From 39d8c4dc2c6f66af5243c6256740399cab272353 Mon Sep 17 00:00:00 2001 From: NicolasPce Date: Thu, 13 Jan 2022 17:57:06 +0100 Subject: [PATCH] =?UTF-8?q?Nicol=C3=A1s=20-=20Final=203=20horas?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../Main Cobify-checkpoint.ipynb | 992 ++++++++++++++++++ .../MainCobify-checkpoint.ipynb | 992 ++++++++++++++++++ MainCobify.ipynb | 992 ++++++++++++++++++ 3 files changed, 2976 insertions(+) create mode 100644 .ipynb_checkpoints/Main Cobify-checkpoint.ipynb create mode 100644 .ipynb_checkpoints/MainCobify-checkpoint.ipynb create mode 100644 MainCobify.ipynb diff --git a/.ipynb_checkpoints/Main Cobify-checkpoint.ipynb b/.ipynb_checkpoints/Main Cobify-checkpoint.ipynb new file mode 100644 index 0000000..948486c --- /dev/null +++ b/.ipynb_checkpoints/Main Cobify-checkpoint.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "19e233cb", + "metadata": {}, + "source": [ + "### Importamos librerías" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "f04297b3", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn import metrics\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline\n", + "%config Inlinebackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set_context('poster')\n", + "sns.set(rc={'figure.figsize': (16., 9.)})\n", + "sns.set_style('whitegrid')\n", + "\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "21019f1f", + "metadata": {}, + "outputs": [], + "source": [ + "# Nos traemos el data set, teniendo en cuenta que los decimales están separados por coma\n", + "df = pd.read_csv('measurements.csv',decimal=',')\n" + ] + }, + { + "cell_type": "markdown", + "id": "089a188e", + "metadata": {}, + "source": [ + "### Analísis y limpieza de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "ed6cfec1", + "metadata": {}, + "outputs": [], + "source": [ + "#df2 = pd.readcsv(\"measurements2.xlsx\",encoding = \"ISO-8859-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "6c5970a4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(388, 12)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "1b7b5cd1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
028.05.02621.512NaNE1000045.0E10
112.04.23021.513NaNE10000NaNNaN
211.25.53821.515NaNE10000NaNNaN
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside specials gas_type AC \\\n", + "0 28.0 5.0 26 21.5 12 NaN E10 0 \n", + "1 12.0 4.2 30 21.5 13 NaN E10 0 \n", + "2 11.2 5.5 38 21.5 15 NaN E10 0 \n", + "\n", + " rain sun refill liters refill gas \n", + "0 0 0 45.0 E10 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN " + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "7655fa36", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "specials 295\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "refill gas 375\n", + "dtype: int64" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "4dd5bb12", + "metadata": {}, + "outputs": [], + "source": [ + "#Eliminamos la columna refill gas, ya que es la misma que gas_type, aunque en algunas falten datos se observa como cambia a la par\n", + "#Elimanamos tambien la columna specials, porque nos da la misma información que las columna AC, rain y sun\n", + "df.drop(['refill gas', 'specials'], axis = 1 , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "ab3d31ac", + "metadata": {}, + "outputs": [], + "source": [ + "#Para la columna refill liters le asignamos en los valores que faltan el valor que coincide con el gas_type" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "86c99310", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "dtype: int64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "4eb23638", + "metadata": {}, + "outputs": [], + "source": [ + "#Para los 12 datos de temperatura interior que nos faltan al ser un 3% de la muestra le asignaremos el valor medio" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "6cfa05b0", + "metadata": {}, + "outputs": [], + "source": [ + "df['temp_inside'] = df['temp_inside'].fillna(22)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "a0f6405a", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
028.05.02621.512E1000045.0
112.04.23021.513E1000045.0
211.25.53821.515E1000045.0
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside gas_type AC rain \\\n", + "0 28.0 5.0 26 21.5 12 E10 0 0 \n", + "1 12.0 4.2 30 21.5 13 E10 0 0 \n", + "2 11.2 5.5 38 21.5 15 E10 0 0 \n", + "\n", + " sun refill liters \n", + "0 0 45.0 \n", + "1 0 45.0 \n", + "2 0 45.0 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Para la columna refill liters, completamos el valor con el valor previo, ya que también parece ir correlacionada con elgas type\n", + "df = df.fillna(method='ffill')\n", + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "305762b9", + "metadata": {}, + "outputs": [], + "source": [ + "#Normalizamos los dos tipos de combustible para facilitar en análisis de su correlación\n", + "\n", + "combus= {\"E10\": 1,\n", + " \"SP98\": 0,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "e77d01d6", + "metadata": {}, + "outputs": [], + "source": [ + "df.gas_type = df.gas_type.map(combus)" + ] + }, + { + "cell_type": "markdown", + "id": "7e97b7b1", + "metadata": {}, + "source": [ + "### Correlación" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "414cc0af", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
distance1.000000-0.1289670.5622990.0747340.0881750.053411-0.025738-0.0197910.081120-0.097117
consume-0.1289671.000000-0.227866-0.161470-0.3208110.0153270.0965910.248118-0.1706670.139765
speed0.562299-0.2278661.0000000.0591390.0154110.097360-0.0354080.0094890.081618-0.086277
temp_inside0.074734-0.1614700.0591391.0000000.359649-0.0109210.296719-0.0372040.242237-0.077320
temp_outside0.088175-0.3208110.0154110.3596491.000000-0.1487050.167562-0.1863150.346903-0.356930
gas_type0.0534110.0153270.097360-0.010921-0.1487051.000000-0.105285-0.060328-0.0227610.133453
AC-0.0257380.096591-0.0354080.2967190.167562-0.1052851.0000000.2429150.088598-0.079404
rain-0.0197910.2481180.009489-0.037204-0.186315-0.0603280.2429151.000000-0.112650-0.083591
sun0.081120-0.1706670.0816180.2422370.346903-0.0227610.088598-0.1126501.000000-0.055477
refill liters-0.0971170.139765-0.086277-0.077320-0.3569300.133453-0.079404-0.083591-0.0554771.000000
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside \\\n", + "distance 1.000000 -0.128967 0.562299 0.074734 0.088175 \n", + "consume -0.128967 1.000000 -0.227866 -0.161470 -0.320811 \n", + "speed 0.562299 -0.227866 1.000000 0.059139 0.015411 \n", + "temp_inside 0.074734 -0.161470 0.059139 1.000000 0.359649 \n", + "temp_outside 0.088175 -0.320811 0.015411 0.359649 1.000000 \n", + "gas_type 0.053411 0.015327 0.097360 -0.010921 -0.148705 \n", + "AC -0.025738 0.096591 -0.035408 0.296719 0.167562 \n", + "rain -0.019791 0.248118 0.009489 -0.037204 -0.186315 \n", + "sun 0.081120 -0.170667 0.081618 0.242237 0.346903 \n", + "refill liters -0.097117 0.139765 -0.086277 -0.077320 -0.356930 \n", + "\n", + " gas_type AC rain sun refill liters \n", + "distance 0.053411 -0.025738 -0.019791 0.081120 -0.097117 \n", + "consume 0.015327 0.096591 0.248118 -0.170667 0.139765 \n", + "speed 0.097360 -0.035408 0.009489 0.081618 -0.086277 \n", + "temp_inside -0.010921 0.296719 -0.037204 0.242237 -0.077320 \n", + "temp_outside -0.148705 0.167562 -0.186315 0.346903 -0.356930 \n", + "gas_type 1.000000 -0.105285 -0.060328 -0.022761 0.133453 \n", + "AC -0.105285 1.000000 0.242915 0.088598 -0.079404 \n", + "rain -0.060328 0.242915 1.000000 -0.112650 -0.083591 \n", + "sun -0.022761 0.088598 -0.112650 1.000000 -0.055477 \n", + "refill liters 0.133453 -0.079404 -0.083591 -0.055477 1.000000 " + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#observamos la colerracion\n", + "corre = df.corr()\n", + "corre" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "8287e7bb", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " mask = np.triu(np.ones_like(corre, dtype=np.bool))\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.triu(np.ones_like(corre, dtype=np.bool))\n", + "heatmap = sns.heatmap(corre, mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG');" + ] + }, + { + "cell_type": "markdown", + "id": "ccf99683", + "metadata": {}, + "source": [ + "#### Observamos que el consumo se relaciona principal y negativamente con la velocidad y la temperatura exterior, sin ser ninguna superior al 0.5 (valor absoluto). Además, la velocidad se correlaciona positivamente con la distancia (trayectos largos a mayor velocidad). La temperatura exterior se correlaciona positicamente con el sol, y la temperatura interior, y negativamente con los litros de relleno. No existen otras correlaciones superiores a 0.35\n" + ] + }, + { + "cell_type": "markdown", + "id": "b7a31b48", + "metadata": {}, + "source": [ + "### Predicciones consumo" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "d668efbe", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos la regresión líneal" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "fd46bd3f", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "22d7de78", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "dd9af45a", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_train = lr.predict(X_train)\n", + "y_pred_test = lr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "5a5aabcc", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.22815357510490153" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train_predict = lr.predict(X_train)\n", + "metrics.r2_score(y_train, y_train_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "e9fb732c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1020698110029008" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test_predict = lr.predict(X_test)\n", + "metrics.r2_score(y_test, y_test_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "984494b0", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9154795542880089" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train, y_train_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "69d01cd4", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9375077349245255" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics.mean_squared_error(y_test, y_test_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "e3fc29b2", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos el random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "c1be8ed3", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "ac6a5ed5", + "metadata": {}, + "outputs": [], + "source": [ + "forest = RandomForestRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "a84cc540", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor()" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "5bb449ad", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.24289915602982237" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train,forest.predict(X_train)).round(3)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "a17fee39", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6985699678629192" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_test,forest.predict(X_test)).round(3)**0.5" + ] + }, + { + "cell_type": "markdown", + "id": "027fc881", + "metadata": {}, + "source": [ + "#### Obtenemos un MSE más bajo en el random forest, 0.24 en el test, y de 0.69 en el train, aún así no parece un gran modelo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c402484a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c36a66d", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/.ipynb_checkpoints/MainCobify-checkpoint.ipynb b/.ipynb_checkpoints/MainCobify-checkpoint.ipynb new file mode 100644 index 0000000..69d9ab2 --- /dev/null +++ b/.ipynb_checkpoints/MainCobify-checkpoint.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25980055", + "metadata": {}, + "source": [ + "### Importamos librerías" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "f63a2ff3", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn import metrics\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline\n", + "%config Inlinebackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set_context('poster')\n", + "sns.set(rc={'figure.figsize': (16., 9.)})\n", + "sns.set_style('whitegrid')\n", + "\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "e6e0415c", + "metadata": {}, + "outputs": [], + "source": [ + "# Nos traemos el data set, teniendo en cuenta que los decimales están separados por coma\n", + "df = pd.read_csv('measurements.csv',decimal=',')\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ce8eeeb", + "metadata": {}, + "source": [ + "### Analísis y limpieza de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "f3c01757", + "metadata": {}, + "outputs": [], + "source": [ + "#df2 = pd.readcsv(\"measurements2.xlsx\",encoding = \"ISO-8859-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "6275ac7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(388, 12)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "eaf5b089", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
028.05.02621.512NaNE1000045.0E10
112.04.23021.513NaNE10000NaNNaN
211.25.53821.515NaNE10000NaNNaN
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside specials gas_type AC \\\n", + "0 28.0 5.0 26 21.5 12 NaN E10 0 \n", + "1 12.0 4.2 30 21.5 13 NaN E10 0 \n", + "2 11.2 5.5 38 21.5 15 NaN E10 0 \n", + "\n", + " rain sun refill liters refill gas \n", + "0 0 0 45.0 E10 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN " + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "43d9089e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "specials 295\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "refill gas 375\n", + "dtype: int64" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "db8bcbb5", + "metadata": {}, + "outputs": [], + "source": [ + "#Eliminamos la columna refill gas, ya que es la misma que gas_type, aunque en algunas falten datos se observa como cambia a la par\n", + "#Elimanamos tambien la columna specials, porque nos da la misma información que las columna AC, rain y sun\n", + "df.drop(['refill gas', 'specials'], axis = 1 , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "9464bdde", + "metadata": {}, + "outputs": [], + "source": [ + "#Para la columna refill liters le asignamos en los valores que faltan el valor que coincide con el gas_type" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "f0459a09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "dtype: int64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "04f84b0a", + "metadata": {}, + "outputs": [], + "source": [ + "#Para los 12 datos de temperatura interior que nos faltan al ser un 3% de la muestra le asignaremos el valor medio" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "a5ecb335", + "metadata": {}, + "outputs": [], + "source": [ + "df['temp_inside'] = df['temp_inside'].fillna(22)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "f24f2c8b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
028.05.02621.512E1000045.0
112.04.23021.513E1000045.0
211.25.53821.515E1000045.0
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside gas_type AC rain \\\n", + "0 28.0 5.0 26 21.5 12 E10 0 0 \n", + "1 12.0 4.2 30 21.5 13 E10 0 0 \n", + "2 11.2 5.5 38 21.5 15 E10 0 0 \n", + "\n", + " sun refill liters \n", + "0 0 45.0 \n", + "1 0 45.0 \n", + "2 0 45.0 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Para la columna refill liters, completamos el valor con el valor previo, ya que también parece ir correlacionada con elgas type\n", + "df = df.fillna(method='ffill')\n", + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "df948205", + "metadata": {}, + "outputs": [], + "source": [ + "#Normalizamos los dos tipos de combustible para facilitar en análisis de su correlación\n", + "\n", + "combus= {\"E10\": 1,\n", + " \"SP98\": 0,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "df8fdbdd", + "metadata": {}, + "outputs": [], + "source": [ + "df.gas_type = df.gas_type.map(combus)" + ] + }, + { + "cell_type": "markdown", + "id": "e30f7ca3", + "metadata": {}, + "source": [ + "### Correlación" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "31628178", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
distance1.000000-0.1289670.5622990.0747340.0881750.053411-0.025738-0.0197910.081120-0.097117
consume-0.1289671.000000-0.227866-0.161470-0.3208110.0153270.0965910.248118-0.1706670.139765
speed0.562299-0.2278661.0000000.0591390.0154110.097360-0.0354080.0094890.081618-0.086277
temp_inside0.074734-0.1614700.0591391.0000000.359649-0.0109210.296719-0.0372040.242237-0.077320
temp_outside0.088175-0.3208110.0154110.3596491.000000-0.1487050.167562-0.1863150.346903-0.356930
gas_type0.0534110.0153270.097360-0.010921-0.1487051.000000-0.105285-0.060328-0.0227610.133453
AC-0.0257380.096591-0.0354080.2967190.167562-0.1052851.0000000.2429150.088598-0.079404
rain-0.0197910.2481180.009489-0.037204-0.186315-0.0603280.2429151.000000-0.112650-0.083591
sun0.081120-0.1706670.0816180.2422370.346903-0.0227610.088598-0.1126501.000000-0.055477
refill liters-0.0971170.139765-0.086277-0.077320-0.3569300.133453-0.079404-0.083591-0.0554771.000000
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside \\\n", + "distance 1.000000 -0.128967 0.562299 0.074734 0.088175 \n", + "consume -0.128967 1.000000 -0.227866 -0.161470 -0.320811 \n", + "speed 0.562299 -0.227866 1.000000 0.059139 0.015411 \n", + "temp_inside 0.074734 -0.161470 0.059139 1.000000 0.359649 \n", + "temp_outside 0.088175 -0.320811 0.015411 0.359649 1.000000 \n", + "gas_type 0.053411 0.015327 0.097360 -0.010921 -0.148705 \n", + "AC -0.025738 0.096591 -0.035408 0.296719 0.167562 \n", + "rain -0.019791 0.248118 0.009489 -0.037204 -0.186315 \n", + "sun 0.081120 -0.170667 0.081618 0.242237 0.346903 \n", + "refill liters -0.097117 0.139765 -0.086277 -0.077320 -0.356930 \n", + "\n", + " gas_type AC rain sun refill liters \n", + "distance 0.053411 -0.025738 -0.019791 0.081120 -0.097117 \n", + "consume 0.015327 0.096591 0.248118 -0.170667 0.139765 \n", + "speed 0.097360 -0.035408 0.009489 0.081618 -0.086277 \n", + "temp_inside -0.010921 0.296719 -0.037204 0.242237 -0.077320 \n", + "temp_outside -0.148705 0.167562 -0.186315 0.346903 -0.356930 \n", + "gas_type 1.000000 -0.105285 -0.060328 -0.022761 0.133453 \n", + "AC -0.105285 1.000000 0.242915 0.088598 -0.079404 \n", + "rain -0.060328 0.242915 1.000000 -0.112650 -0.083591 \n", + "sun -0.022761 0.088598 -0.112650 1.000000 -0.055477 \n", + "refill liters 0.133453 -0.079404 -0.083591 -0.055477 1.000000 " + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#observamos la colerracion\n", + "corre = df.corr()\n", + "corre" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "f3a239ad", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " mask = np.triu(np.ones_like(corre, dtype=np.bool))\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAA44AAAIPCAYAAAA8bIBdAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjQuMywgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/MnkTPAAAACXBIWXMAAAsTAAALEwEAmpwYAACpjUlEQVR4nOzdd3xN9x/H8dfNQIbESmLEpq3R4Wd3WC2qpWqrWq1RrdHasWNvqiE2VbVSglJCUbVXF7E61CYJCZFBbpL7+4Peujdyq+Rm6Pv5eNzHwznnc8/9fr/Ovbmf+/mecwwmk8mEiIiIiIiISCocMroBIiIiIiIikrkpcRQRERERERGblDiKiIiIiIiITUocRURERERExCYljiIiIiIiImKTEkcRERERERGxSYmjiIiIiIhIJhcTE0PDhg25ePFiim0nT56kWbNm1K9fnyFDhpCYmAjA5cuXeffdd3n99df58MMPiY2NfeTXV+IoIiIiIiKSif3yyy+88847nD179oHb+/fvz7Bhw9iyZQsmk4mgoCAARo4cSZs2bQgJCaF8+fIEBgY+chuUOIqIiIiIiGRiQUFBjBgxAm9v7xTbLl26xO3bt3nhhRcAaNq0KSEhIRiNRg4fPkz9+vUt1j8qp0d+poiIiIiIiDyS6OhooqOjU6z38PDAw8PDYt3YsWNT3U94eDheXl7mZS8vL8LCwoiKisLd3R0nJyeL9Y9KiaOIiIiIiIgVwysV7Lr/z1q+z8yZM1Os79GjBz179nzo/ZhMphTrDAZDqusflRJHERERERGRdNahQweaNGmSYr11tfGf+Pj4cO3aNfNyREQE3t7e5MmTh5iYGJKSknB0dDSvf1RKHEVERERERKw52PdyMA+akvooChUqRPbs2fnhhx+oWLEi69ato0aNGjg7O1OpUiU2bdpEo0aNzOsflS6OIyIiIiIiksV06dKFY8eOATBlyhTGjx9PgwYNiI+Pp3379gCMGDGCoKAg3njjDY4cOcInn3zyyK9nMD1o8quIiIiIiMh/mKFWZbvu37TzsF33n9ZUcRQRERERERGbdI6jiIiIiIiINYdHvwLpk0gVRxEREREREbFJFUcRERERERFrdr6qalaj0RARERERERGbVHEUERERERGxZlCN7X5KHEVERERERKxpqqoFjYaIiIiIiIjYpIqjiIiIiIiINVUcLWg0RERERERExCZVHEVERERERKwZDBndgkxFFUcRERERERGxSRVHERERERERazrH0YJGQ0RERERERGxSxVFERERERMSaQTW2+2XK0fDz82POnDl06dIl1Zhbt27x0UcfpWOrRERERERE/psybcXR29ub+fPnp7r95s2bnDp1Kh1bJCIiIiIi/xk6x9FCpkgcTSYTEyZMYOfOnXh7e5OUlESVKlWoU6cOO3bsYMOGDSxYsABHR0d8fX2ZPHkyY8aMITw8nO7duzNr1iymT5/O/v37uXnzJrlz5yYgIAAvLy9efvll6tevzw8//ICjoyOffvophQsXZt++fUyYMAGTyUTBggWZOnUqLi4uTJo0iUOHDpGUlETTpk3p2LFjRg+PiIiIiIhIhsoUafSWLVs4ceIEGzduZMaMGZw/f95i+6effsqiRYsIDg6mePHinDlzhqFDh+Lt7c2sWbM4d+4cZ86cYeXKlWzZsoUiRYqwYcMGACIiIqhevTrr1q2jcuXKLFu2jISEBPr168fEiRPZsGEDTz/9NGvXriUoKAiAtWvXsnr1arZv386RI0fSfTxERERERCSDOTjY95HFZIqK46FDh6hXrx7Ozs7kyZOHGjVqWGyvXbs277zzDq+++ir169enTJkyXLx40by9aNGiDBw4kK+++oo///yTn3/+mSJFipi3v/LKKwCULl2aI0eOcPr0aXx8fChTpgwAffr0AaBXr16cPHmSAwcOABAXF8fp06epVKmSXfsvIiIiIiKSmWWKxNFgMJCcnGxednKybNbQoUM5deoU33//Pf3796dHjx5UrFjRvD00NJS+ffvSsWNH6tevj4ODAyaTybw9e/bs5tcxmUw4Oztb7P/WrVvExsaSlJRE//79qVevHgCRkZG4urqmeX9FRERERCRzMxgMGd2ETCVT1EirV69OSEgICQkJ3Lx5k927d5u3JSYmUq9ePXLnzs0HH3xA48aNOXnyJE5OTiQmJgJw+PBhqlSpwjvvvEOpUqXYu3cvSUlJqb5e8eLFiYyM5PfffwdgwYIFrFixgmrVqhEUFITRaCQ2NpY2bdrwyy+/2LfzIiIiIiIimVymqDi+9tprHDt2jIYNG5IvXz5Klixp3ubk5ESvXr147733yJEjBx4eHkycOJE8efJQsGBB2rVrx5QpU+jRoweNGjXC2dmZp59+2mIqq7Xs2bMzefJkBgwYgNFopEiRIkyaNIls2bJx7tw5mjRpQmJiIk2bNqVq1arpMQQiIiIiIpKZZMHzEO3JYLp/TqeIiIiIiIjg0PwNu+4/efUmu+4/rWWKiqOIiIiIiEimooqjBY2GiIiIiIiI2KSKo4iIiIiIiDWDamz302iIiIiIiIiITao4ioiIiIiIWNM5jhaUOIqIiIiIiFhzMGR0CzIVpdEiIiIiIiJikyqOIiIiIiIi1nRxHAsaDREREREREbFJFUcRERERERFrujiOBY2GiIiIiIiI2KSKo4iIiIiIiDVVHC1oNERERERERMQmVRxFRERERESs6aqqFjQaIiIiIiIiYpMqjiIiIiIiItYcDBndgkxFFUcRERERERGxSRVHERERERERa7qqqgWNhoiIiIiIiNikiqOIiIiIiIg1XVXVghLHNBQfeSKjm5BpueQpm9FNEBERERGRR6TEUURERERExIpB5zha0GiIiIiIiIiITao4ioiIiIiIWHFQxdGCRkNERERERERsUsVRRERERETEiiqOlpQ4ioiIiIiIWFHiaEmjISIiIiIiIjap4igiIiIiImIlM1UcN2zYwOzZszEajXTs2JF3333XvO3kyZP4+fmZlyMjI/H09GTjxo2sW7eOKVOmkDdvXgBq1apF7969H6kNShxFREREREQyqbCwMKZPn05wcDDZsmWjdevWVK1alVKlSgFQpkwZ1q9fD0B8fDwtWrTA398fgGPHjuHn50fDhg0fux2ZJ40WERERERHJJBwcHOz6eFj79u2jWrVq5MqVC1dXV+rXr09ISMgDY+fOnUvlypWpVKkScDdxXLduHW+99Rb9+vXj5s2bjz4ej/xMEREREREReSTR0dFcvHgxxSM6OtoiLjw8HC8vL/Oyt7c3YWFhD9xfUFAQPXr0MK/z8vKiZ8+erF+/ngIFCjBq1KhHbq+mqoqIiIiIiFhxMBjsuv8lS5Ywc+bMFOt79OhBz549zcsmkylFjOEBbduwYQOvvfaa+XxGgFmzZpn/3blzZ1577bVHbq8SRxERERERkXTWoUMHmjRpkmK9h4eHxbKPjw9HjhwxL4eHh+Pt7Z3iedu2beODDz4wL9+6dYs1a9bQsWNH4G4C6uT06OmfEkcREREREREr9r6qqoeHR4ok8UFefPFFAgICiIyMxMXFha1btzJ69GiLGJPJxPHjx6lQoYJ5naurKwsWLKBChQo8//zzfPnll9StW/eR26vEUUREREREJJPy8fGhd+/etG/fHqPRSPPmzXnuuefo0qULvXr14tlnnyUyMhJnZ2eyZ89ufp6joyOffvop/v7+3L59m2LFijFp0qRHbofB9KBJs/JI4iNPZHQTMi2XPGUzugkiIiIiIg8t78CP7br/6xNn2HX/aU1XVRURERERERGbNFVVRERERETEir3PccxqNBoiIiIiIiJikyqOWdCuvUcImP0lCUYjpUsWxX9ID9zdXB8YazKZGD4mgFIlitDh3bcBuBUTy8hxs/jz3EVMySYavVGb99o1TcceiIiIiIhkbqo4WtJoZDGRUTcZMTaAKeMHsH7VLHwL5WdG4NIHxp45e4GuPYfz7fa9FusD563A2ysva5Z9xrJFkwkKDuGXY6fSo/kiIiIiIpIFZUjF0WQyMWXKFLZt24ajoyOtWrWiRo0aDB8+nBs3buDq6sqQIUN47rnn8PPzw93dnePHjxMWFkb37t1p1qwZ+/fvZ/LkyQB4enoydepU4uLiaN++PTt27AAgICAAgJ49e/LSSy9Ru3Ztjhw5gpeXF23atGHp0qVcvXqVCRMmUKVKFc6dO4e/vz83btwgR44cDBs2jLJlM9fVQPcf+plyZUpTtHBBAFo0fZ1W7XozuF9XDAaDReyq1Ztp/Oar5Pfxslg/oHcnkpKSAYi4FoXRmIi7u1v6dEBEREREJAtQxdFShoxGSEgIP/74Ixs2bOCrr74iODiYbt260a5dOzZs2MCgQYP4+OOPSUhIAODq1assX76c2bNnm+89EhgYiL+/P8HBwdSuXZsTJ2zfCuPatWvUqlWLkJAQALZt28by5cvp2bMnS5YsAWDgwIH079+ftWvXMnr0aHr37m3HUXg0YWHXyO+d17zs45WXmNg4YuPiU8QO6teVhg1qpVhvMBhwcnJksP90mrf9mEr/K0exIgXt2WwREREREcnCMiRxPHz4MA0aNCBbtmy4ubmxfPlyoqKiqFevHgAvvPACnp6enDlzBoCXXnoJg8HAU089xY0bNwB49dVX6dGjB6NGjaJkyZK8/PLL//i6NWrUAKBQoUJUq1YNgIIFCxIdHU1sbCyhoaEMGjSIxo0b07dvX+Li4oiKirLDCDy65OQH33bT8RF+ERnn35udm5dwMzqGuYuCHrdpIiIiIiJPDAcHB7s+spoMmarq5GT5shcuXMBkskyITCYTSUlJAGTPnh3AYipmx44dqV27Nt999x2TJ0/m6NGjvPXWWxb7SUxMtHitbNmymf/t6Oho8XrJyclky5aN9evXm9ddvXqVXLlyPWIv007gvOXs3HMYgNjYeEqXLGLeFh5xHY+c7ri45Hjo/e078BOlShbF2ysPrq4uvF73FbZ/tz/N2y0iIiIiIk+GDEl1K1euzLfffovRaCQ+Pp5PPvkEg8HA1q1bAfj555+5du0apUuXTnUfLVq0IDY2lo4dO9KxY0dOnDiBh4cHN2/eJDIykoSEBHbv3v3QbcqZMyfFihUzJ4579+7l3XfffbyOppGPurYh6IvpBH0xnaXzJ3A09FfOXbgMwOq1W6hVo8q/2t/W7XuZu3AVJpOJhAQjW7fvpXLFZ+3RdBERERGRLMnRwcGuj6wmQyqOdevWJTQ0lKZNm5KcnEz79u2pWrUq/v7+BAQE4OzsTEBAgEWF0FqfPn3w8/PDycmJ7NmzM3LkSHLmzEmnTp1o3rw5+fPn59ln/10yNHnyZPz9/VmwYAHOzs5Mnz49xQVnMlqePLkYObQn/QdPxmg04lsoP2OGfwzA8ZO/M3L8LIK+mG5zH316vcfYSXNo3vZjDBioXaMq77ZqmB7NFxERERHJErLidFJ7Mpis54jKI4uPtH2Bnv8ylzyZ6+q0IiIiIiK2FBs9xK77PztsrF33n9YypOIoIiIiIiKSmaniaEmjISIiIiIiIjap4igiIiIiImJFFUdLGg0RERERERGxSRVHERERERERK6o4WtJoiIiIiIiIiE2qOIqIiIiIiFhRxdGSRkNERERERERsUsVRRERERETEioPBkNFNyFRUcRQRERERERGbVHEUERERERGxonMcLWk0RERERERExCZVHEVERERERKyo4mhJoyEiIiIiIiI2qeIoIiIiIiJiRRVHSxoNERERERERsUkVRxERERERESuqOFrSaIiIiIiIiIhNqjiKiIiIiIhYUcXRkhJHERERERERK0ocLSlxTENvfbMno5uQaQXXupDRTcjUchaun9FNEBERERFJlRJHERERERERK46qOFrQaIiIiIiIiIhNqjiKiIiIiIhYUcXRkkZDREREREREbFLFUURERERExIoqjpY0GiIiIiIiImKTKo4iIiIiIiJWVHG0pNEQERERERERm1RxFBERERERseLoqBrb/TQaIiIiIiIiYpMqjiIiIiIiIlZ0jqMljYaIiIiIiIjYpIqjiIiIiIiIFVUcLWk0RERERERExCZVHEVERERERKyo4mhJoyEiIiIiIiI2qeIoIiIiIiJixUEVRwsaDREREREREbFJFUcRERERERErOsfRkhJHG+rUqcMXX3yBr69vRjfFrEqhwnSqUAVnB0f+vBHJ1P3fE2c0WsR8ULEaNYqW4Nad2wBciL7J2N3bAXi5SHHeKf8Czo6OhMfEMHHvd9xKuJPu/bCnPQeOM3PhBhKMiZQuUZBhfd/B3c0lRdymbYdZGrQdg8FAjuzO9OvenLJPF8FoTGTSzNX8fOwPAF6sUpZeXRrj6KgPDxERERH5b1LimIV4Zs9Bvxdr0TtkPZduRdO5QhU6VahCwKG9FnFlvXwYu3s7JyLCLNY/lScfPSq/xMch6wiLjaFbpeq8X6EyMw7uSc9u2FXUjVuMnLKMhZ9+QhFfbz6bv56ZCzbg93FLi7izF8KYMW89y2b3J19eT/YcPE5//4V8s2Ikq9bv4sbNGFYtGESyyUSXT2bw7fc/8XqdihnUKxERERFJb5mp4rhhwwZmz56N0WikY8eOvPvuuxbbZ86cyZo1a/Dw8ACgZcuWvPvuu5w8eZKhQ4cSExNDpUqVGDlyJE5Oj5YCZrnE8erVq/Tr14+4uDgcHBwYOnQoffr0oU6dOhw5cgSAcePGUbZsWc6dO4e/vz83btwgR44cDBs2jLJly3Lt2jWGDx/O1atXMRgM9O3blxdffJEbN27Qv39/rl69SsmSJblzJ3NV4ioW9OXXaxFcuhUNwIZfTzC3YXOLxNHZwYFSefLSouxzFHD34PKtaGYf2UdEXCyvlihNyB+nCIuNAeCLX47gkT1HhvTFXg78cIqyTxWhiK83AM0bvcw7XScysFcLDAaDOS6bsxPD+rxDvryeAJR9qgjXo6IxGhNp27wOrd+uiYODA1FR0dyKjcczp2uG9EdEREREMkZmSRzDwsKYPn06wcHBZMuWjdatW1O1alVKlSpljgkNDWXatGlUqFDB4rn9+/dnzJgxvPDCCwwePJigoCDatGnzSO3IHKPxL6xevZpatWoRHBxM//79+eGHHwDIlSsX69ato1evXgwcOBCAgQMH0r9/f9auXcvo0aPp3bs3AGPHjqVZs2YEBwcze/Zshg8fTkxMDJ999hlly5Zlw4YNvPvuu1y7di3D+vkgXq5uRMTFmJcj4mJxy5YNV2dn87q8rm78fPUyC386RLdv1nDyWhijatcHwNfDE0eDAyNr1WPOm83oWeVl4q2muWZ1YeE38PHObV729spFbNxtYuNuW8QVzJ+Xl6uVA8BkMjF9zlpqVC+Ps/Pd31KcnBwJmP81b7cbRZ5cOanwbMn064SIiIiIPPGio6O5ePFiikd0dLRF3L59+6hWrRq5cuXC1dWV+vXrExISYhETGhrK/PnzadSoEaNGjeLOnTtcunSJ27dv88ILLwDQtGnTFM/7N7Jc4li9enUWLVpE3759CQsLo23btsDdcizcPS8xLCyMq1evEhoayqBBg2jcuDF9+/YlLi6OqKgo9u3bx2effUbjxo3p0qULiYmJXLhwgUOHDvHGG28AULlyZQoXLpxh/XwQh/sqZvdLNpnM/74ac4shO0K4GH0TgK9OHKWAuwf53XPi6OBANd8ifHpgNx9+s4ao+Dh6V6+RLm1PL/ePxf1S+8UoPv4OfqMXc+FSBMP6vmOxrWeXt/hu3UQK5s/D+BlBad5WEREREcm8HB0Ndn0sWbKEV199NcVjyZIlFu0IDw/Hy8vLvOzt7U1Y2N+npMXGxlKmTBkGDhzI2rVriY6OJjAwMMXzvLy8LJ73b2W5qaoVK1bkm2++YefOnWzatIm1a9cCWMzVTU5OJikpiWzZsrF+/Xrz+qtXr5IrVy6Sk5NZsmQJuXLlAu6Wf/Ply4fBYMB0X+Lh6OiYPp16SOGxMTyTz9u8nM/Vjeg7t7mdmGheVzxXHkrmzsu2P38zrzMYIDE5metxcfwZFUnU7XgAtvzxK5Prvpl+HbCTOZ9/w679oQDExt6mZPEC5m0R127ikdMVF5fsKZ53NSyS3sPmUaxIfuZM7UmO7NkA+Dn0DLlzuVPU1xsnJ0ca1qvK5Jmr06czIiIiIvKf0KFDB5o0aZJi/V/nKf7F9IDCyP2nYLm5uTF//nzz8vvvv8/gwYOpWbOmzef9W1mu4jhp0iTWr19PkyZNGD58OCdOnADgm2++AeDbb7+lZMmSFCpUiGLFipkTx71795pPIq1WrRrLly8H4Pfff+ett94iPj6e6tWrm+OPHj3K+fPn07t7Nv1w5SJl8nlTKOfdg6nhU2XYf+GcRYwJEx9VfpH87jkBaPRUWf6MiuRaXCy7z5+haqEi5Mx2N4l6uUgxTl+PSN9O2EG3jm+yfO5Als8dyOKAPoSePMf5i+EArNmwh5ovPpviOTejY+na9zNqv/w844d2NCeNAEd+/pVpgcEkJiWRnJxMyPYjVK7wVLr1R0REREQynqODg10fHh4e+Pr6pnhYJ44+Pj4Wp9CFh4fj7f13Meny5cusXv13kcNkMuHk5JTieRERERbP+7cMpgelsJnYlStX6Nu3L7GxsTg6OtK5c2emTJnC888/z5kzZ3BxcWH8+PEUL16cP/74w3xxHGdnZ/z9/XnuuecICwtj+PDhXL58GYB+/fpRs2ZNYmJi8PPz488//6REiRKcPn2aRYsWPfTtOOounWfPrgNQpWBh3q9QBWdHBy7fimbS3p0UcM9Jn+o16PZNMACvFi9Fq/Iv4GgwEBEXy9R93xMRFwvcTTbferocDhgIi73FtP27uB4fZ/d2B9cqavfX+Mueg8eZtXADxsQkfAvkY+TAtnh6uHHi9HnGTFvB8rkDWbhsC3OXbKJU8YIWzw2c1AM31+xMDQzmx6O/YzAYeKF8CXp3a0KOHNlSecXHl7NwfbvtW0RERET+vQ6bvrLr/pe80eKh4sLCwnjnnXdYvXo1Li4utG7dmtGjR/Pcc88BEBkZSYMGDVi9ejW+vr4MGTKEokWL8sEHH9CwYUNGjhxJxYoVGTp0KMWKFaNz586P1N4slzg+SGa532J6JI5ZVXomjlmREkcRERGRzOX9kDV23f+i15s9dOyGDRuYO3cuRqOR5s2b06VLF7p06UKvXr149tln2bJlCwEBARiNRv73v/8xcuRIsmXLxqlTpxg6dCixsbGULVuW8ePHky3boxVDstw5jiIiIiIiIv8ljRo1olGjRhbr7j+vsX79+tSvn7IQ8cwzz1hMY30cT0TiuGPHjoxugoiIiIiIPEEyy30cMwuNhoiIiIiIiNj0RFQcRURERERE0pIqjpY0GiIiIiIiImKTKo4iIiIiIiJWVHG0pNEQERERERERm1RxFBERERERsaKKoyWNhoiIiIiIiNikiqOIiIiIiIgVVRwtaTRERERERETEJlUcRURERERErKjiaEmjISIiIiIiIjap4igiIiIiImLF0VE1tvspcRQREREREbGiqaqWNBoiIiIiIiJikyqOIiIiIiIiVlRxtKTREBEREREREZtUcRQREREREbGiiqMljYaIiIiIiIjYpIpjGvq8ccuMbkKm5ZRwJqObkKldvXUzo5uQaeXP6ZnRTRAREZH/IAdVHC1oNERERERERMQmVRxFRERERESsOBoMGd2ETEUVRxEREREREbFJFUcRERERERErjgbV2O6n0RARERERERGbVHEUERERERGxonMcLaniKCIiIiIiIjap4igiIiIiImJFFUdLqjiKiIiIiIiITao4ioiIiIiIWHHQVVUtaDRERERERETEJlUcRURERERErOgcR0uqOIqIiIiIiIhNqjiKiIiIiIhYcXRQje1+ShxFRERERESsaKqqJaXRIiIiIiIiYpMqjiIiIiIiIlYcVHG0oIqjiIiIiIiI2PTIFcdbt24xcOBAAgMD07I9/2jFihUAvPPOO2nyvODgYA4dOsSECRPSpoF2cGDPHhbMmk1CQgIlSpei/9AhuLm7P1SM/0A/Ll24aI67evkyz/3vf4ydNoV9u3YzceQovH18zNtnzJ+Lq5tbuvXNHnbt+5GAOStJSEikdKki+A/qirub6wNjTSYTw8fOoVSJwnRo09C8flXwVtZu+I47dxIo83Rx/Ad9QLZszunVhTSzf88e5s0MxHjvuBg4bGiKY8dWzFuv1cPL28sc27pdO+o2eJ3ff/2V6RMmERsTg6u7G50/7Mb/KldO176JiIiI2JOjQTW2+xlMJpPpUZ548eJF2rdvz44dO9K6TekqLRPHS9E3Hr9BVm5ERfF+q3f4bME8fIsUYV7ATOJi4/jEb8C/igE4dfwE/n6D+Gz+PLzz+zB/5ixc3dx4972Oad5ua3kSztj9NQAio6Jp1rY/n8/xp2jhAnwauJzYuHiG9OuUIvbM2UuMn7qIY8d/58POLcyJ4/adh5g5bxWfzxlJTndX+g+dQbkyJXi/XWO7tftm9pJpvs8bUVF0aNmaWQvn41ukCHM+CyAuLo4+fgMfKub82XMM6tOHZcFrUuy7VaPGdOjSmTfeasT1a9f4uGs3ZsybQ958+dK8H/lzeqb5PkVERET+yaJff7Hr/t9/6nm77j+tPXIaPWbMGMLDw+nevTvr1q2jSZMmNG7cmMGDB3Pnzh0AXnrpJYYOHcrrr79Ou3bt2Lx5M23atKFOnTocOnQIgHbt2jFixAiaNGnCG2+8wZ49e2y+bkBAAAEBAQC8/PLLjB49mrfffptmzZpx4cIFACZOnMhbb71FkyZNmDlzZornrVu3jvr169OsWTN27txp3vfRo0d55513aNKkCe+//755fxnpyIGDPF22DL5FigDwVrOmbA8J4f58/2FijEYjE0eOpHuf3njnv1thPH70GD8dPsIH7drzcZeu/PLjT+nYM/vYf+go5cqUoGjhAgC0aFKXzVv38qDfR1at2UrjN2tRt041i/UbQnbTrvWbeHq44+DgwJD+nXjz9VfSpf1p6fCBgzxTtqz5uGjcvBnbNlseF7ZiQo8excHBkY8/+JD3Wrfh8/kLSEpK4saNG4SHhVH/zTcAyJsvHyVKl+LQ/gPp30kRERERO3E0GOz6yGoeOXEcOnQo3t7efPLJJwQFBbFy5UrWr19P3rx5WbhwIQDXrl2jVq1ahISEALBt2zaWL19Oz549WbJkiXlfCQkJrF27lqlTp+Ln50dCQsJDtSEiIoLq1auzbt06KleuzLJly7h06RK7du3i66+/ZuXKlZw9e9acyAKEhYUxZcoUli1bxqpVq4iNjTW3YejQoUydOpW1a9fy3nvvMWzYsEcdnjQTHhZmMZXUy9ub2NhY4u61+2FjNq3/mrz5vHildi3zOg9PTxq3aM7cpV/QuftHjBgwgIiwMLv2x97Cwq+T3zuvednHKw8xsfHExsWniB3U9z0aPiAhPH/hCpFR0XzUZzwt2g9gzqLVeLg/eKprZnb3uPA2L6d+7Dw4JikpiUpVqzA5YAafzZ/L4f0HCF4VRK5cuShQsCAhG78B4PLFSxz7+WeuX7uWfp0TERERkXT12FdVPXjwIOfOnaNly5bA3cpW2bJlzdtr1KgBQKFChahYsSIABQsWJDo62hzz13PLlCmDl5cXp0+f5tlnn32o13/llbtf/EuXLs2RI0fw8fEhe/bstG7dmtq1a/PJJ5+QPXt2c/xPP/1EhQoVyHdvSl2jRo04cOAAZ8+e5cKFC3z44Yfm2JiYmH89HmkttZnEDo6O/ypmzYoV9Bk8yGL7qMkTzf9+9oUXKPvscxw5eIgGbzV6nCZnqOTkB4/Fv7mBqzExiQOHj/HpxL5kz5aNYWMCCZi7igGfdEirZqaL5OTkB66//7iwFdOoydvm5WzZstHy3TasWbWKFm3eYdy0KQR++hlfLV9BqadKU+2ll3B2znrngIqIiIikRuc4WnrsxDEpKYkGDRowdOhQAGLvVSr+ki1bNvO/He/7wno/R6svsk5OD9+sv5JCg8GAyWTCycmJr776ikOHDrFr1y5at27N0qVLzfEGg8Hiy/Jfr5WcnIyvry/r16839+taJqigePv4cDI01LwcERFBTg8PXFxcHjrmt9OnSUpM4vn//c8cE3PrFutXr6FNxw4YzKVy078a+8wicP5X7NzzAwCxcfGULlHYvC38WiQeOd1wccnx0PvzzpeLOjUrmy+o80b9l5m3ODhtG50OfPLn52TocfPytQccO7ZitnyziVJPlaZk6dIA5vcX3E3Qx02bYl7u3+tjXrr3I5GIiIiIPHkeOY12cnIiMTGRqlWr8u2333L9+nVMJhP+/v4W01AfxqZNmwA4duwY0dHRPPXUU4/aLE6cOEHbtm2pXLkyAwcOpGTJkvz555/m7RUrVuSXX34hLCyM5ORk82uXKFGCmzdvcuTIEQDWrFlDv379HrkdaaVStaqcDA3l4vnzAGxYE8yLNV75VzG//PAjFSpXui9BBBdXV9Z9tZrd330H3E0uTx0/QZUXq9u7S2nuoy4tCFoygaAlE1g6bxRHj//GuQtXAFi9dhu1Xqn0r/b3Wq2qfLvjALfvJGAymfhu1xHKPZP2F6+xt8rVqnLivuPi6zXBvFSzxkPH/PnHHyyaM5ekpCTu3L7N2qCvqF23LgBTxo1jz87vAQj95Sh//vEHFatWSa+uiYiIiNidznG09Mjlpbx581KwYEHGjh1Ljx496NChA8nJyZQpU4auXbv+q31duHCBJk2aADB9+vRUK5MPo2zZsrzwwgs0bNgQFxcXypQpQ40aNTh+/G5VJV++fAwdOpSOHTvi4uJCqVKlgLuV0RkzZjB27Fju3LmDu7s7EydOtPVS6SJ3njz0Hz4Mf79BJBoTKehbCD//EZw+cZIpY8Yyf/mXqcb85dKFC/gUKGCxX0dHR8ZMmUzAlCl8Pnc+jo6ODBs3Bs9cudK5h2krT25PRg7uRv+hn2I0JuJbyIcxwz4C4PjJPxg5YT5BS2xfQbdl03rcvBVDm/cHk5SUTJmni9G3Z9v0aH6ayp0nD37DhzF8oB9GYyKFfAsxeKQ/p06cYPKYsSxcvizVGICOXbvw6cTJvNe6DYmJidR67VUavn33yrL9Bg9m8pixfD5/AS6uLoydMtmikikiIiIiT5ZHvh1HWmnXrh09evSgatWqGdmMNGGP23E8KdLrdhxZlT1ux/Gk0O04REREJCOsOnPCrvtvVaLsPwdlIpnyhLbPP/+ctWvXpljv7e3N/PnzM6BFIiIiIiIi/10ZXnF8kqjimDpVHG1TxTF1qjiKiIhIRlh99pRd99+82DN23X9a0zVmRURERERExKZMOVVVREREREQkI2XFK5/akyqOIiIiIiIiYpMqjiIiIiIiIlYcDZmnxrZhwwZmz56N0WikY8eOvPvuuxbbt23bRkBAACaTCV9fX8aPH4+npyfr1q1jypQp5M2bF4BatWrRu3fvR2qDEkcREREREZFMKiwsjOnTpxMcHEy2bNlo3bo1VatWNd+PPiYmBn9/f9asWYOPjw8zZswgICCAoUOHcuzYMfz8/GjYsOFjtyPzpNEiIiIiIiKZhKPBYNdHdHQ0Fy9eTPGIjo62aMe+ffuoVq0auXLlwtXVlfr16xMSEmLebjQa8ff3x8fHB4Cnn36aK1euAHDs2DHWrVvHW2+9Rb9+/bh58+Yjj4cSRxERERERESsOBge7PpYsWcKrr76a4rFkyRKLdoSHh+Pl5WVe9vb2JiwszLycO3duXnvtNQBu377NvHnzzMteXl707NmT9evXU6BAAUaNGvXI46GpqiIiIiIiIumsQ4cONGnSJMV6Dw8Pi2WTyZQixvCAK77eunWLjz76iGeeeca831mzZpm3d+7c2ZxQPgoljiIiIiIiIlbsfTsODw+PFEnig/j4+HDkyBHzcnh4ON7e3hYx4eHhdOrUiWrVqjF48GDgbiK5Zs0aOnbsCNxNQJ2cHj3901RVERERERGRTOrFF19k//79REZGEh8fz9atW6lRo4Z5e1JSEt26daNBgwYMGTLEXI10dXVlwYIF/PLLLwB8+eWX1K1b95HboYqjiIiIiIiIFXtXHB+Wj48PvXv3pn379hiNRpo3b85zzz1Hly5d6NWrF1evXuXEiRMkJSWxZcsWAMqXL8/YsWP59NNP8ff35/bt2xQrVoxJkyY9cjsMpgdNmpVHcin6RkY3IdPKk3Amo5uQqd3MXjKjm5Bp5c/pmdFNEBERkf+g7ZfP2nX/rxYsZtf9pzVVHEVERERERKw4OuisvvtpNERERERERMQmVRxFRERERESsOGSScxwzC1UcRURERERExCZVHEVERERERKw4GlRju59GQ0RERERERGxSxVFERERERMRKZrmPY2ahxDENJeqWmKmKvnQoo5uQqbmVejqjm5BpTTt2IKObkKn1ebZaRjdBRERE/gOUOIqIiIiIiFhxQBXH++kcRxEREREREbFJFUcRERERERErDio4WlDFUURERERERGxSxVFERERERMSKQec4WlDFUURERERERGxSxVFERERERMSKg+7jaEEVRxEREREREbFJFUcRERERERErqrBZUuIoIiIiIiJiRVNVLSmRFhEREREREZtUcRQREREREbGiCpsljYeIiIiIiIjYpIqjiIiIiIiIFQM6x/F+qjiKiIiIiIiITao4ioiIiIiIWNFVVS2p4igiIiIiIiI2qeIoIiIiIiJiRRU2SzYTx1u3bjFw4EACAwPTqz2P5GHaOWTIEFq3bs2zzz5rsd7Pz48qVarQtGlTezfzkR3cs5dFgYEYE4wUL1WKPkOH4Obu9lAxSUlJzJo8haM//QRAlRdfpEuvnhgMBs6d+ZNPx48nPi4eg8FAp+4fUal6tYzoYpra/+MZ5i7fi9GYRMmi+RjYrS5urtlTxK0J+Zn1W3/BYDBQ0MeTAR/UJbenK3cSEpm+YAcn/7iKyWSibKkC9O5ch+zZst7vLHt272ZmQAAJRiOlS5dm2PDhuLu7/6uYq1ev8l6HDqxYuZJcuXMDsOv77/EfMYL8+fOb4+YvXIibm+VxmZWc++FnDi37iqTERPIWKUzNjzqRzdXFIiZ087ec2LIDDAY8fLyp+eH7uHh6AHA8ZDuntn9PYkIC+UoUo9ZHnXB0ds6IroiIiIikOZuJ9M2bNzl16lR6teWRPUw7x44dmyJpzApuREUxZfQYhk8Yz6LVQRQoVJCFs2Y9dMz2zZu5cO48c5cvY86yLzn640/s3r4DgIBJk6jfqBFzli2l77AhjBk8hKTExHTvY1q6ER3H+MCtjO7bkGUzOlLA25O5y/ekiDt9JoxVG34gcExrlkxtj2/+3CxYtQ+AL4IPkpSczOLJ7Vg8pR13EhL5cu2h9O7KY4uKimKkvz+TpkwheO1aChUqxMyAgH8Vs3HjRrp06kRERITF844ePUrbdu1YvnKl+ZGVk8b4m9HsnLWAev170vqzieT08eLgsiCLmIg//uSXr0NoPHYYLaePw7OAD4dXrgHgzIEjhG7+ljeHD6Dl9HEkJRg5unFLRnRFRERE0oiDwWDXR1ZjM3EcM2YM4eHhdO/enXXr1tGkSRMaN27M4MGDuXPnDgAvvfQSQ4cO5fXXX6ddu3Zs3ryZNm3aUKdOHQ4duvtlu127dowYMYImTZrwxhtvsGdPyi/y94uPj6dv3740bNiQRo0asW7dOgCCg4Px8/Mzx7Vr146DBw9atDMmJoauXbvStGlTmjZtyvbt2y1iTSYT48ePp379+rRr147z58+b95daHzPSDwcP8nTZMhQqUgSAhs2asiNkCyaT6aFikpKSuX07HqPRiDEhgUSjEefs2QBITkom5lY0AHGxcWS7tz4rO/TLOZ4pmZ/CBe5Wxt6u9xzf7j5lMV4AT5fwYfmMjri7ZudOQiLXImPwdM8BwPNlfGnftCoODgYcHRwoXdyLqxHR6d6Xx3Vg/37KlitHkXvHRfMWLdi8ebPFWNiKiYiI4PvvvmOGVbIJcPSXXzhy+DBt27Sh8/vv8+MPP6RPp+zk4i+heJcqgWeBuxXUcvXr8Pvu/RZj5VWyOK0DJpLdzZXEhARiI6PIfq8y+9v3e3mu0evkyOmOwcGBV7p24KkaL2ZIX0RERETswWbiOHToULy9vfnkk08ICgpi5cqVrF+/nrx587Jw4UIArl27Rq1atQgJCQFg27ZtLF++nJ49e7JkyRLzvhISEli7di1Tp07Fz8+PhISEVF83ICCA3Llzs3HjRpYsWUJAQIDNiuJf7Zw1axbffvsthQoVIjg4mMmTJ3PkyBGL2C1btnDixAk2btzIjBkzzInjb7/9lmofM1JEWDhe3j7mZS9vb+JiY4mLjXuomHoN3yRnTg/avNmI1m80pGBhX6q/8goAPQb0Y+XnX9CmYSP8evSk18ABODplvemY9wu/fgvvvH9Ps/TKm5PY+ATi4lMeb05Ojuw+9DvNP5zPLycv0qB2OQCqPF+UwgXvJp5XI6L5atNP1K7+VPp0IA2FhYXh4/P3ceHt7U1sTAyxsbEPFePl5cXkqVMpUaJEin17enrSomVLvly+nB49e9K/Xz/CwsLs2yE7irkeiVvePOZlt7x5SIiLxxh/2yLO0cmJPw/9wLIPenPl5GmeqXP3vXTjylXib97imzFT+KrPEI4ErSNbFq7AioiICDhgsOsjq3mocz4PHjzIuXPnaNmyJY0bN2b79u2cOXPGvL1GjRoAFCpUiGrV7p4jV7BgQaKj/67StGzZEoAyZcrg5eXF6dOnU329AwcO0Lx5cwDy5MnDq6++aq5e/pMKFSqwbds2PvroI3744Qe6d+9usf3QoUPUq1cPZ2dn8uTJY277P/Uxo5iSkx+43sHR4aFivlywEM/cuVgVsonlG7/mVnQ0q5ctI+HOHcYOGUq/4cNYvnEDU+fOYcb4iYRn4S//AKYHDwUODg8+1F+pUooNCz/kvRbV6Tc2mOTkvytMp8+E0WN4EE3rv8CLFVMmT5ldcirHhaOj47+KeZDJU6dSu04dAF6oUIHnnnuOgwcOPGJLM54p2fTA9YYHHDfFq1Skw+JZVGrRhG9GT8GUnExyYhKXjoZSt093mk4cyZ2YGA6tWG3vZouIiIikm4cqLyUlJdGgQQOGDh0KQGxsLElJSebt2bL9PcUxtS+c1l9WnWxUtqynFd6dcpmEwWCw2GY0GlM8t1ixYmzevJndu3fz3XffsWjRIjZv3mzebjAYLL4s/9WOf+pjRvHK78Op48fNy9ciIsjp4YGLi8tDxez5bifd+/XF2dkZZ2dn6r75Bru3f8dzFf7Hndt3qPbKywCUebY8RUsU51Tocbzvq0BlBQtX7WPvkbtJfmz8HUoUyWfedi0yhpxu2XHJYXmRkotXbxB5I5bnnikEwBt1yjF1/nZuxd7GM6cL2/eeZtqC7XzSqQ51X34m/TqThvLnz09oaKh5OSI8HA+rY+dhYqzdunWLr4KCeO/99zHcm59vMplsvqczO3evPIT/9od5+e40VDecc/x9UaWbV8KIu3GTAmXuVp+frlOD3fM/505sHG55clGsSkXzxXRK13iRH79an76dEBERkTSVBU9DtCubFUcnJycSExOpWrUq3377LdevX8dkMuHv728xDfVhbNq0CYBjx44RHR3NU0+lPvWvWrVqrF5999f6yMhItm/fTpUqVcidOzd//PEHJpOJCxcumKuWf7UT4MsvvyQgIIAGDRowYsQIIiMjuXXrlnnf1atXJyQkhISEBG7evMnu3bsB0qSP9lCxalVOhoZy6d6U2o3Ba6le45WHjin99NPs2rYNgMTERPbv2s0z5ctRsLAvsTExHD96FIDLFy9y/uxZSj2d9aZkdmr1Iosmt2XR5LbMGfsOJ367yoUrUQCs//YoL1cumeI516NiGPnpJm5ExwPw7e5TFC+SF8+cLuw88CszFn/H1KFNs2zSCFCtenVCjx0zT8des2YNNWvW/Ncx1lxdXfkqKIgdO+5eZOnUqVMcP36cF1/Muuf0FX7+WcJ/+4ObV64CcGLrDopWrmARExd1g+3TA4mPvvt58vvufeQu7EuOnO6UqFaZM/sPk3gnAZPJxNlDP+JVqni690NERETEXmyWCPLmzUvBggUZO3YsPXr0oEOHDiQnJ1OmTBm6du36r17owoULNGnSBIDp06fbnArXvXt3/P39adSoEUlJSXTr1o1y5cqRkJDAmjVreP311ylevDgVK1a0aGe7du2YPXs2ffr0oVGjRjg5OdGjRw88PDzM+37ttdc4duwYDRs2JF++fJQseTepeOaZZx67j/aQO08e+g0bxmi/wRgTjRQs5Et//+H8euIk08aOY86ypanGAHTr/Qmzpkzl/RatcHRw4IXKlWjVoT1OTk6MmDSR2VOnk5CQgJOTIx/7+VHQ1zeDe/x4cnu64vdhPYZP24gxMZlCPp4M6fE6AKf+uMqkOdtYNLktz5fxpV3TKnw88iscHRzIm8eNsf3fAmDu8r2YTDBpzjbzfss/XZA+netkSJ8eVZ48eRju78/A/v0xGo34+voycvRoTpw4wZhRo1i+cmWqMbY4Ojoydfp0Jk+cyNw5c3BydGT8hAnmW3VkRS6eHtTq3pmtU2aSnJiIh483tXt2JeL3P/l+ziKaTxlNgbJPU6FZIzaMGI+DoyOuuXNRf8DHAJSt/yq3Y2JYM3AEpuRk8hUvSvUO72Vwr0RERORxZMXzEO3JYLKeF2oH7dq1o0ePHlStWtXeL5Whzt2MyugmZFo5zq7K6CZkaq6l2mV0EzKt+WeOZXQTMrU+z2b9e6+KiIhkRvb+bl/UM2v96J5hJyV9/vnnrF27NsV6b29v5s+fnwEtEhERERERueuhriL6H5IuFcf/ClUcU6eKo22qOKZOFUfbVHEUERGxjwt2/m5fWBVHERERERGRrM1Bl1W1oMRRRERERETEii6OY0lTd0VERERERMQmVRxFRERERESsaKaqJVUcRURERERExCZVHEVERERERKzoHEdLqjiKiIiIiIiITao4ioiIiIiIWNHtOCyp4igiIiIiIiI2qeIoIiIiIiJiRRU2SxoPERERERERsUkVRxERERERESu6qqolVRxFRERERETEJlUcRURERERErOiiqpZUcRQRERERERGblDiKiIiIiIhYccBg18e/sWHDBt544w3q1q3LsmXLUmw/efIkzZo1o379+gwZMoTExEQALl++zLvvvsvrr7/Ohx9+SGxs7GOMh4iIiIiIiGRKYWFhTJ8+neXLl7N+/XpWrVrF77//bhHTv39/hg0bxpYtWzCZTAQFBQEwcuRI2rRpQ0hICOXLlycwMPCR26HEUURERERExIqDwWDXx8Pat28f1apVI1euXLi6ulK/fn1CQkLM2y9dusTt27d54YUXAGjatCkhISEYjUYOHz5M/fr1LdY/Kl0cR0REREREJJ1FR0cTHR2dYr2HhwceHh7m5fDwcLy8vMzL3t7eHD16NNXtXl5ehIWFERUVhbu7O05OThbrH5USxzTk4uCY0U3ItFxKtcvoJmRq1xLvZHQTMq1uJcpldBMytfhrP2d0EzItl3wvZHQTREQkCzNgsuv+lyxZwsyZM1Os79GjBz179jQvm0wp22G4r2KZ2vZ/et6/pcRRREREREQknXXo0IEmTZqkWH9/tRHAx8eHI0eOmJfDw8Px9va22H7t2jXzckREBN7e3uTJk4eYmBiSkpJwdHQ0r39UOsdRRERERETEminZrg8PDw98fX1TPKwTxxdffJH9+/cTGRlJfHw8W7dupUaNGubthQoVInv27Pzwww8ArFu3jho1auDs7EylSpXYtGmTxfpHZTA9qIYpjyT8Vso5ynKXpvHapqmqqfNxcs7oJmRqhvg/MroJmZamqoqIyOOIi71p1/27unk+dOyGDRuYO3cuRqOR5s2b06VLF7p06UKvXr149tlnOXXqFEOHDiU2NpayZcsyfvx4smXLxqVLl/Dz8+P69esUKFCAadOm4en58K97PyWOaUiJY+qUONqmxDF1ShxtU+KYOiWOIiLyODJT4pgZ6BxHERERERERa6bkjG5BpqJzHEVERERERMQmVRxFRERERERSUMXxfqo4ioiIiIiIiE2qOIqIiIiIiFjTOY4WVHEUERERERERm1RxFBERERERSUEVx/up4igiIiIiIiI2qeIoIiIiIiJiTec4WlDFUURERERERGxSxVFERERERCQFVRzvp4qjiIiIiIiI2KSKo4iIiIiIiDWd42hBFUcRERERERGx6YmrOB49epQtW7bQv3//jG5Kmtm3Zw9zZ87CmJBAydKl8Rs2FDd394eOafhaXby8vcyx77RrR70GDfj9t9+YOn4Ct2/HY8BA1+4fUe2ll9K1b2lhz+7dzAwIIMFopHTp0gwbPhx3q/H5p5irV6/yXocOrFi5kly5cwOw6/vv8R8xgvz585vj5i9ciJubW/p0LA0c3LOXxYGzMSYYKV6qJL2HDsHN3e2hYm7dvEnAxMn88etv5HDJQb2GDWncqgXnzvzJxGEjzM9PTk7i7B9nGDpxPC/XrpW+HUwDu3fvISBgJgnGBEqXLs2I4cNSHD8PE3f16lXad3iPVStXkDt3LvP6/fsP8OmMz1i1cnl6dMdudu37kYA5K0hIMFK6VBH8B3XD3c31gbEmk4nhY2dTqkRhOrRpZF5f+83OeOXLY17u0KYRb9Z/xe5tFxEReTSqON7PYDKZTBndiLQUHBzMoUOHmDBhQrq/dvit6DTfZ1RUFO1btiJw4QIKFynC7M8CiIuLpa+f30PFnD97loF9+rIieE2KfXdo/Q6dun1AjVq1OPP773R7vxPfbN+Gs7NzmvfDxcExzfcJd/vesnlzFi5eTJEiRfhsxgzi4uLwGzTooWM2btzI3NmzuXLlCtu2bzcnjjMDAnB1deX9Tp3s0vb7XUu8k+b7vBEVxQet32Xa/LkUKlKYhQGziI+Lo8fA/g8VM8V/FA6Ojnw82I/k5GRG9R9Iw2ZNqfrKyxavM+/Tz4i8dg2/MaPSvA8APk5pfzz+JTIqiubNW7J48UKKFinCjBmfERsXx+BBfv8qbsPGjcyePZcrV66wY/s2cufOxe3bt1m4cBErVwXh4+PN6q+C7NIHQ/wfdtnv/SKjomnWti+fzxlF0cIF+DRwGbFx8Qzp1zlF7JmzFxk/dRHHjv/Gh51bmhPHs+cu02vgJL5e+and2/sXl3wvpNtriYjIkyfu5jm77t/Vs6hd95/WMmyq6tSpU6lXrx6tWrWiR48eBAcHM336dFq2bEn9+vVp3bo1ERERGI1G+vfvz9tvv83bb79NUFDqX76io6P57LPP2LFjB7Nnz6ZNmzbs2bMHuPsLeL169QgLC6NOnTqMGTPGvM8TJ04AcO7cOd577z2aNGnCO++8Y16fkQ4fOMAzZctSuEgRAN5u3oxvN4dwf75vK+bY0aM4OjjQ64NudGj9DovnzycpKQmAhV8u5ZWaNQG4dPESOXO64+CQtWYvH9i/n7LlylHkXt+bt2jB5s2bLcbHVkxERATff/cdMwICUuz76C+/cOTwYdq2aUPn99/nxx9+SJ9OpZEfDx7iqbJlKFSkMABvNmvKjpAtFmNjK+a3U6d59Y0GODo64uzsTOWXXmT3ju8sXiP0p5/Zs+M7evoNTL+OpaED+w9QrlxZit47Nlq0aJ7i+PmnuPCICHZ+9z0BATMsnrN//wHi4+PxHzE8fTpjR/sP/UK5MiUpWrgAAC2a1GXz1j0pxglg1ZqtNH6zFnXrVLdY/3PoaRwdDHTuMZIW7fszd9FqkpL0S66IiGRipmT7PrKYDJmqumPHDn744Qc2btxIfHw8TZo0oWbNmpw5c4aVK1fi4ODAgAED2LBhA+XLl+fmzZusW7eOqKgoJk6cSMuWLR+4Xw8PD3r16sWhQ4f48MMP8fb25uuvv+bll1/myJEjFClSBB8fHwBy5crFunXr2LFjBwMHDmTDhg0MHDiQ4cOHU7ZsWX7//Xe6d+/Oli1b0nNoUggPCzO3GcDL25vY2FjiYmPNU1FtxSQlJVGpalU++rgXd+7cYcDHn+Dm5kbLNm1wcnLCZDLRqvHbXL1yhV59++LoaJ/KoL2EWfXd29ub2JgYYmNjzdMIbcV4eXkxeerUB+7b09OTN958k9p16vDzTz/Rt08flq9cabGvzCwiLAwvb2/zspe3F3GxscTFxpmnq9qKebpcWbZv2ky555/DmJDA3h07cXSy/MiY/1kAHT78IMX016zi6gOOjZiYWIvj55/ivL28mDp1cop9165di9q1a3HkyBH7dSCdhIVfJ793XvOyj1deYmLjiY2LTzFddVDf9wE4eCTUYn1SUjLVKj9H7+5tuX0ngZ79J+Dm5kLbVm/avwMiIiLy2DIkcdy3bx8NGjQgW7ZsZMuWjddeew1HR0cGDhzIV199xZ9//snPP/9MkSJFKF26NH/++SedOnWiRo0a9OvX76Ffp0GDBkyfPp34+HjWrl1L06ZNzdv+Sj7r1KmDn58fV69eJTQ0lEH3TXGMi4sjKiqK3PemLmaE5OQHzyR2uC/BsxXzVpMm5uVs2bLR6t13Wb1qJS3btAHAYDCwav06Ll+6RI8uXSlWojgVK1dOwx7YV3Lyg3+tcbQYn3+OeZD7E8oXKlTgueee4+CBA7zVuPEjtDT9mVLtt8NDxXT9pBfzZwTQvW0H8uTLS4WqlTl59Jg55sTRo0TfuEnt+vXStuHpKPX+Oz5S3JMqtc8Yx38xQ6HZW6+a/50tmzPtWjVkxerNShxFRCQTy3pVQXvKkMTRwcEhxZf5Gzdu0KlTJzp27Ej9+vVxcHDAZDKRO3duvvnmG/bu3cv3339PkyZN+Oabb/Dw8PjH13F1daVGjRqEhIRw4MAB/P39zduc7qucJCcnk5SURLZs2Vi/fr15/dWrV8mVK9dj9/dx+OT34WTo37/cX4uIIKeHBy4uLg8VE/LNJko9VZpSpUsDd6fsOjk5YTQa+X7Hd9Sp+xoODg4ULFSIilUq89vp01kqccyfPz+h9/U9IjwcD6vxeZgYa7du3eKroCDee/99DAYD8PfYZRVe+fNz6vjf062vRUTg7pGTHPf121ZM+NWrdO7ZnZyengAELVlKQV9fc+z3327n1TcaZLnpzYGz5/D997sAiI2NpVSpkuZt4eERDzw28ufPz7H7jqHU4p4kgfOD2LnnbrU0Ni6e0iWKmLeFX4vEI6cbLi45Hnp/G0N28VSpojxV6u75HCZMODn9NxJvERHJmgxP1qVgHluGfON76aWX2Lp1KwkJCcTExLBz507i4uKoUqUK77zzDqVKlWLv3r0kJSWxfft2+vXrR61atRg6dCiurq5cuXIl1X07OjqSmJhoXm7WrBnTp0/nlVdeIVu2bOb133zzDQDffvstJUuWpFChQhQrVsycOO7du5d3333XTiPw8KpUq8bx0FAunD8PwLo1a3i5Zo2Hjvnzjz9YOGcuSUlJ3Ll9m+CgIOrUrYuzszMLZs9m+9atwN2E4acjP/DC//6Xjr17fNWqVyf02DHO3+v7mjVrqHnvvM1/E2PN1dWVr4KC2LFjBwCnTp3i+PHjvPjii3bohX1UrFqFU6GhXDp/AYBvgtdSvUaNh475JngtX8ybD0DU9Ug2r19Prdf/ri4e+/EnXqhcKT26kqY++rAbq1YuZ9XK5XyxZDHHjoVy7t6xsXrNGmo94NioXr3aQ8U9ST7q0pKgJZMIWjKJpfPGcPT4b5y7cPezd/Xab6n1yr/7v//9zAUCFwSRlJTM7TsJrFyzhXqvZp33k4iIyH9dhl1Vdfr06Wzbtg1PT08cHBxo1qwZy5cv5/bt2zg7O1OqVCmSk5MZP348Q4YMITQ0lOzZs/Pyyy/Tt2/fVPf7559/0rVrV+rXr2+e1lqzZk0CAgJ47rnngLvTU59//nnOnDmDi4sL48ePp3jx4vzxxx/4+/tz48YNnJ2d8ff3Nz/nYdjjqqoA+/fsZe6sWSQajRT09WXoSH8uX7rExDFjWLx8eaoxHp6e3L59m+kTJ3E8NJSkxERqvfYqXT/6CIPBwB+//870iZOIjY3BYHCgbccO1Klb1y59sNdVVQH27NnDrIAAjEYjvr6+jBw9mkuXLjFm1CiWr1yZaoznvUraXyr9738WV1U9ceIEkydOJDYuDidHR/r07UslO1Vj7XFVVYBDe/exeNZsEhONFChUiP7+w7ly6TKfjh1P4LIvUo3J6elJXGwsk0eM4vLFi3fPhe3YnlcbvG7ed+MatVnw1Sq8fLxTe/k0Yc+rqgLs3rOHgIC77x1fX19Gjx6Jp6cnx0+cYNSoMebbaKQWd78K/6tkvqrqX44cOcKEiZOy9FVVAXbv+4mAucsxGhPxLZSfMcO64+nhzvGTfzBywlyClkyyiB82JtDidhzxt+8wYdrdq60aE5OoW7saPT9oba7o24OuqioiIo8jPupXu+7fJfdTdt1/WsuQxPGnn37i7NmzNGnSBKPRSKtWrRg3bhzPPPNMmr6OyWTi119/ZeDAgaxbt868vk6dOnzxxRf43jftLi3YK3F8EtgzcXwS2CtxfBLYO3HM6tIrccyKlDiKiMjjUOJoKUNO2CpevDgzZ85k8eLFmEwm3n777X+VNH7++eesXbs2xXpvb2/mz59vXl6yZAkLFixgxowZKWJFRERERERSlQVvmWFPGTZV9UmkimPqVHG0TRXH1KniaJsqjqlTxVFERB5HfOQpu+7fJU/azra0t6xziUgREREREZF0o4rj/bLWdfRFREREREQk3aniKCIiIiIiYk3nOFpQxVFERERERERsUsVRREREREQkBVUc76eKo4iIiIiIiNikiqOIiIiIiIg1neNoQRVHERERERERsUkVRxERERERkRRUcbyfKo4iIiIiIiJikyqOIiIiIiIi1nSOowVVHEVERERERMQmVRxFRERERERSUMXxfqo4ioiIiIiIiE2qOIqIiIiIiFjTOY4WlDiKiIiIiIikoMTxfpqqKiIiIiIiIjap4igiIiIiImJNU1UtqOIoIiIiIiIiNqnimIbcDUkZ3YRMKywxMaObkKn5OJoyugmZ1vqr5zO6CZnaM7kKZHQTMq1non7P6CZkWi65S2V0E0REMj2TSd/t76eKo4iIiIiIiNikiqOIiIiIiIgVU7LOcbyfKo4iIiIiIiJikyqOIiIiIiIiVnSOoyVVHEVERERERMQmVRxFRERERESsmJJVcbyfKo4iIiIiIiJikyqOIiIiIiIiVjL7OY6XL1+mf//+XL9+neLFizNlyhTc3NwsYsLDwxk0aBDXrl3DwcGBAQMGUL16dYxGI1WrVqVw4cLm2ODgYBwdHVN9PVUcRUREREREspiRI0fSpk0bQkJCKF++PIGBgSliJk2aRO3atVm/fj1Tp06lX79+JCUlcfr0aSpUqMD69evND1tJIyhxFBERERERSSk52b6Px2A0Gjl8+DD169cHoGnTpoSEhKSIq1evHo0aNQKgaNGi3Llzh7i4OI4dO0ZkZCQtW7akZcuWHDp06B9fU1NVRURERERE0ll0dDTR0dEp1nt4eODh4WHzuVFRUbi7u+PkdDed8/LyIiwsLEVcvXr1zP9euHAhZcqUIWfOnBgMBl599VW6d+/OyZMn6dKlCxs2bCBPnjypvqYSRxERERERESv2PsfxiyVLmDlzZor1PXr0oGfPnublzZs3M378eIuYYsWKpXiewWBI9bU+//xzVq1axZdffglA69atzdvKli3Lc889x48//shrr72W6j6UOIqIiIiIiKSzDh060KRJkxTrrauNDRo0oEGDBhbr/rq4TVJSEo6OjkRERODt7f3A15k0aRLff/89y5YtI3/+/ACsW7eO//3vfxQpUgQAk8mEs7OzzfYqcRQREREREbFi7/s4ej7ElNTUODs7U6lSJTZt2kSjRo1Yt24dNWrUSBH3+eefc/DgQVasWGHxWqdPn+bnn3/G39+fM2fOcPLkSSpWrGjzNQ0mk8n0SK2VFOJiojK6CZlWWJIOM1t8HDU+qVkfdjWjm5CpPZMrX0Y3IdN6xuFWRjch03LJXSqjmyAikund+CPYrvvPVbLpYz3/0qVL+Pn5cf36dQoUKMC0adPw9PRkxYoVhIeH06tXL6pUqYK7u7tF0jhv3jzc3NwYPHgwZ86cwWAwMGTIEKpVq2bz9ZQ4piEljqlT4mibEsfUKXG0TYlj6pQ4pk6Jo4jIP4v6/Su77j93qRZ23X9a0+04RERERERExCad4ygiIiIiImLF9Jj3WnzS/CcTx19//ZVGjRrx2WefmW+aCXevLrR06VISExNJTk6mRYsWtG/fPgNb+rfdu/cSMDOQBKOR0qVKMWL4ENzd3R467vbt20yYOIXjx0+SbErm2fLl8BvYjxw5cnD79m0+nTGTn385Snx8PE2bNKZD+7YZ0MtHc3DPXhYHzsaYYKR4qZL0HjoEN6uxSS3m1s2bBEyczB+//kYOlxzUa9iQxq1acO7Mn0wcNsL8/OTkJM7+cYahE8fzcu1a6dvBx3T3mJhz75goyYjhg20cOynjbt2KYeTocZw9e47kZBONGjbgvY7tAPh+1x6GjxhtvkIXwKIFgbi5pdx/VvLroSNs+3wZSUYjPsWL8tYn3cnh6moRc3DDJo58swUMkKdAfhr1+hD3XLkypsF29uPe/aycMxej0UiRkiX5YPBAXB/wf2wymZg9djyFSxSnUZt3AJg2eBhhly6ZY8IvX6FshefpP2lCurU/Pezae4iAwCX33j/F8B/yCe5urg+MNZlMDB89nVIli9Lh3Wbm9dG3YujUbSD+Qz+hXJnS6dV0ERFJhb1vx5HV/CenqgYHB1O/fn1WrlxpXrdq1SqWLFnC7NmzWb9+PcuWLePrr7/mq6/sO7f5YURGRTFi5BgmTx7PuuAgfH0L8lnArH8Vt3DR5yQmJbFq5VKCVn7J7Tt3WLT4CwBmfDaLmzejWbZ0MV9+sZhVQas5eiw0Xfv4qG5ERTFt9FiGTRjPwtWrKFCoEItnBT50zNzpM8jh4sK8Vcv5dNECjuzfz8HdeyhaojiBy74wP/5XtSq16tXNcknj3WNiLJMnj2Nd8Mp7x0Tgv4oLnD0PH29vVgctY9nShXy1ei2/HD0GwC+/HKN9uzasWrHE/MjqSWPszZusmz6TVkP603P+THLn92Hb4qUWMZd/+4N9a9bTaeo4us+eQZ6CBfhu6YoMarF9RUfdYM7Y8fQeN5rpK5fhXbAAKwLnpoi7dPYsY3p+woHt31ms7zNuNBOXLGLikkV0HdgfN3d33uvbO72any4io24yYsynTBk/mPVB8/AtmJ8ZsxY/MPbMn+fp2mMw327fY7F+977DtH2/N3+eu5geTRYREfnX/nOJY2JiIl9//TW9e/fmxIkTnD9/HoDZs2fj5+dnvv+Jh4cHEydO5KmnnsrI5gJwYP9BypUtQ9F791lp0bwpmzdvwfq6Rrbi/lehAl06vYeDgwOOjo488/RTXLlyFZPJxDebQvjwwy44OjqSM6c78+cGUrxY0XTv56P48eAhnipbhkJFCgPwZrOm7AixHBtbMb+dOs2rbzTA0dERZ2dnKr/0Irt3WH7xDf3pZ/bs+I6efgPTr2Np5MD+Q/eOibt9v3tMbH3AsZN63ID+ven9SQ8AIq5dx5hgxN3dHYBfjh7j0OEfaPPue7zf6UN++PGndOydffzx488UeqoUeQsVBKDSm69z7LvdFmNWsHRJei2YRQ43N4wJCdy6HolLzpwZ1WS7OnroECXLPEOBwnePjbpN32bP1m9THENb1qyl5ptvUO3V2g/cT6LRSOCYcbT/pCf5fHzs3u70tP/gj5QrU5qiRQoB0KLpm2zesjPFGAGsWvMNjRvWpe6rL1usXxH0NaOH98ErX550abOIiPwzU3KSXR9ZzX8ucdy5cycFCxakePHivPbaa6xcuZLIyEiuXLnC888/bxFbsmTJFOsywtWwcHzy//1Fy9vbm5jYWGJj4x46rnr1qhQtejehvHzlCsuWr6Lua3WIiooiLi6OgwcP07nrh7R6px3ff7+bnFnkS3BEWBhe993s1Mvbi7jYWOLuGxtbMU+XK8v2TZtJTEwkPi6OvTt2EnntusVrzP8sgA4ffpBi+mtWcDUszOqY8Erl2Ek9zmAw4OTkxJCh/rRo2ZaKlSpQ7N6xlMvTk1YtmrF82WJ69uhG336DCAsLT5/O2cnNiOt45Pv7SqUe+fJyJy6OO/HxFnGOTk6c3HeQae27cC70BBXq1knvpqaL62Hh5PX5+/2T18uL+NhY4uMsj6H3+/amRoP61k8327HhG3Lny0eVminvMZXVhYVHkN/Hy7zs452PmNg4YuPiU8QO6vchDRukPFYCPx3N88+WsWs7RUREHsd/LnEMDg6mYcOGALzxxhusXbvW/KtwZr0zicn04BNzHR0d/nXciZOn6NSpG61bNadGjZdJTEwkKSmJixcvMW/OLAJnfsrqNcF89933adcBO0rtpOX7+2wrpusnvTAYDHRv24FRA/yoULUyzs5/n/p74uhRom/cpHb9emnb8HSS2jGd8tj557ixY/z5bvsmom9GM2/+3Wl4U6eMp06dmgBUqPA8zz/3LAcOHkqLpmeY1N5HDg4pPy7LvFiVgSuXUOvdViwdNprkJ/Ak+uRUjo0HjYctm1YF0aRj5jhnPK0lJ6fy/vmXYyQiIpmLyZRk10dW85+6OM7169fZtWsXoaGhfPHFF5hMJqKjo9m/fz+FCxcmNDSUypUrm+MPHTrErl276NevX7q3NXD2PL7ftRuA2NhYSpUqad4WHhGBh4cHLi4uFs/Jn9+HY6HHU40L2fIt4ydMxm9AXxrcqwzkzp0bJycn3nzzdRwcHMibNy+vvPIyR48do3btmvbu5mPzyp+fU8dPmJevRUTg7pGTHPeNja2Y8KtX6dyzOzk9PQEIWrKUgr6+5tjvv93Oq280+NdfkjNS4Oz5fL/r7vlTd4+dEuZtd4+JnA957NyN27fvAKVKl8TbywtXV1der1+X7Tt2cuvWLYK+Cub999pjMBiAuwmok1PW/ljx9PLi0unfzMu3rl0nh7s72XLkMK+7fvkKMVE3KFruboWoQt06bJw5l9sxsbh6ZI1q/cPK5+PD7/e9fyIjruGW0/I99k/+PP0ryUlJlK3wgh1amDEC5y1l5+6DAMTGxlG6ZDHztvCI63h4uOPikiOVZ4uIiGQ9WefbcBr4+uuvqVatGrt27WLHjh189913dOvWjVWrVtGpUycmTJhAREQEAJGRkUyYMIGiRTPmXL+PPuzKqhVLWbViKV98voBjx0I5d+98zNWr11Kr5ispnlO9WtVU477dtoNJk6cROGuGOWkEcHZ2pkaNl9m4cTMAcXFxHDh4iLJly9q7i2miYtUqnAoN5dL5CwB8E7yW6jVqPHTMN8Fr+WLefACirkeyef16ar3+d3Xx2I8/8ULlSunRlTTz0YddzBeq+eLzeRw7dpxz9/q+evW6VI6dKqnGbd22g3nzFmEymUhISGDrth1UrlwRV1dXVgWtYfuOnQCcOnWa0OMneLF6tfTpqJ2U/N/zXDz1K9cvXQbgyKatPFOtskVMTGQUqydMJfZmNABHd+7Cu2jhJy5pBHiuSmV+P36CKxfuHhvb1q2n0isv/8OzLJ38+WfKVfyf+QeGJ8FHXdsRtHQmQUtnsnTBNI6Gnubc+btXj129dhO1Xsna7wMREbk7a82ej6zGYMqs8zPtoFGjRvTu3Zs6df4+v+T69evUqVOH4OBg9u7dy1dffYXBYMBkMtGqVSvatn3421LExUTZo9kA7N6zj4CZgSQajfj6+jJ61HA8PT05fuIko0aPY9WKpTbj3nq7ObduxeDt/fd5OC88/xyD/Ppz8+ZNJk+ZzomTp0hOTqbB6/X4oGvnNG1/WJL9DrNDe/exeNZsEhONFChUiP7+w7ly6TKfjh1P4LIvUo3J6elJXGwsk0eM4vLFi3f/zzu259UGr5v33bhGbRZ8tQqv+87xsgcfR/uNz91jYs69Y6LQvWPC496xM4FVK5bYjLt16xZjxk3ijz/OYMBArVo1+LBbZxwcHDh+4iQTJ00jLi4OR0cn+vXpReXKFdO0/evDrqbp/h7Gr4d/YPvnX5KUmEju/Plp0q8XUVfC+PqzQD6cOQ2Aw9+EcGjjZhwcHcmZJw9vftSF3PnT/6Ivz+TK989Bj+mnfftZMWceiUYjPoUK0X34EMIuXWbehElMXLLIIjZwzDiL23EALJoyjVx589L0vQ52b+v9nnG4lW6vtXvfYQICl2A0GvH1LcCY4X3x9MzJ8ZO/MXLcDIKWzrSIHzZqWorbcQA0ePs9powfbPfbcbjkLmXX/YuIPAnCQxf9c9Bj8C7/vl33n9b+U4mjvdkzcczq7Jk4PgnsmThmdRmROGYl6ZE4ZlXpmThmNUocRUT+Wdix+Xbdv8+zXey6/7T2n5qqKiIiIiIiIv9e1r6KhYiIiIiIiD1kwXst2pMqjiIiIiIiImKTKo4iIiIiIiJWsuK9Fu1JFUcRERERERGxSRVHERERERERK1nxXov2pIqjiIiIiIiI2KSKo4iIiIiIiBWd42hJFUcRERERERGxSRVHERERERERKybdx9GCKo4iIiIiIiJikyqOIiIiIiIiVnSOoyUljiIiIiIiIlZ0Ow5LmqoqIiIiIiIiNqniKCIiIiIiYkVTVS2p4igiIiIiIiI2qeIoIiIiIiJiRbfjsKSKo4iIiIiIiNikiqOIiIiIiIgVneNoSRVHERERERERsUkVx7RkUB6emlO3rmd0EzK1fLl9MroJmVZjn/wZ3YRMzRD3W0Y3IdMyORfN6CZkWpsvnsnoJmRqDXxLZHQTRCQT0DmOlpTpiIiIiIiIiE2qOIqIiIiIiFgxJanieD9VHEVERERERMQmVRxFRERERESs6BxHS6o4ioiIiIiIiE2qOIqIiIiIiFjROY6WVHEUERERERERm1RxFBERERERsZKscxwtqOIoIiIiIiIiNqniKCIiIiIiYkXnOFpSxVFERERERERsUsVRRERERETEiiqOlpQ4ioiIiIiIWDElJ2Z0EzIVTVUVERERERERm1RxFBERERERsZKsqaoWVHEUERERERERm1RxFBERERERsWJKVsXxfqo4ioiIiIiIiE2qOIqIiIiIiFjR7TgsKXEEtm/fTmhoKB9//HFGN+Wh7N69h4CAQBKMCZQuXYoRw4fi7u7+0HG3b99mwoTJHD9xguTkZJ4tXx4/v/7kyJEjA3qTto4fOMTGBYtJNBopWKI47/T7hBxubhYxR77dwY6g1WAwkC17dpr26EaRp58CYEq3nhjv3MHR2RmASq/Wpk6r5unej8exZ/duZgYEkGA0Urp0aYYNH57i+EgtJikpienTprF//36SEhNp2749zZvf7f+Rw4eZPn06SYmJeObKRd9+/Xjqqbvj1r9fP3779VdcXV0BqFipEn379Uvfjj+C3bv3EjBzzt1xKFWSEcMH4+7u9tBxt27FMHL0OM6ePUdysolGDRvwXsd2/HHmTwYP8Tc/Pzkpid//OMOUyeN4tU6t9OtgGtq172cC5n5FgjGR0iUL4+/XCXc3lwfGmkwmho9bQKkShejwzhsA3IyOYezUJZz+7TwuObLT+I1XeKd53fTsgt3s3rOfgFnzSUgwUrp0CUYMHfDg48hGXNBX61i7/hvu3LlDmWeeYsSwAWTLli29u5LmHvcz+S9rA+cScfEyXceNTM/mi4jIfQwmk8mU0Y14UsTF3rT7a0RGRdG8eWsWL55P0SJFmDEjgNi4OAYPGvjQcbNmzebK1auMGjkCk8nEkKEjKFKkMB99+IHd2v191HW77fsvMTduMKFTNz6eMRUv30J8PW8hd+LjafFxD3NM2IWLzOozgL5zZuKZNw8nDh4i6NOZ+K/4gjvxtxnRqi1jg1fi6JS+v6m8nNsnTfYTFRVFy+bNWbh4MUWKFOGzGTOIi4vDb9Cgh4r5KiiI3bt3M236dOLi4nivY0dGjhpFsaJFadSwIRMnTaJK1aqc/fNP+vTpw8pVq8iWLRuv16vH0mXL8PLySpN+3M/RdDvN9wn33iMt3mXxorkULVKYGZ/NIjY2jsGD+j903MRJ03BwcKB/v0+Ij4+nWYt3GT9uJM8/96zFPqZO+4xr164z3g5feg1xv6X5Pq1FRkXTrP1gPg8cStHC+fl09ipi424zpG+HFLFnzl5m/PQvOHb8Dz7s1MScOA4dOw8nR0eG9X+P5ORkPhk8g1Zvv0qNl16wW7tNOYrabd9/iYy6QfNWHVm8YCZFi/gyI2Du3ePDr/dDx23fsYtZsxeweMFMcuZ0p7+fP+XKPs37Hd+1W7u/j463277/8rifyX/5aecuVn82i6LPPJNuiWMD3xLp8joikrkdXd7Grvt/rs3yx3r+5cuX6d+/P9evX6d48eJMmTIFN6sf5y5fvsybb75JkSJFAMiXLx8LFy4kISGBIUOGEBoaSo4cOZgyZQolS5a0+XpP/DmOBw8epHnz5jRt2pT33nuPTp060bJlS2rXrs2UKVMACA4Oxs/PD4A6derw6aef0rx5c958801CQ0MzsvkpHNh/kHLlylL03n9+ixbN2Lw5BOv831bc//5XgS6d38fBwQFHR0eeefoprly5ku59SWunjvxIkaefwsu3EAAvvdWQH7Z/ZzE2Ts7OtOr7CZ558wBQ+KmnuBUZRaLRyPlTp8nukoN5g4czsfOHrA2cS8KdOxnSl0d1YP9+ypYrZ/5waN6iBZs3b7YYA1sx3333HW+99RZOTk54eHhQr149Nm/axPkLF3B3d6dK1aoAFCteHHc3N44ePcqlS5eIi4tj3NixtG7ZkpEjRnDzpv1/RHlcB/YfolzZMhQtUhiAFs2bsnnz1ge8l1KPG9C/N70/ufslOOLadYwJxhTV3R9/+plt279jyOAB6dAr+9h/OJRyz5SgaOH8ALR4uw6bv92fYqwAVq3dRuMGr1C3dhWL9SdPn+XN+i/i6OiAs7MTr1R/nm93Hk6X9tvTgQOHKVf2GYoW8QWgRbO32ByyLeVxZCNu46YttH23JZ6eHjg4ODBkUB8avlEv3fuS1h73Mxng6rnz7Fi1mvrt7PvlTUQkKxo5ciRt2rQhJCSE8uXLExgYmCLm2LFjNGrUiPXr17N+/XoWLlwIwNKlS3FxcWHz5s0MHjzYnAvZ8sQnjgBnz55lyZIlvPzyyzRs2JCgoCC+/vprli9fTmRkZIr4XLlysXr1alq3bs3cuXMzoMWpuxoWho+Pt3nZ29ubmJhYYmNjHzquevVqFC1695f4y5evsGz5Suq+9mr6dMCObkRcI9d9Fa9cXvm4HRvHnbg487q8+X0oV+3uF1qTycS62fMoX70qTs7O3ImPp/QLz/PeiCH0CZxBVHgEGxcsTvd+PI6wsDB8fP6uXnp7exMbE2NxfNiKsd7m4+NDeFgYRYoUIS4+ngP79wNw/Phx/jhzhmvXrhEVGUmVqlUZMmQIy1aswMXVlVEjM/90sqthYfjkv38cvIiJjSU2Nu6h4wwGA05OTgwZ6k+Llm2pWKkCxYoWsXj+9Okz6dH9gwdOXcwqwsIjye+Tx7zs45WHmNh4YuNSVoMH9W5Pw9dfSrH+2bIl+WbLPoyJicTF3Wb790e4dv2GPZudLq6GhePj8/fnTurHUepx585fJCrqBt179qflO+8zd97n5MyZ8vSDrCYtPpOXTZhCmwF9yO7imu7tFxFJTkqy6+NxGI1GDh8+TP369QFo2rQpISEhKeKOHTvGr7/+StOmTWnfvj2nT58GYOfOnbz11lsAVK5cmaioKC5fvmzzNf8TiWPx4sXJmTMnnTp1okCBAixcuJCxY8diNBqJj085XeeVV14BoHTp0ty4cSOdW2ubKTn5gesdHR3/ddyJEyfp1LkrrVu1oEaNV9KukRkktT4bHBxTrLsTf5vPR43j2uXLtOr3CQDlX6xG20H9yeHmhnO2bNRt04pje/bbs8lpLvkh/t9txTxoDB0cHXF3d2fqtGksWrSId1q14puNG6lcqRLOzs6Uf/ZZpkydSj4vLxwdHen6wQfs2bMH472KQWaV2ix9R0eHfx03dow/323fRPTNaObN//vHhp9/OcaNGzdo8HrWrh4lJ6cyBg4P/yekT/fWGIDW7w+n95DPqFapHM7OWf80+7Q4jhITEzlw8AgTx/uz7Iu53IyOZmbggjRva3p73M/kFVM+5ZW3G1GgeDE7tlJEJONER0dz8eLFFI/o6Oh/fG5UVBTu7u443Tu9ysvLi7CwsBRx2bNn5+233yY4OJhOnTrRvXt3EhISCA8PtzjFyMvLi6tXr9p8zaz/V/sh/HXRlwkTJnDhwgUaNmzIa6+9xr59+x74xzx79uwAGAyGdG1nagJnz+X773cBEBsbS6lSpczbwsMj8PDwwMXF8iIV+fPn51jo8VTjQrZsZfz4SfgN7EeDBq+nQy/sL7e3N+dOnTYv37x2Ddec7mR3sbzoT1RYOPOH+uNTpDDdp04k273/79B9B3Bxd6PkvfPTTCYTjk4pv+BkZvnz57eYXh0RHp7i+LAVkz9/fq5du2beFh4ejre3N8nJybi6ujJv/nzztuZNm1K4cGF++vFHom/dombNmnc3mEw4GAw4/IukIr0Ezp7P97v2AH+9l/4+jyk8IgIPj5wPeC/5WL6X7ovbt+8ApUqXxNvLC1dXV16vX5ftO3aaY7du3UbDhg0y5Vj8k8AFwezc+xMAsbHxlC7pa94Wfi0Kj5xuuLhkf+j9xcbd5pOPWuHpcbeStnjZNxQu5P0Pz8qcAucs4vtdewGIjY2zOo6uPfg48vHmWOjJB8Z55ctH7VqvmKvSbzaoy7wFX5DVPc5n8o2ICM4cCyX8wkV2rllH3K1b3I6NZe6gYXwwfnR6d0VE/qPsfR/HJUuWMHPmzBTre/ToQc+ePc3LmzdvZvz48RYxxYoVS/G8B+Uu9++nZs2aTJ06lTNnzjywPf/0fSXrfZt5DHv37qVTp040aNCAK1euEBYWlmr1JTP56MMPWLVyGatWLuOLJYs4diyUc+fPA7B6TTC1atZI8Zzq1aumGvfttu1MmjSVwMDPnpikEeDpSv/j7IlTRFy8BMDeDZso/2J1i5jY6FsE9BnAc6+8RIdhg8xJI8CNa9dYP2cBCXfukJyUxM7Va6lQK+XYZmbVqlcn9Ngxzt/7f1+zZs3fCd1DxNSoVYuv168nMTGRW7dusXXLFmrVro3BYODjnj05ceIEANu+/RYnJydKly5NXHw8kydONJ/X+MUXX/Dqa6+lqIJnBh992IVVK5awasUSvvh8HseOHefc+QsArF69jlo1U1beq1erkmrc1m07mDdvESaTiYSEBLZu20HlyhXNz/3hx5+pUrlSOvQs7X3UuSlBi0cTtHg0S+cO5+jxPzh34e4vkavX7aDWyxX+1f6+WreDwIXBAFyPvEnwhp00qFvd9pMyqY+6vc+q5QtZtXwhXywO5FjoCc6dvwjA6jVfU6tGyqm61atVTjXutVdrsm37Tm7fvnP3XOOdeyhX9un065CdPM5nci4vL0YFLWPAvFkMmDeLBh3aUaJ8eSWNIvJE6dChA9u3b0/x6NDB8uJzDRo0YNeuXRaPhQsXEhMTQ9K9Ka8RERF4e6f8QXbp0qVERUWZl00mE05OTnh7exMREWFen9rz7/efqDj+5YMPPmDAgAF4eHiQN29eypcvz8WLFzO6Wf9Knjx58PcfRv/+fiQaE/H1LcTo0f4AHD9xglGjxrJq5TKbcQEBgZhMJkaNGmve7wvPP8+gQVn34h0AOXPnos2A3iweOZbExETyFSjAu379OH/6V1ZOncGAebPYu2EjUeERHN2zj6N79pmf233yeF5s+AbXr1xlSreeJCclUfqF57LcBRny5MnDcH9/Bvbvj9FoxNfXl5GjR3PixAnGjBrF8pUrU40BaN68OZcuXKBN69YYjUaaNmtGxYp3E6Ex48YxZvRoEo1G8uXLx5Rp0zAYDLz00ku0fucdOr33HskmE6VKlWLosGEZOQwPJU+ePPiPGEL/AUNINBrvvkdGDQfg+ImTjBo9gVUrltiM69u7J2PGTaJFq7YYMFCrVg3avNPS/Brnz1+gYMECGdK/tJQntwcjB3Wm/7CZGBMT8S3ozZihXQE4fupPRk5cRNBi21/oO7VryJDR82jWfjAmk4lu771N+TJZ/8qVefLkxn/4QPr7jbh3fBRktP9gAI6fOMWoMZNZtXyhzbiWzRsTHR1Nm/ZdSU5K5plnStPnk48ysltp4nE/k908PTKw9SIi9r+Po4eHBx4ej/ZZ5+zsTKVKldi0aRONGjVi3bp11KiRsuBx+PBhbt++TZcuXTh06BDJycmUKFGCmjVrsn79eipVqsSRI0fInj07BQsWtPmauh1HGkqP23FkVelxO46sLK1ux/EkstftOJ4U6XE7jqwqPW7HkVWlx+04sjLdjkNEAH5c1MSu+//f+2sf6/mXLl3Cz8+P69evU6BAAaZNm4anpycrVqwgPDycjz/+mLCwMPz8/IiIiCB79uyMHTuWZ555hjt37jB8+HBCQ0PJli0bY8aMoVy5cjZfT4ljGlLimDoljrYpcUydEkfblDimTolj6pQ42qbEUUQAfpjfyK77r9hlg133n9b+U+c4ioiIiIiIyL/3nzrHUURERERE5GEk2/mqqlmNKo4iIiIiIiJikyqOIiIiIiIiVux9VdWsRomjiIiIiIiIFZOmqlrQVFURERERERGxSRVHERERERERK5qqakkVRxEREREREbFJFUcREREREREruh2HJVUcRURERERExCZVHEVERERERKzoHEdLqjiKiIiIiIiITao4ioiIiIiIWDElJ2Z0EzIVVRxFRERERETEJlUcRURERERErOgcR0uqOIqIiIiIiIhNqjiKiIiIiIhY0X0cLaniKCIiIiIiIjap4igiIiIiImJF5zhaUuKYhq4nJmd0EzKtWtkjM7oJmdq1xNwZ3YRMK/TWzYxuQqb2+03HjG5CptWpmGtGNyHTKpszR0Y3IVOLj/o1o5uQabnkfiqjmyAiGUSJo4iIiIiIiJXkZFNGNyFT0TmOIiIiIiIiYpMqjiIiIiIiIlaSk3Ua2v1UcRQRERERERGbVHEUERERERGxonMcLaniKCIiIiIiIjap4igiIiIiImJFFUdLShxFRERERESsJJt0cZz7aaqqiIiIiIiI2KSKo4iIiIiIiBVNVbWkiqOIiIiIiIjYpIqjiIiIiIiIleRkneN4P1UcRURERERExCZVHEVERERERKzoHEdLqjiKiIiIiIiITao4ioiIiIiIWFHF0ZIqjiIiIiIiImKTKo4iIiIiIiJWdFVVS6o4ioiIiIiIiE2qOGYBB/bsZWFgIMYEIyVKlaLv0CG4ubs9VExSUhIBk6dw9KefAKj64ot07dUTg8Fgfu7mrzewd+dOxkybmq79spdd+34iYO4qEhISKV2yMP6DuuDu5vrAWJPJxPBxcylVvDAd2rwJQL+hn3L+Ypg55vKVCCq+UIYZE/umS/vTkr2OnVMnTjB72nRux98mKTmZ1u3b8lqDBhnRxTRz/MAhNi5YTKLRSMESxXmn3yfkcLMcqyPf7mBH0GowGMiWPTtNe3SjyNNPWcSsDZxLxMXLdB03Mj2bb1dnj/zE/mVBJBmN5C1ahFe7dyabq+V76uimrYRu2Q4Y8MzvTe0PO+GayxOABR0/xD1PbnNshcZv8nTNl9KzC2lu9+49BAQEkmBMoHTpUowYPhR3d/eHjrt9+zYTJkzm+IkTJCcn82z58vj59SdHjhz8+utvjBs/kfj4eAwGAz16fMTLL72YAb18NAf37GXRvc+U4qVK0ecBnzupxSQlJTHrvs+dKi++SJd7nzs/H/mBeTNmkJSUhIenJ91696bkU6UzootpatfewwQEfkGC0UjpUsXwH9LL9t+s0Z9SqmRROrzb1Lw++lYMnboNwn9oL8qVyfpjIpJZ6BxHS6o4ZnI3oqKYMnoMIyaM5/PVQRQoVJAFs2Y9dMy2zZu5eO4885cvY96yL/nlx5/YtX0HANE3b/Lp+InMmjIV0xPyvoiMimbEuHlMGfMJ61dMwbegNzNmr3pg7Jmzl+j68Ti+3XHQYv2UMZ8Q9Pl4gj4fz/CBncnp7sqgPh3TofVpy17HjslkYuTAQbTv2oW5y5Yy/tPpzPn0My6eP58R3UwTMTdusGLyNN73H8qQJQvIWyA/GxYstogJu3CRr+ct4IMJYxgwbxb12rZmkf8Yi5ifdu7iyLYd6dl0u4u/Gc32mfNp0P9j2s6cgqePN/uWWr6nwv/4k5/Wb6LZuBG0mTEBzwL5ObhiNQBRly6T3c2N1tPGmR9ZPWmMjIpihP9oJk+ZwLq1q/EtVIjPAmb9q7iFCxeTmJTIqpXLCFq1nNt37rBo8RIAhg4dQYf2bVm1chljRo9k4MDBGI3GdO3jo/rrM2X4hPEsuveZsjCVz50HxWzfvJkL584zd/ky5iz7kqM//sTu7TuIjYlh1EA/uvTsydzly+g5cABjBw8hISEhI7qZZiKjbjJizAymjB/E+qA5+BbMz4xZnz8w9syfF+jaYyjfbt9jsX73viO0fb8vf567mA4tFpH/sic+cbx69Spt27aladOmNG/enJ9//pk6depw8eLdD9iDBw/Srl07ANq1a8ekSZNo1aoVdevW5fvvv8/IpgPww8GDPFW2DL5FigDQqFlTtodswXRfpmcrJjkpmdu34zEajRgTEkg0GsmWPRsA32/bTp58eenaq2f6d8xO9h8+RrkyJShaOD8ALZq8xuZv91qM119WBX9L4zdqULdO1Qfuy2hMZPjYOfTv1Y78Pnnt2m57sNexY0xIoH3nTlSsUgUALx9vPHJ5ci08Iv07mUZOHfmRIk8/hZdvIQBeeqshP2z/zmKsnJydadX3Ezzz5gGg8FNPcSsyisR7X+ivnjvPjlWrqd+uTfp3wI7O/3wM71LFyVXw7nuq/Ouv8uvufRZj412yOG1nTSG7myuJCQnERkaSI+fd6tuVU7/h4ODA2uFjWdF7EIeC1pKclLXPGTmw/yDlypWl6L33TYsWzdi8OSTF54ytuP/9rwJdOr+Pg4MDjo6OPPP0U1y5cgWA5cu/oFatmgBcuHiRnDndcXDIGn+ufzh4kKfLlqHQvT43bNaUHQ/43EktJukBnzvO2bNx6fwF3NzdqFClMgBFihXD1c2Nk8eOpX8n09D+gz9RrkxpihYpCECLpg3YvOX7B//NWvMNjRu+St1XX7ZYvyJoA6OH98YrX550abPIf0lyssmuj6zmiZ+qunr1amrVqkXnzp05ePAgP/zwg814o9HIqlWr2LFjBzNmzKBmzZrp1NIHCw8Lx9vbx7zs5e1NXGwscbFx5qk/tmLqNXyT77fvoPWbjUhKSqJi1SpUf+UV4G6SALBl48Z07JF9hYVdJ7/33388fbzyEBMbT2xcfIqpP39VEQ/+cPyB+1q7cSdeeXNTp2Zlu7XXnux57DRo/Jb5ORvXriM+Lp4y5culU8/S3o2Ia+Ty8jIv5/LKx+3YOO7ExZmnq+bN70Pe/HfHymQysW72PMpXr4qTszN34uNZNmEKbQb04fzp3zKkD/YSc/067vn+/uHEPW8eEuLiMcbHW0xXdXRy4szBI+wIXICjszNVWzcHwJScTOHny/NSh3dITEhgw5gpZHNx4YVGr6d7X9LK1bAwfHy8zcve3t7ExMQSGxtrMV3VVlz16tXM6y9fvsKy5SsZNnQQAE5OTphMJhq91YTLl6/Qv18fHB0d06Fnjy8iLByvf/jcsRVTr+Gb7N6+gzZWnzuxMbHEx8Vz5MBBKlWryukTJzh35gyR166nex/TUlh4BPl98pmXfbzzERMb9+C/Wf26AXDw8C8W6wM/fXKmxYtI5pY1fsJ8DNWrV2fRokX07duXsLAw2rZtazP+lXtfjEuXLs2NGzfSoYW2mVK5mpODo8NDxSxdsJBcuXPxVcgmVmz8mlvR0Xy1bJld2poZJKcy59bxEX6t/3LVZjp3ePsxW5Rx0uPYWbHkC76YN58xU6eQPUeOtGt8OkttHAwOKb+s34m/zeejxnHt8mVa9fsEgBVTPuWVtxtRoHgxO7YyY5hS+UXU8ID3VImqlei8ZA5VWjXl69ETMSUnU65ubWp0bo+jszPZ3dx44a0GnDl4xN7NtqvUjhfr5O5h4k6cOEmnzl1p3aoFNWq8Yl5vMBjY8PVavl6/hsWff8GhQ4fToOX297ifO18uWIhn7lysCtnE8nufO6uXLcPN3Y2RUyax8vPP6damLdu+2cQLlSrh5Jy1f/9OreLwKH+zRCTtJZuS7frIap74T6aKFSvyzTff8PLLL7Np0ya6dbv7i91f00ASExMt4rNnzw5gcfGYjOSd34fr16+Zl69FRJDTwwMXF5eHitnz3U5eb9QIZ2dn3N3dqffmG/x85Md07YO9BS5YTcuOg2jZcRBrN3zHtes3zNvCr0XikdMNF5d/l9Sc+vUsSUlJVKpQJo1bm37seewkJCQwdugwvtuylc8Wzs/yF6jI7e1NdGSkefnmtWu45nQnu9VxExUWzoxefXBwcKD71Im4urtzIyKCM8dC2blmHZO6dmfzkqWcCQ1l7qBh6d0Nu8jplZe4qBvm5ZjrUWR3d8P5vh8Kbly5yuWTp83LZerU5FbENW7HxnJq5x6unb3v/FcTODhljerZ/QJnz6VV63dp1fpd1q5bz7X7Kl3h4RF4WL23APLnz28zLmTLVj78qCe9enanU6f3gLuzXkK2bDVfAr5QoUJUrVqZU6d/tXcX04RXfh8i/+Fzx1bMnu92Uv/e546buzt133yDX478SHJyMjlcXJkyZzZzln9J9/79uHzpIgV9C6dr/9JC4LwvadmuFy3b9WLt11u5di3KvC084joeHu7/+m+WiEh6eOITx0mTJrF+/XqaNGnC8OHDOXHiBLlz5+b3338HYPv27RncQtsqVq3KydBQ84VHNgSv5cX7fpX+p5hSTz/Nzm3bgLtJ8v5du7P0lMIH+ahzc/PFbJbOG8nR479z7sJVAFav206tVyr+630e+fkkVSqWyzQ/IDwKex47owcNJjY2lhkL55O/YMH06pLdPF3pf5w9cYqIi5cA2LthE+VfrG4RExt9i4A+A3julZfoMGwQ2e79yJTLy4tRQcsYMG8WA+bNokGHdpQoX54Pxo9O937YQ+Hnn+Xqr79z4/Ld91To1u0Ur/w/i5i4qBtsmTqT+OhbAPy6ay95ChfGJWdOIs9f5ODKNSQnJZN4J4Gjm7dS+qVqKV4ns/voww9YtXIZq1Yu44slizh2LJRz9943q9cEU6tmjRTPqV69aqpx327bzqRJUwkM/IwGDf6etuvs7EzgrDls2bIVgPCICA4f/oGKFSvYu4tp4q/PlEv3+rwxeC3VU/nceVBM6aefZpfV584z5e9+Fg/t3YdfT5wEYNe27Tg5OVGidKn06lqa+ahrW4KWfkbQ0s9YumAKR0NPc+78ZQBWr91MrVcefN69iKQ/neNoyWB60BnYT5ArV67Qt29fYmNjcXR0pHPnzri5uTF69Gg8PT15+eWX+fHHH1m6dCnt2rWjR48eVK1alYsXL9K+fXt27Hj4KyReuBn1z0GP4ODefSycFUhiopEChXwZ6D+cK5cuM23sOOYuW5pqjIenJzdv3GTmlKn8fvo0Dg4OVKhciW6ffIyT09/Te7Zs3Miu7d8xdrr9bseRL+EPu+3b2u79PxMwZxXGxER8C3kzZuiHeHq4c/zUGUZOmE/Q5+Mt4oeNnWNxOw6AcVMX45U3F106NkmXNl/LVtIu+7XHsXPq+HE+6fIBvkWKmCv0AJ17dKdy9bRPCEJv2ed9Ze3EwUNsXPA5iYmJ5CtQgHf9+nH9yhVWTp3BgHmz2LpsBZs//zLFdNTuk8fj5ulhXj4Y8i2/7NqTbrfj+P2m/c/xOvvDz+z/MojkxEQ88ntTt1c3boaF813gAlpPGwfAsZBtHNu8DQdHB9zy5KZmlw54+HhjvHOHXfOXcPXX30lOSqJU9SpUe7dluvwo06nYU/8c9Ih279lLQMAsEo2J+PoWYvRofzw9PTl+4gSjRo1l1cplNuPeatyMW7du4e3997m1Lzz/PIMGDeC3335n/IRJxMbG4uDgwHsd21OvXt00bX9Eov2mSB3au49FswIxJhopWMiX/v7DuXrvc2fOvc+dB8V4eHoSfeMms6ZM5bfTp3F0cOCFypX44N7frKM//sjsaZ+SaDSSJ19ePhk8iAKFCtmlD97J6Xexr937jhAQuASjMRFf3/yMGd4HT8+cHD/5GyPHBRC09DOL+GGjpqe4HQdAg7c7MWW8n91vx+GS237vK5HM5stu9vmM+UvbOZfsuv+09sQnjunJXonjkyA9E8esyF6J45MgvRLHrCo9Esesyp6JY1Znz8TxSZCeiWNWo8RR/ku+6FrArvtvP++KXfef1rL2WeUiIiIiIiJ2kBWnk9rTE3+Oo4iIiIiIiDweVRxFRERERESsqOJoSRVHERERERERsUkVRxERERERESt/3VM3s7p8+TL9+/fn+vXrFC9enClTpuDm5mYR061bN65cuXsRnuTkZH799VdWr17NM888Q9WqVSlc+O/74QYHB+PomPq9lpU4ioiIiIiIZDEjR46kTZs2vPnmm8yaNYvAwED69+9vETNnzhzzv2fMmMELL7zAs88+S2hoKBUqVGDhwoUP/XqaqioiIiIiImIlOdlk18fjMBqNHD58mPr16wPQtGlTQkJCUo3/448/WLduHQMHDgTg2LFjREZG0rJlS1q2bMmhQ4f+8TVVcRQREREREUln0dHRREdHp1jv4eGBh4eHzedGRUXh7u6Ok9PddM7Ly4uwsLBU42fPnk2nTp1wd3cHwPD/9u49Lufzf+D4qxMirKYY5jgM4ztDmGPlsEgU2xxqTDNGzlqNvmkMpTAK85vDMMxG7IAZOeSwMqc5hxmyKZGKou7u+/r90dxzd8LUXX33fj4ePR7dn8/1+Xze13V/rutzX/d1fT63iQlOTk6MHj2ac+fOMXz4cL7//ntsbGzy3Yd0HIUQQgghhBAih6J+quqqVasIDw/Ptdzb25sxY8boX2/fvp3Zs2cbpKlTp06u7UxMTPI8TkpKCgcPHmTmzJn6ZQMGDND/36RJE5o3b86xY8fo2rVrvvFKx1EIIYQQQgghjGzIkCG4ubnlWp5ztNHZ2RlnZ2eDZRqNhjZt2qDVajEzMyMxMRE7O7s8j7Nv3z46depE2bJl9cu2bNnCa6+9Rq1atQBQSmFhYVFgvNJxFEIIIYQQQogcivqpqk8yJTU/FhYWtGrVim3bttG7d2+2bNlCp06d8kx74sQJWrVqZbAsNjaWEydOEBgYyOXLlzl37hwtW7Ys8JjycBwhhBBCCCGEKGWmTZvG119/Tc+ePTly5Ajjx48HYP369SxYsECfLi4ujqpVqxpsO3r0aJKSknBxcWHcuHEEBwfr73/Mj4lSqmgn7/6LxKXcKe4QSqwqmb8Vdwgl2q0y9Ys7hBLr9F2pVwW5lHK7uEMosbzqNCzuEEqsxKyS/dtkxc1Ol1jcIZRYltZSr8S/R/jA54p0/97rk4t0/4VNRhyFEEIIIYQQQhRI7nEUQgghhBBCiByK+h7H0kZGHIUQQgghhBBCFEhGHIUQQgghhBAih6L+HcfSRkYchRBCCCGEEEIUSEYchRBCCCGEECIHGXE0JCOOQgghhBBCCCEKJCOOheh5lVTcIZRYv5vVLu4QSrQ6Krm4QyixOle0KO4QSrTmN6KKO4QS60pGneIOocSqY5Ze3CGUaMq0bHGHUGKlp94o7hBKtPKVXijuEEQhkhFHQ9JxFEIIIYQQQogcpN9oSKaqCiGEEEIIIYQokIw4CiGEEEIIIUQOMuJoSEYchRBCCCGEEEIUSEYchRBCCCGEECIHrQw5GpARRyGEEEIIIYQQBZIRRyGEEEIIIYTIQQYcDcmIoxBCCCGEEEKIAsmIoxBCCCGEEELkICOOhmTEUQghhBBCCCFEgWTEUQghhBBCCCFykBFHQzLiKIQQQgghhBCiQDLiKIQQQgghhBA56HTFHUHJIiOOQgghhBBCCCEKJCOOQgghhBBCCJGDVslNjo+SEUchhBBCCCGEEAWSEUchhBBCCCGEyEGeqmpIRhyFEEIIIYQQQhRIRhyFEEIIIYQQIgd5qqqhEtlx1Gq1vP/++8THx+Pu7s7ly5eZOXMmnp6eeHt7AxAeHs6aNWsMtnu4vnz58nz11VfMnDmTDRs2UKFCBVxcXIojK4Vm/4HDhC35gsxMDQ1eqsu0qeOxsir/xOlSUu4ya044sRcuY2lZDleXbgx8y5XfLl9jSsAc/fY6nZZLv10lNGgqTg7tjZnFQnHk4CG+XLIUjSaT2vXr4z31I8pXqJArnVKKsE9mUateXfoOHpRrfZDfFGyqVOH9yRONEXaR2n8ghrAlKx85JyZgZZW7TPJLp9VqCQpdzNFjpwDo8HprJox9DxMTE1JS7hI8dzGXf79GRkYGXkMH4NKzq7Gz+I/tPxBN2OLlf+W5HtP8J+VTNnmnm+z3MXFxf+rT/fnnDV577T+MHf0eU/47S788u15dITR4Gk4OHY2St8IWffway74+TKZGS71aNvi815kK5cvkSrf5p9N8F3kOExOobleJSV6dsK5sCcC3O8+wde95MjVaGtapwuThnSljYWbsrBS6Z2l30u7dY9GsIK5fvYrSKRx6voG7p4exs1Do9h+IIWzxir/bE/+J+bc7eaSb7Dc9R92K57XXmrNg7nR+OXKCeQv+D61WS+XKlZg8YSSNGtY3ZvaeybOWjVarJShkEUePnwSgw+v2TBg7HBMTE/22f/xxg0FDvFm8cDZNmzQ0Wt6e1f4DPxO26PPsPDeoxzT/D/Mpm/zTOXTrg51tFX3aIZ4D6OncjYuXLjNk2CherFlDvy541jTq1KlV9BkT4n9EiZyqmpCQQGxsLFu3bsXLy4uZM2c+1fbNmjXTb3P8+HEyMzOLIkyjSbqTwrRP5hMyeypbvvmcmjWqsXDxyqdKF/rp/2Fpacmmrz5j9fJ5HDx0hKgDMdSvV4sNX4br/9q2eY03unculZ3GlDt3CJs5iw9nf8KiDeupVqM6axYvyZUu7soVAsaM42Dk7jz3s/nLtZz79WRRh2sUSXeSmfbJPEJm/5ct3yynZo0X8jl38k+3dXskV69e55t1S9iwdjFHj59k1+79AATMCKWqXRW+WrOIz8JmM2feZyQkJBo1j/9U0p1kps0IJSRoGls2fpGd50XLnipdaNA0Nqxdyoa1SwmYOgGrilZ89OEY6terrV++Ye1S2rZpxRvdHUptpzE59T5zPt9L4LhurA59m+p2lfh8w+Fc6S78nsjX204SNq0PK4LepEa1yqzc+AsAUb/8zuadZwj9qBcrgt4kQ5PFxu2njJ2VQves7c76/1vG87a2LFy7hpAVn/NjxBbOnzptrPCLxN91JoAtG1f8VWeWP1W60KAANqz9jA1rP3ukbnlz914ak3ynM37McL5et5QpvmPwnTKz1FznC6NsstvkOL5Zt5QNaz/j6LGT7Ircr982IyOTqdOC0Wg0RstXYUi6k8y06cGEBE9ny6Y11KxRnYXh//dU6a5cuUalShXZsG65/q+nczcAfj15GuceXQ3WSadRPI5OFe1faWPUjmNMTAz9+/fH3d0dX19f0tLS8PX1xd3dnT59+vDDDz8AMGLECJKTk3F3dycmJgZPT8+nPo6npyeHDh1i9+7dLFy4kP3793Pr1i1GjRqFu7s7/fr149ChQwCEhYXh5eVFz549Wbt2LStXrsTV1ZW+ffsSEBBQ6OXwtKJjjtG0cUNq18r+luxN915s/3EPKscjggtKd+78JVycHTEzM8PCwoKO7Vuza/dBg+2PHT/Nrt0HmOo7xjgZK2QnDv9Cg8aNqf7iiwC84e5G1I6ducpp+8YInHr1pL2TY659nDp6jGPRMfTo28coMRe1vM+J3U947mSn0+p03H/wgEyNBk2mBo0mizJlypCScpeYw8d5/73BAFStasuaFZ9SqXJF42byH4qOOUrTJg2pXasmAG/26832HyPzKJvHp9NoNPz34zn4TBhFtap2BtsfO36KXbujmOo3vmgzVISOnLpOo7q21KxWGQBXpyZEHrqYq6wa1rVlTegArMqXITMzi1tJaVSyKgfAzgMXeNO5OZWsymFqasKEdzvSrUMDo+elsD1ru+M1YRxDx4wG4M6t22RpNFTIY4SlNMmuM43+bk/6ueTT7jw+XXbdCsFnwkiqVbXj2rU/sLKqQBv7FgDUrVOLChXKc/LUOSPl7tkURtlotdrcbXJZC/22s+eE4erSneeeq2y8jBWC6OhfaNrk5UfaWle2/7grd9kUkO7Xk2cwMzVl+MjxvDVwGEs/X4VWqwXg15Nn+P3KVTyGjMRjyEgid0cZN4OiVJKOoyGjT1W9cuUKe/bsoWLFioSGhtK0aVOCg4O5d+8eAwYM4D//+Q9LlizhnXfeISIigpiYmH98rNdffx1HR0fs7e3p2LEjEyZMoF+/fjg5OXHz5k0GDRrEli1bAMjMzGTbtm1kZWXRoUMH9u/fj5mZGR9//DEJCQlUrVq1kErg6cUnJFK16t/TLuzsqnAvLZ20tPsG01ULSvdK00b8sH03//lPEzSZGiL3HMTc3PDtnx+2DO+RQ/KcAlsa3EpI4Hm7vz+0P29rS3paGvfT0w2mjT2cfnryyFGD7ZMSb7F8/gICPp3LT1u+NU7QRSz7nLDVv7azs/3rnEg3mP5TUDrXXt3YFbmfHi4eaLVa2tq/RueObTl9JpYqz9vw5boIDv58hMxMDe8M7qe/mJd08Qk3qfrI+ZJ/2Tw+3eZvt2Nb5XkcHTrkOs78hUvx/mBYntOtSoubt9Owe95K/9rWpgJp9zWk39fkmq5qbm7KgSNXCF22DwsLM97t3wqA6zdSSK53H9/gbdxOTqdZo2q8P6CNUfNRFJ613TExMcHM3Jz5gdP5ec9e2nTuSPVapXsUJD4hkap2T9juPCbd5m9/NKhbtWvV4H76fX6OPkK7tq04czaWy5evkngryUi5ezaFUTauLt2z2+Reg7Lb5DYt6dyxHQARW7aTlaXFvW9Plq1cb7yMFYL4hJt5XIfS8m6T80mn1Wpp06YVE8aOJCMjgzHjP8KqQnkGD3oTy3LleKNHV97q34fLv19l+MjxvPBCVZo0bmTUfApRmhl9qmrdunWpWDF7ROLQoUN89dVX9OnTh8GDB5Oens7FixeL7NiHDh1i4cKF9OnTh+HDh5OVlUVcXBwAzZs3B8Dc3JwWLVrQv39/wsPDGTx4cLF2GoFc37Y9ZGZm+sTpJo17DxMTGOg5hom+M2hj3wILi787jidOniU5ORXnHl0KLW5jU/l8dWNq+vjTPCsri7kB0xg2fiw2Vao8Nn1pkf85YfbE6ZYuW4v1c5WJ3L6eH7//ktTUu6xeu4msrCz++DOeChXK88Xn8wj65CPmfrqUs+eKrg4XpvzOl1z16gnSrV2/ieHDBudKc+LkGZKTU3DukXt0uzTJ7/wwNTXJc3mHVnXY8tkQhri3xDd4GzqdIkur4+jp6wSM6cqSGW6k3stg+Te/FGXYRvEs7c6jJgQGsGr7D9xLvcvXK74ohMiKT+HWrQiGD/v7PnQrqwrMD/2Y5V98xVuDRvL91l20bvWqwfWsJCuMslm67EusrSsT+eMGfvxh3V9t8kbOnb/IxogfmPrR2EKP2xgK47OOu5sLvpPHUqZMGSpWrIjH4DfZvfcAAFP8JvBW/+zZRPXq1qZb1y7sizpUiDkQ/4u0ShXpX2lj9Ja2XLly+v91Oh0hISE0bdoUgFu3blG5cmUSEhKK5Ng6nY5Vq1bx3HPPAdn3UlapUoVdu3YZxLV48WJOnDhBVFQU7733HqGhodjb2xdJTPlZvHQN+/Znj7ampaXzUv06+nU3E29RqZIVlpblDLapVtWWU6dj80x3I/4m4729qPzXNMKVq7/hxZrV9Wl/2hmFS0+np/6wU5JUqVaVC2fP6l/fTryFVcWKlLO0fOy2l86dJ+HPG6xcGAZA8u0kdDodmsxMRk/xK7KYi8LipavZtz8aeNpz53ye6XbvPYjvpFFYWFhgYWFB715d2bX7gP4+WNde2feP1HqxOq/+pymnz8bSpHHJnIK4eOkX7Iv6GYC0tDReeqmufl12nitimeN8qVbNjlNnzuWb7nzsRbRaLS1f+0+u4/20cy8uPbuVynq1cuMRDh27CkD6/UzqvmijX5d4J42KFcpiWc7CYJs/4lNISrlPs0bVAHDu3IhPVxzgbloGz1tXoEOruvoRym7tG7B6i+HoW2n0LO0OwPHoGGrXr4+NbRUsy5enY7eu/LxnbxFFW3QWL131SN1Kf8K6ZcupMznbnUfr1qW/6lZzfRqdToelZTmWfRaqX+b+lpfB9aykKeyy2b3nAL6TRz/SJmfPCklISCQtLZ2hXuMBSEy8zdSAIMaPHU6XTu2KPqP/wOLPVrAvKvu2meyyqadfl2/ZVLXj1Om82+Qftv1Ewwb1adgg+2FJSinMzc3QarWsXLWOgW/3o0KFv2ZV/bVOCPHkivXTTNu2bVm/Pnsqxc2bN3F1deXGjRuFegwzMzP9/Pa2bduybt06AC5duoSrqyv37983SJ+UlISzszMNGzZk3LhxtG/fntjY2Fz7LWqjRnjqH1izevk8Tp0+z9VrfwCwMWIbXTq2zbVNuzav5ZtuY8Q2lvxf9lNob9++w+ZvfzQYXTx6/DT2rV4t2kwVsVft7blw+gx//jWKvGPzFuw7PdnDSF5u9grLvo1g/uovmL/6C3q49aG9k2Op6zQCjBrxDhu+XMyGLxezevmnOc6JrXTpmPsDRLs2LfNN93Kjl/gpMvteEE1WFvv2R9PslZepUb0ajRu9xPfbdgHZ59Wvp87RtIR2GgFGjRiqf2DN6hVhnDp9jqvXrgOwMeJ7unR6Pdc22WWTf7qjx07SulULgycaPrrOvnWLIspN0Xq3fys+n9WPz2f1IzywL+cu3eR6fAoA30ee4/XXaufa5nZyOjPCI0m5+wCAyIOXqPOiNZUrlqOTfV32xVwmIzMLpRQHjl6hUT3bXPsobZ6l3QE4GLmbDctXoJRCk5nJwcjdNGvZsqjCLTKjRgzRP8xm9YoFf9WZh+3JD3l2XP6uW3mny65brxrULRMTE8ZM8OfM2QsA7NwVhbm5OQ0b1KOkKuyyeblRA37a9UibHPUzzV55GZ+JH/DtppX6Y9naPs/M6X4lttMIMGrkMP2DalavXMyp02f/bms3fUeXTrkf1Neubet80/322+8sWboCrVbLgwcZbPh6Mz26ZT/fYV/UITZt/h6AP2/EE7k7CifHzkbKqSitdLqi/SttinVuh7e3N4GBgbi4uKDVavHx8aFWrVpcv3690I7x+uuvM2/ePCpWrIi/vz8BAQH07t0bgDlz5mBlZWWQ3sbGhgEDBtC/f38sLS154YUXcHNzK7R4/gkbm+cI/O8EfD6aRVZWFjVrVGPGtMkAnDl3gekzF7Lhy/AC0w0b8hb+gaH0H/gBSilGvDfY4BHd1+L+oPoLxTsl91k9Z2PNGP8phEzxR6PJolqNGowL8OfSufMsmh3E/NVfFHeIRpd9TkzE56NP/jonXmDGNB/g4bnzKRu+XFxguskTRhAcuhi3t97D1NQU+9avMvSdtwCYOyeAoJBFbIzYilKK94cNommT0nG/iI2NNYH/9cHHb/rfeQ70BeDM2Vimz5zHhrVLC0wHBded7HXVjJKfomRd2RKf9zsTuHAnWVk6qttVwm9kFwBiLycSuiyKz2f1o/nLLzC4TwsmzPweM1NTnrcuz4zx3QHo07UJd+9lMNI/Aq1O0aBOFT4YVDqfMvuoZ2133h3rzWdzQhnn8Q4mmNCmU0dc3n7TOMEXkew6MxkfvxlkZWmoWaM6MwL/anfOXvirbn1WYDrIu26ZmJgwa8ZHzJg1H40miypVbJgXEpjnFzclUWGUzeQJIwkOXYTbm8MwNTXLbpOHvF2c2SoUNjbWBAb44uM3jSyNhpo1qzMjcAoAZ86eZ/onIWxYt7zAdO8PH0LwnAW8OXAYWVlZdHPqglvfXgDMnDGVmbPn8f0PP6LV6Zg80Zt6dXN/ASaEyJ+Jym+yuHhq6cm/FXcIJdYVXaXiDqFEq2N6r7hDKLlMSse9S8XlzoWvizuEEiul/jvFHUKJVccsvbhDEKWVSe7fbxV/K1/pheIOQRSi0Z3KPT7RM1gU9aBI91/YSt+NN0IIIYQQQgghjEq+yhdCCCGEEEKIHErjby0WJRlxFEIIIYQQQghRIBlxFEIIIYQQQogcSuOTT4uSjDgKIYQQQgghhCiQjDgKIYQQQgghRA5a+fEJAzLiKIQQQgghhBCiQDLiKIQQQgghhBA5yFNVDcmIoxBCCCGEEEKIAsmIoxBCCCGEEELkIE9VNSQjjkIIIYQQQgghCiQjjkIIIYQQQgiRg9zjaEhGHIUQQgghhBBCFEhGHIUQQgghhBAiBxlxNCQjjkIIIYQQQgghCiQjjkIIIYQQQgiRg1bJkOOjZMRRCCGEEEIIIXLQ6Yr2r7AsWLCAsLCwPNdlZmbi4+ODs7Mzbm5u/PbbbwAopQgODuaNN96gZ8+eHD169LHHkY6jEEIIIYQQQpQyd+/eZcqUKaxYsSLfNGvWrMHS0pLt27czZcoU/Pz8ANixYwe//fYb27ZtY9GiRfj5+ZGVlVXg8aTjKIQQQgghhBA56FTR/j2ryMhI6tSpw7vvvptvmr179+Lq6gpA69atuXPnDn/++Sf79u2jZ8+emJqaUrduXapXr87x48cLPJ7c4yiEEEIIIYQQRpaamkpqamqu5ZUqVaJSpUqP3b5v374A+U5TBbh58ya2trb617a2tsTHx3Pz5k3s7OxyLS+IdBwLUfnn6hd3CCVWk+IOoMSzfXwSIfJQvvWk4g6hxKpR3AEIIYQo1b45nlmk+w8LCyM8PDzXcm9vb8aMGaN/vX37dmbPnm2Qpl69enzxxRf/6LimpqaoPB78Y2pa8GRU6TgKIYQQQgghhJENGTIENze3XMtzjjY6Ozvj7Oz8j45hZ2dHYmIitWvXBiAxMRE7OzuqVq1KYmKiPt3D5QWRjqMQQgghhBBCGNmTTkl9Fp07d+bbb7+lVatWHDlyhLJly1K9enU6derEpk2bcHFx4fr161y5coVmzZoVuC/pOAohhBBCCCHE/4j169dz8+ZNxo0bh6enJwEBAfTq1YsyZcowZ84cAN544w1Onjypf3DOzJkzKVeuXIH7NVF5TXAVQgghhBBCCCH+Ij/HIYQQQgghhBCiQNJxFEIIIYQQQghRIOk4CiGEEEIIIYQokHQchRBCCCGEEEIUSDqOQgghhBBCCCEKJB3HEszPz4/PPvuM4cOH55vm7t27jBo1yohRCZGbo6Mj169fN+oxi+vcX79+PevXry+07SIiIvDz83vmuEpLW/AkcU6dOpVTp07lWu7n50dERERRhWYUJ0+eJCQkpLjDKHYXLlygUaNG7Nixw2D5li1b6NevH3369KF3796sXr26mCIsWSIjI1mwYEFxhyGKkVarxcvLi169erF8+XKmTp0KgKenJzExMcTExODp6Zlru4frT506pd9mw4YN/PDDD0aNX/xvkN9xLOHs7Oz4/PPP812fkpLC+fPnjRiRECVDcZ37AwcONOp2T6q0tAVPEufMmTONFI3xXbp0idu3bxd3GMUuIiKCHj168NVXX9GjRw8g+8PsV199xdKlS7GzsyM1NZVhw4ZhaWnJm2++WcwRFy8nJyecnJyKOwxRjBISEoiNjeXAgQP/aPtmzZrpf9z9+PHj2NvbF2Z44l9COo4liFKKoKAg9u7di52dHVqtFnt7exwdHdm9ezfff/89y5Ytw8zMjJo1axISEsInn3zCzZs3GT16NIsWLWL+/Pn8/PPPpKSkYG1tTVhYGLa2tnTo0IEePXpw9OhRzMzM+PTTT3nxxRc5dOgQQUFBKKWoXr06c+fOxdLSkjlz5nD48GG0Wi3u7u4MHTq02MokNDSUXbt2YWZmxttvv02nTp0ICAggOTmZ8uXLM3XqVJo3b46fnx9WVlacOXOGhIQERo8eTb9+/fj555/13/BXrlyZuXPnkp6ezjvvvMPu3bsBCAsLA2DMmDG0b98eBwcHjhw5gq2tLYMGDWLNmjXEx8cTFBSEvb09V69eJTAwkOTkZMqVK8d///tfmjRpUixllJ/4+HgmT55Meno6pqam+Pv7M3HiRBwdHTly5AgAs2bNokmTJvnm59atWwQEBBAfH4+JiQmTJk3i9ddfJzk5GR8fH+Lj46lfvz4ZGRlGz9+j5363bt1YtWoVOp2Opk2bMm3aNMqWLftE76Wnpyf16tXj5MmTZGRkMGXKFDp06JDvcR89V/KrV8HBwRw8eBAzMzOcnJzw9vY22G7Lli0sWbIEKysratSoQfny5YHs0ajZs2fz4MEDrK2t+fjjj3nxxRdLdHncv38ff39/YmNjMTExwcvLi759+xIREcHhw4cJCgoCsr/19vb2ZuXKlfo4g4ODmThxIrdu3QJg9OjRODk56dPa29vn2SZC9shUXnksLHPnzmXHjh1YW1tja2uLo6MjV69ezdW+Pvfcc0yZMoWLFy8CMGjQIN56660895mamsrChQtJT09nyZIl7N+/n1GjRtGhQweUUvTo0YM1a9YwcODAp6qnpU1WVhbfffcda9euZcCAAVy7do1atWqxZMkSgoODsbOzA6BSpUoEBwdz7969Yo64aMXExBASEoJOp6Ny5cqYmppy9+5dEhMT6dWrF5MnTzaoT46Ojri6unLgwAHu379PcHAwr7zySnFno8jkdy1bvXo1NWvWJCYmhvDwcNasWYOnpyfNmjXj6NGjJCUl4e/vT+fOnYs7CwV69P1v0KABAQEBTJ8+nYsXL6LVahk+fDguLi6MGDGC5ORk3N3d8fX11ef5aY4THh7OBx98wO7du4mOjsbW1pbGjRvneZ0PCwvjxIkT3Lhxg8GDB5OZmcnmzZsxNTWlefPmTJ8+vQhLRZRYSpQY27dvVx4eHiozM1Pdvn1btW/fXm3atEk5ODgopZRydHRUt27dUkopNW/ePHX27FkVFxenX3/lyhXl7e2ttFqtUkopHx8ftXz5cqWUUg0bNlQ7d+5USik1e/ZsNXv2bJWRkaHatWunzp49q5RSau7cuWr16tVq3bp1atasWUoppTIyMpSHh4f65ZdfjFcQj9i2bZsaMGCAysjIUPfu3VOurq6qe/fuaseOHUoppY4fP666dOmiMjIylK+vrxo9erTS6XTq/Pnzyt7eXimllIeHh/r111+VUkqtWrVK7d+/36DclFJq4cKFauHChUopw7Ly8PBQEydOVEopFRERoUaNGqWUUurtt99WZ86cUUopdfHiRdW9e3cjlMbTCQsLU59//rlSSqno6Gi1bNky5eDgoMLCwpRSSkVGRioXFxelVP75GT9+vNq1a5dSSqmEhATl5OSk7t69qz7++GM1b948pZRShw8fVg0bNlRxcXFGzd/D9/DChQtq4MCB6sGDB0oppUJDQ9WiRYuUUk/2Xnp4eCg/Pz+llFJnz55V7du3VxkZGfkeN79z5WG9un79uurZs6dSSqkHDx6oSZMmqQcPHui3i4+PV+3bt1eJiYlKo9GoYcOGKV9fX5WRkaF69+6t/vjjD6WUUlFRUWrIkCElvjyCg4PVjBkzlFJK3b59Wzk6Oqpz586pTZs2KV9fX306Dw8PFR0dbVD3IiIiVGBgoFJKqUuXLqmgoCCDtPm1iQXlsTBERkaqgQMHqoyMDJWcnKwcHBzU119/nWf7GhMTo4YPH66UUiopKckgz3l5tFw2btyofHx8lFLZ9cjLy0sppZ66npY2O3fuVP369VNKKTVlyhQVHBysbt++rRo2bKju379fzNEZX3R0tGrZsqVKTU1Vy5YtUxEREUoppVJTU1WLFi3U7du3Dc4bBwcHtXLlSqWUUqtXr1be3t7FFbpR5Hcte3jNiY6OVh4eHkqp7Lbjk08+UUpl1x03N7fiCfopPPr+K6VUSEiIWrVqlVJKqbt376pevXqpa9euGbSdOfMcHR1tsOxRea339fVVmzZtUkrlf51fuHChPr1Go1Ft2rRRmZmZSqvVqoCAABUfH1+EpSJKKhlxLEEOHz5M9+7dsbCwwMbGhk6dOhmsd3BwYODAgTg5OdGjRw8aN25scF9Z7dq18fX15ZtvvuH333/nxIkT1KpVS7++Y8eOADRo0IAjR44QGxtL1apVady4MQATJ04EYOzYsZw7d47o6GgA0tPTiY2NpVWrVkWa/7z88ssvODs7U6ZMGcqUKcO6detwcHCge/fuALz66qtUrlyZy5cvA9C+fXtMTExo2LAhycnJAPoRn65du+Lk5ET79u0fez/ew7KvUaMGLVu2BKB69eqkpqaSlpbG6dOn+eijj/Tp09PTuXPnDtbW1oVdBP9Yu3btGDNmDOfOnaNz5854eHiwdu1a/WiIo6Mjfn5+xMfH55ufQ4cOcfnyZRYuXAhkjxTExcVx+PBh5s6dC0Dr1q2feFSsKMTExHD16lV9vjQajcEoTEHv5UMPt23cuDG2trbExsbqp/Q8Ts56VbVqVcqWLcuAAQNwcHBg/PjxBiNhx48fp0WLFlSpUgWA3r17Ex0dzZUrV4iLi+ODDz7Qp/0nIy3GLo/o6GhmzZoFgI2NDU5OThw+fBgrK6vHxtqiRQvmzZtHQkICXbp0YfTo0Qbr82sTH5fHZ3Xo0CGDdqdr166YmZnl2b42aNCA33//HS8vLzp16sTkyZOf+DjOzs7Mnz+f+/fvs3nzZtzd3fXrnqaelqR250lERETg4uICQM+ePZk8eTJeXl5A9iyTf6O6detSsWJFvLy8iI6OZvny5Vy8eBGNRsP9+/dzpX+03fnpp5+MHa5R5Xcty8+jZfPwc0BJ9/D9h+z258GDB2zatAnIrucXL16kYcOGRXLs/K7zAM2bNwfA3NycFi1a0L9/f5ycnBg8eDBVq1YtknhEySYdxxLExMQEnU6nf21ubvj2+Pv7c/78efbt24ePjw/e3t76D34Ap0+fZtKkSQwdOpQePXpgampqcBF++OHVxMQEpRQWFhYG+7979y5paWlotVp8fHz0nbOkpCT9VDpjy1kGcXFxuT5YKKXQarWAYR4fGjp0KA4ODuzZs4eQkBBOnjyJq6urwX6ysrIMjlWmTBn9/2ZmZgbH0+l0lClThm+//Va/LD4+nueee+4f5rJotGzZkq1bt7J37162bdvG5s2bAcMy1el0aLXafPOj0+lYtWqVPm8JCQlUqVJFfw49lLOMjEmr1eLs7Iy/vz+A/hx+qKD3Mq/lOp0u13lXkJz1ytzcnG+++YbDhw8TFRXFgAEDDKYT5VfPdTodNWvW1L8PWq1WP4XzaRi7PPKrjznPEY1Gk2vbOnXqsH37dvbv38+ePXtYsWIF27dv16/Pr6wel8dnZWpqanBcgOTkZLy8vHK1r9bW1mzdupWDBw+yb98+3Nzc2Lp1K5UqVXrsccqXL0+nTp348ccfiY6OJjAwMFde4fH1tDS5ffs2UVFRnD59mtWrV6OUIjU1lZ9//pkXX3yR06dP07p1a336h/XoaTrkpVG5cuUACAoKIi4uDhcXF7p27cqhQ4fy7Eznda37X5XftexhuWRlZRmkL41l8/D9h+z6HhISQtOmTQG4desWlStXJiEhoUiOnd91fteuXQZxLV68mBMnThAVFcV7771HaGio3Cf5LyRPVS1B2rVrx48//khmZiYpKSns379fvy4rK4vu3btjbW3NiBEj6NOnD+fOncPc3FzfaP7yyy/Y29szcOBAXnrpJQ4ePFjgh6m6deuSlJTEpUuXAFi2bBnr16+nbdu2fP3112g0GtLS0hg0aBC//vpr0WY+H61bt2bnzp36b13Hjx+PiYmJ/hvWEydOcOvWLRo0aJDvPt58803S0tIYOnQoQ4cO5ezZs1SqVImUlBSSkpLIzMw0KOvHqVixInXq1NF/gDt48CCDBw9+towWgTlz5vDtt9/i5uZGQEAAZ8+eBWDr1q0A7Ny5k/r161OjRo1889O2bVvWrVsHZD/Uw9XVlfv379OuXTt9+pMnT3Lt2jVjZ09/7rdp04adO3dy+/ZtlFIEBgayatWqp9rXtm3bADh16hSpqanP9M3u2bNn8fDwoHXr1vj6+lK/fn1+//13/fqWLVvy66+/kpCQgE6n0x+7Xr16pKSk6O9r27Rp01N9WC6u8mjbti0bN24Esr9kioyMxN7eHmtra3777TeUUsTFxREbG2sQJ8CXX35JWFgYzs7OTJs2jaSkJO7evavfd35tYmHksSDt27fnp59+IjMzk3v37rF3717S09PzbF8jIyOZPHkyXbp0wd/fn/Lly3Pjxo18921mZmbwQbdfv37Mnz+fjh07GnTqn6aelibfffcdbdu2JSoqit27d7Nnzx5GjhzJhg0b8PLyIigoiMTERCD7fAoKCqJ27drFHLXxHDx4EC8vL5ydnblx44a+nfg3y+taZm1trf/sEhkZWcwRFq62bdvqn8B98+ZNXF1dC2xT/gkzMzP958P8rvOPSkpKwtnZmYYNGzJu3Djat2+vb9PFv4uMOJYgXbt25dSpU7i4uFClShXq16+vX2dubs7YsWN59913KVeunP6hATY2NlSvXh1PT09CQ0Px9vamd+/eWFhY0KhRowKnZJYtW5aQkBA+/PBDNBoNtWrVYs6cOZQpU4arV6/i5uZGVlYW7u7utGnTxhhFkEu3bt04ffo07u7u6HQ63nnnHdq0aUNgYCBhYWFYWFgQFhZm8IErp4kTJ+Ln54e5uTlly5bl448/1k8J6t+/P9WqVXviaYkPhYSEEBgYyLJly7CwsGD+/Pkl7ttNT09PJk2axObNmzEzM2PatGmEhoZy7NgxNm7ciKWlpf7BJfnlx9/fn4CAAHr37g1kX8CtrKwYO3Ysfn5+9OrVi3r16hXLVNXnn3+e6tWrM3PmTLy9vRkyZAg6nY7GjRvz/vvvP9W+4uLicHNzA2D+/PnPNILapEkTXn31VVxcXLC0tKRx48Z06tSJM2fOAFClShX8/f0ZOnQolpaWvPTSS0D2SOCCBQuYOXMmGRkZWFlZERwc/MTHLa7yGD16NIGBgfTu3RutVsvIkSNp2rQpmZmZbNq0iTfeeIO6devqZ0c8jNPT05MlS5YwceJEevfujbm5Od7e3gYjdfm1iS+//PIz57EgnTt35tixY7i5uVG5cmXs7OyoUaMG+/bty9W+jh49mh07dtCrVy/Kli1L9+7dadSoUb77bt68OeHh4YSGhjJ58mRatmyJiYkJ/fr1M0j3NPW0NImIiGDChAkGywYNGsSyZcsIDAxEo9EwbNgw/Yj122+//a96ouqIESP48MMPqVSpEs8//zyvvPKK0X/qqKTJ61pWoUIFZsyYQXh4eIEP7yqNvL29CQwMxMXFRT8DrFatWoV6Hrz++uvMmzePihUr5nudf5SNjQ0DBgygf//+WFpa8sILL+ivEeLfxUT9W28oEOJfyNHRUf8kOpHt4RM8i+vLkZJGyiP7PtQrV67g5uaGRqPh7bffZtasWbz88suFehylFBcuXMDX15ctW7bol0s9FUIIURLJiKMQQuTwxRdf6O+jedTjflf1f9W/rTzq1q1LeHg4K1euRClF3759n6rT+KTltWrVKpYtWyY/7C6EEKJUkBFHIYQQQgghhBAFkofjCCGEEEIIIYQokHQchRBCCCGEEEIUSDqOQgghhBBCCCEKJB1HIYQQQgghhBAFko6jEEIIIYQQQogCScdRCCGEEEIIIUSB/h/NF0j5glHmuQAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.triu(np.ones_like(corre, dtype=np.bool))\n", + "heatmap = sns.heatmap(corre, mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG');" + ] + }, + { + "cell_type": "markdown", + "id": "118ff471", + "metadata": {}, + "source": [ + "#### Observamos que el consumo se relaciona principal y negativamente con la velocidad y la temperatura exterior, sin ser ninguna superior al 0.5 (valor absoluto). Además, la velocidad se correlaciona positivamente con la distancia (trayectos largos a mayor velocidad). La temperatura exterior se correlaciona positicamente con el sol, y la temperatura interior, y negativamente con los litros de relleno. No existen otras correlaciones superiores a 0.35\n" + ] + }, + { + "cell_type": "markdown", + "id": "d72e3839", + "metadata": {}, + "source": [ + "### Predicciones consumo" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "1f31b290", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos la regresión líneal" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "305a98fa", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "5cbefd90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "4451dfa4", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_train = lr.predict(X_train)\n", + "y_pred_test = lr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "8bbff554", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.22815357510490153" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train_predict = lr.predict(X_train)\n", + "metrics.r2_score(y_train, y_train_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "d2968680", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1020698110029008" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test_predict = lr.predict(X_test)\n", + "metrics.r2_score(y_test, y_test_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "0c83c6cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9154795542880089" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train, y_train_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "1c1920a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9375077349245255" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics.mean_squared_error(y_test, y_test_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "2e93fef8", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos el random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "09f429a5", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "d9ddef9b", + "metadata": {}, + "outputs": [], + "source": [ + "forest = RandomForestRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "e44a95a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor()" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "ca9015d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.24289915602982237" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train,forest.predict(X_train)).round(3)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "84b57363", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6985699678629192" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_test,forest.predict(X_test)).round(3)**0.5" + ] + }, + { + "cell_type": "markdown", + "id": "793a694f", + "metadata": {}, + "source": [ + "#### Obtenemos un MSE más bajo en el random forest, 0.24 en el test, y de 0.69 en el train, aún así no parece un gran modelo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2583fd64", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be457321", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/MainCobify.ipynb b/MainCobify.ipynb new file mode 100644 index 0000000..69d9ab2 --- /dev/null +++ b/MainCobify.ipynb @@ -0,0 +1,992 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "25980055", + "metadata": {}, + "source": [ + "### Importamos librerías" + ] + }, + { + "cell_type": "code", + "execution_count": 134, + "id": "f63a2ff3", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "\n", + "from sklearn import metrics\n", + "from sklearn.linear_model import LinearRegression\n", + "from sklearn.model_selection import train_test_split\n", + "import warnings\n", + "warnings.filterwarnings(\"ignore\")\n", + "from sklearn.svm import SVC\n", + "from sklearn.metrics import mean_squared_error, r2_score\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.ensemble import GradientBoostingRegressor\n", + "\n", + "\n", + "from matplotlib import pyplot as plt\n", + "%matplotlib inline\n", + "%config Inlinebackend.figure_format = 'retina'\n", + "\n", + "import seaborn as sns\n", + "sns.set_context('poster')\n", + "sns.set(rc={'figure.figsize': (16., 9.)})\n", + "sns.set_style('whitegrid')\n", + "\n", + "import numpy as np\n" + ] + }, + { + "cell_type": "code", + "execution_count": 118, + "id": "e6e0415c", + "metadata": {}, + "outputs": [], + "source": [ + "# Nos traemos el data set, teniendo en cuenta que los decimales están separados por coma\n", + "df = pd.read_csv('measurements.csv',decimal=',')\n" + ] + }, + { + "cell_type": "markdown", + "id": "2ce8eeeb", + "metadata": {}, + "source": [ + "### Analísis y limpieza de datos" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "id": "f3c01757", + "metadata": {}, + "outputs": [], + "source": [ + "#df2 = pd.readcsv(\"measurements2.xlsx\",encoding = \"ISO-8859-1\")" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "id": "6275ac7c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(388, 12)" + ] + }, + "execution_count": 120, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 121, + "id": "eaf5b089", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidespecialsgas_typeACrainsunrefill litersrefill gas
028.05.02621.512NaNE1000045.0E10
112.04.23021.513NaNE10000NaNNaN
211.25.53821.515NaNE10000NaNNaN
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside specials gas_type AC \\\n", + "0 28.0 5.0 26 21.5 12 NaN E10 0 \n", + "1 12.0 4.2 30 21.5 13 NaN E10 0 \n", + "2 11.2 5.5 38 21.5 15 NaN E10 0 \n", + "\n", + " rain sun refill liters refill gas \n", + "0 0 0 45.0 E10 \n", + "1 0 0 NaN NaN \n", + "2 0 0 NaN NaN " + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 122, + "id": "43d9089e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "specials 295\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "refill gas 375\n", + "dtype: int64" + ] + }, + "execution_count": 122, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 123, + "id": "db8bcbb5", + "metadata": {}, + "outputs": [], + "source": [ + "#Eliminamos la columna refill gas, ya que es la misma que gas_type, aunque en algunas falten datos se observa como cambia a la par\n", + "#Elimanamos tambien la columna specials, porque nos da la misma información que las columna AC, rain y sun\n", + "df.drop(['refill gas', 'specials'], axis = 1 , inplace = True)" + ] + }, + { + "cell_type": "code", + "execution_count": 124, + "id": "9464bdde", + "metadata": {}, + "outputs": [], + "source": [ + "#Para la columna refill liters le asignamos en los valores que faltan el valor que coincide con el gas_type" + ] + }, + { + "cell_type": "code", + "execution_count": 125, + "id": "f0459a09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "distance 0\n", + "consume 0\n", + "speed 0\n", + "temp_inside 12\n", + "temp_outside 0\n", + "gas_type 0\n", + "AC 0\n", + "rain 0\n", + "sun 0\n", + "refill liters 375\n", + "dtype: int64" + ] + }, + "execution_count": 125, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isna().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 126, + "id": "04f84b0a", + "metadata": {}, + "outputs": [], + "source": [ + "#Para los 12 datos de temperatura interior que nos faltan al ser un 3% de la muestra le asignaremos el valor medio" + ] + }, + { + "cell_type": "code", + "execution_count": 127, + "id": "a5ecb335", + "metadata": {}, + "outputs": [], + "source": [ + "df['temp_inside'] = df['temp_inside'].fillna(22)" + ] + }, + { + "cell_type": "code", + "execution_count": 128, + "id": "f24f2c8b", + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
028.05.02621.512E1000045.0
112.04.23021.513E1000045.0
211.25.53821.515E1000045.0
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside gas_type AC rain \\\n", + "0 28.0 5.0 26 21.5 12 E10 0 0 \n", + "1 12.0 4.2 30 21.5 13 E10 0 0 \n", + "2 11.2 5.5 38 21.5 15 E10 0 0 \n", + "\n", + " sun refill liters \n", + "0 0 45.0 \n", + "1 0 45.0 \n", + "2 0 45.0 " + ] + }, + "execution_count": 128, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#Para la columna refill liters, completamos el valor con el valor previo, ya que también parece ir correlacionada con elgas type\n", + "df = df.fillna(method='ffill')\n", + "df.head(3)" + ] + }, + { + "cell_type": "code", + "execution_count": 129, + "id": "df948205", + "metadata": {}, + "outputs": [], + "source": [ + "#Normalizamos los dos tipos de combustible para facilitar en análisis de su correlación\n", + "\n", + "combus= {\"E10\": 1,\n", + " \"SP98\": 0,\n", + " }" + ] + }, + { + "cell_type": "code", + "execution_count": 130, + "id": "df8fdbdd", + "metadata": {}, + "outputs": [], + "source": [ + "df.gas_type = df.gas_type.map(combus)" + ] + }, + { + "cell_type": "markdown", + "id": "e30f7ca3", + "metadata": {}, + "source": [ + "### Correlación" + ] + }, + { + "cell_type": "code", + "execution_count": 131, + "id": "31628178", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
distanceconsumespeedtemp_insidetemp_outsidegas_typeACrainsunrefill liters
distance1.000000-0.1289670.5622990.0747340.0881750.053411-0.025738-0.0197910.081120-0.097117
consume-0.1289671.000000-0.227866-0.161470-0.3208110.0153270.0965910.248118-0.1706670.139765
speed0.562299-0.2278661.0000000.0591390.0154110.097360-0.0354080.0094890.081618-0.086277
temp_inside0.074734-0.1614700.0591391.0000000.359649-0.0109210.296719-0.0372040.242237-0.077320
temp_outside0.088175-0.3208110.0154110.3596491.000000-0.1487050.167562-0.1863150.346903-0.356930
gas_type0.0534110.0153270.097360-0.010921-0.1487051.000000-0.105285-0.060328-0.0227610.133453
AC-0.0257380.096591-0.0354080.2967190.167562-0.1052851.0000000.2429150.088598-0.079404
rain-0.0197910.2481180.009489-0.037204-0.186315-0.0603280.2429151.000000-0.112650-0.083591
sun0.081120-0.1706670.0816180.2422370.346903-0.0227610.088598-0.1126501.000000-0.055477
refill liters-0.0971170.139765-0.086277-0.077320-0.3569300.133453-0.079404-0.083591-0.0554771.000000
\n", + "
" + ], + "text/plain": [ + " distance consume speed temp_inside temp_outside \\\n", + "distance 1.000000 -0.128967 0.562299 0.074734 0.088175 \n", + "consume -0.128967 1.000000 -0.227866 -0.161470 -0.320811 \n", + "speed 0.562299 -0.227866 1.000000 0.059139 0.015411 \n", + "temp_inside 0.074734 -0.161470 0.059139 1.000000 0.359649 \n", + "temp_outside 0.088175 -0.320811 0.015411 0.359649 1.000000 \n", + "gas_type 0.053411 0.015327 0.097360 -0.010921 -0.148705 \n", + "AC -0.025738 0.096591 -0.035408 0.296719 0.167562 \n", + "rain -0.019791 0.248118 0.009489 -0.037204 -0.186315 \n", + "sun 0.081120 -0.170667 0.081618 0.242237 0.346903 \n", + "refill liters -0.097117 0.139765 -0.086277 -0.077320 -0.356930 \n", + "\n", + " gas_type AC rain sun refill liters \n", + "distance 0.053411 -0.025738 -0.019791 0.081120 -0.097117 \n", + "consume 0.015327 0.096591 0.248118 -0.170667 0.139765 \n", + "speed 0.097360 -0.035408 0.009489 0.081618 -0.086277 \n", + "temp_inside -0.010921 0.296719 -0.037204 0.242237 -0.077320 \n", + "temp_outside -0.148705 0.167562 -0.186315 0.346903 -0.356930 \n", + "gas_type 1.000000 -0.105285 -0.060328 -0.022761 0.133453 \n", + "AC -0.105285 1.000000 0.242915 0.088598 -0.079404 \n", + "rain -0.060328 0.242915 1.000000 -0.112650 -0.083591 \n", + "sun -0.022761 0.088598 -0.112650 1.000000 -0.055477 \n", + "refill liters 0.133453 -0.079404 -0.083591 -0.055477 1.000000 " + ] + }, + "execution_count": 131, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#observamos la colerracion\n", + "corre = df.corr()\n", + "corre" + ] + }, + { + "cell_type": "code", + "execution_count": 132, + "id": "f3a239ad", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":1: DeprecationWarning: `np.bool` is a deprecated alias for the builtin `bool`. To silence this warning, use `bool` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.bool_` here.\n", + "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n", + " mask = np.triu(np.ones_like(corre, dtype=np.bool))\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "mask = np.triu(np.ones_like(corre, dtype=np.bool))\n", + "heatmap = sns.heatmap(corre, mask=mask, vmin=-1, vmax=1, annot=True, cmap='BrBG');" + ] + }, + { + "cell_type": "markdown", + "id": "118ff471", + "metadata": {}, + "source": [ + "#### Observamos que el consumo se relaciona principal y negativamente con la velocidad y la temperatura exterior, sin ser ninguna superior al 0.5 (valor absoluto). Además, la velocidad se correlaciona positivamente con la distancia (trayectos largos a mayor velocidad). La temperatura exterior se correlaciona positicamente con el sol, y la temperatura interior, y negativamente con los litros de relleno. No existen otras correlaciones superiores a 0.35\n" + ] + }, + { + "cell_type": "markdown", + "id": "d72e3839", + "metadata": {}, + "source": [ + "### Predicciones consumo" + ] + }, + { + "cell_type": "code", + "execution_count": 164, + "id": "1f31b290", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos la regresión líneal" + ] + }, + { + "cell_type": "code", + "execution_count": 191, + "id": "305a98fa", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 192, + "id": "5cbefd90", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "LinearRegression()" + ] + }, + "execution_count": 192, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "lr = LinearRegression()\n", + "lr.fit(X_train, y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 193, + "id": "4451dfa4", + "metadata": {}, + "outputs": [], + "source": [ + "y_pred_train = lr.predict(X_train)\n", + "y_pred_test = lr.predict(X_test)" + ] + }, + { + "cell_type": "code", + "execution_count": 194, + "id": "8bbff554", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.22815357510490153" + ] + }, + "execution_count": 194, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_train_predict = lr.predict(X_train)\n", + "metrics.r2_score(y_train, y_train_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 195, + "id": "d2968680", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.1020698110029008" + ] + }, + "execution_count": 195, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_test_predict = lr.predict(X_test)\n", + "metrics.r2_score(y_test, y_test_predict)" + ] + }, + { + "cell_type": "code", + "execution_count": 196, + "id": "0c83c6cb", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9154795542880089" + ] + }, + "execution_count": 196, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train, y_train_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 197, + "id": "1c1920a6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.9375077349245255" + ] + }, + "execution_count": 197, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "metrics.mean_squared_error(y_test, y_test_predict)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 198, + "id": "2e93fef8", + "metadata": {}, + "outputs": [], + "source": [ + "#Probamos el random forest" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "id": "09f429a5", + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(\"consume\", axis=1)\n", + "y = df.consume\n", + "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=123)" + ] + }, + { + "cell_type": "code", + "execution_count": 200, + "id": "d9ddef9b", + "metadata": {}, + "outputs": [], + "source": [ + "forest = RandomForestRegressor()" + ] + }, + { + "cell_type": "code", + "execution_count": 201, + "id": "e44a95a9", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "RandomForestRegressor()" + ] + }, + "execution_count": 201, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "forest.fit(X_train,y_train)" + ] + }, + { + "cell_type": "code", + "execution_count": 202, + "id": "ca9015d1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.24289915602982237" + ] + }, + "execution_count": 202, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_train,forest.predict(X_train)).round(3)**0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "id": "84b57363", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.6985699678629192" + ] + }, + "execution_count": 203, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mean_squared_error(y_test,forest.predict(X_test)).round(3)**0.5" + ] + }, + { + "cell_type": "markdown", + "id": "793a694f", + "metadata": {}, + "source": [ + "#### Obtenemos un MSE más bajo en el random forest, 0.24 en el test, y de 0.69 en el train, aún así no parece un gran modelo" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2583fd64", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "be457321", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +}