estim8.error_models

This module defines the blueprint for modeling measurement noise.

class estim8.error_models.BaseErrorModel(error_distribution: rv_continuous = <scipy.stats._continuous_distns.norm_gen object>, error_distribution_kwargs: dict = {})

Bases: ABC

Abstract base class for error modeling.

Methods

generate_error_data(values)	Abstract class method for calculating errors of experimental data given the datapoints.
get_sampling(values, errors, n_samples)	Resamples values of data given the class instance error_distribution.

abstractmethod generate_error_data(values: array) → array

Abstract class method for calculating errors of experimental data given the datapoints.

Parameters:

valuesnp.array

Values of the data on which to apply error model

Returns:

errorsnp.array

Error calculated according to specified model

get_sampling(values: array, errors: array, n_samples: int) → List[array]

Resamples values of data given the class instance error_distribution.

Parameters:

valuesnp.array

The values to resample.

n_samplesint

The number of samples to generate.

Returns:

resamplingList[np.array]

The generated Monte Carlo samples of values.

class estim8.error_models.LinearErrorModel(slope: float = 0, offset: float = 0, error_distribution: rv_continuous = <scipy.stats._continuous_distns.norm_gen object>, error_distribution_kwargs: dict = {})

Bases: BaseErrorModel

An ErrorModel with linear relationship between measurement value and noise given by;

\[\sigma = slope \cdot y + offset\]

Attributes:

error_model_params

Error model parameters given by slope and offset.

Methods

generate_error_data(values)	Generates error data based on the linear error model.
get_sampling(values, errors, n_samples)	Resamples values of data given the class instance error_distribution.

property error_model_params: dict

Error model parameters given by slope and offset.

generate_error_data(values: array) → array

Generates error data based on the linear error model.

Parameters:

valuesnp.array

Values of the data on which to apply error model.

Returns:

errorsnp.array

Error calculated according to the linear model.