Rename of some methods in normalization class and integration into...

Rename of some methods in normalization class and integration into 'read_frames_and_save_tf_records'-routine

video_prediction/datasets/era5_dataset_v2.py

@@ -201,7 +201,7 @@ class norm_data:
                 
         self.status_ok = True
                 
-    def normalize_var(self,data,varname,norm):
+    def norm_var(self,data,varname,norm):
         
         # some sanity checks
         if not self.status_ok: raise ValueError("norm_data-object needs to be initialized and checked first.")
@@ -217,7 +217,7 @@ class norm_data:
         elif norm == "znorm":
             return((data[...] - getattr(self,varname+"avg"))/getattr(self,varname+"sigma")**2)
         
-    def denormalize_var(self,data,varname,norm):
+    def denorm_var(self,data,varname,norm):
         
         # some sanity checks
         if not self.status_ok: raise ValueError("norm_data-object needs to be initialized and checked first.")        
@@ -238,27 +238,28 @@ def read_frames_and_save_tf_records(output_dir,input_dir,partition_name,vars_in,
     # ML 2020/04/08:
     # Include vars_in for more flexible data handling (normalization and reshaping)
     # and optional keyword argument for kind of normalization
-    known_norms = ["minmax"]     # may be more elegant to define a class here?   
+    
+    if n
 
     output_dir = os.path.join(output_dir,partition_name)
     os.makedirs(output_dir,exist_ok=True)
     
-    norm = norm_data(vars_in)
+    norm_cls  = norm_data(vars_in)
     nvars     = len(vars_in)
-    vars_uni, indrev = np.unique(vars_in,return_inverse=True)
-    if 'norm' in kwargs:
-        norm = kwargs.get("norm")
-        if (not norm in knwon_norms): 
-            raise ValueError("Pass valid normalization identifier.")
-            print("Known identifiers are: ")
-            for norm_name in known_norm:
-                print('"'+norm_name+'"')
-    else:
-        norm = "minmax"
+    #vars_uni, indrev = np.unique(vars_in,return_inverse=True)
+    #if 'norm' in kwargs:
+        #norm = kwargs.get("norm")
+        #if (not norm in knwon_norms): 
+            #raise ValueError("Pass valid normalization identifier.")
+            #print("Known identifiers are: ")
+            #for norm_name in known_norm:
+                #print('"'+norm_name+'"')
+    #else:
+        #norm = "minmax"
     
     # open statistics file and store the dictionary
     with open(os.path.join(input_dir,"statistics.json")) as js_file:
-        norm.check_and_set_norm(json.load(js_file),norm_name)        
+        norm_cls.check_and_set_norm(json.load(js_file),norm_name)        
     
         #if (norm == "minmax"):
             #varmin, varmax = get_stat_allvars(data,"min",vars_in), get_stat_allvars(data,"max",vars_in)
@@ -295,7 +296,7 @@ def read_frames_and_save_tf_records(output_dir,input_dir,partition_name,vars_in,
             # a) normalization should be cast in class definition (with initialization, setting of norm. approach including 
             #    data retrieval and the normalization itself
             for i in range(nvars):    
-                sequences[:,:,:,:,i] = norm.normalize_var(sequences[:,:,:,:,i],vars_in[i],norm_name)
+                sequences[:,:,:,:,i] = norm_cls.norm_var(sequences[:,:,:,:,i],vars_in[i],norm_name)
 
             output_fname = 'sequence_{0}_to_{1}.tfrecords'.format(last_start_sequence_iter, sequence_iter - 1)
             output_fname = os.path.join(output_dir, output_fname)