"z2\":z2,\n",
+    "            \"r1\":r1,\n",
+    "            \"r2\":r2,\n",
+    "            \"et_1\":et_1,\n",
+    "            \"et_2\":et_2,            \n",
+    "        }\n",
+    "        return dictionary\n",
+    "    \n",
+    "    ## Saving the catalog to file\n",
+    "    if file != None:\n",
+    "        hdu = fits.BinTableHDU.from_columns(\n",
+    "         [fits.Column(name='No.', format='I', array=np.arange(N)+1),\n",
+    "          fits.Column(name='z1', format='E', array=np.round(z1,3)),\n",
+    "          fits.Column(name='z2', format='E', array=np.round(z2,3)),\n",
+    "          fits.Column(name='x1', format='E', array=np.round(x1,3)),\n",
+    "          fits.Column(name='y1', format='E', array=np.round(y1,3)),\n",
+    "          fits.Column(name='x2', format='E', array=np.round(x2,3)),\n",
+    "          fits.Column(name='y2', format='E', array=np.round(y2,3)),\n",
+    "          fits.Column(name='r1', format='E', array=np.round(r1,3)),\n",
+    "          fits.Column(name='r2', format='E', array=np.round(r2,3)),\n",
+    "          fits.Column(name='phi_1', format='E', array=np.round(phi_1,3)),\n",
+    "          fits.Column(name='phi_2', format='E', array=np.round(phi_2,3)),\n",
+    "          fits.Column(name='e1_1', format='E', array=e1_1),\n",
+    "          fits.Column(name='e1_2', format='E', array=e1_2),\n",
+    "          fits.Column(name='e2_1', format='E', array=e2_1),\n",
+    "          fits.Column(name='e2_2', format='E', array=e2_2),\n",
+    "          fits.Column(name='et_1', format='E', array=et_1),\n",
+    "          fits.Column(name='et_2', format='E', array=et_2),\n",
+    "          fits.Column(name='et_int_1', format='E', array=et_int_1),\n",
+    "          fits.Column(name='et_int_2', format='E', array=et_int_2),\n",
+    "          fits.Column(name='gt_1', format='E', array=gt_1),\n",
+    "          fits.Column(name='gt_2', format='E', array=gt_2)])\n",
+    "        hdu.writeto(file, overwrite=True)\n",
+    "        print('Catalog of shears and redshifts written to %s ' %file)\n",
+    "        "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "54cec253-29e4-40b2-84aa-8d95985bba2c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "sigma_v of cluster= 2073.8652969844206\n",
+      "Passed arguments: sigma_v_cluster= 2073.8652969844206  z_cluster= 0.5 fid_cosmology= FlatwCDM(H0=70.0 km / (Mpc s), Om0=0.3, w0=-1.0, Tcmb0=0.0 K, Neff=3.04, m_nu=None, Ob0=None)\n",
+      "Passed arguments: N= 5000  z1z2_delta= 0.3  z_min= 0.55  z_max= 2.5\n",
+      "Want_pairs is set to:  False\n",
+      "Generated Redshift Pairs successfully ...\n",
+      "Passed arguments: N= 5000  r_min= 1.0  r_max= 3.0\n",
+      "Want_pairs is set to:  False\n",
+      "Generated Position Pairs successfully ...\n",
+      "Passed arguments: N= 5000  g_sigma= 0.0001\n",
+      "Generated 5000  Intrinsic shapes successfully ...\n",
+      "Catalog of shears and redshifts written to /global/u2/s/sriniva/Elliptical_lenses_data/Rad_NFW_halos/Individual_Galaxy_catalog_5000_sigma_0.0001_r_min_1.0_r_max_3.0_z_cluster_0.5.fits \n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'\\nr=np.linspace(0.6,3.0,9)\\n\\nfor i,r_i in enumerate(r):\\n    if i == len(r) - 1: break\\n    r_min = r_i\\n    r_max = r[i+1]\\n    N = int(np.ceil(1000*(r_max**2 - r_min**2)/(r[1]**2-r[0]**2)))\\n\\n    file_loc = \"/global/u2/s/sriniva/Shear_ratio_project_new/Shear_catalogs/SIS/\"\\n    file=file_loc+\"Galaxy_catalog_\"+str(N)+\\'_sigma_\\'+str(g_sigma)+\"_r_min_\"+str(\\n        np.round(r_min,1))+\"_r_max_\"+str(np.round(r_max,1))+\".fits\"\\n\\n    generate_shear_catalog(sigma_v_cluster, z_cluster, cosmo, file, N=N, z1z2_delta=z1z2_delta, z_min=z_min, \\n                           z_max=z_max, r_min=r_min, r_max=r_max, g_sigma=g_sigma)\\n'"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "M_cluster = 10**15 # Solar Masses\n",
+    "R_cluster = 1.5 # Mpc\n",
+    "rs_cluster=0.418 # in Mpc\n",
+    "c = R_cluster/rs_cluster\n",
+    "#z_cluster=0.182 ## Default\n",
+    "z_cluster = 0.5\n",
+    "\n",
+    "#SOURCE: Taylor 2007, \"Probing dark energy with the shear-ratio geometric test\"\n",
+    "sigma_v_cluster=np.sqrt((3*const.G*(M_cluster*units.M_sun))/(2*(R_cluster*units.Mpc))).to('km/s').value\n",
+    "print(\"sigma_v of cluster=\", sigma_v_cluster)\n",
+    "\n",
+    "#M_cluster=10.671*10**14\n",
+    "w0=-1.0  #fiducial cosmology\n",
+    "r_min=1.0\n",
+    "r_max=3.0\n",
+    "g_sigma=0.0001\n",
+    "z1z2_delta=0.3\n",
+    "z_min=z_cluster+0.05\n",
+    "z_max=2.5\n",
+    "N=5000\n",
+    "want_pairs_choice = False\n",
+    "\n",
+    "file_loc = \"/global/u2/s/sriniva/Elliptical_lenses_data/Rad_NFW_halos/\"\n",
+    "catalog_name = \"Galaxy_catalog_\"+str(N)+'_sigma_'+str(g_sigma)+\"_r_min_\"+str(\n",
+    "    np.round(r_min,1))+\"_r_max_\"+str(np.round(r_max,1))+\"_z_cluster_\"+str(\n",
+    "    z_cluster)+\".fits\"\n",
+    "\n",
+    "if want_pairs_choice == False:\n",
+    "    misc=\"Individual_\"\n",
+    "else:\n",
+    "    misc=\"\"\n",
+    "file = file_loc + misc + catalog_name\n",
+    "cosmo = FlatwCDM(H0=70, Om0=0.3, w0=-1.0)\n",
+    "\n",
+    "generate_shear_catalog(R_cluster,c,M_cluster, z_cluster, cosmo, file, N=N, z1z2_delta=z1z2_delta, z_min=z_min, \n",
+    "                       z_max=z_max, r_min=r_min, r_max=r_max, g_sigma=g_sigma, \n",
+    "                       want_pairs=want_pairs_choice)\n",
+    "\n",
+    "'''\n",
+    "r=np.linspace(0.6,3.0,9)\n",
+    "\n",
+    "for i,r_i in enumerate(r):\n",
+    "    if i == len(r) - 1: break\n",
+    "    r_min = r_i\n",
+    "    r_max = r[i+1]\n",
+    "    N = int(np.ceil(1000*(r_max**2 - r_min**2)/(r[1]**2-r[0]**2)))\n",
+    "\n",
+    "    file_loc = \"/global/u2/s/sriniva/Shear_ratio_project_new/Shear_catalogs/SIS/\"\n",
+    "    file=file_loc+\"Galaxy_catalog_\"+str(N)+'_sigma_'+str(g_sigma)+\"_r_min_\"+str(\n",
+    "        np.round(r_min,1))+\"_r_max_\"+str(np.round(r_max,1))+\".fits\"\n",
+    "\n",
+    "    generate_shear_catalog(sigma_v_cluster, z_cluster, cosmo, file, N=N, z1z2_delta=z1z2_delta, z_min=z_min, \n",
+    "                           z_max=z_max, r_min=r_min, r_max=r_max, g_sigma=g_sigma)\n",
+    "'''"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "id": "21bd0320-2d8b-4c5b-ab17-243761a83e11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Starting guesses for  12  chains:\n",
+      "[[2.74451871e+15 1.13608912e+01 1.17179854e-01 2.75443075e+01]\n",
+      " [3.57623164e+15 1.85119328e+01 6.33557819e-01 2.77620299e+01]\n",
+      " [3.01421412e+15 1.42072116e+00 1.42005419e-01 4.24686635e+01]\n",
+      " [2.72487103e+15 1.74258599e+00 9.35227761e-01 3.06819135e+01]\n",
+      " [2.11885034e+15 4.04367949e-01 5.16677654e-01 1.61778555e+01]\n",
+      " [3.22982467e+15 1.66523969e+01 4.10573854e-01 1.96664379e+01]\n",
+      " [2.18849847e+15 1.55631350e+01 2.61983600e-01 3.13934038e+01]\n",
+      " [4.45897323e+15 1.74002430e+01 7.66513929e-01 2.71014622e+00]\n",
+      " [4.81835014e+15 1.95723668e+01 4.51643214e-01 3.00045022e+01]\n",
+      " [1.91782415e+15 1.59831713e+01 5.62792766e-01 3.01787041e+01]\n",
+      " [3.95883347e+15 9.22958725e+00 1.87000235e-02 9.46721525e+00]\n",
+      " [2.64494570e+15 1.56105835e+01 6.11497379e-01 5.80168339e+00]]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "  4%|▍         | 262/6000 [00:06<02:14, 42.53it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 17%|█▋        | 1027/6000 [00:24<01:55, 43.13it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 18%|█▊        | 1107/6000 [00:26<01:55, 42.36it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 22%|██▏       | 1307/6000 [00:30<01:47, 43.69it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 34%|███▎      | 2022/6000 [00:47<01:31, 43.55it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 35%|███▌      | 2102/6000 [00:49<01:30, 43.07it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 50%|█████     | 3002/6000 [01:10<01:10, 42.44it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 50%|█████     | 3012/6000 [01:10<01:12, 41.40it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 67%|██████▋   | 4012/6000 [01:33<00:46, 42.38it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 73%|███████▎  | 4352/6000 [01:41<00:38, 42.65it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      " 93%|█████████▎| 5602/6000 [02:10<00:09, 43.31it/s]/tmp/ipykernel_2155535/169669499.py:7: RuntimeWarning: invalid value encountered in divide\n",
+      "  return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)\n",
+      "100%|██████████| 6000/6000 [02:20<00:00, 42.77it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Time taken:140.9 seconds\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 16 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "f=None\n",
+    "\n",
+    "#cosmo = FlatwCDM(H0=70, Om0=0.3, w0=-1.0)\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.3, Omega_b0=0.000, Omega_k0=0.0)\n",
+    "\n",
+    "## DATA:\n",
+    "file_loc = \"/global/u2/s/sriniva/Elliptical_lenses_data/Rad_NFW_halos/\"\n",
+    "catalog_name = \"Individual_Galaxy_catalog_5000_sigma_0.0001_r_min_1.0_r_max_3.0_z_cluster_0.5.fits\"\n",
+    "#file=file_loc+\"Galaxy_catalog_10000_sigma_0.24.fits\"\n",
+    "\n",
+    "file=file_loc+catalog_name\n",
+    "hdul=fits.open(file)\n",
+    "data = Table(hdul[1].data)\n",
+    "data\n",
+    "#z_cluster=0.182\n",
+    "z_cluster=0.5\n",
+    "g_sigma=0.01\n",
+    "r = np.append(data['r1'].data,data['r2'].data)\n",
+    "x = np.append(data['x1'].data,data['x2'].data)\n",
+    "y = np.append(data['y1'].data,data['y2'].data)\n",
+    "phi = np.append(data['phi_1'].data,data['phi_2'].data)\n",
+    "z = np.append(data['z1'].data,data['z2'].data)\n",
+    "e_t = np.append(data['et_1'].data,data['et_2'].data)\n",
+    "e_1 = np.append(data['e1_1'].data,data['e2_1'].data)\n",
+    "e_2 = np.append(data['e1_2'].data,data['e2_2'].data)\n",
+    "\n",
+    "gamma1 = e_t*(-1*np.cos(2*phi))#np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = e_t*(-1*np.sin(2*phi))#np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = x/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = y/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "#MCMC parameters:\n",
+    "params_key=[\"M200m\",\"c200m\",\"e\",r\"$\\theta_{e}$ in degrees\"]\n",
+    "ndim = len(params_key)\n",
+    "nwalkers = ndim*2+4\n",
+    "nsteps = 6000\n",
+    "nburns = 3000\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "limits=[[1.0E12,5.0E15],  # mdelta\n",
+    "       [0.0,20.0],     # cdelta\n",
+    "       [0.0001,0.99],   # e\n",
+    "       [0.0, 45.0]]    # theta_e #in degrees\n",
+    "z_cl=0.47\n",
+    "#z_gal=0.8\n",
+    "q_true=1.0 #default = 2/3 for the files in the same directory as this notebook\n",
+    "ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "theta_e_true = 0.0\n",
+    "true_value=[1.0E15, 1.5/0.418, ell_true,theta_e_true]\n",
+    "\n",
+    "'''\n",
+    "Axis ratio q:  0.6666666666666667\n",
+    "c:  3.890554441834905\n",
+    "r200_Mpc: 1.026773208537997\n",
+    "ARCSEC_TO_MPC:  0.00588784182755241\n",
+    "BOX_WIDTH_ARCSEC*ARCSEC_TO_MPC:  5.88784182755241\n",
+    "SIGMA_C=3.77988e+15\n",
+    "arcsec_per_pix:  10.0\n",
+    "'''\n",
+    "# PREP DATA:\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=z_cluster, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=1)\n",
+    "\n",
+    "\n",
+    "# CALL EMCEE\n",
+    "post, mean, err_plus, err_minus = emcee_wrapper(gamma1, gamma2, x_arcsec, y_arcsec,z_cl=z_cluster,\n",
+    "                                                params_key=params_key,limits=limits,true_value=true_value, ndim=ndim,nwalkers=nwalkers,nsteps=nsteps, nburn=nburns, filename=f)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "61cb40e8-6f2e-41cd-9ffd-92e50aed081c",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'mean' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[10], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m mdelta_fit \u001b[38;5;241m=\u001b[39m mean[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;66;03m#2.5E14#\u001b[39;00m\n\u001b[1;32m      2\u001b[0m cdelta_fit \u001b[38;5;241m=\u001b[39m mean[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;66;03m#3.89#\u001b[39;00m\n\u001b[1;32m      3\u001b[0m ell_fit \u001b[38;5;241m=\u001b[39m mean[\u001b[38;5;241m2\u001b[39m]\u001b[38;5;66;03m#ell_true#\u001b[39;00m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'mean' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "mdelta_fit = mean[0]#2.5E14#\n",
+    "cdelta_fit = mean[1]#3.89#\n",
+    "ell_fit = mean[2]#ell_true#\n",
+    "print(\"ell_fit = \",ell_fit)\n",
+    "theta_e_fit = mean[3] #in degrees\n",
+    "\n",
+    "## DATA:\n",
+    "file_loc = \"/global/u2/s/sriniva/Elliptical_lenses_data/Rad_NFW_halos/\"\n",
+    "catalog_name = \"Individual_Galaxy_catalog_5000_sigma_0.0001_r_min_1.0_r_max_3.0_z_cluster_0.5.fits\"\n",
+    "#file=file_loc+\"Galaxy_catalog_10000_sigma_0.24.fits\"\n",
+    "\n",
+    "file=file_loc+catalog_name\n",
+    "hdul=fits.open(file)\n",
+    "data = Table(hdul[1].data)\n",
+    "data\n",
+    "#z_cluster=0.182\n",
+    "z_cluster=0.5\n",
+    "g_sigma=0.01\n",
+    "r = np.append(data['r1'].data,data['r2'].data)\n",
+    "x = np.append(data['x1'].data,data['x2'].data)\n",
+    "y = np.append(data['y1'].data,data['y2'].data)\n",
+    "z = np.append(data['z1'].data,data['z2'].data)\n",
+    "e_t = np.append(data['et_1'].data,data['et_2'].data)\n",
+    "e_1 = np.append(data['e1_1'].data,data['e2_1'].data)\n",
+    "e_2 = np.append(data['e1_2'].data,data['e2_2'].data)\n",
+    "\n",
+    "gamma1 = e_t*(-1*np.cos(2*phi))#np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = e_t*(-1*np.sin(2*phi))#np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = x/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = y/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "\n",
+    "theta_e_fit = theta_e_fit*np.pi/180.0\n",
+    "gamma1,gamma2,x_arcsec,y_arcsec = halo_rotation(gamma1,gamma2,x_arcsec,y_arcsec,theta_e_fit)\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=0.5, sigma_c=3.77988e+15)\n",
+    "\n",
+    "\n",
+    "\n",
+    "plt.figure(figsize=[13,5])\n",
+    "plt.subplot(131)\n",
+    "plt.errorbar(r_mono, ds_mono, yerr=np.abs(ds_mono_err), label='data', color='orange')\n",
+    "ds_model = compute_delta_sigma_excess(ell_fit, r_mono, mdelta_fit, cdelta_fit, z_cl=z_cluster, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "ds_model_without_e_square = clmm.compute_excess_surface_density(r_mono, mdelta_fit, cdelta_fit, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "plt.plot(r_mono, ds_model, label='model[FIT]', color='darkblue')\n",
+    "\n",
+    "\n",
+    "ds_model = compute_delta_sigma_excess((1-0.99**2)/(1+0.99**2), r_mono, 1.0E15 ,1.5/0.418, z_cl=z_cluster, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "plt.plot(r_mono, ds_model, label='model[TRUTH]', color='gray')\n",
+    "#plt.plot(r_mono, ds_model_without_e_square-ds_model, label='model*(1+e^2) - model', color='darkblue', ls='dashed')\n",
+    "#plt.yscale('log')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.title(r'$\\Delta\\Sigma^{mono}$')\n",
+    "plt.legend()\n",
+    "\n",
+    "\n",
+    "plt.subplot(132)\n",
+    "ds_model = compute_delta_sigma_const(ell_fit, r_quad, mdelta_fit, cdelta_fit, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "plt.plot(r_quad, ds_model, label='model[FIT]', color='darkblue')\n",
+    "print(ds_model)\n",
+    "plt.errorbar(r_quad, ds_quad_const,  yerr=ds_quad_const_err, label='data', color='orange')\n",
+    "plt.yscale('linear')\n",
+    "plt.xscale('linear')\n",
+    "plt.title(r'$\\Delta\\Sigma^{quad}_{const}$')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(133)\n",
+    "ds_model = compute_delta_sigma_4theta(ell_fit, r_quad, mdelta_fit, cdelta_fit, z_cl, cosmo=cosmo, hpmd='nfw', \n",
+    "                                             sample_N=10000, delta_mdef=200)\n",
+    "plt.plot(r_quad, ds_model, label='model[FIT]', color='darkblue')\n",
+    "\n",
+    "plt.errorbar(r_quad, ds_quad_4theta,  yerr=ds_quad_4theta_err, label='data', color='orange')\n",
+    "plt.yscale('linear')\n",
+    "plt.xscale('linear')\n",
+    "plt.title(r'$\\Delta\\Sigma^{quad}_{4\\Theta}$')\n",
+    "plt.xlabel('r (Mpc)')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "f08820b6-a918-4ded-bec1-2410822c9481",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'g_nfw' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[9], line 13\u001b[0m\n\u001b[1;32m     10\u001b[0m R_cluster \u001b[38;5;241m=\u001b[39m (M_cluster\u001b[38;5;241m/\u001b[39m(\u001b[38;5;241m200\u001b[39m\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m4\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m\u001b[38;5;241m*\u001b[39mnp\u001b[38;5;241m.\u001b[39mpi\u001b[38;5;241m*\u001b[39mrho_c))\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39m(\u001b[38;5;241m1\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m3\u001b[39m)\n\u001b[1;32m     11\u001b[0m z_cluster \u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0.47\u001b[39m\n\u001b[0;32m---> 13\u001b[0m g \u001b[38;5;241m=\u001b[39m g_nfw(r,z,R_cluster,c_cluster,M_cluster,z_cluster,cosmo_t)\n\u001b[1;32m     15\u001b[0m cosmo \u001b[38;5;241m=\u001b[39m Cosmology(H0\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m71.0\u001b[39m, Omega_dm0\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.2648\u001b[39m, Omega_b0\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.000\u001b[39m, Omega_k0\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m0.0\u001b[39m)\n\u001b[1;32m     17\u001b[0m \u001b[38;5;66;03m#g_ccl = clmm.compute_excess_surface_density(r, 2.00E14, 3.89, z_cluster, cosmo, delta_mdef=200,\u001b[39;00m\n\u001b[1;32m     18\u001b[0m \u001b[38;5;66;03m#                                     halo_profile_model='nfw', massdef='critical', alpha_ein=None, \u001b[39;00m\n\u001b[1;32m     19\u001b[0m \u001b[38;5;66;03m#                                     verbose=False, validate_input=True)/cosmo.eval_sigma_crit(z_cluster, z)\u001b[39;00m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'g_nfw' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "cosmo_t = FlatwCDM(H0=71, Om0=0.2648, w0=-1.0)\n",
+    "cosmo = FlatwCDM(H0=71, Om0=0.2648, w0=-1.0)\n",
+    "r = np.linspace(0.3,2.5,1000)\n",
+    "z = np.full(len(r), 0.8)\n",
+    "M_cluster = 2.0E14#10**15 # Solar Masses\n",
+    "c_cluster = 3.89\n",
+    "#rs_cluster=0.418 # in Mpc\n",
+    "H = cosmo.H(0.47).to(units.s**-1).value\n",
+    "rho_c = 3*H**2/8/np.pi/const.G.to_value(units.Mpc**3*units.M_sun**(-1)*units.s**(-2))\n",
+    "R_cluster = (M_cluster/(200*4/3*np.pi*rho_c))**(1/3)\n",
+    "z_cluster = 0.47\n",
+    "\n",
+    "g = g_nfw(r,z,R_cluster,c_cluster,M_cluster,z_cluster,cosmo_t)\n",
+    "\n",
+    "cosmo = Cosmology(H0=71.0, Omega_dm0=0.2648, Omega_b0=0.000, Omega_k0=0.0)\n",
+    "\n",
+    "#g_ccl = clmm.compute_excess_surface_density(r, 2.00E14, 3.89, z_cluster, cosmo, delta_mdef=200,\n",
+    "#                                     halo_profile_model='nfw', massdef='critical', alpha_ein=None, \n",
+    "#                                     verbose=False, validate_input=True)/cosmo.eval_sigma_crit(z_cluster, z)\n",
+    "\n",
+    "ell = 1.0\n",
+    "\n",
+    "g_ccl = compute_delta_sigma_excess(ell=ell, r=r, mdelta=2.00E14 , cdelta=3.89, z_cl=z_cluster, cosmo=cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200)/cosmo.eval_sigma_crit(z_cluster, z)\n",
+    "print(\"Sigma_c =\", cosmo.eval_sigma_crit(z_cluster, z[0]))\n",
+    "\n",
+    "\n",
+    "#plt.figure(figsize=[10,10])\n",
+    "f, (a0, a1) = plt.subplots(2, 2, gridspec_kw={'height_ratios': [3, 1]}, figsize=(12, 8))\n",
+    "\n",
+    "plt.subplot(221)\n",
+    "#plt.plot(r, g, label='NFW - RAD')\n",
+    "plt.plot(r, g_ccl, label='NFW - monopole (CLMM)')\n",
+    "from gamma import gamma\n",
+    "gt = gamma(r)\n",
+    "plt.plot(r, gt, ls='dashed', label='NFW - COLOSSUS', color='red')\n",
+    "#plt.plot(r, gt-g_ccl, label='NFW_Tae - NFW_ccl')\n",
+    "plt.ylabel('$g_{NFW}$')\n",
+    "plt.xlabel('r in Mpc')\n",
+    "plt.yscale('log')\n",
+    "plt.title('e = '+str(ell))\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(223)\n",
+    "plt.plot(r, gt-g_ccl, label='NFW_Tae - NFW_ccl')\n",
+    "\n",
+    "plt.subplot(222)\n",
+    "#plt.plot(r, g, label='NFW - RAD')\n",
+    "ell = 0.0\n",
+    "g_ccl = compute_delta_sigma_excess(ell=ell, r=r, mdelta=2.00E14 , cdelta=3.89, z_cl=z_cluster, cosmo=cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200)/cosmo.eval_sigma_crit(z_cluster, z)\n",
+    "\n",
+    "plt.plot(r, g_ccl, label='NFW - Monopole (CLMM)')\n",
+    "from gamma import gamma\n",
+    "gt = gamma(r)\n",
+    "plt.plot(r, gt, ls='dashed', label='NFW - COLOSSUS', alpha=0.8, color='red')\n",
+    "#plt.plot(r, gt-g_ccl, label='NFW_Tae - NFW_ccl')\n",
+    "plt.ylabel('$g_{NFW}$')\n",
+    "plt.xlabel('r in Mpc')\n",
+    "plt.yscale('log')\n",
+    "plt.title('e = '+str(ell))\n",
+    "plt.legend()\n",
+    "\n",
+    "plt.subplot(224)\n",
+    "plt.plot(r, gt-g_ccl, label='NFW_Tae - NFW_ccl')\n",
+    "\n",
+    "write_to_file = False\n",
+    "if write_to_file == True:\n",
+    "    file_loc = '/global/u2/s/sriniva/'\n",
+    "    file_name = 'CLMM_model_monopole_q1_0.fits'\n",
+    "\n",
+    "    hdu = fits.BinTableHDU.from_columns(\n",
+    "             [fits.Column(name='r', format='E', array=np.round(r,3)),\n",
+    "              fits.Column(name='Sigma', format='E', array=g_ccl)])\n",
+    "    hdu.writeto(file_loc+file_name, overwrite=True)\n",
+    "    print('Catalog of shears and redshifts written to %s ' %file_loc+file_name)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d2892978-2725-435a-8e00-fd138155355b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 145,
+   "id": "6444a88b-340d-45b8-abb0-bde5cacc20a5",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7ff2664a0810>]"
+      ]
+     },
+     "execution_count": 145,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "abf47c33-b8bf-45b7-a720-fcd651c4de22",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "f=None\n",
+    "\n",
+    "#cosmo = FlatwCDM(H0=70, Om0=0.3, w0=-1.0)\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.3, Omega_b0=0.000, Omega_k0=0.0)\n",
+    "\n",
+    "## DATA:\n",
+    "file_loc = \"/global/u2/s/sriniva/Elliptical_lenses_data/Tae_NFW_halos/\"\n",
+    "catalog_name = \".fits\"\n",
+    "#file=file_loc+\"Galaxy_catalog_10000_sigma_0.24.fits\"\n",
+    "\n",
+    "file=file_loc+catalog_name\n",
+    "hdul=fits.open(file)\n",
+    "data = Table(hdul[1].data)\n",
+    "data\n",
+    "#z_cluster=0.182\n",
+    "z_cluster=0.47\n",
+    "g_sigma=0.01\n",
+    "\n",
+    "x = data['x1'].data\n",
+    "y = data['y1'].data\n",
+    "#phi = np.arctan2(y/x)\n",
+    "z = np.full()#np.append(data['z1'].data,data['z2'].data)\n",
+    "e_t = np.append(data['et_1'].data,data['et_2'].data)\n",
+    "e_1 = np.append(data['e1_1'].data,data['e2_1'].data)\n",
+    "e_2 = np.append(data['e1_2'].data,data['e2_2'].data)\n",
+    "\n",
+    "gamma1 = e_t*(-1*np.cos(2*phi))#np.load(dir_loc_mcmc+\"gamma1.npy\").flatten()\n",
+    "gamma2 = e_t*(-1*np.sin(2*phi))#np.load(dir_loc_mcmc+\"gamma2.npy\").flatten()\n",
+    "\n",
+    "x_arcsec = x/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"x_arcsec.npy\").flatten()\n",
+    "y_arcsec = y/(cosmo.eval_da(z_cluster) * np.pi/180.0 * 1/3600)#np.load(dir_loc_mcmc+\"y_arcsec.npy\").flatten()\n",
+    "\n",
+    "\n",
+    "#MCMC parameters:\n",
+    "params_key=[\"M200m\",\"c200m\",\"e\",r\"$\\theta_{e}$ in degrees\"]\n",
+    "ndim = len(params_key)\n",
+    "nwalkers = ndim*2+4\n",
+    "nsteps = 6000\n",
+    "nburns = 3000\n",
+    "np.random.seed(0)\n",
+    "\n",
+    "limits=[[1.0E12,5.0E15],  # mdelta\n",
+    "       [0.0,20.0],     # cdelta\n",
+    "       [0.0001,0.99],   # e\n",
+    "       [0.0, 45.0]]    # theta_e #in degrees\n",
+    "z_cl=0.47\n",
+    "#z_gal=0.8\n",
+    "q_true=1.0 #default = 2/3 for the files in the same directory as this notebook\n",
+    "ell_true= 1*(1-q_true**2)/(1+q_true**2)\n",
+    "theta_e_true = 0.0\n",
+    "true_value=[1.0E15, 1.5/0.418, ell_true,theta_e_true]\n",
+    "\n",
+    "'''\n",
+    "Axis ratio q:  0.6666666666666667\n",
+    "c:  3.890554441834905\n",
+    "r200_Mpc: 1.026773208537997\n",
+    "ARCSEC_TO_MPC:  0.00588784182755241\n",
+    "BOX_WIDTH_ARCSEC*ARCSEC_TO_MPC:  5.88784182755241\n",
+    "SIGMA_C=3.77988e+15\n",
+    "arcsec_per_pix:  10.0\n",
+    "'''\n",
+    "# PREP DATA:\n",
+    "ds_mono,ds_mono_err,r_mono,ds_quad_4theta,ds_quad_4theta_err,ds_quad_const,ds_quad_const_err,r_quad = make_estimators(gamma1, gamma2, x_arcsec, y_arcsec, z_cl=z_cluster, \n",
+    "                                                                                                                      sigma_c=3.77988e+15, method=1)\n",
+    "\n",
+    "\n",
+    "# CALL EMCEE\n",
+    "post, mean, err_plus, err_minus = emcee_wrapper(gamma1, gamma2, x_arcsec, y_arcsec,z_cl=z_cluster,\n",
+    "                                                params_key=params_key,limits=limits,true_value=true_value, ndim=ndim,nwalkers=nwalkers,nsteps=nsteps, nburn=nburns, filename=f)\n",
+    "\n",
+    "\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda-clmmenv",
+   "language": "python",
+   "name": "conda-clmmenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/triaxiality/Elliptical_lenses_model.ipynb b/examples/triaxiality/Elliptical_lenses_model.ipynb
new file mode 100644
index 000000000..60754b209
--- /dev/null
+++ b/examples/triaxiality/Elliptical_lenses_model.ipynb
@@ -0,0 +1,990 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "id": "5e9caded-6c8d-49c4-9636-f4663a733f08",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import clmm\n",
+    "from clmm import Cosmology\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.3 - 0.045, Omega_b0=0.045, Omega_k0=0.0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eb239054-f2be-4a86-9752-49ea533563e6",
+   "metadata": {},
+   "source": [
+    "<font size=5> <font color='red'>[INCOMPLETE]</font> MODELLING of Quadrupole and Monopole from Adhikari et al.  </font>\n",
+    "\n",
+    "Can be skipped! The same code has been implemented by Callum!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "d2c70c16-282b-4277-ac16-f14325771a2b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\Sigma$')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "r_proj = np.linspace(0.1,1.0,1000)\n",
+    "mdelta=10E14\n",
+    "cdelta=4.0\n",
+    "z_cl=0.3\n",
+    "sigma = clmm.compute_surface_density(r_proj, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "\n",
+    "plt.plot(r_proj, sigma)\n",
+    "plt.xlabel('r', fontsize=15)\n",
+    "plt.ylabel('$\\Sigma$', fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 220,
+   "id": "e9639fec-8fcc-4396-ac35-0fe26ff37e1e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "import scipy\n",
+    "def gamma_tangential_monopole(r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw'):\n",
+    "    kappa_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    \n",
+    "    f = lambda r_i: r_i*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                          verbose=False, validate_input=True)\n",
+    "    integrate = np.vectorize(pyfunc = scipy.integrate.quad_vec)\n",
+    "    \n",
+    "    integral, err = integrate(f,0,r)\n",
+    " \n",
+    "    \n",
+    "    return (2/r**2)*integral - kappa_0\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "ab0582ee-dd50-437c-ab8f-c82d47bffd86",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "CPU times: user 2min 5s, sys: 43.8 ms, total: 2min 5s\n",
+      "Wall time: 2min 5s\n"
+     ]
+    }
+   ],
+   "source": [
+    "%%time\n",
+    "\n",
+    "g_t_monopole = gamma_tangential_monopole(r_proj, mdelta, cdelta, z_cl, cosmo, hpmd='nfw')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "1620353e-5cc3-4656-a45b-88f25e608017",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\Delta\\\\Sigma$  (MONOPOLE)')"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.plot(r_proj, g_t_monopole)\n",
+    "plt.xlabel('r', fontsize=15)\n",
+    "plt.ylabel('$\\Delta\\Sigma$  (MONOPOLE)', fontsize=15)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "447d07d6-05a8-48f9-bcbc-5df204b516ed",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def g_tangential_quadrupole(r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw'):\n",
+    "    kappa_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    \n",
+    "    f1 = lambda r_i: (r_i**3)*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                          verbose=False, validate_input=True)\n",
+    "    f2 = lambda r_i: (r_i**3)*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                          verbose=False, validate_input=True)\n",
+    "    \n",
+    "    integrate = np.vectorize(pyfunc = scipy.integrate.quad_vec)\n",
+    "    \n",
+    "    integral1, err = integrate(f1,0,r)\n",
+    "    integral2, err = integrate(f2,r,np.inf)\n",
+    " \n",
+    "    \n",
+    "    return (2/r**2)*integral - kappa_0"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c041e089-186d-4415-8fb9-ce36a2e7a33b",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<font size= 8> ## TAE's IMPLEMENTATION ##</font>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a7643fc8-f68b-4fbe-a11b-193befddfc28",
+   "metadata": {},
+   "source": [
+    "<font size=5> MODELLING of Quadrupole and Monopole from Shin et al. </font>\n",
+    "\n",
+    "Using the expressions for $\\Delta\\Sigma^{4\\theta}$ and $\\Delta\\Sigma^{Const}$ (estimators for quadrupole) and implementing them using CLMM functionality. "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "a1774b04-2289-486d-9f17-883c9c2be372",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "from scipy.interpolate import InterpolatedUnivariateSpline\n",
+    "def compute_delta_sigma_4theta(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200):\n",
+    "\n",
+    "    ### DEFINING INTEGRALS:\n",
+    "    r_arr = np.linspace(0.01, np.max(r), sample_N)\n",
+    "    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr\n",
+    "    f = InterpolatedUnivariateSpline(r_arr, (r_arr**3)*sigma_0_arr*eta_0_arr, k=3)  # k=3 order of spline\n",
+    "    integral_vec = np.vectorize(f.integral)\n",
+    "    ###\n",
+    "    \n",
+    "    ### ACTUAL COMPUTATION:\n",
+    "    I_1 = (3/(r**4)) * integral_vec(0, r)\n",
+    "    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    #eta_0 = np.gradient(np.log(sigma_0),r)\n",
+    "    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)\n",
+    "    eta_0 = eta_0_interpolation_func(r) \n",
+    "    \n",
+    "    return (ell/4.0)*(2*I_1 - sigma_0*eta_0), eta_0_interpolation_func\n",
+    "\n",
+    "def compute_delta_sigma_const(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000 ,delta_mdef=200):\n",
+    "\n",
+    "    ### DEFINING INTEGRALS:\n",
+    "    r_arr = np.linspace(0.01, np.max(r), sample_N)\n",
+    "    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr\n",
+    "    f = InterpolatedUnivariateSpline(r_arr, sigma_0_arr*eta_0_arr/r_arr, k=3)  # k=3 order of spline\n",
+    "    integral_vec = np.vectorize(f.integral)\n",
+    "    ###\n",
+    "    \n",
+    "    ### ACTUAL COMPUTATION:\n",
+    "    I_2 = integral_vec(r, np.inf)\n",
+    "    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)\n",
+    "    #eta_0 = np.gradient(np.log(sigma_0), r)*r\n",
+    "    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)\n",
+    "    eta_0 = eta_0_interpolation_func(r) \n",
+    "    \n",
+    "    return (ell/4.0)*(2*I_2 - sigma_0*eta_0), eta_0_interpolation_func\n",
+    "\n",
+    "def compute_delta_sigma_excess(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000, delta_mdef=200):\n",
+    "    return clmm.compute_excess_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, \n",
+    "                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "eade1fb4-13d8-487c-b845-48be0c0b7033",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.49102485026816\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fbad6ba0390>"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "q=2/3\n",
+    "ell = (1-q**2)/(1+q**2)\n",
+    "#ell=0.47\n",
+    "r = np.logspace(-1,1,100)*0.7\n",
+    "\n",
+    "mdelta=2.5*1E14\n",
+    "delta_mdef = 200\n",
+    "cdelta=5.0\n",
+    "z_cl=0.3\n",
+    "\n",
+    "rdelta = (mdelta/((4*np.pi/3)*cosmo.get_rho_m(z_cl)*delta_mdef))**(1/3)\n",
+    "rs=rdelta/cdelta\n",
+    "print(rdelta)\n",
+    "\n",
+    "plt.axvline(rs, label='rs (Scale radius)')\n",
+    "plt.axhline(0, linestyle='--', color='orange')\n",
+    "\n",
+    "ds4theta, eta_func = compute_delta_sigma_4theta(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "label=r'$\\Delta\\Sigma^{4\\theta}$'\n",
+    "plt.plot(r, ds4theta/0.7, 'b-',label=label)\n",
+    "#plt.plot(r, eta_func(r), 'b.', label=label+str(' eta_0'))\n",
+    "\n",
+    "\n",
+    "\n",
+    "dsconst, eta_func = compute_delta_sigma_const(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "label=r'$\\Delta\\Sigma^{Const}$'\n",
+    "plt.plot(r, dsconst/0.7, 'r-',label=label)\n",
+    "#plt.plot(r, eta_func(r), 'r.', label=label+str(' eta_0'))\n",
+    "\n",
+    "\n",
+    "plt.xlabel('r in (Mpc/h)')\n",
+    "plt.ylabel(r'$\\Delta\\Sigma$ ($M_{\\odot}/Mpc^{2}$), [QUADRUPOLE]')\n",
+    "plt.xscale('log')\n",
+    "plt.title('MODEL')\n",
+    "plt.xlim([0.1,10.0])\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b8c2cd2c-9624-4f2d-b462-fda462c9a79b",
+   "metadata": {},
+   "source": [
+    "<font size= 8>## CALLUM's IMPLEMENTATION ## </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "9c591818-3468-43a3-9937-415bbf103407",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_46607/2077186905.py:22: RuntimeWarning: invalid value encountered in arctanh\n",
+      "  ( 1 - 2 * np.arctanh( np.sqrt( ( 1- x ) / ( 1 + x ) ) ) / (  1 - x**2 )**0.5 )\n",
+      "/tmp/ipykernel_46607/2077186905.py:45: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
+      "  i = integrate.quad( I_1_integrand , 0 , x )[0]\n",
+      "/tmp/ipykernel_46607/2077186905.py:52: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
+      "  If increasing the limit yields no improvement it is advised to analyze \n",
+      "  the integrand in order to determine the difficulties.  If the position of a \n",
+      "  local difficulty can be determined (singularity, discontinuity) one will \n",
+      "  probably gain from splitting up the interval and calling the integrator \n",
+      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
+      "  i = integrate.quad( I_2_integrand , x , 5e2 )[0]\n",
+      "/tmp/ipykernel_46607/2077186905.py:52: IntegrationWarning: The occurrence of roundoff error is detected, which prevents \n",
+      "  the requested tolerance from being achieved.  The error may be \n",
+      "  underestimated.\n",
+      "  i = integrate.quad( I_2_integrand , x , 5e2 )[0]\n",
+      "/tmp/ipykernel_46607/2077186905.py:52: IntegrationWarning: The algorithm does not converge.  Roundoff error is detected\n",
+      "  in the extrapolation table.  It is assumed that the requested tolerance\n",
+      "  cannot be achieved, and that the returned result (if full_output = 1) is \n",
+      "  the best which can be obtained.\n",
+      "  i = integrate.quad( I_2_integrand , x , 5e2 )[0]\n",
+      "/tmp/ipykernel_46607/2077186905.py:45: IntegrationWarning: The maximum number of subdivisions (50) has been achieved.\n",
+      "  If increasing the limit yields no improvement it is advised to analyze \n",
+      "  the integrand in order to determine the difficulties.  If the position of a \n",
+      "  local difficulty can be determined (singularity, discontinuity) one will \n",
+      "  probably gain from splitting up the interval and calling the integrator \n",
+      "  on the subranges.  Perhaps a special-purpose integrator should be used.\n",
+      "  i = integrate.quad( I_1_integrand , 0 , x )[0]\n",
+      "/tmp/ipykernel_46607/2077186905.py:45: IntegrationWarning: The algorithm does not converge.  Roundoff error is detected\n",
+      "  in the extrapolation table.  It is assumed that the requested tolerance\n",
+      "  cannot be achieved, and that the returned result (if full_output = 1) is \n",
+      "  the best which can be obtained.\n",
+      "  i = integrate.quad( I_1_integrand , 0 , x )[0]\n"
+     ]
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy import integrate\n",
+    "from astropy.cosmology import FlatLambdaCDM\n",
+    "from astropy import constants\n",
+    "import sys\n",
+    "sys.path.insert( 0 , '/pbs/home/c/cmurray/magnification_and_dust/')\n",
+    "\n",
+    "\"\"\"\n",
+    "This is a pure python code to calculate the shear profiles around elliptical mass density following Adhikari 2014 \n",
+    "(https://arxiv.org/pdf/1407.1128.pdf)\n",
+    "\"\"\"\n",
+    "\n",
+    "\n",
+    "# surface mass density {dimensionless}\n",
+    "def sigma_0( x ):\n",
+    "\n",
+    "\t# this is my way of dealing with the discontinuity at x=1...\n",
+    "\t# if the change is smaller than this it does not help \n",
+    "\tif x < 1:\n",
+    "\t\treturn ( 2 / ( x**2 -1 ) ) * \\\n",
+    "\t\t\t   ( 1 - 2 * np.arctanh( np.sqrt( ( 1- x ) / ( 1 + x ) ) ) / (  1 - x**2 )**0.5 )\n",
+    "\t#if x == 1:\n",
+    "\t#    return 2/3\n",
+    "\t#else:\n",
+    "\tx = x + 7e-5 \n",
+    "\treturn ( 2 / ( x**2 -1 ) ) * \\\n",
+    "\t\t\t   ( 1 - 2 * np.arctan( np.sqrt( ( x - 1 ) / ( x + 1 ) ) ) / (  x**2  - 1)**0.5 )\n",
+    "\t\n",
+    "sigma_0 = np.vectorize( sigma_0 )\n",
+    "\n",
+    "\n",
+    "def eta_0( x ):\n",
+    "\tdx = 1e-6\n",
+    "\txh = x + dx\n",
+    "\txl = x - dx\n",
+    "\tsh = np.log( sigma_0( xh ) )\n",
+    "\tsl = np.log( sigma_0( xl ) )\n",
+    "\treturn x * ( sh - sl ) / ( 2 * dx )\n",
+    "\n",
+    "def I_1_integrand( x ):\n",
+    "\treturn np.power( x , 3 ) * sigma_0( x ) * eta_0( x )\n",
+    "\n",
+    "def I_1( x ):\n",
+    "\ti = integrate.quad( I_1_integrand , 0 , x )[0]\n",
+    "\treturn i * 3 / x ** 4\n",
+    "\n",
+    "def I_2_integrand( x ):\n",
+    "\treturn sigma_0( x ) * eta_0( x ) / x\n",
+    "\n",
+    "def I_2( x ):\n",
+    "\ti = integrate.quad( I_2_integrand , x , 5e2 )[0]\n",
+    "\treturn i\n",
+    "\n",
+    "I_2 = np.vectorize( I_2 )\n",
+    "\n",
+    "# does the heavy lifting for quad_shear_t\n",
+    "def __quad_shear_t__( x ):\n",
+    "\ta = sigma_0( x ) * eta_0( x )\n",
+    "\treturn ( a -  I_1( x ) - I_2( x ) ) \n",
+    "\n",
+    "__quad_shear_t__ = np.vectorize( __quad_shear_t__ )\n",
+    "\n",
+    "def __alt_qst__( x ):\n",
+    "\treturn np.interp( x , xx , qst )\n",
+    "\n",
+    "def quad_shear_t( x , psi , e ):\n",
+    "\treturn ( e / 2 ) * __alt_qst__( x ) * np.cos( 2 * psi )\n",
+    "\n",
+    "quad_shear_t = np.vectorize( quad_shear_t )\n",
+    "\n",
+    "def __quad_shear_x__( x ):\n",
+    "\treturn ( -  I_1( x ) + I_2( x ) )\n",
+    "\n",
+    "__quad_shear_x__ = np.vectorize( __quad_shear_x__ )\n",
+    "\n",
+    "def __alt_qsx__( x ):\n",
+    "\treturn np.interp( x , xx , qsx )\n",
+    "\t\n",
+    "def quad_shear_x( x , psi , e ):\n",
+    "\treturn ( e / 2 ) * __alt_qsx__( x ) * np.sin( 2 * psi )\n",
+    "\n",
+    "def av_sigma_0_integrand( x ):\n",
+    "\treturn x * sigma_0( x )\n",
+    "\n",
+    "def av_sigma_0( x ):\n",
+    "\ti = integrate.quad( av_sigma_0_integrand , 0 , x )[0]\n",
+    "\treturn 2 * i / x ** 2\n",
+    "\n",
+    "__av_sigma_0__ = np.vectorize( av_sigma_0 )\n",
+    "\n",
+    "\n",
+    "def __mono_shear_t__( x ):\n",
+    "\treturn av_sigma_0( x ) - sigma_0( x )\n",
+    "\n",
+    "__mono_shear_t__ = np.vectorize( __mono_shear_t__ )\n",
+    "\n",
+    "def __alt_mono_shear_t__( x ):\n",
+    "\treturn np.interp( x , xx , mst )\n",
+    "\n",
+    "def mono_shear_t( x ):\n",
+    "\treturn __alt_mono_shear_t__( x )\n",
+    "\n",
+    "# monopole + quadrupole\n",
+    "def shear_t( x , psi , e ):\n",
+    "\treturn mono_shear_t( x ) + quad_shear_t( x , psi , e )\n",
+    "\n",
+    "\n",
+    "# only the quadrupole is non zero\n",
+    "def shear_x( x , psi, e ):\n",
+    "\treturn quad_shear_x( x , psi, e )\n",
+    "\n",
+    "\n",
+    "#shear = np.vectorize( shear )\n",
+    "\n",
+    "direc = ''\n",
+    "\n",
+    "\n",
+    "\n",
+    "# rather than computing each time we have \n",
+    "# computed and saved these values\n",
+    "xx = np.logspace( -5 , 5  , num = 200 )\n",
+    "#qst = np.load( direc + 'qst.npy')\n",
+    "#qsx = np.load( direc + 'qsx.npy')\n",
+    "#mst = np.load( direc + 'mst.npy')\n",
+    "\n",
+    "# average sigma for deflection angle\n",
+    "as0 = __av_sigma_0__( xx )\n",
+    "qst = __quad_shear_t__( xx )\n",
+    "qsx = __quad_shear_x__( xx )\n",
+    "mst = __mono_shear_t__( xx )\n",
+    "\n",
+    "\n",
+    "def av_sigma( x ):\n",
+    "\treturn np.interp( x , xx , as0 )\n",
+    "\n",
+    "\n",
+    "class triaxial_lens:\n",
+    "\t\n",
+    "\tdef __init__( s , M_lens , c , z_lens , e , H0 = 70 , Om0 = 0.3 ):\n",
+    "\t\t# lens mass [M_sun] \n",
+    "\t\ts.M_lens = M_lens\n",
+    "\t\t# lens concentration\n",
+    "\t\ts.c = c\n",
+    "\t\t# lens redshift\n",
+    "\t\ts.z_lens = z_lens\n",
+    "\t\t# lens ellipticity\n",
+    "\t\ts.e = e\n",
+    "\t\t# set up the cosmology\n",
+    "\t\ts.cosmo = FlatLambdaCDM( H0 = H0 , Om0 = Om0 )\n",
+    "\t\t# critical density of the universe [M_sun / Mpc^3]\n",
+    "\t\ts.rho_c = s.cosmo.critical_density( s.z_lens ).to('M_sun/Mpc^3').value\n",
+    "\t\t# mean matter density of the universe [M_sun / Mpc^3]\n",
+    "\t\ts.rho_m = s.rho_c * s.cosmo.Om( s.z_lens )\n",
+    "\t\t# halo definition\n",
+    "\t\ts.delta = 200\n",
+    "\t\t# \n",
+    "\t\ts.delta_c = s.char_overdensity( c )\n",
+    "\t\t# [Mpc]\n",
+    "\t\ts.r200 = s.r200_calc()\n",
+    "\t\t# scale radius [Mpc]\n",
+    "\t\ts.rs = s.rs_calc()\n",
+    "\t\t# scale radius radians\n",
+    "\t\ts.theta_s = s.rs / s.cosmo.angular_diameter_distance( s.z_lens ).value\n",
+    "\t\n",
+    "\t# characteristic overdensity, dimensionless\n",
+    "\tdef char_overdensity( s , c ):\n",
+    "\t\treturn ( s.delta / 3. ) * ( c**3 / ( np.log( 1 + c ) - c / ( 1 + c ) ) )\n",
+    "\t\n",
+    "\t# calculate r 200\n",
+    "\tdef r200_calc( s ):\n",
+    "\t\tr2003 = ( 3.0 * s.M_lens ) / ( 4.0 * np.pi * s.delta * s.rho_m )\n",
+    "\t\treturn r2003 ** ( 1. / 3. )\n",
+    "\t\n",
+    "\t# scale radius [Mpc]\n",
+    "\tdef rs_calc( s ):\n",
+    "\t\treturn s.r200 / s.c\n",
+    "\t\n",
+    "\t# this is the prefactor which gives the correct amplitude\n",
+    "\t# to the signal calculated above\n",
+    "\tdef A( s ):\n",
+    "\t\treturn s.rs * s.rho_m * s.delta_c\n",
+    "\t\t\t\t\t\t\t\t   \n",
+    "\t# excess surface density\n",
+    "\t# theta in radians\n",
+    "\t# psi in radians\n",
+    "\t# e ellipticity\n",
+    "\t# units [M_sun/pc^2]\n",
+    "\tdef esd_t( s , theta , psi):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\t# factor of 1e12 changes units from M_sun/Mpc^2 -> M_sun/pc^2\n",
+    "\t\treturn s.A() * shear_t( x , psi, s.e ) / 1e12\n",
+    "\n",
+    "\n",
+    "\tdef esd_x( s , theta , psi):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\t# factor of 1e12 changes units from M_sun/Mpc^2 -> M_sun/pc^2\n",
+    "\t\treturn s.A() * shear_x( x , psi, s.e ) / 1e12\n",
+    "\n",
+    "\tdef gamma_t( s , theta , psi, z_gal ):\n",
+    "\t\treturn s.esd_t( theta , psi) / s.sigma_cr( s.z_lens , z_gal )\n",
+    "    \n",
+    "\tdef gamma_x( s , theta , psi, z_gal ):\n",
+    "\t\treturn s.esd_x( theta , psi) / s.sigma_cr( s.z_lens , z_gal )\n",
+    "\n",
+    "\t# units M_sun/pc^2\n",
+    "\tdef sigma_cr( s , z_lens , z_source ):\n",
+    "\t\tG = constants.G\n",
+    "\t\tc = constants.c\n",
+    "\t\tD_s = s.cosmo.angular_diameter_distance( z_source )\n",
+    "\t\tD_d = s.cosmo.angular_diameter_distance( z_lens )\n",
+    "\t\tD_ds = s.cosmo.angular_diameter_distance_z1z2( z_lens , z_source )\n",
+    "\t\treturn ( ( c**2 / (4*np.pi*G) ) * D_s / ( D_d * D_ds ) ).to('M_sun / pc^2').value\n",
+    "\n",
+    "\t# monopole convergence\n",
+    "\tdef kappa_0( s , theta , z_gal ):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\treturn s.A() * sigma_0( x ) / 1e12 / s.sigma_cr( s.z_lens , z_gal )\n",
+    "\n",
+    "\tdef sigma( s , theta  ):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\treturn s.A() * sigma_0( x ) / 1e12 \n",
+    "\n",
+    "\t# the deflection angle in radians\n",
+    "\tdef alpha( s, theta , z_gal ):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\treturn s.A() * theta * av_sigma( x ) / s.sigma_cr( s.z_lens , z_gal ) / 1e12\n",
+    "\n",
+    "\t# dimensionless esd\n",
+    "\tdef template( s , theta ):\n",
+    "\t\tx = theta / s.theta_s\n",
+    "\t\treturn shear_t( x , 0 , s.e )\n",
+    "    \n",
+    "    # Gamma 1 (r, theta):\n",
+    "\tdef gamma_1( s, theta, psi, z_gal):\n",
+    "\t\treturn -1*s.gamma_t(theta , psi=psi, z_gal=z_gal )*np.cos(2*psi) + s.gamma_x( theta , psi=psi, z_gal=z_gal )*np.sin(2*psi)\n",
+    "    \n",
+    "    # Gamma 2 (r, theta):\n",
+    "\tdef gamma_2( s, theta, psi, z_gal):\n",
+    "\t\treturn -1*s.gamma_t( theta , psi=psi, z_gal=z_gal )*np.sin(2*psi) - s.gamma_x( theta , psi=psi, z_gal=z_gal )*np.cos(2*psi)\n",
+    "    \n",
+    "\tdef delta_sigma_4theta(s, theta, psi, z_gal):\n",
+    "\t\treturn s.sigma_cr(z_lens, z_source=z_gal)*np.sum(s.gamma_1(theta, psi=psi, z_gal=z_gal)*np.cos(4*psi) + s.gamma_2(theta, psi, z_gal)*np.sin(4*psi), axis=0)/np.shape(psi)[1]\n",
+    "    \n",
+    "\tdef delta_sigma_const(s, theta, psi, z_gal):\n",
+    "\t\treturn s.sigma_cr(z_lens, z_source=z_gal)*np.sum(s.gamma_1(theta, psi=psi, z_gal=z_gal), axis=0)/np.shape(psi)[1]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "c9f0ee79-6ae2-4e14-833a-b7b325fb5248",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [],
+   "source": [
+    "## Define a cluster:\n",
+    "\n",
+    "M_lens = 2*1E14\n",
+    "c = 4.0\n",
+    "z_lens = 0.3\n",
+    "q = 2/3\n",
+    "e = (1 - q**2)/(1 + q**2)\n",
+    "z_gal = 1.0\n",
+    "t = triaxial_lens(M_lens, c, z_lens, e)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "4a28780a-6a3a-4b2c-9802-0e064e082c6e",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0, 0.5, '$\\\\Delta\\\\Sigma^{const}$')"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1500x750 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "theta = np.linspace(0.5/60*np.pi/180, 50/60*np.pi/180, 100)\n",
+    "psi = np.pi/2\n",
+    "\n",
+    "delta_sigma_t = t.esd_t(theta, psi=psi)\n",
+    "shears_t = t.gamma_t(theta, psi, z_gal)\n",
+    "plt.figure(figsize=[15,7.5])\n",
+    "plt.subplot(121)\n",
+    "plt.plot(theta,shears_t, label='Quadrupole t')\n",
+    "delta_sigma_x = t.esd_x(theta, psi=psi)\n",
+    "shears_x = t.gamma_x(theta,psi=psi, z_gal=z_gal)\n",
+    "plt.plot(theta,shears_x, label='Quadrupole x')\n",
+    "plt.legend()\n",
+    "plt.xlabel(r'$\\theta$ in radians')\n",
+    "plt.ylabel(r'$\\gamma$')\n",
+    "\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "gamma_const = t.esd_x(theta, psi=psi) - t.esd_t(theta, psi=psi)\n",
+    "delta_sigma_const = gamma_const*t.sigma_cr(t.z_lens, z_gal)\n",
+    "plt.plot(theta, delta_sigma_const)\n",
+    "plt.xlabel(r'$\\theta$ in radians')\n",
+    "plt.ylabel(r'$\\Delta\\Sigma^{const}$')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "c938d62b-a7fa-4146-9492-4d4f2bf5076d",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_46607/302144706.py:26: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
+      "  plot = plt.pcolormesh(1000*x, 1000*y, Z, cmap='RdBu', shading=\"auto\", vmin=-0.05, vmax=0.05)\n",
+      "/tmp/ipykernel_46607/302144706.py:37: UserWarning: The input coordinates to pcolormesh are interpreted as cell centers, but are not monotonically increasing or decreasing. This may lead to incorrectly calculated cell edges, in which case, please supply explicit cell edges to pcolormesh.\n",
+      "  plot = plt.pcolormesh(1000*x, 1000*y, Z, cmap='RdBu', shading=\"auto\", vmin=-0.0005, vmax=0.0005)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.colorbar.Colorbar at 0x7fbae0d80690>"
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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AAAAAgJwYZAMAAAAAAAByYpANAAAAAAAAyIlBNgAAAAAAACAnBtkAAAAAAACAnBhkAwAAAAAAAHJikA0AAAAAAADIiUE2AAAAAAAAICcG2QAAAAAAAICcGGQDAAAAAAAAcmKQDQAAAAAAAMjp/wf5eysnlT2nQwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1500x750 with 3 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "theta = np.linspace(0.5/60*np.pi/180, 50/60*np.pi/180, 1000)\n",
+    "psi =  np.linspace(0.001*np.pi/180, 2*np.pi, 1000)\n",
+    "x = theta*np.cos(psi)\n",
+    "y = theta*np.sin(psi)\n",
+    "#x_center = 0.5 * (x[:-1] + x[1:])\n",
+    "#y_center = 0.5 * (y[:-1] + y[1:])\n",
+    "\n",
+    "# pcolormesh needs the pixel edges for x and y\n",
+    "# and with default flat shading, Z needs to be evaluated at the pixel center\n",
+    "plt.figure(figsize=[15,7.5])\n",
+    "plt.subplot(121)\n",
+    "def z_func(x, y):\n",
+    "    theta = np.sqrt(x**2+y**2)\n",
+    "    psi = np.arctan2(y,x)\n",
+    "    return t.gamma_t(theta, psi, z_gal)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "Z = z_func(X, Y)\n",
+    "\n",
+    "plot = plt.pcolormesh(1000*x, 1000*y, Z, cmap='RdBu', shading=\"auto\", vmin=-0.05, vmax=0.05)\n",
+    "\n",
+    "plt.subplot(122)\n",
+    "def z_func(x, y):\n",
+    "    theta = np.sqrt(x**2+y**2)\n",
+    "    psi = np.arctan2(y,x)\n",
+    "    return t.gamma_x(theta, psi, z_gal)\n",
+    "X, Y = np.meshgrid(x, y)\n",
+    "\n",
+    "Z = z_func(X, Y)\n",
+    "\n",
+    "plot = plt.pcolormesh(1000*x, 1000*y, Z, cmap='RdBu', shading=\"auto\", vmin=-0.0005, vmax=0.0005)\n",
+    "\n",
+    "# contour needs the centers\n",
+    "#cset = plt.contour(X, Y, Z, cmap='gray')\n",
+    "#plt.clabel(cset, inline=True)\n",
+    "\n",
+    "plt.colorbar(plot)\n",
+    "#plt.savefig('plot_z_flat.png')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2943ca17-b2d3-4f48-83b1-a9bababf759e",
+   "metadata": {
+    "tags": []
+   },
+   "source": [
+    "<font size=8> COMPARING THE TWO IMPLEMENTATIONS! </font>"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "45591488-4d4c-4c94-95f1-7adc6714372b",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fbad89eef10>"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "######## ADHIKARI IMPLEMENTATION BY CALLUM ##########\n",
+    "# Create a triaxial lens by the class definition for CALLUM's code:\n",
+    "\n",
+    "M_lens = 2.5*1E14\n",
+    "c = 4.0\n",
+    "z_lens = 0.3\n",
+    "q = 2/3\n",
+    "e = (1 - q**2)/(1 + q**2)\n",
+    "z_gal = 1.0\n",
+    "t = triaxial_lens(M_lens, c, z_lens, e)\n",
+    "\n",
+    "# Make the grid in distance and angle to compute the monopole + quadrupole shears:\n",
+    "theta = np.linspace(0.1/60*np.pi/180, 50/60*np.pi/180, 1000)\n",
+    "psi =  np.linspace(0.001*np.pi/180, 2*np.pi, 1000)\n",
+    "Theta, Psi = np.meshgrid(theta, psi)\n",
+    "\n",
+    "# PLOTTING Delta Sigma:\n",
+    "\n",
+    "delta_sigma_4theta = t.delta_sigma_4theta(theta=Theta, psi=Psi, z_gal=z_gal)\n",
+    "r_dist = theta*cosmo.eval_da(t.z_lens)\n",
+    "plt.plot(r_dist, delta_sigma_4theta, label='$\\Delta\\Sigma^{4\\Theta}$')\n",
+    "\n",
+    "\n",
+    "delta_sigma_const = t.delta_sigma_const(theta=Theta, psi=Psi, z_gal=z_gal)\n",
+    "plt.plot(r_dist, delta_sigma_const, label='$\\Delta\\Sigma^{Const}$')\n",
+    "plt.xlabel(r' R in MPc')\n",
+    "plt.ylabel(r'$\\Delta\\Sigma$')\n",
+    "plt.xscale('log')\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "id": "34f95c0a-38fa-4248-9c8d-f4363dd6997e",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1.49102485026816\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fbad75d02d0>"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "######### TAE's IMPLEMENTATION ###########\n",
+    "\n",
+    "#Define properties of a triaxial lens using clmm-infused functions for TAE's method:\n",
+    "q=2/3\n",
+    "ell = (1-q**2)/(1+q**2)\n",
+    "mdelta=2.5*1E14\n",
+    "delta_mdef = 200\n",
+    "cdelta=4.0\n",
+    "z_cl=0.3\n",
+    "\n",
+    "rdelta = (mdelta/((4*np.pi/3)*cosmo.get_rho_m(z_cl)*delta_mdef))**(1/3)\n",
+    "rs=rdelta/cdelta\n",
+    "print(rdelta)\n",
+    "\n",
+    "plt.axvline(rs, label='rs (Scale radius)')\n",
+    "plt.axhline(0, linestyle='--', color='orange')\n",
+    "\n",
+    "# Grid of distances to compute delta sigma:\n",
+    "r = np.logspace(-1,1,100)*0.7\n",
+    "\n",
+    "# PLOTTING Delta Sigma:\n",
+    "\n",
+    "ds4theta, eta_func = compute_delta_sigma_4theta(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "label=r'$\\Delta\\Sigma^{4\\theta}$'\n",
+    "plt.plot(r, ds4theta/0.7, color='blue',label=label, linestyle=(0, (5, 12)))\n",
+    "plt.plot(r_dist, delta_sigma_4theta*1E12/0.7, label='$\\Delta\\Sigma^{4\\Theta} [CALLUM]$', color='blue', linestyle=(0, (1, 2)))\n",
+    "#plt.plot(r, eta_func(r), 'b.', label=label+str(' eta_0'))\n",
+    "\n",
+    "\n",
+    "\n",
+    "dsconst, eta_func = compute_delta_sigma_const(ell, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "label=r'$\\Delta\\Sigma^{Const}$'\n",
+    "plt.plot(r, dsconst/0.7, color='red',label=label, linestyle=(0, (5, 12)))\n",
+    "plt.plot(r_dist, delta_sigma_const*1E12/0.7, label='$\\Delta\\Sigma^{Const}$ [CALLUM]', color='red', linestyle=(0, (1, 2)))\n",
+    "#plt.plot(r, eta_func(r), 'r.', label=label+str(' eta_0'))\n",
+    "\n",
+    "\n",
+    "plt.xlabel('r in (Mpc/h)')\n",
+    "plt.ylabel(r'$\\Delta\\Sigma$ ($M_{\\odot}/Mpc^{2}$), [QUADRUPOLE]')\n",
+    "plt.xscale('log')\n",
+    "plt.title('MODEL')\n",
+    "plt.xlim([0.1,10.0])\n",
+    "plt.legend()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cd47cf76-7eb3-4cda-8320-0282f1ca4fb3",
+   "metadata": {},
+   "source": [
+    "<font size=4 color='green'> And, they are consistent ever after! <\\font>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "conda-clmmenv",
+   "language": "python",
+   "name": "conda-clmmenv"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/triaxiality/catalogs.zip b/examples/triaxiality/catalogs.zip
new file mode 100644
index 000000000..d3d104e3c
Binary files /dev/null and b/examples/triaxiality/catalogs.zip differ
diff --git a/examples/triaxiality/data_functions.py b/examples/triaxiality/data_functions.py
new file mode 100644
index 000000000..1c0be8f87
--- /dev/null
+++ b/examples/triaxiality/data_functions.py
@@ -0,0 +1,39 @@
+import numpy as np
+import clmm
+
+
+def weights(Sigma_crit, theta, Sigma_shape=1, Sigma_meas=0) :
+	## EQUATION 35, 32, 33
+	w  = 1 / (Sigma_crit**2 * (Sigma_shape**2 + Sigma_meas**2))
+	w1 = np.cos(4*theta)**2 * w
+	w2 = np.sin(4*theta)**2 * w
+	return w, w1, w2
+
+def calc_theta(x, y, rotation=-np.pi/2) :
+	return np.arctan2(y, x) + rotation
+
+
+def make_radial_bins(x, y, Nbins=10) :
+	r = np.sqrt(x**2 + y**2)
+	#r_bins = np.linspace(np.min(r), np.max(r), Nbins+1)
+	rbin_edges = np.logspace(np.log10(np.min(r)), np.log10(np.max(r)), Nbins+1)
+	inds = np.digitize(r, rbin_edges, right=True) - 1
+	rbin_mean = np.array([np.mean(r[inds==i]) for i in range(Nbins)])
+	return r, rbin_edges, rbin_mean, inds
+
+def Delta_Sigma_const(w, gamma1, Sigma_crit) :
+	## TODO: sum over clusters
+	return w * Sigma_crit * gamma1 / w
+
+def Delta_Sigma_4theta(w1, w2, gamma1, gamma2, theta, Sigma_crit) :
+	## TODO: sum over clusters
+	return Sigma_crit * (w1*gamma1/np.cos(4*theta) + w2*gamma2/np.sin(4*theta)) / (w1 + w2)
+
+def Delta_Sigma_4theta_cross(w1, w2, gamma1, gamma2, theta, Sigma_crit) :
+	## TODO: sum over clusters
+	return Sigma_crit * (w1*gamma1/np.cos(4*theta) - w2*gamma2/np.sin(4*theta)) / (w1 + w2)
+
+def Delta_Sigma_const_cross(w, gamma2, Sigma_crit) :
+	## TODO: sum over clusters
+	return w*Sigma_crit*gamma2 / w
+
diff --git a/examples/triaxiality/delta_sigma_test.ipynb b/examples/triaxiality/delta_sigma_test.ipynb
new file mode 100644
index 000000000..508aef896
--- /dev/null
+++ b/examples/triaxiality/delta_sigma_test.ipynb
@@ -0,0 +1,263 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "c176f9f7-534d-427d-b2e9-bf2661319118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import clmm\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "c136f015-0e5d-4ae4-b22f-c0add048d9a0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sigma_crit = 3.77988e+15\n",
+    "ARCSEC_TO_MPC = 0.00588784182755241\n",
+    "h = 0.71"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6a05969e-2d03-4ff9-ada1-a82fef843623",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#35\n",
+    "def weight_ij(sigma_crit_ij=sigma_crit, sigma_shape=1, sigma_meas_ij=1):\n",
+    "    return sigma_crit_ij**-2 / (sigma_shape**2 + sigma_meas_ij**2)\n",
+    "\n",
+    "w_ij = weight_ij()\n",
+    "\n",
+    "#32\n",
+    "def weight_1_ij(theta_ij, sigma_crit_ij=sigma_crit, sigma_shape=1, sigma_meas_ij=1):\n",
+    "    return np.cos(4*theta_ij)**2 * weight_ij(sigma_crit_ij, sigma_shape, sigma_meas_ij)\n",
+    "\n",
+    "#33\n",
+    "def weight_2_ij(theta_ij, sigma_crit_ij=sigma_crit, sigma_shape=1, sigma_meas_ij=1):\n",
+    "    return np.sin(4*theta_ij)**2 * weight_ij(sigma_crit_ij, sigma_shape, sigma_meas_ij)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "01bb66e5-b622-4ad1-97f6-ed82e13355ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "gamma1 = np.load(\"gamma1.npy\") * -1\n",
+    "gamma2 = np.load(\"gamma2.npy\")\n",
+    "\n",
+    "x_arcsec = np.load(\"x_arcsec.npy\")\n",
+    "y_arcsec = np.load(\"y_arcsec.npy\")\n",
+    "\n",
+    "theta = np.arctan2(y_arcsec, x_arcsec)\n",
+    "theta -= np.pi/2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "65695a2d-30ea-42c0-93ea-8de8ba5cf000",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#34\n",
+    "def delta_sigma_const(weight_ij, gamma_1_ij, sigma_crit_ij=sigma_crit):\n",
+    "    # TODO: summation over i\n",
+    "    return weight_ij*sigma_crit_ij*gamma_1_ij / weight_ij\n",
+    "\n",
+    "#31\n",
+    "r = (x_arcsec**2 + y_arcsec**2)**0.5\n",
+    "num_bins = 10\n",
+    "r_bins = np.linspace(0, np.max(r), num_bins+1)\n",
+    "r_bins_mid = (r_bins[1:] + r_bins[:-1])/2\n",
+    "\n",
+    "r_inds = np.digitize(r, r_bins, right=True)\n",
+    "def delta_sigma(weight_1_ij, weight_2_ij, gamma_1_ij, gamma_2_ij, theta_ij, sigma_crit_ij=sigma_crit):\n",
+    "    # TODO: summation over i\n",
+    "    return sigma_crit_ij*(weight_1_ij*gamma_1_ij/np.cos(4*theta_ij) - weight_2_ij*gamma_2_ij/np.sin(4*theta_ij)) / (weight_1_ij + weight_2_ij)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "bec47489-3f42-41fb-8237-5f403c862686",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#37\n",
+    "def delta_sigma_cross(weight_1_ij, weight_2_ij, gamma_1_ij, gamma_2_ij, theta_ij, sigma_crit_ij=sigma_crit):\n",
+    "    # TODO: summation over i\n",
+    "    return sigma_crit_ij*(weight_1_ij*gamma_1_ij/np.cos(4*theta_ij) + weight_2_ij*gamma_2_ij/np.sin(4*theta_ij)) / (weight_1_ij + weight_2_ij)\n",
+    "\n",
+    "#38\n",
+    "def delta_sigma_cross_const(weight_ij, gamma_2_ij, sigma_crit_ij=sigma_crit):\n",
+    "    # TODO: summation over i\n",
+    "    return weight_ij*sigma_crit_ij*gamma_2_ij / weight_ij"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "2e90dc70-3178-4bd7-92ed-e2da79ee91ca",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delta_sigmas = []\n",
+    "delta_sigma_consts = []\n",
+    "delta_sigma_crosses = []\n",
+    "delta_sigma_cross_consts = []\n",
+    "for i in range(num_bins):\n",
+    "    select = (r_inds-1) == i\n",
+    "    theta_i = theta[select]\n",
+    "    gamma_1_i = gamma1[select]\n",
+    "    gamma_2_i = gamma2[select]\n",
+    "    weight_i = weight_ij()\n",
+    "    weight_1_i = weight_1_ij(theta_i)\n",
+    "    weight_2_i = weight_2_ij(theta_i)\n",
+    "    \n",
+    "    delta_sigma_i = delta_sigma(weight_1_i, weight_2_i, gamma_1_i, gamma_2_i, theta_i)\n",
+    "    delta_sigmas.append(np.mean(delta_sigma_i))\n",
+    "    \n",
+    "    delta_sigma_const_i = delta_sigma_const(w_ij, gamma_1_i)\n",
+    "    delta_sigma_consts.append(np.mean(delta_sigma_const_i))\n",
+    "    \n",
+    "    delta_sigma_cross_i = delta_sigma_cross(weight_1_i, weight_2_i, gamma_1_i, gamma_2_i, theta_i)\n",
+    "    delta_sigma_crosses.append(np.mean(delta_sigma_cross_i))\n",
+    "    \n",
+    "    delta_sigma_cross_i = delta_sigma_cross_const(w_ij, gamma_2_i)\n",
+    "    delta_sigma_cross_consts.append(np.mean(delta_sigma_cross_i))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "47299fb0-eb0d-49bf-9fe7-ba39b5b34d49",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(r_bins_mid*ARCSEC_TO_MPC/h, delta_sigmas)\n",
+    "plt.xlabel('R [Mpc/h]')\n",
+    "plt.ylabel(r'$\\Delta \\Sigma^{4 \\theta }$ [$M_\\odot$h / $Mpc^2$]')\n",
+    "plt.title('Excess Surface Density as a function of Radius')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "b8b79c06-a93c-4543-9be2-ced9aaf3b43c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(r_bins_mid*ARCSEC_TO_MPC/h, delta_sigma_consts)\n",
+    "plt.xlabel('R [Mpc/h]')\n",
+    "plt.ylabel('$\\Delta \\Sigma^{const}$ [$M_\\odot$h / $Mpc^2$]')\n",
+    "plt.title('Excess Surface Density (Const) as a function of Radius')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "c977e706-e6d6-4f74-a3c1-20f9e0f041f5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(r_bins_mid*ARCSEC_TO_MPC/h, delta_sigma_crosses)\n",
+    "plt.xlabel('R [Mpc/h]')\n",
+    "plt.ylabel(r'$\\Delta \\Sigma_X^{4 \\theta }$ [$M_\\odot$h / $Mpc^2$]')\n",
+    "plt.title('Excess Surface Density (Cross) as a function of Radius')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "3a66e275-89f5-4b34-853c-50904f31ffb9",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.scatter(r_bins_mid*ARCSEC_TO_MPC/h, delta_sigma_cross_consts)\n",
+    "plt.xlabel('R [Mpc/h]')\n",
+    "plt.ylabel('$\\Delta \\Sigma_X^{const}$ [$M_\\odot$h / $Mpc^2$]')\n",
+    "plt.title('Excess Surface Density (Cross Const) as a function of Radius')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/triaxiality/fitting_elliptical_halo.ipynb b/examples/triaxiality/fitting_elliptical_halo.ipynb
new file mode 100644
index 000000000..a5e875474
--- /dev/null
+++ b/examples/triaxiality/fitting_elliptical_halo.ipynb
@@ -0,0 +1,583 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "id": "72bc88e4-941f-4150-821c-06e389a55326",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "The autoreload extension is already loaded. To reload it, use:\n",
+      "  %reload_ext autoreload\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1064,
+   "id": "c176f9f7-534d-427d-b2e9-bf2661319118",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# import corner\n",
+    "import numpy as np\n",
+    "import clmm\n",
+    "import matplotlib.pyplot as plt\n",
+    "from data_functions import *\n",
+    "from model_functions import _delta_sigma_4theta, _delta_sigma_const, _delta_sigma_excess, gamma_tangential_monopole, _sigma, gamma_tangential_monopole_e2corrected\n",
+    "from clmm import Cosmology\n",
+    "import scipy\n",
+    "from scipy.optimize import minimize\n",
+    "from multiprocessing import Pool\n",
+    "import emcee\n",
+    "from scipy.interpolate import InterpolatedUnivariateSpline, splrep, splev\n",
+    "from IPython.display import display, Math\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1108,
+   "id": "e4c5abc2-1008-449d-8417-93857a2ded63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "q = 2/3\n",
+    "ell = (1-q**2)/(1+q**2)\n",
+    "r_ = np.logspace(-1,1,200)*0.7\n",
+    "\n",
+    "mdelta = 2e14\n",
+    "delta_mdef = 200\n",
+    "cdelta = 3.89\n",
+    "z_cl   = 0.8\n",
+    "\n",
+    "sigma_crit = 3.77988e+15"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1109,
+   "id": "3ca92be4-2de5-4834-ab45-f76839fc46b3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "cosmo = Cosmology(H0=70.0, Omega_dm0=0.27 - 0.045, Omega_b0=0.045, Omega_k0=0.0)\n",
+    "\n",
+    "r_proj = np.linspace(0.1,1.0,1000)\n",
+    "sigma = clmm.compute_surface_density(r_proj, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, \n",
+    "                                     halo_profile_model='nfw', massdef='mean', alpha_ein=None, \n",
+    "                                     verbose=False, validate_input=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1110,
+   "id": "e3a4bef3-9794-4156-ab8d-9eed3f2c0beb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "rdelta = (mdelta/((4*np.pi/3)*cosmo.get_rho_m(z_cl)*delta_mdef))**(1/3)\n",
+    "rs=rdelta/cdelta\n",
+    "\n",
+    "ds4theta, eta_func = _delta_sigma_4theta(ell, r_, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "dsconst, eta_func = _delta_sigma_const(ell, r_, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1111,
+   "id": "f8b226f2-c84a-4973-8959-22fbc9eb3213",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def rotate(x, y, phi) :\n",
+    "    x_ = x*np.cos(phi) - y*np.sin(phi)\n",
+    "    y_ = x*np.sin(phi) + y*np.cos(phi)\n",
+    "    return x_, y_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1172,
+   "id": "01bb66e5-b622-4ad1-97f6-ed82e13355ec",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "phi = np.pi   ## rotation angle\n",
+    "\n",
+    "indir = 'axis_ratio_2_3/'\n",
+    "gamma1_ = np.load(f'catalogs/{indir}gamma1.npy')\n",
+    "gamma2_ = np.load(f'catalogs/{indir}gamma2.npy')\n",
+    "gamma1_, gamma2_ = rotate(gamma1_, gamma2_, phi)\n",
+    "gamma1 = np.hstack(gamma1_)\n",
+    "gamma2 = np.hstack(gamma2_)\n",
+    "\n",
+    "gammat_ = np.load(f'catalogs/{indir}gamma_T.npy')\n",
+    "gammax_ = np.load(f'catalogs/{indir}gamma_X.npy')\n",
+    "gammat_, gammax_ = rotate(gammat_, gammax_, phi)\n",
+    "gammat = np.hstack(gammat_)\n",
+    "gammax = np.hstack(gammax_)\n",
+    "\n",
+    "x_arcsec_ = np.load(f'catalogs/{indir}x_arcsec.npy')\n",
+    "y_arcsec_ = np.load(f'catalogs/{indir}y_arcsec.npy')\n",
+    "x_arcsec_, y_arcsec_ = rotate(x_arcsec_, y_arcsec_, phi)\n",
+    "x_arcsec = np.hstack(x_arcsec_)\n",
+    "y_arcsec = np.hstack(y_arcsec_)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1173,
+   "id": "998dd5e5-84ac-48fd-90b6-ae84c5c42e56",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "Nbins = 15\n",
+    "arcsec_to_Mpc = 0.00588784182755241\n",
+    "r, rbin_edges, r_bins, r_inds = make_radial_bins(x_arcsec*arcsec_to_Mpc, y_arcsec*arcsec_to_Mpc, Nbins)\n",
+    "theta = calc_theta(x_arcsec, y_arcsec, phi)\n",
+    "\n",
+    "delta_sigmas_4theta = []\n",
+    "delta_sigmas_const = []\n",
+    "delta_sigmas_4theta_cross = []\n",
+    "delta_sigmas_const_cross = []\n",
+    "gammat_avg = []\n",
+    "for i in range(Nbins):\n",
+    "    select = (r_inds == i)\n",
+    "    theta_i = theta[select]\n",
+    "    gamma1_i = gamma1[select]\n",
+    "    gamma2_i = gamma2[select]\n",
+    "    w_i, w1_i, w2_i = weights(sigma_crit, theta_i)\n",
+    "    DS4thetai = Delta_Sigma_4theta(w1_i, w2_i, gamma1_i, gamma2_i, theta_i, sigma_crit)\n",
+    "    DSconsti = Delta_Sigma_const(w_i, gamma1_i, sigma_crit)\n",
+    "    DS4thetacrossi = Delta_Sigma_4theta_cross(w1_i, w2_i, gamma1_i, gamma2_i, theta_i, sigma_crit)\n",
+    "    DSconstcrossi = Delta_Sigma_const_cross(w_i, gamma2_i, sigma_crit)\n",
+    "    delta_sigmas_4theta.append([np.mean(DS4thetai), np.std(DS4thetai)/np.sqrt(len(DS4thetai))])\n",
+    "    delta_sigmas_const.append([np.mean(DSconsti), np.std(DSconsti)/np.sqrt(len(DSconsti))])\n",
+    "    delta_sigmas_4theta_cross.append([np.mean(DS4thetacrossi), np.std(DS4thetacrossi)/np.sqrt(len(DS4thetacrossi))])\n",
+    "    delta_sigmas_const_cross.append([np.mean(DSconstcrossi), np.std(DSconstcrossi)/np.sqrt(len(DSconstcrossi))])\n",
+    "    gammat_avg.append([-2*np.mean(gamma1_i*np.cos(2*theta_i)), abs(2*np.std(gamma1_i*np.cos(2*theta_i))/np.sqrt(len(gamma1_i)))])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1174,
+   "id": "50316750-0b43-4d0b-9570-7152e9b45410",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "priors = np.array([[-1,1],[12,17],[0,1.5],[-15,0],[-15,0],[-15,0]])\n",
+    "ndim = len(priors)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1175,
+   "id": "4b3b4221-ef86-4738-b38f-b75c1739adbb",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def delta_sigma_monopole(r, B) :\n",
+    "    q_ = (1-B[0])/(1+B[0])\n",
+    "    sigma_nfw = _sigma(r, 10**B[1], 10**B[2], z_cl, cosmo, hpmd='nfw', sample_N=1000, delta_mdef=200)\n",
+    "    lnS = np.log(sigma_nfw)\n",
+    "    eta_0 = r * np.gradient(lnS, r)\n",
+    "    deta_0 = r * np.gradient(eta_0, r)\n",
+    "    def sigma_0(r_, sigma_, eta_0_, deta_0_) :\n",
+    "        return sigma_ * (1 + B[0]**2 * (eta_0_/8 + eta_0_**2/16 + deta_0_/16))\n",
+    "    integral = np.array([scipy.integrate.simps(r[r<=ri]*sigma_0(r[r<=ri], sigma_nfw[r<=ri], eta_0[r<=ri], deta_0[r<=ri]), r[r<=ri]) for ri in r])\n",
+    "    return ((2/r**2) * integral - sigma_0(r, sigma_nfw, eta_0, deta_0)) / sigma_crit / q_**(eta_0/2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1176,
+   "id": "e00433b3-0b9c-4e83-8b2c-4eb87d47588a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def MLE(B,x,y1,dy1, y2, dy2, y3, dy3) :\n",
+    "    ell_, log10_mdelta_, log10_cdelta_, ln_f1, ln_f2, ln_f3 = B\n",
+    "    mdelta_ = 10**log10_mdelta_\n",
+    "    cdelta_ = 10**log10_cdelta_\n",
+    "    \n",
+    "    yPred1 = _delta_sigma_4theta(ell_, x, mdelta_, cdelta_, z_cl, cosmo=cosmo, hpmd='nfw', sample_N=1000)[0]/0.7/1e12\n",
+    "    s1 = dy1**2 + yPred1**2*np.exp(2*ln_f1)\n",
+    "    \n",
+    "    yPred2 = _delta_sigma_const( ell_, x, mdelta_, cdelta_, z_cl, cosmo=cosmo, hpmd='nfw', sample_N=1000)[0]/0.7/1e12\n",
+    "    s2 = dy2**2 + yPred2**2*np.exp(2*ln_f2)\n",
+    "    \n",
+    "    _yPred3 = splrep(np.logspace(-2,2,50), delta_sigma_monopole(np.logspace(-2,2,50), B), s=0)\n",
+    "    yPred3 = splev(x, _yPred3) / 0.7\n",
+    "    s3 = dy3**2 + yPred3**2*np.exp(2*ln_f3)\n",
+    "\n",
+    "    LL = - np.sum((y1-yPred1)**2/2/s1 + np.log(2*np.pi*s1)/2 + (y2-yPred2)**2/2/s2 + np.log(2*np.pi*s2)/2 + (y3-yPred3)**2/2/s3 + np.log(2*np.pi*s3)/2)\n",
+    "    return LL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1177,
+   "id": "e568a030-61b9-44f1-a529-a87577d84869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "order = np.isfinite(r_bins) & [(np.sum(r_inds==i)>10) for i in range(len(r_bins))]  #& (r_bins >= rs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e6006ff5-28e7-432f-ba5c-88dcd732332d",
+   "metadata": {},
+   "source": [
+    "## Implementing emcee"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1178,
+   "id": "2fc962c6-331f-46b2-bff7-4a1d10c3f15b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "nwalkers = 32\n",
+    "nsteps = int(1e3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1179,
+   "id": "e9289b51-9ffc-4f83-ad86-95badb2a0d00",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_prior(theta):\n",
+    "    if np.all([(priors[i,0] < theta[i] < priors[i,1]) for i in range(ndim)]) :\n",
+    "        return 0.0\n",
+    "    else :\n",
+    "        return -np.inf"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1180,
+   "id": "73fc5fd3-305f-4fb5-8cd0-85b2c588d09e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def log_probability(theta, x, y1, dy1, y2, dy2, y3, dy3) :\n",
+    "    lp = log_prior(theta)\n",
+    "    if not np.isfinite(lp):\n",
+    "        return -np.inf\n",
+    "    else :\n",
+    "        return lp + MLE(theta, x, y1, dy1, y2, dy2, y3, dy3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1181,
+   "id": "b15cdbdb-3d0d-4a94-bc0a-55f6caf31f11",
+   "metadata": {
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1000/1000 [00:58<00:00, 17.05it/s]\n"
+     ]
+    }
+   ],
+   "source": [
+    "pos = np.array([[priors[i,0] + np.ptp(priors[i])*np.random.random() for i in range(ndim)] for _ in range(nwalkers)])\n",
+    "\n",
+    "with Pool() as pool :\n",
+    "    sampler = emcee.EnsembleSampler(nwalkers, ndim, log_probability, args=(r_bins[order], np.array(delta_sigmas_4theta)[:,0][order]/0.7/1e12, np.array(delta_sigmas_4theta)[:,1][order]/0.7/1e12, np.array(delta_sigmas_const)[:,0][order]/0.7/1e12, np.array(delta_sigmas_const)[:,1][order]/0.7/1e12, abs(np.array(gammat_avg)[:,0][order]), abs(np.array(gammat_avg)[:,1][order])), pool=pool)\n",
+    "    sampler.run_mcmc(pos, nsteps, progress=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1182,
+   "id": "93a0c80f-36f5-41b3-8f4b-af0138fd6b8c",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "flat_samples = sampler.get_chain(discard=300, flat=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1183,
+   "id": "b1a2342c-dbe3-458d-945e-04a6b97b0214",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 1000x500 with 6 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(ndim,1, figsize=(10,5), sharex=True);\n",
+    "[[axs[i].plot(sampler.chain[j,:,i], c='k', lw=0.1) for i in range(ndim)] for j in range(nwalkers)];\n",
+    "[ax.set_xlim(-100, nsteps+100) for ax in axs];\n",
+    "\n",
+    "axs[0].axhline(ell, lw=0.5, c='r');\n",
+    "axs[1].axhline(np.log10(mdelta), lw=0.5, c='r');\n",
+    "axs[2].axhline(np.log10(cdelta), lw=0.5, c='r');\n",
+    "\n",
+    "axs[0].set_ylabel(r'$\\epsilon$');\n",
+    "axs[1].set_ylabel(r'$\\log m_{\\Delta}$');\n",
+    "axs[2].set_ylabel(r'$\\log c_{\\Delta}$');\n",
+    "axs[3].set_ylabel(r'$\\ln f_1$');\n",
+    "axs[4].set_ylabel(r'$\\ln f_2$');\n",
+    "axs[5].set_ylabel(r'$\\ln f_3$');"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1184,
+   "id": "f13d116b-8f1f-47fc-80a1-0c2be2a2a5b5",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{\\epsilon} = 0.331_{-0.038}^{0.036}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{\\log M_\\Delta} = 14.249_{-0.042}^{0.041}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{\\log c_\\Delta} = 0.662_{-0.105}^{0.146}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{\\ln f_1} = -8.156_{-3.935}^{4.579}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{\\ln f_2} = -5.646_{-4.863}^{4.833}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/latex": [
+       "$\\displaystyle \\mathrm{ln f_3} = -1.284_{-0.277}^{0.315}$"
+      ],
+      "text/plain": [
+       "<IPython.core.display.Math object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "labels = ['\\epsilon', '\\log M_\\Delta', '\\log c_\\Delta', '\\ln f_1', '\\ln f_2', 'ln f_3']\n",
+    "for i in range(ndim):\n",
+    "    mcmc = np.percentile(flat_samples[:, i], [16, 50, 84])\n",
+    "    q_ = np.diff(mcmc)\n",
+    "    txt = \"\\mathrm{{{3}}} = {0:.3f}_{{-{1:.3f}}}^{{{2:.3f}}}\"\n",
+    "    txt = txt.format(mcmc[1], q_[0], q_[1], labels[i])\n",
+    "    display(Math(txt))\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1185,
+   "id": "29d885ab-9374-458b-a08c-90db9ff93078",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ps = [np.percentile(flat_samples[:,i], [16,50,84])[1] for i in range(ndim)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1186,
+   "id": "f19fa07e-0871-4e1f-83b2-8092805bb8df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds4theta, eta_func = _delta_sigma_4theta(ps[0], r_, 10**ps[1], 10**ps[2], z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "dsconst, eta_func = _delta_sigma_const(ps[0], r_, 10**ps[1], 10**ps[2], z_cl, cosmo, hpmd='nfw', sample_N=1000)\n",
+    "\n",
+    "# sigma_nfw = _sigma(r_, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000, delta_mdef=200)/0.7\n",
+    "_dsmono = InterpolatedUnivariateSpline(np.logspace(-3,3,2000), delta_sigma_monopole(np.logspace(-3,3,2000), ps), k=3)\n",
+    "dsmono = _dsmono(r_)\n",
+    "# eta_0 = np.gradient(np.log(sigma_nfw),r_)*r_"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1187,
+   "id": "98569a0f-a691-477a-b021-a1f93839f140",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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/+lf897//hb//jT8A5eXlcHFxMUUJREQtmofSHkoHWw7laEXMPUSnpgbIzQVycoALF6QwnFZYhAP2BRD1wOajBVg1twjXsm70+NraSmOIfX2BDh2A++6Tvvf1Bby9b4TjuwnG+uCwCbJEJgnRU6dOBSBN8v7444+je/fuqKurw7/+9S8sXLjQFCUQEZGRmDsMtlTGHqKjVgPnz0sh+fx57SUnRxp+cfOdIlxdAVVcKRAozTYhgwyPTCzFhG6emqCsUkk3pzAHDpsgS2OSEF1YWIhDhw7hyJEjOHz4MFavXo2srCzIZDK89957+Omnn9CtWzd069YNQ4YMMUVJRERkIJY8XtuSA/69DtG5cgU4exY4c+bGcnNovvkmfTY2gJ8f4O8PhIYCcXFAx47S444dpYDs5KQ924SAwF9HWcZsE0SWyCQh2sPDA4MHD8bgwYM166qqqpCeno7Dhw/jyJEj2LRpExYsWKBza04iIrJsljxe25ID/p2G6NTXS73FZ84Ap09rh+XTp6WZLBo4OACBgVIgjo0FxozRDsne3nc3M0XDsImF207iH/FhHDZBdBsmvdnKzezt7dGzZ0/07Mlb6xIRtWSWPF7bkgN+SmYRThZWYO1vRWhf44msLODUqRuB+exZoKrqxvY+PkBQkDR7xeDBQHCw9DgoSBqLbKgxyLlllShSVyO3rNIwOyRqpcwWoomIiIzNEgK+ENLUbqdOQROUD+QV4UyHVIh6GV7fmori72Jw/bwngoOBkBBpuMXNITkgQLrDnSkk9PZHXLgnPBRy0xyQqIViiCYiIjKAqirg5Eng+HFpuTk0V1RI28hk0vAK14GlkAkZYCVgBRmS5pbivac9YWMBf5U9lPYW+b8KRJbGAj6uRERELYdafSMoZ2be+P7MmRuzXXh5SRfwRUUBTz8tDcEIDZV6le3ttS/gq4fA4Ch3iwjQRHT3jPqRfe211zBq1CjeUIWIiFqckpIbIfnmsJyXd2ObgACgSxdg1Cjpa8Pi5nb7fceFe2LlszF4e8sxvDm8Ky/gI2qBjBqiCwoKMHz4cFhbW2PEiBF49NFH8fDDD0Mu5zgrIiKyDJcvA+npN5aMDCksl5ZK7dbW0jjl8HBgwgTpa5cuQFiYNC1cc8WFe+KbAzkM0EQtlFFD9BdffAEhBHbt2oXNmzfjlVdeQV5eHuLi4jBy5EgMHz4cKpXKmCUQEREBAKqrgRMntANzerp0tz5Amku5c2cgIgKIj5eCcni4FKDt7MxbOxFZHqOPwJLJZOjfvz/69++P999/H8ePH8fmzZuxcuVKPP/88+jduzdGjhyJsWPHwtfX19jlEBFRKyeEdLOR9HRg88Ei/HrKCp2/KsLp3z1x/bq0jZ8fEBkJPPOM9DUyUupZNmVYLlZXQV1Zi2J1FS/kI2qBTH4ZQ5cuXdClSxfMnDkTJSUl2LRpEzZt2gQAmDFjhqnLISKiFuzaNWn4xaFDwOHDwJEj0uOKCsAhpAgeT6RC+AJVfoV48ZEYPN7bExER0i2uzW3t/hz8ca4Ma/fnWNyNYIjozsx6LXD79u0xadIkTJo0yZxlEBFRC1BSIgXlhuXQIWlKufp6adxyly5At27Ao49KPcvby0vx/VEZ6qwErGUyuIWV4v77LWf8MedjJmrZOKEOERFZlPp66W59N4flw4dvzIrh5ATcdx8waBAwfTrQvbs0jtn+lhERtpnu+PaINI1cnRDoE+RuwldxZ5yPmahlY4gmIiKzuX5dmgkjNVV7SIZaLbV7e0sh+dlnpTmXu3eX7uRnZXXnfXMaOSIyJoZoIiIyifp6IDtbCswHDkjLoUPSuGaZTLoZSffuwLBh0tfu3QHPe8y9nEaOiIyFIZqIiAxOCCAnRwrKDaE5LQ0oL5fag4OBnj2Bxx6TvvboATg7m7dmIiJ9mCxE//rrr/j1119RXFyM+vp6rbZVq1aZqgwiIjKwlMwiHMu5igWri1B1xhOpqVJwLimR2v38gJgY4NVXpa/R0UC7duatmYjoXpkkRM+bNw/JycmIiYmBt7c3ZDKZKQ5LRERGUFoq9SofOAD8erwIZzqkQtTL8OmJVFSnxKCHlyeSkqTAHBMDeHmZu2IiIsMzSYj+5JNPsHr1aowfP94UhyMiIgOpqJACc8OQjNRU4MwZqc3VFeg4qhQyyAArASuZDElzS/HmCI4/JqLWzyQhuqamBn379jXFoYiIqJkqK6XZMW4OzCdOSOObnZykccuPPiqNYY6JkW6H/ctxdyR+JU0jVy8E+gRb1jRyRETGcheTBN27yZMn4+uvvzbFoRp17tw5TJo0CYGBgXBwcEBwcDDmzJmDmpoare1ycnIwYsQIODk5QaVS4aWXXtLZhoioNaipAQ4eBFasABITpZkwFAqgb19gxgzpJiaDBgGrVkl3ACwvB37/HViyBBg7FujUSZpRo2EaOf92Dlj5bIzFzYJx8621iYgMyWg90dOnT9d8X19fj08//RS//PILunXrBltbW61tlyxZYqwyAAAnTpxAfX09VqxYgZCQEGRkZCAxMRFXr17FokWLAAB1dXUYNmwY2rdvj127dqG0tBQTJkyAEAJLly41an1ERMZUVyf1KDdMK5eaKs3FXF0t3ekvIkLqXf7736WvERGAnd3d79+Sp5HjrbWJyFiMFqIPHTqk9bh79+4AgIyMDK31prjIcMiQIRgyZIjmcVBQEE6ePImPP/5YE6K3bduGzMxM5ObmwsfHBwCwePFiTJw4EfPnz4dSqTR6nURE90oI7bmYU1OlHuerV6We486dpaEYzzwjfe3eHXBwMHfVxsNbaxORsRgtRG/fvt1YuzaI8vJytLtpjqW9e/ciIiJCE6ABYPDgwaiurkZaWhoGDhyos4/q6mpUV1drHqsbbrFFRGQCQgC5udo3L0lLAy5fltqDgqSgPHeu1MMcFQW0tf4A3lqbiIylTd5s5fTp01i6dCkWL16sWVdYWAjPW26N5ebmBjs7OxQWFja6n3fffRfz5s0zaq1ERA0qy22wZYt2L3NxsdTm6ysF5RkzpK/R0YA7r/EjIjKaFh2i586de8cQe+DAAcTExGge5+fnY8iQIXjyyScxefJkrW0bG1oihGhyyMns2bO1xn6r1Wr4+fnp8xKIiBp16dKNuZgbQvOFC1H4GoBKJQXl55+/MVOGt7e5KyYialtadIieMmUKxowZc9ttAgICNN/n5+dj4MCBiI2Nxaeffqq1nZeXF/bv36+1rqysDLW1tTo91A3kcjnkco6zI6J7U1EBHDqkHZhPn5balEopJCckAAeuZGPVP0Lg7y+NbyYiIvMxe4g+/edfiuDgYL2fq1KpoFKp7mrbvLw8DBw4ENHR0fjiiy9gZaU9u19sbCzmz5+PgoICeP/ZpbNt2zbI5XJER0frXRsRUWMqK6WZMW4eknH8uDS+2cFBmot5xAgpOPfsKc3F3HC6mvxlGTp2NG/9REQkMVuIvnTpEp599lmEhIRACIHs7Gz861//gpubm8GPlZ+fjwcffBD+/v5YtGgRSkpKNG1ef96PNj4+HuHh4Rg/fjwWLlyIS5cuYcaMGUhMTOTMHETULNXVwNGjUlBOTZWGZ2RkSFPO2dkB990HDBggjWOOiQG6dAFsmjgrp2QW4WRRBVIyiyxyKjkiorbGbCG6YTzxoEGDAAC//vorZsyYgc8//9zgx9q2bRuys7ORnZ2NDh06aLUJIQAA1tbW+Omnn5CUlIR+/frBwcEB48aN00yBR0R0OzU1QHr6jVtkp6ZKgbm2VgrGkZE35mKOjpYe3+1osJTMIiR+lQoASPwq1SJvakJE1NaYLUQfO3YMgwYNQnJyMqZMmYKHHnoIb775plGONXHiREycOPGO2/n7+2PLli1GqYGIWoa76fGtrQWOHdPuYT56VArS1tZA165Sz3JiohSYu3UD7O9hlrW9p0thLZOhTghYy2TYd6aUIZqIyMzMPia6oScYkO5sSERkLo31+A4M9URm5o2wfPPd/qysgPBwKShPmCAF5/vuM/zNS2KD3bFq91kAQJ0Q6BPEueuIiMzNbCG6b9+++PrrrzFnzhwAwL///W/069fPXOUQEWHXqVJYyWSoFwIyIcPUBaU4850nqqq07/aXkCAF5+7dAScn49cVF+6Jlc/G4O0tx/Dm8K7shSYisgBmC9Hz5s3DlClT8N133wEAXFxcsHz5cnOVQ0RtjFot9SgfOnRjOV3lDvdRZyHqZYCVgJ/cHZMX3Lg9tkJhvnrjwj3xzYEcBmgiIgththDt4OCAzz//HFVVVRBCwMHQ//9JRPSnoiLtsHzoEJCdLbXZ2QEREX+OYY7yRL13DNadyMTcUeEMrERE1CSzj4m2v5erbYiIblJXB5w5c6OH+fBh6WtBgdSuVEo9ysOGAVFR0tKlC2Bre/NePHHkS/b4EhHR7Zk9RBMRNUdpqTSl3NGj0pKeLk0pd+2a1O7lJYXkv/zlRmAODLxx4xIynGJ1FdSVtShWV8FDyY4RImobjBqi586dix49eiA6Ohq+vr7GPBQRtVI1NcCJE9qB+ehRID9farezk6aUi4wEnnpKmk4uMlIK0WQaa/fn4I9zZVi7PwfT4kLNXQ4RkUkYNUQnJydDJpMBkG7RHR0djR49emiCdUfev5aI/iQEcOGC1Jvc0LN89Kh0S+zr16Vt/P2lkDxxohSUu3UDOnW6dTgGmVpCb3/EhXvCQ3GXd48hImoFjBqie/bsiYKCAvzlL3+Bl5cXDh48iJ9//hkLFy7E9evX4ebmhh49emDbtm3GLIOILEh9PXD+PJCZqb0cPw5UVEjbODtLIblvX+Bvf5PCckQE4Opq1tKpCR5Kew7jIKI2x6ghev/+/Vi9ejVee+01REVF4YMPPkBoaChqa2tx9OhRHDx4EIcOHTJmCURkJg0X+d0clP+zMxz/TroxbtnJSbpZSXg48MQTN74PCODYZSIismxGv7Bw4sSJGD16NJKTkxETE4Pnn38ec+bMQXR0NKKjo419eCIysspKabq4kyelscuZmdItsU+elO7qB0izYnTtCrh3vIbnX3LShOUOHRiWiYioZTLJ7BzOzs54//33kZiYiOnTpyMkJAQLFizAc889Z4rDE9E9qqsDcnKAU6ek5eTJG9/n5EjjmQHAzU0Ky336AM89J30fHg54e0t3/Jv85TlMn9DevC+GiIjIAEw2xV1tbS0qKysxZswYFBUVITExEaNGjUK7du1MVQIR3YYQ0k1JTp/WDcvZ2Td6le3spIv5QkOBMWOkr2Fh0rr27aWwTERE1NoZNUTPnz8f6enpSE9Px6lTp+Dk5IRu3bqhd+/eeP755+Hi4mLMwxPRLWpqpIv6zpyRwnLDcuaMtFy9Km0nk0kzYYSGAg8+CPz1r1JQDg2V1ltbm/VlEBERmZ1RQ/Sbb76JgIAATJw4EWPHjkWnTp2MeTiiNk8I6SYk588DZ8/qBuWcHGl2DACwsZEu4AsOBvr3l6aNCwqSHoeEAA4O5nwlREREls2oIfr+++/HkSNHMHfuXPzzn/9Et27dNHNFR0dHIyIiAtbs0iK6a/X1QGGhFJLPnwfOndP9vmHmC0C6oC84WFp69rwRkoODpYv6bHjPUiIiomYx6p/Q33//HQCQlZWFtLQ0HDx4EGlpafj6669x+fJlyOVyREZG4o8//jBmGUQtghBAeTmQlyfddKTha07OjYCckyMNyWjg6gp07CgtDz8s9Sw3PA4IANzdOUaZiIjIGEzSD9WpUyd06tQJY8aM0aw7e/YsUlNTOU80tQn19UBxsXY4buxrw5jkBh4eN0Jx9+7aIbljR4CXFRAREZmH0UL00aNHERERAasmJoENDAxEYGAgnnzySQDAsWPHEBYWBhv+/zK1IDU1UjguLJRmtigsvPF9QQFw5rw9LhYB+fk3bl0NSLep9vGRhlT4+koB2df3xuMOHaRp4ezszPbSDC4lswgniyqQklmEuHBPc5dDRER0T4yWWKOiolBYWIj27e9uTtjY2FgcPnwYQUFBxiqJ6K5cvw5cvKgbihv7/tIl3eerVICXF+DpCQQG1WNInLVWOPb1laaCa0s3GUnJLELiV6kAgMSvUrHy2RgGaSIiatGMFqKFEHjzzTfh6Oh4V9vX3DzQk8hA6uuBy5elUHy3S1mZ7n5cXW8EYy8vIDLyxvcNX728pHBsa3vjeecu1iBAZau7wzZm7+lSWMtkqBMC1jIZ9p0pZYgmIqIWzWgh+oEHHsDJkyfvevvY2Fg4cE4tukevvAKkpt4IxKWl0t32buXqKvUYNyxhYUC/flIIbljXEIw9PAB7e5O/lFYlNtgdq3afBQDUCYE+Qe5mroiIiOjeGC1E//bbb8baNVGTbGwAPz8gKko7JN8cjtu10+4tJuOLC/fEymdj8PaWY3hzeFf2QhMRUYvHq/ioVfnnP81dATUlLtwT3xzIYYAmIqJWoQ1d2iSprq5G9+7dIZPJcPjwYa22nJwcjBgxAk5OTlCpVHjppZc4VpuIiIiIdLS5nuiZM2fCx8cHR44c0VpfV1eHYcOGoX379ti1axdKS0sxYcIECCGwdOlSM1VLRERERJaoTfVE/+c//8G2bduwaNEinbZt27YhMzMT//rXvxAVFYWHH34YixcvxsqVK6FWq81QLRERERFZqjYToouKipCYmIg1a9Y0Ou3e3r17ERERAR8fH826wYMHo7q6GmlpaY3us7q6Gmq1WmshIiIiotbP4MM51Go11q9fj9OnT6Ndu3bo3r07BgwYADsz3npNCIGJEyfib3/7G2JiYnDu3DmdbQoLC+HpqX3Bk5ubG+zs7FBYWNjoft99913MmzfPGCUTERERkQUzeE/00KFDcebMGYSEhGDJkiVYuHAhAgICsHDhQgghDHqsuXPnQiaT3XZJTU3F0qVLoVarMXv27NvuTyaT6awTQjS6HgBmz56N8vJyzZKbm2uQ10VEREREls3gIfry5ctYsGABJk2aBC8vL81Y4/z8fEybNs2gx5oyZQqOHz9+2yUiIgL/+9//sG/fPsjlctjY2CAkJAQAEBMTgwkTJgAAvLy8dHqcy8rKUFtbq9ND3UAul0OpVGotRERERNT6GXw4R79+/fDvf/8bY8eO1fTgurq64oMPPkBYWBg+/PBDgx1LpVJBpVLdcbv/+7//wzvvvKN5nJ+fj8GDB+Obb75B7969AUh3TJw/fz4KCgrg7e0NQLrYUC6XIzo62mA1ExEREVHLZ/AQ/dFHH2HOnDn4+OOPUVRUhLVr18LBwQF//PEHnJ2dDX24u+Lv76/1uKGO4OBgdOjQAQAQHx+P8PBwjB8/HgsXLsSlS5cwY8YMJCYmsoeZiMyuWF0FdWUtitVV8FDyPvREROZm8BBta2uLBQsWQK1WIyUlBUeOHEF5eTkCAwOxdetWQx/OYKytrfHTTz8hKSkJ/fr1g4ODA8aNG9fodHhERKa2dn8O/jhXhrX7czAtLtTc5RARtXlGu9mKUqnEE088gSeeeALvvPMOpk6daqxD6S0gIKDRixz9/f2xZcsWM1RERHR7Cb39ERfuCQ+F3NylEBERjBCiZ86cqbPu5huWvP/++4Y+JBFRq+ehtOcwDiIiC2LwEL1+/Xr06dMHjzzyiKa318bGBl27djX0oYiIiIiIzMLgU9wdP34cISEh2Lx5M/r164cJEyZAoVBgwoQJmunkiIiIiIhaMoP3RDs4OOCdd95BdnY2ZsyYgbCwMNTV1Rn6MEREREREZmO0CwtDQkLw448/YtOmTbC2tjbWYYiIiIiITM5oIbrByJEjMXLkSGMfhoiIiIjIZAw+JvpmgwYNwuXLl5tsv3jxIoKCgoxZAhERERGRwRk1RP/222+oqanRPK6urtZqr6urw/nz541ZAhERERGRwRk1RN8sIyMDISEheOONNxq90QkRERERUUth9DHRALBz5048+uijGD58OFauXIk9e/Zg3bp1pji0STX846DhxjJEFRVXobbj7DQNaiqvtIjPR0upk4iIDK/h/H/HTl9hRDKZTHzyySfC0dFRLF68WAghxIULF0S/fv2Et7e3WLdunbCysjJmCSaVm5srAHDhwoULFy5cuHBp4Utubu5tc59MCOONrbCysoKdnR0+//xzJCQkaNbX1dVh5syZ+PDDDzWPW4P6+nrk5+dDoVBAJpMZ/Xg9e/bEgQMHjH4cUxzPkPs2xL6auw99n6dWq+Hn54fc3FwolUq9j0dNM/Xnw5gs7bWYsh6edwz/PJ53jMvSPq/NZWmvw5T1CCEQHR2NU6dOwcqq6ZHPRh3OMWHCBIwbNw5xcXFa662trbF48WL0798fGzduNGYJJmVlZYUOHTqY7HjW1tYmPQEa83iG3Lch9tXcfTT3eUqlkn/MDMzUnw9jsrTXYsp6eN4x3vN43jEOS/u8NpelvQ5T12NnZ3fbAA0YOUR/8cUXt20fNWoUAgICjFlCq/bCCy+0muMZct+G2Fdz92Hqnwk1rTX9LCzttZiyHp53THNsMpzW8vOwtNdhiZnHqMM5mlJeXo61a9fi888/x5EjR3D9+nVTl0BkEdRqNVxcXFBeXm5R/+InotaL5x0iwzDZFHcA8L///Q/PPPMMvL29MW/ePAQEBHC6O2rT5HI55syZA7lcbu5SiKiN4HmHyDCM3hN94cIFrF69Gl988QWKiorw6KOPIiEhAYMHD8bx48dx3333tZoLC4mIiIiobTDqmOhHHnkE27dvx6BBg5CcnIxRo0bByclJ026KGSyIiIiIiAzNqCF669atGDduHKZOnYqYmBhjHoqIiIiIyGSMOiZ69+7dcHBwwKBBgxAWFobk5GRkZ2cb85BEREREREZnktk5rl27hnXr1mHVqlXYu3cvevbsiYSEBHTt2hVxcXEcE01ERERELYrJp7g7efIkPv/8c6xZswZFRUWQyWQM0URERETUophlnmhAutX35s2bsWrVKmzatMkcJRARERERNYvZQjQRERERUUtl0putEBERERG1BgzRRERERER6YogmIiIiItITQzQRERERkZ4YoomIiIiI9MQQTURERESkJ4ZoIiIiIiI9MUQTEREREemJIZqIiIiISE8M0UREREREemKIJiIiIiLSE0M0EREREZGeGKKJiIiIiPTEEE1EREREpCcbcxfQmtTX1yM/Px8KhQIymczc5RARERGRnoQQqKiogI+PD6ysmu5vZog2oPz8fPj5+Zm7DCIiIiK6R7m5uejQoUOT7QzRBqRQKABIb7pSqTRzNWQJzpdeRUd3J3OXYTGmfJ2GZeOizV3GHbWUOomIyPDUajX8/Pw0ua4pDNEG1DCEQ6lUMkQTAEBRYw2lkiG6gZ2Dc4v4bLSUOomIyHjuNDS32RcWVldXN/epREREREQtWrNDdN++fXXWnTp16p6KISKixhWrq5CRV45idZW5S9FhybURERmL3iF6y5YtWLRoEa5evYr8/HyttieffNJghRER0Q1r9+dg+NJdWLs/x9yl6LDk2oiIjEXvEN21a1c4OjqiuLgYY8eORVBQEB544AE8/fTTsLa2NkaNJrd8+XIEBgbC3t4e0dHR2Llzp7lLIqI2LqG3P3oFuCGht7+5S9FhybWxl5yIjEXvCwsDAwORlJSEiIgIPPDAAwCAvLw8nD17FhEREQYv0NS++eYbTJ06FcuXL0e/fv2wYsUKDB06FJmZmfD3t7w/EETUNngo7aF0sIWH0t7cpeiw5NrW7s/B//s1Cy8/1AnT4kLNXY6WYnUViiuq4aGQW+R7R9QcKZlF2Hu6FLHB7ogL9zR3OUbV7DHRn376KdRqNQDgyJEjKCkpgaurq6HqMpslS5Zg0qRJmDx5Mrp06YIPP/wQfn5++Pjjj81dGhER6cmSe8ktdRiMpfbeb0jNxcvrDmFDaq65S9GSklmE5M2ZSMksMncpGuaqKSWzCIlfpWL1nrNI/CrVot4TY2j2FHdHjx6FUqlEZmYmZsyYgf79+2PHjh348MMPDVieadXU1CAtLQ2zZs3SWh8fH489e/bobF9dXa01S0nDPyoOHz4MZ2dnzXo3NzcEBgaiqqoKmZmZOvvp0aMHAODkyZO4evWqVltAQADatWuHkpIS5OZqnzgUCgU6deqEuro6HDlyRGe/kZGRsLW1xenTp1FeXq7V5uvrC09PT5SVleHs2bNabQ4ODujSpQsA4NChQxBCaLV36dIFDg4OOH/+PEpLS7XaPD094evri4qKCmRlZWm12draIjIyEgCQnp6O2tparfZOnTpBoVAgLy8PRUXaHzx3d3d07NgRlZWVOH78uFabTCZDVFQUAOD48eOorKzUag8MDISbmxuKioqQl5en1ebi4oLg4GDU1tYiPT0dt7rvvvtgbW2NrKwsVFRUaLX5+fmhffv2uHTpEs6dO6fV5uTkhLCwMADAwYMHdfYbHh4Oe3t7nD17FmVlZVpt3t7e8Pb2hlqtRnZ2tlabXC5H165dAUifwevXr2u1h4aGwtnZGRcuXEBxcbFWm0qlgr+/P65du4YTJ05otVlZWaF79+4AgMzMTFRVaf/xDAoKgqurKwoLC3WuhXB1dUVQUBBqamqQkZGh81q7d+8OKysrnDp1ChfPncDBgzeGffn7+0OlUuHixYvIydEOEs7OzggNDUV9fT0OHz6ss9+IiAjY2dnhzJkzuHz5slabj48PvLy8cPnyZZw5c0arzd7eHuHh4QCkz2p9fb1We+fOnQEAOTk5uHjxolabh4cHOnTogCtXruhcSG1jY4Nu3boBAI4dO6Yzg1FISAiUSiUKCgpQUFCg1Xa354jLBee13j/Acs4RN/9sLe0cUVN8Bh5K6WJ4SzpH+Lk5wlMpR93lAhw8eEWr/a7PEVlZQF0dUFsL1NVBbmODrsHBwPXr0jmipga4fl1a6uoQ6uMDZzs7XMjPR/HFi1ptKkdH+Lu6YkVaKX4urkZv+VU8630dqK+HlRDo7uMD1NcjMy8PVTU10nHr64H6egS5uMBVLkdheTnyy8s161FfD1dbWwQpFNI54uLFG211dYAQ6O7iAqv6epwqL8eVmhpACKkdgL+9PVR2dvjOzhszgoZDVl+HjYfzcXHemxhUfgahjo7SOUKt1jwHQgBCIMLREXYyGc5cu4bLtbWa9QDgY2MDL1tbXK6pwZnq6httQsBeJkO4XA7U1+PwtWuov+l5EAKdbWzgKJMhp6YGP3t3xXsPvgCr+jqs2n0W7/22HGMKj+FKfT1O3fL7ayMEutnZAQCO1dSg+qZ9AkCIjQ2UMhkK6upQUFen9Vw3AIE2NqgSApkN+73pb3MPGynOnbx+Hdv9o/DekOmamj747xI8lnsEJXV1yL35PQKgkMnQycoKdULgyC3HhBCItLKCrUyG03V1KL+lXl+ZDJ4yGcqEwNk/97vxgYmwum8o6q2sYV1fh33T5kL12yqIm/YJAF1kMjgAOC8ESm/JGJ5/7rtCCGifIQBbAJF/TjuXLgRqb2nvJAQUMhnyhMCt8d0dQEeZDJVC4PgtbTIAUX/u97gQKLmlpiaJZurRo4eor68X//jHP8SaNWs061qyvLw8AUDs3r1ba/38+fNFaGiozvZz5swRAO64JCQkCCGEyMrKarS9QZ8+fXTaGt7bZcuW6bTFx8cLIYQoLy9vdL/FxcVCCCFGjBih07Z48WIhhBDr16/XaYuKitLUZGdnp9OekZEhhBBi0qRJOm2zZs0SQgixfft2nTZfX1/Nfn19fXXat2/fLoQQYtasWTptkyZNEkIIkZGRodNmZ2en2W9UVJRO+/r164UQQixevFinbcSIEUIIIYqLixt9D8vLy4UQQsTHx+u0LVu2TAghxJo1a3Ta+vTpI4QQ4mzJlUb3m5WVJYQQIiEhQadtzpw5Qgghtm7dqtMWHBysea0qlUqnfc+ePUIIIaZNm6bTlpSUJIQQIi0tTadNoVBo9hseHq7TvnHjRiGEEAsWLNBpGz16tBBCiNzc3EZfa1VVlRBCiAEDBui0rVy5UgghxMqVK3XaBgwYIIQQoqqqqtH95ubmCiGEGD16tE7bggULhBBCbNy4UactPDxc81oVCoVOe1pampi0+g+RlJSk0zZt2jQhhBB79uzRaVOpVJr9BgcH67Rv3bpVCNH4eeNuzxHtgyN02niOuPM5wsrGVrNfg58jUlOFOHRIxPfqpdO2bMwYIf7v/8SasWN12rrHxIuOr24RgTM3iY6vbhEOIdrPz4qIECIyUiQ08js6x9ZWCLlcbLWy0mkLxo04qGqk3j1/tk1rpC0JENtCeomOr24R/v/YqFWXAhDCzk4Ie3sRLpPpPHejq6sQPj5igYuLTttohUKIiAiR27lzo+9h1f33C/Hgg2KAq6tO28qICCEeeUQ8+dQsTU3+/9go3AZNFgNUKiGeeEJUPfZYo/vNfewxIZ55Roz299dpWxATI8TkyWJjI+f2cDc3IaZMEeKll4Sikd/vtGefFWLmTJEUFSXcBk3WquuhcW8I8frrYs/EiTrPUzk6CvHWW0K89ZYIdnPTad/6zDNCzJ0r5jz4oE5bQrduQrz9tsiaOrXR1yrmzxdi/nzRx89Pp6axf3tfiHffFcsefVTnefGdOgnx/vuiPDm50f0Wz50rxKJFYkQjfxcWjxwpxJIlYv2ECZp1Dn/+/nT88/jb/rlS2Flb6zw347XXhFi6VEyKjdVpmxUfL8RHH4ntL7+s0+br6irERx8J8dFHwreR35ftU6cKsXy5mNXIz3VS375CfPyxyHjzTZ02OxsbIT7+WIiPPxZRfn43Pt9/ZoCmyIS427it7dNPP8Wnn36KS5cuIT09HU5OTujcubNOD1dLkp+fD19fX+zZswexsbGa9fPnz8eaNWt0XltjPdF+fn7YsWMHe6LZE42wsDCcu3gVl3JO6uy3rfZEz/z3frw1oqumzVJ7ol/69hjeGuhpcT3Ro9/bgNfig7TaLOUckbz5mOZnaxHniLo6oLISf+RcxdpDJXhrQBDiFLU4npmJyrIyoLISuHoVuHYNgba2cKutRVFJCfJKS4Fr14DqaqCqCi61tQiurUXttWtIv3YNqKnROu59AKwBZAHQPkMAfgDa29vjkq0tzllbA3Z20mJjgzUxT2FT4P2os7KCVX09hhWk4i95OzXbhLdvD3snJ5ytrERZfT1gawvY2AA2NvB2c4O3uzvU168j+9Ilab21NWBtDbmDA7oGBQG2tjiak4PrwI12GxuEBgfDWanEhdJSFF++rPVclacnVp+6jtUH8lAvACsZMCzEEX+JcjHZOeLKFe0e+YZzxHf7svDKj6cghIBMJsOsfm4YFKYy2znC0dEROTk5+PnIBby3uwwySKnrvWEBGNO/q9nOESdPnsT2U6VaNX3wWBge6x1isnPEH3lVOFlWj0diOiEu3LNF5oiSkhIMGDAA5eXlt73xVrNDNABcvnwZNjY2cHZ2RnZ2Nt5++218+eWXzd2d2dXU1MDR0RHffvstHnvsMc36l19+GYcPH8aOHTtu+3y1Wg0XF5c7vunUdpy7eBUBKt6xsMHkLw/gswk9zV3GHVlqnZZaF2CE2mprgcuXgbIy6evN399unVoNVFQA164hJaQXEp94C9b1daizssbK75IRl/2HtH8rK8DZGVAopOXm752cAEdHaXFwkJaG7+/09ebvbWyAJu541jB2tMHKZ2Ms4iIsS60LkMZE78y+iP4hKoyO8TN3ORopmUXYd6YUfYIs50I6S6ypJbnbPNfsMdEnTpzA5s2b4erqiq5duyIiIqJFB2gAsLOzQ3R0NFJSUrRCdEpKCh599FEzVkZEZLlSMotwsqgCKZlFjf/Brq+XAu7Fi0BJibQ0fN/Uulv+V07DygpwdZUWN7cbX/39pe9dXDSBeG+5O6yLgTora1jLgH1zPkBcXLAUlB0cmgy4phAX7olFo7th4baT+Ed8mMUEnYa6GsKqpdQFAKNj/CwqPDeIC/e0qPcJsMyaWqNmh+ihQ4di8uTJuHz5MlasWIH09HRcvXoVJ0/q/td1SzJ9+nSMHz8eMTExiI2NxaeffoqcnBz87W9/M3dpRESWpaICKfuzkPhLAWRCIPGrVKysz0BcQQZQUAAUFkqB+OJFaXjFrdq1A9q3lxaVCoiKkr6qVFLbrUHZ1VUKyFZ3N7FUbGYRVv3Zq1ongD49ggFPywkWuWWVKFJXI7es8s4bm5ClhlUiS9PsEO3l5YXXX39da11dYyfJFubpp59GaWkpkpOTUVBQgIiICPz888/o2LGjuUsjIjINIYBLl4CcHGnJzb3xfX6+FJALCoCrV7F30GRYR4+Qenvr67AvuwRxJecAb28gPFw7JN/8fbt20nAHI4oL98TKZ2Pw9pZjeHN4V4vrmUvo7Y+4cE94KOTmLoWImqHZZ7DBgwdjzZo1GD9+vGZda7ljYVJSEpKSksxdBhGRcdTXAxcuAKdPA+fONR6Wb75A184O6NAB8POTll69pJDs7Y1YGw+sOlgNGaRhE33e+QdgQWE1LtwT3xzIsbgADUg3qeFNVoharmaH6D/++AOrVq3CvHnz0KtXL0RGRiIyMhLDhw83ZH1ERNQclZXA2bNSUD5zRvrasJw9qz3LhKenNKbY3x8YOvTG935+0lcPjyaHUMQBWBlRZLG9vcXqKqgra1GsrmJgJSKDanaI/vnnnwFIVzBmZGQgIyMDv/zyC0M0EZGpCCH1KB8/fmM5cQLIzgZuns5RLgeCgoDgYGDIEOlrw9KxI2B/b+HSknt71+7PwR/nyrB2f47F3fabiFq2Zofo69evY926dSgpKUF4eDgSExMhM+OVzkRErVZtLZCdjai07UBeinZgbpjFQi4HwsKAzp2B++/XDsre3nd9MV5rw3HHRGQszQ7RY8eOhbu7Ozp37owNGzbg1VdfxbfffotOnToZsj4iorZDCOnCvaNHpeXIEenryZPA9et4AZBmqejSBbjvPmDMGOn7zp2BgADpphmkheOOichYmh2iT548iaNHj2oeHzx4EImJifjtt98MURcRUetWWwtkZgIHD94Iy0ePAg1371IogG7dgAEDgBdeALp0wfSjVVjy4mCzzm9MRESSZodoZ2dnnD59GsHBwQCkW05eunTJYIUREbUa169Lwy/S0oDUVGk5cgSoqpICcUiIFJhffln62q2b1LN8U1hOySzCgapjSDlebJFjj4mI2ppmh+gVK1Zg1KhRGDp0KLp06YLjx4/D39/fkLURkYGVV9biavV1OMlt4OJga+5yWichpKni9uwB9u4FDhwADh+WZsuQyYDQUCAmBnj6aelr9+5Sr/Nt3Hwr5sSvUi3qVsxERG1Vs0O0SqXCvn37sGXLFhw/fhzBwcGYO3euAUsjIkMqr6zFgXOXUHalBm7OdugZ0I5BuhF3vIX1raqrpSEZDaF5715pXDMgzYjRuzcwejQQHS3dkU+p1LumvadLYS2ToU4IWMtk2Hem1KJCNKeRI6K2qNkhOj4+Hjt37sTTTz8NACgrK0NmZiZiYmIMVhwRGc7V6us4XXwF1bX1uHStBl19lAzRt7irHt/ycmDXLmDHDmDnTilA19QADg5Az57A+PFAbKy0eHgYpK7YYHes2n0WAFAnBPoEuRtkv4bCaeSIqC1qdoi2sbGBq6ur5rGLiwuef/55pKWlGaIuIjIwJ7kNgj2cNT3RjnbGveVyS9Roj6+3nRSWd+wAfvtNGppRXw/4+AAPPACMGycF5vvuA2yN848S3r6aiMjyNPuvaIcOHbB7927069cPAGBlZYWam++ARUQWxcXBFj0D2uFazXU42nFMdGMaenxlQqAOQJ//lwyMXC+Nc/b3l2bKSEqSvgYHm3SWDEu+oQmnkSOitqjZIfqjjz7CsGHDEBsbi549eyI9PZ0XFhJZOBcHW4bnW9XVSUMytm1DXEoKVhbWYp9vOPpUFiAurD2wahXw4IPSbBlERER/anaI9vf3x8GDB/HDDz8gPT0doaGhWLBggSFrIyIyjpwc4L//BVJSgF9+AcrKpBkyBg1C3JNx2H7FC3EzH+d8zERE1KR7GhRpa2uLp556Ck899ZSh6iEiMry6OuCPP4AtW6Tl6FHpNti9egEvvgjExUmzaPw5prn4ywMM0EREdFv3fGXRoEGD8P3332tdZHizixcvolevXjhz5sy9HoqI6O6p1cC2bcDmzcDPPwMXLwLu7sAjjwCvvy4FZzc3c1dJREQt1D2H6N9++03rgsLq6mrI5Teu0K6rq8P58+fv9TBERHeWnw98/z3w44/SbBrXrwMREcDkycCIEVJvs7W1uaskIqJWwMqQO8vIyEBISAjeeOMNCCEMuWsiosadOwcsXgz07Qv4+gLTpklB+cMPgbNngfR04N13pXYGaCIiMhCDTRS7c+dOPProoxg+fDhWrlyJPXv2YN26dYbaPRHRDadOAd99Jy1paYBcDgweDHz5pdTjzGEaRERkZAbpif7hhx8wZMgQvPHGG/jqq69w8OBB1NTUoHv37vjtt98McQgiauvOnwfeew/o1g0ICwPeeQcIDAT+/W+gpATYuBF49lkGaCIiMgmD9ES//PLL+Pzzz5GQkAAA8PX1xY4dOzBz5kyMGzfOEIcgorbo4kXg22+Br7+WbrXt4ACMHAnMmyf1PDs6mrtCIiJqo+45RE+YMAHjxo1DXFyc1npra2ssXrwY/fv3x8aNG+/1METUVly9KvUqf/21NJezEEB8PLBmDTBqFODsbO4KiYiI7j1Ef/HFF7dtHzVqFAJ4py8iup36euD336W7A373HXDtmnQh4IcfAk8+CXh4mLtCIiIiLQa7sPBW5eXlWLt2LT7//HMcOXIE169fN9ahiKilysmRLgZcvRo4cwYICQFeew0YN04a70xERGShDB6i//e//2HVqlX4/vvvoVAocP/99+Pw4cOGPgwRtVRVVdJwjVWrpNtuOzoCTz0lhel+/XinQCIiahEMMjvHhQsX8M477yA4OBgjR46EEAIbNmxAfn4+5s2bZ4hDEFFLl5GBsWsWAj4+wJgx0tjnzz4DCgqkQH3//QzQRETUYtxzT/QjjzyC7du3Y9CgQUhOTsaoUaPg5OSkaZfxjyJR21VTI91BcPlyYOdOxCjbAX//K/CXv0jT1NFdK1ZXQV1Zi2J1FTyU9uYuh4iozbvnEL1161aMGzcOU6dORUxMjCFqIqKWLicHWLFC6mkuLgYGDAC++QYzKzpgxaS+5q6uRVq7Pwd/nCvD2v05mBYXau5yiIjavHsezrF79244ODhg0KBBCAsLQ3JyMrKzsw1RGxG1JPX1wNatwKOPShcFLlsmjXXOyAB++w0pEQOQWVqFlMwic1faIiX09seWF+9HQm9/c5dCREQwQIiOjY3FypUrUVhYiFdffRXbtm1DWFgY+vTpg6VLl6KoiH8wiVq1K1ekwBwWBgwdKt1Z8OOPgbw8YOlSoGtXpGQWIfGrVOReqkTiV6kM0s3gobRHhK8Lh3IQEVkIg1xYCACOjo547rnnsGvXLmRmZuKBBx7AggUL8PDDDxvqEERkSS5cAF59FfDzA6ZOBaKjgd27gUOHgL/+VeumKHtPl8L6z+sjrGUy7DtTaqaiiYiIDMNgIfpmYWFheP/993HhwgV8//33GDZsmDEOQ0TmkJoKJCRIQzY++QSYPFma43ndOukGKY1cTBwb7I46IQAAdUKgT5C7qasmIiIyKKOE6AbW1tYYNWoUNm3aZMzDEJGx1dUBP/4IPPAA0LMnsHcvsHix1Bu9cCHgf/txunHhnlj5bAz82zlg5bMxiAv3NE3dRERERmLUEE1ELVxNjTSHc3g48NhjgBDSlHVZWcBLLwEKxV3vKi7cE6GeCgZoIiJqFYx2228iasEaboSyaJHU2/zYY8CaNUCvXuaujIiIyCIwRBPRDZcvAx99BHz4IVBWJo19fvVVqSeaiIiINBiiiQgoKgI++EC6s2BNDTBpEvCPfwABAeaujIiIyCIxRBO1ZUVFwD//Kc3rbGsLJCVJ09V5eZm7MrPh7bWJiOhutJkLCwMCAiCTybSWWbNmaW2Tk5ODESNGwMnJCSqVCi+99BJqamrMVDGREZWUADNnStPUff65NGTj/HngvffadIAGtG+vTURE1JQ21ROdnJyMxMREzWPnm24GUVdXh2HDhqF9+/bYtWsXSktLMWHCBAghsHTpUnOUS2R4paXSxYJLlwJWVsCMGcC0aYCbm7krsxgJvf0RF+4JD4Xc3KUQEZEFa1MhWqFQwKuJXrZt27YhMzMTubm58PHxAQAsXrwYEydOxPz586FUKk1ZKpFhlZUBS5YA/+//AfX10vR0r7wCuPOmJ7fyUNpzGAcREd1RmxnOAQD//Oc/4e7uju7du2P+/PlaQzX27t2LiIgITYAGgMGDB6O6uhppaWmN7q+6uhpqtVprIbIoV64AycnSBYKLFwN/+xtw9iywYAEDNBER0T1oMz3RL7/8Mnr06AE3Nzf88ccfmD17Ns6ePYvPPvsMAFBYWAhPT+2bQLi5ucHOzg6FhYWN7vPdd9/FvHnzjF47kd5qa4GVK6UAXVYmXTA4axbgyRudEBERGUKL7omeO3euzsWCty6pqakAgGnTpmHAgAHo1q0bJk+ejE8++QSff/45SktLNfuTyWQ6xxBCNLoeAGbPno3y8nLNkpuba5wXSnS3hAC+/Vaa13nKFGDIEODUKWn6OgZoIiIig2nRPdFTpkzBmDFjbrtNQBPz3Pbp0wcAkJ2dDXd3d3h5eWH//v1a25SVlaG2tlanh7qBXC6HXM6Lj8hCbN8uzbJx4ADwyCPS7bkjI81dFRERUavUokO0SqWCSqVq1nMPHToEAPD29gYAxMbGYv78+SgoKNCs27ZtG+RyOaKjow1TMJExHDkiDdXYulW6Lff27cCDD5q7KiIiolatRYfou7V3717s27cPAwcOhIuLCw4cOIBp06Zh5MiR8Pf3BwDEx8cjPDwc48ePx8KFC3Hp0iXMmDEDiYmJnJmDLFN+PvD668CXXwKdOgEbNgCPPw40MfyIiIiIDKdNhGi5XI5vvvkG8+bNQ3V1NTp27IjExETMnDlTs421tTV++uknJCUloV+/fnBwcMC4ceOwaNEiM1ZO1IjKSmmM84IFgL09sGwZkJgo3XGQiIiITKJNhOgePXpg3759d9zO398fW7ZsMUFFRM0ghNTb/I9/AHl50lzPb74JuLqauzIiIqI2p02EaKIWLy0NmDoV2LULGDEC2LYNCA01d1VERERtVoue4o6o1SsoAJ57DujZU5rv+b//BTZtYoAmIiIyM4ZoIktUWyvdpjs0VArNy5YBhw8D8fHmroyIiIjA4RxEluf334EXXgAyM4G//x14+23Azc3cVREREdFN2BNNZCmKioBnnwUGDACcnKSbpixbxgBNRERkgRiiiczt+nVg6VJp6MbPPwMrVwJ79gA9epi7MiIiImoCQzSROe3dK100+PLLwJgxwMmTwOTJgBU/mkRERJaMf6mJzKGsDPjrX4G+fQFra2DfPmDFCsDd3dyVERER0V3ghYVEpiQE8N13wJQpwLVrwEcfAc8/LwVpIiIiajHYE01kKhcuAKNGAU8+CcTGAsePA0lJDNBEREQtEEM0kbHV10s9zuHh0owb330H/PAD4Otr7sqIiIiomRiiiYzI9kQmcP/90vCNceOkuZ8ff9zcZREREdE9YogmMobqauCtt+AzqJ90EeHvvwOffAK4upq7MiIiIjIAXlhIZGipqcDEicCpUyh/+RW4vjMXkMvNXRUREREZEHuiiQyluhp4/XWgTx/Azg5ITcXlV99ggCYiImqFGKKJDCEtDYiJARYuBObMAfbvB7p1M3dVREREZCQM0UT3oqYGePNNoHdvwMZGGsrx5puAra25KyMiIiIj4phoouY6eFAa+3z8OPDWW8Ds2QzPREREbQR7oon0VVsrDdno1Uu6UUpqqhSiGaCJiIjaDPZEE+nj5EngmWeAQ4eAN96QLiRkeCYiImpz2BNNdDeEAJYvB6KiALUa2LsXmDuXAZqIiKiNYogmupPCQmDYMOCFF6Qx0AcPAj17mrsqIiIiMiMO5yC6nR9+ABITpZk3fvoJeOSRu35qeWUtitVVcHOyg4sDe6yJiIhaE/ZEEzWmogJ47jng8ceB++8H0tP1DtDpFy7jeIEa6Rcuo7yy1ojFEhERkamxJ5roVnv3ShcPFhUBn30mhWmZTK9dXK2+joqq6/BycUBF1XVcq7nO3mgiIqJWhD3RRA3q6oAFC4D+/QEPD+DIEWDSJL0DNAA4yW2gsLdBYXklFPY2cLTjv1eJiIhaE/5lJwKA/Hxg/Hhg+3bppin3OPOGi4MtIju4ws7GCmFeSvZCExERtTIM0UQ//wxMmCCF5l9+AQYNMshuXRxs4aGwZ4AmIiJqhTicg9qu6mpg+nRp+rpevaThGwYK0ERERNS6MURT25SVBfTtCyxbBnzwAbBlC9C+vbmratWK1VVQ/zntHxERUUvHEE1tz5o1QI8e0jR2+/YBU6c26+JB0s/a/Tn441wZ1u7PMXcpRERE94xjoqntuHYNePFFYNUq4NlnpV5ohcLcVbUZCb39ERfuCQ+F3NylEBER3TOGaGobTp0CRo8GsrOBL76Qbt9NJuWhtIeH0t7cZRARERkEh3NQ67d+PRAdDdTUAPv3M0ATERHRPWOIptaruloavvH008Dw4cCBA0BkpLmrIiIiolaAwzmodTp3DnjqKWnauo8+Av7+d148SERERAbTKnqi58+fj759+8LR0RGurq6NbpOTk4MRI0bAyckJKpUKL730EmpqarS2SU9Px4ABA+Dg4ABfX18kJydDCGGCV0AGtWWLNPtGSQmwezeQlMQATURERAbVKkJ0TU0NnnzySfz9739vtL2urg7Dhg3D1atXsWvXLqxbtw7fffcdXnnlFc02arUacXFx8PHxwYEDB7B06VIsWrQIS5YsMdXLoHslBPDqq8CIEUD//sDBg0BMjLmrIiIiolaoVQznmDdvHgBg9erVjbZv27YNmZmZyM3NhY+PDwBg8eLFmDhxIubPnw+lUom1a9eiqqoKq1evhlwuR0REBE6dOoUlS5Zg+vTpkLEn0/LJZMCVK8D77wMzZrD3mYiIiIymVYToO9m7dy8iIiI0ARoABg8ejOrqaqSlpWHgwIHYu3cvBgwYALlcrrXN7Nmzce7cOQQGBurst7q6GtXV1ZrH5eXlAKRebTKTd9+VvlZUmLeOP1VUXIXars7cZRAREdFdashxdxrS2yZCdGFhITw9PbXWubm5wc7ODoWFhZptAgICtLZpeE5hYWGjIfrdd9/V9ILfzM/Pz0CVExEREZE5VFRUwMXFpcl2iw3Rc+fObTSg3uzAgQOIucsxr40NxxBCaK2/dZuGf4E0NZRj9uzZmD59uuZxfX09Ll26BHd3d5MM/+jZsycOHDhg9OOY4niG3Lch9tXcfej7PLVaDT8/P+Tm5kKpVOp9PGqaqT8fxmRpr8WU9fC8Y/jn8bxjXJb2eW0uS3sdpqxHCIHo6GitEQyNsdgQPWXKFIwZM+a229zac9wULy8v7N+/X2tdWVkZamtrNb3NXl5eml7pBsXFxQCg04vdQC6Xaw3/ANDk7CDGYG1tbdIToDGPZ8h9G2Jfzd1Hc5+nVCr5x8zATP35MCZLey2mrIfnHeM9j+cd47C0z2tzWdrrMHU9dnZ2sLK6/fwbFhuiVSoVVCqVQfYVGxuL+fPno6CgAN7e3gCkiw3lcjmio6M127z22muoqamBnZ2dZhsfH5+7Duum9sILL7Sa4xly34bYV3P3YeqfCTWtNf0sLO21mLIenndMc2wynNby87C012GJmUcmWsFEyDk5Obh06RI2bdqEhQsXYufOnQCAkJAQODs7o66uDt27d4enpycWLlyIS5cuYeLEiRg1ahSWLl0KQLooMCwsDIMGDcJrr72GrKwsTJw4EW+99ZbWVHhEhqRWq+Hi4oLy8nKL+hc/EbVePO8QGYbF9kTr46233sKXX36peRwVFQUA2L59Ox588EFYW1vjp59+QlJSEvr16wcHBweMGzcOixYt0jzHxcUFKSkpeOGFFxATEwM3NzdMnz5da8wzkaHJ5XLMmTNHZ1gQEZGx8LxDZBitoieaiIiIiMiUWsUdC4mIiIiITIkhmoiIiIhITwzRRERERER6YogmIiIiItITQzQRERERkZ4YoomIiIiI9MQQTURERESkJ4ZoIiIiIiI9MUQTEREREemJIZqIiIiISE8M0UREREREemKIJiIiIiLSE0M0EREREZGeGKKJiIiIiPTEEE1EREREpCeGaCIiIiIiPTFEExERERHpiSGaiIiIiEhPDNFERERERHpiiCYiIiIi0hNDNBERERGRnmzMXUBrUl9fj/z8fCgUCshkMnOXQ0RERER6EkKgoqICPj4+sLJqur+ZIdqA8vPz4efnZ+4yiIiIiOge5ebmokOHDk22M0QbkEKhACC96Uql0szVkDm9958T+Pf+HNQJAWuZDOP6+OPVIZ3NXZbZTfk6DcvGRZu7jDuy1DottS7AsmsjItKHWq2Gn5+fJtc1hSHagBqGcCiVSoboNm5gZEd8fagEVgAEgAcjOvJ3AoCdg3OLeB8stU5LrQuw7NqIiJrjTkNzDRqi1Wo11q9fj9OnT6Ndu3bo3r07BgwYADs7O0MehsjixYV7YuWzMXh7yzG8Obwr4sI9zV0SERERGZBBZ+cYOnQozpw5g5CQECxZsgQLFy5EQEAAFi5cCCGEIQ9FZPHiwj0R6qlggCYiImqFDBqiL1++jAULFmDSpEnw8vLCtm3bkJmZifz8fEybNs2QhyIiIiIiMhuDhuh+/frh3//+N4Ab40hcXV3xwQcf4D//+Y9mu3PnzuGNN97Qeu6GDRvw/fffG7IcIiJq44rVVcjIK0exusrcpRBRK2PQEP3RRx8hPT0dDzzwAIqKirB27Vp8//33mDVrFpydnTXbBQQEoKqqCk8//TRqa2uxbNkyLF26FIMGDTJkOURE1Mat3Z+D4Ut3Ye3+HHOXQkStjEEvLLS1tcWCBQugVquRkpKCI0eOoLy8HIGBgdi6davWtosWLcLChQvRpUsXhIeH47///S/s7e0NWQ4REbVxCb39sff0RST09jd3KUTUyhjltt9KpRJPPPEEkpOT0b59e0ydOhXt27fX2kYIgezsbAQEBKCoqAhXrlwxRilERNSGeSjtoXSwhYeSnTREZFgG7YmeOXOmzrqVK1dCrVYDAN5//30AQHV1NcaOHYvOnTtjxYoV2LJlCx566CH8+OOPCAwMNGRJREREREQGZ9AQvX79evTp0wePPPKIZko7GxsbdO3aVWu70tJSjBgxAn/5y18AAMOHD4e7uzvOnTvHEE1ERG1CsboKxRXV8FDI2VNO1AIZdDjH8ePHERISgs2bN6Nfv36YMGECFAoFJkyYgAkTJmi28/Hx0QToBrGxsRg4cKAhy2m25cuXIzAwEPb29oiOjsbOnTvNXRIREbUyvOiRqGUzaE+0g4MD3nnnHWRnZ2PGjBkICwtDXV2dIQ9hdN988w2mTp2K5cuXo1+/flixYgWGDh2KzMxM+PvzwhQiolulZBbhZFEFUjKLeHMhPVjqRY/sISe6O0a5sDAkJAQ//vgj+vXrh4SEBGMcwmiWLFmCSZMmYfLkyejSpQs+/PBD+Pn54eOPPzZ3aUQt2s1Bi1qPlMwiJH6VitxLlUj8KtXifr6W/Ht35EI5CtRVOHKh3NylaHl7SyaGL92Ft7dkmrsULRtSc/HyukPYkJpr7lK0WGJdKZlFSN6caZbfe3Me29QM2hN9q5EjR2LkyJHGPIRB1dTUIC0tDbNmzdJaHx8fjz179uhsX11djerqas3jhgsoDx8+rDUvtpubGwIDA1FVVYXMTN2TUo8ePQAAJ0+exNWrV7XaAgIC0K5dO5SUlCA3V/sDqlAo0KlTJ9TV1eHIkSM6+42MjIStrS1Onz6N8nLtk7Svry88PT1RVlaGs2fParU5ODigS5cuAIBDhw7p3LK9S5cucHBwwPnz51FaWqrV5unpCV9fX1RUVCArK0urzdbWFpGRkQCA9PR01NbWarV36tQJCoUCeXl5KCrS/vC5u7ujY8eOqKysxPHjx7XaZDIZoqKiAEhDiiorK7XaAwMD4ebmhqKiIuTl5Wm1ubi4IDg4GLW1tUhPT8et7rvvPlhbWyMrKwsVFRVabX5+fmjfvj0uXbqEc+fOabU5OTkhLCwMAHDw4EGd/YaHh8Pe3h5nz55FWVmZVpu3tze8vb2hVquRnZ2t1SaXyzXXGBw9ehTXr1/Xag8NDYWzszMuXLiA4uJirTaVSgV/f39cu3YNJ06c0GqzsrJC9+7dAQCZmZmoqtK+MUVQUBBcXV1RWFiI/Px8rTZXV1cEBQWhpqYGGRkZOq+1e/fu+PVECRK/SgUgkPhVKmb1c0MvX3v4+/tDpVLh4sWLyMnR/i9tZ2dnhIaGor6+HocPH9bZb0REBOzs7HDmzBlcvnxZq83HxwdeXl64fPkyzpw5o9Vmb2+P8PBwANJntb6+Xqs9X6bCyaIKfL0jA50VNVptHh4e6NChA65cuYJTp05ptdnY2KBbt24AgGPHjmmdGwCpc0GpVKKgoAAFBQVabXd7jrhccB4HD1prtZn7HLFp33FYyYB6AVjJpMf3Byot4hzxR14V3ttdhobfu5XPxqCD7JJFnCOOlgJzf5V+D27+TDQw1zni5BU5Nh8tACCw+WgBuir2oJevvdHPEVZWVjh16pTOTF0N54jv9mVhxo+nIITAxsP5uFiQi0FhKrOcIzp37gxHR0fk5OTg5yMX8N7uMk1d1yvVGNO/q9nOESdPnsT2U6V4b3cZZABW7T6LDx4Lw2O9Q0xyjmj4zFnJpGOvfDYGqur8FpcjSkpKdN6LRgkjGThwoCgrK2uyvaSkRAQGBhrr8M2Sl5cnAIjdu3drrZ8/f74IDQ3V2X7OnDkCwB2XhIQEIYQQWVlZjbY36NOnj07bmjVrhBBCLFu2TKctPj5eCCFEeXl5o/stLi4WQggxYsQInbbFixcLIYRYv369TltUVJSmJjs7O532jIwMIYQQkyZN0mmbNWuWEEKI7du367T5+vpq9uvr66vTvn37diGEELNmzdJpmzRpkhBCiIyMDJ02Ozs7zX6joqJ02tevXy+EEGLx4sU6bSNGjBBCCFFcXNzoe1heXi6EECI+Pl6nbdmyZUIIIdasWaPT1qdPH+k9Wv1Ho/vNysoSQgiRkJCg0zZnzhwhhBBbt27VaQsODta8VpVKpdO+Z88eIYQQ06ZN02lLSkoSQgiRlpam06ZQKDT7DQ8P12nfuHGjEEKIBQsW6LSNHj1aCCFEbm5uo6+1qqpKzNt0THScuUl0fHWL8P/HRuE2aLIAIFauXCmEEGLlypU6zxswYIAQQoiqqqpG95ubmyuEEGL06NE6bQsWLBBCCLFx40adtvDwcM1rVSgUWm0OIb1Ex1e3aBaHkF5a7dOmTRNCCLFnzx6d/apUKs1+g4ODddq3bt0qhGj8vHG354j2wRE6beY+Ryg699P8XBveM0s5R7gNmqypK2jWTyJ58zGLOUeEJ7wpgmb9pPOZaFjMdY6Yt+mYCJy1RacuY58jhBBiwIABOm0N54gnF6zT/Cwb6jLHOQKASEtLE0IIkZSUpPU75v+PjeKhGcuFEOY7R/Tp00enprHvbxBCmOYccfOxA17dIpI3H2vROaLh890UmRC3/PPAQKysrFBYWAgPDw8AUq+tXC7XtBcVFcHHx8eixkzn5+fD19cXe/bsQWxsrGb9/PnzsWbNGp3eu8Z6ov38/LBjxw72RLMnGmFhYZj85QEkRWr3HAJtuycakFlsT/SqQ2r8J/sq6oTUqzqskxP+0l2paTd3T/To9zbgtfggrTZLOEfsv1CJz/cVYFIfb/TytbeYc8TNPdGAzGJ7ogFYVE90Y59VS+iJfuXPnmiZTKrL0nqiZTIZ3hsWYFE90QIwS090w/vRknuiBwwYgPLyciiVSjTFJCE6IyMDQ4cOxYQJE/D2229DJpNZZIiuqamBo6Mjvv32Wzz22GOa9S+//DIOHz6MHTt23Pb5arUaLi4ud3zTqe2Y/OUBfDahp7nLsAgpmUV4e8sxvDm8q8VefNYwvrfBymdjLKpWS/59stTaLPn3zlJr25Cai4XbTuIf8WEYHeNn7nI0NqTmYmf2RfQPUbGuO0jJLMK+M6XoE+Ru8t8tS3w/9HW3ec4oFxbebOfOnXjggQcwcOBArFy5Eg899JDOv34thZ2dHaKjo5GSkqK1PiUlBX379jVTVUStQ1y4J0I9FRYVFm4VF+6Jlc/GwL+dg8UFaGoeS/69s9TacssqUaSuRm5Z5Z03NqHRMX74f2OiLC6YWWJdceGeeHN4uFl+tyzx/TAWo4boH374AUOGDMEbb7yBr776CgcPHkRNTQ26d++O3377zZiHbrbp06fjs88+w6pVq3D8+HFMmzYNOTk5+Nvf/mbu0ojIBCw12FjyLBPUPMXqKqgra1GsrrrzxiaU0NsfW1683+Km3iOyNEadnePll1/G559/rpnmztfXFzt27MDMmTMxbtw4Yx662Z5++mmUlpYiOTkZBQUFiIiIwM8//4yOHTuauzQiaqNuHmbSMMuEpYV80t/a/Tn441wZ1u7PwbS4UHOXo+GhtOf80ER3wWghesKECRg3bhzi4uK01ltbW2Px4sXo378/Nm7caKzD35OkpCQkJSWZuwwiIgDA3tOlsJbJUCcErGUy7DtTyhDdCiT09kdcuCc8FPI7b0xEFsdoIfqLL764bfuoUaMQEBBgrMMTEbUascHuWLVbuvq9Tgj0CXI3c0VkCOzxJWrZjH5h4a3Ky8uxfPlyREdHIyYmxtSHJyJqcXjBY/NZ6rhjImr5TBai//e//+GZZ56Bt7c35s2bh4CAAJ15A4mIqHGWesGjpbt53DERkSEZNURfuHAB77zzDoKDgzFy5EgIIbBhwwbk5+dj3rx5xjw0ERERZ5ogIqMx2pjoRx55BNu3b8egQYOQnJyMUaNGwcnJSdMuk8mMdWgiIiIAHHdMRMZjtBC9detWjBs3DlOnTuXYZyIiIiJqVYw2nGP37t1wcHDAoEGDEBYWhuTkZGRnZxvrcEREREREJmO0EB0bG4uVK1eisLAQr776KrZt24awsDD06dMHS5cuRVER77pFRERERC2T0WfncHR0xHPPPYddu3YhMzMTDzzwABYsWICHH37Y2IcmIiIT4DRyRNQWmXSe6LCwMLz//vu4cOECvv/+ewwbNsyUhyciIiPgNHJE1BYZ7cLC27G2tsaoUaMwatQocxyeiIgMiLevJqK2yCwhmoiIWg9OI0dEbZHJb/tNRERERNTSMUQTEREREemJIZqIiIiISE8M0UREREREemKIJiIiIiLSE0M0EREREZGeGKKJiIiIiPTEEE1EREREpCeGaCIiIiIiPTFEExERERHpiSGaiIiIiEhPDNFERERERHpiiCYiIiIi0hNDNBFRC1CsroK6shbF6ipzl0JERGCIJiJqEdbuz8Ef58qwdn+OuUshIiIANuYugIiI7iyhtz/iwj3hoZCbuxQiIkIb6Yk+d+4cJk2ahMDAQDg4OCA4OBhz5sxBTU2N1nYymUxn+eSTT8xUNRHRDR5Ke0T4usBDaW/uUoiICG2kJ/rEiROor6/HihUrEBISgoyMDCQmJuLq1atYtGiR1rZffPEFhgwZonns4uJi6nKJiIiIyMK1iRA9ZMgQrWAcFBSEkydP4uOPP9YJ0a6urvDy8jJ1iURERETUgrSJ4RyNKS8vR7t27XTWT5kyBSqVCj179sQnn3yC+vr6JvdRXV0NtVqttRARERFR69cmeqJvdfr0aSxduhSLFy/WWv/222/joYcegoODA3799Ve88soruHjxIt54441G9/Puu+9i3rx5piiZiIiIiCxIi+6Jnjt3bqMXA968pKamaj0nPz8fQ4YMwZNPPonJkydrtb3xxhuIjY1F9+7d8corryA5ORkLFy5s8vizZ89GeXm5ZsnNzTXK6yQiIiIiy9Kie6KnTJmCMWPG3HabgIAAzff5+fkYOHAgYmNj8emnn95x/3369IFarUZRURE8PT112uVyOeRyTjdFRERE1Na06BCtUqmgUqnuatu8vDwMHDgQ0dHR+OKLL2BldedO+EOHDsHe3h6urq73WCkRERERtSYtOkTfrfz8fDz44IPw9/fHokWLUFJSomlrmIlj8+bNKCwsRGxsLBwcHLB9+3a8/vrr+Otf/8reZiIiIiLS0iZC9LZt25CdnY3s7Gx06NBBq00IAQCwtbXF8uXLMX36dNTX1yMoKAjJycl44YUXzFEyEREREVmwNhGiJ06ciIkTJ952m1vnkiaitqlYXQV1ZS2K1VW8OyARETWpRc/OQWTJbg5j1HKs3Z+DP86VYe3+HHOXQkREFqxN9EQTmcPNYWxaXKi5y6G7lNDbH3HhnvBQ8FoIIiJqGkM0kZEwjLVMHkp7DuMgIqI7YogmMhKGMSIiotaLY6KJiIiIiPTEEE1EREREpCeGaCIiIiIiPTFEExERERHpiSGaiIiIiEhPDNFERERERHpiiCYiIiIi0hNDNBERERGRnhiiiYiIiIj0xBBNRERERKQnhmgiIiIiIj0xRBMRERER6YkhmoiIiIhITwzRRERERER6YogmIiIiItITQzQRERERkZ4YoomIiIiI9MQQTURERESkJ4ZoIiIiIiI9MUQTEREREemJIZqIiIiISE8M0UREREREemKIJiIiIiLSE0M0EREREZGe2kyIDggIgEwm01pmzZqltU1OTg5GjBgBJycnqFQqvPTSS6ipqTFTxURERERkqWzMXYApJScnIzExUfPY2dlZ831dXR2GDRuG9u3bY9euXSgtLcWECRMghMDSpUvNUS5Rq1KsroK6shbF6ip4KO3NXQ4REdE9aVMhWqFQwMvLq9G2bdu2ITMzE7m5ufDx8QEALF68GBMnTsT8+fOhVCpNWSpRq7N2fw7+OFeGtftzMC0u1NzlEBER3ZM2M5wDAP75z3/C3d0d3bt3x/z587WGauzduxcRERGaAA0AgwcPRnV1NdLS0hrdX3V1NdRqtdZCRI1L6O2PLS/ej4Te/uYuhYiI6J61mZ7ol19+GT169ICbmxv++OMPzJ49G2fPnsVnn30GACgsLISnp6fWc9zc3GBnZ4fCwsJG9/nuu+9i3rx5Rq+dqDXwUNpzGAcREbUaLboneu7cuToXC966pKamAgCmTZuGAQMGoFu3bpg8eTI++eQTfP755ygtLdXsTyaT6RxDCNHoegCYPXs2ysvLNUtubq5xXigRERERWZQW3RM9ZcoUjBkz5rbbBAQENLq+T58+AIDs7Gy4u7vDy8sL+/fv19qmrKwMtbW1Oj3UDeRyOeRyuf6FExEREVGL1qJDtEqlgkqlatZzDx06BADw9vYGAMTGxmL+/PkoKCjQrNu2bRvkcjmio6MNUzARERERtQotOkTfrb1792Lfvn0YOHAgXFxccODAAUybNg0jR46Ev790kVN8fDzCw8Mxfvx4LFy4EJcuXcKMGTOQmJjImTmIiIiISEubCNFyuRzffPMN5s2bh+rqanTs2BGJiYmYOXOmZhtra2v89NNPSEpKQr9+/eDg4IBx48Zh0aJFZqyciIiIiCyRTAghzF1Ea6FWq+Hi4oLy8nL2XhMRERG1QHeb51r07BxERERERObAEE1EREREpCeGaCIiIiIiPbWJCwtNpWF4OW//TURERNQyNeS4O102yBBtQBUVFQAAPz8/M1dCRERERPeioqICLi4uTbZzdg4Dqq+vR35+PhQKRZO3Cjeknj174sCBA0Y/jimOZ8h9G2Jfzd2Hvs9Tq9Xw8/NDbm4uZ3QxMFN/PozJ0l6LKevhecfwz+N5x7gs7fPaXJb2OkxZjxAC0dHROHXqFKysmh75zJ5oA7KyskKHDh1Mdjxra2uTngCNeTxD7tsQ+2ruPpr7PKVSyT9mBmbqz4cxWdprMWU9PO8Y73k87xiHpX1em8vSXoep67Gzs7ttgAZ4YWGL9sILL7Sa4xly34bYV3P3YeqfCTWtNf0sLO21mLIenndMc2wynNby87C012GJmYfDOYjMiDfoISJT43mHyDDYE01kRnK5HHPmzIFcLjd3KUTURvC8Q2QY7IkmIiIiItITe6KJiIiIiPTEEE1EREREpCeGaCIiIiIiPTFEExERERHpiSGaiIiIiEhPDNFERERERHpiiCYiIiIi0hNDNBERERGRnhiiiYiIiIj0xBBNRERERKQnhmgiIiIiIj0xRBMRERER6YkhmoiIiIhITwzRRERERER6YogmIiIiItITQzQRERERkZ4YoomIiIiI9MQQTURERESkJ4ZoIiIiIiI9MUQTEREREemJIZqIiIiISE825i6gNamvr0d+fj4UCgVkMpm5yyEiIiIiPQkhUFFRAR8fH1hZNd3fzBBtQPn5+fDz8zN3GURERER0j3Jzc9GhQ4cm2xmiDUihUACQ3nSlUmnmaoiIiIhIX2q1Gn5+fppc1xSGaANqGMKhVCoZoomIiIhasDsNzdX7wsLq6upmF0NERERE1BroHaL79u2rs+7UqVMGKYbuTbG6Chl55ShWV5m7FCIiIqJW7a5D9JYtW7Bo0SJcvXoV+fn5Wm1PPvmkwQsj/a3dn4PhS3dh7f4cc5dCRERE1Krd9Zjorl27IicnB8XFxRg7dqzmikVvb29YW1sbs0a6Swm9/bH39EUk9PY3dylERERErdpdh+jAwEAkJSUhIiICDzzwAAAgLy8PZ8+eRUREhNEKpLvnobSH0sEWHkp7c5dCRERE1KrpPSb6008/hVqtBgAcOXIEJSUlcHV1NXRdREREREQWS+8QffToUSiVSmRmZmLGjBnYunUrpk6daoTSiIiIiIgsk97zRNva2kIIgdWrV+O1117DM888g+joaGPURkRERERkkfQO0c8//zx69uyJS5cuYc6cOQCAq1evGrwwIiIiIiJLpXeI/utf/4qnnnoKNjY2cHJyQnZ2Nnr37m2M2oiIiIiILJLeIfrEiRPYvHkzXF1d0bVrV0RERODLL780Rm1ERERERBZJ7wsLhw4dipqaGly+fBkrVqzAgw8+iLCwMGPURkQGxrtaEhERGYbePdFeXl54/fXXtdbV1dUZrCAiMp61+3Pw/37NwssPdcK0uFBzl0NERNRi6d0TPXjwYKxZs0ZrHe9YSNQyJPT2R68AN97VkoiI6B7p3RP9xx9/YNWqVZg3bx569eqFyMhIREZGYvjw4caoj4gMiHe1JCIiMgy9Q/TPP/8MAFCr1cjIyEBGRgZ++eUXhmgiIiIiajP0DtHXr1/HunXrUFJSgvDwcCQmJkImkxmjNiIiIiIii6T3mOixY8di165dkMlk2LBhA6KiopCVlWWM2oiIiIiILJLePdEnT57E0aNHNY8PHjyIxMRE/Pbbb5p1586dw2effYZ33nlHs27Dhg2wsrLC448/fm8VExERERGZmd490c7Ozjh9+rTmcY8ePXDp0iWtbQICAlBVVYWnn34atbW1WLZsGZYuXYpBgwbde8VN+P333zFixAj4+PhAJpPhxx9/1GoXQmDu3Lnw8fGBg4MDHnzwQRw7dkxrm+rqarz44otQqVRwcnLCyJEjceHCBaPVTEREREQtk94hesWKFRg1ahRmzpyJL774AjNnzoS/v+50WYsWLUJMTAy6dOmCbdu24b///S9cXV0NUXOjrl69ivvuuw/Lli1rtP3999/HkiVLsGzZMhw4cABeXl6Ii4tDRUWFZpupU6fihx9+wLp167Br1y5cuXIFw4cP5zzYRERERKRF7xCtUqmwb98+REdH4/z58wgODsb69et1thNCIDs7GwEBASgqKsKVK1cMUnBThg4dinfeeafR4SJCCHz44Yd4/fXX8fjjj2tuVX7t2jV8/fXXAIDy8nJ8/vnnWLx4MR5++GFERUXhX//6F9LT0/HLL78YtXYiIiIialn0DtHx8fGora3F008/jblz5+Kpp55CZmam1jbV1dV44okn4O7ujl9++QVvvvkmHnroIZw9e9Zghevj7NmzKCwsRHx8vGadXC7HgAEDsGfPHgBAWloaamtrtbbx8fFBRESEZptbVVdXQ61Way1ERERE1PrpHaJtbGy0hmW4uLjg+eef19qmtLQUI0aMwIIFCwAAw4cPxyeffIJz587dU7HNVVhYCADw9PTUWu/p6alpKywshJ2dHdzc3Jrc5lbvvvsuXFxcNIufn58RqiciIiIiS6N3iO7QoQN27959YwdWVqipqdHaxsfHB3/5y1+01sXGxmLgwIHNLNMwbp3PWghxxzmub7fN7NmzUV5erllyc3MNVisRERERWS69p7j76KOPMGzYMMTGxqJnz55IT09v9MJCS+Ll5QVA6m329vbWrC8uLtb0Tnt5eaGmpgZlZWVavdHFxcXo27dvo/uVy+WQy+VGrJyIiIiILJHePdH+/v44ePAgHn74YeTk5CA0NBTffPONMWozmMDAQHh5eSElJUWzrqamBjt27NAE5OjoaNja2mptU1BQgIyMjCZDNBERERG1TXr3RAOAra0tnnrqKTz11FOGrqfZrly5guzsbM3js2fP4vDhw2jXrh38/f0xdepULFiwAJ06dUKnTp2wYMECODo6Yty4cQCksd2TJk3CK6+8And3d7Rr1w4zZsxAZGQkHn74YXO9LCIiIiKyQM0K0QAwaNAgfP/9903O/Xzx4kX06tULZ86cae4h9JKamqo15nr69OkAgAkTJmD16tWYOXMmKisrkZSUhLKyMvTu3Rvbtm2DQqHQPOeDDz6AjY0NnnrqKVRWVuKhhx7C6tWrYW1tbZLXQEREREQtg0wIIZrzRCsrKxQWFsLDwwOANN3bzeODi4qK4OPj06ZuVKJWq+Hi4oLy8nIolUqz1DD5ywP4bEJPsxybWgb+jhARETXtbvOc3mOiG5ORkYGQkBC88cYbaGYmJ2p1itVVyMgrR7G6ytylEBERkYHdc4jeuXMnHnjgAQwcOBArV67EQw89hOLiYkPURtSird2fg+FLd2Ht/hxzl0JEREQGdk8h+ocffsCQIUPwxhtv4KuvvsLBgwdRU1OD7t2747fffjNQiUQtU0Jvf/QKcENCb8ueApKIiIj0d08h+uWXX8ann36quYjP19cXO3bswNixYzWzXhC1VR5KeygdbOGhtDd3KURERGRgzZ6dY8KECRg3bhzi4uK01ltbW2Px4sXo378/Nm7ceM8FEhERERFZmmaH6C+++OK27aNGjUJAQEBzd09EREREZLEMMjvHzcrLy7F8+XJER0cjJibG0LsnIiIiIjI7g4Xo//3vf3jmmWfg7e2NefPmISAggNPdEZEGp/wjIqLW5J5C9IULF/DOO+8gODgYI0eOhBACGzZsQH5+PubNm2eoGomoFeCUf0RE1Jo0e0z0I488gu3bt2PQoEFITk7GqFGj4OTkpGmXyWQGKZCIWoeE3v7Ye/oip/wjIqJWodkheuvWrRg3bhymTp3Ksc9EdEec8o+IiFqTZg/n2L17NxwcHDBo0CCEhYUhOTkZ2dnZhqyNiMjkOHabiIjuRrNDdGxsLFauXInCwkK8+uqr2LZtG8LCwtCnTx8sXboURUVFhqyTiMgkOHabiIjuxj3PzuHo6IjnnnsOu3btQmZmJh544AEsWLAADz/8sCHqIyIyKd6unYiI7oZB54kOCwvD+++/jwsXLuD777/HsGHDDLl7IiKjs9Sx2xxmQkRkWQx+sxVAuvX3qFGjsGnTJmPsnoiozeEwEyIiy2KUEN3SLV++HIGBgbC3t0d0dDR27txp7pKIqI2z5GEm7CUnoraIIfoW33zzDaZOnYrXX38dhw4dQv/+/TF06FDk5LD3h4jMx1KHmQCW3UvOgE9ExsIQfYslS5Zg0qRJmDx5Mrp06YIPP/wQfn5++Pjjj81d2h2lZBbhZFEFUjI5M4olsMSfhzlrssT3ozGWWqel1gUAfm4O8FTK4efmYO5SdLy9JRPDl+7C21syzV2Kjg2puXh53SFsSM01dylaWJd+UjKLkLw506I+m+asyRLfD2ORCSGEuYuwFDU1NXB0dMS3336Lxx57TLP+5ZdfxuHDh7Fjxw6t7aurq1FdXa15rFar4efnhx07dsDZ2Vmz3s3NDYGBgaiqqkJmpu6JvEePHgCAkydP4urVq1ptAQEBaNeuHUpKSpCbq33iUCgU6NSpE+rq6rDy5/14b3cZAAFAhln93DBpSE/Y2tri9OnTKC8v13qur68vPD09UVZWhrNnz2q1OTg4oEuXLgCAQ4cO4dZfkS5dusDBwQHnz59HaWmpVpunpyd8fX1RUVGBrKwsrTZbW1tERkYCANLT01FbW6vV3qlTJygUCuTl5elMkeju7o6OHTuisrISx48f12qTyWSIiooCABw/fhyVlZVa7YGBgXBzc0NRURHy8vK02lxcXBAcHIza2lqkp6fjVvfddx+sra2RlZWFiooKrTY/Pz+0b98ely5dwrlz57TajpYCc38t0Dye1c8NvXxv9CCGh4fD3t4eZ8+eRVlZmdZzvb294e3tDbVarTP3ulwuR9euXaVjHD2K69eva7WHhobC2dkZFy5cQHFxsVbbiQo7vPaf843WZGVlhe7duwMAMjMzUVWl3WsXFBQEV1dXFBYWIj8/X6vN1dUVQUFBqKmpQUZGBm7VvXt3/HqiBIlfpeLm389evvbw9/eHSqXCxYsXdf63x9nZGaGhoaivr8fhw4d19hsREQE7OzucOXMGly9f1mrz8fGBl5cXLl++jDNnzmi12dvbIzw8HABw+PBh1NfXa9r+yKv683Ok+x4BgIeHBzp06IArV67g1KlTWvu1sbFBt27dAADHjh3TOjcAQEhICJRKJQoKClBQUKDVdqdzRKm9b6PvH3D354gjR47o7DcyMvKezxErtuzFu7suadU24eEoizhH3Ph5SrWtfDYGHWSXeI5o4hyhUqlw8oociV+lQggBmezG75qxzxFWVlY4deoUrly5otXWcI74bl8WXvnxlFZdg8JUJj9HAEDnzp3h6OiInJwc/HzkAt7bXQYZpN+y94YFYEz/riY/R9ycI7afKtWq6YPHwvBY7xCTnCMaPnMNx175bAxU1fktLkeUlJRgwIABKC8vh1Kp1HlfNARp5OXlCQBi9+7dWuvnz58vQkNDdbafM2eO+PP35LZLQkKCEEKIrKysRtsb9OnTR6dtzZo1Qgghli1bptMWHx8vhBCivLxcuA2aLPz/sVF0fHWL8P/HRuE2aLIoLi4WQggxYsQInecuXrxYCCHE+vXrddqioqI0NdnZ2em0Z2RkCCGEmDRpkk7brFmzhBBCbN++XafN19dXs19fX1+d9u3btwshhJg1a5ZO26RJk4QQQmRkZOi02dnZafYbFRWl075+/XohhBCLFy/WaRsxYoQQQoji4uJGfzbl5eVCCCHi4+N12pYtWyaEEGLNmjU6beEJb4qgWT9p/Txubs/KyhJCCJGQkKDz3Dlz5gghhNi6datOW3BwsOa1qlQqnfY9e/YIIYSYNm2aTtuD05Y2WZNCodDsNzw8XOe5GzduFEIIsWDBAp220aNHCyGEyM3NbfQ9rKqqEvM2HRMdZ27SOfbKlSuFEEKsXLlS53kDBgwQQghRVVXV6H5zc3OFEEKMHj1ap23BggVCCCE2btyo+7MJD9e8VoVCodXmNmiyCJy1RXR8dYsImLlJ5+c2bdo0IYQQe/bs0dmvSqXS7Dc4OFinfevWrUKIxs8bdzpHzNt0rMmf3d2eIxrbryHOEe5xz+uceyzlHHHzeTFo1haRvPmYRZ0jAl7dYlHniKSkJDFv0zER8OpmnbqMfY4QQogBAwbotDWcI55csE7n98wc5wgAIi0tTQghRFJSks7f3odmLDfLOaJBnz59dGoa+/4GIYRpzhE3H7vjq9JnriXniIbPd1PYE32T/Px8+Pr6Ys+ePYiNjdWsnz9/PtasWYMTJ05obc+eaMv8FyR7mdgTzZ5oCXuipZ+n+LP30hJ7oq1kQL2wjHPEzT3RDT2JltYTfXNd7Im+gT3RElP3RDNE30Tf4Ry3UqvVcHFxuXP3v5GkZBbh7S3H8ObwrogL9zT58UmbJf48zFmTJb4fjbHUOi21LgB48euD2Hy0ACO6eWPpuB7mLkfLhtRc7My+iP4hKoyO8TN3OVpSMouw70wp+gS5W9TPlHXpxxLrMmdNlvh+6Otu8xxD9C169+6N6OhoLF++XLMuPDwcjz76KN59993bPtfcIRoAJn95AJ9N6GmWY5MuS/x5mLMmS3w/GmOJdRarqzDl64NYNq6Hxc3QUayuQnFFNTwUcourjYhIX3eb52xMWFOLMH36dIwfPx4xMTGIjY3Fp59+ipycHPztb38zd2lE1Iat3Z+DP86VYe3+HEyLCzV3OVo8lPYMz0TU5jBE3+Lpp59GaWkpkpOTUVBQgIiICPz888/o2LGjuUsjojYsobc/4sI94aGQm7sUIiICQ3SjkpKSkJSUZO4yiIg02NtLRGRZeLMVIqKbFKuroK6s5R3uiIjothiiiYhucvPYYyIioqZwOAcR0U049piIiO4GQzQRmcTNwyQseWwvxx4TEdHd4HAOIjIJDpMgIqLWhD3RRGQSHCZBREStCUM0EZkEh0kQEVFrwuEcRERERER6YogmIiIiItITQzQRERERkZ4YoomMhHe+IyIiar0YoomMhFO6ERERtV6cnYPISDilGxERUevFEE1kJJzSjYiIqPXicA4iIiIiIj21ihA9f/589O3bF46OjnB1dW10m5ycHIwYMQJOTk5QqVR46aWXUFNTo7VNeno6BgwYAAcHB/j6+iI5ORlCCBO8AiIiIiJqSVrFcI6amho8+eSTiI2Nxeeff67TXldXh2HDhqF9+/bYtWsXSktLMWHCBAghsHTpUgCAWq1GXFwcBg4ciAMHDuDUqVOYOHEinJyc8Morr5j6JRERERGRBWsVIXrevHkAgNWrVzfavm3bNmRmZiI3Nxc+Pj4AgMWLF2PixImYP38+lEol1q5di6qqKqxevRpyuRwRERE4deoUlixZgunTp0Mmk5nq5RARERGRhWsVwznuZO/evYiIiNAEaAAYPHgwqqurkZaWptlmwIABkMvlWtvk5+fj3Llzje63uroaarVaayEiIiKi1q9NhOjCwkJ4enpqrXNzc4OdnR0KCwub3KbhccM2t3r33Xfh4uKiWfz8/IxQPRERERFZGosN0XPnzoVMJrvtkpqaetf7a2w4hhBCa/2t2zRcVNjUUI7Zs2ejvLxcs+Tm5t51PURERETUclnsmOgpU6ZgzJgxt90mICDgrvbl5eWF/fv3a60rKytDbW2tprfZy8tLp8e5uLgYAHR6qBvI5XKt4R9ERERE1DZYbIhWqVRQqVQG2VdsbCzmz5+PgoICeHt7A5AuNpTL5YiOjtZs89prr6GmpgZ2dnaabXx8fO46rBMRERFR22Cxwzn0kZOTg8OHDyMnJwd1dXU4fPgwDh8+jCtXrgAA4uPjER4ejvHjx+PQoUP49ddfMWPGDCQmJkKpVAIAxo0bB7lcjokTJyIjIwM//PADFixYwJk5iIiIiEiHxfZE6+Ott97Cl19+qXkcFRUFANi+fTsefPBBWFtb46effkJSUhL69esHBwcHjBs3DosWLdI8x8XFBSkpKXjhhRcQExMDNzc3TJ8+HdOnTzf56yEiIiIiy9YqQvTq1aubnCO6gb+/P7Zs2XLbbSIjI/H7778bsDIiIiIiao1axXAOIiIiIiJTYogmIiIiItITQzQRERERkZ4YoonakGJ1FdSVtShWV5m7FCIiohaNIZqoDVm7Pwd/nCvD2v055i6FiIioRWsVs3MQ0d1J6O2PuHBPeCh4p00iIqJ7wRBN1IZ4KO3hobQ3dxlEREQtHodzEBERERHpiSGaiIiIiEhPDNFERERERHpiiCYiIiIi0hNDNBERERGRnhiiiYiIiIj0xBDdivBudERERESmwRDdivBudERERESmwZuttCK8Gx0RERGRaTBEtyK8Gx0RERGRaXA4BxERERGRntgTbUBCCACAWq02cyVERERE1BwNOa4h1zWFIdqAKioqAAB+fn5mroSIiIiI7kVFRQVcXFyabJeJO8Vsumv19fXIz8+HQqGATCYz+vF69uyJAwcOGP04pjieIfdtiH01dx/6Pk+tVsPPzw+5ublQKpV6H4+aZurPhzFZ2msxZT087xj+eTzvGJelfV6by9JehynrEUIgOjoap06dgpVV0yOf2RNtQFZWVujQoYPJjmdtbW3SE6Axj2fIfRtiX83dR3Ofp1Qq+cfMwEz9+TAmS3stpqyH5x3jPY/nHeOwtM9rc1na6zB1PXZ2drcN0AAvLGzRXnjhhVZzPEPu2xD7au4+TP0zoaa1pp+Fpb0WU9bD845pjk2G01p+Hpb2Oiwx83A4B5EZqdVquLi4oLy83KL+xU9ErRfPO0SGwZ5oIjOSy+WYM2cO5HLeIIeITIPnHSLDYE80EREREZGe2BNNRERERKQnhmgiIiIiIj0xRBMRERER6YkhmoiIiIhITwzRRERERER6YogmIiIiItITQzQRERERkZ4YoomIiIiI9MQQTURERESkJ4ZoI/n9998xYsQI+Pj4QCaT4ccffzTq8d5991307NkTCoUCHh4eGDVqFE6ePGnUYxIRERG1VQzRRnL16lXcd999WLZsmUmOt2PHDrzwwgvYt28fUlJScP36dcTHx+Pq1asmOT4RERFRWyITQghzF9HayWQy/PDDDxg1apRmXU1NDd544w2sXbsWly9fRkREBP75z3/iwQcfNMgxS0pK4OHhgR07duCBBx4wyD6JiIiISGJj7gLaqr/85S84d+4c1q1bBx8fH/zwww8YMmQI0tPT0alTp3vef3l5OQCgXbt297wvIiIiItLGnmgTuLUn+vTp0+jUqRMuXLgAHx8fzXYPP/wwevXqhQULFtzT8YQQePTRR1FWVoadO3fe076IiIiISBd7os3g4MGDEEIgNDRUa311dTXc3d0BAOfOnUNgYOBt9/PCCy80OuZ6ypQpOHr0KHbt2mW4oomIiIhIgyHaDOrr62FtbY20tDRYW1trtTk7OwMAfH19cfz48dvux83NTWfdiy++iE2bNuH3339Hhw4dDFc0EREREWkwRJtBVFQU6urqUFxcjP79+ze6ja2tLTp37nzX+xRC4MUXX8QPP/yA33777Y692ERERETUfAzRRnLlyhVkZ2drHp89exaHDx9Gu3btEBoaioSEBDz77LNYvHgxoqKicPHiRfzvf/9DZGQkHnnkEb2P98ILL+Drr7/Gxo0boVAoUFhYCABwcXGBg4ODwV4XEREREfHCQqP57bffMHDgQJ31EyZMwOrVq1FbW4t33nkHX331FfLy8uDu7o7Y2FjMmzcPkZGReh9PJpM1uv6LL77AxIkT9d4fERERETWNIZqIiIiISE+8YyERERERkZ44JtqA6uvrkZ+fD4VC0eTwCiIiIiKyXEIIVFRUwMfHB1ZWTfc3M0QbUH5+Pvz8/MxdBhERERHdo9zc3NtOF8wQbUAKhQKA9KYrlUozV0NERERE+lKr1fDz89PkuqYwRBtQwxAOpVLJEE1ERETUgt1paG6LDNHXr1/HunXrUFJSgvDwcMTHx3MMMhERERGZTIucnWPs2LHYtWsXZDIZNmzYgKioKJw6dcrcZRFpqa2rR2VNHWrr6s1dChERERlYi+yJPnnyJI4ePap5fPDgQfz1r3/Fb7/9Zr6iiG5x6WoNThdfQbCHMzyV9uYuh4iIiAyoRfZEOzs74/Tp05rHPXr0wKVLl8xYEZGudk52moWIiIhalxbZE71ixQqMGjUKQ4cORZcuXXD8+HH4+/ubuywiLbbWVrC3tYatdYv8tyoRERHdRov86x4ZGYnU1FRER0fj/PnzCA4Oxvr1681dFhERERG1ES2yJ/rEiRPYvHkzXF1dER8fj4iICDg6Opq7LCIiIiJqI1pkT/TQoUNRU1ODy5cvY8WKFXjwwQcRFhZm7rLIEhz6F3DkG6D8grkrISIiolasRfZEe3l54fXXX9daV1dXZ6ZqyKIc+xHITpG+dwsAOt4PBNwPBPQDXDlunoiIiAyjRYbowYMHY82aNRg/frxmnbW1tRkrIovxzAbg6kXg/G7g3G7g3C7g8L+kNlf/m0L1/YBbR/PWSkRERC2WTAghzF2Evh555BFkZGTAzs4OvXr1QmRkJCIjIzF8+HCz1qVWq+Hi4oLy8nLe9tuSXLt0I1Sf3wUUZgAQgIufFKY79vszVAcABrzzZXllLU4WqBHmrYSLg63B9ktERETGc7d5rkWG6AZqtRoZGRnIyMhAZmYmPvzwQ7PXwxDdAlSWAef3Sr3U53cBBUcBCEDZAQh8AAh6EAgaACi8mn2I8spapF+4rLnZSmQHVwZpIiKiFuBu81yLHM5x/fp1rFu3DiUlJQgPD0diYiJkBuxBpFbOwQ3o/Ii0AEDlZSBnH3BuJ/D/27v36Cjre9/j75lJMgm5AQmESxIIIQQCIiUgBgULNECQi25ta0FEsa692+qqG5VtT+2qctza2trSnharyKXu4z5YqYBVkEa5yJb7TeQqEEK4hMRwGwhkkszM+eMXciEBkjCZyQyf11rPmpnneeaZ71QYP/76fX6/vLXw5X+b/fHpJkz3+LYZrY5o2+iPKHVWcqGskk6xEVwoq+RSeaVCtIiISBAJyJHo7373u8TFxdG7d2/27NnDli1beP/990lLS/NrXRqJDhIXv4H8z02gzlsD546CxQpdvgUpVaE6aQiEXnspb41Ei4iIBKagbufo378/u3btqn69fft2ZsyYwZo1a/xXFArRQetsfk2gPvI5XCqBkHATpK+MVHceANa6N7eev1zBgVMO0jupJ1pERCRQBHU7R1RUFIcPHyY1NRWAgQMHcubMGT9XJUGrXXfI7A6Z08DthuK9VYF6LXz+Onw2y7SI9Pg2pI6CnqMgpguxEaF0jA5XgBYREQlCARmi33zzTe677z5ycnLo06cP+/btIzlZcwCLD1it0Kmf2YY+CZXlcGIrHF4Fhz6DD58CPNAxA1JHEt7hLogded3WDxEREQk8AdnOAeB0Olm6dCn79++nc+fOPPzww35f+lvtHELpachbXROqL56CkAiz2MuVUer4Xl6dSk9ERES8J6h7ot9++20WLFhAbGws/fr1q54nesCAAX6tSyFa6vB4OHFgG11Pr4fDn5lp9VxOM5Vez5EmVPe4x7SCiIiISKsQ1CE6JSWFpUuXYrfb+eqrr9i9ezd79uxh8eLFfq1LIVqull9SSvf4SPOi/JJZ9OXQZyZUl3xtZv3oOsiMUPfMNjOAWK3+LVpEROQWFtQhetKkSSxatIiIiAh/l1KHQrRcrU6Ivtq5YyZMH/rMzP7hPA9t4iEt22ypo5o0N7WIiIjcvKAO0Vu3buX555/npz/9KUOGDKFjx47+LglQiJa6Klzu6nmiQ203GF12VcLxLXBwJRzMhaLdYLGZafR6jYa00eZmRfVSi4iItKigDtEZGRkMHjyY8PBwvvrqK4qKikhJSeHTTz/1a10K0VJbkaOsOkQnxDRxdo7zx+HgP02gzlsDFZdML3VaNvQaY5YnD7vGCLeIiIg0W1CH6KysLDZs2FBnX35+Pt27d/dPQVUUoqW2CpebSpeHEJvlxiPR171QmemlPvhP+HolnD0CNjt0v9sE6rRsaN/De4WLiIjcwoI6RD/zzDMMGDCAqVOn+ruUOhSixSdKDlWNUq+E/C/AXQFxaablo9do6HYX2LTAi4iISHMEdYgeN24cu3fvxm63M3jw4Oop7saPH+/XuhSixeecF8xNiVd6qS8Ugj0Gen4H0nPMY5v2/q5SREQkYAR1iL7C4XCwe/dudu/ezd69e5k9e7bf61GIFr/xeODULjjwCRxYDoU7zc2JyVmQPhbSx0Fcqr+rFBERadVuiRDd2ihES6viOAlff2JC9ZG1UFlm2j6uBOrEO8AW4u8qRUREWpWgD9EjR47kgw8+oG3btg0eLykp4Y477iAvL89nNSlES6tVXmpm+TiwwgTr0m/MSolpo03bR+ooCG/ZP7PFjjKKLzjpGG2nY1NnKxEREfGRxua5gB2GWrNmDeXl5dWvnU4ndru9+rXL5eLo0aP+KE2k9QmLhN73ms3thpPbTaA+sAJ2vQfWUDPbR3oO9BoL7bp5vYR3NxXwh88O8tNRafx7di+vX19ERMSXgmJ94d27d9OzZ09eeOEFAnRgXcR3rFZIHASjfgE/Xg8/3QVj/tMcW/lz+EN/mDMUPpsFx7ea0O0FU4Ykc0f3dkwZkuyV64mIiPhTwI5EX7Fu3TomTZrE+PHjmTt3LuvXr2fRokX+LkskcLTrBkP+1WxlDrMU+YFPYOt8WPc6RHUyI9S97zWLvITYb3zNBnSMCScmIlStHCIiEhQCeiR6yZIljB07lhdeeIF33nmH7du3U15ezoABA1izZo2/yxMJPOEx0Pd++Jc34dlD8OhyuO1ByFsN7z4Ir/WAv02DXe/D5XP+rlZERMRvAvbGQqvVSlhYGPPmzWPKlCnV+10uFzNnzqye7s7lcvmsJt1YKEHL44HifbD/YzjwMZzcAdaQqj7qe6H3OIhNvOFlfvjXLbw9bbAPChYREWmeoL+xcNq0aUyePJns7Ow6+202G6+//jrDhg1j2bJlfqpOJMhYLJCQYbZ7noPzJ8xc1Ps/hpU/gxXPQefbqwL1vZDQ17xHREQkSAXsSHRj7Ny5kwEDBvjs8zQSLbeky+fg0KcmUB/MhfIL0DYZeo83gTrpzur5qDUSLSIirV3Qj0Rfy/nz53n33XeZN28eX375JZWVlf4uSSS4RbQ1fdO3PQiVTshfB/uXw54lsHGOmY+6Vw70HkeYO9bf1YqIiHhF0IxEr1q1ivnz5/PBBx8QHR3N3XffzdKlS9UTLeIvbjcU7jAj1PuXk3sqgi/ct3FXYhjZQ/qbYB3Vwd9VioiI1BH0KxYCHD9+nIULF7JgwQKKioqYNGkSU6ZMYcyYMezbt4/bb79dIVqkFcjdW8QT72zFghsPVuaGvk62bTskDalZBCYu1d9lioiIBH87x7hx41i9ejUjR45k1qxZ3HfffURGRlYft+imJpFWY8Ph09gsFlweKzaLhY0DXyO72z5zc+Lq/4TcX0CH3pA+zvRSd/mWWRRGRESklQrYEP3JJ58wefJknn76aQYNGuTvckTkOrJS45j/xREAXB4Pd6YnQcYgGDgVykvh8GoTqLcthP/5nVngpfc4M9tHyrBmL/AiIiLSUgJ2qOeLL74gIiKCkSNHkp6ezqxZszh06NBNXfPFF1/EYrHU2Tp16uSlikVuXdkZCcx9ZBDJ7SOY+8ggsjMSag6GRUKf8XDfHHj2oFngpd8DcHgVvPsAvJYK7z+qBV5ERKRVCeieaIBLly6xaNEi5s+fz4YNGxg8eDBTpkyhb9++ZGdnN6kn+sUXX2Tx4sV8+umn1ftsNhsdOjTu5if1RItcX5OmuKu9wMv+j6Bwp1ngpdtdpoc6PcdMpSciIuJFQd8TfUWbNm2YPn0606dP58CBA8ybN49XXnmFoqKiZvVFh4SEaPRZpDW41gIvB1bAyp/DipnQ6TbT8pGeYxZ70b0QIiLiIwHbztGQ9PR0XnvtNY4fP84HH3zAvffe2+RrHDx4kC5dupCSksJDDz1EXl7eNc91Op04HI46m4i0kNiucMcTMPUDmJkHDy4wNyNufAPeugd+3w8+fta0gVSW+7taEREJcgHfzuFNK1as4NKlS/Tq1YuioiJefvll9u/fz549e4iLi6t3/osvvshLL71Ub7/aOUQa1iIrFroq4OgXZoGXA8vh/DGwx0DP75i2j57fMQvCiIiINMItMU90SystLSU1NZWZM2cyY8aMesedTidOp7P6tcPhICkpSSFa5BpafNlvjwdOfVXV9rEcCr80fdTd765p+2ib1HKfLyIiAe+W6YluSZGRkdx2220cPHiwweN2ux27XVNvibQaFgt07m+2bz8P54+bHur9H8PKn8GK56BT/6r5qMeZ5+qjFhGRZlCIvg6n08m+ffsYNmyYv0sRkeaITTR91Hc8AWXn4WCuGaHeOAfW/gpik8zodPo4M1ptC/V3xSIiEiAUomt59tlnmTBhAsnJyRQXF/Pyyy/jcDiYNm2av0sTCXjFjjIclysodpTRMSbc9wWEx8JtD5qtstz0UR9YbnqpN78F9lhIyzahOi3bnC8iInINCtG1HD9+nB/84AeUlJTQoUMH7rzzTjZu3Ei3bt38XZpIwHt3UwGb88/y7qYC/j27l3+LCQmD1BFmy3kNTu2qujHxY9i9GKyhVX3UOdBrDLTr7t96RUSk1dGNhV6kxVZErq3YUUbxBScdo+3+GYlurHMFpo/6wHLI/wLcFdChjwnTvcZC4mCwafxBRCRYaXYOP1CIFgl8dcJ+WDnkrYavV5rtUglEtIOe2SZU9xxlXvu6rtb8HyEiIgFOs3OIiDTDu5sK+MNnB/npqDTTdpIxyWxuN5zcbkapv14JX/0NLDboNrRmlDquZ4vN9lGvLhER8SuNRHuRRqJFAl+xo4wn/3s7f5o88PojvueP14xQH1kLlWXQvocJ073GQPJQ03vt67pEROSmaCRaRKQZOsaEExMReuOgGpsIgx83W/klOPI5fP0J7FlqptALi4aeI02o7pkNUR18U5eIiPiEQrSIyM0KawPpY83m8ZjZPr5eaUL10h8BFkgcBGmjzTLknQeA1dqkj8jdW8SBogvk7i0iOyOhRb6GiIg0nkK0iIg3WSzQ+Xaz3TMTLhTBoVwTqL/4I6z+T2gTb25K7JkNqSMhMu66l8zdW8QT72wF4Il3tjL3kUEK0iIifqYQLSJSi9dHfKMT4FsPm81VAcc2waFP4eCnsOs9wAJdM80IdVo2dPkWWG11LrHh8GlsFgsujwebxcLGvNMK0SIifqYbC71INxaKBLbaI75Ay4/4OgpNoD70KRxeDc7zENHejE6nZUPqKIjq4Pu6RERuYbqxUESkiXw+4hvTGQZONZurEo5vqQrVuWblRIDOA8hOy2ZuznBe3ljBCxP6KUCLiLQCCtEiIlWyUuOY/8URAFweD3f2uH6vslfZQqBbltlG/cL0Uh9eZQL1lrfJvvwbsixRhOwcCZdHQY8R0K6b7+oTEZE61M7hRWrnEAl8uXuL+N8f7eEX4/u2nhFft4v/t3Qpxds/4rvtDtLl4h7wuM281KkjTaBOGQbhsf6uVEQk4KmdQ0SkGbIzEnhvS0HrCdAAVhujvnMvxUO+Q0i0HUIvw5F1ZknyQ5/BlrfN6oldMyF1hAnViYPAFurvykVEgpZCtIhIAOgYE15roZVwyJhoNoCz+ebGxLzVsOlNWPtrs9hL97vNSHXqiBZdklxE5FakEC0iEujadYdBj5nN7YKTOyFvFRxeAyv/F7grICYRUoabto+U4WbFRRERaTaFaBGRWoodZTguV1DsKAvMJbatNkjMNNvw58B5EY6uN6PUR9bBl/9tzmuXUhWqh0P3YWY+axERaTSFaBGRWt7dVMDm/LO8u6mAf8/u5e9ybp49CnqNNhtA6Wk4+j8mUB/5HLb/1eyPT68Zqe4+DNq091/NIiIBQLNzeJFm5xAJfMWOMoovOOkYbQ/MkeimulAE+VWBOn8dnMkDLJDQryZUJ2dBRFt/Vyoi4hONzXMK0V6kEC0iAe/88ZpR6vx1cP4Y1aG6WxZ0GwrJQ+u0f9xy/+EhIkFNIdoPFKJFJKh4PHD2CBzdYPqqC9ZXjVQD7VNNoO42lJk7O/C3/U4m9O/C/5k80L81i4jcJIVoP1CIFpGg5yiEgiuhegO5J+08UfEMNly4sDE36wzZd2ZCh95gtfq7WhGRJtNiKyIi4n0xnaHfv5gN2LBkO7bNhbg8Nmy42bhlM9k7noSIdqaXOnEwJN0BXQZCWBs/Fy8i4j0K0SIi0mxZ6V2Zv6kQABdW7vzesxD1oBmpPrYR1v0Oyi+YFRU79YOkIZB4ByQNhrbdtACMiAQshWgREWm27IwEJvTvzD92FTKhf2ey+3cDukGPe8wJbhcU74Pjm+HYFji8Cja/ZY5FdjSj1ImDTbjuMgBCI/z1VUREmkQ90V6knmgRuRU1eXaO0tNwfEtVsN4MJ7ZBxSWwhkCn/iZYd800LSDte6i3WkR8SjcW+oFCtIhIM7gqoXiPCdTHt5jHs0fMMXusGaHuOhC6fMsE69hEtYGISItRiPYDhWgRES+5dAZO7oCT2+FE1eMF03tNZAcTprsOrHmMjG/wMou3HmPdoRKG9YznwUFJPvwCIhKoNDuHiIgErjbtoecos13hKKwVrLfDpr/A5bPmWGxS1Uj1AOh0O3TuT26Bh2cX7wJg2c6TxLYJIzsjof5niYg0g0K0iIgEhpjOZus9zrz2eOBsfk2oPrkD1v3ezAYCbOBfsTEMF1asFg8b9xwmu3cH9ViLiFcoRIuISGCyWKB9itn6PWD2ud1wLh9OfUXWl0eZ/6UVKy7cHht37vo5HPzaLGHeuT90us3cyNixD4TY/fpVRCTwqCfai9QTLSLSulT3RCeF8mBCEZz6Ck7tMo8lBwGPmRUkPh0SMkyg7lj1GJusUWuRW5BuLPQDhWgRkQBSXgpFe2tCdfE+sznPm+NhUWb58trBumMGRHXU7CAiQUw3FoqIiFxPWKRZOTFpcM0+jwccJ6sC9R7zeGoXfPU+VJaZcyLaQ0LfqlDdx4xix/cyM4QoXIvcMhSiRURErrBYILar2dK+U7Pf7TI3MRbvNaPXxXshby1smQcelzknvK0J0/G9ID6t5nm77mCr/69bTb8nEtgUokVERG7EaoO4VLP1mVCzv9IJZ45AyddV20ETsPcuq54lBGuoWXmxVrDOvZDMsx+fAzT9nkigUogWERFprhA7dOxttto8Hrhwqm64Lvkadv0NHMfZUPEwNsbgwoYNNxtXLiK76JIJ21e2Nu3VHiLSiilEi4iIeJvFUjOvdY976h5zXiRry1fM/+gMFty4sHKnbT9s+wwuFtWcZ4+pmsKvR92tXQpEJWjmEBE/U4gWERHxJXsU2Xdn8dvwmp7o7EH3mmPOi3D2CJzJM20iZ/LMdmwLOI7XXMNmh7ZJ0Da51tat5nlkx2aH7Ny9RWw4fJqs1Di1mIhch6a48yJNcSciIi2m4jKcPWpC9fljcK4Azh2teiyoWQIdrh2yYxMhpitEdwJbaL2PyN1bxBPvbMVqAbcH5j4ySEFabjma4k5ERCSYhEY03H99RZmjVrguqAnZJ3eYGx1rh2wsJkjHdKnaEiGmCxsOdsOCDbfHggXYmHdaIVrkGhSiRUREgkF4DIT3NXNYN6TMAY4TcP6EebyynT8Bh1eB4wRZl3sxn2ex4cKFjTt3/QIKz0J0ZxO6ozpBdELdx8h4M3uJyC1GIVpERORWEB5jto59Gj7u8ZDtdPC79XvZcLiEu9udJjsusypsn4SjG+DiKbh0uu77LFbTgx3VsSpoJ1z12KnmWIi92eWrV1taG/VEe5F6okVEJOhVlkPpNyZQXyiq+3ix2Eztd7HIbO7Kuu+1x0CbODN6Hdmh5nmb+Kp9tZ63iYfQcEC92uJb6okWERER7wsJq1nV8Xrcbrh8pm6oLi2BSyXmsbTELExTWmJGt8sv1r9GWBRExrPh0gNY+RZujxUrbjau+yfZlyohol39LSxS82uLTyhEi4iIiPdZrTWjy/S78fkVl2uF7NNVj99AaQlZxz3MP2/FdmVe7eL34eN14HE38LmhDYfr6q2tWaI9PMaMjNd+DItu8tSAajO5damdw4vUziEiItIycvcWsTHvNHf2qAqrbjc4HWbWkXrbuVrPz9Q/fnWbSTUL2KPrh+sGH2PJLYrmic/cNW0m30sj+7YkCAnXaHgAUzuHiIiIBI3sjIS6I71WqxlVjmgLpDT+Qh4PlJeaAF7mqPV4/qrXtR4vnIKSA3X3e1w1y7d7bNhwsfHvfyD7w/9rbrYMizKtJdVb9FWvq47boxo4N8pMaRgSYR5D25j+8JCIRo2Ua3TcNxSiGzBnzhx+85vfUFhYSN++fZk9ezbDhg3zd1kiIiJysywWE1ztUWaO7ObweKDiElm7Cpi/OA8rmCkB7xkHne4x/d3lpVVb1XNn1eOl02YO7+rjF8yjq7xxnx0SbrbQNnUDdtXz3Is9eCLvbqy4mf/FEeYOOEx214pagTzCzJJis5vHq583dMymuNgQ/a9ylffee4+nn36aOXPmcNddd/Hmm2+Sk5PD3r17SU5O9nd5IiIi4m8WC4RFkj2oD3PbtK/bZtJcleVQUVoTrisuQUVZ1eNlqKz1/OpjFZeh0jxuOBeLFTdurFhxsfHACbIL3q95X7O+r+2qgB1mgnwD4Tv3Ync2lHYhq+05sjucBVsYWEPMoy3U9KzbrmxXHas+HmaCuy2s6nXt57Xfe/Vr385Xrp7oqwwZMoSBAwfyxhtvVO/r06cP9913H6+++up13+vvnujzlysodVYSaQ8hNqL+cq4i+jMiIhLcrjsdoMdjwnil04x8V5aZ8O5y1jyvLKs65qw6r5HHXE5yT3fgiaMjq28AndtxMdnh+817XBVmc1dc9byRI/CNZQ2ptdnMo8VWf1/1c1u99zicHmL/9WP1RDdFeXk527Zt4/nnn6+zf/To0axfv77e+U6nE6fTWf36/PnzgAnTvnb+cgXbC85y7mI5baPCGJjcTiFJ6tCfERGR4DckMYLZ/5LOlvwzDO7eniGJEQ3kEisQDtZwCPPeZ69esR/P10epACzAmsQfMWTsNZapv8LjAbfLhGl3BbhqPXdXgKuyJmxf89iVUF5pNo+76rnrqsdazz1u8NTeV3OOw3W5qrTrjzMrRNdSUlKCy+UiIaHu/x2TkJDAqVOn6p3/6quv8tJLL9Xbn5SU1GI1ioiIiASCV2bDK/4u4iZcuHCB2NjYax5XiG6A5appaTweT719AD/72c+YMWNG9Wu3282ZM2eIi4tr8HxvGzx4MFu2bGnxz/HF53nz2t64VnOv0dT3ORwOkpKSOHbsmKZF9DJf//1oSa3tu/iyHv3ueP99+t1pWa3t72tztbbv4ct6PB4PmZmZdOly/RtPFaJriY+Px2az1Rt1Li4urjc6DWC327Hb7XX2tW3btiVLrMNms/n0B7AlP8+b1/bGtZp7jea+LyYmRv8y8zJf//1oSa3tu/iyHv3utNz79LvTMlrb39fmam3fw9f1hIWFYb3BdIJNW5YnyIWFhZGZmUlubm6d/bm5uQwdOtRPVV3bT37yk6D5PG9e2xvXau41fP3PRK4tmP5ZtLbv4st69Lvjm88W7wmWfx6t7Xu0xsyj2Tmu8t577zF16lT+8pe/kJWVxVtvvcXcuXPZs2cP3bp183d5EmT8PaOLiNx69Lsj4h1q57jK97//fU6fPs2sWbMoLCykX79+LF++XAFaWoTdbueXv/xlvbYgEZGWot8dEe/QSLSIiIiISBOpJ1pEREREpIkUokVEREREmkghWkRERESkiRSiRURERESaSCFaRERERKSJFKJFAsj9999Pu3btePDBB/1diogEqY8++oj09HTS0tJ4++23/V2OSKulKe5EAsjq1au5ePEif/3rX1m8eLG/yxGRIFNZWUlGRgarV68mJiaGgQMHsmnTJtq3b+/v0kRaHY1EiwSQESNGEB0d7e8yRCRIbd68mb59+9K1a1eio6MZN24cK1eu9HdZIq2SQrSIl3z++edMmDCBLl26YLFYWLp0ab1z5syZQ0pKCuHh4WRmZrJu3TrfFyoiQetmf4dOnjxJ165dq18nJiZy4sQJX5QuEnAUokW8pLS0lNtvv50//elPDR5/7733ePrpp/n5z3/Ojh07GDZsGDk5ORQUFFSfk5mZSb9+/eptJ0+e9NXXEJEAdrO/Qw11eFoslhatWSRQhfi7AJFgkZOTQ05OzjWP/+53v+Pxxx/nhz/8IQCzZ89m5cqVvPHGG7z66qsAbNu2zSe1ikhwutnfoa5du9YZeT5+/DhDhgxp8bpFApFGokV8oLy8nG3btjF69Og6+0ePHs369ev9VJWI3Eoa8zt0xx13sHv3bk6cOMGFCxdYvnw5Y8aM8Ue5Iq2eRqJFfKCkpASXy0VCQkKd/QkJCZw6darR1xkzZgzbt2+ntLSUxMRElixZwuDBg71drogEocb8DoWEhPD6668zYsQI3G43M2fOJC4uzh/lirR6CtEiPnR1b6HH42lSv6HukheRm3Wj36GJEycyceJEX5clEnDUziHiA/Hx8dhstnqjzsXFxfVGhUREWoJ+h0S8SyFaxAfCwsLIzMwkNze3zv7c3FyGDh3qp6pE5Fai3yER71I7h4iXXLx4kUOHDlW/PnLkCDt37qR9+/YkJyczY8YMpk6dyqBBg8jKyuKtt96ioKCAf/u3f/Nj1SISTPQ7JOI7WvZbxEvWrFnDiBEj6u2fNm0aCxcuBMwiB6+99hqFhYX069eP3//+9wwfPtzHlYpIsNLvkIjvKESLiIiIiDSReqJFRERERJpIIVpEREREpIkUokVEREREmkghWkRERESkiRSiRURERESaSCFaRERERKSJFKJFRERERJpIIVpEREREpIkUokVEREREmkghWkRERERuyqOPPorFYsFisbB06VJ/l1PHjWrKz8+vrn3AgAGNvq5CtIiIiIgAdcNwSEgIycnJ/OhHP+Ls2bM3fO/YsWMpLCwkJyenet+Va23cuLHOuU6nk7i4OCwWC2vWrPH21+DFF1/koYceatS5SUlJFBYW8swzzzTpMxSiRURERKTalTCcn5/P22+/zT/+8Q9+/OMf3/B9drudTp06Ybfb6+xPSkpiwYIFdfYtWbKEqKgor9Zd24cffsikSZMada7NZqNTp05NrkchWkRERESqXQnDiYmJjB49mu9///v885//bPb1pk2bxqJFi7h8+XL1vvnz5zNt2rQ6511pq1i0aBFDhw4lPDycvn371hup3rNnD/feey8xMTFER0czbNgwDh8+XH382LFj7N69u86IeElJCffffz9t2rQhLS2NDz/8sNnf5wqFaBERERFpUF5eHp988gmhoaHNvkZmZiYpKSn8/e9/B0zI/fzzz5k6dWqD5z/33HM888wz7Nixg6FDhzJx4kROnz4NwIkTJxg+fDjh4eGsWrWKbdu2MX36dCorK6vf/+GHHzJ8+HDatm1bve+ll17ie9/7Hrt27WLcuHFMmTKFM2fONPs7gUK0iIiIiNTy0UcfERUVRUREBKmpqezdu5f/+I//uKlrPvbYY8yfPx+ABQsWMG7cODp06NDguU8++SQPPPAAffr04Y033iA2NpZ58+YB8Oc//5nY2FgWLVrEoEGD6NWrF4899hjp6enV71+2bFm9Vo5HH32UH/zgB/Ts2ZNXXnmF0tJSNm/efFPfSSFaRERERKqNGDGCnTt3smnTJp566inGjBnDU089dVPXfPjhh9mwYQN5eXksXLiQ6dOnX/PcrKys6uchISEMGjSIffv2AbBz506GDRt2zZFxh8PB2rVrmThxYp39/fv3r34eGRlJdHQ0xcXFN/OVFKJFREREpEZkZCQ9e/akf//+/PGPf8TpdPLSSy/d1DXj4uIYP348jz/+OGVlZXX6lRvDYrEAEBERcd3zVqxYQZ8+fejWrVud/VeHbovFgtvtblINV1OIFhEREZFr+uUvf8lvf/tbTp48eVPXmT59OmvWrOGRRx7BZrNd87za0+FVVlaybds2evfuDZgR5XXr1lFRUdHge5ctW1ZvFLqlKESLiIiIyDV9+9vfpm/fvrzyyis3dZ2xY8fyzTffMGvWrOue9+c//5klS5awf/9+fvKTn3D27Nnq9o8nn3wSh8PBQw89xNatWzl48CD/9V//xYEDB6isrGTFihWNntruZilEi4iIiMh1zZgxg7lz53Ls2LFmX8NisRAfH09YWNh1z/vVr37Fr3/9a26//XbWrVvHsmXLiI+PB0xbyKpVq7h48SL33HMPmZmZzJ07l9DQUNauXUtUVBSZmZnNrrEpQnzyKSIiIiLS6i1cuLDB/ZMnT2by5MlNvp7H47nmsbZt2zZ4vE+fPvVWOKytf//+rFy5st7+2bNnM2HChEbVcO7cuWtev7EUokVERETkpl2ZGm/RokWMHz/e55/fr1+/OjN7NFZBQQEZGRmUl5eTkZHR6PdZPNf7TwQRERERkRsoLi7G4XAA0LlzZyIjI5t8jfz8fFJSUtixYwcDBgzwcoXXVllZSX5+PmBWa0xKSmrU+xSiRURERESaSDcWioiIiIg0kUK0iIiIiEgTKUSLiIiIiDSRQrSIiIiISBMpRIuIiIiINJFCtIiIiIhIEylEi4iIiIg0kUK0iIiIiEgT/X+6w2QKR6Bh+AAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 700x700 with 5 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, axs = plt.subplots(5,1, figsize=(7,7), sharex=True);\n",
+    "plt.ticklabel_format(style='sci', axis='y', scilimits=(-2,2))\n",
+    "plt.tight_layout();\n",
+    "\n",
+    "## Delta Sigma 4theta\n",
+    "axs[0].errorbar(r_bins[order], np.array(delta_sigmas_4theta)[:,0][order] / 0.7 / 1e12, yerr=np.array(delta_sigmas_4theta)[:,1][order] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1);\n",
+    "axs[0].errorbar(r_bins[~order], np.array(delta_sigmas_4theta)[:,0][~order] / 0.7 / 1e12, yerr=np.array(delta_sigmas_4theta)[:,1][~order] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1, alpha=0.2, c='C0');\n",
+    "axs[0].plot(r_, ds4theta/0.7 / 1e12, 'b-', lw=1);\n",
+    "axs[0].set_ylabel(r'$\\Delta \\Sigma^{4\\theta}$ [$M_\\odot$h / $pc^2$]', loc='top');\n",
+    "axs[0].set_xscale('log');\n",
+    "axs[0].set_ylim(-50, 10);\n",
+    "# axs[0].set_ylim(-10, 50);\n",
+    "axs[0].axhline(0, lw=1, ls='--', c='k');\n",
+    "# axs[0].axvline(rs, lw=1, ls='-', c='k');\n",
+    "\n",
+    "## Delta Sigma const\n",
+    "axs[1].errorbar(r_bins[order], np.array(delta_sigmas_const)[:,0][order] / 0.7 / 1e12, yerr=np.array(delta_sigmas_const)[:,1][order] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1);\n",
+    "axs[1].errorbar(r_bins[~order], np.array(delta_sigmas_const)[:,0][~order] / 0.7 / 1e12, yerr=np.array(delta_sigmas_const)[:,1][~order] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1, alpha=0.2, c='C0');\n",
+    "axs[1].plot(r_, dsconst/0.7 / 1e12, 'r-', lw=1)\n",
+    "axs[1].set_ylabel(r'$\\Delta \\Sigma^{const}$', loc='top');\n",
+    "axs[1].set_xscale('log');\n",
+    "axs[1].set_ylim(-100, 10);\n",
+    "# axs[1].set_ylim(-10, 100);\n",
+    "axs[1].axhline(0, lw=1, ls='--', c='k');\n",
+    "# axs[1].axvline(rs, lw=1, ls='-', c='k');\n",
+    "\n",
+    "## Delta Sigma 4theta X\n",
+    "axs[2].errorbar(r_bins, np.array(delta_sigmas_4theta_cross)[:,0] / 0.7 / 1e12, yerr=np.array(delta_sigmas_4theta_cross)[:,1] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1);\n",
+    "axs[2].set_ylabel(r'$\\Delta \\Sigma^{4\\theta}_\\times$', loc='top');\n",
+    "axs[2].set_xscale('log');\n",
+    "axs[2].set_ylim(top=10);\n",
+    "# axs[2].set_ylim(-10, 100);\n",
+    "axs[2].axhline(0, lw=1, ls='--', c='k');\n",
+    "# axs[2].axvline(rs, lw=1, ls='-', c='k');\n",
+    "\n",
+    "## Delta Sigma const X\n",
+    "axs[3].errorbar(r_bins, np.array(delta_sigmas_const_cross)[:,0] / 0.7 / 1e12, yerr=np.array(delta_sigmas_const_cross)[:,1] / 0.7 / 1e12, fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1);\n",
+    "axs[3].set_ylabel(r'$\\Delta \\Sigma^{const}_\\times$', loc='top');\n",
+    "axs[3].set_xscale('log');\n",
+    "axs[3].axhline(0, lw=1, ls='--', c='k');\n",
+    "# axs[3].axvline(rs, lw=1, ls='-', c='k');\n",
+    "\n",
+    "axs[4].errorbar(r_bins[order], abs(np.array(gammat_avg)[:,0][order]), yerr=abs(np.array(gammat_avg)[:,1][order]), fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1);\n",
+    "axs[4].errorbar(r_bins[~order], abs(np.array(gammat_avg)[:,0][~order]), yerr=abs(np.array(gammat_avg)[:,1][~order]), fmt='o', markersize=2, ls='none', elinewidth=0.5, capsize=1, alpha=0.2, c='C0');\n",
+    "# axs[4].plot(r_, dsmono/sigma_crit/(q)**(eta_0/2), lw=1);\n",
+    "axs[4].plot(r_, dsmono/0.7, lw=1);\n",
+    "axs[4].set_xlabel('R [Mpc/h]', loc='right');\n",
+    "axs[4].set_ylabel('$\\Delta \\Sigma^{mono}$', loc='top');\n",
+    "axs[4].set_xlim(right=5);\n",
+    "axs[4].set_ylim(bottom=0);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cb3e487f-763c-44e4-87db-84ceffc4593f",
+   "metadata": {},
+   "source": [
+    "## Calculate monopole term"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "80376214-7c4d-4a0b-8edd-a7669534f343",
+   "metadata": {},
+   "source": [
+    "The corrected monopole term should look something like:\n",
+    "$$ \\Sigma_0(r) = \\Sigma_c r^\\eta_0 \\left(1 + \\epsilon^2 \\left(\\frac{1}{8} \\dfrac{d \\ln\\Sigma}{d \\ln r} + \\frac{1}{16}\\dfrac{d^2\\ln\\Sigma}{d\\ln r^2} + \\dfrac{1}{16}\\left(\\dfrac{d \\ln\\Sigma}{d \\ln r} \\right)^2 \\right) \\right)$$\n",
+    "which n terms of things we've already calculated is:\n",
+    "$$ \\Sigma_0(r) = \\Sigma_c r^\\eta_0 \\left(1 + \\epsilon^2 \\left(\\frac{1}{8} \\eta_0 + \\frac{1}{16}\\dfrac{d\\eta_0}{d\\ln r} + \\dfrac{1}{16}\\eta_0^2 \\right) \\right)$$.\n",
+    "What we need to compare then is:\n",
+    "$$ \\langle \\gamma_{+} \\rangle = \\frac{\\bar{\\Sigma}_0(<r) - \\Sigma_0(r)}{\\Sigma_{crit}} \\equiv \\bar{\\kappa}_0(<r) - \\kappa_0(r) $$\n",
+    "where\n",
+    "$$ \\bar{\\kappa}_0(<r) = \\frac{2}{r^2}\\int^r_0 r' \\kappa_0(r')dr' $$"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "desc",
+   "language": "python",
+   "name": "desc"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/triaxiality/gamma1.npy b/examples/triaxiality/gamma1.npy
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diff --git a/examples/triaxiality/gamma_T.npy b/examples/triaxiality/gamma_T.npy
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diff --git a/examples/triaxiality/gamma_X.npy b/examples/triaxiality/gamma_X.npy
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diff --git a/examples/triaxiality/kappa.npy b/examples/triaxiality/kappa.npy
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diff --git a/examples/triaxiality/model_functions.py b/examples/triaxiality/model_functions.py
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index 000000000..09d9d7c48
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+++ b/examples/triaxiality/model_functions.py
@@ -0,0 +1,135 @@
+import numpy as np
+import clmm
+import scipy
+from scipy.interpolate import InterpolatedUnivariateSpline
+
+
+
+## CALCULATE MONOPOLE COMPONENT OF GAMMA_T
+def gamma_tangential_monopole(r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw'):
+    ## Calculate the monopole component of gamma_T given by
+    ## 2 * r^-2 * Integrate[ r'*Sigma(r', mdelta, cdelta, z_cl, **kwargs), 0, r] - Sigma(r, mdelta, cdelta, z_cl, **kwargs)
+
+    kappa_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    
+    f = lambda r_i: r_i*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                          verbose=False, validate_input=True)
+    integrate = np.vectorize(pyfunc = scipy.integrate.quad_vec)
+    
+    integral, err = integrate(f,0,r)
+ 
+    
+    return (2/r**2)*integral - kappa_0
+
+
+
+## CALCULATE MONOPOLE COMPONENT OF GAMMA_T WITH EPSILON^2 CORRECTIONS
+def gamma_tangential_monopole_e2corrected(r, ell, mdelta, cdelta, z_cl, cosmo, hpmd='nfw'):
+    ## Calculate the monopole component of gamma_T given by
+    ## 2 * r^-2 * Integrate[ r'*Sigma0(r', mdelta, cdelta, z_cl, **kwargs), 0, r] - Sigma0(r, mdelta, cdelta, z_cl, **kwargs)
+    ## where Sigma0 = Sigma*(1 + e^2/8 * (eta_0 + eta_0^2/2 + r*(deta_0/dr)/2))
+
+    kappa = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200,
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    
+    eta_0  = r * np.gradient(np.log(kappa), r)
+    Deta_0 = r * np.gradient(eta_0, r)
+    
+    kappa_0 = kappa * (1 + ell**2/8 * (eta_0 + eta_0**2/2 + Deta_0/2))
+    
+    f = lambda r_i, kapp_0_i: r_i * kappa_0_i
+
+    integrate = np.vectorize(pyfunc = scipy.integrate.quad_vec)
+    
+    integral, err = integrate(f,0,r)
+ 
+    
+    return (2/r**2)*integral - kappa_0
+
+
+
+def g_tangential_quadrupole(r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw'):
+    kappa_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    
+    f1 = lambda r_i: (r_i**3)*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                          verbose=False, validate_input=True)
+    f2 = lambda r_i: (r_i**3)*clmm.compute_surface_density(r_i, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                         halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                          verbose=False, validate_input=True)
+    
+    integrate = np.vectorize(pyfunc = scipy.integrate.quad_vec)
+    
+    integral1, err = integrate(f1,0,r)
+    integral2, err = integrate(f2,r,np.inf)
+ 
+    
+    return (2/r**2)*integral - kappa_0
+
+
+
+
+def _delta_sigma_4theta(ell_, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000, delta_mdef=200):
+
+    ### DEFINING INTEGRALS:
+    r_arr = np.linspace(0.01, np.max(r), sample_N)
+    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr
+    f = InterpolatedUnivariateSpline(r_arr, (r_arr**3)*sigma_0_arr*eta_0_arr, k=3)  # k=3 order of spline
+    integral_vec = np.vectorize(f.integral)
+    ###
+    
+    ### ACTUAL COMPUTATION:
+    I_1 = (3/(r**4)) * integral_vec(0, r)
+    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=200, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    #eta_0 = np.gradient(np.log(sigma_0),r)
+    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)
+    eta_0 = eta_0_interpolation_func(r) 
+    
+    return (ell_/4.0)*(2*I_1 - sigma_0*eta_0), eta_0_interpolation_func
+
+
+
+def _delta_sigma_const(ell_, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=10000 ,delta_mdef=200):
+
+    ### DEFINING INTEGRALS:
+    r_arr = np.linspace(0.01, np.max(r), sample_N)
+    sigma_0_arr = clmm.compute_surface_density(r_arr, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    eta_0_arr = np.gradient(np.log(sigma_0_arr),r_arr)*r_arr
+    f = InterpolatedUnivariateSpline(r_arr, sigma_0_arr*eta_0_arr/r_arr, k=3)  # k=3 order of spline
+    integral_vec = np.vectorize(f.integral)
+    ###
+    
+    ### ACTUAL COMPUTATION:
+    I_2 = integral_vec(r, np.inf)
+    sigma_0 = clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+    #eta_0 = np.gradient(np.log(sigma_0), r)*r
+    eta_0_interpolation_func = InterpolatedUnivariateSpline(r_arr, eta_0_arr)
+    eta_0 = eta_0_interpolation_func(r) 
+    
+    return (ell_/4.0)*(2*I_2 - sigma_0*eta_0), eta_0_interpolation_func
+
+
+def _sigma(r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000, delta_mdef=200):
+    return clmm.compute_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
+
+def _delta_sigma_excess(ell_, r, mdelta, cdelta, z_cl, cosmo, hpmd='nfw', sample_N=1000, delta_mdef=200):
+    return clmm.compute_excess_surface_density(r, mdelta, cdelta, z_cl, cosmo, delta_mdef=delta_mdef, 
+                                     halo_profile_model=hpmd, massdef='mean', alpha_ein=None, 
+                                     verbose=False, validate_input=True)
diff --git a/examples/triaxiality/x_arcsec.npy b/examples/triaxiality/x_arcsec.npy
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