add unitest for testing eval_metric_ds

test/test_visualize_postprocess.py

@@ -56,9 +56,12 @@ def test_get_data_params(vis_case1):
     assert vis_case1.future_length == 12
 
 def test_run_deterministic(vis_case1):
+    vis_case1.num_samples_per_epoch = 20
     vis_case1.init_session()
     vis_case1.restore(vis_case1.sess,vis_case1.checkpoint)
-    vis_case1.sample_ind = 0
+    print("fcast-product",vis_case1.fcst_products)
+    eval_metric_ds = Postprocess.init_metric_ds(vis_case1.fcst_products, vis_case1.eval_metrics, vis_case1.vars_in[vis_case1.channel], vis_case1.num_samples_per_epoch, vis_case1.future_length)
+
     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]
@@ -87,6 +90,7 @@ def test_run_deterministic(vis_case1):
     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")
@@ -105,6 +109,30 @@ def test_run_deterministic(vis_case1):
     assert t2_per_max == per_image_max
     
 
+    ##Test evaluation metric
+    for ivar, var in enumerate(vis_case1.vars_in):
+        batch_ds["{0}_persistence_fcst".format(var)].loc[dict(init_time=init_times[0])] = \
+                        persistence_seq[vis_case1.context_frames-1:, :, :, ivar]
+        
+    eval_metric_ds = vis_case1.populate_eval_metric_ds(eval_metric_ds,batch_ds,sample_ind,vis_case1.vars_in[vis_case1.channel])
+    ##now manuly calculate the mse and see if values is the same as the ones in eval_metric_ds
+    #calculate the mse between generateed images and reference images
+    sample_gen = gen_images_denorm[0,vis_case1.context_frames-1:,:,:,vis_case1.channel]  
+    sample_ref = input_images_denorm_all[0,vis_case1.context_frames:,:,:,vis_case1.channel]
+    sample_gen_ref_mse_t0 = np.mean((sample_gen[0] - sample_ref[0])**2)
+    metric_name = "2t_savp_mse"
+    print("eval_metric_ds",eval_metric_ds)
+    assert eval_metric_ds[metric_name][0,0] == sample_gen_ref_mse_t0
+    sample_gen_ref_mse_t5 = np.mean((sample_gen[5] - sample_ref[5])**2)
+    assert eval_metric_ds[metric_name][0,5] == sample_gen_ref_mse_t5   
+
+
+
+       
+
+
+    
+       
 
 #def test_run_determinstic_quantile_plot(vis_case1):
 #    vis_case1.init_metric_ds()