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()