{ "cells": [ { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958" ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import numpy as np\n", "from datetime import datetime\n", "from scipy.signal import argrelextrema\n", "from scipy.signal import savgol_filter\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Importation des données\n", "\n", "Les données sont disponibles sur le site du [Programme Scripps CO2](https://scrippsco2.ucsd.edu/data/atmospheric_co2/mlo.html) au format *csv*." ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_url = \"https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\"" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Pour nous protéger contre une éventuelle disparition ou modification du serveur du Programme Scripps CO2, 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. Étant donné l'évolution temporelle des données, qui sont mises à jour régulièrement, celles-ci sont téléchargées à nouveau une fois par jour." ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "data_file = \"donnees-CO2-\" + str(datetime.today().date().year) + \"-\" + str(datetime.today().date().month) + \"-\" + str(datetime.today().date().day) + \".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": { "hideCode": false, "hidePrompt": false }, "source": [ "Les 44 premières lignes de données contiennent une description de leur contenu (auteurs/institut, date, citation, informations données).\n", "Après ces 44 lignes, les données sont affichées directement sans titre de colonne.\n", "\n", "Les informations sur les données sont les suivantes : *The data file below contains 2 columns indicaing the date and CO2 concentrations in micro-mol CO2 per mole (ppm), reported on the 2008A SIO manometric mole fraction scale. These weekly values have been adjusted to 12:00 hours at middle day of each weekly period as indicated by the date in the first column.*" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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01958-03-29316.19
11958-04-05317.31
21958-04-12317.69
31958-04-19317.58
41958-04-26316.48
51958-05-03316.95
61958-05-17317.56
71958-05-24317.99
81958-07-05315.85
91958-07-12315.85
101958-07-19315.46
111958-07-26315.59
121958-08-02315.64
131958-08-09315.10
141958-08-16315.09
151958-08-30314.14
161958-09-06313.54
171958-11-08313.05
181958-11-15313.26
191958-11-22313.57
201958-11-29314.01
211958-12-06314.56
221958-12-13314.41
231958-12-20314.77
241958-12-27315.21
251959-01-03315.24
261959-01-10315.50
271959-01-17315.69
281959-01-24315.86
291959-01-31315.42
.........
31542020-01-25413.36
31552020-02-01413.99
31562020-02-08414.83
31572020-02-15413.81
31582020-02-22414.17
31592020-02-29413.89
31602020-03-07414.00
31612020-03-14414.30
31622020-03-21414.62
31632020-03-28415.57
31642020-04-04415.61
31652020-04-11416.47
31662020-04-18416.60
31672020-04-25415.86
31682020-05-02417.20
31692020-05-09416.99
31702020-05-16416.54
31712020-05-23417.49
31722020-05-30417.19
31732020-06-06416.49
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31752020-06-20416.11
31762020-06-27415.75
31772020-07-04415.20
31782020-07-11414.91
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31802020-07-25413.63
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3184 rows × 2 columns

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" ], "text/plain": [ " 0 1\n", "0 1958-03-29 316.19\n", "1 1958-04-05 317.31\n", "2 1958-04-12 317.69\n", "3 1958-04-19 317.58\n", "4 1958-04-26 316.48\n", "5 1958-05-03 316.95\n", "6 1958-05-17 317.56\n", "7 1958-05-24 317.99\n", "8 1958-07-05 315.85\n", "9 1958-07-12 315.85\n", "10 1958-07-19 315.46\n", "11 1958-07-26 315.59\n", "12 1958-08-02 315.64\n", "13 1958-08-09 315.10\n", "14 1958-08-16 315.09\n", "15 1958-08-30 314.14\n", "16 1958-09-06 313.54\n", "17 1958-11-08 313.05\n", "18 1958-11-15 313.26\n", "19 1958-11-22 313.57\n", "20 1958-11-29 314.01\n", "21 1958-12-06 314.56\n", "22 1958-12-13 314.41\n", "23 1958-12-20 314.77\n", "24 1958-12-27 315.21\n", "25 1959-01-03 315.24\n", "26 1959-01-10 315.50\n", "27 1959-01-17 315.69\n", "28 1959-01-24 315.86\n", "29 1959-01-31 315.42\n", "... ... ...\n", "3154 2020-01-25 413.36\n", "3155 2020-02-01 413.99\n", "3156 2020-02-08 414.83\n", "3157 2020-02-15 413.81\n", "3158 2020-02-22 414.17\n", "3159 2020-02-29 413.89\n", "3160 2020-03-07 414.00\n", "3161 2020-03-14 414.30\n", "3162 2020-03-21 414.62\n", "3163 2020-03-28 415.57\n", "3164 2020-04-04 415.61\n", "3165 2020-04-11 416.47\n", "3166 2020-04-18 416.60\n", "3167 2020-04-25 415.86\n", "3168 2020-05-02 417.20\n", "3169 2020-05-09 416.99\n", "3170 2020-05-16 416.54\n", "3171 2020-05-23 417.49\n", "3172 2020-05-30 417.19\n", "3173 2020-06-06 416.49\n", "3174 2020-06-13 416.50\n", "3175 2020-06-20 416.11\n", "3176 2020-06-27 415.75\n", "3177 2020-07-04 415.20\n", "3178 2020-07-11 414.91\n", "3179 2020-07-18 414.29\n", "3180 2020-07-25 413.63\n", "3181 2020-08-01 413.42\n", "3182 2020-08-08 412.85\n", "3183 2020-08-15 412.75\n", "\n", "[3184 rows x 2 columns]" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data = pd.read_csv(data_file, skiprows=44, header=None)\n", "raw_data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Vérification des données" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
\n", "\n", "\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "
01
\n", "
" ], "text/plain": [ "Empty DataFrame\n", "Columns: [0, 1]\n", "Index: []" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "raw_data[raw_data.isnull().any(axis=1)]" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Toutes les lignes contiennent des informations non nulles.\n", "\n", "### Formattage de la date" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [], "source": [ "def convert_week(year_month_day_str):\n", " year = int(year_month_day_str[:4])\n", " month = int(year_month_day_str[5:7])\n", " day = int(year_month_day_str[8:10])\n", " w = datetime(year, month, day)\n", " return pd.Period(w, 'W')\n", "\n", "raw_data['period'] = [convert_week(yw) for yw in raw_data[0] ]" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/html": [ "
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conc
period
1958-03-24/1958-03-30316.19
1958-03-31/1958-04-06317.31
1958-04-07/1958-04-13317.69
1958-04-14/1958-04-20317.58
1958-04-21/1958-04-27316.48
1958-04-28/1958-05-04316.95
1958-05-12/1958-05-18317.56
1958-05-19/1958-05-25317.99
1958-06-30/1958-07-06315.85
1958-07-07/1958-07-13315.85
1958-07-14/1958-07-20315.46
1958-07-21/1958-07-27315.59
1958-07-28/1958-08-03315.64
1958-08-04/1958-08-10315.10
1958-08-11/1958-08-17315.09
1958-08-25/1958-08-31314.14
1958-09-01/1958-09-07313.54
1958-11-03/1958-11-09313.05
1958-11-10/1958-11-16313.26
1958-11-17/1958-11-23313.57
1958-11-24/1958-11-30314.01
1958-12-01/1958-12-07314.56
1958-12-08/1958-12-14314.41
1958-12-15/1958-12-21314.77
1958-12-22/1958-12-28315.21
1958-12-29/1959-01-04315.24
1959-01-05/1959-01-11315.50
1959-01-12/1959-01-18315.69
1959-01-19/1959-01-25315.86
1959-01-26/1959-02-01315.42
......
2020-01-20/2020-01-26413.36
2020-01-27/2020-02-02413.99
2020-02-03/2020-02-09414.83
2020-02-10/2020-02-16413.81
2020-02-17/2020-02-23414.17
2020-02-24/2020-03-01413.89
2020-03-02/2020-03-08414.00
2020-03-09/2020-03-15414.30
2020-03-16/2020-03-22414.62
2020-03-23/2020-03-29415.57
2020-03-30/2020-04-05415.61
2020-04-06/2020-04-12416.47
2020-04-13/2020-04-19416.60
2020-04-20/2020-04-26415.86
2020-04-27/2020-05-03417.20
2020-05-04/2020-05-10416.99
2020-05-11/2020-05-17416.54
2020-05-18/2020-05-24417.49
2020-05-25/2020-05-31417.19
2020-06-01/2020-06-07416.49
2020-06-08/2020-06-14416.50
2020-06-15/2020-06-21416.11
2020-06-22/2020-06-28415.75
2020-06-29/2020-07-05415.20
2020-07-06/2020-07-12414.91
2020-07-13/2020-07-19414.29
2020-07-20/2020-07-26413.63
2020-07-27/2020-08-02413.42
2020-08-03/2020-08-09412.85
2020-08-10/2020-08-16412.75
\n", "

3184 rows × 1 columns

\n", "
" ], "text/plain": [ " conc\n", "period \n", "1958-03-24/1958-03-30 316.19\n", "1958-03-31/1958-04-06 317.31\n", "1958-04-07/1958-04-13 317.69\n", "1958-04-14/1958-04-20 317.58\n", "1958-04-21/1958-04-27 316.48\n", "1958-04-28/1958-05-04 316.95\n", "1958-05-12/1958-05-18 317.56\n", "1958-05-19/1958-05-25 317.99\n", "1958-06-30/1958-07-06 315.85\n", "1958-07-07/1958-07-13 315.85\n", "1958-07-14/1958-07-20 315.46\n", "1958-07-21/1958-07-27 315.59\n", "1958-07-28/1958-08-03 315.64\n", "1958-08-04/1958-08-10 315.10\n", "1958-08-11/1958-08-17 315.09\n", "1958-08-25/1958-08-31 314.14\n", "1958-09-01/1958-09-07 313.54\n", "1958-11-03/1958-11-09 313.05\n", "1958-11-10/1958-11-16 313.26\n", "1958-11-17/1958-11-23 313.57\n", "1958-11-24/1958-11-30 314.01\n", "1958-12-01/1958-12-07 314.56\n", "1958-12-08/1958-12-14 314.41\n", "1958-12-15/1958-12-21 314.77\n", "1958-12-22/1958-12-28 315.21\n", "1958-12-29/1959-01-04 315.24\n", "1959-01-05/1959-01-11 315.50\n", "1959-01-12/1959-01-18 315.69\n", "1959-01-19/1959-01-25 315.86\n", "1959-01-26/1959-02-01 315.42\n", "... ...\n", "2020-01-20/2020-01-26 413.36\n", "2020-01-27/2020-02-02 413.99\n", "2020-02-03/2020-02-09 414.83\n", "2020-02-10/2020-02-16 413.81\n", "2020-02-17/2020-02-23 414.17\n", "2020-02-24/2020-03-01 413.89\n", "2020-03-02/2020-03-08 414.00\n", "2020-03-09/2020-03-15 414.30\n", "2020-03-16/2020-03-22 414.62\n", "2020-03-23/2020-03-29 415.57\n", "2020-03-30/2020-04-05 415.61\n", "2020-04-06/2020-04-12 416.47\n", "2020-04-13/2020-04-19 416.60\n", "2020-04-20/2020-04-26 415.86\n", "2020-04-27/2020-05-03 417.20\n", "2020-05-04/2020-05-10 416.99\n", "2020-05-11/2020-05-17 416.54\n", "2020-05-18/2020-05-24 417.49\n", "2020-05-25/2020-05-31 417.19\n", "2020-06-01/2020-06-07 416.49\n", "2020-06-08/2020-06-14 416.50\n", "2020-06-15/2020-06-21 416.11\n", "2020-06-22/2020-06-28 415.75\n", "2020-06-29/2020-07-05 415.20\n", "2020-07-06/2020-07-12 414.91\n", "2020-07-13/2020-07-19 414.29\n", "2020-07-20/2020-07-26 413.63\n", "2020-07-27/2020-08-02 413.42\n", "2020-08-03/2020-08-09 412.85\n", "2020-08-10/2020-08-16 412.75\n", "\n", "[3184 rows x 1 columns]" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data = {'period' : raw_data['period'], 'conc' : raw_data[1]}\n", "data = pd.DataFrame(data=data)\n", "data = data.set_index('period').sort_index()\n", "data" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "### Vérification de l'écart temporel entre les données" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "hideCode": false, "hidePrompt": false }, "outputs": [ { "data": { "text/plain": [ "[7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 14,\n", " 7,\n", " 42,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 14,\n", " 7,\n", " 63,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 14,\n", " 7,\n", " 7,\n", " 7,\n", " 14,\n", 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7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " 7,\n", " ...]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def ecart_temporel(date1, date2):\n", " year1 = int(str(date1)[:4])\n", " month1 = int(str(date1)[5:7])\n", " day1 = int(str(date1)[8:10])\n", " year2 = int(str(date2)[:4])\n", " month2 = int(str(date2)[5:7])\n", " day2 = int(str(date2)[8:10])\n", " return (datetime(year1,month1,day1)-datetime(year2,month2,day2)).days\n", "\n", "delta_t = [ecart_temporel(data.index[i+1],data.index[i]) for i in range(0,len(data)-1)]\n", "delta_t" ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "Certaines données sont manquantes.\n", "Tous les delta_t sont cependant des multiples de 7 ce qui signifie qu'il n'y a pas eu de décalage dans les données." ] }, { "cell_type": "markdown", "metadata": { "hideCode": false, "hidePrompt": false }, "source": [ "## Un premier coup d'oeil aux données" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['conc'].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Caractérisation de l'oscillation périodique\n", "\n", "On observe en effet une oscillation superposée à une évolution temporelle plus lente.\n", "Zoomons sur quelques années afin de voir l'oscillation plus clairement." ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['conc'][-500:].plot()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Période d'oscillation\n", "\n", "Une observation visuelle nous indique que l'oscillation a une période proche d'un an.\n", "Les cycles terrestres (saisons, courants, etc...) étant pour la plupart cycliques avec une période d'un an, je fais l'hypothèse que c'est le cas ici.\n", "Afin de vérifier cette hypothèse, je vais localiser le maximum et le minimum de la concentration en CO2 chaque année (l'année est déterminée par le premier jour de la semaine considérée - cela ne devrait pas amener de problème car les extremums annuels ne sont pas situés fin décembre/début janvier).\n", "L'écart entre les maximums et entre les minimums nous indiquera si mon hypothèse est vérifiée.\n", "\n", "Les années 1958 et 2020 sont ignorées car elles sont incomplètes." ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "year = [int(str(data.index[i])[:4]) for i in range(0,len(data))]\n", "max_index = [int(year.index(i)) + int(np.array(data[year.index(i):year.index(i+1)-1]).argmax(axis=0)) for i in range(1959,2020)]\n", "min_index = [int(year.index(i)) + int(np.array(data[year.index(i):year.index(i+1)-1]).argmin(axis=0)) for i in range(1959,2020)]\n", "\n", "delta_t_max = [ecart_temporel(data.index[max_index][i+1],data.index[max_index][i]) for i in range(0,len(max_index)-1)]\n", "delta_t_min = [ecart_temporel(data.index[min_index][i+1],data.index[min_index][i]) for i in range(0,len(min_index)-1)]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(365.4, 18.06488306078952)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(delta_t_max), np.std(delta_t_max)" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(365.28333333333336, 11.816784202518422)" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(delta_t_min), np.std(delta_t_min)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'écart entre les maximums annuels étant exactement égal à 1 an (365,25 j), l'hypothèse faite plus haut et validée et je conclus que **la période des oscillations est d'un an.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Amplitude d'oscillation\n", "\n", "Afin de calculer l'amplitude des oscillations, nous devons soustraire la moyenne annuelle de la concentration à cette concentration. Cela permet d'isoler les oscillations dans le signal. Pour les mêmes raisons que précédemment, les années 1958 et 2020 sont ignorées." ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "yearly_average = [np.mean(data['conc'][year.index(i):year.index(i+1)-1]) for i in range(1958,2020)]\n", "yearly_average.append(np.mean(data['conc'][year.index(2020):]))\n", "data['yearly_average'] = [yearly_average[int(year[i])-1958] for i in range(0,len(data))]\n", "data['oscillations'] = data['conc'] - data['yearly_average']" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "data['oscillations'].plot()\n", "Amplitude = [0.5 * ( data['oscillations'][max_index][i] - data['oscillations'][min_index][i] ) for i in range(0,len(max_index))]" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(3.3075609079445134, 0.2844505794603768)" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "np.mean(Amplitude), np.std(Amplitude)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**L'amplitude d'oscillation est en moyenne de 3,30 ppm, et varie avec un écart-type de 0.28 ppm.**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Modèle simple de l'évolution lente de la concentration\n", "\n", "### Visualisation des données" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(range(1958,2021),yearly_average)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Deux modèles envisagés\n", "\n", "A vue d'oeil il est compliqué de savoir si l'évolution lente de la concentration suit une évolution parabolique ou exponentielle.\n", "Nous allons donc comparer ces deux modèles afin de déterminer celui qui est le plus adapté." ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "# Définition des fonctions pour le fitting\n", "def func_parab(x, a, b, c):\n", " return a*np.power(x,2) + b*x + c\n", "def func_exp(x, a, b, c):\n", " return a * np.exp(b * x) + c\n", "\n", "# Définition des variables à fitter\n", "xdata = range(0,len(yearly_average)-2)\n", "ydata = yearly_average[1:len(yearly_average)-1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Estimation des points de départ pour le fitting :**\n", "\n", "A partir de la courbe précédente, j'ai choisi les points suivants afin d'estimer grossièrement les paramètres du fitting : (0, 320), (20, 340), et (30, 360).\n", "Un changement de variable est fait pour les valeurs de x en faisant commencer x à 0 au lieu de 1959. \n", "\n", "Il faut résoudre le système d'équation suivant : { f(0) = 320, f(20) = 340, f(30) = 360 pour f représentant les fonctions parabolique et exponentielle.\n", "\n", "Le résultat obtenu pour le modèle parabolique est a = 5.45e-2 ppm/an2, b = - 9.1e-2 ppm/an, et c = 320 ppm.\n", "Pour le modèle exponentiel, le résultat obtenu est a = 4 ppm, b = 0.3 an-1, et c = 316 ppm." ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [], "source": [ "# Fit\n", "popt_parab, pcov_parab = curve_fit(func_parab, xdata, ydata, p0 = [0.0545, -0.091, 320])\n", "popt_exp, pcov_exp = curve_fit(func_exp, xdata, ydata, p0 = [4, 0.3, 316])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Visualisation des données fittées" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Plot\n", "plt.plot(range(1959,2020), ydata, label='Données')\n", "plt.plot(range(1959,2020), func_parab(xdata, *popt_parab), 'r--', label='Fit parabolique')\n", "plt.plot(range(1959,2020), func_exp(xdata, *popt_exp), 'k--', label='Fit exponentiel')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Il n'y a pas de différence visuelle significative entre les deux fit.\n", "Il est cependant intéressant de noter que la tendance globale de l'évolution lente est bien reproduite dans les deux cas.\n", "\n", "Nous allons maintenant comparer la moyenne et l'écart-type de l'erreur entre les données et leur fitting." ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(1.230054309774862e-13,\n", " -7.491403490259144e-11,\n", " 0.6795969684950424,\n", " 0.6623123190021285)" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "err_parab = ydata - func_parab(xdata, *popt_parab)\n", "err_exp = ydata - func_exp(xdata, *popt_exp)\n", "\n", "np.mean(err_parab), np.mean(err_exp), np.std(err_parab), np.std(err_exp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dans les deux cas, l'erreur moyenne est très faible (inférieure à 1e-10 en valeur absolue) et les écarts-types sont similaires (avec un léger avantage pour la méthode exponentielle).\n", "**La méthode choisie pour la modélisation est la méthode exponentielle**, mais elle restera comparée à la méthode parabolique (par curiosité essentiellement)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Extrapolation jusqu'à 2025" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.plot(range(1959,2020), ydata, label='Données')\n", "plt.plot(range(1959,2026), func_parab(range(0,len(range(1959,2026))), *popt_parab), 'r--', label='Extrapolation parabolique')\n", "plt.plot(range(1959,2026), func_exp(range(0,len(range(1959,2026))), *popt_exp), 'k--', label='Extrapolation exponentielle')\n", "plt.legend()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "L'année 2025 correspond à l'index 67 du vecteur (1959 1960 ... 2025)." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(427.44379606384007, 429.7446447207408)" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "func_parab(67,*popt_parab), func_exp(67,*popt_exp)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Un modèle simple basé sur une fonction exponentielle permet d'extrapoler la valeur de la concentration de CO2 en 2025 et donne la valeur suivante : 429.7 ppm.**\n", "\n", "Le modèle parabolique donne une valeur proche (mais qui commence à diverger du modèle exponentiel) : 427.4 ppm.\n", "L'inspection visuelle donne aussi l'impression que l'extrapolation exponentielle suit mieux la dynamique de la courbe de concentration." ] } ], "metadata": { "hide_code_all_hidden": false, "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 }