CoCalc Public Filestmp / saber.ipynbOpen in with one click!
Authors: Harald Schilly, ℏal Snyder, William A. Stein

Pruebas Saber 2011 y 2012

Este es un cuaderno para explorar las herramientas básicas de Pandas utilizando los datos de las pruebas Saber (de último grado de colegio en Colombia) de los años 2011 y 2012.

In [1]:
import numpy as np import pandas as pd import matplotlib.pyplot as plt import re from scipy import stats

¡Datos a mí!

Los datos saqué del especial de "Mejores Colegios" de la Revista Dinero. Los archivos de Excel para ambos años tenían formatos distintos así que necesitaban un poco de limpieza y latonería. Eso es lo que viene acá.

In [2]:
names2011 = ['Puesto', 'Colegio', 'Municipio', 'Departamento', 'Oficial', 'Periodo', 'Jornada', 'Calendario', 'Evaluados', 'Promedio_Total', 'Matematica', 'Quimica', 'Fisica', 'Biologia', 'Filosofia', 'Ingles', 'Lenguaje', 'Sociales', 'DE_Matematica', 'DE_Quimica', 'DE_Fisica', 'DE_Biologia', 'DE_Filosofia', 'DE_Ingles', 'DE_Lenguaje', 'DE_Sociales', 'CSE_2009'] data2011 = pd.read_csv("http://www.finiterank.com/saber/2011.csv", names=names2011) data2011 = data2011.drop(0) names2012 = ['Puesto', 'Colegio', 'Calendario', 'Evaluados', 'Promedio_Total', 'Matematica', 'DE_Matematica', 'Quimica', 'DE_Quimica', 'Fisica', 'DE_Fisica', 'Biologia', 'DE_Biologia', 'Filosofia', 'DE_Filosofia', 'Ingles', 'DE_Ingles', 'Lenguaje', 'DE_Lenguaje', 'Sociales', 'DE_Sociales', 'Municipio', 'Departamento', 'Jornada'] data2012 = pd.read_csv("http://www.finiterank.com/saber/2012.csv", names=names2012) data2012 = data2012.drop(0)
In [3]:
def clasificador_oficial(x): if x[-4:] == '(Of)': return 'SI' else: return 'NO'
In [4]:
data2012['Oficial'] = data2012['Colegio'].map(clasificador_oficial)
In [5]:
def nombre_colegio(x): if x[-4:]=='(Of)': return x[:-5] else: return x
In [6]:
data2012['Colegio'] = data2012['Colegio'].map(nombre_colegio)

Comparación entre 2011 y 2012

In [9]:
def dos_graficos(x,y,titulo_x,titulo_y): f, axarr = plt.subplots(2, sharex=True, figsize=(10,15), dpi=100) axarr[0].hist(x, bins=100, color='red') axarr[0].set_title(titulo_x) axarr[1].hist(y, bins=100, color='orange') axarr[1].set_title(titulo_y) plt.show()
In [10]:
x = data2011.Matematica y = data2012.Matematica titulo_x = 'Distribucion promedios matematica 2011' titulo_y = 'Distribucion promedios matematica 2012' dos_graficos(x,y,titulo_x,titulo_y)
--------------------------------------------------------------------------- KeyError Traceback (most recent call last) <ipython-input-10-692ae98375e7> in <module>() 5 titulo_y = 'Distribucion promedios matematica 2012' 6 ----> 7 dos_graficos(x,y,titulo_x,titulo_y) <ipython-input-9-f726c277f714> in dos_graficos(x, y, titulo_x, titulo_y) 1 def dos_graficos(x,y,titulo_x,titulo_y): 2 f, axarr = plt.subplots(2, sharex=True, figsize=(10,15), dpi=100) ----> 3 axarr[0].hist(x, bins=100, color='red') 4 axarr[0].set_title(titulo_x) 5 axarr[1].hist(y, bins=100, color='orange') /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/matplotlib-1.3.1-py2.7-linux-x86_64.egg/matplotlib/axes.py in hist(self, x, bins, range, normed, weights, cumulative, bottom, histtype, align, orientation, rwidth, log, color, label, stacked, **kwargs) 8247 # Massage 'x' for processing. 8248 # NOTE: Be sure any changes here is also done below to 'weights' -> 8249 if isinstance(x, np.ndarray) or not iterable(x[0]): 8250 # TODO: support masked arrays; 8251 x = np.asarray(x) /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/core/series.py in __getitem__(self, key) 506 def __getitem__(self, key): 507 try: --> 508 result = self.index.get_value(self, key) 509 510 if not np.isscalar(result): /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/core/index.py in get_value(self, series, key) 1413 1414 try: -> 1415 return self._engine.get_value(s, k) 1416 except KeyError as e1: 1417 if len(self) > 0 and self.inferred_type in ['integer','boolean']: /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:3097)() /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_value (pandas/index.c:2826)() /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/index.so in pandas.index.IndexEngine.get_loc (pandas/index.c:3692)() /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7201)() /usr/local/sage/sage-6.4/local/lib/python2.7/site-packages/pandas/hashtable.so in pandas.hashtable.Int64HashTable.get_item (pandas/hashtable.c:7139)() KeyError: 0
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El acantilado y la ladera

La diferencia en las distribuciones de puntajes entre los colegios públicos y privados ilustra a la perfección la desigualdad inherente al sistema educativo. Aunque los promedios de las distribuciones son cercanos (con una ligera ventaja para los privados), el sesgo hacia la derecha de los colegios privados es más alto que el de los públicos.

Esto sugiere que aunque en general los públicos y los privados sean igualmente regulares (promedios de cuarenta y algo sobre cien), dentro de los privados hay una cola gruesa de colegios con resultados mucho mejores que los de cualquier colegio público.

In [54]:
publicos2011 = data2011[data2011.Oficial =='SI'] privados2011 = data2011[data2011.Oficial =='NO'] publicos2012 = data2012[data2012.Oficial =='SI'] privados2012 = data2012[data2012.Oficial =='NO']
In [55]:
x = publicos2012.Matematica y = privados2012.Matematica titulo_x = 'Distribucion promedios matematica 2012 - Colegios publicos' titulo_y = 'Distribucion promedios matematica 2012 - Colegios privados' dos_graficos(x,y,titulo_x,titulo_y)
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ELKAIB7xeX+gBAKAkCFgAAAAOI2ABAAA4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\nK1dq165deuSRR3zbg4ODNXz4cD3xxBOnHfvQoUPq37+/tmzZovT0dB07dkx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yox/9SMuX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\nAACWEbAAAAAsI2ABAABY9v+nxHBBpaSwKgAAAABJRU5ErkJggg==\n"}
In [65]:
print "Sesgo en públicos: ", publicos2012.Matematica.skew() print "Sesgo en privados: ", privados2012.Matematica.skew() print "Promedio en públicos: ", publicos2012.Matematica.mean() print "Promedio en privados: ", privados2012.Matematica.mean() print "Mediana en públicos: ", publicos2012.Matematica.median() print "Mediana en privados: ", privados2012.Matematica.median()
Sesgo en públicos: 0.513152819792 Sesgo en privados: 1.10905758369 Promedio en públicos: 43.5846841332 Promedio en privados: 47.3824865108 Mediana en públicos: 43.4 Mediana en privados: 45.7
In [62]:
x = publicos2011.Matematica y = privados2011.Matematica titulo_x = 'Distribucion promedios matematica 2011 - Colegios publicos' titulo_y = 'Distribucion promedios matematica 2011 - Colegios privados' dos_graficos(x,y,titulo_x,titulo_y)
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GqlKliurWravDhw/rl19+kXT2tNaePXvceTt16pTj\nF3t2gYGBatasmb755hv3tW+++UYdOnTIc7mLqe/111/vPj93W9u2bVtoZXfu3FkzZszQTz/95P4d\nP35ctWvXVmxsrHsqSpJ27Niho0eP5riedu3auaeFpbOhZtu2be52/O53v9OiRYu0adMm7dy50z2N\nkZOc3u+cHD9+XPfdd5/69eunLVu26PDhw6pbt667/LnHTpUqVRQbG6sFCxa423rkyBH99NNP+Srv\nXGPHjlVGRob+/e9/6+jRoxoyZIjfP6wyZcrkui3XX3+9/vOf/+jHH388b9pLL72krVu3au7cuTpy\n5IgmTZqU5z/C06dP69Zbb1V4eLimT5/uN61hw4Z+VyFu2LBBlSpVUnh4uN98Be3onnXlr8/n07Fj\nx3Kcp0OHDlq4cKF76vVc7dq1U0pKit9rq1evzvHK0c6dO2vkyJHnHbetW7dWbGys1q5d68578uTJ\nXK/Ua9u2bZ5ltmnTRu+995527dql8uXL59kFIHuZ6enp2rFjh/uD8Oqrr9aBAwfc6SkpKX77PGv+\n7HVo06aN+zyn9yevYyin74imTZsqMDBQZcuW1cMPP6z169fr448/1rRp0/TJJ5/kul0o/ghapVTW\nP5iUlBQ99dRTGjNmjAYNGqQGDRr4TZekjz76SNu3b9eZM2dUpUoVVahQQZUqVZJ09pdY9n/g+S17\n8eLF+vjjj/X999/r+eef18033yxJatCggWrWrKnZs2crNTVVo0aN8vsS++Mf/6jExER98sknyszM\n1Lp169z+Pdn17NlTEyZM0HfffaekpCS9+eab6tWr18XtpGz1feedd7R8+XKtW7dO//jHP9SjR49c\n5+/Zs6emT5+u1atXa82aNZo+ffoll923b18999xzWrZsmX755RelpqZq/vz5ks72XUlJSdHrr7+u\nffv2acyYMbkG3C5dumjjxo2aNWuWDh06pJEjRyomJkYhISH67rvv9Pnnn+v06dOqUKGCKlasqMDA\nwFz3RX75fD6lp6crNDRUZ86c0bhx49zWJunssbNt2zalp6f7be/TTz+tlJQUnTlzRnv37tXChQvz\nXWZ2+/btU3BwsEJCQpSUlKRXX33Vb3qrVq38/tllV716dXXp0kVDhw7V9u3bderUKbe1Z9++fapR\no4Zq1aqlVatW6cUXX8y1DhkZGfrDH/6ggICAHIdf6Nevn1auXOmGt+HDh6t3795un5wzZ87o1KlT\nysjI0JkzZ3T69Gm/IJSRkaFTp07pzJkz+vnnn3Xq1KlLvgJu6NChql27trp166Z58+bp9OnT2rZt\nm+6//36tX79eLVq0UIUKFTRu3DilpaXpueeeU7ly5XLsF9W3b18lJiZq4cKF+vnnn3X06FG9/fbb\nks62SJ06dUqTJ09WamqqRo8enWtQjY6OzrXMQ4cO6YMPPtDx48dVtmxZVapUKdfjVpIOHDigSZMm\nKTU1VU8//bSio6NVs2ZNSdJvf/tbzZ49W0eOHNHMmTPPC35ly5bVX/7yFx04cECvvvqqNmzY4PYV\nM7Mc93lex1DPnj315ptv6vPPP9f27ds1YcIE3XrrrZLOttqvX79ev/zyi6pWraoyZcrkuV0o/gha\npdTNN9+soKAg9erVS8uWLdPzzz+viRMnutOzdwbdvn27unTpomrVqum+++7T2LFj3Wb0Bx98UB99\n9JGCg4Pd5XP6dZf9Ncdx9Mgjj2jixIlq3769OnXqpBEjRrjTp02bplmzZql169Zq1qyZ6tWr5067\n5ZZbNH78eL344osKCQnRfffdp1OnTp1Xxv/8z/+oV69euu222/TMM89o4sSJ6tix43nbllP9cqr7\nww8/rKFDh6pXr14aOHCg+vfvn+uyffr00ZAhQ/TQQw8pISFBjz32mO6+++5c58+r7N/97nf6y1/+\nohdffFFXXXWV2rRpo5UrV0o6+0W+YMECzZ49W23atFHr1q399lX27axSpYo+//xzffnll4qJiVHl\nypX1+uuvSzrb4vLEE0/oqquu0nXXXafq1av7nd46t67nvpe5bU+dOnU0btw49e3bV82bN9fPP/+s\ndu3aufM1atRIvXr1UuPGjVWrVi1J0n333acBAwbo6aefVnBwsLp06aLvvvvukvbd6NGj9e2336pe\nvXqaMGGCHnnkEb/5hw0bpueff141atTQvHnzztuWmTNnqkmTJurRo4fq16+vuXPnSpKGDBmikydP\nKjw8XI8//rgeeuihXOuxYsUKffzxx/rss89UvXp19/Td8uXLJZ0dh+6NN97QtGnT1LZtW4WFhWnK\nlCnu8q+++qoCAgL00EMPaenSpapcubIeeOABd3qXLl0UEBCgr7/+Wvfff78CAgL8TlNfjEqVKumb\nb75RVFSURo4cqZo1a+qWW25R/fr1de2110qSPv30U+3du1fR0dH64Ycf9Omnn+a4rkaNGumVV17R\n3LlzVa9ePTVt2tQdG6tMmTJatGiRlixZoubNm6ts2bJq3ry5e9r73PchtzLPnDmjSZMmqW7duoqM\njNThw4c1ZsyYHOvjOI5+//vfa9OmTWrSpInS09P9LmAYPny4jhw5osjISKWkpPhd+CCdPWWd1Ro3\nZ84cLVy4UFWrVs2xvvk5huLj4zVp0iQ9++yz6tWrl3r27Om2xh04cEC33367qlevrp49e6p///4F\nao1H0XPsUn/+AKXEr371K82cOVOdO3cu6qoAV5yjR4+qVq1a2rdvX6H3TcsyZswYbd++Xa+99pon\n6wfykmeL1qlTpxQbG6sWLVooLi5OkyZNknT2l2K9evUUHR2t6Ohov/PHkydPVoMGDdSoUaML9qUB\nAJQ+Cxcu1JEjR7Rnzx4NHz5cTZs29SxkSQwoiqKVZ4/lSpUq6YsvvlBAQIBOnz6tVq1aqUePHnIc\nR0OHDtXQoUP95j906JCmTp2qxYsXa+fOnRo0aNB5HRkBAKXbV199pd69e6t8+fK6++679eabb3pa\nnle3/QLyI+9Lw3T2ijPp7FUXmZmZqlixoqScfyEkJyerW7duCgsLU1hYmMxMPp+Pjnwo0fK6ZBzA\nxRs1apRGjRp1WcsDisoFO8OfOXNGzZs3V+3atfXII48oLCxMkjRlyhTFxcVp/Pjx8vl8kqSVK1f6\njYfSsGFDt+MuAABAaXPBFq0yZcpo7dq12rVrl7p37662bdsqISFBTz/9tI4dO6Y///nPSkxM1LBh\nw3Js5brQFWgAAADF3aX29cv38A4RERHq3r27kpOTVatWLTmOo2rVqunhhx/We++9J+nsaOBZA1lK\n0pYtWxQTE5NrhUvb36hRo4q8Dmw32812s91sN9vNdl/cX0HkGbTS0tJ05MgRSdKPP/6ohQsXqmfP\nntq/f78kKTMzU2+88Ya6d+8uSWrdurUWLFig3bt3KykpiYHWAABAqZbnqcP9+/erX79++uWXX1Sn\nTh0NGzZMoaGhuueee/Ttt9+qQoUK6tChgxISEiSdvc9YQkKCOnfurAoVKigxMfGybAQAAEBxlGfQ\natq0aY7DM5x7K4vsBg8erMGDBxe8Zleg+Pj4oq5CkWC7Sxe2u3Rhu0uX0rrdBVEkI8M7jlPgc54A\nAACXQ0FyC/c6BAAA8AhBCwAAwCMELQAAAI8QtAAAADxC0AIAAPAIQQsAAMAjBC0AAACPELQAAAA8\nQtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCMELQAAAI8QtAAAADxC0AIAAPAI\nQQsAAMAjBC0AAACPELSAEi44KEiO48hxHAUHBRV1dQAA2ThmZpe9UMdRERQLXJEcx1HWp8mR+GwB\nQCErSG6hRQsAAMAjBC0AAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhB\nC7hCZR8x3nEcVcj2mBHkAeDyYGR4oITLbWT47K+703KYDwCQN0aGBwAAKIYIWgAAAB4haAEAAHiE\noAUAAOARghYAAIBHCFoAAAAeIWgBAAB4hKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISgBQAA4BGC\nFgAAgEfyDFqnTp1SbGysWrRoobi4OE2aNEmS5PP51LNnT4WFhalXr15KT093l5k8ebIaNGigRo0a\nadmyZd7WHgAAoBhzzMzymuHEiRMKCAjQ6dOn1apVK7333nt677339MMPP+j555/X448/roiICA0b\nNkyHDh1Shw4dtHDhQu3cuVNDhgxRSkrK+YU6ji5QLIB8chxHWZ8mR3I/W9lfd6flMB8AIG8FyS0X\nPHUYEBAgSUpPT1dmZqYqVqyolStXauDAgapYsaIGDBig5ORkSVJycrK6deumsLAwdezYUWYmn893\nSRUDAAAo6S4YtM6cOaPmzZurdu3aeuSRRxQWFqZVq1YpMjJSkhQZGamVK1dKOhu0oqKi3GUbNmzo\nTgMAAChtyl1ohjJlymjt2rXatWuXunfvrrZt215U85njODm+Pnr0aPdxfHy84uPj871OAAAAryQl\nJSkpKalQ1nXBoJUlIiJC3bt3V3JysmJiYrR582ZFR0dr8+bNiomJkSTFxsZq0aJF7jJbtmxxp50r\ne9ACAAAoLs5tABozZswlryvPU4dpaWk6cuSIJOnHH3/UwoUL1bNnT8XGxmrWrFk6efKkZs2apbi4\nOElS69attWDBAu3evVtJSUkqU6aMAgMDL7lyAAAAJVmeLVr79+9Xv3799Msvv6hOnToaNmyYQkND\nlZCQoD59+qhhw4Zq2bKlxo8fL0mqXbu2EhIS1LlzZ1WoUEGJiYmXZSOAkiw4KEg//feikRqBgTp8\n7FgR1wgAUFguOLyDJ4UyvAPgym14hoIuz/AOAFA4PB3eAQAAAJeGoAUAAOARghYAAIBHCFoAAAAe\nIWgBAAB4hKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISgBQAA4BGCFlBCBAcFyXEcOY6j4KCgoq4O\nACAfuNchUMTye6/D/NzTkHsdAkDh416HAAAAxRBBCwAAwCMELaAYy94vCwBQ8hC0gGLsJ59PJone\nVABQMhG0gFKonMQVjABwGZQr6goAuPwyle0KRJ+vKKsCAFc0WrQAAAA8QosWUIxkndIDAFwZCFpA\nMZL9lJ50dmBRAEDJxalDAAAAj9CiBZRAnGIEgJKBFi2gBMo6xcj4WgBQvBG0AAAAPELQAgAA8AhB\nCwAAwCMELaCU43Y8AOAdrjoESjluxwMA3qFFCwAAwCMELQAAAI8QtAAAADxC0AIAAPAIQQsAAMAj\nBC0AAACPELQAAAA8wjhawBUka/BRAEDxQIsWcAXJGnzULjQjAOCyIGgBAAB4hKAFAADgEYIWAACA\nRwhaAAAAHiFoAQAAeISgBQAA4BGCFgAAgEfyDFo//PCDOnXqpMaNGys+Pl5vvPGGJGn06NGqV6+e\noqOjFR0drU8++cRdZvLkyWrQoIEaNWqkZcuWeVt7AACAYswxs1zHNjxw4IAOHDigFi1aKC0tTa1b\nt9batWs1ceJEBQYGaujQoX7zHzp0SB06dNDChQu1c+dODRkyRCkpKecX6jjKo1igVHEcxx1g1JH/\nYKPZnxfkcX7nK6+zg55KUo3AQB0+duwStggAriwFyS153oKnTp06qlOnjiSpZs2aaty4sVatWiVJ\nORaYnJysbt26KSwsTGFhYTIz+Xw+BQYGXlLlAFxeWSPLS5Lj8xVlVQDgipDvPlrbt2/Xxo0bFRsb\nK0maMmWK4uLiNH78ePn++4W8cuVKRUVFucs0bNhQK1euLOQqAwAAlAz5Clo+n0933nmnJk2apCpV\nqighIUE7d+7UggULtGPHDiUmJkrKuZWLG9wCAIDSKs9Th5KUkZGh3//+9+rbt6969uwpSapVq5Yk\nqVq1anr44Yf10EMPadiwYYqNjdWiRYvcZbds2aKYmJgc1zt69Gj3cXx8vOLj4wuwGQAAAIUjKSlJ\nSUlJhbKuPDvDm5n69eunmjVrauLEie7r+/fvV2hoqDIzMzVixAgFBQVpxIgROnjwoDp27KiFCxfq\n+++/19ChQ+kMD1xAceoMf95jPqcA4F1n+OXLl2vOnDlq1qyZoqOjJUnPPvus3nzzTX377beqUKGC\nOnTooISEBElS7dq1lZCQoM6dO6tChQruKUUAAIDSKM8WLc8KpUULpVxwUJB+ynZVHy1aAFB8FSS3\nMDI8UAQa5kxpAAAgAElEQVR+8vlk8g9CAIArD0ELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAA\nwCMELcBDwUFBchxHjuMoOCioqKsDALjMGEcL8NB5o77/97jPbTR4xtECgOKHcbQAAACKIYIWAACA\nRwhaAAAAHiFoAQAAeISgBQAA4BGCFgAAgEcIWgAAAB4haAEAAHiEoAUAAOARghYAAIBHCFoAAAAe\nIWgBAAB4hKAFAADgEYIWAACARwhawCUIDgqS4zhyHEfBQUFFXR0AQDHlmJld9kIdR0VQLFBoHMdR\n1hHsSLkez7nNd97rOTzOa9rFPr7k5fmcAkCBcgstWgAAAB4haAEAAHiEoAUAAOARghYAAIBHCFoA\n8oUrLQHg4pUr6goAKBl+8vn+74pEn69I6wIAJQUtWgAAAB6hRQu4TMrp7FgsAIDSgxYt4DLJ1NnB\nQBkCFABKD4IWAACARwhaAAAAHiFoAYUo+xAI9McCANAZHihE2YdAkM7emBkAUHoRtADkiKskAaDg\nCFoAcpR1lWQWIhcAXDz6aAEAAHiEoAUAAOARghYAAIBHCFoAAAAeoTM8UEBcnQcAyA0tWkABcQ9D\nAEBuCFoAAAAeyTNo/fDDD+rUqZMaN26s+Ph4vfHGG5Ikn8+nnj17KiwsTL169VJ6erq7zOTJk9Wg\nQQM1atRIy5Yt87b2AAAAxVieQat8+fKaNGmSNm7cqHnz5mnkyJHy+XyaNm2awsLCtG3bNtWrV0/T\np0+XJB06dEhTp07V4sWLNW3aNA0aNOiybAQAAEBxlGfQqlOnjlq0aCFJqlmzpho3bqxVq1Zp5cqV\nGjhwoCpWrKgBAwYoOTlZkpScnKxu3bopLCxMHTt2lJnJ5/N5vxUAAADFUL77aG3fvl0bN25U69at\ntWrVKkVGRkqSIiMjtXLlSklng1ZUVJS7TMOGDd1pAAAApU2+hnfw+Xy68847NWnSJFWtWlVm+b++\nKrfL3kePHu0+jo+PV3x8fL7XCQAA4JWkpCQlJSUVyrouGLQyMjL0+9//Xn379lXPnj0lSTExMdq8\nebOio6O1efNmxcTESJJiY2O1aNEid9ktW7a4086VPWgBAAAUF+c2AI0ZM+aS15XnqUMz08CBA9Wk\nSRM99thj7uuxsbGaNWuWTp48qVmzZikuLk6S1Lp1ay1YsEC7d+9WUlKSypQpo8DAwEuuHAAAQEnm\nWB7nAZctW6YOHTqoWbNm7inAcePGqW3bturTp4/WrFmjli1bas6cOapataok6YUXXtCUKVNUoUIF\nJSYmqn379ucX6jgXdfoRKG4cx3EHKHWkHB/nNS0/jwu6vOd14TMMoJQoSG7JM2h5haCFko6gRdAC\nUHoUJLcwMjwAAIBHCFoAAAAeIWgBAAB4hKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISgBQAA4BGC\nFgAAgEcIWgAAAB4haAEAAHiEoAUAAOARghaQh+CgIDmOI8dxFBwUVNTVAQCUMI5d6u2oC1JoAe6C\nDVxOjuMo60gtLykz27Ss151cHuc1LT+PC7q853XhMwyglChIbqFFC8inTJ0NGsQLAEB+EbQAAAA8\nQtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCzhH9kFKAQAoCIIWcI6ffD7GywIAFAqC\nFgAAgEcIWgAAAB4haAEAAHiEoAXgopWT3AsGgoOCiro6AFBslSvqCgAoebJusC1Jjs9XlFUBgGKN\nFi0AAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCMELQAAAI8Q\ntAAAADxC0AIAAPAIQQsAAMAjBC0AAACPELRQ6gUHBclxHPcPAIDCUq6oKwAUheCgIP3k87nPLds0\nohYAoLDQooVS6SefTyb/gAUAQGEjaAEAAHgkz6A1YMAA1a5dW02bNnVfGz16tOrVq6fo6GhFR0fr\nk08+cadNnjxZDRo0UKNGjbRs2TLvag0AAFACOGaW69mTpUuXqmrVqrrnnnu0fv16SdKYMWMUGBio\noUOH+s176NAhdejQQQsXLtTOnTs1ZMgQpaSk5Fyo4yiPYgHPOY7jnjZ0dH4frZymXezjol7+staF\nzzOAK1hBckueneHbt2+vXbt2nfd6ToUlJyerW7duCgsLU1hYmMxMPp9PgYGBl1QxAACAku6S+mhN\nmTJFcXFxGj9+vHz/vXJr5cqVioqKcudp2LChVq5cWTi1BFBslZPcoTGCg4KKujoAUKxc9PAOCQkJ\nevrpp3Xs2DH9+c9/VmJiooYNG5ZjK1deYxKNHj3afRwfH6/4+PiLrQqAYiBT2U4jZhsyAwBKqqSk\nJCUlJRXKuvLsoyVJu3bt0s033+z20cpu7dq1euihh7R8+XJ9+OGHWrRokV544QVJUosWLbR06dIc\nTx3SRwtFjT5aHi7PZxvAFaYgueWiTx3u379fkpSZmak33nhD3bt3lyS1bt1aCxYs0O7du5WUlKQy\nZcrQPwsAAJRqeZ46vOuuu/Tll18qLS1N9evX15gxY5SUlKRvv/1WFSpUUIcOHZSQkCBJql27thIS\nEtS5c2dVqFBBiYmJl2UDAAAAiqsLnjr0pFBOHaKIceqQU4cAkF+X9dQhAAAA8oegBQAA4BGCFgAA\ngEcIWriiBQcFMZgmAKDIXPSApUBJ8pPPx2CaAIAiQ4sWAACARwhaAAAAHiFoAQAAeISgBaDQlJO4\n+AAAsqEzPIBCkylx8QEAZEOLFgAAgEcIWgAAAB4haAEAAHiEoAUAAOARghYAAIBHCFoAAAAeIWgB\nAAB4hKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISgBQAA4BGCFgAAgEcIWgAAAB4haAEAAHiEoAUA\nAOARghYAAIBHCFoAAAAeIWgBAAB4hKAFAADgEYIWAE+Uk+Q4jhzHUXBQUFFXBwCKRLmirgCAK1Om\nJPvvY8fnK8qqAECRoUULAADAIwQtAAAAjxC0AAAAPEIfLZQaWZ2zAQC4XGjRQqmR1TnbLjQjAACF\nhKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISgBQAA4BGCFgAAgEcIWgAAAB4haAEAAHgkz6A1YMAA\n1a5dW02bNnVf8/l86tmzp8LCwtSrVy+lp6e70yZPnqwGDRqoUaNGWrZsmXe1BgAAKAHyDFp/+tOf\n9Omnn/q9Nm3aNIWFhWnbtm2qV6+epk+fLkk6dOiQpk6dqsWLF2vatGkaNGiQd7UGAAAoAfIMWu3b\nt1eNGjX8Xlu5cqUGDhyoihUrasCAAUpOTpYkJScnq1u3bgoLC1PHjh1lZvL5fN7VHAAAoJi76D5a\nq1atUmRkpCQpMjJSK1eulHQ2aEVFRbnzNWzY0J0GAABQGl100DLL/y15Hce52NUDAABcMcpd7AIx\nMTHavHmzoqOjtXnzZsXExEiSYmNjtWjRIne+LVu2uNNyMnr0aPdxfHy84uPjL7YqAAAAhS4pKUlJ\nSUmFsq6LDlqxsbGaNWuWnnvuOc2aNUtxcXGSpNatW+vPf/6zdu/ere+//15lypRRYGBgruvJHrQA\nAACKi3MbgMaMGXPJ68rz1OFdd92l66+/Xt99953q16+v2bNnKyEhQbt371bDhg21d+9ePfjgg5Kk\n2rVrKyEhQZ07d9ZDDz2kF1544ZIrBQAAcCVw7GI6XRVWoY5zUX29gEvlOI6yjjRHuuDj/M53OdZ1\nxdWFzzyAEqoguYWR4QEAADxC0AIAAPAIQQsAAMAjBC0AAACPELQAAAA8QtACAADwCEELAADAIwQt\nAAAAjxC0AHiunM4O+Jf1FxwUVNRVAoDL4qLvdQgAFytT54wy7/MVVVUA4LKiRQsAAMAjBC0AAACP\nELQAAAA8QtACAADwCEELV5zgoCD36jYAAIoSQQtXnJ98Ppn8r3IDAKAoELQAAAA8QtACAADwCEEL\nAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCMELQAAAI8QtAAAADxC0AIAAPAIQQsAAMAjBC2U\nSNlvHB0cFFTU1QEAIEfliroCwKXIunG0JDk+X5HWBQCA3BC0UOKVk+Q4TlFXAwCA83DqECVepiTL\n9ofiLyscc+oXwJWOFi0Al11WOJY49QvgykaLFgAAgEcIWgAAAB4haAEAAHiEoAUAAOARghYAAIBH\nCFooMbKPBg8AQElA0EKJkTUaPGNlAQBKCoIWAACARwhaAAAAHiFoASg2svfD49Y8AK4E3IIHQLGR\n1Q9P4tY8AK4MtGgBAAB4hKAFAADgEYIWAACARy45aEVERKhZs2aKjo5W69atJUk+n089e/ZUWFiY\nevXqpfT09EKrKIDSpZxEx3gAJd4lBy3HcZSUlKQ1a9Zo5cqVkqRp06YpLCxM27ZtU7169TR9+vRC\nqyiAK1P2QJVdpuQOUPsTHeMBlFAFOnVo5j9G98qVKzVw4EBVrFhRAwYMUHJycoEqB+DKlz1QAcCV\npkAtWp07d1avXr00f/58SdKqVasUGRkpSYqMjHRbugAAAEqjSx5Ha/ny5QoNDdXmzZt18803q3Xr\n1ue1cOVl9OjR7uP4+HjFx8dfalUAAAAKTVJSkpKSkgplXY5dTDrKxdChQxUVFaVPP/1UI0eOVHR0\ntFavXq1x48Zp3rx55xfqOBcVygDpv8dN1mMpx8d5TcvP44IuT108XBffGQCKSEFyyyWdOjxx4oR8\n/+2cmpqaqgULFqhbt26KjY3VrFmzdPLkSc2aNUtxcXGXVCkgS/ZbsgAAUNJcUovWzp07deutt0qS\nQkJCdPfdd2vAgAHy+Xzq06eP1qxZo5YtW2rOnDmqWrXq+YXSooV8yk8r1hXXckNdcn7MdwaAIlKQ\n3FIopw4vulCCFvKJoEVd3Md8ZwAoIpf91CEAAAAujKAFAADgEYIWAACARwhaAAAAHiFoAQAAeISg\nBQAA4BGCFgAAgEcIWgAAAB4haAEAAHiEoAWg2Csnufe8dBxHwUFBRV0lAMiXckVdAQC4kEydcwuf\n/97UHgCKO1q0AAAAPELQAgAA8AhBC8VOcFCQ2xcHAICSjKCFYucnn08m/z45AACURAQtAAAAjxC0\nAAAAPELQAgAA8AhBCwAAwCMELQAAAI8QtACUONlvycPteAAUZ9yCB0CJk/2WPNyOB0BxRosWAACA\nRwhaAAAAHiFoAQAAeISgBQAA4BGCFopE9htHO46jCtkeAwBwpeCqQxSJrBtHZ3GU7SqyIqgPAABe\noEULAADAIwQtAFeM7KekGcgUQHHAqUMAJVrWKPFZGMgUQHFCixaAEi1rlHi70IwAUAQIWgAAAB4h\naAEAAHiEoAUAAOARghYum+xXhAEAUBoQtHDZZA1SSqdlXA5ZVyOee+cBhn4AcDkRtOApWrFQVLJf\njZiR7bFJ8vl8hC4AlwXjaKHQBQcF6adsYxhxax0UN1khTGK8LQDeImihUBCuAAA4H6cOUSjofwUA\nwPkIWgBKteyd5umvBaCwEbSQb9ywF1ei7J3m6SQPoLARtJBv2U8P/kQHYlyBsoeuSznG+TEC4FwE\nLVyS7KdbGLoBV7r8Bih+jAA4V6EHrSVLligqKkoNGjTQlClTCnv1JVpSUlJRV+Gi5TYOVvZf/hfq\nAJ/kTdWKvaSirkARSSrqChSS7D8m8hOgSuLnuzCw3aVLad3ugij0oDV48GAlJiZq0aJFeumll5SW\nllbYRZRYRX2AZg9N+T21URhXEyYVYNmSLKmoK1BEkoq6AoUk+4+J7M5tzc36HBX157uosN2lS2nd\n7oIo1HG0jh49Kknq0KGDJKlr165KTk7WTTfdVJjF4BJlhaYs5f/b8VeSagQG6vCxY5LOHxMLwP/J\nPtip5P85AoBzFWqL1qpVqxQZGek+b9Sokb7++uvCLMIzRdGJNXuZFS6ypenc5S+lzrldbcWYWED+\nZX2ORp3zem73Wjz3s5rb5zg/3w/ntlJXyOVxbsvQYR/wnmNmhfb/dNGiRZo5c6befPNNSdL06dO1\nd+9e/fWvf/UvlF9/AACgBLnUuFSopw5jYmL05z//2X2+ceNGdevW7bz5CjHbAQAAFFuFeuqwWrVq\nks5eebhr1y599tlnio2NLcwiAAAASoxCv6n0//7v/+qBBx5QRkaGBg0apJo1axZ2EQAAACVCofbR\nAgAAwP9hZHgAAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCME\nLQAAAI8QtAAAADxC0AIAAPAIQQsAAMAjBC0AAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0\nAAAAPELQAgAA8AhBCwAAwCMELQAAAI8QtAAAADxC0AIAAPAIQQsAAMAjBC0AAACPELQAAAA8QtAC\nAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCMELQAAAI8QtAAAADxC0AIAAPAIQQsA\nAMAjBC0AAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0AAAAPELQAgAA8AhBCwAAwCMELQAA\nAI8QtAAAADxC0AIAAPAIQQsAAMAjBC0AAACPELQAAAA8QtACAADwCEELAADAIwQtAAAAjxC0AAAA\nPELQAgAA8AhBCwAAwCMELZwnISFBY8eOLZR17d69W4GBgTIzSVJ8fLxmzpxZKOvOLjAwULt27Sr0\n9ZYkERER+vzzzyVJzz77rO67774irlHJsXTpUkVGRhZ1NUqswvpcv/7667rxxhsLoUYF06RJEy1Z\nsqTQ19u/f3899dRThb5eFG8ErVImIiJCAQEBCgoKUnh4uDp37qx58+b5zTNt2jSNHDkyX+vK+see\nm7CwMPl8PjmOI0lyHMd9XJh8Pp8iIiIKfb0lSfb9+uSTT2rGjBlFWJu8/fOf/1T79u2LrPwyZcro\n+++/d5+3b99eW7ZsKdQyUlNTddddd6lu3bqqW7euHnjgAa1fv95vng8//FBNmjRRzZo11adPH504\nccKdNnfuXF1//fWqUqWKOnXqdN7677//fkVGRqps2bJ65ZVXClzfo0eP6uGHH1ZUVJSCgoIUFRWl\n0aNH+9UpN4X1ub777ru1YMGCAq+noDZs2KAOHToU+nq9+v5D8UbQKmUcx9FHH32kY8eO6Z133lFc\nXJwee+wxDRs27JLWldVSlZPMzMyCVLXEKW3bW9LldewWhvT0dMXGxiolJUVbt25V3bp1/VoZd+7c\nqd69e+uBBx7QihUr9MMPP+jRRx91p4eEhGjo0KEaPnx4jutv0aKFpk6dqpYtWxb4n/fJkyd13XXX\nadOmTRozZozS0tL0/vvva/fu3dq+fXuB1l2SXI7PsNfHHYohQ6kSERFhixcv9nvt5ZdftrJly9q2\nbdvMzKxfv342cuRIMzM7fvy4DRw40MLDwy04ONg6dOhgZ86csT59+liZMmWscuXKVrVqVZswYYLt\n3LnTHMexuXPnWuPGjS0+Pt527dpljuPYL7/8YmZm8fHxNnbsWOvUqZPVrVvXxo0bZ+np6WZm9sUX\nX1i9evX86hYeHm6LFi0yM7MzZ87Y/PnzrWfPnlatWjVr1aqV7dmzx8zMHMexHTt2uHWeOnWqNWnS\nxLp06WLz58931zd79mxr27atjRkzxq6++mq78cYbbcWKFbnur/DwcJsyZYrFxMTYNddcY9OmTbOf\nf/7ZrW/dunVt2rRp1qBBA7vnnnssIyPDXnvtNYuNjbW4uDibM2eOZWRknDd/RESERUVF2eLFi+3L\nL7+06667ziIjI+3111/3K//f//633Xzzzfab3/zGJk6caD6fz522YsUK69Kli4WHh9vkyZP93ttR\no0ZZnz593HlXr15t/fr1s/DwcBs5cqTt3bvX7/2Pi4uzoKAga9iw4XnHR5Z+/frZkCFD7Pbbb7eQ\nkBC76aab7Pjx4/bUU09Z/fr17dZbb7WtW7e6848bN85+/etfW3BwsPXu3duWLFliZmabNm2ySpUq\nWdmyZa1q1apWo0YNMzPLyMiwt956yzp16mTNmze3l19+2U6fPn1J+y45Odni4uKsevXqFhcXZ1Om\nTHHfh/bt25vjOFalShWrWrWqzZ0797xjLzU11f7+979bkyZNLCQkxB555BEzM9u+fbt16tTJQkJC\nrGnTpva3v/3N7z3JS0ZGhgUEBNiWLVvc9+iGG27wez8rV65sJ06c8FtuxowZFh8fn+t627VrZ6+8\n8kq+6pCbsWPHWlBQkHts52Tr1q326KOPWv369W3QoEH23XffudPi4+Nt5syZ7vOlS5da7969LSIi\nwkaPHm2pqanutA0bNtjtt99uoaGh9sQTT1jHjh3t5ZdfNrOzn8927drlq8z333/f4uPjrVq1avar\nX/3qvM9OllGjRtmdd95p9957r9WuXdvuv/9+2717tzs9PDzcXnrpJWvTpo1VqVLFMjMzLTw83BYv\nXmx79+61ypUr2+HDh935U1JSrGbNmpaZmXnB42HXrl123333We3ate3ee++1Pn36uN+tZmeP69tu\nu82uvfZaGz9+vF8548aNs+bNm1tQUJA1bdrUNmzYkOt7g+KNoFXK5BS0UlNTrVy5cvavf/3LzMz6\n9+9vTz31lJmZvfjii9a7d287evSoZWZm2rJly3JdV1bQ6tWrl+3YscNOnTrlvpYVtDp27Gh16tSx\n+fPn2/bt2+23v/2tDR8+3MxyDlrZy3j33XetQYMG9uGHH9ovv/xia9eutR9//NHM/IPW008/bZ06\ndbKtW7fa4sWLLSIiwr744gszO/tFXqFCBRszZowdPnzYRo0a5ffFfq7w8HBr2LChLV261L799luL\njo626dOnu/UtV66cDRgwwPbv328nT560WbNmWbNmzWzVqlW2evVqa9Gihc2ePdudv3z58vboo49a\nWlqa/fWvf7U6derYbbfdZtu3b7fPP//cqlSp4v6z++CDD6xZs2b21Vdf2b59++yOO+6wJ5980szM\nfvrpJwsICLBXXnnF9u7da3fffbeVL1/e3VejR492g9bx48etatWqNmPGDDt06JANGjTIOnbs6L73\n9erVc/+B/ec//3H347n69etn1apVsw8++MD27dtnsbGx1qhRIxs3bpwdPnzYHnjgARswYIA7/9tv\nv2379++3EydO2MSJE/3e23/+85/n7fcXXnjBOnfubBs2bLDt27dbfHy8/eMf/7ikfbd69WpLTk62\nzMxMW758uYWHh9tnn33mlpX9eMlaf/b63XLLLda3b1/btm2bnT592j3ut2/fbosWLbKff/7Z1q5d\nay1btrQZM2bkevxkt2rVKgsICLBjx46Zmdkf//hHGzJkiDv96NGj5jiObd682W+5yxG0unbtan/4\nwx/ynCc8PNzGjh1raWlpNm7cOIuIiHCnZQ9aa9eutXr16tlnn31mhw8ftkcffdR69+5tZmd/LNWq\nVcsmTZpkqampNmzYMKtQoYK77LlBK7cyf/75ZwsPD7evv/7azMwOHDhgGzduzLHeo0aNsvLly9vz\nzz9vhw4dssGDB1tcXJw7PSIiwho1amRLliyxU6dOua9lfZY6d+7s9x4PGzbMEhISzOzCx0OrVq3s\n8ccft9TUVJswYYJVqFDB/W79/vvvrXr16vb222/bnj177M4777R+/fqZ2dkw2rBhQ/cH0ZYtW2z/\n/v15vj8ovghapUxOQcvMrEmTJjZhwgQz8w9akydPtq5du9qmTZsuuK6sUJXVcpH9tewtWn379nWn\nL1iwwJo0aWJmFw5ad9xxh02aNCnH7cr+j7N58+a2YMECd9qIESNs0KBBZnb2i7xGjRpuffbt22fl\ny5d3W9Vy2sasfWFmlpiYaD169HDr6ziO36/jW265xRITE93nM2bMsFtuucWdv2zZspaWlmZmZnv2\n7DHHcfxa3Bo0aGBJSUlmZta7d2+/X+lr1qyxRo0amZnZW2+9Ze3bt3en7dixwxzHybFF691337U2\nbdq48x4/ftwCAgIsLS3N0tLSLCQkxD766KM8WzPMzgatrG0xM3vmmWfsqquucp9nBZqcnDlzxurX\nr2/ffPONmZ3/D9XMrG3btrZ8+XL3+XvvvWfdu3e/qH2XFajPNWLECLdVyizvoHXkyBF3/1zIjBkz\n3OMhL0eOHLGoqCibOHGi+1rXrl1t8uTJfvNdddVVfj9mssrwOmg1atTIr27nSklJsdDQUL/X6tat\naykpKWbmH7SefPJJe+aZZ9z50tLS3Bag5ORkq1+/vjvt5MmTVrFixRyDVm5lrlmzxjIyMiw0NNTm\nzJljx48fz3PbRo0aZWFhYe7z9PR0q1SpktvKFhERYX/5y1/8lsn+vfPyyy9b586dzez/juOlS5fm\nWFb24+HAgQNWqVIlO3nypDu9fv367vfJxIkT7a677nKnbdu2zUJCQtwfkeHh4ZaUlOR+V6Hkoo8W\nlJqaqi1btqh+/frua/bffgQDBw5UfHy8evTooaZNm+bryqLY2Ng8p7do0cJ9HB0drY0bN+r48eMX\nXG9SUpLatm2b5zw+n0/r1q1Tq1at3NdatWqlpUuXus8bN26sMmXOHvqhoaHKzMzUwYMH813fr776\nyn1eu3Ztv/22YsWKPMsODQ1VSEiIu6wkNW/e3G99e/fulSQtWrRICQkJqlGjhmrUqKFOnTpp165d\nOnjwoJKTk/2Wu+aaa1StWrUc6798+XK1bNnSfR4QEKAGDRpoxYoVCgkJ0WuvvaZJkyYpNDRUjz32\nmFJTU3Ncj+M4fmXWqlVLjRs39nueVXdJmj9/vm677TZdffXVCg4O1v79+7Vu3boc1338+HGtWLFC\nN910k7u9/fv314oVKy5q3+3bt0+StHfvXj344INq1qyZgoKCNGnSpFzLzml/hYeHu2Vll56ersGD\nBysmJkbVqlXTkCFDLrjeEydOqEePHurQoYOGDBnivh7y/9u7/+iY7vyP46+J+E1IKHVEVMuGIKQk\nUY743Y4AACAASURBVPUjcpaj3UbSao+qOharbbSH0h+nPxTZb7e22iNKK7QrTttFF91dP4podKd+\nVBM/orZEJcVStosSJggh9/uHNWtkEvLjk0lmno9z5hy5c3Pv+52MyWs+93PvbdbMZUL+uXPndOrU\nKbf7rYjrZ/42btxYAQEBbtdp06aNy8/6Zje/hiSpZ8+e2rJlS7F109PTNWPGDOfvsX379rpw4YJ2\n7typjIwMl/9P9erVU6dOncq0z82bN8vf31+ff/65VqxYoeDgYI0dO1aHDh0qsf7w8HDnvxs2bKh7\n7rlHGRkZzmWlvWc98sgj2rZtm37++Wdt2rRJfn5+6tOnj6TSXw+ZmZlq37696tWr59zWjf3c/F7R\nvn17XblyRXv37lV4eLj+8Ic/6JVXXlHr1q01derU2zopAdUTQQtatWqVLMsq9qYmXfuj/Oqrr+rH\nH39UamqqJk+erH379kmSatWq5XZip7+/f6n7y8rKcv57165d6ty5sxo2bKjWrVvr9OnTunr1qiTp\n1KlT+umnn5zrDhgwwO0b+40aN26s8PBw7dixw7lsx44dFTqD6OZ6e/fu7fz65l7vv//+Stt3bGys\nPvroI505c8b5OH/+vFq2bKno6Gjt3r3bue6PP/6os2fPut1Onz59tHPnTufX58+fV05OjrOPBx54\nQOnp6dq3b58OHTqkmTNnlliTu9+3O+fPn9e4ceM0atQo7d+/X6dPn1br1q2d33/za6dhw4aKjo5W\nWlqas9e8vDydOXPmtvZ3szfffFOFhYVau3atzp49q0mTJqmoqMj5vJ+fX4m99O7dW//617/0yy+/\nFHvugw8+0A8//KBly5YpLy9PycnJLtu92aVLl/Twww+rbdu2mj9/vstzoaGhLmchfv/996pXr57a\ntm3rsl5FJ7pfP/PX4XDo3Llzbtfp16+fNmzYoMLCQrfP9+nTR7t27XJZtnPnTrdnjsbGxmrKlCnF\nXrdRUVGKjo7Wd99951z34sWLJZ7tef/995e6z/vuu09/+9vfdPjwYdWuXVsvv/xyiT+DG/eZn5+v\nH3/80SVclfaeFRgYqEGDBukvf/mLlixZouHDhzufK+31EBkZqdzcXF28eNG5/o393PxekZOTo1q1\najk/vIwYMULbtm3Tt99+qw0bNmjRokUl1ojqjaDlg67/gdm1a5feeOMNJSUlacKECerQoYPL85K0\nZs0a5ebmqqioSA0bNlSdOnWcn9B69Ojh8gf8dve9ceNGffHFFzp48KDeffddxcXFSZI6dOig5s2b\na9GiRTp58qSmTZvm8kfm8ccf14IFC7Ru3TpduXJFe/bs0enTp4vtIz4+Xu+8844OHDggu92upUuX\nKiEhoWw/pBvq/fzzz7V161bt2bNHH374oR566KES14+Pj9f8+fO1c+dOZWVlaf78+eXe98iRIzVz\n5kxt2bJFV69e1cmTJ7Vq1SpJ0qBBg7Rr1y4tXrxYx48fV1JSUol/LAYOHKi9e/cqNTVVJ06c0JQp\nUxQZGalmzZrpwIED+uqrr3Tp0iXVqVNHdevWVePGjUv8Wdwuh8Oh/Px8tWrVSkVFRZoxY4ZztEm6\n9trJyclRfn6+S79Tp07Vrl27VFRUpGPHjmnDhg23vc8bHT9+XEFBQWrWrJnsdrs++eQTl+d79Ojh\n8kfuRk2bNtXAgQM1efJk5ebmqqCgwDnac/z4cQUGBqpFixbavn273n///RJrKCws1KOPPqoGDRq4\nvfzCqFGjlJmZ6fxj/corr+iJJ55Q/fr1JUlFRUUqKChQYWGhioqKdOnSJZcgVFhYqIKCAhUVFeny\n5csqKCgo9xltkydPVsuWLTV48GCtWLFCly5dUk5Ojp566in985//VPfu3VWnTh3NmDFDp06d0syZ\nM+Xv7+8yOnXdyJEjtWDBAm3YsEGXL1/W2bNntXz5cknXRqQKCgo0Z84cnTx5UtOnTy8xqEZERJS4\nzxMnTmjlypU6f/68atWqpXr16pX4upWkn3/+WcnJyTp58qSmTp2qiIgINW/e/LZ/Pk888YQ+/vhj\nff7553riiSecy0t7Pdx5553q3Lmzpk2bppMnT2rWrFkuI+dDhgxRWlqa/vrXv+rYsWOaNm2a4uLi\n5Ofnpx07digjI0OFhYWqX7++/P39S+0P1RtBywfFxcUpICBACQkJ2rJli959913NmjXL+fyN13rJ\nzc3VwIED1aRJE40bN05vvvmm7r77bknSM888ozVr1igoKMj5/e4+fd+4zGaz6bnnntOsWbPUt29f\nDRgwQK+//rrz+ZSUFKWmpioqKkrh4eEKDg52PjdkyBC9/fbbev/999WsWTONGzdOBQUFxfbx8ssv\nKyEhQY888oj+8Ic/aNasWerfv3+x3tzV5672Z599VpMnT1ZCQoLGjh2r3/72tyV+75NPPqlJkyZp\n/PjxSkxM1PPPP68RI0aUuH5p+37ggQf0+9//Xu+//77uuOMO3XfffcrMzJR0LQykpaVp0aJFuu++\n+xQVFeXys7qxz4YNG+qrr77S119/rcjISNWvX1+LFy+WdG3E5dVXX9Udd9yhnj17qmnTpi6Ht26u\n9ebfZUn93HnnnZoxY4ZGjhypbt266fLly87DLZIUFhamhIQEde7cWS1atJAkjRs3TmPGjNHUqVMV\nFBSkgQMH6sCBA+X62U2fPl27d+9WcHCw3nnnHT333HMu67/44ot69913FRgYqBUrVhTrZeHCherS\npYseeughtWnTRsuWLZMkTZo0SRcvXlTbtm31wgsvaPz48SXW8c033+iLL77Ql19+qaZNmzoP323d\nulXStevQLVmyRCkpKbr//vsVEhKiuXPnOr//k08+UYMGDTR+/Hht3rxZ9evX19NPP+18fuDAgWrQ\noIG+/fZbPfXUU2rQoIHLYeqyqFevnnbs2KFOnTppypQpat68uYYMGaI2bdqoffv2kqT169fr2LFj\nioiI0NGjR7V+/Xq32woLC9PHH3+sZcuWKTg4WF27dnVeG8vPz0/p6enatGmTunXrplq1aqlbt27O\nw943/x5K2mdRUZGSk5PVunVrdezYUadPn1ZSUpLbemw2m4YOHap9+/apS5cuys/P12effVamn8+Q\nIUOUm5urVq1aqWvXrs7lt3o9LF++XKdPn1aXLl20f/9+DRs2zPnc3XffreXLl+vTTz9V//79FR4e\n7nwfPXfunJ566ikFBQVpwIABioqK0pNPPlmmmlF92KzyfgQCfEC7du20cOFCxcbGeroUwOucPXtW\nLVq00PHjxyt9btp1SUlJys3N1aeffmpk+8CtlDqidfToUQ0YMECdO3dWTEyMlixZIunaYYH4+HiF\nhIQoISHBZfh/zpw56tChg8LCwm45nwYA4Fs2bNigvLw8/fTTT3rllVfUtWtXYyFL4gKh8LxSg1bt\n2rWVnJysvXv3asWKFZoyZYocDodSUlIUEhKinJwcBQcHOyd5njhxQvPmzdPGjRuVkpKiCRMmVEkT\nAICaYdu2bWrfvr0iIyPVsGFDLV261Oj+uO0NPK1Mhw7j4uI0adIkzZs3T1OmTFH37t21a9cuzZgx\nQ8uXL9fq1au1ceNGzZ49W9K1yYybNm1iEh8AAPBJtz0ZPjc3V3v37lVUVJS2b9/uvNN9x44dnRN0\nMzIyXK6JEhoa6nwOAADA15R+waP/cjgcGjZsmJKTk9WoUaMyHfO+1VloAAAA1V155/vdckSrsLBQ\nQ4cO1ciRIxUfHy/p2oXYsrOzJUnZ2dmKjIyUdO3qutcvZilJ+/fvdz7nrmBfe0ybNs3jNdA3fdM3\nfdM3fdN32R4VUWrQsixLY8eOVZcuXfT88887l0dHRys1NVUXL15UamqqevXqJUmKiopSWlqajhw5\nIrvdLj8/P+ZnAQAAn1XqocOtW7fqz3/+s8LDwxURESFJmjFjhhITE/Xkk08qNDRU9957r95++21J\n1+41lpiYqNjYWNWpU0cLFiww3wEAAEA1VWrQ6tOnT4m3R1i5cqXb5RMnTtTEiRMrXpkXiomJ8XQJ\nHkHfvoW+fQt9+xZf7bsiPHJleJvNVuFjngAAAFWhIrmFex0CAAAYQtACAAAwhKAFAABgCEELAADA\nEIIWAACAIQQtAAAAQwhaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFgAAgCEE\nLQAAAEMIWgAAAIYQtAAAAAwhaAEAABhC0AKqgaDAANlsNpdHUGCAp8sCAFSQzbIsq8p3arPJA7sF\nqi2bzSZr8U3LRoj/JwBQDVQktzCiBQAAYAhBCwAAwBCCFgAAgCEELQAAAEMIWgAAAIYQtAAAAAwh\naAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYAAIAhBC0AAABDCFoAAACGELSAasrf79od4298BAUG\neLosAEAZ+Hu6AADuXSmSrMWuy2wjHJ4pBgBQLoxoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFmBI\nUGBAsbMGOXMQAHwLZx0ChpzJcxQ7a1DizEEA8CWMaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYA\nAIAhBC0AAABDCFoAAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEELqOHc3byaG1cDQPXATaWB\nGs7dzau5cTUAVA+MaAGVwN2oEgAAjGgBlcD9qJJnagEAVB+MaAEAABhC0AIAADCEoAUAAGAIQQsA\nAMAQghYAAIAhBC0AAABDuLwDUIP4+4lrdAFADULQAmqQK0Xiel0AUINw6BAAAMAQghYAAIAhBC0A\nAABDCFoAAACGMBkeqGKcOQgAvoOgBVQxzhwEAN/BoUMAAABDCFoAAACGELQAAAAMIWgBXuj6hPsb\nH3X8bcWW2Ww2BQUGeLpcAPBaTIYHvFBJE+5vXnZtuaNqigIAH8SIFgAAgCEELQAAAEMIWoCPczef\ni3lbAFA5mKMF+Dj387mYtwUAlYERLQAAAEMIWgAAAIYQtAAAAAwhaAEAABhC0AIAADCEoAUAAGAI\nQQsAAMCQUoPWmDFj1LJlS3Xt2tW5bPr06QoODlZERIQiIiK0bt0653Nz5sxRhw4dFBYWpi1btpir\nGgAAoAYoNWiNHj1a69evd1lms9k0efJkZWVlKSsrSw888IAk6cSJE5o3b542btyolJQUTZgwwVzV\nAAAANUCpV4bv27evDh8+XGy5ZVnFlmVkZGjw4MEKCQlRSEiILMuSw+FQ48aNK61YAACAmqRcc7Tm\nzp2rXr166e2335bDce1WHZmZmerUqZNzndDQUGVmZlZOlQAAADVQme91mJiYqKlTp+rcuXN66aWX\ntGDBAr344otuR7lsNluJ25k+fbrz3zExMYqJiSlrKQAAAJXObrfLbrdXyrbKHLRatGghSWrSpIme\nffZZjR8/Xi+++KKio6OVnp7uXG///v2KjIwscTs3Bi0AAIDq4uYBoKSkpHJvq8yHDv/9739Lkq5c\nuaIlS5bowQcflCRFRUUpLS1NR44ckd1ul5+fH/OzAACATyt1RGv48OH6+uuvderUKbVp00ZJSUmy\n2+3avXu36tSpo379+ikxMVGS1LJlSyUmJio2NlZ16tTRggULqqQBAACA6qrUoLV06dJiy8aMGVPi\n+hMnTtTEiRMrXhUAAIAX4MrwAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBC8BtCQoMkM1m\nc3kEBQZ4uiwAqNbKfGV4AL7pTJ5D1mLXZbYRDs8UAwA1BCNaAAAAhhC0AAAADCFoAQAAGELQAgAA\nMISgBQAAYAhBCwAAwBCCFgCP4dpcALwd19EC4DFcmwuAt2NECwAAwBCCFoBi/P1U7JDe7a7HoT8A\n+B8OHQIo5kqR3BzSu931OPQHANcxogUAAGAIQQsAAMAQghYAAIAhBC0AAABDCFoAjHN3YdKSzmQE\nAG/CWYcAjHN3YVLJ/ZmMAOBNGNECAAAwhKAFAABgCEELAADAEIIWAACAIUyGB1Cprt//EABA0AJQ\nyW73PokA4As4dAgAAGAIQQsAAMAQghYAAIAhBC0AAABDCFpAGbm7bx8AAO5w1iFQRu7u28dZdVUv\nKDBAZ/IcLssCmzbW6TPnPFQRABRH0AJQI7kPvA73KwOAh3DoEAAAwBCCFgAAgCEELQDVyvVb+Nz4\nCAoM8HRZAFAuzNECUK24v4UPc68A1EyMaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghZQCm63AwCo\nCM46BErB7XYAABXBiBYAAIAhBC0AAABDCFoAAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEEL\nAADAEIIWAACAIQQtAAAAQwhaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFgAA\ngCEELQAAAEMIWgAAAIYQtAAAAAwhaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYAAIAhBC0AAABD\nCFoAAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEELAADAEIIWAACAIQQtAAAAQwhaAAAAhhC0\nAAAADCFoAQAAGELQAgAAMISgBaDa8/eTbDaby+N21wsKDKjiagHgf0oNWmPGjFHLli3VtWtX5zKH\nw6H4+HiFhIQoISFB+fn5zufmzJmjDh06KCwsTFu2bDFXNQCfcqVIsha7Pm53vTN5jqotFgBuUGrQ\nGj16tNavX++yLCUlRSEhIcrJyVFwcLDmz58vSTpx4oTmzZunjRs3KiUlRRMmTDBXNQAAQA1QatDq\n27evAgMDXZZlZmZq7Nixqlu3rsaMGaOMjAxJUkZGhgYPHqyQkBD1799flmXJ4eCTJAAA8F1lnqO1\nfft2dezYUZLUsWNHZWZmSroWtDp16uRcLzQ01PkcAACALypz0LIs67bXLWnCKgAAgC/wL+s3REZG\nKjs7WxEREcrOzlZkZKQkKTo6Wunp6c719u/f73zOnenTpzv/HRMTo5iYmLKWAgAAUOnsdrvsdnul\nbKvMQSs6OlqpqamaOXOmUlNT1atXL0lSVFSUXnrpJR05ckQHDx6Un5+fGjduXOJ2bgxaAAAA1cXN\nA0BJSUnl3laphw6HDx+u3r1768CBA2rTpo0WLVqkxMREHTlyRKGhoTp27JieeeYZSVLLli2VmJio\n2NhYjR8/Xu+99165iwIAAPAGpY5oLV261O3ylStXul0+ceJETZw4seJVAQAAeAGuDA8AAGAIQQsA\nAMAQghYAAIAhBC0AAABDCFoAAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEELAADAEIIWAACA\nIQQt4L+CAgNks9lcHgAAVESpN5UGfMmZPIesxa7LbCM8UwsAwDswogXAq/n7qdhIZVBggKfLAuAj\nGNEC4NWuFMnNSKXDM8UA8DmMaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYAAIAhBC0AAABDCFoA\nAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEELAADAEIIWAACAIQQtAAAAQwhaAAAAhhC0AAAA\nDCFoAQAAGELQAgAAMISgBQAAYAhBCwD+KygwQDabzeURFBjg6bIA1GD+ni4AAKqLM3kOWYtdl9lG\nODxTDACvwIgWAACAIYxoAfA5/n6SzWbzdBkAfABBC4DPuVKkYocIJck2ouprAeDdOHQIAABgCEEL\nAADAEIIWAACAIQQtAAAAQwhaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFgAA\ngCEELQAAAEMIWvBJQYEBstlsLg/AHX8/FXutBAUGeLosADWEv6cLADzhTJ5D1mLXZbYRnqkF1duV\nIrl5rTg8UwyAGocRLQAAAEMIWgAAAIYQtODV3M3FYj4WAKCqMEcLXs3dXCyJ+VgAgKrBiBYAAIAh\nBC0AAABDCFoAAACGELQAAAAMIWgBAAAYQtACAAAwhKAFAABgCEELXoMbRQMAqhsuWAqvwY2iAQDV\nDSNaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFgAAgCEELQAAAEMIWgAAAIYQ\ntAAAAAwhaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYAAIAhBC0AAABDCFoAAACGELQAAAAMIWgB\nAAAYQtACAAAwhKAFAABgSLmD1l133aXw8HBFREQoKipKkuRwOBQfH6+QkBAlJCQoPz+/0goFgOos\nKDBANpvN5REUGODpsgB4WLmDls1mk91uV1ZWljIzMyVJKSkpCgkJUU5OjoKDgzV//vxKKxQAqrMz\neQ5Zi+XyOJPn8HRZADysQocOLcty+TozM1Njx45V3bp1NWbMGGVkZFSoOAAAgJqsQiNasbGxSkhI\n0KpVqyRJ27dvV8eOHSVJHTt2dI50AQAA+CL/8n7j1q1b1apVK2VnZysuLk5RUVHFRrhKM336dOe/\nY2JiFBMTU95SAAAAKo3dbpfdbq+UbZU7aLVq1UqS1KlTJw0ZMkSrV69WZGSksrOzFRERoezsbEVG\nRpb4/TcGLQAAgOri5gGgpKSkcm+rXIcOL1y4IIfj2iTPkydPKi0tTYMHD1Z0dLRSU1N18eJFpaam\nqlevXuUuDAAAoKYrV9D6z3/+o759+6p79+56/PHH9cILL6hNmzZKTEzUkSNHFBoaqmPHjumZZ56p\n7HoBwOP8/VTsUg4A4E65Dh22a9dOu3fvLra8cePGWrlyZYWLAoDq7ErRtcs33Mg2wjO1AKjeuDI8\nAACAIQQtAAAAQwhaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQAAYAhBCwAAwBCCFgAAgCEELQAA\nAEMIWgAAAIYQtAAAAAwhaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQghYAAIAhBC0AAABDCFoAAACG\nELQAoAoFBQbIZrO5PIICAzxdFgBD/D1dAAD4kjN5DlmLXZfZRjg8UwwA4xjRAgAAMISgBQAAYAiH\nDgHAEH8/yWazeboMAB5E0AIAQ64Uyc18LM/UAsAzOHQIAABgCEELAADAEIIWqj2uOwQAqKmYo4Vq\nj+sOAQBqKka0AKCaYjQXqPkY0QKAaorRXKDmY0QLAADAEIIWAACAIQQtAAAAQwhaAAAAhjAZHgA8\njHsiAt6LoAUAHubunogS90UEvAGHDgEAAAxhRAs1EodaAAA1AUELNZK7Qy0cZgEAVDccOgQAADCE\noAUAAGAIQQsAAMAQghYAAIAhBC0AqEGun3F74yMoMMDTZQEoAWcdAkAN4v6MW4dnigFwS4xoAUAN\nxygXUH0xogUANRyjXED1xYgWAACAIQQtAAAAQwhaAAAAhhC0AAAADCFoAQAAGELQAgAAMISgBQBe\niGtrAdUD19ECAC/EtbWA6oERLVQrQYEBxT6FAwBQUzGihWrlTJ7Dzadwz9QC+IKgwACdySs+0hXY\ntLFOnznngYoA70LQAgAf5u7DjcRhRqCycOgQAADAEIIWAACAIQQtAAAAQwhaAAAAhhC04DFcygEA\n4O046xAew6UcAADejhEtAEAx7m7hU8ffxm19gDJiRAsAUIz7W/hwWx+grBjRQqVyN++KT70AAF/F\niBYqFVeZBgDgfwhaAOAjrs+7AlB1CFoA4CNKmncFwBzmaAEAABhC0AIAADCEoAUAAGAIQQsAAMAQ\nghYAAIAhBC0AAABDCFoAAACGELQAAAAMIWgBAAAYQtBCMe5uDF3Rm0Jfv/XHjQ8AALwdt+BBMe5u\nDF3Rm0Jz6w8AgC9iRAsAUG7uRqvdjYCbGCmvyDZN1AO4w4gWAKDc3I9WFx8BNzFSXpFtmqgHcIcR\nLZSbu0+EAHC7bnc0TKrY+w3vVfAkRrRQbu4/EXqmFgA1z+2OhkkVe7/hvQqeVOkjWps2bVKnTp3U\noUMHzZ07t7I3X6PZ7XZPl+AR9n2ergBAVfLkWcZVte/Kfj+vKXPGfPXvWEVUetCaOHGiFixYoPT0\ndH3wwQc6depUZe+ixvLVF6g929MVAKhK10eqbnx4274r+/38+qjbjY8zedVvzpiv/h2riEoNWmfP\nnpUk9evXT23bttWgQYOUkZFRmbsoVU35RHAzd3XX8bdVSS+3O3eB62AB8Hbu3udKeu99++0Zxt+3\ny1JPTf37V1E1oe9KnaO1fft2dezY0fl1WFiYvv32W/3mN7+pzN2UqKaeRVLS/IGq6OV25y5wHSwA\n3s7d+5zk/r23oOCy8fftstRTU//+VVRN6NtmWZZVWRtLT0/XwoULtXTpUknS/PnzdezYMf3f//2f\n604ZDQEAADVIeeNSpY5oRUZG6qWXXnJ+vXfvXg0ePLjYepWY7QAAAKqtSp2j1aRJE0nXzjw8fPiw\nvvzyS0VHR1fmLgAAAGqMSr+O1uzZs/X000+rsLBQEyZMUPPmzSt7FwAAADVCpV/eoX///srOzlZu\nbq52796tli1bqmvXrs7nHQ6H4uPjFRISooSEBOXn51d2CR539OhRDRgwQJ07d1ZMTIyWLFkiyft7\nLygoUHR0tLp3765evXopOTlZkvf3fd3Vq1cVERGhuLg4Sb7R91133aXw8HBFREQoKipKkm/0ff78\neY0aNUq/+tWvFBYWpoyMDK/v+4cfflBERITz0aRJE82ZM0f5+fle3bckffTRR+rdu7d69Oih559/\nXpJvvM6XLFmi/v37q3PnzvrTn/4kyTv7HjNmTJmyypw5c9ShQweFhYVpy5Ytt9y+0VvwjB49WuvX\nr3dZlpKSopCQEOXk5Cg4OFjz5883WYJH1K5dW8nJydq7d69WrFihKVOmyOFweH3v9erV0z/+8Q/t\n3r1bX3/9tRYuXKicnByv7/u69957T2FhYc6TPXyhb5vNJrvdrqysLGVmZkryjb6nTZumkJAQ7dmz\nR3v27FHHjh29vu/Q0FBlZWUpKytLO3fuVIMGDfTwww9r3rx5Xt336dOn9dZbb+nLL7/U9u3b5XfX\nFgAABXNJREFUdeDAAaWlpXn97/vs2bNKSkrS3//+d2VkZOjDDz/U2bNnvbLvsmSVEydOaN68edq4\ncaNSUlI0YcKEW27faNDq27evAgMDXZZlZmZq7Nixqlu3rsaMGVOl19mqKnfeeae6d+8uSWrevLk6\nd+6s7du3+0TvDRo0kCTl5+frypUrqlu3rk/0/dNPP2nt2rX63e9+5zzZwxf6loqf3OILfaenp+u1\n115TvXr15O/vryZNmvhE39elp6erffv2atOmjdf3Xb9+fVmWpbNnz+rixYu6cOGCmjZt6vV9f/PN\nN7r33nsVGBioRo0aacCAAdq2bZtX9l2WrJKRkaHBgwcrJCRE/fv3l2VZcjhucTkJy7BDhw5ZXbp0\ncX4dEhJiXbx40bIsyzp//rwVEhJiugSPysnJsdq1a2c5HA6f6P3q1atWeHi4VatWLWvu3LmWZfnG\n7/zRRx+1du3aZdntduuhhx6yLMs3+m7Xrp0VHh5uxcfHWytXrrQsy/v7Pnr0qBUaGmqNGjXKioqK\nsv74xz9aFy5c8Pq+bzR69Gjrgw8+sCzL+3/flmVZa9eutWrXrm01atTIeu211yzL8v6+8/Pzrbvv\nvts6ePCgdfz4catLly7WG2+84bV9325Wef3116358+c71xs2bJiVnp5e6raNjmiVEOyqepce43A4\nNGzYMCUnJ6tRo0Y+0bufn5++++475ebmat68ecrKyvL6vtesWaMWLVooIiLCpVdv71uStm7dqu++\n+04zZszQ5MmT9fPPP3t93wUFBTpw4ICGDh0qu92uvXv3atmyZV7f93WXL1/W6tWr9dhjj0ny/tf5\nyZMnlZiYqH379unw4cPatm2b1qxZ4/V9N2zYULNnz9azzz6rRx99VF27dlXdunW9vu/rytLnra4N\nWuVBKzIyUtnZ125+l52drcjIyKouoUoUFhZq6NChGjlypOLj4yX5Tu/StUnSDz74oDIyMry+72++\n+UarVq1Su3btNHz4cH311VcaOXKk1/ctSa1atZIkderUSUOGDNHq1au9vu/27dsrNDRUcXFxql+/\nvoYPH67169d7fd/XrVu3Tj169NAdd9whyfvf1zIzM9WrVy+1b99ezZo102OPPabNmzd7fd+SFBcX\np7Vr12rr1q0qKirS4MGDfaJvqeTXdXR0tPbt2+dcb//+/bf8GVR50IqOjlZqaqouXryo1NRU9erV\nq6pLMM6yLI0dO1ZdunRxnqEieX/vp06dUl5eniTpl19+0YYNGxQfH+/1fb/11ls6evSoDh06pM8+\n+0yxsbH69NNPvb7vCxcuOOcmnDx5UmlpaRo8eLDX9y1JHTp0UEZGhoqKivTFF1/o17/+tU/0LUlL\nly7V8OHDnV97e999+/bVjh07dPr0aV26dEnr1q3ToEGDvL5v6drEb+nanLzvv/9e9957r0/0LZX8\nuo6KilJaWpqOHDkiu90uPz8/NW7cuPSNVdLhTbcef/xxq1WrVladOnWs4OBgKzU11Tp37pw1ZMgQ\nq02bNlZ8fLzlcDhMluARmzdvtmw2m9WtWzere/fuVvfu3a1169Z5fe979uyxIiIirPDwcGvQoEHW\nxx9/bFmW5fV938hut1txcXGWZXl/3wcPHrS6detmdevWzYqNjbUWLlxoWZb3921ZlvXDDz9Y0dHR\nVrdu3awXXnjBys/P94m+8/PzrWbNmlnnzp1zLvOFvhctWmT169fP6tmzpzVlyhTr6tWrPtF33759\nrdDQUKtnz55WRkaGZVne+fsua1aZPXu2dc8991idOnWyNm3adMvtV+q9DgEAAPA/VX7oEAAAwFcQ\ntAAAAAwhaAEAABhC0AIAADCEoAUAAGAIQQsAAMCQ/wfRQUK90OS98gAAAABJRU5ErkJggg==\n"}
In [64]:
print "Sesgo en públicos: ", publicos2011.Matematica.skew() print "Sesgo en privados: ", privados2011.Matematica.skew() print "Promedio en públicos: ", publicos2011.Matematica.mean() print "Promedio en privados: ", privados2011.Matematica.mean() print "Mediana en públicos: ", publicos2011.Matematica.median() print "Mediana en privados: ", privados2011.Matematica.median()
Sesgo en públicos: -0.052012984336 Sesgo en privados: 0.574996008708 Promedio en públicos: 43.2461475202 Promedio en privados: 47.5885674994 Mediana en públicos: 43.49 Mediana en privados: 46.64

Y claro, no es sólo en matemática

In [75]:
x2012 = publicos2012.Promedio_Total y2012 = privados2012.Promedio_Total titulo_x2012 = 'Distribucion promedios totales 2012 - Colegios publicos' titulo_y2012 = 'Distribucion promedios totales 2012 - Colegios privados' x2011 = publicos2011.Promedio_Total y2011 = privados2011.Promedio_Total titulo_x2011 = 'Distribucion promedios totales 2011 - Colegios publicos' titulo_y2011 = 'Distribucion promedios totales 2011 - Colegios privados' f, axarr = plt.subplots(2, 2, sharex=True, sharey=True, figsize=(15,10), dpi=100) axarr[0, 0].hist(x2012, bins=100, color='red') axarr[0, 0].set_title(titulo_x2012) axarr[1, 0].hist(y2012, bins=100, color='orange') axarr[1, 0].set_title(titulo_y2012) axarr[0, 1].hist(x2011, bins=100, color='green') axarr[0, 1].set_title(titulo_x2011) axarr[1, 1].hist(y2011, bins=100, color='cyan') axarr[1, 1].set_title(titulo_y2011) plt.show()
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0euC1k+YBcCqu+ZXD/gKAhqGy13tq3AAA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In [76]:
print "Sesgo en públicos 2012: ", publicos2012.Promedio_Total.skew() print "Sesgo en privados 2012: ", privados2012.Promedio_Total.skew() print "Promedio en públicos 2012: ", publicos2012.Promedio_Total.mean() print "Promedio en privados 2012: ", privados2012.Promedio_Total.mean() print "Mediana en públicos 2012: ", publicos2012.Promedio_Total.median() print "Mediana en privados 2012: ", privados2012.Promedio_Total.median() print "Sesgo en públicos 2011: ", publicos2011.Promedio_Total.skew() print "Sesgo en privados 2011: ", privados2011.Promedio_Total.skew() print "Promedio en públicos 2011: ", publicos2011.Promedio_Total.mean() print "Promedio en privados 2011: ", privados2011.Promedio_Total.mean() print "Mediana en públicos 2011: ", publicos2011.Promedio_Total.median() print "Mediana en privados 2011: ", privados2011.Promedio_Total.median()
Sesgo en públicos 2012: 0.526599249105 Sesgo en privados 2012: 1.02662898427 Promedio en públicos 2012: 42.8033300686 Promedio en privados 2012: 46.0227293165 Mediana en públicos 2012: 42.7 Mediana en privados 2012: 44.65 Sesgo en públicos 2011: 0.207080369486 Sesgo en privados 2011: 0.673856432328 Promedio en públicos 2011: 42.2170533907 Promedio en privados 2011: 45.6859889984 Mediana en públicos 2011: 42.26 Mediana en privados 2011: 44.65

Todos los colegios son iguales, pero hay unos mejores que otros

Entre los puntajes de cada área hay correlaciones altas. Cuando un colegio obtiene resultados altos (bajos) en un área, generalmente obtiene resultados altos (bajos) en todas las demás.

In [100]:
areas = ['Matematica', 'Lenguaje', 'Biologia', 'Fisica' ,'Ingles', 'Quimica'] puntajes2012 = data2012[areas] puntajes2011 = data2011[areas]
In [101]:
puntajes2012.corr()
Matematica Lenguaje Biologia Fisica Ingles Quimica
Matematica 1.000000 0.851994 0.877380 0.832650 0.853915 0.881737
Lenguaje 0.851994 1.000000 0.871563 0.772343 0.851910 0.852104
Biologia 0.877380 0.871563 1.000000 0.805226 0.834049 0.890977
Fisica 0.832650 0.772343 0.805226 1.000000 0.825350 0.839733
Ingles 0.853915 0.851910 0.834049 0.825350 1.000000 0.834422
Quimica 0.881737 0.852104 0.890977 0.839733 0.834422 1.000000
In [103]:
puntajes2011.corr()
Matematica Lenguaje Biologia Fisica Ingles Quimica
Matematica 1.000000 0.867654 0.869163 0.850349 0.734763 0.872090
Lenguaje 0.867654 1.000000 0.856485 0.760507 0.665898 0.785545
Biologia 0.869163 0.856485 1.000000 0.795072 0.687669 0.807628
Fisica 0.850349 0.760507 0.795072 1.000000 0.655000 0.808700
Ingles 0.734763 0.665898 0.687669 0.655000 1.000000 0.695131
Quimica 0.872090 0.785545 0.807628 0.808700 0.695131 1.000000

Pero las áreas sí miden cosas distintas (o algo así)

Usemos una prueba t con muestras dependientes. La hipótesis nula es que los valores esperados de las distribuciones son iguales.

In [105]:
print 'Matemática versus Lenguaje 2012:' print 'Promedio Matemática:', puntajes2012.Matematica.mean() print 'Promedio Lenguaje:', puntajes2012.Lenguaje.mean() t_val, p_val = stats.ttest_rel(puntajes2012.Matematica,puntajes2012.Lenguaje) print "Estadística t: {0:8.6f}. Valor de p: {1:8.6f}".format(t_val, p_val)
Matemática versus Lenguaje 2012: Promedio Matemática: 44.9236683576 Promedio Lenguaje: 45.8776395054 Estadística t: -33.282382. Valor de p: 0.000000
In [108]:
x = puntajes2012.Matematica y = puntajes2012.Lenguaje titulo_x = 'Puntajes matematica 2012' titulo_y = 'Puntajes lenguaje 2012' dos_graficos(x,y,titulo_x,titulo_y)
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urKwspaamqnPnzlq6dOlFOQkAAIC2pFUmGrUsq9lN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\n"}
In [107]:
print 'Matemática versus Física 2012:' print 'Promedio Matemática:', puntajes2012.Matematica.mean() print 'Promedio Física:', puntajes2012.Fisica.mean() t_val, p_val = stats.ttest_rel(puntajes2012.Matematica,puntajes2012.Fisica) print "Estadística t: {0:8.6f}. Valor de p: {1:8.6f}".format(t_val, p_val)
Matemática versus Física 2012: Promedio Matemática: 44.9236683576 Promedio Física: 44.0144023462 Estadística t: 29.726227. Valor de p: 0.000000
In [109]:
x = puntajes2012.Matematica y = puntajes2012.Fisica titulo_x = 'Puntajes matematica 2012' titulo_y = 'Puntajes fisica 2012' dos_graficos(x,y,titulo_x,titulo_y)
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NehIAAABtXZt6OKrjOEE11+EYn88nr9fb2tUISVy7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\nQmCC\n"}

¿Progreso?

Para terminar, comparemos los puntajes promedios de los colegios en 2011 y 2012.

Dado que el formato del nombre del colegio cambió en los archivos de 2011 a 2012, primero debo intentar pegarlos identificando nombres de 2012 en la lista de 2011. La solución es burda pero la muestra que resulta es suficientemente grande.

In [111]:
# Estas funciones permiten seleccionar un nombre de colegio de 2011 (sin abreviaturas) # para un nombre de colegio de 2012 (con abreviaturas). Es horriblemente lento. # Se aceptan sugerencias de soluciones más dignas a este problema. def simplificador(x): return set(a for a in x.split() if a[-1] != ".") def listacandidatos(x): return [t for t in data2011.Colegio if simplificador(x).issubset(set(t.split()))] def candidato(x): if not re.search(r'\.', x): return x elif len(listacandidatos(x))==1: return listacandidatos(x)[0] else: return np.nan
In [112]:
data2012['Nombre_sin_abreviaturas'] = data2012.Colegio.map(candidato)
In [119]:
print "Número total de colegios en 2012: ", len(data2012.Colegio) print "Número de colegios con nombre completo identificado: ", len(data2012.Nombre_sin_abreviaturas.dropna())
Número total de colegios en 2012: 12616 Número de colegios con nombre completo identificado: 8972
In [124]:
# Ahora peguemos las dos DataFrames usando los nombres de la columna nueva en 2012 datoscombinados = pd.merge(data2011, data2012, left_on='Colegio', right_on='Nombre_sin_abreviaturas')
In [125]:
# El DataFrame resultante: datoscombinados
<class 'pandas.core.frame.DataFrame'> Int64Index: 7760 entries, 0 to 7759 Data columns: Puesto_x 7760 non-null values Colegio_x 7760 non-null values Municipio_x 7760 non-null values Departamento_x 7760 non-null values Oficial_x 7760 non-null values Periodo 7760 non-null values Jornada_x 7760 non-null values Calendario_x 7760 non-null values Evaluados_x 7760 non-null values Promedio_Total_x 7760 non-null values Matematica_x 7760 non-null values Quimica_x 7760 non-null values Fisica_x 7760 non-null values Biologia_x 7760 non-null values Filosofia_x 7760 non-null values Ingles_x 7759 non-null values Lenguaje_x 7760 non-null values Sociales_x 7760 non-null values DE_Matematica_x 7760 non-null values DE_Quimica_x 7760 non-null values DE_Fisica_x 7760 non-null values DE_Biologia_x 7760 non-null values DE_Filosofia_x 7760 non-null values DE_Ingles_x 7759 non-null values DE_Lenguaje_x 7760 non-null values DE_Sociales_x 7760 non-null values CSE_2009 6538 non-null values Puesto_y 7760 non-null values Colegio_y 7760 non-null values Calendario_y 7760 non-null values Evaluados_y 7760 non-null values Promedio_Total_y 7760 non-null values Matematica_y 7760 non-null values DE_Matematica_y 7760 non-null values Quimica_y 7760 non-null values DE_Quimica_y 7760 non-null values Fisica_y 7760 non-null values DE_Fisica_y 7760 non-null values Biologia_y 7760 non-null values DE_Biologia_y 7760 non-null values Filosofia_y 7760 non-null values DE_Filosofia_y 7760 non-null values Ingles_y 7759 non-null values DE_Ingles_y 7759 non-null values Lenguaje_y 7760 non-null values DE_Lenguaje_y 7760 non-null values Sociales_y 7760 non-null values DE_Sociales_y 7760 non-null values Municipio_y 7760 non-null values Departamento_y 7760 non-null values Jornada_y 7760 non-null values Oficial_y 7760 non-null values Nombre_sin_abreviaturas 7760 non-null values dtypes: float64(36), int64(4), object(13)
In [126]:
# Número de colegios: len(datoscombinados.Colegio_x)
7760
In [127]:
# Ahorxa creemos una columna donde se reste el promedio de 2012 menos # el promedio de 2011 y grafiquemos la distribución. datoscombinados['Resta_de_promedios'] = datoscombinados.Promedio_Total_y - datoscombinados.Promedio_Total_x
In [138]:
x = datoscombinados.Resta_de_promedios titulo_x = 'Distribucion de la resta de promedios entre 2012 y 2011 por colegio' fig = plt.figure(figsize=(11,6), dpi=100) ax = fig.add_subplot(1,1,1) ax.hist(x, bins=100, color='red') ax.set_title(titulo_x)
<matplotlib.text.Text at 0x7592e10>
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IEHm9XvXv319mJr/ff/6rBgAAQFi46nnM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\nAMA1wiMAAABc+18Fo90y7pUZKQAAAABJRU5ErkJggg==\n"}
In [140]:
print "Promedio: ", datoscombinados.Resta_de_promedios.mean() print "Mediana: ", datoscombinados.Resta_de_promedios.median() print "Desviación estándar: ", datoscombinados.Resta_de_promedios.std()
Promedio: 0.536192010309 Mediana: 0.555 Desviación estándar: 2.92110044054
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