Skip to content

M
mooc-rr
Commit 1d408102 authored by fac8f4cc02bfb4e3dfb02802cea5d62a's avatar fac8f4cc02bfb4e3dfb02802cea5d62a
Browse files
Options

add extrapolation

parent 797c4043
Show whitespace changes
Inline Side-by-side
Showing with 110 additions and 45 deletions
module3/exo3/Exercice_evaluation_pairs_sujet_1_CO2.ipynb
View file @ 1d408102
...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 1, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -12,7 +12,8 @@ ...@@ -12,7 +12,8 @@
"pd.options.mode.chained_assignment = None # default='warn'\n", "pd.options.mode.chained_assignment = None # default='warn'\n",
"import isoweek\n", "import isoweek\n",
"import os\n", "import os\n",
"import numpy as np" "import numpy as np\n",
"import scipy.optimize"
] ]
}, },
{ {
...@@ -24,7 +25,7 @@ ...@@ -24,7 +25,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 4, "execution_count": 10,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
...@@ -43,7 +44,7 @@ ...@@ -43,7 +44,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 11,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -946,7 +947,7 @@ ...@@ -946,7 +947,7 @@
"[2590 rows x 9 columns]" "[2590 rows x 9 columns]"
] ]
}, },
"execution_count": 5, "execution_count": 11,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -965,7 +966,7 @@ ...@@ -965,7 +966,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1011,7 +1012,7 @@ ...@@ -1011,7 +1012,7 @@
"Index: []" "Index: []"
] ]
}, },
"execution_count": 6, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1029,10 +1030,8 @@ ...@@ -1029,10 +1030,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 35, "execution_count": 13,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -1329,7 +1328,7 @@ ...@@ -1329,7 +1328,7 @@
"1782 -999.99 " "1782 -999.99 "
] ]
}, },
"execution_count": 35, "execution_count": 13,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -1340,10 +1339,8 @@ ...@@ -1340,10 +1339,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 36, "execution_count": 14,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -2245,7 +2242,7 @@ ...@@ -2245,7 +2242,7 @@
"[2572 rows x 9 columns]" "[2572 rows x 9 columns]"
] ]
}, },
"execution_count": 36, "execution_count": 14,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -2264,10 +2261,8 @@ ...@@ -2264,10 +2261,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 9, "execution_count": 15,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -2336,7 +2331,7 @@ ...@@ -2336,7 +2331,7 @@
"Length: 2572, dtype: datetime64[ns]" "Length: 2572, dtype: datetime64[ns]"
] ]
}, },
"execution_count": 9, "execution_count": 15,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -2355,7 +2350,7 @@ ...@@ -2355,7 +2350,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 10, "execution_count": 16,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -2390,7 +2385,7 @@ ...@@ -2390,7 +2385,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 32, "execution_count": 17,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -2399,7 +2394,7 @@ ...@@ -2399,7 +2394,7 @@
"Text(0,0.5,\"$\\\\mu$ mol/mol de CO2 dans l'atmosphère\")" "Text(0,0.5,\"$\\\\mu$ mol/mol de CO2 dans l'atmosphère\")"
] ]
}, },
"execution_count": 32, "execution_count": 17,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -2432,10 +2427,8 @@ ...@@ -2432,10 +2427,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 37, "execution_count": 18,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -3399,7 +3392,7 @@ ...@@ -3399,7 +3392,7 @@
"[2572 rows x 10 columns]" "[2572 rows x 10 columns]"
] ]
}, },
"execution_count": 37, "execution_count": 18,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -3420,10 +3413,8 @@ ...@@ -3420,10 +3413,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 39, "execution_count": 19,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -3753,7 +3744,7 @@ ...@@ -3753,7 +3744,7 @@
"48 2022.0 418.514615" "48 2022.0 418.514615"
] ]
}, },
"execution_count": 39, "execution_count": 19,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -3779,10 +3770,8 @@ ...@@ -3779,10 +3770,8 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 15, "execution_count": 20,
"metadata": { "metadata": {},
"collapsed": true
},
"outputs": [ "outputs": [
{ {
"data": { "data": {
...@@ -4870,7 +4859,7 @@ ...@@ -4870,7 +4859,7 @@
"[2572 rows x 12 columns]" "[2572 rows x 12 columns]"
] ]
}, },
"execution_count": 15, "execution_count": 20,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -4890,7 +4879,7 @@ ...@@ -4890,7 +4879,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 27, "execution_count": 21,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -4899,7 +4888,7 @@ ...@@ -4899,7 +4888,7 @@
"Text(0.5,1,\"Oscillation pendant l'année\")" "Text(0.5,1,\"Oscillation pendant l'année\")"
] ]
}, },
"execution_count": 27, "execution_count": 21,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -4934,7 +4923,7 @@ ...@@ -4934,7 +4923,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 34, "execution_count": 44,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -4943,7 +4932,7 @@ ...@@ -4943,7 +4932,7 @@
"Text(0.5,1,'La moyenne annuelle de la concentration de CO2')" "Text(0.5,1,'La moyenne annuelle de la concentration de CO2')"
] ]
}, },
"execution_count": 34, "execution_count": 44,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -4971,7 +4960,83 @@ ...@@ -4971,7 +4960,83 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"Cette courbe montre très bien comment le $CO_2$ monte dans notre atmosphère chaque année depuis les années 1970. Ceci implique l'effet de serre qui augmente la température globale. Pour que nous humains peuvent toujours habiter sur la terre, il est nécessaire que le niveau de $CO_2$ finisse d'augmenter." "Maintenant, on peut faire une extrapolation jusqu'à 2025 comme indiqué dans le sujet. Car il est déjà 2024, nous allons faire la extrapolation jusqu'à 2030."
]
},
{
"cell_type": "code",
"execution_count": 59,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f8b8ead3da0>]"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"def exponential(x, a, b):\n",
" return np.exp(a*x)+b\n",
" \n",
"param, cov = scipy.optimize.curve_fit(exponential, yearly_avg[\"year\"], yearly_avg[\"yearly average\"], p0=[0.003, 10])\n",
"\n",
"yearly_avg.plot(x=[\"year\"], y=[\"yearly average\"])\n",
"plt.xlabel(\"year\")\n",
"plt.ylabel(r\"$\\mu$ mol/mol de CO2 dans l'atmosphère\")\n",
"plt.title(\"La moyenne annuelle de la concentration de CO2\")\n",
"\n",
"year = np.arange(1974, 2031, 1)\n",
"plt.plot(year, exponential(year, param[0], param[1]))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les valeurs estimés dans les prochaines années."
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Estimation de la concentration en 2025: 420.2018865870856 ppm\n",
"Estimation de la concentration en 2030: 430.20268782943265 ppm\n"
]
}
],
"source": [
"print(\"Estimation de la concentration en 2025:\", exponential(2025, param[0], param[1]), \"ppm\")\n",
"print(\"Estimation de la concentration en 2030:\", exponential(2030, param[0], param[1]), \"ppm\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"En conclusion, cette courbe montre très bien comment le $CO_2$ monte dans notre atmosphère chaque année depuis les années 1970. Ceci implique l'effet de serre qui augmente la température globale. Pour que nous humains peuvent toujours habiter sur la terre, il est nécessaire que le niveau de $CO_2$ finisse d'augmenter."
] ]
} }
], ],
......
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment