adding change of clusters number

Visualisation_des_donnes_de_mapping.py

@@ -5,7 +5,7 @@ import matplotlib.pyplot as plt
 import seaborn as sns
 
 #### Importation des données
-def Distance_matrix_clustering (input_file,val_infini=50.2):
+def Distance_matrix_clustering (input_file,val_infini=50.2, n_clusters=5):
 
     dataframe= pd.read_excel(input_file,index_col = 0)
     print(dataframe.head())
@@ -48,7 +48,7 @@ def Distance_matrix_clustering (input_file,val_infini=50.2):
 
 
 ##### Traitement de la matrice de distance
-    nombre_de_clusters=7
+    nombre_de_clusters=n_clusters
     clustering = AgglomerativeClustering(n_clusters = nombre_de_clusters,  metric = "precomputed",linkage="complete").fit(matrice_pour_traitement)
 #results_clustering=pd.DataFrame ({'in': [clustering.labels_] })
     results_clustering=clustering.labels_
@@ -71,7 +71,7 @@ def Distance_matrix_clustering (input_file,val_infini=50.2):
 
 ###### visualisation
 
-    l_identifiants_de_graphes=[111,111,111,111,111,111,111,111]
+    l_identifiants_de_graphes=[111,111,111,111,111,111,111,111,111,111,111,111,111,111]
 
     plt.figure(figsize=(15, 3))
     for num_graph in range (nombre_de_clusters):
@@ -82,8 +82,8 @@ def Distance_matrix_clustering (input_file,val_infini=50.2):
             print(matrice_pour_traitement[Liste_d_indice_trier_selon_cluster[num_graph][0]][Liste_d_indice_trier_selon_cluster[num_graph][sous_indice]])
         plt.subplot(indentifiant_graphe)
         plt.plot(Liste_de_nom_trier_selon_cluster[num_graph],donnes_graph )
-
-    plt.suptitle('les differents clusters n=7 ')
+        plt.tick_params(axis='x', rotation=90, size=0.08,labelsize = 7)
+    plt.suptitle('les differents clusters n='+str(n_clusters))
     plt.show()
 
 #Distance_matrix_clustering("C:\\Users\\mumec\\Desktop\\Mini_codes\\DistanceMatrix_compose_interet.xlsx")