BaseCurveAnalysis¶
- class BaseCurveAnalysis[source]¶
Abstract superclass of curve analysis base classes.
Note that this class doesn’t define the
_run_analysis()method, and no actual fitting protocol is implemented in this base class. However, this class defines several common methods that can be reused. A curve analysis subclass can construct proper fitting protocol by combining following methods, i.e. subroutines. See Curve Analysis workflow for how these subroutines are called.Subclass must implement following methods.
_run_data_processing
This method performs data processing and returns the processed dataset. Input data is a list of dictionaries, where each entry represents an outcome of circuit sampling along with the metadata attached to it.
_format_data
This method consumes the processed dataset and outputs the formatted dataset. For example, this may include averaging Y values over the same X data points.
_run_curve_fit
This method performs the fitting with the predefined fit models and the formatted dataset. This is a core functionality of the
_run_analysis()method that creates fit result objects from the formatted dataset.Optionally, a subclass may override following methods. These methods have default implementations as described below.
_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. This criterion can be updated by subclass.
_run_curve_fit
This method performs the fitting with predefined fit models and the formatted dataset. This method internally calls
_generate_fit_guesses()method. Note that this is a core functionality of the_run_analysis()method, that creates fit result object from the formatted dataset._create_analysis_results
This method creates analysis results for important fit parameters that might be defined by analysis options
result_parameters._create_figures
This method creates figures by consuming the scatter table data. Figures are created when the analysis option
plotisTrue._initialize
This method initializes analysis options against input experiment data. Usually this method is called before other methods are called.
Initialize the analysis object.
Attributes
- models¶
Return fit models.
- name¶
Return name of this analysis.
- parameters¶
Return parameters estimated by this analysis.
- plotter¶
A short-cut to the curve plotter instance.
Methods
- config()¶
Return the config dataclass for this analysis
- Return type:
- copy()¶
Return a copy of the analysis
- Return type:
- classmethod from_config(config)¶
Initialize an analysis class from analysis config
- Return type:
- run(experiment_data, replace_results=False, **options)¶
Run analysis and update ExperimentData with analysis result.
- Parameters:
experiment_data (ExperimentData) – the experiment data to analyze.
replace_results (bool) – If True clear any existing analysis results, figures, and artifacts in the experiment data and replace with new results. See note for additional information.
options – additional analysis options. See class documentation for supported options.
- Returns:
An experiment data object containing analysis results, figures, and artifacts.
- Raises:
QiskitError – If experiment_data container is not valid for analysis.
- Return type:
Note
Updating Results
If analysis is run with
replace_results=Truethen any analysis results, figures, and artifacts in the experiment data will be cleared and replaced with the new analysis results. Saving this experiment data will replace any previously saved data in a database service using the same experiment ID.If analysis is run with
replace_results=Falseand the experiment data being analyzed has already been saved to a database service, or already contains analysis results or figures, a copy with a unique experiment ID will be returned containing only the new analysis results and figures. This data can then be saved as its own experiment to a database service.