y_label=f'Scaled {DELTA}I (a.u.)'))
U.save_multiple_save_info_to_itx(filename, save_infos)
############################# Weak signal in Gamma broadened
save_infos = []
dat = get_dat(2213)
data2d = get_2d_data(dat, 'entropy')
data2d.x = data2d.x / 100 # Convert to real mV
data = Data1D(data=np.nanmean(data2d.data, axis=0), x=data2d.x)
data_single = Data1D(data=data2d.data[0], x=data2d.x)
fig = p1d.plot(data.data,
x=data.x,
xlabel='V_D (mV)',
ylabel='Delta I (nA)',
mode='lines',
trace_name='Avg Data')
fig.add_trace(
p1d.trace(data_single.data,
x=data_single.x,
mode='markers',
name='Single sweep'))
fig.show()
fig = p2d.plot(data2d.data, data2d.x, data2d.y, 'V_D (mV)', 'Repeats')
fig.show()
save_infos.append(
U.IgorSaveInfo(x=data2d.x,
data=data2d.data,
nan_policy='omit')
fig = plotter.figure(
xlabel=dat.Logs.xlabel,
ylabel=f'{C.DELTA}Gate /mV',
title=
f'Dat{dat.datnum}: After mapping and subtracting line fit (using Dat{csq_dat})<br>'
f'ESS={dat.Logs.fds["ESS"]:.1f}mV, '
f'ESC={dat.Logs.fds["ESC"]:.1f}mV, '
f'ESP={dat.Logs.fds["ESP"]:.1f}mV')
fig.add_trace(plotter.trace(x=x, data=data - fit.eval(x=x)))
fig.show()
if plot_dot_tune:
dat = get_dat(3063)
plotter = TwoD(dat=dat)
fig = plotter.plot(data=dat.Data.i_sense,
title=f'Dat{dat.datnum}: Dot Tune')
fig.show()
data = dat.Data.i_sense
diff_data = np.diff(dat_analysis.hdf_util.T, axis=0)
# x = U.get_matching_x(dat.Data.x, diff_data)
x = dat.Data.x
y = U.get_matching_x(dat.Data.y, shape_to_match=diff_data.shape[0])
fig = plotter.plot(diff_data,
x=x,
y=y,
title=f'Dat{dat.datnum}: Differentiated Dot Tune',
trace_kwargs=dict(zmin=-0.1,
zmax=np.nanmax(diff_data)))
fig.show()
par = 'g'
for fs in [near_zeros, near_fifteens, others]:
print(
np.mean([
f.best_values.get(par) for f in fs
if f.best_values.get(par) is not None
]))
x = [f.best_values.mid for f in fits if f.best_values.mid is not None]
z = [f.params['mid'].stderr for f in fits if f.best_values.mid is not None]
fig = plotter.plot(
data=z,
x=x,
xlabel='Center in ACC*100 /mV',
ylabel='Uncertainty in Fit value /mV',
title=
f'Dat{dat.datnum}: Correlation of Center to fit value uncertainty',
trace_kwargs=dict(marker=dict(size=3)))
fig.show()
z = [f.reduced_chi_sq for f in fits if f.best_values.mid is not None]
fig = plotter.plot(
data=z,
x=x,
xlabel='Center in ACC*100 /mV',
ylabel='Reduced Chi square of Fit',
title=
f'Dat{dat.datnum}: Correlation of Center to Reduced Chi squaure of Fit',
trace_kwargs=dict(marker=dict(size=3)))
fig.show()