{"cells": [{"cell_type": "markdown", "metadata": {}, "source": ["# Pr\u00e9diction, COVID en France, s\u00e9rie temporelle\n", "\n", "On s'int\u00e9resse aux s\u00e9ries temporelles de l'\u00e9pid\u00e9mie du COVID en France r\u00e9cup\u00e9r\u00e9es depuis data.gouv.fr : [Chiffres-cl\u00e9s concernant l'\u00e9pid\u00e9mie de COVID19 en France](https://www.data.gouv.fr/en/datasets/chiffres-cles-concernant-lepidemie-de-covid19-en-france/)."]}, {"cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [{"data": {"text/html": ["<div id=\"my_id_menu_nb\">run previous cell, wait for 2 seconds</div>\n", "<script>\n", "function repeat_indent_string(n){\n", "    var a = \"\" ;\n", "    for ( ; n > 0 ; --n)\n", "        a += \"    \";\n", "    return a;\n", "}\n", "// look up into all sections and builds an automated menu //\n", "var update_menu_string = function(begin, lfirst, llast, sformat, send, keep_item, begin_format, end_format) {\n", "    var anchors = document.getElementsByClassName(\"section\");\n", "    if (anchors.length == 0) {\n", "        anchors = document.getElementsByClassName(\"text_cell_render rendered_html\");\n", "    }\n", "    var i,t;\n", "    var text_menu = begin;\n", "    var text_memo = \"<pre>\\nlength:\" + anchors.length + \"\\n\";\n", "    var ind = \"\";\n", "    var memo_level = 1;\n", "    var href;\n", "    var tags = [];\n", "    var main_item = 0;\n", "    var format_open = 0;\n", "    for (i = 0; i <= llast; i++)\n", "        tags.push(\"h\" + i);\n", "\n", "    for (i = 0; i < anchors.length; i++) {\n", "        text_memo += \"**\" + anchors[i].id + \"--\\n\";\n", "\n", "        var child = null;\n", "        for(t = 0; t < tags.length; t++) {\n", "            var r = anchors[i].getElementsByTagName(tags[t]);\n", "            if (r.length > 0) {\n", "child = r[0];\n", "break;\n", "            }\n", "        }\n", "        if (child == null) {\n", "            text_memo += \"null\\n\";\n", "            continue;\n", "        }\n", "        if (anchors[i].hasAttribute(\"id\")) {\n", "            // when converted in RST\n", "            href = anchors[i].id;\n", "            text_memo += \"#1-\" + href;\n", "            // passer \u00e0 child suivant (le chercher)\n", "        }\n", "        else if (child.hasAttribute(\"id\")) {\n", "            // in a notebook\n", "            href = child.id;\n", "            text_memo += \"#2-\" + href;\n", "        }\n", "        else {\n", "            text_memo += \"#3-\" + \"*\" + \"\\n\";\n", "            continue;\n", "        }\n", "        var title = child.textContent;\n", "        var level = parseInt(child.tagName.substring(1,2));\n", "\n", "        text_memo += \"--\" + level + \"?\" + lfirst + \"--\" + title + \"\\n\";\n", "\n", "        if ((level < lfirst) || (level > llast)) {\n", "            continue ;\n", "        }\n", "        if (title.endsWith('\u00b6')) {\n", "            title = title.substring(0,title.length-1).replace(\"<\", \"&lt;\")\n", "         .replace(\">\", \"&gt;\").replace(\"&\", \"&amp;\");\n", "        }\n", "        if (title.length == 0) {\n", "            continue;\n", "        }\n", "\n", "        while (level < memo_level) {\n", "            text_menu += end_format + \"</ul>\\n\";\n", "            format_open -= 1;\n", "            memo_level -= 1;\n", "        }\n", "        if (level == lfirst) {\n", "            main_item += 1;\n", "        }\n", "        if (keep_item != -1 && main_item != keep_item + 1) {\n", "            // alert(main_item + \" - \" + level + \" - \" + keep_item);\n", "            continue;\n", "        }\n", "        while (level > memo_level) {\n", "            text_menu += \"<ul>\\n\";\n", "            memo_level += 1;\n", "        }\n", "        text_menu += repeat_indent_string(level-2);\n", "        text_menu += begin_format + sformat.replace(\"__HREF__\", href).replace(\"__TITLE__\", title);\n", "        format_open += 1;\n", "    }\n", "    while (1 < memo_level) {\n", "        text_menu += end_format + \"</ul>\\n\";\n", "        memo_level -= 1;\n", "        format_open -= 1;\n", "    }\n", "    text_menu += send;\n", "    //text_menu += \"\\n\" + text_memo;\n", "\n", "    while (format_open > 0) {\n", "        text_menu += end_format;\n", "        format_open -= 1;\n", "    }\n", "    return text_menu;\n", "};\n", "var update_menu = function() {\n", "    var sbegin = \"\";\n", "    var sformat = '<a href=\"#__HREF__\">__TITLE__</a>';\n", "    var send = \"\";\n", "    var begin_format = '<li>';\n", "    var end_format = '</li>';\n", "    var keep_item = -1;\n", "    var text_menu = update_menu_string(sbegin, 2, 4, sformat, send, keep_item,\n", "       begin_format, end_format);\n", "    var menu = document.getElementById(\"my_id_menu_nb\");\n", "    menu.innerHTML=text_menu;\n", "};\n", "window.setTimeout(update_menu,2000);\n", "            </script>"], "text/plain": ["<IPython.core.display.HTML object>"]}, "execution_count": 2, "metadata": {}, "output_type": "execute_result"}], "source": ["from jyquickhelper import add_notebook_menu\n", "add_notebook_menu()"]}, {"cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": ["%matplotlib inline"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Donn\u00e9es"]}, {"cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["c:\\python387_x64\\lib\\site-packages\\IPython\\core\\interactiveshell.py:3146: DtypeWarning: Columns (17,18) have mixed types.Specify dtype option on import or set low_memory=False.\n", "  has_raised = await self.run_ast_nodes(code_ast.body, cell_name,\n"]}, {"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>date</th>\n", "      <th>granularite</th>\n", "      <th>maille_code</th>\n", "      <th>maille_nom</th>\n", "      <th>cas_confirmes</th>\n", "      <th>cas_ehpad</th>\n", "      <th>cas_confirmes_ehpad</th>\n", "      <th>cas_possibles_ehpad</th>\n", "      <th>deces</th>\n", "      <th>deces_ehpad</th>\n", "      <th>reanimation</th>\n", "      <th>hospitalises</th>\n", "      <th>nouvelles_hospitalisations</th>\n", "      <th>nouvelles_reanimations</th>\n", "      <th>gueris</th>\n", "      <th>depistes</th>\n", "      <th>source_nom</th>\n", "      <th>source_url</th>\n", "      <th>source_archive</th>\n", "      <th>source_type</th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>43080</th>\n", "      <td>2021-02-10</td>\n", "      <td>region</td>\n", "      <td>REG-75</td>\n", "      <td>Nouvelle-Aquitaine</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2510.0</td>\n", "      <td>NaN</td>\n", "      <td>213.0</td>\n", "      <td>1524.0</td>\n", "      <td>78.0</td>\n", "      <td>20.0</td>\n", "      <td>10227.0</td>\n", "      <td>NaN</td>\n", "      <td>OpenCOVID19-fr</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>opencovid19-fr</td>\n", "    </tr>\n", "    <tr>\n", "      <th>43081</th>\n", "      <td>2021-02-10</td>\n", "      <td>region</td>\n", "      <td>REG-76</td>\n", "      <td>Occitanie</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>2882.0</td>\n", "      <td>NaN</td>\n", "      <td>276.0</td>\n", "      <td>1886.0</td>\n", "      <td>121.0</td>\n", "      <td>25.0</td>\n", "      <td>12801.0</td>\n", "      <td>NaN</td>\n", "      <td>OpenCOVID19-fr</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>opencovid19-fr</td>\n", "    </tr>\n", "    <tr>\n", "      <th>43082</th>\n", "      <td>2021-02-10</td>\n", "      <td>region</td>\n", "      <td>REG-84</td>\n", "      <td>Auvergne-Rh\u00f4ne-Alpes</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>8453.0</td>\n", "      <td>NaN</td>\n", "      <td>400.0</td>\n", "      <td>3699.0</td>\n", "      <td>213.0</td>\n", "      <td>42.0</td>\n", "      <td>34579.0</td>\n", "      <td>NaN</td>\n", "      <td>OpenCOVID19-fr</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>opencovid19-fr</td>\n", "    </tr>\n", "    <tr>\n", "      <th>43083</th>\n", "      <td>2021-02-10</td>\n", "      <td>region</td>\n", "      <td>REG-93</td>\n", "      <td>Provence-Alpes-C\u00f4te d'Azur</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>5010.0</td>\n", "      <td>NaN</td>\n", "      <td>438.0</td>\n", "      <td>3581.0</td>\n", "      <td>240.0</td>\n", "      <td>33.0</td>\n", "      <td>24080.0</td>\n", "      <td>NaN</td>\n", "      <td>OpenCOVID19-fr</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>opencovid19-fr</td>\n", "    </tr>\n", "    <tr>\n", "      <th>43084</th>\n", "      <td>2021-02-10</td>\n", "      <td>region</td>\n", "      <td>REG-94</td>\n", "      <td>Corse</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>133.0</td>\n", "      <td>NaN</td>\n", "      <td>7.0</td>\n", "      <td>58.0</td>\n", "      <td>4.0</td>\n", "      <td>3.0</td>\n", "      <td>587.0</td>\n", "      <td>NaN</td>\n", "      <td>OpenCOVID19-fr</td>\n", "      <td>NaN</td>\n", "      <td>NaN</td>\n", "      <td>opencovid19-fr</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["            date granularite maille_code                  maille_nom  \\\n", "43080 2021-02-10      region      REG-75          Nouvelle-Aquitaine   \n", "43081 2021-02-10      region      REG-76                   Occitanie   \n", "43082 2021-02-10      region      REG-84        Auvergne-Rh\u00f4ne-Alpes   \n", "43083 2021-02-10      region      REG-93  Provence-Alpes-C\u00f4te d'Azur   \n", "43084 2021-02-10      region      REG-94                       Corse   \n", "\n", "       cas_confirmes  cas_ehpad  cas_confirmes_ehpad  cas_possibles_ehpad  \\\n", "43080            NaN        NaN                  NaN                  NaN   \n", "43081            NaN        NaN                  NaN                  NaN   \n", "43082            NaN        NaN                  NaN                  NaN   \n", "43083            NaN        NaN                  NaN                  NaN   \n", "43084            NaN        NaN                  NaN                  NaN   \n", "\n", "        deces  deces_ehpad  reanimation  hospitalises  \\\n", "43080  2510.0          NaN        213.0        1524.0   \n", "43081  2882.0          NaN        276.0        1886.0   \n", "43082  8453.0          NaN        400.0        3699.0   \n", "43083  5010.0          NaN        438.0        3581.0   \n", "43084   133.0          NaN          7.0          58.0   \n", "\n", "       nouvelles_hospitalisations  nouvelles_reanimations   gueris  depistes  \\\n", "43080                        78.0                    20.0  10227.0       NaN   \n", "43081                       121.0                    25.0  12801.0       NaN   \n", "43082                       213.0                    42.0  34579.0       NaN   \n", "43083                       240.0                    33.0  24080.0       NaN   \n", "43084                         4.0                     3.0    587.0       NaN   \n", "\n", "           source_nom source_url source_archive     source_type  \n", "43080  OpenCOVID19-fr        NaN            NaN  opencovid19-fr  \n", "43081  OpenCOVID19-fr        NaN            NaN  opencovid19-fr  \n", "43082  OpenCOVID19-fr        NaN            NaN  opencovid19-fr  \n", "43083  OpenCOVID19-fr        NaN            NaN  opencovid19-fr  \n", "43084  OpenCOVID19-fr        NaN            NaN  opencovid19-fr  "]}, "execution_count": 4, "metadata": {}, "output_type": "execute_result"}], "source": ["from pandas import DataFrame, read_csv, to_datetime\n", "df = read_csv(\"https://www.data.gouv.fr/en/datasets/r/0b66ca39-1623-4d9c-83ad-5434b7f9e2a4\")\n", "df['date'] = to_datetime(df['date'].apply(lambda s: s.replace(\"_\", \"-\")))\n", "df.tail()"]}, {"cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["from datetime import datetime\n", "gr = df[[\"date\", \"cas_confirmes\"]].groupby(\"date\").sum().sort_index()\n", "gr.loc[datetime(2020, 11, 11), 'cas_confirmes'] = (\n", "    gr.loc[datetime(2020, 11, 10), 'cas_confirmes'] + gr.loc[datetime(2020, 11, 12), 'cas_confirmes']) / 2\n", "gr.loc[gr.index >= datetime(2020, 9, 1), 'cas_confirmes'].plot(figsize=(14, 4), grid=True, title=\"Cas positifs\");"]}, {"cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["covsept = gr.loc[gr.index >= datetime(2020, 9, 1), 'cas_confirmes']\n", "covsept.diff().plot(figsize=(14, 4), grid=True, title=\"Cas positifs quotidiens\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Pr\u00e9diction : AR"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Autocorr\u00e9logramme"]}, {"cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 864x576 with 2 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["import matplotlib.pyplot as plt\n", "from statsmodels.graphics.tsaplots import plot_acf, plot_pacf\n", "data = covsept.diff()[1:]\n", "fig = plt.figure(figsize=(12,8))\n", "ax1 = fig.add_subplot(211)\n", "fig = plot_acf(data, lags=40, ax=ax1)\n", "ax2 = fig.add_subplot(212)\n", "fig = plot_pacf(data, lags=40, ax=ax2);"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### ARIMA"]}, {"cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [{"name": "stderr", "output_type": "stream", "text": ["c:\\python387_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n", "  warnings.warn('No frequency information was'\n", "c:\\python387_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n", "  warnings.warn('No frequency information was'\n", "c:\\python387_x64\\lib\\site-packages\\statsmodels\\tsa\\base\\tsa_model.py:524: ValueWarning: No frequency information was provided, so inferred frequency D will be used.\n", "  warnings.warn('No frequency information was'\n"]}, {"name": "stdout", "output_type": "stream", "text": ["                               SARIMAX Results                                \n", "==============================================================================\n", "Dep. Variable:          cas_confirmes   No. Observations:                  162\n", "Model:                 ARIMA(7, 1, 1)   Log Likelihood               -1665.458\n", "Date:                Thu, 11 Feb 2021   AIC                           3348.917\n", "Time:                        00:17:40   BIC                           3376.649\n", "Sample:                    09-02-2020   HQIC                          3360.177\n", "                         - 02-10-2021                                         \n", "Covariance Type:                  opg                                         \n", "==============================================================================\n", "                 coef    std err          z      P>|z|      [0.025      0.975]\n", "------------------------------------------------------------------------------\n", "ar.L1          0.2273      0.162      1.407      0.159      -0.089       0.544\n", "ar.L2         -0.1927      0.077     -2.508      0.012      -0.343      -0.042\n", "ar.L3         -0.0039      0.103     -0.038      0.970      -0.206       0.199\n", "ar.L4         -0.1243      0.102     -1.216      0.224      -0.324       0.076\n", "ar.L5          0.0696      0.078      0.895      0.371      -0.083       0.222\n", "ar.L6         -0.0285      0.076     -0.374      0.708      -0.177       0.121\n", "ar.L7          0.3801      0.056      6.750      0.000       0.270       0.491\n", "ma.L1         -0.6858      0.167     -4.098      0.000      -1.014      -0.358\n", "sigma2      5.996e+07   1.57e-09   3.81e+16      0.000       6e+07       6e+07\n", "===================================================================================\n", "Ljung-Box (L1) (Q):                   0.00   Jarque-Bera (JB):               289.45\n", "Prob(Q):                              1.00   Prob(JB):                         0.00\n", "Heteroskedasticity (H):               1.46   Skew:                             0.31\n", "Prob(H) (two-sided):                  0.17   Kurtosis:                         9.54\n", "===================================================================================\n", "\n", "Warnings:\n", "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n", "[2] Covariance matrix is singular or near-singular, with condition number 3.36e+32. Standard errors may be unstable.\n"]}], "source": ["from statsmodels.tsa.arima.model import ARIMA\n", "mod = ARIMA(data, order=(7, 1, 1))\n", "res = mod.fit()\n", "print(res.summary())"]}, {"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>cas+</th>\n", "      <th>pred</th>\n", "    </tr>\n", "    <tr>\n", "      <th>date</th>\n", "      <th></th>\n", "      <th></th>\n", "    </tr>\n", "  </thead>\n", "  <tbody>\n", "    <tr>\n", "      <th>2021-02-06</th>\n", "      <td>20586.0</td>\n", "      <td>20346.037004</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-07</th>\n", "      <td>19715.0</td>\n", "      <td>19741.530365</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-08</th>\n", "      <td>4317.0</td>\n", "      <td>14212.567962</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-09</th>\n", "      <td>18870.0</td>\n", "      <td>14869.262634</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-10</th>\n", "      <td>25387.0</td>\n", "      <td>23740.270465</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "</div>"], "text/plain": ["               cas+          pred\n", "date                             \n", "2021-02-06  20586.0  20346.037004\n", "2021-02-07  19715.0  19741.530365\n", "2021-02-08   4317.0  14212.567962\n", "2021-02-09  18870.0  14869.262634\n", "2021-02-10  25387.0  23740.270465"]}, "execution_count": 9, "metadata": {}, "output_type": "execute_result"}], "source": ["import numpy\n", "pred = DataFrame({\"cas+\":data.copy()})\n", "pred['pred'] = numpy.nan\n", "pred['pred'] = res.forecasts.ravel()\n", "pred.tail()"]}, {"cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [{"data": {"text/plain": ["array([20346.03700422, 19741.53036487, 14212.56796166, 14869.2626336 ,\n", "       23740.27046459])"]}, "execution_count": 10, "metadata": {}, "output_type": "execute_result"}], "source": ["res.forecasts.ravel()[-5:]"]}, {"cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [{"data": {"text/plain": ["2021-02-11    21924.732356\n", "2021-02-12    21223.595082\n", "2021-02-13    18260.072157\n", "2021-02-14    18045.532220\n", "2021-02-15    13187.175698\n", "2021-02-16    17328.572392\n", "2021-02-17    22102.419937\n", "Freq: D, Name: predicted_mean, dtype: float64"]}, "execution_count": 11, "metadata": {}, "output_type": "execute_result"}], "source": ["res.forecast(7)"]}, {"cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [{"data": {"image/png": 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WgdNmgT1rYaA9LQaBpE4t1WhyIdcVyBrNSUswmsDrtCFiIfjjO+GsD4DDQ1LYCKcsOrVUo8kBbRloTnmC0YRyEQWaAQmHX4RYiJjVS0qCtBkxA3nMC+tqNKcsWgw0pzzBiCEG/ca2GG07INBCwqrqFCVtLpBJSMZPYC81mpMbLQaaU4bdrQEuvWM9fYOxYcdDsYRaY2CKARIOPUfc6gEgaXWpwzpuoNGMiRYDzXHjyw/v4Ln9R74p0QsHujjYGeJQV2jY8WA0qdJKA82AAIsNov0kbEoMUhZDDHTcQKMZEy0GmuNCMJrg1/9uYN2eIxeD+u5Q+l7D7h2J43NalWXgr4TKMwBIGmKQsDrVhVoMNJox0WKgOS40GAN5OJ484nuYFoFZfsIkFE0OxQzyq2H6uQAkbF71rxE7IJFlrcGmX8Fd5+jgsua0R4uB5rhwuFttNhN9DWJQ36XuERwlBiq1VIlBDcxYA0DKsAySFtMyGBx9045d0LkbooEj7pdGMxXQYqA5LtQbYhBJHJkYROJJmvuUmycQGcoKklISjCXwO6wqZpBXDdPPASBpNyyDtBhksQyMUtcEWo6oXxrNVEGLgea4cLhHuXgi8dTEF3cdgF2PDDvU0D00q8+MGQzGkkgJxZagcgPlTwNPEVz0BVqnXQ1AwjpOAFmLgUYDaDHQHCdMF084NrFlEFr/feSD74bIkOvmUFcw/Xumm8gUhtKUEZjOr1b/XnQ7gXJlIcSEYRlkSy013UMDrbk8hkYzZdFioDkuHO7JzU2USkkO7N2BkEloeCF9/JAhJn6nbVgA2RSD4mSHOpBfkz5ns6qPd3y81FJtGWg0gBYDzXEgmkjS0q8G4oncRH/Z3ERBVM3SkwfXpY8f6gpS6ndSnu8a5iYy9zIoiBlikJcpBgKAmHCoA1nFwLA4tBhoTnO0GGiOOY09YaQEq0WMm000EInz3cd2USW6AZAHn0mfq+8aZGaxF5/TNiyAbLqM/LF2sDrBW5I+Z7cYloHQloFGMxFaDDTHHHONwcwS77jrDO557hC2UBt2kaQuVYGtew8MtAFQ1xViZokXv8s2zDJIb2wTaVUuIiHS56wWwzKwGJZB1piBIQYDWgw0pzdaDDTHHDMTaH6Fn8g4YrCjuZ81JeraB5IXqYN1zzAQidMVjDKzVIlBtpiBc7B1KHhsYDfdRJhuohGppakkxI3SFgEdQNac3mgx0BxzGrpD+J02qvJd48YMukMx5ji6APhnajVxRwHUrU9nItUWe/E77cOyicyYgT3YotJKMzADyAmJ2uBm5KIz0ypwF8JgFySir+EpNZpTGy0GmmNOQ88g04s9uO1WIokkcozSDz2hGNMtXUgETbKUjpKzoW49dZ1q0J5V6sXnsjGQGTOIJrGRwBJsUwvOMrAZbqJ4UoLdPbochbEbGiXz1b86vVRzGqPFQHPMaegepLbYi9NuRUqIJrJbBz2hGBWyg5Svghh2GgrOgoEWIm17AajMd+Fz2gjFkiRTSlCC0TjVohuBhILhloHdtAySEmzusS2D0nnqXx1E1pzGaDHQHFMSyRRNvcoycNmtAESzuIqiiSTBaILSRBuicDoA+zyrAChoexGnzYLPacPvUju1mrGCUDTJQqex4Kx4zrB7mqmliVRKWQYjYwamGJiWwThiEIjEOesb/2JjfU+OT67RnFpoMdAcU7Y29RNPShZX5eGyq49btoVnvSHl+imItWEprMXjsNJIORRMp6rnJUp8ToQQ+F02Cgng+PO7INhJMJpgnq1d3WSEGKRTS0030cjUUnP1cenEYtARiNIxEGVP28Bk/wQazSmBFgPNMeX5/V0IAWtml+A2LINsJSm6Q1FsJPCG26BgOgVuO/3hOMy6iNnBzZR6VVu/y87Zlj24D66Fg08RjCSYJdrA4Qdv6bB7Wg3LIJm2DIZvipO2DPKqwOEbN2YQTyprZmTFVI1mqqDFQHNMef5AJ0uq8in0OtJuomyWQU8oRoXoQZCCgunkue30DSox8MgQq+z1APicNqYLwxJo204olmC6aIPi2cPWGADYMwPI3lIIdgx/U3P1sdOvNsUJtEDderjn9RAbLhwxI84Rimox0ExNtBhojhnBaIJXD/dx3ly1KjjtJsoSM+gJxZgmDN9/wQzy3XYC4TjMvBCAVantAPhdtqHr2ncwEElQk2we5SKCjNTSZEplGvU3D7/AtAycfmUddO2Hv30Eml7O2E9ZkbYMtBhopihaDDTHjA0Hu0mkJOfPMcTAZlgGWRaedQdj1KTFYDr5hptIeorZJWtZFNkMjBCDth3EImFKku1jiIEZQJZqQVq0f0gAYOh3hyEGHTshYIhALDjsXqZlMHKXNY1mqqDFQHPMeP5AFy67hVW1hQC4HEbMIIsYpNcYCAvk11DgUWIQjCZ4PrmYaQPbIDaIz2lnmjDcPYNdzI7swILMKgbDAshmAbtM6yAaALsHrDYlBpDePzntQjKIJbWbSDO10WKgOWY8t7+Ts2YW4zQsAtMyyFasrjsUY66tA5FXDVY7+W47feEY3cEYm1LzsMo4tO/E77RQIzpp9y8BYE3cKHNdPGvUPdOWQTI1VNo6kOH+iQ6owDHAnMtg7uVw+TfU6zFiBtpNpJmqaDHQHHW2NPbxkd9t5mBniAvmDlURHT9mEGU5+6BqBQD5bjuReIqW/jA7U7XqorZteKKdOEWCfYXnA3CpfEmdK5o96p7pFcimmwiGWwaxoIoXAMx4Hdz0IPgrjHPDxSCeVIvcBrQYaKYoWgw0R5WDnUGuu+sFntvfyf+7cDY3nzMjfS6dTZStWF2glQrZAdPPBZQYANR1hmimhKQjD9q2I/oaAKh3zCXlr6JU9DNoLwJ3wahbCiGwWoRKLfVXAkLtk2wSHRgSAxOH2jd5VMwgqfqs3USaqUrOYiCEsAohXhVC/N14PVMI8ZIQ4oAQ4k9CqB1EhBBO4/UB43xtxj2+YBzfK4S4IuP4lcaxA0KI24/i82mOM239apXvz29Zxe1XLUgLADC0ziCLGFQPbFW/TD8bgLwMMQBBomwJtG2DXiUGTbKceOkiAAa8M0bdz8RmEaochdWuZv39E4mB4TYaIQbxhFH+QgeQNVOUyVgGHwd2Z7z+X+AHUso5QC/wPuP4+4Be4/gPjOsQQiwC3gEsBq4EfmoIjBW4C7gKWATcaFyrOQUxi8gVuB2jzg1ZBqPdRHMiO4hZXFCxTLX3qPYHO9WgbKteDu07oecgKQSNqSLChepjEs6bOWZ/7FZL2sVDXvXomIEzb3iDtGUw3E0U1QFkzRQnJzEQQtQAbwDuMV4L4BLgz8Yl9wPXGb9fa7zGOH+pcf21wB+llFEp5SHgAHCW8XNASlknpYwBfzSu1ZyCBMJqsMxz20adc9rMmMFwyyCZkixN7abNv0TN4MlwE3UFyXPZsFYuU1VHDzxFj6WYvpiFRocSAUvx6HiBic0qVG0iUHGDkdlETt/wBharKmo3yjIwAsixBKlU9qqrGs2pTK6WwZ3A5wBzSlcM9EkpzWlSE2DWD64GGgGM8/3G9enjI9qMdXwUQogPCCE2CSE2dXZ25th1zfHE3JLSdPNkYrEIHDbLqBXIfX09LBIN9BavTB8zxaCpN0yJzwkVS9WJls102ysZiCT4d2w2EWmnbNH5Y/bHZsm0DGpUzMAsoR0NjnYTgRKIMVJLpYTBcTbo0WhOVSYUAyHENUCHlPKV49CfcZFS3i2lXC2lXF1aWjpxA81xJxBJIAT4HKMtA1Bxg8iI2kThQy9hFZJwxZnpY6YYSAnFPocqJmdVrqM+ZxXBaILnOlxcX/gArrkXjtkfu1Wo1FJQlkF8EMK96sbZYgagXEUjs4kyym5rV5FmKpKLZbAGeJMQoh7lwrkE+CFQIIQwv/E1gGl/NwPTAIzz+UB35vERbcY6rjkFCYTj+Jw2LBYx+mQqicsmRsUMZMMGklIgaobEIM81JCbFXqdyH5UtBGDAXU0gHGdLYx9LZ4w/KbBahFqBDEOb3wSa1a5mqfgYYuAbvc4gOdRnvQpZMxWZUAyklF+QUtZIKWtRAeCnpZQ3AeuAtxmX3Qo8bPz+iPEa4/zTUm1t9QjwDiPbaCYwF3gZ2AjMNbKTHMZ7PHJUnk5z3AlE4uS5RruISMbhh8t5Lw+PchM5219ln6whv6g4fcxmteB3KkEo9hnBaCO4HPbW0B2KMRBJsHxawbj9cdgsQwN5fsYq5MxSFKMa+bKklg6JgV54ppmKvJZ1Bp8HPiWEOICKCdxrHL8XKDaOfwq4HUBKuRN4ANgFPA58REqZNOIKHwWeQGUrPWBcqzkFGYgk0hvQDKPhReg/zNXJp0cFkO2hFhplGUXe4RlIZtyh2OdUBwwxiPqmp69ZMYEYeB02Bs3BO20ZNA3tZTCmmyh7ailIil/6DnQdGPd9NZpTjeyO3TGQUq4H1hu/16EygUZeEwGuH6P9N4BvZDm+Flg7mb5oTk4C4XjW4DF7/gHAtFQzhYOHgNXpU65IJ+2ylkLPcDHId9tp7gtTYloGi98MvfUMepcBB/A7bcwuHZENNAKPw0ooaoiPrwwsNmUZmIP9WGIQGO6pNBedFRBk2o67oKwQLvjsuO+t0ZxK6BXImqNKIJIY5u8HVLB271qoUtlCZww8N3QuEcWd6KffVpTes9ikwGNYBl7DMvCVwpXfxONRawHOmFaQPTaRgdoz2bAMLFbwV6mBPrN89UiyxAxMy8BNTB0Y7B33fTWaUw0tBpqjykC2mEHbduhvhNXvYb9jEWeGnx86F1Qb1YSdZaPulZ92Ew23GHxGLGGieAGAx2kbnv2TX632KkiLQRbLwukbXuoaFTPwOW24hCEGYb0XsmZqocVAc1TJ6ibauxYQMO9KtvrPZ07yYLqsBANtACS9Y4tByQgxMF+vmlE4YX98TivBaEaMomIZNG2CvsPq9cgVyJA1tTSWTFHoteNKWwbdE763RnMqocVAc9RIpSQD0YwAcus2FSvY8RBMOxt8ZezOv0Cd2/0oAH0dar3hzJmj9yNIWwamm8hg1YxCfvcfZ3PR/InXmngdNgZjGZbBipshGYWNRr7DWDGDVBwSsfShWCKF12HDZzXFQFsGmqmFFgPNUSMUSyAlyk3U1wj/dz788Z3QtVcFf4GQdzqHqYTGDQAcOLgfgFWLF46635m1RZw9sygtCiZCCNbMKUGI8eMFoNxEg7HkUAmJymWqTHbXXvU6qxgYxzIyiuLJFE6bhUK7YWVoN5FmijGpbCKNZjwCkYy6RG1qz2Le8guoORMKawFVrK6OKqZ31wHQ1txAAguza0dXHr1sUTmXLSp/TX3yOVVxvFAsgd+MZax6N7S8ihQWUlY3FimHC0tmGWtPkfo1kcJhs1BgT0AMbRlophzaMtAcNQJhVZfI77JDxy51cN6VUDQTjMHWabdQl6yAnjrC0TjR3mYG7cUIi3Ws274mvEaweTCzBMaStxK3uBlIuZj9xce44s5nhzfKUrk0nkxht1rItxn3ifRDStco0kwdtBhojhpmmYY8lx06dkP+dHAND9C67VYOpCogEWbj9p2UyF5EXuUx65PXqJE0bNWw088zeddQL2q4YF4p+9qDhDPFIr2nwZAYmJZBvj1uHJEQ7jtm/dZojjdaDDRHjSHLwKbEoGx0HMBlt3JIqq0l9+x4lUpLH97immPWJ9MyGFlc7l73e/h6+Q94w1LVl+5QdOikmW6akV4aS0rsVgt+a8Z9dNxAM4XQYqA5agxEjfLVDqBrH5QtGHWNy2ahPqUG4FDrHiqtfVjyKo5Zn7xmzCA63KXTG06S73WlM5W6g0OZQ9ncRLFEEofVgt8aH7pOxw00UwgtBpqcCMeSdA5Ex73G3NimMHxYpWaWjd6wzmW30kYhKZuLksE6/KmAsT/xscF0E420DHoHYxR67OkFbcMsgyxuonhS4rBZ8Fq0ZaCZmmgx0OTEd5/Yy9U/eo5YIjXmNaabyBswirhlcRO5HVYkFsK+GawWe9RB32vLGBqPtJsoY62BlJLewTiFHofaOAfoMiyDUDTBEweMlNKM1NJYIoXdKvBatGWgmZpoMdDkxP6OAToHojx/YOwd5gaiCVx2C/buPSAsUDJv1DVOm3LbdNhrWGgxVgEfQ8vAl44ZDLmJwvEksUSKAo8jbRn0hJQYPLK1hU/+1RCzEesMHDYLHhEjJY00VG0ZaKYQWgw0OdHUGwbg4S0tY14TCBt1iTp2QdEssLtHXeOyq4+cGUQGwH/sLANPOmYwZBn0DqrZfaHHjsdhw2W30B1UbqLm3jBhjPIXmTEDI7XULWL04kNabNoy0EwptBhospJIpogbG7qkUpLm3jAWAf/c2T7mto+BSHwok6h0dPAYVMwAYEekZOjgcYgZZKaW9hpWQIFRMrvY60wHkFv7I0gsRC2erKmlLmKEcZJyFWjLQDOl0GKgycrH/vgqn/jTFgA6g1FiyRTXLKsiHE/yr93tWdsMRBKUuFLQU5c1eAxqnQHAhn6jyJywgqck67VHA6tF4LZbh9Un6suwDEAVvusyBKItoCygQZzDUkvjyRQOqwUnMSLSQcJZqC0DzZRCi4EmKzuaA2w8pAa7pt5BAK5bUUVVvmtMV1EgHGeetRVkKmvwGIYsg31xwzXkKwfLsf0YekdULu0dVAN/obGzWrHPmXYTtfZHAAiknEjDMkgkU6QkSgxkhAgOYo4CCOs9DTRTBy0GmlGkUpK2/ggdA1H6B+PpeMH0Ig9XLKnguf2dqG2thxOIJFiUMoKvlWdkvbcZM+gij4jFC/5jt8bAxDtiT4O+QdNNpCyDIq+DnlAMKdVz+502giknkZDaGjOeVM9qt1mwyxhhHETt+doy0EwptBhoRtEdiqU3gD/QOZAWg+oCD+V5LuJJSTg+ui7PQCTOnMR+cBWoAHIWTMsABN0FS6E8uzvpaDKyjLXpJipwm5aBg+5gjEA4wWAsyQXzSgniJhzsA0in0zqsFuypCBHpIGzL1zEDzZRCVy3VjKK1P5z+fX97kKbeMMVeB26HNZ2qGYwk8DiGPj5SSgLhBNMde1SJ6DHKSw+JAey95B6qFx4Py8A6PIA8GMfntOGwqblQiddJLJliX4eKEVw0v5TwHiexsHptCqPdZsGajBLBRciSpywDKcd8Vo3mVEJbBppRtPQNicG+9iBNvYPUFKo0UXPjmoERGUXRRApLMkzpYB1Urxzz3qabCGB6WSFY7WNee7RQbqIhS6ZvMDZsjwRzrcGO5n4AZpV6welDRlXMwBQDp9WCNRkhgp2A8KtNcuKDx7z/Gs3xQIuBZhQtfSqIWl3gZn/HAM29YWoKPQDDLINMApE4i0QDFpLpje+z4bBaEEJNpqcVjV6HcCzwOm3DViD3DsYo9GaKgVqFvLNFxQgq8t04vflY4koM4gnTMhBYkhFiwkVHwqhfpOMGmimCFgPNKFr7wzhtFs6aWcS+9gGa+sJpyyAtBiMsg0A4wRmWg+rFOJaBEAKXzUplniu9GvlY43VYRy06K/QM7atc7B2yDISAMr8Try8ftwzTPxgfchNZLYh4GJvTQ1PEpRrruIFmiqDF4DQikUzxvSf2ptMox6KlP0JVgZu55T7aA1FiidSQGJhuoiyWwVLLIaKuMsirGvf+boeV6cWe1/AkkyObm6ggUwwMN9H+jiBlfqcqVZ2Xj4cIdZ0DwwLIxMM43F7qBo19mbVlcFrzUl03A5H4xBeOYEdzP999Yk/WrLwThRaD04htzf38ZN0BntrdMe51rX1hKvNdzC0b2h/YdBP5ncq9MtIy6B+Mc4Y4SLgse0ppJtMK3SyfVjjZ7h8xPsNNZH7xlGUw5CYqMiyDZEpSka9Ez+b2YxMpQuHBDMtAQCKMy+3lwIAhJtoyOKqs29vB2u2tJ7obOdHUO8gNd2/gnucOTbrtH14+zF3rDrK/IzjxxSPovfet9D9/z6TbTYQWg9OIhm7lA+8Oxca9rrU/QmW+m7llPgQpbCRGWQbBEbOh1vZ2ZltasU1bNWE/Hvx/r+Mzl48uYnes8DhsSKkK1CVTkkAkPswycNqs6cB4ZZ5y/9hcSgijof50zMCNemaP10ej6SbSlsFR5RfP1vG9J/ae6G7kxPq9qmjjy4cm/xloa6rjKstLPL1n/InZSIKRGHmHn6JHi4HmtVDfpTJfxnMTJZIp2gMRqgpcTCvy8F3HPTzq+CLVRon/sWIGsaYtAHhrz5ywHw6bBZv1+H30fEaxumA0QX84jpQMswxgKG5Qka8GebvHEIPBgbRl4BBKRH2+PPow/iBaDI4qgUicxt5BkqlJuk8SMcJ//QQyMHYhxaPN+r0d2EmwpbE3XccrF5IpyarOh/mZ44fs2rFlUu+5YedBrEIyPbyHaHByn72uCdzDWgxOI3KxDNoHoqQkVBW4sZLiCusmFloa8Tz/bUAN5E6bZVRqaaJbmcqiZM4x6v2RY+5pMBhNDpWiyLAMYCijqKpAiYHDrfZujg8OpL/oLqm+THl+P3FsBP2z4ODTx/4BTiMC4QTxpByW3pwLrQe24N76K3av/+Mx6tlwookkLx9oY5Prw7wp9RS7jEy0XDjUFaIo1Q1AdeuT9Idzjzls3LUfAKuQ7NuwdlJ9nsgK0WJwGtHQY1gG44hBq/ElrMx3QetW/DJIh2Ma/PsueOFH8ItL+ZftE4TCkWHtrIEmUgjwjx88PhF4MiqXjixFYTJkGSh3mMtfBEBqsHsogCwNIcnPB2BX+ZugcQN07DnGT3D6YAZjG7ont37jQJOKM3Qd3nfU+5SNlw/14Iv3kE+QN1ufZ1ND7nWqdrUGKBFqTcuVlpd4fn9XTu2SKcm+Qw3p16HdT06qz+ZkcCy0GJxGmF+w8dxELUahtqoCN9StA6DsQ3+Hwhnw5Jegcw/TaCOvf+hLF00k8UXbCDlKwebIet8TydAGNwl6Q2bF0uyWQaXpJiqsUf8GW4kZtYkchmXg9XrxOKw847kMLHZ49TfH/iFOA6SUBIwstfoJBq6RtHYo/73onXwwl55DcOi5STVZv7eTMpvq42rLXnYfzP19d7b0UyqUJXGGpY6t27ekz+1o7ueS763ncBYxfPVwL/aIsih6bSVUdW+YlDutfgKB1WJwmhCIxNO7eQ3b/H0EwyyDg+ugfCkU1sJNf4a3/RI+9CIA0wKvpts09gxSSRcx78lnFYAqRwEwGEvSF84uBiVGemmFEUAW+dUAuAZb0wFkUwyEw0tNoZt9QTcsuBq2/B4S4/tjNRNjBvhh4lnsSDq71Oy6ON5KRyAywdXDaf3714n/5q0Qy90aWbe3g3MrVRkSGyk8h9els9Va+sJ86LevcM9zdVnb7moJUGkNwIw1AHgO/oN4MkUqJfni33ZQ1xViQ133qHZP7m6nxKKyj3pqr2E6bezatT33B23fOe5pLQanCeZMY2aJN12hMxutRtVOvyUOjS/B7IvUiZK5sOStUDiDdmslswa3ptsc6hqkWnRhKZh2rB/jiPA6s7iJvMPdROfOKmbNnOK0ZYDDSwAfrkj7UADZEANsLqYVelQBv5XvUumlux45Pg8zhQmEh+JQE81iM5FS0terBs/poj3rQDoeXc112FNRunfk5nbpCESo6wxxZpmx+ZOwcVbsZQ51hXhgYyNX3Pksj+1o46HNzVn7uqu5nyL6oGY1gcIlXJZ4jjt+9Qf+/vQzbG3sA2B32+gYxFO7O1hWpNbLlK+5CYDGDX+B1MTBaykl5X1bx71Gi8Fpgml2r5heQCyZGhUANmnpC1NZ4IKGFyEZg1kXjbrmgHsZC2I7VJE24FBngCrRg6d0xjHr/2vBm+kmGoxhswj8zuE1Gl83p4Tf/cc5w7KcOi0l+KPt6QCyLWnMOO0eagrdNPUMImddTMg/k9QjH4Wtf1Ib+/zrq7DtwePybFOJgBEvsAiyuknGonMgioipooI+EWH7/oM5t02lJK6IcjF1bPprTm3MbVPLDTdRqPYyLrRs5a0/eYbP/WUb88v9vGFpJQc7g6PcOB0DUeKDfdhkHHzl5K2+gSWWem5v+jBvev5arq/u5oyafPa2DQxrF4knOdARZJ4vCnYvvtoz6bOVcnXTnaS+UQlP/feEfc5Pji+SWgxOE8x4wYrparHXWK6ilv4wlflGvMDqgOmvG3XNYf9y8mUAOlXgtLOtGaeI4yw+ScXAYeyDHEvSOxinwGNH5FBptNdWSn68Mx1AtpuWgd1FTaGHgWiCV5sCXNz1WQ7a58FfPwA/WgHP/wD+/eNj9jxTFTN4PLfMT0NPiFSO/vBdrQF8Yij7qOXQ7pzfc39HUM3SgbLWZ3KaZZt1rjyJfkDgPesW/CLMLf5X+MU7F/PAB8/lwnmlRBMpGnuGi9qulgClQr0f3jI4+4Nw80P8a9kPiGHj9sotLKjIY0/bwDDrfTCmLII82Q+eYhAC23se4bu2D7JTzkS+9HOIj52BVd8dopzxU1G1GJwmNHSHKPU7mV6kVhL3hLL7uDsCUeU3r38Opp0NjtFlI1oLjNpDDS8AEOqsV69PcjdRKJqgIxAdtuBsPPrtZRQkOtNuImvaMnCni+x9/s/b6JAFfMjyVXj9/8DFX4R5V0IoYxaWTAzbT1mTHdNNtKQ6n0g8RcdAbnGYXa0B/AwNhNa+hpzjBlvqOygWA3Q7aiiWPTTt/veEbcLGwOxJ9IG7EMucS8GZx6eCd/D6h5ZjefFO5pardSj72ofP8He29FOC4QLylYLNCXMu5bK3vBf7wqspPvQIC8vd9IRidGYkepi1tbyJfvCoTDdf9SLOf+fn+d/IdYhYCPaP7eZq6A5RLsbPeNJicJpQ3z1IbbEnnULZlcUySKYkXcEolR4JbTtg2llZ7xXPm0GbLEQ2qGCy7GtUJ/Jrjk3nXyN2qwWHzUJ9V4hn93VyzqyinNqFnGUUyH6SMTXQ2FJmzMCdLs+xvyNIsdfBwZ4Ig2d+GC78nIqvDHal3Wg8+134+flH/blOdiLxJF99ZGfOawZMN9HSarXGI9eMol0tAcqccTVjBqaLDl48mFvcYF+dcik5zryVpBQ0bXgofS4cS2bNvDMHZmfcGJjtbvh/z8PbfgW158Ez32VunhKMkeUmWvojzHQZz+UrH3ZOLLsBQp2cJZVvf0/rkJCY1og70QfeoT3Dz5lVzLyzr6JL5tG/6U9jPmd91yDlpkUyBhOKgRBimhBinRBilxBipxDi48bxIiHEk0KI/ca/hcZxIYT4kRDigBBimxBiZca9bjWu3y+EuDXj+CohxHajzY9ELja8ZlI0dIeYXuRNF2XL5ibqCcVISVjAQZBJqF6d9V5+t52XUguh/kVCkTiesFFL5iQVA1Cuoke2thBPpXjfedl3YRvJoFttvOMKt2OzCCwJY1Czu9LlOQo8dr5w9UKkZMjP6ymBRGRor4POPdBzECK5L0yaCmxu6OW+F+v5zINbcyrIZqaVLq0pwEGc5rb2nN5nV2uAClccvGVIfyWLXN386sX6nN6ztakeAP/0Zex3LqKw8UmuvvMZzv3WUyz88uOc/c2nhm32BEMuG0esNy1AFM6AJW+BK78F8RC+bfdRle+ivrVz2P97KJqgym689pYN78zcy8FdyOzWtVxneZ4z/nqJmpRButCiM/M9DT5++SLWWc7FWfckMpq91lFDd4hKy2u3DBLAp6WUi4BzgI8IIRYBtwNPSSnnAk8ZrwGuAuYaPx8AfgZKPICvAGcDZwFfMQXEuOb9Ge2uzKFfmhwJx5K0B6LUFnvSRdmyuYk6DbN8RtjwudaMIQZOGy+lFiKCrbQc3Eq16CZh86jtLk9SvE4biZTk8kXlzCzx5tQm6q4EwDXYht2oWAqA3UO+2855c0r4wlULOHumsjR2mzM5c+YWMhYThVSAkt76o/EopwzmIscXD3bzu5cOT3i9GTNYUOHnS/bfccFzN03YZjCW4FBXiBJ7DJx+RGEtq/L62drYx/MHxl/M1TcYI95nTGR8ZVhX3Mx8Grg98XNeN6uQW86ZQSIl2XK4b1g7c5Zuj44emKlYCrMvhZf+j6sKDnP7/nfCn9+TPh2MJCizBEBYwT2iWKPNAYvfjHP3Q9zp+Cn54cNpV6y5basjy3vmu+2UnH0DLqJsfDL7Cuymrn4KGX8yMqEYSClbpZSbjd8HgN1ANXAtcL9x2f3Adcbv1wK/looNQIEQohK4AnhSStkjpewFngSuNM7lSSk3SCXlv864l+Yo0NCjzNIZJV5VlM1py+om6hhQftbygR2QPx18ZaOuAVWsbl1yOQDx3Y9TJbpJ+GtO6u0fzYVn7z8/N6sAIO5TYuCJtKstMhMRQIDVgRCC3/7H2dxw5nSqC9z4nDb2mOmA5pd10BiMgkYZgNNNDLoHsVsFa+YU8621u2mewF0UCCdwWC14HFZW2espDddBb8O4bfa3B5ES8i0RcPqgcCZliRYq8lz85OkDJFOSl+q602tsMnn1cB+lxkpg/BXMvfJDcMFnuSC4ljvc9/HFqxdgtYj0pkcmZszAGulJ+++HseZjEOrgv9o/SbHsQbZuS58KRhPqPX1lYMky/K56Nzj9/DXvZiI4oU+JaCiawEkMayI0WoCACy+7lh5LIWLjPWzaO/pvNtjdNNafMM2kYgZCiFpgBfASUC6lNGvNtgGmA6waaMxo1mQcG+94U5bjmqPE4zvaAFheUwBAhVdwSd13RpVRMC2DvO6tY1oFAD6nnVaKCRctJL/xaapFJ7bCkzN4bFKZ7+Ks2iJWzci9dHbKb1oGrUOWgd0zSvQsFsGCCj+7W00xMC0Dw28dMsXgCFbHnsIc7glRU+jha29aTCiWZP3e8WvjDETi+F02BDADVXCub/f6cdv0GOtGnKkQOP1QWIsYaOXD51VT0rCWN33zAW64ewPfeXx0yZDNh3spF71IhHLZCKESANZ8Ajbfj+vweuaU+tjZ0j+snXLZSES4F9xZxGDmhTDjPLoLlvGrxBWIUEd6QVsolqCYPvCWZn+gyjPg9sPsXvBRmmQJKUMMQ9EkhZhuyNFiYLHZsF70eVaKvVT9/mK2PTe07qV/MJ5Onx2PnMVACOED/gJ8Qko5TCqNGf0x36VBCPEBIcQmIcSmzs6JH04D8WSKP7x8mAvmlaY3lLnR8iQX9P0Ndg9fKNUxEKWUPmwDzROIgZpld1ddREX/FmZZ2rAVTj9mz3A0+Mk7V3Lfe8/MKaXUxOXx0yt9uMJtOG2mGLiyXruwMo89rUY6oNe0DLrVyuSIMZichpbB9CKPSlVm9FapIwlEEuS57TDYgzelfN/rnniIhzaPPas172mPB5UYFM0E4J0dd3CX40d8wL2OBRX+rLWD9rQNMNsdQnhLwGqsOxECLv5PyJ8G677J4kr/KMtgMJagwBZHJCJZB2aEgFsfpfEtD7M5NVcd68sY1FN9Y1rdZvv55X4aUyXEuuvT71kkRrghR5B/wYcYuOkfpKwOqp7+WPp4Q8/EmUSQoxgIIewoIfidlNIMt7cbLh6Mf03ZbwYyp4k1xrHxjtdkOT4KKeXdUsrVUsrVpaVjKKsGIJ2j/dTudtoDUW45x1gDEOnn+kHDr9g33JzsHIhyrtOYvY4RPAbStf+bSy/AShIvkZM6eAwqZmAWrMu5jcNGmyzCH+tQG9uYlkEWFlbmMRBNqFXJmW6iUMak5TQSAyklh7sHmVHsweOwIsTosucjCYSVZUD3AQCSznxeZ93Nlx8eu4yCeU9LPAjOPFU6BbDteACAa2fK9AKwwIg9OALhOBWWvlFZPdiccMFnofkVrnBupWMgQmfH0IY7oViCaofh8somBgAWC3PKfByWxqBv/N8HownyU72j33ME88r9NMlSLP2GmyiWpFCMbRmYFMx9HbuqrqdE9qZjVvXdg1SIictd55JNJIB7gd1Syu9nnHoEMDOCbgUezjj+LiOr6Byg33AnPQFcLoQoNALHlwNPGOcCQohzjPd6V8a9NEfAur0dLPjS4/z4qf3c92I91QVuLllgfCifvxN/KkAXBWl/pElnMMrZjkNgsUHlsjHvb1oGzb7F9AtjN7T8k9tNdCR4nTZaZREF8U4jZhAGW3bLYEGl+jvsbg2oQcliV19GM17g8KmCaKcJvYNxBqIJphd5EEIwwxEctSHSSAYicfJc9rQYWJffSHmqnfxo65j7BQQjCQQpLDHDMiieoz6/C66B6lUw0MLy6QVICYGHb4eN9w61jSYokn3ZB+bl74TCWi468B2ecXySkp8uhE616c5gLJlefTzewOx32Yn7je+FIQahaBxfondsN5FBsc9BkyzFEeuHSIBQNEFxDmIAEMpXZeQT7SoRpLk3TLnoQ1rHX1+Ti2WwBrgFuEQIscX4uRr4NvB6IcR+4DLjNcBaoA44APwC+DCAlLIH+B9go/Hz38YxjGvuMdocBB7LoV+aMdjW2E8smeKOJ/exoa6Hd549HatFqMFpw8/YVXIFLyQXI0cE5zoDUZaJA1C+ROVOj0F6H+SoTAeST3bL4Ejwu2y0ymJKUp0ZMYPsf5cFFX6EMDKKhFCm/GD3kGVQvRL6G9UCtFOIQ10h3nH3v/nCQ5MoiMZQobkZxV5o38WT4sOsOfx/47YJRBJDloHFpgZk4GzL7jFdTAOROF5hZMY5/Sqg+9GNcP39aoISaOWMaQUIAcX7HxxWYXYgkqAw1QP+itE3ttrh0i/jGGyjhzwEElpV/v9gNEm5zUjhzBZAzqCotIqwcENvA6mUxBYLGKUoxnETAXluO03SEIz+RkLRJBVpAcruJjKJFqpdBGOtu1TzcJwKS2/258wgl2yi56WUQkq5TEq53PhZK6XsllJeKqWcK6W8zBzYjSyij0gpZ0spl0opN2Xc65dSyjnGz68yjm+SUi4x2nxUnky7RJ+CtAXClPic3PXOlbx+UTk3nmX48/f8AxJh9s1+L42yBALNwwanzoEIsxMHoGrFuPc3LYNDXSH+klhDzOaH0vnH7HlOFKZlUCQG8FoT44qBx2Gjttg7PIg82D1kGUw7G1IJCEyc1XGy8OjWFq7+4XNsqOvhwU2N6Yyctv4Imw+P74M+bKSVzij2wIafYifBxV2/TQ+o2RhmGRTOhPKlRO0FnGPZzcBYYhBNUO40LA6nYaUWzVIxgLwqGGglz2VncYkVd6If2ndBUl0fisTIS/SM7bJZ8lbEf3XwWe831V4d3WqBWiiWoNQ6sWUAUOB10CLKobdetTOzlyZwE3kdVpoxBv2+wwzGEpRZBwAB7oJx21rzqxmQbpLtKmgeiMSptvQhJthrRK9AnoK09KltK9+wrJJfvGt1em0Be/4BBdMRFUtokqWIVAIGhnyh1oFmPKmgypUeB6fNgt0q2N0a4LnUMl546ytjBrVOZXxOK61SfdnL6VFiMIabCGBRVR47W40vu6dIWWJmJlGNsZr7FIobfPuxPcws8fKrd59JIiX5h7FR/cf++Cq33vvyuLWDzEJz050h2PYAzzovZMBSAA9/JD0YjyQQNi2Dg4a7x0Jv2VmcY9k1yt9vEowkKLMbaaMO3/CT/kqIBSES4MIyozxFMgpd+5BSYov2YiU5/sBstTG3uoR2UaIWDqLcRGYp6YnEIM9tpzFVqsQgmhwSgwncREII+h3G4N13WKWkWkJqbYLFOm5bv9vBAVmN6DLEIBxXAeTXahloTj6+8NA2PvibTWOeb+uPqPpCA23w909CuA+iQahbD/PfQInfNWSCZuQxz0gY9dcnEAMhBH6XPT0LrinKHlQ91fE6bbSi3ACVdKmYwRgBZIAlVfk09oTpH4wbbqIuCHaqQapsobroFBGDcCxJc1+YK5dUcNH8UuaV+3hkSzMvH+rh5UM9DEQT45aLaOgZpDzPiWvL/ZCM8vfCW/i5/8PQth22/mHU9fFkinA8SZ7Lqgbd4tkAREuWMN3SyWAw+4KpYDRBqcN0E+UNP5lnDKYDrawqyFiZ27qNaCJFQcqwbvzjz9IXV+VxIFFOskvFMgbNYK6wgCt/3Lb5bjt1yVJkXwPBSJwSTMtgfDcRQMJdREw4DcsgSZFlIKdJV57Lxv5UNY4etQFVIJKghB4ljuOgxeA4kmsVxol4Zm8nz+zrJDFGUK21P6zq8u/8K2z6Jaz7ptqrNxmFBVdT5HXQJE0TVMUNuoJRFokGlXNdtmjCPvictnT5ALNOz1TD67SxN6UCgHMS+yEeGTO1FNSgASjrwHQThTrULDCvCml1ULdvx3Hp+2vlUJca6GeWeBFCcO3yajbW9/K1R3fiMMp8j0y5zORwV4iL/C2w8R6Y83oC3lk8Lc9UlpURiM3EdANV0q0W9xUbe2kbopDszr5RTDCaoNieETPIxBz8Ai0scPcBqM932zYGIgnKzFo9vvFnzAsq8qiXFcjugyAlg7GEyvnPYZae57JTnypFxAeJ9relt7ucyE0EkOd20GUrh74GQtGEWkE8gSUCKnC9T9bgiHTBYA+xUD8eGYY8LQYnBQ9vaWbefz3GLfe+xJ9facqpbko2ekMxWvojROKpUUWwQM3wA5EElQVuaHxZHdz4C3jxx6pcxPTXUexz0CJL1BfDsAw6BqIstBwm7JuuVnJOgBk3KPE5cDvG/0KcqngdNrrJ50CqioXRbeOmlkKGGDQbX9pIPwRajNWmVnrsFezatW1UWeOTEVMMZpWq0h1vOkPNsne2BPjoJXOwW0evzE0TaOW77e/jf7tvg3AvnP8pfC4bwajhkjHjKBmYpSgqEkZWuSEGthIlBqInuxgEIgkKbYabaKQYmIPfQCsVqQ5i0kqzZyG0biMYTVBmlK6eyDIo8TuplxXYYgEY7CEUTZInA9kXnI0g321Pp5cmuw9RJvpICVtOpVv8LhttojxtGRTIXMXAxgFprNvt3IszYtR40pbBycHfXm3G77JxuGeQzzy4lT9ubJy4URZ2tQ59Abc19Y0632rsYVyZ74KmTWpzGlcBNL2sSitbbZR4nUirgwF7SVoMOgeiLBQNJEoX59QPM6NoqloFAFaLwG238nJqAXMiO5T/eZyYQbHPSWW+S61YNReede5J+4dbLRXMEO1p3/vJzKEuNdEw6zhNc0X4S96d3O/6Hu953QzmlvlHrcw1ie9eywxaWD/rM/DpPTDjdficNrWhkq8cgqML0Jnlq0tixvfCEANXuVq0Ze/PnpYbjMQptBrxgHEsA0ugiW5rKfuts6FtG8FwLMMyGF8MCtx2DknDeug5yGAsgT+V28Cc57bRaIiB6DnIGywbiJQuzV6KYmRbl10FkfsOE4omyEv15/iedvanTDHYgytsZLTpmMGJZzCW4IWD3bx5RQ3rPjCfHxf/hR/9Y1POpX0z2WXMxlx2C1ubRn8Z2wwxmGYLQP9hVQnx0i+rkwuvAVT5hMp8N5228rQY9PT0MEN0YK0aP15gYu4UZlbvnKr4XDY2pBbiToUg0jduyi3A4qp8drQEhtL/wr1p/3BDqowZooPHtp78GUV1nSEq811qoV7bDrj7QlbFXuZCNuPvfJXFVXnUNXcg20a7vUKHtxCQbvqXvift4/Y5bYSiCaR/DDEwLIOCcAPYvemBy5dfSKfMwzWQvUZRMJog3zKGm8juVq6cgVboO0yPvZzdzIRogFhXHWWil4TdB47xCxcWeOzUG2KQ6jpAOJ7El8xtYM7PSBGdvuceZlg6GFh924TtQA3qh1OlEO7FH23Bm8zdMmimhJjVA5178MdNMdDZRCec5/d3EUukuHRhGZaXfsYbQ3/hm/yYLz6UW2nfTHa1BqjIc7F6RlFWy6DFKLc7PWys2qw5UxW/et+TaiGOQXWBW31IjbUGonMXFiFx15yRUz9OB8sA1CD2cmrB0IEJxSCPus4gEUfB0EGjVPFT0QXkiUFWtP950hu+HykNrz5J17eXEeicnCVa1xVSVkHbDrjvDSoF+dZHVcB0w10srvTz9fh34ReXqlhKBqmWreyWM5hdNjQ4+1w2UhIS7tKsYmC6iXzBBiiela7/5LRZOSwr8IWyVz0NRhLkmbucjRQDUANgoBX6Ggk4K9mWUGnWjpaNrLLsI+mZ2Hfvd9lpoowUFpJdB5DS2OVsgjUGoGb3URxE3GXkDxxgd2o6lgVXT9hOva+NQ3H1HnfG/gcpLLDwjRO2s1stuOxWuly1pOrW8yHxEHGLU8cMTgae2t2B32njzBmFsPNv4CnmYrGZM+r+jw11Ey8TD73yJ+LP/gD2/ZMDzR0sqspjWU0+e1oHiMSTw641LYOCni1q28rKM9QXa9pZwwqsVRe6qYsVp9cauHvUakVLZW6WgRkzMHf8mqp4nVbaKKbHTPObQAyWVOeTknAwlHGdr5RgNMFD4RXszTuXz9r+xDMbN0+qHy8c6Jq0Jbm1sY+/PfwQJZEGnrjny+mqtCbZKnmCKiVR1xnkzLwe+M2bVZzkvY/DzAvUxGL3o1zecS8XW7cikhHoyggIp5J4+/Zy0DqbRZVD2T3mbnNRl5rpkhheQt10E7mCTWqNQQat1koKIqPFLJmShGJJteWlzaUWio0kr1IlSQTbGPRU8WqkEoSVRa98mSWinoGzPjHWny+N1SJwu9z0OSpIdh0EJK54X05ikO9WfQq6ldvmJ4nr8Lly22kvz2Vnf0y9x0zRwpOzv6AWL+aA32Wn1T4dS+cevCLC+lU/mdAC0mLwGglE4kQTyTHPp1KSp/Z0cMH8Uhxtm5Xr5vKvE19yA5+wPUTTS38Z9/6NDXU4Hv0Q9qe/Cr+/nk/0fYvFVXksqykgkZLDYgigYgYlPge25leUENicWe9bXeBmT7RQbWITaKYgsJeg8OZcVuJ0sQy8Rj2jRr+xEM82sWUAsKsvow6St8wIGgsaX/d1LEKwZNOXct4KM5mSvO/+jbz3vo3p/ZgnYk9bgJvveYkZNlU59erIWm796T/508bDHOwM8vE/vsrK/3mSh7eMLgPWE4oRiCS4qfmb6vPxrr+pzVsAznw/IKja9hMOpAyBbN811NfO/ThlBCqXYbEMTT5Mt2LYabjPRgSRlZtIYhtogoLhRQ/bbdXkxzvTlT9NzLpEXgazWwWg4gYdqn9xXw2dEYEsW4iQST4W/yiW5e/I3m4EBR477fZqRM9BPESxynjO/nuAVv8y2rwLeEKehcue27CbZ8QqpNXFzxNvpL7mupzagbIqnva9kb5l/8GV0W8zWL1mwjZaDI6Qx3e0csP//ZvlX/snn31w25jXbWvupysY5bKFZSrV0+qABW/Afu0PqbPP4ep9XwEjf3kkA5E4z/7uW1hlimuiX2f39Bu5SLzK8sIoZ0zLp0Z00rzjhWFtWvvD1OTZoeVV5SIag+oCN43ptQYNVIf30eSYnfOeBKdNzMCsw5RviMEElkFlvosir4PNnQIw/pa+snQGUWn1HDYv+DQrE68SuWPpsFo5Y9HcGyYST7GnbYCfPL0/p36v3d5GMJbgymlJcBfhFRHennyMz/9lO5fe8QyP7WijwGPndxtGu1/qukK4iFIysAtWv3f46vKCabDkreDM4yu+LxPHnh5sARp2bQCgeuHZw+5p/h2DdmMAHSUGCUoIIBJhKJgx7Fy30yh1MmKNhikGHhkeWwzyqkAqAU36a0hJCF75Yx48417+njo33a+JKPA4aBJV2PoOcYVlozo4gQ8eVM4/wNPTbuP/5t2D2+nIuXKu32UjgI/t73yFbyduxOvMPWvP77KzTcylbvV/0Yc/LUrjocXgCOgfjHPbH16lPRBhSXU+/9zVlt7wYiRP7W7HIuCiuSVKDOZcpvyudjfPr7qTiLSS+P2NalHYCP7rwY1cFV1LV/UlNLvn8+lDq7EKyYqBZ6jwO/mF84dc+MptQ3vtotxEq9wtaoHUeGJQ6B5aePbQB5mf3EdjXm4mKMCqGUWcNbOIaVPdMjDFoOhs5YooGN9yEsLY26AjPFQ2wFuqqpmixPPMt32GLxXfwY5ICfzjU8iGf4874z9oZPYsqPBz1/qD7GgeShy4a90Bvrl296g2XcEoRR4HrsE2VQpj3pW82/IPts65mxfK7+DZG328//xZtDTsIXzvm6BxY7rtoc4Qi0QDFpnMXprk2rvgts0U1synXlQPE4Ou/RuJShvLVwwXA/PvGLAZ+0mMiBsMROLMcxou0xGWQb/L+JsbK4ABkJJwj9rzwG3uZZCNjHRKq1Fmvds/nzrHfJw2tTd2LhS4VRDZGg/yv/a76Ss7ExZdO2E7m9WC12GlPxwnGEvlLD6g3EQALWElAt5JVN3Nc9kYiCQIhOPD7jUeWgyOgMd3thJPSn5840o+d8UCIvHUmFvsPbW7g1UzCins2gQDLbD4zelzK5cu45PxD2Pr2Qc7/jys3UAkjmfPnykSQcou/xTvv2AWu+KV7JEzKKx7BHHgSRZSR16yZ1hJidb+CCuEMXscZ0+C6gI3rbKYhNVNKj7If8Xfw+ElH8n5b3Du7GIe+OC5OX+ZTlXMQSzmrYLP1aktDSegMt9NeyAylFHkK6OxdxCPw0qR14HDZuGT730XX/R8mbi0cv8vf8LCLz8+ZqpmXadyJ/3s5lWc7WnhWw+sJ5FMUdcZ5PtP7uPuZ+uG9l826BqIUuJzQn8T5FfDRV9AuIvIT/ZRnWql4m83cLPzeR5w/A/uxmdg6+/TbQ92BVluM1I5s4mBzQG+UhZU+NmeqCHVPlRi2t6xg2Z7Lfm+4ZMEs+x5n8W0DNqGnQ+EE8y2G5sBjRCDoNd43VPHjuZ+3v2rl4ntf5rZv1nNHNGEMzk4evWxibkKGYGzWFkYvYOxoaJ4OVLgsbM/oRIBWmUx9Zf8XP0dciDfbScQiROKJdKfp1zIc6trW/pUrGcylkGey85AJJ5eGJrvnvh9p/Y3+Rjx8JYWZpZ4WVKdx9mzivC7bPxzZ9uo61r6wuxqDXBdbRIe+oDKN59/Vfr8oso8drlX02crgQNPDWu7uyXAu61PEChYBDPWcOu5tRR67GzOuwTR9DI88Z/EhYoHyJYtgEph7Q/H1WpZb+m4/v/KAhdxbPz+jN/w/FX/5LfJ17Nk2sQ+0NMNn/EFdFgtKgCXg4lfke+kYyCK9JSoGIPDR2NPmGmFnrSLoMjr4K73XMgB3yre6HwVi5A8vKUl6/3qOoMUuG3M3H8/v0t8lv/u/Ty/e243d/xzH06bmnnetW64q7E7FKPKk1TpsPk1ULUcPr4FPvgMfGAdFEwn/58fx2+Nc1BMQ5oLFFGWwTmuwyr/fpyFStOKPOxNTcMy0ArhXtr6wkyPHyRaumTUteYg2CuMQXuEm2ggEqfWll0MbN5CesmDnjqeP9DF+r2d9NW9gpAp1lh2Yk8GJ7YM8qrI96vFlH2DcYLRBP4cZssmBW47z0fn0DL9jbw3/lkceROXkzDJc9uVZRBNTk4MjP61BZQYTGY/Dr9LVQjQlsExpCMQ4d913bzxjCqEENj/+Z98q/RJ/rW7fVR5iKf2dFBGL2/f9WEVLLzlr8M+tBaL4Px5ZaxPLkPWrR9WQbTxwHbmW5pgxc0gBF6njd/9xzmsue6D6oLuA2yZdxspKRhsUJkp6QVnoT1QtXLcgctps1Lmd7IjVsbmToEQsLh6/DorpyM+p/oS2a2575BWkecimZJEXcVqdasQNPUOjsq8mlPmZ+HFN1Ica+bt00Os3d6aNdX4YGeQb7h+C098AWpfx0xLO/lP384/trfyH+fP4uZzZvD3bS3Udw0FpLuCUeY4DUsjb0R5cX8FvPsfcPaH2Hzxb/h7/Ezl6okq66KuK8QS6pRVMM5naHqRh73SmHC072LLrl0UiwEKZq4ada3pHhmICRV4HeEmCkTiTBNd6tyIFfB+l43DlEP3QWVxAYku5TJabdmLLTFOANm0DPKnUehRM/newRgDkfikXDb5HgetUTsvLPsWB2X1pGfpgXCcUDSRnlzk2g6GMgQnIyR+l82wDAwx0DGDo8/ft7UipbE8v/55eOlnXNP5C86KvMgrI7bWe2p3O5/1PYYt1AY3P5S1ANwF80r4Z3QJIhqA5lfSxy11TwPgX3Jl+tiiqjxmzF4I08+F/OkElrybQ7KCePMWAFr7IriJkBc8OGEZaoCqAjfNfWG2N/Uzp9Q3qS/H6YL5pXfYcv8Sl+epVcoNSz8Gb/oJUkoaewazZ17NVznn78jbRlNvmB3No0s8NHf0cFX4UTjjRsS7HmXgrE9yneVZ3u1+nvefP5P3nT8Tm9XCz9YP+dS7BqLMsBufx/wsW4p7i+Gqb3PW2eezOTUXIVPQ8ipSSnp6e6iMH57wMzStyMMeo3YTHbuIH/o3AKXzRrsn/ek9MBKqFtDAcDHoG4xTSccoq8BsezBZjuw5REdApaRajf2kV1v2YYsNjK5YauIpVkkbBdMo9KgBsXcwTnCybiK3HSlJi9FkSrCkLYNIYnIxA8O102qsHZpsADkST9ETjOGwqXUHE6HFAOgbjPG3V5snXACWSkn+tqWZRZV5zCn1wr++Bv5KkhVn8B373bz86pb0tYOxBC8e7OZ8+x7E9HOhZvRsCWDV9CKeTy0hhQUODrmKqrpfpN1WhTAKdQ3j+vvhvY9TVZLPDjkTZ6daBdraH2axqFdf7BzEoLrQTXNvmG3N/Syt0VZBNswv76Qsg3xDDKwzYOb59A3GCcWSTMtW3dVfATVnsrD/OawWwdodw0tVDETi5IXqsSBVORGLhfwr/pP+inP5Cj/Hv/1+yvwu3riknKd2qbbhWJJQLEm1xXC75GURAwO3w0q9y6io2vgy/eE4sxN1ajOXCT5DxV4H/fZSwlY/tGzhrPqf0SwqsGWJVTltFqwWQSiaUCuyR1gG7YEIFan2McWgPlWBCDTR19+n+h1sIClsVIoeLJGesS0DIeDCz8GKm8lz2bEI9X0PRic3MBcYQtJs+u8nE8x1q2BuMDq5mIHZP9Pin2wAGaC5L5yTiwi0GJBIpvjgb17hE3/awrYs5R1MIvEkH//dBj7Q/t98P+/3qhpj08tw4eewvv1+7BbJlbs+l86Ffm5/F+5EgPLwQag9f8z7Vha4CAofbb5F6bhBLBphSWwbTUXnZm/kL4f8aqoL3exMzcAdboVQN029Yc6wGgW9chCDmgI3DT2DdA5EWaZdRFkxv7yTCZRXGJaBOYts7FWfiTHTcBe8AVvbFq6bEeWxEa6iQ10h5gljwZVZTdZqI/+9DyHmXg7/+DT8/Hy+tf8N/DrxWcLROF1BNXsuS3UBIiOIOsYz5pfQYp8OTRtpC0RYZsntMySEYHqRl0ZbLWz9PeXxJh4o+1jWwKoQAp/TpnYsG1GsLhJP0jsYozA+lhjY2S3VcX//HpzE8EXbqSvK+F6NJQag9jOedREWiyDfbTfcRJOMGRhiYC78c+cw0zbJNyyDUGxyAmRmIpnWkGcS1oj5bE294bSFMRGnpRg8tLmJW+59iS1P/pZDP7yavYdUSYaxMoISyRS3/vJl5uy9m2usL7Gg8QFY+xm1UnLFLVA0k7/P+RqzEwdJPfRBSKV4dl8nFzr3qRlW7dgLPuxWC5X5brY5V0HLZhjsoXn7OrwiSmLWJeM+R57LziG7Ueq3bSsHOoKc4zysZoITVGIEZRmY487SmoIJrz8dMb+8ZtnmXCj2ObFaBO3Gl7ixRw0gY6bhLnkbOHz8Z+g7tHb3sScjM6iuM8R8SxMpi0Pt4GXi8MANv4PX3QYOHz0lZ7LI0kDvrqfSYlCY6FADb7aVuRlU5LvYIeZB00Za+8IstdSp7Kkcau5PK3KruIFM8UTqLMIzxv7MpovV+ctVNpHx4Wvrj1BKP7ZUdNQaA1CWwc5Urerr4H5qRCcWJDv85zMgDYEdK5toBIUeh9qfORKflJso360ErqUvjMdhHbagbiLyXHaC0QTByOQsA1AuppgRi5xszACgqXdQWwZjsactwO0Pbaetbidznv80cwP/5q8l/8fSCg/P7etIB9EyeXRbC4H6V7nN/ggsewd8cqcq/vaWX6S/aNYFV/ONxDux7HkE1n2DHc39XOU7oHLTq7O7iEymFbl5JnWGWhzz3B1Edz1BXFopXXrZhM/Tl2/MFlu3sq99gKWWupysAlDppaCW22eWDtAMYa60noxlYLUISn3OdBaIaRmMWbqjYBq89R6KArv5rv3/2NwwVKKkrjPIPNEEJXPVVo7D3sgGl38d3vsYjZf9nD7pxfbq/XQFVZkJf7Q9p72pK/JdvByfDYPd2PatZY1lJ8mK5Tk967QiD/+KLCDpKeVrsZuZVTJ2yQOzWB2+ckjGVKYTKlumRhjF1LKIgc9po4Vi4o4C5stD1AqVudcoqthpMRbEjWcZZFDgsdMbihnZRJN3E5liMBnMkhSJlJx0XM4cyK0WgXMSn0HTMugdjOcUPIYpIAbRRJJ3/+plvvS3Hemt9sYiEk/ysT+8SrET1lb/CrvDybppH2VmcDM/lN/may0fQH57BmTkTadSkl+s28OP3PcgPEVw5beUn/f8T8O0oUVd8yv83Ju8msYZb0E+/32SbTtZmdqpagKNURLCZHqRh6cHpsHym+DfP2Fe3f28ynxqKyee3ecXldEmyki2bKWrq4OKeFPOYlBliMHcMt+U3ZPgtbKsJp//d+Fszp41ubTb8nxX2k3U0B2i0GMf3y0x/yrkJV/iTdZ/Y9v/WPrwwc4QC21NWMrH33CoqrSQvyQvoLjxnwS7VYqqK9yWPXg8gso8F89GlNVx/uZPIJDYz8+tsua0Qg8Px1bzyGXraKGEWaVj74Wh9jRIpEtGtzbVw0MfxLv57gwxyO4mAkG7dz5LLIdY5FQWfAMV7HYYJddzFINCj4PW/ggpyaQGZjMTKRRLTirFE4Zn8ngn+T0zBcvjsOa8cjmzHQzFDybilBeDP21sZP3eTv7w8mEu+t467n+xfsxrf/3IE7y7+07WOT+FvWMbzrf+nIvf9w0471PM6t/AoHSqDV+2P5hu89SeDq7r+SVzUwcR1/xgzOJUc8p8WITg0fIPk3Lk8T+WuykLH4AZ5034DNMKPbQH40Su/hFc8wOSWNiad1FOpmhNoZsdqVrkoed5v+URdTBXy8DwYZ+hXURj4rRZuf2qBZOe0VXkOdMpgbtaAizMwfKyrPk4A8JHVdtQIkFrRweVshNKF4zTEsr9Tv6UuhSrTFBW9xdAYgu2jE4rzdY238V+WU20fAWv5r+eG+13Yqt93YTtQE1kANbvUwO0uRlONnxOtcFN3K3cT42PfR+2/ZHFO77LFVZjG9csK7zNweyAdRbzRBOrvJ30SS8Ng052e4xgdQ4WEEC+x06zsRp8MjGDzAH1SC0DmJyrB4aEZLKfv8z3PC0sg0g8yU/XHeTM2kJeuP0S1swp4Ztrd2e1EPrDcWq33cn19udwTV8J198HZinZS79M5BP7eEfq69T5V6nKolIipeTlx3/LB2z/IHXm+9P7AWTDZbdSW+xlW7eFbfM+ygrLASNekIMYGF+opr4wqZXv4XXylxye9c6c/gY1hW7+HH8diUScD9seUWVucxSDPJedj10yh5vOGT0b07w2KvJctAUixJMpdrcNsCSXAL3Vxl7/OSwJvQSpJKmUxG7sYzvRVqQ2q4VQ3mwOupexqPkvzHQFEfHB3CyDfBcSC1uvfIg78z+Hu2Bii9TE/Ow+t7+LPJeNYu/Yq3JVADlOc0LN4s/qeQTKFhGy5XONdYNasZ2lsqbpqtscm4ZTJFgdfZkGWc7+jiBt/iXw6X1qUV0OFHocaR+8bxJuIpvVkp5tT3pAz3ifybuJhiyDyTDcMjgNxOCBTY20BSJ84rJ5lOe5+M7blmGzCL708I5RaaJ/ePkwc2U9g7WXwTt+N6wsBELgKijnrJlFPBI/E3oPQds2Nmzdzkf676A7byGWK74xYX/mlfvZ1z7Ao/bL2SunI3OIF8CQL7mxJ8yBziCdURvLphXk9DeoLnDzeOosPlr1ANfGv0Hs5kdyKq1r8qnL57NMWwZHnfJ8FwORBNua+oklUulqphPRWXkxhQSI1L/E/o4gM1JGIbmyhRO2rS5w80f32ymMtfB16z3q4DhppSZm9lNrf5i2/kj6dS6YGVI9oRizSn3jujKUZZBgfygjkH7lt/hDsVEGJYuLCIZm8OsDKivKF++iQZbTH46rgok5JEuYmGsN1H0nNzCbcYNJWwaeI7cMzGefbLtM0Zny2USt/RF++K/9nFVbxOtmF4OUVL78LX64vJln9nXy2I6h8hCxRIo/Pb+bGZYO8mvHLsZ23pwSftO7BCmsyB1/xfn323CIJL6bfzOh3x9U3KC+O8SmwwP8qOQriHf8ftwN1E3MLJPG3kE21auFQqtrcxvQTVfP83U9DBQtwTl77DRWzfHDHFCf3qPy6RdX5Za6a533euLSysDWR9l8uJf5oomUzZ01sDqSqgIXawcXs979etYkjcJzOQaQQWX1tAUi6de54HXaKPEpa2A8F5F5bSiaZE+voF96+FdyBf0Va/hH4mye8l0zZuE3r8OKRcD2SAmDqO9hvVQCMNmZdoFnyHLxT7atkVE0WTHInJlPxhqBoYF8MmsMQFkyZj+nvGXQE4pRVeDmy29cpGYjdevhhR9y6Y7Pc21hA/c+P7Rn6j+2t1AY3K8W7pSPrptics0ZVUhPMa9YlpB68SesTGxh/xmfw1k2N6c+za/wk5Kwramf4ukLYM7ERc0ASv1OnDYLjT2DvNLQS7HXQW1xbtVAzVWtkXiKeWW5BdE0xx5zFfJTuzvwOKzpvYQnYkZ1FRtT83EeepJXGnpZbGtGlC3Iac/c6kI3bYEI37e8mz6rEfDOQQz8Ljs+p4367hD94fikxACGPoPjZRLBUAB5f2eIt8W+ysfjH+VgV5DWQJQnaj8H530iaztzjYLEwiGr2vimIVWRvudkKMwUg0nEDGDIMpjswJzpvz/SbKLJrD42MS2fKR8zWFyVx6O3nTfki332e+CvRBTO4Nvx/6WvcScdRjbHfS82cEG+seKxYmwxqC5wc8+7VvNw7EysMs4r9pUsfdMnc+7TvPKhwXhJjjNBUB/2aUUeDvcM8kpDDytnFOacOVDosacXwMwrHzuTQ3N8McVgT9sAiyrzsOaYl15b4mGdXEFeYD+VB/7EYksDYoJ4gUl1gYdkSrK9R/CXmV9Ta2C8uRVUK89z8urhPoBJuYlgKIg8XiYRDM3EtzX1ES2cRwg3e9sG6AxGqcgff58Ic+Bucc8DoNPYeW6yg2umm2iyQmIO6p5JDswehzX9/3+kbqLJZjDBkJCcHtlEkQCkUtDwIjQ8D2s+Djf+CYdF8pTjM/CrK2nY9DhbG/u4orgLnPkT7uS1uraIC9/6IX7H1aTedBeWSSw2qi32pPPRF1dPLm9/WqGbbU391HcPsnpGYc7thBBpV9G8Cm0ZnCxkzq5zjReAyl7a5T+PFIJPR+/Cn+ofdwV7JubnQEoYqDgbrv1JThYFqLLb+9oHRvU9F8yYVy5uIoD67kEunl+K3SrYUNeNlCqIPR7mLLex8Gxw5hHMmzPseK4McxMdccxgcu2EEGkh8U06LfXIgtYw9Hz5U90yIJWAO+bD9xfC3z6sMhFW3golc7B86Fl+6XgnlkATBU98DJcV5sp6KF+cUwniy5bP4cYv/54zl+Y2IzOxWS3MKfXhsFqYO0mXzbQiT7oGyapJiAEMLR7LtEw0Jxaf05aetU62Gqy7Yi7XxL7JldFv8/INW2D5jTm1qy4YGlBLfBPHuDKpyHeRMnIuKieYpY/kkgVlXDS/lFkl41sGmTPx+RV51BZ7eeGAqp80kTViDmxd1ZfB5+vxFZQOO54rhd7MnP/jEzOAodn5ZN09aTfREbynaVVMeTcR8QjEB5VPdKBN1R9xKHNVFNbSvuJj/Hf0RvLj7dw2swlb1+5xXUQjmcxy80wuXVjGpQvLJr3pixlEdlgtuaUhZjC9yIPdKqgtzs0vrTk+lOepAXkyLkOA2aU+dqVmsF/MYMns3PakhqFFhHAEYpAxGE/WTbRqRhH3veesCT/zmQHbOWU+Zpf60qUzJrJGzIGtPM8FFiuVRh/NEuO5YsYMfE5bzq47kyONGYCanTttFmyT8DRAxqKz12AZ5BpAPnVrFifULJobfqM2sBgx479ycQXveGYlfVYvt4R/D7HguMHjo8WnL58/8UVZME3tpTX5OZWbzeSDF87ikiMQIM2xpSLfRWNPmLmTjOXMLlPXL6z0T8ol4XHYKPI66AnF0hk+k+krqEHrWK1G944UgzIvGIv9J3ITmVZWmV+JXLlx/WT9/i67FZfdckTl2k0X02RjBqBm50fynkOLzo7cMsjVejq1xcCZl1UIQK2qLczL41/JC3lb91p18DiIwZFiLt6ZTLzApKbQk71WvuaEcvH8MqoL3NgnORucbQRiV02f/GehqsBliMGRWQYTDcqvBXMwLPI6KPI60s/pslsm9GubA1rZiH4eyQBb6HEcmRi4j9wyKPE505bFZCj1O/E4rEe013hNoZsSnzPnyeWpLQalS8aMAVgsgh+/cwV5vR+Gh9eCsOS0cOdEMafMxxuWVvLmlRMvEtKcGvzH+bMmvigLCyv9LKrM46qlY285ORbVBW52NAco8U8+ZgBDWVDHAnNAn2OIgCkGFXmuCbPnhtxE6rnOmlnEGdMK0veaDEdq/ZjxhiMJ5n7mivn0D8Yn3S7PZWfDf1466cAzwPvOm8n1q3Mr0wGnshjEI1A6vkvmzNoiVRtow1JVJdFx8s6enTYrd9009oI4zemDx2Fj7cePbPHgnDIfLx3qmXTA0ZxpH0vLwBxEZ5ep2JaZfZRL9tKMYg8FHjtlfnXt7FIfD39k7NLw43H5ovJJ++4Blk8r5EvXLOL8uSWTbltd4E4nekyWXH3+I1Eusdw/B6euGKTiExbvApTl8Pb7h2IMGs0U5sMXzeHtq6dNqsIlKNdNdYF70skLkyHPZWN2qZfz5piZQHZqCt3MKJo48eHtq6fxhmWVRyUu9qkjjOtZLYL3nTfzNb//yYqYaKvHk5XVVVa56ZnHYe7rT3RXNJopQSolEYJJC8lroaE7hN9lp2icAneao4cQ4hUp5eh9STmVLQOY0E2k0Why50jTqV8LM3Q69EnDqZuLKKw51WrXaDQazcScNGIghLhSCLFXCHFACHH7hA1szpyX2ms0Go1mfE6K0VQIYQXuAq4CFgE3CiHGrwWRQ2lojUaj0eTGSSEGwFnAASllnZQyBvwRyF7c3MSmxUCj0WiOFieLGFQDjRmvm4xjwxBCfEAIsUkIsSkwGDtundNoNJqpzskiBjkhpbxbSrlaSrk6r1Sv1NVoNJqjxckiBs1AZnnGGuPY2IiTpesajUZz6nOyjKgbgblCiJlCCAfwDuCRE9wnjUajOW04KRadSSkTQoiPAk8AVuCXUsqdJ7hbGo1Gc9pwUogBgJRyLbD2RPdDo9FoTkdOFjeRRqPRaE4gWgw0Go1Go8VAo9FoNFoMNBqNRsMpvJ+BEGIA2JvDpflA/xG+zZG2PRbvWQJ0Hef3PFbtjqSt+fwn4jlfS9uj8Z4T/d8fi/c8Xu1yaTvW859On4XJfgbGes/5Ukp/1iullKfkD7Apx+vufg3vcURtj8V7TvS8U+U5J/r/PhHPeaL/Rrl+1k/155zo//5keM4T+Dea1GdgrPcc7z6ng5vo0RPQVr/n1HrP19JWv+fUes/X0va1vOeRkvN7nspuok1yjO3bpiKn2/OO5HR+/tP52UE/Pxy9v8F49zmVLYO7T3QHjjOn2/OO5HR+/tP52UE/Pxy9v8GY9zllLQONRqPRHD1OZctAo9FoNEcJLQYajUaj0WJwMiGEqBFCPCyE2C+EOCiE+KFR0nus6z8hhPAczz4eK4QQUghxR8brzwghvnoCu3RcEEIkhRBbhBA7hRBbhRCfFuL03KxDCBE80X04UWR8Dsyf2nGuXS+EOOoB9dPyQ3cyIoQQwEPA36SUc4F5gA/4xjjNPgFMCTEAosBbhBAlJ7ojx5mwlHK5lHIx8HrgKuArJ7hPmuOP+Tkwf+qPdwe0GJw8XAJEpJS/ApBSJoFPAu8VQniFEN8TQuwQQmwTQtwmhPgYUAWsE0KsO4H9PlokUJkOnxx5QghRK4R42nj2p4QQ04UQ+UKIBnMWbfyNGoUQ9uPd8aOFlLID+ADwUaGwCiG+K4TYaDz7B81rhRCfF0JsN6yJb5+4Xh9dhBA+4/94s/F81xrHa4UQu4UQvzCsqH8KIdwnur/HEiHEKiHEM0KIV4QQTwghKjNO32JYEDuEEGcdjffTYnDysBh4JfOAlDIAHAb+A6gFlksplwG/k1L+CGgBLpZSXnyc+3qsuAu4SQiRP+L4j4H7zWcHfiSl7Ae2ABca11wDPCGljB+vzh4LpJR1qA2eyoD3Af1SyjOBM4H3G7sBXgVcC5wtpTwD+M4J6/DRJwK8WUq5ErgYuMOwmgHmAncZVlQf8NYT08VjgjvDRfRXY1LzY+BtUspVwC8Z7iXwSCmXAx82zr1mTprNbTTjchHwUyllAkBK2XNiu3NskFIGhBC/Bj4GhDNOnQu8xfj9NwwNfn8CbgDWobZK/elx6urx4nJgmRDibcbrfNSAeBnwKynlIEy5z4MAvimEuABIAdVAuXHukJRyi/H7K6gJ0lQhbAzuAAghlgBLgCcNLbQCrRnX/wFASvmsECJPCFEgpex7LR3QYnDysAt4W+YBIUQeMB2oPxEdOkHcCWwGfpXDtY+gBo4iYBXw9DHs13FBCDELSAIdqIHxNinlEyOuueJE9O04cRNQCqySUsaFEPWAyzgXzbguCUxlN5EAdkopzx3j/MgFYq95wZh2E508PAV4hBDvAhBCWIE7gPtQe0N/UAhhM84VGW0GgOwVCE9RjFnuAygXicmLqJk/qMHiOePaILAR+CHwdyPOcsoihCgFfg78RKrVoE8AHzLjIEKIeUIIL/Ak8B4zkyzj8zAVyAc6DCG4GJhxojt0gtgLlAohzgUQQtiFEIszzt9gHD8P5Uo80mqoabRlcJIgpZRCiDcDPxVCfAkl1GuB/0TNguYB24QQceAXwE9QAdfHhRAtUyhuAEoEP5rx+jbgV0KIzwKdwHsyzv0JeBDlSjsVcQshtgB2VBD9N8D3jXP3oFwhmw2/eSdwnZTycSHEcmCTECLG0OfklMWY6ERRMaFHhRDbgU3AnhPasROElDJmuAd/ZMTQbCireadxSUQI8Srqc/Peo/GeuhyFRqM54QghzgB+IaU8Kpkxmsmj3UQajeaEIoT4f6iA6H+d6L6czmjLQKPRaDTaMtBoNMcXIcQ0IcQ6IcQuYwHZx43jRUKIJ4Uqx/KkEKLQOH6TsehuuxDiRcOlZN7rl0KIDiHEjhP1PFMFLQYajeZ4kwA+LaVcBJwDfEQIsQi4HXjKKMfylPEa4BBwoZRyKfA/DK/Jfx9w5fHq+FRGi4FGozmuSClbpZSbjd8HgN2oxWXXAvcbl90PXGdc86KUstc4vgGoybjXs8BUWnR3wtBioNFoThhCVedcAbwElEspzVW2bQytPM7kfcBjx6d3pxd6nYFGozkhCCF8wF+ATxilSNLnjHU3csT1F6PE4Lzj2tHTBG0ZaDSa446xqvovqKKLDxmH283KnMa/HRnXL0MtwrtWStl9vPt7OqDFQKPRHFeM1dT3ArullN/POPUIcKvx+63Aw8b101F7fdwipdx3PPt6OqHXGWg0muOKUU/nOWA7qjIpqHIaL6HqUk0HGoC3Syl7hBD3oMpVNxjXJqSUq417/QFViqQEaAe+IqW89zg9ypRCi4FGo9FotJtIo9FoNFoMNBqNRoMWA41Go9GgxUCj0Wg0aDHQaDQaDVoMNJojQgjxVSHEZ8Y5f51RfE2jOSXQYqDRHBuuA7QYaE4Z9DoDjSZHhBBfRK2M7QAagVeAfuADgAM4ANwCLAf+bpzrRy2YArgLKAUGgfdLKU/L/X01JydaDDSaHBBCrELVzj8bVeBxM/Bz4FdmrRwhxNeBdinlj4UQ9wF/l1L+2Tj3FPD/pJT7hRBnA9+SUl5y/J9Eo8mOrlqq0eTG+cBfpZSDAEKIR4zjSwwRKAB8wBMjGxrVOV8HPJhRmdN5rDus0UwGLQYazWvjPuA6KeVWIcS7UXVyRmIB+qSUy49ftzSayaEDyBpNbjwLXCeEcAsh/MAbjeN+oNUoyXxTxvUDxjmklAHgkBDielBVOzP38dVoTga0GGg0OWBs0/gnYCtqp62NxqkvoaptvgBkBoT/CHxWCPGqEGI2SijeJ4TYCuxEbfGo0Zw06ACyRqPRaLRloNFoNBotBhqNRqNBi4FGo9Fo0GKg0Wg0GrQYaDQajQYtBhqNRqNBi4FGo9Fo0GKg0Wg0GuD/A1Wy8LIacwJKAAAAAElFTkSuQmCC\n", "text/plain": ["<Figure size 432x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["pred.plot();"]}, {"cell_type": "markdown", "metadata": {}, "source": ["## Approche machine learning"]}, {"cell_type": "code", "execution_count": 12, "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>cas+</th>\n", "      <th>l1</th>\n", "      <th>l2</th>\n", "      <th>l3</th>\n", "      <th>l4</th>\n", "      <th>l5</th>\n", "      <th>l6</th>\n", "      <th>l7</th>\n", "    </tr>\n", "    <tr>\n", "      <th>date</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>2020-09-10</th>\n", "      <td>9843.0</td>\n", "      <td>8577.0</td>\n", "      <td>6544.0</td>\n", "      <td>4203.0</td>\n", "      <td>7071.0</td>\n", "      <td>8550.0</td>\n", "      <td>8975.0</td>\n", "      <td>7157.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-09-11</th>\n", "      <td>9406.0</td>\n", "      <td>9843.0</td>\n", "      <td>8577.0</td>\n", "      <td>6544.0</td>\n", "      <td>4203.0</td>\n", "      <td>7071.0</td>\n", "      <td>8550.0</td>\n", "      <td>8975.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-09-12</th>\n", "      <td>10561.0</td>\n", "      <td>9406.0</td>\n", "      <td>9843.0</td>\n", "      <td>8577.0</td>\n", "      <td>6544.0</td>\n", "      <td>4203.0</td>\n", "      <td>7071.0</td>\n", "      <td>8550.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-09-13</th>\n", "      <td>7183.0</td>\n", "      <td>10561.0</td>\n", "      <td>9406.0</td>\n", "      <td>9843.0</td>\n", "      <td>8577.0</td>\n", "      <td>6544.0</td>\n", "      <td>4203.0</td>\n", "      <td>7071.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2020-09-14</th>\n", "      <td>6158.0</td>\n", "      <td>7183.0</td>\n", "      <td>10561.0</td>\n", "      <td>9406.0</td>\n", "      <td>9843.0</td>\n", "      <td>8577.0</td>\n", "      <td>6544.0</td>\n", "      <td>4203.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>...</th>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "      <td>...</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-06</th>\n", "      <td>20586.0</td>\n", "      <td>22139.0</td>\n", "      <td>23448.0</td>\n", "      <td>26362.0</td>\n", "      <td>23337.0</td>\n", "      <td>4347.0</td>\n", "      <td>19235.0</td>\n", "      <td>24392.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-07</th>\n", "      <td>19715.0</td>\n", "      <td>20586.0</td>\n", "      <td>22139.0</td>\n", "      <td>23448.0</td>\n", "      <td>26362.0</td>\n", "      <td>23337.0</td>\n", "      <td>4347.0</td>\n", "      <td>19235.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-08</th>\n", "      <td>4317.0</td>\n", "      <td>19715.0</td>\n", "      <td>20586.0</td>\n", "      <td>22139.0</td>\n", "      <td>23448.0</td>\n", "      <td>26362.0</td>\n", "      <td>23337.0</td>\n", "      <td>4347.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-09</th>\n", "      <td>18870.0</td>\n", "      <td>4317.0</td>\n", "      <td>19715.0</td>\n", "      <td>20586.0</td>\n", "      <td>22139.0</td>\n", "      <td>23448.0</td>\n", "      <td>26362.0</td>\n", "      <td>23337.0</td>\n", "    </tr>\n", "    <tr>\n", "      <th>2021-02-10</th>\n", "      <td>25387.0</td>\n", "      <td>18870.0</td>\n", "      <td>4317.0</td>\n", "      <td>19715.0</td>\n", "      <td>20586.0</td>\n", "      <td>22139.0</td>\n", "      <td>23448.0</td>\n", "      <td>26362.0</td>\n", "    </tr>\n", "  </tbody>\n", "</table>\n", "<p>154 rows \u00d7 8 columns</p>\n", "</div>"], "text/plain": ["               cas+       l1       l2       l3       l4       l5       l6  \\\n", "date                                                                        \n", "2020-09-10   9843.0   8577.0   6544.0   4203.0   7071.0   8550.0   8975.0   \n", "2020-09-11   9406.0   9843.0   8577.0   6544.0   4203.0   7071.0   8550.0   \n", "2020-09-12  10561.0   9406.0   9843.0   8577.0   6544.0   4203.0   7071.0   \n", "2020-09-13   7183.0  10561.0   9406.0   9843.0   8577.0   6544.0   4203.0   \n", "2020-09-14   6158.0   7183.0  10561.0   9406.0   9843.0   8577.0   6544.0   \n", "...             ...      ...      ...      ...      ...      ...      ...   \n", "2021-02-06  20586.0  22139.0  23448.0  26362.0  23337.0   4347.0  19235.0   \n", "2021-02-07  19715.0  20586.0  22139.0  23448.0  26362.0  23337.0   4347.0   \n", "2021-02-08   4317.0  19715.0  20586.0  22139.0  23448.0  26362.0  23337.0   \n", "2021-02-09  18870.0   4317.0  19715.0  20586.0  22139.0  23448.0  26362.0   \n", "2021-02-10  25387.0  18870.0   4317.0  19715.0  20586.0  22139.0  23448.0   \n", "\n", "                 l7  \n", "date                 \n", "2020-09-10   7157.0  \n", "2020-09-11   8975.0  \n", "2020-09-12   8550.0  \n", "2020-09-13   7071.0  \n", "2020-09-14   4203.0  \n", "...             ...  \n", "2021-02-06  24392.0  \n", "2021-02-07  19235.0  \n", "2021-02-08   4347.0  \n", "2021-02-09  23337.0  \n", "2021-02-10  26362.0  \n", "\n", "[154 rows x 8 columns]"]}, "execution_count": 13, "metadata": {}, "output_type": "execute_result"}], "source": ["def lagged_dataframe(data, lag=7):\n", "    res = data.copy()\n", "    data = res.iloc[:, 0]\n", "    for i in range(1, lag):\n", "        res['l%i' % i] = data.shift(i)\n", "    return res[lag:].copy()\n", "\n", "lagged_dataframe(DataFrame({'cas+': data}), 8)"]}, {"cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [{"data": {"text/plain": ["((154, 7), (154,))"]}, "execution_count": 14, "metadata": {}, "output_type": "execute_result"}], "source": ["feat = lagged_dataframe(DataFrame({'cas+': data}), 8)\n", "X, y = feat.drop('cas+', axis=1), feat['cas+']\n", "X.shape, y.shape"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### R\u00e9gression lin\u00e9aire"]}, {"cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [{"data": {"text/plain": ["Pipeline(steps=[('standardscaler', StandardScaler()),\n", "                ('linearregression', LinearRegression())])"]}, "execution_count": 15, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn.linear_model import LinearRegression\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.pipeline import make_pipeline\n", "\n", "lr = make_pipeline(StandardScaler(), LinearRegression())\n", "lr.fit(X, y)"]}, {"cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.639550667481653"]}, "execution_count": 16, "metadata": {}, "output_type": "execute_result"}], "source": ["from sklearn.metrics import r2_score\n", "r2_score(y, lr.predict(X))"]}, {"cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [{"data": {"text/plain": ["0.4573617856901514"]}, "execution_count": 17, "metadata": {}, "output_type": "execute_result"}], "source": ["r2_score(y, X.values[:, 0])"]}, {"cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [{"data": {"image/png": 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\n", "text/plain": ["<Figure size 1008x288 with 1 Axes>"]}, "metadata": {"needs_background": "light"}, "output_type": "display_data"}], "source": ["fig, ax = plt.subplots(1, 1, figsize=(14, 4))\n", "ax.plot(y.values)\n", "ax.plot(lr.predict(X))\n", "ax.set_title(\"Pr\u00e9diction avec un mod\u00e8le lin\u00e9aire\");"]}, {"cell_type": "markdown", "metadata": {}, "source": ["### Autres mod\u00e8les\n", "\n", "Overfitting ?"]}, {"cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [{"name": "stdout", "output_type": "stream", "text": ["TRAIN: (29, 7) TEST: (25, 7)\n", "TRAIN: (54, 7) TEST: (25, 7)\n", "TRAIN: (79, 7) TEST: (25, 7)\n", "TRAIN: (104, 7) TEST: (25, 7)\n", "TRAIN: (129, 7) TEST: (25, 7)\n"]}], "source": ["from sklearn.model_selection import TimeSeriesSplit\n", "tscv = TimeSeriesSplit(n_splits=5)\n", "for train_index, test_index in tscv.split(X, y):\n", "    X_train, X_test, y_train, y_test = (\n", "        X.iloc[train_index, :], X.iloc[test_index, :],\n", "        y.iloc[train_index], y.iloc[test_index])\n", "    print(\"TRAIN:\", X_train.shape, \"TEST:\", X_test.shape)"]}, {"cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": []}], "metadata": {"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"}, "language_info": {"codemirror_mode": {"name": "ipython", "version": 3}, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.7"}}, "nbformat": 4, "nbformat_minor": 4}