From 71832034b5f2aa0371302b0df5c4d83af83c1971 Mon Sep 17 00:00:00 2001 From: BNU computer <201921160007@bnu.mail.edu.cn> Date: Sun, 27 Sep 2020 07:37:42 +0800 Subject: [PATCH] Upload New File --- VItamin_bilby_BBH_waveform_test.ipynb | 460 ++++++++++++++++++++++++++ 1 file changed, 460 insertions(+) create mode 100644 VItamin_bilby_BBH_waveform_test.ipynb diff --git a/VItamin_bilby_BBH_waveform_test.ipynb b/VItamin_bilby_BBH_waveform_test.ipynb new file mode 100644 index 0000000..8ed7b70 --- /dev/null +++ b/VItamin_bilby_BBH_waveform_test.ipynb @@ -0,0 +1,460 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "This is a notebook to generate bilby BBH waveforms for Vitamin box to test. \n", + "We specify injection parameters first, and bilby will give waveforms. Then we whiten the waveforms. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import bilby\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# We first establish a dictionary of parameters that includes \n", + "# all of the different waveform parameters, including masses \n", + "# of the two black holes (mass_1, mass_2), spins of both black \n", + "# holes (a, tilt, phi), etc. \n", + "\n", + "# Here we set the value of each parameter as the median of its prior's \n", + "# range, which is given to train VItamin_B\n", + "parameters = dict(\n", + " mass_1=57.5, mass_2=57.5, a_1=0., a_2=0., tilt_1=0., tilt_2=0.,\n", + " phi_12=0., phi_jl=0., luminosity_distance=2000, theta_jn=3.141592653589793/2, psi=3.141592653589793/2,\n", + " phase=6.283185307179586/2, geocent_time=0.25, ra=6.283185307179586/2, dec=0.)\n", + "\n", + "\n", + "\n", + "# Set up interferometers. In this case we'll use three interferometer \n", + "# L1 H1 and V1. Duration and sampling frequency follow VItamin_B's\n", + "# requirement.\n", + "\n", + "ifos_list = [ 'L1','H1','V1']\n", + "duration = 1.\n", + "sampling_frequency = 256\n", + "\n", + "\n", + "# Set up a random seed for result reproducibility. \n", + "np.random.seed(8870)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "ifos = bilby.gw.detector.InterferometerList(ifos_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "# Use Bilby to generate BBH waveform\n", + "def generate_bbh_waveform(parameters,ifos_list,sampling_frequency,duration):\n", + "\n", + " \n", + " # Fixed arguments passed into the source model\n", + " waveform_arguments = dict(\n", + " waveform_approximant='IMRPhenomPv2',reference_frequency=50., minimum_frequency=20.)\n", + " \n", + " # Create the waveform_generator using a LAL BinaryBlackHole source function\n", + " # the generator will convert all the parameters\n", + " waveform_generator = bilby.gw.WaveformGenerator(\n", + " duration=duration, sampling_frequency=sampling_frequency,\n", + " frequency_domain_source_model=bilby.gw.source.lal_binary_black_hole,\n", + " parameter_conversion=bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters,\n", + " waveform_arguments=waveform_arguments)\n", + " \n", + " ifos = bilby.gw.detector.InterferometerList(ifos_list)\n", + " \n", + " ifos.set_strain_data_from_power_spectral_densities(\n", + " sampling_frequency=sampling_frequency, duration=duration,\n", + " start_time=parameters['geocent_time'] - 0.1)\n", + " \n", + " ifos.inject_signal(waveform_generator=waveform_generator,\n", + " parameters=parameters)\n", + " \n", + " \n", + " waveform = {}\n", + " waveform['times'] = {}\n", + " waveform['y_data_noisy'] = {}\n", + "\n", + " \n", + " k = 0\n", + " for ifo in ifos_list:\n", + " \n", + " waveform['times'][ifo] = np.linspace(parameters['geocent_time'], \n", + " parameters['geocent_time']+duration,int(sampling_frequency) )\n", + " waveform['y_data_noisy'][ifo] = ifos[k].time_domain_strain\n", + " \n", + " k += 1\n", + " \n", + " \n", + " return waveform" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "07:36 bilby INFO : Waveform generator initiated with\n", + " frequency_domain_source_model: bilby.gw.source.lal_binary_black_hole\n", + " time_domain_source_model: None\n", + " parameter_conversion: bilby.gw.conversion.convert_to_lal_binary_black_hole_parameters\n", + "/home/changfeng/.local/lib/python3.8/site-packages/bilby/gw/detector/psd.py:356: RuntimeWarning: invalid value encountered in multiply\n", + " frequency_domain_strain = self.__power_spectral_density_interpolated(frequencies) ** 0.5 * white_noise\n", + "07:36 bilby INFO : Injected signal in L1:\n", + "07:36 bilby INFO : optimal SNR = 8.25\n", + "07:36 bilby INFO : matched filter SNR = 8.55+0.66j\n", + "07:36 bilby INFO : mass_1 = 57.5\n", + "07:36 bilby INFO : mass_2 = 57.5\n", + "07:36 bilby INFO : a_1 = 0.0\n", + "07:36 bilby INFO : a_2 = 0.0\n", + "07:36 bilby INFO : tilt_1 = 0.0\n", + "07:36 bilby INFO : tilt_2 = 0.0\n", + "07:36 bilby INFO : phi_12 = 0.0\n", + "07:36 bilby INFO : phi_jl = 0.0\n", + "07:36 bilby INFO : luminosity_distance = 2000\n", + "07:36 bilby INFO : theta_jn = 1.5707963267948966\n", + "07:36 bilby INFO : psi = 1.5707963267948966\n", + "07:36 bilby INFO : phase = 3.141592653589793\n", + "07:36 bilby INFO : geocent_time = 0.25\n", + "07:36 bilby INFO : ra = 3.141592653589793\n", + "07:36 bilby INFO : dec = 0.0\n", + "07:36 bilby INFO : Injected signal in H1:\n", + "07:36 bilby INFO : optimal SNR = 5.16\n", + "07:36 bilby INFO : matched filter SNR = 4.79+0.13j\n", + "07:36 bilby INFO : mass_1 = 57.5\n", + "07:36 bilby INFO : mass_2 = 57.5\n", + "07:36 bilby INFO : a_1 = 0.0\n", + "07:36 bilby INFO : a_2 = 0.0\n", + "07:36 bilby INFO : tilt_1 = 0.0\n", + "07:36 bilby INFO : tilt_2 = 0.0\n", + "07:36 bilby INFO : phi_12 = 0.0\n", + "07:36 bilby INFO : phi_jl = 0.0\n", + "07:36 bilby INFO : luminosity_distance = 2000\n", + "07:36 bilby INFO : theta_jn = 1.5707963267948966\n", + "07:36 bilby INFO : psi = 1.5707963267948966\n", + "07:36 bilby INFO : phase = 3.141592653589793\n", + "07:36 bilby INFO : geocent_time = 0.25\n", + "07:36 bilby INFO : ra = 3.141592653589793\n", + "07:36 bilby INFO : dec = 0.0\n", + "07:36 bilby INFO : Injected signal in V1:\n", + "07:36 bilby INFO : optimal SNR = 0.45\n", + "07:36 bilby INFO : matched filter SNR = 1.23-0.61j\n", + "07:36 bilby INFO : mass_1 = 57.5\n", + "07:36 bilby INFO : mass_2 = 57.5\n", + "07:36 bilby INFO : a_1 = 0.0\n", + "07:36 bilby INFO : a_2 = 0.0\n", + "07:36 bilby INFO : tilt_1 = 0.0\n", + "07:36 bilby INFO : tilt_2 = 0.0\n", + "07:36 bilby INFO : phi_12 = 0.0\n", + "07:36 bilby INFO : phi_jl = 0.0\n", + "07:36 bilby INFO : luminosity_distance = 2000\n", + "07:36 bilby INFO : theta_jn = 1.5707963267948966\n", + "07:36 bilby INFO : psi = 1.5707963267948966\n", + "07:36 bilby INFO : phase = 3.141592653589793\n", + "07:36 bilby INFO : geocent_time = 0.25\n", + "07:36 bilby INFO : ra = 3.141592653589793\n", + "07:36 bilby INFO : dec = 0.0\n" + ] + } + ], + "source": [ + "bbh_waveform = generate_bbh_waveform(parameters,ifos_list,sampling_frequency,duration)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "times = bbh_waveform['times']\n", + "y_data_noisy = bbh_waveform['y_data_noisy']" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'waveform')" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(times['L1'],y_data_noisy['L1'])\n", + "plt.title('L1')\n", + "plt.xlabel('times')\n", + "plt.ylabel('waveform')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "# Load ASD file of L1.\n", + "asd_file_Ligo = './aLIGO_ZERO_DET_high_P_asd.txt'\n", + "asd_file_Virgo = './AdV_asd.txt'\n", + "freq_Ligo = []\n", + "asd_Ligo = []\n", + "freq_Virgo = []\n", + "asd_Virgo = []\n", + "\n", + "with open(asd_file_Ligo, 'r') as f1:\n", + " while True:\n", + " lines = f1.readline() \n", + " if not lines:\n", + " break\n", + " pass\n", + " freq_tmp, asd_tmp = [float(i) for i in lines.split()] \n", + " freq_Ligo.append(freq_tmp) \n", + " asd_Ligo.append(asd_tmp)\n", + " pass\n", + " \n", + " # Transfer data format from list to array.\n", + " freq_Ligo = np.array(freq_Ligo) \n", + " asd_Ligo = np.array(asd_Ligo)\n", + " pass\n", + "\n", + "with open(asd_file_Virgo, 'r') as f2:\n", + " while True:\n", + " lines = f2.readline() \n", + " if not lines:\n", + " break\n", + " pass\n", + " freq_tmp, asd_tmp = [float(i) for i in lines.split()] \n", + " freq_Virgo.append(freq_tmp) \n", + " asd_Virgo.append(asd_tmp)\n", + " pass\n", + " \n", + " # Transfer data format from list to array.\n", + " freq_Virgo = np.array(freq_Virgo) \n", + " asd_Virgo= np.array(asd_Virgo)\n", + " pass" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "def whiten_signal(signal, asd):\n", + " \n", + " signal_rfft = np.fft.rfft(signal)\n", + "\n", + " whitened_signal_rfft = signal_rfft / asd\n", + " whitened_signal = np.fft.irfft(whitened_signal_rfft, n=Nt)\n", + " return whitened_signal" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# Interpolate asd data linearly.\n", + "\n", + "# Vitamin box requires sampling frequency = 256Hz, duration = 1 second.\n", + "Nt = 256\n", + "dt = duration/sampling_frequency\n", + "\n", + "freq_Ligo_rfft = np.fft.rfftfreq(Nt, dt)\n", + "asd_Ligo_interp = np.interp(freq_Ligo_rfft, freq_Ligo, asd_Ligo) \n", + "plt.loglog(freq_Ligo, asd_Ligo,'-o',label='Ligo ASD')\n", + "plt.loglog(freq_Ligo_rfft, asd_Ligo_interp, 'x',label='Interpolation')\n", + "\n", + "freq_Virgo_rfft = np.fft.rfftfreq(Nt, dt)\n", + "asd_Virgo_interp = np.interp(freq_Virgo_rfft, freq_Virgo, asd_Virgo) \n", + "plt.loglog(freq_Virgo, asd_Virgo,'-o',label='Virgo ASD')\n", + "plt.loglog(freq_Virgo_rfft, asd_Virgo_interp, 'x',label='Interpolation')\n", + "\n", + "plt.grid('on')\n", + "plt.ylabel('ASD (strain/rtHz)')\n", + "plt.xlabel('Freq (Hz)')\n", + "plt.legend(loc='upper center')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "# Whiten signal in 3 detectors.\n", + "whitened_signal_L1 = whiten_signal(y_data_noisy['L1'],asd_Ligo_interp)\n", + "whitened_signal_H1 = whiten_signal(y_data_noisy['H1'],asd_Ligo_interp)\n", + "whitened_signal_V1 = whiten_signal(y_data_noisy['V1'],asd_Virgo_interp)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "x_data_list=[parameters['mass_1'],parameters['mass_2'],parameters['luminosity_distance'],\n", + " parameters['geocent_time'],parameters['phase'],parameters['theta_jn'],\n", + " parameters['psi'],parameters['ra'],parameters['dec']]\n", + "x_data=np.array(x_data_list)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'whitened waveform')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SrL/ZqtPk8w8sB+98mxbrVLmOqdLCuJqCbCX/3lgiBYYiHBJIFA6OLxyymVgfIvuVR9itFNlDOpvxUlnNOgfAW+h/88gEPnr5/amL4MyKUDNdLq0GdNV9+6AU8JjhDtg1WcZnfrkdP38gufaw0VBwGgoD/kLiRjYxAoIbrxMWq30GEC2CSyccagkCsR2dprLy2LYfmMNjRro0f06l1h3hWXVcDOazoTGkgYXWznlaDvfvmcb9e6Yx7Qtu/jzHt8KTx8tupaBgL6nWYa7WbDnw97AwNpWS933nTrzn2wvTSTWIOXifv2+BepF1GxEOCSTGHBwXBe1War6BeAEYLdrIR4RDzfWL4DIW6o2IcHAa+O979uIzv9yute52i6SZusc+8Krjps6nvvJez23x+ERJm+NJQtGEA9DFmFRWfl9pHsJhMdxK8UVwbuh3EjWnYWRodZCt1KFbyXRZmZsp6XNbT2857Jkq453fuD2VsK46DQwVfOEwL7fS/CyHF37qOrzwU9dpxUrHhZwGZipOYmUxn1czazBJmJSqbpPlUNJuJRaIwffsn6mGhE4cNaeBj15xf0uhr5TSawh/P2dXZSxq+fm9JrVwIKIRf6+F5US0vJuD6gc4E6kp5uC7lZIC0rwAjBQ4IB22HHIZgm2RvxNc8L66q7QJPVnyJnu7RdK84U3tJE2Oeqnm4MaHD2HlUA41p4F9fjtqXXHd4jN48YoLSGvtdh6mM793sdtn6DqHFG4lPuaOiuD875qtOqliMebYzAaBvBh2YpX9atsh/PCO3bh/73Tb19acRmA5dOBWYm19Yq7WcTqpCd87JSMDz22oWCWMxwuEr0VSIdxsTMyh2a0UdjW2m4d375rEZ36xHdc/dDDxNeHEB+/72HJY5hfK9itthQMRvYmI9gK4C8Ct/s8t3R5Yr+GJE9XCq46LfDaDfEIqa+BW8uocokVwWcvSbiWF8KLKC9fhOe8z2gkHM3WPb5TopG40FN7x9dv1PhTMZKkOp6HwlONXAgAePei5BHgxaqVF8+eziyV0U7lHYjl4x9s7yyF5MXD9nfsCV1onqaze5yoVCKeWrzcWlHLMNU4SDjWngT/72q2hjB9WNA4n1OSYVJ0GhvIs/NILh1ljQZ5P22yGYzJzEddfUtwhbo7GFcI5fuM7t6FCrqNmt1J4IY8mHdy9c0qfTwDaDdYqOy9cT+P9zQH3FYNLXDgA+AsApyultiiljvN/tnZ7YL1Gu1diMonytheQjkt95Ak+GhNzqOuYg+Xt52B8dM1p6IWLzeT2MYfgRqj7e1JXHTf0nZPlOn5052788rfhtuZsUp+ydhgA8PiE574op7Ac+JwUYrRovYCltBwOzVZ1CuRixBx0V9a4VNZWrjQ37ErrZIzmHErjWkqyHPhzks7t3qkKLr97L379SJAiyXPpUErhMJhrdiu5DdVSWJjWwnzjDkDgktUxBxYOCdZI3PWKs1xCSpQpHOpBKivQbE2a/yul8IeX3YjPXR/UXbAi2ErgxzV45P1PuDdZJ9ScRkhAdZM0o3sYQP/udN8lyglVs162UsYTDnGWQyUISEdjDk6jgVzG8t1K0YC0qzWkCf/it8ulNye94weJGyp8A3B2TtQvyub3CeNDyFqExw5FLYdWC6U3bm05mKms/g2Q1vXx0SsewJu+4m22wlZHmhTe+RYUBdlKhrtGu5WaP9NtKHzwe3fhbr9Sfj79pMw5kCYoXXcbGPTPbazlkCAcOBZhuvQO+xk3h+dq+PdrH8atj00kfm/VcTEU41b6xysfwO//+w1Nr79jxySuvHcv5moO1o8VAaApy81xG3jPt+7Etv0zTe+Pwu+N7lGStPia9yaPO64QzhQY5rUoR1JZTYHvJXYE/89WHZRqbqhVP4+rlXAIp0yHA9LzcZ+++1t34My/+1lX959gsile80EANxDRrwHoM6OUekfXRtUHJFkOXp2DhUI2vs5hqlyHRcBQjlNZzYyiwHLwiuCC99Uc1dT3p93cKUU2FeLJZ97YM9VwHIMxc7w3LCvqjKU0MQf+/IEYLbqddhtl33RFL5j8ue38+Z+4+iF84zeP48YPPrujoF6joXRb5zhfcJxA3DFRwtd/swOjRc8FEHfM7ai7DYwN2Jgs1VNaDgrDBRtz/l4DwThbu+xY4Js+er7uB2aq+MINj+JV52/COZvjQ4ZVI+ZgKiY3bZ/Ag/tmoJQCUXC+/+Pah3H3rimsHslj/VgRuybLTemfuybL+O5tO3HK2mGcsGq45XEHwsEJBXJnk4SDcYOsGMphturEWw7GY+Y11m6lGMuh6rjIZYJjnfSPa9oYy4x2K7WyHJot1D1GHUenXOHXvuw8XMamFQMdv78T0lgOnwHwcwA3IYg53NrNQfUD5QRtkusc8glFcNPlOoYLNiyLkM9mwi0J/JbddoZQbzTXNkxHboJ2GTFm8K3uNrSbK85yOBy5aTkNdjCXxcblA9q1kybmwAtHXEFYp9lKU+V6034KrY57qlzH569/BPtnqk1NEdtRqrtQCl5xYoxGF3ezcmEXL+rardRhERz7l9O00Ki7XlaUnaGIcGidJ8/KgnntDvvC4cF9M3AbKvG6KOXtOaCzlfzjazQUHto3g0q90fTeyXINB2ermKu6GBvwLOXoQslu1sMpXCFsMc5V3ZDwTYw5GOeBz29bt1LIcnBRsK2g9XxkITdda1MxLiSeE0nCCwgrajzefSwc5mE5bF7uCYQHUiQYHClphIOtlHq3UuoLSqkv8U/XR9Yj7twxiS/d8Gii792rc8igkI0vgpuuODo1Lloh7bh+y27LsxzM5cWLOYRvgnbaKS8GQ/lsaLcpUzviyTwZdSv57x3IZzCUz+rjraSJOfi9gvLZDCwKa5mdxhymynXtHmDNrdVxf/Wmx/QCdPfOqcTXxcE38bJBG25D6cWoVcyBA6y8qA/MM5V1uV682p8XL3GBULQzHQWkefE25yUnN9y3e7rle3mhYvcMf9eOwyX9udFGk5OlOqpOAwdmqxjKZzFcyDYpONpVOpe+oKxUc0ILdVLMwZyjK4byAOLPb9hyCJ6fqzkYyGWRtXhfEu/zGg1PUJquQxZupnDnY5tJCEh/4zePh2qIqo7XOWC/30V2PpbD+mWe+85MOugWaYTDFf5+zWuPhVTWf/n5Nnzkp/e3CEh72oYXc3CbcrCn/b5KAGIb72WN9hlmzKFUc5qETduAtH/TjhZtOK4K3Eqm5eDfGFMRzY3fO5jLws5Y+j38eJrgbC7rCbrYgHTKKl7Pcghr7q208hsePohT1o6gaGd0HCAtLHyXD+ZDx1FpYS1xgJWF7HxTWdktlVTBG3q947VZKeYiwqFNJhgrC+GYQzggnVRdzec+nw26BgPAA8YiFN0al7XpibkaBvIZDBfsZsuhEsQ8olx+955YYTVXc0P33UzFSwH+4q8eCR2bOUeH8lnks1bs8bVyKxXtDLK++4ivadAdOLjG7FYyrRi2iuJiDpOlGj7wvbvxrZuD7gNVp4HpSt3oapBOONyza0rPVct36z3QJ8LhlfDjDjjKU1kdt4Gbth9CzW3omyq+fYZXBNdQzYvEVLmu86abspUaCnaGvJvP2M8BiG//3T6V1UHOd3HV3KC+wfxO1rqaLIdqYDlkMxRUNqcKSHvP6T5Rkd5KAFBOUcXbaChMl+uouQ2trQGtj3u24mB8OI/T1o00tVNvB6dcLhuwQ2Nt5VZiy4EXBXaldboTHCsMaYQm7/tRtDPhbKU2NSRRwa6U0ota9DVRqoZw8JQF7/hMDdVsUwEg9NmD+SyG8tkmFxAvoBMR5WTHRAl/9rXbcNV9e2FnwnGjUtUJKzgVB/funsbf/vg+XPtgkHVnztGC7VnAcf5/M73VfE+55mIgl4GdCbuVopYsENw/M6GYQ3K2Eu8xMVkOjtssUC3amVS1JDdsO4jf+Zfr8ZUbH9NjBvrArUREFoAPGCmsR20q659/43b8fz+8R0+uvTFBo0bDC5JxbyWguYfQdCViOYSK4LjOwfL3cwgWmOiNB6RrnzGYyyDna/6s+TaUkQboT+Cpcj2U4cCLxICdgW1ZWlPkxYuP+6bth/B3Pw63Oq6ZwsEirQk1GkovKrPVOj573faWWUuzNSfY8MhtGF1Zk2+a2aqD4XwWp68fxb27pztapMuGpWUeY7WFQNRuJX8B4DqHNAHpvVMV7J2qoOYEwiFNLKbuNmBbhGIuGyscEi0HHZD2frMGbsSQE919fOy5rCfw+bse3Duj90M3K4Yr9XCwfCjnuZWi/vcky4GtjnItaNmhj6PmhhbmmUpd32dJLqKincFAPhMbczDrMGoRy2Egl0HWT2qoG10GgEivJXYrVUy3UnJAmoWDmZ1WqQcK3FAhm8py+J7fRJOVVT7njx4qdb1HU0vhoJRqAHhvV0fQBzQaCj+4Yze+/pvABGQz3Fww+GLmbQt5P4gVvUDTZSewHHwNjBdl7q1kR/ZzAICDMaX6adpnDGi3kAqNVS/S/gSOFmDN1RzPhZCxYGfjLAfv95X37sXnI+Y8a1g2tx/3bypzst/w8CF8+Kf346r7kju+mpktVePGaajwXhaNhsLHr3wQe6bK/l4ZGZy+fhTluhvqPdSOcj2oQfHOUdhyiNuMhd1K0ZhDmlTWCz76P3jGP12DmtPwXZFW7MJ+62OHQ8dbZ7dSJOlBV0gnLApz2q3kvY4XZE4zBZIFS+BW8pQNvqbb9s/inM3LAIRrJaKB9QFtOUQD0r5wSLBg6kZ3YsC7Z6Ixh9mqYwjGeCugYHO/s+brUkpwK5Vrrh/4D1sOFaO3GV8XtpLM+ofpSrOriTngu+BM66rquKHYjrk2xFF3G7jG73HGawEfv9tQR9wivR1p3EpXE9FfENHGozXmMGtMuKiJG8pq8SdNIZtB3rccogvKVLkeBKR9AWLuU5C1CBmrOTtiIsZyaCT0lGFKVReDeS+rxctWag4Mm1qNmTHivdcbZ9ZqjjlEm8WZ/uZmt1KzD/Vxf+K2msCmVlV1PG2RtVxTMO6eKuNfr9mGK+/Zizl/3Mv9bVhb5Zi/8xu34+cP7NP/s6vLtBy8molm4cZj2jddDX2PztBqI7g5r7/meJ1zc1kLA7lsk0/8wb0zePm/34DrtwUtGLysNkIxlwkt5u2C/UFVv/ebF6bjx4eaXhOF35P3LQfeoGj3VBnHjw9hIJcJWbdRN+VQYswhyFaamKtpjVovcm448LtqJN8cczCEA1sBnF3FeLEDKzYWlFTnUKr7AWn/nne0smBkAfpCcrLcbDFoy6GFWyk0xw0FaJAr0SNW8j9f9aAWCL95ZEILZE5JLtdcrBkpNI2pG6QRDn8I4K0AfomjNObAGuxbLz4e//fSs0LPhfYa5hvIDtxKoYnkNlCuu9pyYOuibrhLshkLvqISconExRzapUuW6q4/ucNuJQCo+hvMmBPXnEye1eEdg20s8JWIi4V9xjzZzePx3ErBDRmKdfjfy8V1cZjaZ6nmeu0pYnz6rMlNlR3M1Ty3Ep9/Xjw/e912fPqabcZ7XPzwjt24/qFDxnc09+8Pqr2tpvYKuyeD7pm8IOeyFjJGwDaJH98Z3pTHswQyTZo7F0RFz2/Of31cQDppgWfLgZUEVgZM4dAuIJ3TMQevYn+m4mDtaBErhnJ6xzUATbGMQT9bqTnmELSCefNXbsVb/+s2fxyB68YUtGtHC00xh5mKY9R4cIV7WNBz6m/cdWnlVirmMjpbiVNXw232w5aDeUy6UWDNbXJvsuUQKBVWqGNyNCuMj+1fr9mGK/xdAHnr0uWDOZ11Vq67WDvmCYc0LVGOhLbCISbecNTFHFi6n7FhDGdsHAs9F9f330tlZbeS8TwHm/xFV/sy/f0a6n4qKxcSmTfFQf9Cc2Us4N1QuyfL2J2wt0LJd7HkWriVzNTCyajl4PvP47KV9AY1/rkxF6+aditx5lVyQPfxlJaDzgbKNRdhscDaO12BUt5CxFlD/NyHf3o//unKB3Uch8drLlb8Wl5QqkbhIAt08xzumfLOey4T3Ca5DAuH1oL7cn/HNrZE81kLA5HsI/McmL5sdj8Wc9mQWyl1KquvJGjhsGqw6TVRgoC0P58aSsfd1o4WsGIwH3IrRYsqBznmUA13UTXn3y2PTWC7X5tiVkGbC+vqkQLmaq62XABgtlLXwoBTVXm8nCJcsDM60ePmRyfwhi/foj837IoKjr9cc72YWyvLwX/MPN6ZigPHbWCu5mLYX+SjFtPBmfD5GcrbodY2uhLdUADv3T2NhgpqrHZNlpHPWjh+fFBfy1LNxbrRoj+mHlsORGQT0TuI6Dv+z9uIyO7qqBYZs802LxxM3I5hBcNyMG9e17/QXLVr6+Kahp6o2Yyl09EaoYC0t5itGS3ox5xGA3/5/bvxwe/dHTvuuZqLop0N3EpO80IyW61j5VAudJzeex0M+KatFyD3BFg0W4kXrQOmW4m1TA5Ix1gOTFQ4NBoK3799Jxy3ERYOVS4yiytI8sbEi/VgPqsDw1EXCxfGsRvMvGnLEeFQd5UW+CORIDUQXNuxgWBO2FkvZuS2serYYuKbP+cLh+jiHFdcVXMbOuYQF5BObJ8RCUizZsmWw8qhnG5AF4XPQ067lRpaOKwZLWDlUC7WrcTFZ2w5NFRYAJlCr6GAg7M1VOquXrAdtxEStGtGCijVHO1uGchlMFNx9JzTvbH8/znzrKDdSg3c/OgEfnbfPh1An606Wpkwr+9sxcFwwUY2IeYABNdvslzXgmimUtfzap0fz4kKhwORtN/hQtbrmOyyW6nZcrhzx6T//d7523W4jPVjRc9yKNXQaHgK4NpRdiv12HIA8O8AzgHwb/7POf5jRw18g44N2Bg0sheAaPfOQLvKx1gOPKH5/RzoMndsy1gE/nhTa2C30uqRQDg0lDe2OJcT4GlEXszBCi105rhnqw42LPOqKk1No1QLLAduE+A0VFM32qkYy4G1ejtLIasjLvti73QlJEBvfnQC7/rmnbhu28FYy2HAbs4G4nPMFtRwIbAcolr0TdsnQuM1F91oF86a09Bj4xYK1RhLccRQGNJYDjV/EeAsH35ftG4BCBZP08XmuKrJDcWZcuYxP7RvBvtnAtdXU0C6VAcRcM7mZXjns0/Ey87e4D/fLFx4n+u84Vba7QvjdaNF33II5gC7YreOe1bJYD6DoXxzHGi6XMeq4Xzou3ZPlo223MH5Hi3aGBuwUXeVFnTLBnKhdtvadaYtB++zi3aQtcdWB8/3uaqjF3ZzC96ZqlewaidkK/H4dk2WMVmqY6NfnTxdcfQxrvNdPFF3mnm/AJ6lEI45NAuHu/yiTi0cJstYN1bEsoEcDpfqWikYH84ja1FTkH+hSSMczlNKvUYp9XP/53UAzuvqqBYZ03IgIq0pDuTC2Q980UKprKbloAWAd1pto7iGFxM7Q9qyMKtsSzUXuayle7yTX3lccxqJfuK5qmtkKzUibiXv75mKo6sqTeEwVw1iDqw5ccwECG6QVm4lbj/Ox80T3Wx3pFS4jfNBX/vcPVkOCYdZ7VaKizn4loMfAxjMZVHwLYySEQsAgF8/MhH6npmI5ZDLWtolaJ6zEe1qavbxm9aknbFCGVpx8OJtCno74wekIxv1xFkOdbcBOxNOZTUXUX7stV+4Gf/43w82fW/VcCuNFGzYGQvveu5J2OjPgzjXknaZ2r5wMNxKq0byfsyhpl1Gk+UaMhZh03JfOPhuJSDcwnqm4mDLikHzq7B7sqIziPjavvEZW/H1N1ygLULznpwLZSv5x1gP16wUc0FxKV8btpzmai6WDYYtQ17MR2IsB1PJ+v5tO/HUj/0cB2er2KSFQ9AjS1sOkaB0nHCoxLiVzOt6187J0DnZNelZDmMDOUyWakH6eS6jH+smaYSDS0TH8z9EtBVAdxNsFxleNHkRMH/HtVkwhUN4vwZeNMOWg2NkZGQtKzbmAHjaK2cxDNgZuK7XKjnZx+xgMJeJdSvxuGYrDsaKNobz2XC2Us3MVgqEmBlzqPt+VSASMDXcShnL0oE8nui80HIKJbcDBwI/+N6pSsRyiKSKNpoXQ17ohwqBW6lSc0O+3Ju1cPBdCmbMwa+I1VlkTnDOOOYQZymawkEHpP2F5NoH9+PRg+F0Ws5+Wz1cCL0vmn0EGMFNY5x1v5K+aGdQ891AoVbTNRdzVQe7Jsuh7+ZrZVoOywyXGMdz4uZT1bimtu9W2jNVwYrBHAp2BssHc3AaSscQJkt1jBVt7bIczGd1X6bpiOWw2W8Qx0qDaTnwWFcM5nDquhE9/3luDLE7JpJ9x+M9Y+MYztm8DKesHdHJEXyuDhuWAytdgdITJCcEFdKN0GcDQZM8AFq4zlQc/X4WDqZwdxsKE5HU9KFC1t9rxZs30YD0VKmOR43OyJW6iwMzVaxfVsQy35riOV3MZXUjx26SRji8F8A1RHQtEf0CXhO+93R1VIvMVLmus0OAYDGILhhBzCGjXQatYg6cBVFzg0lhZwK3UtRvPVyw9YJdzGXh+kFss+VCqebghZ+6Dj+8Y5dXxJP3LQcnPpV1pur5VUcH7HBA2shWyhmadMVwK5mujgNxqazZYFc78zv5/HF+vJmxxGPY0yQcuMisubFdtK3IUD6rtf9y3dXvXTtawN7pCuaqjr6Rom4lsyK2ZlgOw3FuJR2sDoq0zMK/xw+V8Nov3Ix3fvOO0PhYK149GhYOA0b20VX37sWHf3JfbAuGuqt8N1Qwx2rG4l2pu/qcmlZZuRbWxidLNYwZu43xuY3bZlTXOdgZbYnunSrrGNhARLBMlesYHbBx/PgQhgtZjBSz+jyxFs2um7VjRdgZwpkbx0AE7Jwsa0HGgp+1d/4e9qeP+MKB771SpEhz/VgB333LhVg7WvSz7hp67kz5nzFXdfR50IkWlUAhtHVvpXDGXhRWCGcqda3MsAJkWqgTczU0VJCtCADD+axf5+B9dnSv7rt2TQLwXNvlmquFEruVgMCtWrQzWDZgp2pmeCQkCgcieoX/53YAJwJ4B4C3A3iCUuqaro7K+/4XENGDRLSNiD7Qze/yahNsrdHzRGqupA1M77gKadZ2WRPJZQON3HQ5WUmWQ9HWcYCBXMbTGJ1wN8xPXv0Q7t8zjS/d8CgAL7vJznrae9StxBr1cCGLFUPhbBOvmCyocwC8BblUd3WtAb8+a1FyKmumOSDN5+0Ja4ZRsK1QthVrc2w5DEWyPeLqCKI362DeS9/NZayQcDh17QgAb8FMCkgX7SBeVDME6oiRwcREjwfggK2X9vqv1zwEILwIAIEGv9rwtXNAmgOqP7tvH75046N6EYxaDnaGtLJSqgXCYaRoo1x38ahf/LdvpqIXzjlDsCulcLhU0752IHDZxbqV/M/IZQK30p6pCtb6mTFRZWiq7FkOLz9nA65//7OQzzbHHFhIjBSyOGn1MJ5+4jhWDxewe7KsBRmfb7ZeWYBxTIPnKC++pUi2Uj4bZPdxYkU9YjnMVl0MF7zeS0GKNruV4uocjKC1/73rx4p47qmrMZTPYrrsaOsosBwMRcq/V9gNBQQWEMdDeMc9tnI43nDu5uWo1Bv6nvHcSt555ccCt1LvLIcP+r+/q5SqKqXu8n9a77q9ABBRBsCnAbwQwKkAXklEp3br+6bKtVBGyhhbDrxguOEUQe7KCoS1Wp2RpGMOgVuJJ2w2Q7B0zMF7/TuffSJe/7Tj8De/e6q+GQZyGT3R2bWwb7qid6LiRXQgn21qnwH4XV4rQdfW8aG8nrS8L28Qc/DGU6o5cBtKL4b8+s0rBnBgpqr9zXU3cJ/Z/q52/J1AsJiOD+WxZqQQMs0Pa8uhHApWTkcsh7iYA8MCpWBbKNdcfaOfts4TDo9PlIxiK1ff9NGKWPOcBamspmsuHMAGoHtjTczV8L3bdoXGzGjLwYg5eJZAEEMo1V3UXYXtB7xF3rQcvFRWS7uBTMthtJiF21D47T6vyE6pIBYTrQQ+PFcPzeuBhCA+vx7gmEPgVuLMmKgyNFmqY2wgh4xF+npHYw7mxlc/ftvT8M5nn4h1Y55wCFxg3u+MFg5Z/flAcK2jrSq0MDMEs9d5ILDSea7NVOoYzvP+KuFEi5GiHXKrmp8NBNlR1/zFM3HK2hFdy7HrcBlEQUDejDmwlb3Z2G9hKO9bQAnZSnftnMSWFQNYPZJHpe5i1+FAOLCA3+3fRwU7g7Fi991KrTb7OUREVwE4joh+FH1SKfWS7g0LTwawTSm1HQCI6BsALgFwX8t3zZMpo5MqECwGSZYDN7sDwguXmZEEhN1KZkCa38KP/elTj8OofxPf6zeTG8h5MYcaOBDnYPuBOb1oai3Cz++uO+GAdM0Nth0dymcxPpzHHTsO688CYGQreePkBZonHi+wx48P4eEDc152R8HWmi0RhVNZIwHclcM5rBkt6MAmEAQJ905VMFTI4oRVQ9h+cE6PdSCmziHaEmFIC1Cv1bi2HNaNAvCEw0Ej7XKu6mJ0wNKWQzjmkOxW0tp6wRAOfhD+wExVX79oMJIXvlUjzZZDPRJHYsHJAo43ubENN2fZ34cCYKt2Dg/sCRri7TxcxuYVAyjVXd18rlr3mkcuC7mVvGOMtxyCeJrtW2RT5TrGfeEdWA6B73/LynCgmWMOfD2mjaAvK0Trxoq4Z9eUzqDj+4cX6GjMYbgQtkaidTim1ZbNsHDwnpucq+tr7FkOwf4qpuDieaxjDvWw5UAUJJd4wsHB9oOzfhZXDnYmnDl0UFsO3vlhK9BtKO22HYwEpO/aOYXztizXzRZ3T3nCZ81oQb/GtByW+emt3aSV5fBiAH8N4CCA/xPz003WA9hh/L/Tf0zjtxG/hYhuOXDgAI6EyVJYOLC2FRUOulV1JuitFFdNyRPddCuZAWl2K+lsJSO7ZyAXLHyuUjr4W665WiNZP1bU1buDed+tFEllrbsNfUMNFzzhcGiuBscN3FRBnYM3ADaNR/0FhYXDCau8PPn9fiuJur94ee+1ghbEEcth5VAea0eLEcshqCrdN13FU7auANDcu+jKe/bimzc/DiAsgDMW6YWqmMv4biXvvZtXDGAon8WOiRIOzlQDrbMaVJdGLQedyhpT51BzOGsoKGq0/PYnpruK/56tOvjsddv1eKKWw4Dh1olmoM1UvOIxFv5e4z1Lv57HxVbtA3untVa/83AJlXoDSkFn5UxX6ijV3EhAmr+/OeYQDkhbeuFhoRlYysHibBYHAl7zPT4WwAz6Bjooz11tAfhzNmo5BMLBD3LramQn1PLEdCvZGQuOca8dLtVCClI+a+nvM/d6B6AznYCwq3iu6iKfDZJIVg7lsWeqjO0H5rB1fBBEhFXDBeyfDuY436eblhf1uFiZZKFkBqT3z1SwZ6qCMzaMomBnUKm7mCx5LlcvgzHsVirmMhgbsFE1UrG7QaJwUErVlFI3AbhQKfULADcrpX7BP10bUUqUUpcppc5VSp07Pj5+RJ/F/lMmcCuFpbsuaMp4kyWftfC567bjbf91GxpGCl0mE5OtZGQycUCaHzNTP8/aNIbztizDlpUDvluJqzxdPQFPXD2kx6RTWRsNVBxX32RmQHm4YGN8OA+lvGAZm8psObCFwzcMHz9P8lN8Xz5v41j38/D5eKIB6XVjRVgErB0tYs1oAftnKkYDs1qof9VFJ60CEGQXsQn9xRsexb9d+zCAsHAYzGX0jVrw6wD4hhsueLvabds/i5mqg+N8zZYXq3IkW8mskB6NiTlU/QWQXSr8PjtDetFZPpjTn/+z+/biwz+9Hzc/6llo0VRWXpzLNTfk2rEzpIPjOnEha6FoB0FgDmTyOB89VML5xy1HxiLsPFzWC/5yX7DzJvbLBpsD0nFupZrTQM5fBLMZatLc85HU7brb0MoPY1kUaps9ZyzMzKoRTxPmgkZeiFlBYcuBi+yiMSmlPCFv9oIyz7HpVposBYHj4YId6pLM2/lyRwLb6C9mKllzVSckgM7YMIb79kzj4QOzurhw9Uhen2/AU6oGchms9K0u0wrke5LvvZrTwP2+FXj6+lEUcxk0lCfY+Nj5mrNC6AWkvevaTeshTbbSCUR0H4AHAICInkRE/9a1EXnsArDR+H+D/1hX4IA0c/LaEYwUstjom75Ry4EncsHOYK7m4id37cGP7txtxBzCbqW6kUFhVki7WjgEN9nW8SF8+80XYqyYC6Uwluue5ZDLWnqrQAB++wyC8itTWdOqOQ3dQ3/5YA7j/k5Z+2eqoXxpILBwzAwOILAc2JfPHUprCZYD+1N/76z1+Ok7no7x4TzWjhZQd5UObh8u1fVewpy+CATCgSf9TNXR5rkZ1zEXmoGcp2UFFpKNjcuKuMOvNGW3By8sbDnktOWg9GKn3UqRiuS8ndE3Nh9zxiK96IwP5fXns2XF52nFUE4LfnYrAZ7mbm5dyUHNax/cj3t2e27FrBVYLJV6sA/AqGEJHD8+hLWjBew0dmtj4crWmulW4uM4OFsNdbP9yV27sW3/LPL+8eUylnZjmfEdbyzBvWBnmpeP0aKN+/dM62peIKzdc4yJGxoGbqVwthK3yA5iDmaNjhty8TKcRVYzLAeeG0MckPa/b7oSTkIxEyuiAWlTAJ29aUynfB/vxxtWjxR0jyzAu2/Gh/NG7zJLC9fpiuem4ufqbkPP87WjBf1dB2cDyzebsbB8MKcF0EAuoxW4wx3ssNcpaYTDJwE8H8AhAFBK3QngGV0bkcfNAE4kouOIKAfgUgBNcY+FwG0ozFSckFvpgq0rcNffPl/7W/UOZboFhjehWLvKZS187IoH9KTKxLiV6kYmUzRbyRQOjGVRKChbqrk4MF3F+FA+kp6Y1WmAsxXH6NnS0JXVywdz+lgOzFYDyyEftRyCSnHAm+R2hrBlxSByGQs7Jjxtz3EbuqraNny1uvozl9XWBneQ3DtVgeN6O2GdstYTDudv9TRfO0O6NQO7RZTyXE/lmhuyHIaMtFL2z/INN5zPYtPyAcxWHVgEPO0Ez2XFCwtv7sILSqXu4tu37MSakYIep1lPUNOWQzjBIGsFLc5XDud0PyEWphxMHMxl9fXI+11Z+Vqa2vsGP3/+zV+9Da/4jxsB+HURRsyBz+1yo0jy5edswIZlRew4XNaVw2wp7PU187EYt9LHr/otXvrpX0Epr2XK2/7rdlx9/z7t+jAXfe1WijSaNK1Hk9c/7Tjc8PAh/Nu122K1+2i1NAubpmwlo84BCKxa7/wFFdPhmEPUrVQPuVZNy2E6EmfkPVbMYwQ84WAKoLM2LdN/b9WWQ0ErBoC3sK8cymvLL2dknk37afNm3Iu1/2WDOX2NDs7UQnN984oBvR4U/WwloLstNNIIByildkQe6moRnFLKAfA2AFcCuB/At5RS93bju6ILool5AQEjhdMKn7bXP+047J2uGKmf4ZvMtBxsywK/nescYmRDqIUH4C1s+32NxBwrb/MJeBPZ9GWycBgbsPVNeWCmGuwfHclWYsthzLAcRoteMHH9siJ2HA76BXHfqLhUVvNm4lTIPVNeRbRSXsrpSauH8LtnrAPgaZZOQyFjkXZjMAdnq6g4DSNgGdwwBb9uYKZSx1AuC8sibPIzRN7wjK04Z7PXWd50KxXsDDKWV6X+rVt24O5dU/jAC0/WZr7pUqg6fkU1u5UyYYsQ8CwH19eS2Q23a7KMgm2Fjse0HMr1cMxhw1hgCTJZywqlsrLiceEJK/Gxlz0Rd/3N87BurIhVwwUcnA2sQe51xC4I03LIZy1tyRwu1XForhZq4ZLLBNeUCSyHcIv6WoLl8LqnbsEFW5fjh3fsDmVAMaarDWjOVrL9FGW2rIZjLAcOuHufbcYcCPWG6VaqBW6lvB2OOVScSKJBIPCjW4maAmh8OI+Nfixhq2E5zFQdrXQdmPGUOG05GIJ+uhIWDqzEZS3yug37VtYBw3IAoF2kgBf/4TVgqosZS62ylZgdRHQhAOU33HsnvAW7qyilLgdwebe/5/493nZ7K4fyTc9p37QbCIeMFaSiMqz5aROZFxFTODQCqyJqOcQJh0zkO0o1Bwdmqti8YiCcnui7lQDPP8rHwcJhtOi1T+DHD8xU9WRnrZAXBV5ER4yYAwuKDcuK2DkRuJW068zc7CdGOHAR1d7pig5Gjw/ncdW7LtKvyWctzFa98USF4sHZKip1FyuHPL9u1K1UrruYLjv6WF5w2hocnK3hz555vBb8Wjj42UqAt5Dsmapg68pBXHLmOmN/5nADPK8a3godl7l4skU2U3G05VB1GrpymMdrNwWkA/2KFxsTMxA+Va7razZSyOLSJ2/Sr+NKWd2LSFsOnnAw6xyIPA2WF94dEyUcvypo580C3ww0D+mAtG9t+fum1w3r0YSIsGakgN2TlVDhHmNmcAHN9wzgzelaydvbg8+B6YYr1eJjDlnLc4eZO7mxu2fItxyC9u/1UKDcszoaekxmTYTpFgO8WoTJuX3a2lwzyq6yCraOD+HAbBUXbF0RciuZ15LbogN+ynGphmWDOe/6+K8zYw5A0DyxYFuwLArNpW6RxnJ4M7z9HNbD8/uf6f+/5FFK4eNXPYjVI3k855TVTc/zpDY3vjcXr5+8/Wn4+Xsu0pZENZKWF+qtFKqQDmcrxbmVosKhXHexf6biWw7hICNPtJlq0GmVNRLWJIu5DIbzWRyYqRoZHJypEbilgCDmMDFX04Jiw7IBXY1bdxrhgLRun+FqrZzhVL89UxVdHW1qswBCwiqqjXIXT16EzRumaFgOrKGvGing3c89ydtTWOfdO3rTHfOGBTw3gZdckMFIIRsq9qvWGyHLwTxmhoXubNUJvZddSBxgzRkB5plKPaSdclqniSlM/v4n9+FvfnSv/hyTsaKN6Updu2FW+o3o2D8dtYjNRXbH4XLIZce+75BbKWI5VOre3gVKIdZyALzF1Nsv2V/ADe1+wHC1AUBFu2KDzzJbyRcilgEA3WuJKHwt7CzX6wTHxF2BOZXV7BlmWg7ZDIX2cxg2XDqm5QMA73vBE/CF152n4xXcJmXvtCcQJ0t1z61kzDVWMKbKdS8GYWwENjFX0+5CVl6UQqzlwPMqaZvihSSNcCCl1KuUUquVUquUUq9WSh3q2ogWkZ/dtw+3PT6Jdz77JH0hTfgClmusLamQFnT6+lFsHR/SWk8lkpaXi7EczIB0q5hDVIOeKtdxuFTHquGC1uaJPBOTF/e5qoNC1vOpV/1JZ2arjA/ncdCIOfDiyTcdV6GawXnuOrlxeRGH/EwnL1OFF0or5FaKpjdaFmH1SAHfvmUn3v712wHECAd/oo8U7CaheHC2imq94Xf9zIbcSkEqqxPSAvXzvgtptuLo4rOgBYJ3rKevH9Gv9wKLZoPBeOFgLmRaOFScUIsRXtiHjC1j+TF297HFuWlFvHAYzGfx4Zeejk3LB/R7moTDQA5KQVdMc3B771QFA7lMk9ZrsvNwCZWakZlTa9bieY6YXYjNjKo48v7GNnGWAxCOO/BrzPmuEyUiwoGVormq52YzU0yBwN1bjhEOQ36xqNk+I9RQ0QosB2++GcIhcpxrR4s4d8ty/T+3Sdk/XdWda72AdBBzKOiYgxOyHOqOwuG5uo61mYLIjDmwcGDhEVeEu9CkEQ6/IqKriOj1RDTWtZH0gFPXjeCNz9iKPzh3Q+zzfCP++TfvwB985ka/IVrMQu5f6HIk88J0K5lVxbq3UkwqKxNdJB/3e+msGgkshwE7A8sP6AK+f9S2kPdvgom5cPuElcNelfQsZ0xEFr2g3YEhHPyYAWu3Ow+X4TSCYCQ3/QOCuoAor71wC44fH9QZNFFt1rQcmtxKM1XtDnr1BZvx/NPW6OcKOiBdb4pVAJ6Lw9vXuK41ZL5hmSeuH9V/rx4pYJ/RApsXIC0ctEBsdisdmquGKlZ5cRnOBwKYFz0u0HvV+ZvxqUvPxBNWDzeNnefZqy/YjHM3B0HQ6ELL55L3seBdwvZOV5qEsIlXD1KO1Tz52loULEZZv6dUpR5s4ZlkORTsDKp1LzXXLCBjxoebXbgh4aCzdCi0MLN2PVdz/GsTFnx8zsqGpfnIwTktZFhoAf5e78Ww5RBkK7khrT3XQsACQRxl73RFW4/RbCUzuYCbN2YsQs11Q8WKRUMYmmPgzrasxMYV4S40bYWDUuokAH8F4DQAtxHRT4jo1V0b0SKyYdkA/vJFp+hFPIqppd386GG9zWcU7gffHFxr7q2UzTTvBEcpLIfHfA1ofCivi2L4JjIXjHzW8oviwuYqv/fAbBUzVUcHcM3vmtWWQzAp1/qaKHek3DFRwkzFMRYN063UiL2R/tfTt+Kbb3oKPvzS03HO5mVNQcnAn95sORya89xKBTuDD7zwZDz31MD9N5DLaDPedAOYDBeymKk62tXAhWUMZ1UBvnCYCguHnLHrnw5I+78tCqyg6HaoLBxYYzX9ybyx06rhPC45cz0Gchn8zhlr8SRjF0LzmrI1AMRZDt5c2H5gDsOFrBZGQJD5Fcfx44N+8Vzz4sLfPZTPhuamV6AVVCDHxRwA+P76eO0e8Fx/UUylazDkjjEtB+94SlVHf3b4M4KiwS2+Nbb9wJzWwNlyqDm8nW845sBupUobyyHKUD6LwVwG+wzhsHIo5x970JGXYaHK4+GYA4DQ8Q5FrOR1owUtcPizo90DFpK02Uq/UUq9G15biwkAX+raiPqIqGZSc1ST5gYEkzKalmdbpuUQVEgH+zmo2GA0gKagN+elrxrJY7hggyh8E5ljNifd8qFAOCwb9IKXsxUnZLKa2U5AuNHcel8T3brSC4g9uG8GD+2b0VXTWcvyfdAq9oY1efUFm/Hdt1zYtMDxeR729x4wOTBbRcVxQzcNwwJq/0wlUTh4loOjXQ3FyOeYi8DqkTz2z1R1wV7NPx6+sXMRy6FoZ/T3PuK3zuZzx9dm3VhRa7FstXDaLscjiAj/+kdn4/mnBYLPPA+8HwfQPCdHi9713X5gFisGcyH//ilrRhDl7196Ov7md0/FhuUD2DFR0nP2T596HP7rf53vHZ9uFREWLgXbQsVxQ40X48hnLTSU5+aMu19WRVpyAGFXnelWMufTMm05uKH9SBhW0sp1F2tHi8j5NTh8jfK2d1/c8tgEgKATAL/X3CZ0qAPhAHiupX3TFV2vs3IoDyLytyG1QvPOLKb0AtL1ppgDEHYrAcA5W5Zjs29BcBFutYuWQ9tsJSIaAfB78GoNjgfwfXhC4qgnlHUzUvAthzi3km85OGHLwfJNR6cpIO29z2mo2HgD0Gw5cK/3NSMFZCzCSMHWi405pnzWS5Ob8NMUTcthrOhtEDJdqYcmvw5o++4m8znWWkcHbGxaPoAr7tmDuZqLk9cMh8bp+B1kowt/GthEHi5km2MOM1VU6uFd1ZiCLiRSOmU2yprRAnYeLmuXHzeze+mZ65oWv9UjBTgNhYlSDSuH8qhxKms2EpD2fxdzQdCbff5PWD2M3zw6oa/Nmy7aij8638suylhethAHi4sRF9dgztRkg/NgWg5RFw1r09MVByesGgqdp7MNdxTzxxdsBuC5QK66d6/OznrRE9doPzofZ1Tg5rNe0SF3Fm0VkOYx5WOEOguH0aKNSt1bTMMxh8AVZy7MXMg2Xa7jcKQdORBcF56Ha8cKeOxQSc/nXCaDQ3M1vP6Lt2DrykG88PQ1xnvDRXBh4dDarQR4iReH5+qY8IvSOBZV9FPNTeWGLa5cNoNDszW4DdXWcgCAT/7hmWanHX09ukWaO/lOeBlKf6eUOkkp9X6l1K1dG1EfYWo9ywdzTdlKDFsIcWl57JOP20PacRux8QYgrEkBgRbLWuiyATukYTHcVZMXIDPmMDZgo6GCpncMj3e26qVMmjeDuTA9cf0o7tnlpf6yO8bcRctLb5yHcGC3ktEhk4+RU1njbtAB40Y60UjJNDlt3Qge2jejM6VYM/vkpWfh7196eui1q/00S04DrToN5DNe6iD3HAKChcxrUx22HE5c7Y2DrYKCnQn52FcO57DDdxFGNd/BGIENBHsGAM0uSLPty/LBfOj8nxMjHJiNywZQd5UO2MZlBUUXp4Lt1QnomEOLgDQQFHxF4fTmsWIwN02lYNDY25xjHQD0TokTczWv5U0kdmVH7js+byzkOJtpIJfBN9/0lFD6utlduOo09BjM42nFaDGHyXIdk+Wan8Rg6WPJZcNCjhWofNbSqbbLfRdgUsyBz5HpUSjYVs8D0luVUu9SSt3YtVH0KeZkq7mNxMIfXlyjTcQAT3DU3EZof2kyLAdCvHSIW2M3LCvqxWHj8gEdD7Ajpncum8HeqXCvIiDI9th5uBxrOXgaeiak/a8w3n+6H7y1CDjJD6LquIq/pel8LAd+z0jEcti0YhAHZ2uo+uOKYvpxT4wJ6gLA6etG4TSUbqkRXZBNOBbC+zJ77TP8G9m29ELHYyzmvL0hshbpmAO726KBb2bc2FejycWVa16ggbBwiGK6AFf4ufLMCePxAhMI5sVe3QY6uG48H6JuDW4KlybmAHgZQXEL6/NPW4OPveyJui0LkGQ5BIuo932W3420jslSvSngbt6bdsbS541Ttjme9N7nP6EpKM7dheuu1+q+aGf1NUij8IwWbUyVapjyd8jj6/Cu55yEP75gi64xMcdpKnF8LKFspXxrx07BznQ1lTVNEdxKInofvIC0jiQppZ7VtVH1CUSEV5yzAVfcs9evco4XDjyJ2HIwK6jtrBVyK2WNOoBGi5hD1HIAoDc4B4BPv+psbYGYN9Yfnb8JP75rT1MjOyDYb/fQXC3kMjAXokI2E1qgzcWGM3u2rBwMdSoFPMuh5s7TrZQNUlnNgP9xKwZwp7+ox7mVzMV147L4BZQF2m/87UPjhAzDwoHTWWuGJWQ27LONx4gIQ4UsJkt1HLdyUH/GUD7+e0xtNSqoBhIsh7g0ayabsXQbaTO+BDTHrUzYSuH4R7S7qXcMMcIhRcwhSNusx1p8BTuDS5+8Cbc/Phk6Dj02bRH7FppfuJf3O5QeLtVwuFQLCUYgfB9kLQvrxrzzwYHnN110PNYvK+IPzt2IKNmMl8TxiwcPoFx38dQTVuAbNz+OuuumshzGBmxMlev+PhfBuF56VtBMmlOveU7lshZ2H/SUCr5POdCsVLNwjlLoslspjXD4GoBvAvgdeAVxrwFwZD2ylxD/9IongQi4/qGDcBoqPubA+dWcrRTjVgqa9oXrHNLEHNh83GgUS5nppievGcGrzt+EN190PAZy2ZBGF3UrMeaNb7aDaHUjcE2AGegM3EqN2DqHNCSlsp66bgQ/uGM3gPhF3XwsKeNsw7IiRou2thwGExZtIEixZFOfi+AA4PfP2aBdaSw8deW0/91PP3Gl1gATLQdDYx3MR2MO8ZZDO8YGbMxUHG3lvff5T9BCMQkWXmzFxLmVojEHnodpAtKAF8NaO5acMWXeS3GprPz5BUMoLxvI4Z7dU5ipODFuJVMpIx3I50X2lLUjoey08Hu9rLvv3b4TKwZzeMZJ4yH3YTvGijbm/Lb6prvMhJUZU8lgF90y3TPLszBKNTeF5dB7t9IKpdTnANT9dt1/CuCotxpM+CLWnNZupejGJd7f3nt1n/6M6VZqFXMInuDJFtdmAfA0ko/83hO1ZZEzzHBzMTIDeGxq85iYQosbYWwghzddtBWXPjnQvAK3kkK5nk7LihIIh3Aq62nrggWuEGORsObdylVERDh9/QiqTgPPPnmVbnkQh9dmJKddLV77DO+z3/eCk/G7T/J6QZldeQGv0y0APP3EcZ0+miSETMshahGYAiVp4Y2D5wcvMG+9+ARcdFLrNvbacpjjTetTWA5+hXEtdUC63jLTx3x/JuRWCvf8yhuL6rJBW1fqR91KoVifZWFDJObQiqxlYbJUw9X37cdLzlwH24gxpclWYkH12KFSqHOuST6iTJhWtlmsyvOqnXDI20HFdzdIYzlwZc8eInoxgN0AlndtRH2I7aeGOg0V694wffZAeKLn2K1k7BIXBKRVbI0DAGSMx0eLNvZOV0KWQyt48p29eSycJ140LYfgcb2jm3F833rTU0IplMwHX3hKeJy+1eG6ClPleks/dxK6QroYsRwMLS/OcuBT1MonDwAveuJaKAV86pVnJZ5v5rR1o7jmwf26TUScm8xMZTW5YOtyFOwM/uQpm/H0E+MXZ1NYD0TeH7LmIgvv9e+/ONSZ1IQXpqhbqRWcGcXuR1P4BsIhmsoaqXPIxp9Ls5q61cIaVqJSxBz8gDQrWlHLwbSA7YylkymixxE7lgzp4sTzIllbaYQDp8UeNPqRRYlaDnyfHrdyMGQ18uvauZXyWSu0vexCk0Y4fJiIRgG8B8C/ABgB8K6ujagP4Va/jttANkaa88TWG5eEJmnYrWQbdQ4N1SLmkAkLByAcc2jFnN+E7WknrAw9bvpooxOPi9lY63vycenkvxmQnvL3Fe6UOMuhYFux2pQJV22/53kntfz8V52/Ga86f3Oqsbz2wi143Rdvxg9u97YPiRcOQSorADzr5FV4cO+MTo39u0tOb3oPw5ZDzs/EMRnIJ7uVNiwbABKSj/icm8kD7RgyYg5Zi0JjYQ08OkfythUKSCdaDoYC1WphNed4KFspkoWnLYeMhZyZmt0UkDaETYawcfkAXvnkTXjmE9pvBmbGCVnoaMsljeUQs5NklGhAmt2Xv3/OhpDSwskP7dxZBTsT6ue10KQRDlcrpSoApgBc3LWR9DG5jLcNZy2hh73usFjnRnrBc1nL0o33LPKChJ3WOXCZf1rL4Wa/yOepEeFgBi+j2pSd8fyXnbqF9D7ZTsNrg5ygNbXC7DjKVsygbjBmJdY5LB/M4dGPvbjj72vFRSeNY+vKQXzu+kdCYzPha8MuuM+/9jwo3h2nDWw5xAWZzTqHaFv4VvDCtLwD4cCCKM63zYvycFxAOlXMIRP7dxTzGE2Fymyf4X1GYDmY7rqohm6OJ5fxlLCPvuyJid9vYrqk2F0VWA7tYw7hbYYTYg6RYkquXfo9I2gNeEKkVWyMKdiZnldI30NEvyKijxHRi30r4pjCznhVwDXHjQ0UmjEHL1XV8H36rSy8YLZ3uslwKyXGHMyUxFVD2LxiINGXGYX7IT0xJijJWk1UK9TBvxbZPHHwseu9I+YhHM7Y4G2NyjdYxiLtE+fitrhiqm5gWYQLjl+BbX6vojjLgTVec4Fv565ieEe+wRjhULAtbUkm1RDEwVloKwabexYlkc9m9FyOCt7EVNZsBtW6i5rbOuZgfl6r7LVsRNNnopX/5jat5sLbMubQQUDf/C4gELK2jnmkjzkAaMqiYqLbzf7dJafhlU/eFKol4te1cykBniuwp9lKSqkTiGgTgKcDeDGATxPRpFLqzK6Nqs8wWwHHBqSNbKVohS/vlOYYeyCY24QmWQ7m57z14uPx5885MfV4v/3mp+DgbDU2g2fZQA47JspNWqHdgQkd9z72XSfdGK14xknjeIYRQDXbVa8eyeORg3MJ1SDdYeVQXm+TGac1ssbbqSAFvCI4IN5yIPIsptmq09Hi9odP3oTjxgdbprzGMZj3UnCjx3jy2mG89sItuPD4FaHH89w+I6Hbqn5dyHKYT0A63DOMPyOftUIV/1FlyQ65xjq1gI0EEHYrWR0EpIvxWYEm0e6+f/KULbGvK9qZkBWZBNeddIs07TM2AHgqPOHwJAD3Ari+ayPqQ3iSzlWd2FRWs84hGqS0M0FAmidgxhAOSQqn+T0FO5OopcWxbqzYpI0wvHg3xRzmueBxQJqDeUk3RmefSdrVcdq6Udy0fSK0Y1m3GTcCu7GWgxWvcadhIOc1aUtKdR3IZTzh0IFbaf1YEb93Vnxn4VYM5jzhEBUq+WwGf/uS05peX8hmvH23/dia3SYgDbS2HDKJAenwIhoqgvOFg9dCJjqHjUSQToWD/3qzzXmgMLW/J4YLWV2fkJzKmk7YvPbCLSilWPS7ncqaJubwOLw9nf9/pdSbuzaSPoYneLnuxt60PLEaqlljsbMWyuV6aEP2UIV0gnQwLYq4lh3zhW+uqJ85ycXQDm52xp1GF0I4ZC3Sfuf3Pv8JOG7lIJ598qoj/ty0mBlFcYsMn6uoItDJ5ydp+UP5LA7N1VoWsC0UwRag6a45v44zZNLFHNJlK4UsB90+I5wyzKmsgKfkRO+dsOXQqVvJe73pqtLdU1NYDpZFGC3aTUVwJuYuhK14jtF5uBVclKhU8jpyJKSZFWcB+DKAPyKiG4noy0T0+gUfSR8T7AWtYrWlpJQ8IHAruUYBXVAEl1znwJo8t3teKHTMoUk4pA++mbAw5Erb+biVoniWQ9Cb6NUXbF6UxZIxaxHi/M2ZSLZSpzzl+BU4y2jPbTKQzyyoMtAK3f8p5TUPNkrystvTZSu1CEgb7UjMOT7YIpV1KO+lO8fFtsIxh/klVpgLeyfZSkAw9xNjDjG90I6EfNbbFrVbVnWamMOdRPQwgIfhuZZeDeAiAJ/ryoj6EPNiZmMshyTfKT/n9WxR+r38Ea6rYNmtYw6dBtbawTdVtDCIF/lOLYdsU8yh81TWKGtGC6kzs7pBSDjExpjC2Uqd8tGXnZH4nFfhvjCLRzsGteWQVjiELYekcZqPpwlIR++ZaD1AwUhlJSKMDeRitfNoCnknZFtYDmkVprGijceQIpV1Hi1m4jC3Cp1P25p2pIk53AIgD+AGANcBeIZS6rEFH0kfY17MuEmX8ZvpKRVjOehspaDdd0ZbDiox0MqvXaiJxFz0hHE8eqgUar8BGG6lDhc8drMdnFs4y+Hrb7igY7fAQrLSdCvFpbLyuZqn5dCKwVxm0Y69c7cSWw7sVkpwifpdbL0K8/ZuJTtyz1gW4dLzNupUbP4MvhdWDedDApwxhVKnlgMfi7mwa+GQ8vyMDuRC8bIoppBbCDiDr1J3m+7nhSBNzOGFSqljppdSHLkU5ip3X81EbhjbItQbjVC7bzah3Ra+QnY9LbQWec7m5Thn8/Kmxzu9ERjW+g7OVDGQyyyIBhPtObTYDOYyOtgXpzXqrqxdSK8dyGc7XtjmC5/ntGnCfC5mKnUQNWv8odfaVttGjKzpx33Ox14eWFf8vXwvfOIPz4xtmRJOjZ2fW8m0HILeSuk+a6xox8ZCmKhFdKRwVXu1S0HpNG6lY1owAOGLmTTpshlCzW12O9kZC3VHwWkEAWldBOc2kJSUorWqRVootKukwwWPg7e7p8pY26Jv0VKCiDA+nMeOiXLsjWzP0wWXhtPXjWKu2r2WCCZcT5BWyPHxTlcc2G1iYflsBjNwWrpkeDFvt5AXIlk+T1gT3579yOoc2K3UbDmkXcz/6PxNLTsLFBfYcigYlkM36K2KtkQIdXtM0JZ0mmqTW4nrHFSwQ5x/UzUU2tY5dMOXGEfQAbNz4bB+rIhdk+V5VUf3KyuHkoVDNy2HtzzzeLzlmccv+OfGMXgEbqV2C5xZm5CEGZBO81nt7gU70lupE1hAjYViDulTWQHggq0rcMHWFYnPF3ILbDlo4dAdy2FxVp4lTrgVcIJbyX9NNObgtc9ooG5USIf2S0j4zm4FpJPopBo0ypM2epXYC5HG2i+wTztucTtx1RC2rhzElpWDiz2sBUULh5SLH2cRTZVqbeel7kCaos6hXXbWmtGCVx3dJtnBbE3TebaSbzkMmtlK6eoS0hLtrXSk6AaHXerMmmg5ENHLWr1RKfW9hR9OfxJyKyVZDgmZF7ks91ZqaKvDNBba9VZaNLfSPFNZAeBJG8Zw+d172968SwndIC9mYdg6PoSf/8UzF3lEC89Qh9lKnOF2aK55o50oPI9aWw7sVmotHJ536hpc//6LU7WPyfodlDtNB7ZbWg4Lcw9y0d5CxdT4uvUi5vC7/u9VAC4E8HP//4vhZS7NWzgQ0T/5n1+DlyL7OqXUpP/cBwG8HoAL4B1KqSvn+z0LRZosCI41NBXB+V1ZnZg6BwDJXVkX3a10JJbDGICFyVTqF7hKerHOfy/o1K3EwqGasK+JSRpXkL5n2lSDWxZh1XC6eFbOFw6dXrekVNZMpGPtkXDeluX4zz85F2dvGluQz+PrtugxB6XU6wCAiK4CcKpSao///1oAXzzC7/0ZgA8qpRwi+gcAHwTwfiI6FcCl8LYkXQfgaiI6SSnVvQYiKYjuTRv/mngTOWtZcBre3rRslpvCoV3MYbEsh/nGHACvwV/WIqzoYD+BfudFZ6xFqeY29aA6mjALDdMwbKRLtlt8gyBy+4B0u5hDJ+gU8A7vm6dsXYHXPGUzTlkbBLuLdqblRlKdYlmE56asfk6DWefQDdLM/I0sGHz2Adh0JF+qlLrK+PcmAL/v/30JgG8opaoAHiGibQCeDODGI/m+IyWcrZTkVooPrunWGzVXa9amotQ2IL1o2Urzz8AZzGfxldefj+NXLW0fvMnJa0bwV79zaq+H0VU6LYLLZS2d4ts25sBupRbzKbAcFlA4zPMzVwzl8b8j+3C89qlb2u6o10tYketWQDqNcPgfIroSwNf9//8QwNULOIY/hbdHNQCshycsmJ3+Y00Q0RsBvBEANm06IlnVFjtFilw2IbimNwKqu0GFdAq3Er92sQLSvKvXfGIOgNcSQlhadCocAM96qNSr6d1KLV7XDcuB75eFcAeuHS3qlvH9SM/cSoxS6m1E9HsAnuE/dJlS6vvt3kdEVwNYE/PUh5RSP/Rf8yEADoCvpR+yHtdlAC4DgHPPPberLTvTxByS0vL48bK/1wMQFQ794VY6EstBWJqsHinAIq/iOC3DhSwOzKQQDja7rFoEpLthOSS4d49G8n1S53AbgBml1NVENEBEw0qpmVZvUEo9p9XzRPRaAL8D4Nkq2EZrF4CNxss2+I/1lHC2UnIRnPd8c/sMwHMrBQHp4PnEzX4WOSAd7YApHP2sHyviF++9GBti9gpPguMOaesccpkjL4LrBBY4C912ph/hc9yt3eDankEiegOA7wD4jP/QegA/OJIvJaIXAHgfgJcopUrGUz8CcCkR5YnoOAAnAvjNkXzXQhDafjChh72tWwFEspX8Rb5cDzYKMjuMJsUc+CWLFXOIbqwiHBtsXD7QUddfTsdM2suB0UVwLWMOXQxId7AfxlIln/V2Duyl5fBWeEHhXwOAUuohIjrS5vr/Cq+Z38/8iXmTUurNSql7iehbAO6D5256a68zlYBIn/hOLQej3Xe8Wyn+O3kv5cWrcxDLQWgPp7O2m5c8j1o23ksoHD0SeFyLFavrJUSEVcP5tqnA8yWNcKgqpWqsXRBRFsAR+fiVUie0eO4jAD5yJJ+/0JgTrV22UvR5OybTybwXWmltlkVtNbSFYihvI5e1xHIQWjKc99xKC1Pn0A3LIV1LjqOFX/9lS+/9EZFGOPyCiP4SQJGIngvgzwD8uGsj6kOIghbESW4eOyFbyezFFJet1GoO5zJWS5/tQvJH52/CBVuXL6j/Vzj6YMuhnbtz+WAOg8aWm3EktZw5Emy/XXg3dkY71kgjHD4Ar2L5bgBvAnA5gM92c1D9iM1dV1t0ZQViYg4xWxemKYIDgH94+Rk4eW18B8qFZrRo46xNyxblu4SlCwek27ltXn3BZjznlNUtNfiMFX/PHAnZDPV0L5CjiTSprA0A/+n/HLPYWQuouYlaTpL/NLxREFsOwfOtpvGLz1g7v8EKQpfoJObQrjGhrauZFzbmsFhxuqOdNDvBPRXA3wLY7L+eACil1NbuDq2/YDM6yYfK7qPoZj+rR4Ic8mjL7ujfgtDvcFv2hUgV7UZ8YDGTOI520riVPgfgXQBuhdcM75iknX80yXI4YXwI+azlNStj4WC27BbZICwh0sYc0pDUVeBI8CwHuakWgjRXeEopdYVSar9S6hD/dH1kfQZbDO0a70W1oGzGwsn+zlVmvIJfJpaDsJQI3EpHPm+zXYg5iFtp4UhjOVzjt9j+HoAqP6iUuq1ro+pDcpnWwqFVw69T1o7gzp1TkcpoQkMpsRyEJcVIIV0qaxrSbvbTCReesAKrRtK3AxGSSSMczvd/n2s8pgA8a+GH079wvUFynUOyFnTK2hEAwOMTQTG4ZzEosRyEJUXagHQaiAh2hpridEfCq87fvGCfdayTJlvp4sUYSL+jKy8TTOBWlZmnrfOEw8RcXT9mWQBciTkISwvdW2mBiiWzlpW4L7vQW9L0VlpNRJ8joiv8/08lotd3f2j9hXYrJVQst6r2PHvTMvzF807C374k2B+ALQaxHISlxFjRxgtOW4NzNy9MTcy6sQLWjvVvW+xjmTRupS8C+AKAD/n//xbe/guf69KY+hLWlJJ7KyXHHCyL8LZnnRh+TAuHhRylIHQXyyL8xx+fs2Cf99N3PF0CyH1KmquyUin1LQANAFBKOTgGU1rbNfSyO8y8YINByvyFY5mCnTlm+iAtNdKsZHNEtAJ+sz0iugDAVFdH1YfkMhayFiUu5p12mAwK4hZmfIIgCAtJGrfSu+Hts3A8Ef0KwDiAV3R1VH2InbVa9mxJqnNIwgq63B754ARBEBaYNMLhXgAXAXgCvNYZDyKdxXFUYWdal+Xras+UaXlBEdwRD00QBGHBSbPI36iUcpRS9yql7lFK1QHc2O2B9Rv5bOvKy077xOj9MVq23hMEQegNiZYDEa2BtyVokYjOQtBAdATAwCKMra94xbkbcfr60cTn7YSd4JLIcLbSMWeDCYKwFGjlVno+gNcC2ADgn43HZwD8ZRfH1JecvWkZzm6x30HQPiPdam9JtpIgCH1MonBQSn0JwJeI6OVKqe8u4piWJHoP6ZQxB5IiOEEQ+phWbqVXK6W+CmALEb07+rxS6p9j3nbMYncYc2ADQ0SDIAj9SCu3Em/jNLQYA1nqdNqbPiMV0oIg9DGt3Eqf8X//78UbztIlsBzSxhzErSQIQv+SZpvQcQBvALDFfL1S6k+7N6ylR7bDbCVpnyEIQj+TpgjuhwCuA3A1jsGeSmnhLKVOK6TFrSQIQj+SRjgMKKXe3/WRLHFOXz+Cl565Dk/aMJbq9SxExHAQBKEfSeMg/wkRvajrI1niDBdsfPLSszA6YKd6vaSyCoLQzyQKByKaIaJpAO+EJyDKRDRtPH7EENF7iEgR0Ur/fyKi/0tE24joLiI6eyG+px+RIjhBEPqZVtlKw938YiLaCOB5AB43Hn4hgBP9n/MB/DuCPayPKiTmIAhCP5Nmm9CvENEbiOjkBf7uTwB4H/x9InwuAfBl5XETgDEiWrvA39sXWBJzEAShj0kTc/g8gLUA/oWIthPRd4nonUfypUR0CYBdSqk7I0+tB7DD+H+n/1jcZ7yRiG4holsOHDhwJMPpCUHLbpEOgiD0H22zlZRS1xDRLwGcB+BiAG8GcBqAT7V6HxFdDWBNzFMfgte473kdjzY8rssAXAYA5557rmrz8r5DiuAEQehn0hTB/Q+8Vho3wqt3OE8ptb/d+5RSz0n4vCcCOA7AnX4wdgOA24joyQB2AdhovHyD/9hRRxCQ7u04BEEQ4kjjVroLQA3A6QDOAHA6ERXn+4VKqbuVUquUUluUUlvguY7OVkrthbcd6Z/4WUsXAJhSSu2Z73f1M5Zs9iMIQh+Txq30LgAgomF4+zt8AZ67KN+F8VwO4EUAtgEoAXhdF76jL5BsJUEQ+pk0bqW3AXg6gHMAPAovQH3dQg3Atx74bwXgrQv12f0M9+ezRDoIgtCHpGmfUYC3E9ytSimny+M5ZtBuJZENgiD0IWncSh9fjIEca0i2kiAI/Yxsb98jdLZSb4chCIIQiwiHHiGWgyAI/YwIhx5Bkq0kCEIfI8KhR/i7ikpXVkEQ+hIRDj1CspUEQehnRDj0CIk5CILQz4hw6BGku7L2dhyCIAhxiHDoEcEe0iIdBEHoP0Q49AiJOQiC0M+IcOgRgVtJpIMgCP2HCIceIV1ZBUHoZ0Q49IiMZCsJgtDHiHDoEZYUwQmC0MeIcOgRpHeCEwRB6D9EOPQIS+ocBEHoY0Q49AgdcxDpIAhCHyLCoUdot5LEHARB6ENEOPQIS2IOgiD0MSIceoQlRXCCIPQxIhx6BPdWkpCDIAj9iAiHHkFSBCcIQh8jwqFHaItBZIMgCH2ICIceIZv9CILQz4hw6BGWxBwEQehjeiYciOjtRPQAEd1LRP9oPP5BItpGRA8S0fN7Nb5uI9lKgiD0M9lefCkRXQzgEgBPUkpViWiV//ipAC4FcBqAdQCuJqKTlFJuL8bZTWSzH0EQ+pleWQ5vAfAxpVQVAJRS+/3HLwHwDaVUVSn1CIBtAJ7cozF2FbYcpEJaEIR+pFfC4SQATyeiXxPRL4joPP/x9QB2GK/b6T921CExB0EQ+pmuuZWI6GoAa2Ke+pD/vcsBXADgPADfIqKtHX7+GwG8EQA2bdp0ZIPtAZKtJAhCP9M14aCUek7Sc0T0FgDfU0opAL8hogaAlQB2AdhovHSD/1jc518G4DIAOPfcc9VCjXuxkJbdgiD0M71yK/0AwMUAQEQnAcgBOAjgRwAuJaI8ER0H4EQAv+nRGLtKYDGIdBAEof/oSbYSgM8D+DwR3QOgBuA1vhVxLxF9C8B9ABwAbz0aM5UA063U44EIgiDE0BPhoJSqAXh1wnMfAfCRxR3R4iN1DoIg9DNSId0jdLaSXAFBEPoQWZp6hN4JTmIOgiD0ISIcekRGKqQFQehjRDj0CIk5CILQz4hw6BFSBCcIQj8jwqFHEIV/C4Ig9BMiHHoE7yEtwkEQhH5EhEOPELeSIAj9jAiHHkESkBYEoY8R4dAjpH2GIAj9jAiHHiExB0EQ+hkRDj1CdoITBKGfEeHQI0gC0oIg9DEiHHpERmIOgiD0MSIcesT5W5fjTRdtxclrRno9FEEQhCZ6tdnPMc9wwcYHX3hKr4chCIIQi1gOgiAIQhMiHARBEIQmRDgIgiAITYhwEARBEJoQ4SAIgiA0IcJBEARBaEKEgyAIgtCECAdBEAShCVJK9XoMRwwRzQB4sNfjWGRWAjjY60EsMnLMxwZyzIvHZqXUeNwTR0uF9INKqXN7PYjFhIhukWM++pFjPjbox2MWt5IgCILQhAgHQRAEoYmjRThc1usB9AA55mMDOeZjg7475qMiIC0IgiAsLEeL5SAIgiAsICIcBEEQhCaWlHAgohcQ0YNEtI2IPhDz/LuJ6D4iuouI/oeINvdinAtJu2M2XvdyIlJE1FfpcPMhzTET0R/41/peIvqvxR7jQpNibm8iomuI6HZ/fr+oF+NcKIjo80S0n4juSXieiOj/+ufjLiI6e7HHuNCkOOZX+cd6NxHdQERPWuwxhlBKLYkfABkADwPYCiAH4E4Ap0ZeczGAAf/vtwD4Zq/H3e1j9l83DOCXAG4CcG6vx70I1/lEALcDWOb/v6rX416EY74MwFv8v08F8Givx32Ex/wMAGcDuCfh+RcBuAIAAbgAwK97PeZFOOYLjTn9wl4f81KyHJ4MYJtSartSqgbgGwAuMV+glLpGKVXy/70JwIZFHuNC0/aYff4ewD8AqCzm4LpEmmN+A4BPK6UOA4BSav8ij3GhSXPMCgBvOD4KYPcijm/BUUr9EsBEi5dcAuDLyuMmAGNEtHZxRtcd2h2zUuoGntPog/VrKQmH9QB2GP/v9B9L4vXwNI+lTNtj9s3tjUqpny7mwLpImut8EoCTiOhXRHQTEb1g0UbXHdIc898CeDUR7QRwOYC3L87Qekan9/vRRs/Xr6OlfUYIIno1gHMBXNTrsXQTIrIA/DOA1/Z4KItNFp5r6ZnwtKtfEtETlVKTvRxUl3klgC8qpf4PET0FwFeI6HSlVKPXAxMWFiK6GJ5weFovx7GULIddADYa/2/wHwtBRM8B8CEAL1FKVRdpbN2i3TEPAzgdwLVE9Cg83+yPlnhQOs113gngR0qpulLqEQC/hScslippjvn1AL4FAEqpGwEU4DVrO1pJdb8fbRDRGQA+C+ASpdShXo5lKQmHmwGcSETHEVEOwKUAfmS+gIjOAvAZeIJhqfuhgTbHrJSaUkqtVEptUUptgeenfIlS6pbeDHdBaHudAfwAntUAIloJz820fRHHuNCkOebHATwbAIjoFHjC4cCijnJx+RGAP/Gzli4AMKWU2tPrQXUTItoE4HsA/lgp9dtej2fJuJWUUg4RvQ3AlfCyOz6vlLqXiP4OwC1KqR8B+CcAQwC+TUQA8LhS6iU9G/QRkvKYjypSHvOVAJ5HRPcBcAG8t9da1pGQ8pjfA+A/iehd8ILTr1V+WstShIi+Dk/Ar/TjKH8DwAYApdR/wIurvAjANgAlAK/rzUgXjhTH/NcAVgD4N3/9clQPO7VK+wxBEAShiaXkVhIEQRAWCREOgiAIQhMiHARBEIQmRDgIgiAITYhwEARBEJoQ4SAI84CIxojoz/y/1xHRd3o9JkFYSCSVVRDmARFtAfATpdTpvR6LIHSDJVMEJwh9xscAHE9EdwB4CMApSqnTiei1AF4KYBBeS4+Pw2vD/ccAqgBepJSaIKLjAXwawDi8Iq83KKUeWOyDEIQkxK0kCPPjAwAeVkqdCeC9kedOB/AyAOcB+AiAklLqLAA3AvgT/zWXAXi7UuocAH8B4N8WY9CCkBaxHARh4blGKTUDYIaIpgD82H/8bgBnENEQvI1duM0LAOQXf5iCkIwIB0FYeMxuwA3j/wa8e84CMOlbHYLQl4hbSRDmxwy8lukdo5SaBvAIEb0C0Psl93a/YEGIIMJBEOaB3wX2V/5m8f80j494FYDXE9GdAO5F/PavgtAzJJVVEARBaEIsB0EQBKEJEQ6CIAhCEyIcBEEQhCZEOAiCIAhNiHAQBEEQmhDhIAiCIDQhwkEQBEFo4v8BzE6hp/nEmJQAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(times['L1'],whitened_signal_L1)\n", + "plt.title('L1')\n", + "plt.xlabel('time')\n", + "plt.ylabel('whitened waveform')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "y_data_noisy_array=np.zeros((3,256))\n", + "y_data_noisy_array[0,:] = whitened_signal_H1\n", + "y_data_noisy_array[1,:] = whitened_signal_L1\n", + "y_data_noisy_array[2,:] = whitened_signal_V1" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "import h5py\n", + "\n", + "with h5py.File('data_092501.h5py','w') as f1:\n", + " \n", + " \n", + " d1 = f1.create_dataset('x_data',data = x_data)\n", + " d2 = f1.create_dataset('y_data_noisy',data = y_data_noisy_array)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} -- GitLab