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module2/exo1/Projet_info.ipynb 0 → 100644
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module2/exo1/Untitled.ipynb 0 → 100644
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module2/exo1/module2-exo2.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Données fournies\n",
"donnees = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,\n",
" 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n",
" 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n",
" 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
" 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0,\n",
" 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4,\n",
" 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de la moyenne\n",
"moyenne = np.mean(donnees)\n",
"moyenne"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de l'écart-type (4.42/4.31)\n",
"ecart_type = np.std(donnees, ddof=1)\n",
"ecart_type"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul du minimum\n",
"minimum = np.min(donnees)\n",
"minimum"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de la médiane\n",
"median = np.median(donnees)\n",
"median"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul du maximum\n",
"maximum = np.max(donnees)\n",
"maximum "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14.113000000000001, 4.334094455301447, 2.8, 14.5, 23.4)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Affichage des résultats\n",
"moyenne, ecart_type, minimum, median, maximum"
]
}
],
"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": 4
}
module2/exo1/toy_notebook_fr.ipynb → module2/exo1/module2_exo1.ipynb
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......@@ -73,7 +73,7 @@
},
{
"cell_type": "code",
"execution_count": 16,
"execution_count": 9,
"metadata": {},
"outputs": [
{
......@@ -116,7 +116,7 @@
},
{
"cell_type": "code",
"execution_count": 17,
"execution_count": 4,
"metadata": {},
"outputs": [
{
......@@ -125,7 +125,7 @@
"3.122"
]
},
"execution_count": 17,
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
......@@ -134,6 +134,131 @@
"4*np.mean(accept)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.4 Écrire le lien \"aiguilles de Buffon\" vers wikipedia"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"## [Aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguilles_de_Buffon) c'est la syntaxe pour avoir le resultats suivant :"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[Aiguilles de Buffon](https://fr.wikipedia.org/wiki/Aiguilles_de_Buffon)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.5 Écrire le code de la méthode de Buffon pour Python et pour R"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.9873039581777445\n"
]
}
],
"source": [
"import random\n",
"\n",
"def buffon_needle(num_needles):\n",
" count_crossed = 0\n",
" for _ in range(num_needles):\n",
" # Générez une longueur aléatoire de l'aiguille\n",
" needle_length = random.uniform(0, 1)\n",
" # Générez une position aléatoire pour le centre de l'aiguille\n",
" needle_center = random.uniform(0, 1 / (2 * needle_length))\n",
" # Vérifiez si l'aiguille croise une ligne\n",
" if needle_center < needle_length / 2 or needle_center > 1 - (needle_length / 2):\n",
" count_crossed += 1\n",
"\n",
" # Estimez pi en utilisant la formule de Buffon\n",
" pi_estimate = (2 * num_needles) / count_crossed if count_crossed > 0 else 0\n",
" return pi_estimate\n",
"\n",
"# Nombre d'aiguilles à lancer\n",
"num_needles = 100000\n",
"pi_estimate = buffon_needle(num_needles)\n",
"print(pi_estimate)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 1.5 ECRIRE DANS LE LANGUAGE R"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"%reload_ext rpy2.ipython"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[1] 3.003138\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"\n",
"buffon_needle <- function(num_needles) {\n",
" count_crossed <- 0\n",
" for (i in 1:num_needles) {\n",
" # Générez une longueur aléatoire de l'aiguille\n",
" needle_length <- runif(1, 0, 1)\n",
" # Générez une position aléatoire pour le centre de l'aiguille\n",
" needle_center <- runif(1, 0, 1 / (2 * needle_length))\n",
" # Vérifiez si l'aiguille croise une ligne\n",
" if (needle_center < needle_length / 2 || needle_center > 1 - (needle_length / 2)) {\n",
" count_crossed <- count_crossed + 1\n",
" }\n",
" }\n",
"\n",
" # Estimez pi en utilisant la formule de Buffon\n",
" pi_estimate <- (2 * num_needles) / count_crossed\n",
" return(pi_estimate)\n",
"}\n",
"\n",
"# Nombre d'aiguilles à lancer\n",
"num_needles <- 100000\n",
"pi_estimate <- buffon_needle(num_needles)\n",
"pi_estimate"
]
},
{
"cell_type": "code",
"execution_count": null,
......
module2/exo1/module2_exo3.ipynb 0 → 100644
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
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module2/exo1/module3-exo2.ipynb 0 → 100644
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
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module2/exo2/module2-exo2.ipynb 0 → 100644
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"# Données fournies\n",
"donnees = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, 10.9,\n",
" 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, 17.9, 12.2,\n",
" 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n",
" 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
" 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0,\n",
" 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4,\n",
" 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.113000000000001"
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de la moyenne\n",
"moyenne = np.mean(donnees)\n",
"moyenne"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"4.334094455301447"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de l'écart-type (4.42/4.31)\n",
"ecart_type = np.std(donnees, ddof=1)\n",
"ecart_type"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"2.8"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul du minimum\n",
"minimum = np.min(donnees)\n",
"minimum"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"14.5"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul de la médiane\n",
"median = np.median(donnees)\n",
"median"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"23.4"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Calcul du maximum\n",
"maximum = np.max(donnees)\n",
"maximum "
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14.113000000000001, 4.334094455301447, 2.8, 14.5, 23.4)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Affichage des résultats\n",
"moyenne, ecart_type, minimum, median, maximum"
]
}
],
"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": 4
}
module3/exo1/Untitled.ipynb 0 → 100644
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{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
module3/exo1/analyse-syndrome-grippal.ipynb
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module3/exo2/co2_mm_mlo.txt 0 → 100644
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# --------------------------------------------------------------------
# USE OF NOAA GML DATA
#
# These data are made freely available to the public and the scientific
# community in the belief that their wide dissemination will lead to
# greater understanding and new scientific insights. To ensure that GML
# receives fair credit for their work please include relevant citation
# text in publications. We encourage users to contact the data providers,
# who can provide detailed information about the measurements and
# scientific insight. In cases where the data are central to a
# publication, coauthorship for data providers may be appropriate.
#
#
#
# Contact Xin Lan (xin.lan@noaa.gov)
#
# File Creation Thu Oct 5 035533 2023
#
#
# --------------------------------------------------------------------
#
#
# See gml.noaa.govccggtrends for additional details.
#
# Data from March 1958 through April 1974 have been obtained by C. David Keeling
# of the Scripps Institution of Oceanography (SIO) and were obtained from the
# Scripps website (scrippsco2.ucsd.edu).
#
# The estimated uncertainty in the annual mean is the standard deviation
# of the differences of annual mean values determined independently by
# NOAAESRL and the Scripps Institution of Oceanography.
#
# NOTE In general, the data presented for the last year are subject to change,
# depending on recalibration of the reference gas mixtures used, and other quality
# control procedures. Occasionally, earlier years may also be changed for the same
# reasons. Usually these changes are minor.
#
# CO2 expressed as a mole fraction in dry air, micromolmol, abbreviated as ppm
#
# NOTE Due to the eruption of the Mauna Loa Volcano, measurements from Mauna Loa Observatory
# were suspended as of Nov. 29, 2022 and resumed in July 2023.
# Observations starting from December 2022 to July 4, 2023 are from a site at the
# Maunakea Observatories, approximately 21 miles north of the Mauna Loa Observatory.
#
# year mean unc
1959 315.98 0.12
1960 316.91 0.12
1961 317.64 0.12
1962 318.45 0.12
1963 318.99 0.12
1964 319.62 0.12
1965 320.04 0.12
1966 321.37 0.12
1967 322.18 0.12
1968 323.05 0.12
1969 324.62 0.12
1970 325.68 0.12
1971 326.32 0.12
1972 327.46 0.12
1973 329.68 0.12
1974 330.19 0.12
1975 331.13 0.12
1976 332.03 0.12
1977 333.84 0.12
1978 335.41 0.12
1979 336.84 0.12
1980 338.76 0.12
1981 340.12 0.12
1982 341.48 0.12
1983 343.15 0.12
1984 344.87 0.12
1985 346.35 0.12
1986 347.61 0.12
1987 349.31 0.12
1988 351.69 0.12
1989 353.20 0.12
1990 354.45 0.12
1991 355.70 0.12
1992 356.54 0.12
1993 357.21 0.12
1994 358.96 0.12
1995 360.97 0.12
1996 362.74 0.12
1997 363.88 0.12
1998 366.84 0.12
1999 368.54 0.12
2000 369.71 0.12
2001 371.32 0.12
2002 373.45 0.12
2003 375.98 0.12
2004 377.70 0.12
2005 379.98 0.12
2006 382.09 0.12
2007 384.02 0.12
2008 385.83 0.12
2009 387.64 0.12
2010 390.10 0.12
2011 391.85 0.12
2012 394.06 0.12
2013 396.74 0.12
2014 398.81 0.12
2015 401.01 0.12
2016 404.41 0.12
2017 406.76 0.12
2018 408.72 0.12
2019 411.65 0.12
2020 414.21 0.12
2021 416.41 0.12
2022 418.53 0.12
\ No newline at end of file
module3/exo2/module3-exo2.ipynb 0 → 100644
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module3/exo3/co2_annmean_mlo.csv 0 → 100644
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# --------------------------------------------------------------------
# USE OF NOAA GML DATA
#
# These data are made freely available to the public and the scientific
# community in the belief that their wide dissemination will lead to
# greater understanding and new scientific insights. To ensure that GML
# receives fair credit for their work please include relevant citation
# text in publications. We encourage users to contact the data providers,
# who can provide detailed information about the measurements and
# scientific insight. In cases where the data are central to a
# publication, coauthorship for data providers may be appropriate.
#
#
#
# Contact: Xin Lan (xin.lan@noaa.gov)
#
# File Creation: Thu Oct 5 03:55:33 2023
#
#
# --------------------------------------------------------------------
#
#
# See gml.noaa.gov/ccgg/trends/ for additional details.
#
# Data from March 1958 through April 1974 have been obtained by C. David Keeling
# of the Scripps Institution of Oceanography (SIO) and were obtained from the
# Scripps website (scrippsco2.ucsd.edu).
#
# The estimated uncertainty in the annual mean is the standard deviation
# of the differences of annual mean values determined independently by
# NOAA/ESRL and the Scripps Institution of Oceanography.
#
# NOTE: In general, the data presented for the last year are subject to change,
# depending on recalibration of the reference gas mixtures used, and other quality
# control procedures. Occasionally, earlier years may also be changed for the same
# reasons. Usually these changes are minor.
#
# CO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm
#
# NOTE: Due to the eruption of the Mauna Loa Volcano, measurements from Mauna Loa Observatory
# were suspended as of Nov. 29, 2022 and resumed in July 2023.
# Observations starting from December 2022 to July 4, 2023 are from a site at the
# Maunakea Observatories, approximately 21 miles north of the Mauna Loa Observatory.
year,mean,unc
1959,315.98,0.12
1960,316.91,0.12
1961,317.64,0.12
1962,318.45,0.12
1963,318.99,0.12
1964,319.62,0.12
1965,320.04,0.12
1966,321.37,0.12
1967,322.18,0.12
1968,323.05,0.12
1969,324.62,0.12
1970,325.68,0.12
1971,326.32,0.12
1972,327.46,0.12
1973,329.68,0.12
1974,330.19,0.12
1975,331.13,0.12
1976,332.03,0.12
1977,333.84,0.12
1978,335.41,0.12
1979,336.84,0.12
1980,338.76,0.12
1981,340.12,0.12
1982,341.48,0.12
1983,343.15,0.12
1984,344.87,0.12
1985,346.35,0.12
1986,347.61,0.12
1987,349.31,0.12
1988,351.69,0.12
1989,353.20,0.12
1990,354.45,0.12
1991,355.70,0.12
1992,356.54,0.12
1993,357.21,0.12
1994,358.96,0.12
1995,360.97,0.12
1996,362.74,0.12
1997,363.88,0.12
1998,366.84,0.12
1999,368.54,0.12
2000,369.71,0.12
2001,371.32,0.12
2002,373.45,0.12
2003,375.98,0.12
2004,377.70,0.12
2005,379.98,0.12
2006,382.09,0.12
2007,384.02,0.12
2008,385.83,0.12
2009,387.64,0.12
2010,390.10,0.12
2011,391.85,0.12
2012,394.06,0.12
2013,396.74,0.12
2014,398.81,0.12
2015,401.01,0.12
2016,404.41,0.12
2017,406.76,0.12
2018,408.72,0.12
2019,411.65,0.12
2020,414.21,0.12
2021,416.41,0.12
2022,418.53,0.12
module3/exo3/co2_mm_mlo.txt 0 → 100644
View file @ 2554bf1d
# --------------------------------------------------------------------
# USE OF NOAA GML DATA
#
# These data are made freely available to the public and the scientific
# community in the belief that their wide dissemination will lead to
# greater understanding and new scientific insights. To ensure that GML
# receives fair credit for their work please include relevant citation
# text in publications. We encourage users to contact the data providers,
# who can provide detailed information about the measurements and
# scientific insight. In cases where the data are central to a
# publication, coauthorship for data providers may be appropriate.
#
#
#
# Contact Xin Lan (xin.lan@noaa.gov)
#
# File Creation Thu Oct 5 035533 2023
#
#
# --------------------------------------------------------------------
#
#
# See gml.noaa.govccggtrends for additional details.
#
# Data from March 1958 through April 1974 have been obtained by C. David Keeling
# of the Scripps Institution of Oceanography (SIO) and were obtained from the
# Scripps website (scrippsco2.ucsd.edu).
#
# The estimated uncertainty in the annual mean is the standard deviation
# of the differences of annual mean values determined independently by
# NOAAESRL and the Scripps Institution of Oceanography.
#
# NOTE In general, the data presented for the last year are subject to change,
# depending on recalibration of the reference gas mixtures used, and other quality
# control procedures. Occasionally, earlier years may also be changed for the same
# reasons. Usually these changes are minor.
#
# CO2 expressed as a mole fraction in dry air, micromolmol, abbreviated as ppm
#
# NOTE Due to the eruption of the Mauna Loa Volcano, measurements from Mauna Loa Observatory
# were suspended as of Nov. 29, 2022 and resumed in July 2023.
# Observations starting from December 2022 to July 4, 2023 are from a site at the
# Maunakea Observatories, approximately 21 miles north of the Mauna Loa Observatory.
#
year mean unc
1959 315.98 0.12
1960 316.91 0.12
1961 317.64 0.12
1962 318.45 0.12
1963 318.99 0.12
1964 319.62 0.12
1965 320.04 0.12
1966 321.37 0.12
1967 322.18 0.12
1968 323.05 0.12
1969 324.62 0.12
1970 325.68 0.12
1971 326.32 0.12
1972 327.46 0.12
1973 329.68 0.12
1974 330.19 0.12
1975 331.13 0.12
1976 332.03 0.12
1977 333.84 0.12
1978 335.41 0.12
1979 336.84 0.12
1980 338.76 0.12
1981 340.12 0.12
1982 341.48 0.12
1983 343.15 0.12
1984 344.87 0.12
1985 346.35 0.12
1986 347.61 0.12
1987 349.31 0.12
1988 351.69 0.12
1989 353.20 0.12
1990 354.45 0.12
1991 355.70 0.12
1992 356.54 0.12
1993 357.21 0.12
1994 358.96 0.12
1995 360.97 0.12
1996 362.74 0.12
1997 363.88 0.12
1998 366.84 0.12
1999 368.54 0.12
2000 369.71 0.12
2001 371.32 0.12
2002 373.45 0.12
2003 375.98 0.12
2004 377.70 0.12
2005 379.98 0.12
2006 382.09 0.12
2007 384.02 0.12
2008 385.83 0.12
2009 387.64 0.12
2010 390.10 0.12
2011 391.85 0.12
2012 394.06 0.12
2013 396.74 0.12
2014 398.81 0.12
2015 401.01 0.12
2016 404.41 0.12
2017 406.76 0.12
2018 408.72 0.12
2019 411.65 0.12
2020 414.21 0.12
2021 416.41 0.12
2022 418.53 0.12
\ No newline at end of file
module3/exo3/exercice.ipynb
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module3/exo3/gml.noaa.gov/webdata/ccgg/trends/co2/co2_annmean_mlo.txt 0 → 100644
View file @ 2554bf1d
# --------------------------------------------------------------------
# USE OF NOAA GML DATA
#
# These data are made freely available to the public and the scientific
# community in the belief that their wide dissemination will lead to
# greater understanding and new scientific insights. To ensure that GML
# receives fair credit for their work please include relevant citation
# text in publications. We encourage users to contact the data providers,
# who can provide detailed information about the measurements and
# scientific insight. In cases where the data are central to a
# publication, coauthorship for data providers may be appropriate.
#
#
#
# Contact: Xin Lan (xin.lan@noaa.gov)
#
# File Creation: Thu Oct 5 03:55:33 2023
#
#
# --------------------------------------------------------------------
#
#
# See gml.noaa.gov/ccgg/trends/ for additional details.
#
# Data from March 1958 through April 1974 have been obtained by C. David Keeling
# of the Scripps Institution of Oceanography (SIO) and were obtained from the
# Scripps website (scrippsco2.ucsd.edu).
#
# The estimated uncertainty in the annual mean is the standard deviation
# of the differences of annual mean values determined independently by
# NOAA/ESRL and the Scripps Institution of Oceanography.
#
# NOTE: In general, the data presented for the last year are subject to change,
# depending on recalibration of the reference gas mixtures used, and other quality
# control procedures. Occasionally, earlier years may also be changed for the same
# reasons. Usually these changes are minor.
#
# CO2 expressed as a mole fraction in dry air, micromol/mol, abbreviated as ppm
#
# NOTE: Due to the eruption of the Mauna Loa Volcano, measurements from Mauna Loa Observatory
# were suspended as of Nov. 29, 2022 and resumed in July 2023.
# Observations starting from December 2022 to July 4, 2023 are from a site at the
# Maunakea Observatories, approximately 21 miles north of the Mauna Loa Observatory.
#
# year mean unc
1959 315.98 0.12
1960 316.91 0.12
1961 317.64 0.12
1962 318.45 0.12
1963 318.99 0.12
1964 319.62 0.12
1965 320.04 0.12
1966 321.37 0.12
1967 322.18 0.12
1968 323.05 0.12
1969 324.62 0.12
1970 325.68 0.12
1971 326.32 0.12
1972 327.46 0.12
1973 329.68 0.12
1974 330.19 0.12
1975 331.13 0.12
1976 332.03 0.12
1977 333.84 0.12
1978 335.41 0.12
1979 336.84 0.12
1980 338.76 0.12
1981 340.12 0.12
1982 341.48 0.12
1983 343.15 0.12
1984 344.87 0.12
1985 346.35 0.12
1986 347.61 0.12
1987 349.31 0.12
1988 351.69 0.12
1989 353.20 0.12
1990 354.45 0.12
1991 355.70 0.12
1992 356.54 0.12
1993 357.21 0.12
1994 358.96 0.12
1995 360.97 0.12
1996 362.74 0.12
1997 363.88 0.12
1998 366.84 0.12
1999 368.54 0.12
2000 369.71 0.12
2001 371.32 0.12
2002 373.45 0.12
2003 375.98 0.12
2004 377.70 0.12
2005 379.98 0.12
2006 382.09 0.12
2007 384.02 0.12
2008 385.83 0.12
2009 387.64 0.12
2010 390.10 0.12
2011 391.85 0.12
2012 394.06 0.12
2013 396.74 0.12
2014 398.81 0.12
2015 401.01 0.12
2016 404.41 0.12
2017 406.76 0.12
2018 408.72 0.12
2019 411.65 0.12
2020 414.21 0.12
2021 416.41 0.12
2022 418.53 0.12
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