Adopt the unitest for main_visulization_postprocess for Michael update version

test/test_visualize_postprocess.py

@@ -59,9 +59,9 @@ def test_run_deterministic(vis_case1):
     vis_case1.init_session()
     vis_case1.restore(vis_case1.sess,vis_case1.checkpoint)
     vis_case1.sample_ind = 0
-    vis_case1.input_results,vis_case1.input_images_denorm_all, vis_case1.t_starts = vis_case1.get_input_data_per_batch(vis_case1.inputs) 
-    assert len(vis_case1.t_starts) == batch_size
-    ts_1 = vis_case1.t_starts[0][0]
+    input_results,input_images_denorm_all,t_starts = vis_case1.get_input_data_per_batch(vis_case1.inputs) 
+    assert len(t_starts) == batch_size
+    ts_1 = t_starts[0][0]
     year = str(ts_1)[:4]
     month = str(ts_1)[4:6]
     filename = "ecmwf_era5_" +  str(ts_1)[2:] + ".nc"
@@ -72,36 +72,42 @@ def test_run_deterministic(vis_case1):
     t2_var = np.array(t2_var)    
     t2_max = np.max(t2_var[117:173,0:92])
     t2_min = np.min(t2_var[117:173,0:92])
-    input_image = np.array(vis_case1.input_images_denorm_all)[0,0,:,:,0] #get the first batch id and 1st sequence image
+    input_image = np.array(input_images_denorm_all)[0,0,:,:,0] #get the first batch id and 1st sequence image
     input_img_max = np.max(input_image)
     input_img_min = np.min(input_image)
     print("input_image",input_image[0,:10])
     assert t2_max == input_img_max
     assert t2_min == input_img_min
-   
-    feed_dict = {input_ph: vis_case1.input_results[name] for name, input_ph in vis_case1.inputs.items()}
+    sample_ind = 0 
+    feed_dict = {input_ph: input_results[name] for name, input_ph in vis_case1.inputs.items()}
     gen_images = vis_case1.sess.run(vis_case1.video_model.outputs['gen_images'], feed_dict=feed_dict)
-
+    gen_images_denorm = vis_case1.denorm_images_all_channels(gen_images, vis_case1.vars_in, vis_case1.norm_cls,
+                                                                norm_method="minmax")
     ############Test persistenct value#############
-    vis_case1.ts = Postprocess.generate_seq_timestamps(vis_case1.t_starts[0], len_seq=vis_case1.sequence_length)
-    vis_case1.get_and_plot_persistent_per_sample(sample_id=0)
-    ts_1_per = (datetime.datetime.strptime(str(ts_1), '%Y%m%d%H') - datetime.timedelta(hours=23)).strftime("%Y%m%d%H")
+    times_0, init_times = vis_case1.get_init_time(t_starts)
+    batch_ds = vis_case1.create_dataset(input_images_denorm_all, gen_images_denorm, init_times)
+    nbs = np.minimum(vis_case1.batch_size, vis_case1.num_samples_per_epoch - sample_ind)
+    times_seq = (pd.date_range(times_0[0], periods=int(vis_case1.sequence_length), freq="h")).to_pydatetime() 
+    persistence_seq, _ = Postprocess.get_persistence(times_seq, vis_case1.input_dir_pkl)
+    ts_1_per = (pd.to_datetime(times_0[0]) -  datetime.timedelta(hours=23)).strftime("%Y%m%d%H")
+   
     year_per = str(ts_1_per)[:4]
     month_per = str(ts_1_per)[4:6]
     filename_per = "ecmwf_era5_" +  str(ts_1_per)[2:] + ".nc"
-    fl_per = os.path.join("/p/project/deepacf/deeprain/video_prediction_shared_folder/extractedData",year_per,month_per,filename_per)
+ 
+    fl_per = os.path.join("/p/scratch/deepacf/deeprain/ambs_era5/extractedData",year_per,month_per,filename_per)
     with Dataset(fl_per,"r")  as data_file:
        t2_var_per = data_file.variables["2t"][0,117:173,0:92]    
      
     t2_per_var = np.array(t2_var_per)
     t2_per_max = np.max(t2_per_var)
-    per_image_max = np.max(vis_case1.persistence_images[0])
+    per_image_max = np.max(persistence_seq[0])
     assert t2_per_max == per_image_max
 
 
 
-def test_run_determinstic_quantile_plot(vis_case1):
-    vis_case1.init_metric_ds()
+#def test_run_determinstic_quantile_plot(vis_case1):
+#    vis_case1.init_metric_ds()