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
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. |
PurityBenchmarkingQubitAnalysis
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. |
rotate_to_probability_axis |
Rotates the S21 IQ points to the real - normalized axis |
plot
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
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