def entropy_vs_time_fig(title: Optional[str] = None):
plotter = OneD()
if title is None:
title = 'Entropy vs Time'
fig = plotter.figure(xlabel='Time', ylabel='Entropy /kB', title=title)
fig.update_xaxes(tickformat="%H:%M\n%a")
return fig
def get_integrated_fig(dats=None, title_append: str = '') -> go.Figure:
plotter = OneD()
if dats:
title_prepend = f'Dats{dats[0].datnum}-{dats[-1].datnum}: '
else:
title_prepend = ''
fig = plotter.figure(
xlabel='ESC /mV',
ylabel='Entropy /kB',
title=f'{title_prepend}Integrated Entropy {title_append}')
return fig
def plot_stdev_of_avg_data(dat: DatHDF,
data_type: str = 'transition',
data_name: str = 'default') -> go.Figure:
"""Plot the stdev of averaging the 2D data (i.e. looking for whether more uncertainty near transition)"""
plotter = OneD(dat=dat)
fig = plotter.figure(
ylabel=f'{dat_analysis.dat_analysis.characters.SIG}I_sense /nA',
title=
f'Dat{dat.datnum}: Standard deviation of averaged I_sense data after centering',
)
fig.add_trace(trace_stdev_of_avg_data(dat, data_type, data_name))
return fig
def plot_linear_theta_comparison(
e_dats,
t_dats,
a_dats,
fit_name='gamma_small') -> Tuple[List[FitInfo], go.Figure]:
csq_mapped = True if 'csq' in fit_name else False
efit = plot_linear_theta_nrg_fit(e_dats,
show_plots=False,
csq_mapped=csq_mapped)
tfit = plot_linear_theta_nrg_fit(t_dats,
show_plots=False,
csq_mapped=csq_mapped)
fit = plot_linear_theta_nrg_fit(a_dats,
show_plots=False,
csq_mapped=csq_mapped)
plotter = OneD(dats=all_dats)
fig = plotter.figure(
xlabel='ESC /mV',
ylabel='Theta /mV (real)',
title=f'Dats{all_dats[0].datnum}-{all_dats[-1].datnum}: Theta vs ESC')
for dats, name in zip((e_dats, t_dats), ['entropy', 'transition']):
xs = [dat.Logs.dacs['ESC'] for dat in dats]
thetas = [
dat.NrgOcc.get_fit(name=fit_name).best_values.theta for dat in dats
]
datnums = [dat.datnum for dat in dats]
fig.add_trace(
plotter.trace(data=thetas,
x=xs,
name=name,
mode='markers',
text=datnums))
xs = [dat.Logs.dacs['ESC'] for dat in t_dats]
for f, name in zip((efit, tfit, fit),
['entropy fit', 'transition fit', 'all fit']):
fig.add_trace(
plotter.trace(x=xs,
data=f.eval_fit(np.array(xs)),
name=name,
mode='lines'))
return [efit, tfit, fit], fig
def transition_fig(dats: Optional[List[DatHDF]] = None, xlabel: str = '/mV', title_append: str = '',
param: str = 'amp') -> go.Figure:
plotter = OneD(dats=dats)
titles = {
'amp': 'Amplitude',
'theta': 'Theta',
'theta real': 'Theta',
'g': 'Gamma',
'amp/const': 'Amplitude/Const (sensitivity)',
}
ylabels = {
'amp': 'Amplitude /nA',
'theta': 'Theta /mV',
'theta real': 'Theta /mV (real)',
'g': 'Gamma /mV',
'amp/const': 'Amplitude/Const'
}
fig = plotter.figure(xlabel=xlabel, ylabel=ylabels[param],
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: {titles[param]}{title_append}')
return fig
def dcbias_multi_dat(dats: List[DatHDF]):
plotter = OneD(dats=dats)
fig = plotter.figure(
xlabel='Heater Current Bias /nA',
ylabel='Theta /mV',
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: DCbias')
thetas = []
biases = []
for dat in dats:
fit = dat.Transition.get_fit(which='avg',
name='default',
check_exists=False)
theta = fit.best_values.theta
thetas.append(theta)
biases.append(dat.Logs.fds['HO1/10M'] / 10)
hover_infos = common_dat_hover_infos(datnum=True)
hover_infos.append(
HoverInfo(name='Bias',
func=lambda dat: dat.Logs.dacs['HO1/10M'] / 10,
precision='.2f',
units='nA'))
hover_infos.append(
HoverInfo(name='Theta',
func=lambda dat: dat.Transition.get_fit().best_values.theta,
precision='.2f',
units='mV'))
hover_group = HoverInfoGroup(hover_infos=hover_infos)
fig.add_trace(
plotter.trace(x=biases,
data=thetas,
mode='markers+lines',
hover_template=hover_group.template,
hover_data=hover_group.customdata(dats)))
return fig
def plot_fit_integrated_comparison(dats: List[DatHDF],
x_func: Callable,
x_label: str,
title_append: Optional[str] = '',
int_info_name: Optional[str] = None,
fit_name: str = 'SPS.0045',
plot=True) -> go.Figure():
if int_info_name is None:
int_info_name = 'default'
plotter = OneD(dats=dats)
fig = plotter.figure(
title=
f'Dats{dats[0].datnum}-{dats[-1].datnum}: Fit and Integrated (\'{int_info_name}\') Entropy{title_append}',
xlabel=x_label,
ylabel='Entropy /kB')
hover_infos = [
HoverInfo(name='Dat',
func=lambda dat: dat.datnum,
precision='.d',
units=''),
HoverInfo(name='Temperature',
func=lambda dat: dat.Logs.temps.mc * 1000,
precision='.1f',
units='mK'),
HoverInfo(name='Bias',
func=lambda dat: dat.AWG.max(0) / 10,
precision='.1f',
units='nA'),
# HoverInfo(name='Fit Entropy', func=lambda dat: dat.SquareEntropy.get_fit(which_fit='entropy', fit_name=fit_name).best_values.dS,
# precision='.2f', units='kB'),
HoverInfo(
name='Integrated Entropy',
func=lambda dat: np.nanmean(
dat.Entropy.get_integrated_entropy(
name=int_info_name,
data=dat.SquareEntropy.get_Outputs(
name=fit_name).average_entropy_signal)[-10:]),
# TODO: Change to using proper output (with setpoints)
precision='.2f',
units='kB'),
]
funcs, template = _additional_data_dict_converter(hover_infos)
x = [x_func(dat) for dat in dats]
hover_data = [[func(dat) for func in funcs] for dat in dats]
entropies = [
dat.Entropy.get_fit(name=fit_name).best_values.dS for dat in dats
]
# entropy_errs = [np.nanstd([
# f.best_values.dS if f.best_values.dS is not None else np.nan
# for f in dat.Entropy.get_row_fits(name=fit_name) for dat in dats
# ]) / np.sqrt(dat.Data.y_array.shape[0]) for dat in dats]
entropy_errs = None
fig.add_trace(
plotter.trace(data=entropies,
data_err=entropy_errs,
x=x,
name=f'Fit Entropy',
mode='markers',
trace_kwargs={
'customdata': hover_data,
'hovertemplate': template
}))
integrated_entropies = [
np.nanmean(
dat.Entropy.get_integrated_entropy(
name=int_info_name,
data=dat.SquareEntropy.get_Outputs(
name=fit_name).average_entropy_signal)[-10:])
for dat in dats
]
### For plotting the impurity dot scans (i.e. isolate only the integrated entropy due to the main dot transition)
# integrated_entropies = []
# for dat in dats:
# out = dat.SquareEntropy.get_Outputs(name=fit_name)
# x1, x2 = U.get_data_index(out.x, [-40, 40], is_sorted=True)
# integrated = dat.Entropy.get_integrated_entropy(name=int_info_name,
# data=out.average_entropy_signal)
# integrated_entropies.append(integrated[x2]-integrated[x1])
fig.add_trace(
plotter.trace(data=integrated_entropies,
x=x,
name=f'Integrated',
mode='markers+lines',
trace_kwargs={
'customdata': hover_data,
'hovertemplate': template
}))
if plot:
fig.show(renderer='browser')
return fig