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
def theta_slope_in_weakly_coupled(dats: List[DatHDF], show_intermediate=False, fit_name: str = 'simple') -> go.Figure:
"""Calculates and plots slope of theta in weakly coupled for all temperatures (option to show the linear fits for
each temp) """
plotter = OneD(dats=dats)
fig = plotter.figure(xlabel='Fridge Temp /mK', ylabel=f'Slope ({DELTA}{THETA}/{DELTA}ESC)',
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}:'
f' Slope of thetas in weakly coupled')
dats_sorted_by_temp = sort_by_temps(dats)
line = lm.models.LinearModel()
slopes = []
temps = []
for temp, dats in sorted(dats_sorted_by_temp.items()):
if len(dats) > 0:
dats = [dat for dat in dats if dat.Logs.fds['ESC'] < -235]
escs = np.array([dat.Logs.fds['ESC'] for dat in dats])
thetas = np.array([dat.Transition.get_fit(name=fit_name).best_values.theta for dat in dats])
pars = line.guess(thetas, x=escs)
fit = calculate_fit(x=escs, data=thetas, params=pars, func=line.func)
slopes.append(fit.best_values.slope)
temps.append(temp)
p = OneD(dats=dats)
f = p.figure(xlabel='ESC /mV', ylabel='Theta /mV',
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: Temp = {temp}mK, Fit of theta slope')
f.add_trace(p.trace(x=escs, data=thetas, name='Data'))
f.add_trace(p.trace(x=escs, data=fit.eval_fit(x=escs), name='Fit', mode='lines'))
if show_intermediate:
f.show()
print(f'{temp}mK:\nSlope = {fit.best_values.slope:.3g}{PM}{U.sig_fig(fit.params["slope"].stderr, 2):.2g}\n'
f'Reduced Chi Square = {fit.reduced_chi_sq:.3g}\n')
fig.add_trace(plotter.trace(data=slopes, x=temps, mode='markers+lines'))
return fig
def plot_dT_comparison(dats: List[DatHDF], plot=True):
plotter = OneD(dats=dats)
fig = plotter.figure(xlabel='Fridge Temp /mK',
ylabel='% Difference between calculated dTs',
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: Difference between DC bias calculated dT and '
f'Square Entropy Calculated dT')
hover_infos = [
HoverInfo(name='Dat', func=lambda dat: dat.datnum, precision='.d', units=''),
HoverInfo(name='Temperature', func=lambda dat: dat.Logs.temps.mc * 1000, precision='.1f', units='mK'),
HoverInfo(name='Bias', func=lambda dat: dat.AWG.max(0) / 10, precision='.1f', units='nA'),
]
funcs, template = _additional_data_dict_converter(hover_infos)
for bias in sorted(list(set([dat.AWG.max(0) for dat in dats]))):
ds = [dat for dat in dats if dat.AWG.max(0) == bias]
diffs = [compare_dTs(dat, verbose=False) for dat in ds]
hover_data = [[func(dat) for func in funcs] for dat in ds]
fig.add_trace(plotter.trace(data=diffs, x=[dat.Logs.temps.mc * 1000 for dat in ds],
name=f'Bias={bias / 10:.0f}nA',
mode='markers+lines',
trace_kwargs={'customdata': hover_data, 'hovertemplate': template}))
if plot:
fig.show(renderer='browser')
return fig
def one_d_data_vs_n(self):
if self.datnum:
self.which = ['i_sense_cold', 'dndt', 'occupation']
try:
x, data_dndt = _get_x_and_data(self.datnum, self.experiment_name, 'dndt')
except NotFoundInHdfError:
logger.warning(f'Dat{self.datnum}: dndt data not found, probably a transition only dat')
return go.Figure()
nrg_func = NRG_func_generator(which='dndt')
nrg_dndt = nrg_func(x, self.mid, self.g, self.theta, self.amp, self.lin, self.const, self.occ_lin)
nrg_func = NRG_func_generator(which='occupation')
occupation = nrg_func(x, self.mid, self.g, self.theta, self.amp, self.lin, self.const, self.occ_lin)
# Rescale dN/dTs to have a peak at 1
nrg_dndt = nrg_dndt * (1 / np.nanmax(nrg_dndt))
x_max = x[get_data_index(data_dndt, np.nanmax(data_dndt))]
x_range = abs(x[-1] - x[0])
indexs = get_data_index(x, [x_max - x_range / 50, x_max + x_range / 50])
avg_peak = np.nanmean(data_dndt[indexs[0]:indexs[1]])
# avg_peak = np.nanmean(data_dndt[np.nanargmax(data_dndt) - round(x.shape[0] / 50):
# np.nanargmax(data_dndt) + round(x.shape[0] / 50)])
data_dndt = data_dndt * (1 / avg_peak)
if (new_max := np.nanmax(np.abs([np.nanmax(data_dndt), np.nanmin(data_dndt)]))) > 5: # If very noisy
data_dndt = data_dndt / (new_max / 5) # Reduce to +-5ish
interp_range = np.where(np.logical_and(occupation < 0.99, occupation > 0.01))
if len(interp_range[0]) > 5: # If enough data to actually plot something
interp_data = occupation[interp_range]
interp_x = x[interp_range]
interper = interp1d(x=interp_x, y=interp_data, assume_sorted=True, bounds_error=False)
occ_x = interper(x)
plotter = OneD(dat=None)
fig = plotter.figure(xlabel='Occupation', ylabel='Arbitrary',
title=f'dN/dT vs N: G={self.g:.2f}mV, '
f'{THETA}={self.theta:.2f}mV, '
f'{THETA}/G={self.theta / self.g:.2f}'
f' -- Dat{self.datnum}')
fig.add_trace(plotter.trace(x=occ_x, data=data_dndt, name='Data dN/dT', mode='lines+markers'))
fig.add_trace(plotter.trace(x=occ_x, data=nrg_dndt, name='NRG dN/dT', mode='lines'))
return fig
def plot_entropy_vs_temp(dats: List[DatHDF], integrated=False, plot=True):
fit_name = 'SPS.0045'
plotter = OneD(dats=dats)
_tname = 'Integrated' if integrated else 'Fit'
fig = plotter.figure(
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: {_tname} Entropy',
xlabel='Heating Bias /nA',
ylabel='Entropy /kB')
temps = list(range(0, 300, 10))
hover_infos = [
HoverInfo(name='Dat', func=lambda dat: dat.datnum, precision='.d', units=''),
HoverInfo(name='Temperature', func=lambda dat: dat.Logs.temps.mc * 1000, precision='.1f', units='mK'),
HoverInfo(name='Bias', func=lambda dat: dat.AWG.max(0) / 10, precision='.1f', units='nA'),
]
funcs, template = _additional_data_dict_converter(hover_infos)
for temp in temps:
ds = [dat for dat in dats if np.isclose(dat.Logs.temps.mc * 1000, temp, atol=5)]
if len(ds) > 0:
x = [dat.AWG.max(0) / 10 for dat in ds]
hover_data = [[func(dat) for func in funcs] for dat in ds]
if integrated is False:
entropies = [dat.Entropy.get_fit(name=fit_name).best_values.dS for dat in ds]
entropy_errs = [np.nanstd([
f.best_values.dS if f.best_values.dS is not None else np.nan
for f in dat.Entropy.get_row_fits(name=fit_name) for dat in ds
]) / np.sqrt(dat.Data.y_array.shape[0]) for dat in ds]
fig.add_trace(plotter.trace(
data=entropies, data_err=entropy_errs, x=x, name=f'{temp:.0f}mK',
mode='markers+lines',
trace_kwargs={'customdata': hover_data, 'hovertemplate': template})
)
else:
integrated_entropies = [np.nanmean(dat.Entropy.integrated_entropy[-10:]) for dat in ds]
fig.add_trace(plotter.trace(
data=integrated_entropies, x=x, name=f'{temp:.0f}mK',
mode='markers+lines',
trace_kwargs={'customdata': hover_data, 'hovertemplate': template})
)
if plot:
fig.show(renderer='browser')
return fig
def plot_avg_thetas(dats: Iterable[DatHDF]) -> go.Figure:
dats = list(dats)
thetas = [dat.Transition.avg_fit.best_values.theta for dat in dats]
x = [dat.Logs.fds['ESS'] for dat in dats]
plotter = OneD(dats=dats)
fig = plotter.figure(
xlabel='ESS /mV',
ylabel='Theta /mV',
title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: Avg Theta')
fig.add_trace(
plotter.trace(data=thetas, x=x, mode='markers', name='Avg Fit Theta'))
return fig
def plot_gamma_dcbias(datnums: List[int], save_name: str, show_each_data=True):
"""
Makes a figure for Theta vs DCbias with option to show the data which is being used to obtain thetas
Args:
datnums (): Datnums that form DCbias measurement (i.e. repeats at fixed Biases)
save_name (): Name of fits etc to be loaded (must already exist)
show_each_data (): Whether to show the fit for each dataset (i.e. to check everything looks good)
Returns:
go.Figure: A plotly figure of Theta vs DCbias
"""
# if calculate:
# with ProcessPoolExecutor() as pool:
# list(pool.map(partial(do_transition_only_calc, save_name=save_name, theta=theta, gamma=None, width=600,
# t_func_name='i_sense_digamma', overwrite=False), GAMMA_DCbias))
dats = get_dats(datnums)
plotter = OneD(dats=dats)
# fig = plotter.figure(ylabel='Current /nA',
# title=f'Dats{dats[0].datnum}-{dats[-1].datnum}: DCbias in Gamma broadened' )
# dat_pairs = np.array(dats).reshape((-1, 2))
# line = lm.models.LinearModel()
# params = line.make_params()
# for ds in dat_pairs:
# for dat, color in zip(ds, ['blue', 'red']):
# params['slope'].value = dat.Transition.avg_fit.best_values.lin
# params['intercept'].value = dat.Transition.avg_fit.best_values.const
# fig.add_trace(plotter.trace(x=dat.Transition.avg_x, data=dat.Transition.avg_data-line.eval(params=params, x=dat.Transition.avg_x),
# name=f'Dat{dat.datnum}: Bias={dat.Logs.fds["HO1/10M"]/10:.1f}nA',
# mode='lines',
# trace_kwargs=dict(line=dict(color=color)),
# ))
fig = plotter.figure(
ylabel='Current /nA',
title=
f'Dats{dats[0].datnum}-{dats[-1].datnum}: DCbias in Gamma broadened')
line = lm.models.LinearModel()
params = line.make_params()
for dat in dats[1::2]:
params['slope'].value = dat.Transition.avg_fit.best_values.lin
params['intercept'].value = dat.Transition.avg_fit.best_values.const
fig.add_trace(
plotter.trace(
x=dat.Transition.avg_x,
data=dat.Transition.avg_data -
line.eval(params=params, x=dat.Transition.avg_x),
name=
f'Dat{dat.datnum}: Bias={dat.Logs.fds["HO1/10M"] / 10:.1f}nA',
mode='lines',
))
fig.show()
def plot_csq_trace(dat: DatHDF, cutoff: Optional[float] = None) -> Data1D:
plotter = OneD(dat=dat)
plotter.TEMPLATE = 'simple_white'
fig = plotter.figure(xlabel='CSQ Gate (mV)',
ylabel='Current (nA)',
title='CS current vs CSQ gate')
x = dat.Data.x
data = dat.Data.i_sense
if cutoff:
upper_lim = U.get_data_index(x, cutoff)
x, data = x[:upper_lim], data[:upper_lim]
fig.add_trace(plotter.trace(data=data, x=x))
fig.show()
return Data1D(x=x, data=data)
def check_centers():
"""Assuming 'simple' exists as a Transition fit name, will just plot centers and print any that deviate far from
0 """
global all_dats
for dat in all_dats:
fit = dat.Transition.get_fit(name='simple')
if fit.best_values.mid > 5 or fit.best_values.mid < -5:
print(f'Dat{dat.datnum}: mid = {fit.best_values.mid:.1f}mV')
plotter = OneD(dats=all_dats)
fig = plotter.figure(xlabel='Datnum', ylabel='Center /mV',
title=f'Dats{all_dats[0].datnum}-{all_dats[-1].datnum}: Centers')
fig.add_trace(plotter.trace(data=[dat.Transition.get_fit(name='simple').best_values.mid for dat in all_dats],
x=[dat.datnum for dat in all_dats], mode='markers'))
fig.show()
def check_rough_broadening_vs_temp() -> go.Figure:
global all_dats, dats_by_temp
dats = all_dats
plotter = OneD(dats=dats)
fig = plotter.figure(xlabel='ESC /mV', ylabel='Theta /mV', title=f'Dats{dats[0].datnum}-{dats[-1].datnum}:'
f' Rough idea of broadening')
for temp, dats in dats_by_temp.items():
if len(dats) != 0:
dat = dats[0]
dats = U.order_list(dats, [dat.Logs.fds['ESC'] for dat in dats])
x = [dat.Logs.fds['ESC'] for dat in dats]
thetas = [dat.Transition.get_fit(name='simple').best_values.theta for dat in dats]
fig.add_trace(plotter.trace(x=x, data=thetas, mode='markers+lines', name=f'{temp}mK'))
return fig
def one_d_data_subtract_fit(self):
if self.datnum:
dat = get_dat(self.datnum, exp2hdf=self.experiment_name)
plotter = OneD(dat=dat)
xlabel = 'Sweepgate /mV'
fig = plotter.figure(xlabel=xlabel, ylabel='Current /nA', title=f'Data Subtract Fit: G={self.g:.2f}mV, '
f'{THETA}={self.theta:.2f}mV, '
f'{THETA}/G={self.theta / self.g:.2f}')
for i, which in enumerate(self.which):
if 'i_sense' in which:
x, data = _get_x_and_data(self.datnum, self.experiment_name, which)
nrg_func = NRG_func_generator(which='i_sense')
nrg_data = nrg_func(x, self.mid, self.g, self.theta, self.amp, self.lin, self.const, self.occ_lin)
data_sub_nrg = data - nrg_data
fig.add_trace(plotter.trace(x=x, data=data_sub_nrg, name=f'{which} subtract NRG', mode='lines'))
return fig
return go.Figure()
def get_integrated_trace(dats: List[DatHDF], x_func: Callable,
trace_name: str,
int_info_name: Optional[str] = None, SE_output_name: Optional[str] = None,
) -> go.Scatter:
if int_info_name is None:
int_info_name = 'default'
if SE_output_name is None:
SE_output_name = 'default'
plotter = OneD(dats=dats)
x = [x_func(dat) for dat in dats]
integrated_entropies = [np.nanmean(
dat.Entropy.get_integrated_entropy(name=int_info_name,
data=dat.SquareEntropy.get_Outputs(name=SE_output_name).average_entropy_signal
)[-10:]) for dat in dats]
trace = plotter.trace(
data=integrated_entropies, x=x, name=trace_name,
mode='markers+lines',
)
return trace
def plotting_center_shift():
nrg_func = NRG_func_generator('occupation')
params = lm.Parameters()
params.add_many(
('mid', 0, True, -200, 200, None, 0.001),
('theta', 3.9, False, 1, 500, None, 0.001),
('amp', 1, True, 0, 3, None, 0.001),
('lin', 0, True, 0, 0.005, None, 0.00001),
('occ_lin', 0, True, -0.0003, 0.0003, None, 0.000001),
('const', 0, True, -2, 10, None, 0.001),
('g', 1, True, 0.2, 2000, None, 0.01),
)
model = lm.Model(nrg_func)
x = np.linspace(-10, 5000, 10000)
gs = np.linspace(0, 200, 201)
thetas = np.logspace(0.1, 2, 20)
# thetas = np.linspace(1, 500, 10)
# thetas = [1, 2, 5, 10, 20]
all_mids = []
for theta in thetas:
params['theta'].value = theta
mids = []
for g in gs:
params['g'].value = g
occs = model.eval(x=x, params=params)
mids.append(x[U.get_data_index(occs, 0.5, is_sorted=True)])
all_mids.append(mids)
plotter = OneD(dat=None)
fig = plotter.figure(xlabel='Gamma /mV',
ylabel='Shift of 0.5 OCC',
title='Shift of 0.5 Occupation vs Theta and G')
fig.update_layout(legend=dict(title='Theta /mV'))
for mids, theta in zip(all_mids, thetas):
fig.add_trace(
plotter.trace(data=mids, x=gs, name=f'{theta:.1f}', mode='lines'))
fig.show()
return fig
def entropy_vs_gate_trace(dats: List[DatHDF], x_gate, y_gate=None):
fit_name = "SPS.0045"
plotter = OneD(dats=dats)
entropy = [dat.Entropy.get_fit(which='avg', name=fit_name).best_values.dS for dat in dats]
entropy_errs = [np.nanstd([f.best_values.dS if f.best_values.dS is not None else np.nan
for f in dat.Entropy.get_row_fits(name=fit_name)]) for dat in dats]
x = [dat.Logs.fds[x_gate] for dat in dats]
trace = plotter.trace(data=entropy, data_err=entropy_errs, x=x, mode='markers+lines',
name=f'Dats{dats[0].datnum}->{dats[-1].datnum}')
hover_infos = [
HoverInfo(name='Dat', func=lambda dat: dat.datnum, precision='.d', units=''),
HoverInfo(name=x_gate, func=lambda dat: dat.Logs.fds[x_gate], precision='.1f', units='mV'),
# HoverInfo(name='Time', func=lambda dat: dat.Logs.time_completed.strftime('%H:%M'), precision='', units=''),
]
if y_gate:
hover_infos.append(HoverInfo(name=y_gate, func=lambda dat: dat.Logs.fds[y_gate], precision='.2f', units='mV'))
funcs, hover_template = _additional_data_dict_converter(info=hover_infos)
hover_data = [[f(dat) for f in funcs] for dat in dats]
trace.update(hovertemplate=hover_template,
customdata=hover_data)
return trace
def plot_stacked_square_heated(datnums: List[int], save_name: str, plot=True):
dats = get_dats(datnums)
# Plot Integrated
integrated_plot_info = PlotInfo(
title_append='Integrated Entropy',
ylabel='Entropy /kB',
data_func=lambda dat: dat.Entropy.get_integrated_entropy(
name=save_name,
data=dat.SquareEntropy.get_Outputs(
name=save_name, check_exists=True).average_entropy_signal),
x_func=lambda dat: dat.SquareEntropy.get_Outputs(name=save_name,
check_exists=True).x,
trace_name=lambda dat: f'Dat{dat.datnum}')
fit_plot_info = PlotInfo(
title_append='Fit Entropy',
ylabel='Entropy /kB',
data_func=lambda dat: dat.SquareEntropy.get_Outputs(
name=save_name, check_exists=True).average_entropy_signal,
x_func=lambda dat: dat.SquareEntropy.get_Outputs(name=save_name,
check_exists=True).x,
trace_name=lambda dat: f'Dat{dat.datnum}')
figs = []
for plot_info in [integrated_plot_info]:
plotter = OneD(dats=dats)
dat = dats[0]
fig = plotter.figure(
xlabel=dat.Logs.xlabel,
ylabel=plot_info.ylabel,
title=
f'Dats{dats[0].datnum}-{dats[-1].datnum}: {plot_info.title_append}'
)
for dat in dats:
data = plot_info.data_func(dat)
x = plot_info.x_func(dat)
hover_infos = [
HoverInfo(name='Datnum',
func=lambda dat: dat.datnum,
precision='d',
units=''),
HoverInfo(name=dat.Logs.xlabel,
func=lambda dat: plot_info.x_func(dat),
precision='.2f',
units='/mV'),
HoverInfo(name=plot_info.ylabel,
func=lambda dat: dat.datnum,
precision='d',
units=''),
]
hover_funcs, template = _additional_data_dict_converter(
hover_infos)
hover_data = []
for func in hover_funcs:
v = func(dat)
if not hasattr(v, '__len__') or len(
v) == 1: # Make sure a hover info for each x_coord
v = [v] * len(x)
hover_data.append(v)
fig.add_trace(
plotter.trace(x=x,
data=data,
name=plot_info.trace_name(dat),
hover_data=hover_data,
hover_template=template,
mode='lines'))
if plot:
fig.show()
figs.append(fig)
return figs
sweep_gate_divider=100)
# print('done')
p1d = OneD(dat=None)
fig = p1d.figure(xlabel='ESC /mV', ylabel='dT')
dats = get_dats(VS_GAMMA)
escs = [dat.Logs.dacs['ESC'] for dat in dats]
# dts = [dat.SquareEntropy.get_fit(fit_name='forced_gamma_zero_non_csq_hot').best_values.theta -
# dat.SquareEntropy.get_fit(fit_name='forced_gamma_zero_non_csq_cold').best_values.theta for dat in dats]
dts = [
dat.Entropy.get_integration_info(name='forced_theta_linear_non_csq').dT
for dat in dats
]
line = lm.models.LinearModel()
fit = calculate_fit(x=np.array(escs[:11]),
data=np.array(dts[:11]),
params=line.make_params(),
func=line.func)
fig.add_trace(
p1d.trace(x=escs, data=dts, text=[dat.datnum for dat in dats]))
fig.add_trace(
p1d.trace(x=escs,
data=fit.eval_fit(x=np.array(escs)),
mode='lines',
name='fit'))
fig.show()
def _get_param_trace(fits: List, param: str, y_array: np.ndarray):
plotter = OneD(dat=None)
trace = plotter.trace(x=y_array, data=[getattr(fit.best_values, param) for fit in fits], mode='markers+lines')
return trace
# dat = get_dat(1619)
dat = get_dat(2174)
csq_dat = 2174
plotter = OneD(dat=dat)
calculate_csq_map(dat.datnum, csq_datnum=csq_dat, overwrite=True)
fig = plotter.figure(
xlabel=dat.Logs.xlabel,
ylabel='Current /nA',
title=f'Dat{dat.datnum}: Before mapping i_sense to CSQ<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')
x = dat.Data.get_data('x')
data = dat.Data.get_data('i_sense')
fig.add_trace(plotter.trace(x=x, data=data, mode='lines'))
fig.show()
fig = plotter.figure(
xlabel=dat.Logs.xlabel,
ylabel='Gate /mV',
title=
f'Dat{dat.datnum}: After mapping i_sense to CSQ (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')
data = dat.Data.get_data('csq_mapped')
fig.add_trace(plotter.trace(x=x, data=data, mode='lines'))
fig.show()
line = lm.models.LinearModel()
# fig.show(renderer='browser')
# datnums = [702, 703, 707, 708]
datnums = [7436, 7435]
all_dats = get_dats(datnums)
plotter = OneD(dats=all_dats)
fig = plotter.figure(
xlabel='Time /s',
ylabel='Current /Arbitrary',
title=
f'Dats{all_dats[0].datnum}-{all_dats[-1].datnum}: Transition ReadVsTime<br>Decimated to 10Hz',
)
for dat, name, bias in zip(all_dats, [
'On Transition 300uV', 'Off Transition 300uV',
'On Transition 500uV', 'Off Transition 500uV'
], [300, 300, 500, 500]):
data = dat.Data.get_data('i_sense')
numpts = data.shape[-1]
time_elapsed = numpts / dat.Logs.measure_freq
x = np.linspace(0, time_elapsed, numpts)
data = data - np.mean(data)
data = data / bias
data = U.decimate(data, dat.Logs.measure_freq, desired_freq=10)
x = U.get_matching_x(x, data)
fig.add_trace(plotter.trace(data, x=x, mode='lines', name=name))
fig.show(renderer='browser')
run_dat_init(dat, overwrite=False)
# for dat in dats:
# plot_occupation_fit(dat).show()
save_infos = []
for dat, name, igor_name in zip(dats, names, igor_names):
fig = p1d.figure(xlabel='Occupation',
ylabel=f'Scaled {DELTA}I (a.u.)',
title=name)
real_data = get_dndt_vs_occ(dat)
fit_data = get_expected_dndt_vs_occ(dat)
for data, trace_name in zip([real_data, fit_data], ['Data', 'Fit']):
fig.add_trace(
p1d.trace(data=data.data,
x=data.x,
mode='lines',
name=trace_name))
# fig.show()
save_infos.append(
U.IgorSaveInfo(x=real_data.x,
data=real_data.data,
name=igor_name,
x_label='Occupation',
y_label=f'Scaled {DELTA}I (a.u.)'))
save_infos.append(
U.IgorSaveInfo(x=real_data.x,
data=fit_data.data,
name=f'{igor_name}_fit',
x_label='Occupation',
y_label=f'Scaled {DELTA}I (a.u.)'))
def testing_fit_methods():
# Weakly coupled entropy dat
# dat = get_dat(2164)
# dat = get_dat(2167)
dat = get_dat(2170)
out = dat.SquareEntropy.get_Outputs(name='default')
x = out.x
data = np.nanmean(out.averaged[(
0,
2,
), :], axis=0)
plotter = OneD(dat=dat)
fig = plotter.figure(
ylabel='Current /nA',
title=f'Dat{dat.datnum}: Fitting Weakly coupled to NRG')
fig.add_trace(plotter.trace(x=x, data=data, name='Data', mode='lines'))
print(dat.SquareEntropy.get_fit(fit_name='default').best_values)
params = lm.Parameters()
params.add_many(
# ('mid', 2.2, True, None, None, None, None),
('mid', 0, True, -200, 200, None, 0.001),
# ('mid', 1, True, -100, 100, None, 0.001),
('theta', 3.9, False, 1, 6, None, 0.001),
('amp', 0.94, True, 0, 3, None, 0.001),
# ('lin', 0.0015, True, 0, 0.005, None, None),
# ('lin', 0.0, True, 0, 0.005, None, 0.00001),
('lin', 0.01, True, 0, 0.005, None, 0.00001),
('occ_lin', 0, True, -0.0003, 0.0003, None, 0.000001),
# ('const', 7.2, True, None, None, None, None),
('const', 7, True, -2, 10, None, 0.001),
# ('g', 0.2371, True, 0.2, 200, None, 0.01),
('g', 1, True, 0.2, 200, None, 0.01),
)
dfs = []
for method in [
# 'leastsq',
'least_squares',
'differential_evolution',
# 'brute',
# 'basinhopping',
# 'ampgo',
'nelder',
# 'lbfgsb',
'powell',
# 'cg',
# 'newton',
'cobyla',
# 'bfgs',
# 'tnc',
# 'trust-ncg',
# 'trust-exact',
# 'trust-krylov',
# 'trust-constr',
# 'dogleg',
# 'slsqp',
# 'emcee',
# 'shgo',
'dual_annealing'
]:
try:
t1 = time.time()
fit = calculate_fit(x,
data,
params=params,
func=NRG_func_generator(which='i_sense'),
method=method)
total_time = time.time() - t1
# fig.add_trace((plotter.trace(x=x, data=fit.eval_init(x=x), name='Initial Fit', mode='lines')))
fig.add_trace((plotter.trace(x=x,
data=fit.eval_fit(x=x),
name=f'{method} Fit',
mode='lines')))
df = fit.to_df()
df['name'] = method
df['duration'] = total_time
df['reduced chi sq'] = fit.fit_result.redchi
dfs.append(df)
except Exception as e:
print(f'Failed for {method} with error: {e}')
df = pd.concat(dfs)
df.index = df.name
df.pop('name')
print(df.to_string())
fig.show()
dats_50 = get_dats((8710, 8729+1), overwrite=False, exp2hdf=Sep20.SepExp2HDF)
for all_dats, temp in zip([dats_100, dats_50], [100, 50]):
dc_info = dc_bias_infos[temp]
for dat in all_dats:
try:
info = dat.Entropy.integration_info
except NotFoundInHdfError:
set_integration_info(dc_info, dat)
plotter = OneD(dats=dats_100)
# fig = plotter.figure(xlabel='LCB /mV', ylabel='Entropy /kB', title=None)
fig = plotter.figure(xlabel='LCT /mV', ylabel='Entropy /kB', title=None)
for all_dats in [dats_100, dats_50]:
plotter = OneD(dats=all_dats)
fits = [entropy_fit_sp_start(dat, 50) for dat in all_dats]
data = np.array([dat.Entropy.avg_fit.best_values.dS for dat in all_dats])
data_50 = np.array([fit.best_values.dS for fit in fits])
# x = np.array([dat.Logs.fds['LCB'] for dat in dats])
x = np.array([dat.Logs.fds['LCT'] for dat in all_dats])
fig.add_trace(plotter.trace(data=data, x=x, mode='markers', name='sp_0'))
fig.add_trace(plotter.trace(data=data_50, x=x, mode='markers', name='sp_50'))
fig.show(renderer='browser')
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)
params.add('g', value=0, min=-50, max=1000)
params.add('amplin', value=0)
params['theta'].vary = False
params['theta'].value = 4
for func, name in zip([i_sense, i_sense_digamma],
['i_sense', 'i_sense_digamma']):
fit = dat.Transition.get_fit(x=x,
data=data,
occ_x = interper(x)
plotter = OneD(dat=None)
fig = plotter.figure(
xlabel='Occupation',
ylabel='Arbitrary',
title='dN/dT vs Occupation at Temp/Gamma = 0.04 (Temp = 4e-5 in NRG)')
fig = plotter.figure(
xlabel='Occupation',
ylabel='Arbitrary',
title='dN/dT vs Occupation at Temp/Gamma = 0.19 (Temp = 1.9e-4 in NRG)'
)
fig.add_trace(
plotter.trace(x=occ_x,
data=(data_dndt - 0.2) * 1.2,
name='Data',
mode='lines+markers'))
fig.add_trace(
plotter.trace(x=occ_x, data=nrg_dndt, name='NRG', mode='lines'))
fig.show()
#
#
# nrg = NRGData.from_mat()
# occs = nrg.occupation
# dndts = nrg.dndt
# ts = nrg.ts
#
# fig = plotter.figure(xlabel='Occupation', ylabel='Arbitrary', title='dN/dT vs Occupation for various NRG T')
#
# # Plot Data dN/dT
# fig.add_trace(plotter.trace(x=occ_x, data=(data_dndt*1.40-0.25), mode='lines+markers', name='Data'))
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