notebooks/quickstart.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from kinemathic.animation import ianimate, tweening"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel('../datasets/WPP2015_FERT_F01_BIRTHS_BOTH_SEXES.XLS', skiprows=16, index_col = 'Country code')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename(columns={df.columns[2]:'Description'}, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(df.columns[[0, 1, 3]], axis=1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>1950-1955</th>\n",
       "      <th>1955-1960</th>\n",
       "      <th>1960-1965</th>\n",
       "      <th>1965-1970</th>\n",
       "      <th>1970-1975</th>\n",
       "      <th>1975-1980</th>\n",
       "      <th>1980-1985</th>\n",
       "      <th>1985-1990</th>\n",
       "      <th>1990-1995</th>\n",
       "      <th>1995-2000</th>\n",
       "      <th>2000-2005</th>\n",
       "      <th>2005-2010</th>\n",
       "      <th>2010-2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>900</th>\n",
       "      <td>WORLD</td>\n",
       "      <td>487364.363</td>\n",
       "      <td>512937.177</td>\n",
       "      <td>561132.795</td>\n",
       "      <td>597172.105</td>\n",
       "      <td>611175.699</td>\n",
       "      <td>607055.970</td>\n",
       "      <td>646458.598</td>\n",
       "      <td>698316.802</td>\n",
       "      <td>676437.337</td>\n",
       "      <td>650312.457</td>\n",
       "      <td>658430.353</td>\n",
       "      <td>680566.922</td>\n",
       "      <td>699213.776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>901</th>\n",
       "      <td>More developed regions</td>\n",
       "      <td>93850.232</td>\n",
       "      <td>95448.675</td>\n",
       "      <td>92629.195</td>\n",
       "      <td>85134.137</td>\n",
       "      <td>82271.418</td>\n",
       "      <td>78923.492</td>\n",
       "      <td>79161.433</td>\n",
       "      <td>78297.163</td>\n",
       "      <td>71535.899</td>\n",
       "      <td>66248.331</td>\n",
       "      <td>66076.568</td>\n",
       "      <td>69551.907</td>\n",
       "      <td>68687.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>902</th>\n",
       "      <td>Less developed regions</td>\n",
       "      <td>393514.131</td>\n",
       "      <td>417488.502</td>\n",
       "      <td>468503.600</td>\n",
       "      <td>512037.968</td>\n",
       "      <td>528904.281</td>\n",
       "      <td>528132.478</td>\n",
       "      <td>567297.165</td>\n",
       "      <td>620019.639</td>\n",
       "      <td>604901.438</td>\n",
       "      <td>584064.126</td>\n",
       "      <td>592353.785</td>\n",
       "      <td>611015.015</td>\n",
       "      <td>630526.654</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>941</th>\n",
       "      <td>Least developed countries</td>\n",
       "      <td>49463.116</td>\n",
       "      <td>54921.227</td>\n",
       "      <td>61422.115</td>\n",
       "      <td>68881.235</td>\n",
       "      <td>76829.959</td>\n",
       "      <td>85551.485</td>\n",
       "      <td>95364.057</td>\n",
       "      <td>104201.246</td>\n",
       "      <td>113396.831</td>\n",
       "      <td>123647.646</td>\n",
       "      <td>133323.975</td>\n",
       "      <td>141890.152</td>\n",
       "      <td>149996.813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>Less developed regions, excluding least develo...</td>\n",
       "      <td>344051.015</td>\n",
       "      <td>362567.275</td>\n",
       "      <td>407081.485</td>\n",
       "      <td>443156.733</td>\n",
       "      <td>452074.322</td>\n",
       "      <td>442580.993</td>\n",
       "      <td>471933.108</td>\n",
       "      <td>515818.393</td>\n",
       "      <td>491504.607</td>\n",
       "      <td>460416.480</td>\n",
       "      <td>459029.810</td>\n",
       "      <td>469124.863</td>\n",
       "      <td>480529.841</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                    Description   1950-1955  \\\n",
       "Country code                                                                  \n",
       "900                                                       WORLD  487364.363   \n",
       "901                                      More developed regions   93850.232   \n",
       "902                                      Less developed regions  393514.131   \n",
       "941                                   Least developed countries   49463.116   \n",
       "934           Less developed regions, excluding least develo...  344051.015   \n",
       "\n",
       "               1955-1960   1960-1965   1965-1970   1970-1975   1975-1980  \\\n",
       "Country code                                                               \n",
       "900           512937.177  561132.795  597172.105  611175.699  607055.970   \n",
       "901            95448.675   92629.195   85134.137   82271.418   78923.492   \n",
       "902           417488.502  468503.600  512037.968  528904.281  528132.478   \n",
       "941            54921.227   61422.115   68881.235   76829.959   85551.485   \n",
       "934           362567.275  407081.485  443156.733  452074.322  442580.993   \n",
       "\n",
       "               1980-1985   1985-1990   1990-1995   1995-2000   2000-2005  \\\n",
       "Country code                                                               \n",
       "900           646458.598  698316.802  676437.337  650312.457  658430.353   \n",
       "901            79161.433   78297.163   71535.899   66248.331   66076.568   \n",
       "902           567297.165  620019.639  604901.438  584064.126  592353.785   \n",
       "941            95364.057  104201.246  113396.831  123647.646  133323.975   \n",
       "934           471933.108  515818.393  491504.607  460416.480  459029.810   \n",
       "\n",
       "               2005-2010   2010-2015  \n",
       "Country code                          \n",
       "900           680566.922  699213.776  \n",
       "901            69551.907   68687.122  \n",
       "902           611015.015  630526.654  \n",
       "941           141890.152  149996.813  \n",
       "934           469124.863  480529.841  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "test = df[df.index <900][1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f47f8cc7e10>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47f89d92b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test.T[1:].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Description</th>\n",
       "      <th>1950-1955</th>\n",
       "      <th>1955-1960</th>\n",
       "      <th>1960-1965</th>\n",
       "      <th>1965-1970</th>\n",
       "      <th>1970-1975</th>\n",
       "      <th>1975-1980</th>\n",
       "      <th>1980-1985</th>\n",
       "      <th>1985-1990</th>\n",
       "      <th>1990-1995</th>\n",
       "      <th>1995-2000</th>\n",
       "      <th>2000-2005</th>\n",
       "      <th>2005-2010</th>\n",
       "      <th>2010-2015</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country code</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>174</th>\n",
       "      <td>Comoros</td>\n",
       "      <td>37.869</td>\n",
       "      <td>43.858</td>\n",
       "      <td>47.306</td>\n",
       "      <td>50.659</td>\n",
       "      <td>55.594</td>\n",
       "      <td>65.953</td>\n",
       "      <td>78.042</td>\n",
       "      <td>85.920</td>\n",
       "      <td>91.089</td>\n",
       "      <td>98.722</td>\n",
       "      <td>108.099</td>\n",
       "      <td>118.535</td>\n",
       "      <td>128.497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>Djibouti</td>\n",
       "      <td>13.491</td>\n",
       "      <td>16.392</td>\n",
       "      <td>21.958</td>\n",
       "      <td>30.850</td>\n",
       "      <td>42.948</td>\n",
       "      <td>61.240</td>\n",
       "      <td>77.648</td>\n",
       "      <td>101.534</td>\n",
       "      <td>121.562</td>\n",
       "      <td>113.961</td>\n",
       "      <td>112.392</td>\n",
       "      <td>110.948</td>\n",
       "      <td>110.445</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Description  1950-1955  1955-1960  1960-1965  1965-1970  \\\n",
       "Country code                                                           \n",
       "174              Comoros     37.869     43.858     47.306     50.659   \n",
       "262             Djibouti     13.491     16.392     21.958     30.850   \n",
       "\n",
       "              1970-1975  1975-1980  1980-1985  1985-1990  1990-1995  \\\n",
       "Country code                                                          \n",
       "174              55.594     65.953     78.042     85.920     91.089   \n",
       "262              42.948     61.240     77.648    101.534    121.562   \n",
       "\n",
       "              1995-2000  2000-2005  2005-2010  2010-2015  \n",
       "Country code                                              \n",
       "174              98.722    108.099    118.535    128.497  \n",
       "262             113.961    112.392    110.948    110.445  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tweening two lines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dropping Description\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "\n",
       "            <video width=\"640\" height=\"480\" controls loop>\n",
       "              <source src=\"test_30.mp4\" type=\"video/mp4\">\n",
       "            </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47f8a1aba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tweening(test, 'test_30.mp4', fps=10, transitions=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/fdion/anaconda3_5/lib/python3.6/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = sm.datasets.co2.load_pandas()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "co2 = data.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>co2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1958-03-29</th>\n",
       "      <td>316.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-05</th>\n",
       "      <td>317.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-12</th>\n",
       "      <td>317.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-19</th>\n",
       "      <td>317.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1958-04-26</th>\n",
       "      <td>316.4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              co2\n",
       "1958-03-29  316.1\n",
       "1958-04-05  317.3\n",
       "1958-04-12  317.6\n",
       "1958-04-19  317.5\n",
       "1958-04-26  316.4"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "co2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f47e64e5470>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47e64bfcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "co2[0:11].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "## Time series animation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <video width=\"640\" height=\"480\" controls loop>\n",
       "              <source src=\"tmp.mp4\" type=\"video/mp4\">\n",
       "            </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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YOw9GHxWNt27WXwsbl2r+mjjxSlHjT0y/9xGf3ru59gM643VT7VfyiEgxcBqwBLgfmOlPOwkICZ4fBK7w3jczgEbnXEo1AMMwsppXfqGycdWuz4sTingAtGxOuk4GCgdDwWBNWLbKV4+KK/qFfu256tVkQFSgpBKQ5Ep/ANCZFf1I4A4RyUW/GO5xzj0sIi8Cd4nIV4CtwOf8+Y8CZwHLgGbgyq6ftmEYfZrR02H13PSVc6ry3t4c2c5r34md1w5t3g9+8cPJayoPgPoPojJ+R8VUzOCRGhAFGkCVypWPZf4CyXJ2q+idcwuAIzL0NwAfy9DvgKu7ZHaGYfRPVs9V2bAi2R8v1A1qetnPR6z+7nyVIw7XnDNNG1XR3+2TlYXUBCUVsK0hSkoW95g54/vwp1naLsuwok+tEDVAyL7tZcMwepf4inndwuRx3TKVJ/2LytatpBE8a570JfzG+M3bzzyusqhMXSgbVkJOPgweEV0bt8On2ugHMKboDcPoWrbEtuRaG2Hz6ug4ZJ4c5Y0Ebc3R2IGnqwy+8jne4CC5agIKSrytBere1Zw2ZaOTyn1ne9QeYoo+YIreMIy9Y2stPPfDdM+a+/8h5byYuSZs0obVdtu2aGzzGhh3nNrgcwsiG/vmNTAk5ve+fqHKVa+lK/OdsblUH4ShmKI3DGPvePZ78MyNcMfZyf6691WW76cyXuCjdonKUPIvrOgba2Dly5H7ZG4htG/XFfqWNUkPmmD2aVyZbp6ZeKoq/1kPq4eOAZiiNwxjb9np0xusfDnZXzlB5ZFXqNzWoDK+8g9VnMKK/qeHqXz7AZV5BRq81FSrq/T4ir5oSNRO3XAtHgrXLoL9M/jJD2BM0RuGsXf89U6V8cRhEKUlOOJylWFFH7JPAuQXq4ybbgCOuUplUbna+oN9P76ij2/uDklJZWBkxBS9YRgds3kt1Lyx63M0r2E6BSmr9hUvqpz+GcjNh/zSqNrT2BmaHvi0b+lxxf6q5Bu9oo+v3CtiXyyZfOWNNEzRG4bRMb+/AP57Jjz7/WT/ureidsOKzEFIeX7VHvLHPPLPKg/wAfVlYyIPnYaVmrdGfKqsZbNh7fwosja+oo9XjQo2fWOXmKI3DKNjNvh0v89+L9n/6w8ljxc/mDwurY4yQrY1qRzlaxBN+rjKvAJNTtbepgq/PLY6n3Cyf//FujFbUpl5fmHD19glpugNw9h3tnvvmZCvZvpno7EXf6qycRUcdGa0al/3FqyaA/XLAZdctU/5lMr1i9QOL5mS4gL5RV31BFmNKXrDMDombhpJ3TiFyNUx5It/4ScqK1I2aHfu1GCpignp91g7X2XcVTJkr1y/KHNyshsa9GV0ClP0hmHAgnvUlz2V5pgP/OO+mlO8atT0z6gM+eJf9Ir+oDNUHnKObrJuWQs7tkWulxB55YQslPEvlaDo21sze9aIdLzKN9IwRW8YA5365fCXv4982QNb1mkKg8Mu8B1+w3W7t7kfd41GsGYiFNsuHupruPogqviKfsrFKt97RvPXVMSqQcXrtoZ9AmOvMUVvGAOd1MjWQLOvEnXwmSpDuoFQPar6YMiL2cgbYzltAiUV6kcfSvvFlXn4Mqh7V33s4yX8imPFuo+O2fuNvcIUvWEMdOKFuHfGfOLDyr14qHq+bPcbrWt9ecDhhyXrsL77hMpQqDtcu7MNHvqSHmeyw2ciuGZC5KVj7DWm6A3DiGiNRa8+8I8qC4eorTwUBvnrnSA5MHyyHh93jQY/5fqqTWf+MLpHqjKPZ5qMu0xOOCV5XqLW6zCMfcMUvWEYEVvWqdzWoHVXAYYfqnKpzwe/dQMMHhUp4+Jy9ZUPuebj+eFDEW9IVoKCKA0CwHEptYrCRuvBabWNjL3AFL1hDGRe9mmDQ+73FT5B2Q9igUiFg5Or77YmGDcjOg62+/eeUs+ZgtJoLF5wu3pSx/M48CPpfdevgk/esdtHMHaPKXrDGMj83zdUTr1E5ZP/lhwvG6fykLOh1JtQWrcklXnYON20PDonEF/dDxm5Z3MrGqI5cYx9xhS9YQwEnIPFDyUrMMUJm6TjfWqDiX6F/SUfzFQ4RItxb1mviciqYkU9Qt731saOUxWAFu5OZchoqJzY+ecw9ordFgc3DCMLWHgv3PtZrbH6bxu1L56ILCdXy/uFCk2tmzWjZHB5LBoCO1pg+Qt6PPaY6NrCWH74XSr6Eel9X1mUOSGa0aXYit4wBgLBVXJnLKq1JZZC4KAzoNj7vO/cqdGqq+ZE44VlKu/1Pu3xaNV4JaeSivT3HnG4ykEZFL1I0n/e6BZsRW8YA4HmjRn6NkXtEYerK2T9B7qaTyVe1QmiUoCQXNHnl5DGZfdq3di8DqJojW5nt1+lIlIkIq+JyHwRWSQi3/b9/yMiH4jIPP+a5vtFRG4SkWUiskBEjuzuhzAMYzf89XdRe7PPAR8U/Xk3qwzpCprr9Pgj34muCcq8YDCMPTbp5x5f0R+Q4g8PMHg4jJyyb/M39onOrOhbgZnOua0ikg+8KCKP+bHrnHN/Tjn/TOBA/zoWuNlLwzB6i/oPonbjKvWACQq96mCVJRVqzgnnjj4quib4tW/fAiVVyXvHV+pxt0ujz7DbFb1TfA5S8v1rV7sn5wJ3+uvmAOUisod+VYZh7DENq+AXR8O6hcn+nSml/kK64dW+RGCwq298V2VY/cdrwQ4aHrVTUxADfHEefD1DrhujT9CpXRARyRWRecAG4EnnnM8ryo3ePPNTEQm/5UYDq2KX1/g+wzC6k5tP0GjWX5+Q7G9pyHwc7PZhY3XKJ1Wuna/pDOLukKNjFthMVZ0q9ofCQXs/d6Nb6ZSid861O+emAWOAY0RkMvB1YBJwNFAB+AoEZEoSnfYLQESuEpG5IjK3trZ2ryZvGEaM4ANfmLJxGgpwH/9PKmd/S2VznfrDB3v7sENU1n+gEa4decO0t3bVjI0eYo/8mpxzDcCzwBnOubXePNMK3A4Ex9oaIF6afQywJsO9bnHOTXfOTa+urk4dNgwjE+074MkbNHAplXceUbm9SdP+Bv5WlPtUlSE3fFNd0t5eHHON3PhOx3M45vN7Pm+jV+mM1021iJT7djFwGrAk2N1FRIDzgGAYfBC4wnvfzAAanXNru2X2hjHQqHkdXvov+PFByf64q6RrV3t9IAQ5xe3sAJtXq0dMIO49k4nzbtZfBeYm2e/ojNfNSOAOEclFvxjucc49LCJPi0g1aqqZB3zBn/8ocBawDGgGrsxwT8Mw9oY1b2buTy0D2LQh/Zx4MNO6t9REc/hFUV+8NN9p30q/ftqlnZ2l0cfYraJ3zi0AjsjQP7OD8x1wdaYxwzD2kW31mftD9sljvwCv/hq2ert8U110TjwFQbDTD08pHxgYZeEv2YTFHhtGf+L5WFGPeJHukNpglF+TbfU2/EeuVTns0OR9Qn6ZeDWoOBNO2rd5Gn0KU/SG0RdZ/BC0bk32taSkJogX9QhKv3ioluELppuauSqDDf+MH6hsXAUFg5LphgEuvy8ZEWtkBaboDaOvMf9uuPvT8Oh1yf4t3qfhsPP98bpoLCj63HwtvbfZO7qN80Hp/+ALiux3nMqNS9UfXlK8oQ+YCSd8qWuew+gzmKI3jL7GDh+5Wvdusn+zjzwd69MMNMY8a14JlaLyNb973Xt63LJZ7e2lPn1wXlF0zdAMgU9GVmKK3jD6GkfO0spONa8nc7WHZGT7Ha8ynr9mycMqCwf5nDWN+lr2ZNLEkxtzjcwU4WpkJaboDaOvIQKTL9D25lisYWhXHQRF5dDoV/gh1zxo6oKick1zEOq/ulium3gaYVvRDxhM0RtGXyR4vcRX7ZtXa/RqfpGu2rf5Ddb/iBUBGXaI1nBtaYT27dp38e+j8XiAVFk8gN3IZkzRG0ZvUfdeuidNIJhV4hGub9weKe/iimQ0LMD5t+ivgaIyXcXX+jQGQ8cnzwuVnva0WLfRbzFFbxi9wc52+PmR8JNDUvp3wt2XRxGwwU0yuFpu9zK+og9MvVhlrk9StvwF3XxNLe9X7fPPl1qOqYGClRI0jN7gvadVbt+qSj8nV4+bNsDiB2H/D6s9PWyktjUnr598oUbJ1q9Iv3e+96z54HmomJDuQnnhbfDuk1A+ruuex+jT2IreMHqDmtej9rZYvviQs6ZsrPrDhwjX+uUqz75J5dSLYcYXYMPb6feeeknUHjwqfby0CqZdkt5vZC2m6A2jN9gU22RdG0tUtu4tlZUHaLbJsKIPXwwjJqtc8gjc+lFo8sVDLotV9MzNj9qDzDxjmKI3jO7DOS3b5zJU3mzeGNVqXTMv6n/4yyorJ/oVvVf0r/1WKz6FOq7t22HVHFj+oh6PSss7qJgd3sAUvWF0H3N+BTeOgP/5ePrYe09rNsn80igjZfuOaFzEr+jX6xdFUy2MnBqNj5iicsEfQXKTRUMAhvjqnTtauu55jH6LKXrD6C5e/C+VK15M9tcuVfnBcxrJ2rpFj2teU/nxn6osHaaeNRvf1U3bCSdH94h70gwall72b+Q0lVutTKdhit4wuo+4Mo6v1nf69glf1lV7CIq6/UyVReUqBw1T+cyNKvOLo3sUlkXt1MpRABfdrlkoP3nH3s/fyBpM0RvGvrJ5jfq/pxIPVNq4NGqH4tpjj1G7+8aU5GXjfNKyoMDF/zcNWStBV/Ahl/zgDIFPeYWahTIU/jYGNKboDWNfWL9Ig57iBUECm1drbnhIlvZbOUdlbiG8+4SmH27bpgnHTvgyDPEukaW+cHfdMq0gVTgkef9QVWrMUV33PEZWYoreMPaFm30myWe/l/SuWfmqukoeeo4ex23lj1+vMjcWrxhy08QLdBcMUtmwAkoq0wOfgmtl5YH7/hxGVmOK3jC6ing64BDIdNSVKpsybIrml8DRf5+8Nr5qD9WfWhqhpKrj96ucuPdzNgYEpugNY1+Q3KgdN8+8+huVww8DRAOcIFr1FwyGMUdHtV6f/b7K+Io+3m5pTH/vg/3mbcX+ez19Y2Bgit4wOsPNJ8BvUgpm79wJOBh/oh7HV/S1i1UWDNIVd/CVb/HpDk76mppiwkbrO/6LoKQyuke8nuvmmvQ5ffQ/4KvL0uu+GkYKpugNY3d88DysXwhr52kCskBLg6YDrjpIj5vrVMaLeufkaEWoZm9Pf+JfVQaXypnfVBns7FUxe3vcY2byJ9LnlZtnKQ6MTmGK3jB2x4P/FLVDcjGAF36sMrg3BvNKvGg36Cq9uU7NNkHhB9fIYJMP9V+Lh2aew3FX79XUDQM6oehFpEhEXhOR+SKySES+nTL+cxHZGjsuFJG7RWSZiLwqIuO7ftqG0YPElXvcPBMKchf54KWQhXKLr+164ldVllRqkFTrZtjRqukJJn1Mx3JyAdFUBWXjtDpUJjIFRRlGJ+nMir4VmOmcmwpMA84QkRkAIjIdSP3L/CxQ75ybCPwU+EEXztcwepbUQKiVL6efc8RlWuAjmG5CquEpvhBIsLs316n558DTo2tFooLdu6rhWjZmz+duGJ7dKnqnhBV7vn85EckFfgR8LeWSc4EQd/1n4FSRVAdgw+iDtLdBW0oSsLCJOuwwlSEvTZyCUl2Rv3qzHi95WGWZTywWFP2W9Xq/1EjWkBIhk5vktxr1ZRj7QKds9CKSKyLzgA3Ak865V4FrgAedc2tTTh8NrAJwzu0AGoHKlHMQkatEZK6IzK2ttcRLRh/gu1VwY4qJJNjUT7xW88tsb06/LpXmOjXDBG+YoOjff0Zl6gaq8xu8w1LKChpGF9EpRe+ca3fOTQPGAMeIyIeBi4CfZzg90+o9LSG3c+4W59x059z06mrzHDB6mcUPR+14ArIQ6FRSCQUl0Nakx8Ef/oQvJe/jHKx/G0ZNi/qC3X3ZUyrHzkhec9TfqQwbtIbRxeyR141zrgF4FjgFmAgsE5HlQImILPOn1QBjAUQkDygDNqXdzDD6Em/9KWrHfdbDBmtJhUayhhX9ipdUhlJ9p94Q9bc2au6aQHCTDGUBgztm4OyfqXmm8oB9fw7DyEBnvG6qRaTct4uB04A3nHMjnHPjnXPjgWa/+QrwIDDLty8EnnYuU4kdw+hDvH1/1A555CEKcCoqg03vwaK/6HEo7Rf+vnzNAAAgAElEQVQySgbPmzq/3pl2aXSPPF+sO7hQxnPcGEYP0Jm/uJHAHX7zNQe4xzn38C7OvxX4nV/hbwI+te/TNIwuonWLrtLLx3Z8TlEs38wD3n+9qCx5ztoFUD4OBg9Pjm/xq/a4vT141RhGL7FbRe+cWwB0UJDyb+cMirVbUPu9YfQ9fn6UmlD+rT6qyhS3yeeXRNGvG5dF/fFCHzu2a0bJiglRX1D0m95TGQ98Cit6w+glLDLWGDi8/t+RnTyeTbJ9e9QuHhrZ5bfGIlxzcuCs/9R2SwM0rNQVfSAo+nVvqWKPBzjlxVb08S8Hw+ghTNEbA4dH/jlqxwOfQkrhgz/mFb33na+Zq/KC36oMJf4W3qtfFGUxRR9SGWx4W78AOgod2fT+vj2DYewFpuiNgcmf/i5qB//2+g/Uuyb4zs/2njQhkOlA7/64bLbK4YdF9yiNuQjHV/qBsOIPXxqG0YOYojeyj/YdySyTmQjpCUDLAQJccAs018OqV/X64FEzym9RFQ/VtMPvP5vsByithOGHazuTog+OZxNO2aNHMYyuwBS9kV1sWQffrYTvVCT7d/iC3JUTVVmHRGXbm2DRfdoeuj+sf0vb9cu16PeYY5JmmKJyTVlQXAFDUlIZhJQH5Rly1oTskx0lLTOMbsQUvZFdbFwateMJycIG67Ff0GIfoeBHqLsKmrLg3F9qu3kTbFiSjHCFSFGnBj1B9GWSaUV/8vVwQwPk5nf+WQyjizBFb2QXYSMVYMOiqL3gbpWtW+CAU1SRQ5Rx8qPf05V7sLtvel8jXFOzRobVfXUGRb/N37OsAx99y+1n9BKm6I3s4oMXovbqv6p0Dp70lZwOPlPNLkHBB+U8+iiVwXtm4zsqh4xO3n+dN+1Ihv86I6eq3FUwlmH0Aqbojf7Jildg1Wvp/Y2rIrNKKNa9PVbar3qSetZsq1fTzuY12h8yTIYo1k2+1N+QUZnf/9h/SO8784fwhRdh8Ig9exbD6GZM0Rv9k9vPgFs/kt6/9HH1mCkcorVeISrxB2o+GTRCUwM3b4TXbtH+kDo4JCCr70DR5xWrrD44/b3zi2HE4Xv3PIbRjVh2JaP/EbfDx9nuUwhveg/yS6Mi3S0phTuCt8yWtWqrLxwS+bmHFX3wykktEvL552HVHLO3G/0KU/RG/+MH46N28yY1xUDkQXPG92HlnMg//qWfqfzk71QG5d2wUk03J8WKpIUV/bZ6DYIKx4HqgzJvxBpGH8ZMN0bfZVsD1L2363NCumCIIlqHjtcVeutmPQ4eNyH/TPCKWfYU4JLukPEEZGZrN7IEU/RG32RnO/xgP/j5kVFUaSq5BckkYU3ek6akSlMNt3hFHzJJjpyicvBw9bxZ8ogexwOc4iaZ4GFjGP0cU/RG32TNm1G7OVagLARBjZwGkz6e9LwJK/rSSk0rvGObphQuqYRDz9PN0kBJZeSVkyn4CdTObxhZgCl6o3fZsDja+Ixzf8x9sS6WFz5knVw7T+3wIe9M/JrBo6LiIa2bobEmPfCp0JdQKK2OiocEgvnmc7P35EkMo89iit7oPWqXwq9mwM+mpo/FUxnEFf3vzo/a5ePUcybcK5BfFAU+1b0HO1rSFX2BV/RVGdwkQ16aTC6UhtEPMUVvdD9rF0DNG+n963dhA59wskrJTSr6UCTkzB9BaVUU4RoKel/5mMoRk1U+932Vqf7wIYhq6Pj09575Tc1Lk5Pb8fwMox9hit7ofn5zIvz3TGhYlexf+JeoHS/nB+r2eMg5MGhYshpUoGKCt7N7u3xwocwvURkCl9b7oiKp/vBhD2DFS+n3FjE/eSOrMEVvdC/xjdS6d6O2c7AkVmP+jduTY4016gZZUBoFQsWZcLIq76Za3XANgU7xYiAQlQNMVfSB1PMNIwsxRW90L3E/+OD+CMn8MwDvPBq1V7wEbc26YVowKDo3XD92BuTmebu7gy1rANFVfEdpgFN94q98TL8czr5pb57KMPoVFhlrdC9xs8vsG2DKRdoOPu6B4OsO8Adf/am0Wr1rAu94v/cQyRoCoLbW6i+ATHngAcYem/4FsN/x8M0MJiHDyEJsRW90L3N+FbU3r47aIWr1iE/D8MnqZhkIK/ijPxf17dgebcqOO05lgbfHtzXrZmxZSkrhQEdmG8MYIOxW0YtIkYi8JiLzRWSRiHzb99/q+xaIyJ9FZJDvLxSRu0VkmYi8KiLju/cRjD7Ncp8ffsIpMGJK1P+O94457HxYvxA2vK0VmuKbsvEN0ZZG3cytmBAp+BAA1bxRx1Nzx0+9RGVIWGYYA5TOrOhbgZnOuanANOAMEZkBfMU5N9U5NwVYCVzjz/8sUO+cmwj8FPhBN8zb6G8Ul+vKO/DUt1WWVEV925ugzW+8nvBllRfeprJ2SXrgU4hcXfAnlammm6oDVZqiNwY4u1X0Tgk7Z/n+5ZxzmwFERIBiICQkORe4w7f/DJzqzzGyFefUvfH1/072t7epHHdcuj98cIMcGQuWamuG7f7LIPi3j/Q1Wzev9oo+psxD6oKQd370kcn3D9cecMoeP5JhZBOdstGLSK6IzAM2AE865171/bcD64BJwM/96aOBVQDOuR1AI1DZxfM2+hJLH4cn/w0e+edkf8NKldMug9VztR28cIorYOqlap4572bta94UuVyGL4KwSdtUq1Gw8RV9jv/zDb8ChqREv048Fa57Hw6YuW/PZxj9nE4peudcu3NuGjAGOEZEJvv+K4FRwGLAu0qQafWeln5QRK4SkbkiMre21rwf+jWhHB9o8e1ASDg2ciqc/P+0Xb9cE5M1bdDIVkgW5H70q9oOZpeQqqD2HcClpzKIk1eQ3ldqawzD2COvG+dcA/AscEasrx24G/iE76oBxgKISB5QBmwiBefcLc656c656dXV1Xs1eaOHWbdQlXEq22L/vFvWR+1Vr6p9fPjkqPh200b1e2/fHplngp2+pQFyC7VcXzDDBOX9pi8akqro9z9pnx7JMAYCnfG6qRaRct8uBk4D3hGRib5PgLOBJf6SB4FZvn0h8LRzHSUUN/oVvz4BbjoivT8e/bo1puhrl0D1IWpiCav3pg3w+wu1XbG/yuJyldvqAQfTr+x4DqkbrvF89IZhZKQzK/qRwDMisgB4HXgSeAS4Q0TeAt7y53zHn38rUCkiy4Brgeu7fNZG95Ep3UAqa+Ylj+OKvsaba5zTPDPDDtHjojLIyYcNS6DW+8yHFX1+iUapzr1NV/pbN3T83pUHZJ7vmKN3P2/DGKB0xutmgXPuCOfcFOfcZOfcd5xzO51zJzjnDvd9lwUvHOdci3PuIufcROfcMc65DL/1jT7J+kXwH6PgoS+nj62OZZ+885zkWHNdtLKe/S2Va96E1sbI/i6ika7r5kfXhc1TEd10DZu3E1LMMQeernLqpR3P/UPXdjxmGAMci4w1Im4+XmU8wVhg3cKo3dIYtXe0qt1+6P7J80PqgjHTo77SqmR5vvjmaVF51J708eS9Gn0K4rhZKHD6v8Px/wQHfiR9zDAMwBT9wOS+L8CcX+/6nI3vJo9DxSaAccdH7QeugU3vJYOS2rap8s8rghExP/l1C6L238WSmEHkZZNXHBUNCRzlbfZTP5U+z8HDVdl3lMzMMAxT9AOS+f8Lj//Lrs8JkauBl3+hcvRRUa1VgLfuUVn7jvrGg3rW1PlVfk4Hf2LjZiSPQ3bJUdM0M2WcY6+CbzXClE/ues6GYWTEFP1AY2ssZmFbQ8fntW2L2q1bo4CnoeOTpptA/XI4138ZNG/UVX6qR8zp/x61U6s3rfWr/ZWv7Gr2hmHsBaboBxr3fiZqb6uP2jt3xk4S9ZAJvP1A1C6pTKYeDpkhL/5d5A+/tRY2fQCVKYo+FN3ORIiAjSc+MwyjSzBFP9AIni0Az/xHrH+FylFHwNhjkgnIHvhHlVe/Dq/dou018+C9ZzQtwfFf1HQDwVd+9Vxob4WKFFfIEOU681/T53X+r7X/c0/t/bMZhpERKzwy0NjvBDWzQFRoG2CRr9965CxdwS9/0Z8TSxscNkxBvzCCy+VRf6dy6HhNXvbcD6LjOFM+CTu2wbRPp8+rpAI+fN2eP49hGLvFVvTZSlNdVAA7zls+pe+445Kr+2V+JR2iVV27Bj1t97lrTrpe/d3P/JG/wKnf/fDDoyCmnNwoRzxA9cHJ987JhemfyZyTxjCMbsMUfbbyowlwy8k+GViMsIofdYSOhewUQ8dDYZkW3T5gJridmqBshd8cHesjTyd9TOW2elj2ZHqu93gt2CGjuu55DMPYa0zR92dWvQ6Nq9P7Qx54SHqxBE+ak7+uir2tSTdW696DeXdpJCtEtvbGGlg7HyRHTT6gJhaA2qUqV7zYZY9jGEb3YDb6/opzcOtp2r6hIVl2b9WrUTvuQhnSCQ8dr8obtEh3zevJe4ei26vf0MRkQ8dHJpn8Ys1NU+tz2B21iwRkhmH0CWxF319provaqVGsz8WqN8YDn0IqgSGjocCX4du+NSrUHbxixh6rctlsePt+qJ6UvH9JZfSeh52fHAvulpMv7PyzGIbRrZii7+tsWQ8L/5Lev3Fp1N5ckxyrim2Cuph//JxfqSwbHa3QV8+Feu9a+SWfcKxwkHrPhMCoELUaKKmARr+RW5pSSyCs8CtSct8YhtFrmOmmr/NjXxd1v+OTCjdum9/Rmrym/gMYcTiMng6LH9K+thYt+Qe6og+FtZ1TH/rqSZFtHvQ4lP2bkFJztSRWtalyYnIsuFSm9huG0WuYou/LxDM9bng7qehbYrb3HS1Re+dONbkccraaUZo3wo7tKgN5hZHppq1ZV/Tl+yXfu6AENizSdqr3TMhpM3xyuqvk5E9A+VgYc0znn9MwjG7FTDd9mbACh/RiH3HTzbz/Te8vqYxW6M11kX3+zB+qDMW3tzdrANXQFEUf36DNZLqB9CyToAnJ9js+PTGZYRi9hv1v7MvE66+GXOzB7z2kIgB494mo/UdfnKO4IjKxNNfBHb5YyIjDVYaskm/crkFRqVGscYIXTiBUdYoXIzEMo89iK/q+TOvmyKTy6q/VLNO2Db5dnjxv2GFRe5O3q0/+RJQorKUhKqo9clry2vAFkmq6iZOa6/2kr6lM/RVgGEafxFb0fYH1b6snTGqU6db1an6ZcrF6yeTkJCNPAcrG6uYrJAOlRkzWYCfQKNbysar4C0oyzyFVaf/jHJj3h3T3SdDV/4W3R26YhmH0aWxF35XMvQ2+XZE0uQTa2zSvezxJGKjt/Obj4PvjYGd7cqz2Hag8EGZ+A070NVFbt0Tj18zViNW2Zi3QHfznD7tAZVjRb6vXgtupJpg4qSv6YYfA6d+F0UdmPn/yBfrlZBhGn8cUfVexrR4e/oomA6t5LX18wd3wvdGw4qVkfzy46aEvRe2mOk0BPGJy8vz4ir7qwCj3TP1yeN4nHPuoTz9cOkwjYBtr9BXMN4FT/y1qF2XYWDUMIyswRd9VxP3a41khA6OO1KIam95PGYilLnjzd9Gq/unvqKxKyQAZVvRn/afKEJgUf8/gJZNfpOX8al5Xe3/qCnzKxbt8JMMwsgNT9HuKc8kye4H2WNBSplJ7ww9V882y2cn+vMLkcbh3k/d7H/8hXY3fejq8OztysxzlTSplY1XGs1TG895UTIhyy4f0BIHglfOR76TP1zCMrMEU/Z6wrV49Xm4coTbxOHHl37I58/WDh0deLqBeNMFNMuR5D8FPO1qh6iDdPM0t0ERl9R9EXjUh13tRmaYr2PC2Hp+WUtR72ZNRauJBw5Jj+cWaEO34L+76uQ3D6NfsVtGLSJGIvCYi80VkkYh82/ffJSLviMhCEblNRPJ9v4jITSKyTEQWiEgHu3l9mEX3wTPfS+9fcE/UXpsSwBR8yyFKEpZKwSANUAqEyFNQMwtEXxjrF2oKA4giUZs2wuKHtURfoU9AJgKFg6MkY6NS3CfjDBqR3ieS/AVgGEbW0ZkVfSsw0zk3FZgGnCEiM4C7gEnA4UAx8Dl//pnAgf51FXBzV0+62/nT38Fz30830eTHXBOXp2yqvv+cytyCzKYbUH/0xlXR8bqFUTvPJxnb0aJ5abasjYpr5+bpfdtb1cNm+KHJ+xYNiSJiMynzQLwUoGEYA4bdKnqnBFePfP9yzrlH/ZgDXgOCS8e5wJ1+aA5QLiIj0+/cy6xdAD+ckNkVMrD6r8njFS9H7Rf+Mzk255cqR0xJmmfiLLpPV/vB7POId5k8+6bkij7kpSmNmVp2tutG7vatkX0+UFgGO70P/eAUF8qjPxe1c3Izz8swjKymUzZ6EckVkXnABuBJ59yrsbF84HIgJGYZDcSWrdT4vtR7XiUic0Vkbm1t7d7Of+/5zYmaGuBXMzo+J9U8M/8PKsOmZtwnfshojVCtnhTllQH4xdHw5A3J+4QAp7AZeuQVUdrgHS1aixWinDKgbptvP6DtVH/4+EZwUUrUbKrXjmEYA45OKXrnXLtzbhq6aj9GROLO3b8CnnfOveCPMxl8XYZ73uKcm+6cm15dXZ3hkm7ExaazLWVT9Z1YIrH65Zmv/9BXVAYTzbYG2LwaDjhFfdW3rNOMkTu2q+08eNZccrfKYKfPL4FDzlEbeTDdbHofXv65totjij5O6qr9wNOjdqq9/aDTYcgYuOLBzPcyDCPr2aMUCM65BhF5FjgDWCgiNwDVwOdjp9UAY2PHY4A1+zjPruXt+6P2hJOTY49eF7U3LI7aYbOzelIy4rS0EtYt0OOqg3yJPgdb1nifeBdFnQavl+1N+mtg4zsw3tdiDav09Qthuf/O3O/49Lkf8/mOc8BnYuh4uHZRx+OGYWQ9nfG6qRaRct8uBk4DlojI54CPApc4Fy9jxIPAFd77ZgbQ6Jxb2w1z3z1b1kXVk+Jsjn3vpFZIaowFHgWXxfg1H/pKZB4JOeHvOFvlpI9HQUmNq7WgB0R5ZIrKoGyc2tPn3qZ9w/zGaghyCqt5yOwNc9YP0xV7sNmnJiwzDMOgc6abkcAzIrIAeB210T8M/BoYDrwiIvNEJMTTPwq8DywDfgv8Y9dPu5P8+GD42RR4/9lkfzyP+lt/SppyAsf/k67Yd/rvsLBBOnJqZDrZ9EHSZ760MtpAbaqNvmTCir7yAPWa+eCFqLj29M+ozCtKvv/FdyWPP+x/afzquPS5Dj8M9v8wnHx9+phhGAOe3ZpunHMLgCMy9Ge81nvhXL3vU+tC1s5PmmjiicFAV/5D/AZrSSWMP1FNKW6n5movKots9yVVkfdKU21kZjn/NyoHxRT95tWQk5+s0FRSqa6TW9Zp9sdwr9RCHdUpm6ihbuyGt/WLKb7azy+CWQ915pMwDGMA0v8jY9+dDXeemzTHpJIaqfrE15PHYUO2sUY9cSoPiFb5q15TW/tbPliqtCpafbe3QoN3MJr4EZXFFYCoom9YpaacuFvj4BGq6Dev3rVtvWBQ8vgjsYjXnSkZMA3DMHZB/1b025vgrk+oaSa4Jwbe/H3Ujtva41x+n8pt9SpD/vZxx0PdMm0//vWkX7xI5EXT0qgmHcmJNmhz89QtsqnWZ4yM70ujq/ua11XRpyY/O/Gfo3ZhiqI/+GOahOzSe9ILgRiGYeyC/q3oV8XSAaf6j790U9RuivnpxwtuB/fFbX5TdesGlcMOUbMN6C+FoJA/8l2VYYX+4k/VDj9kdFSaD3SDd+sGWDUnPb9M3Af+oDOSYwd/LGqnruhzcuCCW+Cgj2IYhrEn9G9FH6+oFLdZ1y5V18VAUOCQjHaNl9oDeNbntymtVldJUPv80/+u7UPPia4tGAzHXaNBVdWTkvMqqYps9yOmJMfiaRRO+FJyLPxSKCq3/DOGYXQZ/VvRtzVn7o+bRCZ9XN0cQ573h3ymxmOuihT9nF/7KkzrYfjhkFegShxg5JRIaZeNi+571TMqNy6FMdOT77/ixSiYKnUsnvAsVZkH85OZZgzD6EL6t6LvKEvkyKkqqw6G4T6It7kuec4Z39esjwDr34rs86f73Ow5OTB2hn6Z5OTBmKOT5pmGlfDKL7SdWrkpTmVKIrGcXTg6le+nvyBO//eOzzEMw9hD+ndx8OAmmRreP6gavuVX1Iv8hmtTbWTHn/6ZyM4+YopukIYo2OGHR/fJK9B0BTt3wIhYPyRt6HH3SYAjLtdqUZBuow/vOyNDeEFuHlyfoTqVYRjGPtC/V/TVB8O0T8PSJ+B746Kc8P/3r/CwzwwZIl+bamGJ9zUP9ndQm3lbs1ZoKh6q7pOB3EJYPVfbg1OUeTx9T6pnzX4nRO1U80xY0RdajVbDMHqG/q3oJ54G5/0SVr8BrY3w0JfV//3ln8PcW/Wcvyn6jbD0/7Q95ujoHgUlumrfuNTnqokp5tyCqD0+prxBo1EDFROSY6EWa2q0K0QZK1PdJw3DMLqJ/m26CZz7S/jFUWqzj7tSOhcp+gX3wLtPaDu+QZpfonlpVs9Vk0ucUIIP0hOMBfs+pOd5z8mBf1mROf/79M/qvELqA8MwjG4mOxR91UQtu9e2LQp0uuxeXZ0Hu3xQ8qnkl0SumKl53ne1cQpw7BeSK/s4xeWZ+3PzYMYXdn1fwzCMLiQ7FD3AeTfrKvvxf9HjygNU5qRYp85KqQwVZ7+UhGHDD4Olj8FhF2Q+/8wf7N1cDcMwepD+baOPs32LKuVQhal8XObzKvZPHh92XtROXdEfdzUccjac9aOum6dhGEYPkz0r+nceh+d/5DdaJWkfHzQ8yleTmn8+pBCG9LJ7JRVw8e8xDMPoz2TPir64HHBa/q8spUTtRXdE7ZKq5FhBadTOK8AwDCPbyCJF79MZNNXCoBHJsWCvh6SfPGR2gTQMw8giskfRT/4EXP26tqsPSo7Fo1ND4rDA4BGafTJkpjQMw8gyssdGn1eodvlDz4tqqMb52I/T886AumBe20G+esMwjCwgexQ9qInmk3dkHjv6cz07F8MwjD5C9phuDMMwjIyYojcMw8hyTNEbhmFkOaboDcMwspzdKnoRKRKR10RkvogsEpFv+/5rRGSZiDgRqYqdLyJykx9bICIZXGAMwzCMnqIzXjetwEzn3FYRyQdeFJHHgJeAh4FnU84/EzjQv44FbvbSMAzD6AV2q+idcw7Y6g/z/cs5594EkNQKSnAucKe/bo6IlIvISOfc2q6btmEYhtFZOmWjF5FcEZkHbACedM69uovTRwOrYsc1vs8wDMPoBToVMOWcawemiUg5cJ+ITHbOLezg9LQlPokCq/4kkauAq/xhq4h0dL8yoLGLx8YBHVXh7o7325u57OqavX2//v6Z2FxsLjaXJB1UPkrBObdHL+AG4Kux4+VAVez4N8AlseN3gJG7uefcXYzd0g1jtT38fns8l11ds7fv198/E5uLzcXm0vn3i78643VT7VfyiEgxcBqwZBeXPAhc4b1vZgCNbt/s8w91w1hDD7/f3sxlV9fs7fv198/E5mJzsbl0/v3+hvhvhY5PEJkC3AHkojb9e5xz3xGRLwJfA0agtvtHnXOfE92d/QVwBtAMXOmcm7ub95jrnJu+q3O6kp5+v13RV+bSV+YBNpeOsLlkZiDPpbPv1xmvmwXAERn6bwJuytDvgKs7Oc/ALXt4/r7S0++3K/rKXPrKPMDm0hE2l8wM5Ll06v12u6I3DMMw+jeWAsEwDCPLyQpFLyK3iciGuIumiEwVkVdE5C0ReUhEhsTGpvixRX68yPcf5Y+X+TQOmVxFe2ouz4rIOyIyz7+GZXq/rpqLiFwWe695IrJTRKb1xueym7n09OeSLyJ3+P7FIvL12DVn+LksE5Hr93QeXTyX5b5/nojsck+si+ZSICK3+/75InJy7Jp9+nvpwnl0xd/KWBF5xn/ei0TkS76/QkSeFJF3vRzq+0U6SAEjIrP8+e+KyKw9ncs+0RnXnL7+Aj4MHAksjPW9Dpzk258BvuvbecACYKo/rgRyffs14Dg0FuAx4MxenMuzwPSe+lxSrjsceD923KOfy27m0qOfC3Ap8EffLkHdicejzgnvAROAAmA+cGhvzMUfLyfm5twDn8vVwO2+PQx4A8jpir+XLpxHV/ytjASO9O3BwFLgUOCHwPW+/3rgB759ln9mAWYAr/r+CuB9L4f69tB9mduevLJiRe+cex7YlNJ9MPC8bz8JfMK3TwcWOOfm+2vrnHPtIjISGOKce8Xpv8ydwHm9MZc9fc8umkucS4D/BeilzyXjXLqKPZyLA0pFJA8oBrYDm4FjgGXOufedc9uBP6LpP3pjLl3CHs7lUOApf90G1M1velf8vXTFPPbk/XYzl7XOub/69hZgMRrpfy7qjYiX4Rn/lgLGOTcHKPefyUfRrAKbnHP1/hnO6Kp57o6sUPQdsBA4x7cvAsb69kGAE5EnROSvIvI13z8aTdcQ6MrUDXs6l8Dt/ifnN/f05+9ezCXOxUTKtTc+l47mEujJz+XPQBOwFo14/E/n3Ca6N9XHns4F9Evg/0TkDdGo866io7nMB84VkTwR2R84yo9119/Lns4j0GV/KyIyHvVAfBUY7nx8kJfBLNTR30WvpobJZkX/GeBqEXkD/cm13ffnAR8CLvPyfBE5lU6mbuihuQBc5pw7HDjRvy7v5rkAICLHAs0uSnHRG59LR3OBnv9cjgHagVHA/sA/i8gEeudz6WguACc4545Es8deLSIf7ua53IYqq7nAfwEvAzvovs9lT+cBXfi3IiKDgHuBLzvndvUrqqPn786/l92SXcXBYzjnlqCmEUTkIOBjfqgGeM45t9GPPYraA38PjIndYgywppfm8pRzbrW/douI/AH9T35nN84l8CmSK+gaev5z6Wgu9MLncinwuHOuDdggIi+hpoFVJFeOPfG5dDSX951za/y1G0TkPvRzeT7t5l00F+fcDuAr4TwReRl4F6inG/5e9mIeXfa3Ipqe/V7gLufcX3z3evFZeb1pZoPvr2N82XUAAAGrSURBVCHz30UNcHJK/7N7Ope9JWtX9GGHXURygH8Ffu2HngCmiEiJt3WeBLztf35tEZEZ/ifeFcADvTEX/zO0yl+TD3wc/enanXMJfReh9mbgbz9Le/pzyTiXXvpcVgIzvTdFKbrBtgTdHDxQRPYXkQL0S+nB3piLiJSKyGB/TSmqELv1c/F/s6W+/RFgh3Ou2/4f7ek8uupvxT/DrcBi59xPYkMPAsFzZhbRM3aUAuYJ4HQRGSrqoXO67+sZemrXtztf6KpvLdCGfnN+FvgSukO+FPg+PjjMn/9pYBH6D//DWP903/cemsZBemMuQCnqPbDAj/0M743TzXM5GZiT4T698bmkzaU3PhdgEPAn/35vA9fF7nOWP/894Bvd/bfb0VxQz5/5/rWoh+YyHk1YuBiYDezXVX8vXTGPLvxb+RBqYlkAzPOvs1APuafQXw9PARX+fAF+6Z/9LWJeP6j5aZl/Xbk3/0Z7+7LIWMMwjCwna003hmEYhmKK3jAMI8sxRW8YhpHlmKI3DMPIckzRG4ZhZDmm6A3DMLIcU/SGYRhZjil6wzCMLOf/A5d1NQLSFKIqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47e6450358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ianimate(co2, n=len(co2), axis=1, step=25, autoscale=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "            <video width=\"640\" height=\"480\" controls loop>\n",
       "              <source src=\"hist.mp4\" type=\"video/mp4\">\n",
       "            </video>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f47e64234e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ianimate(co2, filename='hist.mp4', n=len(co2), axis=1, step=25, autoscale=True, kind='hist')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}