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_2d(dat: DatHDF, differentiated=True) -> go.Figure:
plotter = TwoD(dat=dat)
x, y, data = _get_data(dat, dat.Logs.measure_freq, differentiated=differentiated)
title_prepend = ''
if differentiated:
title_prepend = 'Differentiated '
fig = plotter.plot(x=x, y=y, data=data, title=f'Dat{dat.datnum}: {title_prepend}Res Potential vs ACC<br>'
f'ESC={dat.Logs.fds["ESC"]:.1f}mV')
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
params=params,
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)))
from typing import Optional, TYPE_CHECKING
from progressbar import progressbar
from dat_analysis.analysis_tools.general_fitting import FitInfo
from dat_analysis.dat_analysis.characters import DELTA
from dat_analysis.plotting.plotly.dat_plotting import OneD, TwoD
from dat_analysis.core_util import Data1D
from dat_analysis.analysis_tools.nrg import NRGParams, NrgUtil
import dat_analysis.useful_functions as U
from temp import get_avg_entropy_data, get_avg_i_sense_data, get_linear_theta, get_initial_params, get_2d_data
if TYPE_CHECKING:
from dat_analysis.dat_object.make_dat import DatHDF
p1d = OneD(dat=None)
p2d = TwoD(dat=None)
p1d.TEMPLATE = 'simple_white'
p2d.TEMPLATE = 'simple_white'
NRG_OCC_FIT_NAME = 'csq_forced_theta'
CSQ_DATNUM = 2197
class StrongGammaNoise:
def __init__(self, dat: DatHDF):
self.dat = dat
# The averaged data which does show some signal, but quite small given the number of repeats and duration
self.avg_data = get_avg_entropy_data(self.dat, lambda _: False,
CSQ_DATNUM)
from scipy.interpolate import interp1d
import lmfit as lm
import dat_analysis.analysis_tools.nrg
from dat_analysis.dat_analysis.characters import DELTA
from dat_analysis.plotting.plotly.dat_plotting import OneD, TwoD
from dat_analysis.core_util import Data1D, Data2D
from dat_analysis.analysis_tools.nrg import NRG_func_generator
from dat_analysis.analysis_tools.nrg import NRGParams, NrgUtil, get_x_of_half_occ
import dat_analysis.useful_functions as U
from temp import get_avg_entropy_data, get_avg_i_sense_data, _center_func
kb = 0.08617
p1d = OneD(dat=None)
p2d = TwoD(dat=None)
p1d.TEMPLATE = 'simple_white'
p2d.TEMPLATE = 'simple_white'
NRG_OCC_FIT_NAME = 'forced_theta'
# NRG_OCC_FIT_NAME = 'csq_forced_theta'
CSQ_DATNUM = None
# CSQ_DATNUM = 2197
GAMMA_EXPECTED_THETA_PARAMS = NRGParams(
gamma=23.4352,
theta=4.5,
center=78.4,
amp=0.675,
lin=0.00121,
def transition_2d(self) -> go.Figure:
if not self._correct_call_args():
logger.warning(f'Bad call args to GraphCallback')
return go.Figure()
if not self.calculated_triggered or self.calculated.analysis_params.transition_only_datnum is None:
dat = self.dat
plotter = TwoD(dat=dat)
out = dat.SquareEntropy.get_row_only_output(name='default',
calculate_only=True)
x = out.x
y = dat.Data.get_data('y')
data = np.nanmean(out.cycled[:, (
0,
2,
), :], axis=1)
fig = plotter.plot(data=data,
x=x,
y=y,
title=f'Dat{dat.datnum}: 2D Cold Transition')
if self.calculated_triggered:
out = self.calculated.calculated_entropy_fit.output
x = out.x
data = np.nanmean(out.cycled[:, (
0,
2,
), :], axis=1)
ys = y_from_rows(
self.calculated.analysis_params.entropy_data_rows,
y,
mode='values')
fig.add_trace(
plotter.trace(data=data,
x=x,
y=np.linspace(ys[0], ys[1],
out.entropy_signal.shape[0])))
for h in ys:
plotter.add_line(fig, h, mode='horizontal', color='black')
else:
dat = get_dat(
self.calculated.analysis_params.transition_only_datnum)
plotter = TwoD(dat=dat)
x = dat.Transition.x
y = dat.Data.get_data('y')
data = dat.Transition.data
ys = y_from_rows(
self.calculated.analysis_params.transition_data_rows,
y,
mode='values')
fig = plotter.plot(data=data,
x=x,
y=y,
title=f'Dat{dat.datnum}: 2D Transition only')
for h in ys:
plotter.add_line(fig, h, mode='horizontal', color='black')
return fig
from deprecation import deprecated
import dat_analysis.useful_functions as U
from dat_analysis.dat_analysis.characters import DELTA
from dat_analysis.dat_object.make_dat import get_dat
from dat_analysis.plotting.plotly.dat_plotting import OneD, TwoD
from dat_analysis.analysis_tools.general_fitting import calculate_fit
from dat_analysis.analysis_tools.nrg import NRG_func_generator
from OLD.new_dash.pages import invert_nrg_fit_params
import plotly.io as pio
pio.renderers.default = 'browser'
p1d = OneD(dat=None)
p2d = TwoD(dat=None)
@dataclass
class Params:
gamma: float
theta: float
center: float
amp: float
lin: float
const: float
lin_occ: float
vary_theta: bool = False
vary_gamma: bool = False
datnum: Optional[int] = None