def charge_matrixelem(datastore,
qbt_index_subsys,
initial_state_idx=0,
**kwargs):
"""
Parameters
----------
datastore: DataStore
qbt_index_subsys: tuple(int, QuantumSystem)
index of the qubit system within the underlying HilbertSpace, and qubit object
initial_state_idx: int
index of initial state
**kwargs: dict
standard plotting option (see separate documentation)
Returns
-------
Figure, Axes
"""
(qbt_index, qbt_subsys) = qbt_index_subsys
label_list = [(initial_state_idx, final_idx)
for final_idx in range(qbt_subsys.truncated_dim)]
return plot.matelem_vs_paramvals(datastore,
select_elems=label_list,
mode='abs',
**defaults.charge_matrixelem(
datastore.param_name, **kwargs))
def charge_matrixelem(
specdata: "SpectrumData",
qbt_index_subsys: Tuple[int, "QuantumSystem"],
initial_state_idx: int = 0,
**kwargs
) -> Tuple[Figure, Axes]:
"""
Parameters
----------
specdata:
qbt_index_subsys:
index of the qubit system within the underlying HilbertSpace, and qubit object
initial_state_idx:
index of initial state
**kwargs:
standard plotting option (see separate documentation)
"""
(qbt_index, qbt_subsys) = qbt_index_subsys
label_list = [
(initial_state_idx, final_idx) for final_idx in range(qbt_subsys.truncated_dim)
]
return plot.matelem_vs_paramvals(
specdata,
select_elems=label_list,
mode="abs",
**defaults.charge_matrixelem(specdata.param_name or "", **kwargs)
)
def charge_matrixelem(sweep, qbt_index, initial_state_idx=0, title=None, fig_ax=None):
data_key = 'n_op_qbt{}'.format(qbt_index)
specdata = copy.deepcopy(sweep.bare_specdata_list[qbt_index])
specdata.matrixelem_table = sweep.sweep_data[data_key]
xlabel = sweep.param_name
ylabel = r'$|\langle i |n| j \rangle|$'
label_list = [(initial_state_idx, final_idx) for final_idx in range(sweep.hilbertspace[qbt_index].truncated_dim)]
return plot.matelem_vs_paramvals(specdata, select_elems=label_list, mode='abs', xlabel=xlabel, ylabel=ylabel,
title=title, fig_ax=fig_ax)
def plot_matelem_vs_paramvals(self,
operator,
param_name,
param_vals,
select_elems=4,
mode='abs',
x_range=None,
y_range=None,
filename=None,
fig_ax=None):
"""Generates a simple plot of a set of eigenvalues as a function of one parameter.
The individual points correspond to the a provided array of parameter values.
Parameters
----------
operator: str
name of class method in string form, returning operator matrix
param_name: str
name of parameter to be varied
param_vals: ndarray
parameter values to be plugged in
select_elems: int or list, optional
either maximum index of desired matrix elements, or list [(i1, i2), (i3, i4), ...] of index tuples
for specific desired matrix elements (default value = 4)
mode: str, optional
entry from MODE_FUNC_DICTIONARY, e.g., `'abs'` for absolute value (default value = 'abs')
x_range: (float, float), optional
custom x-range for the plot (default value = False)
y_range: (float, float), optional
custom y-range for the plot (default value = False)
filename: str, optional
write graphics and parameter set to file if path and filename are specified (default value = None)
fig_ax: tuple(Figure, Axes), optional
fig and ax objects for matplotlib figure addition (default value = None)
Returns
-------
Figure, Axes
"""
if isinstance(select_elems, int):
evals_count = select_elems
else:
flattened_list = [index for tupl in select_elems for index in tupl]
evals_count = max(flattened_list) + 1
specdata = self.get_matelements_vs_paramvals(operator,
param_name,
param_vals,
evals_count=evals_count,
filename=None)
return plot.matelem_vs_paramvals(specdata,
select_elems=select_elems,
mode=mode,
x_range=x_range,
y_range=y_range,
filename=filename,
fig_ax=fig_ax)
def plot_matelem_vs_paramvals(
self,
operator: str,
param_name: str,
param_vals: ndarray,
select_elems: Union[int, List[Tuple[int, int]]] = 4,
mode: str = "abs",
num_cpus: int = settings.NUM_CPUS,
**kwargs,
) -> Tuple[Figure, Axes]:
"""Generates a simple plot of a set of eigenvalues as a function of one
parameter. The individual points correspond to the a provided array of
parameter values.
Parameters
----------
operator:
name of class method in string form, returning operator matrix
param_name:
name of parameter to be varied
param_vals:
parameter values to be plugged in
select_elems:
either maximum index of desired matrix elements, or
list [(i1, i2), (i3, i4), ...] of index tuples
for specific desired matrix elements (default value = 4)
mode:
entry from MODE_FUNC_DICTIONARY, e.g., `'abs'` for absolute value
(default value = 'abs')
num_cpus:
number of cores to be used for computation (default value = 1)
**kwargs:
standard plotting option (see separate documentation)
"""
if isinstance(select_elems, int):
evals_count = select_elems
else:
flattened_list = [index for tupl in select_elems for index in tupl]
evals_count = max(flattened_list) + 1
specdata = self.get_matelements_vs_paramvals(operator,
param_name,
param_vals,
evals_count=evals_count,
num_cpus=num_cpus)
return plot.matelem_vs_paramvals(specdata,
select_elems=select_elems,
mode=mode,
**kwargs)
def plot_matelem_vs_paramvals(self,
operator,
param_name,
param_vals,
select_elems=4,
mode='abs',
**kwargs):
"""Generates a simple plot of a set of eigenvalues as a function of one parameter.
The individual points correspond to the a provided array of parameter values.
Parameters
----------
operator: str
name of class method in string form, returning operator matrix
param_name: str
name of parameter to be varied
param_vals: ndarray
parameter values to be plugged in
select_elems: int or list, optional
either maximum index of desired matrix elements, or list [(i1, i2), (i3, i4), ...] of index tuples
for specific desired matrix elements (default value = 4)
mode: str, optional
entry from MODE_FUNC_DICTIONARY, e.g., `'abs'` for absolute value (default value = 'abs')
**kwargs: dict
standard plotting option (see separate documentation)
Returns
-------
Figure, Axes
"""
if isinstance(select_elems, int):
evals_count = select_elems
else:
flattened_list = [index for tupl in select_elems for index in tupl]
evals_count = max(flattened_list) + 1
specdata = self.get_matelements_vs_paramvals(operator,
param_name,
param_vals,
evals_count=evals_count)
return plot.matelem_vs_paramvals(specdata,
select_elems=select_elems,
mode=mode,
**kwargs)