analysis

tergite_autocalibration.lib.nodes.characterization.purity_benchmarking.analysis

Module containing classes that model, fit and plot data from the purity benchmarking experiment.

Classes:

Name	Description
ExpDecayModel	Generate an exponential decay model that can be fit to purity benchmarking data.
PurityBenchmarkingQubitAnalysis	Analysis that fits an exponential decay function to purity benchmarking data.

ExpDecayModel

ExpDecayModel(*args, **kwargs)

Bases: Model

Generate an exponential decay model that can be fit to purity benchmarking data.

Methods:

Name	Description
guess	Generate initial guesses for the model parameters based on the data.

guess

guess(data, **kws) -> Parameters

Generate initial guesses for the model parameters based on the data.

PurityBenchmarkingQubitAnalysis

PurityBenchmarkingQubitAnalysis(name, redis_fields)

Bases: BaseQubitAnalysis

Analysis that fits an exponential decay function to purity benchmarking data.

Methods:

Name	Description
plot	Plot the fitted values from the analysis
plotter	Plot the normalized data and fitted exponential decay curve.
process_qubit	Setup the qubit data and analyze it.

plot

plot(primary_axis)

Plot the fitted values from the analysis Args: primary_axis: The axis object from matplotlib to be plotted Returns: None, will just plot the fitted values

plotter

plotter(ax: Axes)

Plot the normalized data and fitted exponential decay curve.

process_qubit

process_qubit(dataset, qubit_element) -> QOI

Setup the qubit data and analyze it. Args: dataset: xarray dataset with the qubit data qubit_element: name of the qubit element Returns: QOI: Quantity of interest as QOI wrapped object