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En febrero de 2019 participamos en el datatón carcelario organizado por Documenta AC, una asociación civil que promueve los derechos humanos de las personas recluidas en las cárceles del país. En esta ocasión obtuvimos el primer lugar con un proyecto que consistió en crear un índice de condiciones de vida en las cárceles al que denominamos INDHP…
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indhpol.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Imports"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Imports"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"init_cell": true
},
"outputs": [],
"source": [
"from simpledbf import Dbf5\n",
"import pandas as pd\n",
"import glob\n",
"import os\n",
"import re\n",
"import matplotlib.pyplot as plt\n",
"import json\n",
"import seaborn as sns\n",
"sns.set(style=\"whitegrid\")\n",
"%matplotlib inline\n",
"datos_dir = 'D:/datos/enpol/'\n",
"# Diccionario con nombre de entidades y códigos\n",
"with open('diccionario_cve_entidad.json', encoding='utf-8') as d_ent_file:\n",
" diccionario_cve_entidad = json.load(d_ent_file)\n",
"# Catalogo entidades\n",
"catal_ent = pd.read_csv('catalogo_entidades.CSV', encoding='latin1')\n",
"componentes_dicc = {'corrupcion': 'Corrupción', 'inseguridad': 'Malos tratos', 'servicios': 'Condiciones dignas', 'sobrepoblacion_rel': 'Hacinamiento', 'violencia': 'Gobernabilidad',\n",
" 'ddhh': 'Derechos Humanos',\n",
" 'presupuesto_pi': 'Presupuesto',\n",
" 'interno_pt': 'Personal'}"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Funciones\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"init_cell": true
},
"outputs": [],
"source": [
"def dbf_to_csv(inputfile, outputfile='output'):\n",
" Dbf5(inputfile, codec='latin-1').to_dataframe().to_csv(outputfile, sep=',', index=False)\n",
" \n",
"def asigna_cve_entidad(x, nacional=True):\n",
" \"\"\"Función que asigna la clave Inegi si detecta el nombre de la entidad.\n",
" nacional indica si se asigna también un código cuando se refiere al valor Nacional\"\"\"\n",
" if not nacional:\n",
" del diccionario_cve_entidad['00']\n",
" for c in diccionario_cve_entidad:\n",
" out = ''\n",
" if re.search(sin_acento(diccionario_cve_entidad[c]),sin_acento(x), flags=re.I|re.A):\n",
" out = c\n",
" break\n",
" return out\n",
"\n",
"def sin_acento(x):\n",
" output = x.replace('á','a').replace('é','e').replace('í','i').replace('ó','o').replace('ú','u')\\\n",
" .replace('Á','A').replace('É','E').replace('Í','I').replace('Ó','O').replace('Ú','U')\n",
" return output\n",
"def radial_estado(est1, est2):\n",
" labels = np.array(indice.keys().map(componentes_dicc))\n",
" stats_H = indice.loc[est1].values\n",
" stats_M = indice.loc[est2].values\n",
" angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)\n",
" # close the plot\n",
" stats_H = np.concatenate((stats_H,[stats_H[0]]))\n",
" stats_M = np.concatenate((stats_M,[stats_M[0]]))\n",
" angles=np.concatenate((angles,[angles[0]]))\n",
" fig= plt.figure()\n",
" ax = fig.add_subplot(111, polar=True)\n",
" ax.plot(angles, stats_H, 'o-', linewidth=2)\n",
" ax.fill(angles, stats_H, alpha=0.25)\n",
" ax.set_thetagrids(angles * 180/np.pi, labels)\n",
"\n",
" plt.plot(angles, stats_M, 'o-', linewidth=2)\n",
" plt.fill(angles, stats_M, alpha=0.25)\n",
" ax.grid(True)\n",
" ax.legend([est1, est2], bbox_to_anchor=(1,1))\n",
" return ax\n",
"\n",
"\n",
"def radial_sexo(est):\n",
" columnas = ['corrupcion', 'inseguridad', 'servicios']\n",
" labels = np.array([componentes_dicc[x] for x in columnas])\n",
" stats_H = indice_H.loc[est, columnas].values\n",
" stats_M = indice_M.loc[est, columnas].values \n",
" angles = np.linspace(0, 2*np.pi, len(labels), endpoint=False)\n",
" # close the plot\n",
" stats_H = np.concatenate((stats_H,[stats_H[0]]))\n",
" stats_M = np.concatenate((stats_M,[stats_M[0]]))\n",
" angles=np.concatenate((angles,[angles[0]]))\n",
" fig= plt.figure()\n",
" ax = fig.add_subplot(111, polar=True)\n",
" ax.plot(angles, stats_H, 'o-', linewidth=2, label='Hombre')\n",
" ax.fill(angles, stats_H, alpha=0.25)\n",
" ax.set_thetagrids(angles * 180/np.pi, labels)\n",
"\n",
" plt.plot(angles, stats_M, 'o-', linewidth=2, label='Mujeres')\n",
" plt.fill(angles, stats_M, alpha=0.25)\n",
" #ax.set_title([df.loc[386,\"Name\"]])\n",
" ax.grid(True)\n",
" ax.legend(['Hombres', 'Mujeres'] )\n",
" return ax\n"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# De dbf a csv"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"files = glob.glob1(datos_dir, '*.dbf')\n",
"for f in files:\n",
" csvname= datos_dir+f.replace('dbf', 'csv')\n",
" if not os.path.exists(csvname):\n",
" dbf_to_csv(datos_dir+f, outputfile=csvname)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Índice corrupción"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"corrup = pd.read_csv(datos_dir+'ENPOL_SEC8_9_10.csv', index_col='ID_PER')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"* 8.2 Durante 2016, un servidor público, empleado del gobierno o custodio: \n",
" * 8.2.1 ¿INTENTÓ apropiarse o le PIDIÓ de forma directa algún benefcio como dinero, bienes, regalos o favores?\n",
" * 8.2.2 ¿a través de un interno o abogado, le PIDIÓ dinero, bienes, regalos o favores?\n",
" * 8.2.3 ¿le INSINUÓ o GENERÓ las condiciones para que usted le proporcionara dinero, bienes, regalos o favores?\n",
"* 8.3 Dígame por favor, durante 2016, ¿por cuáles servicios, bienes, benefcios o permisos, paga usted dentro del Centro penitenciario\n",
" * 8.3.1 Usar baños, mingitorios y/o regaderas\n",
" * 8.3.2 Tener agua potable\n",
" * 8.3.3 Tener energía eléctrica en su celda\n",
" * 8.3.4 Tener una cama, colchoneta y/o cobijas\n",
" * 8.3.5 Recibir comida (rancho)\n",
" * 8.3.6 Salir a patio de visita\n",
" * 8.3.7 Ir a juzgados\n",
" * 8.3.8 Ir a locutorios\n",
" * 8.3.9 El pase de lista\n",
" * 8.3.10 Acceder a visita conyugal\n",
" * 8.3.11 Acceder a servicios médicos, psicológicos o escolares\n",
" * 8.3.12 Participar en algún taller\n",
" * 8.3.13 Tener aparatos eléctricos\n",
" * 8.3.14 Tener dispositivos electrónicos de comunicación\n",
" * 8.3.15 Cambiar de celda (evitar o cambiar de celda)\n",
" * 8.3.16 Tener acceso a un teléfono\n",
" * 8.3.17 Protección\n",
" * 8.3.18 Otro\n",
"* 8.4 ¿A quién(es) ha tenido que pagar durante 2016 por lo que mencionó anteriormente?\n",
" * 8.4.2 Custodios\n",
" * 8.4.3 Personal técnico penitenciario\n",
" * 8.4.4 Personal médico\n",
" * 8.4.5 Personal administrativo\n",
"* 8.7 De los pagos antes mencionados, ¿usted denunció ante alguna autoridad?\n",
"* 8.9 Durante 2016, la(s) persona(s) que ha(n) venido a visitarlo, ¿ha(n) tenido que pagar por..\n",
" * 8.9.1 pasar la comida?\n",
" * 8.9.2 pasar la ropa?\n",
" * 8.9.3 pasar otros objetos?\n",
" * 8.9.4 mandarlo a llamar?\n",
" * 8.9.5 entrar al centro?\n",
" * 8.9.6 tener visita conyugal?\n",
" * 8.9.7 Otro\n",
"* 8.10 De lo mencionado anteriormente, ¿a quién(es) ha(n) tenido que pagar sus visitas?\n",
" * 8.10.2 Custodios\n",
" * 8.10.3 Personal técnico penitenciario\n",
" * 8.10.4 Personal administrativo\n",
" * 8.10.5 Médicos\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"code_folding": [],
"scrolled": true
},
"outputs": [],
"source": [
"# Corrupción\n",
"varP8_2 = [f'P8_2_{x}' for x in range(1, 4)]\n",
"varP8_3 = [f'P8_3_{str(x).zfill(2)}' for x in range(1, 19)]\n",
"varP8_4 = [f'P8_4_{x}' for x in range(2, 6)]\n",
"varP8_7 = ['P8_7']\n",
"varP8_9 = [f'P8_9_{x}' for x in range(1, 8)]\n",
"varP8_10 = [f'P8_10_{x}' for x in range(2, 6)]\n",
"varP8 = varP8_2 + varP8_3 + varP8_7 + varP8_9\n",
"dfs_corrup = {'general': corrup,\n",
" 'mujeres': corrup.query('SEXO==2'),\n",
" 'hombres': corrup.query('SEXO==1'),\n",
" 'federal': corrup.query('FUERO==1'),\n",
" 'est_mun': corrup.query('FUERO==2')}\n",
"L_corrup = dict()\n",
"# Población privada de libertad\n",
"for d in dfs_corrup:\n",
" corrup = dfs_corrup[d]\n",
" pob = corrup.groupby('CVE_ENT')['FAC_PER'].sum()\n",
" score_corrup = corrup[varP8+['CVE_ENT','FAC_PER']]\\\n",
" .replace({'P8_7': {1:3}})\\\n",
" .replace({'P8_7': {2:1}})\\\n",
" .groupby(['CVE_ENT'])\\\n",
" .agg(lambda x: ((x[varP8]==1).apply(lambda y: y*x['FAC_PER'])).sum())\\\n",
" .apply(lambda x: 100*(x/pob))\\\n",
" .assign(corrupcion=lambda x: x.sum(axis=1))\\\n",
" .assign(corrupcion_n=lambda x:\n",
" (x['corrupcion']-x['corrupcion'].min())/(x['corrupcion'].max()-x['corrupcion'].min()))\n",
" L_corrup[d] = score_corrup"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Indice Inseguridad"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"hidden": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"seg = pd.read_csv(datos_dir+'ENPOL_SEC7_1.csv', index_col='ID_PER')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"* 7.47 Durante 2016, ¿a usted le ha sucedido el (CÓDIGO DE SITUACIÓN)?\n",
" * 7.47.1 Le han robado objetos personales\n",
" * 7.47.2 Lo han amenazado o presionado para exigirle dinero o bienes (extorsión)\n",
" * 7.47.3 Lo han amenazado o presionado para que hiciera algo o dejara de hacerlo\n",
" * 7.47.4 Alguien, solo por actitud abusiva o por una discusión, lo golpeó generándole una lesión física (moretones, fracturas, cortadas, etcétera)\n",
" * 7.47.5 Lo han agredido mediante hostigamiento sexual, manoseo, exhibicionismo o intento de violación\n",
" * 7.47.6 Lo han obligado mediante violencia física o amenaza a tener una actividad sexual no deseada (violación sexual)\n",
"* 7.49 ¿Quién(es) los han agredido de la forma que mencionó? \n",
" * 7.49.1 Le han robado objetos personales\n",
" * 7.49.1.2 Custodios\n",
" * 7.49.1.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.1.4 Personal médico\n",
" * 7.49.2 Lo han amenazado o presionado para exigirle dinero o bienes (extorsión)\n",
" * 7.49.2.2 Custodios\n",
" * 7.49.2.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.2.4 Personal médico\n",
" * 7.49.3 Lo han amenazado o presionado para que hiciera algo o dejara de hacerlo\n",
" * 7.49.3.2 Custodios\n",
" * 7.49.3.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.3.4 Personal médico\n",
" * 7.49.4 Alguien, solo por actitud abusiva o por una discusión, lo golpeó generándole una lesión física (moretones, fracturas, cortadas, etcétera)\n",
" * 7.49.4.2 Custodios\n",
" * 7.49.4.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.4.4 Personal médico\n",
" * 7.49.5 Lo han agredido mediante hostigamiento sexual, manoseo, exhibicionismo o intento de violación\n",
" * 7.49.5.2 Custodios\n",
" * 7.49.5.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.5.4 Personal médico\n",
" * 7.49.6 Lo han obligado mediante violencia física o amenaza a tener una actividad sexual no deseada (violación sexual)\n",
" * 7.49.6.2 Custodios\n",
" * 7.49.6.3 Personal técnico penitenciario (psicólogos, trabajadores sociales, criminólogos, secretarios)\n",
" * 7.49.6.4 Personal médico "
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"# seguridad\n",
"varP7_47 = [f'P7_47_{x}' for x in range(1, 7)]\n",
"varP7_49 = [f'P7_49_{i}_{x}' for x in range(2, 5) for i in range(1,7)]\n",
"varP7 = varP7_47+varP7_49\n",
"\n",
"dfs_seg = {'general': seg,\n",
" 'mujeres': seg.query('SEXO==2'),\n",
" 'hombres': seg.query('SEXO==1'),\n",
" 'federal': seg.query('FUERO==1'),\n",
" 'est_mun': seg.query('FUERO==2')}\n",
"L_seg = dict()\n",
"for d in dfs_seg:\n",
" seg = dfs_seg[d]\n",
" # Población privada de libertad\n",
" pob = seg.groupby('CVE_ENT')['FAC_PER'].sum()\n",
" score_seg = seg[varP7+['CVE_ENT', 'FAC_PER']]\\\n",
" .groupby('CVE_ENT')\\\n",
" .agg(lambda x: ((x[varP7]==1).apply(lambda y: y*x['FAC_PER'])).sum())\\\n",
" .apply(lambda x: 100*(x/pob))\\\n",
" .assign(inseguridad=lambda x: x.sum(axis=1))\\\n",
" .assign(inseguridad_n=lambda x:\n",
" (x['inseguridad']-x['inseguridad'].min())/(x['inseguridad'].max()-x['inseguridad'].min()))\n",
" L_seg[d] = score_seg"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Índice de servicios básicos"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"hidden": true,
"scrolled": true
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py:2728: DtypeWarning: Columns (5) have mixed types. Specify dtype option on import or set low_memory=False.\n",
" interactivity=interactivity, compiler=compiler, result=result)\n"
]
}
],
"source": [
"serv = pd.read_csv(datos_dir+'ENPOL_SEC_5_6.csv', index_col='ID_PER')"
]
},
{
"cell_type": "markdown",
"metadata": {
"hidden": true
},
"source": [
"* 6.2 ¿Cuenta con cama propia?\n",
"* 6.3 Regularmente, ¿comparte su/alguna cama, camastro o colchoneta con otra persona?\n",
"* 6.4 Regularmente, ¿su celda está limpia?\n",
"* 6.5 Por favor, dígame si SU CELDA cuenta con:\n",
" * 6.5.1 Agua potable\n",
" * 6.5.2 Drenaje\n",
" * 6.5.3 Luz eléctrica (Energía eléctrica)\n",
" * 6.5.4 Lugar para bañarse y hacer del baño\n",
"* 6.6 ¿Hay un área común para bañarse y hacer del baño?\n",
"* 6.7 De acuerdo con su experiencia, ¿el agua potable en su celda...\n",
" * 6.7.1 es pura y transparente?\n",
" * 6.7.2 es bebible sin temor a enfermarse?\n",
" * 6.7.3 tiene servicio constante?\n",
"* 6.9 De acuerdo con su experiencia, ¿el drenaje en su celda..\n",
" * 6.9.1 está conectado de tal forma que los desechos se descargan adecuadamente?\n",
" * 6.9.2 recibe mantenimiento frecuente que evita olores desagradables y plagas (cucarachas, ratas, etc.)?\n",
" * 6.9.3 presenta fugas de aguas negras por ruptura en el drenaje?\n",
"* 6.11 De acuerdo con su experiencia, ¿el servicio de luz eléctrica (energía eléctrica) en su celda...\n",
" * 6.11.1 ilumina adecuadamente?\n",
" * 6.11.2 genera la luz sufciente para que usted pueda leer o trabajar sin lastimarse la vista?\n",
" * 6.11.3 en caso de falla se da atención inmediata a la falta de energía eléctrica?\n",
"* 6.13 De acuerdo con su experiencia, ¿el lugar para bañarse y hacer del baño...\n",
" * 6.13.1 cuenta con regaderas para que usted pueda bañarse?\n",
" * 6.13.2 cuenta con un espacio para hacer del baño (sanitarios)?\n",
" * 6.13.3 cuenta con lavamanos?\n",
" * 6.13.4 está limpio?\n",
"* 6.15 ¿El Centro le ha proporcionado (RENGLÓN)...\n",
" * 6.15.1 servicios médicos?\n",
" * 6.15.2 medicamentos?\n",
" * 6.15.3 alimentos?\n",
"* 6.17 ¿El Centro le ha proporcionado (RENGLÓN)...\n",
" * 6.17.1 ropa (uniformes)?\n",
" * 6.17.2 calzado?\n",
" * 6.17.3 cobijas?\n",
" * 6.17.4 artículos de limpieza personal (jabón, papel higiénico, pasta de dientes, etcétera)? "
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"# seguridad\n",
"varP6_G1 = ['P6_2', 'P6_3', 'P6_4', 'P6_6']\n",
"varP6_G2 = [f'P6_{j}_{x}' for x in range(1, 5) for j in [5, 13, 17]]\n",
"varP6_G3 = [f'P6_{j}_{x}' for x in range(1, 4) for j in [7, 9, 11, 15]]\n",
"varP6 = varP6_G1+varP6_G2+varP6_G3\n",
"\n",
"dfs_serv = {'general': serv,\n",
" 'mujeres': serv.query('SEXO==2'),\n",
" 'hombres': serv.query('SEXO==1'),\n",
" 'federal': serv.query('FUERO==1'),\n",
" 'est_mun': serv.query('FUERO==2')}\n",
"L_serv = dict()\n",
"for d in dfs_serv:\n",
" serv = dfs_serv[d]\n",
" # Población privada de libertad\n",
" pob = serv.groupby('CVE_ENT')['FAC_PER'].sum()\n",
" score_serv = serv[varP6+['CVE_ENT', 'FAC_PER']]\\\n",
" .replace({'P6_3': {2:3}})\\\n",
" .replace({'P6_3': {1:2}})\\\n",
" .groupby('CVE_ENT')\\\n",
" .agg(lambda x: ((x[varP6]==2).apply(lambda y: y*x['FAC_PER'])).sum())\\\n",
" .apply(lambda x: 100*(x/pob))\\\n",
" .assign(servicios=lambda x: x.sum(axis=1))\\\n",
" .assign(servicios_n=lambda x:\n",
" (x['servicios']-x['servicios'].min())/(x['servicios'].max()-x['servicios'].min()))\n",
" L_serv[d] = score_serv"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# sobrepoblación"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {
"hidden": true,
"scrolled": true
},
"outputs": [],
"source": [
"sobrepob = pd.read_csv('../observatorio/privadas_libertad.csv')\\\n",
" .query('year==2017 & entidad!=\"Sistema Federal\"')[['sobrepoblacion_rel', 'entidad', 'g_a_total']]\\\n",
" .assign(cve_ent=lambda x: x['entidad'].apply(asigna_cve_entidad).astype(int),\n",
" sobrepoblacion_n = lambda x:\n",
" (x['sobrepoblacion_rel']-x['sobrepoblacion_rel'].min())/(x['sobrepoblacion_rel'].max()-x['sobrepoblacion_rel'].min()))\\\n",
" .rename(columns={'g_a_total': 'pob'})\\\n",
" .dropna(subset=['cve_ent'])\\\n",
" .set_index('cve_ent')"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# Violencia"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {
"hidden": true,
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
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"\n",
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" vertical-align: top;\n",
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"\n",
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" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Intentos de fuga</th>\n",
" <th>fuga</th>\n",
" <th>motines</th>\n",
" <th>riñas</th>\n",
" <th>Homicidio</th>\n",
" <th>Intento de homicidio</th>\n",
" <th>intento de suicidio</th>\n",
" <th>Suicidio</th>\n",
" <th>Huelgas de Hambre</th>\n",
" <th>Decesos</th>\n",
" <th>Intentos de Violación</th>\n",
" <th>Violaciones</th>\n",
" <th>Agresiones a terceros</th>\n",
" <th>Autoagresiones</th>\n",
" <th>violencia</th>\n",
" <th>violencia_n</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cve_ent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>0.018448</td>\n",
" <td>0.041507</td>\n",
" <td>0.216759</td>\n",
" <td>4.570380</td>\n",
" <td>0.313608</td>\n",
" <td>0.027671</td>\n",
" <td>0.096850</td>\n",
" <td>0.225982</td>\n",
" <td>0.069178</td>\n",
" <td>1.277493</td>\n",
" <td>0.013836</td>\n",
" <td>0.009224</td>\n",
" <td>2.112244</td>\n",
" <td>1.457356</td>\n",
" <td>10.450535</td>\n",
" <td>0.145680</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.786782</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.786782</td>\n",
" <td>0.010968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>0.079202</td>\n",
" <td>0.000000</td>\n",
" <td>0.633613</td>\n",
" <td>7.682560</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.079202</td>\n",
" <td>0.000000</td>\n",
" <td>0.079202</td>\n",
" <td>0.475210</td>\n",
" <td>0.237605</td>\n",
" <td>0.000000</td>\n",
" <td>8.632980</td>\n",
" <td>1.108823</td>\n",
" <td>19.008395</td>\n",
" <td>0.264977</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1.293661</td>\n",
" <td>0.646831</td>\n",
" <td>0.646831</td>\n",
" <td>5.821475</td>\n",
" <td>1.293661</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.587322</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.055627</td>\n",
" <td>1.940492</td>\n",
" <td>23.285899</td>\n",
" <td>0.324605</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.700280</td>\n",
" <td>15.406162</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.700280</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.700280</td>\n",
" <td>2.801120</td>\n",
" <td>1.400560</td>\n",
" <td>21.708683</td>\n",
" <td>0.302619</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>17.191977</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.220410</td>\n",
" <td>1.102050</td>\n",
" <td>18.514437</td>\n",
" <td>0.258091</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.750563</td>\n",
" <td>12.884663</td>\n",
" <td>0.125094</td>\n",
" <td>0.375281</td>\n",
" <td>1.000751</td>\n",
" <td>0.875657</td>\n",
" <td>0.000000</td>\n",
" <td>2.501876</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.752815</td>\n",
" <td>4.003002</td>\n",
" <td>26.269702</td>\n",
" <td>0.366200</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.909504</td>\n",
" <td>6.366530</td>\n",
" <td>0.454752</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.183265</td>\n",
" <td>5.002274</td>\n",
" <td>25.920873</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.457026</td>\n",
" <td>0.000000</td>\n",
" <td>47.294225</td>\n",
" <td>0.659281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>32.608696</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.418060</td>\n",
" <td>2.090301</td>\n",
" <td>35.117057</td>\n",
" <td>0.489532</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.065161</td>\n",
" <td>0.032581</td>\n",
" <td>0.000000</td>\n",
" <td>0.097742</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.065161</td>\n",
" <td>0.065161</td>\n",
" <td>0.325807</td>\n",
" <td>0.004542</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.817197</td>\n",
" <td>1.015572</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.015572</td>\n",
" <td>0.338524</td>\n",
" <td>0.338524</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.031144</td>\n",
" <td>5.754909</td>\n",
" <td>20.311442</td>\n",
" <td>0.283142</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.077045</td>\n",
" <td>0.423745</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.038522</td>\n",
" <td>0.231134</td>\n",
" <td>0.770446</td>\n",
" <td>0.010740</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>0.000000</td>\n",
" <td>0.223015</td>\n",
" <td>0.000000</td>\n",
" <td>10.258698</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.446030</td>\n",
" <td>1.115076</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.892061</td>\n",
" <td>1.561106</td>\n",
" <td>14.495986</td>\n",
" <td>0.202074</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.776197</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.776197</td>\n",
" <td>0.010820</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>0.000000</td>\n",
" <td>0.206384</td>\n",
" <td>0.000000</td>\n",
" <td>1.375894</td>\n",
" <td>0.206384</td>\n",
" <td>0.206384</td>\n",
" <td>0.275179</td>\n",
" <td>0.137589</td>\n",
" <td>0.000000</td>\n",
" <td>0.275179</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.063841</td>\n",
" <td>0.068795</td>\n",
" <td>4.815630</td>\n",
" <td>0.067130</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.036921</td>\n",
" <td>0.701495</td>\n",
" <td>0.147683</td>\n",
" <td>0.000000</td>\n",
" <td>0.036921</td>\n",
" <td>0.184604</td>\n",
" <td>0.000000</td>\n",
" <td>0.996862</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.849179</td>\n",
" <td>0.147683</td>\n",
" <td>3.101348</td>\n",
" <td>0.043233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.604493</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.200562</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.203369</td>\n",
" <td>0.000000</td>\n",
" <td>3.008424</td>\n",
" <td>0.041937</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.853403</td>\n",
" <td>0.872600</td>\n",
" <td>0.000000</td>\n",
" <td>0.290867</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.271670</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>7.271670</td>\n",
" <td>38.394415</td>\n",
" <td>61.954625</td>\n",
" <td>0.863647</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.348189</td>\n",
" <td>6.963788</td>\n",
" <td>2.089136</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.348189</td>\n",
" <td>0.000000</td>\n",
" <td>3.830084</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.133705</td>\n",
" <td>16.713092</td>\n",
" <td>0.232981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.548546</td>\n",
" <td>1.097093</td>\n",
" <td>2.468459</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.548546</td>\n",
" <td>0.000000</td>\n",
" <td>3.839824</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.411410</td>\n",
" <td>0.000000</td>\n",
" <td>8.913878</td>\n",
" <td>0.124259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>18.372034</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.255167</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.551671</td>\n",
" <td>21.178872</td>\n",
" <td>0.295233</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>0.134336</td>\n",
" <td>0.000000</td>\n",
" <td>1.209027</td>\n",
" <td>16.657711</td>\n",
" <td>0.134336</td>\n",
" <td>0.000000</td>\n",
" <td>0.134336</td>\n",
" <td>0.403009</td>\n",
" <td>0.000000</td>\n",
" <td>1.209027</td>\n",
" <td>0.000000</td>\n",
" <td>0.134336</td>\n",
" <td>16.389038</td>\n",
" <td>2.955400</td>\n",
" <td>39.360559</td>\n",
" <td>0.548686</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.960154</td>\n",
" <td>0.480077</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.480077</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.920307</td>\n",
" <td>0.026769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>0.000000</td>\n",
" <td>0.571429</td>\n",
" <td>0.571429</td>\n",
" <td>1.142857</td>\n",
" <td>0.571429</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.571429</td>\n",
" <td>0.000000</td>\n",
" <td>1.428571</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>4.857143</td>\n",
" <td>0.067709</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>0.000000</td>\n",
" <td>0.184809</td>\n",
" <td>0.000000</td>\n",
" <td>6.283497</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.554426</td>\n",
" <td>0.000000</td>\n",
" <td>1.663279</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.554426</td>\n",
" <td>4.435409</td>\n",
" <td>13.675845</td>\n",
" <td>0.190641</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>6.734007</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.247038</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>3.741115</td>\n",
" <td>0.498815</td>\n",
" <td>12.220975</td>\n",
" <td>0.170360</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.511509</td>\n",
" <td>3.580563</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.511509</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.023018</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.767263</td>\n",
" <td>0.511509</td>\n",
" <td>6.905371</td>\n",
" <td>0.096261</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>0.000000</td>\n",
" <td>0.176523</td>\n",
" <td>0.706090</td>\n",
" <td>1.235658</td>\n",
" <td>4.060018</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.529568</td>\n",
" <td>0.000000</td>\n",
" <td>3.883495</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>10.591350</td>\n",
" <td>0.147643</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>5.738881</td>\n",
" <td>27.259684</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>34.433286</td>\n",
" <td>4.304161</td>\n",
" <td>71.736011</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>9.487397</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.141603</td>\n",
" <td>0.708015</td>\n",
" <td>0.000000</td>\n",
" <td>3.115265</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.708015</td>\n",
" <td>1.699235</td>\n",
" <td>15.859530</td>\n",
" <td>0.221082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.680272</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.680272</td>\n",
" <td>0.009483</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.610966</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>2.610966</td>\n",
" <td>0.036397</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Intentos de fuga fuga motines riñas Homicidio \\\n",
"cve_ent \n",
"0 0.018448 0.041507 0.216759 4.570380 0.313608 \n",
"1 0.000000 0.000000 0.000000 0.786782 0.000000 \n",
"2 0.079202 0.000000 0.633613 7.682560 0.000000 \n",
"3 1.293661 0.646831 0.646831 5.821475 1.293661 \n",
"4 0.000000 0.000000 0.700280 15.406162 0.000000 \n",
"7 0.000000 0.000000 0.000000 17.191977 0.000000 \n",
"8 0.000000 0.000000 0.750563 12.884663 0.125094 \n",
"5 0.000000 0.000000 0.909504 6.366530 0.454752 \n",
"6 0.000000 0.000000 0.000000 32.608696 0.000000 \n",
"9 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"10 0.000000 0.000000 0.000000 9.817197 1.015572 \n",
"15 0.000000 0.000000 0.077045 0.423745 0.000000 \n",
"11 0.000000 0.223015 0.000000 10.258698 0.000000 \n",
"12 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"13 0.000000 0.206384 0.000000 1.375894 0.206384 \n",
"14 0.000000 0.000000 0.036921 0.701495 0.147683 \n",
"16 0.000000 0.000000 0.000000 1.604493 0.000000 \n",
"17 0.000000 0.000000 0.000000 7.853403 0.872600 \n",
"18 0.000000 0.000000 0.348189 6.963788 2.089136 \n",
"19 0.000000 0.000000 0.548546 1.097093 2.468459 \n",
"20 0.000000 0.000000 0.000000 18.372034 0.000000 \n",
"21 0.134336 0.000000 1.209027 16.657711 0.134336 \n",
"22 0.000000 0.000000 0.000000 0.960154 0.480077 \n",
"23 0.000000 0.571429 0.571429 1.142857 0.571429 \n",
"24 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"25 0.000000 0.184809 0.000000 6.283497 0.000000 \n",
"26 0.000000 0.000000 0.000000 6.734007 0.000000 \n",
"27 0.000000 0.000000 0.511509 3.580563 0.000000 \n",
"28 0.000000 0.176523 0.706090 1.235658 4.060018 \n",
"29 0.000000 0.000000 5.738881 27.259684 0.000000 \n",
"30 0.000000 0.000000 0.000000 9.487397 0.000000 \n",
"31 0.000000 0.000000 0.000000 0.000000 0.000000 \n",
"32 0.000000 0.000000 0.000000 2.610966 0.000000 \n",
"\n",
" Intento de homicidio intento de suicidio Suicidio \\\n",
"cve_ent \n",
"0 0.027671 0.096850 0.225982 \n",
"1 0.000000 0.000000 0.000000 \n",
"2 0.000000 0.079202 0.000000 \n",
"3 0.000000 0.000000 0.000000 \n",
"4 0.000000 0.000000 0.700280 \n",
"7 0.000000 0.000000 0.000000 \n",
"8 0.375281 1.000751 0.875657 \n",
"5 0.000000 0.000000 3.183265 \n",
"6 0.000000 0.000000 0.000000 \n",
"9 0.000000 0.065161 0.032581 \n",
"10 0.000000 0.000000 1.015572 \n",
"15 0.000000 0.000000 0.000000 \n",
"11 0.000000 0.000000 0.000000 \n",
"12 0.000000 0.000000 0.000000 \n",
"13 0.206384 0.275179 0.137589 \n",
"14 0.000000 0.036921 0.184604 \n",
"16 0.000000 0.000000 0.200562 \n",
"17 0.000000 0.290867 0.000000 \n",
"18 0.000000 0.000000 0.348189 \n",
"19 0.000000 0.000000 0.548546 \n",
"20 0.000000 0.000000 0.000000 \n",
"21 0.000000 0.134336 0.403009 \n",
"22 0.000000 0.000000 0.000000 \n",
"23 0.000000 0.000000 0.571429 \n",
"24 0.000000 0.000000 0.000000 \n",
"25 0.000000 0.000000 0.554426 \n",
"26 0.000000 0.000000 0.000000 \n",
"27 0.000000 0.511509 0.000000 \n",
"28 0.000000 0.000000 0.529568 \n",
"29 0.000000 0.000000 0.000000 \n",
"30 0.000000 0.141603 0.708015 \n",
"31 0.000000 0.000000 0.680272 \n",
"32 0.000000 0.000000 0.000000 \n",
"\n",
" Huelgas de Hambre Decesos Intentos de Violación Violaciones \\\n",
"cve_ent \n",
"0 0.069178 1.277493 0.013836 0.009224 \n",
"1 0.000000 0.000000 0.000000 0.000000 \n",
"2 0.079202 0.475210 0.237605 0.000000 \n",
"3 0.000000 2.587322 0.000000 0.000000 \n",
"4 0.000000 0.000000 0.000000 0.700280 \n",
"7 0.000000 0.000000 0.000000 0.000000 \n",
"8 0.000000 2.501876 0.000000 0.000000 \n",
"5 5.002274 25.920873 0.000000 0.000000 \n",
"6 0.000000 0.000000 0.000000 0.000000 \n",
"9 0.000000 0.097742 0.000000 0.000000 \n",
"10 0.338524 0.338524 0.000000 0.000000 \n",
"15 0.000000 0.000000 0.000000 0.000000 \n",
"11 0.446030 1.115076 0.000000 0.000000 \n",
"12 0.000000 0.776197 0.000000 0.000000 \n",
"13 0.000000 0.275179 0.000000 0.000000 \n",
"14 0.000000 0.996862 0.000000 0.000000 \n",
"16 0.000000 0.000000 0.000000 0.000000 \n",
"17 0.000000 7.271670 0.000000 0.000000 \n",
"18 0.000000 3.830084 0.000000 0.000000 \n",
"19 0.000000 3.839824 0.000000 0.000000 \n",
"20 0.000000 0.255167 0.000000 0.000000 \n",
"21 0.000000 1.209027 0.000000 0.134336 \n",
"22 0.000000 0.480077 0.000000 0.000000 \n",
"23 0.000000 1.428571 0.000000 0.000000 \n",
"24 0.000000 0.000000 0.000000 0.000000 \n",
"25 0.000000 1.663279 0.000000 0.000000 \n",
"26 0.000000 1.247038 0.000000 0.000000 \n",
"27 0.000000 1.023018 0.000000 0.000000 \n",
"28 0.000000 3.883495 0.000000 0.000000 \n",
"29 0.000000 0.000000 0.000000 0.000000 \n",
"30 0.000000 3.115265 0.000000 0.000000 \n",
"31 0.000000 0.000000 0.000000 0.000000 \n",
"32 0.000000 0.000000 0.000000 0.000000 \n",
"\n",
" Agresiones a terceros Autoagresiones violencia violencia_n \n",
"cve_ent \n",
"0 2.112244 1.457356 10.450535 0.145680 \n",
"1 0.000000 0.000000 0.786782 0.010968 \n",
"2 8.632980 1.108823 19.008395 0.264977 \n",
"3 9.055627 1.940492 23.285899 0.324605 \n",
"4 2.801120 1.400560 21.708683 0.302619 \n",
"7 0.220410 1.102050 18.514437 0.258091 \n",
"8 3.752815 4.003002 26.269702 0.366200 \n",
"5 5.457026 0.000000 47.294225 0.659281 \n",
"6 0.418060 2.090301 35.117057 0.489532 \n",
"9 0.065161 0.065161 0.325807 0.004542 \n",
"10 2.031144 5.754909 20.311442 0.283142 \n",
"15 0.038522 0.231134 0.770446 0.010740 \n",
"11 0.892061 1.561106 14.495986 0.202074 \n",
"12 0.000000 0.000000 0.776197 0.010820 \n",
"13 2.063841 0.068795 4.815630 0.067130 \n",
"14 0.849179 0.147683 3.101348 0.043233 \n",
"16 1.203369 0.000000 3.008424 0.041937 \n",
"17 7.271670 38.394415 61.954625 0.863647 \n",
"18 0.000000 3.133705 16.713092 0.232981 \n",
"19 0.411410 0.000000 8.913878 0.124259 \n",
"20 0.000000 2.551671 21.178872 0.295233 \n",
"21 16.389038 2.955400 39.360559 0.548686 \n",
"22 0.000000 0.000000 1.920307 0.026769 \n",
"23 0.000000 0.000000 4.857143 0.067709 \n",
"24 0.000000 0.000000 0.000000 0.000000 \n",
"25 0.554426 4.435409 13.675845 0.190641 \n",
"26 3.741115 0.498815 12.220975 0.170360 \n",
"27 0.767263 0.511509 6.905371 0.096261 \n",
"28 0.000000 0.000000 10.591350 0.147643 \n",
"29 34.433286 4.304161 71.736011 1.000000 \n",
"30 0.708015 1.699235 15.859530 0.221082 \n",
"31 0.000000 0.000000 0.680272 0.009483 \n",
"32 0.000000 0.000000 2.610966 0.036397 "
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"viol = pd.read_csv('../observatorio/violencia_prisiones.csv')\\\n",
" .query('year==2017')\\\n",
" .rename(columns={'state_code': 'cve_ent', 'total': 'violencia'})\\\n",
" .set_index('cve_ent')\\\n",
" .drop(['entidad', 'year'], axis=1)\\\n",
" .apply(lambda x: 1000*(x/sobrepob['pob']))\\\n",
" .assign(violencia_n=lambda x:\n",
" (x['violencia']-x['violencia'].min())/(x['violencia'].max()-x['violencia'].min()))\n",
"viol"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"# DDHH"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"hidden": true
},
"outputs": [],
"source": [
"dh = pd.read_csv('../observatorio/supervision_penitenciaria.csv')\\\n",
" .query('year==2017 & cal_general!=\"No visitado\"')\\\n",
" .dropna(subset=['direccion'])\\\n",
" .assign(cve_ent=lambda x: x['entidad'].apply(asigna_cve_entidad))\\\n",
" .astype({'cal_general': float, 'integridad_fisica_moral': float, 'estancia_digna': float, 'gobernabilidad': float, 'reinsercion_social': float})\n",
"dh.loc[dh['entidad']==\"Centros Federales\", 'cve_ent'] = dh.loc[dh['entidad']==\"Centros Federales\", 'direccion']\\\n",
" .fillna('')\\\n",
" .apply(asigna_cve_entidad)\n",
"ddhh = dh.astype({'cve_ent': int})\\\n",
" .groupby('cve_ent').mean()\\\n",
" [['cal_general', 'integridad_fisica_moral', 'estancia_digna', 'gobernabilidad', 'reinsercion_social']]\\\n",
" .rename(columns={'cal_general': 'ddhh'})\\\n",
" .assign(ddhh_n=lambda x:\n",
" (x['ddhh']-x['ddhh'].min())/(x['ddhh'].max()-x['ddhh'].min()))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {
"hidden": true
},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>ddhh</th>\n",
" <th>integridad_fisica_moral</th>\n",
" <th>estancia_digna</th>\n",
" <th>gobernabilidad</th>\n",
" <th>reinsercion_social</th>\n",
" <th>ddhh_n</th>\n",
" </tr>\n",
" <tr>\n",
" <th>cve_ent</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>7.613333</td>\n",
" <td>8.436667</td>\n",
" <td>7.636667</td>\n",
" <td>8.186667</td>\n",
" <td>7.853333</td>\n",
" <td>0.868769</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>7.510000</td>\n",
" <td>6.902500</td>\n",
" <td>7.425000</td>\n",
" <td>8.300000</td>\n",
" <td>6.822500</td>\n",
" <td>0.842352</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>5.090000</td>\n",
" <td>5.700000</td>\n",
" <td>5.066667</td>\n",
" <td>4.443333</td>\n",
" <td>5.283333</td>\n",
" <td>0.223690</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>6.825000</td>\n",
" <td>7.495000</td>\n",
" <td>5.940000</td>\n",
" <td>6.740000</td>\n",
" <td>6.545000</td>\n",
" <td>0.667235</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>7.738000</td>\n",
" <td>7.766000</td>\n",
" <td>7.616000</td>\n",
" <td>8.480000</td>\n",
" <td>7.322000</td>\n",
" <td>0.900639</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7.090000</td>\n",
" <td>6.783333</td>\n",
" <td>7.010000</td>\n",
" <td>7.176667</td>\n",
" <td>7.340000</td>\n",
" <td>0.734981</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>6.166000</td>\n",
" <td>6.570000</td>\n",
" <td>6.342000</td>\n",
" <td>6.720000</td>\n",
" <td>6.014000</td>\n",
" <td>0.498764</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>7.272857</td>\n",
" <td>7.287143</td>\n",
" <td>7.510000</td>\n",
" <td>8.548571</td>\n",
" <td>7.295714</td>\n",
" <td>0.781727</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>6.875455</td>\n",
" <td>6.629091</td>\n",
" <td>7.220000</td>\n",
" <td>6.481818</td>\n",
" <td>7.152727</td>\n",
" <td>0.680133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>6.822500</td>\n",
" <td>6.695000</td>\n",
" <td>6.525000</td>\n",
" <td>8.017500</td>\n",
" <td>6.382500</td>\n",
" <td>0.666596</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>8.060000</td>\n",
" <td>8.140000</td>\n",
" <td>8.396667</td>\n",
" <td>8.928333</td>\n",
" <td>7.431667</td>\n",
" <td>0.982957</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>4.215000</td>\n",
" <td>4.407500</td>\n",
" <td>3.762500</td>\n",
" <td>3.272500</td>\n",
" <td>5.175000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>4.800000</td>\n",
" <td>4.730000</td>\n",
" <td>4.380000</td>\n",
" <td>4.032500</td>\n",
" <td>5.277500</td>\n",
" <td>0.149553</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>6.986667</td>\n",
" <td>6.751667</td>\n",
" <td>6.725000</td>\n",
" <td>6.986667</td>\n",
" <td>6.753333</td>\n",
" <td>0.708564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>6.395833</td>\n",
" <td>5.975833</td>\n",
" <td>6.033333</td>\n",
" <td>6.384167</td>\n",
" <td>6.795000</td>\n",
" <td>0.557520</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>6.406667</td>\n",
" <td>7.166667</td>\n",
" <td>5.876667</td>\n",
" <td>5.433333</td>\n",
" <td>6.706667</td>\n",
" <td>0.560290</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>7.006667</td>\n",
" <td>6.961667</td>\n",
" <td>6.968333</td>\n",
" <td>7.676667</td>\n",
" <td>6.505000</td>\n",
" <td>0.713677</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>7.348333</td>\n",
" <td>6.946667</td>\n",
" <td>7.691667</td>\n",
" <td>7.585000</td>\n",
" <td>7.388333</td>\n",
" <td>0.801023</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>5.016667</td>\n",
" <td>4.663333</td>\n",
" <td>3.796667</td>\n",
" <td>5.133333</td>\n",
" <td>5.243333</td>\n",
" <td>0.204942</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>6.238000</td>\n",
" <td>6.680000</td>\n",
" <td>6.574000</td>\n",
" <td>6.262000</td>\n",
" <td>5.424000</td>\n",
" <td>0.517171</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>6.390000</td>\n",
" <td>7.320000</td>\n",
" <td>5.214000</td>\n",
" <td>7.062000</td>\n",
" <td>6.504000</td>\n",
" <td>0.556029</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>8.126667</td>\n",
" <td>8.173333</td>\n",
" <td>8.403333</td>\n",
" <td>8.543333</td>\n",
" <td>8.146667</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>5.030000</td>\n",
" <td>4.235000</td>\n",
" <td>3.545000</td>\n",
" <td>4.400000</td>\n",
" <td>4.750000</td>\n",
" <td>0.208351</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>6.552500</td>\n",
" <td>6.320000</td>\n",
" <td>7.350000</td>\n",
" <td>6.420000</td>\n",
" <td>5.942500</td>\n",
" <td>0.597571</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>5.955000</td>\n",
" <td>6.287500</td>\n",
" <td>5.695000</td>\n",
" <td>5.065000</td>\n",
" <td>5.487500</td>\n",
" <td>0.444823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>6.581667</td>\n",
" <td>6.593333</td>\n",
" <td>6.171667</td>\n",
" <td>6.496667</td>\n",
" <td>6.578333</td>\n",
" <td>0.605028</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>5.882000</td>\n",
" <td>6.334000</td>\n",
" <td>4.954000</td>\n",
" <td>5.826000</td>\n",
" <td>6.064000</td>\n",
" <td>0.426161</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>4.715000</td>\n",
" <td>5.060000</td>\n",
" <td>5.062500</td>\n",
" <td>2.235000</td>\n",
" <td>4.565000</td>\n",
" <td>0.127823</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>6.990000</td>\n",
" <td>6.860000</td>\n",
" <td>7.115000</td>\n",
" <td>6.720000</td>\n",
" <td>7.050000</td>\n",
" <td>0.709416</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>6.222000</td>\n",
" <td>6.782000</td>\n",
" <td>5.590000</td>\n",
" <td>6.166000</td>\n",
" <td>6.404000</td>\n",
" <td>0.513081</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>6.812500</td>\n",
" <td>7.610000</td>\n",
" <td>6.920000</td>\n",
" <td>7.865000</td>\n",
" <td>6.092500</td>\n",
" <td>0.664039</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>5.520000</td>\n",
" <td>6.210000</td>\n",
" <td>5.710000</td>\n",
" <td>5.153333</td>\n",
" <td>5.730000</td>\n",
" <td>0.333617</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" ddhh integridad_fisica_moral estancia_digna gobernabilidad \\\n",
"cve_ent \n",
"1 7.613333 8.436667 7.636667 8.186667 \n",
"2 7.510000 6.902500 7.425000 8.300000 \n",
"3 5.090000 5.700000 5.066667 4.443333 \n",
"4 6.825000 7.495000 5.940000 6.740000 \n",
"5 7.738000 7.766000 7.616000 8.480000 \n",
"6 7.090000 6.783333 7.010000 7.176667 \n",
"7 6.166000 6.570000 6.342000 6.720000 \n",
"8 7.272857 7.287143 7.510000 8.548571 \n",
"9 6.875455 6.629091 7.220000 6.481818 \n",
"10 6.822500 6.695000 6.525000 8.017500 \n",
"11 8.060000 8.140000 8.396667 8.928333 \n",
"12 4.215000 4.407500 3.762500 3.272500 \n",
"13 4.800000 4.730000 4.380000 4.032500 \n",
"14 6.986667 6.751667 6.725000 6.986667 \n",
"15 6.395833 5.975833 6.033333 6.384167 \n",
"16 6.406667 7.166667 5.876667 5.433333 \n",
"17 7.006667 6.961667 6.968333 7.676667 \n",
"18 7.348333 6.946667 7.691667 7.585000 \n",
"19 5.016667 4.663333 3.796667 5.133333 \n",
"20 6.238000 6.680000 6.574000 6.262000 \n",
"21 6.390000 7.320000 5.214000 7.062000 \n",
"22 8.126667 8.173333 8.403333 8.543333 \n",
"23 5.030000 4.235000 3.545000 4.400000 \n",
"24 6.552500 6.320000 7.350000 6.420000 \n",
"25 5.955000 6.287500 5.695000 5.065000 \n",
"26 6.581667 6.593333 6.171667 6.496667 \n",
"27 5.882000 6.334000 4.954000 5.826000 \n",
"28 4.715000 5.060000 5.062500 2.235000 \n",
"29 6.990000 6.860000 7.115000 6.720000 \n",
"30 6.222000 6.782000 5.590000 6.166000 \n",
"31 6.812500 7.610000 6.920000 7.865000 \n",
"32 5.520000 6.210000 5.710000 5.153333 \n",
"\n",
" reinsercion_social ddhh_n \n",
"cve_ent \n",
"1 7.853333 0.868769 \n",
"2 6.822500 0.842352 \n",
"3 5.283333 0.223690 \n",
"4 6.545000 0.667235 \n",
"5 7.322000 0.900639 \n",
"6 7.340000 0.734981 \n",
"7 6.014000 0.498764 \n",
"8 7.295714 0.781727 \n",
"9 7.152727 0.680133 \n",
"10 6.382500 0.666596 \n",
"11 7.431667 0.982957 \n",
"12 5.175000 0.000000 \n",
"13 5.277500 0.149553 \n",
"14 6.753333 0.708564 \n",
"15 6.795000 0.557520 \n",
"16 6.706667 0.560290 \n",
"17 6.505000 0.713677 \n",
"18 7.388333 0.801023 \n",
"19 5.243333 0.204942 \n",
"20 5.424000 0.517171 \n",
"21 6.504000 0.556029 \n",
"22 8.146667 1.000000 \n",
"23 4.750000 0.208351 \n",
"24 5.942500 0.597571 \n",
"25 5.487500 0.444823 \n",
"26 6.578333 0.605028 \n",
"27 6.064000 0.426161 \n",
"28 4.565000 0.127823 \n",
"29 7.050000 0.709416 \n",
"30 6.404000 0.513081 \n",
"31 6.092500 0.664039 \n",
"32 5.730000 0.333617 "
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ddhh"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CNGSPSP"
]
},
{
"cell_type": "markdown",
"metadata": {
"heading_collapsed": true
},
"source": [
"## población"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"hidden": true,
"scrolled": true
},
"outputs": [],
"source": [
"cn_pob = pd.read_excel('../CNGSJSP/CNGSPSPE2018_M3.xlsx', sheet_name='3.3', header=4, skipfooter=7)\\\n",
" .dropna(subset=['Entidad Federativa'])\\\n",
" .assign(cve_ent=lambda x: x['Entidad Federativa'].apply(asigna_cve_entidad).astype(int))\\\n",
" .set_index('cve_ent')\\\n",
" [['Total']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Presupuesto ejercido (miles de pesos)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"# Presupuesto en miles de pesos\n",
"presup = pd.read_excel('../CNGSJSP/CNGSPSPE2018_M3.xlsx', sheet_name='3.6', header=5, skipfooter=10)\\\n",
" .dropna(subset=['Entidad Federativa'])\\\n",
" .assign(cve_ent=lambda x: x['Entidad Federativa'].apply(asigna_cve_entidad).astype(int))\\\n",
" .assign(presupuesto=lambda x: x['Ejercido3'].replace({'-': pd.np.nan}))\\\n",
" .set_index('cve_ent')\\\n",
" .join(cn_pob)\\\n",
" .assign(presup_pi=lambda x: x['presupuesto']/x['Total'])\\\n",
" .pipe(lambda df: df.fillna({'presup_pi': df['presup_pi'].mean()}))\\\n",
" [['presupuesto', 'presup_pi']]"
]
},
{
"cell_type": "code",
"execution_count": 231,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"6.447328153592051"
]
},
"execution_count": 231,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"(presup.join(cn_pob).sum()['presupuesto']/presup.join(cn_pob).sum()['Total'])/12"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Recursos humanos (internos por cada trabajador)"
]
},
{
"cell_type": "code",
"execution_count": 234,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"interno_pt = pd.read_excel('../CNGSJSP/CNGSPSPE2018_M3.xlsx', sheet_name='3.5', header=5, skipfooter=6)\\\n",
" .dropna(subset=['Entidad Federativas'])\\\n",
" .assign(cve_ent=lambda x: x['Entidad Federativas'].apply(asigna_cve_entidad).astype(int))\\\n",
" .rename(columns={'Total': 'personal'})\\\n",
" .set_index('cve_ent')\\\n",
" .join(cn_pob)\\\n",
" .assign(interno_pt=lambda x: x['Total']/x['personal'])\\\n",
" [['personal', 'interno_pt']]\n",
"cng = presup.join(interno_pt)"
]
},
{
"cell_type": "code",
"execution_count": 237,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.986040468819106"
]
},
"execution_count": 237,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"interno_pt.join(cn_pob).sum()['Total']/interno_pt.join(cn_pob).sum()['personal']\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Indice multidimensional"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [],
"source": [
"W_corrupcion = 0.15\n",
"W_inseguridad = 0.15\n",
"W_servicios = 0.1\n",
"W_violencia = 0.15\n",
"W_sobrepoblacion = 0.1\n",
"W_ddhh = 0.15\n",
"W_presup = 0.1\n",
"W_interno = 0.1\n",
"W = pd.np.array([W_corrupcion, W_inseguridad, W_servicios, W_violencia, W_sobrepoblacion, W_ddhh, W_presup, W_interno])\n",
"\n",
"indice_multi_pre = dict()\n",
"indice_multi = dict()\n",
"for d in ['general', 'mujeres', 'hombres', 'federal', 'est_mun']:\n",
" indice = pd.concat([L_corrup[d], L_seg[d], L_serv[d]], axis=1)\n",
" indice_multi_pre[d] = indice[['corrupcion', 'inseguridad', 'servicios']]\\\n",
" .join([viol[['violencia']], sobrepob[['sobrepoblacion_rel']], ddhh[['ddhh']], cng[['presup_pi', 'interno_pt']]])\n",
" indice_multi[d] = indice_multi_pre[d]\\\n",
" .apply(lambda x: (x-x.min())/(x.max()-x.min()))\\\n",
" .assign(ddhh=lambda x: 1-x['ddhh'], presup_pi=lambda x: 1-x['presup_pi'])\\\n",
" .assign(indice=lambda x: (x*W).sum(axis=1))\\\n",
" .apply(lambda x: 1-x)\n",
" indice_multi[d].to_csv(f'indice_multidimensional_{d}.csv')\n",
"indice = indice_multi['general']\n",
"indice.index = indice.index.map(catal_ent.set_index('CVE_ENT')['NOM_ABR'].to_dict())\n",
"indice_H = indice_multi['hombres']\n",
"indice_H.index = indice_H.index.map(catal_ent.set_index('CVE_ENT')['NOM_ABR'].to_dict())\n",
"indice_M = indice_multi['mujeres']\n",
"indice_M.index = indice_M.index.map(catal_ent.set_index('CVE_ENT')['NOM_ABR'].to_dict())"
]
},
{
"cell_type": "code",
"execution_count": 206,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>corrupcion</th>\n",
" <th>inseguridad</th>\n",
" <th>servicios</th>\n",
" <th>violencia</th>\n",
" <th>sobrepoblacion_rel</th>\n",
" <th>ddhh</th>\n",
" <th>presup_pi</th>\n",
" <th>interno_pt</th>\n",
" <th>indice</th>\n",
" </tr>\n",
" <tr>\n",
" <th>CVE_ENT</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>Ags.</th>\n",
" <td>0.937673</td>\n",
" <td>0.202207</td>\n",
" <td>0.683749</td>\n",
" <td>0.989032</td>\n",
" <td>0.948622</td>\n",
" <td>0.868769</td>\n",
" <td>0.109208</td>\n",
" <td>0.993671</td>\n",
" <td>0.723177</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BC</th>\n",
" <td>0.980196</td>\n",
" <td>0.765865</td>\n",
" <td>0.227940</td>\n",
" <td>0.735023</td>\n",
" <td>0.904309</td>\n",
" <td>0.842352</td>\n",
" <td>0.267569</td>\n",
" <td>0.372282</td>\n",
" <td>0.675725</td>\n",
" </tr>\n",
" <tr>\n",
" <th>BCS</th>\n",
" <td>0.863711</td>\n",
" <td>0.200615</td>\n",
" <td>0.531979</td>\n",
" <td>0.675395</td>\n",
" <td>0.894373</td>\n",
" <td>0.223690</td>\n",
" <td>0.718251</td>\n",
" <td>0.949958</td>\n",
" <td>0.603968</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Camp.</th>\n",
" <td>0.973942</td>\n",
" <td>0.669434</td>\n",
" <td>0.679563</td>\n",
" <td>0.697381</td>\n",
" <td>0.921934</td>\n",
" <td>0.667235</td>\n",
" <td>0.365959</td>\n",
" <td>0.578841</td>\n",
" <td>0.705828</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Coah.</th>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.720284</td>\n",
" <td>0.340719</td>\n",
" <td>0.963926</td>\n",
" <td>0.900639</td>\n",
" <td>0.424395</td>\n",
" <td>0.445309</td>\n",
" <td>0.741595</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Col.</th>\n",
" <td>0.953667</td>\n",
" <td>0.562894</td>\n",
" <td>0.335667</td>\n",
" <td>0.510468</td>\n",
" <td>1.000000</td>\n",
" <td>0.734981</td>\n",
" <td>0.502584</td>\n",
" <td>0.754453</td>\n",
" <td>0.673572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chis.</th>\n",
" <td>0.944608</td>\n",
" <td>0.796405</td>\n",
" <td>0.480561</td>\n",
" <td>0.741909</td>\n",
" <td>0.871006</td>\n",
" <td>0.498764</td>\n",
" <td>0.110240</td>\n",
" <td>0.987593</td>\n",
" <td>0.692193</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Chih.</th>\n",
" <td>0.873352</td>\n",
" <td>0.699709</td>\n",
" <td>1.000000</td>\n",
" <td>0.633800</td>\n",
" <td>0.854928</td>\n",
" <td>0.781727</td>\n",
" <td>0.395705</td>\n",
" <td>0.572632</td>\n",
" <td>0.730615</td>\n",
" </tr>\n",
" <tr>\n",
" <th>CDMX</th>\n",
" <td>0.000000</td>\n",
" <td>0.042666</td>\n",
" <td>0.001418</td>\n",
" <td>0.995458</td>\n",
" <td>0.796984</td>\n",
" <td>0.680133</td>\n",
" <td>0.665539</td>\n",
" <td>0.763344</td>\n",
" <td>0.480467</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Dgo.</th>\n",
" <td>0.992207</td>\n",
" <td>0.878992</td>\n",
" <td>0.873189</td>\n",
" <td>0.716858</td>\n",
" <td>0.789644</td>\n",
" <td>0.666596</td>\n",
" <td>0.067610</td>\n",
" <td>0.483374</td>\n",
" <td>0.709580</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gto.</th>\n",
" <td>0.958747</td>\n",
" <td>0.345727</td>\n",
" <td>0.662985</td>\n",
" <td>0.797926</td>\n",
" <td>0.829489</td>\n",
" <td>0.982957</td>\n",
" <td>0.918646</td>\n",
" <td>1.000000</td>\n",
" <td>0.803916</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Gro.</th>\n",
" <td>0.959638</td>\n",
" <td>0.823239</td>\n",
" <td>0.286763</td>\n",
" <td>0.989180</td>\n",
" <td>0.684067</td>\n",
" <td>0.000000</td>\n",
" <td>0.034124</td>\n",
" <td>0.536133</td>\n",
" <td>0.569917</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Hgo.</th>\n",
" <td>0.887450</td>\n",
" <td>0.699754</td>\n",
" <td>0.371747</td>\n",
" <td>0.932870</td>\n",
" <td>0.766003</td>\n",
" <td>0.149553</td>\n",
" <td>0.188125</td>\n",
" <td>0.201479</td>\n",
" <td>0.553179</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Jal.</th>\n",
" <td>0.972184</td>\n",
" <td>0.839646</td>\n",
" <td>0.654580</td>\n",
" <td>0.956767</td>\n",
" <td>0.606975</td>\n",
" <td>0.708564</td>\n",
" <td>0.599235</td>\n",
" <td>0.523863</td>\n",
" <td>0.760040</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mex.</th>\n",
" <td>0.371454</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.989260</td>\n",
" <td>0.000000</td>\n",
" <td>0.557520</td>\n",
" <td>0.332537</td>\n",
" <td>0.470708</td>\n",
" <td>0.368060</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mich.</th>\n",
" <td>0.853186</td>\n",
" <td>0.539101</td>\n",
" <td>0.641692</td>\n",
" <td>0.958063</td>\n",
" <td>0.988067</td>\n",
" <td>0.560290</td>\n",
" <td>0.691400</td>\n",
" <td>0.992793</td>\n",
" <td>0.767991</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Mor.</th>\n",
" <td>0.763067</td>\n",
" <td>0.414793</td>\n",
" <td>0.456363</td>\n",
" <td>0.136353</td>\n",
" <td>0.697673</td>\n",
" <td>0.713677</td>\n",
" <td>0.444752</td>\n",
" <td>0.858740</td>\n",
" <td>0.549936</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Nay.</th>\n",
" <td>0.921413</td>\n",
" <td>0.675497</td>\n",
" <td>0.696334</td>\n",
" <td>0.767019</td>\n",
" <td>0.601882</td>\n",
" <td>0.801023</td>\n",
" <td>0.372731</td>\n",
" <td>0.552846</td>\n",
" <td>0.697122</td>\n",
" </tr>\n",
" <tr>\n",
" <th>NL</th>\n",
" <td>0.985046</td>\n",
" <td>0.824573</td>\n",
" <td>0.812120</td>\n",
" <td>0.875741</td>\n",
" <td>0.869358</td>\n",
" <td>0.204942</td>\n",
" <td>0.538308</td>\n",
" <td>0.663136</td>\n",
" <td>0.721837</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Oax.</th>\n",
" <td>0.931102</td>\n",
" <td>0.451177</td>\n",
" <td>0.432067</td>\n",
" <td>0.704767</td>\n",
" <td>0.884237</td>\n",
" <td>0.517171</td>\n",
" <td>0.350900</td>\n",
" <td>0.760684</td>\n",
" <td>0.633421</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Pue.</th>\n",
" <td>0.596616</td>\n",
" <td>0.201021</td>\n",
" <td>0.148359</td>\n",
" <td>0.451314</td>\n",
" <td>0.807844</td>\n",
" <td>0.556029</td>\n",
" <td>0.526577</td>\n",
" <td>0.846251</td>\n",
" <td>0.503650</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Qro.</th>\n",
" <td>0.946503</td>\n",
" <td>0.344277</td>\n",
" <td>0.692793</td>\n",
" <td>0.973231</td>\n",
" <td>0.960705</td>\n",
" <td>1.000000</td>\n",
" <td>0.828873</td>\n",
" <td>0.802974</td>\n",
" <td>0.818136</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Q. Roo</th>\n",
" <td>0.941986</td>\n",
" <td>0.749127</td>\n",
" <td>0.426786</td>\n",
" <td>0.932291</td>\n",
" <td>0.777237</td>\n",
" <td>0.208351</td>\n",
" <td>0.194318</td>\n",
" <td>0.000000</td>\n",
" <td>0.564597</td>\n",
" </tr>\n",
" <tr>\n",
" <th>SLP</th>\n",
" <td>0.912830</td>\n",
" <td>0.386076</td>\n",
" <td>0.580709</td>\n",
" <td>1.000000</td>\n",
" <td>0.908328</td>\n",
" <td>0.597571</td>\n",
" <td>0.109716</td>\n",
" <td>0.942372</td>\n",
" <td>0.688584</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Sin.</th>\n",
" <td>0.851973</td>\n",
" <td>0.787972</td>\n",
" <td>0.560631</td>\n",
" <td>0.809359</td>\n",
" <td>0.915169</td>\n",
" <td>0.444823</td>\n",
" <td>0.000000</td>\n",
" <td>0.475817</td>\n",
" <td>0.629281</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Son.</th>\n",
" <td>0.762799</td>\n",
" <td>0.381739</td>\n",
" <td>0.612369</td>\n",
" <td>0.829640</td>\n",
" <td>0.861868</td>\n",
" <td>0.605028</td>\n",
" <td>0.475951</td>\n",
" <td>0.523409</td>\n",
" <td>0.634241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tab.</th>\n",
" <td>0.820537</td>\n",
" <td>0.402486</td>\n",
" <td>0.299135</td>\n",
" <td>0.903739</td>\n",
" <td>0.824695</td>\n",
" <td>0.426161</td>\n",
" <td>0.447874</td>\n",
" <td>0.879240</td>\n",
" <td>0.628033</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tamps.</th>\n",
" <td>0.973011</td>\n",
" <td>0.878419</td>\n",
" <td>0.884004</td>\n",
" <td>0.852357</td>\n",
" <td>0.916367</td>\n",
" <td>0.127823</td>\n",
" <td>0.096151</td>\n",
" <td>0.598656</td>\n",
" <td>0.674259</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Tlax.</th>\n",
" <td>0.948403</td>\n",
" <td>0.264370</td>\n",
" <td>0.411796</td>\n",
" <td>0.000000</td>\n",
" <td>0.945177</td>\n",
" <td>0.709416</td>\n",
" <td>0.822085</td>\n",
" <td>0.906368</td>\n",
" <td>0.596871</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Ver.</th>\n",
" <td>0.886392</td>\n",
" <td>0.728569</td>\n",
" <td>0.762215</td>\n",
" <td>0.778918</td>\n",
" <td>0.863142</td>\n",
" <td>0.513081</td>\n",
" <td>0.264362</td>\n",
" <td>0.523906</td>\n",
" <td>0.677406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Yuc.</th>\n",
" <td>0.922881</td>\n",
" <td>0.661321</td>\n",
" <td>0.907828</td>\n",
" <td>0.990517</td>\n",
" <td>0.995157</td>\n",
" <td>0.664039</td>\n",
" <td>1.000000</td>\n",
" <td>0.980189</td>\n",
" <td>0.874131</td>\n",
" </tr>\n",
" <tr>\n",
" <th>Zac.</th>\n",
" <td>0.921375</td>\n",
" <td>0.424743</td>\n",
" <td>0.509957</td>\n",
" <td>0.963603</td>\n",
" <td>0.945077</td>\n",
" <td>0.333617</td>\n",
" <td>0.715775</td>\n",
" <td>0.877759</td>\n",
" <td>0.701358</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" corrupcion inseguridad servicios violencia sobrepoblacion_rel \\\n",
"CVE_ENT \n",
"Ags. 0.937673 0.202207 0.683749 0.989032 0.948622 \n",
"BC 0.980196 0.765865 0.227940 0.735023 0.904309 \n",
"BCS 0.863711 0.200615 0.531979 0.675395 0.894373 \n",
"Camp. 0.973942 0.669434 0.679563 0.697381 0.921934 \n",
"Coah. 1.000000 1.000000 0.720284 0.340719 0.963926 \n",
"Col. 0.953667 0.562894 0.335667 0.510468 1.000000 \n",
"Chis. 0.944608 0.796405 0.480561 0.741909 0.871006 \n",
"Chih. 0.873352 0.699709 1.000000 0.633800 0.854928 \n",
"CDMX 0.000000 0.042666 0.001418 0.995458 0.796984 \n",
"Dgo. 0.992207 0.878992 0.873189 0.716858 0.789644 \n",
"Gto. 0.958747 0.345727 0.662985 0.797926 0.829489 \n",
"Gro. 0.959638 0.823239 0.286763 0.989180 0.684067 \n",
"Hgo. 0.887450 0.699754 0.371747 0.932870 0.766003 \n",
"Jal. 0.972184 0.839646 0.654580 0.956767 0.606975 \n",
"Mex. 0.371454 0.000000 0.000000 0.989260 0.000000 \n",
"Mich. 0.853186 0.539101 0.641692 0.958063 0.988067 \n",
"Mor. 0.763067 0.414793 0.456363 0.136353 0.697673 \n",
"Nay. 0.921413 0.675497 0.696334 0.767019 0.601882 \n",
"NL 0.985046 0.824573 0.812120 0.875741 0.869358 \n",
"Oax. 0.931102 0.451177 0.432067 0.704767 0.884237 \n",
"Pue. 0.596616 0.201021 0.148359 0.451314 0.807844 \n",
"Qro. 0.946503 0.344277 0.692793 0.973231 0.960705 \n",
"Q. Roo 0.941986 0.749127 0.426786 0.932291 0.777237 \n",
"SLP 0.912830 0.386076 0.580709 1.000000 0.908328 \n",
"Sin. 0.851973 0.787972 0.560631 0.809359 0.915169 \n",
"Son. 0.762799 0.381739 0.612369 0.829640 0.861868 \n",
"Tab. 0.820537 0.402486 0.299135 0.903739 0.824695 \n",
"Tamps. 0.973011 0.878419 0.884004 0.852357 0.916367 \n",
"Tlax. 0.948403 0.264370 0.411796 0.000000 0.945177 \n",
"Ver. 0.886392 0.728569 0.762215 0.778918 0.863142 \n",
"Yuc. 0.922881 0.661321 0.907828 0.990517 0.995157 \n",
"Zac. 0.921375 0.424743 0.509957 0.963603 0.945077 \n",
"\n",
" ddhh presup_pi interno_pt indice \n",
"CVE_ENT \n",
"Ags. 0.868769 0.109208 0.993671 0.723177 \n",
"BC 0.842352 0.267569 0.372282 0.675725 \n",
"BCS 0.223690 0.718251 0.949958 0.603968 \n",
"Camp. 0.667235 0.365959 0.578841 0.705828 \n",
"Coah. 0.900639 0.424395 0.445309 0.741595 \n",
"Col. 0.734981 0.502584 0.754453 0.673572 \n",
"Chis. 0.498764 0.110240 0.987593 0.692193 \n",
"Chih. 0.781727 0.395705 0.572632 0.730615 \n",
"CDMX 0.680133 0.665539 0.763344 0.480467 \n",
"Dgo. 0.666596 0.067610 0.483374 0.709580 \n",
"Gto. 0.982957 0.918646 1.000000 0.803916 \n",
"Gro. 0.000000 0.034124 0.536133 0.569917 \n",
"Hgo. 0.149553 0.188125 0.201479 0.553179 \n",
"Jal. 0.708564 0.599235 0.523863 0.760040 \n",
"Mex. 0.557520 0.332537 0.470708 0.368060 \n",
"Mich. 0.560290 0.691400 0.992793 0.767991 \n",
"Mor. 0.713677 0.444752 0.858740 0.549936 \n",
"Nay. 0.801023 0.372731 0.552846 0.697122 \n",
"NL 0.204942 0.538308 0.663136 0.721837 \n",
"Oax. 0.517171 0.350900 0.760684 0.633421 \n",
"Pue. 0.556029 0.526577 0.846251 0.503650 \n",
"Qro. 1.000000 0.828873 0.802974 0.818136 \n",
"Q. Roo 0.208351 0.194318 0.000000 0.564597 \n",
"SLP 0.597571 0.109716 0.942372 0.688584 \n",
"Sin. 0.444823 0.000000 0.475817 0.629281 \n",
"Son. 0.605028 0.475951 0.523409 0.634241 \n",
"Tab. 0.426161 0.447874 0.879240 0.628033 \n",
"Tamps. 0.127823 0.096151 0.598656 0.674259 \n",
"Tlax. 0.709416 0.822085 0.906368 0.596871 \n",
"Ver. 0.513081 0.264362 0.523906 0.677406 \n",
"Yuc. 0.664039 1.000000 0.980189 0.874131 \n",
"Zac. 0.333617 0.715775 0.877759 0.701358 "
]
},
"execution_count": 206,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"indice_multi['general']"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 720x576 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig1, ax1 = plt.subplots(figsize=(10,8))\n",
"sns.barplot(y='CVE_ENT', x=\"indice\", ax=ax1,\n",
" data=indice.reset_index().sort_values('indice'),palette=\"Blues_d\")\n",
"ax1.set_ylabel('')\n",
"ax1.set_xlabel('(0=mejor, 1=peor)')\n",
"fig1.suptitle('Ranking ÍNDHPOL por estados, 2017')\n",
"fig1.savefig('graficas/ranking_indhphol.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 169,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"image/png": 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JnoXAspZPPqgYc1+nffJBxZimg4v3VugnkV3d8ckHFWMuDWpWpNNY2frQQrnPmpv5F/9pLAUFBQUFBYXiz6uwQLn4bilSUFBQUFBQUHgKlJEdBQUFBQUFhcdQ/Ed2lM6OAb1ez+RJixAj47G0tGDy1M8KuEovXrSZ7duPyM62vT6geYvaXL58g5HD5yEhUbKkhlmzB5uss61er2fp7N+5FHMZc0tz+oxsR/mK8rx2fFQyK+flGffFhCUwZHoPQk9EkhBzGYB7t1Kw1VgzZdGXRtH/JFQqmNSrLlXdS8lO7gtPkHAtNbe8x1tVeLuhnAzsYMhl5v92HhsrNXM+b0RJjRXazGyG/nCM2ymmuZZBr9ezds5vJMVextzCnG7D2lPOUH+J0cls+GFz7rEXwxPoP7UnNerJXmFRobEsmbqaGRtN0zU7P3q9nhlTNhAdlYyFhTljJ3fC1a1cbvmKJbvZteMMGo01XXu0oUlzXyOqfTr0ej1zp28iNuoKlpZqvhrXDpd8WZF/XXmAfX8FY2amolPPVjRumRfTpbjrDOw+j9/+noCllYUx5D8RlQqmdAqkSsVS6LL1jFp5hoQbabnlPVv78E4dOUnkgfNXmLctgn5vCDStLqeeKGFrgWMJa+oNK9LUak+NCpjW0oeqZTXocvQM3yOSkM/+opufM22rOSFJMPdUAnvjbtO/tivN3EsDUMLKHEdbS2ovPm6kCBTACJ0dg+/Vr0A48nlkAfQURTFSEIQ+yMkI9YbXx4iieKAodO3dcwpdpo51G6YTGhLFzBkr+HGBbB0QJSawfdth1v/6DQCdOo6mXn1fVq7YxptvNaJjpzf4fs4afv9tL126vlUUcp+ZM4cukKXLZvIvg4i+EM/q+VsZOqMXAB6VXRj/wwAATuwLwaFsCQLqVyWgvnyzzM7OYeJn8/l0pGk6ZgO0qeOKlYUZbcftJsCnDKO61qTf7EMAuJbT8F5jD/43ZhcSEusnvcbuU4k0qOHEhbjb/PD7BT5qVon+H9Vg6oogI0fycEKOyPU3csGXXAyLZ+NPWxkwTa4/Vx8Xhs4dCMCZAyGUKlsyt6Nz+/od/v71ADnZpm/mCnBg7zkydVksXTOU86FxfD9rE9/OlxOyxUQls2v7GZatGwZAry7fUqeegLWNaS+WPro/DJ0umx9WfE74uQR+nvMnU+bIruepKVo2rz/Cyi0jydDq6NPhu9zOTlpqBj/P2YqFheka1AK8FuCMpYWaj2fsJ8CzNKPb+tN3wTEAXMva8X5dVz6cvg8J2DCsObuDL/PzXyI//yUCsHhgI2b8ft6IETye173KYqU248Nfgwksb8+4Jl703iabADhYm9PNz5k31gZhpTZjb9fa7I07yYIziSw4I5sRL3uvBtOPXDRmCC+M9ApsPTfWmp19oig2F0WxGTARmC0IQgegDdBKFMXmyJ2eVYIgFIkxzNmgCBo3CQTAP6AyYRfyMn/GXkyiTt3qBVylo8QEqlTx4N59efQgNVWLuQlflMRzcfjXl52XfWp4cDEy8V/HZGgz+W3JLrp/+WGB13f9dhi/OgJuXs7/eo+pUFtw5FDoFQBCom9Rw6tMbtmVW2n0nL4fvfTAyV1FZlYOy3eILNgkX7Scy9py6yFmhaZCzPmLVK8r11+l6h4kiP+uv0xtJn8u+4v2n8v1l5WZxervNtLpy/8VqdYXITQ4loaNqgHg6+9JRNil3LK4i1epWccHKysLrKwscHVzJDoq2VhSn5rzIXHUaSjbPlTzc0cMz6s7a2tLypV3IOOB67nBkFeSJL6b+hu9Br6FlbVpd+Zqe5flUJichDEk7ja+7nm7EK/cSeeTeUfQSyBJYKE2IzMrr+P9eqAz99J1HA6/VuS6n5Y6ziU5kHAbgOCrKfg52eeW3cnI5vU1Z8jWSzjaWXI/M7vAe9/wKsu9jGwOXSpoyVP8KP52EaawQNkBiAf6Al+LopgFIIpiHBAgimKRGK2kpmnR2OdzXc7vbFvZnTNnIkhL1XL3TgohwSLp2gycypdh7ZqdvPvOIA4fDuaN1xsWhdTnQpuWga1dnvOymdrsX0/7B7adpF4Lf0qU0uS+lp2Vzd4/jvNOp+ZFJfW50NhakJKe38lderiTe5dAwuPvEH9FzgCslyRWjWtFtzcEDgRfLnrhT0lGWgY2mvyu7qp/1d+RHSep1dwfe0P9rZv7O6+1b4GDY6ki1foipKVmYGefF2d+93NvHxeCg2JIS8vg7t1UzoVcLBZu0ulpGdhpCrqe5xRwPS9Fz49n0a/THD7s0ASAlQt3U79xVbwqm+4DxgM01hakaPNu8nrpH23P4Ig+6mM/whLvEnc9b3r5szerMO/P8KIV/IxoLNWkZObVV44kkc/UnRwJuvs5s6VdIDv+4Qs2oI4rc07GF5FShcdhrDU7LQVBOABYAX7A28ijOgXG+kRRvFVUgjR2NgWcbSW9Pp+zbUU6d36Dvn2m4uZWHj8/HxwcSjBm9A98/fVAGjcJ5OCBIEaOnMfPC8cUleRnwsbOGm163o1B0kuozQuORB3ZfZbBUwvmeDx/OooqAZWwzXejNUVS07PQWD/Byb1fA9Iyspiw+HSB93adspdKziVYPKI5LQdtLTLNz4K1nXUBo9KH1d/JPUH0m/QJAHdv3iP6/EWuJ99k2/JdpKWk88uklfSZ0K0oZT8zdhrrAu7nkpTnMO3pVZ52HZsxqN+PVHRzpIafB6XydcxNFVs7a7T/cD1/UHenjkVy6+Z91vw5GoARAxZRI8CDPTvO4uhUkp1bTnL7VgrD+//C90sGGEX/k0jNyMLOKq/tqVQUbHvmZszsXpvUzGzGrzmb+7p3BXvup2cVWN9jiqTqctBY5rU1M1T809R9xbnLrL1whZUf+NKgYimOJ93Fp7Qt9zNzCqzvKa4U93x8YPxprAZATWATcAlwzX+QIAivCYJQJAZKgTWrcPig3BBDQ6LweYiz7eq10xg1pidXrt7Cx8eVEiU02NvbAeBYzoH790y30Vb29SDkuOy8HH0hHlevCgXK01O1ZOuyKeNUMBHehTPRuWt3TJkg8QbNAuWn4ACfMoiXCiYsXDisGZEJdxi36BR6Q8Pt90F1PmgiuzVrM7MLXKBNDa8anpw/IdffxbB4XCo9vP5Kl5Prr1TZkkxZNZqhcwcydO5A7OxtTb6jA+AfWImjh+WpxfOhcXj55I1s3Lmdwt27qSxe9RVDR7bl2tU7BcpNlRoBHpw8GglA+LkEPL3zLmkaexusrCywsDTH0soCjb0NqSlaVm0dxXeL+vPdov6ULmPPzAV9jCX/iQTF3qK5rxxTgGdpxOT7Bcp/GdCQiKR7jF19lvxNrFFVJw5eMH0PsjNX7tHCQ15sHFjenshbedf5SqVsWPi2PO2apZfIzNHnXl8auzqwP/520QtWeCimsBvrwWTtUmCcIAidRVHMFgShMrAEqFUUIlq3qcexY+fo1GE0kiQxbfoAli/bipt7BVq0qE1i4jXafTxcdrYd1hW1Ws2Ysb2YNmUxOTl6JCTGjjdN12WAOs18OX86ivF954Ek0XdMB7avP4CTS1lqN6nBlcQbOFYo/a/3Xb50nSZv1DaC4mdj9+lEGvlV4NfJr8lO7j+doOfbVUi4anByr+qEpbmaZgHyzXH2uhA27o9lVv8GtG3hhZmZihE/nzByFI8msIkvEWdEvhkwFySJ7iM68vevB3B0KUtAoxpcT7pBmfL/rr/iRvNW/pw8FknPzrMBGD+lC2tW7KWimyNNm/uSnHSTbu1nYGFhzhdffYhabQoz8Y+ncYsaBJ2I4vNP5iNJMHxiezauPoiLa1kaNqvO2ZPRDOw+D5XKDN8AD2oVA6fz/OwKTqZx1XJsHNECFTB8xRl6tfYh/kYqapWKepUd5bZXQ+4Qzdp8nuCLt6nkpOFIxHXjin8K/oq5SRM3Bza1DUClUjH070h6B1Yk4a6Wv+NuEXEzjT/aBSIBB+JvczL5HgCVHGw4XOzX6jzAdB8En5Yit4v4x26sHMAeWCCK4nJBEAYD7ZGdz9XAaFEUDz7u8xS7iOKLYhdRfFHsIoo3il1E8aao7SIyc04Vyn3WSl331bWLMGwlL/eIsjnAnCIVpKCgoKCgoPBIXoWt56YwjaWgoKCgoKBgshT/CZRi39m5nVm8kzU9jnY9X5X53ofjP6iSsSUUKjH3040todAIKJP95IOKMc173H/yQcWY+d8W//Vdj6O1s+2TD1L4T1HsOzsKCgoKCgoKhYey9VxBQUFBQUFBwcRRRnYM6PV6vp22mZioy1hYmjNyQlsq5jPrW7viAHt2hmBmpqJrr5Y0a+VLTo6e+bP/RAxPRKfLoWe/NjRqVs2IUTwdKhVM6lefqp4OsmnmD8dIMGQUBujxXjXebuIBwMGgZOavDzWS0qdH0uu5vH4NGcmJqMzNcencHatyTrnll39dS3psDGbWciZb934D0euySFq+CCk7B/OSJanYrQdmlqZp5Crp9ez+eSM34pJRW5jzxsCOODjn7Ti5GBTO0XU7AXDycqVNv7bo0jPYOms5WRk61BZq3h7SDY1DCWOF8Ej0ej0zpm4kWryMpaU5YyZ1wNUtL7bVy/exe0cQKjMVn3zahhat/Ll3L40JI1eRlpZByZJ2jJ7YgdJl7B/zLcZDpYJJvfOZ1P78D5Pat/OZ1AbLJrUaGwu++6IhGhsLLMzN+HrFWYKjiySZ/DOj1+v5fd5vXI5NxtzCnHZfdcDRRa6/5Jgk/liQZ1KbEJFAj0m9KO9envWz16HPyUEC2g1uRzlXp0d8g3HR6/VMm7wcUbyEpaU5Eyf3xs09L1fS0sV/snP7cew0NvTo9Q7Nmgdy724q7741FG+figC0bF2bLl3fMFYIL4ykrNl5OM9j9ikIgg3wE+CMvBrqHvBZUWVRPrwvDJ0ui4WrPufCuQR++PZPvpkrm/Wl3Nfy29qjbNg2Aq1WR492c2jWypdd24LIzs7hpxUDuXHtHvv/PlcUUl+YNvXdsLJU03b4TgKEsozqWZt+0/YD4Oqk4b1mnvxv2A4kSWL9N2+y+8QlxHjTXj90PzQYKTsLr2GjSY+L5eqmjbj3G5hbnpF4CY/PB2OuybshXt+2nlL1GuJQvyHXtm3h9uGDlG31mjHkP5HoE+fJ0WXRZdYQLkfGsX/pZj4aKyeay0zP4MCyLXT4+nNsS2g4+fsetPdTCT8YhKO7M817vE/ormOc2rSXlr0+fMI3FT0H951Hl5nN0jWDOR8az9xZfzB7/qcApNxPZ8Oag2zaMQ5tuo4ubWfSopU/yxf9jX/NSvT49DVOHRdZMG8bYyd1NHIkDyfXpHaswaS2W036zfqHSe3ogia1b9Rz5dj5qyzfIeJZwZ7vBzXm/ZE7jRzJw7lw9DzZuiwGzR9MfHg8W3/eQq8pcs4xF++KDPjucwBCDoZQokxJqtatytoZa2j8QWN8G/kReTqC7Uu202NiT2OG8Uj27Q0iU5fF6nUTCQ2NYfbMtcz7cQgAUVGJ7Nh+nDXrJwLQtdMk6tarRkR4PG++1YBRY7s/5pOLE8purMexTxTFDiBnQkY2+1xNntlnliAInsAhQRACgXbAVVEUPzG850tgPDCoEDXmci44jnoNZaPFGn7uRIYl5ZbZ2FhSvoID2gdmfSo5NcDJY1F4+ZRn2MAlSBIMHvl+UUh9YWpXLcehs7KBYoh4kxreeSNYV26m0XPiHvSGVKfmajMydabvmJ0eG4OmWg0AbD290CbE55ZJej2Z169xec1KslPu49CwCQ4NG1P+4/YgSUh6PVl37mDlVCTJup+LpIhYPGvKmaydq3hyNSbPTPJyZBxl3Suwf8kf3Lt2E782DbAtaY+juzO3k+ScnZnpGf+ylzAVQs5epEFjOTZffw8i8hll2thYUd65NNp0HVqtDpXBcyku9iqfffEOAH6Bnsz6+reiF/6U1K7iyKGQx5jUfr0/N+uuublsUrt0eyS6LPkGY/4P80xTI+7CRarUkevPo5oHiVEPN6ndtWInA+bIHZ/3+r2PjZ1sQaPP0WNuYbqTDMFnRRo19gPA39+b8LC43LK42GRq16mClZWcd8rdvTxRUYmEh8cRERFPj25TKV26BCPHdMXR8dXNu1VGIU5gAAAgAElEQVQcKKo1O09j9pkAvCYIwruCIJQA5gNfFZE+0tIysbMvaJSZXcCsryRdP5xNz/bf83GnxgDcu5tGUsJNZs7vSecezfl6/K9FJfeF0NhakJKW3zRT/3DTzB61Cb94i/jLpr8zRZ+hRW2T3yjTDClHrj+9TkeZ5q2o2KM37gMHc+vQfjKSElGpVCDpiZk6gbSoSGwreRtL/hPJTM/Ayq5gfHpDfOn3U0k8H02zT97j4wmfcWbrAW4nX8emhB3xIZEsGTCN05v34tumvrHkP5a0tAw0+YwyzcxUBdqek1Mp2n8wnW7tZ9G+U1MAKldx4dCB8wAcPnCBjAxd0Yp+BjQ2T2lS2zWQ8DjZpDYlPYvMrBzKlrTm288bMnttiFG0Pw0Z6ZlY2xWsv5ycgp2zk3+dwL9ZAJqSspeZpqQGtbma64nX2PrLFl7v9nqRan4WUlO1aDT5TKLzmdP6VHbl7BmRtDQtd++mEBISjTY9A09PZ/oP+B/LVo6lZataTJ+20ljyXwpSIf0rSgqzs9NSEIQDgiAcR7aC+A15iuqhZp+iKG4HpgK9gDhgL1Bkpkx2dlakpxU0ynxgQHjiaCS3bqTw645R/L5rDIf3hxF+/hIlS9rSsFlVVCoVgbW9SEy4UVRyX4jU9Cw0No83zfzuqybY2Zgz4eeTxpD4zJhZ26DPKGggqVLL9WdmaUmZFq0xs7RCbW2NpnIVtMnyyJ1KbY7P+Cm4dO5G0oolRtH+NFjZWqPT5o9Pj5khPht7O8r7uKFxKIGljRWuNby5HpfE0XU7qftRa3r9OIa2k/qzZfpSY8l/LHZ21qQ9ou0dOxLOrZv3+eOv8WzdPZGD+84Tdj6B7r3bcCX5NgN6/8i1q3dxKm+6T82p2qdob180ws7aooBJbWXXUqwa34rZ60I5ZcK2Cta2VmTmNxmWJNTqgqOIZ/cGUe/Ngp3t6JBolk5YQucRXUx2vQ6ARmNDej6TaL2UZxJdycuFDp3b0L/vLGbPXIuvnxcODvbUrV+NOvXk9ZstW9cmMuLVzoZfHCjMzs4zmX0KgtAA2CuK4gfIGZaXG36KBN9AD04ckY0WL5xLoJJP3pSGfQkbrKwtsLQ0x8rKAo29NakpWvwCPTl+WDb4ixYvm/QFNz9BEddpVlteOBcglEVMKLgeZ+GYlkTG3WHcghO501mmjq2XNylh8pN+elws1s4uuWWZ164S9+03SHo9Uk42abEx2Li6cXndalJFuf7MrKzlkR4TxaVqJS6eCQfkaStH9zwDzPLertxMuEL6/VT0OTlcFuMp41oea40tVrbyE7dtKXsytabpvuwf6Mmxw3Js50PjC5h72pewxcoqr+3Z29uQkqIlOCiWt96ry4+LB+DsUhq/QE9jyX8iz2NS6+1SgvlDGjN43lEOhVwucs3Pgkf1SkSckusvPjyeCp4FTWq1qVqys7JxKJd3fYwOieaPHzfRZ3o/XAW3ItX7rAQEVubwYXmTRmhoDD4+ebew27fvc/dOCitWj2fEqK5cvXIbbx9XJo5bzJ7dpwA4eSKMatU8jCH9JSIV0k/RUSjeWIYFyv3yrdkphTyiMwD4AMhv9rkX2exzLHBfFMWxhvfUBH4QRbHh477rRsbWlxLAg91YsdFXkCSJ0ZPbc/xwBBXdytK4eXWWLNjFiaMiZmYq/AI96T/4bbKycpg9dRPxF68hSRJDx36EULXiy5ADQIN2hbMo+MFurCoeDrJp5tyjNK9dkYQrsmnm90ObEiLmjVLNXnmWYPHlj1q9zKSCebuxkgCJil17kBJ2HkvHcpTwC+DG7p3cPxsEajUO9RpQumlzMq9eIXndKlSowExFhXadsK7w8ly036r48pIK5u7Gik8GCd4c1JmLZ8IoVcERn3q+RBwK4tTmvQBUaRxIvf+1IeXWPXb9sA6dNhN9Tg6NO72FR2CVl6LnY8+Xd914sBsrJuoykgTjp3Ti6OFwXN3K0rSFL7/8uIPjRyNRqVQE1KzE50PeIynxJhNHrwbAsVwpxk7uWGAq7EWp1eXl7Yt4sBurilspub0tOEHzQOdck9rvBzUmJN9Oq9lrQ+j7QTWquDuQfEN22E5J1+Uuan4ZzH2JSQUf7Ma6cvEykiTRYVgnIk6FU9bZkRoNa3Ap8hJ71u6m5+Q8o+RZfWaSk5WNvYO8YcDRtRztBrd/aZpaO5d98kFPyYPdWFFRiUiSxJRpfTh8KARXNyeat6jJlIlLiYiIl81pB7endu0qJCVdZ8LYRUiShI2NFROn9H6pa3as1HWK9MksNetAofRMNBbNiyyOwuzsPJPZpyAI9sAPgD+QZvgZJIpixOO+62V1dkyRwursmAqvegbll9nZMTVeZmfHFHmZnR1T5GV2dkyRl9nZMUWUzs6zUyhL4J/H7FMUxRTgVdmnp6CgoKCg8IpQ/LeeKxmUFRQUFBQUFF5pTDe5gYKCgoKCgoLRUTIomwClrTyMLaHQGD7TNK0LXhYWZq/umhYAO/Pif4F4FOZmr7ar9NBJr/ag92cbLYwtoVB5p37qkw8qxix47LYdhYdR7Ds7CgoKCgoKCoWHMrKjoKCgoKCg8IqjdHZeGfR6PZMnLUaMjMfS0oLJU/vh7p6XHGvxoj/Yvv0IGo0tvXq9T/MWtZj+9TIiI+IBuHnzLvYl7Fi/4WsjRfB4JL2ePYY8LWoLc14b2BGHCnnO0nFB4RxfLxsNlvNypVXftpz6fQ/xwfLO/8w0LWl37vPZimlG0f8kJL2eXT9t5LrBFfytzwu6gseeCeeoIT6nSq689llbMtMz2DpzObpMHWpzNe9+ZZqu4PnR6/Vs+3EjVy9eRm1hzgdfdqCMIc4rsUnsWJjnMJ0UGU+n8b3xqV1kicifC71ez/Qpa4gSk7C0NGfcpO64uedt5ly+eCd/7TiFncaG7j1fp2lzf65cvsWkcSvIyclBkmDsxK54eJqmt5mk17N34a/cjE9GbW5Om4GdKFWg7YVxYsNfAJSrVJGWfdtxetPfJJzN1/bu3qfvctO8tqiAaa0qU9VRgy5Hz/C/RRLu5mUc7ubvQtvq5ZGAucfj2Rt3i/513GjmIW9/L2FljqOdJbUXHjOK/ich6fWIq9aRkpiEmbk5VXt0xdYp7/wU12zgXnQsamt52YH/F/3J1moJW7QMJAlzOztq9O2F2uCfpWAcnquzIwhCdWAmYAtogB3ARFEUi233b++e0+gydazb8DWhIVHMnLGSHxeMACBKTGD7tiOs/1W+2HTqOJZ69WswarTsip6VlU2XzuOYPLmv0fQ/iZiT58nJyqLTzCFcFuM4uHQzH4yRXbN16RkcXL6FdtNk1+xTm2TX7Hoft6Hex20A2DxlIU27vWfMEB5L1AnZebnb7CEkR8axd+lmPs7nCr5/2RY6ff05tiU1nDC4gocdDMLRw5kWPd4nZNcxTm7aSysTdAXPT8Tx82TrsukzZzCJEfH8tegPOk+QHcIreFWk10zZaPHC4WDsy5Qw+Y4OwP69Iegys1ixdhTnQmOZM+tX5vwgO9ZHRyWxc/spVq4fDUCPzt9Qp14Vfpq/hfadWtCiVSDHjlxg/veb+HZuf2OG8UhiTp4jR5dFhxlfcUWM4+Cyzbw/2tD2tBkcXrGFtlO/wKaEhtOGtlf3f69R93+vAfDH1J9p3N10TYZf9y6LlbkZH64/S2CFEoxr6kXvrRcAcLC2oJu/C2+sPo2V2oy9n9Rl76LjLDh9iQWnLwGw7ANfph+++LivMCo3zoaiz8qiztgR3Iu9SPT63/AflHeupSRcIuCrL7C01+S+dnHLnzjVrUXFls2J/f0PLh8+imvrFkZQ/3KQpOK/9fyZOzuGbMjrgY9EUYwWBEENbEQ2+fz5JesrMs4GRdC4SSAA/gGVCbsQm1sWezGZOnWrFXS2FRPwD6gMwJrVO2nUyJ/KgnvRC39KksNj8Qg0uGYLnlx7iGv2waWya7avwTX7AdHHQ7HS2OBR03RvnEnhsVSqJetzqeLJ1ei8+JIj43D0qMC+pX9w9+pN/F97hCu42jRdwfNzKewi3oY4Xat6kBz9b4dpXUYm+1btpNesL4pa3nMRcjaaho1lx3o/fy/Cw/J8hOIuXqF2XQErK3lBrat7OaKjkhg8vC0ajWyMmpOjx8rSdBfcXo64iEdN2SepguDJtZhLeWUP2t6yzdy7epMabRr+o+2FYGVnm9t2TZE6LqU4EH8bgOAr9/Ernzc6eicji9dXnSZHkqhYwpL7mdkF3vuGd1nuZWRzKOF2kWp+Fu5Gx1DatzoAJb0qkRKfd35Kej3p164TuXw1uvv3cW7SCOemjbB3dSXjjpwUNlubgVXpVzuJY3HgeUZ23kf2vYoGEEUxRxCEbkCWIAiLkb2vygA7RVEcJwjCciALcAeskDtK7wJuhs9yBcYgZy0qD/wiiuKPLxTVc5CapkVjn8/Z1uB6bm6upnJlNxb9spm0VC1ZWdmEBEfRtp1sfKfTZfHrhr/ZsHF6UUt+Jh7lmm2mVqM1uGZ3+34EFtZWrB/1PRUET0q7yEO1J3/7m7eHmna+x8z0DKxs8+Iz+0d8l85F02PeCCytrVg98ntcqnhiU8KOuOBIFvWfRkZKOp1nDDJiBE9HZnrGQx2m83fUgnadoHqTAOxKah72ESZHWloGGvu8ulOb5bU9b5+KLFu0k7S0DLKysjkXEstHbZviYLAZiI+7ypzZG/lu3gBjyX8iuvQMLG3z19m/216XOSOxsLbi19Hf4yx44mBoe6d//5u3vvrESMqfDo2lmpR8nZgcvYRapSLHkJ0/R5LoHuDCkAaeLAtOKvDeAXXdGbgjvEj1Pis52gzMbfLOT/LVX06mDtdWLXB7vTWSXs/Zmd9h7+mOVWkHYn7bzLUTp9FnZ1Ppg3eMF8BLodhO2uTyPPsrH+ZcngpUAE6Iovg60Bj4LN8h8aIovgZEAJ6iKL4F/I7c6QFwAd4D6gODBUF4aPblwkRjZ0NaPmfb/M7LXl4V6dz5Dfr2mcbMGSvw8/POvdgeP36e2nWqYW9vV9SSn4nHuWZbl5Bds+0MrtkVq3tzI06+KN26dAUrO5sC63tMkWdyBa/uzbWLsit4vY9a8+mCMbSf3J/NJuoKnh8rW2sytQUdwv85InVufxC132hQ1NKeG9n1PK/uCrpKV6B9pxZ83ncuc2b+Sg1fTxxKyZ240ycjGfLFj0yd3stk1+sAWNpao9MWdAXPf246ebvntj2X6l5cf9D2EuW2V8rE216qLgeNZd45aKYit6PzgBUhydReeJR6FUvRwLUUAD6lbbmfmV1gfY8poraxJicjn4luvvpTW1ni2qYlaitLzG2scagqkJqYRMyG36nWqzv1p02gcqd2hC1abhzxLwmpkP4VJc/T2Ung387lnsgjNXUEQViDbAeRP0nMWcN/7yL7ZQHcAR487hwTRTFTFEUtcAHweg5dL0RgzSocPijLDA2JwqdynhPv7dv3uHMnhdVrpzJqTA+uXL2V63x74tg5mhimv0wZ56qViAsyuGaLcZTN55rt5FXQNfuKwTUbICE0Cs9apjuE/gCXqpWINbiCJz/KFfxenit4WTeDK7hdPlfwdNN0Bc+PWzVPok/LcSZGxOPkWdC4NCNNdpgu+RJNBwubgEBvjh6SHevPhcbi7ZNnpnvndgp376SydPUIho7qwLWrd/DyceH0yUhmfbOeHxZ+SbUaHkZS/nQ4V6lEfFAYAFdEedrqAeW8XLl16TJaQ9u7mq/tXQoVc6e/TJkzl+/RwrMMAIEVShB5My23rJKDDQvflacos/QSmTl69IZ7XGN3B/bHme701QNK+Xhx65y8Bule7EU0FV1yy9KvXiNo+iwkvR59dg73omKxd3fD3M4Wc8NIs1WpkmSnvdo5xYoDzzONtQ0YLQjCT6IoxgqCYAF8B+wH7oqi2FcQBG+gjyAID0y+ntSFCzCs/bECqgPRz6HrhWjdpi7Hjp2jU4cxSJLEtOkDWL7sT9zcy9OiRW0SE6/R7uORWFiYM2xY19yn6bj4y7z3QbOilvvM+NT3IyFEZO3w7wB4/YvOnNmyj1LlHfGu50uTru/y+8QFAAiNAnM7Q7eTr+Ee8HKcsgsToYEf8SEiq4Z9hyTB24M6c+qPfTgYXMGbdX+XDRPk+Ko2DsTR3Zkmnd9m5/x1nN1xBH12Dm8O7GDkKJ5M1YZ+xAaL/DJkDkjw4ZBOHN20n9LOZala35ebSdcp5VS81ge0aB3IiePhfNL5GyRJYuLUT1i9fDeubuVo2sKfpKSbdGk3FQsLcwYN/Ri12ozZ32wgOyuHCaPl0Th3j/KMndjVyJE8HO/6fiSERrJ+xHeAxGufdyZoyz5KVXDEq64vjbu+xyZD26vcOK/t3Um+jpu/YETlT8df0Tdo4ubApg41UQFDd0XSu6YrCXfT+fviLSJupPJHx5pIEhyIv83JpLsAVHKw5XCC6ZsdO9YM4HZYBGemzkRColqv7lzatQebco44BvpTvn5dTk+ZgZlaTflG9dG4OFO5c3uiVm+QF/ZKEkJX07+2PJ7iP431XK7ngiDUAmYhjwzZA38iL1JeD6QgO5a7Aq2AacB6URT/EgThGyDS4H7+JfLIzglgLnANea3PPFEUVwiC0AnQiKL4y+O05Ejnin8tPIIl4hVjSyhULF7tJLWvdAblt91e7QzKq6JNf5TvRfh6p+ku6H4ZvFPf2AoKlwUNWxSp6/ld3Y5CuZiVsnzLtF3PRVEMAlo+pMj3Ia99ku99I/P9/j2AIAjNgQhRFAt0fUVRXPs82hQUFBQUFBReHv/JrecKCgoKCgoK/x1eBbuI55rGMi2iinsA/1n+Sop98kHFmL7LX91niZjRZY0toVDR5tw0toRCpeEa6ycfVIyxfMWTFZ/t1KRIp7FuZ/5ZKPfZ0lbvmvY0loKCgoKCgsJ/heI/pvCKLxFVUFBQUFBQ+K+jjOw8A6GhIrNnL2fVKtPOlvy8FOf49Ho9G+f+xuXYZMwtzenwVQccXeRkbEkxSWxekGeQGR+eQO/JvXByL8+62evQG8wk2w9ph5Ork7FCeCwqYOqbVanmpCEzR8+IbeEk3MlntlirIh/7OyNJMPfwRfbF5E3DeJWx5Y8edak95xCZOaa30FCv1zNl8lKiIhOwsDRn8pS+uLnnJQlcsmgLO3YcQ2NnQ49e79K8RS3S0zOYMmkJycnXycrKZvSYHvj6eRsxiqdDr9czY8oGoqOSsbAwZ+zkzri65SUNXLHkb3btOINGY03XHq1p0vxhez5MCxUwroE3lR3syNLrGX80msSUjH8ds6B1dfYn3uJX8SoaCzXfNBWwszDHwkzFrNMXCb2RYhT9T0IFjKojx6fL0TPlZDSJqf+Ob17z6hxIusXvMVcB+OuDulxKkdvouZsp/BAaX7TCXyLFfrULL6Gz8zJMQQVBuCqKoummQAUWLfqdrVv3Y2Pzas51F/f4zh+VjUAH/zCY+PB4/vh5C59O6Q1ARe+KfP6dbJAZfDCEEmVKUrVuVVZ/s4Ym7zfGr7EfEacj2LZ4O70m9TRmGI/kdaGcbLa4/DSBLiUZ27oyn24MBcDBxoKutV15c9EJrMzN2NOvIQ3mHQbkVP5jW1dGl2O6V6u9e86gy9SxZv0UQkOimTVzFfN/HAZAVNQltm8/yroNUwHo0nE89erXYNnSP/HxcWX6jAGIYgJiZEKx6Owc2HuOTF02S9cM5XxoHN/P2sS382UD4ZioZHZtP8OydUMB6NXlW+rUE7C2Me0FKK3cymCpNqPLjlD8HO0ZVqcSX+wraAHxRU13Slrl3W66V3fhxJW7rA6/jEcJG2Y2q0K7P4OLWvpT0aKiHN8nu0PxLWPP4JqVGHKoYHwD/N0pYZkXn6vGmsg7qXx50LStMP5LvNA0Vj5T0C9FUWyBbPfgi2wK+krh5lae+fNHG1tGoVHc47t4/iJV68iZnj2qeZAo/tsgM1Obyc7lO/nfwI8A+OCz96leXzb40+foMbc03YHOOq6lOBgrj9YEJ9/Dr0I+s0VtFm/8coJsvYSjnRX3M7Jyy6a/XY2Z+2PQZuUUueanJfhsJI0aBwDgH+BD2IU8N5qLsXkmvFZWlri5lydKvMSxI+ewsDCnT++vWfjTJho19jeW/GciNDiWho3k89TX35OIsDxT0LiL16hZxwcrKwusrCxwdStHdFSysaQ+NYFOJTiaLCcHPHcjheplCnqytXEvi16CI8l5CQRXhiezUZRHQNRmKnQmOOL4gIByJTh2RdZ+/lYK1UoXjK+Vqxzfg2MAqpbW4GhjxcJWvsxrXh33fN5vxRF9If0UJS+6ZudfpqBAN2CpIAjfCoJw0vAzCEAQhBqCIOwWBGGPIAhnBEFoaPgcK0EQ1gqCcEQQhC2GrMwmxeuvN8r163kVKe7xZaRnFjDIVKllg8z8nNh5goBmAWgMBpmakhrU5mquJV5jy8ItvNHt9SLV/CxorMwLmi1Kstli/r+713bljx512BFxHYAvm1ZiX8xNIq6nFrneZyE1VYt9vpvBAxNeAJ/KbgSdiSQtTcvdOymEhESh1WZw504K9+6n8cvi0TRrXovZM1cbS/4zkZaagZ19QcPaB7F6+zgTHBRDWloGd++mci7kIlqtzlhSnxqNhTkpurxzUy9JqA2npncpW96u5MgPwQkF3pOiyyEzR08ZGwu+aSLwfVB8ESp+NuzMzUnV/bPtyb97lbTlTQ9HfjpXML6bWh3LwhLpu/c8S8MSmdrQ9DNhPw6pkH6Kkhd9lH2oKaggCO8AnsgjPebAEUEQ9gHVgK9EUTxvyJDcAziGPP01WhTFeEEQDgCBwKkX1KbwH8La1uqJBplBe4PoMaFHgdeig6PZOG8jXUZ2Mdn1OgCpmdnY5Rt5MsvnKv2AFWcSWXs2iRUdA2ng7sCHNSpwJSWD9gHOOGosWdW5Ju1Wnilq6U9Eo7EpYARa0ITXhY6dXqNfn29wc3PCz8+bUg4lKFVKQ4uWtQBo3qImSxZvMYr2Z8VOY016WkFT0AexenqVp13Hpgzqt4CKbo7U8POgVCnTNhgGSM3Kxs4ir62pVCoezJq+51WOcraWLH3DF2eNNVk5epJTMzmafAefUrbMalaF2WfiOHPtnpHUP5m07ILxmeWL7x3PcjjaWLKwlS/OdtZk6fVcScvk7PV7ZBvaZ8iN+5Qz8anI/wIv2tlJAGrmf8FgCloLOGxYt5MlCMIJ5I5OMjBOEAQtss3EfcPbbouiGG/4/Sry+h8FhafGs0Ylwo5fILB5IPHh8Th7VihQrk2VDTIdyuUZZEYHR/P7j5vo900/Spu4n9SZpLu08nFke8Q1Al1KIuYbralU2pYRLb3p+9s5svQSuhwJvQTNFhzNPebIwMZ0XXP2YR9tdAJrChzYH8QbbzYgNCQan8p5PsO3b9/n7p0UVq2ZREpKOn16T8PHx5XAWgKHDwZTvXolgs5E4u1d8THfYDr4B1bi0IELtHmjJudD4/DyyTNyvXM7hbt301i8agipKVoG9vmhQLmpEnz9Ps1dy7Ar/iZ+jvZE38kzAv0u34hN/wA3bmp1HE2+Q6WStnzboirDDkQi5jveFAm5cZ+mLmX4+9JNfMvYE3M3T+/ckPjc3/v6yvEdu3KHLwI8uJeZzYqIJHxK2XE1PfMhn1x8UBYoP94UtBkwx/BaQ2AFsBjoLIpihCAIkwAPw+e8Av8rFYyJX2NfxCCROZ9/D5JEp+Gd2L9xP2VdHPFtWIPrSTf+1aHZtGAzOdk5rJmxBoByFcvRfkh7Y8h/In9FXqexZxk2da+DSgVD/wyjdz034m9r2RN9g/BrqWz+pA4A+2NvcfKS6RssPqBV6zocO3aezh3HgQRTvu7HiuXbcXNzonmLWiQlXad929FYWJjz1dAuqNVm9OnzIePHLaRzh3GYm6v5ekZ/Y4fxVDRv5c/JY5H07PwtIDF+ShfWrNhLRTdHmjb3JTnpJt3az8TCwpwvvvoQtdr0s4PsTbhFQ2cHVr8lr5sadzSKbtVcuJSi5UDiw13Nv6zlgZXajJH1KgHytNY/FzWbCvsTb1G/vAPL2vijUsHEE1F0ruJCYoqWQ8kPj29ZWBJTGwo0di5NjiQx4URUEatW+CcvnEH5EaagkwyvNQIsgV9FUZwhCMIQoB+y6WcSUFYUxTb5d2MJgrAe+Bm4DgwURfEJVzElg3JxRcmgXHxRMigXb5QMysWbos6gfFW7tVDus+Vt3is+GZQfYwo69CHHfoc88vPP18vn+z2/IWjxeFxTUFBQUFB4RZGkIu1bFQqmP0aqoKCgoKCgoPACvLrj7AoKCgoKCgovzKuwVqTYd3Z0+vtPPqiY0u+oaedHeVH27Xu1Bxar1C/eicQeh4WZ6W+JfhGOXbtibAmFipPTq73hdXrtu8aWoGBiFPvOjoKCgoKCgkLhoX8FhnaUzo6CgoKCgoLCI3kF+jpKZ+cBer2eqZOXIUZewtLSgklTev/DeflPdu44hp2dDT17vUOzFjW5dzeVd978Cm8fOQlaq9a16dLtDWOF8FgkvZ7YNetIT0xEZW6Bd/eu2DiVyy2/uHY992NiUVvLW1KrDuyPSm1G7Oq1ZN68iT47m0odO2BfydNYITwWFTD17WpUdbJHl6NnxNYwEu6k55Z3rePKx/4uSMC8g7Hsi76RW+ZVxo7NvetRZ/YBk3QFBzm+z6t54WlvR5Ze4vuwaC6n52Ud/qxKJaqXKkG6wSJjYnA46QYbgg/dnXGwtGBpdMLDPrpYEBoqMnv2clatmm5sKc+MXq9n7ZzfSIq9jLmFOd2GtadcRdnpPDE6mQ0/bM499mJ4Av2n9sTV24Ul01aTnZVNyTIl+GRkJ6ysTX8/tQoYVN0LL3tbdHqJby/EFDhPB1T1pIZDidxzc/zZCNKyTde3DeT6W/Ht71yKkeuv98h2OBnqLyE6mdVz/8g9NjY8gS+/7kGlamC6gPEAACAASURBVG4M6/gNFT3le0jtpr683q6pUfQryBR6Z+dZXNEFQVgOrBdF8a/C1vVP9u0JIjMzizXrJxmcl9cw/8evANl5ecf2Y6zdMAmArh0nUbd+dcLD43nz7YaMHtu9qOU+M7eDQ5CysvAbPZKU2IvEb/yNqgPzdvanXrpE9cGDsLDPM7m7tOVPbJ2dqdyrB2mJSaQlJZlsZ+e1KrIr+EdLT8qu4K8JfLpBdlGWXcHdeGvhMazMzfi7fyMafi93djSWasa8Jpi0ESFAw3JlsDAzY/DJc1QpaU8fwZOJwRG55d4l7BgddIH7WXkePpZmZnxZ3ZsqJe05cq345o1ZtOh3tm7dj41N8cwNE3LkAlm6bEYu+JKLYfFs/GkrA6b1AsDVx4WhcwcCcOZACKXKlqRGvapsmL+ZBq/XocHrddi67C8O/XmMNm2bGzGKp6ORU2kszVR8fuI8VUtp6FfFg/FnI3PLfUpoGHE6rMB5auoEHb6ATpfNhIWDiLkQz9oftjL4G7n+3H1cGPPDAID/s3eeUVFcbQB+FpZeBBUFkabIWACVKHZjS4wpptpbLNFoNMYUu1GjsaCJUdPV2HvXqNEo9ooFFIQBpCj2gtKWtrvfj0F2QbFF2IVvnnM4untnZt97Zmbn7i3vw8ngUBwr2uPfuBbhIdE0aVef3iM+MGToL42ykEG5WGeIliYr+tmzIs3zzMl169XgYnh8flncpWs0DKylZ16uTLR4mYsR8URejOfjXlP48ou53L5lvFlrU2JjcfCVDN921auRlqD7la/VaMi8eYvYZcs5Pz2Im0ckzcD9iAhMlKZEzJnLlb934FCntkFifxYaujtyMFZnBferUtAK3uH3Y5IV3NaClEzdF+30d+owKziGzBzjbuzUcbTn9B3p+op6kEoNe12jVAG4WlsxvI43Pwb687qr5PgyN1Gw99otVsc9aoAvTbi7OzN//lhDh/HCxF6Io05gTQCq1fEkUXz0fGSpsti++B+6DHsfgM5D36PRa6+g0WhIvn0fe0e7Eo35RfFztCfkjjQ5OPJ+GkK5gtdpVRtLvvT1Zm5jP96oWqmIoxgX0efj8W8knT9vX0/iox49f5mqLDb9tZteX0jnL0G8QkJ0ElOH/sy88Uu5f6fsLqQpLRT3cpjnsqIbkvQ0FbZFmJd9fNwKmZdjUKmy8KrmwpChH7Fk+QTatG3A9O+XGir8p5KrykRppbc6yESBNm/IQ52djUvb1vgM6E/tEZ9zff8B0q8kkZOWTm5GBnVGDKd8XX8S1m80UPRP51ms4L0burO5fyN2Rd4E4ItXqxMcc5vIm6klHu/zYq00LdDdr9FqMcmrnqWpKVsvXyfofDTjzkTwjpszXrbWpOWqOXu39K9Kad++Wb4sszSSmZ6Jla3u3lOYKFAXGro5svMkr7Sqi52D1DhQKBRoNFom9w1CPBeDt69x9qgWxlqpJD1H/z6kwHW6OfE608OiGR1ykY7uLlSzM/5VYar0TKxtdL2KJiYmj5y/g3+fJLC17vy5eFTmg35vMP7nobzS0pdlP20q0ZhfNmXBel7cjZ3HWtGB19FZ0ZsD3QVB8CvmWJ6ITSHzskajyf+CrZZnXh48MIhZQSvx96+Oo6MdjRrXIbCR1NvRtl0DIiONd06E0soSdaaufmi1KPKs4Kbm5ri0a4uphTlKS0scatYkPSkJpa0N5etKvV3l6/oX6A0yNiQreGEzccHbaVnIZQJ/OECguyNNPMvznn8VutSvypo+DXGyNWdZr1dKOuxnJiNXjbVpQbP0wxUSWWo1WxKvkaXRoFKrCb33gGp2ZXtpeGnC0saSrIyCVnfTQo23k3vP0PytxgXeUypNmbx0ND2/6sxf01eVSKz/lYzcXKyU+vchBa7TTQnXddfp3ful4jq1srEkU0/kqdE+ev6O7TlLq7cb5b+u/Yo3tQO8AWm+TmL01ZIJVqZIiruxkwi46b9R2IouimIO8NCKbjDqB/hw+FAowGPNy8nJqSxbOZHRY3tz48Y9vGu4MXH8Av7dcwqAkyciqF3HeH992Xl7k3whHIDUS3FYu7rml6lu3OTCjFloNRo0uWpSYmOxdXfHXm+fB9ExWFdxeeyxjYHTV+7TuoY0abC+azlEvd6aahWs+b1zPYA8K7gGjVZLq/mH6bo0hK5LQ7idlk3v5WcMEvuzcPF+Cg2dJGN7zXJ2JKTqzMuuNlb82MgfE8BUocDXwZ7YFOM2Sf8/Ud3XiwsnpPlVcREJuFYreB9lpKnIzc6lfCXH/PdWztlA1LkYACytLVEoSke6/vDkVBrlXae1HGyJT9UtEqhqY8Xcxn6669TRnpgU488l5uPnSWje+YsNT8DtcecvJ5cKlXXnb9GMdYQcOA9AxOkYPIWqJRdwMaDVFs9fSVLcE5Sf14reoZjjKZK27Rpw/NgFenabhFarZcq0QSxdsjPPvBxAUtItunaagJmZKV9+3Q1TUxO++Kor3477k7Wr92JlZcHkKQMMFf5TqVC/HvcvRnJ++kzQavHu+zFX9/yLZaVKVKhXF6dGgZyfNgOFqSlOTRpj7VqFqm92IHbpsvz3a/Tva+hqFMnuyJu0qFaBjf0CUaDgm63h9G/sQeK9DPZGS0NVm/s3QquFA7F3OJlovPOrHsfRm3cJqODAnEB/UMCP4TF84FGFaxmZnLh9j+Brt/ipcV3UGi17r90iMT3j6QeVKRHqt/Aj8rTIjM/mglZLn1Hd+HfdAZxcK1KvmS+3km5Twbl8gX3afNCClT+u5++lezAxUdBjxEcGiv75OHLzLq9UdGBeYz8UQNCFWD7yrMLVjEyO37rHvmu3md/EH7VWy56rt0lMUxk65KfySks/wkOimfzpPNBq+WRsV3atOUDlqhUJaO7LjSu3qehS8Px1/vQtFk5fw97NR7GwMmfAqC4Giv7lYNwzGp+N/2w9fxrPaUVfgjSh+RSwUBTFp05lz9acLgPzxB9P2c+gnPX0jUoxQmPj76J/UXa3Lx2TS1+Ug9djDR1CsfJdaDlDh1CslPUMyoFOb5VoV19c6vZiec5Ws3un9FjPn8ZzWtE/1ntZNtbsycjIyMjIlGLkpecyMjIyMjIyMkZOqc+grNGWnuRUz8vXfmV7GCsm2cHQIRQr4+s+MHQIxcbdzHuGDqFYmXSugqFDKFYSx580dAjFSoMzxpnJvrRSBjp2Sn9jR0ZGRkZGRqb40GpLx2rAJyEPY8nIyMjIyMiUaeSeHRkZGRkZGZkiMcQwliAIJsCvQF0gCxggimKsXvnXQDeklfHTRFHc/NgD5SE3dvLQaDR8/90yokXJej7xu364e1TOL/9r4Q7+2XECG1tLPu7/Fq+2qpdfduZ0FGNG/sGe4DmGCP250Wg0/BG0iYSYa5iZK/lsbGdc3Crml29ZsZ/De0IxMVHw4cdtadzKoMmtnwkF8KV/dbztJSv4zLAYruplxH64TVCj2hy5cY+tiTewUZoy6RUBS6UpuRotU86K3MvKMUj8T0Oj0bByzkauxF5Daa6kzzc68/LlmKus+VlnXo67mMjQqX3xbVQLgOiwSyyYspJZG741SOxPQ6PRMPv7zcREX8PcXMmYiZ2o6q67HlctPcC/u0JRmCjo078Nr7b1Y9miYE4eEwFIS1Vx904qfwdPNFQVnhkF8IWvZAXP0WiZdaGgFXxY7YJW8PFnjN8KrlDAlDFtqenjRHa2mjFT/iXxSsGl3+UdrNiwpCtvdF5GdrauPq+39ubNdj58MW5nSYf9zGg0Gr6b/AdRUQmYm5sxZepneHjoEgsuWLCJnTsOY2NrTf/+79G6dUOSkm4yerSUl6dKFScmfzcEKysLA9aiVPIeYCmKYhNBEBoDPyApqB56Nz8HvAEbIBQoucZOEYbzA8AgURS7Ftr2J+BHURQvv8wYXpTgfWfJzs5h+epvOR8Wyw9Bq5n7yxcAxERfYdeO46xYIz0senefSmCjWlhZWXDj+l2WLf6H3Bzj/kLS5+TBcHKyc5i56HPEC4ksnruNsbP7AZCeqmLHuiP8unEMWapsvuz1Y6lo7LRwqYCFiQmDj5yntqMdn9X2YmxIZIFtPqnpgb25Wf7rDm6ViUvN4LeLCbzjXplu3lX5JSK+8KGNgnN55uyxvw3nUkQC63/dxtBpknnZvYYrI+dK5uXT+0NxqGCf39C5dyuZPWsPoFYb7/V5KDiC7OwcFiwfRvj5ROb9sJ2guVICy9QUFetXHWXd36NQqbLp03kOr7b1o3f/NvTuL2W0+HroXwwe/pYhq/DMNM+zgg89LlnBh9TyZPyZglbwb06VLiv46629MTdX8tHHa6jn58LYES0Z9OW2/PIWTTwYOawFFcoX9GBN+LoVLZt4cjH6VkmH/Fzs3XuSrKwc1qydSWioSNDMxfzyqySmjRYT2fH3YdaumwlAt25jaNzYn1lBS+napT1vv9OS9ev/ZcmSbQwe3MmQ1fhPaAwzQ7k58A+AKIonBEFooFeWjmRosMn7e2rew5c2Z+cJhnPhcduLoviFsTR0AM6djaZpc+mh7l/Xmwi9h17cpWs0aFizgPU8JvoKWVnZTJ28lLHf9jZU2C9EZFg89RtLFl/Bz4NLehZfCytznJwdyVJlk6nKLjVp6v3L23Myzzp/MTmVmg62BcpbuVRAg5YTemb6uNR0rPMcN9ZmSnI1xpsnNPZ8PL555uzqdTxJKMKcvXXxbrp9LpmXc7JyWP7DBnqM+LBEY31ews7F06ipVDdffw+iIpLyy6yszHF2cUSVdz2aFLoeD+y9gJ29FY2bPfZrxujwK2/Pqds6K7hPISu4q40lX/l5M7+xHx1KiRW8QT1XDh1LACD0wnX8ajsXKNdqtPQavIEHKQV7Ws+ev8aE6ftKKswX5uyZSJq3qA9AvXoC4eGX8ssuxSURGFgn/9ng4eGCKCZw6dIVWrQMACAgoCZnz0Q+9tilBQOJQO0B/SWtakEQ9DtorgAXgbPAvKcd7GVOUC7KcB4H1BAEYZcgCGcEQZgEIAjCAUEQagqC0EwQhBOCIBwWBGGbIAh2LzGmZyY9TYWdnpnY1ERnPa/h48aZ06JkPb+fRlhoLKqMLKZPXU7vvh2oXLl8UYc1SlTpmVjbFm3xrVjZgc+7BvFVnzm81aW5IUJ8bmyUpqQVsoKb5j0XveysaVfViUVRBdvWD7JzaejkwPLWAXSr7sqOyzdLMuTnQpWRidVTzMuHd5ykgZ45e+XcTbTv0gpHJ+Ne4p+RnoWtna5upqa6ew+gknM5erw/m4+7/ESn7gWvx2V/BdP/09dKLNb/irVSSXqurtdGU9gKnnCdaaHRjAy5yLsepcMKbmtjTmqanihTrcHUVNcoPXLyMvcfZD6y34490RR3Bv+XQVq6Cju986B/ffr4uHP69EXS01QkJ6cQei4KlSqLmjW92B8seRODg0NQqR6tv8xTSUGyLjzERBTFhzdPB8AFSSjuDrwnCELgkw72Mhs7RRnOswFLpPG3FsDQQvu9B2xCcmX9BThiAB6xnmu1etbzKnTt0Y4hg37gh6DV+PlXw1Rpytkz0fzx6xb695nOgwdpjPzqV0OE/twUtvjqW5jPHosk+U4Kv28ex4Kt4zl5MJzoCKPpgCuS9Fx1fi8NSFZwdd736BtulXCytGBuUz86uFWiS3VXAp0c6Cu4sSr2Kr32n+WrE+FMbVjLQNE/HSvrQufsMeblk3vP0iLPvHz/zgNizsexbekegob/QnpKBn9MXlaiMT8r1jYWZKTrPSw1unvv+NEo7t5OZcPOMWzePY5D+yO4eEG6HuMv3cTOzqrA/B5jJyM3t4C93oSCVvCNelbwc3fuU70UWMHT0rOxsTHPf60wUaBWG38j5lmxtSn0bNC7PqtXd6N7jzcZOPA7gmYuwd/fB0dHO0aO+pjg4BA+GfAdJiYKHBztDRX+S8FAPTtHgTcB8ubsXNArSwZUQJYoipnAfeCJv+peZmOnKMN5SyBcFMUsURQzgMKD0dOASsA+4CPAIDNE69evwZHDkqX2fFgsNWroLLX37qVwPzmNpSvGM3JMD25cv0f9AB+27ZzJoqVjWLR0DOXK2RL0wxBDhP7c1PL34swxqVtVvJCIu7dusp2tnTXmFmaYmSsxtzDDxtaKjFTjl/VduJdCkzxrdG1HO+L0rN+/XUxg0OEwPj92gV1XbrH20lVO3b5PanYu6XlzI5Kzcgo0lowNbz9PLpyUztmliARcvR5vXn5oznaoWI7vV4xh5NzPGDn3M2zsrRk00TiHW/3re3L8iFS38POJVK+hGwaxs7fCwtIMc3MlFhZm2NpZkpp3PYaciCk1w1cPCU9OpVElnRU8rpAVfH4TPSt4+dJhBT8Teo1WzbwAqOfnghh7x8ARvVwCAmpx6OAZAEJDRXx83PPL7t17QHJyCitXTWfsuP7cuHGHGjXcOXYsjM+GdmHBwm8xUZjQtGldQ4VfmtkMZAqCcAyYA4wQBOFLQRA6iqJ4GAgBTgiCcByIBv590sFe5gTlogzn//LkRlwPYIkoil8LgjAGGIgkCi1R2rR7hePHIujdfQparZbvvh/AsiX/4O5eiVdb1yfpyi26d54kWc+/6YKpaelNUdSolS+hp6IZPWAeWi0Mm9CFrasO4lK1AoEtfQkLiWZU/3koFApq1fWibiMfQ4f8VA5dv0sDJwd+be6PApgeGkOXalVISs/k6M3HZ/tdGHWZUfW8ec/LBaVCQVCY8cof67fw4+LpaKYPmYdWq6Xv6K7sWXuASlUlc/bNK4+as0sLr7bxJeR4DAN7/4xWq2Xcd11YvewgVd0r0qJVHU6fiOGTnvNRmCioW9+LwCbS9Xg58RYNGxv/tanP4RuSFXx+E8kKPvN8LJ28qnA1PZNjt+6x99ptfmnqT26eFTyhFFjBd++PoXljd9Yv7opCASMn7aZ/jwASrtxn36G4px/AyGn3WiOOHQulW9fRaLVapk0fxpLFW3H3cJFWXl25SaePvsHMTMnX3/TB1NQULy9Xxo39GXNzM7y93Zjw7UBDV+M/YYjRRlEUNcCnhd6O0iufCDzzEsyXaj0vwnB+EL3VWIIg3BBF0VkQhANIFSmHtJb+4ZDXQKRJSc9kPc9Unyg7/aWFiEstW7+QCjPokHHPJfmvTAssu7qI2g7Gu7rrZfBRsKyLKM3ElnFdhImidomuHIlI/rtYnrN1HN8undbzJxjO9+tt45z3byu98lces49sPZeRkZGRkTEwZaFHQU4qKCMjIyMjI1MkBsqz81Ip9Y2djFzjTkj1X7iYbP70jUoxhzuWjjwiL8q0UOOfb/Gi1ChXdusGMLdx8tM3KsV8MLDB0zcqxXx6tOw+FwD+bF7b0CGUOkp9Y0dGRkZGRkam+CgDHTuy9VxGRkZGRkambCP37OSh0WiY9f1mYkVJjjlmUifc9JKVrVwiyQhNTBT0HtCGVnkywhNHC8oId+w3fhmhRqNh28/ruRF3DaWZkvdHdKVCFUkqee1SEjt/1/nUrkQl0GPiAHwaGG/CvWchLExk9uwlLF8+3dChvBBajYYTi9aSnHgVEzMlTQf1wN7ZKb886VwEYRt2AVDBy41G/TuTm5XN4flLyErLQGlhTouhvbG0N0iC8iei0WiYM20zsdHXMDdT8k0hEeiapQfY908oCoWCngPa0LKNH1qtlo9en5q/XZ26Hgz8/E1DVeGJaDQaFs7aRELsNczMlHw6RifejY++ypKftuZvGxORyDcz+lLVqzLzJ68CwNbeiuGTe2JhafzD2gpgcgtvalWwJVujYeyBaBJTHhXyLnzTl70Jd1l98bpB4nwetBoNMStWk3blCiZmZgh9emFVWTcEH7tqDQ9iL2FqKWUB9x06hISt20i7ImlPsh88QGltTcC40QaJ/2VQChJdPxWDi0CBfsANURR/f5mxPC+HgiPIzsphwYphhIclMn/2doLmFZQRrt+RJyPsNIdWhWSEXw39iyFflA4ZYeSxC+Rm5/LpTyO4HJnAzj+30GvSJwBUqV6VAbOGAXDh0DnsKtiX+obOggUb2bZtP1ZWlk/f2Ei5HHIedU4ub079mtvR8Zxevok23wwCIEeVyZkVW2g/cTiW9raEb/2XrNQ04g6fpoKXO3U/6kDsgROc37SbwI8/MnBNHuXIfune+23ZMCLOJ/Lrj9uZ9pPu3tu4+iirto8iU5VN/y5zaNnGj6tX7lKjlisz5vUzcPRPJ+RQONnZOUxb8DnR4Yksm7+NUUFS3F4+rkz+VUpGenxfGI4V7anfpCZLftpKs3b1aP9hM1b9vpPg7Sfp0KmFIavxTLzmVQELpQmdtoRSr5IdY5pU49PdFwts82WgJ+UsSs/v7DvnQtHk5BAwbjQpl+K4tG4DvsN0CWRTEy/jP2I4ZnY6z5l3ty4AaHLVhM4IwqdPrxKP+2VSBto6sgj0IWHn4mncLE9GWNeDyItFyAgzHpVjHth7AftSJCNMjIjLb8C41/LkasyjUsnszCz2rdjF24ONWyL5LLi7OzN//lhDh/GfuCVewrWudM6cfLy4c+myXlkcDu5VOL18E7smzsHSwQ5Leztqv9Uavw/aA5B+JxnLcsbXqwNw/lw8gXn3Xh1/D8RCItDKLo5kqrJR6YlpoyOTuHMrheEDfmfkZ4u4nGC8E1L1xbs+vh5cinz0fstUZbF24W76ffkeAJ41qpCWlylalZ6JqanxZvfWp4FzOQ5dliZ3h95KxbdSwWvujWoV0Wjh0JXSMwH8QUws5X3rAGBfvRqpCYn5ZVqNBtWtW0QvW8656UFcP3y0wL5X9wXjWKc2tlVdSzRmmUd5mc3rR0SggiD0BpoCnwiCsAtJC7FdFMVJekkFAd4VBKETUAGYIIri9pcY1zORnpaFrZ4c86EI9KEDpZJzObq/NxuNWkOvAQVTCS1bFMzkmT1KNN7/QmZGJhYFpJIK1Gp1gS/U0/+cwLdFPWzK2T7uEKWK9u2bkZRkvJLPZyEnIxMza52o1sTEBI1ajYmpKVmp6dyIiKZj0BiUlhb8M3EOTjW8KFelMiYmJuz+bh7Jl6/x+vjCWjrjICM9Cxt9Ma1poXuvcjl6fyDdez36SfdehYr29OjXmtav1+X8uXimjl3Nn6uGGyT+p/GIeNdUkrjqu82Ct5+iSRt/7PMkrhUqlWPlbzs4sucsOdm5dB7QvsTjfhFszU1JzdYTnWokIa9aCzUcrXnHuxJD91xkWAMPA0b5fKgzMzHVu/cUJgq0ajUKU1PUWdm4tmlN1ddfQ6vREDbrB+w8PbB1q4omN5frBw8TMH6MAaN/OWi1JZrDsFgwBhEowFVRFNsCXwCDX2JMz4yNrQXpGUXICI9EcfdOKht3jWHznnEcCo4gQk9GaGtnVWB+j7FjaW1JtqqQVLLQL8ew/Wdo8EaTkg5NpgjMrC3JzSx4zkzyzpmFnQ0Vq3tg5WCPmaUFlWt5k5x4NX/b9t9+TofJX7D/x4UlHvezUFgEqtW7904ele69NTvGsO6fcRw5EEHkhcsItavSvLX0a9u/vhd3bqcYrUHbysYSVfrjxbsPObz7LG07Nsp/vfznv/lsfFfmrBpJ3xHvMf+71SUW738hLVuNrbme6FRPyPu+UJnKNuas6OjPB0Jl+vm70tLNIN7n58LU0hJ1pm7ekVarRZF375lamOPari2mFuYorSxxqFWT9Ly5OskXIynnUwOlXkNJxnAYgwgU4EzevzeQ5vuUOP71PDl+OE9GGPYYGaGFTkZoZ2eZ38UcciKGJs1Lx/DVQ9zreCGeksbRL0cmUNmzSoHyzHRJKungZPxfRP8vVBKqkXQuAoDb0fE4uuvOWYVqbty/cp3MlDQ0ajW3Y+Ip5+rMhc27uXToFABKCwtMTIzz15lfPU9O5olAI84n4qV379kWuvds8+69JX/8y/oVhwGIFa9R2dnhkeFlY6Gmvxdnj0v1iw5PxL16QYlrepqKnJxcKlbW3W82dlZY20oPyfIV7UlPyaA0cOZGCq+6S462epXsEO/phLxBJ+L5aHMoPbadZ5N4k7/OXy0Vw1nlvL25dz4cgJRLcdi46oakMm7cJHTGLLQaDZpcNSkxsdh6SKLQ5IuRlPerY5CYXzaaYvorSYxBBMozlBc7r7b15dSJGD7p9TNotYybkicjdKtIi9Z1CDkRw4Ae8zExUeCvJyNMTLiV///SQu2m/sSeFfljxBy0Wvjwq+4c2bifClUqUquJH3eSbuFYuXRKJcsq7g3rcu18FDsn/ABaLc0G9yTi733YOTvh3sCfgG7v8O+0XwDwbBKAo3sVLO1tOfLrcmL2H0Or0dLs054GrsXjadHGl9MnYhjS+2e0aBk9uQtrl0v3XrNWdThzIobBvSQRqH89Lxo08aGmrxtTx67mxJEoTE1NGP1dF0NXo0gCX/Xl/Kloxn0yDy3w2bgubF99EOeqFWjYwpfrl29TyaXgD4t+X73Poh82o1FLj4QBX5cOe86e+Ds0q+rAuvfqokDBqAMi/fxdSXygYl/i44W8xk7FgHokX4zk3LSZaLVaavb7mCu7/8WqciUq1qtLpcaBnPt+BgpTUyo3bYyNq/RDRHXjJs5Ny0bvuJF2mj4XxiAC7UreaixBEGoCv4ui2EoQhCBggyiKp570mfeytpWB0/B4gq8Z/1LT/8JHXtUMHUKxMi008ekblVL6CWU7g/ItVdlOQfbBOntDh1CstPEt6X6DkuXP5q1KtBsz5PaOYnnONnR66/9KBDpJrywKaJX3/5EvMzYZGRkZGRmZ56cs9CiU7Z8vMjIyMjIyMv/3lJ7MTjIyMjIyMjIlTlmYs1PqGzuuNUrHkswXwcqigqFDKFZ6Zd01dAjFSsblSYYOodiIuh9j6BCKlXN3zQwdQrHy9auZT9+oFNPAKcfQIcgYGaW+sSMjIyMjIyNTfJSF6d5yY0dGRkZGRkamSDTyMNb/BwqFgrnf98O/ljtZiBKjxAAAIABJREFU2bkMHvkncYnGrx9QKBTMntwF35quZGXnMnzsSuIv38kv7925KX26NketVjP7193s2R+eX9akYXX+/OFj/FpOAOCd9vUYPvA1tFoty9YeZfn64yVeH3j6uejbrQ39e7RFnatmxvzN7Np3jgqOdiyZPxQrS3Ou30xm4Fe/o8rMBqBieTv2b55Mg9dHkZWl6/r2qV6FQ1un4PHKpwXeN1Ykq/tSli+fZuhQ/hMajYY/gjaREHMNpbmSoWN1hnCALSv2c2hPKCYmCj76uC2NW/kZMNpnQ6vR8M+v67kVfxVTMyVvft6N8lV0xvpLpy9yeLVkrHeu7kb7wZ3IyshkS9AScjKzMVWa0vHr3tg6Gudyca1Gw74/1nEn4SqmSiWvDe2Og4uufvFnIjix9h8AKlWrSptBnQnZ9C+JZ6VEi1npKtLvpzBoiXFeuxqNhsWzN3I59hpm5koGjO6Mc1WpfgnRV1kxb0v+trERiYyY3hehbjUWz97I7Wt3yc1V02fE+1SvXXoUGWWRF2rsFGE3XwKEAWcBBWABrBBF8ee8fbRIOXQG6x1nHtBRFEVPQRCWA6IoilPzyvoDbUVR7P6CdXtpdGzfAEsLM1q9P5HA+t7MmNCTzgN+MHRYT+Wt1/yxsFDSvvMPNKjnyZQxH9Bz8J8AVKpox8DerWjzQRAW5kp2rfmSA0ejyM7OxdXZgc/6tc1P2W9iouDbrzvS5v0g0jOyOL5rPDv2nudecvqTPr5YeNK5qOxUjiF929Ps7XFYWpixb+Mk9h2+wNgvPmDtlqOs2HCIr4d0ZECPtsxftIt2Lf2ZMroblSqWK/AZdrZWzBjfk6xs42/kACxcsJGt2w5gbWVh6FD+MycPSobwmYs+R7yQyOK52xg7WzKEp6Wq+HvdEX7bOIYsVTYjev1YKho74okL5Obk0OeHL7kaFc++RZvpNGEgAFkZmez7ays9pw/DupwtxzfsJSMljYgDZ6jkUYU2/d7l3D/HOLFxH+0GvG/gmjye2JPnUWfn0HXmV1wX4zm4eDPvjpXql63K5PDSrXSa+jlW9raEbNqLKiWNwA9fJ/DD1wHYMvV3mvd515BVeCJnDoWTk53L5D+HExOewMr52/hqZn8APH1cGf/zZwCcDA7FoaI9dRvXYuOif6hazZnBE7pzOfYaibHXSnVjpyz07Dz30vMn2M3bAxdFUWwliuKrSB6sDoIgvJO3613gVUEQlHnHMQUa6B16CNBTEISmgiAEAJ8Bn7xgvV4qTRsK/HsgDIBT52J5xb90JMNr/Ep1gg9Jv55OhyZQz9c9vyzA35OTZ+PIzs4lNS2TuMTb1BGqYGGu5IcpXflm0tr8bTUaLY3fmEpqWiblHWxQKBSk67l+SpInnYsG9bw5fjqa7OxcUlJVxCXcxK+mO00aCPx7UNpn9/5QWjeXHpAarZa3un9P8v2CjbZfZgxgYtAaVKrsEqrVf8PN3YX580u/bBAkQ3hAniFc8PMgNkpnCLe0MsfJ2ZEsVTaZegZ0Yycp4hLVAiRjvWtNL67H6Op0NTKeSp4u7Fu0hWUjf8LG0Q6bcnZU8qxClkqaRJydkfmIS8uYuBYZh2dAbQBcBC9uxl7WlUXFU9HDhYOLN7N2zBysHeywLqczocccD8XCxhrP+rVKPO5nRTwfT928a7KGryfxUY+31m9YtJs+X0gN0vMnRZRKU2aM+IPNS/7Fv1HpUgqVRV4kz84jdnOgNxCsv5EoijnAXOBhHvdc4ADwWt7r14G9etunAr2ABcBCoKcoiiXfdfAY7GyteJCqc9Oo1RpMTY0/RZGdrSUpqbpMtxqNLu7CZWnpmdjbWRE0sTM/L9zH9ZsPChxLrdbw9ut1ObR9DMdDYsnJVZdMJQrxpHNhZ2tFil5ZaroKe3tr7O2seJDnFkpNz6ScveQcCj58gXv30wocf9yID9kVfI4LkZcpLbRv3zS/F660k1HYEG4iGcIfUrGyA8O6BvFVnzm83aW5IUJ8brJUmVja6BnrTSVjPUBGShqJ52No3bcjXScPJmTrAe5evYWVnQ3x56L4Y/D3nNi0j7qvNTZU+E8lOyMTc+uC5+xh/VQpaVy5EEOL3u/y/rdDOLf9AMlXb+VvG7LxX5p07VDiMT8PqvRMrGwetdbrc+DvkzRqXRe7PGt96oN00lNVjJ4ziIBmtVn18/YSjfllo9EqiuWvJHmRJ/aT7OaFuQno68BXIekhALoDKwsdJwS4B9wRRfHiC8RWLKSmqbCz1fuyMlGgVhv//PTUtExsbXRDG/pxFy6ztbEkOyeXxg2qM3LYm2xbMRzHctYsnNM3f5u/94RRp/l4zMxM6fq+ztBckjzpXKSmqbDVe6jY2UiNnJRU3T52Npbcf4JUsdv7zfm4S2t2r51AZady/L2ibPSYlBasbSxRZTzeEH72WCTJd1L4Y/M4Fmwdz8mD4URHGH+j1MLKMr+XBqQ5Lg+N9Vb2NrjUcMfW0R5zKwvc6nhzMy6Jw6t30fjDdgz6bRzdpgxh4/S/DBX+UzG3tiRbpXfOtFpd/exsqOztgU1e/VzrVOdWvGQFv3vlOhY2VgXm9xgjVjaWZOpdk5rHWOuP7TlL63d034m29ta80lySgAY0q0PcY3qDShNqbfH8lSQv0tgpym7u/phtPYAkvddHgfqCIFQAKuQdS/84nwMJgFYQhIEvEFuxcPx0NO1b1wMgsL434aXkwj15No52raQbrkE9Ty6K1/LLzp5PoEkDbyzMldjZWuJTvTJnwxJp1H4KHXvOpWPPuSQ/yGDAiMXY2VqyfeVwzM2VaLVaMlTZaAw0iPukc3E6NJZmgQIWFmbY21kheFchQrzCCb192reux7FTUUUe37flCNp3mUL7LlO4efsBb/ecXrwVkilATX8vzhyThl7FC4l4eOsM4TZ21phbmGFmrsTcwgwbWyvSU43f0VW1djUunZZ+u12NisfJU2esd/Z24/bl62Q8kIz118QEKro5Y2VrjUVeb4m1gx3ZGcabF6dKzWoknIkA4LooDVs9pFJ1N+5evoYqRarfDTGBCm6S1f5ymJg//GXM+Ph5EppnrY8JT8CtkLU+I01FTnYuFfSs9ULdavn7RIbFUdXLueQClnksLzJB+Ul283wEQbAAhgP5TwtRFLWCIOwEfgO2FNo+EBiENAfIFjghCMJRURQjXiDGl8rWf0Jo08KP/Zsmo1DAwK//MHRIz8Tfe8Jo1awm/6z9EoVCwdDRKxjStw1xibf5J/gCfy47wI7VIzAxUfD9nL/Jys597HFS0zLZsO00O1Z9QU6OmgjxKuu2PtHPWmw87lx8PuBNLiXeZMe/Z/h18W72bZiIwkTBpFnryMrKYcb8zSz4cTD9urfhzr1UPh72s0Fil3k6jVv5EnYqmlED5oEWhk3owtZVB3GpWoHAlr6cD4lmZP95mCgU1KrrRb1GPoYO+akITfyJPyey9OsfQQtvfdGDk5uDcazihE8jP1r1foc13/4KQK0W9ankWYWWPd9i57zVnN15BE2umjeHdX3KpxgO78b+JIZFsWbUj4CW14f14MzWYBxcnKge6EfzXh3ZNEmqn0/z+lT0kBp7yVdv4V7X+OeyNHjVjwsh0UwaNA+tVsugcV3ZueYAlV0r8koLX65fuY2TS/kC+7zbuy0Lpq9j4sC5mCpNGTzB4Ots/hNlYYLyC1nPi7CbL0W3GksDmAErRVH8PW+fh7ZzP+A04AyogCggADgBdMuTiZI3sXkaECiKYpE/36zcu5WB0/B4ynoGZZWcQbnUUtYzKJ+6XbYzKKtyS8fk7helrGdQblCx5GzhAHuv7iyW52w71zeN23r+BLt5kYkg9GznF5CWpT/EM+9fn0Lbb0dqRMnIyMjIyMgYCOOfofp05KSCMjIyMjIyMkVS0iunioNS39gpy0MF97PK9lCBtkz8XiianVfinr5RKcXT1tARFC9lfZhn0of7DR1CsVJjxluGDqFYOdzR0BGUPkp9Y0dGRkZGRkam+CjpZeLFgfFnxpORkZGRkZGR+Q/IPTt5aDQaJk/6nSgxHnNzM6ZOHYqHhy4fxoI/N7JjxyFsba3oP+BDWrduSNKVG4we/RNaLVSp4sR3U4ZiZaR+Io1Gw6zvNxEjSjK7sZM64+auy/e4csl+9uySBIt9BrSlVVs/0lJVjB+5gkxVNkozUyZP706FisYpI5Tqt5nYvPqNmdSpUP0O8G9e/XoPaEOrtn4sWxTMiaMiIHmX7t5JZcf+iYaqwhPRaDRsnLeBa5euojRT0vmrrji5SsnYrsYmseXXzfnbJkYm0ndyf5w9nFkzezUatRot0HlEZyq5VTZQDZ6MRqPhz1mSANTMTMkQPQFofPRV/pqzNX/b6IhERs3si6uHE/O/W4NWq8XJ2ZHBYzphYWluqCoUSVkUZSoUCmZ8+y51BBeys3P58ttNJFzWra7s8VFDencOJFet4aff9/PvwSisrcyY+e17uFV1xNxMybjvt3HuQhIfvF2PTz9ujkatZfWm0yxde9KANXsUBfClf3W87W3I0WiZGRbD1fTMR7YJalSbIzfusTXxBjZKUya9ImCpNCVXo2XKWZF7pUAoXBRlYen5S2/sCILQClgHXES6BsyAn0RRXPeyP+tlsnfvCbKys1m7dhahoVHMnPEXv/42HgBRTODvvw+ybv1sALp1HUnjxv4EzVpCl64deOedV1m/fg9LFm9h8JAuT/oYg3EwOJysrBwWrvic8LBE5s3exqx5kmAxNUXFulVH2LBjDCpVNr07/Uirtn7s2BpC9RrODPvyHbZsOMGKJQcY/rVxDhYfCo4gOyuHBSuGER6WyPzZ2wmaJ2V/Tk1RsX7VUdbvGIVKlU2fTnNo1daP3v3b0Lu/tKjwq6F/MeQL4x3nDz96gdzsHIbPH0HCxQS2/b6V/lMGAODqXZXPfhwGQOjBUOwrlKNWYC1WzVxJ8/ea49fMn6iQSHYs2kHfSf0MWY0iOXUwnJysHGYs/BwxPJEl87YxZpYUq5ePK1N+GwLAsX1hlHeyJ6BJTYLGLOX195vQsn0A/249wbZVB+nU77UnfYxBKIuizA5ta2NpruTt7r8R4O/GpJFv8vHQ5QA4VbRlQM+mtO/0MxYWSrat+JSDx2IY0q8lUTE3GTZmPbV8nKkjuHDuQhITv+nAqx1/Ij0jm0PbR7BlVxgPUowniWILlwpYmJgw+Mh5ajva8VltL8aGRBbY5pOaHtib69IVdHCrTFxqBr9dTOAd98p0867KLxHxJR26jB7FNYwVrCcEfR0YJQhCvWL6rJfCmTORtGgRAEC9ejUJD4/NL4u7lERgoB8WFuZYWJjj4VEFUYznUuwVWrZ8BYCAgFqcOWM0hotHCDsXT5NmkszOt64HURd1mYetrMxxdnFEpcomM0MnWKxew4WMPOFnenomSqXxjnqGnYunsV79Ii/qEncXVb+HHNh7AXt7Kxo3M94EZ/HhcdRsKMkSPWt7ciX60SzeWaosdi/dxftDPwCg46fvUruRlEFbo9agNDPejtzIsHjqN8kTgPp6cKkI2eKaBbvp/+V7ACTF3yQgb59a/l5EnjfOh0lZFGUGBngSfCQagLPnr1C3jmt+WX0/N06dSyQ7R01qWhbxl+9SW3ChVTMfsnPUrP6zL18ObsOBo9L+F8Ub2NtaYmGuRAG8QOq3YsW/vD0nbyUDcDE5lZoOBWfnt3KpgAYtJ/K2AYhLTcc6TylhbaYkV1O6F2NotMXzV5IU+7efKIppgiD8AXTKU0A0AG4AXsBDI/oipB4gLfC5KIphxR1XYdLTMrCztcl/bWpqQm6uGqXSFB/Bgz//XE9aWgY5ObmcOxdF5y7tqVnLi+Dgk7z/fluC951CpTKMCfxZSE/LxKaQYPFh/QAqOzvQ7b0gNGotvQdIvR3lHGw4dTyaru8FkfIgg9+XfGaQ2J+F9LQsbPXqZ1qofpWcy9H9vdlo1Bp6DSiYImrZomAmz+xRovE+L5kZWVjqywhNFKjVakxNdY6ek/+coO6r9bAtJ30ZP/z31pWbbPtzK/0m9y/ZoJ+DjPRMrG0eFYDqO4j2bTtF07b+2Oc9bLx8qhByOILWbzXk1OEIsozUUl+UKNPE1DRflNlzzmjMLC1YN/YnqgheOLpWAiRR5ptffWygyIvGztaC1DRd74tao8XU1AS1WiOVperK0tOzsLOzoLyjNQ7lrOg2cDGdOtZn4jdvMmzMesTYm+zeMJSMjGx27o0gJdV4enUAbJSmpOmJPzVaLaYKadKul5017ao6MSEkio8FnTHpQXYuDZ0cWN46ADszJUOPnjdE6C+NsjCMVVI/1W8iiT8riKIYCPRH59eaDcwTRbElkl5iUQnFVAAbW2vS0/UN4dr8B2X16m706PEWAz+ZzMyZf+Ff1wdHR3tGjepHcPApBvSfiImJAkdH45zPAmBja0lGIZndw/odOxLJnTspbNo1ji17xnMoOJyIC5dZ+PseevZtzZotI5n7x0DGfLnUUOE/FRtbC9KLqN/xI1HcvZPKxl1j2LxnHIeCI4i4IP26jr90E1s7qwLze4wRS2sLsjIKyhb1GzoAZ/edoVGHgnbsmNAY/pq4iB6jehrtfB14VAD6ONniod1naddRJ1vs83lHQg5H8N0Xf2JiosDOwQZjpCyKMlPTsgpKhhX6Qt6CZTY2FqSkZJJ8P4PdwVLv954DkdT1daWWjzNtWwoEvhZEw9eCqFjelnfa+5ZsZZ5Ceq46v5cGpPlKD1cnveFWCSdLC+Y29aODWyW6VHcl0MmBvoIbq2Kv0mv/Wb46Ec7UhsbVM/f/SEk1djyQGjHHAURRvI2kiQCoBRzKez+UQpLRkiIgoBYHD50GIDQ0Ch8fj/yye/cekJycyqrVMxk37hNuXL9DjRruHDsWytDPurJw0WQUJgqaNjXekTr/el4cOyyNM4eHJVK9hk5mZ29vjYWFGebmSiwszLC1syItVYW9vVV+b0n58rakpxvXLy59/Ot5crxA/XTiPTt7qwL1s7OzJC1PIBlyIoYmzY13+OohnnWqEXlKelAkXEzAxaugjFCVpiI3JxfHSjoZYUxoDFt+2cTA6Z/iJjzO02s81PT34uxDAWh4Ih6FZIvpaSpycnKpqCdbDDsVTef+r/PtTwNRKBTUbWicnqyyKMoMOZdA2xbSfRPg70ZUzI38snMXrtDoFc88ybAFNao5ERVzk1NnE2jbUhp2bNzACzH2FqlpmWRm5ZKZlYtGo+XOvTTK2VsZpE5FceFeCk3y7qvajnbEpaTnl/12MYFBh8P4/NgFdl25xdpLVzl1+z6p2bmk50iuweSsnAKNpdKIWqsolr+SpNiHsQRBsAM+ARYCTYGfBEFwRKeHiARaANvy5vXceOyBipnXXmvMsaOhdO06Eq1Wy/Rpw1m8eAse7i60bhPIlaQbfPThl5iZKflm5MeYmpri5eXK2LHzMTdX4l3DnW+//dQQoT8Trdr6EnIimk96zUOrhfFTurBq2UGqulWgZWuprH+PeZiYKKhb34vAJj5U83Zm2qR1bFx7jNxcNWMmdjJ0NYrk1ba+nDoRwye9fgatlnFTurB62UGqulWkRes6hJyIYUCP+ZiYKPDPqx9AYsKt/P8bM37N/Yg+KzLv85/QarV0/aY7Bzbsp2IVJ3yb+nI76TblKxeUEW75dTPqXDWrZ64EwMmtEp1HGOcE+katfAkLiWbMJ9L1OXR8F7atOohzngD02uXbVHJxLLCPq7sTv3y/FqWZEvdqznzyzQcGiv7JlEVR5s69F2nZtAbbV36KQqHgi3EbGNSnOfGX77JnfyQLVxxj6/JBKEwUzJi7h6zsXOb+eYAfvvuAv1cNJidXzeej15F07T7L151k2/JBZOeoSbxyj7Vbzhq6egU4dP0uDZwc+LW5PwpgemgMXapVISk9k6M37z12n4VRlxlVz5v3vFxQKhQEhcU+djuZkuOFRKBPotBqLDVSg2ousBn4GaiP1KAJBBohzdVZgOTLMgOGiaJ4WhCEIGCDKIpP1GtrEcvAaOLjkTMol26O3yq7MklPW/XTNyrFHL5hfEvYXyZyBuXSzeGOzUu0W2Rd3D/F8pztXO0N4xaBPglRFA8AlQq/LwhCTeCwKIqfCYJQAYgA7oiimAU8sl5UFMWRLzs2GRkZGRkZmedDnqD8fFwBugmCcAL4BxiV19CRkZGRkZGRkSk2SizxhiiK6YBxZcaSkZGRkZGReSJlYcKB8WYZe0ZyNGmGDqHYqPeDcaonXhaj3isDfaNPoHUV48z78jKwVpbtczewpuvTNyrF9Dr/hqFDKFZslI/MpJD5P6fUN3ZkZGRkZGRkio+SXiZeHMiNHRkZGRkZGZkiKQsTlOXGTh4ajYap3y1GjErE3NyMyVM+wd1Dl5hu0YJt7Np5HBsbK/r1f5tXWweQkZHJ1Ml/cfXqbXJychkzrg9+/t4GrEXRKICpb9SidiVbstQaRu28SGKyLmN071eq8pFfFbTA3CNxBMfeAeDksBbE38sA4OzVBwQdMM58EVqNhuCHZmkzJe0+K2iWTjgTwcl1klnaqVpVWg/sjFaj5fDiTdy8dBl1Ti6NurxJtYbGlb21MBqNhj+CJDu40lzJUD07OMCWFfs5tEeyu3/0cVsat/IzYLTPhkajYf6MTcRFX8fM3JQREzrjqlen9csOsH/3OUxMFHTt25bmbfxQqbKYMW4VqQ8ysLQyZ+SUbjg42j7hUwyHRqPhu8l/EhWVgLm5GVOmDsFDL7HgggWb2LnjCDa21vTv/x6tWzfg2rXbjBo5Fy3gUM6WWbNHYGVlnMPaGo2G6VNWES0mYW6uZMLk3rh76IaRliz8h392nsLG1oo+/drTspU/16/dZfKEpajVGrRaLeMn9cLTy/kJn2K8hIWJzJ69hOXLpxs6FJknUKyNHUEQqgFBQFUgA1ABI4FUoK4oituL8/Ofh+C9p8nKymHlmu8IC41hVtBK5v/yFQDR0ZfZueMYq9Z+B0CvbpMIbFyHJX/9jXcNN6bNHIIoXiY6KtFoGzvthUpYKE14f1kI9auUY3xbHz7ZICnIHK3M6BXgRodFJ7BQmrB3YFOa/HwYD0crwm+k0n99qIGjfzqXTp5HnZNDlzyz9OHFm3lHzyx9ZOlWPswzS5/eLJmlE85EoFar6Tz9S9Lu3ifm2DkD1+LpnDwYTnZ2DjMXfY54IZHFc7cxdrZkB09LVfH3uiP8tnEMWapsRvT6sVQ0do4diCA7K5e5S4YReSGRP+dsZ/KPkrE+LVXFljVHWLJ1NJmqbAZ3+5HmbfzYtfkkNWq60nPg6+zZFsKqhXsZ8s17Bq7J49m79xRZWTmsWTuD0FCRoJlL+OXXMQBEi4ns+Pswa9fNBKBbtzE0buzH0qXb6fBmM7p378BPc1ayccNeevYyztwx+/eFkp2Vw9JVozkfFsecWeuZ87Pk0YuJTmLXjlMsWyPVt2+PGTRsJPDb/G106d6a1m3rc+xIBPN/2swPcwcbshovxIIFG9m2bT9WVpZP37gUI/fsPAFBEKyBbcAnoigez3svEPgFWALUBIymsXP2rEjz5v4A1K1Xg4vhcfllcZeu0TCwFhYWUqIxdw9nosXLHD1ynjc6NGHQgOnY2FoxbkJfg8T+LDSs6sDBOKm35ty1B/i76Dxeyaoc3lh4ArVWS1UbC1IycwDwc7bH2c6CNT1eITNHw3d7ReLyenmMjWuRcXjU1zNLX9KZpa9HxVPBw4XDizfz4OYd6rRrinU5OxLPRVLBowpbp/6GVgutBnxkqPCfmciweAIa59nB/TyI1bODW1qZ4+TsSJYqm0zVo3Z3YyU8NJ4GTaVMwbX8PIi+qFcnS3MquziS+bBOJlKdPujeMt/FdOtGMo4V7B49sJFw9kwkzVvUB6BePYHw8Ev5ZZfikggM9M3/bvHwcEEUE6hV04sbN+8CkJaWgbOL8brbQs/G0rR5HQD861bjYkRifll83A0aBPpgYSEl2HTzqExM9FVGjPwIW1tJC6FWq7EwL50JON3dnZk/fywjR/5o6FBknkJx5tl5Bwh+2NAByMuG3BYYDXQXBKGjIAj1BUE4IgjCQUEQdguCYSQ+6WkqbO2s81+b5FnPAXx83DhzOor0dBX3k1MJDY1GpcrifnIqKSnp/LFwDK1aBfBD0EpDhP5M2FooSc3MzX+t1mgx1XsYqrVa+rzixpY+DdkZdQuAW2lZ/HIsnq4rz/DzsXh+6mi8QzzZqkws9MzSijyzNIAqJY2k8Bia9X6XdycMIfTvAyRfvYUqNZ3712/TcdynNHi/Hf/+bLzn7yEZ6ZlY2z5qB39IxcoODOsaxFd95vB2l+aGCPG5yUjLxOYJdXKq7MAnH81iSI85vNe1Rf77pqYmfDPoN7auPUpgs5olGvPzkJaegZ3ed4tpge8WD06fvkh6mork5FRCz4moVFlUdq7AypU7efvt4Rw+fI727ZsaKvynkp6eia2dzmdlaqLIr593DVfOno4hPT2T+/fTOB96CZUqC0dHO8zMlCTE32DO7A0MHPK2ocL/T7Rv3yxfOFyW0WiL568kKc5hLC8gf4KHIAhbgXKAC7AcsBVFcZsgCKeBAaIohgqC8C7wI1DiP7FtbK0KiC71rdnVqrvSrfvrDB44Ezd3Z/z9vXF0tMPBwY5WbQIAeLV1AIsWGk1H1SOkZeViY6E73SYKBepCqpClZ66w6lwSS7vWp4mHI+euPkCdd0WeTrqPs53xdtWaW1mSnamXo1LPLG2pZ5YGcK1dndsJSVjZ2eDVwBeFQkFV3xrcv3bLEKE/F4Xt4Fo9O/jZY5Ek30nhj83jAJg8/E9q+nvhU8e4JaDWtpao0gsZ3fPqFHIsint3Uli2fSwAY4YuoE5dT2r6SnWa9cdgLsffYsLwRSzdNqbkg38GbG2sSU/XzY/TaDT53y3Vq1ele48ODBw4BXcszM9fAAAgAElEQVR3F/z9a+DoaMfYsT8zfdowmreoz4EDpxk9ei5//DHeUFV4IjY2lqTrnT+NVv+704Uu3VszbNBc3Nwr4evnhaODNLcq5GQU06euYur0fqV2vs7/C+oyMIxVnD07V5AaPACIoviuKIqtgGQgSW+7Knm2c5Ds53WKMaYiqR8gcPiQFEZYaAw1fHTy9Xv3UkhOTmXZykmMHtubGzfu4l3DjfqvCBw+KO1z5nQU1b2NNzfH6aT7tK4udYXXr1IO8bYuP1G18tb88aE0hJej0ZKdq0WjhS9aVKNfoPRQqVXJlqspqkcPbCS41Cpolq7gXrRZ+np0AuWrOlNFb5/b8UnYVXR87LGNiZr+Xpx5aAe/kIiHt66eNnbWmFuYYWauxNzCTGrApxrvOXtInbqenDoaBUDkhUQ8vXUPPlt7K8wtdXWytbMiLVXF6r/2sXfHGUAavjMxNd4hu4CAmhw6KMktQ0NFfHw88svu3XtAcnIKK1dNY+y4fty4cZcaNdwpZ2+b39NcqVJ5Uh6kP/bYxkC9+tU5eugCAOfD4vCuofseTL6Xyv3kNP5aMYqvx3Th5o17VK/hSsjJKGbNWMvPfwyntq+ngSKX+X+iOHt2tgKjBUFoLIriCQBBELyRJitr0TW0rgmC4C+K4nngVSC6GGMqkrbtGnD82AV6dpuIVqtlyrRBLF2yA3d3Z1q1DiAp6RZdO43HzEzJl193x9TUhE8GvsvECQvo0fVblEol02Ya7wS7f8RbNPeqwKbeDVEAX++IYECgOwnJKvbG3ObizTQ292kIWtgfd5eTl5OJvJXK3I6+tPF2Qq3R8PXfEYauRpF4N/LncmgU60b/iFar5bVhPTibZ5auFuhH054d2TJZMkvXaCaZpR2qOHHr93WsHfUDWq2WNp92NXAtnk7jVr6EnYpm1IB5oIVhE7qwddVBXPLs4OdDohnZfx4mCgW16npRr5HxG92btfbl7Mlovug7H60WvprYhQ0rDuLqVpEmr9bh3MkYPu8zDxMTE+rU8+SVxj5U96nCrIlr+GfrSTRqLV9PNE6bO0C71xpx7FgY3bqOQavVMm36UJYs3oa7hzOtWzck6cpNOn30DWZmSr7+pjempqaMGz+AqVMWSKuVgAnffmLoahRJ63b1OXE8ko97zECrhUlT+7Biyb+4uTvRsnVdkpJu07Pz95iZKRn+9UeYmpowe8Y6cnNymTh2MQAenpUZP6mXgWsiUxSaMpBn56Vbz/URBMETmIE0dKUEcoGZwHVgDTDxf+ydd3gU1duG791Nr4QkJKSTwiSUJECo0gX1Z69UpYvSVBSRIlWkiihWBAWRDqIgTaR3kECoyaQnhJBQAqRtsklmvz8mZBO6SrKbfHNfVy7ZOTO7z+ucnXn3zDnvA4jIruiq0vaBoigmCoKwFPhYFMXUu7x1GTopsgYMsN2doJlZxpZQqSgVlKsvNb2Csretv7ElVCrakqvGllCp2JrVffBO1Zr6VZp9fB+9vVK+8G+HPFF9Xc/LI4piMnCvn8tCuX+3v8uxfSpDk4KCgoKCgsLDUxOWnlel67mCgoKCgoKCQpWjVFA2YXaPKHjwTtWYLuNLHrxTNWbIfOHBO1VTlsXf9+lytWdw52nGllCpWFo4GltCpdLlh17GllCprOtctXPxasKVWkl2FBQUFBQUFO6J8hhLQUFBQUFBQcHEUUZ2SqnpRqCSJPHNzPUkxl3C3FzDexO64VHObHHdL3vY++dJVGoV3fs/zmOdGpOXq2X2hBXk5xVSXFTC4JHPERLqZ7wg7oNKBZ+8FkawpyO6YomxK0+SctVQm2RAxwCebSrX/9hzPpP520QcbcyZ90Yz7KzMuZ6vY9zKk1zLNc0VVJIkMWXyAmLEZCwszJg2bXhFM8kf1rN58z7s7GwYOOilsiXNY8Z8iV6vx8OjDlM/GWqSZpJ6SWLrt2vJTJJNXJ99pye1PQwmrvHHz7N/xVYA3AO8eWroaxTmF/DbrCXoCnRozDS8OKoPdrUd7vURJotKpeLLTwcQGuJDoa6YIaN/IDEl09iyHohKpWLulJ40CvFCpytmxLhfSEq5Utbep3tb+vdoR3FJCZ99s5U/d5+hjosDCz8fgLm5hswr2QwdvQRtqTUNwBfTenP9Zh5T5vxujJDuiV6SSF+1HG1aGmozMzxf74tlHYPRafrqleQlxKOxkouu+g4ZhsZarpGUFxfLhcWLCJ4+2yjaHxU1Yel5pY7sCILgLwjCOkEQjgiCsEsQhM2CIBilaOCDKG8E+t77PZhTzvrhlhHo8lVT+OHHMXzz1Tq02sIyI9Cfl01i0tQ3SU66ZMQI7s/hPefQ6YqZt3gE/Uc8w8J5hmrPuTlaNq46wOeLR/Dp14P5Ye4GANYv30d48yDm/DCUDyZ155tZvxlL/gN5onFdLMw1vDpvH7P/OMe4lwzWFt7ONrwQ4cWr8/bxyrx9tA2uQ7CHA0O71ud4YhbdvtzP0r2JjHqugREjuD87dhylUKdj9epZfPBBH2bNXFzWJorJbNq0j9VrZvPjT5P5av5KtNpCZs9ZQvceT7J8xQxatGzEksUbjBjBvREPn6FYV0T/ue/Tud9z7Fhk6GeF+QXs/GkD3Se9Rf/PP8DRrTb52bmc3nGUOn4e9J39Lg3aN+Xw+p1GjODf8/yTEVhZmtPxpUlMmLmSmRNeN7akh+LZrmFYWZrzxGuzmTznNz4dayh6X8fFgbf6dOLJ7nN4pd98Jo56EQsLM0a+9SQr1x/m6Z5zEeMv0b+nYRFuvx7taCCYZlHW7FNRSEVFBI4ei/uLL3Pp1zUV2rUXUqn3znv4v/8h/u9/WJbo6LKyuLJjO/qSmjDjpfpTaclOOSPQuaIothJFsTMwBdkI1OR4WCNQS0uLCkag5uZmvDVoBgu+W0+b0uNNkXNRSTRrbTBbjIuuaCBZ5y5miy/1as/TL7cGoKREwsLSdAcCIwKc2Rct/yKOSr5OY+9aZW2Xrmvp991hJD3o9WCuUVNYVEKQuz17So85nnSNCH9no2h/GCIjo2nXTrYmkc0ky5xYSEwwmElaWlqUmUkmxF+gfftmgFzFNzIy2ijaH8SF8wkENAsBwCu4HpfiDX0zLToJV9+6/PXj7/w8+gtsa9lj62iPq58HhVp5Ar8uvwCNpnr6E7VpLvDXnlMAHDsZT7PQ6lHfp1VEIDv2yUVGj0clEd7IUBW6WZgfRyMT0OmKyc4tICnlCg0FT8Z+upbVG46hUqnwrOvE5avZADRvUo/m4fVYsnK/UWJ5EHkJcdg3kH882fgHoE0xGJ3qJQnd5UwuLv+FhDkzyTp0AACpqIj0lcvw7NnbKJofNTXBG8sYRqCdBEFYIgjCH4IgHBIEwUkQhLmCIBwt/Xu3EjXdk5puBJqf92Czxbdem8OI1+fxQnfZbNHO3hpLK3OyrmYze8IK+g17usp1Pyx2VmbkaA1D4pKkR1OatBVLeq7nyY+nxr7QkHNpN0i6ksf5izfp0kh+VNmlUV2szU33hpmXm4+93T3MJAVfjh8/R26uluvXszl5MgattoDgkHrs2nUMgF07/0arNc3VfYX5BVjaGowky5u45mfnknI6jsf7PU/PKUM4tmEP1y5exsbelsQTMXz/9qcc/nUn4U+0Mpb8/4S9nTU3c/LLXpeUSGg0pj+V0t7OiuxyViQlkkH37W25eQU4lBqFajQqDm+dSLuW9TkamYCbqwNj3nmWUZNXVm0A/wBJW4DGumL/vDVaI+l0OHfsjHf/gfiNeI+svXvQpqWRvmoFLl2ewLyW6VvQPAwl+sr5q0qMZQR6AdgsiuI8QRCeLd23VameA4Ig7BJF8UwlaruDmm4EeruBpFTObPH4QdlscclG2Wxx/IiFNAjzQ2jkQ1L8JWaOW8agd58ltFmAUbQ/DLkFxdhaGbqzSq0qMzEFsDBTM7tXE3ILi5m4Rv4l/d1fcUx6pTG/DGvD3ujLpN8wXR8pW7vbzST15cwkvend+2kGvzkVH193QsPq4+TkwEcf9eeTT35g86b9tG4dipOTac5psbSxQlcuEdNLUpmJq429LR71fcrm4/g0CiQzMY1ze0/Q+tUuNPvfY2QmXWTd9J8Y/M0Yo+j/L+TkarG3M9xI1WoVJSWSERU9HDm5BdhV+PFk0H17m52tFTez5YSuuFii1VNT6NAmmO8/68fGbSdxdrJj7Y8jqOPigLW1BXEJmaxYfxhTQW1tRUlhuf6pl1CV9k+1hQXOnbugtpDnwtkKwRRcSCUvPg7dlctc3vwHJXl5pC76AZ9Bg42iX0HGmEagYmlTCLBfFEW9KIpFwBGgyidP1HQj0AZhfvxdzmyx3u1mi+UMJO3srMnN1ZKSmMH0j5by0bTeNH8sxFjSH4rIxCw6NpBjCvdzQkzPrtD+w5stiU7P5uPVp8qGT1sEOLP+7wu88c0h0q7lE5louvYcTZsGs3efbHx5dzPJHFasnMH48YPIuHSVoCAfDh06xfBhPVj04yRUahVt2oQZS/598WrgT/zf5wFIi0mijp9HWZt7kDeXUy6Rf1M2cb0Yk4yLtztWdjZY2cg3VFtHewrzTXPU6kEcPh7Lk53CAWjRJJCzMRcecIRpcDQygSc6yI92IsLrcV68WNYWeSqZ1hGBWFqY4WBnRf0Ad6Jj0/lsSk/atZLrw+TmFSBJehYs3U3HF2fwbO/P+WLBn6z745hJJToAtv6B5JyVf3vnJyZg5eFV1laYmUniZ7PQSxL6kmLyE+Kx9vVFmDLNMIfH1rbaJzo14TGWsYxAtcCtny/RQH9gniAI5kAb4OdK1HVXaroRaJtOjTh5NJb3B8hmi+9P6s76ZXvx8HahVYeGnDwWx8h+81GVmi02bVmfqR8sQacr5vvP5NURtnbWTPq8v5EjuTt/nk6nreDK2pHtUKFi9PITDOwUQPKVPDRqFS0DXbAw09AhxA2AOX+cI/FyLp+9IY/MZd4oYMzKk8YM4b507dqKQwdP0aPHR+j1MGP6CBYv3oCvT106dW7OhbQMXn1lFObmZnw4uh8ajYZ69TwZN+4rLCzMCQzyZuLEt4wdxl0Jbh1K0kmRJR98jh547r3eHPltF7XrulK/VWM6932OFRNkE9cG7ZpQx8+Djm88w6b5Kzm++QBSSQnPvGP6Jq53Y8O2v+ncrjG7109BpYLBoxYYW9JD8cf2KDo+FsKfaz5EpVIx7KOfGTbgcRJTrrB152kWLN3N1lWjUKtVTPt8A4W6Yhb8vIt5n/Rm9HA9kqTng0mm++iqPA7hTciNOU/CnJno9Xq8+vTjyo7tWLrWwSEsnFotWpIwezoqjRm1WrbCysN0f/T+f8ZYRqDdgFWiKG4r3e8z4DHAAlgjiuIsQRA6A21FUZx6v8+oyUagaXnpxpZQqdT0CsoJSgXlasvgzkuMLaFSUSooV2/WdW5fpWvBp0f9VSn32XHhXWu8EeiW2/YbdZdjdwG7KkeZgoKCgoKCwsOg1NlRUFBQUFBQUDBxTLdwioKCgoKCgoLRqQkTDqp9spNffNnYEiqNE1fNjS2hUikOMM2l0I8KbbHpru76rzSsVWxsCZWKl29HY0uoVPT2FsaWUKlEfZFkbAmVS+f2D95HoQLVPtlRUFBQUFBQqDwU13MFBQUFBQUFBRNHGdkpRZIkZk9bR5x4EQsLM8ZN6YG3j8F5edmSXWzfcgK1WkW/N7vS8fFQbt7MY9KYZeTlFeDoaMu4yd2p7WxvxCgeDkmS2Pj1WjIS0zEzN+OlkT1wLnWZTk9IY8v3BiPGCzHJ9J40iPoRpl1UUAV82jmIEBc7dCUSo3eIpNw0FJrrE+rBaw3c0Ovhy2Mp7EzKYmiENx18awPgYGmGq40FEYtMq6DZLSRJYvony4gVL2BhYcbEKf3w8XUra1+8aAvbthzF1s6afgOeon3HcK5eucn4j36gqKgYF1dHpnw60CRdz0GO76fPfiU1Ph0zCzMGj+mGu5fcJ5NjL7J0vsEJO/5cCu/P6M+pIzGkxMvlGW5ey8HGzopPFr5nFP33Q6WCKe+3IzjQGV1RCeNm7SX1YsWil7VrWbH62xd5pt9adDp5hsT+9a+TknYTgJPnMpm74FiVa38YVCqYMqw1IfVqy/F9eZCUSzkV9qntYMmauc/y9NDf0RWV4GhnwdwPO2BnY86N7ELGzT9I1k3TLwypUsGUt1sRUs8JXZHEuK8PVYi1//MNeKadHwB7Iy/y1apTRlL6aKkJIzuPPNkRBKEj8LYoij3KbZsJxIiiuORRf96jYu+uM+gKi/hx+UjOnErmyzkb+OyrQQDkZOezZvk+ft3yMdp8HW+8NoeOj4eyZOEOwpv60+/Nrhw7LPLd/M2Mn2L6xc2iD52hWFfM21+MJDU6mS0//M4bk98EwCPAi0FzRgBwZt9J7J0dTD7RAXgywAVLjZqX1pykibs9E9oFMGiTbFToZGVGn1APnloRiaVGzc43ItiZdJRvj1/g2+NyxdrFzzdixoHE+32EUdm98yS6wiKWrhjP6VMJfD5nNV98/Q4AcbFpbN18hF9WTQCgX+9Pad4yhMWLNvPsC2147oXH+P6b3/l1zV5e7/uEMcO4J8f3naVIV8zUH94l7mwyy77ayKhZAwHwq+/JxK+HAXBkVxROLg6EtwohvJXcL4uLS5g85CveHNPNaPrvR9d29bC01NBtyO+EN6jD2GGtGTLuz7L2ti28+PCtlrjUNthG+Hg6cD72Km+N2WYMyf+Irq19sTTX8NoHmwkXXBk7qAVvf2JwoW/X1IMP+0fg4mSwkBjSPYzIc5l8t+Y0bcLrMqpfM8Z9edAY8v8RXVv5YGmh4bXRWwkXXBg7IIK3P90NgLebHc93qMcrH25Br9ezaub/2H4kFTH5upFV/3eUpec1iFMnEmnVVr54Ng7zI+a8oWy7tbUl7h610ebr0JZzBU9KyKB16TGhTepx6oTp3izLk3IusSyB8Qnx42LcnSXqdQWF7Fy2lWeHvFLV8v4VzT0c2ZMiTwg+mZFDqJthhO16QTFPLj9OsaTH1daC7MKKk2ufCnDhZkEx+1JN96J08kQcbdrK5flDwwI4fy65rC0pMZ2IFsFYWppjaWmOj68bcbFpjBrTk2eea40kSWRkZOHsbLoTwsXTSYS1CgYgqJEfiXexTSjQFrLuxz/p+95LFbb/uW4/oc0FfAI87jjGFGgW6s6+o3I8Uecv0yjYtUK7XtLTd+QmbmQbvOsaCa64udrwy5fPsnD2/6jnbbpFACMa1mFfpGwXESVeoVGQc4V2SQ99xv3JjRxDfIE+tdh7PA2AyPOXadagTtUJ/g9EhNRh34lbsV6lUaBLWdulq3kMmLwDSdKj14OZRk2hriasY6oZVOVjLJUgCN8CEUAGsm/Wc6VtPwLmgB54RxTFKh/7y8srvMPYrri4pMxs0c2tFj1enIkkSfQd2AWA+sGe7N9zFiHEi/17zlJQoKtq2f+KgvwCLG1vN/ErQaMxuH4f33aERu3CsXW0M4bEf4ydhYacQsOFpUSvR6MyOOuW6KFvqAfvt/Jj8amLFY4d1tyb4Vujq1LuPyYvT4udveGXv0atLuufgUFe/LRwC3l5WoqKSjgVFc/Lr3VApZL7cPeXJ6HTFTF4yPNGjOD+aPMKsCnfJzVqSopLysxqAfZsOkrLTmE41DL0yeKiYnb+fphpi0zv8dUt7GzNyck1XBskSUKjUVFS2jkPHr94xzFXruXz/S9RbNuTSLPG7nw2oTOvDP7tjv1MATsbC3Lyy8enR1POiPfgyTsrwUcnXuPxVj6cT8zi8VY+WFtWjxkVdjbm5OQVlb2WJKks1uISPddLE7ox/SM4n3iN5Ns8+qorpm9N+2Aqa2SnsyAIe279Ab0AB8BZFMUWwEDgltPmZ8B8URTbA+8iJz5Vjq2tJfl55VzBy7lKHzoQzdWr2fy2bQIbtk9i764znDuTQt9BXUi/mMXwQd+SmXEDN3cnY0j/x1jZWKHTGmLV6/UVEh2AU7sjiXiqdVVL+9fk6kqwszDEoEZVlujc4ufT6UQsOkxLT0dae9UCIKi2DdmFJRXm95gitrbW5OcZNEp6Q//0D/Cge6/ODH9rHp/PXk2jxv7UKk0IzM3NWP/Hp3w8uS8Txi4yivaHwdrWCm1+uT4p6SskOgAHtp+g83MtK2w783csweH+2JRzDjc1cvOKsLUxlJFQqwyJzr04E3OFnQeSAYg8k4Gbq21lSvxP5ObrsLMuF1+5ROdefL/mNJ5udvz86ZPUdbHl0tW8ypb5SMjNL8LO2pCYqVUVY7UwV/P5B+2wtTZj0vdHjSFR4R5UVrKzSxTFjrf+gBWANXAYQBTFK0BM6b4hwL7S7VEYkqAqJbSJP4f2y87LZ04lExhUt6zN3sEaS0tzLCzMsLQ0x87empwcLScjE3j6+eZ8vWgoHp7OhDapd6+3Nyl8GtZDPCbHmhqdjJtfxeH/gjwtxUXF1HKtHskbwPFLN+nkJ082buJuT8w1w8XTv5Y1C55pAECRpKewREIq9YRr6+3E7mTTr4cT3iSQA/tk5+XTpxIIDDKYDWZlZXPjei6Ll43jw7E9yczIIjDIi+lTf+Hvo/KIla2tFWqV6T61rt/Yj6jDsta4s8l4B9St0J6fq6VYV4yzW8U+efZ4XNncHVPlxJkMOrb2ASC8QR3ExAf3txH9m9GvW2MAggNqcykzt1I1/hciz1+mQ4TsBB4uuD7UHJXmjdz5fWc8fcf/SVpmDpHnq0e9tMjo8rG6IKZUjHXB+M7EJF1nwrdHkGrCrN5SjOF6LgiCWhCE7wVBOFw6cBJ4W/v/BEE4Uvr3rSAI951YVJVjh7lAO+ALQRCcgPql26NLt28UBCEc+RFXldPx8cYcOywy6PUv0Ov1TPikFyt+3o2XjyvtOzXi7yOxDOw9D5VKRVhTf1q2Fki7cJUp45YD4FrHkfFTexpD+j+mQZtQ4k+ILBg5D70eXvmgFwd+3Y2zhwshrRtzNe0yTm61jS3zH7Et/irtfJxY/1o4KpWKUX/FMKiJFyk3tPyVdI3oq3n83q0JemBPchZHL8qrXPydrNlvwnN1btG5S1OOHD5P396fotfDlGkD+GXJn3j71KFDp3Aupl2hd7epmJub8d6obmg0anq+3oVPpy7lh+/+QK1WMXbC68YO454079CYM3/HMvGt+aDX89b4HmxetQc3Txci2jXi0oUruNa9s0+mp16m3VMRRlD88Gzfl8RjEV6s/vYFVCoVY2bsoX/3xqSkZbPrYMpdj1mw7CRzJ3amY2sfikv0fDR9T5Vq/idsP5TCY008WPPZM6hU8NG8Awx4qSEp6dnsPHrn3CuApLSbzPlALoyXeS2PsV+Y/uRkgO1HUnks3IM1s/4nx/rlQQa80ICUSzmo1SpaNHLHwlxDh2byj5HPlp7gpHjFyKr/O3rjTFB+EbASRbG1IAitgLnACwCCINgDc4COoiheFQRhNOAC3PN/9iN3Pb/PaiwReb5OE+SEpgXQEnmuzkLAsvTfI0RRPC4IwmxgnSiK911veUO3teakz7ex42L1nwF/P97faLqPHh4FMcOqxzyEf0PMjRvGllCpdH8pzdgSKpWaXkEZM9MdxXwUxG/sW6U3h9HHdlXKfXZ2i873jEMQhM+BY6Iorip9fVEURc/Sfz8J9AN0gD+wSBTFn+/3WY/8aiyK4h5gz23bxgiCEAxoRVEcJgiCM3AOuCqKYiHQ9S7vM/pRa1NQUFBQUFD4ZxjpiZwDcLPc6xJBEMxEUSxGHsXpBIQjPzXaLwjCYVEUY+/1ZlWZ/l4AegqCcATYBnxUmugoKCgoKCgoKJQnGyhfpVddmugAXAP+FkUxQxTFXOR5v+H3e7MqG2cXRTGP0udtjxI7M9OsrfEoaFkn1dgSKpUPutTsoWYztemuoPmvrE6yevBO1ZjPlvkYW0Klsj/DNCtpPyricmq2iXJVY6Sl5weRy9OsKZ2zc6ZcWyTQSBAEF+AG0Ap5Osw9qbmTChQUFBQUFBT+M0Z6jPUb0FUQhEPIjkD9BUF4H4gXRXGjIAhjgVulyNeIonj2fm+mJDsKCgoKCgoKJoUoihLw9m2bY8q1rwJWPez7KcmOgoKCgoKCwj2pCd5YSrJTiiRJfDJ1EWJMChYW5kz55G18fd3L2hct/J0tWw5iZ2vDgIHP07FTM2ZMX4IYkwzA1as3sLe3ZeXqT40Uwf2RJIn5M9aTEHsJcwsNH0zohqePwddlzdI97N52EpVaRa8Bj9O2c+OyttSkywzvO591f03CwtI0n4XrJYm9P6zhWvJFNOZmdBraC8e6Bg+ilBPnOL5aNlV08fei/WDZNHLpmxPK9nMT6tH6ddO0VJAkiWlTFyPGpJb2z0H4lOufPy78g61bDmFra82Agc/SoVNT8vMLmDZlMRcvXqGoqJix4/vSODTAiFHcHb0kcWrJKm6mXkRtZkaTQb2xczd4JWWeOkfM+s0A1PLzJrRfD1Qq+eKb/ncU6cdOEDFsgFG0PwySJPHbV+u4lHgRM3MzXh3ZAxdPuc+lJ6Sx8TuDDURqdAp9Jw9EaC4XSty/fg8513N4euBzd31vU0MvSZz5eRXZqWmozc0IG/g6tm7lz+VZYn+Xz6Wjrw+N+xrOpamilyQyVy+n4OIFVGZm1O3VF4s6bmXtmWtWkJ8Yj9pKnsfm9dZwJK2WS8uWoJdKQA/uvfpg6eZ+r49QqAKqPNkptY94WxTFmLu0JQPBoihWee3+nTv+prCwiBWrPuVUVCxzZi/l62/k1e+xsals3nyQVaWJTO+eE2jZqhFjx/UDoKiomDden8iUT96qatkPzcHd59Dpivnq5xGcP53C9/P+4JN5/QHIzdHy26oDLN0whgKtjrd6fF6W7OTlFvD9vI1YmGvu9/ZGJ/HYaUqKinhl5gdkiEkcXPIbT48dDIBOW8DhnzfwwifvYO1gx8nfdlCQncRUxvoAACAASURBVIsuvwAXf2+eGWe65+0Wu3ZEUlhYxPJVUzgVFcec2cv56psPALl/btl8iBWrpwDwRs8ptGjVkCU/bZYrKc8agiimEhuTYpLJzqXIU5QUFdNh8odkxSdxdsV6Wr0vj14XaQs4u3I9bcePxNLejrhN29Hl5GLpYM/ppWu4fCYaR18vI0dwf84dOkOxrojhX44kJTqZTT9soN+UQQB4BHjx9mcjADi9LwoHZ0eE5iEUFepYN281qTEpNG4XZkz5/4iMyFOUFBXRdtJorscncm7Fr7QYOQSAYm0B0avW03rc+1ja2xG/2XAuTZnc0yeRiorwGzUObVICl9evxevt4WXtBRdS8R4+EjM7QxyZa1fi1KEz9mFNyD1/lisbfsVr8DBjyH8k1ARvLGVkp5QTJ2Jo21ZeuRYWXp9zZxPK2hIT0mjRogGWlnIhLl9fd2LFFMLC5SLQy5dvo02bUOrXN90VHGejkmjeRgCgQagvseVc3a2sLHBzd6JAq6NAq0Nd6uqu1+uZN20dA4c/zcSRi42i+2HJiE7Ep4lsCeEu1ONKgmElW0ZMErV963JoyW9kZ14lpEsbrB3tSTsTS961G/w+cT5mFuY81v9lnDzd7vURRuXECZG2beWbXlh4EOfPJpW1JSak07xFSFn/9PF1I1ZM5eCB0zz1v1a8NWgmtnbWjJ/QzxjSH8g1MQG3UPnc1Q6sx40kQ1XhrLhEHLw8Obv8V/IvX8W342NlN8fa9f2pGxFG8q4DRtH9sCSfTUSIkEdqfEP8SIu9s6qwTlvI9qVbGTJXTnyKdMU069KcoKb1uXyhelgpAGTFJlCn9Fw6BfpzM7niubT39uT8inXkX7mKT4fHTD7RAchPiMeuQSMArOsFUJCaXNamlyR0VzLJWLGUkpxsHFu3o1abttR5uRsa69KiqZKE2tw0R8T/P2Gstb8ugiD8IQjCX4IgnBAE4UUj6SgjL1eLvb1N2Wu1RnaVBgiq78Px49Hk5Wm5cT2HqKhYtKVGmjpdMWtX/0X/Aab5+OMW+XkF2Nrd6Sp9C1f3Wgx8dQ5Des3jxR7tAFi6YDst24YQUN/0l/fr8guwsDHEp1KrkUrk+Aqyc7l4No7Wb7zAsx8P5fSmPdxIv4ytkyNNX3mCF6e+Q7NXnmDHF0uNJf+B5OVWdD0v3z/r1/cm8nhMuf4Zh1ZbyI3rOWRn57Fg0Rg6dmzK3NkrjCX/vhRrCzC3McRW/tzpcnK5Gh1Lwx4v0nr0MBL+3EXupUwAvFpFIC/SMG0K8guxKu/orlZRUlJSYZ9j244Q2j4cW0fZwNXG3ob6EcFVqvNRUFygxcy6XGV0VblzmZvLtehYQrq/RMtRw0ksdy5NGalAi7p8TGo1+tKYJJ0Opw6P49FvEF7DRnJj/24KLl7AzM4elcaMwswMLq9fg/PTpn1/eBDG8MZ61BhrZCccmCuK4h5BENoAU4DfjaQFAFs7a/LytGWv9eVczwMCvOjV6yneGjwdHx93GocGUstJ/kVy5PBpmkWEVEiUTBEbW6sKru7lXaWPHYoh62o2y/4YB8CYYQtpFO7Hzi0ncHFzZOuGo2Rdy+GjoT8w70fTHIq1sLGiSFsxPnWpk7uVvS11An2xcXIAoG6DAK4mpeEb0Qi1Ws7364YEkJd1A71eb5JzCOT+Wc71XJLKuZ570rPXEwwZPBtvHzdCQwNwcrKnVi07OnZuBkCHTk35cdFGo2h/EGbWVhRrDbGVP3cWdrbU8vfBqpYjAM5CIDdT0rCra5ojcHfDysaSwvJ9U69Ho6n4WPjkrkjemNC/qqU9csysrCkpKFcrVn/buaznaziXwUFkp5r+uVRbWSMVlJtZodejKo1JbWGBU6cuqC3kukU29YMpTEvDytObvNgYMlcto27fQdV+vk5N8DStkpEdQRDsBEEoP463H3hLEIRfkJeWGX2Mr0lTgX37TgJwKiqWoHKPpLKysrl+PZtlyz9h7Lj+ZGRcIyhIbj98+Azt2jUxiuZ/QsNwP44dlKdJnT+dQr1Aw5fP3l52dTe3MMPC0hxbe2tyc7Qs3TiWzxcO5fOFQ6ntbM+sbwcbS/4DcQ/2J+XEOQAyxCScfQ2u2a4B3mSlpqPNzkUqKSEzNhknL3eOr97K6U17ALialIa9S22TTHQAmjStz/59UQCcioojqL53WZvcP3NYunwSY8b1ISMji8Agb5o0E9i/Vz4m8ng0AYGmObeldv0AMk7J5y4rPgkHb8NIYq16PuSkXaIwRz531xOSsfese6+3Mkn8GvoTc+w8ACnRybj7VdSvzdNSXFRMrTpOdzu8WuFU35/MU3K5k+vxidiXO5eOfj5kp6UbzmV8Enaepp8EWPsHkntOrmenTUrA0sOzrE13OYPUz2eilyT0JcVoE+Kx8vYhLzaGy2tX4j1sJNa+fkZSrlCeqhrZ+Rn4WhCEvUAdYB4wRxTFrYIg9Ec29DIqXbq04PCh0/Tu+TF6vZ5p04eyZMkmfHzc6dSpGWlpl+n22ljMzc0YNep1NBo5T0xOSuf5FzoYWf2DadupESeOxPJOv6/Q6+HDyd1Zt2wvHt4utOnQkBNH4xjRdz4qlZpG4X40a1X/wW9qQvi3DOXCqRh+Hfs56PV0Ht6bqI27cHR3pV6LxrR6/Xk2Tf0WgIA2TXD29aDJy13Z8cXPpESeQ6VR03lEbyNHcW8e7xLB4UNneL3nZPR6PZ9Mf4ufl2zBx8eNjp2akpZ2mR6vTcDcXMP7o3qi0ah5c/ALTJqwkN49JmFmpmH6rCHGDuOueESEceVsNPumzEGvh6aD3yB+y05s3Vyp2yyUBt1e4NCsrwDwbNmsQjJUHWj4WGNiT4h8894X6PV6un3Qi33rduPs6UrD1o24mnaF2m53OrpXR+o2C+fq2RgOTJ0Dej1hb/YhYesObN1ccW8aRki3Fzg6Zz4AHi2a4eDl+YB3ND72YU3IjzlPymcz0KOn7uv9ydq5HXPXOtiHhuPQvCUpc6aj0mhwaNkaSw9P0qdPRl9SQvovPwJgWccd9159jBzJv6cmLD1/5K7nd6P0UdV8oADYiOyTNRXZ/fwCECaKYsNbq7GANkBbURSnPui9i6VTNWCA7e5c0tZsu4j1yTW7ZP2QkJpxA7sbEyJvPninakybOjpjS6hUFLuI6s2GLu2qNPsYfGBPpdxnf2jbscriqJKRHVEUDwERt21eeZf9/Er/uav0T0FBQUFBQcGIKEvPFRQUFBQUFGo0NWGCcrVPdvy/umFsCZXGlbmmuVRY4eF4J3WisSVUGvNfmWtsCZXKpV9ut+SpWezbU+V1W6sUj8YWxpagYGJU+2RHQUFBQUFBofKoCROUjVVUUEFBQUFBQUGhSlBGdkpRAZ92DiLExQ5dicToHSIpNw1DvX1CPXitgRt6PXx5LIWdSVkMjfCmg6+84sbB0gxXGwsiFh02UgT/HpVKxZefDiA0xIdCXTFDRv9AYorpVzZ9WGpCfJIkMWXyAmLEZCwszJg2bTi+5WoJLfxhPZs378POzoaBg16iU6fmpF3IZMyYL9Hr9Xh41GHqJ0Oxtq5eq3BqwrnTSxLJK1aQn5aGyswM/z59sKpjMMdMXrWKnPh4NKVGkvWHDkWl0ZC8fDmFV68iFRfj17MndvXqGSuE+6ICpj3TgBA3e3QlEh9tPEfK9fyy9jeae/NqmCd6YP7eBHbFXcHaXMP8l0NxtDZHW1TCyN9Ok5VfZLQY7ocKGBUeQJCjLTpJz4wTcVzMK7hjn8/aNGD/pSx+T8rgjfpetHST6ybZmWtwtrLguS3Hql78I6ImzNl55CM7giB0FARBLwhC99u2nxYEYcmj/rxHxZMBLlhq1Ly05iQzDyYyoZ3BMNHJyow+oR68tCaKnutP82mnIAC+PX6B7r+eovuvp8jILeT97Xd4m1YLnn8yAitLczq+NIkJM1cyc8Lrxpb0SKkJ8e3YcZRCnY7Vq2fxwQd9mDXT4FUmisls2rSP1Wtm8+NPk/lq/kq02kJmz1lC9x5PsnzFDFq0bMSSxRuMGMG/oyacu+tRUUhFRTQcMwbvl18mZe3aCu15KSkEv/suDUaNosGoUZjZ2HDpzz+x9vCgwejR+PfpgzYjw0jqH8wTwXWwNFPz8k9HmbUjlo+fEMranKzNeSPCh1d+OkrvpX8z7RnZI6xnUy/OXMqm25Jj/HH2EiPam55B7S3aezhjoVEzeO9pvjubzDuN70w6Bzf0xcHCsNz9l9g0hu8/w/D9Z7ii1THteGxVSn7kSJX0V5VU1mOsGKDnrReCIDQGbCvpsx4JzT0c2ZOSBcDJjBxC3QwGddcLinly+XGKJT2uthZkFxZXOPapABduFhSzL/V6lWp+VLRpLvDXnlMAHDsZT7NQfyMrerTUhPgiI6Np164pAOHhAmfPxpe1yUa1jbC0tMDS0gJf37qIYjIJ8Rdo3162i2jaNJjIyGijaP8v1IRzlxMfT62GDQGw9/cnL8VgjqmXJAouXyZp2TLOzZrF5QOyqenN8+dRmZkR88UXXNy0qex4U6S5jxN7468CcPLiTRp7OJS1XdcW8b/vD8nXTjtLsgvka+dPR1P4er9stuzhaM2V3MI739hECHN24GimfG0/dz2HYCe7Cu2dPJzR6/Ucybzz+t/Bw5mcomKOXq65C2mqC5WV7JwCfARBqFX6+nVgOYAgCK8JgnBYEIQDgiDMLN02RxCE6YIgqAVB2CkIwjOVpOue2FloyCk0mPOV6PVoys3JKtFD31APNnRrwpbSL/YthjX3Zt7R5CpS+uixt7PmZo5h2LmkRCqrEF0TqAnx5eXmY29n8F/TlDcCFXw5fvwcublarl/P5uTJGLTaAoJD6rFrlzx0vmvn32i11W8FTk04dyUFBQYHbORHc+WNJN07dyZgwACC332Xy3v3kp+WRlFuLiX5+QS/9x61wsJIXbfOWPIfiJ2lGTnlfgDK105Vhdd9mvvw28CWbI02PIKU9LCiTwR9W/iwJ67iNdWUsDHXkFt093uDv4MNXb1dWXj+7gVg+whe/Bhd/YvD1gQj0Mq8aqwHXhIEQQW0AA4BtZFNPx8XRbEt4CkIQldgHNAZ2VbimCiKmytR113J1ZVgZ2Ew51OjouS2k/Hz6XQiFh2mpacjrb3kPC6otg3ZhSUV5vdUN3JytdjblXPUVqsoKakJZaRkakJ8tnY2FYxqpQpGtd707v00g9+cyqxZiwkNq4+TkwMffdSfXbuOMWjgFNRqFU5ODvd6e5OlJpw7jZUVJYUVjUDLG0m6P/44GktLNFZWOAgCeRcuYGZri1NYGABOoaHklhsNMjVyC4uxLX/tVKkoua0y/9K/U2kxdw8tfJxo7WeoLN5r6XG6LT7Gd93Cq0zvPyW/qAQbs9vjk//9lE8dXK0t+apdY572qUOPQE9ausn3Bj97a3J0xXfM71EwDpWZ7KwAegDtkY0/QZ4Q7QpsEQRhD9AA8BdFsQj4AugOfFmJmu7J8Us36VT6JWzibk/MtbyyNv9a1ix4pgEARZKewhIJqfTL3Nbbid3JWVUv+BFy+HgsT3aSLzYtmgRyNuaCkRU9WmpCfE2bBrN3XyQAUVEi9ev7lrVlZd3k+vUcVqycwfjxg8i4dJWgIB8OHTrF8GE9WPTjJFRqFW3ahBlL/r+mJpw7+4AAbpyRjSRzEhOx8TT4QRVkZnJ+9mz0koRUXExOfDy2vr7YBwaWHZMdF4dNXdM1Pz1+4QadglwBaOLpiJiZU9bm72zD96WJTJGkR1d67Rzath4vhcox5ReV3JEcmRKnr2XT2l2ebNzQyZ6Em4Z7w7dnk3lzzymG7z/DltTLrIq/yNFM+ZFV8zq17vpoqzoi6VWV8leVVNpqLFEUEwVBsAXeAcYC/oAe2QurqyiKRYIg9AOiBEFwQh7deR9YCDxXWbruxbb4q7TzcWL9a+GoVCpG/RXDoCZepNzQ8lfSNaKv5vF7tybogT3JWRy9KHsD+TtZs7+aztW5xYZtf9O5XWN2r5+CSgWDRy0wtqRHSk2Ir2vXVhw6eIoePT5Cr4cZ00ewePEGfH3q0qlzcy6kZfDqK6MwNzfjw9H90Gg01KvnybhxX2FhYU5gkDcTJ75l7DD+MTXh3Dk1acLN6GjOzZwJgH/fvlz66y+sXF1xCg/HuUULzs2YgUqjwaV1a2w8PPB8+mkSly7l3MyZqDQaAvr3N3IU9+bP6Eza+Tvz64AWqFDx4YazDGzlS0pWPjtirxCdmcNvA1ui18Oe+KscTblOwtU85r7YmO5NvFCr5GNMlb3p12hepxYLOoTKq3Yj4+gR6EFaXgEHLt37h66PnTV/K3N1TIZHbgQqCEJH4G1RFHsIgjACeEMUxRaCIDyFPNKzAxgKaIBkoD+wDFgviuKy0hVbJ0r3Gy6K4tD7fZ7Pl3tN9yfBf+TK3O+NLUHhP5Bfgyso2/g80KO3WvOaUkG5WuPR2KTXw/xnDr3ctkqHRbrt3lcp99k1ndpXXyNQURT3AHtK//0V8FXpv7cB20p3W3bbYS+XO75fue33TXQUFBQUFBQUKhelzo6CgoKCgoKCgomjVFBWUFBQUFBQuCf6GuCNVe2TnWODbxpbQqXxTugQY0uoVCKcTbeQ2KOgSMp/8E7VlLyUCcaWUKkM2H/F2BIqlemv14DnEvehZ4CrsSUomBjVPtlRUFBQUFBQqDxqwpwdJdlRUFBQUFBQuCfVq4zn3VGSnVIkSWLe9N+Ij03HwtyMDye9hpePS1n7qp/3sHNbFCqVitcHdaZ958bo9XpefWJa2X4Nw3wZ/M7TxgrhvugliYsrl6MtdV72fqMvluWcly+uXkleQjxqS9l5ud7QYWisZXuC3LhYUn9aRIMZs42i/WHQSxKHFq3mWvJFNOZmtHu7Nw51DUPZF06e4+TarQA41/OmzaBuqEpL2t+4mMHGsZ/Ra9EMzMqZ+ZkSkiQxbepixJgULCzMmfLJm/j4upe1/7hwI1u3HMbW1poBA5+lQ6em5OcXMG3KT1y8eIWiomLGju9L49BAI0ZxbyRJYuqUBcTEJGNhYc4n04ZVdHVfuJ4tm/dja2fDwIEvyq7uaZmMGTMf9Ho8PFyZMtU0Xd31kkTS8hXkpaWhNjPDv28frMt995JWVnQ9F4bJrueJy2TXc32J7Hpub6Ku53pJYvM3a8lMSkdjbsbz7/agtofhuxf393n2rpAX4tYN9OLpoa9RkJvP+jm/UJhfgI2DLc+90wPbWvb3+gijIkkSUyZ/T4yYhIWFOdOmDcfX16OsfeEPv7J58z7s7KwZOOgVuW9eyGDMmC/Q68HDw5Wpnww3yb75/4lHluyU1tfZDfQQRXF1ue2nkevmOIii+PI9jt2DXJvHaLbhB3afQ1dYxHdLR3DudArffv4H07+QC3nlZGv5deVBVvzxEQVaHQO7z6N958ZcvHCNoBBPZs4fYCzZD83NU7LzctBHY8lLTCB93RrqDR1e1p6fmor/O+9hZlfxgqPLyuLKX9vLvHxMlZS/T1OiK+b56aO4HJvE0aXr6fqRXERPpy3g2C+/88zkd7FysOP0hr8oyM7F2tEeXb6Woz//hsbctPP+XTuOU1hYxPJVUzkVFcec2cv56psPAIiNTWXL5kOsWC3Xvnmj52RatGrIkp82ERjkzfRZQxHFVGJjUkw22dmx4yiFhUWsWj2LqCiR2bMW88234wCIFVPYvGk/q9fMAqBnz7G0ahXKnNk/06P7kzz7XHvWrv2LJUs2MmTIa8YM465klbqeNx47hpyERFLWrCV4+LCy9rzUFELeexdze8N378LGjdh4ehA0cAB5aWnkX7hgsslOzOEzFBcVM/DzkaTFJLN90e/0mPgmAIX5Bfz10wb6zRyBjaMdB9fuJD87j4Nrd+DT0J923Z8g8aTIziWbeP69ng/4JOOwY8cRCnU6Vq+eQ1RUDLNm/sS3330MgCgms2nTXtas/QyAnj1G06pVKLPnLKF7j//x3HMdWLt2O0sW/86Qod2NGcZ/oiY8xnrUS8/v6XZ+r0THVDh9MokWjwUD0DDUF/FcWlmbtbUFbnWdKNDq0Gp1ZSMCsdFpXL2czbuDvmf0sB9JTb5sFO0PQ158HPYNGwFg6x9A/m3Oy7rLmaQt+4W42TO5dlB2XpaKikhbsQyvnr2NovmfkBGdgGeTEADq1K/H1QSD+d5lMZHaPh4cXbqeTRPmYe1oj7WjPXq9ngMLVhLR6znMLC2MJf2hOHFCpG3bUADCwoM4fzaxrC0xIZ3mLULKXM99fN2JFVM5eOA05uZmvDVoBgu+W0+b0uNNkROR0bRt1wS45eqeUNaWkJhGixYN73R1T7hAu/ayE3zTpsGcMFFX95y4eGo1KnU9D/Cv4HOllyQKMi+T+Msyzs40uJ7fOHcetZkZ5+d9QZqJu56nnksksJn83fMK9iM9zmDpcSE6CTc/D7Yv+p3FH36JrZM9to52XEnNIDBCtuDxblCP1POJd31vUyAyMpp27eR+Fh4ezNmz8WVtiQlptGjRuFzf9EAUk0iIv0D79s0AaNo0hMjI80bRrmDgUSc793M7zyj9b0tBEI4IgnBUEIT1giDccvmbJAjCrtLt/o9Y1wPJzyvE1s6q7LW6nKs0QB03R/q8/Blv9viCV3q2BcDZxYHeAzrx5aK3eX1QZ6aNW1nVsh8a6XbnZbW6gvOyS6fO+AwYiP8773Ft7x60aWlcXLWCOl2fwNzJyViyH5oibQEWNhXjk0rjK8jJ49LZWJr3fpEnxw3l7OY93EzP5OTaLXg3bYSzn5exZD80ebla7OwNrufl+2f9+t5EHo8hL0/Ljes5REXFotUWcuN6DtnZeSxYNJaOHZsyd/ZyY8l/ILl5Wuzt7+HqXt+H48fPk1fq6h51MgattpDg4HrsvuXqvst0Xd3vcD1X3+l6HjhwACHvvUvGnr3kpaVRnJtLcV4+DUa+R+3QMJLXmq7reWF+AZY2hmunSq0q++7lZ+eRdDqOLv2fp/fUtzn6+x6upV3G3d8T8Yjs/SUeOUtRoc4o2h+GvNx87O0MFZkr9E3Bl+PHz5Kbm8/169mcvNU3Q+qxa9dRAHbtPIZWW71XntYEb6zKKCp4N7fz8vwA9BdFsSWyJURI6fbNoih2BrYCr1aCrvtiY2tJfl45Z+JyrtJHD8Zw7WoOqzaPZc228RzYc47oM6kIDbxo20n+xRXapB5Xr2TzqO03HhVqKyukgnI3A71UwXnZpXMX1Bay87KdEIz2Qip58XFkbPqD+LlzKMnLI2XRD0ZS/2DMra0o0lZ0llaXxmdlZ4tLoC82Tg6YW1viHhLIteSLxO/7m9hdh9g86Qu0N7LZNu1rY8l/ILZ21uSVc08u73ruH+BJz15PMGTwLObMXk5oaCBOTvbUqmVPx87yL9IOnZpy7lySUbQ/DHa2944vIMCbXr2fZvDgqcyetYTQ0Po4Odkz+qN+7Nr1N28OmoparaKWibq6a6ysKCkod7OTKrqe1+1icD13DBbIv+V6Hl7qeh4WSp4Ju55b2lih01a8dt767tnY2+IZ5INdbQcsrC3xaRRARuJF2nbrys3MLJaO+4bsqzdwdDHdH1S2djbk5WnLXt/eN3v3fobBb05h1qyfCA2rj5OTAx99NIBdu44xaOAk1GoVTibaNx8WqZL+qpLKSHbu5nZeHjdRFKMBRFH8VhTFE6XbI0v/mwHY3OW4SqVxuB9HD8jD4OdOp1AvyDD5087BGktLcywszLC0NMfO3orcHC1LFvzF2mVyiPFiOm7utcoecZkatgGBZJ+Vf0nlJSZg5WkYzSjMzCR+ziz0koS+pJi8hHhsfH0JnjKNwA8+JPCDD9HY2uI7aLCx5D8Qt2B/0k6cA+BybBK1fQwTCJ39vbmeeomC7FykkhKuxCVRy8udbl9P5pkp7/HMlPewruXAUx8Pv9fbG50mTQX274sC4FRUHEH1vcvasrKyuX49h6XLJzNmXB8yMq4RGORNk2YC+/fKx0QejyEg0POu720KNG0awr695V3dfcraZFf3bJavmMG48QPJyDC4ug8b3p2FiyaiVqlN1tXdPrCc63lCIjZehvOgzczk7CyD63l2fDy2Pr7YB1V0Pbf2MF3Xc+8G9Yg7Lj+mSYtJxs3P8N2rG+TN5ZRL5N+Uv3sXY1Jw9XEj5WwCoY+3oM/0YdRyq413A9OcjwRy39y77zgAUVEx1K/vW9Ym980cVqycxfjxb5Jx6VbfjGL4sB4s+nEKKrWKNm3CjSVfoZTK8Ma6m9t5edIFQQgSRTFOEISPgNjS7UYdEmnXuRHHj8QxtM/X6NEzZkp3Vv+yFy9vFx7r2JDII3EMeeMrVGoVoeH1iGhdn+BG3kwbt5IjB2LQaNSMmWq6E9Acw5uQG32euNkzQa/Hu28/ruzYjoVrHRzDwnFq0ZK4WdNRacxwatUKKw/TvTHeDb8WYaSfjuGP8XPR6/W0H/Y6Z/7YiYO7K77NQ4no/Rzbpn0DQL02TSskQ9WBx7tEcPjQGV7vOQm9Xs8n09/i5yWb8fFxp2OnpqSlXabHax9jbm7G+6N6odGoeXPwC0yasJDePSZiZmbG9FmmW6SyS9eWHDoURc8eY9Dr9UyfMYIlizfg41u3dHVLJq+9+iHm5maM+rBvmav7+HFfy67ugd5MmGiayXjtJk24eT6aMzNngh4C+/UlfftfWNVxpXZ4OC4tW3Bm+gxUZhpcW7XGxrPU9fznpZyZIbueBw4wXdfzkDahJJ4U+fGDeaCHF0b24vD63dT2cEFo1ZjH+z3HsgnfAdCgXRPq+HlgZmHOb3Nli0QH51omOzkZoGvXVhw6GEWPHqPR6/XMmP4uixf/jq9PXTp1bsGFtAxefeV9zM3N+HB0v7K+OW7cV1hYmBEY5MPEidXbWLYmTFB+ZK7nD+F2/pQoiu6CIDQHPkcexboE9AX+LD02RhCEtwF3URQnC4KwHPLfpQAAIABJREFUHXhWFMV7PtDN0G6sAafh7rxzpNaDd6rG1PQKyu81qm1sCZWGmcr6wTtVY2p6BeUnPLUP3qka0zPAdEeKHgUqhCp9hPDknwcq5T7755NV597+yJIdY6EkO9UXJdmpvijJTvVGSXaqN1Wd7HTddrBS7rN/PfVYlcWhuJ4rKCgoKCgo1GhMu5LaQ5CWpzG2hErjrOkunnkkJF2p2aMDiwYfNbaESmPaiiqvDlGlvOJXYweMAZh0wM7YEiqVxz1EY0uoVNyshSr9vJowZ6faJzsKCgoKCgoKlUdNSHaUx1gKCgoKCgoKNRplZKcUSZJY/NmvpManY25hxqAx3XD3ks3skmMvsmz+72X7xp9LYeSM/ghh/iz+7FeupF+juLiEviNfIqCB770+wqiogIltAhFq26ErkZh4IJbUnII79vn+iUbsSrnGavFS2fbHfZ150s+V0XuNZl32QFTAR80CCHK0pUjSM+14HGm5d8b3RbsG7E3PYn1CBmoVjAzzJ6S2HeZqFQvPpXLg0nWj6H8QKhVM/rADwUHO6IpKGD99N6lp2RX2caplxeqFL/Ns79XodIbq3/6+tVj74yu0fnpJhe2miCRJbPpmLRmJsqnki+/1wLnUVPJSQhpbFvxWtm9aTDK9Jg4iKCLkXm9nEkiSxPr567iUKJvUdnu/By6eckwX49PY8J0hptToFPpNGUhwczmmfev3kJOVwzODnjOK9odBBYxtHkh9J1t0JRKfHI3jwl2+e/M7NmRP2jV+jc8o2+7nYM3PT4TTdf0RdCY6fCBJEp9P/42E2HTMzc0YfZtJ9MpSk2h1OZPokhKJrz/7A/H8BYqKSuj/dlfatG9gxCj+G9V8HRNQScnOg0xBRVHsVxmf+1+I3HeWIl0xU354l7izySz/aiMfzBoIgF99Tz7+WjbuO7orilouDoS1CuHXH7fh5e/OkAm9SI1PJyU+3WSTncd9nbHQqOm1KYpQV3tGt/Bn+M6Kfi3vNvPD0bJilxjbMoDHPJ2IycqtSrn/mI6ezliq1QzcdZpGte15L6weow5W9Eoa0tgXh3Ku5k/71sFMrWLQrtO4WlvQxcsFMM1kp2sHfywtNXR/cz1hDd0Y885jDB29tay9bUtvRg1thUvtivU4bW3MGfPOYyaf5Nwi+vAZinXFDJ43kgvRyWxb+Du9J8mmknUDvBg4ewQAZ/efxN7ZweQTHYBzB89QrCtixPyRpJxP5o8FG+g/dRAAnoFeDJ0rx3RqbxSOzo4ENw+hqFDH2nmrSY1OoXE70yyWeItOXvK1pd/2UzR2tmdkU3/e31fx2jIszBcHi4rXFlszDSOb+FMkVXUt3X/G/ttMor/5/A9m3GYSvbLUJHpAqUn09k2RlBSX8O3Pw7mSeZPdf502chQKlfkY656moKaIeDqJsFayEWhQIz+SYi7csU+BtpB1P/5J3/deAuD0UREzMw0zRy7gtyV/EdqyaieN/ROaujlyIE2+kZ++kkNDl4ru5k/4uSDpYX9axZv9ycvZTD0UV2U6/y1hLg4cypC1n83KIcSp4gTMzl7OSHp92T4ArdydyNQWMq9tA8ZHBLIvPatKNf8TmoW5s/+wbG566lwmjYNdK7RLej39RmzkRnbFX9TTxnbk8++OoC0srjKt/4XyppLeIX5cjLvze6grKGTXL1t55u1XqlrevyLpXCJC6UiNbwM/LsTeGVOhtpA/l27lxWGyX3KRrphmXZrzeK+uVar13xBex4FDpSOiZ67l0KB2xe/e497yteXQbaOmH7cM4utTyRQUm3ayc+ZkEi3vYxLtfheT6GOHY3F1c2T08B+ZPXUdj3Uw/aT8fuj1lfNXlVRmsnM/U9DegiD8LQjCAUEQFguCYC4IQj9BEPaVbnu8EnXdFW1eAf/H3nmHR1G1ffje3fRNQgLpIT0wlFR6777qq2J5lV6kKIoFRFGKKCAIAhbELkgREFGKKBakSpeWUJJMeiO00NI22WRnvj8mZBNqUJLd5Nv7unKxu1P293DOmTl7zpnnZ6+tagRqKKv6a3jnLwdp3zMSJxelMedfLaQwX8ekD8fQqnMLVn/yc61qvhscrTUUlBpveJIsoynPcBDq4sBDwR4sOpp+w3G/p10wbWrraqK11lBYaiyvyvGFODvwgL87X57MrHKMi40V/o72vLInjhUJ2bzVrkltSr4rHLU25Bcac2saJBmNxpiiYt/f2VzJq5q36KXRbdm5N4OE5Iu1pvPfUlJUjF3ldqhWYTBUbYdH/jhAy65RaBvUjSeKigtL7hjT378fILKbMSYHJweENs1qVec/RWtlRYHeeG0xVG57DRx4MNCdz49X9fYaE+7PntOXSLpSWJtS/xGF1TCJHvrEAkYP+Igny02ir14uJDszl/cWjWTwiB7MeXttreu+l9SHzk5Nr9m5Zgq6DMUU9D0gGpgBRIuimC8IwofAGKAAuCyK4qM1rOmm2GvtKC4y3iwkSUZjVfWx9n1bjjJu1vCK947ODrTuohiBturckk0rt9eO2H9AQakBrbUxHpVKhaG8sj0a6omn1oalD0bg62hHqSRxuqCYPafNc0rnZhSWGnCwunl8/w30wN3els97hOOttaVUkjlTWMxVfRm7y0dzjl7II8DRfB+FLyjUo3UwTsEpN8zbXy363t+UsxcKeLJvc9wbOrB04SMMfn7jbY8xNbYOdpRcZyqp0VRth8d3HGHAVPO1T7geO61t1ZjkG2M6uv0Iw6fVnZgqU1hWVuXaoq7U9h4O8sDd3oYve4fjo1WuLWcKS/hvoAfnikp4NMSTRvY2fNYrnNFbzXOqR3sbk+gD5SbR32+eDMBrYxcTFhWIs4sDHbs1R6VSEdUmhOyM+p2ksi5Q052d1cDnQCpGU1A1cEoUxfzy938B/wEOAiZLjtA0PJCje+Po0DuKpJPp+IVUNd4rKtBRqi+jkafRnVeIDCZmfzxBzfyIj02lcZDX9ac1G46dy6OHf0N+T8slwt2JpMvGX1TvHzYm9HkhOoDcIn2d6ugAxObm0c2nIVuzcwlr6ETKVWN8i46nV7x+pqU/F4v17D97hcaO9nT2dmXH6Ys0aaDlbJH5ZnQ+cvwsvboE8tu2FCJbepKYcufRmvueWlXxevuGIYwYZ74jj9fwbxGEePAU4d2iyYpPxzOoqodZcaGOstIyGribr0v29QS2DCZu/0miukeTEZeOV1DVa4uuUIdBX4aLR92JqTIxF/Lo5tuIPzNzCW/kRHKl0ZqFMekVr8eE+5Or07PvzGUe/flwxee/9G3L2O0nalPyXREWFci+XXH0uj+SU8czCK5kEu3kbI9NuUm0SqWqMIkOjw7iwJ4EevSJIFnMwcOrbpbtNeq60wLUcGfnFqagMtBCEAStKIqFQHeMZqAmm7xt0z2cE4cSmT7mY2RZZszUAfy6Zieevm607hrGmawLuHtXTf//6LDefD1nLW8/uxCNlYbnpw0ykfo7szUjl06+Lqx6KBKVSsXU3SLDW/qSmadjR5b5rlWpLjtPX6S9lwtLekUAMPNQEoOa+pBdUHzLtTgbU88yqXUo3/SOQIWKOUeSa1PyXfHnzlQ6t/VjzVdPoFLB5FnbGTEwkozsq2zfnW5qefeM5p0iSDkm8tUExVTy8QmD2FtuKtm8Qzi52edx8axbNhxhncNJPCKyaNxHIMv0f20Qu37cgZuPOy07hXEh+wKuXnUrpsrsyLpIBy9Xlt4XqTw1eCCRwc18ycrX8dfpun9t6VZuEv38sE+gkkm0r58bXcpNop8bugi1WkV4VBBtOzYlqk0IH8xez3NDFyHLMq+++YSpw/h/T414Y1XDFHQLMB6lc5MMjC7/vJkoipPKzzEP+FEUxb9v912HczfX/S7nLRi2yenOO9VhtI71O83T1Q/Mc1j+XlDfMyjbW9XbywpQ/zMo//Z43RqZvls87fvWqjdWl59qxgh0z6O1ZwRaIyM7oijuBHaWv14ELCp//Tvwe/luq687bNl153i9JrRZsGDBggULFqqPbN4PzFWL+v3T2oIFCxYsWLDw/x5LBmULFixYsGDBwi2pB+uT635np7lL/Z17frWzztQSahQn63rQgm5D6C/mmU37XrAm1cbUEmqUkU2LTC2hRsm9UA/mJW7Dq383MLWEGmVld1MrqHvU+c6OBQsWLFiwYKHmMFPbsrvCsmbHggULFixYsFCvsYzslCNJEnPeWU2imI2NjRXTZgzDP8CjYvuyxb/z+69/o3W0Z/jI++nWI4IzOReZMW05BoOELMu8OX0ogWaaWFCWJLZ88QMX0hTn5QdeHIirj9FfKfVIHHu/U4wlPUP8uO+5p9AXFbNp/jJKi/VorDU8NGEYjq7Opgqh2tRX5+xvFqwjMzkHKxsrnp3UD6/GSkzpiadZ8bExM3LyqQwmzBlB7IEEMpJzALh6MR8HRzve+Xq8SfRXF1mSOL58DVczs1FbWRE1egiOnsZ2eC72JOKGzQA0CPQnYviACj+iuoAkSXwxbz3pSTlY21jx4pR+ePsZHbQ3rNzB7i0xqNQqnny6Nx17hJtQbfVQAbN7NaG5myN6g8TrW0Uyrho92oZF+PBUC09kGRb+ncG2tEuMbeNH9wAlt5CzrRXuDja0WbzfRBHcHlmSyFi9mqJspU4GDhuGnYexTmasWUNBcjIaO8VSInTsWJBlTrz5Jva+vgC4REfj1bvWXZDuGZY1O7egPM/ORiBcFMWs8s/mAgmiKC6rie/8t+zYFoO+pJTlqydxPDaVD+f/wIflTudJidn8tvlvVqxRUoKPGDyXtu0FPl+0if6DetKzdzT79pxi0UcbeH/h86YM45YkHTiBQV/KkPkTyElIY8c3G3jizWcBxY9o59KfGPDuSzg4O3Jw3VZ0eQXE7TqCe4APPUY8Suwf+/h7/TZ6jXrcxJHcmfronH34r5OU6suY+dU4kk6ms3LRJl57bxQAgU19eau8rh7YHoOrmzNRHZoT1UGJq6zMwPTnF/HMpH4m019dzhyJxaAvpdvbr3MpOZVTq9fR/hWlTZXqijn13Xo6T52ArZMjSb9sQZ9fgK1z3clHdXDXSUr1pcxb8jLiiQy+WbiJqQtGAlCQr+OXtXv4Yt1kSnR6xg/9oE50du4PccNWo+bxtceI9nJiWtcQRv9yCgBXOyuGRfjwwOoj2GrUbBvahm1pB/nscBafHVYMUZf2DWPOnlRThnBbLsfEIJWW0mLSJApSU8n64QeavPBCxfaijAyajhuHtZOxHl6Ni6Nhu3YEDBx4s1PWOSydndujB5YKgnCfKIpm/18VczSZTuU+VxGRwcSdMhrXpaWepU27ptjaKt5EfgGeJCWe5pXXn8Sx3E/JYDBga2N944nNhOz4FIJaKTc/n2ZBnE02Oi/nJKThFuDNjiUbuXoul4j7OuLQwAn3AB8uZZ8DlA7R9V5h5srdOGePmv9ybcv7R4jH04jsoBhDNgkLJDXhxpiKdSX8uOQP3vr0hSqf//HjbiLaCviH+NxwjLlxKTEFj4gWADQMDeZKmrEdXk5KxdnPl1Orf6TwfC4BPTrXqY4OQFxsGtHl5SiEB5BcqRzt7G3w8HKlRKenuJKDtrnT1qcBOzOUTMnHzuYT4Wksk8vFZdy/6jAGGRo725BXUlbl2AdC3LhaXMZfmeabBLAgOZkGLZV7g2NwMIUZxjopSxLF58+TvnIlZXl5uHXujHuXLhRlZlKUmUnC/PlYOTnhP2AANi4ut/oKC7VATa7Z2Q5cAqpceQVBmCMIwp+CIBwQBGFp+Wf7BEFoWf76QUEQPq1BXTelsLAYRyejEaRGrapwtg1t4svRw0kUFhZz5UoBx2NS0OlKcHV1wtraivS0s3y44EeeHftwbcuuNiVFxdhqjfGp1GqkcuflorwCsk4k0f3pvjz59vMc3rSTS6fPY++sJT0mgSUvzObQhm2E39fBVPLvivronK0rLMZBW9V52VBWNaadvxykfc9InCs9oVhWWsa2jft5eFCP2pL6ryjV6bB2uHk9LckvIDc+kRb9H6fjxBdJ/WM7BWfOmUrqP6KosLiqg7a6ajm6ebrw4oB5TBj+IQ/372IKiXeNo42G/BJjDJVdz5X3MDzCh5/6RfNrcm6VY19o68eHB9NrSek/w1BcjMa+Up1UqZDL66Sk1+PZqxfBI0fSdNw4zu/aRVF2NnZeXvg88gjNJk7ENTqazDVrTCX/nlAfXM9reoHy88ArgiA0KX/vjOJsfh/QCeggCIIv8DVwzU58JLCkhnXdgFZrR2ElZ1tJNjrbBod4039QT14as5AP5/1AWHgQruU3lEMHE5jw8mfMmjPSbNfrgOImrdcZ59FlWUJd7rxs76TFq4k/jq7O2Njb4hcWyvm0bPZ+9xvtnujDqE+n8tSMsfw05xtTyb8rquuc3eaBjrUt7R9jr7VDV3RdTNeNtO3ZcpRej7Sv8tmJQ4k0iwrGwYwd3StjbW9PWXHVOK/VUxsnLS5BAdi5NMDKzo5GQhOuZmabSuo/wuE25XhkXzyXcvP4asNUFv/0Jgd3nSTxVKappFabAr0BR5tKrucYXc+vsfx4Dm0W76e9bwM6NlZGOJo0dCCvxFBlfY85orGzQyqp6lqvKq+TahsbPHv3RmNri8bODmdBoCgrC+dmzXBupozguURFUZRp/uVY36nRzo4oihdRPLCWlX+XDvAQBOE74EvAEbAGvgf6CoLgAfiJoni0JnXdjKjoEPb+pTjvHo9NJbSJb8W2y5fyuXK5gG9WvsFrk/tz7uwlQpr4cuhgAvPnfs8nX46jRVhgbUu+K3ybB5N6OA5Qpq3cA4xTGl6hfuRmnKEorwDJYCBHTKeRnxd2jg7YOii/Qh1cnCjRmfdF6Rr+LYJIOqTEWl+cs5uGBxKzPx6ApJPp+IVUdc4uKtBRpi+jkWfVmE4eTqpYu1MXaNg0mHMxJwG4lJyKs5+x7FwC/cnPzqEkX6mnl5LTcPIx3x8YN6N5RBBH9inlKJ7IICDUWI6OTg7Y2lpjbWOFja01Wkd7CvPNP9fW4TNX6RmoLDaO9nIi4aLR9TzYxZ4vH1KmJUslmRKDhFT+k76Lnys70s3fKNQxJIQrJ5R7Q0FqKg6+xntD8blzxM+bhyxJSGVl5Ccnow0IIG3FCi4fVW5j+QkJOATU7ZxbsiTXyF9tUuNPY4mi+LMgCI8DTwOzUDoz/QVBcAceB1SiKBYJgrADWAh8W9OabkbPPtEc2B/P04PnIsswfdZwVi77Ez9/d7r1jCQ7+wJD+s3G2tqKca89iUajZsHctZSVlvH2lKUABAR68ub0oaaQf0eadoggPUZk5esfgAwPjhvMoY3bcfF2p0n7cLoNe4Qf3v4MgGZdonEP8KHL4If445PvOPbrHiSDgQdeGGDiKKpHfXTObts9nBOHEnlrzMcgy4yZOoDNa3bi6etGm65hnMm6gLv3jTHlZJ6n6wNtTKD4n+HdOooLJxP4a8Z8QCb6mWEk/7YVrac73q0iad7vUfbP+xgA3/atcfbzvf0JzYwOPcKI+TuR10d/DDK8PK0/P63ehVfjRrTvFkbsoUQmjvoYtUpF88ggoto3NbXkO/J7ci5d/V1Z/1QUKpWK1/5MYHR0YzKu6Pgz7SLxuYVs7BeNDOxMv8TB01cBCHa1Z7cZr9W5hmt0NHnx8cTNnQtA0PDhnP3zT2zd3XGNiqJRu3bEzZmDSqPBrWNH7H188HviCdKWLeP8zp2obWwIHDbMxFH8O+rDAuUadz0vf+8MnAA+B/4HGIASwB54RRTFvYIgtAL2At6iKF4RBKEF8KIoimNv912FZbvqQTHcnDUpdWMk5Z9S7zMoOxvuvFMdZU1q3ZgW+6fU9wzK/1letxZ23y3dokytoGZZ2b17ra5eb/3d7hq5WB8Z2LX+uJ6Xv88Dro3jzb3FYRrgB1EUr5QfEwfctqNjwYIFCxYsWKhZ6sPIjlkkFRQE4UWUhcn/M7UWCxYsWLBgwUL9wiw6O6IofgJ88k+Otde433mnOkpvnxRTS6hRSqW6kUfkn9LIrh78HLoFb0QUmFpCjdJ+wBVTS6hR2r1s/pnQ/w2d3Ov3EoDapj54Y5lFZ8eCBQsWLFiwYJ7Uh2ksixGoBQsWLFiwYKFeYxnZsWDBggULFizckvowsmPp7JQjSRIzZ3xJQkI6NjbWvDPrBQICjAm/vv56Pb9u3o3W0YFRox6jZ8+2ZGefY9IkJe+Jj487M2aOxd7e1oRR3BpJklg0dz1pSWewttYwflo/fCu5Lf/w7U52/nEMtVrFgBG96dwznMICHe9NW01RYQllpQaefeURWkQEmi6I2yBJEp+9t560cjfpl6f2w6dSfOtX7mDXlhjUKhVPPd2bTj2NBov7dpxg77ZYJs4aYgrp1UKSJObPXk+yeAZrGw2Tp/fDz98Y36plO/nzN6X8ho3uTY/e4RTk65j2+kqKdXqsrK2YPmcgjdzMb62GJEksmL2BpMQcbGysmPz2UzSuFNvq5Tv58zfFCXz4qF507x3OiiXbObhPBBQDzYu5+fyy/W1ThVBtVCqYMaY9zQMboi8zMOWT/WScza/YPuKR5jzUNRCAXUdOs+j74yZSWn1kSSJnzSp05a7gvkOGY1vJFTzn++8oTDG6ggc8/wIaewcACpMSyVq6mGbvzjOJ9uogSxLbv1xLbvppNNZW9HlhEC7exrWi6UdOcXDt7wC4Bzem57P9kCWZ3UvXcy4lE0NpGe37/5fgtmGmCsECJurs3M4VHZgrimKtp0XduvUgJSWlrPn+PWJiROa9t5RPP5sCQKKYweZfdvP92vcAGDhwMh06RDB/3nIG9L+fhx/pxg8//MmyZZt4/vmnalt6tdi38xSl+jI+WvoS8Scy+OrDn5nxwQhAuVn8tGYPSzdOolinZ+ygD+jcM5x1q/4iqm0TnhjUjaz088yduopPV71i4khuzoFyN+n3v3mZhBMZLFm4iWmV3KR//n4PX61X3KRfGvJBRWfny/c3cuxAAkFNzTs53V/bT6EvKePrlS9xMjaDRQt+Zt7HSvnl5+n4YfUeftg8CZ1Oz/CnPqBH73A2/3SYkCbevDjhYX768QCrlu3k5df6mjiSG/lr+yn0+lK+/vYlTh7P4OP3f2bewsqx7WXtL28osfX7kO69wxk2qhfDRvUC4LUXv+H5cQ+ZMoRqc197f2xtNDw16TeimroxeUQbnpuzAwA/T0f6dg/if6//hizLrHn3AbYcyETMMO/F0nmxiit46OuTKUpN4cy6tQQ+/2LFdl1WJkEvj8fKsWpuH/2lS1zYuqXCZ8pcSTl4HENpKf3fe5UzYhq7l27gkSnPAqDXFbNn+U/8b9bL2Ds7cnjDVnR5BaQfOYXBYKDfnAkUXLxC0r5jJo7i31EfRnZMuWbnmiu6WTySc/RIPF26RgMQFSVw8qTxSaiU1GzatWuJra0NtrY2BAR4I4rppKRk0bVbKwBatWrG0SPxJtFeHU7FpNGmowBA8/AAkuKvc1v2dqX4mtuyWimSJwZ146EnFP8og0HC2tZ8BwJPxaTRqqPiRdPsJvG5V3KTVldyk24eEcDYN56sdb13S+yxNDp0VsovLDKA+DhjfPb2Nnh5u6LT6SkuMrplhzTxoqjc762wsLjC683ciD2WRvtOStmFRQSQcMrod1UltuvKDmDn1hM4OdtX/N+YO22ae/DX0RwAYhJzCQttVLHtTG4hI2dsQ5JkZBmsNGpKSs27IwBQmJKEUwtl1MIhOATdda7g+vPnOL3qW1Lmz+XSvj0ASKWl5Hy3Et+Bg02i+W7IiU8lIFqxvPAWgjiXYvS5OpOQRqMAb3Yv3cAPUz7EoYETDg2cyDgWj2MjF36a9TlbP/uOoDaWUR1TY8q713aUztYL/MPHzu8lBYU6nJwcKt5rNGrKygxYWWlo2tSfr79aR2GBDn1pKTHHEujX7z80axbEju1/89jjvdi+/RA6M/aOupXb8jUTQndPF559aj4GSWLA070BKlzgL+XmMW/aap579dHaF15NdNfFp7lJfM/3n4ckyTw1vFfFft3ui+b4keRa13u3FBYU43hdfNfqJ4CHlwuDHpuPZJAYOlopvwYuWv7en8jAx+aRd1XHF8vMM0dnUWEJjk6VYtNcH1sDBj++AINBqhjNucaKb7Yzc6753zCv4ehgTX6RvuK9JMlo1CoMkkyZQeZyvtI5nfR0a+LSLpGek3+rU5kNku46V3C1GtlgQKXRIOn1NOrRC7c+9yFLMmkfLsDeP5CLO7bh1uc/WLuYvz+dXldc4REISnySwYBao0GXV0D2ySQGfTAJaztbfpz6Ed5CELr8Qq6cuUDfqc9x+lQyf36yiqdmjzdhFP8OWTK1gn+PqX+qPw/8LQjCHybWgaPWnsJCY2dFkoyu5yEhfgwa/F+efXYm/v7eREQ0xdXVidffeJpZ73zN5s176NAxHBdX81sPcQ0HrR1FRVWde691BA7tTeBSbh7LNynTdlNe+poWkYE0C/MnLfkM705ZybPjHiaidYhJtFcHe60duutc66u4SV/MY8nGqQC89fJXNI8MQmjpbxKt/wStox2Flcqvcv3cvyeBi7l5rPtNKb/xz31NRFQg336zncEjevD4Ux1JTsxh8oQVrFz3qkn03w4HrW3FCBRcF9veBC5eyOfHXycD8Mrzi4mICqRFuD9pKedwcrKvsr7H3CkoKsXR3rrivVoFhkpJTGys1cx9qTOFulLe/vKgKSTeNWp7OwwlxmunLEtVXMEb9eqD2kZZy6gVmlGclUlhchL6C+c5v/lnDIWFZC7+Cv/Rz5pE/52wsbdDX2ysn8gy6vL47Jy0eIYGoC2/9vu2COFCejb2TlqC2oShUqloHNaEKznnTSH9nlETtlK1jUkfPb+JK7rJaNWqOX/tOgJATIxI06bGG+GlS1e5fDmPVavnMGXqKM6ezaVJE3/27YvlhRfQjygpAAAgAElEQVT78/Xit1Cr1HTqFGkq+XekRWQgh/YmABB/IoPAUOOyKEdn+xvdlgt0ZKSeZdYbK5g0azBtO5u3c3aLyCAOl7tJJ5zIILCSK7jWyQGbyvE51Q036cpERAWyf7dSfidjMwhpYiw/p/Lys7GxwtbWGicnewrydTg5O1SMBrk2dKzSmTcnIqID2b9HKbuTx28Sm50xNkcnO/LLy+7QgaQ6M311jSMJ5+neWlkfFtXU7Yb1OF9O6UVC2iWmfX4AqY5kctMGh5J/UnEFL0pNwc6nccW2knPnSF3wHrIkIRvKKEpJxj4gAGHGLIInTCR4wkQ0Wq3ZdnQAvJsHk37kFABnxDQa+RuvLR4hflzMzEGXV4BkMHAmMZ2Gjb3wqXTMhbRsnNzMfwSrvmPqkZ3rXdFfN5WOPve1Z9++GAYOmIQsy7w75yWWLf0J/wBv5cmrrHM89eRErK2teG3icDQaDUFBvkyd8gk2NtaEhvox7S3zbbCde4Zx9GAi40cuAhkmvN2fdSt34ePnRsfuLTn2dxLjnv4YtVpNy6hAWrVvyvRXl1GqL+OLBRsBcHC0r1jUbG507BHGsYOJvDbqY2QZxr/Vnw2rduHjZ3STfnXkx6hUKlpGBRFdB9ykK9O9dxh/H0jkmaFK+U19pz/frdhFYz83uvZsyaEDSYwerJRfRHQg7To2JTjUiznTf2D99/soK5OY/LZ5Lp7v3iuMQ/uTeHbYJ8iyzNSZ5bH5u9G1R0sOH0jimSGLUKlVREYH0a6jUnaZGedp26FuleOWA5l0jvRm7dwHUKHijUV7Gdm3ORln8lGrVbRr6YmNtbqiQ7Tg26McE3NNrPr2OEdFU5AQR8r8uciyTONhT3Nh6xZs3T1wjozCpV17Uua9i0pjhUv7Dtj5mPfDANcT2j6CzJgE1k76AFmWue+lwRz9aTsu3u4Etwun05C+bJzxGQBNOkfjFuCDi487579Yy/dvvI8sy/R6boCJo/h31IOBnZpxPb8Tt3FFfxv4GoittPv7oih+d6tzSXJcPSiGm5NZYLGLqMvUZ7uI+jCsfTvqu11E1MuBppZQo/TyMs9RzHvF2Bb/qdWLZ4tv/qqRBh83slvddj2/E3dwRV9W+4osWLBgwYIFCzejPvy2Mfk0lgULFixYsGDBfKkjy8duS53v7BQbLptaQo3x5GYXU0uoUfSlplZQs8zoWn+dwXv61IOr320QxgabWkKNcuhg/Z7meWN0Pb+4/D9AEAQ18BkQCZQAo0VRTL7JPpuBn0RR/OJ257MYgVqwYMGCBQsWboks18zfHXgMsBNFsSMwCXj/JvvMAhpWJwZLZ8eCBQsWLFiwYG50AX4HEEXxANCm8kZBEJ4EJOC36pyszk9j3SskSeLdd1aSKGZhY2PFWzOexj/As2L70sW/8vuvB9E62vP0yAfo1iOK3AtXmfrGV5SWluHm3oAZs0eZrRGoCpjUJpQmLlpKJYl3/k4iu6D4hn0+6taSv05fZF3KWQB+7duOrAIlr8nx3Hw+PZ5eu8KriQqY2j4UwVWL3iAx/UASWfk3xvdpr5bsyLrID0lnsbdSM7dLMxrYWKErMzBlbyKXS8x7+FuSJH5a9CNn0hRTwv+NH4Cbr2JKmJOSzc9fbKjYNys+g6Fvj0Joa945kiRJYt6sH0kSFSPQKTP64+dvNFpcuWwHW349ilqt4uln+tCjdwQF+TrenLgCnU6PtbWGGXOHmKXJKSj1bmzzEIKctJRKMh+fSuJMpWzrY4RgWrg6U1SmWEO8ExOH1sqK8S2boFapUAGL4pI5XWSeuaFUwKz7m9HC05GSMpk3fosj47JR67BWjXkywhtZhoV709ierDxKf/DFLqRdUvY7evoK83aZ59OnkiSx7P11ZCbnYGVtxehJ/fBqrNTPjKTTfLtwY8W+KXEZjH93BEJkMMsWrOP8mYsYSg0Me+VxQloE3OorzB4TZVB2Bq5Wem8QBMFKFMUyQRDCgEHAk8Bb1TlZjXR2BEEIAhYAjQBrlEfJ3xBF0Wxzn+/Ydgx9SSkrVk/leGwKH8z/no8+eRmApMRsftt8gG/XTAPg6cGzadu+OUsXb+bhRzvxyKOd+eLTjaxbu4shw/9jyjBuSY/GjbDRqBm5NZawRk68EhXMq3viquzzfEQADSr5XzV2tCPhcgETdsddfzqzo5dfI2w1aob+HkuEmxOvtQ5m3M6qul+Kqhrf/0K9iLtYwJcnMukb7MGz4X68dzi1tqXfFXH7TlBaWsrYj14hMz6dzV/9xPAZowHwCWnMmPkvAXD8rxicGzYw+44OwK7tJ9GXlLFk1XhOxKazcP4mFiwaBShGoGtX/cW6X6eiK9Iz9KkF9OgdwS8//U1IU29emtCXjT/uZ+XSHYybaJ52Jh09GmGjVvPa38cRGjgxWgjinRijj16Is5ZpR06SV1pW8dmYZsH8nJnDgQuXaNXIhaebBDA7NsEU8u/I/U3dsbVS8/iKw0T7OPNmr6Y8s07JHuJqb83QVo158JuD2Fqp2fpMRzom7yHA1Z6TZ/MZ9WPsHc5ueo7sPkmpvozpX44j+WQ6qz/ZxIS5Sv0MaOLLm5+8AMDB7TG4ujkT2aE565b8TuNgL56bNojM5Bwyk3PqdmfHNEv08oDK7rFqURSvNZJhgC+K7VQgoBcEIV0Uxd9vdbJ73tkRBMEe2ISymOhg+WfDge+Ah+/1990rjh1NolMXxawtIjKEuFPpFdvSUnNo064ZtrZKmnf/AE+SErN5bdJAZFlGkiTOnr1E+/a1btZebaLcnNl/RlnMffJiPs0bOlbZ3ruxG7IM+84YF3w3d3XEw96WL3qGU2KQ+OBYKhlmmnk42sOZvTmK9uO5+bRoVDW++/zdkIA9p43xrUzIodzzFG+tHReLzXtUByD9VCpCG6UD4988kNNJWTfsoy8uYeu3vzFmwUu1Le8fEXs0lQ5dFCPQ8MhAEq43OfVxRVekR1fJpDa0iQ8ZaUoKfsXk1Hxn5Fu4OHPkolLvxKv5hDob66YK8HGw56UWobjY2LDl9Dn+zDnHEjGNwvKRHo1KhV4yX3Oitn4u7Eq9CMCxnDwivI33p8u6Uh5YchCDLNNYa0NesXKvCvdyxsvJljWDWlFcJjFzayKpl4pMov9OiMfTiGiv1M/QsEDSEm5sc8W6EtZ98wfTyjs+J/4W6dA7ivcmfIm9gx3DX32iVjXXE/YCjwBrBUHogJKLDwBRFCsSEAuCMB04e7uODtTMmp2HgF3XOjrlwpYDboIgRAiCsEUQhK2CIBwWBKGTIAh+giAkCYLgLQhCL0EQdguCUOvTa4WFugrjSzAaLQKENmnM0cOJFBbquHKlgNiYZHS6ElQqFQaDxJOPTuPw3wlEtgqtbdnVRmttRUGlX46SLKMpv9GHNHDg/gB3vjiRUeWY3GI9S+OzeG7HCb6Jy2JmB/NNze9obUWB/ubxhbo48GCQO5/GZNxwnCTD4vvCGdjMh92nL9WW3H9McVEJdtrKpoQqDIaqztiHfj9AeNcotA0crz/cLCksLMbR0dj21GpVRdsD8PR0ZcBjcxne/336D+oKgLOLAwf3ifR/dC4rl+7gkSc61Lru6uJgpanouIBSN691su00Gn7OPMOCE4m8dfQUD/l5EejoQF5pGQZZxtfBnlFNg1idcuMN1lxwtLEiv8TY9gyS0kGreC/LDG/dmI3D2/KreA6A8wUlfLo/nQGrj/LJvjQ+6tuytmVXG11hMQ7aG02UK7Prl4O07xmJk4vS5vKvFlKYr+OND8YQ3bkF333yc61qvteYaIHyBqBYEIR9wIfAK4IgTBAEoe8/iaEmOhXBwM0mX9OA5sCroiieEARhEDBCFMVnBEF4HVgOeAIPVRqqqjW0WnuKKhuBykYzwuAQH/oP6sWLYz7Ez9+TsPBgXMortbW1Fet/ns2B/aeYNnkxS5ZPqm3p1aKwtAyH8ngApaNWXtkeCvTAw8GGL3qF4621o9QgkVNYwtELVzGU18jY3Dw8HGxMIb1aFJSW4WBtjE+NMb5Hgj3wdLBh8X3h+DjaUSop8V0bCRr95wkCne35tFdLHtp42BTyq42dgy0l1xu6ajRV9onZfoTBb5qnrcfN0Grtqra9Skag+/bEk5ubx4bflSnkcWO+JCI6iBVLtjFkRC+e6NeJJDGHya8sZdV6k7nN3JaiMgP2lcpIrVJV5C0pMRjYlJlDSfnITeylqwQ5aUkvKCLCtQFjm4ew4GSi2a7XASjQl6G1qRwfFdeNayw/ks3qY6dZ3j+ajv5XOJZztcIA9XD2Vbwc7TBX7LV26IpubjJ8jX1bjvLyrOEV7x2dHWjVWenAterckp9Xba8dsfUIURQl4LnrPr5hLlcUxenVOV9NjOycRplDu54mwBlgmiAIy1EWFl2z/90INEYZEcquAU13JCo6lD1/KaNkx2NTCG1i9G+5dCmPK5cLWLpyChMnD+Tc2UuENmnMuzO/5dBBZe5dq7VDrTLfofTY3Dw6+yhP6IU1ciL5SmHFto9j03n6z1jGbD/BL2nnWC2eZv/Zyzwb5s+gpsr/QxMXLWcrOVObGzEX8ujqq8QX4eZEUqX4PjyazuDfYhn15wk2pZzj27jT7M25zKiwxjwc5AGArsxwwwXaHAloEUzCIWUtUmZ8Ol6B3lW2FxfqKCstw8Wj7hgPRkQHsW+30o5OxKYT2sQY0/Ump45O9uRfMzl1Um6QDRuZr8kpQNyVPNqWG0EKDZxILzDWTV+tPfPaRaBGGQ1p6epMSl4hEa4NeLZZMNOOniI5z7zzNR3OvkrPEMV5PtrHGfGCUW9wQwe+fCICgFJJRm+QkGSZ8V2CGdlWMVtu7uHI6TzzLb+m4YHEHlDqZ/LJdPyCq7a5ogIdpaVlNPI0tjkhIpiY8mMSYlNpHGi+SxyqgyzLNfJXm9TEyM5PwFRBENqJovg3gCAIo4ELwEfAYFEU4wVBmIGxU/QqsAVoJwhCh/LHzGqVXn1acWB/HMMHz0aWYcaskXy77A/8/D3o3jOK09kXGNxvJtbWVox/rR8ajZqBQ/owe+YKvvr8Z9RqFZOnDalt2dVmR/ZF2nu5sqRPJCpgxsFEBgu+ZOXr+Cvn5tM3y+KyeaejQGefhhhkmekHE2tX9F2wLfMiHbxdWXF/JCoVTNuXyNDmSnw7s28e34bkc8zu1JTHQz3RqFS8tS+pllXfPS07h5N8VOSz8R8BMk9OGMTudTto5ONOi45hXMi+gKtntdJOmA09eofz936R0UMWIssy094ZyOrlO2ns70a3nmEcOpDIqMEfoVKpiGwVTPuOAiGh3sx++3vWrdlLWZmByW/3N3UYt2T/+YtEN3JhQTvlpv/RySQeC/DhTFExBy9cYueZ87zfPhKDLLMt5zyZhUVMjIjGSq1iQphidHq6sIhP4s3zaaXfxfN0CWzI+qFtUKngtV/iGN3Wn/TLRWxNziXufD4bhrUFZHakXORg1hXiLxSw8JEweoW6YZBkXvvllKnDuCVtuoVz8lAiM577GFmWeXbKAH5dsxPPxm607hLGmawLuHtXbXN9h/Vm8dy1TB+zEI2VhufeHGQi9feGOvA78I7UiBGoIAghKHNsjVA6VMeBicBIlGGpc0A24AZMBr4COqJMga0rfz0V+PFah+lWFJXtrQfFcHO6/Wi+ixLvBZYMynWX+p5BefBO83yM/V5x8oj5jqTcC34cbb6j0PeCtu4P1aoRaNCnu2qkwae90L1uG4GKopgC3GwR0Qflf9fTqvzfeKBF+WvznIC3YMGCBQsW/h9RH0Z2zHeRiQULFixYsGDBwj3AkkHZggULFixYsHBLzDjNU7Wp852dN4/U37nZK6vNN7fGvUB9yXwfp70XzLY239wh/5aWD1698051mEa2hjvvVIeJamuetjb3Cl9t/b62WLh76nxnx4IFCxYsWLBQg9SDNTuWzo4FCxYsWLBg4dZIdb+3Y+ns3ARZkji5fA15mdmora2IGDUEradHxfbzsSdJ2rgZAOcAf8KGD0ClqtUnAf8VKhXMeK4DzYNc0ZdKTPlkHxlnjB6tI/q24KGugQDsOnKaRWvM36xPpYLpr3alWWgj9KUSU+fuJPN0XpV9XF3s+P7zx3l4+Fr0emWaYveGoaRnK1MyMSfP8v6Xt810YDLqs2u9JEl8/t560pJysLax4qWp/fDxc6vYvn7lDv7aEoNKpaLf073p2DO8Ytv+HSfYsy2WibPMN8eVLEmkr15NUXY2KisrgocNw87DeD1JX7OG/ORkNHZKksSmY8ei0mhIX7WKktxcpLIyAgcOxDEoyFQh3BZZkjj3/SqKT2ehsrLCe9BwbDw8K7afW7uaotRk1OXxNR7zIrK+lJxlXyMbDFg5N8B72AjUNuY5tSZJEh+9u56UxDNY22iY+FY/fP2N9fP7FTvZ9vsx1CoVg0f1pmuvcGRZpt/971Ts1zIikGde/q+pQrBAzbme9wDWAnEoA2D2wCpRFBfVxPfda84eiUUqLaXz269zOTmV+NXraPPK8wCU6YqJX7OejlMmYOPkSMrmLejzC7B1drrDWc2H+zr4Y2uj4anXfyNKcGPyyDY8N3sHAH6ejvTtHsT/Jv6KLMusmfsgWw5kIqZfvsNZTct9XYOwtbGi/3MbiWzpwaQXOzJ28h8V27u0a8xrz3XAraHRg8nf15lTiRd47o3b+seZBfXZtf7ArpPo9aUs+OZlEk5k8M3CTby5YCQABfk6fv5+D1+tn0yJTs/LQz6o6Ox89f5Gjh5IILip7+1Ob3Iux8QglZbSctIk8lNTyfjhB4QXXqjYXpiRQbNx47B2Ml5Dsjdtwt7Hh5CRIynKzqYwK8tsOzsFx48hlZYS+NoUdGkpnF//A42fe7Fie3FWJn4vvoKVozG+c5vX0KBDJxq078SFzT9xZc8uGvb6jynk35E9O06h15fx6YqXiDuewWcf/MzsjxQ7loJ8Heu/28PKTZMo1ul5pv8HdO0VTk7WRZo09+XdhaNMrP4eUQ+ePa/JkZ3toigOABAEwRYQBUH4VhTFKzX4nfeEy4kpuEco6X5cQ4O5km40kLyclIqzny9xq3+k6EIu/t0716mODkCb5h78dfQ0ADFiLmGhxl8pZ3ILGTl9K1L5sKWVRk2J3vwXa7aO8GL3wUwAYk+dJ7yZR5XtkgRPj/+Z9UuerPgsTHDH003Lio8foaTEwLsf7yUtyzwX3tZn1/q4mDRad1RcpZuFB5AUb1yYb2dvg4eXKyU6PcU6fZUR1OYRAXToHsbvG/bXuua7IT85GZeWymJ1p+BgCjOM1xNZkig+f560lSspzcvDvXNnPLp04WpcHA3btCHho4/Q2NkROMh8M/AWpSTj2CIMAPugEIoz0yu2yZKE/sI5zq5egSE/jwYdu+LSqQse/+sPsowsSZRdvoyNh/naKZw4lka7TooJcouIABLjKtVPOxs8vV0pvlY/yx1exfhscs/n8cozn2Nra83Y1/riH+hx0/NbqB1qaxrLCTAAWwVBGCKKYoIgCM8BXqIoThcE4SVgEMoo0BpRFD+uJV03paxYh5W9cQRApVIjGQyoNRr0BQVcjE+kyztTsLKzZf+s93EJDcbR2/M2ZzQvHB2syS80pi+WJAmNWoVBkikzyFzOV55wmzSiDXGpF0nPybvVqcwGR60N+YX6ivcGSUKjUWEodwPdd/hGy7XzF4v4cuUxft+RSusILxa81Zv/PbO+1jTfDbdyrTfIRtf6N/bG80yYf8U+11zrt2XlEunmzMwOAsP/jDGF/NtSVFiMg+ONrtLXzBbdPF0Y238ekiTz5PBeFft1vS+aE0eSa13v3WIoLkZT5XqiQjYYUGk0SHo9Xr164dWnD8gy8e+/j2NgIKUFBRiKimg2fjwX9u8n88cfCRk50oRR3BqpWIe6Unyo1VXic+3em4a970OWZLIWzscuIAA7Xz9kyUDauzOQS0txe/AR0wVwB4oKi9FWrp+aqvXT3dOFp/83H8kgMWhkbwAauTkxaGQvetwXyYljabw7dTVfrBpvEv33ApVlzc5t6SUIwk5AAkqBl7hJVmRBEFoA/YEuKJ2drYIg/CGKoliD2m6LlZ09ZcWVHmmXZdTlrsXWjloaBAVg59IAgIbNmpCXmV2nOjsFRaU42huLXq1SVTgQA9hYq5n7cmcKdaW8/cVBU0i8awoK9WgrubKrVcaOzq04mXABg0FJIHHk+Fk83LU1qvHfUJ9d6x20dugKr3NyL4/1yL54Ll/MY/HGqQC89fJXtIgMomlL/5ueyxzR2NlhKKkan6r8eqK2scGrd280tsp6FWdBoDArCyutFtfISABcIyLI+d18p1rVdvZIxZXWj10Xn2vPPhXrcRyaNqMkOxs7Xz9UGiuCp71DYUIcOSuWEPCKeSbNd9DaUVTZ9Vwy1s+DexO4lJvHd79MAWDi2K8JiwpEaOGHxkrJ2RseHUTu+Tyl3OvQ2s4q1P2+To1mUN4uimIPURR7iaJ4vyiKv163/VqphwEBwDZgO4qfVmgN6rojrk2DuRB7EoDLyak4+flUbGsQ6E9+dg76/AIkg4EryWk4+ZrvEOzNOBJ/nu5tGgMQJbghZlRdj/Pl1F4kpF1m2mcHKqazzJ0jJ87SvYNyA4xs6UFi6s3NPyvz4sjWDO+nmDM2C23EmXPm62VVn13rm0cGcXhfuUP0iQwCQoyu0o5ODtjYWmNtY4VNuet5gRlOxd0Op5AQrpw4AUB+aioOvsY1RsXnzhE3bx6yJCGVlZGfnIw2IACn0NCKY/KSknDw9r7puc0B++BQCk4pWnVpKdj6GOPTnz9L5gdzkSUJ2VCGLiUZOz9/zq5ZSWFiAgBqWzuz7gSERQVycI+iNe54BsGhxuu9k7M9tjepn8u/2sKPq3YDkCzm4OHtYtYx/n+gtp/GKga8gQQUP6zTgAicAh4URVEWBOEV4EQt66qCV+sock8msHfmfJBlIp8ZRupvW9F6uuPZKpJm/R7l4Hxlps2nXWucGpv3Asnr2XIgk85RPqx970FUKnhj4V5GPtqCjDP5qNUq2oV5YWOtoXtrJa4FK45yTLxgYtW358+/0ujctjFrPn8MlQomv7uTEf0jyMi+yva9GTc95quVx1gwrTc9OvpjMEhMKl+kbY7UZ9f6jj3CiDmYyMRRHyPLMO6t/mxctQtvv0a07xZGzKFEXhv5MSqVihZRQUS3b2pqyXeFa3Q0V+PjOTV3LgDBw4dz5s8/sXN3xzUqikbt2nFqzhxUGg1uHTvi4OOD73//S+qKFZyaOxeVRkPIiBEmjuLWOEVGU5QQR8aCOcjIeA8ZwaVtW7B298ApIgrntu3JmP8uKo0G5/YdsfXxxbVHb86u+ZaLv/4MKhWeAwabOoxb0rVXGEcOJPLi8EXIMrwxoz9rv92Fr58bnXu05MjBJMYO+xi1Sk14dCBtOjSlWUs/Zk9dzYHd8Wis1EyaMcDUYfw76siP3ttRU67nPYDnri1QrvT5f4EFQBZKRyezfM3OROAxwBb4G2XKqzvQRRTFmbf7rgkHt9f9UrgFm2ZbMijXZZxfrL8ZlFfX8wzKs2Lq1kMHd8vV0vpti/h5J7N/DuZf4ePwSK0OEwW8t61G7rMZb/Su867nO4GdN/n8V+D66SxEUZwPzL/u4+3lfxYsWLBgwYIFU1EPRnYsSQUtWLBgwYIFC7fGkmfH9Mxp43jnneoo6zr43HmnOsx7/c0/f8+/4angBqaWUGMYZAdTS6hRVnSv39NYGQUme9i1VsjT1+/FwD71u/nVCHW+s2PBggULFixYqEEkUwv499TvVWoWLFiwYMGChf/3WEZ2ypEkidkzlyOKmdjYWDF95mj8A4yJAr9Z/Au/bd6P1tGeEaMeonuPaK5eKeCR/04ktImSs6ZXnzYMGXq/qUK4LSpg1gPNaeHhSIlB4o1f48i4bHwaaljrxjwZ7oMMLNyTyvbk3IptIY0c2Di8HW0W/kWJwTy7+JIksfnTHzibloOVtRV9xw2gkY97xfakQ3HsXK0kZvMObcxDY59CV1DE+vnfUlJUjL2zlr4vD8DRxTynLyRJYuaMxYgJ6djYWDNz1nMEBBhzryz+eiObN+/B0dGBUaMepUfP1sx5dykJ8ekA5OZewclZy5rv3zVRBLdGkiRmzVyKmJCBjY01M955Bv8AYy6TJV9v4rdf96PV2jNy1MN079mKoqJiZs34htOnL1BaWsbkqcMJjzBpeq5/RWysyIIFy/j22zmmlnLXSJLEornrSS03ynxlWj98Kxm5/rBiJzv+OIZarWLAiN506RWOTlfC3Kmryb9ahJ29Da+/MxAXV/NfkiBJEl/MW096uWnti1P64V0p1g0rd7B7SwwqtYonn+5Nxx7htzlb3UFlWbNTf9i+7Qglej0rv3ub2NhkFsxbzcefvgJAYmIWv27ex6o10wEYOmgm7dq3ID4unQf/25HJbw4zofLqcb/gga2VmsdXHCLapwFv9m7KMz8qbuau9tYMbeXHg0sOYGulZuuznej4iZIQy9FGw5u9m6K/QzZiU5Ow/wRlpWU888ErZCWk88fijQx66xkASoqK2fLNTzw99yW0DRzZ88M2ivIK2fPDVvxbBtOt/39IOSaybdkvPDp+oIkjuTnbth5CX6Lnu+/fJTYmkXnvreDTz94AIFHMYPMve1izVunIDBr4Ju07hDF5ipKbpbS0jCGDpzFz5hiT6b8d27cepqSklFVrZhIbk8T8eatY9OmrACQmZvLr5n2s/l7JQDF04HTadWjJsm9+IbSJH+++NxZRzCQxIaPOdna+/nodmzbtwN7e7s47myH7dp5CX1LGwmUvEX8ig68+/JkZHxiNMjeu2cOynxSjzOcHfkCXXuH8tuEgTZr5MuTZ/7Bl0yFWL97K2ImPmTiSO3Nw10lK9aXMW/IyYrlp7dRKprW/rN3DF+sU09rxQz+oN52d+l9J7jwAACAASURBVECNTmMJgtBaEIQtgiDsEQRhnyAIswVBMMuc9ceOJtK5i5JNNzIylLhTaRXb0lJyaNO2Oba2Ntja2hAQ4EViYhZxcenEx6czYtgsXh3/MRcumG9uh7aNXdiVqozWHMu5SoS3c8W2y7pSHlh8gDJJxl1rS16x0Tdrzn9bMG9nMrpS815MnHkqldDWzQHwaxZITpIxR1FWfBoegT78sXgjSyYuxNHVCW0DRy5knqVJG8Xw1b9FEJlxqSbRXh2OHomnS9doACKjmnLqZErFtpTU07Rt16Jq/RSNiRRXrfyNzp0jaSoE1Lru6nD0qEiXa20vqglxJ43lkJqSQ9t2xrbnH+BFopjJ3j3Hsba2YszoOXz5+Xo6lR9fF/H392LRoimmlvGPORmTRptyo8zm4dUzynxiUDcGjuoDwPmzl3FtZJ4jqtcTF5tGdAfFtFYIDyA5oXqmtXUeSa6Zv1qkxjo7giA0BlYCL4qi2AXoDJQAH9bUd/4bCgp0ODoal7ir1WrKypQbfJOmjTl6WKSwUMeVK/nExCShKyohKMibsS88wdIVb9Krd2vmzF5hKvl3xNHWivxio5GkQZLRVGqMBllmeGs/Ng5vy68J5wEY3zWY7cm5xJ83XxuFa5QUFWPnUNlMUoXBoJRfUV4h6ceTuG9EX4bMfI79G3eSm30er2BfEg4oybrFAycpLdHf9NzmQEGhDkenSvVTY6yfTZv6c/hwPIUFOq5czifmWCJFOsUaQq8vZe33fzJipPkaLRYW3C42P44cTlDa3uV8YmIS0elKuHI5n7y8Qr5cPJkePVrx/rxVppL/r7n//s5YVfI9q2sUFVxnlFlu5HoNd08XnnlyPmMHf8hjA7pWfK7RqJk45nN++n4v7To3q1XN/5QbTEGvi9XN04UXB8xjwvAPebh/F1NIrBnqQWenJqexhgGLRVFMBCi3gngHSBUE4SCQCbgCDwGLgRBAA3wgiuL3Najrpjg62lNUaDSzk2Sp4gIUHOLLgMF9GDtmAX7+HoRHhODq6kh4RDB2dorBXa8+bfj0E/N0zAYoKClDa3ud+ed187DLj2Sx+lg2ywdE0zHAlcdbenMmv5j+kT64O9rw7cBW9Ft5uLalVwtbBztKdJXMFiUZTbkZob2TFp8m/jg1VEazAsJCOJt6mq797uPXL9axfMqnhLZujrObq0m0VwdHrT2FhcY1VrIkV9TPkJDGDB78AGOenY2/vxcREaG4uiq/lPfvP0Gbti1wcjJfk1Otoz2FldtepdiCQ3wZOOg/PP/se/hVis3FxYkevVoB0L1nK5Ys/tkk2i2Ag+OtjVwP7VOMMlf8rIxcTX7xa1pGBtIsTPGxm//l82SmnWfauCUs3zS59sXfJQ5aO3RF111nKpnWXsrN46sNimnt9HFf0TyibpnW1mdqchorAKgyLyCKogycA7yA1aIo9gGeAXJFUewE9AFmCYLgdv3Japqo6Kbs3h0DQGxsMk2a+FVsu3QpjyuXC1i+chpvTB7K2TMXCW3ix/RpS9i65RAABw+cokWLwNqWXW0OZ1+hZ4jy3xrt0wDxgnG0JrihA1/+T5kGKJVk9GUykgzdv9jLgFVHGLDqCBcK9Az97qhJtFcH/xZBJB2OAyArIR2PQGOOIp8mfpzPOEPh1QIMBgPZCRm4+3uScTKFqN7tGP7uC7h6NsS/RZCp5N+R6FbN2L1L+f+PjUmkSVPjBfTSpatcvpzPytWzmDx1BGfOXqyovwf2Hadr+fSXuRLdSmD3X+VtLyaJJk2rtr3Ll/NZsWo6k6YM4+xZpe1FtxbYvUs55sjhBEJC65Y/XX2iZWQgf+9VjDLjT2QQWMko09HZHhu7G40yv/tmG1s3HwGU6R+1pm5M+TSPCOJIuWmteCKDgNCqprWVTUG1jvYU1jHT2lsiyzXzV4vU5MhOBhBc+QNBENSAP0YDUIDmwFYAURTzBUGIQxnlyaUW6d2nNQf2nWTooBnIMrwz+xlWLPsNP39PevSMJjvrPAP7vYW1tRUTJg5Eo1EzbkI/3n5zMd+v2Yq9vS3T3xldm5Lvit/F83QJasT6YW1RAa9tPsXodv6kX9axNekCcecK2DC8LciwI/UiBzMv3/Gc5kSzThGkHBNZ/OqHyDI89sog9q3fQUMfN5p1CKfP04/w7bTPAWjZNRrPQB+sbKzZ8P5KAJwauZjt4mSAPve1Y9++4wwaMBVZlpk95wWWLf0Z/wAvevZsQ1bWOfo9OQlraysmThxaMaqVlp5D38e6m1j97endpw37951gyMC3kWWZd94dw/Jlm/H396JHz1ZkZ59nwFNvKm3vtUFoNGqeefZR3p72NYMHvIWVlRXvvve8qcP4f0vnnmEcPfh/7J13eFPV/4DfpHvSsgqFTtpeVhcb2SDurYCyZCiKCoqAMuSLLAEBB+Ae7KECCggCyt67g1JuWrpLGS2jK23aJL8/bmhSKFDQNkl/932ePjS5o5/DPefm5JxzP6+K94ZKosyxU/uxbqUkyuzYrQWnjyYw+tWFKJVKWkT407pDCE1CvJk3dS3bNh5Fp9Uzbmo/cxejUnTo3pKoYyo+eG0h6GH0lH5sXL2XBo0laW30cUloq1QoaBYeQISVSWtrMlUiAgUQBMEH2AE8A1wBfgUygFIgBEkUek4QhHeAQFEU3xcEwQ2IBjqIoni5Mn+nWHvMsh8T+heEzK3ZssWan0G55mbA1upL7r2TFWOvtI4Fsw9KTc+grC61jpGiB6Wpx1PVWkD/j/6qks/ZlJmPV1s5qmwaSxTFdGAgsBjYAtQDGgL2QG2TXb8H6giCcABJHjpNFMXLgiD0FwRhRFXFJyMjIyMjI1MJ5GmsuyOK4kmgXJY9QRDCgCRRFPMN+2iAVys4dnVVxiYjIyMjIyPz/4NqTyooimJMdf9NGRkZGRkZmQekmh8TrwqsPoOyviYYyu7A2tcsN+/Lf8GIbe733smKecYvz9whVBmqG9a1gP1+eem1OHOHULWU1Nz7JkBmzHZzh1ClqNOeMncIVofVd3ZkZGRkZGRkqg6FPLIjIyMjIyMjU6Ox/r6O3Nm5iWQ9X45KTMPe3o6p04fdYj3fwrYtR3BxdWTI8Cfp1j2ibNvJE+eY+MF37NhlkSYMQCrf8s/Wk54oWcGHfdgXr8aSFTw1IZPVC/8o2/f82VRGfzKUwGa+TOg/h0YBUpKw1l1DeaRPV7PEfy8UwEftgxBqu6DR6ph6OIH0vKLb9vm6Vwt2pefwm+oiTrZK5nZpiru9LepSLZMOqLhWbJmPVOt0Oj6ZsRKVmI69vS3/mzakXP1c8uNWtm09iourE0OGPUbX7hFkX7nB5A+/p6SklLr1ajFt1nCcnBzMWIqK0el0/DR/A6kGk/QbE/vSoLGUADNFlcmyLzeW7ZsQl8q4OUNp7O/F4umr0eulxHWjpw3EwdEitXsoFDDtjfY086+NpkTLpK8Ok3rROMU59OlmPNnZH4C9JzNZ9KtxWWPv9j48/pA/73++v7rDrjQKBUx7qyPNAjzRlOiYtPAgqVnlp3Bruzvw6/wneeLtjWhKtNRytWfBuK64OtlxPa+YSYsOcfVG0R3+guWiUCj4ctYwwpr5UqwpZeQH35OUesncYclUQKU7O4IgtAZmA85Ij6zvRnpMvMKFJYIg+AMxwM20u45APtBHFEWLm/DftfMUGk0JK9b8j5joRBZ8uoYvv3oPgARVOn9tOczKtf8DYHD/mbRr3wwnJwcuZuWwfMk2Si1clHlq/xlKikuZ8u27JMalsParTbw7ezgAfsGNmLjobQCO7Y7Cs647Ye2bEXdCRftekQwa84I5Q68UPX3r4GCjZOBf0YTVdWN8m0BG7z5bbp9RkX642xur/IvBDTibk8+3MWk826Q+I8J8mHvcMmWgu3eeRlNcwvLVk4mJPs9n837hi8WjAUhQZfDXliOsWDsFgCEDZtG2fTOW/LiFp559iKef7cS3X/3B+l/3MvDVR8xZjAo5vk8ySc/8YTSqM6msWLiJ8Z9KJmn/kEZM/eotAA7visazrjsRHZqy7MuNdOwVwaMvdmLtt1vZtfkoj/fpcrc/YzZ6t/fFwc6GPhP+IiKkLhOHtuHN2bsB8PFy5ZmuAbz44V/o9XrWznqMHUfTEFOv89HwtnSJ8CY++aqZS3B3encwlG/cViKEekwc3pY3Z+4q296llTfjX21NXQ+jU2pk3zBOxl3im99ieSi8IeMGt2LSokPmCP9f8cyjbXB0sKP781NpFxnEnCkD6fvaAnOH9d9TA6axKpVn519IPc+Kotjd8NMBOA4M/zcBVxWnT6l4qHMoAGHhQcSZWM+Tzl+gTdumJuZlLxJU6RQXa5g5bRmT/jfYXGFXmoSYZELbS7K9oBb+JJvYem9SrC7mj5+2M+Dd5wFIEdNJVWUw+53FLJ6yjOvZudUa8/3Qqr47By5IfeiY7Dya13Ett723b130esr2AVgZf4HvY9MAaOjiSI7aMkd1AE6fSuChzi0BCAtvwtm4lLJtyUkXaNOuKQ4Odjg42BnqZwbjJrzCk093RKfTcfHiVerUscwF4WJ0MuGGuhnS0o/zFdTNInUxv/24nSFjngPAP9i7LBV/YWGRRYs02zSrz77TFwCIUmXTskmdsm1Z2QUMm74TnU6PXg+2tkqKDV+cTp27wtTvjpgl5vuhTQsv9p3KBCBKvELL4Drltut0egZ/tIPrecbvxUG+Huw9KR1zMv4yrZt7YY081Fbg7z3RABw7nUjrsMB7HCFjLiqbVPA2qScwA3hCEASnypxAEAQF4ANcM7weKwjCcUEQDguCMNfwnocgCH8KgrBPEIRDgiD0vN8CPSgF+WrcXI1FsSlnPffhZJn1PJ/oqETUhcXMnrmCwUMfx8ur9p1OazGoC4pwvoutF2DflqO07RGOm4fUUWjo68Xzwx9j4uJ3aNWlJSu/sFzRqYudLfkao9Vdp9dzU7cT5OHMEwH1WByVettxOj382DuUV5p6sz/Tcr9BFxSocXWruH4GBTfm1AlV+fqpLkahUKDV6njp2SmcOHaO8FZB5gr/rhQW3lI3bW6vm7s3H6NDzzDcDXWzdv1abF9/gLEDPiXq8Dk69Ayv1pjvB1cnO/IKjR/0Op0eG6VUOUu1eq7lSWLJCa+25mzSVVIuSFNAWw+mVHfetQfC1cmOvAKT8mmN5QM4GJXF9bzicsfEJ12lV3vJgdarvQ9ODpbbWb0bbq5O3MgrLHut1eqwsalK5aSZqAFJBSt7Ve4l9bwTzQVB2CMIQgygAhKBZYIghAJ9gYcMP8GCIDwFfAT8LYpiV6AP8JPBp1Xl3GZe1pual70N1vMFLPh0DaFhgdjY2nDqpIrvvv6D4a/O5saNfD4Y+3V1hPpAOLk4UlRYsZn4Jod3nKLrU+3LXjdrHUSzSOkDsnXXUFITMqsn2AegoKQUFztjeZQo0Bra0tOB9anvbM9Pj4TybBMvBjdvRCdvo+H8tb9jGbItms+7N6vusCuNi4sThXepn/369+SdNz7ns09/oWVoIB6GToGdnS0bNs/io49fZcrEH80S+71wdr6lbupur5sHdpyi59PGurly8Z+MnPwyC1Z9wJD3nuOr6WuqLd77JV9dgqujXdlrpQK0JtMC9nZKPhvTBRcnO6Z+f9QcIf4r8tUluDqZlE+pKFe+ivj2txga1Xdl2cxHaFjXhazsgqoOs0rIu+VLslIpfcGQsTwq25G4m9Tzbg6rs6IodgfaG85xSRTFUqApcEQUxRJDp2k/0AJJCroPQBTFTCAXSTNR5URGBnNgv7QwMCY6keDgxmXbjNbzj/hg4gAuZl0lslUIm7bO5adlE/lp2URq1XLl0wVvVUeoD0RQqD/RhyVbb2JcCo0DG5bbXpivpqSklDpexk7Akrm/cmKv9H9y9mQC/kJjLJXTl3Pp0kgaYQur60bCdePN8/NTKQz4K5phO2LZeP4Sy89mcvDCNYa3bMxTgfUBKCzVorXgr9ERkUEc2BcLQEz0eYKCjZbvm/VzycpJjJ/4CpcuXiUouDGfTF/B8aPSNXdxcUSpsMxvnEJYAKcNdVN1JhXfJhXXzbomddPVzQlnw4eMZ113Cky+XVsaJ+Mv0621dL0iQuoipl0vt/27iT05l3KVKd8eQWeFayNOnr1MtzbSvSFCqIeYcu8lmW1bNOCPXed59aMdZFzK52R8pVSIFsfhEyoe7SE9rNIuMogzFUzB1gh0+qr5qUYqu0B5BbBDEIRNlJd6/imK4j275KIoqgVBGABECYJwCDgHjBUEwRbQAl2B5Ugdmy7AaUEQGgGeQM59lumB6Plwaw4fimNw/xno9Xqmz3qN5Uu34etbn24G63n/vh9jZ2fD++P7Wd1QZeuuocSdUDFz5EL0ej3DJ77MtrV78Gpcl8jOLbmYfoW6DcpPx/V540l+mrOWnb8fxMHRnmEfWq6ZeGdaDh0berLisXAUCphyUMXgZo1Iy1OzJ6Pi6ak/Ei8xs1MILwR5oVQomHIwoZqjrjw9H27FkcNneXXALPR6mDZzGCuWbsfHtz7dekSQmXGFAX2nY2dny3vj+mJjo+SVgQ8za/pyvv9mM0qlgolTBpq7GBXStltLYo6rmDJiIXo9jJzcjz/XSCbpNl1aciHtCvUaeJY7Zuj7z/Pzgt/R6XTo9TBsrOUuot9xNI1OEQ35dfZjKBQKPlx0kGHPNCM1Kw+lUkG7Fl7Y2ynp1krqEM1feYrTYraZo648Ow6n0inSm1/nPYFCAR9+cZBhzzUn9UIeO49V/OGfnHmDee9LC8ov5RQy8cuD1Rnyf8bGbcfp2SWU3RumoVDAiHHfmTukqqEGDFZV2npueBrrE8AV6Ymsi0jTWO8DjwGuoih+b7K/P7DWsDD55nsDgFFIU1fvAf2QRpcOGM7jCfyMJAp1AqaIorjtbnEVaY9Y31ehSnI6p1r6eWajpmdQPtrfujrE90PNz6Bsueu3/hPkDMpWjTptTbVazwPe3Vgln7PJXz5bbeWo9KPnd5F6aiqSdoqimAJ0uOW9VcAqw8vPDD+mXAWeq2xMMjIyMjIyMlWLwoKn+CvLv0oqKEs9ZWRkZGRkZCwdq8+gXKqz3IWJ/5b+v7neeycr5vHI0nvvZMU42za6905WShN368t2ez88M8FyF+P/F/wxMsrcIVQp15LfNXcINQsrXDh/K1bf2ZGRkZGRkZGpQmrANFbNXUEpIyMjIyMjI4M8siMjIyMjIyNzN2rAw3tyZ8eATqdjzoy1qFQZ2NvZMmX6QHx865dtX/rTdrZvPYGLqyODhz5C1+6hZdtWr9hJdnYuo8c8b47QK4UCmNEtmGZ1XdFodUzYLZJqYhke1NKbF5tKfpqFx1PZlXqVWg62fN67Ka52tlwvLmHibpXF+qP0Oh3iijXkpWegtLWl2dBBOHsZr5+46hduJJzHxlGyfoePfotStZq4H5aAXo+tiwst3xiOjYNlmrPvRXS0yPz5S1mxYra5Q7lvbra9BFUmdna2TJk+4Ja2t4MdZW2vN13Ktb1d5GTnMmqMdTzEqdfpOLNsLblpGSjtbAkbPhAXk3p6OfoMCX9sAcDdz5eWr76MQlGtTxnfNwoFzJjYi6Yh9dBotEyc8Tep6eUTJ9b2cGLd0pd5rO9yNBqjCuSRHkE88XAI703eWt1hVxqdTses6ctRiWnY29sxdfowfP2MLq+ff9zCti1HcHF1ZMjwJ+nWPaJs28kT55j4wXfs2HUvjaRMVVOlnR1BECYADyP1C/XAJKQ8O2tN8+fcYkjXIxnSd4uiOKkq4zNlz85oijUlLF31AbHRSXw+bz2fLRoJQIIqk21bjrNszYcADB04j7btBRQKmDl1FWdik+nZO7K6Qn0gHgmsi4ONkhfXnybCy43JnZowYmscAJ6OtgwM9ebJX07iYKNkxytt2LX8KG+39uVEVi5fn0yjU2MPxncIYMJulZlLUjFXTkWjKymh7UcfcuN8Eglr1xH+rjGjdV5qGhFjR2PvZlz0nbRxM17tWtO4Z3fOr/+DC/sP4vNwDzNE/+/44Yf1bNq0Gycnx3vvbIHs2RmNRlPKklXjiY1O5vN5G/hs0ZsAJKoy2b7lOEvXfADAsIHzadteAAXMmrqKM7EpFt/2TLl4UqqnnaZ+wLXEJOJXr6fNGOk+U6ouIn7tBjpOeh97N1fOb9mBJi8fB3c3M0d9dx7pEYS9vS0vDVlLRGhDJo3pyhvvbyrb3qWjHx+M6kKd2s7ljpsyrjtdO/pzVmXZ2ZN37TyFRlPCijX/IyY6kQWfruHLr94DIEGVzl9bDrNy7f8AGNx/Ju3aN8PJyYGLWTksX7KN0hLt3U5vHdSABcpVtmZHEITmwDNAb1EUHwE+REoYeCduGtJ7IFnVexjy+FQLUafP81Cn5gCEhgdyNs4ojUxOukjrtiFGq7RvfRJVGWiKS3jymfYMG/F4dYX5wLRpWIu9aVKitKhLeYTWM95ArxWV8sTaE5Tq9NRztifXINQMqu3MnlTpmBNZubRpWKv6A68k1xMSqR3aAoBaTQLJSzFeP71OR+Gly5xbupITsz7lwj4pW6ubjw8lBdLTfKXqIhQ21ikj9PVtwKJF1fa94D8n6vR5Opa1vQDib2t7wSZtrx4Jqkw0xaWGtveYucJ+IK6pzlMvTCqrZ1Ag103q6bWEJNx9GnF29ToOzZyPg7ubxXd0ANpENGLfoRQAomKzCG1eXpeo1+kZNHIdN3LLP8F3KuYCU2bvrK4wH5jTp1Q81FkaTQwLDyIuLrlsW9L5C7Rp2xQHB3scHOzx9fMiQZVOcbGGmdOWMel/g80V9n/L/yMR6INwGcmdNUwQhEaiKEYB7Sp5rBPgAFTbc+X5+eWt0kpT63mwN6dPJlBQUMT16/nERCWhVmtwr+VSdpO2dNzsbcgzGT7WmljBpdcwONSbDS9F8td5KVX92ewCHg6oA8DDAXVwtLXc9exadRG2Tsbrh1KJTiuVV1uswadXD1qMGEbE+6PJ2L2XvPQMHGp7krFzD0cmTyMnNg6vtq3MFP2/49FHO5VJQa2RgvyiO7a9oGBvTp1MvKXtFeNey5kOVtL2TCktUperpwqFsZ5q8vPJiVfRtN/ztBv3Dsnbd5GfdclcoVYaVxd78vKNIledVoeNyc3lwNE0rt+4PVXBlh0qKpvB35wU3CL7tDH9bAjx4eQJkYICNdev5xMdlYi6sJjZM1cweOjjeHnVvtNpZaqZKpvGEkUxWxCEZ4B3gKmCIBQCk+9ySHNBEPYgTWNpgS9FUUysqvhuxdXViYKC8lbwmx8gAU0a0veV7ox6czE+vvVoGeZfZpW2FvI0WlztTazgCqMV/CbLYy+wJi6LJU+H0qGRB9+cTGNqlyBWPBPGvrSrZJnc0CwNGydHtEUmN1S9HqVhpMbGwR6f3j3L1uN4NhPIT88gfftOmg9/lTqhLciOjiXuh6VEjHnHHOH/v8bF1bGc0f32tteN0W9+hY9vPVqEBVhd2zPF1tGJ0iKTdmRST+1cXagV4IejhzSCWrtpMLlpGbg29KroVBZDfoEGFxfjWjeFUoH21puLFePi6kSBSf3UmdTPwCbevDzgYd56YwG+vl6EhgViY2vDqZMq0tMu893Xf3DjRj4fjP3aokXR90SexrozgiAEAbmiKA4TRdEXGAh8g+S9qoiyaSxRFB8WRXFZVcVWEeGRgRzcfwaA2OgkgoK9y7Zdu5rH9ev5/LxiHOMm9OXixWs0MdluDZzMukF3X+m/PsLLDTHH6G8N9HDim8ekb8klOj0arQ69Xk8771psEC8xaFMM6blFnMzKNUvslcEjuAk5MdL1u3E+CdfGxoR+hRcvcXL2PPQ6HbpSLTdU53Hz88XWxRlbZ+kbm4NHLUoLam6CSksmPLIJB/dL68dio5MraHsF/LRiLOMm9OGSFbY9UzxDArkSLdXTa4lJuPkYy1LL35e8jAto8vLRabVcT0zGrVGDO53KYjgZdYHunQIAiAhtiJhoPRLTyhAZGcyB/ZIsICY6keBgY8LJq1dzuX4tn2UrP+KDiQO4mHWVyFYhbNo6l5+WTeSnZROpVcvVujs6NYSqXKAcBowUBOFpURSLABVwA2nUxuLo0SuCo4fOMXTAPPTomTpjMCuX/YOPbz26dg8jMyObQf3mYGdnw3tjX7A66/n2pGw6+3iy7oUIFAoF43eeY3h4Y1JvqPknJYf4nAI2vBiJHtiTepWjF27gV8uRBb2aAnCpQMOHu0TzFuIu1GsVwdW4eE7M/BQ9epoPf5W07f/gVL8e9SLDadChHcdnzEVpY0ODTh1wbeRNyIB+qFb+gl6vA70eYdDL5i7G/0t69Arn6KF4hg2Yhx6YOmMQK5ftNLS9UDIzshncbw62dra8O/Z5q2t7pjRoHUH2mXMcnD4P9HrCXx9M0l//4OJVD69W4TTt+yxH5y0EwLtda9waW34W7u27E+jcwZfflryMQgEffLyd4QNakZJ+nZ37kswd3r+m58OtOXwojsH9Z6DX65k+6zWWL92Gr299uvWIJCP9Mv37foydnQ3vj+9n1fXzTiisf2Cn8tbzB0EQhMlAXyAfaRRpLpLo8yHg5jCBiDS9Vc6QXlnyS3bVgMtQMaHfW+86jMrweGQNSN5wF75+yPI/qB6UvJJ0c4dQpUw9ZdmPe/9barou4uzxh8wdQpXiaNOhWitok+G/Vcnn7Pmf+lie9fxBEEVxFjDrlrf/uMPu993RkZGRkZGRkaliasCaHTmpoIyMjIyMjMydkTs75ie/NN/cIVQZm/vXvLlfU+bHWn4OkX9Dwg3LTMD4X9DQ2XqfiKoMe87X7CnkrOzj5g6hSum1qbJZTqyTg5abrN9isfrOjoyMjIyMjEwVYv0DO7L1XEZGRkZGRqZmI4/sGNDpdHz+ye8kqi5gb2fL+Kl9aOxbt2z72mV72LktCoVCwcDXetK1GhalQQAAIABJREFUZyh6vZ6XHplZtl+LcD9GjH7CXEW4Kzqdjh/mbSAl4QJ2draMnNSXhj5S3MmqTJZ8vrFsX1VcKh/OHYq3Xz0WTV8Lej31Gnjy5sQ+ODhapihTr9ORvGo1BRmSCDTw1cE41TcKFpPXrCUvMREbR8kfJbz9FgobG5JWrqI4Oxu9thT/V17BLSDAXEW4Kzqdjq/nbiA54QJ29raMntwXbx9j/dywcjd7d0ShVCjoM6QXD/UwyjIP7Y7l4M5oxs8caI7Q74kkAl2DSpWOvZ0dU6YPukUEuo3tW48bRKCP0rV7GFlZV5k+ZRnaUh169EyeOhD/AMvMSaMAJrYNIsTTBY1Wx4yjCaTnF922z8LuLdiTkcP6xItl7/u7O7HskQh6bziCxgrXTSgUCr6cNYywZr4Ua0oZ+cH3JKVaflZoUxTAuIgmBNVyQaPVM+d0ApkFt1+/eR2bcyDrKn+kXMTF1oZpbQWcbG0o0emZfkLkarFlSpQrhRVkur4X1dLZuYMQdBDwmSiKadURw704sDsOTXEJ3ywfRVxMKl9/tplPvhgKQF6umvVrDrJ684cUqTUM7/c5XXuGkpmeQ3CzRsxZOMzM0d+bY3vPoCkuYfaPo1GdSWXZwk1MmCfFHRDSiOnfSEmvDu2MxrOeO5EdmzJv4jIefb4jXR5txT8bj7B59V5eGtbbnMW4I1ejotCVlBA6cQJ555NI/fU3mr7zdtn2grRUmr33LnZuxnVC6Zs24dzIm+DhwyjIyKAwPd1iOztH9p6hRFPCgp9Hcy42lZ++3MSU+dL1y89Ts/mXA3y/YSLFag2jBn5W1tn5bsEfnD5yjoAQy30Mfs/OKIOEd4JBwruOzxZJ9dEo4Z0AwNCBc2nbvinfLNpI31d60KNXBIcOxrH4i9+Z/+VIcxbjjvRoXAd7GyVDdkQTWseNMa0CeX/f2XL7vB3uh7t9+duxi60NYyIDKdFZb4qGZx5tg6ODHd2fn0q7yCDmTBlI39cWmDus+6Krdx3slUre2BtDC083RoUGMOFIfLl9RjT3w93eruz1E35eJOUW8nVcCk/7e9E/uDGLzyTfemrrwQo72rdS5dNYdxKCiqL4nqV0dABiTifTrpOUQK9FmB9iXEbZNicne7waelKk1qBWa1AopNQAqvgMsi/n8u5r3/LB2z+RlmK59t746GQiO0rlC2npx/lzt+dJKVIX88sP2xn+/nMAZCRfKjumaVgA8TGW21jzEhLxaCmJQN2aBJKfWl4EWnTpMkkrVnJmzlwuHzgAwPW4syhtbTn7+Rdk/PknHi1amCX2yhAXlUyrm9ci1I+EeOP1c3Syp14DT4rVGorUGpQKY+qKZmF+vPXhS9Ue7/0QdTqRhzpJ//e3S3izKpTwjhnfh85dpQ6dtlSHvYNdhee2BCLqu3Mo6xoAsTl5NK9dfnF3L5+66PSU7XOTj9oHszg6haJS6+3sPNRW4O890QAcO51I67BAM0d0/4TVcefIJenaxF3Lo+ktupLu3nXQ6fVl+wCcv1GAs0Ep4WJrS6neeq9hTaE61uxUKAQVBGGPIAhNBUH4WBCEZYIg/CUIwllBEB6thphuo7CgGBdXx7LXShuj7A2gvlctBr8wn9df/oIXX+kMQJ267gwY1oMvf3yTga/1ZOakNdUed2VRFxTh7GJSPqUSbWn5ZNY7Nx2jY68w3A2N2T/Em+OGNP7H98dRrNZUX8D3ibaoCBtTwaJSgd4gWNRpNDTo2ZOg4cNo9t67XNyzl4KMDErz8yktKKT5mPeoHRZOym/rzBX+PVEXFJWrnza3XL96Xh6M7Pcp7w7+nKf7dS57v2vvSGmM3YLJv00EqjCR8DaqQMJbjKenK3Z2NqQkX+SL+esYMfIpc4V/T1xsbcnXlJa9NpXwNqnlzOP+9fgmJrXcMW+E+nIg8yoJ1wuwZtxcnbiRZ9SwaLU6q8sw7GJrQ0FpxRLlADdnHvGpx4/x5b+352pKaVffg5W9WtE/uBF/pljX1N1t6Kvopxqp8mmsSgpBi0VRfFwQhN7AWGB7Vcd1K84uDhSaikB1Rtnb0YPnyMnOY+2WiQCMf+tHQiP8EZo3xsZgAg+LDCD7Si56vb5s5MeScHJxRF1oYibW6bG5xZS9f/spxs0eXPZ6yOhn+GH+Bg78fZrQNsG4ebhUW7z3i42jI1pTwaJOj8IgWFTa29Pw4V7YODgAUKupQGF6OrYuLnhGhAPgGR5G5rZt1R53ZXFycURtUj91euP1O3konqs5ufz0h9Ss/jf6e5qFByC08DVLrPeLq6tjOdFixRLehfj41qeliQj0+DGROTNWM2P2UItdrwNQUFqKi13FEt6nAupTz8me73qF4u3iSIlOR1ZBMU/41+dSYTHPNvGijpM9X/cM5bV/YsxUggcn7xZjuFKpQKu1rlGOglJt2SgNlL9+j/vWp66jAws7h9LQ2YESvZ6swiKeDWjAqoRMNqZcpIm7M7PaN+PVXafNVAIZqJ5prMoIQW/WgnTAETMQGuHP0QPSPGxcTCoBwcabp6u7Ew4Odtjb2+LgYIermyP5eWqWfvc3v63cD0CieAGvBh4W2dEBaRrq1CGpfKozqfg1aVhue0G+mpKSUup6eZa9F31MRd/hjzDlixEoFQrC24ZUa8z3g1tQE67HxgKQdz4JZxOnkPrSJc7M/dQgAi0lNzERF18/3IKDyo7JTUjAybthhee2BJqHB3DCcP3Oxabib3L9XNycsXeww87eFnsHO1zcnCjIU5sr1PsmPDLoFgmv8doZJbwfMG5CPy5evEqT4EYcPyYyf/YvLP5uNM1b+psp8soRdSWXTt7S7S60jhuJJqM1X0al8OqOaEbsjGVz8iVWnsvkUNY1nt18ghE7YxmxM5YctYa3dsWaK/x/xeETKh7tEQFAu8ggzlQwfW7pxObk0tFwX2zh6cb5G8br93VcCiP2RjPqQCxb0y7zS0ImRy9fJ1dTSn6JNJp3rbgEF1srz9uk11fNTzVSHQuUKyMENfvqpy49W3LiSAJvDV6MHj0TpvXjlxV7aexTl07dW3DySAIjBy1CoVQQFhFAm44hNG3pw8xJazhy4Bw2NkomTO9n7mLckfbdWxJzXMWk1xei18PbH/Vj0+q9NGxch7ZdW5KVdoV6DT3LHePtW4+vZ/2CrZ0tPoENeH38C2aK/t7Ujozkxtl4YufMAT0EDXmVCzv+xrF+PWpHRFC3fTtiP5mNwtaGeh064tzIm0ZPPEHSsuXEzp6DwsaGoGFDzV2MO9Kxe0tOH1Uxbrh0/d77Xz9+X7UXb586tO/akujjKsYOW4hCoaBFRACR7S23Y3orkoQ3nqED5hokvENYuexvfHzrm0h4P8HOzpb3xr6IjY2SBXN+paS0lKmTlgLgF+DF5KmW+bTZ7vQcOjTwZEnvcBQK+PiIigFNG5Gep2Zf5lVzh1elbNx2nJ5dQtm9YRoKBYwY9525Q7pv9l7IoW19D77tGoZCAbNOJtAvyJvM/CIOXKz4+v0Yn8aEyCBeCGyIrVLB3NOJ1Rz1f4uiBixQrlIR6E3uIAR9D3gTeBm4KIrit4IgNAW+FUWxuyAI7wOJoihuutu5L6o3Wf9VuAPZRdY1t32/1PQMypPD88wdQpVR0zMod/3Nyr+J34P4CV+bO4QqpdWXb997Jyvm4POdq3UKIajfqir5nE38ZUDNEIHe5B5C0I9N9jsHdDf8/ll1xCYjIyMjIyNzF2rAkELNHjqQkZGRkZGR+X+PnEFZRkZGRkZG5s7IGZTNT0ZBzZ1bf2ltzV4X8UqH0nvvZMXY21j/DeJOFGpzzR1ClfJwUM1ue041fE1Lbdea2/ZkHgyr7+zIyMjIyMjIVCHWlRqpQuTOjoyMjIyMjMydkaexag46nY4l89eTlihZpV+b0JcGjesBkKLKZOXCP8r2TYxLZczsoQjhgSyZv54rF3IoLdXy6pjnadLcz1xFuCsKYGb3YJrVdUWj1fHhLpHUG8astYNCvXmpqRd6YOHxVHalXMXN3oZFjzbHyVZJiU7PmL/juVJomeZevU5H1JK13EjLRGlnS6vXBuDawGjOvhgVR/yGLQB4BPgQMeTlsgSQmcejyDx6inbvWK7QVafTsWjOBpJUWdjZ2zBmSl8amVjPf1u+h93bT6NUKnh5aC869wxFrS5mzuTV5N0oxNHJng9mvIKHp+VNz+h0Oj6b9TuJKqntfTi1D419jWVbs2wP//wVhVKpYNDwnnTtFYpWq2Px/M2cO5tOiUbL0Dd706lbczOW4s7odTpilq3lRloGSltbIl4biKuXsW5eij6D+LtUN2v5+xL2qrFuXjgRxYVjJ2nz1nCzxF4ZaroVXAGMbBZEgKsLJTodi84mkKU2lm+EEEgzD3fUBqXEzOizuNjaMrp5MDYKBQqFgsVnE8gstJ5EnzWRKuvsVGQ6F0XxZFX9vX/LyX1nKNGUMu37d0k4k8KqRZsYO1e6wfiHNOKjxdIc99FdUXjUdSe8QzPW/7SNxoENGDmlP2mJF0hNvGCxnZ1HAuviYKPkhXWnifRy46NOTXh9q+S98nS0ZVBLb5745SQONkr+7t+Gh1KO8lLTBpzLKWDOoSRebt6AEZE+zDqYZOaSVMyFk9FoS0rpPm08VxOSiV21gY5j3wSgRF3EmTUb6PLRGBzcXFFt3oEmLx8Hdzeil//KpZh4PPwam7kEd+fQnjg0xaV8uXQU8bGpfP/5ZqZ9JiVBzM9T88faAyzdOIEitYaRr3xG556h/PX7UYKbNmLgiEfYsek4q3/8h7fGP2fmktzO/l1xFGtK+HbFKOJiUvlqwWZmfymVLS9XzbrVB1n754cUqTUM7fs5XXuFsv3Pk5SWavlm2TtcuXSD3X9brkoh62Q0Wk0JXad+wNXEJOJWr6f9GMnQXqIuIm7NBjpNfh8HN1cS/jTWzdgVv3I59iy1fC27btZ0K3iH+lL5xh+PRqjlxrCQQGZFG631TdxcmXrqDLklxjWII4QmbEnP4siVHCLreDA4yJ/ZMfEVnd46kEd2KsbEdN5JFEW9IAgRwDIgvCr+3n+BGJNMeAfJKh3c0p/kO1jB1/20nf99JXV8Yo6KdOgVwZwx3+Hk4siQsZabYbitdy32pknZPk9fyiO0vjGh37WiUh5fewKtHhq72ZNrkBaeyymgiaczAK72tpRacBbNHPE8XuHSN/vawQFcSzaKFa8mJOHu04jYVespuJyNf/dOOLhL5a8THIh363CSdx0wS9yV5UxUMm0eEgBoFuqH6qyJ9dzRHq+GnhQZrOcKpTQq8EL/rmUeossXr+FZxzKTOMacTqb9Q1LbaxHmx7m4jLJtTk72NDCUTW1idD92SEVgcAPGv/MT6OG9Cc+aJfbKcFV1nvphhroZFMh1k7p5zVA341avo+ByNn4mdbN2cCANWoeTumu/WeKuLA9qBfczOLMs3Qre3MOdk9lS7OKNPILdjeVTAA2dnXi7WTAeDnb8nXmJfy5c4mdVUpk81EahoERnueWzVARBUAJfI/UbioHXRFFMNNk+BikpMcBWURSn3e18VTWyY2o63yaKYpQgCO0EQYgEFiGpIoqA15Fy/axB8mI1AY6JojiyiuK6I+qCIpxcylvPtaXacrLMPX8epX2PcNwMjTnvRgEFeWomfP4G+/86zurFmxk5pX91h14pXO1syCu+3dx7U2in1cPgUG/GtPdnaXQmANeLSuji68nf/dvg4WBHnw1R5gi9UpSoi7ArZz1XotNqUdrYUJyXz5WzKnp9MhFbRwf2Tv+M2sEBuDX0onHHNlw5qzJj5JWjML+89fymtf5m/azn5cHrL81Dq9Px8tBeZfvZ2CgZ/8Y3pCReZM7XI6o97spQUFCMq1v5tldaqi2TgdZvUIuBz89Hp9UxcHhPAG5cLyAjNZtPFw0j6mQSs//3K4uXvGWW+O9FiVqNnfOd62Z2vIruMydh6+jAgZkLqB0UiGtDLxp1aEN2vOXXzTtZwbV6oxV88tFzDG1qFNOaWsHd7W15a5/ljsw529pSWGoctdHp9SgVoNODo40Nf6ZfYGNqJkqFglltQknMzSMlXzK9N3J2Ylhw+ZEgq8Q833OfAxxFUewoCEIHYAHwLIAgCIHAAKC9Ibr9giD8LoriHStSlSQVFEUxG8PIDnBYEIRzwFPAD8A7oih2Q+qx3cySHAIMB9oBTwiCUO0KYycXR4ruYQU/tOMUPZ5uX/ba1d2Z1p1bANCqUwuSLFhyl1+ixcW+YnPvTZbHXqDdz4dp512Ljo08eLedH9+dSqf36hMM2hTDN49b5poIADsnR0qLTMzZOj1Kg/Xc3tUFz0BfHD1qYevoSN2mQdxIzbjTqSwSZ9fy1nO9ifX8+KFzXM3OZfnmSaza8hGH9pzh3Jm0sn3nfTeSBT++zfTxy6s97srg4uJAoWnZdEbr+ZGD58i5ksevWyeybvtk9u+O42xsGu61nHmoWzMUCgWRbZqQnnrFXOHfEzsnJ0qLypevrG66ueAR4FdWN+sIwdxIs666WVkr+BO+9ekX3Ij29T0Y1syHVQmZDNx5ijEHzzCrfTMzRX9vCktLcTIpn0Kh4OYgd7FWy+a0CxTrdKi1WmKuXifATfoyHOpZi8nhzfksTrT+9To6fdX83J3OwDYAURSPAG1MtqUDj4miqBVFUQfYIQ2g3JEq6ezcxXTeXBTFm8MD+4AWht8TRVHME0VRC2RhBvN5SKg/UYelOdWEMyn43GIFL8xXU6IppY6JFVwIDyw7Jj46icYB1d5HqzQnsm7Qw08yL0d6uSHmGM29gR5OfGvoyJTo9Gi0OnR6PTeKS8kzTGnlqDW42VvuevY6IU24GCWtQbqakEwtH++ybZ4BvuRmZFGcl49Oq+VqYgpujSzXcF4RLcL9OXbwHADxsan4Bxnrmqu7E/aORuu5q5sT+Xlq1vy8k3+2SMvkHJ3sUdpUq06n0oRG+nP4gNSO4mJSCQw2ls3N3QkHRzvs7W1xcLDDzc2R/Dw1YZEBHNkv/X8kihfwauBZ4bktgdohgVyKkqzuVxOTcDepmx7+vuRlXDCpm8m4eVvufaQiaroVPP56Lm3qSvdOoZYbqfnG8nm7ODG3bRhKpOmq5h61OJ+bT6hnLV4XmjD19BkSc/PNFLnV444kDb+JVhAEWwBRFEtEUcwWBEEhCMJ84LQoincdBq2qT687mc4zBUEIMww1dTO8DxZg3mjTLZTY4yo+fmMher2eNya/zNa1e/BqVJfWXVqSlX6Feg1rlzvm2cG9+GH2r0wd8SU2tjYWO4UFsP18Nl18PFn/YgQKhYLx/5xjeERjUq+r+Sclh/jsAn5/KRI9sCf1Kkcv3CD5hpq5PUIY2NIbOxslE3Zb7pC6d5twLsfGs+fjeaCH1m8MImHrTly86uHdOowW/Z7l4JxFADTq0LpcZ8ga6NSjJaeOqnhv6CL0ehg7tR/rVu6lkU9dOnZrwemjCYx+dSFKpZIWEf607hBCkxBv5k1dy7aNR9Fp9Yyb2s/cxaiQrj1bcuJwAiMHL0av1zNxej/WLt9LY9+6dO7eghNHEnhj4CKUSgWhkQG07RhCRJsmLJi5gTcGLkKv1zN2iuWul2vYOoIrZ86xb9o8QE/k64NJ/OsfXLzq0bBVOM36PsvhTxcC0Kh9a9x9Gpk34PukplvBD1/OIaKOJ5+2DUcBfBmn4lnfRmSp1Ry7cpU9WVeY3y6CUr2eXVmXSCsoZGFoJHZKBWNahACQWajmq3jLLeM9Mc8C5VzAdKGhUhTFsvlEQRAcgZ+BPOCec9hVZj2/g+k8FfgSaV1XKdLUlQ5YK4piB8NxR5AWHTkjTXndtRAnsreYvaNUVcgZlK2bN5sWmjuEKsPJtsY2OwDmx9Tstncw097cIVQpNT2D8ubeXarXev700qqxnm8ecsdyCILwIvC0KIpDDGt2poqi+LhhmwJpimuXKIpzK/O3qmxe4g6mc4CuFbzXweS4DibvW+aKQxkZGRkZmf8vmKfv+DvQWxCEQ0gDJEMFQXgfSARskGaHHARBeNyw/0RRFA/f6WSWuwhDRkZGRkZGxvyYIe2IYeHxm7e8fc7k9/ta22v1nZ3aDjU3f4G9g2UuKP2vWLum4N47WTET5zibO4Qq42qRlT9dcg/+Omd3752sGHu7mj3Ns/yJG/feSeb/FVbf2ZGRkZGRkZGpOhQ1IINylTx6LiMjIyMjIyNjKcgjOwZ0Oh1fzdlAUkIWdnY2vDelL94mosV1K/awd/tpFEoF/Yb2olOPUAry1Xw6ZTWFBcWUlmgZMeZpmoX5m68QlUQBfNw5iKa1JSno5P0q0nJvF/f98FhL/knNYW18llnifFAUCpjxQhjNvN3RlOqY8Gs0qSZ5hQZ18ufFNj4ALNyhYlf8JXOFet/odDrmzviFBFUmdna2fDS9Pz6+Rqnksp92sH3rCVxdHRk0tDdduoeaMdrKcVNymmzS9spJTlfsYY+J5PRm25t7S9trbqFtTwF81D4IobYLGq2OqYcTSM+7vb193asFu9Jz+E11ESdbJXO7NMXd3hZ1qZZJB1Rcs2BR5oQ2QYR4SuWbcSyBjPzby/dltxbszcxhfeJFAP56th1pedJ0aGxOHoujU6o38Eqi0+mYN2sDCaIkqp30cV98TES1q5buZodBVPvqa73o3iuU/Dw1H32wkiK1Bls7G6bN7k+duu5mLMW/xPoHdqosqeACQRD2CIJwThCENMPvv91h35WCIDxcFXHcD4f3xKHRlPL5klEMHfUkP3y+uWxbfp6aTWsP8NmSUcxaPILvF2wEYMOqfUS0DWbe928xdmo/vpr7u7nCvy96+9fBwUZJv01RzD+ezIT2gbftM6aNP7UcrLMv/EjLhjjYKXlx0QHmboln8jPGzM+eLvYMfMiflxYdYMC3h5jxUpgZI71/9uyMoVhTws+rxvHOmGf5Yt6Gsm2Jqky2bznBktXjWfT9KL77agtFao0Zo60ch/bEUaIp5Ysloxg26km+v6XtbVx7gC+WjOKTxSP41tD21hva3nwraHs9faX2NvCvaL44lcL4Nre3t1GRfribJO18MbgBZ3PyGbI9hm0pVxgR5lOdId8X3RtL5Rv6dzSLolMYE3l7+d4K86OWSfkauzpy7lo+b+yK5Y1dsRbb0QHYu+sMxcUl/LhyNG+/+yQL528q25aXq+bX1Qf4ceUovvxuBF98KtXPLRuP0yS4Ad8ufZuHH41g5dI9Zope5iZV8mkmiuJYAEEQhgBNRVGcUBV/578kLiqZ1h2NosWEeBPRopM99SsQLT7fvyt2dtJ/oVarw95KOgetG9Rif7oktou+nEdovfKCyEcD6qID9qVfq+Boy6dNQG32nrsMQFTaNUJ9PMq2XSvQ8MSCvWh1ehq7OZKrtsxvy3ci+vR5Huokdd5CwwOIjzNqIZKTLtKqbTAODtLiWh/feiSoMgkNDzBLrJUlLiqZNvfZ9l64pe3ZWXDba1XfnQMXpLYUk51H8zrlc/j09q2LXk/ZPgAr4y9gKCoNXRzJseB6GlHPnUNZUuxncvJoXrt8+Xr51EWnh4NZxvI1q+1KfScHvusZSpFWx2enkkjNs8xF79Gnk+nYSRLVtgz345yJhPemqFat1lBUqEFhENU2CW5ISrJ0DyooKMLW1spXjFiwBLqyVNsdwpDm+XugMeAJbBFF8WPD5lGCIExEenZ+mCiKSdUV100KC+4tWnyjzzx0Oh39hkiiRVc3Se53NTuXT6es5o2xlmteNsXVzqZMAwHlxX3Bns483aQ+o/45yzut/MwY5YPj5mhLXpFJ+XR6bJQKtIYGq9XpGdzJn/cebcrSA9Ve1f4VBflFuLgZpZJKpVGaGRTciKU/7qCgoIiSklJiopJ4vk8nM0ZbOSrT9kb0MUhO79D23rTgtudiZ0u+prxI8mZ7C/Jw5omAery/N543w33LHafTw4+9Qwn2dGHE37HVHXalcbWzLVM/QPnyNanlzGN+9fjgQDyvtzSWL1utYcnZdP5JzyairjszOgoM3mGZouGCCiS8pqJarwYevPLcp+i0ega/Jolqa3m4cOywipef+5TcG4V8u/Rts8T+n2H9fZ1qXbPjCxwQRfFnQRCcgDTgY8O2/aIozhcE4RlgDlLm5WrF2cURtakI1ES0eOKgJFpcumkSAJNH/UDzcH+Elr4kJ2YxZ9JKXnv3KcJaN6nusB+I26SgGMV9zwV74eViz/Inw2jk5kiJTkdmXhH7M6xnlCevqBRXk2/6SoWxo3OT5QdTWHMklSWvd6BDkzocOZ9T3WE+EC6ujhQWmAhP9UZpZkCTBvR9pRvvvvkVjX3r0TLMHw8Py88E7OziSGHhHSSnhra3zND2JhnaXlND2/tk0kpGWHjbKygpxcWu4vb2dGB96jvb89MjoXi7Su3tQn4xBw2jPK/9HUuAuxNf9WrBE7+fMEf49yS/pLSc20phIgJ9MqA+9Z3s+bZnKN4uhvIVFHP68g1KDU/4RGXnUt/JcjM6u7iWr586E1HtoQPxZGfnsuGvyQC89+b3hEUEsPznXQwc2oPn+3QkQXWBie8vY9X6cWaJX0aiOjs7OUBHQRB6IXmyTGv3PsO/h4AZ1RhTGc3D/Tm6/yxde0cQH5tKwK2iRQdJtKhQKHB1dSI/X01q0kU++XA5E2cPIjDEelxLJy/m0tOvNn8lZRNe3w3VNePi3XnHkst+H9XKjytqjVV1dABOJl+lVwsvtkRfIMLXEzErt2xbYD0Xxj/RnJHLjlOi1aMp1ZlJ+/JghEcGsm9PLL0fa01sdDJNgo317trVPK5fz+fHFWPJz1PzzohF5bZbKjfbXjdD27tVcupg0vZcXJ0oMLS9mR8uZ9LsQTSx8LZ3+nIu3RvXYXtqNmF13Ui4bmxvn59KKft9ZLgv2WoNBy9cY3jLxlwq1PBn0mUKS7VoLbiSRl/JpWujOvydnk3CRHr/AAAgAElEQVTLOm4kmpRvYVRK2e8jWvqSU6ThcNY1RoX7c0NTyvL4DII9XLho0pmwNMIiAjiwN46HH43gTHQqTYKNEmF3d2ccHCRRrUKhKJPwurs74WoYDapd25WCgrsKuS0fC65/laU6OzvDgcuiKE4WBEEARphsawccA7oAZ6oxpjIe6tGS00dVvD9MEi2+P7UfG1buxdunLh26teD0sQTGDFmIwiBabNU+hOljl6LRlPLt/D8AcHF1YupnQ80R/n3xd0o2nRp7sPaZcBQomLhXZGhoI1JvqNmVVrG4z5rYfiaLziH1WDeqMwpg/C9RDO8aSGpOAf/EXSI+6wYbRndGr4c95y5zNMk6RnUAuvcK5+ihcwwbMB+A/80YyKplO2nsW4+u3UPJzMhmcL+52NnZMnrs89jYWP5agTLJ6bBFYGh76w1tr6Oh7b07xCg5bdU+hI/HLqXEpO05uzoxzULb3s60HDo29GTFY+EoFDDloIrBzRqRlqdmT0bF7e2PxEvM7BTCC0FeKBUKphxMqOaoK8/ujBzaN/Dk54el8k07omKA0Ij0fDX7Misu39KzGczsKNDZuzZanZ6Pj1iuZLh7r5YcP6Li9UEL0evhoxn9WL18L4196tC1h7Rt+ICFKJUKwiMDaNcxhMCgBnzy8a+s/+UQpaVaJk7tY+5i/L+nykSgUH6BsiAIYcBqpFGdAsAfqXOzAGmtTgNACwwVRTFdEIRVwAeiKGbe7W8k5W22/i7nHXhsbS1zh1CllJy7bu4QqpRoOYOy1fL07x733smKsa/ZCaL554WanUHZ0+Gpak2vH9zzhyr5nE3Y9Xq1laNKR3ZEUVxq8nsM0LKC3Qbe4dgBVRSWjIyMjIyMTGWpAUMKlj/GLSMjIyMjIyPzL7Dc5BQyMjIyMjIy5kdeoGx+/FytMxdMZTgyONXcIVQpSoXVV7+7UlhaaO4Qqoy6jlac+r4SFC+KMXcIVUqupmavaZkV2MHcIVQp89ubOwLro2Z/2sjIyMjIyMj8O+QMyjIyMjIyMjI1Guvv68idnZvodDqmT/sR8VwK9vZ2TJ/5Jn5+xuRRP/7wB1u2HMDV1Znhw5+le4/WzP5kCefiUwDIzr6Om7sLa3/5xEwluDs3zb2JYhZ29jZMvM3cu4e//5LM0oNNzL1Tysy9tnw8+xWLNffqdDo+nbmOBPEC9va2TJrWDx/femXbVy7dzY6tp1AqFQx5/WG69wqTzMTjl6NWa7Czs2HanIEWXb7PP/mdRNUF7O1sGT+1D41Nrt/aZXvYuS0KhULBwNd60rVnKHq9npcemVm2X4twP0aMfsJcRbgjOp2OOTPWoFKlY29nx5Tpg8qZ3Jf+tI3tW4/j4urI4KGP0rV7GFlZV5k+ZRnaUh169EyeOhD/gAZ3+SvmQ6GAjz/sRtPgumhKtEyeuZu0jPLTSJ4ejvzy04s89cpaNBotTo62LJj5CB7uDhQWlTD+f/9w7brlJKZTKGDm5MdoHlKfYo2WD6dtJdXEpffyCxEMeCmSUq2ORT8cZNe+RHwa1WLBjKdRKBRkZt1gwvStFBWV0r1TIO++2QWAuPiLfPTJdnMV657odTpil60lNy0DpZ0t4cMH4uJlrKuXos+g+mMLALX8fAl99eUyX5aMeblrZ0cQhAVAa6QcOM5AEnBFFMUalyFp5z/H0RRrWPPLJ0RHqfh07nK++vpDAFRiKlv+PMDaX6WOTP9XPqJ9h5ZMnCQlMSspKWXggClMn/6G2eK/F/t2xaEpLuWHlaM4E53Kovmb+XShFH9erprfVh/gty0TUKs1vNrnM7r3CmXLxhM0CW7IO+8/xcZ1R1i1dA+jxz1j5pJUzN5dZ9AUl/LTqveIjU7hy3mbmL9oOGAwE6/ax/qtk1EXahjUZz7de4Xx58ZjNAlpyKj3n+GPdYdZuWQ37463TMfSgd1xaIpL+Gb5KOJiUvn6s8188oXx+q1fc5DVmz+kSK1heL/P6dozlMz0HIKbNWLOwmFmjv7u7NkZRbGmhKWrJhAbncTn89bx2aK3AEhQZbJty3GWrZFcwkMHzqVt+6Z8s2gjfV/pQY9eERw6GMfiL35n/pcjzVmMO9K7eyAODjb0G76e8JZeTHivE2+N21q2vXMHH8a905G6tY15mfo+35y4c5f56scTPP9UU94a3oZZCw6YI/wKebSngIO9Lc8PXk5kqDcfje3F6++tA6BeHReG9m/D068swcHBlnVLB3HgcDKTxvRi1W+n2PjXWV5+PpzXB7Xn51XHmfR+L/oNX8m162reGNKB2p7OXL1mmevdLp6MRltSQuepH3AtMYm41etpN0aqd6XqIuLXbqDjpPdxcHMlccsONHn5OLi73eOslo+ipi9QtkZ7+YNy6mQ8nbtEAhAeEULcmfNl284nZdK2XXMcHCTDhZ9fA1RiKuERIQCsWvkXnTqFEyJY7mLp6NPJdOgkmaVbhvsRXylzbwNSy5l7bW4/sYUQfSqJDp0lM3FouP/tZmJvT9SFGtQm5uygYO9byme5mRhiTifTzmBebhHmhxiXUbbNyckeL4MZXK02Xj9VfAbZl3N597VvcXCw453xT+PrX7/C85uTqNOJPNSpBQCh4YGcjTMuzE9OyqJ125Ayk7uvb30SVRmMGd8HV1dJBqot1WHvYLlZ8lqHN2T/IclOH33mEqHN6pXbrtPpGfL2RjYsNyoBl62JQWmop94NXMnJsawkjm0jG7P3kCTRPR17gbAWxlHw8JbenIjKQFOiRVOiJSX9Gk1D6hMcWJcPp0nHnIjKYMr4h4mOu8C5hMt8NLYXvo09WbshymI7OgBXVeepH9YcAM+gQG6kGOvq1YQk3HwacXb1OgqvZOPbrVON6OjUFO57GutO9nJBEFYChUiZke2A34CnDfs9DQQB4w2naQh8LYrit4IgjAYGADpgr7k6VPkFalzdjN+slDZGs21IiC8/fP87BflqSkpKiTqtok9fyeWi0ZTw6y9/88tvs80RdqUpyC8qc7UA2Nxi7q3fwIP+z81Dp9Ux6DXJLH3T3PvKc5+Se0PNt0vfMkvslaGgoKjsww9AqVSUNxN7efLyc3PQ6fS8Olwqn7uHM0cPifR7dg65Nwr5btkos8ReGQoLisubl21uuX5etRj8wnx0Wh0Dhknm5Tp13RkwrAc9Hgkn5nQyMyet4fvV75ol/ruRn19UZjGH8tcuOPj/2Dvv8Ciq9v1/Nj1kA4QOIQ0SDi0JAQIivaivvjb8KiBNKQoKqFip0gUBRQErIL0p4ouKIgLSm5SEmpMEkgAJvaZs2u78/phNU0rQTWazv/l4cSW7Z7J7P86cnWdPeW5fFs3fUMTJ/ZnnsvDxUQ1OExMu8MnMNXw02z5HdQCMXm6kpmfnPzZbFJydDZitbpm795+77d9ZLAqLP38KEVyZfkN/LBWtxcXo5U5qasG0mtlsyY/J2+hGalqB11V6ejbeRndOyIs81CGE7386SpcOIZTzdKNSxXK0igzgsW4LSM/IZs2iPhw6kkxCkn3a1uRmmnDxLLhWMThhMZtxcnYmOy2NqydjaTdpFC4e7uya/BE+wXUw1qyunWBbYdFawL/nn6zZuZt7+Skp5ctCiPlAbSnlo0KIKcDjQAxQC2hufd+jQojvgH7AS8BB4BUhhLOU0vyvovoHGL08SU8v+PakFHK2rVu3Nr16/YdBL0/B378GYWHB+PioGfuePUdpHtkQb2+v0pZ8X3gZPUi/g3Pvnp0xXL1yi+9/VZ2l3xg8j7AmgSz9Zgu9+nWg63OtiI9NYeSbS1j2/Vua6L8XXl5F3cBv50z8w4axALw+6CvCIoJYsmAzvft14pluDxInUxg5fCHL176rif57Uc7LnYz0Qs7gheLbtyuGq1dSWbV+JADvvDqf0CaBiIa1cbaOVoVFBHHl8i0URbG7NQRGo0cRo8SiTu416fZ8B4YNno2ffzUahwXlO7n/uV8ybdIKJk3tZ7frdQDS0rPxKlfge+xkKEh07sULr66jTkBFvv7kcbp0XVZSEu+btPQsvLzc8x87ORXElJqWjbFQvF5ebtxKzWTyR5uYOPIRnny0Ibv3JXLtRgbXb5g4cvw8l6+q5qH7Dp6loahut8mOi4cn5sxCpqWKgpOzeq26Gb2oGBSAR0XV5qdy/RBunTnnGMmOA/BPxu3z3MuXo/paFXYvP2T9eQM4Yf39OpD3lXSXlDJLSpluba8D9AWGAVtRR4E0+SSOaFqfHdtU+dFRsYTU889vu3btJtevp7JsxWRGju7H+QtXCQnxA2Dv7iO0tU5/2TNhTQLZsyMGwOrcW3Bz8LY6S7u5ueDu7oq31bnXu3y5/NEgHzt37g2LCGL3jpMAHI1OJLiQM/Ff4zN6e5KaF5+31Zm4sn3HF9okkH071fiOH0kiKOTvzuAF8XmQlmpi0Ve/892yHQDEyxSq16hod4kOQHhEMLt2qP6/R6NPExzim9+W5+T+zdJ3eXtEdy5cuEbdEF/+3C+ZOXU1c796jYaNAzVSXjwORp+nfWv18yS8cXViT93beHbQi0156lF1mjwjM7fYyVFpceDwOTq2qQtARGgtZNzl/LboYylENvXD3c0Zb6M7wUFViI2/TJsHgvjkyx288OpqLIrCzj0JHD15ARFcFZ+Knjg7G4gIq0Xc6StahXVPfOrV4WK0eq1ejz+Nt1+t/LYKgf7cOpdCVmoaFrOZ6/EJGH3tNwm/LxSlZP6VIv9kZOdu7uX3Ut9ECOEEeAINgHhgAvCylDJLCLEZaAns+ge6/hVdHmrB7t1H6NljNIqiMGXqEBYt/An/gBp07Nics2cv0u3ZEbi6uvDOO31wtmbzCYkpPPl0+9KWe9+079yY/XtjeamP6iw9elJ3Vi7ZRm2/KrTt2Ig/98YxsJfqLB0WEZjv3Dt1/HesXb2b3FyLXTv3dugcyv49koG9P0VRFMZOep4Vi7dS279KIWfiTzAYDIQ3rUPLVoK6wTWZMm4136/aZXUm7q51GHekbafGHNgbx6t956KgMGJCd1YvVc9f6w6NOLg3jlf6zMHgZCCsSRDNW9WjfmM/Jo9ayd6dMTg7OzFion3G17FzE/btPkm/Xh+ioDBu0ossW/w7fv7VaNchjORzV+jT/QNcXV14463/w9nZiY+mfUtObi7jRi0CICCoOqPH3dZmT3N+33qa1i39WLXgGQwYGDlxM/16hpN07iZbtife9m/W/HiSD8d35tmnGuLspP6NPbFhi6RNqyDWLu6LwQBvv7+egX1akHjmOpu2xbFwxQG+W9gHJycDM+dsJSvbzOmka8yY8DjZ2bnEnrrC2Km/kZtr4cPZW1n6RQ8Aft54ktj4y/d4d+2o2awJV47FsHPiDFAUwl/qy6lfN+FVvSo1mobToNtT7JsxG4BaLZpRvrbvPV6xjOAAC5SL5Xp+H+7li6SUm4QQM4EoKeUyIcTb1peJAmYCl4FKwCxr+2BgiPX5s6jJUzughZRyyr20mZUjZf8s3IGb2Y5eQdl+FzzbgkxzrtYSSgyji31u0bcVTR88rrWEEiXLwSsoP/e1o1dQ7lSqQ7T1Hvi8RO6zsXtfLbU4ipXs2AIhRBfgRSmlTb9+6clO2UVPdsouerJTttGTnbJNqSc7LUoo2dlfesmO/e611dHR0dHR0dGxAaVWQVlKuQnYZOvXzbXY76LSf0vzIY4bG4B7K8fepZA44QutJZQY1xPsbwu7LZmxpo7WEkqU0EqOO+oIUNXDYQf8tcEB1uzodhE6Ojo6Ojo6d0RRyn6hHX0aS0dHR0dHR8eh0Ud2dHR0dHR0dO6I4gAllPVkx4rFYmHKxMVIeQY3NxfGTxyIf0DBmpJv5v/Mr+v34GX0pN+A/9K+QwQ3b6TxxGPvEBxSG4BOXZrTu88jWoVwVwwGmNSrKfX9KpKda2bk4gMkXUrPb+//UAiPR6qFErcevcDsn07gZIDR3ZsQGuiDu4sTn/54gi1HzmsVwl0xAO8/GEz9ykayzRbG7ojlTGrm34758uHGbDlzldUxBXF0CajMI0FVeWdrTOmKthEGg4FPp/QnrIE/Wdm5vPLu15xOuqi1rPtC7X9LiJVncHNzZdzE/n/pf+vZsH4vXkYPXhzwX9p3aMLNG2k8+dh7hfpfM3r1eVirEO6IxWLhhzlrOH86GRdXF54d3oMqvqo/Vsqpc/z4xQ/5x545mcQL4wcgIhsAsGPtVlKvp/LYgCc00V4cLBYLn01by+m487i6OvPG2G7U8quS375m6Va2/XYYg5OB7v0607pjKOlpJqaPXUFGeha5OWZeHv4EDcICtQviLlgsFqZNWkVcbDKuri6MndgLP/8Cj7lFCzay8ZcDeBk96NvvIdp2CM1vW7F0C1ev3GLY8Ke1kK5TCJsmO3dwSW8EbJZS9rDle9maLZsPkpWdzbKV44iOjmfm9BXM/mw4ALGxZ/ll/W6WrxoPQJ+eE2nRsiEnTyTy6GOtGDmmr4bKi8fDEb64uTrz7NQtNKlTiVHPhTPos90A+FXx4qmW/nSdshkFWP1eRzYeTqaRf0VcnQ10m/YH1St68FhzP22DuAtdAirj7uzE8z9FEV7Vm3db1mHophNFjnm9WSAV3Ite8iMfqEsbXx9irqWVplyb8uQjzfFwd6VD13G0iAhm2tjedBv4kday7ostmw+RnZ3D0pXvcyQ6no+mr+TTz94AIC72LL+u38OyVe8D0LfnZFq0bMDJE0n857EHGDmmj5bS78nx3UfJzc5h6KfDSTqZyM9fr+PFCQMBqFW3NoNnqp5sR7ZHUb5yBURkA3KyslkzazVnYpIIbRuupfx7smfrcbKzc5m1cBgnjyYxb9ZPjPu4HwBpqSZ+XLWTBf8bQaYpm6E9P6Z1x1DWLt9Ok8gQuvZsx7nES0wbvZy5y4drHMnt2bo5muzsXBYuf4ej0QnMmrGWj+cMBiA+Npnf1v/JopWqzUz/3jOJbCnAAFPGLefY0UQ6PWT/FfbvRWmVqClJbJrs3M4lXQjRARhsy/cpCQ4fiqV1mzAAwsODOXE8Ib8t4VQKzSMbFHU9jz3LiROJnDyZSL++k6lUqTwjRvelatWKmui/F82Dq7D92AUAok5fIzSwUn7b+esZvPjJDizW69nV2YmsHDPtGtVAJt9kwWttMBhg/IrDWkgvFk1rVGBn8nUAoi+n0rhKUbfhhwOroAA7zl0v8nzUxVtsTrpC9/o1Kas8GCn4fWs0APsPx9MsrOztJDp8KJYH26jfiMPCgzleqP+dPpVC88j6+f3PP6A6cdb+F3Mykf59P6BSpfK8N7q3Xfa/xGOnEc3VkZqABoGciz37t2OyTVlsXPIrr3ykJj452bk06xJJSNN6XDp7qVT13i/HoxJo1koA0CA0gLiTBfF5eLpRraYPmaZsMk3ZGKxO7l17tsPVVb39mM0W3Nztd5Ih6vApWrVWnc5Dw4M4ebyg/lnC6Qs0iwzB3d0VAH//qsTFJhMQWJ3/PtmSFq3qk5hQtkZZb4e+QPk+EUIMFUJsEULsEEL8LIRwE0IMEUKssLYvFkJoYq2dlmbCaCzkem51BQcIqVebQwck6ekmbtxIJSoqDlNGFkFBNXl1yDMsXDKGTp2bMXXKEi2kFwujpwupppz8xxaLgrP1gyfXrHA9TXVlHvlcGMfPXCfhYho+3u4EVjcyYPZOvvw1hun9IjXRXhyMrs6kZhdspzUrCs7WclUhPuV4vG41Zh9M/Nvf/ZpwuczvqvQ2enIzNSP/sepAXbb2HqSnmfAu5FrvXKT/+XEwv/+lER0Vn9//XhnyDN8sGUXHzk2ZNmWpVvLvSmZGFh5ehRzrnQyYzUW9jvdv2EtYuyZ4VVBNTst5l6Ne8/qlqvOfkpGeiZexcHxOmHML4qtavSKDnpvBsN6zeKp7WwCM3p64e7hy7cotpo9dwYtDHit13cUlPS0To3fBtVn43hAcUotDB+NJT8/kxo00jkSdxmTKonyFcjxgTZB07INSS6etnliVgS5SSosQ4jcgUkr5mRDiISHEIsBNSvl5aWkqjNHoWdQ1W7HkOy/XqetLj15deHXQTPz8qxEaVhcfHyOhYXXw8FCdfzt1ac5nc9dqIb1YpJly8fIoON0GA5gtBXd5NxcnpveLJC0zh/eXqYao19Oy2BKtrm3ZH3uFoOpFR0vsibQcM16uBRWZnQwG8rwTnwquTrVybix6LAxfowc5FgvJqZn5I0FlndS/JArqzbRsfRPzMnoWMWK1FHI+r1O3lrX/fYS/f3VCw+pQ0cebxkX6XzM+t9P+51HOnSxTIcd6Rcn31svj8JaD9Bnbr7Sl2YRyXh6YMgrisygKztZzd2BXDNeu3GLRj6MAGD1sHg3DAxGN/UmIP8+0UcsY+PrjhDWrq4n24uBl9Chyb1AKXZtBdWvS7fn2vDb4M/z8q9IoLIiKFY1aSS059JGd4iOltADZwEohxAJUh3NXa/M04AVgRmnp+StNIuqxY0cUANHR8fmu5gDXrt3ixvU0Fi8by3sj+3Dh/FWCQ/wYP3YBmzb+CcC+vcdp2DBQC+nF4mD8FTqEqlM1TepUQiYXLRf/9dDWnDx7gzFLD+VPZx2IK/ib+rUrkHItA3vl0MVbtKutTs2FV/Um9lrB4uuZfybQ46coXvjlCD/EXWTRsWSHSXQA9hyI5ZGOTQBoERHMsZi/T5PYOxERIezccQSAI9HxhFgXHUPh/jeGd0f24sL5awSH1Gb82G8K9b8Tdtv/AhvVIWa/un4s6WQiNQKLTpma0k3k5uRSsZqPFvL+NQ3DA/lzl7q4/+TRJIKCC5y+jeU9cXN3xdXNBTd3V4xGT9LSTCSdvsAH7y3hvcm9iGzdQCvpxSI8oi67dqj2IUejEwgOKXA6v34tlRs30lmw9C3eHvEcFy9cp26hdh37oTRHdsKAp6WULYUQ5YCDgEEI4QZ8AgwCvhBCtJVSZpeWrjw6d2nG3t3H6NNzAooCk6a8xJJFv+LnX50OHSM4d/YSz3d7H1dXF95853mcnZ14/c1ujBszn9WrNuHp6c74SQNLW3ax+e1wMm0aVue7ER0xGAy8u/BPBjwUQuKlNJydDLQUVXFzdaJ9qPpBNeP7o6zekcCk3j58P7ITBgOMWXpQ4yjuzKbEKzxYqyIrHg/HYDAwarvkhca+nLll4o8z17SWV6Ks2/AnndqG8sfaCRgM8PLbX2kt6b7p1KUZe3Yfp2/PSSiKwsQpA1myaAP+/tVob+1/PbuNx9XVmTff6V6k/327aguenu6Mm9Rf6zBuS6PWocQeknz2xicoikK3t3qyfc0fVPatSqNWjbly7jKVqle69wvZKQ92bMzhfbG82X8OigJvjuvO2mXbqOVXhQfaN+Lw/jiGvzgbg5MTjZoE0rRlPSa+tYjs7Fy+nPk/QB3Zy1vUbG907BzOvt0n6d9rBgowblIfli3ejJ9/Vdp1CCX53BX6dp+Gi6sLr7/VtcxNIRcHR9h6XiJGoHdYoNwf+BkoD2RZ/y0AmgNXpZSThRDjre2TgflSymfu9V5Z5v1lfMXFnWkwyLGNQHW7iLKLo9tF/HbOcUb+bofj20WUu/dBZRhv186lagQaFDG9RO6zCYffLbU4SmRkR0q5qNDvW4Gt1oedbnP48kLHji/0/D0THR0dHR0dHZ2SxRF2Y9nvfj8dHR0dHR0dzdGTHTvAZL6itYQS4+sJXlpLKFEquqdqLaFEce3aS2sJJcatHPuu/fJv+VKW3TU0xUF+a5+V0G1FaA/7q7dkS9Z10VpB2aPMJzs6Ojo6Ojo6JYcjLFB2vGXjOjo6Ojo6OjqF0Ed2rFgsFqZPXkOcTMHNzYVRE7rj5181v33Zoj/Y+MshnJwMvPhSFzp0DuPmzXTGjVhOenomFSp4MWp8NypVts/CexaLhdWfriH5VAouri70fLs71axmhGfjk/n+swIzwoQTSbw8qT+1g31ZNGUZ5txcylcqT9/3euLm4aZVCHfFYrGwcOb3nIlPwdXNhYEjulGjthpfYmwyy2b/L//Y+ONJDJ/aDxFeh4Uzv+dyylVyc828MLwrdRsGaBXCXbFYLMyfsZak+BRcXV0YPLIbNaxmi4mxySz6ZF3+sXHHk3hnWj9qB1VnzoQVAHiV9+T1Cb1xt8PzZ7FY+HjKD8THqufuvXHPUdu/wEhy5eKtbPo1CicnA30GdKJd51DMZgtzZ/5EzImz5GSb6Tf4IVq3t8+KtQZgSIO61PH2Isei8MnxOM6bCorUDRZ1aOhTHpO1Ku+EqBNkWH9/2r8WPu6uLIyz352Zjm4yrFgsXFy9nMzksxhcXKjZ8wXcqhXsJL347QoyTsfj5KFWka49aCgWk4nzyxahWMygQI2efXGvXuNOb2H36Gt27oIQYjswXkq5pdBznwJHpZTzS+p9/ynbthwjOyuXBcvf4Gh0Ip/O+JGZcwYAkHrLxLfLt/P9L6MxZWTT57mZdOgcxqJ5m2jSNIgXX3qI/XskX8xez+gJ9ul3emTnMXKyc3l77hsknEhk7Rc/MniyGp9fsC9vzBoKwKGtUVSoXIFGLRqwZu4PPPBIJC0fjmT9og3s/Gk3nZ7roGEUd+bgdjW+CV+/TtyxRJbP+ZG3PlTjC6zny5i5QwDYtyWKilXKE/5AA75fsIHadWrwytienIlPISk+xW6TnT+3HyMnO4cp814j9lgSS+b8yLvT1boygfV8Gf+56rKyZ3M0PlXK06RVfRZ9so4HuzThkf9rzcovf2HLT/t49Lm2WoZxW3ZsOU5Wdg5fLh3G8SNJfPbRT0z9VK25knrLxJoVu1j183tkmrLp120W7TqH8tvPB8nNNfPF4qFcvniTP34/onEUd6ZVtcq4OTnx5v4j1K/gzUsiiIlRJ/Pbg8t7MebgMW7lFGwHd3Ny4vWGwYgK3uy6ZN/rEh3dZDjtyGEsOTkEvj0KU8IpLq39jtqDh+a3Z549g9/Q4bgYC864FqEAACAASURBVL7oXvxuJT7tO+EdHkHaiWNcXvc9tV8eooV8m+AIRqAlOY31NZBvB24tHvgEsLIE3/MfE33oNA+0Ub1oQsMDiTlRUIXW09ONGrV8MGVkYypkZpdw6iKt2qjVP8Migog+lPD3F7YTTh07TcNINb6ghoGckX+vsptlymL94g08N6wrAP835GkiuzTDYrFw/fINvCvZ56gVgDySQPgDanwhjQNJuE0V4UxTFmsW/MYLb6jxHdkncXFxZtrwr/hh0e+EtRSlqvl+iIlOoIk1vnqNAzh18vbxfTv/N/q9+TQAgSG1SE81AWBKz/ybRYG9cORwAi0fVGNrFBZAzPFz+W2enm7UsBpJmkzZOBnUvrd/dyxVq1fgnaELmD5xDa3b228V3kYVy3Pwqlq3J+ZmKiHlC+wEDECtcp681jCYmZFhPFxLHTFwczKw+fwlViXYfzXs4poMK0pRk+EL100seK0NU19ozuboFK3k35OMU/EYGzYGwDOoLplnEvPbFIuF7MsXubBiCUkfTeXG7p0AVHumG8bGqrEtFgtOrq5/fVmdUqYkp7HWAFOEEOWklBnAU8BGoI4QYjZqP7+KWmwwAvgQ1U7iayllqTv6padnYvyLv1BurjnfA6V6dR96PD0Ni0XhhQGdAahX35cdW48hGtRmx9bjZGaWeuHnYpOZkYmnV6H4nFUzwsI3wN2/7iOifThGqxmhwWDAYrbwwUszyMnO4dE+D5e67uJiSs/Es7DZorNqRpjn0QOw9ed9tOwYjrfVuyb1ZjrpqSZGzBrEjl//ZMXcn3hlbM9S114cTOmZlDPePb4tP+2nVacwylvjq1ytAiu+WM/OjYfIyc7luYGPlLru4pCenoXRu2hshftetRoV6N11Jhazhd4D1FJdN2+kcy7pCtPn9Cfq4Gmmvv8tcxdq4iF8T8q5OJNeyBjToig4GcCigIezMz+eOc8PSck4GQxMa96Y2FupJKZlcOjqDbrUqqah8uJxJ5Nhs0Uplslwi3pVmN4vkh7Tt2oUwd2xZJpw8iz47MTJCcVsxuDsjCU7G5/2nanU+SEUi8LZT2fgERCAh686UpV18QKX1n6L76Chd3j1soEjTGOV2MiOlDITWAd0tT7VD3W0Zx4wRErZAfgFeNfa7iGlbKtFogPg5VXU7M1iKTB7273zJFeu3OKHDWNZt/F9tm05xvGjSbwwsDMpydcZOvBzLl64QfUa9rvd0aOcB5mF1gkolr+bEf656SCtH3ugyHPOLs6MXTiCnm92Y8m0FaWi9Z/g6eVBZmEzQotSJBEA2L3xEB2faJn/2Fi+HM3aNAKgaetGnLZjTylPLw9M6YXMJG8T387fDtHpyYL4ls39mVfH9ODjFe/Sb/jTzJ1ol4OqeHm5k/GX2PL63t5dMVy9nMq3v4xkzW+j2fHHcU4cPUP5CuV4sH0DDAYDEc3rcjbpslby70lGrhlP56ImtXn+c1lmM+vOpJBlsWAym4m+dpM63mWr5ERxTIY/eaklXh4uZdJk2MnDE0tmwWcnioLBej6d3Nzw6dgFJzd3nD08KFevPlnn1JHJ9NgYkr+aS80XBpbp9TqOQknvxpoH9BFC1AJ8pJSHgAbA50KIraijOnmuabKEtdyVsIggdu9Q59GPRicSHFJg1udd3hN3d1fc3Fxwd3fF6O1JaqqJwwdP8diTzZk7/1Vq+VYiLCJIK/n3pE7jII7vU+NLOJFIrTp/MSNMU80IfQqZEa76ZA2xh+MAcC/nkT99Z4/UCw0kao8aX9yxRPzqFo0vI81ETnYulasXxCfC6+T/zcno09QOst8PJBEWxGGr1thjSfjfLr6cXKoUis/L25Ny1tFKnyrlSb9ln0auoRGB7Nmpxnb8SBJ1QgrOg3d5T9w9Cvqet7cHaakmwiKC2LtDNZ+MlylUr2G/Jponbtwisoqqr34FbxLSChbv+np5MrNFGE6As8FAI5/yxN9Kv8Mr2SeObjLsWSeYtONHATAlnMK9lm9+W/alC5z5eBqKxYJizsV0Kh4PP3/SY2O49N1K/IYMxzMgUCPltkPBUiL/SpMS3Y0lpTwqhPAGXge+yXsa6CulPCOEaA3kfWprOk7WoXMo+/dIBvb+FEVRGDvpeVYs3kpt/yq069iYP/fGMqDXJxgMBsKb1qFlK8G5s1eYMEod7aharQKjJ9rn4mSA8DahxByUzBz6KaDQ+93n2fzdVqrWqkJY68ZcOneZyjWKFlLr8ExbVs36jl+WbsRgMNDj9We1EV8MmrcP5eifsYwfNBtFURg0uge/rNpKdd8qNGvbmPNnL1O1ZtH4nurbmXlTv2Xcy5/i7OJst1NYAC3aN+bI/ljGvDQbBXh1dHd+XrmNGrUr07xtY1LOXKZqzaI3/P5vdWXBRz9gMatda8Db9unA0q5TYw7sieOVvnNRFIWRE7uzask2avtXoU2HRhzYG8eg3nNwcjIQGhFEZKt6NGlel48mr2VQ7zkoisJbY+0zNoDdl64SUbkiH7UIwwB8fCyOrgG1SMnIZN/la/xx/hKzWoaTqyhsTrnEmXT7vfHfDkc3GfYOjyAj5gRJM6eioFCzdz+ubd6Ia9VqeIc1oXxkS5JmfIDB2ZnyLVvhXsuXlA/Go5jNpCxdAIB7tRrU6Nn3Hu9kvzjCNFaJGIEWRggxAJgB+Esp04QQzYCPgLxx3QGoozuDpZQ9rH/zJhAvpfzxXq9/I/uXsr9M/A4csN+ReZtQ0d1hTx0Ark6OG1/NcmX/w+9uvLBdr6BclgntYb+jtLZgXZe2pTrMXrPR6BL5MDt/fErZNgItjJRyAaq7ed7jg0CHvxwWS4FZKFLKj0tal46Ojo6Ojs69cYSRHb2Cso6Ojo6Ojo5DU+YrKLs6Ge99UJkljdibZf4U3ZEW1XIQFTzufWAZ5XSqibBKdbWWUSJczoyjqof91iX691zmidpla+3M/SCB0/PDtZZRYjy16SJrO9vvovWyR9kf2XHcO6kD4MiJDuDQiQ7gsIkO4OCJDg6d6IBjJzqAnujYGH0aS0dHR0dHR0fHznHsoQMdHR0dHR2df4UjjOzoyY4Vi8XC1EnLiZXncHNzYeyEF/APKCjVvmj+r2z4ZT9eRk9e6P8I7TqEcz7lKhPGLsZsNqMoMGZ8HwLttDCdYrGw5atvuZKYjLOrC12G9KRizQJX98SDx9n37QYAqtapTceXu6FYFHYsXMvFU2cw5+TSsvtj1IlsrFUId8VisTBt0iriYpNxdXVh7MRe+PkXOn8LNrLxlwN4GT3o2+8h2nYIzW9bsXQLV6/cYtjwp7WQXiwsFgsTxn9FjEzEzc2FyZOHEhBQUFhw3tdrWb9+O0ZjOQYM7ErHjpGcO3uRESPUulG1alVj4qRX8fR01zCKf050tGTmzMUsXfqB1lLuG0fve8VBPX+LWLp0qtZS7huLxcLECfOQMYm4ubkycfIrRfre/Hk/sH79ToxGTwYMeJoOHZuTknKZEe/ORkGhQgUjM2YOL7N9z1EosWksIcQaIcSIQo+NQggphLDLyeI/NkeRnZXD4hUjGTb8GWbN+Da/LS72HL+u38/ilaP4fN5wvpz7IyZTFl/MWUf3nh2Zt+gd+r/0KHM+WathBHfn1L4jmHNy6P7hW7Tu8yQ7Fv6Q35ZtymTn4nU8OXoQ3T98i/LVKmO6lUbMtv2YzWa6TX2TJ0a+zM0L9lv4Z+vmaLKzc1m4/B2GDX+aWTMKzkV8bDK/rf+ThSveYe7Xw/jys5/JNGWTmZnN2PcW8t3KbRoqLx6bNu0jKzub1as/5K23+vLhtIX5bVIm8vPP21n97XQWfDOeObNXYjJlMX3GIrr3eITlK6bSomVjFi1cp2EE/5z5875nzJi5ZGfZr/fc3XD0vncv5s37njFj5pCVlXPvg+2QzZv2k52VzcrVU3nzrd5M/3BxflusTGL9zztYtXoq8xe8z5w5qzCZsliy+Gcefaw1S5dNJjjYj+/XbNYwgn+PolhK5F9pUpJrdgYDrwghGlofz0Q1+Ywuwff8x0QdiuPBNuo3p7Dwupw4npTflnD6PM1bCNzdXXF3d8UvoBpxsecY/u5ztGmnjhCYzRbc3ezX2Tbl5GkCItRTUVMEcfHUmfy28zEJVA6oyY6FP/DdqFmUq+BNuQreJB0+ibFyRdZN/oJNn68kqLn9frOMOnyKVq3V+ELDgzhZ5PxdoFlkSP758/evSlxsMtlZufz3yZb0f/k/WskuNgcPnqRt26YANGkiOHYsPr/t9KlztGjRGHd3N9zd3QgIqImUiZyKP0u7ds0AaNq0PgcPntRE+7/Fz78mc+aM1FrGP8bR+9698PevwZw5o7SW8Y85dPAkbdpGABDepB7Hj53Kbzt1+hyRLRoV6XuxMon69QO5eSsNgLQ0Ey6uzrd9bZ3SoySNQK8AQ4H5Qoj2QF3gYyHEViFEfQAhxGAhxHjr72OEEAeEEFFCiEElpetOpKdnYvQucLZ1dlKdlwGCQ2pz6EAs6emZ3LiRxpGoU5hM2fj4eOPq6kJiwgVmzfyOl199orRlF5tsUybu5Qp2PxmcnLCY1fhMt9I4dyyO1n2f4qmxrxL181auJ1/ClJrOjfOXeXL0YJp37cLvc5drJf+epKcVPX9ORc5fLQ4djC90/k5jMmVRvkI5Hmjd8E4vaVekp2XgbSyX/9jZuSC+eiKAAweOk5Zm4vr1Wxw+HIPJlEn9BkFs2bIfgC2b/8RUyAi2LPHIIw/mG4OWRRy9792LRx5pXabPX1q6CaN3Qd9zKtz36gVw4MBJ0tNM3LieStRhSYYpk+o1KrNi+a888fjr7NhxmP888qBW8m2CUkL/lSYl7Y31kxCiK7AIaCOlVIT4+5ZVIUQE8CjQEnAHpgohDFLKUvu/4eXlQXph13PFkt9B69StSfeeHRk26FP8/KvSODQIn4pqfZ8/98UwdfJyJk8dYLfrdQDcPD3IzixwlkZRcLI693p4e1E9OAAvn/IA+Dasy+XEc3h6exHUvDEGg4HajUO4kXJJC+nFwstY1LVeUQqcs4Pq1qTb8+15bfBn+PlXpVFYEBUrlq36TF7GcqSnm/IfWwo5g9et60evXo/x8ksT8Q+oQVh4PXx8yvPee/2YNOlr1v+8g1atwvCxnl+d0sXR+56jY/TyLNL3FIulUN+rTa9e/2HQy5Px969BWFgIPj7lGT1qLh98MJQ2bSPYtvUgI0bM5suvRmsVwr/GERYol8bW8yXAPill8m3a8nwxBLBfSmmWUmZIKV8vzUQHoElEMLu2q862R6JPERxSO7/t+rVUblxP45tl7/H2yB5cvHCduiG+/LkvhhnTVjH3qzdo2DiwNOXeNzUb1CHx4HEAzssEKvsXLLCrVtePq2dSMN1Kw2I2cz42kUq1a1Cr0N9cTjiHdxX7rV0RHlGXXTtUrUejEwgOqZXfdv1aKjdupLNg6Vu8PeI56/mrdaeXskuaNq3Ptu2qWWJUlKRevYD8tmvXbnL9eiorVk5l9OiBXDh/hZAQf3bvjmbokB7MXzAOg5OBBx+0y+VyDo+j9z1HJ6JpfXZsOwRAdFQsIbfpe8tWTGHk6P6cv3CVkBA/ypc34u3tBUDVaj7culm2nOwdES12Y2WiOp3HAE2BZOvvrwghnFANQn8BHpdSZt3xVWxMxy4R7N1zghd7TUNRFMZPfpFlizbi51+Ndh3DOXfuCr27TcbV1YXX334WZ2cnZk5bTW6OmXGjVEP3gMAajBnfp7Qk3xfBLcM4ExXDtyM+RlEUHhrWi0PrtlCxZlXqtAjlwd5P8r8JnwMQ0jqCKgG1qFirKpe+/JbV732Eoih0Gmy/ru4dO4ezb/dJ+veagQKMm9SHZYs34+dflXYdQkk+d4W+3afh4urC6291xdm5bJWYeuihB9i9K5oePd5DUWDqB8NYuHAdAf416dgpkrPnLvDs/72Nq6sL77z7Is7OzgQF+TJq1Bzc3FwJDvHj/fdLfXZYB8fve45Ol4dasnv3EXr2GIWiKEyZOoRFC3/EP6AmHTs25+zZi3R79l21773TB2dnZ0aPGcCUSfMxmy0oKIx5f6DWYfwrHGFkpzRczztQ1NH8MdTFymdRE50zUsrxQoiRwJOoo01fSCkXCSGWAGOklGdu/+qQnrvdYa2lF8eWzTUWxaVPSNmdxy8ORteyNXp0f5StZPF++eJE0r0PKsO82jBQawklilkpmzv3iouzoXGpup5XChlaIvfZa3FzHcr1fCtFHc1/QR25+etxU4Gpf3mubwnL09HR0dHR0bkLjjCyoxcV1NHR0dHR0bkjim4Eag847CwWn+/3vPdBZZh1Z0t1JLbU+fkhx52GNCultpxOE6Z/57ifKwAB/U/d+6AyTKtquVpLKFEquZfdukta4QDJjo6Ojo6Ojk5J4QjTWI69ylBHR0dHR0fn/3v0kR0rqhHoikJGoH3/YgS64S9GoGGFjEAtKIpi10agBmBsq2Dq+XiRY7Hw/q44zqZm/u2Yz7s04o+zV/lWXsDo6sy0dgIvVxdcnQzM+PM00ZdTNdF/LwzAsIZ1CfL2Isei8MnxOFIyCuJ7pX4dGlUsT4a1cu34wyfIsFZB7RpQCx83V76Js98dOBaLhUkTvyE2JglXNxcmThqEf0DBtbZg3jp++WU3Ri9P+g14gg4dm5GRkcmkCQtITr5ETk4uo0b3IzQsWMMo7ozFYmHKxCXEyjO4ubkybmJ//AOq57d/M389G9bvxcvowYsD/kv7Dk24eSONJx97L78mVqcuzejV52GtQrgjBmDyfxvSoLo32WYL7/14nKTrGfntfSL9eDbcFwWYve0UW+Iu4+nqzOxnwqjg6Yopx8zwH45wLcM+vaUsFgvfz15DyqlkXFxd6PZWD6r6qkanyfHn+N/nBV5gSSeT6DdhADUCarBq5kosZjMK0G14N6r5Vb/DO2iLxWJhxpS1xMvzuLo5M3J8N/z8q+S3L1+0ld9/PYyTk4G+AzvToXMoaakmxr67jExTNi6uLoyf+jyVq5Tdop6OMLJTIsmO1R5iQqGnagNXpZQtS+L9bEGBEegIjkSfZtaM75g1dwhQYAS6ZJXqz9Ov1zQiWwq+mPMj3Xt2pGPnCHbvPM6cT37go09f0TKMO9LZvzJuzk70/iWasKrevBNZh9e2nChyzGtNA6jgXnBJvNDIl73nb7DsRAqB5T2Z3r4+3X46XNrSi8WD1Srj6uTE8H1HqF/Bm5dFEOMPF3hBBZf3YtTBY9zKKZjLd3Ny4o1GwdSv4M3Oi1e0kF1sNm86QHZWNstXTSI6Ko4Z05cy57N3AIiNPcP69btYuXoyAL2ff5+WDzRm4Tc/ERLix9QPhyBlEjImyW6TnS2bD5GdncPSle9zJDqej6av5NPP3gAgLvYsv67fw7JV7wPQt+dkWrRswMkTSfznsQcYOcY+a1vl8XD9ari7OPHMN/uI8K3AmIcFL61W+5GPpyt9mvvz2Fe7cXdx4vdXW/PgJ5d5vmltjp6/xeztp3g2vBbD2tVlwoYYjSO5Pcd2HSU3O4fX5wwn8UQiP365jgGT1LoyvsG1GfLxMACitkVRvnIFGrRowIoPl9Pm6TaEtg4j5s+TrF+wnn7j+2sZxh3ZvuU42Vm5zFs2jGPRScyZ+RPTZ/cDIPWWie9W7OS79SMwmbJ54bmP6dA5lPXrDlA3pCZD33ycdWv2snzRVl57+0mNI/k36MnObZFSbgM6AAghqgM7gTdL4r1sRdSheB5s0wiAsPA6fzECvUDzFvVwd1eNPv0CqhMXm8zwd5/FaFQXEZvNZrs2Ao2oXp5dydcBOHI5lUaVi9olPBRQBYsCO63HACw5kUy2WV2o6exkINtsvxd8I5/yHLiiao+5mUpI+YL4DIBvOU9ebxSMj5sbG5IvsjH5Im5OBjalXOLw1Rv4edn3YvDDh2Jo3aYJAOFNQjh+7HR+2+lTyUS2aIi7uxsA/gE1iJVn2L3zCP95tBUvD/wAo9GT0WPt82YCcPhQLA+2UU11w8KDOX48Ib/t9KkUmkfWLxRfdeJiz3LiRCIxJxPp3/cDKlUqz3uje1O1akVN9N+NSH8ftsWryfTh5JuE1ir4hn/dlMOjX+7GrCjUNrpzK1NNxr/Zl4STdf1+rQqeXE6z3wXhCcdOUz+yAQCBDQM5G3v2b8dkmbL4bfGvDJmlJj5PDn4KT2ufs5gtuLja7yRD9OEEHmit2hw1Dg/g5ImC+Dw93ahR0weTKZvMjGwMBvWk1Q2pQVKCavGRnp5Zpr3BHIUSXbMjhHAF1gAzgL1CiPlCiN+shp+TrMeECCG2CSH2CCE2CyGqlqSmO/F3I1BDISNJXw4diPuLEWjWX4xA1/Dyq49rIb1YGF1dSM0uGNWwKArO1g/T4Irl+G+dqsw9XHQaJzXbTJbZQmVPV6a1FXxyMLEUFd8f5VycSbeeL1Djy7tZeDg7s+7MeaYfiWX0weM84VeDIGM50nLNHLp6QyPF90damgnvwkanhcwIQ+r5c/BADOnpVjPCqFhMpkyuX0/l5q10vp4/ivYdmjFz+jKt5N+T9DQT3sbbG/GG1PPj4AGpxncjjeioeEwZWQQF1eSVIc/wzZJRdOzclGlTlmol/64Y3V1IzSroe2ZFwdlgKPK4b6Q/Pwxoya8nL+Y/b1FgRd/mvNDCn61x9jvymJmRhYdXgdGpk5MBs9lc5Jh9G/YS3r4JxgrqlxBjBSPOLs5cOnuRH79exyN9HylVzfdDelomRmNBfIWvTYBqNSrS8+kZvNh9Fs/1agtAhYpe7N8Ty/NPT2f5om080bVFqeu2JYqilMi/0qSk0+lPgeNSyq+FEIHAXinlQCGEB3AOGItaTXmqlHKDEKIbEAFsLGFdf0M1Ai349mQpZCRZ1Ai02m2MQFcweWp/u12vA5CWk4uXa8G3C4PBgHXQhifrVqNaOTe++U8otYwe5JgtJKdlsSv5OiEVyzGjfX1mHkjgwMWbGqm/Nxm5Zso5F43PYo0vy2zmf0kpZFnUkamoazep4+1FQlrG7V7KLjEaPYsY1SpFjEB9eb7nwwx+eRr+/tUJCwumok95KlY00rFTMwA6dGzKgvnrNNFeHLz+El/R/leLHr268Oqgj/D3r05oWB0q+njTOKwOHh7ugLpe5/O5azXRfi/SsnLxciu4Np0MBsx/+aBf8ucZVh48y6JezWgVWIk9idcA6LnkAHUre/FNz6a0n7OjVHUXF49y7mRlFHx2KoqCs3PRkYxDmw/ywvv9ijwXFxXH97O/o9d7ve12vQ6oJsPpheIrbMK7Z2cMV6/c4vtfRwHwxuB5hDUJZOk3W+jVrwNdn2tFfGwKI99cwrLv39JEv45KiY3sCCH6AWHAMOtT14BIIcRyYBaquzmoJqB7AKSU30opSz3RAWgSUbeQEehpgkN889uKGoF25+KFa4WMQFcz96vX7d4I9PClW7StXQmAsKrexF0vMKb7+GAiPddH02/DUdbFX2TJiWR2JV+nToVyfNSxAe9tl0Wmt+yREzduEVlVNUusX8GbxNSC+Hy9PPm4ZRhOgLPBQOOK5Ym/VbaM+SKaCnZsV9d5REfFEVLPL7/t2rVb3LieytLlExgx6kUuWM0II5oJdmxT/+bggRiCg2vf9rXtgYiIEHbuOALAkeh4QgoZ8arxpbF42RjeHdmLC+evERxSm/Fjv2HTxj8B2Lf3BA3t1ALhwNkbdAxRB6wjfCsgLxYs8q9TuRxfdlOnJ3MsCtlmCxZF4dU2QXQNUw1DM3LMf0uO7InARnU4uV9d/5d4IpGaQTWLtJvSTOTm5OJTrcDMNC4qjv99tpaXpw7GT/iXqt77JaxJIHt2qOuljkUnUTek4Eutd3lP3N1dcXNzwd3dFW9vT9JSTXiXL5c/GuRTyVgkkS+LKIqlRP6VJiXijSWEiARWAG2klBetz70G+Eop3xNCBAMSdWRpLfCZlHKTEKIXUElKOae475Weu80mAeTtxoqLPYeiwPjJL7Br+zHVSLJjOFMmLCPmxBlcXV0YOrwrzZrXo3vXieTk5FC5SgUAAgKr29QItOUy2+WihXdjAYzdFUtb30qcSTWx9ey1/ONebeLPFVM238oLzO7UEFHJi5Q0taOmZpv/tqj53+Bb03ZFBfN3Yxm9wAAfH4sjsooPKRmZ7L18jecCfWlbowpmi8KmlEusP3ch/28fqlUNPy9Pm+/G+vkhL5u9Vv5uLJkECkz6YDA7tkfh71+dDh2bMXH8fE6cSMDV1YU3hj9P88gG3LyRxvtjv+LK5Ru4uDjzwYev4utb7d5vVgxsXVQwbzdWXOxZFEVh4pSB7Nh+BH//arTvGMGk8YuIOZmEq6szrw1/jmbN63Pu3GXGjZkPCnh6ujNuUn+brdmpP9l2o5h5u7HqVzdiwMA7647RIaQKSdcy2BR7mdfb16VDcBUUBbbGX2H29lNU8XLjo6dDcXdxwslg4MPNsRw8a7sp18/62+5Gk7cb6/zpFBRFocc7PTm5/wRValWl8YONORNzhk0rNtJ/YoEZ5oyXp2POycXbxxuAqn7V6Da8u8002bKoYP5urNjzoMDoSd3Zs+Mktf2q0LZjI+Z99ht7d8Xg5OREWEQgQ998nCuXbzF1/HeYMrLIzbXw0pBHaNGqns00VXJ/olQrshoDXyiRbDstcXGpxVFSyc7vQBDqVFUeClAFSAXSAT+gM+AJfIXqdp4B9AaaoCZKE+/1XrZKduwRWyY79ogtkx17xJbJjr3h6BWUbZns2CO2THbsEcevoFy6yY5XQJ8Suc+mJy0t20agUsqH7vNPOv/l8RbrPx0dHR0dHR0N0cIbSwjhBHwOhANZwEApZXyh9peAQUAuMFlK+fPdXs+xhw50dHR0dHR0yiJPAx5SylbACOCjvAYhRA3gNaA18AgwVQjhfttXsaInOzo6Ojo6Ojp3RKMFym2ADQBSyr1A80JtLYBdUsos9P6NxAAAGypJREFUKeVNIB51Q9Qdsd9KTsXEy6W9wy78OPai1gp0dG6P/ZbPtA2J47RWoKNjP5jOrNTiPlseKLx4ziyEcJFS5t6mLRWocLcX00d2dHR0dHR0dOyNW4B3ocdO1kTndm3ewF23K+rJjo6Ojo6Ojo69sQt4DEAI8QBwtFDbfqCtEMJDCFEBaAAcu9uLlcjWcx0dHR0dHR2df0qh3VhhqOWq+qEmP/FSyh+tu7FeRh20+UBK+f3dXk9PdnR0dHR0dHQcGn0aS0dHR0dHR8eh0ZMdHR0dHR0dHYdGT3Z0dHR0dHR0HBo92dHR0dHRGCGE372P0tHR+afoC5T/P0cI0Qi1QJMF+AB1VftmbVXZDiGEF+AD5KCu3F8ipbStvbkdIISoClyVUjq2w6MDIYR4DTABFVF3mmyQUr6praqSwZGuTyGE253apJTZpalFp/iU+QrKpYEQwht4FPDIe05KuUQ7RTblS+B1YAIwGpgOOEyyAywHFgL/B5wAvkb1UnEIhBAdgQWoRbYqCiFeklL+rrGsf40Qou+d2hyo7z0PtEctid8Ix+p3gMNenxJQULdDF0YB6pS+HJ3ioCc7xWMdkAKctT52pOGwHOA44Cal3CuEcLRrwgf4EXhNStlXCPEfrQXZmElAGyllihDCF1gLlPWbCahFwgAeADKA3UAkqlOFoyQ7ClATuCilVIQQlbQWVAI43PUppQzSWoPO/eNoN7aSwklK2VtrESWEAqwAfhFCdAPSNdZja9yAt4BDQoiGgFFjPbbGLKVMAZBSJgshMrUWZAuklCMBhBAbpJT/zXteCLFRO1U25w9gO/C8EGIWcNeiaGUUh7w+AYQQTwJDUBNwA1BZSnlXM0od7dCTneJxRAjREojCOqrjQHOz3YEWUspfhBAdrI8dibeBp4ApQC/gVW3l2JxbQohhqDfNdsA1jfXYmmpCiIpSyhtCiMpAZa0F2Qop5WghxBigCvCulDJHa00lgCNfn+8Dw4DBqInrQ9rK0bkb+m6s4tEeWAXEoM7Xxmgrx6ZkAx2FEOtRkwKHQkq5C5iFugj7Z8BZW0U2pzfgj5rM+aMudHUkpgAHhBCHgH3AcI312Azrl4tTqNM6cUIIR7xZOvL1eVVKuQdASrkIqK2tHJ27oY/sFAMpZbgQwgDk7Sgwa63JhnwDbENdyNseWAQ8qaUgWyKEWAC0AryAcqg3lwc0FWVbhkkp38l7IISYCozUUI9NsfrdfC+EqIbj9b3JONh6ltvgyNdnlhCiHeAqhHgEdf2Vjp2iJzvFwPoN7BvgJuDjIDsK8qgspZxj/T1KCPGspmpsTwPUnS5fAaOANdrKsQ1CiAHAQKCBEOIx69NOqGuUHOVm8rd1EUKIKlLKUI1l2QpHXs9yu+vTGfU8Osr1+QpQHzVpnYQ6raVjp+jTWHdBCNHT+mveN7AIoLX1saPgKYSoASCEqI7jTfOkSikVwEtKeQU1GXAElqFuXf7W+vN54DnUUSxH4n1gPOpOyMVAtKZqbMstIcQwIUS4dV2LI61nud31+SwOdH1KKZOBo0Aa6vTqbm0V6dwNPdm5O3lbzIt8AwMc5hsYMBbYLYQ4jNpZx2isx9YcFEK8DaQIIVbhIKOZUsosKWUi6uLI6kAAEAS01FJXCfDXdRGOVGm48HoWP6C/tnJsR6HrczhgRv3MfBGooaEsmyKE+BzYj7qec7X1p46d4hAf/CWFlHKl9VdH3lFwXUpZxzo9cEUI0V5rQbZESjnKWhTShFoYcp/GkmzNGqAaRWtAbddOjs1x2HURUsqbQogZFBQr9QauayipJHDkop4tgDqOUBX6/wf0ZKd49EYd8ZiC2mHL/DcwIURboCEwXAjxsfU5J2Ao0FhLbbbE6jn0PAU3lAhgonaKbE4NKeWDWosoQRx2XYR1ZOBR4DxqnRYFcLRzmVfU83UHLOoZj/q5kqG1EJ17oyc7d0EIUVtKeQ51mmBeoaaqlP1vYNdRh5TdKfi2bAHe1UxRyfAdsImCkQ9HI0YIUStvmtVREEL4F3oYZ/3pMNvOrbQA6jr4yEBeUc+DDljU0x9IEkLEWx8rDv7Fo0yjJzt3503rv6/4u0VEp9KXYzuklMeAY0KIeYVvlEIIVw1llQSpUkpHW4dUmDbAGSHEFdRrVJFS1tJYky1Ybf1ZGXV65xjqSORFoKlWomzM/w8jA28BT+OYRT2f11qATvHRk527UMiB+FGggZTysBDiaWC9hrJszRNCiLdQrwUDqldWPW0l2ZRjQogewGEKql/HaivJdkgpHelc5SOlbAUghPgB6CulTLU62K+8+1+WKRx+ZEBKuVsIUQ51p+AOwGH6HurC61moSXgsjjfy6FDoyU7xWIY6FXIYNRHoBvS861+UHV5CLSY4BnXK5w1t5dicJtZ/eSiU8VG5wgghGqE611dEXQx6TEr5s7aqbEptKWUqgJQyXQjhCKNWeTj8yIAQ4gPUysINUKu1j8Rx4p4HfIG6IaADqrt7Zy0F6dwZPdkpHr5Syi8BpJTThRB/aC3IhlyRUp4XQnhLKbcKIRxp8S5Syo5aayhhZqOW4J+H+mH7K6othqOwUQixDTiAusZlqcZ6bIkr6ohHnpFkLWCQpopsTxspZTshxB9SysVCiFe0FmRDPKSUP1p//58Q4s27Hq2jKXqdnWIihKhn/VkXxyq8d9M6NacIIQahLr52GIQQyUKIHCFEihAiWwhxSwjhUD5EUsp41CmQy0Cq1npsiZRyNOo6j33AUCnlpxpLsiVLrD/boNZIchiT00K4CCE8UD9fnFGnfhwFFyFEKID151/XderYEXqyUzzeAL4VQqSgLpx0pAz+JSAJGIE6RedI37xAHWJubF202wD4H+oarEmaqrId16xJqpd1bdINrQXZAiHEQOvPqagLW5sA3a3TIo5ChpRyKnBOSvki6q5PR2MWcBC1nMU+4HNt5diU14BvhBDJqKOqr2usR+cu6NNYxUBKuc86ElAXiJVSOlJRwTVSyoetv7+lqZKSobaUUgJIKU8JIfyllPFCiFythdmIAaieX1eA5tbHjkBeqYAYTVWULAarVYvRuvi6ktaCbI2U8jshxCYgGEiwWrY4BFLKw0AkqPW8pJSOWt7CIdCTnWIghHgVdXTnGNBICDFJSrlMY1m24oYQ4ilAotbZcajdSsB5IcQ0VCuMB4EL1sQ1W1tZ/45CNaBqoJrU5lEFx6jwrQghHkYtuOeoTAC6om6ASKBgWsthEEIspND0jhACKWWZL8oKIIR4DbUye0WgnxBiQ6EdvDp2hp7sFI+XgDApZaZ1G+U21A8oR6AqRXdgOdRuJaAv8DLq1NVRVFPJCMr+jpDCNaAK4yjn707nRwE2lqaQkkJKuZ0Ca49qWmopQfL8ogyo9ZEcbTdde2AD0AjYrK0cnbuhJzvF4yKQN+1hAq5qqMXWfFR4q7IQopuWYmyFEKK5lPIAqpdZDAXTIe2klGX+Zpn3DdKBd5u9JKXMFUI4ikv93xBCTEGddsyvoOwgBSHzkVL+VujhBiFEme97hVBQq89flFIqQgiHm4Z0JPRkp3g4AVFCiN2oowKuQogVAFLKMllvRwjxONAaeF4IkVfIzAl4CvhWM2G2ozPqduW/jhA4zMgAgBBiMuoNM3+qwEFumEtQa1lJCmLL84+qo5UoG/NfIEBKmaW1kJLCOhWZR00caxH2FtSRueeFELOA7zXWo3MX9GSneEwp9PtyzVTYlmjUra4m1BsK/6+9ew+2qjzvOP49XsCI11GTaLxEMvYnoIk2CUWj0OoEtYlRJ1WUMDLesFovSbTBS0wNOloTbeotdawSb8WEknqJNFprUqsSL4xivYQfVQQxEUfFGwoG5PSPd23dnMphE9fmPfs9z2eG2Wtvzpn5Meew17PXet/nIX3C/Okqv6OD2L6oejwqd5Y2+yrw6dJOmI0PEbZ3lNQFbFHS4tbKY6RxEUX97Hpo/rCxlAKGKDf5re0dACTNtN3R6wBLF8VOa3bo+YLtjl5MWO0cuF7SjSUOIpT0IukqwEBgQ9Lunk8BL9v+dMZodSv6hCnpAOAKUj+ojYAJtv8rb6raPElaQL+Q6qqV7VKuWgHpw4ak3QEBT9l+InemGk2g+vAbhU7fF8VOa4ZUj12kfh+LKGTnRImFDoDtrQEk3QScaXtBNWrgR3mT1a70E+a5wAjbL1fbtG8FRuSNVJsxpGaCRfRG+jCSziMtmH8YOEXSLbZ/mDlWXQZKeoyVd7J25LKG/iCKnRbYPrNxXF1SL6kdf+kGN/pf2P69pO1zB6pZ6SfMt6rO0NheKOnt3IFqNB94u7RbkD0cAAy3vaLqoPwboJRiZ2LuAKF1Uey0oMeOkK1JJ5ciVM3MNidNO58A3GB7ft5UtXpa0o2kT5Z7kCYvl6TIE2ZTp+T1JN0B3E+ajVXSv3M74FlJc6vnxU09B14ANgbeIM0AeylvnFo9Sip4tgamA/+TN07oTRQ7rWnsCOkiLegt5ZMJpHvOPwG+DjwNXA3slzVRvRo9doYBP20a3FeKUk+Y7vEIcFuOIG00JneAtWAbYI6kx4GhwB+qXa0U8Hs6mTR4dxRpXMS11XHog6LYac05BXVM7mlz4HbgFNtHSto/d6A6VWuSpld/SlTkCdP29QCS1iO15G+eDF6K8R/y2qS1nqK9Ds0doI22sD1Z0jjbM6olDqGPimKnNcdRTsfkngaQZmI9KmkosFHmPGHNrE86oTQXA8dnTVSvfyP9jn4KWBf4PXBz1kT1adzSaXQXLnEw8/8r6GwXU9BJ2rl63JayJroXJ4qd1pS86v404GDgfGAccGLeOGEN3QD8AtiLVAiUVqxuanuUpGuAk4G7cweqi+2VRn1I+mWuLG1UZEEnaRPS1POfkHbrTiPeO/u0KHZa8/fAa7lDtEN1+XVz0tqWObYfzp2pTpJGkvrsrANcTrolOSVvqlq9Y/tCSTvZPlpSaQuwl1WPg2wvKWl8hKQ/aXq6NVDaTsEiCzpJJ5E+JC4HTrZ9Z+ZIoQVR7LTmdNt75Q7RDpIuBHYi7XYZL2mk7dMyx6rTD4BvAFeSxmNMBUoqdrqq/jMbVTvrSpvPc4uk7wGPS3oQeDN3oBo1FwJLgdNzBWmXQgu6saQmiZsAN5IGgYY+Loqd1iySdCor38YqZb7SSNtfApB0KfBg5jx1W0I1yLXq0zIwd6CafR84hLSm7DkKaXbZYPvKxrGk6cD/ZoxTq4KHuDZrLuiWUEZBt7TqmPxKSVcaSxfFTmteJXVO3q16XtIwyfUlrVPtWmoMWizJm8B/Aj+W9DfA85nz1Mr2f5OGEQJ8PGeWdpC0G+kW6wZNL3f0fCVJz7GK/2eFdb9eqaCTtF2jwWdBYgdWh4hipwXVfJddSH0i5tielTtTjX4GPFDdIviz6nlJDgM+Y/vp6md4Te5AdZJ0JHAmaQYYUNwJ8zrSbKySTpI7V49dwF3A6F6+tqNJOoV0RWcz4ChJd9r+duZYH9UwSVNIP7/GMVDUxpXiRLHTAkknk+7TPgScLmmq7Yszx6qF7Usk3UV6A77W9pO5M9VsS+AsSVuRdkwMIv0cSzEROJCyioFmC20XVaA2d7uW9F5p3a97OILUaO9OUmPPe/LGqcVhTcdXZUsR1kgUO60ZC+xte7mk9YEZQEcXO9XC5J6X0v9UErbPypGpTa4GLgHOId3uuZ5yBkkCzLX9TO4QbTRP0hmk6e7dUNR6uf6gm7Qw+SXb3ZI6fgG97XtzZwhrLoqd1nTZXg5ge5mkZav7hg4wO3eAtWQD27+S9F3blrQ0d6CavVNt553FB8VAScXqQNLOF1XPO369XI8dShtK2olq7YftOXlStc2vSR8yjpD0I+DnmfOEfiqKndbcL2kaaYjkXsADmfN8ZP2kHT/Au5L2A9aVNIK0xbck/547QJu9VsAaj5567lC6ujruBvZZ+3Hax/bZwNlVL6+J1S6mENa6KHZWQ9IE0gLQ0cDngXttX5E3Va1KbscPaSfPxaS1O6cDJ+SNUw9JX7A9E3gxd5Y2GyJpM9uv5w5Sl36y5Rx4v6nnj0nvLf8qab7tazPHCv1QFDu9kHQusAtwk+3pkp4C/kHS5rbPy5uuNsW24wew/QJweO4cbbAvMJO0ALSbldsGdPRtnh6GAq9Kepn07+u2XdrVx5KdD4wk3b66gHRVPIqdsNZFsdO7A4ARthtrIeZJGkNaoFxKsVNsO34ASWcB3wHeoSoISjhZ2r6oOvxbYHfbd1dt7IsaWGt7h9wZwkeywvYiSd22l0p6K3eg0D9FsdO7xY1Cp6FaoFzSf9iS2/FD2ia6je13cgdpk5v5YA3IIlKx89V8ceolaRhpe+9mwL8AT9q+I2+q+khah1SE7wk8VOCalmeqnZ9bVLvq5ucOFPqnKHZ6t0TSYNtzGy9IGkxBXYZLbsdfmUdaBFqqQbanAdieIum43IFqdhlwFPDPpNsfvwSKKHYkXQTMBXYgTQR/CRifNVT9TiR1vL4feBso7fczdIgodno3EbhV0j2kN6Xtgf0o4A1J0uRe/rqj2/H3MAB4QtIT1fPuwrqc/kHSl0kzzYYD72XOUzvbz1S3QV4u7KrqXrYnSvq17b+o3mdKc4ftYjtEh84RxU4vbD8laW/gINKW7EeBSbZLeMP9ArAh6bbHDMqd8XLR6r+kox1L2m12GfA0cHzeOLVbJOl4YJCkw4FidmWR2iEMJzVOHABslTtQG7wu6SBWHqJcWi+h0AGi2FkN229Q2CRpANufrWZFjQPOIDX+uqnAbryPkbonDwXmUM7CciBd9QAObjyXtHXGOO1wDHAW8AqpQD8mb5xa3QBcTrqS+gPg0rxx6iVpE2BH4JtNLxfXSyh0hq7u7mKWn4SPoOqHcTKwne1ixilUzSDvJTWEHAXsa/treVPVR9IkUu+gAaQrdXNsD8ubql6SDiR1UH7C9l2589RJ0hbAYOA526/kzlOXamfgaaTbqifZvjNzpNDPrZM7QMhL0iaSxpM+PX+SwrYuA1vYvtz2LNuXApvnDlSz/YFtSTuVhgC/yxunXpKuBL5BapFwrKQfZo5UG0mHkW4hnw08KGlc5kh1GksqUEcAp2bOEkLcxuqvJB1Kaki3PamL8l/bnpc1VHt8TNInbS+U9AlSJ9eSvGr7XUkbVwt5N8wdqGafs71XdXyppBlZ09TrW8DnbS+WtDHwK8r5sLG02kb/Smm9u0Jniis7/dfPgJ1JW813BS6QNEXSlLyxancOMEPSLNKn6HMy56nbC5KOBt6u+plsnDtQzZ6XtC1AVawuyJynTitsLwaoNj2UNretodTND6GDxJWd/qtfzOexfTcwWNKWha2JOLI6nEFaF/Ek6aRyVbZQNZL0Imkx6wbAIZKeJ92uK+ZnCDwr6RLS5oCRwLOZ89RpWPXBqavpGIDCWj+EDhHFTj9l+97cGdpJ0hW2T5L0G6omkJIAsL1nzmw1GdJ0fATQOLEUsePAdmm7yj7M0aRWAV8mtQ2YmDdOrQ5rOi6iAA+dLYqdUKrGFvPD+aAIGAi8my1RjWyf2TiWNML2WTnztIukXYHJpKs6C4GjbT+WN1VtBpG6Jjf6do2lkDYXpX+YCp0n1uyEItl+qTocDZxqez5wBel2QWmKuJqzCpcBx1ZXeo4i/QxLcQvwl6SrdENIa+hCCG0QV3ZC6U4gDVkE+AppfcSN+eKENbSO7ccBbM+StDx3oBp12S5pNEsIfVYUO6F079leCu9PrC/iKoikm0lXdEpfALqsairYWMTb8bchm7Ziz5W0B2kMTTdAgVPPQ+gTotgJpbtN0n3Aw6TJ0rdnzlOXq1ZxXJpjgGtIC7AfJt3K6nTmg0J1n6bjblI35RBCzWJcRCiepN1I3VxnN26JhL5N0lDgCtv7SJoNvEZapHxsKSMjJHUB29peIOmLth/JnSmEUsUC5VAkScdWjxeStsF+Dhgj6YKswUKrLgK+Ux2/aHsPUm+oM/JFqt0/AeOr43GS/jFnmBBKFsVOKFWj0+5s0m2D5j+h79vQ9szq+A14f8L7+vki1W532+cD2D6VdJs1hNAGUeyEUi2pJrk/9yF/Qt/3scaB7YObXl+WIUu7dFVTz5G0GbGGMoS2if9coVQnVI+fAQYAjwC7A4uBP8+UKbTud5KG23648YKk4aTGgqWYBMyU9BqwKXBi5jwhFCsWKIeiSZoOHGR7uaR1gem298+dK/RO0o6knXP3AM+QdintCxxo+/mc2epU/U5+grQuKd6MQ2iTuI0VStc8Y2k94OO5goTW2X4OGE4adDoImAnsWVihMwp4nFTQfV/SMZkjhVCsuI0VSnct8JSkJ4GhwN9lzhNaZHsJMDV3jjY6j9Qo8efABcADpN/XEELN4spOKJrtK4EvkrYy72275JNn6CwrbC8Cuqsu32+t7htCCH+cWLMTilY1FJwAbNB4LeYRhb5A0jXAy6RhoDcDQ2yP7/27Qgh/jLiNFUp3HWlS9oLVfF0Ia9uJwNHA/cDbwHF544RQrih2QukW2r4md4gQPsQdtkfnDhFCfxDFTijdPElnAI/xwWTp/8gbKQQAXpf0NWAOsALA9py8kUIoUxQ7oXQDSUNAVT3vBqLYCX3BVsC3mp53k6aghxBqFguUQ/Ek7ULadj7H9qzceUJokLQpsAMw1/bi3HlCKFUUO6Fokk4GxgIPAXsCU21fnDdVCCDp68B3SVfYp5K2oJ+fN1UIZYo+O6F0Y0n9db4JfAkYkzlPCA3fBkYArwDnA4fkjRNCuaLYCaXrsr0cwPYyypqaHTrbCtvvkq7odJO2n4cQ2iAWKIfS3S9pGnAfsDepJX8IfcF9km4GtpV0FfBI7kAhlCrW7ITiSfoKMAT4re3pufOE0CBpf2BXYLbtX+TOE0Kp4spOKJakCcBk29MlvQkMy50pBABJnwX+CtgSeAGYnTdRCGWLNTuhSJLOBUYDA6qXFgCjJX0vW6gQAEmHApNJv5N3kgaATpN0UNZgIRQsruyEUh0AjKgWfmJ7nqQxwAxgUtZkob87FRhl+/0FyZKuA24HbssVKoSSxZWdUKrFjUKnodqN9VamPCE0LG8udABsvwW8lylPCMWLYieUaomkwc0vVM9jRX7IbcUqXo/34xDaJG5jhVJNBG6VdA8wF9ge2A8YnzVVCDBM0pQer3WRRpqEENogtp6HYlVzhw4CtgHmA3dUtwtCyEbSqFX9ne1712aWEPqLKHZCCCGEULS4RxxCCCGEokWxE0IIIYSiRbETQgghhKJFsRNCCCGEokWxE0IIIYSi/R9yI1zfGLCxbgAAAABJRU5ErkJggg==\n",
"text/plain": [
"<Figure size 720x576 with 2 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig2, ax2 = plt.subplots(figsize=(10,8))\n",
"sns.heatmap(indice.drop('indice', axis=1), cmap=\"YlGnBu_r\", yticklabels=True, ax=ax2, annot=True,\n",
" xticklabels=componentes_dicc.values())\n",
"ax2.set_ylabel('')\n",
"fig2.savefig('graficas/componentes_indice.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 190,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 210,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax3 = radial_sexo('Yuc.')\n",
"ax3.figure.suptitle('Comparativo del índice por sexo: Yucatán')\n",
"ax3.figure.savefig('graficas/componentes_sexo_yucatan.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax4 = radial_estado('Ags.', 'BCS')\n",
"ax4.figure.suptitle('Comparativo del índice por estados: Ags. vs BCS')\n",
"ax4.figure.savefig('graficas/componentes_entidades_Ags_BCS.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 213,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax5 = sns.regplot(x=\"corrupcion\", y=\"inseguridad\", data=indice_multi['general'].reset_index())\n",
"ax5.set_xlabel('Corrupción')\n",
"ax5.set_ylabel('Malos tratos')\n",
"ax5.figure.suptitle('Relación entre componente de Malos tratos y Corrupción')\n",
"ax5.figure.savefig('graficas/relacion_malostratos_corrupcion.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax6= sns.regplot(x=\"servicios\", y=\"corrupcion\", data=indice_multi['general'])\n",
"ax6.figure.suptitle('Relación entre componente de Condiciones dignas y Corrupción')\n",
"ax6.set_xlabel('Condiciones dignas')\n",
"ax6.set_ylabel('Corrupción')\n",
"ax6.figure.savefig('graficas/relacion_condicionesdignas_corrupcion.png', dpi=600)"
]
},
{
"cell_type": "code",
"execution_count": 218,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"C:\\ProgramData\\Anaconda3\\lib\\site-packages\\scipy\\stats\\stats.py:1706: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax7 = sns.regplot(x=\"interno_pt\", y=\"inseguridad\", data=indice_multi['general'].reset_index())\n",
"ax7.figure.suptitle('Relación entre componente de Malos tratos y Personal')\n",
"ax7.set_xlabel('Personal')\n",
"ax7.set_ylabel('Malos tratos')\n",
"ax7.figure.savefig('graficas/relacion_malostratos_personal.png', dpi=600)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Datos de la historia de María"
]
},
{
"cell_type": "code",
"execution_count": 163,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"26.91356160472303"
]
},
"execution_count": 163,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# sobrepoblacion\n",
"indice_multi_pre['mujeres']['sobrepoblacion_rel'].mean()\n",
"# pago por una cama\n",
"L_corrup['general']['P8_3_04'].max()\n",
"# Servicios de higiene\n",
"L_serv['mujeres']['P6_17_4'].mean()\n",
"# cuenta con drenaje\n",
"L_serv['mujeres']['P6_5_2'].mean()\n",
"# Hostigamiento sexual\n",
"L_seg['mujeres']['P7_47_5'].max()\n",
"# Hostigamiento sexual por custodios\n",
"L_seg['mujeres']['P7_49_6_2'].max()\n",
"# Servicios médicos\n",
"L_serv['mujeres']['P6_15_1'].max()\n",
"# Medicamentos\n",
"L_serv['mujeres']['P6_15_2'].mean()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Datos de la historia de Custodio"
]
},
{
"cell_type": "code",
"execution_count": 226,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>corrupcion</th>\n",
" <th>inseguridad</th>\n",
" <th>servicios</th>\n",
" <th>violencia</th>\n",
" <th>sobrepoblacion_rel</th>\n",
" <th>ddhh</th>\n",
" <th>presup_pi</th>\n",
" <th>interno_pt</th>\n",
" </tr>\n",
" <tr>\n",
" <th>CVE_ENT</th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" <th></th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>36.747463</td>\n",
" <td>93.987991</td>\n",
" <td>643.612345</td>\n",
" <td>0.786782</td>\n",
" <td>-32.57</td>\n",
" <td>7.613333</td>\n",
" <td>21.118717</td>\n",
" <td>2.405063</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13.178673</td>\n",
" <td>38.971249</td>\n",
" <td>871.512049</td>\n",
" <td>19.008395</td>\n",
" <td>-14.82</td>\n",
" <td>7.510000</td>\n",
" <td>49.279730</td>\n",
" <td>7.943860</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>77.742222</td>\n",
" <td>94.143393</td>\n",
" <td>719.495769</td>\n",
" <td>23.285899</td>\n",
" <td>-10.84</td>\n",
" <td>5.090000</td>\n",
" <td>129.423272</td>\n",
" <td>2.794702</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>16.644886</td>\n",
" <td>48.383587</td>\n",
" <td>645.705174</td>\n",
" <td>21.708683</td>\n",
" <td>-21.88</td>\n",
" <td>6.825000</td>\n",
" <td>66.776035</td>\n",
" <td>6.102679</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>2.201829</td>\n",
" <td>16.118122</td>\n",
" <td>625.345001</td>\n",
" <td>47.294225</td>\n",
" <td>-38.70</td>\n",
" <td>7.738000</td>\n",
" <td>77.167550</td>\n",
" <td>7.292929</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>27.882443</td>\n",
" <td>58.782553</td>\n",
" <td>817.649992</td>\n",
" <td>35.117057</td>\n",
" <td>-53.15</td>\n",
" <td>7.090000</td>\n",
" <td>91.071768</td>\n",
" <td>4.537349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>32.903900</td>\n",
" <td>35.990375</td>\n",
" <td>745.203977</td>\n",
" <td>18.514437</td>\n",
" <td>-1.48</td>\n",
" <td>6.166000</td>\n",
" <td>21.302312</td>\n",
" <td>2.459241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>72.398231</td>\n",
" <td>45.428495</td>\n",
" <td>485.489642</td>\n",
" <td>26.269702</td>\n",
" <td>4.96</td>\n",
" <td>7.272857</td>\n",
" <td>72.065807</td>\n",
" <td>6.158019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>556.466654</td>\n",
" <td>109.560299</td>\n",
" <td>984.771184</td>\n",
" <td>0.325807</td>\n",
" <td>28.17</td>\n",
" <td>6.875455</td>\n",
" <td>120.049719</td>\n",
" <td>4.458099</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>6.521114</td>\n",
" <td>27.929272</td>\n",
" <td>548.893977</td>\n",
" <td>20.311442</td>\n",
" <td>31.11</td>\n",
" <td>6.822500</td>\n",
" <td>13.721369</td>\n",
" <td>6.953629</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>25.066816</td>\n",
" <td>79.979480</td>\n",
" <td>653.993751</td>\n",
" <td>14.495986</td>\n",
" <td>15.15</td>\n",
" <td>8.060000</td>\n",
" <td>165.059095</td>\n",
" <td>2.348646</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>24.572902</td>\n",
" <td>33.371125</td>\n",
" <td>842.101298</td>\n",
" <td>0.776197</td>\n",
" <td>73.40</td>\n",
" <td>4.215000</td>\n",
" <td>7.766711</td>\n",
" <td>6.483357</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>64.584272</td>\n",
" <td>45.424088</td>\n",
" <td>799.610036</td>\n",
" <td>4.815630</td>\n",
" <td>40.58</td>\n",
" <td>4.800000</td>\n",
" <td>35.152287</td>\n",
" <td>9.466321</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>17.619280</td>\n",
" <td>31.769734</td>\n",
" <td>658.196120</td>\n",
" <td>3.101348</td>\n",
" <td>104.28</td>\n",
" <td>6.986667</td>\n",
" <td>108.258953</td>\n",
" <td>6.592729</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>350.582595</td>\n",
" <td>113.724784</td>\n",
" <td>985.480018</td>\n",
" <td>0.770446</td>\n",
" <td>347.41</td>\n",
" <td>6.395833</td>\n",
" <td>60.832822</td>\n",
" <td>7.066526</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>83.575674</td>\n",
" <td>61.104967</td>\n",
" <td>664.639983</td>\n",
" <td>3.008424</td>\n",
" <td>-48.37</td>\n",
" <td>6.406667</td>\n",
" <td>124.648469</td>\n",
" <td>2.412890</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>133.525629</td>\n",
" <td>73.238236</td>\n",
" <td>757.302760</td>\n",
" <td>61.954625</td>\n",
" <td>67.95</td>\n",
" <td>7.006667</td>\n",
" <td>80.787627</td>\n",
" <td>3.607781</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>45.759628</td>\n",
" <td>47.791751</td>\n",
" <td>637.319503</td>\n",
" <td>16.713092</td>\n",
" <td>106.32</td>\n",
" <td>7.348333</td>\n",
" <td>67.980322</td>\n",
" <td>6.334385</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>10.490503</td>\n",
" <td>33.240991</td>\n",
" <td>579.428023</td>\n",
" <td>8.913878</td>\n",
" <td>-0.82</td>\n",
" <td>5.016667</td>\n",
" <td>97.424428</td>\n",
" <td>5.351311</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>40.389518</td>\n",
" <td>69.686931</td>\n",
" <td>769.450697</td>\n",
" <td>21.178872</td>\n",
" <td>-6.78</td>\n",
" <td>6.238000</td>\n",
" <td>64.098172</td>\n",
" <td>4.481807</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>225.783520</td>\n",
" <td>94.103819</td>\n",
" <td>911.302115</td>\n",
" <td>39.360559</td>\n",
" <td>23.82</td>\n",
" <td>6.390000</td>\n",
" <td>95.338361</td>\n",
" <td>3.719101</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>31.853429</td>\n",
" <td>80.121086</td>\n",
" <td>639.090423</td>\n",
" <td>1.920307</td>\n",
" <td>-37.41</td>\n",
" <td>8.126667</td>\n",
" <td>149.094831</td>\n",
" <td>4.104854</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>34.356723</td>\n",
" <td>40.605030</td>\n",
" <td>772.090991</td>\n",
" <td>4.857143</td>\n",
" <td>36.08</td>\n",
" <td>5.030000</td>\n",
" <td>36.253595</td>\n",
" <td>11.262222</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>50.517010</td>\n",
" <td>76.041211</td>\n",
" <td>695.130915</td>\n",
" <td>0.000000</td>\n",
" <td>-16.43</td>\n",
" <td>6.552500</td>\n",
" <td>21.209074</td>\n",
" <td>2.862319</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>84.247819</td>\n",
" <td>36.813495</td>\n",
" <td>705.169933</td>\n",
" <td>13.675845</td>\n",
" <td>-19.17</td>\n",
" <td>5.955000</td>\n",
" <td>1.698537</td>\n",
" <td>7.020990</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>133.674161</td>\n",
" <td>76.464469</td>\n",
" <td>679.301295</td>\n",
" <td>12.220975</td>\n",
" <td>2.18</td>\n",
" <td>6.581667</td>\n",
" <td>86.335773</td>\n",
" <td>6.596774</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>101.671781</td>\n",
" <td>74.439509</td>\n",
" <td>835.915632</td>\n",
" <td>6.905371</td>\n",
" <td>17.07</td>\n",
" <td>5.882000</td>\n",
" <td>81.342817</td>\n",
" <td>3.425046</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>17.160757</td>\n",
" <td>27.985241</td>\n",
" <td>543.486776</td>\n",
" <td>10.591350</td>\n",
" <td>-19.65</td>\n",
" <td>4.715000</td>\n",
" <td>18.796848</td>\n",
" <td>5.926056</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30.800059</td>\n",
" <td>87.920522</td>\n",
" <td>779.586127</td>\n",
" <td>71.736011</td>\n",
" <td>-31.19</td>\n",
" <td>6.990000</td>\n",
" <td>147.887844</td>\n",
" <td>3.183246</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>65.170536</td>\n",
" <td>42.611639</td>\n",
" <td>604.379734</td>\n",
" <td>15.859530</td>\n",
" <td>1.67</td>\n",
" <td>6.222000</td>\n",
" <td>48.709287</td>\n",
" <td>6.592344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>44.946062</td>\n",
" <td>49.175462</td>\n",
" <td>531.574668</td>\n",
" <td>0.680272</td>\n",
" <td>-51.21</td>\n",
" <td>6.812500</td>\n",
" <td>179.525997</td>\n",
" <td>2.525234</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>45.780871</td>\n",
" <td>72.266993</td>\n",
" <td>730.506234</td>\n",
" <td>2.610966</td>\n",
" <td>-31.15</td>\n",
" <td>5.520000</td>\n",
" <td>128.983074</td>\n",
" <td>3.438247</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" corrupcion inseguridad servicios violencia sobrepoblacion_rel \\\n",
"CVE_ENT \n",
"1 36.747463 93.987991 643.612345 0.786782 -32.57 \n",
"2 13.178673 38.971249 871.512049 19.008395 -14.82 \n",
"3 77.742222 94.143393 719.495769 23.285899 -10.84 \n",
"4 16.644886 48.383587 645.705174 21.708683 -21.88 \n",
"5 2.201829 16.118122 625.345001 47.294225 -38.70 \n",
"6 27.882443 58.782553 817.649992 35.117057 -53.15 \n",
"7 32.903900 35.990375 745.203977 18.514437 -1.48 \n",
"8 72.398231 45.428495 485.489642 26.269702 4.96 \n",
"9 556.466654 109.560299 984.771184 0.325807 28.17 \n",
"10 6.521114 27.929272 548.893977 20.311442 31.11 \n",
"11 25.066816 79.979480 653.993751 14.495986 15.15 \n",
"12 24.572902 33.371125 842.101298 0.776197 73.40 \n",
"13 64.584272 45.424088 799.610036 4.815630 40.58 \n",
"14 17.619280 31.769734 658.196120 3.101348 104.28 \n",
"15 350.582595 113.724784 985.480018 0.770446 347.41 \n",
"16 83.575674 61.104967 664.639983 3.008424 -48.37 \n",
"17 133.525629 73.238236 757.302760 61.954625 67.95 \n",
"18 45.759628 47.791751 637.319503 16.713092 106.32 \n",
"19 10.490503 33.240991 579.428023 8.913878 -0.82 \n",
"20 40.389518 69.686931 769.450697 21.178872 -6.78 \n",
"21 225.783520 94.103819 911.302115 39.360559 23.82 \n",
"22 31.853429 80.121086 639.090423 1.920307 -37.41 \n",
"23 34.356723 40.605030 772.090991 4.857143 36.08 \n",
"24 50.517010 76.041211 695.130915 0.000000 -16.43 \n",
"25 84.247819 36.813495 705.169933 13.675845 -19.17 \n",
"26 133.674161 76.464469 679.301295 12.220975 2.18 \n",
"27 101.671781 74.439509 835.915632 6.905371 17.07 \n",
"28 17.160757 27.985241 543.486776 10.591350 -19.65 \n",
"29 30.800059 87.920522 779.586127 71.736011 -31.19 \n",
"30 65.170536 42.611639 604.379734 15.859530 1.67 \n",
"31 44.946062 49.175462 531.574668 0.680272 -51.21 \n",
"32 45.780871 72.266993 730.506234 2.610966 -31.15 \n",
"\n",
" ddhh presup_pi interno_pt \n",
"CVE_ENT \n",
"1 7.613333 21.118717 2.405063 \n",
"2 7.510000 49.279730 7.943860 \n",
"3 5.090000 129.423272 2.794702 \n",
"4 6.825000 66.776035 6.102679 \n",
"5 7.738000 77.167550 7.292929 \n",
"6 7.090000 91.071768 4.537349 \n",
"7 6.166000 21.302312 2.459241 \n",
"8 7.272857 72.065807 6.158019 \n",
"9 6.875455 120.049719 4.458099 \n",
"10 6.822500 13.721369 6.953629 \n",
"11 8.060000 165.059095 2.348646 \n",
"12 4.215000 7.766711 6.483357 \n",
"13 4.800000 35.152287 9.466321 \n",
"14 6.986667 108.258953 6.592729 \n",
"15 6.395833 60.832822 7.066526 \n",
"16 6.406667 124.648469 2.412890 \n",
"17 7.006667 80.787627 3.607781 \n",
"18 7.348333 67.980322 6.334385 \n",
"19 5.016667 97.424428 5.351311 \n",
"20 6.238000 64.098172 4.481807 \n",
"21 6.390000 95.338361 3.719101 \n",
"22 8.126667 149.094831 4.104854 \n",
"23 5.030000 36.253595 11.262222 \n",
"24 6.552500 21.209074 2.862319 \n",
"25 5.955000 1.698537 7.020990 \n",
"26 6.581667 86.335773 6.596774 \n",
"27 5.882000 81.342817 3.425046 \n",
"28 4.715000 18.796848 5.926056 \n",
"29 6.990000 147.887844 3.183246 \n",
"30 6.222000 48.709287 6.592344 \n",
"31 6.812500 179.525997 2.525234 \n",
"32 5.520000 128.983074 3.438247 "
]
},
"execution_count": 226,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Extorsión\n",
"L_seg['hombres']['P7_47_2'].max(), \n",
"# Violencia física\n",
"L_seg['hombres']['P7_47_4'].max()\n",
"# Violencia física\n",
"indice_multi_pre['general']\n"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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" vertical-align: middle;\n",
" }\n",
"\n",
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" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>corrupcion</th>\n",
" <th>inseguridad</th>\n",
" <th>servicios</th>\n",
" <th>violencia</th>\n",
" <th>sobrepoblacion_rel</th>\n",
" <th>ddhh</th>\n",
" <th>presup_pi</th>\n",
" <th>interno_pt</th>\n",
" <th>indice</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>corrupcion</th>\n",
" <td>1.000000</td>\n",
" <td>0.636991</td>\n",
" <td>0.621938</td>\n",
" <td>-0.122628</td>\n",
" <td>0.447226</td>\n",
" <td>-0.027756</td>\n",
" <td>-0.138051</td>\n",
" <td>-0.054471</td>\n",
" <td>0.658545</td>\n",
" </tr>\n",
" <tr>\n",
" <th>inseguridad</th>\n",
" <td>0.636991</td>\n",
" <td>1.000000</td>\n",
" <td>0.524834</td>\n",
" <td>0.004026</td>\n",
" <td>0.236313</td>\n",
" <td>-0.209621</td>\n",
" <td>-0.408977</td>\n",
" <td>-0.493840</td>\n",
" <td>0.433042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>servicios</th>\n",
" <td>0.621938</td>\n",
" <td>0.524834</td>\n",
" <td>1.000000</td>\n",
" <td>0.032877</td>\n",
" <td>0.420029</td>\n",
" <td>0.150476</td>\n",
" <td>0.041112</td>\n",
" <td>0.058948</td>\n",
" <td>0.788020</td>\n",
" </tr>\n",
" <tr>\n",
" <th>violencia</th>\n",
" <td>-0.122628</td>\n",
" <td>0.004026</td>\n",
" <td>0.032877</td>\n",
" <td>1.000000</td>\n",
" <td>-0.159898</td>\n",
" <td>-0.277039</td>\n",
" <td>-0.135884</td>\n",
" <td>-0.105803</td>\n",
" <td>0.141598</td>\n",
" </tr>\n",
" <tr>\n",
" <th>sobrepoblacion_rel</th>\n",
" <td>0.447226</td>\n",
" <td>0.236313</td>\n",
" <td>0.420029</td>\n",
" <td>-0.159898</td>\n",
" <td>1.000000</td>\n",
" <td>0.097031</td>\n",
" <td>0.177679</td>\n",
" <td>0.328290</td>\n",
" <td>0.601406</td>\n",
" </tr>\n",
" <tr>\n",
" <th>ddhh</th>\n",
" <td>-0.027756</td>\n",
" <td>-0.209621</td>\n",
" <td>0.150476</td>\n",
" <td>-0.277039</td>\n",
" <td>0.097031</td>\n",
" <td>1.000000</td>\n",
" <td>0.356185</td>\n",
" <td>0.266217</td>\n",
" <td>0.392372</td>\n",
" </tr>\n",
" <tr>\n",
" <th>presup_pi</th>\n",
" <td>-0.138051</td>\n",
" <td>-0.408977</td>\n",
" <td>0.041112</td>\n",
" <td>-0.135884</td>\n",
" <td>0.177679</td>\n",
" <td>0.356185</td>\n",
" <td>1.000000</td>\n",
" <td>0.489293</td>\n",
" <td>0.307272</td>\n",
" </tr>\n",
" <tr>\n",
" <th>interno_pt</th>\n",
" <td>-0.054471</td>\n",
" <td>-0.493840</td>\n",
" <td>0.058948</td>\n",
" <td>-0.105803</td>\n",
" <td>0.328290</td>\n",
" <td>0.266217</td>\n",
" <td>0.489293</td>\n",
" <td>1.000000</td>\n",
" <td>0.296794</td>\n",
" </tr>\n",
" <tr>\n",
" <th>indice</th>\n",
" <td>0.658545</td>\n",
" <td>0.433042</td>\n",
" <td>0.788020</td>\n",
" <td>0.141598</td>\n",
" <td>0.601406</td>\n",
" <td>0.392372</td>\n",
" <td>0.307272</td>\n",
" <td>0.296794</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
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"text/plain": [
" corrupcion inseguridad servicios violencia \\\n",
"corrupcion 1.000000 0.636991 0.621938 -0.122628 \n",
"inseguridad 0.636991 1.000000 0.524834 0.004026 \n",
"servicios 0.621938 0.524834 1.000000 0.032877 \n",
"violencia -0.122628 0.004026 0.032877 1.000000 \n",
"sobrepoblacion_rel 0.447226 0.236313 0.420029 -0.159898 \n",
"ddhh -0.027756 -0.209621 0.150476 -0.277039 \n",
"presup_pi -0.138051 -0.408977 0.041112 -0.135884 \n",
"interno_pt -0.054471 -0.493840 0.058948 -0.105803 \n",
"indice 0.658545 0.433042 0.788020 0.141598 \n",
"\n",
" sobrepoblacion_rel ddhh presup_pi interno_pt \\\n",
"corrupcion 0.447226 -0.027756 -0.138051 -0.054471 \n",
"inseguridad 0.236313 -0.209621 -0.408977 -0.493840 \n",
"servicios 0.420029 0.150476 0.041112 0.058948 \n",
"violencia -0.159898 -0.277039 -0.135884 -0.105803 \n",
"sobrepoblacion_rel 1.000000 0.097031 0.177679 0.328290 \n",
"ddhh 0.097031 1.000000 0.356185 0.266217 \n",
"presup_pi 0.177679 0.356185 1.000000 0.489293 \n",
"interno_pt 0.328290 0.266217 0.489293 1.000000 \n",
"indice 0.601406 0.392372 0.307272 0.296794 \n",
"\n",
" indice \n",
"corrupcion 0.658545 \n",
"inseguridad 0.433042 \n",
"servicios 0.788020 \n",
"violencia 0.141598 \n",
"sobrepoblacion_rel 0.601406 \n",
"ddhh 0.392372 \n",
"presup_pi 0.307272 \n",
"interno_pt 0.296794 \n",
"indice 1.000000 "
]
},
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