début transformée de fourier

module3/exo3/exercice_fr.ipynb

@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,7 +37,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -57,7 +57,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 49,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -69,7 +69,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 50,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -1096,7 +1096,7 @@
        "[804 rows x 11 columns]"
       ]
      },
-     "execution_count": 50,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1114,7 +1114,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1131,7 +1131,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 59,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -1140,7 +1140,7 @@
        "Text(0,0.5,'CO2 (ppm)')"
       ]
      },
-     "execution_count": 59,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1159,7 +1159,7 @@
    ],
    "source": [
     "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"])\n",
-    "plt.xlabel(\"Temps\")\n",
+    "plt.xlabel(\"Temps (Année)\")\n",
     "plt.ylabel(\"CO2 (ppm)\")"
    ]
   },
@@ -1172,7 +1172,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [
     {
@@ -1181,7 +1181,7 @@
        "Text(0,0.5,'CO2 (ppm)')"
       ]
      },
-     "execution_count": 61,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1200,7 +1200,7 @@
    ],
    "source": [
     "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"seasonally\"])\n",
-    "plt.xlabel(\"Temps\")\n",
+    "plt.xlabel(\"Temps (Année)\")\n",
     "plt.ylabel(\"CO2 (ppm)\")"
    ]
   },
@@ -1213,7 +1213,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 62,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -1222,7 +1222,7 @@
        "Text(0,0.5,'CO2 (ppm)')"
       ]
      },
-     "execution_count": 62,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1241,7 +1241,7 @@
    ],
    "source": [
     "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n",
-    "plt.xlabel(\"Temps\")\n",
+    "plt.xlabel(\"Temps (Année)\")\n",
     "plt.ylabel(\"CO2 (ppm)\")"
    ]
   },
@@ -1254,7 +1254,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 63,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
@@ -1263,7 +1263,7 @@
        "(2020, 2024)"
       ]
      },
-     "execution_count": 63,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     },
@@ -1282,7 +1282,7 @@
    ],
    "source": [
     "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"] - data_MLO[\"seasonally\"])\n",
-    "plt.xlabel(\"Temps\")\n",
+    "plt.xlabel(\"Temps (Année)\")\n",
     "plt.ylabel(\"CO2 (ppm)\")\n",
     "plt.xlim([2020,2024])"
    ]
@@ -1311,7 +1311,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1328,7 +1328,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1355,7 +1355,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 94,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [
     {
@@ -1386,7 +1386,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 103,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -1412,24 +1412,163 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 105,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
     "def CO2_comp_lente(t):\n",
-    "    return a + b*t + c*t"
+    "    return a + b*t + c*t**2"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Ajoutons une nouvelle **colonne** à notre jeu de données avec notre estimation:"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
-    "CO2_estimation = CO2_compe_lente(temps)\n",
+    "CO2_estimation_lente = CO2_comp_lente(temps)\n",
+    "data_MLO[\"CO2_comp_lente\"] = CO2_estimation_lente"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Enfin, nous pouvons afficher notre estimation:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0,0.5,'CO2 (ppm)')"
+      ]
+     },
+     "execution_count": 28,
+     "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.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2_comp_lente\"],\"--\",label=\"estimation\")\n",
+    "plt.plot(data_MLO[\"Date.1\"],data_MLO[\"CO2\"],label=\"données\")\n",
+    "plt.xlabel(\"Temps (Année)\")\n",
+    "plt.ylabel(\"CO2 (ppm)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Composante périodique\n",
     "\n",
-    "data"
+    "Afin d'identifier la composante périodique, nous allons soustraire la composante lente à notre jeu de données et ensuite appliquer une transformée de Fourier qui permettra d'identifier la fréquence de l'oscillation"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x7fb15354fa90>]"
+      ]
+     },
+     "execution_count": 29,
+     "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": [
+    "CO2_periode = CO2 - CO2_estimation_lente\n",
+    "plt.plot(data_MLO[\"Date.1\"],CO2_periode)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "x and y must have same first dimension, but have shapes (396,) and (790,)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-41-ebce9b368705>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0mfourier_freq\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfft\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrfftfreq\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mCO2_periode_fourier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfourier_freq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mCO2_periode_fourier\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/pyplot.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3361\u001b[0m                       mplDeprecation)\n\u001b[1;32m   3362\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3363\u001b[0;31m         \u001b[0mret\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3364\u001b[0m     \u001b[0;32mfinally\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3365\u001b[0m         \u001b[0max\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_hold\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwashold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1865\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1866\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1867\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1868\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1869\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1526\u001b[0m         \u001b[0mkwargs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcbook\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalize_kwargs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_alias_map\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1527\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1528\u001b[0;31m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1529\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1530\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_grab_next_args\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    404\u001b[0m                 \u001b[0mthis\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    405\u001b[0m                 \u001b[0margs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 406\u001b[0;31m             \u001b[0;32mfor\u001b[0m \u001b[0mseg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_plot_args\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mthis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    407\u001b[0m                 \u001b[0;32myield\u001b[0m \u001b[0mseg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_plot_args\u001b[0;34m(self, tup, kwargs)\u001b[0m\n\u001b[1;32m    381\u001b[0m             \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mindex_of\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    382\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 383\u001b[0;31m         \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_xy_from_xy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    384\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    385\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommand\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'plot'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;32m/opt/conda/lib/python3.6/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36m_xy_from_xy\u001b[0;34m(self, x, y)\u001b[0m\n\u001b[1;32m    240\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    241\u001b[0m             raise ValueError(\"x and y must have same first dimension, but \"\n\u001b[0;32m--> 242\u001b[0;31m                              \"have shapes {} and {}\".format(x.shape, y.shape))\n\u001b[0m\u001b[1;32m    243\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m2\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    244\u001b[0m             raise ValueError(\"x and y can be no greater than 2-D, but have \"\n",
+      "\u001b[0;31mValueError\u001b[0m: x and y must have same first dimension, but have shapes (396,) and (790,)"
+     ]
+    },
+    {
+     "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": [
+    "CO2_periode_fourier = np.real(np.fft.fft(CO2_periode))\n",
+    "fourier_freq = np.fft.rfftfreq(len(CO2_periode_fourier))\n",
+    "\n",
+    "plt.plot(fourier_freq,CO2_periode_fourier)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {