fix uncertainty whitespace/linting errors

Author: sevyharris

rmgpy/tools/uncertainty.py

@@ -47,7 +47,7 @@ def __init__(self, dG_library=1.5, dG_QM=3.0, dG_GAV=1.5, dG_group=0.1, dG_ADS_c
         """
         Initialize the different uncertainties dG_library, dG_QM, dG_GAV, and dG_other with set values
         in units of kcal/mol.
-        
+
         We expect a uniform distribution for some species free energy G in [Gmin, Gmax].
         dG = (Gmax-Gmin)/2
         """
@@ -82,7 +82,7 @@ def get_uncertainty_value(self, source):
     def get_partial_uncertainty_value(self, source, corr_source_type, corr_param=None, corr_group_type=None):
         """
         Obtain the partial uncertainty dG/dG_corr*dG_corr, where dG_corr is the correlated parameter
-        
+
         `corr_param` is the parameter identifier itself, which is a integer for QM and library parameters, or a string for group values
         `corr_source_type` is a string, being either 'Library', 'QM', 'GAV', 'ADS', or 'Estimation'
         `corr_group_type` is a string used only when the source type is 'GAV' and indicates grouptype
@@ -135,11 +135,12 @@ def get_partial_uncertainty_value(self, source, corr_source_type, corr_param=Non
     def get_uncertainty_factor(self, source):
         """
         Retrieve the uncertainty factor f in kcal/mol when the source of the thermo of a species is given.
-        
+
         This is equivalent to sqrt(3)*dG in a uniform uncertainty interval
         """
         dG = self.get_uncertainty_value(source)
         f = np.sqrt(3) * dG
+        return f
 
 
 class KineticParameterUncertainty(object):
@@ -151,7 +152,7 @@ def __init__(self, dlnk_library=0.5, dlnk_training=0.5, dlnk_pdep=2.0, dlnk_fami
                  dlnk_rule=0.5, dlnk_Surface_Library=2.659, dlnk_family_surface=2.659):
         """
         Initialize the different uncertainties dlnk
-        
+
         We expect a uniform distribution for some reaction kinetics  about ln(k0) in [ln(kmin), ln(kmax)].
         dlnk = (ln(kmax)-ln(kmin))/2
         """
@@ -181,7 +182,7 @@ def get_uncertainty_value(self, source):
         elif 'Training' in source:
             # Should be a single training reaction
             # Although some training entries may be used in reverse,
-            # We still consider the kinetics to be directly dependent 
+            # We still consider the kinetics to be directly dependent
             dlnk += self.dlnk_training
         elif 'Rate Rules' in source:
             family_label = source['Rate Rules'][0]
@@ -231,7 +232,7 @@ def get_uncertainty_value(self, source):
     def get_partial_uncertainty_value(self, source, corr_source_type, corr_param=None, corr_family=None):
         """
         Obtain the partial uncertainty dlnk/dlnk_corr*dlnk_corr, where dlnk_corr is the correlated parameter
-        
+
         `corr_param` is the parameter identifier itself, which is the string identifier of the rate rule
         `corr_source_type` is a string, being either 'Rate Rules', 'Library', 'PDep', 'Training' or 'Estimation'
         `corr_family` is a string used only when the source type is 'Rate Rules' and indicates the family
@@ -303,11 +304,12 @@ def get_partial_uncertainty_value(self, source, corr_source_type, corr_param=Non
     def get_uncertainty_factor(self, source):
         """
         Retrieve the uncertainty factor f when the source of the reaction kinetics are given.
-        
+
         This is equivalent to sqrt(3)/ln(10) * dlnk  in a uniform uncertainty interval
         """
         dlnk = self.get_uncertainty_value(source)
         f = np.sqrt(3) / np.log(10) * dlnk
+        return f
 
 
 class Uncertainty(object):
@@ -340,16 +342,16 @@ def __init__(self, species_list=None, reaction_list=None, output_directory=''):
         if not os.path.exists(self.output_directory):
             try:
                 os.makedirs(self.output_directory)
-            except:
+            except OSError:
                 raise Exception('Uncertainty output directory could not be created.')
 
     def load_database(self, kinetics_families='all', kinetics_depositories=None, thermo_libraries=None, reaction_libraries=None):
         """
         This function loads a single copy of the RMGDatabase with full verbose averaging
-        of the rate rule to trace kinetics sources.  
-        
+        of the rate rule to trace kinetics sources.
+
         By default, this function loads all the kinetics families, only the training kinetics depository,
-        the primaryThermoLibrary, and no reaction libraries.  
+        the primaryThermoLibrary, and no reaction libraries.
         """
         from rmgpy.data.rmg import RMGDatabase
         from rmgpy import settings
@@ -380,7 +382,7 @@ def load_model(self, chemkin_path, dictionary_path, transport_path=None, surface
         """
         Load a RMG-generated model into the Uncertainty class
         `chemkin_path`: path to the chem_annotated.inp CHEMKIN mechanism
-        `dictionary_path`: path to the species_dictionary.txt file 
+        `dictionary_path`: path to the species_dictionary.txt file
         `transport_path`: path to the tran.dat file (optional)
 
         Then create dictionaries stored in self.thermoGroups and self.rateRules
@@ -457,7 +459,7 @@ def extract_sources_from_model(self):
                         # We must retrieve the original saturated molecule's thermo instead of using the radical species as the source of thermo
                         saturated_species, ignore_spc = self.retrieve_saturated_species_from_list(species)
 
-                        if ignore_spc:  # this is saturated species that isn't in the actual model  
+                        if ignore_spc:  # this is saturated species that isn't in the actual model
                             self.extra_species.append(saturated_species)
 
                         if 'Library' in source:
@@ -729,7 +731,7 @@ def sensitivity_analysis(self, initial_mole_fractions, sensitive_species, T, P,
                              sensitivity_threshold=1e-3, number=10, fileformat='.png'):
         """
         Run sensitivity analysis using the RMG solver in a single ReactionSystem object
-        
+
         initial_mole_fractions is a dictionary with Species objects as keys and mole fraction initial conditions
         sensitive_species is a list of sensitive Species objects
         number is the number of top species thermo or reaction kinetics desired to be plotted
@@ -798,7 +800,7 @@ def local_analysis(self, sensitive_species, reaction_system_index=0, correlated=
         output = {}
         for sens_species in sensitive_species:
             csvfile_path = os.path.join(self.output_directory, 'solver',
-                                        'sensitivity_{0}_SPC_{1}.csv'.format(reaction_system_index+1,
+                                        'sensitivity_{0}_SPC_{1}.csv'.format(reaction_system_index + 1,
                                                                              sens_species.index))
             time, data_list = parse_csv_data(csvfile_path)
             # Assign uncertainties
@@ -855,7 +857,7 @@ def local_analysis(self, sensitive_species, reaction_system_index=0, correlated=
                 # Add the reaction index to the data label of the reaction uncertainties
                 # data.index stores the physical index of the reaction + 1, so we convert it to the RMG index here
                 for data in reaction_data_list:
-                    data.label = 'k' + str(self.reaction_list[data.index-1].index) + ': ' + data.label.split()[-1]
+                    data.label = 'k' + str(self.reaction_list[data.index - 1].index) + ': ' + data.label.split()[-1]
 
             if correlated:
                 folder = os.path.join(self.output_directory, 'correlated')