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Commit af742293 authored by cb1e5ba91280d02583d0604166e71c1c's avatar cb1e5ba91280d02583d0604166e71c1c
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module3/exo3/exercice.ipynb
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...@@ -35,7 +35,7 @@ ...@@ -35,7 +35,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 282, "execution_count": 355,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -144,8 +144,87 @@ ...@@ -144,8 +144,87 @@
" <td>317.51</td>\n", " <td>317.51</td>\n",
" <td>314.71</td>\n", " <td>314.71</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>751</th>\n",
" <td>2020</td>\n",
" <td>8</td>\n",
" <td>44058</td>\n",
" <td>2020.6230</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>752</th>\n",
" <td>2020</td>\n",
" <td>9</td>\n",
" <td>44089</td>\n",
" <td>2020.7077</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>753</th>\n",
" <td>2020</td>\n",
" <td>10</td>\n",
" <td>44119</td>\n",
" <td>2020.7896</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>754</th>\n",
" <td>2020</td>\n",
" <td>11</td>\n",
" <td>44150</td>\n",
" <td>2020.8743</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" <tr>\n",
" <th>755</th>\n",
" <td>2020</td>\n",
" <td>12</td>\n",
" <td>44180</td>\n",
" <td>2020.9563</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" <td>-99.99</td>\n",
" </tr>\n",
" </tbody>\n", " </tbody>\n",
"</table>\n", "</table>\n",
"<p>756 rows × 10 columns</p>\n",
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
...@@ -155,16 +234,30 @@ ...@@ -155,16 +234,30 @@
"2 1958 3 21259 1958.2027 315.70 314.44 316.19 \n", "2 1958 3 21259 1958.2027 315.70 314.44 316.19 \n",
"3 1958 4 21290 1958.2877 317.45 315.16 317.30 \n", "3 1958 4 21290 1958.2877 317.45 315.16 317.30 \n",
"4 1958 5 21320 1958.3699 317.51 314.71 317.86 \n", "4 1958 5 21320 1958.3699 317.51 314.71 317.86 \n",
".. ... ... ... ... ... ... ... \n",
"751 2020 8 44058 2020.6230 -99.99 -99.99 -99.99 \n",
"752 2020 9 44089 2020.7077 -99.99 -99.99 -99.99 \n",
"753 2020 10 44119 2020.7896 -99.99 -99.99 -99.99 \n",
"754 2020 11 44150 2020.8743 -99.99 -99.99 -99.99 \n",
"755 2020 12 44180 2020.9563 -99.99 -99.99 -99.99 \n",
"\n", "\n",
" [ppm] [ppm] [ppm].1 \n", " [ppm] [ppm] [ppm].1 \n",
"0 -99.99 -99.99 -99.99 \n", "0 -99.99 -99.99 -99.99 \n",
"1 -99.99 -99.99 -99.99 \n", "1 -99.99 -99.99 -99.99 \n",
"2 314.91 315.70 314.44 \n", "2 314.91 315.70 314.44 \n",
"3 314.99 317.45 315.16 \n", "3 314.99 317.45 315.16 \n",
"4 315.06 317.51 314.71 " "4 315.06 317.51 314.71 \n",
".. ... ... ... \n",
"751 -99.99 -99.99 -99.99 \n",
"752 -99.99 -99.99 -99.99 \n",
"753 -99.99 -99.99 -99.99 \n",
"754 -99.99 -99.99 -99.99 \n",
"755 -99.99 -99.99 -99.99 \n",
"\n",
"[756 rows x 10 columns]"
] ]
}, },
"execution_count": 282, "execution_count": 355,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -184,7 +277,7 @@ ...@@ -184,7 +277,7 @@
" raw_data.to_csv('monthly_in_situ_co2_mlo',index=True)\n", " raw_data.to_csv('monthly_in_situ_co2_mlo',index=True)\n",
"\n", "\n",
"print(raw_data.shape)\n", "print(raw_data.shape)\n",
"raw_data.head()" "raw_data"
] ]
}, },
{ {
...@@ -1812,28 +1905,18 @@ ...@@ -1812,28 +1905,18 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 354, "execution_count": 356,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.08334002695417775"
]
},
"execution_count": 354,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "outputs": [],
}
],
"source": [ "source": [
"df.s1.mean()\n", "df.s1.mean()\n",
"dt" "dt\n",
"dt = 1/12"
] ]
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 353, "execution_count": 367,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -1842,7 +1925,7 @@ ...@@ -1842,7 +1925,7 @@
"<StemContainer object of 3 artists>" "<StemContainer object of 3 artists>"
] ]
}, },
"execution_count": 353, "execution_count": 367,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
}, },
...@@ -1873,6 +1956,91 @@ ...@@ -1873,6 +1956,91 @@
"plt.stem(freq, s1_fft.real, label=\"real\")\n" "plt.stem(freq, s1_fft.real, label=\"real\")\n"
] ]
}, },
{
"cell_type": "code",
"execution_count": 371,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<StemContainer object of 3 artists>"
]
},
"execution_count": 371,
"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": [
"plt.stem(freq[0:100], s1_fft.real[0:100], label=\"real\")"
]
},
{
"cell_type": "code",
"execution_count": 407,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Estimation de la frequence lente : 12.0 \n",
" Esitmation de la fréquence rapide : 744.0\n"
]
},
{
"data": {
"text/plain": [
"array([ 1, 2, 62])"
]
},
"execution_count": 407,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"type(s1_fft.real)\n",
"N = (len(s1_fft.real)+1)/2\n",
"N = int(N)\n",
"indide_f = np.where(s1_fft.real[0:N] > 500)\n",
"f_lente = indide_f[0][0] * (1/dt)\n",
"\n",
"f_rapide = indide_f[0][-1] * (1/dt)\n",
"print(f' Estimation de la frequence lente : {f_lente} \\n Esitmation de la fréquence rapide : {f_rapide}')\n"
]
},
{
"cell_type": "code",
"execution_count": 384,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"372.0"
]
},
"execution_count": 384,
"metadata": {},
"output_type": "execute_result"
}
],
"source": []
},
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": null,
......
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