{ "cells": [ { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "import pandas as pd\n", "import matplotlib.pyplot as plt\n", "from scipy.optimize import curve_fit\n", "import numpy as np\n", "import isoweek # Pour gérer les semaines ISO" ] }, { "cell_type": "code", "execution_count": 65, "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 = \"monthly_in_situ_co2_mlo.csv\"\n", "# Vérifier si le fichier local existe, et s'il n'existe pas, le télécharger depuis l'URL\n", "import os\n", "import urllib.request\n", "\n", "# Vérifier si le fichier local n'existe pas\n", "if not os.path.exists(data):\n", " # Télécharger les données depuis l'URL et les enregistrer dans le fichier local\n", " urllib.request.urlretrieve(data_url, data)" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonallySta
0adjustedadjusted fitfilledadjusted filledNaN
1Excel[ppm][ppm][ppm][ppm][ppm][ppm]NaN
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8195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
9195808214121958.6219314.93316.21313.97315.29314.93316.21MLO
10195809214431958.7068313.21316.11312.44315.35313.21316.11MLO
11195810214731958.7890-99.99-99.99312.42315.41312.42315.41MLO
12195811215041958.8740313.33315.21313.61315.46313.33315.21MLO
13195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
14195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
15195902215961959.1260316.49315.84316.29315.63316.49315.84MLO
16195903216241959.2027316.65315.38316.98315.70316.65315.38MLO
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21195908217771959.6219314.80316.09314.80316.13314.80316.09MLO
22195909218081959.7068313.84316.75313.30316.22313.84316.75MLO
23195910218381959.7890313.33316.34313.31316.31313.33316.34MLO
24195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
25195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
26196001219301960.0410316.43316.37316.63316.56316.43316.37MLO
27196002219611960.1257316.98316.33317.29316.64316.98316.33MLO
28196003219901960.2049317.58316.28318.03316.72317.58316.28MLO
29196004220211960.2896319.03316.70319.14316.79319.03316.70MLO
....................................
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767202110444842021.7890413.55417.16413.14416.74413.55417.16MLO
768202111445152021.8740414.82417.08414.69416.92414.82417.08MLO
769202112445452021.9562416.43417.36416.19417.10416.43417.36MLO
770202201445762022.0411418.01417.94417.34417.26418.01417.94MLO
771202202446072022.1260418.99418.21418.21417.42418.99418.21MLO
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773202204446662022.2877420.02417.25420.50417.71420.02417.25MLO
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780202211448802022.8740417.04419.31416.74418.98417.04419.31MLO
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794 rows × 11 columns

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" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "0 adjusted \n", "1 Excel [ppm] [ppm] [ppm] \n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.71 314.44 316.19 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", "6 1958 05 21320 1958.3699 317.51 314.69 317.89 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.27 \n", "8 1958 07 21381 1958.5370 315.87 315.20 315.86 \n", "9 1958 08 21412 1958.6219 314.93 316.21 313.97 \n", "10 1958 09 21443 1958.7068 313.21 316.11 312.44 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.42 \n", "12 1958 11 21504 1958.8740 313.33 315.21 313.61 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.77 \n", "14 1959 01 21565 1959.0411 315.58 315.52 315.64 \n", "15 1959 02 21596 1959.1260 316.49 315.84 316.29 \n", "16 1959 03 21624 1959.2027 316.65 315.38 316.98 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.09 \n", "18 1959 05 21685 1959.3699 318.29 315.46 318.68 \n", "19 1959 06 21716 1959.4548 318.15 316.00 318.07 \n", "20 1959 07 21746 1959.5370 316.54 315.87 316.67 \n", "21 1959 08 21777 1959.6219 314.80 316.09 314.80 \n", "22 1959 09 21808 1959.7068 313.84 316.75 313.30 \n", "23 1959 10 21838 1959.7890 313.33 316.34 313.31 \n", "24 1959 11 21869 1959.8740 314.81 316.69 314.53 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.37 316.63 \n", "27 1960 02 21961 1960.1257 316.98 316.33 317.29 \n", "28 1960 03 21990 1960.2049 317.58 316.28 318.03 \n", "29 1960 04 22021 1960.2896 319.03 316.70 319.14 \n", ".. ... ... ... ... ... ... ... \n", "764 2021 07 44392 2021.5370 416.65 415.85 416.95 \n", "765 2021 08 44423 2021.6219 414.34 415.89 414.78 \n", "766 2021 09 44454 2021.7068 412.90 416.40 413.04 \n", "767 2021 10 44484 2021.7890 413.55 417.16 413.14 \n", "768 2021 11 44515 2021.8740 414.82 417.08 414.69 \n", "769 2021 12 44545 2021.9562 416.43 417.36 416.19 \n", "770 2022 01 44576 2022.0411 418.01 417.94 417.34 \n", "771 2022 02 44607 2022.1260 418.99 418.21 418.21 \n", "772 2022 03 44635 2022.2027 418.45 416.92 419.11 \n", "773 2022 04 44666 2022.2877 420.02 417.25 420.50 \n", "774 2022 05 44696 2022.3699 420.77 417.36 421.27 \n", "775 2022 06 44727 2022.4548 420.68 418.09 420.60 \n", "776 2022 07 44757 2022.5370 418.68 417.87 418.98 \n", "777 2022 08 44788 2022.6219 416.76 418.31 416.80 \n", "778 2022 09 44819 2022.7068 415.41 418.91 415.07 \n", "779 2022 10 44849 2022.7890 415.31 418.93 415.18 \n", "780 2022 11 44880 2022.8740 417.04 419.31 416.74 \n", "781 2022 12 44910 2022.9562 418.57 419.49 418.27 \n", "782 2023 01 44941 2023.0411 419.24 419.17 419.46 \n", "783 2023 02 44972 2023.1260 420.33 419.55 420.38 \n", "784 2023 03 45000 2023.2027 420.51 418.97 421.34 \n", "785 2023 04 45031 2023.2877 422.73 419.95 422.81 \n", "786 2023 05 45061 2023.3699 423.78 420.36 423.65 \n", "787 2023 06 45092 2023.4548 423.39 420.80 423.03 \n", "788 2023 07 45122 2023.5370 -99.99 -99.99 -99.99 \n", "789 2023 08 45153 2023.6219 -99.99 -99.99 -99.99 \n", "790 2023 09 45184 2023.7068 -99.99 -99.99 -99.99 \n", "791 2023 10 45214 2023.7890 -99.99 -99.99 -99.99 \n", "792 2023 11 45245 2023.8740 -99.99 -99.99 -99.99 \n", "793 2023 12 45275 2023.9562 -99.99 -99.99 -99.99 \n", "\n", " seasonally CO2 seasonally Sta \n", "0 adjusted fit filled adjusted filled NaN \n", "1 [ppm] [ppm] [ppm] NaN \n", "2 -99.99 -99.99 -99.99 MLO \n", "3 -99.99 -99.99 -99.99 MLO \n", "4 314.91 315.71 314.44 MLO \n", "5 314.99 317.45 315.16 MLO \n", "6 315.07 317.51 314.69 MLO \n", "7 315.15 317.27 315.15 MLO \n", "8 315.22 315.87 315.20 MLO \n", "9 315.29 314.93 316.21 MLO \n", "10 315.35 313.21 316.11 MLO \n", "11 315.41 312.42 315.41 MLO \n", "12 315.46 313.33 315.21 MLO \n", "13 315.52 314.67 315.43 MLO \n", "14 315.57 315.58 315.52 MLO \n", "15 315.63 316.49 315.84 MLO \n", "16 315.70 316.65 315.38 MLO \n", "17 315.77 317.72 315.42 MLO \n", "18 315.85 318.29 315.46 MLO \n", "19 315.94 318.15 316.00 MLO \n", "20 316.03 316.54 315.87 MLO \n", "21 316.13 314.80 316.09 MLO \n", "22 316.22 313.84 316.75 MLO \n", "23 316.31 313.33 316.34 MLO \n", "24 316.40 314.81 316.69 MLO \n", "25 316.48 315.58 316.35 MLO \n", "26 316.56 316.43 316.37 MLO \n", "27 316.64 316.98 316.33 MLO \n", "28 316.72 317.58 316.28 MLO \n", "29 316.79 319.03 316.70 MLO \n", ".. ... ... ... ... \n", "764 416.18 416.65 415.85 MLO \n", "765 416.36 414.34 415.89 MLO \n", "766 416.55 412.90 416.40 MLO \n", "767 416.74 413.55 417.16 MLO \n", "768 416.92 414.82 417.08 MLO \n", "769 417.10 416.43 417.36 MLO \n", "770 417.26 418.01 417.94 MLO \n", "771 417.42 418.99 418.21 MLO \n", "772 417.56 418.45 416.92 MLO \n", "773 417.71 420.02 417.25 MLO \n", "774 417.86 420.77 417.36 MLO \n", "775 418.03 420.68 418.09 MLO \n", "776 418.21 418.68 417.87 MLO \n", "777 418.40 416.76 418.31 MLO \n", "778 418.59 415.41 418.91 MLO \n", "779 418.78 415.31 418.93 MLO \n", "780 418.98 417.04 419.31 MLO \n", "781 419.18 418.57 419.49 MKO \n", "782 419.38 419.24 419.17 MKO \n", "783 419.59 420.33 419.55 MKO \n", "784 419.79 420.51 418.97 MLO \n", "785 420.01 422.73 419.95 MLO \n", "786 420.23 423.78 420.36 MLO \n", "787 420.46 423.39 420.80 MLO \n", "788 -99.99 -99.99 -99.99 MLO \n", "789 -99.99 -99.99 -99.99 MLO \n", "790 -99.99 -99.99 -99.99 MLO \n", "791 -99.99 -99.99 -99.99 MLO \n", "792 -99.99 -99.99 -99.99 MLO \n", "793 -99.99 -99.99 -99.99 MLO \n", "\n", "[794 rows x 11 columns]" ] }, "execution_count": 66, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Lire les données depuis le fichier local CSV en sautant la première ligne (commentaire)\n", "raw_data = pd.read_csv(data, skiprows=57)\n", "\n", "# Afficher les données brutes\n", "raw_data" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonallySta
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YrMnDateDateCO2seasonallyfitseasonallyCO2seasonallySta
2195801212001958.0411-99.99-99.99-99.99-99.99-99.99-99.99MLO
3195802212311958.1260-99.99-99.99-99.99-99.99-99.99-99.99MLO
4195803212591958.2027315.71314.44316.19314.91315.71314.44MLO
5195804212901958.2877317.45315.16317.30314.99317.45315.16MLO
6195805213201958.3699317.51314.69317.89315.07317.51314.69MLO
7195806213511958.4548-99.99-99.99317.27315.15317.27315.15MLO
8195807213811958.5370315.87315.20315.86315.22315.87315.20MLO
9195808214121958.6219314.93316.21313.97315.29314.93316.21MLO
10195809214431958.7068313.21316.11312.44315.35313.21316.11MLO
11195810214731958.7890-99.99-99.99312.42315.41312.42315.41MLO
12195811215041958.8740313.33315.21313.61315.46313.33315.21MLO
13195812215341958.9562314.67315.43314.77315.52314.67315.43MLO
14195901215651959.0411315.58315.52315.64315.57315.58315.52MLO
15195902215961959.1260316.49315.84316.29315.63316.49315.84MLO
16195903216241959.2027316.65315.38316.98315.70316.65315.38MLO
17195904216551959.2877317.72315.42318.09315.77317.72315.42MLO
18195905216851959.3699318.29315.46318.68315.85318.29315.46MLO
19195906217161959.4548318.15316.00318.07315.94318.15316.00MLO
20195907217461959.5370316.54315.87316.67316.03316.54315.87MLO
21195908217771959.6219314.80316.09314.80316.13314.80316.09MLO
22195909218081959.7068313.84316.75313.30316.22313.84316.75MLO
23195910218381959.7890313.33316.34313.31316.31313.33316.34MLO
24195911218691959.8740314.81316.69314.53316.40314.81316.69MLO
25195912218991959.9562315.58316.35315.72316.48315.58316.35MLO
26196001219301960.0410316.43316.37316.63316.56316.43316.37MLO
27196002219611960.1257316.98316.33317.29316.64316.98316.33MLO
28196003219901960.2049317.58316.28318.03316.72317.58316.28MLO
29196004220211960.2896319.03316.70319.14316.79319.03316.70MLO
30196005220511960.3716320.03317.20319.70316.87320.03317.20MLO
31196006220821960.4563319.58317.45319.04316.93319.58317.45MLO
....................................
764202107443922021.5370416.65415.85416.95416.18416.65415.85MLO
765202108444232021.6219414.34415.89414.78416.36414.34415.89MLO
766202109444542021.7068412.90416.40413.04416.55412.90416.40MLO
767202110444842021.7890413.55417.16413.14416.74413.55417.16MLO
768202111445152021.8740414.82417.08414.69416.92414.82417.08MLO
769202112445452021.9562416.43417.36416.19417.10416.43417.36MLO
770202201445762022.0411418.01417.94417.34417.26418.01417.94MLO
771202202446072022.1260418.99418.21418.21417.42418.99418.21MLO
772202203446352022.2027418.45416.92419.11417.56418.45416.92MLO
773202204446662022.2877420.02417.25420.50417.71420.02417.25MLO
774202205446962022.3699420.77417.36421.27417.86420.77417.36MLO
775202206447272022.4548420.68418.09420.60418.03420.68418.09MLO
776202207447572022.5370418.68417.87418.98418.21418.68417.87MLO
777202208447882022.6219416.76418.31416.80418.40416.76418.31MLO
778202209448192022.7068415.41418.91415.07418.59415.41418.91MLO
779202210448492022.7890415.31418.93415.18418.78415.31418.93MLO
780202211448802022.8740417.04419.31416.74418.98417.04419.31MLO
781202212449102022.9562418.57419.49418.27419.18418.57419.49MKO
782202301449412023.0411419.24419.17419.46419.38419.24419.17MKO
783202302449722023.1260420.33419.55420.38419.59420.33419.55MKO
784202303450002023.2027420.51418.97421.34419.79420.51418.97MLO
785202304450312023.2877422.73419.95422.81420.01422.73419.95MLO
786202305450612023.3699423.78420.36423.65420.23423.78420.36MLO
787202306450922023.4548423.39420.80423.03420.46423.39420.80MLO
788202307451222023.5370-99.99-99.99-99.99-99.99-99.99-99.99MLO
789202308451532023.6219-99.99-99.99-99.99-99.99-99.99-99.99MLO
790202309451842023.7068-99.99-99.99-99.99-99.99-99.99-99.99MLO
791202310452142023.7890-99.99-99.99-99.99-99.99-99.99-99.99MLO
792202311452452023.8740-99.99-99.99-99.99-99.99-99.99-99.99MLO
793202312452752023.9562-99.99-99.99-99.99-99.99-99.99-99.99MLO
\n", "

792 rows × 11 columns

\n", "
" ], "text/plain": [ " Yr Mn Date Date CO2 seasonally fit \\\n", "2 1958 01 21200 1958.0411 -99.99 -99.99 -99.99 \n", "3 1958 02 21231 1958.1260 -99.99 -99.99 -99.99 \n", "4 1958 03 21259 1958.2027 315.71 314.44 316.19 \n", "5 1958 04 21290 1958.2877 317.45 315.16 317.30 \n", "6 1958 05 21320 1958.3699 317.51 314.69 317.89 \n", "7 1958 06 21351 1958.4548 -99.99 -99.99 317.27 \n", "8 1958 07 21381 1958.5370 315.87 315.20 315.86 \n", "9 1958 08 21412 1958.6219 314.93 316.21 313.97 \n", "10 1958 09 21443 1958.7068 313.21 316.11 312.44 \n", "11 1958 10 21473 1958.7890 -99.99 -99.99 312.42 \n", "12 1958 11 21504 1958.8740 313.33 315.21 313.61 \n", "13 1958 12 21534 1958.9562 314.67 315.43 314.77 \n", "14 1959 01 21565 1959.0411 315.58 315.52 315.64 \n", "15 1959 02 21596 1959.1260 316.49 315.84 316.29 \n", "16 1959 03 21624 1959.2027 316.65 315.38 316.98 \n", "17 1959 04 21655 1959.2877 317.72 315.42 318.09 \n", "18 1959 05 21685 1959.3699 318.29 315.46 318.68 \n", "19 1959 06 21716 1959.4548 318.15 316.00 318.07 \n", "20 1959 07 21746 1959.5370 316.54 315.87 316.67 \n", "21 1959 08 21777 1959.6219 314.80 316.09 314.80 \n", "22 1959 09 21808 1959.7068 313.84 316.75 313.30 \n", "23 1959 10 21838 1959.7890 313.33 316.34 313.31 \n", "24 1959 11 21869 1959.8740 314.81 316.69 314.53 \n", "25 1959 12 21899 1959.9562 315.58 316.35 315.72 \n", "26 1960 01 21930 1960.0410 316.43 316.37 316.63 \n", "27 1960 02 21961 1960.1257 316.98 316.33 317.29 \n", "28 1960 03 21990 1960.2049 317.58 316.28 318.03 \n", "29 1960 04 22021 1960.2896 319.03 316.70 319.14 \n", "30 1960 05 22051 1960.3716 320.03 317.20 319.70 \n", "31 1960 06 22082 1960.4563 319.58 317.45 319.04 \n", ".. ... ... ... ... ... ... ... \n", "764 2021 07 44392 2021.5370 416.65 415.85 416.95 \n", "765 2021 08 44423 2021.6219 414.34 415.89 414.78 \n", "766 2021 09 44454 2021.7068 412.90 416.40 413.04 \n", "767 2021 10 44484 2021.7890 413.55 417.16 413.14 \n", "768 2021 11 44515 2021.8740 414.82 417.08 414.69 \n", "769 2021 12 44545 2021.9562 416.43 417.36 416.19 \n", "770 2022 01 44576 2022.0411 418.01 417.94 417.34 \n", "771 2022 02 44607 2022.1260 418.99 418.21 418.21 \n", "772 2022 03 44635 2022.2027 418.45 416.92 419.11 \n", "773 2022 04 44666 2022.2877 420.02 417.25 420.50 \n", "774 2022 05 44696 2022.3699 420.77 417.36 421.27 \n", "775 2022 06 44727 2022.4548 420.68 418.09 420.60 \n", "776 2022 07 44757 2022.5370 418.68 417.87 418.98 \n", "777 2022 08 44788 2022.6219 416.76 418.31 416.80 \n", "778 2022 09 44819 2022.7068 415.41 418.91 415.07 \n", "779 2022 10 44849 2022.7890 415.31 418.93 415.18 \n", "780 2022 11 44880 2022.8740 417.04 419.31 416.74 \n", "781 2022 12 44910 2022.9562 418.57 419.49 418.27 \n", "782 2023 01 44941 2023.0411 419.24 419.17 419.46 \n", "783 2023 02 44972 2023.1260 420.33 419.55 420.38 \n", "784 2023 03 45000 2023.2027 420.51 418.97 421.34 \n", "785 2023 04 45031 2023.2877 422.73 419.95 422.81 \n", "786 2023 05 45061 2023.3699 423.78 420.36 423.65 \n", "787 2023 06 45092 2023.4548 423.39 420.80 423.03 \n", "788 2023 07 45122 2023.5370 -99.99 -99.99 -99.99 \n", "789 2023 08 45153 2023.6219 -99.99 -99.99 -99.99 \n", "790 2023 09 45184 2023.7068 -99.99 -99.99 -99.99 \n", "791 2023 10 45214 2023.7890 -99.99 -99.99 -99.99 \n", "792 2023 11 45245 2023.8740 -99.99 -99.99 -99.99 \n", "793 2023 12 45275 2023.9562 -99.99 -99.99 -99.99 \n", "\n", " seasonally CO2 seasonally Sta \n", "2 -99.99 -99.99 -99.99 MLO \n", "3 -99.99 -99.99 -99.99 MLO \n", "4 314.91 315.71 314.44 MLO \n", "5 314.99 317.45 315.16 MLO \n", "6 315.07 317.51 314.69 MLO \n", "7 315.15 317.27 315.15 MLO \n", "8 315.22 315.87 315.20 MLO \n", "9 315.29 314.93 316.21 MLO \n", "10 315.35 313.21 316.11 MLO \n", "11 315.41 312.42 315.41 MLO \n", "12 315.46 313.33 315.21 MLO \n", "13 315.52 314.67 315.43 MLO \n", "14 315.57 315.58 315.52 MLO \n", "15 315.63 316.49 315.84 MLO \n", "16 315.70 316.65 315.38 MLO \n", "17 315.77 317.72 315.42 MLO \n", "18 315.85 318.29 315.46 MLO \n", "19 315.94 318.15 316.00 MLO \n", "20 316.03 316.54 315.87 MLO \n", "21 316.13 314.80 316.09 MLO \n", "22 316.22 313.84 316.75 MLO \n", "23 316.31 313.33 316.34 MLO \n", "24 316.40 314.81 316.69 MLO \n", "25 316.48 315.58 316.35 MLO \n", "26 316.56 316.43 316.37 MLO \n", "27 316.64 316.98 316.33 MLO \n", "28 316.72 317.58 316.28 MLO \n", "29 316.79 319.03 316.70 MLO \n", "30 316.87 320.03 317.20 MLO \n", "31 316.93 319.58 317.45 MLO \n", ".. ... ... ... ... \n", "764 416.18 416.65 415.85 MLO \n", "765 416.36 414.34 415.89 MLO \n", "766 416.55 412.90 416.40 MLO \n", "767 416.74 413.55 417.16 MLO \n", "768 416.92 414.82 417.08 MLO \n", "769 417.10 416.43 417.36 MLO \n", "770 417.26 418.01 417.94 MLO \n", "771 417.42 418.99 418.21 MLO \n", "772 417.56 418.45 416.92 MLO \n", "773 417.71 420.02 417.25 MLO \n", "774 417.86 420.77 417.36 MLO \n", "775 418.03 420.68 418.09 MLO \n", "776 418.21 418.68 417.87 MLO \n", "777 418.40 416.76 418.31 MLO \n", "778 418.59 415.41 418.91 MLO \n", "779 418.78 415.31 418.93 MLO \n", "780 418.98 417.04 419.31 MLO \n", "781 419.18 418.57 419.49 MKO \n", "782 419.38 419.24 419.17 MKO \n", "783 419.59 420.33 419.55 MKO \n", "784 419.79 420.51 418.97 MLO \n", "785 420.01 422.73 419.95 MLO \n", "786 420.23 423.78 420.36 MLO \n", "787 420.46 423.39 420.80 MLO \n", "788 -99.99 -99.99 -99.99 MLO \n", "789 -99.99 -99.99 -99.99 MLO \n", "790 -99.99 -99.99 -99.99 MLO \n", "791 -99.99 -99.99 -99.99 MLO \n", "792 -99.99 -99.99 -99.99 MLO \n", "793 -99.99 -99.99 -99.99 MLO \n", "\n", "[792 rows x 11 columns]" ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Supprimer les lignes contenant des valeurs manquantes (NaN) à partir des données brutes\n", "data = raw_data.dropna().copy()\n", "\n", "# Afficher les données nettoyées (sans valeurs manquantes) et en créer une copie\n", "data" ] }, { "cell_type": "code", "execution_count": 69, "metadata": {}, "outputs": [], "source": [ "data = data.dropna(subset=[' CO2'])" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Index([' Yr', ' Mn', ' Date', ' Date', ' CO2', 'seasonally',\n", " ' fit', ' seasonally', ' CO2', ' seasonally', ' Sta'],\n", " dtype='object')\n" ] } ], "source": [ "print(data.columns)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Objectif: regrouper et analyser les émissions de dioxyde de carbone (CO2) en fonction des années.\n", "On commence par extraire les années uniques à partir d'un ensemble de données, puis somme les émissions de CO2 pour chaque année. Les résultats sont ensuite stockés dans une série pandas pour une analyse ultérieure. Enfin, le code génère un graphique de dispersion pour visualiser l'évolution des émissions annuelles de CO2 au fil du temps. Cette approche permet de mettre en évidence les tendances et les variations annuelles des émissions de CO2, facilitant ainsi une compréhension plus approfondie de l'impact environnemental sur une période donnée." ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:13: SettingWithCopyWarning: \n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", "Try using .loc[row_indexer,col_indexer] = value instead\n", "\n", "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", " del sys.path[0]\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 71, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Grouper par année (Yr) et sommer les valeurs de CO2\n", "annees = data[' Yr'].unique() # Obtenir la liste des années uniques\n", "\n", "# Initialiser les listes pour stocker les années et les émissions de CO2 annuelles\n", "year = [] # Liste des années\n", "yearly_CO2 = [] # Liste des émissions annuelles de CO2\n", "\n", "for annee in annees:\n", " # Filtrer les données pour l'année spécifique\n", " donnees_annee = data[data[' Yr'] == annee]\n", " \n", " # Somme des émissions de CO2 pour l'année spécifique\n", " donnees_annee[' CO2'] = donnees_annee[' CO2'].astype(float)\n", " somme_co2_annee = donnees_annee[' CO2'].sum()\n", " \n", " # Ajouter l'année et la somme des émissions à leurs listes respectives\n", " year.append(annee)\n", " yearly_CO2.append(somme_co2_annee)\n", "\n", "# Créer une série pandas avec les données annuelles et les années comme index\n", "yearly_CO2 = pd.Series(data=yearly_CO2, index=year)\n", "# Tracer un graphique de dispersion des données d'incidence annuelle avec un style en étoile\n", "yearly_CO2.plot(style='*')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le code fourni a pour objectif d'effectuer une analyse des composantes périodiques d'un jeu de données de concentrations de CO2 à l'aide de la transformation de Fourier en utilisant Python. Il commence par charger les données à partir d'un fichier CSV dans un DataFrame Pandas. Ensuite, il identifie la colonne appropriée contenant les données de concentration de CO2 et la convertit en format numérique (float) pour s'assurer que les données sont traitées correctement. Une fois les données préparées, le code applique la transformation de Fourier pour analyser les composantes périodiques dans les données. La sortie de cette analyse est ensuite visualisée dans le domaine de fréquence, montrant les amplitudes des différentes composantes périodiques. Ce type d'analyse est utile pour détecter des modèles saisonniers ou cycliques dans les données de CO2, ce qui peut avoir des implications significatives dans le domaine de la climatologie et de l'environnement. Assurez-vous de personnaliser le code en remplaçant 'CO2' par le nom de la colonne réelle contenant les données de CO2 de votre ensemble de données." ] }, { "cell_type": "code", "execution_count": 72, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Fréquence dominante (période la plus importante) en années: inf\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:20: RuntimeWarning: divide by zero encountered in true_divide\n", "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py:27: RuntimeWarning: divide by zero encountered in double_scalars\n" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dates = data[\" Yr\"] # Colonne des datess\n", "co2_concentration_series = data[\" CO2\"].astype(float) # Colonne de concentration de CO2 (avant ajustement saisonnier)\n", "\n", "# Appliquer la transformation de Fourier pour identifier les composantes périodiques\n", "co2_concentration = co2_concentration_series.values\n", "fourier_transform = np.fft.fft(co2_concentration)\n", "frequencies = np.fft.fftfreq(len(co2_concentration))\n", "amplitudes = np.abs(fourier_transform)\n", "\n", "# Trouver les fréquences dominantes (composantes périodiques)\n", "# Dans cet exemple, nous considérons les fréquences positives seulement (ignore les négatives)\n", "positive_frequencies = frequencies[:len(frequencies) // 2]\n", "positive_amplitudes = amplitudes[:len(amplitudes) // 2]\n", "\n", "# Identifier la fréquence dominante (correspondant à la période la plus importante)\n", "dominant_frequency = positive_frequencies[np.argmax(positive_amplitudes)]\n", "\n", "# Créer un graphique pour montrer les composantes périodiques\n", "plt.figure(figsize=(12, 6))\n", "plt.plot(1 / positive_frequencies, positive_amplitudes) # Période au lieu de fréquence\n", "plt.title(\"Composantes Périodiques de la Concentration de CO2\")\n", "plt.xlabel(\"Période (années)\")\n", "plt.ylabel(\"Amplitude\")\n", "plt.grid(True)\n", "\n", "# Afficher la fréquence dominante\n", "print(\"Fréquence dominante (période la plus importante) en années:\", 1 / dominant_frequency)\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Extrayez les colonnes \"dates\" et \"saison\"\n", "dates = data[' Yr']\n", "saison = data['seasonally']\n", "\n", "# Créez un graphique avec les données\n", "plt.figure(figsize=(10, 6))\n", "plt.plot(dates, saison, label='Saison', color='blue')\n", "plt.title('Évolution de la Saison au Fil du Temps')\n", "plt.xlabel('Dates')\n", "plt.ylabel('Saison')\n", "plt.legend()\n", "plt.grid(True)\n", "\n", "# Affichez le graphique\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On réalise une analyse des données de concentration de CO2 en utilisant des techniques de traitement de séries chronologiques. On commence par extraire les données d'année et de concentration de CO2, puis appliquer la transformation de Fourier pour identifier les composantes périodiques. Les fréquences principales sont détectées pour caractériser ces oscillations. Pour modéliser la tendance à long terme, une régression linéaire est employée, et ses paramètres sont estimés. L'extrapolation de cette tendance est effectuée jusqu'en 2023 à l'aide du modèle. Le résultat est illustré dans deux graphiques : le premier expose la concentration de CO2 avec ses oscillations périodiques, tandis que le second dépeint la contribution lente avec une extrapolation jusqu'en 2023. Cette analyse permet de visualiser les tendances périodiques et à long terme dans les données de CO2, offrant un aperçu utile pour des analyses et des prévisions futures." ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy.fft import fft\n", "from scipy.signal import find_peaks\n", "from scipy.optimize import curve_fit\n", "import matplotlib.pyplot as plt\n", "# Extraire la colonne des dates et de CO2\n", "dates = year\n", "co2_data = yearly_CO2.astype(float).values\n", "#co2_data = np.pad(co2_data, (0, 2), 'constant', constant_values=(0, 0))\n", "\n", "# Appliquer la transformation de Fourier aux données pour identifier l'oscillation périodique\n", "co2_fft = fft(co2_data)\n", "frequencies = np.fft.fftfreq(len(co2_data))\n", "amplitudes = np.abs(co2_fft)\n", "\n", "# Trouver les fréquences principales (les périodes des oscillations)\n", "peaks, _ = find_peaks(amplitudes)\n", "periods = 1 / frequencies[peaks]\n", "\n", "# Créer un modèle simple pour la contribution lente: une régression linéaire\n", "def linear_model(x, a, b):\n", " return a * x + b\n", "\n", "# Adapter le modèle aux données pour estimer les paramètres a et b\n", "popt, _ = curve_fit(linear_model, np.arange(len(co2_data)), co2_data)\n", "\n", "# Créer un graphique pour visualiser l'oscillation périodique\n", "plt.figure(figsize=(15, 6))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(dates, co2_data)\n", "plt.title('Concentration de CO2 avec Oscillation Périodique')\n", "\n", "# Extrapoler la tendance lente jusqu'à 2023\n", "years = np.arange(1958, 2024)\n", "extrapolated_data = linear_model(len(co2_data)+ years - 1958, *popt)\n", "\n", "# Créer un graphique pour visualiser la contribution lente et l'extrapolation\n", "plt.subplot(2, 1, 2)\n", "plt.plot(dates, co2_data, label='Données originales')\n", "plt.plot(dates, extrapolated_data, label='Extrapolation', linestyle='--', color='red')\n", "plt.title('Contribution Lente et Extrapolation jusqu\\'à 2025')\n", "plt.xlabel('Année')\n", "plt.ylabel('Concentration de CO2')\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Prévision de 2024 et 2025:" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from numpy.fft import fft\n", "from scipy.signal import find_peaks\n", "from scipy.optimize import curve_fit\n", "import matplotlib.pyplot as plt\n", "# Extraire la colonne des dates et de CO2\n", "dates = year\n", "dates = np.append(dates, [2024, 2025])\n", "\n", "co2_data = yearly_CO2\n", "#.astype(float).values\n", "#co2_data = np.pad(co2_data, (0, 2), 'constant', constant_values=(0, 0))\n", "\n", "\n", "# Utilisez le modèle linéaire et les paramètres estimés pour faire la prédiction\n", "prediction_2024 = linear_model(68 + 2024 - 1958, *popt)\n", "prediction_2025 = linear_model(68 + 2025 - 1958, *popt)\n", "\n", "# Ajoutez ces prévisions à co2_data\n", "co2_data = np.append(co2_data, [prediction_2024, prediction_2025])\n", "\n", "\n", "\n", "# Appliquer la transformation de Fourier aux données pour identifier l'oscillation périodique\n", "co2_fft = fft(co2_data)\n", "frequencies = np.fft.fftfreq(len(co2_data))\n", "amplitudes = np.abs(co2_fft)\n", "\n", "# Trouver les fréquences principales (les périodes des oscillations)\n", "peaks, _ = find_peaks(amplitudes)\n", "periods = 1 / frequencies[peaks]\n", "\n", "# Créer un modèle simple pour la contribution lente: une régression linéaire\n", "def linear_model(x, a, b):\n", " return a * x + b\n", "\n", "# Adapter le modèle aux données pour estimer les paramètres a et b\n", "popt, _ = curve_fit(linear_model, np.arange(len(co2_data)), co2_data)\n", "\n", "# Créer un graphique pour visualiser l'oscillation périodique\n", "plt.figure(figsize=(15, 6))\n", "plt.subplot(2, 1, 1)\n", "plt.plot(dates, co2_data)\n", "plt.title('Concentration de CO2 avec Oscillation Périodique')\n", "\n", "# Extrapoler la tendance lente jusqu'à 2025\n", "years = np.arange(1958, 2026)\n", "extrapolated_data = linear_model(len(co2_data)+ years - 1958, *popt)\n", "\n", "# Créer un graphique pour visualiser la contribution lente et l'extrapolation\n", "plt.subplot(2, 1, 2)\n", "plt.plot(dates, co2_data, label='Données originales')\n", "plt.plot(dates, extrapolated_data, label='Extrapolation', linestyle='--', color='red')\n", "plt.title('Contribution Lente et Extrapolation jusqu\\'à 2025')\n", "plt.xlabel('Année')\n", "plt.ylabel('Concentration de CO2')\n", "plt.legend()\n", "\n", "plt.tight_layout()\n", "plt.show()" ] } ], "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 }