def evals_vs_paramvals(specdata, which=-1, subtract_ground=False, label_list=None, **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
----------
specdata: SpectrumData
object includes parameter name, values, and resulting eigenenergies
which: int or list(int)
number of desired eigenvalues (sorted from smallest to largest); default: -1, signals all eigenvalues
or: list of specific eigenvalues to include
subtract_ground: bool
whether to subtract the ground state energy
label_list: list(str), optional
list of labels associated with the individual curves to be plotted
**kwargs: dict
standard plotting option (see separate documentation)
Returns
-------
tuple(Figure, Axes)
matplotlib objects for further editing
"""
index_list = process_which(which, specdata.energy_table[0].size)
xdata = specdata.param_vals
ydata = specdata.energy_table[:, index_list]
if subtract_ground:
ydata = (ydata.T - ydata[:, 0]).T
return data_vs_paramvals(xdata, ydata, label_list=label_list,
**defaults.evals_vs_paramvals(specdata, **kwargs))
def plot_wavefunction(self,
which=0,
mode='real',
esys=None,
phi_grid=None,
scaling=None,
**kwargs):
"""Plot 1d phase-basis wave function(s). Must be overwritten by higher-dimensional qubits like FluxQubits and
ZeroPi.
Parameters
----------
esys: (ndarray, ndarray), optional
eigenvalues, eigenvectors
which: int or tuple or list, optional
single index or tuple/list of integers indexing the wave function(s) to be plotted.
If which is -1, all wavefunctions up to the truncation limit are plotted.
phi_grid: Grid1d, optional
used for setting a custom grid for phi; if None use self._default_grid
mode: str, optional
choices as specified in `constants.MODE_FUNC_DICT` (default value = 'abs_sqr')
scaling: float or None, optional
custom scaling of wave function amplitude/modulus
**kwargs: dict
standard plotting option (see separate documentation)
Returns
-------
Figure, Axes
"""
fig_ax = kwargs.get('fig_ax') or plt.subplots()
kwargs['fig_ax'] = fig_ax
index_list = process_which(which, self.truncated_dim)
phi_wavefunc = self.wavefunction(esys,
which=index_list[-1],
phi_grid=phi_grid)
potential_vals = self.potential(phi_wavefunc.basis_labels)
scale = set_scaling(self, scaling, potential_vals)
amplitude_modifier = constants.MODE_FUNC_DICT[mode]
kwargs = {
**defaults.wavefunction1d_discrete(mode),
**kwargs
} # if any duplicates, later ones survive
for wavefunc_index in index_list:
phi_wavefunc = self.wavefunction(esys,
which=wavefunc_index,
phi_grid=phi_grid)
phi_wavefunc.amplitudes = standardize_sign(phi_wavefunc.amplitudes)
phi_wavefunc.amplitudes = amplitude_modifier(
phi_wavefunc.amplitudes)
plot.wavefunction1d(phi_wavefunc,
potential_vals=potential_vals,
offset=phi_wavefunc.energy,
scaling=scale,
**kwargs)
return fig_ax
def plot_wavefunction(
self,
which: Union[int, Iterable[int]] = 0,
mode: str = "real",
esys: Tuple[ndarray, ndarray] = None,
phi_grid: Grid1d = None,
scaling: float = None,
**kwargs,
) -> Tuple[Figure, Axes]:
"""Plot 1d phase-basis wave function(s). Must be overwritten by
higher-dimensional qubits like FluxQubits and ZeroPi.
Parameters
----------
which:
single index or tuple/list of integers indexing the wave function(s) to be
plotted.
If which is -1, all wavefunctions up to the truncation limit are plotted.
mode:
choices as specified in `constants.MODE_FUNC_DICT`
(default value = 'abs_sqr')
esys:
eigenvalues, eigenvectors
phi_grid:
used for setting a custom grid for phi; if None use self._default_grid
scaling:
custom scaling of wave function amplitude/modulus
**kwargs:
standard plotting option (see separate documentation)
"""
wavefunc_indices = process_which(which, self.truncated_dim)
if esys is None:
evals_count = max(wavefunc_indices) + 1
evals = self.eigenvals(evals_count=evals_count)
else:
evals, _ = esys
energies = evals[list(wavefunc_indices)]
phi_grid = phi_grid or self._default_grid
potential_vals = self.potential(phi_grid.make_linspace())
amplitude_modifier = constants.MODE_FUNC_DICT[mode]
wavefunctions = []
for wavefunc_index in wavefunc_indices:
phi_wavefunc = self.wavefunction(esys,
which=wavefunc_index,
phi_grid=phi_grid)
phi_wavefunc.amplitudes = standardize_sign(phi_wavefunc.amplitudes)
phi_wavefunc.amplitudes = amplitude_modifier(
phi_wavefunc.amplitudes)
wavefunctions.append(phi_wavefunc)
fig_ax = kwargs.get("fig_ax") or plt.subplots()
kwargs["fig_ax"] = fig_ax
kwargs = {
**self.wavefunction1d_defaults(mode,
evals,
wavefunc_count=len(wavefunc_indices)),
**kwargs,
}
# in merging the dictionaries in the previous line: if any duplicates,
# later ones survive
plot.wavefunction1d(
wavefunctions,
potential_vals=potential_vals,
offset=energies,
scaling=scaling,
**kwargs,
)
return fig_ax
def plot_wavefunction(self,
esys,
which=0,
phi_range=None,
phi_count=None,
mode='real',
scaling=None,
xlabel=r'$\varphi$',
ylabel=r'$\psi_j(\varphi),\, V(\varphi)$',
y_range=None,
title=None,
filename=None,
fig_ax=None):
"""Plot 1d phase-basis wave function(s). Must be overwritten by higher-dimensional qubits like FluxQubits and
ZeroPi.
Parameters
----------
esys: ndarray, ndarray
eigenvalues, eigenvectors
which: int or tuple or list, optional
single index or tuple/list of integers indexing the wave function(s) to be plotted.
If which is -1, all wavefunctions up to the truncation limit are plotted.
phi_range: tuple(float, float), optional
used for setting a custom plot range for phi
phi_count: int, optional
number of points on the x-axis (resolution) (default value = 251)
mode: str, optional
choices as specified in `constants.MODE_FUNC_DICT` (default value = 'abs_sqr')
scaling: float or None, optional
custom scaling of wave function amplitude/modulus
xlabel, ylabel: str, optional
axes labels
y_range: tuple(float, float), optional
used to set custom y range for plot
title: str, optional
plot title
filename: str, optional
file path and name (not including suffix) for output
fig_ax: Figure, Axes
Returns
-------
Figure, Axes
"""
modefunction = constants.MODE_FUNC_DICT[mode]
index_list = process_which(which, self.truncated_dim)
if fig_ax is None:
fig = plt.figure()
ax = fig.add_subplot(111)
else:
fig, ax = fig_ax
phi_wavefunc = self.wavefunction(esys,
which=index_list[-1],
phi_range=phi_range,
phi_count=phi_count)
potential_vals = self.potential(phi_wavefunc.basis_labels)
if scaling is None:
if isinstance(self, qubits.Transmon):
scale = 0.2 * self.EJ
elif isinstance(self, qubits.Fluxonium):
scale = 0.125 * (np.max(potential_vals) -
np.min(potential_vals))
else:
scale = scaling
for wavefunc_index in index_list:
phi_wavefunc = self.wavefunction(esys,
which=wavefunc_index,
phi_range=phi_range,
phi_count=phi_count)
if np.sum(phi_wavefunc.amplitudes) < 0:
phi_wavefunc.amplitudes *= -1.0
phi_wavefunc.amplitudes = modefunction(phi_wavefunc.amplitudes)
plot.wavefunction1d(phi_wavefunc,
potential_vals=potential_vals,
offset=phi_wavefunc.energy,
scaling=scale,
xlabel=xlabel,
ylabel=ylabel,
y_range=y_range,
title=title,
fig_ax=(fig, ax),
filename=filename)
return fig, ax
def evals_vs_paramvals(specdata,
which=-1,
x_range=None,
ymax=None,
subtract_ground=False,
filename=None,
title=None,
label_list=None,
fig_ax=None,
**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
----------
specdata: SpectrumData
object includes parameter name, values, and resulting eigenenergies
which: int or list(int)
number of desired eigenvalues (sorted from smallest to largest); default: -1, signals all eigenvalues
or: list of specific eigenvalues to include
x_range: (float, float)
custom x-range for the plot
ymax: float, optional
custom maximum y value for the plot
subtract_ground: bool
whether to subtract the ground state energy
filename: str
write graphics and parameter set to file if path and filename are specified
title: str, optional
plot title
label_list: list(str), optional
list of labels associated with the individual curves to be plotted
fig_ax: tuple(Figure, Axes), optional
fig and ax objects for matplotlib figure addition
**kwargs:
keyword arguments passed on to axes.plot()
Returns
-------
tuple(Figure, Axes)
matplotlib objects for further editing
"""
index_list = process_which(which, specdata.energy_table[0].size)
xdata = specdata.param_vals
ydata = specdata.energy_table[:, index_list]
if subtract_ground:
ydata = (ydata.T - ydata[:, 0]).T
xlabel = specdata.param_name
ylabel = 'energy [{}]'.format(DEFAULT_ENERGY_UNITS)
return data_vs_paramvals(xdata,
ydata,
xlabel=xlabel,
ylabel=ylabel,
x_range=x_range,
ymax=ymax,
title=title,
label_list=label_list,
filename=filename,
fig_ax=fig_ax,
**kwargs)
