{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ " # Interpolation de la concentration de CO2 dans l'atmosphère à Mauna Loa" ] }, { "cell_type": "code", "execution_count": 106, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import numpy as np\n", "from scipy.optimize import curve_fit\n", "import math" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis sont disponibles sur le site Web de l'institut Scripps. Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine de la période demandée. Nous téléchargeons toujours le jeu de données complet, qui commence en 1958 et se termine avec un mois récent." ] }, { "cell_type": "code", "execution_count": 149, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YearMonthDate (Excel)Date(Numeric)CO2 (raw)CO2 without seasons(raw)CO2 (fitted)CO2 without seasons(fitted)CO2 (interpolated)CO2 without seasons(interpolated)
019581212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99
119582212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99
219583212591958.2027315.70314.43316.19314.90315.70314.43
319584212901958.2877317.45315.16317.30314.98317.45315.16
419585213201958.3699317.51314.71317.86315.06317.51314.71
519586213511958.4548-99.99-99.99317.24315.14317.24315.14
619587213811958.5370315.86315.19315.86315.22315.86315.19
719588214121958.6219314.93316.19313.99315.29314.93316.19
819589214431958.7068313.21316.09312.45315.35313.21316.09
9195810214731958.7890-99.99-99.99312.43315.41312.43315.41
10195811215041958.8740313.33315.20313.61315.46313.33315.20
11195812215341958.9562314.67315.43314.76315.51314.67315.43
1219591215651959.0411315.58315.54315.62315.57315.58315.54
1319592215961959.1260316.49315.85316.27315.63316.49315.85
1419593216241959.2027316.65315.37316.98315.69316.65315.37
1519594216551959.2877317.72315.41318.09315.77317.72315.41
1619595216851959.3699318.29315.48318.66315.85318.29315.48
1719596217161959.4548318.15316.02318.05315.94318.15316.02
1819597217461959.5370316.54315.87316.67316.03316.54315.87
1919598217771959.6219314.80316.07314.82316.12314.80316.07
2019599218081959.7068313.84316.73313.31316.22313.84316.73
21195910218381959.7890313.33316.33313.32316.31313.33316.33
22195911218691959.8740314.81316.69314.53316.39314.81316.69
23195912218991959.9562315.58316.35315.72316.47315.58316.35
2419601219301960.0410316.43316.39316.61316.56316.43316.39
2519602219611960.1257316.98316.35317.28316.64316.98316.35
2619603219901960.2049317.58316.27318.03316.71317.58316.27
2719604220211960.2896319.03316.70319.15316.79319.03316.70
2819605220511960.3716320.04317.21319.68316.86320.04317.21
2919606220821960.4563319.58317.47319.02316.93319.58317.47
.................................
73820197436612019.5370411.78410.97412.29411.51411.78410.97
73920198436922019.6219410.01411.55410.16411.74410.01411.55
74020199437232019.7068408.48411.98408.45411.96408.48411.98
741201910437532019.7890408.37411.99408.57412.18408.37411.99
742201911437842019.8740410.22412.49410.16412.40410.22412.49
743201912438142019.9562411.78412.71411.70412.61411.78412.71
74420201438452020.0410413.38413.32412.89412.83413.38413.32
74520202438762020.1257414.03413.26413.81413.03414.03413.26
74620203439052020.2049414.44412.87414.82413.23414.44412.87
74720204439362020.2896416.11413.29416.27413.43416.11413.29
74820205439662020.3716417.10413.69417.03413.63417.10413.69
74920206439972020.4563416.23413.68416.36413.83416.23413.68
75020207440272020.5383414.47413.68414.78414.03414.47413.68
75120208440582020.6230412.53414.10412.63414.23412.53414.10
75220209440892020.7077411.19414.70410.90414.44411.19414.70
753202010441192020.7896411.15414.78411.02414.63411.15414.78
754202011441502020.8743412.88415.15412.58414.82412.88415.15
755202012441802020.9563413.89414.82414.09415.00413.89414.82
75620211442112021.0411415.17415.11415.24415.17415.17415.11
75720212442422021.1260416.47415.70416.13415.35416.47415.70
75820213442702021.2027417.14415.59417.06415.50417.14415.59
75920214443012021.2877418.24415.44418.48415.66418.24415.44
76020215443312021.3699418.92415.50419.23415.81418.92415.50
76120216443622021.4548418.73416.14-99.99-99.99418.73416.14
76220217443922021.5370-99.99-99.99-99.99-99.99-99.99-99.99
76320218444232021.6219-99.99-99.99-99.99-99.99-99.99-99.99
76420219444542021.7068-99.99-99.99-99.99-99.99-99.99-99.99
765202110444842021.7890-99.99-99.99-99.99-99.99-99.99-99.99
766202111445152021.8740-99.99-99.99-99.99-99.99-99.99-99.99
767202112445452021.9562-99.99-99.99-99.99-99.99-99.99-99.99
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768 rows × 10 columns

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" ], "text/plain": [ " Year Month Date (Excel) Date(Numeric) CO2 (raw) \\\n", "0 1958 1 21200 1958.0411 -99.99 \n", "1 1958 2 21231 1958.1260 -99.99 \n", "2 1958 3 21259 1958.2027 315.70 \n", "3 1958 4 21290 1958.2877 317.45 \n", "4 1958 5 21320 1958.3699 317.51 \n", "5 1958 6 21351 1958.4548 -99.99 \n", "6 1958 7 21381 1958.5370 315.86 \n", "7 1958 8 21412 1958.6219 314.93 \n", "8 1958 9 21443 1958.7068 313.21 \n", "9 1958 10 21473 1958.7890 -99.99 \n", "10 1958 11 21504 1958.8740 313.33 \n", "11 1958 12 21534 1958.9562 314.67 \n", "12 1959 1 21565 1959.0411 315.58 \n", "13 1959 2 21596 1959.1260 316.49 \n", "14 1959 3 21624 1959.2027 316.65 \n", "15 1959 4 21655 1959.2877 317.72 \n", "16 1959 5 21685 1959.3699 318.29 \n", "17 1959 6 21716 1959.4548 318.15 \n", "18 1959 7 21746 1959.5370 316.54 \n", "19 1959 8 21777 1959.6219 314.80 \n", "20 1959 9 21808 1959.7068 313.84 \n", "21 1959 10 21838 1959.7890 313.33 \n", "22 1959 11 21869 1959.8740 314.81 \n", "23 1959 12 21899 1959.9562 315.58 \n", "24 1960 1 21930 1960.0410 316.43 \n", "25 1960 2 21961 1960.1257 316.98 \n", "26 1960 3 21990 1960.2049 317.58 \n", "27 1960 4 22021 1960.2896 319.03 \n", "28 1960 5 22051 1960.3716 320.04 \n", "29 1960 6 22082 1960.4563 319.58 \n", ".. ... ... ... ... ... \n", "738 2019 7 43661 2019.5370 411.78 \n", "739 2019 8 43692 2019.6219 410.01 \n", "740 2019 9 43723 2019.7068 408.48 \n", "741 2019 10 43753 2019.7890 408.37 \n", "742 2019 11 43784 2019.8740 410.22 \n", "743 2019 12 43814 2019.9562 411.78 \n", "744 2020 1 43845 2020.0410 413.38 \n", "745 2020 2 43876 2020.1257 414.03 \n", "746 2020 3 43905 2020.2049 414.44 \n", "747 2020 4 43936 2020.2896 416.11 \n", "748 2020 5 43966 2020.3716 417.10 \n", "749 2020 6 43997 2020.4563 416.23 \n", "750 2020 7 44027 2020.5383 414.47 \n", "751 2020 8 44058 2020.6230 412.53 \n", "752 2020 9 44089 2020.7077 411.19 \n", "753 2020 10 44119 2020.7896 411.15 \n", "754 2020 11 44150 2020.8743 412.88 \n", "755 2020 12 44180 2020.9563 413.89 \n", "756 2021 1 44211 2021.0411 415.17 \n", "757 2021 2 44242 2021.1260 416.47 \n", "758 2021 3 44270 2021.2027 417.14 \n", "759 2021 4 44301 2021.2877 418.24 \n", "760 2021 5 44331 2021.3699 418.92 \n", "761 2021 6 44362 2021.4548 418.73 \n", "762 2021 7 44392 2021.5370 -99.99 \n", "763 2021 8 44423 2021.6219 -99.99 \n", "764 2021 9 44454 2021.7068 -99.99 \n", "765 2021 10 44484 2021.7890 -99.99 \n", "766 2021 11 44515 2021.8740 -99.99 \n", "767 2021 12 44545 2021.9562 -99.99 \n", "\n", " CO2 without seasons(raw) CO2 (fitted) CO2 without seasons(fitted) \\\n", "0 -99.99 -99.99 -99.99 \n", "1 -99.99 -99.99 -99.99 \n", "2 314.43 316.19 314.90 \n", "3 315.16 317.30 314.98 \n", "4 314.71 317.86 315.06 \n", "5 -99.99 317.24 315.14 \n", "6 315.19 315.86 315.22 \n", "7 316.19 313.99 315.29 \n", "8 316.09 312.45 315.35 \n", "9 -99.99 312.43 315.41 \n", "10 315.20 313.61 315.46 \n", "11 315.43 314.76 315.51 \n", "12 315.54 315.62 315.57 \n", "13 315.85 316.27 315.63 \n", "14 315.37 316.98 315.69 \n", "15 315.41 318.09 315.77 \n", "16 315.48 318.66 315.85 \n", "17 316.02 318.05 315.94 \n", "18 315.87 316.67 316.03 \n", "19 316.07 314.82 316.12 \n", "20 316.73 313.31 316.22 \n", "21 316.33 313.32 316.31 \n", "22 316.69 314.53 316.39 \n", "23 316.35 315.72 316.47 \n", "24 316.39 316.61 316.56 \n", "25 316.35 317.28 316.64 \n", "26 316.27 318.03 316.71 \n", "27 316.70 319.15 316.79 \n", "28 317.21 319.68 316.86 \n", "29 317.47 319.02 316.93 \n", ".. ... ... ... \n", "738 410.97 412.29 411.51 \n", "739 411.55 410.16 411.74 \n", "740 411.98 408.45 411.96 \n", "741 411.99 408.57 412.18 \n", "742 412.49 410.16 412.40 \n", "743 412.71 411.70 412.61 \n", "744 413.32 412.89 412.83 \n", "745 413.26 413.81 413.03 \n", "746 412.87 414.82 413.23 \n", "747 413.29 416.27 413.43 \n", "748 413.69 417.03 413.63 \n", "749 413.68 416.36 413.83 \n", "750 413.68 414.78 414.03 \n", "751 414.10 412.63 414.23 \n", "752 414.70 410.90 414.44 \n", "753 414.78 411.02 414.63 \n", "754 415.15 412.58 414.82 \n", "755 414.82 414.09 415.00 \n", "756 415.11 415.24 415.17 \n", "757 415.70 416.13 415.35 \n", "758 415.59 417.06 415.50 \n", "759 415.44 418.48 415.66 \n", "760 415.50 419.23 415.81 \n", "761 416.14 -99.99 -99.99 \n", "762 -99.99 -99.99 -99.99 \n", "763 -99.99 -99.99 -99.99 \n", "764 -99.99 -99.99 -99.99 \n", "765 -99.99 -99.99 -99.99 \n", "766 -99.99 -99.99 -99.99 \n", "767 -99.99 -99.99 -99.99 \n", "\n", " CO2 (interpolated) CO2 without seasons(interpolated) \n", "0 -99.99 -99.99 \n", "1 -99.99 -99.99 \n", "2 315.70 314.43 \n", "3 317.45 315.16 \n", "4 317.51 314.71 \n", "5 317.24 315.14 \n", "6 315.86 315.19 \n", "7 314.93 316.19 \n", "8 313.21 316.09 \n", "9 312.43 315.41 \n", "10 313.33 315.20 \n", "11 314.67 315.43 \n", "12 315.58 315.54 \n", "13 316.49 315.85 \n", "14 316.65 315.37 \n", "15 317.72 315.41 \n", "16 318.29 315.48 \n", "17 318.15 316.02 \n", "18 316.54 315.87 \n", "19 314.80 316.07 \n", "20 313.84 316.73 \n", "21 313.33 316.33 \n", "22 314.81 316.69 \n", "23 315.58 316.35 \n", "24 316.43 316.39 \n", "25 316.98 316.35 \n", "26 317.58 316.27 \n", "27 319.03 316.70 \n", "28 320.04 317.21 \n", "29 319.58 317.47 \n", ".. ... ... \n", "738 411.78 410.97 \n", "739 410.01 411.55 \n", "740 408.48 411.98 \n", "741 408.37 411.99 \n", "742 410.22 412.49 \n", "743 411.78 412.71 \n", "744 413.38 413.32 \n", "745 414.03 413.26 \n", "746 414.44 412.87 \n", "747 416.11 413.29 \n", "748 417.10 413.69 \n", "749 416.23 413.68 \n", "750 414.47 413.68 \n", "751 412.53 414.10 \n", "752 411.19 414.70 \n", "753 411.15 414.78 \n", "754 412.88 415.15 \n", "755 413.89 414.82 \n", "756 415.17 415.11 \n", "757 416.47 415.70 \n", "758 417.14 415.59 \n", "759 418.24 415.44 \n", "760 418.92 415.50 \n", "761 418.73 416.14 \n", "762 -99.99 -99.99 \n", "763 -99.99 -99.99 \n", "764 -99.99 -99.99 \n", "765 -99.99 -99.99 \n", "766 -99.99 -99.99 \n", "767 -99.99 -99.99 \n", "\n", "[768 rows x 10 columns]" ] }, "execution_count": 149, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/monthly/monthly_in_situ_co2_mlo.csv\"\n", "data_file = \"monthly_in_situ_co2_mlo.csv\"\n", "\n", "import os\n", "import urllib.request\n", "if not os.path.exists(data_file):\n", " urllib.request.urlretrieve(data_url, data_file)\n", " \n", "raw_data = pd.read_csv(data_file, skiprows=54,header=[0,2])\n", "raw_data.columns=[\"Year\" ,\"Month\" , \"Date (Excel)\" ,\"Date(Numeric)\" , \"CO2 (raw)\" ,\"CO2 without seasons(raw)\" , \"CO2 (fitted)\" , \"CO2 without seasons(fitted)\",\"CO2 (interpolated)\", \"CO2 without seasons(interpolated)\"]\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Voici l'explication des colonnes données:\n", "\n", "| Nom de colonne | Libellé de colonne |\n", "|----------------|-----------------------------------------------------------------------------------------------------------------------------------|\n", "| Year | Année\n", "| Month | Mois\n", "| Date (Excel) | Date au format Excel\n", "| Date (Numeric) | Date au format decimal\n", "| CO2 (raw) | Concentration de CO2 en ppm brute\n", "| CO2 without seasons(raw)\t | Concentration de CO2 en ppm en lissant les effets de saisons \n", "| CO2 (fitted)\t | Concentration de CO2 lissée par une fonction polynomiale de degré 3 associée à une fonction à 4 harmoniques\n", "| CO2 without seasons(fitted)\t | Concentration de CO2 lissée par une fonction polynomiale de degré 3 uniquement |\n", "| CO2 (interpolated)\t | Interpolation de la concentration de CO2 à l'aide de la somme des 2 fonctions décrites précédemment pour les points manquants |\n", "| CO2 without seasons(interpolated) | Interpolation de la concentration de CO2 à l'aide de la fonction polynomiale décrite précédemment pour les points manquants\n", "\n", "Les premières lignes du fichier CSV sont un commentaire, que nous ignorons en précisant `skiprows=54`." ] }, { "cell_type": "markdown", "metadata": { "hideCode": true }, "source": [ "Les concentrations égales à -99.99 sont les concetrations non mesurées , nous les remplacont par NaN ( Not a Number) pour plus de clarté." ] }, { "cell_type": "code", "execution_count": 150, "metadata": {}, "outputs": [], "source": [ "raw_data=raw_data.replace(-99.99,np.nan)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On trace ensuite l'évolution des concentrations mesurées en fonction du temps" ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 151, "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": [ "raw_data.plot(x='Date(Numeric)',y='CO2 without seasons(raw)')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut ensuite calculer les variations de ces concentrations pendant en un an en comparant à la valeur moyenne de l'année. On retrie ensuite ces valeurs pour plus de facilités pour les calculs suivants." ] }, { "cell_type": "code", "execution_count": 152, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 152, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "raw_data['CO2 variations'] = raw_data['CO2 (raw)']-raw_data['CO2 without seasons(raw)']\n", "sorted_data = raw_data.sort_values('Month')\n", "sorted_data.plot(x='Month',y='CO2 variations',style='o')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut ensuite interpoler la concentration pour calculer celle-ci en 2025. Pour cela on va interpoler les valeurs à l'aide d'un polynome de degré 3. Il est néanmoins préférables de supprimer les valeurs NaN des calculs auparavant" ] }, { "cell_type": "code", "execution_count": 153, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 153, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "x=raw_data['Date(Numeric)']\n", "y=raw_data['CO2 without seasons(raw)']\n", "idx = np.isfinite(x) & np.isfinite(y)\n", "coefficients=np.polyfit(x[idx], y[idx],deg=3)\n", "xmod = np.linspace(min(x), max(x), 100)\n", "modele = [coefficients[3] + coefficients[2] * val + coefficients[1] * val**2 + coefficients[0] * val**3 for val in xmod] \n", "plt.scatter(x, y, marker = \"o\", color = \"blue\", label = \"Positions\")\n", "plt.plot(xmod, modele, color = \"red\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut alors calculer la valeur en 2025 à l'aide de notre interpolation qui est de :" ] }, { "cell_type": "code", "execution_count": 154, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "425.0003961302573" ] }, "execution_count": 154, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Prevision=coefficients[3] + coefficients[2] * 2025 + coefficients[1] * 2025**2 + coefficients[0] * 2025**3\n", "Prevision" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour réaliser l'interpolation des variations mensuelles , il nous faut définir la fonction initiale. Nous avons choisi d'utiliser une fonction sinusoidale à 3 harmoniques d'une période de 12 mois qui est la suivante" ] }, { "cell_type": "code", "execution_count": 155, "metadata": {}, "outputs": [], "source": [ "def func(x, a1, a2 , a3):\n", " return a1*np.sin((2*3.14/12)*x)+a2*np.sin((4*3.14/12)*x)+a3*np.sin((6*3.14/12)*x)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On vient alors de meme qu'auparavant identifier les coefficients de la fonction." ] }, { "cell_type": "code", "execution_count": 156, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 156, "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": [ "x=sorted_data['Month']\n", "y=sorted_data['CO2 variations']\n", "idx = np.isfinite(x) & np.isfinite(y)\n", "popt,pcov = scipy.optimize.curve_fit(func, x[idx], y[idx])\n", "plt.plot(x, popt[0]*np.sin((2*3.14/12)*x)+ popt[1]*np.sin((4*3.14/12)*x)+ popt[2]*np.sin((6*3.14/12)*x))" ] } ], "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 }