Merge pull request #575 from wbinventor/mgxs-better-comparisons

Remove NumPy Elementwise Comparison Warnings from openmc.mgxs

openmc/mgxs/mgxs.py

@@ -673,14 +673,14 @@ def get_xs(self, groups='all', subdomains='all', nuclides='all',
         filter_bins = []
 
         # Construct a collection of the domain filter bins
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
             for subdomain in subdomains:
                 filters.append(self.domain_type)
                 filter_bins.append((subdomain,))
 
         # Construct list of energy group bounds tuples for all requested groups
-        if groups != 'all':
+        if not isinstance(groups, basestring):
             cv.check_iterable_type('groups', groups, Integral)
             for group in groups:
                 filters.append('energy')
@@ -838,7 +838,7 @@ def get_subdomain_avg_xs(self, subdomains='all'):
         """
 
         # Construct a collection of the subdomain filter bins to average across
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains)
@@ -881,7 +881,7 @@ def print_xs(self, subdomains='all', nuclides='all', xs_type='macro'):
         """
 
         # Construct a collection of the subdomains to report
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
@@ -1010,7 +1010,7 @@ def build_hdf5_store(self, filename='mgxs.h5', directory='mgxs',
             xs_results = h5py.File(filename, 'w')
 
         # Construct a collection of the subdomains to report
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
@@ -1192,7 +1192,7 @@ def get_pandas_dataframe(self, groups='all', nuclides='all',
 
         """
 
-        if groups != 'all':
+        if not isinstance(groups, basestring):
             cv.check_iterable_type('groups', groups, Integral)
         if nuclides != 'all' and nuclides != 'sum':
             cv.check_iterable_type('nuclides', nuclides, basestring)
@@ -1252,7 +1252,7 @@ def get_pandas_dataframe(self, groups='all', nuclides='all',
             columns = ['group in']
 
         # Select out those groups the user requested
-        if groups != 'all':
+        if not isinstance(groups, basestring):
             if 'group in' in df:
                 df = df[df['group in'].isin(groups)]
             if 'group out' in df:
@@ -1789,21 +1789,21 @@ def get_xs(self, in_groups='all', out_groups='all',
         filter_bins = []
 
         # Construct a collection of the domain filter bins
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
             for subdomain in subdomains:
                 filters.append(self.domain_type)
                 filter_bins.append((subdomain,))
 
         # Construct list of energy group bounds tuples for all requested groups
-        if in_groups != 'all':
+        if not isinstance(in_groups, basestring):
             cv.check_iterable_type('groups', in_groups, Integral)
             for group in in_groups:
                 filters.append('energy')
                 filter_bins.append((self.energy_groups.get_group_bounds(group),))
 
         # Construct list of energy group bounds tuples for all requested groups
-        if out_groups != 'all':
+        if not isinstance(out_groups, basestring):
             cv.check_iterable_type('groups', out_groups, Integral)
             for group in out_groups:
                 filters.append('energyout')
@@ -1887,7 +1887,7 @@ def print_xs(self, subdomains='all', nuclides='all', xs_type='macro'):
         """
 
         # Construct a collection of the subdomains to report
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
         elif self.domain_type == 'distribcell':
             subdomains = np.arange(self.num_subdomains, dtype=np.int)
@@ -1926,12 +1926,6 @@ def print_xs(self, subdomains='all', nuclides='all', xs_type='macro'):
             bounds = self.energy_groups.get_group_bounds(group)
             string += template.format('', group, bounds[0], bounds[1])
 
-        if subdomains == 'all':
-            if self.domain_type == 'distribcell':
-                subdomains = np.arange(self.num_subdomains, dtype=np.int)
-            else:
-                subdomains = [self.domain.id]
-
         # Loop over all subdomains
         for subdomain in subdomains:
 
@@ -2135,14 +2129,14 @@ def get_xs(self, groups='all', subdomains='all', nuclides='all',
         filter_bins = []
 
         # Construct a collection of the domain filter bins
-        if subdomains != 'all':
+        if not isinstance(subdomains, basestring):
             cv.check_iterable_type('subdomains', subdomains, Integral)
             for subdomain in subdomains:
                 filters.append(self.domain_type)
                 filter_bins.append((subdomain,))
 
         # Construct list of energy group bounds tuples for all requested groups
-        if groups != 'all':
+        if not isinstance(groups, basestring):
             cv.check_iterable_type('groups', groups, Integral)
             for group in groups:
                 filters.append('energyout')