Ajout regression linéaire du cas > MTU

module3/exo3/exercice_python_fr.org

@@ -526,9 +526,41 @@ f"Les coeff sont L = {lmodel.intercept_} et C =  { 1 / lmodel.coef_}"
 #+END_SRC
 
 #+RESULTS:
-: Les coeff sont L = 3.257592785874401 et C =  [2761.3155395]
+: Les coeff sont L = 113.19744441078485 et C =  [9479.50730539]
 
 *** Cas supérieur à la MTU
+#+BEGIN_SRC python :session :results replace
+import pandas as pd
+import matplotlib.pyplot as plt
+from matplotlib import dates
+import numpy as np
+from scipy import stats
+import seaborn as sns
+import statsmodels.api as sm
+from sklearn import linear_model
+
+@plt.FuncFormatter
+def fake_dates(x, pos):
+    """ Custom formater to turn floats into e.g., 2016-05-08"""
+    return dates.num2date(x).strftime('%Y-%m-%d')
+sns.set(color_codes=True)
+df = pd.DataFrame({
+'date': pd.to_datetime(date_g1500),
+'datenum': dates.date2num(date_g1500),
+'T': T_g1500,
+'S': S_g1500})
+fig, ax = plt.subplots()
+sns.regplot(x="datenum", y="T", color='purple', data=df, ax=ax)
+# here's the magic:
+ax.xaxis.set_major_formatter(fake_dates)
+# legible labels
+ax.tick_params(labelrotation=30)
+fig.savefig('stacko_g1500_reglineaireT-f(S).png')
+#+END_SRC
+
+#+RESULTS:
+: None
+
 #+BEGIN_SRC python :session
 from sklearn.linear_model import LinearRegression
 from sklearn.metrics import mean_squared_error
@@ -543,4 +575,4 @@ f"Les coeff sont L = {lmodel.intercept_} et C =  { 1 / lmodel.coef_}"
 #+END_SRC
 
 #+RESULTS:
-: Les coeff sont L = 5.867233082184833 et C =  [441.71908009]
+: Les coeff sont L = 120.04921279789215 et C =  [-555.38712112]