TODO: store git sha

filter_functions.py

@@ -10,6 +10,7 @@ transient dimension, e.g. time, should be 4th dimension of input data
 
 
 TODO: add GPU support (CUDA, cupy, cucim)
+TODO: store git commit sha
 
 """
 
@@ -76,6 +77,10 @@ class image_filter:
         self.chunks = chunksize
         self.outchunks = outchunks
         
+        #wheter considering means for first and last time step
+        self.take_means = True
+        self.num_means = 7
+        
         # not sure if this is clever, does dask understand that this data is reused?
         self.Gaussian_4D_dict = {}
         self.Gaussian_space_dict = {}
@@ -209,10 +214,15 @@ class image_filter:
             self.calculated_features.append(DG)
             self.feature_names.append(name)
 
-    def diff_to_first_and_last(self):
+    def diff_to_first_and_last(self, take_mean, means):
+#         TODO: take temporal mean/median for first and last
         DA = self.data
-        first = DA[...,0]
-        last = DA[...,-1]
+        if take_mean:
+            first = DA[...,:means].mean(axis=-1)
+            last = DA[...,-means:].mean(axis=-1)
+        else:
+            first = DA[...,0]
+            last = DA[...,-1]
         if type(first) is not np.ndarray:
             first = first.compute()
             last = last.compute()
@@ -358,6 +368,7 @@ class image_filter:
     
     # TODO: include feature selection either in compute (better) or save
     # TODO: maybe add purge function
+    # TODO: maybe add iterative segmentation results, i.e. median filter of segmentation
     def prepare(self):   
         self.Gaussian_4D_dict = {}
         self.Gaussian_space_dict = {}
@@ -366,7 +377,7 @@ class image_filter:
         self.calculated_features = []
         self.feature_names = []
         
-        self.diff_to_first_and_last() 
+        self.diff_to_first_and_last(self.take_means, self.num_means) 
         self.Gaussian_4D_stack()
         self.diff_Gaussian('4D')
         self.Gradients()
@@ -376,6 +387,7 @@ class image_filter:
         self.Gaussian_space_stack()
         self.diff_Gaussian('space')
         # self.rank_filter_stack() #you have to load the entire raw data set for this filter --> not so good for many time steps
+        
         self.prepared = True
 
     

segmentation.py

@@ -1,3 +1,8 @@
+"""
+TODO: stor git_sha
+"""
+
+
 import xarray as xr
 import pickle
 import os

training_functions.py

@@ -4,7 +4,7 @@ Created on Wed Aug 17 16:04:34 2022
 
 to be loaded in Jupyter
 
-
+TODO: store git commit sha
 @author: fische_r
 """
 import xarray as xr
@@ -293,6 +293,9 @@ class train_segmentation:
         plt.xticks(rotation=90)
         plt.ylabel('importance') 
         
+    def pickle_training_dict(self):
+        pickle.dump(self.training_dict, open(os.path.join(self.training_path, 'training_dict.p'),'wb'))
+        
     def pickle_classifier(self):
         pickle.dump(self.clf, open(os.path.join(self.training_path, 'classifier.p'),'wb'))
     
@@ -303,6 +306,7 @@ class train_segmentation:
         labelnames = os.listdir(path)
         flag = True
         for label_name in labelnames:
+            print(label_name)
             X, y = training_set_per_image(label_name, path, feat_data, self.lazy)
             training_dict[label_name] = X,y
             if flag:
@@ -318,6 +322,7 @@ class train_segmentation:
         self.clf = clf
         self.training_dict = training_dict
         
+        
     def train_parallel(self):
     #come up with a way to train() in parallel
     # maybe with dask.delayed