Curve Analysis (qiskit_experiments.curve_analysis)

Curve analysis provides the analysis base class for a variety of experiments with a single experimental parameter sweep. This analysis subclasses can override several class attributes to customize the behavior from data processing to post-processing, including providing systematic initial guess for parameters tailored to the experiment.

Base Classes

BaseCurveAnalysis()	Abstract superclass of curve analysis base classes.
CurveAnalysis([models, name])	Base class for curve analysis with single curve group.
CompositeCurveAnalysis(analyses[, name])	Composite Curve Analysis.

Data Classes

ScatterTable()	A table-like dataset for the intermediate data used for curve fitting.
SeriesDef(*args, **kwargs)	A dataclass to describe the definition of the curve.
CurveData(*args, **kwargs)	A dataclass that manages the multiple arrays comprising the dataset for fitting.
CurveFitResult([method, model_repr, ...])	Result of Qiskit Experiment curve analysis.
ParameterRepr(name[, repr, unit])	Detailed description of fitting parameter.
FitOptions(parameters[, default_p0, ...])	Collection of fitting options.

Visualization

BaseCurveDrawer()	Abstract class for the serializable Qiskit Experiments curve drawer.
MplCurveDrawer()	Curve drawer for MatplotLib backend.

Standard Analysis Library

BlochTrajectoryAnalysis([name])	A class to analyze a trajectory of the Bloch vector of a single qubit.
DecayAnalysis([name])	A class to analyze general exponential decay curve.
DampedOscillationAnalysis([name])	A class to analyze general exponential decay curve with sinusoidal oscillation.
OscillationAnalysis([name])	Oscillation analysis class based on a fit of the data to a cosine function.
ResonanceAnalysis([name])	A class to analyze a resonance peak with a square rooted Lorentzian function.
GaussianAnalysis([name])	A class to analyze a resonance, typically seen as a peak.
ErrorAmplificationAnalysis([name])	Error amplification analysis class based on a fit to a cosine function.

Fit Functions

fit_function.cos(x[, amp, freq, phase, baseline])	Cosine function.
fit_function.cos_decay(x[, amp, tau, freq, ...])	Cosine function with exponential decay.
fit_function.exponential_decay(x[, amp, ...])	Exponential function
fit_function.gaussian(x[, amp, sigma, x0, ...])	Gaussian function
fit_function.sqrt_lorentzian(x[, amp, ...])	Square-root Lorentzian function for spectroscopy.
fit_function.sin(x[, amp, freq, phase, baseline])	Sine function.
fit_function.sin_decay(x[, amp, tau, freq, ...])	Sine function with exponential decay.

Initial Guess Estimators

guess.constant_sinusoidal_offset(y)	Get constant offset of sinusoidal signal.
guess.constant_spectral_offset(y[, ...])	Get constant offset of spectral baseline.
guess.exp_decay(x, y)	Get exponential decay parameter from monotonically increasing (decreasing) curve.
guess.rb_decay(x, y[, b])	Get base of exponential decay function which is assumed to be close to 1.
guess.full_width_half_max(x, y, peak_index)	Get full width half maximum value of the peak.
guess.frequency(x, y[, filter_window, ...])	Get frequency of oscillating signal.
guess.max_height(y[, percentile, absolute])	Get maximum value of y curve and its index.
guess.min_height(y[, percentile, absolute])	Get minimum value of y curve and its index.
guess.oscillation_exp_decay(x, y[, ...])	Get exponential decay parameter from oscillating signal.

Utilities

utils.is_error_not_significant(val[, ...])	Check if the standard error of given value is not significant.
utils.analysis_result_to_repr(result)	A helper function to create string representation from analysis result data object.
utils.convert_lmfit_result(result, models, ...)	A helper function to convert LMFIT MinimizerResult into CurveFitResult.
utils.eval_with_uncertainties(x, model, params)	Compute Y values with error propagation.
utils.filter_data(data, **filters)	Return the list of filtered data
utils.mean_xy_data(xdata, ydata[, sigma, ...])	Return (x, y_mean, sigma) data.
utils.multi_mean_xy_data(series, xdata, ydata)	Take mean of multi series data set.
utils.data_sort(series, xdata, ydata[, ...])	Sort data.
utils.level2_probability(data, outcome)	Return the outcome probability mean and variance.
utils.probability(outcome)	Return probability data processor callback used by the analysis classes.