Added loading bars to featurize

ElM2D/ElM2D.py

@@ -422,9 +422,19 @@ def import_embedding(self, path):
 
     def featurize(self, compositions, how="mean"):
         elmd_obj = ElMD(metric=self.metric)
-        vectors = np.ndarray((len(compositions), len(elmd_obj.periodic_tab[self.metric])))
 
-        for i, formula in enumerate(compositions):
-            vectors[i] = ElMD(formula, metric=self.metric, feature_pooling=how).feature_vector
+        vectors = np.ndarray((len(compositions), len(elmd_obj.periodic_tab[self.metric]["H"])))
+
+        if self.verbose:
+            print("Constructing compositionally weighted feature vectors for each composition")
+            for i, formula in tqdm(list(enumerate(compositions))):
+                vectors[i] = ElMD(formula, metric=self.metric, feature_pooling=how).feature_vector
+
+            print("Complete")
+
+
+        else:
+            for i, formula in enumerate(compositions):
+                vectors[i] = ElMD(formula, metric=self.metric, feature_pooling=how).feature_vector
 
         return vectors

setup.py

@@ -2,13 +2,13 @@
 setup(
   name = 'ElM2D',        
   packages = ['ElM2D'],  
-  version = '0.3.3',      
+  version = '0.3.4',      
   license='GPL3',       
   description = 'A high performance mapping class to embed large datasets of ionic compositions with respect to the ElMD metric.',  
   author = 'Cameron Hagreaves',              
   author_email = 'cameron.h@rgreaves.me.uk', 
   url = 'https://github.com/lrcfmd/ElM2D/',   
-  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.3.3.tar.gz',    
+  download_url = 'https://github.com/lrcfmd/ElM2D/archive/0.3.4.tar.gz',    
   keywords = ['ChemInformatics', 'Materials Science', 'Machine Learning', 'Materials Representation'],   
   install_requires=[            
           'cython',