qblox_scheduler.analysis.conditional_oscillation_analysis

Module containing an analysis class for the conditional oscillation experiment.

Classes

ConditionalOscillationAnalysis

Analysis class for the conditional oscillation experiment.

Functions

_add_center(→ None)
_center_and_fit_sinus(→ lmfit.model.ModelResult)

Module Contents

_add_center(param_name: str, data: numpy.typing.NDArray, params: lmfit.parameter.Parameters) → None

_center_and_fit_sinus(y: numpy.typing.NDArray, x: numpy.typing.NDArray) → lmfit.model.ModelResult

class ConditionalOscillationAnalysis(dataset: xarray.Dataset | None = None, tuid: quantify_core.data.types.TUID | str | None = None, label: str = '', settings_overwrite: dict | None = None, plot_figures: bool = True)

Bases: qblox_scheduler.analysis.base_analysis.BaseAnalysis

Analysis class for the conditional oscillation experiment.

For a reference to the conditional oscillation experiment, please see section D in the supplemental material of this paper: https://arxiv.org/abs/1903.02492

Example

import warnings

from qblox_scheduler.analysis.conditional_oscillation_analysis import (
    ConditionalOscillationAnalysis
)
with warnings.catch_warnings():
    warnings.simplefilter("ignore")
    from qblox_scheduler.analysis.data_handling import OutputDirectoryManager as mng

# load example data
test_data_dir = "../tests/test_data"
mng.set_datadir(test_data_dir)

# run analysis and plot results
analysis = (
    ConditionalOscillationAnalysis(tuid="20230509-165523-132-dcfea7")
    .run()
    .display_figs_mpl()
)

process_data() → None

Process the data so that the analysis can make assumptions on the format.

run_fitting() → None

Fit two sinusoidal model to the off/on experiments.

analyze_fit_results() → None

Check fit success and populates .quantities_of_interest.

create_figures() → None

Generate figures of interest.

matplolib figures and axes objects are added to the .figs_mpl and .axs_mpl dictionaries., respectively.