def fit_single_esc_varying_width(show_figs=True) -> List[go.Figure]:
"""Fits transition with varying width based on temperature at a single ESC"""
global dats_by_coupling_gate
dats = dats_by_coupling_gate[-260]
dats = U.order_list(dats, [dat.Logs.temps.mc for dat in dats])
figs = []
for dat, w in progressbar(zip(dats, [20, 30, 50, 100, 200])):
save_name = 'varying_width'
do_transition_only_calc(dat.datnum, save_name=save_name, theta=None, gamma=0, width=w,
t_func_name='i_sense')
plotter = OneD(dat=dat)
fig = plotter.figure(title=f'Dat{dat.datnum}: I_sense at {dat.Logs.temps.mc * 1000:.0f}mK',
ylabel='Current /nA')
fig.add_trace(plotter.trace(data=dat.Transition.avg_data, x=dat.Transition.avg_x, mode='lines', name='Data'))
fig.add_trace(plotter.trace(data=dat.Transition.get_fit(name=save_name).eval_fit(x=dat.Transition.avg_x),
x=dat.Transition.avg_x, mode='lines', name='Fit'))
[plotter.add_line(fig, value=xx, mode='vertical', color='black', linetype='dash') for xx in [w, -w]]
if show_figs:
fig.show()
figs.append(fig)
w_theta = dat.Transition.get_fit(name="simple").best_values.theta
n_theta = dat.Transition.get_fit(name=save_name).best_values.theta
print(f'Temp {dat.Logs.temps.mc * 1000:.0f}mK:\n'
f'Wide Theta: {w_theta:.2f}mV\n'
f'Narrow Theta: {n_theta:.2f}mV\n'
f'Change: {(n_theta - w_theta) / w_theta * 100:.2f}%\n'
)
return figs
fig_minus = plotter.figure(
title=
f'Dat{dat.datnum}: Transition Data minus Fit (width={fit_width})'
+ title_row_2,
ylabel=f'{C.DELTA}{y_label}')
fig_minus.add_trace(
single_transition_trace(dat,
label=None,
subtract_fit=True,
fit_name=fit_name,
transition_only=transition_only,
se_output_name=save_name,
csq_mapped=csq_map))
for fig in [fig_minus, fig_fit]:
plotter.add_line(fig, value=fit_width, mode='vertical')
plotter.add_line(fig, value=-fit_width, mode='vertical')
if fit_func == 'i_sense_digamma_amplin':
add_print = f'\tGamma: {fit.best_values.g:.1f}mV\n' + \
f'\tAmpLin: {fit.best_values.get("amplin", 0):.3g}{y_units}/mV\n'
elif fit_func == 'i_sense_digamma':
add_print = f'\tGamma: {fit.best_values.g:.1f}mV\n'
else:
add_print = ''
print(f'Dat{dat.datnum}:\n'
f'\tWidth: {C.PM}{fit_width}mV\n'
f'\tAmp: {fit.best_values.amp:.3f}{y_units}\n'
f'\tTheta: {fit.best_values.theta:.2f}mV\n'
f'\tLin: {fit.best_values.lin:.3g}{y_units}/mV\n'
def one_d(self, invert_fit_on_data=False) -> go.Figure:
"""
Args:
invert_fit_on_data (): False to modify NRG to fit data, True to modify Data to fit NRG
Returns:
"""
plotter = OneD(dat=None)
title_prepend = f'NRG fit to Data' if not invert_fit_on_data else 'Data fit to NRG'
title_append = f' -- Dat{self.datnum}' if self.datnum else ''
xlabel = 'Sweepgate /mV' if not invert_fit_on_data else 'Ens*1000'
ylabel = 'Current /nA' if not invert_fit_on_data else '1-Occupation'
fig = plotter.figure(xlabel=xlabel, ylabel=ylabel, title=f'{title_prepend}: G={self.g:.2f}mV, '
f'{THETA}={self.theta:.2f}mV, '
f'{THETA}/G={self.theta / self.g:.2f}'
f'{title_append}')
min_, max_ = 0, 1
if self.datnum:
x_for_nrg = None
for i, which in enumerate(self.which):
x, data = _get_x_and_data(self.datnum, self.experiment_name, which)
x_for_nrg = x
if invert_fit_on_data is True:
x, data = invert_nrg_fit_params(x, data, gamma=self.g, theta=self.theta, mid=self.mid, amp=self.amp,
lin=self.lin, const=self.const, occ_lin=self.occ_lin,
data_type=which)
if i == 0 and data is not None:
min_, max_ = np.nanmin(data), np.nanmax(data)
fig.add_trace(plotter.trace(x=x, data=data, name=f'Data - {which}', mode='lines'))
else:
if data is not None:
scaled = scale_data(data, min_, max_)
fig.add_trace(
plotter.trace(x=x, data=scaled.scaled_data, name=f'Scaled Data - {which}', mode='lines'))
if min_ - (max_ - min_) / 10 < scaled.new_zero < max_ + (max_ - min_) / 10:
plotter.add_line(fig, scaled.new_zero, mode='horizontal', color='black',
linetype='dot', linewidth=1)
else:
x_for_nrg = np.linspace(-100, 100, 1001)
for i, which in enumerate(self.which):
if which == 'i_sense_cold':
which = 'i_sense'
elif which == 'i_sense_hot':
if 'i_sense_cold' in self.which:
continue
which = 'i_sense'
nrg_func = NRG_func_generator(which=which)
if invert_fit_on_data:
# nrg_func(x, mid, gamma, theta, amp, lin, const, occ_lin)
nrg_data = nrg_func(x_for_nrg, self.mid, self.g, self.theta, 1, 0, 0, 0)
if which == 'i_sense':
nrg_data += 0.5 # 0.5 because that still gets subtracted otherwise
# x = (x_for_nrg - self.mid - self.g*(-1.76567) - self.theta*(-1)) / self.g
x = (x_for_nrg - self.mid) / self.g
else:
x = x_for_nrg
nrg_data = nrg_func(x, self.mid, self.g, self.theta, self.amp, self.lin, self.const, self.occ_lin)
cmin, cmax = np.nanmin(nrg_data), np.nanmax(nrg_data)
if i == 0 and min_ == 0 and max_ == 1:
fig.add_trace(plotter.trace(x=x, data=nrg_data, name=f'NRG {which}', mode='lines'))
min_, max_ = cmin, cmax
else:
scaled = scale_data(nrg_data, min_, max_)
fig.add_trace(plotter.trace(x=x, data=scaled.scaled_data, name=f'Scaled NRG {which}', mode='lines'))
if min_ - (max_ - min_) / 10 < scaled.new_zero < max_ + (max_ - min_) / 10:
plotter.add_line(fig, scaled.new_zero, mode='horizontal', color='black',
linetype='dot', linewidth=1)
return fig
