concentration de CO2 dans l'atmosphere

parent 8a660175
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......@@ -1113,7 +1113,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On supprime les premieres quatre lignes. Les preòieres deux lignes sont vides, et les lignes 3 et 4 n'ont pas d'echantillon."
"On supprime les premières quatre lignes. Les premières deux lignes sont vides, et les lignes 3 et 4 n'ont pas d'échantillon."
]
},
{
......@@ -1129,7 +1129,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On Supprime le format de data 'data1' et 'data2', qui ne sont pas interessantes pour notre analse. "
"On Supprime le format de data 'data1' et 'data2', qui ne sont pas intéressantes pour notre analyse."
]
},
{
......@@ -1145,7 +1145,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous verifions qu'il n'y a pas des valeurs null dans le tableau."
"Nous vérifions qu'il n'y a pas des valeurs nulles dans le tableau."
]
},
{
......@@ -1208,7 +1208,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On voit qu'il n'y a pas des valeurs nulls. On verifie le type de donné :"
"On voit qu'il n'y a pas des valeurs nulles. On vérifie le type de donné :"
]
},
{
......@@ -1243,7 +1243,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On voit que le tableau est composé par des 'object'. On va le convertir en valeurs numeriques. "
"On voit que le tableau est composé par des 'object'. On va le convertir en valeurs numériques."
]
},
{
......@@ -1302,7 +1302,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"Les 6 dernieres lignes sont vides, on peut les retirer."
"Les 6 dernières lignes sont vides, on peut les retirer."
]
},
{
......@@ -1334,7 +1334,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On reinitialise les index de nos listes."
"On réinitialise les index de nos listes."
]
},
{
......@@ -1395,15 +1395,17 @@
"source": [
"## Point 2 - Prevision jusq'à 2025\n",
"\n",
"Dans ce paragraphe on va developper un modele pour prevoir l'evolution de la concentration de CO2 jusq'au 2025, avec les informations hystoriques qu'on a à disposition. \n",
"\n",
"On peut utiliser la méthode des moindres carrés pour identifier le'evolution lineaire de la tendence evidencié en rouge dand le graphique precedent. La methode des mindres carrés permet d'indetifier la ligne droite qui s'approche le mieux aux differentes points de l'étude. Cette ligne droit presente lq forme suivqnte: \n",
"Dans ce paragraphe on va développer un modèle pour prévoir l'évolution de la concentration de CO2 jusqu’au 2025, avec les informations hystériques qu'on a à disposition. \n",
"\n",
"On peut utiliser la méthode des moindres carrés pour identifier l’évolution linéaire de la tendance montrée en rouge dans le graphique précèdent. La méthode des moindres carrés permet d'identifier la ligne droite qui s'approche le mieux aux différentes points de l'étude. Cette ligne droit présente la forme suivante:\n",
"\n",
"\n",
"\\begin{align}\n",
"y=ax+b\n",
"\\end{align}\n",
"\n",
"La theorie de la methode des moindres carrées, nour permet de definir la forme des coefficients a et b. \n",
"La théorie de la méthode des moindres carrées, nous permet de définir la forme des coefficients a et b.\n",
"\n",
"\\begin{equation}\n",
"a=\\frac{N\\sum(xy)+\\sum(x)\\sum(y)}{N\\sum(x^2)-(\\sum x)^2}\n",
......@@ -1415,13 +1417,16 @@
"b=\\frac{\\sum(y)- a\\sum(x)}{N}\n",
"\\end{equation}\n",
"\n",
"Le lien suivant nous montre ça dans le detail.(https://www.mathsisfun.com/data/least-squares-regression.html)\n",
"Le lien suivant nous montre ça dans le détail.(https://www.mathsisfun.com/data/least-squares-regression.html)\n",
"\n",
"\n",
"Il est intéressant de simplifier cette équation. Pour ce faire on peut rendre 'barycentrique' la série historique, comme montré dans l'image suivante:\n",
"\n",
"Il est interessant de simplifier cette equation. Pour ce faire on peut rendre 'baricentrique' la serie historique, comme montré dans l'image suivante: \n",
"\n",
"![Screenshot%202020-07-19%20at%2009.15.53.png](attachment:Screenshot%202020-07-19%20at%2009.15.53.png)\n",
"\n",
"Cette operation nous permette de reduire la complexité des termes 'a' et 'b' car les sommes \n",
"Cette opération nous permet de réduire la complexité des termes 'a' et 'b' car les sommes\n",
"\n",
"\\begin{equation}\n",
"\\sum x\n",
"\\end{equation}\n",
......@@ -1432,7 +1437,7 @@
"(\\sum x)^2\n",
"\\end{equation}\n",
"\n",
"deviennent nulle.Donc on peut calculer a et b avec les formes suivantes: \n",
"deviennent nulle. Donc on peut calculer a et b avec les formes suivantes:\n",
"\n",
"\\begin{equation}\n",
"a=\\frac{\\sum(xy)}{\\sum(x^2)}\n",
......@@ -1444,25 +1449,23 @@
"b=\\frac{\\sum(y)}{N}\n",
"\\end{equation}\n",
"\n",
"On commence par calculer le terme 'a'. Pour ce faire on réalise un tableau en normalisant les périodes prises dans l'étude: chaque mois représente une période normalisé, on aura donc 744 (12*62 ) périodes, équivalentes à la longueur des vecteurs de 'raw_data_new'. \n",
"\n",
"\n",
"On commence par calculer le terme 'a'. Pour ce faire on realise un tableau en normalisat les periodes prises dans l'étude:chaque mois represente une periode normalisé, on aurà donc 742 periodes,èquivalentes à la longueur des vecters de 'raw_data_new'. \n",
"\n",
"On va donc definir tous les operateurs necessaires pour calculer a."
"On va donc définir tous les opérateurs nécessaires pour calculer a."
]
},
{
"cell_type": "code",
"execution_count": 40,
"execution_count": 90,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.12794974175705662"
"0.1511674880564176"
]
},
"execution_count": 40,
"execution_count": 90,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1472,7 +1475,7 @@
"\n",
"\n",
"for i in range(len(raw_data_new)):\n",
" x[0]=-371\n",
" x[0]=-368\n",
" x[i]=x[i-1]+1\n",
" \n",
"sumx =len(raw_data_new)\n",
......@@ -1482,8 +1485,7 @@
"for j in range(len(raw_data_new)):\n",
" y[j]=raw_data_new.seasonally_adjusted_filled[j]\n",
" \n",
"sumy = np.sum(y)\n",
"\n",
" \n",
"xy=np.multiply(x,y)\n",
"sumxy=np.sum(xy)\n",
"\n",
......@@ -1495,19 +1497,20 @@
"#on passe a calculer a\n",
"\n",
"a=(sumxy)/(sumx2)\n",
"a"
"a\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"La valeure de a est:0.12794974175705662 et ça rapresente le coefficient anguaire de la ligne droite qu'on cherche à calculer. On passe à caluculer b."
"La valeure de a est:0.1511674880564176 et ça représente le coefficient angulaire de la ligne droite qu'on cherche à calculer. On passe à calculer b."
]
},
{
"cell_type": "code",
"execution_count": 42,
"execution_count": 64,
"metadata": {},
"outputs": [
{
......@@ -1516,7 +1519,7 @@
"355.3829380053908"
]
},
"execution_count": 42,
"execution_count": 64,
"metadata": {},
"output_type": "execute_result"
}
......@@ -1530,16 +1533,16 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"On definit donc la ligne droite calculée comme : \n",
" \n",
"On définit donc la ligne droite calculée comme :\n",
"\n",
"\\begin{equation}\n",
" y=0.12794974175705662*x+355.3829380053908\n",
" y=0.1511674880564176*x+355.3829380053908\n",
"\\end{equation}\n",
"\n",
"Avec x qui represente une unité temporelle d'un mois. Les 742 mois donnent l'information jusq'au 2020. Donc pour chercher l'evolution de la concentration de CO2 au 2025, il faut considerer qu'il nous font 5*12 mois, soit 60unité temporelles normalisées. Ces 60 unitées temporelles normalisées il faut les sommer aux 371 qui donnent la quantité de CO2 au 2020, en arrivant à 431 unité de temps normalisé. A la fin du 2025, la concentration de CO2 sera: \n",
"Avec x qui représente une unité temporelle d'un mois. Les 742 mois donnent l'information jusqu’au 2020. Donc pour chercher l'évolution de la concentration de CO2 au 2025, il faut considérer qu'il nous font 5*12 mois, soit 60unité temporelles normalisées. Ces 60 unités temporelles normalisées il faut les sommer aux 371 qui donnent la quantité de CO2 au 2020, en arrivant à 431 unités de temps normalisé. A la fin du 2025, la concentration de CO2 sera:\n",
"\n",
"\\begin{equation}\n",
" y=0.752767160240205*431+354.63\n",
" y=0.1511674880564176*431+355.3829380053908\n",
"\\end{equation}\n",
"\n",
"Soit, "
......@@ -1547,77 +1550,44 @@
},
{
"cell_type": "code",
"execution_count": 145,
"execution_count": 78,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"438.8513728192164"
"420.53612535770685"
]
},
"execution_count": 145,
"execution_count": 78,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y2025=0.12794974175705662*431+355.3829380053908\n",
"\n",
"y2025=(0.1511674880564176*431)+355.3829380053908\n",
"\n",
"y2025"
]
},
{
"cell_type": "code",
"execution_count": 127,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f46e2af2438>]"
]
},
"execution_count": 127,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.plot(x,y) "
]
},
{
"cell_type": "code",
"execution_count": 146,
"execution_count": 81,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f46e261c160>]"
"[<matplotlib.lines.Line2D at 0x7fe9d0ef6b70>]"
]
},
"execution_count": 146,
"execution_count": 81,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
......@@ -1629,18 +1599,17 @@
}
],
"source": [
"xnew=np.append(x,804)\n",
"xnew=np.append(x,431)\n",
"ynew=np.append(y,y2025)\n",
"plt.plot(xnew,ynew) "
"plt.plot(xnew,ynew) \n",
"plt.plot(431, y2025, marker='o', markersize=3, color=\"red\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"cell_type": "markdown",
"metadata": {},
"outputs": [],
"source": [
"\n"
"Le point en rouge représente le niveau de concentration de CO2 en ppm à la fin du 2025."
]
}
],
......
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