candl.interface

`candl.interface`#

Tools to help interface the likelihood code with other software.

Overview:#

Theory Code Interface Functions:

MontePython Interface:

get_montepython_nuisance_param_block_for_like()

Cobaya Interface:

CandlCobayaLikelihood
get_cobaya_likelihood_class_for_like()
get_cobaya_info_dict_for_like()

Cobaya Theory Code Classes:

CobayaTheoryCosmoPowerJAX
CobayaTheoryCosmoPower
CobayaTheoryPyCapse
CobayaTheoryBBTemplate
CobayaTheoryCosmoPowerJAXLensing
CobayaTheoryCosmoPowerLensing

class candl.interface.CandlCobayaLikelihood(*args: Any, **kwargs: Any)#

Wrapper for a candl likelihood into a cobaya.likelihood.Likelihood class. Based on example likelihood provided by Cobaya (https://cobaya.readthedocs.io/en/latest/likelihoods.html), Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290). See the different attributes below that can be set when passing the class to Cobaya.

Attributes:

data_set_filestr: Path of the data set info yaml file.
variantstr: Variant of the likelihood (if requested).
clear_internal_priorsbool: Whether to clear internal priors.
clear_specific_priorsstr or list: String of list of strings of specific priors to clear. If a given parameter appears in an internal prior, the full prior will be cleared (beware for multi-dimensional priors). Doesn’t do anything if clear_internal_priors is True.
lensingbool: Whether to use the lensing likelihood.
feedbackbool: Whether to print feedback when initialising the likelihood.
data_selectionany: Data selection to be used. String, list of string, binary mask, or path to a mask are supported.
candl_likecandl.Like or candl.LensLike: Candl likelihood.
wrapper: any: Set if wanting to access a wrapper likelihood (e.g. clipy).
additional_argsdict: Additional arguments to be passed to the likelihood at initialisation.

Methods

`__init__`(args, *kwargs)
`initialize`()	Called from __init__ to initialise and complete the setup.
`get_requirements`()	Return dictionary of parameters that are needed
`logp`(**params)	Calculate the log-likelihood by calling candl.

get_requirements()#: Return dictionary of parameters that are needed

initialize()#: Called from __init__ to initialise and complete the setup. Loads the candl likelihood.

logp(**params)#: Calculate the log-likelihood by calling candl.

class candl.interface.CobayaTheoryBBTemplate(*args: Any, **kwargs: Any)#

Wrapper for a two template BB calculator in a cobaya theory class.

To initialise, pass ‘template_filenames’ in the relevant cobaya block: A dictionary with ‘lensing_B_modes’ and ‘r’ template file names.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290).

This is a specified version of CobayaTheoryTemplate.

Attributes:

template_filenamesdict: File names of templates.
providerProvider: Cobaya provider.
current_statedict: Dict containing current parameters and results.
templatesdict: Dict containing cropped BB spectrum templates.
ellsarray (int): Ell range provided.

Methods

`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Sums the two templates with their respective amplitudes.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Loads the templates requested and crops them to the right ell range.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryCosmoPower(*args: Any, **kwargs: Any)#

Wraps regular CosmoPower model into a cobaya theory class. See A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2106.03846).

To initialise, pass ‘emulator_filenames’ in the relevant cobaya block: A dictionary of spectrum type (TT, TE, …) and file names of corresponding emulator models.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

emulator_filenamesdict: File names of emulators.
cp_emulatorsdict: CosmoPower emulators.
cp_parslist: List of parameters required by the emulators.
providerProvider: Cobaya provider.
current_statedict: Dict containing current parameters and results.

Methods

`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Calls the CosmoPower emulator models.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Loads the emulator models.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryCosmoPowerJAX(*args: Any, **kwargs: Any)#

Wraps CosmoPower-JAX model into a cobaya.theory.Theory class. See D. Piras, A. Spurio Mancini 2023 and A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2305.06347, https://arxiv.org/abs/2106.03846).

To initialise, pass ‘emulator_filenames’ in the relevant cobaya block: A dictionary of spectrum types and emulator file names (if they are placed in cosmopower-jax’s trained_models/ folder), or the full path of the files, including in either case the file ending ‘.pkl’ or ‘.npz’.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

emulator_filenamesdict: File names of emulators.
cp_emulatorsdict: CosmoPower emulators.
cp_parslist: List of parameters required by the emulators.
providerProvider: Cobaya provider.
current_statedict: Dict containing current parameters and results.

Methods

`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Return the Cls or Dls

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Calls the CosmoPower-JAX emulator models.

get_Cl(ell_factor=False, **kwargs)#: Return the Cls or Dls

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#

Called from __init__ to initialise. Loads the emulator models.

Notes

For CosmoPower-JAX emulator models are expected to be placed in the package directory. TE emulators are loaded as PCA+NN, other spectra as NN-only models.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryCosmoPowerJAXLensing(*args: Any, **kwargs: Any)#

Wraps CosmoPower-JAX model for lensing into a cobaya theory code. See D. Piras, A. Spurio Mancini 2023 and A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2305.06347, https://arxiv.org/abs/2106.03846).

To initialise, pass ‘emulator_filenames’ in the relevant cobaya block: A dictionary of spectrum types and emulator file names as placed in cosmopower-jax’s trained_models/ folder.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

emulator_filenamesdict: File names of emulators.
cp_emulatorsdict: CosmoPower emulators.
cp_parslist: List of parameters required by the emulators.
providerProvider: Cobaya provider.
descriptorstr: A short descriptor.
par_nameslist: Names of parameters involved in transformation.
current_statedict: Dict containing current parameters and results.

Methods

`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Calls the CosmoPower-JAX emulator models.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Calls the CosmoPower-JAX emulator models.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryCosmoPowerLensing(*args: Any, **kwargs: Any)#

Wraps CosmoPower model for lensing into a cobaya theory code. See A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2106.03846).

To initialise, pass ‘emulator_filenames’ in the relevant cobaya block: A dictionary of spectrum types and emulator file names as placed in cosmopower-jax’s trained_models/ folder.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

emulator_filenamesdict: File names of emulators.
cp_emulatorsdict: CosmoPower emulators.
cp_parslist: List of parameters required by the emulators.
providerProvider: Cobaya provider.
descriptorstr: A short descriptor.
par_nameslist: Names of parameters involved in transformation.
current_statedict: Dict containing current parameters and results.

Methods

`__init__`(args, *kwargs)
`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Calls the CosmoPower emulator models.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Calls the CosmoPower emulator models.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryPyCapse(*args: Any, **kwargs: Any)#

Wraps a capse model into a cobaya theory class through the pycapse interface. See Bonici, Bianchini, Ruiz-Zapatero 2023 for more (https://arxiv.org/abs/2307.14339).

To initialise pass in the relevant Cobaya block: base_path : str

Base path where emulator files are stored.

specs_to_emulatelist: Spectrum type (TT, TE, …) of corresponding emulator models.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

base_pathstr: Path of the dictionary containing the emulator files.
specs_to_emulatelist: List of spectrum types (from TT, TE, EE, pp) indicating which emulators to load.
pc_emulatorsdict: Pycapse emulators.
pc_parslist: List of parameters required by the emulators. May not match expected parameter order of emulators, as it can be the collection of parameters for a series of emulators requiring different inputs.
pc_pars_to_reg_parslist: Dictionary translating the pycapse parameter names to more commonly used ones.
providerProvider: Cobaya provider.
current_statedict: Dict containing current parameters and results.

Methods

`__init__`(args, *kwargs)
`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Hands off to pycapse emulators.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Loads the emulator models.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

class candl.interface.CobayaTheoryTemplate(*args: Any, **kwargs: Any)#

Wrapper for a scaled template in a cobaya theory class.

To initialise, pass ‘template_filenames’ in the relevant cobaya block: A string with the template file name. Figures out spectrum types from the indices supplied as ix_in_file. Template is assumed to be in Dls.

This code is taken from the Cobaya example for custom theory codes and only slightly modified. See https://cobaya.readthedocs.io/en/latest/theories_and_dependencies.html. Torrado and Lewis, 2020 (https://arxiv.org/abs/2005.05290)

Attributes:

template_filenamestr: File name of the template.
ix_in_filedict: Indices of the spectrum in the template files, e.g. {“ell”: 0, “TT”: 1, “TE”:2}.
providerProvider: Cobaya provider.
current_statedict: Dict containing current parameters and results.
templatesdict: Template spectra.
ellsarray (int): Ell range provided.

Methods

`initialize`()	Called from __init__ to initialise.
`initialize_with_provider`(provider)	Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).
`get_requirements`()	Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.
`must_provide`(**requirements)	Return dictionary of parameters that must be provided.
`get_can_provide`()	Return list of quantities that can be provided.
`calculate`(state[, want_derived])	Calculate the CMB spectra.
`get_Cl`([ell_factor])	Get the Cls or Dls.

calculate(state, want_derived=True, **params_values_dict)#: Calculate the CMB spectra. Sums the two templates with their respective amplitudes.

get_Cl(ell_factor=False, **kwargs)#: Get the Cls or Dls.

get_can_provide()#: Return list of quantities that can be provided.

get_requirements()#: Return dictionary of derived parameters or other quantities that are needed by this component and should be calculated by another theory class.

initialize()#: Called from __init__ to initialise. Loads the templates requested and crops them to the right ell range.

initialize_with_provider(provider)#: Initialization after other components initialized, using Provider class instance which is used to return any dependencies (see calculate below).

must_provide(**requirements)#: Return dictionary of parameters that must be provided.

candl.interface.get_CAMB_pars_to_theory_specs_func(CAMB_pars)#

Helper that returns a simple python function that moves from parameters to spectra using CAMB.

Parameters:

CAMB_parscamb.model.CAMBparams: CAMBparams for the model. Defines accuracy, ell range, etc.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_CLASS_pars_to_theory_specs_func(CLASS_cosmo)#

Helper that returns a simple python function that moves from parameters to spectra using CLASS.

Parameters:

CLASS_cosmoclassy.Class: Class for the model to be evaluated. Need to set desired output options, accuracy settings, ell range, etc.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_CobayaTheory_pars_to_theory_specs_func(theory_calc)#

Helper that returns a simple python function that moves from parameters to spectra using a cobaya.theory.Theory instances.

Parameters:

theory_calccobaya.theory.Theory: Theory code to calculate theory Dls.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_CosmoPowerJAX_pars_to_theory_specs_func(emulator_filenames)#

Helper that returns a simple python function that moves from parameters to spectra using CosmoPower. See D. Piras, A. Spurio Mancini 2023 and A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2305.06347, https://arxiv.org/abs/2106.03846). Assumes that all emulators have the same input parameters in the same order.

Parameters:

emulator_filenamesdict: Dictionary of spectrum types and emulator file names (if they are placed in cosmopower-jax’s trained_models/ folder), or the full path of the files, including in either case the file ending ‘.pkl’ or ‘.npz’.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_CosmoPower_pars_to_theory_specs_func(emulator_filenames)#

Helper that returns a simple python function that moves from parameters to spectra using CosmoPower. See A. Spurio Mancini et al. 2021 for more (https://arxiv.org/abs/2106.03846).

Parameters:

emulator_filenamesdict: Dictionary of spectrum types and emulator file names.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_PyCapse_pars_to_theory_specs_func(capse_base_path, specs=['TT', 'TE', 'EE'])#

Helper that returns a simple python function that moves from parameters to spectra using PyCapse. See Bonici, Bianchini, Ruiz-Zapatero 2023 for more (https://arxiv.org/abs/2307.14339).

Parameters:

capse_base_pathstr: Path where the PyCapse is located.
specslist (optional): Which spectra (TT, TE, EE, BB) to try to load.

Returns:

func: Function that takes a dictionary of parameter values, ell_max, and ell_min (optional) as input and returns a dictionary of CMB spectra (Dl).

candl.interface.get_cobaya_info_dict_for_like(like, name='candl_like', data_selection=Ellipsis, clear_internal_priors=True, clear_specific_priors=[], feedback=True, wrapper=None, additional_args={})#

Thin wrapper for CandlCobayaLikelihood that returns the class with the requested data set in the format expected by Cobaya. Note that since Cobaya prefers to instantiate likelihoods itself, this will instantiate a new instance of the likelihood. That means, any modifications to the likelihood object you pass will not be reflected in the likelihood that Cobaya uses. Still, the wrapper will try to copy over any data selection - it’s worth double checking this with the feedback printout. Use the optional keywords to pass arguments to the new Cobaya likelihood object during initialisation. Note that candl internal priors are cleared by default, be sure to either apply these in the Cobaya parameter block or override the default behaviour.

Parameters:

like: candl.Like, candl.LensLike: A candl likelihood to be used.
name: str: Name to give the likelihood in Cobaya
data_selectionany: Data selection to be used. None, string, list of string, binary mask, or path to a mask are supported.
clear_internal_priorsbool: Whether to clear internal priors.
clear_specific_priorsstr or list: String or list of strings of specific priors to clear. If a given parameter appears in an internal prior, the full prior will be cleared (beware for multi-dimensional priors). Doesn’t do anything if clear_internal_priors is True.
feedbackbool: Whether to print feedback when initialising the likelihood.
wrapperstr: Identifier of a supported external wrapper likelihood.
additional_args: dict: Additional arguments to be passed to the likelihood at initialisation.

Returns:

dict:: Dictionary to use in Cobaya’s ‘likelihood’ entry.

candl.interface.get_montepython_nuisance_param_block_for_like(like)#

Prints out info needed by Montepython .param file for nuisance parameters.

Parameters:

like: candl.Like: Likelihood to be used.

Returns:

None

candl.interface

Contents

candl.interface#

Overview:#

`candl.interface`#