{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Part 2: Normalization and background removal\n", "\n", "*by Morgane Desmau & Marco Alsina*\n", "\n", "*Last update: June 2021*\n", "\n", "This notebook explains the following steps:\n", "\n", "1. Normalization of a spectrum.\n", "3. Background removal of spectrum.\n", "\n", "**Important:** This tutorial assumes you have succesfully completed the previous tutorial in the series:\n", "- [Part 1: Basics of data processing](01.basics_data_processing.ipynb)" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Python version : 3.9.4\n", "Numpy version : 1.20.3\n", "Scipy version : 1.6.3\n", "Lmfit version : 1.0.2\n", "H5py version : 3.2.1\n", "Matplotlib version : 3.4.2\n", "Araucaria version : 0.1.9\n" ] } ], "source": [ "# checking version of araucaria and dependencies\n", "from araucaria.utils import get_version\n", "print(get_version(dependencies=True))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 1. Retrieving the database filepath\n", "\n", "`araucaria` contains spectra from different beamlines as examples and for testing purposes.\n", "The [testdata](../../testdata_module.rst) module offers routines to retrieve the respective filepaths.\n", "\n", "In this case we will be reading and processing a sample from a minerals database measured at the Fe K-edge in the P65 beamline of DESY, Hamburg (data kindly provided by Morgane Desmau):\n", "\n", "1. Fe_database.h5\n", "\n", "We will use the [get_testpath()](../../testdata_module.rst#araucaria.testdata.utils.get_testpath) function to retrieve the filepath to the database." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# retrieving filepath\n", "from pathlib import Path\n", "from araucaria.testdata import get_testpath\n", "\n", "fpath = get_testpath('Fe_database.h5')\n", "\n", "# checking that filepath is a Path class\n", "isinstance(fpath, Path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " **Note**\n", " \n", " If you prefer to process your own database, just modify the filepath to point to the location of your file.\n", "
" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 2. Summarizing a HDF5 database\n", "\n", "It is illustrative to first summarize the data contained in an `HDF5` database. Here we use the [summary_hdf5()](../../io/io_hdf5.rst#araucaria.io.io_hdf5.summary_hdf5) function to produce a summary report." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "=================================\n", "id dataset mode n \n", "=================================\n", "1 FeIISO4_20K mu 5 \n", "2 Fe_Foil mu_ref 5 \n", "3 Ferrihydrite_20K mu 5 \n", "4 Goethite_20K mu 5 \n", "=================================\n" ] } ], "source": [ "# summarizing database\n", "from araucaria.io import summary_hdf5\n", "\n", "report = summary_hdf5(fpath)\n", "report.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As seen in the report, spectra in the database were acquired in transmission mode (mu), and were merged from at least 5 scans. `araucaria` also distinguises a reference measurement `mu_ref`, which is convenient to verify that spectra is properly aligned within a database.\n", "\n", "The [read_hdf5()](../../io/io_hdf5.rst#araucaria.io.io_hdf5.read_hdf5) function allows us to read a single [Group](../../main/main_group.rst#araucaria.main.group.Group) from a HDF5 database.\n", "In this case we will read the dataset of ferrous sulfate measured at 20 K." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "True" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from araucaria import Group\n", "from araucaria.io import read_hdf5\n", "\n", "name = 'FeIISO4_20K'\n", "group = read_hdf5(fpath, name)\n", "\n", "# checking the group class\n", "isinstance(group, Group)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 3. Spectrum normalization\n", "\n", "Analysis and comparison of spectra acquired under different conditions requires normalization.\n", "In this regard, XAFS spectra is commonly normalized by setting the absorption edge step to one $(\\Delta \\mu_0 \\sim 1.0)$.\n", "Such approach depends on the value of the absorption threshold ($E_0$).\n", "\n", "The [find_e0()](../../xas/xas_normalize.rst#araucaria.xas.normalize.find_e0) function allows to find $E_0$ for a single scan group:" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "e0 value is 7124.723 eV\n" ] } ], "source": [ "from araucaria.xas import find_e0\n", "\n", "e0 = find_e0(group)\n", "print('e0 value is %1.3f eV' % e0) " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the previous value to normalize the spectrum and automatically update the group with the [pre_edge()](../../xas/xas_normalize.rst#araucaria.xas.normalize.pre_edge) function.\n", "\n", "Here we use a dictionary to specifiy the normalization parameters, including the pre-edge and post-edge fitting ranges, as well as the coefficients for the pre-edge Victoreen and the post-edge polynomial functions." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "
\n", " \n", " **Note**\n", " \n", " We specified the upper end of the post-edge polynomial to be `inf`.\n", " This value defaults to the maximum recorded energy in the scan.\n", " Please check the documentation of [pre_edge()](../../xas/xas_normalize.rst#araucaria.xas.normalize.pre_edge) for further details.\n", "
" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "edge step is 0.1821 a.u.\n" ] } ], "source": [ "from numpy import inf\n", "from araucaria.xas import pre_edge\n", "from araucaria.plot import fig_pre_edge\n", "\n", "# pre-edge parameters\n", "pre_edge_kws = {'pre_range' : [-160, -40],\n", " 'post_range': [150, inf],\n", " 'nvict' : 2,\n", " 'nnorm' : 3}\n", "\n", "pre_data = pre_edge(group, e0=e0, update=True, **pre_edge_kws)\n", "print('edge step is %1.4f a.u.' % group.edge_step)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we can plot the normalized spectrum with the [fig_pre_edge()](../../plot_module.rst#araucaria.plot.fig_xas.fig_pre_edge) function. The function accepts a dictionary of parameters for the figure, so in this case we specify the figure size with the `figsize` key." ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcUAAAFeCAYAAAD0YAQEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8rg+JYAAAACXBIWXMAAAsTAAALEwEAmpwYAABuj0lEQVR4nO3dd5xU5dXA8d+Z7cAWeocFpLelCooUQWwIFhRssUSRJBpjogm+GmOMJkaNGjXWqFjBgiAgIkIABUW6gPTO0nvdvuf9495dZtfdZdi5u7PlfD+fYWdueebMMHfO3Oc+RVQVY4wxxoAv1AEYY4wxZYUlRWOMMcZlSdEYY4xxWVI0xhhjXJYUjTHGGJclRWOMMcZlSbECEJGxIvJ4qOMoiIicEJHmoY7DGK+JyBwRucO9f6OIzPC4/EQRUREJL8a+t4rIPC/j8YqIfCkit4Q6jsJYUixn3APxsIhEhTqW/Py/JHKoajVV3RyqmEz5JSJbRWSviFT1W3aHiMwJYVgFUtUPVHVwqOMoa0TkURF533+Zql6qqu+EKqYzsaRYjohIInABoMDQUn7us/61aowHwoF7gy1EHPZ956GK+p1gH5Ly5RfAAmAskL/6oZaIfC0ix0Vkrog0hdwvg+dEZJ+IHBWRFSLSwV0XLyLvish+EdkmIg/nfHG41S/z3X0PAY/6LXvRLWutiAx0t38CJ2G/5FaZvuQuVxE5J8Dnmyciz7hnwltE5NKSfkNNmfc0cL+IJBS0UkTOE5FF7udxkYic57dujog8ISLzgVNAc/fz+GsR2eAeK38TkRYi8r2IHBORj0Uk0t2/uohMdT+vh937jQqJI7e6UkT+6B4DObcMERnrrosXkTdFZLeI7BSRx0UkzF0X5n7+D4jIZuDyot4YERkjIpvc17FaRK76+SY/P1b94t3s7rtFRG50l/vc43Kb+53xrojEu+tyqnN/KSLbgf/5LRslIrvc1/UHd/tLgP8DRrjvw49+/y93nMXz3SIi29335aGi3hNPqKrdyskN2Aj8GugGZAB13eVjgeNAXyAK+Dcwz113MbAESAAEaAvUd9e9C3wOxAKJwHrgl+66W4FM4B6cX+sxfsvuAyKAEcBRoIa7zxzgjnwxK3BOgM+XAdwJhAG/AnYBEur33W4h+7xvBQYBnwGPu8vuAOa492sAh4Gb3c/o9e7jmu76OcB2oL27PsL9PE4G4tzlacAsoDkQD6wGbnH3rwlcA1RxP7OfAJP84sv9vLuf33kFvIbG7uf4MvfxJOA1oCpQB1gI3OWuGw2sdfepAcx24w0v5P25FmiAc3IzAjjpd2wXeqy6z30MaO1uWx9o796/Hed7pjlQzX3v33PXJbrxvOuWEeO3bJy7rCOwHxjk7vMo8H6+uP3ft0Ce7w33uTq7/19tS/RzF+oPvt0C/I+CPjhJo5b7eC1wn3t/LDDeb9tqQJZ7cF2Ik3x6AT6/bcLcD1g7v2V3cfoL51Zge74YbiVfonIP6pvd+7kfdr/1CpwT4PNt9FtXxd23Xqjfe7uF5sbppNjB/UKvTd6keDOwMN8+3wO3uvfnAI/lW6/A+X6PlwB/8nv8L+D5QuJJAg77Pfb/cr+VfEnR/SLPLR+o6x4DMX7bXA/Mdu//Dxjtt24wRSTFAuJbDgzzi6fAYxUneR3BSfgx+cqYBfza73FrnO+dcE4nqeZ+63OWtfFb9hTwpnv/UYpOioE8X6N8r2FkSX7urPq0/LgFmKGqB9zHH5K3CnVHzh1VPQEcAhqo6v+Al4D/AHtF5HURiQNqAZHANr8ytgENCyrTz051P51++zQIIP5Anm+P32s45d6tFkDZpgJT1VXAVGBMvlUNyPt5gsA+w3v97qcU8LgagIhUEZHX3Kq9Y8A3QEJOdWcA3gTWqeo/3cdNcc7adovIERE5gnPWWMfv9fjHm/+15SEivxCR5X5ldcA5znIUeKyq6kmcM8fRbixfiEgbvxjyH6PhOAk9R0Hvaf64A/lOCPT59vjdP0UJfydYUiwHRCQGuA7oJyJ7RGQPTrVIZxHp7G7W2G/7ajjVJLsAVPUFVe2GU13UCngAOIDzi6yp31M1AXb6PS5oCpWGIiL59tlVxPY5Ank+YwrzF5yqdf+Et4u8nycI7DMcqD/gnLmcq6pxOJcnwLkMUSQRGePu+0u/xTtwzhRrqWqCe4tT1fbu+t34Hcc4r6Ww8pviVCvejVNdnACsyhdboceqqn6lqhfhVJ2udcuCn7+nTXCqYf1/OBT0nuaPO5DvhECfr1RZUiwfrsSpDm2HU4WThHNt8FucxjcAl4lIH7eRwN+AH1R1h4j0EJFzRSQC55pDKpClqlnAx8ATIhLrHmS/B/I0ny5AHeC3IhIhIte6cUxz1+3FuTbwM0E8nzGo6kbgI+C3founAa1E5AYRCReRETjHyFSPnjYW58zxiIjUwEnMZyROA7HfAleqaorfa9gNzAD+JSJxbiOTFiLSz93kY5xjq5GIVOfnZ8b+quIknP3uc96Gc6bor8BjVUTqishQcbq6pAEncL5fwLk2eJ+INHN/XP8d+EhVM8/wsv/snlm3B27D+b8C5zshUQpv+Vvc5ysxlhTLh1uAt1V1u6ruybnhVIveiFPd8CHOQXsIpyHOje6+cTi/Ag/jVE0cBJ5x192Dkyg3A/PcMt46Qyw/AC1xzvyeAIar6kF33b+B4eK01HuhgH2L83zG5HgMJxkA4H7uhuCc0R0E/ggM8bvEEKznca4LHsBp9T09wP1G4Fz/XCOnW6C+6q77Bc5lhNU4x+SnOGdr4BynXwE/AktxGp0USFVX41z//B4n8XQE5ufbrLBj1Yfznu3C+b7oh9OAD5zj8T2cquItOD+i7wngNc/FaTAzC3hGVXMGMvjE/XtQRJYWsF9xn6/ESN4qZ2MKJyK34lwg7xPqWIwxoSdO3+ktQEQoz+68ZGeKxhhjjMuSojHGGOOy6lNjjDHGZWeKxhhjjMuSojHGGOOqkKOc16pVSxMTE0MdhjGFWrJkyQFVrR3qOEqKHYOmrCvsGKyQSTExMZHFixeHOgxjCiUiRQ7hVd7ZMWjKusKOQas+NcYYY1yWFI0xxhhXhaw+NcacJiJv4QyHtk9V84+PiYg8wOlhAcNxxsisraqHRGQrzlydWUCmqnYvnaiNCQ1LiiZXRkYGycnJpKamhjqUCiM6OppGjRoRERERyjDG4oyT+25BK1X1aZwZ7hGRK3Dm6Tzkt8kAD8cTNSFUGY/xsz0GLSmaXMnJycTGxpKYmEjeGWdMcagqBw8eJDk5mWbNmoUyjm/cMSoDcT3OzAWmAqpsx3hxjkG7pmhypaamUrNmzUpxsJQGEaFmzZrl5le5iFQBLgEm+C1WYIaILBGRUaGJzHilsh3jxTkG7UzR5FFZDpbSUs7ezyuA+fmqTs9X1V0iUgf4WkTWquo3Be3sJs1RAE2aFDo/rgmxcvaZDNrZvl47UzTG5BhJvqpTVc2ZqX0fMBHoWdjOqvq6qnZX1e61a1fYcQlMBWdJ0RiDiMTjTDb7ud+yqiISm3MfGAysCk2ExkBmZslP2WhJ0ZQpW7dupU2bNtxxxx106NCBG2+8kZkzZ3L++efTsmVLFi5cyKOPPsozzzyTu0+HDh3YunVr6IIu40RkHM4M7a1FJFlEfikio0VktN9mVwEzVPWk37K6wDwR+RFYCHyhqoHOPm9MgbZu3Urbtm258847ad++PYMHDyYlJYXly5fTq1cvOnXqxFVXXcXhw4cB6N+/P//3f/9Hv379+Pe//03//v2577776Nu3L23btmXRokVcffXVtGzZkocffjjo+CrVNcXs7GxWrVpF69atiYqKCnU4Zdpfp/zE6l3HPC2zXYM4/nJF+zNut3HjRj755BNef/11evTowYcffsi8efOYPHkyf//730lKSvI0ropOVa8PYJuxOF03/JdtBjqXTFTe2vP3Thyu0Zm2o98LdSjlRiiP8Q0bNjBu3DjeeOMNrrvuOiZMmMBTTz3Fiy++SL9+/XjkkUf461//yvPPPw/AkSNHmDt3LgBTpkwhMjKSb775hn//+98MGzaMJUuWUKNGDVq0aMF9991HzZo1i/0aKtWZ4t69e5k4cSIrV64MdSimCM2aNaNjx474fD7at2/PwIEDERE6duxoZ4SmQPXSt9F2z+RQh2EC1KxZs9wft926dWPTpk0cOXKEfv36AXDLLbfwzTen23ONGDEiz/5Dhw4FoGPHjrRv35769esTFRVF8+bN2bFjR1CxVaozxfr163PbbbfRuHHjUIdS5gXya6+k+J/F+3y+3Mc+n4/MzEzCw8PJzs7O3aa8dHkwpiwpK8d4WFgYR44cKXL7qlWrFri///dDzuNgrztWqjNFcJqKiwiqGupQTDElJiaydOlSAJYuXcqWLVtCHJExJhjx8fFUr16db7/9FoD33nsv96yxtFWqM8Uc69atY86cOdx2221ERkaGOhxzlq655hreffddkpKS6NGjB61atQp1SMaYIL3zzjuMHj2aU6dO0bx5c95+++2QxCEV8Yype/fuWtRcbjt27GDmzJlceeWVVK9evRQjK9vWrFlD27ZtQx1GhVPQ+yoiSyry4NpnOgY992i8+/do6T1nOVRZj/GzOQYr5Zli48aNue2220IdhjHGmDKm0l1T9JeWlsbOnTtDHYYxxpgyolInxUmTJjF+/HiysrJCHYoxxpgyoFJWn+bo168f2dnZhIWFhToUY4wxZUClTor16tULdQjGGGPKkEpdfQqQlZXFzJkzc/u9GWOMqbwqfVL0+Xzs3LmT/fv3hzoUEyL5Bxg3xpScF154gbZt29KwYUPuvvvuIredM2cO3333XSlF5qjU1afgTEB500032XXFciQrK8v+v4wpp15++WW+/PJL5s6dy5n6ss6ZM4dq1apx3nnnlVJ0dqYIkPsFe/To0VKZr8sULmfqqFtuuYVOnToxfPhwTp06RWJiIo899hh9+vThk08+YcaMGfTu3ZuuXbty7bXXcuLEiQLLe/rpp+nRowedOnXiL3/5S+7yJ554gtatWzNo0CDWrVuXu3zRokV06tSJ3r1788ADD9ChQwfAScQPPPBAblmvvfZayb4RxlRAo0ePZvPmzQwdOjR3aihwZr4499xz6dKlC4MGDWLv3r1s3bqVV199leeee46kpKTcIeBKWqU/U8xx4MABXnnlFS699FK6dz+7gUamrthFvbhouifWKKHoQmPs2LE/W9a+fXt69OhBRkYGH3zwwc/WJyUlkZSUxKlTp/j444/zrLv11lsDet5169bx5ptvcv7553P77bfz8ssvAxAdHc28efM4cOAAV199NTNnzqRq1ar885//5Nlnn+WRRx7JU86MGTPYsGEDCxcuRFUZOnQo33zzDVWrVmX8+PEsW7aMzMxMunbtSrdu3QC47bbbeP311znvvPMYM2ZMbllvvvkm8fHxLFq0iLS0NM4//3wGDx5Ms2bNAnpNxpQ5X46BPR7PGFSvI1z6ZKGrX331VaZPn87s2bOZOnVq7vI+ffqwYMECRIT//ve/PPXUU/zrX/9i9OjRVKtWjfvvv9/bOItgSdFVs2ZNBgwYQMuWLc9637s/XAbA1icv9zqsSqlx48acf/75ANx000288MILwOnpYxYsWMDq1atzt0lPT6d3794/K2fGjBnMmDGDLl26AHDixAk2bNjA8ePHueqqq6hSpQpwehqaI0eOcPz48dyqmhtuuCH3wJ0xYwYrVqzg008/BZxahQ0bNlhSNMYDycnJjBgxgt27d5Oenh7S4yqkSVFELgH+DYQB/1XVAn9iiEgPYAEwQlU/LaFY6NOnT0kUXW4VdWYXERFR5PoqVaoEfGaYn4gU+Dhn+hhV5aKLLmLcuHF5tvvhhx+46667AHjsscdQVR588MHcZTmef/75nz1HTrmFUVVefPFFLr744rN/QcaURUWc0ZW2e+65h9///vcMHTqUOXPm8Oijj4YslpBdUxSRMOA/wKVAO+B6EWlXyHb/BL4qjbgOHTrEF198YdcWQ2j79u18//33AIwbN+5nP1Z69erF/Pnz2bhxIwCnTp1i/fr1nHvuuSxfvpzly5czdOhQLr74Yt56663c6407d+5k37599O3bl4kTJ5KSksLx48eZMmUKANWrVyc2NpYFCxYAMH78+NznvPjii3nllVfIyMgAYP369Zw8ebJk3whjKomjR4/SsGFDwJktI0dsbCzHjx8v1VhC2dCmJ7BRVTerajowHhhWwHb3ABOAfaUR1NGjR/nxxx/ZvXt3aTydKUDbtm1555136NSpE4cOHeJXv/pVnvW1a9dm7NixXH/99XTq1IlevXqxdu3an5UzePBgbrjhBnr37k3Hjh0ZPnw4x48fp2vXrowYMYKkpCSuueYaLrjggtx93nzzTUaNGkXv3r1RVeLjndkX7rjjDtq1a0fXrl3p0KEDd911l/1wMsYjjz76KNdeey0XXHABtWrVyl1+xRVXMHHixFJtaBOyqaNEZDhwiare4T6+GThXVe/226Yh8CFwIfAmMLWw6lMRGQWMAmjSpEm3bdu2FSsuVSU1NZWYmJiA90kc8wVQ/q8ploVpZbZu3cqQIUNYtWpVSJ7/xIkTVKtWDYAnn3yS3bt38+9//zuoMm3qqFJgU0cFpCwc46FwNsdgKM8Uf35RB/Jn6OeBP6nqGUfsVtXXVbW7qnavXbt28YMSyU2Ip06dKnY5pnz64osvSEpKokOHDnz77bc8/PDDoQ7JGFOKQtnQJhlo7Pe4EbAr3zbdgfFuo4hawGUikqmqk0o6uDlz5rBo0SLuvfdeIiMjS/rpjCsxMTFkZ4ngtHDNaeVqjKl8QpkUFwEtRaQZsBMYCdzgv4Gq5rbLFZGxONWnk0ojuJYtW+Lz+QpspWiMMaZiCllSVNVMEbkbp1VpGPCWqv4kIqPd9a+GKjaAhg0b5raGClR2tuLzWRI1xpRdqlqpfuyfbbuZkPZTVNVpwLR8ywpMhqp6a2nElN+WLVs4duwYnTt3LnB9dvbpNzw1M4sqkTYegjGmbIqOjubgwYPUrFmzUiRGVeXgwYNER0cHvI99g5/BwoULOXjwIJ06dSrwQ5TpnxQzsqlilx+NMWVUo0aNSE5OrlSzAkVHR9OoUaOAt7ekeAaXX3450dHRhf6q2jZ5Cjlv48yhV3Leb++i0bCCulua0jJp0iRatWpFu3Y/GwsiYGPHjmXx4sW89NJLHkZmTGhFRETY0IRnYLNknEG1atUIDw9HVX/WWTv5889Z8djfch8fO3CYFQ89RPLnn5d2mMbPpEmTWL16dajDMMaUQ5YUA5CZmckbb7zB7Nmz8yxf+8wzZKSl5T7OCIsgKyWFtTZhbbEVNnXUrFmz6NKlCx07duT2228nzX3fx4wZQ7t27ejUqRP3338/3333HZMnT+aBBx4gKSmJTZs2/ew53n//fXr27ElSUhJ33XUXWVlON9i3336bVq1a0a9fP+bPn5+7/aZNm+jVqxc9evTgkUceye3cD4VPTWWMKZ8sKQYgPDycFi1aUL9+/TzLU3bvJktOT3abERaRu7wiGDt2LMuXLwec+QTHjh3LihUrAMjIyGDs2LG5fQpTU1MZO3Ysa9asAZyBD8aOHZs7V2Fh8x0WZN26dYwaNYoVK1YQFxfHs88+y6233spHH33EypUryczM5JVXXuHQoUNMnDiRn376iRUrVvDwww9z3nnnMXToUJ5++mmWL19OixYt8pS9Zs0aPvroI+bPn8/y5csJCwvjgw8+YPfu3fzlL39h/vz5fP3113nONO+9917uvfdeFi1aRIMGDXKX+09NtXz5cpYsWcI333xz9m90CRORt0Rkn4gU2AFURPqLyFERWe7eHvFbd4mIrBORjSIypqD9jalILCkGaODAgbkTzuaIqV+fLN/ppJgeHpm73BRf/qmjZs2aRbNmzWjVqhUAt9xyC9988w1xcXFER0dzxx138Nlnn+VOBVWUWbNmsWTJEnr06EFSUhKzZs1i8+bN/PDDD/Tv35/atWsTGRmZpwP/999/z7XXXgs400nl8J+aqmvXrqxdu5YNGzZ4+VZ4ZSxwyRm2+VZVk9zbYxD4oP1liWZnhzoEU85ZQ5uzkJ2dzYoVK2jevDlxcXG0uf9+dj9+uqo0IyyCsJgY2pTihJglyX/qp7CwsDyP808dFR0dnedx/qmj/KsczyTQpuLh4eEsXLiQWbNmMX78eF566SX+97//5dlmx44dXHHFFYAz67eqcsstt/CPf/wjz3aTJk066ybqhU1NVdao6jcikliMXXMH7QcQkZxB+8vsBdvs7CzCfPZb3xSffXrOwrFjx5gyZUpulWKjYcNo+YfTCVBq1aHTE09Y69Mg5Z86atCgQWzdujV3qqj33nuPfv36ceLECY4ePcpll13G888/n/v/4j/dTOPGjXOnkxo9ejQDBw7k008/Zd8+Z9KVQ4cOsW3bNs4991zmzJnDwYMHycjI4JNPPsmNp1evXkyYMAH4+XRSBU1NVU71FpEfReRLEWnvLmsI7PDbJtldVmZlZ59xmGRjimRJ8SwkJCRw55135plqqNaFA3Pvn/PnRy0heiD/1FH33Xcfb7/9Ntdeey0dO3bE5/MxevRojh8/zpAhQ+jUqRP9+vXjueeeA2DkyJE8/fTTdOnS5WcNbdq1a8fjjz/O4MGD6dSpExdddBG7d++mfv36PProo/Tu3ZtBgwbRtWvX3H2ef/55nn32WXr27Mnu3btzp5MqbGqqcmgp0FRVOwMvApPc5YEM2p9LREaJyGIRWRyqfnBZNp2XCVLIpo4qSaUxbU1WVhZhYWGs23Oci593Glc8fmUHburVtESftySVhWllQj11VEFOnTpFTEwMIsL48eMZN24cn59Ft5uyMHWUW306VVU7BLDtVpzB+FsCj6rqxe7yBwFU9R+F7+0I1dRRKffvIKZaXOk9rym3CjsG7ZpiMSQnJ/Pxxx9zww03kJF1et7F1AyruqmIlixZwt13342qkpCQwFtvvRXqkDwlIvWAvaqqItITpwbpIHCEMwzaX9ZkZduZogmOJcViqFmzJvXq1QMgK88wb5YUgxXqqaMKcsEFF/Djjz+GOoxiE5FxQH+glogkA38BIiB3rOHhwK9EJBNIAUaqU4VU4KD9IXgJRdLs7Nx63mxrfWqCZEmxGGJiYnKb5u/cdjh3eYolRVMGqer1Z1j/ElDgeHYFDdpf1qhqblLULDtTNMGpPA1tPgAScV5xovs4SGlpaWxdt4qctgdHTmUEX6gx5qz4t4uwM0UTrMqRFD8ARkH2NmV1rWOwzXkcbGJcuXIlq77/HzXlFAAHT6QHG6kx5iypnk6EWXamaIJUOZLiQ8ApeHLgCv76y4/YXHc/nHKXB6FLly50v3g4B7Uq4T7h0ElLisaUtjwt6O1M0QSpciTF7c6fKzbUok3Ubt658ss8y4srLCyMKgm1AagbG8WBk2ln2MMY4zX/pGitT02wKkdSbOL86bi9IbqnGdTbwsqmO3OXByMzW2kTto8+2cs5dCI1+AKNMWfFv/rUxj41waocSfEJwB0r+raJl5GFj/FXTnOWBykrO5uTGkl4lQROpaRx8ISdLQbrtddeo379+iQlJeXeVq5cGeqwTBmVp6FNlrUAN8GpHEnxRuB1oCk021+T6D1tiKy+i3kdg5/RICNL2ZGdwDk9LySdcNbtKZfDfJUpK1as4PHHH88ds3T58uV07Ngx1GGZssovKaqNfWqCVDmSIjiJcSuQDaOevITt1GLs98lBF5vTeb9jw3iqShrLfloXdJnlRfLnnzPzgguYcs45zLzgApLPYuizoqxcuZKkpCRPyjIVX94zRbumaIJTeZKinzoJsfTsfwkzt5zi+00Hgyor002KdeOi6RudzL5V8ytFX6nkzz9nxUMPkbJrF6iSsmsXKx56yJPE+NNPP3HbbbflVp2+/vrrHkRsKir/a4rZ2RVvLGdTuirtiDa3nJfIx/PX8M6ELzj3/pvwFXMOtiw3AYb5hCM12nIsLKzYZZUna595hqyUlDzLslJSWPvMM0HNFLJjxw7q1KnDihUr8iw/efIkv/71r4mMjKR///7ceOONxX4OU7FonupTO1M0wan4396FiI4IY0SbGOqnbGH8zB+KXU5GlnNAhvuE+nVrs/mo87gizj7iL2X37rNaHqgVK1bQpk2bny3/7LPPGD58OG+88QaTJ08O6jlMxZInKWrFr6UxJavSJkWAW6/oxylfDMt/mEd6RvF+YeZcUwzzCY0SYth7LIXPJk5k/ITPmb4quARRlsXUr39WywO1cuXKApNicnIyjRs3Bpz+ocbksGuKxkuVOilGRoTTuWcfqmaf4u0pc4tVRs41xfAwHw2rx5CtgkTEMGP9EUa/v4R5Gw54GXKZ0eb++wmLicmzLCwmhjb33x9UuStXruS9997LvZ7YpUsXTpw4QaNGjUhOdhpGVYZrtiZwaq1PjYcqdVIEGHlRL06Fx7Jl5SJOpJx9H8Oca4rhPqFhgtMZsnbr7sw+VhsQHpq0skImxkbDhtHpiSeIadAARIhp0IBOTzwR1PVEgA8++IDk5OTcrhjLli2jWrVqXH311UyYMIFf/epXXHHFFR69ClMR2IDgxkuVtqFNDp/PR7/+F/LO9O9597vN/Hrg2c08n+lXfdqwunPmNH+TkwQvbx7Jwm0HuOPdRXw/ZiDVq0Z6G3yINRo2LOgkGKiqVavy9ttvl8pzmXLGGtoYD1X6M0WAy85PomqL7rz67XaOnuX0T5n+DW3iowGYvXYfPrJpenQFd5yTSmpGNv9bu8/zuI0xgA3zZjxkSdH1x0taUyX9MC9+OvOs9vM/U4yOCKNWtSjW7jmOio9rrxvBL0ZeS42qkXwXZH9IY0zB8lxTtGHeTJAsKbra1ItjUM1jpG5ezKZdgV8DzMrOJtwniDhzf+dUoTauXoVmTRsRExNN72Y1+H7j/grfTcOYULAuGcZLlhT9jLzyMsLIZuyEaQHvk5mthPkk93GjBCcptqxTDYCUlBQaH/iB2JM72HkkpcAyjDHFl2eWDOuSYYIU0qQoIpeIyDoR2SgiYwpYf6OIrHBv34lI55KMp0OLRvhqt8B3cAtL1wU22WJmlhLulxRrVXMa07SsGwtAdHQ0tWokkE4YK5KPeh+0x+xs1lv2fpa8PEnR3m8TpJAlRREJA/4DXAq0A64XkXb5NtsC9FPVTsDfcOa6KFG3X+tMLfXJ5MDOFrPynSn2al4TgH6tnMmHRYSbbxhBMrX4MfmI5/F6KTo6moMHD9oXi0dUlYMHDxIdHR3qUCq0vF0y7EzRBCeUXTJ6AhtVdTOAiIwHhgGrczZQ1e/8tl8ANCrpoBrXqUFc8yTmrt/Hkm2H6Na0RpHbZ2ZnExF2+rfFpR3rs+LRwcRFR+QuiwoPo03dWLZuWMeJCxpTrVq1Eos/GDkd5Pfv3x/qUCqM6OhoGjUq8Y9t5WbXFI2HQpkUGwI7/B4nA+cWsf0vgS9LNCLXb0ZcysdPz+ap6esYP6pXbiOaguQ/UwTyJMQcnepGEL5mBYsXJ9C/f3+vQ/ZEREQEzZo1C3UYxpyVPDUb1vrUBCmU1xQLyjQF1tuJyACcpPinQgsTGSUii0VkcbBnOlWjwrnnwnM4uH09n/5vcZHbZuS7pliYHq0aMy2tNbGJNlmuMV6y1qfGS6FMislAY7/HjYBd+TcSkU7Af4FhqlpoZz9VfV1Vu6tq99q1awcd3MgejekavY/F82eTklZ4h/6sbCUs7MxJ8cK2dTjqi2Pi8l1k2a9ZYzyTp6GNjX1qghTKpLgIaCkizUQkEhgJ5JkTSESaAJ8BN6vq+tIMLioinJ7n96OKpvD6Z4V36M/IyiYigPkT46IjuKZbI6YvXs+/nvs3W7du9TBaYyqvPNWnlhRNkEKWFFU1E7gb+ApYA3ysqj+JyGgRGe1u9ghQE3hZRJaLSNF1mR67un93TkXVYO/6pew9fKzAbTKzNE9Dm6LcP7gVCQkJbDwRxsb9p7wM1ZjKy7/1qVpSNMEJaT9FVZ2mqq1UtYWqPuEue1VVX3Xv36Gq1VU1yb11L834fD4fV15+KVFk8spHBbfxycjKJiL8zNWnADWrRfHhqPNZV7UTj8zczYk0az5uTLDyXEfMtu5EJjg2os0Z9O54Dpm1WzE7OZON+078bH16VjbhAVSf5qgTF82z1yWx79gpnnt/Kunp6V6Ga0yl4197arNkmGBZUgzAvb+4mkPZtfj7H9Y471gi8IGzLiMrm8gAq09zdGtanevaVSM7+UemfbvE63CNyUNE3hKRfSKyqpD1hY4cJSJbRWRlKC5fBCxvVgxdHKZCsKQYgFqfR/HreYkcarqcLzuvhW3AKOAD95pigNWn/sZc24dFMd35+4KT7Due6nnMxvgZC1xSxPozjRw1IBSXLwKVp0uGNbQxQbKkGIiH4PqlDekQto+vLp5BhmTBKWd5xllWn+aIi47gmV9cwLHUDO4au4CjKWc3j6MxgVLVb4BDRaz/TlUPuw9LZeQob/lfU7QzRRMcS4qB2A4JJ6vRYlkPqscc5u2LFuQuTz+L1qf5takXx+MD69Lu4Dzuen2WNbwxZUH+kaMUmCEiS0RkVFE7ejmAxtnQbP/O+3amaIJjSTEQTZw/N0+7kLT0KqzvOZ/D0anQBDKzsoksRvVpjiG92tGgaXN+2nOS/3672aOAjTl7hYwcdb6qdsUZuP83ItK3sP29HkAjUIr1UzTesaQYiCeAKhCeFU6/WQOJDU/hP9fOgCeKX32aIzo6mt/cdgPntmnC2O+2ctLOFk0IFDZylKrucv/uAybiDORftuSZOsqqT01wLCkG4kacpgdNYfDCLqQdaMmURulsv+QUJ9KyqBoVFvRT/LJXA5qnb+XDBVuDLsuYs1HYyFEiUlVEYnPuA4OBAluwhpKNaGO8ZEkxUDcCW0GyhXufuJrj4dW48c0FHDiRRlzMz2fFOFs1OEGniD1M+PZH0jLtwDbeEZFxwPdAaxFJFpFfBjhyVF1gnoj8CCwEvlDV6aX+As4gT48MO1M0QQrl1FHlVt24aB68qBnTZ3zNUalP3djgJ5Ft1aoVfYbdzNjxq/ls8E6un9PEuZb5BE5CNqaYVPX6M6y/A7ijgOWbgc4/36OM8UuEYmeKJkh2plhMVyY1oFXkUQbG7eXyTvWDLk9EGLgqkQ774nj3nHVkq+bpD2mMKYRNHWU8ZEmxmGJjY7mwf18S0vdz6uBuT8qUh4VrdmfTvcH3fJnkXtpx+0MaYwqWp/WpJUUTJEuKQejduzfx8fF89dVXZHvRaXg7XDmvK9tTGjCu7b48y40xBcvb0MaSogmOJcUghIeHM2jQIPbu3cvy5cuDL7AJVD8RywWzL2Rek2MsaHwwd7kxphD+Z4fWed8EyZJikNq3b8+AAQNo2bJl8IW5/SFvWtaU5qeyGH/NRDKrZTrLjTEF8j9RtOpTEyxLikESEfr27UtsbGzwhbn9IaMbhnHthhpUid/DzH9ssNanxhTB+ikaL1lS9MjBgwd57733OHLkSHAFuf0hb5vcm7kR3Xkr4kjeg94Yk1ees0M7UzTBsaTokfDwcLZv387XX3/tSXnREWGMGtCGhVsOMn3ROk/KNKYi8v/RKFk2TKIJjiVFj8THx9OnTx9Wr17Nli1bPClzRI/G9Io7yg9ffsS6LTs8KdOYiscvKWbbFGwmOJYUPXTeeeeRkJDA9OnTPemiER0Rxh9HXsTizKY8P2+PBxEaU/HkubyQlR66QEyFYEnRQxEREQwePJh9+/axbNkyT8rs2rwOlw3sw5c/7WPWmr2elGlMheJffZptSdEEx5Kix9q0acPQoUPp1KmTZ2XeeUFzkmpkMuXT8Zw4lepZucZUBP5nir4sqz41wbGk6DERoUuXLkRERHgzyg0QEebj1t6J+DJTeGNWmZu5x5gQ80uKdqZoglTkLBkiUiOAMrJV9Yg34VQce/bs4ZNPPmH48OHUrx/8gOFXXtCZ2clZvPjdbs5rf5Bzm9f0IEpTXojICwFsdkxVHy7xYMoY9eubaNWnJlhnOlPcBSwGlhRxW1GSAZZXCQkJpKamMn36dM/6GT5xdScSa8Tw+IczOXzSDv5KZhhFH4dLgGtCFl1InT6+wqz1qQnSmeZTXKOqXYraQES8aVFSwURHRzNw4ECmTJnCqlWr6NixY9BlVosK596kcJZ/t5bnPl/AYzf09SBSU048p6rvFLWBiFQvrWDKEs32rz61pGiCc6Yzxd4BlBHINpVSly5dqF+/Pl9//TXp6d6c2V0+4Dyymp3HB6uOs37vcU/KNGWfqj7vxTYVkn9DG7WkaIJTZFJU1TM2dQxkm8pKRLj00ks5fvy4N7No4Iyc87tr+lE1Mpy/TVllQ8AZRGRIqGMIpZz5FDPVR3hBZ4pHdsCKT/KNHG5MwYrd+lREpnoZSEXVuHFjbr31Vnr06OFZmTWqRjK6SzXq7ZjDtCWbPCvXlFvefbjKoZwfhulEEFbQmeK4kfDZHbDXWm6bMwumS8adnkVRwTVt2hQRITXVu5Pq6/q0JT2iGi/9bwPpmTYIcmWmqn8JdQwh5SbFDMILToo5yXDv6lIMypRXxU6Kqrrby0Aqul27dvH888+zYcMGT8qrVbMGV147gjWHsnn3+62elGnKPhH5RUG3UMcVSrlnihJJeP6kmOl3Lf/Q5lKMypRXZ2p9CoCIbMG/3bNLVZt7HlEFVadOHapVq8aXX35Js2bNCA8P6K0v0oDWdRhwTgIzZs1maKd61Imv4kGkpozzryqNBgYCS4F3QxNOGZB7phhBBPmS4uGtp+9bUjQBCPRMsTvOwdgDuAB4AXi/pIKqiMLDw7nssss4fPgw8+fP96zc25JiaUcy//78O8/KNGWXqt7jd7sT6AJEhjquUFJ3DsUMiSRc/aaOykyD1ZMAOKHRpO3fGILoTHkTUFJU1YN+t51u0+8Lg31yEblERNaJyEYRGVPAehGRF9z1K0Ska7DPGUrNmzenffv2zJs3j8OHD3tSZt/unaD9JYxbk8aa3cc8KdOUK6eAlqEOIqTcM8VMiSAcv6S4+C2Y/QQAc7M7weFtoYjOlDMBJUUR6ep36y4io4HYYJ5YRMKA/wCXAu2A60WkXb7NLsU54FsCo4BXgnnOsmDw4MH4fD7Wrl3rWZm/u7wLcTER/P3zpdZFo4ITkSkiMtm9TQXWAZ+HOq6Q8rumGKV+1xB3/whAikaySRsQkXYYPBqP2FRcgV7Y+pff/UxgC3BdkM/dE9ioqpsBRGQ8zlBW/k3EhgHvqvNNv0BEEkSkfnlu5BMXF8fdd99NbGxQvynySKgSya+6xrJz8dd8Mrsa113Y3bOyTZnzjN/9TGCbqiaHKpiyIOd3YGpYLFFZGWhGChIRg6afQoDB6f/kQt9yfGRDymGoWsC4wSmH4ZtnoP3V0KhbqcZvypaAkqKqDiiB524I+E8nnwycG8A2DYFymxSB3IS4b98+qlevTkRERNBl3nJRN+5ZsYbvFh9kWN8sosLDgi7TlD2qOjfUMZQ9ztlfWkQcpEPq8SPE1Ihh7/697MxuyQ6tyyF1f4Se3F9wUpz7NCz4Dyx7D0bNhRrNSjF+U5YE03k/2Ot7UsCy/HV/gWyTE88oEVksIov3798fZGgl78iRI7z22mt8++23npQXHRXJzcOvYOOhTN6ev9WTMk35ICKvhzqGkMq5phgZD8DJYwcASD1+mGPqtMhu2qSps+3JAr4bVOGnz6BuR0Bg3PVg8zJWWsF03v9VkM+dDDT2e9wIZ1aOs90GAFV9XVW7q2r32rVrBxlayUtISKBDhw589913HDx40JMy+7aqzeBzYlk8exqbd+7zpExTLrxW1EoReUtE9olIgUO6FNWg7UyN4cqCnOvoWVFOUkw56hxPVbKPc5SqPHl1R6pUr+dsfOrAzws4vAWO74but8Kwl2D/Glgz5UxPCn5TVpmKI5jO+8GOaLMIaCkizUQkEhgJTM63zWTgF+5B2ws4Wp6vJ+Z30UUXER4ezrRp0zxrIHP3hedQi2O88uUST8ozZZ+qnuk/eyxwSRHrC2zQFmBjuNDLOXaiEwBIPeG07I7JOkFUtRqM7NmEyLg6AGQcL+DH4tZ5zt8m50HryyC2Pvw4vvDnW/kpPNkEHqsBS991kmPKEY9ejAm1gHuQu9PStMTpMAyAqn5T3CdW1UwRuRv4CggD3lLVn9yWrajqq8A04DJgI07T89uK+3xlUbVq1RgwYADTp09n9erVtG/fPugyOzVvwIxeV/HSnC1ct/UQPRIDmSfalBciUhv4E06S8j8WC+0iparfiEhiEcUW2KANSOTMjeFCLmdAcF+VBADSc5Ji9kmyIp1riTHxtclWIe3IXn52Bf+niRDXCOq0BRHocpPT6ObgJqjZIu+2pw7BF3+AiCpOFevke5wbQIsLYfjbEJNQeLAZqZC8CDQLdi2H9BNQqzW0vhSiqgX1PhSbKmSlQ+oxSD0KUbEQHgm+cAiPhrAIyMqEsOAHHCk1OxZB2lE4Z9BZ7xroiDZ3APfiVF8uB3oB3xNkX0VVnYaT+PyXvep3X4HfBPMcZV2PHj1YuXIlx45518fwNxe25pOlu3hx0nze+NVgoqKiPCvbhNwHwEfA5cBo4BYg2IvohTVoC6QxXOi5Z4rhbgOazFOHITuLcLLwRcQAUCO2CoephhzLd6a4fz1smg3n3e0kRIAed8CCV+H1AXDHTKjdCrZ9D+u/dM4q047D7dMhoSlM+pWzn2bD6s/hn02hQVfYtdQp656lUL0ZrJkMM/4MR7fnC14AdZJsq0ugeT/n/sn9kJ0JYZHO36wMOLHP6WYSFg4nD8DRneDzQZVaEB3nbOsLd+LJyoDoeKjTDuIbOYku7bhTTXxkO+xY6Czfs8JJ1Flphb+/YZFO0oyKg9h6TnyZaU41c3QCJDQB8TllaXbefep2OH0/55ad7byG9FOQftJJulVrQ0Q0uc1IRIq4775vOfcPbYaIGKhax/lhERXnvN8Ajx4t+rNTgEBT/704o9ksUNUBItIG+OtZP5v5GZ/Px+23347PF8zl3bxiIsO4u3ddNn/zGWMnCXeNqNQzC1U0NVX1TRG5122JOldEgm2RWliDtoAbuoHT2A2n+pUmTZoEGdLZcL6II2OdWpHsU4edL20gPMpNilUjOahx1Die7/fDt884X6jn3Xt6WWw9uPkz+PA6J+nd8JEz00bqEWd9vzHOWSXAdX7zPu9YCO8OO50QAV7M1x6xaR/IOAW9f+OcWUbHO/utGA+rPnMa/BTGFw7xjZ0EUrWWk4ii4pykIj7n7DM7C7Lca50HN8GGGacTVY4qtZxrq9mZTtKs0xbiGrrJLsUpIyoWjiY7zwmw7TsnucUkQGaqcwaZesTdL8Z9fr/nyUqHqHjISIFqdU+fceYkefFBZFUIj3LOTtNPQtoxCI8B1G+ar3z3wX3sdz+hqRNrVKxTzvE9hb+HAQg0KaaqaqqIICJRqrpWRFoH9cwmV05C3LBhAwkJCXjRUOjGfh24ddkmvlgfwciUDOJjgu/2YcqEnGaRu0XkcpyGZ42CLLOwBm2RhSwvkKq+DrwO0L1791IbRSLnOzOmSlVSNQJNOeJ8cQNhEU4Nc81qkRwgjpqn/JLi/vWw8hPo9Wuolu+Ya9wTBj0KU+6Fp90q1Nu/cr7Qm55fcCCNe8JDu50zoLBI+Jtf148L/wx97gNfAV2lmvZ2bpc/5zT6ST/hJD9fuPN8x3dDXAPnrEwK+p1ShKwMJ0lkpjlVotXqOomoopv6e6fvaTEEmhSTRSQBmAR8LSKHKeLgMGcvLS2NiRMnUqtWLW677TbkbD/8+fh8wh9HDGDoS/N4Yuoq/jk8KegyTZnwuIjEA38AXgTigPuCLHMycLd7zfBc3AZtIrIftzEcsBOnMdwNQT6X99ysGBEWxjGqIalHyExPIRzwRTgJoHqVSNZqHG1S3erTJe/AlN86Z1rn/47UjCwmL9/F5Z3qUzXK/VpMutE5e9syF3qOgia9Aosn0h2Y/2yr7ny+n1/DBKgSRLuAsAhIaHzm7SqaIc8We9dAO+9f5d59VERmA/HA9GI/q/mZqKgoBg8ezOeff87SpUvp1i34UTU6NIznrvMasnPxDD6scoQbLy+JMRhMaVLVnMm9jwIB/YeKyDigP1BLRJKBv4DT3qSoBm2FNYbz7MV4JedUUYQTvmqEpR0lPdVJihLunCkmVInkEHFEpa1xppOa+Ren6vDKl6Fabf762UrGLdzO0u2HefKaTk55YRFw0wSn6rB5v9C8NlPqzvpClqrOVdXJqv6DDBovdO7cmaZNmzJz5kxOnDjhSZn3XtKB7Kg4xi3dz9EU65BcXrnX64q1japer6r1VTVCVRup6puq+mpOozZ1/EZVW6hqR1Vd7LfvNFVt5a57wrtX5B11r2WJCKd8sURmHCU9LcVZFukkxTCfcCq8OjGZx2DTLKdqbeBfoEEX0jOzmbbS6ek1ftEO5m/068sYFmEJsZIpMimKyNKi1ge6jQmMiDBkyBAyMjKYMWOGJ2VGR4Rz1y+uZ/Wpqjz55RpPyjQhMUZEri7idg1Og7hKTEgLq0pkVgoZaacA8EXk9lohLcqthvxxvNOgo3l/ABZsPsjRlAz+c0NXmteqyl8m/2QD61diZ6o+bSsiK4pYLzhVqcYjtWrVYuDAgURFRaGqnlwH7Ngonjv6NGP2dwuZWlcZcn5nDyI1pWwucMUZtvm6NAIpa3ISmIiQFRZFRHoqme6ZYk6XDIDjMY0hFWeOxXMucrsAwNer9xITEcbAtnU4lprBg5+tZPXuY7RvYF9tldGZkmKbAMqwsY481rt3b8/LvLt/c44tmcKU2d8zsEcHYiJtwPDyRFUr1MAVXhK3SwbiIzMsmghNIyM9b+tTgAPxHSGnQWJrZ4Cf1buO8fHiHQxqW5foiDAubl+PhyetYtrK3ZYUK6kiq09VdVsAt0o9bU1JWrx4sWcDhsdVjebSq0fy1YlGPD9rvSdlGlMW5D1TjCFSU8lMd84Uw6NOJ8Uq8TX4TAZB3Q5oh+GkZmTxpwkriIuJ4K/DnNGkalSN5LwWNZm6YrdVoVZS3vUYN57buXMnc+bMYd8+bwb37tehKSO6N+Gdbzfww1r7LWMqiNykCNnhMURrGpk5Z4qRp6tPa1aN5IG0X5J91zz+MHkrbf48nZU7j/J/l7WhVrXTffcu71ifbQdPsXhb8fq5mfLNkmIZdtFFFxEVFcXUqVM9+9X64KVtGBK1jo8+nUB6ps1Cbsq/3ENDfGh4NFGkk+1eUwyPPH2mWL1KJFnZyo/JR/hs2U6qV4ngr0Pbc2VSwzzlDU1qQK1qkfxj2hoys+wYqWwsKZZhVapUYfDgwezYsYOlS71p5JtQNZIe513A3JP1eXXuJk/KNKHlwdym5dzpLhkaHkMY2WiaM5ZwRE5HeqBunJMgcz73X/z2Am45L/FnjdmqRIbz8OXtWLr9CK99s7k0XoApQwJKim6T7w0iclREjonIcRHxbgRrU6jOnTuTmJjIzJkzSU1N9aTMERf1olfHc3hp9kY27/emP6QJqWDnNi3fcqpPEWf8Tsgd4isi+nT1aadGTsOZr37aS4/E6jRIiKEwV3ZpyMA2dfjvt5tJzchi//E0Vu10RqgZt3A7Xf/2NX+d8hOpGdbOsKIJdJi3p4ArVNU6upWynL6LR48eJTo6+sw7BOiRIe3Yt24ZL7/9IU8/cKcNAVeOeTC3afmWU3/q8/08KUadTnyNqsdQNTKMk+lZXNP1zMPF3t6nGbP+u49/Tl/LxGU7OXIqg/PPqcn8jc4kxm/P30q4T3jo8rI3xaQpvkCrT/daQgydmjVr0rx5cwAyMzM9KbNuXDR9WtVh7/F0Ji/f6UmZpuSJyPkiUtW9f5OIPCsiTUMdVyj5j2gjkXmTYmTU6epTEeG5EUnc1KsJV3Zp+LNy8juvRU06N4rn7flbOXIqgwta1mL+xoNc1K4u6x6/hOt7NuaNb7fw8pyNHDllA3xVFIEmxcUi8pGIXO8/ikaJRmZ+ZuHChbz88sukpRUx99lZ+M3IyzlaN4nHp621IeDKj1eAUyLSGfgjsA14N7QhhZhflwyf29rUl3YEgMiovLUrg9vX4/ErOxIdceZ+uiLC67/oTtOaVfhoVC/evb0nix4axOs3dyMqPIw/XtyGJjWq8NT0dfR8YhYfL9pxxjJN2RdoUozDGSh4MM6oGlcANklfKatfvz6HDx9m1qxZnpQXHubjiSs7knryGM9++KUnZZoSl+lOvj0M+Leq/huIDXFMIZYzILiPMPfMMDz9GGkaQVQAya8odeOimfvAAM5tXhMRoXZsVO6lhupVI5l27wW8c3tPEmtV4dEpP7Fuz/Ggns+EXkBJUVVvK+B2e0kHZ/Jq3LgxPXv2ZNGiRWzfnn8G7+Lp2CieaxqnkbnjR35Yb7OBlQPHReRB4GbgCxEJw53xorJSv4Y2YZFVAYjKOEoaEUSFl2wD+2pR4fRrVZt3bu9JbHQ4t729kH3HvWkQZ0Ij0NanjURkoojsE5G9IjJBRIKd2NQUw8CBA4mPj2fKlCmeXV+858ahzAvvwmPTN1m/rLJvBJAG3K6qe4CGwNOhDSnEcpKiTwiPdpJiTOYx0oggPKx0ep3Vj4/hv7/oweFTGfR8YhYtH5pGSrq1TC2PAv3EvI0zEWkDnINwirvMlLLIyEiGDBnCoUOH2LHDm2sYNeOq8seh3fhp1zHe+l/Zmy7PnOYmwg+B6iJyBZCuqpX7mmJO9Sk+ItykWCX7BOlElmoUHRvF8/4d5+ITyMhS2j4ynW837C/VGEzwAk2KtVX1bVXNdG9jgdolGJcpwjnnnMO9995Ls2bNPCvzso71uKJBCsnzJ7Jqsw0BV1aJyB3AQuBqYDiwQEQq9aUM/7FPc5JiNGmkS+nXKndrWp3N/7ichm4fyJvfXMi8DQfOsJcpSwJNigfc5t9h7u0m4GBJBmaKFhcXB8CWLVvIzg6+ylNEuOfqfqzWhvzzfztsMOSy6wGgi6reqqq3AN2AP4U4phDLaWgjRMVUy12aGYKkmGP+mAu5o4/zo/WmN38gccwXJI75gv5Pz+bPk1ZZp/8yLNCkeDtwHbAH2I3zC7VS/zotC3bs2MG7777LggULPCmvVaPaDLt0EN9uPMQHP2zzpEzjuWTAv4njcaBS9wUQvwHBo2Kq5i7PkNKtPs3v4SHtePu2HlzQshbXdXeaYGw9eIr3FmyjzZ+nM/Bfc5izbp/9AC1jAhrRRlW3A0NLOBZzlho1akTr1q2ZPXs2bdq0oUaNGkGXedO5TZi9bCM/TJ9Al7ojad/szJ2cTckTkd+7d3cCP4jI5zinSMNwqlMrrdPVp748STErhGeKOQa0rsOA1nUAeGp4Z7KzlSkrdvHXKavZtP8kt769iMs71ueZazvbHKdlRJFniiLyR/fviyLyQv5b6YRoCiMiXHbZZYSFhTFlyhRPfnGKCGOGdCSSLJ6e+iPZ2fYrtoyIdW+bgEmcbl3yOU7tTSV2uvo0JjqKNHV+62f4vBsW0Ss+nzAsqSFL/3wR6x6/hN9f1Ippq3Zzw38XsOeodeUoC850ppgztNvikg7EFE9cXBwXXXQRU6dOZdmyZXTtGvyECa0a16HHZSMY89kq3vl+K7ed712DHlM8qvrXUMdQZvk1tIkO93GSSKLIJNMXdYYdQysqPIzfDmxJyzrVuP+TH7nyP/N57eZudG6cEOrQKrUik6KqTnHvnlLVT/zXici1JRaVOStdu3Zl48aNRER4V100okcTvlq1h0+/+obu9aPo2LyBZ2Wb4hOR2jjDu7UHck+FVPXCkAUVYv7Vp+FhPlKJIp5TZPlCe00xUJd2rE/TmlW5893FDPvPfH7ZpxlXd21I+wbxoQ6tUgq0oc2DAS4zISAijBgxgo4dO3pa5p8vaU4X33ZemTCTLKtGLSs+ANYCzYC/AluBRaEMKPROnykCpIpzhphdxs8U/bVrEMfUe/rQM7EGb87bwuUvzGPUu4tZmXw01KFVOkWeKYrIpcBlQMN81xDjAG+GUzGeUVUWL15MTEwMHTp0CLq85g1q0/7Cq3hw2laS5m1mVN8WHkRpglRTVd8UkXtVdS4wV0TmhjqokPKfTxFIlyhQyA4rP0kRnLFUPx7dm73HUnlr/hbenr+VWWv3cXWXhvyqfwua16525kJM0M50TXEXzvXEocASv+XHgftKKihTPKrKypUr2b9/P02bNiU2Nvhxokde0J7ZW1N5YcZqejWuSqdm9TyI1AQhZzqT3SJyOc4xWsmHXMyZTzEnKUaXy6SYo25cNA9e2pbhXRvxrxnrmfzjLj5dmkxS4wSGd2vE1V0aWUvVElRk9amq/qiq7wDnAOOAZcBSYKqqHi6F+MxZ8Pl8DBs2jMzMTKZOnepZa9THhrbj0ojVvD1ugo2NGnqPi0g88AfgfuC/VPYfqLmfczcputWmGl4+k2KOlnVjefXmbsz704X8pv85HDiRxkMTV3Hek7N49uv17D1mrVVLQqDXFC/CaQr+AvASsNGtWjVlTM2aNRk4cCDr16/nxx9/9KTMeglV6Ni9N98cr81r32z2pExTPKo6VVWPquoqVR2gqt1UdXKo4wqtvNcUs3KTYtnrklEctWOjuP/i1nzzwAA+uONcOjSM54VZG+j71GwenfwT2w6eDHWIFUqgSfFZYICq9lfVfsAA4LmSC8sE49xzz6VJkyZ89dVXnk1I/IvL+9Kr4zk8P3M9q3fZxX9ThmjepOjzuV9r5bT6tDAiwvnn1OK9X57LnPv7c3nH+rz7/Vb6PT2H33+0nP3HvTnWK7tAk+I+Vd3o93gzsK+4TyoiNUTkaxHZ4P6tXsA2jUVktoisEZGfROTe4j5fZSMiDBs2jGuvvZaoKO++GP42rAPdo/bw6tgPSbOxG00Z4d8lw/8vERXjTLEgibWq8uyIJL4bM5ABrWvz2bKd9H1qNn+buprdR1NCHV65dqYRba4WkauBn0RkmojcKiK34EwdFUwz8DHALFVtCcxyH+eXCfxBVdsCvYDfiEi7IJ6zUqlRowbNmzcHIDXVm2sPNapGMrhDAw6lZPHS/9Z7UqYpHSJyiYisE5GNIvKz401EHhCR5e5tlYhkiUgNd91WEVnpriuDA3nkPVPE/SsVpPq0KPXio3n7tp7M/H1fLu1Yj7HfbaXvU7P546c/svOIJcfiONOZ4hXuLRrYC/QD+gP7gZ+d3Z2FYcA77v13gCvzb6Cqu1V1qXv/OM7oOjYQ51latmwZL7zwAseOHfOkvFuvupg6Hfrw8twt1oeqjBCRIocxEpEw4D/ApUA74Pr8PzBV9WlVTVLVJJw+yHNV9ZDfJgPc9d29jd4D+apPw9XpLZYVVXk6v59TJ5Znr0tizv39ubZ7YyYt38Wgf83llTk2cfjZOlPr09uAO4AVqnpbvlsws2TUVdXd7nPsBuoUtbGIJAJdgB+K2GaUiCwWkcX799vEnjmaNm1KRkaGp2Oj/uWK9jSqms2/35tIaoZ1Vy0DfnWG9T2Bjaq6WVXTgfE4P0wLcz1Oa/NyImfsU+frLELTnaXRCSGKJ3Qa16jC36/qyOz7+3NBy1r8c/pahrw4j69X77XZOAJ0xmuKqppFMWbIEJGZbjVM/ltRB2NB5VQDJgC/U9VCT3dU9XVV7a6q3WvXtvmPc9SoUYNBgwaxceNGli9f7kmZ8VUiuLNTDPXSdvDc1GWelGmKT1XvPMMmDck7vVQyhdS6iEgV4BKcYy73KYAZIrJEREYFE2uJyPmyd88U94c7fWkjqgZTmVW+NUyI4fVfdOfVm7pxLCWDO99dzMBn5/La3E1WrXoGAU0dBXwnIi8BHwG57X9zqjcLoqqDClsnIntFpL6q7haR+hTSaEdEInAOzg9U9bMAYzX59OzZkzVr1vDVV1/RokWL3AmKg3HjkAt58EQ13li4j4u7HaZrk8r7BVTaRKQTkIjf8XuG40MKWFbYacMVwPx8Vafnq+ouEakDfC0ia1X1mwLiGgWMAmjSpEnRL8JTeatPP6h2K7MO12FAg6RSjKFsuqRDPS5qV5dJy3bywQ/b+MeXa/nHl2tpWrMKD13Wlgvb1CE8LND2lpVDoO/GeTgDED8G/Mu9PRPE804GbnHv34Iz/U0e4nzC3wTWqOqzQTxXpSciDB06FBFhxw5v5qMVER66qiv142P42/i5pKRbNWppEJG3gLeAazh9zX/IGXZLBhr7PW6EMxJOQUaSr+pUVXe5f/cBE3GqY38mZLU1+a4pJtRuyNisS4ivUrG6ZBRXmE+4plsjPvv1+Xz7xwHc1bc52w6eYtR7SzjnoS/565SfWLXT2gfkkFDUM4tITeBjoAmwHbhWVQ+JSAPgv6p6mYj0Ab4FVgI5V4r/T1Wnnan87t276+LFZbCRXIilpqYSHe1ti7wp81ewdOZEpGkPHrn1Mk/LrshEZElxGq2IyGpVPatW2CISDqwHBuJMUrwIuEFVf8q3XTywBWisqifdZVUBn6oed+9/DTymqtOLes7SPAYXvP8Xem18nhO/30q1uOrsP57G58t3ctv5zQjzFXSSbE6mZfL16r08NnU1x1IyyMxWujWtTt+WtRndvzlR4RV/GLnCjsGAqk/dg+UvQF930VycA6NYPy9U9SDOAZp/+S6cAchR1XkUXO1jiiknIa5du5ZatWpRq1atoMsccl5HFq7bzofrsrhkyyF6NqsRdJmmSN+LSDtVXR3oDqqaKSJ3A18BYcBbqvqTiIx217/qbnoVMCMnIbrqAhPds7Bw4MMzJcRSl+9MsXZsFHdc0DyUEZV5VaPCubJLQ67s0pCjKRl8sngH736/jedmrue5met5engnhiU1JDK88lWtBvqK38IZBPw693YMeLukgjIlJzU1lcmTJzNp0iSys4Nvqi0ijLnxEhpWr8YfP1nGidR0D6I0RXgHJzGuE5EVbv/BFWfaSVWnqWorVW2hqk+4y171S4io6lhVHZlvv82q2tm9tc/Zt0zJlxTN2YmPieCOC5oz+/7+/GZACxrER/PApysY8Mwc3p6/hdRKNlBHoEmxhar+xT1ANruzgNtPsXIoOjqayy+/nJ07d/Ltt996UmbVqHD+PrQ1SSeX8PS7lXwYzpL3FnAzTgvRnOuJV4Q0ohDLHQ5cKt9ZjZfCfMIDF7dh3p8u5L+/6E6DhGj+OmU1A56Zw2NTVnP0VMaZC6kAAv0UpbjX+AAQkfMBa9dbTrVv356OHTsyd+5cdu0qrL3F2enTpgHV6zZk5tZ05m884EmZpkDbVXWyqm5R1W05t1AHFVLq1HjYmaI3fD5hULu6fDL6PMbd2YuGCTG8NX8LnR+bwXsLtpGSXrHPHANNir8C/uMO97QNZ6aM0SUXlilpl156KdWqVWPixIlkZnrTcvRPd44kqkYD/vjpCo6nVo5flSGwVkQ+FJHrc4ZhdIdirMTydt433undoiaf/uo8nriqAy1qV+XPk1ZxwVOzeWHWBvYdr5hTVwX0KVLV5araGegEdFTVLqrqzbxEJiRiYmK48sor6dOnD2Fh3rQ0i44I45nrOhN7Yjv/HDvRkzLNz8QAacBgAu+SUaGJe00xd3YM47kbz23KzN/348M7z6Vdgzie/Xo9PZ+YxQOf/MiOQ6dCHZ6nAm19ei9Ow5rjwBvuWItjVHVGSQZnSlbOgOEA2dnZnnypdG1SnfMb+Ni+ayez1+xhQNt6QZdpHO4YpgdU9YFQx1KWqFWflgoR4bwWtTivRS027T/BgxNW8tmynXyyJJnezWvy5yHtaNcg+IFBQi3Qb8Hb3SHWBuOMU3ob8GSJRWVK1U8//cQrr7zi2Wwa9902gq3xXXhw4k8cTbFqVK+4Qy4WOfh3ZWZJsfS0qF2Nj0f35ts/DuDi9nVZtPUQl73wLYljvuDRyT+RlV1+x1kNNCnmfNouA952q07tE1hBJCQkcPDgQb788ktPyqsWE8WzI7pw9MRJ/vHuVE/KNLmWi8hkEbnZrim68s2naEpPg4QYXru5O0sevii3j/LY77bS4v+mMWbCCo6cKn9dtAL9FC0RkRk4SfErEYnl9Cgzppxr2LAhffv2ZcWKFaxeHXCf8CJ1bBTPLS2zidq9ks8XrPWkTANADeAgcCF2TdFl/RRDLb5KBB/f1ZuNT1zKXf2cyzLjF+0g6bGveeTzVWw7ePIMJZQdgQ4I/ksgCdisqqfcYdpuK7GoTKm74IIL2LBhA1OmTKFRo0aeDBr+uxuHcONLUXwxM5m+HZtTvWqkB5FWbu50bsafdd4vM8LDfDx4aVvGXNKGT5ck8/KcTbz7/TbeW7CNAa3r8IveTenbsja+Mjz8XqCtT7NxRuV/RET+BfRV1TOOomHKj7CwMK6++mqys7NZv369J2VGR4bzt5HnczQlnb9+8r3N5+YBEWkkIhNFZJ8728wEEWkU6rhCy02K1vq0zBARru3emNn392fBgwO558KWrEg+yq1vL+LCf83hzXlbymx7g4A+RSLyMk6/xJXAKuAuEflPSQZmSl/NmjW5++676d7du8nV2zWI49c9axK/dQ5vTZrpWbmV2Ns4s8w0wJkTcQqVfMhFsR9bZVq9+Gh+f1ErvhtzIS9c34Va1aL429TV9Pr7LB78bCUb9h4PdYh5BFp92g/ooO5PfRF5BydBmgomNjYWgOTkZMLCwqhfv37QZd59eXfu3riFycvTGdzvFI1rVAm6zEqstqr6J8GxIvK7UAVTFihKtkrADSRMaESG+xjauQFDOzdg1c6jvPf9Nj5ZvINxC7dzRecG/GZAC9rUC32XjkA/R+twpnnK0Riw6tMKKisriwkTJjBhwgTS04NvPRYRHsafbx9KpoTz23FLSUsvm9Um5cQBEblJRMLc2004DW8qL9VCZ0w2ZVOHhvH8c3gnFvzfQO7q25yvV+/hkue/5YY3FvDVT3tC2qWjyKQoIlNEZDJQE1gjInNEZDawBijFWURNaQoLC2PYsGEcPHiQr776ypMyG1WvwuPD2lNn70KeeftTT8qspG7HmalmD7AbGO4uq8QUtR5i5VKtalE8eFlbvv3jhfzholZsPXCSu95bQt+nZvPq3E0cPln6XTrOVH36TBHr7MdZBZaYmMj555/P/PnzOeecc2jbtm3QZQ7r0ogf5tdj7o40Ltx0kN4tanoQaeWiqtuBoaGOo0xRS4rlXe3YKO4Z2JJf9W/BzDV7eee7bTz55Vqe+3o9w5IacMt5ibRvEF8qsRSZFFV1bkHL3VkybgC+KYmgTNkwYMAAtmzZkttNI+d6YzAeuvM6rnhxHr/7aBlf3tuXGtZNIyAi8kgRq1VV/1ZqwZQ5Vn1aUYSH+bikQ30u6VCfdXuO8873W5m4dCcfL06mR2J1burVlCs6NSjRLh0BX5sWkSQReUpEtgKP41Shmgosp5tG7969qVq1qidlVo0K54XruxBzaj//ePU9TyY6riROFnADpw/xn0IVVJngNP8LdRTGY63rxfL3qzqy4MGBPHx5W/YcS+Xe8csZ/Pw3vPf91hLr0nGma4qtROQREVmDM13UDkBUdYCqvlQiEZkypWbNmlxwwQX4fD7Pppjq0DCeq9rFk37sIGO/8aZPZEWnqv/KuQGv48yWcRswnko/4Xe2VZ9WYPFVIrjjgubMuX8Aj1/ZgegIH3/+/CfOf/J/PPHFanYd8XZq3zOdKa4FBgJXqGofVX0RqNgzTJoC7dy5kxdeeMGzSYnvGXkJxxP78uTMLazedcyTMis6EakhIo/jtPwOB7qq6p9UdV+IQwspsdanlUKYT7ipV1Om3N2HyXefz4A2dXhr/lb6PjWbe8cvY9XOo548z5mS4jU4rdxmi8gbIjIQq6eolGrUqIGIMGHCBNLS0oIuz+fz8fS1XagRHcZT70ziyImKNSeb10TkaWARzvRtHVX1UVU9HOKwygw7U6w8RIROjRJ48fouzH2gP7eel8isNfsY8uI8rn55PjNX72XT/hPFTpJFJkVVnaiqI4A2wBzgPqCuiLwiIoOL9YymXIqJieGqq67i8OHDTJs2zZMya1aL4pEL65OYtoWnPprjSZkV2B9wRrF5GNglIsfc23ERqdyn2mrVp5VVo+pVeHhIO7578EIeuLg1e46mcse7ixn4r7kMeXFescoMdOzTk6r6gaoOARoBy4ExxXpGU24lJibmzqaxfPlyT8q87LyOVEm6jA83KJN/9KZqtiJSVZ+qxqhqrKrG+d1iVTX0w4AYE0Jx0RH8ZsA5zP3jAP49Mimoss56ZCRVPaSqr6nqhUE9symX+vbtS2JiIlu2bPGszN9f0Y1uTavz988WsnrLbs/KNZWF9VM0jogwH8OSGnJei5q0rFOtWGXYcIHmrPh8Pq6//nquvPJKz8oMD/Px7PAO9JM1/HfcBNIzrZuGOQvWed/k8+Gdvfj69/2Kta8lRXPWIiMjEREOHTrEd99950mZTWvH0fWCQUw/1pB/zVjnSZmmsrDWp8Y7gc6SYczPLFu2jHnz5hEfH0/79u2DLm/EwB6sPB7Na99spmvDGC7unBh8kKbiU0VtgmHjETtTNMXWv39/GjZsyJQpUzh82JveAQ9f3o6LEvYze9I4tu2zHgfmzATFeooZr1hSNMUWFhbGNddcA8CECRPIygp+XIfoiDDuvOICNmXX5sFJa0M6hYwpJ6zzvvGQJUUTlOrVqzN06FB27tzJggULPCmzZ/sWXDvkYr7bfIj//M+GgTNnYg1tjHcsKZqgtWvXjquvvpqePXt6Vua13RtxZds4tnw7iekLVnpWbmUlIpeIyDoR2SgiP+tjLCL9ReSoiCx3b48Eum9ZYEnReMWSovFEx44diYiIIC0tjRMnTgRdnojw8JVJEB7FczM3cOBE8EPLVVYiEgb8B7gUaAdcLyLtCtj0W1VNcm+PneW+oaPZ2DVF45WQJEV3YOOvRWSD+7d6EduGicgyEZlamjGas6eqvPvuu3z88ceeXF+sFR/LHb+8jS2pMdz30XKy7fpicfUENqrqZlVNx5lZY1gp7Ftq7JNhvBKqM8UxwCxVbQnMough4+7F5m4sF0SE3r17s2PHDmbNmuVJme0bxPPIkLYc3PwT//rAfhcVU0Ocad9yJLvL8ustIj+KyJciktPHJtB9EZFRIrJYRBbv37/fi7gDZNcUjXdClRSHAe+4998BrixoIxFpBFwO/Ld0wjLB6tChAz169OD7779nzRpvfsvc0LMJSTWyWL1hCws2HfCkzEqmoIyR/+RqKdBUVTsDLwKTzmJfZ6Hq66raXVW7165du7ixnj0b0cZ4KFRJsa6q7gZw/9YpZLvngT8CNu5XOTJ48GAaNGjA559/zqFDh4Iuz+fz8cBdN7E1tiP3frTcri+evWSgsd/jRkCe0ddV9ZiqnnDvTwMiRKRWIPuGmtiZovFQiSVFEZkpIqsKuAV0PUJEhgD7VHVJgNuHqOrG5BceHs61115L48aNCQsL86TM6tVieOnGrpw6dYrH3viUrCz7nXQWFgEtRaSZiEQCI4HJ/huISD0RZ1gYEemJ891wMJB9Q86mjjIeKrFh3lR1UGHrRGSviNRX1d0iUh8oaObw84GhInIZEA3Eicj7qnpTIc/3OvA6QPfu3e26e4glJCRw4403Ak4DHPFgGK72DeL5dZcq7Fq5nJe+WMy9Q73rAlKRqWqmiNwNfAWEAW+p6k8iMtpd/yowHPiViGQCKcBIVVWgwH1D8kIKo3a4G++EauzTycAtwJPu38/zb6CqDwIPgtOHCri/sIRoyq60tDQ+++wz2rVrR+fOnYMu766rLuT3J6L54Pv9nNvhIL2a1/QgyorPrRKdlm/Zq373XwJeCnTfssbOFI1XQnVN8UngIhHZAFzkPkZEGohImT74zNnJ6bv4xRdfsG9fQRUCZ8fn8/H4yF40rVmVv34wm+17g79maco3u6ZovBSSpKiqB1V1oKq2dP8ecpfvUtXLCth+jqoOKf1ITbB8Ph/XXHMNkZGRfPzxx6SlBd9IplpUOM9e3ZZuWWt4/t2JNj5qZac2ILjxjo1oY0pcbGwsw4cP59ChQ0ydOhX14BpQl+Z1adH7YiYfrM3zM2181MrNBgQ33rGkaEpFYmIiAwYMYOvWrZ4MAwdw28U9ubp7U17633omf1+22n6Y0mTzKRrv2CTDptT06dOHbt26UaVKFc/KfGxYB05sXMTCr36kdeM6tG5Uip3GTZkgVn1qPGRniqbUiAhVqlQhOzubuXPncvz48aDLjI4I457rh7BMmvO7CWtJSQ9+zFVT3lj1qfGOJUVT6o4cOcL8+fP55JNPPBk4vG3Tetw/chBr9xzj4U8WkZ1tHfsrG2t9arxiSdGUuho1ajBs2DB27NjBV1995UmZA1rX4Z7e9YjaMJNXJsz0pExTTlj1qfGQJUUTEu3bt6d3794sWrSI5cuXe1LmvZd3ITWuCW8sP8nS7Yc9KdOUfdZP0XjJkqIJmUGDBpGYmMj06dNJTU0NurywMB9/GX09cfEJ/Pr9pew5fNKDKE2Zp9nW+tR4xpKiCRmfz8fw4cO5+eabiY6O9qTM+CoRvHpTN5qlbuDZV98iPSPTk3JN2WVnisZLlhRNSFWtWpWGDZ05azdt2uRJI5l2DeIY2KUVm09G8syMdUGXZ8o6S4rGO5YUTZmQnJzM+++/z//+9z9Pyrvjqgtp2/18Xv92K9NWlKnp/4zXrKGN8ZAlRVMmNGrUiG7dujF//nxWr17tSZl/HtKOcxtE8tVnH7BozWZPyjRlj1N9al9lxhv2STJlxiWXXELDhg2ZNGmSJzNqRIb7+PvwJMJEeGLKSo6lZngQpSlzNNs67xvPWFI0ZUZ4eDjXXXcdUVFRjB8/noyM4JNYiwa1ue6mW1l1JIL7xi8n22bUqJCs9anxiiVFU6bExcUxYsQI+vXrR0REhCdl9mpRi0euaEfyhlX8481PPCnTlB1WfWq8ZJ8kU+Y0atSIzp07A86QcF64uVdTetYLY8uOXUxeluxJmaZsELVh/Yx3LCmaMmvnzp289NJLLF26NOiyRIT/G3U9R+r14I+freSnXUc9iNCUDdYlw3jHkqIps+rXr0/Tpk354osv2L59e9DlxURF8PLN3agV7eP5N8eTvP9I8EGa0FNFxb7KjDfsk2TKrJwRbxISEvj44485ejT4s7s6sdE8cXkidbP28ei4uWRkWdVbeSfW9tR4yJKiKdNiYmIYOXIkGRkZnrVI7ZfUmk6X3MDMXeE88cUaD6I0oWXVp8Y74aEOwJgzqV27Ntdccw3btm0jLCzMkzKv69WCdQfS+OK7H2nIAe4c2s+Tck3pE8221qfGM/ZJMuVCq1atuOiii/D5fJ6cLQKMuaQ1feMOsWrJQpZsPehJmab0CQrWT9F4xJKiKVcOHDjAiy++yLp1wQ/0HREexn133MSKKkn86oNl7D0W/PRVJjSs+tR4xZKiKVfi4+OJjY1lwoQJ7NmzJ+jy6teK59VbenEyLYOHX/uMYydTPIjSlCan+tSSovGGJUVTrkRERDBy5EhiYmIYN24cx48fD7rM1vVi+eugBjQ4uYHH3puBqrVmLF+s+tR4x5KiKXdiY2O5/vrrSUlJ8axF6vC+nanZ43I+3RrOy3M2eRClKS2CVZ8a71hSNOVSvXr1uOaaa6hataonExMD3HNZN4YlNeDVr1cw7usfPCmzrBCRS0RknYhsFJExBay/UURWuLfvRKSz37qtIrJSRJaLyOLSjfzMnGHeLCkab1iXDFNutW7dmlatWiEiZGdn4/MF9xtPRPjnNZ14cMv3LJ+/lrYtzyEpsaZH0YaOiIQB/wEuApKBRSIyWVX9J67cAvRT1cMicinwOnCu3/oBqnqg1II+K2qzZBjP2JmiKddEhFOnTvHmm2/y448/Bl1edEQY991+PUsjO3HXh8vYc7RCtEjtCWxU1c2qmg6MB4b5b6Cq36nqYffhAqBRKcdYbFZ9arxkSdGUe1FRUURFRTF58mS2bdsWdHlN6lbnpdv7cTItiz+8MY0jJ8p9i9SGwA6/x8nussL8EvjS77ECM0RkiYiMKoH4gmLVp8ZLlhRNuRcWFsa1115L9erV+eijjzh06FDQZbauF8uTlzel5YmVPPbWZLLK9+TEBWWMAl+QiAzASYp/8lt8vqp2BS4FfiMifQvZd5SILBaRxfv37w825rNgA4Ib79gnyVQIMTEx3HDDDQCMGzeO1NTgqz2H9GxD3S4DmbirCv+cvjbo8kIoGWjs97gRsCv/RiLSCfgvMExVc4f4UdVd7t99wESc6tifUdXXVbW7qnavXbu2h+EXzRkQ3M4UjTcsKZoKo0aNGlx33XX4fD5PkiLAr4b14ebezXjrmw28OW2BJ2WGwCKgpYg0E5FIYCQw2X8DEWkCfAbcrKrr/ZZXFZHYnPvAYGBVqUUeAEFtngzjmZAkRRGpISJfi8gG92/1QrZLEJFPRWStiKwRkd6lHaspXxITE7nrrrtISEhAVT3piP/IkHYMrX2QrQu/ZvbK4Od1LG2qmgncDXwFrAE+VtWfRGS0iIx2N3sEqAm8nK/rRV1gnoj8CCwEvlDV6aX8EoqmClZ9ajwSqk/SGGCWqrYEZrmPC/JvYLqqtgE64xzQxhTJ5/ORlZXFpEmT+Oabb4IuLzzMx5hfXsOaKh25d8IaNu0/4UGUpUtVp6lqK1VtoapPuMteVdVX3ft3qGp1VU1yb93d5ZtVtbN7a5+zb1kiNnWU8VCokuIw4B33/jvAlfk3EJE4oC/wJoCqpqvqkVKKz5RzPp8Pn8/HnDlzWL58edDl1UmI47k7BhMR5uN3b85i1/4jQZdpvGGzZBgvhSop1lXV3QDu3zoFbNMc2A+8LSLLROS/7jWNAoWu5Zspi0SEIUOG0Lx5c6ZMmcLmzZuDLrNxjSr8Z2RHOqWu5Om3PiY905uRdEyw7EzReKfEkqKIzBSRVQXchp15b8AZbacr8IqqdgFOUng1a8havpmyK6erRq1atfjoo4/Yu3dv0GX2almPtuddxLTDdfi/iStt8PAywKfW+tR4p8SGeVPVQYWtE5G9IlJfVXeLSH1gXwGbJQPJqpozCOWnFJEUjSlIdHQ0N954I++//z4pKd50wr9p8Lnsl/X8e9Z6GoUf43dXXeBJuaa4rJ+i8U6oPkmTgVvc+7cAn+ffQFX3ADtEpLW7aCCwOv92xpxJXFwco0ePJjExEcCTAcR/N6gl1zbN4OiK//HJ3OVBl2eKT7ARbYx3QpUUnwQuEpENOIMUPwkgIg1EZJrfdvcAH4jICiAJ+HtpB2oqhpzBwr///ns+/PBDsrKygipPRHj0tsvZFtuBP8/cxYrkIx5EaYpDcv8xJnghSYqqelBVB6pqS/fvIXf5LlW9zG+75e51wk6qeqXfgMXGFEt0dDSbNm1i6tSpQV8PrBodyT/vHELNqtHcPXY+a7bu9ChKc3YUtXFIjEfsk2QqlS5dutC3b1+WL1/O3Llzgy6vdmwUb97Sna6Zq3n7/fGcSA1+wmNzdnyabV0yjGcsKZpKp3///iQlJTF37lwWLw5+ztw29eMYePGlzDzVhN/960eymqlzZCUCHwRdvAmIJUXjDUuKptLJ6cPYqlUrTxrdAFzRuwP31u3BzON7+XuXuWRLNmwDRmGJsYSJtT41HrJPkqmUwsLCGDlyJD17OhM+ZGZmBl3mLc8lcsvaeLI7z+XSMe86C08BDwVdtCmC0/rUGG9YUjSVlrjXobZu3coLL7zAnj17gitwO/x58nnMTDuH9Vm18iw3Jcen2aiUWJdrU8lYUjSVXvXqziQtH3zwAUeOHCl+QU0gXH18+48b2fLPIXmWm5ITRpZVnxrP2CfJVHrx8fHcdNNNZGZm8v7773Py5MniFfQEUCXfsiruclNifGSjPjtTNN6wpGgMUKdOHa6//nqOHj3Khx9+SHp6+tkXciPwOtAUpzFkU/fxjV5GavILIxssKRqP2CfJGFeTJk0YPnw469evJzy8mIfGjVgSLGVhZNkkw8YzlhSN8dO6dWtat3aG201JSSE6Ojq3QY4pm3xq1afGO/bzypgCpKSk8PrrrzN9+nSbHqqMc84Uw0IdhqkgLCkaU4Do6GjatGnDwoULPRkOzpScMLJQnyVF4w2rczCmACLC4MGDSU1NZe7cuURFRdG7d+9Qh2UKYA1tjJfsk2RMIUSEK664gvT0dGbMmEFcXBzt27cPdVjGT3ZWFmGiVn1qPGNJ0Zgi+Hw+rrrqKuLj42nevHmowzH5pKacoAogETGhDsVUEHZN0ZgzCA8PZ/DgwcTExJCZmcmuXbtCHZJxpZw8DoBEVQ1xJKaisKRozFn4+uuvefvtt9m+3QY0LQtS3aToi6oW4khMRWFJ0Ziz0LdvX+Lj4/nwww/ZvXt3qMOp9NJOHQMgLNqSovGGJUVjzkLVqlW5+eabiY6O5v333+fAgQOhDqlSO3V0HwARVaqHOBJTUVhSNOYsxcfHc/PNNyMifPrpp9a5P4QSZj4AQMM23UMciakorPWpMcVQs2ZNbrrpJkTEhoELoUbqVGHXqNMwxJGYisLOFI0ppnr16lG3bl1UlYULF3LixIlQh1Tp7JK6LI4bFOowTAViSdGYIB09epSZM2fy3nvvcerUqVCHUyARuURE1onIRhEZU8B6EZEX3PUrRKRroPuGimZnUz37MJkxtUMdiqlALCkaE6SEhARGjhzJwYMHee+990hNTQ11SHmISBjwH+BSoB1wvYi0y7fZpUBL9zYKeOUs9g2Jn76bSoykE1a3bahDMRWIXVM0xgPNmzdnxIgRjB8/ng8++ICbbrqJqKioUIeVoyewUVU3A4jIeGAYsNpvm2HAu+q0GlogIgkiUh9IDGDfs5adlUXKKbfjvQgRkdFkZWWi2dlkZWWSnZ2Nun+zs7PQrCyyNZvsrEwObF/D8TWz6Jj8EbulNm0H/SKYUIzJw5KiMR5p2bIlw4cPZ8KECWzfvp2WLVuGOqQcDYEdfo+TgXMD2KZhgPsGbMEHj9F6w+vE6QmqSt5WuxEBllEPyFQfa6M7Ufvmt6gWZ90xjHcsKRrjobZt2/Lb3/6WuLi4UIfir6Dmsfn7kRS2TSD7OgWIjMKpeqVJkyYFBhJTvw3rD19EdnQCRFYDzSb88CYyYxtBeCQiYeDzOQN8iw/Eh/jC8vyNql6P5t0G0yG+RmGv15his6RojMfKWEIE5+yusd/jRkD+AVwL2yYygH0BUNXXgdcBunfvXmDi7HzhdXDhdWcTuzGlyhraGFPxLQJaikgzEYkERgKT820zGfiF2wq1F3BUVXcHuK8xFYadKRpTwalqpojcDXwFhAFvqepPIjLaXf8qMA24DNgInAJuK2rfELwMY0qFJUVjKgFVnYaT+PyXvep3X4HfBLqvMRWVVZ8aY4wxrpAkRRGpISJfi8gG92+BbapF5D4R+UlEVonIOBGJLu1YjTHGVB6hOlMcA8xS1ZbALPdxHiLSEPgt0F1VO+BczxhZqlEaY4ypVEKVFIcB77j33wGuLGS7cCBGRMKBKhTSFNwYY4zxQqiSYl23uTfu3zr5N1DVncAzwHZgN04T8RmlGqUxxphKpcSSoojMdK8F5r8NC3D/6jhnlM2ABkBVEbmpiO1HichiEVm8f/9+b16EMcaYSqXEumSoaqGTnInIXhGpr6q73UGH9xWw2SBgi6rud/f5DDgPeL+Q5zvjaBrGGGNMUUJVfToZuMW9fwvweQHbbAd6iUgVcaY2HwisKaX4jDHGVELi9Nkt5ScVqQl8DDTBSX7XquohEWkA/FdVL3O3+yswAsgElgF3qGpaAOXvB7adRUi1gANn9yoqLHsvTivJ96KpqlbY2XHtGAyKvRenlfoxGJKkWNaIyGJV7R7qOMoCey9Os/ei9Nh7fZq9F6eF4r2wEW2MMcYYlyVFY4wxxmVJ0fF6qAMoQ+y9OM3ei9Jj7/Vp9l6cVurvhV1TNMYYY1x2pmiMMca4KmRSFJHWIrLc73ZMRH5X1OwcIvKgiGwUkXUicrHf8m4istJd94LbZ7LcKOK9eFREdvotv8xvnwr5XkDBM69Uxs9FSbNj8DQ7BvMq88egqlboG87sGnuApsBTwBh3+Rjgn+79dsCPQBTOsHKbgDB33UKgNyDAl8CloX5NHr0XjwL3F7BNhX0vgIbAFiDGffwxcGtl/1yU8ueuUr/XdgyW/WOwQp4p5jMQ2KSq2yh8do5hwHhVTVPVLcBGoKc4Q9DFqer36vwvvEvhM3qUB/7vRWEq+ntR0Mwrlf1zUdLsGDzNjsEyfgxWhqQ4Ehjn3i9sdo6GwA6/fZLdZQ3d+/mXl1f+7wXA3SKyQkTe8quuqLDvhRY+80pl/1yUNDsGT7NjsIwfgxU6KYpIJDAU+ORMmxawTItYXu4U8F68ArQAknA+nP/K2bSA3SvEeyFnOfMKFfi9KC12DJ5mx2D5OAYrdFIELgWWqupe9/Fe97QbyTs7RzLQ2G+/Rjin9Mnu/fzLy6M874Wq7lXVLFXNBt4AerrbVeT3InfmFVXNAHJmXqnMn4uSZsfgaXYMloNjsKInxevJW1VR2Owck4GRIhIlIs2AlsBC9zT+uIj0cls2/YKCZ/QoD/K8FzkfQNdVwCr3fkV+LwqbeaUyfy5Kmh2Dp9kxWB6OwVC3RiqpG84F3INAvN+ymsAsYIP7t4bfuodwWjatw68VE9Ad58O6CXgJd8CD8nQr5L14D1gJrHA/ePUryXvxV2Ct+zrew2nVVik/FyH63FXK99qOwTzvRZk+Bm1EG2OMMcZV0atPjTHGmIBZUjTGGGNclhSNMcYYlyVFY4wxxmVJ0RhjjHFZUjTGGGNclhSNMcYYlyXFckhEsvLNzzamDMSUKCIpIrL8DNudIyIr8y2LEpEtItLefT3pIlKrRAM2Jgh2DFZc4aEOwBRLiqomeVWYO0ySqDMGYzA2BRDXZqCxiPj8nm8UMFdVfwKSRGRrkHEYU9LsGKyg7EyxgnB/Ja4RkTfEmdV6hojEuOtuEpGF7i/A10QkzG/7l4GlOAfJn0VkrTgzX48Tkfvd/f8mIvf6PdcTIvLbAGJqJiKfi8hi9/lbuwfhdiDR3SYG+APOhKvGlFt2DFYMlhTLp5h8VTcj3OUtgf+oanvgCHCNiLQFRgDnu78gs4Ab3e1bA++qahegNnAN0AW4GmdcwRxv4g7WKyI+nDnhPigqQBGJAP4L/F5Vu+MccDlVTGuANu793wCTVXXrWb4HxoSSHYMVlFWflk8/q7oRkUScKVmWu4uW4PwSTAC6AYucGhpicKZl+QbYpqoL3O37AJ+raopb3pScslV1q4gcFJEuQF1gmaoePEOMVwLtgQnu84YD37rr1gCtReQbnAOyV8Cv3JiywY7BCsqSYsWS5nc/C+fgE+AdVX3Qf0P3AD7pv+gMZf8XuBWoB7wVQCydgYdU9c0C1q0BLgTuBT7Q03PtGVPe2TFYzln1acU3CxguInUARKSGiDQtYLt5wBUiEi0i1YDL862fCFwC9AC+CuB5dwMXu1U9iEhHtzEBOAdkT+B24OmzfUHGlDN2DJYjdqZYPsXka3Y9HXi1oA1VdbWIPAzMcA+ODJzqkj35tlskIpOBH4FtwGLgqN/6dBGZDRxR1awAYnwLGACsEZEUYJWq3uSuWwd0xPkVe7SwAowpw+wYrKBsPkWTS0SqqeoJEamCc71jlKouddf5cFrIXauqGwrYNxGYqqodPIhjK9BdVQ8EW5Yx5Ykdg6Fn1afG3+vur9+lwAS/g7EdsBGYVdDB6MoC4uUMHYeLIiI5v74jgGD7axlTHtkxGGJ2pmiMMca47EzRGGOMcVlSNMYYY1yWFI0xxhiXJUVjjDHGZUnRGGOMcVlSNMYYY1yWFI0xxhiXJUVjjDHG9f92wns8XMxEwwAAAABJRU5ErkJggg==\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "import matplotlib.pyplot as plt\n", "\n", "# figure size in inches\n", "fig_kws = {'figsize' : (6.4, 4.8)} \n", "\n", "fig, ax = fig_pre_edge(group, **fig_kws)\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### 4. Background removal\n", "\n", "Once a XAFS spectrum is normalized we can compute the Extended X-ray Fine Structure (EXAFS) $\\chi(k)$. For this we need to remove background signal that accompanies the EXAFS. \n", "\n", "`araucaria` implements background removal with the [autobk()](../../xas/xas_autobk.rst#araucaria.xas.autobk.autobk) function, which accepts a dictionary to provide the parameters. Please check the documentation of [autobk()](../../xas/xas_autobk.rst#araucaria.xas.autobk.autobk) for further details." ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "from araucaria.xas import autobk\n", "\n", "# autobk parameters\n", "autobk_kws = {'rbkg' : 1.0,\n", " 'k_range' : [0, 14],\n", " 'kweight' : 2,\n", " 'win' : 'hanning',\n", " 'dk' : 0.1,\n", " 'nclamp' : 2,\n", " 'clamp_lo': 1,\n", " 'clamp_hi': 1}\n", "\n", "# background removal\n", "autbk_data = autobk(group, update=True, **autobk_kws)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once we have removed the background signal, we can visualize it along with the $\\chi(k)$ spectrum using the [fig_autobk()](../../plot_module.rst#araucaria.plot.fig_xas.fig_autobk) function. The function also accepts a dictionary of parameters for the figure, provided in this case with the `fig_kws` dictionary." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "image/png": "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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# plot background and EXAFS\n", "from araucaria.plot import fig_autobk\n", "\n", "fig, ax = fig_autobk(group, show_window=False, **fig_kws)\n", "plt.show()" ] } ], "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": "3.9.4" } }, "nbformat": 4, "nbformat_minor": 4 }