no commit message

module3/exo3/exercice.ipynb

@@ -9,7 +9,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 281,
+   "execution_count": 409,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -35,7 +35,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 355,
+   "execution_count": 410,
    "metadata": {},
    "outputs": [
     {
@@ -257,7 +257,7 @@
        "[756 rows x 10 columns]"
       ]
      },
-     "execution_count": 355,
+     "execution_count": 410,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -289,7 +289,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 283,
+   "execution_count": 411,
    "metadata": {},
    "outputs": [
     {
@@ -310,7 +310,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 284,
+   "execution_count": 412,
    "metadata": {},
    "outputs": [
     {
@@ -425,7 +425,7 @@
        "4  1958   5   21320  1958.3699  317.51  314.71  317.86  315.06  317.51  314.71"
       ]
      },
-     "execution_count": 284,
+     "execution_count": 412,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -443,7 +443,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 285,
+   "execution_count": 413,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -486,7 +486,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 286,
+   "execution_count": 414,
    "metadata": {},
    "outputs": [
     {
@@ -601,7 +601,7 @@
        "75  1964   4   23482  1964.2896 NaN NaN  321.83  319.45  321.83  319.45"
       ]
      },
-     "execution_count": 286,
+     "execution_count": 414,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -612,7 +612,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 287,
+   "execution_count": 415,
    "metadata": {},
    "outputs": [
     {
@@ -727,7 +727,7 @@
        "6  1958   7   21381  1958.5370  315.86  315.19  315.86  315.22  315.86  315.19"
       ]
      },
-     "execution_count": 287,
+     "execution_count": 415,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -746,7 +746,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 288,
+   "execution_count": 416,
    "metadata": {
     "scrolled": true
    },
@@ -892,7 +892,7 @@
        "1958-08-01  315.29  314.93  316.19  "
       ]
      },
-     "execution_count": 288,
+     "execution_count": 416,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -918,7 +918,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 289,
+   "execution_count": 417,
    "metadata": {},
    "outputs": [
     {
@@ -949,7 +949,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 290,
+   "execution_count": 418,
    "metadata": {},
    "outputs": [
     {
@@ -1001,7 +1001,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 291,
+   "execution_count": 423,
    "metadata": {},
    "outputs": [
     {
@@ -1020,19 +1020,16 @@
    "source": [
     "from scipy.optimize import curve_fit\n",
     "\n",
-    "def func(x,a,b,c):\n",
+    "def func_cube(x,a,b,c):\n",
     "    return a*(x-b)**(2)+c\n",
     "\n",
     "data_cube = df.copy()\n",
+    "popt, pcov = curve_fit(func_cube,data_cube['Date 2'],data_cube['s1'])\n",
     "\n",
-    "x = data_cube['Date 2']\n",
-    "y = data_cube['s1']\n",
-    "popt, pcov = curve_fit(func,x,y)\n",
-    "\n",
-    "def fa(x):\n",
+    "def fcube(x):\n",
     "    return popt[0]*(x- popt[1])**(2)+popt[2]\n",
     "\n",
-    "data_cube['reg_cube'] = fa(data_cube['Date 2'])\n",
+    "data_cube['reg_cube'] = fcube(data_cube['Date 2'])\n",
     "\n",
     "fig = plt.figure(figsize=(18, 5))\n",
     "ax1 = fig.add_subplot(121)\n",
@@ -1891,32 +1888,37 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 340,
+   "execution_count": 426,
    "metadata": {},
-   "outputs": [],
-   "source": [
-    "nb = len(df['Date 2'])\n",
-    "ecart = []\n",
-    "for i in np.arange(0,nb-1):\n",
-    "    ecart.append(df['Date 2'][i+1] - df['Date 2'][i])\n",
-    "# definition du signal\n",
-    "dt = np.mean(ecart)"
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.08390542740841234"
       ]
      },
-  {
-   "cell_type": "code",
-   "execution_count": 356,
+     "execution_count": 426,
      "metadata": {},
-   "outputs": [],
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "df.s1.mean()\n",
-    "dt\n",
-    "dt = 1/12"
+    "data_cube = df.copy()\n",
+    "popt, pcov = curve_fit(func_cube,data_cube['Date 2'],data_cube['s1'])\n",
+    "data_cube['reg_cube'] = fcube(data_cube['Date 2'])\n",
+    "data_cube['co2'] = data_cube['reg_cube']-data_cube['s1']\n",
+    "\n",
+    "nb = len(data_cube['Date 2'])\n",
+    "ecart = []\n",
+    "for i in np.arange(0,nb-1):\n",
+    "    ecart.append(data_cube['Date 2'][i+1] - data_cube['Date 2'][i])\n",
+    "dt = np.mean(ecart)\n",
+    "dt"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 367,
+   "execution_count": 429,
    "metadata": {},
    "outputs": [
     {
@@ -1925,13 +1927,13 @@
        "<StemContainer object of 3 artists>"
       ]
      },
-     "execution_count": 367,
+     "execution_count": 429,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 1296x720 with 1 Axes>"
       ]
@@ -1943,22 +1945,22 @@
     }
    ],
    "source": [
-    "s1 = df.s1 - df.s1.mean()\n",
+    "s1 = data_cube.co2 - data_cube.co2.mean()\n",
     "\n",
     "# calcul de la transformee de Fourier et des frequences\n",
     "s1_fft = np.fft.fft(s1)\n",
     "n = s1.size\n",
-    "\n",
+    "dt = 1/12\n",
     "freq = np.fft.fftfreq(n, d=dt)\n",
     "\n",
     "# affichage de la transformee de Fourier\n",
     "plt.figure(figsize=(18, 10))\n",
-    "plt.stem(freq, s1_fft.real, label=\"real\")\n"
+    "plt.stem(freq, abs(s1_fft.real), label=\"real\")\n"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 371,
+   "execution_count": 431,
    "metadata": {},
    "outputs": [
     {
@@ -1967,13 +1969,13 @@
        "<StemContainer object of 3 artists>"
       ]
      },
-     "execution_count": 371,
+     "execution_count": 431,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": "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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -1985,7 +1987,7 @@
     }
    ],
    "source": [
-    "plt.stem(freq[0:100], s1_fft.real[0:100], label=\"real\")"
+    "plt.stem(freq[0:100], abs(s1_fft.real[0:100]), label=\"real\")"
    ]
   },
   {