diff --git "a/1. Python\347\256\200\344\273\213.ipynb" "b/1. Python\347\256\200\344\273\213.ipynb" new file mode 100644 index 0000000..8886b9d --- /dev/null +++ "b/1. Python\347\256\200\344\273\213.ipynb" @@ -0,0 +1,845 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 4, + "id": "13ef297c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World!\n" + ] + } + ], + "source": [ + "print('Hello World!')" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "ef9e6ce2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello World!\n" + ] + } + ], + "source": [ + "print('Hello World!')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2aeae8d0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello! Machine Learning\n" + ] + } + ], + "source": [ + "print('Hello!','Machine','Learning')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8cb1be3e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "I'm fine\n" + ] + } + ], + "source": [ + "print(\"I'm fine\")" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "0e273d32", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "abcde\r", + "f\n" + ] + } + ], + "source": [ + "print('abcde\\rf')" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "708e07ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\t2\t3\n", + "100\t200\t300\n" + ] + } + ], + "source": [ + "print('1\\t2\\t3')\n", + "print('100\\t200\\t300')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "27e4641f", + "metadata": {}, + "outputs": [], + "source": [ + "print?" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "f52c4e09", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello world!\n" + ] + } + ], + "source": [ + "a = 'Hello world!'\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "eddd9d8f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Huang\n", + "Hello, Huang !\n" + ] + } + ], + "source": [ + "name = input()\n", + "print('Hello,',name,'!')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "cadb98a3", + "metadata": {}, + "outputs": [ + { + "ename": "SyntaxError", + "evalue": "leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers (, line 1)", + "output_type": "error", + "traceback": [ + "\u001b[1;36m File \u001b[1;32m\"\"\u001b[1;36m, line \u001b[1;32m1\u001b[0m\n\u001b[1;33m a = 010\u001b[0m\n\u001b[1;37m ^\u001b[0m\n\u001b[1;31mSyntaxError\u001b[0m\u001b[1;31m:\u001b[0m leading zeros in decimal integer literals are not permitted; use an 0o prefix for octal integers\n" + ] + } + ], + "source": [ + "a = 010" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "ff688c69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "str" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = '755.1'\n", + "type(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "1e300ec7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "140713379113040\n", + "2786278258928\n" + ] + } + ], + "source": [ + "a = 10\n", + "print(id(a))\n", + "a = 'python'\n", + "print(id(a))" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4c608b4b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2786278258928\n", + "2786278258928\n" + ] + } + ], + "source": [ + "a = 'python'\n", + "print(id(a))\n", + "print(id('py'+'thon'))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "a3792c31", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2786310891888\n", + "2786310891888\n" + ] + }, + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 'H2O'\n", + "b = 'H2O'\n", + "print(id(a))\n", + "print(id(b))\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "c9a2d511", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2786310879536\n", + "2786310878384\n" + ] + }, + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 'H2O!'\n", + "b = 'H2O!'\n", + "print(id(a))\n", + "print(id(b))\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "bb4da100", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a==b" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "32325425", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 256\n", + "b = 256\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "b9c28b80", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "False" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = 257\n", + "b = 257\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "4f455060", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a, b = 257, 257\n", + "a is b" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "94f49da3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "a = '5'\n", + "b = 1\n", + "a = int(a)\n", + "print(a + b)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "f692a8d2", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1, 2, 3, 4, 5]\n" + ] + } + ], + "source": [ + "a = [1, 2, 3, 4, 5]\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "fece9881", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n" + ] + } + ], + "source": [ + "print(a[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "fd9422ae", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "print(a[-1])" + ] + }, + { + "cell_type": "code", + "execution_count": 73, + "id": "86aaee3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['uv', 255, 345, 'cm-1', 10, 'uv', 255, 345, 'cm-1', 10]\n", + "['uv', 255, 345, 'cm-1', 10, 'res', 123]\n" + ] + } + ], + "source": [ + "list_a = ['uv', 255, 345, 'cm-1', 10]\n", + "list_b = ['res', 123]\n", + "\n", + "print(list_a * 2)\n", + "print(list_a + list_b)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "7c516e98", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a = list(range(10)) # list(range(0, 10, 1))\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "a584e8b2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.append(11)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "24b1305c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 88, 3, 4, 5, 6, 7, 8, 9, 11]" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.insert(3,88)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "id": "f1750db4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11]" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.remove(88)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "id": "f2072607", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 11, 3]" + ] + }, + "execution_count": 66, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.append(3)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "c9c16a82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 4, 5, 6, 7, 8, 9, 11, 3]" + ] + }, + "execution_count": 67, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.remove(3)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "8b114e44", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 4, 5, 6, 7, 8, 9, 11, 3]" + ] + }, + "execution_count": 68, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.pop(2)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "af351193", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 4, 5, 6, 7, 8, 9, 11]" + ] + }, + "execution_count": 69, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a.pop()\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "32cd0084", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 4, 6, 7, 8, 9, 11]" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del list_a[3]\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "id": "6d5888a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 4, 6, 7, 8, 11]" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "del list_a[-2]\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 72, + "id": "dfed2cdc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" + ] + }, + "execution_count": 72, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a = []\n", + "for i in range(10):\n", + " list_a.append(0)\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 74, + "id": "08132e73", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a = [0] * 10\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "fde28633", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = [i for i in range(10)]\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "e3f4f821", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0]" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list_a = [0] * 10\n", + "list_a" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "36f86ad0", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/2. Python\347\232\204\345\237\272\346\234\254\344\275\277\347\224\250.ipynb" "b/2. Python\347\232\204\345\237\272\346\234\254\344\275\277\347\224\250.ipynb" new file mode 100644 index 0000000..9c34b19 --- /dev/null +++ "b/2. Python\347\232\204\345\237\272\346\234\254\344\275\277\347\224\250.ipynb" @@ -0,0 +1,1373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "c6b01711", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(1, 2, 3, 4, 5)" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup1 = (1, 2, 3, 4, 5)\n", + "tup1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "1b22bd53", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tuple" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup1 = (1,)\n", + "type(tup1)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "68abe2d5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "('x', 3)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "tup2 = 'x', 3\n", + "tup2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "8aeafea3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "oe\n" + ] + } + ], + "source": [ + "s = 'molecule'\n", + "print(s[1:5:2])" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2f689de8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['m', 'o', 'l', 'e', 'c', 'u', 'l', 'e']" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "list(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a68e68eb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "new is ['H', '2', 'S'] and old is ['H', '2', 'S']\n" + ] + } + ], + "source": [ + "old = list('H2O')\n", + "new = old\n", + "new[2] = 'S'\n", + "print('new is ', new, 'and old is ', old)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "3810d89a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "new is ['H', '2', 'S'] and old is ['H', '2', 'O']\n" + ] + } + ], + "source": [ + "old = list('H2O')\n", + "new = old[:]\n", + "new[2] = 'S'\n", + "print('new is ', new, 'and old is ', old)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "7167d7b6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n" + ] + } + ], + "source": [ + "mole1 = 'LiFePO4'\n", + "mole2 = 'LiCoO2'\n", + "\n", + "print('Fe' in mole1)\n", + "print('Fe' in mole2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0badb98e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['L', 'i', 'F', 'e', 'P', 'O', '4']\n" + ] + } + ], + "source": [ + "l1 = list(mole1)\n", + "print(l1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2b79c502", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'LiFePO4'" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = ''.join(l1)\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "4060e89c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'a', 'c', 'd', 'n', 'o'}" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1 = set('anaconda')\n", + "set1" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "c2cbe32b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'h', 'n', 'o', 'p', 't', 'y'}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set2 = set('python')\n", + "set2" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "68b2a3e0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'n', 'o'}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "set1 & set2" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "212d292c", + "metadata": {}, + "outputs": [], + "source": [ + "dict1 = {'H' : 1, 'O' : 16, 'Na': 23}" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "285c99fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "40" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mass = dict1['Na'] + dict1['O'] + dict1['H']\n", + "mass" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bf232819", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{12: 'C', 14: 'C14'}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict2 ={}\n", + "dict2[12] = 'C' # key 12, value 'C'\n", + "dict2[14] = 'N'\n", + "dict2[14.0] = 'C14'\n", + "dict2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b7c8fdc9", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "4e9bb47c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 50\n", + "1 50\n", + "2 100\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "s = pd.Series([50, 50, 100])\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "06476e47", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Red 50\n", + "Purple 50\n", + "Blue 100\n", + "dtype: int64\n" + ] + } + ], + "source": [ + "s.index = ['Red','Purple', 'Blue']\n", + "print(s)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6cb0a4b1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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2blue100100
\n", + "
" + ], + "text/plain": [ + " name score1 score2\n", + "0 red 50 60\n", + "1 purple 60 50\n", + "2 blue 100 100" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = [['red', 50, 60], ['purple', 60, 50], ['blue', 100, 100]]\n", + "df = pd.DataFrame(data, columns = ['name', 'score1', 'score2'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5aefcbfe", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a is positive\n" + ] + } + ], + "source": [ + "a = 5\n", + "if(a > 0):\n", + " print('a is positive')" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "cfe01e36", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "liquid\n", + "abc\n" + ] + } + ], + "source": [ + "temp = 25\n", + "if(temp > 100):\n", + " print('gas')\n", + "elif(temp>0):\n", + " print('liquid')\n", + "else:\n", + " print('solid')\n", + "print('abc')" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0b156b6b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a= 1\n", + "a= 2\n", + "a= 3\n", + "a= 4\n", + "a= 5\n", + "a= 6\n", + "a= 7\n", + "a= 8\n", + "a= 9\n" + ] + } + ], + "source": [ + "a = 1\n", + "while(a < 10):\n", + " print('a=',a)\n", + " a+=1" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "89b1ee73", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a= 3\n", + "a= 6\n", + "a= 9\n", + "a= 12\n", + "a= 15\n", + "a= 18\n", + "a is larger than 20\n" + ] + } + ], + "source": [ + "a = 1\n", + "while(a < 20):\n", + " if(a % 3 == 0):\n", + " print('a=',a)\n", + " a+=1\n", + "else:\n", + " print('a is larger than 20')" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d31662c5", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a= 1\n", + "a= 2\n", + "a= 3\n", + "a= 4\n", + "a= 5\n", + "a= 6\n", + "a= 7\n", + "a= 8\n", + "a= 9\n" + ] + } + ], + "source": [ + "a = 1\n", + "while(a < 10):\n", + " print('a=',a)\n", + " a += 1" + ] + }, + { + "cell_type": "markdown", + "id": "fe3c6860", + "metadata": {}, + "source": [ + "1, 1, 2, 3, 5, 8, 13, 21, ..." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "cfbf0fd4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 1 2 3 5 8 13 21 34 55 89 " + ] + } + ], + "source": [ + "a = 1\n", + "b = 1\n", + "while a < 100:\n", + " print(a, end = ' ')\n", + " a, b = b, a + b" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "b0b098a8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['red', 'purple', 'blue']\n" + ] + } + ], + "source": [ + "a = ['red', 'purple', 'blue']\n", + "while 'blue' in a:\n", + " print(a)\n", + " a.remove('blue')" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "8a73b25c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "H\n", + "C\n", + "N\n" + ] + } + ], + "source": [ + "a = ['H', 'C', 'N']\n", + "for b in a:\n", + " print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "eee85b8c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 H\n", + "1 C\n", + "2 N\n" + ] + } + ], + "source": [ + "for i, ele in enumerate(a):\n", + " print(i, ele)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "807eccdc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = list(range(10))\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "d1e65b50", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1 3 5 7 9 " + ] + } + ], + "source": [ + "for i in range(1, 10, 2):\n", + " print(i, end = ' ')" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4c2a6c62", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 C\n", + "1 H\n", + "2 3\n" + ] + } + ], + "source": [ + "mole = 'CH3COOH'\n", + "for i, j in enumerate(mole):\n", + " if i == 3:\n", + " break\n", + " print(i, j)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5e84eba7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 C\n", + "1 H\n", + "2 3\n", + "4 O\n", + "5 O\n", + "6 H\n" + ] + } + ], + "source": [ + "mole = 'CH3COOH'\n", + "for i, j in enumerate(mole):\n", + " if i == 3:\n", + " continue\n", + " print(i, j)" + ] + }, + { + "cell_type": "markdown", + "id": "322fb313", + "metadata": {}, + "source": [ + "zip()函数" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "ae1c80a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "C C\n", + "H H\n", + "3 3\n", + "C C\n", + "O H\n", + "O 2\n", + "H O\n" + ] + } + ], + "source": [ + "mole1 = 'CH3COOH'\n", + "mole2 = 'CH3CH2OH'\n", + "for i, j in zip(mole1, mole2):\n", + " print(i, j)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "02b4bb82", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{0: 'K', 1: 'C', 2: 'N'}" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "dict3 = {}\n", + "s = 'KCN'\n", + "for i, dict3[i] in enumerate(s):\n", + " pass\n", + "dict3" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "5bc99e21", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "4\n" + ] + } + ], + "source": [ + "for i in range(5): # 0 1 2 3 4\n", + " print(i)\n", + " i = 10" + ] + }, + { + "cell_type": "markdown", + "id": "b7e8ec55", + "metadata": {}, + "source": [ + "A. 0\n", + "B. 0\n", + " 1\n", + " 2\n", + " 3\n", + " 4" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "7d5042de", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "liquid\n" + ] + } + ], + "source": [ + "temp = 25\n", + "if(temp > 100):\n", + " print('gas')\n", + "elif(temp>0):\n", + " print('liquid')\n", + "else:\n", + " print('solid')" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "2193eb86", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "liquid\n", + "solid\n" + ] + } + ], + "source": [ + "def phase(temp):\n", + " if(temp > 100):\n", + " print('gas')\n", + " elif(temp>0):\n", + " print('liquid')\n", + " else:\n", + " print('solid')\n", + "\n", + "phase(25)\n", + "phase(-5)" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "a2a5b839", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1\n", + "1\n", + "2\n", + "1\n" + ] + } + ], + "source": [ + "def fun(a):\n", + " print(a)\n", + " a += 1\n", + " print(a)\n", + "\n", + "a = 1\n", + "print(a)\n", + "fun(a)\n", + "print(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "4c1a59c6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[525]\n", + "[525, 'cm-1']\n" + ] + } + ], + "source": [ + "def fun(a):\n", + " a.append('cm-1')\n", + "\n", + "b = [525]\n", + "print(b)\n", + "fun(b)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "cce04af5", + "metadata": {}, + "outputs": [], + "source": [ + "z_max = 3e-10\n", + "z_min = 2.9e-10\n", + "I_max = [2.27e-12, 2.24e-12, 2.30e-12, 2.25e-12, 2.22e-12, 2.25e-12, 2.24e-12]\n", + "I_min = [2.81e-12, 2.84e-12, 2.99e-12, 2.58e-12, 2.61e-12, 2.80e-12, 2.50e-12]" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "95a25ef0", + "metadata": {}, + "outputs": [], + "source": [ + "def avg_list(I):\n", + " sum = 0\n", + " for current in I:\n", + " sum += current\n", + " return sum / len(I)" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "0df42442", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.253,2.733\n" + ] + } + ], + "source": [ + "I1 = avg_list(I_max)*1e12\n", + "I2 = avg_list(I_min)*1e12\n", + "\n", + "print('%.3f,%.3f'%(I1, I2))" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "98716b88", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.1415926\n", + "3.142\n" + ] + } + ], + "source": [ + "a = 3.1415926\n", + "print(a)\n", + "print('%.3f'%a)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "4dca4847", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.0 2.9\n" + ] + } + ], + "source": [ + "z_max *= 1e10\n", + "z_min *= 1e10\n", + "print(z_max,z_min)" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "64646489", + "metadata": {}, + "outputs": [], + "source": [ + "dI = I1 - I2\n", + "dz = z_max - z_min\n", + "I_avg = 0.5 * (I1 + I2)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "eefb41ec", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = pow(2, 3)\n", + "a" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "33984ae9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.530\n" + ] + } + ], + "source": [ + "WF = 0.952 * dI * dI / dz / dz / I_avg / I_avg\n", + "print('%.3f'%WF)" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "aaa11f8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "3.529593090368709" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def Cal_WF(z_max, z_min, I_max, I_min):\n", + " z_max *= 1e10\n", + " z_min *= 1e10\n", + " I1 = avg_list(I_max)*1e12\n", + " I2 = avg_list(I_min)*1e12\n", + " dI = I1 - I2\n", + " dz = z_max - z_min\n", + " I_avg = 0.5 * (I1 + I2)\n", + " return 0.952 * dI * dI / dz / dz / I_avg / I_avg\n", + "\n", + "z_max = 3e-10\n", + "z_min = 2.9e-10\n", + "I_max = [2.27e-12, 2.24e-12, 2.30e-12, 2.25e-12, 2.22e-12, 2.25e-12, 2.24e-12]\n", + "I_min = [2.81e-12, 2.84e-12, 2.99e-12, 2.58e-12, 2.61e-12, 2.80e-12, 2.50e-12]\n", + "\n", + "Cal_WF(z_max, z_min, I_max, I_min)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "e6554ec3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 0, 0]" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "r = [0] * 3\n", + "r[0] = 1\n", + "r" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "82f5a356", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[0, 0, 0], [0, 0, 0], [0, 0, 0]]" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f = [r] * 3\n", + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "644c0356", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 0, 0], [1, 0, 0], [1, 0, 0]]" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[0][0] = 1\n", + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "7bf32b24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0, 0, 0], [0, 0, 0], [0, 0, 0]]\n", + "[[1, 0, 0], [0, 0, 0], [0, 0, 0]]\n" + ] + } + ], + "source": [ + "f = [[0]*3 for i in range(3)]\n", + "print(f)\n", + "f[0][0] = 1\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "80bd6219", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[[1, 0, 0], [0, 0, 0], [0, 0, 0]]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "71a9b423", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[1, 0, 0]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "5a3cd6a3", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "1" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f[0][0]" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "9322aeee", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n", + "6\n" + ] + } + ], + "source": [ + "def fun():\n", + " a = 5\n", + " b = 6\n", + " return a , b\n", + "\n", + "c, d = fun()\n", + "print(c)\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "92cc6a5d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/3. Python\346\226\207\344\273\266\350\257\273\345\206\231\357\274\214\345\272\223.ipynb" "b/3. Python\346\226\207\344\273\266\350\257\273\345\206\231\357\274\214\345\272\223.ipynb" new file mode 100644 index 0000000..56d742a --- /dev/null +++ "b/3. Python\346\226\207\344\273\266\350\257\273\345\206\231\357\274\214\345\272\223.ipynb" @@ -0,0 +1,2400 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "41acabd6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiment\n", + "3.3\n", + "2.2\n", + "2.5\n", + "3.1\n", + "3.0\n", + "eV\n" + ] + } + ], + "source": [ + "f = open('sample.txt', mode = 'r')\n", + "res = f.read()\n", + "f.close()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30795687", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Experiment\n", + "\n" + ] + } + ], + "source": [ + "f = open('sample.txt', mode = 'r')\n", + "res = f.readline()\n", + "f.close()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ffaea046", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['Experiment\\n', '3.3\\n', '2.2\\n', '2.5\\n', '3.1\\n', '3.0\\n', 'eV']\n" + ] + } + ], + "source": [ + "f = open('sample.txt', mode = 'r')\n", + "res = f.readlines()\n", + "f.close()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f724af51", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.3, 2.2, 2.5, 3.1, 3.0]\n" + ] + } + ], + "source": [ + "f = open('sample.txt', mode = 'r')\n", + "f.readline()\n", + "data = []\n", + "for i in range(5):\n", + " data.append(float(f.readline()))\n", + "f.close()\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c1837a74", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.3, 2.2, 2.5, 3.1, 3.0]\n" + ] + } + ], + "source": [ + "f = open('sample.txt', mode = 'r')\n", + "res = f.readlines()\n", + "f.close()\n", + "res = res[1:-1]\n", + "for i in range(len(res)):\n", + " res[i] = float(res[i])\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "b7fb6bfb", + "metadata": {}, + "outputs": [], + "source": [ + "def avg_list(I):\n", + " sum = 0\n", + " for current in I:\n", + " sum += current\n", + " return sum / len(I)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "f0af9465", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.82" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "avg_list(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "eb38c84e", + "metadata": {}, + "outputs": [], + "source": [ + "f = open('res.txt', mode = 'w')\n", + "f.write('avg = '+str(avg_list(res)))\n", + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "6f3f1074", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.3, 2.2, 2.5, 3.1, 3.0]\n" + ] + } + ], + "source": [ + "with open('sample.txt', mode = 'r') as f:\n", + " f.readline()\n", + " data = []\n", + " for i in range(5):\n", + " data.append(float(f.readline()))\n", + "\n", + "print(data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "122b2e3f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3.00E-10,2.27E-12,2.24E-12,2.30E-12,2.25E-12,2.22E-12,2.25E-12,2.24E-12\n", + "\n" + ] + } + ], + "source": [ + "with open('didz.csv', mode = 'r', encoding = 'UTF-8') as f:\n", + " res = f.readline()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "988d84a9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['\\ufeff3.00E-10,2.27E-12,2.24E-12,2.30E-12,2.25E-12,2.22E-12,2.25E-12,2.24E-12\\n', '2.90E-10,2.81E-12,2.84E-12,2.99E-12,2.58E-12,2.61E-12,2.80E-12,2.50E-12\\n']\n" + ] + } + ], + "source": [ + "with open('didz.csv', mode = 'r', encoding = 'UTF-8') as f:\n", + " res = f.readlines()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5d6fdec8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['3.00E-10,2.27E-12,2.24E-12,2.30E-12,2.25E-12,2.22E-12,2.25E-12,2.24E-12\\n', '2.90E-10,2.81E-12,2.84E-12,2.99E-12,2.58E-12,2.61E-12,2.80E-12,2.50E-12\\n']\n" + ] + } + ], + "source": [ + "with open('didz.csv', mode = 'r', encoding = 'UTF-8-sig') as f:\n", + " res = f.readlines()\n", + "print(res)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "2f17c186", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "['2.27E-12', '2.24E-12', '2.30E-12', '2.25E-12', '2.22E-12', '2.25E-12', '2.24E-12']\n" + ] + } + ], + "source": [ + "with open('didz.csv', mode = 'r', encoding = 'UTF-8-sig') as f:\n", + " res = f.readline()\n", + " I_max = res.strip('\\n').split(',')\n", + " I_min = f.readline().strip('\\n').split(',')\n", + "\n", + "z_max = I_max[0]\n", + "z_min = I_min[0]\n", + "I_max = I_max[1:]\n", + "I_min = I_min[1:]\n", + "print(I_max)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "8e191cdd", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[2.27e-12, 2.24e-12, 2.3e-12, 2.25e-12, 2.22e-12, 2.25e-12, 2.24e-12]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "I_max_f = list(map(float, I_max))\n", + "I_max_f" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "4bd16706", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "2.252857142857143e-12" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "\n", + "I1 = np.mean(I_max_f)\n", + "\n", + "I1" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "79bed67c", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "f6ac1786", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5 6]\n" + ] + }, + { + "data": { + "text/plain": [ + "numpy.ndarray" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([1, 2, 3, 4, 5, 6]) # a = np.array(1, 2, 3, 4, 5, 6)是错误的\n", + "print(a)\n", + "type(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "f6e31240", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n" + ] + } + ], + "source": [ + "b = np.array([[1,2,3],[4,5,6]])\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "946ac95c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2\n" + ] + } + ], + "source": [ + "print(b.ndim)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "7da639d1", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(2, 3)\n" + ] + } + ], + "source": [ + "print(b.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "5412d031", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(6,)\n" + ] + } + ], + "source": [ + "print(a.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "da6926c9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4 5 6]\n", + "[1 2 3 4 5 6]\n" + ] + } + ], + "source": [ + "print(a)\n", + "print(a.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "e9c3ca6e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n", + "[[1 4]\n", + " [2 5]\n", + " [3 6]]\n" + ] + } + ], + "source": [ + "print(b)\n", + "print(b.T)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "403c7a43", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0.]\n" + ] + } + ], + "source": [ + "c = np.zeros(5)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "05d0deaf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]]\n" + ] + } + ], + "source": [ + "d = np.ones((5, 3))\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "ffce722a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 3 5 7 9]\n" + ] + } + ], + "source": [ + "f = np.arange(1, 10, 2)\n", + "print(f)" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "abb578b9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]\n" + ] + } + ], + "source": [ + "g = np.arange(0, 2.01, 0.25)\n", + "print(g)" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "3f9f601e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.25 0.5 0.75 1. 1.25 1.5 1.75 2. ]\n" + ] + } + ], + "source": [ + "h = np.linspace(0, 2, 9)\n", + "print(h)" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "9fb97ea6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2, 3)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "c7cf0e16", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n", + "(3, 2)\n", + "[[1 2]\n", + " [3 4]\n", + " [5 6]]\n" + ] + } + ], + "source": [ + "b = np.array([[1,2,3],[4,5,6]])\n", + "print(b)\n", + "b = b.reshape(3, 2)\n", + "print(b.shape)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "f622c80a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1]\n", + " [2]\n", + " [3]\n", + " [4]\n", + " [5]\n", + " [6]]\n" + ] + } + ], + "source": [ + "b = b.reshape(-1,1)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "16e0b6e2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + "[[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", + " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]]\n" + ] + } + ], + "source": [ + "c = np.zeros(24)\n", + "print(c)\n", + "c = c.reshape(2, -1)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "369ed4e4", + "metadata": {}, + "outputs": [], + "source": [ + "c = c.reshape(-1)" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "82ebfbf6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n", + " 0., 0., 0., 0., 0., 0., 0.])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "id": "2bcfc35f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]]\n", + "\n", + " [[0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]\n", + " [0. 0. 0.]]]\n" + ] + } + ], + "source": [ + "c = c.reshape(2, 4, 3)\n", + "print(c)" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "c6d7ba72", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 ... 9997 9998 9999]\n" + ] + } + ], + "source": [ + "b = np.arange(10000)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "id": "af18d803", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 ... 97 98 99]\n", + " [ 100 101 102 ... 197 198 199]\n", + " [ 200 201 202 ... 297 298 299]\n", + " ...\n", + " [9700 9701 9702 ... 9797 9798 9799]\n", + " [9800 9801 9802 ... 9897 9898 9899]\n", + " [9900 9901 9902 ... 9997 9998 9999]]\n" + ] + } + ], + "source": [ + "b = b.reshape(100, -1)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "a134ae1c", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13\n", + " 14 15 16 17 18 19 20 21 22 23 24 25 26 27\n", + " 28 29 30 31 32 33 34 35 36 37 38 39 40 41\n", + " 42 43 44 45 46 47 48 49 50 51 52 53 54 55\n", + " 56 57 58 59 60 61 62 63 64 65 66 67 68 69\n", + " 70 71 72 73 74 75 76 77 78 79 80 81 82 83\n", + " 84 85 86 87 88 89 90 91 92 93 94 95 96 97\n", + " 98 99]\n", + " [ 100 101 102 103 104 105 106 107 108 109 110 111 112 113\n", + " 114 115 116 117 118 119 120 121 122 123 124 125 126 127\n", + " 128 129 130 131 132 133 134 135 136 137 138 139 140 141\n", + " 142 143 144 145 146 147 148 149 150 151 152 153 154 155\n", + " 156 157 158 159 160 161 162 163 164 165 166 167 168 169\n", + " 170 171 172 173 174 175 176 177 178 179 180 181 182 183\n", + " 184 185 186 187 188 189 190 191 192 193 194 195 196 197\n", + " 198 199]\n", + " [ 200 201 202 203 204 205 206 207 208 209 210 211 212 213\n", + " 214 215 216 217 218 219 220 221 222 223 224 225 226 227\n", + " 228 229 230 231 232 233 234 235 236 237 238 239 240 241\n", + " 242 243 244 245 246 247 248 249 250 251 252 253 254 255\n", + " 256 257 258 259 260 261 262 263 264 265 266 267 268 269\n", + " 270 271 272 273 274 275 276 277 278 279 280 281 282 283\n", + " 284 285 286 287 288 289 290 291 292 293 294 295 296 297\n", + " 298 299]\n", + " [ 300 301 302 303 304 305 306 307 308 309 310 311 312 313\n", + " 314 315 316 317 318 319 320 321 322 323 324 325 326 327\n", + " 328 329 330 331 332 333 334 335 336 337 338 339 340 341\n", + " 342 343 344 345 346 347 348 349 350 351 352 353 354 355\n", + " 356 357 358 359 360 361 362 363 364 365 366 367 368 369\n", + " 370 371 372 373 374 375 376 377 378 379 380 381 382 383\n", + " 384 385 386 387 388 389 390 391 392 393 394 395 396 397\n", + " 398 399]\n", + " [ 400 401 402 403 404 405 406 407 408 409 410 411 412 413\n", + " 414 415 416 417 418 419 420 421 422 423 424 425 426 427\n", + " 428 429 430 431 432 433 434 435 436 437 438 439 440 441\n", + " 442 443 444 445 446 447 448 449 450 451 452 453 454 455\n", + " 456 457 458 459 460 461 462 463 464 465 466 467 468 469\n", + " 470 471 472 473 474 475 476 477 478 479 480 481 482 483\n", + " 484 485 486 487 488 489 490 491 492 493 494 495 496 497\n", + " 498 499]\n", + " [ 500 501 502 503 504 505 506 507 508 509 510 511 512 513\n", + " 514 515 516 517 518 519 520 521 522 523 524 525 526 527\n", + " 528 529 530 531 532 533 534 535 536 537 538 539 540 541\n", + " 542 543 544 545 546 547 548 549 550 551 552 553 554 555\n", + " 556 557 558 559 560 561 562 563 564 565 566 567 568 569\n", + " 570 571 572 573 574 575 576 577 578 579 580 581 582 583\n", + " 584 585 586 587 588 589 590 591 592 593 594 595 596 597\n", + " 598 599]\n", + " [ 600 601 602 603 604 605 606 607 608 609 610 611 612 613\n", + " 614 615 616 617 618 619 620 621 622 623 624 625 626 627\n", + " 628 629 630 631 632 633 634 635 636 637 638 639 640 641\n", + " 642 643 644 645 646 647 648 649 650 651 652 653 654 655\n", + " 656 657 658 659 660 661 662 663 664 665 666 667 668 669\n", + " 670 671 672 673 674 675 676 677 678 679 680 681 682 683\n", + " 684 685 686 687 688 689 690 691 692 693 694 695 696 697\n", + " 698 699]\n", + " [ 700 701 702 703 704 705 706 707 708 709 710 711 712 713\n", + " 714 715 716 717 718 719 720 721 722 723 724 725 726 727\n", + " 728 729 730 731 732 733 734 735 736 737 738 739 740 741\n", + " 742 743 744 745 746 747 748 749 750 751 752 753 754 755\n", + " 756 757 758 759 760 761 762 763 764 765 766 767 768 769\n", + " 770 771 772 773 774 775 776 777 778 779 780 781 782 783\n", + " 784 785 786 787 788 789 790 791 792 793 794 795 796 797\n", + " 798 799]\n", + " [ 800 801 802 803 804 805 806 807 808 809 810 811 812 813\n", + " 814 815 816 817 818 819 820 821 822 823 824 825 826 827\n", + " 828 829 830 831 832 833 834 835 836 837 838 839 840 841\n", + " 842 843 844 845 846 847 848 849 850 851 852 853 854 855\n", + " 856 857 858 859 860 861 862 863 864 865 866 867 868 869\n", + " 870 871 872 873 874 875 876 877 878 879 880 881 882 883\n", + " 884 885 886 887 888 889 890 891 892 893 894 895 896 897\n", + " 898 899]\n", + " [ 900 901 902 903 904 905 906 907 908 909 910 911 912 913\n", + " 914 915 916 917 918 919 920 921 922 923 924 925 926 927\n", + " 928 929 930 931 932 933 934 935 936 937 938 939 940 941\n", + " 942 943 944 945 946 947 948 949 950 951 952 953 954 955\n", + " 956 957 958 959 960 961 962 963 964 965 966 967 968 969\n", + " 970 971 972 973 974 975 976 977 978 979 980 981 982 983\n", + " 984 985 986 987 988 989 990 991 992 993 994 995 996 997\n", + " 998 999]\n", + " [1000 1001 1002 1003 1004 1005 1006 1007 1008 1009 1010 1011 1012 1013\n", + " 1014 1015 1016 1017 1018 1019 1020 1021 1022 1023 1024 1025 1026 1027\n", + " 1028 1029 1030 1031 1032 1033 1034 1035 1036 1037 1038 1039 1040 1041\n", + " 1042 1043 1044 1045 1046 1047 1048 1049 1050 1051 1052 1053 1054 1055\n", + " 1056 1057 1058 1059 1060 1061 1062 1063 1064 1065 1066 1067 1068 1069\n", + " 1070 1071 1072 1073 1074 1075 1076 1077 1078 1079 1080 1081 1082 1083\n", + " 1084 1085 1086 1087 1088 1089 1090 1091 1092 1093 1094 1095 1096 1097\n", + " 1098 1099]\n", + " [1100 1101 1102 1103 1104 1105 1106 1107 1108 1109 1110 1111 1112 1113\n", + " 1114 1115 1116 1117 1118 1119 1120 1121 1122 1123 1124 1125 1126 1127\n", + " 1128 1129 1130 1131 1132 1133 1134 1135 1136 1137 1138 1139 1140 1141\n", + " 1142 1143 1144 1145 1146 1147 1148 1149 1150 1151 1152 1153 1154 1155\n", + " 1156 1157 1158 1159 1160 1161 1162 1163 1164 1165 1166 1167 1168 1169\n", + " 1170 1171 1172 1173 1174 1175 1176 1177 1178 1179 1180 1181 1182 1183\n", + " 1184 1185 1186 1187 1188 1189 1190 1191 1192 1193 1194 1195 1196 1197\n", + " 1198 1199]\n", + " [1200 1201 1202 1203 1204 1205 1206 1207 1208 1209 1210 1211 1212 1213\n", + " 1214 1215 1216 1217 1218 1219 1220 1221 1222 1223 1224 1225 1226 1227\n", + " 1228 1229 1230 1231 1232 1233 1234 1235 1236 1237 1238 1239 1240 1241\n", + " 1242 1243 1244 1245 1246 1247 1248 1249 1250 1251 1252 1253 1254 1255\n", + " 1256 1257 1258 1259 1260 1261 1262 1263 1264 1265 1266 1267 1268 1269\n", + " 1270 1271 1272 1273 1274 1275 1276 1277 1278 1279 1280 1281 1282 1283\n", + " 1284 1285 1286 1287 1288 1289 1290 1291 1292 1293 1294 1295 1296 1297\n", + " 1298 1299]\n", + " [1300 1301 1302 1303 1304 1305 1306 1307 1308 1309 1310 1311 1312 1313\n", + " 1314 1315 1316 1317 1318 1319 1320 1321 1322 1323 1324 1325 1326 1327\n", + " 1328 1329 1330 1331 1332 1333 1334 1335 1336 1337 1338 1339 1340 1341\n", + " 1342 1343 1344 1345 1346 1347 1348 1349 1350 1351 1352 1353 1354 1355\n", + " 1356 1357 1358 1359 1360 1361 1362 1363 1364 1365 1366 1367 1368 1369\n", + " 1370 1371 1372 1373 1374 1375 1376 1377 1378 1379 1380 1381 1382 1383\n", + " 1384 1385 1386 1387 1388 1389 1390 1391 1392 1393 1394 1395 1396 1397\n", + " 1398 1399]\n", + " [1400 1401 1402 1403 1404 1405 1406 1407 1408 1409 1410 1411 1412 1413\n", + " 1414 1415 1416 1417 1418 1419 1420 1421 1422 1423 1424 1425 1426 1427\n", + " 1428 1429 1430 1431 1432 1433 1434 1435 1436 1437 1438 1439 1440 1441\n", + " 1442 1443 1444 1445 1446 1447 1448 1449 1450 1451 1452 1453 1454 1455\n", + " 1456 1457 1458 1459 1460 1461 1462 1463 1464 1465 1466 1467 1468 1469\n", + " 1470 1471 1472 1473 1474 1475 1476 1477 1478 1479 1480 1481 1482 1483\n", + " 1484 1485 1486 1487 1488 1489 1490 1491 1492 1493 1494 1495 1496 1497\n", + " 1498 1499]\n", + " [1500 1501 1502 1503 1504 1505 1506 1507 1508 1509 1510 1511 1512 1513\n", + " 1514 1515 1516 1517 1518 1519 1520 1521 1522 1523 1524 1525 1526 1527\n", + " 1528 1529 1530 1531 1532 1533 1534 1535 1536 1537 1538 1539 1540 1541\n", + " 1542 1543 1544 1545 1546 1547 1548 1549 1550 1551 1552 1553 1554 1555\n", + " 1556 1557 1558 1559 1560 1561 1562 1563 1564 1565 1566 1567 1568 1569\n", + " 1570 1571 1572 1573 1574 1575 1576 1577 1578 1579 1580 1581 1582 1583\n", + " 1584 1585 1586 1587 1588 1589 1590 1591 1592 1593 1594 1595 1596 1597\n", + " 1598 1599]\n", + " [1600 1601 1602 1603 1604 1605 1606 1607 1608 1609 1610 1611 1612 1613\n", + " 1614 1615 1616 1617 1618 1619 1620 1621 1622 1623 1624 1625 1626 1627\n", + " 1628 1629 1630 1631 1632 1633 1634 1635 1636 1637 1638 1639 1640 1641\n", + " 1642 1643 1644 1645 1646 1647 1648 1649 1650 1651 1652 1653 1654 1655\n", + " 1656 1657 1658 1659 1660 1661 1662 1663 1664 1665 1666 1667 1668 1669\n", + " 1670 1671 1672 1673 1674 1675 1676 1677 1678 1679 1680 1681 1682 1683\n", + " 1684 1685 1686 1687 1688 1689 1690 1691 1692 1693 1694 1695 1696 1697\n", + " 1698 1699]\n", + " [1700 1701 1702 1703 1704 1705 1706 1707 1708 1709 1710 1711 1712 1713\n", + " 1714 1715 1716 1717 1718 1719 1720 1721 1722 1723 1724 1725 1726 1727\n", + " 1728 1729 1730 1731 1732 1733 1734 1735 1736 1737 1738 1739 1740 1741\n", + " 1742 1743 1744 1745 1746 1747 1748 1749 1750 1751 1752 1753 1754 1755\n", + " 1756 1757 1758 1759 1760 1761 1762 1763 1764 1765 1766 1767 1768 1769\n", + " 1770 1771 1772 1773 1774 1775 1776 1777 1778 1779 1780 1781 1782 1783\n", + " 1784 1785 1786 1787 1788 1789 1790 1791 1792 1793 1794 1795 1796 1797\n", + " 1798 1799]\n", + " [1800 1801 1802 1803 1804 1805 1806 1807 1808 1809 1810 1811 1812 1813\n", + " 1814 1815 1816 1817 1818 1819 1820 1821 1822 1823 1824 1825 1826 1827\n", + " 1828 1829 1830 1831 1832 1833 1834 1835 1836 1837 1838 1839 1840 1841\n", + " 1842 1843 1844 1845 1846 1847 1848 1849 1850 1851 1852 1853 1854 1855\n", + " 1856 1857 1858 1859 1860 1861 1862 1863 1864 1865 1866 1867 1868 1869\n", + " 1870 1871 1872 1873 1874 1875 1876 1877 1878 1879 1880 1881 1882 1883\n", + " 1884 1885 1886 1887 1888 1889 1890 1891 1892 1893 1894 1895 1896 1897\n", + " 1898 1899]\n", + " [1900 1901 1902 1903 1904 1905 1906 1907 1908 1909 1910 1911 1912 1913\n", + " 1914 1915 1916 1917 1918 1919 1920 1921 1922 1923 1924 1925 1926 1927\n", + " 1928 1929 1930 1931 1932 1933 1934 1935 1936 1937 1938 1939 1940 1941\n", + " 1942 1943 1944 1945 1946 1947 1948 1949 1950 1951 1952 1953 1954 1955\n", + " 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969\n", + " 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983\n", + " 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997\n", + " 1998 1999]\n", + " [2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013\n", + " 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027\n", + " 2028 2029 2030 2031 2032 2033 2034 2035 2036 2037 2038 2039 2040 2041\n", + " 2042 2043 2044 2045 2046 2047 2048 2049 2050 2051 2052 2053 2054 2055\n", + " 2056 2057 2058 2059 2060 2061 2062 2063 2064 2065 2066 2067 2068 2069\n", + " 2070 2071 2072 2073 2074 2075 2076 2077 2078 2079 2080 2081 2082 2083\n", + " 2084 2085 2086 2087 2088 2089 2090 2091 2092 2093 2094 2095 2096 2097\n", + " 2098 2099]\n", + " [2100 2101 2102 2103 2104 2105 2106 2107 2108 2109 2110 2111 2112 2113\n", + " 2114 2115 2116 2117 2118 2119 2120 2121 2122 2123 2124 2125 2126 2127\n", + " 2128 2129 2130 2131 2132 2133 2134 2135 2136 2137 2138 2139 2140 2141\n", + " 2142 2143 2144 2145 2146 2147 2148 2149 2150 2151 2152 2153 2154 2155\n", + " 2156 2157 2158 2159 2160 2161 2162 2163 2164 2165 2166 2167 2168 2169\n", + " 2170 2171 2172 2173 2174 2175 2176 2177 2178 2179 2180 2181 2182 2183\n", + " 2184 2185 2186 2187 2188 2189 2190 2191 2192 2193 2194 2195 2196 2197\n", + " 2198 2199]\n", + " [2200 2201 2202 2203 2204 2205 2206 2207 2208 2209 2210 2211 2212 2213\n", + " 2214 2215 2216 2217 2218 2219 2220 2221 2222 2223 2224 2225 2226 2227\n", + " 2228 2229 2230 2231 2232 2233 2234 2235 2236 2237 2238 2239 2240 2241\n", + " 2242 2243 2244 2245 2246 2247 2248 2249 2250 2251 2252 2253 2254 2255\n", + " 2256 2257 2258 2259 2260 2261 2262 2263 2264 2265 2266 2267 2268 2269\n", + " 2270 2271 2272 2273 2274 2275 2276 2277 2278 2279 2280 2281 2282 2283\n", + " 2284 2285 2286 2287 2288 2289 2290 2291 2292 2293 2294 2295 2296 2297\n", + " 2298 2299]\n", + " [2300 2301 2302 2303 2304 2305 2306 2307 2308 2309 2310 2311 2312 2313\n", + " 2314 2315 2316 2317 2318 2319 2320 2321 2322 2323 2324 2325 2326 2327\n", + " 2328 2329 2330 2331 2332 2333 2334 2335 2336 2337 2338 2339 2340 2341\n", + " 2342 2343 2344 2345 2346 2347 2348 2349 2350 2351 2352 2353 2354 2355\n", + " 2356 2357 2358 2359 2360 2361 2362 2363 2364 2365 2366 2367 2368 2369\n", + " 2370 2371 2372 2373 2374 2375 2376 2377 2378 2379 2380 2381 2382 2383\n", + " 2384 2385 2386 2387 2388 2389 2390 2391 2392 2393 2394 2395 2396 2397\n", + " 2398 2399]\n", + " [2400 2401 2402 2403 2404 2405 2406 2407 2408 2409 2410 2411 2412 2413\n", + " 2414 2415 2416 2417 2418 2419 2420 2421 2422 2423 2424 2425 2426 2427\n", + " 2428 2429 2430 2431 2432 2433 2434 2435 2436 2437 2438 2439 2440 2441\n", + " 2442 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455\n", + " 2456 2457 2458 2459 2460 2461 2462 2463 2464 2465 2466 2467 2468 2469\n", + " 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483\n", + " 2484 2485 2486 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497\n", + " 2498 2499]\n", + " [2500 2501 2502 2503 2504 2505 2506 2507 2508 2509 2510 2511 2512 2513\n", + " 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527\n", + " 2528 2529 2530 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541\n", + " 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552 2553 2554 2555\n", + " 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569\n", + " 2570 2571 2572 2573 2574 2575 2576 2577 2578 2579 2580 2581 2582 2583\n", + " 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596 2597\n", + " 2598 2599]\n", + " [2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613\n", + " 2614 2615 2616 2617 2618 2619 2620 2621 2622 2623 2624 2625 2626 2627\n", + " 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640 2641\n", + " 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655\n", + " 2656 2657 2658 2659 2660 2661 2662 2663 2664 2665 2666 2667 2668 2669\n", + " 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683\n", + " 2684 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694 2695 2696 2697\n", + " 2698 2699]\n", + " [2700 2701 2702 2703 2704 2705 2706 2707 2708 2709 2710 2711 2712 2713\n", + " 2714 2715 2716 2717 2718 2719 2720 2721 2722 2723 2724 2725 2726 2727\n", + " 2728 2729 2730 2731 2732 2733 2734 2735 2736 2737 2738 2739 2740 2741\n", + " 2742 2743 2744 2745 2746 2747 2748 2749 2750 2751 2752 2753 2754 2755\n", + " 2756 2757 2758 2759 2760 2761 2762 2763 2764 2765 2766 2767 2768 2769\n", + " 2770 2771 2772 2773 2774 2775 2776 2777 2778 2779 2780 2781 2782 2783\n", + " 2784 2785 2786 2787 2788 2789 2790 2791 2792 2793 2794 2795 2796 2797\n", + " 2798 2799]\n", + " [2800 2801 2802 2803 2804 2805 2806 2807 2808 2809 2810 2811 2812 2813\n", + " 2814 2815 2816 2817 2818 2819 2820 2821 2822 2823 2824 2825 2826 2827\n", + " 2828 2829 2830 2831 2832 2833 2834 2835 2836 2837 2838 2839 2840 2841\n", + " 2842 2843 2844 2845 2846 2847 2848 2849 2850 2851 2852 2853 2854 2855\n", + " 2856 2857 2858 2859 2860 2861 2862 2863 2864 2865 2866 2867 2868 2869\n", + " 2870 2871 2872 2873 2874 2875 2876 2877 2878 2879 2880 2881 2882 2883\n", + " 2884 2885 2886 2887 2888 2889 2890 2891 2892 2893 2894 2895 2896 2897\n", + " 2898 2899]\n", + " [2900 2901 2902 2903 2904 2905 2906 2907 2908 2909 2910 2911 2912 2913\n", + " 2914 2915 2916 2917 2918 2919 2920 2921 2922 2923 2924 2925 2926 2927\n", + " 2928 2929 2930 2931 2932 2933 2934 2935 2936 2937 2938 2939 2940 2941\n", + " 2942 2943 2944 2945 2946 2947 2948 2949 2950 2951 2952 2953 2954 2955\n", + " 2956 2957 2958 2959 2960 2961 2962 2963 2964 2965 2966 2967 2968 2969\n", + " 2970 2971 2972 2973 2974 2975 2976 2977 2978 2979 2980 2981 2982 2983\n", + " 2984 2985 2986 2987 2988 2989 2990 2991 2992 2993 2994 2995 2996 2997\n", + " 2998 2999]\n", + " [3000 3001 3002 3003 3004 3005 3006 3007 3008 3009 3010 3011 3012 3013\n", + " 3014 3015 3016 3017 3018 3019 3020 3021 3022 3023 3024 3025 3026 3027\n", + " 3028 3029 3030 3031 3032 3033 3034 3035 3036 3037 3038 3039 3040 3041\n", + " 3042 3043 3044 3045 3046 3047 3048 3049 3050 3051 3052 3053 3054 3055\n", + " 3056 3057 3058 3059 3060 3061 3062 3063 3064 3065 3066 3067 3068 3069\n", + " 3070 3071 3072 3073 3074 3075 3076 3077 3078 3079 3080 3081 3082 3083\n", + " 3084 3085 3086 3087 3088 3089 3090 3091 3092 3093 3094 3095 3096 3097\n", + " 3098 3099]\n", + " [3100 3101 3102 3103 3104 3105 3106 3107 3108 3109 3110 3111 3112 3113\n", + " 3114 3115 3116 3117 3118 3119 3120 3121 3122 3123 3124 3125 3126 3127\n", + " 3128 3129 3130 3131 3132 3133 3134 3135 3136 3137 3138 3139 3140 3141\n", + " 3142 3143 3144 3145 3146 3147 3148 3149 3150 3151 3152 3153 3154 3155\n", + " 3156 3157 3158 3159 3160 3161 3162 3163 3164 3165 3166 3167 3168 3169\n", + " 3170 3171 3172 3173 3174 3175 3176 3177 3178 3179 3180 3181 3182 3183\n", + " 3184 3185 3186 3187 3188 3189 3190 3191 3192 3193 3194 3195 3196 3197\n", + " 3198 3199]\n", + " [3200 3201 3202 3203 3204 3205 3206 3207 3208 3209 3210 3211 3212 3213\n", + " 3214 3215 3216 3217 3218 3219 3220 3221 3222 3223 3224 3225 3226 3227\n", + " 3228 3229 3230 3231 3232 3233 3234 3235 3236 3237 3238 3239 3240 3241\n", + " 3242 3243 3244 3245 3246 3247 3248 3249 3250 3251 3252 3253 3254 3255\n", + " 3256 3257 3258 3259 3260 3261 3262 3263 3264 3265 3266 3267 3268 3269\n", + " 3270 3271 3272 3273 3274 3275 3276 3277 3278 3279 3280 3281 3282 3283\n", + " 3284 3285 3286 3287 3288 3289 3290 3291 3292 3293 3294 3295 3296 3297\n", + " 3298 3299]\n", + " [3300 3301 3302 3303 3304 3305 3306 3307 3308 3309 3310 3311 3312 3313\n", + " 3314 3315 3316 3317 3318 3319 3320 3321 3322 3323 3324 3325 3326 3327\n", + " 3328 3329 3330 3331 3332 3333 3334 3335 3336 3337 3338 3339 3340 3341\n", + " 3342 3343 3344 3345 3346 3347 3348 3349 3350 3351 3352 3353 3354 3355\n", + " 3356 3357 3358 3359 3360 3361 3362 3363 3364 3365 3366 3367 3368 3369\n", + " 3370 3371 3372 3373 3374 3375 3376 3377 3378 3379 3380 3381 3382 3383\n", + " 3384 3385 3386 3387 3388 3389 3390 3391 3392 3393 3394 3395 3396 3397\n", + " 3398 3399]\n", + " [3400 3401 3402 3403 3404 3405 3406 3407 3408 3409 3410 3411 3412 3413\n", + " 3414 3415 3416 3417 3418 3419 3420 3421 3422 3423 3424 3425 3426 3427\n", + " 3428 3429 3430 3431 3432 3433 3434 3435 3436 3437 3438 3439 3440 3441\n", + " 3442 3443 3444 3445 3446 3447 3448 3449 3450 3451 3452 3453 3454 3455\n", + " 3456 3457 3458 3459 3460 3461 3462 3463 3464 3465 3466 3467 3468 3469\n", + " 3470 3471 3472 3473 3474 3475 3476 3477 3478 3479 3480 3481 3482 3483\n", + " 3484 3485 3486 3487 3488 3489 3490 3491 3492 3493 3494 3495 3496 3497\n", + " 3498 3499]\n", + " [3500 3501 3502 3503 3504 3505 3506 3507 3508 3509 3510 3511 3512 3513\n", + " 3514 3515 3516 3517 3518 3519 3520 3521 3522 3523 3524 3525 3526 3527\n", + " 3528 3529 3530 3531 3532 3533 3534 3535 3536 3537 3538 3539 3540 3541\n", + " 3542 3543 3544 3545 3546 3547 3548 3549 3550 3551 3552 3553 3554 3555\n", + " 3556 3557 3558 3559 3560 3561 3562 3563 3564 3565 3566 3567 3568 3569\n", + " 3570 3571 3572 3573 3574 3575 3576 3577 3578 3579 3580 3581 3582 3583\n", + " 3584 3585 3586 3587 3588 3589 3590 3591 3592 3593 3594 3595 3596 3597\n", + " 3598 3599]\n", + " [3600 3601 3602 3603 3604 3605 3606 3607 3608 3609 3610 3611 3612 3613\n", + " 3614 3615 3616 3617 3618 3619 3620 3621 3622 3623 3624 3625 3626 3627\n", + " 3628 3629 3630 3631 3632 3633 3634 3635 3636 3637 3638 3639 3640 3641\n", + " 3642 3643 3644 3645 3646 3647 3648 3649 3650 3651 3652 3653 3654 3655\n", + " 3656 3657 3658 3659 3660 3661 3662 3663 3664 3665 3666 3667 3668 3669\n", + " 3670 3671 3672 3673 3674 3675 3676 3677 3678 3679 3680 3681 3682 3683\n", + " 3684 3685 3686 3687 3688 3689 3690 3691 3692 3693 3694 3695 3696 3697\n", + " 3698 3699]\n", + " [3700 3701 3702 3703 3704 3705 3706 3707 3708 3709 3710 3711 3712 3713\n", + " 3714 3715 3716 3717 3718 3719 3720 3721 3722 3723 3724 3725 3726 3727\n", + " 3728 3729 3730 3731 3732 3733 3734 3735 3736 3737 3738 3739 3740 3741\n", + " 3742 3743 3744 3745 3746 3747 3748 3749 3750 3751 3752 3753 3754 3755\n", + " 3756 3757 3758 3759 3760 3761 3762 3763 3764 3765 3766 3767 3768 3769\n", + " 3770 3771 3772 3773 3774 3775 3776 3777 3778 3779 3780 3781 3782 3783\n", + " 3784 3785 3786 3787 3788 3789 3790 3791 3792 3793 3794 3795 3796 3797\n", + " 3798 3799]\n", + " [3800 3801 3802 3803 3804 3805 3806 3807 3808 3809 3810 3811 3812 3813\n", + " 3814 3815 3816 3817 3818 3819 3820 3821 3822 3823 3824 3825 3826 3827\n", + " 3828 3829 3830 3831 3832 3833 3834 3835 3836 3837 3838 3839 3840 3841\n", + " 3842 3843 3844 3845 3846 3847 3848 3849 3850 3851 3852 3853 3854 3855\n", + " 3856 3857 3858 3859 3860 3861 3862 3863 3864 3865 3866 3867 3868 3869\n", + " 3870 3871 3872 3873 3874 3875 3876 3877 3878 3879 3880 3881 3882 3883\n", + " 3884 3885 3886 3887 3888 3889 3890 3891 3892 3893 3894 3895 3896 3897\n", + " 3898 3899]\n", + " [3900 3901 3902 3903 3904 3905 3906 3907 3908 3909 3910 3911 3912 3913\n", + " 3914 3915 3916 3917 3918 3919 3920 3921 3922 3923 3924 3925 3926 3927\n", + " 3928 3929 3930 3931 3932 3933 3934 3935 3936 3937 3938 3939 3940 3941\n", + " 3942 3943 3944 3945 3946 3947 3948 3949 3950 3951 3952 3953 3954 3955\n", + " 3956 3957 3958 3959 3960 3961 3962 3963 3964 3965 3966 3967 3968 3969\n", + " 3970 3971 3972 3973 3974 3975 3976 3977 3978 3979 3980 3981 3982 3983\n", + " 3984 3985 3986 3987 3988 3989 3990 3991 3992 3993 3994 3995 3996 3997\n", + " 3998 3999]\n", + " [4000 4001 4002 4003 4004 4005 4006 4007 4008 4009 4010 4011 4012 4013\n", + " 4014 4015 4016 4017 4018 4019 4020 4021 4022 4023 4024 4025 4026 4027\n", + " 4028 4029 4030 4031 4032 4033 4034 4035 4036 4037 4038 4039 4040 4041\n", + " 4042 4043 4044 4045 4046 4047 4048 4049 4050 4051 4052 4053 4054 4055\n", + " 4056 4057 4058 4059 4060 4061 4062 4063 4064 4065 4066 4067 4068 4069\n", + " 4070 4071 4072 4073 4074 4075 4076 4077 4078 4079 4080 4081 4082 4083\n", + " 4084 4085 4086 4087 4088 4089 4090 4091 4092 4093 4094 4095 4096 4097\n", + " 4098 4099]\n", + " [4100 4101 4102 4103 4104 4105 4106 4107 4108 4109 4110 4111 4112 4113\n", + " 4114 4115 4116 4117 4118 4119 4120 4121 4122 4123 4124 4125 4126 4127\n", + " 4128 4129 4130 4131 4132 4133 4134 4135 4136 4137 4138 4139 4140 4141\n", + " 4142 4143 4144 4145 4146 4147 4148 4149 4150 4151 4152 4153 4154 4155\n", + " 4156 4157 4158 4159 4160 4161 4162 4163 4164 4165 4166 4167 4168 4169\n", + " 4170 4171 4172 4173 4174 4175 4176 4177 4178 4179 4180 4181 4182 4183\n", + " 4184 4185 4186 4187 4188 4189 4190 4191 4192 4193 4194 4195 4196 4197\n", + " 4198 4199]\n", + " [4200 4201 4202 4203 4204 4205 4206 4207 4208 4209 4210 4211 4212 4213\n", + " 4214 4215 4216 4217 4218 4219 4220 4221 4222 4223 4224 4225 4226 4227\n", + " 4228 4229 4230 4231 4232 4233 4234 4235 4236 4237 4238 4239 4240 4241\n", + " 4242 4243 4244 4245 4246 4247 4248 4249 4250 4251 4252 4253 4254 4255\n", + " 4256 4257 4258 4259 4260 4261 4262 4263 4264 4265 4266 4267 4268 4269\n", + " 4270 4271 4272 4273 4274 4275 4276 4277 4278 4279 4280 4281 4282 4283\n", + " 4284 4285 4286 4287 4288 4289 4290 4291 4292 4293 4294 4295 4296 4297\n", + " 4298 4299]\n", + " [4300 4301 4302 4303 4304 4305 4306 4307 4308 4309 4310 4311 4312 4313\n", + " 4314 4315 4316 4317 4318 4319 4320 4321 4322 4323 4324 4325 4326 4327\n", + " 4328 4329 4330 4331 4332 4333 4334 4335 4336 4337 4338 4339 4340 4341\n", + " 4342 4343 4344 4345 4346 4347 4348 4349 4350 4351 4352 4353 4354 4355\n", + " 4356 4357 4358 4359 4360 4361 4362 4363 4364 4365 4366 4367 4368 4369\n", + " 4370 4371 4372 4373 4374 4375 4376 4377 4378 4379 4380 4381 4382 4383\n", + " 4384 4385 4386 4387 4388 4389 4390 4391 4392 4393 4394 4395 4396 4397\n", + " 4398 4399]\n", + " [4400 4401 4402 4403 4404 4405 4406 4407 4408 4409 4410 4411 4412 4413\n", + " 4414 4415 4416 4417 4418 4419 4420 4421 4422 4423 4424 4425 4426 4427\n", + " 4428 4429 4430 4431 4432 4433 4434 4435 4436 4437 4438 4439 4440 4441\n", + " 4442 4443 4444 4445 4446 4447 4448 4449 4450 4451 4452 4453 4454 4455\n", + " 4456 4457 4458 4459 4460 4461 4462 4463 4464 4465 4466 4467 4468 4469\n", + " 4470 4471 4472 4473 4474 4475 4476 4477 4478 4479 4480 4481 4482 4483\n", + " 4484 4485 4486 4487 4488 4489 4490 4491 4492 4493 4494 4495 4496 4497\n", + " 4498 4499]\n", + " [4500 4501 4502 4503 4504 4505 4506 4507 4508 4509 4510 4511 4512 4513\n", + " 4514 4515 4516 4517 4518 4519 4520 4521 4522 4523 4524 4525 4526 4527\n", + " 4528 4529 4530 4531 4532 4533 4534 4535 4536 4537 4538 4539 4540 4541\n", + " 4542 4543 4544 4545 4546 4547 4548 4549 4550 4551 4552 4553 4554 4555\n", + " 4556 4557 4558 4559 4560 4561 4562 4563 4564 4565 4566 4567 4568 4569\n", + " 4570 4571 4572 4573 4574 4575 4576 4577 4578 4579 4580 4581 4582 4583\n", + " 4584 4585 4586 4587 4588 4589 4590 4591 4592 4593 4594 4595 4596 4597\n", + " 4598 4599]\n", + " [4600 4601 4602 4603 4604 4605 4606 4607 4608 4609 4610 4611 4612 4613\n", + " 4614 4615 4616 4617 4618 4619 4620 4621 4622 4623 4624 4625 4626 4627\n", + " 4628 4629 4630 4631 4632 4633 4634 4635 4636 4637 4638 4639 4640 4641\n", + " 4642 4643 4644 4645 4646 4647 4648 4649 4650 4651 4652 4653 4654 4655\n", + " 4656 4657 4658 4659 4660 4661 4662 4663 4664 4665 4666 4667 4668 4669\n", + " 4670 4671 4672 4673 4674 4675 4676 4677 4678 4679 4680 4681 4682 4683\n", + " 4684 4685 4686 4687 4688 4689 4690 4691 4692 4693 4694 4695 4696 4697\n", + " 4698 4699]\n", + " [4700 4701 4702 4703 4704 4705 4706 4707 4708 4709 4710 4711 4712 4713\n", + " 4714 4715 4716 4717 4718 4719 4720 4721 4722 4723 4724 4725 4726 4727\n", + " 4728 4729 4730 4731 4732 4733 4734 4735 4736 4737 4738 4739 4740 4741\n", + " 4742 4743 4744 4745 4746 4747 4748 4749 4750 4751 4752 4753 4754 4755\n", + " 4756 4757 4758 4759 4760 4761 4762 4763 4764 4765 4766 4767 4768 4769\n", + " 4770 4771 4772 4773 4774 4775 4776 4777 4778 4779 4780 4781 4782 4783\n", + " 4784 4785 4786 4787 4788 4789 4790 4791 4792 4793 4794 4795 4796 4797\n", + " 4798 4799]\n", + " [4800 4801 4802 4803 4804 4805 4806 4807 4808 4809 4810 4811 4812 4813\n", + " 4814 4815 4816 4817 4818 4819 4820 4821 4822 4823 4824 4825 4826 4827\n", + " 4828 4829 4830 4831 4832 4833 4834 4835 4836 4837 4838 4839 4840 4841\n", + " 4842 4843 4844 4845 4846 4847 4848 4849 4850 4851 4852 4853 4854 4855\n", + " 4856 4857 4858 4859 4860 4861 4862 4863 4864 4865 4866 4867 4868 4869\n", + " 4870 4871 4872 4873 4874 4875 4876 4877 4878 4879 4880 4881 4882 4883\n", + " 4884 4885 4886 4887 4888 4889 4890 4891 4892 4893 4894 4895 4896 4897\n", + " 4898 4899]\n", + " [4900 4901 4902 4903 4904 4905 4906 4907 4908 4909 4910 4911 4912 4913\n", + " 4914 4915 4916 4917 4918 4919 4920 4921 4922 4923 4924 4925 4926 4927\n", + " 4928 4929 4930 4931 4932 4933 4934 4935 4936 4937 4938 4939 4940 4941\n", + " 4942 4943 4944 4945 4946 4947 4948 4949 4950 4951 4952 4953 4954 4955\n", + " 4956 4957 4958 4959 4960 4961 4962 4963 4964 4965 4966 4967 4968 4969\n", + " 4970 4971 4972 4973 4974 4975 4976 4977 4978 4979 4980 4981 4982 4983\n", + " 4984 4985 4986 4987 4988 4989 4990 4991 4992 4993 4994 4995 4996 4997\n", + " 4998 4999]\n", + " [5000 5001 5002 5003 5004 5005 5006 5007 5008 5009 5010 5011 5012 5013\n", + " 5014 5015 5016 5017 5018 5019 5020 5021 5022 5023 5024 5025 5026 5027\n", + " 5028 5029 5030 5031 5032 5033 5034 5035 5036 5037 5038 5039 5040 5041\n", + " 5042 5043 5044 5045 5046 5047 5048 5049 5050 5051 5052 5053 5054 5055\n", + " 5056 5057 5058 5059 5060 5061 5062 5063 5064 5065 5066 5067 5068 5069\n", + " 5070 5071 5072 5073 5074 5075 5076 5077 5078 5079 5080 5081 5082 5083\n", + " 5084 5085 5086 5087 5088 5089 5090 5091 5092 5093 5094 5095 5096 5097\n", + " 5098 5099]\n", + " [5100 5101 5102 5103 5104 5105 5106 5107 5108 5109 5110 5111 5112 5113\n", + " 5114 5115 5116 5117 5118 5119 5120 5121 5122 5123 5124 5125 5126 5127\n", + " 5128 5129 5130 5131 5132 5133 5134 5135 5136 5137 5138 5139 5140 5141\n", + " 5142 5143 5144 5145 5146 5147 5148 5149 5150 5151 5152 5153 5154 5155\n", + " 5156 5157 5158 5159 5160 5161 5162 5163 5164 5165 5166 5167 5168 5169\n", + " 5170 5171 5172 5173 5174 5175 5176 5177 5178 5179 5180 5181 5182 5183\n", + " 5184 5185 5186 5187 5188 5189 5190 5191 5192 5193 5194 5195 5196 5197\n", + " 5198 5199]\n", + " [5200 5201 5202 5203 5204 5205 5206 5207 5208 5209 5210 5211 5212 5213\n", + " 5214 5215 5216 5217 5218 5219 5220 5221 5222 5223 5224 5225 5226 5227\n", + " 5228 5229 5230 5231 5232 5233 5234 5235 5236 5237 5238 5239 5240 5241\n", + " 5242 5243 5244 5245 5246 5247 5248 5249 5250 5251 5252 5253 5254 5255\n", + " 5256 5257 5258 5259 5260 5261 5262 5263 5264 5265 5266 5267 5268 5269\n", + " 5270 5271 5272 5273 5274 5275 5276 5277 5278 5279 5280 5281 5282 5283\n", + " 5284 5285 5286 5287 5288 5289 5290 5291 5292 5293 5294 5295 5296 5297\n", + " 5298 5299]\n", + " [5300 5301 5302 5303 5304 5305 5306 5307 5308 5309 5310 5311 5312 5313\n", + " 5314 5315 5316 5317 5318 5319 5320 5321 5322 5323 5324 5325 5326 5327\n", + " 5328 5329 5330 5331 5332 5333 5334 5335 5336 5337 5338 5339 5340 5341\n", + " 5342 5343 5344 5345 5346 5347 5348 5349 5350 5351 5352 5353 5354 5355\n", + " 5356 5357 5358 5359 5360 5361 5362 5363 5364 5365 5366 5367 5368 5369\n", + " 5370 5371 5372 5373 5374 5375 5376 5377 5378 5379 5380 5381 5382 5383\n", + " 5384 5385 5386 5387 5388 5389 5390 5391 5392 5393 5394 5395 5396 5397\n", + " 5398 5399]\n", + " [5400 5401 5402 5403 5404 5405 5406 5407 5408 5409 5410 5411 5412 5413\n", + " 5414 5415 5416 5417 5418 5419 5420 5421 5422 5423 5424 5425 5426 5427\n", + " 5428 5429 5430 5431 5432 5433 5434 5435 5436 5437 5438 5439 5440 5441\n", + " 5442 5443 5444 5445 5446 5447 5448 5449 5450 5451 5452 5453 5454 5455\n", + " 5456 5457 5458 5459 5460 5461 5462 5463 5464 5465 5466 5467 5468 5469\n", + " 5470 5471 5472 5473 5474 5475 5476 5477 5478 5479 5480 5481 5482 5483\n", + " 5484 5485 5486 5487 5488 5489 5490 5491 5492 5493 5494 5495 5496 5497\n", + " 5498 5499]\n", + " [5500 5501 5502 5503 5504 5505 5506 5507 5508 5509 5510 5511 5512 5513\n", + " 5514 5515 5516 5517 5518 5519 5520 5521 5522 5523 5524 5525 5526 5527\n", + " 5528 5529 5530 5531 5532 5533 5534 5535 5536 5537 5538 5539 5540 5541\n", + " 5542 5543 5544 5545 5546 5547 5548 5549 5550 5551 5552 5553 5554 5555\n", + " 5556 5557 5558 5559 5560 5561 5562 5563 5564 5565 5566 5567 5568 5569\n", + " 5570 5571 5572 5573 5574 5575 5576 5577 5578 5579 5580 5581 5582 5583\n", + " 5584 5585 5586 5587 5588 5589 5590 5591 5592 5593 5594 5595 5596 5597\n", + " 5598 5599]\n", + " [5600 5601 5602 5603 5604 5605 5606 5607 5608 5609 5610 5611 5612 5613\n", + " 5614 5615 5616 5617 5618 5619 5620 5621 5622 5623 5624 5625 5626 5627\n", + " 5628 5629 5630 5631 5632 5633 5634 5635 5636 5637 5638 5639 5640 5641\n", + " 5642 5643 5644 5645 5646 5647 5648 5649 5650 5651 5652 5653 5654 5655\n", + " 5656 5657 5658 5659 5660 5661 5662 5663 5664 5665 5666 5667 5668 5669\n", + " 5670 5671 5672 5673 5674 5675 5676 5677 5678 5679 5680 5681 5682 5683\n", + " 5684 5685 5686 5687 5688 5689 5690 5691 5692 5693 5694 5695 5696 5697\n", + " 5698 5699]\n", + " [5700 5701 5702 5703 5704 5705 5706 5707 5708 5709 5710 5711 5712 5713\n", + " 5714 5715 5716 5717 5718 5719 5720 5721 5722 5723 5724 5725 5726 5727\n", + " 5728 5729 5730 5731 5732 5733 5734 5735 5736 5737 5738 5739 5740 5741\n", + " 5742 5743 5744 5745 5746 5747 5748 5749 5750 5751 5752 5753 5754 5755\n", + " 5756 5757 5758 5759 5760 5761 5762 5763 5764 5765 5766 5767 5768 5769\n", + " 5770 5771 5772 5773 5774 5775 5776 5777 5778 5779 5780 5781 5782 5783\n", + " 5784 5785 5786 5787 5788 5789 5790 5791 5792 5793 5794 5795 5796 5797\n", + " 5798 5799]\n", + " [5800 5801 5802 5803 5804 5805 5806 5807 5808 5809 5810 5811 5812 5813\n", + " 5814 5815 5816 5817 5818 5819 5820 5821 5822 5823 5824 5825 5826 5827\n", + " 5828 5829 5830 5831 5832 5833 5834 5835 5836 5837 5838 5839 5840 5841\n", + " 5842 5843 5844 5845 5846 5847 5848 5849 5850 5851 5852 5853 5854 5855\n", + " 5856 5857 5858 5859 5860 5861 5862 5863 5864 5865 5866 5867 5868 5869\n", + " 5870 5871 5872 5873 5874 5875 5876 5877 5878 5879 5880 5881 5882 5883\n", + " 5884 5885 5886 5887 5888 5889 5890 5891 5892 5893 5894 5895 5896 5897\n", + " 5898 5899]\n", + " [5900 5901 5902 5903 5904 5905 5906 5907 5908 5909 5910 5911 5912 5913\n", + " 5914 5915 5916 5917 5918 5919 5920 5921 5922 5923 5924 5925 5926 5927\n", + " 5928 5929 5930 5931 5932 5933 5934 5935 5936 5937 5938 5939 5940 5941\n", + " 5942 5943 5944 5945 5946 5947 5948 5949 5950 5951 5952 5953 5954 5955\n", + " 5956 5957 5958 5959 5960 5961 5962 5963 5964 5965 5966 5967 5968 5969\n", + " 5970 5971 5972 5973 5974 5975 5976 5977 5978 5979 5980 5981 5982 5983\n", + " 5984 5985 5986 5987 5988 5989 5990 5991 5992 5993 5994 5995 5996 5997\n", + " 5998 5999]\n", + " [6000 6001 6002 6003 6004 6005 6006 6007 6008 6009 6010 6011 6012 6013\n", + " 6014 6015 6016 6017 6018 6019 6020 6021 6022 6023 6024 6025 6026 6027\n", + " 6028 6029 6030 6031 6032 6033 6034 6035 6036 6037 6038 6039 6040 6041\n", + " 6042 6043 6044 6045 6046 6047 6048 6049 6050 6051 6052 6053 6054 6055\n", + " 6056 6057 6058 6059 6060 6061 6062 6063 6064 6065 6066 6067 6068 6069\n", + " 6070 6071 6072 6073 6074 6075 6076 6077 6078 6079 6080 6081 6082 6083\n", + " 6084 6085 6086 6087 6088 6089 6090 6091 6092 6093 6094 6095 6096 6097\n", + " 6098 6099]\n", + " [6100 6101 6102 6103 6104 6105 6106 6107 6108 6109 6110 6111 6112 6113\n", + " 6114 6115 6116 6117 6118 6119 6120 6121 6122 6123 6124 6125 6126 6127\n", + " 6128 6129 6130 6131 6132 6133 6134 6135 6136 6137 6138 6139 6140 6141\n", + " 6142 6143 6144 6145 6146 6147 6148 6149 6150 6151 6152 6153 6154 6155\n", + " 6156 6157 6158 6159 6160 6161 6162 6163 6164 6165 6166 6167 6168 6169\n", + " 6170 6171 6172 6173 6174 6175 6176 6177 6178 6179 6180 6181 6182 6183\n", + " 6184 6185 6186 6187 6188 6189 6190 6191 6192 6193 6194 6195 6196 6197\n", + " 6198 6199]\n", + " [6200 6201 6202 6203 6204 6205 6206 6207 6208 6209 6210 6211 6212 6213\n", + " 6214 6215 6216 6217 6218 6219 6220 6221 6222 6223 6224 6225 6226 6227\n", + " 6228 6229 6230 6231 6232 6233 6234 6235 6236 6237 6238 6239 6240 6241\n", + " 6242 6243 6244 6245 6246 6247 6248 6249 6250 6251 6252 6253 6254 6255\n", + " 6256 6257 6258 6259 6260 6261 6262 6263 6264 6265 6266 6267 6268 6269\n", + " 6270 6271 6272 6273 6274 6275 6276 6277 6278 6279 6280 6281 6282 6283\n", + " 6284 6285 6286 6287 6288 6289 6290 6291 6292 6293 6294 6295 6296 6297\n", + " 6298 6299]\n", + " [6300 6301 6302 6303 6304 6305 6306 6307 6308 6309 6310 6311 6312 6313\n", + " 6314 6315 6316 6317 6318 6319 6320 6321 6322 6323 6324 6325 6326 6327\n", + " 6328 6329 6330 6331 6332 6333 6334 6335 6336 6337 6338 6339 6340 6341\n", + " 6342 6343 6344 6345 6346 6347 6348 6349 6350 6351 6352 6353 6354 6355\n", + " 6356 6357 6358 6359 6360 6361 6362 6363 6364 6365 6366 6367 6368 6369\n", + " 6370 6371 6372 6373 6374 6375 6376 6377 6378 6379 6380 6381 6382 6383\n", + " 6384 6385 6386 6387 6388 6389 6390 6391 6392 6393 6394 6395 6396 6397\n", + " 6398 6399]\n", + " [6400 6401 6402 6403 6404 6405 6406 6407 6408 6409 6410 6411 6412 6413\n", + " 6414 6415 6416 6417 6418 6419 6420 6421 6422 6423 6424 6425 6426 6427\n", + " 6428 6429 6430 6431 6432 6433 6434 6435 6436 6437 6438 6439 6440 6441\n", + " 6442 6443 6444 6445 6446 6447 6448 6449 6450 6451 6452 6453 6454 6455\n", + " 6456 6457 6458 6459 6460 6461 6462 6463 6464 6465 6466 6467 6468 6469\n", + " 6470 6471 6472 6473 6474 6475 6476 6477 6478 6479 6480 6481 6482 6483\n", + " 6484 6485 6486 6487 6488 6489 6490 6491 6492 6493 6494 6495 6496 6497\n", + " 6498 6499]\n", + " [6500 6501 6502 6503 6504 6505 6506 6507 6508 6509 6510 6511 6512 6513\n", + " 6514 6515 6516 6517 6518 6519 6520 6521 6522 6523 6524 6525 6526 6527\n", + " 6528 6529 6530 6531 6532 6533 6534 6535 6536 6537 6538 6539 6540 6541\n", + " 6542 6543 6544 6545 6546 6547 6548 6549 6550 6551 6552 6553 6554 6555\n", + " 6556 6557 6558 6559 6560 6561 6562 6563 6564 6565 6566 6567 6568 6569\n", + " 6570 6571 6572 6573 6574 6575 6576 6577 6578 6579 6580 6581 6582 6583\n", + " 6584 6585 6586 6587 6588 6589 6590 6591 6592 6593 6594 6595 6596 6597\n", + " 6598 6599]\n", + " [6600 6601 6602 6603 6604 6605 6606 6607 6608 6609 6610 6611 6612 6613\n", + " 6614 6615 6616 6617 6618 6619 6620 6621 6622 6623 6624 6625 6626 6627\n", + " 6628 6629 6630 6631 6632 6633 6634 6635 6636 6637 6638 6639 6640 6641\n", + " 6642 6643 6644 6645 6646 6647 6648 6649 6650 6651 6652 6653 6654 6655\n", + " 6656 6657 6658 6659 6660 6661 6662 6663 6664 6665 6666 6667 6668 6669\n", + " 6670 6671 6672 6673 6674 6675 6676 6677 6678 6679 6680 6681 6682 6683\n", + " 6684 6685 6686 6687 6688 6689 6690 6691 6692 6693 6694 6695 6696 6697\n", + " 6698 6699]\n", + " [6700 6701 6702 6703 6704 6705 6706 6707 6708 6709 6710 6711 6712 6713\n", + " 6714 6715 6716 6717 6718 6719 6720 6721 6722 6723 6724 6725 6726 6727\n", + " 6728 6729 6730 6731 6732 6733 6734 6735 6736 6737 6738 6739 6740 6741\n", + " 6742 6743 6744 6745 6746 6747 6748 6749 6750 6751 6752 6753 6754 6755\n", + " 6756 6757 6758 6759 6760 6761 6762 6763 6764 6765 6766 6767 6768 6769\n", + " 6770 6771 6772 6773 6774 6775 6776 6777 6778 6779 6780 6781 6782 6783\n", + " 6784 6785 6786 6787 6788 6789 6790 6791 6792 6793 6794 6795 6796 6797\n", + " 6798 6799]\n", + " [6800 6801 6802 6803 6804 6805 6806 6807 6808 6809 6810 6811 6812 6813\n", + " 6814 6815 6816 6817 6818 6819 6820 6821 6822 6823 6824 6825 6826 6827\n", + " 6828 6829 6830 6831 6832 6833 6834 6835 6836 6837 6838 6839 6840 6841\n", + " 6842 6843 6844 6845 6846 6847 6848 6849 6850 6851 6852 6853 6854 6855\n", + " 6856 6857 6858 6859 6860 6861 6862 6863 6864 6865 6866 6867 6868 6869\n", + " 6870 6871 6872 6873 6874 6875 6876 6877 6878 6879 6880 6881 6882 6883\n", + " 6884 6885 6886 6887 6888 6889 6890 6891 6892 6893 6894 6895 6896 6897\n", + " 6898 6899]\n", + " [6900 6901 6902 6903 6904 6905 6906 6907 6908 6909 6910 6911 6912 6913\n", + " 6914 6915 6916 6917 6918 6919 6920 6921 6922 6923 6924 6925 6926 6927\n", + " 6928 6929 6930 6931 6932 6933 6934 6935 6936 6937 6938 6939 6940 6941\n", + " 6942 6943 6944 6945 6946 6947 6948 6949 6950 6951 6952 6953 6954 6955\n", + " 6956 6957 6958 6959 6960 6961 6962 6963 6964 6965 6966 6967 6968 6969\n", + " 6970 6971 6972 6973 6974 6975 6976 6977 6978 6979 6980 6981 6982 6983\n", + " 6984 6985 6986 6987 6988 6989 6990 6991 6992 6993 6994 6995 6996 6997\n", + " 6998 6999]\n", + " [7000 7001 7002 7003 7004 7005 7006 7007 7008 7009 7010 7011 7012 7013\n", + " 7014 7015 7016 7017 7018 7019 7020 7021 7022 7023 7024 7025 7026 7027\n", + " 7028 7029 7030 7031 7032 7033 7034 7035 7036 7037 7038 7039 7040 7041\n", + " 7042 7043 7044 7045 7046 7047 7048 7049 7050 7051 7052 7053 7054 7055\n", + " 7056 7057 7058 7059 7060 7061 7062 7063 7064 7065 7066 7067 7068 7069\n", + " 7070 7071 7072 7073 7074 7075 7076 7077 7078 7079 7080 7081 7082 7083\n", + " 7084 7085 7086 7087 7088 7089 7090 7091 7092 7093 7094 7095 7096 7097\n", + " 7098 7099]\n", + " [7100 7101 7102 7103 7104 7105 7106 7107 7108 7109 7110 7111 7112 7113\n", + " 7114 7115 7116 7117 7118 7119 7120 7121 7122 7123 7124 7125 7126 7127\n", + " 7128 7129 7130 7131 7132 7133 7134 7135 7136 7137 7138 7139 7140 7141\n", + " 7142 7143 7144 7145 7146 7147 7148 7149 7150 7151 7152 7153 7154 7155\n", + " 7156 7157 7158 7159 7160 7161 7162 7163 7164 7165 7166 7167 7168 7169\n", + " 7170 7171 7172 7173 7174 7175 7176 7177 7178 7179 7180 7181 7182 7183\n", + " 7184 7185 7186 7187 7188 7189 7190 7191 7192 7193 7194 7195 7196 7197\n", + " 7198 7199]\n", + " [7200 7201 7202 7203 7204 7205 7206 7207 7208 7209 7210 7211 7212 7213\n", + " 7214 7215 7216 7217 7218 7219 7220 7221 7222 7223 7224 7225 7226 7227\n", + " 7228 7229 7230 7231 7232 7233 7234 7235 7236 7237 7238 7239 7240 7241\n", + " 7242 7243 7244 7245 7246 7247 7248 7249 7250 7251 7252 7253 7254 7255\n", + " 7256 7257 7258 7259 7260 7261 7262 7263 7264 7265 7266 7267 7268 7269\n", + " 7270 7271 7272 7273 7274 7275 7276 7277 7278 7279 7280 7281 7282 7283\n", + " 7284 7285 7286 7287 7288 7289 7290 7291 7292 7293 7294 7295 7296 7297\n", + " 7298 7299]\n", + " [7300 7301 7302 7303 7304 7305 7306 7307 7308 7309 7310 7311 7312 7313\n", + " 7314 7315 7316 7317 7318 7319 7320 7321 7322 7323 7324 7325 7326 7327\n", + " 7328 7329 7330 7331 7332 7333 7334 7335 7336 7337 7338 7339 7340 7341\n", + " 7342 7343 7344 7345 7346 7347 7348 7349 7350 7351 7352 7353 7354 7355\n", + " 7356 7357 7358 7359 7360 7361 7362 7363 7364 7365 7366 7367 7368 7369\n", + " 7370 7371 7372 7373 7374 7375 7376 7377 7378 7379 7380 7381 7382 7383\n", + " 7384 7385 7386 7387 7388 7389 7390 7391 7392 7393 7394 7395 7396 7397\n", + " 7398 7399]\n", + " [7400 7401 7402 7403 7404 7405 7406 7407 7408 7409 7410 7411 7412 7413\n", + " 7414 7415 7416 7417 7418 7419 7420 7421 7422 7423 7424 7425 7426 7427\n", + " 7428 7429 7430 7431 7432 7433 7434 7435 7436 7437 7438 7439 7440 7441\n", + " 7442 7443 7444 7445 7446 7447 7448 7449 7450 7451 7452 7453 7454 7455\n", + " 7456 7457 7458 7459 7460 7461 7462 7463 7464 7465 7466 7467 7468 7469\n", + " 7470 7471 7472 7473 7474 7475 7476 7477 7478 7479 7480 7481 7482 7483\n", + " 7484 7485 7486 7487 7488 7489 7490 7491 7492 7493 7494 7495 7496 7497\n", + " 7498 7499]\n", + " [7500 7501 7502 7503 7504 7505 7506 7507 7508 7509 7510 7511 7512 7513\n", + " 7514 7515 7516 7517 7518 7519 7520 7521 7522 7523 7524 7525 7526 7527\n", + " 7528 7529 7530 7531 7532 7533 7534 7535 7536 7537 7538 7539 7540 7541\n", + " 7542 7543 7544 7545 7546 7547 7548 7549 7550 7551 7552 7553 7554 7555\n", + " 7556 7557 7558 7559 7560 7561 7562 7563 7564 7565 7566 7567 7568 7569\n", + " 7570 7571 7572 7573 7574 7575 7576 7577 7578 7579 7580 7581 7582 7583\n", + " 7584 7585 7586 7587 7588 7589 7590 7591 7592 7593 7594 7595 7596 7597\n", + " 7598 7599]\n", + " [7600 7601 7602 7603 7604 7605 7606 7607 7608 7609 7610 7611 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7966 7967 7968 7969\n", + " 7970 7971 7972 7973 7974 7975 7976 7977 7978 7979 7980 7981 7982 7983\n", + " 7984 7985 7986 7987 7988 7989 7990 7991 7992 7993 7994 7995 7996 7997\n", + " 7998 7999]\n", + " [8000 8001 8002 8003 8004 8005 8006 8007 8008 8009 8010 8011 8012 8013\n", + " 8014 8015 8016 8017 8018 8019 8020 8021 8022 8023 8024 8025 8026 8027\n", + " 8028 8029 8030 8031 8032 8033 8034 8035 8036 8037 8038 8039 8040 8041\n", + " 8042 8043 8044 8045 8046 8047 8048 8049 8050 8051 8052 8053 8054 8055\n", + " 8056 8057 8058 8059 8060 8061 8062 8063 8064 8065 8066 8067 8068 8069\n", + " 8070 8071 8072 8073 8074 8075 8076 8077 8078 8079 8080 8081 8082 8083\n", + " 8084 8085 8086 8087 8088 8089 8090 8091 8092 8093 8094 8095 8096 8097\n", + " 8098 8099]\n", + " [8100 8101 8102 8103 8104 8105 8106 8107 8108 8109 8110 8111 8112 8113\n", + " 8114 8115 8116 8117 8118 8119 8120 8121 8122 8123 8124 8125 8126 8127\n", + " 8128 8129 8130 8131 8132 8133 8134 8135 8136 8137 8138 8139 8140 8141\n", 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8318 8319 8320 8321 8322 8323 8324 8325 8326 8327\n", + " 8328 8329 8330 8331 8332 8333 8334 8335 8336 8337 8338 8339 8340 8341\n", + " 8342 8343 8344 8345 8346 8347 8348 8349 8350 8351 8352 8353 8354 8355\n", + " 8356 8357 8358 8359 8360 8361 8362 8363 8364 8365 8366 8367 8368 8369\n", + " 8370 8371 8372 8373 8374 8375 8376 8377 8378 8379 8380 8381 8382 8383\n", + " 8384 8385 8386 8387 8388 8389 8390 8391 8392 8393 8394 8395 8396 8397\n", + " 8398 8399]\n", + " [8400 8401 8402 8403 8404 8405 8406 8407 8408 8409 8410 8411 8412 8413\n", + " 8414 8415 8416 8417 8418 8419 8420 8421 8422 8423 8424 8425 8426 8427\n", + " 8428 8429 8430 8431 8432 8433 8434 8435 8436 8437 8438 8439 8440 8441\n", + " 8442 8443 8444 8445 8446 8447 8448 8449 8450 8451 8452 8453 8454 8455\n", + " 8456 8457 8458 8459 8460 8461 8462 8463 8464 8465 8466 8467 8468 8469\n", + " 8470 8471 8472 8473 8474 8475 8476 8477 8478 8479 8480 8481 8482 8483\n", + " 8484 8485 8486 8487 8488 8489 8490 8491 8492 8493 8494 8495 8496 8497\n", + " 8498 8499]\n", + " [8500 8501 8502 8503 8504 8505 8506 8507 8508 8509 8510 8511 8512 8513\n", + " 8514 8515 8516 8517 8518 8519 8520 8521 8522 8523 8524 8525 8526 8527\n", + " 8528 8529 8530 8531 8532 8533 8534 8535 8536 8537 8538 8539 8540 8541\n", + " 8542 8543 8544 8545 8546 8547 8548 8549 8550 8551 8552 8553 8554 8555\n", + " 8556 8557 8558 8559 8560 8561 8562 8563 8564 8565 8566 8567 8568 8569\n", + " 8570 8571 8572 8573 8574 8575 8576 8577 8578 8579 8580 8581 8582 8583\n", + " 8584 8585 8586 8587 8588 8589 8590 8591 8592 8593 8594 8595 8596 8597\n", + " 8598 8599]\n", + " [8600 8601 8602 8603 8604 8605 8606 8607 8608 8609 8610 8611 8612 8613\n", + " 8614 8615 8616 8617 8618 8619 8620 8621 8622 8623 8624 8625 8626 8627\n", + " 8628 8629 8630 8631 8632 8633 8634 8635 8636 8637 8638 8639 8640 8641\n", + " 8642 8643 8644 8645 8646 8647 8648 8649 8650 8651 8652 8653 8654 8655\n", + " 8656 8657 8658 8659 8660 8661 8662 8663 8664 8665 8666 8667 8668 8669\n", + " 8670 8671 8672 8673 8674 8675 8676 8677 8678 8679 8680 8681 8682 8683\n", + " 8684 8685 8686 8687 8688 8689 8690 8691 8692 8693 8694 8695 8696 8697\n", + " 8698 8699]\n", + " [8700 8701 8702 8703 8704 8705 8706 8707 8708 8709 8710 8711 8712 8713\n", + " 8714 8715 8716 8717 8718 8719 8720 8721 8722 8723 8724 8725 8726 8727\n", + " 8728 8729 8730 8731 8732 8733 8734 8735 8736 8737 8738 8739 8740 8741\n", + " 8742 8743 8744 8745 8746 8747 8748 8749 8750 8751 8752 8753 8754 8755\n", + " 8756 8757 8758 8759 8760 8761 8762 8763 8764 8765 8766 8767 8768 8769\n", + " 8770 8771 8772 8773 8774 8775 8776 8777 8778 8779 8780 8781 8782 8783\n", + " 8784 8785 8786 8787 8788 8789 8790 8791 8792 8793 8794 8795 8796 8797\n", + " 8798 8799]\n", + " [8800 8801 8802 8803 8804 8805 8806 8807 8808 8809 8810 8811 8812 8813\n", + " 8814 8815 8816 8817 8818 8819 8820 8821 8822 8823 8824 8825 8826 8827\n", + " 8828 8829 8830 8831 8832 8833 8834 8835 8836 8837 8838 8839 8840 8841\n", + " 8842 8843 8844 8845 8846 8847 8848 8849 8850 8851 8852 8853 8854 8855\n", + " 8856 8857 8858 8859 8860 8861 8862 8863 8864 8865 8866 8867 8868 8869\n", + " 8870 8871 8872 8873 8874 8875 8876 8877 8878 8879 8880 8881 8882 8883\n", + " 8884 8885 8886 8887 8888 8889 8890 8891 8892 8893 8894 8895 8896 8897\n", + " 8898 8899]\n", + " [8900 8901 8902 8903 8904 8905 8906 8907 8908 8909 8910 8911 8912 8913\n", + " 8914 8915 8916 8917 8918 8919 8920 8921 8922 8923 8924 8925 8926 8927\n", + " 8928 8929 8930 8931 8932 8933 8934 8935 8936 8937 8938 8939 8940 8941\n", + " 8942 8943 8944 8945 8946 8947 8948 8949 8950 8951 8952 8953 8954 8955\n", + " 8956 8957 8958 8959 8960 8961 8962 8963 8964 8965 8966 8967 8968 8969\n", + " 8970 8971 8972 8973 8974 8975 8976 8977 8978 8979 8980 8981 8982 8983\n", + " 8984 8985 8986 8987 8988 8989 8990 8991 8992 8993 8994 8995 8996 8997\n", + " 8998 8999]\n", + " [9000 9001 9002 9003 9004 9005 9006 9007 9008 9009 9010 9011 9012 9013\n", + " 9014 9015 9016 9017 9018 9019 9020 9021 9022 9023 9024 9025 9026 9027\n", + " 9028 9029 9030 9031 9032 9033 9034 9035 9036 9037 9038 9039 9040 9041\n", + " 9042 9043 9044 9045 9046 9047 9048 9049 9050 9051 9052 9053 9054 9055\n", + " 9056 9057 9058 9059 9060 9061 9062 9063 9064 9065 9066 9067 9068 9069\n", + " 9070 9071 9072 9073 9074 9075 9076 9077 9078 9079 9080 9081 9082 9083\n", + " 9084 9085 9086 9087 9088 9089 9090 9091 9092 9093 9094 9095 9096 9097\n", + " 9098 9099]\n", + " [9100 9101 9102 9103 9104 9105 9106 9107 9108 9109 9110 9111 9112 9113\n", + " 9114 9115 9116 9117 9118 9119 9120 9121 9122 9123 9124 9125 9126 9127\n", + " 9128 9129 9130 9131 9132 9133 9134 9135 9136 9137 9138 9139 9140 9141\n", + " 9142 9143 9144 9145 9146 9147 9148 9149 9150 9151 9152 9153 9154 9155\n", + " 9156 9157 9158 9159 9160 9161 9162 9163 9164 9165 9166 9167 9168 9169\n", + " 9170 9171 9172 9173 9174 9175 9176 9177 9178 9179 9180 9181 9182 9183\n", + " 9184 9185 9186 9187 9188 9189 9190 9191 9192 9193 9194 9195 9196 9197\n", + " 9198 9199]\n", + " [9200 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9380 9381 9382 9383\n", + " 9384 9385 9386 9387 9388 9389 9390 9391 9392 9393 9394 9395 9396 9397\n", + " 9398 9399]\n", + " [9400 9401 9402 9403 9404 9405 9406 9407 9408 9409 9410 9411 9412 9413\n", + " 9414 9415 9416 9417 9418 9419 9420 9421 9422 9423 9424 9425 9426 9427\n", + " 9428 9429 9430 9431 9432 9433 9434 9435 9436 9437 9438 9439 9440 9441\n", + " 9442 9443 9444 9445 9446 9447 9448 9449 9450 9451 9452 9453 9454 9455\n", + " 9456 9457 9458 9459 9460 9461 9462 9463 9464 9465 9466 9467 9468 9469\n", + " 9470 9471 9472 9473 9474 9475 9476 9477 9478 9479 9480 9481 9482 9483\n", + " 9484 9485 9486 9487 9488 9489 9490 9491 9492 9493 9494 9495 9496 9497\n", + " 9498 9499]\n", + " [9500 9501 9502 9503 9504 9505 9506 9507 9508 9509 9510 9511 9512 9513\n", + " 9514 9515 9516 9517 9518 9519 9520 9521 9522 9523 9524 9525 9526 9527\n", + " 9528 9529 9530 9531 9532 9533 9534 9535 9536 9537 9538 9539 9540 9541\n", + " 9542 9543 9544 9545 9546 9547 9548 9549 9550 9551 9552 9553 9554 9555\n", 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9899]\n", + " [9900 9901 9902 ... 9997 9998 9999]]\n" + ] + } + ], + "source": [ + "np.set_printoptions(threshold=1000)\n", + "print(b)" + ] + }, + { + "cell_type": "code", + "execution_count": 76, + "id": "db069a58", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 2, 3])" + ] + }, + "execution_count": 76, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.array([20, 30, 40, 50])\n", + "b = np.arange(4)\n", + "b" + ] + }, + { + "cell_type": "code", + "execution_count": 77, + "id": "7d05b6c0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([20, 31, 42, 53])" + ] + }, + "execution_count": 77, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a + b" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "id": "b0a0704a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([0, 1, 4, 9], dtype=int32)" + ] + }, + "execution_count": 78, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b ** 2" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "id": "787c4adb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0.91294525, -0.98803162, 0.74511316, -0.26237485])" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.sin(a)" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "id": "bd771f7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ True, False, False, False])" + ] + }, + "execution_count": 80, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a < 25" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "id": "eb0bab6e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 0],\n", + " [0, 4]])" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.array([[1, 1], [0, 1]])\n", + "d = np.array([[2, 0], [3, 4]])\n", + "\n", + "c * d" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "id": "74b70195", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5, 4],\n", + " [3, 4]])" + ] + }, + "execution_count": 82, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c @ d" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "id": "e39730df", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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"source": [ + "z = np.array(df[0]) * 1e10\n", + "lnI = np.log(np.array(df[3]) * 1e12)" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "id": "cab6dbf2", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-2.1268743 7.14673331]\n" + ] + } + ], + "source": [ + "fit_res = np.polyfit(z, lnI, 1)\n", + "print(fit_res)" + ] + }, + { + "cell_type": "code", + "execution_count": 100, + "id": "a843b0cf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "4.306461770837489" + ] + }, + "execution_count": 100, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "wf_res = 0.952*pow(fit_res[0],2)\n", + "wf_res" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c2ecbf94", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/4. \346\234\272\345\231\250\345\255\246\344\271\240\347\256\200\344\273\213.ipynb" "b/4. \346\234\272\345\231\250\345\255\246\344\271\240\347\256\200\344\273\213.ipynb" new file mode 100644 index 0000000..5710928 --- /dev/null +++ "b/4. \346\234\272\345\231\250\345\255\246\344\271\240\347\256\200\344\273\213.ipynb" @@ -0,0 +1,684 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "id": "a5283380", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9b7b2392", + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(0, 20, 5)\n", + "y = 2 * x + 1" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "d5449b69", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 0., 5., 10., 15., 20.])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "x" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "0979dddf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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G6Mwo64X1JlcmIq2gAI+w2Wuz5Iv5qvrki3nOXzvfpIpEpJUU4BE2eXGy4mGTSnL5HGdfPtukikSklRTgEVXcLJLJZmrqm1nJ6BRDkV1AAR5RuXyOeCxeU9/uru6q99xFpPMowCMqmUiyUdyoqW9hs0AykWxwRSLSajsGeHDR4hUzu7plWZ+ZvWRmbwT3FS9qLM0R64oxlBqqqe9Q/xCxrliDKxKRVguzB/41YHTbsjPABXd/CLgQPJcWm3h0ouo96WQiyZnDertEdoMdA9zdfwisblv8JDAdPJ4GnmpsWRLG2OAYiViiqj6JWIITgyeaVJGItFKtx8AfdPcbAMF9f6WGZnbKzBbNbDGbzda4OSmnp7uHufE5euO9odr3xnuZG5/TnCgiu0TTv8R093Punnb3dCqVavbm9pyRgRHmT87Tt6+v4uGUZCJJ374+5k/Oay4UkV2k1gC/aWYHAIL7lcaVJNUaGRhh+fQyU8enGO4fxjDiXXEMY7h/mKnjUyyfXlZ4i+wytU4n+yJwEjgb3H+7YRVJTXq6exj/4DjjHxynuFkkl8+RTCR1tonILhbmNMKvA/8T+E0ze8vMPk0puH/LzN4Afit4Lh0i1hVj/337Fd4iu9yOe+Du/nSFVY83uBYREamCfokpIhJRCnARkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISEQpwEVEIkoBLiISUQpwEZGIUoCLiESUAlxEJKIU4CIiEaUAFxGJKAW4iEhEKcBFRCKq1kuqAWBmbwK/BIpAwd3TjShKRER2VleAB464+y8a8DoiIlIFHUIREYmoegPcge+b2WUzO1WugZmdMrNFM1vMZrN1bk5ERO6oN8APu/vDwDHgs2b20e0N3P2cu6fdPZ1KpercnIiI3FFXgLv7cnC/AnwLeKQRRYmIyM5qDnAz6zWz99x5DPw2cLVRhYmIyL3VcxbKg8C3zOzO6/x3d59rSFUiIrKjmgPc3f8G+FADaxERkSroNEIRkYhSgIuIRJQCXEQkohTgIiIRpQAXEYkoBbiISERFIsALmwVuvXuL4max3aWIiHSMRkwn2xTrhXVmr80yeXGSTDZDPBZno7jBUP8QE4cnGBsco6e7p91lioi0jbl7yzaWTqd9cXFxx3aXli5xbOYY+WKeXD531/pkIkkilmBufI6RgZFmlCoi0jHM7HK5C+Z03CGUhaUFjk4fZfX2atnwBsjlc6zeXuXI9BEWlhZaXKGISGfoqABfL6wzOjPK2sZaqPZrG2uMzoyyXlhvcmUiIp2nowJ89tos+WK+qj75Yp7z1843qSIRkc7VUQE+eXGy4mGTSnL5HGdfPtukikREOlfHBHhxs0gmm6mpb2Ylo1MMRWTP6ZgAz+VzxGPxmvp2d3VXvecuIhJ1HRPgyUSSjeJGTX0LmwWSiWSDKxIR6Wx1BbiZjZrZ/zazn5rZmXpeK9YVYyg1VFPfof4hYl2xejYvIhI59VwTMwb8GaUr0g8CT5vZYD3FTDw6UfWedDKR5Mzhuv7vEBGJpHr2wB8Bfuruf+PueeAvgCfrKWZscIxELFFVn0QswYnBE/VsVkQkkuoJ8AHg77Y8fytY9ivM7JSZLZrZYjabvecL9nT3MDc+R2+8N1QBvfFe5sbnNCeKiOxJ9QS4lVl218Qq7n7O3dPunk6lUju+6MjACPMn5+nb11fxcEoykaRvXx/zJ+c1F4qI7Fn1BPhbwPu3PH8fsFxfOSUjAyMsn15m6vgUw/3DGEa8K45hDPcPM3V8iuXTywpvEdnTap6N0My6gb8GHgeWgAXgX7t7xV/jhJ2NcLviZpFcPkcykdTZJiKy51SajbDm+cDdvWBmzwD/A4gBz98rvOsR64qx/779zXhpEZHIaul84GaWBf62xu4PAL9oYDmNorqqo7qqo7qq06l1QX21/RN3v+tLxJYGeD3MbLHcnxDtprqqo7qqo7qq06l1QXNq65if0ouISHUU4CIiERWlAD/X7gIqUF3VUV3VUV3V6dS6oAm1ReYYuIiI/Koo7YGLiMgWCnARkYjquADfaY5xK/lPwfrXzezhFtT0fjObN7PrZpYxs8+VafOYmd0ysyvB7Y+aXVew3TfN7MfBNu/6mWubxus3t4zDFTN7x8ye3damJeNlZs+b2YqZXd2yrM/MXjKzN4L7+yv0bdh89yHr+oKZ/SR4n75lZu+t0Pee73kT6nrOzJa2vFdPVOjb6vH6yy01vWlmVyr0beZ4lc2Gln3G3L1jbpR+0fkz4DeABPAaMLitzRPA9yhNpvUR4JUW1HUAeDh4/B5KUwhsr+sx4DttGLM3gQfusb7l41XmPf0/lH6I0PLxAj4KPAxc3bLsPwBngsdngMlaPotNqOu3ge7g8WS5usK8502o6zng90O8zy0dr23rvwj8URvGq2w2tOoz1ml74GHmGH8S+K9e8iPgvWZ2oJlFufsNd381ePxL4Dplps7tUC0fr20eB37m7rX+Arcu7v5DYHXb4ieB6eDxNPBUma4Nn+9+p7rc/fvuXgie/ojSBHEtVWG8wmj5eN1hZgb8K+DrjdpeWPfIhpZ8xjotwMPMMR5qHvJmMbODwIeBV8qs/mdm9pqZfc/Mars+XPUc+L6ZXTazU2XWt3W8gN+h8j+sdowXwIPufgNK/wCB/jJt2j1u/5bSX07l7PSeN8MzwaGd5yscDmjneP0L4Ka7v1FhfUvGa1s2tOQz1mkBHmaO8VDzkDeDmSWBbwDPuvs721a/SukwwYeA/wz8VStqAg67+8OULm33WTP76Lb17RyvBPAJYLbM6naNV1jtHLc/BArATIUmO73njfZl4APAIeAGpcMV27VtvICnuffed9PHa4dsqNitzLKqxqzTAjzMHONNm4f8XswsTukNmnH3b25f7+7vuHsuePxdIG5mDzS7LndfDu5XgG9R+rNsq7aMV+AY8Kq739y+ol3jFbh55zBScL9Spk27PmcngY8D4x4cKN0uxHveUO5+092L7r4JfKXC9to1Xt3AvwT+slKbZo9XhWxoyWes0wJ8AXjIzH492Hv7HeDFbW1eBP5NcHbFR4Bbd/5UaZbgGNtXgevu/qcV2vzDoB1m9gilsX27yXX1mtl77jym9CXY1W3NWj5eW1TcM2rHeG3xInAyeHwS+HaZNmE+iw1lZqPABPAJd/+/FdqEec8bXdfW70w+WWF7LR+vwMeAn7j7W+VWNnu87pENrfmMNeOb2Tq/1X2C0je5PwP+MFj2u8DvBo8N+LNg/Y+BdAtqepTSnzavA1eC2xPb6noGyFD6JvlHwD9vQV2/EWzvtWDbHTFewXb/AaVA3r9lWcvHi9J/IDeADUp7PJ8Gfg24ALwR3PcFbf8R8N17fRabXNdPKR0TvfMZm9peV6X3vMl1/Xnw2XmdUsAc6ITxCpZ/7c5nakvbVo5XpWxoyWdMP6UXEYmoTjuEIiIiISnARUQiSgEuIhJRCnARkYhSgIuIRJQCXEQkohTgIiIR9f8AKQRRQKCWnQ0AAAAASUVORK5CYII=\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.xlim((0,30))\n", + "plt.ylim((0,50))\n", + "\n", + "x_ticks = np.linspace(0, 30, 11)\n", + "plt.xticks(x_ticks)\n", + "\n", + "plt.xlabel('x0')\n", + "plt.ylabel('x0')\n", + "\n", + "plt.scatter(x, y, s = 200, c = 'green', marker = ',')" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "4e23215d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-10, 10, 40)\n", + "y = x * x + 1\n", + "plt.plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "24e4c025", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-10, 10, 40)\n", + "y = x * x + 1\n", + "y0 = -x * x\n", + "\n", + "plt.plot(x, y)\n", + "plt.plot(x, y0, c = 'red')" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "6572b679", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-10, 10, 40)\n", + "y = x * x + 1\n", + "\n", + "x2 = np.linspace(-10, 10, 5)\n", + "y2 = 3 * x2 + 50\n", + "\n", + "plt.scatter(x2, y2, c = 'red')\n", + "plt.plot(x, y)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "859b9f76", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = [1, 2, 3]\n", + "y = [5, 3, 4]\n", + "plt.bar(x, y, color = 'r', width = 0.2)" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "02b0aee0", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]]\n", + "[[0 0 0 0 0 0 0 0 0 0]\n", + " [1 1 1 1 1 1 1 1 1 1]\n", + " [2 2 2 2 2 2 2 2 2 2]\n", + " [3 3 3 3 3 3 3 3 3 3]\n", + " [4 4 4 4 4 4 4 4 4 4]\n", + " [5 5 5 5 5 5 5 5 5 5]\n", + " [6 6 6 6 6 6 6 6 6 6]\n", + " [7 7 7 7 7 7 7 7 7 7]\n", + " [8 8 8 8 8 8 8 8 8 8]\n", + " [9 9 9 9 9 9 9 9 9 9]]\n" + ] + } + ], + "source": [ + "x0, y0 = np.meshgrid(np.arange(0, 10, 1), np.arange(0, 10, 1))\n", + "print(x0)\n", + "print(y0)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "d7c7c50c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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ZE9dXS3powqSpPSrpStu7V/+9uVrb8JOrg24mbrWIeNr2RyXdq+cO6P5m4qwpXSXpQ5KO2P7V6o99bvV2JfAxSXeu/qHmEUk3TtwzmYi43/Zdkh7UyrulDmsbfjk5X0IOAMllefQBAFgHQw0AyTHUAJAcQw0AyTHUAJAcQw0AyTHUAJDcfwAUhupim3EMvAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(x0, y0)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2fa16d41", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", + " plt.pcolormesh(X, Y, Z)\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(-5, 5, 20)\n", + "y = np.linspace(-5, 5, 20)\n", + "X, Y = np.meshgrid(x, y)\n", + "Z = (1 - X/2 + X**3 + Y**4)*np.exp(-X**2-Y**2)\n", + "plt.pcolormesh(X, Y, Z)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "cc0a440f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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"pygments_lexer": "ipython3", + "version": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/5.1 \346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.ipynb" "b/5.1 \346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.ipynb" new file mode 100644 index 0000000..c8513ef --- /dev/null +++ "b/5.1 \346\234\272\345\231\250\345\255\246\344\271\240\347\256\227\346\263\225.ipynb" @@ -0,0 +1,177 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "4ea499ba", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.linear_model import LinearRegression" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b38adfd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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HVYHjatuSXpJ0LiKe3/VWpY7pQeOs4jE9DFehDGH7L7V91i1J10j6TUR0Smxpqmy/IumUtpfhvCDpaUn/KelVSWuSNiU9FBFJfwF4wDhPaftX7ZC0IemRnXniVNn+qaTfSfpE0qWi/JS254crc0wPGefDqtgxPQwBDgCJYgoFABJFgANAoghwAEgUAQ4AiSLAASBRBDgAJIoAB4BE/QnjDOwxq9x9HwAAAABJRU5ErkJggg==\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x_val = np.random.uniform(low = 0, high = 30, size = 10)\n", + "y_val = x_val + 3 * np.random.normal(size=len(x_val))\n", + "plt.scatter(x_val, y_val, c='black')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "a2f06191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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formuladb_centerad_energy
0Cu12Co-1.33-0.547
1Cu12Rh-1.57-0.591
2Cu12Ir-1.74-0.420
3Cu12Ni-1.21-0.674
4Cu12Pd-2.01-0.500
5Cu12Pt-1.78-0.391
6Cu13-2.33-0.316
7Cu12Ag-4.13-0.201
8Cu12Au-3.12-0.298
\n", + "
" + ], + "text/plain": [ + " formula db_center ad_energy\n", + "0 Cu12Co -1.33 -0.547\n", + "1 Cu12Rh -1.57 -0.591\n", + "2 Cu12Ir -1.74 -0.420\n", + "3 Cu12Ni -1.21 -0.674\n", + "4 Cu12Pd -2.01 -0.500\n", + "5 Cu12Pt -1.78 -0.391\n", + "6 Cu13 -2.33 -0.316\n", + "7 Cu12Ag -4.13 -0.201\n", + "8 Cu12Au -3.12 -0.298" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "f = open('db_center.csv', encoding = 'UTF-8')\n", + "names = ['formula', 'db_center', 'ad_energy']\n", + "data = read_csv(f, names = names)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "3eb63b53", + "metadata": {}, + "outputs": [], + "source": [ + "f.close()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "b31d6f0f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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formuladb_centerad_energy
0Cu12Co-1.33-0.547
1Cu12Rh-1.57-0.591
2Cu12Ir-1.74-0.420
3Cu12Ni-1.21-0.674
4Cu12Pd-2.01-0.500
\n", + "
" + ], + "text/plain": [ + " formula db_center ad_energy\n", + "0 Cu12Co -1.33 -0.547\n", + "1 Cu12Rh -1.57 -0.591\n", + "2 Cu12Ir -1.74 -0.420\n", + "3 Cu12Ni -1.21 -0.674\n", + "4 Cu12Pd -2.01 -0.500" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "a342608b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.547, -0.591, -0.42 , -0.674, -0.5 , -0.391, -0.316, -0.201,\n", + " -0.298])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y = data['ad_energy'].values\n", + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "3ae54d7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " db_center\n", + "0 -1.33\n", + "1 -1.57\n", + "2 -1.74\n", + "3 -1.21\n", + "4 -2.01\n", + "5 -1.78\n", + "6 -2.33\n", + "7 -4.13\n", + "8 -3.12\n" + ] + } + ], + "source": [ + "excluded = ['formula', 'ad_energy']\n", + "X = data.drop(excluded, axis = 1)\n", + "print(X)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "1146529f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "#from sklearn.metrics import mean_squared_error\n", + "\n", + "lr = LinearRegression()\n", + "lr.fit(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "22883e64", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.42839089]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "predicts = lr.predict([[-2.2]])\n", + "print(predicts)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "05663398", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "R2=0.7646612597264624\n" + ] + } + ], + "source": [ + "R2 = lr.score(X,y)\n", + "print('R2='+str(R2))" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a8201419", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-0.14221031]\n", + "-0.7412535628655474\n" + ] + } + ], + "source": [ + "coef = lr.coef_\n", + "intercept = lr.intercept_\n", + "print(coef)\n", + "print(intercept)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "03f0fa7d", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']\n", + "plt.rcParams['axes.unicode_minus'] = False\n", + "\n", + "y = data.iloc[:,2]\n", + "x = data.iloc[:,1]\n", + "plt.scatter(x, y, label='实际值')\n", + "plt.plot(x, lr.predict(X), color = 'red', label = '预测值')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "f19e8372", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.rcParams['font.sans-serif'] = ['SimHei']\n", + "plt.rcParams['axes.unicode_minus'] = False\n", + "\n", + "y = data.iloc[:,2]\n", + "x = data.iloc[:,1]\n", + "plt.scatter(x, y, label='实际值')\n", + "plt.plot(x, lr.predict(x.to_frame()), color = 'red', label = '预测值')\n", + "plt.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1099ae98", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but LinearRegression was fitted with feature names\n", + " warnings.warn(\n" + ] + }, + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " 0 1 2\n", + "0 3.000000e-10 2.270000e-12 2.240000e-12\n", + "1 3.000000e-10 2.060000e-12 1.900000e-12\n", + "2 3.000000e-10 2.790000e-12 1.560000e-12\n", + "3 2.990000e-10 2.600000e-12 1.930000e-12\n", + "4 2.990000e-10 1.740000e-12 9.890000e-13" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('iz.csv', mode = 'r', encoding = 'UTF-8') as f:\n", + " df = pd.read_csv(f, header = None)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "00c012a6", + "metadata": {}, + "outputs": [], + "source": [ + "df[3] = 0.5 * (df[1] + df[2])\n", + "z = np.array(df[0]) * 1e10\n", + "current = np.array(df[3]) * 1e12" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "fcd90eab", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from scipy.optimize import curve_fit" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "922886ba", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 3.60225002e+00 -2.56599244e-03]\n", + " [-2.56599244e-03 1.89800824e-06]]\n", + "4.306461770837465 3.919756213516532\n" + ] + } + ], + "source": [ + "def func(x, a, b):\n", + " return a * np.exp(b*x)\n", + "\n", + "def cal_wf_lin(z, current):\n", + " lr = LinearRegression()\n", + " lr.fit(z.reshape(-1, 1), np.log(current))\n", + " wf = 0.952 * pow(lr.coef_[0], 2)\n", + " return wf\n", + "\n", + "def cal_wf(z, current):\n", + " pfit, pcov = curve_fit(func, z, current)\n", + " print(pcov)\n", + " wf = 0.952 * pow(pfit[1], 2)\n", + " return wf\n", + "\n", + "res1 = cal_wf_lin(z, current)\n", + "res2 = cal_wf(z, current)\n", + "print(res1, res2)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e8d4bea2", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/5.4 \344\272\244\345\217\211\351\252\214\350\257\201.ipynb" "b/5.4 \344\272\244\345\217\211\351\252\214\350\257\201.ipynb" new file mode 100644 index 0000000..ad91f80 --- /dev/null +++ "b/5.4 \344\272\244\345\217\211\351\252\214\350\257\201.ipynb" @@ -0,0 +1,387 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "5189436a", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import datasets\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import cross_val_score\n", + "\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "fdee034a", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function load_boston is deprecated; `load_boston` is deprecated in 1.0 and will be removed in 1.2.\n", + "\n", + " The Boston housing prices dataset has an ethical problem. You can refer to\n", + " the documentation of this function for further details.\n", + "\n", + " The scikit-learn maintainers therefore strongly discourage the use of this\n", + " dataset unless the purpose of the code is to study and educate about\n", + " ethical issues in data science and machine learning.\n", + "\n", + " In this case special case, you can fetch the dataset from the original\n", + " source::\n", + "\n", + " import pandas as pd\n", + " import numpy as np\n", + "\n", + "\n", + " data_url = \"http://lib.stat.cmu.edu/datasets/boston\"\n", + " raw_df = pd.read_csv(data_url, sep=\"\\s+\", skiprows=22, header=None)\n", + " data = np.hstack([raw_df.values[::2, :], raw_df.values[1::2, :2]])\n", + " target = raw_df.values[1::2, 2]\n", + "\n", + " Alternative datasets include the California housing dataset (i.e.\n", + " func:`~sklearn.datasets.fetch_california_housing`) and the Ames housing\n", + " dataset. You can load the datasets as follows:\n", + "\n", + " from sklearn.datasets import fetch_california_housing\n", + " housing = fetch_california_housing()\n", + "\n", + " for the California housing dataset and:\n", + "\n", + " from sklearn.datasets import fetch_openml\n", + " housing = fetch_openml(name=\"house_prices\", as_frame=True)\n", + "\n", + " for the Ames housing dataset.\n", + " \n", + " warnings.warn(msg, category=FutureWarning)\n" + ] + } + ], + "source": [ + "house = datasets.load_boston()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "fc17bd34", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "samples total = 506\n" + ] + } + ], + "source": [ + "print('samples total = ' + str(len(house.target)))" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "9057ed49", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[6.320e-03 1.800e+01 2.310e+00 0.000e+00 5.380e-01 6.575e+00 6.520e+01\n", + " 4.090e+00 1.000e+00 2.960e+02 1.530e+01 3.969e+02 4.980e+00]\n", + "24.0\n" + ] + } + ], + "source": [ + "print(house.data[0])\n", + "print(house.target[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "6d8e30f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[6.3200e-03, 1.8000e+01, 2.3100e+00, ..., 1.5300e+01, 3.9690e+02,\n", + " 4.9800e+00],\n", + " [2.7310e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9690e+02,\n", + " 9.1400e+00],\n", + " [2.7290e-02, 0.0000e+00, 7.0700e+00, ..., 1.7800e+01, 3.9283e+02,\n", + " 4.0300e+00],\n", + " ...,\n", + " [6.0760e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", + " 5.6400e+00],\n", + " [1.0959e-01, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9345e+02,\n", + " 6.4800e+00],\n", + " [4.7410e-02, 0.0000e+00, 1.1930e+01, ..., 2.1000e+01, 3.9690e+02,\n", + " 7.8800e+00]])" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X = house.data\n", + "y = house.target\n", + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "33762754", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[-1.08011358e-01 4.64204584e-02 2.05586264e-02 2.68673382e+00\n", + " -1.77666112e+01 3.80986521e+00 6.92224640e-04 -1.47556685e+00\n", + " 3.06049479e-01 -1.23345939e-02 -9.52747232e-01 9.31168327e-03\n", + " -5.24758378e-01] 36.45948838509001\n" + ] + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X, y)\n", + "print(lr.coef_,lr.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "5d0d76dc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE = 4.679\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "print('RMSE = %.3f'%np.sqrt(mean_squared_error(y_true = y,\n", + " y_pred = lr.predict(X))))" + ] + }, + { + "cell_type": "markdown", + "id": "4acc792f", + "metadata": {}, + "source": [ + "#### 留出法" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "f2300f57", + "metadata": {}, + "outputs": [], + "source": [ + "X_lo_train = house.data[:-200]\n", + "y_lo_train = house.target[:-200]\n", + "X_lo_test = house.data[-200:]\n", + "y_lo_test = house.target[-200:]" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "9e0f253d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.22531206 0.01236249 0.01996516 0.67898399 -8.10624608 9.14475345\n", + " -0.04698834 -0.99589822 0.114772 -0.01468724 -0.61916486 0.01680992\n", + " -0.11589961] -13.026212481552207\n" + ] + } + ], + "source": [ + "lr_lo = LinearRegression()\n", + "lr_lo.fit(X_lo_train, y_lo_train)\n", + "print(lr_lo.coef_,lr_lo.intercept_)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "8d373705", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE = 19.241\n" + ] + } + ], + "source": [ + "print('RMSE = %.3f'%np.sqrt(mean_squared_error(y_true = y_lo_test, \n", + " y_pred = lr_lo.predict(X_lo_test))))" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "1639f4cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1 2 3 4] [0]\n", + "[0 2 3 4] [1]\n", + "[0 1 3 4] [2]\n", + "[0 1 2 4] [3]\n", + "[0 1 2 3] [4]\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import LeaveOneOut\n", + "\n", + "sample_x = range(5)\n", + "loo = LeaveOneOut()\n", + "for train, test in loo.split(sample_x):\n", + " print(train,test)" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "a41e0c1b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Folds: 506, mean RMSE: 3.383\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "\n", + "crossvalidation = KFold(n_splits = len(X))\n", + "scores = cross_val_score(lr, X, y, scoring = 'neg_mean_squared_error',\n", + " cv = crossvalidation)\n", + "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n", + "print('Folds: %i, mean RMSE: %.3f'%(len(scores), np.mean(rmse_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "5cbdeaab", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[2 3 4] [0 1]\n", + "[1 3 4] [0 2]\n", + "[1 2 4] [0 3]\n", + "[1 2 3] [0 4]\n", + "[0 3 4] [1 2]\n", + "[0 2 4] [1 3]\n", + "[0 2 3] [1 4]\n", + "[0 1 4] [2 3]\n", + "[0 1 3] [2 4]\n", + "[0 1 2] [3 4]\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import LeavePOut\n", + "\n", + "sample_x = range(5)\n", + "loo = LeavePOut(p = 2)\n", + "for train, test in loo.split(sample_x):\n", + " print(train,test)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "2634f12d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Folds: 10, mean RMSE: 5.181\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold\n", + "\n", + "crossvalidation = KFold(n_splits = 10)\n", + "scores = cross_val_score(lr, X, y, scoring = 'neg_mean_squared_error',\n", + " cv = crossvalidation)\n", + "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n", + "print('Folds: %i, mean RMSE: %.3f'%(len(scores), np.mean(rmse_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "99fab561", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/5.5 KNN\345\210\206\347\261\273.ipynb" "b/5.5 KNN\345\210\206\347\261\273.ipynb" new file mode 100644 index 0000000..3c80d5d --- /dev/null +++ "b/5.5 KNN\345\210\206\347\261\273.ipynb" @@ -0,0 +1,568 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "588220f0", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn import neighbors\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from pandas import read_csv" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "e80a455b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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\n", + "
" + ], + "text/plain": [ + " x y labels\n", + "0 0.847328 1.100095 1\n", + "1 0.846215 1.073316 1\n", + "2 0.981437 1.079204 1\n", + "3 0.914274 1.150070 1\n", + "4 0.884353 0.948567 1\n", + ".. ... ... ...\n", + "595 0.755693 0.313843 3\n", + "596 0.834400 0.574275 3\n", + "597 0.837354 0.610552 3\n", + "598 0.740000 0.610826 3\n", + "599 0.799171 0.558804 3\n", + "\n", + "[600 rows x 3 columns]" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('knn.csv', encoding = 'UTF-8') as f:\n", + " names = ['x','y', 'labels']\n", + " data = read_csv(f, names = names)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "7b8355fb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "x1 = data['x'].iloc[:200,]\n", + "y1 = data['y'].iloc[:200,]\n", + "x2 = data['x'].iloc[201:400,]\n", + "y2 = data['y'].iloc[201:400,]\n", + "x3 = data['x'].iloc[401:600,]\n", + "y3 = data['y'].iloc[401:600,]\n", + "plt.scatter(x1, y1, c = 'b', marker = 's', s = 50)\n", + "plt.scatter(x2, y2, c = 'r', marker = '^', s = 50)\n", + "plt.scatter(x3, y3, c = 'g', s = 50)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d81dac24", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " x y\n", + "0 0.847328 1.100095\n", + "1 0.846215 1.073316\n", + "2 0.981437 1.079204\n", + "3 0.914274 1.150070\n", + "4 0.884353 0.948567\n", + ".. ... ...\n", + "595 0.755693 0.313843\n", + "596 0.834400 0.574275\n", + "597 0.837354 0.610552\n", + "598 0.740000 0.610826\n", + "599 0.799171 0.558804\n", + "\n", + "[600 rows x 2 columns]\n", + "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n", + " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n", + " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3\n", + " 3 3 3 3 3 3 3 3]\n" + ] + } + ], + "source": [ + "excluded = ['labels']\n", + "X = data.drop(excluded, axis = 1)\n", + "y = data['labels'].values\n", + "print(X)\n", + "print(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "acdfe98a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "KNeighborsClassifier(n_neighbors=30)" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "clf = neighbors.KNeighborsClassifier(30)\n", + "clf.fit(X,y)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "feef2b56", + "metadata": {}, + "outputs": [], + "source": [ + "xx, yy = np.meshgrid(np.arange(0.2, 1.31, 0.01), np.arange(0.2, 1.31, 0.01))" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "efcbd197", + "metadata": {}, + "outputs": [], + "source": [ + "coords = np.c_[xx.ravel(), yy.ravel()]" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "6c7e79f4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]\n", + " [0 1 2 3 4 5 6 7 8 9]]\n", + "[0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6\n", + " 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3\n", + " 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9]\n", + "[0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3\n", + " 3 3 3 4 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 6 7 7 7 7\n", + " 7 7 7 7 7 7 8 8 8 8 8 8 8 8 8 8 9 9 9 9 9 9 9 9 9 9]\n" + ] + } + ], + "source": [ + "x0, y0 = np.meshgrid(np.arange(0, 10, 1), np.arange(0, 10, 1))\n", + "print(x0)\n", + "print(x0.ravel())\n", + "print(y0.ravel())" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1253fd77", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n", + "[[ 7 8 9]\n", + " [10 11 12]]\n", + "[[ 1 2 3 7 8 9]\n", + " [ 4 5 6 10 11 12]]\n", + "[[ 1 2 3]\n", + " [ 4 5 6]\n", + " [ 7 8 9]\n", + " [10 11 12]]\n" + ] + } + ], + "source": [ + "x_temp = np.array([[1,2,3],[4,5,6]])\n", + "y_temp = np.array([[7,8,9],[10,11,12]])\n", + "print(x_temp)\n", + "print(y_temp)\n", + "print(np.c_[x_temp,y_temp])\n", + "print(np.r_[x_temp,y_temp])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "82ec0fdc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0.2 0.2 ]\n", + " [0.21 0.2 ]\n", + " [0.22 0.2 ]\n", + " ...\n", + " [1.29 1.31]\n", + " [1.3 1.31]\n", + " [1.31 1.31]]\n" + ] + } + ], + "source": [ + "print(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "459c58e3", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", + " warnings.warn(\n", + "C:\\ProgramData\\Anaconda3\\lib\\site-packages\\sklearn\\base.py:441: UserWarning: X does not have valid feature names, but KNeighborsClassifier was fitted with feature names\n", + " warnings.warn(\n" + ] + } + ], + "source": [ + "Z = clf.predict(coords)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "7c6752bc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(112, 112)\n" + ] + } + ], + "source": [ + "print(xx.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "86b358c7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(12544,)\n" + ] + } + ], + "source": [ + "print(Z.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6478359d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(112, 112)\n" + ] + } + ], + "source": [ + "Z = Z.reshape(xx.shape)\n", + "print(Z.shape)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "07a66180", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", + " plt.pcolormesh(xx, yy, Z)\n" + ] + }, + { + "data": { + "text/plain": [ + "(0.2, 1.3, 0.2, 1.3)" + ] + }, + "execution_count": 15, + "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.pcolormesh(xx, yy, Z)\n", + "plt.scatter(x1, y1, c = 'b',marker = 's', s = 50, alpha = 0.8)\n", + "plt.scatter(x2, y2, c = 'r', marker = '^', s = 50, alpha = 0.8)\n", + "plt.scatter(x3, y3, c = 'g', s = 50, alpha = 0.8)\n", + "plt.axis((0.2, 1.3, 0.2, 1.3))" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "fdac2d18", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":5: MatplotlibDeprecationWarning: shading='flat' when X and Y have the same dimensions as C is deprecated since 3.3. Either specify the corners of the quadrilaterals with X and Y, or pass shading='auto', 'nearest' or 'gouraud', or set rcParams['pcolor.shading']. This will become an error two minor releases later.\n", + " plt.pcolormesh(xx, yy, Z, cmap = light_rgb)\n" + ] + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.colors import ListedColormap\n", + "\n", + "light_rgb = ListedColormap(['#AAAAFF','#FFAAAA','#AAFFAA'])\n", + "\n", + "plt.pcolormesh(xx, yy, Z, cmap = light_rgb)\n", + "plt.scatter(x1, y1, c = 'b',marker = 's', s = 50, alpha = 0.8)\n", + "plt.scatter(x2, y2, c = 'r', marker = '^', s = 50, alpha = 0.8)\n", + "plt.scatter(x3, y3, c = 'g', s = 50, alpha = 0.8)\n", + "plt.axis((0.2, 1.3, 0.2, 1.3))\n", + "plt.rcParams['figure.figsize']=(80, 80)\n", + "plt.savefig('test.jpg')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7cd15fde", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/6. \351\242\204\346\265\213d\345\270\246\344\270\255\345\277\203.ipynb" "b/6. \351\242\204\346\265\213d\345\270\246\344\270\255\345\277\203.ipynb" new file mode 100644 index 0000000..81e989f --- /dev/null +++ "b/6. \351\242\204\346\265\213d\345\270\246\344\270\255\345\277\203.ipynb" @@ -0,0 +1,2524 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "167530a7", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pandas as pd" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "35f3fec5", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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FeCoNiCuRuRhPdAgIrPtAu
Fe-0.920.05-0.20-0.13-0.29-0.54-1.24-0.83-0.36-1.09-1.42
Co0.01-1.17-0.28-0.16-0.24-0.58-1.37-0.91-0.36-1.19-1.56
Ni0.090.19-1.290.19-0.14-0.31-0.97-0.53-0.14-0.80-1.13
Cu0.560.600.27-2.670.580.32-0.64-0.700.58-0.33-1.09
Ru0.210.260.010.12-1.41-0.17-0.82-0.270.02-0.62-0.84
Rh0.240.340.160.440.04-1.73-0.540.070.17-0.35-0.49
Pd0.370.540.500.940.240.36-1.830.590.530.190.17
Ag0.720.840.670.470.840.860.14-4.301.140.50-0.15
Ir0.210.270.050.210.09-0.15-0.73-0.13-2.11-0.56-0.74
Pt0.330.480.400.720.140.23-0.170.440.38-2.25-0.05
Au0.630.770.630.550.700.750.170.210.980.46-3.56
\n", + "
" + ], + "text/plain": [ + " Fe Co Ni Cu Ru Rh Pd Ag Ir Pt Au\n", + "Fe -0.92 0.05 -0.20 -0.13 -0.29 -0.54 -1.24 -0.83 -0.36 -1.09 -1.42\n", + "Co 0.01 -1.17 -0.28 -0.16 -0.24 -0.58 -1.37 -0.91 -0.36 -1.19 -1.56\n", + "Ni 0.09 0.19 -1.29 0.19 -0.14 -0.31 -0.97 -0.53 -0.14 -0.80 -1.13\n", + "Cu 0.56 0.60 0.27 -2.67 0.58 0.32 -0.64 -0.70 0.58 -0.33 -1.09\n", + "Ru 0.21 0.26 0.01 0.12 -1.41 -0.17 -0.82 -0.27 0.02 -0.62 -0.84\n", + "Rh 0.24 0.34 0.16 0.44 0.04 -1.73 -0.54 0.07 0.17 -0.35 -0.49\n", + "Pd 0.37 0.54 0.50 0.94 0.24 0.36 -1.83 0.59 0.53 0.19 0.17\n", + "Ag 0.72 0.84 0.67 0.47 0.84 0.86 0.14 -4.30 1.14 0.50 -0.15\n", + "Ir 0.21 0.27 0.05 0.21 0.09 -0.15 -0.73 -0.13 -2.11 -0.56 -0.74\n", + "Pt 0.33 0.48 0.40 0.72 0.14 0.23 -0.17 0.44 0.38 -2.25 -0.05\n", + "Au 0.63 0.77 0.63 0.55 0.70 0.75 0.17 0.21 0.98 0.46 -3.56" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('data_impurities.csv', encoding = 'UTF-8') as f:\n", + " df = pd.read_csv(f, index_col = 0)\n", + " \n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "361d0139", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.63" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.loc['Au','Fe']" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c8a6889e", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Fe', 'Co', 'Ni', 'Cu', 'Ru', 'Rh', 'Pd', 'Ag', 'Ir', 'Pt', 'Au'], dtype='object')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.index" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a7915ab0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['Fe', 'Co', 'Ni', 'Cu', 'Ru', 'Rh', 'Pd', 'Ag', 'Ir', 'Pt', 'Au'], dtype='object')" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "e2ae81ff", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Fe\n", + "host Fe, guest Fe, val -0.92\n", + "Fe\n", + "host Fe, guest Co, val 0.05\n", + "Fe\n", + "host Fe, guest Ni, val -0.2\n", + "Fe\n", + "host Fe, guest Cu, val -0.13\n", + "Fe\n", + "host Fe, guest Ru, val -0.29\n", + "Fe\n", + "host Fe, guest Rh, val -0.54\n", + "Fe\n", + "host Fe, guest Pd, val -1.24\n", + "Fe\n", + "host Fe, guest Ag, val -0.83\n", + "Fe\n", + "host Fe, guest Ir, val -0.36\n", + "Fe\n", + "host Fe, guest Pt, val -1.09\n", + "Fe\n", + "host Fe, guest Au, val -1.42\n", + "Co\n", + "host Co, guest Fe, val 0.01\n", + "Co\n", + "host Co, guest Co, val -1.17\n", + "Co\n", + "host Co, guest Ni, val -0.28\n", + "Co\n", + "host Co, guest Cu, val -0.16\n", + "Co\n", + "host Co, guest Ru, val -0.24\n", + "Co\n", + "host Co, guest Rh, val -0.58\n", + "Co\n", + "host Co, guest Pd, val -1.37\n", + "Co\n", + "host Co, guest Ag, val -0.91\n", + "Co\n", + "host Co, guest Ir, val -0.36\n", + "Co\n", + "host Co, guest Pt, val -1.19\n", + "Co\n", + "host Co, guest Au, val -1.56\n", + "Ni\n", + "host Ni, guest Fe, val 0.09\n", + "Ni\n", + "host Ni, guest Co, val 0.19\n", + "Ni\n", + "host Ni, guest Ni, val -1.29\n", + "Ni\n", + "host Ni, guest Cu, val 0.19\n", + "Ni\n", + "host Ni, guest Ru, val -0.14\n", + "Ni\n", + "host Ni, guest Rh, val -0.31\n", + "Ni\n", + "host Ni, guest Pd, val -0.97\n", + "Ni\n", + "host Ni, guest Ag, val -0.53\n", + "Ni\n", + "host Ni, guest Ir, val -0.14\n", + "Ni\n", + "host Ni, guest Pt, val -0.8\n", + "Ni\n", + "host Ni, guest Au, val -1.13\n", + "Cu\n", + "host Cu, guest Fe, val 0.56\n", + "Cu\n", + "host Cu, guest Co, val 0.6\n", + "Cu\n", + "host Cu, guest Ni, val 0.27\n", + "Cu\n", + "host Cu, guest Cu, val -2.67\n", + "Cu\n", + "host Cu, guest Ru, val 0.58\n", + "Cu\n", + "host Cu, guest Rh, val 0.32\n", + "Cu\n", + "host Cu, guest Pd, val -0.64\n", + "Cu\n", + "host Cu, guest Ag, val -0.7\n", + "Cu\n", + "host Cu, guest Ir, val 0.58\n", + "Cu\n", + "host Cu, guest Pt, val -0.33\n", + "Cu\n", + "host Cu, guest Au, val -1.09\n", + "Ru\n", + "host Ru, guest Fe, val 0.21\n", + "Ru\n", + "host Ru, guest Co, val 0.26\n", + "Ru\n", + "host Ru, guest Ni, val 0.01\n", + "Ru\n", + "host Ru, guest Cu, val 0.12\n", + "Ru\n", + "host Ru, guest Ru, val -1.41\n", + "Ru\n", + "host Ru, guest Rh, val -0.17\n", + "Ru\n", + "host Ru, guest Pd, val -0.82\n", + "Ru\n", + "host Ru, guest Ag, val -0.27\n", + "Ru\n", + "host Ru, guest Ir, val 0.02\n", + "Ru\n", + "host Ru, guest Pt, val -0.62\n", + "Ru\n", + "host Ru, guest Au, val -0.84\n", + "Rh\n", + "host Rh, guest Fe, val 0.24\n", + "Rh\n", + "host Rh, guest Co, val 0.34\n", + "Rh\n", + "host Rh, guest Ni, val 0.16\n", + "Rh\n", + "host Rh, guest Cu, val 0.44\n", + "Rh\n", + "host Rh, guest Ru, val 0.04\n", + "Rh\n", + "host Rh, guest Rh, val -1.73\n", + "Rh\n", + "host Rh, guest Pd, val -0.54\n", + "Rh\n", + "host Rh, guest Ag, val 0.07\n", + "Rh\n", + "host Rh, guest Ir, val 0.17\n", + "Rh\n", + "host Rh, guest Pt, val -0.35\n", + "Rh\n", + "host Rh, guest Au, val -0.49\n", + "Pd\n", + "host Pd, guest Fe, val 0.37\n", + "Pd\n", + "host Pd, guest Co, val 0.54\n", + "Pd\n", + "host Pd, guest Ni, val 0.5\n", + "Pd\n", + "host Pd, guest Cu, val 0.94\n", + "Pd\n", + "host Pd, guest Ru, val 0.24\n", + "Pd\n", + "host Pd, guest Rh, val 0.36\n", + "Pd\n", + "host Pd, guest Pd, val -1.83\n", + "Pd\n", + "host Pd, guest Ag, val 0.59\n", + "Pd\n", + "host Pd, guest Ir, val 0.53\n", + "Pd\n", + "host Pd, guest Pt, val 0.19\n", + "Pd\n", + "host Pd, guest Au, val 0.17\n", + "Ag\n", + "host Ag, guest Fe, val 0.72\n", + "Ag\n", + "host Ag, guest Co, val 0.84\n", + "Ag\n", + "host Ag, guest Ni, val 0.67\n", + "Ag\n", + "host Ag, guest Cu, val 0.47\n", + "Ag\n", + "host Ag, guest Ru, val 0.84\n", + "Ag\n", + "host Ag, guest Rh, val 0.86\n", + "Ag\n", + "host Ag, guest Pd, val 0.14\n", + "Ag\n", + "host Ag, guest Ag, val -4.3\n", + "Ag\n", + "host Ag, guest Ir, val 1.14\n", + "Ag\n", + "host Ag, guest Pt, val 0.5\n", + "Ag\n", + "host Ag, guest Au, val -0.15\n", + "Ir\n", + "host Ir, guest Fe, val 0.21\n", + "Ir\n", + "host Ir, guest Co, val 0.27\n", + "Ir\n", + "host Ir, guest Ni, val 0.05\n", + "Ir\n", + "host Ir, guest Cu, val 0.21\n", + "Ir\n", + "host Ir, guest Ru, val 0.09\n", + "Ir\n", + "host Ir, guest Rh, val -0.15\n", + "Ir\n", + "host Ir, guest Pd, val -0.73\n", + "Ir\n", + "host Ir, guest Ag, val -0.13\n", + "Ir\n", + "host Ir, guest Ir, val -2.11\n", + "Ir\n", + "host Ir, guest Pt, val -0.56\n", + "Ir\n", + "host Ir, guest Au, val -0.74\n", + "Pt\n", + "host Pt, guest Fe, val 0.33\n", + "Pt\n", + "host Pt, guest Co, val 0.48\n", + "Pt\n", + "host Pt, guest Ni, val 0.4\n", + "Pt\n", + "host Pt, guest Cu, val 0.72\n", + "Pt\n", + "host Pt, guest Ru, val 0.14\n", + "Pt\n", + "host Pt, guest Rh, val 0.23\n", + "Pt\n", + "host Pt, guest Pd, val -0.17\n", + "Pt\n", + "host Pt, guest Ag, val 0.44\n", + "Pt\n", + "host Pt, guest Ir, val 0.38\n", + "Pt\n", + "host Pt, guest Pt, val -2.25\n", + "Pt\n", + "host Pt, guest Au, val -0.05\n", + "Au\n", + "host Au, guest Fe, val 0.63\n", + "Au\n", + "host Au, guest Co, val 0.77\n", + "Au\n", + "host Au, guest Ni, val 0.63\n", + "Au\n", + "host Au, guest Cu, val 0.55\n", + "Au\n", + "host Au, guest Ru, val 0.7\n", + "Au\n", + "host Au, guest Rh, val 0.75\n", + "Au\n", + "host Au, guest Pd, val 0.17\n", + "Au\n", + "host Au, guest Ag, val 0.21\n", + "Au\n", + "host Au, guest Ir, val 0.98\n", + "Au\n", + "host Au, guest Pt, val 0.46\n", + "Au\n", + "host Au, guest Au, val -3.56\n" + ] + } + ], + "source": [ + "for i in df.index:\n", + " for j in df.columns:\n", + " print(i)\n", + " print(f'host {i}, guest {j}, val {df.loc[i, j]}')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "fad31f0c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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FeCoNiCuRuRhPdAgIrPtAu
Fe-0.92-0.87-1.12-1.05-1.21-1.46-2.16-1.75-1.28-2.01-2.34
Co-1.16-1.17-1.45-1.33-1.41-1.75-2.54-2.08-1.53-2.36-2.73
Ni-1.20-1.10-1.29-1.10-1.43-1.60-2.26-1.82-1.43-2.09-2.42
Cu-2.11-2.07-2.40-2.67-2.09-2.35-3.31-3.37-2.09-3.00-3.76
Ru-1.20-1.15-1.40-1.29-1.41-1.58-2.23-1.68-1.39-2.03-2.25
Rh-1.49-1.39-1.57-1.29-1.69-1.73-2.27-1.66-1.56-2.08-2.22
Pd-1.46-1.29-1.33-0.89-1.59-1.47-1.83-1.24-1.30-1.64-1.66
Ag-3.58-3.46-3.63-3.83-3.46-3.44-4.16-4.30-3.16-3.80-4.45
Ir-1.90-1.84-2.06-1.90-2.02-2.26-2.84-2.24-2.11-2.67-2.85
Pt-1.92-1.77-1.85-1.53-2.11-2.02-2.42-1.81-1.87-2.25-2.30
Au-2.93-2.79-2.93-3.01-2.86-2.81-3.39-3.35-2.58-3.10-3.56
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" + ], + "text/plain": [ + " Fe Co Ni Cu Ru Rh Pd Ag Ir Pt Au\n", + "Fe -0.92 -0.87 -1.12 -1.05 -1.21 -1.46 -2.16 -1.75 -1.28 -2.01 -2.34\n", + "Co -1.16 -1.17 -1.45 -1.33 -1.41 -1.75 -2.54 -2.08 -1.53 -2.36 -2.73\n", + "Ni -1.20 -1.10 -1.29 -1.10 -1.43 -1.60 -2.26 -1.82 -1.43 -2.09 -2.42\n", + "Cu -2.11 -2.07 -2.40 -2.67 -2.09 -2.35 -3.31 -3.37 -2.09 -3.00 -3.76\n", + "Ru -1.20 -1.15 -1.40 -1.29 -1.41 -1.58 -2.23 -1.68 -1.39 -2.03 -2.25\n", + "Rh -1.49 -1.39 -1.57 -1.29 -1.69 -1.73 -2.27 -1.66 -1.56 -2.08 -2.22\n", + "Pd -1.46 -1.29 -1.33 -0.89 -1.59 -1.47 -1.83 -1.24 -1.30 -1.64 -1.66\n", + "Ag -3.58 -3.46 -3.63 -3.83 -3.46 -3.44 -4.16 -4.30 -3.16 -3.80 -4.45\n", + "Ir -1.90 -1.84 -2.06 -1.90 -2.02 -2.26 -2.84 -2.24 -2.11 -2.67 -2.85\n", + "Pt -1.92 -1.77 -1.85 -1.53 -2.11 -2.02 -2.42 -1.81 -1.87 -2.25 -2.30\n", + "Au -2.93 -2.79 -2.93 -3.01 -2.86 -2.81 -3.39 -3.35 -2.58 -3.10 -3.56" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i in df.index:\n", + " for j in df.columns:\n", + " if i != j:\n", + " df.loc[i, j] += df.loc[i, i]\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "8581b566", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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namegroupbulk wigner-seitz radiusatomic numberatomic massperiodelectronegativityIionization energy(eV)H_fusdensity
FeIron82.662655.8450041.837.9024247.37.87
CoCobalt92.622758.9332041.887.8810272.58.86
NiNickel102.602858.6934041.917.6398290.38.90
CuCopper112.672963.5460041.907.7264203.58.96
RuRuthenium82.7944101.0700052.207.3605381.812.10
RhRhodium92.8145102.9055052.287.4589258.412.40
PdPalladium102.8746106.4200052.208.3369157.312.00
AgSilver113.0147107.8682051.937.5762104.610.50
IrIridium92.8477192.2170062.208.9670213.922.50
PtPlatinum102.9078195.0780062.208.9588113.621.50
AuGold113.0079196.9665562.409.225564.619.30
\n", + "
" + ], + "text/plain": [ + " name group bulk wigner-seitz radius atomic number atomic mass \\\n", + "Fe Iron 8 2.66 26 55.84500 \n", + "Co Cobalt 9 2.62 27 58.93320 \n", + "Ni Nickel 10 2.60 28 58.69340 \n", + "Cu Copper 11 2.67 29 63.54600 \n", + "Ru Ruthenium 8 2.79 44 101.07000 \n", + "Rh Rhodium 9 2.81 45 102.90550 \n", + "Pd Palladium 10 2.87 46 106.42000 \n", + "Ag Silver 11 3.01 47 107.86820 \n", + "Ir Iridium 9 2.84 77 192.21700 \n", + "Pt Platinum 10 2.90 78 195.07800 \n", + "Au Gold 11 3.00 79 196.96655 \n", + "\n", + " period electronegativity Iionization energy(eV) H_fus density \n", + "Fe 4 1.83 7.9024 247.3 7.87 \n", + "Co 4 1.88 7.8810 272.5 8.86 \n", + "Ni 4 1.91 7.6398 290.3 8.90 \n", + "Cu 4 1.90 7.7264 203.5 8.96 \n", + "Ru 5 2.20 7.3605 381.8 12.10 \n", + "Rh 5 2.28 7.4589 258.4 12.40 \n", + "Pd 5 2.20 8.3369 157.3 12.00 \n", + "Ag 5 1.93 7.5762 104.6 10.50 \n", + "Ir 6 2.20 8.9670 213.9 22.50 \n", + "Pt 6 2.20 8.9588 113.6 21.50 \n", + "Au 6 2.40 9.2255 64.6 19.30 " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "with open('data_impurities_features.csv', encoding = 'UTF-8') as f:\n", + " feat = pd.read_csv(f, index_col = 0)\n", + "feat" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "b1ef8c89", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "name Cobalt\n", + "group 9\n", + "bulk wigner-seitz radius 2.62\n", + "atomic number 27\n", + "atomic mass 58.9332\n", + "period 4\n", + "electronegativity 1.88\n", + "Iionization energy(eV) 7.881\n", + "H_fus 272.5\n", + "density 8.86\n", + "Name: Co, dtype: object" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feat.loc['Co']" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "d657882e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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groupbulk wigner-seitz radiusatomic numberatomic massperiodelectronegativityIionization energy(eV)H_fusdensity
Fe82.662655.8450041.837.9024247.37.87
Co92.622758.9332041.887.8810272.58.86
Ni102.602858.6934041.917.6398290.38.90
Cu112.672963.5460041.907.7264203.58.96
Ru82.7944101.0700052.207.3605381.812.10
Rh92.8145102.9055052.287.4589258.412.40
Pd102.8746106.4200052.208.3369157.312.00
Ag113.0147107.8682051.937.5762104.610.50
Ir92.8477192.2170062.208.9670213.922.50
Pt102.9078195.0780062.208.9588113.621.50
Au113.0079196.9665562.409.225564.619.30
\n", + "
" + ], + "text/plain": [ + " group bulk wigner-seitz radius atomic number atomic mass period \\\n", + "Fe 8 2.66 26 55.84500 4 \n", + "Co 9 2.62 27 58.93320 4 \n", + "Ni 10 2.60 28 58.69340 4 \n", + "Cu 11 2.67 29 63.54600 4 \n", + "Ru 8 2.79 44 101.07000 5 \n", + "Rh 9 2.81 45 102.90550 5 \n", + "Pd 10 2.87 46 106.42000 5 \n", + "Ag 11 3.01 47 107.86820 5 \n", + "Ir 9 2.84 77 192.21700 6 \n", + "Pt 10 2.90 78 195.07800 6 \n", + "Au 11 3.00 79 196.96655 6 \n", + "\n", + " electronegativity Iionization energy(eV) H_fus density \n", + "Fe 1.83 7.9024 247.3 7.87 \n", + "Co 1.88 7.8810 272.5 8.86 \n", + "Ni 1.91 7.6398 290.3 8.90 \n", + "Cu 1.90 7.7264 203.5 8.96 \n", + "Ru 2.20 7.3605 381.8 12.10 \n", + "Rh 2.28 7.4589 258.4 12.40 \n", + "Pd 2.20 8.3369 157.3 12.00 \n", + "Ag 1.93 7.5762 104.6 10.50 \n", + "Ir 2.20 8.9670 213.9 22.50 \n", + "Pt 2.20 8.9588 113.6 21.50 \n", + "Au 2.40 9.2255 64.6 19.30 " + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "feat.drop('name', axis = 'columns', inplace = True)\n", + "# feat = feat.drop('name', axis = 'columns')\n", + "feat" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5f0c1cc3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "host(Fe), guest(Co), input=[ 8. 2.66 26. 55.845 4. 1.83 7.9024 247.3\n", + " 7.87 9. 2.62 27. 58.9332 4. 1.88 7.881\n", + " 272.5 8.86 ], output=-0.87\n" + ] + } + ], + "source": [ + "x = list()\n", + "y = list()\n", + "\n", + "for i in df.index:\n", + " for j in df.columns:\n", + " vec_i = feat.loc[i].to_numpy()\n", + " vec_j = feat.loc[j].to_numpy()\n", + " x_val = np.concatenate((vec_i, vec_j))\n", + " y_val = df.loc[i][j]\n", + " x.append(x_val)\n", + " y.append(y_val)\n", + " if i == 'Fe' and j == 'Co':\n", + " print(f'host({i}), guest({j}), input={x_val}, output={y_val}')" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "4a5c6bcf", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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x1x2x3x4x5x6x7x8x9x10x11x12x13x14x15x16x17x18y
08.02.6626.055.845004.01.837.9024247.37.878.02.6626.055.845004.01.837.9024247.37.87-0.92
18.02.6626.055.845004.01.837.9024247.37.879.02.6227.058.933204.01.887.8810272.58.86-0.87
28.02.6626.055.845004.01.837.9024247.37.8710.02.6028.058.693404.01.917.6398290.38.90-1.12
38.02.6626.055.845004.01.837.9024247.37.8711.02.6729.063.546004.01.907.7264203.58.96-1.05
48.02.6626.055.845004.01.837.9024247.37.878.02.7944.0101.070005.02.207.3605381.812.10-1.21
............................................................
11611.03.0079.0196.966556.02.409.225564.619.3010.02.8746.0106.420005.02.208.3369157.312.00-3.39
11711.03.0079.0196.966556.02.409.225564.619.3011.03.0147.0107.868205.01.937.5762104.610.50-3.35
11811.03.0079.0196.966556.02.409.225564.619.309.02.8477.0192.217006.02.208.9670213.922.50-2.58
11911.03.0079.0196.966556.02.409.225564.619.3010.02.9078.0195.078006.02.208.9588113.621.50-3.10
12011.03.0079.0196.966556.02.409.225564.619.3011.03.0079.0196.966556.02.409.225564.619.30-3.56
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121 rows × 19 columns

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" + ], + "text/plain": [ + " x1 x2 x3 x4 x5 x6 x7 x8 x9 x10 x11 \\\n", + "0 8.0 2.66 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 8.0 2.66 \n", + "1 8.0 2.66 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 9.0 2.62 \n", + "2 8.0 2.66 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 10.0 2.60 \n", + "3 8.0 2.66 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 11.0 2.67 \n", + "4 8.0 2.66 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 8.0 2.79 \n", + ".. ... ... ... ... ... ... ... ... ... ... ... \n", + "116 11.0 3.00 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 10.0 2.87 \n", + "117 11.0 3.00 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 11.0 3.01 \n", + "118 11.0 3.00 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 9.0 2.84 \n", + "119 11.0 3.00 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 10.0 2.90 \n", + "120 11.0 3.00 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 11.0 3.00 \n", + "\n", + " x12 x13 x14 x15 x16 x17 x18 y \n", + "0 26.0 55.84500 4.0 1.83 7.9024 247.3 7.87 -0.92 \n", + "1 27.0 58.93320 4.0 1.88 7.8810 272.5 8.86 -0.87 \n", + "2 28.0 58.69340 4.0 1.91 7.6398 290.3 8.90 -1.12 \n", + "3 29.0 63.54600 4.0 1.90 7.7264 203.5 8.96 -1.05 \n", + "4 44.0 101.07000 5.0 2.20 7.3605 381.8 12.10 -1.21 \n", + ".. ... ... ... ... ... ... ... ... \n", + "116 46.0 106.42000 5.0 2.20 8.3369 157.3 12.00 -3.39 \n", + "117 47.0 107.86820 5.0 1.93 7.5762 104.6 10.50 -3.35 \n", + "118 77.0 192.21700 6.0 2.20 8.9670 213.9 22.50 -2.58 \n", + "119 78.0 195.07800 6.0 2.20 8.9588 113.6 21.50 -3.10 \n", + "120 79.0 196.96655 6.0 2.40 9.2255 64.6 19.30 -3.56 \n", + "\n", + "[121 rows x 19 columns]" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame(x, columns = [f'x{i+1}' for i in range(18)])\n", + "df['y'] = y\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "baf542e6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aaaaa, Hello world!\n" + ] + } + ], + "source": [ + "a = 'Hello'\n", + "b = 'world!'\n", + "print('aaaaa, %s %s'%(a,b))" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "1f0eaac9", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aaaaa, Hello world!\n" + ] + } + ], + "source": [ + "a = 'Hello'\n", + "b = 'world!'\n", + "print(f'aaaaa, {a} {b}')" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "f1f8b213", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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fmkszDYP3Drp61iW5x2DtpdwY89hLXyv23N8XET8pedwDbIiI5ZJ68se3t+D3JCPF+T1rf4P33j320jYU0zILyL5zFWAV8DQu7m/jJ5ONBI+7saXZUyEDeErSJkmL87ZzImIvQP7z7CZ/h5mZ1anZPfdLI2KPpLOB9ZJ+UHTF/MVgMcCMGTOaTMPMzEo1teceEXvyn/uBR4C5wD5JUwDyn/srrLsyIrojoruzs7OZNMzMbJCGi7ukd0qaePw+cCXwErAWWJR3WwQ81mySZmZWn2amZc4BHpF0PM5XI+KvJD0PrJF0I/AKcF3zaY6siRf0cPGqnhp9APyGlbVWrbHncWeVNFzcI+JHwOwy7X8HXN5MUu3mjR3L/WUdNiJqjT2PO6vEFw4zM0uQi7uZWYJc3M3MEuTibmaWIBd3M7MEubibmSXI13MvyNfDtpHi7wCwRigiRjoHuru7o7e3d6TTqKnSk8xX27OhVm7sedyZpE0R0V12mYu7mdnoVK24e1rGkuO9XBsJ7XZk7z13S5a/Ss5GynCNPU/L2Jgw+66nOHDoSNU+Z0wYz5Y7rxymjGwsGMlx52kZGxMOHDriC7zZsGvXcefz3M3MEuQ9d0uGr7tvI6Fdx13bFnfPn1q9WnHdfY87q1e7ft9D2xb3N7tuY2KtPgBsHfpkrKx2LITNfpLY4250aLex146fYB+ys2UkzQe+AIwD7o+I5ZX6Vjtbxucst6+LV11cqN/WRcNbCFs1Zjz22lc7jr2ROM992E+FlDQO+CFwBdAPPA9cHxHby/X3qZDFteOgtrHBY6/9jMSpkHOBvvx7VpG0GlgAlC3uQyXFwfjGjooHQL9QzyGg906HhsdebR57Q2uo9tyvBeZHxEfyxzcAvxYRt5Tr7z334Zdi8bH253FX28c+9jHuu+8+Dh8+TEdHBzfddBN333132b4jseeuMm1vexWRtBhYDDBjxowhSsMqGctPHhs5HneVzb7rKXatvZs3vv8kky77EGfP+Rcc3PwkX/wfX2LV/97NWVcsqetN4qHac/914DMRMS9/vBQgIv5Luf7eczezsa6Ro5qR2HN/HpglaSbwGrAQ+P0h+l1mZqPe1kVbkcT0f/sNTjrl1F+0v/n/fs6rn7+WenfEh6S4R8RRSbcAf012KuSDEbFtKH6XmVkqOjo6+MS0H/PJT37yF20rVqzgjo6OumMN2YeYIuIJ4Imhim9mlpqbbrqJ22+/HYAlS5Zw7733cvvtt7NkyZK6Y7XtJ1TNzMaa42fF3HHHHdx22210dHSwZMmSimfLVOPruZuZjVLV3lD1JX/NzBLk4m5mliAXdzOzBLm4m5klyMXdzCxBbXG2jKQB4Mc1uk0GftKCX9eKOO2US6viOJehjdNOubQqjnMZ2jhFYvxyRHSWXRIRo+IG9LZLnHbKJcVtaqdcvE3OZbRuk6dlzMwS5OJuZpag0VTcV7ZRnHbKpVVxnMvQxmmnXFoVx7kMbZymYrTFG6pmZtZao2nP3czMCho1xV3SX0l6XdLjTcSYI+l7krZJelHS7zUY55clbZK0OY9V//U434p1uqTXJH2xiRjH8lw2S1rbYIwZkp6StEPSdkldDcR4X0kemyX9XNLVDebzX/O/7Q5J/11Sua9urBXjTyS9lN/q+l9XGm+SZkp6TtLLkr4u6ZQGYtwiqU9SSJrcRC5fkbQz374HJVX9duoqcR6QtCV/TnxD0mn1xihZfrekg01s00OSdpWMoTkNxJCkZZJ+mI+fjzeYy3dL8tgj6dEG41wu6YU8zjOS3lUtTsu04rSf4bgBlwP/Cni8iRjnA7Py++cCe4EzG4hzCtCR3z8N2A2c22BOXwC+Cnyxie062IK/79PAFSXb9I4m450F/H0jcYB/Cvwt2Re9jAO+B1xWZ4yrgPVkl7V+J9ALnN7seAPWAAvz+/cCf9hAjEuArnzcTG4ilw+QfV+xgK9Vy6VGnNNL7q8AeuqNkS/rBv6iyHiskstDwLVN/o8+DPw5cFL++OxG4gzq803ggw3m80Pggvz+vwEeqvc50cit7fbcJf1qvgdxqqR35ntv746IDcAbzcQBTomIlwEiYg+wHyj/AYDqcc6PiMN5lw5qHAFV2iZJ/wQ4B3iq0W2S9O4i69aI8R7g5IhYDxARByPiZ03mci3wZCNxyAr6qeQvosB4YF+dMf4x8L8i4mhE/F9gCzC/6HaUG2+SBLwf+EbetAq4ut4xGxHfj4jdzeSSx3kicsBGYFqDcX5asn0TgKg3hqRxwH8DPtXMNpXTQIw/BP5TRLyZb9/+ZnKRNJHs//5og3ECOD2/fwawp8b2/rGkW0seL1ONo49y2u7LOiLieWVTC/+ZbKB9OSJeanUcSXPJisf/aSSOpOnAOuBdwB/lLxaFYwDbgW8DN5C94je8Tfkg6wWOAssj4tE6c/kV4HVJDwMzgW+R7b0dqzeXki4LyfYCG9mm70r6DtmRlciOanbUuU2bgDslrQDeAbyP7G9e73aU+iXg9Yg4mj/uB6YO15itRNl0zA3ArY3GkfRnZEcC24HbIuJndca4BVgbEXtVMoPW4DYtk/RpYAPZOKw3xnnA70m6BhgAPh4RLzfxf7oG2HD8RbCBOB8BnpB0CPgp8N4av+8B4GHgC5JOInsuzS2Q59sNx+FBvTeyorsFeA4YV9J+GXVMy1SJMwXYCby3mTj5snPJ9prOqScG2ZPhU/myD1FwWqZcLuRTQmRFejdwXp25XAscyNc/mewQ9MYm/74DwPhGtonsBXMd2fTQaWTTMv+8gb/Lvwc2k03PfAW4tZnxRnaU11fyeDqwtdExS5lpmQbj3Af8aQvijAPuAT5c59/lXOAZsqM/GDQtU08u+dgR2RHbKuDTDcQ4SPYCBfDbwHeb/Ls8CfxOE9v0MPBr+f0/Au4v8JxYTzZ9Nx/4RpHn0eBb203L5M4ie1JPJDs8b1kcSaeTFY7/EBHPNptPZHvs24B/VmeMXwdukbQb+CzwQUnLG8klz4GI+BHZ3PkldcboB74fET+KbK/0UbJpjbpzyf0u8EhEHCkQo1yca4BnI5seOkj25Kq1t1Pu77IsIuZExBVkBePlOrdjsJ8AZ0o6fsQ7jbcOsYdszFYj6U6yF51PDlpUdz6RHal9HfidOmNcQvaC3JeP53dI6mskl4jYG5nDwJ/x1h5rPdvTT7aDAvAI8J5GcgGQ9Et5DusGLSoUR1InMDsinsubvk72nlIt95Pt9H0YeLBA/xO0a3FfCfxHsr2tP2lVHGVnNjwC/HlE/GUTcaZJmgAgaRJwKdmRQOEYEfEHETEjIrqAf5fn1NNALpMkdeS5TM5zOWH6oVoM4HlgUj4QIZtfrBWjXJzjrid7g6+owXFeAX5D0sn5lMNvABWnZcrFkDQuf2Ki7D2F91D5vY1C4y2yXarvkB3pACwCHqsnRgGF40j6CDAPuD7y+eV64yjzruP3yd4Q/EE9MSJiXUT8o4joysfzzyKi9IyQerZpSkkuVwPHpzvq+fs+SjaGIRs7P2wkl9x1ZHviPx/UXjTOPwBnSDo/f3wFtccyZHVqPvCrwF8X6H+iRnb3h/IGfBB4ON46THyO7B/1XbJD/UNkr8zzGojzQeAI2aH68ducBuLMA14kOyx7EVjcyDaVLP8QBaZlKsS5A9ia57KVGtMpVf6+V+TbspXsjIVTGozTBbxGfqZCg9t0OfA/yZ4E24EVDcQ4Pn+8HXi20v+53vFGNnW1EegD/pJs+qDeGB/PHx8l2/O/v8FcjpK9Z7Q5v3263jhkO3h/m//fXyIrVqfXm8ugv+nBJv6+3y7J5ctke8f1xjiTbE97K9mU3uxGawvZkfD8JsfMNbz1HH0a+JWCz417yd5Da6iW+hOqZmZtJn8j9QXgusjP8KtXu07LmJmNSZIuJDsy3NBoYQdfW8bMLEneczczS5CLu5lZglzczcwS5OJuZpYgF3czswS5uJuZJej/A1Jod5eRy1e9AAAAAElFTkSuQmCC\n", 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\n", 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mean9.6363642.79727347.818182112.6857144.9090912.0845458.093945209.80000013.1718189.6363642.79727347.818182112.6857144.9090912.0845458.093945209.80000013.171818-2.115868
std1.0723810.13861820.12336954.1264800.7958220.1881220.64099789.8049385.1280331.0723810.13861820.12336954.1264800.7958220.1881220.64099789.8049385.1280330.818380
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25%9.0000002.66000028.00000058.9332004.0000001.9000007.576200113.6000008.9000009.0000002.66000028.00000058.9332004.0000001.9000007.576200113.6000008.900000-2.580000
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75%11.0000002.90000077.000000192.2170006.0000002.2000008.958800272.50000019.30000011.0000002.90000077.000000192.2170006.0000002.2000008.958800272.50000019.300000-1.460000
max11.0000003.01000079.000000196.9665506.0000002.4000009.225500381.80000022.50000011.0000003.01000079.000000196.9665506.0000002.4000009.225500381.80000022.500000-0.870000
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28.000000 \n", + "50% 7.881000 213.900000 12.000000 10.000000 2.810000 45.000000 \n", + "75% 8.958800 272.500000 19.300000 11.000000 2.900000 77.000000 \n", + "max 9.225500 381.800000 22.500000 11.000000 3.010000 79.000000 \n", + "\n", + " x13 x14 x15 x16 x17 x18 \\\n", + "count 121.000000 121.000000 121.000000 121.000000 121.000000 121.000000 \n", + "mean 112.685714 4.909091 2.084545 8.093945 209.800000 13.171818 \n", + "std 54.126480 0.795822 0.188122 0.640997 89.804938 5.128033 \n", + "min 55.845000 4.000000 1.830000 7.360500 64.600000 7.870000 \n", + "25% 58.933200 4.000000 1.900000 7.576200 113.600000 8.900000 \n", + "50% 102.905500 5.000000 2.200000 7.881000 213.900000 12.000000 \n", + "75% 192.217000 6.000000 2.200000 8.958800 272.500000 19.300000 \n", + "max 196.966550 6.000000 2.400000 9.225500 381.800000 22.500000 \n", + "\n", + " y \n", + "count 121.000000 \n", + "mean -2.115868 \n", + "std 0.818380 \n", + "min -4.450000 \n", + "25% -2.580000 \n", + "50% -2.020000 \n", + "75% -1.460000 \n", + "max -0.870000 " + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "b2e93793", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "plt.figure(figsize = (15,1))\n", + "sns.heatmap(df.corr()['y'].to_frame().T, annot = True)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "2bd53e93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CkfZ+9CiezZqWWd738kvJ+cfR3u1JSWR8+gWR336OmZtL7srV5K5cXanxnqpWdBSHDhaexB0+dIRa0VFlJimGDh3En38tIz298ITukkvasWb1bxyJi2fiw9PYsuXc9Ugoi2fNEPKKtPe8+GT82zQ6zRqnsFhoNv95vOpHkfi/+WSu3XnmdSpR9KUdOLp0U8GFUJMJQzm6dDOx983BI9CXnvOnkbhkU7m6l/vVDaf3bzPIz8hmy8wvSF6x/ZzH3fzhq2l6/5UkLtnE5umfYc+znjexl+ZsY/dvEAWmSZdPH8YrNICD3//Dztd/OmfxtXluHGsnziNzbzw12jSk9cybWXrVdKfX929QE4AePzyB4WZh63Nfk/DXhnMWnzPavTSeyD6tSd9xiI1PfgS4fr1XZuxQ+e29/qieJPy5HoCARtHUHtKJvwc/iWm10XrmzdQd3pUDXy4p1zbbvTQe02Yn7udVbHvx23MWX2kq43fmbNrMRdOuZ9O0TwuG6p1qz7xf6fTBA1y6/nXc/X1YOf4VMM1zVvcAdYZ14tAP/5R7vTOJqhlJ3OHC86YjcQlE1YwskaSoF1OHwcMGMfCyPhxLPsaUh59h74kbegMv68PDkycQFh7KDSPvOOcxipRHRXtSvAvULWuhYRi3ArcCvPH804y74doK7uY8ZJT2YfF8pPc1t5P7zdzSM3Kmncypt4OPH753PIEluj72uH2VEWlJRinBl5FK9WrfCv+hA4kfcx8APt06Yj+WQt7WnXi1a1WJQZah1NhLD967fSv8hw4i/uZ7AUfstuOO2L3bX1SJQZahtNidZAn0J6DvJezsOQZbWia1X3uEoCG9SP3+r3MY4ClKidcs0VBK//8w3N3wbxnDjkfnkRa7i0ZP30S9u4eyd1bh/CERw7qQ8O2ycxx0aUr5HmW0Gc+2rfEdfClJtzmGBxkB/nh360ziVddiT8+gxvQn8RnQl+yFlTjM5hRGaf8Pp+lVMPLqIbz7XuGcG7FrN9LggovJzMxi0MDefP3lPJo271opsRZXWtzlWN1uZ8uA+3AL9KPh3IfxblyXnO3Oj9c+12oP68S+jwuPt4ieLYka0JZGt18GgJuXB761Qp2+G5uTkMKCdveQdzyD4ItiuOS9+/m9x8Rid4PP1ubpn5OTmILF0502z43jwrsGs+2Fb8+L2MtytrFb3N0I7diYvwZOxpadS9cvHyNl/V6OLt185pXPwM3Xi9D2F9LxncLhhRZPj3Jtw3C34B8TxeIrn8YnOoQe303h956TCsb7V4U1974NFoPWM26i9pBO7P/sb5eu98qOvbLbe1iXZtS7tieLhzwFQHi3FgRfFEOvBdMc8Xp7lrtHzao7Xicn/jjuft50fPde6o7oVqEL7dLiO1Vl/c5UtM2cnAckZcNewjqXfkMiotdFpGzaz5Lh0/GrH0nXLx4h6d9Hzkndn1R7aCdW3/VmhdY9HWfPCTw9PcnNzeWyPtcw6PK+PPfqNIZf5hj2ueDnP1jw8x907NSOhx65i2uvvOWcxynirDKTFIZh/FDWIiD0dBs1TXMOMAcgP2nPf6rHkHk8CUtI4TACo0Y49pRjxcq41b8Qn1scY9MM/yDcW1xMjt2Gdd3ywkLZmVh3bMC9RXvyqihJYU08intURGGcEeHYjpbswu7RKIbQyQ+QePcj2FMdP9JerVrg06MTtbpejOHpieHnS+jTD5P8+Mwqid2WcBT3qMJ6d48MKzv2J+4n4c5Hsac6Jlzzat0c3x6d8C0Se9j0SSQ9NqtKYrfGJ+FRs7Drp3tUGPlODtnw69KavIMJ2I45/h/SFy7Hp23TSk1S5B5Jxiu68CfAKzqUvPjjJcp41wojFcddJa+aoeTGHwfTJDcumbQTXZoTf/yXencPLVjPcLMQcdnFrOr3cKXFf5Lt6FHcIou09/DwUodsuDdsQPAjD5J8/8OYaSfae/t2WOPisaekApCzaAmeLVtUepLi9ttuZOzY0QCsXr2O2nWiC5bVql2TuDKGwIWE1KBDhzYMHzGu4LOiPSrmL/iTV1+ZQWhoDZKTj5e2iXMm70gynkXau2dUKPnxx06zRulsaZmk/7OJoJ5tzjpJ0eDmftQf3QuA5aNnk5OQ4tR6njX8qdG6If/e/GKRTw1WjH2JjN1HipVt99J4glrUIychheWjZ5e5TXuelbw8x/9Nyoa9ZO5PwL9hVJljyCsSe05iSsG+9n/2d8HFwvkQe9nOLvbsuGMk/bO1YCLOhD/WEXxRzDm5WDYsFvLTMkvOXWAx6P2rozfFkV9j2Tr7q9PGdyx2F6bVRtaBo6TvPoJ/gyiOr9tT5jplOat6t5sc+v4fGt1x+YkJBau23l0p9vK29/IIbFqHts/fwvJRswqHMxhw4IvFbJ5RfFLw6EHtafKAY+6h2AfeOe3+c078rbZm5nDw2+XUaNPQqSTFqfXuGRJQMr5T91VJvzMVPVZDO1xIzf5tiezTGjcvD9z9fWj/2h2svuuNgjL1R/Zg+6uOy5/MfQlkHjhKQKPoc1L3AEHN6mK4uZGy4ezbCMCNY0cy6gbH8Or1azcRXSuqYFnN6EgSSulZeSQunl9+cHSGn//T7zz/2rQSZVb8s4Z6MXWoERLM8WMp5yRWkfI6XU+KbsB1wKm/PgZwcaVFdJ6z7duOJaIWRlgU5vEkPDr0IHtu8Qv1jEduKHjtffODWNevwLpuOYZ/EKbNCtmZ4OGJe9M25C34ospiz9u8Hfc6tXCPjsKamITfgJ4kPTqjWBm3qAjCn3uS5MkzsR4onIMg5bV3SXntXQC82rUi8IYRVZagAMjdvB33usVjP/roM6fEHk7E80+Q9Pis4rG/Oo+UVx0zcnu3v4jAG0ZUWYICIHvDDjzr18KjdiT5CckEXd6dw/c969S61rij+LRujOHthZmTi1/nVmRvLH1M87mSvnY3vg1q4l03nNwjx4gY2pktt79SrEzSwtXUHjOQhG+XEdiuEbb0LPJOnLDkxiXj27AmWbuPENKtJZk7CuciqNG9JZk748g9Uv6L1vLK37oN99q1cKsZhe1oEj59e3P8yaeLlXGLjCDkmakcf+oZbAcL47QlJOLZvJljTorcXLzatyV/W+V3a3/zrf/x5lv/A+DSQX244/ab+Pzz7+l4cVvSUtPKHOpx1fDL+fmX38nNLexOGxkZTkKCY4hUh/atsVgslZ6gAMhcvxPvmJp41okgP/4YIUO6sucu556K4R4SiGm1YUvLxPD2JLBrK+Lf+ObMK57Bnvd+Y8975R/BWGtwR+J/X1tsVvnERRtoOHZAwazuQS3qkbppf8GcFWfiGRrgOOm3m/jWjcA/JorM/aX/v1Y0du+I4IILiOiB7Unbdui8ib0sZxt7wqINXHjn5bj5eGLPsxLWqSm75vxyTmKzZmSTeeAotQZ35PCPKxzxNatL6pYDTk+6eGTBamoP68yBzxfjGRKAf4Oap63b06lIvfvVjyRznyMJGtW/Lekn5tOo6np3pdjL296d5VMrlEvm3cfqu94gY09h9/2jSzbT6f0H2DVnPrlJaXgE++Hu70Pc/NXEzT/zcEPDzYJHkB95x9Ix3N2o2a8NiYtLPhGqNEXrvaz4TlVZvzMVPVY3z/i8IMkQ1rkpjW6/rFiCAiDrcDIR3VqQvGI7XmGBBDR0HGfWzJyzqvuTag/rzKHvlp+5oJP+9+5n/O9dxxOuevfrzs23XMv338ynbfuLSE/LKDHUA2DhL3/SpXtHPv/4Wzp16cCeXY55furH1GHfXsdcaC0uaoqnh4cSFKeyOzeBqpwbRlndgw3DmA/MNk2zxC1ZwzAWm6bZ3ZkdVFZPioeemMmqtRtISUkjNCSYO8Zez/DBA87pPrIfua1C67m36IDXyNsxDAt5yxaS98unePRwZJDz/y7+iKyCJEXsEiy1YvAZ8xBYLGBYyF/9N3k/fVyhGI6vqdjkLt5dLnY8gtRiIeOHBaS9+wn+wy8HIOPrnwiZfD++fbphO3HH1rTZiL/uzmLbOJmkqOgjSE17xYY/+HS9mJCHbnfE/v1CUud+QsBVjtjTv/qJ0Cn349u3K7YjjpMI02rjyOjisZ9MUlT0EaRZ6Z5nLlQK/57tiXz8VgyLhZSvfiPpjc+pce0gAI5/Oh+3sBo0+O4lLP6+YNqxZ+awe+Bt2DOyCZ8wmsDLumHabORs3sORR1/GzCv/rPTxGX5Olw3t04ZG027EcLMQ9+lf7H/pW6Jv6AdA3AeOE5kLnxlLaO9W2LLz2DrhDdLXO+74+TevR5MXbsPi6U72/kS2TngD64nJ4Jq+fAepa3YWbMNZTRoePXOhUnh16kjQhDvBzULWT/PJ+N/H+A51PCEj67sfCXr4QXx6dscWX9jek8Y6fhcCxt6Ed99eYLORv2MnKc88B/nlf1xh3dUVnwfilZenM6B/T7Kysxk37n7WxDrGqP/4/QfcettDHDlxnP7x25fMfvZ1Fv66qGDdO26/ifHjb8BqtZGTncODDz3FP/+Wb16NfyNKTuTqjKDe7ajz5BiwuJH8+e8cefUrwq9z/H4f/Wgh7uHBNPvlOdz8fTHtJvasbDb1uhuvOhHEvDgB3CwYhsGxn5Zx5KWKJXIPWEufO8crPIjeC5/GPcAH025iy8zht+6OLtydP55I7P1zCu7edvvmcXa8+kOxuQEs3h60mnoDIR0agWGQdfBoicfcgeMOXKvpN+IZGkh+Whapm/az7NqZRF/WgWYTR2C32sBmZ8uzXxP/W6xT38nZ2Lt+9RheoQFgGKRu2s/aie9iy8o9L2JvOHYAF955OV4RweQmpZHwxzpiH3jnrGMHx8R4je8ZgmmaJPyxjk3TPi2xvjM6vHkX4Z2b4hkSQO7RVLY8+zVHl22mzcwxeEUGY/Fw49B3/7DthZLzAbSYfC11hnXGO6oGOfHH2ffJIrY+9zXgeDJFZO+LMG12tr/0PYe+P/tx7U7Ve2IqPb6f4ngagmGQuvkA6ybNw5qRXa31Xt2xV7S9l9Y+LB5uAOz94A/aPn8L0ZddTNYhx98102bnrwGO86laQy6h8d1XYFgs2PNtrHvkvVInWx2w6mU8/H2weLqTn5rJ0pEzyTqURPdvp2DxcMNws5C4eBMbnviwxOSPZ3K6+KridwbO/lg9maT45/rniLmhD+Coe+/IYNq9fBvekcFgGOx49QcOfu0Yfno2dZ9+YmLxASteYtno2aVOmnvSPXkVn2vm6dmP0bNPV3Kys7n/rslsWOfoXfLB52/w0IQnSIg/SmBgAK/OmUWt2lFkZmbx8P3T2Lp5O3fcM4bhI6/Amm8lJyeHp6c8X+5HkB46tqni45fPA3n7Y/9fjg7wrNfWJf/fykxSFBQwjGamaW455bOepmkucmYH5/Nwj4omKVxBRZMUrqCiSQpXUNEkhSsoT5LC1VQ0SeEKziZJUd0qmqRwBWUlKURERKrL2SQpqpuSFOcnV01SWJwo84VhGJMMBx/DMF4FnjnjWiIiIiIiIiIi5eDM0z06ArOA5UAA8DHQpTKDEhEREREREXEJpT2VUSqNMz0p8oFswAfwBvaapv6XREREREREROTcciZJsQpHkqID0BW41jCMsp+TJSIiIiIiIiJSAc4M9xhrmubJKd/jgSGGYVxfiTGJiIiIiIiIyH/QGZMURRIURT/7sHLCEREREREREXEhds12UJWcGe4hIiIiIiIiIlLplKQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJTjzdA8RERERERGR/yTT1MSZVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpCx2zUlRldSTQkRERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZegOSlEREREREREymJqToqqpJ4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuATNSSEiIiIiIiJSFrutuiP4T6n0JEX2I7dV9i4qjc8zb1V3CBXmseaX6g6h4qzW6o6gwozg8OoOocJidqyv7hAqzLYxp7pDqDCPtedvrrjZpOjqDqHCDkxPqe4QREREign3DKruEERcgoZ7iIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hLO38HQIiIiIiIiIpXNtFd3BP8p6kkhIiIiIiIiIi5BSQoRERERERERcQlKUoiIiIiIiIiIS9CcFCIiIiIiIiJlsWtOiqqknhQiIiIiIiIi4hKUpBARERERERERl6AkhYiIiIiIiIi4BM1JISIiIiIiIlIWU3NSVCX1pBARERERERERl6AkhYiIiIiIiIi4BCUpRERERERERMQlaE4KERERERERkbLYNSdFVVJPChERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hI0caaIiIiIiIhIGUzTVt0h/KeoJ4WIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuwWXnpHBr3h7vkbdjWCzkLVlA3oLPSy1nqX8hfo+8TPbbM7DGLgF3D/wmPg/uHuDmhnXNEnJ/+LCKoz+9x2e8wOJlKwmpEcx3H71V3eEUs2zLfmZ/sxi73WRYp2aM6de+2PL3/4jll9XbAbDZ7eyNP85fM8ZxPCObie8vKCh3OCmV2y+9hOt6ta662LceYPZ3Sx2xX9KUMX3aFo/9z7X8EruzMPaEFP6aehNBft4MmvYRfl4eWCwG7hYLn9x/VZXFDbBs4y5mfbIQu2lnWLc2jL2sa7Hl6Vk5PPrOt8Qnp2G127lxQCeGdmtN/LFUHpv7HcmpmRiGwVU92jK6X8eqjX1PArN/34jdDsNa1WVMpwtLlFm1P4ln/9iI1W5Sw8eTd0c7vl9aTj5T569l19F0DAOevLQNrWqFVFnsbk3a4jV0HFjcyP/3V/L//LrUcpY6F+Az4VlyPngW24blAHh0vwL3S/qDaWI/sp/cz14Ga36VxQ7w/PNPMmBAL7Kysrn11gdZt25TiTK///4l/v5+AEREhLF69TquvvpWRo4cyv333wZAZmYW99zzGBs3bq2SuJftT+bZJTuwmyZDm0Uzpl39EmVWHzrOs0t3YLWbBHt78O6V7QB48o8tLN6XRIiPJ1+NuuScx1bnyi5ceNdgAKyZOaybNI/ULQdKlGv7wi0Et2qAYRhk7DnC6nvewpaV6/R+ag3uSNMHhxPQKJq/Bk0mZf1eAHzrhNFv8XOk744D4NiaXaybNO//fexhnZvS6f0HyDyQCEDcL6vY9sK3VRY7QGDTOrR5dhweAT6Ydjt/DZyMPffMx/SAVS9jzcjGtNkxbXb+GvB4seX+F0TT7qXxBLesz5aZX7DzzZ+djhfAs4Y/HedOoEbrhuz/fDHrH30fAHc/b7p/P6WgnE/NUA5+vZQNU5w/56k5oB3NJo3AtDti3zD5Q5JXbi9RLrxrc1pOGQUWA2tmLmsmvEXmvgSn91MZ9e5sew/v0oyWT4zG8HQnZcNeYu+bg2mzn3XsFW3vPtEhtH/1drzCg8E02fvhn+yeu6BYmcpqMwDdvnkc74hgbDl5ACwbOZPcpLRybR+gRusG9Px5KivGv0LcTytLLG8wpj8X3DIQ/5gofmo2nrxj6eXa/rluM87Uu7PHQ1lOV++GhxutZ9xMWOemYDfZPPNz4n5e5fS2nTHx6Xvp0qcTOdk5PDFhOts27ihR5qmXH6Ndp9ZkpGUCMGXCdHZs3knPAV25fdItmHYTm83Gs5NfZt3KDec0vvOe6fzvhpw910xSGBZ8Rt1F5osPYx5Pwu+xV7Gu/wf7kQMlynkPH4d185rCz6z5ZD4/EXJzwM0Nv4kvYt20CtuebVX7HU5j6KX9GDX8Ch6d9lx1h1KMzW7nmS8X8dadQ4kM9mf0c5/To0UDGtYsvGi8qU9bbjpx8f/3xr18tGgdQX7eBPl588Wkawu203/ye/Ru1aBqY/9mCW/dNpjIID9Gv/g1PZrXp2FUkdh7t+Gm3m0csW/ex0d/ryfIz7tg+Tt3XEENf58qi7lo7DM+ms/bD1xHZEggo6bOpWfrxjSsFV5Q5vM/V9EgOpxXJ1zLsbRMhjz2Opd1aombxcKD1/Snab2aZGbnMnLqO1zSrEGxdSs3dpNnft3AWyM7Exngw+j3/6ZHoygahgUWlEnLyeeZX9fz+tWdqBnky7HMwguK2b9vpHODSJ4bdjH5NjvZ+VU4KZFhwevK8WS/NQUzNRmf+57HunklZsLBEuU8L78J2/a1hR8FheDRbTBZs++E/Dy8bpiIe5tuWFf9WWXhDxjQi4YNY2jRogcXX9yGV155mu7dh5Yo17fviILXn376Fj/++CsA+/YdpH//q0lJSaN//568/vozpa5/rtnsJjP/3s6bQ9oQ6e/F6C9W0SMmjIYh/gVl0nPzmfH3Nl6/og01A7w5lpVXsGxwk5pc07I2k3/fUinxZR5IZPGwaeSnZhLZuxVtnhvHokunlCi3YcpHWDOyAWj55HU0HNOfHa/96PR+0rYd5N8xL9Lm2bEllmXsT+DPvo/+p2IHSFqxjX+uP/3fxcqK3XCz0OH1O1l91xukbjmAZw1/7PlWp7e7ZPj0Mi/C8lMy2PD4/6g5sH2py8/ElpvPlllfEdikNoFN6hR8bs3MKVbXvRZO5/Av5bvoSVyyiSMLHedQgU3r0HHOBH7r9mCJcq1njeHfm54nfWccDW7qS5P7hrJmwttO76cy6t2p9m4YtHvldpaOmE7GnniaTryKuld3Z/+ni846dqhYezetdjY++TEpG/fh7udNr1+nk7h4I+k7DheUqaw2c9KqO18vdtFfbhaD5o9fS8Kisi9ik1duJ/63WLp9M7lCuzjXbcaZenf2eCjL6eq9yb1DyU1K5bcuD4Bh4FnDv4ytVEzXPp2o26A2QzpdQ8u2zXl01oPccOmtpZZ9aerr/P7TomKfrViyhkULlwLQqGlDZs2ZxpXdRp3TGEXKwyWTFG4xjbEfjcNMigcgf9XfuLfuTN4pSQrP3kPIX7MEt5jGxTeQm3NiQ+7g5gZmVUTtvPatW3L4iPN3IKrKpv0J1AkPpnZYEAAD2l7Ioo17iiUpipofu4OB7RqV+HzF9kPUDgsiOiSwlLUqx6YDidQJC6J2qGOfA9pcwKJN+4olKYqaH7uTgW1Kxl4dNu05TJ2IGtSOqAHAwI7NWbRue7FEg2EYZOXkYZomWbl5BPn54GaxEB4cQHhwAAB+Pl40qBlGYkpalSUpNh05Tp0aftQOdtypH9CsFot2xhdLUszfcojejaOpGeQLQIifFwAZufnEHkxm2mWOxJGHmwUPt6obgWap2wh70hHMY45j0bp2Ce4tOpJ/SpLCo9vl2DYsx1LnlPZisYCHJ9isGB5emKnHqip0AC6/vB+ffOLo+bFy5VqCggKJioogPj6x1PL+/n706NGZW291nHD9+29hcnflylhq1apZ+UEDmxLSqBPkQ+0gR0JwQKNIFu1JKpakmL8jgT4NI6gZ4Egihvh6FixrV6sGcWnZlRbfsdU7C1+v2YVPGb9/Jy+UAdx8CuNz8/Wi1fQbCWpaB8PNja3PfV1w0ltU+s64cxj1iXjP49idVVmxR/S8iNQtBwruxOcdzzhnMecmpZGblEZU3zYlltUZ3oWG4wZi8XDjeOxu1j48D+zFT1psWbkkr9yOX0xkmfvwi4nCKyyQ5H/Ld0OmaC8Ud19vTLOMEybTxP1EEt89wJec+BSgeuvdmfbuGeKPPS+fjD2O88nEvzfS+J4h7P90UbW195zEFHISUwBHoil952F8omoUu1iuijZzNhqOHUDczyup0brsm1Gpm/aX+nl1tRln6v10x8PZ1nu9kT0LEx6mWe6eJWfSY0BXfvrC0TNkY+xmAgIDCIsIJSkx2an1s7MKf1t9TvdbIFJFTpukMAwjEAg3TXP3KZ9fZJpmpfUBMoLDsB87WvDePH4Ut5gmp5QJxb1NF7Ken1gySWFY8Jv8OpbwaPIW/YBtr+v0onBliSmZRAUXXihEBvuzcX98qWWz8/JZvnU/j1zVo8SyhbE7GFRK8qIyJaZmEnXiQhkgMtiPjftLv1jLzstn+baDPHJlt4LPDANuf/snDAOGd2rOVZ2aVXrMJyWmpBMVElTwPqJGIBv3HC5WZmTvDtzz6mf0vf9FMnNymX3bcCwWo1iZw0kpbDsQT8sGtaskboDE9ByiAgp7n0QG+LAx7nixMvuPZWC12xn78VKy8qyMat+AwS3rciglixq+nkz5eS07EtNoFhXExL4t8fGsmtypERSKmZJU8N5MScJSr/EpZUJwb3kJ2W88jtc1hW3aTD1G/qLv8Jv8LuTnYd2+FtuOdVUS90nR0VEcOlR4Enf4cDzR0ZFlJimuuGIAixYtIz295AndTTeNZOHCRZUVajGJmTlEBhT2YIr092JTQvGuxvtTsrDa7Yz7Zg1Z+TaubVWHwU2qJolSVP1RPUn4c32Zy9u9NJ7IPq1J33GIjU9+BECTCUM5unQzsffNwSPQl57zp5G4ZFO5hiT41Q2n928zyM/IZsvML0he4Xx34/M59pB2jej9xzPkJBxn41Mfk779cKnlKiN2/wZRYJp0+fRhvEIDOPj9P+x8/SfnAjdNun72MKYJez/8g30fOdejKqBRNLWHdOLvwU9iWm20nnkzdYd35cCXS5zbbxF1hnXi0A//lHs9gOhB7Wn+6Ei8wgJZft2zpZaJfeAdOn88EXtOHvkZ2Sy69Amgmuu9iLLae15yOhYPN4JbxZCyfi+1Lu+IT3TIOYkdzv5Y9a0TRnCL+hyL3X3mwpy7NtPupfGYNjtxP69i24slh1WdjndUDaIv7cCS4U/TrnXpd+pPxxXazOnqvbTj4Wzr3SPQcZOm2cQRhHduSsb+RNY/8l6FhtmUJaJmOPFxhX//E44kElEzvNQkxZ0Pj+eW+29m5ZI1vDL9TfLzHENleg3qzt2P3kZIWA3uuc75HiQilaHMqwHDMK4GXgISDcPwAG4yTfNkP8L3gbZlrIphGLcCtwK81LUpNzcp50WTUdqHxTN63tfcTu43c0sfH2TayZx6O/j44XvHE1ii62OP21e+GP6DzFK6nBhGqf8ZLN60l9YxNYsNlwDIt9r4e9Ne7hncuVJiLEtpCd8yQmfx5v20jokqFvv7dw8jIsiPY+lZ3PbWT8REBNOuYXQlRVucM7Ev37ybJnWimPvQDRxMPM745z+i7YX18Pdx9ErIysnjgde/5KFrBxR8VhVKbTOnvLfZTbbGpzJnZGdyrDZu+HAJF9UKwWa3sy0+lYf7taRldAizftvIvH93cmf3plUTfGkN5JT/DK8ht5D70/9K/s74+OHWoiOZT98C2Zl43zgJ93Y9sa5ZVHnxnqK0Y/N0dz6uvnoI77//WYnPu3fvxI03XkOfPsPPaXxnw2Y32ZqYzttD25JjtXHjV6u5KDKIejV8qyyGsC7NqHdtTxYPearMMmvufRssBq1n3ETtIZ3Y/9nfRPRsSdSAtjS6/TIA3Lw88K0V6vTd2JyEFBa0u4e84xkEXxTDJe/dz+89JhbrQfD/MfaUDftY0P4ebFm5RPZpTaf3HuDXzvdXWewWdzdCOzbmr4GTsWXn0vXLx0hZv5ejSzefcd2/Bz9JTkIKXmGBdPn8EdJ3xTnVoyG8WwuCL4qh14Jpjpi9PSt80VJ7aCdW3/VmhdaNm7+auPmrCb2kCc0mjWDp1TNKlLng1kEsHz2b42t30+iOy7noqeuIfeCdaq33k87U3leOf42Lnroei5cHiYs2YFodv+fVfay6+XrRce59bJjyodPrnIs2s+qO18mJP467nzcd372XuiO6lSvJcdG0G9g07dMSvQicVd1t5kz1XtrxcLb1bri74VsrlORV29n45EdcMP5SWj4xmtV3V+yYLXUfTp4TvDr9LZISk/Hw9GDyc5O4+a7rmPPCewD8NX8xf81fTNtLWnHHpFu47ep7z1l8/y/YNSdFVTrdLctHgXamaR4xDONi4EPDMB41TfMbykgjnGSa5hxgDkDaLf3L/StmHk/CElKkq3uNcOwpxbtSu9W/EJ9bHOMADf8g3FtcTI7dhnXd8sJC2ZlYd2zAvUV78pSkOKPIYH/iUwrvsiakZBAe6Fdq2QWxOxnYruQEiUu37KdJ7XBCA6vuYgIcPSfiUzIL3iekZJYd+9pdDGxzQbHPIoIcZUMCfOnVMoZNBxKrLEkRWSOA+GOpBe8Tj6cRcWIIx0nfL13HmEu7YBgGdSNDqBUWzN4jSbRsUIt8q437X/+CSy9pQd92VXSBfzL2AB/i0wv/yCekZxMe4H1KGW+CfSLw8XTHx9OddnVC2Z6YStvaoUQEeNPyxF2tfk2imffvTqqKmZKEERxW8N4IDsNMK/47Y6lzAd7XO+4mGH6BuDVtR67dBm7ujmEimY6TFOvGf3Cr36TSkxTjx9/AzTePBGDNmg3Url3YRmvViuLIkdJ7UYSEBNO+fSuuuab4Xa8WLZrw5puzGDLkRo4dS6m0uIuK8PMmIT2n4H1CRi7hfsUTaxH+XgR7h+Lj4YaPhxtto4PZkZxeaUmKBjf3o/7oXgAsHz0bz5AA2j5/C8tHzTpzV2K7yaHv/6HRHZez/7O/AYMVY18iY/eRYsXavTSeoBb1yElIYfno2WVvLs9KXp5jnykb9pK5PwH/hlFljh///xR7TkIKAAl/rMOYeTOeIQFld4k+x7Fnxx0j6Z+tBftL+GMdwRfFOHXhczLu3KQ0jsxfTUibhs4NuzDgwBeL2Tyj+MTg0YPa0+SBKwFHD4YzzR0Q1KwuhpsbKRucm2OgrHpP/ncbfvUjStS7Z2gAQc3rcXyt467zoe//ocunkwq+RFXWe0Xa+7E1O1k8dCoAET1a4t/wZK+sqm3vRRnublzy7n0c/GYZceWZR+QctJmceEdvR2tmDge/XU6NNg3PmKQoWu8egb5c/PbdAHiFBBDZpzWm1c6RBaud/hLVdayWp96LHg9nW+95x9KxZuUQ94ujjg7/+C/1R/U8Y7xncvXNV3Ll6CsA2LxuK1HREQXLImtGcDQ+qcQ6J3tW5Ofl8/1nP3PD7deWKBP773pq169FcEgQKUXOT0Wq0umSFG6maR4BME1zpWEYvYCfDMOoTSXP8mDbtx1LRC2MsCjM40l4dOhB9tyZxcpkPHJDwWvvmx/Eun4F1nXLMfyDMG1WyM4ED0/cm7Yhb8EXlRnu/xvN60Zy4GgKh5NTiQjyZ2HsDmbcOKBEufTsXNbsOsyM6/uXWLYgdkepyYvK1rxOxInY04gI8mPh2l3MuL5viXLp2bms2R3HjNF9Cj7Lzs3Hbpr4eXuSnZvPPzsOMv6Up5pUauwxtTiQcIxDR48TWSOQBSs288z4YcXKRIUEsWLLXtpeWI/k1Az2xSdTO7wGpmny5Hs/0qBmODcM6FRlMRfEXjOYA8cyOZySSUSADwu3HGbGFe2KlenZqCYzf9uA1W4n32ZnY9xxruvQkDB/b6ICfdiXnE790ABW7DtKg9CAMvZ07tkP7sQSHo0REomZmox7m27kflh80r6s6bcUvPYaOQHrllXYNq3AUvdCx9AQD0/Iz8OtUSvsBys/wfL22x/w9tsfADBwYG9uu+1GvvjiBy6+uA1paellDvW48srLmD//D3JzC7vT1qkTzWefvc3Ysfexa9dZTKBWTs0jAziQmsXhtGwi/LxYuDOBZ/o3L1amZ0w4sxZvP9FmTDYlpHFd67qVFtOe935jz3u/AeBTK5RL5t3H6rveKBjHXhq/+pEFTzeI6t+W9F2Ou4CJizbQcOyAglndg1rUI3XTfsfdfyd4hgY4LrbsJr51I/CPiSKzjKFr/59i9wovHPJWo01DDMMoNUFRWbEnLNrAhXdejpuPJ/Y8K2GdmrJrzi9nXM/N1wvDMLBm5uDm60VEj5Zse+Ebp/Z5dMlmOr3/ALvmzCc3KQ2PYD/c/X0K7uQ6q/awzhz6bvmZC55QtN796heOnQ9uWR+Lh3uJes9PycQjwBf/BlFk7IknontL0ndUT71XpL17hQWSm5SGxdOdC+8azPaXvjsnsZe3vRfV9sVbSd95mF1vn7mNFXW2bcZws+AR5EfesXQMdzdq9mtD4uKST4U6VdF6L6rdy+M58tvaciQoqu9YhTPXe9Hfl6LHw7k4Vo/8upbwzk05umwL4d1akLaj9OFs5fHFe9/wxXuO35uufTsxcsxwFnz3Oy3bNicjPaPUoR5F56noNbA7u7ftAaBO/Voc3OeIqUnLC/Hw8FCCQqqVUVb3YMMwlgPXF52PwjCMAOA7oKtpmk71Ka9ITwoA9xYd8Bp5O4ZhIW/ZQvJ++RSPHo6uYfl/F38UU0GSInYJllox+Ix5yDGpnWEhf/Xf5P30cUVCwOeZynk86ENPzGTV2g2kpKQRGhLMHWOvZ/jgksmAs2FdU74/fCct2byPZ79Zgt1uZ8glzbhlQAe+XLoRgBFdWwLw/YqtLN+6n1k3DSy2bnZePgOnvM9PT9xAwNkMObA6P6N6sdi37OfZ75dht5sMubgJt/Rrx5fLHZn1EZ0dF0Hfr9zG8m0HmXVDv4L1DiWncf88x2RDVrudQW0bcUu/diV34AQjuGITVi7ZsJPZny7EbjcZ2rU1twzuxhd/Of7wXd2rPYnH05k873uSUjIwMRlzaRcu73QRsTsOcPPM92lUOwLLia5+dw/vTbeLyj8niLmj7PHrp419dwLP/r4Ru2ky5KK63NK5MV+udVz0jmgTA8D7K3byw4YDGIbBsFb1uK5DQwC2JaQydf5a8m0mtYJ9mXpZGwK9PcvcV1lsGys274xb03Z4DRkHFgv5K38n//cvce/kaNfWf4o/mqwgSXHiEaSeA67FvXU3TLsN++E95H7+KtjK33bD36xYvQO8+OI0+vfvQVZWNuPHP0hsrONY/fbb97njjokFPSsWLvyM5557k99++7tg3TfemMXQoYM4cOAQAFarja5dB5dr/8mzL6tQ3Ev2JfHckh3YTRjSrCbj2sfw5SZHHCNaOIYH/i92P99vjcNiGAxrFs3oE0mKhxduYs3h46Tk5BPi48ltHRswrFn5ez0tmJ5S6udtn7+F6MsuJuuQY16koo+U7PzxRGLvn0NOYio9vp+Ce4APGAapmw+wbtI8rBnZWLw9aDX1BkI6NALDIOvg0VKfWBE9qD2tpt+IZ2gg+WlZpG7az7JrZxJ9WQeaTRyB3WoDm50tz35N/G+xTn2n8zn2BmP60+DGvtitNuw5eWx44qOCiRGrInZwTIzX+J4hmKZJwh/rHF3az8C3bgSXvHcf4OiGfvCbZWx/+XtibnAkwvd+8Ade4UH0Xvg07gE+jsf7ZebwW3fHsIBaQy6h8d1XYFgs2PNtrHvkPY7H7iqxnwGrXsbD3weLpzv5qZksHTmzYMK/ASteYtno2WTsKv8EjxfeNZi6I7phz7diy8ln09RPCh65WFDvCSlED2pP04lXYdpN8lMzWXPvHLIOJFZbvYOT7T0hhRZTRhHVtw2GxWDP/35n9zuO3/bqau+hFzemxw9PkLrlAOaJ7uObn/kC31qhQOW2maxDSXT/dgoWDzcMNwuJizex4YkPKzx042SS4uQjSIvWe8OxA7jwzsvxiggmNymNhD/WEfvAO9XWZpyp99MdD2d7rPrUDqPDq7fjEeRHbnIaa+59m+zDJZMI0yh9wlFnPPzM/XTudQk52Tk8ee8Mtqx3nBu9+vFzTL1/JkcTknj7q1eoERqMYRhs37ST6ROfJTsrm5vuGs3lIwZhzbeSm5PLi1NfL/cjSNfGLzttT/vzXU7sD/8vZxP1bnuFS/6/nS5J0QrIAjxM09xS5HMPYKRpmk49iLuiSQpXUFlJiqpQ0SSFS6hgksIVVDRJ4QoqmqRwBRVNUriCs0lSVLeKJilcQVlJChERkepyNkmK6vb/Pkmx5rvz9pr2dLzbDXXJ/7cyn/dnmuZ60zR3Al8YhjHJcPABXgDuqLIIRUREREREROQ/ocwkRREdgTrAcmAVEAd0qcygREREREREROS/x5kkRT6QDfgA3sBe0yztuZ8iIiIiIiIiIhXnTJJiFY4kRQegK3CtYRhfVWpUIiIiIiIiIvKfc7pHkJ401jTNk8/XiQeGGIZxfSXGJCIiIiIiIuIa7LbqjuA/5Yw9KYokKIp+5tSTPUREREREREREnOXMcA8RERERERERkUqnJIWIiIiIiIiIuARn5qQQERERERER+W/Swy2rlHpSiIiIiIiIiIhLUJJCRERERERERFyCkhQiIiIiIiIi4hI0J4WIiIiIiIhIWeyak6IqqSeFiIiIiIiIiLgEJSlERERERERExCUoSSEiIiIiIiIiLkFzUoiIiIiIiIiUxdScFFVJPSlERERERERExCUoSSEiIiIiIiIiLkFJChERERERERFxCUpSiIiIiIiIiIhL0MSZIiIiIiIiImWxa+LMqqSeFCIiIiIiIiLiEpSkEBERERERERGXUOnDPY6vOX+7xnis+aW6Q6gw93aXVncI/022/OqOoMKsOZnVHUKFuTdpU90hVFjtDw9XdwgV5j78zuoOoeKmT6/uCERERIq52Du6ukMQcQmak0JERERERESkLJqTokppuIeIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuQXNSiIiIiIiIiJTBNG3VHcJ/inpSiIiIiIiIiEgJhmEMNAxju2EYuwzDeLiU5UGGYfxoGMZ6wzA2G4Zx89nuU0kKERERERERESnGMAw34HVgENAMuNYwjGanFLsT2GKaZiugJ/C8YRieZ7NfJSlERERERERE5FQXA7tM09xjmmYe8Bkw5JQyJhBgGIYB+APHAOvZ7FRzUoiIiIiIiIiUxW6v7giqSy3gYJH3h4COp5R5DfgBiAMCgGtM0zyrClNPChEREREREZH/GMMwbjUMY3WRf7eeWqSU1cxT3g8A1gHRQGvgNcMwAs8mLvWkEBEREREREfmPMU1zDjDnNEUOAXWKvK+No8dEUTcDM03TNIFdhmHsBZoAKysal3pSiIiIiIiIiMipVgGNDMOIOTEZ5kgcQzuKOgD0ATAMIxJoDOw5m52qJ4WIiIiIiIiIFGOaptUwjLuAhYAbMM80zc2GYdx2YvlbwDTgfcMwNuIYHjLJNM2ks9mvkhQiIiIiIiIiZTm7eSDPa6Zp/gL8cspnbxV5HQf0P5f71HAPEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXILmpBAREREREREpi/2/OydFdVBPChERERERERFxCUpSiIiIiIiIiIhLUJJCRERERERERFyC5qQQERERERERKYupOSmqknpSiIiIiIiIiIhLcNmeFN6dOxDy4B3gZiHj2/mkvf9ZseV+g3oTeNNIAOxZ2Ryb8TL5O/cUFrBYqPnRG1iPJnF0wuNVGTrLtuxn9jeLsdtNhnVqxph+7Ystf/+PWH5ZvR0Am93O3vjj/DVjHMczspn4/oKCcoeTUrn90ku4rlfrqgz/tB6f8QKLl60kpEYw3330VnWHUy6uHPvSFWuY+cpcbHYbwy/rz7jrriq2PDU9g8kzX+Hg4SN4eXoy7eF7aNSgHgAffPE9X//0K4Zh0KhBPZ5+eAJeXp5VFvuyzXuZ/eVf2E2TYZ1bMGZAx2LL3/9tFb+s2gqAzWZnb/wx/pp9O0F+Pnz4xxq+Xb4RA2hUK4ynrh+Il0fV/Swt27CTWZ8swG63M6x7W8Ze3q3Y8vSsHB59+xvij6Vitdm5cVBnhnZrQ3xyKo+98y3JqRkYhsFVPdsxuv8lVRb3SZNnPESPvl3Izsph0j1PsmXDthJlZr36JB06tSUjPQOASXc/ydZNO/AP8Of5N6dRs1YU7u5uvPvGh3z96Y9VEvfSlbHMem0eNpudKy/ry7hRVxZbnpqewZTZr3EwLgEvTw+mTryTRjH12HvgMA9Nfb6g3KEjCdx580iuv2rwOYutzpVduPAux/asmTmsmzSP1C0HSi3b7OGrqTW4I6bNzt7//c7udxc6vZ9agzvS9MHhBDSK5q9Bk0lZvxcA3zph9Fv8HOm74wA4tmYX6ybNc2qbNQe0o9mkEZh2O6bNzobJH5K8cvt5EftJNVo3oOfPU1kx/hXiflpZYnl41+a0nDIKLAbWzFzWTHiLzH0JZx07QGDTOrR5dhweAT6Ydjt/DZyMPTffuQ1bDHovnE52/DH+uf65Yos8gvxo9+Kt+NWPxJabT+x9b5O27ZDTMXvW8Kfj3AnUaN2Q/Z8vZv2j7wPg7udN9++nFJTzqRnKwa+XsmHKh05v29n2Ht6lGS2fGI3h6U7Khr3E3jcH0+b8XcXKqPfz9Vj1iQ6h/au34xUeDKbJ3g//ZPfcBcXK+F8QTbuXxhPcsj5bZn7Bzjd/djpeKLvNAHT75nG8I4Kx5eQBsGzkTHKT0pzedljnpnR6/wEyDyQCEPfLKra98G2Jcg3G9OeCWwbiHxPFT83Gk3csvVzf4Vy3GWfq3dnvVpbT1bvh4UbrGTcT1rkp2E02z/ycuJ9XOb1tZ1zzxM206NWWvOxc3n/wdQ5u3luizJiX7qFey4bYrFb2rd/FR4/OwW614Rvoxw3P3kF43Ujyc/P5YOIbxO04eE7jEykP10xSWCyETLqbxDsmYU04Ss2PXif77+Xk7y3842M9HE/CuPuxp2fg3bkDoY/fR/yNdxcsD7h2GPl7D2D4+1Zp6Da7nWe+XMRbdw4lMtif0c99To8WDWhYM6SgzE192nJTn7YA/L1xLx8tWkeQnzdBft58Menagu30n/wevVs1qNL4z2Topf0YNfwKHp323JkLuxhXjd1ms/H0i2/zzgtTiQoP5ZpbH6BX14tpWL9uQZl3PvySJhfE8Mr0R9mz/xDTX3yLd196moSjyXz81Y98/+HreHt58cATs5j/5xKGDupTNbHb7Tzz+R+8dc9VRAYHMHrWx/S46AIa1gwtKHNTvw7c1K8DAH9v2M1Hf64hyM+HhJR0Pl0UyzeTb8Lb04OH5v7IgtXbGNKpRZXFPuPDX3j7oeuJDAlk1FPv0LNNYxrWiigo8/kfK2lQK5xX7xvFsbRMhjzyKpd1aombm4UHR/anaf1oMrNzGfnk21zSvEGxdStbj75dqNegDn0vHkrrdi2YOvsRrhp4Y6llZz/1Mgt+/KPYZ9eNHcGu7XsYf919hIQGs/Cfb/jhq/nk51srNW6bzcb0l99hzrNPEBUeysjbJtKrcwca1q9TUGbux1/T5IIYXp72MHsOHGLGS+8w94WniKlbi6/mvlCwnT4jbqFP145l7apCMg8ksnjYNPJTM4ns3Yo2z41j0aVTSpSrN7IHPrVC+a3rg2CaeIUFlms/adsO8u+YF2nz7NgSyzL2J/Bn30fLHXvikk0cWbgGcJzEd5wzgd+6PXhexA6AxaD549eSsGhDmUVazxrDvzc9T/rOOBrc1Jcm9w1lzYS3nd5FWbEbbhY6vH4nq+96g9QtB/Cs4Y+9HMfCBbcMIn3nYdwDfEosazxhCCmb9/PvmBfxvyCa1s/cxNIRM5zeti03ny2zviKwSW0CmxQeJ9bMnGJ13WvhdA7/Ur6LHqfau2HQ7pXbWTpiOhl74mk68SrqXt2d/Z8ucno/lVHv5+uxalrtbHzyY1I27sPdz5tev04ncfFG0nccLiiTn5LBhsf/R82B7U+zpbKV1WZOWnXn68Uu+ssracW2Esm4UyWv3E78b7F0+2ZyhfZxrtuMM/UOzn23spyu3pvcO5TcpFR+6/IAGAaeNfwrtI+ytOjZhoiYmkzueTcxbRoxevotzBxasm2u/G4J8+59BYCxr0yg68g+LP7oVwbdeSWHtuzlrfHPEtkwmlFTx/Hi6KnnNEaR8nDJJIVni8ZYD8VhPXwEgMyFi/Dp2aVYkiJ3w5aC13kbt+IWGV7w3i0iDJ9uHUl79xMCTrkjXdk27U+gTngwtcOCABjQ9kIWbdxTLElR1PzYHQxs16jE5yu2H6J2WBDRIeX7Y1rZ2rduyeEjzt+1ciWuGvvGrTupW6smdaKjABjUpxt/Ll1RLEmxe99BbjnRlhvUq83h+ESSjh0HwGqzk5ubh7ubO9k5uYSHlt7WKsOmffEn2nswAAPaNWbR+l3FkhRFzV+9jYHtmxS8t9ns5OZbcXdzIyfPSnjQuf2jfTqb9hymTmQItSMc9TWwYwsWrd1eLNFgGAZZObmYpklWbh5Bfj64WSyEBwcQHhwAgJ+PFw2iw0k8nl6lSYq+A3vw3eeOu2vr1mwiIMif8MgwjiYkObW+aYKfvx8Avn6+pKakYbXaKi3ekzZu20Xd6CLtvXdX/lq2sliSYve+g4wbPRyABnVrczghkaRjKYSFBBeUWRG7kTrRkURHnds6P7Z6Z+HrNbvwKeO3O+bGvqy6/TVHRULBnUg3Xy9aTb+RoKZ1MNzc2Prc1wWJg6LSd8ad07gBbFm5Ba/dfb0xT8R2PsQO0HDsAOJ+XkmN1qdJzpsm7v6ORIB7gC858SnA2cce0fMiUrccKLgTn3c8w+m4fWqGENW3Ndtf+o4Lbru0xPLAC2ux/ZUfAMjYFYdvnXC8wgLJTUqjzvAuNBw3EIuHG8djd7P24XlgL/7/ZsvKJXnldvxiIsuMwS8mCq+wQJL/Ldmb6nScae+eIf7Y8/LJ2BMPQOLfG2l8zxD2f7qoWuv9fD1WcxJTyElMARyJpvSdh/GJqlHsYjk3KY3cpDSi+rYpsf65ajOVLXXT/lI/r64240y9n87Z1nu9kT0Lk8amWe6eJWfSqn8H/v3mbwD2rt2JT4AfgeHBpB1NKVZu06K1Ba/3rd9FjSjH+VrNRrWZ/4aj10jC7jhCa4cTEBZEelLqOY3zvGbXnBRVySXnpHAPD8Man1jw3pZ4FLeI0i96APyHDiJ7WWG30BoP3kHKy+9g2ks/QatMiSmZRAUXXmhFBvuTmFr6D2h2Xj7Lt+6nb6sLSixbGLuDQaUkL+T/n8SkZKIiwgreR4aHkXg0uViZxhfU5/fF/wCwccsOjiQkknA0mcjwUG4aOZS+I8bSa9iNBPj50eXikic1lRZ7SgZRNQIKY68RcPr2vmUffds42nVkcAA39O3AwMffod8jb+Hv40nnZvWrImwAEo+nEVUkCRhRI5CE48W7vI7sczF74pLoe+/zXPX4G0wcNQiLpfjP5uGjx9m2/wgtG9aqkrhPiqwZwZG4wqRbfFwikVHhpZa979E7+HHRZzw67X48PT0A+Gju5zS8MIZlmxby0+LPefqx58q8qD2XHO298Pc8MjyUhKRjxco0blif3xf/CziSeEfij5JwyjEx/8+lDOpTfHjOuVZ/VE8S/lxf6jK/ehHUHnIJvRY+TedPJuIX40i6NJkwlKNLN/PXwMksGf40LaaMws3Xq1z79asbTu/fZtDt28mEdmxcrnWjB7Wn35Ln6PzRQ8TeN+e8id07qgbRl3Zgz/9+P2252AfeofPHExkU+yp1R3Rl+6s/nJPY/RtEgWnS5dOH6f3rdBrdebnT61407Xo2Tfu0zOMndfMBoi919Car0aYhvrXD8IkOJaBRNLWHdOLvwU/yZ99HMe126g7v6vR+i6ozrBOHfvinQuueVFZ7z0tOx+LhRnCrGABqXd4Rn2hHQqA6692Z2ME12/tJvnXCCG5Rn2Oxu50qf67aTLuXxtP79xk0uW9YudcFCGnXiN5/PEPnTyYS0Lh8f/tcoc2crt5L+25nW+8egY5e3c0mjqD3r9O5+J0J5e7RcybBkSEciyv8O5kSn0yNqLJvWlnc3bhkWHc2/+1IWhzauo+2Ax09E+u3uoCQWuEFCQyR6nDanhSGYUQBmKYZbxhGONAN2G6a5uZKjcowSn5WxrmzV/tW+A8dSPyY+wDw6dYR+7EU8rbuxKtdq0oMsnRmKYEapX0fYPGmvbSOqUmQn3exz/OtNv7etJd7BneulBjFtZR2Yntqmxk3+ipmvvIOw8dMoFGDejRp1AA3NzdS0zP4a+kKFn7+DgH+fjwwZRY//voXg/v3qprYS2vvZZRdvGE3rRtEE+TnuAualpXDog27+HnqOAJ8vXjonR/5ecUWLuvYrBIjLlTa9YRxSvTLN+2iSd0o5k66kYOJxxj/7Ie0bVwXfx/HMZuVk8sDr33BQ6MGFnxWVUr7XSmtLT339GscTUjC09ODaS88zq1338Rrz79Dt96d2LppO9cPG0/dmNq8/+UbrP5nLRkZmZUad6n1fspXGTvqSma+9i5Xjbv/RHuPwd2tMDmUn5/PouWrmHDLdZUWZ1iXZtS7tieLhzxV6nI3Lw9sufn8NeBxoi/tQLsXb2Xx0KlE9GxJ1IC2NLr9soJyvrVCnb4bm5OQwoJ295B3PIPgi2K45L37+b3HRKwZ2U6tHzd/NXHzVxN6SROaTRrB0qtLDitwxdgvmnYDm6Z9WuLO5KkuuHUQy0fP5vja3TS643Iueuo6Yh9456xjt7i7EdqxMX8NnIwtO5euXz5Gyvq9HF16+tOdqH5tyE1KI2XDXsdY81Jsf/UHWj19A71/n0Ha1oOkbtqHabUR3q0FwRfF0GvBNEfM3p7lmhugqNpDO7H6rjcrtC6cub2vHP8aFz11PRYvDxIXbcC0Ou4oVle9lyd2V2zv4OhR0HHufWyY8qHT65yLNrPqjtfJiT+Ou583Hd+9l7ojunHgyyVOr5+yYR8L2t+DLSuXyD6t6fTeA/za+X6n16/uNnO6ei/ru51tvRvubvjWCiV51XY2PvkRF4y/lJZPjGb13RU/Zkvso9RzgrLLj5o2jp0rt7JrlaP31YI3v+OaJ27m8V+e5fC2AxzcvBe7rfJ7V4qUpcwkhWEY44GHHS+NWcBNwGbgGcMwZpum+e5p1r0VuBVgRp0mjAorX5bVmngU9yJdeN0iwrGdchcNwKNRDKGTHyDx7kewpzp+LLxatcCnRydqdb0Yw9MTw8+X0KcfJvnxmeWKoaIig/2JTym8k5yQkkF4oF+pZRfE7mRguwtLfL50y36a1A4nNLBq59OQ6hEZHkZ8YmEX/YSjSYSHFc9++/v58vQjEwDHheiAa26hds1Ilq2MpVbNSEKCHcOL+nTvxLpN26osSREZHED88cIuiwnH08scsrFgzXYGdigc6vHvtv3UCg0iJMDRzvu0bsS6PXFVlqSIDAkk/ljhSUbi8TQiivQKAfh+yTrGXNYVwzCoGxlKrfBg9h5JomWD2uRbbdz/2hdc2qklfdtXTcyjx4zgmusdd742rN1CzejCLqVR0REkljLU4+Twj7y8fL7+5AfG3Xk9AMOvvYK3X3kPgAN7D3HoQBwNGtVnw9rKzUFHhocSn1j4e55wNJmI0FLa+yTHHEOmaTLw2tuoVbPwuy5ZsZamFzYoNvzjbDS4uR/1RzuOmeWjZ+MZEkDb529h+ahZZXYlzo47RtzPjh58cb+sot1L408sMVgx9iUydh8pVr7dS+MJalGPnIQUlo+eXWYs9jwreXmOfaZs2Evm/gT8G0aVOX781NhzElIASP53G371I/AMCSjRrdgVY/cI9OXitx3/514hAUT2aY1ptXNkweqC8p6hAQQ1r8fxtY67n4e+/4cun046J7Fnxx0j6Z+tBXWV8Mc6gi+KOeOFT2iHC6nZvy2RfVrj5uWBu78P7V+7g9V3vVFQxpqRzZp7C+fNGLDqZTIPHCW0UxMOfLGYzTM+L7bN6EHtafKAYzLZ2AfeOePcAUHN6mK4uZGywbk5BirS3o+t2cnioY7x6RE9WuLfsOaJJVVb7+fzsVqU4e7GJe/ex8FvlhFXnnlEDM66zeTEnxgqmpnDwW+XU6NNwzMmKcr6nUn4Yx3GzJtL/Z053ZeojmMVzlzvRZMWRb/b2dZ73rF0rFk5xP3i+D07/OO/1B/V84zxnknP6wfQ9dq+gGPoRkh0KCf7hgRHhZKScKzU9S6fcBUBoYG8Nb5w7o2cjGz+91Dh79b0pa+TdDCxtNVFqsTpelLcBTQHfID9wAUnelTUAP4CykxSmKY5B5gDsL9t33L3H87bvB33OrVwj47CmpiE34CeJD1a/G6QW1QE4c89SfLkmVgPFI4nS3ntXVJec4Tm1a4VgTeMqLIEBUDzupEcOJrC4eRUIoL8WRi7gxk3DihRLj07lzW7DjPj+v4lli2I3VFq8kL+f2rRpBEHDsVxKC6eyPBQ5v+xhNlTik92l5aegY+3Fx4eHnz906+0a9Ucfz9fakaGs2HLdrJzcvH28mTFmvU0b1J1w4Sa14viQGIKh5NSiQj2Z+Ga7cy4ueSY7PTsXNbsPMSMmwqX1awRyIZ9R8jOy8fbw50V2w/QvG7VjZ9tHhPNgYRkDh09TmSNABas2MQztw0vViYqNIgVW/bQtnE9klMz2HckmdrhNTBNkyfnfU+DmmHcMLDqejx9PO9LPp73JQA9+3XlurFX89O3C2ndrgXpaRmlzkdRdJ6Kfpf2ZMc2xylM3KF4OnW7mNX/riM0PISYC+pxcL9zY3PPRosmF7D/8BEOHUkgMiyE+X8uZdbj9xUrk5aRiY+Xp6O9//w77S5qhr9fYdJ2/p9LGNS7Yt3iS7Pnvd/Y895vAPjUCuWSefex+q43CsbglyZuwWrCuzZn/6d/E9a5KRl7HCfciYs20HDsgIJZ3YNa1CN10/5iF6mn4xka4LjYspv41o3APyaKzP1lnygWjd2vfuHxE9yyPhYP91IvHFwx9qLavTyeI7+tLZagAMhPycQjwBf/BlFk7IknontL0nfEnZPYExZt4MI7L8fNxxN7npWwTk3ZNeeXM663ecbnBRcuYZ2b0uj2y4olKMCRgLFm52Lm26g/uhdJ/27DmpHN0SWb6fT+A+yaM5/cpDQ8gv1w9/cp6A3jrNrDOnPou+VOl69Iez85h4bF050L7xrM9pe+A6q+3s/nY7Woti/eSvrOw+x6+8xtrKizbTOGmwWPID/yjqVjuLtRs18bEhdvOuN6RevdKzyo4PMabRpiGEa55leormMVzlzvXuFB5B51zMFQ9Ludi2P1yK9rCe/clKPLthDerQVpTs6FcTqLPlzIog8dT6pp0astvW4cyKoflhHTphHZ6Vkl5qMA6HJNb5p1b82Lo6YW633pE+hLXnYetnwrXUf2YeeKreQ42cPnP8PUnBRVyShrDKVhGLGmabY98Xq9aZqtiixba5qmUwPfK5KkAPDucrHjEaQWCxk/LCDt3U/wH+4Yd5bx9U+ETL4f3z7dsJ2YCNG02Yi/7s5i2ziZpKjoI0gjnhlaofWWbN7Hs98swW63M+SSZtwyoANfLt0IwIiuLQH4fsVWlm/dz6ybBhZbNzsvn4FT3uenJ24gwKd8YyOLcm9X8kLxXHjoiZmsWruBlJQ0QkOCuWPs9QwfXDIJ44qqJHabk4+rO8Xif1Yz69W52Ox2hl3al/E3XM3n388H4Johg1i3aRuPTn8RNzcLDerVYerD9xAU4Oix8Nq8T1j45xLc3Nxo0qgBUyfeXTDvQHlYN/1dodiXbNrDs18tcrT3Ti24ZdAlfLnYMTZ4RHfHz8b3/2xi+ZZ9zBpbfOzoGz8t49c123GzWGhSJ4InRvfHswKPIDV8A85cqLTY1+9g9icLsNtNhnZrwy1XdOeLPx13V67u3YHE42lMnvsdSakZmKbJmMu6cnnnVsTu2M/NM96jUe0ILCe6WN59VR+6tSp/crHlFRV/2swTsybRvVdnsrNzePieJ9m03vGo13c+fZnH7p1GYkISH3zzFiGhNTAM2LppB1MemkFWZjYRkWHMevUpwiPDMAx4+5X3+eGr+eXa/+b1H1Qo7sX/rmH26/Mc7X1QH2697iq++MFxonX1FQNYt3k7jz3zChaLhYb1a/PUQ3cWtPfsnFz6XXML8z9+kwD/0nupOeOnttNL/bzt87cQfdnFZB06CoBps/PXAMffkM4fTyT2/jnkJKTgEehLhzfuxKdWKNbMXNZNfJfULQeweHvQauoNhHRoBIZB1sGjpc4UHz2oPa2m34hnaCD5aVmkbtrPsmtnEn1ZB5pNHIHdagObnS3Pfk38b7FOfacL7xpM3RHdsOdbseXks2nqJwWPIHX12Is6maQ4+QjSorFHD2pP04lXYdpN8lMzWXPvHLIOJJ517OCYGK/xPUMwTZOEP9Y5hp+Uw8kkxT/XP0fMDY4nLO394A9C2jWi/au3Y9rspO04ROz975Cf6hhWVWvIJTS++woMiwV7vo11j7zH8dhdJbY9YNXLePj7YPF0Jz81k6UjZxZM+DdgxUssGz2bjF3ln+DR2fbeYsooovq2wbAY7Pnf7+x+x/Hoxuqs9/P1WA29uDE9fniC1C0HME9MxLf5mS/wreUY/7/3gz/wCg+i98KncQ/wwbSb2DJz+K27YyjJ2bSZrENJdP92ChYPNww3C4mLN7HhiQ/POMyqqAZj+tPgxr7YrTbsOXlseOKjgklMi9Z7w7EDuPDOy/GKCCY3KY2EP9YR+8A71dZmnKn30323sz1WfWqH0eHV2/EI8iM3OY01975N9uGSvcQXelfsPBLg2qljad6jNXnZefzvodfZv3EPAHe99wgfTnqL1MTjvLHrM44dPkpOZg4Aaxes4OdXvqJB2wu56fm7MO12juw8xAcT3yQrrXzDP9/e92VZI37/X8he+FrVT3ZYBXwG3OWS/2+nS1KsBjqZpplvGEZt0zQPnfjcG1hRNGlxOhVNUriCiiYpXEFlJSnkDCqYpHAFFU1SuIKKJilcwdkkKapbRZMUrqCsJIWIiEh1OZskRXVTkuL85KpJitM93eNKAMMwmp1MUJwQApR88LqIiIiIiIiIyFkoM0lhmuYB0zTzgS8Mw5hkOPgAjwBTqyxCEREREREREflPcGbwd0dgFrAcCAA+BrpUZlAiIiIiIiIiLsGuiTOr0umGe5yUD2TjeMqHN7DXNDW9qYiIiIiIiIicW84kKVbhSFJ0ALoC1xqG8VWlRiUiIiIiIiIi/znODPcYa5rmyYcAxwNDDMO4vhJjEhEREREREZH/oDMmKYokKIp+9mHlhCMiIiIiIiLiQjQnRZVyZriHiIiIiIiIiEilU5JCRERERERERFyCkhQiIiIiIiIi4hKcmThTRERERERE5L/J1JwUVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpCx2zUlRldSTQkRERERERERcgpIUIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuARNnCkiIiIiIiJSFlMTZ1Yl9aQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJWhOChEREREREZGy2DUnRVWq9CSFaTcqexeVx2qt7gjkfHM+/4CdzxMCuSnfWh0MQ53xREREzhUL5/F1k8g5pDNMEREREREREXEJSlKIiIiIiIiIiEtQH2kRERERERGRspzPw6LPQ+pJISIiIiIiIiIuQUkKEREREREREXEJSlKIiIiIiIiIiEvQnBQiIiIiIiIiZbFrToqqpJ4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuAQlKURERERERETEJWjiTBEREREREZGyaOLMKqWeFCIiIiIiIiLiEpSkEBERERERERGXoCSFiIiIiIiIiLgEzUkhIiIiIiIiUhbTrO4I/lPUk0JEREREREREXIKSFCIiIiIiIiLiEpSkEBERERERERGXoDkpRERERERERMpit1d3BP8p6kkhIiIiIiIiIi5BSQoRERERERERcQlKUoiIiIiIiIiIS3DZOSl8OrcnZOIdYLGQ8e18Ut/7vNhyv0t7E3TTNQDYs7NJnv4K+Tv2FBawWKj5yevYEpNIvGdyVYbOsq0HmP3dUux2k2GXNGVMn7bFlr//51p+id0JgM1uZ29CCn9NvYkgP28GTfsIPy8PLBYDd4uFT+6/qkpjP5PHZ7zA4mUrCakRzHcfvVXd4ZSLK8e+dEUsM1+bi81mZ/hl/Rg3enix5anpGUye9SoH4+Lx8vRk2sS7aNSgHgAffPkDX//8GwYGjRrU4+lJd+Pl5VllsS/bvI/ZXy3CbrczrEsLxvS/uNjy939bzS+rtgEn2nv8Mf6adRvHM7KY+O4vBeUOJ6dy+2WduK538eOlUmPfsINZH/7iiL1nO8YO7lFseXpWDo+++SXxyalY7XZuvLQLQ7u3K1hus9u5dsqbRNQI5LUHrq+yuE+aPOMhevTtQnZWDpPueZItG7aVKDPr1Sfp0KktGekZAEy6+0m2btqBf4A/z785jZq1onB3d+PdNz7k609/rJK4Xbm917myCxfeNRgAa2YO6ybNI3XLgVLLNnv4amoN7ohps7P3f7+z+92FTu+n1uCONH1wOAGNovlr0GRS1u8FwLdOGP0WP0f67jgAjq3ZxbpJ85zaZs0B7Wg2aQSm3Y5ps7Nh8ockr9x+XsR+Uo3WDej581RWjH+FuJ9Wllge3rU5LaeMAouBNTOXNRPeInNfwlnHDhDYtA5tnh2HR4APpt3OXwMnY8/NP+M2B6x6GWtGNqbNUe9/DXi8Qt+tLJ41/Ok4dwI1Wjdk/+eLWf/o+wC4+3nT/fspBeV8aoZy8OulbJjyodPbdrbNNBjTnwtuGYh/TBQ/NRtP3rF0p/cBlVPv5/Oxeiqf6BDav3o7XuHBYJrs/fBPds9d4PT6zR6+mrojuuEZ7McPDccUfF73mu60nDKK7CPHANgz71f2fbKoQjGe6ozHapdmtHxiNIanOykb9hJ73xxMm/Nj+iujzZyqsuodoNYVjvgxIXXzflbd8Xq54yuvq5+4mea92pCXncsHD77Bwc17S5S5+aW7qdeyITarlX3rd/Pxo3OwW234Bvpx/bO3E1Y3EmtuPh9OfJO4HQcrPebziuakqFKumaSwWAh55G4SbpuENSGJ6I9fI+vvf8jfU/jHx3o4nvixD2BPz8CnSwfCJt/LkevvKVgeOGoY+XsPYPHzrdLQbXY7z3yzhLduG0xkkB+jX/yaHs3r0zAqpKDMTb3bcFPvNgD8vXkfH/29niA/74Ll79xxBTX8fao0bmcNvbQfo4ZfwaPTnqvuUMrNVWO32Ww8/fLbvPPcU0SFh3LNbQ/Rq8vFNKxfp6DMOx99RZMLYnjl6UfYs/8Q019+m3dfmEbC0WQ+/vonvv/fq3h7efHAk7OZ/+cShg7qUzWx2+0888WfvHX3lUQGBzB69if0aNmQhjVDC8rc1K89N/VrD8DfG3fz0Z9rCfLzJsjPmy8eva5gO/0ffYferS6okrhP7nPG/37k7Uk3ExkSyKgpb9GzbVMa1oooKPP57//SoFYErz5wPcfSMhky8SUu69wKD3fHT+fHC/+hQXQ4Gdm5VRb3ST36dqFegzr0vXgordu1YOrsR7hq4I2llp391Mss+PGPYp9dN3YEu7bvYfx19xESGszCf77hh6/mk59vrdS4Xb29Zx5IZPGwaeSnZhLZuxVtnhvHokunlChXb2QPfGqF8lvXB8E08QoLLNd+0rYd5N8xL9Lm2bEllmXsT+DPvo+WO/bEJZs4snAN4DiJ7zhnAr91e/C8iB0Ai0Hzx68lYdGGMou0njWGf296nvSdcTS4qS9N7hvKmglvO72LsmI33Cx0eP1OVt/1BqlbDuBZwx97OY6FJcOnn/7C3YnvVhZbbj5bZn1FYJPaBDYpPE6smTnF6rrXwukc/mVVubbtbJtJXrmd+N9i6fZNxW76VEa9n8/H6qlMq52NT35MysZ9uPt50+vX6SQu3kj6jsNOrX/k11j2zPuV/v+8UGLZoe//LUhsnTNnas+GQbtXbmfpiOlk7Imn6cSrqHt1d/Z/usjpXVTWsVpUZdW7X0wUje8ewt+DnyI/NbPcba4imvdsQ0RMFE/0vIeYNo24dvo4Zg99rES5ld8t5b17XwVgzCsT6DqyN4s/+o2Bdw7j0JZ9vD3+OSIbRjNy6lheHj2t0uMWKYtLJim8WjTGejAO6+F4ADIXLsK3Z2dSiyQpctdvKXy9YStukeEF790iwvDp1pHUuZ8QeH3xO3SVbdOBROqEBVE71PGDNKDNBSzatK9YkqKo+bE7GdimUVWGeFbat27J4SPO37VyJa4a+8ZtO6lbqyZ1oqMAGNS7K38uW1Hsom33/oPcMsrRlhvUq83h+ESSjqUAYLXZyM3Nw93NneycPMLDSm9rlWHTvnjqhAdTOywYgAHtGrNow+5iSYqi5q/ezsD2jUt8vmL7QWqHBxEdWvl/yE/atPsQdSJDqR3hqK+Bl7Rk0ZqtxZIUBgZZObmYpklWTi5Bfj64WRyj5BKOpbJk3XbGXdGDDxcsr7K4T+o7sAffff4zAOvWbCIgyJ/wyDCOJiQ5tb5pgp+/HwC+fr6kpqRhtdoqLd6TXL29H1u9s/D1ml341Cx9+zE39mXV7a85KhLITUoDwM3Xi1bTbySoaR0MNze2Pvd1wUVgUek7485p3AC2rMJkmbuvN+aJ2M6H2AEajh1A3M8rqdG6QdmFTBP3E0l89wBfcuJTgLOPPaLnRaRuOVBwJz7veMbZfZlTlPXd6gzvQsNxA7F4uHE8djdrH54H9uL/b7asXJJXbscvJrLM7fvFROEVFkjyvyV7U52Os20mddP+Uj+vzno/n4/VU+UkppCTmAI4kk/pOw/jE1UDe24+rZ+5Gc/QQGzZucQ+OJeMXSXjOR67q9JjLOpMx6pniD/2vHwy9jjO4xP/3kjje4aw/9NFLnWsVla9x1zXiz3v/Up+aiZQ2OYqU6v+7fn3m8UA7F27E98APwLDg0k7mlKs3OZFawte71u/i+Aox/laVKPaLHzjWwASdscRWjucgLAg0pNSKz12kdK45JwUbhFhWOOPFry3JiThFhFWZnn/YQPJXlp49yDkods5/tI7YFZ9t5zE1Eyigv0K3kcG+5F44kfqVNl5+SzfdpC+FxX+yBsG3P72T1z7wpd89c+WUteT/18Sjx4jKrywfUeGh5J49FixMo0b1uf3Jf8CsHHrDo7EHyXhaBKR4aHcdM1Q+l59C72G30yAvy9dOrSputhTMoiqEVAYe7A/iSmlnzBk5+WzfMs++rYumZRbuHo7g9o1qbQ4S5N4PI2okKCC9xEhgSQcL34iMbLfJeyJO0rfu2dx1aOvMfH6y7CcSFLM/ugX7hs5AIvFqNK4T4qsGcGRuMKkW3xcIpFR4aWWve/RO/hx0Wc8Ou1+PD09APho7uc0vDCGZZsW8tPiz3n6sefKvEA5l86n9l5/VE8S/lxf6jK/ehHUHnIJvRY+TedPJuIX40i6NJkwlKNLN/PXwMksGf40LaaMws3Xq1z79asbTu/fZtDt28mEdiyZ1Dud6EHt6bfkOTp/9BCx9805b2L3jqpB9KUd2PO/309bLvaBd+j88UQGxb5K3RFd2f7qD+ckdv8GUWCadPn0YXr/Op1Gd17u9LqYJl0/e5heC6dT/7reTn+3gEbR1B7Sib8HP8mffR/FtNupO7yr8/stos6wThz64Z8KretMmylLtdZ7EefjsVoW3zphBLeoz7HY3bR5bhzrHvsffw14jI1PfUzrmTeXe3u1LutAnz9n0nHuBHyizz6p68yxmpecjsXDjeBWMY4YLu9YsG9XaTOnOpf17t+gJv4Na9Ljhyfo+fNTRPa66JzEeDrBkSEcjyu8SXE8PpngMm6QAljc3eg4rBtb/l4HwOGt+2k9sCMA9Vo1JKRWODVOs75IZStXTwrDMGaYpnn2/drOvKOSn5Vx8uzdvhX+QwcRf/O9APh064jteAp5W3fi3b7yfxROVVqYpX0dgMWb99M6JqrYUI/37x5GRJAfx9KzuO2tn4iJCKZdw+hKilZcgUnJRnNqmxk3ajgzX53L8LH30qhBPZo0aoCbmxup6Rn8tWwlCz97mwB/Px54YjY//rqIwf17VlHsJRllNPjFG/fQukF0sfYOkG+18ffG3dwzpEslRFi20o/V4rEv37iTJnVrMveRMRxMPMb4me/RtnE91mzbR0igH81iarFq656SG6oCpdVzaUmG555+jaMJSXh6ejDthce59e6beO35d+jWuxNbN23n+mHjqRtTm/e/fIPV/6wlI6P0pOq5cr6097Auzah3bU8WD3mq1OVuXh7YcvP5a8DjRF/agXYv3srioVOJ6NmSqAFtaXT7ZQXlfGuFOn03NichhQXt7iHveAbBF8VwyXv383uPiVgzsp1aP27+auLmryb0kiY0mzSCpVfPOC9iv2jaDWya9mmJXgSnuuDWQSwfPZvja3fT6I7Lueip64h94J2zjt3i7kZox8b8NXAytuxcun75GCnr93J06eYzrvv34CfJSUjBKyyQLp8/QvquuGI9Gsr6buHdWhB8UQy9Fji6VLt5e1b4jmvtoZ1YfdebFVrXmTZTluqs95PO12O11Fh9veg49z42TPkQ024ntP2FdHyncCiz5USS2Vnxv8Zy6Nvl2POsxNzQxzEE46rpFY4PnD9WV45/jYueuh6LlweJizZgWh03Dl2hzZzqXNe74W7BPyaKxVc+jU90CD2+m8LvPSeRn5ZV4RjPvFPnr50Arp02jl0rt7LrxJxhC9/8jhFP3MSjv8wmbtsBDm7ei60cc4iInGtlJikMw3jl1I+A6w3D8AcwTfOekmsVrHsrcCvA9NpNGBVau1xB2RKO4l7kjqB7ZBi2o8klynk0iiH0iftJuPNR7KmOsaBerZvj26MTvl0vxvD0xPDzJWz6JJIem1WuGCoqMtiP+JTCk/yElEzCA/1KLbtg7S4Gtik+Bj8iyFE2JMCXXi1j2HQgUUmK/+ciw0OJP1qY/U44mlyiC7u/ny9PP+w45EzTZMDIW6ldM5Jlq9ZSq2YEIcGOHgF9undi3eZtVZakiAz2J/544TjshJQMwoPKaO9rtjOwfcneEks376NJnQhCyzhOKktkSCDxxwq7MSYeSyMiOKBYme8XxzJmcHcMw6BuZCi1wmuwNy6JdTsOsCh2G0vX7yA330pmdi6PvPklz9w+olJjHj1mBNdcPwyADWu3UDO6sPt3VHQEiaUM9Tg5/CMvL5+vP/mBcXc6Jvgcfu0VvP3KewAc2HuIQwfiaNCoPhvWVvxEzxmu2N4b3NyP+qN7AbB89Gw8QwJo+/wtLB81q8yuxNlxx4j72TFZXNwvq2j30vgTSwxWjH2JjN1HipVv99J4glrUIychheWjZ5cZiz3PSl6eY58pG/aSuT8B/4ZRxSaNO13sOQkpACT/uw2/+hF4hgSUmCvBFWP3CPTl4rfvBsArJIDIPq0xrXaOLFhdUN4zNICg5vU4vnY3AIe+/4cun046J7Fnxx0j6Z+tBXWV8Mc6gi+KcerC52Sd5yalcWT+akLaNCyWpKjRKqbU74YBB75YzOYZxScGjx7UniYPXAk4eo6UVX8nBTWri+HmRsqG05c7qSJtpmxVW+/n87F6Joa7G5e8ex8Hv1lG3C+rcPf3IT8ts+ScFxaD3r86Eg1Hfo1l6+yvytxm0TrZ+9GftHj82grFVt5jFeDYmp0sHjoVgIgeLfFvWPPkN622Y7U0lVHv2XHHOBa7C9NqI+vAUdJ3H8G/QRTH153bmxo9rh9Al2sdczLtX7+bGtFhgGPi2xpRoaQkHC91vcsmXIV/aCBzxhf2nMrJyObDhwoTnU8vfY3kg4nnNN7zXjX00P8vO11PiiuBRcCvOBIUACOBkgPHTmGa5hxgDsC+1v3K3X84d/N23OvWwj06CmtiEn4DenL00WeKlXGLCifi+SdIenwW1gOFE9ykvDqPlFcdsyt7t7+IwBtGVFmCAqB5nQgOHE3hcHIaEUF+LFy7ixnX9y1RLj07lzW745gxunDCt+zcfOymiZ+3J9m5+fyz4yDjT0w4KP9/tWjciAOHjnDoSAKRYSHM/3Mpsx+/v1iZtPQMfLy98PDw4Ouff6Ndq+b4+/lSMyKcDVt2kJ2Ti7eXJytiN9C8ccMqi715vSgOJB7ncFIqEcH+LFyznRk3DSpRLj07lzU7DzHjxpLLFqzZVmryorI1b1CLA/HJHEo8RmRIIAv+3cgzdxRPMkSFBrNi827aNq5PcmoG++KTqB1RgwnX9GfCNf0BWLV1D//7ZVmlJygAPp73JR/P+xKAnv26ct3Yq/np24W0bteC9LSMUuejKDpPRb9Le7Jjm+MCL+5QPJ26Xczqf9cRGh5CzAX1OLjfucnCzoYrtvc97/3Gnvd+A8CnViiXzLuP1Xe9UTCeujRxC1YT3rU5+z/9m7DOTcnY4zjhTly0gYZjBxRMVBfUoh6pm/az5l7nJnf0DA1wXFjYTXzrRuAfE0Xm/rJPFIvG7le/MGkV3LI+Fg/3Ui82XTH2otq9PJ4jv60tcdGTn5KJR4Av/g2iyNgTT0T3lqTviDsnsScs2sCFd16Om48n9jwrYZ2asmvOL2dcz83XC8MwsGbm4ObrRUSPlmx74ZtiZRZefG+p3y3gwlp0ev8Bds2ZT25SGh7Bfrj7+xT0bHBW7WGdOfSd8/PiVKTNlKWq6/18PlbPpO2Lt5K+8zC73nZ8f2tGNpkHjlJrcEcO/7jCEWOzuqRuOeD0ZJ3eEcEFcy5ED2hH+s6K/caX91gF8AoLJDcpDYunOxfeNZjtL30HVN+xWpbKqPcjC1ZTe1hnDny+GM+QAPwb1DyrtlGWvz9cyN8fOp5U06JXG3reOJDVPywjpk0jstOzSsxHAdDlmt407d6Kl0dNLdb70ifQl7zsXGz5NrqM7MPOFVvJOYteQSJnyyhrDLJhGAHANCACeMg0zcOGYewxTfM0M1qVVJEkBYBP14sJeeh2xyNIv19I6txPCLjKMe4s/aufCJ1yP759u2I74jjoTauNI6PvLLaNk0mKij6CNHL6ZRVab8mW/Tz7/TLsdpMhFzfhln7t+HK5I8M7onNzAL5fuY3l2w4y64Z+BesdSk7j/nmORx9Z7XYGtW3ELf3aldyBE9w7XlGh9c7koSdmsmrtBlJS0ggNCeaOsdczfPCAStnXuVYlsedX7CkPi/9dzazX5mGz2xg2qC/jrx/B59872sI1QwaybvM2Hp3xMm4WCw3q12HqxLsICvAH4LX3PmXhn0txc3OjSaMYpj50V8G8A+Vh3fx3hWJfsmkvz369yNHeOzXnloEd+XKJY2zwiG6tAPj+n80s37qPWWOKH1PZefkMfHwuPz01hgCf8o0FLsoILH2izjPGvm47sz92PIJ0aPd23DKkJ1/84bjjdnWfi0k8nsbkOV+TlJKBaZqMGdydy7u0LraNk0mKij6CtOVlMyu0HsATsybRvVdnsrNzePieJ9m0fisA73z6Mo/dO43EhCQ++OYtQkJrYBiwddMOpjw0g6zMbCIiw5j16lOER4ZhGPD2K+/zw1fzy7X/LRs+qlDcrtDef2xT+qzlbZ+/hejLLibrkGNepKKPlOz88URi759DTkIKHoG+dHjjTnxqhWLNzGXdxHdJ3XIAi7cHrabeQEiHRmAYZB08yj/Xl3yiUPSg9rSafiOeoYHkp2WRumk/y66dSfRlHWg2cQR2qw1sdrY8+zXxv8U69Z0uvGswdUd0w55vxZaTz6apnxQ8TtLVYy/q5IXPyccaFo09elB7mk68CtNukp+ayZp755B1IPGsYwfHJJaN7xmCaZok/LHO0aX9DHzrRnDJe/cBjm7oB79ZxvaXvyfmBscNiL0fFH+qzqnfrdaQS2h89xUYFgv2fBvrHnmv1Mn4Bqx6GQ9/Hyye7uSnZrJ05MyCJxAMWPESy0bPLnVivzNxts00HDuAC++8HK+IYHKT0kj4Yx2xD7xTbfUO5/exeqrQixvT44cnSN1yAPPEYw43P/MF6TsP02bmGLwig7F4uHHou3/Y9sK3JdZvMfla6gzrjHdUDXLij7Pvk0Vsfe5rmj96DTUHtMNutZGfksHaSe9VqJ2U5XTHaospo4jq2wbDYrDnf7+z+x3Hb3x1tplTVVa9A7R88joie1+EabOz/aXvOfS9c3PG/OZd8SdsjZw6lmY9WpGXnccHD73BgY2Onht3vvcwH016m9TE47y261OOHT5KTmYOAOsWrOCXV74mpm0jbnr+Lux2O0d2HuKjiW+RlVa+4Z9v7vuieibpqiLZHz1W+RN3VQOf66a75P9bmUmKggKG0RZ4HvgZuMs0zfrl2UFFkxSuoKJJCldQWUkKOYMKJilcQUWTFK6gokkKV3A2SYrqVtEkhSsoK0khIiJSXc4mSVHdlKQ4P7lqksKZiTNzgN7AHcBSAMMwepqmuagS4xIRERERERGpfnbNSVGVnElSfAF8ADwLvGcYxqtAe6BTZQYmIiIiIiIiIv8tFifKdATqAsuBlUAcULXPChQRERERERGR//ecSVLkA9mAD+AN7DVNPYNFRERERERERM4tZ5IUq3AkKToAXYFrDcMo++HAIiIiIiIiIv9fmOb/z38uypk5KcaapnnyAcjxwBDDMCr2rD0RERERERERkTKcsSdFkQRF0c8+rJxwREREREREROS/ypnhHiIiIiIiIiIilc6Z4R4iIiIiIiIi/012PTeiKqknhYiIiIiIiIi4BCUpRERERERERMQlKEkhIiIiIiIiIi5BSQoRERERERERcQmaOFNERERERESkLJo4s0qpJ4WIiIiIiIiIuAQlKURERERERETEJShJISIiIiIiIiIuQXNSiIiIiIiIiJTF1JwUVUk9KURERERERETEJShJISIiIiIiIiIuQUkKEREREREREXEJmpNCREREREREpAym3azuEP5T1JNCRERERERERFyCkhQiIiIiIiIi4hIqfbhHVrpnZe+i0hjB4dUdQsXZ8qs7goqzn8eP+PHwqu4IKszwr1HdIVSYGb+/ukOoMH837+oOocLM3MzqDkFEROT/DQ+M6g5BxCVoTgoRERERERGRspzPN1HPQxruISIiIiIiIiIuQUkKEREREREREXEJSlKIiIiIiIiIiEtQkkJEREREREREXIImzhQREREREREpi6mJM6uSelKIiIiIiIiIiEtQkkJEREREREREXIKSFCIiIiIiIiLiEjQnhYiIiIiIiEhZ7GZ1R/Cfop4UIiIiIiIiIuISlKQQEREREREREZegJIWIiIiIiIiIuATNSSEiIiIiIiJSFru9uiP4T1FPChERERERERFxCUpSiIiIiIiIiEgJhmEMNAxju2EYuwzDeLiMMj0Nw1hnGMZmwzD+Ptt9ariHiIiIiIiIiBRjGIYb8DrQDzgErDIM4wfTNLcUKRMMvAEMNE3zgGEYEWe7XyUpRERERERERMry352T4mJgl2maewAMw/gMGAJsKVJmFPCNaZoHAEzTTDzbnWq4h4iIiIiIiIicqhZwsMj7Qyc+K+pCoIZhGIsMw1hjGMYNZ7tT9aQQERERERER+Y8xDONW4NYiH80xTXNO0SKlrGae8t4daAf0AXyAfwzD+Nc0zR0VjUtJChEREREREZH/mBMJiTmnKXIIqFPkfW0grpQySaZpZgKZhmEsBloBFU5SaLiHiIiIiIiIiJxqFdDIMIwYwzA8gZHAD6eU+R7oZhiGu2EYvkBHYOvZ7FQ9KURERERERETKYp46wuG/wTRNq2EYdwELATdgnmmamw3DuO3E8rdM09xqGMYCYANgB+aaprnpbParJIWIiIiIiIiIlGCa5i/AL6d89tYp758Fnj1X+3TZJIVf93ZETb4Vw83C8c9/JfntL4st92xQm+hZ9+Ld/AKOvvAByXO/KVgWcvNQgq/uD5jkbt9P3MQXMfPyqyz2ZRt3MeuThdhNO8O6tWHsZV2LLU/PyuHRd74lPjkNq93OjQM6MbRba+KPpfLY3O9ITs3EMAyu6tGW0f06VlncAEtXrGHmK3Ox2W0Mv6w/4667qtjy1PQMJs98hYOHj+Dl6cm0h++hUYN6AHzwxfd8/dOvGIZBowb1ePrhCXh5eVZh7LHMfG0uNpud4Zf1Y9zo4SVjn/UqB+PiHbFPvKsw9i9/4Ouff8PgROyT7q7S2M/k8RkvsHjZSkJqBPPdR2+deYUqtGzDTmZ9/At2u8mwHm0Ze3n3YsvTs3J49O2viE9OxWqzc+OgLgzt3rZguc1u59on3iKiRiCv3X9d1ca+7SCzf/jHEfvFjRnTu3Wx5e8vWs8vsbtOxGmyNzGFv568jiBf74LYR738HRFBvrw6ZmCVxg4w8el76dKnEznZOTwxYTrbNpYc+vfUy4/RrlNrMtIyAZgyYTo7Nu+k54Cu3D7pFky7ic1m49nJL7Nu5YYqiXvpqvXMeutDbDY7Vw7qybhrrii2PDU9kykvzOHgkQS8PDyY+sCtNKpfOBzSZrMz8u7HiQitwevTHjqnsdW5sgsX3jUYAGtmDusmzSN1y4Eyy7eafiP1Rvbgh4ZjyrWfWoM70vTB4QQ0iuavQZNJWb8XAN86YfRb/Bzpux3DPY+t2cW6SfOc2mbNAe1oNmkEpt2OabOzYfKHJK/cXqJcgzH9ueCWgfjHRPFTs/HkHUuv9thPqtG6AT1/nsqK8a8Q99PKKosdILBpHdo8Ow6PAB9Mu52/Bk7Gnlv+cwef6BDav3o7XuHBYJrs/fBPds9d4PT6zR6+mrojuuEZ7FeiXdW6whE/JqRu3s+qO14vd3xFVXd7h4rXe3lib/bw1dQa3BHTZmfv/35n97sLzzr2irZ3Z9qHs8dyWTxr+NNx7gRqtG7I/s8Xs/7R9wuWdfvmcbwjgrHl5AGwbORMcpPSnN52WOemdHr/ATIPOJ4wGPfLKra98G2JcuFdm9NyyiiwGFgzc1kz4S0y9yU4vZ9z3WYsXh50/24KFk93LO5uHP5pBVuf/brUsmf6HSrL6erd8HCj9YybCevcFOwmm2d+TtzPq5zetjOGP3ETzXq1IS87l48ffJNDm/eWKHPDS3dTp2UDbFYbB9bv4rNH38FutQFwwSXNuHLKjbi5u5F5PJ1XrnnqnMYnUh6umaSwWKj55O3sv/Fx8uOTaPDti6T/8S95uwqffmJLTSd+6tsE9O9UbFX3yFBCbhzM7gG3Y+bmUeuVhwkc3IPUr3+vktBtdjszPprP2w9cR2RIIKOmzqXn/7F33+FRVG8bx7+zm957QgolAekQeofQmwiKIMVCE6wUUVB+CiiCgA1FVEBFBcQuKtJLpNfQO6EECOm9Z3fn/WMhJCSBTUhZXp/PdXlJds7s3DuZmew8c+ZMcG2C/Dzz2vy09QCBvp4snDCUhJR0+v9vEX3bNESr0fDqEz2oW60K6ZnZDHlnKa3rBRaYt1yz6/W8+/Filn70Dj6e7jwxdjKd27ckqHrVvDZLl/9CnZo1+HT2NC5eucbsj7/k6wXvEh0bz8pf/+bP5YuwsbZm8ox5rNu6gwG9u1Zc9k8Ws/SDt43Zn3uNzu1aEpTvxGbpil+N2d99w5j9k8V8/dEsY/bf1vDndwuN2WfOr9DsphjQpzvDBj7CtFkfVHaUAvQGA3O+X8PiKc8Yt/eZiwlpUocgP6+8Nj9t2UegrxcLJz1p3N5f/5S+bRthaWE8/KzcuIdAX0/SMrMrPPt7f+ziy7F98Ha2Z/inq+lUvxpB3q55bUaENGZESGMA/j11hRXbj+cVKAB+2HGCGl4upGfnVGh2gPZd21A10J/+bZ6gYdP6TJv3Kk/3GVtk2wXvLGLzmtACr+3bcYjQDTsBqFU3iHlLZvFYh2HlHRu93sDsRd+y5L038PFwY8jLb9G5dVOCqvnntfnqxz+pE1SVT2ZM4mJEJHMWfctX86blTV+xej01AnxJz8gs83zpETFsf3QWucnpeHdpTJMPxhDaZ3qRbV0a18DSya5Uy0k5c5W9oz6myfujC01LuxLN1m7Tipjr7mJ2nODGhkOA8Ut8qyUT2NTh1ULt4vefJWpTGB1+f6vkwSmf7ABoFOq/OZTo0OKLZeWVXdFqaLHoRQ6+9DnJpyKwcnXAkKsr1TJUnYHjM1eSdPwyFvY2dN44m5jtx0k9d92k+W9sDOPiNxvpseejAq/b1/Ch9sv9+bff2+Qmp2Pt4VSqfPlV9vZ+P+vd1OzVhnTC1s+dTe1fBVUt8Xor6+3dlO3D1H25OPrsXE7N+xWnOv441QkoNP3Ai4sKnPSXVNy+M+x56u7fR4LnjWLviA9JPR9J4Ihu1Jk0gEMTFpu8jLLeZgzZuewY+C76jGwUCy2d/ppB1JajJN68EJHHhONQce623utMHEB2XDKb2k0GRcHK1aHE73839UKC8azhw6yQCVRvUovBs0fz0YA3C7U7uHoH309cCMAzn46n7ZAu7FyxCVsnOwbPGs0Xz8whMTIeB/f7P74IcT/Mskhh2/ghcq5Ekns1CoDkNdtx7Naa+PxFivhk9PHJOHZuUWh+xUKLYmOFqtOhsbVGFx1fYdlPXLxOgJcr/l7GE51ereoTeuRsgUKDoihkZOWgqioZ2Tk429ui1WjwdHHE08URAHtbawKreBCTlFJhRYrjp89T1a8KAb4+APTu2oGtO/cVKFKEX77Kszd7VwRW8+d6VAxxCYkA6PQGsrNzsNBakJmVjae7W4XkBjh+5o7sXdqzdde+AkWK8CtXeXbYwDuyJ93Mrs+XPQdPj4rLbormwQ25fsP0KxAV5cTFawR4u+HvZVxfvVo1JDTsTIEihYJCRlZ2oe0dIDohmR1HzzGmXyeWr99dsdkjYgnwcML/5h/insFBhJ68UqBIkd+6w+H0alIz7+fopDR2nLnKmK7BLN9+vEIy59epZ3vW/Gy8+nY87CSOTo54eLkTF2Pa8S4z3wm+rZ0NagXda3n8bDhVfb0JqGLcRnqHtGbbnkMFihThEdfzelcEVvXlenQscYnJeLg6ExUbz479R3h2aH++/21tkcu4HwkHz9/+96EL2FYp5ligUWg4fRgHnl+Eb5/bf4e0dtY0nv0MznUDULRaTn/wW97JRn6p5+8cGPv+6TNuF/os7vI7TT5xpcjXKzM7QNDonkT+sx/X4MBi25RXdq+QRiSfisi7Ep+TmFaKT2CUFZNEVkwSYLzCn3r+OrY+rhiycwl+byRW7k7oM7MJe/Ur0i4UzlPopOmmGk925uKyjeQmG3tFleTqd3Eqe3u/n/VuavYaz3TjwPOf5d1Pfmu9Vdb2Xtz2kb9Icbd9OWBgO4LG9EJjqSUxLJzDr38DhoL7uj4jm/j9Z7Gv4V2m2UtEVbFwsAXAwtGOrKgkoHK3mVvrVWOpRWOhLXKMgeKOQ/e73qsNCbldaFLVEvcCu5eGPVqw//ftAFw+fB5bR3ucPF1IiU0q0O5U6JG8f185egFnH+N+0+yR9hxdv5/ESON3iLT4+z++/L9jMFR2gv8Uk4sUiqLUAJoAp1RVPVN+kYy9IXJvxOX9rIuKw7ZxbZPm1UXHE//V7zy041sMWTmk7wwjfefh8opaSExSKj5uznk/e7k6cfxiwasnQ7q0YPzCH+n2ysekZ2Uz/7mBaDQFH0F7PS6JMxFRNAz0p6LExMXj4+WR97O3pwfHTxXsXli7ZnU2b99D00b1OH7qHDeiY4iOjad+7ZqMGDKAboNGY2NlRdsWTWjXsknFZY9NwMczf3Z3jp86X6BN7aDqbN6x15j99DluRMUSHRtnzP7EALoNfhYbayvatgimXYuKy/4gi0m8Y3t3c+J4+LUCbYZ0a8X4BSvpNuF90rNymP/CIDQ3ixTzV65j0uCepGdVbC8KgJiUdHxcbl/J8Ha25/jN7qt3yszRsfvsNd54tG3ea+//tZeJfVuSXoru4GXBq4onUZG380bfiMGrimeRRYoXXx/Hs6+MZP+OQ3w6+wtyb97+1rl3R16e9hxuHq6Mf9L0q3T3IyY+AR9P97yfvT3cOHYmvECb2jWqsnnXAZo2qM3xM+HciI4jOi4BD1dn5n+5nEljhpJRDr0o7lR9WAjRW48WOS1oVE9ubAjLO9m4pc6EAcTuPEnYpCVYOtkRsm4WMTtOFDjpuBf7qp502TSH3LRMTs39mfh9pnfz9u3dnPrThmDt4cTuJ0t2a2hlZrfxccW3Twt2DHyXZsFF9wgqz+wOgT6gqrRb9TrW7o5c/XMP5xetKXGOO9kFeODSoDoJYeG0XjaJw1O+If1SFK5NggieO5Kdj882+b0cAqsA0OmvGShaDac/+I3obWV3i1ZlbO9ltd7vlt2+mhf+/Vvj26cF2fEpHP3f96Rfiqr0fRUKbh93Kmpfdqzli3//NvzbbyaqTk/w3JFUHdieiF92lGi5zRaMQ9UbiPznAGc+Lnyrxr24NatFly3vkRWdyPG3V5J6tnAvobDJS2m7cgqGrBxy0zIJ7TMDqORtRqPQZeNsHGr4EL5sI4mHC6734o5D97veb/VAqjdlEJ5t65J2JYajbywrk0LjLc7eriRF3v77nxQVj7OPW6EixS0aCy0tHu3Ib29/C4BXYBW0Flpe/nE6Nva2hC5bx4GbRQ8hKkOxRQpFUVarqjrg5r/7AwuAUOA9RVHeU1X127vMOxYYCzDDowGDnaoW17S4NyhZ+3w0Tg44dmvN+ZBR6FPS8f/sDZz7dyb5z22lfs+SKOrC1Z0fZ/fJcOoE+PDVa09zNSaRcR+uoOlD1XCwtQYgIyuHyYt+4bWhPfNeqwhFXXVT7gg/ZvjjzP10KQNHTaBWYDXq1ApEq9WSnJrGtp372PDTUhwd7Jk8fR5/b9xGvx6dKyY7RWUv+POYYQOZu/ArBo6eWDj7rv1s+HGxMfuM+fy9MZR+PUIqJPuDzJRtZveJC9SpWoWvXh/J1ZgExs3/jqa1q3Ho7BXcnOypV8OXA6dL3+20tEzZV2/ZfuoKwdW982712H7qCq4ONtTz9+RAePlcVb6XO9czFP37WDj7S+Ji4rG0suStD6Yy8qUnWfLRMgC2rdvOtnXbadq6MS9MfZbnBk8s79jFrPeCn2X0E/2Y+8VyHn/+DWrVCKBOzepYaDT8uzcMNxdn6teqwYGjp8o1p0e7elQbGsL2/oXvybXxdsGvXyt2PDar0DSvkIb49GxKref7AqC1tsTOz93kq7FZ0UmsbzaenMQ0XBrVoPWyV9jcaQq6NNOKMpHrDhK57iDuretQb+ogdg6eY9J8lZ290aynOTFrVaErkxWVXWOhxb1Vbbb1egt9Zjbtf/kfSUcvEbvzZKnygPGKcauvJnFs+nJUgwH35g/Raun428u0sizR+ykWGhxq+LD9sXex9XWj0+rpbA6ZSm5KRqkz3lJZ23tZrPe7Zb+VSZ+dy7aeb+LbpwXNPh7L9gHvVPq+mn/7KGqeovZlzw4NcGlUg87rjb8LrY1ViU90D7ywiKyoRCzsbWj19USqDupQoiJH0rHLrG8+Hn1GNt5dg2mzbDIb275SqF3Nsb3ZPXw+iYfDqfXCwzR6+0nCJi+t3G3GoLK12zQsnexovWwSTnX8STlz+8JKcceh+13vioUWOz934g+c5fjMFdQc14eGM4Zz8OUvTH6Pey6jqC8wd+khOXjWaML3n+biAeN1Z41WQ0DDQD4bNgtLGysm/T6Ly4fPE3vpRpllFKIk7taTolq+f08FuqiqeklRFA9gC/BtcTOqqroEWAJwKqhvib9x6KLisKxy+6q4hY8HuSbesmHfLpicq9HoE4wHj9QNu7FtWrfCihTero5EJSTn/RyTmILXzVs4bvlz5xFG9WmHoihU9XbDz8OFSzfiaBjoR65OzyuLfqZP6wZ0a1a3QjLnZff0ICrmdg+W6Ni4Qrc9ONjb8e4bEwDjCVHPJ57Fv4o3u/aH4VfFGzcX41X1rh3bcOTEmQorUnh7uhMVmz97fNHZXx9/O/uQscbsBw7jV8WrYPaTZ6RIYQJvN6eC23tCEdv7jjBG9e1wc3t3x8/TlUuRcRw5F0Ho4bPsPHae7Fwd6ZnZvPHlr7z33ON3LqZ8sjvbE5V0u5todHI6nk72RbZdfyScXk2C8n4+cjmaf09FsPPMKnJy9aRn5zDth23MGVa+2/vgkY/x2HDjbRAnj5zGx/f2bTXeVbyIjYorNM+tnhW5Obn8+eM/PP380EJtwvYexb+6Hy5uziTl+32WB28PN6Jibx/Po+MS8HJ3KdDGwd6Od18dBxj31V7PTMTPx5N1/+5h295D7DhwhOycXNIzMnl93ufMnfrCfWUKHNmd6sONv7vdw+dj5eZI0w+fZfeweUV2JXZpWB2HGt702PsxAFpbK3rs+YiNbV4BFPaNXkBaeMEvds0WjMO5QTWyopPYPXx+sVkMOTpycozLTDp2ifQr0TgE+RR7//id2bOikwCI33sG++peWLk5lqBbceVlt3Syo+XilwGwdnPEu2swqs7AjfUHKyR7ZmQCcXtO562r6C1HcGlUo9RFCsVCS+uvJ3H1911Erj2AhYMtuSnphccvuHllF4xjUZye/+tdMyaEXUDV6cmIiCU1/AYOgT4kHrlYomzmtL2XdL2XNPutZUT+Yxz8MHLtAZotGHdzSsVu7/nduX3cTf59GQUift7OyTk/FWjj27s5dSY/Bhh7MNwtQ1bUzVt007O4+sduXJsE3bNIUdxxJnrLEZS5IwsdZ6zcHXGuXy2vp8K1P/fQbtXUW5++0vfV3JQMYnefxrtz4wJFCtfGNYo8Dt3ves9JSEWXkUXkWuPx7Prfe6k+LMTkvMXp8FQP2gw1jp8WcTQcF9/bvRRdfNxJjk4scr5eEx7Hwd2Jr8d9mPdaUlQC6Ymp5GRmk5OZTfj+0/jVrSZFClFp7lakyF9csFBV9RKAqqpxiqKU6005mcfOYVXdD0t/b3Kj43F+uCPXJ5nWbVUXGYttcG0UG2vUrGzs2zYm83jR93eWh/o1/IiITuBabCLerk6s33eS98Y9WqCNj5sz+05doulD1YhPTuNyVDz+nq6oqsrMZX8TWMWTp3u2KWYJ5adBnVpEXIvkWmQU3p7urNuyg/nTC3YBT0lNw9bGGktLS35bs5FmjevjYG9HFW9Pjp06S2ZWNjbWVuw7dJT6dWpVXPbatYi4doNrN6Lx9nBj3dadzH+zYGW/QPZ/Nt3O7uXJsVPnbmcPO0b92kHFLEnkV3B7d2T9vuO899ygAm183FzYd+oiTWtXN27vN+Lw93JlwuDuTBjcHYADpy/x3bpdFVagAKgf4ElEXArXE1LwcrJnw5HwIosMqZk5HLoYVWDa+D4tGd+npTF7eCTf/3us3AsUAD8v+52flxmfZNS+WxuGjBrI+tWbadi0PmmpaUXe6pF/nIrOvToSfsZ4QhNQ3Y+rl41ddOs0fAhLS8tyL1AANKgdyJXrUVyLisHb3Y11oXuZ9/qLBdqkpKVja22NpaUFv63bRrMGdXCwt2PiqCFMHDUEgANHT/Htr//cd4EC4OKyTVxctgkAWz93Wn8ziYMvfU7axagi20dtPsLaRreX+0j4NzdP2CAm9BhBo3vmjeru3KAaySeucGiiaQPGWbk7Gk+2DCp2Vb1wqOFD+pWib0O6M7t99dv3Qbs0rI7G0qJE9z1XZvb8mn0yjhubDpegQHH/2aNDj/HQiw+jtbXCkKPDo01dLiwp/ZgnTT8eS+r561xYbHwPXVom6RGx+PVrxfW/9xkz1qtK8qkIkwdevLH+IP6PtiXip+1YuTniEFjlruu3OOa0vZd0vZc0O0Dk+oN4tq/PlVX/4tG2LmkXb5RJ9pJu7/nduX3cyb66d96TMPLvy7E7TtLm28lcWLKO7LgULF3ssXCwzet1cS+KVoOlsz05CakoFlqqdG9CzPYT95wv/3q39rx9i6drkyAURSl0nMlNSsfS0Q6HQB/SLkbh1bEhqeeMPSUqa1+1cndEzdWTm5KBxsYSrw4NOLfo7wJtNrScmPfv/Mchx4f87mu9A9zYeBjPtnWJ3XUKzw4NSDFxIN272bF8IzuWbwSgXucmdHymJ2F/7aZ6k1pkpWYUeatHmye6ULdjIz4bNqtA78vjGw/y+Dsj0Wg1aC0tqBZci9Cvy37cpwdaKXv6idK5W5GisaIoKYACWCuK4qOqapSiKFaAtlxT6Q1Evf0FVb+dhaLRkPTrJrLPR+A6tDcAiavWofVwJXD1AjQOdqAacBvRn/Bez5F59Cyp63cR+NcnqHo9WScvkvTjunKNm5+FVsMbT/bm+Y9WYjCoDGgfTE0/L37eZjyIDe7cnLH9OvLWN38y8K0vUVGZOKgrro52hJ2LYM2eY9Ty92LwDOPB+uWBXejQqGJO9i0stEybOI5xr85EbzDwaJ9u1KxRlZ/+NK6/J/r35uKVa0yb/TFarYbAagG8c7NnQqN6teke0o7BYyai1WqpUyuQQf16VkjuvOwTnmXca2+jN+h5tPet7OtvZu/FxYhrTJvzCVqNhsDqAbwz5aWb2R+ie6e2DH72lZvZazDo4YrLborXZszlwOFjJCWl0HXAk7ww+ikGVuD6LY6FVssbT/Xl+fe/x2AwMKBjU2r6e/HzVuOVocFdWjC2fyfeWvoHA//3GaoKEwf3wNWx6B4LFclCq+H1AW15fuk6DAaV/i1rU9PHjV/2GG8jGNSmHgBbT1ymzUN+2Jawa3Z527l5D+27tuGvvT+TlZnFzIm3u/UvXPkB77wyl9joOGZ/PgNXdxcUReHsifPMnmIs+HZ9OISHB/VGl6sjOyubqeOKHtG/rFlotUx7cQTPTZtnPM706ETN6v78vMb4BKbBD3fjYkQk/3v/CzQaDUHV/Hh7UsnHKCituq88hpWrI8FzRwKg6g1s62kcIb3tyimEvbIk7ypiUU5//DuN33martvmgqKQcTW2yFHwfXs3p/HsZ7Byd6Ltiikkn7jCrqFz8Whdh3pTBhkfCac3cHjKN+QmpZuU3e/hllQd1AFDrg59Vi77xy3Mm5Y/e9Donjz04sNYe7nQdetcorccIWzy0krNfjcVkT03OZ3zi9fSef27qKpK9JYjRG0+Uqq87i1rU21QB5JPRdBls3G/PPnezxx4cRFN5o6i9sQBaCy1XFu9p8hHZjZ4aygBj7ZFa2tF77CFXP4hNG/8Ca9Ojei2fT6q3sCJd364rwE+ofK39/tZ76ZmP7fwL1p8/iI1x/ZGl55N2CtLyyR7abf34rYPOz/jVfBL328pdl9OPXedk/N+pt2Pr6NoNBhy9Rx5YxmZ1wr3out54BMsHWzRWFng26sZO4fMJeNaHO1WvY7GUoui1RCz/QSXVmw1aX3f4tevFYHPdMOg02PIymH/c0UfZw6/upRWX09ENajkJqdzaOKSMlnvpd1mbLxcaP7p8yhaDWgUrv+1l6hNh6nxdNe89V6c+13vqeeuc+LdVbRY+DyNZj1NdnyKyUUZU53adpj6nZsw/d9PyMnMYeVrt28lGbfsdVZNXUxKTCKDZ48h8Xosk/54F4Bj6/ez/tPfiA6/zul/j/L6+vcxGFT2/rSVG+euFrc4Icqdcq8R3RVFqa+q6sl8P7sAz6mqOteUBZTmdg9zEfh9+T+Or7xoazav7Ail9yCPnmtZcWOIlDX9hbJ9XndFUmMe3D+kbceWfNAyc7Fv7yeVHaHU1rT+6N6NhBBCiAoUaqOv7Ail9unln0o/qOADIOODMQ/sOe3d2L36lVn+3jQmtPlJUZQpipEtMAvoX865hBBCCCGEEEII8R9jyiNIWwHzgN2AI7ASaFeeoYQQQgghhBBCCLOgPsA9vR9ApvSkyAUyAVvABrikqvJbEkIIIYQQQgghRNkypUhxAGORogXQHhiqKErxz8gSQgghhBBCCCGEKAVTbvcYrarqrefrRAH9FUV5qhwzCSGEEEIIIYQQ4j/onj0p8hUo8r+2vHziCCGEEEIIIYQQ4r/KlJ4UQgghhBBCCCHEf5Ph/+UTSM2WKWNSCCGEEEIIIYQQQpQ7KVIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBxqQQQgghhBBCCCGKoRoMlR3hP0V6UgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALMiaFEEIIIYQQQghRHINa2Qn+U6QnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgY1IIIYQQQgghhBDFUQ2VneA/RXpSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQYoUQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZKPeBM6PS7Mt7EeWmxrmjlR2h1HRZ6ZUdofQe4IFpFAfXyo5QatqaLSo7QqnlrF9d2RFKrbWNX2VHKDX9sdDKjiCEEEL8v9EuW55pYLYMamUn+E+RnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgtz4JIQQQgghhBBCFMfw4I6Z9yCSnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgoxJIYQQQgghhBBCFMegVnaC/xTpSSGEEEIIIYQQQgizIEUKIYQQQgghhBBCmAUpUgghhBBCCCGEEMIsyJgUQgghhBBCCCFEcVRDZSf4T5GeFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAihRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFmTgTCGEEEIIIYQQojgGtbIT/KdITwohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQcakEEIIIYQQQgghiqEaDJUd4T9FelIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBihRCCCGEEEIIIYQwC2Y1JoVb58bUenckilbDjZVbuLLwz0Jtas0eiXvXJhgyszk1/nPSjl8CwMLJjjofPYd9nQBQVU5P+oKUg+epv2QidkG+eW10KRkc6DqlXD/HrovRzN98HIMBHm1clVFtHirU5sCVON7fchydQcXV1oqvh7cHICUrl3fWHeZCbCqKAjP7NKGxn1u55i2Q/eQl5v+yDYOq8mjbBozq2arA9G83HWDtgdMA6PUGLkUlsG3+8zjb27J8yyH+2H0cBajl58HbT/XC2rLiNrFdJy8z/9dQDAYDj7ZrwKgeLe/IfpC1B84YsxtuZp/3HIlpGUz5em1eu+vxyTzftw1PdmlacdmPnWfeyrUYDCqPdmrK6Ic7FpiempHFtMW/EhWfjE5v4Jne7RjQ8XY+vcHA0Blf4uXqxGevPFlhuU3x5pyP2L5rP26uLqxe8WVlxylAW7MxVr2eBo0GXdg2cnf+VWC6pnpdbIa8iiEpBgD96QPk/vs7WFhiM3I6aC1RNFp0p/aRG/prhecfPGMk9Ts3ISczm+9f/ZyrJy8VajNywctUaxiEXqfj8tFwVk5bgkGnx8bRlpEfj8fNzx2NVsvmpX+z55fQCsm968xV5v+1x7i9t6zNqC7BBaZ/G3qUtWEXANAbVC7FJLFt5pPYWFow6os15Or06AwGujUM5IWezco0m0NNX5otGIdLw+qcmvsz57/4p8h2nu3r03D6MNAo6NKzOTThS9IvR5u8HL9+raj76kAca/myrfdbJB01/u7sAjzovv0DUsMjAUg4dIEjU78x6T0DHmvHQy/1A0CXnsWRqd+QfCqiyLb1Xh+MX79WqHoDl77bTPjXGx6I7J7t6tFwxnAUKwuSjl0ibNISVL3p9wkXlx3AqW4ATd4fg6WjLarBwLZeb2HIzr3r+zX9eCw+3ZuQHZfClpCpRbbxaFuXRu88hcbSguyEVHY8OsvkvFauDrT6agKuwUFc+Wk7R6d9mzdNsdQSPGckHm3rgkHl5NyfiPzngMnvDaav946rp2PhYAOAtYcziYfD2TvyI5OXU9br/X4/T3Hutr47/P4mNl4u6LNyANg1ZC7ZcSmlynknW183mi98HmtPF1BVLi3fSvhX602ev97rg6k6qANWLvb8FTQq7/WqT3Sk4fRhZN5IAODiNxu5/ENomWR2DQ4k5J932DfuUyLX7C803dy2GVPWca0XHibgsbYAKBZanGr5sab+OHKT0k3KXN77a1GazHqaKl0bo8/MYf/ExSQev1yojX2AJ22+fAkrFwcSj19m38ufY8jVU+2xttR58fb+cuj1ZSTd3F8e3r+A3LQsVL0BVa9nU6+37jvrA8+gVnaC/xTzKVJoFGrPHc3hwe+SHRlP8w3vEbvhIBnnruc1ce/aBLsaPuxtPR6nZrWoPX8Mh3r/D4Ba744kftsRToz5CMVSi9bWGoCTYxfkzV9z5lPoUjLK9WPoDSrvbTzGl0Pa4u1oy/Bv/6VTLR+CPJzy2qRk5fLexqMsGtyGKs52JKRn502bv/k4bQO9+eDRluTqDWTm6ss1b8HsBt77aQtfjn8cbxdHhs9bSadGNQmq4p7XZkT3Fozo3gKAf4+Fs2LrIZztbYlOSmVVaBi/vzUCGytLXvvqb9YfPEP/Ng0qLvvPW/ny5ceM2ef/QKeGQXdkb86I7s2N2Y+Hs2LrYZztbXC2t+HnaU/mvU+PaUvp0rhmheS+tcw5369h8ZRn8HZzYtjMxYQ0qUOQn1dem5+27CPQ14uFk54kISWd/q9/St+2jbC0MO7CKzfuIdDXk7TM7OIWU2kG9OnOsIGPMG3WB5UdpSBFwarPSLKWz0FNicfm2dnozh5Cjb1eoJk+4gzZP7xfcF5dLlnfvQs52aDRYjNqJvoLRzBcu1Bh8euHNMGrhg8zQsZTo0kths4ew/wB/yvUbv/qnSybuBCAUZ9OoP2QLmxfsYmQp3px48I1vhgzDwc3R2Zu/YT9q3egL+djjt5g4L0/dvHl2D54O9sz/NPVdKpfjSBv17w2I0IaMyKkMQD/nrrCiu3HcbazQVVVlo7ri521Jbl6AyMX/UX7Ov40quZdZvlyk9I49uZ3VOnV/K7tgueNYu+ID0k9H0ngiG7UmTSAQxMWm7yclDNX2TvqY5q8P7rQtLQr0WztNq3E2dMjYtj+6Cxyk9Px7tKYJh+MIbTP9ELtqg3phK2fO5vavwqqinW+v09mnV1RaPbp8+wcNJu0i1HUnfI4VQd35Mqq0PvOrmg1tFj0Igdf+pzkUxFYuTpgyNXd8/2u/LSdi99spNnC54ucbulkR/DckewaOo/M6/ElXtf67FxOzfsVpzr+ONUJKDCtzsQBZMcls6ndZOPxzNWhRO8Npm8z2we8k/fvVl9N5MaGQyVaTlmv9+KY+nmKc7f1DXDgxUUFTpbLiqozcHzmSpKOX8bC3obOG2cTs/04qeeu33tm4MbGMC5+s5EeewoXAa79ubfAyXKZ0CjUf3Mo0aHHim1ibtuMKev4/OdrOP/5GgB8ujel5rjeJhcooPz31ztV6dIYx0Af1radjHvTmjSbO5LNfWcUatfozSGcXbKOq3/updm8UdQYGkL491tIi4hl62OzyE3OwKdLY5q/P7rA/Nsef5echLT7zilEaZhNkcKpaU0yLkWRdcV4xTJm9W48e7XgSr6Dh0ev5kT9sh2AlEPnsXCyx8rLBX1GNi5t6nJ6/CIA1Fw9utzCxQivR9pweOA7hV4vSyduJBLgao+/iz0APev5EXo+qkCRYt2pa3Sp7UsVZzsA3OyNBZW07FzCrsYzq28TACy1Giy1FXdHzonLUQR4uuDv4WLM3qw2oUcvFDjRz2/dwTP0al4n72e93kB2rg4LrZasHB2ezvd/ADZVkdmPhd8l+1l6Na9d6PV9Z6/i7+mMr3vJvkjejxMXrxHg7Ya/l7HHTK9WDQkNO1OgSKGgkJGVjaqqZGTn4Gxvi1Zj3DaiE5LZcfQcY/p1Yvn63RWW21TNgxty/YbpV5grisavJoaEKNTEm70kTuzBonZzcmNN+1JIzs2CkFZr/E+t2Ap74x7N2fu78Xh46fB57BztcfJ0ISU2qUC7k6GH8/59+egFXHyM+4SKio39zatcdjakJ6Vh0JX/yNUnImIJ8HDC/+Y+1jM4iNCTVwoUKfJbdzicXk2MRUNFUbCztgRApzegMxhQFKVM82XHpZAdl4JPtyZ3b6iqWDjYAmDhaEdWVBIAWjtrGs9+Bue6AShaLac/+K3IL+ep5yPLNDdAwsHzt/996AK2VYruhVfjmW4ceP6zvG321tVgc89u5eaAISeXtItRAMT8e5za4/tzZVXofWf3CmlE8qmIvKvuOYmmfTGP33sGuwCPYqcHPNaWyH8OkHk9HqDAlfeAge0IGtMLjaWWxLBwDr/+TaErdfqMbOL3n8W+RuFCXLUhIWzq8KrxB1UlJyHVpMz5mbrN3GJhb4Nn+/ocmmgsyFXWei/O3T7P/a7v8pQVk0RWTBJgvKKdev46tj6uGLJzCX5vJFbuTugzswl79SvSLhRel4lhFVcgBwga3ZPIf/bjGhx4z7bmss0Ut46LKwQFPNqGa3/c/k5lDvvrnfx6NePyLzsAiA+7gKWTHTZeLnmf8xbv9vXZ+4LxHOnyz9up/+pAwr/fQny+/SX+0Pl77v9CVKRiixSKolQFYlRVzVKM3wJHAE2BU8BSVVVLX+ougrWPG9mR8Xk/Z0fG49S0VsE2VdzIuh53u82NeKyruKHq9OTGp1D3kxdwqF+N1GMXOffmtxgybl9Vdmldl5zYZDIvRZVl7EJiUrPwcbTN+9nb0ZbjkYkF2lxJSENnMDB65U4ycnQMax5Iv4ZVuZaUgaudFdP/Ocy5mBTq+TgzpVtDbK0qppYUk5SGj6vj7eyujhy/fKPItpk5uew+dZk3nuhibOviyNPdWtDrzaXYWFrQum412tarXhGxgSKyuzhw/HLRv+u87IO7FJq24eBZejerU8Rc5ScmMRUfN+e8n73cnDgefq1AmyHdWjF+wUq6TXif9Kwc5r8wCM3NIsX8leuYNLgn6Vnm14vCnClOrqgpt485ako8Gv/CPWi0/rWweW4uamoiORtXosbe/N0oCjbj5qBx8yF3/0YM18MrKjoALt5uJEbePh4mRsXj4uNWqEhxi8ZCS6tHO/DL298CEPrdep7/agpz9y/G2t6Wr1/6GLUCCi0xKen4uNwuYHo723M8IqbItpk5OnafvcYbj7bNe01vMDB0wR9cjU/hibb1aFjVq8h5y1vY5KW0XTkFQ1YOuWmZhPYxXn2qM2EAsTtPEjZpCZZOdoSsm0XMjhPoM0zfP+2retJl0xxy0zI5Nfdn4vedLXG+6sNCiN56tOj3r+aFf//W+PZpQXZ8Ckf/9z3pl6LMPntOfCoaSy0ujWuQdPQSfg+3wtbX+KX6frM7BPqAqtJu1etYuzty9c89nF+0psTZC79vFRRLLR1+fxMLe1vCv1pPxC87cKzli3//NvzbbyaqTk/w3JFUHdieiJsnHPdi6WS8yFFvyiA829Yl7UoMR99Ydl+3H9xtm7nFt08LYneeQJeWCZjveoeCn+d+1/ctzRaMQ9UbiPznAGc+/qNMct7JLsADlwbVSQgLp/WySRye8g3pl6JwbRJE8NyR7Hx8donez69vCzxa1yHt4g2OTV9OZmTCfeWz8XHFt08Ldgx8l2bBY+/Z3hy3mfzruChaWyu8OzfmyM0eKOa4vwLY+riRke/cKfNGArZVXAsUKazcHMhJTs+7LS7jRgJ2PoUvCgQODSEq3/6vqiohP76OqkL48i1cXLHtvrIKUVJ3O/tdC9y6qX8uEASsBroALYBRRc8GiqKMBcYCTHJsxsO29660UsTVMJU7vzAXccVMVVEstDg0rMG5ad+QEnaBWu+OoNrLA7g076e8Zl6PtiP6j133znGfCmcunFpvUDkdlcySIW3J0ul5evkOGvm5oTcYOBOVzOvdG9LQ1415m47zzd7zvNixbrnnNjX7LduPhRMc6IuzvbEgk5KRReixC/zzzhgc7ax5benf/LPvFH1b1SvHxLcVdWpV3BXW7ccv3sxuU+D1XJ2ef4+HM75/u3JIWLyiTgzvzL77xAXqVK3CV6+P5GpMAuPmf0fT2tU4dPYKbk721Kvhy4HTZd8F9f+3oo4nBX803LhMxoKXIScbba1gbIa8QubCV262Vcn68g2wscPmiVfQefmjxlwr/J7lpajt+y5FhqGzxnBh/2ku3ByXpV7Hxlw7dYUFQ9/Bs5o341e8xYXer5F184tkeSkqYnGdIbafukJwdW+c7W7vq1qNhp9fGUhKZjavfLeJC1EJ1PSp+Ks/Ncf2Zvfw+SQeDqfWCw/T6O0nCZu8FK+Qhvj0bEqt5/sa81pbYufnbnLvg6zoJNY3G09OYhoujWrQetkrbO40Je8Lvik82tWj2tAQtvd/u8jpWmtL9Nm5bOv5Jr59WtDs47FsH/DOA5F9/7jPaPT2U2isLYkJPYZ6s/fP/WbXWGhxb1Wbbb3eQp+ZTftf/kfS0UvE7jxpcvaiKBZaXBvVYMegOWhtrAhZ8zYJh87j2aEBLo1q0Hm9cXwKrY1ViU5YFAstdn7uxB84y/GZK6g5rg8NZwzn4MtflCrnvdb7Lf6PtuHyytsnK+a63u/8PPe7vgEOvLCIrKhELOxtaPX1RKoO6lDiIse9aO2safXVJI5NX45qMODe/CFaLR2fN11jZVmi94vaGMa1P3ZjyNFR4+muxtulSljkuFOjWU9zYtYqk+/PN7dtJv86Lu7Y5NOjKfEHzuXd6mFu++vtNy7itTv+yBb1XfjOv8NebesROCyELf1v9zbf8sjbZEUnYe3uRMhPr5N64Qaxe8/cX94HnYxJUaHuVqTQqKp6656JbkALVVUNwApFUe5aaldVdQmwBGCr92CTfqPZN+Kx9r3dNd/a152cqMRCbWz8PEjGeGXGuoo72VGJoKpkR8aTcrO7W8zfe6n28oC8+RStBq++LTnQ/XVTotwXb0dbolJvH/SiUzPxdLS5o40NLrZe2FpZYGtlQbMAd87GJNPU3x0vRxsa3rwy1L2OL9/sPU9F8XZxJCrxdvez6MTUYm/ZWH/oLL1a3O5xsPfMFfzcnXFzNFaLuwbX4sjFyAorUni7OBTMnpSGp7N9kW3XHzpb4DaVW3aevEydAC/cnYqer7x4uzkRlZCc93NMQgpeLo4F2vy5I4xRfTugKApVvd3x83TlUmQcR85FEHr4LDuPnSc7V0d6ZjZvfPkr7z33eIV+hgeRmpKA4nT7mKM4uaOmFjzmkH17X9afPwJ9R4GdI2Tk66aZlYH+8mm0NRujK+ciRaenetJuaFcArhwNx9XXA24eD1193EmKTixyvr4THsfB3Ykl45bkvdZmUGc2frEagNgr0cRfjcE7yJcrR8u3R4i3sz1RSbe750Ynp+NZzD63/kg4vZoEFTnNydaa5oFV2HXm2n0XKQJHdqf68M4A7B4+n6zopLu2t3J3xLl+NRIPG9fVtT/30G7VrUETFfaNXkBaeMFeaM0WjMO5QTWyopPYPXx+se9tyNGRk2NcP0nHLpF+JRqHIJ9i74O/M7uVmyNNP3yW3cPmFdsNOjMygch/jAPdRa49QLMF4x6Y7AmHzufd6+7VqSEOQVXKJHtmZAJxe07ndcGO3nIEl0Y17vtkOTMynpyEVPQZ2egzsonbexrn+tVAgYift3Nyzk8F2vv2bk6dyY8Bxt46xa27nIRUdBlZRK49CMD1v/dSfViISZlKs97BOCiga3AQe0d+nO/Vyl/vJn2e+1zfAFk3v5Pq0rO4+sduXJsElWmRQrHQ0vrrSVz9fReRaw9g4WBLbkp64TFeNApdNhoLDTc2hnF6fvGDNuf/fV5asZUGbw4tVbb869jSyY6Wi18GwNrNEe+uwag6AzfWHyw0n7ltM3eu4+IE9G/D1Xy3elTm/nqnmiO6E3jzd5Fw9CJ2+c6dbKu4kXnz1sNbsuNTsXK2R9FqUPUG7Kq4kZnvu4Jz3QBafDiGf4fPL7C93Po7mB2fwrV1B3ELDpQihahQdxvw4KqiKLf6w18GAgAURSn6Jv/7lHo4HLvAKthU9USx1OI1oC1xGwoe8OI2HMRnkPGpB07NaqFPzSAnJomc2GSyI+Oxu/llxa1DQ9LP3T5ZcO3YkPTzkWTfuL8ubqaoX8WFiIR0rielk6s3sOHUdTrV9CnQJqRWFQ5fi0dnMJCZq+N4ZCKB7o54ONjg42TL5XjjgXff5VgC3R2LWkz5ZK/mQ0RMEtfjksnV6dlw6CydGhU+QUjNzObQ+Wt0bnS7a3wVVyeOXb5BZk4uqqqy72wEgRV4ddOYPbFg9oaFe/Dczl74c60/dKbI4kV5q1/Dj4joBK7FJpKr07F+33E6NSmYw8fNhX2nLgIQn5zG5Rtx+Hu5MmFwdzYteJV1H77CvOcH0aJuDSlQmMgQGY7G3QfFxRO0WrQN2qA7W/C+WMXh9m04Gr8g4yX/jFRjocLGWJDDwhJtYAPUuLK/T/9O/y7fwJw+U5jTZwpHN+6n9WPG42GNJrXITM0o8laPdk90oW7Hxnzz8oICvXYSI+Oo3a4hAI4ezngH+hJXzG0XZal+gCcRcSlcT0gx7qtHwulUr2qhdqmZORy6GEXn+tXyXktIyyTl5uCwWbk69l24Tg0v50LzltTFZZvY2m0aW7tNu2eBAiA3KR1LRztjt2PAq2NDUs8Zf/8xoccIGt0zr61zA2P+QxMXs7XbtLt++QZjAQSN8cqXXVUvHGr4kH6l+N9L/uyKhZbW30zi4Euf543bUJTI9QfxbF8fMD55Iu3ijQcm+62BJzVWFjz0Uj8ufbe5TLJHhx7DuW5VtLZWKFoNHm3qknru/ouONzYcwr1VbRStBq2tFa5Na5J6/jqxO07i93CrvM9j6WKPrb8HkesO5q2Tew3QeGPjYTzbGntaenZoQIqJgyyWZr2D8WkLUZsPF3iKgjmsd1M+z/2ub0WrwcrN+H1MsdBSpXsTUs5cLVHOe2n68VhSz1/nwmLj08Z0aZmkR8Ti1+/2U9ac61UFg5qX+W4FCgAbL5e8f/v2bEbqeRPHXLpD/nW8oeVENrSYwIYWE7i+Zh9HXl9WZIECzG+buXMdF8XC0RaPNnULjJNRmfvrnS58u4mN3aexsfs0rq87SPVBHQBwb1qT3NTMQuNRAMTsOoX/w8bO8dUHdyRyvfGz2fm50+7riex9+YsC+4vW1hqLm72NtbbW+HRqSPLZCuwpKgSgFHcPsqIoAcD3gBZIBtoDhwFX4FVVVbeYsgBTe1KA8ekdtWY9g6LVELlqG1cW/IHv090BiPx+EwAPvTca9y7GR+2cnvA5qUeNJ24O9atR56Pn0FhZkHklhtMTPkeXbOymVfeTF0g+dD7vPUzVZm71ErW/ZUd4NO9vPo5BVenfqCrPtq3NL4eNB69BTWoA8O2+8/x1LAJFUXi0cTWebGE8aT4Tncw76w6Tq1fxc7Hjnb5NcLKxKnmIqqV7OsWOExd5/+ZjPPu3acCzvVvzy3Zjx5lBHY2j7f+55wS7T11m3uiHC8z7+ZpdbDx0Fq1GQ50AL2YM74FVaR5BqpZu8L4dJy7x/m+hGAwq/dvU59lerfhlx83sHW5lP8nu05eZN6pvgXkzc3Lp9eZXrHl7FI43nwxTGopD0YP/3TP70XPMX7kOg8HAgI5NefaRTvy81VjlH9ylBTGJKby19A/iklNRVRjVtwMPt2tc4D0OnL7Ed+t2lfoRpNqaLUo13728NmMuBw4fIykpBXc3F14Y/RQD+/W894wlkPNZ4adamEJbK9j4CFJFg+5wKLk7VmPRvBsAuoObsWjZA8vm3VENetDlkLNhOYar51G8q2I94HkUjQYUBd3JvcZHk5bCq9+W/mkaQ94ZTb1OjcnJzOH71z4n4rjxePjistdZMXUxyTGJfHZhFQnXY8lKzwLgyPp9rP30N5y9XHn6gxdw9nJFUWDDF3+yf3XJrgp+9GnLezcqwo7TEbx/8xGk/VvW5tmuTfhlzykABrUx9r7688A5dp+9yrwnu+bNdy4ynrd++heDQcWgqvRoHMi47qV7VPC6sWFFvm7t6UyXDe9i4WiLalDRp2exqaPxloW2K6cQ9soSsqKT8O3dnLpTHkc1qOQmp3No4hIyImLQ2FjS+J2ncWtRCxSFjKux7Hmq8JNtfHs3p/HsZ7BydyI3JYPkE1fYNXQuvn1bUG/KIAw6PegNnHr/N6I2FZ31Tk0/fBbfvi3JuBYLgKo3sK3nmwAFsls62dHi8xex9XNHl57NkSlfk3wq4oHI3mD6MHy6NUHRKFz8bjPhS42PELzf7GAcGK/2+P6oqkr0liPGLu330OKLl/BsWxcrN0eyY5M59f5vaCy1AFz63vg1qdYLD1NtSEdUg8rlldvyMvv1b03tlx9B0Wgw5Oo58sayIgdA7HngEywdbNFYWZCbnM7OIXNJPXcdW38PWix8Hktne7LjUzg0cXHeAJ2mMnW9g/ERnOcW/kX0tttPdKis9V6az3M/6zvjWhwd/5iOxlKLotUQs/0Ex2YsL7Pu3+4ta9Pprxkkn4pANRi/A51872dSz1+nydxRWHu7oLHUcm31Hs58VHgsjAZvDSXg0bbY+LiSFZXI5R9COf3Bb9Sf9gRVejbDoNOTm5TG4anLihx4s7SafTKOG5sO5z2C1Jy3meLWsZ2f8drrrf216hMd8e7cmAPPLSwwf0Xtr7klHAy66ZwRVOncCF1mDvsnLSbxZrGkw4rXODB5KVnRSdhX9aTNly9j5WJP0okr7H3pcww5Olp8MAb/vi1Jv2Yc3+rWo0btq3rS/ptJgLEod+WP3Zz+5M97ZnnixsqyHcnazKS99uj/y/s9HN7/wyx/b8UWKfIaKEpDIBDjrSHXgANAR1VVQ01ZQEmKFOamtEUKs1DKIoVZKGWRwhyUtkhhDsqrSFERSlukMAf3U6SobKUtUpiD4ooUQgghRGUpaZHCnEiR4sFkrkUKUy5zrwKWA/MBG+AToDnQphxzCSGEEEIIIYQQle8Bvoj6ILrbmBS3tMI4HsVujL0oIoGKffyBEEIIIYQQQggh/t8zpUiRC2QCthh7Uly6+ZQPIYQQQgghhBBCiDJjSpHiAMYiRQuMg2cOVRTl7sMJCyGEEEIIIYQQQpSQKWNSjFZV9dazhaKA/oqiPFWOmYQQQgghhBBCCPNQRk/zEaa5Z0+KfAWK/K8tL584QgghhBBCCCGE+K8y5XYPIYQQQgghhBBCiHInRQohhBBCCCGEEEKYBVPGpBBCCCGEEEIIIf6TVBmTokJJTwohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwixIkUIIIYQQQgghhBBmQcakEEIIIYQQQgghiiNjUlQo6UkhhBBCCCGEEEIIsyBFCiGEEEIIIYQQQpgFKVIIIYQQQgghhBDCLEiRQgghhBBCCCGEEGZBBs4UQgghhBBCCCGKYzBUdoL/FOlJIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBSlSCCGEEEIIIYQQwizImBRCCCGEEEIIIURxDGplJ/hPkZ4UQgghhBBCCCGEMAvl3pOiTlBseS+i3OiPZ1V2hFKzqNOksiOUnvbB7eCjRl2p7AillrN+dWVHKDWrl2ZXdoRS+/Xj3pUdodQ+9hlU2RHuQ1hlBxBCCCEKuGSlVHYEIcyC9KQQQgghhBBCCCGEWXhwL1kLIYQQQgghhBDlTcakqFDSk0IIIYQQQgghhBBmQYoUQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkDEphBBCCCGEEEKIYqiqjElRkaQnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBRk4UwghhBBCCCGEKI5BBs6sSNKTQgghhBBCCCGEEGZBihRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQMSmEEEIIIYQQQojiyJgUFUp6UgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALMiaFEEIIIYQQQghRDFXGpKhQ0pNCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAxKYQQQgghhBBCiOLImBQVSnpSCCGEEEIIIYQQwiyYbU8K61YtcJ74Emi1ZPz9D2nLVxWYbtujGw5PDgFAzcwk6f0F6C6EA2D/xOPY9esLqOSGXyRp9jzIya2w7No6TbEeMAY0WnL3biR3629FttME1MR2wvtkff8++mO7AbDs+AgWrXuAqmK4cYXsHz8BXcVl33XsPPN+WI/BYODRjk0Z/XCHAtNTM7KYtvh3ohKS0ekNPNO7LQM6NCEqPpn/Lf2D+OQ0FEXh8ZBmDO/RusJyG7OfY97ytcbsIc0Y3a9T4exf/EJUfDI6g4Fn+rRjQMdmedP1BgNDp3+Bl6sTn01+qmKzn7nK/L/2YDCoPNqyNqO6BBeY/m3oUdaGXbiZU+VSTBLbZj6Js51NXvZhn6zGy9mOhaN6VWh2bc3GWPV6GjQadGHbyN35V4Hpmup1sRnyKoakGGPW0wfI/fd3sLDEZuR00FqiaLToTu0jN/TXCs1+L2/O+Yjtu/bj5urC6hVfVnacQubM+x/denQiIyOL8S+8zrGjp4psN+2tiTwyoBd6vYFvv17F0sXLAWjbviWz35uGhaUFCfGJ9O9bMdu9Oe+rAY+146GX+gGgS8/iyNRvSD4VUahd04+exaVxIIqikHbxBgfHf4k+I9vk5fj1a0XdVwfiWMuXbb3fIunoJQDsAjzovv0DUsMjAUg4dIEjU78x6T2r9GxGvamDUA0GVL2BY28tJ37/2ULtmi96EdfGNTDo9CQeDufwa1+j6vSVmt2hpi/NFozDpWF1Ts39mfNf/FNku/LKDuBUN4Am74/B0tEW1WBgW6+3MGTf/e+vra8bzRc+j7WnC6gql5ZvJfyr9QXamLpNFcfK1YFWX03ANTiIKz9t5+i0b/OmKZZagueMxKNtXTConJz7E5H/HDDpfTXWlnRcPR2NlQUaCy3X1+zj9PsFv6+Ya3YwfZtp9sk4PNrUJTclA4BDExaTfPKKycspj+09aEwvqj/ZGUVRuLRiK+FLH4xtBsCjbV3afDuZ9Ajj3/TItQc489Efhdp5tq9Pw+nDQKOgS8/m0IQvSb8cbfJyynpfNWV7N/WzFac817spus18iqDOweRmZvPPq0uIPnG5UJumz3SnxaheuFb35pPg58hMTAOgauu6PLZ0EslXYwE4t/4Auz5dXab5hCgJ8yxSaDQ4vzqB+AmvoY+JxfPrL8nasRvd5dt/VHSRN4h7cSJqahrWrVviMnUycc++gMbDA/tBjxEzbATk5OA6awa23bqQuXZDxWRXNFg/No7ML6ejJsdjO+lDdCf3o0ZfLdTO6uER6M8evv2SsxuWHfqRMf9FyM3B+ukpWDTpgO7A1gqJrjcYmLN8LYtfewpvNyeGvb2UkCa1CfLzymvz05b9BPp5snDSMBJS0un/xkL6tmmIVqvh1SE9qFvdl/TMbIbMXEzr+oEF5i337N/9zeKpI43Zp39JSNO6BbNv3kugnxcLJz9lzD5lAX3bNsbSwrgbrNywh0BfT9IyTT/RKKvs7/2xiy/H9sHb2Z7hn66mU/1qBHm75rUZEdKYESGNAfj31BVWbD+eV6AA+GHHCWp4uZCenVOh2VEUrPqMJGv5HNSUeGyenY3u7CHU2OsFmukjzpD9w/sF59XlkvXdu5CTDRotNqNmor9wBMO1CxWX/x4G9OnOsIGPMG3WB5UdpZBu3TsSGFSdlk160Kx5Y+Z/NJNeXQcXajd0+GP4+lWhTfPeqKqKh4cbAE7Ojsz/cAZPDBzD9Ws38l4vb+a+r6ZHxLD90VnkJqfj3aUxTT4YQ2if6YXaHZu+Al1aJgANZz5J0KgenPvsb5OXk3LmKntHfUyT90cXmpZ2JZqt3aaVOHvMjhPc2HAIMH6Jb7VkAps6vFqo3dXfd3HwxUUAtPjiJaoP78yl7zZXavbcpDSOvfkdVXo1v2u78squaDW0WPQiB1/6nORTEVi5OmDI1d3z/VSdgeMzV5J0/DIW9jZ03jibmO3HST13+xho6jZVHH12Lqfm/YpTHX+c6gQUmFZn4gCy45LZ1G6y8Xjs6mDy+xqyc9kx8F30GdkoFlo6/TWDqC1HSQy7fQw21+xg+jYDcPydH4hcs79E739LWW/vTnX8qf5kZ0J7v4UhR0e7Va8TtfkI6Zei8tqY83oHiNt3hj1P3f3vYvC8Uewd8SGp5yMJHNGNOpMGcGjCYpOXUdb7qinbu6mfrTjlvd7vJrBzY1xr+LC402R8mwTR890RfD9gZqF21w+eI3zLYYb9+L9C064dOMuvoz4s01zi/wdFUXoBnwBa4CtVVecW064FsBd4QlXV+7ryaJZFCst6ddBdi0QfeQOAzM1bsenQjrR8RYrcEyfz/p1z8hRaL4+8nxWtFsXaGlWvQ7GxxhAXX2HZNVVrYYi7gZpgrBbrDu/AokErcu8oUlh2eBj9sd1oAmrd8QYasLQCvQ7F0ho1OaGionPi4nUCvN3w9zKerPRq1YDQw2cLnDwoikJGVjaqqpKRnYOzvS1ajQZPF0c8XRwBsLe1JtDXk5jE1AorUpwIv0aAt/vt7K0bEnrodMHs5MuelZ2XHSA6IZkdR84y5pFOLF+/u0Iy52WPiCXAwwl/dycAegYHEXrySoEiRX7rDofTq0nNvJ+jk9LYceYqY7oGs3z78QrJfIvGryaGhCjUxJu9JE7swaJ2c3LvKFIUK+fmSaZWa/xPNa/7/ZoHN+T6DdOv/FSkXn278tOq1QAcOngUZ2cnvL09iY6OLdBuxOihPDdmMurNdRsXZzymDBzUj3/+3sT1azcKvF7ezH1fTTh4/va/D13AtkrRxZtbBQoAra3V7X/bWdN49jM41w1A0Wo5/cFveYWD/FLPR5ZhaqP8PTks7Gzyfud3it5yJO/fiYfD8z5jZWbPjkshOy4Fn25N7tquvLJ7hTQi+VRE3tXqnJtXF+8lKyaJrJgkwHjFO/X8dWx9XAsUKe62TQUMbEfQmF5oLLUkhoVz+PVvCt33rM/IJn7/WexreBdafrUhIbcLUapKTkKqSbnzvzeAxlKLxqLwMdics5u6zRSnsrZ3x1p+JB66gD7TeFEhbs9pfPs05/yiNXltzHm9m0xVsXCwBcDC0Y6sqCSg8vZVuPf2fjfmvt5rdW/Gid92AhB5OBxrJ3vsvVxIv3l8uiW6BL2IhABQFEULLAK6A9eAA4qi/KWq6qki2s0DyqRnQLFjUiiK8oiiKDbFTS9PWk8P9NExeT/rY2PRenoU297u4T5k7TFWyA1xcaSt+hnvP37C+6/fMKSlk73/YLlnvkVxdkdNisv7WU2KQ3F2v6ONGxYNW5O7u2D3PjU5gdzQ1di/9TX2M79DzUpHf+5IRcQGICYxBR83p7yfvVydiE5MKdBmSNeWXIyMo9vED3n8zc+ZMqw3Gk3Bzeh6bCJnrtygYZBfheSGW9md8372cisie/fWXIyMpdvL83h82mdMeapvXvb5K9YyaUhPNBqlwjLfEpOSjo/L7Yq6t7M9McnpRbbNzNGx++w1ujWsnvfa+3/tZWLflihKxWdXnFxRU24XAdWUeBSnwsUVrX8tbJ6bi/XwqSie/vneQMHmufewe20x+vDjGK6HV0Ts/xeqVPEm8vrtK2+RkVH4+Bb+YlS9RgADHuvDptDf+PHXpQQGVgMgKKg6Li5OrF7zPZv//Y3BQ/pXSO4HaV+tPiyE6K1Hi53ebME4+hz/AseaVQj/2vg3uc6EAcTuPMm2Xm+xY+C7NJg+DK2ddYmWa1/Vky6b5tDhj7dwb1W7RPP69m5O9x0f0HbFa4RNWnLXtoqFlqqPtyd621GzyF4SZZ3dIdAHVJV2q16ny8bZ1Hrx4RJnsgvwwKVBdRLCij+O5d+mHGv54t+/Df/2m8nWbtNQDQaqDmxv8vIsnewAqDdlEF02zqbl0glYezjdY647aBS6bJ5D3xNfEr39OImHH6DsJVD/9cF03TqXhm8/icbKeH2usrb3lDNXcW9dBytXB7S2Vnh3DcbO173Y9ua43t2a1aLLlvdo+8MUHGsX/V0vbPJS2q6cQu+whVQd1J6zC423glbqvmrC9l7UZzOX9X43jj6upEbe/j6WGpWAYzEXu4rj17Qmo9bNZtB3r+FRq+K+wz8wDP9P/7u3lsAFVVUvqqqaA/wIFPWl8WXgNyCmiGkldreeFD8B6YqirANWARtUVTX9xs/7UvjLZ3FXhKyaBmPXrw9xz403zunogE2HtsQ8PhRDahqus2di27MbmRtM7w56X4o6Ubwju3X/Z8le8x2od2wZtvZoG7Qi/d1nITMdm2emYtEsBN2h0PLLW3xMwHhFM7/dJy5Qp6oPX019hqsxCYx7fzlNa1fFwdZYz8rIymbyZz/z2rBeea9VhCKz3/G72H38PHWqVuGrN0YZs89dRtPa1Th05jJuTvbUq+HHgdMXKyjxbUVnL7rt9lNXCK7unXerx/ZTV3B1sKGevycHwsv+6ua9FbW9F/zRcOMyGQtehpxstLWCsRnyCpkLX7nZViXryzfAxg6bJ15B5+WPGnOt/GP/P1BUUaqo46S1lRVZWdl0DxlI337d+WTRHPr1Ho6FhZZGwfUZ+MgIbGxsWLf5Rw4eOMrF8MvlmvtB2Vc92tWj2tAQtvd/u9g2hyYuBo1C8JwR+Pdvw5Uf/8UrpCE+PZtS6/m+AGitLbHzczf5amxWdBLrm40nJzENl0Y1aL3sFTZ3mlKg58bdRK47SOS6g7i3rkO9qYPYOXhOsW2D544kbu8Z4vcZx62o7OwlUdbZNRZa3FvVZluvt9BnZtP+l/+RdPQSsTtP3ntmjFeHW301iWPTlxf7ee/cpjw7NMClUQ06r59lfA8bK7LjUoqctyiKhRY7P3fiD5zl+MwV1BzXh4YzhnPw5S9Mfg8MKlu7TcPSyY7WyybhVMeflDOFj8Fmmd1EJ2f/RFZMEhorC5p8MIaHXurHmY/+qLTtPfV8JOc++5v2P72BLj2L5JNXMBQzroo5rvekY5dZ33w8+oxsvLsG02bZZDa2faVQu5pje7N7+HwSD4dT64WHafT2k4RNXlq5++o9tvfiPps5rPd7L8T0c6eiRJ24zOdtJ5KbkU1g58Y8tnQSS0IK3y4o/pP8gPy3BFwDWuVvoCiKH/Ao0AVoURYLvVuR4szNBT0OTAaWKYryB7BKVdV/7/amiqKMBcYCzAt8iCe9fUsUSh8bi9b7dtdfradnkbdsWAQF4vLGq8S/8jpqivFgYd28GbrIKAxJyQBkhe7AqmGDCitSqElxKC75bj1x8UBNKdiNWhNQE5unjDu+Yu+Etm4zsg160FoYbxNJN34W3fE9aKvXqbAihbebE1EJtw+6MYkpeLk6Fmjz544jjOrbHkVRqOrtjp+nC5duxNEw0J9cnZ5XPvuZPm0a0q15vQrJXDB78u3sCSl4udyRfXsYo/p1zJfdlUuRcRw5F0Fo2Bl2Hj1Hdq6O9Mxs3vjiF957flDFZHe2JyrpdnfF6OR0PJ3si2y7/kg4vZoE5f185HI0/56KYOeZVeTk6knPzmHaD9uYM6xzuecGUFMSUJxuXwFSnNxRUxMLNsq+/YVNf/4I9B0Fdo6Qka+rY1YG+sun0dZsjE6KFMUaNWYYTz1jHHfi8OHj+Pr55E3z9fUh+kbh4nVkZDRr/toIwD9/b+LTRe/dfD2K+PhEMjIyycjIZM/ugzRoWKfcixTmuK8GjuxO9eHGfWb38PlYuTnS9MNn2T1s3r27EhtUrv25h1ovPMyVH/8FFPaNXkBa+I0CzZotGIdzg2pkRSexe/j84t8uR0dOjnGZSccukX4lGocgnwKDxt0te1Z0EgDxe89gX90LKzfHIrsV15n8GNbuTux97eN8r5pH9nspj+yZkQnE7Tmdt66itxzBpVENk058FAstrb+exNXfdxG5tuiB8JzqBhTephSI+Hk7J+f8VKCtb+/m1Jn8GGC8Gl3c+stJSEWXkUXkWmNv0et/76X6sJB75i1KbkoGsbtP4925caEihTllL802c+t2HEOOjis//pt3clzR23t+V1aFcmVVKAD133iCzBuFv+M+COs9essRlLkjCx1nrNwdca5fLa+nwrU/99Bu1VRufYjK2ldvKW57z19gyv/ZzGlfza/p091oPMT4e7lx7CKO+XrkOPq4kXbHrR53k5Pvs1/cdhTtrBHYujrkDawp/v/Kf95+0xJVVfN3xSzq0umdFbAFwFRVVfVl1bP7bkUKVVXVRGApsFRRFB9gMDBXURR/VVUD7jLjEmAJQGTbziW+yTz39Bks/P3QVvFBHxuHbbcuJM58t0AbrbcXbu+9Q+Lb76G/evsAo4+Owap+PeOYFNnZWDdvSu6ZwqOblxfD1fNoPH1R3LxRk+OxaNKB7OUFB+DJmP1s3r+th0xAd+oA+hP70FR9CE212sYxKXJz0NZqjOHq+TsXUW7q1/AlIjqea7GJeLs6sn7fCd57bmCBNj7uzuw7dZGmtasRn5zG5Rvx+Hu6oqoqM7/5k8AqHjzdq22FZc7LHuhHRFQ812IS8HZzYv3e47z3QsETFx93F/adDKdp7erG7FFx+Hu5MuGJHkx4ogcAB05f5Lu1uyqsQAFQP8CTiLgUriek4OVkz4Yj4UUWGVIzczh0MarAtPF9WjK+T0tj9vBIvv/3WIUVKAAMkeFo3H1QXDxRUxPQNmhD9m+fFWijODijphlPSjV+QcZqf0aqsVBh0ENWBlhYog1sQO6uv4pajLjpm69+4JuvfgCge49OjB77JH/89g/NmjcmJSW10HgUAOv+2UyHjq35YcVvtG3fkvCbRYh1/2xh7gfT0Wq1WFlZ0rRZI75c9G25fwZz3FcvLtvExWWbALD1c6f1N5M4+NLnpF2MKnYe++reeSPV+/RoSuoF41XAmNBjBI3umTequ3ODaiSfuGLsdWECK3dH4wmJQcWuqhcONXxIv1J8z8n82e2r377dx6VhdTSWFkUWKKoPC8E7pBE7Bs0u0LWlMrObqryyR4ce46EXH0Zra4UhR4dHm7pcWLLWpHmbfjyW1PPXubC46PbFbVOxO07S5tvJXFiyjuy4FCxd7LFwsM3rDWOKGxsP49m2LrG7TuHZoQEp50wcDwjj70vN1ZObkoHGxhKvDg04t6jg4K/mlr0024yNl0teocK3V/O8k9KK3t7zs/ZwIjsuBVs/d3z7tCD04RkFppvzerf2vH27nmuTIBRFKXScyU1Kx9LRDodAH9IuRuHVsSGp58rmGFnafdWU7d3a05ns2ORCn62y99XihH2/mbDvjRdhg7oE0/SZ7pz+aw++TYLITs0oNB7F3dh7OpN+87NXaRwIGkUKFP8R+c/bi3ENyH/e7w/c2fWpOfDjzQKFB9BHURSdqqqrS5tLKa4rkKIoh1VVLXI0IkVRqqmqatLIK6UpUgBYt2mF84QXQashY8060r5bid0A4+OYMlb/jfPrr2Ib0hF9lPFLoqrXEzf6OQAcR4/Apltn0OvJPXeepPc+gNySP8bTqbXjvRsVQVu3Gdb9x4BGQ+7+zeRu/gWLNsbHQur2FByHIq9IcfMRpFY9h2IR3AHVoMdw/SLZPy0E/b1HLb6TxaChpcq+4+g55v+wHoNBZUCHJjz7SEd+3mq8MjS4SwtiElN466vVxCWnoaoqo/q25+G2jQk7d4WRc5ZRy98Lzc0K2suPd6VD44dKHkJbuvFcdxw5y/yVxscaDujYjGf7h/DzFuNYJYO7tjRmX/IbcUk3s/fryMPtggu8x60Tn9I+1lCNKt2ARDtOR/D+zUeQ9m9Zm2e7NuGXPcbxaAa1MfZK+fPAOXafvcq8J7sW+R63ihSlfQSpIazwwFWm0NYKNj6CVNGgOxxK7o7VWDTvBoDu4GYsWvbAsnl3VIMedDnkbFiO4ep5FO+qWA94HkWjAUVBd3Kv8dGkpWD10uxSzXcvr82Yy4HDx0hKSsHdzYUXRj/FwH49y3QZvkG9Sz3vvA+m07lbBzIzMhn/4jSOHj4BwKpfljDx5TeJjorBydmRL5d+gJ9/FdLTM3ht0gxOnjAWbl8cP5qhwx/DYDCw8vtfWfzFdyVa/tVN7967URHMYV9d+0jRj5Vr+uGz+PZtScY1Y8FH1RvY1vNNANqunELYK0vIikmm05/TsXC0BUUh+WQER6Z+gy4tE42NJY3feRq3FrVAUci4GlvkSPG+vZvTePYzWLk7kZuSQfKJK+waOhffvi2oN2WQsfu33sCp938jalOYSZ/poZf6UXVQBwy5OvRZuZx454e8R5DmZY9OYsC15WRci8u7anjrMXuVmd3a05kuG97FwtEW1aCiT89iU0dj1/mKyA7GgfFqj++PqqpEbznCiVmrCs1/J/eWten01wyST0WgGoy3cJ5872fs/IxXNC99v+Wu25Rf/9bUfvkRFI0GQ66eI28sK/S0AYCeBz7B0sEWjZUFucnp7Bwyl9Rz17H196DFwuexdLYnOz6FQxMXk3ndtMHCneoG0PzT51G0GtAoXP9rL2c++oMaT3c1++xg+jbT/tf/Ye3uaNxXT1zh8JSv0WdkV+r23nH1dKzcHDDk6jk+YwWxO08+MOs9cFQPAp/phkGnx5CVw7EZK/IG+sy/3n17N6fulMdRDSq5yekcmriEjIiYSttXTdne7/bZKmq9n7MudrjAe+o+6xkCOzUiNzOHta8uIeq4sWfHoG9fZd2Ur0iLSaLZiB60eu5hHDydSY9P4eK2o6yb+hVNn+lOkye7our05GblsvXdlVw/VLILpa9fWVHxg6NVoKThXcxrdPcy4rJy611/b4qiWADngK7AdeAAMExV1SK7LymK8i2w5n6f7nG3IkWIqqqhiqLUK2L0zhBVVUNNWUBpixTmoLRFCnNQ2iKFWShlkcIclLZIYQ5KW6QwB+VVpKgI91OkqGylLVKYg+KKFEIIIURluZ8iRWWTIsWD6V5FCgBFUfpgvKVDC3yjqupsRVGeA1BV9cs72n5LGRQpij0bzFeE+FlRlOXAfMDm5v+bA23uZ8FCCCGEEEIIIYQwX6qqrgXW3vHal8W0HVEWyzSlXNcK430ouzF274gE2pXFwoUQQgghhBBCCCFuMaVffS6QCdhi7ElxSVXvfHamEEIIIYQQQgjx/5Dh/+XdHmbLlJ4UBzAWKVoA7YGhiqLc1z0mQgghhBBCCCGEEHcypSfFaFVVbz1fJwroryhK6YZTF0IIIYQQQgghhCjGPXtS5CtQ5H9tefnEEUIIIYQQQgghxH/Vg/usRyGEEEIIIYQQorzJiIwV6sF9GK8QQgghhBBCCCH+X5EihRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgA2cKIYQQQgghhBDFUA1qZUf4T5GeFEIIIYQQQgghhDALUqQQQgghhBBCCCGEWZAihRBCCCGEEEIIIcyCjEkhhBBCCCGEEEIUx1DZAf5bpCeFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBjUgghhBBCCCGEEMVQDWplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIKMSSGEEEIIIYQQQhTHUNkB/lvKvUhR9eC58l5EubE8/ODWcPyXX6/sCP9JDlqbyo5Qaq1t/Co7Qqn9+nHvyo5QapHh6yo7Qql1Dx5b2RFKbTxelR1BCCGEKGD8C5aVHUEIsyC3ewghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkGKFEIIIYQQQgghhDALD+6gC0IIIYQQQgghRDlTZeDMCiU9KYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBFCiGEEEIIIYQQQpgFGZNCCCGEEEIIIYQojoxJUaGkJ4UQQgghhBBCCCHMghQphBBCCCGEEEIIYRakSCGEEEIIIYQQQgizIGNSCCGEEEIIIYQQxVBlTIoKJT0phBBCCCGEEEIIYRakSCGEEEIIIYQQQgizIEUKIYQQQgghhBBCmAUZk0IIIYQQQgghhCiOjElRoaQnhRBCCCGEEEIIIcyCFCmEEEIIIYQQQghhFqRIIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBRk4UwghhBBCCCGEKIYqA2dWKOlJIYQQQgghhBBCCLMgRQohhBBCCCGEEEKYBbO+3ePjj96hd68uZGRmMnr0JA4fOVGoTejW33FwdADAy9OdAwePMPDx0XTq2Ibff/uGS5evArB69Vrenb2gwrJ/+OFMevbsTEZGJmPHvsqRIrJv3vwLDg72xuxeHhw8eITBg8cyZMgAXnnlOQDS0zMYP/5/HD9+usKyvzXnNTp1a0dmRhZTx8/k1LEzhdrMWziTFm2akpaaBsDUl2dy+sQ5HBwd+PCLWVTx88HCQsvXny/nt1V/S3YTTHl3Iu26tiErM4sZE2Zz5vi5Qm3e/uR/NGsTTFpKOgDTJ8zm3MnzhPRsz/NTn0U1qOj1et5/6xOO7D9WYdkHzxhJ/c5NyMnM5vtXP+fqyUuF2oxc8DLVGgah1+m4fDScldOWYNDpsXG0ZeTH43Hzc0ej1bJ56d/s+SW0wrLPmfc/uvXoREZGFuNfeJ1jR08V2W7aWxN5ZEAv9HoD3369iqWLlwPQtn1LZr83DQtLCxLiE+nf96kKy343b875iO279uPm6sLqFV9WdpxCXn7nRVp3aUlWZjZzJ83n/IkLxbYdP+sleg/uSe/a/fJeC27TmJdmPo/WwoLkxGQmPj75vjPZ+rrRfOHzWHu6gKpyaflWwr9aX6BNlZ7NqDd1EKrBgKo3cOyt5cTvP2vyMqxcHWj11QRcg4O48tN2jk77Nm9ah9/fxMbLBX1WDgC7hswlOy6lzLLf4hocSMg/77Bv3KdErtn/QGT3aFuXNt9OJj0iBoDItQc489EfZZJdsdQSPGckHm3rgkHl5NyfiPzngMnvXZJs9V4fjF+/Vqh6A5e+20z41xtMXo5fv1bUfXUgjrV82db7LZKOGo+zAY+1o9YLffPaOderytbu/yP55BWT3zu/Wi88TMBjbQFQLLQ41fJjTf1x5CalmzR/cevawt6Gjn9Oz2tnW8Wdq7/t5Nj05SXO2PTjsfh0b0J2XApbQqYWmm7pbE+zj8diX90bfXYuYZMWk3LmmsnvX17beknzBY7qQc1ne+FQw4c19caRk5BaouUUt80AONUNoMn7Y7B0tEU1GNjW6y0M2bmmvbFGocuG2WRGJbDnqQ8KTDLnfdWhpi/NFozDpWF1Ts39mfNf/FNkO8/29Wk4fRhoFHTp2Rya8CXpl6NNXk65rfe70NRogFXXYaDRoDu6Hd2+tQWnB9TGeuB41KQ4AHTnDqHb/ZdxorUtVr1HovHwB1Ry1n6DITL8vjMJcT/MtkjRu1cXatWsQZ167WnVsimLPnuPtu37FWoX0uWxvH///NMS/vp7Y97PO3fup/+jz1RI3vx69uxMUFANGjToRMuWTfj003fp2HFAoXbdug3K+/eqVV/y983sly9fpUePwSQlpdCjRwiLFr1X5PzloVO3dlQLDKBbywEEN2vAO/Pf4PFeRa/D+W9/wvq/txR47cnRg7hw9iLjnpyEm7sLG/b8zl+/riM3VyfZ76J91zZUDfSnf5snaNi0PtPmvcrTfcYW2XbBO4vYvCa0wGv7dhwidMNOAGrVDWLeklk81mFYeccGoH5IE7xq+DAjZDw1mtRi6OwxzB/wv0Lt9q/eybKJCwEY9ekE2g/pwvYVmwh5qhc3LlzjizHzcHBzZObWT9i/egf6XH25Z+/WvSOBQdVp2aQHzZo3Zv5HM+nVdXChdkOHP4avXxXaNO+Nqqp4eLgB4OTsyPwPZ/DEwDFcv3Yj73VzMKBPd4YNfIRpsz64d+MK1qpLS/xr+DG8/TPUa1qXSe9N4IV+LxfZtnajh3Bwsi/wmoOTPRNnj2fKk28QExmDi7tLmeRSdQaOz1xJ0vHLWNjb0HnjbGK2Hyf13PW8NjE7TnBjwyHA+GWz1ZIJbOrwqsnL0GfncmrerzjV8cepTkCh6QdeXFTgC21ZZgdAo1D/zaFEh5a8iFnZ2eP2nSl0QlQW2etMHEB2XDKb2k0GRcHK1aHE729KtmpDOmHr586m9q+CqmLt4VSiZaScucreUR/T5P3RBV6/+vsurv6+CwCnOgG0+W5yqQsUAOc/X8P5z9cA4NO9KTXH9Ta5QAHFr2tdehZbu03L+7nzhtlcX2v6CWZ+V37azsVvNtJs4fNFTq89oT9JJ6+wd9THONT0Jfi9EewcNOe+P8Mtpd3WS5ovfv9ZojaF0eH3t0q1nOK2GUWrocWiFzn40uckn4rAytUBQwm+69R8tjep569j4Whb5HRz3Vdzk9I49uZ3VOnV/K7tgueNYu+ID0k9H0ngiG7UmTSAQxMWm7yc8lrvxVIUrLo/RfZPH6CmJmDzzHT0F46gxkcWaGa4eo7s3z4pNLtV1+HoL54gZ/XnoNGCpdX9Z/p/SMakqFhme7tHv349Wb7yVwD27Q/D2cUZHx+vYts7ONjTOaQdf/5Z9JWjivTww9354YffANi//zDOzk73zN6pU9u8IsXevYdISkq5OX8Yfn5Vyj/0Td16dWL1T8bK8pFDJ3B0dsDT28Pk+VUV7G/2DrGztyM5KQWdrvxPNuHBzt6pZ3vW/Gzcdo+HncTRyREPL3eT58/MyMz7t62dDaqqlnnG4jTu0Zy9v28H4NLh89g52uPk6VKo3cnQw3n/vnz0Ai4+xs+nomJjbwOAtZ0N6UlpGHQV85egV9+u/LRqNQCHDh7F2dkJb2/PQu1GjB7Kh/MX5a3XuLgEAAYO6sc/f2/i+rUbBV43B82DG+Ls5FjZMYrUrkdbNvy6CYBTYadxcHLAzatwgUej0fDcm2P5cvbSAq93HdCVHet2EhNpvFKXFJ9UJrmyYpJIOn4ZMJ5QpZ6/jq2Pa4E2+ozsvH9b3LGvBQxsR8i6WXTZPIcm80eDRim0DH1GNvH7z6IvgytnJc0OEDS6J5H/7Cc7LrnA6w9C9uLcb/ZqQ0I4u/DmFUVVLfHValPVeKYbZz783fjHBvKuvmvtrGn68Vg6r59Fl01zqNKzWZHzp56PJC38xl2XEfBoW67+sbvMMgc82oZr+d6vrLYT+xo+WHs4Eb+3cG9HU8TvPUNOUlqx050e8iN2x0kA0i5EYhfgmVcUqsxt3ZR8+SWfuELG1bhCr9/vNuMV0ojkUxEkn4oAICcxDQymfW+wreKGT7dgLq/cZlL7/Cp7X82OSyHxyEXUe323U1UsHIwFGAtHO7KikoDKXe93o6kSiJoUg5ocCwY9utP70dZqYtrMVjZoAh5Cf8z4PQ6DHrIz7z6PEBXAbIsUfr4+XLt6uwJ4/doN/Hx9im0/YEBvtm7bRWrq7T9arVs349DBTaz5azn16j1Urnnz8/X14dq1fNmvR+Hr611s+0ce6UloaMHst4wYMYQNG0LLI2aRvKt4cSPydpe2qMgYvH0Kn7QBTJr2An+H/si0Wa9gZWUJwIqvfiLooRrsOrGBNdt/4t3/fVBhJ8wPcnavKp5E3TzhAoi+EYNXlaKzv/j6OH7a+h2T3x6P5c3sAJ17d+T3HT/w6YoPeHuS6VeM7peLtxuJkbe/RCVGxePiU3yPAo2FllaPduDUv0cACP1uPT41/Zi7fzFvbviQX95eVmHrvUoVbyKvR+X9HBkZhU8R+2r1GgEMeKwPm0J/48dflxIYWA2AoKDquLg4sXrN92z+9zcGD+lfIbkfdJ4+HsRGxub9HHsjFk+fwgXFR0f2Z9fGPSTEFCz+BAT64eDswIJfPmTx2s/pMbB7mWe0C/DApUF1EsIKd3n17d2c7js+oO2K1wibtAQAx1q++Pdvw7/9ZrK12zRUg4GqA9uXeLnNFoyjy+Y51Jn0aJlnt/FxxbdPCy5+t7nA6w9CdgC3ZrXosuU92v4wBcfafmWS3dLJDoB6UwbRZeNsWi6dUOIeDsVlu5N9NS/8+7em84Z3afvDFOxrGL/T1JkwgNidJ9nW6y12DHyXBtOHobWzLnEGAL/+rbm2umyKFFpbK7w7N+b6P8ZbgspqO4GbxY+/9pRJzqIkn4zAt08LAFybBGHn74Gtr7vZbOvF5TPV/W4zDoE+oKq0W/U6XTbOptaLD5s8b6NZT3Fi1qq7/p02533VFGGTl9J25RR6hy2k6qD2eYWRylzvd6M4uqKm3P47qaYmoDgULvRq/GpiM/JtrAdNQvHwNc7r4omakYpVn9HYjJiJVa+R0pNCmIW73u6hKEpHIFpV1bOKorQHWgOnVVUt+iauMqQohaurdzsgDhncn6+Xrcr7OezwcQJrtiQ9PYPevbrw2y/fULd+6f6YllRJsw8e3J9vv/2x0OsdO7bhmWeeoGvXgWWa725Mzf7Bu58RGx2HlZUlsz56k7Evj+CzD5fSoUsbTp84y1OPjqNqDX++/eVzDu45TFqa6V1FJXvx2RfO/pK4mHgsrSx564OpjHzpSZZ8tAyAbeu2s23ddpq2bswLU5/lucETyzu2URHZucv2PnTWGC7sP82FA8YraPU6NubaqSssGPoOntW8Gb/iLS70fo2stPKv5Ju63q2trMjKyqZ7yED69uvOJ4vm0K/3cCwstDQKrs/AR0ZgY2PDus0/cvDAUS6GXy737A80E9a7u7c7IX07MXHQK4Xaai201G70EK888RrWNlYs+utTToWd4tql64XalobWzppWX03i2PTl6IrYDiPXHSRy3UHcW9eh3tRB7Bw8B88ODXBpVIPO62cZ38PGqsT3qR94YRFZUYlY2NvQ6uuJVB3UgYhfdpRZ9kaznubErFWFrtw9CNmTjl1mffPx6DOy8e4aTJtlk9nY9pX7zq5YaLHzcyf+wFmOz1xBzXF9aDhjOAdf/sLk9yguW6HPZ22JPjuXbT3fxLdPC5p9PJbtA97BK6QhPj2bUuv5vnnt7PzcST0fWeg97sa1SRD6zOwSjb1wNz49mhJ/4FzerR5lsZ3c4j+gDQdfMn0dl9TZhX/R+N2n6bJ5Dimnr5J84jKqTm8223px+Ux1v9uMxkKLe6vabOv1FvrMbNr/8j+Sjl4idufJu853axyQpGOXjONCFMGc91VT1Rzbm93D55N4OJxaLzxMo7efJGzy0kpb76VT8DhviL5C5hevQm42msBGWD86nqylr6NotGh8qpG9eSWGGxex7DoMy9Z9yd1h+jgiQpSHYosUiqIsAFoCFoqibAC6AuuASYqihKiq+tpd5h0LjAVQtM5oNPbFNS3g+eeeYfTo4QAcPHgE/wDfvGl+/lWIvFH0oDVubq60aNGEgYPG5L2Wv1fCuvVbWfjpHNzdXYmPTzQpS0mNG/c0I0cOAeDQoWP4++fL7ufDjRsxRc7n5uZC8+aNeeKJguMPNGhQhy++mEf//s+QkJBULplvGT5qEE88ZbwacOzwKarku5Ls4+tFTHThroaxN1/Lycnltx/+YsyLxsECBw59hMWfGk+aIy5d41pEJIG1qnPscHkcgB/s7INHPsZjwx8B4OSR0/j43r4lyLuKF7FRhbPHxcQDkJuTy58//sPTzw8t1CZs71H8q/vh4uZMUkJyoellodNTPWk3tCsAV46G4+rrARgHD3T1cScpuuj9rO+Ex3Fwd2LJuCV5r7UZ1JmNX6wGIPZKNPFXY/AO8uXK0fIZtGnUmGE89Yxx3InDh4/j63e7h5avrw/RReyrkZHRrPnLeDvWP39v4tNF7918PYr4+EQyMjLJyMhkz+6DNGhYR4oURRjwzCM8PKwPAGeOnsPT93ZPIc8qnsRFxxdoX6t+Tfyq+7Jy5/cAWNtas3Lndwxv/wyxN+JITkghKzOLrMwsju47TlC9oDIpUigWWlp/PYmrv+8i8h73y8fvPYN9dS+s3BxBgYift3Nyzk8F2vj2bk6dycaxk8ImL73rfexZUcb9RpeexdU/duPaJKhEJz/3yu7auAYtFxvH/rB2c8S7azCqzvBAZM9ftIjecgRl7sgyWe85CanoMrKIXHsQgOt/76X6sJB75g0c2Z3qwzsDsHv4fLKikwplu7MremZkApE3eyVErj1AswXjbn169o1eUKh7eLMF43BuUI2s6CR2D59/z0z+A9pw7Y/S9U4o6vME9G9T8NaRMthOwDiwp6LVknSs9GM63IsuLZNDE2+PIdDzwCekR8Ti3qZOpW3rd67jovKZ7v62mczIBOL2nM7bRqO3HMGlUY17niy7t3iIKj2a4t01GK21JRYOtjT/7AUOvvR5XpsHZV8tjpW7I871q5F42Pgd5Nqfe2i36tbgrJWz3u9FTU1Ecbrdg1VxdENNSyrYKCcr75+Gi8egx1Ng64AhNQE1NRHDjYsA6M8ewLJ1X0RhMiZFxbrb7R7dgXZACPAS0E1V1VlA75v/FUtV1SWqqjZXVbW5qQUKgC++/I7mLXrQvEUP/vprA08NfxyAVi2bkpKcQlRU0Sf6jw98mH/WbiY7+/a9wvnvK2/RPBiNRlNuBQqAxYu/p3XrPrRu3Ye//97IsGHG3g8tWzYhJSW12OyPPdaXdeu2FMgeEODLjz8uZvToSVy4UH5/xG9Z+c0vPNJ5GI90HsbmdaEMeMJ4cApu1oDUlLS8k/r88o/10L1PCOfOGA/mkdeiaNOhJQDunm7UqFmNq1fK5urm/7fsPy/7nSHdRjCk2wi2rd/Ow4N7AdCwaX3SUtPyChL55R+nonOvjoSfMf5RCah+u3txnYYPYWlpWW4FCoB/l29gTp8pzOkzhaMb99P6sY4A1GhSi8zUDFJikwrN0+6JLtTt2JhvXl5Q4Kp5YmQctds1BMDRwxnvQF/iIoreX8rCN1/9QOcOA+jcYQDr1mzmiaEDAGjWvDEpKalERxf+orjun8106NgaMD7NI/xmEWLdP1to3bY5Wq0WW1sbmjZrxLmzMiJ2UVZ/9xdjej7HmJ7PsXP9Lno+brxFo17TuqSnphe6pWPv1n081nQwQ9o8yZA2T5Kdmc3w9saBcHdu2E3Dlg3QajVY21hTL7gOERciyiRn04/Hknr+OhcWry1yun3124VQl4bV0VhakJOQSuyOk/g93Cqv+7Gliz22/h5ErjvI1m7T2Npt2l1PfBStxvhFHuMJe5XuTUg5c7VMs29oOZENLSawocUErq/Zx5HXl3Fj/cEHIru1p3Pev12bBKEoSpmsd4AbGw/jefOqsGeHBqTcOdhoES4u25T3/mq+nin5s90pcv1BPNvXB4xPQEi7aDzRiQk9RtDonnntnBsYbyc7NHExW7tNM6lAgaLg368VV1eXrkiR//NkRSdh4WiLR5u6eYPEAmWyrgH8H21bZrekFMfSyQ7FUgtA9eGdidt7Bl1aZqVu6/nXsT4zp8h8prrfbSY69BjOdauitbVC0WrwaFOX1HP37oFzcs5PrGv6MhtaTGD/cwuJ3XWyQIECzHtfvVeBAiA3KR1LRzvjrRmAV8eGpJ4z9pSorPV+L4Ybl1BcvVCcPUCjxaJuS/QXDhdsZH/71hhNlRrGHo2ZaZCegpqSgOJm/LzaavUwxJWsF5cQ5UEp7jYERVFOqKraQFEUG+AG4KuqaqaiKFrguKqq9UxZgIWVX6lvLv/0k9n07BFCRmYmY8a8wqEw42jkf//5PWOfe40bN3tWbNn0C/PfX8SGjaF5877w/AjGjXsanU5PVmYWr772Nnv2HizR8i21pX/4yccfz6JHj05kZGQybtyrhIUdB+CPP77lhRem5PWs2LDhRz744As2bfo3b97PP5/HgAG9iYgwHrh0Oj3ti3iyyd34O5g+YOSdZsybSsfObcnMzOL18TM5cdT4+NOlqz7hfxNnERMdx/e/f4mbuyuKAqdPnGP6a3PISM/Ey9uDeQvfxtPbA0WBxZ9+y1+/rit1lgctu4PWptTZX3/vFdp2bk1WZhYzJ87h1FHj7RALV37AO6/MJTY6jsW/foqruwuKonD2xHlmT3mfzIxMRrw0nIcH9UaXqyM7K5uP31lU4keQtrYp+j5qUwx5ZzT1OjUmJzOH71/7nIjjxuLJi8teZ8XUxSTHJPLZhVUkXI8lK91YzT+yfh9rP/0NZy9Xnv7gBZy9jL+TDV/8yf7VJes6+2vi0VJnn/fBdDp360BmRibjX5zG0cPGxwWv+mUJE19+k+ioGJycHfly6Qf4+VchPT2D1ybN4OQJY8+RF8ePZujwxzAYDKz8/lcWf/FdiZYfGV4++8drM+Zy4PAxkpJScHdz4YXRTzGwX897z1gC3YOLfgKNKSa8+zItQ1qQnZXNvFfe5+wx4yN3534/m/df+4j4O3pWrDv7d4FHkD7x3GB6D+6JajDwz6p1/Pr17yVa/nhd4cGM3VvWptNfM0g+FYFqMF4yOfnez9j5GYuDl77fwkMv9aPqoA4YcnXos3I58c4PeY8g9evfmtovP4Ki0WDI1XPkjWUkhhV+tGrPA59g6WCLxsqC3OR0dg6ZS8a1ODr+MR2NpRZFqyFm+wmOzVhu8qBqpmTPr9kn47ix6XDeI0jNPXvgqB4EPtMNg06PISuHYzNWkHDw/H1nTz13HVt/D1osfB5LZ3uy41M4NHExmdcLF4mLc7dsbVdOIeyVJWRFJ2HpZEeLz1/E1s8dXXo2R6Z8TfKpCDQ2ljR+52ncWtQCRSHjamyRT0bw7d2cxrOfwcrdidyUDJJPXGHX0LmAsejR4H9DCO07w+Tcd1P1iY54d27MgecWFnj9ftc1QM99C9g1fD5pF0p/ItTii5fwbFsXKzdHsmOTOfX+b2hunvRf+n4Lbs1q0Xzh86h6AynnrhH2ylJyk9Pv+zPc77Z+y93y5d9mgkb35KEXH8bay4XsuBSitxwhbPLSMtlmAga2o/b4/qiqSvSWI8ZbwUrAo21daj3flz1PfUCNp7vmrXtz3letPZ3psuFdLBxtjY9sT89iU8cp6NIyC6x3397NqTvlcVSDSm5yOocmLiEjIqZC1nuvSaUbj0YT2AirrkNB0aA7vgPdnjVYBIcAoDsSikXTrlg06QwGPaoul9ytP2K4blz3ilcAVr1GomgtMCTFkrP2a8jOKHEGu6nLirj/9/+P6M6dKm5U+grkve1fs/y93a1IMQ9oC9gAoUAdYC/GnhUXVFUt+rlPd7ifIkVlu58iRWW7nyKFKL37KVJUtvspUlS2+ylSVLbyKlJUhPspUlS2oooUQgghRGUqbZHCHEiR4sFkrkWKYs/CVVWdqihKG0CjquouRVGCgEeBpUBJbpwTQgghhBBCCCEeTKpZnsv/v3XXR5CqqroHWKwoyhTgIvA50AGouOcbCiGEEEIIIYQQ4j/hrkWKm1oBVYHdwH4gEuOAmkIIIYQQQgghhBBlxpQiRS6QCdhiHJ/ikqrKQ1iEEEIIIYQQQghRtkwpUhzAWKRoAbQHhiqK8mu5phJCCCGEEEIIIcR/jimPrxitquqtZ3dGAf0VRXmqHDMJIYQQQgghhBBmQe4jqFj37EmRr0CR/7Xl5RNHCCGEEEIIIYQQ/1Wm3O4hhBBCCCGEEEIIUe6kSCGEEEIIIYQQQgizYMqYFEIIIYQQQgghxH+SalAqO8J/ivSkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMghQphBBCCCGEEEIIYRZkTAohhBBCCCGEEKIYqqGyE/y3SE8KIYQQQgghhBBCmAUpUgghhBBCCCGEEMIsSJFCCCGEEEIIIYQQZkHGpBBCCCGEEEIIIYqhqkplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZOBMIYQQQgghhBCiGKqhshP8t0hPCiGEEEIIIYQQQpiFcu9JsderRXkvotzUm+pb2RFKzWLgi5UdodQU5cGtnanZ6ZUdodT0x0IrO0KpfewzqLIjlFr34LGVHaHUNh1ZUtkRSu3vBm9WdgQhhBCigI8/z6nsCKX2v6mVnUD8f/Lgng0KIYQQQgghhBDi/xUZk0IIIYQQQgghhCiGalAqO8J/ivSkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMghQphBBCCCGEEEIIYRZkTAohhBBCCCGEEKIYqlrZCf5bpCeFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWpEghhBBCCCGEEEIIsyBjUgghhBBCCCGEEMVQDUplR/hPkZ4UQgghhBBCCCGEMAtSpBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZOBMIYQQQgghhBCiGDJwZsWSnhRCCCGEEEIIIYQwC1KkEEIIIYQQQgghhFmQIoUQQgghhBBCCCHMgoxJIYQQQgghhBBCFENVKzvBf4v0pBBCCCGEEEIIIYRZkCKFEEIIIYQQQgghzIIUKYQQQgghhBBCCGEWZEwKIYQQQgghhBCiGKpBqewI/ynSk0IIIYQQQgghhBBmwWx7UjiFNKHq22NAqyFu1SaiFv1eYLpNkB/VP3oZuwZBXJ+/gujFfwKgWFtS57fZKFaWKFotiWt3E/nhjxWafdeVeN7fcQ6DqjKgni+jmlUv1ObgtUTe33kOnUHFxcaSrx9rBsDMLafYfjkON1srfh3WukJzA+zcH8a8z75BrzfwWN9ujBn2WIHpyalpTJ//GVcjo7G2suSdKS9Sq0Y1LkVc57V3Psxrd+1GNC+OHMJTj/eruOz7wpj72Vfo9QYG9u3OmOEDC2V/a95CrkZGYW1lxawpL1ErsBoA3//yF7/9swkFhVqB1Xh36stYW1tVXPYDR5n35XLjeu8dwpgnHrkjezrTP1rC1RvRWFta8s7ksdSqHpA3Xa83MOTlN/Fyd2XRrNcqLDfArjNXmf/XHgwGlUdb1mZUl+AC078NPcrasAvGnAaVSzFJbJv5JDaWFoz6Yg25Oj06g4FuDQN5oWezis1+7Bzzlq/FYDDwaEgzRvfrVGB6akYW0774haj4ZHQGA8/0aceAjrcz6g0Ghk7/Ai9XJz6b/FSFZgd4+Z0Xad2lJVmZ2cydNJ/zJy4U23b8rJfoPbgnvWvf3ieD2zTmpZnPo7WwIDkxmYmPT66I2Pf05pyP2L5rP26uLqxe8WWFLjvgsXY89JJxHenSszgy9RuST0UU277x7GeoNqQTfwWNKtFy/Pq1ou6rA3Gs5cu23m+RdPQSAHYBHnTf/gGp4ZEAJBy6wJGp35j0nlV6NqPe1EGoBgOq3sCxt5YTv//sA5H9FtfgQEL+eYd94z4lcs3+QtObL3oR18Y1MOj0JB4O5/BrX6Pq9PedHcCpbgBN3h+DpaMtqsHAtl5vYcjOvev7Nf14LD7dm5Adl8KWkKmFppd0e7qTlasDrb6agGtwEFd+2s7Rad/mTVMstQTPGYlH27pgUDk59yci/zlg8nuXJl9ZbzNQuvVekuxNP3oWl8aBKIpC2sUbHBz/JfqM7PvOXtrt/V7bjKWzPc0+Hot9dW/02bmETVpMyplrJue92zbT4fc3sfFyQZ+VA8CuIXPJjksx+b3zu9e+6tmuHg1nDEexsiDp2CXCJi1B1RtMfv+y3mZsfd1ovvB5rD1dQFW5tHwr4V+tL9TOo21dGr3zFBpLC7ITUtnx6CyTM5f3/novPWY+TVDnxuRm5rDm1cVEnbhcqE3zZ7rTYlQv3Kr78FHwODIT0wBoPa4vDfq3M2a10OBR04+PmzxHVnJ6mWYUwlTmWaTQaKj67jjODZtB7o146v7zPkkb95N1/vZBWpeURsT0r3Dt2arArGp2LmcHT8eQkYVioaX2H++RvC2M9LBzFRJdb1CZ++9ZvujfBG8Ha4b/fIBONTwIcnPIa5Oancucf8+w6JEmVHG0ISEjJ29avzpVeKKhP29tPlUheQtk1+uZ/clSlrw/Ax9Pd4Y8N4XObVsQlO9k+KuVv1GnZg0+mfU6FyOuMWfBUr766G1qVPXj168+ynufroOepWv7VsUtqlyyv/vJYpZ+8DY+nu488dxrdG7XskD2pSt+pU7NGnz67htcvHKN2Z8s5uuPZhEdG8/K39bw53cLsbG2ZvLM+azbuoMBvbtWUHYDsxd9y5L33sDHw40hL79F59ZNCarmn9fmqx//pE5QVT6ZMYmLEZHMWfQtX82bljd9xer11AjwJT0js0Iy52U3GHjvj118ObYP3s72DP90NZ3qVyPI2zWvzYiQxowIaQzAv6eusGL7cZztbFBVlaXj+mJnbUmu3sDIRX/Rvo4/jap5V1j2Od/9zeKpI/F2c2LY9C8JaVqXID+vvDY/bd5LoJ8XCyc/RUJKOv2nLKBv28ZYWhgPnSs37CHQ15O0TNO/8JaVVl1a4l/Dj+Htn6Fe07pMem8CL/R7uci2tRs9hIOTfYHXHJzsmTh7PFOefIOYyBhc3F0qILVpBvTpzrCBjzBt1gcVvuz0iBi2PzqL3OR0vLs0pskHYwjtM73Iti6Na2DpZFeq5aScucreUR/T5P3RhaalXYlma7dpRcx1dzE7TnBjwyHA+CW+1ZIJbOrwapFtzS07ABqF+m8OJTr0WLFNrv6+i4MvLgKgxRcvUX14Zy59t9nkRRSXXdFqaLHoRQ6+9DnJpyKwcnXAkKu75/td+Wk7F7/ZSLOFzxc5vSTbU1H02bmcmvcrTnX8caoTUGBanYkDyI5LZlO7yaAoWLk6FPMuxavs7b20670k2Y9NX4Euzfi3seHMJwka1YNzn/1939mhdNv7vbaZ2hP6k3TyCntHfYxDTV+C3xvBzkFzTH7/u20zAAdeXFTghL9U7rWvKgrNPn2enYNmk3YxirpTHqfq4I5cO8GsrwABAABJREFUWRVq8iLKeptRdQaOz1xJ0vHLWNjb0HnjbGK2Hyf13PW8NpZOdgTPHcmuofPIvB6PtYeTyXmh/PfXuwnq3Bi3Gj580Wkyvk1q0uvdkXw7YEahdlcPnuP8lsM8+eObBV7fu/gf9i7+B4BaXZvQckxvKVCISmWWt3vYB9ci+/INciKiUXN1JPy5E5ceBU94dfHJZBy9UOQVFENGFgCKhRbFQluhD7Y9EZ1CgLMt/s62WGo19KzlTejFuAJt1p2LpmuQF1UcbQBws7t9xb6ZnyvONpYVlje/42cuUNW3CgG+PlhaWtK7S3u27SpYHQ+/fJVWTRsBEFjVn+vRMcQlJBVosy/sOAG+3vj6eFFRjp85T1W/gtm37tpXoE34lau0vpW9mj/Xo25n1+n1ZGfnoNPpyczKwdPDreKynw2nqq83AVW8sLS0oHdIa7btOVQwe8R1WgU3MGav6sv16FjiEpMBiIqNZ8f+Iwzs3bnCMt9yIiKWAA8n/N2dsLTQ0jM4iNCTV4ptv+5wOL2a1ARAURTsrI3buk5vQGcwoCgVd7/fifBrBHi74+/lhqWFBb1aNyT00OkCbRQUMrKyUVWVjKxsnO1t0WqMh83ohGR2HDnLo50qtvfHLe16tGXDr5sAOBV2GgcnB9y8Cm+3Go2G594cy5ezlxZ4veuAruxYt5OYyBgAkuKTyj2zqZoHN8TZybFSlp1w8Dy5N7+YJRy6gG2VYo4FGoWG04dxYtaqAi9r7axp+vFYOq+fRZdNc6jyf+zdd3QUVRvH8e/spveekNASiPQaepFeFQERRVBBQBAVEVBAXkWlCdjFhhQL2BU7RZDeS4AQeg+Q3nvZ3Xn/WNhkSSEJJFnk+ZzjMbtzZ+eX4e5m55k7d4oZHZR2JpL0c1G3NXvBs8NW1wqBRbLA7AB1xvQh8u995MSnFNsm5t/Dpp+TDp0z/fvcanafrk1JOR5hOhOfm5QOhpt/d0jYc5Lc5PRil5fUn2oM6UjXtXPovnE+LRaNAU3hzz99Zg4J+06hL+Isca1hXTm1+A/jA1UlNzHtpnnLks9MBfWZ8u73smS/XqAA0Nrnf9+qqv5+sz7jck8AcduPAZB+NhKHGt6mg+Vb7TO3y83eqzYeThhy80g/Hw1A7NajBNzfBqi6PpMdm0zy0YuAceRN2pmr2Pu5m7Wp8WAHIv/eT9bVBACzUSaW8H4tyT29Qgj7ZTsAkYfOYufigJOPW6F2MccukXIlvtDzBTUc2IFjv+++rfn+C1RV+U/+Z6lKLFIoiuKkKMpDiqJMVhRloqIofRVFqfDChk01D3Kj8t9AudEJ2BT3h7MoGg0N179HsyNfkbr9CBmHzlRAyqLFZmTje634AODrZEtchvlZ1kvJmaTm5DF29UGG/7CPP0/e/i975REbn4Cfj6fpsa+3JzHxiWZt6tWpzcZtewA4euIMUdFxxMQlmLVZu2kH/Xp0rvjABcTGJeLn7WV67OvtSWxcEdm3X89++lr2eHy9PRn1yCB6PvwU3YY8ibOTAx1bt6i87AmJ+HkX2O9eHsTEJ5lnD6zJxp3GYYFHT54jKibe9G+z6LOVTB77KJpKPMC/LjY1Az+3/LMBvq6OxBZTec/K1bHr1BV6Nqltek5vMPDwu7/Q/Y2VtAsOoEnNyitsxSal4ufhanrs4+FCTJL5sNdhvdpxPjKOnhMX8tDMj5j2+H1orhUpFq1aw+RhfdAU8UWlMnj7eREXGWd6HBcVh7efV6F2g58cyM5/dpMYa/5+qBEUgJOrE+//9A5L1nxC7yG9Kjzznab28K7EbDpS5LI6o/sQtT6U7Nhks+frTxpE3I5jbO77KtuHzKXxrOFoHWzLtF3Hmt503zCfzr++imfbemVa179fK3ptf5sOq14idPLnd0x2Oz93/Pu35nwpR0UoVlpqPtSJmM1Hbkt2pyA/UFU6fjeD7v/MI/jZ+0u9bmkV7E/Owf5UH9ierQNeZ1PPmagGAzWHdCr1a10f0dBw2lC6/zOPNksnlfmsb0n5blRRfeZ27feSsgOEvD+e/kc/xbluNc4tX39bssOtvVeLk3IsAv/+rQFwb1EHh+pe2Pt73nKfuS7k/fF03zif+pMHlytfad6ruQlpaKy1uDULBCDg/rbY+xu/x1tCn3Go4YVb49okhp674bWrYe3mSOfVr9Bt/TxqDjV+l7XE9+uNnP08SI3M/z6eGp2Is697CWsUzcrOhjpdmnJybeFLeISoTMVe7qEoysPAS8ARoBuwC2gHLFIUZYSqqkdLWHccMA7gZbdmPOhYu4yxCn/pL9NgCIOB430mo3VxpM6yGdjVq0n2qdJfA1rR9AaVE7FpLBnUkmydnpE/H6Cpryu13Ms3jPJ2KWof33jcO2b4gyz4aDkPjZ1CcFAt6gcHYqXNr1vl5eWxZdd+Jj31WAWnNadSOPyN2ccOH8KCxcsYMuaFa9mD0Gq1pKSls3nnPtZ/vwRnJ0emvraIP//ZwoDeXSsne5H73Tz8mEcGsODTlTw04WWCA2tQv25trDQatu4JxcPNlUbBgew/UvmXCJWmz1y37fglmtf2xdUhv4in1Wj4ccoQUrNymPLVBs5GJ1LXr3JGsZRmv+86eob6Naux7OXRXI5NZPyCL2hZrxYHT17Ew8WRhoEB7D9xvlLyFhG20FM3njn39PWk631deGHolEJttVZa6jW9hymPvIStnQ0f//Ehx0OPc+XC1UJt70ZeHRtS69GubBv4RqFldr5uBAxoy/YHC1+r7NO1CX59WhI84T4AtLbWOAR4knYmslTbzY5JZl3I8+QmpePWNJB2X0xhY5dpZmeDSxK59gCRaw/g2a4+DacPZcfD5sPELTV70zlPGM/Sl/IsevMFTxK/5yQJe0/dluwaKy2ebeuxue+r6LNy6PTT/0g+coG4HcdKtf7N3NifvDs3xq1pIN3WGf8dtHY2ZZobQLHS4hDgScL+Uxx9fRV1x/enyWsjODDx09uSr6CK7DO3Y7+XlP26gy8sAY1C8/mjqD6wPZe+31rl79XinFr8B83mPkH3jfNJPXGZlPCLqDr9LfcZgP3PfEx2dBJWjna0Xf4CNYd2JuKn7WV6jdK+V/eN/4imbzyOxtaa2C1hqDrjfBRV3We0Dra0XTaZsFkrC/1bKVZa3JsGsn3ofLR2NnT96w0SD56xuPdrkdso4rtXsaPpShDcsyVXDpyWSz1ElStpTopXgHaqqmYqiuIFfKOqah9FUZoCS4AOxa2oqurnwOcAB6oPKvM7JDcqAZtq+WcEbfw8yYtOLGGNoulTM0jbHY5r1xaVVqTwcbQjJi3b9DgmPQdvR/MKsY+TLW52nthba7G31tLS343TCWlVXqTw9fYkOja/ChsTl4CPp/kBo5OjA3OnG697V1WVvo8+TUC1/DkEtu89RIN7gvDycKuUzNf5ensSHZc/+iYmLqHQJRtOjg7MnfE8YMzeZ9g4qlfzZef+QwRU88HDzXhWvce97Tl87GSlFSl8vTyILjAaJSY+EZ8b5gdwcnRg7ovjTdn7jnyBAD9v1m7dzeY9B9m+/zA5uXlkZGYxY+EnLJj+TOVkd3UkusCw1ZiUDLxvmPvgunWHz9G3RZ0il7nY29IqqBo7T16ptCKFr4cL0Yn5Q1VjE1PxcTO/xOD3baGMHnAviqJQ09eTAG93LkTGc/h0BFtCT7LjyGly8nRkZOXw8qc/8eaEoRWaedDIB7h/eH8ATh45jbe/t2mZdzVv4mPMRzUFN6pLQG1/vtnxNQC29rZ8s+MrRnQaSVxUPCmJqWRnZZOdlc2RvUep07DOXVmkCHqyF7VHGC+X2jViETYezrR85yl2DV9oHEp8A7cmtXEK9KX3nvcA4xDy3rvf5Z/2UwCFvWPeLzRMOeT98bg2rkV2TDK7RiwqNoshV0durnGbyWEXyLgUg1Mdv2KvIb8xe3ZMMmAcUu5Y2wcbD2ezYcWWmt3axYE2S4x/W2w9nPHt0RxVZyBq3YFC69Wf+iC2ni7seem9As/eWvasyETid58w7auYfw/j1jTwthQpXBrUKNyfFIj4cRvH5v9g1ta/XyvqTzVOWB06dWmx+y43MQ1dZjaRa4z75+qfe6g9vGup8lhSfy/rfi9rdjMGlSu/7yb4mfu59P3WW85e1v5eWrr0LGNR5Zo++z8gIyIOz/b1b6nPAGRHG0dp6jKyufzrLtxb1ClVkaI879XEg2fYNmg2AD5dmuBUp9q1JVX3XlWstLRbPpnLq3cSuabwpJVZkQnkJqahz8xBn5lD/J4TuDaqVaXv15KEPNGLFsOM/y6RYedx8c8flevi50H6DSOfSqPRgHYc+0Mu9RBVr6QihQJcLzFmAD4AqqqGKYpye8co3SDjyBnsAqthU8OHvOhEPAZ24vxz75ZqXSsPF1SdHn1qBoqdDS6dmhH9yeqbr3ibNPJ1JiIlk6upWfg42rL+TAxv9m5k1qZroDcLt51CZzCQp1cJj0nlseY1Ky1jcRrXr8ulq1FciYrB18uDtZt2sPCVyWZtUtMzsLe1wdraml/+3khI04Y4OeYXV9Zu2k6/7mUffnirGtcLJuKKefZFr5ifPU5NS8fezvZa9g2ENGuEk6MD1Xy8CTt+mqzsHOxsbdgbGkajekUfTFdM9iAuXY3mSnQsvp4erN2yh4UznjXPnp6Bva0t1tZW/LJ2MyGN6+Pk6MALo4fxwuhhAOw/cpwvf/670goUAI1qeBMRn8rVxFR8XBxZf/gc84cXnhsjLSuXg+ejzZYlpmdhpdXgYm9Ldp6OvWev8uS1CTYrJXtQABHRCVyJTcTXw4V1e47y5jPmRQY/Tzf2HjtHy3q1SUhJ52J0PNV93Jn0SG8mPdIbgP0nzvPVmp0VXqAA+O2rP/jtK+M1re26t2XwkwPZ9PtmGrZsQEZaRqFLOvZs2suDLR82PV576k9GdBoJwI71u5g09zm0Wg1W1tY0bF6fn5f+UuG/gyU6/8UGzn9hnN/DPsCTdismc+C5T0zXU98oeuNh1jTNf589cG7FtQM2iN0SRp0xfUyzurs2rkVK+CWzg46S2Hg6m66xdqjpg1OgHxmXYkuV3bF2fsHYrUltNNZWha57ttTsBYV8MJ6oDYeKLFDUHt4V365N2T50ntlwqFvNHrMljHuevR+tvQ2GXB1e7Rtw9vM1pVq3JMX1p7jtx2j/5VTOfr6WnPhUrN0csXKyN42EKY2ofw7h3aEBcTuP4925MamnS1dgtKT+Xtb9XtbsYHxfZFyMAcCvd0vSzkbeluxl7e+lZe3igC4rBzVPT+0R3YjfcxJdetYt9xlFq8Ha1ZHcxDQUKy3VerUgdlt4qTKV571q6+VCTnwqGhsr7nluAKfe/w2o2vdqy/fGkXbmKmeXFN0+av1Bms0fhaLVoLGxwr1lXc5+vpbUU1eq7P1akoNfb+Dg18Z/l7rdm9NqZG+O/7Eb/xZ1yUnLKnORwtbZnprtGvD7C7dvhMd/iVr6m9OI26CkIsUaYJ2iKFuBfsBPAIqieFDU9Ri3k95AxKtLueeb10CjJeGHjWSfvoz3Y30AiFu1HitvNxqueRutkwOqQcV37ADCu03E2tedwPcmgVaDoigk/rWTlH9L9wFyO1hpNEy/tx7P/H4IgwoDG1ajjqcTP4Ub70wytHF1gjwc6VDTk4e/24tGURjc0J+6nsbr+mesD+fg1SSSs/Po88UOnm4bxOCG/pWTXatl5vNjeXrabPQGA4P79aBuYE1+/MN4/ebDD/Th/KUr/O/ND9FoNNSpXZ03Xso/mM7KzmH3wSPMmvJ0peQ1y26lZeakpxj/0hvoDXoG9+tJ3cCa/PC78fZSjwzsy/mIK8yc/wFajYag2jWYPe05AJo2vIdeXTrw8FNT0Gq11A8OZOj9fSovu1bLzGdH8fTMhcb93rsLdWtX58e/jNd7Pnx/T85HRPK/tz417vdaAbwxeVyl5SuJlVbDjEEdmLB0LQaDysA29ajr58FPu42Xngxt3xCATeEXaX9PAPY2+ZPCxqdm8uoPWzEYVAyqSu9mQdzbsFYlZtfy8hP3M+GtrzAYDAy6N4S61X358V/jdZgP92jDuEFdefXzXxjy8mJUVeWFR/rg7lz0SJHKtmfTXtp2b8M3O74mJzuHhVPeMi1b8PU83nrpXRJuGFlRUMTZCPZtOcDyDUtRDQb+/m4tF05drITkN/fSawvYfyiM5ORUegx6jGfGPM6QAZXznmww5UFs3J1pvuBJAFS9gc19jLOgd/hmGqFTPjeNVijKifdW02z2E/TYvAAUhczLcex+vPBdSvz7taLZvJHYeLrQYdU0UsIvsfPRBXi1q0/DaUMx6PSgN3Bo2grykks37Dbg/jbUHNoZQ54OfXYe+8YvNi2z9OwlKZi9+aIxZF6Jp+tfxqH9kWv2c/LdX285e15KBmeWrKHburmoqkrMv4eJ3nj4ptlaf/oc3h0aYOPhTL/QxRx/6xc01loALnz9b7H9Ke30VY4t/JGO389A0Wgw5Ok5/PIXZBUxoV2f/R9g7WSPxsYK/74h7Bi2gLTTVwmf+x2tF0+g6ZwnyElILfVBXkFV3d/Lu99LnT02hVYfPo2Vsz0oCinHIky3Ca2q/n6zPuMcHECrxRNQ9QZST18hdIpx0uNb7TOZV+Lp+N0MNNZaFK2G2G3hXFi1qVT7urQK9pngZ+7Hr2cLFI3C+a82ErfT+L2gqvqMZ5t61BramZTjEXTfaLwM7tibP+IQYBx9cOHrf0k7E0nM5jB6bF6AalC5+M1m0+1fLeH9WpKzmw5Tp1tzntn2rukWpNc98uVL/D1tKemxybQa1Yf2T9+Pk7crT61fwLnNh/l7+jIA6vVpzfltR8mrgjuWCXEjpaTrlRRF6Q+0APapqrrh2nMK0EdV1cI3Fy5CeS73sBQNp1dOcaAiWA159uaNLFQlzM1aYdScO/caPn3YlqqOUG6KX+UVN263vg/e3i8qlWnD4aInZrwT/Nn4lZs3EkIIISrRCVvLvdvCzfzv0jd3bvhSONuwzx17TFuSusfXW+S/W4lHg6qqrgEeBVoqRvbAh0DhG+8KIYQQQgghhBBC3ILSnLJuC9TAeHeP/UAk0LEiQwkhhBBCCCGEEKJqKYrSV1GUU4qinFUUZUYRy0coihJ27b9diqLc8iRzJc1JcV0exgk07QE74IKqytQhQgghhBBCCCH++wyqRV4VUeEURdECHwO9gCvAfkVR/lBV9XiBZheALqqqJimK0g/jXT7b3sp2SzOSYj/GIkVroBPwqKIoP9/KRoUQQgghhBBCCGHR2gBnVVU9r6pqLvA9MLBgA1VVd6mqmnTt4R6g+q1utDQjKcaoqnr99hjRwEBFUR6/1Q0LIYQQQgghhBDCYgUAlws8vkLJoyTGAGtvdaM3LVIUKFAUfG7lrW5YCCGEEEIIIYQQVUNRlHHAuAJPfa6qasHbtxV1nUuRdzpRFKUbxiJFp1vNVZqRFEIIIYQQQgghxF1J/Y/OSXGtIFHSPeWvYLyJxnXVMd5Iw4yiKE2BZUA/VVUTbjVXaeakEEIIIYQQQgghxN1lPxCsKEqgoig2wDDgj4INFEWpCawGHldV9fTt2KiMpBBCCCGEEEIIIYQZVVV1iqI8B6wHtMAKVVWPKYry9LXlnwGzAE/gE0VRAHSqqra6le1KkUIIIYQQQgghhBCFqKq6Blhzw3OfFfh5LDD2dm5TihRCCCGEEEIIIUQxVMN/c04KSyVzUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDFWt6gR3FxlJIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQSbOFEIIIYQQQgghiqEalKqOcFeRkRRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsgqKqaoVuYLXf8IrdgBBCCFFGA8LnVnWEcst6+emqjlBuSQcNVR2h3O7k65Ez02yqOkK5Rac7VnWEcqtfJ66qI5RbzQOnqzpCue3xaV3VEcrteyv7qo5Qbm9f/O7O/ZAshfCg+/+Tx7SNz/9lkf9uMpJCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIUqQQQgghhBBCCCGERbCq6gBCCCGEEEIIIYSlUlWLnLrhP0tGUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDFWt6gR3FxlJIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQSbOFEIIIYQQQgghimFQlaqOcFeRkRRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsgsxJIYQQQgghhBBCFEOVOSkqlYykEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsghQphBBCCCGEEEIIYRFkTgohhBBCCCGEEKIYqlrVCe4uMpJCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIUqQQQgghhBBCCCGERaiyOSmc6voT8v543JrU5viCHznz6d9Ftrv3t1lYOdkBYOvlStKhc+x58t1SbydgQFsavDgE52B/Nvd7leQjFwBwqOFFr21vk3YuEoDEg2c5PH1FqV6zxoMduee5AQDoMrI5PH0FKccjCrVr+e5TuDULQlEU0s9HceD5z9Bn5twR2QEazniYgAFtUfUGLny1kXPL10v2SsgO0GzeSGoN68IfdUaXOndFZS/te9W7UyOazBoOGgVdRg4HJ31GxsWYKs1+p75X7f09aLV4ArbebqCqXFi5iXPL1pm1qdYnhIbTh6IaDKh6A2GvriRh36lSZ7Zxd6Ltskm4N6/DpR+2cWTml6ZlnVe/gp2PG/rsXAB2DltATnxqqV/7Tu7vt+qV+e+ybec+PNzd+G3VZ5WyzdLSNmqF3bAJKBoNudvXkbvuhyLbaWrfg+PLH5C1ZD660O1gZY3jtHfAyhq0WnQHt5Pzx8pKzW7XoTUeLz4DWg3pv64l9cvvzZY79uuOy6hhABgys0ic/wF5Z87nN9BoqLbqE3Rx8cRNeqUyo2PfoRUe054BjTF7yhfm+92xf3dcRz0CgCEri4R5H5J3+obs336MPjae2OdfrczoON4bgt+r41C0GpJ++IeEJT+ZLbcJqo7/whewa1SXuHe/JmHZatMyjycH4fZwb0Al59QlIqe9h5qbV6F5Pbo1I3jukyhaDVHf/Mulxb8XahM870k8e7TAkJXD8ec/If2o8XPDysWB+u8+jWP9GqCqnJj8KakHztDo8xdwqONvaqNLzWR/j2kV+nvYtm2N6wvPgVZL5p9/k77yO7Pl9r174vSYsb+rWVkkv/U+urPnAHB85CEcBtwHqOSdO0/yvIVQwfv9Ru+9O5t+fbuTmZXFmDGTOXQ4vFCbLZtW4+TsBICPtyf7DxxmyENj6HJve1b/soILFy8D8Ntva5g77/1Kye3StQU13xgLWg3x320g+uPVZsvt6gRQ+92JODSuw9VFq4hZYuxfiq019X+Zh2JjjaLVkrRmF5HvfF/UJirUwNdG0qBbc3KzcvnhxU+5euxioTbD33+W6k2CMOj0RBw5x88zl2HQ6U3LazQNYuKvc1j13AeErd1Xiektn0FVqjrCXaXKihR5yemEvfIV1fq2KrHdtkGzTT+3XfYCUesPlmk7qScvs2f0e7R4a0yhZemXYtjUc2aZXg8gIyKWbYPnkJeSgW/3ZrR4eyxb+s8q1C5s1ip06VkANHn9MeqM7s3pj/68I7LXGtYF+wBPNnR6EVQVWy+XMm1HspcvO4Bbs0CsXRzKvA2omOylfa82XziaPaPeIe1MJEGjelJ/8iAOTlpSpdnv1PeqqjNw9PVvSD56EStHO7r9M4/YbUdJO33V1CZ2e7jp89ClQQ3afj6JDZ1fLPU29Dl5HF/4My71q+NSv0ah5fuf/dh00F9Wd3J/v1WD+vdi+JAHmDnn7UrfdokUDfbDnyPjvRmoSfE4/m8xuiO7MURFFGpnN2QsumMF/tbq8sh4ZxrkZINWi+O099CF70d//mTlZNdo8Jg+kdhnpqOLiaPaqo/J2rqLvAv52XVXo4kZOwVDWjp2HVrj+cpkokdONC13fnQweRciUJzK19duKfvLE4l5ejq6mHj8v/mIzK27yTtvnj16zFQMaenYd2yN16svEPX486blLsON2TWOlZ+92usTuDTyFfKi4wn69T3S/t1D7tnLpib6lDSiZy/BuXd7s1WtfD3xGDmAc30moObkEvDhDFwGdCHll40VmFeh3oIxHHp4LjmRCbRa/yZx6w+QWeBz07NHCxwC/djT7nlcQoKpt2gsB/v9D4DguU+SsPkw4WPfRbHWorW3BeDYuPdN69d9/XF0qZkV9zsAaDS4vjiJhEkvoY+Nw3v5Z2Rv34Xu4iVTE11kFPHPvoCalo5tuza4TZ9K/FPPoPHywnHog8QOHwW5ubjPeQ37nt3JWlP6EzW3ql/f7gTXDaR+w060bdOSjz96kw6dBhRq17X7g6aff/zhc/748x/T4x079jFw8MhKyWui0VBz7nhOD3+NvKgEGvz9Fsn/7CP7zBVTE11yOhGzluHep63ZqmpOHqcenoUhMxvFSku9X98kZXMoGaGnKy1+/a7N8Q70Y0HXydRsUZch88bw4aDCRc3Q33by7QsfAzDiw4m0HdaN3auM70tFo3DfjOGc2nak0nILUZwqK1LkxKeSE5+KX88WpWpv5WiHd6dGHHzBeNCjdbCl2byRuDaogaLVcuLtX4osYKSdibytuQESD5zJ//ngWeyreRTZ7vpBD4DW3ib/5zsge+DInuyf8JFpKtvrZ1Ele8VmR6PQZNZw9k/4GP/+rU1PV2X2Ur9XVRUrJ3sArJwdyI5OBu6M/W5p79Xs2GSyY5ON2TKySTtzFXs/d7MiRcGRHlYOdqgFpp2uMaQjdcb2RWOtJSn0HIdmrACD+bTU+swcEvadwjHQ97Zmhzu7v9+qVs2bcDWq9COIKos2sB6GuEjU+GgA8vZvxap5B3JvKFLYdB9I3sHtaAPrmb9ATva1F7ICrRYqcZZzm8b10F2JRHc1CoCM9Vuw79rRrEiRE3bc9HPu0RNofb1Nj7U+Xth3bkvq8m9xfuyhygsO2Dauh+5yJLqrxv2esX4LDl07kFKgSJFzJD97TljR2VOWfYvL40MqLzhg3+weci9FknfZmD3lr20492xHQsEiRUIK+oQUnLu1LrS+YqVFsbNB1enQ2Nuii0mo0LwuLeuSeSGa7EuxAMT+tgvvvq25VOBz06tvK6J/2gZA6sEzWLk4YuPjhj4zB7f2DTjxvPHgTc3To8srXIzweaA9h4bMLvT87WTdsD66K5HoI439PWvjJuw6dyS9QJEiL/yY6efcY8fR+niZHitaLYqtLapeh2JniyG+Yvf7jQYM6MPKb34GYO++UFzdXPHz8yE6OrbI9k5OjnTr2pExY6dUZsxCHJsHk3MxitwI4+d34u87cOvdluiCRYqEFHQJKbj1KHzSxpBp/IxUrLQoVtpKvxVEo94hHFi9HYCIQ2exc3bA2duNtLhks3Yntxw2/Rxx5Cyufvl/nzuN6kvY2r3UaFanMiILUaJiixSKolgBY4DBgD/GrySRwO/AclVVK3XsmH//1sTtCDcdTNSfNIi4HccInfw51i4OdF07h9jt4WUaou1Y05vuG+aTl57F8QU/krC39EOlr6s9vCsxm4qvOIa8Px7fHs1JO32Fo6+vumOyO9byofrAdvj3b01OQipH/vc1GReiJXsFZ68zug9R60NNB6jXWUr2koROXUqHb6ZhyM4lLz2LLf1fs6jsd+p71aGGF26Na5MYeq7QMv9+rWg0cxi2Xi7seuwtAJyD/ak+sD1bB7yOqtPTfMGT1BzSiYiftpdpuyHvj0fVG4j8ez8n3/u1TOsW9F/t73caxc0LQ2Kc6bGaFIc2sP4NbTyxatGRzHemFS5SKBocX/0Yjbc/uVv+QH+hkkZRAFbeXugKHODoY+OwaVy/2PZOg/qRtTN/mLL7i8+Q/MFSFIdKHomAscigi87f77qYeGyblJB9cF+yduw3PfZ4aQJJ7y9F42hfoTmLYuXrSV5UvOmxLjoe+2b1Slgjny4mgYRlq7ln+5cYsnPJ2BFKxo5DFRUVAFs/D3Ii8w/IcyITcGkZbN6mmgfZV/N/p5yoBGyreaDq9OQlpNLgg2dwalSLtLDznH7lSwwFPnPc2jUgNy6FrAvRFfp7aL290McU6O9xcdg0bFBse4f7+5O929jfDfHxpH/3I76//oCak0POvgPk7DtQoXlvFODvx5XL+QXkq1eiCPD3K7ZIMWhQPzZt3klaWrrpuXbtQjh4YANRkdFMmzGH48crfkSCTTUPcgv099zoBJxaBJewxg00GhqufQfb2n7EfrWWjENnbr7ObeTq60Fygf6fEp2Iq59HoSLFdRorLSGDO/P7G18B4OLrTuM+rfns0TlSpBAWoaSRFCuBZOB14HoZsTowElgFPFLcioqijAPGAYx3bk1vh7q3HLT64PZc/Gaz6bFP1yb49WlJ8IT7ANDaWuMQ4FnqM2vZMcmsC3me3KR03JoG0u6LKWzsMs3sjOrNeHVsSK1Hu7Jt4BvFtjn4whLQKDSfP4rqA9tz6futd0R2ra01+pw8Nvd5Bf/+rQl5bxzbBs2W7BWY3c7XjYABbdn+4JxCyywh+83UHdePXSMWkXToHMHP3E/TNx4jdOpSi8h+p75XtQ62tF02mbBZK4tcJ3LtASLXHsCzXX0aTh/Kjofn4925MW5NA+m2ztiPtHY2ZZpPAmD/Mx+THZ2ElaMdbZe/QM2hnctc5ID/dn+/4xR5Ka35mT67RyaQs3oZqIYimhrImD0B7B1xeOY1NP61MURerIikhSlFhC/mJKVtq2Y4DepL9OjJANh3boshMZncE2ewDWlWgSGLUWT2osPbtWqG06B+RD/5AmDMrk8yZrdr1bQCQxajqOylpHFxwrlnO850HY0+NYPqH72M68BupPy++eYrl1cRedVCHaXofw/FSotTk0BOz1xBauhZgueOotbEQVxYmD9/iM/gjsT8uvM2hy5KEb9HMX3GpmVzHAb0J/5p4+VBirMTdp07EPvQoxjS0nGf9zr2fXqStb4CL7O5gVLUv0MJowqGPTyQ5V/kz7kReugoQXXbkJGRSb++3fnlpxU0aNSpQrKaKyp3GVY3GDjeZzJaF0fqLJuBXb2aZJ8qfi6m262s+/3BOaM5v+8kF/YbC/gDZz3B3wu+RTVU7ggQIYpTUpGipaqqN5bMrwB7FEUpsaSpqurnwOcAq/2Gm3p70JO9qD2iGwC7RiwiOya5VCFt3J1wb16HPU++V+BZhb1j3if9XJRZ25D3x+PauBbZMcnsGrGo2Nc05OrIzTVWbZPDLpBxKQanOn7FXoN9Y3YbD2davvMUu4YvJDcpvch18jemcuX33QQ/cz+Xvt96R2TPikwk8m9jZT5yzX5C3h9/bYlkr6jsbk1q4xToS+89xn6utbeh9+53+af9lCrPfrP3qo2nM66NapF0yHi2/8rvu+n43fRrSy17v5tvrGrfqwUpVlraLZ/M5dU7iVyzv8S2CXtO4ljbBxsPZ1Ag4sdtHJtvPjmff79W1J9qvAY4dOrSEjNkRycBxktNLv+6C/cWdW5apLiT+/vdQE2KR+ORfxmB4u6NITnRrI229j3YP2Wcx0NxcsWqcRuyDXp0h3flN8rKQHc6DKvGrcitpCKFLjYOKz+f/Jw+3ujjCg9htw4OxPPVqcROfBlDirEwZ9usMfZd2hPQqQ2KjQ2KowOec2eQ8MqCSsmuj4nDyi9/v1v5ehWf/bUpxDw7E0NKmjF780Y4dGmPQ4HsXvOmE/+/hZWSXRcdj3W1/MsIrPy8yCvlJRuOHZuTezkGfaLx3yFt/S7sWzao0CJFTlQCtv6epse2/p7kXvssK9jGLsCLFIwHZrbVPMmJTgJVJScygdTQswDE/rmHWhMHmdZTtBp87mvD/l4zKiz/dfq4OLS+Bfq7t3eRl2xY1QnC7eUXSZgyAzX1Wn9vFYIuMhpDcgoA2Vu2Y9OkcYUXKSY8PZIxY0YAcODAYarX8DctC6hejchiLoHz8HCndesWDBk61vRcwREVa9dtYvGH8/H0dCchIamol7htcqMSsCnQ3238PMmLTixhjaLpUzNI2x2Oa9cWFV6k6PB4L9o+2h2Ay0fO41ag/7v6eZAaU/Q+6zVpCE6eznw1fpnpuRpNg3hssbHY5ejuTIOuzdHrDRz7p3JH4lgyVSbOrFQlFSmSFEUZCvyiqsbTKoqiaIChQLk+Kc5/sYHzX2wo83oBA9oSvfEQhpz8K0xit4RRZ0wf04z0ro1rkRJ+yTRnxc3YeDobvzgbVBxq+uAU6EfGpaKHot2Y3T7Ak3YrJnPguU9IP1/8sD/H2r6muxv49W5J2tnIOyZ75LoDeHdqxKXvtuLVoQHp56MkewVnj954mDVNnzE9fuDcimsHbFWbvTTykjOwdnbAKciP9PPR+NzbhLTTd05/t5T3akEt3xtH2pmrnF2y5qaZ3ZrURmNtRW5iGnHbj9H+y6mc/XwtOfGpWLs5YuVkbxp1cTOKVoO1qyO5iWkoVlqq9WpB7LbCM7Pf6E7u73cD/cVTaHwCULz8UJPisW7dhaxl5gfq6S8/YfrZ7skX0R3Zi+7wLhQnV1S9DrIywNoGqwYtyF33Y6Vlzz12CqsaAVj5+6GLjcexT1fiZ843a6P188H77ddJeHUBuoj8OQiSP1pO8kfLAbANaYbLE0MrrUABkHPsFFY1zbPHzXzzhuze+LzzGvGvLDTPvngFyYuNd6Wxa9UUlyeGVlqBAiAr7DQ2tQOwru5LXkwCrvffy9XJb5VqXV1kHPbN66HY2aJm5+DYoRlZR89WaN60Q+dwCKqGXU1vcqIS8RnUgeMTPjRrE7/+ANVH9yXm1524hASjT8sk99rlZjmRCTjUqUbmuSg8Ojch43T+XATu9zYh40wkOVFlP2gtq7wTJ7GqHoC2mh/6uHjse3Yn6fW5Zm20vj54vDmbpDfeRH85P6c+JhabRg2Nc1Lk5GDbqiV5Jyv+UrdPP/uKTz8zXjbQv18Pnpkwih9++J22bVqSmpJa7KUeDw25n7/XbCQnJ/+yGl9fb2JijJdItW7VHI1GU+EFCoCMI2ewC6yGTQ0f8qIT8RjYifPPle5uglYeLqg6PfrUDBQ7G1w6NSP6k9U3X/EW7Vq5gV0rjX93G3RrQceRvTn8xy5qtqhLdlpmkZd6tHmkG/Xubcpnw+eajbSY33mS6edH3n6aE/+GSoFCVCmluKFAiqLUBhYC3ckvSrgBm4EZqqqW6pRUwZEUBdl6u9J9/VysnO1RDSr6jGw23Gscjtvhm2mETvncdPa28+pXOL34D2I2h5nW19hZ02z2E3i0DgZFIfNyHLsfLzybun+/VjSbNxIbTxfyUjNJCb/EzkcX4H9faxpOG2q87Y7ewPG3fiF6Q2hpfiVavvMU/ve1IfOK8UNU1RvY3Md4SzNT9tgUuvw+Cytne1AUUo5FcHj6CnTpWZafPSYZaxcHWn/yLPYBnugycjg8bTkpxyMkewVnL+iBcytMt2Ssyuylfa/692tFg2kPoRpU8lIyOPjC52RGxFr+frfA96pnm3p0+eM1Uo5HoBqMQ++PvfkjDgHGsyQXvv6Xe54bQM2hnTHk6dBn5xE++1vTLUgDBraj3sQHUDQaDHl6Dr/8BUmhhQ8Q+uz/AGsnezQ2VuSlZLBj2AIyr8Rz76+z0FhrUbQaYreFE/baykITb97yfq/i/j4gfG6h17sdXnptAfsPhZGcnIqnhxvPjHmcIQP63NZtZL38dLnWs2rcGtthE1AUDbk715O75jusuxgvqcnban5rYVORInQ7moBA7Ee/BBoNKBryDmwl969vypUh6WARl5KUgl3HNsZbkGo0pP+xjtTl3+I05H4A0n/5C49Xp+DQozP6a2dsVb2e6MeeNXuN60WK8t6CVDWU7yyafac2eLw0wZj99/WkLPsW54eM2dN+/gvPWVNw6NkJfZTxQE7V6YkaYZ79epGivLcgzUyzuXmjIjh1bYXvK+NQNBqSf95A/Cc/4P5oPwCSvluL1sudoN/eR+PkAKoBQ0Y25/o+jSE9C+9JI3C5rzOqXk/2sfNEzfwANVdX5gzR6Y6lbuvZowXBc0aiaDVEfreZS+//iv8TvQCI/Np4MHfPm2Pw7N4MfVYuJyZ9QtoR4+1enRrVov67T6OxsSLrUiwnJn2CLiUDgAYfPEPKwTOm1yit+nXibt6oCLbt2+I66VnQasj8ay3pX32DwyDjHTIyf/sT1xkvYt/1XvTR+f09fozxc8F5zCjsenYDvZ6802dIfvNtyCv7NHI1D5R/HogPP5hHn95dyczKYuzYKRwMNX5///P3rxn39EtEXXuf/rvhJxa99THr/9liWveZCaMYP/4JdDo92VnZvPjSG+zeU7aD5T0+hSdyLQ3X7iHUeH00aLQk/LCRqMU/4/2Y8fM7btV6rLzdaLjmbbRODqgGFUNmFuHdJmJbw4fA9yaBVoOiKCT+tZOo98tXyP3eqvzzzwye/ST1ujQjLyuHH15awpWjxr495otp/DR9KamxSSw8u4qkq/HkZBgvfwxft58NH5oXVK4XKcp6C9K3L373nx5qsD9g8H/yWpjWV3+1yH+3YosUZo0UxfNa2/ibNr5BcUUKIYQQoqpUVJGiMpS3SGEJyluksATlLVJYgvIWKSxBWYoUlqa8RQpLcCtFiqpW3iKFJbiVIkVVkyLFnclSixSlugWpqqpmF8MpitJLVdWyX7chhBBCCCGEEELcQQwyJ0Wl0pRzveW3NYUQQgghhBBCCCHuesWOpFAU5Y/iFgGexSwTQgghhBBCCCGEKJeSLvfoDDwG3HjvOAVoU2GJhBBCCCGEEEIIcVcqqUixB8hUVXXrjQsURan4+xkJIYQQQgghhBBV7D85a6YFK7ZIoapqPwBFURqqqnr8hsWzKjSVEEIIIYQQQggh7jqlmTjzR0VRpitG9oqiLAberOhgQgghhBBCCCGEuLuUpkjRFqgB7AL2A5FAx4oMJYQQQgghhBBCiLtPSXNSXJcHZAH2gB1wQVVVQ4WmEkIIIYQQQgghLIBBVao6wl2lNCMp9mMsUrQGOgGPKoryc4WmEkIIIYQQQgghxF2nNCMpxqiqeuDaz9HAQEVRHq/ATEIIIYQQQgghhLgL3XQkRYECRcHnVlZMHCGEEEIIIYQQQtytSnO5hxBCCCGEEEIIIUSFK83lHkIIIYQQQgghxF1JlYkzK5WMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGIaqDnCXkZEUQgghhBBCCCGEsAhSpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLILMSSGEEEIIIYQQQhRDRanqCHcVGUkhhBBCCCGEEEIIiyAjKYQQQtx1sl5+uqojlJv9m59VdYRysz64pqojlJ9OV9UJyk1x867qCOUWePpIVUcoN/3R7KqOUG7Wh+7cQ4SG0/2rOkK5Rb4dVdURhLAIMpJCCCGEEEIIIYQQFuHOLZMKIYQQQgghhBAVzKBWdYK7i4ykEEIIIYQQQgghhEWQIoUQQgghhBBCCCEsghQphBBCCCGEEEIIYRGkSCGEEEIIIYQQQgiLIBNnCiGEEEIIIYQQxTCgVHWEu4qMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGKrMSVGpZCSFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERpEghhBBCCCGEEEIIiyBzUgghhBBCCCGEEMUwVHWAu4yMpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZE4KIYQQQgghhBCiGCpKVUe4q8hICiGEEEIIIYQQQlgEKVIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCDJxphBCCCGEEEIIUQxDVQe4y1RZkcKprj8h74/HrUltji/4kTOf/l1ku5APxuPVvgF5qZkAHJy0hJRjl0q9nYABbWnw4hCcg/3Z3O9Vko9cAMChhhe9tr1N2rlIABIPnuXw9BWles0aD3bknucGAKDLyObw9BWkHI8o1K7lu0/h1iwIRVFIPx/Fgec/Q5+ZU6XZq/UJoeH0oagGA6reQNirK0nYd6rItg1nPEzAgLaoegMXvtrIueXrJXsFZw8a3Zu6T/XFKdCPvxqOJzcxrdS5Kyp7afs73Ln7vdXHz+LeLBCDTk/SoXMcemk5qk5fZdnt/T1otXgCtt5uoKpcWLmJc8vWFdnWvXkQXf+ezd7xHxL5175SZ7Zxd6Ltskm4N6/DpR+2cWTml6ZlnVe/gp2PG/rsXAB2DltATnxqqV+7LO9VgGbzRlJrWBf+qDO61NuAiukzN6Nt1Aq7YRNQNBpyt68jd90PRbbT1L4Hx5c/IGvJfHSh28HKGsdp74CVNWi16A5uJ+ePlbcl0+3yyvx32bZzHx7ubvy26rOqjmNm5/FLLFq9DYNBZXD7hozu1cps+Zf/hrLmgLGP6Q0GLkQnsXn+WJLSs5j2Zf5752p8ChP6t+Oxbs0rL/uJCBb9tsOYvV0DRvdoaZ590yHWhJ7Jzx6TzObZo3B1tKPfnFU42lqj0ShYaTR8O+WhSssNsPPoWRZ+ux6DamBw5xaMua+T2fK0zGxmLv2V6IRUdAYDI/u0Z1Dn5kQnpvC/Zb+RkJKBoig81KUlI3q1rdzs52NYtPEoBgMMblaT0e3vKdRm/6V43vr3KDqDiru9DctHGH+/1Ow8Zq89xNm4NBQFXu/fgmYBHpWWXVu/JbaDxoJGS96ef8jb9EuR7TQ16mI/6S2yv34LfdguAKzvfQCrdr1BVTFEXSLn+w9Al1dp2QHeeed1+vTpRmZmFuPGvcjhw+GF2mzc+BNOTo4A+Ph4ceDAYR5+eBzDhg1iypSnAcjIyOT55//H0aMnKiX3zksJvLX9NAZVZVBDf0aH1C7U5sCVJN7acRqdQcXNzprlD4YA8Pq/x9l2MR4Pext+Ht6uUvLe6InXx9C8Wwi5WTl89uJiLoafL9TmqUXPEtSkDoqiEHUhks+mLiYnM5sG7RoxdenLxF6OBWD/uj38+uGPlf0rCGFSZUWKvOR0wl75imp9W9207dHZ35bpy3dBqScvs2f0e7R4a0yhZemXYtjUc2aZXzMjIpZtg+eQl5KBb/dmtHh7LFv6zyrULmzWKnTpWQA0ef0x6ozuzemP/qzS7LHbw4lafxAAlwY1aPv5JDZ0frFQu1rDumAf4MmGTi+CqmLr5VKm7Uj28mVP2HeK6A2hdF79apm3AVXb3+/k/X559U4OPPsxAK0/fY7aI7px4auNVZZd1Rk4+vo3JB+9iJWjHd3+mUfstqOknb5q3lCj0OiVR4nZElam1wfQ5+RxfOHPuNSvjkv9GoWW73/2Y9NBf1mVdr8DuDULxNrFoVzbqYg+UyJFg/3w58h4bwZqUjyO/1uM7shuDFERhdrZDRmL7tjB/Od0eWS8Mw1yskGrxXHae+jC96M/f/L2ZrwFg/r3YviQB5g55+2qjmJGbzDw5k9b+OzZQfi6OTHi7R/o0jiIOtXyDxpH9WjJqGsH/1uPXmDVlsO4Otrh6mjHj9MfNb1O71e/oHuzoMrNvno7nz09AF9XR0a89wtdGtWmjl+B7N1bMKp7C2P2YxdZtfUIro52puVLn3kAdyf7SstcMPv8VWtZMvUxfD1cGD57GV2b16NOgLepzQ+b9hPk783iSY+SmJrBwP99zH3tm6DVaHjxkd40qFWNjKwchs1eSruGQWbrVmx2lTf/CeOzYR3wdbZnxJdb6RLsR50Cf5dSs/N4858jfPxwe6q5OpCYkX8SadHGo3QI8uXtwW3I0xvIyit90fqWKRpsHxxP1mezUFMSsJ/8Drpj+1BjLhdqZ3P/KPSnDuU/5eqBdecBZC56FvJysX1iGlYtOqPbv6nS4vfp0406dQJp3LgLbdq04MMP53LvvYMKtevZc6jp5++++4w///wHgIsXL9O798MkJ6fSu3dXPv74zSLXv930BpUFW0/x6cAW+DrZMuLH/XQJ9KKOh5OpTVpOHvO3nuTjB1pQzdmOxMxc07IB9avxSJPqvLrxeIVnLUrzbi3xC/RnSpdnqNviHkbPHc+sQdMLtVs1ewVZ145NHnv1SXqP7M+fn64G4OT+E7w9el6l5haiOFVWpMiJTyUnPhW/ni3Ktb7WwZZm80bi2qAGilbLibd/MX0pLijtTOStRi0k8cCZ/J8PnsW+WtHV9esFCgCtvU3+z1WYveBIDisHO1RVLbJd4Mie7J/wEVxbfv0sqmSv2Owp4UWPEroT+vudvN9j/j1s+jnp0DnT71hV2bNjk8mOTQaMo1fSzlzF3s+9UJGizpg+RP69D/fm5gddNYZ0pM7YvmistSSFnuPQjBVgMP/d9Zk5JOw7hWOg723Nfv21rytpv6NRaDJrOPsnfIx//9amp6uyz5REG1gPQ1wkanw0AHn7t2LVvAO5NxQpbLoPJO/gdrSB9cxfICf72gtZgVYLxeyWqtKqeROuRsVUdYxCwi/FUMPbjepergD0aXkPW46eNytSFLQ29DR9Q4ILPb/31BWqe7ni71G2AuqtCI+IpYaXK9U9jdvs06IuW8IvmhUpClobeoa+LQpnrwrh569Sw8ed6j7uAPRt24gth0+ZFRoURSEzOxdVVcnMycXV0R6tRoO3mzPebs4AONrbElTNi9jk1EorUoRHJVHD3ZHqbsYz9X0aBrDlTLRZkWLt8St0r+dPNVdjkdTD0RaA9Jw8Qi8nMOc+4/dTa60Ga23lTeGmqRmMIT4KNdH4XtQd2o5V47bk3VCksO58P/qwXWhq3NBfNBqwtgG9DsXaFjUlsbKiA3D//b349lvjyI99+w7h6uqCn58P0dGxRbZ3cnKkS5cOjBtnLGTv2ZP/Wb9vXygBAdUqPjQQHpNKDVd7qrsaC4J9gn3Zcj7erEix9nQMPer4UM3ZWET0cMj/bh8S4E5kahZVJaRXG7b/shmAs4dO4+DiiJuPO8mxSWbtsgocm9jY2pi+swlhae6IOSkazXiYBlMeJHZ7OMfmfY8hV0f9SYOI23GM0MmfY+3iQNe1c4jdHl6myykca3rTfcN88tKzOL7gRxL2Fj8cuTi1h3clZtORYpeHvD8e3x7NSTt9haOvrwKo8uz+/VrRaOYwbL1c2PXYW0W/fi0fqg9sh3//1uQkpHLkf1+TcSFasldw9uJUdfbrSurv/4X9rlhpqflQJ468+jVgGfvdoYYXbo1rkxh6zux5Oz93/Pu3ZvuQuYQ0H2d63jnYn+oD27N1wOuoOj3NFzxJzSGdiPhpe5m2G/L+eFS9gci/93PyvV/LtC6Ubr/XGd2HqPWhpoLMdZaw34uiuHlhSIwzPVaT4tAG1r+hjSdWLTqS+c60wkUKRYPjqx+j8fYnd8sf6C9YzigKSxabnIGfW/6Bgq+bE0cvRRfZNis3j10nLvHyQ10KLVsfepp+RRQvKlJsSgZ+1w6UAXzdHDl6qeiDtazcPHadvMzLD3Y2PacoMGHJXygKDGnfiIfaN6zwzNfFJqfh5+Fqeuzj7sLR8+aF0mHdW/P84u/pOeU9MrJzWPT0EDQaxazN1fhkTkZE0ySoeqXkBohNy8bPOX/0ia+zPUcjzQ/WLiWmozMYGPPNDjJzdQxvFcSAJjW5kpyJu4MNs/4+xOnYVBr6uTKtZxPsbSrnK7Pi6omaHG96rCbHo6lV74Y2Hlg1aUfWJ69g+0h+n1ZTEsnb8huOry6HvFx0pw6hP324UnJf5+/vx5Ur+QXkq1ej8ff3LbZI8cADfdiyZSdpaemFlo0aNYz167dUVFQzsRnZ+Drnj2DydbIlPMb8MsdLyZnoDAbGrj5IZp6eR5vVYED9yimi3Iy7nyeJkQmmx4nRCbj7ehQqUgCMf+s5mncL4crZy6ya+4Xp+eCW9Xhz7bskxSbxzdwvuXrmcqF172YyJ0XlKtcnrqIon6uqOq6E5eOAcQDjnVvT26FuOePBsXk/kB2bjMbGihZvj+We5wZw8t1f8enaBL8+LQmecB8AWltrHAI8S31mLTsmmXUhz5OblI5b00DafTGFjV2mmY1+uBmvjg2p9WhXtg18o9g2B19YAhqF5vNHUX1gey59v7XKs0euPUDk2gN4tqtPw+lD2fHw/EJttLbW6HPy2NznFfz7tybkvXFsGzRbsldw9uJUdXa4eX//L+z35gueJH7PSdMBbVVn1zrY0nbZZMJmrSy0TtM5TxA+57tCIyS8OzfGrWkg3dbNMb6GnU2Z5pMA2P/Mx2RHJ2HlaEfb5S9Qc2jnMhc5brbf7XzdCBjQlu0Pzim0blXv92IpRT1pvv/tHplAzuploBbxdUY1kDF7Atg74vDMa2j8a2OIvHhrme4CahFDThSlyH8MtoVfoHlgNbPLJQDydHq2hl/g+QEdKiRjcYo6SVlMdLYdu0TzQD+z7F9OHIyPqyOJaZk8/dlfBPq4EVLHv4LSmitN9l3HzlG/hh/LXnqCy7FJjH9nFS3vqYWTvXFUQmZ2LlM//omXHu1jeq4yFNlnbnisN6iciE7h82EdyNbpeWLldpoGeKA3GDgZncKMXk1o4u/Bwg1HWbHnDM/e26BywhfVQW74x7Ad+BQ5f31V+HPG3hFt47ZkzH0KsjKwGzkdq5Cu6A5uqbi8NyjqvVnsaDrg4YcH8uWX3xd6/t572zNy5CP06DHktua7FXqDyonYNJYMakm2Ts/Inw/Q1NeVWu7lu2Txdiryc6WY/b7kpY9QNBpGzR5L+wGd2PrTJi6Gn+f5DuPIycymebeWTF06gyldn63Y0EKUoNgihaIoxc0QpAD9S3pRVVU/Bz4HWO033PQOCXqyF7VHdANg14hFZMck3zTg9TNshlwdl77favrSCgp7x7xP+rkos/Yh74/HtXEtsmOS2TViUbGva8jVkZtrrNomh10g41IMTnX8ir0G+8bsNh7OtHznKXYNX0huUuHqr/nGVK78vpvgZ+7n0vdbqzz79f2esOckjrV9sPFwLjRBY1ZkIpF/G+cBiVyzn5D3x19bItkrMnvxLL+/3+n7vf7UB7H1dGHPS+8VeLZysxekWGlpt3wyl1fvJHLN/kLL3ZsF0mbJRABsPZzx7dEcVWcABSJ+3Max+eYTOvr3a0X9qQ8CEDp1aYkZsqONZ150Gdlc/nUX7i3q3LRIUdb97takNk6BvvTeY9zfWnsbeu9+l3/aT6Eq93tJ1KR4NB4Fhrq7e2NINh9Kra19D/ZPGefCUJxcsWrchmyDHt3hXfmNsjLQnQ7DqnErcqVIcVO+bk5EJ+d/7sQkp+Pt4lhk23WhZ+gbUniCxB3HL1G/ujee5Zz/pLx83RyJTs4wPY5Jzig++6Gz9G1hflLHx9XY1sPZgW5NAgmPiK20IoWvuzPRiSmmx7FJqfhcu4Tjut93HGZ0/44oikJNXw8CvNy4EBVPk6AA8nR6pnz8I/3bNaZnSCUd4F/P7mxPdFp+UTImLQtvZ7sb2tjhZu+DvY0V9jZWhNTw5FRsCi2re+LjbEcTf+PX4F71/Vmx5wyVRU2OR3HzMj1W3LxQU80/ZzQ16mL3uPHyCMXRBW2DEHIMetBaGS8TyTAWpnVHd6OtXb/CixTjxz/Bk08OA+DgwTCqV8/vowEBfkRFFT2KwsPDjVatmvHII+bnPRs3rs+nny5k4MCRJCYmV1jugnwc7YhJyzY9jknPwdvRvLDm42SLm50n9tZa7K21tPR343RCWpUVKXo90Y9uw3oBcD7sLB7+nqZlHn6eJBUxiuI61WBg9587uX/8ILb+tMnsMpDDm0N5cs54nN2dSUsq2wTuQtwuJY2kiAMuYV58Vq899inPxs5/sYHzX2wo0zp2Pm6mQoV/31aknrwCQOyWMOqM6WOakd61cS1Swi8ZRy6Ugo2ns/Fgy6DiUNMHp0A/MooZgnljdvsAT9qtmMyB5z4h/XzRQ04BHGv7knHReE2hX++WpJ2NrPLsjrXzrz93a1IbjbVVkQdskesO4N2pEZe+24pXhwakn4+S7JWQvTh3Qn+/k/d77eFd8e3alO1D55mdeajs7AW1fG8caWeucnbJmiKXr2/zgunnkA/GE7XhEFHrDuB8TwDtv5zK2c/XkhOfirWbI1ZO9qaRDTejaDVYuzqSm5iGYqWlWq8WxG4rPDP7jcq636M3HmZN02dMjx84t+JagaJq93tJ9BdPofEJQPHyQ02Kx7p1F7KWLTBrk/7yE6af7Z58Ed2RvegO70JxckXV6yArA6xtsGrQgtx1MnN6aTSq6UtEXDJXE1LwcXVifehp5o/sU6hdWlYOB89eZf7jvQstWxd6usjiRUVrVMPnWvZUfFwdWX/oLPMf71moXVpWDgfPRTJ/RA/Tc1k5eRhUFUc7G7Jy8th9+jLjb7irSYVmDwwgIiaRK3FJ+Lq7sG7vMd4cP9isjZ+HK3uPX6DlPbVISEnnYnQC1b3dUVWV17/4k6Bq3jzRp32lZTZlr+ZGRGIGV5Mz8HG2Z/3xq8x/IMSsTdfgaizYEIbOYCBPb+BoZBKPta6Dl5Mdfi72XExIo7anM3svxhHk6VzMlm4/w+UzaLz9UTx8UVMSsGrRmZyV5pPZZs57yvSz7bBJ6I7vRx++F03Ne4yXhljbQF4u2uBmGC5XfIFlyZKvWbLEeJlk377defrpkfz44x+0adOC1NS0Yi/1ePDB+1i79l9ycvIv5atRw5/vv1/CmDGTOXv21grLZdHI15mIlEyupmbh42jL+jMxvNm7kVmbroHeLNx26lqfUQmPSeWx5jUrLeONNny9lg1frwWgefcQeo/sz+4/dlC3xT1kpWUWeamHby0/Yq5dLteyZysizxmPq1y93UiJSwagTrNgFI0iBQpRpZTihmApinIG6KGqaqF7DSqKcllV1cLTwReh4EiKgmy9Xem+fi5WzvaoBhV9RjYb7jUOx+3wzTRCp3xOdkwynX7+H7aezqAopIRf4tC05egzc9DYWdNs9hN4tA4GRSHzchy7Hy88I7l/v1Y0mzcSG08X8lIzSQm/xM5HF+B/X2saThuKQacHvYHjb/1C9IbQ0vxKtHznKfzva0PmFeO1yarewOY+rwDkZ49Nocvvs7BytjdmPxbB4ekr0KVnVWn2e54bQM2hnTHk6dBn5xE++1vTrQEL7ndrFwdaf/Is9gGe6DJyODxtOSnHIyR7BWevM6YP9zx7P7Y+buTEpxLz72FCpy61/P5+h+/3QVdWknkl3nQ5QOSa/Zx899cqy+7Zph5d/niNlOMRqAbjcN5jb/6IQ4DxLMmFr/81a3+9SHH9LkgBA9tRb+IDKBoNhjw9h1/+gqTQs4W202f/B1g72aOxsSIvJYMdwxaQeSWee3+dhcZai6LVELstnLDXVha6rOR27PeCHji3wnQL0srY7z0HxBd6vdKwatwa22ETUBQNuTvXk7vmO6y7GEf45W01v5W2qUgRuh1NQCD2o18yTmqnaMg7sJXcv74pVwb7Nyvm9qAvvbaA/YfCSE5OxdPDjWfGPM6QAYWLAbdCd7DootvNbD92kbdWb8dgMDCwXUOe6tOan3YcBWBopyYA/L73BLtOXGLhqL5m62bl5tF31pf89doTON/KJQc6XfmyH7/EW7/vxGBQGdimPk/1CuGnXceM2TsYD4J+33eSXScvs/CJXqb1riSkMmWF8fapOoOBfi2DeapXSOENlILiVr4JK7eHnWHRd+sxGFQGdWrOUwM68+NmY7Hz4W6tiE1K49UVvxOfnI6Kyuj+Hbm/fVNCT0fw5IIvCa7ug+baOPSJQ7rTuWnZ5wRRTxc/71eJ2c/F8NbGoxhUlYFNa/JUh3r8dMh40Du0RSAAX+49wx9hESiKwuBmtXisdR0ATsakMHvtIfL0KgFuDsy+rwUudjbFbqs4+qPlm3dG2yAE24FjQaMhb99G8jb+hFV7Y7/W7Ta/HbWpSHHtFqQ2fR7FqnlnVIMew9Xz5PywGPRl77ven5ZvvwO8994cevfuQmZmFuPHv0hoqPG9+uuvX/LMM9NMIyvWr/+et9/+lA0btprW/eSThQwa1I+ICOPBs06np1OnAWXafsKi+27eqAjbL8bz9vbTGFQY2LAaY1sF8lO4McfQxsY5Vb4KvcTvJyLRKAqDG/oz4lqRYsb6cA5eTSI5Ow8PexuebhvE4IZlH/U09u2omzcqxqg542jWpQU5WTkseXExF44a57Ka9uUrfD7tY1Likpn18zzsnRxQFIWIExdY8b8lZKVn0XtkP3o+1he9Tk9udi6r5n7BmYNlm8vp20u/FnMx23/D376P/idnGb0v5juL/HcrqUjxLLBDVdVCn1KKokxUVXVxaTZQXJFCCCGEqCrlLVJYgooqUlSG8hYpLEI5ixSWoLxFCktQ3iKFJShvkcIS3EqRoqqVt0hhCW6lSFHVpEhxZ7LUIkWxl3uoqvpxCctKVaAQQgghhBBCCCGEKK1y3fhZUZReN28lhBBCCCGEEEIIUXrlvenzcqDqZooRQgghhBBCCCEqgcEiL4r47yrpFqR/FLcI8CxmmRBCCCGEEEIIIUS5lDSSojPwGJB+w/MK0KbCEgkhhBBCCCGEEOKuVFKRYg+Qqarq1hsXKIpStnvSCCGEEEIIIYQQQtxESXf36AegKEpDVVWP37B4VoWmEkIIIYQQQgghxF2nNBNn/qgoykpgEWB37f+tgPYVGUwIIYQQQgghhKhqBmTmzMpUmluQtgVqALuA/UAk0LEiQwkhhBBCCCGEEOLuU5oiRR6QBdhjHElxQVVVQ4WmEkIIIYQQQgghxF2nNEWK/RiLFK2BTsCjiqL8XKGphBBCCCGEEEIIcdcpTZFijKqqs1RVzVNVNVpV1YHA7xUdTAghhBBCCCGEqGrqf/S/0lAUpa+iKKcURTmrKMqMIpYriqJ8eG15mKIoLUv50sW6aZFCVdUDRTy38lY3LIQQQgghhBBCCMukKIoW+BjoBzTEeFVFwxua9QOCr/03Dvj0VrdbmpEUQgghhBBCCCGEuLu0Ac6qqnpeVdVc4Htg4A1tBgJfq0Z7ADdFUardykalSCGEEEIIIYQQQogbBQCXCzy+cu25srYpE6tbWVkIIYQQQgghhPgv+6/e2lJRlHEYL9G47nNVVT8v2KSI1W6czqI0bcpEihRCCCGEEEIIIcRd5lpB4vMSmlwBahR4XB2ILEebMpHLPYQQQgghhBBCCHGj/UCwoiiBiqLYAMOAP25o8wfwxLW7fLQDUlRVjbqVjcpICiGEEEIIIYQQQphRVVWnKMpzwHpAC6xQVfWYoihPX1v+GbAG6A+cBTKBJ291u1KkEEIIIYQQQgghimFQipp24e6gquoajIWIgs99VuBnFXj2dm5TLvcQQgghhBBCCCGERZAihRBCCCGEEEIIISyCFCmEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgE2cKIYS46yQdNFR1hHKzPrjm5o0slFVI/6qOcHfS51V1gnLTZWdUdYRys6rfoqojlFv1lVerOkK5WQ25rfP3VSqvd2ZXdQRRDLWqA9xlZCSFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERpEghhBBCCCGEEEIIiyBzUgghhBBCCCGEEMW4c2eyujPJSAohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiJIkUIIIYQQQgghhBAWQeakEEIIIYQQQgghimFQqjrB3UVGUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkGKFEIIIYQQQgghhLAIMieFEEIIIYQQQghRDAMyKUVlkpEUQgghhBBCCCGEsAhSpBBCCCGEEEIIIYRFkCKFEEIIIYQQQgghLIIUKYQQQgghhBBCCGERZOJMIYQQQgghhBCiGGpVB7jLyEgKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkHmpBBCCCGEEEIIIYphUKo6wd1FRlIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBahyi73cKrrT8j743FrUpvjC37kzKd/F9kuaHRv6j7VF6dAP/5qOJ7cxLQybSdgQFsavDgE52B/Nvd7leQjF0zLXBrUoMVbY7F2tkc1GNjc91UMOXk3fU2vDg1o/+VUMiJiAYhcs5+T7/5aqF3Ld5/CrVkQiqKQfj6KA89/hj4zp0qzX+fePIiuf89m7/gPifxrX6Hl3p0a0WTWcNAo6DJyODjpMzIuxkj2Cs5uif29xoMduee5AQDoMrI5PH0FKccjCrXz7tiQJq+NQLGxIjnsAqGTP0fVG6o0e2k/Z1p9/CzuzQIx6PQkHTrHoZeWo+r0VZbd3t+DVosnYOvtBqrKhZWbOLdsnVmb0n4OFcfG3Ym2yybh3rwOl37YxpGZX5qWKdZams9/Eq8ODcCgcmzBD0T+vb/Ur33dzfq7pe330rDr0BqPF58BrYb0X9eS+uX3Zssd+3XHZdQwAAyZWSTO/4C8M+fzG2g0VFv1Cbq4eOImvXLLecpi5/FLLFq9DYNBZXD7hozu1cps+Zf/hrLmwCkA9AYDF6KT2Dx/LEnpWUz7Mr//XY1PYUL/djzWrXllxi/RK/PfZdvOfXi4u/Hbqs+qOk6ZWHL2HXsPsuDDZegNeobc15uxjz1ktjwlLZ1XF3zI5atR2NrYMGfG8wQH1QLg6x9/55e//kFRFIKDajF3xiRsbW0qLfvOYxdY9NNmDKrK4A6NGd2nrdnyLzfsZ83+EwDo9QYuRCeyedEEXB3tWfnvQX7ddRQFCA7w4o3H+2JrXXlfmXeGnWHht+swGAwMvrclY+7vbLY8LTObmUtWE52Ygk5vYGS/Dgzq3ILohBT+t/RXElLSURSFh7qGMKJ3u0rLfd2r81+iS8+OZGVmM/351zkedrJQm4WLX6d1+5akp6UDMH3i65wIP42TsxPvfDqHagF+WFlpWf7JSn757s9Kyb1jXygLP1qBXm/gwft6Mnb4g2bLU9LSmbXoIy5HxmBrY83sac8SHFiLCxFXeWn2O6Z2V6JiePbJYTz+0IBKyX3dkNdG0bBbC3KzcvjmxU+5cuxCoTZPvD+RGk2C0Ov0RBw5y/czl2K49ne3bruGPDhrJForLRlJaXz4yBuVml+IgqqsSJGXnE7YK19RrW+rEtsl7DtF9IZQOq9+tVzbST15mT2j36PFW2PMnle0Glp//CwHnvuElOMR2Lg7YcjTlfp14/eeZPfjb5fYJmzWKnTpWQA0ef0x6ozuzemPSv9BW1HZ0Sg0euVRYraEFduk+cLR7Bn1DmlnIgka1ZP6kwdxcNISyV7B2S2xv2dExLJt8BzyUjLw7d6MFm+PZUv/WeaNFIWQDyewY+g80s9H02DaQ9R8+F4ufbelSrOX9nPm8uqdHHj2YwBaf/octUd048JXG6ssu6ozcPT1b0g+ehErRzu6/TOP2G1HSTt91axdaT6HiqPPyeP4wp9xqV8dl/o1zJbVf2EQOfEpbOg4FRQFG3ensm+gFP3d0vb7TWk0eEyfSOwz09HFxFFt1cdkbd1F3oX8op3uajQxY6dgSEvHrkNrPF+ZTPTIiablzo8OJu9CBIqTw63nKQO9wcCbP23hs2cH4evmxIi3f6BL4yDqVPMwtRnVoyWjerQEYOvRC6zachhXRztcHe34cfqjptfp/eoXdG8WVKn5b2ZQ/14MH/IAM+eU7/1QlSw1u16vZ+57S1j67mz8vD15ZNxUunVqQ53aNU1tlq78ifp1A/lw3kzOX7rCvPc+Y/n7c4mJS+Cbn//k95UfY2dry9TXFrJ203YG9etROdkNBt784V8+e/4hfN2cGbHwG7o0rUudap6mNqN6tWZUr9YAbA07x6pNB3F1tCcmOY3vtoSy+tVR2NlY89KyP1l34CQD2zeutOzzV65hyUuP4+vhwvA3ltK1RT3qBPiY2vzw7z6CArxZPHk4iakZDHx5Mfe1b4JWq+HFYb1pUNufjKwchr2+hHaNgszWrWhdenakVlANerYZRPOQxsxe9DIP9R1ZZNtFb3zAuj//NXvusTFDOXvqPOMfm4yHpxvrd6/mj5/Xknc7PsNLoNfrmffBUj5/6zX8vD0Z9vQ0unVoTZ3a+X8fl33zC/XrBvLBnBmcj7jC/PeXsuzdNwisGcDPy941vU6PoU/Ro1Pb4jZVIRp2bY53oB9zuk6idotgHp43hncHFS6EH/htO1+/sBiAkR8+T4dh3dmxagP2Lg48PGcMn46cT1JkAk6eLpWa/05Q+tNu4naosiJFTnwqOfGp+PVsUWK7lPBLRT6vdbCl2byRuDaogaLVcuLtX4haf7BQu7QzkUWu79O1KSnHI0xnhHOT0sv4G9zc9QIFgNY+/+xBVWevM6YPkX/vw715CV8yVRUrJ3sArJwdyI5OluyVkN0S+3vigTP5Px88i32Bg5rrbDycMOTmkX4+GoDYrUep9/xALn23pUqzl/ZzJubfw6afkw6dM/2OVZU9OzaZ7NhkwDh6Je3MVez93AsVKYpTY0hH6ozti8ZaS1LoOQ7NWAEG1ayNPjOHhH2ncAz0LbR+rWFd2dD5ReMDVS3ziB4oXX+3tP1+MzaN66G7EonuahQAGeu3YN+1o1mRIifsuOnn3KMn0Pp6mx5rfbyw79yW1OXf4nzDGemKFn4phhreblT3cgWgT8t72HL0vFmRoqC1oafpGxJc6Pm9p65Q3csVfw/L+gLbqnkTrkaVfsScJbHU7EdPnKFmQDVq+PsB0K9HZzbt2GtWpDh38TJPXevLQbWqczU6lvjEJAB0egM5OblYaa3Iys7B27PovlYRwi9GX+vvbgD0CanHliNnzYoUBa09cJK+reqbHuv1BnLydFhptWTn6vB2LUehtpzCz1+lhq8H1X2M+6tv28ZsOXTKrNCgKAqZ2TmoqkpmTi6ujvZoNRq83ZzxdnMGwNHeliB/b2KT0iq1SNGzbxd++8E4avHwwXCcXZ3w9vUiLia+VOurKjg6OQLg4OhASnIqujKMsCuvoyfPUtO/QH/v3onNO/eZFSnOXbzM2BFDAAiqWZ2rMbHEJybj5eFmarM39Cg1/H3x96u8fQ7QpHdr9q3eBsDFQ2ewd3bExduN1Lhks3bHtxw2/XzpyFlc/Yz9LOSBThxZt4+kyAQA0hNSKyW3EMW5Y+ekqD9pEHE7jrG576tsHzKXxrOGo3WwLfX6TkF+oKp0/G4G3f+ZR/Cz95dp+x4hwXT/9006fDsN53oBxbYLeX88/Y9+inPdapxbvr7Ks9v5uePfvzXnb3K2MnTqUjp8M41+oYupObQTpxb/IdkrIXtxqrq/X1d7eFdiNh0p9HxuQhoaay1uzQIBCLi/Lfb+HhaVvTQUKy01H+pEzGbj72gJ2R1qeOHWuDaJoecKLSvqc8g52J/qA9uzdcDrbOo5E9VgoOaQTqXenrWL8Qx/w2lD6f7PPNosnYStV9kOSMva3y1xvxfFytsLXXSs6bE+Ng6tT9EHPQBOg/qRtTP/Mhf3F58h+YOlqDcUjCpDbHIGfm75B1q+bk7EphRdvMnKzWPXiUv0bFa30LL1oafpV0TxQvz3xMYn4OfjZXrs6+1FbFyCWZt6dWuzcdtuAI4eP01UTCwxcQn4ensyatggeg4dQ7fBI3F2dKRjm5KLxbc1e3I6fu7O+dndnUvu78cv0rOFsV/7ujnzRM/W9H1lKb1e/gwnexs6NKxdGbEBiE1Kxa9AEdDH3YWYJPMDxmE92nA+Mp6eL7zDQ698wrTh/dBozL/SX41L4uSlKJrUKf47akXwreZDVGR+0S06MhZfP+8i206e+Qx/bvmemXOmYGNjDcCqZT9Q555Adoav569tPzD3f2+jqhX/mWns7/mf577ensTEJ5q1qVenNhu37QGMRbyo6DhibnhPrN20g349zC/PqQyuvu4kR+ZnSY5OMBUgiqKx0tJ68L2c2Gr8u+sTVA0HV0cmfj+Ll/58k9YP3lvhmYUoSbEjKRRF0QJjgerAOlVVdxZY9oqqqnMrIV+xfLo2wa9PS4In3AeA1tYahwDPYs+s3UhjpcWzbT02930VfVYOnX76H8lHLhC349hN100Ou8i6Vs+jz8zBt0dz2n8xlX86TCmy7cEXloBGofn8UVQf2J5L32+t0uxN5zxB+JzvCp1VvVHdcf3YNWIRSYfOEfzM/TR94zFCpy6V7BWcvThVmf06r44NqfVoV7YNLPoaxX3jP6LpG4+jsbUmdksYqs5gMdlLq/mCJ4nfc5KEvacsIrvWwZa2yyYTNmul2cgsKP5zyLtzY9yaBtJt3Rzja9jZkBNf+jMiipUWhwBPEvaf4ujrq6g7vj9NXhvBgYmflvo1ytrfLW2/F0sp4v5jxfyKtq2a4TSoL9GjJwNg37kthsRkck+cwTak2a3lKAe1iKBKUb8PsC38As0Dq+HqaGf2fJ5Oz9bwCzw/oEOFZBSWpagDwxv7zNgRD7Hgw6UMGT2J4KBa1A8OQqvVkpKWzuYde1n/w1KcnRyZOmshf/6zmQG9u1VO9qL6ezFtt4Wdo3mQP66OxhGYqZnZbAk7y9+zx+LsYMtLS//k773Hua9twwpMnK+o43HlhvS7ws9Sv6Yfy6aP5HJsIuPfWknLejVxsje+ZzOzc5j60Y+8NLyv6bnKUtTnSlF96e25HxEXE4+NjTVz3n2FcRNH8dE7S+ncvT0nwk/x+ODx1Ayszpc/fcKB3YdIT8+o0NxF7vcbfpUxwx9kwUfLeWjslGv9PRArbX5xKC8vjy279jPpqccqNGtRivw8L6G48/CcMZzbd4Lz+43zhWi0Gmo0CeKj4XOwtrNh8uo5XDx0hrgLURUVWYgSlXS5xxLAAdgHfKgoylZVVa8fiT8IFFukUBRlHDAOYLxza3o7GM/GBD3Zi9ojjH+gdo1YRHZM8i1EV9g75n3Sz5m/eULeH49r41pkxySza8SiYtfOikwkfvcJ0zDmmH8P49Y0sNgvscVlj/n3MMqCJ7HxcC5+SLRB5crvuwl+5n4ufb+1SrNbuzjQZonx+mhbD2d8ezRH1RmIWnfA1N7G0xnXRrVIOmQ8c3vl9910/G76taWSvaKyl6xq+7uNhzMt33mKXcMXFjt0PvHgGbYNmg2AT5cmONWpZhHZS/s5U3/qg9h6urDnpfcKPFu52QtSrLS0Wz6Zy6t3Ermm8KSVBYsWBT+HUCDix20cm/+DWXv/fq2oP9U4CVjo1KVmk0wWlJuYhi4zm8g1xr559c891B7e9aZ5y9vfLW2/l0QXG4dVgSG8Wh9v9DecRQOwDg7E89WpxE58GUOKsUBk26wx9l3aE9CpDYqNDYqjA55zZ5DwyoJbylRavm5ORCfnv3djktPxdnEssu260DP0Dbmn0PM7jl+ifnVvPF0qdz4NUTV8vb2Ijs0foh8TF4+3l/mZWSdHB+a+PAkwHoj2eeQpqlfzZee+UAKq+eLhZry8qMe97TkcfrLSihS+bs5EJ+V/J4tJSiv2ko11B0/Rt3X+pR57Tl4iwNMVD2djP+/RPJjD5yMrrUjh6+FCdGJ+YTk2KRWfAqNCAH7ffpjR93VCURRq+noS4O3Ghah4mgRVJ0+nZ8pHP9K/fRN6tqqczCNGD+WRxwcDEHboONX88y8j9PP3IbaISz2uX/6Rm5vHL9/+wdhnHwdgyKMPsOTDLwCIuHCFKxGRBAXXJuzQ7T8xUZCvtyfRsfmf5zFxCfh4FtHfpxv/tqmqSt9HnyagWv7vun3vIRrcE2R2+UdF6vx4b9o/apznJeLIOdz880eCuPl5khKTVOR6fSc9hJOnC8vH50/2mRydSEZSGrlZOeRm5XBu3wkCGtSSIkUBlT8G8u5WUpGijaqqTQEURfkI+ERRlNXAoxRfkAZAVdXPgc8BVvsNN/2bnv9iA+e/2HDLoQFit4RRZ0wf04z0ro1rkRJ+yThyoRRitoRxz7P3o7W3wZCrw6t9A85+vqbY9gWz23q7mp53b1EHRVGKLFA41vY13VXCr3dL0s5GVnn2gkI+GE/UhkOFDhzykjOwdnbAKciP9PPR+NzbhLTTkr2is5ekKrPbB3jSbsVkDjz3iWnOiaLYermQE5+KxsaKe54bwKn3f6vy7KVVe3hXfLs2ZfvQeWZnHio7e0Et3xtH2pmrnF1SdHtbb1dy4lIA88+huO3HaP/lVM5+vpac+FSs3RyxcrIncu0BIteWrs9F/XMI7w4NiNt5HO/OjUktxVwY5envlrjfS5J77BRWNQKw8vdDFxuPY5+uxM+cb9ZG6+eD99uvk/DqAnQR+fst+aPlJH+0HADbkGa4PDG00goUAI1q+hIRl8zVhBR8XJ1YH3qa+SP7FGqXlpXDwbNXmf9470LL1oWeLrJ4If6bGtcPJuJKJFcio/H19mTtv9tZNOtFszapaenY29libW3NL3/9Q0izRjg5OlDN15uw46fIys7BztaGvQeP0Kh+5V0m1KiWHxGxyVyNT8HHzYn1B08x/8n+hdqlZeVw8MwV5o/KX1bN3YWwi1Fk5eZhZ23F3lMRNKpZeO6eCsse6E9ETAJX4pLwdXdm3d5w3nx6iFkbP09X9h4/T8t6tUhISediVALVvd1RVZXXV/xOUDUvnuhbeSOevlnxE9+s+AmArr068diYh/nr1/U0D2lMWmp6kfNRFJynolf/rpw+aTy5FHklmvad23Bgz2E8vT0IrFuLy5dKNx/TrWhcvy6XrkZxJSoGXy8P1m7awcJXJpu1SU3PwN7Wxtjf/95ISNOGODnmF23XbtpOv+6lv7zyVm1f+Q/bV/4DQMNuLbh3ZB9C/9hF7RbBZKdlFpqPAqD9I91pcG9TPho+x2yEy9F/DvDQ7CfRaDVora2o1TyYLctv/e+mEOWlFHedl6IoJ1VVrX/Dc7OAPoCPqqql+mtTsEhRkK23K93Xz8XK2R7VoKLPyGbDvdPQpWfR4ZtphE75nOyYZOqM6cM9z96PrY8bOfGpxPx7mNCpS9HYWdNs9hN4tA4GRSHzclyRs9z792tFs3kjsfF0IS81k5TwS+x81PjFsMaQjtR7fiCqqhLz72Hj8ORSCBrdm6CRPTHo9Biycwl7bZVpckFT9tgUuvw+Cytne1AUUo5FcHj6CnTpWVWavaDrBw/Xbw1YcL/792tFg2kPoRpU8lIyOPjC52RGxEr2Cs5uif295TtP4X9fGzKvxAGg6g1s7vNKoeyNZw3Hr2cLFI3C+a82cm6p8ZaFVZm9tJ8zg66sJPNKvGl0wvXbeVZVds829ejyx2ukHI9ANRgvmzn25o84BBjPklz4+t8SP4cCBraj3sQHUDQaDHl6Dr/8BUmhZwttp8/+D7B2skdjY0VeSgY7hi0g7fRV7Kt70XrxBKxdHclJSOXgC0vIulp4xEBplNTfq3K/h/jHFnq90rDr2MZ4C1KNhvQ/1pG6/FuchhjnvEj/5S88Xp2CQ4/O6K9NhKjq9UQ/9qzZa1wvUpT3FqQ+bw4q13rbj13krdXbMRgMDGzXkKf6tOanHUcBGNqpCQC/7z3BrhOXWDiqr9m6Wbl59J31JX+99gTO9qWfH+RGViGFDxRvh5deW8D+Q2EkJ6fi6eHGM2MeZ8iAwkUYS1Qp2fXlu/3utt0HWLh4GXqDgcH9ezL+iYf54fe1ADwysB+Hw08yc957aLUagmrVYPaM53F1No5Y+GjFt6zftB2tVkv94CBmT5tomnegLHThW8uVfXv4ed76eYuxv7dvzFP92vHTNuP190PvNV5y9fvucHYdv8jCMebz1nzy107+OXgKrUZD/Ro+vDaiNzbluAWp4uB880ZFZT9ymkXfrsNgUBnUuQVPPXAvP24yjqh7uHtrYpNSeXXZb8SnpKOqKqPv68T9HZoRevoST87/guDqPmiuDf+f+FAPOjcre3GxyQPlv9vMawunc2+3DmRlZTPj+dcJP2K81evS7z7gfy/MITYmnq9Xf4aHpzuKAifCTzPrpflkZmTh4+vFwsVv4O3rhaLAkg+/5I+f15Zp+8eOfF2u3Nv2HGTRxyuM/b1fD8Y99hA//mGcT+7hB/pw+Ngp/vfmh2g0GurUrs4bLz1r6u9Z2Tn0euQp1n7zKc5ORY9SK40XO8wu97pDZ4+mQZdm5Gbl8s1Ln3L5qPH21+O/mMF305eQGpvEe2e/JelqHNkZ2QCErdvHug9/AaD7uAG0G9oVg0Flzw+b2LKibEWKDy/+UOJJ7DvdFwGP/ScHUzx5dZVF/ruVVKRYBaxSVXXdDc+PBT5VVbVUf2mKK1IIIYQQVaW8RQpLUN4ihSWoqCKFuIlyFiksQXmLFJagvEUKS3ArRYqqVt4ihSW4lSJFVZMixZ3JUosUxd7dQ1XVx24sUFx7fllpCxRCCCGEEEIIIYQQpVX2sWuAoii9VFW9PZNLCCGEEEIIIYQQFspgkeMN/ruKHUlxE8tvawohhBBCCCGEEELc9YodSaEoyh/FLQI8i1kmhBBCCCGEEEIIUS4lXe7RGXgMSL/heQVoU2GJhBBCCCGEEEIIcVcqqUixB8hUVbXQtMqKopyquEhCCCGEEEIIIYRlMFR1gLtMsUUKVVX7ASiK0lBV1eM3LJ5VoamEEEIIIYQQQghx1ynNxJk/KooyXTGyVxRlMfBmRQcTQgghhBBCCCHE3aU0RYq2QA1gF7AfiAQ6VmQoIYQQQgghhBBC3H1KmpPiujwgC7AH7IALqqrKZTlCCCGEEEIIIf7z5OC3cpVmJMV+jEWK1kAn4FFFUX6u0FRCCCGEEEIIIYS465RmJMUYVVUPXPs5GhioKMrjFZhJCCGEEEIIIYQQd6GbjqQoUKAo+NzKiokjhBBCCCGEEEKIu1VpRlIIIYQQQgghhBB3JVWp6gR3l9LMSSGEEEIIIYQQQghR4aRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBClSCCGEEEIIIYQQwiLIxJlCCCGEEEIIIUQxDFUd4C4jIymEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBJmTQgghhBBCCCGEKIbMSVG5ZCSFEEIIIYQQQgghLIKMpBBCCHHXUQ1KVUcoP52uqhOIO43hDj4HqN7B2bXyNbsqKIqcgxXiTifvYiGEEEIIIYQQQlgEKfEKIYQQQgghhBDFUKs6wF1GRlIIIYQQQgghhBDCIkiRQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCDInhRBCCCGEEEIIUYw7+aZgdyIZSSGEEEIIIYQQQgiLIEUKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiSJFCCCGEEEIIIYQQFkEmzhRCCCGEEEIIIYphqOoAdxkZSSGEEEIIIYQQQgiLIEUKIYQQQgghhBBCWAQpUgghhBBCCCGEEMIiyJwUQgghhBBCCCFEMWROisolIymEEEIIIYQQQghhEaRIIYQQQgghhBBCCIsgRQohhBBCCCGEEEJYBJmTQgghhBBCCCGEKIZa1QHuMjKSQgghhBBCCCGEEBZBihRCCCGEEEIIIYSwCFKkEEIIIYQQQgghhEWQOSmEEEIIIYQQQohiGJSqTnB3sZgiRcv3xuHXqwU58an823V6mdZtOONhag7tjI2bI3/UGW22LOCBtjR4cQiokHLsEvuf+fiWs/bZ/wG69CxUvQFVb2Bzn1fMljvV9Sfk/fG4NanN8QU/cubTv8v0+jbuTrRdNgn35nW49MM2jsz8EgArRzvu/X2WqZ19NU8u/7KDsFkry/YLaBS6r59HVnQiux9/22yRtasjIe+Nw7G2L/qcPEInLyH15BWLyH6z/X6de/Mguv49m73jPyTyr30Wkf1G9v4etFo8AVtvN1BVLqzcxLll60q9fmX2+dK8N706NKDp7MfRWFuRk5jG9sFzSv36xe13AMVaS/P5T+LVoQEYVI4t+IHIv/eX6nVLs49rPNiRe54bAIAuI5vD01eQcjyiyrODcZ+2/3IqGRGxAESu2c/Jd38tsm3DGQ8TMKAtqt7Aha82cm75+lJvJ2CAsb84B/uzud+rJB+5ABj3TfAz95nauTasyaZe/yPl2KUSX+9m/cWS93lp2Hdohce0Z0CjIf3XtaR88YPZcsf+3XEd9QgAhqwsEuZ9SN7p8/kNNBqqffsx+th4Yp9/9bZmu5mdJyJY9NsODAaVwe0aMLpHS7PlX246xJrQMwDoDQYuxCSzefYoXB3t6DdnFY621mg0ClYaDd9OeahSs9/MK/PfZdvOfXi4u/Hbqs+qOk6ZWHL2HXtDWfDRMvR6A0Pu68XYEUPMlqekpfPqwsVcjozG1saGOdOeIzioFgBf//QHv/y9AQWF4KBazJ0+EVtbm0rLvvPYRRb9vAWDwcDgjo0Z3buN2fIvNxxgzf6TwLX+Hp3I5oVPk5SeybTla0ztriakMOG+9jzW3fz9UqHZw06zcOUaY/auIYwZ0MVseVpmNjM//YnohBR0BgMj+3dk0L0hpuV6g4FHZ32Kj7sLH019vNJyX/fq/Jfo0rMjWZnZTH/+dY6HnSzUZuHi12ndviXpaekATJ/4OifCT+Pk7MQ7n86hWoAfVlZaln+ykl+++7NSct/J/R1gyGujaNitBblZOXzz4qdcOXahUJsn3p9IjSZB6HV6Io6c5fuZSzHo9ADUbdeQB2eNRGulJSMpjQ8feaNS8wtRkMUUKS79sI3zK/4hZPGEMq8b9U8o51f8Q+/d75o97xjoR72JA9k64A3yUjKw9XK5XXHZPmQeuYlpRS7LS04n7JWvqNa3VbleW5+Tx/GFP+NSvzou9WuYntdlZLOp50zT427r53F1Tdm/gNd9qh9pZ65i5WxfaFm9SQNJPnaJPaPfw6muP83fHMWOofMtJntJ+x0AjUKjVx4lZktYmV+7orMXpOoMHH39G5KPXsTK0Y5u/8wjdttR0k5fLdX6ldnnb/betHZxoPmCJ9n56EKyriaUeZvF7XeA+i8MIic+hQ0dp4KiYOPuVOrXLc0+zoiIZdvgOeSlZODbvRkt3h7Llv6zSnjVysl+Xfzek4UKiTeqNawL9gGebOj0Iqhqmfd/6snL7Bn9Hi3eGmP2/OXVO7m8eicALvVr0P6rqTctUMDN+4ul7/MSaTR4vDyRmKeno4uJx/+bj8jcupu88/lFFt3VaKLHTMWQlo59x9Z4vfoCUY8/b1ruMnwweRci0Dg63N5sN6E3GHhz9XY+e3oAvq6OjHjvF7o0qk0dPw9Tm1HdWzCqewsAth67yKqtR3B1tDMtX/rMA7g7Ff67YQkG9e/F8CEPMHNOye8XS2Sp2fV6PXM/WMLSt9/Az9uTR55+iW4d21Cndv77bumqn6lfN5AP577M+UtXmPfBEpa/O4eYuAS++eUvfv9qMXa2tkx9fRFrN21nUL8elZPdYODNHzfx2cQH8XVzZsSib+nSpA51qnma2ozq1YpRvYzf07YePceqTYdwdbTD1dGOH2c+Znqd3jOX0r1Z3UrJfX2b87/6kyXTn8TXw4Xhsz6ja8sG1AnwMbX5YeMeggJ8WDz1cRJTMxg47X3u69AMayvj1/pv1u8myN+b9KycSst9XZeeHakVVIOebQbRPKQxsxe9zEN9RxbZdtEbH7Duz3/NnntszFDOnjrP+Mcm4+Hpxvrdq/nj57Xk5ekqNPed3N8BGnZtjnegH3O6TqJ2i2AenjeGdwcVPpl34LftfP3CYgBGfvg8HYZ1Z8eqDdi7OPDwnDF8OnI+SZEJOHnevmMmIcrDYuakSNhzktzkdLPnHGv50PHb6XRbP497f5uFU13/ItdNCj1LdmxyoecDH+vG+S/+IS8lA4Cc+NTbnrsoOfGpJB0+j3qtMllQjSEd6bp2Dt03zqfFojGgKTx2SJ+ZQ8K+U+hz8ordhmOgH7ZeLiTsKVydLol9NQ/8ejbn4jebi1zuck8AcduPAZB+NhKHGt6mg56qzl4adcb0IfLvfeTEp5g9b2nZs2OTST56ETAWQdLOXMXez90i+3xR782CajzYgci/95N1NaHQNm91v9ca1pVTi/8wPlDVkgtUNyhuHxeUeOCMaV8lHjyLfbX8A7aqzF4WgSN7cvKd1aAab451ff9rHWxp+d44uq2bQ/cN86nWJ6TI9dPORJJ+LqrEbdQY3IHLv+4qVZ6b9Zc7eZ/bNq6H7nIkuqvRoNORsX4LDl07mLXJOXIcw7UzgzlhJ9D6epuWaX28sO/clvTVa29rrtIIj4ilhpcr1T1dsLbS0qdFXbaEXyy2/drQM/RtEVx5AW9Rq+ZNcHVxruoY5WKp2Y+ePEPNgGrU8PfD2tqaft07sWnnXrM25y5dpl3LpgAE1arO1ehY4hOTAdDp9eTk5KLT6cnKzsXby+PGTVSY8IvR1PB2o7qXm7G/h9RjS9i5YtuvPXCKvq3qFXp+76nLVPd2xb8SD9jCz12hhq8n1X08sLayom+7Jmw5eMKsjYJCZnYOqqqSmZ2Dq6M9Wo3xK31MYgrbD59icJeiP/MrWs++XfjtB+MI4sMHw3F2dcLb16vU66sqODo5AuDg6EBKciq6Ir5P3253cn8HaNK7NftWbwPg4qEz2Ds74uLtVqjd8S2HTT9fOnIW12uF6pAHOnFk3T6SIo3f5dITKueYSYjiWEyRoigt3h7L4f99xeY+/+PoG9/QfMGTZVrfKagaTnWq0eWP1+j69xv4dmt6e4KpKp2+n0G39fOo/Vj3Uq/mHOxP9YHt2TrgdTb1nIlqMFBzSKdyRagxuD1X/thd5vWaznmc8DnfoapF3+035VgE/v1bA+Deog4O1b2w9/e0iOw32+92fu7492/N+a82mj1vEdlL4FDDC7fGtUkMPWe5ff4m27R2c6Tz6lfotn4eNYd2Bm59v1u7GM80N5w2lO7/zKPN0knlHhlScB8Xp/bwrsRsOmJR2T1Cgun+75t0+HYazvUCimzjWMuH6gPb0W39XDp8Ow3HQD8A6k8aRNyOY2zu+yrbh8yl8azhaB1sy5wBIGBgO678VroiRVlY4j4vidbHC110nOmxLiYerU/xX76dBvcla0f+qCuPlyaQ9P5SUA23NVdpxKZk4OfmaHrs6+ZI7LVi0Y2ycvPYdfIyPZsGmZ5TFJiw5C8effcnft59vMLziqoXG5eIn3d+//b19iQ2LtGsTb06tdm4fQ8AR0+cJio6jpi4eHy9PRn1yCB6PvwU3YY8ibOTAx1bt6i87Mnp+LnnF3583ZyILaZ4mpWbx67jF+nZvHBRbv2BU/QLqV9hOYsSm5SKn4er6bGPhwsxSeYHjMN6teN8ZBw9Jy7koZkfMe3x+9BcK1IsWrWGycP6oCmiyFsZfKv5EBUZY3ocHRmLr593kW0nz3yGP7d8z8w5U7CxsQZg1bIfqHNPIDvD1/PXth+Y+7+3i/3Oejvdyf0dwNXXneRrBQaA5OgEUwGiKBorLa0H38uJrca/wT5B1XBwdWTi97N46c83af3gvRWeWYiSFHu5h6IoDsBzgAosBoYBDwIngdmqqhZ/quw20DrY4tnqHtouzR8mq7n2AVZaipUGp0A/tj04F3t/D7r8NouNXaeTl5p5S9m2Dnid7JhkbL1c6PjDy6SdjSzVmXXvzo1xaxpIt3XG6/W1djblPtNdfVB7Djz3aZnWuX6deHLYBeM120U4tfgPms19gu4b55N64jIp4RdRdfoqzw433+9N5zxB+JzvwGD+x8wSshdH62BL22WTCZu1EtVgsNg+X/I2tbg3DWT70Plo7Wzo+tcbJB48c8v7XbHS4hDgScL+Uxx9fRV1x/enyWsjODCxbPu/4D7WpWcV2carY0NqPdqVbQON119aQvbksIusa/U8+swcfHs0p/0XU/mnw5TCv5+tNfqcPDb3eQX//q0JeW8c2wbNxqdrE/z6tCR4wn2mdg4BnqSdiSx1BjAWK/VZOWWam6Y0LHGf33wjRXzpL+bLs12rZjgN6kf0ky8AYN+5LfqkZHJPnMGuVcUXD29UVMyifh2Abccu0TzQz+xSjy8nDsbH1ZHEtEye/uwvAn3cCKlT9Egv8d+gUrjT3Nhnxg4fwoLFyxgy5gWCg2pRPzgIrVZLSlo6m3fuY/33S3B2cmTqa4v4858tDOjdtZKyF6YU0+G3HT1P8yB/s/4OkKfTs/XoOZ4f2LECEhav6PeqefZdR89Qv2Y1lr08msuxiYxf8AUt69Xi4MmLeLg40jAwgP0nzhd+oUpQ1H4uqsjw9tyPiIuJx8bGmjnvvsK4iaP46J2ldO7enhPhp3h88HhqBlbny58+4cDuQ6SnF11UvV3u5P5uzFr6v08AD88Zw7l9Jzh/bV4WjVZDjSZBfDR8DtZ2NkxePYeLh84Qd6HkkZZ3k8o/vXB3K2lOii+By4A98DdwAngbGAB8ChQ7E4+iKOOAcQDjnVvT26Hs1/IpGg15qRlmcwEAxkkf/5kHGK/LP7Ho52JfIysykcTQs6g6PZkRcaSdi8IpyI+kw7f2wZ0dkwwYh1VHrT2AR4s6pRv+r0DEj9s4Nt98ojX/fq2oP/VBAEKnLjVNXFcc14Y1UbRaksNKbncjz9b3UK13S3x7NEdra42Vkz2tPnqGA899YmqjS8/i4AtLTI/77P+AjIg4PNvXr9LscPP97t4skDZLJgJg6+GMb4/mqDpDle/34ihWWtotn8zl1TuJXLMfKyd7i+3zJcmKTCA3MQ19Zg76zBzi95zAtVGtW97vuYlp6DKziVxzAICrf+6h9vCuZcp24z4uikuDGrR85yl2DV9IbtK12msVZQ96she1R3QDYNeIRaY+H/PvYZQFT2Lj4VzoEoasyEQi/zZOEBu5Zj8h74+//tuzd8z7hS7lCHl/PK6Na5Edk8yuEYtumqn6oPZc+fX2jh6ypH1eFvqYOKwKnBG08vVCH5dQqJ11cCCer00h5tmZGFKM/162zRvh0KU9Dp3aoNjYoDg64DVvOvH/W3hbMxbH182R6OT8L/kxyRl4uzgW2XbdobP0bWH+d9vH1djWw9mBbk0CCY+IlSLFf5yvtyfRcfGmxzFxCYWGsDs5OjB3hrGwrqoqfYaNo3o1X3buP0RANR883IwjAnrc257Dx05W2kGbr5sT0Un5n5Uxyel4uxbT3w+eom+rwqMldhy7SP0aPngW8z6pKL4eLkQn5l+yGpuYio+b+eVAv28LZfSAe1EUhZq+ngR4u3MhMp7DpyPYEnqSHUdOk5OnIyMrh5c//Yk3Jwyt0MwjRg/lkccHAxB26DjV/H1Ny/z8fYiNiS+0Tty153Jz8/jl2z8Y+6zxsGLIow+w5MMvAIi4cIUrEZEEBdcm7NCxCv0d7sT+3vnx3rR/1DjvRcSRc7j558+54ubnSUpMUpHr9Z30EE6eLiwf/47pueToRDKS0sjNyiE3K4dz+04Q0KCWFClElSnpco97VFWdCjwLNAImqqq6DZgGNCvpRVVV/VxV1VaqqrYqT4ECjAfLGRFxBAxoa3rOtWFNMKhs6jmTTT1nlniwBhC17gDeHRsCYOPhjFNQNTIuxZYrz3VaB1usrlXbtQ62+HRpQurJy6VaN277MQLub2sagmzt5oh9dS8i1x4w/U43O1AGqD64Q7mGXh+b/wNrW05kfetJ7Ht6MXE7j5kVKMA4ZFqx1gJQe0Q34vecRJeeVeXZS7Pf17d5gfWtJ7G+9SSu/rWXwzO+IGrdgSrPXpyW740j7cxVzi4xziJuqX3+ZqLWH8SzbT0UrQatvQ3uLeuSdubqbdnvUf8cwvvaqB/vzo1JLeXEotfduI9vZB/gSbsVkznw3Cekn482PV9V2c9/scH0+mqBEUHuLeqgKEqRcyxErjuAd6dGgPGOIOnnjV8oYreEUWdMH1M718bGGcgPvrCETT1nlqpAgaJQfUBbLv92+4oUlrbPyyLn2CmsagZg5e8HVlY49ulK5lbzfaP188bnndeIf2Uhuoj87ScvXsGVPsO50v9x4mbMI3v/4UorUAA0quFDRFwyVxNSydPpWX/oLF0a1y7ULi0rh4PnIunWOND0XFZOHhnZuaafd5++TN0ShhGL/4bG9YKJuBLFlagY8vLyWLtpB906mN8hIzUtnbw84/wwv/y9gZBmjXBydKCajzdhx0+TdW3ehL2hYQTVql5p2RvV8iMiNomr8SnG/n7wFF2aBBVql5aVw8EzV+jWtE6hZesOniyyeFHRGgUFEBGdwJXYRPJ0OtbtOUqXluY5/Dzd2HvMePliQko6F6Pjqe7jzqRHerPhw2msfe9FFj77MK0bBlV4gQLgmxU/8UC34TzQbTgb125h0CPGEXzNQxqTlppuKkgUVHCeil79u3L6pPH3ibwSTfvOxn7m6e1BYN1aXL50ez/Li3In9vftK/9hUf/pLOo/nbB/9tPm2iUatVsEk52WSWpccqF12j/SnQb3NuWriR+YjXA5+s8BglrXR6PVYG1nQ63mwcScrfj9LkRxbnp3D1VVVUVR1qjXevK1x7f94rDWnz6Hd4cG2Hg40y90Mcff+oX9z35MiwWjqffCIDTWWq78trvIW9U1fvVRagzugNbehn6hi7n47RZOvP0LMZvD8OnSlJ7bFqHqDYTP/jb/rF052Xq50u6LyYDxeq7Lq3cSszmMwCeMlcwLX/+Lrbcr3dfPxcrZHtWgUvepvmy4dxppp69ybOGPdPx+BopGgyFPz+GXvyDrSuEP7z77P8DayR6NjRX+fUPYMWyB6a4E1R9ox87SHGCUUsHszsEBtFo8AVVvIPX0FUKnLAWo8uyl2e/FqersRfFsU49aQzuTcjyC7huNd0859uaPFtnni3pvaq4Vsi58/S9pZyKJ2RxGj80LUA0qF7/ZbLo04Fb3e/j/2bvP8CiqPgzj92w6pIcUEnqRLr333kRQBCk2imAHu6KigiBgFxvYRcUuYkFEpSO9915DEhJIIT27837YEAhJYAkkWV6en5cX2Z0zs8+ezG52/3PmzEuzaTr9Xq6feAfpcYm5RvlcTEF9XCoiKCd7rUduxj3AJ2fujzOXtr0S+8zlZAf7pUGr3NkFW5YVW1oGq++ZnrOs1VdPsP6RmaRFx7N7+lyavnc/1Ub1JCs5Pec1u+ONn6g/4Q46L5wChkHKkRP5XikkvGcT6k+6E/cgX1p9+QQJWw+xfPAUAMq0rEnq8ZOkHHa80HWx/cWZ+/yirDZOTnmH0Pdftl+C9Jf5ZO47hM8tNwCQ9MNv+I+6HYu/L0Hjso+2ZVk5PvT+K5ujEFxdLDx1c1vunfkbNptJ32Y1qRYWyPcr7EcnB7SyF7r+3XKAljXK4+Vx9lSzuNOpPPKJ/fK9WTYbPRtVp3WtCsX/JC7g8eensGbDZuLjE+nc7zbuG3E7/ft0v/iKTsBZs7u6ujBuzN2MfvxFrDYrN/XsQrXKFfj2F/u+cGvfHuw/fJRxk9/CxWKhSqXyTHjiAQCur30dXdu3YuDdj+Di4kLN6pUZcEPxPSdXFwtPDezEve/+ZN/fW9ahWngZvl9qP/9+QFv7sbZ/N+6lZa2KufZ3sM9TsXLnYZ4d3KXYMp/N7sLTd9zAva98js1mo1+7xlQrF8p3/9hHzA3s3IxR/Trw3Mwf6f/0dEzTZOyt3QnwKd4RHwVZtGAZ7bu05p/Vv5CamsZTD72Qs+zD2W/xzNiJxETH8tr7LxEYFIBhwI6tuxn/uP3v9LuvfcjU6S/y2+JvMQx4ZcLbnMqenLIoXc37O8D2hRuo07Eh4xe/RUZqBl89fvZUx9GfPsXsJ2eQGHOKgZNGcurYCR7++SUANv+5mj/f/pHofcfYsXgTT/35Cjabycpv/+X4bscOwooUBaOgyWgMw/gIGHv+3BOGYVQFPjdN06EZzX4KG1L0s92IiIhcgkZhJy7eyEmFTupd0hEKzbX5jSUd4dqUWfyXorxSsrYtLukIhWb4Bl28kZOq13tKSUcotO2bvyzpCIX2aMsXSjpCob198NuSma21mLxc8bb/y++0Tx/60il/bwWOpDBNc2QB9+8zDKNt0UUSERERERERkWtRYS9BWvzj30RERERERETk/1phixQfX9EUIiIiIiIiInLNK/B0D8Mw5ha0CLh6T7ITERERERERcZCN/8spKZzWha7u0Ra4DTj/0gAG0CxvcxERERERERGRwrtQkWIlkGKaZp5plQ3D2FV0kURERERERETkWnShq3v0BDAMo7ZpmtvPWzy+SFOJiIiIiIiIyDXnQiMpzvjOMIxZwDTAM/vfJkDLogwmIiIiIiIiUtJsJR3gGuPI1T2aA+WBFcAaIBJoXZShREREREREROTa40iRIhNIBbywj6Q4YJqmikkiIiIiIiIickU5UqRYg71I0RRoAww2DOOHIk0lIiIiIiIiItccR+akGGGa5trsn6OAvoZh3F6EmURERERERETkGnTRIsU5BYpz75tVNHFEREREREREnIdZ0gGuMY6c7iEiIiIiIiIiUuRUpBARERERERERp6AihYiIiIiIiIg4BUcmzhQRERERERG5JtlKOsA1RiMpRERERERERMQpqEghIiIiIiIiIk5BRQoRERERERERcQqak0JERERERESkADajpBNcWzSSQkREREREREQcZhhGoGEYCwzD2JP9b0A+bcobhrHQMIwdhmFsMwxjjCPbVpFCRERERERERC7FU8A/pmlWB/7Jvn2+LOBR0zRrAS2A+w3DqH2xDatIISIiIiIiIiKXoi/wefbPnwP9zm9gmuZx0zTXZ/+cBOwAIi62Yc1JISIi15yUJPeSjlBohn9wSUcoPGtmSScoPJutpBMUnptHSScoNMM7z+jhq4YZdaikIxSat4tnSUcoNDM9uaQjFFoWZklHkALY9LvJT6hpmsfBXowwDCPkQo0Nw6gENARWXWzDKlKIiIiIiIiIXGMMwxgFjDrnrpmmac48Z/nfQFg+qz5ziY/jDfwIjDVNM/Fi7VWkEBEREREREbnGZBckZl5geZeClhmGEW0YRtnsURRlgZgC2rlhL1B8ZZrmT47k0pwUIiIiIiIiInIp5gJ3Zv98J/DL+Q0MwzCAj4Edpmm+7uiGVaQQERERERERkUsxBehqGMYeoGv2bQzDCDcM44/sNq2B24FOhmFszP6/18U2rNM9RERERERERAqgaTPzMk0zDuicz/2RQK/sn5cBxqVuWyMpRERERERERMQpqEghIiIiIiIiIk5BRQoRERERERERcQqak0JERERERESkALaSDnCN0UgKEREREREREXEKKlKIiIiIiIiIiFNQkUJEREREREREnILmpBAREREREREpgA2zpCNcUzSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECqAZKYqXRlKIiIiIiIiIiFNQkUJEREREREREnIKKFCIiIiIiIiLiFFSkEBERERERERGnoIkzRURERERERApgK+kA1xiNpBARERERERERp6AihYiIiIiIiIg4hRI73cO7WjiN3xyNf71KbJ/yHXve/z3fdo1evxv/+lUwDIPT+4+z9qEPsKakO/w4EX2aU+ux/vhUD2dhz+eI33QgZ5lvrfI0fGUkbj5emDYbC3s8hy0986LbLNu9MbWfHIBps2FabWx+bhZxq3flaRfcpg71xg8Bi0FWcjrrxnxA8sHoEs1e/ubWXPdAHwCyktPY+OQnJGw/nDd769rUe34ohrsr8ZsPsP7hmZhWxwc6lWS/VxnejWp398C7chi/1R5Nxskkh3MXVXZH+/2M+pPupOKg9sytOvyqyd5uznhcvT0B8Cjjx6kN+1g57PUSy27xcKPdnPFY3F2xuLpw7LdV7Hjlx0I9t4K4B3jT/KMxBDSoyqFvl7Bp3Gc5yww3FxpMHkaZVrXAZrJtyrdE/r7G4W3np/p9N1D+5lb27bu64Fs9gt/qjCYzPvmy8rqW9qTdL+Nz2nmVDeLIj8vYPH5WoXJezfv7xZRu15iw50ZhuFg49e1fxM34Ptdy9yrlCJ86Fs861Tjx+hfEffRTzrLAYf3wH9gNMEnfdYjIJ97AzLj8TI5avmUvU7+ej820cVPbhozo3SbX8qSUNMZ9+DNRcYlk2Wzc2b0l/do2IOpkAs98NIe4hGQMw+CW9o0Y2rV5seUGWLZqHVPe/girzUr/3t0YedstuZYnJJ3muSlvc+TYcTzc3Zn41ENUr1IRgC+++4Uff/sLwzCoXqUiLz01Bg8P92LMvp4p73yE1Wqjf++ujBzaP2/2qdM5Ehllz/7EA2ezfz+XH39fgEF29icfLNbsF/Ls5NdZsnw1gQH+zPnyg5KOk8fyzXuY+tUf2GwmN7VvxIgb2uVanpSSxrgZPxAVl0CW1cadPVvTr12jnOVWm43Bz39ASIAv7zxyW/Fm33mEaXP/s2dvVoPhnRrkWv7Zok38sX5vdk6TAzHxLHzhNvxKeeZkH/LWHEL8SjF9eI9izQ7wxEtjad25JWmpaTw/ZhI7t+zO0+bFt56hccsGnE60//0aP2YSu7ftoUP3Ntz75N2YNhOr1corz73FxtWbiyX3sjWbmPrBLKxWGzf37MDIW2/MtTwhKZnxr8/kyPFoPNzcmPDoKKpXKp+z3Gq1MejBZwkJCuDdiY8XS+ZzDXh+GHU6NiQzNZ0vHnuPI9sO5Glz15sPUrFeVaxZWRzctI+vx83ElmWly6g+NO3XFgAXFwth1crxRKMRpCQ49vlC5EorsSJFZvxpNj/7OWV7NLlgu83jvyTrdCoA9V64jarDu7H7nV8dfpzEnUdYOfwNGr4yItf9houFpu/ez9oH3iNh+2HcA7yxZWY5tM2YpVs5Pn8dYP8g3HzmGBa0fSxPuwZTh7PyrtdI2hNJlbu6UPPhfqwbM6NEsycfjmHJTRPJTEgmtFN9Gr46kkW9xuduZBg0fvtelg2YxOn9UdR64hYqDGzHodmLSjS7o/0et3oXUQvW0/an5xzOW9TZHer3bP71K+PmW+qqy76k34Scn5t/NDbnd1VS2W3pmSzt/xLWlHQMVxfaz32eqH82cSr7g92lPLeCWNMz2T71B3xrlsO3Zvlcy2qO7Ud6bAILWj8KhoF7gLfD2y3Invd+Y897vwEQ1rUR1Ub3dLhAcaG8Wclp/NtlXM7tjvMnceyPwhdUrub9/YIsFsq+cC+H7nyWzKhYqvz8Bkn/rCRj75GcJtaEJKImzMCnW8tcq7qGBhF4Zx/2db8XMz2DiLefwrdPexJ+/PvycznAarMx+ct5zHj0NkIDfRky4SM6NKhB1YjgnDbf/ruGKuHBTB8zmJOJyfR95l16t6yHi8XCY7d2o1bFsiSnpjNowoe0qF0l17pFmt1q5aU3ZvDh6xMICw7i1lGP0rFNM6pWqpDT5sNZ31OzWmXenjSO/YeOMumND/j4zZeIPhHHVz/8yi+z3sXTw4NHn5/KvH+X0q9n5+LL/tYMPnz1RXv2ex6nY+tmVD3ni82HX/5gz/7S0/bsb83g49cn2rP/+Bu/fD7dnv2FacWa/WL69erKkP43Mm7iqyUdJQ+rzcbkL35jxhN32vf3F2bQoWFNqkaE5LT59p9VVAkPYfrDt9n396fepner63FztX80/uqv/6gSHszpVMcPjF2p7C//vJwPRvUi1K80Q9+eQ/s6FakaGpDT5q4O9bmrQ30AFm8/xJdLtuQUKAC+XrqVyiH+JKdnFGt2gDadW1KhSjn6tryVeo3qMG7qY9zRa1S+bd+c8C5//7Yo132rlq5j0fxlAFSvVZWpMydyc9shRR0bq9XGpHc/Y+bLTxNWJpBBDz5HxxaNqFqxXE6bj775hZpVK/DW8w+z/3Akk9/9jI+mnv3b+eWcP6lcPpzklNQiz3u+Oh0aElI5jBc6PESlhtUZNGkkr/R7Jk+7NXOW8dnY6QAMe3sMrQd1YumXC/h75q/8PdP+/ape58Z0GtFbBYrz2DBLOsI1pcRO90iPTeTUxv2YWdYLtjtToABw8Tp79MCllAeN3hhFxz8n0mnBZMp2b5zv+kl7Ijm973ie+0M6XE/C9sM5R/cyTp0Gm2M737kjOVxLeWKaBaxnmrh6e9nb+ZQiLSq+xLOfXLuHzOw3nZPr9uJVNjBPG/dAb2wZmZzeHwVAzOItRNzQrMSzO9rvCVsPkXIkNs/9zt7vAFgM6o0fwtaJs6++7NlcS3sS3KYOkfPWlnj2M/uMxc0Fi6sLnLfPXOi5le/fmg7zJtLp78k0nDYCLEa+249bvQtrPkfoKw7qwK7pc+03TPOSR/RcTPmbWnL05xVXLO8ZpSuH4VHGl7iVOwud7Wre3y/Eq/51ZByKJPNIFGRmkfDbEny6tMjVxhqXQNqWPZBPUcRwdcHwdAcXCxYvD7Ki4y47k6O27j9G+ZAAyoUE4ObqQo/mdVi0MfdINMMwSEnLwDRNUtIz8CvthYvFQrC/D7UqlgWgtJcHVcqWISY+sdiyb9mxhwoRZSkfHoabmxs9O7fl32WrcrXZd/AILRrbv7RVqViOY1ExxJ48BUCW1UZ6egZZWVZS09IJDrrwe9gVzb7zvOyd2vDv8vOyHzpCi0bXn5c9Pju79ZzsGQSXKb7sF9OkQT38fH1KOka+tu4/SvnQQMqFBOLm6kqP5vVYtD73e5qBQUpaep79HSD6ZAJLN+3mpvb5v/cUafbDJyhfxpdyQb64ubrQvUFVFm07VGD7eRv20aNhtZzb0fGnWbrzCDc3r1EccfNo370Nv333JwBb1m/Dx9eHMiFBDq+fes4XfK8Lfca+wrbs2keF8FDKlw3Bzc2Vnh1asPC/3Adb9h0+RvMGdQGoUiGcY9EniD2VAEDUiTiWrt5I/54diyXv+a7v1oRVPy0B4OCGPZTyKY1vsH+edtsWbcj5+dCmvQSE5f3dNLmxNWvnLi+yrCKOuKSRFIZh7DZN87qiClOQxm+OJrRzA5J2H2XLC18CUHNMP04s28b6h2fi5luKDvMmErN0q8OngnhXCQPTpPXsp/AI8uHIL/+x593fHM4U3rMJdcYNwqOMLytueyXfNusf/ZBWXz2BLS2DzNOpLOr1vFNkP6PSkA5E/7spz/0ZcUlY3Fzwr1+Z+E0HiLihOV7hgU6R3ZF+L0hJZz+joH4HqDq8O8fnryctJv6qy35GeK+mnFi2NafAWKLZLQad/pqEd+Uw9n36F6c27HPouflUD6dc35Ys7vMCZpaVBlOGUaF/Gw5/v9Shhz0zMqD2EwMIblWL04di2PT0p6THXpkvdi5e7oR2rM/G7NM1Ljfvucrf1JKjc/+7Ijnh6t7fz+caGkTm8bMF0KyoWLzqO/ZFICs6jriPfuK6pZ9hS8sgedl6kpdtuPiKV0hMfBJhgX45t0MCfNmy/1iuNoM6NeWh6d/Q5ZE3SE5LZ9o9/bGcV+w6FhvPzsNR1KtSjuISExtHWEiZnNuhwWXYsj13gaVGtUr8veQ/Gl1fmy3bd3M8OoboE3HUqVGNuwb1o8uAEXi6u9OqaUNaN2tYfNlPnCQs+NzsQWzZvid39qqV+HvpSnv2Hbs5HnWC6BOx9uy39qPLwLvx9HCnVdMGtG5afNmvZjGnztvfA33Zsu9orjaDujTnoTe/osuYV0hOy2DafQOwZBcppn01j4cHdic5rXhHUQDEJCYT5n925F2oX2m2HI7Jt21qRhYrdh3l6Zta5dz3ytyVjO3djOQrcHpbYYSUDSYq8mze6OMxhJQNJjYmb1H2/qdGc/cjw1i9dB1vT3qfzOzT3zr2bMeD4+4hsEwAD92Wd8RsUYiJO0lY8Nkv7KFlAtm8M/dnhhqVK/D38jU0qluDLTv3cTw6lujYk5QJ8GPaB7N4eORgUkpgFAWAf2ggpyLP/n06FRWHf1ggiSfi821vcXWh2U1t+f7Fz3Ld7+bpTu32Dfh2/MdFmFbk4gosUhiGkQQ541rOfEopdeZ+0zR9L7DuKGAUwGifpnQrVa2gpg5ZN3YGWAwaTL6Lcn1bcuibxYR0qEdY90ZUv7c3AC4ebpSKCCJpT6RD27S4uhDUvAYLezyHNTWdNt8/Q/ymA5xYts2h9SPnrSVy3lqCWtSk9pMDWDZwcp421Ub1ZMXQaZzasI/q993A9S/exvpHPyzx7ABlWtem4uAOLOn7Yr7LV49+h+tfvB2LhxsxizZjZtnnoyjp7I70e0FKOjtcuN89Q/2J6NOcpTdPvOqyn6vcTS05+NVC58huM/m3yzjcfEvR4tOH8a1ZjsSdR/M0O/+5Bbeti//1len4p/134eLpfkkFBsPVhVIRQcSt2cWWF76k2uhe1Ht+KGsffN/hbVxIWLdGxK3ZnXOqx+XmPVe5fi1Z+8CVyXk17+/5MvKOTnGUxdcbny4t2NNhONbEZMq98zR+fTuS8MvCi698BeR3MPL8p7Ni2z5qlg/jo8fv4EjMKUa/9iWNrquIt5cHAClpGTz67vc8Prh7zn3FIb8jqcZ54UcOvYUpb39I/+FjqF6lIjWrV8HFxYWEpNMsXLaK+d9+iI93aR4dP5Vf/1pIn27Fc7TTzGd48Pn9PnJIf6ZM/4j+I8bmzb58NfO/mWHP/vw0fv1rEX26dSiW7FczR/aZFVv3UrNCWT56ahhHYk4yetrnNKpRkXW7DhHoW5ralcNZsyPvOf1FzZHX6hlLth+iQaXQnFM9lmw/RIC3J7XLBbNmn2Pvl1fa+f0M+f8+pk/6gNiYONzc3Xju1ScZ9sBtzHz9UwAWzlvCwnlLaNSiPvc9eTf3DBxb1LEL6Pfcz2XErX2Y8v4sbrn3aapXLk/NapVwtVhYvHI9gf5+1KlemTWbthd51vw42u9nDJo4kr2rd7BvTe4RRtd3acz+tbt0qoeUuAuNpPgM8AMeN00zGsAwjAOmaVa+2EZN05wJzAT4KWxIziukyrCuVBpq/2CwYug00qLjHU9qMzn6y39Uv+8GDn2zGDBYNeLNPEN9G785Gr+6FUmLjmfF0GkFbi418iSx/+3IGYId/c9G/K+vXOCH2IKyx63cSelKIbgH+uQazu0e5INfnYo5R26P/vIfrWc/mb20ZLO7B/rQ6LW7WTFkqn0YdD5OrtuTM8dASPt6eFct6xTZL9bvF+bc/e5frxLelUPptvINwH60vNt/r/NXy0ecPvsZ7gHeBDSoysphb5xzb/Fmz09mYgonVuwgtGP9PEUK31rl8z43Aw5/t4Rtk7/N1Ta8ZxNqPnozYB8pde5EjefKOJlEVkoakX/YT3k59utKKg3p4HDec+X3GijftyVHzjnV43LznuFXuwKGiwvxmy/9g/nVvL87KisqFreyZ4+Ku4aVIdPBUzZKt25AxpForCftxaOk+SvwalSr2IoUoQE+RJ1MyLkdcyqREP/cQ/V/WbaR4b1aYxgGFUIDiSjjz4HjsdSrEkFmlpVH3v2OXi3q0qVxrWLJnJM9uAxRMWePEEafiM1z2oN36VK89PQYwP7BvPutd1OubCjLV68nomwogf72o+qd27Vk49adxVakCA0OIurEudnj8s/+1ENnsw8aZc++ZgMRZUNyZ9+2U0UKB4QG+ube30/ms78vXc/w3m2z9/cgIoIDOBAZy8bdh1m0YRfLNu8hPTOL5NR0nv7gB16+55bzH6ZosvuVJir+7HtmdEIywb6l823758Z99GhYNef2xoPRLN5+mGU7Z5ORaSU5PYNxXy9k8pCi3d8HDruZm4faJ5nctnEHYeFn5/4ILRvCiai8p+CeGVmRmZHJL9/8zh33Ds7TZv3KTZSrFIF/oB/x5/w+i0JomUCiTpx9P4+OPUlIkH+uNt6lS/HSY6MB+2u1x51jiQgLZt7i/1i4ch1L12wkPSOT5JRUnpr6HlOevK9IM7e7vTutB9vnqDm0aR8B4WUA+yizgLAgEqJP5bterzG34BPky8zRM/Msa9ynNWvmLiuyzFczzUhRvAosUpim+aBhGI2B2YZhzAHe4TJ/P/s/XcD+Txdc0jqlK4XmXBEjrFsjkvbaK8MxizZTdUT3nNnp/epWJGHrIfuoCwdEL9rMdfffgIuXO7aMLMq0rMXemX84lL10pdCc+/3rVcLi5prni3JmfDJuPqXwrhLG6f1RhLSrR9Luks/uFRFEi08eZu0D7+XMOZEfjzK+pMcmYnF35boH+rDrzTklnt2Rfr8QZ+/3qL838sf1Z/+g3bjvk+wvbM6f/YyIPs2J+ntDrqsoFHf2M9yDfDAzrWQmpmDxdCOkbV12v5t70t2CntuJpdto+dmj7J05j/TYRNz8S+Pq7ZUzkscRx//aQHCrWpxYvp3gtnVJ3H3s4ivl4/z3TVcfL8q0rMWaB967onkByt3UiqNzVly84UVyXm37u6NSN+/GvVIEbuVCyYyOw++Gdhx72LHTzrIiT+DVoAaGpwdmWjqlW9Undcvei694hdSpHMHh6JMcPXGK0ABf/ly1jZdH35SrTVigH6u2H6DRdRWJSzjNwag4ygUHYJomL3z6K1XKBnNH95YFPELRqVuzOoePRnI0MorQ4CDm/bOUaeNzDwFPTDqNl6cHbm5u/PjbXzSuXwfv0qUoGxrM5u27SE1Lx9PDnVXrNlGnZvXiy16jOoePHufo8WhCywQy799lTHv2kYKz/77gbPaQYDZv3302+/rN1KlRtYBHknPl3t99+HPVFl6+Z0CuNmGB/qzavp9GNSrZ9/fjsZQLCWDMwK6MGdgVgDU7DvD5vOXFVqAAqFM+mMOxiRw7mUiIb2nmb9yXb5EhKTWDdfujci17qFczHupln0Nszb5Ivli8ucgLFADfffoT331qv5JRmy4tGTS8P3/O+Zt6jepwOul0vqd6lAkJyrm/Y4927Nu5H4DylSI4ctD+97Jmvetwc3Mr8gIFQN0aVTh0LIqjUTGEBgUyb9FKpj51f642iaeT8fLwwM3NlR/nLaRx3Zp4ly7F2OGDGDt8EABrNm3nsx9+L/ICBcCSWfNZMmu+PX/HhrS/swdr5y6nUsPqpCal5HuqR6tbO1G7XX3eGjIhz0gLTx8vqjevnTOxpkhJMi42IY1hGBbgAWAAUNU0zfBLeYBzR1KcyyPYj07zX8LVx8t+maHkNBa0e4Ks06m0+uoJ1j8yk7SYBNr/Mh5XHy8wDBK2HWbjk5+QdToVi6cb9SfcQWDT6mAYpBw5wX+3551hOrxnE+pPuhP3IF8yE1NI2HqI5YOnAPaJ5mo81BfTNIn+Z2OeydsKct0DfagwoC22zCysaZlsnfB1zqUwc7JHxxPeswm1nrgF02aSmZDMurEzSTkcU6LZG712N+G9m5Fy9AQAptXGwu7P5sled/wQwro0xLAY7P/8b/Z9aJ8E6Wro96ojunPd/TfgEeJPemwi0f9sZP2jH14V/X6uG/d9knNJxqsle9ufnmX39LlELzx7ubCSyu5bqzxN3r4Xw8UCFoNjc1ey8/WfqXyH/ajDgS/+ueBzi+jbghoP3ohhsWDLtLLx6U9zXRnkjO5r3sLN2wuLuyuZCcksGzSFpN3H8CpXhqbT78XNrzTpcYmsGzuD1GOXP1FihVvbEdqxPmvuyf0h4nLzAnRf9SbLh07j9N7LGyZ8NezvNUsX7kOvd4cmhD47CsNiIf6HBcS+9y0Bg3sCcGr2PFzKBFBlzptYvEuBacOWnMa+HvdgO51K8Jih+PZui2m1krZtP8fHvYWZcelXHanyReFmul+6eQ/TZs/HZjPp16YBd/dpy3cL7UWsgR2bEHMqiec++YXY+NOYmAzv1ZobWl7P+t2HGTblM6qXC8GSPaT4wf6daHv9pX/Zd6l24St6FWTJf2uZOv0jrDYbN/Xqwug7BvLtL/MAuLVvTzZu3cm4SW/g4mKhSsXyTHjqIfx87Of1v/PJ18z/dykuLi7UrF6FCU88iLu726WHsDl+Ge5c2VeuZeo7n2C1WbmpZxdG3z6Ab3/5Mzt7DzZu28m4yW/hYrFQpVJ5JjzxwNnsn85m/r/LsrNXZsLjDxQuu9uVPz3n8eensGbDZuLjEwkK9Oe+EbfTv0/3K/441r2Fu9LQ0k27mfbVPGw2G/3aNeLuG9vz3b/2bQ3s1JSYU4k89+HPxCYkYZowvHdbbmhdP9c2zhQpCnsJUjPmyMUb5Zd9x2Feyb4Ead9mNbi7c0O+/89+GsGAlrUB+GXNblbsOsLU2/K/2suZIkVhL0HaatTPhVoP4KmXH6FVxxakpabxwtjJbN9kP6Vg+levMuGRKZyIjmXGD28TEOSPYRjs2rqHSU+8QmpKKnc9MJQbBvQkKzOL9LR03pjw7iVfgnTVyrcKlXvJ6o1M+2CW/X2mW3tGDenHd7/Zr8A08IYubNy+h2deeR+LxULVihG8+PAo/Hxyj3I5U6Qo7CVIx3YoeJTgxdw6YQS129cnIzWDWY+/x+Et9sLPfZ8+xVdPziAh5hTT987m5LETpCWnAbDxz1XMe9t+efYWt7SndvsGfPJg4frvvYPfFf6cyKvAw5UG/V8Opnjj4DdO+Xu7aJEip6FhlAUamqZ5SYejCipSiIiIlJTCFimcQWGLFM6gsEUKp1DIIoVTKIIiRXEpbJHCGRS2SOEMLqdIUdIKW6RwBpdTpChpKlJcnZy1SOHw1T1M0zwOHAcwDKOraZqXdt6GiIiIiIiIyFXmKi5TX5UshVxP16URERERERERkSvqQpcgnVvQIiCogGUiIiIiIiIiIoVyodM92gK3AedfO84AmhVZIhERERERERG5Jl2oSLESSDFNc/H5CwzD2FV0kURERERERETkWlRgkcI0zZ4AhmHUNk1z+3mLxxdpKhEREREREREnYPJ/eXEPp+XIxJnfGYbxpGHnZRjGdODlog4mIiIiIiIiItcWR4oUzYHywApgDRAJtC7KUCIiIiIiIiJy7XGkSJEJpAJegCdwwDRNXSpWRERERERERK4oR4oUa7AXKZoCbYDBhmH8UKSpRERERERERJyA7f/0f2d1oat7nDHCNM212T9HAX0Nw7i9CDOJiIiIiIiIyDXooiMpzilQnHvfrKKJIyIiIiIiIiLXKkdO9xARERERERERKXKOnO4hIiIiIiIick2yYZZ0hGuKRlKIiIiIiIiIiFNQkUJEREREREREnIKKFCIiIiIiIiLiFDQnhYiIiIiIiEgBNCNF8dJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hQ0caaIiIiIiIhIAWyaOrNYqUghIiLXnKjTpUs6QqFV3r2ppCMUWlZacklHKDzTVtIJCs3wDijpCIXmUq1pSUcotIw/55R0hEJr4RlR0hEKzbp5UUlHEJHLpNM9RERERERERMQpqEghIiIiIiIiIk5Bp3uIiIiIiIiIFODqPeHv6qSRFCIiIiIiIiLiFFSkEBERERERERGnoCKFiIiIiIiIiDgFzUkhIiIiIiIiUgATs6QjXFM0kkJEREREREREnIKKFCIiIiIiIiLiFFSkEBERERERERGnoDkpRERERERERApgK+kA1xiNpBARERERERERp6AihYiIiIiIiIg4BRUpRERERERERMQpaE4KERERERERkQKYmCUd4ZqikRQiIiIiIiIi4hRUpBARERERERERp6AihYiIiIiIiIg4BRUpRERERERERMQpaOJMERERERERkQLYSjrANUYjKURERERERETEKahIISIiIiIiIiJOQUUKEREREREREXEKmpNCREREREREpAA20yzpCNeUEitSeFcLp/Gbo/GvV4ntU75jz/u/59uu0et341+/CoZhcHr/cdY+9AHWlHSHHyeiT3NqPdYfn+rhLOz5HPGbDgBQqnwZui55laR9kQCcXLeXjU9+4tA2y9/cmuse6ANAVnIaG5/8hITth/O0C25dm3rPD8VwdyV+8wHWPzwT0+r4tCslmR2g9lMDiejTHNNq48Dnf7Pv4/nKfg1mv9znkx/3AG+afzSGgAZVOfTtEjaN+yxnWdufnsUzxB9rWgYAywdNIT02sVA5HX2fafzWaMq0rEVmYgoA68bMIGHbIYcf50r3e6M3RhHWtSHpsYn80+HJPMvd/ErT+I1RlK4UijU9k/UPzyBx51GH8xZ1/zu6fzjj+3t+AjvWp/pLwzBcLBz/6h8OTf8lT5vqk4YR1LkhttR0tj/0Hqe32LO4+pai5uv3ULpmeTBNdjz8Polr91Bn5lhKVQ3PaZOVmMKazk8UOqMjlu+PZtrfW7DZ4Kb6FRje8ro8bdYciuWVf7aQZTMJ8HLn46FtAEhMy2TCvA3sPZGEYcALvRpSPyKwSPPmyr7tANO+X4jNNLmpVV2Gd2+ea/lnC9bwx5odAFitNg5EnWThtHvxK+3FrH/W8fOKLRhA9YgyvHh7Dzzciu/jz/JtB5n2wyJsNhs3ta7L8G7Nzsu+lj/W7LRnt2Vnn3oPp06n8MTHf+S0OxaXwL29W3Jbp0bFl33zHqZ+9Qc2m8lN7Rsx4oZ2uZYnpaQxbsYPRMUlkGW1cWfP1vRrdzaf1WZj8PMfEBLgyzuP3FZsuS/m2cmvs2T5agID/Jnz5QclHScPl2r1ce9xB1gsZK1fSOayubmWWyrVwnPQY9jiYwCw7lhD5uKfwNUNz2HjwcUNw+JC1vZVZC76odjzD3x+GHU6NiQjNZ0vHnuPI9sO5Gkz7M0HqVivKtasLA5u2sdX42Ziy7Li6ePFsDceIjAiCIuLC39/+Cv/fb+oWHIv33mEaXP/s+/vzWowvFODXMs/W7SJP9bvBcBqMzkQE8/CF27D082V4e//RmaWlSybjS71qnBf98bFkvlcA7L7PfMC/X7Xef3+9Xn9HnBOv68spn4XyU+JFSky40+z+dnPKdujyQXbbR7/JVmnUwGo98JtVB3ejd3v/Orw4yTuPMLK4W/Q8JUReZadPhTNv13GXVpwIPlwDEtumkhmQjKhnerT8NWRLOo1Pncjw6Dx2/eybMAkTu+PotYTt1BhYDsOzV7k/NmBioPa4xURxII2j4Fp4lHG95IeR9n/f7Lnx9HnUxBreibbp/6Ab81y+NYsn2f5mvvfzfnCeTkcfZ8B2DLhayJ/W12ox7nS/X7o2yXs/+QvGk+/N9/lNcb0JX7bIVYOfwPvauE0ePkulg2Y7PD2i7r/Hd0/nPH9PQ+LQY0pI9gw8CXSI+NoMv9lTsxfS8ruYzlNgjo3pFTlMFa2eAjfxtWpMW0k63o+A0D1l4YRt3AjW0e+juHmgouXBwDbRr2Zs361F24nK7tAVlSsNpOX/9rMB4NaEerjxdDPFtO+ehhVz3mPSUzL5OW/NvHuwJaU9SvFyeSzBaNpf2+hVZVQXr2pGZlWG6mZ1iLNmzu7jZe//YcPHrqFUH8fhk79ivbXV6Nq2aCcNnd1bcpdXZsCsHjzPr78dx1+pb2Ijk9i9qL1/PTcXXi6u/H4R7/y59qd9G1Zt/iyf/cvHzx4sz37tK9pX6/qedmbcFdX+3vU4i37+PLfDfiV9sSvtCffjbstZzvdxn1Ip/rViiX3mcec/MVvzHjiTkIDfRnywgw6NKxJ1YiQnDbf/rOKKuEhTH/4Nk4mJtP3qbfp3ep63FztHy+/+us/qoQHczrV8eJjcejXqytD+t/IuImvlnSUvAwD917DSJs1GTMxDs+7J5G1ax3miWO5mlkP7yT961dyr5uVSdrnL0FGOlhc8Bz+Ata9G7Ed3Vts8et0aEhI5TCe7/AQlRtWZ/CkkUzr90yedqvnLOPTsdMBGP72GNoM6sSSLxfQ4fYeHN97lPdHTsU70IcX/n2L1XOWYi3i9xyrzcbLPy/ng1G9CPUrzdC359C+TkWqhgbktLmrQ33u6lAfgMXbD/Hlki34lfLENE0+HN2bUh5uZFptDHt3Lm1qluP6iqFFmvlcZ/r9hQ4PUalhdQZNGskr+fT7mjnL+Cy734e9PYbWgzqx9MsFtD+v35//9y3WFEO/ixSkxIoU6bGJpMcmEtal4QXbnfkAC+Di5X7251Ie1J90J361ymO4uLDj1R85Pn9dnvWT9kReudDZTq7dc/bndXvxKpv3aJJ7oDe2jExO748CIGbxFmo81JdDsxc5fXaAynd2Yc2970D20KYzR1KV/drLnp8LPZ/y/VtTdWQPLG4unFq/jw1PfQK23EPkrCnpxK3eRenKRfsH3NH3mYKUVL/HrdxJqfJlClzue10Eu962H1k7vTeSUuWD8SjjS3psolP0v6P7uzO+v5/Pt1E1Ug5EkXbIfsQyZs4Kgns05dA5RYoyPZoQ9f0SABLX7cHVtzTuIf5YU9Lxb1mLHQ+9C4CZaSUrM28xIuTGlmzoP6FIn8fW46coH1Cacv6lAeheO4JFe6JyFSnmbT9KpxrhlPUrBUBgaXtB5XR6JuuPxDGxt/115OZiwc2l+Ka02nowivLB/pQr42/P3rgGizbtzfVF/1zz1u6kR5OaObetVhvpmVm4uriQlpFFsJ93ccQGCsi+ed8Fsu+iR5Maee5ftesI5YL9CA+6tML15di6/yjlQwMpF2J//fZoXo9F63fmKlIYGKSkpWOaJinpGfiV9sLFYt83ok8msHTTbkb2ac+sP1cUW25HNGlQj2PHo0s6Rr4sEdWwnYzCPJU9SmLrf7jWaELmeUWKAmVkF4RcXOz/F/MQ9frdmrDyJ/v74YENeyjlUxrfYH8ST8Tnardt0Yacnw9u2ot/mP01YWLiWdoTAI9SniTHn8aWVfQXf9x6+ATly/hSLvs11r1BVRZtO5SrSHGueRv20aOhvWhoGAalPNwAyLLayLLZMAyjyDOf6/puTViV3e8HHez3Q5v2EhB25r2oZPpdpCBXxZwUjd8cTWjnBiTtPsqWF74EoOaYfpxYto31D8/EzbcUHeZNJGbp1ksaKly6QjCdFkwm83Qq26d8R9yqXZecrdKQDkT/uynP/RlxSVjcXPCvX5n4TQeIuKE5XuGBV0V2gNIVQyjXtwXhvZqSHpfIpme+IPlAlLJf49kv9nx8qodTrm9LFvd5ATPLSoMpw6jQvw2Hv196Sdts/OZoTKuNyN/XsPONn69Izoup89RAaj1yMzFLt7Jt0jfYMrKctt8Tth0mvFdT4lbvIqBhVUqVK4NXeBDuAd5O1/8X2t/PPJazvr8DeIQFkh4Zl3M7PTIO30bVc7cpG0jasdizbY7H4VE2EDPLSmZcIrXeug/vOhVJ2ryf3c9+hu2c5+HfohYZJxJIPRBVqHyOiklKI8zHK+d2qI8XWyJP5Wpz6ORpsmw2Rny1jJSMLIY0qUKfehU4Gp9CQCl3xv++gd0xidQO8+OJLvXwci+ejxAx8acJC/A5mz3Ahy0Hj+fbNjUjkxXbD/L0rZ3sbf19uKNLU3o8+yGebq60qFWRVrUrFUdsIJ/s/t5sOZj/7zon+8BOeZbNX7uLno1r5rNW0Yk5lURYoF/O7ZBAX7bsy31a2aAuzXnoza/oMuYVktMymHbfACzZRYppX83j4YHdSU5zrlEUzs7wDcBMPPueYybGYSmXdwSNS7nqeN4zBTPpFBl/fYV5Ivt3Yxh4jp6MJTCMzNV/YTu2r7iiA+AfGsipyLPvh6ei4vAPC8zzZfkMi6sLzW9qy/cvfgbAos//5N6PnmDK6hl4lPbi4wfewCyGQktMYjJh/mcLmKF+pdlyOCbftqkZWazYdZSnb2qVc5/VZmPwmz9zJC6RW1vVpl6FkHzXLSqF6fdm+fT7y9n9/kkx9fvVRL1RvAr8hGEYxvWmaW7O/tkNeBJoBmwFXjJNs8CxqYZhjAJGAYz2aUq3Upc3PHHd2BlgMWgw+S7K9W3JoW8WE9KhHmHdG1H93t4AuHi4USoiyOEja2nR8fzZ+CEyTp3G//rKtPj0Ef5u/0SuI3sXU6Z1bSoO7sCSvi/mu3z16He4/sXbsXi4EbNoM2Z2RfJqyO7i4YY1PZOF3Z8lvFdTGr8xiiX9Jij7NZzdkecT3LYu/tdXpuOfE+05Pd0veT6DNfe9S1rUKVxLe9L847FUGND2kr9kX6ptk74lLSYei7srDV8dyXUP9GHn6z87bb/vmj6X+i/dQae/J5O44wgJWw9iZlmdrv8vtr+D876/58jnaJiZ56NKPkfMTBPD1QXvepXZPe4TEtfvpfpLd1HxwX4cmPptTrOQm1oT/fPyS891ifJmzpvaajPZEZXAzEGtSMuycsespVwfEYjVZmNnVAJPda1HvfBApi7Ywicr93B/u1pFntvR7Gcs2byPBlXC8SttL8gkpqSxaPNefp8wEp9SHjz+4a/8vmo7vZvXLsLEZ+X3obagI6xLtuzPzu6Z6/7MLCuLt+zjob6tiyBhwfL7gnJ+9hVb91KzQlk+emoYR2JOMnra5zSqUZF1uw4R6Fua2pXDWbPj8k/du7bk936S+6bt+EFS3nwQMtJxqd4Az0GPkDr9key2JmkfPA2epfC89RGyQsphxjg+Z9Fly2//vsCX3cETR7J39Q72Zs/LUrtdfY5uP8SbgycQXDGUh758jr09HyftMv9uXkx+EQsaDLFk+yEaVArFr9TZ16qLxcJ3j/QnMTWdRz5fwN6ok1QLK755e/J7X7lQkWFQdr/vO6ffj5zT7w8WU7+LFORCh0E+A87MfjQFCAJeA/oBHwB3FLSiaZozgZkAP4UNyXmFVBnWlUpDOwKwYug00qLjHU9qMzn6y39Uv+8GDn2zGDBYNeJNTu/LfTSl8Zuj8atbkbToeFYMnVbw5jKyyMg4DUD85gMkH4rGu2pYgedhn5/dPdCHRq/dzYohU8k4dTrfdU6u28OSfvYhvCHt6+FdtWz2EufPnhp5ksjf7efnR/6xhsZvjlb2ayh7oZ+PAYe/W8K2yd/mWje8ZxNqPnozAOsf/fCCj5sWZT+6m5WcxpGfVxDQsOolfUkuzPtMWoy9jS0ji0PfLM75cuwM/Z6frNOp9i/32bqveYvkwycIalmzxPq/MPt7jhJ+f7+Q9ONxeISfHZrvER5ERtSpPG08I8qQgH20hkfZINKjToFpkh4ZR2L2RGsxv66k4oP9ctYzXCyE9G7Gmq5PXXKuSxXq40VU0tkPm9FJqQT7eJ7XxhN/rxC83F3xcnelcfkgdsUk0KhcECE+ntTLHg3YtWY4n6zcQ3EJ9fch6lTS2eynkgo8ZePPdbvo0fTsiIOVOw8REeRHoI/9FJbODaqzcX9ksRUpQv29c2ePP02wX+l82/65bleu01TOWLbtIDXLhxDkm/96RSU00Jeokwk5t2NOJhLi75OrzS9L1zO8d1sMw6BCaBARwQEciIxl4+7DLNqwi2Wb95CemUVyajpPf/ADL99zS7E+h6uRmXgSw/fse47hG4SZlPs9h/Szr2Xrno3QeziU8oGUs/saaSlYD+7ApVp9soq4SNH+9u60HtwZgEOb9hEQXgay3w8DwoKIjz6V73q9x9yCd5AvM0fPzLmv5YCO/PX+HABOHIom7kgMoVXDObSpaEeEhPqVJir+7N+q6IRkggt4zf25cR89GlbNd5mvlwdNqpRl+c6jRV6kaHeRfk8ooN97jbkFn3z6fX4J9LtIQS5UpDi3JNcZaGqaZqZhGEuAgsfuXsD+Txew/9MFl7RO6UqhJB+0nzcY1q0RSXvtR9JiFm2m6ojuObPS+9WtSMLWQ7k+uF+Ie5CP/YOzzaRUhRC8K4eRfCj/YV3nZ/eKCKLFJw+z9oH3cuacyM+Zc8Qt7q5c90Afdr0556rJHvnnWoLb1OHQ7MWUaVWL0/uPK/s1lL2wz+fE0m20/OxR9s6cR3psIm7+pXH19iJy3loi56296GMYLhbc/EqTcTIJw9WFsl0bErNka6FzOsozxD+nUBHeo0nOlTKcod/z4+ZbiqzUdMxMK5WGdiR25U6yTqeWaP8XZn93lvf3C0nasI9SVcriWSGY9OMnCenXiu33vp2rTez8tZQb3oPon5fj27g61qQUMrL3p/TIOEpVLUvKvuMEtq1H8u6zXxYC2tUjeU8k6cdPFirbpahT1p/DJ5M5Fp9MiI8X87cfY/KNuWef71C9LFMWbCbLZiPTamNL5Clua1qVMt6ehPl6cTAuiUpBPqw6eIIqQT4FPFIRZK8YxuGYeI7FJhDi7838dbuYPKxXnnZJqems23OUyXedXVY2wJfNB4+TmpGJp5srq3Ydpk6FYpzMrmIYh2NO5c5+V8+Cs9+Zd9mf63bmW7woanUqR3A4+iRHT5wiNMCHP1dt4eV7BuRqExboz6rt+2lUoxJxCac5eDyWciEBjBnYlTEDuwKwZscBPp+3XAUKB9ki92EJCsPwD8ZMOolL3Zak//hOrjaGtx/maXsByRJR1X7IPyXJXqiwWSEtBVzdcKlSl8zlc/N7mCtq8az5LJ5lvxJZ3Y4N6XBnD9bOXU7lhtVJTUrJ95SD1rd2ola7+rw1ZEKuI/6nImOp0boee9fsxKeMH6FVwokt4LSLK6lO+WAOxyZy7GQiIb6lmb9xH5OHdMzTLik1g3X7o3ItO3k6FVcXC75eHqRlZrFq7zGGZU+wWZSWzJrPknP6vX12v1e6QL+3urUTtfPp95ORsdRsXY99xdzvIgUxChoKZBjGfuBRwIL99I5a5yzbZJqmQ6++c0dSnMsj2I9O81/C1ccL02ZiTU5jQTv7cNxWXz3B+kdmkhaTQPtfxuPq4wWGQcK2w2x88hOyTqdi8XSj/oQ7CGxaHQyDlCMn+O/2vLM0h/dsQv1Jd+Ie5EtmYgoJWw+xfPAUwns3pfYTA7BlWcFqY/srPxK1YL0jT4lGr91NeO9mpBw9AYBptbGw+7MAZ7NHx1N3/BDCujTEsBjs//xv9n34J8BVkd3NtxRN37sfr4ggspLT2fjExyRsP6zs12D2S30+EX1bUOPBGzEsFmyZVjY+/Smn1uedWbz7mrdw8/bC4u5KZkIyywZNIeVoLO1+Ho/FzQXDxULMkq1sfn5WnokfHeXQ+0x0PG1+eAaPIB/7+8zWQ2x44mOsKekl1u9N33+A4Fa1cA/0If1EAttf+RGLmwsAB774h8DG1Wky/V5Mq43E3UdZ/8iHZCYkO03/O7S/l/D7u7+Z5fDzCerckOoT78RwsRA5eyGH3vyZ8DvsX8Aiv7AXZq57eQRBnepjTc1gx5j3SNq0HwDvOhWp+fo9WNxdST0Uw44x75GV/buq9dZ9JKzbk7MNR7WcUumS2p+xdF80r/y9BZtp0vf6Ctzdqgbfb7CPLhnQsDIAn63aw9zNhzEMg5vqV+S2pvajhTujE5gwbwOZVpMI/1JM6N0QX0/3Ah+rQBUKd/rn0q37eSX7Mp59W9bl7p4t+H6J/XjJgHb2jyO//LeVFdsPMnXEDbnWfe+35fy1bhcuFgs1y4fw/NBuuBfmEqRm4SaRW7r1AK/8uAibzaRvyzrc3aM53y/Nzt72TPZtrNhxkKnDe+daNzUjkx7PfsRvLw7HJ/vKMIVheOc/+d9Fs2/azbSv5mGz2ejXrhF339ie7/5dA8DATk2JOZXIcx/+TGxCEqYJw3u35YbWuT8enilSFPYSpC7VmhZqvQt5/PkprNmwmfj4RIIC/blvxO3079P9ij9Oxjt5r67gCJfqDeyXIDUsZG1YRObSObg26QJA1tq/cW3WDbcmXTFtVsjKIGP+LGxH9mCEVsCj370YFgsYBlnbVtovTVoIj31W+Ks6DJowgtrt65ORmsEXj7/H4S3298P7P32KL5+cQULMKd7ZO5uTx06QlpwGwMY/V/HH2z/iFxLAHa/eh19IAIYB89//hdVzLu2Uw9ffbnbxRvlYuuMwr2RfgrRvsxrc3bkh3/+3HYABLe2jr35Zs5sVu44w9bbOOevtjozjuW8XY7OZ2EyTbvWrMLpr4S4V/OhDhbvKGMCt5/T7rHP6/b5Pn+Kr7H6fnk+/zzun332z+/2vQvT7ewe/K97ZQovZkIo3/V9OS/H1oZ+d8vd2oSLFp+fd9ZRpmtGGYYQBX5mm2Tm/9c5XUJFCRESkpFxKkcLZFLZI4RQKWaRwCoUsUjiDwhYpnEFRFCmKS2GLFM7gcooUJa2wRQpncDlFipKmIsXVyVmLFAUeSjBNc1gB90dhP/1DREREREREROSKKdSFzg3D6Hqlg4iIiIiIiIjIta1QRQrg4yuaQkRERERERESueQWe7mEYRkHTARvYL0cqIiIiIiIi8n/N5P9ySgqndaHprdsCtwHnX+DeAK7eGWlERERERERExCldqEixEkgxTXPx+QsMw9hVdJFERERERERE5Fp0oat79AQwDKO2aZrbz1s8vkhTiYiIiIiIiMg150IjKc74zjCMWcA0wDP73yZAy6IMJiIiIiIiIlLSbCUd4BrjyNU9mgPlgRXAGiASaF2UoURERERERETk2uNIkSITSAW8sI+kOGCapopJIiIiIiIiInJFOVKkWIO9SNEUaAMMNgzjhyJNJSIiIiIiIiLXHEfmpBhhmuba7J+jgL6GYdxehJlEREREREREnIINs6QjXFMuOpLinALFuffNKpo4IiIiIiIiInKtcuR0DxERERERERGRIqcihYiIiIiIiIg4BUfmpBARERERERG5Jpmak6JYaSSFiIiIiIiIiDgFFSlERERERERExCmoSCEiIiIiIiIiTkFFChERERERERFxCpo4U0RERERERKQAtpIOcI3RSAoRERERERERcQoqUoiIiIiIiIiIU9DpHiIics2pWfVESUcoNOuWtJKOUGiuNRuWdITCc7l6PzKZUYdKOkKhZfw5p6QjFJr7A5NKOkKh/fBGz5KOUGhvhA0o6QiF5snako4g4hSu3r+4IiIiIiIiIkXMNM2SjnBN0ekeIiIiIiIiIuIUVKQQEREREREREaegIoWIiIiIiIiIOAXNSSEiIiIiIiJSABuak6I4aSSFiIiIiIiIiDgFFSlERERERERExGGGYQQahrHAMIw92f8GXKCti2EYGwzD+M2RbatIISIiIiIiIiKX4ingH9M0qwP/ZN8uyBhgh6MbVpFCREREREREpAC2/9P/L1Nf4PPsnz8H+uXXyDCMckBv4CNHN6wihYiIiIiIiIhcilDTNI8DZP8bUkC7N4EnuIS6iK7uISIiIiIiInKNMQxjFDDqnLtmmqY585zlfwNh+az6jIPbvwGIMU1znWEYHRzNpSKFiIiIiIiIyDUmuyAx8wLLuxS0zDCMaMMwypqmedwwjLJATD7NWgM3GobRC/AEfA3D+NI0zdsulEune4iIiIiIiIjIpZgL3Jn9853AL+c3ME3zadM0y5mmWQkYBPx7sQIFaCSFiIiIiIiISIFMzJKO4IymAN8ZhjECOAwMADAMIxz4yDTNXoXdsIoUIiIiIiIiIuIw0zTjgM753B8J5ClQmKa5CFjkyLZ1uoeIiIiIiIiIOAUVKURERERERETEKeh0DxEREREREZEC2DQnRbHSSAoRERERERERcQoqUoiIiIiIiIiIU1CRQkREREREREScguakEBERERERESmAaWpOiuKkkRQiIiIiIiIi4hRUpBARERERERERp1Bip3s0emMUYV0bkh6byD8dnsyzvPzNrbnugT4AZCWnsfHJT0jYftjh7bsHeNP8ozEENKjKoW+XsGncZznL2v70LJ4h/ljTMgBYPmgK6bGJDm3XKzyQJtPvxSPYH0yTA7P+Zd9Hf+Zq410tnMZvjsa/XiW2T/mOPe//7nBuZXecI8/nQmo/NZAKA9ri7l+auVWH59xf4dZ21Bs/hNTjJwHY/8lfHPx6UZHmLNu9MbWfHIBps2FabWx+bhZxq3c5/Bjq9/xVHdmDSrd1xDAMDnz5L/s+zJ2zKN9nDDcXGkweRplWtcBmsm3Kt0T+vsbhbbv5labxG6MoXSkUa3om6x+eQeLOo3naVRnejWp398C7chi/1R5Nxskkhx8DIKJPc2o91h+f6uEs7Pkc8ZsO5CzzrVWehq+MxM3HC9NmY2GP57ClZ15wexd7b3f0eRWkOPf1/Hg0b4rf2AfAxYWUX3/n9KzZuZZ7deuC922DADBTU4l/5U2y9u4DoPStt1CqT2/AJHPffuInTYWMC/fnleRSsxEe/UaCxYXMlX+R+e+P+bazlK+G15hXSPviFaybVwDg1u5GXFt0A9PEdvwQ6d+8BVnFl3355j1M/fpPbDYbN7VrxIgb2uZanpSSxrgZPxF1MoEsq407e7aiX9uGRMUl8MyHPxOXcBrDMLilQ2OGdmtRbLnt2XczddYf9uwdGjOiT/u82d//nqi4BLJsNu7s1Zp+7RrnLLfabAwe/z4hAb688+jtxZt95xGmzf0Pm83kpmY1GN6pQa7lny3axB/r92bnNDkQE8/CF27Dr5RnTvYhb80hxK8U04f3KNbsLtXq497jDrBYyFq/kMxlc3Mtt1Sqheegx7DFx9iz7lhD5uKfwNUNz2HjwcUNw+JC1vZVZC76oVizX8izk19nyfLVBAb4M+fLD0o6Tr4mT32GLt3ak5KSxkP3PcXmTdvzbTfuubHc2K8HVquNzz6ezYczZgHQqk0zJr08Dlc3V07GnaJv7+LZ76/m1yrATc/fSa2ODclITWf2Y+9zbNvBPG2GvvkA5etVwZpl5fCmvXw/7iNsWdac5eWvr8KYn1/iiwfeYvO8VcWYXiS3EitSHPp2Cfs/+YvG0+/Nd3ny4RiW3DSRzIRkQjvVp+GrI1nUa7zD27emZ7J96g/41iyHb83yeZavuf/dXB/GHWVm2djywlfEbzmIa2lPOv41iZglW0jafSynTWb8aTY/+zllezS55O0ru+MceT4Xcvyv9ez/5C+6/fd6nmVHf1mZ68tPUeeMWbqV4/PXAfYvhs1njmFB28ccfgz1e16+NctR6baOLOr5HLaMLFrPfoqovzeSfCAqp01Rvs/UHNuP9NgEFrR+FAwD9wDvS8pfY0xf4rcdYuXwN/CuFk6Dl+9i2YDJedrFrd5F1IL1tP3puUva/hmJO4+wcvgbNHxlRK77DRcLTd+9n7UPvEfC9sO4B3hjy8y66PYu9t7u6PMqSHHu63lYLPg9Noa4MY9jjTlB8McfkLZ0BVkHD+U0yYo8Tuz9YzGTTuPRohn+Tz5K7N33YSlThtIDbiZmyF2QkUHAxOfx6tKJ1D/mF03W8xkWPG4eTeoH4zET4vB6+DWytq3GjD6Sp537DXdh3bXh7F1+gbi17UPKtPshMwOPO57AtWFbstb8WyzRrTYbk2f9wYzHbyc00JchL35Ih4Y1qBoRktPm239WUyUimOkPD+FkYjJ9n55O75b1cHGx8NigbtSqFE5yajqDXphBizpVcq1b5Nk//5UZTw6zZx//AR0a1cqd/e+VVIkIYfqjt9uzP/EmvVvVx83V/hHtq/n/USU8mNOp6cWS+dzsL/+8nA9G9SLUrzRD355D+zoVqRoakNPmrg71uatDfQAWbz/El0u25BQoAL5eupXKIf4kp2cUa3YMA/dew0ibNRkzMQ7PuyeRtWsd5oncf6esh3eS/vUrudfNyiTt85cgIx0sLngOfwHr3o3Yju4tvvwX0K9XV4b0v5FxE18t6Sj56tK1HVWqVqJZw240blKfaa+/QI/OA/O0Gzz0ZsIjytKySU9M06RMmUAAfP18mPba89zafyTHjh7Pub+oXc2vVYBaHRpQpnJZJncYS8WG1bhl0kje6vdsnnbr5yzjq7HvAHDb2w/SYlAnVny5AADDYnDDU0PYtWRTsWa/WthKOsA1psRO94hbuZOM+NMFLj+5dg+ZCcn2n9ftxavs2Tep8v1b02HeRDr9PZmG00aAxcizvjUlnbjVu7Be5KjfpUqLiSd+y0HAfuQ1ac8xvMICcrVJj03k1Mb9mOdUJpX9yivo+ZSuGELrr5+k4/xJtJszHu9q4fmuf2r9XtJi4kss57msKWf/oLmW8sw1OY/6vXB8qkdwat1erKkZmFYbsf/tILxX7uJbUb7PVBzUgV3Ts4/cmeYlj3DwvS6CE0u3AXB6bySlygfjUcY3T7uErYdIORKb536XUh40emMUHf+cSKcFkynbvXGeNgBJeyI5ve94nvtDOlxPwvbDOSNLMk6dBtvFJ4262Hv7hZ6Xs+3r53OrXZOso5FYI49DVhapf/+LZ9vWudpkbt2GmWR//hnbtuMSUiZnmeHiguHhAS4WDE8PbLFxxZbdUqE6ttjjmCejwZpF1oaluNZtnqedW9sbsG5egZmUcN4GLODmDhYLhpsHZsLJYkoOW/cfo3xoIOVCAnFzdaVH87os2pB7pJlhGKSkpWOaJinpGfiV9sLFYiHY34dalezvRaW9PKgSHkzMqUt7LV5W9n1HKR8adDZ7i3osWrcjd3bOyZ6WnpMdIPpkAks37uKm9vm/fos0++ETlC/jS7kgX9xcXejeoCqLth0qsP28Dfvo0bBazu3o+NMs3XmEm5vXKI64uVgiqmE7GYV5KgasVqxb/8O1xiUcfMnI/pvs4mL/34kmzGvSoB5+vj4lHaNAPXp35tvZcwBYt3YTfn6+hIYG52l314jBvDbt3ZzPO7Gx9veU/gP68PuvCzh29Hiu+4va1fxaBajbrQlrf1oCwKENe/HyKYVPsH+edjsWbcz5+fCmffiFnf3c0/auHmyet5qkuCs7AlGkMK6KOSkqDelA9L/2qp5P9XDK9W3J4j4v8G+XcZg2GxX6t7nkbTZ+czSd/p5MzYdvKnSuUuXL4F+3EifX73OovbJfmez5Off5NHx1JBuf+ZyF3Z9hy4tf0WDKsEveXkTvpnT+dwrNPxqDV/iVq+JfqN/Dezah69JXafXl46x/eCagfr8ciTuPENSiJu4B3rh4uRPauQGlwoMKbH8l32fcfEsBUPuJAXT6axLNPhyTb4HhQhK2HSa8V1MAAhpWpVS5MnhdIP/5ao7px4ll21jY4zmW9n+JuuOH4FLKw+H1vauEgWnSevZTdPprEtXvv+GS8hekoOfl7Ps6gEtwGazRMTm3rSdO4BJcpsD2pW7oRdp/qwGwxcZyevZ3hP78LaFzf8R2Opn01WuveMaCGH5BmPFni1lmfCyGX9B5bQJxrdeCzBW5T4syE06SuWgOpZ/7mNIvfI6Zlox198biiA1AzKlEwgLPvn5CAnyJPpX7Q/Sgzs3YHxlLl7Gvccuz7/HEkJ5YLLk/4hw7cYqdh45Tr2pEseSGM9n9cm6HBOaTvWsL9keeoMuDU7ll3Ds8cXvvnOzTvvyDhwd1x5JPwa6oxSQmE+Z/dgRYqF9pYrKLuudLzchixa6jdKlXKee+V+auZGzvZhhG8Wc3fAMwE88WAc3EOAzfgDztXMpVx/OeKXgMfRIjuNw5GzDwvOdlSj0+A+u+LdiOOfZZSaBs2VAij50dsRgZGUVYeGiedpUql6ffzb1YsOhHvvnhQ6pUqQhA1aqV8Pf3Zc5vX/D34h8ZOKhvseS+ml+rAL6hgcRHnt3n46NO5ipAnM/i6kKTm9qyc7H9c49faAD1ujdlxVcLijyriCMKPN3DMIwHgG9M04w1DKMa8AlwPbALGGma5pbiCFimdW0qDu7Akr4vAhDcti7+11em458TAXDxdL/kc47X3PcuaVGncC3tSfOPx1JhQFsOf7/0krbhUsqD5h89zObxs8g6nerQOsp++dnzc+7zMW02gppcR/MPH8pZbnF3u6TtRf21nqM/r8CWkUXlOzrT+O17WXbLpCuaM79+j5y3lsh5awlqUZPaTw5g2cDJ6vfL6PekPZHsfudX2nz7NFnJaSRsO5TrvMtzXen3GcPVhVIRQcSt2cWWF76k2uhe1Ht+KGsffN/hbeyaPpf6L91Bp78nk7jjCAlbD+Y7wqkgIR3qEda9EdXv7W1/Dh5ulIoIImlPpEPrW1xdCGpeg4U9nsOamk6b758hftMBTizb5nCG/BT0vJx5Xz8r74fPgi5J5t6oAaX69CL2HvtrwvDxxrNtK2JuGYwt6TQBk17Aq3sXUuf/fQXzXUB+XxTPy+7R927Sf/sczPMGtXqVxqVuc5JfuhtSk/G880lcG3cga92iostbcEzAfkTzXCu27qVmhTA+evJOjsScZPQrs2hUowLeXvZTD1LS0nn0ne94fEiPnPuKQ77Zz/tdrNiyh5oVyvLR08Pt2ad8SqMaFVm38yCBvqWpXTmCNTv2F1Pis/LPnn/bJdsP0aBSaM6pHku2HyLA25Pa5YJZs8+x95wrK7/9PfdN2/GDpLz5IGSk41K9AZ6DHiF1+iPZbU3SPngaPEvheesjZIWUw4xxfO6ca1l+Ran83ic93N1JS0una4f+9O7TlbfenUyfnkNxdXXh+gZ16H/jXXh6ejLv729Yu2YT+/cdLNLcV/Nr1Z41nzsvMALolonD2b96BwfW7ASg7/g7+W3K15gOjJgUKQ4XmpPiXtM038n++S3gDdM0fzYMowPwAdC6oBUNwxgFjAIY7dOUbqWqFdT0gnxrlafRa3ezYshU+1BjAAMOf7eEbZO/zdU2vGcTaj56MwDrH/3wguckp0WdAuxD1Y/8vIKAhlUv6YOs4epCi48f5shPy4n8w/GJ8JT98rLnG+u85+Pq7UVmYjL/dhmXu6HFoNNf9i+8x/9az45pBU+ClbOvAQe+/Je6zw6+rIz55byQuJU7KV0pBPdAH/X7ZTo0exGHZi8CoM7Tt5J6PO/w+qJ4n8k4mURWShqRf9iPlB/7dSWVhnS4aN4qw7pSaWhHAFYMnca6sTNylnVf8xbJh09cdBtnGawa8WaeUzkavzkav7oVSYuOZ8XQaQWunRp5ktj/duScphL9z0b8r6982UWKrNOp+T6voJY1nXJfP5f1xAlcQs+en+wSHJzvKRuuVavg//RjxD3yFGaivdDi0aQxWZFR2OLtp1GkLVqKe726xVakMONjMfzPOfXEvwxmYu5h1Jby1fC83T4XjlHaF5dajUm3WcHF1X6aSLL9uWRt+Q+XSjWLrUgRGuhL1MmzBauYU4mEBOQe7v7L0o0M790GwzCoEBpERLA/B47HUq9KOTKzrDzyznf0almPLk1qF0vm3NnPnjoTczKREP/zsi9Zz/A+7c7JHsCByFg27j7MovU7WbZpN+mZWSSnpvP0+9/z8r0Diie7X2mizjl1KzohmWDf0vm2/XPjPno0rJpze+PBaBZvP8yynbPJyLSSnJ7BuK8XMnlIxyLPDWAmnsTwPTtSyPANwkw6lbtR+tmDBdY9G6H3cCjlAynnnA6UloL14A5cqtUnS0WKAg0fOYTb77TPO7FhwxbCI8JyloWHhxF9PCbPOpGR0fw29y8Afv91AW+/+3L2/VHExZ0iJSWVlJRU/luxlrr1ahZ5keJqfK22vr0bLQZ3AuDIpn34nzPa0j8skIToU/mu121Mf0oH+fL96LNzg5W/vgq3Tx8DQOkAH2p1aIDNamXrX8U34k/kXBcqUpy7LMQ0zZ8BTNNcZBjGBU+GM01zJjAT4KewIYUqyXlFBNHik4dZ+8B7nN5/dtjYiaXbaPnZo+ydOY/02ETc/Evj6u2VcxT6YgwXC25+pck4mYTh6kLZrg2JWbL1krI1emMUSXuOsXfGH5e0nrJfXvb8nP98sk6nknz4BBF9mnPsV/usxH61K5Cw/XDeL9AF8Azxz5kzIbx7Y5L2ODYh5KXkPF/pSqEkH4wGwL9eJSxurmScTFK/XyaPMr6kxybiFRFEeK+mLLrh+VzLi+p9BuD4XxsIblWLE8u3E9y2LokOTCy6/9MF7P/UPtTSzbcUhpsLZqaVSkM7Ertyp8MjnwBiFm2m6ojuOROR+tWtSMLWQ7kKBBcSvWgz191/Ay5e7tgysijTshZ7Z17a+0Z+3HxLkZWanud5Oeu+fq7MHTtxLReBS9kwrCdi8erSiVMvvJSrjUtoCIEvT+DUiy9jPXL2S401Ogb3OrUxPDww09PxaNKIzJ2OX8HnctmO7MESHI4RGIqZEIdrw7akz8o98V7KpLtzfvYYNIas7Wuwbl2FpcJ1WCrWsM9JkZmBS/X62I7sKbbsdSqHczg6jqMnThEa4MOfq7by8j39c7UJC/Jj1fb9NKpRkbiE0xw8Hke54ABM0+SFT36hStky3NGjVbFlzsleJYLDUXEcjTlJaKAvf67cwsv35f7iEhbkz6pt+2hUo5I9e1Qs5UICGHNrN8bc2g2ANTv28/kfy4utQAFQp3wwh2MTOXYykRDf0szfuC/fIkNSagbr9kflWvZQr2Y81KuZPfu+SL5YvLnYChQAtsh9WILCMPyDMZNO4lK3Jek/vpOrjeHth3na/qXUElHVfig6JcleqLBZIS0FXN1wqVKXzOVz83sYyfbJR1/zyUdfA9C1W3tGjLqNn3/8ncZN6pOYmER0dN4C+7zf/6ZtuxZ8/eWPtGrTjH3ZRYh5v//DlFfH4+Ligru7G40aX88H735W5M/hanytLp/1F8tn2Qs9tTo2pM2d3dkwdwUVG1YjLSmFpBPxedZpfmtHarSrzwdDJuYa4TKp7dmRsINevZft/6xXgeI85vnDsaRIGQUNVTUMYxIQAUwABgEpwE9AZ6C/aZoOnaBcUJGi6fsPENyqFu6BPqSfSGD7Kz9icXMB4MAX/9DotbsJ792MlKP2NzbTamNhd/sstRF9W1DjwRsxLBZsmVY2Pv0pp9bnnXW5+5q3cPP2wuLuSmZCMssGTSHlaCztfh6Pxc0Fw8VCzJKtbH5+lkMTwgEENatB+7nPk7D9MKbNPiR228vfUSoiKCe7R7Afnea/hKuPF6bNxJqcxoJ2T5B1OlXZC5n9Up5P0p5jNJwyHI9QfyxuLhyd8x87X/85z/p1nxtM+Zta4RkWQFrUKQ5+vYgdr/5InXG3UrZ7Y2xZVjLjT7PhyU85vbfww1Ud6ffrHuhDhQFtsWVmYU3LZOuEr3MuQap+L7x2c8bjHuiNLdPKlue/5MSybVS+ozNQtO8zSbuP4VWuDE2n34ubX2nS4xJZN3YGqcccnygxsHF1mky/F9NqI3H3UdY/8mHOJJ+tvnqC9Y/MJC06nqojunPd/TfgEeJPemwi0f9sZP2jH2LxdKP+hDsIbFodDIOUIyf47/a8s8GH92xC/Ul34h7kS2ZiCglbD7F88BTAPpFljYf6Ypom0f9sZOvE2XnWP9/F3tsv9LyKc19vUSXvZKGO8GjZHL8x94OLhZTf5nH6868o1c9+GduUOb/i99RjeHVohzXKXnQ0rVZiR9wDgM+Iu/Ds0hGsVjJ37yH+5Vch89InAPVtUbhJ81xqNcaj70iwWMhc/TeZf3+Pa0v7ZSGz/ss9D0VOkSL7EqTu3Qfj2qAtps2K7dh+0r+dDtaLX+3lfK4DCjdCaumm3Uz7+k9sNpN+bRty943t+O5f+6i0gZ2aEnMqkec+mkNswmlM02R47zbc0Ko+63cfYtjkT6leLgRL9njoB2/pTNv61116CJfCXRBt6cZdTPvKflnDfu0ac3ffDnz3j32ukoGdm9mzz/yR2Pjs7H3acUPrBrm2ceaLT2Eva2hGFTzh5QWz7zjMK9mXIO3brAZ3d27I9//ZLyc5oKV9VMova3azYtcRpt7WOd9tnClSFPYSpLb16wq1nkv1BvZLkBoWsjYsInPpHFybdAEga+3fuDbrhluTrpg2K2RlkDF/FrYjezBCK+DR714MiwUMg6xtK+2XJi0E9wcu/1TR8z3+/BTWbNhMfHwiQYH+3Dfidvr36X7FHye8as9Crzv11fF07NKW1JRUHrp/HJs22AvGs7+fydgHnyU6KgZfPx8++PBVIsqVJTk5hccffp5tW+2fe+5/aASDh96MzWbjqy9+YMb7n1/S4x9Z8NLFG+XDGV6r4wbmf2loR9w8YRg12zcgMzWd2Y9/wNEt9lNP7v70Sb59ciaJMad4Ze9XnDoWS3qy/aDHlj9X89fbuffvM0WKS70E6esHvymZCTmKSbfyPf4vqxR/HfnTKX9vBRYpAAzDuAu4F6gKeABHgDnAVNM0Ewpc8RyFHUkhIiJSVApbpHAGhS1SOIPCFimcQiGLFM6gsEUKZ1DYIoUzKIoiRXG5nCJFSStskcIZXE6RoqSpSHF1ctYixQX/4pqm+RnwWbEkEREREREREZFrWqEOCxiG0dU0TV2jRkRERERERP6v2TQnRbGyXLxJvj6+oilERERERERE5JpX4EgKwzAKmsrYAIIKWCYiIiIiIiIiUigXOt2jLXAbcPq8+w2gWZElEhEREREREZFr0oWKFCuBFNM0F5+/wDCM4ru4u4iIiIiIiEgJudAVMeXKK7BIYZpmTwDDMGqbprn9vMXjizSViIiIiIiIiFxzHJk48zvDMJ407LwMw5gOvFzUwURERERERETk2uJIkaI5UB5YAawBIoHWRRlKRERERERERK49F5qT4oxMIBXwAjyBA6Zp2oo0lYiIiIiIiIgTsKE5KYqTIyMp1mAvUjQF2gCDDcP4oUhTiYiIiIiIiMg1x5GRFCNM01yb/XMU0NcwjNuLMJOIiIiIiIiIXIMuOpLinALFuffNKpo4IiIiIiIiInKtcuR0DxERERERERGRIufI6R4iIiIiIiIi1yRTE2cWK42kEBERERERERGnoCKFiIiIiIiIiDgFFSlERERERERExCloTgoRERERERGRAthMzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECqAZKYqXRlKIiIiIiIiIiFPQSAoREbnmVFi7u6QjFJrbhqv3T3e5WcdKOsI1ydvFs6QjFFoLz4iSjlBoP7zRs6QjFFrkvnklHaHQujYYVdIRCu2HepklHUHEKWgkhYiIiIiIiIg4hav3cIyIiIiIiIhIEbNpVopipZEUIiIiIiIiIuIUVKQQEREREREREaegIoWIiIiIiIiIOAUVKURERERERETEKWjiTBEREREREZECaOLM4qWRFCIiIiIiIiLiFFSkEBERERERERGnoCKFiIiIiIiIiDgFzUkhIiIiIiIiUgDT1JwUxUkjKURERERERETEKahIISIiIiIiIiJOQUUKEREREREREXEKmpNCREREREREpAA2NCdFcdJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyC5qQQERERERERKYCpOSmKlUZSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hRUpBARERERERERp1BiE2eWv7k11z3QB4Cs5DQ2PvkJCdsP52nX+K3RlGlZi8zEFADWjZlBwrZDDj9ORJ/m1HqsPz7Vw1nY8zniNx3IWeZbqzwNXxmJm48Xps3Gwh7PYUvPdHjbAQ2q0OH3Cawa/TaRv63Os7zK8G5Uu7sH3pXD+K32aDJOJjm87aLKXqZVLVp+9ijJh2MAiPxjDTtf//mqyH7Gxfo9uHVt6j0/FMPdlfjNB1j/8ExMq61Eszva78Ft6lBv/BCwGGQlp7NuzAckH4wu0exnXKzf280Zj6u3JwAeZfw4tWEfK4e9XqLZr/p+txh0mj+J1KiT/Hf7q4V6bgVxD/Cm+UdjCGhQlUPfLmHTuM9ylhluLjSYPIwyrWqBzWTblG+J/H2Nw9s+42p8rTrijdcn0LNHJ1JSUxkx4mE2bNyap82if3/C28cbgJDgINas3Uj/W0bQvl1LfvrxEw4cPALAnDl/8NKkN69ILke89toLdO/ekZSUVEaNeoyN+WT/++/v8fYubc8eUoa1azcycOAoBg3qxyOP3ANAcnIKDz30DFu27Ci27M9Nfpz2XVqTmpLGkw+9wPbNO/O0mTr9BZq2bMTppNMAPPngC+zYuhtvH29ee38iZSPCcHV14eP3ZvHj7F+V3QFPvDSW1p1bkpaaxvNjJrFzy+48bV586xkat2zA6cRkAMaPmcTubXvo0L0N9z55N6bNxGq18spzb7Fx9eZiyz7w+WHU6diQjNR0vnjsPY5sO5CnzbA3H6RivapYs7I4uGkfX42biS3LiqePF8PeeIjAiCAsLi78/eGv/Pf9omLLPnnqM3Tp1p6UlDQeuu8pNm/anm+7cc+N5cZ+PbBabXz28Ww+nDELgFZtmjHp5XG4urlyMu4UfXvfXmzZC/Ls5NdZsnw1gQH+zPnyg5KOk68HJ9xPi07NSEtNZ8rD09izdW+BbR+a+AA9B3anZ40+Ofc1aFmfB164FxdXVxJOJTD2lkeLIzZujZtR+p4HMSwW0v78ndTvv8613L1Fa0rdMQJsNkyrleSZ75C1bQu4ueP3ytsYbm7g4kLGssWkfPlpsWS+2pimJs4sTiVWpEg+HMOSmyaSmZBMaKf6NHx1JIt6jc+37ZYJX+f7AdcRiTuPsHL4GzR8ZUSu+w0XC03fvZ+1D7xHwvbDuAd4Y8vMcnzDFoM6zw4melHBf2zjVu8iasF62v70nFNlj121M88Xnqsl+0X73TBo/Pa9LBswidP7o6j1xC1UGNiOQ7MXlXh2R/q9wdThrLzrNZL2RFLlri7UfLgf68bMKPHsjuzvS/pNyPm5+UdjOT5/nePbL8LsV3O/V7u7J0l7juHq45XvckeeW0Gs6Zlsn/oDvjXL4VuzfK5lNcf2Iz02gQWtHwXDwD3A+9If4Cp+rV5Izx6dqF6tMjVrt6F5s0a8+87LtGrTJ0+7Dp1uzvn5u29nMvfXv3JuL1u2mr433XlF8lyK7t07UrVqZerWbU+zZg15++2XaNeuX552XboMyPl59uwP+DU7+8GDR+jWbSDx8Yl069aBd999Od/1i0L7Lq2pWKU8XZr1o0HjukyY9jS39Mi/D6e9+BZ//vpPrvtuGzGAvbv2M/q2hwkM8mf+fz8x94d5ZF6h/eL/NXubzi2pUKUcfVveSr1GdRg39THu6DUq37ZvTniXv39blOu+VUvXsWj+MgCq16rK1JkTubntkKKODUCdDg0JqRzG8x0eonLD6gyeNJJp/Z7J0271nGV8OnY6AMPfHkObQZ1Y8uUCOtzeg+N7j/L+yKl4B/rwwr9vsXrOUqyZ1iLP3qVrO6pUrUSzht1o3KQ+015/gR6dB+ZpN3jozYRHlKVlk56YpkmZMoEA+Pr5MO2157m1/0iOHT2ec39J69erK0P638i4iYX7u1XUmndqRrnKEQxtcye1G9Xi4ZfHcF+fB/NtW+P66/D2LZ3rPm/f0oyd9BBP3PY0MZEx+Af5F0NqwGLB+/6xJIx7FFvsCfzfmkHGquVYD589qJuxcT0ZK5cD4FKpCj7jXiB+1B2QmUHCUw9DWiq4uOD36ju4rl1F1s78i2IixaXEihQn1+45+/O6vXiVvbQ3UJdSHtSfdCd+tcpjuLiw49Uf8/1SlLQnMt/1QzpcT8L2wzmjNzJOnb6kx686ojuRv68moEGVAtskbM1/xEdJZ3eEs2a/WL+7B3pjy8jk9P4oAGIWb6HGQ305NHtRiWd3iGni6m3/QurqU4q0qHjA+fv9XK6lPQluU4d1Y2c4RXaHOGG/e5UNJKxLA3a9OYdq9/S6pKdTvn9rqo7sgcXNhVPr97HhqU/AlvsIgDUlnbjVuyhdOTTP+hUHdWBB28fsN0zzkkdSwf/va7VPn+7M+uoHAFatXo+fvx9hYSFERcXk297buzQdO7RmxMhHrliGwrrhhq58/fWPAKxevQE/P9+LZm/fvhWjRtn3hZUrz/4OVq9eT0RE2aIPna1Lj/bM+fZ3ADau24qPnzfBoWU4ER3r0PqmCaWzR4eUKl2KhPhEsrKK/ssmXN3Z23dvw2/f/QnAlvXb8PH1oUxIELExcQ6tn5qSmvOzVynPYj0SWb9bE1b+tASAAxv2UMqnNL7B/iSeiM/VbtuiDTk/H9y0F/+wIMB+uUHP0tmjA0t5khx/GluW4yO9LkeP3p35dvYcANat3YSfny+hocFER5/I1e6uEYO5Z+SjOf0aG3sSgP4D+vD7rws4dvR4rvtLWpMG9Th23PFRisWtdbdWzP9hAQDb1+/A29ebwJBATsbk7j+LxcI9z45i4gOTadujTc79nft1Zum8ZcRE2t9T4+PiiyW363W1sEYewxZl/32nL/4X9xZtSD2nSEHa2dei4elFrqtpnlnm6orh6mp/0xEpYU4xJ0WlIR2I/ndTgcvrPDWQzv9Ood6Lt2Fxt9dVao7px4ll21jY4zmW9n+JuuOH4FLKw+HH9K4SBqZJ69lP0emvSVS//waH1/UMCyC8V1P2f/63w+ucqySzAwQ2rk6nf16m1ddP4FMj4qrJ7ki/Z8QlYXFzwb9+ZQAibmiOV3hgiWcHx/p9/aMf0uqrJ+i5fjoVBrRh1/S5JZ79Uvf38F5NObFsK1mnU0s8O1y9/X79xNvZOnH2BT/U5/fcfKqHU65vSxb3eYF/u4zDtNmo0L9Ngds4n5tvKQBqPzGATn9NotmHY/Ao4+vw+nD1v1YvJCI8jKNHzhZHjh09TkR4WIHt+/Xryb8Ll5OUdLZQ0qJFY9atXcBvc2dRu/Z1VyzbxYSHh3H06DnZj0URHp63SHXGjTd2Z9Gi3NnPuOuuQcyfv6goYuYrtGwIxyPPfrmJiowhNCw437YPj7uPXxd9w7iJj+Du7gbAlx99S9XrKrN863x+W/ItLz3zarF9Yb6as4eUDSYq8mwRK/p4DCFl889+/1Oj+fbfz3n0xYdwy84O0LFnO35a+jVvf/kqLz48ucgzn+EfGsipyLOFoFNRcfiHFXxAzOLqQvOb2rJ98UYAFn3+J2HVIpiyegbPzn+N71/8tNj6vWzZUCKPReXcjoyMIiyf12qlyuXpd3MvFiz6kW9++JAqVSoCULVqJfz9fZnz2xf8vfhHBg7qWyy5r3bBYWU4EXm2EHTi+AmCw8rkaXfTsL4s/+u/PMWL8lUi8Pbz5s3vX2PGH+/RrX/XIs8MYClTBtuJs69TW+wJLEF5c7u3aov/zC/wnTCF029MPWcDFvzf+Yig2XPI2LCWrF3FdxqfSEEKHElhGMZPwE/AHNM0i+DQpV2Z1rWpOLgDS/q+mO/ybZO+JS0mHou7Kw1fHcl1D/Rh5+s/E9KhHmHdG1H93t4AuHi4USoiqMAja+ezuLoQ1LwGC3s8hzU1nTbfP0P8pgOcWLbtouteP/EOtk6cnefIpKNKMnv85oP82eQhrCnphHZuQMtPH+WvVo4f4bsa+n316He4/sXbsXi4EbNoM2b2kY+rod+rjerJiqHTOLVhH9Xvu4HrX7yN9Y9+eFX0+xnlbmrJwa8W5txWv1969rCuDUmPTSR+8wH7vBCX8NyC29bF//rKdPxzoj2zpzvpsYkO5QUwXF0oFRFE3JpdbHnhS6qN7kW954ey9sH3Hd7G1fxavRjDMPLcd6EvLoMG9uXjT2fn3F6/YQtVqjUjOTmFnj068eP3n1CrjuNFpMtxqdkHDuzLZ599k+f+du1acuedt9K5c/8rmu9CHM3+6kvvcCI6Fnd3Nya+/iyjHryLd177kLadWrJj6y5uv2k0FSqX47Pv32Ptfxs4fTpZ2a9A9umTPiA2Jg43dzeee/VJhj1wGzNft5/XvnDeEhbOW0KjFvW578m7uWfg2KKObZdP9gsdIR48cSR7V+9g7xr7fCG129Xn6PZDvDl4AsEVQ3noy+fY2/Nx0k6nFriNK8XRfvdwdyctLZ2uHfrTu09X3np3Mn16DsXV1YXrG9Sh/4134enpyby/v2Htmk3s33ewyLNf1Rzo96DQIDr0bs/YAXk/S7i4ulDj+ut45NbH8fB05925b7N9/XaOHjhWZJHt8tnX85GxYikZK5biWvd6St0xnMRx2fNl2GzEPzASo7Q3Ps+9hEvFylgP5Z2/5VpnQyNMitOFTvdoDtiAtw3D+BuYDfxummbGxTZqGMYoYBTAaJ+mdCtVDYAqw7pSaWhHAFYMnYZ7oA+NXrubFUOmFjgcNy0mHgBbRhaHvlmc86EVDFaNeJPT+47nat/4zdH41a1IWnQ8K4ZOKzBjauRJYv/bkTOMOfqfjfhfX7nAD7HnZnfzLUWzGfZz1DwCfQjt3AAzy8bxP9depGfOKLnsK4ZOIy06Pmc9Y8ow3AN9LmE4t/P3+8l1e3LmRwhpXw/vqmeGJDt3v7sH+eBXpyKnNuwD4Ogv/9F69pMlnv1S9nf3AG8CGlRl5bA3zrlX/e5o9jOCml5H2W6NCO3cABcPN1y9vWjyzn2sfeC9nDZZ53xQPve5YcDh75awbfK3ubYZ3rMJNR+1z5Ow/tEPc00yea6Mk0lkpaQR+Yf993vs15VUGtLhgnnh6n6tXsy999zJiBFDAVi7diPlyofnLIsoV5bIAoYvBwYG0LRpQ/oPGJlz37mjEub9+S/T355MUFAAcXGnCpXtYkaPvoNhwwYBsG7dZsqVOyd7RBjHj+d/qkdgoD9NmtTn1ltzzz9Qt25N3n9/Kn373snJk/FFkvmMocMHcOvtNwGwecN2yp5zJDksPISYfE6XOHMKRUZGJj9+PZeR99snC+w/+EZmvG3/0nz4wFGOHo6kSvVKbN5w+YWr/7fsA4fdzM1DbwRg28YdhIWH5CwLLRvCiai82c+c/pGZkckv3/zOHfcOztNm/cpNlKsUgX+gH/EnE4oke/vbu9N6cGcADm3aR0B4GWAXAAFhQcRH5/866z3mFryDfJk5embOfS0HdOSv9+cAcOJQNHFHYgitGs6hTfuKJPvwkUO4/U77vBMbNmwhPOLsCK3w8DCi83mtRkZG89tc+5wxv/+6gLfffTn7/iji4k6RkpJKSkoq/61YS916NVWkyEe/O2/khiH2Uyp3btpNcPjZkULBZYOJjc59alP1OtWIqBTOV8u+AMDDy4Ovln3O0DZ3cuJ4LAknE0lLTSMtNY1Nq7ZQtXbVIi9S2GJPYAk++zq1lAnGFlfw6WRZWzfjUjYCw9cPM/Hsa9FMPk3m5g24N2lGqooUUsIuVKSIMU3zFsMwfIB+wN3ATMMwfgNmm6b5V0ErmqY5E5gJ8FPYkJyy0/5PF7D/U/u5Xl4RQbT45GHWPvBezjnJ+fEM8c8pVIT3aELizqP2cIs2U3VE95wZ6f3qViRh66Gc8+AvJnrRZq67/wZcvNyxZWRRpmUt9s78o8D252Y/V+O3RnN8wYZLKFCUbHaPYL+c+wMaVsUwjEs63/xq6HePMr6kxyZicXflugf6sOvNOSWe3ZF+z4xPxs2nFN5Vwji9P4qQdvVI2h1Z4tnPdbH9PaJPc6L+3pDrKgrqd8ezn7Ft8rc5RYYyrWpR/d7euQoUZ55b+omEPM/txNJttPzsUfbOnEd6bCJu/qVx9fYict5aIuc59j51/K8NBLeqxYnl2wluW5fE3Rf/gHU1v1Yv5v0PPuf9Dz4HoFfPztx37118++0vNG/WiMSExALndLil/w38/sffpKen59x37nnlTZs0wGKxFFmBAmDGjC+YMcP+YbpHj07cc8+dfPfdXJo1a0hiYlKB2W++uTfz5v2TK3v58uF8880MRox4mL17i/4D7FeffM9Xn3wPQIeubbhtxEB++3k+DRrXJSnxdL5zOpw710PXXh3YvdP+hTLyaBQt2zZj7cqNBAUHUrlaRY4cKrovDldz9u8+/YnvPv0JgDZdWjJoeH/+nPM39RrV4XTS6Xznozh3noqOPdqxb+d+AMpXiuDIQXvWmvWuw83NrcgKFACLZ81n8az5ANTt2JAOd/Zg7dzlVG5YndSklDzzUQC0vrUTtdrV560hE3IdNT8VGUuN1vXYu2YnPmX8CK0STuzh/F8vV8InH33NJx/Zr8jQtVt7Roy6jZ9//J3GTeqTmJiUZz4KgHm//03bdi34+ssfadWmGfuyixDzfv+HKa+Ox8XFBXd3Nxo1vp4P3v2syLJfzeZ8Ppc5n9tP8WzRqTk3DevLv78spHajWiQnJec5pWPlv6u4udHZSUzn7fqVoW3sE+Eum7+CMS89gIuLBVc3N2o3qMkPH/5Y5M8ha/dOXMLLYQkNwxYXi0f7TiRNnZirjaVsBLbj9teiS9Xq4OqKmZiA4ecHWVbM5NPg7o57wyaknHdlEJGSYBQ01NMwjPWmaTY6775AYCAw0DTNTo48wLlFinM1eu1uwns3I+Wo/U3XtNpY2P1ZAFp99QTrH5lJWnQ8bX54Bo8gHzAMErYeYsMTH2NNScfi6Ub9CXcQ2LQ6GAYpR07kO8t9eM8m1J90J+5BvmQmppCw9RDLB08B7JPL1XioL6ZpEv3PRvvw5Et05gP4mauPnJu96ojuXHf/DXiE+JMem0j0PxtZ/+iHJZq9yvBuVLmzC7YsK7a0DDY//2XOJKbOnt3Rfq87fghhXRpiWAz2f/43+z60T/p1NfR7eM8m1HriFkybSWZCMuvGziTlcMxV0e8AbX96lt3T5xK98OwVHdTvl9fvZ4oU/93+KpXvsB8hPPDFPxd8bhF9W1DjwRsxLBZsmVY2Pv0pp9bnvYxa9zVv4ebthcXdlcyEZJYNmkLS7mN4lStD0+n34uZXmvS4RNaNnUHqMccmyjufs75WB55cXKjnA/D2W5Po3q0DKampjBz5COvW2/f3X3/5glH3PM7x7JEV/yz4nmmvvMv8vxblrHvfvXcxevQdZGVZSUtN47HHX+S/lY4XuQHcXAo/5/Ubb0ykW7f2pKSkMnr0Y6xfvwWAn3/+jPvueyJnZMX8+d/w6qvvs2DB2X56772p9OvXk8OH7QcLsrKstMnnyiYXUs4773nSjnp+6pO069iK1NQ0nnroBbZusp83/eHst3hm7ERiomP54qcPCAwKwDBgx9bdjH98MinJqYSElmHq9BcJDi2DYcCMtz9j7g/zCp3lasvu7eJZ6OxPvfwIrTq2IC01jRfGTmb7JvvpENO/epUJj0zhRHQsM354m4AgfwzDYNfWPUx64hVSU1K564Gh3DCgJ1mZWaSnpfPGhHcv+RKkLTwvbe6scw2aMILa7euTkZrBF4+/x+Et9uLJ/Z8+xZdPziAh5hTv7J3NyWMnSEtOA2Djn6v44+0f8QsJ4I5X78MvxP47mf/+L6yes/SSHv+HUwXPt3YxU18dT8cubUlNSeWh+8exaYP9csGzv5/J2AefJToqBl8/Hz748FUiypUlOTmFxx9+nm1b7SNH7n9oBIOH3ozNZuOrL35gxvufX9LjR+678q+Px5+fwpoNm4mPTyQo0J/7RtxO/z7dr/jjdG2Q/xVoHDHmpQdp1qEp6WnpTH3kFXZttl9yd8oXk3jl8deJO29kxbxdv+a6BOmt9wyk58DumDYbv8+exw8f/3RJj/9DvcJdKtutaXO8Rz0ILhbS/vqD1G++xLOXfURU2h9z8RowGI/O3SErCzMjg+SP3ydr2xb7lT4eGwcWCxgG6UsXkfr1pe0rZ5SZt9ix806uUo3Ktvm/PN9j/fFlTvl7u1CRYolpmu0u9wEKKlKIiIiUlMspUpS0yylSlLTLKVJI4V1OkaKkXU6RoqRdTpGipBVFkaK4XE6RoqQVtkjhDP7fixQNw1r/X36n3RC13Cl/bwVe3eNKFChERERERERERBxVqEuQGoZRPNfUEREREREREZFrRqGKFMDHVzSFiIiIiIiIiFzzCjyx1TCMuQUtAoKKJo6IiIiIiIiI87DxfzklhdO60OxbbYHbgNPn3W8AzYoskYiIiIiIiIhcky5UpFgJpJimmWcKdMMwdhVdJBERERERERG5FhVYpDBNsyeAYRi1TdPcft7i8UWaSkRERERERESuOY5MnPmdYRhPGnZehmFMB14u6mAiIiIiIiIicm1xpEjRHCgPrADWAJFA66IMJSIiIiIiIuIMzP/T/5yVI0WKTCAV8AI8gQOmadqKNJWIiIiIiIiIXHMcKVKswV6kaAq0AQYbhvFDkaYSERERERERkWvOha7uccYI0zTXZv8cBfQ1DOP2IswkIiIiIiIiItegixYpzilQnHvfrKKJIyIiIiIiIuI8bKbzzt/w/8iR0z1ERERERERERIqcihQiIiIiIiIi4hRUpBARERERERERp+DIxJkiIiIiIiIi1yQTzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECmAzNSdFcdJIChERERERERFxCipSiIiIiIiIiIhTUJFCRERERERERJyC5qQQEZFrzsqQpiUdodBqPxle0hEKzbX//SUdodAM4+o9rmOmJ5d0hEKzbl5U0hEK7Y2wASUdodC6NhhV0hEKbcHGmSUdodAmNnmupCMU2oSSDiD/V1SkEBERERERESmAiSbOLE5X72EBEREREREREfm/oiKFiIiIiIiIiDgFFSlERERERERExCloTgoRERERERGRAthMzUlRnDSSQkREREREREScgooUIiIiIiIiIuIUVKQQEREREREREaegOSlERERERERECmCiOSmKk0ZSiIiIiIiIiIhTUJFCRERERERERJyCihQiIiIiIiIi4hQ0J4WIiIiIiIhIAWym5qQoThpJISIiIiIiIiJOQUUKEREREREREXEKKlKIiIiIiIiIiFNQkUJEREREREREHGYYRqBhGAsMw9iT/W9AAe38DcP4wTCMnYZh7DAMo+XFtq0ihYiIiIiIiEgBzP/T/y7TU8A/pmlWB/7Jvp2ft4A/TdOsCdQHdlxswypSiIiIiIiIiMil6At8nv3z50C/8xsYhuELtAM+BjBNM8M0zfiLbVhFChEREREREZFrjGEYowzDWHvO/6MuYfVQ0zSPA2T/G5JPmyrACeBTwzA2GIbxkWEYpS+2YddLCCEiIiIiIiIi/wdM05wJzCxouWEYfwNh+Sx6xsGHcAUaAQ+aprnKMIy3sJ8W8tzFVhIRERERERGRfJimraQjlAjTNLsUtMwwjGjDMMqapnncMIyyQEw+zY4CR03TXJV9+wcKnrsih073EBEREREREZFLMRe4M/vnO4Ffzm9gmmYUcMQwjBrZd3UGtl9swypSiIiIiIiIiMilmAJ0NQxjD9A1+zaGYYQbhvHHOe0eBL4yDGMz0ACYfLENl+zpHhaDTvMnkRp1kv9ufzXXIlcfL5q+ez9eEUFYXF3Y8/7vHPpmscObdg/wpvlHYwhoUJVD3y5h07jP7Nst7Um7X8bntPMqG8SRH5exefwsh7brFR5Ik+n34hHsD6bJgVn/su+jP3O1Kdu9MbWfHIBps2FabWx+bhZxq3dddnYAw82FBpOHUaZVLbCZbJvyLZG/r7li2cu0qkXLzx4l+bB9tE7kH2vY+frPJZ69sM/nQmo/NZAKA9ri7l+auVWH51oWcWNzaj3WH0xI2HaINfe9W+icFg832s0Zj8XdFYurC8d+W8WOV37Mt21Agyp0+H0Cq0a/TeRvqx1+jJLcZ6rfdwPlb25lfyxXF3yrR/BbndFkxieXaHZH+t2Z93fvauE0fnM0/vUqsX3Kd+x5//d82wW3qUO98UPAYpCVnM66MR+QfDDa4ceJ6GPf132qh7Ow52Z2BtIAAEQrSURBVHPEbzqQs8y3VnkavjISNx8vTJuNhT2ew5aeecHtOfq6LNOqFtdPuB2LmyvpJ5NYetNEhzMX5/vM+Xw7NKTCiyPBxULs7AVEvftTruWeVSOo9PqDlKpblWPTviR6hv2gguHhRs0fJ2G4u2G4uHDqjxVEvvbNFcvliOWH4nhl6W5spkm/2uEMb1wpT5u1R0/xyrLdZNlM/D3d+PjmxgC88M92lhyMJdDLnR+GtCjW3ADLVq9n6jufYLXauLl3F0YOuTnX8oSk04yf9g5HIqPxcHdjwhP3U71yRQ4cPsbjE17LaXf0eDT3DxvE7bf0Kb7sq9Yz5Z2PsFpt9O/dlZFD++fJ/tzU6RyJjMLD3Z2JTzxA9SoVAfji+7n8+PsCDAyqV6nIS08+iIeHe/FlX7OJqR/Msvd7zw6MvPXG87InM/71mRw5Ho2HmxsTHh1F9Urlc5ZbrTYGPfgsIUEBvDvx8WLLDbB85xGmzf0Pm83kpmY1GN6pQa7lny3axB/r99pz2kwOxMSz8IXb8HRzZfj7v5GZZSXLZqNLvSrc171x8WbfvJups/7AZrNxU4fGjOjTPtfypJQ0xr3/PVFxCWTZbNzZqzX92p3NaLXZGDz+fUICfHnn0duLNTvAgxPup0WnZqSlpjPl4Wns2bq3wLYPTXyAngO707PG2ddkg5b1eeCFe3FxdSXhVAJjb3m0OGJf0LOTX2fJ8tUEBvgz58sPSjrORfV6/g6qd6xPZmoGPz82g+PbDuZp0//N+4ioVxlrlpVjm/Yxd9wn2LKsxR9WrlqmacZhHxlx/v2RQK9zbm8EmlzKtku0SFHt7p4k7TmGq49XnmVVh3UjcfdR/rvjVdyDfOi27DUO/7gMM9OxF481PZPtU3/At2Y5fGue/YOZlZzGv13G5dzuOH8Sx/5w/AOsmWVjywtfEb/lIK6lPen41yRilmwhafexnDYxS7dyfP46wP4hv/nMMSxo+5jDj1FQdoCaY/uRHpvAgtaPgmHgHuB9RbMDxK7amadoVNLZ8+Po8ynI8b/Ws/+Tv+j23+u57i9dOYwaD/ZlcZ8XyUxIxqOM72XltKVnsrT/S1hT0jFcXWg/93mi/tnEqfXn/dG2GNR5djDRizZf8mOU5D6z573f2PPebwCEdW1EtdE9HS5QFGV2R/vdWff3zPjTbH72c8r2uPB7eoOpw1l512sk7Ymkyl1dqPlwP9aNmeHw4yTuPMLK4W/Q8JURue43XCw0ffd+1j7wHgnbD+Me4I0tM+ui23Nkn3HzLUWDKcNYPngqqcfiLvk1VpzvM7lYLFR4aTS7hzxP5vE4/tfefYZHUbVxGL9PGukJCSGFmkAUkBZ6J6GjohQVpCMoFpoFRCwozYKvCqgIqCiCvQBKL4L03nuRGgiBENLrzvthlpCQtglsCT4/r1xmd2eX/56cnZl95syZ6kumErtyOyknLmQtkhGbwLm3vqR0x8Y5nqqlpnPsibcwJKWgHOy5/493ufH3bhJ3H797+QqQadB4b/0xZj4ahr97Kfr8vIPWwWWo4nOrfeJT05my/iifPRJGoIczMUlpWY91qRZIz1rleXN1oSM07372zEwmT5vD7KnjCfDzpdezY4ho1pAq2b4Mf7ngN6pVDWbaxLGcPneBKZ/M4cuP3iG4Yjl+/fKjrNdp+/jTtG3ROL9/yizZJ02bxZwP3yHAz5eez44monmjHNnnzP+ValWDmT7pNU6fvcDkabP46qOJREVfY8Fvf7Ho2xk4lyrFy29/wLK1G+jaOdf+oJmyG5j82TfMfvc1Asr40Gv4m0Q0qUeVSuWzlvnyx0VUq1KRaeNf5PS5SKZ89g1fvn9rH2v+wuUEVwgiMSnZIpmzshsMvPvHJr545kH8vdzoM30hrR+oRBX/0lnLDAyvw8DwOgCsP3yW+f8cwMvVGU3TmDP0IVxLOZKeaWDQZ4tpUa08tSv5Wyz7lG//ZNarg/D38aT3W18QXq86VcrdmjT/p9VbCSlXlhkv9yMmLpFHx3zCQ83q4Oig79YvWLGFkCA/EpJTLZI5u8ZtGlE+uBx9WgygRr3qvPjuSJ7vMjzPZe+vfR/unjkn+Xf3dGPU5BGM6fsaVyKv4O3rbYHUhev6YHt693iEcROLt79gSaHhdfANDmBa+MuUD6tKl8mDmN11fK7l9i/cxG+jPgfgsekvUL9XODvmr7F03BLDgGbtCP8pVjvdwyXQh4B2dTmz4O88H9c0DUd3vXjh4OZMWmwCWoY+YUmFHs0JXzaRNqunEPbBYLBTuZ6fmZTKte3HyCzgqJ9bcAClynhybetRk3OnXIkl9sAZQC94xJ+4iEtA6RzLZCbd2ig4GDd4N91p9kq9wjk2Y7F+Q9NIi4m/q9kLYs3secnv/bhVKkvz718lYsVkWi18C/eqQXk+//ruk6Rcic11f3DfCE7PXUn6Df2LdurVuDvKCbf6hJ2jPXYO9qDlXtFVGdyRyCXbSb16I8f9JanPVOjWlAt/bLaJ7DdfGwpu93zfi5Wzp16N4/re02iFHdXQNBxuris9XEm5HAuAvWsp6n38DBHLJ9Jm1RQC8zkSGH8ikoRTl3LdXza8NjcOn+PG4XMApF1PAEPh7WdKn6nQvRmRS3aQfPFa1nvNeszG1jPZudUNJfXMJdLORaGlZxCzaCPeHXJ+4c24doOkfSfz/LsZklIAfcSRKmJ/vFMHo+Ko4OVCeS8XHO3t6Bjqz7rTV3Mss+x4FG2rlCXQwxkAH9dbR+zrlyuNl7OjxfJmd+DoSSoGBVIhKABHR0c6t2nB35tyjjQ7deY8jevVBiCkYnkuRl3hakxsjmW27T5AhSB/ggLyukKaeRw4eoKK5XJmX7tpW45lTp09T5Ob2SuV5+LlW9kzMjNJTU0jIyOT5JQ0/Mr4WC77sVNUDPKnQmBZHB0d6BzehL+37MqZ/dxFGtetqWevGMTFqGiuXte3YZejr7Fh+156dI6wWOabDp6LpkIZT8r7euLoYE/HulVYd+hsvssv23OKTmFVAVBK4VpK7+sZmQYyDAaUyr0eMpeDpy5Qwd+X8mV9cHRwoFOTWqzbdSTHMgpFUkoqmqaRlJKKl5sL9nb6Ln1UzA027D1Gt9aWHf1xU/MOzVjx6yoADu8+grunOz5lc/dbOzs7nn3jGb6YPCfH/W27tmXDso1cidRHOMZeizV7ZlM0qFsLL08Pa8cwSbUO9dn7+wYALuw5ibOHK+5+3rmWO7FuX9bvF/edwjPAcusXIQqTb5FCKRWilPpaKTVJKeWulJqjlDqolPpFKVX5Tv/h2hP7cXDiDzm+wGd3+uuVeIQG8eC+z2j39/vsf3MeaBoeoUGUf7Qp67u8zdp249AMBir2aFGsDBW6NeXC4i3Ffg+uFcrgXbMyMbtP5XosqHMD2m/4kGbzR7P7Rf2qLnea3dHTFYAaYx6nzcrJNJozsthH+QvK7lM/lDZr3qXZ92PwuL+czWXPS/b3E/bhEPa+/i1/d3ydA+8soO57g4r0Wu4hgbhXCaT14vGEL3kH/4jadx7QTtFm9RQeOvgFUf8c4PqenO3uHFCaoAcbcvrb1Tnut6V2L6jPANi7OOEfUYeLS7bbTvZC2h1KZn/PbvfLc2i2YAydd8+g4uMtsr6gVxvZleiNh/i705ts6DGJmm/1xt61lMmv6x4SAJpG8x/G0mblZEJfeLjI2fLrM+4hgTh6u9Hy9zeIWDGZio+3BGy/3Z0CfUi7dOuLfdrlazgFFmGnzs6OGis+ps6+b4nbsI/EPSfuWrbCXElMwd9YfADwdy9FdGLOo6xnY5OIS01nyO+76P3Tdv48mrt4ZQ1Xrl4joKxv1m1/P1+irsbkWOb+KpVZ/c9WAA4cOcGly9FERV/LscyytRvp3Lal+QNncyU6hgC/Mlm3/f18uRKdR/YNN7MfN2a/ir+fLwN7dqXdE08T0WMQHu6uNG8YZrns12II8MvW7mV8iLp6PWf24Iqs3qSPRj1w9BSXoq5m/W0++OI7XhzyJHYW/IJ/05W4RAK8b40S8vdy48qNvEf4JadlsPnYBdrVqpx1X6bBwBMf/Uabd76jSWg5alW0XGHryvU4Any8sm6X9fEk6nrOgyW92jfhdGQ07Ya/z2PjPmVMv4ewMxYpPpi/lBd7dcQujwKvJfgFlCE6MjrrdvSlaPwCyuRartugR9m0cgsxV3J+HiqElMPdy51Pfvkfs5Z+Toce7c2e+V7j6e/Djchb67+4yzF4FnCAyc7BnjrdWnByfdFH8gphLgWNpPgG2AEkAFuBo0BnYDnwdUEvqpR6Rim1Uym1c2VS7vPQAtqHkXo1jtj9/+bxbF3ZiNrEHjzL0jovsKbta9SZMhAHdxf8WtbEu3awfnRw9RT8WjyAW6XibTzKd23KhT+KV6Swdy1F4y9fZP9b35GRkHsYY+Synaxq+QpbBn1EjVcfB7jj7MrBHtdyvlzbcYy1HV4nZucJao3vc1ezx+4/w/IGI1jb9jVOfbWSpnNftqnshb0fzWDAt8F9NJ4zQj8SO3UwzmW9i/R6ysEO9+AA/uk+ie3PfUq9/z2d9eWn2Awaa9uNY1nYMHzCquBZrXyOh2tP7M/BiT/kOlJtK+1eWH8HCOhQj2s7jmed6mET2Qtp95LY329X9ZnObO7zAcvqDefsj/9Q+52+AJQNr8V9wx+hzeoptPz9DexLOeJazreQV7vFzsEe38b3s+OFz1j/6DsEdW6IX4sHTH5+QX1GOdhTunYwm/tOZdOT71HtxW64hwSUgHbPvdNfpMEQBgOHO77I/oZDcKsbivP9Fe9etLsg06Bx5Eo8M7rU5bNH6jJnx7+cvZ5k7Vh5tvHt33sH9+5OXEICjw15ie//WEq10GAc7G/t4qSnp7Nu8w46tG5m5rQ5aXkMD749+5DePYiLT6DH4FEs+H0J1UJDsLe350Z8An9v2s6KH2ex9revSU5O4c+V6ywTnPzaPWf4wT27EBefyGPPvcb3i1dQrWplHOzsWL91Nz7eXjwQGmyhtDmZ0mdu+ufwWepW9sfL9VYRz97Ojp9f6sGKN3pz8Hw0Jy/H5P1kMzCl3TcfOEG1ioGsnvEqP09+gXe//ZOE5BTW7zmKj6cbNYLLWShtHvJo6NsPSPr6+xL+UGv+mJt7Dih7B3vur30fY/u/zpg+Y+k/qg/lrfl+SqC8+np+B4UBHp44iLPbj3J2h+nz5wlhbgXNSeGhadpMAKXU85qm3Zx56iul1LCCXlTTtNnAbIDfA3rn+lT4NryPwA718G9bF/tSjji4u9Dg0+fZOezzrGUq92qddUQw8UwUieei8QgNAgXnfv6HQ1N+yvGaQZ0bUO1lfSKt3S/PyTH5W168alRE2dsXWCjJj3Kwp8lXL3L+901EFjKfxbWtR3GrXBYnH487zp4WE09GUgqRS3cCcPHPrVTuHX5Xs2f/MhG1Zi/qvUE2k92U9+Pg7kJ6XGKOeUcA/Yj6ysmAPhfFkQ9+zfc1kyNjiNmtD9dOOhdN/KlLuIcEcH3v6TvOmx6XRPTmI/hH1CHu6K3z2EvXCabRLP2czVI+Hvi3rauf3mQD7W5qf6/waFPOZzvVwxay35Rfu9tafw8Z1J7KffSh0Zv7fEBKVGyByzv5euD1QKWsESIXFm2h+Q+vGh9VbBv8Sa5TOep/MhSvmpVIiYplc58P8n3t5MgYrm45knW6RNSavXjXDiZ646FC30dhfSY58hppMfFkJqWSmZTK1a1H8Hqgkk31mTxf/9I1nAJvHRF0CvAlvRhfXjLjEonfchCv8DBSjp27a/kKUtbNmaj4lKzbUQmp+LnlHFlT1r0U3s6+uDja4+JoT70gb45fi6dS6Tss0t4hfz9fLl+5dVQwKvoaZX1zjmBxd3Nl0qv6OlTTNDo9+SzlAm/NIbBh2x6q3xdCGR9vi2S+yd/Pl8vRt0bfREVfy3XKhrubK5PGjgD07B17PUP5QH827dhDucCy+HjrR9XbtmrK3kNH6dIh3DLZy/hwOdtolKirMZS9bX4AdzdXJr0yNCt7pwGjKBfgx7L1W/h76y427NhLalo6iUnJjH3/c9579XnLZPdy43Jswq3sNxLxu23ug5uW7z1Fp7AqeT7m6VKKBiGBbDp6gaoWGgrv7+PJ5Zhbp31eiYmjrHfO0wwW/bObp7q0QilFRX9fyvmV5t/Iq+w9fo51u4+ycd9xUtMzSExO5bWZv/Duc4+bNXPXAY/wcG99jryj+47jF+SX9ZhfoB9Xo3KOagp9oCrlKgexYOM8AEq5lGLBxm/p02IA0ZeuciMmjpTkFFKSU9i37QBValThwr+mzTf2X9WoX3vqP6nvP1zcdxqvoFsHJTwDfIjPZ38ifGR33Hw9+HHoV5aIWaIVVOgRd19BIykMSqn7lFINAVelVAMApVRVwP5O/tFDU35iWb3hrGg4ku3PziB606EcBQqApIvXKNtSP8+xVBlPPKoEknj2CtEbDlHu4cZZQ3gdvd1wKV+GyGU7WdtuHGvbjSu0QAFQvlszLizcXOhyean38TPEn7jIyVlL83zcrfKtHSPvWpWxc3QgLSb+rmS/tHIPfs2qA/rR3jgTJ4k0NXspv1tDDEuHVUEpZTPZTXk/GQnJJJ6LplyXW+eJe9WomHVEfW27cQUWKAAuLd+JX/MaADj5eOAeove94nLy9cgaiWHn7EjZljWJPxmZY5kVjUaxouFIVjQcycW/trF37FwuLd9pE+1eWJ8B/Wo8ZZpWz5owFrB6dlPa3db6++m5q7Jev7ACBUB6bCKOHq76qRlA2Va1iD+uv8cr6/ZTZXDHrGW9aupXC9g1ahZr240rsEABELVuP17VK2Lv4oSyt6NM0+rEH79Q4HNuKqzPXFqxC9/G96Ps7bB3caJ0varEn7ho9T5TmMR9J3AODsSpQlmUowM+j7YgdpVpV+Fx8PHE3vglSTk74dmiDiknLbfT/YC/B+duJHExLpn0TAMrTkQRHpxzCHZ4sB97LsWSYTCQnJ7Jwag4gkvn/cXOkmpWq8rZi5e4cCmK9PR0lq3dSHizhjmWiUtIJD1dn6PktyWrqV+7Bu5ut4ory9ZuoHOb4p0aeidq3h/KuQs5s0c0a5Rjmbj4hGzZV1G/zgO4u7kSWNaP/YePk2yce2Db7v2EVCqf1z9jpuwhnL14mQuXr5CensGydVsJb5JzngO93fUJdX9b9jf1a1bD3c2VUU/1Ys2CT1kxbxpTXxtGozo1LFagAHiggh/nrsZxMSaO9IxMVuw9ResauUcuxSensev0ZSIeqJR1X0xCMnHGCSdT0jPYdvIiwWW9cj3XbNlDynHu8jUuXIkhPSOD5VsP0LpetRzLBPh6s+2QXpy+diOBM5evUr5saUb27MCq6WNY9vErvP/CEzSsEWL2AgXAwm8XM6Tjswzp+Cwbl2+i42P6KRo16lUnMT4x1ykdW9duo3u9J+jVtC+9mvYlNTmVPi0GALBxxWZqNaqJvb0dpZxLUaNuNc6dtEwxtyTb/t0qZj44jpkPjuPoyp3U7a6f2lY+rCop8ckkRMfmek69nuFUbVWLX4Z/Kl/Ahc1R+XVKpVRb4HPAADwNvAjUATyBpzVNW2TKP5DXSIrsyjSrTuhzD7Gl34cE99dnrP533hqc/b2pP+1ZnP29QSmOz1jM+d82AVDu0SbcP/wRlJ0dhvRM9r42N/eVEoCOO6bh6O6CnZMD6TcS2djrvawZ5jtu+4RNfT4g4bYvLYXxbXQ/rReP58bhc2gGfSLPQ+/+nDWM+t95a7hvWBcqPt4SQ3oGmSnpHJzwfdYlSO80u0v5MjSc8RyOXm6kXotj16hZWZPP3Y3sIU91IGRAOwwZmRhS0tg/fj4xO09YPXtR3k/8iYuEvfcUpfy9sXO058LCLXleVrLmm09SoVsznANKk3L5Ome+X8eRD/VLVNZ6uy/+bWqjZRo49skiLiwq/twlntUr0GD6cyh7O7BTXFy8laMf/ZGjv2dXf9pQLq3ak3UJUlvvMwAVe7bCP6IOO56dkeP51sxuSrvbcn8v5edFmxWTcPBwQTNoZCamsKrVGDISkmm2YAy7X5pNSlQsQZ0bUH3MY2gGjfQbiewaNZukc1ewc3akzoT++DQMBaVIOh+d51VMgjo3oM7kATj5epIel8SNg2fZ9OR7gD6J5f0jHkXTNKLW7NVPSSqEqX0m9PmHqdSrFZpB48yCvzk1Z7lF272iQ/FOY/BqU58Kbz8FdvZc+2k1l2b8il9fvRgUPX8FDn7e1Fj6IfburmgGDUNSMgcjhlOqQlmCPx4J9nYopYj5axOXPvm5WBlqvJr3ZMCF2XDmKh9uOI5Bg0drBDKkQTC/HNQLT4/X1L/8frv7LIuORGKnFN1qBNGnrv7FbuyKg+y6eJ3YlHR8XJx4tnEI3WoUPYdDjxeKlf2frbv44LOvyTQY6Na5Lc/0fYyfF68A4IlHOrL30DFef3c6dnZ2VKlcnndGv4CXhz4nQXJKKu17Ps2yBTPxcC9+0UWp4s01/s/WnfrlUw2ZdOvcjqH9HuenRXp/7/loJ/YeOsq4KdOwt7MjpHIFJowZlpX907k/sGLtRuzt7akWGsyE0cNwcir6BKZaqulXXMqRfftePvjiO73dO7Tmmd5d+fkvfe6kJx5ux97DJ3h96ky93SuV450Xn8HLI2cb79h3mG9+XVLsS5Bm7l9XrOdtOHKOqcZLkD7a6H6ebhvGL1v0q9M83lQ/ELFox3E2HzvP+31vXTHleOQ13vxpPQaDhkHT6FAnhKHt6xUrgwqoVPhCeWXfe4wPFuiXIO3aqj5PPxrOz2v0fYIn2jbiyvU43pz9G1djE9A0jae6tOLh5nVzvMaOI6f5dummYl+CtFN3068SdbuRk4bTKLwhqSmpvP/SVI7t169i9N68yUwd/RHXbhtZsezYnzkuQdrz2Sfo/ERHNIOBJT8s49evcl7quTCr9s4udvb8jB7/Hjv27Cc2Ng5fH2+eH9yPHl06Fv7EIprY4M278joPTRhIaOva+iVIR88i8oBe4O87dzSLXp1D/JVYxp+cx42LV0lN1EfZHVm+g3XTTb8M++0mnFlgnYlQLKSiT617spJzLuaATf7d8i1S5LmwUmWA65qmmXwR3cKKFEIIIYSlFbdIYQuKW6SwBcUtUtiC4hYpbEFxixS2oLhFCltQ3CKFLbiTIoW1maNIYSl3q0hhDVKkKJlstUhRpC2upmlXNU3LVErJVLtCCCGEEEIIIYS4qwqaOLMgXwG2NS25EEIIIYQQQghxlxnyuFqTMJ98ixRKqcX5PQSYfh07IYQQQgghhBBCCBMUNJKiJdAXSLjtfgU0yr24EEIIIYQQQgghRPEVVKTYCiRpmrb+9geUUsfMF0kIIYQQQgghhBD/RfkWKTRN6wyglKqhadrh2x5+y6yphBBCCCGEEEIIG1CUK2KKO2fK1T1+Vkq9qnQuSqkZwLvmDiaEEEIIIYQQQoj/FlOKFI2BCsBmYAcQCTQ3ZyghhBBCCCGEEEL895hSpEgHkgEXwBn4V9M0g1lTCSGEEEIIIYQQ4j+noIkzb9oBLAIaol96dJZS6jFN0x4zazIhhBBCCCGEEMLKDDInhUWZUqQYrGnaTuPvl4FHlVL9zJhJCCGEEEIIIYQQ/0GFnu6RrUCR/b7vzBNHCCGEEEIIIYQQ/1WmzEkhhBBCCCGEEEIIYXamnO4hhBBCCCGEEEL8J2nInBSWJCMphBBCCCGEEEIIYROkSCGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImyMSZQgghhBBCCCFEPjRNJs60JBlJIYQQQgghhBBCCJsgRQohhBBCCCGEEELYBClSCCGEEEIIIYQQwibInBRCCCGEEEIIIUQ+DMicFJakzD0JSHmfmiX2L+rn5GXtCMXWyDnI2hGKzQ5l7QjF5liCszdPLbk1y3+dSm67j3je0doRiu3jz9OsHaHYriuDtSMUW6SWYu0IxVZGOVk7wn9ShuxcW4VzCR6wPK76JWtHKLbpR8pbO0KxvblzorUjFJtjmZCSuzNmAj+v++/JFWn0jWM2+XcruWtPIYQQQgghhBBC3FOkSCGEEEIIIYQQQgibUHLHdwshhBBCCCGEEGZm7ikSRE4ykkIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAlSpBBCCCGEEEIIIYRNkDkphBBCCCGEEEKIfBhkTgqLkpEUQgghhBBCCCGEsAlSpBBCCCGEEEIIIYRNkCKFEEIIIYQQQgghbIIUKYQQQgghhBBCCGETZOJMIYQQQgghhBAiH5pMnGlRMpJCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETZA5KYQQQgghhBBCiHwYkDkpLElGUgghhBBCCCGEEMImSJFCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJMieFEEIIIYQQQgiRD02TOSksSUZSCCGEEEIIIYQQwiZIkUIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAkyJ4UQQgghhBBCCJEPg8xJYVE2XaSY8O5rtGnfkuTkFF584XUO7j+S53JjXh/Bw492INNg4Luvf+Lr2Qvo0DmC0eOGYzAYyMjI5O1x77Fj2x6LZR8zaRTN2zYlJTmF8SMnc/TA8VzLvDPtdeo3rUtCXCIAb42czPFDJwjv2ILnXn0azaCRmZnJ1DensXf7fotl7zl+EDUj6pGWnMo3r3zG+UP/5lrmqU9GUKlWFTIzMjiz7yTzx83GkJGJq6cb/ac+j19Ff9JT05k35nMij5+3WPYnxg/igYgw0pJTmffK53lmH/TJ8GzZT7EgW/Z+U5+jTEV/MlLT+W7MTItm7zF+IDWM2Re8MpMLeWTv/8lwKtQKITMjk3P7TvLjuDkYMjIBqNqkBt3fGoC9gz2J1+OZ3vMds+YNm9ifwLZ1yExOY/uoWVw/cCbXMm4V/Gj6xTCcvN25fuAM24Z/jiE9k0rdm1HthS4AZCSmsGvsXGIPnwPg4e2fkJ6QgpZpQMvMZFWnN836Ptq93Y8qEXVJT05lySuziTqY+33UG9Cehk91onRlf6bVfZbk6wkAVGxSne5zXuTG+WgAji/fwabpC82a9ya74Jo4te0NdnZk7PuHjG1Lcz5e4X5K9RiBFnsVgIzju8jYvFh/sJQLTp0HYVemPKCRtvRrDJGnLJL7pg5v96dKRB3Sk9P465VZXM6j3RsY292ncgAf1R2a1e5Nhj5EzUebA6Ac7ChTtRwfhz1Lyo1Ei2R/dPwAqkfUJS05jZ9emcnFQ7mz9/7kBcrXCsGQkcm5faf4ddyXWZ9VgAq1Qxj+x0TmD5vG/mXbLZIboP/bg6kbUZ+05FS+eGUGZw6ezrXM0x+8QEitKiiluPRvJF+8PIPUpBSqN3mAl+e8xpXzVwDYsXwrf0z/2WLZS9o68l7J/rhxu5pewHZ14G3b1e+N29V2z3ShYdeWANjb2xFQtTxj6g0myUKf1TvJ7uzhwqCPR1C6nC929vasnvMnW39ZZ5HcAN3GD6C6sc/8kM96ps8nw3L0mV/yWM+M/GMS84ZNY/+ybRbJ7Vi/EW7PDkfZ2ZGyfAnJv3yf43GnJs1x7T8YDPo2PnH2p2QcOgCOTnhNnY5ydAR7e9I2ridp/lyLZM7Pg+P7E2rcTv3xyiwu5fE36PHJ85SrFUxmRiYX951i8bivc/wNbMEbUz7in03b8SntzcL5X1g7jhAmsdkiRZt2LQmuUpEWDR6kXoPavPu/N+nSvneu5Z7o3ZWgcgG0btwFTdPwLeMDwMZ/trJy2d8AVK9xHzO//pDwJo9YJHuLtk2pGFKeR5v2pFa9Bxj3/iv0f/CZPJf9ZMJnrP5rXY77tm3YxboVGwEIrV6F92dPpHvL3O/dHGqGh1E2OJA3w4cTHBZKn8lP817XcbmW275wA1+Pmg7A4OkjadGrLf/MX0nnF7pz4fC/fDF0Kv5Vgug9YQgf95lgkewPhIdRNjiA8eEjCA4L5cnJQ/ig6+t5ZN/I3FEzAHhq+kha9GrDP/NX0emFblw4fIZZQz/Ev0oQvSYMZlqfiRbJXiO8Ln7BAUwMH0nlsFCemDyYj7q+kWu5nQs3MM+YfcD0ETTr1YaN81fh4unKExMHM3PAFK5HXsPd19OseQPb1MEjJIClzV7Gt15V6r83iNUPjc+1XO03enFs9jLOL9pK/fefIvjJcE7NW0PCuWjWdp9I+o0kAtrUocHUwTme//djk0iLSTDrewAIiahD6eAAZrV+maCwKnScNJB5Xd/OtdzFncc5tWYPvX/M3Z8u7DjGr0/9z+xZc1AKp/b9SP3pQ7T4GJwHvEXmyb1o1yJzLGY4f5zU36blerpT2z5knj5I2sLPwc4eHJ0slRyAKhF18AkOYGbrlwkKq0qnSYP4pmvu/nN+53FOrNlD3x9zfha2zlrC1llLAAhtG0ajIZ0tVqCoZvysvhf+IhXDqtJj8mCmd81dSNu9cBPfj/oMgD7Th9O4VwRb5q8GQNkpHhrbm2P/7LNI5pvqRtQjIDiIl1o/T9Ww+3hq0lDe6vpqruXmT/ia5IRkAPq+OYgOAx7kz5m/A3B0xxE+fGqyRXNDyVtH3ivZb25X3w4fQeWwUHpNHsLUPLarOxZu5Btj9kHTR9K8Vxs2zF/F6tl/snr2nwDUalufNoMfsliB4k6zt+7XiUsnLzBzyPu4+3gwfu00dizcQGa6+b+AVg+vS5ngQKaEj6JSWFUemzyEaXn0md0LN7Jg1KcA9J0+nCa92rB5/ipAX888bOn1jJ0d7i+M4sa4lzFcjcZ72izStm0i89zZrEXS9u4mbesmAOwrh+Ax7m1in+kP6WncGPsipCSDvT1eH36Kw85tZBw9bLn82YSG18E3OIBp4S9TPqwqXSYPYnYe26n9Czfx26jPAXhs+gvU7xXOjvlrLB23QF0fbE/vHo8wbuKH1o4ihMkKnZNCKTVMKVXaEmGy6/BgBL/+qB/1271zP56eHpT1L5Nruf6DevLJ1JlZl4W5djUGgKTE5KxlXNxcsOQAndYdW/DXz8sBOLD7EB6eHpQp62vy85OTsmV3dbboJW/qdGjI1t/XA/DvnhO4eLjh6eeda7mD626NSjmz7ySlA/T3FxhaniObDgIQdSoS3/J+eJTxMn9woE6HBmz9/R9Az+6aT/ZDt2X3NmYPCC3P0U0HAMtnr9WhIduN2c8U0O6H1+3N+v3svpN4BehFufqPtGDf8u1cj7wGQMK1OLPmLdepPmd+2QDAtd0ncfR0xbls7rz+LR7gwl/6UeIzP/9Duc4N9OfsPEH6jST9910ncAn0MWve/IS2r8/B3/SCYOSeU5TydMMtj/cRdegsNy5ctXC6/NkFhqDFXkG7EQ2GTDKObMc+NMy0Jzs5Y1fhPjL36/0NQyakJhf8nLvsvvb12f+b3n8i95zE2dMV92K2e41Hm3Fo0RZzxMzTAx3qs/N3Pfu5PSdx9nDFI4/P6tFsn9Vz2T6rAC0GdmL/sm1m/5zern77Rmz4TS/en9xzHFdPN7zL5t683yxQADiVcgIbGOJa0taR2ZXk7LU7NGBbtuymbFfPZtsnyK7BI83ZuXiT2bLe7s6zazi7OQNQytWZxNgEDBkGc8cGoGaHBuw0Zj+75yQu+axnjuRYz5zKsZ5pObAT+5dtJ96C/cXhvupkRl7EcPkSZGSQun4tTk1a5Fwo5db6RTm7kGMH/eZjDg4oBwerrnuqdajPXuO6/oJxXe+ex9/gxLpbRaCL+07hGWCd/ZmCNKhbCy9PD2vHEKJITJk4MwDYoZT6WSnVSSmlzB0KICDQn8iLl7NuX4qMIiDQP9dylYIr0KVbZ5as+Ynvfp5JcEjFrMc6PdSWdVsXM+/Hz3l5uHmHjGdXNtCPy5FXsm5HXbpC2UC/PJd9YexQflr7LS+/MwJHJ8es+yM6t+L3Dd8zff6HvPPiFLNnvsnb34cY444QQOzla5QuYIVr52BPk26tOLRe38hfOHKGep0aA1C5TlV8yvnlubNiDt7+PlyPvPWF5vrla3gXkr1xt5YcXr8XgItHzlLXmL1SnSrG7JbZ2Hj5lyb2tnb3KiR7w26tOLJe3ziWDQnE1cuN4T++xeg/36Vh91ZmzesS4ENStrzJl2JwCcz5ZcfJx520G4lomfpOXdKlGFwDcn8hCnkynMtrb23kNU0j/MextF8xiZC+EWZ6BzqPgNLEZ3sf8Zdj8PAvWk22XL2qPLVsMo9/O5oyoeXudsQ8KY/SaHExWbe1+BiUe+7cduWq4jzoHUo9/iKqTJD+XG8/tKR4nB4cjPPAt3HqNMjiIyk8AnyIy9buccVodwAHZyeqtK7NUQueLuHl75Pjs3rjckyhn9X63VpyzPhZ9fQvTc2ODdmyYLXZs96udIBvjvV7zOVrlPbPO/vQqcOYuXMugVXLseKbJVn3h9a7n3eXfcSYb9+kXGgFs2e+qaStI7MrydmLs11t1K0lh4zb1ZscnZ2o0boue5ZtNVfUXO40+7pvlxNQtRzvbp/F6yv+x6/vzLXYQSPP29YzsSasZxp0a8lRY5/x8i9NrY4N2bxgldmz5shRpgyG6Fv7v4ar0dj55j7A6NSsJd6z5+E54T0SPn4/2wvY4f3pl/j+sJC0PTvJOJb3ad6W4Onvw43btlOeeezD3GTnYE+dbi04ud5yp2cLcS8rtEihadobQCjwFTAQOKGUmqKUqpLfc5RSzyildiqldiamxuS3WIHyqoXktXFwcnIiNTWVh9r25Pt5v/HhjFvD85cvWUN4k0cY3HcEo18bVqwcxWFq9hmTv6Bbiyfp22kIXqU9GTSsb9Zjfy/7h+4te/PSoLE8/+rTZs2bXd7Z81++98QhnNh+hJM7jgKwfOZCXL3ceGPpVCIGdOb8oX8xZFro3Ly86mcFhH9y4hBOZsu+wph93NIPsrJnZlrmqEmetb8Csj8xcTCnth/htDG7nb0dFWqFMGvQ+3zefwodh3fHLzjQXHEhr1LlbXlN6Utlm9UgpHc4+yb/mHXfmkfeYWWHN/in9weEDmyPX5NqdyNx3kz8rObn8sEzfN5sFF93fp1d36yk+5wX72a6IsqZ2xB1luSZr5Aydzzpu9ZQqtsIAJSdPXYBlcjY8zcp37yNlp6KY5OHLJo07+5e9J3/0Hb1uLDzuMVO9QDT1+83dZ/4FKe3H+XfHccAePSt/ix573s0g+WPEOZ5iCGf7LNGf8rzjQYTefICTbvoR0LPHDzNiGbP8Frnl1j5zRJenjPWjGlzKnHryGzutewF9fdexu3qKWP2m2q3q8/pnccsdqoH3Hn2Gq3qcP7wWV5rNJR3HxzNExMG4+zuYra82RXlswrw2MSnOL39CP8asz/61gD+ssp6xrTjmGmbNxD7TH/iJryOa/+nbj1gMBA7bAgx/R7H4b7q2FcKNlPOwhV1O/XwxEGc3X6Us8Z1vbj3aPfof7bKpDkpNE3TlFKXgctABlAa+FUptUrTtDF5LD8bmA1Q3qemye9+wOBe9O7/GAD79hwkqFxA1mOBQf5EXb6S6zmXIi+zdLFeKV7212r+92nuOQS2bdlFpeAKlPbx5npMrKlxiuSJQd3p3kef8+LQ3iMEBJXNesw/sCzRl3MPWb56Ra/Qpqels+jHJfR/7slcy+zeuo/ylcvh7eNFbMwNs2QP79eRFk+2A/TTH3yCfLk5hZ53gC+xUXkXmh4e+Rgevp58MfTWOW4pCcl8O/rzrNuTN37G1fO5/253S+t+HWn+ZFsAzu47RemgMoC+gSgd4Ets1PU8n/fQyMdw9/Vk9tDZObJ/N3pm1u1JGz/lmhmzt+zXgabG7Of2ncI76NaIE+8AX27kk72TMftXQ2/NgxB7OYbE6/GkJaeSlpzKqe1HKFe9EtH/XrpreasObE9IH31kQ8y+07hmy+sS6EPy5dgcy6dei8fJyw1lb4eWacA10IfkbO/Jq3oFGv5vCOv7fEDa9VvzT6RExRqfH8eFZTvxqRtC9NacO7x3ol7/dtTppb+PS/tP45HtfXgE+JBwJTafZ+aWlm1Y/Om/92E/cSAupd2zJng0Fy3+Osrz1lE15eGDlhB7W7iUrF8Np/dDh37g4o4hPgYt/jqGS/qEiZnHdlikSFG/f3vCjO0euf80ntna3bOI7X7TA12acGix+U/1aNavPY2fbAPA+X2nc3xWvQJ8iMvns9p+ZA/cfT34duiXWfdVqB1C3xl6wcittAfVw+uSmWng0MqdZsnevn9nInq1B+D0fn39fpNPgC/Xr+SdHUAzGNjy5yYeHtqV9b+szXEayN6/dzNo4lA8SnsQfz3eLNlL2jryXsneqpDtan7ZHzTuE2Tfrt5Uv0tzdizeaJa82d3N7E0fj2DFzIUARJ+N4tr5K/hXCeLsPvNMMty8XweaZK1nbu8zPvlm7zCyB26+nvwy9KOs+yrUDqHfjJHArfWMITOTg2Zaz9xkuBqNnd+t/V+7Mn4YruV/yl7Gwf3YB5ZDeXqhxd3ax9USE0jfvwenBo1IPpt7slNzadSvPfWf1LdTF/edxuu27VS8cf/kduEju+Pm68GPQ7+yREwh/hMKLVIopUYAA4CrwJfAaE3T0pVSdsAJIFeRori+/epHvv1KP6Lapn0rBj39JIt+X0a9BrWJj0vgSlTuFd2KpWtp3qoxPy34g6bNG3L6pD45T+XgCpz5V78yQ83a1XFydDRbgQLg57m/8/NcfWKxFu2a0uupHixfuJpa9R4gIT4hqyCRXZmyvln3R3Rqxamj+peGCpXLcf7MRQCq1boPR0dHsxUoANZ9t4J1360AoGZEPSIGdGLH4k0Eh4WSHJ9EXHRsruc079mGGq3q8nHvCTkqyy6erqQlp5GZnkGLXm05se0IKQnmO999/XcrWJ+VPYzwAZ3YaUL26q3qMC3P7KlkpmfS3ALZN3y3kg3frQSgRkQYrQZ0ZPfizVQOCyUln+xNe7aheqvafNp7Yo7sB1bu5LEJg7Czt8Pe0YFKdUNZ99XSXM+/Eye/WcXJb/SCYGDbuoQ+1YFzC7fgW68q6fHJpOTxJfPKpsOUf7gR5xdtpfITrYhcvgsA13K+NP9qFFuHzyTh9K3TuuxdSqHsFBmJKdi7lCKgdS0OffzHXX0fu+etZvc8fah9lTZ1qTegPUcWbyEorAqp8UkkFuHLspufF4nR+mczsE4I2CmzFygADJf+RZUui/IqgxZ/HYfqjUj9c9Zt4TwhUT8X2S4wWD8slKxn0+JiUD4BaDGXsa9UA8PVyNv/ibtu17xV7Jqn95+qberSYEAHDi/eQlBYVVLjk4tcpCjl4ULFJtVZNGpm4Qvfoc3frWLzd3r26hFhNB/Qgb2LN1MxrCop8UnE5/FZbdQzgvtb1eaL3pNyfFantByZ9XvPD5/lyJrdZitQAKyat4xV85YBULdNfToMeJAtizdSNew+kuOTiM2jSOFfKYCos/rnsl67BkSeugCAl583N4zvtUqdUJSdMluBAkreOvJeyf7Pdyv4J9t2tbVxu1q5gO1qs55tqJHHdhXA2cOF0MY1sianNKe7mT0m8irVmtfi1I6jeJTxwj8kiKvnzHfgYtN3K9lk7DPVI8JoMaAjexZvplIB65nGPSO4v1Udvritz0xuOSLr914fPsfhNbvNXqAAyDh+FPug8tj5B2C4dpVSrdsQ/37Og4d2geUwXNL3ce2rhIKDA1rcDZSXF2RkoiUmgJMTTmENSLrtyiDmtv27VWw3ruvvi6hL4wEdOLB4C+XDqpISn0xCHn+Dej3DqdqqFt/0nmLROeSEuNepwj5QSqkJwFeapp3N47HqmqYVeMJYUUZS3G7SB68T3rYFKcnJvDTsTfbvPQTAvJ8+Z/TI8URdjsbT04MZs9+nXPkAEhOTGPvSRI4cOsbzI56iR69HyEjPICUlhUlv/a/IlyD1cyr+pIlj332JZhFNSElO4e1RUzi8Tz8SPGPBh0x46T2io64y69fplPb1RinFsYMnmDxmKslJyQwc1oeHH+9MRnoGqSmpfDzhsyJfgrSRc1Cxsz85YTAPtNYvr/ft6M84e0Avngyb+xrfvfoFN65c5/OTPxJzMZqURP1o7Z7l21gy/VdC6t3HwP8NQzMYuHTiAvPGzCQprmjDO+1MHC6Yl14TBlOjdR3SktOYN/pzzhmzvzB3LPNfncWNK9f59OQPObLvXb6NpdN/I7heKAP/NwyDMfv8MV8UObvjHWR/fMJTVDdmXzB6JueN2YfOHcsPr84i7sp1Pj75PdezZd+/fDvLp/8GQJtnutDk8XAMBo2tP61l3ddF24ltnlq0i/3UmzKQwIjaZCSnsf3FWVzfpx/taDl/NDtenkNKVCxuFf1o+sVwnLzdiD14lq3DPseQlkHDD4dQ/qFGJBonRbx5qVG3in60+Fo/ZUI52HP2j80cmbao0Cz/OhW/3dtPHEBI69qkJ6ex9JXZXD6gv4/Hv3mFZWO+JOFKLPUHdqDxsw/j7udF4rU4Tv+9j2Wvfkm9Ae0J69sWLSOT9JR01k5awMVdJ4r074943rHwhfJgF1Ibp7ZPgrIj48AGMrb8hUPdcAAy9q7DoV5bHMIiwJCJlpFO+tofMVw8CYAqWwGnToNQ9g4YYqNJW/oVpCYVOcPHn6cVKztAx4kDqWJs979emcUlY7v3/GY0S8bMIeFKLA0GdqRptnY/9fdelryqj0qo/VgrQlrXZuHwT4v1719XxT+Vq9uEQdzfug7pyan8NHoWF4yf1cFzx/DLq3OIu3Kd90/O5/rFq6QaJ3E+uHwHq6b/nuN1bhYpinoJ0kgtpfCF8jFw4jPUaR1GanIqs16Zwb8H9KPCY755g9ljPuNGdCxv/ToZF3dXlFKcO/IvX78+i+SEZDoM6Ey7vp3IzMgkLSWN+ZPmcmJX0YY2l1HFn//E2uvIO2Ht7Bl3MKS3Z7bt6nfZtqvPzx3LAuN2dUYe29VlxuxNHmtNjdZ1+Xp47isNmdudZPcqW5r+Hz6PZ9nSKAUrZy5i+8INRfr3nU2a+i1v3ScMolpr/fLYP4z+Ims98/TcV/np1dnEXbnO1JMLcqxnDizfzsrb1jM3ixRFvQTpuOrFG6nj2LAx7s8MB3s7UlYuJfnH+Tg/qI80Tlm6GJfHn6RU246QkYGWlkbiVzPJOHRAv9LHK+PAzg6UInXDOpK//7ZYGaYfKV+s593uoQkDCTVup/4YPYtI43aq79zRLHp1DvFXYhl/ch43Ll4l1dh/jizfwbrpxT+48ubOu39VudHj32PHnv3Exsbh6+PN84P70aNLx7v+7ziWCbHIvIXW4uZa+Z6sQiUmnbHJv1uhRYo7dSdFCmu7kyKFtd1JkcLa7qRIYW13UqSwtqIWKWzJnRQprK24RQpbcCdFCmu7kyKFtd1JkcLa7qRIIYrvTooUovjupEhhbcUtUtiCu1WksAZzFCks5V4vUri4VLonV6TJyWdt8u9WcteeQgghhBBCCCGEuKdIkUIIIYQQQgghhBA2QYoUQgghhBBCCCGEsAkl9yR0IYQQQgghhBDCzOTqLZYlIymEEEIIIYQQQghhE6RIIYQQQgghhBBCCJsgRQohhBBCCCGEEELYBJmTQgghhBBCCCGEyIeGzElhSTKSQgghhBBCCCGEEDZBihRCCCGEEEIIIYSwCVKkEEIIIYQQQgghhE2QIoUQQgghhBBCCCFsgkycKYQQQgghhBBC5EPTZOJMS5KRFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETZAihRBCCCGEEEIIIWyCzEkhhBBCCCGEEELkQ+aksCwZSSGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImyJwUQgghhBBCCCFEPmRGCsuSkRRCCCGEEEIIIYSwCVKkEEIIIYQQQgghhE2QIoUQQgghhBBCCCFsgirp13xVSj2jadpsa+coDsluHZLdOiS7dUh265Ds1iHZrUOyW4dktw7JLoT53QsjKZ6xdoA7INmtQ7Jbh2S3DsluHZLdOiS7dUh265Ds1iHZhTCze6FIIYQQQgghhBBCiHuAFCmEEEIIIYQQQghhE+6FIkVJPq9KsluHZLcOyW4dkt06JLt1SHbrkOzWIdmtQ7ILYWYlfuJMIYQQQgghhBBC3BvuhZEUQgghhBBCCCGEuAeU+CKFUmq5UipWKfWXtbMUhVKqrlJqi1LqkFJqv1Kqp7UzmUopVUkptUsptdeY/1lrZyoqpZSnUuqiUupTa2cpCqVUprHd9yqlFls7T1EopSoqpVYqpY4opQ4rpSpbO5MplFIR2dp8r1IqRSnV1dq5TKWU+sD4OT2ilJqulFLWzmQqpdT7SqmDxh+bX0fmtz1SSgUrpbYppU4opX5SSjlZK2N+Csg+TCl1UimlKaXKWCtfQQrIvkApdczYf75WSjlaK2N+Csj+lVJqn3H/4FellLu1MuansP0vpdQMpVSCpXOZooB2/0Yp9W+29X1dK0XMVwHZlVJqslLquHF9P8JaGfNTQPYN2do8Uim10EoR81VA9rZKqd3G7BuVUlWtlVGIe0mJL1IAU4F+1g5RDElAf03THgA6AZ8opbytG8lkl4BmmqbVBRoDY5VSQdaNVGQTgfXWDlEMyZqm1TX+PGLtMEU0D5iqaVp1oBFwxcp5TKJp2t832xxog/7ZXWndVKZRSjUDmgO1gZpAQ6C1VUOZSCn1EFAPqIu+nhmtlPK0aqjC5bc9eh/4WNO0UOA6MNiiqUyTX/ZNQDvgrGXjFEl+2RcA1YBagAswxJKhTJRf9hc1TaujaVpt4BwwzLKxTJLv/pdSqgHgbdE0RVPQvuPobNvZvRbMZKr8sg8EKgDVjNvZHy0ZykR5Ztc0rWW27ewW4HdLBzNBfu0+E+hjzP498IYlQwlxryoxRQqlVEPjEQVnpZSb8chgTU3T1gDx1s5XkLyyA06app0A0DQtEv0Lm59Vg+Yhn+z3aZqWalykFDbaj/LrM0qp+oA/NvxFM7/s1s5linyy1wYcNE1bBaBpWoKmaUlWjpqLCe3+GLCspGQH7AFnwAn9s+oIRFkzZ17yyV4PWK9pWoamaYnAPvSCrtUVZXuklFLoxa1fjXd9C3S1bOIceYq0LdU0bY+maWcsnzS3YmRfqhkB24HyFg9tVIzsccbnKfQCi9UmECtqdqWUPfoXujEWD5s7yz2171hI9ueACZqmGQA0TbPagYDitrtSygN9fbnQUlnzyFDU7Bpws4DuBURaLKyJlFITlVIjs92erGxwpI0Q2TlYO4CpNE3bofTh7ZPQN9jzNU07aOVYJiksu1KqEfqXiFNWipiv/LIrpSoAS4Cq6EcdbG6lnFd24DCwFr0a3taK8QpUQLs7K6V2AhnAe5qmLbRmzrzk0+4hQKxS6ncgGFgNjNU0LdN6SXMzYT3TC/jIKuEKkU/2DUqpv9FHPyngU03TjlgzZ17y6TO7gPFKqY8AVyAC/fNrdUXcHvkCsZqmZRhvXwDKWSBmnu7lbWl+lH6aRz9gZGHLmktxsiul5gIPovf7l82fMm/FyD4MWKxp2iVl5bPLitlnJiul3gLWoG+nUgtZ3iyKkb0K0FMp1Q2IBkbcPBhmaXewnukGrLlZpLOGYmQfAixVSiUDcUATC8Qsqq/QR6dMU0rZoe/LNLJuJCEKoWlaiflB/yK/D9gG2Ge7Pxz4y9r5ipk9EDgGNLF2xqJmNz4WhH6Eyt/aOU3Jjr7zNMb42ED0L21Wz2lquwNBxv+HAGeAKtbOaWK7PwbcMOZ2AH4DBls7p6ntbrw/EH3Hz9HaGYvQ7lXRi4nuxp8tQCtr5zS13YHXgb3AKvSh+yOtndOEfpJje4Q+Qu5kttsVgAMlIfttzzkDlCkp7X7bc+YAn5TQ7PbA58CgkpAdfZ9gI/rIOYCEktTuxvW8Qh959i3wVgnKngC8bPy9O7ChpGTP9tgyoEcJ6zO/A42Nv48GvrR2/nze0yogDH1E4q/WziM/8lPYj00O0y+AD/qOtgf6EOaSJFd2pZ9fvQR4Q9O0rVbMVph8213TR1AcAlpaIZcpbs/eFBimlDoDfAj0V0q9Z714BcrV7sb2RtO008A69A2OLbo9+wVgj6ZppzX9iPJC9OH8tii//v4E8IemaelWSWWa27N3A7Zq+uk1Ceg7gLZ4lAfy7u+TNf085fboXxysclQwH6Zuj64C3kqpmyMXy2P94cD31La0IEqp8eiFopfMnMsURW53TR9t9hPQw4y5TGFq9jD04uhJ43bWVSl10vzxCmRyu2uadknTpQJzsf7R5qL0mQvoBwAA/kCfi8iaivpZ9UVv7yVmzmUKk7IrpfyAOpqmbTPe9RPQzPzxiuVL9INzg4CvrRtFiMKVtCLFbOBN9CNq71s5S1HlyK702d3/AOZpmvaLVZMV7vbs5ZVSLgBKqdLoE/Mds2K+guTIrmlaH03TKmqaVhl4Bb39x1ozYAFub/fSSqlSAEqfZb85NjL8PQ+3f1Z3AKWNG3TQzzktKdlvehL4wSqJTHd79nNAa6WUg3HIe2vA5k73MLq9v9sbd1pR+pwmtbGteWRM2h5pmqYBf6OPJgIYACwye7qC3TPb0oIWVEoNAToCT2rG8/StzKTsSlf15u9AF+CoRRLmz9T+vkTTtABN0yobt7NJmqZZ+2oHRekzgcb/K/S5Y6x9KlRRPqsL0betoK/rj5svlkmKup55HH2UQopZU5nG1OzXAS+l1H3G2+2x3W3sH+ijKBoCK6ycRYjCWXsoh6k/QH/gd+Pv9uhDsNoAG9CHYCejV5E7Wjuridn7A+noQ5lv/tS1dlYTs3cE9qMPhdsPPGPtnEXpM9keH4iNnu6RT/ZxwAFjux/Adk+XyO+z2t7YXw4A36BPHmv1vCZmrwxcBOysnbGI2dsCs9B3mg4DH1k7ZxGy3zwX/zCw1ZbWj0XdHqGf5rQdOAn8ApQqQdlHGG9noI8AsdpQ5mJkz0Cf62mv8cdqQ/eLkh39ANIm47ryIPoXJc+SkD2P51r1dI9i9Jm12dp9PuBegrJ7o49COIB+al+dkpLduNw6oJM1+0sx270bt/bN1gEh1n4PBby3L9DnM7N6FvmRn8J+lKZZbcJoIYQQQgghhBBmZJwwczfwuGalCVWFKIqSdrqHEEIIIYQQQggTKKVqoI/kWyMFClFSyEgKIYQQQgghhBBC2AQZSSGEEEIIIYQQQgibIEUKIYQQQgghhBBC2AQpUgghhBBCCCGEEMImSJFCCCGEEEIIIYQQNkGKFEIIIYQQQgghhLAJUqQQQgghhBBCCCGETfg/ehuasH/cnwcAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.figure(figsize = (20, 20))\n", + "sns.heatmap(df.corr(), annot = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "698dab3a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]]\n" + ] + } + ], + "source": [ + "a = np.array([1,2,3])\n", + "b = np.array([4,5,6])\n", + "print(np.stack((a,b)))" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "4ad35e26", + "metadata": {}, + "outputs": [], + "source": [ + "X = np.stack(x)\n", + "y = np.array(y)" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "e065b456", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 8. , 2.66 , 26. , ..., 7.9024, 247.3 , 7.87 ],\n", + " [ 8. , 2.66 , 26. , ..., 7.881 , 272.5 , 8.86 ],\n", + " [ 8. , 2.66 , 26. , ..., 7.6398, 290.3 , 8.9 ],\n", + " ...,\n", + " [ 11. , 3. , 79. , ..., 8.967 , 213.9 , 22.5 ],\n", + " [ 11. , 3. , 79. , ..., 8.9588, 113.6 , 21.5 ],\n", + " [ 11. , 3. , 79. , ..., 9.2255, 64.6 , 19.3 ]])" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "1ae3606a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.92, -0.87, -1.12, -1.05, -1.21, -1.46, -2.16, -1.75, -1.28,\n", + " -2.01, -2.34, -1.16, -1.17, -1.45, -1.33, -1.41, -1.75, -2.54,\n", + " -2.08, -1.53, -2.36, -2.73, -1.2 , -1.1 , -1.29, -1.1 , -1.43,\n", + " -1.6 , -2.26, -1.82, -1.43, -2.09, -2.42, -2.11, -2.07, -2.4 ,\n", + " -2.67, -2.09, -2.35, -3.31, -3.37, -2.09, -3. , -3.76, -1.2 ,\n", + " -1.15, -1.4 , -1.29, -1.41, -1.58, -2.23, -1.68, -1.39, -2.03,\n", + " -2.25, -1.49, -1.39, -1.57, -1.29, -1.69, -1.73, -2.27, -1.66,\n", + " -1.56, -2.08, -2.22, -1.46, -1.29, -1.33, -0.89, -1.59, -1.47,\n", + " -1.83, -1.24, -1.3 , -1.64, -1.66, -3.58, -3.46, -3.63, -3.83,\n", + " -3.46, -3.44, -4.16, -4.3 , -3.16, -3.8 , -4.45, -1.9 , -1.84,\n", + " -2.06, -1.9 , -2.02, -2.26, -2.84, -2.24, -2.11, -2.67, -2.85,\n", + " -1.92, -1.77, -1.85, -1.53, -2.11, -2.02, -2.42, -1.81, -1.87,\n", + " -2.25, -2.3 , -2.93, -2.79, -2.93, -3.01, -2.86, -2.81, -3.39,\n", + " -3.35, -2.58, -3.1 , -3.56])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "bc35658c", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.utils import shuffle\n", + "X, y = shuffle(X, y)\n", + "X_train, y_train = X[:-30, :], y[:-30]\n", + "X_test, y_test = X[-30:,:], y[-30:]" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9699b84a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([-0.92, -2.42, -1.66, -3.1 , -2.22, -1.58, -1.92, -1.17, -1.28,\n", + " -1.41, -2.06, -2.02, -2.67, -4.45, -3.16, -3.8 , -1.39, -1.85,\n", + " -1.29, -1.47, -1.49, -3.31, -1.1 , -2.85, -3.44, -1.05, -1.9 ,\n", + " -0.89, -1.43, -1.39])" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "e75a3aea", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "\n", + "lr = LinearRegression()\n", + "lr.fit(X_train, y_train)\n", + "y_pred_train_lr = lr.predict(X_train)\n", + "y_pred_test_lr = lr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "49330c08", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'ML')" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(4,4))\n", + "plt.scatter(y_train,y_pred_train_lr, alpha = 0.5, color = 'blue',\n", + " label = 'training')\n", + "plt.scatter(y_test,y_pred_test_lr, alpha = 0.5, color = 'red',\n", + " label = 'test')\n", + "plt.legend()\n", + "plt.xlabel('DFT')\n", + "plt.ylabel('ML')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "f2aa898e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE(training)0.221\n", + "RMSE(test)0.198\n" + ] + } + ], + "source": [ + "from sklearn.metrics import mean_squared_error\n", + "\n", + "rmse_tr_lr=mean_squared_error(y_train, y_pred_train_lr, squared = False)\n", + "rmse_te_lr=mean_squared_error(y_test, y_pred_test_lr, squared = False)\n", + "print('RMSE(training)%.3f'%rmse_tr_lr)\n", + "print('RMSE(test)%.3f'%rmse_te_lr)" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "ae8dfc13", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100 times mean RMSE: 0.253\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import cross_val_score, ShuffleSplit\n", + "\n", + "cvf = ShuffleSplit(n_splits = 100, test_size = 0.25)\n", + "score_lr = cross_val_score(lr, X, y, cv = cvf,\n", + " scoring = 'neg_root_mean_squared_error')\n", + "\n", + "print('100 times mean RMSE: %.3f' % -score_lr.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "47381493", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "gbr = GradientBoostingRegressor()\n", + "gbr.fit(X_train, y_train)\n", + "y_pred_train_gbr = gbr.predict(X_train)\n", + "y_pred_test_gbr = gbr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "2c1c75ac", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'GBR')" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize=(4,4))\n", + "plt.scatter(y_train,y_pred_train_gbr, alpha = 0.5, color = 'blue',\n", + " label = 'training')\n", + "plt.scatter(y_test,y_pred_test_gbr, alpha = 0.5, color = 'red',\n", + " label = 'test')\n", + "plt.legend()\n", + "plt.xlabel('DFT')\n", + "plt.ylabel('GBR')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "2ba4898c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "RMSE(traing)0.036\n", + "RMSE(test)0.155\n" + ] + } + ], + "source": [ + "rmse_tr_gbr = mean_squared_error(y_train, y_pred_train_gbr, squared = False)\n", + "rmse_te_gbr = mean_squared_error(y_test, y_pred_test_gbr, squared = False)\n", + "print('RMSE(traing)%.3f'%rmse_tr_gbr)\n", + "print('RMSE(test)%.3f'%rmse_te_gbr)" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "11bec4da", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(lr) 100 times mean RMSE: 0.253\n", + "(gbr) 100 times mean RMSE: 0.156\n" + ] + } + ], + "source": [ + "cvf = ShuffleSplit(n_splits = 100, test_size = 0.25)\n", + "score_gbr = cross_val_score(gbr, X, y, cv = cvf,\n", + " scoring = 'neg_root_mean_squared_error')\n", + "print('(lr) 100 times mean RMSE: %.3f' % -score_lr.mean())\n", + "print('(gbr) 100 times mean RMSE: %.3f' % -score_gbr.mean())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a8892e52", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git "a/7. \346\234\272\345\231\250\345\255\246\344\271\240\344\270\216\351\253\230\351\200\232\351\207\217\347\255\233\351\200\211.ipynb" "b/7. \346\234\272\345\231\250\345\255\246\344\271\240\344\270\216\351\253\230\351\200\232\351\207\217\347\255\233\351\200\211.ipynb" new file mode 100644 index 0000000..9b68d2b --- /dev/null +++ "b/7. \346\234\272\345\231\250\345\255\246\344\271\240\344\270\216\351\253\230\351\200\232\351\207\217\347\255\233\351\200\211.ipynb" @@ -0,0 +1,6444 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "ed7de9da", + "metadata": {}, + "outputs": [], + "source": [ + "from matminer.datasets import load_dataset\n", + "df_mp = load_dataset('boltztrap_mp',data_home = '.')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "0654c51b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpidpf_npf_ps_ns_pformulam_nm_pstructure
0mp-100700.8650.0125-421.0760.0BaAg(PO3)30.57872.80[[ 0.08245398 10.58009491 11.61923254] O, [3.1...
1mp-100861.0500.6440-393.0567.0YSF0.5414.02[[2.84699546 0.94899849 0. ] F, [0.9489...
\n", + "
" + ], + "text/plain": [ + " mpid pf_n pf_p s_n s_p formula m_n m_p \\\n", + "0 mp-10070 0.865 0.0125 -421.0 760.0 BaAg(PO3)3 0.578 72.80 \n", + "1 mp-10086 1.050 0.6440 -393.0 567.0 YSF 0.541 4.02 \n", + "\n", + " structure \n", + "0 [[ 0.08245398 10.58009491 11.61923254] O, [3.1... \n", + "1 [[2.84699546 0.94899849 0. ] F, [0.9489... " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mp.head(2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "25c453af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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mpidpf_npf_ps_ns_pformulam_nm_pstructure
7mp-10221.010.0227-363.0913.0K2S0.3716.4[[0. 0. 0.] S, [4.57110998 3.23226287 7.917394...
\n", + "
" + ], + "text/plain": [ + " mpid pf_n pf_p s_n s_p formula m_n m_p \\\n", + "7 mp-1022 1.01 0.0227 -363.0 913.0 K2S 0.37 16.4 \n", + "\n", + " structure \n", + "7 [[0. 0. 0.] S, [4.57110998 3.23226287 7.917394... " + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_mp.loc[df_mp['formula']=='K2S']" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "1063b090", + "metadata": {}, + "outputs": [], + "source": [ + "from matminer.data_retrieval.retrieve_MP import MPDataRetrieval" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1faa02d8", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.22019999999999995, 'material_id': 'mp-1244869'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.5673, 'material_id': 'mp-1456'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.4248000000000003, 'material_id': 'mp-715276'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.37980000000000014, 'material_id': 'mp-1245154'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-1078361'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.1123, 'material_id': 'mp-1245078'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-716814'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.3464999999999998, 'material_id': 'mp-715572'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-1068212'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.4464000000000001, 'material_id': 'mp-510080'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-776606'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.30180000000000007, 'material_id': 'mp-1245277'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.12109999999999999, 'material_id': 'mp-1245084'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.5655999999999999, 'material_id': 'mp-1178392'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.5554000000000001, 'material_id': 'mvc-12005'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.9977, 'material_id': 'mp-685153'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.6876999999999998, 'material_id': 'mp-1181824'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.4021999999999997, 'material_id': 'mp-565814'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.4382000000000001, 'material_id': 'mp-542896'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.0606, 'material_id': 'mp-1205415'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.2831999999999999, 'material_id': 'mp-1244911'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 1.1378, 'material_id': 'mp-1245019'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-557546'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.8159000000000001, 'material_id': 'mp-705547'}\n", + "{'formula': {'Fe': 2.0, 'O': 3.0}, 'band_gap': 0.0, 'material_id': 'mp-19770'}\n" + ] + } + ], + "source": [ + "mpd = MPDataRetrieval(api_key = 'lKGaqovS50D38bRQ')\n", + "data = mpd.get_data('Fe-O',['formula', 'band_gap'])\n", + "for d1 in data:\n", + " print(d1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "50a9b494", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method get_data in module matminer.data_retrieval.retrieve_MP:\n", + "\n", + "get_data(criteria, properties, mp_decode=True, index_mpid=True) method of matminer.data_retrieval.retrieve_MP.MPDataRetrieval instance\n", + " Args:\n", + " criteria: (str/dict) see MPRester.query() for a description of this\n", + " parameter. String examples: \"mp-1234\", \"Fe2O3\", \"Li-Fe-O',\n", + " \"\\*2O3\". Dict example: {\"band_gap\": {\"$gt\": 1}}\n", + " \n", + " properties: (list) see MPRester.query() for a description of this\n", + " parameter. Example: [\"formula\", \"formation_energy_per_atom\"]\n", + " \n", + " mp_decode: (bool) see MPRester.query() for a description of this\n", + " parameter. Whether to decode to a Pymatgen object where\n", + " possible.\n", + " \n", + " index_mpid: (bool) Whether to set the materials_id as the dataframe\n", + " index.\n", + " \n", + " Returns ([dict]):\n", + " a list of jsons that match the criteria and contain properties\n", + "\n" + ] + } + ], + "source": [ + "help(mpd.get_data)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "c930fde2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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formulagap expt
0Hg0.7Cd0.3Te0.35
1CuBr3.08
2LuP1.30
3Cu3SbSe40.40
4ZnO3.44
.........
6349Tm2MgTl0.00
6350Nb5Ga40.00
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6352Lu2AlTc0.00
6353CeZnPO0.00
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formulagap expt
11Sr2Be2B2O78.00
27CsH3Se2O64.77
28CsB3GeO75.76
32Si3N45.10
62CuN34.17
.........
3753KScSe2O65.40
3775MgSe5.60
3810SrO5.70
3865KI6.38
3871RbI6.36
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formulagap expt
27CsH3Se2O64.77
28CsB3GeO75.76
32Si3N45.10
62CuN34.17
64MnI24.40
.........
3720Ba2CdB6O124.59
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3810SrO5.70
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formulagap expt
0Hg0.7Cd0.3Te0.35
1CuBr3.08
2LuP1.30
3Cu3SbSe40.40
4ZnO3.44
.........
6349Tm2MgTl0.00
6350Nb5Ga40.00
6351Tb2Sb50.00
6352Lu2AlTc0.00
6353CeZnPO0.00
\n", + "

6354 rows × 2 columns

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formulagap expt
27CsH3Se2O64.77
28CsB3GeO75.76
32Si3N45.10
62CuN34.17
64MnI24.40
.........
3720Ba2CdB6O124.59
3726Y2O35.60
3753KScSe2O65.40
3775MgSe5.60
3810SrO5.70
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\n", + "
" + ], + "text/plain": [ + " formula gap expt\n", + "27 CsH3Se2O6 4.77\n", + "28 CsB3GeO7 5.76\n", + "32 Si3N4 5.10\n", + "62 CuN3 4.17\n", + "64 MnI2 4.40\n", + "... ... ...\n", + "3720 Ba2CdB6O12 4.59\n", + "3726 Y2O3 5.60\n", + "3753 KScSe2O6 5.40\n", + "3775 MgSe 5.60\n", + "3810 SrO 5.70\n", + "\n", + "[184 rows x 2 columns]" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_46 = df.loc[(df['gap expt']>4.0) & (df['gap expt']<6.0)]\n", + "df_46" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "c64cb79c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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formulagap expt
186CaSnO34.40
328CaS5.38
515Ca10Ge16B6O515.47
664CaS5.40
848Ca3B2N44.10
886Ba2Ca2B4O105.60
1014CaI25.98
1047CaTe4.30
1759CsCaBO34.20
2634CaSe4.87
2779CaB64.50
3197CaSe5.00
3237Ca3BiP3O124.20
3513CaTe4.07
\n", + "
" + ], + "text/plain": [ + " formula gap expt\n", + "186 CaSnO3 4.40\n", + "328 CaS 5.38\n", + "515 Ca10Ge16B6O51 5.47\n", + "664 CaS 5.40\n", + "848 Ca3B2N4 4.10\n", + "886 Ba2Ca2B4O10 5.60\n", + "1014 CaI2 5.98\n", + "1047 CaTe 4.30\n", + "1759 CsCaBO3 4.20\n", + "2634 CaSe 4.87\n", + "2779 CaB6 4.50\n", + "3197 CaSe 5.00\n", + "3237 Ca3BiP3O12 4.20\n", + "3513 CaTe 4.07" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_46Ca = df_46.loc[df_46['formula'].str.contains('Ca')]\n", + "df_46Ca" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "35c08202", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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formulagap expt
32Si3N45.10
62CuN34.17
109Na3MoO4Cl4.69
293AlN5.90
547LaH2NO54.76
805Na2Zn3Se4O124.90
848Ca3B2N44.10
877NaI5.80
1017Nd2O34.40
1049RbNbSe2O74.10
\n", + "
" + ], + "text/plain": [ + " formula gap expt\n", + "32 Si3N4 5.10\n", + "62 CuN3 4.17\n", + "109 Na3MoO4Cl 4.69\n", + "293 AlN 5.90\n", + "547 LaH2NO5 4.76\n", + "805 Na2Zn3Se4O12 4.90\n", + "848 Ca3B2N4 4.10\n", + "877 NaI 5.80\n", + "1017 Nd2O3 4.40\n", + "1049 RbNbSe2O7 4.10" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_46N = df_46.loc[df_46['formula'].str.contains('N')]\n", + "df_46N.head(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "8d6ec01d", + "metadata": {}, + "outputs": [], + "source": [ + "from matminer.featurizers.conversions import StrToComposition" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "25f4b4bb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1c2ffa9cfddd410fa923c4b30b92c9df", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "StrToComposition: 0%| | 0/184 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
formulagap exptcomposition
27CsH3Se2O64.77(Cs, H, Se, O)
28CsB3GeO75.76(Cs, B, Ge, O)
32Si3N45.10(Si, N)
62CuN34.17(Cu, N)
64MnI24.40(Mn, I)
............
3720Ba2CdB6O124.59(Ba, Cd, B, O)
3726Y2O35.60(Y, O)
3753KScSe2O65.40(K, Sc, Se, O)
3775MgSe5.60(Mg, Se)
3810SrO5.70(Sr, O)
\n", + "

184 rows × 3 columns

\n", + "" + ], + "text/plain": [ + " formula gap expt composition\n", + "27 CsH3Se2O6 4.77 (Cs, H, Se, O)\n", + "28 CsB3GeO7 5.76 (Cs, B, Ge, O)\n", + "32 Si3N4 5.10 (Si, N)\n", + "62 CuN3 4.17 (Cu, N)\n", + "64 MnI2 4.40 (Mn, I)\n", + "... ... ... ...\n", + "3720 Ba2CdB6O12 4.59 (Ba, Cd, B, O)\n", + "3726 Y2O3 5.60 (Y, O)\n", + "3753 KScSe2O6 5.40 (K, Sc, Se, O)\n", + "3775 MgSe 5.60 (Mg, Se)\n", + "3810 SrO 5.70 (Sr, O)\n", + "\n", + "[184 rows x 3 columns]" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_46_com = StrToComposition().featurize_dataframe(df_46, 'formula')\n", + "df_46_com" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "cb3eec5b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
formulagap exptcomposition
32Si3N45.10(Si, N)
62CuN34.17(Cu, N)
293AlN5.90(Al, N)
547LaH2NO54.76(La, H, N, O)
848Ca3B2N44.10(Ca, B, N)
1592GdH2NO54.84(Gd, H, N, O)
1659MgGeN24.20(Mg, Ge, N)
1922YH2NO54.83(Y, H, N, O)
2000MgSiN24.05(Mg, Si, N)
3320MgGeN24.40(Mg, Ge, N)
3562NH7Se2O64.81(N, H, Se, O)
\n", + "
" + ], + "text/plain": [ + " formula gap expt composition\n", + "32 Si3N4 5.10 (Si, N)\n", + "62 CuN3 4.17 (Cu, N)\n", + "293 AlN 5.90 (Al, N)\n", + "547 LaH2NO5 4.76 (La, H, N, O)\n", + "848 Ca3B2N4 4.10 (Ca, B, N)\n", + "1592 GdH2NO5 4.84 (Gd, H, N, O)\n", + "1659 MgGeN2 4.20 (Mg, Ge, N)\n", + "1922 YH2NO5 4.83 (Y, H, N, O)\n", + "2000 MgSiN2 4.05 (Mg, Si, N)\n", + "3320 MgGeN2 4.40 (Mg, Ge, N)\n", + "3562 NH7Se2O6 4.81 (N, H, Se, O)" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for i, row in df_46_com.iterrows():\n", + " if row['composition'].get_atomic_fraction('N') == 0:\n", + " df_46_com = df_46_com.drop([i])\n", + "\n", + "df_46_com" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "3b819778", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([3.604e+03, 1.432e+03, 8.470e+02, 2.590e+02, 9.100e+01, 6.700e+01,\n", + " 3.100e+01, 1.300e+01, 8.000e+00, 2.000e+00]),\n", + " array([ 0. , 1.17, 2.34, 3.51, 4.68, 5.85, 7.02, 8.19, 9.36,\n", + " 10.53, 11.7 ]),\n", + " )" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.hist(df['gap expt'])" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "ded36498", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([4.84787831e-01, 1.92623800e-01, 1.13933211e-01, 3.48390811e-02,\n", + " 1.22407582e-02, 9.01242639e-03, 4.16992863e-03, 1.74867975e-03,\n", + " 1.07611061e-03, 2.69027653e-04]),\n", + " array([ 0. , 1.17, 2.34, 3.51, 4.68, 5.85, 7.02, 8.19, 9.36,\n", + " 10.53, 11.7 ]),\n", + " )" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4mp-10025SiRu21262160.300999[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930100.110773101.947798103.784823255.055257256.768081258.4809040.324682[[0.00410214297725, -0.001272204332729, -0.001...[[349.3767766177825, 186.67131003104407, 176.4...[[407.4791016459293, 176.4759188081947, 213.83...#\\#CIF1.1\\n###################################...7000Si4 Ru8\\n1.0\\n4.037706 0.000000 0.000000\\n0.00...
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" + ], + "text/plain": [ + " material_id formula nsites space_group volume \\\n", + "0 mp-10003 Nb4CoSi 12 124 194.419802 \n", + "1 mp-10010 Al(CoSi)2 5 164 61.987320 \n", + "2 mp-10015 SiOs 2 221 25.952539 \n", + "3 mp-10021 Ga 4 63 76.721433 \n", + "4 mp-10025 SiRu2 12 62 160.300999 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_Reuss G_VRH G_Voigt K_Reuss K_VRH K_Voigt \\\n", + "0 96.844535 97.141604 97.438674 194.267623 194.268884 194.270146 \n", + "1 93.939650 96.252006 98.564362 173.647763 175.449907 177.252050 \n", + "2 120.962289 130.112955 139.263621 295.077545 295.077545 295.077545 \n", + "3 12.205989 15.101901 17.997812 49.025963 49.130670 49.235377 \n", + "4 100.110773 101.947798 103.784823 255.055257 256.768081 258.480904 \n", + "\n", + " poisson_ratio compliance_tensor \\\n", + "0 0.285701 [[0.004385293093993, -0.0016070693558990002, -... \n", + "1 0.268105 [[0.0037715428949660003, -0.000844229828709, -... \n", + "2 0.307780 [[0.0019959391925840004, -0.000433146670736000... \n", + "3 0.360593 [[0.021647143908635, -0.005207263618160001, -0... \n", + "4 0.324682 [[0.00410214297725, -0.001272204332729, -0.001... \n", + "\n", + " elastic_tensor \\\n", + "0 [[311.33514638650246, 144.45092552856926, 126.... \n", + "1 [[306.93357350984974, 88.02634955100905, 105.6... \n", + "2 [[569.5291276937579, 157.8517489654999, 157.85... \n", + "3 [[69.28798774976904, 34.7875015216915, 37.3877... \n", + "4 [[349.3767766177825, 186.67131003104407, 176.4... \n", + "\n", + " elastic_tensor_original \\\n", + "0 [[311.33514638650246, 144.45092552856926, 126.... \n", + "1 [[306.93357350984974, 88.02634955100905, 105.6... \n", + "2 [[569.5291276937579, 157.8517489654999, 157.85... \n", + "3 [[70.13259066665267, 40.60474945058445, 37.387... \n", + "4 [[407.4791016459293, 176.4759188081947, 213.83... \n", + "\n", + " cif kpoint_density \\\n", + "0 #\\#CIF1.1\\n###################################... 7000 \n", + "1 #\\#CIF1.1\\n###################################... 7000 \n", + "2 #\\#CIF1.1\\n###################################... 7000 \n", + "3 #\\#CIF1.1\\n###################################... 7000 \n", + "4 #\\#CIF1.1\\n###################################... 7000 \n", + "\n", + " poscar \n", + "0 Nb8 Co2 Si2\\n1.0\\n6.221780 0.000000 0.000000\\n... \n", + "1 Al1 Co2 Si2\\n1.0\\n3.932782 0.000000 0.000000\\n... \n", + "2 Si1 Os1\\n1.0\\n2.960692 0.000000 0.000000\\n0.00... \n", + "3 Ga4\\n1.0\\n2.803229 0.000000 0.000000\\n0.000000... \n", + "4 Si4 Ru8\\n1.0\\n4.037706 0.000000 0.000000\\n0.00... " + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_el = load_dataset('elastic_tensor_2015',data_home = '.')\n", + "df_el.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "93a222de", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "x = np.array(df_el['G_VRH'])\n", + "y = np.array(df_el['K_VRH'])" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "4915d4ee", + "metadata": {}, + "outputs": [], + "source": [ + "z = np.array(df_el['poisson_ratio'])" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "8dbef820", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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4mp-10025SiRu21262160.300999[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930100.110773101.947798103.784823255.055257256.768081258.4809040.324682[[0.00410214297725, -0.001272204332729, -0.001...[[349.3767766177825, 186.67131003104407, 176.4...[[407.4791016459293, 176.4759188081947, 213.83...#\\#CIF1.1\\n###################################...7000Si4 Ru8\\n1.0\\n4.037706 0.000000 0.000000\\n0.00...
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" + ], + "text/plain": [ + " material_id formula nsites space_group volume \\\n", + "0 mp-10003 Nb4CoSi 12 124 194.419802 \n", + "1 mp-10010 Al(CoSi)2 5 164 61.987320 \n", + "2 mp-10015 SiOs 2 221 25.952539 \n", + "3 mp-10021 Ga 4 63 76.721433 \n", + "4 mp-10025 SiRu2 12 62 160.300999 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_Reuss G_VRH G_Voigt K_Reuss K_VRH K_Voigt \\\n", + "0 96.844535 97.141604 97.438674 194.267623 194.268884 194.270146 \n", + "1 93.939650 96.252006 98.564362 173.647763 175.449907 177.252050 \n", + "2 120.962289 130.112955 139.263621 295.077545 295.077545 295.077545 \n", + "3 12.205989 15.101901 17.997812 49.025963 49.130670 49.235377 \n", + "4 100.110773 101.947798 103.784823 255.055257 256.768081 258.480904 \n", + "\n", + " poisson_ratio compliance_tensor \\\n", + "0 0.285701 [[0.004385293093993, -0.0016070693558990002, -... \n", + "1 0.268105 [[0.0037715428949660003, -0.000844229828709, -... \n", + "2 0.307780 [[0.0019959391925840004, -0.000433146670736000... \n", + "3 0.360593 [[0.021647143908635, -0.005207263618160001, -0... \n", + "4 0.324682 [[0.00410214297725, -0.001272204332729, -0.001... \n", + "\n", + " elastic_tensor \\\n", + "0 [[311.33514638650246, 144.45092552856926, 126.... \n", + "1 [[306.93357350984974, 88.02634955100905, 105.6... \n", + "2 [[569.5291276937579, 157.8517489654999, 157.85... \n", + "3 [[69.28798774976904, 34.7875015216915, 37.3877... \n", + "4 [[349.3767766177825, 186.67131003104407, 176.4... \n", + "\n", + " elastic_tensor_original \\\n", + "0 [[311.33514638650246, 144.45092552856926, 126.... \n", + "1 [[306.93357350984974, 88.02634955100905, 105.6... \n", + "2 [[569.5291276937579, 157.8517489654999, 157.85... \n", + "3 [[70.13259066665267, 40.60474945058445, 37.387... \n", + "4 [[407.4791016459293, 176.4759188081947, 213.83... \n", + "\n", + " cif kpoint_density \\\n", + "0 #\\#CIF1.1\\n###################################... 7000 \n", + "1 #\\#CIF1.1\\n###################################... 7000 \n", + "2 #\\#CIF1.1\\n###################################... 7000 \n", + "3 #\\#CIF1.1\\n###################################... 7000 \n", + "4 #\\#CIF1.1\\n###################################... 7000 \n", + "\n", + " poscar \n", + "0 Nb8 Co2 Si2\\n1.0\\n6.221780 0.000000 0.000000\\n... \n", + "1 Al1 Co2 Si2\\n1.0\\n3.932782 0.000000 0.000000\\n... \n", + "2 Si1 Os1\\n1.0\\n2.960692 0.000000 0.000000\\n0.00... \n", + "3 Ga4\\n1.0\\n2.803229 0.000000 0.000000\\n0.000000... \n", + "4 Si4 Ru8\\n1.0\\n4.037706 0.000000 0.000000\\n0.00... " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matminer.datasets import load_dataset\n", + "df = load_dataset('elastic_tensor_2015',data_home = '.')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "4f32a242", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Index(['material_id', 'formula', 'nsites', 'space_group', 'volume',\n", + " 'structure', 'elastic_anisotropy', 'G_Reuss', 'G_VRH', 'G_Voigt',\n", + " 'K_Reuss', 'K_VRH', 'K_Voigt', 'poisson_ratio', 'compliance_tensor',\n", + " 'elastic_tensor', 'elastic_tensor_original', 'cif', 'kpoint_density',\n", + " 'poscar'],\n", + " dtype='object')" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.columns" + ] + }, + { + "cell_type": "markdown", + "id": "b8d8a75d", + "metadata": {}, + "source": [ + "df = df['cif','kpoint_density']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "e44f02ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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material_idformulaspace_groupstructureelastic_anisotropyG_VRHK_VRHpoisson_ratio
0mp-10003Nb4CoSi124[[0.94814328 2.07280467 2.5112 ] Nb, [5.273...0.03068897.141604194.2688840.285701
1mp-10010Al(CoSi)2164[[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278...0.26691096.252006175.4499070.268105
2mp-10015SiOs221[[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os]0.756489130.112955295.0775450.307780
3mp-10021Ga63[[0. 1.09045794 0.84078375] Ga, [0. ...2.37680515.10190149.1306700.360593
4mp-10025SiRu262[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930101.947798256.7680810.324682
\n", + "
" + ], + "text/plain": [ + " material_id formula space_group \\\n", + "0 mp-10003 Nb4CoSi 124 \n", + "1 mp-10010 Al(CoSi)2 164 \n", + "2 mp-10015 SiOs 221 \n", + "3 mp-10021 Ga 63 \n", + "4 mp-10025 SiRu2 62 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_VRH K_VRH poisson_ratio \n", + "0 97.141604 194.268884 0.285701 \n", + "1 96.252006 175.449907 0.268105 \n", + "2 130.112955 295.077545 0.307780 \n", + "3 15.101901 49.130670 0.360593 \n", + "4 101.947798 256.768081 0.324682 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "unwanted_columns = ['volume','nsites','compliance_tensor','elastic_tensor',\n", + " 'elastic_tensor_original','G_Reuss','G_Voigt','K_Reuss',\n", + " 'K_Voigt','cif','kpoint_density','poscar']\n", + "df = df.drop(unwanted_columns, axis = 1)\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "30170480", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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space_groupelastic_anisotropyG_VRHK_VRHpoisson_ratio
count1181.0000001181.0000001181.0000001181.0000001181.000000
mean163.4038952.14501367.543145136.2596610.287401
std65.04073319.14009744.57940872.8869780.062177
min4.0000000.0000052.7221756.4761350.042582
25%124.0000000.14503034.11795976.4353500.249159
50%193.0000000.35528759.735163130.3827660.290198
75%221.0000000.92311791.332142189.5741940.328808
max229.000000397.297866522.921225435.6614870.467523
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" + ], + "text/plain": [ + " space_group elastic_anisotropy G_VRH K_VRH \\\n", + "count 1181.000000 1181.000000 1181.000000 1181.000000 \n", + "mean 163.403895 2.145013 67.543145 136.259661 \n", + "std 65.040733 19.140097 44.579408 72.886978 \n", + "min 4.000000 0.000005 2.722175 6.476135 \n", + "25% 124.000000 0.145030 34.117959 76.435350 \n", + "50% 193.000000 0.355287 59.735163 130.382766 \n", + "75% 221.000000 0.923117 91.332142 189.574194 \n", + "max 229.000000 397.297866 522.921225 435.661487 \n", + "\n", + " poisson_ratio \n", + "count 1181.000000 \n", + "mean 0.287401 \n", + "std 0.062177 \n", + "min 0.042582 \n", + "25% 0.249159 \n", + "50% 0.290198 \n", + "75% 0.328808 \n", + "max 0.467523 " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "52ee1f04", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6d70ada1f547499f9f79a926e5aeedad", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "StrToComposition: 0%| | 0/1181 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
material_idformulaspace_groupstructureelastic_anisotropyG_VRHK_VRHpoisson_ratiocomposition
0mp-10003Nb4CoSi124[[0.94814328 2.07280467 2.5112 ] Nb, [5.273...0.03068897.141604194.2688840.285701(Nb, Co, Si)
1mp-10010Al(CoSi)2164[[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278...0.26691096.252006175.4499070.268105(Al, Co, Si)
2mp-10015SiOs221[[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os]0.756489130.112955295.0775450.307780(Si, Os)
3mp-10021Ga63[[0. 1.09045794 0.84078375] Ga, [0. ...2.37680515.10190149.1306700.360593(Ga)
4mp-10025SiRu262[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930101.947798256.7680810.324682(Si, Ru)
\n", + "" + ], + "text/plain": [ + " material_id formula space_group \\\n", + "0 mp-10003 Nb4CoSi 124 \n", + "1 mp-10010 Al(CoSi)2 164 \n", + "2 mp-10015 SiOs 221 \n", + "3 mp-10021 Ga 63 \n", + "4 mp-10025 SiRu2 62 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_VRH K_VRH poisson_ratio composition \n", + "0 97.141604 194.268884 0.285701 (Nb, Co, Si) \n", + "1 96.252006 175.449907 0.268105 (Al, Co, Si) \n", + "2 130.112955 295.077545 0.307780 (Si, Os) \n", + "3 15.101901 49.130670 0.360593 (Ga) \n", + "4 101.947798 256.768081 0.324682 (Si, Ru) " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matminer.featurizers.conversions import StrToComposition\n", + "df = StrToComposition().featurize_dataframe(df, 'formula')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "a974600a", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b041b831537b4bf9ae64342e87169240", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "ElementProperty: 0%| | 0/1181 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
material_idformulaspace_groupstructureelastic_anisotropyG_VRHK_VRHpoisson_ratiocompositionMagpieData minimum Number...MagpieData range GSmagmomMagpieData mean GSmagmomMagpieData avg_dev GSmagmomMagpieData mode GSmagmomMagpieData minimum SpaceGroupNumberMagpieData maximum SpaceGroupNumberMagpieData range SpaceGroupNumberMagpieData mean SpaceGroupNumberMagpieData avg_dev SpaceGroupNumberMagpieData mode SpaceGroupNumber
0mp-10003Nb4CoSi124[[0.94814328 2.07280467 2.5112 ] Nb, [5.273...0.03068897.141604194.2688840.285701(Nb, Co, Si)14.0...1.5484710.2580790.4301310.0194.0229.035.0222.8333339.611111229.0
1mp-10010Al(CoSi)2164[[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278...0.26691096.252006175.4499070.268105(Al, Co, Si)13.0...1.5484710.6193880.7432660.0194.0227.033.0213.40000015.520000194.0
2mp-10015SiOs221[[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os]0.756489130.112955295.0775450.307780(Si, Os)14.0...0.0000000.0000000.0000000.0194.0227.033.0210.50000016.500000194.0
3mp-10021Ga63[[0. 1.09045794 0.84078375] Ga, [0. ...2.37680515.10190149.1306700.360593(Ga)31.0...0.0000000.0000000.0000000.064.064.00.064.0000000.00000064.0
4mp-10025SiRu262[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930101.947798256.7680810.324682(Si, Ru)14.0...0.0000000.0000000.0000000.0194.0227.033.0205.00000014.666667194.0
\n", + "

5 rows × 141 columns

\n", + "" + ], + "text/plain": [ + " material_id formula space_group \\\n", + "0 mp-10003 Nb4CoSi 124 \n", + "1 mp-10010 Al(CoSi)2 164 \n", + "2 mp-10015 SiOs 221 \n", + "3 mp-10021 Ga 63 \n", + "4 mp-10025 SiRu2 62 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_VRH K_VRH poisson_ratio composition \\\n", + "0 97.141604 194.268884 0.285701 (Nb, Co, Si) \n", + "1 96.252006 175.449907 0.268105 (Al, Co, Si) \n", + "2 130.112955 295.077545 0.307780 (Si, Os) \n", + "3 15.101901 49.130670 0.360593 (Ga) \n", + "4 101.947798 256.768081 0.324682 (Si, Ru) \n", + "\n", + " MagpieData minimum Number ... MagpieData range GSmagmom \\\n", + "0 14.0 ... 1.548471 \n", + "1 13.0 ... 1.548471 \n", + "2 14.0 ... 0.000000 \n", + "3 31.0 ... 0.000000 \n", + "4 14.0 ... 0.000000 \n", + "\n", + " MagpieData mean GSmagmom MagpieData avg_dev GSmagmom \\\n", + "0 0.258079 0.430131 \n", + "1 0.619388 0.743266 \n", + "2 0.000000 0.000000 \n", + "3 0.000000 0.000000 \n", + "4 0.000000 0.000000 \n", + "\n", + " MagpieData mode GSmagmom MagpieData minimum SpaceGroupNumber \\\n", + "0 0.0 194.0 \n", + "1 0.0 194.0 \n", + "2 0.0 194.0 \n", + "3 0.0 64.0 \n", + "4 0.0 194.0 \n", + "\n", + " MagpieData maximum SpaceGroupNumber MagpieData range SpaceGroupNumber \\\n", + "0 229.0 35.0 \n", + "1 227.0 33.0 \n", + "2 227.0 33.0 \n", + "3 64.0 0.0 \n", + "4 227.0 33.0 \n", + "\n", + " MagpieData mean SpaceGroupNumber MagpieData avg_dev SpaceGroupNumber \\\n", + "0 222.833333 9.611111 \n", + "1 213.400000 15.520000 \n", + "2 210.500000 16.500000 \n", + "3 64.000000 0.000000 \n", + "4 205.000000 14.666667 \n", + "\n", + " MagpieData mode SpaceGroupNumber \n", + "0 229.0 \n", + "1 194.0 \n", + "2 194.0 \n", + "3 64.0 \n", + "4 194.0 \n", + "\n", + "[5 rows x 141 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matminer.featurizers.composition import ElementProperty\n", + "ep_feat = ElementProperty.from_preset(preset_name = 'magpie')\n", + "df = ep_feat.featurize_dataframe(df, col_id = 'composition')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e39d9d7c", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Help on method from_preset in module matminer.featurizers.composition.composite:\n", + "\n", + "from_preset(preset_name) method of abc.ABCMeta instance\n", + " Return ElementProperty from a preset string\n", + " Args:\n", + " preset_name: (str) can be one of \"magpie\", \"deml\", \"matminer\",\n", + " \"matscholar_el\", or \"megnet_el\".\n", + " \n", + " Returns:\n", + " ElementProperty based on the preset name.\n", + "\n" + ] + } + ], + "source": [ + "help(ElementProperty.from_preset)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "e2d6f85d", + "metadata": {}, + "outputs": [], + "source": [ + "from matminer.featurizers.conversions import CompositionToOxidComposition\n", + "from matminer.featurizers.composition import OxidationStates" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "531675d5", + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2de30313a34d42139db9a42b9378aaec", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "CompositionToOxidComposition: 0%| | 0/1181 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
material_idformulaspace_groupstructureelastic_anisotropyG_VRHK_VRHpoisson_ratiocompositionMagpieData minimum Number...MagpieData maximum SpaceGroupNumberMagpieData range SpaceGroupNumberMagpieData mean SpaceGroupNumberMagpieData avg_dev SpaceGroupNumberMagpieData mode SpaceGroupNumbercomposition_oxidminimum oxidation statemaximum oxidation staterange oxidation statestd_dev oxidation state
0mp-10003Nb4CoSi124[[0.94814328 2.07280467 2.5112 ] Nb, [5.273...0.03068897.141604194.2688840.285701(Nb, Co, Si)14.0...229.035.0222.8333339.611111229.0(Nb0+, Co0+, Si0+)0000.000000
1mp-10010Al(CoSi)2164[[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278...0.26691096.252006175.4499070.268105(Al, Co, Si)13.0...227.033.0213.40000015.520000194.0(Al3+, Co2+, Co3+, Si4-)-4373.872983
2mp-10015SiOs221[[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os]0.756489130.112955295.0775450.307780(Si, Os)14.0...227.033.0210.50000016.500000194.0(Si4-, Os4+)-4485.656854
3mp-10021Ga63[[0. 1.09045794 0.84078375] Ga, [0. ...2.37680515.10190149.1306700.360593(Ga)31.0...64.00.064.0000000.00000064.0(Ga0+)0000.000000
4mp-10025SiRu262[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930101.947798256.7680810.324682(Si, Ru)14.0...227.033.0205.00000014.666667194.0(Si4-, Ru2+)-4264.242641
\n", + "

5 rows × 146 columns

\n", + "" + ], + "text/plain": [ + " material_id formula space_group \\\n", + "0 mp-10003 Nb4CoSi 124 \n", + "1 mp-10010 Al(CoSi)2 164 \n", + "2 mp-10015 SiOs 221 \n", + "3 mp-10021 Ga 63 \n", + "4 mp-10025 SiRu2 62 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_VRH K_VRH poisson_ratio composition \\\n", + "0 97.141604 194.268884 0.285701 (Nb, Co, Si) \n", + "1 96.252006 175.449907 0.268105 (Al, Co, Si) \n", + "2 130.112955 295.077545 0.307780 (Si, Os) \n", + "3 15.101901 49.130670 0.360593 (Ga) \n", + "4 101.947798 256.768081 0.324682 (Si, Ru) \n", + "\n", + " MagpieData minimum Number ... MagpieData maximum SpaceGroupNumber \\\n", + "0 14.0 ... 229.0 \n", + "1 13.0 ... 227.0 \n", + "2 14.0 ... 227.0 \n", + "3 31.0 ... 64.0 \n", + "4 14.0 ... 227.0 \n", + "\n", + " MagpieData range SpaceGroupNumber MagpieData mean SpaceGroupNumber \\\n", + "0 35.0 222.833333 \n", + "1 33.0 213.400000 \n", + "2 33.0 210.500000 \n", + "3 0.0 64.000000 \n", + "4 33.0 205.000000 \n", + "\n", + " MagpieData avg_dev SpaceGroupNumber MagpieData mode SpaceGroupNumber \\\n", + "0 9.611111 229.0 \n", + "1 15.520000 194.0 \n", + "2 16.500000 194.0 \n", + "3 0.000000 64.0 \n", + "4 14.666667 194.0 \n", + "\n", + " composition_oxid minimum oxidation state maximum oxidation state \\\n", + "0 (Nb0+, Co0+, Si0+) 0 0 \n", + "1 (Al3+, Co2+, Co3+, Si4-) -4 3 \n", + "2 (Si4-, Os4+) -4 4 \n", + "3 (Ga0+) 0 0 \n", + "4 (Si4-, Ru2+) -4 2 \n", + "\n", + " range oxidation state std_dev oxidation state \n", + "0 0 0.000000 \n", + "1 7 3.872983 \n", + "2 8 5.656854 \n", + "3 0 0.000000 \n", + "4 6 4.242641 \n", + "\n", + "[5 rows x 146 columns]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = CompositionToOxidComposition().featurize_dataframe(df, 'composition')\n", + "\n", + "os_feat = OxidationStates()\n", + "df = os_feat.featurize_dataframe(df, 'composition_oxid')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "1c98ca78", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "10a964f9f991457e88604d353642041a", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "DensityFeatures: 0%| | 0/1181 [00:00\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
material_idformulaspace_groupstructureelastic_anisotropyG_VRHK_VRHpoisson_ratiocompositionMagpieData minimum Number...MagpieData avg_dev SpaceGroupNumberMagpieData mode SpaceGroupNumbercomposition_oxidminimum oxidation statemaximum oxidation staterange oxidation statestd_dev oxidation statedensityvpapacking fraction
0mp-10003Nb4CoSi124[[0.94814328 2.07280467 2.5112 ] Nb, [5.273...0.03068897.141604194.2688840.285701(Nb, Co, Si)14.0...9.611111229.0(Nb0+, Co0+, Si0+)0000.0000007.83455616.2016540.688834
1mp-10010Al(CoSi)2164[[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278...0.26691096.252006175.4499070.268105(Al, Co, Si)13.0...15.520000194.0(Al3+, Co2+, Co3+, Si4-)-4373.8729835.38496812.3974660.644386
2mp-10015SiOs221[[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os]0.756489130.112955295.0775450.307780(Si, Os)14.0...16.500000194.0(Si4-, Os4+)-4485.65685413.96863512.9762650.569426
3mp-10021Ga63[[0. 1.09045794 0.84078375] Ga, [0. ...2.37680515.10190149.1306700.360593(Ga)31.0...0.00000064.0(Ga0+)0000.0000006.03626719.1803590.479802
4mp-10025SiRu262[[1.0094265 4.24771709 2.9955487 ] Si, [3.028...0.196930101.947798256.7680810.324682(Si, Ru)14.0...14.666667194.0(Si4-, Ru2+)-4264.2426419.53951413.3584180.598395
\n", + "

5 rows × 149 columns

\n", + "" + ], + "text/plain": [ + " material_id formula space_group \\\n", + "0 mp-10003 Nb4CoSi 124 \n", + "1 mp-10010 Al(CoSi)2 164 \n", + "2 mp-10015 SiOs 221 \n", + "3 mp-10021 Ga 63 \n", + "4 mp-10025 SiRu2 62 \n", + "\n", + " structure elastic_anisotropy \\\n", + "0 [[0.94814328 2.07280467 2.5112 ] Nb, [5.273... 0.030688 \n", + "1 [[0. 0. 0.] Al, [1.96639263 1.13529553 0.75278... 0.266910 \n", + "2 [[1.480346 1.480346 1.480346] Si, [0. 0. 0.] Os] 0.756489 \n", + "3 [[0. 1.09045794 0.84078375] Ga, [0. ... 2.376805 \n", + "4 [[1.0094265 4.24771709 2.9955487 ] Si, [3.028... 0.196930 \n", + "\n", + " G_VRH K_VRH poisson_ratio composition \\\n", + "0 97.141604 194.268884 0.285701 (Nb, Co, Si) \n", + "1 96.252006 175.449907 0.268105 (Al, Co, Si) \n", + "2 130.112955 295.077545 0.307780 (Si, Os) \n", + "3 15.101901 49.130670 0.360593 (Ga) \n", + "4 101.947798 256.768081 0.324682 (Si, Ru) \n", + "\n", + " MagpieData minimum Number ... MagpieData avg_dev SpaceGroupNumber \\\n", + "0 14.0 ... 9.611111 \n", + "1 13.0 ... 15.520000 \n", + "2 14.0 ... 16.500000 \n", + "3 31.0 ... 0.000000 \n", + "4 14.0 ... 14.666667 \n", + "\n", + " MagpieData mode SpaceGroupNumber composition_oxid \\\n", + "0 229.0 (Nb0+, Co0+, Si0+) \n", + "1 194.0 (Al3+, Co2+, Co3+, Si4-) \n", + "2 194.0 (Si4-, Os4+) \n", + "3 64.0 (Ga0+) \n", + "4 194.0 (Si4-, Ru2+) \n", + "\n", + " minimum oxidation state maximum oxidation state range oxidation state \\\n", + "0 0 0 0 \n", + "1 -4 3 7 \n", + "2 -4 4 8 \n", + "3 0 0 0 \n", + "4 -4 2 6 \n", + "\n", + " std_dev oxidation state density vpa packing fraction \n", + "0 0.000000 7.834556 16.201654 0.688834 \n", + "1 3.872983 5.384968 12.397466 0.644386 \n", + "2 5.656854 13.968635 12.976265 0.569426 \n", + "3 0.000000 6.036267 19.180359 0.479802 \n", + "4 4.242641 9.539514 13.358418 0.598395 \n", + "\n", + "[5 rows x 149 columns]" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from matminer.featurizers.structure import DensityFeatures\n", + "\n", + "df_feat = DensityFeatures()\n", + "df = df_feat.featurize_dataframe(df, 'structure')\n", + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "6e0e5846", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "['density', 'vpa', 'packing fraction']" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df_feat.feature_labels()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "006c9106", + "metadata": {}, + "outputs": [], + "source": [ + "y = df['K_VRH'].values" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "520e6310", + "metadata": {}, + "outputs": [], + "source": [ + "excluded = ['G_VRH','K_VRH','elastic_anisotropy','poisson_ratio',\n", + " 'formula','material_id','structure','composition',\n", + " 'composition_oxid']\n", + "X = df.drop(excluded, axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "c86d982d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "There are 140 possible descriptor: \n", + " ['space_group' 'MagpieData minimum Number' 'MagpieData maximum Number'\n", + " 'MagpieData range Number' 'MagpieData mean Number'\n", + " 'MagpieData avg_dev Number' 'MagpieData mode Number'\n", + " 'MagpieData minimum MendeleevNumber' 'MagpieData maximum MendeleevNumber'\n", + " 'MagpieData range MendeleevNumber' 'MagpieData mean MendeleevNumber'\n", + " 'MagpieData avg_dev MendeleevNumber' 'MagpieData mode MendeleevNumber'\n", + " 'MagpieData minimum AtomicWeight' 'MagpieData maximum AtomicWeight'\n", + " 'MagpieData range AtomicWeight' 'MagpieData mean AtomicWeight'\n", + " 'MagpieData avg_dev AtomicWeight' 'MagpieData mode AtomicWeight'\n", + " 'MagpieData minimum MeltingT' 'MagpieData maximum MeltingT'\n", + " 'MagpieData range MeltingT' 'MagpieData mean MeltingT'\n", + " 'MagpieData avg_dev MeltingT' 'MagpieData mode MeltingT'\n", + " 'MagpieData minimum Column' 'MagpieData maximum Column'\n", + " 'MagpieData range Column' 'MagpieData mean Column'\n", + " 'MagpieData avg_dev Column' 'MagpieData mode Column'\n", + " 'MagpieData minimum Row' 'MagpieData maximum Row' 'MagpieData range Row'\n", + " 'MagpieData mean Row' 'MagpieData avg_dev Row' 'MagpieData mode Row'\n", + " 'MagpieData minimum CovalentRadius' 'MagpieData maximum CovalentRadius'\n", + " 'MagpieData range CovalentRadius' 'MagpieData mean CovalentRadius'\n", + " 'MagpieData avg_dev CovalentRadius' 'MagpieData mode CovalentRadius'\n", + " 'MagpieData minimum Electronegativity'\n", + " 'MagpieData maximum Electronegativity'\n", + " 'MagpieData range Electronegativity' 'MagpieData mean Electronegativity'\n", + " 'MagpieData avg_dev Electronegativity'\n", + " 'MagpieData mode Electronegativity' 'MagpieData minimum NsValence'\n", + " 'MagpieData maximum NsValence' 'MagpieData range NsValence'\n", + " 'MagpieData mean NsValence' 'MagpieData avg_dev NsValence'\n", + " 'MagpieData mode NsValence' 'MagpieData minimum NpValence'\n", + " 'MagpieData maximum NpValence' 'MagpieData range NpValence'\n", + " 'MagpieData mean NpValence' 'MagpieData avg_dev NpValence'\n", + " 'MagpieData mode NpValence' 'MagpieData minimum NdValence'\n", + " 'MagpieData maximum NdValence' 'MagpieData range NdValence'\n", + " 'MagpieData mean NdValence' 'MagpieData avg_dev NdValence'\n", + " 'MagpieData mode NdValence' 'MagpieData minimum NfValence'\n", + " 'MagpieData maximum NfValence' 'MagpieData range NfValence'\n", + " 'MagpieData mean NfValence' 'MagpieData avg_dev NfValence'\n", + " 'MagpieData mode NfValence' 'MagpieData minimum NValence'\n", + " 'MagpieData maximum NValence' 'MagpieData range NValence'\n", + " 'MagpieData mean NValence' 'MagpieData avg_dev NValence'\n", + " 'MagpieData mode NValence' 'MagpieData minimum NsUnfilled'\n", + " 'MagpieData maximum NsUnfilled' 'MagpieData range NsUnfilled'\n", + " 'MagpieData mean NsUnfilled' 'MagpieData avg_dev NsUnfilled'\n", + " 'MagpieData mode NsUnfilled' 'MagpieData minimum NpUnfilled'\n", + " 'MagpieData maximum NpUnfilled' 'MagpieData range NpUnfilled'\n", + " 'MagpieData mean NpUnfilled' 'MagpieData avg_dev NpUnfilled'\n", + " 'MagpieData mode NpUnfilled' 'MagpieData minimum NdUnfilled'\n", + " 'MagpieData maximum NdUnfilled' 'MagpieData range NdUnfilled'\n", + " 'MagpieData mean NdUnfilled' 'MagpieData avg_dev NdUnfilled'\n", + " 'MagpieData mode NdUnfilled' 'MagpieData minimum NfUnfilled'\n", + " 'MagpieData maximum NfUnfilled' 'MagpieData range NfUnfilled'\n", + " 'MagpieData mean NfUnfilled' 'MagpieData avg_dev NfUnfilled'\n", + " 'MagpieData mode NfUnfilled' 'MagpieData minimum NUnfilled'\n", + " 'MagpieData maximum NUnfilled' 'MagpieData range NUnfilled'\n", + " 'MagpieData mean NUnfilled' 'MagpieData avg_dev NUnfilled'\n", + " 'MagpieData mode NUnfilled' 'MagpieData minimum GSvolume_pa'\n", + " 'MagpieData maximum GSvolume_pa' 'MagpieData range GSvolume_pa'\n", + " 'MagpieData mean GSvolume_pa' 'MagpieData avg_dev GSvolume_pa'\n", + " 'MagpieData mode GSvolume_pa' 'MagpieData minimum GSbandgap'\n", + " 'MagpieData maximum GSbandgap' 'MagpieData range GSbandgap'\n", + " 'MagpieData mean GSbandgap' 'MagpieData avg_dev GSbandgap'\n", + " 'MagpieData mode GSbandgap' 'MagpieData minimum GSmagmom'\n", + " 'MagpieData maximum GSmagmom' 'MagpieData range GSmagmom'\n", + " 'MagpieData mean GSmagmom' 'MagpieData avg_dev GSmagmom'\n", + " 'MagpieData mode GSmagmom' 'MagpieData minimum SpaceGroupNumber'\n", + " 'MagpieData maximum SpaceGroupNumber' 'MagpieData range SpaceGroupNumber'\n", + " 'MagpieData mean SpaceGroupNumber' 'MagpieData avg_dev SpaceGroupNumber'\n", + " 'MagpieData mode SpaceGroupNumber' 'minimum oxidation state'\n", + " 'maximum oxidation state' 'range oxidation state'\n", + " 'std_dev oxidation state' 'density' 'vpa' 'packing fraction']\n" + ] + } + ], + "source": [ + "print('There are %s possible descriptor: \\n %s'%(X.shape[1], X.columns.values))" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "d3ca8a15", + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.metrics import mean_squared_error\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "8081ca59", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training R2 = 0.926\n", + "training RMSE = 19.773\n" + ] + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X,y)\n", + "\n", + "print('training R2 = '+ str(round(lr.score(X, y), 3)))\n", + "print('training RMSE = %.3f' % np.sqrt(mean_squared_error(y_true = y,\n", + " y_pred = lr.predict(X))))" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "9168dae7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.figure(figsize = (5, 5))\n", + "plt.plot([0,400],[0,400],'r--')\n", + "plt.scatter(y, lr.predict(X), s = 80, c = 'b', edgecolor = 'k',\n", + " alpha = 0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "fccf23cb", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cross-validation results:\n", + "Folds: 10, mean RMSE: 22.388\n" + ] + } + ], + "source": [ + "from sklearn.model_selection import KFold,cross_val_score\n", + "\n", + "crossvalidation = KFold(n_splits = 10, shuffle=True, random_state = 1)\n", + "scores = cross_val_score(lr, X, y, scoring = 'neg_mean_squared_error',\n", + " cv = crossvalidation, n_jobs = 1)\n", + "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n", + "\n", + "print('cross-validation results:')\n", + "print('Folds: %i, mean RMSE: %.3f' %(len(scores), np.mean(rmse_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "2aecbda4", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "training R2 = 0.989\n", + "training RMSE = 7.687\n" + ] + } + ], + "source": [ + "from sklearn.ensemble import RandomForestRegressor\n", + "\n", + "rf = RandomForestRegressor(n_estimators = 50, random_state = 1)\n", + "rf.fit(X, y)\n", + "\n", + "print('training R2 = ' + str(round(rf.score(X, y),3)))\n", + "print('training RMSE = %.3f' % np.sqrt(mean_squared_error(y_true = y,\n", + " y_pred = rf.predict(X))) )" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "e5333df1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 30, + "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.figure(figsize=(5,5))\n", + "plt.plot([0,400],[0,400],'r--')\n", + "plt.scatter(y,rf.predict(X), s= 80, c = 'b', edgecolor = 'k',alpha = 0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "41898498", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cross-validation results:\n", + "Folds: 10, mean R2: 0.924\n", + "Folds: 10, mean RMSE: 19.277\n" + ] + } + ], + "source": [ + "crossvalidation = KFold(n_splits = 10, shuffle=True, random_state = 1)\n", + "\n", + "r2_scores = cross_val_score(rf,X, y, scoring = 'r2', cv = crossvalidation,\n", + " n_jobs = 2)\n", + "scores = cross_val_score(rf, X, y, scoring = 'neg_mean_squared_error',\n", + " cv = crossvalidation, n_jobs = 1)\n", + "rmse_scores = [np.sqrt(abs(s)) for s in scores]\n", + "\n", + "print('cross-validation results:')\n", + "print('Folds: %i, mean R2: %.3f' % (len(r2_scores), np.mean(r2_scores)))\n", + "print('Folds: %i, mean RMSE: %.3f' %(len(scores), np.mean(rmse_scores)))" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "d9d0597b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "from sklearn.model_selection import cross_val_predict\n", + "plt.figure(figsize=(5,5))\n", + "plt.plot([0,400],[0,400],'r--')\n", + "plt.scatter(y, cross_val_predict(rf, X, y, cv = crossvalidation), s = 80,\n", + " c = 'b', edgecolor = 'k', alpha = 0.7)" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "987e87e4", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "importances = rf.feature_importances_\n", + "included = X.columns.values\n", + "indices = np.argsort(importances)[::-1]\n", + "\n", + "plt.figure(figsize=(10,5))\n", + "plt.bar(x = included[indices][0:10], height = importances[indices][0:10],\n", + " color = 'g', edgecolor = 'k')\n", + "plt.xticks(rotation = -45, fontsize = 15, ha='left')\n", + "plt.yticks(fontsize = 15)\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9145b9a6", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0 4 2 1 3]\n", + "[0 2 3]\n", + "[3 1 2 4 0]\n" + ] + } + ], + "source": [ + "a = [1,5,3,6,2]\n", + "b = np.argsort(a)[::1]\n", + "c = np.argsort(a)[::2]\n", + "d = np.argsort(a)[::-1]\n", + "print(b)\n", + "print(c)\n", + "print(d)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "180130ef", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "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": "3.8.8" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}