{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958\n", "\n", "On s'intéresse à la concentration en CO2 au cours du temps depuis 1958; les données sont disponibles sur le site web [scrippsco2.ucsd.edu](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html)\n", "\n", "Commençons par importer les bibliothèques nécessaires à l'analyse" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import pandas as pd\n", "import isoweek\n", "import os\n", "from urllib.request import urlretrieve" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Chargement des données\n", "\n", "Chargeons à présent les données. On vérifie si le fichier existe avant; si il n'existe pas on le charge avec `urlretrieve`\n" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n", "data_path = \"./monthly_in_situ_co2_mlo.csv\"\n", "\n", "if not os.path.exists(data_path):\n", " urlretrieve(data_url, data_path)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Notons qu'un entête est présent qu'il faut supprimer (jusqu'à la ligne 60), ainsi que deux lignes 62 et 63 qui précisent le contenu de la colonne et son unité mais ne sont pas utiles ici" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "def skiprows(x):\n", " return x < 61 or x in [62,63]\n", "\n", "data = pd.read_csv(data_path,skiprows=skiprows,skipinitialspace=True,na_values=\"-99.99\")" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDate.1CO2seasonallyfitseasonally.1CO2.1seasonally.2Sta
019581212001958.0411NaNNaNNaNNaNNaNNaNMLO
119582212311958.1260NaNNaNNaNNaNNaNNaNMLO
219583212591958.2027315.71314.43316.20314.91315.71314.43MLO
319584212901958.2877317.45315.16317.30314.99317.45315.16MLO
419585213201958.3699317.51314.69317.89315.07317.51314.69MLO
519586213511958.4548NaNNaN317.27315.15317.27315.15MLO
619587213811958.5370315.87315.20315.86315.22315.87315.20MLO
719588214121958.6219314.93316.22313.96315.29314.93316.22MLO
819589214431958.7068313.21316.12312.43315.35313.21316.12MLO
9195810214731958.7890NaNNaN312.42315.41312.42315.41MLO
10195811215041958.8740313.33315.21313.60315.46313.33315.21MLO
11195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
1219591215651959.0411315.58315.52315.64315.57315.58315.52MLO
1319592215961959.1260316.49315.84316.30315.64316.49315.84MLO
1419593216241959.2027316.65315.37316.99315.70316.65315.37MLO
1519594216551959.2877317.72315.41318.09315.77317.72315.41MLO
1619595216851959.3699318.29315.46318.68315.85318.29315.46MLO
1719596217161959.4548318.15316.00318.07315.94318.15316.00MLO
1819597217461959.5370316.54315.87316.67316.03316.54315.87MLO
1919598217771959.6219314.80316.09314.80316.13314.80316.09MLO
2019599218081959.7068313.84316.75313.29316.22313.84316.75MLO
21195910218381959.7890313.33316.35313.31316.31313.33316.35MLO
22195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
23195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
2419601219301960.0410316.43316.37316.62316.56316.43316.37MLO
2519602219611960.1257316.98316.33317.30316.64316.98316.33MLO
2619603219901960.2049317.58316.27318.04316.71317.58316.27MLO
2719604220211960.2896319.03316.70319.14316.79319.03316.70MLO
2819605220511960.3716320.03317.20319.70316.86320.03317.20MLO
2919606220821960.4563319.59317.45319.04316.93319.59317.45MLO
....................................
77420227447572022.5370418.71417.91418.94418.18418.71417.91MLO
77520228447882022.6219416.75418.30416.77418.36416.75418.30MLO
77620229448192022.7068415.42418.91415.04418.55415.42418.91MLO
777202210448492022.7890415.31418.92415.15418.74415.31418.92MLO
778202211448802022.8740417.03419.29416.71418.95417.03419.29MLO
779202212449102022.9562418.46419.38418.25419.15418.46419.38MKO
78020231449412023.0411419.13419.06419.45419.37419.13419.06MKO
78120232449722023.1260420.33419.55420.40419.61420.33419.55MKO
78220233450002023.2027420.51418.97421.39419.83420.51418.97MLO
78320234450312023.2877422.73419.96422.89420.10422.73419.96MLO
78420235450612023.3699423.78420.38423.77420.37423.78420.38MLO
78520236450922023.4548423.39420.81423.23420.66423.39420.81MLO
78620237451222023.5370421.62420.82421.73420.96421.62420.82MLO
78720238451532023.6219419.56421.12419.67421.27419.56421.12MLO
78820239451842023.7068418.06421.56418.06421.58418.06421.56MLO
789202310452142023.7890418.41422.02418.28421.88418.41422.02MLO
790202311452452023.8740420.11422.38419.95422.19420.11422.38MLO
791202312452752023.9562421.65422.57421.58422.48421.65422.57MLO
79220241453062024.0410422.62422.55422.85422.77422.62422.55MLO
79320242453372024.1257424.34423.56423.85423.06424.34423.56MLO
79420243453662024.2049425.22423.65424.91423.31425.22423.65MLO
79520244453972024.2896426.30423.50426.41423.58426.30423.50MLO
79620245454272024.3716426.70423.29427.25423.84426.70423.29MLO
79720246454582024.4563426.63424.06426.65424.11426.63424.06MLO
79820247454882024.5383425.40424.62425.10424.36425.40424.62MLO
79920248455192024.6230422.71424.30423.00424.63422.71424.30MLO
80020249455502024.7077421.60425.12NaNNaN421.60425.12MLO
801202410455802024.7896NaNNaNNaNNaNNaNNaNMLO
802202411456112024.8743NaNNaNNaNNaNNaNNaNMLO
803202412456412024.9563NaNNaNNaNNaNNaNNaNMLO
\n", "

804 rows × 11 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date.1 CO2 seasonally fit seasonally.1 \\\n", "0 1958 1 21200 1958.0411 NaN NaN NaN NaN \n", "1 1958 2 21231 1958.1260 NaN NaN NaN NaN \n", "2 1958 3 21259 1958.2027 315.71 314.43 316.20 314.91 \n", "3 1958 4 21290 1958.2877 317.45 315.16 317.30 314.99 \n", "4 1958 5 21320 1958.3699 317.51 314.69 317.89 315.07 \n", "5 1958 6 21351 1958.4548 NaN NaN 317.27 315.15 \n", "6 1958 7 21381 1958.5370 315.87 315.20 315.86 315.22 \n", "7 1958 8 21412 1958.6219 314.93 316.22 313.96 315.29 \n", "8 1958 9 21443 1958.7068 313.21 316.12 312.43 315.35 \n", "9 1958 10 21473 1958.7890 NaN NaN 312.42 315.41 \n", "10 1958 11 21504 1958.8740 313.33 315.21 313.60 315.46 \n", "11 1958 12 21534 1958.9562 314.67 315.43 314.77 315.52 \n", "12 1959 1 21565 1959.0411 315.58 315.52 315.64 315.57 \n", "13 1959 2 21596 1959.1260 316.49 315.84 316.30 315.64 \n", "14 1959 3 21624 1959.2027 316.65 315.37 316.99 315.70 \n", "15 1959 4 21655 1959.2877 317.72 315.41 318.09 315.77 \n", "16 1959 5 21685 1959.3699 318.29 315.46 318.68 315.85 \n", "17 1959 6 21716 1959.4548 318.15 316.00 318.07 315.94 \n", "18 1959 7 21746 1959.5370 316.54 315.87 316.67 316.03 \n", "19 1959 8 21777 1959.6219 314.80 316.09 314.80 316.13 \n", "20 1959 9 21808 1959.7068 313.84 316.75 313.29 316.22 \n", "21 1959 10 21838 1959.7890 313.33 316.35 313.31 316.31 \n", "22 1959 11 21869 1959.8740 314.81 316.69 314.53 316.40 \n", "23 1959 12 21899 1959.9562 315.58 316.35 315.72 316.48 \n", "24 1960 1 21930 1960.0410 316.43 316.37 316.62 316.56 \n", "25 1960 2 21961 1960.1257 316.98 316.33 317.30 316.64 \n", "26 1960 3 21990 1960.2049 317.58 316.27 318.04 316.71 \n", "27 1960 4 22021 1960.2896 319.03 316.70 319.14 316.79 \n", "28 1960 5 22051 1960.3716 320.03 317.20 319.70 316.86 \n", "29 1960 6 22082 1960.4563 319.59 317.45 319.04 316.93 \n", ".. ... .. ... ... ... ... ... ... \n", "774 2022 7 44757 2022.5370 418.71 417.91 418.94 418.18 \n", "775 2022 8 44788 2022.6219 416.75 418.30 416.77 418.36 \n", "776 2022 9 44819 2022.7068 415.42 418.91 415.04 418.55 \n", "777 2022 10 44849 2022.7890 415.31 418.92 415.15 418.74 \n", "778 2022 11 44880 2022.8740 417.03 419.29 416.71 418.95 \n", "779 2022 12 44910 2022.9562 418.46 419.38 418.25 419.15 \n", "780 2023 1 44941 2023.0411 419.13 419.06 419.45 419.37 \n", "781 2023 2 44972 2023.1260 420.33 419.55 420.40 419.61 \n", "782 2023 3 45000 2023.2027 420.51 418.97 421.39 419.83 \n", "783 2023 4 45031 2023.2877 422.73 419.96 422.89 420.10 \n", "784 2023 5 45061 2023.3699 423.78 420.38 423.77 420.37 \n", "785 2023 6 45092 2023.4548 423.39 420.81 423.23 420.66 \n", "786 2023 7 45122 2023.5370 421.62 420.82 421.73 420.96 \n", "787 2023 8 45153 2023.6219 419.56 421.12 419.67 421.27 \n", "788 2023 9 45184 2023.7068 418.06 421.56 418.06 421.58 \n", "789 2023 10 45214 2023.7890 418.41 422.02 418.28 421.88 \n", "790 2023 11 45245 2023.8740 420.11 422.38 419.95 422.19 \n", "791 2023 12 45275 2023.9562 421.65 422.57 421.58 422.48 \n", "792 2024 1 45306 2024.0410 422.62 422.55 422.85 422.77 \n", "793 2024 2 45337 2024.1257 424.34 423.56 423.85 423.06 \n", "794 2024 3 45366 2024.2049 425.22 423.65 424.91 423.31 \n", "795 2024 4 45397 2024.2896 426.30 423.50 426.41 423.58 \n", "796 2024 5 45427 2024.3716 426.70 423.29 427.25 423.84 \n", "797 2024 6 45458 2024.4563 426.63 424.06 426.65 424.11 \n", "798 2024 7 45488 2024.5383 425.40 424.62 425.10 424.36 \n", "799 2024 8 45519 2024.6230 422.71 424.30 423.00 424.63 \n", "800 2024 9 45550 2024.7077 421.60 425.12 NaN NaN \n", "801 2024 10 45580 2024.7896 NaN NaN NaN NaN \n", "802 2024 11 45611 2024.8743 NaN NaN NaN NaN \n", "803 2024 12 45641 2024.9563 NaN NaN NaN NaN \n", "\n", " CO2.1 seasonally.2 Sta \n", "0 NaN NaN MLO \n", "1 NaN NaN MLO \n", "2 315.71 314.43 MLO \n", "3 317.45 315.16 MLO \n", "4 317.51 314.69 MLO \n", "5 317.27 315.15 MLO \n", "6 315.87 315.20 MLO \n", "7 314.93 316.22 MLO \n", "8 313.21 316.12 MLO \n", "9 312.42 315.41 MLO \n", "10 313.33 315.21 MLO \n", "11 314.67 315.43 MLO \n", "12 315.58 315.52 MLO \n", "13 316.49 315.84 MLO \n", "14 316.65 315.37 MLO \n", "15 317.72 315.41 MLO \n", "16 318.29 315.46 MLO \n", "17 318.15 316.00 MLO \n", "18 316.54 315.87 MLO \n", "19 314.80 316.09 MLO \n", "20 313.84 316.75 MLO \n", "21 313.33 316.35 MLO \n", "22 314.81 316.69 MLO \n", "23 315.58 316.35 MLO \n", "24 316.43 316.37 MLO \n", "25 316.98 316.33 MLO \n", "26 317.58 316.27 MLO \n", "27 319.03 316.70 MLO \n", "28 320.03 317.20 MLO \n", "29 319.59 317.45 MLO \n", ".. ... ... ... \n", "774 418.71 417.91 MLO \n", "775 416.75 418.30 MLO \n", "776 415.42 418.91 MLO \n", "777 415.31 418.92 MLO \n", "778 417.03 419.29 MLO \n", "779 418.46 419.38 MKO \n", "780 419.13 419.06 MKO \n", "781 420.33 419.55 MKO \n", "782 420.51 418.97 MLO \n", "783 422.73 419.96 MLO \n", "784 423.78 420.38 MLO \n", "785 423.39 420.81 MLO \n", "786 421.62 420.82 MLO \n", "787 419.56 421.12 MLO \n", "788 418.06 421.56 MLO \n", "789 418.41 422.02 MLO \n", "790 420.11 422.38 MLO \n", "791 421.65 422.57 MLO \n", "792 422.62 422.55 MLO \n", "793 424.34 423.56 MLO \n", "794 425.22 423.65 MLO \n", "795 426.30 423.50 MLO \n", "796 426.70 423.29 MLO \n", "797 426.63 424.06 MLO \n", "798 425.40 424.62 MLO \n", "799 422.71 424.30 MLO \n", "800 421.60 425.12 MLO \n", "801 NaN NaN MLO \n", "802 NaN NaN MLO \n", "803 NaN NaN MLO \n", "\n", "[804 rows x 11 columns]" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier indique qu'à cause d'une éruption en 2022, la station MLO n'a pas pu faire de relevés et sont alors relevés par MKO. Nous nous intéresserons ici qu'à MLO et allons donc supprimer les entrées correspondantes à MKO. De plus, des NAN sont présents dans le jeux de données, on va donc supprimer les lignes correspondantes." ] }, { "cell_type": "code", "execution_count": 58, "metadata": {}, "outputs": [], "source": [ "data_MLO = data[data[\"Sta\"]==\"MLO\"].dropna()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Un premier affichage des données\n", "nous allons à présent pouvoir plotter les différentes courbes:" ] }, { "cell_type": "code", "execution_count": 59, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 59, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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lNDBitRyeONBBemIsywrT6R7yMxEYZXGBNb5QmZtCQ4+XsqzkM76eOjvtYlJKzRv1dsmMtr4RRscDPH24k5vWFlOalUT3oJ9697AzAF2Va40zaE2lt08ThFJqXpgIBDnZZSWIrqFRjnYMMjIe4PJFueSmJdDWP4JvLMAiexHcDasLyUyOY3O1TmF9uzRBKKXmrB+8WMt3nzsJQG33sJUQFudiDGw92QPAovzUSVVYQwniwops9v9/7+aa5QUzH/h5QhOEUmpOGhkL8J1nT/LvL9TS5x3jpL0t6BZ7CusrJ7uJcQkVOSlOUgAmHat3RhOEUmpO2t/S7xyf7Bqiwe1FBC6pyQVgb3M/FTnJxMe6WFEcLs2dkxI/47GerzRBKKXmhJNdQ1z73Vc41eMF4EBrOEE0e3w09HgpyUyiIieZ0MLnRfaAdEF6Il+/aQX/+32rENFV0eeKJgil1Jzw3NEu6rqH+d7z1phDXfcw2SnxuMQqn3G4bYAVRenExbjIscccIruTPnVZFbdtrpiV2M9XmiCUUnPC0OgEAL4xayPJpl4vi/JTKcpI4mjHII09XtaUZgBWRVbQ8YZo0wShlJoVrX0+vP4J53FLnw+AriE/3UOjHGgZYFVxBhU5ybxaa81YCq1xWFaYBsBFldkzHPXCoglCKTXj6rqHuOxbL/Gl3x4AYHQ8wJMHOwBrP4edjR7GAkHev76E8uxkp8VQkWMtfvs/H1nHY3deSlm2rpKOJi21oZSacftbBgD4w6FOxiaC/GRbIwDZKfF0D/k50j6IS2BxQeqkJBDa4Kc4M4niTF0hHW3aglBKRd3htgG+88wJp0x3W9+Ic63Z4+Wl493kpibwN1uWYAxsq+2hMjeFxLgYJykApCTo37QzaVoJQkSyRGSliFSLiCYVpdRb8v77tvODl+o41WuNM7T3hxNEvdvLsY5BblpT5NRNOtQ2wNICa5yhOlcHomfLGdOxiGQAdwJ/CsQDbiARKBCRN4D7jDEvzUiUSql5bTxgtRx2NXqoyk3haMcgi/JTqese5mBrP96xADV5KZO2BV1sJ4jlRWn8xeVVVOdpophpZ2uv/Rb4BXC5MaY/8oKIXAjcJiLVxpifRDNApdT8NhSxqU+Tx8vQ6DhH2gf43JU11LuH2dFg7elQk5fqVGAFnBaEiPC1G1fMbNAKOEuCMMZsOcu1PcCeqESklJr3jDHOiua2iO6kFs8Ie5r6CBqrZMZvdrawu6kPsNY0xMa4yE9LoHvIz9JCbTHMtmmN+IjIGqAy8n5jzCNRikkpNY+d6Bzi4z/ZwUc3lvPFLUucfaFzUxNo7fOx65SHWJewvjyTnJR4PN4x0hJiyUuzVkc/8b8u4+nDnc6aBzV73nTAWUR+CvwU+BPgvfbXTdN4XqKI7BSRAyJyRETusc+vE5E3RGS/iOwWkY0Rz7lLROpE5ISIXPe235VSatY8eagD95Cfn79+CoDf7W9naUEaVy/Lo7VvhF2NfawqySA5Ppb8dCsp1OSnOi2OgvREbr+kUmsqzQHTaUFsNsa8nQ5AP3C1MWZYROKAbSLyFPBPwD3GmKdE5AbgX4ErRWQFcCuwEigGnheRJcaYwNv42UqpGTIRCDIRNCTGxQDQYXcp9fvG6R4cZU9TH5+/ZjGxLqF7yM+wf4Jb1pUAsKY0k+11vbrgbY6azpTV1+0P77fEWIbth3H2l7G/QrV5M4B2+/gW4AFjjN8Y0wjUARtRSs1ZgaDhim+/zId/9LpzrmNg1Dl++YQbgCUFqZRlWzOUfGMBKu21DdevLKQqN4UbVxfOYNRquqbTgvg5VpLoxGoVCNbn/5o3e6KIxGANZi8C/tMYs0NEvgA8IyLfwUpQl9i3lwBvRDy91T6nlJqjeob9tPWP0NY/wsDIOCnxMRzvHKIqN4XGHi9vNPQCUJmTwsh4uDMgNFtpbVkmL/3dlbMRupqG6bQgfgrcBlxPePzhvdN5cWNMwBizDigFNorIKuBzwBeNMWXAF4HQNNmpOhzN6SdE5A577GK32+2eThhKqSjpjGgtHOsY5EDrAD3Dfj5xsVV2+3U7QVTlpjiL4ABW21VZ1dw2nQTRbIx53BjTaIxpCn29lR9ir6N4GSvJ3A6EZkD9D+FupFagLOJppYS7nyJf635jzAZjzIa8vLy3EoZS6hyIXNfQORhOEK19IxzvHATgmmUFxLiEjoFR8tMSSEmIpSAt0bm3MD18rOau6SSI4yLyaxH5UxH5QOjrzZ4kInkikmkfJwHXAsexPvSvsG+7Gqi1jx8HbhWRBBGpAhYDO9/i+1FKRdH+ln5W/+Oz/OiVesBKCiFtfSMcbR8kNSGWsuwk8u1pq5V2d5LLJfz0zzbwypeu1BlK88R0xiCSsMYe3h1xzhBuBZxJEfBzexzCBTxkjPm9iPQD3xeRWGAUuAPAGHNERB4CjgITwJ06g0mpueUPhzqc75+5ooYjbQNOImjr97GvuZ8LK7IQEQrSE+kYGKUqJ7w6+uplBbMSt3p73jRBGGP+/O28sDHmILB+ivPbgAvP8Jx7gXvfzs9TSkXfsQ6rC8k95GciEGRrXQ8XlmfROThKg9tLnXuYG9cUAZCSYE17rcpLOePrqbltOgvlqkXkCRFxi0i3iPzO7gJSSi0wp3q9ALQPjLK3uR/3kJ8b1xRRkpXE7qY+jLFmLAGIPe9kjQ5Iz1vTGYP4NfAQVpdRMdbA8gPRDEopNTd0DozSPWQNRHcPjdLWN+JUXH31pDWLcE1pBqURVVjL7TUOd9+0nLtvXM7F1TkzHLU6V6aTIMQY8/+MMRP21y+ZYvqpUur8Yozhwz96nY33voDXP8EzhzsJGvjUZVYHwqu1bpLiYijLSqYkYgprhb0qellhOp++vFoHpOex6SSIl0TkqyJSKSIVIvJl4EkRyRYR3TFcqfNUvdtLs8fa4Keue5gdjR6KMhK5dFEuAAdbB1hSkIrLJZP2cchOiZ+VeNW5N51ZTB+xv3/mtPOfxGpJVJ/TiJRSs6JzYJSH97by2StqiHEJR+0BacDat6HRwyU1ORRlhtcwLC209myI3B9aWwznj+nMYtIBaaUWgH9+8ii/P9jBmtIMLl+cx8nOIefajgYP7iE/GyqzSU+Mc84vLbTKqtXkpfKBC0p475riGY9bRc90ZjElisjfiMgjIvKwiHxBRHQZpFLnmWH/BGBtC2qM4ZkjnawvzyQ9MZa9zdamPjX2lNXQ2ofQrm/xsS6+++F1XLUsfxYiV9EynTGIX2CV4P4P4AfACuD/RTMopdTMCgQNh9sGAGt19P6Wfmq7h7n1ojJy0xKo7bYKM5dlWQPQ975/NbdtrmBDZdasxayibzpjEEuNMWsjHr8kIgeiFZBSKvqGRsf52qOHuaQmh1s3lrOvuY+e4THAShAHWqxt6K9als/De9pocHtxCRRmWJ0HW1YUsGWFroo+302nBbFPRDaHHojIJmB79EJSSp1rTx7sYNcpj/P4sf3tPH6gna8+cghjDC+d6CbWJbx7RQGtfT4aerzWNqCpCeTZu74VZSQRFzOdjwx1vpjO/+1NwGsickpETgGvA1eIyCERORjV6JRS79hEIMidv97Lh374Ol57nOFw64BzvWd4jCPtgyzKT2VZYRqdg6Oc6ByiOi8FEWFjpTWb3T+hpdEWmukkiOuBKqwKrFfYxzfwFvaFUErNnsiKq1trrdXPh9vDCaLZ4+Nw2yDLi9IpzUomaGBHo4fqvFQA3rPK2u2tQEt0LzhnHIMQkVRjzPDZ9n4QkdTohKWUeif2NHlYW5pJbIyLxh6vc/5A6wBXLMnnZNcQ1y7P5/lj3Tx7tJOeYT8X1+RMKplRbZfpzk9P5H8+ezFFGZogFpqztSB+JyL/JiLvEhGnHKNdvO9TIvIMVutCKTWHHGjp50/+63X+8YkjgNUaiHUJ5dnJHO8Y5Nc7mxkPGD6+2dr1LVTCe1NVNqX2LCXAaUEAXFQ5+ZpaGM6YIIwx1wAvYK2gPiIiAyLSC/wSKARuN8b8dmbCVEpN1wvHu63vx6zvu095WFuWyaL8VLoG/exp8lCencyVS/PJS0ugxTNCfKyL0qxkZ5YSQLWW6V7wzjrN1RjzB+APMxSLUuocCO3Z0DPsxz8R4FjHIB+8sBT/RJBDbQN4xyZYVWKtgC7LSsI95Kc6N4UYlzhfgaChKlcTxEI3nXUQSql55IRdImM8YNhW24N3LMDK4gxa+3y4h/wAvG9dCQBl2cnsbe6nJqI76dG/vIQYl5AYFzPzwas5RSc1KzXPfeW3B7n7sUMA+MYmaPb4uHSRtQfDc0e7AFhRnE6eXR4DoCbfSgildpnuytzw+MKa0kxWFusmP0oThFLzWs+wnwd3t/DLN5oxxnCyyyqJcdVSqybSs0e7iIsRlhSkkZcWMb5gdx+9Z1URW1YU8NFNFTMfvJrzNEEoNY8M+yf48dYGZ9FaU294Cmvn4Cg7GnoBuH5VIS4Bj3eMxflpxMe6yE8PtyBCA9CrSjL4709smLSfg1IhZ0wQIrJaRN4QkRYRuV9EsiKu7ZyZ8JRSkX66rZF/fvIYP912Cpi8CO5I2yC7m/qozkuhNCvZWdi2stgakM5LDSeI5HgdflRv7mwtiP8C/hFYDZwEtolIjX0t7kxPUkpFz2v1PQDsb7HKb0cugjvaMUhjj5dF9oBzjMvauCeUIEItiMsX585YvGp+O9ufEanGmKft4++IyB7gaRG5Dd2TWqkZ8aNX6llRnM7li/MYHB1nZ6NVcC9UfntbbQ+rStLpHvTT7PHR3OvjmuXW+IO1sc8IG6usAeuE2Bhe/dJVk3aEU+pszpYgREQyjDEDAMaYl0TkT4CHAd2LWqkoO9DSzzeeOg5A4zdu4Gj7IEEDK4rSOdk1hMc7xt7mPv7qqkW8fNLN3uY+xgJBqnKs8YV/+/BaaruHWWG3IADKc3Q1tJq+s3UxfQtYHnnCGHMQuAZ4JJpBKaXgRFd4y0+Pd8zZ0OfaFQVMBA2P7G0laODKZfkUpCfS4La6myrtGUrLi9K5ea1uAarevjO2IIwxvw4d20X5jDHGa4xpBv5iJoJTaiGLHICu6x7mSPsghemJzpjCw3vbyEyOY21pJgURM5R0BbQ6V846zVVEPicizUAT0CIiTSLyl9N5YXsv650ickBEjojIPRHX/peInLDP/2vE+btEpM6+dt3bfVNKzUeP7G3lhu9vdaawtnp8zrWmXh+H2wZYVZLuVFU91jHIkvw0YlxCoT1jKcYlzn7RSr1TZyv3fTdwCXClMabBPlcNfF9Eso0x//wmr+0HrjbGDItIHNYsqKeAJOAWYI0xxi8i+fZrrwBuxdr/uhh4XkSWGGN0lxK1IPzNQ9ZOvm80eLhiSR77W/q5dFEO2+t6afJ4qXcP857VRU4yAKiwxxRCU1qT42IQkZkPXp2XztaCuA34QCg5ANjHHwY+8WYvbCzD9sM4+8sAnwO+aYzx2/d12/fcAjxgjPEbYxqBOmDjW3w/Ss0bTx/uoM9r7QM9bO/0BrC/uZ/uwVEaerxcsSSPtMRYttb2EDSwqjidnIj1DKHxhuyUeAAyU3QGujp3ztrFZIwZneLcCBCczouLSIyI7Ae6geeMMTuAJcDlIrJDRF4RkYvs20uAloint9rnTn/NO0Rkt4jsdrvd0wlDqVnX3j/iDDIDnOrx8tlf7uWvfrMXCBfYA2joGeYNezrr5uoc8tISOGhvEbqqJMNZ3wA4M5RCtZP+6eZV0X0jakE5W4JoFZFrTj8pIlcDHdN5cWNMwBizDigFNorIKqxurSxgM/Al4CGx2sRTtYv/aL2FMeZ+Y8wGY8yGvLy86YSh1IzqHhrltp/soL0/PMj8Zz/byU3/sc2ppvpavVUSI7Su4XinVaK7Oi+FBreXNxp6SUuIZUVRurMCOjM5zhl/CJXrXluaCUBhRiKnvnkjVy3Ln4F3qBaKs62D+DzWrnLbgD1YH9YXAZdidQdNmzGmX0RextqBrhV4xBhjgJ0iEgRy7fNlEU8rBdrfys9Rai74ybZGttb28KsdTXzpumWTiujta+7j3SsLnT2h42JcGGPY29RPWmIsm6qyeeZIFxNBw/qKLGJjXFTmpLCj0UNFdrIzvvCj2zYQDBqna0nZNESvAAAgAElEQVSpaDjbjnJHgFXAq0AlUG0fr7KvnZWI5IlIpn2cBFwLHAceA662zy8B4oEe4HHgVhFJEJEqYDGgNZ/UvNPeb/XMhloL9e5wOYyuQeva0XarxeAbC9DnG+epwx1cv7KQ0qxkPN4xjnUMOiUzFtmlueNjw/9cSzKTKMvWRW8qus42i2kRUGCM+elp5y8XkXZjTP2bvHYR8HMRicFKRA8ZY34vIvHAT0XkMDCGtXWpwdrW9CHgKDAB3KkzmNR81NhjtRZaPFYX0+5THuda5+AogaDheKe1pqFzcJTdpzz4xgJsqMwiITa8SU+VvUfDZXbtpBVF4RXRSs2Es3UxfQ/4+ynOj9jX3nu2F7ZXXa+f4vwY8PEzPOde4N6zva5Sc80H/+s1SrKS+P6t6zHG0Gi3GDrt1sKuU31kp8QTH+Oic8DPsY5BRseDXH1BPr/e0eyMR1RH7OoGk1dEP/vFdznjD0rNlLMNUlfaH/KTGGN2Y3U5KbXgef0T7G7q43f72zHG0D3kxzsWICHWRcfACMYYdjd52FCRRWFGIp2DIzy8t5XEOBcftzfp2VZnVWitzk2ZtC9DZU54RfSSgjTSEnUKq5pZZ0sQZ/tzRXcXUQvS4Og4L53oxuoVhT1Nfc61tv4Rpx7SpYtyGR0PUts9TFOvj4sqs60upYFRjrYPsqIonSUFqbjEKqORnhhLdkr8pFXQxbqJj5plZ0sQu0Tkj2ouicinsGY1KbXg/MXPd/PnP9vF6/bObTsae51rDW4vxzqswefLFlnjBr8/YE3Eu7DSakF0Dfo52TXE0sI0YmNczgro6rxURITYGBd337ic//zoBZPWOyg1G842BvEF4FER+RjhhLABa9bR+6MdmFJzUYO9Qc8Lx7q5pCaXNxo8lGUn0eIZocE9zJ7mPooyEllbZq1PeOpwJ/GxLlYVZ7Cz0eOsmF5SkAZAUUYiHQOjzh7RAJ++vHqG35VSUzvbNNcuY8wlwD3AKfvrHmPMxcaYzpkJT6nZY4zhCw/s48mD1rrQiUAQj10a40TnECNjAQ629nPj6mISYl2c6Bri+aNdXLUs3xlQru0epjo3hfhY16QaSkvtBJGeZI0rhPaIVmouedONaY0xLwEvzUAsSs0ph9sGeWx/O4/tb+fGNTfS2jdCIGiNPdR2D7GvpY/xgGFTdTbPHunk5RNu/BNBrl2eT15aAi6BoAmvYyiISBArS6zSGFnJ1kK3CyqyUGquOWstJqUWsj1N4fULA75xnjxktSRuWlOEe8jPgRZrNfTa0kyKM5PoGLCmtVbmpBAX43JmHYUSRGHENNUMu+XwpeuWct/HLuCSGt0nWs09b9qCUGqhCAYNY4EgiXHWYrU697Bzrb5nmJ9tb+SyRblcUpPL7w92sLXWTUF6Atkp8RRnhvdjCK1wHhmz1nmGEkR5djJ/fmklH7koXFGmODNJZyupOUtbEErZvvHUMZZ9/Wn8EwGMMexs9JBj1zra39xPz/AYVy7Nc8YXXqvvZVmhtbq5KMP6kC9MTyQuxvpnddOaIgBW291JMS7hH9670nmOUnOdJgilbP+9tRGAbbU9NHt8nOwa5o53WTOKXjlplZavyU+d1FW03C5/EWpBJMaF/0n9ywdWs/frW6jI0QFoNT9pF5NasJp6vZTbFVInAuEtTmq7h7HXwXFRVTY5KfFOglicn0pqQvifzfIiazZSjV0m48bVRc61xLgYp7tKqflIWxBqQTrWMcgV336ZH71qbZgYqsAKVuKo7bbGHxblp1KaZXUfpSXGUpKZ5AwwA0530YUVWbz4t1fwxS1LZuotKBV1miDUgvTicWun28f3WyudG3vDJbmben3Udg1RmJ5IemIcJXaCWFaYhoggItTY6xZC6xdExFkNrdT5QruY1ILw+d/swzc2wY9vt3a4rbdnKA2MjAM424FetTSPk13DDI1OsLjA6jYKDUAvLUxzXu/Bz1xMQqzLGZBW6nykv93qvDfgG+fxA+08f6ybnmFrE58Wjw+A9oERRscDvHLCTU1eCmtKM2nrH+FoxyCL862EEJqF9K7F4S1uc1MTtLqqOu9pglDnnT7vGN97/iSj49Y6hMiCeie7hjDG0OD2Eh/rwhhrX+idpzx88MIyKnKsNQyBoHFaELesK2bv17fw7pWFM/9mlJpFmiDUeecfnzjC956vdcYXDrT2O9daPSMc7xyi1zvGhy4sBeAJu+LqxqpsJ0GANWMJrPEF3ftZLUSaINR5p63P2upze721Ec+pHh8lmUm4BFr6fM6U1dsvqQTg6cOdiFhbepZnh9cshFZAK7VQ6SC1mveePtxJSWYSq0utsYJWO0GENu9p7PE6H/YtHh9dg36WF6WzpCCNjKQ4BkbGKc9OJik+ZtJCt8xkbTWohU0ThJrXRscDfPaX1nYljd+4Af9EkK4ha01DY4+X0fEAJ7uGuGJpHqPjAerdXo53DvLJS6sAqMxJ5kDrgJNARITtX70az/DY7LwhpeYQ7WJS89ohe3oqgHvIz97mPoyBjZXZDPsn2Fbbw0TQsL4sk7LsZA61DTAeMKyyZyaV2oX1Fkd0J0W2RpRayDRBqHnlQEs/dz92iKC9L8OpnvACt4YeL1tre4h1CbesLwbggV0txMUIF9fkUJYVHoAOtRhCq6TTk3TKqlKn0y4mNa/cev8bjIwH+PCGMmfNQkhjj5ettW4uqMhyaiO9dKKbFUXppCXGOckAoMre4vNzV9TQOTDKhzaUzuwbUWoe0BaEmrOCQcMPX6mnyS6DMToeYMRe2/BGg7W2ocUzQm5qAvGxLnaf6uNw2yDvWpzr7N4WCBqnHEZonwbAKaKXmRzP929dT35auEKrUsqiCULNWS8c7+abTx3nrkcOAVb3UsjJLqtUxr6WPlaXpFOVk8JTh60d39aXZ5GfluDcG2pNhFZEb1lRMCPxKzXfaReTmjOMMQSChli7vtH2OmsdQ6M9zrDfThDLCtOo7R6me2iUBreXj2woY9cpDye6hgBrXCEloiR3qAWRFB/D4XuuI9alBfWUmo6otSBEJFFEdorIARE5IiL3nHb970TEiEhuxLm7RKRORE6IyHXRik3NTf/85DGu+PbLDI1aBfRCM5S6h/wEgoaDrQOUZSextjSTjv4RdjRYe0Zvrs4hNzXcYojc0AegOjc8Qyk1IVb3aFBqmqLZxeQHrjbGrAXWAdeLyGYAESkDtgDNoZtFZAVwK7ASuB64T0T0X/ICEQgafrKtkbb+EV6r72UiEORI+wCJcS4CQUP30Cj7W/pZU5pJYUYi7mE/2+t6SE2IZWVxOjmp1qK2gvQEEmKtX5sPXljK8qL0SVVYlVLTF7UEYSyhXd/j7C97ny7+D/DliMcAtwAPGGP8xphGoA7YGK341Nyyt7nPOW7tG6He7WV0PMj1doG8Ay0DtPWPsK40k6KMRIyBp490sr48k9gYl9OCKM4Mz1T69gfX8IfPX0aMdikp9bZEdZBaRGJEZD/QDTxnjNkhIjcDbcaYA6fdXgK0RDxutc+d/pp3iMhuEdntdrujFruKrtHxAP/1cj3t9jTVyAHo1j4fB+0Ce9evshLE0/YA9NqyTKcLqd837uwJHRqIvmFVeMvP0OY+Sqm3J6qD1MaYALBORDKBR0VkDfA14N1T3D7Vv2TzRyeMuR+4H2DDhg1/dF3ND7/b38a3nj7O9roefvnpTbT1j5ASH0NJVhKtfSMEgoaU+BguXWQNUYV2gFtWlEZHxPagoRXQly/OZefXrtHpqkqdQzMyi8kY0y8iL2N1I1UBB+y/7EqBvSKyEavFUBbxtFKgfSbiU9EXDBpEcP6if/6Y9YHfbddNau71UZKVRFlWMq19I7iH/KwsySAtMY7M5Dj6feMUpCeQnhiHiaiCsaTAGl8QEU0OSp1j0ZzFlGe3HBCRJOBaYJ8xJt8YU2mMqcRKChcYYzqBx4FbRSRBRKqAxcDOaMWnZo5/IsD779vOTf+xjdHxAMYYdp+yZiA1e3x0D43yykk3F1ZkU5qVxKkeL8c6Blljr1sosccVQuUx0hPDf9doSW6loieaLYgi4Of2TCQX8JAx5vdnutkYc0REHgKOAhPAnXYXlZrn9jX3c6DVmrJa2zVMblo8fb5xlhSkcrJrmFdOuJkIGm5ZV8yh1gFntXSoYF5RRhJH2gdZlBeuuPqDj66nqdc3ab2DUurcitq/LmPMQWD9m9xTedrje4F7oxWTmhmHWgf45tPHuO+jF5KRHMfxjkHnWp17CPewVRjv2uUFnOwadsYXFuWn0ucNl9kOrXzOsAvpLSoIT1e9aU1x1N+HUgudltpQ59x3nzvB9rpeHthlLXM53jlEWmIsLoHGHh9PHOggLTGW9661PuRfONZNZnIcOSnxlEZUXK3MsVZA/9kllXzi4gret06TglIzSdvn6pwL7eh21G45HOsYZHVJBo09Xtr7R9jb3Mdli3KdqaljgSBr8jIQESpyk0m2Zy+57PULq0szdH8GpWaBtiDUO2KM4Tc7m6nrtuogTQSCnLKrr9Z1D3Oqx8vh9kEuKM+iODOJ2q4hmnp9LC9KJz7WRU6KtQI6PAAdx567t3D/bRfOzhtSSjk0Qah3ZFtdD3c9cog//7+7AGjy+BgPGNISYmnx+Nhe30MgaPjQhlKKM5OcwepldvmL+FjrVzByNlJSfIwucFNqDtAEod6Sn21v5Jp/CxfUe70+vC/D6HiAWrsM96WLchkcnWB/cz+pCbGUZyc701UBZwV0gp0ganS6qlJzjiYIdUbBoOGxfW1OMgC454mj1Lu9PHukC4ADreESGY09Xg7bFVgvWZQDwNbaHpYUpCIilGSGF7KFksU3PrCGj24qZ1NVdtTfj1LqrdEEoRynerwc7wxPSX36SCdfeHA/33++FgCvf8K51uTxEQwaDrYMsKEiC4B69zC/O9DG5YtznRLbnYOjTjXVUCG9hFiXMwB9cU0O//L+1STH63wJpeYaTRDK8b77tnP997Y6LYattVYxxGN20jhpb8gD0Orx0dAzzJB/ghvXWAXyTnYO0eIZ4eKaHGeTHoCl9vqFzdU5bKrK5ivXL5uR96OUemf0zzYFwHggSL/PSgwHWga4bHEuR9qtxHC4bRBjDK/b+0BX5iTT7PGxv8XqTrp8cS5pibG8ctJKKIvz0yiK2LRnid2CSEmI5cHPXDxj70kp9c5oC0IBcLwj3Dqodw/j9U9wtH2QrOQ4BkbGae0bYevJHlYWp3NRZTYtfT4OtFgD0NW5qZREzFAKjTmEVkIvK0yflfeklHpnNEEsYF2D4bLZkRv21HUPc6Cln4mg4cMXWQV2m3p9HGjtZ0NFFmXZyXQN+tnZ6GF1SQYul0waXwithv7NHZt58I7NZNtrHZRS84smiAXqZ9sb2fQvL3DI/qt/X3Mf+WkJrCvLpN49zAl7vGHL8gIAXj7RjW8swPryLMqzrQRwomuItWWZQHhWUk1eqrODW2pCLJuqc2b0fSmlzh1NEAuExzvG6Hi4OO6j+9oA2NFojSvsbe7ngvIsFuWnUtc9zInOITKT45wSF78/aO3otr48k7LscL2ktfb1UAsiKyUu+m9GKTUjNEEsEO/615e4+jsvO4/HJoIA7G/pp2fYT7PHx/ryTCpzkuke8vPMkU42VmaTEBtDXloCnYOjZKfEU56dTFl2eMFbqAVx9bJ8AC6q1PUMSp0vdBbTeejbzxyno3+Ub31wDXExLvp9Ywz7Jxj2T9A9NEp+WiIdA9b4w77mfvY1W4vdLqjIos0utNfnG+dmu3pqSWYS7iE/F5RnIiLkpSYAVhdSaLbS0sI0tn75KgrSdVc3pc4XmiDOA4/ta+OSmhzy0xMJBA3/+VI9AJctzuUDF5Sy61R4APpE5xDHO4YYGBknJyWetv4RttW6iXUJq4ozJm0M/u4VhQCk2Tu4rS+3FsSJCAf/8d14/ROTaiZFdj0ppeY/7WKa5+rdw3zhwf187ld7AWsGUsjhNmsdw4P2vgwA9d3DPLa/jZT4GL76HmvB2pOHOlhckEZSfIxTI6kqN8UppPfetcXkpsZz1dJ853XSE+Moygh3NSmlzj/agpjnttqL0/Y292GMcWojxce6ONVr7b/w4vFuPndlDT/Z2kj7wChba3u4enkBK4qtZNAzPMaV9od/SkIs275yFXEx4b8dPryhjA9vKJvhd6aUmm3agphnPN6xSfWSDturnY2Bhh4vB1v7SUuI5eql+TT2eHn2SCdBAx/ZUEZhRiKvnnTjHvJz+aJcKnLC5TBWFIUXs5VmJetYglJKE8RcZoxhe521n0LI5365h+u/t5Vj9m5tR9sHnT2bT/V4Odg6wKqSDGryU2j2+Nh1qo+C9AQqc1MoykjkeKe1vmFtWSapCeEG5PIiXe2slJpME8Qc9sTBDj724x38eqc1huAbm2BHoweAnY0exiaC1HYPcdXSPABaPD6OdQyypiyDqtxUAkHDk4c6WGdPRQ3NOIqLEaeYXihJrNEtPZVSp9ExiDnkv19tIDHOxW0XVwKwvbYHgIMt/bC5wmk1ABzvHOTlE92MBwzXrijgdwfaeeWkm/GAYW1pJgXpCc6968qs2UdFEaudQ2MMj915KYGgISVBfxWUUpPpp8Ic0drn494/HAPgulWF5KclUu+2ZiQ19Fh7PId2byvNSqLFM8JThzvJTU3g+pWF5KQk8NIJa8B6dUkGSfExzmuHWhCF9rhCXlo4eSzSndyUUmegCWKOCE1JBajtGiYnJcHZf6Gp1wdY5S42VGRRkJHIsfZBOgdHWVeWQWyMi+rcFHqG/WSnxFOaNXn6aahcxrqyTPLSEvjclTUz9K6UUvOZjkFEQb17mN/uaZ107sFdzc52nGCtV3h0XytBewD6ROfQpGu/eP0Ug6MTrC7JoGfYz8HWfo53DnHTmiJKMpNo9vhocA87pbSXFFotgVUlGYgIIsIvPrmR//rYBc44w9qyTHZ97VouqcmN5ttXSp0nopYgRCRRRHaKyAEROSIi99jnvy0ix0XkoIg8KiKZEc+5S0TqROSEiFwXrdii7bYf7+Dv/ueAU067e3CUrzx8iJv+Y5tzz98+tJ8vPniAX+1oAmB3k4fF+akkxcXQ7PHx8N5WlhSk8unLqwD4762NiMANq4sozkhkImgIGijPsVYvX2Cvcg6tegZ415I83rO6aEbes1Lq/BPNFoQfuNoYsxZYB1wvIpuB54BVxpg1wEngLgARWQHcCqwErgfuE5GYKV95DjHG8NShDmebToB2u87Ra/XWIPMzRzqda0Oj4wSDhlp7xfPDe9sIBA17m/rYXJ1DcWYiLR4fdd3DXL44z9nb+alDHVRkJ5OfnuhUTgWosMtb3LC6iI9sKONvtyyJ7htWSi0YUUsQxhKq+xBnfxljzLPGmAn7/BtAqX18C/CAMcZvjGkE6oCN0Yrv7Xr+aBf/s7vFefzKSTef+9Vevvzbg4A1FTWkudcqfPfU4XCCaOr1caR9EN9YgLTEWE52DdHYM4x3LMDaskxKspLZecrD6HiQJQWpTgthImhYYu/tHJkgQl1MiXExfOuDa6jO00FnpdS5EdUxCBGJEZH9QDfwnDFmx2m3fBJ4yj4uAVoirrXa52aNxzvGVx8+iMc75pz79C9286XfHmQiYJXLfvZoFwBvNPRijOFUj8+5t7XPh8c7xo5Gj7NWodnj44mD7cTHuLhtcwW+sYAzO2ltaQYlmUnO3tDVealkJMWRbM9IWmrv7Vxm79iWlRxHRrLuv6CUio6oJghjTMAYsw6rlbBRRFaFronI14AJ4FehU1O9xOknROQOEdktIrvdbnc0wnb84VAHD+xq4Z+eOAKA1x9uHRy11yQcaLFqH/X5xukcHHXKYKQmxNLaN8Lzx7oIBA2fucKaOdTU62NvUx+rStKdKabPHesmOT6G6rxUSjLDJS6qc63FbBMB6z/DYrsFkZEcx6N/eQm/uWNz1N67UkrNyCwmY0w/8DLW2AIicjtwE/AxY0woCbQCkRXhSoH2KV7rfmPMBmPMhry8vKjG3dRrrT8I7Z0QKoQHcKxjkNHxAMc7h5xVyK19I/zhUAeZyXFctSyf1n4fB1r6SU+MZVNVNtkp8RxuH2B/Sz+bqnOc9QivnnRzUWU2MS6hJGKKamgv5z/daP1n2VQV3oxnfXmW072klFLREM1ZTHmhGUoikgRcCxwXkeuBrwA3G2N8EU95HLhVRBJEpApYDOyMVnxT+flrp3goYnwhVLeo2WOFubcpcl+FYfY29xEIGmff5kOtAzx/rJuPbSqnPDuJjv5RDrcPsqwoHRGhPDuZJw92MBE0XLU0n4rscLG861Zaey9EFtAL7bVw900rOPnP79ECekqpGRXNFkQR8JKIHAR2YY1B/B74AZAGPCci+0XkhwDGmCPAQ8BR4GngTmNMYOqXfue6h0Y51Dow6fE/PH6EL//2IAMj1hhAKEF0DIwyOh5gb3M/NXkpLCtMo6XPxx8OdZAcH8P7L7CGSkKzla5ZXkBpVjITQcOBln6nUmqx3X0U6xLWlGZM2rozVBtpbak16/emNeHpqXExLmdvBqWUmilRW0ltjDkIrJ/i/KKzPOde4N5oxRTpfT/YTvvAKG/cdQ2FGYnOQDFYRe9642NwD/lZU5rBwdYBWvt87Gz08N61xbiHRmntG6HBPcymqmxKMpOIj3Wxo9FDrEtYUZQ+abwilCBCG+ysLE4nMc4aeC7OSKR9YJRKu+UQ4xKO/dP1xMZMNSSjlFIzZ0H+WWqMcdYqvN5grVUIzUYCK0GEpqbebhfOe3x/O8P+Ca5elk9pVjLHOgapd3vZWJWDiDhJYFF+KolxMZRmhbffDG3ME6qmuqk6x7n26J2X8s0PrKYwI9x9lBQfM2nDHqWUmg0L8lMoctrq6/W9eLxjPHmwg/esssYBWvp8PHe0i7Vlmawvt7p8frOrhfhYF5cuyqEkYh3CJTXWh31oJXNoKmpxxGyk0GylP7mglLtvXM5fX7PYuVaQnsitG8uj8TaVUuodWZAJoqXPWsAW4xJeq+9lZ6PVvfSpy6rITI5jT1Mf+1v62bI83/nL3j3kZ2VxOsnxsZOK4a0qsWYwVeZaLYYku+soITaGu29czm2bK5zupKyUeD59ebWW1lZKzQsL8pOqKieFH912IUfaBvj3F+v47Z42EuNcrCnNpCwrmWeOWN1N164oIDk+loykOAZGxp1d10LdR9W5KcS4rLGCW9aW8MoJN3deFR5i+fTl1TP8zpRS6txZkC2IjOQ4rltZyJXL8gF4/lgX68oyiY91UWPPJspNjWepvTAttFQjNM6wsjidv79hGQ9+5uJJr/mTP7uIsuxklFLqfLAgE0RIWcRAcigZXFhhjSWESmZDuPZRqAXhcgl3vKtm0sY7Sil1vlnQCSI3Nd45XmQniGtXWIveIgvi/eJTG/nSdUudndmUUmohWJBjECEiwj03r6Rn2M8t64oBa63Crz69iarc8Irm/LTESWMLSim1ECzoBAFw+yWVf3Tu0kW645pSSi3oLiallFJnpglCKaXUlDRBKKWUmpImCKWUUlPSBKGUUmpKmiCUUkpNSROEUkqpKWmCUEopNSUJFaKbj0TEDTRF8UfkAj1RfP1omY9xz8eYQeOeaRr3uVFhjMl7s5vmdYKINhHZbYzZMNtxvFXzMe75GDNo3DNN455Z2sWklFJqSpoglFJKTUkTxNndP9sBvE3zMe75GDNo3DNN455BOgahlFJqStqCUEopNaUFlSBE5Kci0i0ihyPOrRWR10XkkIg8ISLpEdfW2NeO2NcT7fMX2o/rROTfJbQ36RyIW0Q+JiL7I76CIrJuHsQdJyI/t88fE5G7Ip4zl+OOF5Gf2ecPiMiVsxG3iJSJyEv2f7sjIvLX9vlsEXlORGrt71kRz7nLju2EiFw3H+IWkRz7/mER+cFprzWX494iInvs+PaIyNWzEfdbZoxZMF/Au4ALgMMR53YBV9jHnwT+t30cCxwE1tqPc4AY+3gncDEgwFPAe+ZK3Kc9bzXQEPF4zsYNfBR4wD5OBk4BlfMg7juBn9nH+cAewDXTcQNFwAX2cRpwElgB/CvwVfv8V4Fv2ccrgANAAlAF1M/G7/fbiDsFuAz4LPCD015rLse9Hii2j1cBbbMR91t+n7MdwIy/Yag87R/+IOGxmDLgqH18A/DLM/xiHI94/KfAj+ZK3Kc951+Ae+dD3HY8T2Al5hz7H1z2PIj7P4GPR9z3ArBxtuKO+Hm/A7YAJ4CiiN+BE/bxXcBdEfc/Y39Izem4I+77MyISxHyJ2z4vQC9Wcp7VuN/sa0F1MZ3BYeBm+/hDWP/4AZYARkSeEZG9IvJl+3wJ0Brx/Fb73Ew7U9yRPgL8xj6e63H/FvACHUAz8B1jjIe5H/cB4BYRiRWRKuBC+9qsxS0ilVh/se4ACowxHQD293z7thKgZYr45nrcZzKf4v4TYJ8xxs/c+f2ekiYIq7vgThHZg9VUHLPPx2I1ZT9mf3+/iFyDlf1PNxtTwc4UNwAisgnwGWNC/ehzPe6NQAAoxury+FsRqWbux/1TrH/Uu4HvAa8BE8xS3CKSCjwMfMEYM3i2W6c4Z85yPqreQtxnfIkpzs25uEVkJfAt4DOhU1PcNmemlsbOdgCzzRhzHHg3gIgsAW60L7UCrxhjeuxrf8Dql/4lUBrxEqVA+4wFbDtL3CG3Em49gPV+5nLcHwWeNsaMA90ish3YAGxlDsdtjJkAvhi6T0ReA2qBPmY4bhGJw/qw+pUx5hH7dJeIFBljOkSkCOi2z7cyudUZim/Gf0/eYtxnMufjFpFS4FHgE8aY+tmK+61Y8C0IEcm3v7uAu4Ef2peeAdaISLKIxAJXYPU7dwBDIrLZnm3wCaz+x7kSd+jch4AHQufmQdzNwNViSQE2Y/XNzum47d+PFPt4CzBhjJnx3xP7Z/wEOGaM+SMDXkIAAALaSURBVG7EpceB2+3j2yNieBy4VUQS7K6xxcDOeRD3lOZ63CKSCf9/e3cPGkUUhWH4/ewURRERQRDBTgJKiBGsBMUijQgiIoKFKey0sVIhWshixEosbQI2KRRUJJoUggERi0DUEEFJZyH4AxHRSI7FvdFRLoaBsJOY74GBZHZnOJufPXvvmTmX+6S6z2hTcdfWdBGknRvpE/U7YIaUuU8Cp0kF0ddAi1yIzM8/DrwkzT9fqezvyvveANerxyySuPcCTwvnWbRxA6uBwfzzfgWcXSJxbyUVJieAYVKXzLbHTZoGDdKVd2N56yEV/EdIo5oRYH3lmHM5tkkqV84sgbingA/AdP79bF/scZM+VHypPHcM2NjE33edzXdSm5lZ0bKfYjIzszInCDMzK3KCMDOzIicIMzMrcoIwM7OiZX+jnFmJpLnLFQE2ke7yfp+/746I78UDzf4jvszVbB6S+oDpiLjadCxm7eQpJrOaJJ2Q9ExpvY0bklbkZn2fJPXn5o5DknZLeizpraSefGyvpNv58UlJ5/P+NZIeKK0p8ULS4WZfpZkThFktkjqAQ8CeiNhJmqY9mh9eCzyMiE5SM78+YB+p7cmlymm68zGdwDGlBZ16gKmI2BERHcCjNrwcs39yDcKsnv3ALuB5XvhrJb/bZn+NiLk39nHgc0T8kDROaskxZygiPgJIukNq2zACtCS1gLtR6ddj1hQnCLN6BNyMiAt/7EwNHauF61ngW+Xr6v/a34W/iIgJSV2kkUS/pHsRcXlhQzerx1NMZvUMA0ckbYBfayRvqXmOA5LWSVoFHARGJW0mFcIHgGuk6SezRnkEYVZDRIxLuggM59bfM6T1kev08H8C3AK2AQMRMZaL2C1Js6SRyKkFDt2sNl/matZGknqBjog403QsZvPxFJOZmRV5BGFmZkUeQZiZWZEThJmZFTlBmJlZkROEmZkVOUGYmVmRE4SZmRX9BLyw3+8HOuRrAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"])\n", "plt.xlabel(\"Temps\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le fichier propose aussi directement les données de la quantité de CO2 dans l'atmosphère en enlevant la composant saisonnière ; nous allons donc dans un premier temps afficher ces données déjà traitées" ] }, { "cell_type": "code", "execution_count": 61, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 61, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En soustrayant les deux nous pouvons alors en déduire les variations saisonnières" ] }, { "cell_type": "code", "execution_count": 62, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "Text(0,0.5,'CO2 (ppm)')" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps\")\n", "plt.ylabel(\"CO2 (ppm)\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Zoomons un peu autour de 2020 - 2024 pour mieux se rendre compte des variations" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(2020, 2024)" ] }, "execution_count": 63, "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": [ "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n", "plt.xlabel(\"Temps\")\n", "plt.ylabel(\"CO2 (ppm)\")\n", "plt.xlim([2020,2024])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Analyse des données\n", "\n", "Bien que le fichier d'origine nous fournisse un jeu de données pré-traitées; nous allons désormais tenter de retrouver ces résultats en:\n", "- identifiant la composante lente en un polynôme de degré 2 en fonction du temps\n", "- identifiant par la suite la composante périodique en effectuant une analyse spectrale une fois la composante lente enlevée." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Composante lente\n", "\n", "on considère un polynôme de la forme $C(t) = a + b t + c t^2$. Nous allons appliquer une régression linéaire (grâce à [`np.linalg.lstsq`](https://numpy.org/doc/stable/reference/generated/numpy.linalg.lstsq.html))\n", "\n", "Commençons par récupérer les tableaux numpy" ] }, { "cell_type": "code", "execution_count": 87, "metadata": {}, "outputs": [], "source": [ "temps = np.array(data_MLO[\"Date.1\"])\n", "CO2 = np.array(data_MLO[\"CO2\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour que les temps soient de taille plus raisonnable, nous allons soustraire le temps initial à tout les temps afin de commencer à zéro:" ] }, { "cell_type": "code", "execution_count": 93, "metadata": {}, "outputs": [], "source": [ "temps = temps - temps[0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Construisons ensuite la matrice qui nous permettra d'effectuer la régression linéaire:\n", "\n", "$$ A_{i,j} = t_i^j $$\n", "\n", "Cad,\n", "$$ A = \\begin{bmatrix}\n", " ... & ... & ... \\\\\n", " 1 & 2020^2 & 2020^2 \\\\\n", " 1 & 2020,1^2 & 2020,1^2 \\\\\n", " 1 & 2020,2^2 & 2020,2^2 \\\\\n", " ... & ... & ...\n", " \\end{bmatrix}$$" ] }, { "cell_type": "code", "execution_count": 94, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[[1.00000000e+00 0.00000000e+00 0.00000000e+00]\n", " [1.00000000e+00 8.50000000e-02 7.22500000e-03]\n", " [1.00000000e+00 1.67200000e-01 2.79558400e-02]\n", " ...\n", " [1.00000000e+00 6.62536000e+01 4.38953951e+03]\n", " [1.00000000e+00 6.63356000e+01 4.40041183e+03]\n", " [1.00000000e+00 6.64203000e+01 4.41165625e+03]]\n" ] } ], "source": [ "A = np.column_stack([temps**0,temps, temps**2])\n", "print(A)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons à présent résoudre le système linéaire: $$ Ax = b$$" ] }, { "cell_type": "code", "execution_count": 103, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Estimation des coefficients de la régression: a = 314.830 ppm, b = 0.734 ppm/annee, c = 0.014 ppm/annee^2 \n" ] } ], "source": [ "param = np.linalg.lstsq(A,CO2,rcond=None)\n", "a,b,c = param[0] \n", "print(f\"Estimation des coefficients de la régression: a = {a:.3f} ppm, b = {b:.3f} ppm/annee, c = {c:.3f} ppm/annee^2 \" )" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous allons désormais afficher le CO2 au cours du temps et y superposer notre estimation. Commençons par définir une fonction qui renvoie la composante lente:" ] }, { "cell_type": "code", "execution_count": 105, "metadata": {}, "outputs": [], "source": [ "def CO2_comp_lente(t):\n", " return a + b*t + c*t" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "CO2_estimation = CO2_compe_lente(temps)\n", "\n", "data" ] } ], "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 }