{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Initial imports" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "# import necessary modules\n", "# uncomment to get plots displayed in notebook\n", "%matplotlib inline\n", "import matplotlib\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "from classy_sz import Class\n", "from scipy.optimize import fsolve\n", "from scipy.interpolate import interp1d\n", "import math\n", "\n", "font = {'family':'STIXGeneral'}\n", "axislabelfontsize='large'\n", "matplotlib.rc('font', **font)\n", "\n", "plt.rcParams.update({\n", " \"text.usetex\": True,\n", " \"font.family\": \"sans-serif\",\n", " \"font.sans-serif\": [\"Helvetica\"]})\n", "\n", "\n", "\n", "\n", "import os \n", "path_to_class_sz = os.environ['PATH_TO_CLASS_SZ_DATA']+'/class_sz/class-sz/'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Settings for the calculations" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "\n", "common_settings = {\n", "\n", " 'H0':67.556,\n", " 'omega_b':0.022032,\n", " 'omega_cdm':0.12038,\n", " 'ln10^{10}A_s': 3.047,\n", " 'n_s': 0.9665,\n", " 'tau_reio':0.0925,\n", " 'cosmo_model': 0, # This parameter is important if you want to compute with emulators. Set to 0 for lcdm with \\Sigma mnu=0.06 eV.\n", "\n", "}\n", "\n", "# best-fit from Kusiak et al. https://arxiv.org/pdf/2203.12583.pdf\n", "\n", "HOD_blue = {\n", "'sigma_log10M_HOD': 0.68660116,\n", "'alpha_s_HOD': 1.3039425,\n", "'M1_prime_HOD': 10**12.701308, # Msun/h\n", "'M_min_HOD': 10**11.795964, # Msun/h\n", "'M0_HOD' :0,\n", "'x_out_truncated_nfw_profile_satellite_galaxies': 1.0868995,\n", "'f_cen_HOD' : 1., \n", "'full_path_to_dndz_gal': path_to_class_sz + 'class_sz_auxiliary_files/includes/normalised_dndz_cosmos_0.txt',\n", "}\n", "\n", "\n", "unWISE_common = {\n", "'galaxy_sample': 'custom',\n", "'M0_equal_M_min_HOD':'no',\n", "'x_out_truncated_nfw_profile': 1.0,\n", " \n", " \n", "'z_min': 0.005,\n", "'z_max': 3.,\n", "'M_min': 1e10,\n", "'M_max': 3.5e15,\n", "\n", "'nfw_profile_epsabs' : 1e-33,\n", "'nfw_profile_epsrel' : 0.001,\n", " \n", " \n", "'x_min_gas_density_fftw' : 1e-5,\n", "'x_max_gas_density_fftw' : 1e4,\n", " \n", " \n", "'redshift_epsabs': 1.0e-40,\n", "'redshift_epsrel': 0.001,\n", "'mass_epsabs': 1.0e-40,\n", "'mass_epsrel': 0.001,\n", "\n", "\n", "\n", "'hm_consistency': 1,\n", "\n", "\n", "'delta_for_galaxies': \"200c\",\n", "'delta_for_matter_density': \"200c\",\n", "'mass_function': 'T08M200c',\n", "'concentration parameter': 'B13' ,\n", " \n", "\n", "}\n" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# the parameters needed for the ksz calculations:\n", "ksz_params = {\n", "#fiducial ksz params\n", "\n", "'k_min_for_pk_class_sz' : 0.001,\n", "'k_max_for_pk_class_sz' : 50.0,\n", "'k_per_decade_class_sz' : 50,\n", "'P_k_max_h/Mpc' : 50.0,\n", "\n", "'nfw_profile_epsabs' : 1e-33,\n", "'nfw_profile_epsrel' : 0.001,\n", "\n", "\n", "'ndim_masses' : 80,\n", "'ndim_redshifts' : 30,\n", "\n", "\n", "\n", "\n", "'n_k_density_profile' : 50,\n", "'n_m_density_profile' : 50,\n", "'n_z_density_profile' : 50,\n", "\n", "# 'n_k_density_profile' : 200, # default 80\n", "# 'n_m_density_profile' : 150, # default= 100 decrease for faster\n", "# 'n_z_density_profile' : 150, # default= 100 decrease for faster\n", "\n", "'k_per_decade_for_pk' : 50,\n", "'z_max_pk' : 4.0,\n", " \n", "## some settings to try more points to avoid numerical noise in some cases:\n", "## for example\n", "# 'ndim_masses' : 100,\n", "# 'ndim_redshifts' : 100,\n", "# 'n_ell_density_profile' : 100,\n", "# 'n_m_density_profile' : 100,\n", "# 'n_z_density_profile' : 100,\n", " \n", "\n", "# slow:\n", "# 'n_z_psi_b1g' : 100,\n", "# 'n_l_psi_b1g' : 400,\n", "\n", "# 'n_z_psi_b2g' : 100,\n", "# 'n_l_psi_b2g' : 400,\n", "\n", "# 'n_z_psi_b2t' : 100,\n", "# 'n_l_psi_b2t' : 400,\n", "\n", "# 'n_z_psi_b1t' : 100,\n", "# 'n_l_psi_b1t' : 100,\n", "\n", "# 'n_z_psi_b1gt' : 100,\n", "# 'n_l_psi_b1gt' : 100,\n", " \n", " \n", "# fast:\n", "'n_z_psi_b1g' : 50,\n", "'n_l_psi_b1g' : 50,\n", "\n", "'n_z_psi_b2g' : 50,\n", "'n_l_psi_b2g' : 50,\n", "\n", "'n_z_psi_b2t' : 50,\n", "'n_l_psi_b2t' : 50,\n", "\n", "'n_z_psi_b1t' : 50,\n", "'n_l_psi_b1t' : 50,\n", "\n", "'n_z_psi_b1gt' : 50,\n", "'n_l_psi_b1gt' : 50,\n", "\n", "'N_samp_fftw' : 1024, # fast: 800 ; slow: 2000\n", "'l_min_samp_fftw' : 1e-9,\n", "'l_max_samp_fftw' : 1e9,\n", " \n", "}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Halo model and effective approach" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Calculate" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "CPU times: user 17min 26s, sys: 5.37 s, total: 17min 31s\n", "Wall time: 2min 7s\n" ] } ], "source": [ "%%time\n", "M = Class()\n", "M.set(common_settings)\n", "M.set(HOD_blue)\n", "M.set(unWISE_common)\n", "M.set(ksz_params)\n", "M.set({\n", "'output':'mean_galaxy_bias,kSZ_kSZ_lens fft (1h),kSZ_kSZ_lens fft (2h),kSZ_kSZ_lens fft (3h),kSZ_kSZ_lens_hf', \n", "\n", " \n", "'projected_field_filter_file' : path_to_class_sz + 'class_sz_auxiliary_files/includes/s4_fl_A_170422.txt', \n", "\n", "'dlogell' : 0.1,\n", "'ell_max' : 10000.0,\n", "'ell_min' : 10.0,\n", "\n", "'gas_profile' : 'B16', # set Battaglia 2016 density profile\n", "'gas_profile_mode' : 'agn',\n", "'normalize_gas_density_profile' : 0,\n", "'use_xout_in_density_profile_from_enclosed_mass' : 1,\n", "\n", " \n", "## with current settings, the calculation seems more stable without use_fft_for_profiles_transform\n", "## i.e., 'use_fft_for_profiles_transform' : 0\n", "'use_fft_for_profiles_transform' : 0, \n", "\n", "# 'use_bg_at_z_in_ksz2g_eff' : 1,\n", "'non_linear' : 'halofit',\n", "\n", "\n", "'N_kSZ2_gal_multipole_grid' : 70,\n", "'N_kSZ2_gal_theta_grid' : 70,\n", "'ell_min_kSZ2_gal_multipole_grid' : 2.,\n", "'ell_max_kSZ2_gal_multipole_grid' : 2e5,\n", "\n", "\n", " })\n", "\n", "# M.compute_class_szfast()\n", "M.compute() ## we do both effective approach (fast not available as of now) and hm, so chose compute()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "cl_kSZ_kSZ_X = M.cl_kSZ_kSZ_kcmb()" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['ell', '1h', '2h', '3h', 'hf', 'covmat', 'lensing term'])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "cl_kSZ_kSZ_X.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plot results" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": 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5HpMjRwI0aB96CMltt7WMRoKG6m23IdGkCZL0stILyGvQmLRfkwdfyafykrKnhyZXkjbPBrtHjdvy5ajFdFS23fyV/Tt23FymHXfcVFb+TdemjWHeN8uWoaahtZoMQBdk6h+0fjNmyJI4PYk0VvmqN3UzF+NlGR/7KWM2BI5Az7WOH+EE6kk8++yzVbwxwQVQu3ZtrF69GmX6SQ58+1Fbfbdy5Url/V7FeBq+SqhXD8XFxWqu7Auifv36qjACn3LsoEe4oqJC9dc02I/tvB6vq8F2PiExL5w9pUa1DcHjNGZ5aDB3XJ06dVRfnqNhl0lfk7LbZSJPGozx0TLZ++eSiXQ02N+NTJwXjiVvQrcyZZqnTDK5mScvMpE+x40ykX5cZLLPdT7miTLoN0tBzRPpsJ10tMfCj0ylpaWV48a+XudJrxntrAhDJvt8O5kne/8w76c46wg9r1rHhypTeTnqrl2L4ubNsWbIEFT76isUzZqFor//RuLrr1H01FOoeOYZVLRsqY51CxZgi06dUP7ww5jP67dogUT16pZMH3yAstLSjfO0di2KunSx5qmkBGttY2BCJo6b/n9Q80TeqW/s942ftVdSUlJ5j/Bh3OvaY7umQ15MyfTwww/j8ccf34QOv7PzF0hBBHpdubmLG78ylanl94xVpMEryB5asHTp0k1CC/w8MXGhkJbdIHZCxwQN/ZDDxZn6+iAbHS58+zVN9Y+cpyECHlk9blQUcZDJ7doIyiOrX3OlgymPrA4xst87+dAFmei4XTNeZApKF2Rqj9K9bao9ErqAf+fPV57QBF+XMy/8v/5lxXvSy9q1q/KyJpnLdNo0oHVr6/X6LrsgkYafOOuCTO1x1gWJDHRMysT+fLPvJLTAc2UvQgvl9ftCRxAxsnwC49NVqiEZBg2v57o9z4SchYg4jlsUeA6DB9PXMEUvbH0guiAcGB03xor+8gvw88/WBiRuWuImp6lTrdjTffcFbrrJ2mnPV8tt2qiwgLzy7BGiC9aGpgvcxMj6MmQFZgxZJxMlEAgEAkHeQFPh668tg5WGK1M8XXSRZbRyY1TnzpbReuihQGnp5jvoBYKA7CPPJWo1rr32Wl/nH8w6vgUOU88SpLNmzRpf9PzQ8Hqu2/NMyFmIiOO4RYHnMHgwfQ1T9MLWB6ILwkHOcaMhytf+DAfgzv0HH+T7XuDuu4Fff7WMVhqwxIgRAFNSDR5sGbFEAEZsFOZadMGaUHWBU/g2ZJlD1itoaes8nwIzsAer54OG13PdnmdCzkJEHMctCjyHwYPpa5iiF7Y+EF0QDirHbd064K236JWiZ4k/6txtA7z8MncTMecmcPLJVl8at488YrXRmM0Xz3mE6ILojZtvQ5Yble677z7X57399tuqaIJUXhIIBAKBIARwd/+bbwJXXom6Rx1lGavcpc4E+9yncfnlwAknWOVL2XbjjVayfeY+FQgiCiM1w5hqq2vXrpul4soU98CUUm+99ZZyHUuAvkAgEAgEhrFmjfXaf8wY66AXtXdvy5Dt0QMlV16J6qw6VaeOZbQKBDGFb4/sZ599hk8++QRTp05VJWpzeWHbtGmjStwOHjxYpdPoy1JtAmNIV4wgTBpez3V7ngk5CxFxHLco8BwGD6avYYpe2PpAdIEHMDxg1CiAb0dZoYoG7MKFVhgAYw0vvFCVK1WVpWi0XnEFqjElVsw2GUdhrkUXRG/cjGYtePrpp9GuXbvNPLP0wvbr10/llqXnloYsDdpCh2QtEAgEAoEr8CebuVi//dY6mO6KoQAXXADssYfytqoKVaxsJRDEFKFmLbCDIQOpntlnnnlGJbWl55ZVv7i5S4zYTWFydyIrb/jNWuCVhtdz3Z5nQs5CRBzHLQo8h8GD6WuYohe2PhBdkCGulSVZmRWApVu/+Qbgm8wPPlClTrH11kCnTlafe+4B+vfPacTGcdyiwLPoglWh6oJQY2RTjVl6Zlm+9sknn8S4ceNwwAEHqP+LARs83JR1C4KG13PdnmdCzkJEHMctCjyHwYPpa5iiF7Y+KHhdsGCBFSbA9Fa//Walv9p1V6BnTysFFuNcWQ3LJ+I4blHgWXRB9MbNuCGrjVmm5aIRy7/n8tWHQCAQCASCjaDXacYMYNttrcpXZ5wBsKz7XntZ6a169QIWLZLiAgJB2IYscd5556mMBEyxJRD4BeOrGZ7CdG309h9++OG45JJL1Hfjx4/H1VdfrcJWGH/NNwACgUAQScOVx++/A3fcYXle6WVlflaGCbzzjvVXsvkIBI7hO0b2/PPPz+qZZZ7ZbNkM/FYGiwJoRPHghraBAwfmNTcuaxnnk4bXc7OdRyOWxiozXTBE5Y033sDDDz+sxprYbbfdKo1cQfDroxB5DoMH09cwRS9sfeD2nCisj6yYM8eqjHXccVY+1vHjgfr1rSpYNGTpkd1lF6BBA2CnnUIzYiM/bhHlWXRB9MbNtyFL71g2HH/88crAyGTM0kCJM2hM0RinkUVv4JIlS1ynFDOVS5d0mOLCDz0/NLyem+s8Gq9XXXVV5WduHrzmmmsqY7E1GvCHQBDo+ihEnsPgwfQ1TNELWx+4PScK62MTsHoRMwlwYxYLCdDzyphXbtI69VSrvGv37kDLltbnVq3ywmbkxi0mPIsuqBmqLgg1j2yjRo2yHvTMHnjggWm/o7ctrqCBTv7txhSNWhrnbgx0k7sTmbLCb9YCrzS8npvrPBbPoMfb3n8nei42eGsF4a2PQuQ5DB5MX8MUvbD1gdtz8r4+mL91+HDg3/8G5s0D6LD55z+tOFfmbqXB2rUr8NBDGz2yEUDexy2mPIsuWBGqLggtRpZeMGYjaNiwoetzFy9ejB9//NHX9WlE8pU+vXN8xZwJ3HRGw5P8MkUYDWsTsZT0wJIHfW3tFbS3hQkWmcgnDa/nZjvvhBNOUPmJnS5yzjXBeSboLReYWx+FyHMYPJi+hil6YesDt+eEuj7ocf3+e8s4peeV5VyZUYAZBuhBOuQQ64gBonBfxZFn0QXRGzffhiyNQcYsekV/5rzz+EqfRiQ3k+XyfrJvt27dNnk9TeOX8GPM0mhlDLAdmhfZcGQODNlIxYQJE9KOs64apx8oGIbQo0cPZQwLBAKBK5SWAkVFAB0u3M/BUq877ww8/7xVKevvvxn8l28uBYKChu/QAhoJ+TifcZM0WnJtFqNhydfPzKJgB40dbcyaBF+B2w2pMHDbbUCtWpY+3W67LdVffj7iCOt7/mWubNp+bE93lJQATzwBaFt/++3T9xsyBJg9G+jY0erHxAH6O/v1zzrL+v7994OR+aGHHlIFNlKzYnDc7WPfvXt3jB07NhgmBAJB1QMVHAsLcDMWQwGYFqtJE+Dyy63vvv4a2GEHS+GJESsQ5B1GS9TmAwwXoNeNOWvTvcrXO9tp+KaC59nTNTEW8/XXX895TRqq6dKK6ThOp6+ydQk2ysC/fsGpZNLh6tWrew7s9kPD67luz+MDCI1VhhDY+3M+GWJi976yL+dKwgvMrI9C5DkMHkxfwxS9sPWB23N8y1lebnlbGef61VfA3XdbXtiXXgL228/K51oFy4dH4b6KI8+iC8pC0wUsTcvfeSclagPLIxsV0Bub6bUyDRxuVtOGLPt5fQVNo4qb1+zhC05hcndisc/E2X5oeD3XzXkcZ8Zjp3swEQS/PgqR5zB4MH0NU/TC1gduz3F9DRquP/1kGa0nn2z9n2/1aLTS6cEsAnXrWumwqjCicF/FkWfRBcWhnRtq1gIvYOokhgT43ejlBNx0lbpRSIMGkYn0XzSWSUsbsTqbgVOY3J3Ipxe/WQu80vB6rtPz6DHn2D7xxBOV/SV3bLjroxB5DoMH09cwRS9sfeD2nJz92c6UV6tXAyNGWKEC55wDzJpleV4PPtjK63rffVYpWBqxBYAo3Fdx5Fl0wfJQdYFTBOaRZaEEuoUPOugg7MenXRsY20gMGTJEVWPKZwlbbhjzAxrCDE/gK2xtvNLLq0MawoaJxe/XEA7iPI4z54oPC+yrjdgvvvhCNnK5QJx+uKLEcxg8mL6GyR/CMM/18iCc0gD88Qdw++1WOiyGbXFDMvO3Tp5MD4ZrnqoionBfxZFn0QXRG7fADNlFixap1793M+ZoQ/UlnfJq//33ryyWEGRlr1zeOhrauQo65KLP4gf8q1M+abiJyeSTSqpLnZPOBMI8UttTodvth5P+pmnoz077p7tuuv6cozvvvFNluKBXlmk81qxZg2+++UY9MLCffg2RyjvnhpklMtF2w2MU2v3QsI9znGTKtjbC4NF+/aDmKfU6fnn3cx9nohOWTG7nO0lPz2efWUbrF18g8fzzSDZvDhx4IL0mQLNmG/vXqWMZug5px7E9zrogl0xx1AW5ZIqjLjAp07p161DC3eYp7al2UV4MWXopyQi9kzzoOaMnVht4jE/lke9qTH6uny79lhe0ylDdhZvHGIZB0KBlibfVq1eroGkNtvG7lStXory8HKtWrVLt9erVUzEpqUmI69evj6Kios0WCTeb0UBkf02D/djO6/G6Gmxn8PX69euxdu3ayvZq1aqpv1yU9oVZo0YN1KlTR/XlORp2mfQ1uYDtMpEnpk4jvzRiU8EHJXr1ucmLRuvtt9+OOXPm4Nhjj8Wjjz6qvmPWAl73wQcfVPTdyMTgdI5l6s3mRKZM82TPp1e3bl1P80Q6Gk5lIn2OM2Ui/bjIZF8b+ZgnysB7iwhqnkiH7aSjH8r8yFRaWlo5buzrdZ70mtH6LgyZ7POddp5WrkTdCRNQPHo0Vg8YgJJ581D23/+ibJ99UHzBBSju1MmS6eijrf7LlwdyP8VZR+h51To+LjLZ10Y+5om86v8HNU/knfrGft/4kamkpKRy3GrVquV5nuzjT15MyXTbbbep3+3YZC2wG7Y0dJl6izlo/ezYz5a1QH9Hz3Bq+i2CHmK+rua5+YDOWjBjxoxNxsCPR5aLiAtE3+henni90rA/4acGamejw+vZr2mqf5Q8ClHxyOpx4xEHmdyujaC8MORBP6j5lSmTyqWit8vph3c/93EmOm7XjBeZ0s43H8KY8oqFB/780yr92rMnEvvth4pTTkHFdtt5Xh9RurdNtcdZF2STKa66IJtMcdUFiQx0vMiUySNL+41OvshlLaCxpjMD0DNLD22qwROEtzVbiIGXimRB8JlrojQyjZdeWKlGZLb+pmnoRezmmoT9xjDd3y0vcWj3SiN1buMgk9u1EUR7unvCCR231+SPo6n59qsLMtEJWiY13+vXI8EiBDRaWeb1xx+R6NYNePBBYPfdgQUL6HKy+pNWGp0Txv0U5fY464JM7XHWBZna46wLiFw80iPMN6Q8Zs2apaq50g7cZpttsPXWW1ceftOPVs+n8UZD9t5778UVV1wR2HXopeXgpQNjL/O1KcsOPuXYn2K0QZiKXO08+PTCReHHI+uVBj/Ty0yjPJORm44O5bdf01R/r+MY5XY/NOxzGyUvjMm1EZQXRuc0TAcT80TofNKpPxRh64JMdLx4ZB3LNG8eEv/9L5JffIHk2LFIDhuGROfOwM03I9GrF5K2N1MKG4xXv+sjSve2qfY464JsMsVVF2STKa66ILGBDg3UX375BbNnz1bH3LlzK//P71JtL4Y20AObCoY20KC1G7hOnXuRyCObaSJNgZvLMm3oYnsUSsmy+hRjXQj7Qjv77LPxf//3fyj0GNl08Tr2/hL/JjGyEiMboxjZOnVQQmP1iy9QfeRIrPvnP1G9WTPUXr4cJRdfjEU77oi622yjzqnZsydq16qF1atWZZwn+/oI836Ks47Q8yoxshIj63SeeHz33Xf47bff8NNPP2HMmDHKWNWgEdqiRQs0bdpUhY0eddRRaNasmfq8ww47qGvrtUYDlwf5e+mll/DJJ58oHinzH3/8oeS3z0XeYmS33357FZ/KOFQeqSm4NJi1gDvSg6rsxbRNzCyQuikrU3s+YmTJQ7oY2VSIR1Y8suKRjYcXRjyyNpmoD6ZMsbIKHHQQEosXI8lcrsxew4O/DfXrB6oLMrVH6d421R5nXZBNprjqgmwyRVUXlJeX4/fff8f333+vDFb+peeVc0Djlobq7rvvro6uXbsqg5XGs0mZaBcx9DOvMbIMG9CHzlRAQ5MeUA4CFwI3fflJf+UkDyyvybRNTI9l3/DFjADccBYF6MWV2papb7Z2Tcv+2SkdEzQyyeOkf+p5Jvp7Hccot/uh4Xdeg2o3uTaCaPdzPzlppzJPJ6cf3k3cx3Y6rmjMmYNkvXqo9ttvSJx4IhK1am00XHffHYmffw5dF2Rqj9J9YKo9zrogU3ucdUGm9qjoghUbvK1ff/01Ro8erYxX7bHfeeedsccee+Ciiy5Cx44d1f/1G2SnvHiRideOVNYCFgrQKbjoCbUzzo1f1113HXZxWRKQhqiuoEVjmKm8dJhAuvKlOp8tDeipU6dW5rTNJ7RHlnLYnzj8et3si9XL06FXGtmQi479mqb6+xnHqLb7pWFXKHGRye3aCKKdn914rrxck96OdEo+bF2QiU7OvrNnA3y7Rs/rihVIvPYaKrp3R2LhQqBly7zqgkztUboPTLXHWRfkkimOuiCXTPnQBbNnz1YGKw/mY2eoAK/ZqFEj9OnTBz179lQGK8MeGRrhVKeka2OYE721EydOVB5Whhr8/PPP6NKly2Y82+loz7cTj2yo6beI1NyyNEL1wNCwZCUwFkpo3bo1qjq0IcsSuk5jZPmUlC62hbT4OkCnx9Axsqll4ZzEyNrTsrCdCzFdDBJjb1JjZBmXw9iWdPE6LGKQLgaJcTls17tS7TKli9eh4W9P5eElVsypTDoGSccaOZUp2zylk8ntPHmRifR5Lnkn/TjIRD7Yruc6H/NEGeztQcyTjlnmofWhH5l4DX0f6xhZL/Ok1ww/bxIju24dqn/zDYpHjkTN0aNRdv31WN25M4rffhtle+8NdOiA+lts4Vomjgv76/nONU/kke26f5j3Uxhrz+k8uZVJzyt5Z3scZNJxo/bsBWHPE3llrCePTPGkfufJzru+b/zItGrVKhV3qnOqf/vttyrlJ0EHII3WvfbaC/vuuy+aNGmyCe92mezpz+x5ZH/88UflqWVWAjoJab9dcsklGDBggOLj0ksvVcbyQw89pK7HN/Innngi3nvvPeV0/Oqrr9R5jLfdbrvt1Hzy4LUnT54cTUPWiWHLmNdMmQaqEiRGduPTp8TIBu9BimpcnMm1EUS7HreCjpGtXRtF3KBFb+uRR3I3KXDBBSpUIHHggUh27conWd8ySYxsODoiqrogm0xx1QXZZDKtC1asWKG8qzQu9V/GttJAp9HLUMs999yz8th2220d0+cxcuRIZfTSXuOmr8suuwynnXYa7rnnHmUwT5gwAf369VPfNW/eXBnxWoZMMmmnFM+ll3b69OnqoDeYtmDk8sjmyi1LkGm/cbNxg/6hSW3L1Ddbu6Zl/+yUjgkameRx0j/1PBP9vY5jlNv90PA7r0G1m1wbQbT7uZ+ctOtXdlHRBSgrQ2L8eKB5c1SbM0cZq+jUSRUhAB+627VjzNhGGmnoepUpKF2QqT1K94Gp9jjrgkztcdYFmdq96gIa9fRgamP1xw0HPZsEPaGdOnXCrrvuijPOOENtvt97772V9zYbNH3yMGXKFLRv3x4ffvgh5s+fjzPPPFOFaDIMlHT5Hfu//PLLleezjWBcrVOZdLYjvVmfvPIIpUTtwQcfrFImBGHYchdcUPQFAoFAkAK+ImWKsb//Bi66CBg1yjJW77sPFfxRmjVLZRYQCATBg4YfjUeGAPz111+VXkr9f7brUIKtttpKGZZMd8W/PDp06KBCAjQtGoV8VZ8JDM3g9zRa6b09+uijleHKPUgMG2BIAPHKK69s5lWNAjwbskF7TQvJK6vd9hp+XgNqem7pmKChn7r8vPo22d8PL1FtNzG+fucpHzKZuD+8tmf6v1+Z0sHUfDu6jxctQuL555EcPRr45hvg8ceBww4DzjwTiRdeQLJBA+scxgXXrau8rmHJZFoXZGqP0n1gqj3OusCJTHHTBantNED//vtvdcybN6/y4Gd6WbXRao+FpfHYpk0btXfosMMOU3/5ma/iW7Vqtdn17Dxm0gXMUMD/M4UWvbd//vknWrZsWRkPzI1gBK/lVKcErd+MG7K8CKtyBQHSzpVWqyqBwc/ava5d714KIujAc8bJ+E3OzPO8JJxm/0yB55kSTjMgXV8zk0yElsnOoxuZCiXZeS6ZON5xkcleiCDf80SeTGy4yTRPvLaW05RMpKdkqqjAmo8+QrVvvkH1777DuosuQs299kLxwoVYc9JJKHvgASQbNeIWY9Q/6qjNZNLx/EHLpKv+6HNyzZNO9K77S0EEdzJx/OIik+6n5zqf88Rrp25gI51FixZh0qRJqsIVjVP+pS2jjVR6WVNz1/O3n8UEGK/KzU7MqMTCAvw/jVQal/S6ZtrslUgkcv7mkideh6/+mY2AeWEZs8pzKR9zxnI+eC0di5xpnvh/L2uPtOy6QM/Tww8/jMf5EG0Dv7OPeS543uw1ZMgQBA3ufqvKCGKzFydfL26vHlmvNPiZhke6OtHZ6HDh269pqn+UPApR8cjqcYvSBg+TayMoLwx50K/q/MqUSeVyp65dTj+8JxcsQPmXX6rMAjj1VCQaN0bywguBPfcE9tpL5XLFhh9kpzrFS0EEtzIFpQsytUfp3jbVHmddkE2mfOoCXpvGKONGedBApWGq//JYsGDBJjSY2UBXtmI2ABqq/EsDlX/ZzrbGjRtvljPVjy4YO3as2oxG+jSK6Vl99dVXlfHJ8AMa1kyD5XYMnNgGJnWBLnYVi6wFhQx7Hlm7IesVnMrUXZ1h0vB6rtvzTMhZiIjjuEWB5zB48H0NZnkZMYI7Jlg7E8n990dpr14o7tsXCW6kbdYsdL68nCu6IBzEcdyC5pkeTcaecsNU6sHYVLsndOutt1ZGaraDRpgXPp3IqR1GjGFt27at8hLT8cc0V9x8RY/uPvvso/imV1fbGn7GLWxd4CaPbN6zFggETsAblsU0CD4RU1HcdNNN6jPbWSCDefJYrS3fhS4EgsCxbBnfKwLz5gH9+lmbsehpZf7tXXdVm7bWrFxpPSDHxFARCMIAQ6v4Kv3XX39Vqal4MF3UzJkzK72DfPvCGFTmeN9///3VXx40GunNpFc1yAcA7QFduHChMq4Zfnj77ber7E70bJ5//vkYNWqUCl9o1KiRimMdNmyYOvfUU0+tpEMZCsFXKYZsBKAXrYnQAjstr6EFXmnYX1t55T1dfxqqDL+48sorK18xUanwdQ4TKjPLBfPa2Wn5GceqHFqgjzjJZOr+8Npuv35Q85R6nbT9abTefz/w1VdITJ2K5GuvWcbrs88CrIy4IaRH0UmhZ0KnBCJTlmsG1d8NL3Fsj7MuyCWTm7mmh5UbmOzGKv/Sw6rPoQHIjU5M3q+NVR7MgapD5FJ1gY4L9SOTDpOgwfzuu+/imGOOUTlaWVTglFNOUcYzwzf5u8f8+sypyvyt9PLScB0xYoSiR6M1sYE229ONjZ/fdLd0TOsCpxBDNgLgInVa2StbMD1fN+iNU7qyl9sNAuyvaejKXk6D6Xnj8zsqELfB9PqalD11g8B//vMfdePyZtcyMZnz008/rfLaaZnIK2lp2Qp9I0eqTKTPcaZMupJUHGSyr418zJNdAQc1T6SjKxcpT8+qVag5ahRqjxmD5PDhWHPVVSjfYw/UqFcPifvvR63evbF2/XpLJqbJYn+bTPTa6HHTlb28zJNeM8Qmlb28yORwnuzz7WSe7P1ls5czmfS8ar0ZF5nsc22XieF5rF5FpwcT6zO/Ko1WzQtjUpmS6sgjj8QOO+ygNj3xL6+XrrIXN8Glk4m86g2o2WTimDFmlgYxPae8HmmxytXzzz+PwYMHqxjWCy64AG+//bb6PeN4s41gGizG2NL7S8Oa12IeWNK1z6uTeSopKakcN9L0Ok/28bdX9nKy9thfPwRoXRCJzV4CszGy9hiQqD7F56v9rbfeUh5Xel91+8CBA5Uhq286tvNp9Y033qgMLYgC76bbo8SLyGRQJm4U+eorK8719NMZhAdcfjkS++yDJL2uDBewVc6KhUwxaI8SLyKTM5mo8xlexk1NuuwqDVhd2pjJ+JlSim/qOnfurD7rzU0meaRnl7v8mSqURvNxxx2H8847TxmnNCRvu+02lXeVv1877rijysdKfpk1IGrjG8W15yZGVgzZKrbZi0+UvIn0U0+YNLye6/Y89qdiuuaaa1RsrAYNWcbI6hzEusoJn34FZtZHIfIcCA9z51pGK3/UVq1C8sADUdGnD4r23RcJZmvZ4JnJN89h6wMvuiDf6yOOiNO40bvHTUz0bH777bfKaOVvJkFvJY1WxpDyoPGaLfG/W2jziLv+TzrpJOU9ZSqra6+9FkcccQSeeeYZxR+ravXv31/lf2UmAno4ozI3yZjqAtnsVcDgqwQulnzR8Hquk/P4ZKufvvl6htVHUkFDloarroVN45YKTpdALnSYWB+FyLNvHubMYbyPVTnrqKNUkQHssw/QsSPQpYuKf1254cHW1OYsU+MWtj5we04U1kccEdVx4wYnGq36YKgAvbDMFMAwvMsvv1wZr/x/ujRSXsDX73yVzfA4vuq+6qqr8MADDyjji84S5l2lx5Wv9xlTS3zwwQeVYQsMU9Axt1Gcm7Ux1QVOIYZsVcAvt1kHY1T4z1HTgIWjgG/P2Nhn/0+B6vWBT3ttbOv+CND6JOCtRhvbdvgH0PZ64L3tgTVzrLamhwB7vwMMPxSYP9xqq9cWOOI34IdLgClPbjz/oNnAn48CE66wPne6wToMgMYoDxqrN998s3ptxCdyO2jAaiNWidi9uzJ+xZAVhAp6cmbPBm65RW3OAmMt//c/oFcvgLuL+cMXcU+YQBA06Knjrny74cpYV20UcoMTw8j4l8aiiTRSjHHlwTjRxx57DNdffz3+9a9/qd+Tk08+ufJ196BBg1TcKPHQQw+pv/ycKZ+0IH8QQ7YqQBuLOldb7S2BVgOsIxUnrsvexh9gBnAfOWXzH9r9Ptr83O4PWYf93B0uBHa8CEGBRimTPNNIZZ48u+FK76sd9u8EgsDAaj1DhwIjRwKjRgH33QfsvTcXJHDllUD79hvvpx13zDe3AkHewMICX375pUqpyB35THtFMJZ13333xY033qgMV+ZCtcNrFCTPYxEAZgFgDOtFF12k9lGwOADTaBF33XVXZX9mxyH8hAcIwoUYshGAfVe0n4BpHnw1ovt4CdL2Q4OfuYvSC+/2azrp37dvX/UKiBvA+BpIP6HbxzJdm1uZotbuh4Z9nOMkk9u1Ybpd86D/D24w/Plny2AdNQqJiy9GkgUH+JnG6w03INGunUqBhQ1ZRzacnPGaRKqcfnj3cx9nouOWhleZ3M63n/URpfvAVHvUdAEzADDrjDZcuTGKoAf02GOPVcZrnz59VPooO510c+pkrmm0Mpb28MMPx7nnnqsM1v3226+y/PEjjzxSWUmLmYHcjMFmusDH2MRZF+RLpkwQQzYCoBdR3xycQK/pt/QOfr5+8Zuyhed5TUOTq+6zhpaJKT34qkfXYU6Via+YLrvsMlx33XVKJrbrzXFMbM0+uswiadnTb7GNvOg2rzJFLQ2Nn3RBHKO4yKRfA+q1kZd5Wr8e9SZNQuLbb7Fyv/0Y7IU655+Pst69UXzssUCHDljO+/eBBzbKtGF3tZt5Iq/2WuQmZCI9E/OkX+kGLRPb7fOda55oJOm0gW5lKuT0WxocP9My8btx48Yp45W5UceMGaOuyyI2NFovvvhiHHLIIcrjqlO8Efx/NpnYz742KBN5Z5orXv/ee+9VaawYgsDsACzPyswB5Ivjwd9Tgnz7nSdeW6ekCmKemCbLft+YWHsrVqwwsvb4f5MySfqtmEP/ODDpsT1rgZ8nJi5oLhi7QeyEjgka2mjkDZEaw5SNDhe+/Zr2/tydymoqTz31lAop0P25i5TJoJ944gn1uojt3Nil+2ka/fr1U9VN7JkLouQ9Cdsjq8c5SvXVva6NwHhkFoFvvrE2YTG7wP77o2LHHVHEzVnnnQdsv70vmTKpXP7o2OX0I5Of+zgTHbdrxotMbufb7/qI0r1tqj0fuoDhAR9//DE++ugj5XWlgcywLib252t8vkHbfvvtPa/t1Ln+5JNPVG5Wgvqf12TBA4YKcANYUOOrxy1T2IEpj2zUdUEiAx2TMvG3n7/pkrUgJtCLIrUtU99s7Xz60k+MbumYoEFDNnWxOuE99Zq6nQuZiqpbt26b8MO0J7vttpvaCJBKy06DN4M9CbMTXqLe7oeGHuegeXTb7mVtGOOFmQTq1wf++gs44wzmbUNi992Bu+9WxmxyzhysLCvLusnE7zxRmaeT04+sfu7jdHTCksntfPtdH1G6D0y1B60LeC73KdBw5UEjkm8Ve/XqpVJT0RtK/azfNGaCk+uyuAc9sfPnz1dv5nTFK3oVu3TpouJtdYytW9pe2jOtaS/046oLElnoBC1TOoghK4g86E2lp5Wvq/iUzzyxVGAPPvig+p7puBgrS6P1zjvvVDFSzOnH//MVEw+CfQQFjg1VecBcw3fcAYwezUSV1kYtptV56imr3OuGSnsK/H/KKzSBoNDA7ALacKUHlIYkU08xTIBZZGi8mtpcS8/fd999p0IR/vnPf6oYV8a66o1YzC4gEGhIaEEVK4igshb4SE/ih4bXc92eZ0LOQkQcx80Iz9OnA889B3z7LcCHmvffB9q2BT7+GOjTxwoVyEI7jHEzfQ1T9MLWB6ILwoGTcWOMIgsRvPfee6pkqt3rethhh+HQQw/FLrvsYnTc33//fcUT38Cdf/75eOGFFxSvOvwh33MtumB5aLpACiIUMEwkHPZDw+u5bs+LYiLvOCCO4+aKZ4YJME8rjdbvvgNefJE7EADmfrzqKoAhA/qh8ayzguHBI0xfwxS9sPWB6IJwkG7caDQw1pXGKz2vfLu1zTbbqAwApr2uNG4YN0nj+OCDD1ZZDRhKxg1G3Kj03//+V/WzGz5RmGvRBdEbN/HIRsAj6+SJQyAQpIC7celh3ZAGC/ffz6Bo4JlnrMIDPFh4YEOqHYFAsDlYypuGKw9mGaAnlqFbRx55pDqYVUenq/ILmhsseNChQwdVZICGLEuNM/vMTjvtJF51gSf7SAzZKpa1gIpBB1S7oWOCRuquRqe8czeq/Zqm+nsdxyi3+6Fhn9s4ZS2onGumDfr2WyRGjULywgstI/b2263wgL32QqJvXyTr1DHOox43eonSwcQ86bRo6TYtha0LMtHxkrXArUxB6YJM7VG6t0215+rLjVPMszp06FDlfaURSW8Z41CPOOIIdbRq1cooj7Nnz8asWbOwxx57KPovvvii8uym2wyWSSa/c50vXZBNprjqgoSHrAVuZZKsBTEDK1QxHxzhJ48sFRTTnjCvHA8vOe3YX9NgPzf5FKmUyAP5d5unj4uW1+S5TnLa8TvdX3JEOpOJ9HVaHNKPvExr16J81Cgs7dwZyQkTUHfQIJR37Yrq++1nyUQD9qOPNs5T3bpY5zKHsROZ9I8/6QQ1T6RDRU8etaL3s/a401vfx+zrdZ70miEdrpswZOLrbH1vO5kn8q/7Sx7Z9DKRN5bqpvFKz+uiRYvQuHFj9UqfuVb33nvvyt8ggryZkOnll19WG28nT56sNuvSE/vKK68omXg9ex5ZJzLZ10Y+5om88v/sH5TeI++aF33f+JGppKSk8h6mk8nr2mO7pkNeTMkkeWRjjiA8svaAaq8eWa80+JkycXG69cimBoKb6B8lL0lUPLJ63CLnkS0ttaplMYXOV18B55+PBI3vXr2w4uabUb99eyTovSkuzosXRm88CNIjqzd9mvLCeL2PM9Hx4pF1K1NQuiBTe5TubVPtum3x4sVq89SwYcNU3lU+VLBYwNFHH60yAPBVPnOumtYFU6ZMwfPPP4/bb78d//vf/5Snt2nTpr5l8jvX+dIF2WSKqy5IePDIupVJPLIxg14UqW2Z+mZr17Tsn53SMUEjkzxO+qeeZ6K/13GMcrsfGn7n1Vj70qUAfwxGjgRuuQWJceOANm2Ad98FunUDvvgCaN1a9U8yD3CaRNph8u7nfnLSTmWebk374d3EfWynE5ZMQemCTO1Rurf9tv/1119499138c4772AUQ3CSSfTs2RM33XSTMmBpvBLaKDGpC2i47rrrripl1kEHHaT6sGhNNridJ79zbaLd5JjFVRckstAJWqZ0EEO2ioGvB/JJw+u5bs8zIWchIq/jNmGClaeVG7PmzQMmTgRatgSuv551mq2iBBqNG1t/k8lIzHUYPJi+hil6YesD0QXOQQNhwoQJlcbrxIkT1XiwkharHnKzFqtdBTFuDLfhK20arsz5etJJJ2HbbbdVr6PZFgSiMNeiC6I3bhJakEdI1gJBlQVLuzJPKz2uPFh4gG1jx6qNWejYUbIJCAQewPhneltpuNKAZXlYvupmiqxjjjlGxb0yLjMIMLaShupzzz2H6dOn45ZbbsHcuXPThg4IBH4gWQsKuCCCVjRu3PKmaHg91+15JuQsRAQ2btwc8OuvG43Wp5+24lzfeQfYe2/rYNiAh2tGYa7D4MH0NUzRC1sfiC5ID8rIOFcar4x75b6K5s2bK8OVBzdrcVOOU7gZN/ZdsGCB8rYy68Bbb72FRo0aKQ9brhK0JhGFuRZdsDo0XSAFEQoYbnb6BUHD67luzzMhZyHCyLhxYxbDBBgi8I9/WFWzHnkE2Gcf4LjjrJKuRx1lHQYQhbkOgwfT1zBFL2x9ILrAwsKFC1WGARqvn332mXqN36lTJ1x44YXKeN1tt918GSa5xo0GBA3Vn3/+GY899hheeuklfP3115tkNwgbUZhr0QXRGzcxZAUCQXasW2flbN1zT+CVVwDmcG3f3vK0rlkDDBwIDBqUby4FgipRnEDHu9JopCdrzz33VCmyuFlre5ZTDhjM+UpvLzeIsQwtQxVYlpbIpxErEGSCrEqBQLApmKewdm3gk0+AO+4AfvwR6NLFChU47DBg1qxNN2YJBAJPoKE6fvx4Zbjy+OWXX9Qre5aCfeqpp1TxAL7SDxrMNcocrXyVe/zxx2P06NF48MEHq3S4hqDqQAzZCEDnaNPwk+eNCY91Hzd0TNDgZyZW9sK7/Zqm+nsdxyi3+6FhH7fN+v/2G/Dqq0iMGIEk87lOmQK0agX8+9/A7rsjUafOpv09rA+vMrldG6bbNQ/6/yZkSodUOf3w7uc+zkQnLJmC0AWZ2vNxD3OzFkvBsjgBc7zSA8p8mTRab775ZpW6iknnNdyOvRtdMGPGDOV9HTJkCKZNm4brr79eZSDI19jkkimOuiCXTHHUBfmSKfTNXueffz4GDx4su/EdbPbi6yIdNK/zrGWq7MXKGukqcujKG6kVOdxWTklX4YaKN13lFD7Fp6syoquJpFYZ0VV+NJzK1Lp1a6XoGRPGajSkwR8BtjGxN6/HzQ+MIzvjjDPwbxpfDmTiblvdz41MP/30k7oG094wbqwPK0y5lCmv81Rainq//ILSTz9FYuRIrHrlFVT/8UfU+OEH1DjwQKzp3BnrbZ6YQl57IpPIZEImxrt+8cUX+Oijj/D555+rDb4tWrRQr+6ZbYCv7kk7DJk4lsx0wDK05557rso80K5dO5knkQn5kOmhhx7KWNmLFeHymrWAjHB343Hc/CEIrbIXFxAXmd0gdkLHBA1+1iXsUl9JZaOjF76+pr0/Ff4BBxyAHxijaetPw59G/pNPPlnZzhKId911F9544w1H48UfFFa2oYHsZdy5xj/99FPFX6Q9slRw33+P5PDhWHnBBag/dCiKXnkFSW7O2m8/oHdvVTEraN69yJRtbYTFo74nMilTUx5Z6gO7nH5493MfZ6LjpbKXW5nczrff9RHkWqKxyM1a9Lp+9dVX6oecBQPoeeXv4i677OJKT/qViQYNDQmO1wUXXKBKxXqZ13zpiLjqgmwyxVUXJDxU9nIrk1T2ihn0okhty9Q3WzsXhZ2eGzomaFDZpJMnF53Ua+p2GvnXXnvtZu3p/t+tWze0bds24/ep1+SD1sCBAx33TwXjybzI6rc9Z18+Uf/0E7Drrla4wPnnW6Vf998fybVrkaCX/7zzkCn6LUjevciUaW2EyaP9dZpTOm766h+KqOiCdHTCksntfPtdH6bWDMF4VxquPH788UflMWOJ1vvvv18VJ6AXNBdM3080CKir/vOf/yiD4LzzzlMpvExX9gq6Pc66IFN7XHVBIgudoGUK3ZB1w4hAkA6M26Jx6hTMb+gE9MZyMwUN2diDipX32jffAPffD4wYATRrBnz6KXDIIRs3Z7FfyqsggUDgHUyJRW+rNl7nzJmjjMbDDjsM11xzDQ455BAjOcK9gK+V+cqYvPAt1eWXXy6/yYIqiUANWZbI46tXxt/YjZGxY8eqJ1caE2xnHIRAkA7du3dXPwxOccIJJ6i/XF+vv/66WnvaK3HVVVep/3Pdvfnmm+r/d955Z+XaZEy3Bo1cDYYs0ODVIQiRwKJFVuWszz8HvvhChQ6AG0T69wcYb7T11vnmUCCokpg3bx4++OAD5dVkXD4NRsbxU/cwRRZj5t0UJzAJer0YWzhp0iRcccUV+Pjjj1UGAoYPCARVFaHEyDLInYYFc+TRw8Y0HzQsaEDQQGC6j0KEicpeVKJ//PGH+j+nkvWvGT/q9cnbD41M5+60004q8DyX8uVrOCfXZH+uL8bI2g1ODRqqNEr5I6PBtUjDVhuwBK9FIzXVQCVNrlVt2HLN0iBmm/2BjPE7pJcaIxsImK+VVbMo0403Wl7X114DDjwQ4PVbtsxJwu04RwFR4DkMHkxfwxQ9P3S8nOtFFwQ9NwyX4sZOGq48GK9P/dO7d28V78qjY8eOeb2n9GvbG2+8EW3atMFZZ52lxiWTQR2F+8otosCz6IKy0HRB5Cp79e3bVx2C9PCzuGjEMjY0ykhnLKbK78aDkSvOhw9HehOYBr0lNHxp0GqvbTbQGNag8cobig9jbsIcfKG83KqexfytH3wAnHWWSoOlDFfi6KOtwwXcjnMUEAWew+DB9DVM0fNDx8u5XnRBEHPDTVHUATRc6X2lF5bOBoYKXHLJJeovM6hEATS0WTqW3lfuJ2A4Qa5xicJ95RZR4Fl0QXFo50YmRpYeMRowfO0iyAw/TnF6O2ko5soa4IYXrzQynUsec51H7zSfupx6Yex/7aCxSQ8qQxJSQc8pvbK5DFluhuBB0FtOegTfJAQOGq8sQjB8uOVlfftt4KCDgL//ZiI+X6TdjnMUEAWew+DB9DVM0fNDx8u5XnSBKTl///13ZQzyYJ5XpjPaYYcdcPLJJyuvKyts5duQsoNpAFm04Pnnn1dpvZzuD4jKfeUWUeBZdMGKUHVBJAxZerH46pUxsWSe1Ur69+8vuWUNgq/stbeTE083PD0HfgxZrzT8nmsCOk1XprhabZTmAj23jJ+lQUwPL9N0GQfzATJEgBW0eLz+uhXnypR1jz0WSJxrQJFEgSIKPIfBg+lrmKLnh46Xc92e45U/PqQy7I2G6yeffIJZs2appO377LOPuveZ35WGbNTw9NNPY7vttlOlYxlKQHjxDkfhvoojz6ILojdugRqyPXr0qMwj+9dff6nXvfTO8smR3jEatUxNIhCYgn71r1POZPo+nYHLg+vy7rvvVmuVbxSMhhLwRv7lF8toPeUUq5oWswwcfLBlxHbqZGUfaN/e3DUFAsEmsa7a6/rtt9+qmH6+MeI+DYYL7L333urVfNRAI4Cbt2677Ta1mYzx+Ux6H1qok0AQYQS2lZFeQu0dIxiAzmT1fD3Lp116aRk3S2NXIDAFGqI0YO0xrhps41uBTNBe16uvvloZsvYfCR1WQAM5He2MWLyYOXoAln1kSqwBA4A5cwBWVWHcOFNkXX450LmzZcQKBAJjYGzriy++iFNPPRVNmjRRb1i4iXPrrbfGY489hunTp6uQggceeEB5OKNmxNJL/N///le94WJoA43xDh06KI+sQCAI2CPLvHWsGc3gc1ZzsIMxijwYz6jjOwVmkDrWYdPweq4Jvu2v3viwZI+FZSYC/ojp2Ff7wxb/ci3a+9NgTc1lyzYatPYwA3u/Ssybxwta6bEmTQI++sjytjJFVosWyCdMjnMh8RwGD6avYYpe2PrA7Tn2/izVyfhWpn3k8csvvygjkBtizznnHOV1ZTnYKMW6pgP1EcPGaHCzIhhhukpmFO6rOPIsuiB64xaYIcsffioSvrKhEZFuwxcNiEjl5swTTAWNkw7Twvih54eG13OdnkdPKF/3U8kTjL/mOfTA8oFJhxLQIOX6o2eVabO0sWlPx6UNXvbRqeA02I8eWRqwXJ80XHkt9tVeX8bN8jwazEumT8d59JDQcL3wQoD/Z33ru+8GevasLP+q4l/zCBProxB5DoMH09cwRS9sfeDlnJ9//lnds/y9GTVqlNqk1axZM+VIueGGG9Sbv6hkGMgFhj6wjC1zqzMEj2EP5557bpW8r+LIs+iColB1Qd7zyNrB+FiGFgjM55G1I66bvdyeZ0JO36mxxoyxsgow5GD//a1croceChxxBGMUEEXkfdxiynMYPJi+hil6YeuDXOfwexp5rKbF48svv8T8+fNVSMC+++6rjFcefP0elzVOLFq0SBmuzJBw7733KkO8qt9XceRZdMHy0HRB3vLI0jDjq1p6sPhUqZFqxFL50AMmabk2rUOswYlO93yRq91+uKVjgka2tFhOeTfZ3+s4ZmyfPx+49lorVKBFCyQefBBJelwZSlCt2sb+HsfASbsfGvZxMz42Acpk6v7w2m6/flDzlHodv7z7uY8z0QlLptTx/vPPPzF8+HAVMkDj9e+//1ZFVxgucMYZZ6jCBDRemXEglU6u6+b7PmCS+JkzZ6qQJ75xevXVV32vgzjrglwyxVEX5JIpjrogXzIFasjSgO3Xr99mm2D4+pUbvFKtaRq2jI3lK2CeS0VVyGDsJite6AnUk3j22WerJP5EzZo1ldeBibqp/DTYxu9WrlypduAyjyvBXK6MA+P42hcE41To4udTjh18UuJGAvbXNNiP7bwer6vBds7p+vXrVUyaBn9cCL7a46HB3bWM92JfnqNhl0lfk7LbZSJPGnXr1q2Uyd4/l0yko8H+TmVKzJmDmp99hlqfforS44/HmsMOQ40uXVB65ZUobt3akmnNmowyZZqnTDK5mScvMpE+x41rjfRZJ97tPOVDJvtcZ1p7lIlrPgiZKAPvLSKoeSIdtpOO9lj4kYlxlXrc2NfrPOk1Q9A7ErRMrFTI0KEff/wRX3/9tToWLFigdEvXrl0xYMAAtWuf3leGC5A2+df0wryfNLzO00MPPYTFixfj8ssvV6FL7BOWjtDzqnV8mPeTH5nsuiCsebLLpB8+iKDmibxT39jvGz8ylZSUVI4bH/a8zpN9/MmLKZkefvhhPM6y6jbwOzt/oYQW0APL1+N8quRTMv8/ZcoUFYPIGEUauK1atdrsPKY5Ymyj/pEo1NCCpUuXbhJa4OeJye6+9/rE65VGtqTH2ehw4ae+djDR3/U4Un5uPqxb14pzZVWtgw9G4qijkGSGAbb7oR8Bj6weNyqKqHlbTKyNoLww+jVXOpjyyOoQI/u9kw9dkImO2zXjRCYa3DRaGdtKo3X06NFYuHCh+oFmRhvmdKXRyg1aqZtFTKyPfKz3008/Hddddx1atGihDAvtADBFP866IJtMcdUF2WSKqy5IZKBjUib2Z5o5J6EFvg3Za665Rj1B84kyXZwnN8g888wzKpxgl1122eS7e+65R51f6Iask4lyCk5nqhEZJg2v57o9z4SclWB6rIsvtkrBclPIv/8NHHOMlfc1JvFjeRm3AuI5DB5MX8MUPZP6gJ6g7777ThmsNF75f7bRoGOZVXpb99prL5Vqip6moPkLC3RW3HrrrbjvvvswdepU9VbSbsDmA3EYtyjyLLogEcq5buwj36EFrIzCI9OFmLOPr4OYvYAeWnsBhExPNYUGA07xSjp8avWzQ9EPDa/nuj3Pt5wzZ1pG6/vvA3vsAbA6Tu/ewPXXA/Y3BzFT8mGsj0LkOQweTF/DFD2/+oDxrNpw5UGnB18Z0tNCY/Wmm25Sxivf5PHVpv16QfMXBiZNmoQZM2ao/NU00Mnv9ttvn2+2Ij9uUeVZdEFFaLaBG7vItyFLhZTLWmYKI4YaMCCfzO3PXd6CQMDYFL8ZEPzQ8Hqu2/Nc9WfMz9ixVhGCXr2szAK77w4wpyxDBnhjnXkmCgEm1kch8hwGD6avYYqeUzr8oWJxgW+++aYyvpV6n2jevLky5M4880z1t2PHjhmN1UB1QUjgOGy77bYqdpG5p/njfeyxxyJKiOK4xYFn0QVb5pWHQAxZN3lgmedv0KBB6v9izAoCB5/oLrgAGDrUqqp1ySXAnnsCEyfmmzOBIPbgxqwxY8Yog5XGK0u+8hU6DVSGkbFSFkPLmHe5UDLUcPMW02gx1I75a+l1Tg2pEwgEETNkuZmLZf6cKqonnnhCxcYKBMaxaJEVLvDOO3xVADz/vOV9veEGy5AVCASewDdpzAf+/fffq1ABGq1M3s8wAb6R42asSy+9VBluu+++u9qYZd8cUtVBbzQPvnVkqjCGTAgEgpgYskwPxQ1bJ554osody6dwvkb5mFWOMuDKK6/EkCFD1FOrwCxMxNT4oeH1XLfnVfafNg2YPt0qSnDqqUCjRsAppwCHHGJ9f/jhnvipqohLPFzUeA6DB9PX8EOPrwHHjh2rDFbGtjJdIrMJEIzx7Nmzp0oPyByuO++8c8aNS1548KwL8gRmXfj3v/+NoUOHqjFzGt+bb+R73OLKc6HpgnzbBqFW9qJhypuY5WiZNuWTTz7JeQ4D//naiTFEhYggshYUDP7zH9aY5XZgPk0B4gERCDyBXlXGdFJ/09vK45dfflEeVeolZhOg4cqD3ta4lHsNGrp4AZ04TBXENJQCgSB8+yiUErWC8ErU8keJORj9ZC3wSsPruTnPY3q2r7+2Yl2nTUPynXdQPmwYqjVrhkS3blUuu0BQMLE+CpHnMHgwfY1M9JgcnUYqnQj6mDhxotqUxH6dOnWqNFp57LjjjuqVeVj6wO05+VgfzL7DAkD0UDNtGPPBxg1RuK/iyHNV0gVRtw3yVqJWkH8wJ6Nfo9gPDa/nbnYeK6z89BPQowdw/vnWBq3jjrM2bwFYtc8+Vv+YKOGqtD4KkecweDB9DRpaTP3EWFZttNKIZRECvv7u0KGD2qx78sknq78MDUstOsAfID+7jb3I5PacMOaGxjw91vROc+x4zfbt2yPOiMJ9FUee46gLVhuilw/bwAk8G7LMHcswAmYhsOeGFQh8YeRI4MknAcZYM0UW870+9hhr8W3sIy8RBIJNjM158+bhp59+UvGa/MuDpb+1N5WeVuZqPffcc5XR2qVLF1XCUpB7bPnmjMYrixm88sorOOecc/LNlkAgMGHIMrUIX4nfeeedOOGEE1R5WhY+4FO9QOAYy5YB776LOsOGMeiMu0yAAw8EHn5YbdziK7y33nrLWNEIDdJl6WSmC/ICphXigxzj4lipZ9q0aSqDx5NPPqnuBYEgCNCb+scff1Qaq9pw1Rux6FFluifq5/PPP19txurcubMqNiBwD+71ePvtt9W9/sYbb+SbHYFAYDq0gNW6eDCGgTc5n/b5/4EDB6ofc9nAFD5M7Jj1Q8PRuStW8BcXePllK7frAQeg7MgjUUxj1WGWgXzuDKYBfPfddyvPFtd6tn40eBlT5ybfcpCIy47qqPEcBg+p1+CrOMavMjRAHwwNYJwr0apVK+U4oMHKvzRgmQaRdHRIAA1bv3F2gesDn+cEMTe33HKLig/mAy9TalVFROG+iiPP+dAFUaFXFLIucAojMbKMe2AaLh7MNUivFH+46aGiIqCBW1VBrzSf1hmUrD1zLMvbtm3bvKTG8Pvw4IdG1nNpvL77LkCvBjduMf6VOV6POw6JunVRJ2Q5/YBzTNgNVM53qteYa4NxiVFBvsctrjyHwQMT6dsNVnpaWd6Ua4qhAUxx1bVrV5x22mnqL0MDspX4NsVzYPrA0Dkm54ZjzVSS1113nfotY2GDKBhOVfW+iiPPYfBg+hqJGOsCpzC+2atNmza466671EGFTKP2qquuUrWm6b2qahW9qPAom36dTBn5mUatU5h6bU4669evR40aNXxlLfBKY7NzabwyZIAejZ9/Bt57Dzj9dOC114C6dT1f04ScJpDNkIgiojJucePZNA9z586t3HzF/Kz8O3v2bPVd3bp1K0MDrrjiCmW00oh1Gxpgimej+iCAc0zIyQplX331FQ477DAVikHjtUmTJqjKiMJ9FUeew+DB9DWSMdYFThHo4yaVMCt5MU9s//791f/5lMtXYfQ4VBXwx0iDXmjtscsH1q5dm1ca6lzKf/TRAKu90QtLg7ZvX8sb26/fJkas02vSu8mHBI4vf2i4ezidt5Njz7cA7Mcbhn95Hs/PBfaxn8vNMfS228E2HU7Afjz0dXmO7s+HGdLS52zFSmMRgIn1UYg8e+GBinjmzJkqUf6NN96Iww8/XBlIzZo1w5FHHomHHnpI0WXWAOYk5a54hmax5OsjjzyiNhXR4+81vtXUuPnWBwGf45W/8vJyLFiwQMUdMxaW83X00UdvlrmhqiIK91UceQ6DB9PXWBtTXeAUoaXfyhZPy81iXmtxa8MlVxwiDQwaKjoEgIYGizH4RWp1ss8++0zJU1BYt87KMvDqq6jOFFncrHXSScArr6Q1Wr2AxiDnjuuFP/jcANa9e3e1WUt7RmnY6n58cOJftnHuf/jhh00eONKtI57LNcL548EYV16Pc6rnmWEj/D9p8v+Z1hy/49uIXP0EVQPMDsA1xPXGN1Ha08qQAWKbbbbZJGsAj5YtW1Z6J3Q516r6KjtK0J6ed955R+kFbljmQ4VAIIgnQs8jmxpPS4OEBiU9VjQaaIA4iaVgX3p6GZuYKw6RffkjwhAHDe0tM2HMatBoIT80YKo8Skv5Ts7K99qhA9C9OzBgAMp69gTofTzxRKOX4w+/Nib5Q8Qfnssuu0wZm/rBgT9IOjWc3XDk/NsfZNJBe21pgNjP1VkTeB2uFR7a487rZoqFJg0+LPG6PEcM2aoDJvZm/KoOD9BxrUzTRDRv3ly9jfrHP/6h9A7nnp7YuLzCrergBsxevXrhuOOOU84VgUAQb+T18Z/xtFdeeSWmTJlS6TWjZ5ZpvL788sus59JYpGFz7bXXZu3HHxoaIakpkegl08asCWhDiQg9tOC224BatYDatbHldtsB8+YBr79utemD+VknTNi07ZlnuDV607arrlKbS7D99hvbjjnGus4hhwA1alg5XWvXBl580br2qlXAV1+pwgXVmfD40Uc3nsvvHUBdMws4X3bQG0vYyxvTK08PbarRmKsEMudNG8Sp55ImUVUeTnKNcxSRT575Oox66aWXXsIFF1ygql3xQZt5WU8//XS89957yttKPcTX03xVPWvWLAwbNgz/+te/cMQRR6Bp06aOjFjTcpqi54eOl3PdnuOkPx9++WDBhw3GHh911FEF/2AhuiC6PIgucIf8r4qUeFoe9Kjxda6JjWE0QNJ5XbUnTXvaCHreXqcBmAPpshJoQ5lpmRg3yfAFp5kLfCvUG25QxyZUBgywjnRhAFnaSKMe/6M3q/E1HA3gESOs4gSMfeV48SGgWTOrz4bXcpXnXnSRdTgE5a9XT52ZEfaxZP90ydzpbeVDBB8qdAYJGiG54mP1g0cPVhFLQ1PTjTucjHMh88xcrDo3qz6Ys5WxlLoSFvUUH7R1JSxTlWpMy2mKnh86Xs51e06u/qtWrcKIESNUfDI3cxUXF+d953sUILogujyILohA1gIT4I5dHiZgf/WczjiiwawNWR0b6RQ0kPjq0G4s241ieyhDWFkLWDOd9b/9ZC2opHH//cCzzzIAkO/jgH32Ad5/3/j13Z6n+6eCBixDBDivnAd63PkakXPMB4xCh4n1URV4pmHKt0DM0Wo3XOfMmbNJ5oB99tkHl1xyifr/9ttvr8KfgtypbFJOU/T80PFyrlddkNqfc8w3MfQCDR8+XBmxhx56qCv+qzJEF0SXB9EF7u2iQA1Z/jjku9KXrriUDqzK5CfPJ2mnvrbWnrt8xUQyWToXiycsWqQyCyQowz33AI0bAy+9RGH4eBTo9d2ex53GqaARSwOWhqsdueZYe3u5iSzdwwoPN3mBowxf6yOGPM+fP18ZrD///LM6+P/ffvut8kGIr/2po8444wz1l4fOjKGhN2LFbW5M0fNDx8u5bs9J7c/5YqYIxrszbv7ee+91df1CQaHpgjjxILrAHVwbsizXxyB5J+Br+nwbsrmQK34yG2iscnOaPXSBMutNQU6R+iPJJxYqY6bdsafe0e2p0O32w0l/BXpbi4qQuO46JBkPesQRKD3uONTg96efvrFvCp10tPXnnNfMwnum/qky2a9lNzQYd23vy5hZHd/KHeR8FWx/ImRftnEu6UVP3eylveqc53Typbal8qWvy/87mo8c7X5o2MfZBC+m2v2uDYKGKQ1UPjxro5WHLt1au3ZtlY+V3tVTTz1VlW1lUYGtt946J4+p682ETOmQKqcXOr50QQ46YcnkZL4z9b/++utViBB/o3jkohOl+8BUe5x1QS6Z3K4N0+1edEEumeKoC0zKRN2tqxXa2904D1wbsjQKnBiyjHPlq169kzwfyBUbaSL2kfGyfH3N/Lg0WOjlTU3JlQssNZkOpMtKMwQNWv4Ys2wld01rsI3fsRwlX6cxJoxgPArjwbi5wb54mCOxKJHAqo8+Qg2my/rhB6z85htsec45qLj+eqwoLVU0yjakAqKRx+vxuhpsZ5wZExzbc8NVq1ZN/eWitC9MJkFmTCv78hwNu0yaby5gu0za88pFzVe+WiYmMSdIk6mPyCeNFG7O4oYbxjLyVTHnQo/vrbfeqjIdsB/PsRvADzzwgHqVzE1kzCXJcxhbx1fPDDfhph3d1y6zlom8EppfykReiTvuuAN//vknLrzwQtWP19awy7TZPBUVbXYzU06er6+nx8zJPJE+x5mvW0k/VYE4madMay9Imexrg+dzjOk950OHNlg5vuzLPvSed+zYEWeffbYyVmnYbLfddur+SJVJrfUcMlEGfW5Q80Q6bCcd/aDFeeJ97GWeOEZ63NjX6zzpNaP1ZRgy2efbydrjoe/tQYMGqQwRbAv6ftLwM0/5uJ94XT2vWsfHRSb72sjHPJFX/f+g5om8U9/Y7xs/MpWUlFSOGz2iXufJPv7kxZRMt912G26//Xb4QtIlEolE8t577834/fLly5ODBg1S/YqKipJBY+nSpVwxyXHjxmX87sknn0x77gknnJDcbbfdkvkCx4r8zZgxI7ls2bLKg+38u3bt2mRFRUXlQdg/p7aXl5cnV61apf6m7T97ttV+223Jim7dkhUPP5ysWLjQHY0svPCc1atXV57rlPfUa9r7c444Rqn9R48erdqfeOKJyvYlS5ao/g0aNFDHAQcckPz000/V95xn9udnQvez88L1wva2bduqvjyH9FN5Zxu/nzp1amXblClTKvmx99f8k6absczU7oeGfZxN8GKqPVNfHhzjV155JXnjjTcmjzzyyGSLFi3UePKoXbt2co899kied955ycceeyz57bffJleuXGmcRz1umXg0MU88Uu8BP7z7uY8z0QlDpmy6IPVYt25d8vPPP1f9X3/9daU3Ta29OLfHWRdkk8nN2giq3YsucDtPcdAFFQZlop1jt3+0DUS7iHqe/8+FBP9xY/jSY8VUM9wNzr/2HaBMmUXvFS1uemP5mp3FD4L2unITRuorYft39CKnpt8imOeToQXZEuUHCT7l8AmGTymB7aSl53LoUOCFF6wysRMnWnleN3gLBYIogGpoxowZlYUr9KFDf5jiil52HcfKo3379pVvAQSFA+1NpNeZb6yo3+OyYUkgKDTMmcOy3NYWHKbaZrIjbltiptBst60b+8h1aAFDBkiciaTvuecetTGC8aCsWMMYQxqyfL2r++QTOvl9thADhgLkG/o1t4afGBa+SqjNnYFjxiCx005Ifvcdy48BF14IcNfuhlrH2WKQFI3atStf3zjlJdvOxGx0KL/9mqb6RynGy1S73xhZPW58rZMvmTh/06dPrzRWuRnPbrRyAxYfmJkxgMYrwwQY+5xursOIl9Pjli7lWyY6XuJJGS5jX9N+ePdzH2ei43bNeJHJyb3N3L0ff/wxHnvsMZWuMfUaYd1PUW2Pky5wI5Pb34mo6IJsMsVVFwAJzJ6dwLhxSfzxB7BggWWoLlyYAF9YTpliJTxicU9uM/nrL9pa5dhyy2qYPdvK7skSAP/3fwmceWYS//sfkwNwc65l5LZubc23U7g2ZO15E1nMgE/ETHNEo5G7xU2lzTIFeml1mchUMD6WO93zDf5o62TB9oXGGD9WQHMcI7t0KRJPPYVab76JBAsXvPQSVvTujWSvXlbntWtRv3r1rLEtfAqit4NxN15iZBkHQ/7dxiDxYYPXTI2RzRSvw+90f4l/cxcjy3s1zBhZykBjlRkDGNv67bffbma0Mlk987TS07rttttWykQ+mHlAx1blY550jCzpBBkjyx8v8mgyRpbnm4iRJR0vMbJeZOLa0Pe2XaZHHnkEzZo1U6m09GZa8kL+df8w76c46wg9r5QnTjGy9rWRzxhZ9g8yRlbzYjJGtl69ep5iZKtXL8Zvv61E06blGDq0DLVrJ3HMMbXRt28RdtqpFDvtVIEmTSrQqVMSrVrVQcOGFdhll5UqvTxFfuwxS6bFi5cpG2HjQwgzw2yBrbZ6Gx06XI8VKw7FihVHo7S0JUpLt0O1alY6RCdwHVqgwcmikcXNNHxtT88sc6qaKGJgKrRAb5iiwZpuAxYHNNN5YUC7zrmz3v6A4OqJqaICic8/R7JhQyRbt8b6a65BjYEDkejeHQkXT9r2py7ecHp3v1uPLGXionXrkbVf01T/KHkUouKR1eMWlBeGBh+zB3z//ffKYOXf33//XfWhIcTKWLvvvrvagEV9wU1YJtdGUF4Y8pCpvLEpjyx1WWpWDT/eE6/3cSY6XjyybmVKN98MUzv55JPVA03jxo0rdaWJ9RGle9tUe1R0gWmZ4qoLsskUNV1QUZEE978zEyWzVvKYMCGBxo2TmDgxiR9+4MNPPXTunAhUpoqKBH76iXn6twomtIBVbejF1IUD7F5Yhh0wblbHzmqjJp8gv+m8xPQQcTHmy4i1Qy+u1LZMfRW4i/qOOyz/PUtgshRs48ZYd999qJlyo2elk6ZN8+OFRiZ5nPRPPc9Efy+8R73dDw2/85oKKplvvvkGo0ePxnfffYcxY8ZU7oZmxoC9995bpTCjAcuYVnt+1qDWRhDtfu4nJ+1U5unk9MO7ifvYTicsmfTBohVcM3w4ogeJRSmCWB9RurdNtedDFwTdHmddkKk937pg/Xqr6vxbbwEtWzJ+NaEq0e++O9Ctm1U0tGtXhmnS4AQ6dWKGoOzrxoRM3PrQrp3zuHfXhiw9m4yFZfyrjoVNrcjFcAMqHQbhp0syH2YeWJ3rlU/19g1f9NS6TZMVFPRTk31i0z4xlZcjyTKxS5cCZ5zBdx+q0laiU6dKGnyloM/NSCeHR9YrDX7m6wkvT432a5rq74b3uLT7oWEfN6905s2bh1GjRqmDxivDBdjOzVi9e/dWeTxptDJcgK+l0sGtTG7Xhul2zUM63jPRcXtNIlVOP7z7uY8z0QlLJr4upffm/PPPx0cffYQLLrhgMx1pan1E6d421R6WLsiHTHHUBblkCksXVFQkVWzqd98l8M03SXz/vfXqn/GsjRoxZWcCTZsmMXmyd50StEzGQgu4OYoGbK6NXDQcqYjsuRtNgoYolR1L0DJ0QJclJXQCfDt0iVJ6YadOnaoyFrgpWhBkaAGNfr37Wj+xpMbI1n3iCRQ/8gjK2rbFukGDUHbYYZvEtug8b6nxOjrOzU9cFePt0sUgMfYmXbyOjstJjdfR8XJOc3mKTPmVifzwXuHDKEMERo4cib8Ytb+hGtqee+6JfffdV23Iat26deWTdZRlqorzVJVkYuYb6m+tw6uCTFVxnkSmaMtUXFwDdevWwXvvrUOrVszrnsChh9ZTCYt69SrCbrutQ7dupejQoRzcnpNPmR566CE8/vjjm9DhdxzzyZMnOwotcG3I0tt61113OepLT2jQ6bfijKwxspzwt98Gvv0WePRR4MMPkdhhByQzvF7TT0xcKFx8doM4U3/TNPiZyoBKId3rg0x0uPDt1zTVP0oehah4ZPW4pYuL4+dJkyap2vRfffWVKgjB4hLsyyISNFz32msv9OnTRyWdD0Mmt2sjKC8MeeAPRjqY8sjyB9Mupx/e/dzHmeh4iZF1KtOwYcPUDyYdDPT6N2/e3KguyNQepXvbVLsJXRBFmeKqC7LJ5Pe+4ccFCxhPyhhW4JdfrAybfFlLz+uzzybRrVsSLVtyc1ddbL11IlCdYlK/6f1PgcXIBtG3kKEXRSWeekqVjMVeewFnnWW1HX641TcLDUJnDbDf6Nn6m6ahqytlo5+uPfWapvq74T0u7X5o6HHT4BMvjVZtvP7999/qKZ8bss455xwV49qrV69NHrTClsnt2giiXb9ZCmqeqMzTyemHdz/3cTo6QcjEuGrGUrOSHg1Zrj29WdS0LsjUHqV721S7W10QJd4ztcdZF2Rqz3XflJUl8PffVh5WHg0bJrDPPgDNg3/8w9qUdf75QJcuCXTpwtz4wOWXAzvtZNE65xwaigwhKK80YoPWKUHrNyOGLF8jOkW+88jGBkzC9swzVtK1zz8HmPnht9+YBT7fnAmqEKhMGBrATB00WnmwjC/DWhjTeuaZZ6pQAXpeM3kcBAIT0PkomROWa41p1wiXLwgFgliAy5p2GXOoclsP38jbDxqnrKTOPdt33gk8+GBNPPEE08tZhQOaNrWOgw+2+u63H0tPA0ceCRx1VL6lyz9cG7KCANCzJ5L9+gGME2HGga22shS6y1dG9sNJf9M07K+tnPRPd12T/d3wHpd2tzS4GZJx5Mwuwr+snsVXidwEedJJJynDlaEC+tWNfXyjIpPbtWG63X79oOYp9Tp+efdzH2eiY0omvrXZY4891GZB1lnX/YLUBZnao3Rvm2p30tfPvOZTJtO6gP9lWCxjSKtVS4L7U//8E2jfHsoTOnlyAmvXstAP1MH/863Bqady43sSHTsCW29tJf1ftQpYvTqB1auTG/4PjBrFbE7A8OEJPPlkEkxbT7p8wcXd/1tumQRVb/fuVrHNTp0svk47rQT/+EdNFdOqHZMbxwDYdHtPInRdkGuegtJvvmJk7733XvVDyB8/nVqLnh3+MDJ+4YorrnB8QcHmMbI7t22Lsg0lY7XrPVNBhGyB53TdM6Car+jo5fASpE06moYuiOA08JyePWt3ZIWrYHoGhpMOr0nZcwWeM3aGdHR/2SCwUSYartyYpUMFfvrpJ0V3xx13xD777KOMVhqvLVq0iIVM5IPzrec6H/NEGTQdE0np06090tG86FdqfmTiNfR9rAsipM5T7dp1UbNmMWbMWI5atZJMSY0pU4pQvXpdrF9fhCVLVqsf7zVrynHMMRWYO7cBVqyowM47r8RTT9XA8uXWq8/q1Wth3bpyrFtXihNPXI9mzZJ44olaGDy4Bh5/fB0+/7yaor1q1RpMmTIdXbrsij32WIMLL1yPyy6rjX/+cx0WL66Jf/2rFkpLy1FRwQ0onGtuzi3CsGHV8Mwz61CjRjmOOqoUZ59dB0VF1VBcXA1lZeuVTEVFCVx5ZQm23LI2XnutGJdeuhyPPloD48dbm2iLi6tveDVdioMOKsXxx5eq6912W0388ksFXnqpVKX84cYXHnXr1lKViE4/fSW++KI6ttuuQhk3r766BZLJUpSVWf15FBcX4eSTa2HRorWYNKkMvXuX49tvq2H1am64qYnS0nU035Wh1Lp1Bdq2rYHffquNHXdchb//LseiRRxDethqqv5r165EjRoVaNiQMYmsKF4XtWoVY+HC5SgqSqprcmz8rD2uYf6f64LtQd1PtWrVRnExE+3z94mVubg7ng8zdVFSUoylS/l7YxmT/NusWV20bl2E4cNXoWPHcixblsCECfxdqYt16yqwdCmLfVRDzZoJnHJKKb7/fkvUq1eG9u1X4YYbaoOslpZynmuodbt6dQUGD16LlSsT+N//auGZZ4oxYEAZPvusSBmvZJVJCGrVSuDyy9dj0KA1OOigenj//VX48svaeO65mqhevQw1a1aouNOaNZOq8tSNN9bA66+vRbt2ZWjatAIffliMhg1rokGD6kgmV6FOnQplFDdrVoEGDeqjWjV3BRFSC4n40XslJSWVukAXRPCiy9mu6ZAXt0UeMskUymavQYMGqR9HJjDnDmbmiqUBy5gmezUngXdDlj/U9omK6lN83NoLQSb+/+eff670ujKzAJUa02ExKwc3zzAlHjfPxEWmqtBuivb69QksX86E5tarRxYppEena9cEhg5NYuZMy/OjjzVrEujXL4m99+YeBWDoUKhXlA8/bHmW+BvIg795Bx3Ecq8JnHpqUlWwppeIWf1q1eIPNqtqJTf8H3jhBe47TWDx4iSOPZbODYsOn7+Liy1vFv/PHJTbbgt8+ilDyxL44YckvvtuHlatWo6ddmqPyZMnoUuXndGkSRKdOwM//AB06GAZMn/+yYd4y+PE31L+rPA3r0+fBCZPpieMpStVxkH1HZOml5dvHDO+cmWfiRMZR5hU+2QZV2gbZZSVJbHDDlZuzNdeA447LoFp05LKa0aa/G3nUV6eQIMGSRV/OGwYwNuHcYc332xdk33txy23MJYxiREjgIsvBm66Cfj9d4sOr6lpn3MON0Hz1XACw4cn8eKLfI286XXZn/wxtye3SJxyCuc+ge7dN15341JJYN68JPcCq7F84AG+4Etg6tSkMnY5fvrvgw8mcOCBSRVHydhKpiF/+mmLBkDPrEWR64JrZsCABK6/PqnWCtcS58Q6rMT5pMtX5Q8/bHkjr78+gZYtk+rVOWXRPFarlsDbbyfRti3ADJjDhwP//Cfw7rsb140+GI147bUJnHVWUvHHJCn33UfDLIHiYqsv12TjxpwLejstrybTwT/1lLVW+T0NU65fVlOlt5O88t7hOC5bZvHOfhv8R7HWEYkItZukrQtPGDFkU7MU0OhiPtZ77rlHGbO0nN3UxBWkN2SzbaZxCk4ln4T4pK6fesKk4fVct+eZkDPO4IMljVbWm+fBDVp80ubGLBquPDp37rxZ8YE4jlsUeDbBw4oV1o8yf0ztB394+ULr4YeTaNNmDXr1qoNevRKqPw8aZtThVA80DmfNAr7+2jqHGQUZXk/Pj/1gRer27ZP45JM1OOSQOsrjR1r8UdcHr+tEFD+y6zctzIQxa9YsVZ3L9PWisD7yCf3rrQ1/ehf5QKONXbbrv/SE8vtFi/g6PIk5c1aiqGjzcePaaNgQWLjQWnc8n+doT7A+eBpffWvHLGnT6GU7122G2ic+ZM3/XIfBg+lrJA3RC9s2cGPI5oyRZelZO0iYKbVoyNLjU4jKI8ow8VDhh4bXc92eV0gPT5SVleiYHJ4HQwfYRmP1tNNOw8EHH6w2aNGYdUIrbogCz3YeaCTQQ8of+3HjrJQ38+dvehx6qOV14utoGp7ffQfcequVeNx+aEc593fyFSYrX3K/Jw2I+vUt48EO9qehSjAuLxNofPTqVar+zxg+HiZkdwMWx2DGixNPPNHV74ToAufQw6rDHIgMdUgqwT3EXB/16rFKU+YHGvuaadYsM70N9QEqjeAgEYW5DoMH09eoMEQvH7aBE1R3UgCBRiuNV3uVriuvvBJDhgxxFZArEAicYdGiRfjkk0+Ux5V/Fy5cqJ5K6W1lsvhDDjlks3ABgT/wdSg9SjQE+Pp6+nTr1fScOcCMGXWVV+qhh6xSjhdcwPLcwDffAD/9ZL1O56t/lnbk/3W6Z75epneKO4uz7S7eeWcax1b844aorViCvwdMu8h9FQ888MAmsXICgUCQF0OWKbS4sStdFSx+x7hZgT+Y2onpZ3eiCRr6sx/eTfaPUuxQrnY+rXJjFtMRffDBB+qhkX2Zloib/g499FDl3bKX+XO7ZuzjFqUxML02MrUzppNlGRnXZx0JzJnDV6xQsY3Mfkfj9bbbEvjoIysej56onXdOYsst16F9+3oqBpCOb77m56Uuuii7TDRM7V9l4pFIldOPrH7u40x0svXl5pFXXnlFbU696aab0KxZs8pE+0HPt5/1EaX7wFR7nHVBLplM6QKv7fbrBzVPqdeJoi7Il0zGKnu9/fbbOO6449J+N2HCBPzwww+KCXpyGYawP9+ZCbLGyDJ8g7sPCY6dn6wFPLgBz0/WAk3DS9YCvt4mf26zFrA/z3eatYDX0P3jmrVg3bp16n758MMPlQE7e/ZsJQvvmSOPPFIZr7yGKZm4Dji35IPXiUvWAtLXc51unsjSggXc1V0Xkyevx4wZ5Zg7twh16iQxeHAF/vGPOjj44BJ06rQOF15YR+0ybtGiCG3acKfxWmy7bZnaIZ2pVCNl0GMQZNaC1F29frMW6Ps4U9YCJ/Ok1wzpUJ+nysTvKQv1/R133IFLL71U8e1VJtLid3q+c6098khZdf8olQmN4v1kz1rAeSXdILMWmJSJ/FL367nOxzyRV92f4xvEPJF3zaOprAXlG+5hv1kLNB0vWQsyyZSXErW8AJOnv/nmm5WZC9IZtFdffbXKcCAZDTJDshYUnkzz589XHtf3338fn376qVLArVu3xhFHHKGMV6bIohKKk0xBzxN/W7jJacYMy2Papk0C++1n7Z7nbnqGAXAHc+vW1q7pFi2sWFLGp3InO9PxVK++cVd2FGSKY3u6NsZrM9TlueeeqzIyxb09SryITCJTJLIWpKJ///6YNm1apbHKp/BM4FM6d1gLwstaQJq6xGPYNLye6/Y8E3KGBfL666+/Ko8rD5bkJJgQnoYrj06dOoUiR5THjQ/29IjSScCUUWeeaaUHYhJxxqbSMKWhyvRLRxxhVbT58UeohOT2zSZxHTfT1zBFLxOdZ599Vnld6IFln3TX8MJDVdYFUUIcxy0KPIsuWBGabWA0a0Eq+BqcG7+eeuoplZqLnln+fz/WTEtBurhaQbBw+VxinIbXc92eZ0LOoMDXU8znqo1XxpjzNQ+zC9AAOOyww7AtdwTlAfkcNxqpNFaHDLE2SDG8Xh98m0Xv6r77Wrv+CVbPYV7T2rWXY6utttzMo7qhqmmVGTfT1zBFz07nxRdfVAU1jjrqKPU6ksj2w+SFh6qkC6KMOI5bFHgWXRC9cfNcopa5ZOmdZQwnE67z/08//bSKB9FoywzIAkEBgG8emBpr2LBhKtMAnz6ZVUB7Xfmg5yQ9VtzBSCKGAPzxBzBpkvX3lluAMWOsBO///a/VzqHgLn7u7meGP6ad0rj6ausvswNQ96WEYQnygClTpqB9+/bKeGXs2tZ+8nkJBAJBPg1ZvgbXoNuXHllWFRo4cKD6TO/sOSxfInD1pGJ/WvEaZ2I/3NIxQUN/9sO7yf5BxwgxEJ2GK72uX3/9tYoHZ/z45ZdfrmJemXEg1VuVzxgk+7j5HQN6UJnwfPx4qxoTa4Szgs60aUDTpqyClMAOOyTRo4f16v+ww4DDD1eUcO217mQydX94bbdfP6h5Sr2OX9793Mf2dr3B48ILL1RZCU444YTNxsG0TEHogkztUYovNNUeti4IU6Y46oJcMsVFFyQjnLXAtSHLXdbpQgiYhosxs/TQMvif4Qdxib3JN2j8OM1awM1B6XYbMl6NPzj84WFMic5a4HaXtd61yO911gJeL92uUL5CT81aQPrcmZhuByX7ptsVStr6mpTdLlO6HZR2Htnfy+5dpzLpXaHMMMB2xoZzoxa9r6xYRC8rN2gxbyZDB7hxS+8KJZ/p5imTTG7myYtM2jBhH9KnTLnmiSwtWlQTU6fWxg8/rMevvybw229FmD27CLNmlWLBgppYt24N2rYtxXPPFaFlywpVG97a6bpRJrLLayaT7mWyz7WTeXK79jLdT/asBfo1elDzRDpcS1pOvzJxJ7ceN521wO3a+/PPP5X+Yaaa119/PW3WgiBkss+303nS/cO8n8JYe07myYtMWhewj84OEgeZ7HOdj3kir/rNWlDzRN55Xft940emkpKSynHTWQu8zJN9/L1kLcgk08MPP5wxa4FTeMpawBCCTF5XbgTr168ffvzxRxVPS2UoCC9rgX7CdkvHBI1scPK0Z3/wMdHfxFM5DYIRI0Zg6NChyvs6Z84cNGrUSIULHHPMMeohLjVFVr69Ldna9bil60+9MW9eAi1aJEG9Qg8r9yCyNnvnzgl06mR5Xjt3Bnbc0QoPCEMmt2sjiHZ+1nlRTciUrp0/AKkP//nQBb///rvS3yeddJJKCcecsJnWTBAyBaELMrVHyetoqt2ELoiqTHHUBblkirIucEvHpEyBZi2gkUrCNFK7deuGc889N20/vRlMshaEm7WAc0NaqQsmDBpez3V7ngk5s4FPq4xzfeedd5T3lfPDVHPHHnusMl5ZDlZ70OME+7gxJRVLq44fb5VZ5V9WoerTB/jwQ6tiFWNU8108LOi5jgoPpq/hhd7ixYvVWx2d35j5wsPWB1HTBVUVcRy3KPBcKLrANB0v5waatYAxsU43g/EQCOIAGqs0XOl5ZX5X/ph37twZF198sTJe08W7xmkDFlNYscb64ME1wWgV5mW9/HKgWzdgt92As8+2PK16P1rv3vnmWhAWtJeFGQm222475YltyacYgUAgiAGMupW+/PJLjB8/XmUryFT9SyCIkkec4QKM//vkk09UTE7v3r1x2223KeOVbx3iCBqtTFfL49tvLY8rc6++9BKw557l4MMtCwWkCXcXFCBOPfVUXHbZZeoQCASCuKHI7QkMF+Cmnh49euCZZ56pbD/ooINw4IEHYsyYMcqjxe/tm10EgiiAYQOvvfaaChPYZpttcNppp6nwF27WYjzg6NGjVdaBuBmxzMG6ahXfmAAdOrCQAAP4geuvt7yvL79s9evTpwyNGuWbW0G+wYe2W2+9VcWAc+0zTEwgEAjiCNcxsty1zfgpvZueuOeee5SBy/auXbtWvqq99tprN9uNJtg8RtZJDIhTpAbDh03D67luz3PTnzs3P/zwQ+V5ZcwrP/NBa8CAASrmO06vUblh9fvvgVGjrIMxrKwO+uCDwEknQRmp1aoxWD649RE2osBzGDyYvkY6emxjto2ddtpJOSJOPvnkzTYrmuTLy7lB6gJBvMctCjxXFV3gBWHqAjf2kevQAqbVuvPOOzdpo4HA3dvaiCUYpGtiA1MhgLv57M8TXncV8iAt7qh0uxvVBA37rlI3uxN1Wg99TRP9mabks88+w6uvvop3331XeWK5Pm+66SZlwDJNlp1vJ+Obj/Zly5IqFODLL4F//Qv45ZcEdt01qTZlMca1Vy8rRdall6buSN6ctn1uTey6NdVucm0E0a7HjW+i0sHPznE7uNHKLqcf3tPdx/zMrBt0MAwZMkRlnnFDx9WaKV0BlK5C+foVKKpWA4n6bYGVk1Wb6l9UA9iyI7B6FrB+cSWdii27oGLNfBStX4hEUXUgUQ2Jem2QrCgFShapz0hUB2o0QKKoGBXrV6AimUBRtepIVKsJFFVHAuRbETQ+T3Fod7r7PIq6IJtMcdUF2WTKly5IBkTHpEz2NGC54NqQTXdRxsXefffdm7U3btzYLfmChMk8ssxfyrxyfvLIahpe8siSB/LjNk8fPfi8JmV3ktOO3+n+WqalS5fim2++wVtvvaViX/l55513VvXgGfO6/fbbq/7Wrv3SSOaIXLw4gdGjec1aOO64IvTtW46XX16NbbZJ4IYbEthvP87rxjx9JLVihfM8spxbPmQ6zSNrQia/eS/nz59fOdf5yiPLdc20a0HmkeV6pRym8sjq+5h9mUKOxuvgwYPx/PPPKzk2m6eK9UiUrkC9hi1RVLYMq2d/DZQuw/pVC1DU4ghsyTzAv92FsjULVb+SVuegvFEfbPntAcpATZatwvqWZ2Ldjjeh/qjeKKpYA6AW1m+9P9Z1vAO1fxmM6isnolpRNZTXaoZVuzyLmlOeQfH8D5EoSqBaURHW9vkS6ya9gS3nPYdEslwZpYn9PsC6Rb+hxo8XAWxLlqGs+1Oo0bAD8PHuSFSUc5awdodrUbzzP1H8dgMgac3p+mYnY23nB7DlN/sjuWoyktXqoqxhb6zp+hxqTboNNdf8hmT1uiit1ghrO96F6guHo/qKH1GrXmOUJ2pjVaMjkShdgqI1M5CosQXqNWiC9dUaY+26DWsmkYhtHlmuD95LccojO2/evEpdkK88svw/bZsg88jy95C/p6byyK7aoAv85pHVdLzkkc0kU17yyDKE4K677qr8TOXI8rTjxo1TO7uz9RWkd53zB8zuvfbzxGRPceHVI+uVBj9TJi5Otx7Z1NQcTvvrBynGvfKgx4mpsrjzmq9NOzHxqctxDLN9zRrg668T+OyzJL74Apg509qINWAA0L8/ZaWH28w17XMbJS+MybURlBdGp4IJ0iOr0/Bl9Vjw/+VrkShbhWTpcqB6faD2dsDcD4H1S5EoW4lk7eZINj0c6368HYumjkbtGsBWTTtgWZsb0Gje48CcYYoGaSWO+BXJXwcDP/9LeTNR3AA4eAwSK/9E8vd71OeSZB3U6HwFimo2QnLW28obiuItgS07qWsnVv+lDERUrwtUqwMUVXMnkyFdkLV/BY3iMiRLV1tGbq1tgBV/ILF2HpL0HicrgBbHAn9/CSwcuWF8VwLdHgbmfwH8fi9QttJq3/NNYP0SYPhBlrzF9ZHo8RiSjXsDYwYCNRsCNRsDTQ5FYps+SM792Bqvmo2AWtsCxVtEwiMbRV2QTaa46oJsMvnSBQ7bkz5+093SMSkT+2+11VbBhBZwEwxjq5g/llkK6DHcbbfdNjNi2WfQoEFuyRck9KJIbcvUN1u7pmX/7JSOCRqZ5HHSP/W8bP1Z+53xrjReWXSDtd8ZMkDjtWfPnhnPdUI7jPb772eKOuD994FnnwX69k3gqacARufY31oVFbkbx1ztfuc1qHaTayOIdj/3k5N2KvNERYkyChNr5wJb7gyUrQb+uB+JtXOANXOADlcA2+wDDGtrGUPF9YH2FwA7XAjMGqK8kjRsE9XrY9pff2HrWq2wuu4alNdtiK1b7m69Iat5MtD8KKBabesgPx0uBzpepbyLlajTFIlt91V8rVu+HDW32FJ9n2ifJqVi/e2RTlIlU5q5Mz3fjvpX408dww825Jcjtuygjk16NulrHaSh25odbh2atv7PgLXWAwENXhqz3Dvd5lSgZLEVBgHLs5WY+gywboEVQtHkEGC3+4AvDkCCxnCt7YCtewOdbgBo8JYuR4LGbu2mwBY7OB4zJ21x0QWZ2uOsCzK1Z9QFHu6bdDD5m+6UTtAyGTFkabhyc1fDhg2VxczYWHtuWVb9YmwiX+8y3OCKK65wewmBD7iZ/CBoeD3XyXlz585VhitrvvMNAF/ZMM3bf/7zH+y///6RLFLAh83Jk4FPPwU+/xxo2NDanMUHer4JOvFE64jT+ggbUeDZCA/0+iWKgJVTgPlfWn959HgMWPQdtvzmNGVAok4zYLf7LWOmXjtgm72BOs2B+jsANbYE+i3bnHbP5zb5wbjtrLOUs6H3oadtynv9NNk46IkNSHYv57o9J2/rg3NJLzQPjeZHb95vr7c2b9vzNWDd39bBuF9i9Qxg4WirjUbx/p8AE64G6AWv29I6dn8KWDUNWDXd+lynBVBcL7b3VRx5DoMH09dIGKKXD9sgkNACQbSzFlQ1cGxY6/1///ufegPAeJ3DDz8cp5xyCg477DAV2xNF/Por8PDDwCefMI6J6emAAw4A9t3XMmYFVRQ0VtfMsjyo9LTNHAJMf8na6ERD5bgFwIIRwJz3gHrbK28mtuu7wTua2NQr6hKMi+UD3QcffCD6pKqgfD1AD/2amdaaan0SMOcDYPLjVhs3yx06AVg9Hfj9PqBeG+vhp1V/K7whWQ7YvdACQRW0j3wbstOnT99kB7gdOgRBEF6JWgZI2zeMhEnD67mp5zE4/aOPPlLG63vvvacC3Pfdd19lvB5//PGVAf5+5DQJxqT//beVCuuUU4Abb7SqaY0caRmwUUlJa2J9FCLPaXngq+IVk4CVfwLlJcAOFwDjrwCmPAHU3AZo0AnYZxiwZBywdp5ltNLI4M56p9dwiEWLFqm9CgMHDsTMmTNVOjlT4xa2PnB7ThTWRySwbhGwZOwGb+00K+Rk/VLgi32BGlsB9doCXW4FGvYAZr+LZL22KKvTFtVrN4rNuEVhrsPgwfQ1kjHVBYGWqE3F1VdfrdJvpeKvv/5SO2TFkA0X3I3o1yj2Q8Prudzl+PPPP6uwAYaqcAPcLrvsoqpsnXjiiWhOK9F2U5iQ0w9mzwY++sgKGRg+HDjsMODFF4FbbwVa8G1fMbDzzogc8j1useGZntWyNdZr25lvoHz+OFRfPwuovS3Q7UFg/OXAmtlWHONWG9IO7nwtsMvtmxqrDbsFJqfO6MANEdy1zPvCnhPZ1LiFrQ/cnhPHNW0ctRoDTQ/dvL3fSitUYeVUK6ykbBUw9331udqKP4Aut1gx1uP/CWyxI7BFB6BBZ2tjWgQRhbkOgwfT11gdU13gFL4NWRod3NS13377beKJPe+882LzpCfIH3777TdV453eV1bWYsYBrid6X5k6Kyqgh5XvLiZOBM46C1i4EDjkEOCEEwBmDtGZ5tq2zTenAsfgbvVVf1keLMYaNuoOjDxG7WZXr2zbnQN0f1iFBSSLalq72vkjT/R+aXN6If/4cy8Cw224Z+GSSy4J9dqCmIC/wbWbWIdG75eVMlvBN4Fb1AOYo7d+e2DZr8DMtyyDmBsLRxxlbUZjvt+t+1j3h0AQQfg2ZGmwTp06VXkF6AY+4YQTlAHLzTjTpk0zw6WgSoFeJBYqeOmll1TqLK6do48+WuXN3XPPPVU6mChg6VIa2sCee1pVsy68EOjSxdqsxewCEWFTkA4VZVZsYY2G1s7yqc9uiCmcCbQ7F9juAODdNhtjClufzIzOwE5XbNhw1QIoKrZo7Xw9SpYvRy16EyLwcM68i9zoeOaZZ+LQQ9N44QQCJ+BaVkUnEpZX1g4+tXe8BljxO7D8N2DJD5Yh+8X+KmOGegux7X7WRkSBIO6G7BNPPFGZrYCeNIYT6EwFcatXXxVgwgj0QyPTuUzMzApbNF4/+eQT1e+II47ADTfcoH6MGQfLH2enXvwgjF3qbhquTIn1wQesogUce6xlyP7vf1a4ABHnsvRReUgwwjM3wqyaanlQGa/a7mzr7zenWOmP6IXq+YL1yrR8DbDVbkDzY4AGu1ghAydYFaU2wTZ93PFgELmu8fLLL+OQQw5RzoLUfIxe6Jniy/S5bs+J45qOArKOG9cWNyvysKPXi8CSCcDS8cDSnyxDdvghVigOjVumh2t2WH54DglR0AX5olcUsi7IS9YCGrMHHnhg5eYvlkJMLWfLHei85JIlS5Shy122hQr7Zi97MHPUktJ7bWcC65EjRyrjlenYGAfbq1cvnHbaaaqIBlO45Vum0lLrmD+fWQUSKC5OqnjXI46AKgNbo0Z+5sOPTHFrd9SXu/7nfwWsnITEiklI9v4fMOd94Le7NsT27YjEDhchyQIBDBlgLk6mR4qyTA7bWc1ou+22w3PPPaf0JfVr3GWKSnuUeImtTMybu/RHJJZOQJLlg3e6FBjdDyhfBzTuicS2+1nFIuIkUwjtUeIlijK52ezl2pBlmU9uxEkHXfmhbdu26v/c8MXSjnYwnuvKK69U/2ffN954YxPjV4O7cLt165YxI0JVMmRp0DstUcsScelKy5EWx5opeJiiSpeo5Ri7LVGraegStZnKuTK7QGqJWpbGY6gJf3S5CZBxr23atMHpp5+uMg604E6oDdAy0cAlHV6TsttlSlcuj4Y/Pbi6v9sSgInElhg5sgx9+qzCjTfWwi67lOO448qxcOEWaN26JG0JQF3qL7UE4Jo1a9KWNcw2T+lkcjtP6cpP5pon0mcf8kL6kZNpxXJlfFZb8i1qLPgYNdf8juTy37Gw13eos/QLFC8ajmT9HVFrm11Q0qA31q5PhjJPlEHTMVEmNN08kY7mRXtaKRNLSjLchnpSb5RwIhOvoe9j9vU6T3rNsB9/VEzIlG2eSIOHvrdzzRN5JB3dP8z7Kc46Qs8rr8d20zIl1i9GtaVjUWvVBFQvAla0ux41frsBifK1KGuwO2o02xfFDdq7kom/b3SA6bnOxzyRV00nUzlXv/NE3jUv+r7xI1NJSUnlPaxL1HpZe2zXdLyUqM0k00MPPZSxRO3kyZODMWQPOuggFT6QqUSbBo1dlqelArbjiy++wFNPPYXrrrtO7UonJkyYoPqnemerevquqlSilgv0nXfewZNPPonhw4er81lpi97X3r17Zy2DGFRZSnsbswy89x4wbBjw/fcJ7LtvEq+9ZoUL6De0UXqydSKT2/ZIlaVkWEC1GsqrmpjzHpJLf7R2Vx813cqxunIyEg27omLLzli+rjh2ZSndXjO1hCOLf1x66aVKf7K/lj0MXZCJjts1kypTXkvUilcs/7pg6URV/pfFP1CxHom93kRy0n+sinTbHgBssZNSxplkkhK14dsFbunEpkQt02115U6XHKAXjnkNU9G3b190795deRppwDLeiwYPvbiphitfPRcC9KJIbcvUN1u7pmX/7JSOFxp8YmJIyQsvvICFCxdijz32wPPPP69CB/hE6JT31Gv67c97Y9061Yq99gJmzbLCBbhha+hQPoFGp/Si23Y/NLyuDSPtTOQ++x0kWL2Ir/0P/9Uq49lodyS2P8/Kvco4qhYbqyMlaMSVLHe1NoJo93M/OWnXxirfYEyaNElVTLzxxhvT6oYwdEE6Ol5lcqvfTOuCXNeN0r1tqj2SuqDhLtax4z82tjMsaPa7wOTHgFpNgAOGA4vHIsH4dlazMzzXJtpNjpnJ+yZMXZCNTtAyGTFkaYj67UurnJ4GZjVgGVt6YnVIAlPK8C+9lAxjEEQPzFk5dOhQ5Vn/6quv1FMTQwf4EMJ8r042ogQBGq/jxgG77gq88AKLdQC33cbYbaBzZ4Y+hM5S4WLZz8DfX1olN1lTvu8XVoL2RrsDO11meV8ylfQsQPCVHV/38ZUcQ3MYbtWFKTIEgqqMJgdZB1G64XX07GHAtGet7AgtBwBd/g1UlFfGvAsExrMWMOaVHlZ7HlmnoMHKrAc8GE/LVEyMgaExS++slFl0Dx1rGwSN33//XXlf//vf/6p52nvvvdVOasa+MvaGT132WCUT18zVnyHYX3/NTYTWsdVWVgjBmWduNFxp2ArMrY/NwE0dC78BFo4CSpYA3R+y6sPz/yyV2XhPq1+bU6PDc8R4uO+++9QbjIsvvthY/mRTPPuh4+Vcr7pAgHiPW3F96+8ut1qFGpj6i/mciZFHAesWok7jvkDb/kDD3G+F4zxupq9RPaa6ILSsBQwPoOeVHlV6EfhKjMeuPqwHGrX06FXljV5uawnnC4xL+vDDD/Hggw+q+ObGjRvjjDPOUN7XnXba4FXLE5jl7eWXGcYCHHeclSpLnPghgKl26HFl6qsWxwFfnwKsnWOl3uGxXeFmInELZnbp16+f2i/AzZICgSBDXujFY4C5H1rVyVhdb8ozQN1Wls5hvL2gYO0jI+m3GCLAMAAGQNM7y7/cFFaVN2qZTr9lonQbp5Kv/ekd9fpqX9Pgq07Gvf7nP//BlClTsPvuuytvEWOaubvR5PWdnMfNpNxM+fvvwODBSTz//DqMGFELnTolYKteKwhqfTClFV/1/XoHMOlhoGZjoPmxlveEKiSgUBITazpqPJDesGHDcOSRR+Knn37CDjvsoLyxpq9hgp4fOl7OdXtOFNZHHBHHcduM58lPADNesx6qd/gH0OVmy+BlkYeweIjBNZIx1QVu0m8ZmXHmhuVudZ0qKpOXlTGV3ABW1T2t+QRTbXCx+Hko4WtOloxlmAANV+aB7dmzZ6DXT3cejdfPPmMZZKtIAeNeDzqIlY2s/gcfzJvC9aUKGq7mZ8FoYNYQ4O9PgaIawKETgKZHAG3OtKpfaQQ8CX7XdJR4YJoc0mFseZ8+fSo3zlLRm5bTFD0/dLyc6/acKKyPOCKO47YJz+0HWce6RcC6eVbbR12B2k2t2HsedZoFy0NAKFhdkGRGmzVAGVOMbahA5AC+o6cZy8oMBdmMWIJ9WM6WKbl4jiA64I8oU2Ydc8wxaN++PV577TVccMEFmD59uvq/UyPWFEaNAs46C2jaFHjgAaB3b8sTy6wDLFCw9dahslM4WDkFYDqcMRuyjbBiFncO934VOGS81bZVl02NWIFjMKvHXnvtpe63Bx54AI0aNco3SwJB/FGrMdCgs/X/g8dYWRGWTgCmPme1MV5//ab5TgXhI7F2piqcoTDhKuCb04CvjgAm3my1fXkw8EYdYGhz4LszXNH27ZGlJ3bs2LGO+tKY5cYuxlree++9laVsBfkBXf2vvPKKSkg8ceJEtcmE88PSsU2aNAn1lRNTZd16K3DvvVa2AdrOgwcD22wTGguFB27SomJp3NNSLDPfAJoeBjQ70noy3l5Cg0zg559/xvvvv6/iYUePHh2JMpsCQZVE9dpAsyOsgygvAWa+CXx/LrBtX6DDFUDjPfLNZeEgmQSmPg389TLqr/gT2O1+a6PeljsDDbsBNbYC6m54Q7/XEKBabaBow16BlGILgRqyTL3kFtwcxrjQL7/8sqBL1AYBVvTIBZa8ZCUNHosXL8bhhx+O+++/v3Iu7JVDgri+zjZAz+srr1ibtS65pIbKLsD2004zdx1BmnGb+xEw+XErNRbTYe37IdD5ZmDXwYGHCXhBFObaCw8sasBqNi1btlRFQYhsr9ZMy2mKnh86Xs51e04U1kccEcdxc81ztZrAnq9aqb1mvwMky6w0gD9eC7Q+Gdi6j+u0XmGMW6x1Qdlaq6hN2Uqg3TnAmrnAztdh3Ra9UbvuhswUbdN4XIvreebL92avHj16OPbIpuKaa65RoQaFirCzFjC9Gb2vr776qlqQLIH7j3/8Q4UThJnnlcYrC75xk9ZJJwH9+wNNmoTCQmFmGOBuX+ZmnP8FcAAr63wLrF8GbNd3Y8obgTGwlCa9rgzL4f9PPdVb2jGBQBAAuHF16vPA9JeBkoVA71eAxr0i+RAfO/x4DTDlaaBxb4BFbpofGYp95Psdlx87mBW9GGJQ6DCQOKKSDj1Adnr8IWXxgn322QfdunXDiBEjcOedd6oKQg8//PBmRmw6Gn6uT0yaRC8v8NZbVn5XhgaOGAF89x09scB227m7ph8eCwJla6wqWhyf3+8Dxl4IVKuFZI8nsGZdGZJMV9PimFgYsVGYa7c8ML78008/xUknneTYiDUtpyl6QegDk+dEYX3EEXEcN2M8M/vKTpcAh4wF9vsE2LID8PfnwKe9gWkvbthoFDAPVUUXrJoGjLsMGH6o9bn5McCRfwL7vreJEetVFziFb0OWOWO9bt7ijl0WPyh06DrG+kjX5rSdOwN1TWTmfqWhetxxxymDltXUWFL28ssvV086uWh44WX9+vXq78yZSbzzjtX24INJTJnCXK9J/PxzEtddl0TbtpvSsV/TyXWd9PczjlFtz9p34TdIjjgKyaHNkPzzEST5Cq3D5Uge8gOSnW5EssEuKNkwP7GRycPaCKKdPGTrz3zLN910kwqZGjx4sEo/6PaaqXL65d3PfZyOjhcaXmQKQhd4XXtxbHfa18+85ksm47qg/g5IFjdQb6eSnW5CcvZQJN9theSauUiWl3nSBSbmKeq6IMn2yU8i+WlvJGs0QLL7Y1Zb455I1mhoTKbQYmTpVe3fv7/kjPUB5t7VVS90LWJOIl/962wQzN1au3ZtlRKrrMy6wQi28TuWtqSx+uuvv6rUWTwY63rssceqB43ddttN9ecrT23o2kHDlj/GdOezVKbuy3Zez16xi+109dNotcfTrllTDe+8UwNvvpnEb78xZGA99t13He67r4bKk7lmzVp1joZdJn1Nym6XiTxp1K1bF8XFxZvwyP7169dXPGWSiXQ02N+NTJyXevXqqY1xWoERDM2gTOybSaZM85RJJvuN60SmolV/ovjv91C84GNU32cIyhK1sH7bfijd+VGgen0UlRRhi5pFWF9SovgkfY4bZSL9KMqUbp7sc52PeaIMvLeIVJl4TVa8a9GihapUyO/1azA3a49jw3aOjd5k6UcmXfKWYF+v86TXDMGcjmHIZJ9vJ/Nk72967cVdR2SSSc+r1vFxkck+18bnqcHeWN+lJxIlC5BcXwd1J92C4sUjUNL8TKzb+jAVb0tetXxBzRN5p76x3zd+1l5JSUnluDFG3+s8sX31srmo9eftqLHzJShqcghWNDgEyeItAarH5cs9ycQ3w9yvYwe/s/OXC0YKItD7QGPsjjvuCDXGNu7QMSBLly7dpCCCNmRTkamdbXyVyeIFH330kdqAx1RnF110EZoyh5UDOrpNG7nkx25UZ6PB+/Oii4APPwT22acUZ51VHYcckkCxLQ1cNjpc+PZrmurvdhwj355MIrHsRyTrtbfiXcf/E2hxrHqdk2jcG8k0mxbsNOxzqx9o8i6T4bURRLseNxpydrCdCvvkk09W4TtU+F6vSejCKPZsIV5593If56Ljds14kSkoXZCpPTL3tsF2J32jqguyyRS6LqgoQ2Lex0hOeRJYPQM49Ccky9dh+aqSzXSBV5liowvK1qq3fPjtXiRaHgd0/jcStbcJVCb2py0TWkEEpmxiZa927drhnHPOcXWuATs69tCLK7UtU1+7IczqW48++ij+/PNPVeaS/z/99NPVk1Sua2Zq09U37MoiHYYNS+DvvwE6jQ88EPjPf5KoVasctWoVZ6Wfrj31mqb6OxnHyLez3vgfDwCz31ae1gR34TJFFhN+2+VPS2FT2vbKKlGS1eTaCKJdZxvQ7Uypdeutt6pwnQ8++MD3NakH08nph3en97FTOmHJFJQuyNQepfvAVLuTvlHVBZnaQ9cF1YpVnGeCsZ6lq+gmBEadiC3WrwJ2vAgJ6l+dKsqHTJHWBRVlwJp5SDBN1srJKNnnC9RsvHNWOkHLFJhHlmDVrkGDBqkwA/7fyS58bvRi+VMawoUIr1kL+CrzkUcewYsvvqheHRx//PEq+8Cee+7pavLdYt48K+PASy9ZG7eYiYNvIaRQm2GUrwfmD7cM1zZnAHVaADNesUrCbrFDvrkraDDzx4IFC9RbKKaxa9bMfOUggUAQUVSUWqml6J2s2wbo+ayVj7tavCqk5QTNQhaSmHg90ORQoNsDCBuhZi3Q0FW76KFgrBjLnJKRTBgyZIjaPc8UXIUOJ88SjC9599131ea6jh074q233sJll12GGTNm4PXXX1flLgnGwvh5NtGxU5oGw6CGDrWqanXpArVpi+Es7doBLVtuasSmnuv1mqb7xwJM3M1ch6umA+80BX4fDGzZCai/PVC3BdDxat9GbBzHLQo889qzZs1SYQT6VRhfx5o0Yk3LaYqeHzpezhVdEA7iOG5R4DmZqI5VWx2E5P5fAD0es4zY99pbBReW/lR1dMH4y4A/7gN6PFlpxOZDFziFkdACjauuukrFjtAzy//zoOHFjUaMhSWmTZumMhV8/vnnandva3HnZQWfRujhZsgAjVaWi3355ZdxwgknqODsVLgJkM4E0li40KqqdeKJNI4BRozQC5urVLLX67s9z4SckXi6n/uxVXlm3ofAbg8ArU8FDv8dqBVMHd44jlsUeObbo/3220+VcY6LnKbo+aHj5dyC1AV5QBzHLQo8V/LAYgvEYROtcrgjjwV2uQ1odSKQLAeKiv1fwxDKnNCbPwL49Q6g90sqBlalKUt5wxu2LsiLIas9szReBw4cqErRaqM11cpmaVu9I1+wOebMmaOKFzDsgrsOTzzxRBU+wE11QYEOdB4lJQlVqOD774HXXrNCCASGwCf4eZ8A3OnJ0rBTn7HiXVm6jzXDiYCMWIF7nHHGGbjuuutw8803Z9zgIRAIChiMH+1wObDjpVblsEXfA1/3B9qfD7T7v+jr89UzgO/OAdbOsQzYmo1dVzvLN4wbsgRDC2jA0ptIg/WHH35QO9D4Q0DPLL2JbViXVLAZmD6L3h+mz2L6jAsvvBAXX3wxmgRU+orPFSwV++yzwPvvA7fcApxySlIVK+DDmBixBitsfXsmMPcDoFEPYMfLrLiqfd7NN2eCFDB1FcN1TjnlFFx99dVqE6tOXyMQCARpoTZ+VQO27gXs96kVR/tBB+CQCZaxW6122s1hecPisZYBwIIQbc+yPMlR4i/fhqwGA3UZXiDIDnqpR40ahbvvvhvvv/++ir1j/DA91m5L1zIfnBMsWgQ89xzw9NOcJ+Dss4EHH2SuSMbF1vZcrc/p9f2e5/U6oaFkiWW0zhoK1GkGdP8P0GoA0O1BoGbDvLEV+XHLM8+M62fGDz5QciMl49F5f4bBg+lrmKLnh46Xc6ucLogo4jhuUeDZEQ80Dns8Cux6l1VB8c/HrNf2rU8B2p4BbNnR/zVcoLad3tIfgYn/ApZNtH6XincH2pzino4fHgzDWNYCgfddeQwXoNd65513VoY/wwiY0DgIjB4N7Lgj8McfwKuvMhQE2HXXQC5VeGCarDWzgK17A5/tBdRuZuV5bXqoFW8kiDT++usvnHXWWRg+fHig2T8EAkEBYuVU4K8XgbkfAQd9DayYBNTabmNIWRjOlZoNrTeDjXtZXthq0X3lmpesBQLv4KYt5qJkbkrmgPVqxPKZJLWqCLF4sRXrSowZw/hbYK+9gMce29yIzUTDz/VNn+eHx0Dw56PAx92BT3pYNbuJA0YCfV6zvLARMWIjN24R4fnrr7/GPffco8KdGBKVasSGwYPpa5iiF7Y+iL0uiAniOG5R4NkXD/XbAV3+DRwyxtoIxjd27+9obRKb876Za6RixZ9ITnoYpV8cocrJoqIc6PUC0H6gayM2H7og76EFZJpeRmYpYHzs4sWL0ahRIxU/y1hZ/pWMBRZoxNore/nBxrKawDffsFgF8NFHwLHHAiecAPzzn85p+Ll+0Of54dF3rOviH6wcr2vmAr1fBGptA3R7CGjUc2OMUUQ9enkbtwjyPHnyZFXicccdd1RlHgl7da4weAjyGqboha0PYqMLYo44jlsUeDbGA1Mq7nCRFX7G35RmRwC/3wdUrEe1oqZAk25Ag07O6ZWttjaaLRxthQ/s9Raw6Bvl+V3f5FhUb+8/BjYftkHohiyNV27u4sHXdEy7pTd1NWzYUJViHTNmjDJsaeDys85wsP/++5tkpWBBA/aRR5gVwipEMmiQ9dmQnVyYYHUTvgZqsDPw3dnAsp+A5scBHTfEf7fsl28OBS43c9GAHT16NBo3bowjjzxS/RUIBIJQUb0u0ObUjZ/rtQWWjEPxkmHAilHAHs8AYwYCy36xcoozt3jHK4G184HyNcCS8cCyn4EuNwO/3wssGAlsvSfQfpBlDLQ9UxXVKWUd+ep1UFVR3ZQBe+655yrjlUYpX885zUrAFF0sosDzmFf2uOOOM8FSwWHuXL4itUrFcs1yE1fPnpF1DMYDC78Bpj1nVXJp0MXaibr7E1WvikuB4dRTT8Wll16q4mEFAoEgMuCeiubHYM3y5Rvf0u5ypyoPi5VTgNJlVtu0Z4EpzwANuwJb97HeFHb+FwoVvjd7sUIXDVFW6OratasvZp5++mmVc5Z/3e7Wjwr69eun+HeSc1IHM9ND7SW0oLycYQlWha3ttgNefz2JCy4oU94mr5tVuByYuNgLDa/nuj3PD49ZUbbGCsT/+zOrasvMt4CSRUqxoE5TxB2BjVsMeOZrLeaDvemmmxQ9N/oljHEzfQ1T9MLWB5HRBVUccRy3KPAsuqAsNF3ATV60owLf7MUNEmSKORf9GrEE002xihV37k+fPh1xA8vG8nALt4ti6VLgvvuA9u1ZcQhYs8YyZC+5JKFi/PwsVp7rlYbXc92e54fHzaCf4/76H/BOC2DaC0CjPazKLK36AztcUCWMWOPjFhOeqUC5iZIlZRnqRLh9SA5j3ExfwxS9sPVBXnVBASGO4xYFnkUXFIeqC5zCsyE7YcIEFd9qOhSAnklWs2J4Qpyg4369wKlTfOpUYOBAy4D9/Xdg6FBg5Eigd++NdPj04sfJ7oeG13PdnmdCTqyeCfx8q1Une9V0YLv9gSP/BPZ9D2h3lq/yglGFkXELGX7XIzeZ3nDDDcoj279/f1VkJEwe8nUNU/TC1gd50QUFiDiOWxR4Fl2wPFRdELghSw+sCS9sJjgtX0vjsVu3bhg/fnzWfvT0suAA/7Jaj71srgmQLsvzBoEPPwTmzwcWLmTVNGDSJOCZZ4Bddtm8r4nF79cQDuM8T9dh6ABLxM7/Cvhkd2D9UmCvt4F6rYHaTYCajVDVEacfLj88c1PpmWeeqTZxvfvuu8ojGzYP+b6GyR/CMM8NRRcIYjluUeBZdEH0xi3Qyl5BgpvDlixZotJ45TJi2ZfGrr3KGGNZCXqV/YJGsQk6dqxeDXzyCUCHN6MsmKmMm7d4CFyAN8+ib4FpzwOzhwK9XwG23Q84ZlaV9LoWOmjArl69GnvttRfuuuuufLMjEAgEgoCR94IIb7/9tqfzmOLrzTffxLXXXpu1H41cGpqp3lJmSNDGrF/QK6zj7/yCxQooEpM+DBnCVEHABRcAHbNXtBOkYu3fVsjAugXADxcBW+4MHP4b0OQgy4AVI7ZKgan81q1bp0IIysvL1aaCJk2a5JstgUAgEASMvHtkuVEsyJRbNHjTeUvpyU31pnKjFvnJBRrB+nxTIQU//gjcfz/w8cfAmWcC48YBLVq4p1O/fn3fvPih4fVct+el7U/vK6ulTH3WSgrd9V6rrvWh2T32hQQT6yOKPDMbweGHH45DDjkkbzxE7Rqm6IWtD4zoAkGVHLco8Cy6IHrjVj0Km6SCBA3VE1jSKg1ojHJTmTZk2S9T30ze3u7du/vmkbvzHnoI6NYNePRRTrh3OowF9Ju1wCsNr+e6PW+z/qxhvXo6sO3+wKwhQIvjgd7/q9IJoPO1PqLGM0s633rrrXj44YcDkyuMcTN9DVP0wtYHvnWBoMqOWxR4Fl1QFKouCM2Q5at9r8YoY1xNb7pK99q/Xbt2ab9jtbFc8bW5+KchrL24ehzuvPNOdU2nntqKiiSefx6+oXcGMvODn3RFXml4PdfteZX9V3yBxJ+PACv/BDpcAWzXF+hpYCCrKEysjyjwzMpc7733nnqTc/nll6N58+aByhPGuJm+hil6YesDz7ogRms6CojjuEWBZ9EFy0PVBaEZsoxNozHqpABA2N5Yp8aoV9CTaw9boNHMUAMa927GIyZ6JP/gwl46Hpg/AmhyNlCyGNjpn0DTQ4GivL9cEISANWvWqHyEI0eOxGGHHYZd0qXuEAgEAkHBwPevP+NFabxdeeWVns438Wreq6FMY9Nr7tdUcAx07lumDhswYIDjMAU+qdjBJxY+jdSsWVMdqe2p0O32w0l/0zT0Z6f90103Y//Z7wITb0IiuR7JNmchWVGBZLtzK58C+G8mmdzyEuV2PzTs4xwnmfTx559/4oILLlBlrR944IFKmYLm0b42g5qn1Ov45d3PfZyJTlgy5dQFPvu74SWO7XHWBblk8jPX+dIFuWSKoy4wKRM36ZaUlGzWnmoXBWrI0lX8ww8/eD5fb5rKF7x4ktOBYQReN321atUqbTvz3bL0L0GDtnbt2iq1EEu9abCN361cuVLt1l61apVqr1evnvJcsQyuffEw4JqxKqmLhPPIHd/sr2mwH9t5PV5Xg+2sjrR+/XqsXbu2sr1atWrqLxelfWHWqFFDJaJnX56jYZdJX5MLWMlUXA1rpw5B9dmvY03nh1GtfAvU7vYoqm/bByuWL8eqlSuVEcv+uWTi2GiwvxuZuPudY5l6szmRKdM8kSeNunXreponLzKRPseZMpF+XGT66quv1APnOeecg9dee019H+Y8UQbeW0RQ80Q6bCcd/erNj0wMv9D3FPt6nSe9ZrSuDEMmuy5wMk/2/mHeT3HWEXpetY6Pi0z2uc7HPJFX/f+g5om8U9/Y7xs/MpWUlFSOW61atTzPk338yYspmW677Tbcfvvt8INEMpP57AI0trzmbGSZW6/eXO113WqrrTBu3LjNUmDp75i5IJ2ReeCBB6rQAp6bD3BxcOJnzpy5SdlMrx5ZQj9hO+1vkkY2OHnaq4ydmT8ciW9PRbL+TkDbM4GW/YBqtTL3F2+LK4+C/hGIukyzZs1SD0f8bs6cOdh9993zxiM/U/EGOU/8AbCvab+8+7mP09EJSyYn97af/m7lj1t7nHVBLpn8zHW+dEEumeKoC0zKlM0j27JlS/U3V1lxI4GFfhKP+zFinXpbs4UYcMNXvsFJokHrBKkLIdONbl9sTun4pcHzuFgz7U7MRAfrlwHTXwWmv4hE13uArXYBDvwaiXpt0vdPUcK5ZHLCe5zavdJI/SGIqkx6DQ0bNkxt5DryyCPRrFmznHMdVLset9QfUTcy5WpPd9/54d2vLkhHJwyZ3NzbXvqLjoi+LsjW7neu86ULMrXHVRckstBxKxO9xDzSnRObgghBg15a1ltPB76upFc237DfHPYnHy/t+rWGWzomaPDgKwVH/cvLkCxdDaycAry3PUrnfYVk51uRbNwbyRoNgXptstKx8+hEpqrS7peG/bVXVGU65ZRT8P333+Oiiy7CUUcd5Wiug24nD0HPU6qcfnn3cx+noxOWTG7n28/6iNJ9YKo9zrogl0xx1AVe5inquiAZkkxOUeW3ejOrQKYNXWw3XVrW64Y3xroQ+omFk3j22WerjWNVKUa2bMmvqDHnVdSY8zrKOlyPGjudj1X7T8Sy1RWoV7seEitWOorXscdKSfxb/GNkGdPJfLA0Xvm3cePG6jtezz7X+ZgnyiAxshIjW5V0hJ5XiZGVGNl8x8gyB/jjjz++CR1+Z+cvlBjZfCJbjCzBPLF9+/ZVacKctOcjRpY82EMLtCGbilztqbna3NAxQUM/sXFxbvL6YP1yJEoWIlmnBfDJ7kDzI4E2ZwD126t+XPipOeayXddpf6/jGOV2PzTsc2sixstUO2PEGQv19NNP46STTlKKWfd1uzaCaNfjlmljqIl50rosNc9iPnRBJjpu14wXmYLSBZnao3Rvm2qPsy7IJlNcdUE2meKqCxIZ6JiUSdt2ocXIRjkPLI3b/v37b1ZKlhkB3nzzTUQB6eJG0sWZOGnX8an2G90pHVM0Ks/9+3Ng6nPAvE+Ajlch0fFq4PCJac9JvWau6zrt73Uco9zuh4Y9fjkKMi1cuFC9eWDqunQbMr2sjSDayYNbOm76Upmnk9MP737u43R0wpIpKF2QqT0K94Hp9jjqglztcdYFmdrjqgsSHmJkTclUpTyyNERpsbMYA0MEmMZLhwkwS0Eq7r77bvWXT1JTp05VsbH5DivQHlnKkS5rQSqCbDdGu2wVkovHAtvuB0y4AthiJ6BFPyRqNoivTBFqjxIvftqZsu+5557DY489tlmfqPDopz1KvIhMIlPc26PEi8gUjkza8+3EIxtbQ7YqQBuyLGfrNEaWMSrpYltIizEoPBirqmNkdZybmxhZTUPHTzHeLl0MEmNvdLxOYt081Jr5DGrMegnlTY7Cqo73bhavw6pM6WKQGE9DWrwmZbfLlC5eh4Y/x0D39xIr5kQmewySjjVyKlO2eUonk9t58iIT6XNuyQfp50Om33//HfPnz0enTp1USi0+gGaTiXyQvp7rfMwTZdD9TcQpppsn0tE8ak+EH5l4DX0f6xhZL2tPrxnSyRYja0om0uB3er5zzRN55LV1/zDvpzjrCD2vpMv2OMhEfqn79VznY57Iq+6fKZ7U7zyRd82j3RPqVaaSkpLKe1jHyHqZJ7ZrOl5iZDPJ9NBDD2WMkZ08ebIYslFHlYiRXfEHUH9HYPJjwOrpWNHkLNTftkPlYnXCu8TIhvPEm8+4OCoxKt1Ro0apdX/MMcc4ohHXuDi314xDXJzEyMajPeq6wKtMcdUF2WSKqy5IFGqM7L333osrrrgirMvFCnpRpLZl6putXdOyf3ZKxzENLry/v0Ti93uB5b8AB44GdrzIWuwbdiVmo5+uPfWapvp7Hccot/uh4XVt+G1nvuhDDz0URxxxhGsabtdGEO1+7icn7bx30smZL12Qjk5YMgWlCzK1R+neNtUeZV3gtT3OuiBTe1x1QcJDjKwpmfKaRzZd3KogZqgoBUpXAct+Asb/E2h9MnDUVKBuy3xzJogoLrnkEhVC8OCDD2Y0YgUCgUAg8IrQPLISwZAZ2m1vfxLx6vq393FDJyuNinJg+svAzzcDO18HtDsXiUMnoJKC7XWV19dUqd+b6O91HKPc7oeGfdyC5JGfv/76a/Tp00eFEPD1EMMKMl3X9Now3a550P93QsftNU3RMaELMtEJS6YgdIFXXuLYHiVdYFqmOOqCXDLFURfkS6a8G7Ju3MSFhh49elQWE9Cudy8FEXSQNmMQ/SZn5nnkY8sttkDyywNRUbYea3d5FuUNdkPRypVWcuY0gef2oG6nCacZkG6vmJIrObOuHqb7S7JzdzJxvIOSiZsBSOeJJ55A+/bt0bVrV0WLhxeZSM8+1/mcJ/JkYsNNpnniRgwtpymZSM/E2tPx/EHLxHb7fOeaJ25asfeXggjuZOL4xUUmbUzpuc7nPPHamTZGmZgn8mO/b0zItGLFCiPzxP+blClWBRH4o8YdaIJgN3tx0XIR2w1iJ3Q2o7HmDyT+ehGJ3e5DctV0oG4rdspKg5+5g5QLP/XBJRsvXPh2vk31j5JHISoeWT1uQWzwYFq7QYMGqZywpmRyuzaC8sKQByr6ID0N/CGyy5kvXZCJjpfNXm5lCkoXZGqP0r1tqj0KuiAImeKqC7LJFFddkPCw2cutTJHc7CXIDL0oUtsy9c3Wzicz3mT2G90pHdW2Zi4S465GYvFXSHT+F29dJOq3cUyDT4Cpi9UJ76l8m+rvdRyj3O6Hhh43kzxOnDgRv/32m6rK9cYbb3hfe4bWRhDt6XjIRcdNXyrzTNfIhy5IRycsmYLSBZnao3Rvm2rPly4IWqa46oJM7XHVBQkPm71MyZQOYsgKLJRucPmvno6K2i2Aw38HatTPN1eCCINPzHz1z6dl/UahYcOG+WZLIBAIBAUEo4bsl19+qX7c0lnjLCX79ttvZzyXVbZyuY+rKrTbXsOP699OyxEdZiKY8iQSv96BZLeHkWxxPNYVd0TN6nXVJi43vNhfW3nl3WR/r+MY5XY/NOzjZoIXln1u2rQpTjnlFLRu3drzGja9Nky3268f1DylXicvuiAHnbBkCkIXZGqP0r1tqj0fuiAsmeKoC3LJFEddkC+ZQjFkGSPHIx3I1NixYzMyxwo/u+66KwoR3bt3d1zZK1uQNr1jOhBcV/bKGnieTKLeNwciWXNrVN//C1TU3xErli+vpKErezkNpueGNcrBWBi3wfSVFcJslb2yBZ7b+8tGDmcykb4uTED6XmViPugBAwao1FrZqsGYksk+1/mYJ/1jTwQ1T6RDuqSjr+VHJsaq6/HRlb28rD29Zohslb1MymSfbyfzZO8vm72cyaTnVev4uMhkn+t8zJPdGAtqnsg7/9rvGz8yldg2ZevKXl7myT7+Xip7ZZIpVpu9tt9+e0yZMiWMS8Vusxe92HZvdKBPmfxRnvsBknPeBXo8BaydB9RpGqmnb1PtUeIl7jKx7aWXXsLJJ5+M6dOnKw8slWicZaqK8yQyiUxR4kVkEpm8tusKapHa7KWtcIGz8ck0XtnauSD4ZManLt1vk/6LvgfGXQIky5Hochu/BOo2c0cjCy/6Kd9+rhPeidRrmurvZRyj3u6Vhn1u3dDR6VZmzpypnrSZgSQsmdyujSDa090TTui46ZvtGoHoAg90wpApSF2QqT1K97ap9qB0Qb7b46oLMrXHVRckPG72cqsLIlfZSxAO7K8XKrFgFFCyxPp/x2uAg8cATQ+2DFmnNPxcP4Dz/PBYyHA7bkwNt99++6nXRTfeeGNeNnNFYa7D4MH0NUzRC1sfiC4IB3EctyjwLLogeuMmhmxVxuKxwPBDgO/PBdbMAhrvAbQ4JqMBKxBoMJ0WDVfm8fvuu+8qC3YIBAKBQBAlSPqtqojy9UCyFBhzHrDjJUDrU4EimWpBbsyfP19lGGEM7EEHHaTaGOwvEAgEAkEUEZp1E9KesljCvhPSc8B0eRmS8z5BvT+fAIprI9nndSQOGY/K3jno6zYeNFz092544WfulvQS7G2/pqn+UQqCN9Xuh4Z93FL767mn95XG7E477YQ+ffp4WgemZXK7Nky3ax70/03IlA6pcvrh3c99nIlOWDIFoQsytUfp3jbV7lcXRFmmOOqCXDLFURfkS6a8G7LMEytIjx49elS+uuUEukq/VVyOmnUboWzUSUisnYOSlqejdLujUbeszEqb4bE2PNP3eE1volN+OE1vsmbNGnUNXtNJKhC2UybdX1LruJOJ10mV6V//+pdKA3faaaep+bBfN58yMaUc51nPdT7niTzplFRBzBNlsNciNyETx83EPJFOGDKx3T7fueZp1apViifdX9JvuZNJFzSJg0xa5+u5zuc88dpBph0kT/b7xoRMpaWlRuaJdEzKFKv0W4LM6be4oUZXRnL0lFJRDsz7CJj6NLD8dySO+APJ8hIkq9VSC1ffaF6eDnl4pZFrZ2ImOjo/nb6mqf5R8ihExSOrx03XV7/vvvtwxhlnKIVIpcIHqijJ5HZtBOWFIQ/8YUgHUx5ZPkDY5fTDu5/7OBMdvWaClCkoXZCpPUr3tql2r7og6jLFVRdkkymuuiCRgY5JmZiWlHs0IpV+S5AZelGktm2GNXORqN0E+PNBYM57QLvzgJbHA0XVkCiqo8IH+LTEm8x+o2e6ZqY2PzT49Je6WLPKtKE99Zqm+rvhPS7tfmjocZs2bZoqQtKiRQulSKgwwuA9U7vJtRFEu90L5JSOm77a25Qqpx/e/dzH6eiEJVNQuiBTe5TubVPtbnRBvnh02x5nXZCpPa66IOEh/ZYpmdJBshZEHXxamfMh8NWRwMddgTUzgZ3+CRwwAmhzClDNygMoEDgFHzYYtsLXsqzQtc022+SbJYFAIBAIPEEM2agiWQGUrwMWfw/8fDPQ+mTg6BlA3VZAQqZN4B6zZs3CqaeeqmKevvrqKxVbJRAIBAJBnCEWUdRQUQb89TLwQSfgrxeBxj2Bg78HWp/kyPvKgG2/8EPD67luzzMhZyEZsJ999hmaN2+O2267zdUrmyggCnMdBg+mr2GKXtj6QHRBOIjjuEWBZ9EF0Rs32ewVgc1elcHMnIqPuwM1GwOdrge22TvfLApiDO5I5uY7lpYdMWIEBg0alG+WBAKBQCBwbx9lgWz2igAqJj+J5LqfgN4vAfu8j0SdJtZOPpe7UXkwrYZOFeRmd6IJGvzMnYmZArqz7Ty2X9NUfze8x6XdTd8HHnhAhRFcfPHFKi+sfdyitFPZ5NoIaqcyecgUimFi7RGMWbbL6Yd3P/dxJjpesha4lSkoXZCpPUr3tql2J339zGu+ZIqrLsgmU1x1QcJD1gIvusApfBuyQ4YMwdixYysvzs0ju+66q1+yBYXPX/k3XvqhMSbN7+A+j6wt/xs9cFws/J43mpecduyvabCfmzx9TN1EHpjPzm2ePqba4HUou5OcdvxO95cckZvK9OCDD2LffffFWWedhYYNG1bKRPqcW00/LjLZ10Y+5okycF2TflD5SfV8aDn9ysQdwvo+Zl+v86TXDOk0aNAgFJns8+1knsi/7i95ZJ3JpOeV/dgeF5nsayMf80Re9e9rUPNE3smf/b7xI1NJSUnlPczUmF7nie2aDnkxJVMk8shee+21uPPOOys/P/300yq1z8CBA1WZy0z44osv0KhRo4I2ej3nkc3QzoMLi7T8eGS90uBnysTF6dYja7+mqf5R8iiYas/Wd/To0ejVq5d6sGzfvr0yYlO9MHrcouSFMbk2gvLCkAcackF6ZPkjbZfTD+9+7uNMdLx4ZN3KFJQuyNQepXs7bI9sFHVBNpniqguyyRRXXZDw4JF1K1OoeWRphM2YMQOtWrVSn7UHkQYtGbjiiivSnte3b18Vs3f33XfnZLKqQy+K1LZMfbO1a1r2z07pmKCRSR4n/VPPM9Hf6zhGuT21TT9Bv/DCC2jTpg169uyZsb/feQ2q3eTaCKLdz/3kpJ3KPJ2cfng3cR/b6YQlU1C6IFN7lO4DU+1O+kZVF2Rqj7MuyNQeV12QyEInaJkCyVowePBgHH/88bjuuus2KT9Gg5YHjVV7ux1PPPEEnnrqKb8sCGzgK4J80vB6rtvzTMhZFcBXPSxxzFfJzzzzDJo1a1blxi0KPIfBg+lrmKIXtj4QXRAO4jhuUeBZdEH0xs23IUtX8Q8//IDJkycrN/D555+P4cOHV35HY/WOO+7Ajz/+aILfKgk3Tx656DDOxQ89PzS8nuv2PBNyxh2zZ89WcUWMj/r0009VTFRVHLco8BwGD6avYYpe2PpAdEE4iOO4RYFn0QU1Q9UFoeeRffPNN1Vs3tSpU1XYAONfTzzxRAwdOlR5ZV9//XX89NNPm503ZcoUFDp8hilvQic10DxMGl7PdXueCTnjCgb7z507V4XjMHCf4L1WVcctCjyHwYPpa5iiF7Y+EF0QDuI4blHgWXTBilB1gVMYTb+12267Ke/Q+PHj8eSTTyrj9o033lCWNYOjGQ973nnnqb5LlixR/RiaIDAHNykrgqDh9Vy355mQM4547733MHHiRPz73/9WWQncIo7jFgWew+DB9DVM0QtbH4guCAdxHLco8Cy6IL88hJZHlgYtDVkef/31l6oqRKOVxiu9tjRs+/fvr+Jj7bv1BQJBejz00EPYZptt1FsOxqQLBAKBQCAIoSACd1FrL6zGNddcg379+okRKxDkwLBhw7D33nvjmGOOqUynJRAIBAKBII+Vve666y5VSCFbntlCgs7RpuEnzxuTIOs+buiYoMHPTKDshXf7NU319zqOUWjX1WMYQ86qXMwLq+GVtn3coiSr6bVhul3zoP9vQqZ0SJXTD+9+7uNMdMKSKQhdkKk9SveBqfY464JcMsVRF+SSKY66IF8yBVYQQeC/IML222+vqmLpCcxW2UtX1kityKErb6RW5NDVefxUg2Fqp3SVU1ipI12VEV1NJLXKCMvXpqucIjJtKhOrSB100EEq3lyvi7jLVBXnSWQSmUQmkUlk8icTw+YyVfZiNiwnBRE8G7KMfeVu6aCKGXz55ZfYf//9UZURRGUve2Utrx5ZrzT4mQuYizz1FXg2Oro0rr0imIn+UfIoOGlfvHgxbrnlFjzwwAPqJqZiMSmTfW6jVM3H5NoIyguj79V0MOWR1Qrbfu/kQxdkouOlspdbmYLSBZna833PB9EeZ12QTaa46oJsMsVVFyQ8VPZyK1Molb0Y+8q0Wox3NR0iwMTubdu2RaFAL4rUtkx9c7Xb6bmhY4IGF2U6eZzQST3PRH8/4xhWO5+mJ02ahC5duihPLH9YaMRm6u/3ml7mNeh202sjCu1u75so6YJ0dMKUKQhdkKk9SmvGVHucdUGm9jjrgkztcdUFiSx0gpbJeB5ZFjtgvOu9994LE6CXl9kMaMRWdW+sQEDQy0Aj9uWXX1YG7BFHHOHqBhYIBAKBoJDhe7MXjVlu3OrevTsOPPBADBw40LWHluczVVe7du3w9NNPSzYDQUHgv//9L2bOnIkbb7wRu+66a77ZEQgEAoEgdjC62UsbpOPGjVOGLfPJ0jglmDqIYC5Zxj7oKmD0wjI9Fw+GKxQSdKwNx8OE8c6ppIePnj2vXj0/NLye6/Y8E3LmE2+99RY6duyIFi1aqM1cehds0IjjuEWB5zB4MH0NU/TC1geFpgvyhTiOWxR4Fl1QEZouYGwsC2kFutkrFwOff/45pk2bpoxVbbxqMHSgW7duytjt2rUrChXakHUyUU6hY1TzRcPruW7PMyFn2JgzZw6aNm2KDz74QN0DNGbDRhzHLQo8h8GD6WuYohe2PigEXRAFxHHcosCz6IJEKOe6sY8k/VYV88hy0knLz2LzSsPruW7PMyFnPnDUUUepePIddtghL9eP47hFgecweDB9DVP0wtYHhaIL8o04jlsUeBZdsDw0XeDGI+trs5dAIMgO5vajAcv8ee+++27ejFiBQCAQCKoiqpuIi2W8K0FLe8CAAbJxRVDwYMLpESNG4PDDD8e///1vFQcbF8+HQCAQCAQFY8j+8MMPKgWXBrMOvP766zmzF3zxxReqoIIYvYKqBuaFXbduHUaOHInDDjusoOPABQKBQCAIEr5jZFkU4dprr0WrVq02aadBy9iGK664Iuu5d999d2DVwaIO2ezl/bwoBP2nwyeffKKyEnD9RxFRHbeo8ywbPGSzV1VDHMctCjyLLkhUvc1evEjfvn1VRSJW+bJfkN9dffXVWY1Vbn7JZuxWZUj6LW/nRSENSyreeeedyoIGrCmtK3NFCVEctzjwLCl3JP1WVUMcxy0KPIsuqIhk+i3fm71ogDG8YPLkyaou7vnnn4/hw4dXfseCCXfccQd+/PFHv5cSOMDKlSvzSsPruW7PMyGnCbAqFzdyMV8yN3LZy8tGEVEZt7jxHAYPpq9hil7Y+iCuuiBuiOO4RYFn0QXRGzffMbIab775JsaPH6+8svTQ0qhlpS9u/mIIAQsl0BLfZZddNjlvypQpKHTwacXuGOc4pXOU52q3H27pmKChP/vh3WR/r+PoVFYarc888wyOO+449OzZs7I9yOv6oWEftyB5NC2TqfvDa7v9+kHNU+p1/PLu5z7ORCcsmYLQBZnao3QfmGqPsy7IJVMcdUEumeKoC/IlU+CGLMFKXp9++qkyaGm40rh94403FFN0ETPEgBW8CBZJYL/Bgwej0MHCENWrW1PBsdKTePbZZ+P//u//VDu9fLVr11bev7Kysspz2cbv+LRTXl6udssT9erVQ3FxsQpfsC+I+vXrKwOM7no76D2n65/9NQ32Yzuvx+tqsJ2u/vXr16uNTRqsUqVTTvHQqFGjhtq1z748R8Muk74mZbfLRJ406tatWymTvX8umexPguzvRibOC8eSm7e0TBzPY489Fv/73/9URgKeo6/tZJ4yyeRmnrzIRPocN8pE+naZnM5TPmSyz7WbeTIlE2XgvUUENU+kw3bS0a/e/MjE8BY9buzrdZ70miGow8OQyT7fTubJ3j/M+ymMted0ntzKpOdV6/i4yGSf63zME3nV/w9qnsg79Y39vvEjU0lJSeW41apVy/M82cefvJiS6eGHH8bjjz++CR1+Z+cvFwIviMAStJ999pkyWmm8stoXBenfv78yak3EhsY9Rnbp0qWbjIOfJybS5OKwG8RO6Jigwc9cwLxxU+NgstHRBrS+pqn+pp/K16xZg+eeew4XXngh5s6di+bNm0fS25Kp3T63VBRR87aYWBtBeWH0vZoOpjyyOhbMfu/kQxdkouN2zXiRKShdkKk9Sl5HU+1x1gXZZIqrLsgmU1x1QSIDHZMyce8Q3+xHtrIXww8YbpAtPVchIIisBQLz4C2yYMEClS7ugQcewKWXXqqeUgUCgUAgEOTXPspLZS/mnR03blw+Lh1JmHqWIB2+UvRDzw8Nr+e6Pc+EnG7w22+/qbcHfI1z5ZVXxtaIDXvcqgrPYfBg+hqm6IWtD6KuC6oK4jhuUeBZdEFpqLrAKfJWovb444/P16WrNOyxQvmg4fVct+eZkDMXGBLDGO6dd94ZQ4cORVVAGONWFXkOgwfT1zBFL2x9EEVdUBURx3GLAs+iC6I3bkY3ewkEVQETJ05Ucb6sOte2bVvVxjgygUAgEAgE0YL8OgsEG6B3rDLshWnhtt56a5UfViAQCAQCQTQhhmwVgwnPoR8aXs91e14QHlLmPKYRe9ZZZ6kcyFURcfQsR4HnMHgwfQ1T9MLWB1HQBYWAOI5bFHgWXRC9cctL1gKBBclakH8wt921116Lm2++WaV3YU4+gUAgEAgE+UPksxYINoXJ3YlMfuw3a4FXGl7PdXueKTl//fVXVcSBBSmYt66qG7Emxq0QeQ6DB9PXMEUvbH2QD11QiIjjuEWBZ9EFJaHqAqcQQ7aKwV71Ix80vJ7r9jw/PPIGWbRokfLE0gvL4hysbFIIMLE+CpHnMHgwfQ1T9MLWB2HqgkJGHMctCjyLLojeuIkhKygojB07Fuecc47ayDVs2LBIxFwJBAKBQCDwBkm/JSgITJgwQdWJ7tWrF2655ZZ8syMQCAQCgcAAxB3lE9OmTcPnn3+u6gLbP+cLrD6VTxpez3V7ntP+NF4Zm8NkzCtXrlTnNW/eHIUKE+ujEHkOgwfT1zBFL2x9EJQuEMR/3KLAs+iC6I2bZC3wibfeegv9+vWr/LzbbrvhzTffrEyknw2StSB4XHzxxSqV1pFHHplvVgQCgUAgEDiAZC0IGTRceTAHKQ8nRqwdJncnMqDa725+rzS8nuv2PCf9L7zwQkyfPh0PPPCAGLEG10ch8hwGD6avYYpe2PogCF0gqBrjFgWeRResDVUXOIUYsgZAL+wJJ5yg/uYbfI2eTxpez3V7Xrr+XPg6rOO0005DkyZNVHotgdn1UYg8h8GD6WuYohe2PjChCwRVc9yiwLPoguiNW/4DTnyCMal8tf/0009nNSSfeuopFcfaoEEDTJ06Vb1uPuCAA4zxMX78eCxZskTlJOU1BOGirKxMpdJ69dVX0bt3b/Ts2TPfLAkEAoFAIAgYsfXIDhw4UBmwTz75pDIic/UlrrrqKpx33nkYPHiwOs/UpizSIhhSQJ7yudmLBn0+aXg91815fLK76667Kp/wZsyYgb59+6K4uBjPPvss6tSp44mHQoCJ9VGIPIfBg+lrmKIXtj7wowsE8bqv4siz6IIIjlsy5li6dCkDKZLjxo1L+z3b27Ztu1n71KlTkw0aNDDOz5tvvumY7vLlyxXvy5YtM3LtioqK5E477aT+5oOG13Pdnsfx4rh9/fXXyeeff76yTRD8+ihEnsPgwfQ1TNELWx941QVy/8fvvoojz6ILdgpNF+h7m3ZSLsQ+tMCJtzRdCIHekEXvqf6eGQhef/31nDTp0c20oYvhDQxhoJfYacwsy6OaAOnoIx80vJ7r9jzuZiS22morVdiA4O5GQfDroxB5DoMH09cwRS9sfeD2HN0vTms6CojCfRVHnkUXJELVBU5R5Q1ZGqrciJUONEY/++yzSkOW/TL1zQQaU8xYoGk0bNhQ/WW8rFOY3J2oD6+L1g8Nr+e6Pe+VV15Rf5s2bYoOHTq44rGQYWJ9FCLPYfBg+hqm6IWtD9yeo3VnnHbfRwFRuK/iyLPogmSouqDKx8i62QzWrl27tN/R6MwVX5sN9LzSGOYGL/v1CHtbmDCh0P2m7wrqvMsuuwwjR47EoEGDPF1DEM8f/CjwHAYPpq9h8gE5zHOjMN+FgDiOcxR4Fl0QvXGr8oZsLrjxnKaC2QnoibVnKWAoAzeUSeYCM+Dif/HFF7F+/XpV3IAlZgUCgUAgEAiqfGiBLhubCTQ2tQfVKxgve/fdd6v/L168WHl/mR3BzRPK7NmzK+M+Cbre+V3NmjXVkdqeCt3OgymoWAlDx6Nk62+aBj+Xl5dXnpurv27n9ezX1O26osfkyZMxc+ZMFQ/LpMq6H/86kckJ73Fp90PDPrdFRUWxkCnT2giTR72u7feoH5kyeSbS3TteefdzH2ei43bNeJHJ7Xw71QWZ2qN0b5tqj7MuyCZTXHVBNpniqgsSGeh4kWndunWbZR2x39NOPLmxL1FLY5Vxqqyolbq5Sn+nvaSpYKosGrI8Nx+gAduiRYu8XFsgEAgEAoEgypg1axaaN29euB5Zvx7boMHNSizOwPyn6QKgUz2yAoFAIBAIBFUFJSUlGSt1lpaWKjspF6q0IavjVLMZrDrLQD7AVzqZ0ngJBAKBQCAQCAp8sxfDDRi7mg4MK2CpWoFAIBAIBAJB/FDlDVlmFci0oYvt6YolCAQCgUAgEAiijyodWkAMGDAAffv23ayd+WMZeuC0+pZAIBAIBAKBIFooqup5YGmo9u/fH0899dQm7VdffbWqyCUQCAQCgUAgiCdim36Lhig3cbEELUMEuGlKhwkw3VYqdK5XemGZKYCxsRJWIBAIBAKBQBBfxNaQFQgEAoFAIBAUNmIfWiAQCAQCgUAgKEyIISsQCAQCgUAgiCWqfNYCQeGB8dMEY6dZ8GLw4MGVxTEEAkFhgHsouMlX74ugPqAukCI0AkHVgsTICqoUBg4cuInh2q9fP/UDNm7cuHyzJhAIQgTvfW7qPe+88yp1AzcH06gVCARVBxJaIKgysGex0Lj22mtVzmAeAoGgsGB/gG3Xrl3G4jgCgSC+kNACQd7BHxd6T55++umsBSr4mpDGarYUaswrTHqajvbM2tsEAkHV1wWpecI/++wznHDCCYHxLhAI8gMxZAV5A1/10fBkzFoujyn7duvWDVdddVVlG3/wCP0Dxh+1pUuXbnKepis5gwWCwtEF6Qxf0k6XY1wgEMQbEiMryDvoWdlqq63Ua8B0Xhj+sPGHKjW2jd4b/qClGq928HUif/jsP3oCgaBwdIH23vIcbgSVzV4CQdWCxMgKIg96UdJ5WvQPEuNi04E/WnyVKEasQFC4uoCbvagD+FArcbICQdWDGLKCyIM/TpnSZ/EHjLFv6bwwjRo1UhkMBAJBYekCemBptNoNW20Av/XWWyFxKxAIwoAYsoLIgx4U/iilA/PEpsbU8ceL7doTq7MZCASCwtAF7MeY29RzCdn0KRBULYghK4g97D9Y/CHjbmV6bWi88rjzzjslLk4gKCBdQGO1f//+m4QhvP766+qzbPwUCKoWJGuBINKgNzUbaLBqTwv79u3bt7Kijx0SYiAQFI4u0Pc84+QZYrR48WLlsU1NySUQCOIPMWQFsYeOmUuXfksgEBQO7PGz/L88wAoEVR8SWiCo0l4agUBQGBBdIBAUJsSQFcTCw5LtR4qvDAUCQdWG6AKBQJAOYsgKIg9u3GCMWzowJo7lKQUCQdWH6AKBQJAKMWQFkQd3GWdKYs522YUsEBQGRBcIBIJUyGYvQeQxYMAAlY0gFUy1xdeNkhdSICgMiC4QCASpEI+sIO9ITVyeCp0TMjWlFlPrSDodgaDqQHSBQCBwi0QymUy6PksgMAD++OiqW3wtyKIF+tUga6qn4u6771Z/6XmZOnWqioeTV4kCQfwhukAgEHiFGLICgUAgEAgEglhCQgsEAoFAIBAIBLGEGLICgUAgEAgEglhCDFmBQCAQCAQCQSwhhqxAIBAIBAKBIJYQQ1YgEAgEAoFAEEuIISsQCAQCgUAgiCXEkBUIBAKBQCAQxBJiyAoEAoFAIBAIYgkxZAUCgUAgEAgEsYQYsgKBQCAQCASCWEIMWYFAIBAIBAJBLFE93wwIBAKBIDpYtmwZrr76ajRo0AA9evTACSeckG+WBAKBICPEIysQCASCSvTt21cZsoMHD8bYsWPzzY5AIBBkhRiyAoFAIFDo168fDjjgALRt2zbfrAgEAoEjSGiBQCAQCDB+/Hi89dZbWLp0ab5ZEQgEAsdIJJPJpPPuAoFAIKiKOPDAA5Un9sknn1Sfn3rqKXTv3h277bZbvlkTCASCjJDQAoFAIChw0Bv7+eefq9ACjalTp4oRKxAIIg8xZAUCgaDAQS8ssxQwPlZ7YwcOHJhvtgQCgSAnxJAVCASCAscbb7xRacQy/RYP2fAlEAjiANnsJRAIBAWMadOmKcN1wIAB6jNTb+k4WYFAIIg6xCMrEAgEBQzGxhKMh9X5YwUCgSAuEENWIBAIChjjxo1T8bH0wtIry/8LBAJBXCCGrEAgEBQwlixZUhlaIFkKBAJB3CCGrEAgEBR4jOx5550nRqxAIIglxJAVCASCAgVjYgUCgSDOEENWIBAIChAsR8tcsQwpYHhBpk1gAoFAEGVIiVqBQCAoYDA+tlu3bqqSlwYNXB4SbiAQCKIOMWQFAoGgwEHv7GeffYZ27dqpz4yZlewFAoEgDhBDViAQCAQCgUAQS0iMrEAgEAgEAoEglhBDViAQCAQCgUAQS4ghKxAIBAKBQCCIJcSQFQgEAoFAIBDEEmLICgQCgUAgEAhiCTFkBQKBQCAQCASxhBiyAoFAIBAIBIJYQgxZgUAgEAgEAkEsIYasQCAQCAQCgSCWEENWIBAIBAKBQBBLiCErEAgEAoFAIIglxJAVCAQCgUAgEMQSYsgKBAKBQCAQCGIJMWQFAoFAIBAIBIgj/h9ENBZ9Azn0RwAAAABJRU5ErkJggg==", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "label_size = 17\n", "title_size = 18\n", "legend_size = 13\n", "handle_length = 1.5\n", "fig, (ax1) = plt.subplots(1,1,figsize=(7,4))\n", "ax = ax1\n", "ax.tick_params(axis = 'x',which='both',length=5,direction='in', pad=10)\n", "ax.tick_params(axis = 'y',which='both',length=5,direction='in', pad=5)\n", "ax.xaxis.set_ticks_position('both')\n", "ax.yaxis.set_ticks_position('both')\n", "plt.setp(ax.get_yticklabels(), rotation='horizontal', fontsize=label_size)\n", "plt.setp(ax.get_xticklabels(), fontsize=label_size)\n", "ax.grid( visible=True, which=\"both\", alpha=0.2, linestyle='--')\n", "\n", "ax.set_xscale('log')\n", "ax.set_yscale('log')\n", "ax.set_ylim(1e-5,5e-2)\n", "ax.set_xlim(50,9000)\n", "\n", "fac = (2.726e6)**2*np.asarray(cl_kSZ_kSZ_X['ell'])*(np.asarray(cl_kSZ_kSZ_X['ell'])+1.)/2./np.pi\n", "\n", "ax.plot(cl_kSZ_kSZ_X['ell'],fac*np.asarray(cl_kSZ_kSZ_X['1h']),label = r'$1\\mathrm{h}$',c='k',ls=':',lw=0.7)\n", "ax.plot(cl_kSZ_kSZ_X['ell'],fac*np.asarray(cl_kSZ_kSZ_X['2h']),label = r'$2\\mathrm{h}$',c='b',ls='-.',lw=0.7)\n", "ax.plot(cl_kSZ_kSZ_X['ell'],fac*np.asarray(cl_kSZ_kSZ_X['3h']),label = r'$3\\mathrm{h}$',c='orange',ls='--',lw=0.7)\n", "ax.plot(cl_kSZ_kSZ_X['ell'],fac*(np.asarray(cl_kSZ_kSZ_X['1h'])+np.asarray(cl_kSZ_kSZ_X['2h'])+np.asarray(cl_kSZ_kSZ_X['3h'])),\n", " label = r'$\\mathrm{Total}$',c='k',ls='-',lw=1.)\n", "ax.plot(cl_kSZ_kSZ_X['ell'],fac*np.asarray(cl_kSZ_kSZ_X['hf']),label = r'halofit',c='red',ls='--',lw=0.7)\n", "ax.legend(loc=2,ncol = 1,frameon=False,fontsize=14)\n", "ax.set_xlabel(r\"$\\ell$\",size=title_size)\n", "ax.set_ylabel(r\"$\\ell(\\ell+1)C_\\ell^{\\mathrm{kSZ^2X}}/2\\pi\\quad [\\mathrm{\\mu K^2}]$\",size=title_size)\n", "fig.tight_layout()\n" ] } ], "metadata": { "kernelspec": { "display_name": "Python (venv_class_sz)", "language": "python", "name": "venv_class_sz312" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.9" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "state": {}, "version_major": 2, "version_minor": 0 } } }, "nbformat": 4, "nbformat_minor": 4 }