Parameters

Parameters#

Parameter#

Represents a single nested-sampling parameter with its prior, kind, and scope.

class ForMoSA.parameter.parameter.Parameter(name, prior, kind, scope='global', obs_index=None, title=None, vsini_function=None, logger=None, log_level='INFO')[source]#

Bases: object

ForMoSA Parameter class. Handles a single parameter for the nested sampling algorithm.

Parameters:

name (str) – Name of the parameter (‘par1’, ‘par2’, ‘rv’, ‘d’, …)
prior (Prior) – Prior object associated with the parameter (UniformPrior, GaussianPrior, ConstantPrior, LogUniformPrior)
kind (ParameterKind) – Type of parameter used to identify the parameter
scope (str) – ‘global’ if this is a global parameter or ‘local’ if it is applied to specific observations
obs_index (list[int] | None) – Index of the obervation the parameter is applied to (None if scope is ‘global’)
title (str | None) – Title of the parameter (used to connect grid parameters to their associated title)
vsini_function (VsiniFunction | None) – Vsini function used for the prior (required if name starts with ‘vsini’)
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Notes

Authors: Allan Denis

property logger: Logger#: Logger.

property name: str#: Name of the parameter.

property kind: ParameterKind#: Parameter type.

property scope: str#: Scope (‘global’ or ‘local’).

property is_local: bool#: Whether the parameter is local.

property title: str#: Title.

property obs_index: list[int] | None#: Index of the observation the parameter is applied to.

property prior: Prior#: Prior object associated with the parameter.

property vsini_function: str#: Vsini function used for the prior.

property is_fixed: bool#: Whether the parameter is fixed (constant prior) or free.

property to_dict: dict#: Dictionary representation of the parameter.

classmethod from_dict(data, logger=None, log_level='INFO')[source]#

Reconstruct a Parameter from a dictionary of Parameter.

Parameters:

data (dict) – Dictionary containing parameter parameters
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Return type:

Parameter

Returns:

Parameter – An instance of class Parameter

Notes

Authors: Allan Denis

Parameter Set#

Container for the full set of parameters explored during nested sampling.

class ForMoSA.parameter.parameter_set.ParameterSet(logger=None, log_level='INFO')[source]#

Bases: object

Container for nested sampling parameters.

Parameters:

logger (Logger | None) – Logger
log_level (str) – Level of the logging

Notes

Authors: Allan Denis

property logger: Logger#: Logger.

property parameters: list[Parameter]#: List of parameters.

property free_parameters: list[Parameter]#: list of free parameters.

property fixed_parameters: list[Parameter]#: list of fixed parameters.

property names: list[str]#: list of parameter names.

property free_names: list[str]#: list of parameter names.

property titles: list[str]#: List of titles.

property free_titles: list[str]#: List of titles.

property kinds: list[ParameterKind]#: list of parameter kinds.

property n_free_parameters: int#: Number of free parameters.

property to_dict: dict#: Dictionary representation of the set of parameters.

classmethod from_config(config_params, logger=None, log_level='INFO')[source]#

Generate instance of ParameterSet from an instance of ConfigParameters.

Parameters:

config_params (ConfigParameters) – Instance of ConfigParameters
logger (Logger | None) – Logger
log_level (str) – Level of the logging

Return type:

ParameterSet

Returns:

“ParameterSet” – As instance of ParameterSet

Notes

Authors: Allan Denis

classmethod from_dict(data, logger=None, log_level='INFO')[source]#

Reconstruct a ParameterSet from a dictionary of ParameterSet.

Parameters:

data (dict) – Dictionary containing ParameterSet parameters
logger (Logger | None) – Logger
log_level (str) – Level of the logging

Return type:

ParameterSet

Returns:

‘ParameterSet’ – An instance of class ParameterSet

Notes

Authors: Allan Denis

classmethod from_json(path, logger=None, log_level='INFO')[source]#

Reconstruct a ParameterSet from a json file.

Parameters:

path (str | PathLike) – Path to the json file
logger (Logger | None) – Logger
log_level (str) – Level of the logging

Return type:

ParameterSet

Returns:

‘ParameterSet’ – An instance of class ParameterSet

Notes

Authors: Allan Denis

prior_transform(theta)[source]#

Transform a list/array theta in [0,1]^N into physical values using each free parameter prior.

Parameters:: theta (ndarray[float]) – list/array of floats in [0,1]^N where N is the number of free parameters
Return type:: list[float]
Returns:: list[float] (physical values in the same ordering as self.free_parameters)

Notes

Authors: Allan Denis

add_parameter(parameter)[source]#

Add a Parameter to the set.

Parameters:: parameter (Parameter) – Instance of class Parameter to add
Return type:: None

Notes

Authors: Allan Denis

summary(as_dataframe=True)[source]#

Return a summary of parameters.

Parameters:: as_dataframe (bool) – If True, return pandas.DataFrame, else formatted string
Return type:: DataFrame | str
Returns:: pandas.DataFrame | str – Summary of parameters

Notes

Authors: Allan Denis

to_json(path)[source]#

Save the set of parameters to a given path as a json file.

Parameters:: path (str | PathLike) – Path to save the set of parameters
Return type:: None

Notes

Authors: Allan Denis

Priors#

Prior distribution classes used to define parameter search ranges.

Inheritance diagram of ForMoSA.parameter.prior.UniformPrior, ForMoSA.parameter.prior.LogUniformPrior, ForMoSA.parameter.prior.ConstantPrior, ForMoSA.parameter.prior.GaussianPrior

class ForMoSA.parameter.prior.Prior(logger=None, log_level='INFO')[source]#

Bases: ABC

Abstract base class for prior distributions.

Notes

Authors: Allan Denis

Parameters:

logger (Logger | None)
log_level (str)

property logger: Logger#: Logger.

property to_dict: dict#: Dictionary representation of the prior.

classmethod from_dict(data, logger=None, log_level='INFO')[source]#

Reconstruct a Prior from a dictionary of Prior.

Parameters:

data (dict) – Dictionary containing prior parameters
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Return type:

Prior

Returns:

Prior – An instance of Prior

Notes

Authors: Allan Denis

abstractmethod sample(theta)[source]#

Sample from the prior distribution.

Parameters:: theta (float) – A value between 0 and 1 to sample from the prior
Return type:: float
Returns:: float – Sampled value from the prior distribution

Notes

Authors: Allan Denis

abstract property is_fixed: bool#: Whether the prior is fixed.

abstract property prior_type: PriorType#: Type of prior.

parse_prior(prior_type, params, logger=None, log_level='INFO')[source]#

Parse prior parameters into corresponding prior object.

Parameters:

prior_type (PriorType) – Type of the prior
params (dict) – Dictionary of prior parameters
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Return type:

Prior

Returns:

Prior – Prior object corresponding to the specified type and parameters

Examples

>>> Prior = Prior.parse_prior(prior_type, params)

Notes

Authors: Allan Denis

class ForMoSA.parameter.prior.UniformPrior(lower, upper, logger=None, log_level='INFO')[source]#

Bases: Prior

Class defining a Uniform prior.

Parameters:

lower (float) – Lower bound of the uniform prior
upper (float) – Upper bound of the uniform prior
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Notes

Authors: Allan Denis

property lower: float#: Lower bound of the uniform prior.

property upper: float#: Upper bound of the uniform prior.

property bounds: list#: Bounds of the uniform prior.

property prior_type: PriorType#: Type of prior.

property is_fixed: bool#: Whether the prior is fixed.

sample(theta)[source]#

Sample from uniform prior and theta value.

Parameters:: theta (float) – theta value between 0 and 1
Return type:: float
Returns:: float – Sampled value

get_params_dict()[source]#

Return a dictionary representation of the uniform prior.

Return type:: dict
Returns:: dict – Dictionary of the parameter

Notes

Authors: Allan Denis

class ForMoSA.parameter.prior.LogUniformPrior(lower, upper, logger=None, log_level='INFO')[source]#

Bases: Prior

Class defining a Log-Uniform prior.

Parameters:

lower (float) – Lower bound of the log-uniform prior
upper (float) – Upper bound of the log-uniform prior
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Notes

Authors: Allan Denis

property lower: float#: Lower bound of the log-uniform prior.

property upper: float#: Upper bound of the log-uniform prior.

property bounds: list#: Bounds of the log-uniform prior.

property prior_type: PriorType#: Type of prior.

property is_fixed: bool#: Whether the prior is fixed.

sample(theta)[source]#

Sample from loguniform prior and theta value.

Parameters:: theta (float) – theta value between 0 and 1
Return type:: float
Returns:: float – Sampled value

get_params_dict()[source]#

Return a dictionary representation of the loguniform prior.

Return type:: dict
Returns:: dict – Dictionary of the parameter

Notes

Authors: Allan Denis

class ForMoSA.parameter.prior.ConstantPrior(value, logger=None, log_level='INFO')[source]#

Bases: Prior

Class defining a Constant prior.

Parameters:

value (float) – Constant value of the prior
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Notes

Authors: Allan Denis

property value: float#: Constant value of the prior.

property prior_type: PriorType#: Type of prior.

property is_fixed: bool#: Whether the prior is fixed.

property bounds: float#: Bounds of the constant prior.

sample(theta)[source]#

Sample from constant prior and theta value.

Parameters:: theta (float) – theta value between 0 and 1
Return type:: float
Returns:: float – Sampled value

get_params_dict()[source]#

Return a dictionary representation of the uniform prior.

Return type:: dict
Returns:: dict – Dictionary of the parameter

Notes

Authors: Allan Denis

class ForMoSA.parameter.prior.GaussianPrior(mean, stddev, logger=None, log_level='INFO')[source]#

Bases: Prior

Class defining a Gaussian prior.

Parameters:

mean (float) – Mean of the Gaussian prior
stddev (float) – Standard deviation of the Gaussian prior
logger (Logger | None) – Logger
log_level (str) – Level of the Logger

Notes

Authors: Allan Denis

property mean: float#: Mean of the Gaussian prior.

property stddev: float#: Standard deviation of the Gaussian prior.

property prior_type: PriorType#: Type of prior.

property is_fixed: bool#: Whether the prior is fixed.

property bounds: tuple[float, float]#: Bounds of the Gaussian prior (mean, stddev).

sample(theta)[source]#

Sample from gaussian prior and theta value.

Parameters:: theta (float) – theta value between 0 and 1
Return type:: float
Returns:: float – Samples value

get_params_dict()[source]#

Return a dictionary representation of the uniform prior.

Return type:: dict
Returns:: dict – Dictionary of the parameter

Notes

Authors: Allan Denis

Parameters

Contents

Parameters#

Parameter#

Parameter Set#

Priors#