API Reference

Core data containers and result objects

class labfit.AsymmetricError(lower: 'np.ndarray', upper: 'np.ndarray')[source]
__init__(lower: ndarray, upper: ndarray) → None
property effective: ndarray
lower: ndarray
upper: ndarray
class labfit.DataSeries(x: 'np.ndarray', y: 'np.ndarray', sigma: 'MaybeArray | AsymmetricError | None' = None, y_err: 'MaybeArray | AsymmetricError | None' = None, label: 'str' = '', sigma_low: 'np.ndarray | None' = None, sigma_high: 'np.ndarray | None' = None, sigma_cov: 'np.ndarray | None' = None)[source]
__init__(x: ndarray, y: ndarray, sigma: ndarray | Sequence[float] | float | AsymmetricError | None = None, y_err: ndarray | Sequence[float] | float | AsymmetricError | None = None, label: str = '', sigma_low: ndarray | None = None, sigma_high: ndarray | None = None, sigma_cov: ndarray | None = None) → None
property effective_sigma: ndarray | None
error() → ndarray | None[source]
label: str = ''
sigma: ndarray | Sequence[float] | float | AsymmetricError | None = None
sigma_cov: ndarray | None = None
sigma_high: ndarray | None = None
sigma_low: ndarray | None = None
with_label(label: str) → DataSeries[source]
x: ndarray
y: ndarray
y_err: ndarray | Sequence[float] | float | AsymmetricError | None = None
property y_error: ndarray | None
labfit.Series

alias of DataSeries

class labfit.Dataset(series: 'list[DataSeries]' = <factory>)[source]
__init__(series: list[~labfit.types.DataSeries] = <factory>) → None
append(series: DataSeries) → None[source]
extend(items: Iterable[DataSeries]) → None[source]
series: list[DataSeries]
class labfit.FitResult(reduced_chi2: 'float', params: 'dict[str, float]', covariance: 'np.ndarray | None' = None, p_value: 'float' = nan, uncertainties: 'dict[str, float]' = <factory>, success: 'bool' = True, message: 'str' = '', model_name: 'str' = '', param_names: 'tuple[str, ...]' = <factory>, x: 'np.ndarray | None' = None, y: 'np.ndarray | None' = None, sigma: 'np.ndarray | None' = None, y_fit: 'np.ndarray | None' = None, series: 'DataSeries | None' = None, model: 'Any' = None, is_weighted: 'bool' = True)[source]
__init__(reduced_chi2: float, params: dict[str, float], covariance: ~numpy.ndarray | None = None, p_value: float = nan, uncertainties: dict[str, float] = <factory>, success: bool = True, message: str = '', model_name: str = '', param_names: tuple[str, ...] = <factory>, x: ~numpy.ndarray | None = None, y: ~numpy.ndarray | None = None, sigma: ~numpy.ndarray | None = None, y_fit: ~numpy.ndarray | None = None, series: ~labfit.types.DataSeries | None = None, model: ~typing.Any = None, is_weighted: bool = True) → None
covariance: ndarray | None = None
is_weighted: bool = True
items()[source]
keys()[source]
message: str = ''
model: Any = None
model_name: str = ''
p_value: float = nan
param_names: tuple[str, ...]
property parameter_uncertainties: dict[str, float]
params: dict[str, float]
predict(x: ndarray | Sequence[float] | float) → ndarray[source]
reduced_chi2: float
property residuals: ndarray
series: DataSeries | None = None
sigma: ndarray | None = None
success: bool = True
uncertainties: dict[str, float]
values()[source]
x: ndarray | None = None
y: ndarray | None = None
y_fit: ndarray | None = None
class labfit.Fitter(model: 'str | Any' = 'linear', p0: 'Any' = None, bounds: 'Any' = None)[source]
__init__(model: str | Any = 'linear', p0: Any = None, bounds: Any = None) → None
bounds: Any = None
fit(x, y=None, **kwargs) → FitResult[source]
fit_multi(dataset, **kwargs)[source]
model: str | Any = 'linear'
p0: Any = None
class labfit.Plotter(series: 'list[DataSeries]' = <factory>, figure: 'Any' = None, axes: 'Any' = None)[source]
__init__(series: list[~labfit.types.DataSeries] = <factory>, figure: ~typing.Any = None, axes: ~typing.Any = None) → None
add_series(*args, **kwargs) → Plotter[source]
axes: Any = None
figure: Any = None
plot(result=None, **kwargs) → Plotter[source]
save(path, **kwargs)[source]
series: list[DataSeries]