if __name__ == '__main__':
# dat = get_dat(2214)
# x = dat.Data.get_data('x')
# data = dat.Data.get_data('i_sense')[63]
# all_data = dat.Transition.data
# dat = get_dat(2216)
dat = get_dat(2164)
out = dat.SquareEntropy.get_row_only_output(name='default')
x = out.x
all_data = np.nanmean(np.array(out.cycled[:, (0, 2), :]), axis=1)
single_row = 10
data = all_data[single_row]
plotter = OneD(dat=dat)
plotter.MAX_POINTS = 100000
fig = plotter.figure(
ylabel='Current /nA',
title=f'Dat{dat.datnum}: Checking Accuracy of Center from fit')
# Whole row of data
fig.add_trace(
plotter.trace(x=x,
data=data,
name=f'All data of row{single_row}',
mode='lines'))
# Fits
reports = []
fits = []
params = dat.Transition.get_default_params(x, data)
#
# [plotter.add_line(fig, v, color='black', linetype='dash') for v in [0, 1]]
#
# fig.update_layout(template='simple_white')
# fig.show()
#
# fig.write_image('dndt_vs_Occ_many.svg')
#
dats = get_dats([
2102, 7046, 7084, 7094
]) # Last CD, This CD slow sweep, This CD same sweep, This CD fast sweep
plotter = OneD(dats=dats)
plotter.RESAMPLE_METHOD = 'downsample' # So jumps are still obvious instead of binning
plotter.MAX_POINTS = 2000
single_figs = []
row = 0
fig = plotter.figure(xlabel='ACC /mV',
ylabel='Current /nA',
title=f'Comparing Transition Noise: Row{row}')
for dat in dats:
fig.add_trace(
plotter.trace(
data=dat.Transition.data[row],
x=dat.Transition.x / 100,
name=f'Dat{dat.datnum}: {dat.Logs.sweeprate/100:.1f}mV/s',
mode='lines'))
single_fig = plotter.figure(