From bb8ef22e5ad4f323e26b1329334ab283ab438337 Mon Sep 17 00:00:00 2001 From: 7eba932125d7468e05c00632ef18215f <7eba932125d7468e05c00632ef18215f@app-learninglab.inria.fr> Date: Thu, 10 Jun 2021 14:08:01 +0000 Subject: [PATCH] =?UTF-8?q?Premi=C3=A8re=20analyse=20de=20l'=C3=A9volution?= =?UTF-8?q?=20lente?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- module3/exo3/exercice.ipynb | 552 ++++++++++++++++++++++++++++-------- 1 file changed, 439 insertions(+), 113 deletions(-) diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb index 19eaaa6..c4cc194 100644 --- a/module3/exo3/exercice.ipynb +++ b/module3/exo3/exercice.ipynb @@ -16,14 +16,14 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", - "import isoweek" + "import numpy as np" ] }, { @@ -32,12 +32,12 @@ "source": [ "## Importation des données\n", "\n", - "Les données sont accessible sur le site de [l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Elles sont téléchargée en date du 02/06/2021. Si le fichier de données n'a plus de version locale, il sera téléchargé." + "Les données sont accessible sur le site de [l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Ils s'agit d'un suivi des teneurs en CO2 mesurées au Mauna Loa Observatory à Hawaii depuis 1958. Elles sont téléchargée en date du 02/06/2021. Si le fichier de données n'a plus de version locale, il sera téléchargé." ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -84,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 3, "metadata": {}, "outputs": [ { @@ -206,7 +206,7 @@ "4 314.91 315.70 314.44 " ] }, - "execution_count": 23, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -220,12 +220,12 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Les lignes 0 et 1 vont gêner l'analyse et ne contiennent que des indications sur les données. Nous les retirons:" + "Les lignes 0 et 1 vont gêner l'analyse et ne contiennent que des indications sur les données. Nous les retirons. Les titres de colonnes contiennent des espaces qui gêne leur appel. Nous renommons donc également les colonnes:" ] }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 4, "metadata": { "scrolled": true }, @@ -251,16 +251,16 @@ " \n", " \n", " \n", - " Yr\n", - " Mn\n", - " Date\n", - " Date\n", - " CO2\n", - " seasonally\n", - " fit\n", - " seasonally\n", + " year\n", + " month\n", + " Date1\n", + " Date2\n", " CO2\n", - " seasonally\n", + " CO2 overall\n", + " C02_3\n", + " CO2_4\n", + " C02_5\n", + " CO2_6\n", " \n", " \n", " \n", @@ -334,41 +334,47 @@ "" ], "text/plain": [ - " Yr Mn Date Date CO2 seasonally fit \\\n", - "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", - "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", - "4 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", - "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", - "6 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", + " year month Date1 Date2 CO2 CO2 overall C02_3 \\\n", + "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", + "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", + "4 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", + "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", + "6 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", "\n", - " seasonally CO2 seasonally \n", - "2 -99.99 -99.99 -99.99 \n", - "3 -99.99 -99.99 -99.99 \n", - "4 314.91 315.70 314.44 \n", - "5 314.99 317.45 315.16 \n", - "6 315.07 317.51 314.70 " + " CO2_4 C02_5 CO2_6 \n", + "2 -99.99 -99.99 -99.99 \n", + "3 -99.99 -99.99 -99.99 \n", + "4 314.91 315.70 314.44 \n", + "5 314.99 317.45 315.16 \n", + "6 315.07 317.51 314.70 " ] }, - "execution_count": 24, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.drop(labels=[0,1], axis=0).copy()\n", - "data.head()" + "\n", + "col_list = data.columns\n", + "data.rename(columns={col_list[0]: 'year', col_list[1]: 'month', col_list[2]: 'Date1',\n", + " col_list[3]: 'Date2', col_list[4]: 'CO2', col_list[5]: 'CO2 overall',\n", + " col_list[6]: 'C02_3', col_list[7]: 'CO2_4', col_list[8]: 'C02_5',\n", + " col_list[9]: 'CO2_6'}, inplace=True)\n", + "data.head()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Les titres de colonnes contiennent des espaces qui gêne leur appel. Nous renommons donc les colonnes comme suit:" + "On converti les colonnes 'year' et 'month' en période que l'on défini ensuite comme index" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -403,10 +409,23 @@ " C02_5\n", " CO2_6\n", " \n", + " \n", + " period\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " 2\n", + " 1958-01\n", " 1958\n", " 01\n", " 21200\n", @@ -419,7 +438,7 @@ " -99.99\n", " \n", " \n", - " 3\n", + " 1958-02\n", " 1958\n", " 02\n", " 21231\n", @@ -432,7 +451,7 @@ " -99.99\n", " \n", " \n", - " 4\n", + " 1958-03\n", " 1958\n", " 03\n", " 21259\n", @@ -445,7 +464,7 @@ " 314.44\n", " \n", " \n", - " 5\n", + " 1958-04\n", " 1958\n", " 04\n", " 21290\n", @@ -458,7 +477,7 @@ " 315.16\n", " \n", " \n", - " 6\n", + " 1958-05\n", " 1958\n", " 05\n", " 21320\n", @@ -475,45 +494,67 @@ "" ], "text/plain": [ - " year month Date1 Date2 CO2 CO2 overall C02_3 \\\n", - "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", - "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", - "4 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", - "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", - "6 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", + " year month Date1 Date2 CO2 CO2 overall C02_3 \\\n", + "period \n", + "1958-01 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", + "1958-02 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", + "1958-03 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", + "1958-04 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", + "1958-05 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", "\n", - " CO2_4 C02_5 CO2_6 \n", - "2 -99.99 -99.99 -99.99 \n", - "3 -99.99 -99.99 -99.99 \n", - "4 314.91 315.70 314.44 \n", - "5 314.99 317.45 315.16 \n", - "6 315.07 317.51 314.70 " + " CO2_4 C02_5 CO2_6 \n", + "period \n", + "1958-01 -99.99 -99.99 -99.99 \n", + "1958-02 -99.99 -99.99 -99.99 \n", + "1958-03 314.91 315.70 314.44 \n", + "1958-04 314.99 317.45 315.16 \n", + "1958-05 315.07 317.51 314.70 " ] }, - "execution_count": 25, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "col_list = data.columns\n", - "data.rename(columns={col_list[0]: 'year', col_list[1]: 'month', col_list[2]: 'Date1',\n", - " col_list[3]: 'Date2', col_list[4]: 'CO2', col_list[5]: 'CO2 overall',\n", - " col_list[6]: 'C02_3', col_list[7]: 'CO2_4', col_list[8]: 'C02_5',\n", - " col_list[9]: 'CO2_6'}, inplace=True)\n", - "data.head()\n" + "def convert_month(year,month):\n", + " return pd.Period(year = int(year), month = int(month), freq='M')\n", + "\n", + "data['period'] = [convert_month(year,month) for year,month in zip(data['year'],data['month'])]\n", + "data = data.set_index('period')\n", + "data.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "On converti les colonnes 'year' et 'month' en période que l'on défini ensuite comme index" + "On vérifie qu'il n'y a pas de trou dans les périodes:" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "periods = data.index\n", + "for p1, p2 in zip(periods[:-1], periods[1:]):\n", + " delta = p2.to_timestamp() - p1.end_time\n", + " if delta > pd.Timedelta('1s'):\n", + " print(p1, p2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Toutes les périodes sont bien renseignées. Quand il n'y a pas de données pour la période, la valeur -99.99 est entrée. Nous enlevons pour le moment ces valeurs. Mais avant cela, il faut convertir les valeurs de CO2 en données numériques:" + ] + }, + { + "cell_type": "code", + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -564,32 +605,6 @@ " \n", " \n", " \n", - " 1958-01\n", - " 1958\n", - " 01\n", - " 21200\n", - " 1958.0411\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " \n", - " \n", - " 1958-02\n", - " 1958\n", - " 02\n", - " 21231\n", - " 1958.1260\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " -99.99\n", - " \n", - " \n", " 1958-03\n", " 1958\n", " 03\n", @@ -628,79 +643,390 @@ " 317.51\n", " 314.70\n", " \n", + " \n", + " 1958-07\n", + " 1958\n", + " 07\n", + " 21381\n", + " 1958.5370\n", + " 315.86\n", + " 315.19\n", + " 315.86\n", + " 315.22\n", + " 315.86\n", + " 315.19\n", + " \n", + " \n", + " 1958-08\n", + " 1958\n", + " 08\n", + " 21412\n", + " 1958.6219\n", + " 314.93\n", + " 316.19\n", + " 313.99\n", + " 315.29\n", + " 314.93\n", + " 316.19\n", + " \n", " \n", "\n", "" ], "text/plain": [ - " year month Date1 Date2 CO2 CO2 overall C02_3 \\\n", - "period \n", - "1958-01 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", - "1958-02 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", - "1958-03 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", - "1958-04 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", - "1958-05 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", + " year month Date1 Date2 CO2 CO2 overall C02_3 \\\n", + "period \n", + "1958-03 1958 03 21259 1958.2027 315.70 314.44 316.19 \n", + "1958-04 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", + "1958-05 1958 05 21320 1958.3699 317.51 314.70 317.87 \n", + "1958-07 1958 07 21381 1958.5370 315.86 315.19 315.86 \n", + "1958-08 1958 08 21412 1958.6219 314.93 316.19 313.99 \n", "\n", " CO2_4 C02_5 CO2_6 \n", "period \n", - "1958-01 -99.99 -99.99 -99.99 \n", - "1958-02 -99.99 -99.99 -99.99 \n", "1958-03 314.91 315.70 314.44 \n", "1958-04 314.99 317.45 315.16 \n", - "1958-05 315.07 317.51 314.70 " + "1958-05 315.07 317.51 314.70 \n", + "1958-07 315.22 315.86 315.19 \n", + "1958-08 315.29 314.93 316.19 " ] }, - "execution_count": 26, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "def convert_month(year,month):\n", - " return pd.Period(year = int(year), month = int(month), freq='M')\n", + "data_valuesonly = data.copy()\n", + "data_valuesonly['CO2'] = pd.to_numeric(data_valuesonly['CO2'])\n", "\n", - "data['period'] = [convert_month(year,month) for year,month in zip(data['year'],data['month'])]\n", - "data = data.set_index('period')\n", - "data.head()" + "periods_novalue = []\n", + "for i in data_valuesonly.index:\n", + " if data_valuesonly['CO2'][i] == -99.99:\n", + " periods_novalue.append(i)\n", + "data_valuesonly = data_valuesonly.drop(periods_novalue)\n", + "data_valuesonly.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "data_valuesonly['CO2'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "On vérifie qu'il n'y a pas de trou dans les périodes:" + "## La contribution périodique \n", + "On remarque une évolution périodique, due à des variations saisonnières, superposées à une variation continue et plus lente. On veut maintenant caractériser l'évolution périodique" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "periods = data.index\n", - "for p1, p2 in zip(periods[:-1], periods[1:]):\n", - " delta = p2.to_timestamp() - p1.end_time\n", - " if delta > pd.Timedelta('1s'):\n", - " print(p1, p2)" + "data_valuesonly['CO2']['2015-01':'2019-01'].plot()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## La contribution lente \n", + "On veut maintenant extraire la contribution lente et l'extrapoler à 2025. Une première approche est une évolution linéaire à partir de l'année 2000." ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "-99.99" + "" ] }, - "execution_count": 30, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" + }, + { + "data": { + "image/png": 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552KLoaIGguFRfvlaK9cs8TldJ1+u62FxbioiQoLbxcqiDLasLiQlUTPPk6X/p5RS0+bxg2187peHuKoyl5/eeyX+4VF+sqvJeX5/8wBt/jB/dl0lZdlW2eRY1LAobk/X3370WjRTc350Rq+Umjb1XVY1zZ4TfUSjhv+xg/wfXGKtYH2uuguAqoI0ijK9TkBfHFcr73KJ5uTPkwZ6pdSU+d6LE4ueANr81g5Qo+OGjkCYl+t6WJqfxhffuRqA52OBPj8dj9uFyw7o8Yui1PnT1I1Sakr0DY5w/++OAnDHhhIA2v3DzvMN3YPUdAbZvCyP7JQEUhPdHO8dIt3roSDDamvg9bgYHBnXQH+BdEavlJoSr7dObAwSCI9ijKG6Pchli7MBa0/X7mCEyjzrRmuZHcyr8tOc1MzPPnQV1y/LY21p5vRfwDyigV4pNSVi9fEATb1DNPQM0js4wpbVhQDssdsbxBqRlWbHAv1ES4M1JZn81z2bSPdOLJRS508DvVJqSsQH+taBYV5t7APghpX5JLiFPcetx7GFT/l2uqYsJxl1cWmOXil1Ufzv3hbqu0N8+m3LcbuEo+0B0r0eguExWvqHOdzmx5eWSKUvleyURNr9YdwuYVGOFeg/dfMyBoZGuHWd9pC/2DTQK6UuWCgyxqd/fgCATRU5bCjLYlttNx+4qpyf7m6itX+YV4/3sXFxDiJCbloSXcEIZdnJJHqsxIIvLYlvv/+ymbyMeeucqRsR8YrIbhE5ICKHReSL9vn1IrJTRPaLyB4R2RT3nvtEpE5EakTklqm8AKXUzGuPS9PUd4V45mgXo+OG29cXU5qdzNH2AM19w6xflAVYN1wBKu38vJpak8nRR4AbjDGXAOuBLSJyJfBV4IvGmPXAF+zHiMgqYCuwGtgCfFtE3FMxeKXUzNh7op+Hdhx3+tO02/XxAPXdg9R2Bkn0uFhTbDUie6WhF4Bye9PuS+2AH3usptY5UzfG+k6G7IcJ9h9j/8mwz2cCbfbx7cDDxpgI0CgidcAm4JWLOG6l1Az6o//cAcCNK/MpzU5x6uNzUhNpGxgmyeNicU4KLpc4PeQByn1WZc0fX15GRnICm5flTf/gF6BJ5ejtGfleYCnwgDFml4h8AnhKRP4V6zeDq+2XlwA7497eYp879TPvBe4FWLRo0Zu+AKXU9IpGjXN8qNVPaXYKz1V3kZOayLrSTDr8YYyBxfZsPVY2CRMz+JRED++6tHR6B76ATaq80hgzbqdoSoFNIrIG+Evgk8aYMuCTwA/sl5+uCYV5wwljHjTGbDTGbMzL05/qSs0VfUMjzvGxzhDGGF6p7+WW1YWUZCXTGQhzom+QcrsRWayHvEusTULU9DuvOnpjzADwAlbu/W7gF/ZTP8dKz4A1gy+Le1spE2kdpdQcY4zhaHvAycd3BSLOc23+MF3BCIHwGCuL0slP99I/NEp4NMpiu198bEHUnVfob+4zZTJVN3kikmUfJwM3AdVYwXuz/bIbgGP28WPAVhFJEpEKoArYfbEHrpSaHk8e6uDt33iJ/3i2DoCmvkHnubaBYY60BQBYVpDu9KgBnBn9quIMfv/J6/ny7WumcdQq3mRy9EXAQ3ae3gU8Yox5XEQGgG+IiAcIY+fbjTGHReQR4AgwBnzEGDM+NcNXSk21p490AlZrYYADLX48LuHaKh+t/cPsPt6HxyVcUprF8MjEP/XFORMVNcsK0lEzZzJVNweBDac5vx047eoGY8z9wP0XPDql1IyrsTfnju0K9WJtN2tLM6nwpfJqYx/V7QGqCtJJTnSTlz4xoy/O8p7289T00143SqkzMsbQ1GsF+Jb+YVoHhjncFuDWtUWUZCUzODLOobaAk6YpyJgI7h63hpfZQlsgKKXOqKV/mGBkjEpfKg09g+yo6wGsrpJ9g1b1TXcw4vSL96Ul8t6NZfzRZVo6OZvoj1yllKMnFOGKf3yGbzxj1Vb8al8rInDXlYsB2G4H+mUF6RRnTSyEKrUDvYjwz+9ex6aKnGkeuTobDfRKKcdzR7voDET492dqAdjV2MeKwgxns5Dtx3rwpSWRk5p4Ug5ed4Ca3TTQK7WAhUfHGY9b6bqvecA5DkXG2NfUz8bF2U7uvXdwhGUFVl28L3XixqsG+tlNA71SC5QxhhWff5JPPbLfOVfXFXSOnzzUweDIOBvLs8lPTyLRvrkaK5V0uYQKX6zNgW4WMptpoFdqgWqzO07+er+1cD0UGeNoe9BpIRy78bq6OBOXS3C7rO4m8TXxj3zoKg598RYStMJmVtPvjlIL1OG4zbuHRsb49f5WQpExPrR5CQAH7edjs/XL7Rusa0oynPflpSeRlqTFe7OdfoeUWqCer+l2jtsGwjx7tIvy3BRuWpkPQF1XCF9aotOI7OvvXU9L/xBrSzJnZLzqzdNAr9QCEB4d567v70IEfvxnV5DgcvH0kU7y0pPoDkZoHRimrivEJWVZZHgT8LiEsahxOk+C1Ws+JzVxBq9CvVmaulFqHvr2C3VOjxqwdoTac6KfV4/3U9MRpKYzSE8owgfs+vjjPYO09A9R4UvF5RKnyqZEb7LOCxrolZpnAuFRvvpkDX/+X3sYjIwB1gYhMc19w1R3WB0nb1xZgNslvFLfS9RApV1Fc0WllY/PS0tCzX0a6JWaZ2K9aWBiJeuhtgDpXitT29I/xJG2AAluoaogjcIMLy/bryu3A/3b1xQBkJGcMJ1DV1NEA71Sc1w0augKTGzOfbx3ol/86y3WTP5Qq5+rKnPJTE6guX+Il471cNnibBLcLkqykgnaM/8Ke6u/m1bm8/0PbHQqcNTcpoFeqTnuhy83sukfn6Wmw1rstLuxj+QENxW+VI60B9hR30NjzyBXLcmlLCeZuq4QNZ1BNlXkAhPthHNTE8lMsWbwIsJNqwq0dHKe0ECv1Bz36GutAPxqv/V1R30vV1bmUJ6bQmcgzI66XtwuYevliyjNSmFnQx/GwFJ7YVTshmuZtjGYtzTQKzWHGWNo6bdy8rUdQcKj4zR0h1hbmuWUTh5pD7A0L43kRPdJrQpiN15LsqwAn6n5+HlLfy9Tag5r94cJhq38ek1nkJqOIFEDq4rSGY9G6R0coaYjyIZFWcDJs/bKPCvQv3VFHretK+ITN1VN/wWoaaEzeqXmkJGxKP+7t4XR8SiAk5ffVJ5Duz/MYXuj7pVFGfjSkhiPGloHhqnMs9I08TP6lERrnleUmcy37ryUpfm6r+t8pYFeqTnkG8/W8umfH+C3B9uBif1cNy/PYzxq2FbbRWqim7LslJP2b42laZbYAX+j3V9eLQyaulFqDjnQbJVLNvZYJZSHWv0UZXpZWWTNxp+r7mJdaRYul+CLW+wUaydc7kvldx+7znmsFgad0Ss1S0XGxvmTH+xi+7Ee51yvvU/rkfYAxhh2NvRyRUWOs63f6Lhxgn78jL4ibyKwryrOIDnRPR2XoGYJDfRKzVKv1Pfy0rEe7vrBLudc28AwAEfbA3QHI/SERlhflkVR5kTufVWR1V0yPtBneLWiZiHT1I1Ss9QzRyeako1HDV3BMP7hUbJSEmjpH3ZuvFbkpZHhnfinHJvRpyd5yE5J4D0by6Z34GrW0Rm9UrOAMYYv/eYIvznQ5pzbVjvRL76lf8i5AXvPNRUA7GzsBaA8NwURcV67ssjaGEREeO3zN3Pf21dM+fjV7KYzeqVmgVeP9/PDlxvhZbhtXRH+4VGa+4a5cUU+z1Z3cbx3iO11PSzJS2VjuVUxs7uxD49LnJ7xj/3VNSR53M5GIcBJPwDUwqUzeqVmgfrukHPcFYxwxE7LvHWFtdtT28Awuxv7uHqJz+kVv69pgNLsZDz2fq3rSrNYXqi18OqNNNArNQu09g87x/VdISf/Hgv0z1V3MTQyzlVLcsmPu8m6OFfLJNW5nTPQi4hXRHaLyAEROSwiX4x77qMiUmOf/2rc+ftEpM5+7papGrxSc9V/7zzB915scB63DQzjsrMs9T2DHG7zU5jhpSQrmczkBF6pt/LxywvTSUvykGynZ8pztRGZOrfJ5OgjwA3GmJCIJADbReQJIBm4HVhnjImISD6AiKwCtgKrgWLgGRFZZowZn5pLUGpu8Q+P8vlfHQLg7qvLSfS4ONwW4MrKXF5p6KU7GOFwW4DVxdZN1YKMJGo7Q4hYLQxEBI9bYFRn9GpyzjmjN5ZYAjHB/mOAvwS+YoyJ2K/rsl9zO/CwMSZijGkE6oBNF33kSs1Rh9v8Jx33DY5Q0xnkmqU+slMSaekfor47FBforZx8cWYySR5rJh9rZBa/EEqpM5lUjl5E3CKyH+gCnjbG7AKWAdeJyC4R2SYil9svLwGa497eYp9TakH65rPHWPn5JxmzG5EdbQ86z9V3D7K7sQ+AKypyyE1NZEedtX/rqmJr4VN+uhXolxWkOe8rzvQ671HqXCYV6I0x48aY9UApsElE1mClfbKBK4HPAI+IVct1unouc+oJEblXRPaIyJ7u7u7TvEWp2a+6w2pFEPNaUz93PPAy3cGIc+5rT9cyPDrOq8f7rfe0B8hKScDjEhq6Q+xs6MWb4GJdaRa+tCQ67G0BYzP63LREAFbY9fEAD997FY/+5dVOB0qlzua8qm6MMQPAC8AWrJn6L+zUzm4gCvjs8/FL8UqBtlM+CmPMg8aYjcaYjXl5eW9y+ErNnO3Hetjy9Zd4ZM/EL7D//EQ1+5sH+O+dJwCrrXBMdUfA/hpkbUkmpdnJNPdbZZOXLc4m0eOi0J6pJ3lcTkvhm1cVcM3SXO7ctMj5rEW5KVymHSjVJE2m6iZPRLLs42TgJqAa+BVwg31+GZAI9ACPAVtFJElEKoAqYPfUDF+pmfNSnfWb6EF7A25jDLV22+DmPmvXp2NdE2majkCYrkCYI+0BLim1+tM09w1R1xViTYmVpom1E85OSXQWO11ensNP/uxK3epPvWmT+b2vCHhIRNxYPxgeMcY8LiKJwA9F5BAwAtxtrN9hD4vII8ARYAz4iFbcqPmovsuqUWi1G4219A/TPzQKQKedfoktfALo8Id56VgP41HDreuKaPeHefS1FgAq7OqZJfY+rn12l0qlLoZzBnpjzEFgw2nOjwB3neE99wP3X/DolJpF+gdHSPd6nJWoDXZP+PYBK6jHZval2clOoD/aHiQ5wc2q4gza/WEaewZxibUBSHGW1/nsRXY9/HVVPtKSPHzgqsXTdl1q/tOVsUpNwvDIOBu+/DRffvwIAGPjUZp6rfRM7ObpwdYBEt0uNi/LoysQcfrFryxKpyQrmQ5/mMbeQcpyUkj0uJwe8gDl9ow+3ZvA/i/czGduWT7NV6jmMw30Sk3CwZYBAB56xbrJ2tw/zFjUUOlLxT88Snh0nIPNflYWpVOWk0IwMsbBFj9H2gPcsaGEokwvHYEwjd2DTlAvypyY0RdmTBx73C5tRqYuKg30Sp3Gsc6gk34BnFp3sG66NvZY+fkrl+QCVguDQ61+1pZmUpBh9aJ50W4zfNnibAozvYyMRTnSHnC28SuJm9G7XBrY1dTRQK/UKVr6h7j531/kfQ/udM69ENcbvm9whOoOq5rmmiU+AHY19hGMjLGuNIsCe4HTS8d6nHx8/Ow9FugrfKncc00F//NnV0z5NamFTVdbKHWK1+2bqg09g3QFwyS53exr6mdtSSavt/pp6BnkQPMA5bkpzmrVZ45Yu0GtLckkwb5Zu/t4HxW+VLwJbqeNAVgbdIOVovnCH6yazktTC5TO6NWC91pTP08e6nAet8S1DK7pCLLnRB9RA3deYS1Yqm4P8NKxHq6szKXAnqlvr+vB7RKW5KU5i54Aquxyyfg9XSu0EZmaZhro1YL3rm/v4C9+vJcOv5WTb+kfcp6r6QhyoMWPS+CmlQUAPHW4k6GRcW5dV0R6koeURDeRsSiL7GqatKT4/VuttgXxG3XHl1UqNR000KsFrSc00ZPmtaZ+jDHsauzj8vJsUhLdtA2EOdLmZ0leGnnpSWR4PexssHrDL8lLQ0Scipklp+kkeUWl1XTM7RLet6mMf3n3OqcOX6npon/j1IIWW90K0NAd4tmjXVR3BLljQwkFGV46A2FqOoPOFn3FWcmMRY3Vl8YO8LHZemXeRHfJm1Zd9xnHAAAgAElEQVTmk+H1cHn5RHfJf3rXOt6zMb4NlFLTQwO9WlCa+4b42atNzuNj8YG+Z5CX63vwJrh478YyCjKSqO8O0dw3zAo70Mfy74tzUpySyFQ7VRPrUwPwnbsuY8/f3ezcmFVqJmnVjVpQPvGz/ew90c8lZVmsKMzgNwfaKMtJJj/dS2v/MN3BCFX56Xjc1ox9Z4NVP7+swAr0sTLJ8rig/sFryslKTuBGO4cPaHpGzSoa6NWCctzuT7P9WA9V+ensax7g7qsW0xWM8FpTP6NjhquXWougCuKqZ1YU2r3hU600TXHcc9dV5XFdlbbaVrOXTjvUvBUeHedX+1qJRq2NQcajhkDY6i55oneI5r4hRsaiVOWnU5yVTHPfMB2BMFX51uw9tvAJcHrDX70kl7KcZO68QpuOqblDZ/Rq3nrg+Tq++VwdHrdw27pi2gaGGR23gn6T3QcerNbAkfGJDUJite/xm4DE8vFXL/Xx0t/cMJ2XodQF0xm9mrcO2Ctcd9l59hN2t8mslASa+6wNuAGW5qdRElfbXmWvdl1qB/wPbV4ybWNWaipooFfzQkv/EOV/+1unFQFAq73wqdHOyx+wO1C+fU0hLf3D1HaGyEtPIjM54aSWwaXZVm/4ZQXpHPyHt/Gpm5dN12UoNSU00Kt54df7rW2JH3ihDrA6TMZaGZzoswL9tppuqvLTWFuSxch4lB31Pc4ip1hwX1mUgTuuk2SGN2HarkGpqaI5ejUn7ajvYWleGvn2oqXYatVgeAyAzkCEyFiUdK+HtoEwJ3oH2X28j89uWcEie+/Vdn+YG1fmA5CW5GH3/72R7NTEGbgapaaWzujVnFPTEeTO7+3ir366D7Bm7/ubrLRMS/+Qs7MTwB3rSxiPGp46bDUtu6Iyxwn0YLUxiMnP8OoCJzUv6d9qNee8Ut8DWJuBGGPoCkYIRsao9KUSHo3SOzjCi7Xd5KQm8o61RQA8eagDEVhZmHFSU7H4QK/UfKWBXs164dFxImPjzuPWgYk2wt3BCA3dVg7+uiprE5DmviFePNbNtUt9ziYfrzUNUJadQnKi+6RVq7HKGqXmMw30atb7w2/v4B3feMl5HB/o67sHqekIAPCWFVa+/ekjnfSERti8LI/89CSSPNZf8/jukj/98yv5442lJ+38pNR8pTdj1awWCI9ytN0K5M19Q5TlpFDbGaIqP41jXSGa+4c40OInPz2JyxZnA/CEvYnIpoocXC6hJDuZhu7Bk9I0Vy3J5Sp7v1el5jud0atZJRo1jMWtUq2x92YFqO8O0eEPU9cV4vb1xQB0BcK8Ut/LxvJsMrwJZHg9NPYM4k1wOZtvJye4AWsFrFILkQZ6Nat8+ucHuPKfnsUYq1VBW1ya5njPIC/XWTdib1hRQIbXw67GPjoCYa63m4rF6uErfGlO24LNy2LPTSyKUmoh0dSNmjVGx6P8Yl8rYK1mrcxLo23A2t7PJXC8d4jA8Ci5qYmsKEwnP8PLjnqrjHKFvWVfaXYyR9oDJ+XjP3XzMtaXZXHtUt80X5FSs4PO6NWM2Xuin4/9dB+jdqqmun0iTbPProtv6hskKyWBVcUZNNobg1y91IfLJeSnJzFud6aMVdcU2Auo4m+yetwu3ra6EJGJFa9KLSQa6NWMed/3dvLYgTZnVr6vud95rs5uOLbneD+XlGZR4UvjUKufzkCE1cXW7D3f3sLPl5ZIZrLVquB9mxaR7vXwluX503kpSs1qGujVtInNvgEiY+OMjFkz+b3Hre6S+5sGyEtPYml+GnVdIXpDEY51hbiiMofiLC+9gyMATgOy2Oy9Im63p1XFGRz8+7dxjaZplHKcM9CLiFdEdovIARE5LCJfPOX5T4uIERFf3Ln7RKRORGpE5JapGLiaW15v8bP8757guWqru2Rtx8RerW1+Kw+/r3mA9WVZlGQl0+EPs7vR+gFwRUUuPntnJ8BpKZyZYs3ic+OeAzRFo9QpJjOjjwA3GGMuAdYDW0TkSgARKQNuBpzdlkVkFbAVWA1sAb4tIu6LPXA1t/xyXytjUcMPtx8H4PVWq1e8Ly2JtoFhBoZGaOwZZMOiLAozvHQEwuxq7MOb4GJtSSY5cc3GYpU1a0syAbjzikXTezFKzTHnDPTGEpt+Jdh/Yr+D/zvwN3GPAW4HHjbGRIwxjUAdsOniDVnNRS/UdAHgH7a28jvU5ifD6+GKihza/WFnk5D1pVkUZHrpCUXY1djHpYuySfS4yE2bCPSx3Px1VXnU/L8tXL9M92tV6mwmlaMXEbeI7Ae6gKeNMbtE5J1AqzHmwCkvLwGa4x632OdO/cx7RWSPiOzp7u5+k8NXs1VDd4jwqNWfpjsYocHe/CPWvuBQq581JZmUZCfTOjDMvqZ+RGBtaSYFGUkYA0fbA6wsit14tdI1i3JSTkrNJHn0l0WlzmVSgd4YM26MWQ+UAptEZB3wOeALp3n56RKk5g0njHnQGLPRGLMxL09nZPNJQ3eIG762jQ/+6FXAah0McOmiLPoGRwiER6luD7K2JJOiTC8jY1G2H+uhIjeVdG8ChRkTpZGxpmMri9J54M5LefQvr57+C1JqjjuvqhtjzADwAlZ6pgI4ICLHsX4AvCYihVgz+LK4t5UCbRdjsGpuiPWaecXuCR/b6WlThdVb5oWabkbGo6wuyXQqaPac6HeCesFpAr2IcOu6IvLST77xqpQ6t8lU3eSJSJZ9nAzcBOwzxuQbY8qNMeVYwf1SY0wH8BiwVUSSRKQCqAJ2T9kVqBn3y30t7LB7xAPsbx5wjoPhUV5p6MUl1qYfgLMJyNqSTIozJ9oSVNpNxwrjFjst1X7xSl2wybRAKAIesitnXMAjxpjHz/RiY8xhEXkEOAKMAR8xxoyf6fVqbtvV0Msnf2bdpqn+8ha8CW5eb/GT6HExMhalqW+IJ15v57Z1xSwrSAfg94c7SEvysDgnhf6hEeezYm0LclISSU5wk5OaqFv7KXURnDPQG2MOAhvO8ZryUx7fD9x/QSNTs9LwyDgGQ0qi9Vdnz4mJ1awneofITkmgIxDmXRtK+MW+Vg40++kfGmVdaSYF6Um4XcLouOHSRRm4XHJS2WSsu6TLJez9/E1ERqMopS6croxV5+WPv/sKm//lBae7ZHVcG+G6rhC77EVOt11ibeH3vF1WuSQ/DY/b5ZRGrrFr4EWE1ESrcqYqro1wSqJHZ/NKXSTavVJNmn9o1FnoVNsZYnlhOtXtAa6szGFnQx/N/UPsa+qnKNPL5mX5ZKUksK3GKp1d4rNn63ZpZGyxE8CjH74aj0tI9yZM8xUptTDojF5N2hF7pyeA2s4g4dFxGnoGubw8h/QkD+0DwxxtD3Lp4mzcLqEww8vIeJREj4sSuxf8B68p59JFWbw1runYisIMluanT/v1KLVQaKBXZ7T3RD8P7TjuPK7rOjlN8/jBdsajhk0VORRleWnoGaS5f8hJweTHmo7lpuK2NwH5s+sq+cWHr3H61Cilpp6mbtRpRaOGP/rPHQBcs9TndJRMS/KQluShdWCYlv5hfGlJXLvUR1FmMttqrTRNrLqmwM7HL8lPPf1/RCk1LXRGrwDY2dDLLf/+Ih12J8mmviHnuViNfF13iCX5aRRkJNEZCHOsK8jywjREhOKsidr3WNuC3DQr0Me3EVZKTT8N9AtUdUfAqZwB+M8X6qnpDPLfO48DcNC+6QpWPj4aNdR0hFial0ZeupeuQIRjnSFn9l5kL3xyu4RFOVZ3yTs2FHPzqgLesbZomq5KKXU6GugXgPDo+ElB/bEDbWz5+kv85mC7c67e3tGpqc9qV/BqYx8piW7WlWZS1xViR30vPaEI1y/zkZ+RRE1nkOHRcSfQx1oZuEWcfPyKwgy+94GNrC6eqLBRSk0/DfTz3PDIOCs+/yRfeaLaOfc7O8C/Ym/hNxgZc/rRNNspm92NfVy2OJvFuam0DYQ50GK1NXjrinxKsyfaFiwrsG68blycDcB7NpZO8RUppc6XBvp57qAdoL/7YoNzrtaunnm91XouFsR9aYm09A/TPzhCTWeQKytzKc700uEPU9cVojDDS4Y3wcnBA05ZZLkvlV3/90Y+d+vKabkupdTkaaCf5+IbjMVaBB+3e8PXdASJjI3z9JFOvAku/nhjmbPhB8CGRVlWG+HxKLsb+5xOkuviFjvFNuUGq+tkrDWCUmr20H+V81x8oK/pCOIfHiVqYOvlZTz8ajMt/cO81mTt1bq80JqdP33E2td1dVEmwfAYYG0YcvOqAsCqpvnrm5cxbt6wzYBSahbSGf088+v9reyNazS2r2mADYuyAGjqG+RImx+XwNvtSpgTvYMcbQtwSVmWk3v//eEOSrKSyUxJOKmN8JK4XjQfvbGKT9y0bDouSSl1gTTQzyP13SE+/vB+Pvgjq/1/u3+YjkCYd6wpwiXWBiB13SEW56ZSkWvVtj97tIuR8SjrS7MoszfdDkbGWFVs5eHj6+O1N7xSc5MG+jmsvjvEvqaJ2fuRNqsXTSA8RjRq2N9kpW02lmdTlJlMc98Qh9sCLM1PoyDTWsz0pL0b1PpFWSft3rTKvuEa30Z4ab4GeqXmIg30c9jWB3fyh9/eQU8oAkBnIOw8d7x3kP3NAyS6XawqzqAsJ5nna7o50TvEdVU+kjxufGlJ9A6OkJ+eRGGG96RNt2MzehHhz6+rYHlBOr40bRus1FykgX6OuPe/9nDP//cqo+PWZhzGGLqDVoB/1a6SidXCg1Uyua9pgFXFGSR53JRmp+AfHiXR7eLta6z8fCwtc0lZlhPkP3DVYutcaZbzWZ+7dRVPfuK6k34QKKXmDg30s9BgZIwf7zzBeNSqavEPjfL7I508V93lpFrqukLO66s7ggyNjPHzPc0sL0jH4xKqO4K83upnfZkVsGP595VF6U6KJtUuhYy9BuBLt6/hwBfedtK+rYAGeaXmMA30s9C/PFXD3/3qEM8etcocD7VN9J05aveE/9+9LXhcQkqim4aeQXY19jE4Ms5Hb1xKSXYy22q6GR4ddzb42Lw8D4ANi7Kdz7pjQzG+tCTeYj8Xoy2ElZpftI5+FordVH2loZe3rS7kYIsV6LNTEjjRO4Qxhl/vb+Mty/MJhEfp9IfZUddDosfFTSsL+Nmrzbx0zOo4GVvFur4si8c/ei3lcZ0k33v5It57+aJpvjql1HTTGf0sMDI2sQm2McZpURDbhu/11gEW5aRwSVkWjT2DNPUN0REIs3l5HkWZXtoDw+yo7+WyRdl4E9yU2d0jE9xyUqXMmpJM0pL0Z7tSC40G+hlW3x1i/Zd+zwPP1wHQ7g8zMDSKLy2Jhp5BRsaivN7qZ21pJuW5qRzvHWS3ffN1U3kOhRleWvqHqekIOgujYm2CCzK8JHr0W6zUQqdRYBpFxsZZ+w9P8cPtjc65X7zWwtDION97yWo6dthO29ywwsqb13YGae4bZm1JJuW5KQyNjPPEoQ4ykxOoyk+jMNOLMTAWNU4Lg1jL4KJTbqgqpRYmDfRTqDMQZmBoxHn8Ym0PwfAYX3r8iHPuQLOVfw+GxwiPjvNaUz8icF2VFeifr+4CrEZisfz6c9VdbFycjcvegDsmFuivXerjtnVFfGPrhqm9QKXUnKAJ2ykyNDLGFf/4LGlJHvZ/4WY8bheH7F2bvAnWz9do1HC4zU9KopuhkXGO9w7yyKvNvHV5PuWxFgV2oF9dkkmvvTAKYGN5DgAFcbP2Sp+Vj89JTeRbd1469ReplJoTdEY/RfYct1oThCJj1Nm7NzXY7YHDo1H8Q6Psbeqnf2iUd19mbdbxamMfvYMj3LSywKlj3988QIUvlczkBErtWniATRVWmWR80zHNxyulTkcjwxQ5FregqaF7kLHxKK/FdZVs6hvitwfbSfS4uOtKazWqM3svzsCXlkiWXc++xq6FT/S4WFWUQYbX45wryEjiy7evZttn3jIdl6WUmoM00J9Dc98Qf/zdV07qI7OttputD75CKGL1ajfG8I+/O8q//b7G2Zu1uj1AcoIbgPquEN99sYHWgWE+dmMVYPWieeJQO5uX5VHpS8UlsMPe2q8yLxURwZdmrWCN3+jjsb+6ht2fu4kkj/XZIsKfXFXO4tyJ+nillIp3zkAvIl4R2S0iB0TksIh80T7/LyJSLSIHReSXIpIV9577RKRORGpE5JapvICp9oPtjexu7ON/djU5577yRDU7G/p4dG8LYJVEPvhiA//xXB0v1/VijGFHfS/XL/PhS0ukzT/MzoZelhWkce/1lYC1uUdnIMKW1YV43C7y072MjEXJSU0k3WvN5C+1yyVjN1kBPG4XXvsHiFJKTcZkZvQR4AZjzCXAemCLiFwJPA2sMcasA2qB+wBEZBWwFVgNbAG+LSJzIjKNRw0/3N5IV9zsvd1vNQprtPPrg5Ex6u2ce+xrTWfQef0Th9rpDERoHRjmyspcijKTaRsIc6wz5CxYyk1NdHZxWltqzdZjOflYDTzA3//Bav7fHWu4dqlvqi5ZKbUAnDPQG0ss4Zxg/zHGmN8bY8bs8zuBUvv4duBhY0zEGNMI1AGbLvK4L4r/ePYYL9f1OI9/f7iDLz1+hI8/vN85FwvwrQNWwH+hpttZydrUNwTAy8d6cAlU5afR2DPoVNesK82kOMtLXVeIjkCYJfbGHWU5KQyPjuNxiVNdE6t5j994OzXJw11XLsbl0oZiSqk3b1I5ehFxi8h+oAt42hiz65SX3AM8YR+XAM1xz7XY52bUo3tbeGTPxLAGI2P829O1vP/7E5cSuxl6qM2PMYZo1HCi1wrmrXYL4KcOd5CTmsgtqwto6rP6zjx2oI23rSqkMi+VnlCEQ21+RKygXZSZ7PyQqLTr4GOz9nJfqlMpE5vRX1I6kY9XSqmLYVJ19MaYcWC9nYf/pYisMcYcAhCRzwFjwE/sl59u+vmGXaRF5F7gXoBFi6a+sdZf//wAALesLiQzOcHpAglWysbtEg7YG2kHw2Oc6B0iagyRsSiFGV46g2GGRsZ4vrqLd6wtIis1geeru2kdGKYrGOGqJbkc6wqyu7GPQ60BluSlkZLoOWkrvtiCp7IcqySyMq7B2GduWc6Vlblv6CSplFIX6ryqbowxA8ALWLl3RORu4Dbg/SZWbmLN4Mvi3lYKtJ3msx40xmw0xmzMy5va4Ba/0CgW4PfZ2+yBVQETDI9S1x3i6iW5gLWJxzN2m+B3X1aKMbD9WA/ByBhXL81lUU4KI+NRfnuwHYANi7LwpSXRPzTK9rpuLrPbARfF1bnH0jQrCq30zKaKHOe5lEQPt6wudKpplFLqYplM1U1erKJGRJKBm4BqEdkCfBZ4pzFmKO4tjwFbRSRJRCqAKmD3xR/6mXUFrLa9MbWdEzXtsRuou+zGYAA1HUEONPsxBm5eVWB9RjDMD7Y3sqk8xwn+z9mpnRWFGU765ZE9zaQneVhVlOH0mAmPRnn72kJgou8MQHKiFcRvXVvE05+8nj+9tuLiXrhSSp3GZGb0RcDzInIQeBUrR/848C0gHXhaRPaLyHcAjDGHgUeAI8CTwEfs1M+0+dQjB7jz+7vYUW8F++O9g85zjd2DRKOGPSf6eIcdjJv6hnjqcAfeBBe3rrW22TvY4qczEOHWdUWUZFvB+pmjXSS4hQpfqjNTr+8e5PKKHDxuF2uKJ/LrsfbAa0vemHN3uYSqgnTdtUkpNS3OmaM3xhwE3tAdyxiz9CzvuR+4/8KGNjn/9LujvFDTzaMfvpq0JA/h0XG227P5I20Brl7io6YjSJLHRWGml/ZAmOqOIANDo9ywooDtx3po7R9me10P1yzxkZ/hJSXRzbPVE+WPRZnJiEBPKMKKwnQS7c+KubLSSsFUFUz0fo/9IEj0uPjNX11LcqKuTVNKzYw5H32++2IDNZ1BZ4Psw3Hb7rX0DxONGp481MF1VT6KMr10+sNsq7U29Li+ykdJdgoHW/009gw6jcIW56bS3DeMS2BlYQaJHhcF6VZgX2WXP8Zv4HFFhZXaSXC7+PiNVdy6tgh3XEnk2tJMluZPLHpSSqnpNKcD/ej4xM5MOxus9gEPPF+PxyXkpibS3DfE4bYAHYEwb19TRGGGl45AmG21XawsyiA/w0tJlteptrnCnpnHNsuuzEtz8uopSdbXFUUTAfv6ZXksL0hndfFE7fsnb17GA+/XzpFKqdljTrcp7vBPrGDd2dBLZGycl+t6eO/lZXQFIzT1DvFsdSci8JbledR2BWkdGKYzEOb/XF0OQIl9szQl0e3k0zeUZfHT3U3O7B2smviG7kFWFU3k3B/64OWaZ1dKzXpzekbfE4rgTXBx2eJsDrUF2NnQR2QsynVVeZRmJ9PcP8Rz1V2sL8siNy2JwgxrN6bRceN0fyy0c+krCtNJcFv/O2Jb8q2Km6l/7T2X8J27LuUquwIH0CCvlJoT5nSg37Aom6Nf2sInbqpiPGr43ovWdnwby7Mpy7a23TvY4ufGFfnAyVvrxWrZF+daZZJ/uGFi8W5VQTr/8b4NvG/TxEIub4KbLWtOzr0rpdRcMKdTN2DNqpcVWHnz7XU9lGYn40tLOqnj46WLrcVLBXHb7lXmWYuXblldyG8/di2ri08ug3znJcVTPXSllJoWc3pGH5OXlkSinXapsuvXLylzuiZTYbcaiNW2A06axu2SNwR5pZSaT+b8jB6sBUi5aYm0+8PO7D4tyUOCWxgdN05pZLo3gV9++GrNrSulFpR5EegBPrtlBdUdQe65ttw5t/2zN9DUN3RSm98Ndg8apZRaKOZNoL9jwxs7IRdkeE/Kyyul1EI0L3L0SimlzkwDvVJKzXMa6JVSap7TQK+UUvOcBnqllJrnNNArpdQ8p4FeKaXmOQ30Sik1z4kxZqbHgIh0Aycu4kcuApo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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" } ], - "source": [] + "source": [ + "data_valuesonly['CO2'].plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
Generalized Linear Model Regression Results
Dep. Variable: CO2 No. Observations: 753
Model: GLM Df Residuals: 751
Model Family: Gaussian Df Model: 1
Link Function: identity Scale: 19.998
Method: IRLS Log-Likelihood: -2195.3
Date: Thu, 10 Jun 2021 Deviance: 15019.
Time: 13:48:15 Pearson chi2: 1.50e+04
No. Iterations: 3 Covariance Type: nonrobust
\n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "\n", + " \n", + "\n", + "
coef std err z P>|z| [0.025 0.975]
Intercept 306.1259 0.326 938.290 0.000 305.486 306.765
index 0.1329 0.001 177.232 0.000 0.131 0.134
" + ], + "text/plain": [ + "\n", + "\"\"\"\n", + " Generalized Linear Model Regression Results \n", + "==============================================================================\n", + "Dep. Variable: CO2 No. Observations: 753\n", + "Model: GLM Df Residuals: 751\n", + "Model Family: Gaussian Df Model: 1\n", + "Link Function: identity Scale: 19.998\n", + "Method: IRLS Log-Likelihood: -2195.3\n", + "Date: Thu, 10 Jun 2021 Deviance: 15019.\n", + "Time: 13:48:15 Pearson chi2: 1.50e+04\n", + "No. Iterations: 3 Covariance Type: nonrobust\n", + "==============================================================================\n", + " coef std err z P>|z| [0.025 0.975]\n", + "------------------------------------------------------------------------------\n", + "Intercept 306.1259 0.326 938.290 0.000 305.486 306.765\n", + "index 0.1329 0.001 177.232 0.000 0.131 0.134\n", + "==============================================================================\n", + "\"\"\"" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import statsmodels.api as sm\n", + "\n", + "data_valuesonly[\"Intercept\"]=1\n", + "data_valuesonly['index'] = np.linspace(1,len(data_valuesonly['CO2']), len(data_valuesonly['CO2']))\n", + "logmodel=sm.GLM(data_valuesonly['CO2'], data_valuesonly[['Intercept','index']]).fit()\n", + "logmodel.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ], + "text/plain": [ + " Intercept index CO2 period\n", + "748 1 749.0 405.648073 2020-12\n", + "749 1 750.0 405.780947 2021-01\n", + "750 1 751.0 405.913820 2021-02\n", + "751 1 752.0 406.046693 2021-03\n", + "752 1 753.0 406.179567 2021-04" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data_pred.tail()" + ] }, { "cell_type": "code", -- 2.18.1