added verbose boolean variable

dingo/preprocess.py

@@ -9,27 +9,32 @@
 
 class PreProcess:
 
-    def __init__(self, model, tol=1e-6, open_exchanges=False):
+    def __init__(self, model, tol = 1e-6, open_exchanges = False, verbose = False):
 
         """
-        model parameter gets a cobra model as input
+        model -- parameter gets a cobra model as input
         
-        tol parameter gets a cutoff value used to classify 
-        zero-flux and mle reactions and compare FBA solutions
-        before and after reactions removal
+        tol -- parameter gets a cutoff value used to classify 
+               zero-flux and mle reactions and compare FBA solutions
+               before and after reactions removal
         
-        open_exchanges parameter is used in the function that identifies blocked reactions
-        It controls whether or not to open all exchange reactions to very high flux ranges
+        open_exchanges -- parameter is used in the function that identifies blocked reactions
+                          It controls whether or not to open all exchange reactions 
+                          to very high flux ranges.
+                          
+        verbose -- A boolean type variable that if True 
+                   additional information for preprocess is printed.
         """
 
         self._model = model
         self._tol = tol
-
-        if self._tol > 1e-6:
-            print("Tolerance value set to",self._tol,"while default value is 1e-6. A looser check will be performed")
-
+
         self._open_exchanges = open_exchanges
+        self._verbose = verbose
 
+        if self._tol > 1e-6 and verbose == True:
+            print("Tolerance value set to",self._tol,"while default value is 1e-6. A looser check will be performed")
+
         self._objective = self._objective_function()
         self._initial_reactions = self._initial()
         self._reaction_bounds_dict = self._reaction_bounds_dictionary()
@@ -204,8 +209,9 @@ def reduce(self, extend=False):
 
         elif extend == False:
 
-            print(len(self._removed_reactions), "of the", len(self._initial_reactions), \
-            "reactions were removed from the model with extend set to", extend) 
+            if self._verbose == True:
+                print(len(self._removed_reactions), "of the", len(self._initial_reactions), \
+                "reactions were removed from the model with extend set to", extend) 
 
             # call this functon to convert cobra to dingo model
             self._dingo_model = MetabolicNetwork.from_cobra_model(self._model)
@@ -266,10 +272,11 @@ def reduce(self, extend=False):
                             # restore bounds
                             self._model.reactions.get_by_id(reaction).bounds = self._reaction_bounds_dict[reaction]
 
-
-            print(len(self._removed_reactions), "of the", len(self._initial_reactions), \
-            "reactions were removed from the model with extend set to", extend)
-            print(additional_removed_reactions_count, "additional reaction(s) removed")
+
+            if self._verbose == True:  
+                print(len(self._removed_reactions), "of the", len(self._initial_reactions), \
+                "reactions were removed from the model with extend set to", extend)
+                print(additional_removed_reactions_count, "additional reaction(s) removed")
 
             # call this functon to convert cobra to dingo model
             self._dingo_model = MetabolicNetwork.from_cobra_model(self._model)

dingo/utils.py

@@ -205,7 +205,7 @@ def get_matrices_of_full_dim_polytope(A, b, Aeq, beq):
 
 
 def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, indicator_cutoff = 10, 
-                         cells = 10, cop_coeff = 0.3, lower_triangle = True):
+                         cells = 10, cop_coeff = 0.3, lower_triangle = True, verbose = False):
     """A Python function to calculate the pearson correlation matrix of a model
        and filter values based on the copula's indicator
 
@@ -217,6 +217,7 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind
     cells -- Number of cells to compute the copula
     cop_coeff -- A value that narrows or widens the width of the copula's diagonal
     lower_triangle -- A boolean variable that if True plots only the lower triangular matrix
+    verbose -- A boolean variable that if True additional information is printed as an output.
     """
 
     if cop_coeff > 0.4 or cop_coeff < 0.2:
@@ -328,7 +329,8 @@ def correlated_reactions(steady_states, reactions=[], pearson_cutoff = 0.90, ind
                                                                'indicator': indicator, 
                                                                'classification': "no correlation"}
 
-            print("Completed process of",i+1,"from",corr_indices.shape[0],"copulas")
+            if verbose == True:
+                print("Completed process of",i+1,"from",corr_indices.shape[0],"copulas")
 
         if lower_triangle == True:
             filtered_corr_matrix[np.triu_indices(filtered_corr_matrix.shape[0], 1)] = np.nan

tests/correlation.py

@@ -19,7 +19,8 @@ def test_correlation(self):
                                         reactions = reactions,
                                         indicator_cutoff = 5,
                                         pearson_cutoff = 0.999999,
-                                        lower_triangle = False)
+                                        lower_triangle = False,
+                                        verbose = False)
 
         # sum values in the diagonal of the correlation matrix ==> 95*pearson ==> 95*1
         self.assertTrue(np.trace(corr_matrix) == len(reactions))
@@ -38,7 +39,8 @@ def test_correlation(self):
         corr_matrix = correlated_reactions(steady_states, 
                                            indicator_cutoff = 0,
                                            pearson_cutoff = 0,
-                                           lower_triangle = True)
+                                           lower_triangle = True,
+                                           verbose = False)
 
         # sum values in the diagonal of the correlation matrix ==> 95*pearson ==> 95*1
         self.assertTrue(np.trace(corr_matrix) == len(reactions))

tests/preprocess.py

@@ -21,7 +21,7 @@ def test_preprocess(self):
 
         # call the reduce function from the PreProcess class 
         # with extend=False to remove reactions from the model        
-        obj = PreProcess(cobra_model, tol=1e-5, open_exchanges=False)  
+        obj = PreProcess(cobra_model, tol=1e-5, open_exchanges=False, verbose=False)  
         removed_reactions, final_dingo_model = obj.reduce(extend=False)      
 
         # calculate the count of removed reactions with extend set to False        
@@ -46,7 +46,7 @@ def test_preprocess(self):
 
         # call the reduce function from the PreProcess class 
         # with extend=True to remove additional reactions from the model        
-        obj = PreProcess(cobra_model, tol=1e-6, open_exchanges=False)        
+        obj = PreProcess(cobra_model, tol=1e-6, open_exchanges=False, verbose=False)        
         removed_reactions, final_dingo_model = obj.reduce(extend=True)        
 
         # calculate the count of removed reactions with extend set to True