{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Sujet 1 - Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Récupération des données et analyse préliminaire" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données sur la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa sont disponibles sur le site web de l'[institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). La série de données s'étend de 1958 à nos jours. Les données considérées sont les relevés avec une granularité hebdomadaire. Elles sont disponibles à l'adresse suivante:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur de l'institut Scripps, nous faisons une copie locale de ce jeux de données que nous préservons avec notre analyse. Il est inutile et même risquée de télécharger les données à chaque exécution, car dans le cas d'une panne nous pourrions remplacer nos données par un fichier défectueux. Pour cette raison, nous téléchargeons les données seulement si la copie locale n'existe pas." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "data_file = \"CO2_Mauna_Loa_Hebdo.csv\"" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le jeu de données est téléchargé le 08/10/2020 à 16h30." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un examen préliminaire des données permet d'en observer la structure.\n", "\n", "On note que les 44 premières lignes décrivent le contexte général. Ellent devront donc être supprimées lors de l'extraction des données.\n", "\n", "Les lignes 40 à 44 précisent que les unités de concentration de CO2 sont des micro-moles de CO2 par mole (ppm). Ces valeurs sont considérées à 12h00 le samedi de chaque semaine.\n", "\n", "On note également que les dates sont au format \"AAAA-MM-JJ\"." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1 \"-------------------------------------------------------------------------------------------\"\n", "\n", "2 \" Atmospheric CO2 concentrations (ppm) derived from in situ air measurements \"\n", "\n", "3 \" at Mauna Loa, Observatory, Hawaii: Latitude 19.5°N Longitude 155.6°W Elevation 3397m \"\n", "\n", "4 \" \"\n", "\n", "5 \" Source: R. F. Keeling, S. J. Walker, S. C. Piper and A. F. Bollenbacher \"\n", "\n", "6 \" Scripps CO2 Program ( http://scrippsco2.ucsd.edu ) \"\n", "\n", "7 \" Scripps Institution of Oceanography (SIO) \"\n", "\n", "8 \" University of California \"\n", "\n", "9 \" La Jolla, California USA 92093-0244 \"\n", "\n", "10 \" \"\n", "\n", "11 \" Status of data and correspondence: \"\n", "\n", "12 \" \"\n", "\n", "13 \" These data are subject to revision based on recalibration of standard gases. Questions \"\n", "\n", "14 \" about the data should be directed to Dr. Ralph Keeling (rkeeling@ucsd.edu), Stephen Walker\"\n", "\n", "15 \" (sjwalker@ucsd.edu) and Stephen Piper (scpiper@ucsd.edu), Scripps CO2 Program. \"\n", "\n", "16 \" \"\n", "\n", "17 \" Baseline data in this file through 05-Oct-2020 from archive dated 06-Oct-2020 12:01:19 \"\n", "\n", "18 \" \"\n", "\n", "19 \"-------------------------------------------------------------------------------------------\"\n", "\n", "20 \" \"\n", "\n", "21 \" Please cite as: \"\n", "\n", "22 \" \"\n", "\n", "23 \" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and \"\n", "\n", "24 \" H. A. Meijer, Exchanges of atmospheric CO2 and 13CO2 with the terrestrial biosphere and \"\n", "\n", "25 \" oceans from 1978 to 2000. I. Global aspects, SIO Reference Series, No. 01-06, Scripps \"\n", "\n", "26 \" Institution of Oceanography, San Diego, 88 pages, 2001. \"\n", "\n", "27 \" \"\n", "\n", "28 \" If it is necessary to cite a peer-reviewed article, please cite as: \"\n", "\n", "29 \" \"\n", "\n", "30 \" C. D. Keeling, S. C. Piper, R. B. Bacastow, M. Wahlen, T. P. Whorf, M. Heimann, and \"\n", "\n", "31 \" H. A. Meijer, Atmospheric CO2 and 13CO2 exchange with the terrestrial biosphere and \"\n", "\n", "32 \" oceans from 1978 to 2000: observations and carbon cycle implications, pages 83-113, \"\n", "\n", "33 \" in \"A History of Atmospheric CO2 and its effects on Plants, Animals, and Ecosystems\", \"\n", "\n", "34 \" editors, Ehleringer, J.R., T. E. Cerling, M. D. Dearing, Springer Verlag, \"\n", "\n", "35 \" New York, 2005. \"\n", "\n", "36 \" \"\n", "\n", "37 \"-------------------------------------------------------------------------------------------\"\n", "\n", "38 \" \"\n", "\n", "39 \" \"\n", "\n", "40 \" The data file below contains 2 columns indicaing the date and CO2 \"\n", "\n", "41 \" concentrations in micro-mol CO2 per mole (ppm), reported on the 2008A \"\n", "\n", "42 \" SIO manometric mole fraction scale. These weekly values have been \"\n", "\n", "43 \" adjusted to 12:00 hours at middle day of each weekly period as \"\n", "\n", "44 \" indicated by the date in the first column. \"\n", "\n", "45 1958-03-29, 316.19\n", "\n", "46 1958-04-05, 317.31\n", "\n", "47 1958-04-12, 317.69\n", "\n", "48 1958-04-19, 317.58\n", "\n", "49 1958-04-26, 316.48\n", "\n", "50 1958-05-03, 316.95\n", "\n", "51 1958-05-17, 317.56\n", "\n", "52 1958-05-24, 317.99\n", "\n", "53 1958-07-05, 315.85\n", "\n", "54 1958-07-12, 315.85\n", "\n", "55 1958-07-19, 315.46\n", "\n", "56 1958-07-26, 315.59\n", "\n", "57 1958-08-02, 315.64\n", "\n", "58 1958-08-09, 315.10\n", "\n", "59 1958-08-16, 315.09\n", "\n", "60 1958-08-30, 314.14\n", "\n" ] } ], "source": [ "Lignes = []\n", "with open(data_file, \"r\", encoding='utf-8') as entree:\n", " for ligne in entree:\n", " Lignes.append(ligne)\n", "for Cpt in range(60):\n", " print(Cpt+1, Lignes[Cpt])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On charge les données à partir de la ligne 45 et on examine leur teneur." ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateCO2
01958-03-29316.19
11958-04-05317.31
21958-04-12317.69
31958-04-19317.58
41958-04-26316.48
\n", "
" ], "text/plain": [ " Date CO2\n", "0 1958-03-29 316.19\n", "1 1958-04-05 317.31\n", "2 1958-04-12 317.69\n", "3 1958-04-19 317.58\n", "4 1958-04-26 316.48" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=44, names=['Date', 'CO2'], parse_dates=[0], infer_datetime_format=True)\n", "raw_data.head()" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateCO2
31852020-08-29411.79
31862020-09-05411.55
31872020-09-12411.45
31882020-09-19411.17
31892020-09-26411.06
\n", "
" ], "text/plain": [ " Date CO2\n", "3185 2020-08-29 411.79\n", "3186 2020-09-05 411.55\n", "3187 2020-09-12 411.45\n", "3188 2020-09-19 411.17\n", "3189 2020-09-26 411.06" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data.tail()" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "On remarque que les données couvrent la période allant du 29/03/1958 au 19/09/2020.\n" ] } ], "source": [ "date_début = raw_data.loc[raw_data.index.min(), 'Date']\n", "date_fin = raw_data.loc[raw_data.index.max()-1, 'Date']\n", "print(f\"On remarque que les données couvrent la période allant du {date_début.strftime('%d/%m/%Y')} au {date_fin.strftime('%d/%m/%Y')}.\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons vérifier si des données sont manquantes. Il semble que non (pas de valeur nulle)." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateCO2
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [Date, CO2]\n", "Index: []" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Manque-t-il des données (des semaines non enregistrées)? Pour cela, nous allons vérifier que l'écart entre deux dates consécutives est bien de 7 jours.\n", "\n", "On note que de nombreuses dates sont manquantes. Dans certains cas, ce sont plusieurs semaines qui sont manquantes." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "5 : 1958-05-03 00:00:00 - 6 : 1958-05-17 00:00:00 - 14 days 00:00:00\n", "7 : 1958-05-24 00:00:00 - 8 : 1958-07-05 00:00:00 - 42 days 00:00:00\n", "14 : 1958-08-16 00:00:00 - 15 : 1958-08-30 00:00:00 - 14 days 00:00:00\n", "16 : 1958-09-06 00:00:00 - 17 : 1958-11-08 00:00:00 - 63 days 00:00:00\n", "29 : 1959-01-31 00:00:00 - 30 : 1959-02-14 00:00:00 - 14 days 00:00:00\n", "33 : 1959-03-07 00:00:00 - 34 : 1959-03-21 00:00:00 - 14 days 00:00:00\n", "43 : 1959-05-23 00:00:00 - 44 : 1959-06-06 00:00:00 - 14 days 00:00:00\n", "53 : 1959-08-08 00:00:00 - 54 : 1959-08-22 00:00:00 - 14 days 00:00:00\n", "210 : 1962-08-18 00:00:00 - 211 : 1962-09-15 00:00:00 - 28 days 00:00:00\n", "225 : 1962-12-22 00:00:00 - 226 : 1963-01-05 00:00:00 - 14 days 00:00:00\n", "231 : 1963-02-09 00:00:00 - 232 : 1963-02-23 00:00:00 - 14 days 00:00:00\n", "241 : 1963-04-27 00:00:00 - 242 : 1963-05-11 00:00:00 - 14 days 00:00:00\n", "269 : 1963-11-16 00:00:00 - 270 : 1963-11-30 00:00:00 - 14 days 00:00:00\n", "277 : 1964-01-18 00:00:00 - 278 : 1964-05-30 00:00:00 - 133 days 00:00:00\n", "279 : 1964-06-06 00:00:00 - 280 : 1964-06-27 00:00:00 - 21 days 00:00:00\n", "285 : 1964-08-01 00:00:00 - 286 : 1964-08-15 00:00:00 - 14 days 00:00:00\n", "385 : 1966-07-09 00:00:00 - 386 : 1966-08-06 00:00:00 - 28 days 00:00:00\n", "398 : 1966-10-29 00:00:00 - 399 : 1966-11-12 00:00:00 - 14 days 00:00:00\n", "408 : 1967-01-14 00:00:00 - 409 : 1967-02-04 00:00:00 - 21 days 00:00:00\n", "898 : 1976-06-19 00:00:00 - 899 : 1976-07-03 00:00:00 - 14 days 00:00:00\n", "1302 : 1984-03-24 00:00:00 - 1303 : 1984-04-28 00:00:00 - 35 days 00:00:00\n", "1368 : 1985-07-27 00:00:00 - 1369 : 1985-08-10 00:00:00 - 14 days 00:00:00\n", "2299 : 2003-06-07 00:00:00 - 2300 : 2003-06-21 00:00:00 - 14 days 00:00:00\n", "2315 : 2003-10-04 00:00:00 - 2316 : 2003-10-25 00:00:00 - 21 days 00:00:00\n", "2385 : 2005-02-19 00:00:00 - 2386 : 2005-03-26 00:00:00 - 35 days 00:00:00\n", "2431 : 2006-02-04 00:00:00 - 2432 : 2006-02-25 00:00:00 - 21 days 00:00:00\n", "2479 : 2007-01-20 00:00:00 - 2480 : 2007-02-03 00:00:00 - 14 days 00:00:00\n", "2775 : 2012-09-29 00:00:00 - 2776 : 2012-10-20 00:00:00 - 21 days 00:00:00\n", "3153 : 2020-01-11 00:00:00 - 3154 : 2020-01-25 00:00:00 - 14 days 00:00:00\n" ] } ], "source": [ "# Calculer les écarts de dates entre deux index contigüs. Si l'écart n'est pas égal à 7 jours, afficher les deux lignes.\n", "for cpt in range (raw_data.index.min(), raw_data.index.max()-1):\n", " début = raw_data.loc[raw_data.index.min():raw_data.index.max(), 'Date'][cpt]\n", " fin = raw_data.loc[raw_data.index.min():raw_data.index.max(), 'Date'][cpt+1]\n", " if str(fin-début) != '7 days 00:00:00':\n", " print(cpt, ':', début, ' - ', cpt+1, ':', fin, ' - ', fin-début)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Si nous tentons d'analyser les données et surtout de modéliser leur tendance en les approchant par un polynôme, nous allons obtenir des résultats erronés. En effet, la variable d'entrée du polynôme pour obtenir une évaluation sera la colonne des indice des lignes du tableau. Comme les dates et les indices n'évoluent pas de manière conjointe, les résultats des prévisions en seront affectés.\n", "\n", "On se propose donc de créer un data frame avec toutes les semaines depuis le début des mesures, le 29/03/1958, jusqu'à la fin de l'année 2025. Les données déjà disponibles seront alors jointées dans ce data frame. Les nouvelles prévisions seront également ajoutées. Le modèle de prévision de la tendance de long terme pourra alors être ajusté." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Recontruction d'un jeu de données complété" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Création d'un data frame avec toutes les dates depuis le 29/03/1958 jusqu'à la fin de l'année 2025." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "tab_date = pd.date_range(start='1958-03-29', end='2026-01-01', periods=None, freq='7D', normalize=False, name=None)\n", "df = pd.DataFrame(tab_date, columns=['Date'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour chaque date, le numéro de la semaine est calculé. Cette colonne est ajoutée au data frame." ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [], "source": [ "tab_week = []\n", "\n", "for cpt in range(df.index.min(), df.index.max()+1):\n", " date = df.loc[df.index.min():df.index.max(), 'Date'][cpt]\n", " tab_week.append(pd.Period(date, 'D').week)\n", "\n", "df = df.assign(Week = tab_week)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons intégrer les mesures dans ce data frame." ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateWeekCO2
41958-04-2617316.48
51958-05-0318316.95
61958-05-1019NaN
71958-05-1720317.56
81958-05-2421317.99
91958-05-3122NaN
101958-06-0723NaN
111958-06-1424NaN
121958-06-2125NaN
131958-06-2826NaN
141958-07-0527315.85
151958-07-1228315.85
\n", "
" ], "text/plain": [ " Date Week CO2\n", "4 1958-04-26 17 316.48\n", "5 1958-05-03 18 316.95\n", "6 1958-05-10 19 NaN\n", "7 1958-05-17 20 317.56\n", "8 1958-05-24 21 317.99\n", "9 1958-05-31 22 NaN\n", "10 1958-06-07 23 NaN\n", "11 1958-06-14 24 NaN\n", "12 1958-06-21 25 NaN\n", "13 1958-06-28 26 NaN\n", "14 1958-07-05 27 315.85\n", "15 1958-07-12 28 315.85" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.merge(raw_data, on='Date', how='outer')\n", "df[4:16]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Observation du rendu des données disponibles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons afficher les données. Nous observons une tendance globale de hausse et un phénomène qui semble périodique ou saisonnier." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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AdpfX8cnWUvf1pP5pNLUYhvdJYkK/FPqmxHLG0Az3fH5a8JcyOF4a6JVSAauyvomCmXN429756StPfuye21NR525+cd6IbPJSrRz8hn2V9Em2ZtZ4g3t2km+2TW/Te3+XUUoFvBv+9jlg7Qx1aF59T3kdB+3CYzefMZD6JmsDvPqmVvfBa3REOCvuP9cvhdMbaaBXSgWspTvKDnuuvqmV779obf2XnRRDi6dyWT/PSD4lLooUz2rY3khTN0qpgPHBxmJqG9tu83eouy4c7vc6JznWTdcADOsTOpuGdAYN9EqpgFBUVsu3/vY5d9gbgrQcUlvYuxXg1EG+h6znjsgmPEyIjgh320b11UDvpYFeKRUQPthgrXSds2ovra2G0poGv/P/3XTAPe5rP3gF/GrXfDJzOs/fMJm4KM1Ke2mgV0oFhPveXOseVzc288UOayrleSOyAdh+wCpY9tS1E0iN85UxyPQE+tyUWM4Ymtkd3Q0qGuiVUt2upKqBgplzeGN5Ubvnq+qb+c4/lwEwZWA6AAvWWxUoB2Um+G0GcsqA9C7ubfDTQK+U6nZOlckfvbISgOaWVr/zlZ5NRJzSBU5pg9wU66GrM6o/KSc0K052Jk1kKaW6nXdmTWur4e43VvudX2IH9chw8UvNDMlKIDHGCvDL7j2XyvqmXj91siN0RK+U6nJzV+/lHc+uT8mxvuBc3djMR54HrQBvrdoDwB3nDyPFk4/3buIdFiYa5DtIA71SqksZY/juC1+4OXeAqnpfamb9nkoS7IJjQ+wSws52fznJscRE+qZNFngCveo4DfRKqS61qqjCPTbGYIzhhcU73bbC0hpy7MVOf75uIsmxkSzaYo3wR+Ra8+HPGmbNpBmogf64aKBXSnWpgzWN7nFJdQNbiqv9zpfVNrF6dwVXTMhjYGYCKXGR1Da2kBAdwYB0K7CHh1mhysnPq2OjgV4p1aUqPWma3WV1VNgzaoZkJRAZLqzbU0l5bRMT+6cCUNNgrYAd1ieRMHv3KGcbwORYDfTHQwO9UqpTrdtTyaylu9zX/1m5xz2uqGti4UZrT9ff/M84UuOimG2f759uFSJz0jjREb7w9KNzhvLHaycwbbDOmT8eGuiVUp2mpqGZi37/X+58bRW1jc1UNzSzYH2xe76irsnd83VodiJp8b5ZM86I/rlvnczI3CR+cv4w91xURBgXjs7xWyilOk7n0SulOs37G3xBfeO+KlqNf2GyB2f7yhyECdTbG3fnJMe4s2vSE6KZc+vp3dDb3uOoI3oRiRGRJSKyUkTWishDdvs4EflMRFaIyFIRmex5z10iskVENorI+V15A0qpnrNxXxWLNvvmwHsD+6b9Ve7c+ZvPGAjgtwFIc6uhqcW63luYTHW+jqRuGoDpxpixwDjgAhGZAjwOPGSMGQfcb79GREYAVwMjgQuAp0QkvN1PVkoFtfN/+xFff3axOzL3DuB3lNa6D1NnXuBfP/65b51MTGQ4V07MA2CbXbBMdY2jBnpjceZDRdp/jP3HKfqcDDhPXC4FXjbGNBhjtgNbgMkopUJKY7OvPs3SQmsnqB++sgKwShfsOFjLmt0V9EuLQ0S4/tT+7vVObv66U/uTmRjNX795cjf2vPfpUI7eHpEvAwYDTxpjFovID4F5IvIrrB8YU+3L+wKfed5eZLcd+pk3ATcB9OvX77hvQCnVMw5U++rF//mjrZw2JIN+aXGM75fCwZpGdpbWsrWkmnNOssoMe2vWZCVaM2syEqL5/J5zurfjvVCHZt0YY1rsFE0eMFlERgHfBX5kjMkHfgQ8a1/e3mNx06bBmKeNMZOMMZMyM7V+tFKBrqmllUJPiuUnr650j50KkxV1TSTFRNI/PY41eyqobWxxd3tygjvgt+2f6nrHNL3SGFMOLMTKvV8P/Ms+9Sq+9EwRkO95Wx6+tI5SKkh9/8UvOPNXC9158Zv2V7nnqhuaKalqoKKuif7pceSnxrn5+uwkK6g3tba2+UzVPToy6yZTRFLs41jgHGADVvA+w75sOrDZPp4NXC0i0SIyABgCLOnsjiulute8tdbGH84smwtH5QAgAvsr6/lok7UQasrAdLKSfGkaJ9BfMcF68Prit0/ptj4rS0dy9DnA83aePgyYZYx5S0TKgd+JSARQj51vN8asFZFZwDqgGbjFGNNymM9WSgWZV5bu4rErx/CPz3YA1tTIN1fsITEmgriocE7KSXLLHAD0sQN9TGQ4hY/O6JE+93ZHDfTGmFXA+HbaFwETD/OeR4BHTrh3SqmA5A3k+yuth7KriyoYl59CeJj4zYvXfHzP05WxSqmj+u/mEr/Xb6/eC8CM0Vb6Zs7qvawsqiAjwQrw3gev3nryqmdorRulVBvbSqopmDnHfeB63bP+j9nu+pe19d/XJvdj+vAst92ZcpkUq2PIQKKBXinVxvT/+xCA837zkV/7zV8a6Pf6tCEZflv9/e7qcQCICL+6aixzbj2ti3uqOkIDvVLqiFpbfctgJg9Ia3PeG+gL0n07QF05MY+Rucld2znVIRrolVK8vqyIukbf5LjB9t6tAB968vPtPVj1bvStD14DkwZ6pXq5/24u4cevrmT0g/PcNu92fweqrLz7Wz84zZ0qCXD5BKuySXy072Fruqe+vAoc+sREqV5u7Z5KwCobDPDQf9a2e74gI574KF9Qdx7C9kmKISsxmtMGZxARrmPHQKSBXqle7tG5G/xe/+3jQgBuPXsIv39vM8t2lJGbHENCtH+4mNDP2hFKRFiihckCmv74VaoXKatp5ORHFvDZtlIAymsb/c47deUBpgy0Hryu2VNBTkqs2/7NqQV8eWwu6QmapgkWGuiVClHFVfX84u31NLf4iond++YaSqoauPppq5L4Hz/c6vceJzf//bMGkx5vLX4yBkqqfCWJH7xkJH/42niiI3QhVLDQQK9UiPrxrJX8+aNtDL5nrtv23vr97nFDcwt//nAbAJPsjbkXbrT2fD11UDoZnhF7S2ubSuMqiGigVypErd9b2aatvsk3ut9TXs+pA9MBeODLIwFYvP0gAIMyE0iN8wX64qr6ruyq6mIa6JUKUbGeGTLLd5ax62AtALn2XPeislo+tXP1zu5PK3aVExURRnZStLvfK8C1p/i2AVTBRwO9UiHiwdlrKZg5BwBjDAnRvhWrP561ktMf/wCAfulxAPz90x3u+fSEKESgqr6ZrMRoRKwgHxlu/X3VpLxuuQfVNTTQKxUCjDE890khAFX1TXy0+YBf6ub8UX3c48vH5xERJsxfZ+Xrf3XVWCLDw+hrz6yJ8syFX37/eTx17QQtZRDkNNArFQLeWbPPPS48UMv2Et/K1jCBiDBhWHYiAF89OZ+cFCt9ExEmfGVcLgADM62yB1ERvrCQEB3BRXYpYhW8NNArFQLeXeebTbN+XyW1nvnwBthRWsvG/VVcPt4qW5CfaqVv+qbGuqtZo+0Af95I3+hfhQYN9EqFgG0Hatzjn/5nHSt2lgPw8czpGAOz7Q29x+RZKZi8VCtN0y8tzn2fs5l3pWf3KBUatASCUkHo/z33OTsO1rLg9jMAWLmr3D3XPz2OZTvKuHx8Xzfv7pg2OAOAPHtE790J6olrxvPK57v4xqk6wybU6IheqSDT1NLKexuK2VJc7bfqFeCck7KprG+itKaRIXZO3snBgy/AOwugdpfXuudiIsO5fmqBO+NGhQ4N9EoFuN3ldbzy+U73tbNdH1iLng7W+OrVZCREsetgHeBLyzh1ajISot259f9zcj4A984Y0bWdVwFBUzdKBbhpj74PwIicZEbnJVNc6Qv0q3dXEBHuG4Ene3Z7yk+zAryz7qnV+MoY5KbEUvjojK7stgogOqJXKkjcPmsFADsO+tIt20qqWbT5AACL7z6bcE/axRnRv7VqL4DfyF/1LhrolQogFXVN/GreRjeH/uaK3e65kblJANz60nK3befBWjbsq2RgZjzZSTG8tqzIPZdi16qJtKdPntLOfq+qd9BAr1QAGfvQuzzxwRZueeELAG57eYV77kC1/4h8fL8Udhys5fPCMmIjrdz7Y1eOafOZf/jaeGaMyeGfN57ShT1XgUwDvVIBotVTCnjj/iq/c2cPz2JfZT3b7fnysZHhDMiIp9B+7cyPn2iXG/7OGYPc956Uk8ST10xwR/aq99GHsUoFiIZm31TJ8fkpfufy0+JYvP2gu5/rr786lk37q/lXlZXaGZJlTaVMiolkxf3nkhwbiVIO/RGvVA95c8VuCmbOoaahGYDlu8rcc1sP1NDU0kpUeBjXTelPQ3Mr1Q3NLNxYAsD5I/uQnRTtXu+d+p4SF6Vz4ZWfowZ6EYkRkSUislJE1orIQ55zPxCRjXb74572u0Rki33u/K7qvFLBzMm/3/HaSgAefmu9e25bSTWriipobGllysB05qza456bOiidsDChptFXz6Z/uq+UgVKH6kjqpgGYboypFpFIYJGIzAVigUuBMcaYBhHJAhCREcDVwEggF1ggIkONMS2H+XylegVjTLsj7U+2Wpt/eMsKV9U3s8nO0w/NTuDXXx3HjX9fCvjq1HhH9FMHZXRZv1XwO+qI3licmqeR9h8DfBd41BjTYF9XbF9zKfCyMabBGLMd2AJM7vSeKxXA6hpbqPdUkKysb2LUA/N4demuNtcOsssDO74+pR9gLYYCa2XrULucAfjKCTsPXsEqX6DU4XQoRy8i4SKyAigG5htjFgNDgdNFZLGIfCgiJ9uX9wW8381FdptSIenZRduZYy9KAmvkftL97zDVXtEKsGjzAWoaW3j4rXWA/2bb+yrqafQ8iD1tcCYAn2w5QL+0OBKiI9xVrgA59laA3j1dlTqSDgV6Y0yLMWYckAdMFpFRWGmfVGAKcAcwS6zfS9t7CtRmC3kRuUlElorI0pKSkuO+AaV62sNvreOWF79wXz8+byPgvxL1gdnWbJnKeuvB63xP/fj9lfUUllrTJM85KZsUu4xBYWktw/tYI3lvysdZOOWM4k8brGkbdWTHNL3SGFMuIguBC7BG6v8yxhhgiYi0Ahl2e77nbXnAnnY+62ngaYBJkya1+UGgVDAorqxv07bGTrl4lVRZ9WlS7SB+35tr3HPNrYYnP9gCWGmbFE+9moKM+DafNTjLl8bZ8PAFhIfpDBt1ZB2ZdZMpIin2cSxwDrAB+Dcw3W4fCkQBB4DZwNUiEi0iA4AhwJKu6b5SPWuLZ8s+x+AsX87dm6IBKKu1NvVwAv9l9o5Pe8utHxinD8kkJdaXkslP9aVsXvz2Kfz5uol+nxcTGa4LodRRdWREnwM8LyLhWD8YZhlj3hKRKOCvIrIGaASut0f3a0VkFrAOaAZu0Rk3KlRd85fF7nF1QzMJ0RFsKfYF/4q6JiLD/Ufc2zw/HG6YNoA3lu9mSeFBBmclEB4mfiP6fM8OUDqzRh2vowZ6Y8wqYHw77Y3A1w/znkeAR064d0oFmFc+30l8dAQXj8ltc27z/ipG5ibzX7uaJFh5+uZW60FrQXochaW17myaey46yd2k2zpvpWm8M2gGZvjPyFHqeGgJBKWOwf++vhqAi0blEHZIbnznwVp2l9f5tZXVNnLVnz4F4JazBnPHa6tYv9eaH5+fFkdGgm8ufIFn0dPlE/qyv7KefroQSnUCDfRKHUZtYzNR4WFE2Dlw785OJdUNpMf7T288WNPoV6/GaXOMtguPrd5t7e966GpW74PXX391XCfcgVIWfYqj1GGMuH8eg++Z6y58mvSzBe65ZTvKqKhrcl+HCZRWN/Lo3A0AvPTtKYDVJmKN0AdlJiACnxdaNW28+XeAAe3MsFGqM2igV6odTZ5Ntzfuq2pz/q+LtlNn/wC45pR+pMZFUVrT6C5sGmdXn7z7jdUYAxP6pRIZHkZmQjSNza2kx0eREG39Qj2+n3XtwEwN9KpraKBXCmhsbmVVUbn72pn+CHD/7LV+teIBSmsaOe2xDwD4yri+pCdEUVJVT2l1I9+cWuBuwu1wRu/Oqta+nmmTT14zgSevmUBOcixKdQUN9EoB5/7mQy554mP+u9lapb3fsxBq5a5yau3R+0A7veJsAAIwqX8qfVNi+bywjNrGFrcuzWTP1n1Ds63ZM9lJVqDfW+FA9dYxAAAgAElEQVT7/NyUWGaMyemK21IK0ECvFAA7Sq0Nt9fstipIrtnjqyR52uAMd2787ecNbfPesDAhJS7Kzdk7C6YSo31zHbITrQC/bIeVn3cWSinVHTTQq17Pu3q1qKwWYwz3/dsqUTAsO5FFWw64aZ2J/VO5YkKee/3t51qB37tIaoRdi2arZ2GUMxXTyf3fcubgrrgVpdqlgV71eo/P2+Ae7y6v4++f7nBfJ8ZYo/L1e6tIjo2kT1KM33z3UwelA75CY4D7kPX8UX0AuNiTlnn9u1N5/IoxJMfpVn+q+2igV73Ovop6bnx+qfuA9emPtvmda7bbzxqWydknZQOwfGcZw/skIiKkeubPOw9Xr5hojfJjIn3/pEblWvPmrz2lv9s2JDuRr57srfmnVNfTQK96ncuf+pgF6/fz+/c3A2DszE1UeBj7K+uZt3YfAL/9n/FkJFhBfcO+KjfF461F46xs7WM/ZPXWo7l4TA4fz5zujvqV6ika6FWvk2xv2FFe2+TX/oPpgymrbWLJ9oOAlbbJSPSVKBhiz6bxbhLi1KXJT4vjyWsm8NAlI91zIkLfFJ0yqXqeBnoV0lpbDSt3lWOM74Grszfr5uIqd9Xr/zttgN9IfdpgawPuTE8tmuunWimYvFQrR5+b7CtIBjBjTE6b1a5KBQKtdaNC2k9eW8m/vtjNDdMGcP+XR/iteP14S6k7n31kbhLeNVHOXPhMz4jeCfCTB6Txzg9PZ5hnH1elApmO6FXIMMbw5ord1DQ0u23/+mI34BvFO/PYHdsPWFMgc1NiOXdEttvuBPU0z4PXBM+8+OF9kvy291MqkOmIXoWM9XuruO3lFQAUPjrD71yJXXny5n8s82v/zXzrgWy/tDiSYnz/HAZkWIE+MjyM/ulxfGlIZpf1W6mupoFehYw5q9tsTezaX1GPMcZdvTpjTA5zVu11NwHJSY7xG6FPHuCbKfPhHWd1UY+V6h6aulFBa9bnuyj01Jx58oOtfueNMYhAZLhQ1dDM/kpfobIHvjzCPZ48IM0N8s5iKG+aRqlgp9/NKigtLTzIna+vIjEmgtUPng9YAb2pxRAVEYYxhoM1jRhjTX3cVlLjljGIiQwjIz6aqIgwGptbGWtvCAIw//YzaDWm3a+pVLDSQK+CkrNbU1W99eC1qaWVVgNxUeHUNrZQUtXAUwutEf7ecmtmzW8XWPn4P147kbAwIdoO9E5FSbBy8kqFGv2uVkEpOiKca07pB0B5bSN7y+tpaTVMH54FwJaSap77pBCAW88eAsA6e+bNKQOt8sHOD4kRnjo1SoUiDfQqKNzx6kpmvr7Kff3C4h0s3lYKQGFpLfe+aVWbPCnHqRzpy91fd6qv1kzflFjioqxfZFPtBVIDMxK6tvNK9TBN3aiAV1XfxKvLigCYeeFwUuKieG99MdvsB7F7yuvYvN/a7m/a4AyW7SjjeXs0PzAznnjPbk/eDbn/8f9O4c0Vu8lO8i2KUioU6YheBbx1nk1A1u+1iou9v6GYif1SAWu3puiIMBJjIhiRk0REmFBW0wjALy4b7Tdt0rsB96i+ydwzY4QufFIhT0f0KuAUzJwD+BY9fWqnaAA27qtkd3kdYBUUi4kMY9O+KgpLa7lgZB+iIsJ4d91+wCpK5mznNzgrgS3F1X4FyZTqLXRErwKKU2TMe+zMlgEoq23iJ6+uBOCnl44kJzmWV5buAvArYQDW6N0Zrd9mP5D17uOqVG+hgV71qJW7ynlv/X739c6Dte7xp1utkfyovr5ZMa9/UeQe90+PJzfFNzXy5AIriH91krUJiHfa5JfH5rLh4Qu4apJu+qF6H03dqB5jjOHSJz8G4L93nkV+Whz/9+5G9/z+ynpqG5vdDbsBisqstM2Pzx1KeJiQFm89SI2KCCM/zar9PryP9YPBW3kSfLXjleptdESvupW3TLB3CqSz2ce8tb7R/bIdZeyzywgfytlz1dnTtbG51U3TNLdaX6O/1oZXCuhAoBeRGBFZIiIrRWStiDx0yPmfiIgRkQxP210iskVENorI+V3RcRV8fjlvA0PumcvCjcWANS3SUVzVQLWnvHB2UjR7KupYYKd1/vKNSX6f5WwIEtXOStbrphTwo3OGcv3Ugs6+BaWCUkdG9A3AdGPMWGAccIGITAEQkXzgXGCnc7GIjACuBkYCFwBPiYj+ztzLtbQat+iYU5rgF3M3uOf3V9ZTVObLz4/PT6W4soFfzdsEWNUlb/7SQPf80D7Wph/z1/l+A3DERoVz2zlDNFWjlO2ogd5Yqu2XkfYfp+rTb4A7Pa8BLgVeNsY0GGO2A1uAyZ3XZRWMvEF8u73QydkMpE9SDF/sLGPXQWuE/9gVo8lMjKakuoGTchJJjIlgVN9kCjxz4PPtjUGGZFurWmdeOLxb7kOpYNShHL2IhIvICqAYmG+MWSwilwC7jTErD7m8L7DL87rIblO9SG1jMwUz57B2j1XvvdRewAQQGea/QGlfZT2riipYvbuCMIGLRueQlRhNeW0TK4sq+PLYXAC8RSWjIqxv3T9eO5EXbjyF75wxqIvvSKng1aFAb4xpMcaMA/KAySIyBrgHuL+dy9tbZtim7quI3CQiS0VkaUlJybH0WQWBEffPA2DG7xcBUGzXgh9up1wamlv8XgNs3l9Fv7Q4EmMiSfdsyh1rp2DG90sB/OfLx0aFM22w+3hIKdWOY5peaYwpF5GFWOmZAcBKe6ZDHvCFiEzGGsF7JyvnAW22/jHGPA08DTBp0iQtAB5CTDv13J1aNCflJDFn1V6+2GHVhv/qpHzqmlr45byNrCqqcOfMG8/Y4Mxhme57Nz9yIRFhWrJAqWPRkVk3mSKSYh/HAucAy40xWcaYAmNMAVZwn2CM2QfMBq4WkWgRGQAMAZZ02R2oHmeMYdDdb/M7ewVrQztlBv5vvvVQdUh2Ao0trXyy9QBgLWRKjbM24N5dXsfgLCvnvnCj77c85zxY9eK1No1Sx6YjqZsc4AMRWQV8jpWjf+twFxtj1gKzgHXAO8AtxpiWw12vgt+XfvkBLa2G3yywgrlT5x2s3ZxaW32j874p1qKmf362g+ykaDITo0mK9f1iOSjTCvR3nD/MbRup9eKVOiFHTd0YY1YB449yTcEhrx8BHjmhnqmg4cyWAWt0/+66fe7r+qZW5q21Xg/OSmBotpWTL6tt4pyTrE1Cwj0jdCfQO38DOoJX6gTpylh1TJzZNH9cuLXd89UNzdzzhrUJiDMSf/7TQgAeu2IMw7J9D19H5lrbAZ59ku/h6sBMawpluObhleo0GujVMflok5U7f+wda7FTRV2T3/mDnmmUv7h8NABr91QSGS6MyUsmzBPAR/e1Ar0zVRIgMSbSPX7x26fw8czpnXwHSvU+WtRMHVFVfRMRYWHE2rs0bdhX5Z5raTWMfehdADISojlQ3UBJVQPxUeGcMyLbTdNU1TczICO+zcbbo+xAfzhTB+m0SaU6g47o1RGNfvBdzvrVQve1txCZt1bNTy8dCcCqogpqGlsYmp1ITGS4W4smLzW2zWf3SfaVEV5899l8fs85nd19pRQ6oldHMHultfxhX6WvguQuT714b6CfNjgDEXj5c6vs0ZSB6YBVXbK0ppF8TyXJV26aQnOr/1x7b+14pVTn0hG9cv3+vc3c/cZq9/WtLy13j2vsypJFZbVM6m/t1bppvy+NkxwbSd+UWDbtryY6IoyxeVZaxil9kOsZvZ8yMF1XsyrVjTTQ91ItrcZv/9SahmZ+PX8TLy7e6bedn2PT/irKaho5UN3obsf3mV1D/n8vsAqKORtvD8pMIOKQfHxclP7yqFRP0UDfSw26+22G3jvXfe3MdQfYUlzt1qJx7DxY6472Ty5IIzk2ko+3WKtbvzTUGp0nx1ozZpzVreAbyV87pV8X3IVSqiM00PcCc1btpWDmHLaWWNWmmz27PNU1WgG90jNNcnNxFRW1/tMmS6oamLvG+mFw1vAshmYnUG5f0z/dGsm/tWov4D9dcs6tp/Puj75EdITWhleqp2ig7wVuefELAJ58fwsAD8xe65777XtW2QInSAMcrGlic7H1Q+GSsblEhotfmWHADfJp8VEkRPunZbx7tabGR7nTLJVSPUMDfS/gjLA/tguJvbjE3RCMMBFaWw1Ld5S5bb94ez0/f3s9AI9cNor0+Gh2llqzbe68wKpB4/wgaPHMnvn0rumcNSyT284e0oV3o5Q6VhroQ1xlfZP70HW/XRPeu2n2HxduZduBar/3NLcadxplYkwkCTERvLfB2rKvn/3eeHsBlTcfn5Mcy9++NVm38FMqwGigDzH1TS18suWAWxN+dVGFey43OQZjDIWltX7vcTYFueeik3Dqhxnguin9ASuPX99k/bAosPPxD39lFOD/Q0MpFZg00IeY4fe9wzXPLHZz7it2WRt8XH9qf/ZV1ru1aL4+pR9Zdi799lnWbpDnjsjmptOtDbir6pvdPVqrG3xlh52qkheNzuGaU/px94yTuuGulFInQgN9EKtvauHpj7b6zYd37KuwVrOu2FXOwIx4RuYm02rg462lAEwekM43pxVY19orX/umxvoVKStIt0brzg5PgFvzJiYynJ9fNpoMz5Z/SqnApIE+SLS2GsoOmfny/CeF/PztDXzvhWVuW2KMNQNmw74qjDGs2FXOuPwUBmVZo3NntevQ7AT6eMoOzBiTQ2R4GPPX7XfbhudYZYa/cWr/rrkppVS30EAfJG558QvGPzyfBZ5A/Iu5VqngBeuLAWhsbnV3d3r9iyK2HaihpKqBsfkpbUbeBenxfkXFnC1a//A13x4zzm5QzsYfQzwPXpVSwUMDfQBqbmnl1peWs3aP70Gqs1jpxr8vbfc9xhh+9MoKv7ZP7TTNlIHppB8S6GMiw90cPcAtZw0GfJuBeI3PT+GhS0by+vemHsfdKKV6mhYgCUCfF5Yxe+UeZq/cQ+GjM9q9xvuAFKCyvpk5q/f6tS3afICoiDAGZyXg3bDp9/aoPTPBN6Ifmm2N1pPjInnzlmn0T/fNphERrp9acCK3pJTqQTqiD0C3vbzc73XLISV9m1pa+b692tWxu8xXMtiZ+vjhphKGZCUQHiZ++66ebleO9G7K7S1CNjY/hZS4qBO8C6VUoNARfQAwxtBqfPukJsVGUlxlzW0vrqxnV5n/vPd9FfUs3Ght6ZefFsuug3V8829LALj5jIFMHWTVgq9ravGrH3/FhDwKS2tIjbeCuIjw6ndOJTel7aYgSqnQoSP6HmaMYcBdbzPo7rfdRU5bin0rVVfvruDjLVau/Vv2dEgn8E8ZmMbvr7bSMM4Phu+dMZhUz2j89KG+qZH/99WxvP5d/zz7yQVp7kNXpVRo0kDfzQpmzvHbmm+3Z5emck/FSGeGy+bian493yo89rXJVqnf/9g7P10wsg8jcpP8Pj85LpJoT/VInSmjlNJA34VaWw1V9b7g7ZQH3n6gxt3c47mPC93zi7cfpMQemX9lfF8iw6VN8M9OiualJbsAOG1IJtER4W4deGcKZayn1sxVk/K74M6UUsFEA30XGnj324x+8F13tWlhqW9jbacUQaRn9F1SVc9Vf/oEgD5JMSTHRrLfXrV6zknZiIibZkmIjmBQprUIyikwdlKOVQ44LExIibOCv3cLP6VU76QPY7tIq2emzNaSaib0S2Wn58HogeoGclNi+ePCrURHhNHQ3EpZbZNbcGx8vxSSYyP5vNDaru/KiXkAfLHTql2TFBPhzqRxplrOGJ3jfv6/vzeNXWW1frNtlFK9kwb6LuIN6i8t3klkWBg3POdb7DRv7T6y7RIEDc2tpMVHuTVnAAZmJrC1xPcbwPA+/pt37KnwXVtr7xLl1KEBKMiId4uSKaV6N03ddMC8tfv86swYY9rMbX/+k0Ie9OzctG5vpXtcVtvEur0VfteHh4Xxgxet+fJfm2xVklxvv8cpD+zl1IH/wXRrBesvrxzjnnvx21OICg9jmj0/XimlvDTQH8Wjczdw8z+Wcd+ba9y2/ZUNjH5wHn//tBCwAv8Ds9fy3CeF3G6XIXBKFiy99xyevm4iX+wod99fkB7H1uJqlthpmZ9eOpKc5BiW22mZL3mmRDrC7Dn2Pz5vGIWPzvB7yDp5QBqbHrlQK0kqpdp11EAvIjEiskREVorIWhF5yG7/pYhsEJFVIvKGiKR43nOXiGwRkY0icn5X3kBnO1jT6Ff2908fbgXgE7tuDECrMdQ2tvDLdzYCvtQJwL+W7wZ8UyA376/mqYVb3EVKqx88j8FZiSzfaW3dNyw7kcjwMIZ49lXNT7MeuP77lmmdfn9Kqd6nIyP6BmC6MWYsMA64QESmAPOBUcaYMcAm4C4AERkBXA2MBC4AnhKRgNxbbs3uCneaI8BfPtrGhIfnc9M/fLn0C0b2AXy12QFyU2I5a1gm/TOsthcX+/ZgzUyMpsZ+OHrZ+L4s3l7Kr97dxEq7LnxiTCQDMuLcHPtt51j7q27cV+X3+QBj+ibz/bMG89ldZ3fqfSulepejBnpjcZZqRtp/jDHmXWOMU1nrMyDPPr4UeNkY02CM2Q5sASZ3cr+PWWNzq9+mGqXVDVz8h0UMv+8dt+0Re0Nsp7wAwDtrrRSMdz47WEXE1uyu9HsfWNvuORtnXziqD+n2SP7TbaVM7J8K4FdHxike5i1zkBhtPSMPCxN+cv4w/3LCSil1jDqUoxeRcBFZARQD840xiw+55AZgrn3cF9jlOVdkt/WooffOZexD77bZKPtwdpfX+aVw9lfWY4zhumcX85ePtrFsh5V68f7w+PbpA6htbGZ1kZVrz0uNo8HzGYPsVareqZf97T1Y77t4hNumUyKVUp2pQ4HeGNNijBmHNWqfLCKjnHMicg/QDLzgNLX3EYc2iMhNIrJURJaWlJS085bOU+QZLZfXWrNnZv5rldvWcsgKVoAdB2qYs9rKs4/qm0RNYwsHaxr5ZGspFXVNfPv0AYA1Hx7ghmkDSI6NpNXAfW9as2/y02L5eMsB9zOd/Van2EXHwFr4BDDJHu3rbk5Kqc52TLNujDHlwEKs3Dsicj1wMXCtcSpyWSN477r7PGBPO5/1tDFmkjFmUmZm21kmJ+K3CzbxwJtr3CJhzgNVgCK7tsyqIt90x8LSGtbusdIwF422cvIl1Q386BVr0+yLx+QCVp34llZD39RYhvexasz85aNtAAzKiic6wv9RRGJMJBeO8i1icoL56L5tN/dIjImk8NEZ/PTSUW3OKaXUiejIrJtMZ0aNiMQC5wAbROQC4H+BS4wx3jq6s4GrRSRaRAYAQ4Alnd91y76KetbtqXTryAD8dsFmnv90h1tmwLvBxvf++UWbz9iwt4r3N1jb8X3vTGueupOaATh1oDUCX7De2sZvRE4SY/KsYP3K0l1ERYRxxYQ8t1wBwO3nDgXgvJHZbpuTkYmJDOeWswbx9HUTj/OulVKq4zqyMjYHeN6eORMGzDLGvCUiW4BoYL6dU/7MGPMdY8xaEZkFrMNK6dxijGk53IefqAt+9xHltU2seeh8EsLD/FIwu8vrSE+I5jcLNrlt/dLj3MVPF47qw9w1+7jFs4nHYDuPvmizlXKZeeFwdxaMk4YZlJVAbGQ4EWFCc6uhsbmVmMhwJvZP5ZlF2wEYYK9KTYyJdD+72ZObv+P84Z33H0EppY7gqIHeGLMKGN9O++AjvOcR4JET69rRVTc0u7NhEqIj7Ielvl8eisrq/NIkAzPiyUzwrUCdOijdXdjkiLErP247UONekx4fRVREGHvtKZFOXr35kNWxF3pqzTizacLDhAW3f4nK+mZd0KSU6hFBXevGO+PFsWKXbwXq7rI6Zq/0PR7ISIympKqBt+y9VU8b4v9swHkQOr5fCst3lhMmMDQ7kbAwcWfgjMjxr/8O8KevT2jTNibPXT/G4KzENueVUqq7BHUJhArP3PbG5lZExC3ZGxMZxu7yOl5bVgTAX74xiczEaA5UN/DRphJOLkhlQEY8F47q437GV+2yAk4VyNyUWHeE7xjXL4VDje+X6h7fcf4wpg1Ob3ONUkr1lKAO9FERYZxmF/Iaeu9clhYepKaxhWevn0RBejxFZbX81861nzsim8yEaLYdqKGorI6zhmcB/ptiO6N1p+aMt5CZwzu33uFUoQS45azBvHDjlE66Q6WUOnFBHegHZyXwzxtPcV+/utQava/YVU5xVQML1hf7XZ+Z6MuRO0H98+0H3TancJjzQ2BiQZp7bvWD5/GloZncO+Mkt+3hS0dy42kDOut2lFKqSwR1oHe89O0pjMlL5pWl1oLcm88Y5NabAbj1bKueTKbnYagzDz49wSpHMMGTkpkxOodLx+Xy6OWj3bbEmEj+fsNkv/IF151awL2eFa1KKRWIQiLQZyZGs7vMt8l2QnQEZw3Lcl9fbz9kHZLt2yg7O8kK+k9dO4Fpg9N57gZfOZ7YqHB+d/V4d1qlUkoFs5AI9AnREZTa+XRnY477v+wbaafZhcW80xudejL90+N54cYpJHnmuyulVCgJiUCfFh9Flp1/d4qE5XgqPjpB3dkwWymlepOgnkfviIoI48Vvn8Lv3tvCuHwr1y4iPH7lGLdMMFh59jUPnU9sZECWx1dKqS4hvlpkPWfSpElm6dKlR79QKaWUS0SWGWMmHe26kEjdKKWUOjwN9EopFeI00CulVIjTQK+UUiFOA71SSoU4DfRKKRXiNNArpVSI00CvlFIhLiAWTIlIFbCxC79EP2BnF35+MlDRhZ8Peg8d0dX30NX9h+C/B/0+OrrO7P8wY8xRt7ALlEC/tCOru07g80uMMZlHv/K4P/9pY8xNXfX59tfQezj653fpPXR1/+2vEdT3oN9HHfr8Tut/R2Nnb0ndlB/9khPyny7+fNB76Iiuvoeu7j8E/z3o99HRdcf3kZ/eEui79FdJY0x3fHPrPRxFN9xDV6ckgv4e9PuoQ7r8++hQgRLonw7yz+8Oeg89L9j7D3oPgaAz+9+hzwqIHL1SSqmuEygjeqWUUl0kaAO9iPxVRIpFZI2nbayIfCoiq0XkPyKS5Dk3xj631j4fY7dPtF9vEZHfi7MdVQD1X0SuFZEVnj+tIjKuJ/t/HPcQKSLP2+3rReQuz3uC5R6iRORvdvtKETmzp+9BRPJF5AP7v+laEbnNbk8Tkfkistn+O9Xznrvsfm4UkfOD7R5EJN2+vlpEnjjks7r9Ho6j/+eKyDK7n8tEZHqX998YE5R/gC8BE4A1nrbPgTPs4xuAh+3jCGAVMNZ+nQ6E28dLgFMBAeYCFwZa/w9532hgm+d1j/T/OP4fXAO8bB/HAYVAQZDdwy3A3+zjLGAZENbD30c5wAT7OBHYBIwAHgdm2u0zgcfs4xHASiAaGABsDYB/C8d6D/HAacB3gCcO+axuv4fj6P94INc+HgXs7ur+d8s/pi78D1xwyD/QSnzPHfKBdfbxRcA/D/M/aIPn9deAPwda/w95z8+BRwKh/8f4/+BrWNPWIrB+0G4C0oLsHp4Evu657j1gciDcg+drvwmci7UAMcfzfbLRPr4LuMtz/Tw7sATNPXiu+yaeQB8o99DR/tvtApRi/eDtsv4HbermMNYAl9jHV2H9IwUYChgRmSciX4jInXZ7X6DI8/4iu62nHK7/Xv8DvGQfB1r/4fD38BpQA+zFWhX4K2PMQYLrHlYCl4pIhIgMACba5wLiHkSkAGu0uBjINsbsBbD/zrIv6wvs8rzN6Wsw3cPh9Pg9HEf/rwCWG2Ma6ML+h1qgvwG4RUSWYf0K1Wi3R2D9qnet/fdlInI21k/TQ/XkNKTD9R8AETkFqDXGOPnkQOs/HP4eJgMtQC5WyuDHIjKQ4LqHv2L941sK/Bb4BGgmAO5BRBKA14EfGmMqj3RpO23mCO3d5hju4bAf0U5bt93DsfZfREYCjwE3O03tXNYp/Y/ojA8JFMaYDcB5ACIyFJhhnyoCPjTGHLDPvY2Vl/0nkOf5iDxgT7d1+BBH6L/janyjebDuK2D6D0e8h2uAd4wxTUCxiHwMTAL+S5DcgzGmGfiRc52IfAJsBsrowXsQkUisAPOCMeZfdvN+EckxxuwVkRyg2G4vwv83RaevPfq9dIz3cDg9dg/H2n8RyQPeAL5hjNlqN3dZ/0NqRC8iWfbfYcC9wJ/sU/OAMSISJyIRwBlYede9QJWITLGfbn8DK7/WI47Qf6ftKuBlpy3Q+g9HvIedwHSxxANTsPKRQXMP9vdPvH18LtBsjOnR7yP76z0LrDfG/NpzajZwvX18vac/s4GrRSTaTj8NAZYE2T20q6fu4Vj7LyIpwBysZyUfd0v/e+JhSyc98HgJK9/bhPWT8P8Bt2E95NsEPIr9QM2+/uvAWqz86+Oe9kl221bgCe97Aqz/ZwKftfM5PdL/Y70HIAF41f5/sA64IwjvoQDrAdt6YAHQv6fvASsVabBmla2w/1yE9cD7PazfON4D0jzvucfu50Y8szqC7B4KgYNAtf3/bURP3cOx9h9r8FDjuXYFkNWV/deVsUopFeJCKnWjlFKqLQ30SikV4jTQK6VUiNNAr5RSIU4DvVJKhTgN9KpXEpEWsSqBrhWrEuXt9rz5I72nQESu6a4+KtVZNNCr3qrOGDPOGDMSqwDVRcADR3lPAdYKX6WCis6jV72SiFQbYxI8rwdilSfOAPoD/8AqhwvwfWPMJyLyGXASsB14Hvg91oKqM7GqDz5pjPlzt92EUh2kgV71SocGerutDBgOVAGtxph6ERkCvGSMmSTWRiM/McZcbF9/E9aKxp+JSDTwMXCVMWZ7t96MUkcRUkXNlDpBTvXASOAJsXbxasEqc92e87BqKF1pv07Gqh2jgV4FFA30SuGmblqwKgw+AOwHxmI9x6o/3NuAHxhj5nVLJ5U6TvowVvV6IpKJVaHyCWPlMpOBvcaYVuA6INy+tAqrPr1jHvBduwQgfcoAAACCSURBVEQtIjLUqW6pVCDREb3qrWJFZAVWmqYZ6+GrU2L2KeB1EbkK+ACr0iBY1QmbRWQl8BzwO6yZOF/YZWVLgK901w0o1VH6MFYppUKcpm6UUirEaaBXSqkQp4FeKaVCnAZ6pZQKcRrolVIqxGmgV0qpEKeBXimlQpwGeqWUCnH/H+p8KaNeilIBAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df.plot('Date', 'CO2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Un graphique sur une période plus réduite permet de mieux observer le phénomène saisonnier.\n", "\n", "Il semble que le point bas du phénomène saisonnier se situe vers le mois d'octobre et son haut vers le mois de mai." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[-500:-300].plot('Date', 'CO2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe l'absence de données sur certaines périodes." ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 16, "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": [ "df[200:350].plot('Date', 'CO2')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Elaboration d'un modèle prévisionnel de la tendance de long terme" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous nous proposons de tenter d'approcher cette courbe par un polynôme du second degré. L'ajustement du polynôme sera réalisé en minimisant la somme du carré des écarts. Cela devrait placer la courbe résultante comme la position moyenne du phénomène. On note un écart significatif entre la dynamique de la tendance de long terme et celle du phénomène saisonnier. Les deux parties du phénomène devraient donc pouvoir être correctement séparées. La tendance de long terme devrait pouvoir être approchée par un polynôme du second degré." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Toutefois, le data frame contient de nombreuses données manquantes ('NaN'). La présence de ces données manquantes pose problème à l'algorithme d'ajustement du polynôme. Il faut donc limiter l'ajustement à la période de mesure disponible (indice 0 à 3261, cf. ci-dessous). Pour les données manquantes à l'intérieur de cette période, nous utiliserons une interpolation linéaire qui ne devrait trop affecter le résultat final." ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateWeekCO2
32582020-09-0536411.55
32592020-09-1237411.45
32602020-09-1938411.17
32612020-09-2639411.06
32622020-10-0340NaN
32632020-10-1041NaN
32642020-10-1742NaN
32652020-10-2443NaN
\n", "
" ], "text/plain": [ " Date Week CO2\n", "3258 2020-09-05 36 411.55\n", "3259 2020-09-12 37 411.45\n", "3260 2020-09-19 38 411.17\n", "3261 2020-09-26 39 411.06\n", "3262 2020-10-03 40 NaN\n", "3263 2020-10-10 41 NaN\n", "3264 2020-10-17 42 NaN\n", "3265 2020-10-24 43 NaN" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df[3258:3266]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe que les données manquantes à l'intérieur de la période de mesure sont maintenant renseignées." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "df_lt = df[0:3261]\n", "df_lt = df_lt.interpolate()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateWeekCO2
41958-04-2617316.480000
51958-05-0318316.950000
61958-05-1019317.255000
71958-05-1720317.560000
81958-05-2421317.990000
91958-05-3122317.633333
101958-06-0723317.276667
111958-06-1424316.920000
121958-06-2125316.563333
131958-06-2826316.206667
141958-07-0527315.850000
151958-07-1228315.850000
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" ], "text/plain": [ " Date Week CO2\n", "4 1958-04-26 17 316.480000\n", "5 1958-05-03 18 316.950000\n", "6 1958-05-10 19 317.255000\n", "7 1958-05-17 20 317.560000\n", "8 1958-05-24 21 317.990000\n", "9 1958-05-31 22 317.633333\n", "10 1958-06-07 23 317.276667\n", "11 1958-06-14 24 316.920000\n", "12 1958-06-21 25 316.563333\n", "13 1958-06-28 26 316.206667\n", "14 1958-07-05 27 315.850000\n", "15 1958-07-12 28 315.850000" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df_lt[4:16]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous nous proposons de tenter d'approcher cette courbe par un polynôme du second degré." ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([4.83541627e-06, 1.45158071e-02, 3.14685628e+02])" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "p = np.polyfit(df_lt.index, df_lt['CO2'], deg=2, full=False)\n", "p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Avec ces éléments, nous définissons une fonction qui pour un index donné, retourne une valeur de concentration en CO2 pour la tendance de long terme." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "def taux_lt(x):\n", " return np.polyval(p, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous calculons maintenant le taux prévisionnel de long terme pour l'ensemble des index du data frame. Enfin, nous associons ces données au data frame \"df\"." ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [], "source": [ "prév_lt = []\n", "for cpt in range(df.index.min(), df.index.max()+1):\n", " prév_lt.append(taux_lt(cpt))\n", "\n", "df = df.assign(Prév_LT = prév_lt)" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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DateWeekCO2Prév_LT
01958-03-2913316.19314.685628
11958-04-0514317.31314.700149
21958-04-1215317.69314.714679
31958-04-1916317.58314.729219
41958-04-2617316.48314.743769
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" ], "text/plain": [ " Date Week CO2 Prév_LT\n", "0 1958-03-29 13 316.19 314.685628\n", "1 1958-04-05 14 317.31 314.700149\n", "2 1958-04-12 15 317.69 314.714679\n", "3 1958-04-19 16 317.58 314.729219\n", "4 1958-04-26 17 316.48 314.743769" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.head()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateWeekCO2Prév_LT
35312025-11-2948NaN426.228725
35322025-12-0649NaN426.277393
35332025-12-1350NaN426.326071
35342025-12-2051NaN426.374759
35352025-12-2752NaN426.423456
\n", "
" ], "text/plain": [ " Date Week CO2 Prév_LT\n", "3531 2025-11-29 48 NaN 426.228725\n", "3532 2025-12-06 49 NaN 426.277393\n", "3533 2025-12-13 50 NaN 426.326071\n", "3534 2025-12-20 51 NaN 426.374759\n", "3535 2025-12-27 52 NaN 426.423456" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.tail()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons tracer quelques graphiques pour observer la tendance de long terme qui a été évaluée. La tendance évaluée semble correctement coller au phénomène." ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/pandas/plotting/_core.py:1716: UserWarning: Pandas doesn't allow columns to be created via a new attribute name - see https://pandas.pydata.org/pandas-docs/stable/indexing.html#attribute-access\n", " series.name = label\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 25, "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": [ "df.plot('Date', ['CO2', 'Prév_LT'])" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[-500:-300].plot('Date', ['CO2', 'Prév_LT'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Observation du phénomène saisonnier" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour séparer le phénomène saisonnier de la tendance de long terme, nous calculons la différence entre les mesures de CO2 et la tendance de long terme précédemment évaluée." ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
DateWeekCO2Prév_LTSaisonnier
01958-03-2913316.19314.6856281.504372
11958-04-0514317.31314.7001492.609851
21958-04-1215317.69314.7146792.975321
31958-04-1916317.58314.7292192.850781
41958-04-2617316.48314.7437691.736231
\n", "
" ], "text/plain": [ " Date Week CO2 Prév_LT Saisonnier\n", "0 1958-03-29 13 316.19 314.685628 1.504372\n", "1 1958-04-05 14 317.31 314.700149 2.609851\n", "2 1958-04-12 15 317.69 314.714679 2.975321\n", "3 1958-04-19 16 317.58 314.729219 2.850781\n", "4 1958-04-26 17 316.48 314.743769 1.736231" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df = df.assign(Saisonnier = df['CO2'] - df['Prév_LT'])\n", "df.head()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Quelques graphiques pour observer le phénomène saisonnier. Le calcul de la moyenne sur la partie saisonnière devrait être proche de 0, si tout va bien. Ce qui semble être le cas.\n", "\n", "On notera toutefois une sorte de pseudo période avec ce qui semble être des pics vers 1960, 1990 et 2020." ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "-0.01077814129813126" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df['Saisonnier'].mean()" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 29, "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": [ "df[-500:-300].plot('Date', ['Saisonnier'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation du phénomène saisonnier moyen depuis 1958" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous avons observé que le phénomène saisonnier atteint un pic minimum vers le premier octobre et un point haut vers le mois de mai. Nous avons également observé que de nombreuses semaines de relevés sont manquantes.\n", "\n", "Une manière d'aborder le phénomène sans trop se préoccuper ni de son phasage dans l'année ni des données manquantes, serait de calculer la valeur moyenne de la partie saisonnière en fonction de la position de la semaine dans l'année. Cela permettrait d'utiliser au mieux les données disponibles. Une moyenne sur de nombreuses annèes de devrait pas être sensiblement affecter par l'absence de quelques données.\n", "\n", "Cela permettrait de raffiner les prévisions ultérieures." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour cela, on construit un tableau qui va permettre, pour chaque numéro de semaine de l'année, de cumuler la part saisonnière du taux de CO2 ainsi que le nombre de valeurs considérées. Il suffira ensuite de calculer la valeur moyenne observée pour chacune des semaines." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "# Parcourir le tableau.\n", "# Pour chaque date, calculer la position de la semaine dans l'année.\n", "# Ajouter la valeur du CO2_Saisonnier dans le tableau de résultats et incrémenter le nombre de valeurs\n", "# disponibles pour ce numéro de semaine.\n", "\n", "année = [] # Créer le tableau de résultats.\n", "for i in range (0, 55):\n", " # La première information est le numéro de semaine,\n", " # le second, le cumul de taux, le troisième le nombre de valeurs cumulées.\n", " # Le quatrième contient la moyenne calculée.\n", " année.append([i, 0.0, 0, 0.0])\n", " \n", "for cpt in range(df.index.min(), df.index.max()):\n", " # Récupérer les informations.\n", " sem = df.at[cpt, 'Week']\n", " taux_w = df.at[cpt, 'Saisonnier']\n", " if pd.isna(taux_w):\n", " pass\n", " else:\n", " année[sem][1] = année[sem][1] + df.at[cpt, 'Saisonnier']\n", " année[sem][2] = année[sem][2] + 1\n", "\n", "for i in range (0, 55): # Calculer la moyenne pour chaque semaine.\n", " if année[i][2] != 0:\n", " année[i][3] = année[i][1] / année[i][2] " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On affiche le tableau résultant afin de vérifier que toutes les semaines ont été correctement traitées et que le nombre des données est suffisant pour que la moyenne ait un sens (de même que l'approche proposée)." ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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3418.412093590.312069
4527.679068610.453755
5632.803158590.555986
6744.930523590.761534
7851.803820600.863397
8961.016571601.016943
91069.198742601.153312
101186.623840591.468201
1112101.870964611.670016
1213124.475283612.040578
1314145.750013612.389344
1415153.384154612.514494
1516167.767704612.750290
1617177.032279622.855359
1718178.875935612.932392
1819186.693953613.060557
1920191.224544623.084267
2021188.647433623.042701
2122175.434397612.875974
2223162.293974622.617645
2324138.385324602.306422
2425121.343508611.989238
2526101.233399611.659564
262780.422833631.276553
272850.741402620.818410
282916.750510620.270170
2930-4.02098262-0.064855
3031-38.45060462-0.620171
3132-66.75036062-1.076619
3233-97.59442062-1.574104
3334-127.32024961-2.087217
3435-154.80935362-2.496925
3536-179.06646362-2.888169
3637-192.02770262-3.097221
3738-215.95803362-3.483194
3839-219.17896462-3.535145
3940-210.42127960-3.507021
4041-196.89237959-3.337159
4142-186.71464760-3.111911
4243-182.56231861-2.992825
4344-158.11210060-2.635202
4445-144.14365862-2.324898
4546-121.58750162-1.961089
4647-102.09335061-1.673661
4748-89.76698562-1.447855
4849-71.42262662-1.151978
4950-55.46886762-0.894659
5051-42.79570862-0.690253
5152-27.53020261-0.451315
5253-4.50329311-0.409390
\n", "
" ], "text/plain": [ " Num_semaine Cumul Nbre_valeurs Moyenne_semaine\n", "0 1 -10.435116 62 -0.168308\n", "1 2 -0.433861 62 -0.006998\n", "2 3 10.883325 60 0.181389\n", "3 4 18.412093 59 0.312069\n", "4 5 27.679068 61 0.453755\n", "5 6 32.803158 59 0.555986\n", "6 7 44.930523 59 0.761534\n", "7 8 51.803820 60 0.863397\n", "8 9 61.016571 60 1.016943\n", "9 10 69.198742 60 1.153312\n", "10 11 86.623840 59 1.468201\n", "11 12 101.870964 61 1.670016\n", "12 13 124.475283 61 2.040578\n", "13 14 145.750013 61 2.389344\n", "14 15 153.384154 61 2.514494\n", "15 16 167.767704 61 2.750290\n", "16 17 177.032279 62 2.855359\n", "17 18 178.875935 61 2.932392\n", "18 19 186.693953 61 3.060557\n", "19 20 191.224544 62 3.084267\n", "20 21 188.647433 62 3.042701\n", "21 22 175.434397 61 2.875974\n", "22 23 162.293974 62 2.617645\n", "23 24 138.385324 60 2.306422\n", "24 25 121.343508 61 1.989238\n", "25 26 101.233399 61 1.659564\n", "26 27 80.422833 63 1.276553\n", "27 28 50.741402 62 0.818410\n", "28 29 16.750510 62 0.270170\n", "29 30 -4.020982 62 -0.064855\n", "30 31 -38.450604 62 -0.620171\n", "31 32 -66.750360 62 -1.076619\n", "32 33 -97.594420 62 -1.574104\n", "33 34 -127.320249 61 -2.087217\n", "34 35 -154.809353 62 -2.496925\n", "35 36 -179.066463 62 -2.888169\n", "36 37 -192.027702 62 -3.097221\n", "37 38 -215.958033 62 -3.483194\n", "38 39 -219.178964 62 -3.535145\n", "39 40 -210.421279 60 -3.507021\n", "40 41 -196.892379 59 -3.337159\n", "41 42 -186.714647 60 -3.111911\n", "42 43 -182.562318 61 -2.992825\n", "43 44 -158.112100 60 -2.635202\n", "44 45 -144.143658 62 -2.324898\n", "45 46 -121.587501 62 -1.961089\n", "46 47 -102.093350 61 -1.673661\n", "47 48 -89.766985 62 -1.447855\n", "48 49 -71.422626 62 -1.151978\n", "49 50 -55.468867 62 -0.894659\n", "50 51 -42.795708 62 -0.690253\n", "51 52 -27.530202 61 -0.451315\n", "52 53 -4.503293 11 -0.409390" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sem = pd.DataFrame(année[1:54], columns=['Num_semaine', 'Cumul', 'Nbre_valeurs', 'Moyenne_semaine'])\n", "sem" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons donc éventuellement envisager de compléter les données manquantes dans les mesures en utilisant ce tableau." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut afficher la courbe résultante sur le cycle annuel." ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sem.plot('Num_semaine', 'Moyenne_semaine')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Elaboration d'un modèle prévisionnel du cycle annuel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons tenter d'approcher le cycle annuel moyen par un polynôme de degré 7. Le résultat semble correct après quelques essais effectués avec des polynômes de degré inférieur." ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [], "source": [ "p = np.polyfit(sem['Num_semaine'], sem['Moyenne_semaine'], deg=7, full=False)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [], "source": [ "def taux_semaine(x):\n", " return np.polyval(p, x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calculer une année de prévision saisonnière." ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "prév_sem = []\n", "for cpt in range(sem.index.min()+1, sem.index.max()+2):\n", " prév_sem.append(taux_semaine(cpt))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afficher le plot. Le phénomène saisonnier moyen est affiché avec le modèle de prévision proposé. Le résultat semble correct." ] }, { "cell_type": "code", "execution_count": 37, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 37, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "sem = sem.assign(Prév_Sem = prév_sem)\n", "sem.plot('Num_semaine', ['Moyenne_semaine', 'Prév_Sem'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous disposons donc d'un modèle de prévision du phénomène saisonnier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Comparaison entre les mesures et les prévisions du modèle" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous calculons pour chaque date du data frame \"df\", la part saisonnière du taux de CO2 en fonction du numéro de semaine, en utilisant le data frame \"sem\". Puis nous ajoutons la colonne correspondante au data frame \"df\"." ] }, { "cell_type": "code", "execution_count": 38, "metadata": {}, "outputs": [], "source": [ "prév_sem = []\n", "for cpt in range(df.index.min(), df.index.max()+1):\n", " w = df.at[cpt, 'Week']\n", " prév_sem.append(sem.at[w-1, 'Moyenne_semaine'])\n", "df = df.assign(Prév_Sem = prév_sem)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant calculer la somme des contributions saisonnière et long terme et ajouter la colonne correspondante au data frame \"df\"." ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "df = df.assign(Prév_total = df['Prév_LT'] + df['Prév_Sem'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant afficher quelques graphiques pour comparer un peu les données mesurées avec les données issues de la prévision." ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 40, "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": [ "df.plot('Date', ['CO2', 'Prév_total'])" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df[-500:-300].plot('Date', ['CO2', 'Prév_total'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous calculons l'écart entre les mesures et les prévisions lorsque cela a un sens (hors valeurs 'NaN')." ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "Tab_écart_prév = []\n", "for cpt in range(df.index.min(), df.index.max()):\n", " # Récupérer les informations.\n", " Ecart_prév = df.at[cpt, 'CO2'] - df.at[cpt, 'Prév_total']\n", " if pd.isna(Ecart_prév):\n", " pass\n", " else:\n", " Tab_écart_prév.append(Ecart_prév)\n", "df_écart_prév = pd.DataFrame(Tab_écart_prév)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous affichons un histogramme afin d'évaluer l'ampleur des erreurs. L'hitogramme ne semble pas être tout à fait une gaussienne. On note cependant que la majorité des erreurs sont proches de 0. La gamme des écarts de prévision vont de -2ppm à +3ppm environ. Les erreurs les plus importantes sont en faible nombre. Pour une valeur de taux de CO2 qui est autour de 300ppm au moins, ces erreurs ne représentent que 1% environ." ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([[]],\n", " dtype=object)" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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3v7/ou6ZXnVFBD+wGrqiqXwG+B9zecz2j8B3gt4Gv913IsJwht9T4W2Bd30WM2DFgc1W9A7gGuLWx3+PLwHur6p3AlcC6JNf0XBNwhgV9VX21qo51qw8yc35/U6rqQFWN+irjxfb/t9Soqp8Br95SoxlV9XXgv/quY5Sq6kdV9Y1u+afAAWBlv1UNT8042q2+ofu3JM52OaOC/jh/ADzQdxEayErgh7PWD9NQQJyJkqwCfhV4qN9KhivJsiSPAkeA3VW1JObX3BePJPkn4BfneOgTVXVf1+cTzPwZee9i1jYsg8yxMQveUkOnjyRjwBeAj1fVT/quZ5iq6hXgyu7435eSXFFVvR93aS7oq+p9J3o8yUbgA8C1dZpeRLDQHBvkLTUakeQNzIT8vVX1xb7rGZWqej7JNDPHXXoP+jNq102SdcCfAR+sqv/uux4NzFtqNCBJgLuBA1X16b7rGbYkb331TL4kZwPvA77bb1UzzqigB+4E3gLsTvJokr/uu6BhS/JbSQ4D7wLuT/KVvms6Vd0B9FdvqXEA2LEEbqkxVEk+D/wr8PYkh5Pc0ndNI/Ae4MPAe7v336NJ3t93UUN0EfC1JN9mZuNkd1V9ueeaAG+BIEnNO9O26CXpjGPQS1LjDHpJapxBL0mNM+glqXEGvSQ1zqCXpMb9H0yFUnXxcvTwAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "df_écart_prév.hist()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prévisions pour l'année 2025" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons maintenant établir notre prévision pour l'année 2025. Commençons par afficher le graphique correspondant." ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 44, "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": [ "df[-52:].plot('Date', ['Prév_LT', 'Prév_total'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On observe un taux moyen annuel (lié à la tendance long terme, choisie au début du mois de juillet), de l'ordre de 425.5ppm. Avec un minimum annuel de 422ppm et un maximum de l'ordre de 428ppm." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Recherchons les valeurs numériques plus précisément." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Le taux moyen de CO2 prévu pour l'année 2025 est de 425.18ppm, avec un minimum de 422.26ppm et un maximum de 427.96ppm.\n" ] } ], "source": [ "moy2025 = round(df[-52:]['Prév_total'].mean(), 2)\n", "min2025 = round(df[-52:]['Prév_total'].min(), 2)\n", "max2025 = round(df[-52:]['Prév_total'].max(), 2)\n", "print(f\"Le taux moyen de CO2 prévu pour l'année 2025 est de {moy2025}ppm, avec un minimum de {min2025}ppm et un maximum de {max2025}ppm.\" )" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.4" } }, "nbformat": 4, "nbformat_minor": 2 }