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Demo.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"DEMO REGIME SWITCHING\n",
"======================"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n",
"from scipy.stats import norm\n",
"from datetime import timedelta\n",
"import statsmodels.api as sm\n",
"import matplotlib.pyplot as plt\n",
"from matplotlib.dates import DateFormatter"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"def process_data(title_graph, draw_data, col_name, serie_name,\n",
" bAddToGlobalReport, ylim_min, ylim_max, yTicks, axhline,\n",
" y_label, bStickMode, color, bSavePng, bExportGraph,\n",
" bExportData):\n",
"\n",
" global figure_row_idx\n",
"\n",
" ########################################################\n",
" ### Pour rendre le graphique plus lisible on peut tracer une courbe baton\n",
" ### intercalle des 0 avant et après (1ms) pour avoir des batons\n",
" ### tmp contient les clignements baton\n",
" ########################################################\n",
" if bStickMode == True:\n",
" draw_data = draw_data.reset_index()\n",
" timeDelta = np.timedelta64(1, 'ms')\n",
" #Insert 0 avant\n",
" tmp = draw_data.copy()\n",
" tmp = tmp.reset_index()\n",
" tmp['Debut'] -= timeDelta\n",
" tmp[col_name] = 0\n",
" tmp = pd.concat([draw_data, tmp], sort=True)\n",
" #Insert 0 après\n",
" tmp2 = draw_data.copy()\n",
" tmp2 = tmp2.reset_index()\n",
" tmp2['Debut'] += timeDelta\n",
" tmp2[col_name] = 0\n",
" tmp = pd.concat([tmp, tmp2], sort=True)\n",
" tmp = tmp.set_index('Debut')\n",
" tmp = tmp.sort_index()\n",
"\n",
" draw_data = tmp[col_name]\n",
"\n",
" #si la donnée est à afficher dans le plot global\n",
" if bAddToGlobalReport == True:\n",
" maxLoop = 2\n",
" figure_row_idx = figure_row_idx + 1\n",
" else:\n",
" #sinon juste en mode solo\n",
" maxLoop = 1\n",
"\n",
" for x in range(1, maxLoop):\n",
" if x == 0:\n",
" #plot en mode individuel\n",
" fig_tmp = plt.figure(title_graph + ' ' + ref, figsize=(6, 1.5))\n",
" ax1 = fig_tmp.add_subplot(111)\n",
" else:\n",
" #plot en mode groupé sur une page\n",
" #si premier plot on crée l'axe des x en commun\n",
" if figure_row_idx == 1:\n",
" ax1 = fig.add_subplot(figure_row, 1, figure_row_idx)\n",
" global axCommon\n",
" axCommon = ax1\n",
" else:\n",
" ax1 = fig.add_subplot(figure_row, 1, figure_row_idx, sharex=axCommon)\n",
" ax1.grid(True)\n",
" if ylim_min != None and ylim_max != None:\n",
" ax1.set_ylim(ylim_min, ylim_max)\n",
" ax1.set_ylabel(y_label, fontdict={'fontsize': 6})\n",
"\n",
" ax1.set_xticklabels(draw_data.index, rotation=0, ha='right')\n",
" if x == 0:\n",
" ax1.set_xlabel('Time (min.)', fontsize=6)\n",
"\n",
" ax1.plot(\n",
" draw_data.index,\n",
" draw_data,\n",
" label=serie_name,\n",
" color=color,\n",
" linewidth=1.0,\n",
" marker='o',\n",
" ms=1)\n",
"\n",
" if yTicks != None:\n",
" ax1.yaxis.set_ticks(yTicks)\n",
"\n",
" if axhline != None:\n",
" for y_line in axhline:\n",
" ax1.axhline(y=y_line, color='orange', linewidth=0.5)\n",
"\n",
" ax1.xaxis.set_major_formatter(hour_formatter)\n",
" plt.setp(ax1.get_xticklabels(), fontsize=6)\n",
" plt.setp(ax1.get_yticklabels(), fontsize=6)\n",
" ax1.legend(fontsize=6)\n",
" if x == 0:\n",
" if bSavePng == True:\n",
" fig_tmp.savefig(expe_path + title_graph + '.png')\n",
" else:\n",
" #l'acxe des x est commmun on le l'affiche que sur le dernier plot\n",
" if figure_row_idx != figure_row:\n",
" plt.setp(ax1.get_xticklabels(), visible=False)\n",
" else:\n",
" ax1.set_xlabel('Time (min.)', fontsize=6)\n",
"\n",
" #sauvegarde le graphe individuel dans un fichier\n",
" if bExportGraph == True:\n",
" fig_tmp.savefig(path_export + ref + ' ' + expe_file + '_' + title_graph +\n",
" '.png')\n",
"\n",
" #sauvegarde la donnée dans un fichier\n",
" if bExportData == True:\n",
" draw_data.index.names = ['start_time']\n",
" new_header = col_name\n",
" df = pd.DataFrame(draw_data)\n",
" df.to_csv(\n",
" path_export + ref + ' ' + title_graph + '.csv',\n",
" index=True,\n",
" header=new_header,\n",
" sep='\\t')\n",
"\n",
" return"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"data_path = '/home/innovplus/Dream/InnovPlus/CodeBase/Python/sprim_hmm/data/'\n",
"reference = pd.read_csv(data_path + 'reference.txt', delimiter='\\t')\n",
"def load_csv(exp):\n",
" row = reference.loc[reference['Ref'] == exp]\n",
" blink = pd.read_csv(data_path + row['ID'].values[0] + '/' + \n",
" row['Expérimentation'].values[0] + '/bkg.csv', sep='\\t')\n",
" blink['Debut'] = pd.to_datetime(blink['Debut'])\n",
" blink = blink.set_index('Debut')\n",
" blink.rename(columns={'eye_cli': 'blink'}, inplace=True)\n",
" return blink"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"code_folding": []
},
"outputs": [],
"source": [
"def process(blink):\n",
" string_windows = '60s'\n",
" int_windows = 60\n",
" blink['Blink_rounded'] = blink.round({'blink': -1})['blink']\n",
" data_stat = blink['Blink_rounded']\n",
" data_stat_markov = data_stat.rolling(window=string_windows).mean()\n",
" start = blink.first_valid_index()\n",
" row_transition = data_stat_markov.index.get_loc(\n",
" data_stat_markov.loc[data_stat_markov.index >= start +\n",
" timedelta(seconds=int_windows * 2)].index[0])\n",
" data_stat_markov = data_stat_markov.iloc[row_transition:]\n",
"\n",
" k = 2\n",
" MarkovRegession = sm.tsa.MarkovRegression(\n",
" data_stat_markov, k_regimes=k, switching_variance=False)\n",
" res_MarkovRegession = MarkovRegession.fit()\n",
" \n",
" return data_stat_markov, res_MarkovRegession\n",
"\n",
" '''\n",
" # bug si courbe à tendance trés plate retoure que des NaN\n",
" data = res_MarkovRegession.smoothed_marginal_probabilities[1]\n",
" data = data[np.isfinite(data[0])]\n",
"\n",
" # bug si courbe à tendance trés plate retoure que des NaN\n",
" data = res_MarkovRegession.smoothed_marginal_probabilities[1]\n",
" data = data[np.isfinite(data[0])]\n",
"\n",
" # bug si courbe à tendance trés plate retoure que des NaN\n",
" # the usual way to test for a NaN is to see if it's equal to itself:\n",
" if data == data:\n",
" # si toujours même etat alors toujour High\n",
" tmp = res_MarkovRegession.smoothed_marginal_probabilities[1]\n",
" if tmp.loc[tmp[0:] <= 0.09].size == 0:\n",
" res_MarkovRegession.smoothed_marginal_probabilities[\n",
" 1] = res_MarkovRegession.smoothed_marginal_probabilities[1] - 1\n",
"\n",
" #filtre tout ou rien (1 ou 0)\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k - 1][\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k - 1] > 0.5] = 1\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k - 1][\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k - 1] <= 0.5] = 0\n",
"\n",
" #Si après filtrage on a que des hauts alors\n",
" #c'est que les bas était des ultra bas donc haut devient bas\n",
" if res_MarkovRegession.smoothed_marginal_probabilities[k - 1][\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k -\n",
" 1] == 0].count() == 0:\n",
" res_MarkovRegession.smoothed_marginal_probabilities[k - 1] += -1\n",
"\n",
" res = data_stat.max(\n",
" ) * res_MarkovRegession.smoothed_marginal_probabilities[k - 1]\n",
" return res.tolist()\n",
" '''"
]
},
{
"cell_type": "code",
"execution_count": 238,
"metadata": {},
"outputs": [],
"source": [
"def fit(data, initial_probs, transition_matrix, m0, std0, m1, std1): \n",
" Probs = data.copy()\n",
" P = np.array([[initial_probs[0]], [initial_probs[1]]])\n",
" \n",
" for i in range(data.shape[0]):\n",
" P_norm_0 = norm.pdf(data[i], m0, std0)\n",
" \n",
" P_norm_1 = norm.pdf(data[i], m1, std1)\n",
" \n",
" # Handle P_norm too small that can be NaN\n",
" if(P_norm_0 < pow(10, -9)):\n",
" P_norm_0 = pow(10, -9)\n",
" if(P_norm_1 < pow(10, -9)):\n",
" P_norm_1 = pow(10, -9)\n",
" if((P_norm_0 == pow(10, -9)) and (P_norm_1 == pow(10, -9))):\n",
" if(abs(data[i] - m0) > abs(data[i] - m1)):\n",
" P_norm_0 = pow(10, -10)\n",
" else:\n",
" P_norm_1 = pow(10, -10)\n",
" \n",
" P_norm = np.array([[P_norm_0], [P_norm_1]])\n",
" P_next = np.matmul(transition_matrix.transpose(), P)\n",
" l_t = np.matmul(P_norm.transpose(), P_next)\n",
" # update the filter for next period\n",
" P = 1/l_t * P_norm * P_next # inference prob\n",
" Probs[i] = P[0]\n",
" return Probs"
]
},
{
"cell_type": "code",
"execution_count": 123,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1782: RuntimeWarning: divide by zero encountered in true_divide\n",
" return 1. / (1 - np.diagonal(self.regime_transition).squeeze())\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_regression.py:212: RuntimeWarning: invalid value encountered in sqrt\n",
" tmp = np.sqrt(result.smoothed_marginal_probabilities)\n",
"/usr/local/lib/python3.5/dist-packages/numpy/linalg/linalg.py:1942: RuntimeWarning: invalid value encountered in greater\n",
" large = s > cutoff\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
}
],
"source": [
"all_res = []\n",
"\n",
"for exp in reference['Ref']:\n",
" blink = load_csv(exp)\n",
" data, res = process(blink)\n",
" all_res.append(res)\n",
" res.save(fname= exp + '.csv')"
]
},
{
"cell_type": "code",
"execution_count": 133,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:1092: RuntimeWarning: invalid value encountered in sqrt\n",
" bse_ = np.sqrt(np.diag(self.cov_params()))\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1782: RuntimeWarning: divide by zero encountered in true_divide\n",
" return 1. / (1 - np.diagonal(self.regime_transition).squeeze())\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:1092: RuntimeWarning: invalid value encountered in sqrt\n",
" bse_ = np.sqrt(np.diag(self.cov_params()))\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_switching.py:1144: RuntimeWarning: divide by zero encountered in log\n",
" return np.log(results[5])\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:1092: RuntimeWarning: invalid value encountered in sqrt\n",
" bse_ = np.sqrt(np.diag(self.cov_params()))\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/regime_switching/markov_regression.py:212: RuntimeWarning: invalid value encountered in sqrt\n",
" tmp = np.sqrt(result.smoothed_marginal_probabilities)\n",
"/usr/local/lib/python3.5/dist-packages/numpy/linalg/linalg.py:1942: RuntimeWarning: invalid value encountered in greater\n",
" large = s > cutoff\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/base/model.py:508: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
" \"Check mle_retvals\", ConvergenceWarning)\n",
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"(71, 4)\n"
]
},
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>exp</th>\n",
" <th>mean0</th>\n",
" <th>std0</th>\n",
" <th>mean1</th>\n",
" <th>std1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>N1</td>\n",
" <td>66.843367</td>\n",
" <td>0.290885</td>\n",
" <td>90.817547</td>\n",
" <td>0.184893</td>\n",
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" <th>2</th>\n",
" <td>N5</td>\n",
" <td>93.221993</td>\n",
" <td>1.077217</td>\n",
" <td>150.044160</td>\n",
" <td>1.295145</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>N6</td>\n",
" <td>80.740094</td>\n",
" <td>0.269698</td>\n",
" <td>101.973355</td>\n",
" <td>0.281623</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>N7</td>\n",
" <td>61.615789</td>\n",
" <td>0.481280</td>\n",
" <td>78.674447</td>\n",
" <td>0.558405</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>N8</td>\n",
" <td>76.716320</td>\n",
" <td>0.288782</td>\n",
" <td>90.528944</td>\n",
" <td>0.355028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>N9</td>\n",
" <td>78.406160</td>\n",
" <td>2.230096</td>\n",
" <td>107.494441</td>\n",
" <td>1.443944</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>N10</td>\n",
" <td>77.107256</td>\n",
" <td>1.408776</td>\n",
" <td>110.680138</td>\n",
" <td>1.268224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>N11</td>\n",
" <td>80.551994</td>\n",
" <td>0.659896</td>\n",
" <td>128.989290</td>\n",
" <td>1.060003</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>N12</td>\n",
" <td>55.048853</td>\n",
" <td>14.436028</td>\n",
" <td>141.144519</td>\n",
" <td>1.365578</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>N14</td>\n",
" <td>62.652738</td>\n",
" <td>0.957071</td>\n",
" <td>80.970827</td>\n",
" <td>1.816364</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>N16</td>\n",
" <td>87.561233</td>\n",
" <td>1.242599</td>\n",
" <td>189.895643</td>\n",
" <td>3.289091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>N17</td>\n",
" <td>59.287312</td>\n",
" <td>1.263117</td>\n",
" <td>90.341767</td>\n",
" <td>1.409745</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>N18</td>\n",
" <td>63.822208</td>\n",
" <td>0.380093</td>\n",
" <td>77.048018</td>\n",
" <td>0.222271</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>N20</td>\n",
" <td>69.554011</td>\n",
" <td>1527.255055</td>\n",
" <td>75.013340</td>\n",
" <td>1.049117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>N21</td>\n",
" <td>80.487675</td>\n",
" <td>0.787807</td>\n",
" <td>106.322142</td>\n",
" <td>0.913383</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>N22</td>\n",
" <td>98.283724</td>\n",
" <td>0.951606</td>\n",
" <td>119.267633</td>\n",
" <td>0.774082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>N23</td>\n",
" <td>68.840826</td>\n",
" <td>0.488251</td>\n",
" <td>90.536014</td>\n",
" <td>0.590652</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>N24</td>\n",
" <td>30.121880</td>\n",
" <td>0.400222</td>\n",
" <td>58.930464</td>\n",
" <td>1.657124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>N26</td>\n",
" <td>94.410924</td>\n",
" <td>0.573406</td>\n",
" <td>199.607634</td>\n",
" <td>2.234396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>N27</td>\n",
" <td>96.154327</td>\n",
" <td>3.480412</td>\n",
" <td>120.493469</td>\n",
" <td>1.355304</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>N29</td>\n",
" <td>56.513358</td>\n",
" <td>0.529698</td>\n",
" <td>74.115040</td>\n",
" <td>0.228640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>N31</td>\n",
" <td>67.762052</td>\n",
" <td>1.306011</td>\n",
" <td>98.851175</td>\n",
" <td>2.567455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>N33</td>\n",
" <td>72.920038</td>\n",
" <td>1.440899</td>\n",
" <td>97.326753</td>\n",
" <td>0.826172</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>N34</td>\n",
" <td>74.541373</td>\n",
" <td>0.988154</td>\n",
" <td>100.208816</td>\n",
" <td>0.844006</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>N36</td>\n",
" <td>57.027860</td>\n",
" <td>0.461087</td>\n",
" <td>71.852219</td>\n",
" <td>0.382393</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>N39</td>\n",
" <td>38.748153</td>\n",
" <td>0.587869</td>\n",
" <td>58.067272</td>\n",
" <td>0.530956</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>N40</td>\n",
" <td>30.121880</td>\n",
" <td>0.400222</td>\n",
" <td>58.930464</td>\n",
" <td>1.657124</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>N41</td>\n",
" <td>106.884516</td>\n",
" <td>0.656645</td>\n",
" <td>131.006806</td>\n",
" <td>0.936313</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>N42</td>\n",
" <td>75.153946</td>\n",
" <td>1.032952</td>\n",
" <td>174.268407</td>\n",
" <td>4.421267</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>N43</td>\n",
" <td>71.921145</td>\n",
" <td>1.547868</td>\n",
" <td>106.784980</td>\n",
" <td>1.854286</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>N45</td>\n",
" <td>109.135858</td>\n",
" <td>0.798922</td>\n",
" <td>142.322972</td>\n",
" <td>0.902396</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>N46</td>\n",
" <td>56.852574</td>\n",
" <td>0.485272</td>\n",
" <td>76.623768</td>\n",
" <td>0.670294</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>N47</td>\n",
" <td>61.661862</td>\n",
" <td>0.459771</td>\n",
" <td>79.657565</td>\n",
" <td>0.355418</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>N48</td>\n",
" <td>179.092895</td>\n",
" <td>2.546115</td>\n",
" <td>471.154037</td>\n",
" <td>10.449235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>N49</td>\n",
" <td>90.973950</td>\n",
" <td>0.147126</td>\n",
" <td>109.020690</td>\n",
" <td>0.204056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>N50</td>\n",
" <td>74.287624</td>\n",
" <td>0.644214</td>\n",
" <td>99.836455</td>\n",
" <td>0.657204</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>N53</td>\n",
" <td>250.799159</td>\n",
" <td>5.667835</td>\n",
" <td>588.437665</td>\n",
" <td>11.996989</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>N56</td>\n",
" <td>66.576945</td>\n",
" <td>1546.984759</td>\n",
" <td>78.968241</td>\n",
" <td>0.238230</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>N57</td>\n",
" <td>106.495890</td>\n",
" <td>3.324574</td>\n",
" <td>368.415097</td>\n",
" <td>10.995440</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>N58</td>\n",
" <td>101.039357</td>\n",
" <td>1.440128</td>\n",
" <td>157.727525</td>\n",
" <td>1.300730</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>N59</td>\n",
" <td>90.022120</td>\n",
" <td>1.057204</td>\n",
" <td>144.318983</td>\n",
" <td>0.859499</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>N61</td>\n",
" <td>66.397042</td>\n",
" <td>0.891873</td>\n",
" <td>94.184774</td>\n",
" <td>1.387419</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>N62</td>\n",
" <td>78.895549</td>\n",
" <td>0.281430</td>\n",
" <td>104.286863</td>\n",
" <td>0.896677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>N63</td>\n",
" <td>65.594252</td>\n",
" <td>0.943414</td>\n",
" <td>102.926318</td>\n",
" <td>1.410714</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>N64</td>\n",
" <td>84.513041</td>\n",
" <td>0.764535</td>\n",
" <td>115.511793</td>\n",
" <td>0.875732</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>N66</td>\n",
" <td>57.062742</td>\n",
" <td>0.545609</td>\n",
" <td>87.204441</td>\n",
" <td>0.850591</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>N68</td>\n",
" <td>104.364473</td>\n",
" <td>1.141991</td>\n",
" <td>296.711303</td>\n",
" <td>5.227323</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>N69</td>\n",
" <td>110.094703</td>\n",
" <td>1.391173</td>\n",
" <td>140.768943</td>\n",
" <td>2.276014</td>\n",
" </tr>\n",
" <tr>\n",
" <th>64</th>\n",
" <td>N70</td>\n",
" <td>71.053878</td>\n",
" <td>0.612224</td>\n",
" <td>95.814165</td>\n",
" <td>0.597756</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>N72</td>\n",
" <td>134.496546</td>\n",
" <td>1.969580</td>\n",
" <td>226.141458</td>\n",
" <td>6.939982</td>\n",
" </tr>\n",
" <tr>\n",
" <th>67</th>\n",
" <td>X1</td>\n",
" <td>70.362916</td>\n",
" <td>1.202380</td>\n",
" <td>119.322067</td>\n",
" <td>1.806156</td>\n",
" </tr>\n",
" <tr>\n",
" <th>70</th>\n",
" <td>X4</td>\n",
" <td>62.168339</td>\n",
" <td>3.361366</td>\n",
" <td>90.194988</td>\n",
" <td>1.315657</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" exp mean0 std0 mean1 std1\n",
"0 N1 66.843367 0.290885 90.817547 0.184893\n",
"2 N5 93.221993 1.077217 150.044160 1.295145\n",
"3 N6 80.740094 0.269698 101.973355 0.281623\n",
"4 N7 61.615789 0.481280 78.674447 0.558405\n",
"5 N8 76.716320 0.288782 90.528944 0.355028\n",
"6 N9 78.406160 2.230096 107.494441 1.443944\n",
"7 N10 77.107256 1.408776 110.680138 1.268224\n",
"8 N11 80.551994 0.659896 128.989290 1.060003\n",
"9 N12 55.048853 14.436028 141.144519 1.365578\n",
"10 N14 62.652738 0.957071 80.970827 1.816364\n",
"12 N16 87.561233 1.242599 189.895643 3.289091\n",
"13 N17 59.287312 1.263117 90.341767 1.409745\n",
"14 N18 63.822208 0.380093 77.048018 0.222271\n",
"15 N20 69.554011 1527.255055 75.013340 1.049117\n",
"16 N21 80.487675 0.787807 106.322142 0.913383\n",
"17 N22 98.283724 0.951606 119.267633 0.774082\n",
"18 N23 68.840826 0.488251 90.536014 0.590652\n",
"19 N24 30.121880 0.400222 58.930464 1.657124\n",
"21 N26 94.410924 0.573406 199.607634 2.234396\n",
"22 N27 96.154327 3.480412 120.493469 1.355304\n",
"24 N29 56.513358 0.529698 74.115040 0.228640\n",
"26 N31 67.762052 1.306011 98.851175 2.567455\n",
"28 N33 72.920038 1.440899 97.326753 0.826172\n",
"29 N34 74.541373 0.988154 100.208816 0.844006\n",
"31 N36 57.027860 0.461087 71.852219 0.382393\n",
"34 N39 38.748153 0.587869 58.067272 0.530956\n",
"35 N40 30.121880 0.400222 58.930464 1.657124\n",
"36 N41 106.884516 0.656645 131.006806 0.936313\n",
"37 N42 75.153946 1.032952 174.268407 4.421267\n",
"38 N43 71.921145 1.547868 106.784980 1.854286\n",
"40 N45 109.135858 0.798922 142.322972 0.902396\n",
"41 N46 56.852574 0.485272 76.623768 0.670294\n",
"42 N47 61.661862 0.459771 79.657565 0.355418\n",
"43 N48 179.092895 2.546115 471.154037 10.449235\n",
"44 N49 90.973950 0.147126 109.020690 0.204056\n",
"45 N50 74.287624 0.644214 99.836455 0.657204\n",
"48 N53 250.799159 5.667835 588.437665 11.996989\n",
"51 N56 66.576945 1546.984759 78.968241 0.238230\n",
"52 N57 106.495890 3.324574 368.415097 10.995440\n",
"53 N58 101.039357 1.440128 157.727525 1.300730\n",
"54 N59 90.022120 1.057204 144.318983 0.859499\n",
"55 N61 66.397042 0.891873 94.184774 1.387419\n",
"56 N62 78.895549 0.281430 104.286863 0.896677\n",
"57 N63 65.594252 0.943414 102.926318 1.410714\n",
"58 N64 84.513041 0.764535 115.511793 0.875732\n",
"60 N66 57.062742 0.545609 87.204441 0.850591\n",
"62 N68 104.364473 1.141991 296.711303 5.227323\n",
"63 N69 110.094703 1.391173 140.768943 2.276014\n",
"64 N70 71.053878 0.612224 95.814165 0.597756\n",
"66 N72 134.496546 1.969580 226.141458 6.939982\n",
"67 X1 70.362916 1.202380 119.322067 1.806156\n",
"70 X4 62.168339 3.361366 90.194988 1.315657"
]
},
"execution_count": 133,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res_dict = {}\n",
"exps = []\n",
"params = []\n",
"\n",
"for exp in reference['Ref']:\n",
" blink = load_csv(exp)\n",
" data, res = process(blink)\n",
" exps.append(exp)\n",
" params.append([res.params['const[0]'],res.bse['const[0]'], \n",
" res.params['const[1]'],res.bse['const[1]']])\n",
"params = np.array(params)\n",
"print(params.shape)\n",
"res_dict = {}\n",
"res_dict['exp'] = exps\n",
"res_dict['mean0'] = params[:, 0]\n",
"res_dict['std0'] = params[:, 1]\n",
"res_dict['mean1'] = params[:, 2]\n",
"res_dict['std1'] = params[:, 3]\n",
"\n",
"res_df = pd.DataFrame(res_dict, columns = ['exp', 'mean0', 'std0', 'mean1', 'std1'])"
]
},
{
"cell_type": "code",
"execution_count": 135,
"metadata": {},
"outputs": [],
"source": [
"res_df = res_df.dropna()"
]
},
{
"cell_type": "code",
"execution_count": 143,
"metadata": {},
"outputs": [],
"source": [
"res_df_filtered = res_df[res_df['std0'] < 1500]"
]
},
{
"cell_type": "code",
"execution_count": 192,
"metadata": {},
"outputs": [],
"source": [
"def set_axis_style(ax, labels):\n",
" ax.get_xaxis().set_tick_params(direction='out')\n",
" ax.xaxis.set_ticks_position('bottom')\n",
" ax.set_xticks(np.arange(1, len(labels) + 1))\n",
" ax.set_xticklabels(labels)\n",
" ax.set_xlim(0.25, len(labels) + 0.75)\n",
" ax.set_xlabel('Sample name')"
]
},
{
"cell_type": "code",
"execution_count": 200,
"metadata": {},
"outputs": [
{
"data": {
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AoMmKjJcOrnY8nrbZt7UAgA1wBRYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAAANgCARYAAAAAYAseC7DGmABjzGFjzP84t3saY/YZY04ZY94xxjRzjgc7t08594d5qgYAAAD4J2OMEhMTXduLFy/WggULJEnJycnq9NRWRf1qt6KiovTGG29IkrZv366oqCjXR/PmzbVp0yZflA/AT3jyCuz/kXS81PbvJS21LOtfJV2S9DPn+M8kXXKOL3U+DwAAAI1YcHCwNm7cqNzc3Ar3T/puV6X9+h6lpaVp+vTpkqSYmBilpaUpLS1N27ZtU8uWLTVq1Chvlg3Az3gkwBpjQiSNlfSGc9tIul/SeudTVkua4Hz8I+e2nPtHOp8PAACARiowMFAzZszQ0qVL6/T69evXa8yYMWrZsqWHKwNgJ566ArtM0lxJt5zbHSRdtiyr2LmdLam783F3SVmS5Nyf53y+G2PMDGNMqjEm9cKFCx4qEwAAAL6SkJCgNWvWKC8vr9y+DQe/UuQLuxUXF6esrKxy+9euXauHHnrIG2XCX6wa6/gASql3gDXG/FDS15ZlHfRAPS6WZf2XZVlDLMsa0qlTJ09+agAAAPhA69atNXXqVC1fvtxtfNy4ccr8v/cp/cV7FBsbq0cffdRtf05Ojo4cOaLRo0d7s1wAfsgTV2DvkjTeGJMpaa0ctw6/KqmtMSbQ+ZwQSeecj89JCpUk5/42kr7xQB0AAADwczNnzlRSUpKuXbvmGuvQoYOCgwIkSdOnT9fBg+7XRdatW6eJEycqKCjIq7UC8D/1DrCWZc23LCvEsqwwSZMlbbMs6/+TtF1SnPNpj0p6z/k4xbkt5/5tlmVZ9a0DAAAA/q99+/aKj49XUlKSaywnJ8f1OCUlRf369XN7zdtvv83twwAkSYHVP6XO5klaa4z5raTDkkp+SiVJ+pMx5pSki3KEXgAAADQRiYmJWrFihWt7+fLlSnlzlwJvM2r/r9eVnJzs2peZmamsrCzde++9PqgUgL/xaIC1LGuHpB3Ox19I+m4Fz/lW0k88+fcCAADAv+Xn57sed+nSRdevX3dtL1y4UAv7pjs2pm12e11YWJjOnTsnAJA8uw4sAAAAAAANhgALAAAAALAFAiwAAAAAwBYIsAAAAAAAWyDAAgAAAABsgQALAAAAALAFAiwAAAB8K32dlH1AOrNHWtrfsQ0AFSDAAgAAwHfS10nvPyXdvOHYzstybBNiAVSAAAsAAADf+fhFqajAfayowDEOAGUQYAEAAOA7edm1GwfQpBFgAQAA4DttQmo3DqBJI8ACAACg3jJy8pSRk1f7F458QQpq4T4W1MIxDjQGq8Y6PuARBFgAAAD4TmS8NG65FBDs2G4T6tiOjPdtXQD8UqCvCwAAAEATFxkvHVzteDxts29rAeDXuAILAAAAALAFAiwAAAAAwBYIsAAAAAAAWyDAAgAAAABsgSZOAAAAqLcXOyySJL3j4zoANG5cgQUAAAAA2AIBFmgAk17bq0mv7fV1GQAAAECjQoAFAAAAANgCARYAAAAAYAsEWAAAAACALRBgAQAAAAC2QIAFAAAA4HXGGCUmJrq2Fy9erAULFri21+3PUfhzuxQREaGHH37YNT537lxFRESoX79+euqpp2RZljfLho8RYAEAAAB4XXBwsDZu3Kjc3Nxy+06ePKmFf/m7Pnl2uDIyMrRs2TJJ0t/+9jd98sknSk9P19GjR3XgwAHt3LnT26XDhwiwAAAAALwuMDBQM2bM0NKlS8vte/3115Vw/7+o3e1BkqTOnTtLcly1/fbbb1VYWKgbN26oqKhIXbp08Wrd8C0CLAAAAACfSEhI0Jo1a5SXl+c2fuLECZ346pru+t1eRUdHa8uWLZKk4cOHKyYmRl27dlXXrl01evRo9evXzxelw0cCfV0AAAAAgKapdevWmjp1qpYvX64WLVq4xouLi3Xy/DXtmDtM2d//o0aMGKEjR44oNzdXx48fV3Z2tiQpNjZWu3fv1j333OOrLwFexhVYAAAAAD4zc+ZMJSUl6dq1a66xkJAQjY/qrKDA29SzZ0/17dtXJ0+e1Lvvvqvo6Gi1atVKrVq10pgxY7R3714fVg9vI8ACAAAA8Jn27dsrPj5eSUlJrrEJEyZox+cXJUm5ubk6ceKEevXqpR49emjnzp0qLi5WUVGRdu7cyS3ETQwBFgAAAIBPJSYmunUjHj16tDq0ClL4c7sUExOjRYsWqUOHDoqLi1Pv3r01YMAADRw4UAMHDtS4ceN8WDm8jTmwAAAAALwuPz/f9bhLly66fv26a9sYoyWTw7VksqRpm13jAQEBeu2117xZJvwMV2ABAAAAALZAgAUAAAAA2AIBFgAAAABgCwRYAAAAAIAtEGABAAAAALZAgAUAAADgX9LXSdkHpDN7pKX9HduACLAAAAAA/En6Oun9p6SbNxzbeVmObUIsRIAFAAAA4E8+flEqKnAfKypwjKPJI8ACAAAA8B952bUbR5NCgAUAAADgP9qE1G4cTQoBFgAAAID/GPmCFNTCfSyohWMcTR4BFgAAAID/iIyXxi2XAoId221CHduR8b6tC34h0NcFAAAAAICbyHjp4GrH42mbfVsL/ApXYAEAAAAAtkCABQAAAADYAgEWAAAAAGALBFgAAAAAgC0QYAEAAAAAtkCABQAAAADYAgEWAAAAAGALBFgAAAAAgC0QYAEAAAAAtkCABQAAAADYAgEWAAAAAGALBFgAAFB7q8Y6PlB/0zY7PgDYjjFGiYmJru3FixdrwYIFkqRZs2YpKipKUb/arb7zd6ht27aSpLS0NA0fPlwRERGKjIzUO++844vSbSvQ1wUAAAAAgB0FBwdr48aNmj9/vjp27Oi2b+nSpY4Hq8bqP7Zm6nDwMElSy5Yt9eabb6pPnz768ssvdeedd2r06NGugIuqcQUWAAAAAOogMDBQM2bM+EdYrcTb+77UQw89JEnq27ev+vTpI0nq1q2bOnfurAsXLjR4rY0FARZorLi9DwDgJZsOn9Phs5e17/RF3fXyNm06fM7XJQFek5CQoDVr1igvL6/C/WdyC3Q6t0D3339/uX379+9XYWGhevfu3dBlNhoEWAAAANTZpsPnNH/jERXevCVJOne5QPM3HiHEoslo3bq1pk6dquXLl1e4f+3+LxU35J8VEBDgNp6Tk6NHHnlEq1at0m23EctqincKAAAAdbbog89VUHTTbayg6KYWffC5jyoCvG/mzJlKSkrStWvXyu1buz9HDw3r5jZ25coVjR07Vi+99JKio6O9VWajQIAFAABAnX15uaBW40Bj1L59e8XHxyspKclt/LPPPtOla0Ua3vsfDZoKCws1ceJETZ06VXFxcd4u1fYIsAAAAKizbm1b1GocaKwSExOVm5vrNrZ27VpN/m5XGWNcY+vWrdOuXbuUnJzsWGYnKkppaWneLte2WEYHAAAAdTZn9B2av/GI223ELYICNGf0HT6sCvCO/Px81+MuXbro+vXrbvsXLFggrTrgNjZlyhRNmTLFG+U1SgRYALCDko7S0zb7tg4AKGPCoO6SpLnr01V485a6t22hOaPvcI0DgCcRYAEAAFAvEwZ119v7z0qS3nl8uI+rAdCYMQcWAAAAAGALBFgA8DFjjBITE13bixcvdsyZkbRkyRKFh4cr8oXdGrlon86cOeN63tmzZzVq1Cj169dP4eHhyszM9HLlAAAA3kWABQAfCw4O1saNG8t1LpSkQYMGKTU1Vekv3qO4If+suXPnuvZNnTpVc+bM0fHjx7V//3517tzZm2UDAAB4HQEWAHwsMDBQM2bM0NKlS8vti4mJUcuWLSVJ0b3aKjs7W5J07NgxFRcXKzY2VpLUqlUr1/MAAICfSF8nZR+QzuyRlvZ3bKNeCLAA4AcSEhK0Zs0a5eXlVfqcpN3ZGjNmjCTpxIkTatu2rR588EENGjRIc+bM0c2bNyt9LQD/Nem1vZr02l5flwHA09LXSe8/Jd284djOy3JsE2LrhQALAH6gdevWmjp1qpYvX17h/rf2nlNqZp7mzJkjSSouLtbu3bu1ePFiHThwQF988YWSk5O9WDEAAKjSxy9KRQXuY0UFjnHUGQEWsKmqGv/s2rVLgxfsUeD0v2r9+vVur6Pxj/+aOXOmkpKSdO3aNbfxrVu36qX/OaWUp+5UcHCwJCkkJERRUVHq1auXAgMDNWHCBB06dMgXZQMAgIrkZdduHDVCgAVsqqrGPz169FDyzyL18LBu5fbR+Md/tW/fXvHx8UpKSnKNHT58WI8//rhSnhqizq2DXeNDhw7V5cuXdeHCBUnStm3bFB4e7vWaAQBAJdqE1G4cNUKABWyqqsY/YWFhigxtrdvK/A+n8Y//S0xMdDspMWfOHOXn5+snfzykqF/t1vjx4yVJAQEBWrx4sUaOHKkBAwbIsiz9/Oc/91XZAACgrJEvSEEt3MeCWjjGUWeBvi4AQN0lJCQoMjLSbWmVqpRu/HP69Gl9//vf18svv6yAgIAGrhRVyc/Pdz3u0qWLrl+/7treunWr48GqsY4/p6W49sXGxio9Pd0rNQIAgFqKjHf8+d6TjkZObUId4bVkHHXCFVjAxqpr/FMWjX8AAAC8KDJeChkq/cvd0qyjhFcPIMACNldZ45+K0PgHAAAAdkaABWyuosY/laHxDwAAAOyMAAs0AmUb/xw4cEAhidv05wNf6fHHH1dERIQkGv8AqLmqluoqsSE1R8YYpaamSpI++ugj3XnnnRowYIDuvPNObdu2zZslAwCaAJo4ATZVVeOfoUOHKvuV+x0b0za7vY7GPwBqomSprvnz56tjx47l9l8tKNarWzM1bNgw11jHjh31/vvvq1u3bjp69KhGjx6tc+fOebNsAEAjV+8rsMaYUGPMdmPMMWNMhjHm/zjH2xtjPjLGnHT+2c45bowxy40xp4wx6caYwfWtAQAatfR1UvYB6cweaWl/xzbQwKpaqkuSnn/3hOaN6a3mzZu7xgYNGqRu3RzrT0dERKigoEA3btzwSr0AgKbBE7cQF0tKtCwrXFK0pARjTLikZyR9bFlWH0kfO7claYykPs6PGZL+0wM1AEDjlL5Oev8pR/t9ScrLcmwTYuEFCQkJWrNmjfLy8tzGDx06pKxLBRo7sHOlr92wYYMGDx6s4ODghi4TANCE1DvAWpaVY1nWIefjq5KOS+ou6UeSVjuftlrSBOfjH0l603L4VFJbY0zX+tYBAI3Sxy9KRQXuY0UFjnGggVW0VNetW7c0e/ZsvTKpX6Wvy8jI0Lx58/Taa695o0wAQBPi0TmwxpgwSYMk7ZPUxbKsHOeuryR1cT7uLimr1MuynWM5pcZkjJkhxxVa9ejRw5NlAoB95GXXbhzwsJkzZ2rw4MGaNm2aJOnq1as6evSo7jvqmHf/Vf4tjR8/XikpKRoyZIiys7M1ceJEvfnmm+rdu7cvSwcANEIe60JsjGklaYOkmZZlXSm9z7IsS5JVm89nWdZ/WZY1xLKsIZ06dfJUmQBgL21CajcOeFjZpbratGmj3NxcZS6KUeaiGEVHR7vC6+XLlzV27Fi9/PLLuuuuu3xcOQCgMfJIgDXGBMkRXtdYlrXROXy+5NZg559fO8fPSQot9fIQ5xgAT6HpT+Mx8gUpqIX7WFALxzjgJWWX6qrMihUrdOrUKb344ouKiopSVFSUvv7662pfB9RUVcs7JScnq1OnTq7vvTfeeMP1vLlz5yoiIkL9+vXTU089Jce1FQB2VO9biI0xRlKSpOOWZS0ptStF0qOSXnb++V6p8SeNMWsWmk6RAAAgAElEQVQlDZOUV+pWYwD1VVnTH0mKjPddXaibkn+z9550/Ju2CXWEV/4t0cCqWqqrtB07drge//KXv9Qvf/nLhi4NTVh1yztNmjRJK1ascBv729/+pk8++cS1hNzdd9+tnTt36r777vNGyQA8zBNXYO+S9Iik+40xac6PH8gRXGONMSclfd+5LUl/kfSFpFOSXpf0Cw/UAKAETX8an8h4KWSo9C93S7OOEl4BNFnVLe9UEWOMvv32WxUWFurGjRsqKipSly5dqn8hfG/a5nLr2QP1vgJrWdYeSaaS3SMreL4lKaG+fy+AStD0BwDQiCUkJCgyMlJz584tt2/Dhg3atWuX+vbtq6VLlyo0NFTDhw9XTEyMunbtKsuy9OSTT6pfv8q7aAPwbx5r4oSaq+v8DaBGaPoDAGjEKlreSZLGjRunzMxMpaenKzY2Vo8++qgk6dSpUzp+/Liys7N17tw5bdu2Tbt37/ZF6QA8gADrAyXzNypriDFp0iSlpaUpLS1N06dP93J1sD2a/gCAbWw6fE6Hz17WvtMXddfL27TpMH0ta2LmzJlKSkrStWvXXGMdOnRQcHCwJGn69Ok6ePCgJOndd99VdHS0WrVqpVatWmnMmDHau3evT+qGl60a6/hAo0KA9YG6zN8AaiwyXhq3XApw/BJXm1DHNvMmAcCvbDp8TvM3HlHhzVuSpHOXCzR/4xFCbA2UXd5JknJyclyBJSUlxXWbcI8ePbRz504VFxerqKhIO3fu5BZiwMYIsD6SkJCgNWvWKC8vr9y+DRs2KDIyUnFxccrKyvJBdbA9mv4AaEgs1eURiz74XAVFN93GCopuatEHn/uoInspu7zT8uXLFfHLXRr4wm4tX75cycnJkqS4uDj17t1bAwYMULNmzXT58mWNGzdOUs2ncc2bN0/9+/dX//799c4773jtawRQHgHWR2o7fwP+o6o5zLt27dLgwYP151/co6yD21zPSUtL0/DhwxUREaHIyEh++QGwr8qW6mqAEFvVz9sSGzZskDFGqampkqTMzEy1aNHCFUL+7d/+zeN1ecqXlwtqNY6Kl3cq+Z5YuHChMn47Qv/74j3avn27vvOd70iSAgIC9Nprr+n48eMKDg7WxYsXazWNa/PmzTp06JDS0tK0b98+LV68WFeuXGnYLxQ1Ut3PiHX7cxT+3C5FRETo4Ycfdo1zQsLeCLA+5DZ/4/AaadXYSudvwH9UNYe5R48eSk5OVo+hsW7jLVu21JtvvqmMjAxt2bJFM2fO1OXLl71VMgB4jheX6qquZ8TVq1f16quvatiwYW7jvXv3doWQlStXerwuT+nWtkWtxlF/dZnGdezYMY0YMUKBgYG6/fbbFRkZqS1btjRglaipqn5GnDx5Ugv/8nd98uxwZWRkaNmyZZI4IdEYEGB9qNL5G06l52/Af1T1yy8sLEyRkZEyxv2/Vt++fdWnTx9JUrdu3dS5c2dduHDBK/UCgEd5camu6sLG888/r3nz5ql58+Ye/7u9Yc7oO9QiKMBtrEVQgOaMvsNHFTUNtZ3GNXDgQG3ZskXXr19Xbm6utm/fzhQvP1HVz4jXX39dCff/i9rdHiRJ6ty5syTfnJDIyMlTRk757zfUDQHWxyqcvxERoYEDB7rN34B/qeqXX3X279+vwsJC9e7duwEqA4AG5uWluir7eXvo0CFlZWVp7NjyHUZPnz6tQYMG6d577/Xr5VImDOquhQ8OULMAx+FY97YttPDBAZowqLuPK/MNbx3k13Ya16hRo/SDH/xA3/ve9/TQQw9p+PDhCggIqOhTwwcq+xlx4sQJnfjqmu763V5FR0e7QionJOwv0NcFNEUVzd8oafG9cOFCLVy40FeloYZK//Jr0aLmt3rl5OTokUce0erVq3XbbZw/AmBDI19wzHktfRtxAy7VVdHP21u3bmn27NkVnuTt2rWrzp49qw4dOujgwYOaMGGCMjIy1Lp16wapr74mDOqut/eflSS98/hwH1fTdMycOVODBw/WtGnTXGMdOnRwPZ4+fbrmzp3r2n7uuef03HPPSZIefvhh9e3b13vFokqVHZMVFxfr5Plr2jF3mLK//0eNGDFCR44c0ahRo3TgwAF973vfU6dOnTghYUMcQQN1VNEadCUG3kjVT/L/5DZ25coVjR07Vi+99JKio6O9VSaAJqouDZC++eYbxcTEqFWrVnryyScr/sQ+WKqr7M/bq1ev6ujRo7rvvvsUFhamTz/9VOPHj1dqaqqCg4NdQeTOO+9U7969deLEiQarDfZUm2lcN2/e1DfffCNJSk9PV3p6ukaNGuXdglGlio7JQkJCND6qs4ICb1PPnj3Vt29fnTx5UpLjhERaWpo++ugjWZbFCQmbIcCiyalJF+HAwECtX7/e7XUPPPCA2rZtq2+//VZSxb/8KlNYWKiJEydq6tSpiouL89wXA9TQpNf2atJre31dBryoLg2Qmjdvrt/85jdavHhx1Z/cy0t1lf1526ZNG+Xm5iozM1OZmZmKjo5WSkqKhgwZogsXLujmTcfSNF988YVOnjypXr16NWh9sKeaTuMqKirSPffco/DwcM2YMUNvvfWWAgO5idGfVHRMNmHCBO34/KIkKTc3VydOnFCvXr04IdEIEGDR5NSki3DpVusl5syZoz/9yf2qatlffgcOHFBISIg+PJqrX2/6uyIiIiRJ69at065du5ScnOxa2iEtLc3DXxkA/ENdGiDdfvvtuvvuu/2yKVLZn7eV2bVrlyIjIxUVFaW4uDitXLlS7du390KF8LkarE9c7TI8GRn63//9X7dleJo3b65jx47p2LFj+vTTTxUVFeWVLwe1U/ZnxOjRo9WhVZDCn9ulmJgYLVq0SB06dOCERCPAvxaanNIHdS+99JLbvrCwMEmqcH7qyJEjtWPHDj3wwAOuMdccZqehQ4cqOztbGb+7W5IU8eweSdKUKVM0ZcoUT38pAFClhIQERUZGus3lk9wbIC1atMhH1VWvwp4RFdixY4fr8Y9//GP9+Mc/bujS4G8qW59Yqt0dAs6eJJq22bP1oUFU9TPCGKMlk8O1ZLLc/j1LTkjAvrgCiyapPl2EAcAuKuq2WtIA6ZVXXvFhZR6yauw/AgeaNi+uTwzAtwiwaJIqa6EP+6lqTvOSJUsUHh6uyMhIjRw5UmfOnJEkpaWlafjw4YqIiFBkZKTeeecdX5TuUdW9D1sWPKwPfvOI2/uwfft21y3tUVFRat68uTZt2uSL8tGAatMACbAtL65PDDSkrKws9ezZUxcvOubvXrp0ST179lRmZqarH8sPf/hDH1fpWwRYf1CDORvwvKq6CMM+qprTPGjQIKWmpio9PV1xcXGu2yhbtmypN998UxkZGdqyZYtmzpypy5cve7v02pm2ucpb2qp7H77/7P/T6Of/5PY+xMTEKC0tTWlpadq2bZtatmxJI4tGqDYNkADb8vL6xEBDCQ0N1RNPPKFnnnlGkvTMM89oxp3NFLY9ocJ+LE0RAdbXKpuzQYhtcLXpIgz/VVWjmpiYGLVs2VKSFB0drexsx5n4vn37qk+fPpKkbt26qXPnzrpw4YL3im4A1b0Pgc0cTXlKvw+lrV+/XmPGjHG9X2hcatoASXL0AihZYzUkJIS5YrCHkS841iMurQHXJ24Mqltqa926dQoPD1dERES55pZXrlxRSEhI5cttoV5mzZqlTz/9VMuWLdOePXv09GhHJ/WRI0fqn/7pn3xcne8RYH2NORs+VWEX4fYt9Oe339Ljjz/u6iIsSffcc49+8pOf6OOPP1ZISIg++OADX5SMCtRkTnNSUpLGjBlTbnz//v0qLCxU7969G7JEr6jP+7B27Vo99NBDDVke8xW9rKpuq6Xt2LHD7eprZmamLl68qPz8fGVnZys8PNwb5QL144n1iZvYHXFV3blz8uRJLVy4UJ988okyMjK0bNkyt/3PP/+8RowY4a1Sm5ygoCAtWrRIs2bN0rJlyxQUSGQrjXfD15izUSt1XcN19erV6tOnj/r06aM//OEPrvGyB3VDhw5V9iv369rK0frmm2+UkZHheu7u3bt14cIFFRQUKDs7W6NHj264LxS1Ut2c5rfeekupqamaM2eO23hOTo4eeeQRrVq1qsLO03ZT3ftwZt+WSt+HI0eO8D3d1Nj9hEITCxtNwYsdFunFDvXoil2f9Ymb4B1xVd258/rrryshIUHt2rWTJHXu3Nm17+DBgzp//jxTThrYX//6V3Xt2lVHjx71dSl+x/5HbHbHnI1aqcsarhcvXtSvf/1r7du3T/v379evf/1rXbp0qeGKTF+nPoWfKbzwCAdVXlTZnOatW7fqpZdeUkpKioKDg13jV65c0dixY/XSSy8pOjra2+U2mMreh9h9P9fX/7Oo3PsgOW4TmzhxooKCgrxZKlB3TTBsVHe7pyRt2LBBxhhXQ67CwkJNmzZNAwYM0MCBA92WG0IZTfSOuMru3Dlx4oROnDihu+66S9HR0dqyZYskRxfzxMRELV682Bfl1o6NT3KlpaXpo48+0qeffqqlS5cq5/K3vi7JrxBgfa0Jz9mo6pfxmTNnNHLkSEVGRuq+++5zzdmr6mxhWFiYIiMjy11J++CDDxQbG6v27durXbt2io2Ndf0g9jjnQVUzFclITeKgqj7q0kFYUoVd+Cqa03z48GE9/vjjSklJcTt7XFhYqIkTJ2rq1KmKi4trwK/Q+yp7H3793t+1Yko/t/ehxNtvv93wtw+jSao2dK0aqw0Jg91C1/79+12dsQcOHKh33323/CdugmGjqhO4kqO79Kuvvqphw4a5xl5//XVJ0pEjR/TRRx8pMTFRt27d8kq9ttNE74ir7M6d4uJinTx5Ujt27NDbb7+tn//857p8+bL++Mc/6gc/+IFCQvz8QouNT3JZlqUnnnhCy5YtU48ePTRnzhw9ve4zX5flVwiwvuaJORs2VdUv46efflpTp05Venq6XnjhBc2fP9+1r7ZruJ47d06hoaGu7ZCQEJ07d67+X0BFmuBBVX3UpYOwpEq78JWd0zxnzhzl5+frJz/5iaKiojR+/HhJjiuOu3btUnJysutAOS0trQG+Qt+o6H24fuOmZr/9udv7IDnmO2ZlZenee+/1Ralo5KoNXQXFenVrplvo6t+/v1JTU5WWlqYtW7bo8ccfV3FxsfsLm2DYqOoEruSYkzhv3jw1b97cNXbs2DHdf//9khy3gLZt25blkirjxTvi6tI8qSGXf6vozp2QkBCNHz9eQUFB6tmzp/r27auTJ09q7969WrFihcLCwvT000/rzTffdHXL9Ss2Ph57/fXX1aNHD8XGxkqSfvGLX+j4l/na+fk39GNxIsD6g1rO2ahP1zh/UtUv49K/dGNiYvTee++59vn1Gq5N8KCqPurSQVhy78JXVaOarVu36vz5866lYlJSUiRJU6ZMUVFRkWs8LS1NUVFRDfVlekV178OuZ7+rDf8e5fY+SI47F86dO9co5gDD/1Qbut49oXljeruFrpYtWyowMFCS9O2338oYU/6FTXT6TWUncA8dOqSsrCyNHes+p3ngwIFKSUlRcXGxTp8+rYMHDyorK8ubJduHF++Iq0vzpLLLv02ePNmtA3BNjgXPnDmjwYMHKyoqShEREa4TQxXduTNhwgTXLee5ubk6ceKEevXqpTVr1ujs2bPKzMzU4sWLNXXqVL388suefovqz4+OxzYdPqefXv2FfnjlGd318jZtOlz1RZQZM2a4naAICAjQoQV36947OtCPxYkjFhuqT9c4f1PZL+OBAwdq48aNkqR3331XV69e1TfffOPaX5s1XLt37+72Czs7O1vdu3f30FdQRhM9qKqP+nTOBcqq6gTfypUrNWDAAEVFRenuu+8utzzM2bNn1apVK3vM7aovL84NqzJ0XSrQ2IHlb2vft2+fIiIiNGDAAK1cudIVaF2a6PSbik7g3rp1S7Nnz9Yrr7xS7vmPPfaYQkJCNGTIEPXq1Uvt2rVTQECAJPf/G8nJyerUqZPrjpQ33njD9TlKN0FcvXp1lfW98/hwvfP4cA98pT7gxTvi6tI8qezyb8YYvffee7U6Fuzatav27t2rtLQ07du3T4WFhfryyy8llb9zZ/To0erQoYPCw8MVExOjRYsWqUOHDp59IxqSnxyPbTp8TvM3HtEFq40sGZ27XKD5G49UG2JRNQKsDdW1a5w/KvfL+PAaadVYLV68WDt37tSgQYO0c+dOde/e3fVLV6piDddVY6VTW92GRo8erQ8//FCXLl3SpUuX9OGHH1Z+xqq+B3VN9KCqPuraQRj+p9rw+PwuRf1qt1t4/OabbxQTE6NWrVp5ZD3Bqk7wPfzwwzpy5IjS0tI0d+5czZ49223/7Nmzm8aJEi/PDasydE3qV+Frhg0bpoyMDB04cEALFy7Ut9+WaWDShKfflD2Be/XqVR09elT33XefwsLC9Omnn2r8+PFKTU1VYGCgli5dqrS0NAUHBysnJ0edOnWq8PNOmjTJdUfK9OnTJfmgCaKv1aeLcS3VtnlSafv375ckPfHEE7U6FmzWrJmrid+NGzfc5rGWvXPHGKMlS5bo2LFjOnLkiCZPnlzu7/npT3+qFStW1OGr9wI/OR5b9MHnKii66TZWUHRTiz743Kt1NDYEWJuqzw8+f1PR1dRu3bpp48aNOnz4sF566SVJUtu2bd1eV+Earonb9OcDX7mt4dq+fXs9//zzGjp0qIYOHaoXXnhB7du3L1+IJw7qnAdVhQqSJTWpg6r6qG0H4Vqx+1IhNlJtePzNCKX9+h638Ni8eXP95je/cV31rMsV1NJNf4qKinTXXXdVeFDXunVr1+Nr16653Zq6adMm9ezZ023t50bLB3PDKg1dv9+nsDnb3UJXaf369VOrVq0qXkbCi2HDn5Q9gdumTRvl5uYqMzNTmZmZio6OVkpKioYMGaLr16+73nNjjLp27aq//OUvNf67vNoEsYmpbfOkEiXLvzVv3lxPPvlkrY8Fs7KyFBkZqdDQUM2bN0/dunWrvEg7//6sxfFYVb93Zs2a5fr90rdvX7dj0ZrcnfDl5YJajaNmCLA2VdcffP6ooqupubm5rk6JCxcu1GOPPSap6nl+Va3h+thjj+nUqVM6deqUpk2bVnEhnjqoi4zXyWbf0bFmA5rUQVV91KaDMPxXVXeHVBYeb7/9dt19992uOZB1uYJauulP8+bNtWXLlkpvS//DH/6g3r17a+7cua6fn/n5+fr973+vX/3qV/V/E+zAB3PDKg1di2KUuSjGLXSdPn3aNTfvzJkz+uyzzxQWFtZgtdlR2RO4lfn66681ePBg9evXT0VFRXr//fcr/b+xYcMGRUZGKi4uzjXtxqtNED1l2mbHRwOqay+Skg76JXcU1KZ5kuS+/Nttt91W9bHg1je146etyx0LhoaGKj09XadOndLq1at1/vx5j78/fqOGx2NV/d4puYMhLS1N//7v/64HH3xQUs3vTujWtkW5sarGUTMEWBur7Q8+f1b2l/GOHTt0xx13qG/fvjp//ryee+65hi/Cjyb8N0U17SAsiS58fqyqOc1/+DhTveftcAuPZdUlBJdu+iM5Gl5MmTKlwr8jISFBf//73/X73/9ev/3tbyVJCxYs0KxZs9SqVavafbF25aO5YTUNXXv27NHAgQMVFRWliRMn6o9//KM6duzYoLXZQVUncEvbsWOHhgwZIsnRpO3zzz/X8ePH1bx5c/Xv37/CwDNu3DhlZmYqPT1dsbGxevTRRxv0a2lIk17bq0mv7W3Qv6OuvUjKdtCvTfOkypZ/q/RYMKqzggJvq/RYsFu3burfv792795d7/fD7qprNlei9JJzNb07Yc7oO9QiKMBtrEVQgOaMvsNzX0ANeeP/hrcQYGupqrNuu3bt0uDBgxUYGKj169c3eC21+cHnaVW9D2fPnlVMTIwGDRqkyMjISm9XqvCX8YS+kqS4uDidPHlSJ06c0BtvvFH320drw08m/HtLXb6Xt2/f7rqVJioqSs2bN9emTZvqXENdOghL8mgXvrqsRVu2k+PKlSvr/Pd7Rfo69Sn8TOGFRxq8YU9Vc5oTRobp77+/zy08VqTKEFzBFVTpH01/rl+/rpUrV2r27NlVNnqbPHmy63t33759mjt3rsLCwrRs2TL97ne/8995XZ7gxblhdQldjzzyiDIyMpSWlqZDhw5pwoQJHq/L33iz+VFFgadDhw6u37PTp0/XwYMHJXm5CaK/qMEV3Lr2IindQb9ETZsnlV3+raCgQGlpaZUfC35+UZL7sWB2drYKChx3ml26dEl79uzRHXd4P0jVhrdCV3UNJc+cOaPTp0+7Vsio6d0JEwZ118IHB6iTyZORpe5tW2jhgwM0YVDD/T/KyspSz549dfGi43vg0qVL6tmzp67l5khyXMkPCQnxSN8JXyHA1lJVZ9169Oih5ORkry5d46uucVW9D7/97W8VHx+vw4cPa+3atfrFL37h8b+/QfjJhH9vqcv3ckxMjCtQbtu2TS1bttSoUaMarsj6zr+pQVOuuqxFW7aT48svv+zq5Oh3nHO7m6lIRvLKYu7VdQkvHR4rUmUIruAKqvSPpj8tWrTQwoUL1bJly3IHdaWvQGzevNnV0XP37t2u+YMzZ87Us88+a+tf7NVqRA2QMnLylJFTszXB4VBR4MnJyXE9TklJUb9+juZatWqC2MTUpxfJAw884Hpc0+ZJZZd/a9GihWv5twqPBVsFKfy5XW7HgsePH9ewYcM0cOBA3XvvvXr66ac1YMCAOr8Hdb2V+uzZsxo1apT69eun8PBwZWZm1rkGT6muoeTatWsVFxfn1lC0piYM6q7kf/qj/qf1y/rkmftrH15r2WA0NDRUTzzxhGt93meeeUYzZszQ7R27SnKsGT1ixIhafx3+JLD6p6C00mfdSpoLlSiZo1On9RRrMV+jojPaJUp+8C1ZsqT2NdRCVe+DMUZXrlyRJOXl5VXdIKCMkgMRn7RRKTl4e+9JRyOnNqGO8GrDgzpJtTqDXJfv5fXr12vMmDGutVr9TmVNuSS3f9Oq3oeYmBjX4+joaL311luSHJ0cS9y4ccM1X9svVTW3u4G+t0sfIJfMXz958qT6OPeXDo+VmTlzpgYPHlzpnPXJkyfriSeeKDd+2223uZr+JCYmul1JXbFihbZu3aqgoCC1a9eu2iVBGrXIeOmg8+tv4PmCDenFDoskSe9U8zy4K/t/Y/ny5UpJSVFgYKDat2+v5ORkSe5NECVV3gSxCSodeFq0+MfJ79K9SLKzszVixAgdOXKkXCPKapWcvK3k/2e1x4KTw7VksvvrY2NjlZ6eXrs6qlByAnj+/PnlbvMvfSt1u3bt9PXXX7v2TZ06Vc8995xiY2OVn5/vN+uQV/V7Z+3atfrDH/7g2u7evbvrjkfJcXfCfffd5/miangsU9asWbN05513atmyZdqzZ49WrFihKf8vVRfPfCZz/rweeOCBck3z7IQAWwcJCQmKjIx0XY1pqip7HxYsWKBRo0bpP/7jP3Tt2jVt3bq1ks/ghxrJQV1N1ed7ee3ateWWIfErtQhuNXkfyq5Fm5WVpbFjx+rUqVNatGhRrU7UeJWP5nZXGB7X71JQgFG73tfdwmNYWJiuXLmiwsJCXb9+XceOHVN4eHjFIdgZfEuH4NOnTys0NFSBgYHKyMjQ8OHDFRYWpo4dO7od1L366qvV1l3R7a1AY1BV4Fm4cKEWLlxY4esee+wx1/9BuKso8ISEhGhY4d8U9NYE9Zy22TX/tOQkgG2UXPW7ecNx1a+CE/pVnQCu7FbqY8eOqbi4WLGxsZLkV70HKjr5KkmfffaZLl26pOHD/3Gb/+jRo/Xss8+6Gjd9+OGHlf4fqpc6noQOCgrSokWL9MADD+jDDz9UUFCQrFu39L/r/0OHtqXY69i8Av5xysNmqrvNoKmo7H14++239dOf/lTZ2dn6y1/+okceecS/r1D5QH3mUnty7kKV38sVrKlbIicnR0eOHPHvW8lqEdzqshatbTo5enFud1XzHV999VVl/NaxjM727dvdlqvJzMzUxYsXlZ+fr5YtWyo8PFxS+dviVqxYoYiICEVFRWnJkiWuEEzTn7rh9lugfmoz/9RWarGsYG1vpT5x4oTatm2rBx98UIMGDdKcOXN08+bNcp/XVypqNrd27VpNnjzZbem1Gi/RWF/1OAn917/+VV27dnUtQ3Zq50Z17T/cbf1fuyLA1lF1c7z8XV3XvCqrovchKSlJ8fGOs0LDhw/Xt99+W6POk/VWyzkCvlTt/NMJt+vh7/5zha/19NyFunwvr1u3ThMnTlRQUJDH6vC4Wga3uq5F6/edHG02t7vaEOxs7lM6BNeq6Y+d1zWET1Q3z09yLEFjjHHdkrdmzRq3hne33Xab0tLSvFk2vKSm808lG3XQr8WygrVd1rG4uFj/f3v3HmVldad5/NlUGS2kRWOMoxSkUIyT4lJgwIAY4iUTFI2he2UIqCtNnBUYLyudlovQtDNOOi4xlklIkBDtyUITIpCeULI0iTeURCPK/VIQhRgilLag3UQFuRTs+eOcUzlVnLqcs3fVu/db389aLuucuvDWObve93325bd/97vfqba2VmvWrNHrr7/eNGXdt7oNDZr8/i269r1ZGj13peo2FN4Cqr1ic3fddZfmzp17wvd1aItGVyV2Qm/cuFFPP/20Vq9ere9973t66623dO4fHtHelf+qqqoqTZ8+XY888kjTOtnYEGBLVKjXzUVXl7YuZc+rQgq9Dv369dOzzz4rSdq+fbsOHTqks846q/2DcqmUWkRvYVf41pn3Na3NKqStCoZVVVUa0vc0FVoSsm7dOr399tteCyeV0pZzpeRLHUmeOXOmBg4cqE996lP6xje+IWutl9+lmSKDWzF70UZVybGIzdyjQABFVt2GBm14YwQrYHsAACAASURBVL9e/tN/tHlzWkoAlaTNmzdr1KhRMsbohz/8ofbsKTzi8f7772vevHn6zGc+0/TcDTfc0HQd/elPf6r+/fs3FdzpFJuXZa6bd50efAduGrQVeHLrT7fdPaZZESbJbwX9TlXkqF8x2zpWVlZq6NChOu+881ReXq7x48dr/fr13n+Fug0Nmv3LLdpne8vKqGH/h5r9yy2tnieCVUIntLVWN998s77//e+rX79+mjFjhqZPn657J1yoZ2aO0K5du1RbW6uvfvWrBYN5DAiwDlr2uq1Zs0aVlZX6xS9+oalTpzabIheaUva8ak3L1+H+++/XQw89pJqaGk2aNEmLFi1qNu2iINdKqUX0FoaivZLtLR0/flzTpk1TbW1th76+mE6Rgm152kr9Ys2/n9CWd+3apd27d+tzn/tcSZWMf//73+vFF1/U5s2btXXrVq1Zs0arVq068aBcR9RLCG4d3YvWdyXHTtfBzdxL2VJIku644w4NGjRIgwYN0tKllNOJQd2GBv3tgTm65r3ZbYa/TuXwN567OT1yLLM8pa2b07bOU1LhANrY2Kgbb7xRCxcuVM+ePXXHHXc0K+CS784779Qdd9yhU045peDnH3300WYhxrtcB+5fdkuypXfgJtw51NEOCXSBIkf9itnWccSIEdq/f7/27dsnSVq5cmXTshGf7nvyVX14tPnU5A+PHtN9T77q/d/qVCVUjX/ooYfUr1+/pnXGt9xyi7Zv3641f0rPkhECbJHa6nUbMWKE9uzZowMHDujdd99VfX19QkfZMcXueZWvrdehurpaL774ojZt2qSNGzd2bLTQNYAmVKjGRbFrqRcsWKBx48Z5W7vQblu+/wodWDj2hLZcVVWlhoYG9ejRo/2R5CFDTqguaIzRoUOHdOTIER0+fFhHjx7V2Wef3fybfY2odyC4lbIXba6S46ZNm7R582ZNmTKluOMKVClbCj3xxBNav35905ZCtbW1TVXIY9VWkF+4cKEGDx6soUOH6tJLL9W2bduavi43Yjdw4EANHjxYhw4d6upD75Biwl+ncfwbL+bmtL0O20IB9KmnntKQIUNUU1MjKdOZ9eijj55wvVy/fn1TQbfWLF26tN2OYCcRduC2FESbzOrqGXEFuXbgun5/CaN+Hd3WsaysTLW1tbryyis1ePBgWWv19a9/veDPdOnUeHP/h0U9H7QhE6TKEdInLm2zEzpnypQpzTqTy8rKtH79eo3o37vpucmTJ0e93zkBNg02L5P+5ePSXb2LOlF15p5XRXMNoD4L1XRgE3Nfill/+tJLL2n+/PnBrV0odiR51KhRuvzyy3XOOefonHPO0dixY5v2HGySghuyGLV1o3/55Zc3bZk0cuTIpumU27Zt05gxY1ReXq5TTz1VQ4YMKbjvYUzaCvLXX3+9tmzZoo0bN2rmzJlNlbjzR+zq6+v1/PPPB7tGPIiRCce/8WJvTls7T7UWQF977TUZYzR27Fh9+OGHWrhw4QnXy+PHj+v222/X/fff3+pxvvzyy+rZs6cGDRrUod+rJBF24Lbkq02mYhTXtQPXRwdwB0f92p1KXWA/W+mvncBbtmzRokWLmm1Nl+PaqXHu6RVFPd8mlq4EhwAbO8cTVVsBasmSJZ3ba5zPNYBGVqgmp5j1p4sXL9Ybb7wR3NqFYkeSd+7cqe3bt2vPnj1qaGjQypUrTyyAlIIbMt+6alSgIx0S+VsK1dTU6De/+Y0OHjyod955R88995x279594jeFUGStg8fQVpA/7bTTmj4+cOBA0/KIliN2uZGGEAUxMuH4N17szWmh81RbAbSxsVEvvPCCFi9erIqKCi1fvlzDhg1rdr18//33tXXrVl122WWqqqrS6tWrdd111zVbR9sl11EfHbgJ/336aJMhjeI6ce3A9dUBXOSon2+unRozxl6oipOan4MrTirTjLGB1qtAUQiwsXM8UbUWoArtedWpXANoxIVqill/GqpiRpKXL1+ukSNHqlevXurVq5euvvpqvfRSi2DmaUQ9iHV+kSl2S6EvfOELGjdunC655BJNmjRJo0aNOjG4+ZoS7nKTXeQxtBXkH3jgAZ1//vmaOXNm0+uUP2J30UUX6Tvf+U5xv1sX8joyUSrHv/FSbk5bnqfaCqCVlZUaM2ZM03ZM48aN086dO5tdL3v37q133nlHu3bt0q5duzRy5EitWLFCw4cPl5QJyMuWLevc9a+S+/UzgCKIPtpkEDMLfHDtwA2pA9hh5NK1U2P8sD665+8G6yzzFxlZ9Tm9Qvf83WCNH9anpONBWAiwsfNwouronledykcA7WChmhCUuv40J7S1C8WMJPfr10+rVq1SY2Ojjh49qlWrVp04hdjDiLq33vgApg65Tovr6FYCOcVuKTRnzpymkv3WWn3yk59s/gN9jAi43mQXeQxtBflbb71Vf/zjH3Xvvffq29/+tqTmI3YvvPCCli9f3lSNPTRBjEw4/o3nbk4/Upa5jenIzWnL81RbAXTs2LHasmWLDh48KElatWqVqqurC14vW/Pb3/5Wffv27fw9QHPTPXv3lWSKv34GsGTDR5sMYmaBD64duF2493dn8tGpMX5YHy36mwV6/LS5enHWFaWF1xBmD+EEBNgAON2clniiKnXPq04VUQDFiTpalfvLX/6yzj//fA0ePFg1NTWqqanRF7/4xeY/rISqey2lpTfeNYiXspVAMVsKHTt2TO+++66kTBGjzZs3n1i4zceIQAKF3tqbWTBx4kTV1dVJUrMRu549e2rcuHGdsjWED6WEP+88dFqOH9ZHw/qdrs/0/2iHb047GkDPOOMM3X777RoxYoSqqqp00UUX6Zprril4vcx5/vnnm0ZfJemyyy7T6tWrO/z7OBkyIXPdvGt/8dfPAEbsfLTJIGYWSIkUUPL6/Xnq3/qL6t9KpnJtEB1tAcxOQGEEWFeOe685jxKFsvazxEJSCFQHL8ClVOUuKyvTj3/8Y23fvl3btm3Td7/73cLH4Lj+xktvfAdfh1K3n5Gk9957T5WVlbrtttsK/mzXIF7q93d0S6GjR4/qs5/9rKqrqzVlyhT97Gc/U3l5efMf5mNEIIFCb4WC/I4dO5o+fuKJJ3TBBRdIUrMRu8bGxqYRu1KVunfpPffcowEDBujCCy/Uk08+2erPLyX8eddFnZbtddjmtAygN954o+rr67V169agp4RLmXuJ0XNXqv+sJ7qsI9w31zbpI/A4F4HqwgJKnfb9edrb074zBdHRFsDsBG82L9MFR/6g6iNbUnGfXt7+l6BVuRNVrnHnTlRSh08Ubd1cduiPNPfvPPutzI1c78pMeO3K0cvWTtj5x4d4BPR+5np+S1kFfO7pFWooEFY73BtfxOuQq1o7e/bspjVzObntZ3r27Kkf/ehHmjlzZrPy9nfeeafGjBnT6mG4BvFivr/QjX7OM888U/DnnHLKKc22kinoyv/V/FwpFd/R1rsyu89lgec7osRjmDZtWrPp+vPnz9czzzyjk046SWeccYYefvhhSc1H7Iwxqq+vV01NTVN129raWn3wwQe66667tHDhQj3wwAMqKytTr1699OCDD6q6ulpHjhzR1KlTtXbtWhlj9POf/7xgm5IK7126bds2LVmyRPX19XrzzTf1+c9/Xq+99lqnFZNy+fuEP7mO8Ny9RK4jXFLH7iN8/H0GIPe7zvy3zTpy7Lj6nF6hGWMv7HDgaW1AIf9nt6utwFPkkiity5xbStoVwfX7AzF+WB89+sobkqSlU0uryZIL4CXtUO5xdsLAc3q3/0VtcDrfZu9nPqKjmccpuE9nBNaFh54ZL6NELlOHfEhTD5UC2QPOkVMvckreT+fe+CJeh1K2n5GkdevW6e23325zr2TXaXFBTKvzsca9Cwu9tTViN2/ePNXX12vjxo167rnnmhVZyx+xK2UrnoceekiStGXLFlVUVKhHjx6tzlAotHfpY489pokTJ+rkk09W//79NWDAAL3yyiutviRLp44q+aYwFGn4HVw5L5fwOGKXNJdRXC/LTgKYjh2MNKwdDWR2grOU3NflI8C68HCiCuLm0hUn7KA4T0tPyfvpPP2oyNehze1nsoWg8refOX78uKZNm6ba2to2D8M1iAexjkhyny4aWaG3Urbi2bZtm6644gpJmSnEAwYM0KJFiwrvXfrCEl2zd0Gz5xsaGtS3b9+mx5WVlWpooPJ2uwIo1ObCW0d4glumhMDL65iWwCPHjvC0rB0NZZmeq5Tc1+UjwLrwcKIK5uZSKv0inqITdho49yIH9H66rr9xWlNV5OvQ7vYzLzU0235mwYIFGjdunCor235dXYN4EOuIfIms0FuxW/HU1NRoxYoVamxslLVWmzZt0ujRowvvXfqVT53wM9E9paIjPABeXseUBB7njvC0jPilZXZCQPd1vhBgXXg4Ufm4uazb0KBPzvm1qkop3uBDSk7YkocCDj44Trtx7kUO5P1M/L0o4XVodfuZ+nd09+M7m20/89JLL2n+/PmqqqrS9OnT9cgjj2jWrFkFf65rcZMgCvZ0Q8VuxXPTTTepsrJSw4cP1+HDh3XJJZfouuuuK7x36b0vq2rGc832Lu3Tp4927/7rOuE9e/aoT5/Oe6+TLPASlISLGPrqCP/KkX/WV478s89Di4qX19Fn4PnaE4mtX3XuCE/TiJ+v2QkJvp+h3Nf5RBEnF54KKLksUvdSdED6a2g6djhzAS7m98h93WO3Zb6/d9+SXoe6DQ26+/1b9I49TefOXVlU8YXc9888MCdTvKHE7/fyWrrwUEDJuXhR9t858stbdJKOypT4froI4r0ooV3nV6296aabJGW3n3lkq35z+4hm288sXry46eNFixZp7dq1Xb91FTrdN7/5TV100UX62te+VvDzEydO1M033ywpM+04N+W4V69e2r9/vz796U83a1O5vUtzs2Uue/iAamtrNXz4cFVUVOj666/X7bffrjfffFM7duzQxRdf3DW/aHcVQNG73Dnxvidf1Zv7P9S5RRYvQoZrEagmQyao/vEfSGXSwH98oaRjqdvQ4HwcLkV/nDvCXYvuhSbiQliSgriv840A62rIhEQbgHMVY8nLBbju2GjNPPRw5mR7SoVmHLtQ4zv6SyiviqLNVGkrNrD4CDxeXktXHioYzhh7YbOKlFJpvcg7Hs+MGpV6AdbmZSV3agTxXkglVXJsWbV2xowZ+uBwo/77gvXSkqHq16+fVqxYUfShdPdCNb44VaQsQaFOjR07djRtv5O/Fc/BgwdlrdWpp56qY8eOqby8XNXV1Se0qdYMHDhQEyZMUHV1tcrLy5sqHXeG3AyJI8eOa3QJHYbBcOm8lfxVnXU0flifOF//wPioeusqhA5c545wj5WtXd8HH+9jrrBnktdh52uXj/u6gBBgI+el6IDjBTiE8Ogj8Hh5LV15mHbjrRfZhWOniM/3oisuOO1uP5NbW95KCJ48ebImT57cmYfobvOyZLfrkp9ZFl5CVzvvZ0sd3Ypn7969Gjt2rHr06KHRo0c37T/bsk3le/7555s9njNnjubMmVPkL1ScEG6wvfAxepqSqZKp6ZBIgRA6cJ07wj3NzANaQ4CNnHMvmeR8AQ4hPPoIPF5eS1eept0k3ovs2CkSxHuRlVsT1lUjdkHysOe1K9fQ1NXLLdrq1Jg3b17BH11VVaVXXy1iy46E+LrBdl024szH6GkKpkqmpkMiJULoTPfSEZ6SvWjp3AkTRZwi56XogGN1Ml/hsZjnfX+/FEhF6LQstHfsFAnivVAAhaRCEUBFSdeiIl72eEzL1hCOfJzzc6Fpn+0tK1N8lVMffIyepuCc7eVvA96EUlWa4n8eqjF7PA7uRZojwDqq29Cg0XNXqn9CFYBzVYz7nF4hoxK3yHC8AIcQHn0EniC2G/Gx12UIHDtFQngvQrlwBcHDjX7dhgZNfv8WXfverJLOlSHM0gghyIfAxzk/iNDkY2uJFGyzEcKIX5q4nut8deBSJdxdCOcp7kUKYwqxg6bCQ9nG7TLtxmWap3PxBsfqZD6KBrlOV/G17jPxqbdSOhbaeyjgkPR7EcI6pGA4TpN0LdImuU8rD2G5RVr4OOcHEZp8FZoZMkFfeamvpDiLrYW0ZMOHJN8DH+c6H1WlmfbqRwjnKe5FCiPAOkhVo3IITb5K+LsGlqQDjy+JrwvzYcgErdn1nzp33Xd0jt7VXvMx7R48QyMYlYiT442+j3Ola2jyUp3b53rHIgtBhcRHh2EQoYlCM5I8/W2kiMv9g6/7QpeBCZ9rmp3vpSI8v+UL4Tzl614kFfeWeQiwDtJ0g+ta3TNNJfxj770NQd2GBs1e8wl9ePQHTc9VrCnTPX0bovk9QrhwBSN7Q7+vbo7OPL5PPYqsQuzjXBnELA2PW0PEzrXDMJjQ5GnPzphHu4KoXJ8SIdwXhjS4EsL2My5COE/5uBdJy71lPtbAOghlob0r5teHI4T1FpL7Gh5fv8fSqaMSu/B5KySVq1r75xcyVWtjLfgzZIJuO/sRTTrn19I/bi1qlMrXudK1qIhzUZLsesd9PT6u4zKlr3d0bRMpaFMhrHP3IS3XTwr2+BHCfWEIITotQjhP+bgXCeXe0icCrINQKqW6SmPDjpXv/U9LCX8+qoOm4QLq5cKVoqq1LlUQ03KulKS6Y6M1+vAPdN6hxRp9+AeqOza6uB/g2iZS1KZCCE11Gxr0twfm6Jr3ZifaWYd0COFcF0KIltJTOTfp85SPe5E03JO1RIB14KUCcADS2LCT5HLSDuHC4+OGLITfwwfnC1dKqta6jjLlzpVnmb/IyEZ7rvQy2ubaJlLSpkLg4/1M0/UzyRkvaZGWETtXaZmZEArXe5G03JPlYw2sozSs/WStnz+uxRNCWG/h44YshN/DF6cbupRUrfWxpmr8sD664NcLJEkDZ8VZXdvL2jLXNpGSNhUCH+8n10+0lHRByRDWNIe0DteH2Dt20nRPlsMILILorUsL19HLEEaqfPTUpWV2gjMf+0wGIE2jTC68vA6ubSIlbSoEvjrruH4iNElPe+WaEZYQZgb4xggsguitSwtf1VaTHKny1VOXhtkJzlJStTZNo0wuPeleXgfXNhFQm4p9VMLH+8n1EzhRmq4ZaZH0zADfCLCQRNjwJQ0nbW7IPErJPpNpnH5UCi+vg2ubSEmbCgGddUDn4JqBzkaABTxKy0k7bT11iRoyQVr3cObjSDd1p1Mjw9vr4Lr3aAraVAho10Dn4G8LnY0Ai1RJOmxx0kZa+ejU+NaZ92W+39tRdT1fo21peC3SgNFTdIak70VCQEc4OhMBFvCMkzbSivbsR26rrSPHjmv03JV0cgHwLoTzdQjHkBa8ls0RYIEADfynOLcZATpb7Bdx1622AADo7giwAIAuEXv49CFt+yO6ok0AAIpFgAUAoIuwPyLQOjo0AHREj6QPAADSrG5Dg0a8doP6v3qzRs9dqboNDUkfEhLU2pZaxWy1RZsCAHRnBFgA6CS59Y77bG9Zmab1jgSO7mvG2AtVcVJZs+eK2WqLNgUA6O6YQoygMH0oHLwX7ljviJZct9qiTQEAujsCLAB0EtY7ohCXrbZoUwCA7o4pxADQSXysdwTy0aYAAN0dARYAOonrekeflk4dxbTwgJT6foTUpgAASAJTiAGgk7iudwRaok0BALo7AiwAdKLxw/oQLuAVbQoA0J0xhRgAAAAAEAUCLAAAAAAgCgRYAAAAAEAUCLAAAAAAgCgQYAEAAAAAUSDAAgAAAACikFiANcZcZYx51Riz0xgzK6njAAAAAADEIZEAa4wpk/SApKslVUuaZIypTuJYAAAAAABxSGoE9mJJO621r1trj0haIulLCR0LAAAAACACSQXYPpJ25z3ek32uiTFmijFmrTFm7b59+7r04AAAAAAA4Qm2iJO19kFr7XBr7fCzzjor6cMBAAAAACQsqQDbIKlv3uPK7HMAAAAAABRUntC/u0bSBcaY/soE14mSrk/oWAAAAAAgtZZOHZX0IXiTSIC11jYaY26T9KSkMkk/sdbWJ3EsAAAAAIA4JDUCK2vtryT9Kql/HwAAAAAQl2CLOAEAAAAAkI8ACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEgQALAAAAAIgCARYAAAAAEAUCLAAAAAAgCgRYAAAAAEAUCLAAAAAAgCgQYAEAAAAAUSDAAgAAAACiQIAFAAAAAESBAAsAAAAAiAIBFgAAAAAQBQIsAAAAACAKBFgAAAAAQBQIsAAAAACAKBBgAQAAAABRIMACAAAAAKJAgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEgQALAAAAAIgCARYAAAAAEAUCLAAAAAAgCgRYAAAAAEAUCLAAAAAAgCgQYAEAAAAAUSDAAgAAAACiQIAFAAAAAESBAAsAAAAAiAIBFgAAAAAQBQIsAAAAACAKBFgAAAAAQBQIsAAAAACAKBBgAQAAAABRIMACAAAAAKJAgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEgQALAAAAAIgCARYAAAAAEAUCLAAAAAAgCgRYAAAAAEAUCLAAAAAAgCgQYAEAAAAAUSDAAgAAAACiQIAFAAAAAESBAAsAAAAAiAIBFgAAAAAQBQIsAAAAACAKBFgAAAAAQBQIsAAAAACAKBBgAQAAAABRIMACAAAAAKJAgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEgQALAAAAAIgCARYAAAAAEAUCLAAAAAAgCk4B1hhznzHmD8aYzcaY5caY0/M+N9sYs9MY86oxZmze81dln9tpjJnl8u8DAAAAALoP1xHYpyUNstYOkfSapNmSZIypljRR0kBJV0laYIwpM8aUSXpA0tWSqiVNyn4tAAAAAABtcgqw1tqnrLWN2YerJVVmP/6SpCXW2sPW2j9J2inp4ux/O621r1trj0hakv1aAAAAAADa5HMN7E2Sfp39uI+k3Xmf25N9rrXnT2CMmWKMWWuMWbtv3z6PhwkAAAAAiFF5e19gjHlG0n8p8Kk51trHsl8zR1KjpMW+Dsxa+6CkByVp+PDh1tfPBQAAAADEqd0Aa639fFufN8ZMlnStpCuttbmg2SCpb96XVWafUxvPAwAAAADQKtcqxFdJminpOmvtwbxPrZA00RhzsjGmv6QLJL0iaY2kC4wx/Y0xH1Gm0NMKl2MAAAAAAHQP7Y7AtmO+pJMlPW2MkaTV1tr/aa2tN8Ysk7RNmanFt1prj0mSMeY2SU9KKpP0E2ttveMxAAAAAAC6AacAa60d0Mbn7pZ0d4HnfyXpVy7/LgAAAACg+/FZhRgAAAAAgE5DgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEoTzpAwDSaOnUUUkfAgAAAJA6jMACAAAAAKJAgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFAiwAAAAAIAoEGABAAAAAFEgwAIAAAAAokCABQAAAABEgQALAAAAAIgCARYAAAAAEAUCLAAAAAAgCgRYAAAAAEAUCLAAAAAAgCgQYAEAAAAAUSDAAgAAAACiQIAFAAAAAESBAAsAAAAAiAIBFgAAAAAQBQIsAAAAACAKBFgAAAAAQBQIsAAAAACAKBBgAQAAAABRMNbapI+hXcaYfZL+nPRxtONjkt5J+iCAFmiXCA1tEiGiXSJEtEuEprPb5CestWe190VRBNgYGGPWWmuHJ30cQD7aJUJDm0SIaJcIEe0SoQmlTTKFGAAAAAAQBQIsAAAAACAKBFh/Hkz6AIACaJcIDW0SIaJdIkS0S4QmiDbJGlgAAAAAQBQYgQUAAAAARIEA64Ex5ipjzKvGmJ3GmFlJHw+6H2PMT4wxe40xW/Oe+6gx5mljzI7s/89I8hjR/Rhj+hpjnjPGbDPG1Btj/iH7PG0TiTDGnGKMecUYsynbJv9P9vn+xpiXs9fxpcaYjyR9rOh+jDFlxpgNxpjHs49pl0iUMWaXMWaLMWajMWZt9rnEr+EEWEfGmDJJD0i6WlK1pEnGmOpkjwrd0CJJV7V4bpakZ621F0h6NvsY6EqNkqZZa6sljZR0a/b8SNtEUg5LusJaWyNpqKSrjDEjJd0r6XvW2gGS/lPS/0jwGNF9/YOk7XmPaZcIweXW2qF52+ckfg0nwLq7WNJOa+3r1tojkpZI+lLCx4Ruxlr7W0n/0eLpL0l6OPvxw5LGd+lBoduz1r5lrV2f/fh9ZW7M+oi2iYTYjA+yD0/K/mclXSHp37LP0ybR5YwxlZKukfSv2cdGtEuEKfFrOAHWXR9Ju/Me78k+ByTtbGvtW9mP/13S2UkeDLo3Y0yVpGGSXhZtEwnKTtPcKGmvpKcl/VHSfmttY/ZLuI4jCd+XNFPS8ezjM0W7RPKspKeMMeuMMVOyzyV+DS/v6n8QQNez1lpjDCXHkQhjTC9J/0/SN62172UGFjJom+hq1tpjkoYaY06XtFzSf034kNDNGWOulbTXWrvOGHNZ0scD5LnUWttgjPm4pKeNMX/I/2RS13BGYN01SOqb97gy+xyQtLeNMedIUvb/exM+HnRDxpiTlAmvi621v8w+TdtE4qy1+yU9J2mUpNONMblOfa7j6GqjJV1njNmlzFK0KyTNUzZ66wAAAWBJREFUE+0SCbPWNmT/v1eZDr+LFcA1nADrbo2kC7KV4j4iaaKkFQkfEyBl2uHfZz/+e0mPJXgs6Iaya7j+r6Tt1trv5n2KtolEGGPOyo68yhhTIem/KbM2+zlJX85+GW0SXcpaO9taW2mtrVLmPnKltfYG0S6RIGPMqcaYv8l9LOkLkrYqgGu4sZaZW66MMeOUWbtQJukn1tq7Ez4kdDPGmEclXSbpY5LelvS/JdVJWiapn6Q/S5pgrW1Z6AnoNMaYSyX9TtIW/XVd1z8psw6WtokuZ4wZokzRkTJlOvGXWWu/ZYw5T5mRr49K2iDpRmvt4eSOFN1VdgrxdGvttbRLJCnb/pZnH5ZL+rm19m5jzJlK+BpOgAUAAAAARIEpxAAAAACAKBBgAQAAAABRIMACAAAAAKJAgAUAAAAARIEACwAAAACIAgEWAAAAABAFAiwAAAAAIAoEWAAAAABAFP4/GYoPIfySu6QAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f54ed202748>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# create stacked errorbars:\n",
"fig = plt.figure(figsize=(16, 10))\n",
"ax = fig.add_subplot(111)\n",
"plt.errorbar(np.arange(res_df_filtered.shape[0]), \n",
" res_df_filtered['mean0'].values, \n",
" res_df_filtered['std0'].values * 20, fmt='o')\n",
"plt.errorbar(np.arange(res_df_filtered.shape[0]), \n",
" res_df_filtered['mean1'].values, \n",
" res_df_filtered['std1'].values * 20, fmt='o')\n",
"\n",
"# zip joins x and y coordinates in pairs\n",
"for x,y in zip(np.arange(res_df_filtered.shape[0]), res_df_filtered['mean1'].values):\n",
" label = res_df_filtered['exp'].values[x]\n",
" plt.annotate(label, # this is the text\n",
" (x,y), # this is the point to label\n",
" textcoords=\"offset points\", # how to position the text\n",
" xytext=(0,10), # distance from text to points (x,y)\n",
" ha='center') # horizontal alignment can be left, right or center"
]
},
{
"cell_type": "code",
"execution_count": 236,
"metadata": {},
"outputs": [],
"source": [
"def plotData(data, initial_probs, transition_matrix, m0, std0, m1, std1):\n",
" # Set up the axes with gridspec\n",
" fig = plt.figure(figsize=(15, 6))\n",
" grid = plt.GridSpec(1, 8, hspace=0.2, wspace=0.2)\n",
" main_ax = fig.add_subplot(grid[:-1, 1:])\n",
" y_hist = fig.add_subplot(grid[:-1, 0], xticklabels=[], sharey=main_ax)\n",
"\n",
" # scatter points on the main axes\n",
" main_ax.plot(data)\n",
" main_ax.plot(res.smoothed_marginal_probabilities[1] * max(data) / \n",
" max(res.smoothed_marginal_probabilities[1]))\n",
" p_filter = fit(data, initial_probs, transition_matrix, m0, std0, m1, std1) < 0.5\n",
" main_ax.plot(p_filter * max(data) / max(p_filter))\n",
"\n",
" n, bins, patches = y_hist.hist(data.values, 100, histtype='stepfilled', \n",
" orientation='horizontal',color='grey')\n",
"\n",
" d_0 = norm.pdf(bins, m0, std0)\n",
" d_1 = norm.pdf(bins, m1, std1)\n",
" if(max(d_0) < max(d_1)):\n",
" r = max(n) / max(d_1)\n",
" else:\n",
" r = max(n) / max(d_0)\n",
" y_hist.plot(d_0 * r, bins, color='red')\n",
" y_hist.plot(d_1 * r, bins, color='green')\n",
"\n",
" y_hist.invert_xaxis()"
]
},
{
"cell_type": "code",
"execution_count": 239,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/usr/local/lib/python3.5/dist-packages/statsmodels/tsa/base/tsa_model.py:225: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
" ' ignored when e.g. forecasting.', ValueWarning)\n"
]
},
{
"data": {
"image/png": 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TvwEARqXH8JPJEQNisOSKfNhpGpcWpKJZb4ZE3Hmy3psBXLuRWWi48/xBmDOcOSbzBzCTnLImHUxWO6pbDYhRMp/zTK2m18bWZxAz3x0lpAaOtBEQTFmTHgBQ2qRHq4EJ5lr0FpQ16ZGlVnm3sYhYRoEL+bKojuTJ3mRfaQtq2oxuH5s5NBG17R2LlHdPz8b93zLB3pGKEOutx/XPM4WB4QqLxwCOoqj/gDEsUVMUVQngGTCB2yqKou4AUAbgWvbpvwK4BMA5AHoAC3tgzD3CM8880+3jVe1V+OzwZ1h5eCWKW4oRq4jFPQX34K6xd2FUyiiP2w+0OYlQIjoCuBEAWtEH9hWBEBTsVr4GxdtVXC5oU0fKeHt5AGjUMoFdHqvAtBsENvUlBJ0oBXP51BgtvAqVHhfBPx7NPj5IrcKym8bhQFkLrvpgJ/94tkPjbpGIwm1TsvjbSdEKp/d6YPYQvL2hEEq5GNWtBj71sie54/N9AIARaZ1XybkecKdq2mFhlbozdSSA8xqvauDspI2AAGraOhaty5p0aNNbkJOoQlGDDkcqW30I4OIYExNliEdwvIkJ+08vmZlUNDPB8pZHZ2D6a5udHvvwlvG48ePdAIDMeCUGOXz3RytDTOmSs1kQpvA5jwlxobyhi4cudPNcGsB9/g4qVDDbzPjfmf9h5eGV+P3c77DTdmQhCwuwAMOMwyDdJ8XqfauxGquDPdQucQjgjtM0PZu70df2FYHQ4/jRRkBvtkIiohCnlEFv7gjSTlQzF7GBCcyFLRR6fhG65lRNOzLjlVDJJRCLmQlSs9aMza31iJCKnYKdaFaBU7MpsUlRzqmxw1OjBb/vA7NzYbPTeG/TOUx5ZRP2PHEhkl2CvEDSojPjUHkrUmMUmO4mLbIgPQZRcgnu//YwbpzUURetM1mhkvtdWt9/YFMoKTaA86zA+baA1J8wOGQ4/HqsFgBw7fh0FDfqnIx3BKOIAdqrEPoSHBfA9W6abWWrARTF1PP++6qR+HhbMQrrtRCLKMgkIn6BZ1xWnFPwXNtuRH27sdNiVdDgA7g+pMD1R47XH8fKQyvx5dEv0ahvRFpUGh4//3EsHL0QX733VbCH5xUqvR5mqRSwEJcwAsEv/DAx0ZlsiJCJoZJLUM0aWByvasPSTecwe1gSMljlxmQNg4an/QyN0YKZr2/m1dILchPxyW3j8cMBpmS4QWvCnuJmXJCrhsKhBQAXYHF1YfEq5/T6hVOzPL73tn/O5FsO5A/oCPha9ZYeDeC4OpU3rhmFOFXnsgC5RIzhA6Kxp6QZn2wvcXrdhKz4HhtXOPHL0WpsPtOA16/pJkOHNzERWgMn7t7opJ+zt6QZyzaf63R/vEqO9LgIrNpXgb/NyHH6nXpEJOlQSEP5q+fScHkhrneCfI3Rgki5BFKxCNdOyEB0hBT3fHWAd/28enwGUmIUuH1KFpQyCRaMTYM6Uo6PthbjSGUb5gx3Po+1GSyIkksgEvXyIoUsEgAVVgocqYhlaTO2Yfn+5Zj48UQUfFCApXuXYvrA6fj1xl9R9kAZXpj1AnLic/DMM894TLcMJZQ6HXQqL1MGCARCZxxqULzFYLZBJZNAJRdDxypwj/1wFFY7jQfn5PITCqLAhR6najR88DZ7WBK2nm3Af/aW8/WLB8paUNtuxMyhSU6vmzQoHhsemo67L2Bax3DtIgBg66Mz3da4uZIRr+QL/R0DI62pZxfkuFq75Jiug8RYtvbNkZBLiwoi607U4ddjHsrKOQXOJsCFkiaNvD1x7Ye7sPlMAwDg3RvG4NbzBgJggoLcpCjUthuR9/Tv+OFApfBAWCR2cHgMYXqxD5zRYsNLv56CxmiB3sRc2zgcDZkA4JbJA/HhLeOhZJ/z5rWj8eDsXIgo4Filcx1cVasBo5b8gSd+Otbjn6ETFAXIo0kAFy7QNI0tpVtw60+3IvWNVNy79l4YrUa8ffHbqH64Gt9f+z3mDZkHsZuTZrgEclEaDbSRnS2qCQSCl/iRQqkzW6GUiaGUSaAz2aA1WXGqph03TspE/oAYyCXMdo2WMJgo9DPq2jsK9N++nrEh/3p3OQAgQipGZQujqKbFOdelURSFwUmRfKDmmPaWmeB9OpajO6ljT7lA06Ax4ffjTOpZghv1jaPAIV102hA1ElSyThOy/kx1qwFGi637QMEbBc7BRMlVXTlW2YYnfzrGqx4fbC7CznONPo27r5ARF4FnLs/HQ3Nycd/MHCy9cSz/2MP/PYJlm4uEbYgShUcA59IHrisqmvX4aGuRX0ruT4eq8NHWYhQ8+wd+OlQFpbxjjpwZr8SQpEgsmjqoy9dHyMTITY7i25Rw/P2bgwCAb/dVuHtZz6MIrwCuX6ZQ1mnr8PmRz7Hi4AoUNhciWh6N20bdhjvG3oFxqeP6VH55lEaDhkRi7Uwg+I0fAZzBbINSLkakXAy92Yqiei3sNDCDTa+TiEWQiCgYrUSB85UtZxtw28q92LF4FtICaPJR0cIU6X+2cAIi5RKkxUbwhh0F6TF8c23XFMme4MSSi1Hw7DqcqG7DJQWpAd++zmTFhBc38LejFZ1VNo57pudgaEo0Zg9LAkVRuPPz/ThaRRQ4jqoWA+w0YLXTkIq7mFPwNXACAoQu2ggYLTZcvnQ7AODOadlIjVHg1d9PAwAuG5mK924Y06fmNEJRySUQiyj848Ih/H1TBydgx7kmAMAn20tw38zBnjdEiYSZzAQbgQrcbZ/uRXGDDvNGpCIj3re6PqtDU3Szze6kwIlFFP548AKPx9z5g9X4YlcZtCYrItm62UMOAZ1PLR/8Zer9QHRa776nH/SbAM5O27GheAM+OvAR1pxZA6vdimmZ0/D0BU/jquFXQSnt/kAOloukX9A0otvbUZyTE+yREAjhj93qcwqTzmyFUiqBUiaB3mzj6zQcJ/1RCgnaDKRW1Vc+31kKAPhmTxkevTgvYNs9WNaKATEKzGBTJF+5qgC3fLIXADAmM5YP4NQC+vctu2ksFFLfE19Ucgmy1CqUNPpgxiAAVzvw7upQJGIR314AAEZnxGDDqTq0Gy3dBn79AbPVjjoN810aLTZIu0qXdWnk3S12JoXStZH3ruIm/u+aNgNa9Wb+9i9Ha/DUpcOR0k0qbF/BbndWlBxTljm+umMSnlx9HN/sKRdeIUaFSQolr8Axn6yr4KeYNXI516D1OYD73+Fqp9uu37WQwOvCYclYsb0E2wsbMGWwGhaX+u+aNmOvuO06MfGu3n0/P+nzAVyTvgmfHv4Uy/cvR1FLERIiEnD/pPtx59g7kafOw5IlS/Da6teCPcweQWEwQG42ozUmhJolEgjhiN0OgOZXwL0tENebbYhXyZDIOhGuO1EHoMOpEGB6iHHpeATv4VaFdxY1eXimcNr0Fmw5W4/bHSz+pw3pyGgYnR7L/y1EgQuEahYplzj1EgwknMGOL4zKYL6L+74+iIx4JRZOycKQ5CgPr+qb1LUbeUHEZLWjy2/BxYWyW2hbR8DnwLazHamSf5yow9iBcU6PFzdo+0UA16hzTiuOdOOGSlEUnrl8OEobddhf1gKrze65FtUhhTKkWzjwQWbX16aT1R0Oi+fqtJ3qdoVgttpxsLzF6T4hi1eujBsYB7GIwj1fHXS6/4pRA/DzkWqcq9f2fgAXZvTZGrjDtYexcM1CpL2ZhkfXP4rUqFR8veBrVD1Uhdcveh156sCt0IYqca2MHN0aF+fhmQQCoVvs7Aq5z20EbFDKxBiTGet0v+OFLyM+AufqNHwdC0EY5+q1OO/ljdh6ljEvqA9gfdjukiZYbDQuyk9xuv/zRRPx7OXDnQr2u1RZAoxSJobe1DMB3K0r9/J/L795nFevnZKjxr0zclDapMN/9pYHr44lBHBciDFabKho1mNPsZuFBS4gs3vXyNtxjn62ToOhbKD82c5S1LJ90H64dwoAoKiH1NpQg/vO779wCG6fkoWYCPcqsFwixtXj0mG22lEs5LsJlxo4dsWA5vrAuQnkLnl3G/+3oM/uhpJGnVMKZV5KFF67ZqTX25FJRIhTdnbmffqy4QCY45rQPX1KgbPZbfj5zM94e8/b2Fq2FSqpCovGLMK94+9FQXKB29cEyogkFFMs45uZ1J5mEsARCP7BBXA+plDqTVYoZRLkpXRYwT968VAn1UYdKUd1mxH/WnMcL17p/nzVn9GbrXhh7Sk8MHsIkqI6FIXPd5byqX8UxfQXMlltkEv8d+w7UNYCmUSEkenOWQzTcxMxPTcRNjuNufkpvNtdb6CUSVCvMXp+oheUNuqwrbCBv73hoel8s26hiEUUHpubh8fm5mH2m1uwt6QZ2wsbYadpjB0Y51YR4bDbaby67jSuGZeOwUnhr9o5Kpkmqx0XvrEFAFD6yqXOT+TOJ0JSKLtoI6A1WZEULUdGfAQ2nKrHwbJWKKQijMmIhVImRnGD1q/PEorY7TSWby3CZQUDeEOgKjaAu3RkKnI9KL/c4+fqtR6fCxETwIVPHaH7cboeN+0+putz9b+L5+WBAnD3dN9LdNSRMt7N9/cHpiEvJRo0TSMrQYkX1p6CiKKw6Hz3ZihNWpOTsVN/pE8ocCarCSsOrsCw94dhwaoFKGstw+tzXkflQ5VYdumyLoO3vk5CI5Na0RxP+vIQCH7hrwJnYRQ4sUNN0dhM54WVQWyT01X7+69y0R1bzzbgmz3luHb5LnyyvQTv/8nUEdodJiYzhybBZqdRWBeYSeuZWg0GJ0Z2GQyKRRSW3zIOUwarA/J+QoiQiQOeQvnPH47i6TUneNXC2+DNleRoOY5VteHmT/bg1pV78YFLb64WndlJaa5sMeDDLcW496uDeG3daWw4WefX+webqlZnBY7DanNRcngFjm3k3Z0zoIMC56iu6ExWqGQS3DuDMeTYWdSI5GgFRCIKg9QqfLqj1KlXX1/geHUb/v37GVzy7jZYbXbUa4z4v/8cAgBkxHmu6+JqNB/47rDnjIdwU+DYm64BZ4u+I2DLS4lCu9G3AK6imTF1un1Kll/BGwC+pOD5+fn84iZFUbhs5AAAwHO/nHT7uk2n6zDuhQ3YWdS/nVbDWoEzWo346MBHeHXHq6jWVGNs6lh8d/V3WDBsASRucsW9IRQVNW9RNzaiJTYWVlnPu6MRCH0aPoDzXtWhaRp6tpE3ACikIhgtdkQpnM9RN08eiC92lXnXZLYfwfViK23S43n2wl7SqMP3Byr559w1LRubTtfj0x2leOPabhooC6SoQdsp0A42qh5IoYxi1bE2g4VvPO4PXJuDxfPy8MHmIr4ZOfceY55fj3um52DxPKaUgQt4mnRmvP8nY+9e+sql0JmseGjVYTw2Nw/ZieHTDsdRgato7vi7WW92Uo+9MjHhFDg4T7x1JitUcgmGJDPfT7vRyk+Gh6ZE4UR1O57/5STu6ELJEILRYsO6E7WYOljtU70TMy4LFBIxZBL/dYNaVnHXmqz45w9HkepQ4xfhxrzEFc5IyGy1o6RR273qGy4ulFzo1sUio+MxmRytcDK78YZmnRkqmTgg16nkaGa/Rcicr4VzhidjKbtA12awOKXDthksWL6lGADjWjklp/cWz0KNsAzgLDYLPj38KZ7f+jwq2ysxI2sGPpv/GXZ8tQOnvj+FF/FisIcYEiTX1ZEWAgRCIOAu4CLvJx9FDTqYbXZkJTAK202TBuKT7SUY6NILTCoWYXJ2Av44Uev3cAON0WLDss1FsNrsGJkei4vzk3s9pYhzT3PEMXgDgMnZTLZBs87/OjiD2YaqVgOuGZfh97YCCeNkKmDC7wWOk944Nw26veXCvCScq9fihomZ+HJXmVN/wyY2Zep/R6qxeF4erDY73/C6WdcxqTxTq8HhihasO1GHSLk0IAF5b1HWpOf/3u1Q+9akdQngOFMkdu7dbQ0cTbMKnHMAx9iwixGtkCI1RoGaNiOSopkga0xGLH48WAUAsNjsPtdp/nK0Bo/89wjmjx6Ad9heiEJ4d2MhRmfE4oLcRIx89g8AHaly/vDBFibIHzcwDn8lvABMAAAgAElEQVScqMPCqVlevd5RUT9Vo/EcwIWViQkzRtcaOK5GMFohQXSEFOXNevhCi86MuAC1TFk8L49JQx/hXGM8fEDH8XGuXotxDsY8f/1iP+/8GzZZrT1E2KVQbijegFHLR+HuX+5GenQ6Nt66EX/e9ifm5Mzx2hmuLyO2WKBubERdSornJxMIhO5hG+3SPqRQcjbf52UnAGAuWvuenI0oN1brydFyNOnMsLimWgWZX47W4N2NhVi2uQj3fHUAqw8zk0Kz1e5XQ1ihWGx2rD1WjQtyE7s0J3j+LyNAURQuyE10CgR8pahBC5r2P50w0DimUH5/oBKna9uh8TEdisOxfUUg+tk9cvFQ7H9qNmIipJBLRU79DVvZ9+LSif/+zSF8ubus0zZWH67ig/YBseHjoniovMXJ2t8xgGvQuCwscOcTIb8huw2gKKc2AjY7DY3JihjWDIJz/eSCRMeJ8OpDVV5/Fo5S1vBizeFqPgD3RIPGhDfXn8WtK/c6pZH++/czMFvtMFvtnVNKBaAzWfl+YZeNTIXWZMXhCub2hoemC9qGQtZxHj9d297NMxE+bQQ89IHjFLjNj85ElELicw1cs94csJ6X6kg53rpudKf6WKlYhBsnZQIACl3MTA6UdThgmixhsF96kJBU4NylL2qhxa/4FSdxErGIxXW4DnmVedj2xTZswzY3W+nfJNfVQUTTqE4NfLNXAqHf4UcK5e7iJqTGKHjFTSoW8bn/rnApJQ0aU0hZKP9+nFEFB7E9yIobdDCYbZj7zlbEREjx2cKJPdrI+stdZahrN+GVq7Iwc2gSvtxdhqdXH+cfP/XcXF5FUqtkATFuKGK3EWoBnEomhtVOY+hTv8HE9k6aOTQRny6c6NP2rDY7Sps61M1AxONSsYhPtVNIxE4TrRY2uOYUgN8dFOdBahVevWok3v/zHD7YXMTfv3xLEa4ckxYWaZTH2WbmFMV8l6drNUiLjUBVqwFn6zTOKaq8AscqPN19+WwKpSNtBgtoukM1zU2KxNazDUhmFTjHxY5Hvz+Ka8Z7pyYfr2pDTmIkihs7fk+/n6jFTZM8m/ZsP8eY4sjEIr5HIwAcrWxF7lO/8bf3PnmhsyrpAa5NyAc3jeXPOdsKGzEmM1bwb1UuEePlBQV44ZeTOFXjwe2QEgG0LQwEArYGrotDqLrVAIVUhDilFNEKKdqNFp+aZbfozJ3cI3uCF+aPwI8HK1FYr0Wzzox/rTmO6yZkQCoWwcpmxDQIXEzoq4RkAOfqDHnDkhvwM36GCSbMxExMwRRI0b8bhXoivYpZbatOC5+u8gRCyOJqYiLwmkfTNPYUN2HakERBF8okNrCraw9CE1MH9pc2Y/OZBjxy8VAAQGG9BpeNTMXb143GiGfXwWS1Y/2pOj5VbOzz67Hx4enI6aEJ9mc7SzE5Ox4z2MnvKBdXSMcUwHiVDE1a/xU4bhtJXQTbwSKWnTyZHBrf/nmmoaune2TL2QZUNBswbYga2wobu1xc8BWFVASTgwLX5KCOOqpT10/IwCtXMXbkVa0DsOVsx2ey2Gjc980h/Hb/tICOrSew2JgZ9Nd3TsKNH+8BAPzfrMF4e0MhH9zxcCnZQhQe2g5QIqdJdwtbx8QFMpyjIrcQlKDq2JcKqcirCXt1qwGXvbcdV45Jw8ZT9bhufAZWHajg6xs9cZY1ElLKxTjGfm6ZWMTXsnKUNOgEB3CNWhPu+mI/AGBYarRTgBrbhTLfFTdMzMTmM/Uo8WSnT4kCs6rR03SjwH26owQrtpcgO1EFiqIQHSGBxUZDb7ZB1Y07rDsateYeO887IhJRyFZH4pPtJShr0mHDqXrsK22GVEyBEw9P1XhQT/s4IRnAcdhpO57a9BS+xbdIQQoWYAGS4H3jwf5IekUF2qOi0E6aeBMI/uNjDdy5ei0atWY+fdIT3MSr3jXVqpe5evkuAMADs4dALKJQ02bE3PwUSMQiKKRiGC02nKlth0RE8T2BNp2q75ELu8lqQ0WLHleOSeMnnyPTY3Ho6Tlo0pmcDDIAICFSDoPFBr2Zad3gKzoTE7R7O8HpaabkOB9LeSlRvDOcLxTWMxPtpTeOxZ+n6wPSbNwR7njhKHWYMHO9nh69eCj+NqPD0e7i/BRcmFeDURmxeHP9WQCA2RoORhJMHZ+IAiYPSsDb143GILUKozJiseFUHR/I8HAKnJDaKpruZFDBqZmcIjI+Kw5KmZhPnYxTyXDkXxfhvwcq8MLaU2jVWwTXL3F28VvONsBktWNarhqbztSjvl1YCwtOBW/VW9CgMSF/QDSsNprfLketwO0BwMu/nub/zmJdezl8SZuOV8mdUvLcIupIoeyNdHHf4frAMedIx0B9yf8Y06c0dlEwm/3uTtdqnOrLPGGx2VHTZkB6XO8sLiZGyYEaYMOpegDotHhwvKoNRout3xp/hdaVyQGr3YrbVt+Gb459g7vG3oWllyyFTNxzsm1fcJ3koWkMLCtD2cDe601EIPRpeBcy7wK4UlahyksV1tuKMx8QOknqaTRGK2gwtW4prNObQiJGSaMO2wobkZOowpOXDsOiz/b3WOPVimYDaBrIUjubvsSpZG4nownsfU1aM5Txvl3iDGYb3lh/FmIRFRDXvECSnRiJD28Zh7u/PAAAmDQoHqdrNayZhfeft6RBB3WkHDERUvxlTOAzNuQSEXYWNWH1oSr8ZUwajlS28o+16JjgOzla4TThVMok+OT2CQDAB3BFDTo0ak04XtWGZZuL8OUdEwPS6y/QNOuZFDORiHL6PkekxWDj6XreNRKAQw2ccAUO6DCoaHYJ4LITI3HyublOL4tRSpHOWutXthgEB3BlbKDNtenISlBBZ7Li230VeHlBQZdKHqfyFTfo+DTSY1VtGJ4aDT1bS/zBTWNx79cH+TEJhVPcvljUkS78w73nYcW2Etw/e4jg7XAkqGRo0Vtgt9MQibpQJikqTGrguDE6fw7HdgFcAJc/gFnYf3jVYWx+dKbgt6hqMcBOA+nxnls1BIKnLh3GK/HpcRFOx8rk7HjsLm7Gsao2TMjqn62yQjaAe+SPR/DNsW/wwswX8MS0J7qV/ftU8BUAEpqaEK3RoCwrK9hDIRD6CFx6inev4gr+hVpvJ6jkEFGdVxp7E8dV5jHPr8ebrPsfZ9WtkIqwrZDpv3P39BzMykvGjKGJOFrZ1nljAaCMrc8amKDy8EyGhEhmgtqsMyPDx4nGjnPM5xsXYi0EOC7O7zCnKkiPBVCG2jajT/V6xY1aZCcK+259gTNceeC7w/jLmDSnmqO3NjDBmVKA9TsA/Ha8lq99rGwx9Eoql7c0a92bPBSkxYCmgZM17R0TTr4GTogCZ++swLEplHGq7tMHOcWkskWPgnRhWTlcaiGncGepVfy+bNKZ3Z7T7HYaE1/agFl5SSis1+Li/GSsO1EHvZlpo3L71Cz8/ZtDmJydgNevGYVH/nsEVa0GWGx2tOjMSIruPpVyT0kThqdGO9URjhsYj3EDfZvAx6tksNlptBm6USbDpY2Aax849mJ1rr6jfpFLy8+IVyI1RuHkDisEbvFleKp/LqJCGZIchYmD4rG3pBnDU6P5AO7xeXm4elw6xr2wAQfKWvptABdaS4ssP576Ee/seQcPTHoAT17wZK/bVYc7OUVM8XdRdnaQR0Ig9G+4eh+hBh9iEYXEKDnqNcFT4NoNzhb1b/zBTLJTYpiLv4S1Ir981ABcy5oiDEmKRFmzLuApRjY7zbunJQoMgrnvusmPVgJHK1shooDPFk3weRs9zeZHZmDpjWP4VfXL3vPezOvP0/XYV9qCQQKDY18wOzgN6s1WNOvMeHhOrtNzuuvdtffJC/Epq8at2FbM39/J0TFEaNa7t1kvSGMCp2OOCx0uCpzHNgJcwEdxChwTXHk6v2Q4KHBCKXSY+CdGyREpl+BeNs21sQvziFaDBY1aM1btZ9p73DYli39Ma7LispEDUPrKpYhTyXD1uHSMSItGVYsBT68+jokvbXRKtXVlW2EDTlS34/qJgWvrwS32NHWXfsm6UIaLiYnrKqOji2OKQ7+8uSNS+DRxoRwqb4VSJsawXgrgHMl0WIxTR8qRECnHILUKm07Xg6ZpVDTrkbV4rVPtbF8n5BQ4m92GJzY+gfzEfLx+0euCXuNqeuINfVG9G3L2LBoTEtAa3z9XJQiEgMOvbnp3EW/UmhAll3iVo58crcDRyjafHMICQVmzc1F/VasBkXIJBrF1E9zE+XaHyVlyNLOa2260dmnz7wuv/3GGdyMUqtJwyoA/RibHqtowJCnKrxq6niZLrUKWWoWaNmZSbrTYcbC8xavG4+9tKgQA3HJez6XbPzQnF7d8shcA8BXbLiDTpQdid8F5UpQCSXkKjB8Yh/0O9Urc5w4UxQ1axCplfrupNuvMGOJGCU2KViAmQupsmuFVDZydaSPgsEjSojdDLhEhwsP5JTpCgii5hG+YLgTHAI7LLpyem4gPNhehUWMG3HQocgzsVDIxpuSoMTQ5CmfqNDhT2znFOi02AkUNOpyoZoLaJp2ZX5BwpEVn5o+h+aMCl+bL7eurPtiJ3x+YhtQYN7VdDn3gQhruGsXuKy7gLKzr2I+OKdZRcgm0Zmv36aMuNGhNSIlW8C1AeoOXrhyBJ348jnkFKVixvQRARyB663kDseR/J/HHyTrY2Vrsz3aUYHpu/+h/HHIK3IGaAzjTdAaPTX0MYlHg8tuXLFni9r++htxoRFZpKc7m5np+MoFAEIhvylKT1syv8gqlRW/G6VoNvtpT7tN7+svJ6s7OXleNTeMDs7evH407zh+EsZmx/ONc6lOga/f2OLgUCg2muEnZu5sKcaSi1cOz3VPcqMPg5NBLz3NHakwEvr5zEgBgwbKdglegy5v0OFjeiicvGYYRaT1ndjVtSCIeYhW3l1gTCleHVVdDCneMYY+3grQYKGVivhdYoJj1xhZc8O8//d5Od42OoxQSZ9XDpQ9c9wo23clhsIlN1/S00ENRFHKSIrG/rNnj+AHGHdRR4eTUD25xpCsFrtHhNVzd3a/3T8P03EQsvbFzA/C0WCWqWgx8T0zH88df3t+Bx388BqDD6OTuC7IRE4BG8xzDUqORGqNAm8GC9Sfr3D+JbSMAhHgjbz7N30WBcwjECxx+55EKCWga0HejeroSyCbeQhmcFIVV95yH0RkdC1PcItUtkwciW63Cyu0lsLABXKvBghadGTlP/Io/z9T36lh7m5AL4I7VMT/YKRlTgjyS8CT3zBlIbDacGj482EMhEPoOtHN6ims6TVGDFlvdTJybfbjgVTQzq+SHPLmj9RDtbppCO9amzByahKcvG+40aUzm2x8ELq3Nbqdx2mHVXiEVdrnilLqKZgPu/eqA1+9rtdlR1WLAwF4q1A8EUwerMSuPcWh2dxy6o9XAKJQ9Wf/GccPETKfbrgGcEPOVMeykrabNiHED47C3RFgw4g1aL1PKXLHbabTozbyRjisqmcT5Pbxp5M21EXAIImrbDbxzrSdmDE3E8ar2btMUOd7ZUMgrYepIOd6/cSyADqXUXQDXpDXhlMPvlWsdIRZR+HzRRMzKS+70mrS4CBgsNtjYyfexqjbsLWmGjm3O/Z+9zCKWxsh8Z9OGBFZZUUfKsXPxLKgjZThS0UUNbwCFhB6Fd8pkbnLn53P1Wvxl9ACce3GeU01wpJwJhDVuzvdtrHOoK8291APOHWIRhfwB0RiRFs2nXEvEIkwcFI89Jc188C+mKByvboPNTuNt1gCprxJy+SGRMmbVU2/x3RbZHa5pln1RfQOAEcePoy06GpWk/xuBEEC6NzG58I0tAIDSVy51ur/daPH6gvf3mYOx9M9zAV1p9gat0cq7xwmFm0TWBVCBK2vW86YJAASnk1IUhYEJSpQ16X1KQa1pM8Jqp/nG6+HCR7eMw9XLd2FfqbDAxsLWpnE1jT1JYpSc3yfZahVSoxX8cS4UToFr1JowJiMD7/15DhabHdIAjJ8LIAD41X7iQHkL7DS6NONQycXQmR0DOPb4tAtQeNyYmNS0GgXXI3FBstlm7zal22ix4UB5C26dPBBPXDIMFNXx24uOkEAqpjr1cgOAm1bscVpwMVs9px1yQSK3aPSvNScAANeOT3d6HhdkRCkCP2WlKAoj02NxtLILRZdPcw1x+DXGjpFqTVZUtRpwY3Jmp995JPtdao1WwEWAP//fm6AxWjtdz1r0ZoxKj0Ww+Pnv53dSqbmWOy+sPQWA+Rq4ummTgGMwnAm5AG5UCuN49tu531CQXNBj7+NP3RwQmgGgSqPB4HPnsHPKFK/7VREIhG5wUeC6wmanneoDNEarU/G1EB6+KBf/PVDRqb9Zb9FutCJSJkF8pAx6sw0NGhMmZ3dfT5sSo4CIAkqbPDTF9QIulXP1fVO9Vom+uWsyHvz2MI5WtXpV4wGAb07uq4NlsJCIRZg0KB4rd5SAphlnvZ+PVOOmSQPd1qxwDaelvVTP8vKCAry7sRCfL5oIkYjC32cNxuGKVjx0kbB0/9SYCChlYiycmgV1lBw0DbQZLIIdXrvDMag6VaNBtlqFX47V4OZJmYIXAWiaxgu/nERabASu7KIdg0ou4dUkAD61EaBBgwJTC1fdZuCVV09w7TAsXUxqv9hViki5BGmxETBb7ZiUndDpd0NRFFJiFKho6bzAftqlxs1s87wCxLljOp7r8lKieBMUDu47i+yBAA4ARqbH4M8z9e5bcfD7P5TTJwHX8VGgUMSmT7pzp+WCYY0b1dnpGOW2TtM+ZZQEEuY85nxM3jM9B5tOd6RKNmhMvCOyu8/Rlwi5AC5PnYc52XPwxq43sHD0QiSq+kcxYiAYfeQIRDSNw2M655oTCAR/cF9f4Eqr3owEdkL5vyPVKGnUYbLAJt4cFEUhNSaiyzqTnkZrsiJKIcEWtj+QEDMVhVSM3OQoHPax5swdhytaIBOLkJcS5XWj1rTYCPxlTBr2ljajtt3YKWWvO/aXNYOigNxkYb37QolYpQwWGw2jxY7Pd5bhrQ1n8c6GQux/aja/D40WG97eUMgbbUh7qc/dlBw1puSo+dsKqRhfsbV7QuF6nK05XAWAmfgHIoBbf6Kj/ulMrQYrt5dg7bEajEyLwagMYYrDruImHKlsw0tXFnSZEhopl6C2zUGlppjjmjMx8UaBa9FbYLTYBR/bEnZRt6RRx5+jOJp1Zl794pjYhTX78NRonHBtSA4gTilFi96C6bmJOFHdhn9d7rmMw7Uh9KMXD0W7weIUDGpN1h5V4ABgVHosaBp4fd0ZPHtFvvODVLikUDpfo6pa9bjnux0A4NZUJ0ruoMB1gdVm55W707UaWGy01wuSPc3EQfG4Z3oOvt5dBqVcjNp2IzacYn7PVa0G/HK0GpeNHBDkUfYMIRfAAcCrs1/FeZ+ch+u+vw7rbl4Hqdg5lSgU1a9gQ9ntGLd/P0oHDkSTWu35BQQCQTjd5BO2OaweN+k6Ari32R5XUwd7F8ABTG1GpZtV7t5AY7Q4rXQLVSDGZMZi7dGagLln7ilpxujMWK+DN47UWCaNraZNeABnttrx1e4ynD9YHZDAoLfh9pvGaEF0BPN3k86M6jYjn6725+l6LN9SxL9G0ouOcoGCS0tu1fvuNOrIw/89wv/9xE/H+L9Lm3RdBnA0TWNbYSMGqVXIiFfio63FUEfKsWBs1+ULSllXJiYCFTiuBpei8DXr6Ck0wGxm22os/HQftj82C6+uO42nLh0GpUziVjnvKoW7IC0G607U4ffjtZg7grGiLG3UoUVvwTOXD8fCqYMEjQeAk2PtVWPTcd/Mwdh8ph4fbu1oF1HfbkQ7G2REK3omrXwk2xvvs52lbHsDh5xCh6A5HExMOKfkT7aXAhgKAG6DLj6Fkj0e//37aUzOTnDqsffIf4/g7esZQWD14SpIRBS/z0OJxfPysHheHj7ZXoLnfzmJunYT0mIjUNVqwN+/OUQCuN5kTOoYfHT5R7ht9W2Y/+18rLpmFV8bB/if/uiOcA8Kc8+eRVxrKzbMmRPsoRAIfZCu2wg42u43ak3ITY6CzU6jotmAe6bn+HTxSIyS4XBFcExMGAXO+4lSTmIk2o1WaEzWgEy0Cuu0ncwvvGEAawnO2M0Ls9Zff7IOjVpzSE5ShBDNTsrajVanui6bQzrb1kJnk5NA1JD1NrFscNHTacbFDZ0Dm/ImPQ6UN6NZZ8Hzv5wEAOx6fBa2nG3A32cO7nbBIVIuhs6hrtO1kXe3LpRsHzjuOWuP1eC87ASMGyjs2NaamPfVmKx4d1MhvtlTjqHJUbhtShYqmp0XizjTEncMTWFq7u756gBfI8UpHnOGdzYq6Q7HhR4u8J04yFn5+/NMA15bdwYUBch7SC12VCS3nG1wH8AFuMdlwOEWAdjv1LEG0V2dK6cSa4wWWG12LNtchGWbi1D80iX8c347Xou32AW5jafqMWWw2u82Gz1JikPtaZZa6VXbjHAkZM/ct466FR9f/jHWFa3D9M+m41yz8GLn/sh5O3eiNSYGp/Lygj0UAqHv0c3Fm6uZAoC1R2vwt68P4N2NhTDb7Mjy0QhjQEwEGrVm6M29n8OvMbqpAxFANLua3haASbXOZIXBYkNilO8qGNcrqKZVuLHKfd8cBAAYzMKttUMJLsWsVW92aqBttnV8niqX7yMnMTzaJTgSG8FMIlsCoMBZbV2rX+4aX1+1fCce/O4I1h6t5u+b9uqfoOnOwYcrKjmjwPGBmlcKXEcjbzvNuAuOzhRuKGFwOJdwaXMSMTPZd0rrBHDpyNQut+OuHvZYVRsGxCj41gHewAV9nEmNUibBh7eMw8e3jgcAPkimaeHZAL5w+vm5iFZIeAMMnrAxMXFeBLB5qEGM4l0orahzcJzkDGWyEpQwWe1oYFP52wwWtz36QgnHRuWOqqPdHuLBt4+EpALHcefYO5ESmYJbfroFYz4cg/fmvYfbRt3m04843BW27sgsLcXA8nL8NncuaHGY5GsTCGGFc30BBQqnatqxv7QZL/56CiIKuHJMOr7me7fVAgAGJvhm0Z7D1iwUN+h6tEeXO7Q+GK8AHelNbQYLMvwcA1f/p/ayh57zeCRQycSo9qHhs84UrgEcsw+uXr4L/5g1mL/fbKUd/rZh/MA4TMqOx/iseN6SO5yIVXUca/7Swi44PDc/Hz8erMLhilbsfeJC3PfNQVS06LHhZB2OVbXh6z3l+Pavk3h79YNsH7qCtBgcY2vChnqom1TJJbDaaZisrBOkiwLXLQ41cFqjFVY7jeECHSgB4P8uHILPd5VhVEYsjKzFv0LC7PtGrQkSEQWrnXZKa3RHlEKK+2bm4P0/i/Dn6XrIJCIUNWgx2Mea0XevH4PadqOT8+fF+SmC2h0EEoVUjPQ4Jb7eUw6JiMKzV+Qzc81waSMAGt6EmY4plFUOCxXHqxjzqFEZsSht0qO8SY+kKAUMZhvfoiVUyUvpOAaHD4gBUAGA6Q0Xp5T26AJAMAjpAA4ALsu9DEfuOYJbfroFC9csxJdHv8TSeUsxLHFYp+f25SCtO2Zs3gytSoWD48YFeygEQt/EjQvltct38Q5eEhGFf84dil+OVjtZF2epfVPgONewogZtrwdwGtbExFu4id+xqjb872g1/nlxnlv3QyHwAZwfChxFUUiLi8DhCmFOlFzNYWKUHHdOE17HE0qoHCbB727qyFqxOKpxVjuUMgkevTh8szWi5BKIRVRAFDhOcYiJkOKjW8ZhR1EjkqIVyIhTYk9JM+78Yj//3NlvbnV67fN/GYFbJg/E6Of+QKve4lExVrETYJ3J6hTACXI4pO38AhIX3Hiz0KKOlOPSglScqmmHycK1kGC216g1IzlagacvGyaoLYGMXShe+Nk+/r7rxvu2bBMhE2OQm0buCqkYsUopnyZb0AvnwZQYBU7WtOPzXWWYPjSR6V3n2LohlIUcRqLk6/Qo9nx34yT3aejcufnnI9VOLVNu/mQPAMbYZc3hapQ16TFuYBwMFhsifKxH7i1UDpkj+QM6juM3/jiDQ+Wt+PX+acEYVo8RsimUjmTGZGLTrZuw7JJlOFRzCCOXj8SDvz+IBp2whqV9mUFFRRhUWopt06bBKg1O3ygCoe/T+crtaL9stdNIjlbg80UT8X8OykdylLAmu64MTFBCRIG3ge5NNEaLTzVwnGnGW+vP4sMtxTjSVV8lATRomIl5op9GIrdPGYRD5a34wMG0oys4B83PFk5wmgiEE8NSo/DeDWOQ5BJIaIxW3vDDZLX3WC1Rb0FRFBIj5QFpHN/OqnjRCimSohW4cgzTgywtLoKtn+yaUaz5xeZHZmDjw9M9rvBzxxXf35BT9PkmzJ5dKGnQ4DLCvDX4SY5WoLbdyDfZ5gLBRq0J6ig55o5IFZQ1cO2E9E73+ZPu3BVc8DZ/9ACvHUt94Y7zB+H6CUwguuEUa03vTZAdVGinYNNoseOacel4ztVV04XiBh3Kmzof5yPTY0BRTD9Oi42GzU6HhVr/6MWMcYtjuueq/RU4U6fp/vcVhoTNVUosEuPeCffi6uFX48lNT+Ldve9ixaEVeGjyQ3jovIcQo4jpEXMTjlBU9yi7HXPWr0drTAwOEPWNQOg1aNCQSUS4ZfJAGC02vhfT5OwEJEcr8N6mc5CJRV71H3NELhEjM16JIjcmCj2JxWaH0WL3qQaOU+C4xqq7i5swNlOYwYIrDRqmJsffSeENEzPwx8lafLmrDH+bkdNpgr3lbAPilTKMSItGYZ0WIio8a8I4KIrC5aMGoLLFgFd/P83fz62ql75yKcxWO98TLJwZEKvoXK/kA7zDYYTzMR8pl8BT6UwGW/MVq5QhVuk53Zf7XXHOf973gWOOX66mx9v9mBIjh95s4xtxG1klrlFrRlqs8MWm1JgIPH3ZcL4+DeiZAC4vJQqnazV469rRPp9LvWHqYDWmDkLxcf8AACAASURBVFajVW/BuuO1ePbyfMjCysSkQ4EzW+3IjFe6NTDhePHKEXjyp+PYVthZDMlOjARNA+9uLOQbq/vqCNyb3DdzMG6eNBAxSkZR/+uXB/iel3zqch8h7M7iiapEfHT5RzjxtxOYN3gentv6HDLfzsQTG59Ava7e8wb6EKMPHUJqbS02zJ4NG1HfCISeg792M5OIFp2Fv0C+eGUBLhzW4b6WlaDEc/Pz8enCCX69ZWpMBOrahRtwBALO3MCXFMpoh9oZsYjCrqImn8ZgttrxNNuTyl8FjqIoXDQ8BbXtRpQ0OgfDDRoTblu5F5cv3Y4fDlbhnY2FiJRL+sQFPiPevdmA3myF2dZXAriITvvUFxwVOEe6UilfvHIE/7e3ioSSDeB0vgRw6HCh5AJLb5XUZNalj2sb4KjAJai8+63dcf4gzHNwa3VVfQPBd389D7sen9UrwZsj103IQJPOzLhr8qpWiNdPsSmUjsR20QqCY/xAxpBmf1kLsh3SWIelRju5Tb649hQAoNxNu4lQhGuB4drvUB+m5lRdETYKnCt56jysumYVDtUcwsvbX8Yr21/BW7vfwqLRi3D/5PuRm5Dr1/ZDUXFzRKHXY/aGDSjLzMSJESM8v4BAIPgB6/DFXh/rWYXIXX8xiqJw63lZfr+jOkqOY36kIfoCpwz4osBFyiQQUYx6eM34dHyzpxxNWlOni6gnuO8WQEAmbuflMH34dhY1IdtBXdtZ1Mj//cWuUgDAkDBs3u2OjC7cAE9UtzMKXBi2DnAlJVqBmjYjDpa3+Kz0AsB7mwoBOC9AAIDcJZCfm5+C+2cPwbDUaBwoa8GPB6u8DqA4EwhO+eINMrgUSoGNvDmHUdcxe4IL4DTsQo3RYkeb3oIGjQnqKO8Ngz64eRyyFq8F0DMKXIxSihj0/uL0BbmJiJJLsKuoCZdkhFEKJSiHNEEKGR5qJLMTVZBJRDBb7RiSHIlidkHk9WtGAuhQQLnrwjU+1jkGC+46JhOLYLbZoTNZQ7oNgreEbQDHMSZ1DFZdswpnGs/g1R2vYsWhFfhg/we4NPdSPDDpAcwaNMttXnqoB2ieuGj9eiiMRvx6ySWdVl0IBEKAcTEx4azpB3iRduQt6kgZn+rkCzRN45PtJTgvJwH5A4QZAHCGDr7UwIlEFBIi5Zg4KB43TRqIL3aVYc3haiw6v3tDELudxsP/PQKN0YqlN47hP3OgGkxnJSiRGqPArqIm3Dx5IACmTcD93x7mn3O0knERXH5z30hFz0mKhERE4aUrC7C7uAk/HqoCAFyzfBcAQC4N/wBuwdh0rNhegmOVbbDZaVhtNB+se8PZOqbO1NV90TXIfWlBAT/5e+3qUXhu/givXe24gI+rQfPFhZIGzac5e7vQ4tgnCwCMVhvu/foAACAt1jfDJY6eCOCChVhEIVIhYRRKBxfKkG7k7aLAiSgKU3LU3b5EKhZhaHIUjlW1IS1WidX3TUVqjIIP9K8am44Xfz2FbYWNiJJLet1Qy18GxCqQrVZh2hA1Pt9VBl0Q2vL0JOF/FmcZqh6KlfNXouyBMvxr+r+wp3IPZn85G/nL8rF071K0GduCPcSAMaioCGMOHcLOKVNQnxKeDWcJhPDC+cJ9tLINSpkYQ5J6TrFRR8qhNVl9ttP+/XgtXlh7Cot/OCb4Nf6kUALA5wsn4rkr8jE0JQoj02Ow5nCVx9d8t78CPx2qwoZTdShp1KGRraH78W9TfBqDKxRF4bycBOwqbuJrh+74vMM9j0sdilJI/GpbEEpEyiU499IluHZCBp65Ih/Zic7GFKkxod3PSQjDUqOglIlR2qTDNct34YaPd3u9Da4lwKUFqZ1SZ20OQdVz8/OdVu7FIsonlVoucVHg+PQ87xQ4o8WG5GjvAybHPlkA8MHmIuwsasIFuYm4alya19tzRO1nunOoIZMwqk24mpio5BJBqdKp7DExIFaB0RmxfPAGOGsDSnn4pZZHKaTY9MgMzGJLHMK1PUxXhL0C50pKZAqenfEsFp+/GN8e/xbL9i3D//32f1i8YTFuKrgJd4+/G2NTx/pseBJs5U5uNGL+mjVoTEjAlhkzgjoWAqHf4LJCfrC8BZMGZfVoLREXTDRoTB5TYdyxaj/TA6eiRS/IRh/oSKH0NYAb7mDdPG5gHFbtqwBN090qFcs2d9jdt+jMWHusBkBgV/Sn5Kjx48Eq3LRiD5Kj5djJ1uc9c/lw7C9tQXGjDqMzYvtcnyCAUZY2PTyDT3UDeqZeqbehKAoDE1TYVtiRCmuy2vggyRMmqw2TXtoAALhpcmerdcdm7oGysO9KgfNYA+eSAWCw2JEd4736r5CKESmXICZCiioHA5gHZw8R/L25khEfgYpmQ9g6t3aFXCJi2i2EVSNvij+2NEZhPRI55dndOV/kcD4MhONrsHBs39GX6Fu/OAcUEgVuH307bh99O/ZV7cOy/cvw5dEv8dHBjzAudRzuGnsXbii4AdFyZsIR7MBMKPN+/RVRGg1WLlpE2gYQCL2GcyPveo0JF+X2rFshF8DU+xDA6UxW7C9tgVhEoVVvwZk6jaD+ThtPM0ZQcQIc9TyRGqOAzmxDu9HaZXPgunYjKpoNmJufgt9P1OLGFYxbolIm9tpUoTvOH8ykEu0qdjZWmTM8mW9cO2lQfMDeLxTZ88SFWHeiFv9acwK5faTW7/oJGXjm5xP87cI64X0Ty5r0vBlIfmrn1zjG8oGqm+FUPr5XJJ9Cydzs0uacu581MTGabUhO8C19+9d/TINCJsLEFzfy9/mTGrf6b1NR28tmS71BhwLH1SkGdzweYVMoK9h+lnzBtgceuigXOrMV8wpSOz12xegBeM7BaTRc4ZrE60kKZfgxIW0CPp3/KaofrsbSeUthtplxz9p7kPpGKhatWYQd5TtCO7eZpeDIEYw6ehRbpk9HVUZ4FZMSCGGNy8TKaLF3GZQECs6Igmsw7Q0/HaqCxmTFO9ePBgBeceoOm53GN3vKAQDpcf6n2HFpet310uJ6rznWyc0bkYL1D00PqLqZEqNAbrJzwP32daORHqcEV+oUbvUd3pIcrcCt52Vh9+MXYlRGbLCHExBum5KFCVkdBiacK+XjPx7D0k2FMFu7VrZq2pigY+mNY3jXuq4IVADHK3AWbxW4joDPRtMwWOwY6mMQnpmgRJJDf8pnLx8OqR+mNgmRcsE1tuGEXCJm1CyHSD60+4gxClwte1zPHJoo6FWpMRFYdtO4Ti6sAJMWu/+p2bh7ejZeu3pkIAfbq6jknAJHUijDllhFLO6beB/+NuFv2FO1B58c/AT/Of4ffHr4U+Sp87Bo9CLcOupWJEcme96Yj/iq9CU0NOCyX35BWWYmtk3rW93kCYTQx1mBAzqbHgQaTnUrb/IugKNpGl/uKkP+gGhcWpCKxfJjgoLAFrbRM9PA1f+EoQQ2BbRF13Uqzzm2UfnwAdGYnpuILWcbcOe0bKcmrIHigiGJvGEF0PH9/nNuHgYnRWLaEGETnnDHtQ4q3PnqzkmoaDZg9ptbUN7MHOf/2cssRLz+x1k8Pi8Pd0/P6fS6WnZhYVS6+2DWsYWFL6Y+7uDMY4y8Asc18mZdbrtaSKY7ns/VxPpi2OKO26d2bzLUX5GJGXfGsKmBYxU47rieP7pzs3VfUEfK8fi8YQHZVrDg0nv7mgLXrwI4DoqiMDl9MianT8Zbc9/CqhOrsOLgCvxzwz/xxKYncOmQS3HHmDswb8g8SETCvqKeTMGUmUy47rvvYJFK8cPVV4MWh18xKYEQ1rATLIOlY6XcWwtvb1FIxVBHylDd5l160t6SZpyp0+DVqwpAURQiZGJBRigtOiaAu3Natk/jdSVKznw/3dVinKvXIjVGgUi5BC8vKMD3ByoxpofUoZsmM86YnAU71ytNHSnHXy/oPMEnhAdyiRiDkyKRGCVHaaOuo76M5Y+TdZ0CuLN1Gl5tTo52H9DOHZGC5+bnY0JW4FJruTqzVfsqcPPkgYwRCiUC7B5+nw4KnMlig1RM+a16rbr7vD7RD7CnkElE0OgsDi6UIR7AgQngOGU5HE1HegoVm0KpdVHg2gwW6ExWt+2AwoF+/+uNlEVi0ZhF2HnHTpz820k8OPlB7K7cjSu+vQIZb2XgsfWP4XTj6eAN0G7HX376CQlNTfj+6quhifZcx0IgEAINc/FeuaOEvyc2AHVinohSSHljEU9UtRrw/YFKfL2nHNEKCa4YxbjKKWViQQ1M/3ugEgAQH6DPxRXFdzf+E9VtfO+1AbER+MeFQ3qsae8gtQqnn5+LKTkJyElU+d0knBBaZCUocbZeixXbSpzur2jurD6/+cdZHKlsw53nD+oyiOH6OQqpHRWKmD22ixt1+Of3R9g3EoESmELZarTBZLUjTinnt+UrEwfFY3QfSaXtCTLjlThR3Y5mA3P+okLdxoS2A6BQ3MhkGYT8eHsRhVQEiuqswF34xhZMeWVTkEblP/0+gHNkWOIw/HvOv1HxYAVWX7cak9Im4Y1db2DY+8Nw/srzsfLQSmjNWs8bCiAztmzBsNOnsX7OHJRmB2ZlnEAgeImb2oeeTqEEmNx9Ic5ZJeyE8JH/Hvl/9u47Tor6/AP457u7t3v9jivAAUdHehURQRDEgmDDmlijRE1TY/xpNDGiJr9UEzX5GU00JorGGBt2VFCwgCAd6R3u4CrX65b5/TEze7t32+5u2t5+3q9XInfbht1lZp55nu/z4J1tx7FwQgFSlM5bKUl2NLV64fb68Lvlu7GvtC7kc7ypzAoblNu9eVAqNYBThwYH8vkkPLBsO/aW1mPW8MizirRkswn8+5bpWHn3nB7ZcTKRTSrMxtZj1fjDh3v8v+ufnYKyupagjosvfnUEy3eUYOH4Ajxw4RgzNhUAsL1YGW0k7FDHCISlBHB/XXUAHp8PmV3sEkuxu2B8X0gSUFqrzuK0eAZOkiAJgYPlxp6jxgMhBNKcjg5r4Crq47ezJpCgJZTRJNmTcMmoS3DJqEtQUl+CpVuX4rktz2Hx24tx5/I78a2x38J3p3wX0/pP858EdHUsQUQvvgg89BBw0034amDHNsdEZCwp4KqmESdR6S6HfzZbOHXNbsx9dBUApfW1x4dzx7St401x2tHk9mJ/WT2eWnUAq/eU4/07O66jHZqXht4Zri6NLAi57f4ArmMJ5eHKBrz4lVzCtmBCx+5nRJ01fWgungnIvq2572zUt3hw3mOf4Yt95bj6tIGQJAkPKV0rzx9n7gzVk8rAejkDp6yBC9skQ/69D3IXynSN1uRReP2UJkwnG5X9ryRZvNmdBAkCrV4fkgBeoGon1ouh8YQZuCj6pvfFPTPvwc4f7MSXN3+JK8dciX9/829M/8d0THh6Av6y7i+oaqrS/oVXrwYWLwbmzAGefjq4pzERGUw9wZJ/EgKG1M2nuxyoUw46L6w9jMH3vYcnP90fdJ93t53w/3np4tPxwMLRmB3QkEMtoXQra792nqgNeaJYUtuMYfnajUZwOezyOhIlAN1xvAa3v7wZJ2qacLxaXqfxn1un69KwhBLPaQFjIB68cAz6ZadgRO909M1MxmfKrLiGVi88Pgk/WzAKF0/sZ9am+rcFgLwGLsYSSvUCUleGiFPnqM1+qpripHOhJIUqFCFFmtOBhjBNTLy++HzjuBeIkRACMwpnYEbhDDw+/3G88s0reGbTM7hj+R24d8W9uHLMlfje1O/hjAFnhLzy0ZkmJ/mlpbj5uedQl5mJ56ZNQ/NvfqPlX4WIOqvdkfHV22b4ZzrpKd3l8F81fPAtOXPwhw/34DszBvs7a7237QT6ZSXjsasnYdqQHExrN8+sd0Yy1h2shCfgIDX30VV4745Z/ueQJAknapoxf5y2HQozk9sC0A+2l+CdrcfRJ8OFEUpLfwZvpJXANujqWAohBOaMzMebm4ux9Vi1v+xZyxmDnXXumD74eGcpAChD7m0hS7SDKAGcTwngujp0m2KXnGRHdmoSKhvjJWsjwRtQIcI1cMFSXeHXgm88UtXhuBkPmIHrgkxXJm459Rasv2U9Nt26CTdNugnLdi/DzOdmYtLfJuHpDU+jriV4ncmSJUuC/hdOdlUVrl+6FK1OJ1687jo0p2pTzkRE3aG2+ZYPiqlOY3adaS6HvwQxcBbVrhPy8GlJkrDjeA1mn5KP04eGbis+vHc6jtc0o6ZRfp4pA7NxuLIR+8ra1kqcbGhFq8eHgjAd+boqIznJn4ErVYb9vrG5GMVVTRCi57W0J+u5+7yRSLLb8IcP9+Cyp9YAaBtxYYY/XTUR3zpNnuNaXt+iZOCijRFoK6GEYEGOUfpmJqOyQW1iYnGSD3GaSDJEqtMRVELZFBDMxeu6QQZw3TS5YDL+uvCvOH73cfz9wr/DJmz4/nvfR/8/9ccdH9yBvZV7Y36u9Lo6XLd0KRweD1687jrUZrNDFJElKCdQ6rq3lCRjihfy0l2obnKjxeMN6pr4jdIAobyuBVWNbozsG36or9ppbuVu+ar/oslyd8oNh0/676O2nu6bpW1GLD0gAFVf42RDK15adxT9s1O6NUCYqL1QA7fzM1woyErGF/srcFIZlZFnYgfSjOQknD2qNwDgjU3FaPJIkGIcI6Cen9sZwRliUG4qjirl3pavT5Qk+CQB9VvCNXDB0l3BJZSBjY32hGnsZXUsodRIujMdt5x6C7475btYV7wOT379JJ7e8DT+sv4vWDBiAXL25WAYhoVNa6c0NOD6F15ARl0dlt5wA8r76DdMnIg6x+uTYAcwsl8W9jRDt1b37Q3olQJJAk5UNwe149+pZOB2lcgHnlF9w7c6nz40F/2ykvGaMiZgSJ5cvvir93Zh8ZlDIAJmB/XL1joD19aE5URNE2aNyMPn+ypQ2dCKq0cXavpaRF/8dG7ILESvdoGdlms9uyI/Qw4gf/vBblzl8qG0Wh51ELaJSWAGDhJPzg1y2uAcrNzVCihfHys3MTle3QR7iycOUoXmSHXa0RjQhXLrsWoAQJrTjh3Ha3GgvB4DeqXEVXlyty5/CiHuEkLsEEJ8I4R4WQiRLIQYIoRYJ4TYL4R4RQhhXq2CCdQh4UsXLcXRu47i4TkPY9OJTXgRL+IpPIUt2AIPgmuqkxsbcf3SpehVVYWXr7kGRYW6nNj05mdF1DXNHvnfrNPgjNGAXnIJ9eHKBpTXt2DOyHy4HDaU1Mrtj/eUyIHcqAgZOLtN4K5zT8GYgkxcPmUAJg/MxuVTBgCQB5keO9mIW17YAED7ksbM5CTUNLlRVteMA+UNGNE7A7NPkRusnDOGF6lIW6lOR8gGH4GzDZ+8Zop/xIZZ1AAOkIOyxuYo7czbNTHp7gw4is30obnwSeo+37rBGwAUVzUEdUmmYGnOtvXYALC1qBqpTjsuntQf6w+dxLw/rsav3t1l4hZ2XpfPRoQQ/QHcAWCqJEnjANgBfAvA7wA8JknScABVABZrsaHxqG96Xzx41oM48uMjuBSXAgCWYRmewBNYgzVoRStcTU247sUXkV9ejleuvhqHhwzRa3P6gJ8VUZc0K6UXScrQX6MWiA/oJZc0vr/9BFo9Plw1tRAzh+ehvK4FNY1u7DpRh94Zrg4ZhvaunFqIN34wE3+8aiLSXA7MPkWevVZe14JnPj/ov1+exs0deqU5UdnQimn/uxIA0CfThb9ffyqW/3gWzhndW9PXIgpH/feRl+7CQguMrQgM4CQIf5Y6XIbHp5RY+iBgE0CqQSXciW50QSZSXHKwb/XQyOWw+ZvcUEfpyQ6U17XgDx/uBiBn4Mb3z8Kkwiz/fdYerDRr87qku3sBB4AUIYQbQCqAEwDOBnCNcvvzAB4C8FQ3XyeuOe1OvLnkTUiShI8OfITfffk7fHT4I2xM3oC79qQgo6YMjmXLcN2FF4Z9js50sQxDgJ8VUZe0uJUAzm7slfuCrGTYbQJvbJKHbJ8xNBef7i7DJ7vLMPGRjwAAF3WhHbp6ArnzRK2/yciovhmal4bmpjn9644AoE9mMpKT7BFLPom0lpMmd5/MSrFG4BNYpuWFDY0trRHuDZTXNaEPgMkDc/FVusv60UQPYbcJjC7IAk5YPf8GuOxCycApa+D4JQly3fRB+McXh7DrRB1aPF7sPFGLm88cgksm9YfLYcePX9mCwl7x1RW5y3szSZKKhRCPAjgKoAnARwA2AqiWJEnNUxYB6N/trYxjoQKvs3AWRjX3w54jr+GBkSfx2584MX3jE5i2cS2c0K2KsQT8rIi6pFEpvXA5jC2hdNhtKMhKRlFVEzJcDvRKcyIvIzhLdl4XShELlGYld/5nCwBgfP8svPq9M7q/we0ENpV45oapmDMyP8K9ifTRSymhdNis1zTHbrejqaXjsPtAxScb0AfAxIG9YKvnybmRZgzPA04AzW4vnBaO4rw+HyQIZKYkIU4m1xlqSF4apg/NQX2zB7tO1MHtlTBpQDaSk+y4dHJ/LNtSjIr6yBdSrKY7JZS9AFwCYAiAfgDSAMzvxONvFUJsEEJsKC8v7+pmxKXUhgb84l8r8eELHvzyyHz0tQ/ECqzAE3gCX+NrePX555cNflZEnebzSfj9crnswmXA7Lf21FlpWalyFiG/XQe9gTmdHzUyJC8Nz31nKh69ciIevXIi/vztybrMtVPbtTtsAueO6cOuk2QK9UKCz4KdBJPsdjSFGTCsOq40Ock1sXtmohqUKze8cXuiDFs3WUlNEwQE/vLtyQDYhTKUdFcS6lo8/gYmEwvbOr3npbtQUR9lLarFdKee4BwAhyRJKgcAIcQbAGYCyBZCOJTMzgAAxaEeLEnS3wH8HQCmTp1qvb2qRjrMfCspAc45B6iuBt5/Hw+cdx4eAPDl0S9x/8r78d7R93Ao/xAePfdRXDDigg7P141SyhZ+VkSdV9/qgdvrA+zGZ+CAtsYiahbhqtMKUZCVjG3FNXhq1YEOGblYnT1K/yYiarv2sf2zotyTSD9qxnl0gfVKdx0OB0SL0qQkTIB5okoO4LLT5H0BT86Nk63sd70WH7LW6vEi2WlHpkXKhK0oI9mB+hY3thyr9o8XUeWmO1FZ3wpJip8ur905GzkKYLoQIlXIf9t5AHYC+BTAFcp9bgTwVvc2sQc5dgyYPRs4fBj44APgvPP8N80cOBOrv7Mab1z1Blq9rVjw7wW49D+X4kj1Ea1ePZ2fFVHnNbR4IJR1BU4TMnC9lQAtW8nApbscuGB8AX46fxTW/2yeP0NnRacNzsE954/En66aaPamUAI7fUgO3vjBDPzvonFmb4rf0sXTcM/5I+Gw2/37l3DUDJzDbrf+YqweJt0l73etHsC5vV4IYfNfBGCZbUfpLnmszdZj1ZhUmB0UqOWnu9Dq9aG2KXI23Eq6swZunRDiNQCbAHgAbIacpXkPwH+EEL9SfvcPLTY07h04AMybB1RVAR99BMyY0eEuQggsGr0IC09ZiMe/ehwPr34Yo58cjUfmPoK7pt8Fu83eMaMXo4ceeqgK/KyIOq0hoPWwGRm4Ppnhr7r3ztS27b/WnA4bfjh3uNmbQQnOZhOYMrCX2ZsRZNaIfMwakQ/3FjtsUMcEhMnAVTfIfxDGdsElwKasm/RYOIDz+SR4vV7/tlJo6ckOVDW6UdXoxuWnDgi6Ta0WKa9v8S9XsLpu5VolSVoCoH1EcRDAtO48b4+zc6dcNtnaCnzyCXDqqRHv7rQ7ce/Me/Gtcd/C7R/cjns+vgdPfPwELsWlyENeV7fiuCRJU9v9jp8VUQQnappwzp8+wzybfPB22JQMnIHnT2qQ1urh0nSinsbhsMMh5P1Ls9uLX7+/CwN6pWB473TMGCYf7yvqmuU7C5ulh0n3ZK0er2Xf+7pmDwSCL/IxyO8ocEbkxAHZQbepAVxFfQuG9043dLu6iuG63jZtkssmAWD16qjBW6CBWQOx7OpleHHRi6hABf6Gv2ELtui0oUTU3t9WyzPS1EOhGbXxeUojkFaLL6Inos4TwobUJHm/8tm+Cvz9s4N48K0duOaZdf77NDS71TubsYmJTXnPW9w+y5ZR1jS5ISAxAxdFRnJbADd+QPC6bHWJQk1T5I6wVsJPW09ffgnMnQukpwOffw6MHdvppxBC4NoJ1+LATw5g5uCZWIZlODzpMNyIny8ZUbz6Shns6V+jYsL5k3rV0MEOjkQ9j7Ah2S7vWNp3o5QkCa0eHzxeNfsuLJsF6rnkz0YCUNdszfOuqsZWOYATAd8PxvodBL4lWSnBZZJqcFffHD9r4HhGoJcVK+QmJX37ysHbsGHderp+Gf2w4voVeHD2g/jXln9h5YCVuO3u2zTaWCJqr9ntxe6SOqQ6A5sMGH9UHNsvC7fMGoJHr2AjEKIeR9jhUs7E2q+z2nS0CnXN7oALSDxlM5xo+09jqzXL2PeW1kEASEpiB8pI6pT17LfOHtrhtoxkOaCzapAeCvcGenjnHWDhQmD4cOCzz4DCQk2e1m6z4+G5D+P1q17HttJtmP7sdFSiUpPnJqJg337mKwDAQxePxZPXTDZtO+w2gZ8vHIOBuZ2f90ZEFidscCoZuPbrXLcV1aC+xQNbQAAXT23Oewb1vZYsW0K543gtkmwSkux2dqGMYEJ/ed3bmcM79pJQK13qmIFLYK++Clx2GTBxIvDpp0Af7WctXTb6Mlznvg7lNeV4Ds+hFKWavwZRojta2YjcNCcumtAPSba2MhoiIs0IAafSG6n9IOHaJg/qmj3+LpXMwJkgIFg+UduE1zYWYXdJrYkb1NGO4zXITHYwsI/izBF52PSLczH7lPwOtzkdNrgcNn+WLh4w36qlF18EbrxRHhHw3ntAZveHhoYb3N0P/XATbsILeAFLsRQ34SbkIrfbr0fm++0Hu/FNcQ1+c9l4FOYw62IGj9eHk42tuOPsEUhx2sHQjYh0ETC7q77dyeNjK/ZiZN+MDiWUzK4Yqe299ngl/M+rNYBDaAAAIABJREFUWwEAh3+70KwN6qC60Y1khw2BayQZzIWWk+YMe1tGsoMZuIT0/PPADTcAc+YAy5drErxFk498XI/r4YUXS7EUDWjQ/TVJX1uPVePp1Qfwxf4KzPr9p3h76/EOV2VJfxX1rZAkIF8Zog1JPYGSD4o8gSIiTQgb+mfJJ5U3nzkYhTkpQTe/s/V4QAklm5gYLg4CIa8kyZsZB9tqZfIogfj598UMnBb++U9g8WJ51ttbbwEpKdEf0064TFs0vdEb1+Ja/BP/xH/xX9yAG2CHvUvPReZq8XhxyZNfIskucMWpA/Dy+mO44+XNyE5NwpYHzzN78+LexztLMW1wTkxDOsvr5KC5txrAmdjEhIh6MGFDRpINkOQTyDRn8GnZe9tPYIJgBs48IuD/rUmSlE7JAdlcfkc6b/mPZ5u9CZ3CAK67XnhBDt7OPRdYtqxLwRsALFnSfh56ZIEB3wAMwCW4BG/gDazCKszDvC5tA5nr0Q/3AAAcNht+c9kEvLftBGqbPahudMPj9bGNfCfsL6vHFU+vwbu3n4kBvVJRXteCW17YgOG907HiJ2dFfXyZMji3QwaOiEhLwgZIbTMeU50dL8DOGJoDFINr4MygZLUG5qSiud6OJpM3JxSvT4LNJsHaYSZpjXuD7njlFeCmm4Czz+5W8KaFCZiASZiEz/E5juGYadtBXXeiRg4amtxyJ7I0V9v1laMnG03Zpnj16oZjqG5046V1RwEARyrl8uL9ZfVYtrk46uPV93tAL3UNIjNwRKQDYYNQmpRIkhS031fde94I5b7c/xhPfs+Tk2xIS2oLriULXdTzSZKSgUPbGjgeq3o8ZuC66t13geuuA2bOBN5+W7fgrTOllRfgAhzEQbyDd3AbbmMpZZxxOYI/r8Arsav3lmNofrrRmxS31JOgp1YdwL3nj8SRyrYA+MevbMGUgb0ituU/XNGAdJcDeentFjzzBIqItCRsag0cgLaBwqp3bz8TNu8u/305RsBg6nstBe/+3V4JToc1PgdJUrMx1tgeMgYDuK744gvgyiuBSZPkQC5Vv06B0UorAwM8F1yYj/n4L/6LzdiMqZiq23aR9lxJwQnx4b3TcaBczhw9vmIfFowvQJ/MZDM2Le6kB1zF/uCbEhyubIBNAH+9dgq+9+Im7DxRGzGAO1TZiMF5qW0nSha62kpEPYjNDiF5/dmTBy8cC5fDjkcuGYskuw3JSXbgCMcImEdZAyeCOzt6fD44LVLE5vVJECI4wmSQ3/NZ49sXT3buBC66CBg4EHj/fUO6TXbGaIxGIQqxGqvhQfy0QyXA5Qj+5/jbyyZg4fgC/GjucNQ0ubH2AIe2x8oWcOz6wUub8JdP9qMgKwWzRsjzX/aW1kV8/OGKBgzOTevwe4ZxRKQpIYLWwPXNSsZjV09CRnKSHLwBAbezC6XhAjJw9oADi9tjnc/BJ0lyp1IG+AmFGbjOKC0FFiwAXC7gww+B/I7DAPUSaymlgMAczMFSLMV2bMdkTNZ5y0gr7Usoe6U58eS1U9Ds9uLJVftxuJJjImK1JkSwOyg3FWkuB/pnp+BAeX3Yx7Z6fCiqasQlk/q1/VIKXgPHq5tEpImAJiZh11VJwV0oyUht+/r+2cmoSElCTZMbbp8vwmNkDS0e3PfGdkwfmoNrTx+k2xb618BBWGptHumLAVysWlqARYuA8nJg9Wpg8GBNnrar4wMiGYqh6I3eWI/1DODiSJI9dFCQnGRH38xkHK2Mr0Yme0rq8NDbO/Dcd05ThmEbY9PRKny0sxQAsPGBc/DsF4fw1KoDuGzKAABAXroTVY3uDo9b8tY3OFDegIcvGQufhHYZOPUESu+tJ6KE0q4LZUhScAklG1QYKOBinc0mcN8Fo3D/G9ux43gthuSmRSzF/3BHCd7Zehybj1bpGsC5vZL8jeCFxYTCAC5WP/oRsHYt8NprwFRrry0TEJiCKViO5ShFKfqgj9mbRDGoqG8Ne9vAnFQcibNOlA+9vQNrD1Zi09EqzByeZ8hrSpKExz7eCwD4v2smIzfdhR+fMwLnj+2LSYXZAIBM5QpqoMr6Fjy/9ggA4KCy7nBwXlrgExuw9USUcIQNohMBHEsozTVnZD4cNoEbn1sPANjwwDnKAOiO1M7SepIkCQ0tHjgygcASWwb5PR8DuFj861/As88CP/sZcPnlmj51uCYl3c3MjcM4fIgPsRM7GcDFidLa8Dv7Qbmp+O+GIvzzy0O4aeYQA7cqvry6sQif76vAOaN748IJcgmky2H3B28AkJvmxJaj1VhzoAIzhsmB5Se7y/y3bzhyEgAwJC9UBo4HRSLSUGAJZbjgrH0GjvshAwUP8i7ISsHAnFQcrJAv9K0/dBILxheEfGSJEsA1tOjXj6DF44PHJ8FhEzw+JRgWVEezdy/wwx8Cc+cCjzxi9tbELB3pKEQh9mCP2ZtCMSqqkjNsOWnODrepwcgLSpYoHrRdCTTON8U1AIBfLxof9j7fnzMcuelOXPvsOvzry0NYc6ACn+5pC+DW7K9EZrIDvVKT2h7EDBwR6SGmDFzbBSSucTJYQFCkvvd5GW0Zt81Hq8I+VL0oW9XoxvRfr8RrG4s037y6Zjk4dNikoAwtg/yejwFcJB4PcP31QHIy8OKLgD2+5qoNx3CUoAQNYPMLqyurbcbe0noMzk3FWz+c2eH22afk457zR+JQRQPqmjuu3yLZwfIGTByQhd4Rxi2M7JuB9++chTOG5uKhd3bimmfW4f3tJf6Zb9uLazAkP73dAZCDvIlIB13IwJGROu7zH754LCYVZqN/dgoOVYQ/vwqsqimpbcY9r23Ff78+punWqecD8kg6Hp8SCUsoI/nzn4H164H//Afo1y/6/TUUbf5be6FKLgdBXjR7DNruMEh70369EgBw6+xhKMwJvSg6X6mzr2lyIyM5KeR9rES9UOzxGXfF+EB5Pc4Ymhv1fqlOB66aWog1BypRkJWMx6+ehPwMF87+42oAwJD2C9OVvwyvfRORpoQNiNrRMCADB4nrm4zkv5DXtvcfXZCJZT+cie+/uBF7IoykKaltRlbAmuvTBufgvje2YeaIPPTPTtFk86qV57YrJZRqlpDfkZ6PAVw4x48Dv/iFPPPtqqvM3hq/zqyN64d+EBAoRrGOW0TdVR9QH1+YE36nnpEs/3NVSyasTj3ctXiit1vWQrPbixM1zcHNRyJQ5+7NHdUbpw/NhSRJsAnIHSg7PEdw6MaDIxFpIrCEMtwVImbgLGlofho+3lkKt9eHJHvwZ9Pq8aG8rgVnDMvFl/vlsTZ/vHIiZv3+U7yxsQi3zxuhyTas2l0Gu00g3cnvRqLhJx7Oz38ul1A+/njcLgxNQhLykIcSlJi9KRTB9qIa/5/H9ssKez8161bbFCcllMrJSKtBAVxFfQsAoE9m6I5g7c0b3Qc/OfcU/GzBaADymgH1PR7SPoCTWEJJRDro7BgBieubDCWCm5gEGpKXDo9Pwqo95R1u+9eaQ/BJwPyxff2/K8xJxfShOXhtU5FmaxlLapuRn+6C027jGrgEwwAulL17gRdeAG6/HRg61OytASBn3rrSmbI3eqMCFTpsUfx4YsU+bDwSfqGx2bYWVQMANv3i3JANTFR5GfJtJbXNeO6LQ/j98t2GbF93tXi8hrxOeZ0cwOVnxBbAOR023DFvBNJdbYUIv7t8PG47ayjmjurd7t4sniQiHQRk4LgGzorCB0KnD8kBAKzeW9bhtnUHT2JE73T//FHVlacW4khlI74+rM05SX2LB+nJDvk7wqAtoXBvEMof/gC4XMA995i9Jd2Wi1xUo9rszTBNfYsHj63Yi8ufWmP2poTU7Pbitx/IgVik4A0AhuenIznJhne2Hscj7+7EX1cdgM/A9WXttXp8eHNz+CuJ6smIUSWU6hy9cDN5YjF/XAHuv2A0MsOtMeQBkoi01JkMnL+lPfdDhomwzy/MScWpg3phb2l9h9vcPgmpLgfSXA5cP30QXrl1OgDggvF9kea047WN2vQmqGv2KBchleOwulyS35Eej2vg2quqAl56CbjuOqCP+fPTujsPLhvZCT3482B58I5VkiRM/dUK/HDucNx8pvnz1L46WBnzfR12G8b3z8KKXW1X++qaPchKNaehyZ8+3ounVx9AdoozRMaqreqwxW1MBk4toexOABcWSyiJSA/CBuFvkhQuAyf575vIx3NzRN7nj+idjuU7SuD1SXIjEYXX55NnswH45aXj/L9PdTowZ2RvrDkQ+7E/kvoWj7w+XpJ4gTHBMIBr7/XXgaYm4LbbDH3Z7gZq4WQiU5fntbqmVi+EkNvKq4qqGmETApUNrXjk3Z2WCOA6W6c+cUA2vj5chZw0J042tKKqsdW0AG6TMv+mtt1Yg72ldXj284MoUVoon2w0Zs1ehVJCmZseOZPZNf7LmkRE2unsGjgylnqMDhM3zxmZj/98fQyTH/kIa+6f5y/Jd3slfwDXXprLDo9Xm0C8vtmDvpnJQIsEQDDATyDcG7T3+uvAsGHAlClmb4km0pFu9iaY4tInv8S5j63GgYAM3Jm/+xQ3Prc+6H7F1U14Z+txozfPr7MlkOeO6YNRfTNw3/xRAICqxlY9NismJxvk1z5c0Rj0+4ff2YH/bihCUVUTAOBopb5zCLceq8YLaw+jvL4FWSlJcDl0mNcoBZenEBFpQtjaxpSEa2wREMDxBN1oHccIBDp3TF9cMK4vaps92HD4pP/3Xp/UoTOlym4T8GrUxKS+RSmhlCQG+AmGGbhAra3AZ58BixfrlorWK9MWTipCzxTryZrdXv9slne3nQi6bV9ZW0D35f4K3PmfLaiob0FjqwdXnzYw5tcor2uJuVlGJOqMtGdvmBrT/U8fmovlP57tz35VK9ktSZIM7TrV7PbisDLA9LEVe3HB+L44pU8GAKBPRvAQ7WVbjmP+uALMHZWvS3D17We+QmOrF3abwLh+emWc2+YwERFpxp+Bi3DyHVBCSQYL2OeHCp7tNoE/XjURH+8sxfpDJzFnpLycwOP1we4KfYptE0Kz9ev1zUoTk2qff06gvNk8VvV0DOAC7dgBNDYCZ57Z7acyOlALJ9ECuKVrDwcFbYcqGjCmIBM/Xzgab285jlc2tC0cvv4f66DuQ3/6+vaYA7jHV+zF4yv24b+3nYFpSheqrvIqG1CQnRzlnsF6pcplglWNrfjpa9uwrbgGH9w5q1vb0hnfFNcEDej+ZHeZP4DrmyX/Xf530Tj8/M1vAADfe3EjbjhjEB65ZFzHJ+sml8OGxlYvvD4Jpw3u3ucRVrurpVwgTkSaEAKQvBCIcHHLn4EThl+so+jvdarTgfEDsrD+UFsGzu2VkGQP/VitMnDVja2ob/UgKyUJ8kVGfi8SCS/nBNqxQ/7v+PHmboeGHHDAHunAEIdaPT6UKeurAnm8PvzirR1YF7ATBeR685nD8zB/XN+g32emBK8d21NSh5fWHYn6+qv3yjNfNhw5GeWe0fmUnbg9TK18OL2UdW+r95bjlQ3HsOtErWHt+gFgyzG5s+miyf0BAGkBVxqb3F5kuBy49vRB+MeNbZnFF9YewWMf79Vs/o1qhBI4AsA980dq+txtWLZERDqw2f0BWvQxAjxBN1yM7/m0wTnYWlSNZqVpl9cnwWELfYqtVQZuxa4ySBIwb1QffxMT9fjKi4w9HzNwgY4ra6EKCzv9UKtk3EJxwYVGNEa/Y5y44+XNWL6jBPfOH4kfzBnu//3LX4duy3tIWaM1eWA2ACA7NQmLZw7BHz/eG3S/8x//DIC8M9xeXINzx4TuQmpXduh7Suq69xcJeI7kTpYWZiYnwSaAt7a0rd+rqG9F/+yUbm9TLLYV1aB/dgp+c9l4vLm5OGi4eLPbi2Sn/PeZN7oPPr93LtYcqMBPX9+OJ1buQ3F1Ex69cqJm26Iepj65+yx91r8BASVMPCgSkYaUEsrIWbXgLpQ8OTdetHd82pAc/O2zg9hyrBrTh+bC7fPBHiYD53TY0OLxdTubWqysMx/ZNwPMwCUeBnCBamoAux1I73zjjyVLloT8vRUCOyecPSqA215cAwD4ZFdZUADX0OIJut+Zw/Pwxf4Kf3v57FQnhvdOh10IfHfW0A4BnOo7/1yP3SV1WPGT2RjeO6PD7fXK6+w6Udutv8faA5X4v0/344JxfTEot3OlrjabQPsLeBV1LYYFcJUNLeiblYzkJDtcDhtqAgK4oqom5Ae08i/MScXVOQPx55X7UVzdhNc2FuH3l0+ArZNZx1B8PgnF1U04d0wfDM03omEPD5BEpKGALpSxNDEhowWsgYtQPTJ1UA6EAD7eWYrpQ3Ph8UpICnOM653hQovHh9pmtfyxa0rrmpGT5oTTYWvLwIEZuETBvUECcPSgOL3Z7cXxGvmqU/sOjL52O9fnb56GXy8aj5e+e7r/d/9z3kj86OzhSHG2ZWrevyN47dhuJSu25kAlNh+t8q9TU6kB3L6yelR3owvkbz7YhcJeKfjDlRM1WdNQrrTR11NpbTNqGt2obfIgM1ltl+zD6xuLAAA7jtfg830VmDIou8Njv7zvbFyglLGWhCiB7YrVe8tRVNWECycUaPJ8YWlc9klEBKAtAxfphLtdAMeTcwPFeGzOSk3Cokn98dyXh/DiV0dwrKoxbBdKdZ14SU33joNltS3o7W+mxgxcouk5Z/ZayMoCvF6goQFISwt5Fytk1DorCebMCdPD0ZONkCQgw+Xwd2BU1TZ5kGQXcHvb1pVdc3pwY5L26+AAYEy/TGx84Bx8/6VNQYuQH3xLXhN56+yh+NmC0f7fN7R4MKpvBnaX1OGrgydDPmcsjlc349wxvf1zY7rqylMH4NWNRSiv1zeAK6trxum/XgkAcNptGKL8vX0SUNnQio1HTuK5Lw4DAOaPDR1QXT99ED74pgSHKhrQT4Ns4eubipCT5sSC8ToHcJAC/p+ISCOcA2dxkccIBPrVonHYVlyDB5bJzbsunNgv5P0KlADuRE2TUv7YeeV1LfhsbzlmDM8N2FQR8EcGcz0dA7hA/ZR/bEePAqNHh7xLuFJJlRUDvJ7UxOSQ0rp+8qBe+GJfOXw+yV+K1+z2IiXJDsCLgTnRSxI/v3euXHoAIDfdhf/edgYG3/ee//bzx/aB2yvhmc8PYlBuKk7pk4HTBuegocWLM4fn4UhlI9YeqMD5Y/t0emfp80moamxFTlrXh05/67RCFFc34X8XjZcDOJ0zcJuOVLX9IIDBefJFjr9dfypuW7oRlz+1FgAwa0QezhyRF/I5huTLjzlY0YCZw0PfJ1ZNrV6s3FWGy6b0D3ulUzNcA0dEegiYAxeWP8CT9z88OTdQJ97rVKcDj189CRf+5QsA8jKOUAblysfBl9YdxewR+V1aTnDhXz5Hq9eHkWoTL1aJJBwGcIHUoO2bb8IGcPGoJwVw6uyxyYXZ+GxvOd7dfgIXK1e53F4fkuw2rP/5ObDFsNMtjBDkfXnf2eifnYI1Byrwye4yfzv83b+cj1avD73SnJg8MBvPrz2Ck41u/OXbk/2PfX1jEZZtKcbSxaeHe3q8t/0EvD4J2SldD+B+e/kE/5+zU5P0D+COyp0nb545BP9z/ilwKkHT+WP7wqUsygaAxtbw3TD7ZCQjJcmOwxUNONnQijc3F2N8/6wujWPYWlSNJrcX80b37sLfprPajRHgCRQRaSGghDJ8F8qAJiY8UTeYCPj/6Mb1z/L/OVx36TxljfjHO0vx7/VHcd30QZ3aoq3HqlFaKx/vL5syQPmtXELJLpSJgwFcoHHjgJQU4IsvgCuvNHtrNNOTArhDFQ3ITXP6M2x3vLwZM4flIjfdJS8attuQnNT1v+9L3z0ddc0efzOQiQOC13IVV8vr73LSnLj6tEKsOVCJd7YeDwrg7n51a9TXuf3lzQAAty9K6UyM8tNdugdwG49UYcrAbDx40ZgOt735g5lY8OfPAQA/Ont4h9tVNpvAoNxU/OOLQ9h1ohZrDlRCCGDB+AI8sHA0CrJiL6vcXiQ3s5kwoON6OyKiuCAESyitTL1YJ0UY89DOqv+Zg4ZWT/Q7ou2idGd8fVhe6rH2/rPbjpkSWCGSYBjABXK5gFmzgA8+AB5/vEv/GKKVWHZVd0ozbT2oV83M4XkYmJuKjOS2r+5jK/biV5eOh9vrgyNM297OPH+gtHbr0/aV1gMActOcOG9sXxypbMSfPt4rt86PMXBUm6Ckuxy4vpNX3sLJz3DpugauxePF9uIa3HhG6O0d0y8TT14zBbNPyUNGcuQ1l+p7uuZAJa48dQDyM1z415rDOFBWjzd+MAOpzth2S1uLqtE/O8V/NVNX/qvePEASkYaEDfB5ARGpCyXHCJin8++1urwgkl9eMha/eGsHenVhGUWTUuUSfOxTMnD+kROdflqKMz3nzF4rl18O7NsHbN5s9pZopicFcBdN7IcfzBkeVCL58vpjaGz1wO2TdFkL9e7tZ+L+C0YBAD7bJw/xzk2Xd7pq+/9jJzuOaQh3MD5ULl9x++NVE6MGO7HKz3D5xyXoYcfxWrR6fJgysFfY+yycUBDT32dsv0wAwGf3zMUfrpyIe+ePwp+umojdJXVYs78y5m3aU1KH0QWZMd+/e7gGjoh0IOydyMBx/2M4nd7zq0+TG6z5fBIq61vw++W74fbGVpHT5PbCYRPB5zvKGAFKHMzAtXfFFcCddwLPPAM89ZRhL6tn85OeeLVu7qje+NmCUbAJgV+9tws1TW6s2Fnq7+6kpXH9szCybwaeWn0A/153FAAwMCdN+a8cwB2pbMSIPsHdpLw+CXtKa3Hl02tx/wWjcOaIfAzJS8O6Q3KQouW26l1CqTYwmTIofAAXq/svGI2bZg7BwIDZd+q6gZMNsY1lcHt9OFzZgHPCDFvXHNedEJEeYloD11ZCKUkSsysm0PotdzpsSEmyo7bZjbe3HsdfVx3AOWP6RLxIqiqtbQnbAI1r4BIHA7j2cnKAa64BXngBeOQRID/fkJfVcxB4T/yHbLcJ3Dp7GN7aUgwAeGNTMZrcXhzsQj15LJLsNlx56gA88/khuBw25CuzV9R5LmUhgiePT8LTqw+isdWLX7y1A7NG5OGe80fiV+/tAgD0ydQugMvLcKGx1YuGFk+Hsk9VY6sHdpuAy9H5NYKbjlahf3aKJtuc4rRjSLsSE/VgdDLGuXrHq5vg9koYGkOpijaUMQIM5IhIS50aI9DzjuWWF/Cex7oGLlaZKQ7UNnlwsqEWAFBc1RQxgGt2e/HwOzvx1cFKDM1vf+zjsSnR9JzaOi3dey/Q1AQ8+qjZW6KJnlRC2V6asl7qc6W0UU/3XzAaV08txN+uP9X/O3/g0dCCVo8PG4+0zZHz+CTsL6v3/7zmQCXe3Fzs/1nLtVv5ynNFysKNefBDXPjnL7r0/JuOVONUDbJv4aQk2eF02FAVQwZub2kdNisdMbUMgiPiGAEi0oOwAVHXtXENnHlinwPXWZnJSahtdmPHcbkhV1FVU8T7bz5ajZfXH0VxdROG5qcH36iUUKpBJr8jPR8zcKGMHAlcdx3wxBPA978PDB6s+0tacX5cPFCzTTuP1+r+WjabwO+umBD0O5dDDjwOVTTilAc+CLpt3JIPg372+iQsXXsEeelOfPHTs8O2GO4KNSN4oLweXx2sxOWnDgi5HnBfQEAZq/oWD0pqm3VdbyaEQE6qE1VRMnC1zW6c99hn/p8NaWACoO3gzYMiEWkooLNkLCWUZDAdL9plpsjjf9TjclFVx7X0gQIrQKYNbj96R25iQomDAVw4v/418NprwF13AW++6f91PAZaPflKTIpTLgesbZY7O2aEKR/UU6vHh9c3FUW9X166ExX1rSjISunWqINQ1EzU4uc3AAC8koRrT2/rGNnqaSvRqWl0Iys19uYpn+wuAwAkJ+l78tArzYmTDe6I9znQLgDNy+j6HL1OkdjZi4h0oARlEWdLtgvgevIx3Xp0DOCSHfh0T1v10EvrjuKhi8eGbcamzlh99Xtn4LT2AVz7DByrRXo8Xs4JZ8AA4KGHgGXLgNdfN3trKAxHuyzWrbOHmrQl4f1w7jDcfe4p/oGbepT9De8dXE7x9OoDcHt9KKlpxqo9Zfhyf4X/tm3F1TE/7/JvSnCHMrPOoUOHz0C9UpOiZuAOtVvjmJvGDBwRxbHAE+1wVXqBTUy41slYonODvDsjM6XtQqo6GkldHhCKOluuV9gLsDw+JRJm4CK56y7glVeA224DZswACgo0m/NmZCavJ1+tC7xS9a3TCiMOkdbL7l/Ox6hfLMfAnFS8e8eZmPDQR0G3zxiWh5nD81BS0wyvT8KC8QWab4PdJpCVkoSaJjf+75rJ+NG/N+PtLcfxwtrD2FpUg3mjevvvu7e0HrNGxNac55Pdpf4/O7s5Yy+aXmlO7DoRuRRWXSPwy0vHYd3BSk3LUCNi8xIi0oM/qxaBv8mJEkwwu2KgGALsLspUxu4U5qTgzR/MxNRfrcDmo1WYNqR9eaRMnf+WEnJWqhT0n5583kcyZuAiSUoCXnoJaGwEbrgB8HrN3iJqZ1h+GqYqzTUWTigw5cCWnGTHx3fNxjs/OhOZyUmYNUIeBn7P+SPxv4vG4YyhuQDkjpW/uHCMbs1A3v7RTDx93alYOL4A/bNTsHJ3qT9jtXJ3Ga6aOgBJdhHzvDiP14cPvinx/+yw6bu7yEl14mB5A654ag02KmML2quob0FWShKunz4I/3fNFF23JxgzcESkg5jWwLU1MWECrucorW0GAFwzbRBy05ywCaBOWQ4SilpCmRpqCQbnwCUcZuCiGTUK+POfgVtukccKaJQ562omryuZu558JcZht+G1788wezOCZsANzUvD5/sqkOa0B61D09ug3DQMypVbCw/OS8Xx6makuxz+9YH3zh+FD7aXoNkd/kKE1yfhn18ewkUT++Grg5VBB5NFR0amAAAgAElEQVQkh74BXLZSFrLhSBU+3V0WMtCtqG9BXrpB694CsQslEenBFsN66MAAjoyl7vN1qMKYNiQHH+0sxRWnDoAQAj4J+L9P9+OOeSPgDHG8bXKrGbhQ3xm5iQlLbBNHt/YGQohsIcRrQojdQohdQogzhBA5QoiPhRD7lP/q13vcKIsXA9/5jhzALVtm6qYsWbKkq8GfPSE+Kwvw+c/1zTvZ75eVgkMVDThR24xpQ3Lw9o9mIi/dBYddwOMNv4NfuasUv3pvF/62+iCWbS72DyoHgCSdyxXVq5EAcPRkx25c8nq+cv/QbzPw4EhEmgpoTBJ2zmTAHDiOETCafu/14jOHYMfD5/u7SKuOngw9z7ax1QObAFyhLqYyA5dwuns55wkAyyVJGgVgIoBdAO4DsFKSpBEAVio/xzchgKeeAk47TR4vsHmz2VvUFYVIhM/KAtq6QJm3DQXZKahpckOSgOumD8KEAdkA5Iylxxd+aOxzXx4CALy77Ti2F9f4y1PVx+rp4on9AQBD89NwJEQA99jHe+HxSbj73JG6bkdowSWUPIEiIk3EklWTfAAET9DNoON7LoTwj0IKVNUYuhtzY6sXqU5HhIvD7EKZSLp8RiaEyAIwG8A/AECSpFZJkqoBXALgeeVuzwO4tLsbaQnJycBbbwE5OcDChcDRo2ZvUWdlIFE+K5MNVsoYC7JSTNuGwMzZ2H5t89uSbAKtntBXeXccr8FXB09iysBslNW1oKK+FWMCH6tzE5MzR+Th8G8XYvrQXBxrF8C5vT68/80JXDKxHwbmpoZ5Bh0x8UZEevAHcBHK3ySf/36sAjBa23HPqPe+vC70OvWmVm+Y8kmAB6nE051L6kMAlAP4pxBisxDiWSFEGoA+kiSdUO5TAqBPqAcLIW4VQmwQQmwoLy8PdRfrKSgA3nsPaGgAzj8fqKw0fBMefvjhrnaw9CCRPisT3TxzCJYunoZzRveOfmedTB8qd7H69rRCDMtvGzEQKQP3zy8PI9Vpx1+vPdX/u/H9s/zBYLjZNFoblJOKkw2tqAm4Cvn1oZOoa/bgnDEhv6IGYBMTItKBP4CLcAIu+Zh9M4uB7/uGB84BED6AkzNwYQI4dQ6cUobLKpGerztnZA4AUwA8JUnSZAANaFeCJ8nfpJB7JUmS/i5J0lRJkqbm58fW0twSxo8H3n4bOHQIWLAAqKsze4uiUv4hpyLRPiuT2GwCs0bkm1rCMKBXKtbefzZ+vWh80O/DrYFrdnvx9tbjWDS5P/pmJWPyQLnkcsqgXkhXSjzaz9zTy8i+ckOYsx79FGf/cRUAYMWuMjgdNn+HT8NxkDcR6UGdMyYiNaCQgkotWR5nJPW91j/D1SvVCbstfKfoJrcXKaE6UAJQm5hQ4uhOF8oiAEWSJK1Tfn4NclBQKoQokCTphBCiAEBZdzfScs46S54Pd/nlwMUXA++/D6QYUy4XroFJDFm51oT8rBJYqBJOp92GFk/HLpRbj1Wj1ePD3JFy1nDp4tNR1+xGkt3mL53Uew2cSi3brG50o7rRjVaPDyt3l2LGsFykhpx/YwRm4IhIB7GugVNLKDmT0ljCuHXPdptATpozbAau1eODK1wAp2bgwAxcoujyGZkkSSUAjgkh1I4C8wDsBPA2gBuV390I4K1ubaFVXXIJ8MILwOrVwKWXAs3N0R+joxi6U7Ym7GdFfiP7ZmDFrjJc9fTaoHECG5S5a2rr/nSXwx8AqoGbz6ATh94ZychNaxsV8ME3J3CksjFoGLnheNJERHqItQtlYAaOJ+cGasvAGRE8ZyY7UNcSehZci8cLV8QLqfxeJJLuXlK/HcBLQohtACYB+DWA3wI4VwixD8A5ys890zXXAM8+C3z0EXDZZaYHcVEcRSJ/VgQAOFsJgtYfPon9ZfX+3399+CRG9E5Hr7SOM9bU0km3J3z3Sq3dfOYQ/+s+9vFeAMDZo81a/waoGTiJx0ci0lJMa+AkNjExi38OnDEvl+K0o7k19KxWOQMX7rRdOUaxC2XC6FY9kiRJWwBMDXHTvO48b1y5+WbA6wVuvRVYtAh48025Y6VBOtHQpEmSpMT+rAhnndK2hrGsrhmAPFPtm+JazB0Zen2j2rzE4zPuxOGHc4fjB3OGYcovP8bhykaMLshE/2zzunoyA0dEuhBySVzErFq7DBwZydhAKCXJjsYwAVyLx4deqWG+BxLY6CbBcI+ghVtukTNxH34IXHQR0NhxhpVeujHYmxJQdqoT5ymdHF/dUIQD5fXw+iScbGhB36zQFx7uOX8kCnNSMElpbGIUIYR/aPdpg82eMR+8QJxXN4lIE52ZA6c+hPsf4wjjmpgAQIrTgSZ3VzNwwr+ZLLPt+czqCNDzLF4MOBzATTfJ3SnfeQfIyDB7q4g6ePq6U3HWo5/ig29KUN/iwYbDVfBJQF66K+T9JxZm4/N7zzZ4K2V9MuWgsrCXCbPfAikLxImINOVfAxcB9z/mMbCJCQCkJNlQVhs+A+cMtwaO35GEwwyclm68EXjpJeCLL4DzzgOqq83eIqIObDaBVf8zF+P6Z+LzfRX+q33hAjgzqZm3oflpJm8JwAXiRKS5gAxctEHenPFlJsmQ9YcpSfawGbgWjxcuR7gxAkDQMHh+RXo8ZuC09u1vy2vgrr4amDtXbnCi4+y0Lg71pgRntwn0Sg1uWNIrNcmkrQnvqqmFOKVPBiYPNLuEkohIB7FkTbgGzmTGRUMpzvBr4JrdPjgdkZuYUOLgHkEPixbJw75375ZnxhUXm71FRB1kpQQHbDnpHTtQmk0IweCNiHouf2DGMQIEZKYkoabR3eG78ObmItQ0uZETolM0AKWEEszSJhBm4PQyfz6wfDlw4YXA7NnAihXAkCGav0y0BibM0FE4LcpYgHvOH4npQ3Mwqm+myVtkZUqLZnajJCItxTzIW3CEgFkMXFuWn+5Cq9eH2mZP0EXWf355GEPy0vDdWeHOI4MbbVHPxwBOT2edBaxcKQdzs2bJQdyoUbq+JAM2ilVZXQsAYO7I3hjTj8FbVFwgTkRaC2hiEj5Ak4IDPe6KDGZcPktdi15e1+IP4Eprm7GtqAb3zh+JjOQwSx2UJib+OXD8kvR4LKHU27RpwOrVgMcjZ+K2bDF7i4gAAL9ZNB63nTUUowvYLbWzeHAkIk3EnIGzsQLALEIAkjFNTIbkyQ279pbW+X+3ragGAHD6kNwoj+ZxKZEwA2eE8eOBzz4DzjlHbmyyfDlw+um6vFT7kkpm5CicMf0ymXkjIjKTTRnkLSKUaEsSm5iYyrjAaHRBJpwOGzYfrcKC8QUAgMMVDQCA4fnpER6plPmrGThWjPR43CMY5ZRTgM8/B3Jy5EDus8/M3iIiihWvfBORHrqwBo4VAAYzMBhyOmwY3z8Lm462jaEqr2+By2FDZkqEnAvnwCUcBnBGGjRIDuIGDJDXxa1YYfYWEVHMeHAkIo3518BFaFLCMQImk1coGmVcv0zsKWkroSyva0F+hitKVk1uYsIulImDewSj9esHrFoFDB8OXHQR8OGHur7ckiVLMHHCRF1fg4iIiLog1jlwYAbONEJ+x41ag5iVkoT6Fg98Pvn11AAuImbgEg4DODP06QN88onckfKSS+Q1cURkYSyhJCIdBHShDItr4ExmbGCU5pJLJZvc8kDvsrpm5KdHCeAABG4ng/yej3sEs+TlySMGxoyRg7gPPtDlZR5++GFs3bZVl+cmSii8uklEWutEF0r/Q7gvMpbB77cawDW0eHDsZCP2ldVjVEG0hmO8yJhoGMCZKSdHXgc3diywaBHXxBHFCZ5AEZEmYsrAKQEcz9FNYuz+Pl0J4OpbPHhzczEEgG+dVhj5Qe3mwDEB1/MxgDNbTg7w8cfAyJHAxRezOyUREVGiCMishR8j0C4Dx7NzYwkBYWDw3JaB8+J4dRPyM1zol50S5VFyExNKHAzgrCA3V86+DR4MLFwIfP21Zk/NJiZEGuAYASLSQxfGCJDRDC6hdMqzAYurm7DhSJU/IxeRmoFjF8qEwUHeVpGfL2fiZs0CLrhAHjcwenSnnybU4O6t4Bo4ou7jAZGINCbs/j+GHyPAJiamEsaOEVAzcN97cSMAYEzU9W8AM3CJh3sEK+nfXw7iHA7gvPOAoiKzt4iIiIj0ElNgFry+iWtwjWZOExNVeX1LbA/kdyShMANnNcOGybPhAjNx2dkxP3zJkiVBP4fKyBFR1xg1B4iIEgQHeVufwbFQ+5LJSYUxnAPy0JRwGMBZ0cSJwJtvygHcFVfIIwaSkmJ6KAM2Ij3w6EhEOvBnSiLsY5QAjheQzGXUGsQ0V1tZ7e8uH48F4wtieJRcQsk1cImDl3Ssat484Jln5Flxd91l9tYQEUtSiEhrgRm4GLtQktGMbmLSlluZMSwPGckxXMBXmphQ4mAGzspuvBGoqADmzIn5Ie1LKAFg4u6JWPTQIg03jIiIiLot1i6UAUEEsysGM7iJic0m8JdvT0ZdswcDekUbH6AKbmLC70jPxwDO6u6+u9tPcemoSzXYECIiItJUTIO85S6UHCNgFuODoYsm9uv8g5iBSyjMyRMRRcO1J0Skh5jnwAUM8uaJurGEsP4yaOUYxS6UiYMBHBFRTHhAJCKNxZSBYxMTc8kFidZ+/628baQHBnBEREREZrBxkLflGbwGrkuUJibWDjJJS9wjEBFFFVyeQkSkiZhLKNmgwjzx8H4HNzGhno8BHBFRLHgCRURaC9ivRB4jwH2OaeLlvRdtw+B5jOr5GMARERERmSGWNXAI7kLJk3OjxcE7ztLJhMMAjoiIiMgMASWU4dfAcZC3qZQ1cNYuoZdLKNmFMnFwj0BEFA2vbhKRHjhGIA7EwfutNDGhxMEAjogoJjw4EpHGAkooI6+B4xgB08TDHDg1AyexzDZRMIAjIiIiMkOsGTiekJsoTt57ZuASCgM4IqIYWXsNBBHFHTUDJ0WfA8f9j0mEMsjbyu9/u+wsy2x7PgZwRERERGYQ9uj34SBviopz4BIN9whERLEInAPHq5tEpAVlXxJxj6LMgeMYAbPI+TdLU5qYcJ1k4mAAR0RERGSGLnShJIPFRbzMDFyi4R6BiCgaXtUkIj3EMsi7XRdKVgAYLU7eb2ZpEwoDOCKimPCASEQa82fWpChjBLj/MY0QcpMZXsgjC2EAR0RERGQGfwAXKUALbmLC7IrRLL4Gzh9YBmTgGPD3eAzgiIhixCuwRKSpwAxc2DECXANnKqsHQ+pxyerbSZriHoGIKCoGbkSkg06sgfM/hCfqBrP6+x2QgZO4Bi5RMIAjIopF4BgBHhyJSAsBgVm0DBwrAEwi4mSPz8A+oTCAIyIiIjKDjYO8rS9e1sCBXSgTCPcIRERERGYIGOQdsQtlQIMKMpgQlo7fAksoKXEwgCMiioalS0Skl2jZNUlieZypLP7e+5uYtP2K6yR7vm4HcEIIuxBisxDiXeXnIUKIdUKI/UKIV4QQzu5vJmmBnxVRd/CASEQ6EDalSC/yHDiWx5lEWLyEkhm4hKRFBu5OALsCfv4dgMckSRoOoArAYg1eg7TBz4qoG1jCRESaE7YoWX4JPDk3k/zeW76JjBDW30bSTLcCOCHEAAALATyr/CwAnA3gNeUuzwO4tDuvQZpJAj8roi7iQZGIdKJk4MLuZpQSSn+LeJbHGcvq7zeDtoTU3Qzc4wDuBeBTfs4FUC1Jkkf5uQhA/26+BmmjEPysiLoucH0Br4YTkVaidphkBs5cVn/vA+bAgUF+ouhyACeEuBBAmSRJG7v4+FuFEBuEEBvKy8u7uhkUOw8/KyIiIouJugYuuIkJLyAZzBatxNVk/iYm/F4kEkc3HjsTwMVCiAUAkgFkAngCQLYQwqFkdgYAKA71YEmS/g7g7wAwdepUC//L6DGyhRCHwc+KiIjIOmLMwHENrkmEDYAn6t3ME5CBk9joJlF0OQMnSdL9kiQNkCRpMIBvAfhEkqRrAXwK4ArlbjcCeKvbW0la2MbPiqiLrHz1lYjiWyfHCPDk3GDRMqRmYwYuIekxB+6nAH4ihNgPeZ3VP3R4DdIGPyuimPHgSEQ6UAK48AEC18CZStjj5CIeR00kku6UUPpJkrQKwCrlzwcBTNPieUl7/KyIiIgsRO1CGQ4HeZtL2GDtTsRW3jbSix4ZOCIiIiKKhTIHLvwML2UNHMcImCPqGkWThSih5Hek57P4t5KIyAqCT6x4cCQizURdAwdm4MykBtiWzXS1NTGhxMEAjogoFjyBIiI9RG2SwS6UprLZIaz83gdk4NiFMnEwgCMiIiIySye7UJLBhIiTZWb8jiQSBnBEREREZonWxKRdF0qWcBssjpqYMEubOBjAERFFExctpIkoLgm5gDIsJQMXvskJ6crqYwQ4By4hMYAjIooJD45EpAMhr1iKNUDj+iaDCZu8AtHKQRyAoDlwDOZ6PAZwREQxsv4BnIjiT7STbUn5f+5/TBGHYwSo57P4t5KIyAp44kREOlGaZIQN0NjExFzKGAHrYxfKRMIAjogoFjyBIiI9RM3wtGtiwpNzY9nssPZFPCtvG+mFARwRERGRaaKEZMzAmcvq733gHDiugUsYDOCIiIiIzKJ0oQy/xlYZ5C3x5NwUwgZh6RJKK28b6YUBHBFRNJY+eBNRfGMGztKExUsoQzQxYZltz8cAjogoJjwgEpEOlBPv8F0muQbOVHHUxIQSBwM4IqIYsY03EWlO2GLKwHH/YxKrjxEI+F6wC2XisPq3koiIiKgH61wGjgwmbJA/HYsG0JwDl5AYwBERRSP5gtcX8EBJRFqJtj9hBs5cNrvFSyjVbQv4jvAQ1eMxgCMiisbnBmxJZm8FEfVISsFb2BiBGThTKV1CLYsZuITEAI6IKBqfF7A7zN4KIuqJYokPBMcImEbYLB2/tRFcA5dAGMAREUXjdQM2BnBEpAOliUnIEkmprTyOTKKsgbMuK28b6YUBHBFRND4PSyiJSCcRUnAB5XFqgMfsisGEHUKKNGjdZPyOJCQGcERE0fg8gM1h3QM4EcWviHPgmIEzXdxk4PgdSSQM4IiIovF5uAaOiHQS4cQ7RIMKroEzmNUDuFAZOH5HejwGcERE0bRbA8fyFCLSTMRB3gHZFQvHED2azR4n7z2PS4mEARwRUTRcA0dEelFLKGMs0eYFJINZPQMXuG3+MXD8jvR0DOCIiKJR1sAREWkvwum2vzwu3Bo50p0QchMTq77/nAOXkBjAERFFwzVwRKQXoQ4RYBMTS4qbDBzXwCUSBnBERNFwDhwR6SaWDJywbgaopxMWXwPHDFxCYgBHRBSNsgaOJ1BEpDlhAySOEbAsy2fgVAzyEwkDOCKiaHweuRMZEZHWImVOAjNwEsvjTKEEcJwDSlbCAI6IKBqfB7C3daFkhy8i0o6yRwkZHzADZzqb3drvfrsgn8enxMAAjogoGq6BIyK9KE1MQgo1yJsn6MYSVj9VZpCfiKz+rSQiMp/PyzlwRKQbtQ9lRx07DJLBrF6y2q7RDUtsEwMDOCKiaHxuroEjIn1EyvAwA2c+/+dj1QDaqttFemIAR0QUTbs1cEREmhFC7kIZskkGM3CmE3aIsJ+PhXANXEJhAEdEFA3XwBGRbmLrQkkmsfoaOKsHlqQLi38riYgsgGvgiEgvQij5tShz4PyxHIM5Q8VNCaVQ/p/fj0TAAI6IKJp2a+B4AkVEmunkGjgyWLxk4PgdSSgW/1YSEVkA18ARkW5iyZm0rYFjhsVg6sU7y5Yqtlsnya9HQmAAR0QUiSTJARzXwBGRHpTMScgSSmZXzCdsEJYtnwzA70hCYQBHRBSJzyv/lwEcEelCRAgQQgxp5nm6saweGAVkBtmFMnEwgCMiikRSAzjOgSMiHShjBEL3MAkY0mzZEr4eTqj7fqu+/yGCfOrxGMAREUUi+eT/Wn0hOxHFp07uW5hhMZjV9/2BQT6YgUsUFv9WEhGZLCCA4xVwItKefModeYxAuNtJd0oAZ939PzNwiYgBHBFRJMzAEZGeIq2xCtHEhBkWg8XLvl/NwFl9zR5pIk6+lUREJgkRwPEEioi0FX2QNzNwJrH6+mfLZgZJTwzgiIgiYQaOiPTUyQwcGcy/77dqoBRQQinxAmOi4BkJEVEk/hMo7i6JSAfCJq+BC5lJCcjAKbezRM5gVt/3M8hPSF3+VgohCoUQnwohdgohdggh7lR+nyOE+FgIsU/5by/tNpe64RR+VkRdwAwcEelKhC+D48m5+SIG2FYQXGbLAD8xdOeMxAPgbkmSxgCYDuCHQogxAO4DsFKSpBEAVio/k/mK+FkRdYE6yJsHRSLSg4ilCyWbmJgmXi7e8RiVULr8rZQk6YQkSZuUP9cB2AWgP4BLADyv3O15AJd2dyNJE40APyuiTgscI2DZNRBEFL+4Bs7SrB7ASR3LbKnn0+RbKYQYDGAygHUA+kiSdEK5qQRAHy1eg7TBz4qok/wBnMU7kRFRfFJK9EJjBs50Vm9iolaJ2CweaJKmuv1pCyHSAbwO4MeSJNUG3ibJlwJCfuOFELcKITYIITaUl5d3dzMoBvysiLqAa+CISE9CXgMXMnsSkIFjBYBJlDECln3/JbXM3y6vgWOAnxC6dUYihEiCHBC8JEnSG8qvS4UQBcrtBQDKQj1WkqS/S5I0VZKkqfn5+d3ZDIqNAD8ros5jAEdEuop0yt0xA8fzc4OpGVKLxm9tGTiHudtBhupOF0oB4B8AdkmS9KeAm94GcKPy5xsBvNX1zSMNDQI/K6LOYwBHRHqKcQ4c1zeZxOr7fp9H/q/Nzi6UCaQ74fpMANcD2C6E2KL87mcAfgvgv0KIxQCOALiqe5tIGskFcDY/K6JOYgBHRLqKdMIdIgNHxrJ6QBRQQkmJo8sBnCRJXyD8HmVeV5+XdLNRkqSpIX7Pz4ooEg7yJiI9qXPGQtXohVgDxzVOBvMHRhbNgPqUi4w2OySJa+ASBc9IiIgi8Wfg2ESAiHQg5PAtyp2M2BIKRbl4Z9m9vz8Dx1P6RMJPm4goEpZQEpGuBCAh5i6UzLAYzN/ExKIhnL+JiZwp5PcjMfCMhIgokhABHBeJE5FmIrY45Bo409ksvraMa+ASEgM4IqJImIEjIl2J8Gvg/HcJGOTNC0jGsvq+PyADJ0FirJ8gLP6tJCIyGQM4ItJTpH1LYNmeRSv4ejyr7/v9xyhm4BKJxb+VREQmYwBHRHoSAvIKt1ARWsffcY2TwfxNTCwaQQdm4NiFMmHwjISIKBIGcESkqxgHeVs1gOjp1CYmVsUulAmJnzYRUSQBAVzILnFERN0hBETYXQubmJhODYysuv9vtwaOayQTAwM4IqJImIEjIj0JG+Q5AiFuC8zAKX/mCbrBrL7vZxfKhGTxbyURkckCBnmruMaAiLQTaY/CDJzp/GMErJqB88j/tTm4Bi6BMIAjIopEDeCsPguIiOKTUAZ5hwoQuAbOfFZfA+fjMSoRMYAjIoqEJZREpCt5ClxozMCZzur7/oAmJhKYgUsUFv9WEhGZjAEcEelJKc8O2SQpIAPnvztP0I1l9X1/QBMTShz/3969x8hVlnEc/z17b9m2sIUQ6IUW0pgUL0BWUGJUUhMuJsXEBgs1wcSEeNfwVwnGGIwx+AeJfxDURAwmxhLvTSwSQYghkUtFRCtBWgRBoCKUbull2919/OOc2Z7Onjk7654z7ztzvp/kZGbOnNnz7syTOeeZ57zvG3lUAkBgJHAAqlT43XKyAscllIHE/t3fNIgJg9zUQ+RRCQCBkcABqFTBRN55FThO0DurMZF3F0wjgPrgjAQAimTngeMXcABlSwcxyZepwMWaQPQ664t2AEpJp1TgiJH6IIEDgCKzv4Cf/LrkF3AAZUqGMWmvAocOi34aAUahrCMSOAAokjMPHACUxhY2CiWDmHRY7JfPMwplLUUelQAQGH3gAFQpnWcsfxTKxjaclAfT6AMXbQVuOmkjMVIrnJEAQBESOACVaq8C10ggqLB0WOO7P9L8TT49OwKly7nEvyY4IwGAIiRwAKpkJmuZv9EHLjjrl0WbvSmpwNH/rXY4IwGAIiRwACrVTnJmuXfRAbEnzz4j9Q0kd50+cHXBGQkAFCGBA1Al6yuo8JxczxDxgcT+3T8zNXsJJeoj8qgEgMBmTo7wBQClS+eBK55GILM5FZbO6oZBTPoy09wQH7XAGQkAFMmbB44DJIDSLGwQE3RYX3/c3/iZQUwkcYltTZDAAUCRmanklgocgCqkfazypxFgEJPgYv/uzwxiQpJfH5FHJQAENnkouR1eHrYdAHpUUXKWqcClyRzDxHfYbAIXaXLUVIHjCpF6IIEDgCKTB5PbERI4ABVoTORd2AeOk/JgGslRpPmbZmZOVuAY6KY2SOAAoMixCal/WBoYDt0SAL2okZzlnnwzkXdws4OYRMqnT+2jTbJfCyRwAFBkckIaWSGJXzcBVMGowMXMGilzpN//9IGrJRI4AChybILLJwFUpzA38zkbUYHrMDNFPbQjfeBqiQQOAIpMTswZwIQDJIDypBU4RqGMV8zvf7YCx1UitUECBwBFqMABqFLhMPU5FbiYk4meFfF77jNU4GqIBA4AiuRU4ACgNEV9rDIVOKorAZkUdx+4xkArkbYRpSOBA4AiVOAAVKq9eeAQjsV8ujwzJfUNzD6kQlsPEUckAETg2EFpeEXoVgDoVVZw0Vu2Akd1JaxY3/6mQUxQDyRwANDK9JR04vBsBY4TKACla8wzNs88cAgo5qpWZhAT1AcJHAC0MjmR3NIHDkBlGslBXh+4xiZM5B1WxBXQzCAm9JOsDxI4AGilkcA194Hj/AlAWaxgAIrp48lt/2AHG4Q5uqgCR4JfDyRwANDK0beS2yVnhG0HgN7VSM7yiidTR5PbgSWzqxikIoSI33OfLv4RAD2JBA4AWjl6ILklgQNQlb4BmbvcZxdIB/QAAAjjSURBVOY+d+JYcjs4wuVxIcWcNDdX4GJuK0pDAgcArRx9M7ldMha2HQB6V/9QeicnQcurwMVcDeph0Va3MqNQRttGlI4EDgBaoQIHoGr9QzK1GIAiW4Hj5Dwci/h0mT5wtRRxRAJAYCRwAKo2O0BJexU4dF7USRGjUNYSCRwAtHLkgDQ0Kg0klzhxcARQurQCp1Z94KwvSfJm5/SOOJlA581MS30nT+eJj3oggQOAVo4eyK2+Rf1rLIDuUtgH7pg0uDTuQTTqwCx/lNAY0AeulkjgAKCVo29y+SSAavUPFPSBOyINjEgSE3mHZBFP5D0zJfUNzD4kPuqBBA4AWjn8urR0ZehWAOhlRRW4E8ekQfq/hRdxUpQdxCTSHBPlqySBM7OrzOxZM9trZtur2AfKwWcFFJh4RVq+KnQrAPSyRgLXaiLvpgocOi/qqlZmEBPUR+kJnJn1S7pT0tWSNkq63sw2lr0fLB6fFVBgekp6e7+0/NzQLQHQy9JRKFtO5D040uEGYY6+fkVb3soMYkKSXx9VVOAulbTX3Z939+OSdki6toL9YPH4rIBW3tib/LK5/JzQLQHQy5avTio805NznztxeM4UAowyGMDQaVKsoxBnBjGRiI+6GJh/kwVbJemlzOOXJV1WwX6weF33Wd31qxv0yIFnQjcDdTA1Ka06V3rlPmnXg5Kk/Yf3B24UgJ5z2kpp9Cy9dvyQtt39nnTESUsShqmj0tIzpV3bNDE5Ebql9TW8TG8feUPbfniRkv5wESVJyyQdelLatU0vTryosZGx0C1CB1SRwLXFzG6SdJMkrV27NlQz0IaYPqvh/hGN9g3NvyGwWCNLpBVrpCWnz64aPX1Um9ZuUj/9DQCU6OpLb9Zbf74rqcL5TJK8WZ80tEwaO18aHNHo4Kg2nLFBF6y4IHRza2fT+Bf1rz9+Uz59PH++vpCWjEnLV0uDo7pw5YW6/NzLQ7cIHWBlT0xrZu+X9HV3vzJ9fIskufu3Wr1mfHzcd+/eXWo7cCoz+5O7jzet47MCAAAAIpB3vp6nij5wT0jaYGbrzWxI0lZJOyvYDxaPzwoAAADoIqVfQunuU2b2BUn3S+qXdLe77yl7P1g8PisAAACgu1TSB87dd0naVcXfRrn4rAAAAIDuUclE3gAAAACA8pHAAQAAAECXIIEDAAAAgC5BAgcAAAAAXYIEDgAAAAC6BAkcAAAAAHQJEjgAAAAA6BIkcAAAAADQJUjgAAAAAKBLkMABAAAAQJcwdw/dBpnZ65JeDN2OFs6U9N/QjSjBO9x92WL/SOSfVR31SnyiM4gXtItYQbuIFbSLWJnfee5+1nwbDXSiJfNpp6GhmNludx8P3Y7FMrPdZfydmD+rOuqV+ERnEC9oF7GCdhEraBexUh4uoQQAAACALkECBwAAAABdggRuft8P3YCS9Mr/gVPxuWIhiBe0i1hBu4gVtItYKUkUg5gAAAAAAOZHBQ4AAAAAugQJHNABZrbGzB4ys7+b2R4z+3LmuTEz+52ZPZfentHib9xiZnvN7FkzuzKz/qp03V4z297itcNmdm+6zWNmti5dv83MnsosM2Z2Ubn/PRYiglj5oJk9aWZTZrYls/6Kplg5ZmYfK/N/x8JFEC83p/t+2sweNLPz0vXES2QiiJXPmNlf03h4xMw2pus5DkUmdKxktv24mbmZjaePiZUGd2dhYal4kXSOpEvS+8sk/UPSxvTxtyVtT+9vl3R7zus3SvqLpGFJ6yXtk9SfLvsknS9pKN1mY87rPyfpu+n9rZLuzdnmXZL2hX6v6r5EECvrJL1b0o8kbWnRxjFJb0paGvr9qvsSQbxc0YgDSZ9t8d1CvESwRBAryzP3N0v6bc42HIciWELHSma/f5D0qKRxYuXUhQoc0AHu/qq7P5nePyTpGUmr0qevlXRPev8eSXm/Ul8raYe7T7r7PyXtlXRpuux19+fd/bikHem2ea9v7ONnkjaZmTVtc336egQUOlbc/QV3f1rSTEEzt0i6z92PLPgfRKkiiJeHMnHwqKTVOfsgXiIQQaxMZB6eJilvEAaOQxEIHSupb0i6XdKxFs/XOlZI4IAOSy9fvFjSY+mqs9391fT+a5LOznnZKkkvZR6/nK5rtV5mdpuZbW5+vbtPSTooaWXTPj4h6ScL/odQmUCx0o6tIlaiE0G8fFrSfTnriZfIhIoVM/u8me1TUsX5Us4+OA5FJkSsmNklkta4+28KmlbrWBkI3QCgTsxsVNLPJX2l6ddISZK7u5mVMjSsu39tAe26TNIRd/9bGfvG4kUcK+couXTl/jL2jXKEjhcz+6SkcUkfalpPvEQmZKy4+52S7jSzGyR9VdKNmXZxHIpMiFgxsz5Jd0j6VEG7ah8rVOCADjGzQSVfhD92919kntqfnuQ0Tnb+k/Pyf0tak3m8Ol3Xan3L15vZgKQVkt7IPM8v5BEJHCvzuU7SL939xP/xWlQgdLyY2Uck3Spps7tPNj1NvEQkdKxk7NDcS+84DkUkYKwsk/ROSQ+b2QuS3idpZ2Mgk1TtY4UEDuiAtL/ZDyQ94+53ND29Uyd/hbxR0q9z/sROSVstGU1yvaQNkh6X9ISkDWa23syGlHyp7Wzx+sY+tkj6vXvSCzj9tes61fha8phEECvzuV41P3DGJHS8mNnFkr6nJHnLO5EjXiIRQaxsyDz8qKTnMs9xHIpIyFhx94Pufqa7r3P3dUr61m52991p24gViVEoWVg6sUj6gJIO209LeipdrkmfWynpQSUHswckjbX4G7cqGb3pWUlXZ9Zfo2SEqH2Sbs2sv03Jl54kjUj6qZKOxI9LOj+z3YclPRr6PWKJJlbeq6RfwmElVdo9me3WKfm1tC/0+8QSTbw8IGl/Zt87iZc4lwhi5TuS9qT7fUjShZntOA5FtISOlaa/87Ayo1ASK8li6ZsBAAAAAIgcl1ACAAAAQJcggQMAAACALkECBwAAAABdggQOAAAAALoECRwAAAAAdAkSOAAAAADoEiRwAAAAANAlSOAAAAAAoEv8D6ukqDY1AHZOAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f54eeefa048>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"blink = load_csv('N1')\n",
"data, res = process(blink)\n",
"\n",
"plotData(data, res.initial_probabilities, \n",
" res.regime_transition[:, :, 0].transpose(),\n",
" res.params['const[0]'],res.bse['const[0]'] * np.sqrt(data.shape[0]),\n",
" res.params['const[1]'],res.bse['const[1]'] * np.sqrt(data.shape[0]))"
]
},
{
"cell_type": "code",
"execution_count": 220,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f54ed4575f8>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plotData(data, res.initial_probabilities, \n",
" res.regime_transition[:, :, 0].transpose(),\n",
" res.params['const[0]'],res.bse['const[0]'],\n",
" res.params['const[1]'],res.bse['const[1]'] + 30)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f0bc9818e48>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"col_stat = ['Blink_interval']\n",
"y_label = 'Millisecond'\n",
"ylim_max = 100\n",
"ylim_max = None\n",
"axhline = None\n",
"\n",
"figure_row = 8\n",
"figure_row_idx = 0\n",
"ref = 'N53'\n",
"hour_formatter = DateFormatter('%H:%M')\n",
"path_export = ''\n",
"expe_file = ''\n",
"\n",
"# création figure generale avec le format voulu\n",
"fig = plt.figure(\n",
" \"Drowsiness analysis \" + ref,\n",
" figsize=(10, 15),\n",
" dpi=150,\n",
" facecolor='w',\n",
" edgecolor='k')\n",
"fig.suptitle(\n",
" 'Drowsiness ' + ref + ' ' + expe_file, fontsize=6, fontweight='bold')\n",
"\n",
"# definition des marges de la figure\n",
"fig.subplots_adjust(left=0.08, right=0.94, top=0.96, bottom=0.05, hspace=0.08)\n",
" \n",
"\n",
"process_data('Blink_rounded', data, col_stat, 'Blink_rounded', True, None,\n",
" ylim_max, None, axhline, y_label, False, '#1da9ff', False,\n",
" False, False)\n",
"process_data('regime_switching', res.smoothed_marginal_probabilities[1], col_stat, 'regime_switching', True, None,\n",
" ylim_max, None, axhline, y_label, False, '#1da9ff', False,\n",
" False, False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"p[0->0] 0.007160\n",
"p[1->0] 0.023560\n",
"const[0] 5.667835\n",
"const[1] 11.996989\n",
"sigma2 666.123798\n",
"dtype: float64"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.bse"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"p[0->0] 0.979804\n",
"p[1->0] 0.069971\n",
"const[0] 250.799159\n",
"const[1] 588.437665\n",
"sigma2 10301.193543\n",
"dtype: float64"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.params"
]
},
{
"cell_type": "code",
"execution_count": 223,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<table class=\"simpletable\">\n",
"<caption>Markov Switching Model Results</caption>\n",
"<tr>\n",
" <th>Dep. Variable:</th> <td>Blink_rounded</td> <th> No. Observations: </th> <td>259</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Model:</th> <td>MarkovRegression</td> <th> Log Likelihood </th> <td>-1076.273</td>\n",
"</tr>\n",
"<tr>\n",
" <th>Date:</th> <td>Thu, 07 May 2020</td> <th> AIC </th> <td>2162.545</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Time:</th> <td>12:04:16</td> <th> BIC </th> <td>2180.329</td> \n",
"</tr>\n",
"<tr>\n",
" <th>Sample:</th> <td>0</td> <th> HQIC </th> <td>2169.695</td> \n",
"</tr>\n",
"<tr>\n",
" <th></th> <td> - 259</td> <th> </th> <td> </td> \n",
"</tr>\n",
"<tr>\n",
" <th>Covariance Type:</th> <td>approx</td> <th> </th> <td> </td> \n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 0 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 71.9211</td> <td> 1.548</td> <td> 46.465</td> <td> 0.000</td> <td> 68.887</td> <td> 74.955</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime 1 parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>const</th> <td> 106.7850</td> <td> 1.854</td> <td> 57.588</td> <td> 0.000</td> <td> 103.151</td> <td> 110.419</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Non-switching parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>sigma2</th> <td> 193.9078</td> <td> 17.789</td> <td> 10.901</td> <td> 0.000</td> <td> 159.042</td> <td> 228.773</td>\n",
"</tr>\n",
"</table>\n",
"<table class=\"simpletable\">\n",
"<caption>Regime transition parameters</caption>\n",
"<tr>\n",
" <td></td> <th>coef</th> <th>std err</th> <th>z</th> <th>P>|z|</th> <th>[0.025</th> <th>0.975]</th> \n",
"</tr>\n",
"<tr>\n",
" <th>p[0->0]</th> <td> 0.9674</td> <td> 0.016</td> <td> 61.294</td> <td> 0.000</td> <td> 0.936</td> <td> 0.998</td>\n",
"</tr>\n",
"<tr>\n",
" <th>p[1->0]</th> <td> 0.0343</td> <td> 0.019</td> <td> 1.844</td> <td> 0.065</td> <td> -0.002</td> <td> 0.071</td>\n",
"</tr>\n",
"</table><br/><br/>Warnings:<br/>[1] Covariance matrix calculated using numerical (complex-step) differentiation."
],
"text/plain": [
"<class 'statsmodels.iolib.summary.Summary'>\n",
"\"\"\"\n",
" Markov Switching Model Results \n",
"==============================================================================\n",
"Dep. Variable: Blink_rounded No. Observations: 259\n",
"Model: MarkovRegression Log Likelihood -1076.273\n",
"Date: Thu, 07 May 2020 AIC 2162.545\n",
"Time: 12:04:16 BIC 2180.329\n",
"Sample: 0 HQIC 2169.695\n",
" - 259 \n",
"Covariance Type: approx \n",
" Regime 0 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 71.9211 1.548 46.465 0.000 68.887 74.955\n",
" Regime 1 parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"const 106.7850 1.854 57.588 0.000 103.151 110.419\n",
" Non-switching parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"sigma2 193.9078 17.789 10.901 0.000 159.042 228.773\n",
" Regime transition parameters \n",
"==============================================================================\n",
" coef std err z P>|z| [0.025 0.975]\n",
"------------------------------------------------------------------------------\n",
"p[0->0] 0.9674 0.016 61.294 0.000 0.936 0.998\n",
"p[1->0] 0.0343 0.019 1.844 0.065 -0.002 0.071\n",
"==============================================================================\n",
"\n",
"Warnings:\n",
"[1] Covariance matrix calculated using numerical (complex-step) differentiation.\n",
"\"\"\""
]
},
"execution_count": 223,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"res.summary()"
]
}
],
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