diff --git a/pipt/loop/assimilation.py b/pipt/loop/assimilation.py
index be9272b6..305f9a8a 100644
--- a/pipt/loop/assimilation.py
+++ b/pipt/loop/assimilation.py
@@ -536,12 +536,12 @@ def post_process_forecast(self):
                                 self.ensemble.pred_data[i][k] = np.zeros(
                                     (len(self.ensemble.obs_data[i][k]), self.ensemble.ne))
                             for m in range(pred_data_tmp[i][k].shape[1]):
-                                data_array = self.ensemble.compress(pred_data_tmp[i][k][:, m], vintage,
+                                data_array = self.ensemble.compress_manager(pred_data_tmp[i][k][:, m], vintage,
                                                                     self.ensemble.sparse_info['use_ensemble'])
                                 self.ensemble.pred_data[i][k][:, m] = data_array
                             vintage = vintage + 1
             if self.ensemble.sparse_info['use_ensemble']:
-                self.ensemble.compress()
+                self.ensemble.compress_manager()
                 self.ensemble.sparse_info['use_ensemble'] = None
 
         # Extra option debug
diff --git a/pipt/loop/ensemble.py b/pipt/loop/ensemble.py
index b986a76e..1d997a15 100644
--- a/pipt/loop/ensemble.py
+++ b/pipt/loop/ensemble.py
@@ -287,7 +287,7 @@ def _org_obs_data(self):
                     if self.sparse_info is not None and \
                             vintage < len(self.sparse_info['mask']) and \
                             len(data_array) == int(np.sum(self.sparse_info['mask'][vintage])):
-                        data_array = self.compress(data_array, vintage, False)
+                        data_array = self.compress_manager(data_array, vintage, False)
                         vintage = vintage + 1
 
                     # Save array in obs_data. If it is an array with single value (not list), then we convert it to a
@@ -321,7 +321,7 @@ def _org_obs_data(self):
                         if self.sparse_info is not None and \
                                 vintage < len(self.sparse_info['mask']) and \
                                 len(data_array) == int(np.sum(self.sparse_info['mask'][vintage])):
-                            data_array = self.compress(data_array, vintage, False)
+                            data_array = self.compress_manager(data_array, vintage, False)
                             vintage = vintage + 1
 
                         # Save array in obs_data. If it is an array with single value (not list), then we convert it to a
@@ -645,7 +645,7 @@ def save_temp_state_ml(self, ind_save):
         self.temp_state[ind_save] = deepcopy(self.state)
         np.savez('temp_state_ml', self.temp_state)
 
-    def compress(self, data=None, vintage=0, aug_coeff=None):
+    def compress_manager(self, data=None, vintage=0, aug_coeff=None):
         """
         Compress the input data using wavelets.
 
diff --git a/pipt/misc_tools/extract_tools.py b/pipt/misc_tools/extract_tools.py
index c59d2d52..622705d7 100644
--- a/pipt/misc_tools/extract_tools.py
+++ b/pipt/misc_tools/extract_tools.py
@@ -260,8 +260,8 @@ def organize_sparse_representation(info: Union[dict,list]) -> dict:
 
     # Redefine all 'yes' and 'no' values to bool
     for key, val in info.items():
-        if val == 'yes': info[key] == True
-        if val == 'no':  info[key] == False
+        if val == 'yes': info[key] = True
+        if val == 'no':  info[key] = False
 
     # Intial dict
     sparse = {}