diff --git a/video_prediction_tools/main_scripts/main_visualize_postprocess.py b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
index 799a2f997758b24c81883e6def71748d44718ad1..a32461f13a34e6e9f69abd3c6b716cf45390751c 100644
--- a/video_prediction_tools/main_scripts/main_visualize_postprocess.py
+++ b/video_prediction_tools/main_scripts/main_visualize_postprocess.py
@@ -600,12 +600,6 @@ class Postprocess(TrainModel):
         known_eval_metrics = {"mse": Scores("mse", dims), "psnr": Scores("psnr", dims)}
 
         # generate list of functions that calculate requested evaluation metrics
-        for i in self.eval_metrics:
-            print(i)
-
-        print(set(self.eval_metrics).issubset(known_eval_metrics.keys()))
-        print(known_eval_metrics.keys())
-
         if set(self.eval_metrics).issubset(known_eval_metrics.keys()):
             eval_metrics_func = [known_eval_metrics[metric].score_func for metric in self.eval_metrics]
         else:
@@ -1093,7 +1087,7 @@ class Postprocess(TrainModel):
         if not isinstance(ds_in, xr.Dataset):
             raise ValueError("%{0}: ds_in must be a xarray dataset, but is of type {1}".format(method, type(ds_in)))
 
-        if not np.all(varnames in ds_in.data_vars):
+        if not set(varnames).issubset(ds_in.data_vars):
             raise ValueError("%{0}: Could not find all variables ({1}) in input dataset ds_in.".format(method,
                                                                                                        varnames_str))
 
@@ -1104,7 +1098,7 @@ class Postprocess(TrainModel):
             if not isinstance(ds_preexist, xr.Dataset):
                 raise ValueError("%{0}: ds_preexist must be a xarray dataset, but is of type {1}"
                                  .format(method, type(ds_preexist)))
-            if not np.all(varnames in ds_preexist.data_vars):
+            if not set(varnames).issubset(ds_preexist.data_vars):
                 raise ValueError("%{0}: Could not find all varibales ({1}) in pre-existing dataset ds_preexist"
                                  .format(method, varnames_str))