Note

This is the documentation for the current state of the development branch of Qiskit Experiments. The documentation or APIs here can change prior to being released.

CurveAnalysis

class CurveAnalysis(models=None, name=None)

Base class for curve analysis with single curve group.

The fit parameters from the series defined under the analysis class are all shared and the analysis performs a single multi-objective function optimization.

A subclass may override these methods to customize the fit workflow.

_run_data_processing

This method performs data processing and returns the processed dataset. By default, it internally calls the DataProcessor instance from the data_processor analysis option and processes the experiment data payload to create Y data with uncertainty. X data and other metadata are generated within this method by inspecting the circuit metadata. The series classification is also performed based upon the matching of circuit metadata and SeriesDef.filter_kwargs.

_format_data

This method consumes the processed dataset and outputs the formatted dataset. By default, this method takes the average of y values over the same x values and then sort the entire data by x values.

_generate_fit_guesses

This method creates initial guesses for the fit parameters. See Providing initial guesses for details.

_run_curve_fit

This method performs the fitting with predefined fit models and the formatted dataset. This method internally calls the _generate_fit_guesses() method. Note that this is a core functionality of the _run_analysis() method, that creates fit result objects from the formatted dataset.

_evaluate_quality

This method evaluates the quality of the fit based on the fit result. This returns “good” when reduced chi-squared is less than 3.0. Usually it returns string “good” or “bad” according to the evaluation.

_create_analysis_results

This method creates analysis results for important fit parameters that might be defined by analysis options result_parameters.

_create_curve_data

This method creates analysis results containing the formatted dataset, i.e. data used for the fitting. Entries are created when the analysis option return_data_points is True. If analysis consists of multiple series, an analysis result is created for each series definition.

_initialize

This method initializes analysis options against input experiment data. Usually this method is called before other methods are called.

Initialize data fields that are privately accessed by methods.

Parameters:

• models (Optional[List[Model]]) – List of LMFIT Model class to define fitting functions and parameters. If multiple models are provided, the analysis performs multi-objective optimization where the parameters with the same name are shared among provided models. When multiple models are provided, user must specify the data_subfit_map value in the analysis options to allocate experimental results to a particular fit model.

• name (Optional[str]) – Optional. Name of this analysis.

Attributes

CurveAnalysis.drawer	A short-cut for curve drawer instance, if set.
CurveAnalysis.models	Return fit models.
CurveAnalysis.name	Return name of this analysis.
CurveAnalysis.options	Return the analysis options for run() method.
CurveAnalysis.parameters	Return parameters of this curve analysis.
CurveAnalysis.plotter	A short-cut to the curve plotter instance.

Methods

CurveAnalysis.config()	Return the config dataclass for this analysis
CurveAnalysis.copy()	Return a copy of the analysis
CurveAnalysis.from_config(config)	Initialize an analysis class from analysis config
CurveAnalysis.run(experiment_data[, ...])	Run analysis and update ExperimentData with analysis result.
CurveAnalysis.set_options(**fields)	Set the analysis options for run() method.