some modifs + new prms

nutrimorph.py

@@ -80,6 +80,7 @@ data_path = None
 channel = None
 save_images = None
 size_voxel_in_micron = None
+statistical_analysis = None
 soma_low_c = None
 soma_high_c = None
 soma_selem_micron_c = None
@@ -863,7 +864,7 @@ def NutriMorph(data_path: str,
             )
 
             # Keep the connexion only if inferior to the allowed max weighted distance
-            if length is not None:
+            if path.__len__() > 0:
                 length = in_.ToPixel(length, size_voxel_in_micron)
                 if length <= max_weighted_length_c:
                     # Validate and update all the fields + dilate again the whole extension
@@ -1007,8 +1008,9 @@ def NutriMorph(data_path: str,
             soma.skl_graph.add_edge(node, soma_node, root=True)
 
         if save_images is not None:
+
             nx_.draw_networkx(soma.skl_graph)
-            pl_.imsave(f"{save_images}\\graph_{name_file}.png")
+            pl_.savefig(f"{save_images}\\graph_{name_file}_soma{soma.uid}.png")
             pl_.close()
 
         print(": Done")
@@ -1085,11 +1087,47 @@ if __name__ == '__main__':
                     ## TODO + Error in best fitting ellipsoid : JTJ is singular.
                     print(f"WARNING: Error in the running of NutriMorph on {name_file}")
         # Save to .csv in the parent repository
-        concatenated_features_df.to_csv(f"{data_path}_features.csv")
-
-        # --- TODO Clustering with this df and module features_analysis.py
-        # K-means with all the features
-        # PCA with all the features
-        # Select Discriminant features by statistical analysis
-        # K-means with selected features
-        # PCA with selected features
+        concatenated_features_df.to_csv(f"{data_path}\\features.csv")
+
+            # --- TODO Clustering with this df and module features_analysis.py
+        if statistical_analysis:
+            ## -- K-means with all the features (2 conditions)
+
+            ## Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
+            # kmeans = fa_.KmeansOnDF(concatenated_features_df, nb_clusters=(2,), elbow=True, intracluster_var=True)
+
+            ## Between the two conditions, for each duration (2 conditions, 3 durations)
+            # groupby_duration = concatenated_features_df.groupby("Duration")
+            # for duration, values in groupby_duration:
+                # kmeans = fa_.KmeansOnDF(values, nb_clusters=(2,), elbow=True, intracluster_var=True)
+
+            ## -- PCA with all the features
+
+            ## Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
+            # TODO pca = fa_.PCAOnDF(concatenated_features_df)
+
+            ## Between the two conditions, for each duration (2 conditions, 3 durations)
+            # for duration, values in groupby_duration:
+                # pca = fa_.PCAOnDF(values)
+
+            ## -- Select Discriminant features by statistical analysis
+            # TODO filtered_df = SelectFeatures(concatenated_features_df)
+
+            ## -- K-means with selected features
+
+            ## Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
+            # filtered_kmeans = fa_.KmeansOnDF(filtered_df, nb_clusters=(2,), elbow=True, intracluster_var=True)
+
+            ## Between the two conditions, for each duration (2 conditions, 3 durations)
+            # filtered_groupby_duration = filtered_df.groupby("Duration")
+            # for duration, values in filtered_groupby_duration:
+                # filtered_kmeans = fa_.KmeansOnDF(values, nb_clusters=(2,), elbow=True, intracluster_var=True)
+
+            ## -- PCA with selected features
+
+            ## Between the two conditions, regardless the duration of experiment (2 conditions, all durations)
+            # TODO pca = fa_.PCAOnDF(filtered_df)
+
+            ## Between the two conditions, for each duration (2 conditions, 3 durations)
+            # for duration, values in filtered_groupby_duration:
+                # pca = fa_.PCAOnDF(values)
\ No newline at end of file

parameters.ini

@@ -72,6 +72,7 @@ condition
 ; TODO CHO, DIO, ...
 duration
 ; TODO 1H, 3H, 1W, 3W, ...
+statistical_analysis       :    True
 
 [Somas]
 soma_low_c                 :    0.15

search_parameters.py

@@ -54,6 +54,7 @@ channel = parameters['Input']['channel']
 size_voxel_in_micron = IfNotFloat('Input', 'size_voxel_in_micron')
 crop_image = IfNotFloat('Input', 'crop_image')
 save_images = parameters['Input']['save_images']
+statistical_analysis = parameters.getboolean('Input', 'statistical_analysis')
 
 # [Somas]
 soma_low_c = IfNotFloat('Somas', 'soma_low_c')