Prepare for NumPy 2.0 (openmc-dev#2845)

openmc/data/correlated.py

@@ -113,7 +113,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_(self._name)
+        group.attrs['type'] = np.bytes_(self._name)
 
         dset = group.create_dataset('energy', data=self.energy)
         dset.attrs['interpolation'] = np.vstack((self.breakpoints,

openmc/data/energy_distribution.py

@@ -277,7 +277,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('maxwell')
+        group.attrs['type'] = np.bytes_('maxwell')
         group.attrs['u'] = self.u
         self.theta.to_hdf5(group, 'theta')
 
@@ -410,7 +410,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('evaporation')
+        group.attrs['type'] = np.bytes_('evaporation')
         group.attrs['u'] = self.u
         self.theta.to_hdf5(group, 'theta')
 
@@ -556,7 +556,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('watt')
+        group.attrs['type'] = np.bytes_('watt')
         group.attrs['u'] = self.u
         self.a.to_hdf5(group, 'a')
         self.b.to_hdf5(group, 'b')
@@ -728,7 +728,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('madland-nix')
+        group.attrs['type'] = np.bytes_('madland-nix')
         group.attrs['efl'] = self.efl
         group.attrs['efh'] = self.efh
         self.tm.to_hdf5(group)
@@ -846,7 +846,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('discrete_photon')
+        group.attrs['type'] = np.bytes_('discrete_photon')
         group.attrs['primary_flag'] = self.primary_flag
         group.attrs['energy'] = self.energy
         group.attrs['atomic_weight_ratio'] = self.atomic_weight_ratio
@@ -945,7 +945,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('level')
+        group.attrs['type'] = np.bytes_('level')
         group.attrs['threshold'] = self.threshold
         group.attrs['mass_ratio'] = self.mass_ratio
 
@@ -1074,7 +1074,7 @@ def to_hdf5(self, group):
 
         """
 
-        group.attrs['type'] = np.string_('continuous')
+        group.attrs['type'] = np.bytes_('continuous')
 
         dset = group.create_dataset('energy', data=self.energy)
         dset.attrs['interpolation'] = np.vstack((self.breakpoints,

openmc/data/function.py

@@ -364,7 +364,7 @@ def to_hdf5(self, group, name='xy'):
         """
         dataset = group.create_dataset(name, data=np.vstack(
             [self.x, self.y]))
-        dataset.attrs['type'] = np.string_(type(self).__name__)
+        dataset.attrs['type'] = np.bytes_(type(self).__name__)
         dataset.attrs['breakpoints'] = self.breakpoints
         dataset.attrs['interpolation'] = self.interpolation
 
@@ -460,7 +460,7 @@ def to_hdf5(self, group, name='xy'):
 
         """
         dataset = group.create_dataset(name, data=self.coef)
-        dataset.attrs['type'] = np.string_(type(self).__name__)
+        dataset.attrs['type'] = np.bytes_(type(self).__name__)
 
     @classmethod
     def from_hdf5(cls, dataset):
@@ -592,7 +592,7 @@ def to_hdf5(self, group, name='xy'):
 
         """
         sum_group = group.create_group(name)
-        sum_group.attrs['type'] = np.string_(type(self).__name__)
+        sum_group.attrs['type'] = np.bytes_(type(self).__name__)
         sum_group.attrs['n'] = len(self.functions)
         for i, f in enumerate(self.functions):
             f.to_hdf5(sum_group, f'func_{i+1}')

openmc/data/kalbach_mann.py

@@ -366,7 +366,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('kalbach-mann')
+        group.attrs['type'] = np.bytes_('kalbach-mann')
 
         dset = group.create_dataset('energy', data=self.energy)
         dset.attrs['interpolation'] = np.vstack((self.breakpoints,

openmc/data/multipole.py

@@ -1273,7 +1273,7 @@ def export_to_hdf5(self, path, mode='a', libver='earliest'):
 
         # Open file and write version.
         with h5py.File(str(path), mode, libver=libver) as f:
-            f.attrs['filetype'] = np.string_('data_wmp')
+            f.attrs['filetype'] = np.bytes_('data_wmp')
             f.attrs['version'] = np.array(WMP_VERSION)
 
             g = f.create_group(self.name)

openmc/data/nbody.py

@@ -91,7 +91,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('nbody')
+        group.attrs['type'] = np.bytes_('nbody')
         group.attrs['total_mass'] = self.total_mass
         group.attrs['n_particles'] = self.n_particles
         group.attrs['atomic_weight_ratio'] = self.atomic_weight_ratio

openmc/data/neutron.py

@@ -425,7 +425,7 @@ def export_to_hdf5(self, path, mode='a', libver='earliest'):
 
         # Open file and write version
         with h5py.File(str(path), mode, libver=libver) as f:
-            f.attrs['filetype'] = np.string_('data_neutron')
+            f.attrs['filetype'] = np.bytes_('data_neutron')
             f.attrs['version'] = np.array(HDF5_VERSION)
 
             # Write basic data

openmc/data/photon.py

@@ -764,7 +764,7 @@ def export_to_hdf5(self, path, mode='a', libver='earliest'):
         """
         with h5py.File(str(path), mode, libver=libver) as f:
             # Write filetype and version
-            f.attrs['filetype'] = np.string_('data_photon')
+            f.attrs['filetype'] = np.bytes_('data_photon')
             if 'version' not in f.attrs:
                 f.attrs['version'] = np.array(HDF5_VERSION)
 

openmc/data/product.py

@@ -124,8 +124,8 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['particle'] = np.string_(self.particle)
-        group.attrs['emission_mode'] = np.string_(self.emission_mode)
+        group.attrs['particle'] = np.bytes_(self.particle)
+        group.attrs['emission_mode'] = np.bytes_(self.emission_mode)
         if self.decay_rate > 0.0:
             group.attrs['decay_rate'] = self.decay_rate
 

openmc/data/reaction.py

@@ -920,9 +920,9 @@ def to_hdf5(self, group):
 
         group.attrs['mt'] = self.mt
         if self.mt in REACTION_NAME:
-            group.attrs['label'] = np.string_(REACTION_NAME[self.mt])
+            group.attrs['label'] = np.bytes_(REACTION_NAME[self.mt])
         else:
-            group.attrs['label'] = np.string_(self.mt)
+            group.attrs['label'] = np.bytes_(self.mt)
         group.attrs['Q_value'] = self.q_value
         group.attrs['center_of_mass'] = 1 if self.center_of_mass else 0
         group.attrs['redundant'] = 1 if self.redundant else 0

openmc/data/resonance_covariance.py

@@ -239,14 +239,14 @@ def sample(self, n_samples):
         samples = []
 
         # Handling MLBW/SLBW sampling
+        rng = np.random.default_rng()
         if formalism == 'mlbw' or formalism == 'slbw':
             params = ['energy', 'neutronWidth', 'captureWidth', 'fissionWidth',
                       'competitiveWidth']
             param_list = params[:mpar]
             mean_array = parameters[param_list].values
             mean = mean_array.flatten()
-            par_samples = np.random.multivariate_normal(mean, cov,
-                                                        size=n_samples)
+            par_samples = rng.multivariate_normal(mean, cov, size=n_samples)
             spin = parameters['J'].values
             l_value = parameters['L'].values
             for sample in par_samples:
@@ -277,8 +277,7 @@ def sample(self, n_samples):
             param_list = params[:mpar]
             mean_array = parameters[param_list].values
             mean = mean_array.flatten()
-            par_samples = np.random.multivariate_normal(mean, cov,
-                                                        size=n_samples)
+            par_samples = rng.multivariate_normal(mean, cov, size=n_samples)
             spin = parameters['J'].values
             l_value = parameters['L'].values
             for sample in par_samples:

openmc/data/thermal.py

@@ -221,7 +221,7 @@ def to_hdf5(self, group, name):
         """
         dataset = group.create_dataset(name, data=np.vstack(
             [self.bragg_edges, self.factors]))
-        dataset.attrs['type'] = np.string_(type(self).__name__)
+        dataset.attrs['type'] = np.bytes_(type(self).__name__)
 
     @classmethod
     def from_hdf5(cls, dataset):
@@ -294,7 +294,7 @@ def to_hdf5(self, group, name):
         """
         data = np.array([self.bound_xs, self.debye_waller])
         dataset = group.create_dataset(name, data=data)
-        dataset.attrs['type'] = np.string_(type(self).__name__)
+        dataset.attrs['type'] = np.bytes_(type(self).__name__)
 
     @classmethod
     def from_hdf5(cls, dataset):
@@ -464,7 +464,7 @@ def export_to_hdf5(self, path, mode='a', libver='earliest'):
         """
         # Open file and write version
         with h5py.File(str(path), mode, libver=libver) as f:
-            f.attrs['filetype'] = np.string_('data_thermal')
+            f.attrs['filetype'] = np.bytes_('data_thermal')
             f.attrs['version'] = np.array(HDF5_VERSION)
 
             # Write basic data

openmc/data/thermal_angle_energy.py

@@ -43,7 +43,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('coherent_elastic')
+        group.attrs['type'] = np.bytes_('coherent_elastic')
         self.coherent_xs.to_hdf5(group, 'coherent_xs')
 
     @classmethod
@@ -104,7 +104,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('incoherent_elastic')
+        group.attrs['type'] = np.bytes_('incoherent_elastic')
         group.create_dataset('debye_waller', data=self.debye_waller)
 
     @classmethod
@@ -146,7 +146,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('incoherent_elastic_discrete')
+        group.attrs['type'] = np.bytes_('incoherent_elastic_discrete')
         group.create_dataset('mu_out', data=self.mu_out)
 
     @classmethod
@@ -203,7 +203,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('incoherent_inelastic_discrete')
+        group.attrs['type'] = np.bytes_('incoherent_inelastic_discrete')
         group.create_dataset('energy_out', data=self.energy_out)
         group.create_dataset('mu_out', data=self.mu_out)
         group.create_dataset('skewed', data=self.skewed)
@@ -266,7 +266,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('mixed_elastic')
+        group.attrs['type'] = np.bytes_('mixed_elastic')
         coherent_group = group.create_group('coherent')
         self.coherent.to_hdf5(coherent_group)
         incoherent_group = group.create_group('incoherent')

openmc/data/uncorrelated.py

@@ -63,7 +63,7 @@ def to_hdf5(self, group):
             HDF5 group to write to
 
         """
-        group.attrs['type'] = np.string_('uncorrelated')
+        group.attrs['type'] = np.bytes_('uncorrelated')
         if self.angle is not None:
             angle_group = group.create_group('angle')
             self.angle.to_hdf5(angle_group)

openmc/deplete/stepresult.py

@@ -291,7 +291,7 @@ def _write_hdf5_metadata(self, handle):
         # Store concentration mat and nuclide dictionaries (along with volumes)
 
         handle.attrs['version'] = np.array(VERSION_RESULTS)
-        handle.attrs['filetype'] = np.string_('depletion results')
+        handle.attrs['filetype'] = np.bytes_('depletion results')
 
         mat_list = sorted(self.mat_to_hdf5_ind, key=int)
         nuc_list = sorted(self.index_nuc)

openmc/mgxs_library.py

@@ -1964,17 +1964,17 @@ def to_hdf5(self, file):
             grp.attrs['fissionable'] = self.fissionable
 
         if self.representation is not None:
-            grp.attrs['representation'] = np.string_(self.representation)
+            grp.attrs['representation'] = np.bytes_(self.representation)
             if self.representation == REPRESENTATION_ANGLE:
                 if self.num_azimuthal is not None:
                     grp.attrs['num_azimuthal'] = self.num_azimuthal
 
                 if self.num_polar is not None:
                     grp.attrs['num_polar'] = self.num_polar
 
-        grp.attrs['scatter_shape'] = np.string_("[G][G'][Order]")
+        grp.attrs['scatter_shape'] = np.bytes_("[G][G'][Order]")
         if self.scatter_format is not None:
-            grp.attrs['scatter_format'] = np.string_(self.scatter_format)
+            grp.attrs['scatter_format'] = np.bytes_(self.scatter_format)
         if self.order is not None:
             grp.attrs['order'] = self.order
 
@@ -2516,7 +2516,7 @@ def export_to_hdf5(self, filename='mgxs.h5', libver='earliest'):
 
         # Create and write to the HDF5 file
         file = h5py.File(filename, "w", libver=libver)
-        file.attrs['filetype'] = np.string_(_FILETYPE_MGXS_LIBRARY)
+        file.attrs['filetype'] = np.bytes_(_FILETYPE_MGXS_LIBRARY)
         file.attrs['version'] = [_VERSION_MGXS_LIBRARY, 0]
         file.attrs['energy_groups'] = self.energy_groups.num_groups
         file.attrs['delayed_groups'] = self.num_delayed_groups

openmc/plots.py

@@ -465,11 +465,11 @@ def colorize(self, geometry, seed=1):
             domains = geometry.get_all_cells().values()
 
         # Set the seed for the random number generator
-        np.random.seed(seed)
+        rng = np.random.RandomState(seed)
 
         # Generate random colors for each feature
         for domain in domains:
-            self.colors[domain] = np.random.randint(0, 256, (3,))
+            self.colors[domain] = rng.randint(0, 256, (3,))
 
     def to_xml_element(self):
         """Save common plot attributes to XML element

openmc/source.py

@@ -883,7 +883,7 @@ def write_source_file(
     # Write array to file
     kwargs.setdefault('mode', 'w')
     with h5py.File(filename, **kwargs) as fh:
-        fh.attrs['filetype'] = np.string_("source")
+        fh.attrs['filetype'] = np.bytes_("source")
         fh.create_dataset('source_bank', data=arr, dtype=source_dtype)