{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 73, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import numpy as np \n", "from scipy.optimize import curve_fit\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En 1958, Charles David Keeling a initié une mesure de la concentration de CO2 dans l'atmosphère à l'observatoire de Mauna Loa, Hawaii, États-Unis qui continue jusqu'à aujourd'hui. L'objectif initial était d'étudier la variation saisonnière, mais l'intérêt s'est déplacé plus tard vers l'étude de la tendance croissante dans le contexte du changement climatique. En honneur à Keeling, ce jeu de données est souvent appelé [\"Keeling Curve\"](https://en.wikipedia.org/wiki/Keeling_Curve)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evolution de la concentration en CO2 depuis 1958" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les données sont disponibles sur le [site Web de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Nous les récupérons sous forme d'un fichier en format CSV dont chaque ligne correspond à une semaine. 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": 2, "metadata": {}, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"\n", "data_file = \"weekly_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)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme expliqué en entête du fichier de données CSV, les 43 premières lignes sont des commentaires détaillant l'explication des colonnes, nous les ignoront donc en précisant `skiprows=43`." ] }, { "cell_type": "code", "execution_count": 151, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indicated by the date in the first column.
1958-03-29316.19
1958-04-05317.31
1958-04-12317.69
1958-04-19317.58
1958-04-26316.48
1958-05-03316.95
1958-05-17317.56
1958-05-24317.99
1958-07-05315.85
1958-07-12315.85
1958-07-19315.46
1958-07-26315.59
1958-08-02315.64
1958-08-09315.10
1958-08-16315.09
1958-08-30314.14
1958-09-06313.54
1958-11-08313.05
1958-11-15313.26
1958-11-22313.57
1958-11-29314.01
1958-12-06314.56
1958-12-13314.41
1958-12-20314.77
1958-12-27315.21
1959-01-03315.24
1959-01-10315.50
1959-01-17315.69
1959-01-24315.86
1959-01-31315.42
......
2022-04-09419.38
2022-04-16420.57
2022-04-23420.11
2022-04-30419.81
2022-05-07419.64
2022-05-14421.36
2022-05-21420.55
2022-05-28421.34
2022-06-04421.18
2022-06-11420.90
2022-06-18420.45
2022-06-25420.16
2022-07-02419.89
2022-07-09418.92
2022-07-16418.47
2022-07-23418.02
2022-07-30417.64
2022-08-06417.42
2022-08-13416.84
2022-08-20416.24
2022-08-27416.11
2022-09-03415.74
2022-09-10415.85
2022-09-17415.75
2022-09-24414.82
2022-10-01415.12
2022-10-08414.85
2022-10-15415.31
2022-10-22415.60
2022-10-29416.08
\n", "

3299 rows × 1 columns

\n", "
" ], "text/plain": [ " indicated by the date in the first column. \n", "1958-03-29 316.19 \n", "1958-04-05 317.31 \n", "1958-04-12 317.69 \n", "1958-04-19 317.58 \n", "1958-04-26 316.48 \n", "1958-05-03 316.95 \n", "1958-05-17 317.56 \n", "1958-05-24 317.99 \n", "1958-07-05 315.85 \n", "1958-07-12 315.85 \n", "1958-07-19 315.46 \n", "1958-07-26 315.59 \n", "1958-08-02 315.64 \n", "1958-08-09 315.10 \n", "1958-08-16 315.09 \n", "1958-08-30 314.14 \n", "1958-09-06 313.54 \n", "1958-11-08 313.05 \n", "1958-11-15 313.26 \n", "1958-11-22 313.57 \n", "1958-11-29 314.01 \n", "1958-12-06 314.56 \n", "1958-12-13 314.41 \n", "1958-12-20 314.77 \n", "1958-12-27 315.21 \n", "1959-01-03 315.24 \n", "1959-01-10 315.50 \n", "1959-01-17 315.69 \n", "1959-01-24 315.86 \n", "1959-01-31 315.42 \n", "... ... \n", "2022-04-09 419.38 \n", "2022-04-16 420.57 \n", "2022-04-23 420.11 \n", "2022-04-30 419.81 \n", "2022-05-07 419.64 \n", "2022-05-14 421.36 \n", "2022-05-21 420.55 \n", "2022-05-28 421.34 \n", "2022-06-04 421.18 \n", "2022-06-11 420.90 \n", "2022-06-18 420.45 \n", "2022-06-25 420.16 \n", "2022-07-02 419.89 \n", "2022-07-09 418.92 \n", "2022-07-16 418.47 \n", "2022-07-23 418.02 \n", "2022-07-30 417.64 \n", "2022-08-06 417.42 \n", "2022-08-13 416.84 \n", "2022-08-20 416.24 \n", "2022-08-27 416.11 \n", "2022-09-03 415.74 \n", "2022-09-10 415.85 \n", "2022-09-17 415.75 \n", "2022-09-24 414.82 \n", "2022-10-01 415.12 \n", "2022-10-08 414.85 \n", "2022-10-15 415.31 \n", "2022-10-22 415.60 \n", "2022-10-29 416.08 \n", "\n", "[3299 rows x 1 columns]" ] }, "execution_count": 151, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, encoding = 'iso-8859-1', skiprows=43)\n", "raw_data\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The date has been wrongly imported as the index of the dataframe. Let's also add a column for the corresponding day of the year." ] }, { "cell_type": "code", "execution_count": 169, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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indicated by the date in the first column.date
1958-03-29316.191958-03-29
1958-04-05317.311958-04-05
1958-04-12317.691958-04-12
1958-04-19317.581958-04-19
1958-04-26316.481958-04-26
1958-05-03316.951958-05-03
1958-05-17317.561958-05-17
1958-05-24317.991958-05-24
1958-07-05315.851958-07-05
1958-07-12315.851958-07-12
1958-07-19315.461958-07-19
1958-07-26315.591958-07-26
1958-08-02315.641958-08-02
1958-08-09315.101958-08-09
1958-08-16315.091958-08-16
1958-08-30314.141958-08-30
1958-09-06313.541958-09-06
1958-11-08313.051958-11-08
1958-11-15313.261958-11-15
1958-11-22313.571958-11-22
1958-11-29314.011958-11-29
1958-12-06314.561958-12-06
1958-12-13314.411958-12-13
1958-12-20314.771958-12-20
1958-12-27315.211958-12-27
1959-01-03315.241959-01-03
1959-01-10315.501959-01-10
1959-01-17315.691959-01-17
1959-01-24315.861959-01-24
1959-01-31315.421959-01-31
.........
2022-04-09419.382022-04-09
2022-04-16420.572022-04-16
2022-04-23420.112022-04-23
2022-04-30419.812022-04-30
2022-05-07419.642022-05-07
2022-05-14421.362022-05-14
2022-05-21420.552022-05-21
2022-05-28421.342022-05-28
2022-06-04421.182022-06-04
2022-06-11420.902022-06-11
2022-06-18420.452022-06-18
2022-06-25420.162022-06-25
2022-07-02419.892022-07-02
2022-07-09418.922022-07-09
2022-07-16418.472022-07-16
2022-07-23418.022022-07-23
2022-07-30417.642022-07-30
2022-08-06417.422022-08-06
2022-08-13416.842022-08-13
2022-08-20416.242022-08-20
2022-08-27416.112022-08-27
2022-09-03415.742022-09-03
2022-09-10415.852022-09-10
2022-09-17415.752022-09-17
2022-09-24414.822022-09-24
2022-10-01415.122022-10-01
2022-10-08414.852022-10-08
2022-10-15415.312022-10-15
2022-10-22415.602022-10-22
2022-10-29416.082022-10-29
\n", "

3299 rows × 2 columns

\n", "
" ], "text/plain": [ " indicated by the date in the first column. \\\n", "1958-03-29 316.19 \n", "1958-04-05 317.31 \n", "1958-04-12 317.69 \n", "1958-04-19 317.58 \n", "1958-04-26 316.48 \n", "1958-05-03 316.95 \n", "1958-05-17 317.56 \n", "1958-05-24 317.99 \n", "1958-07-05 315.85 \n", "1958-07-12 315.85 \n", "1958-07-19 315.46 \n", "1958-07-26 315.59 \n", "1958-08-02 315.64 \n", "1958-08-09 315.10 \n", "1958-08-16 315.09 \n", "1958-08-30 314.14 \n", "1958-09-06 313.54 \n", "1958-11-08 313.05 \n", "1958-11-15 313.26 \n", "1958-11-22 313.57 \n", "1958-11-29 314.01 \n", "1958-12-06 314.56 \n", "1958-12-13 314.41 \n", "1958-12-20 314.77 \n", "1958-12-27 315.21 \n", "1959-01-03 315.24 \n", "1959-01-10 315.50 \n", "1959-01-17 315.69 \n", "1959-01-24 315.86 \n", "1959-01-31 315.42 \n", "... ... \n", "2022-04-09 419.38 \n", "2022-04-16 420.57 \n", "2022-04-23 420.11 \n", "2022-04-30 419.81 \n", "2022-05-07 419.64 \n", "2022-05-14 421.36 \n", "2022-05-21 420.55 \n", "2022-05-28 421.34 \n", "2022-06-04 421.18 \n", "2022-06-11 420.90 \n", "2022-06-18 420.45 \n", "2022-06-25 420.16 \n", "2022-07-02 419.89 \n", "2022-07-09 418.92 \n", "2022-07-16 418.47 \n", "2022-07-23 418.02 \n", "2022-07-30 417.64 \n", "2022-08-06 417.42 \n", "2022-08-13 416.84 \n", "2022-08-20 416.24 \n", "2022-08-27 416.11 \n", "2022-09-03 415.74 \n", "2022-09-10 415.85 \n", "2022-09-17 415.75 \n", "2022-09-24 414.82 \n", "2022-10-01 415.12 \n", "2022-10-08 414.85 \n", "2022-10-15 415.31 \n", "2022-10-22 415.60 \n", "2022-10-29 416.08 \n", "\n", " date \n", "1958-03-29 1958-03-29 \n", "1958-04-05 1958-04-05 \n", "1958-04-12 1958-04-12 \n", "1958-04-19 1958-04-19 \n", "1958-04-26 1958-04-26 \n", "1958-05-03 1958-05-03 \n", "1958-05-17 1958-05-17 \n", "1958-05-24 1958-05-24 \n", "1958-07-05 1958-07-05 \n", "1958-07-12 1958-07-12 \n", "1958-07-19 1958-07-19 \n", "1958-07-26 1958-07-26 \n", "1958-08-02 1958-08-02 \n", "1958-08-09 1958-08-09 \n", "1958-08-16 1958-08-16 \n", "1958-08-30 1958-08-30 \n", "1958-09-06 1958-09-06 \n", "1958-11-08 1958-11-08 \n", "1958-11-15 1958-11-15 \n", "1958-11-22 1958-11-22 \n", "1958-11-29 1958-11-29 \n", "1958-12-06 1958-12-06 \n", "1958-12-13 1958-12-13 \n", "1958-12-20 1958-12-20 \n", "1958-12-27 1958-12-27 \n", "1959-01-03 1959-01-03 \n", "1959-01-10 1959-01-10 \n", "1959-01-17 1959-01-17 \n", "1959-01-24 1959-01-24 \n", "1959-01-31 1959-01-31 \n", "... ... \n", "2022-04-09 2022-04-09 \n", "2022-04-16 2022-04-16 \n", "2022-04-23 2022-04-23 \n", "2022-04-30 2022-04-30 \n", "2022-05-07 2022-05-07 \n", "2022-05-14 2022-05-14 \n", "2022-05-21 2022-05-21 \n", "2022-05-28 2022-05-28 \n", "2022-06-04 2022-06-04 \n", "2022-06-11 2022-06-11 \n", "2022-06-18 2022-06-18 \n", "2022-06-25 2022-06-25 \n", "2022-07-02 2022-07-02 \n", "2022-07-09 2022-07-09 \n", "2022-07-16 2022-07-16 \n", "2022-07-23 2022-07-23 \n", "2022-07-30 2022-07-30 \n", "2022-08-06 2022-08-06 \n", "2022-08-13 2022-08-13 \n", "2022-08-20 2022-08-20 \n", "2022-08-27 2022-08-27 \n", "2022-09-03 2022-09-03 \n", "2022-09-10 2022-09-10 \n", "2022-09-17 2022-09-17 \n", "2022-09-24 2022-09-24 \n", "2022-10-01 2022-10-01 \n", "2022-10-08 2022-10-08 \n", "2022-10-15 2022-10-15 \n", "2022-10-22 2022-10-22 \n", "2022-10-29 2022-10-29 \n", "\n", "[3299 rows x 2 columns]" ] }, "execution_count": 169, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data['date']=raw_data.index\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Renommons la colonnes de ce jeu de données afin qu'elle coïncide avec la description donnée en entête. La donnée intéressante que nous étudierons est contenue dans la seule colonne qui donne la concentration en $CO_2$ à Mauna Loa en micro-mole de $CO_2$ par mole (ppm). L'index de notre jeu de données est la date. De plus les relevés démarrent le 29 mars 1958, et comme les données sont mises à jour régulièrement, j'utiliserai la version des données disponible sur le site le 01/12/2022 afin d'assurer la reproductibilité de mes analyses. Mon analyse portera donc sur les données du 29 mars 1958 au 29 Octobre 2022." ] }, { "cell_type": "code", "execution_count": 170, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[ppm]day
1958-03-29316.191958-03-29
1958-04-05317.311958-04-05
1958-04-12317.691958-04-12
1958-04-19317.581958-04-19
1958-04-26316.481958-04-26
1958-05-03316.951958-05-03
1958-05-17317.561958-05-17
1958-05-24317.991958-05-24
1958-07-05315.851958-07-05
1958-07-12315.851958-07-12
1958-07-19315.461958-07-19
1958-07-26315.591958-07-26
1958-08-02315.641958-08-02
1958-08-09315.101958-08-09
1958-08-16315.091958-08-16
1958-08-30314.141958-08-30
1958-09-06313.541958-09-06
1958-11-08313.051958-11-08
1958-11-15313.261958-11-15
1958-11-22313.571958-11-22
1958-11-29314.011958-11-29
1958-12-06314.561958-12-06
1958-12-13314.411958-12-13
1958-12-20314.771958-12-20
1958-12-27315.211958-12-27
1959-01-03315.241959-01-03
1959-01-10315.501959-01-10
1959-01-17315.691959-01-17
1959-01-24315.861959-01-24
1959-01-31315.421959-01-31
.........
2022-04-02419.912022-04-02
2022-04-09419.382022-04-09
2022-04-16420.572022-04-16
2022-04-23420.112022-04-23
2022-04-30419.812022-04-30
2022-05-07419.642022-05-07
2022-05-14421.362022-05-14
2022-05-21420.552022-05-21
2022-05-28421.342022-05-28
2022-06-04421.182022-06-04
2022-06-11420.902022-06-11
2022-06-18420.452022-06-18
2022-06-25420.162022-06-25
2022-07-02419.892022-07-02
2022-07-09418.922022-07-09
2022-07-16418.472022-07-16
2022-07-23418.022022-07-23
2022-07-30417.642022-07-30
2022-08-06417.422022-08-06
2022-08-13416.842022-08-13
2022-08-20416.242022-08-20
2022-08-27416.112022-08-27
2022-09-03415.742022-09-03
2022-09-10415.852022-09-10
2022-09-17415.752022-09-17
2022-09-24414.822022-09-24
2022-10-01415.122022-10-01
2022-10-08414.852022-10-08
2022-10-15415.312022-10-15
2022-10-22415.602022-10-22
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3298 rows × 2 columns

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" ], "text/plain": [ " [ppm] day\n", "1958-03-29 316.19 1958-03-29\n", "1958-04-05 317.31 1958-04-05\n", "1958-04-12 317.69 1958-04-12\n", "1958-04-19 317.58 1958-04-19\n", "1958-04-26 316.48 1958-04-26\n", "1958-05-03 316.95 1958-05-03\n", "1958-05-17 317.56 1958-05-17\n", "1958-05-24 317.99 1958-05-24\n", "1958-07-05 315.85 1958-07-05\n", "1958-07-12 315.85 1958-07-12\n", "1958-07-19 315.46 1958-07-19\n", "1958-07-26 315.59 1958-07-26\n", "1958-08-02 315.64 1958-08-02\n", "1958-08-09 315.10 1958-08-09\n", "1958-08-16 315.09 1958-08-16\n", "1958-08-30 314.14 1958-08-30\n", "1958-09-06 313.54 1958-09-06\n", "1958-11-08 313.05 1958-11-08\n", "1958-11-15 313.26 1958-11-15\n", "1958-11-22 313.57 1958-11-22\n", "1958-11-29 314.01 1958-11-29\n", "1958-12-06 314.56 1958-12-06\n", "1958-12-13 314.41 1958-12-13\n", "1958-12-20 314.77 1958-12-20\n", "1958-12-27 315.21 1958-12-27\n", "1959-01-03 315.24 1959-01-03\n", "1959-01-10 315.50 1959-01-10\n", "1959-01-17 315.69 1959-01-17\n", "1959-01-24 315.86 1959-01-24\n", "1959-01-31 315.42 1959-01-31\n", "... ... ...\n", "2022-04-02 419.91 2022-04-02\n", "2022-04-09 419.38 2022-04-09\n", "2022-04-16 420.57 2022-04-16\n", "2022-04-23 420.11 2022-04-23\n", "2022-04-30 419.81 2022-04-30\n", "2022-05-07 419.64 2022-05-07\n", "2022-05-14 421.36 2022-05-14\n", "2022-05-21 420.55 2022-05-21\n", "2022-05-28 421.34 2022-05-28\n", "2022-06-04 421.18 2022-06-04\n", "2022-06-11 420.90 2022-06-11\n", "2022-06-18 420.45 2022-06-18\n", "2022-06-25 420.16 2022-06-25\n", "2022-07-02 419.89 2022-07-02\n", "2022-07-09 418.92 2022-07-09\n", "2022-07-16 418.47 2022-07-16\n", "2022-07-23 418.02 2022-07-23\n", "2022-07-30 417.64 2022-07-30\n", "2022-08-06 417.42 2022-08-06\n", "2022-08-13 416.84 2022-08-13\n", "2022-08-20 416.24 2022-08-20\n", "2022-08-27 416.11 2022-08-27\n", "2022-09-03 415.74 2022-09-03\n", "2022-09-10 415.85 2022-09-10\n", "2022-09-17 415.75 2022-09-17\n", "2022-09-24 414.82 2022-09-24\n", "2022-10-01 415.12 2022-10-01\n", "2022-10-08 414.85 2022-10-08\n", "2022-10-15 415.31 2022-10-15\n", "2022-10-22 415.60 2022-10-22\n", "\n", "[3298 rows x 2 columns]" ] }, "execution_count": 170, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = raw_data.dropna().copy()\n", "data.columns=['[ppm]','day']\n", "data=data[0:3298]\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Y a-t-il des points manquants dans ce jeux de données ?" ] }, { "cell_type": "code", "execution_count": 171, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
[ppm]day
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [[ppm], day]\n", "Index: []" ] }, "execution_count": 171, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'existe pas de points manquants.\n", "\n", "Afin de pouvoir traiter correctement les dates, il est nécessaire de convertir chaque chaîne de caractère en un format datetime compréhensible par pandas." ] }, { "cell_type": "code", "execution_count": 172, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[ppm]day
1958-03-29316.1988
1958-04-05317.3195
1958-04-12317.69102
1958-04-19317.58109
1958-04-26316.48116
1958-05-03316.95123
1958-05-17317.56137
1958-05-24317.99144
1958-07-05315.85186
1958-07-12315.85193
1958-07-19315.46200
1958-07-26315.59207
1958-08-02315.64214
1958-08-09315.10221
1958-08-16315.09228
1958-08-30314.14242
1958-09-06313.54249
1958-11-08313.05312
1958-11-15313.26319
1958-11-22313.57326
1958-11-29314.01333
1958-12-06314.56340
1958-12-13314.41347
1958-12-20314.77354
1958-12-27315.21361
1959-01-03315.243
1959-01-10315.5010
1959-01-17315.6917
1959-01-24315.8624
1959-01-31315.4231
.........
2022-04-02419.9192
2022-04-09419.3899
2022-04-16420.57106
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2022-05-07419.64127
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2022-06-11420.90162
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2022-07-09418.92190
2022-07-16418.47197
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2022-09-24414.82267
2022-10-01415.12274
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3298 rows × 2 columns

\n", "
" ], "text/plain": [ " [ppm] day\n", "1958-03-29 316.19 88\n", "1958-04-05 317.31 95\n", "1958-04-12 317.69 102\n", "1958-04-19 317.58 109\n", "1958-04-26 316.48 116\n", "1958-05-03 316.95 123\n", "1958-05-17 317.56 137\n", "1958-05-24 317.99 144\n", "1958-07-05 315.85 186\n", "1958-07-12 315.85 193\n", "1958-07-19 315.46 200\n", "1958-07-26 315.59 207\n", "1958-08-02 315.64 214\n", "1958-08-09 315.10 221\n", "1958-08-16 315.09 228\n", "1958-08-30 314.14 242\n", "1958-09-06 313.54 249\n", "1958-11-08 313.05 312\n", "1958-11-15 313.26 319\n", "1958-11-22 313.57 326\n", "1958-11-29 314.01 333\n", "1958-12-06 314.56 340\n", "1958-12-13 314.41 347\n", "1958-12-20 314.77 354\n", "1958-12-27 315.21 361\n", "1959-01-03 315.24 3\n", "1959-01-10 315.50 10\n", "1959-01-17 315.69 17\n", "1959-01-24 315.86 24\n", "1959-01-31 315.42 31\n", "... ... ...\n", "2022-04-02 419.91 92\n", "2022-04-09 419.38 99\n", "2022-04-16 420.57 106\n", "2022-04-23 420.11 113\n", "2022-04-30 419.81 120\n", "2022-05-07 419.64 127\n", "2022-05-14 421.36 134\n", "2022-05-21 420.55 141\n", "2022-05-28 421.34 148\n", "2022-06-04 421.18 155\n", "2022-06-11 420.90 162\n", "2022-06-18 420.45 169\n", "2022-06-25 420.16 176\n", "2022-07-02 419.89 183\n", "2022-07-09 418.92 190\n", "2022-07-16 418.47 197\n", "2022-07-23 418.02 204\n", "2022-07-30 417.64 211\n", "2022-08-06 417.42 218\n", "2022-08-13 416.84 225\n", "2022-08-20 416.24 232\n", "2022-08-27 416.11 239\n", "2022-09-03 415.74 246\n", "2022-09-10 415.85 253\n", "2022-09-17 415.75 260\n", "2022-09-24 414.82 267\n", "2022-10-01 415.12 274\n", "2022-10-08 414.85 281\n", "2022-10-15 415.31 288\n", "2022-10-22 415.60 295\n", "\n", "[3298 rows x 2 columns]" ] }, "execution_count": 172, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data['day'] = [pd.to_datetime(date_str) for date_str in data['day']]\n", "data.index = [pd.to_datetime(date_str) for date_str in data.index]\n", "data['day']=data['day'].dt.dayofyear\n", "data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous vérifions la cohérence des données. Entre deux mesures, la différence temporelle doit être d'une semaine. Je décide de garder deux listes pour les années complètes et incomplètes en terme de mesure hebdomadaire. L'année 2022 doit être ajoutée manuellement car incomplète encore au temps de l'analyse." ] }, { "cell_type": "code", "execution_count": 356, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "1958-05-03 00:00:00 1958-05-17 00:00:00 14 days 00:00:00\n", "1958-05-24 00:00:00 1958-07-05 00:00:00 42 days 00:00:00\n", "1958-08-16 00:00:00 1958-08-30 00:00:00 14 days 00:00:00\n", "1958-09-06 00:00:00 1958-11-08 00:00:00 63 days 00:00:00\n", "1959-01-31 00:00:00 1959-02-14 00:00:00 14 days 00:00:00\n", "1959-03-07 00:00:00 1959-03-21 00:00:00 14 days 00:00:00\n", "1959-05-23 00:00:00 1959-06-06 00:00:00 14 days 00:00:00\n", "1959-08-08 00:00:00 1959-08-22 00:00:00 14 days 00:00:00\n", "1962-08-18 00:00:00 1962-09-15 00:00:00 28 days 00:00:00\n", "1962-12-22 00:00:00 1963-01-05 00:00:00 14 days 00:00:00\n", "1963-02-09 00:00:00 1963-02-23 00:00:00 14 days 00:00:00\n", "1963-04-27 00:00:00 1963-05-11 00:00:00 14 days 00:00:00\n", "1963-11-16 00:00:00 1963-11-30 00:00:00 14 days 00:00:00\n", "1964-01-18 00:00:00 1964-05-30 00:00:00 133 days 00:00:00\n", "1964-06-06 00:00:00 1964-06-27 00:00:00 21 days 00:00:00\n", "1964-08-01 00:00:00 1964-08-15 00:00:00 14 days 00:00:00\n", "1966-07-09 00:00:00 1966-08-06 00:00:00 28 days 00:00:00\n", "1966-10-29 00:00:00 1966-11-12 00:00:00 14 days 00:00:00\n", "1967-01-14 00:00:00 1967-02-04 00:00:00 21 days 00:00:00\n", "1976-06-19 00:00:00 1976-07-03 00:00:00 14 days 00:00:00\n", "1984-03-24 00:00:00 1984-04-28 00:00:00 35 days 00:00:00\n", "1985-07-27 00:00:00 1985-08-10 00:00:00 14 days 00:00:00\n", "2003-06-07 00:00:00 2003-06-21 00:00:00 14 days 00:00:00\n", "2003-10-04 00:00:00 2003-10-25 00:00:00 21 days 00:00:00\n", "2005-02-19 00:00:00 2005-03-26 00:00:00 35 days 00:00:00\n", "2006-02-04 00:00:00 2006-02-25 00:00:00 21 days 00:00:00\n", "2007-01-20 00:00:00 2007-02-03 00:00:00 14 days 00:00:00\n", "2012-09-29 00:00:00 2012-10-20 00:00:00 21 days 00:00:00\n", "2020-01-11 00:00:00 2020-01-25 00:00:00 14 days 00:00:00\n", "[1958, 1959, 1962, 1963, 1964, 1966, 1967, 1976, 1984, 1985, 2003, 2005, 2006, 2007, 2012, 2020, 2022]\n", "[1960, 1961, 1965, 1968, 1969, 1970, 1971, 1972, 1973, 1974, 1975, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2004, 2008, 2009, 2010, 2011, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2021]\n" ] } ], "source": [ "date = data.index\n", "incomplete_years=[]\n", "years=np.arange(1958,2022)\n", "for p1, p2 in zip(date[:-1], date[1:]):\n", " delta = p2 - p1\n", " if delta > pd.Timedelta(1,'W'):\n", " print(p1, p2, delta)\n", " if p1.year==p2.year:\n", " if p1.year not in incomplete_years:\n", " incomplete_years.append(p1.year)\n", " else:\n", " if p1.year not in incomplete_years:\n", " incomplete_years.append(p1.year)\n", " if p2.year not in incomplete_years:\n", " incomplete_years.append(p2.year)\n", "incomplete_years.append(2022)\n", "complete_years = [y for y in years if y not in incomplete_years]\n", "print(incomplete_years)\n", "print(complete_years)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous remarquons que beaucoup de semaines (voir mois pour l'année 1964) n'ont pas été mesurées mais cette quantité reste cependant faible devant la plage temporelle et la grandeur du jeu de donnée considéré (3299 mesures).\n", "\n", "Regardons l'évolution hebdomadaire de la concentration de $CO_2$." ] }, { "cell_type": "code", "execution_count": 174, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = date\n", "ppm = data['[ppm]']\n", "fig, ax = plt.subplots()\n", "ax.plot(t, ppm)\n", "\n", "ax.set(xlabel='années', ylabel='CO2 (ppm)',\n", " title='Evolution de la concentration en CO2 à Mauna Loa')\n", "ax.grid()\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Premièrement, on peut constater que les données manquantes influent en effet peu sur la visualisation globale de l'évolution \n", "de la concentration en $CO_2$ à Mauna Loa. De plus, on observe une nette augmentation qui peut se découper en deux phénomènes : une oscillation périodique superposée à une évolution systématique plus lente." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'oscillation périodique" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'oscillation périodique de faible amplitude semble a priori découler de phénomènes saisonnier identiques d'une année à l'autre. Observons quelques années spécifiques pour s'en rendre compte. Nous choisissons les années 1965 et 1999 où les données hebdomadaires sont complètes." ] }, { "cell_type": "code", "execution_count": 175, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ppm_1965 = data.loc['1965-01-01':'1965-12-31']\n", "ppm_1965\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(ppm_1965.index, ppm_1965['[ppm]'])\n", "\n", "ax.set(xlabel='semaines', ylabel='CO2 (ppm)',\n", " title='Evolution de la concentration en CO2 à Mauna Loa en 1965')\n", "ax.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 176, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "ppm_1999 = data.loc['1999-01-01':'1999-12-31']\n", "ppm_1999\n", "\n", "fig, ax = plt.subplots()\n", "ax.plot(ppm_1999.index, ppm_1999['[ppm]'])\n", "\n", "ax.set(xlabel='semaines', ylabel='CO2 (ppm)',\n", " title='Evolution de la concentration en CO2 à Mauna Loa en 1999')\n", "ax.grid()\n", "\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On constate bien pour ces deux années une tendance similaire qui se dégage. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de n'analyser que la composante saisonnière, je crée un jeu de données en soustrayant à chaque mesure \n", "la concentration moyenne annuelle. Les années incomplètes sont d'abord ignorées dans un nouveau jeu de données." ] }, { "cell_type": "code", "execution_count": 295, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[ppm]day
1960-01-02315.722
1960-01-09316.409
1960-01-16316.7316
1960-01-23316.5723
1960-01-30316.6830
1960-02-06316.6137
1960-02-13316.9844
1960-02-20317.4251
1960-02-27317.0058
1960-03-05316.9665
1960-03-12317.7672
1960-03-19318.0779
1960-03-26317.7786
1960-04-02318.6193
1960-04-09319.34100
1960-04-16319.01107
1960-04-23319.03114
1960-04-30319.77121
1960-05-07319.96128
1960-05-14319.82135
1960-05-21320.04142
1960-05-28320.06149
1960-06-04319.46156
1960-06-11320.08163
1960-06-18319.42170
1960-06-25319.09177
1960-07-02318.18184
1960-07-09318.64191
1960-07-16318.41198
1960-07-23317.91205
.........
2021-06-05419.46156
2021-06-12418.90163
2021-06-19418.49170
2021-06-26417.82177
2021-07-03417.70184
2021-07-10417.08191
2021-07-17416.91198
2021-07-24415.92205
2021-07-31414.94212
2021-08-07414.56219
2021-08-14414.66226
2021-08-21414.42233
2021-08-28412.68240
2021-09-04412.58247
2021-09-11413.15254
2021-09-18413.09261
2021-09-25413.05268
2021-10-02413.05275
2021-10-09413.56282
2021-10-16413.97289
2021-10-23413.97296
2021-10-30413.64303
2021-11-06414.32310
2021-11-13414.77317
2021-11-20414.91324
2021-11-27415.59331
2021-12-04415.77338
2021-12-11415.91345
2021-12-18416.58352
2021-12-25417.36359
\n", "

2505 rows × 2 columns

\n", "
" ], "text/plain": [ " [ppm] day\n", "1960-01-02 315.72 2\n", "1960-01-09 316.40 9\n", "1960-01-16 316.73 16\n", "1960-01-23 316.57 23\n", "1960-01-30 316.68 30\n", "1960-02-06 316.61 37\n", "1960-02-13 316.98 44\n", "1960-02-20 317.42 51\n", "1960-02-27 317.00 58\n", "1960-03-05 316.96 65\n", "1960-03-12 317.76 72\n", "1960-03-19 318.07 79\n", "1960-03-26 317.77 86\n", "1960-04-02 318.61 93\n", "1960-04-09 319.34 100\n", "1960-04-16 319.01 107\n", "1960-04-23 319.03 114\n", "1960-04-30 319.77 121\n", "1960-05-07 319.96 128\n", "1960-05-14 319.82 135\n", "1960-05-21 320.04 142\n", "1960-05-28 320.06 149\n", "1960-06-04 319.46 156\n", "1960-06-11 320.08 163\n", "1960-06-18 319.42 170\n", "1960-06-25 319.09 177\n", "1960-07-02 318.18 184\n", "1960-07-09 318.64 191\n", "1960-07-16 318.41 198\n", "1960-07-23 317.91 205\n", "... ... ...\n", "2021-06-05 419.46 156\n", "2021-06-12 418.90 163\n", "2021-06-19 418.49 170\n", "2021-06-26 417.82 177\n", "2021-07-03 417.70 184\n", "2021-07-10 417.08 191\n", "2021-07-17 416.91 198\n", "2021-07-24 415.92 205\n", "2021-07-31 414.94 212\n", "2021-08-07 414.56 219\n", "2021-08-14 414.66 226\n", "2021-08-21 414.42 233\n", "2021-08-28 412.68 240\n", "2021-09-04 412.58 247\n", "2021-09-11 413.15 254\n", "2021-09-18 413.09 261\n", "2021-09-25 413.05 268\n", "2021-10-02 413.05 275\n", "2021-10-09 413.56 282\n", "2021-10-16 413.97 289\n", "2021-10-23 413.97 296\n", "2021-10-30 413.64 303\n", "2021-11-06 414.32 310\n", "2021-11-13 414.77 317\n", "2021-11-20 414.91 324\n", "2021-11-27 415.59 331\n", "2021-12-04 415.77 338\n", "2021-12-11 415.91 345\n", "2021-12-18 416.58 352\n", "2021-12-25 417.36 359\n", "\n", "[2505 rows x 2 columns]" ] }, "execution_count": 295, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seasonal_data=data.dropna().copy()\n", "seasonal_data=seasonal_data.drop(seasonal_data[seasonal_data.index.year.isin(incomplete_years)].index)\n", "seasonal_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Puis j'enlève la moyenne annuelle à chaque concentration." ] }, { "cell_type": "code", "execution_count": 314, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[ppm]day
1960-01-02-1.1788682
1960-01-09-0.4988689
1960-01-16-0.16886816
1960-01-23-0.32886823
1960-01-30-0.21886830
1960-02-06-0.28886837
1960-02-130.08113244
1960-02-200.52113251
1960-02-270.10113258
1960-03-050.06113265
1960-03-120.86113272
1960-03-191.17113279
1960-03-260.87113286
1960-04-021.71113293
1960-04-092.441132100
1960-04-162.111132107
1960-04-232.131132114
1960-04-302.871132121
1960-05-073.061132128
1960-05-142.921132135
1960-05-213.141132142
1960-05-283.161132149
1960-06-042.561132156
1960-06-113.181132163
1960-06-182.521132170
1960-06-252.191132177
1960-07-021.281132184
1960-07-091.741132191
1960-07-161.511132198
1960-07-231.011132205
.........
2021-06-053.374231156
2021-06-122.814231163
2021-06-192.404231170
2021-06-261.734231177
2021-07-031.614231184
2021-07-100.994231191
2021-07-170.824231198
2021-07-24-0.165769205
2021-07-31-1.145769212
2021-08-07-1.525769219
2021-08-14-1.425769226
2021-08-21-1.665769233
2021-08-28-3.405769240
2021-09-04-3.505769247
2021-09-11-2.935769254
2021-09-18-2.995769261
2021-09-25-3.035769268
2021-10-02-3.035769275
2021-10-09-2.525769282
2021-10-16-2.115769289
2021-10-23-2.115769296
2021-10-30-2.445769303
2021-11-06-1.765769310
2021-11-13-1.315769317
2021-11-20-1.175769324
2021-11-27-0.495769331
2021-12-04-0.315769338
2021-12-11-0.175769345
2021-12-180.494231352
2021-12-251.274231359
\n", "

2505 rows × 2 columns

\n", "
" ], "text/plain": [ " [ppm] day\n", "1960-01-02 -1.178868 2\n", "1960-01-09 -0.498868 9\n", "1960-01-16 -0.168868 16\n", "1960-01-23 -0.328868 23\n", "1960-01-30 -0.218868 30\n", "1960-02-06 -0.288868 37\n", "1960-02-13 0.081132 44\n", "1960-02-20 0.521132 51\n", "1960-02-27 0.101132 58\n", "1960-03-05 0.061132 65\n", "1960-03-12 0.861132 72\n", "1960-03-19 1.171132 79\n", "1960-03-26 0.871132 86\n", "1960-04-02 1.711132 93\n", "1960-04-09 2.441132 100\n", "1960-04-16 2.111132 107\n", "1960-04-23 2.131132 114\n", "1960-04-30 2.871132 121\n", "1960-05-07 3.061132 128\n", "1960-05-14 2.921132 135\n", "1960-05-21 3.141132 142\n", "1960-05-28 3.161132 149\n", "1960-06-04 2.561132 156\n", "1960-06-11 3.181132 163\n", "1960-06-18 2.521132 170\n", "1960-06-25 2.191132 177\n", "1960-07-02 1.281132 184\n", "1960-07-09 1.741132 191\n", "1960-07-16 1.511132 198\n", "1960-07-23 1.011132 205\n", "... ... ...\n", "2021-06-05 3.374231 156\n", "2021-06-12 2.814231 163\n", "2021-06-19 2.404231 170\n", "2021-06-26 1.734231 177\n", "2021-07-03 1.614231 184\n", "2021-07-10 0.994231 191\n", "2021-07-17 0.824231 198\n", "2021-07-24 -0.165769 205\n", "2021-07-31 -1.145769 212\n", "2021-08-07 -1.525769 219\n", "2021-08-14 -1.425769 226\n", "2021-08-21 -1.665769 233\n", "2021-08-28 -3.405769 240\n", "2021-09-04 -3.505769 247\n", "2021-09-11 -2.935769 254\n", "2021-09-18 -2.995769 261\n", "2021-09-25 -3.035769 268\n", "2021-10-02 -3.035769 275\n", "2021-10-09 -2.525769 282\n", "2021-10-16 -2.115769 289\n", "2021-10-23 -2.115769 296\n", "2021-10-30 -2.445769 303\n", "2021-11-06 -1.765769 310\n", "2021-11-13 -1.315769 317\n", "2021-11-20 -1.175769 324\n", "2021-11-27 -0.495769 331\n", "2021-12-04 -0.315769 338\n", "2021-12-11 -0.175769 345\n", "2021-12-18 0.494231 352\n", "2021-12-25 1.274231 359\n", "\n", "[2505 rows x 2 columns]" ] }, "execution_count": 314, "metadata": {}, "output_type": "execute_result" } ], "source": [ "seasonal_data['[ppm]']=seasonal_data.loc[:,seasonal_data.columns == '[ppm]'].sub(seasonal_data.loc[:,seasonal_data.columns == '[ppm]'].groupby(seasonal_data.index.year).transform('mean'))\n", "seasonal_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Basé sur le modèle proposé par [Keeling et al.](https://www.rescuethatfrog.com/wp-content/uploads/2017/03/Keeling-et-al-1976-no2.pdf) en 1976, la variation saisonnière proposé est la suivante (on approximera les années bisextiles à 365 jours) : \n", "\n", "$[ppm](t)=a.\\sin(\\frac{2\\pi t}{365})+b.\\cos(\\frac{2\\pi t}{365})+c.\\sin(\\frac{4\\pi t}{365})+d.\\cos(\\frac{4\\pi t}{365})$\n", "\n", "avec $t$ le jour de l'année et $(a,b,c,d)$ le tuple de coefficient inconnus en $ppm$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Observons la variation saisonnière uniquement pour les deux années considérées 1965 et 1999 et essayons de l'approximer au modèle en utilisant un estimateur des moindres carrés (via la fonction `curve_fit` de `scipy.optimize`)." ] }, { "cell_type": "code", "execution_count": 329, "metadata": {}, "outputs": [], "source": [ "ppm_1965_seasonal = seasonal_data.loc['1965-01-01':'1965-12-31']\n", "ppm_1999_seasonal = seasonal_data.loc['1999-01-01':'1999-12-31']" ] }, { "cell_type": "code", "execution_count": 324, "metadata": {}, "outputs": [], "source": [ "def func(t,a,b,c,d):\n", " return a*np.sin(2*np.pi*t/365.0)+b*np.cos(2*np.pi*t/365.0)+c*np.sin(4*np.pi*t/365.0)+d*np.cos(4*np.pi*t/365.0)" ] }, { "cell_type": "code", "execution_count": 330, "metadata": {}, "outputs": [], "source": [ "popt_1965, pcov_1965 = curve_fit(func, ppm_1965_seasonal['day'], ppm_1965_seasonal['[ppm]'])\n", "popt_1999, pcov_1999 = curve_fit(func, ppm_1999_seasonal['day'], ppm_1999_seasonal['[ppm]'])" ] }, { "cell_type": "code", "execution_count": 345, "metadata": {}, "outputs": [ { "data": { "image/png": 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VSimlUpYmOaWUUilLk5xSSqmUlZL35JRSqqTy8/NZvXo1O3bs8DqUkGrWrMmSJUtKtY0qVarQoEED0tPTXYoqeWmSU0qpIKtXr6ZGjRo0btwYZ1zhpLJ161Zq1CjJ+NmWMYaNGzeyevVqmjRp4mJkyUmrK5VSKsiOHTuoVatWUiY4N4gItWrVStqSqtu0JKeS2rSFOUycuZQ1uXnUz8xgeLfm9Gzj9ry7ShWVqgkuINXfXzBNcippTVuYw6ipP5GXXwBATm4eo6b+BKCJTikVE01yKmlNnLm0MMEF5OUXMHHm0tiSXH4+/Por/Pjj3gdAkyb20bTp3r9r1kzAO1CqZB599FGeeuop1q5dy80338zIkSOZNm0ahx12GA0bNvQ6PF/RJKeS1prcvLiWA/DHH/DoozB7Nvz8M+zaZZenp8Phh0PFijB3LmzeXPR1bdtSp3t3OPFE+xylPPTkk0/ywQcfFGkYMm3aNM4++2xNcnHShicqadXPzIh9+cKFcNFFcMghNsnVqQPDhsErr9gS3LZt9t/vvoPcXNi0CRYsgDffhHHjYOtWWtx1FzRvDk8+Cdu3J/jdKRXaoEGDWLFiBT169OChhx5iyJAhfPnll7z33nsMHz6cjh078ttvv3kdpm/oT1aVtIZ3a17knhxARnoaw7s1t/8xBj7+GCZOhFmzoHp1GDrUJrdov3b3398+jjnG/n/ECBaNG8dRM2bAtdfCHXfA9dfDdddBZmaC3qFKesOGwfffu7vN1q3h4YfDrn766af58MMPmTNnDtOnTwegQ4cO9OjRg7PPPptu3bqVqgtBeaMlOZUQ0xbm0HHCbH7K2UzHCbOZtjAn7m30bJPF+F4tycrMQICszAzG92pp78f99ht07gzdusHixTBhAqxaBQ88ED3BhYizyegPmdusNdOefAs+/RTat4fbb4c2beCHH+KOXSmVHLQkp1xXpFVkw9K1iuzZJqvoa4yBf//b/sKuWBGefhr69YPKlUsXJ7CrYA+j3lkEvVrSc/p0+PpruOACOOEEeP556NMn7n0on4tQ4lL+oCU55bpIrSJLZd06OPdcGDCADUe25rxBT9Pk9wZ0fGhuiUqKUeM8/nh7365tW+jbF4YPh927S/celCqhGjVqsHXrVq/D8B1Ncsp1kVpFFlYPjpwRXzXme+9By5bw0Uf8dOOdnHT6LSykBoa9JcV4E11MrTfr1oVPPoFrroH774ezzrKNVpQqY3369GHixImceOKJ2vAkDprklOvCtYqsmZHOqKk/kZObF3ty2r0bhgyxJbisLFiwgEG1O7F9tynytJKUFGNuvVmpEjzxBDz3nL1f166dvQ+oVIKsXLmS2rVr069fPx5//HEAOnbsyM8//8wXX3zBIYcc4nGE/qFJTrlueLfmZKSnFVmWkZ6GCPFVY/7zD/TqZRPMDTfAN99AixYl6z8XR5yFrTeLu+IK+Owz2LEDTj3V9slTSiU1TXLKdcGtImFvq8jc7fkhnx8yOW3YACefDNOnw+OPw4MP2hIVcfafizFOASqlVdjbejOc9u1tt4W8PDjzTPj777j2qZQqW5rkVEL0bJPF3JEn0zKrJnNHnkzPNlmxJ6fffoMOHWzn7alTbb+1IHGXwGKI8/cJ3Wl+UI3YWn+2aAHTptk4zzsPdu6Me79KqbLhiyQnIg1FZI6ILBGRxSIy1OuYVPxiSk7ffmub7G/aZBt89Oy5z3Yi9p8rK126wAsv2Ht0/frBnj1lt2+lVMz80k9uN3CjMeY7EakBLBCRj40xP3sdmIpdIAmFnTrngw9sv7S6de3fzcOXzPbpP+eFvn1tB/Sbb7Yd0O+7b5+n6FRBSnnLF0nOGPMn8Kfz91YRWQJkAZrkfCZscvrwQ1tqa9EC/vtfOOigsg+uJIYPtw1QJk6ERo2KVK1GmioIIiR7pZRrxBgT/VlJREQaA58BRxljtgQtHwgMBKhbt27bKVOmxL3tbdu2Ub16dXcCTbBkiTU3L591m3ewq2APldIqULdmFTIz0gvXxxLn/gsW0HLUKP5p1IgfHnyQ3aUYly9aPJGU+JgWFHDU7bdT66uvWDR2LBtPOAGApWu3sqtg32rMtAqCMbAn6LtXQYSs/TNiijVZPvtY+DHWmjVrcuihh3odTqFx48ZRvXp1rr/+egAKCgpIS9tb7T99+nQOPfRQDj/88Li2u3z5cjYXm42ja9euC4wx7UofdfLwRUkuQESqA28Dw4ITHIAx5lngWYB27dqZLl26xL397OxsSvI6LyRDrNMW5jDqk5/Iy69A4PZuRnoB43sdWVgqiRrnp5/CbbfB4YdTY/ZsTqxdO6HxRFKqY3rCCXDiibR84AFYtAgOOoj+I2dg4rjtnZWZxtyR0fefDJ99rPwY65IlS5JqAOTKlStTuXLlwpi2bt1aJL6ZM2eSnp7OscceG9d2q1SpQps2bVyNNRn5ouEJgIikYxPcZGPMVK/jUS4M3zV3LnTvDo0b21kESpHgXImnNKpWhcmT7ZQ+AwaAMXF3aYi3n59KDiUexSeCsWPH0rx5c0499VSWLrXn76RJkzj22GPp0KED559/Ptu3by8yBU/r1q357bffCp939NFHFz6vPPNFkhMRAZ4DlhhjHvQ6HmWVqlP2t9/afmZZWfDJJ0zLyS/1hcKtTuJQwgvXEUfY2RCmT4fnnw/bmnT/qqGrJOtnZiTkgqkSJ3DfNa5RfKJYsGABU6ZMYeHChUydOpV58+YB0KtXL+bNm8eXX37JEUccwXPPPVc4Bc/EiRP5/vvvOeSQQwqf98MPPxQ+rzzzRZIDOgKXAieLyPfO4yyvgyrvStwp+7vv4PTT4cADYfZspq3d48qFwq1O4qW6cF1/PXTtCsOG0bPmzpBdHe44p0XI5Nf18DquXzBVYiWi9uDzzz/nvPPOo2rVquy333706NEDgEWLFtGpUyeOP/54Jk+ezOIwQ8sFnteyZcuIzysvfHFPzhjzBSBex6GKijqpaSjLl8MZZ9iJSGfPhqwsJr48O+yFIp4WhyWKJ4RIF66o8VSoYPvPtWwJ/frRc86csK8p3rqyVPtVnnCz9iCYrbwqql+/fkybNo2mTZvy9ttvk52dHfK1gecdffTRvPDCC2GfV174pSSnklDcnbLXrbOTnBoDH30EBx8MuHehcKuTeElmUSiy/NXlLLhxDHz+OTz0UNhYAyOtBEaEiXW/S9du1dJdknCr9iBY586deeedd8jLy2Pr1q28//77gG1wUq9ePfLz85k8eXLh84tPwRPueeWVL0pyKnnF3Cl761Y7Tc3atTBnDhx2WOGq+pkZ5IS4wJfkQuFGJ/Fw8QRmUSje723+H5t4e0FOkeWXVGzOJ127UX/0aJvYW7Z0bb+7CvaUeBJa5S63ag+CHXPMMfTu3ZvWrVvTqFEjOnXqBMDdd99N+/btadCgAa1bty5MbH369GHAgAE8+uijvPXWW4XPa9SoES1btiz3c9BpklOJt2sXnH8+/PCDnRfuuOOKrE7EhaI0wsUTbhaF175ZRUGx/qZ5u/cwoMNAZixeCJdeahvaOANMu7Vfrcb0XtRRfEpo9OjRjB49ep/lgwcP3qcLQWAKnuDnDB48uFT7TyWa5FRi7dljp6j5+GP4z39saa6YRF0ogsUzvFa4eG54/fuQzy+e4AJ+3l0Znn3WjuQybhzceWfEGOPdr3Y5SA5JMcScCkuTnEqops8+C6+/bi/y/fqFfV4iLxSRhteKlOiKr5s4c2nI6sQ0kZCJrn5mBpx7MvTuDffea5O9cx8ynHj2W5r7PkqVF9rwRCXOI49w8Ouv25m9R470LAy3mnmH6/fWt33DyLMrBAZuHjEivsCj7Ner6tzywG/DHcYr1d9fME1yKjHeeQduuIENnTvDww9DiCbRZSXRrTfv6dkycqvOgw+2Ce71122LyziVaHJXVWJVqlRh48aNKZsIjDFs3LiRKlWqeB1KmdDqSuW+b76Biy+G9u1Zcsst1ElLi/6aBCqL1ptRq1tHjIDnn4ehQ2HePIjzmARvPzs7my6a4BKmQYMGrF69mg0bNngdSkg7duwodYKqUqUKDRo0cCmi5KZJTrlrxQo45xyoVw/ee489STDaQlK03qxWzVZbXnSR7Sx+5ZVlt28Vl/T0dJo0aeJ1GGFlZ2eXi4GV3aLVlco9mzbZ1pMFBXbS0zp1vI4ISJKZxAH69IEOHeCWW2DLlujPV0qVmpbklDt27oTzzoPff7czCgR19k4GSdHMWwQeeQSOPRbuuSfkTOJKKXdpSU6VnjG2efxnn9mqOGeEBhVCu3a2K8XDD8OyZV5Ho1TK0ySnYhJxCpjbboNXX7V94fr29S5Ivxg3DipXhptu8joSpVKeJjkVVcSpZ/79bxg7Fq66ytO+cL5Srx7ceqsd4uzjj72ORqmUpklORRWuM/Wnj70CgwbZAYiffNLTvnC+M2yYnRF99Ghb3auUSghNciqqUJ2mj1i/grsnj4GjjoI334T00LNdqzAqV7aluXnzYMYMr6NRKmVpklNRFe80fdCWv3j+zTH8k1HdXqCDRkRXcbjsMmjaFG6/XUtzSiWIJjkVVfDYidV3buc/b91JjV3bWfzMZMjSkTdKLD3dJriFC+Hdd72ORqmUpElORRXoTH1wjXSemjaeZhtX8cPDz3Fy79O8Ds3/Lr4YmjWDO+6w0xIppVylSU7FpGfr+ny2/DU6rVxIxX9PouNg7SrgiooVbYL78UeYOtXraJRKOZrkVGxuucV29L7zTujf3+toUkufPnD44fbYamlOKVdpklPRPfIITJgAV19t7yEpd6Wl2dLc4sW2papSyjWa5FRkU6bYPl29esETT2hfuES58EJo0cKW5goKoj5dKRUbTXIpLuJwXNHMmmWbuXfuDJMnxz0HmopDWppNcL/8Yn9YKKVc4YskJyLPi8h6EVnkdSx+EnE4rmgWLLCzChx+uG3eXk5mEfZUr17QqhWMGQO7d3sdjVIpwRdJDngBOMPrIPwm3HBcE2cujfzC5cvtvHC1asGHH0JmZgKjVIUqVLAJbtkyO+C1UqrUfJHkjDGfAZu8jsNvQg3HFVgethrzjz/glFPsfaGZM6F+/TKMWHHuubY0d++92tJSKRf4Ismpkik+HFdAzYz0kNWYH86cbxPc5s3w0UfQvHnZBqxsw54RI+Dnn3VMS6VcIMYnY+aJSGNgujHmqDDrBwIDAerWrdt2Sglu3m/bto3q1auXIsqyE0usuXn55Pydx56gz7iCCBUEdu8p+rlnbM7l/LG3UuPvjfx4//1sOfLIMoszWSRLrLJ7N+0vuYSddeqw8LHH9lmfLHHGQmN1XyLj7Nq16wJjTLuEbNwjFb0OwC3GmGeBZwHatWtnunTpEvc2srOzKcnrvFA81mkLc5g4cylrcvOon5nB8G7N6dklK+TyG17/nuAUl5m3hddeu5Mqf2+g4icfcUznzgmLM5klVay33EKVoUPpkp4OHTsWWZVUcUahsbrPL3Emi5RJcuVZoBVloJFJoPoR7LiTPdsUHUR54syl5Dj36/bbsY2XX7+NpptyGNFvLI+4mOBUKVx5Jdx1F9x3nw7erFQp+OKenIi8BnwFNBeR1SJypdcxJZN4W1EGZhWotnM7L7x5B803/MHQC26l67UXlUW4KhbVqsGQIXb28J9/9joapXzLFyU5Y4yOBhxBpFaUofRsk0X6llwaXXohh/+5jNsuvoMz/u/KfUp8ymNDhtiS3MSJ8J//eB2NUr7ki5KciixcK8pwy9mwge7DLuGotcup+NabjH/5dk1wyah2bbjqKjvazOrVXkejlC9pkksBwZOaBmSkpzG8W4guADk5dpiupUvh/fftKBsqef3f/9n+cg8/7HUkSvmSJrkUEJjUNCszAwGyMjMY36vlvqWzFSugUyeb6D78ELp18yReFYfGje1UPM88A3//7XU0SvmOL+7Jqb0CXQL6NNzK6AmzbVcBpwVlxCrHJUvg1FNhxw745BM49tiyC1qVzogRtsryqafsvH5KqZhpSc5HggdchjgGXF6wwFZRFhTAp59qgvObVq3gjDPsvH55oRsTKaVC0yTnIyUacHnaNJvgqlWDzz+Ho0IOGKOS3c03w/r18NJLXkdwHGDtAAAgAElEQVSilK9okvORuLoKGAMPPGAblhx1FHz9NTRrluAIVcKcdBK0a2cboOjAzUrFTJOcj8TcVSA/HwYPhptuggsugOxsOOigxAeoEkfEztD+yy8cMH++19Eo5Rua5Hwkpq4CmzdD9+62Nd6oUXaW6Yww/eWUv1x4IdSvT4M33/Q6EqV8Q5OcjwR3FYAQXQVWrIAOHWDOHHj+eRg3zk7EqVJDpUpw7bW2JLd4sdfRKOULegX0mZ5tspg78mRaZtVk7siT9ya4d96BY46BP/+0c8H171+i7YedTFUlh6uvpqByZe0crlSMNMn5XX4+3HijbWDSrBl89x107VqiTQV3UQieTFUTXRKpVYt1p58OL78MGzbojxKlotAk52erVtlWdw8+aAfz/eILO0JGCZWoi4Iqc6vPPx927uTnOyfqjxKlotAk51MHfPsttGkDP/1kG5c89hhUrlyqbcY7m4HyxvZGjeCMMzjw5ecoyNtRZJ3+KFGqKE1yfpOXBzfdRMuRI6F+fTuaSe/ermw67tkMlHduuIHaWzfR/ZfP91mlP0qU2kuTnJ/Mm2cblzzwAGvOOcd28D7sMNc2H9dsBspbp53G7wc24sr579qO/0H0R4lSe2mS84Ndu+C22+CEE2DbNpg5k2U33ABVq7q6m5hnM1DeEyF34DUcte432q9aVLhYf5QoVZTOQpDsfvwRLrsMfvgBLr/cNh3PzLSjmCRA1NkMVNJoc8t17Hx8Atf8MJ1vD25J/cyMwlkplFKWJrlklZcH48fDhAlwwAHw7rvQo4fXUalkkpFB5Wuv4aRx4/j9vcPhkEO8jkippKPVlclo5kxo2RLuvhv+9S9YtEgTnArt2muhYkV4/HGvI1EqKblekhOR/YH6QB6w0hijQ6ZHEJgEdU1uHq0q/MMT81+hwcfv2wYls2bBKad4HaJKZvXq2TEtn38e7roLatTwOiKlkoorJTkRqSkit4jIT8DXwDPAG8AfIvKmiJRsCI4UFxhhZO2mbVy24H1eeaAfdWZ/yJLBN/HeSx/QcZ7oSBYquqFDYcsWePFFryNRKum4VZJ7C3gJ6GSMyQ1eISJtgUtFpKkx5jmX9uc7wSW2QAOBiTOXcvRvC7lj1rMcsWElnzVuw22nD2Zz3UbsnP5r4egjgZEsAG1UoPZ13HHQvr0dEOCaa3RQbqWCuJLkjDGnRVi3AFjgxn78KlBiC05ajz0/i1s+nkT3pXNZvd+BDD53JB8072jnDcvL32cbgZEsNMmpkIYOhYsusvdzzzzT62iUShqJuCfXCmgcvG1jzFS39+MnwWNCZuzaweCv3+Tqb6dSUKEC93e6hEnHnsfO9OhDculIFiqs88+39+cefVSTnFJBXE1yIvI80ApYDAQanBig1ElORM4AHgHSgH8bYyaUdptlZU1uHmL20OPnTxmZ/QL1tm1k2pEnMeGk/myuVZedQYMiZ6SnUSW9An9v37c0pyNZqLAqVbKzwd9+OyxdCs21Q7hS4H5J7nhjzJEubxMRSQOeAE4DVgPzROQ9Y8zPbu8rEU7b8juDpz1Omz+X8uNBhzLk3JtZ0OBIO6KIc28u+F4dUKR6E3QkCxWDq6+Ge+6x9+a0S4FSgPtJ7isROTIByec4YLkxZgWAiEwBzgWSO8mtWgUjR/Lsq6+yvvoB3HjWDUw9qitGKhQmrUgjjBRPfno/TkV04IHQty+88AKMHQs1a3odkVKeE1NscNdSbUykM/A+sBbYCQhgjDGtSrndC4AzjDFXOf+/FGhvjBkS9JyBwECAunXrtp0yZUrc+9m2bRvVq1cvTagAVNixg4OnTKHhlCnInj2s6t2bH8+7kDW7hF0Fe6iUVoG6NauQmZFe4n24FWui+SVO8E+skeKs/uuvtLv6apZfcw2rL7ywjCPbl1+OKfgn1kTG2bVr1wXGmHYJ2bhXjDGuPYDlQA+gCdAo8HBhuxdi78MF/n8p8Fi457dt29aUxJw5c0r0ukJ79hjz1lvGHHywMWBM797GrFxZum2GUepYy4hf4jTGP7FGjfPEE41p2tSY3bvLJJ5I/HJMjfFPrImME5hvXMwJyfBwu0PN/4wx7xljfjfG/BF4uLDd1UDDoP83ANa4sF33LFkCp58OF1xgB1D+9FM7mWmjRl5Hpsqb66+HFSvgv//1OhKlPOd2kvtFRF4Vkb4i0ivwcGG784BmItJERCoBfYD3XNhu6W3ZAjfdBK1awfz59qb/ggXQubPXkanyqmdPaNAAHnnE60iU8pzbDU8ysPfiTg9aVuouBMaY3SIyBJiJ7ULwvDFmcWm2WWrGwNtvw3XXwbp1cOWVMG4c1KnjaVhKkZ5uB24eNQoWL4YWLbyOSCnPuJrkjDH93dxesW3/F0iO+pecHHsRefddO1P3u+/aoZUIPXyXtopUZW7AABgzxnYOf+YZr6NRyjOuVleKSFMReV9ENojIehF5V0SauLkPT+3ZYy8YRx4JH30EEyfCN98USXCjpv5ETm4ehr1jTurgyqrM1aoFl1wCL70EGzd6HY1SnnH7ntyr2NkH6mGn23kTiL8tfzJauhS6dIFBg6BdO/jpJ3svruLewnDw8F0BgTEnlSpzQ4fCjh0waZLXkSjlGbeTnBhjXjbG7HYer2DvyfmXMfYG/tFH28T2/PN2nrcQszCHG1tSx5xUnjjqKDsf4RNPQP6+w8Sp1DJtYQ4dJ8zW6bmKcTvJzRGRkSLSWEQaicgIYIaIHCAiB7i8r8Rbtw7OOguGDYPTTrPdBPr3tzMFhBBubEkdc1J5ZtgwWL0appbrMdJTnt4qCc/tJNcbuBqYA2QDg4ErsFPtzHd5X4k1Ywa0bAnZ2faX8HvvwUEHRXzJ8G7NyUhPK7JMx5xUnjrrLDj0UO1OkOL0Vkl4breu9H8jk7w8GDHCDnDbqhXMmRNzE+xAK0ptXamSRoUKtpvL0KHw7beFjaRUatFbJeG5PdVOFeAa4ETsvbjPgaeNMTvc3E+iZKxebS8IixbZi8KECVClSlzbiDTgslKe6N8fbrvNluYmT/Y6GpUA9TMzyAmR0PRWifvVlS8BLYDHgMeBI4GXXd5HwuyuXt22lvzgA3j44bgTnFJJqUYNuOIKeOMN28dTpRy9VRKe20muuTHmSmPMHOcxEDjM5X0kTH5mJnz3HZxxhtehKOWu666DggJ46imvI1EJ0LNNFuN7tSQrMwMBO1dlr5Zaq4T7w3otFJHjjTFfA4hIe2Cuy/tIrDAtJ5Xyk5Aj7/ToYQczGD0aMrQaK9XorZLQ3C7JtQe+FJGVIrIS+Ao4SUR+EpEfXd6XUiqEcM3JvzjrIvjrL8Zfcpv2pVLlhtslOa3nU8pj4ZqTD1lTkykHNqHn52/zzKFdCpMfoCWAVPHLL1CtGjRsGP255YQrJTkRqQ4QPIdc8QegA+gpVQbCNRvP3bGb59qewxEbVtLhjx8A7UuVUubNgxNPhMsu8zqSpOJWdeW7IvKAiHQWkWqBhc6AzVeKyEy0lKdUmYjUbPy9I7uwoWomA+a9U7hM+1Ilp3DDdOXm5e+7fNYs6NrVtqTVsUqLcCXJGWNOAT7BjnayWEQ2i8hG4BXgIOByY8xbbuxLKRVZuObk+1dNZ2fFSrzY9my6rljAYRtWAtqXKhmFu69667SfyPk7r8jyOWOfpOCs7tCkCcyda0e4UYVca3hijPmvMeZiY0xjY0xNY0wtY0wHY8xYY8xat/ajlIosXHPyO85pQUZ6Gq+0OYu8ipW5at407UuVpMLdV33tm1XsMXvHvL/o+w946O3xLK7fDD77DOrXL+tQk57bDU+UUkkgUnPyiTOX8marU+n7w0xq3H8vZ2qjk6QTrgq5IJDgjOHar95g+OcvM7tpO67tOZIl++9fhhH6hyY5pcqRwuR3YSNo1owzs9+C04/xOixfCtkX0aUfDOGG6UoTAWMYPec5BsybxtQWXRlx5lDq1qrhyn5Tkdv95JRSfnDIIdCrlx0BZds2r6PxnURPbRPuvmrf4xrQ6dUXGDBvGv9pew43dr+B9CqVtco5Ak1ySpVXw4dDbq6dCFjFJdFT24S8r3reUdzz9WRaf/Aub53Qk7tOGUj9/avp8F1RuFJdKSItgUlAFvABcLMx5m9n3bfGGF/P75HIagmlPNO+ve1X9dBDcM01dnByFZOSTG0T73WkyH1VY+DWW+G++8jp0YMLpk3lAh2CMCZundVPAXcCXwNXAV+ISA9jzG9Aukv78ESgWiLwqy14lAjQueOUz910E/TsCW+/Db17ex2Nb8Q7tU2k60hM14wxY2DcOBgwgGV9+pClCS5mblVXVjfGfGiMyTXG3A8MAT4UkeOx88r5VrhqiTvfW6zTzSv/O+ccaNYMJk60pQUVk3intilV9ebdd9sk178/PP20nQhXxcytkpyISE1jzGYAY8wcETkfeBs4wKV9JFxgJIHgklnYIZLy8vdZFjhptTSnfKNCBbjxRhg0yPazOukkryPyhcB3PNaanEjVmxGrMe+9F26/3Q7VNWmSJrgScCvJ3Qscga2uBMAY86OInALc5tI+EmrawhxnJAH76yxQMsusms7f2/dNaOHoEEnKdy67zN7vuf9+TXJxiGdqm3DVmzUz0sNXYy74AEaOhL59beOgtLR9Xq+ic2tYr1eD5pCrHhi/0hjzP2PMgNJsW0QuFJHFIrJHRNq5EW8oE2cuLTKSANiSmTGEHSIpFB0iSflORgYMGQLTp8PPP3sdTUoKV70pQshqzK8eeh6uvhq6dYMXXtAEVwqulX1FZLCI/A/4A1glIn+IyDUubHoR0Av4zIVthRWuBLY5Lz/iEEnBdIgk5VvXXgtVq8L48V5H4nuhBlYON9RabohaomNXLWLMa/dAu3bw1ltQqVLZv4kU4lYXgluBDkAXY8wKZ1lT4BEROcAYc09Jt22MWeJsz41Qw7IlsK0hl0cbIklbVyrfq10bBg+23QnuuEMH+S2haK0oi18fJs5cWqQas/mGlTz39t2s278ujWbMgOrVyy74FCXGhRZVIrIUONoYs6PY8gzgB2PMYS7sIxu4yRgzP8z6gcBAgLp167adMmVKXNvPzcsnP287a4MKdBVEyNo/g8yM5OsFsW3bNqr74AvglzjBP7EmKs5KmzbRvm9f1p98MktvvtmVbfrlmII7sS5du5VdBXv2WV4prQLND9p36K3cvHxy/s5jjzHU2LCOC8aMBKnA3AcfpnLjBgmLM5yuXbsuMMYk7LaQF1zr/Vk8wTnL8kRk30+8GBGZhZ2Sp7jRxph3Y9z/s8CzAO3atTNdunSJ5WVFTPvgY6b8muaLkll2djYleY9lzS9xgn9iTWicgwZR78knqffUU9C4cak355djCu7E2n/kDEyIu0AC/D4h9LanLcxh0tvf8NhTd7FnRz7zXppGtwtPSWic5YlbSW61iJxijPkkeKGInAz8Ge3FxphTXYqjVDIz0pk7sovXYSjlnREjbF+sCRPsvyou8XYSB+jZfH96fnwvbN8Is2ZxSseOiQyx3HGr4cn1wDMi8oKIXCciQ0TkRWzJaohL+1BKJVpWFlxxhW2yvmqV19H4TrydxNmzBy69FObNg9deA01wrnOrC8Fi4ChsC8jGQFPn76OcdSUmIueJyGrgBGCGiMwsZbhKqUhGjrSjn9x3X8iWgiq8cK0ow972GDkSpk6FBx6ww6sp17nVuvJQoK4x5vliyzuJyBpnDMsSMca8A7xT2hiVUjFq1Aguu4yCZyfxYNoJ5FSpCZRgvMVyKuZO4s88Y4dTu/ZaGDYs8YGVU25VVz5MqPb3kOesU0r5yahRkJ/PpV++WWSxm9PJlGsffmiT21lnwcMPgw64nDBuJbnGxpgfiy90mvs3dmkfSqmycuihvHvkSVz8/QfU+ie3yKqc3DytwiyNH3+Ef/0LWraEKVN0iqMEcyvJVYmwTse5UsqH3jz9Mqrk7+KqedOKLBfQ2TdKas0a6N4d9tvPDqNWY9++c8pdbiW5eSKyzxiVInIlsMClfSilylDvS0/jwyM7cenCGWTmbQFsgis+fIRWYcZo+3bo0QP+/tsmuCy9r1kW3Epyw4D+IpItIg84j0+xE6gOdWkfSqky1LNNFpXvvJ2q+Tu49qs3ycrMCDs5pM6+EcWePXa2h+++s1WUrVt7HVG54VYXgnXGmA7AGGCl8xhjjDnBGLPWjX0opcreKReeQoXLL2fADzOY26cpWWE6NevsG1Hcfrudff3+++Hss72OplxxdQY+Y8wcY8xjzmO2m9tWSnnk7rvtZJ233hp/Z2cFr7wCY8fCVVfBDTd4HU25o9PMKqUia9DAXpwnT6anWRdfZ+fy7ssv4coroUsXeOIJ7SrgAW27qpSK7uab4dlnYfhwes6apUktFitX2lFMDj5Y54XzkJbklFLR1axp7yvNnm07MqvItmyBc86BXbvg/fehVi2vIyq3NMkppWIzaBAccoidqaCgwOtokldBAVx0ESxZYktwhx/udUTlmiY5pVRsKlWC8eNh0SJ46SWvo0leN94IM2bA44/DqUkxi1i5pvfklFKxu+ACaN8ebrsNeveGqlULV01bmMPEmUuLTDqc6WGonnjqKXjkETvg8qBBXkej0JKcUioeInbk/JwcezF3TFuYw6ipP+0z3FduXr53sZa1mTPhuutsP7j77/c6GuXQJKeUik+nTnDuubbqcv16ACbOXEpeftH7dHn5BazbvMOLCMve4sV20OUWLeDVVyEtLfprVJnQJKeUit+998LOnYWdm8MN67WrYE9ZRpUQUSeOXb/elt6qVtVBl5OQJjmlVPyaN4fRo22pZcaMsMN6VUrz9yUmXDVsYaLbsQPOOw/WrYP33oOGDT2NV+3L32egUso7I0fa6rnBgxl1YlbI4b7q1gw/C1fUElISCFcNO3HmUjvocv/+dlSTl16CY4/1KEoViSY5pVTJVKoEkybB6tWc/frjIYf7ysxID/nSqCWkJBGuGnZNbp7tLzhliq26veCCMo5MxUq7ECilSu6EE2yLwsceo2ffvvQceXKR1dnZy0K+LFIJKZmGDKufmUFOiER3w6IZMOMp+96HD/cgMhUrLckppUpn7Fh7L+qqq2xjlBhELCElkVCzLpz/6+dcP+MpW3p76CEddDnJaZJTSpVO9erw9NN2GKvx42N6SbiGKsk2L13PNllFqmHP2fgL901/yHajePll7SrgA5rklFKld+aZcPHFMG6c7TMWhZ/mpevZJou5I0/m94sa8tgbd5F2WDN4912oEr5RjUoemuSUUu54+GE7W8GVV0YdwLl4CSnp56X73/9sIq9RAz74APbf3+uIVIySvuGJiEwEzgF2Ab8B/Y0xud5GpZTaR+3adqiviy+20/KMHQuEHtOyZ5uswkfSy8mBU06Bf/6Bzz/XvnA+k/RJDvgYGGWM2S0i9wKjgJs9jkmplBIuEcWtb1+YM8dWWx5zDLlV92PUJz8VtqQMdBUA/JHg1qyBk0+GtWvho4+gZUuvI1JxSvrqSmPMR8aY3c5/vwYaeBmPUqnG1T5rInaKmeOPh8svZ9eiCJ2pk92ff9oEl5NjJ4o94QSvI1IlIMYYr2OImYi8D7xujHklxLqBwECAunXrtp0yZUrc29+2bRvVq1cvdZxlwS+x+iVO8E+sbse5dO3WkGNMVkqrQPODSjYOY6W//qLt1Vezq1IlXhtzPzur7Rtvy6yaJdp2ogQf1/RNm2h9ww1UWb+eH++9l82tWnkc3V6JPE+7du26wBjTLiEb90hSJDkRmQUcFGLVaGPMu85zRgPtgF4mStDt2rUz8+fPjzuO7OxsunTpEvfrvOCXWP0SJ/gnVrfjbDJyBqG+UAL8PqF7yTc8dy4FJ3Xhs0ZHc+X5t7Onwt7WlFmZGcwt1nHca4XHdf166NoVVq6E//4XTjrJ69CKSOR5KiIpl+SSorrSGHOqMeaoEI9AgrscOBu4OFqCU0rFJ2F91jp25KdB19B1xQL+74vJhYuTtasAABs22EYmv/9uZxRIsgSn4pcUSS4SETkD29CkhzFmu9fxKJVqEtlnLff8nqw8ry9DvnqDM5fOLdOuAvEOAJ2xahV06ADLl8P779vSnPI9P7SufByoDHwsdvicr40xOq+8Ui4JJBxXWlcWJ0Lj1/4DXX7nqY8fhf87E8oowY2aGkerzuxsjrn2WqhcGWbNgo4dEx6jKhtJn+SMMYd6HYNSqS6hfdYqV4a337YtFU8/Hd54A845JzH7csQ1APQLL8DAgeyqV4/0OXOgadOExqbKVtJXVyqlUkD9+vDFF7af2XnnwYsvJnR3MQ0AvWePnfi1f3/o3JmFTzyhCS4FaZJTSpWN2rXhk0+gSxfo1w8eeCBhu4ramCYvD/r0sZ3WBwyADz5gtw+6j6j4aZJTSpWdGjVgxgw7Tc1NN9nZxRPQYDpiY5pPP4XWreGtt+D+++GZZyA99OSuyv80ySmlylblynZG7UGD7KzaV10F291tOB1qAOj7TzuYnk+NsSXJ/Hw7TNeNN+p8cCku6RueKKVSUFoaPPkk1KkDd98NH38M990HvXu7lnQKG9MYA1OnQu+LbUfv4cPhzjuhalVX9qOSm5bklFLeEIG77oLPPrP36/r2tZORlmC0orB++QV69bLVo/Xqwbx5NpmGSHC5eflx9atT/qBJTinlrU6dbPL5979h2TI47ji44go78n9JbNlit9WhAxxxBMycaRPbt9/CMceEfMm0hTnk/J3nziDVKqloklNKeS8tzU62umyZbZDyyivQqBF07gy33GInKt28OfRrjYGtWyE7Gy6/3JbYBgyA3FyYONEO0TV8OFS0d2dCjYQyceZS9hRrAOOb2RJURHpPTimVPPbbz5a6BgywrR4//9wmqvHjbfVmq1a2r93ff9v7a4FHntP/rUYNuOQSWxI87rh97u+FGwklL78AQsyFGq6/nfIPTXJKqeTTrJlt3g92Ru5vvrEJ74sv7D28WrXgwAPh8MPtvwceCE2aQPfuERuUhBsJJS1MY5dSD1KtPKdJTimV3KpVs0OCnVz6qXnClcwKjKFCsUSX1LMlqJjpPTmlVLkRrmSWlZlB1v4ZRfrVldVsCSqxtCSnlCo3hndrXuSeHOwtsWVuXsbckV28C04lhJbklFLlRqiRULTEltq0JKeUKlcSOq2QSjpaklNKKZWyNMkppZRKWVpdqZTytcCIJWty86ifmcHwbs21OlIV0iSnlPKtcCOYAJroFKDVlUopHws3gomOOakCNMkppXwr3AgmOuakCtAkp5TyrXAjmOiYkypAk5xSyreGd2tORnpakWU65qQKpg1PlFK+FWhcoq0rVTia5JRSvqYjmKhIkr66UkTuFpEfReR7EflIROp7HZNSSil/SPokB0w0xrQyxrQGpgO3ex2QUkopf0j6JGeM2RL032qA8SoWpZRS/iLGJH/OEJGxwGXAZqCrMWZDiOcMBAYC1K1bt+2UKVPi3s+2bduoXr16KaMtG36J1S9xgn9i9UucoLEmQiLj7Nq16wJjTLuEbNwrxhjPH8AsYFGIx7nFnjcKGBNte23btjUlMWfOnBK9zgt+idUvcRrjn1j9EqcxGmsiJDJOYL5Jgpzg5iMpWlcaY06N8amvAjOAOxIYjlJKqRSRFEkuEhFpZoxZ5vy3B/CLl/EopRJLZxVQbkr6JAdMEJHmwB7gD2CQx/EopRJEZxVQbkv6JGeMOd/rGJRSZSPSrAKa5FRJJH0XAqVU+aGzCii3aZJTSiUNnVVAuU2TnFIqaeisAsptSX9PTilVfuisAsptmuSUUklFZxVQbtIkp5TyBe0/p0pCk5xSKulp/zlVUtrwRCmV9CL1n1MqEk1ySqmkp/3nVElpklNKJT3tP6dKSpOcUirpaf85VVLa8EQplfS0/5wqKU1ySilf0P5zqiS0ulIppVTK0iSnlFIqZWmSU0oplbI0ySmllEpZmuSUUkqlLE1ySimlUpYYY7yOwXUisgH4owQvrQ385XI4ieKXWP0SJ/gnVr/ECRprIiQyzkbGmDoJ2rYnUjLJlZSIzDfGtPM6jlj4JVa/xAn+idUvcYLGmgh+iTNZaHWlUkqplKVJTimlVMrSJFfUs14HEAe/xOqXOME/sfolTtBYE8EvcSYFvSenlFIqZWlJTimlVMrSJKeUUiplaZIDROQMEVkqIstFZKTX8RQnIitF5CcR+V5E5jvLDhCRj0VkmfPv/h7F9ryIrBeRRUHLwsYmIqOc47xURLp5HOedIpLjHNfvReQsr+N09t1QROaIyBIRWSwiQ53lSXVcI8SZdMdVRKqIyLci8oMT6xhnebId03BxJt0x9Q1jTLl+AGnAb0BToBLwA3Ck13EVi3ElULvYsvuAkc7fI4F7PYqtM3AMsChabMCRzvGtDDRxjnuah3HeCdwU4rmexensvx5wjPN3DeBXJ6akOq4R4ky64woIUN35Ox34Bjg+CY9puDiT7pj65aElOTgOWG6MWWGM2QVMAc71OKZYnAu86Pz9ItDTiyCMMZ8Bm4otDhfbucAUY8xOY8zvwHLs8fcqznA8ixPAGPOnMeY75++twBIgiyQ7rhHiDMfLz98YY7Y5/013HobkO6bh4gzH03PVDzTJ2S/lqqD/rybyF9ULBvhIRBaIyEBnWV1jzJ9gLzbAgZ5Ft69wsSXjsR4iIj861ZmBqqqkiVNEGgNtsL/ok/a4FosTkvC4ikiaiHwPrAc+NsYk5TENEyck4TH1A01ytnqguGTrV9HRGHMMcCZwrYh09jqgEkq2Y/0UcAjQGvgTeMBZnhRxikh14G1gmDFmS6SnhlhWZvGGiDMpj6sxpsAY0xpoABwnIkdFeLpnsYaJMymPqR9okrO/fBoG/b8BsMajWEIyxqxx/l0PvIOtjlgnIvUAnH/XexfhPsLFllTH2hizzrmg7AEmsbeax/M4RSQdmzgmG2OmOouT7no13c8AAAJWSURBVLiGijOZj6sTXy6QDZxBEh7TgOA4k/2YJjNNcjAPaCYiTUSkEtAHeM/jmAqJSDURqRH4GzgdWISN8XLnaZcD73oTYUjhYnsP6CMilUWkCdAM+NaD+IDCi1rAedjjCh7HKSICPAcsMcY8GLQqqY5ruDiT8biKSB0RyXT+zgBOBX4h+Y5pyDiT8Zj6htctX5LhAZyFbRn2GzDa63iKxdYU23rqB2BxID6gFvAJsMz59wCP4nsNW32Sj/1VeWWk2IDRznFeCpzpcZwvAz8BP2IvFvW8jtPZ94nYKqcfge+dx1nJdlwjxJl0xxVoBSx0YloE3O4sT7ZjGi7OpDumfnnosF5KKaVSllZXKqWUSlma5JRSSqUsTXJKKaVSliY5pZRSKUuTnFJKqZSlSU4pl4nIl17HoJSytAuBUklIRNKMMQVex6GU32lJTimXicg2sSaKyCKxcwH2dtZ1EZHpQc99XET6OX+vFJHbReQL4EJvolcqtVT0OgClUlQv7GC6RwO1gXki8lkMr9thjDkxoZEpVY5oSU6pxDgReM3YQXXXAZ8Cx8bwutcTG5ZS5YsmOaUSI9QUKAC7Kfq9q1Js/T+JCUep8kmTnFKJ8RnQ25kAsw7QGTs6/B/Akc6o8TWBU7wMUqlUp/fklHKfwc77dwJ29ggDjDDGrAUQkTewo8kvw444r5RKEO1CoJSLRKQW8J0xppHXsSiltLpSKdeISH3gK+B+r2NRSllaklNKKZWytCSnlFIqZWmSU0oplbI0ySmllEpZmuSUUkqlLE1ySimlUtb/Awoyw+erAscAAAAAAElFTkSuQmCC\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "\n", "plt.scatter(ppm_1965_seasonal['day'], ppm_1965_seasonal['[ppm]'],label='data')\n", "plt.plot(ppm_1965_seasonal['day'], func(ppm_1965_seasonal['day'], *popt_1965), 'r-',label='fit')\n", "plt.xlabel('jour')\n", "plt.ylabel('CO2 (ppm)')\n", "plt.title('Evolution de la concentration saisonnale en CO2 à Mauna Loa en 1965')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()\n", "\n", "\n", "plt.scatter(ppm_1999_seasonal['day'], ppm_1999_seasonal['[ppm]'],label='data')\n", "plt.plot(ppm_1999_seasonal['day'], func(ppm_1999_seasonal['day'], *popt_1999), 'r-',label='fit')\n", "plt.xlabel('jour')\n", "plt.ylabel('CO2 (ppm)')\n", "plt.title('Evolution de la concentration saisonnale en CO2 à Mauna Loa en 1999')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le modèle proposé semble satisfaisant pour les deux années proposées, on peut notamment regarder la déviation standard associé au calcul de l'estimateur pour les deux années :" ] }, { "cell_type": "code", "execution_count": 348, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[0.09481993 0.09508224 0.09481995 0.09508223]\n", "[0.10420458 0.10449285 0.1042046 0.10449284]\n" ] } ], "source": [ "perr_1965 = np.sqrt(np.diag(pcov_1965))\n", "perr_1999 = np.sqrt(np.diag(pcov_1999))\n", "print(perr_1965)\n", "print(perr_1999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Pour les années 1965 et 1999, l'erreur de deviation standard ne dépasse pas environ 10%. Cependant comme on peut le constater, les valeurs des paramètres $(a,b,c,d)$ sont bien différentes d'une année à l'autre à cause des grandes incertitudes hebdomadaires sur la mesure." ] }, { "cell_type": "code", "execution_count": 350, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.91310813 -0.94674625 -0.37797862 0.52541981]\n", "[ 2.57056481 -0.81960095 -0.57928076 0.67984334]\n" ] } ], "source": [ "print(popt_1965)\n", "print(popt_1999)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Le modèle proposé semble satisfaisant mais il faudrait pouvoir trouver une valeur unique pour le tuple $(a,b,c,d)$ qui donnerait une bonne estimation sur l'ensemble des mesures. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La valeur optimale que j'ai choisi pour le tuple $(a,b,c,d)$ est la moyenne arithmétique de tout les tuples $(a,b,c,d)$ calculables (avec une année complète de mesures hebdomadaires disponible). Vérifions que cette approximation est de bonne qualité.\n", "\n", "Pour la calculer je définis d'abord une fonction qui me calcule ce tuple pour une année $n$ quelconque." ] }, { "cell_type": "code", "execution_count": 373, "metadata": {}, "outputs": [], "source": [ "def parameter_fit_func(year):\n", " ppm_seasonal = seasonal_data.loc[np.str(year)+'-01-01':np.str(year)+'-12-31']\n", " popt,pcov=curve_fit(func, ppm_seasonal['day'], ppm_seasonal['[ppm]'])\n", " return(pcov,popt)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je calcule l'erreur standard et les paramètres du modèle pour chaque année complète de mesure." ] }, { "cell_type": "code", "execution_count": 428, "metadata": { "scrolled": true }, "outputs": [], "source": [ "perr_list=[]\n", "popt_list=[]\n", "for y in complete_years:\n", " pcov,popt=parameter_fit_func(y)\n", " perr_list.append(np.sqrt(np.diag(pcov)))\n", " popt_list.append(popt)\n", "#print(pcov_list)\n", "#print(popt_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Les valeurs choisies pour le tuple $(a,b,c,d)$ sont donc :" ] }, { "cell_type": "code", "execution_count": 425, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 2.16840988, -1.00351247, -0.72485854, 0.67409739])" ] }, "execution_count": 425, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_popt=np.mean(popt_list, axis=0)\n", "norm_popt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut également constater que le modèle proposé correspond bien au comportement sur l'ensemble du jeu de données avec une déviation standard qui ne dépasse pas en moyenne 9%." ] }, { "cell_type": "code", "execution_count": 430, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([0.08913176, 0.08905876, 0.08913162, 0.08905892])" ] }, "execution_count": 430, "metadata": {}, "output_type": "execute_result" } ], "source": [ "norm_pcov=np.mean(perr_list, axis=0)\n", "norm_pcov" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On peut donc définir notre fonction de la variation saisonnière de la concentration en $CO_2$" ] }, { "cell_type": "code", "execution_count": 487, "metadata": {}, "outputs": [], "source": [ "def ppm_seasonal(t):\n", " return 2.16840988*np.sin(2*np.pi*t/365.0)-1.00351247*np.cos(2*np.pi*t/365.0)-0.72485854*np.sin(4*np.pi*t/365.0)+0.67409739*np.cos(4*np.pi*t/365.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On pose comme origine des temps des jours le premier jour de la prémière année de mesure (1958). Attention le premier point de mesure ne correspond pas au premier jour de l'année comme c'était le cas lors du calcul des paramètres du modèle. Je dois donc décaler ma variable temporelle de 88 jours. On trace ensuite la variation saisonnière depuis 1958 jusqu'à 2022." ] }, { "cell_type": "code", "execution_count": 488, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t=(date-date[0]).days+date[0].dayofyear\n", "plt.plot(t, ppm_seasonal(t), 'r-')\n", "plt.xlabel('jour')\n", "plt.ylabel('CO2 (ppm)')\n", "plt.title('Modèle d évolution de la concentration saisonnale en CO2 depuis 1958')\n", "plt.grid()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Etude de l'évolution systématique" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "La deuxième composante d'évolution de la concentration en $CO_2$ mesurée est une variation plus lente qui découle de l'augmentation des émissions de gaz à effet de serre." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Afin de tracer cette évolution, je retire à chaque point de mesure son évolution saisonnière calculée grâce au modèle précédent." ] }, { "cell_type": "code", "execution_count": 500, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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[ppm]day
1958-03-29314.83191588
1958-04-05315.65653495
1958-04-12315.740230102
1958-04-19315.346344109
1958-04-26313.988955116
1958-05-03314.242099123
1958-05-17314.591137137
1958-05-24314.997873144
1958-07-05314.396800186
1958-07-12314.878479193
1958-07-19315.000448200
1958-07-26315.655344207
1958-08-02316.225253214
1958-08-09316.182570221
1958-08-16316.630830228
1958-08-30316.424572242
1958-09-06316.089238249
1958-11-08314.846839312
1958-11-15314.792063319
1958-11-22314.846074326
1958-11-29315.048749333
1958-12-06315.387549340
1958-12-13315.057173347
1958-12-20315.269398354
1958-12-27315.593103361
1959-01-03315.5344713
1959-01-10315.72736410
1959-01-17315.86384017
1959-01-24315.98478624
1959-01-31315.49061831
.........
2022-04-02418.38397392
2022-04-09417.55650099
2022-04-16418.455594106
2022-04-23417.725110113
2022-04-30417.189199120
2022-05-07416.831513127
2022-05-14418.424413134
2022-05-21417.558233141
2022-05-28418.370640148
2022-06-04418.316129155
2022-06-11418.225674162
2022-06-18418.046575169
2022-06-25418.102490176
2022-07-02418.243668183
2022-07-09417.737364190
2022-07-16417.788416197
2022-07-23417.859952204
2022-07-30418.004192211
2022-08-06418.293293218
2022-08-13418.190201225
2022-08-20418.019458232
2022-08-27418.257922239
2022-09-03418.185350246
2022-09-10418.514839253
2022-09-17418.553069260
2022-09-24417.680362267
2022-10-01417.960542274
2022-10-08417.600612281
2022-10-15417.910261288
2022-10-22418.001243295
\n", "

3298 rows × 2 columns

\n", "
" ], "text/plain": [ " [ppm] day\n", "1958-03-29 314.831915 88\n", "1958-04-05 315.656534 95\n", "1958-04-12 315.740230 102\n", "1958-04-19 315.346344 109\n", "1958-04-26 313.988955 116\n", "1958-05-03 314.242099 123\n", "1958-05-17 314.591137 137\n", "1958-05-24 314.997873 144\n", "1958-07-05 314.396800 186\n", "1958-07-12 314.878479 193\n", "1958-07-19 315.000448 200\n", "1958-07-26 315.655344 207\n", "1958-08-02 316.225253 214\n", "1958-08-09 316.182570 221\n", "1958-08-16 316.630830 228\n", "1958-08-30 316.424572 242\n", "1958-09-06 316.089238 249\n", "1958-11-08 314.846839 312\n", "1958-11-15 314.792063 319\n", "1958-11-22 314.846074 326\n", "1958-11-29 315.048749 333\n", "1958-12-06 315.387549 340\n", "1958-12-13 315.057173 347\n", "1958-12-20 315.269398 354\n", "1958-12-27 315.593103 361\n", "1959-01-03 315.534471 3\n", "1959-01-10 315.727364 10\n", "1959-01-17 315.863840 17\n", "1959-01-24 315.984786 24\n", "1959-01-31 315.490618 31\n", "... ... ...\n", "2022-04-02 418.383973 92\n", "2022-04-09 417.556500 99\n", "2022-04-16 418.455594 106\n", "2022-04-23 417.725110 113\n", "2022-04-30 417.189199 120\n", "2022-05-07 416.831513 127\n", "2022-05-14 418.424413 134\n", "2022-05-21 417.558233 141\n", "2022-05-28 418.370640 148\n", "2022-06-04 418.316129 155\n", "2022-06-11 418.225674 162\n", "2022-06-18 418.046575 169\n", "2022-06-25 418.102490 176\n", "2022-07-02 418.243668 183\n", "2022-07-09 417.737364 190\n", "2022-07-16 417.788416 197\n", "2022-07-23 417.859952 204\n", "2022-07-30 418.004192 211\n", "2022-08-06 418.293293 218\n", "2022-08-13 418.190201 225\n", "2022-08-20 418.019458 232\n", "2022-08-27 418.257922 239\n", "2022-09-03 418.185350 246\n", "2022-09-10 418.514839 253\n", "2022-09-17 418.553069 260\n", "2022-09-24 417.680362 267\n", "2022-10-01 417.960542 274\n", "2022-10-08 417.600612 281\n", "2022-10-15 417.910261 288\n", "2022-10-22 418.001243 295\n", "\n", "[3298 rows x 2 columns]" ] }, "execution_count": 500, "metadata": {}, "output_type": "execute_result" } ], "source": [ "global_data=data.dropna().copy()\n", "global_data['[ppm]']=global_data['[ppm]']-ppm_seasonal(global_data['day'])\n", "global_data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Regardons l'évolution hebdomadaire systématique de la concentration de $CO_2$." ] }, { "cell_type": "code", "execution_count": 503, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "t = date\n", "ppm = global_data['[ppm]']\n", "fig, ax = plt.subplots()\n", "ax.plot(t, ppm)\n", "\n", "ax.set(xlabel='années', ylabel='CO2 (ppm)',\n", " title='Evolution systématique de la concentration en CO2 à Mauna Loa')\n", "ax.grid()\n", "\n", "plt.show()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "D'après le modèle proposé par [Keeling et al.](https://www.rescuethatfrog.com/wp-content/uploads/2017/03/Keeling-et-al-1976-no2.pdf) en 1976, je propose le modèle systématique suivant : \n", "\n", "$[ppm](t)=a+b.t+c.t^2+d.t^3$\n", "\n", "avec $t$ l'année et $(a,b,c,d)$ le tuple de coefficient inconnus en ($ppm$,$ppm/an$,$ppm/an^2$,$ppm/an^3$)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je me ramène à l'étude de la moyenne annuelle de cette évolution systématique afin de déterminer les coefficients $(a,b,c,d)$." ] }, { "cell_type": "code", "execution_count": 512, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1958 315.268783\n", "1959 315.991240\n", "1960 316.903900\n", "1961 317.633232\n", "1962 318.442986\n", "1963 318.985702\n", "1964 319.519019\n", "1965 320.032489\n", "1966 321.339473\n", "1967 322.163142\n", "1968 323.053586\n", "1969 324.619867\n", "1970 325.674832\n", "1971 326.315373\n", "1972 327.469752\n", "1973 329.683971\n", "1974 330.258554\n", "1975 331.151982\n", "1976 332.159558\n", "1977 333.922771\n", "1978 335.540347\n", "1979 336.874740\n", "1980 338.706246\n", "1981 339.929063\n", "1982 341.130950\n", "1983 342.789752\n", "1984 344.471765\n", "1985 345.892485\n", "1986 347.113905\n", "1987 348.852525\n", " ... \n", "1993 356.852489\n", "1994 358.730318\n", "1995 360.743232\n", "1996 362.519163\n", "1997 363.657175\n", "1998 366.516755\n", "1999 368.196143\n", "2000 369.393526\n", "2001 370.948971\n", "2002 373.008362\n", "2003 375.561181\n", "2004 377.305409\n", "2005 379.598882\n", "2006 381.768188\n", "2007 383.569289\n", "2008 385.402400\n", "2009 387.346371\n", "2010 389.889604\n", "2011 391.699375\n", "2012 393.971913\n", "2013 396.644900\n", "2014 398.703905\n", "2015 400.857332\n", "2016 404.212768\n", "2017 406.506693\n", "2018 408.528971\n", "2019 411.416631\n", "2020 413.959535\n", "2021 416.084796\n", "2022 418.066532\n", "Name: [ppm], Length: 65, dtype: float64" ] }, "execution_count": 512, "metadata": {}, "output_type": "execute_result" } ], "source": [ "annual_evolution=global_data.groupby(global_data.index.year)['[ppm]'].mean()\n", "annual_evolution" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Je définis ma fonction modèle et j'essaye d'approximer mon évolution à l'aide d'un estimateur des moindre carrés comme précedemment. Je prends comme origine des temps le début des mesures soit l'année 1958." ] }, { "cell_type": "code", "execution_count": 542, "metadata": {}, "outputs": [], "source": [ "def global_func(t,a,b,c,d):\n", " t=t-1958\n", " return a+b*t+c*t**2+d*t**3" ] }, { "cell_type": "code", "execution_count": 543, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[3.14466208e+02 8.46851523e-01 9.66926453e-03 3.84895783e-05]\n", "[[ 8.99233285e-02 -1.04507571e-02 3.24009066e-04 -2.93031446e-06]\n", " [-1.04507571e-02 1.67230097e-03 -5.89502904e-05 5.71705521e-07]\n", " [ 3.24009066e-04 -5.89502904e-05 2.22413968e-06 -2.25062461e-08]\n", " [-2.93031446e-06 5.71705521e-07 -2.25062461e-08 2.34440054e-10]]\n" ] } ], "source": [ "popt,pcov=curve_fit(global_func, annual_evolution.index, annual_evolution)\n", "print(popt)\n", "print(pcov)" ] }, { "cell_type": "code", "execution_count": 544, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2.99872187e-01, 4.08937767e-02, 1.49135498e-03, 1.53114354e-05])" ] }, "execution_count": 544, "metadata": {}, "output_type": "execute_result" } ], "source": [ "perr=np.sqrt(np.diag(pcov))\n", "perr" ] }, { "cell_type": "code", "execution_count": 545, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(annual_evolution.index, annual_evolution,label='data')\n", "plt.plot(annual_evolution.index, global_func(annual_evolution.index, *popt), 'r-',label='fit')\n", "plt.xlabel('années')\n", "plt.ylabel('CO2 (ppm)')\n", "plt.title('Evolution de la concentration systématique en CO2 depuis 1958')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On a à nouveau une estimation satisfaisante avec des erreurs de déviations cependant plus importantes que pour la variation saisonnière." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Prédiction de la concentration à Mauna Loa en 2025" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Maintenant que l'on dispose d'un modèle complet pour les évolutions saisonnières et globales de la concentration en $CO_2$ à Mauna Loa depuis 1958, on peut réunir les deux afin d'assurer la fiabilité du modèle." ] }, { "cell_type": "code", "execution_count": 579, "metadata": {}, "outputs": [ { "ename": "ValueError", "evalue": "Cannot shift with no freq", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscatter\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'[ppm]'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'data'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0mpopt\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'r-'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mlabel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'fit'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'années'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'CO2 (ppm)'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Evolution de la concentration en CO2 à Mauna Loa depuis 1958'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m\u001b[0m in \u001b[0;36mfunc\u001b[0;34m(t, a, b, c, d)\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0ma\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mt\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1958\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mb\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mc\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mt\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/datetimelike.py\u001b[0m in \u001b[0;36m__sub__\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 681\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_add_delta\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 682\u001b[0m \u001b[0;32melif\u001b[0m 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\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_sub_datelike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/indexes/datetimelike.py\u001b[0m in \u001b[0;36mshift\u001b[0;34m(self, n, freq)\u001b[0m\n\u001b[1;32m 780\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 781\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 782\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cannot shift with no freq\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 783\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 784\u001b[0m \u001b[0mstart\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mn\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfreq\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", "\u001b[0;31mValueError\u001b[0m: Cannot shift with no freq" ] }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.scatter(data.index, data['[ppm]'],label='data')\n", "plt.plot(data.index, func(data.index, *popt), 'r-',label='fit')\n", "plt.xlabel('années')\n", "plt.ylabel('CO2 (ppm)')\n", "plt.title('Evolution de la concentration en CO2 à Mauna Loa depuis 1958')\n", "plt.grid()\n", "plt.legend()\n", "plt.show()" ] }, { "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 }