Add verbose keyword

FRETpredict/FRET.py

@@ -149,9 +149,13 @@ def __init__(self, protein, residues, **kwargs):
         self.weights = kwargs.get('weights', False)
         self.user_weights = kwargs.get('user_weights', None)
         self.stdev = kwargs.get('filter_stdev', 0.02)
+        self.verbose = kwargs.get('verbose', False)
 
         # Logging set up
-        logging.basicConfig(filename=kwargs.get('log_file', 'log'), level=logging.INFO)
+        if self.verbose:
+            logging.basicConfig(filename=kwargs.get('log_file', 'log'), level=logging.INFO)
+        else:
+            logging.basicConfig(filename=kwargs.get('log_file', 'log'), level=logging.DEBUG)
 
         # Write the string for the placement residues selection
         for i in range(2):
@@ -420,7 +424,7 @@ def trajectoryAnalysis(self):
                     # If dyes pair has no spectral overlap, skip iteration
                     if self.r0 == 0:
 
-                        print('\nDyes pair R0 = 0!')
+                        logging.debug('Dyes pair R0 = 0!')
                         continue
 
                 # Calculate (r/r0)^6 factor for FRET efficiency calculations
@@ -583,7 +587,6 @@ def save(self, **kwargs):
         # k2, Static, Dynamic1, and Dynamic2 averaging
         # Create DataFrame if only one value of k2 is present (only 1 protein structure/frame provided)
         if k2.size == 1:
-
             df = pd.Series([k2, estatic, edynamic1, edynamic2], index=['k2', 'Estatic', 'Edynamic1', 'Edynamic2'])
 
         # Create DataFrame of weighted averaged values
@@ -595,7 +598,7 @@ def save(self, **kwargs):
                                self.weightedAvgSDSE(edynamic2[np.isfinite(k2)], self.weights[np.isfinite(k2)])],
                               columns=['Average', 'SD', 'SE'], index=['k2', 'Estatic', 'Edynamic1', 'Edynamic2'])
 
-        print(f'Effective fraction of frames contributing to average: {self.fraction_frames()}')
+        logging.debug(f'Effective fraction of frames contributing to average: {self.fraction_frames()}')
 
         # Save DataFrame in pickle format
         df.to_pickle(output_reweight_prefix + '-data-{:d}-{:d}.pkl'.format(self.residues[0], self.residues[1]))
@@ -644,4 +647,4 @@ def run(self):
         # Calculate k2 distribution and k2, Static, Dynamic1, Dynamic2 averaging. Save data to file.
         self.save()
 
-        print('\nDone.')
+        logging.info('Done')

FRETpredict/libraries.py

@@ -22,7 +22,6 @@
 #: Name of the directory in the package that contains the library data.
 LIBDIR = "lib"
 
-
 def find_file(filename, pkglibdir=LIBDIR):
 
     """
@@ -116,18 +115,16 @@ def __init__(self, name):
         self.lib['filename'] = find_file(self.lib['filename'][:-2] + name.split(' cutoff')[1])
 
         # Print logging information
-        logger.debug("[rotamers] ensemble {:s} with topology {:s}.pdb".format(self.lib['filename'],
+        logger.info("[rotamers] ensemble {:s} with topology {:s}.pdb".format(self.lib['filename'],
                                                                               self.lib['filename'].split('_cutoff')[0]))
-        logger.debug("[rotamers] populations {:s}".format(self.lib['filename'] + '_weights.txt'))
+        logger.info("[rotamers] populations {:s}".format(self.lib['filename'] + '_weights.txt'))
 
         # If trajectory is not found
         if not os.path.isfile(self.lib['filename'] + '.dcd'):
-
             raise ValueError("No trajectory named {0}.dcd".format(self.lib['filename']))
 
         # If weights are not found
         if not os.path.isfile(self.lib['filename'] + '_weights.txt'):
-
             raise ValueError("No file named {0}_weights.txt".format(self.lib['filename'] + '_weights.txt'))
 
         # Rotamers parameters
@@ -161,5 +158,4 @@ def __repr__(self):
 
         """
 
-        return "<RotamerLibrary '{0}' by {1} with {2} rotamers>".format(self.name, self.lib['author'],
-                                                                        len(self.weights) - 2)
+        return "<RotamerLibrary '{0}' by {1} with {2} rotamers>".format(self.name, self.lib['author'], len(self.weights) - 2)

FRETpredict/utils.py

@@ -305,10 +305,11 @@ def rotamer_placement(self, universe, prot_atoms, lib):
         universe.load_new(probe_coords, format=MemoryReader, order='afc')
 
         # Save aligned rotamers
-        rotamers = universe.select_atoms("all")
-        with MDAnalysis.Writer(lib.name + ".pdb", rotamers.n_atoms) as W:
-            for ts in universe.trajectory:
-                W.write(rotamers)
+        if self.verbose:
+            rotamers = universe.select_atoms("all")
+            with MDAnalysis.Writer(lib.name.replace(' ','_')+'.pdb', rotamers.n_atoms) as W:
+                for ts in universe.trajectory:
+                    W.write(rotamers)
 
         return universe
 
@@ -491,7 +492,7 @@ def calculate_ws(self):
                           skip_footer=0,
                           delimiter=' ')
 
-        # Check if single-frame structure                  
+        # Check if single-frame structure
         if np.shape(Z) == (2,):
 
             w_s = np.array([1.0])