{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# VIRTUAL CASH MANAGER v1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "El objetivo es obtener una previsión de flujo de caja para los próximos días para lo cual partimos de dos tipos de datos: ciertos e inciertos. Los datos ciertos son aquellas transacciones con fecha de vencimiento futura conocida. Los datos inciertos son todas aquellas transacciones históricas registradas que no tienen fecha de vencimiento.  \n",
    "\n",
    "Una vez separados los datos procedemos a hacer un **scheduling** para los datos ciertos y un **forecasting** para los datos inciertos. El scheduling consiste en una agregar flujos de caja por fecha de vencimiento futura y por cuenta. El forecasting utilizaremos alguna técnica de forecasting sencilla que de mejores resultados."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Importamos los móduglos necesarios\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.mlab as mlab\n",
    "import scipy.optimize as sco\n",
    "from datetime import datetime, timedelta, date\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **1. Scheduling** \n",
    "\n",
    "Para el scheduling utilizamos datos de transacciones registradas con fecha de vencimiento desde hoy en adelante (por ejemplo facturas de clientes y proveedores). Se trata de compromisos ciertos de cobro y de pago."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "121\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>TransDate</th>\n",
       "      <th>DueDate</th>\n",
       "      <th>Cuenta</th>\n",
       "      <th>Importe</th>\n",
       "      <th>Empresa</th>\n",
       "      <th>Tipo</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DueDate</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-12-10</th>\n",
       "      <td>14/07/2016</td>\n",
       "      <td>12/10/2016</td>\n",
       "      <td>banco03</td>\n",
       "      <td>-25.37</td>\n",
       "      <td>emp01</td>\n",
       "      <td>Cierto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-13</th>\n",
       "      <td>15/07/2016</td>\n",
       "      <td>13/10/2016</td>\n",
       "      <td>banco03</td>\n",
       "      <td>395.91</td>\n",
       "      <td>emp01</td>\n",
       "      <td>Cierto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-15</th>\n",
       "      <td>17/07/2016</td>\n",
       "      <td>15/10/2016</td>\n",
       "      <td>banco03</td>\n",
       "      <td>199.56</td>\n",
       "      <td>emp01</td>\n",
       "      <td>Cierto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-17</th>\n",
       "      <td>19/07/2016</td>\n",
       "      <td>17/10/2016</td>\n",
       "      <td>banco03</td>\n",
       "      <td>-3.85</td>\n",
       "      <td>emp01</td>\n",
       "      <td>Cierto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-10-25</th>\n",
       "      <td>27/07/2016</td>\n",
       "      <td>25/10/2016</td>\n",
       "      <td>banco03</td>\n",
       "      <td>216.23</td>\n",
       "      <td>emp01</td>\n",
       "      <td>Cierto</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             TransDate     DueDate   Cuenta  Importe Empresa    Tipo\n",
       "DueDate                                                             \n",
       "2016-12-10  14/07/2016  12/10/2016  banco03   -25.37   emp01  Cierto\n",
       "2016-10-13  15/07/2016  13/10/2016  banco03   395.91   emp01  Cierto\n",
       "2016-10-15  17/07/2016  15/10/2016  banco03   199.56   emp01  Cierto\n",
       "2016-10-17  19/07/2016  17/10/2016  banco03    -3.85   emp01  Cierto\n",
       "2016-10-25  27/07/2016  25/10/2016  banco03   216.23   emp01  Cierto"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"SampleMovs2.csv\",delimiter=\";\")\n",
    "data.index = pd.to_datetime(data.DueDate)\n",
    "print(len(data))\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.datetime(2016, 9, 28, 16, 2, 58, 580828)"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "now = datetime.now()\n",
    "now"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scheduling para los próximos días según vencimiento y partiendo de un saldo inicial. Lista de cuentas y saldos iniciales:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "cuentas = ['banco01','banco02','banco03']\n",
    "inicial = [1000,2000,3000]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculamos los saldos diarios a partir del saldo inicial"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sched = []\n",
    "now = datetime.now()\n",
    "#d = timedelta(n)\n",
    "for elem in cuentas:\n",
    "    df = data[data.Cuenta==elem]\n",
    "    df = df.resample('D') #Resample considera todas las fechas posibles, no solo aquellas en las que hay un flujo de caja\n",
    "    n = len(df[now:])\n",
    "    L = np.tril(np.ones((n,n)))\n",
    "    flow = np.array(df[now:].Importe.values).reshape((n,1))\n",
    "    flow = np.nan_to_num(flow)\n",
    "    i = 0\n",
    "    bal = inicial[i] + np.dot(L,flow)\n",
    "    i += 1\n",
    "    sched.append(bal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Y los representamos gráficamente"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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mb5IkST1i8iZJktQjQ0/ekvx6kieTHNWJrU6yOcl9SU7uxJcluTvJ/Uku6cQPTrKu1bk9\nyTHD7rckSdJcNNTkLcli4E3Ag53YUuBMYClwGnBpBovYAVwGrKqqJcCSJKe0+CpgR1UdB1wCXDzM\nfkuSJM1Vw555+wDw7gmxM4B1VbWzqh4ANgPLkywEDquqja3cVcCKTp0r2/51wElD7bUkSdIcNbTk\nLcnpwJaqumfCoUXAls7nbS22CNjaiW9tsWfUqaongEe6l2ElSZLmi4OmUjnJBmBBNwQU8JvAhQwu\nmQ5D9lxEkiRp9EwpeauqSZOzJC8HXgh8vt3Pthj4myTLGcy0dR84WNxi24CjJ4nTOfaVJAcCh1fV\njsnaXrt27VP7Y2NjjI2N7evXkiRJGoLxtk1NqmrKJ9ljI8mXgWVV9Y0kxwPXACcyuBy6ATiuqirJ\nHcB5wEbgk8CHqmp9knOAl1fVOUlWAiuqauUk7dRMfB9J0nAN/t2/N7/ne1NuusqMentzsU+j3l6o\nqn2+mjilmbd9ULRLnVW1Kcm1wCbgceCcTsZ1LnAFcChwU1Wtb/HLgauTbAYeBp6VuEmSJM0HMzLz\nNlOceZOk0eDM22y0Nxf7NOrt7d/MmyssSJIk9YjJmyRJUo+YvEmSJPWIyZskSVKPmLxJkiT1iMmb\nJElSj5i8SZIk9YjJmyRJUo+YvEmSJPWIyZskSVKPDDV5S/Jvk9yX5J4k7+vEVyfZ3I6d3IkvS3J3\nkvuTXNKJH5xkXatze5JjhtlvSZKkuWpoyVuSMeBfAq+oqlcA/7nFlwJnAkuB04BLM1jEDuAyYFVV\nLQGWJDmlxVcBO6rqOOAS4OJh9VuSJGkuG+bM2zuB91XVToCq+nqLnwGsq6qdVfUAsBlYnmQhcFhV\nbWzlrgJWdOpc2favA04aYr8lSZLmrGEmb0uAH09yR5LbkrymxRcBWzrltrXYImBrJ761xZ5Rp6qe\nAB5JctQQ+y5JkjQnHTSVykk2AAu6IaCA32znPrKqfijJ64CPAS+eSnsT2pEkSZp3ppS8VdWbdncs\nya8AH2/lNiZ5Isn3M5hp6z5wsLjFtgFHTxKnc+wrSQ4EDq+qHZO1u3bt2qf2x8bGGBsb27cvJUmS\nNBTjbZuaVNWUTzLpiZNfBhZV1ZokS4ANVXVskuOBa4ATGVwO3QAcV1WV5A7gPGAj8EngQ1W1Psk5\nwMur6pwkK4EVVbVykjZrWN9HkjRzBs+x7c3v+d6Um64yo97eXOzTqLcXqmqfryZOaeZtDz4KfCTJ\nPcBjwFkAVbUpybXAJuBx4JxOxnUucAVwKHBTVa1v8cuBq5NsBh4GnpW4SZJGx4IFx7J9+978P+0Q\n9nwnzXSV2Zdy0vAMbeZtNjjzJkmabXs3azg3Z4HmXp9Gvb39m3lzhQVJkqQeMXmTJEnqEZM3SZKk\nHjF5kyRJ6hGTN0mSpB4xeZMkSeoRkzdJkqQeMXmTJEnqEZM3SZKkHjF5kyRJ6hGTN0mSpB4ZWvKW\n5JVJbk/yuSR3Jnlt59jqJJuT3Jfk5E58WZK7k9yf5JJO/OAk61qd25McM6x+S5IkzWXDnHm7GFhT\nVa8G1gD/CSDJ8cCZwFLgNODSDFbxBbgMWFVVS4AlSU5p8VXAjqo6DriknVuSJGneGWby9iRwRNt/\nHrCt7Z8OrKuqnVX1ALAZWJ5kIXBYVW1s5a4CVrT9M4Ar2/51wElD7LckSdKcddAQz/1rwKeS/Bcg\nwI+0+CLg9k65bS22E9jaiW9t8V11tgBU1RNJHklyVFXtGGL/JUmS5pwpJW9JNgALuiGggIuAnwTO\nr6pPJHkL8BHgTVNpb0I7k1q7du1T+2NjY4yNjU1Tk5IkSVMx3rapSVVN+SSTnjh5pKqeN/FzkguA\nqqr3t/h6BvfEPQjcVlVLW3wl8IaqeueuMlX1mSQHAl+tqhdM0mYN6/tIkrQ3Brdx7+n/RXtTZm/L\nzWQZ25vuMlW12wmp3RnmPW/bkrwBIMlJDO5tA7gBWNmeIH0R8BLgzqp6CHg0yfL2AMNZwPWdOme3\n/bcCtw6x35IkSXPWMO95+yXgQ22m7J+AXwaoqk1JrgU2AY8D53Smy84FrgAOBW6qqvUtfjlwdZLN\nwMPAyiH2W5Ikac4a2mXT2eBlU0nSbPOyqe3tS5m5dtlUkiRJ08zkTZIkqUdM3iRJknrE5E2SJKlH\nTN4kSZJ6xORNkiSpR0zeJEmSesTkTZIkqUdM3iRJknrE5E2SJKlHppS8JXlLkr9N8kSSZROOrU6y\nOcl9SU7uxJcluTvJ/Uku6cQPTrKu1bk9yTGdY2e38l9MctZU+ixJktRnU515uwd4M/Dn3WCSpcCZ\nwFLgNODSDBZ7A7gMWFVVS4AlSU5p8VXAjqo6DrgEuLid60jgt4DXAScCa5IcMcV+S5Ik9dKUkreq\n+mJVbWaw+mrXGcC6qtpZVQ8Am4HlSRYCh1XVxlbuKmBFp86Vbf864I1t/xTg5qp6tKoeAW4GTp1K\nvyVJkvpqWPe8LQK2dD5va7FFwNZOfGuLPaNOVT0BPJrkqO9yLkmSpHnnoD0VSLIBWNANAQVcVFU3\nDqtjPHs2T5Ikad7bY/JWVW/aj/NuA47ufF7cYruLd+t8JcmBwOFVtSPJNmBsQp3bdtfw2rVrn9of\nGxtjbGxsd0UlSZJm0HjbpiZVNfWTJLcBv1FVf90+Hw9cw+ABg0XABuC4qqokdwDnARuBTwIfqqr1\nSc4BXl5V5yRZCayoqpXtgYXPAssYXOb9LPCadv/bxH7UdHwfSZL21+D5vD39v2hvyuxtuZksY3vT\nXaaq9vlK4x5n3r5rk8kK4PeA5wN/luSuqjqtqjYluRbYBDwOnNPJqs4FrgAOBW6qqvUtfjlwdZLN\nwMPASoCq+kaS32WQtBXw25MlbpIkSfPBtMy8zRXOvEmSZpszb7a3L2X2Z+bNFRYkSZJ6xORNkiSp\nR0zeJEmSesTkTZIkqUdM3iRJknrE5E2SJKlHTN4kSZJ6xORNkiSpR6a0woIkSXqmBQuOZfv2Pb13\n9RAGL3Hdk70pN5NlbG/629t3rrAgSZI0C5JZWGEhyVuS/G2SJ5Is68R/Mslnk3w+ycYkP9E5tizJ\n3UnuT3JJJ35wknVJNie5PckxnWNnt/JfTHLWVPosSZLUZ1O95+0e4M3An0+I/z3wL6rqlcA7gKs7\nxy4DVlXVEmBJklNafBWwo6qOAy4BLgZIciTwW8DrgBOBNUmOmGK/NU3Gx8dnuwvzjmM+8xzzmeeY\nzzzHvD+mlLxV1RerajMTLupW1eer6qG2fy9waJLvSbIQOKyqNraiVwEr2v4ZwJVt/zrgjW3/FODm\nqnq0qh4BbgZOnUq/NX38j33mOeYzzzGfeY75zHPM+2PoT5smeQvwN1X1OLAI2No5vLXFaH9uAaiq\nJ4BHkxzVjTfbOnUkSZLmlT0+bZpkA7CgGwIKuKiqbtxD3ROA/wi8aT/6NpxHNCRJkvqsqqa8AbcB\nyybEFgNfBH6oE1sI3Nf5vBK4rO2vB05s+wcCX+uU+XCnzoeBn9lNP8rNzc3Nzc3NrS/b/uRd0/me\nt6dmytoDBX8GvKeq7tgVr6qHkjyaZDmwETgL+FA7fANwNvAZ4K3ArS3+KeC97ZwHMJjFu2CyDuzP\n47aSJEl9MqX3vCVZAfwe8HzgEeCuqjotyUUMEqxdDzMUcHJVfT3Ja4ArgEOBm6rq/HauQxg8lfpq\n4GFgZVU90I69A7ioneffV9VV+91pSZKkHhupl/RKkiSNupFZ2zTJqUm+0F7m+57Z7s8oSnJ5ku1J\n7u7Ejkxyc3uB8qd8B9/0SrI4ya1J7k1yT5LzWtxxH5IkhyT5TJLPtTFf0+KO+ZAlOSDJ3yS5oX12\nzIcoyQPtZfqfS3JniznmQ5TkiCQfS3Jf+10/cX/GfCSStyQHAL/P4J1wJwBvS/Ky2e3VSPoogzHu\nugC4papeyuA+xdUz3qvRthN4V1WdAPwwcG77u+24D0lVPQb8RFW9GngVcFq7T9cxH77zgU2dz475\ncD0JjFXVq6tqeYs55sP1QQa3jC0FXgl8gf0Y85FI3oDlwOaqerC9T24dg5f+ahpV1aeBb0wId1+u\nfCVPv3RZ06CqHqqqu9r+t4D7GDzJ7bgPUVV9p+0ewuCVSoVjPlRJFgM/BfxhJ+yYD1d4dh7gmA9J\nksOBH6uqjwJU1c6qepT9GPNRSd4mvsi3+/JfDdcLqmo7DBIN4AWz3J+RleSFDGaC7gAWOO7D0y7f\nfQ54CNjQVoVxzIfrA8C7GSTKuzjmw1XAhrYG+b9uMcd8eF4EfD3JR9vtAX+Q5HvZjzEfleRNc4dP\nwAxBkucyWDbu/DYDN3GcHfdpVFVPtsumi4Hl7YXjjvmQJPlpYHubZf5ur3xyzKfX66tqGYMZz3OT\n/Bj+PR+mg4BlwH9t4/5tBpdM93nMRyV52wYc0/m8uMU0fNuTLABoa9d+bZb7M3KSHMQgcbu6qq5v\nYcd9BlTVN4FxBuspO+bD83rg9CRfAv4IeGOSq4GHHPPhqaqvtj//HvgEg1uQ/Hs+PFuBLVX12fb5\nTxgkc/s85qOSvG0EXpLk2CQHM1iV4YZZ7tOoCs/8l/ENwDva/tnA9RMraMo+Amyqqg92Yo77kCR5\n/q6nvZI8h8GLwe/DMR+aqrqwqo6pqhcz+P2+tareDtyIYz4USb63zeiT5PuAk4F78O/50LRLo1uS\nLGmhk4B72Y8xH5n3vCU5lcFTHAcAl1fV+2a5SyMnyf8ExoDvB7YDaxj8a+1jwNHAg8CZVfXIbPVx\n1CR5PfAXDH5Udy2nciFwJ3Atjvu0S/IKBjcNH9C2P66q9yY5Csd86JK8Afj1qjrdMR+eJC8C/pTB\nb8pBwDVV9T7HfLiSvJLBQznfA3wJ+AUGS4Lu05iPTPImSZI0H4zKZVNJkqR5weRNkiSpR0zeJEmS\nesTkTZIkqUdM3iRJknrE5E2SJKlHTN4k9V6SLyd542z3Q5JmgsmbJE2zJD+b5IEk/5Dk40me1zn2\n1iR/leTbSW6dzX5K6ieTN0maRm0R+w8DPwcsAP4RuKxT5GHgA8B/nPneSRoFJm+SRsXyJPcmeTjJ\n5UkOTvK8JDcm+VqL35hk0a4KSW5L8jtJPp3km0nWt+WBdh3/0TZL9o0kDyY5q8UPT3JVO++Xk1zU\n6cfPAjdU1V9V1XeA/wv4V239SKrq1qq6DvjqjIyKpJFj8iZpVPwsg0Xk/xnwUuA3gQAfYbBm4DHA\nd4Dfn1DvbQwWg/4B4BDgNwCSHAvcxGDN5OcDrwLuanV+HzgMeCGD9X7PSvIL7dgJwOd3nbyqvgQ8\nBuxajFqSpsTkTdKo+L2q+kpb0Pm9wNuq6htV9adV9VhVfZvBpcofn1Dvo1X1d1X1GIPFoV/V4m8D\nNlTVtVX1RDvX3UkOAH4GuKCqvlNVDwL/BXh7q/dc4NEJbXyTQbInSVN20Gx3QJKmydbO/oPADyY5\nlMHM2SnA8xjMxD03SaqqWtmHOvW+wyD5gsFs3d9N0s7zGfx2/u8J7e26HPst4PAJdY4A/mGfvo0k\n7YYzb5JGxdGd/WOBrzC4BHoc8Lqqeh5Pz7plL863BXjJJPGvA4+3NrrtbWv79wKv3HUgyT8Dvge4\nfy/alKQ9MnmTNCrOTbKoPXBwIfDHDGbR/hH4Zouv3YfzXQOclOQtSQ5MclSSV1bVkwwur743yXPb\nvXG/Blzdqfcvk7y+PaTwO8CftMu2JDkgySEMEroDkxySxKsgkvaayZukUVDA/wRuBv5fYDPw7xlc\nMv1eBrNl/4vBAwgT601+wqotwE8xmL3bAXwO+Oft8HkMLrF+CfgL4H9U1UdbvU3Ar7T+PAQ8Bzi3\nc+q3M0go/yvwo+08f7DvX1nSfJWnb/uQJEnSXOfMmyRJUo+YvEmSJPWIyZskSVKPjNQTTkm8gU+S\nJPVGVe3Nq4ueYeRm3qrKbQa3NWvWzHof5tvmmDvm82FzzB3z+bDtr5FL3iRJkkaZyZskSVKPmLxp\nSsbGxma7C/OOYz7zHPOZ55jPPMe8P4b+kt4kBwCfBbZW1elJjmSwbM2xwAPAmVX1aCu7GvhFYCdw\nflXd3OLLgCuAQ4Gbqurf7aatGvb3kSRJmg5JqDn6wML5wKbO5wuAW6rqpcCtwGqAJMcDZwJLgdOA\nS5Ps+kKCHH51AAAgAElEQVSXAauqagmwJMkpM9BvSZKkOWeoyVuSxQzWBvzDTvgM4Mq2fyWwou2f\nDqyrqp1V9QCDtQmXJ1kIHFZVG1u5qzp1JEmS5pVhz7x9AHg3z1z8eUFVbQeoqoeAF7T4ImBLp9y2\nFlsEbO3Et7aYJEnSvDO0l/Qm+Wlge1XdlWTsuxSd1pvU1q5d+9T+2NiYN2BKkqQ5YXx8nPHx8Smf\nZ2gPLCT5D8DPM3j44DnAYcCfAq8Fxqpqe7skeltVLU1yAVBV9f5Wfz2wBnhwV5kWXwm8oareOUmb\nPrAgSZJ6Yc49sFBVF1bVMVX1YmAlcGtVvR24EXhHK3Y2cH3bvwFYmeTgJC8CXgLc2S6tPppkeXuA\n4axOHUmSpHllNtY2fR9wbZJfZDCrdiZAVW1Kci2DJ1MfB87pTKOdyzNfFbJ+xnstSZI0Bwz9PW8z\nycumkiSpL+bcZVNJkiRNP5M3SZKkHjF5kyRJ6hGTN0mSpB4xeZMkSeoRkzdJkqQeMXmTJEnqEZM3\nSZKkHjF5kyRJ6hGTN0mSpB4ZavKWZHGSW5Pcm+SeJOe1+JFJbk7yxSSfSnJEp87qJJuT3Jfk5E58\nWZK7k9yf5JJh9luSJGmuGvbM207gXVV1AvDDwLlJXgZcANxSVS8FbgVWAyQ5nsFC9UuB04BLk+xa\n8+syYFVVLQGWJDllyH2XJEmac4aavFXVQ1V1V9v/FnAfsBg4A7iyFbsSWNH2TwfWVdXOqnoA2Aws\nT7IQOKyqNrZyV3XqSJIkzRszds9bkhcCrwLuABZU1XYYJHjAC1qxRcCWTrVtLbYI2NqJb20xSZKk\neWVGkrckzwWuA85vM3A1ocjEz5IkSZrEQcNuIMlBDBK3q6vq+hbenmRBVW1vl0S/1uLbgKM71Re3\n2O7iz7J27dqn9sfGxhgbG5uGbyFJkjQ14+PjjI+PT/k8qRrupFeSq4CvV9W7OrH3Azuq6v1J3gMc\nWVUXtAcWrgFOZHBZdANwXFVVkjuA84CNwCeBD1XV+glt1bC/jyRJ0nRIQlVlzyUn1BtmspPk9cBf\nAPcwuDRawIXAncC1DGbTHgTOrKpHWp3VwCrgcQaXWW9u8dcAVwCHAjdV1fmTtGfyJkmSemFOJm8z\nzeRNkiT1xf4mb66wIEmS1CMmb5IkST1i8iZJktQjJm+SJEk9YvImSZLUI0N/Se9Me3od++/mEOCx\nGSoz6u3NxT6NentzsU+j3t509unZFiw4loceemCf60man0buVSF7t9JW2HO56Soz6u3NxT6Nentz\nsU+j3t509mnyeqP0Wyxp7/iqEEmSpHnA5E2SJKlHTN4kSZJ6pFfJW5JTk3whyf1tQXtJkqR5pTfJ\nW5IDgN8HTgFOAN6W5GWz2ytJkqSZ1ZvkDVgObK6qB6vqcWAdcMYs90mSJGlG9Sl5WwRs6Xze2mKS\nJEnzRp+SN0mSpHmvTyssbAOO6Xxe3GIT7O0KC3sqN11lRr29udinUW9vLvZp1Nub7j49exWGZ64O\nMxdXmZjp9uZin0a9vbnYp/nQ3r7rU/K2EXhJkmOBrwIrgbdNLORbyiXNZQsXvpDt2x+c7W6oNx6j\nnyuJ2N7el9l3vUnequqJJL8K3Mzgcu/lVXXfLHdLkvaJa5hqX+zdet2ab0ZubdNR+j6SpPltkLw5\n8za67bm2qSRJ0sgzeZMkSeoRkzdJkqQeMXmTJEnqEZM3SZKkHjF5kyRJ6hGTN0mSpB4xeZMkSeoR\nkzdJkqQeMXmTJEnqEZM3SZKkHhla8pbk4iT3JbkryZ8kObxzbHWSze34yZ34siR3J7k/ySWd+MFJ\n1rU6tyc5Zlj9liRprliw4FgGa2R+t+2QGSxje9NbZv8MbWH6JD8J3FpVTyZ5H1BVtTrJ8cA1wOuA\nxcAtwHFVVUk+A/xqVW1MchPwwar6VJJ3Aq+oqnOS/Azw5qpaOUmbLkwvSZJ6IZljC9NX1S1V9WT7\neAeDRA3gdGBdVe2sqgeAzcDyJAuBw6pqYyt3FbCi7Z8BXNn2rwNOGla/JUmS5rKZuuftF4Gb2v4i\nYEvn2LYWWwRs7cS3ttgz6lTVE8AjSY4aZoclSZLmooOmUjnJBmBBNwQUcFFV3djKXAQ8XlV/NJW2\nJja9uwNr1659an9sbIyxsbFpbFaSJGn/jI+PMz4+PuXzDO2eN4Ak7wB+CXhjVT3WYhcwuP/t/e3z\nemAN8CBwW1UtbfGVwBuq6p27ylTVZ5IcCHy1ql4wSXve8yZJknphzt3zluRU4N3A6bsSt+YGYGV7\ngvRFwEuAO6vqIeDRJMuTBDgLuL5T5+y2/1bg1mH1W5IkaS4b5tOmm4GDgYdb6I6qOqcdWw2sAh4H\nzq+qm1v8NcAVwKHATVV1fosfAlwNvLqdb2V72GFim868SZKkXtjfmbehXjadaSZvkiSpL+bcZVNJ\nkiRNP5M3SZKkHjF5kyRJ6hGTN0mSpB4xeZMkSeoRkzdJkqQeMXmTJEnqEZM3SZKkHjF5kyRJ6hGT\nN0mSpB4ZevKW5NeTPJnkqE5sdZLNSe5LcnInvizJ3UnuT3JJJ35wknWtzu1Jjhl2vyVJkuaioSZv\nSRYDbwIe7MSWAmcCS4HTgEuT7FrX6zJgVVUtAZYkOaXFVwE7quo44BLg4mH2W5Ikaa4a9szbB4B3\nT4idAayrqp1V9QCwGVieZCFwWFVtbOWuAlZ06lzZ9q8DThpqryVJkuaooSVvSU4HtlTVPRMOLQK2\ndD5va7FFwNZOfGuLPaNOVT0BPNK9DCtJkjRfHDSVykk2AAu6IaCA3wQuZHDJdBiy5yKSJEmjZ0rJ\nW1VNmpwleTnwQuDz7X62xcDfJFnOYKat+8DB4hbbBhw9SZzOsa8kORA4vKp2TNb22rVrn9ofGxtj\nbGxsX7+WJEnStBsfH2d8fHzK50lVTb03e2ok+TKwrKq+keR44BrgRAaXQzcAx1VVJbkDOA/YCHwS\n+FBVrU9yDvDyqjonyUpgRVWtnKSdmonvI0mSNFVJqKp9vpo4pZm3fVC0S51VtSnJtcAm4HHgnE7G\ndS5wBXAocFNVrW/xy4Grk2wGHgaelbhJkiTNBzMy8zZTnHmTJEl9sb8zb66wIEmS1CMmb5IkST1i\n8iZJktQjJm+SJEk9YvImSZLUIyZvkiRJPWLyJkmS1CMmb5IkST1i8iZJktQjJm+SJEk9MtTkLcm/\nTXJfknuSvK8TX51kczt2cie+LMndSe5PckknfnCSda3O7UmOGWa/JUmS5qqhJW9JxoB/Cbyiql4B\n/OcWXwqcCSwFTgMuTbJrXa/LgFVVtQRYkuSUFl8F7Kiq44BLgIuH1W9JkqS5bJgzb+8E3ldVOwGq\n6ustfgawrqp2VtUDwGZgeZKFwGFVtbGVuwpY0alzZdu/DjhpiP2WJEmas4aZvC0BfjzJHUluS/Ka\nFl8EbOmU29Zii4CtnfjWFntGnap6AngkyVFD7LskSdKcdNBUKifZACzohoACfrOd+8iq+qEkrwM+\nBrx4Ku1NaEeSJGnemVLyVlVv2t2xJL8CfLyV25jkiSTfz2CmrfvAweIW2wYcPUmczrGvJDkQOLyq\ndkzW7tq1a5/aHxsbY2xsbN++lCRJ0hCMj48zPj4+5fOkqqbem8lOnPwysKiq1iRZAmyoqmOTHA9c\nA5zI4HLoBuC4qqokdwDnARuBTwIfqqr1Sc4BXl5V5yRZCayoqpWTtFnD+j6SJEnTKQlVtc9XE6c0\n87YHHwU+kuQe4DHgLICq2pTkWmAT8DhwTifjOhe4AjgUuKmq1rf45cDVSTYDDwPPStwkSZLmg6HN\nvM0GZ94kSVJf7O/MmyssSJIk9YjJmyRJUo+YvEmSJPWIyZskSVKPmLxJkiT1iMmbJElSj5i8SZIk\n9YjJmyRJUo+YvEmSJPWIyZskSVKPmLxJkiT1yNCStySvTHJ7ks8luTPJazvHVifZnOS+JCd34suS\n3J3k/iSXdOIHJ1nX6tye5Jhh9VuSJGkuG+bM28XAmqp6NbAG+E8ASY4HzgSWAqcBlybZtSjrZcCq\nqloCLElySouvAnZU1XHAJe3ckiRJ884wk7cngSPa/vOAbW3/dGBdVe2sqgeAzcDyJAuBw6pqYyt3\nFbCi7Z8BXNn2rwNOGmK/JUmS5qyDhnjuXwM+leS/AAF+pMUXAbd3ym1rsZ3A1k58a4vvqrMFoKqe\nSPJIkqOqascQ+y9JkjTnTCl5S7IBWNANAQVcBPwkcH5VfSLJW4CPAG+aSnsT2pnU2rVrn9ofGxtj\nbGxsmpqUJEnaf+Pj44yPj0/5PKmqqfdmshMnj1TV8yZ+TnIBUFX1/hZfz+CeuAeB26pqaYuvBN5Q\nVe/cVaaqPpPkQOCrVfWCSdqsYX0fSZKk6ZSEqtrthNTuDPOet21J3gCQ5CQG97YB3ACsbE+Qvgh4\nCXBnVT0EPJpkeXuA4Szg+k6ds9v+W4Fbh9hvSZKkOWuY97z9EvChNlP2T8AvA1TVpiTXApuAx4Fz\nOtNl5wJXAIcCN1XV+ha/HLg6yWbgYWDlEPstSZI0Zw3tsuls8LKpJEnqi7l42VSSJEnTzORNkiSp\nR0zeJEmSesTkTZIkqUdM3iRJknrE5E2SJKlHTN4kSZJ6xORNkiSpR0zeJEmSesTkTZIkqUemlLwl\neUuSv03yRJJlE46tTrI5yX1JTu7ElyW5O8n9SS7pxA9Osq7VuT3JMZ1jZ7fyX0xy1lT6LEmS1GdT\nnXm7B3gz8OfdYJKlwJnAUuA04NIku9buugxYVVVLgCVJTmnxVcCOqjoOuAS4uJ3rSOC3gNcBJwJr\nkhwxxX5LkiT10pSSt6r6YlVtBiYuqnoGsK6qdlbVA8BmYHmShcBhVbWxlbsKWNGpc2Xbvw54Y9s/\nBbi5qh6tqkeAm4FTp9JvSZKkvhrWPW+LgC2dz9tabBGwtRPf2mLPqFNVTwCPJjnqu5xLkiRp3jlo\nTwWSbAAWdENAARdV1Y3D6hjPns2TJEma9/aYvFXVm/bjvNuAozufF7fY7uLdOl9JciBweFXtSLIN\nGJtQ57bdNbx27dqn9sfGxhgbG9tdUUmSpBkzPj7O+Pj4lM+Tqpr6SZLbgN+oqr9un48HrmHwgMEi\nYANwXFVVkjuA84CNwCeBD1XV+iTnAC+vqnOSrARWVNXK9sDCZ4FlDC7zfhZ4Tbv/bWI/ajq+jyRJ\n0rAloar2+UrjHmfe9tDoCuD3gOcDf5bkrqo6rao2JbkW2AQ8DpzTyarOBa4ADgVuqqr1LX45cHWS\nzcDDwEqAqvpGkt9lkLQV8NuTJW6SJEnzwbTMvM0VzrxJkqS+2N+ZN1dYkCRJ6hGTN0mSpB4xeZMk\nSeoRkzdJkqQeMXmTJEnqEZM3SZKkHjF5kyRJ6hGTN0mSpB4xeZMkSeoRkzdJkqQemVLyluQtSf42\nyRNJlnXiP5nks0k+n2Rjkp/oHFuW5O4k9ye5pBM/OMm6JJuT3J7kmM6xs1v5LyY5ayp9liRJ6rOp\nzrzdA7wZ+PMJ8b8H/kVVvRJ4B3B159hlwKqqWgIsSXJKi68CdlTVccAlwMUASY4Efgt4HXAisCbJ\nEVPst6bJ+Pj4bHdh3nHMZ55jPvMc85nnmPfHlJK3qvpiVW0GMiH++ap6qO3fCxya5HuSLAQOq6qN\nrehVwIq2fwZwZdu/Dnhj2z8FuLmqHq2qR4CbgVOn0m9NH/9jn3mO+cxzzGeeYz7zHPP+GPo9b0ne\nAvxNVT0OLAK2dg5vbTHan1sAquoJ4NEkR3XjzbZOHUmSpHnloD0VSLIBWNANAQVcVFU37qHuCcB/\nBN60H33LnotIkiTNM1U15Q24DVg2IbYY+CLwQ53YQuC+zueVwGVtfz1wYts/EPhap8yHO3U+DPzM\nbvpRbm5ubm5ubm592fYn79rjzNs+eGqmrD1Q8GfAe6rqjl3xqnooyaNJlgMbgbOAD7XDNwBnA58B\n3grc2uKfAt7bznkAg1m8CybrQFU5WydJkkZa2ozV/lVOVgC/BzwfeAS4q6pOS3IRgwRr18MMBZxc\nVV9P8hrgCuBQ4KaqOr+d6xAGT6W+GngYWFlVD7Rj7wAuauf591V11X53WpIkqcemlLxJkiRpZo3M\nCgtJTk3yhfYy3/fMdn9GUZLLk2xPcncndmSSm9sLlD/lO/imV5LFSW5Ncm+Se5Kc1+KO+5AkOSTJ\nZ5J8ro35mhZ3zIcsyQFJ/ibJDe2zYz5ESR5oL9P/XJI7W8wxH6IkRyT5WJL72u/6ifsz5iORvCU5\nAPh9Bu+EOwF4W5KXzW6vRtJHGYxx1wXALVX1Ugb3Ka6e8V6Ntp3Au6rqBOCHgXPb323HfUiq6jHg\nJ6rq1cCrgNPafbqO+fCdD2zqfHbMh+tJYKyqXl1Vy1vMMR+uDzK4ZWwp8ErgC+zHmI9E8gYsBzZX\n1YPtfXLrGLz0V9Ooqj4NfGNCuPty5St5+qXLmgZV9VBV3dX2vwXcx+BJbsd9iKrqO233EAavVCoc\n86FKshj4KeAPO2HHfLjCs/MAx3xIkhwO/FhVfRSgqnZW1aPsx5iPSvI28UW+3Zf/arheUFXbYZBo\nAC+Y5f6MrCQvZDATdAewwHEfnnb57nPAQ8CGtiqMYz5cHwDezSBR3sUxH64CNrQ1yP91iznmw/Mi\n4OtJPtpuD/iDJN/Lfoz5qCRvmjt8AmYIkjyXwbJx57cZuInj7LhPo6p6sl02XQwsby8cd8yHJMlP\nA9vbLPN3e+WTYz69Xl9VyxjMeJ6b5Mfw7/kwHQQsA/5rG/dvM7hkus9jPirJ2zbgmM7nxS2m4due\nZAFAW7v2a7Pcn5GT5CAGidvVVXV9CzvuM6CqvgmMM1hP2TEfntcDpyf5EvBHwBuTXA085JgPT1V9\ntf3598AnGNyC5N/z4dkKbKmqz7bPf8IgmdvnMR+V5G0j8JIkxyY5mMGqDDfMcp9GVXjmv4xvAN7R\n9s8Grp9YQVP2EWBTVX2wE3PchyTJ83c97ZXkOQxeDH4fjvnQVNWFVXVMVb2Ywe/3rVX1duBGHPOh\nSPK9bUafJN8HnAzcg3/Ph6ZdGt2SZEkLnQTcy36M+ci85y3JqQye4jgAuLyq3jfLXRo5Sf4nMAZ8\nP7AdWMPgX2sfA44GHgTOrKpHZquPoybJ64G/YPCjums5lQuBO4FrcdynXZJXMLhp+IC2/XFVvTfJ\nUTjmQ5fkDcCvV9XpjvnwJHkR8KcMflMOAq6pqvc55sOV5JUMHsr5HuBLwC8wWBJ0n8Z8ZJI3SZKk\n+WBULptKkiTNCyZvkiRJPWLyJkmS1CMmb5IkST1i8iZJktQjJm+SJEk9YvImqfeSfDnJG2e7H5I0\nE0zeJGmaJfnZJA8k+YckH0/yvM6x/5Tk/iSPJtmU5O2z2VdJ/WPyJknTqC1i/2Hg54AFwD8Cl3WK\nfAv46ao6gsGSOB9M8kMz3U9J/WXyJmlULE9yb5KHk1ye5OAkz0tyY5KvtfiNSRbtqpDktiS/k+TT\nSb6ZZH1bHmjX8R9N8ldJvpHkweT/b+/egzWrzjqPf39AgGS4i6HLbi5JoCOgQjrSqBnNMQgEHQGt\nBJuMQmKPlgEHxksqXKJ0YhgDjiW5GDJTEm5GWyRGQCnSnYJjRoXQGSEgjaFzoe1u0sRAgBCU4vLM\nH+86sDnpTl/OeU+f/Z7vp+ot9vvsvfZa76pO5+m19torZ7T4Xkmuaff9apILO+14G3BjVf1DVT0F\n/A7w823/SKrqvVW1ph3fCfxf4EeH3z2SRoXJm6RR8TYGm8i/Bngt8B4gwMcZ7Bl4EPAU8JFJ5U5n\nsBn09wK7Ab8NkORg4GYGeybvDxwN3N3KfATYEziEwX6/ZyR5Rzt3JPCFiZtX1VeAp4GJzahf0Da+\nP4bB5tSStFVM3iSNig9X1UNtQ+eLgdOr6ptV9amqerqqvg38PvATk8pdWVVfrqqnGWwOfXSLnw6s\nrKrrquq5dq97kuwE/AJwXlU9VVVrgT8EJp5d2wN4fFIdTzBI9ib7GHBXVa2Y4m+XNIfssqMbIEnT\nZH3neC3wfUl2ZzBydiKwD4ORuD2SpKqqXbuxU+4pBskXDEbrvryJevZn8Hfnv06qb2I69klgr0ll\n9ga+1Q0k+QPgCOAnt/jLJKnDkTdJo+LAzvHBwEMMpkAPA46pqn14cdQtW3G/dcChm4h/A3im1dGt\nb0M7vg84auJEktcALwMe6MTeyyChPL6qntyKtkjSC0zeJI2Ks5PMbwsOLgD+gsEo2r8DT7T4sm24\n3yeA45K8JcnOSfZLclRVPc9gevXiJHu0Z+N+A7i2U+5nk7yhLVJ4H/DJNm1LkvMZTMn+VJvilaRt\nYvImaRQU8GfACuBLwBrg/QymTF/BYLTsHxksQJhcbtM3rFoH/DSD0btHgbuAH2qnz2EwxfoV4LPA\nn1bVla3cauDXWns2Ai8Hzu7c+mIGo4Rfau+BeyLJedv1qyXNSXnxsQ9JkiTNdo68SZIk9YjJmyRJ\nUo+YvEmSJPXISL3nLYkP8EmSpN6oqq1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lzmzHbwVuHWK7JUmSZq1hPvP2K8CH2kjZfwC/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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f8121dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = 60\n",
    "ylim = [-12000,5000]\n",
    "number_of_subplots = 3\n",
    "figure(figsize=(10,10))\n",
    "for v in range(len(cuentas)):\n",
    "    v = v+1\n",
    "    ax1 = subplot(number_of_subplots,1,v)\n",
    "    ax1.bar(range(p),sched[v-1][0:p],width=1.5)\n",
    "    ax1.set_xlabel(str(cuentas[v-1]),fontsize=12)\n",
    "    ax1.set_xlim([0, p])\n",
    "    ax1.set_ylim(ylim)\n",
    "\n",
    "ax1 = subplot(number_of_subplots,1,1)\n",
    "ax1.set_title('Scheduling',fontsize=15,fontweight='bold')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Con estos gráficos el usuario ya sabe cuándo está previsto que una cuenta se quede en negativo. Antes de que llegue la fecha habrá que hacer algún movimiento de compensación entre cuentas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Forecasting**\n",
    "\n",
    "Para el forecasting asumimos que disponemos de un histórico de movimientos \"inciertos\". Es decir, todos los movimientos ciertos con fecha de vencimiento se han eliminado del histórico."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1) Primero conviene hacer un pequeño estudio estadístico de las carácteristicas del flujo de caja para conocer la volatilidad, estacionalidad y eliminar outliers.\n",
    "\n",
    "2) Luego, aplicamos alguna técnica de forecasting. De los 3 modelos probados el que mejor funciona en el ejemplo es el de estacionalidad Día-Semana y Día-Mes\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Empresa</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Debe</th>\n",
       "      <th>Haber</th>\n",
       "      <th>Subcuenta</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>01/01/13 00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>2386.56</td>\n",
       "      <td>572010003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>31/07/13 00:00</td>\n",
       "      <td>82991.04</td>\n",
       "      <td>0.00</td>\n",
       "      <td>572010003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>31/07/13 00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>83961.93</td>\n",
       "      <td>572010003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>01/08/13 00:00</td>\n",
       "      <td>2863.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>572010003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>01/08/13 00:00</td>\n",
       "      <td>5500.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>572010003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Empresa           Fecha      Debe     Haber  Subcuenta\n",
       "0        1  01/01/13 00:00      0.00   2386.56  572010003\n",
       "1        1  31/07/13 00:00  82991.04      0.00  572010003\n",
       "2        1  31/07/13 00:00      0.00  83961.93  572010003\n",
       "3        1  01/08/13 00:00   2863.00      0.00  572010003\n",
       "4        1  01/08/13 00:00   5500.00      0.00  572010003"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data2 = pd.read_csv(\"movimientos.csv\",delimiter=\",\")\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Empresa</th>\n",
       "      <th>Fecha</th>\n",
       "      <th>Debe</th>\n",
       "      <th>Haber</th>\n",
       "      <th>Subcuenta</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>48</td>\n",
       "      <td>01/01/14 00:00</td>\n",
       "      <td>261.07</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-12-31</th>\n",
       "      <td>48</td>\n",
       "      <td>31/12/14 00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>261.07</td>\n",
       "      <td>57200001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>48</td>\n",
       "      <td>01/01/14 00:00</td>\n",
       "      <td>41477.77</td>\n",
       "      <td>0.00</td>\n",
       "      <td>57200003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-02-01</th>\n",
       "      <td>48</td>\n",
       "      <td>02/01/14 00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>37.60</td>\n",
       "      <td>57200003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-03-01</th>\n",
       "      <td>48</td>\n",
       "      <td>03/01/14 00:00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>600.00</td>\n",
       "      <td>57200003</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Empresa           Fecha      Debe   Haber  Subcuenta\n",
       "Fecha                                                           \n",
       "2014-01-01       48  01/01/14 00:00    261.07    0.00   57200001\n",
       "2014-12-31       48  31/12/14 00:00      0.00  261.07   57200001\n",
       "2014-01-01       48  01/01/14 00:00  41477.77    0.00   57200003\n",
       "2014-02-01       48  02/01/14 00:00      0.00   37.60   57200003\n",
       "2014-03-01       48  03/01/14 00:00      0.00  600.00   57200003"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3 = pd.DataFrame(data2[data2.Empresa == 48]) #Seleccionamos una empresa como ejemplo\n",
    "data3.index = pd.to_datetime(data3.Fecha)\n",
    "data3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f4f91a1780>"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+vPxya9+P8NZc1LwVXxtFiM7SW3j797+lb3+7dcsya1br3qvo2i28dWIN77x50qGHZr0U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\n8MJQRvob3mbODKc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s+5AhpddXWCG8/s47PUNmtff74Ad7hrchQ8JndNxxYT5xeKtk551L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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f93f94e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data3['NetCF'] = data3.Debe - data3.Haber\n",
    "data3.NetCF.plot(figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count      2580.000000\n",
       "mean        125.723837\n",
       "std       11089.322145\n",
       "min     -202806.080000\n",
       "25%        -942.900000\n",
       "50%        -202.540000\n",
       "75%         -37.745000\n",
       "max      202806.080000\n",
       "Name: NetCF, dtype: float64"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.NetCF.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Fecha\n",
       "2014-01-01     41477.77\n",
       "2014-03-27     70180.00\n",
       "2014-05-20     71995.00\n",
       "2014-05-09     36000.00\n",
       "2014-02-10    -73032.88\n",
       "2014-10-27    121060.50\n",
       "2014-10-30    -54326.25\n",
       "2014-03-11     84775.62\n",
       "2014-05-11    -35101.69\n",
       "2014-11-19    159720.00\n",
       "2014-11-24    -55110.38\n",
       "2014-11-30    127382.75\n",
       "2014-12-24     37933.50\n",
       "2014-12-31   -202806.08\n",
       "2015-01-01    202806.08\n",
       "2015-01-20    -68186.11\n",
       "2015-01-30    -91520.17\n",
       "2015-09-02     35993.70\n",
       "2015-01-04     36300.00\n",
       "2015-01-04     54450.00\n",
       "2015-01-04    127050.00\n",
       "2015-04-30     71087.50\n",
       "2015-01-06     43983.50\n",
       "2015-09-15     33517.00\n",
       "2015-09-24     73689.00\n",
       "2015-10-14     37631.00\n",
       "2015-10-20    -55403.75\n",
       "2015-10-11     50000.00\n",
       "2015-11-16     92565.00\n",
       "2015-12-31    -68439.31\n",
       "2016-01-01     68439.31\n",
       "2016-05-04    126750.00\n",
       "2016-04-25     58382.50\n",
       "2016-11-05    -74500.00\n",
       "2016-05-25     47674.00\n",
       "Name: NetCF, dtype: float64"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data3.NetCF[abs(data3.NetCF) > 3*data3.NetCF.std()] #Todos estos pueden considerarse valores anómalos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f4f3ec2c18>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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wjjmp8f1oyKQDQgaRZ+dVpI6SMWjVIMjalaZAC7M/KR4sl/qRigVNKfVTAP0ANlBKvQDg\nHADfAvALpdRHATwPmbkJrfUTSqkbADwBYA2AT2v9TtU7FcBVAPoA3K61vrOx/UoA1yil5gN4DcCx\naaSbVI+6WtAYg1YNshBoAwN0cVYBLlRbP1IRaFrr4wN+2j9g/1kAZvls/zOAHX22r0JD4BHSiroO\nPoxBqwZZxIvRxVkt4pYfy7188E0CpFLUvROiQCs39oxL87/9GfecLLPy41cXaBWrNhRopBCkNYDU\n3cVZtWvVjTxi0Fh+5SRpuVHMlQ8KNFIp6jr40MVZDbISaPY6aCy/csPyqw8UaKQQpPV0VwcL2ptv\nDr5PThKoBlkvs0HKCx+Q6gcFGikEdHGGZ731gO98x/83WtCy5aKLgCefzO78Wcy4jOrirHP5FhmW\nS/2gQCOVoi6d2N//3vw/Y9Dy4YwzgNmzszt/1pMEaE0rL3xAqh8UaKRS1FWoMAatGmQ9SYCUH5Zl\nfaBAI5WiDi5OPyjQqgFncZIguMxG/aBAI5WiLoNP0H1ykkD2ZDkoZr1QbVrnJPnDcqsfFGikUtTd\ngpbHNThQZEcey2yQcsL2Vz8o0EilqOtAlGfnXbe8zRN3koC9Pck56eKsDnHLj+7Q8kGBRirFunXy\nWXVLmttJMwatGhRhkgDLt5gkLReWa/mgQCOVwgi0unZGjEErN1nEi9lrq9GCVl74gFQ/KNBIpai7\nizOPa9Qtb/MkaKFaujiJIe4sTro4ywcFGqkUeVrQijTQ0cVZDYrg4iTFhO2vflCgkUpRFxdnJ2LQ\ngq5N0oProJEgWG71gwKNVIq6ujgNtKBlT9broGX5qqek5yKdg+2vflCgkUKQVqdTl1mcLrScVYMs\nLFxcB61aUGzXBwo0UinqMhBxmY1qUgQXJ8u3mLBc6gcFGikEabmN6hKD5kKBVg3ymCTA8isnfnWB\nMzOrDQVaSfjTn4DlyzudiuxI28VZ9UEoqGOmq7Pc5GFBI+WGZVgfKNBKwm67Aeed1+lUFJ+6uzjz\nuGbV87aTBE0SSHpOzuIsP2x/9YMCrUSsXt3pFBSfuljQXPJ0cZLsoIuTBJG03KrqDn32WWDRok6n\nIhso0EoEO9b25DmLs0jlwRi0/MhyoMvjTQKknLAM/fn+94Gf/azTqcgGCrQSwYbZnrq4OIOgQCs3\nWVnQ0nabks7B8mtmYKC6yypRoJFKURcXZydi0DpxrbqR1TpoXGaj/LBc/KFAI4WADbQ9dRFoLnRx\nVoMs3iRgu01ZduWFy2z4U+WJLxRopFLU1cXJSQLVoAiTBFjOxYbl0wwtaKQQsGG2py4WtKD7owWt\n3HAdNBIEy9AfWtAIyZi0F6qt6hNVEHVcB23FCuDVVzudinTJYkkMroNWDVhu/tCCRkhJoIszv2t1\nmuuuA2bOzP+6Wcb9FMHFSYpJ0R6QigIFGikEVW6YZXwXZyfLI2gWZ51i0FavBtas6XQq0sUtR7o4\niUvcMqzqhIIqW4Up0EghSNvFWdUG246f/zy7p8miDfLuoq5VIMgCnOc6aFXL06rgVy5RRFdVy5UW\nNFIIqtrA0qTuLs6bbwbmzcv2GkXJW1d4VIE8LGhFKT8SjaK1v6JQ5TpNgUYqRV0saGlaWKJesyh5\nW8Un5zSFWavzkvKRtAyr6uKsYj9goEArEVXuXNPqPExDrWqDDSLPulGUelhFF2faFjS/46uWZ3WD\n5ddMFfsBAwUaKQSMQUuPrJ6Ui5anVXZxuq56CjTCcvOniv2AgQKtRLCBtqeuAq2uLs5OpCXrZTZa\nfSY5X1HKjcSjaO2vKNDFSUiKaA1ce202567LJAHGoFWzY047Bi2OBa0o5Uv8SdOaWgWq/PBBgUZy\nZ/ly4BOfyObcdVkHzYUCrRoE5XHSQZkvTE+HV18Ffv/7zlzbr/ziWHOrVv5V7AcMFGgloioNK8uY\ngbq6OPOkKHlbxSfnPCYJkPg88ABw4YWduXYW1tQqwEkChKRIlk88dZnF2crCUpdJAlV8cnbrb9JB\n1T5PVQfoPOnkQ0FaYr1qcJIAKQRVaWBZPvHU1YJGF2c1iGo5W7cO+OMfw52vKOVWNu68E7jiCvle\nhDqXliWtKhShTLKCAo3kTh4WtKp1Qu2okkD7zW+A444Ll56qlXNUF+dTTwEf+1j487U6F/HnM5/x\nYmY7KQbo4vSniv2AgQKtRFSlElKgJafV/WV173l18FdfDVx/ffv9OjVYFmmZjYEBz2rc7nxVHaCz\nxs6vIgg0xiM2QwsaISniJ6LS6jTqItBc6mghqWJwcByB1mpwiuPirFqepkkR4p3yiDctE7SgkUJQ\nlUqYZSA/BVr5LWhhKcJgmTb2shj2/0FEEWjuNhKdTj4U0ILmDy1oCVBKPaeUelQp9Rel1EONbWOV\nUncppeYppX6rlBpt7X+WUmq+UupJpdQB1vZdlFKPKaWeVkrNzjrdJDv8BBrfxZkM1w2T5TWy7uDD\nnr+KHXNQHie1oHEdtPgUzcWZ9PiqlX8V+wFDHha0AQD9Wut3aa2nN7Z9CcDvtNbbALgHwFkAoJTa\nDsAxAKYB+ACAHyj1ztB9KYBTtNZbA9haKXVgDmknGeAnosro4uxkR1flGLQoAq1qg02WLk6SnCII\nNM7ibIYuzmQon+scCuAnje8/AXBY4/shAK7XWq/VWj8HYD6A6UqpCQBGaa0fbux3tXVMbahKJaSL\nM33ysKD5XauTVPHJOY8YtKKUXxkpgls9qYuzalSxHzDkIdA0gLuVUg8rpcyE8I201i8DgNZ6MYDx\nje2TALxoHbuwsW0SgAXW9gWNbaSEUKClT54xaEWhik/OQQvUUqAVgyJY0JIeX7Xyr2I/YOjO4Rp7\naq1fUkptCOAupdQ8iGizSS17Z86c+c73/v5+9Pf3p3XqjlOVSugGQqdJXQRaqxilsseghaXKy2xk\nOUmAxKcIAi3pLM6q1YWyWdDmzJmDOXPmhNo3c4GmtX6p8fmKUuoWANMBvKyU2khr/XLDffnPxu4L\nAWxiHT65sS1o+yBsgUaKSZoWtJdfBsaP9zqqOAJt3jxg2DBg002Tp6cIlF2gcZJAZy1oVRvA04Sz\nOItH2foB13B07rnnBu6bqYtTKTVcKTWy8X0EgAMAPA7gNgAnN3Y7CcCtje+3AThWKdWrlNoCwBQA\nDzXcoEuVUtMbkwZmWMfUhqo0rDQF2oQJwDXXJDv3ttsC73tf8rR0krous1GUtKRFEQQaacZtW50W\nAxRozVSxHzBkbUHbCMDNSinduNZ1Wuu7lFJ/AnCDUuqjAJ6HzNyE1voJpdQNAJ4AsAbAp7V+J+tP\nBXAVgD4At2ut78w47SQj0o5B++c/ve9xXZyrV6eTlrzohIsz6NqdomxPzmHIapmNopRZ2SmCi9Nv\nm9at3Z2LFgH/+7/ZpKvTVLEfMGQq0LTW/wCws8/2JQD2DzhmFoBZPtv/DGDHtNNYJqrSyRZxksCQ\nki/ZnLUFTSng9tvTP28Sqtgxh7WcGdata/2qJ7utUazFw3346bRA82vr7QTaRz8KPPvs4OOrQJUt\naCUflkgZSVug2Y3TDFZRG2yVgm3THkDMfcbN26yo8jpoQbM5XdJ4k8DmmwNr10ZOai0pwkNBkEBr\nhS3iq9ZmilAmWUGBlhOzZyd3o1WlYdGClpw8XZx5C7Sw5y9CPFDa5BGD5vLCC62tcEl58snszp03\nWdS5m24KV+fjtLsFC4Cnn05+niJDgUYS881vAq+80ulUFIMiCrSyv3Q4SxenydOiCbROWdDyWGYj\nr0kCZnuWAm277YA338zu/FnjPvykXedOPhl4443w6YhiQbv+euDSS6snymzo4iSJqbLKj0qrVz0l\nbWhxxV/ZLGgueVrQikIV21RY16a9f5JJAqZMs3jtmk1VyimLOjcwEK5txRFoJkYxj1nenaKK/YCh\n5MNSeWgXzBuGqjSstAWanzipuouzFWW3oIWFLs7kFjS3bMNcMwpFqStpkYUYWLcu2jmjCjTX6le1\nMqEFjSQmDYFWFVoJtLTOXXUXZ54xaOZ8JpC8KJ1h0SYJhHFTtSNowM1KoLWyoKWRt1V4s4crbjpt\nQfPb1qp+uOcuSlkceCDw3HPJz0MLGklMGpWoKA0rKa3ckLSgxaNKMWhhKVrHvNNOwOuvJztHVrM4\ng/bJMtwA8OpMVR5OO2lBi+viLNqDjOHFF5O3F6B4/UCalHxYKg+0oHn4DRppxqApVX0LmksdZ3EW\nrWN+801g5cpk5wjr2jSYwTdov7AWtKxcnH4WurJh9w1ZiJ2wFjRDK0uaS5Fj0KK6doOgi5MkhjFo\nHmm7OF1x0tVVfQtaq/vLysVZtAeMvDvmdtdKs41HiUFr9Xsrq4t9PC1owbj5lWb7MnU4igXNb1tZ\nBVoa9aJoD2ppUrJhqbyk9bRQBbKexdndnc8szqJ0dABdnHkQdjBM8xphBVrQdf0saDa0oEUj7Ri0\nKA8/cVycpo1UWaDRgkYSE9WM7UdVKmHaMWjuueNY0Mrm4nTTm4eLM69JAlFcnJ2woLWL90nzGmEF\nWtB127k4s5ywA1TDgmaT9kNBOwGrNfD44953+zNom3v+ouZ9WkYLWtBIIorqIuoUWc/irKOLs44W\ntLStGWGuZ3+6pGlBizJJwP4MOl9Q/aAFrT1ZujjbjQ1z58rkk6D0xJkkUJT2Sxdne0o2LJWTKnRS\naZK1i7MOAq0VWcegFaWDr5OLM4g4FrR2xzMGLZi8LWhvveV9ZwyaP3RxkkSkZUELWwkXLwYuuSTZ\ntbIk7UEhjUkCZXNxumTp4szbAlxUF2c717zWyfMoKOg/rgXNPp9fG8t6HbQqPJy64ibNOteubbUr\nlzAW1qIKtDTCfsx5yly/WkGBlgN5P0XOmwf89Kf5XCsO7QaVpOfu6op+7rIJtDxdnOZ8tKA1f9ok\nbePLlgG//312MWhB4QRZuTiPOgqYNYsWtHaEiUFzv7eypPmdv6jihctstIcCLQfSeooMWwnXrvUC\nuotI1ha07u76uTjzsKAV7U0CRYpBS9rGL7wQ2H//fCYJtDs+DYH2y18C115bDQuaTd4xaO2ELV2c\ntKCRhOT9FFnkmTsAY9Cypi6TBDrl4szCgubW/1bX8ktTEScJdHV5or7I/VE7spwk0E7AVtnFmaZA\nK8o9pU2FhqXikncMWpktaHHoVAxakTqFLC1orVyc69YBTzyR7vXCUiUXZ9A1srCg+dWVLGZUDxlS\nPQta2lbbrGPQij6LMy0XZ1XqlwsFWg7k3UkVXaD5xcW4v8WlLha0PGPQWg0ijzwCnHRSutcLS96x\nJ1kKNPOAkLeLM0sLmi3QymxBs0nbWtNubPDLt6gCrQ4WNAo0Epu0n67DXK/IHaKf+4YuzmTkEYPm\nZ0FbvRpYsybd64UtuyJa0OKmJ6lAC/MmAXebfXwW4QZVtKDlHYPWzrIZ1oJWRLjMRntKPiyVg7yX\nKSi6BS1Lt0pdZnG2Ik+BlsXDQFEFWitrVVYWtCRpcs8XVljSghZM0WLQ3N/8jqcFrbzUVqCdcw5w\n1VX5XCvvp8iyWNCymMW5bl09ZnHm6eI05/ObxdnJh4G8g4PzjEFzrcxJXZx2XvlZ0LKaJFA1gZZ3\nDFq7cimrizPNpZaqPEmgu9MJ6BSvvAKst14+18rbxVlGC5ohDRdnlQVaUIecp4vTppN1Le/g4Dxi\n0IKEWRoxaO42ID8LWpktHG7b6pSL04+yThJIU7hzkkAFWbs2/diZIPJ+ily7Nr1rZVHxqzKLsxOE\nEWhZTxLI2sUZliots8FJAuny6KPArrumf96iuTjDWtCKRpr1gi7OCrJmTX5P/mmZc6NMEkjr3jbf\nXALB0yQtF2eQy6bKkwTC5FOey2xkYUEragxakScJxLWgVXWZjXvvlRnGYfjDH4C33w63b9oPBVm7\nOE0bKaoFLa1lNopwT1lQkmEpfapuQUtr0Fy4EFi1Kp1zGeIKtMWLgRdfHHyMfZ66WNDcji1PF2dR\nYtCq6OIMK8wMcSxofi7OJBY0pYD77x+8vZMWtCjt/33vAy6/PNy5TD6mJQiiWNCC0uNuc89fxBg0\nWtDCUWuBltfAUtZlNkz8QtpCNm4M2u67A1OmtD5P3FmccSxonejoOuni9JskUCcXZ54CLe1JAkGi\nIi1r9t/VDRTwAAAgAElEQVT+NnibPUkg7wE0ar0IOxa0cnPHIekyG+1+q4tAK8I9ZUGtBVpeFrSy\nLrNh8idPF2crXn21OS1+jZyTBMo3SeCuu4C7746XrjD3+u1vy4u7k5LlMhuGMOUbNk1B58tikoDf\nvq0saI88AqxcGf78UYna/lvVo3Yu4SREWag2bgxaEQUMJwmEoyTDUvrkGYOW1lNk2EaW1iQBI9Dy\nsKCFGRTc31pZ0Kru4ozzNJ30mkGTBJK2ozlzJA4oKmEF2plnAl/+cvTzu5QxBs22+ATFbALJJwn4\nXb9VDNquuwKzZ4c/f9ZEiXu0P5MSx4IWNQbNPXcRxFray2xUVaDVdpmNMsWgRe0w05okkJVA84uj\nSjIouBa0OkwSqFIM2po18dpG3sHBWbo4g64RNgat3ZsEgqwonbKgAeED8+MQtV6E3T+o/cWlnbBv\nl66wFjS7fyuCQEvbglaEe8qCWgu0ssSgRe0wjQVN6/iWoWuu8TrQPGPQWpGlBa1sAq0TMWhBAi1p\nJ7tmTbwBJOyTc9rxQlkINHNu09bSsqC1srr84hfAgw/K96QWtHYCLWq8aVKyEmh5x6D5lcvLLw/e\n1i4Gratr8DGdhJMEwlFrgZZ3DFrcShTHgmauZzfMKMyY4X2ni7M4dDIGLWiSQNIHndWr4wu0qljQ\nTB6aGMssJwmYz2OOGXyeVtdqhd8xnXyTQJoxaH775WVB8yuXSy4BvvQlYNKk8AKtCKLMJs3JI0WM\nsUuL2gq0TsSgJbWgha3M5r7Wro0v0GyKItCCzuPn4oza8Msm0FrdX9oCzY1Bs0nDWrtmTbxj8w4O\nzjIGzbTZPCxofuQdg5Y1WVvQOhmDBngTLMLUj1bhEJ2CkwTCUVuBVnUXZ5LrueQp0FoR1oJW91mc\nebs4zT5xHwbi1q+8XRt5WtDMNVzroksSC5rfedxjwhLHglYWF6df2+rELE4b94EmigWtagKtyi7O\nkgxL6VNGF2fY402lT0uAFmUdtKDzdGqSQFYd3WWXAT/9aetrdsLFGTSLE0hW11avbq5jYQVC3q6N\nVvU26YBjjnMF2qpVQF9fNi5Ov/O4x4Sl7DFonXJxxrWguW2xrAItjXy063bVoAUtB2hBayatGDS/\nRl6FGLSnngLGjvX/LUisZ9kBuy5OPwva2rXA0KHxzu/WL7t+tLLKVdGC5ro40xRorUhzkoD53m4W\nZ5EG1aguzrTSHicGDRgs5FsJtCLGaNGCFo7aCrQ1a8q3zEaVLWhJXJxVW2Zj5crgRTzDDJ5ZTxIw\n2BMEknS0a9b4WwDblWERY9DSniSwcmU6As0epP3Odf/9EhpwzDHJBZot5MtiQQv7YJi3Bc1sd5ck\nCSvQBgaKbUFLS6AV4Z6yoEDDUr60sqA9/3y6HX9Scy4taOHOYz+5l1mgvf12dIGWh4vTzefubuCX\nv5TvSV2c9hsiwg6CUTrmNCykrYRj0jZujg+yoLVLUxQXpx/33w/85jfNx4TBrz6aezDuNfM96Ngs\niFoOYdNi9ouzsLIfYWPQhg9vTqN5P3JYC5pNEcQMJwmEo0DDUr60ikHbfHNZBywt3Kek5ctl9fSw\nRLWg2W6nNChKDFo7C9rAgAgtpco9izOMBS1PF2erGLTHH5fPJHVtzZr4Aq2qFrQsXZx+51q+HFi6\nNPiYIPysq34CregDaFQX55FHpnPdsBY0IL6Ls8gWtLRi0Ipev+JSa4HWalBZtiy9a7md9wMPRHv1\nTFSzet1cnPbnkCHlt6DFcXFmaUEz5/ZbB80MFEmehN1JAmHrexyra1S22AJ46aX218tKoIV1cQZd\n18+CFiTQTJ8XJV/ddAPFsKBFtYi1qmt2fqTdtqLM4vRrd36/uccXWaAltaDl0Qd0EsagBZCmRcVt\nhMuXAytWhD++ai5Ov04xiYvTz4JWdoEW9BqcMPmUpwXNkNSCZteFsE/XtsuxXXuN256few7429+A\njTeujgXND2NBUwoYNSrcMXa67T7CfB8YaF6GJWqakuD2CUEExVf67ZOFQGsnsP0sk0C0GLQixmil\nJdDSjgksGgUalvKlnQUtTYHmNsIVK+IJtDpY0OK4OO38jSvQyubi7EQMWqv61CkXZ5j9kmLifVpd\nL+0YNHOeVauAYcPSEWit2tiKFZ6L8803g/dz8RNopu4GWdCyesevX7ra1Uu/9LvYZRulX5k7t/3+\nUSxoph4CgwVaq/O7IqgIYi0tF2fas2qLRq0FWqtGmaZFxe2k4gq0qljQ0hpY07SglU2g5RmD5l4z\nbQta0CSBdvcRpXNOUr5hVm3P0oLWavmSdm3JzqN2/Ygb1hEmX/3E1vLl8tlOoLluujQJ2wf6uWhd\n7H4mSp/1rncB99zTep8oMWh2n5BkmY0iiJm0XZy0oFWMdha0LASaqURZW9DKOEkgDQvawIAssZGX\nizNOR3fNNcBnP9t6n6Ja0NatC87bpMtsJIlBy8uClodAc2dxprXMRpi6aixo7rGt8BM4fgLNTl+e\nAq1dH+haLv2wlz6JWtfa3WMUF2crC1rZYtDa3XfU81CgVYwiWNCiBrKG3T/NKcxAcQWae59JZnHm\nFYN26aXAxRe33ifOMhs2Wbo4gwRaHi7OtWsl/9z9sh5w8rSguddKy8VpW1GCzhVHMPm5CJcv9xap\n7ZQFzZ6o0Ip2Ls61ayWdw4ZFE2hRw1GSujhb1Q9XoBUBThIIR20FWtDL0rMoaPdpYfly2WY3uFaU\n2YJ2442DXSdxZ3G6pDmLMy8XpwnAbkXRXJxhLGh5rIP27LPApz/dfj8/0nBxthKEfgPO6tXRHqqG\nDgWWLGm+Rl6vemp1bCuCBNro0Z44cNNXRBdnUB/35pvSZru6orkKTRxfO09J3V2caYW50IJWAJRS\nBymlnlJKPa2UOjPJuYJcnEY0pWk18nNxAsBbb4U7Pm4MWhEE2tFHAz/7WfO2uBY0lzLO4kxLoCVx\ncf7gB8Dll/v/tmYN8Je/+J977drgfErq4gwj0IzQdwenIrk47bQMHQr8+MfhrrF2rcwUdSckrFwZ\nPgbtttuAe+9t/r1VmtvV+bgC7c03RaCtW+f1cVEnCbz5JrB4cfvrA8CCBfJ6NL90JZ0ksGwZsN56\nkleuBa1V+o27uN1yTe2Eip1+u09wH+5bCTT3/EUSaGm5OItwT1lQGoGmlBoC4BIABwLYHsBxSqlt\n45xLa6kYfg3MNII0n+78XJz2ZzvizuIsiotz5Mjm/1s99cQRaK4FLYpAM/uVzYLWSqC1u/fPfQ74\n5Cf9f7vuOmCXXYBf/xr4+99lm52/aVjQ7rsPeOUV7/+wMWjmGDPoZT1JwJzfiIxWYufjH5dPt83N\nnRvuWkagGeJY0A49FDjkkObfW1nQ3Dxx33vaKl+VAl59NTgGzQi0hQvlvqJa0I4/vjk/WnHoocC0\nac3b0nJxvvlmsEALaqMA8MYb8tlOoEWxoLkuztmzgZtvlv/bCbSg9dQ6BScJhKM0Ag3AdADztdbP\na63XALgewKFxTtRqGQrTCMK6H8PgNkIjzIKsGEHH5+XidBvwmjXAn/8c/e0K7sKXhiJZ0JJ0VnHS\nnJWL06ZdPQkz8B18MHDGGc3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QfH/2E+7w4ZJHb7/dHP9ivo8aFbwMw9Klcu4LL/Rc6IC4\nF84/X6whhr//3XNx7byzxMf94x+D4we32EIsv36sWSNWgtNPl/rx9NNiOTR5DDRbRY45Rj4feKC5\nHb7yilgXP/lJseZ+5jNSZ7q6PFfNwoXSNY4cObgdmYWgN9nEC3zXWvLQuIEA6ROOPFIsQrfc4m1/\n6y2pt6a+T5zouYwNSkkaTaC21rK/CZ9wLS0DA56r76abxNrz29+Kq3uPPaTumHzq6ZH2M26c/D90\nqLTV6dOBr3xlcL7bbcTOxwkTJM1KSRs2/Yjtxvv+9z3X6ujRXtyhSceQIdIWNt9cPAH276+/7n3f\nYQfp2370I7GGmbo1cqQXO2jiG/fd1zvu4IOlTrn1rK9Pwh+22UbSPGmSuLhNHbUthgcd1DzBYf31\nm88zdKjkw+uvS57efnuzO/cf/xB37nHHNadh6lQpo8mT5f8xY+DLTjs113FDb69Xhuee25y2nh5p\nG+6yKIBYUQ2mn3HP62L6LdMHbbaZWHdOOEGsZGbs0rr5uJ4eyRe/OL5WbLqpfI4b5+VPVLq6pC6+\n+qqXT1FJMvt+zz3FA/T5z4ebvOVirMVAdOvhvvtKfWo3y7c0Ag0AtNZ3aq230VpP1Vp/y28fu4Na\nulQqYF+fDJCmsvf0SPwH0NwAenoGB9qaCg80N0K3Qr/8smdi1bpZoAH+As1taMOGiZn93nulQ546\n1XOnKOVVAvPZ2yvCcepU6bBOPFG277qrt1irSZMZxI27EPAEmtaSzkWLvPu1BZrBiCJABqcXX5TO\n1WX99b1B0+Tv5MkyKC1Z4g22w4dLXowZ4w1+RqCdfroMxm55jBolg4GdFiOE3Mba3e3dt7kvpZoF\n2mabyffly6WTM+nu6mp21/X0eALtjTe8ztoINCPgzD0AEhNxwgnN97B8uZxr1CgRq8uXNw8oQHMd\nNu4rN5hUa//Oyd1v8mRxh2+6abPA22orcV3bA9vee8vnCSfIYLVw4eB6vuWW/gHVI0bI9kceEVdK\nd7eIlS228OrShht6AgYQ987cucDuu3v3fM45MigaTIwIIGVy3XUipsy2IUP8H3SWL5e0m/q3erXU\nAVuUrlolDw0f+9jg+5k4sbnTNfdgtu2yi7g97Rme3d2e+/Wpp5o7/YEBudY110jd6euTdj1mjIiP\niROb+6Jx47xBq69PzrfFFsA++wxO6157ed/taxoRYgTaG28MFmgnnui1pfXWa27vRqDZ921jC7Rd\nd5X9Td9mxO3Ikd71jLg75xwJOxgxQu5x2DBPoJmYHNNex4+XfmPkSBG9drsHJM/vuKN5iRC7XfT1\nSbpWr5a+xzw4Dh8u20073nvv5nYHyAPvSy+Jex7wF2iPPirjgulHgjD5Z+pRd7e/uxXwDAlAeIFm\n8taU2ahR4oLv7ZV4YZM/QQLNCLywGIH23vcmE2i33ipjVJD4zZIzzvBiAONw4okSRwhEF2jrrScG\nBBPnGkSpBFoY3Ao9erRUhCVL/CuB3Si7ukRV29jWCXsWo90RjhghMS2mw9pySy9o0uxnYqWefba1\nQPvIRySAemBAOuQxY7zB2DQy24IGeJ3ZlCly7s039yxoJkja7vAMK1d6A7DJG1eg2QJ1/Hiv83vl\nFblf0+EZJk8WwWGONwJu4kTpaI0FravL3/LT1+fFktj3ajDT3s2xS5fK4OA3mcDGnkVm8m3oUBEv\ngIigESO8dAMSG2I6rlYWNDPTzhWDe+whwtx+OrQF2ltvSb1wG7ddJ4cPl7S99VZz5xok0NxlHUxe\nuq+/GjpUttn3a+rlwICUqwlAB+QerrtucPt65hm5/099SgTAiy+KBba3V+qIXRc23tirjyafjEXL\n3POee3pB6EBzkHxXl8Q9uU+u7qCzYoUnuF2xO3Vqc96YQGMXE8htpwPw6s4uu8jgbQYnraVctZbj\nrryy2SpkLN/GOtLTI/k1cqRca9Ik7z5XrGgWaEOHyrk22qh5IDXC3xYt9v0ay5hSUm5+Aq27W0Tr\nNttIbOCZZzb/5ifQjjpKPo1VGBDxudlmg/cfOdJbU8+Iv732kslR48bJPQwbJuU1frxnITRtafx4\nub+hQ+Wh0IhO4wUIsqCYmMS+Ps/zYGK/ALmm8Q4EYUTkLrvI/35iadgwieGyvStBPP64F1fc3S15\nvtVWkj7bggt46XRFI+Dfb5p6oZR3b374CTRTD6MwcaK08z328LcehuFf/sX7ntdC4TZDhgweX6LQ\n2+u92SRO+kePHhzH61I5geZWaCPQXnvNv4HZ27q6xKxuz5yxG8Nee8n5lWruKKdMkcHcBFnaT7km\nPUOGSIf52GNex+o++ZuA1y98QYSDUl7DsV2brkAzaTzmGE+Rm4HLDIh2h2fft0mLuY49k8ikybDR\nRt6gsXSpDMBuB7fdds2D8s47ixvowAMHCzS/5RGGDvXO+aMfDf7d5eWX5dPNS5fRo73FLE2+9fZK\nwDlFrfUAACAASURBVPG//qsMiuap2ubaa719jUB76qlwLk6DybOxYz2BZq5j6kdPj7hKgMEuzqVL\nw7+uxQg0cw5jnXOXHjFptAc4UzfMuwdtN+/uu4s4cp+0t9pK8qanRwTLuHHNoqqnx6sLG23kBeUC\nzeLf5KdrTXQFmotSg90ExsU5cuRggWZbkE25+Am0DTdsvp65B5MfW24p9dSIWq29enX00d5xv/+9\nuOBcQd3d7Q2M++8vgcaGZcsGW9AAqQt2/h9xhLRBu43a92KE25AhngXNFbTd3RLo/9e/SvmbOmju\n1U+g3XCDWB9sC9qBBw4Okgfk/iZMkAc1M7HFnMvcY1+ftyCs+6A4frz89fXJYGbuySzJEiTQPvhB\n+Rw2TPbp7ZUHDtuCNmzY4AdMm0mTpL2atPiJueHDRXS7D/Z+7LCD3ONuu4lVCxAL3GuvNT90AF57\ncB/WgNYuTkDSa7ctG7fP7e2N5+Ls6ZE682//JhNZ4jBmTPSlm4pGKwtzO0aPbr8MT+UEmi24jIvD\nWNDCCLQhQ5q3uYOtedejHYthP8EOHy6zygx2hzl6tLigzDY/CxogItHETpmCX7asvUCzMRYTEwvj\nWtDM07XpoFyB5ufiHD++ucN49llpZCed5D35Tpsmg2x/v5cvu+/uWcZsgea3BpwRQYCIp1YMGeIJ\ntCjYLs4hQ8RMbywuO+/cfI/Tp8uncXEuXgz8+797+WILNNeCZjAi2MTa2CLQdKxvvy1WPmCwqDCi\nxl6o0qzq72IEmm0pMOn0ywN7u+0ONGk09cB1U7n09EgbM3XbrqtBAs2utyYf3AesdgLNLA1jY7s4\n3bw0ggrwrIt+As30G3Y6zH0CUsc326zZ6mja46RJXrzZvvt6bc7Oazv2Z8IEWQrCsGyZuJCMG8nk\n07Bhzfnf1ycCx94WxsVpY/LXtqTaafQbgMwDqt1+J02SmdMGW6AZhg9vTusmm4gF0rg4/UIS+vtF\nBLgPmO6yC0GY8/T0SF9hHgBMWloJNBf7XowlNqg9tOKhh2QJHUDy0W9cctsf0Pyw6GKno5UFze86\ncVyc9nX90h+WQw+NLg6LRFKB1vb80U9bbMxNf+1rMtBEdXECzQ3AHWy7urz4KcOMGZ6l6gtfaF5G\nwO4wTUU0n0ECDRjcIa5YEU2gmSflxx9vvg8jFkxHZ9LnBpn6uTg32qg5v1atkjy96iqZpv2tb8kT\n+BZbyJR59116Y8aINaidQNtzTwkebkdc07qbbyZ/R4wQa4K9po9tbbNXajeiwLg4zZpr9nntc2gt\ndcZY0AzGqtfV5dVd14JmRI3tvmzn4jR1zFzL3dfcl7196FApv/5+7zjzaZ68gzry3l5Jp0m7qT/2\n4D9iRHDbMulwYw7bCbTNNvPWVDIYF6cr0EaO9AS5+R8IFmh+Lk6THxtsIPXPT6C5sax+YqLVwLhs\nmayzZ2JKTT4NG9a8vy3cDHb+mjQYF+eSJYMHklYCxzzAAIOPM+m44ILBAhnwF2iucPj5z8V6aCxo\nJl4Y8D432UQmUrhWLJOeKALt7bcHW9CixD6Za959t2cljSPQolzLsOGG3rjSzoI2bFiwBS2tSQJp\nccQRgyeIlImsBVrlFqo1HfLYsZ5AW7UqvIsTaB5g3cHWT6CNHi0d9oc+NNjUHVeguWk07gmgeZKA\nXxoB6biMG9Iscgo0d/bmfuw0RbGgAd7/SjXHr/hhZiT6CbTddpP1soYOlZiY225rfS5AhKB5X2QU\n3HgiwHNx2pYwex/j4nRN0mEsaIahQwcLNNsdPnq07GOny+54w3RkYQWanwWtr89bd8m4Dc3xxoIW\nJNB6eqSNGeFvrNFDhjQHRpuFde00GPzctmEEmsvy5TIYDx/ePPCYtF97rbgoTTmZNnrCCXLcTTcF\nCzRTNuuvL2VnrFy2i7O3t3lmbpBAe/VV/4HRtWjZQqydQLPzyOTN8OFi4Yq6mGirSQK2xdMvSDxI\noNn9pskvY0EbPTr4Icf8b84X1YJm+hpzTSPQ3vUub0HtsJg1+ezzpU2rAb+dBa2vL3gscV2cnRZo\nZSeJQPOLLxx0/uinLTbmps2T0bBh0lmsXOkv0Owp6mEEmhl07AZgKvevfz04WNQVaEOGeOf0i0Hz\nw3SGUS1od94pi0sOH94sGlau9Ez9djAv0DoGzUwAsIkSZOkKNDPoT5woZv+gewni8MOj7W8wMzlt\nK5JxcbrYlqShQ0Vc9PZ6q6H7TRIISlNfX3Nc10svebOAABH59gQJoDn/3ZW+W1nQzL3YVhQbk0ZT\n/pdf7gV/m/sFPKFgL4Pih58FzdQNW6DZ9xem7MIKNNti9corktduuII94QNoju0CZIblf/yHfG/n\n4txgA1mKxLaW2xa0M87wAoCDBJpfcPYzz3hpMNgPVe5AbLYDYrE2ab74Yq8sTNyTeSNJWIImCQCD\n89LF7G+3KSOKXIwFrbt7sAXN3gdILtAMxpr35S+3X+rAxkw0MfUhyTIPrTjuOLEw+pG2Bc09noQn\niUALM/u1cgKtq0tmhpkA295ef7FhsC1eZj+/TtDep6urOdC71RoqrkAbOdJr1KahPfWUzIYJEmi2\nu8j+bGdBu/lmWU7ErAVkMOsCAYMFmtnPz4J28MEygWLNGlnXLCrm7Qxr1zZb0Ow8DupY/Dj11Gjv\n7LOxl8WwXZx++9n7v/WWxO/ZrhHj4jRLPrSyoAFevZkwobmD33hj7/U4BlvEhxFo224rnyNHinXR\nBOG2s6Ademhzul0XpxHTQQOjsaCZByTbCuRa0Aytytosh2GLBD+Bttde4mq0g/9ffrnZgmjqepBA\nM+dVymv75v2LBrfNuQ8q9m+mz7FnUdqf5r7eemuwQNtqq8FuM1uI2XlgytDsP3Zsc19hjjNlstVW\n3rFh3CthLGhBAs3kuf27KzDt7cuX+8egGVxrZ1iBZvov13JkxKJZkiIsy5eLdTTJ7L8wjBzprRMI\nNN9nkhi0IIFGC1o8kgi0nh5vFmjg+aOftvjMndu8Low9o8zFdkX4CbQgFycgjeDii5sXv3Q58kjv\n+6hRzQ3BNDQj3IIalb02l/3ZzoK2bp0IVXuGocGsZeW6S12rh31cd7c3CBx+eHTz/pAhcr7ly4MF\nWhyLWBxcV+LAgP/gYVx1RqCtWNHsfrJdnOa8QfcQZDm1sZdBcQnzrrxLLpG1hbbcUlzAQbE6bgya\n+7vbZtyXGrv3aGJ82lnQ7DxvVda33z74PH6D4t57y5qDRgxdeqlYrmyxbY5zRYVpb3bnaraFiUEz\nuA9cbvkGWdCAcAOjnyvT3W4eDuy8cgWavWbhttt67z8MIolAGzdOZkfbBAmHvj6pO34xaIa4Ls53\nvUs+3fobZM0Li9+kirzwy3MzmQlo7eKkBS1dkgg0oPk9x77nj3fa4mPHjLgDhY1595n5DvjHeRiM\nixOQRr7PPsGNddttvTV0AE+IGUzjMAHm7SxoriWhnQXNHOMn0FwLmnsPfhY0m913b55VGJbhw8Wd\nYQs0+xp5CTQT9A94edUqvsq4ON96a/CimLZAc62VNnbAchzcBWL9BqeuLnmzxOzZrfd1LWju70o1\nL5HhuojcBYpNXtqzOP0saH4WID/8jmnVCZp9/vVf5bu9xpK5N3v2LuCVh30vRqS3cnG6bdWc327P\nNq0EWpiB0XVl+m03y0nYAs0tE3viTVdXe5dnEoHW2zv4/ZvuLE6Dua9WLk5TdvZ6X/anH6tXy4LX\nQPoCLWsLWivcdnP33c2GgDguziR5UWfC9E1JqKxAs100rQQaMFj4hHFxArIWj/3KEBd3QHUFmu0S\naWWWdl2cYSYJ2JYTP9FgXEJBAq2VWzgJtkAzL3zuhAXNdXGatAXt28qCZlycgL8YNrju46REiX+J\nKtCAZje+PcBp3bxeltkX8Kw13d3tBVqr9Psd02pQtONHL7rI3wV/2GHNaTXltN123sDVzoLW2zvY\nvem2x7QFWhgLmjvpx3YNm/vcc0+Je7TT3IokMWh+BPVxfgLN7QdcK6X9ABpET09wHbNfRxeHTTaJ\nf2wcWrk43XuMY0GjQItHUgta2/Nnc9rOE9aCBgweAMK6OK+/PvgdYn19g4MAR45s7pDtDr1Vo3KF\nmWtB8+sk21nQLrxQgmPbWdCixISFYfhwb023E06Qtc6K4OIEgi1brkBrZUFL6uL0wxVDcTD1wY27\natXBBAk0YHB98bOgue3O3tYOe5KCUs3WIT/s304+WUSXzZo13hsKWoU89PXJa3bsBVXtexg/fnBI\nQxIXZ5hlGlwL2q9/LZMi7LXB9tij+d7s/LDreZQn/jDLbEQRaNtu2+yKM9iW5aAYNPs9qDZRHlLs\nPDnuOBHycTn0UJnk0QnaCbRWFjS/hWqB7JYLqToUaDGxXRJRBVorl5vt4mzFggXSkdoEWdB6esJZ\n0JRqfj1FGAuaiftyG6x53VBQALYbc5MWxoJmu1A7ZUFrF4dlcF2crgXt7bebB5agzjGui/M3v/Ff\nwiSOBc0NiG517729gxeqNbj1JY4FrRXuse3EXbs26ZaZOaeLUt7A7efi3HJLLz7OPV8UC1q7EAIb\n14L2oQ81L0mx/vresjR+As1uU2GskYYkLk4/9t7b37IZxsXpJ9CMcA+LW55JrEZmbbm88LOg9ff7\n7ztpkheT2Q5a0JJBgRYTOx6kXafk/m7v18rF2YoNNhg8GE6e3LxukzHBd3XJvkHrotiDS1iBZg8K\nraw69mtXbLq6gl8nlATbxQkMFjRpW+yC8HNxBnX2tgVt3br2Ls60LWiTJ3uvtrGJI9DGjGleyy2s\ni9ONQYtjQYsi0NxlPpIKtDjH+Ak0v4e8W25pfoF7GAuaaVth8sMvBi2onrWzoEUZUNIWaEHYay8a\nt6rbD/gtah1VoGU1iOaNyXMTX+fmwXnnee98daGLM11azTBPg8otVGtIYkGzaeXijMqBB8qfwQz8\nSgFf/Wpw+uwO375+WAvahhsGu2JN4z7yyOZ1sLJi+HB5GvYTaLNnS4B7Hvi5OMMINKC5/IcMCT9J\nIK5Ai3uMjS0S/ALco8SgAYMfJtwlZ4ImCYSNv4tqQYvTJtsN2H7LbPil37ymybw8PIwFzU9wBOFO\najDf/R5m3L5shx2aJwNEcXGmHYMWhG1BU0qEhxubt/nmg48bMiS+Ba3MuLOQo+SB30K19rlINLK2\noFVeoMWJQTNstdXg+J+wLs4w2G62VqsK2+k2yz6Y44H2szjPPz/43PYTQJiF85JiYh38BNpnP5v9\n9Q1xXZzAYIum7eL8+tf942wAb0JJHLHlV3fjWND8ZmsGncuexekKtK9/HU3vSo3q4rziitbpTdvF\n6Ue7Ads+p7F0tyq7KBa0KALNLyD+sMOa134zuH2dedWb+3tSF6eJ0UsjJMF9cPn2twfvc8gh/rOY\n6yLQ/FyccbwNtKClC12cMYkyizOo0/roR70FM+1902rotoWiFa5Ai2pBa0XWFczFFWhBU++zxnYR\nhXVxumvFmWNWrvR+23ff4LWtzKrzcTpWv3qSh0ALcnGOGNG8hmBUF6d5mXgQfjOX07agtTvGnanX\nzgIYJQYtKOg9iFmz5F24hrPPbn4Lip1GIPjeoro4g2ZLKjX43apxMUKvnXXVT/jWRaDZmHwy/Zf7\n8NQKCrR0oQUtJlEsaEF+ZL+n3KwsaK2II9DCLuSYt0BzlwM46SSxQOWNX96HFWiuBW3NmnCuu7Fj\nZZX7ONP7ky7NEVQf4s7i9NsXaF6otpUFrV1981s4N28X589+1vyCettt64f79gVDGgLtS18Kt1/Y\neNsoAs1MTnIZPTq9gb2vL3odr6tAM3HFYdumDQVaulCgxSSNGDQ/gZamBS2OQLMtCUELY5r97M8g\n8hZorhXK75U5eXD66cC0afI9rIvTfaODOWZgIHydaPWmgFZ0yoIW5OJ0cV317SxoUdtQJwTaqFHN\nE33aWdCUGvy2BLPd/gSiuTijEHVCVCtMnQt6KP2f//G34sVh2LDorv+6ThIAmseOKHWJAi1dKNBi\n4jeLM4pA23PPwS8+N/t0UqDZMWgmLsbPZRZmIUf797w6Lz+R0wnsKepRXZyuYG51bFp0KgYtigXN\nuL1Met03YESxoLm0a3cTJ0Y7X5w0hJnk0NubfgxaFNoJsLCWdaC9QPML3I8LLWjRsOuZG/jfCgq0\ndGEMWkz8XJxRnirvu0/W7XFJ08U5caK3eGYrglycgHT0fk+eYYVDXgLDUBSB5kdQHowdK8tT+KU9\nL4Gb5izOMNvNNYNi0Fx6e8XaZK8xZq/fZ8RNVIFmL0fRqs78+797q+SH4YwzvDcLhKW7u305tHKd\nF0mgteOww2QxViDdPi+IYcMo0KJg17MoLs5ttmn+P6sFyesCLWgxSWuZDb9902ro48a1f1kq0Fqg\nBUELWnjaWRV+/vPBZeAem3X+2W5CM7gXycU5fHizu9pd88wVaHFcnK3yuLu79WvXXL7znWjXN9co\niwUtaczizTd736si0PJcWDZt3Pu84QZg003le1iB5reupTk2rwf0qkGBFpMyCLSwBMWgtSJsxclL\nYBjKKNDcAdfu6PJ2cZpXZUW9ZtaTBCZNAh580PvfDaiPa0Gzz9fpOhNWoGUxizMsYfuyKItQ5yHQ\n+vqyjUGbPz/ce0/Lwl57ed+jWNBckhxLKNBiY8/MjDuLM2jfvINNq2RB81uqoiiE7eztmI+8BK79\ncm0j0KKQxMW5887hrA/2BIggC1q7ZSBcwro48yCMQPuP/xi8PEeRXJxxKLIFLWy6/NaMKxOt+qYk\nImvqVOC00+IfX3cYg5YQ+ykrKBPLZEFr5+oxRI1BowUtnkDLK/9MHXjve71tebk4H3wQuPvu8NcC\nqmtBa2fl+fjHBy9745fHWQVll9mCFlWgRX2TQFVJItCGDQO+97300lI3KNASolT7zqjoAi3oVU+t\nKOoyG1UVaFkPFCbfDjgAePHF6MfHEWgmnsovrqodaQk003bDuvazJMqrqmz88vg3vxG3W9qU2YKW\n9TIbVYVuys4R94EzLJV1cRqGDElXoHXaxRl2oCqqi7OIAs1QZBen7RqMKijtfeNOEohKFpMEOl1n\n0hRoG2+cTppc3MWB06CoFrQ6CbSsXJwkGVn3/7SgofgWtDgxaEV1cRYxBq0MLs6gRXLD0m6SQNA1\n4y7v4WdBs+tuWV2caQm0rCirizOPWZxVhQKts2TZPipvQQvTgKso0KJa0Oq8DloSgZbX4OtnQYtC\nKwtaUB05/HBgs82iXwsItqBFrW9FmiTQ1RVPsBZRoEUhD4H2/vdHX2yYAk0YM6bTKag3WYZf1EKg\ntXtaLPosTjtdUScJ0MXZnjJY0LJ0cQad5+CDo6XRJigGLa5YKYJAq5IFLQp59HlHHx39mDoJtKD7\nXLgwO3c5CUeW7aPSLs6rr5ZZb3V0cdKCFp0iCzS/82ct0JLgihlXoIVh992B/feX7xRo4SirizMO\ndRJoQUycyDzoNBRoMTnxRIl5KrtAs19IHHWh2naN13RyeTXyKsSg2fUpz8EXEHGYtoszi7S77eQr\nX5HXzES51gMPALvt5n++TvD5z8uacFEpokCLAgUaIcFQoCWk7LM4P/EJ7z2DWVjQ8ryfIlrQyuDi\nNPiJwyj4TRLIw4J27LGyyG7ca4V17WfJ4YcDG2wQ/bhOCLSyzeKMAwUaKQIUaAkpuwVtyBDvPYNZ\nrINWd4FmCJsPnZgkYF877jX9BrRWkwSSEFRPkwi0ItaZMBTRgkYXJyHpQIGWkLJb0GzCWhLCDgoU\naOVYqNagdfxrBQm0PCxoSaFAC0edXJx1epNAXe6zjFCgZUzRLWg2UWPQaEFrT9JlNvLMPz/rXVjy\nFGjuLE77enEowpsE4kKBlg20oJEiQIGWkHYWNNPIyyDQsohBy7OTM2kvYsca14KW5+BlW9CKLtDS\nbCedbndJyNPKGvZl9FVxcRYxXaReUKAlJIxAi2KZ6rSLs8wWtCIOskldnHmKzbizOAF/MZZV+oNe\nE8UYtGxhDFo1qct9lhG+SSAhYTqjsANApweKqBa0osWgFbWjtz/b0UkXZ5JZnHla0GbMANas8U9D\nHDrd7pLAWZzZUCeBRooLBVpCqiTQwlaGolrQitjRG8rg4izLLM6g1/ZQoGWLqY+MQSMkH+jiTEia\nAq3TnVXaMWh5W4CK2tHbn60YNkwWXLWPzXOQKMsszlZpiAMFWnjuv7+9BS2Ki7OoeV8ngVaX+ywj\ntKAlZKON2u9TFgta1Bi0ork4i9rR25+t+Oc/m1+YnXf+2ZapIrs4W6UhDp1ud0nIW6Dtvnu65+v0\nQ2kQdRJopLjQgpaQz34WWLSo9T5h3QKdHijK/iaBInb0hjCd/ciRwNCh3v955t/y5cAhh5RjFmfa\ndLrdJSFvgZY2FGiEBJPlW05qYUHr6gI23rj1Pn193nsiW9HpzipsZShqDNq0acDFF+d3vTAkGUDz\nHCRGjPCuGYc8Z3G2SkMcdt+92bVcJooo0DiLs1zU5T7LCF2cOfDAA8C4ce336/STfFhLX1HXQevp\nAT7zmfyuF4YkA2gnB68iTxJIm9NO63QK4lNEgRaFogq0Or1JgBSXLBfRpkBrsPnm4fbrtEA76ihg\n223b71fUGLQiE9eClnf+ldnFWccBlQItG2hBI0WAFrQC0enO6vDDw+1X1Bi0IpLUgpZ351nHWZxl\npogCjS7OchGlvEi+UKAViNGjy9EpFDUGrYjUxcXpd0ydBrlOUUSBFoWi9hGsu6QIlHIWp1LqHKXU\nAqXUI42/g6zfzlJKzVdKPamUOsDavotS6jGl1NNKqdnW9l6l1PWNYx5QSm2aVbrbcdppwJe/3Kmr\nhyfsoFDUzrcT1NHFWZZJAmWGAi0b6iTQ6nKfZaSUAq3BRVrrXRp/dwKAUmoagGMATAPwAQA/UOqd\n6ncpgFO01lsD2FopdWBj+ykAlmitpwKYDeCCjNMdSFE7K5ewFrSyBIlnCV2cydMUJQ11o4gCrSou\nziKmi9SLMgs0vy7pUADXa63Xaq2fAzAfwHSl1AQAo7TWDzf2uxrAYdYxP2l8vxHAftkluRowBi08\nSZfZKIuLswizOA84ADj00PyuVwSKKNCiUNQ+ok4WNFJcyizQTlNKzVVK/UgpNbqxbRKAF619Fja2\nTQKwwNq+oLGt6Rit9ToAbyil1s805SWHMWjRKUsMWpktaFOmALfckt/1igAFWjbUSaDV5T7LSGEF\nmlLq7kbMmPl7vPF5MIAfANhSa70zgMUALkwjwebSKZ6rkkSJQat74y+ri7OMy2zUkSIKtKq4OIuU\np6SeFHYWp9b6/SF3vQLArxrfFwLYxPptcmNb0Hb7mEVKqS4A62mtl/hdaObMme987+/vR39/f8gk\nVgta0MJTZxcnBXr2hA03yJMoaSlqH0GBRopA1PYxZ84czJkzJ9S+mS2zoZSaoLVe3Pj3CAB/bXy/\nDcB1SqnvQlyXUwA8pLXWSqmlSqnpAB4GMAPAxdYxJwF4EMDRAO4Juq4t0OoMBVp06OIkWVA0C9pD\nDwGTJ4ffv6h9RJ0eLupyn2UkavtwDUfnnntu4L5ZroN2gVJqZwADAJ4D8EkA0Fo/oZS6AcATANYA\n+LTW7xjcTwVwFYA+ALebmZ8ArgRwjVJqPoDXABybYborAZfZCE9SC1rZXZx1L/+sKZpA2223aPsX\ntY/gwwUpAoV1cbZCaz2jxW+zAMzy2f5nADv6bF8FWZqDhIQCLTx1WaiWFrTOUDSBFpWi9hF1qrt1\nuc8yUthJAqT4UKCFpywCjS7OckGBlg2su6QIUKCRzKCLq94uzjrF8XQKCrRsoEAjRYACjWRGUTvf\nPKGLM700kcFQoGVDnepuXe6zjHR1yV8WFLDZkTyhBcWjLMtspO3iLOLgWyUo0LKhTgKNFBda0Ehk\nwi5EWdTON0+SrFPFhWpJOyjQsoF1lxSBrq4SzuIk5aConW+e1HmhWg5y2UOBlg20/pIicP75wJZb\nZnNuCrSaU9TOtxOUJQYtTRcnXdzZU3aBdsABwA47dDoVg+HDBSkCOw5aGCw9KNBqDgVa8kkCdHGS\nVpRdoJ14YqdT4A8fLkjVqfnQXF0YgxYeujjTSxMZTNkFWlGpU92ty32SZmo+NBMKNI86ujgZx5M9\nFGjZUCeBRuoJu+aaw06ufAvV2teOuj8taPlDgZYNdaq7dblP0gwFWs15z3uAM8/sdCo6S9kWquWb\nBMoFBVo21EmgkXpCgVZzxo4F9t+/06noLGUTaHGhBa0zUKBlA+suqTolGVoIyR66OEkWUKBlQ53q\nbl3ukzRDgVZRws7iJOW0oMUZnDhJoDNQoGVDnQQaqSfsmkntKeMyG2lZ+zjIZQ8FWjaw7pKqQ4FG\nSIOyLFRroIuzHFCgZUOd6m5d7pM0Q4FGak9ZXZxxjuEszvyhQMuGOgk0Uk8o0CoKY9DCU1YXZxwL\nWhrnIdGgQMuGMs2gJiQOrN6ENKCLk2SBERHM53Rh3SVVhwKN1J6yWtDiHMNZnPlDC1o2UKCRqsOu\nmdSessag0YJWDijQsqFOdbcu90maoUAjpEEdXZycJJA9FGjZUCeBRuoJBRqpPXV3cXKQyxYKtGxg\n3SVVhwKN1B66ONNLFxkMBVo21KXujh8P7Lprp1NBOkF3pxNAsoHLbEQnLatUXvBVT+WAAi0b6iLQ\nFixgG60rFGik9pTVghbnGFrQ8ocCLRvqUnd7ejqdAtIpqMtJ7SmrQOMkgXJAgZYNdRFopL5QoFWU\n970P6O/vdCrKRV1dnBzksoUCLRv4cEGqDgVaRZkyBbj33k6nohyU1YIW5xgKtPyhQMsG1l1SdSjQ\nSO0p6zIbtKCVAwq0bGDdJVWHAo2QBnGEVtkXquUszuyhQMsG1l1SdVi9Se0pqwUtzjGcJJA/FGjZ\nQAsaqToUaKT2lDUGjS7OckCBlg2su6TqUKAR0iCuQCu7i5ODXLZQoGUD6y6pOhRopPbU2cXJ3Mgx\nMAAAEw5JREFUQS57KNCygXWXVB0KNFJ76u7iZKB1tlCgZQMFGqk67JoJaVDHhWo5SSB7KNCygQKN\nVB0KNFJ7ympBi3MMXZz5Q4GWDXy4IFWHAo3UnrIKtLgiLY3zkPAwf7OBdZdUne5OJ4CQohCnsz/i\nCGDffdNPSxg4i7McMI+zgflKqg4FGqk9SSxoEybIX97QxVkemMfZwHwlVYcuTlJ7yhojxFmc5YBC\nIhuYr6TqsGsmpEGZOnu+6qk8UEhkA/OVVB0KNFJ7ymhB46ueygPzOBto/SVVh9Wb1J4yCjSAAq0s\nMI+zgflKqg4FGiENytTZc5JAeWAeZwPzlVSdRAJNKXWUUuqvSql1SqldnN/OUkrNV0o9qZQ6wNq+\ni1LqMaXU00qp2db2XqXU9Y1jHlBKbWr9dlJj/3lKqRlJ0kyISxktaHzVU3mgkMgG5iupOkm75scB\nHA7gv+2NSqlpAI4BMA3ABwD8QKl3mtKlAE7RWm8NYGul1IGN7acAWKK1ngpgNoALGucaC+A/AOwG\n4D0AzlFKjU6YbkLeoYwCDeCrnsoChUQ2sO6SqpNIoGmt52mt5wNwm8mhAK7XWq/VWj8HYD6A6Uqp\nCQBGaa0fbux3NYDDrGN+0vh+IwCz/OeBAO7SWi/VWr8B4C4AByVJNyF+lKmzp4uzPDCPs4H5SqpO\nVs6NSQBetP5f2Ng2CcACa/uCxramY7TW6wAsVUqt3+JchKRCGS1onMVZHpjH2cB8JVWn7ZsElFJ3\nA9jI3gRAA/iK1vpXWSUMg61yhGRCGQUaQIFWFpjH2cB8JVWnrUDTWr8/xnkXAtjE+n9yY1vQdvuY\nRUqpLgDraa2XKKUWAuh3jrk36MIzZ85853t/fz/6+/uDdiUEQDkFGl2c5YF5nA3MV1JG5syZgzlz\n5oTaN813cdpN5TYA1ymlvgtxR04B8JDWWiulliqlpgN4GMAMABdbx5wE4EEARwO4p7H9twC+0ZgY\nMATA+wF8KSgRtkAjJApl6uzTcnHusw8wZUp66SKD4UzZbKBAI2XENRyde+65gfsmEmhKqcMAfA/A\nOAC/VkrN1Vp/QGv9hFLqBgBPAFgD4NNaa9047FQAVwHoA3C71vrOxvYrAVyjlJoP4DUAxwKA1vp1\npdR5AP4Eca2e25gsQEgqlNGCBqQj0KZMoUDLGgqJbGC+kqqTSKBprW8BcEvAb7MAzPLZ/mcAO/ps\nXwVZmsPvXFdBRB0hqVNGgZaWi5NkD/M9G5ivpOrQ8E5IgzJ19mm5OEn2MN+zgflKqg4FGqk9ZbSg\nARRoZYH5ng2M7SNVh9Wb1B4zeJaps6eLszxwxftsYL6SqlOiIYmQbClTZ08XZ3lgvmcD85VUHQo0\nUnvo4iRZwnzPBuYrqToUaKT2lFGg0cVZHpjv2cB8JVWHAo2QBmXq7OniLA/M92xgvpKqk+abBAgp\nJWW0oAEUaGWB+Z4NEyYAI0Z0OhWEZAcFGqk9ZRRodHGWB+Z7Nnzuc51OASHZQhcnIQ3KNIjSxVke\nmO+EkDhQoJHaU0YLGkCBVhaY74SQOFCgkdpTRoFGF2d5YL4TQuJAgUZIgzINonRxlgfmOyEkDhRo\npPaU0YIWBwqFzsB8J4TEgQKN1J4yCjRa0MpDX5/8EUJIFCjQCGlQJvFCgVYeJk0C/vjHTqeCEFI2\nKNBI7SmjBS0OFGidY/31O50CQkjZoEAjtaeMAo0WNEIIqTYUaIQ0KJN4oUAjhJBqQ4FGak8ZLWhx\n6O0Feno6nQpCCCFh4Ls4CWlQJoEWxxr2jW8Aw4dnkx5CCCHpQoFGSEmJKtA22CCbdBBCCEkfujgJ\nKSFlsvYRQgiJDgUaqT1adzoF0WHAPyGEVBsKNEJKCgXa/2/v3mMtK+szjn8fBvDSwghaMTCAtTBW\nrBbHAppiO6UOYNNyCdXYxoBKGhMMbazpxQ5RKEQbUhtaGtRGW5HUWksvDIZwC4z0glVEZOwoYqWt\ngGArQi0I6vDrH/tVNjgXZvY+Z13295OcnLXftd513r3Ou8959vvutZYkjZcBTRogw5kkjZsBTQvP\nKU5JUt8Y0LTw9toL1q/vuhU7z4AmSeNlQNPC2203OO+8rluxcwxnkjRuBjRpgJzilKRxM6BJA2VA\nk6TxMqBJA2Q4k6RxM6BJA+QUpySNmwFNGigDmiSNlwFNGiDDmSSNmwFNGiCnOCVp3Axo0kAZ0CRp\nvAxo0gAZziRp3Axo0gA5xSlJ42ZAkyRJ6hkDmjRAjqBJ0rgZ0KQBMqBJ0rgZ0CRJknrGgCYNkCNo\nkjRuBjRpgAxokjRuBjRJkqSeMaBJA+QImiSN20wBLckvJ/lcki1J1kyVH5zkoSQ3t6+LptatSXJr\nki8muWCqfM8kH0lye5Ibkxw0te60tv1tSU6dpc3SGBjQJGncZh1B2wScDHx8K+u+VFVr2tcZU+Xv\nAU6vqtXA6iTHtfLTgfuq6lDgAuB8gCT7AG8HjgCOAt6RZOWM7ZYkSeqtmQJaVd1WVbcDW3sv/wNl\nSZ4D7FVVn2pFHwJOassnAhe35UuBY9ryccDVVfVAVd0PXA0cP0u7paFzBE2Sxm0pP4P23Da9eX2S\no1vZAcCdU9vc2cq+t+4rAFW1BXggyb7T5c1dU3WkhWRAk6Rx231HGyS5BthvuggoYH1VXb6NancD\nB1XVN9pn0/4hyWE72bZd+vdz9tlnf3957dq1rF27dld2I0mSNFcbN25k48aNT2rbHQa0qlq3sw2o\nqu8A32jLNyf5d2A1k9GvA6c2XdXKmFp3d5IVwN5VdV+Su4C1T6hz/bZ+9nRAk8bKETRJGp4nDhyd\nc84529x2nlOc3/93keRZSXZry88DDgG+XFX3MJm6PDJJgFOBy1q1DcBpbfnVwHVt+SpgXZKV7YSB\nda1MWmgGNEkarx2OoG1PkpOAC4FnAR9LcktVvQr4GeD3k3wbeBR4U/uAP8CbgQ8CTwWuqKorW/kH\ngEuS3A58HXgtQJsmPRe4icnU6jlT+5IWkuFMksYtVdV1G+YmSY3p+UjbcswxcPLJcOaZXbdEkrSr\nklBVW33L7Z0EpIFyFE2SxsuAJg2Q4UySxs2AJg2QZ3FK0rgZ0KSBMqBJ0ngZ0KQBMpxJ0rgZ0KQB\ncopTksbNgCZJktQzBjRpgBxBk6RxM6BJA2RAk6RxM6BJkiT1jAFNGiBH0CRp3Axo0gAZ0CRp3Axo\nkiRJPWNAkwbIETRJGjcDmjRABjRJGjcDmiRJUs8Y0KQBcgRNksbNgCYNkAFNksbNgCZJktQzBjRp\ngBxBk6RxM6BJA2RAk6RxM6BJkiT1jAFNGiBH0CRp3Axo0gAZ0CRp3AxokiRJPWNAkwbIETRJGjcD\nmjRABjRJGjcDmiRJUs8Y0KQBcgRNksbNgCYNkAFNksbNgCZJktQzBjRpgBxBk6RxS1V13Ya5SVJj\nej7SttxwAxxyCOy/f9ctkSTtqiRU1VbfbhvQJEmSOrC9gOYUpyRJUs8Y0CRJknrGgCZJktQzBjRJ\nkqSeMaBJkiT1jAFNkiSpZwxokiRJPWNAkyRJ6hkDmiRJUs8Y0CRJknrGgCZJktQzBjRJkqSeMaBJ\nkiT1zEwBLcn5ST6f5JYkf5tk76l1b0tye1t/7FT5miS3JvlikgumyvdM8pFW58YkB02tO61tf1uS\nU2dpszRGGzdu7LoJ0rKz32vMZh1Buxp4YVUdDtwOvA0gyWHAa4AXAK8CLkqSVuc9wOlVtRpYneS4\nVn46cF9VHQpcAJzf9rUP8HbgCOAo4B1JVs7YbmlU/EelRWS/15jNFNCq6tqqerQ9/ASwqi2fAHyk\nqr5bVf/BJLwdmeQ5wF5V9am23YeAk9ryicDFbflS4Ji2fBxwdVU9UFX3MwmFx8/S7u3Z1Rf8rH8o\nrG/9Ls3y87t+7l3Wn8fvbZHb33W/n7UNQ+67s9bvuu8Mvf6TqTvPz6C9EbiiLR8AfGVq3V2t7ADg\nzqnyO1vZ4+pU1RbggST7bmdfS8KAZv0h1p+V/6S6+dnz2MeQ2991v5+1DUPuu7PW77rvDL3+k6mb\nqtr+Bsk1wH7TRUAB66vq8rbNemBNVZ3SHl8I3FhVH26P388kvP0n8K6qOraVHw38dlWdkGQTcFxV\n3d3WfQk4EngD8JSqemcrPwt4qKr+aCtt3f6TkSRJ6pGqytbKd38SFddtb32S1wO/wGNTkjAZ5Tpw\n6vGqVrat8uk6dydZAexdVfcluQtY+4Q612+jrVt9kpIkSUMy61mcxwO/BZxQVY9MrdoAvLadmfmj\nwCHAJ6vqHiZTl0e2kwZOBS6bqnNaW341cF1bvgpYl2RlO2FgXSuTJEkapR2OoO3AhcCewDXtJM1P\nVNUZVbU5yUeBzcB3gDPqsbnUNwMfBJ4KXFFVV7byDwCXJLkd+DrwWoCq+kaSc4GbmEytntNOFpAk\nSRqlHX4GTZIkSctrkHcSSPLNrtuwyHZ0/JNcn2TNcrVnkdj3u2Xf74b9vnv2/eU3yIDGZKpT3fH4\nd8dj3y2Pfzc87t3zd7DMhhrQSPL0JNcmuSnJZ5Oc0MoPTrI5yZ8l+VySK5M8pev2jkyS/GySy6cK\nLvQ2XMvDvt8p+35H7Peds+8vs8EGNOBh4KSq+ikml/h499S6Q4ALq+ongAeAUzpo39gVvqPqin2/\nW/b9btjvu2ffX0aznsXZpQB/kOQVwKPA/kme3dbdUVWb2vKnged20D5pqdj3tYjs91ooQw1oAV4H\nPBN4SVU9muQOJpfuAJi+JtuWqXLNz3eBFVOPPcbLw77fPfv+8rPf94N9fxkNeYpzb+Br7YX6c8DB\nU+u8o8DSKia37TosyR5JngH8fMdtWiT2/e7Y97tjv++WfX+ZDW4Erd0G6mHgL4GPJfksk4vYfn5q\nM+fIl0g7/o9U1V3tYsSfA+4Abp7azOO/BOz73bLvd8N+3z37fjcGd6HaJD8JvK+qXtZ1WxaRx787\nHvtuefy74XHvnr+DbgxqijPJm5i8i1rfdVsWkce/Ox77bnn8u+Fx756/g+4MbgRNkiRp7Ho9gpZk\nVZLrkvxbkk1Jfr2V75Pk6iS3JbkqycpWvm/b/ptJ/mQb+9yQ5NblfB7Szppn32+3YPlCks8kuTnJ\ns7p4TtKOzLnf75Hkfa3O5iQnd/GcpF3V64DG5JTe36yqFwIvB96c5MeB3wWurarnA9cBb2vbPwyc\nBbx1aztrL9D/XfJWS7Oba98HfqWqXlJVa6rqf5a47dKumme/Xw/cW1XPr6rDgI8veeulOep1QKuq\ne6rqlrb8f0zO2lkFnAhc3Da7GDipbfNQVf0Lj78mDgBJfgh4C3DeMjRdmsk8+37T69e6BHPv928E\n3jW17/uWsOnS3A3mj3aS5wKHA58A9quqe2Hyggaeve2a33cu8IfAt5aoidKSmEPfB/hgm948a0ka\nKc3ZLP3+e1OgwHlJPp3kr5P8yBI2V5q7QQS0JD8MXAr8RntX9cQzG7Z7pkM7RfjHqmoDkwsaelFD\nDcKsfb/51ap6EfAK4BVJXjfnZkpzNYd+vzuTkbd/qqqXMgl5795+Falfeh/QkuzO5IV6SVVd1orv\nTbJfW/8c4Gs72M3LgZcm+TLwj8DqJNctVZuleZhT36eqvtq+Pwh8GDhyaVoszW4e/b6qvg48WFV/\n34r+BnjJEjVZWhK9D2jAnwObq+qPp8o2AK9vy6cBlz2xElOjZFX13qpaVVXPA44GbquqY5aovdK8\nzNz3k6xI8sy2vAfwi0yuAi711cz9vrm83RIK4JXA5nk2Ulpqvb4OWpKfBm4ANjEZ0i7g94BPAh8F\nDmRyb7DXVNX9rc4dwF7AnsD9wLFV9YWpfR4MXF5VL17GpyLtlHn1feC/2n52Z3KT42uZnCXX3xe+\nFtY8/+YnOQi4BFgJ/Dfwhqq6c3mfkbTreh3QJEmSFtEQpjglSZIWigFNkiSpZwxokiRJPWNAkyRJ\n6hkDmiRJUs8Y0CRJknrGgCZpoSTZ0u5L+pn2/aBd2Mf1SdYsRfskCSYXr5SkRfJgVRmuJPWaI2iS\nFs0TbwlEkt2SnJ/kX5PckuTXptb9TpJb24jbO6eqvaZt/4V2BXySHJzkhiQ3ta+XLcPzkTRCjqBJ\nWjRPS3Izk6D25ao6BTgduL+qjkqyJ/DPSa4GXgD8EnBEVT2S5BlT+1nRtn8VcDawDrgXeGVVfTvJ\nIcBfAUcs31OTNBYGNEmL5qGtTHEeC7woyavb472BQ5ncZPsvquoRgO/d/7H5u/b908DBbXlP4E+T\nHA5safuQpJ1mQJOkyWjamVV1zeMKk+O3U+eR9n0Lj/0tfQtwT1W9OMkK4Ftzb6mkheBn0CQtmh/4\nDBpwFXBGkt0Bkhya5OnANcAbkjytle+zg32vBL7alk8FVsynyZIWjQFN0qKprZS9H9gM3JxkE/Be\nJp8xuwrYANzUPrf21u3sA+Ai4PVJPgOsBh6ca8slLYxUbevvjCRJkrrgCJokSVLPGNAkSZJ6xoAm\nSZLUMwY0SZKknjGgSZIk9YwBTZIkqWcMaJIkST1jQJMkSeqZ/wcZv5QavaP25QAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f8282eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "NetCF = pd.Series(data3.NetCF).resample('D').fillna(0) #Agrupamos por fecha y añadimos fechas con flujo de caja cero\n",
    "NetCF[abs(NetCF) > 3*np.std(NetCF)] = 0  #Anulamos outliers sustituyendo por la media\n",
    "NetCF.plot(figsize=(10, 8))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2f4f3eb3da0>"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
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XPrn0rKYuYJaslT659MllvfayTXk0ya9XVRuuf6i19nhV/XaSR6rqvUm+lO3foExr7VRV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\noKAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f4f17668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data4.NetCF.hist(figsize=(10, 8),bins=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Modelo 1. Modelo estacional día-semana + día-mes**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>NetCF</th>\n",
       "      <th>Weekday</th>\n",
       "      <th>DayMonth</th>\n",
       "      <th>DOM-DOW</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Fecha</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2014-01-01</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>(3, 1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-02</th>\n",
       "      <td>-138.500000</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>(4, 2)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-03</th>\n",
       "      <td>-105.475000</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>(5, 3)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-04</th>\n",
       "      <td>-94.385000</td>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "      <td>(6, 4)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-05</th>\n",
       "      <td>-919.745000</td>\n",
       "      <td>7</td>\n",
       "      <td>5</td>\n",
       "      <td>(7, 5)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-06</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "      <td>(1, 6)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-07</th>\n",
       "      <td>-561.336667</td>\n",
       "      <td>2</td>\n",
       "      <td>7</td>\n",
       "      <td>(2, 7)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-08</th>\n",
       "      <td>-207.800000</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "      <td>(3, 8)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-09</th>\n",
       "      <td>-2960.000000</td>\n",
       "      <td>4</td>\n",
       "      <td>9</td>\n",
       "      <td>(4, 9)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014-01-10</th>\n",
       "      <td>-785.916000</td>\n",
       "      <td>5</td>\n",
       "      <td>10</td>\n",
       "      <td>(5, 10)</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  NetCF  Weekday  DayMonth  DOM-DOW\n",
       "Fecha                                              \n",
       "2014-01-01     0.000000        3         1   (3, 1)\n",
       "2014-01-02  -138.500000        4         2   (4, 2)\n",
       "2014-01-03  -105.475000        5         3   (5, 3)\n",
       "2014-01-04   -94.385000        6         4   (6, 4)\n",
       "2014-01-05  -919.745000        7         5   (7, 5)\n",
       "2014-01-06     0.000000        1         6   (1, 6)\n",
       "2014-01-07  -561.336667        2         7   (2, 7)\n",
       "2014-01-08  -207.800000        3         8   (3, 8)\n",
       "2014-01-09 -2960.000000        4         9   (4, 9)\n",
       "2014-01-10  -785.916000        5        10  (5, 10)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data4 = pd.DataFrame(NetCF)\n",
    "data4['Weekday'] = data4.index.weekday+1\n",
    "data4['DayMonth'] = data4.index.day\n",
    "data4['DOM-DOW']=[(int(data4.ix[t].Weekday), int(data4.ix[t].DayMonth)) for t in range(len(data4))]\n",
    "data4.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "estim = data4.groupby(['DOM-DOW']).NetCF.mean()\n",
    "fcast = estim[data4['DOM-DOW']]\n",
    "data4['season'] = fcast.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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IBdTxFqdeG7IVp7oQpTlwb7wBvPde5a9bjWVEKAqVEFA5miEBR4SCem64QT1b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\nXxdwDnPgikWhVmsOXNxn+Rt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uQKeol7+HjiCRSWDztgySSfv9TnPgFIOAy8gZz2+ihTpj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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f3f61cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data4[['NetCF', 'season']].plot(figsize=(10, 8), fontsize=12);\n",
    "legend = plt.legend()\n",
    "legend.prop.set_size(14);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7957969752812916"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_season = ((data4.season-data4.NetCF)**2).sum()/((data4.NetCF.mean()-data4.NetCF)**2).sum()\n",
    "rmse_season  #Este es el modelo que mejor funciona"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21.971249999999991"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estim[dia_pred] #Para un día en concreto"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nextdates = pd.date_range(now,periods=20)\n",
    "nextdays = [((nextdates.dayofweek+1)[i],nextdates.day[i]) for i in range(len(nextdates))] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DOM-DOW\n",
       "(3, 28)      21.971250\n",
       "(4, 29)    -185.532500\n",
       "(5, 30)   -4588.631125\n",
       "(6, 1)     -151.970667\n",
       "(7, 2)     -141.776000\n",
       "(1, 3)     -112.510867\n",
       "(2, 4)     -322.856667\n",
       "(3, 5)      208.745500\n",
       "(4, 6)       44.044155\n",
       "(5, 7)     -348.963333\n",
       "(6, 8)        0.000000\n",
       "(7, 9)     -585.512750\n",
       "(1, 10)    1743.638167\n",
       "(2, 11)      -0.404000\n",
       "(3, 12)    -254.817733\n",
       "(4, 13)    -132.762000\n",
       "(5, 14)     -25.600000\n",
       "(6, 15)     -20.000000\n",
       "(7, 16)       0.000000\n",
       "(1, 17)    1017.853500\n",
       "Name: NetCF, dtype: float64"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estim[nextdays] #Para los próximos 20 días"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Modelo 2. Media móvil**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import statsmodels.api as sm "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kiMFMrgf6AT2Bp4E5Sqm+QHugJODYUiDb+3fg/lLvtmD7As91DU6ppBKFGj2h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\nHUcQfAUX1vAJuGLDIs6/73w2nngQ7PtN2P8iFjQ8TuPKMTD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04Jo8OTnET7YALjhWoteg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\nEYmZ/w/nQYJ2LKkbPQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f80363c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ma = pd.rolling_mean(data4['NetCF'], 20) #Aplicando una media móvil\n",
    "data4['ma'] = ma\n",
    "plot = data4[['NetCF', 'ma']].plot(figsize=(10, 8), fontsize=12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9456573805282852"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_ma = ((data4.ma-data4.NetCF)**2).sum()/((data4.NetCF.mean()-data4.NetCF)**2).sum()\n",
    "rmse_ma"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** Modelo 3. ARIMA **"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t9UalVH/gJaXUUgzRZsW22zt79uzu71OnTmXq1Kl2JZ1z3FIJIwOh7aRQBFq8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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2f4f52233c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = sm.tsa.ARIMA(data4['NetCF'].iloc[1:], order=(1, 0, 0))  #Modelo AR\n",
    "res = model.fit(disp=-1)  \n",
    "data4['fcast'] = res.fittedvalues  \n",
    "plot = data4[['NetCF', 'fcast']].plot(figsize=(10, 8)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9986761623107829"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rmse_arima = ((data4.fcast-data4.NetCF)**2).sum()/((data4.NetCF.mean()-data4.NetCF)**2).sum()\n",
    "rmse_arima  #Un valor por encima de 1 es un resultado peor que la media, la mejor predicción es el valor medio que es cero"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "De los tres modelos el que mejor funciona es el de estacionalidad DOM-DOW.  \n",
    "\n",
    "Finalmente habría que sumar por día, la previsión por scheduling y la previsión por forecasting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}