From 8307b8d15329a706ecd26ea00f62f4e455a7ad77 Mon Sep 17 00:00:00 2001
From: 0f6de7c698c3859424c5f05b941261d9
 <0f6de7c698c3859424c5f05b941261d9@app-learninglab.inria.fr>
Date: Wed, 18 Oct 2023 07:54:43 +0000
Subject: [PATCH] Update exercice.ipynb

---
 module3/exo3/exercice.ipynb | 20 +++++++++++---------
 1 file changed, 11 insertions(+), 9 deletions(-)

diff --git a/module3/exo3/exercice.ipynb b/module3/exo3/exercice.ipynb
index 2664020..187e9c3 100644
--- a/module3/exo3/exercice.ipynb
+++ b/module3/exo3/exercice.ipynb
@@ -213,7 +213,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f19740324e0>]"
+       "[<matplotlib.lines.Line2D at 0x7f914de7d550>]"
       ]
      },
      "execution_count": 6,
@@ -269,7 +269,7 @@
     {
      "data": {
       "text/plain": [
-       "[<matplotlib.lines.Line2D at 0x7f1973f6d898>]"
+       "[<matplotlib.lines.Line2D at 0x7f914ddb9908>]"
       ]
      },
      "execution_count": 7,
@@ -315,7 +315,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -457,20 +457,20 @@
    ],
    "source": [
     "amp_days = all_peaks_sorted[\"date\"][:-1]-all_peaks_sorted[\"date\"].iloc[0]\n",
-    "amp_fit = np.polyfit(amp_days.dt.days,amplitude,deg=1)\n",
-    "amp_fit"
+    "amp_fit = np.polyfit(amp_days.dt.days,amplitude,deg=1)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The amplitude between seasonal CO2 concentration oscillations is growing at a pace of 0.024 ppm of CO2 per year\n"
+      "The amplitude between seasonal CO2 concentration oscillations is growing at a pace of 0.024 ppm of CO2 per year,\n",
+      "with a seasonal variation (peak to peak) of around 6.694 ppm in 1958-04-05 00:00:00 to  8.220 ppm in 2023-07-01 00:00:00 \n"
      ]
     },
     {
@@ -490,8 +490,10 @@
     "plt.plot(all_peaks_sorted[\"date\"][:-1],amplitude)\n",
     "plt.title(\"Amplitude in the CO2 concentration between seasonal peaks\")\n",
     "plt.ylabel(\"CO2 concentration [ppm]\")\n",
-    "plt.plot(all_peaks_sorted[\"date\"][:-1],amp_days.dt.days.multiply(amp_fit[0])+amp_fit[1])\n",
-    "print(\"The amplitude between seasonal CO2 concentration oscillations is growing at a pace of {0:.3f} ppm of CO2 per year\".format(365*amp_fit[0]))"
+    "amp_poly = amp_days.dt.days.multiply(amp_fit[0])+amp_fit[1]\n",
+    "plt.plot(all_peaks_sorted[\"date\"][:-1],amp_poly)\n",
+    "print(\"The amplitude between seasonal CO2 concentration oscillations is growing at a pace of {0:.3f} ppm of CO2 per year,\".format(365*amp_fit[0]))\n",
+    "print(\"with a seasonal variation (peak to peak) of around {0:.3f} ppm in {1} to  {2:.3f} ppm in {3} \".format(amp_poly.iloc[0],data[\"date\"][0],amp_poly.iloc[-1],data[\"date\"].iloc[-1]))"
    ]
   },
   {
-- 
2.18.1