calibr8.contrib.normal

This module implements reusable calibration models with Normal distributions for the dependent variable.

class calibr8.contrib.normal.BaseAsymmetricLogisticN(*, independent_key: str, dependent_key: str, sigma_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, NormalNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of asymmetric logistic model for mu [sigma_degree] parameters for sigma (lowest degree first)

Returns:

muarray-like: values for the mu parameter of a normal distribution describing the dependent variable
sigmaarray-like or float: values for the sigma parameter of a normal distribution describing the dependent variable

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of asymmetric logistic model for mu [sigma_degree] parameters for sigma (lowest degree first)

Returns:

xarray-like: predicted independent values given the observations

class calibr8.contrib.normal.BaseExponentialModelN(*, independent_key: str, dependent_key: str, sigma_degree: int = 0, fixed_intercept: float | None = None, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, NormalNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like: values of the independent variable
thetaoptional, array-like: Parameter vector of the calibration model. Depending on the fixed_intercept setting these are [I, L, k] or [L, k] parameters of exponential model for mu. Followed by parameters for the model for sigma (lowest degree first).

Returns:

muarray-like: values for the mu parameter of a normal distribution describing the dependent variable
sigmaarray-like or float: values for the sigma parameter of a normal distribution describing the dependent variable

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like: observations
thetaoptional, array-like: Parameter vector of the calibration model. Depending on the fixed_intercept setting these are [I, L, k] or [L, k] parameters of exponential model for mu. Followed by parameters for the model for sigma (lowest degree first).

Returns:

xarray-like: predicted independent values given the observations

class calibr8.contrib.normal.BaseLogIndependentAsymmetricLogisticN(*, independent_key: str, dependent_key: str, sigma_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, NormalNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of log-independent asymmetric logistic model for mu [sigma_degree] parameters for sigma (lowest degree first)

Returns:

muarray-like: values for the mu parameter of a normal distribution describing the dependent variable
sigmaarray-like or float: values for the sigma parameter of a normal distribution describing the dependent variable

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: 5 parameters of log-independent asymmetric logistic model for mu [sigma_degree] parameters for sigma (lowest degree first)

Returns:

xarray-like: predicted independent values given the observations

class calibr8.contrib.normal.BasePolynomialModelN(*, independent_key: str, dependent_key: str, mu_degree: int, sigma_degree: int = 0, theta_names: Sequence[str] | None = None)

Bases: ContinuousUnivariateModel, NormalNoise

Attributes:

pymc_dist
theta_fitted: The parameter vector that describes the fitted model.
theta_guess: Initial guess that was used to fit the model.
theta_names: Names of model parameters in a fixed order.
theta_timestamp: The timestamp when theta_fitted was set.

Methods

`infer_independent`(y, *, lower, upper[, ...])	Infer the posterior distribution of the independent variable given the observations of the dependent variable.
`likelihood`(*, y, x[, theta, scan_x])	Likelihood of observation (dependent variable) given the independent variable.
`load`(filepath)	Instantiates a model from a JSON file of key properties.
`loglikelihood`(*, y, x[, name, replicate_id, ...])	Loglikelihood of observation (dependent variable) given the independent variable.
`objective`(independent, dependent[, minimize])	Creates an objective function for fitting to data.
`predict_dependent`(x, *[, theta])	Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.
`predict_independent`(y, *[, theta])	Predict the independent variable using the inverse trend model.
`save`(filepath)	Save key properties of the calibration model to a JSON file.
`scipy_dist`

to_pymc
to_scipy

predict_dependent(x, *, theta=None)

Predicts the parameters mu and sigma of a normal distribution which characterizes the dependent variable given values of the independent variable.

Parameters:

xarray-like

values of the independent variable

thetaoptional, array-like

parameter vector of the calibration model:: [mu_degree] parameters for mu (lowest degree first) [sigma_degree] parameters for sigma (lowest degree first)

Returns:

muarray-like: values for the mu parameter of a normal distribution describing the dependent variable
sigmaarray-like or float: values for the sigma parameter of a normal distribution describing the dependent variable

predict_independent(y, *, theta=None)

Predict the independent variable using the inverse trend model.

Parameters:

yarray-like

observations

thetaoptional, array-like

parameter vector of the calibration model:: [mu_degree] parameters for mu (lowest degree first) [sigma_degree] parameters for sigma (lowest degree first)

Returns:

xarray-like: predicted independent values given the observations