def waterfall_stdev_of_avg(dats: Iterable[DatHDF]) -> go.Figure:
dats = list(dats)
data = np.array([dat.Transition.avg_data_std for dat in dats])
xs = [dat.Transition.avg_x for dat in dats]
data, xs = [
U.resample_data(data=arr, max_num_pnts=500, resample_method='bin')
for arr in [data, xs]
]
plotter = TwoD(dats=dats)
fig = plotter.figure(
title=f'Dat{dats[0].datnum}-{dats[-1].datnum}: '
f'Standard deviation of averaged I_sense data after centering')
fig.add_traces([
go.Scatter3d(mode='lines',
x=x,
y=[dat.Logs.fds['ESS']] * len(dat.Transition.avg_x),
z=row,
name=f'Dat{dat.datnum}')
for x, row, dat in zip(xs, data, dats)
])
fig.update_layout(scene=dict(
xaxis_title=dats[0].Logs.xlabel,
yaxis_title=f'ESS /mV',
zaxis_title=f'{dat_analysis.dat_analysis.characters.SIG}I_sense /nA',
))
return fig
def plot_nrg(which: str,
nrg: Optional[NRGData] = None, plot=True,
x_axis_type: str = 'energy') -> go.Figure:
@dataclass
class PlotInfo:
data: np.ndarray
title: str
if nrg is None:
nrg = NRGData.from_old_mat()
x = nrg.ens
if x_axis_type == 'energy':
xlabel = 'Energy'
elif x_axis_type == 'gate':
xlabel = 'Gate /Arbitrary mV'
x = np.flip(x)
else:
raise NotImplementedError
y = nrg.ts / 0.001
ylabel = f'{THETA}/Gamma'
if which == 'conductance':
pi = PlotInfo(data=nrg.conductance,
title='NRG Conductance')
elif which == 'dndt':
pi = PlotInfo(data=nrg.dndt,
title='NRG dN/dT')
elif which == 'entropy':
pi = PlotInfo(data=nrg.entropy,
title='NRG Entropy')
elif which == 'occupation':
pi = PlotInfo(data=nrg.occupation,
title='NRG Occupation')
elif which == 'i_sense':
pi = PlotInfo(data=1 - nrg.occupation,
title='NRG I_sense (1-Occ)')
elif which == 'int_dndt':
pi = PlotInfo(data=nrg.int_dndt,
title='NRG Integrated dN/dT')
else:
raise KeyError(f'{which} not recognized')
plotter = TwoD(dat=None)
fig = plotter.figure(xlabel=xlabel, ylabel=ylabel, title=pi.title)
fig.add_trace(plotter.trace(data=pi.data, x=x, y=y))
fig.update_yaxes(type='log')
if plot:
fig.show()
return fig