def generate_single_lookup(self, param_index):
"""
For a single parameter value with given index, create a list of the dressed-state indices corresponding to the
canonical bare-state product states (whenever possible).
Parameters
----------
param_index: int
Returns
-------
list of int
dressed-state indices
"""
overlap_matrix = convert_esys_to_ndarray(
self.dressed_specdata.state_table[param_index]
) # overlap amplitudes
dressed_indices = []
for bare_basis_index in range(
self.hilbertspace.dimension
): # for given bare basis index, find dressed index
max_position = (np.abs(overlap_matrix[:,
bare_basis_index])).argmax()
max_overlap = np.abs(overlap_matrix[max_position,
bare_basis_index])
if max_overlap < 0.5: # overlap too low, make no assignment
dressed_indices.append(None)
else:
dressed_indices.append(max_position)
return dressed_indices
def _generate_single_mapping(self, param_index):
"""
For a single parameter value with index `param_index`, create a list of the dressed-state indices in an order
that corresponds one to one to the canonical bare-state product states with largest overlap (whenever possible).
Parameters
----------
param_index: int
Returns
-------
list of int
dressed-state indices
"""
overlap_matrix = spec_utils.convert_esys_to_ndarray(self._dressed_specdata.state_table[param_index])
dressed_indices = []
for bare_basis_index in range(self._hilbertspace.dimension): # for given bare basis index, find dressed index
max_position = (np.abs(overlap_matrix[:, bare_basis_index])).argmax()
max_overlap = np.abs(overlap_matrix[max_position, bare_basis_index])
if max_overlap < 0.5: # overlap too low, make no assignment
dressed_indices.append(None)
else:
dressed_indices.append(max_position)
return dressed_indices
def generate_state_lookup_table(self, spectrum_data, param_index=0):
"""
Create a lookup table that associates each dressed-state index with the corresponding bare-state product state
index (whenever possible). Usually to be saved as self.state_lookup_table.
Parameters
----------
spectrum_data: SpectrumData
param_index: int
indices > 0 become relevant when using ParameterSweep
Returns
-------
list(int), list(tuple)
dressed indices, corresponding bare indices
"""
dims = self.subsystem_dims
product_dim = np.prod(dims)
overlap_matrix = convert_esys_to_ndarray(spectrum_data.state_table[param_index])
dressed_indices = []
for bare_basis_index in range(product_dim):
max_position = (np.abs(overlap_matrix[:, bare_basis_index])).argmax()
max_overlap = np.abs(overlap_matrix[max_position, bare_basis_index])
if max_overlap < 0.5:
dressed_indices.append(None)
else:
dressed_indices.append(max_position)
basis_label_ranges = [list(range(dims[subsys_index])) for subsys_index in range(self.subsystem_count)]
basis_labels_list = list(itertools.product(*basis_label_ranges))
return [dressed_indices, basis_labels_list]
def convert_to_ndarray(entity):
"""Convert the object `entity` to a numpy ndarray of numerical dtype. This is needed in the routines for writing
content of DataStores and SpectrumData to disk.
Parameters
----------
entity: array_like
"""
if is_numerical_ndarray(entity):
return entity
if is_qutip_eigenstates(entity): # entity is output from qt.eigenstates
return convert_esys_to_ndarray(entity)
if isinstance(entity,
(list, np.ndarray)) and is_qutip_eigenstates(entity[0]):
# entity is a list of qt.eigenstates
return np.asarray([convert_esys_to_ndarray(entry) for entry in entity])
# possibly we have a list of numerical values or a list of ndarrays
converted_entity = np.asarray(entity)
if converted_entity.dtype.kind not in set('biufc'):
raise TypeError('Unable to convert data to numerical numpy array: ',
entity)
return converted_entity
def _serialize(self, writer):
"""
Parameters
----------
writer: BaseWriter
"""
np_state_table = None
if isinstance(self.state_table, list):
np_state_table = np.asarray([convert_esys_to_ndarray(esys_qutip) for esys_qutip in self.state_table])
elif isinstance(self.state_table, np.ndarray):
np_state_table = self.state_table
metadata_dict = self._get_metadata_dict()
writer.create_meta(metadata_dict)
writer.add_dataset('energy_table', self.energy_table)
if self.state_table is not None:
writer.add_dataset('state_table', np_state_table)
if self.matrixelem_table is not None:
writer.add_dataset('matrixelem_table', self.matrixelem_table)
