added Conformations.visualise method to visualise clusters, added lines to main

src/projet8.py

         This function returns a distance matrix from the dissimilarity matrix.
         
         Arguments :
-            - diss_matrix : ndarray obtained from Configurations.dissimilarity
+            - diss_matrix : ndarray 
+                obtained from Configurations.dissimilarity
         """
         diss_matrix = -diss_matrix
         diss_matrix = (diss_matrix - np.min(diss_matrix))/np.ptp(diss_matrix)
         Returns clusters and medoids computed with kmedoids on a distance matrix
         Arguments :
             - matrix : str, ('identity' or 'dissimilarity')
-                       corresponding to the desired distance matrix to
+                       corresponding to the desired distance matrix to be
                        computed
             - ncluster number of clusters to be computed
         """
 
         return (clusters, medoids)
 
+    def visualise(clusters, output_name=None):
+        """
+        Generate an image to visualise clusters. Can currently display up to
+        seven different colors
+        Arguments :
+            - clusters : list of lists 
+                output of the small_kmedoids method
+            
+            - output_name : str
+                desired filename for the image output, if none don't save file
+        """
+        nb_confs = sum([len(x) for x in clusters])
+        x = np.arange(nb_confs)
+        y = np.zeros(nb_confs)
+
+        for i in range(len(clusters_diss)):
+            for j in clusters_diss[i]:
+                y[j] = i+1
+        
+        color = []
+        for i in y:
+            if i == 1:
+                color.append('b')
+            if i == 2:
+                color.append('c')
+            if i == 3:
+                color.append('g')
+            if i == 4:
+                color.append('y')
+            if i == 5:
+                color.append('r')
+            if i == 6:
+                color.append('m')
+            else:
+                color.append('k')
+
+        plt.bar(x, y, width=1.0, color=color)
+        
+        if output_name != None:
+            plt.savefig(output_name)
+
 if __name__ == "__main__":
     if len(sys.argv) < 2:
-        sys.exit("Error : usage '$ python3 projet8 md.pdb'")
+        sys.exit("Error : usage '$ python3 projet8 md.pdb ncluster(int)'")
     
+    # Initialization of the class, meaning loading the pdb and writing pd.df
     confs = Conformations(sys.argv[1])
+    nclusters = int(sys.argv[2])
+    
+    # Computation of the distance matrixes
+    confs.identity()
+    confs.dissimilarity()
+
+    # Running the kmedoids algorithm on the identity distance matrix
+    clusters_idt = confs.small_kmedoids('identity', nclusters)
+    medoids_idt = clusters_idt[1]
+    clusters_idt = clusters_idt[0]
+
+    # Running the kmedoids algorithm on the dissimilarity distance matrix
+    clusters_diss = confs.small_kmedoids('dissimilarity', nclusters)
+    medoids_diss = clusters_diss[1]
+    clusters_diss = clusters_diss[0]
+    
     
 
     print(confs.df)