some bug fixes

filter_functions.py

@@ -491,7 +491,7 @@ class image_filter:
             print('prepare first')
         else:
             self.feature_stack = dask.array.stack(self.calculated_features, axis = 4)
-            self.feature_stack_time_independent = dask.array.stack(self.calculated_features, axis=3)
+            self.feature_stack_time_independent = dask.array.stack(self.calculated_features_time_independent, axis=3)
             shp = self.feature_stack_time_independent.shape
             self.feature_stack_time_independent = self.feature_stack_time_independent.reshape(shp[0],shp[1],shp[2],1,shp[3])
             # TODO: rechunk?

training_functions.py

@@ -108,7 +108,7 @@ def training_set_per_image(label_name, trainingpath, feat_data, lazy = False):
         # temporary workaround, make general
         if c1 == 'x' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(x = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, time_0 = p2).data
         elif c1 == 'x' and c2 == 'y':
             feat_stack = feat_data['feature_stack'].sel(x = p1, y = p2).data
             feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, y = p2).data
@@ -120,10 +120,10 @@ def training_set_per_image(label_name, trainingpath, feat_data, lazy = False):
             feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, z = p2).data
         elif c1 == 'y' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(y = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, time_0 = p2).data
         elif c1 == 'z' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(z = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(z = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(z = p1, time_0 = p2).data
         else:
             print('coordinates not found')
         # if lazy:
@@ -175,8 +175,9 @@ class train_segmentation:
         self.raw_data = rawdata
         self.feat_data = data
         # self.feat_data_tme_idp = data['feature_stack_time_independent']
-        self.feature_names = self.feat_data['feature'].data
-        self.feature_names_time_independent = self.feat_data['feature_time_independent'].data
+        self.feature_names = list(self.feat_data['feature'].data)
+        self.feature_names_time_independent = list(self.feat_data['feature_time_independent'].data)
+        
         self.combined_feature_names = self.feature_names + self.feature_names_time_independent
         self.lazy = lazy
 
@@ -283,7 +284,7 @@ class train_segmentation:
         
         if c1 == 'x' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(x = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, time_0 = p2).data
         elif c1 == 'x' and c2 == 'y':
             feat_stack = feat_data['feature_stack'].sel(x = p1, y = p2).data
             feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(x = p1, y = p2).data
@@ -295,10 +296,10 @@ class train_segmentation:
             feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, z = p2).data
         elif c1 == 'y' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(y = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(y = p1, time_0 = p2).data
         elif c1 == 'z' and c2 == 'time':
             feat_stack = feat_data['feature_stack'].sel(z = p1, time = p2).data
-            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(z = p1, time = p2).data
+            feat_stack_t_idp = feat_data['feature_stack_time_independent'].sel(z = p1, time_0 = p2).data
             
                    
         self.current_feat_stack = dask.array.concatenate([feat_stack, feat_stack_t_idp], axis = 2)