GP Class Family

class model.Celerite_GP_Model(pspl_model, filter_index)

Bases: Model

This is nedeed for the GP. Just a wrapper over our model so it is a celerite model.

Attributes

full_size

The total number of parameters (including frozen parameters)

parameter_vector

An array of all parameters (including frozen parameters)

vector_size

The number of active (or unfrozen) parameters

Methods

compute_gradient(*args, **kwargs)	Compute the "gradient" of the model for the current parameters
freeze_all_parameters()	Freeze all parameters of the model
freeze_parameter(name)	Freeze a parameter by name
get_parameter(name)	Get a parameter value by name
get_parameter_bounds([include_frozen])	Get a list of the parameter bounds
get_parameter_dict([include_frozen])	Get an ordered dictionary of the parameters
get_parameter_names([include_frozen])	Get a list of the parameter names
get_parameter_vector([include_frozen])	Get an array of the parameter values in the correct order
get_value(t_obs)	Compute the "value" of the model for the current parameters
log_prior()	Compute the log prior probability of the current parameters
set_parameter(name, value)	Set a parameter value by name
set_parameter_vector(vector[, include_frozen])	Set the parameter values to the given vector
thaw_all_parameters()	Thaw all parameters of the model
thaw_parameter(name)	Thaw a parameter by name

get_gradient

get_value(t_obs)

Compute the “value” of the model for the current parameters

This method should be overloaded by subclasses to implement the actual functionality of the model.

compute_gradient(*args, **kwargs)

Compute the “gradient” of the model for the current parameters

This method should be overloaded by subclasses to implement the actual functionality of the model. The output of this function should be an array where the first dimension is full_size.

freeze_all_parameters()

Freeze all parameters of the model

freeze_parameter(name)

Freeze a parameter by name

Args:

name: The name of the parameter

property full_size

The total number of parameters (including frozen parameters)

get_parameter(name)

Get a parameter value by name

Args:

name: The name of the parameter

get_parameter_bounds(include_frozen=False)

Get a list of the parameter bounds

Args:

include_frozen (Optional[bool]): Should the frozen parameters be

included in the returned value? (default: False)

get_parameter_dict(include_frozen=False)

Get an ordered dictionary of the parameters

Args:

include_frozen (Optional[bool]): Should the frozen parameters be

included in the returned value? (default: False)

get_parameter_names(include_frozen=False)

Get a list of the parameter names

Args:

include_frozen (Optional[bool]): Should the frozen parameters be

included in the returned value? (default: False)

get_parameter_vector(include_frozen=False)

Get an array of the parameter values in the correct order

Args:

include_frozen (Optional[bool]): Should the frozen parameters be

included in the returned value? (default: False)

log_prior()

Compute the log prior probability of the current parameters

property parameter_vector

An array of all parameters (including frozen parameters)

set_parameter(name, value)

Set a parameter value by name

Args:

name: The name of the parameter value (float): The new value for the parameter

set_parameter_vector(vector, include_frozen=False)

Set the parameter values to the given vector

Args:

vector (array[vector_size] or array[full_size]): The target

parameter vector. This must be in the same order as parameter_names and it should only include frozen parameters if include_frozen is True.

include_frozen (Optional[bool]): Should the frozen parameters be

included in the returned value? (default: False)

thaw_all_parameters()

Thaw all parameters of the model

thaw_parameter(name)

Thaw a parameter by name

Args:

name: The name of the parameter

property vector_size

The number of active (or unfrozen) parameters

class model.PSPL_GP

Bases: ABC

PSPL object that has optional support for gaussian process on each photometric filter.

Methods

get_log_det_covariance(t_obs, mag_obs, ...)	Returns photometry with GP noise added in.
get_photometry_with_gp(t_obs, mag_obs, ...)	Returns photometry with GP noise added in.
log_likely_photometry(t_obs, mag_obs, ...[, ...])	Calculate the log-likelihood for the PSPL + GP model and photometric data.

get_photometry_with_gp(t_obs, mag_obs, mag_err_obs, filt_index=0, t_pred=None)

Returns photometry with GP noise added in.

Note

This will throw an error if this is a filter with use_gp_phot[filt_index] = False.

get_log_det_covariance(t_obs, mag_obs, mag_err_obs, filt_index=0, t_pred=None)

Returns photometry with GP noise added in.

Note

This will throw an error if this is a filter with use_gp_phot[filt_index] = False.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Calculate the log-likelihood for the PSPL + GP model and photometric data.

Note

The GP will only be used for filters where use_gp_phot[filt_index] = True.

class model.PSPL_GP

Bases: ABC

PSPL object that has optional support for gaussian process on each photometric filter.

Methods

get_log_det_covariance(t_obs, mag_obs, ...)	Returns photometry with GP noise added in.
get_photometry_with_gp(t_obs, mag_obs, ...)	Returns photometry with GP noise added in.
log_likely_photometry(t_obs, mag_obs, ...[, ...])	Calculate the log-likelihood for the PSPL + GP model and photometric data.

get_photometry_with_gp(t_obs, mag_obs, mag_err_obs, filt_index=0, t_pred=None)

Returns photometry with GP noise added in.

Note

This will throw an error if this is a filter with use_gp_phot[filt_index] = False.

get_log_det_covariance(t_obs, mag_obs, mag_err_obs, filt_index=0, t_pred=None)

Returns photometry with GP noise added in.

Note

This will throw an error if this is a filter with use_gp_phot[filt_index] = False.

log_likely_photometry(t_obs, mag_obs, mag_err_obs, filt_index=0)

Calculate the log-likelihood for the PSPL + GP model and photometric data.

Note

The GP will only be used for filters where use_gp_phot[filt_index] = True.