{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Concentration de CO2 dans l'atmosphère depuis 1958\n", "\n", "M. Keeling a démarré en 1958 une série d'observations de mesures de la concentration de CO2 à Mauna Loa (Hawaii, USA). Ce jeu de données, dit \"Keeling Curve\", est crucial pour constater l'évolution de cette concentration au vu de la durée de l'expérience. Les données sont disponibles sur le [site de l'institut Scripps](https://scrippsco2.ucsd.edu/data/atmospheric_co2/primary_mlo_co2_record.html). Sur celui-ci, nous allons nous intéresser aux mesures hebdomadaires, récupérables sur [ce lien](https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv). Puisque le fichier est mis à jour toutes les semaines, nous nous baserons ici sur une copie locale de ce fichier, récupérée le 16 janvier 2024." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Analyse de la fidélité des données\n", "\n", "La première étape est de charger les données du fichier et de s'assurer de son contenu. D'après les commentaires en en-tête de fichier, la première colonne indique la date et la seconde la concentration de CO2 en micro-moles par mole (ppm). Les mesures sont alignées sur 12h00 sur le premier jour de la semaine." ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import isoweek\n", "import datetime\n", "from scipy.optimize import curve_fit" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# Data taken from\n", "# https://scrippsco2.ucsd.edu/assets/data/atmospheric/stations/in_situ_co2/weekly/weekly_in_situ_co2_mlo.csv\n", "\n", "path = \"weekly_in_situ_co2_mlo.csv\"\n", "raw_data = pd.read_csv(path, comment='\"', names=[\"Date\", \"Concentration\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Vérifions les semaines manquantes dans le dataset." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Missing weeks (74 in total out of 3357 weeks):\n", "1958-05-10,\t1958-05-31,\t1958-06-07,\t1958-06-14,\t1958-06-21,\t1958-06-28,\t1958-08-23,\t1958-09-13,\t1958-09-20,\t1958-09-27,\t1958-10-04,\t1958-10-11,\t1958-10-18,\t1958-10-25,\t1958-11-01,\t1959-02-07,\t1959-03-14,\t1959-05-30,\t1959-08-15,\t1962-08-25,\t1962-09-01,\t1962-09-08,\t1962-12-29,\t1963-02-16,\t1963-05-04,\t1963-11-23,\t1964-01-25,\t1964-02-01,\t1964-02-08,\t1964-02-15,\t1964-02-22,\t1964-02-29,\t1964-03-07,\t1964-03-14,\t1964-03-21,\t1964-03-28,\t1964-04-04,\t1964-04-11,\t1964-04-18,\t1964-04-25,\t1964-05-02,\t1964-05-09,\t1964-05-16,\t1964-05-23,\t1964-06-13,\t1964-06-20,\t1964-08-08,\t1966-07-16,\t1966-07-23,\t1966-07-30,\t1966-11-05,\t1967-01-21,\t1967-01-28,\t1976-06-26,\t1984-03-31,\t1984-04-07,\t1984-04-14,\t1984-04-21,\t1985-08-03,\t2003-06-14,\t2003-10-11,\t2003-10-18,\t2005-02-26,\t2005-03-05,\t2005-03-12,\t2005-03-19,\t2006-02-11,\t2006-02-18,\t2007-01-27,\t2012-10-06,\t2012-10-13,\t2020-01-18,\t2022-12-03,\t2022-12-10\n", "\n", "Missing weeks per year:\n", "1958 15\n", "1959 4\n", "1962 4\n", "1963 3\n", "1964 21\n", "1966 4\n", "1967 2\n", "1976 1\n", "1984 4\n", "1985 1\n", "2003 3\n", "2005 4\n", "2006 2\n", "2007 1\n", "2012 2\n", "2020 1\n", "2022 2\n" ] } ], "source": [ "# Check if every line is the corresponding week\n", "expected_date = set()\n", "w = 0\n", "while True:\n", " new_week = str(isoweek.Week(1958, 13+w).saturday())\n", " if new_week >= '2024-01-01':\n", " break\n", " expected_date.add(new_week)\n", " w += 1\n", "\n", "# Remove line if found\n", "for w in raw_data.index:\n", " stored_date = raw_data[\"Date\"][w]\n", " expected_date.remove(stored_date)\n", "\n", "missing_weeks = sorted(expected_date)\n", "print(f\"Missing weeks ({len(missing_weeks)} in total out of {w} weeks):\")\n", "print(\",\\t\".join(missing_weeks))\n", "\n", "missing_weeks_keep_year = [e[:4] for e in missing_weeks]\n", "missing_weeks_per_year = dict()\n", "for e in missing_weeks_keep_year:\n", " if e in missing_weeks_per_year.keys():\n", " missing_weeks_per_year[e] += 1\n", " else:\n", " missing_weeks_per_year[e] = 1\n", " \n", "print(f\"\\nMissing weeks per year:\")\n", "for y in sorted(missing_weeks_per_year):\n", " print(y, missing_weeks_per_year[y])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous pouvons constater que certaines années ont quelques semaines manquantes. Celles qui se démarquent le plus notamment sont 1958 et 1964 pour lesquelles une grosse part de l'année est manquante. Pour rappel, l'année 1958 n'est mesurée qu'à partir de la première donnée, ainsi les semaines maquantes sont bien des données manquantes _entre_ des mesures et non le début d'année, qui n'a pas été mesuré. Au sens global, seuls environ 2% des mesures sont manquantes, l'influence sur les résultats est donc limitée." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = raw_data.copy()\n", "\n", "data['Date'] = [pd.Period(w, 'W') for w in data['Date']]\n", "\n", "data = data.set_index('Date').sort_index()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Graphique de l'évolution de la concentration de CO2\n", "\n", "Maintenant que notre dataset est vérifié, nous pouvons tracer un premier graphique concernant l'évolution de la concentration en CO2 dans l'atmosphère de Mauna Loa. Nous obtenons le graphique suivant, en nuage de points:" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 6, "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.plot(figsize=(20, 10), style='.', ms=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Nous constatons ainsi deux phénomènes. Le premier est une tendance globale de l'augmentation de la concentration. Le second est une oscillation périodique tout au long de la courbe." ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# Keep data from 1970 to 1975 since we have nothing missing\n", "data_slice = data.iloc[550:850]\n", "\n", "data_slice.plot(figsize=(20, 10), style='.', ms=5)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "En zoomant sur une partie de la courbe pour laquelle aucune donnée n'est manquante, nous pouvons constater que l'oscillation semble périodique sur les années. Cette oscillation sera calculée avec plus de détails par la suite, en ayant enlevé la variation dûe à la tendance globale, et sera considérée avec une période d'un an." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Tendance globale\n", "\n", "Afin de mesurer la tendance globale, puisque l'oscillation périodique observée est annuelle, nous allons calculer la moyenne annuelle de la concentration de CO2." ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# Create an array containing the week period containing the 1st of January\n", "# So that we get the data for each year\n", "week_containing_march_29 = [pd.Period(pd.Timestamp(y, 1, 1), 'W')\n", " for y in range(1958, 2024)]\n", "\n", "year = []\n", "yearly_avg = []\n", "\n", "for week1, week2 in zip(week_containing_march_29[:-1], week_containing_march_29[1:]):\n", " year_data = data['Concentration'][week1:week2]\n", " avg = year_data.sum()/year_data.count()\n", " year.append(week2.year)\n", " yearly_avg.append(avg)\n", "\n", "yearly_avg = pd.Series(data=yearly_avg, index=year)" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "yearly_avg.plot(style='.')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Comme attendu, nous pouvons observer la tendance globale sans la présence de l'oscillation annuelle.\n", "\n", "L'objectif est maintenant de trouver à quelle courbe assimiler ces données afin de les extrapoler pour les années suivantes.\n", "\n", "TODO" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 1.60961150e+00 -2.84730619e+03]\n" ] } ], "source": [ "x_data = yearly_avg.index\n", "y_data = yearly_avg.values\n", "\n", "def ref_func(x, a, b):\n", " return a*x + b\n", "\n", "res, cov = curve_fit(ref_func, x_data, y_data)\n", "print(res)\n", "\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Oscillation annuelle\n", "\n", "A l'origine, l'objectif de ces mesures était d'observer ces variations entre les saisons sur l'année, c'est-à-dire les oscillations visibles sur le graphique précédant. Nous allons maintenant observer ces oscillations en ignorant la tendance globale, et en comparant ainsi les mois au sein d'une même année.\n", "\n", "TODO" ] } ], "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 }