Modules

samplers

Description of module samplers.SamplesPlotting:

name	SamplesPlotting
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Plot samples
long description	Plot samples: histograms, corner plots.
options	save_stats	type	string list
		default	None
		description	if not None, list of paths where to save summary statistics of input samples
	tablefmt	type	string
		default	latex_raw
		description	if save_stats, table formatting
	toplot	type	list
		default
		description	list of SamplesPlotStyle methods (plot_1d, plot_2d, plot_corner, plot_chain, plot_gelman_rubin, plot_autocorrelation_time)
	burnin	type	int float
		default	None
		description	if int, number of steps to remove; if float < 1, fraction of chain to remove
	samples_load	type	string list
		default	None
		description	list of (section, name) in data_block where to find the sample(s) (defaults to standard location) or, if containing / (or ), a path to samples on disk
	$others	options for SamplesPlotStyle

Description of module samplers.ProfilesPlotting:

name	ProfilesPlotting
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Plot samples
long description	Plot profiles: comparisons (aligned) plots, corner plots.
options	save_stats	type	string list
		default	None
		description	if not None, list of paths where to save summary statistics of input samples
	tablefmt	type	string
		default	latex_raw
		description	if save_stats, table formatting
	toplot	type	list
		default
		description	list of ProfilesPlotStyle methods (plot_aligned, plot_aligned_stacked, plot_1d, plot_2d, plot_corner)
	profiles_load	type	string list
		default	profiles
		description	list of (section, name) in data_block where to find the profiles (defaults to standard location) or, if containing / (or ), a path to profiles on disk
	$others	options for ProfilesPlotStyle

Description of module samplers.EvaluateSampler:

name	EvaluateSampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Trial evaluation of the posterior
long description	Tries to evaluate the posterior until a finite value is found.
options	max_tries	type	int
		default	1000
		description	number of tries to find a finite posterior value
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters

Description of module samplers.nested.DynestySampler:

name	DynestySampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Posterior nested sampling with dynesty
url	https://github.com/joshspeagle/dynesty
licence	MIT
requirements	dynesty
bibtex	arXiv:1904.02180 10.1063/1.1835238 10.1214/06-BA127 10.1007/s11222-018-9844-0
options	mode	type	string
		default	static
		choices	static dynamic
		description	if ‘dynamic’, run dynesty.DynamicNestedSampler (using a dynamic number of live points), else dynesty.NestedSampler
	nlive	type	int
		default	500
		description	number of live point. Larger numbers result in a more finely sampled posterior (more accurate evidence), but also a larger number of iterations required to converge. in case mode is ‘dynamic’, gives the number of live points used during the initial (“baseline”) nested sampling run
	bound	type	string
		default	multi
		choices	none single multi balls cubes
		description	Method used to approximately bound the prior using the current set of live points. Conditions the sampling methods used to propose new live points. Choices are no bound (‘none’), a single bounding ellipsoid (‘single’), multiple bounding ellipsoids (‘multi’), balls centered on each live point (‘balls’), and cubes centered on each live point (‘cubes’).
	sample	type	string
		default	auto
		choices	unif rwalk rstagger slice rslice hslice
		description	Method used to sample uniformly within the likelihood constraint, conditioned on the provided bounds. Unique methods available are: uniform sampling within the bounds (‘unif’), random walks with fixed proposals (‘rwalk’), random walks with variable (‘staggering’) proposals (‘rstagger’), multivariate slice sampling along preferred orientations (‘slice’), ‘random’ slice sampling along all orientations (‘rslice’), ‘Hamiltonian’ slices along random trajectories (‘hslice’). ‘auto’ selects the sampling method based on the dimensionality ndim of the problem (i.e. the number of varied parameters). When ndim < 10, this defaults to ‘unif’. When 10 <= ndim <= 20, this defaults to ‘rwalk’. When ndim > 20, this defaults to ‘hslice’ if a gradient is provided and ‘slice’ otherwise. ‘rstagger’ and ‘rslice’ are provided as alternatives for ‘rwalk’ and ‘slice’, respectively.
	update_interval	type	float int
		default	None
		description	If an integer is passed, only update the proposal distribution every update_interval-th likelihood call. If a float is passed, update the proposal after every round(update_interval * nlive)-th likelihood call. Larger update intervals can be more efficient when the likelihood function is quick to evaluate. Default behavior is to target a roughly constant change in prior volume, with 1.5 for ‘unif’, 0.15 * walks for ‘rwalk’ and ‘rstagger’, 0.9 * ndim * slices for ‘slice’, 2.0 * slices for ‘rslice’, and 25.0 * slices for ‘hslice’.
	max_iterations	type	int
		default	None
		description	Maximum number of iterations. Iteration may stop earlier if the termination condition is reached. Default is sys.maxsize (no limit).
	dlogz	type	float
		default	0.01
		description	Iteration will stop when the estimated contribution of the remaining prior volume to the total evidence falls below this threshold. Explicitly, the stopping criterion is ln(z + z_est) - ln(z) < dlogz, where z is the current evidence from all saved samples and z_est is the estimated contribution from the remaining volume.
	seed	type	int
		default	None
		description	random seed to use (MPI-insensitive)
	save	type	string
		default	None
		description	if not None, path where to save profiles
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters
execute output	likelihood	samples	type	cosmopipe.lib.samples.Samples
			description	posterior samples

Description of module samplers.ensemble.ZeusSampler:

name	ZeusSampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Posterior ensemble sampling with zeus
url	https://github.com/minaskar/zeus
licence	GNUv3
requirements	zeus-mcmc
bibtex	arXiv:2002.06212
options	nwalkers	type	int
		default	None
		description	the number of walkers in the ensemble
	thin_by	type	int
		default	1
		description	if you only want to store and yield every thin_by samples in the chain, set thin_by to an integer greater than 1. To compensate, the number of proposals will be multiplied thin_by
	check_every	type	int
		default	200
		description	run diagnostics every check_every samples
	light_mode	type	bool
		default	False
		description	if True then no expansions are performed after the tuning phase. This can significantly reduce the number of evaluations but works best in target distributions that are approximately Gaussian.
	diagnostics	type	dict
		default	{}
		description	“dictionary holding diagnostic options, specifying: ‘burnin’: number (if int) or fraction of samples (default: 0.3) to discard for the tests, ‘nsplits’: number of splits to estimate Gelman-Rubin (default: 4), ‘stable_over’: number of repeatedly satisfied diagnostic tests required to stop sampling (default: 2) ‘eigen_gr_stop’: threshold for the maximum eigenvalue (-1) of Gelman-Rubin test (default: 0.03), ‘diag_gr_stop’: threshold for the maximum diagonal (-1) of Gelman-Rubin test (as for univariate chains) (default: None, typically 0.02), ‘cl_diag_gr_stop’: threshold for maximum Gelman-Rubin test on interval boundaries at ‘nsigmas_cl_diag_gr_stop’ (typically 1) ‘geweke_stop’: threshold for Geweke statistics (typically 0.02), ‘iact_stop’: threshold for sample length divided by integrated autocorrelation time (typically 20), ‘dact_stop’: threshold for integrated autocorreation time variation between two diagnostic tests (typically 0.01). All tests that are not None, should pass for the convergence diagnostic to be met.
	min_iterations	type	int
		default	0
		description	minimum number of iterations (to avoid early stop due to termination conditions being met fortuitously)
	max_iterations	type	int
		default	None
		description	maximum number of iterations. Iteration may stop earlier if the termination conditions are reached. Default is sys.maxsize (no limit).
	seed	type	int
		default	None
		description	random seed to use for initial parameter values (MPI-insensitive)
	max_tries	type	int
		default	1000
		description	number of tries to find a finite likelihood value
	samples_load	type	string
		default	None
		description	if not None, path to samples to resume from
	save	type	string
		default	None
		description	if not None, path where to save profiles
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters
execute output	likelihood	samples	type	cosmopipe.lib.samples.Samples
			description	posterior samples

Description of module samplers.ensemble.EmceeSampler:

name	EmceeSampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Posterior ensemble sampling with emcee
url	https://github.com/dfm/emcee
licence	MIT
requirements	emcee
bibtex	arXiv:1202.3665
options	nwalkers	type	int
		default	None
		description	the number of walkers in the ensemble
	thin_by	type	int
		default	1
		description	if you only want to store and yield every thin_by samples in the chain, set thin_by to an integer greater than 1. To compensate, the number of proposals will be multiplied thin_by
	check_every	type	int
		default	200
		description	run diagnostics every check_every samples
	diagnostics	type	dict
		default	{}
		description	“dictionary holding diagnostic options, specifying: ‘burnin’: number (if int) or fraction of samples (default: 0.3) to discard for the tests, ‘nsplits’: number of splits to estimate Gelman-Rubin (default: 4), ‘stable_over’: number of repeatedly satisfied diagnostic tests required to stop sampling (default: 2) ‘eigen_gr_stop’: threshold for the maximum eigenvalue (-1) of Gelman-Rubin test (default: 0.03), ‘diag_gr_stop’: threshold for the maximum diagonal (-1) of Gelman-Rubin test (as for univariate chains) (default: None, typically 0.02), ‘cl_diag_gr_stop’: threshold for maximum Gelman-Rubin test on interval boundaries at ‘nsigmas_cl_diag_gr_stop’ (typically 1) ‘geweke_stop’: threshold for Geweke statistics (typically 0.02), ‘iact_stop’: threshold for sample length divided by integrated autocorrelation time (typically 20), ‘dact_stop’: threshold for integrated autocorreation time variation between two diagnostic tests (typically 0.01). All tests that are not None, should pass for the convergence diagnostic to be met.
	min_iterations	type	int
		default	0
		description	minimum number of iterations (to avoid early stop due to termination conditions being met fortuitously)
	max_iterations	type	int
		default	None
		description	maximum number of iterations. Iteration may stop earlier if the termination conditions are reached. Default is sys.maxsize (no limit).
	seed	type	int
		default	None
		description	random seed to use for initial parameter values (MPI-insensitive)
	max_tries	type	int
		default	1000
		description	number of tries to find a finite likelihood value
	samples_load	type	string
		default	None
		description	if not None, path to samples to resume from
	save	type	string
		default	None
		description	if not None, path where to save profiles
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters
execute output	likelihood	samples	type	cosmopipe.lib.samples.Samples
			description	posterior samples

Description of module samplers.cosmosis.CosmosisSampler:

name	CosmosisSampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Interface to CosmoSIS
url	https://bitbucket.org/joezuntz/cosmosis/wiki/Home
licence	LGPL
requirements
bibtex	arXiv:1409.3409
long description	Installing CosmoSIS is not straightforward, we recommend you follow instructions at the provided url. There is currently no support to interfacing CosmoSIS theory calculation with cosmopipe.
options	likelihood_name	type		string
		default		cosmopipe
		description		name of the cosmopipe likelihood
	sampler	type		string
		description		sampler to be used within CosmoSIS
	config_cosmosis	type		string
		default		None
		description		if not None, CosmoSIS configuration file
	seed	type		int
		default		None
		description		random seed to use for initial parameter values (MPI-insensitive)
	save	type		string
		default		None
		description		if not None, path where to save profiles
	others	arguments for CosmoSIS sampler
execute input	parameters	list	type		cosmopipe.lib.ParameterCollection
			description		list of parameters
execute output	likelihood	samples	type		cosmopipe.lib.samples.Samples
			description		posterior samples

Description of module samplers.cobaya.CobayaSampler:

name	CobayaSampler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Interface to Bayesian analysis cobaya
url	https://github.com/CobayaSampler/cobaya
licence	LGPL
requirements	cobaya
bibtex	arXiv:2005.05290
long description	There is currently no support for use of Cobaya-provided theory codes camb and class, due to the lack of e.g. fsigma8 prediction in the latter. With several MPI processes polychord sampler will be run in parallel, while mcmc sampler will run several chains, which are further combined into a single set of samples.
options	likelihood_name	type	string
		default	cosmopipe
		description	name of the cosmopipe likelihood
	sampler	type	dict
		default	{}
		description	sampler dictionary, see https://cobaya.readthedocs.io/en/latest/sampler.html (evaluate, mcmc, polychord or minimize)
	seed	type	int
		default	None
		description	random seed to use for initial parameter values (MPI-insensitive)
	save	type	string
		default	None
		description	if not None, path where to save profiles
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters
execute output	likelihood	samples	type	cosmopipe.lib.samples.Samples
			description	posterior samples

Description of module samplers.profiler.MinuitProfiler:

name	MinuitProfiler
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Likelihood profiling with iminuit
url	https://github.com/scikit-hep/iminuit
licence	LGPL-2.1 MIT
requirements	iminuit>=2.0.0
long description	Likelihood profiling using the iminuit interface for the Minuit2 C++ library maintained by CERN’s ROOT team. Best fits are estimated with migrad, confidence intervals with minos.
options	migrad	type	dict
		default	{}
		description	option dictionary for migrad, possibly containing n_iterations (number of fits starting from different values)
	minos	type	dict
		default	{}
		description	option dictionary for minos, possibly containing cl (confidence interval, defaults to 68.3%) and ‘ncall’ (maximum number of likelihood calls)
	torun	type	list
		default	None
		description	list of algorithms to run (migrad, minos). If None, if migrad options are not empty, run migrad. Sample applies for minos.
	seed	type	int
		default	None
		description	random seed to use for initial parameter values (MPI-insensitive)
	max_tries	type	int
		default	1000
		description	number of tries to find a finite likelihood value
	save	type	string
		default	None
		description	if not None, path where to save profiles
execute input	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters
execute output	likelihood	profiles	type	cosmopipe.lib.samples.Profiles
			description	profiles, containing the best fits, and if minos was run, the error intervals

pipelines

Description of module pipelines.SamplesPostprocessing:

name	SamplesPostprocessing
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Run postprocessing on likelihood samples
long description	Set parameters at the mean of the samples, then run modules as a pipeline. Useful e.g. to plot fitted model v.s. data.
options	burnin	type	int float
		default	None
		description	if int, number of steps to remove; if float < 1, fraction of chain to remove
	samples_load	type	section
		default	None
		description	(section, name) in data_block where to find the samples (defaults to standard location) or, if containing / (or ), a path to samples on disk

Description of module pipelines.ProfilesPostprocessing:

name	ProfilesPostprocessing
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Run postprocessing on likelihood profiles
long description	Set parameters at the best fit of the likelihood profiles, then run modules as a pipeline. Useful e.g. to plot fitted model v.s. data.
options	profiles_load	type	string list
		default	profiles
		description	list of (section, name) in data_block where to find the profiles (defaults to standard location) or, if containing / (or ), a path to profiles on disk

likelihood

Description of module likelihood.BaseLikelihood:

name	BaseLikelihood
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Template base likelihood
long description	BaseLikelihood extends BasePipeline, hence is expected to run several modules to e.g. setup data and execute model. Data vector data.y is set in the setup step. The model model.y is read at each execute step and the corresponding likelihood.loglkl computed.
setup input	data	y	type	float_array
			description	data vector
execute input	model	y	type	float_array
			description	model vector
execute output	likelihood	loglkl	type	float
			description	log-likelihood

Description of module likelihood.GaussianLikelihood:

name	GaussianLikelihood
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Gaussian likelihood
long description	Extends BaseLikelihood with a calculation for the log-Gaussian likelihood, based on an inverse covariance matrix provided in the setup step.
setup input	data	y	type	float_array
			description	data vector
	covariance	invcov	type	float_array
			description	inverse covariance
		nobs	type	int
			description	number of observations, to correct for the Hartlap factor. If None, no Harlap correction is applied
execute input	model	y	type	float_array
			description	model vector
execute output	likelihood	loglkl	type	float
			description	log-likelihood

Description of module likelihood.SumLikelihood:

name	SumLikelihood
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Sum of several likelihoods
long description	Extends BaseLikelihood to perform the sum of independent log-likelihoods. Inheriting from BasePipeline, it is expected to setup and execute several independent likelihoods; Adds each likelihood.loglkl together.
execute input	likelihood	loglkl	type	float
			description	peculiar log-likelihood
execute output	likelihood	loglkl	type	float
			description	sum of log-likelihood

Description of module likelihood.JointGaussianLikelihood:

name	JointGaussianLikelihood
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Joint Gaussian likelihoods
long description	Extends GaussianLikelihood to join non-independent Gaussian likelihoods. The data.y vectors of each of the input likelihoods (see join option) are concatenate in the setup step, similarly for the model.y in the execute step. The full inverse covariance matrix should be provided in the setup step (by adding the relevant module to the ‘#modules’ list).
setup input	data	y	type	float_array
			description	data vector
	covariance	invcov	type	float_array
			description	inverse covariance
		nobs	type	int
			description	number of observations, to correct for the Hartlap factor. If None, no Harlap correction is applied
execute input	model	y	type	float_array
			description	model vector
execute output	likelihood	loglkl	type	float
			description	log-likelihood

Description of module likelihood.GaussianLikelihoodFromSamples:

name	GaussianLikelihoodFromSamples
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Gaussian likelihood estimated from samples
long description	GaussianLikelihoodFromSamples extends GaussianLikelihood, with data vector and covariance matrix from previously computed samples.
setup input	samples_load	type	list
		default	None
		description	list of (section, name) in data_block where to find the sample(s) (defaults to standard location) or, if containing / (or ), a path to samples on disk
	parameters	type	list
		default	None
		description	list of parameters from provided samples to use as data vector
execute output	likelihood	loglkl	type	float
			description	log-likelihood

theory

Description of module theory.ModelPlotting:

name	ModelPlotting
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Plot model (v.s. data)
options	covariance_load	type	bool string
		default	False
		description	if True or (section, name), loads a covariance matrix from corresponding data_block entry
	data_load	type	bool string
		default	False
		description	if True or (section, name), loads a data_vector from corresponding data_block entry
	xmodel	type	dict list float_array
		default	None
		description	an array, a list of arrays (for the different projections), a dictionary or a list of dictionary, to override the x-axis sampling of the model (e.g. to get smooth model curves). If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ or ‘step’ (optionally ‘scale’: ‘lin’ or ‘log’). If None, the coordinates of the data vector are used.
	save_model	type	string
		default	None
		description	if not None, a path where to save the model data vector
	$others	options for DataPlotStyle

Description of module theory.galaxy_clustering.LinearModel:

name	LinearModel
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Linear RSD power spectrum model
long description	Linear RSD power spectrum model as given by Kaiser formula and Finger-of-God damping term.
options	FoG	type	string
		default	gaussian
		choices	gaussian lorentzian
		description	Finger-of-God damping term
	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise to multiply the amplitude As, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	base_parameters	type	dict
		default	galaxy_rsd	fsig	value	0.5
					fixed	False
					prior	dist	uniform
						limits	0.2 1.2
					latex	fsigma_{8}
			galaxy_bias	b1	value	2.0
					fixed	False
					prior	dist	uniform
						limits	1.0 4.0
					ref	dist	uniform
						limits	1.5 2.5
					proposal	0.01
					latex	b_{1}
				As	value	0.0
					fixed	False
					prior	dist	uniform
						limits	-2 2
					ref	dist	uniform
						limits	-0.1 0.1
					proposal	0.005
					latex	A_{s}
				sigmav	value	4.0
					fixed	False
					prior	dist	uniform
						limits	0.1 10
					ref	dist	uniform
						limits	3.8 4.2
					proposal	0.5
					latex	sigma_{v}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	galaxy_rsd	fsig	value	0.5
			fixed	False
			prior	dist	uniform
				limits	0.2 1.2
			latex	fsigma_{8}
	galaxy_bias	b1	value	2.0
			fixed	False
			prior	dist	uniform
				limits	1.0 4.0
			ref	dist	uniform
				limits	1.5 2.5
			proposal	0.01
			latex	b_{1}
		As	value	0.0
			fixed	False
			prior	dist	uniform
				limits	-2 2
			ref	dist	uniform
				limits	-0.1 0.1
			proposal	0.005
			latex	A_{s}
		sigmav	value	4.0
			fixed	False
			prior	dist	uniform
				limits	0.1 10
			ref	dist	uniform
				limits	3.8 4.2
			proposal	0.5
			latex	sigma_{v}
setup input	primordial_perturbations	pk_callable	type	callable
			description	Power spectrum
	survey_geometry	zeff	type	float
			description	Effective redshift
	primordial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Current cosmology
	data	shotnoise	type	float
			description	Data shot noise
execute input	galaxy_bias	sigmav	type	float
			description	Velocity dispersion
		b1	type	float
			description	Linear eulerian bias
		As	type	float
			description	Shot noise scaling
		fsig	type	float
			description	Normalisation of velocity power spectrum
	effect_ap	qpar	type	float
			description	Scaling parallel to the line-of-sight
		qper	type	float
			description	Scaling perpendicular to the line-of-sight
	primordial_perturbations	pk_callable	type	callable
			description	Power spectrum
execute output	galaxy_power	pk_mu_callable	type	callable
			description	Linear galaxy power spectrum

Description of module theory.galaxy_clustering.GaussianCovariance:

name	GaussianCovariance
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Compute a Gaussian covariance matrix for the power spectrum.
long description	This module computes a Gaussian covariance matrix.

Description of module theory.galaxy_clustering.EPTMoments:

name	EPTMoments
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd		fsig		value	0.5
							fixed	False
							prior	dist	uniform
								limits	0.2 1.2
							latex	fsigma_{8}
			galaxy_bias		b1		value	1.69
							fixed	False
							latex	b_{1}
					b2		value	-1.17
							fixed	False
							latex	b_{2}
					bs		value	-0.71
							fixed	False
							latex	b_{s}
					b3		value	-0.479
							fixed	False
							latex	b_{3}
					alpha[0:7:2]		value	0.0
							fixed	False
							latex	alpha_[]
					sn		value	0.0
							fixed	False
							latex	s_{n}
					sn2		value	0.0
							fixed	False
							latex	s_{n,2}
					sn4		value	0.0
							fixed	False
							latex	s_{n,4}
					alpha		value	0.0
							fixed	False
							latex	alpha
					alphav		value	0.0
fixed							False
latex							alpha_{v}
alpha_s0					value	0.0
					fixed	False
					latex	alpha_{s,0}
alpha_s2					value	0.0
					fixed	False
					latex	alpha_{s,2}
alpha_g1					value	0.0
					fixed	False
					latex	alpha_{g,1}
alpha_g3					value	0.0
					fixed	False
					latex	alpha_{g,3}
alpha_k2					value	0.0
					fixed	False
					latex	alpha_{k,2}
sv					value	0.0
					fixed	False
					latex	s_{v}
sigma0					value	0.0
					fixed	False
					latex	sigma_{0}
stoch_k0					value	0.0
					fixed	False
					latex	s_{k,0}
counterterm_c3					value	0.0
					fixed	True
					latex	c_{3}
description	base parameters
update_parameters	type	dict
	default	{}
	description	update base parameters
parameters	galaxy_rsd	fsig	value	0.5
			fixed	False
			prior	dist		uniform
				limits		0.2 1.2
			latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha[0:7:2]	value	0.0
			fixed	False
			latex	alpha_[]
		sn	value	0.0
			fixed	False
			latex	s_{n}
		sn2	value	0.0
			fixed	False
			latex	s_{n,2}
		sn4	value	0.0
			fixed	False
			latex	s_{n,4}
		alpha	value	0.0
			fixed	False
			latex	alpha
		alphav	value	0.0
			fixed	False
			latex	alpha_{v}
		alpha_s0	value	0.0
			fixed	False
			latex	alpha_{s,0}
		alpha_s2	value	0.0
			fixed	False
			latex	alpha_{s,2}
		alpha_g1	value	0.0
			fixed	False
			latex	alpha_{g,1}
		alpha_g3	value	0.0
			fixed	False
			latex	alpha_{g,3}
		alpha_k2	value	0.0
			fixed	False
			latex	alpha_{k,2}
		sv	value	0.0
			fixed	False
			latex	s_{v}
		sigma0	value	0.0
			fixed	False
			latex	sigma_{0}
		stoch_k0	value	0.0
			fixed	False
			latex	s_{k,0}
		counterterm_c3	value	0.0
			fixed	True
			latex	c_{3}

Description of module theory.galaxy_clustering.EPTFull:

name	EPTFull
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd.fsig			value	0.5
						fixed	False
						prior	dist		uniform
							limits		0.2 1.2
						latex	fsigma_{8}
			galaxy_bias			b1	value	1.69
							fixed	False
							latex	b_{1}
						b2	value	-1.17
							fixed	False
							latex	b_{2}
						bs	value	-0.71
							fixed	False
							latex	b_{s}
						b3	value	-0.479
							fixed	False
							latex	b_{3}
						alpha[0:7:2]	value	0.0
							fixed	False
							latex	alpha_[]
						sn	value	0.0
							fixed	False
							latex	s_{n}
						sn2	value	0.0
							fixed	False
							latex	s_{n,2}
						sn4	value	0.0
							fixed	False
							latex	s_{n,4}
						bFoG	value	0.0
							fixed	True
							latex	b_{mathrm{FoG}}
		description	base parameters
update_parameters	type	dict
	default	{}
	description	update base parameters
parameters	galaxy_rsd.fsig	value	0.5
		fixed	False
		prior	dist		uniform
			limits		0.2 1.2
		latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha[0:7:2]	value	0.0
			fixed	False
			latex	alpha_[]
		sn	value	0.0
			fixed	False
			latex	s_{n}
		sn2	value	0.0
			fixed	False
			latex	s_{n,2}
		sn4	value	0.0
			fixed	False
			latex	s_{n,4}
		bFoG	value	0.0
			fixed	True
			latex	b_{mathrm{FoG}}

Description of module theory.galaxy_clustering.LPTMoments:

name	LPTMoments
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd.fsig			value	0.5
						fixed	False
						prior	dist		uniform
							limits		0.2 1.2
						latex	fsigma_{8}
			galaxy_bias			b1	value	1.69
							fixed	False
							latex	b_{1}
						b2	value	-1.17
							fixed	False
							latex	b_{2}
						bs	value	-0.71
							fixed	False
							latex	b_{s}
						b3	value	-0.479
							fixed	False
							latex	b_{3}
						alpha[0:7:2]	value	0.0
							fixed	False
							latex	alpha_[]
						sn	value	0.0
							fixed	False
							latex	s_{n}
						sn2	value	0.0
							fixed	False
							latex	s_{n,2}
						sn4	value	0.0
							fixed	False
							latex	s_{n,4}
						alpha	value	0.0
							fixed	False
							latex	alpha_{0}
						alpha_v	value	0.0
fixed							False
latex							alpha_{v}
alpha_s0						value	0.0
						fixed	False
						latex	alpha_{s,0}
alpha_s2						value	0.0
						fixed	False
						latex	alpha_{s,2}
alpha_g1						value	0.0
						fixed	False
						latex	alpha_{g,1}
alpha_g3						value	0.0
						fixed	False
						latex	alpha_{g,3}
alpha_k2						value	0.0
						fixed	False
						latex	alpha_{k,2}
sv						value	0.0
						fixed	False
						latex	s_{v}
sigma0_stoch						value	0.0
						fixed	False
						latex	sigma_{mathrm{stoch}}
counterterm_c3						value	0.0
						fixed	True
						latex	c_{3}
description		base parameters
update_parameters	type	dict
	default	{}
	description	update base parameters
parameters	galaxy_rsd.fsig	value	0.5
		fixed	False
		prior	dist		uniform
			limits		0.2 1.2
		latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha[0:7:2]	value	0.0
			fixed	False
			latex	alpha_[]
		sn	value	0.0
			fixed	False
			latex	s_{n}
		sn2	value	0.0
			fixed	False
			latex	s_{n,2}
		sn4	value	0.0
			fixed	False
			latex	s_{n,4}
		alpha	value	0.0
			fixed	False
			latex	alpha_{0}
		alpha_v	value	0.0
			fixed	False
			latex	alpha_{v}
		alpha_s0	value	0.0
			fixed	False
			latex	alpha_{s,0}
		alpha_s2	value	0.0
			fixed	False
			latex	alpha_{s,2}
		alpha_g1	value	0.0
			fixed	False
			latex	alpha_{g,1}
		alpha_g3	value	0.0
			fixed	False
			latex	alpha_{g,3}
		alpha_k2	value	0.0
			fixed	False
			latex	alpha_{k,2}
		sv	value	0.0
			fixed	False
			latex	s_{v}
		sigma0_stoch	value	0.0
			fixed	False
			latex	sigma_{mathrm{stoch}}
		counterterm_c3	value	0.0
			fixed	True
			latex	c_{3}

Description of module theory.galaxy_clustering.LPTFourierStreaming:

name	LPTFourierStreaming
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd.fsig			value	0.5
						fixed	False
						prior	dist		uniform
							limits		0.2 1.2
						latex	fsigma_{8}
			galaxy_bias			b1	value	1.69
							fixed	False
							latex	b_{1}
						b2	value	-1.17
							fixed	False
							latex	b_{2}
						bs	value	-0.71
							fixed	False
							latex	b_{s}
						b3	value	-0.479
							fixed	False
							latex	b_{3}
						alpha	value	0.0
							fixed	False
							latex	alpha_{0}
						alpha_v	value	0.0
							fixed	False
							latex	alpha_{v}
						alpha_s0	value	0.0
							fixed	False
							latex	alpha_{s0}
						alpha_s2	value	0.0
							fixed	False
							latex	alpha_{s2}
						sn	value	0.0
							fixed	False
							latex	s_{n}
						sv	value	0.0
fixed							False
latex							s_{v}
sigma0_stoch						value	0.0
						fixed	False
						latex	sigma_{mathrm{stoch}}
counterterm_c3						value	0.0
						fixed	True
						latex	c_{3}
description		base parameters
update_parameters	type	dict
	default	{}
	description	update base parameters
parameters	galaxy_rsd.fsig	value	0.5
		fixed	False
		prior	dist		uniform
			limits		0.2 1.2
		latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha	value	0.0
			fixed	False
			latex	alpha_{0}
		alpha_v	value	0.0
			fixed	False
			latex	alpha_{v}
		alpha_s0	value	0.0
			fixed	False
			latex	alpha_{s0}
		alpha_s2	value	0.0
			fixed	False
			latex	alpha_{s2}
		sn	value	0.0
			fixed	False
			latex	s_{n}
		sv	value	0.0
			fixed	False
			latex	s_{v}
		sigma0_stoch	value	0.0
			fixed	False
			latex	sigma_{mathrm{stoch}}
		counterterm_c3	value	0.0
			fixed	True
			latex	c_{3}

Description of module theory.galaxy_clustering.LPTGaussianStreaming:

name	LPTGaussianStreaming
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd.fsig			value	0.5
						fixed	False
						prior	dist		uniform
							limits		0.2 1.2
						latex	fsigma_{8}
			galaxy_bias			b1	value	1.69
							fixed	False
							latex	b_{1}
						b2	value	-1.17
							fixed	False
							latex	b_{2}
						bs	value	-0.71
							fixed	False
							latex	b_{s}
						b3	value	-0.479
							fixed	False
							latex	b_{3}
						alpha	value	0.0
							fixed	False
							latex	alpha_{0}
						alpha_v	value	0.0
							fixed	False
							latex	alpha_{v}
						alpha_s0	value	0.0
							fixed	False
							latex	alpha_{s0}
						alpha_s2	value	0.0
							fixed	False
							latex	alpha_{s2}
						s2FoG	value	0.0
							fixed	False
							latex	sigma_{mathrm{FoG}}^{2}
						counterterm_c3	value	0.0
fixed							True
latex							c_{3}
description		base parameters
update_parameters	type	dict
	default	{}
	description	update base parameters
parameters	galaxy_rsd.fsig	value	0.5
		fixed	False
		prior	dist		uniform
			limits		0.2 1.2
		latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha	value	0.0
			fixed	False
			latex	alpha_{0}
		alpha_v	value	0.0
			fixed	False
			latex	alpha_{v}
		alpha_s0	value	0.0
			fixed	False
			latex	alpha_{s0}
		alpha_s2	value	0.0
			fixed	False
			latex	alpha_{s2}
		s2FoG	value	0.0
			fixed	False
			latex	sigma_{mathrm{FoG}}^{2}
		counterterm_c3	value	0.0
			fixed	True
			latex	c_{3}

Description of module theory.galaxy_clustering.LPTDirect:

name	LPTDirect
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Velocity-based perturbation theory expansions of redshift-space distortions and velocity statistics.
url	git+https://github.com/sfschen/velocileptors
licence	MIT
bibtex	arXiv:2005.00523 arXiv:2012.04636
requirements	velocileptors @ git+https://github.com/sfschen/velocileptors
long_description	This code computes the real- and redshift-space power spectra and correlation functions of biased tracers using 1-loop perturbation theory (with effective field theory counter terms and up to cubic biasing) as well as the real-space pairwise velocity moments.
options	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	one_loop	type	bool
		default	True
		description	do calculation at one loop?
	third_order	type	bool
		default	True
		description	third order bias?
	beyond_gauss	type	bool
		default	True
		description	beyond Gauss?
	kmin	type	float
		default	0.005
		description	minimum wavenumber in [h/Mpc] for power spectrum evaluation
	kmax	type	float
		default	0.25
		description	maximum wavenumber in [h/Mpc] for power spectrum evaluation
	nk	type	int
		default	120
		description	number of k-modes
	reduced	type	bool
		default	True
		description	reduced set of parameters?
	base_parameters	type	dict
		default	galaxy_rsd.fsig			value	0.5
						fixed	False
						prior	dist		uniform
							limits		0.2 1.2
						latex	fsigma_{8}
			galaxy_bias			b1	value	1.69
							fixed	False
							latex	b_{1}
						b2	value	-1.17
							fixed	False
							latex	b_{2}
						bs	value	-0.71
							fixed	False
							latex	b_{s}
						b3	value	-0.479
							fixed	False
							latex	b_{3}
						alpha[0:5:2]	value	0.0
							fixed	False
							latex	alpha_[]
						sn	value	0.0
							fixed	False
							latex	s_{n}
						sn2	value	0.0
							fixed	False
							latex	s_{n,2}
						sn4	value	0.0
							fixed	False
							latex	s_{n,4}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
output	type	list
	default	power
	description	Compute power spectrum ([power]), correlation function ([correlation]), or both ([power,correlation])
parameters	galaxy_rsd.fsig	value	0.5
		fixed	False
		prior	dist		uniform
			limits		0.2 1.2
		latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		bs	value	-0.71
			fixed	False
			latex	b_{s}
		b3	value	-0.479
			fixed	False
			latex	b_{3}
		alpha[0:5:2]	value	0.0
			fixed	False
			latex	alpha_[]
		sn	value	0.0
			fixed	False
			latex	s_{n}
		sn2	value	0.0
			fixed	False
			latex	s_{n,2}
		sn4	value	0.0
			fixed	False
			latex	s_{n,4}

Description of module theory.galaxy_clustering.PyBird:

name	PyBird
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	PyBird: Python code for Biased tracers in redshift space
url	https://github.com/pierrexyz/pybird/tree/dev/pybird_dev
licence	MIT
bibtex	arXiv:2003.07956
requirements	pybird @ git+https://github.com/adematti/pybird.git@dev
long_description	PyBird is designed for evaluating the multipoles of the power spectrum of biased tracers in redshift space. The main technology used by the code is the FFTLog, used to evaluate the one-loop power spectrum and the IR resummation, see Sec. 4.1 in arXiv:2003.07956 for details.
options	output	type	list
		default	power
		description	Compute power spectrum ([power]), correlation function ([correlation]), or both ([power,correlation])
	data_shotnoise	type	float string dict
		default	None
		description	the data shotnoise, or a projection specifier of the data_vector where to find a ‘shotnoise’ argument
	model_attrs	type	dict
		default	{}
		description	additional model attributes, e.g. name (string)
	kmax	type	float
		default	0.25
		description	Maximum wavenumber in [h/Mpc] for power spectrum evaluation
	km	type	float
		default	1.0
		description	Inverse galaxy spatial extension scale in [h/Mpc]
	with_resum	type	string
		default	opti
		description	opti: Resumming only with the BAO peak. True: Resummation on the full correlation function. False: no resummation
	with_stoch	type	bool
		default	False
		description	With stochastic terms
	with_nnlo_higher_derivative	type	bool
		default	False
		description	With next-to-next-to-leading estimate \(k^{2} P_{1-\mathrm{loop}}(k)\)
	with_nnlo_counterterm	type	bool
		default	False
		description	With next-to-next-to-leading counterterm \(k^{4} P_{11}(k)\)
	base_parameters	type	dict
		default	galaxy_rsd	fsig	value		0.5
					fixed		False
					prior		dist	uniform
							limits	0.2 1.2
					latex		fsigma_{8}
			galaxy_bias	b1	value		1.69
					fixed		False
					latex		b_{1}
				b2	value		-1.17
					fixed		False
					latex		b_{2}
				b3	value		-0.71
					fixed		False
					latex		b_{2}
				b4	value		0.0
					fixed		False
					latex		b_{4}
				cct	value		0.0
					fixed		False
					latex		c_{t}
				cr1	value		0.0
					fixed		False
					latex		c_{r,1}
				cr2	value		0.0
					fixed		False
					latex		c_{r,2}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	galaxy_rsd	fsig	value	0.5
			fixed	False
			prior	dist		uniform
				limits		0.2 1.2
			latex	fsigma_{8}
	galaxy_bias	b1	value	1.69
			fixed	False
			latex	b_{1}
		b2	value	-1.17
			fixed	False
			latex	b_{2}
		b3	value	-0.71
			fixed	False
			latex	b_{2}
		b4	value	0.0
			fixed	False
			latex	b_{4}
		cct	value	0.0
			fixed	False
			latex	c_{t}
		cr1	value	0.0
			fixed	False
			latex	c_{r,1}
		cr2	value	0.0
			fixed	False
			latex	c_{r,2}
setup input	primordial_perturbations	pk_callable	type	callable
			description	Power spectrum
	survey_geometry	zeff	type	float
			description	Effective redshift
	primordial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Current cosmology
	data	shotnoise	type	float
			description	Data shot noise
execute input	primordial_perturbations	pk_callable	type	callable
			description	Power spectrum
execute output	galaxy_power	pk_mu_callable	type	callable
			description	Linear galaxy power spectrum

Description of module theory.primordial.GalaxyRSD:

name	GalaxyRSD
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Compute the normalisation of the power spectrum
long description	This module computes the r.m.s. of baryon velocity perturbations (i.e. normalisation of the baryon velocity power spectrum) in sphere of a given radius. Matches the traditional f*sigma8 for a radius of 8 Mpc/h and in absence of neutrinos.
options	radius_sig	type	float
		default	8.0
		description	Sphere radius
execute input	survey_selection	zeff	type	float
			description	Effective redshift
	primordial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Current cosmology
execute output	galaxy_rsd	fsig	type	float
			description	Normalisation of the baryon velocity power spectrum

Description of module theory.primordial.Primordial:

name	Primordial
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Setup current cosmology
requirements	cosmoprimo[extras] @ git+https://github.com/adematti/cosmoprimo
long description	This module sets the current cosmology.
options	compute	type	string
		default	pk_cb
		description	Compute this primordial quantity
	engine	type	string
		default	class
		description	Default engine for the cosmology class
	base_parameters	type	dict
		default	primordial_cosmology		h	value	0.6766
						fixed	True
						prior	dist	uniform
							limits	0.4 0.9
						latex	h
					omega_cdm	value	0.11933
						fixed	True
						prior	dist	uniform
							limits	0.05 0.2
						latex	omega_{c}
					omega_b	value	0.02242
						fixed	True
						prior	dist	uniform
							limits	0.01 0.03
						latex	omega_{b}
					sigma8	value	0.8102
						fixed	True
						prior	dist	uniform
							limits	0.7 1.1
						latex	sigma_{8}
					n_s	value	0.9665
						fixed	True
						prior	dist	uniform
							limits	0.8 1.2
						latex	n_s
					m_ncdm	value	0.06
						fixed	True
						prior	dist	uniform
							limits	0.0 1.0
						latex	m_{nu}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	primordial_cosmology	h	value	0.6766
			fixed	True
			prior	dist	uniform
				limits	0.4 0.9
			latex	h
		omega_cdm	value	0.11933
			fixed	True
			prior	dist	uniform
				limits	0.05 0.2
			latex	omega_{c}
		omega_b	value	0.02242
			fixed	True
			prior	dist	uniform
				limits	0.01 0.03
			latex	omega_{b}
		sigma8	value	0.8102
			fixed	True
			prior	dist	uniform
				limits	0.7 1.1
			latex	sigma_{8}
		n_s	value	0.9665
			fixed	True
			prior	dist	uniform
				limits	0.8 1.2
			latex	n_s
		m_ncdm	value	0.06
			fixed	True
			prior	dist	uniform
				limits	0.0 1.0
			latex	m_{nu}
execute output	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Fiducial cosmology

Description of module theory.primordial.Fiducial:

name	Fiducial
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Setup fiducial cosmology
requirements	cosmoprimo[extras] @ git+https://github.com/adematti/cosmoprimo
long description	This module sets a fiducial cosmology.
options	engine	type	string
		default	class
		description	Default engine for the cosmology class
	base_parameters	type	dict
		default	fiducial_cosmology		h		value	0.6766
							fixed	True
							prior	dist	uniform
								limits	0.4 0.9
							latex	h
					omega_cdm		value	0.11933
							fixed	True
							prior	dist	uniform
								limits	0.05 0.2
							latex	omega_{c}
					omega_b		value	0.02242
							fixed	True
							prior	dist	uniform
								limits	0.01 0.03
							latex	omega_{b}
					sigma8		value	0.8102
							fixed	True
							prior	dist	uniform
								limits	0.7 1.1
							latex	sigma_{8}
					n_s		value	0.9665
							fixed	True
							prior	dist	uniform
								limits	0.8 1.2
							latex	n_s
					m_ncdm		value	0.06
							fixed	True
							prior	dist	uniform
								limits	0.0 1.0
							latex	m_{nu}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	fiducial_cosmology	h	value	0.6766
			fixed	True
			prior	dist		uniform
				limits		0.4 0.9
			latex	h
		omega_cdm	value	0.11933
			fixed	True
			prior	dist		uniform
				limits		0.05 0.2
			latex	omega_{c}
		omega_b	value	0.02242
			fixed	True
			prior	dist		uniform
				limits		0.01 0.03
			latex	omega_{b}
		sigma8	value	0.8102
			fixed	True
			prior	dist		uniform
				limits		0.7 1.1
			latex	sigma_{8}
		n_s	value	0.9665
			fixed	True
			prior	dist		uniform
				limits		0.8 1.2
			latex	n_s
		m_ncdm	value	0.06
			fixed	True
			prior	dist		uniform
				limits		0.0 1.0
			latex	m_{nu}
setup output	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Fiducial cosmology

Description of module theory.primordial.GalaxyBAO:

name	GalaxyBAO
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Compute BAO effect
long description	This module computes the ratio of the BAO angular and radial scale between the current cosmology and a fiducial one.
setup input	survey_selection	zeff	type	float
			description	Effective redshift
	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Fiducial cosmology
execute input	primordial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Current cosmology
execute output	effect_ap	qpar	type	float
			description	Scaling parallel to the line-of-sight
		qper	type	float
			description	Scaling perpendicular to the line-of-sight

Description of module theory.primordial.EffectAP:

name	EffectAP
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Compute Alcock-Paczynski effect
long description	This module computes the scaling between the current cosmology and a fiducial one.
options	engine	type	string
		default	class
		description	Engine for the cosmology class, to override those of primordial and fiducial cosmologies
setup input	survey_selection	zeff	type	float
			description	Effective redshift
	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Fiducial cosmology
execute input	primordial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	Current cosmology
execute output	effect_ap	qpar	type	float
			description	Scaling parallel to the line-of-sight
		qper	type	float
			description	Scaling perpendicular to the line-of-sight

Description of module theory.projections.HankelTransform:

name	HankelTransform
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Hankel transform theory model
long description	Turn power spectra into correlation functions and vice-versa
options	integration	type	list dict
		default	None
		description	if input model in \((x,\mu)\) space, options to go to multipoles
	ells	type	list
		default	0 2 4
		description	if input model in \((x,\mu)\) space, poles to project on
	nx	type	int
		default	1024
		description	number of log-spaced points
	q	type	float
		default	0.0
		description	power-law tilt to regularize Hankel transforms.

Description of module theory.projections.IsotropicScaling:

name	IsotropicScaling
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Rescale power spectrum
long description	Rescale power spectrum by the isotropic ratio between the current cosmology and a fiducial one.
options	pivot	type	float
		default	e’1./3’
		description	Pivot square cosine angle that defines isotropic scaling compared to the anistropic (AP) effect
	base_parameters	type	dict
		default	effect_ap	qpar		value		1.0
						fixed		False
						prior		dist	uniform
								limits	0.9 1.1
						latex		q_{parallel}
				qperp		value		1.0
						fixed		False
						prior		dist	uniform
								limits	0.9 1.1
						latex		q_{perp}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	effect_ap	qpar	value		1.0
			fixed		False
			prior		dist		uniform
					limits		0.9 1.1
			latex		q_{parallel}
		qperp	value		1.0
			fixed		False
			prior		dist		uniform
					limits		0.9 1.1
			latex		q_{perp}
setup input	primordial_perturbations	pk_callable	type		callable
			description		Power spectrum
execute input	effect_ap	qpar	type		float
			description		Scaling parallel to the line-of-sight
		qper	type		float
			description		Scaling perpendicular to the line-of-sight
	primordial_perturbations	pk_callable	type		callable
			description		Power spectrum
execute output	effect_ap	qpar	type		float
			description		Scaling parallel to the line-of-sight (isotropic scaling removed)
		qper	type		float
			description		Scaling perpendicular to the line-of-sight (isotropic scaling removed)
	primordial_perturbations	pk_callable	type		callable
			description		Isotropically rescaled power spectrum

Description of module theory.projections.AnisotropicScaling:

name	AnisotropicScaling
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Rescale power spectrum
long description	Rescale power spectrum by the ratio between the current cosmology and a fiducial one.
options	base_parameters	type	dict
		default	effect_ap	qpar		value		1.0
						fixed		False
						prior		dist	uniform
								limits	0.9 1.1
						latex		q_{parallel}
				qperp		value		1.0
						fixed		False
						prior		dist	uniform
								limits	0.9 1.1
						latex		q_{perp}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	effect_ap	qpar	value		1.0
			fixed		False
			prior		dist		uniform
					limits		0.9 1.1
			latex		q_{parallel}
		qperp	value		1.0
			fixed		False
			prior		dist		uniform
					limits		0.9 1.1
			latex		q_{perp}
setup input	model	collection	type		ModelCollection
			description		Collection of theory models
execute input	effect_ap	qpar	type		float
			description		Scaling parallel to the line-of-sight
		qper	type		float
			description		Scaling perpendicular to the line-of-sight
	model	collection	type		ModelCollection
			description		Collection of theory models
execute output	model	collection	type		ModelCollection
			description		Collection of theory models

tests

Description of module tests.BasicModel:

name	BasicModel
version	0.0.1
date	23/07/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Basic model
long description	Just a scale factor b.
options	base_parameters	type	dict
		default	galaxy_bias		b	value		2.0
						fixed		False
						prior		dist	uniform
								limits	0.0 10.0
						ref		dist	uniform
								limits	1.5 2.5
						proposal		0.01
						latex		b_{1}
		description	base parameters
	update_parameters	type	dict
		default	{}
		description	update base parameters
parameters	galaxy_bias	b	value	2.0
			fixed	False
			prior	dist			uniform
				limits			0.0 10.0
			ref	dist			uniform
				limits			1.5 2.5
			proposal	0.01
			latex	b_{1}

survey_selection

Description of module survey_selection.OddWideAngle:

name	OddWideAngle
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Compute odd wide angle effects for the power spectrum
requirements
long description	So far only order n=1 wide angle effects are implemented, but this is just a question of deriving formulae to include more. Relevant only within the context of a SurveyProjection pipeline.
options	d	type	string float
		default	1.0
		description	distance at the effective redshift. Use \(1\) if already included in window functions. ‘fiducial’ will compute the comoving radial distance at the effective redshift survey_section.zeff.
setup output	survey_selection	effect	type	PowerOddWideAngle
			description	PowerOddWideAngle instance

Description of module survey_selection.WindowConvolution:

name	WindowConvolution
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Apply window convolution
bibtex	arXiv:2106.06324
requirements
long description	Compute Fourier-space window convolution matrix following Beutler and McDonald 2021, provided a window function in configuration space. Relevant only within the context of a SurveyProjection pipeline.
options	srange	type	list
		default	None
		description	\(s\)-range for Hankel transforms
	krange	type	list
		default	None
		description	if srange not provided, equivalent \(k\)-range (taking \(s = 1/k\))
	ns	type	int
		default	1024
		description	number of log-spaced points for Hankel transforms
	q	type	float
		default	0.0
		description	power-law tilt to regularize Hankel transforms
	window_load	type	string
		default	window
		description	either (section, name) in data_block where to find the window function or, if containing / (or ), a path to a window function on disk. If path ends with .npy, none of the arguments below apply. The window function can be in configuration-space or in Fourier-space. In the latter case, if the number of modes (as BinnedProjection “weights”), as well as the physical size of the box used to compute the window function (as BoxSize attribute in BinnedProjection.attrs) are provided, use these to compute the Fourier-volume element in the Hankel transform. Else, use BinnedProjection.edges if provided, else differences in x-coordinates (\(k\)) to compute the Fourier-volume element.
	default_zero	type	bool
		default	False
		description	If a given projection is not provided in window function, set to 0. Else an IndexError is raised.
	projs	type	dict
		default	{}
		description	dictionary holding a mapping between projection names and projection attributes to be added to the corresponding projections (e.g.: shotnoise?)
	comments	type	string
		default	#
		description	the characters used to indicate the start of a comment
	skip_rows	type	int
		default	0
		description	skip the first skiprows lines, including comments
	max_rows	type	int
		default	None
		description	read max_rows lines of content after skiprows lines. The default is to read all the lines
	usecols	type	list
		default	None
		description	if not None, list of columns to read the window function from
	columns	type	list
		default	None
		description	column names corresponding to usecols. Columns ‘x’ and ‘y’ are used as x- and y-coordinates.
	mapping_header	type	dict
		default	None
		description	dictionary holding keyword:regex mapping or (regex, type) to provide the type. The corresponding values will be saved in the attrs dictionary
	mapping_proj	type	dict
		default	None
		description	dictionary holding a mapping from column name to projection specifier e.g.: ‘ell_0’
	attrs	type	dict
		default	{}
		description	global attributes for window function
setup output	survey_selection	effect	type	PowerWindowMatrix
			description	PowerWindowMatrix instance

Description of module survey_selection.BaseBinning:

name	BaseBinning
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Base binning scheme that evaluates model right at the data points
requirements
long description	Relevant only within the context of a SurveyProjection pipeline.
setup output	survey_selection	effect	type	BaseBinning
			description	BaseBinning instance

Description of module survey_selection.SurveyProjection:

name	SurveyProjection
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Project model onto observed data space
requirements
long description	This pipeline takes care of the different survey selection/geometry effects: (odd) wide-angle, window function, binning… as a suite of matrix operations. The user should just specify the list of modules for these different effects through the keyword $modules. Empty list will result in a simple model evaluation at the data points.
options	integration	type	list dict
		default	None
		description	options for integration from \((x,\mu)\) model space to projection space (multipoles, wedges…)
setup input	data	data_vector	type	DataVector
			description	Data vector
execute input	model	collection	type	ModelCollection
			description	Collection of theory models
execute output	model	y	type	numpy array
			description	Model vector

estimators

Description of module estimators.correlation_function.SurveyCorrelationFunction:

name	SurveyCorrelationFunction
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Estimate correlation function using nbodykit
url	https://github.com/bccp/nbodykit
licence	GNUv3
bibtex	arXiv:1712.05834v1 arXiv:1704.02357 arXiv:1911.03545v1
requirements	nbodykit
long description	Compute the two-point correlation function for observational survey data as a function of \(r\), \((r, \mu)\), \((r_{p}, \pi)\), or \(\theta\) using pair counting. The Landy-Szalay estimator (DD/RR - 2 DD/RR + 1) is used to transform pair counts in to the correlation function.
options	mode	type	string
		default	2d
		choices	1d 2d rppi rp angular
		description	if ‘1d’, compute pairs as a function of the 3D separation \(r\); if ‘2d’, compute pairs as a function of the 3D separation \(r\) and the cosine of the angle to the line-of-sight, \(\mu\); if ‘rppi’, compute pairs as a function of distance perpendicular and parallel to the line-of-sight, \(r_{p}\) and \(\pi\); if ‘rp’, same as ‘rppi’, but the correlation function is integrated over \(\pi\); if ‘angular’, compute pairs as a function of angle on the sky, \(\theta\)
	pimax	type	float
		default	80.0
		description	maximum line-of-sight separation (in mathrm{Mpc}/h), in case mode is ‘rppi’ or ‘rp’
	edges	type	dict list float_array
		default	min	1e-12
			max	200
			nbins	5
		description	the separation bin edges along the first coordinate dimension; depending on mode, the options are \(r\), \(r_{p}\), or \(\theta\). Expected units for distances are \(\mathrm{Mpc}/h\) and degrees for angles. If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ (optionally ‘scale’: ‘lin’ or ‘log’)
	muwedges	type	int
		default	3
		description	\(\mu\)-wedges to infer from multipole measurements
	ells	type	list tuple
		default	0 2 4
		description	a list of integer multipole numbers \(\ell\) to compute
	show_progress	type	bool
		default	False
		description	if True, perform the pair counting calculation in 10 iterations, logging the progress after each iteration; this is useful for understanding the scaling of the code
	nthreads	type	int
		default	1
		description	number of OpenMP threads
	z	type	string
		default	Z
		description	if mode is not ‘angular’, redshift column in the input catalog(s)
	ra	type	string
		default	RA
		description	right ascension column (in degree) in the input catalog(s)
	dec	type	string
		default	DEC
		description	declination column (in degree) in the input catalog(s)
	position	type	string
		default	None
		description	if mode is not ‘angular’, position column (in \(\mathrm{Mpc}/h\)) in the input catalog(s). If not provided, cartesian positions are computed from z, ra, dec and the input fiducial cosmology
	weight_comp	type	string
		default	None
		description	column of completeness weights in the input catalog(s); can be specified with operations on columns, e.g. WEIGHT_PHOTO * WEIGHT_NOZ. If None provided, defaults to 1
	nbar	type	string dict
		default	NZ
		description	if mode is not ‘angular’, redshift density (in \((h \ \mathrm{Mpc})^{3}\)) in the input catalog(s), or a dictionary holding fsky the sky fraction and bins (either ‘scott’ to be defined according to Scott’s rule, or an int for the the number of bins, or a list of edges).
	weight_fkp	type	string
		default	None
		description	if mode is not ‘angular’, column of FKP weights in the input catalog(s); if None, defaults to \(1/(1 + \overline{n} P_{0})\)
	P0_fkp	type	float
		default	0.0
		description	reference power for FKP weights
	data_load	type	string list tuple
		default	data
		description	either (section, name) in data_block where to find the data catalog(s) or, if containing / (or ), a path to a catalog on disk. A tuple of list of two strings can be provided for cross-correlations.
	randoms_load	type	string list tuple
default		randoms
description		same as load_data, for the randoms catalog(s)
R1R2_load	type	bool string
	default	None
	description	if not None, (section, name) (or True to default (section,name)) or path to pre-computed estimator from which R1R2 pair counts will be used
save	type	string
	default	None
	description	if not None, path where to save the correlation function (data_vector) measurement
execute input	fiducial_cosmology	cosmo	type		cosmoprimo.Cosmology
			description		fiducial cosmology, used if mode is not ‘angular’ and position or nbar are not provided
execute output	data	data_vector	type		cosmopipe.lib.data.DataVector
			description		correlation function measurement
		correlation_estimator	type		cosmopipe.lib.estimators.correlation_function.LandySzalayEstimator
			description		correlation function estimator, will all pair counts

Description of module estimators.correlation_function.BoxCorrelationFunction:

name	BoxCorrelationFunction
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Estimate correlation function using nbodykit
url	https://github.com/bccp/nbodykit
licence	GNUv3
bibtex	arXiv:1712.05834v1 arXiv:1704.02357 arXiv:1911.03545v1
requirements	nbodykit
long description	Compute the two-point correlation function for observational survey data as a function of \(r\), \((r, \mu)\), \((r_{p}, \pi)\), or \(\theta\) using pair counting. The natural estimator DD/RR - 1 is used to transform pair counts in to the correlation function.
options	BoxSize	type	float list
		description	box size, i.e. physical extent in \(\mathrm{Mpc}/h\) of the cartesian box along each axis, if not provided in the catalog attributes
	mode	type	string
		default	2d
		choices	1d 2d rppi rp angular
		description	if ‘1d’, compute pairs as a function of the 3D separation \(r\); if ‘2d’, compute pairs as a function of the 3D separation \(r\) and the cosine of the angle to the line-of-sight, \(\mu\) if ‘rppi’, compute pairs as a function of distance perpendicular and parallel to the line-of-sight, \(r_{p}\) and \(\pi\) if ‘rp’, same as ‘rppi’, but the correlation function is integrated over \(\pi\) if ‘angular’, compute pairs as a function of angle on the sky, \(\theta\)
	pimax	type	float
		default	80.0
		description	maximum line-of-sight separation (in mathrm{Mpc}/h), in case mode is ‘rppi’ or ‘rp’
	edges	type	dict list float_array
		default	min	1e-12
			max	200
			nbins	50
		description	the separation bin edges along the first coordinate dimension; depending on mode, the options are \(r\), \(r_{p}\), or \(\theta\). Expected units for distances are \(\mathrm{Mpc}/h\) and degrees for angles. If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ (optionally ‘scale’: ‘lin’ or ‘log’)
	muwedges	type	int
		default	3
		description	\(\mu\)-wedges to infer from multipole measurements
	ells	type	list tuple
		default	0 2 4
		description	a list of integer multipole numbers \(\ell\) to compute
	show_progress	type	bool
		default	False
		description	if True, perform the pair counting calculation in 10 iterations, logging the progress after each iteration; this is useful for understanding the scaling of the code
	nthreads	type	int
		default	1
		description	number of OpenMP threads
	position	type	string
		default	None
		description	position column (in \(\mathrm{Mpc}/h\)) in the input catalog(s). If not provided, cartesian positions are computed from z, ra, dec and the input fiducial cosmology
	data_load	type	string list tuple
		default	data
		description	either (section, name) in data_block where to find the data catalog(s) or, if containing / (or ), a path to a catalog on disk A tuple of list of two strings can be provided for cross-correlations.
	save	type	string
		default	None
		description	if not None, path where to save the correlation function (data_vector) measurement
execute output	data	data_vector	type		cosmopipe.lib.data.DataVector
			description		correlation function measurement
		correlation_estimator	type		cosmopipe.lib.estimators.correlation_function.LandySzalayEstimator
			description		correlation function estimator, will all pair counts

Description of module estimators.power_spectrum.SurveyPowerSpectrum:

name	SurveyPowerSpectrum
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Estimate survey power spectrum using nbodykit
url	https://github.com/bccp/nbodykit
licence	GNUv3
bibtex	arXiv:1712.05834v1 arXiv:1704.02357
requirements	nbodykit
long description	Algorithm to compute power spectrum multipoles using FFTs for a data survey with non-trivial geometry. Due to the geometry, the estimator computes the true power spectrum convolved with the window function (FFT of the geometry). This estimator implemented in this class is described in detail in Hand et al. 2017 (arXiv:1704.02357). It uses the spherical harmonic addition theorem such that only \(2\ell+1\) FFTs are required to compute each multipole. This differs from the implementation in Bianchi et al. and Scoccimarro et al., which requires \((\ell+1)(\ell+2)/2\) FFTs.
options	Nmesh	type	int list
		default	512
		description	mesh size, i.e. number of mesh nodes along each axis
	BoxSize	type	float list
		default	None
		description	box size, i.e. physical extent in \(\mathrm{Mpc}/h\) of the cartesian box along each axis. If None, the maximum Cartesian extent of the randoms is used.
	BoxPad	type	float list
		default	0.02
		description	optionally apply this additional (fractional) buffer to the maximum Cartesian extent of the randoms (in case BoxSize is None)
	resampler	type	string
		default	tsc
		choices	tsc cic pcs
		description	name of the resampler to use when interpolating the particles to the mesh
	interlaced	type	bool
		default	True
		description	whether to use interlacing to reduce aliasing when painting the particles on the mesh
	edges	type	dict
		default	{}
		description	“dictionary options for k-edges: min: the edge of the first wavenumber bin (default: 0); max: the edge of the last wavenumber bin (default: Nyquist frequency \(\pi \mathrm{Nmesh} / \mathrm{BoxSize}\)); step, the spacing in wavenumber (\(h/\mathrm{Mpc}\)) (default: the fundamental mode \(2 \pi / \mathrm{BoxSize}\) of the box)”
	muwedges	type	int
		default	3
		description	\(\mu\)-wedges to infer from multipole measurements
	ells	type	list tuple
		default	0 2 4
		description	a list of integer multipole numbers \(\ell\) to compute
	z	type	string
		default	Z
		description	redshift column in the input catalog(s)
	ra	type	string
		default	RA
		description	right ascension column (in degree) in the input catalog(s)
	dec	type	string
		default	DEC
		description	declination column (in degree) in the input catalog(s)
	position	type	string
		default	None
		description	position column (in \(\mathrm{Mpc}/h\)) in the input catalog(s). If not provided, cartesian positions are computed from z, ra, dec and the input fiducial cosmology
	weight_comp	type	string
		default	None
		description	column of completeness weights in the input catalog(s); can be specified with operations on columns, e.g. WEIGHT_PHOTO * WEIGHT_NOZ. If None provided, defaults to 1
	nbar	type	string dict float
		default	NZ
		description	redshift density (in \((h \ \mathrm{Mpc})^{3}\)) in the input catalog(s), or a dictionary holding fsky the sky fraction and bins (either ‘scott’ to be defined according to Scott’s rule, or an int for the the number of bins, or a list of edges). float value is just used for all nbar
	weight_fkp	type	string
		default	None
		description	column of FKP weights in the input catalog(s); if None, defaults to \(1/(1 + \overline{n} P_{0})\)
	P0_fkp	type	float
		default	0.0
		description	reference power for FKP weights
	data_load	type	string list tuple
		default	data
		description	either (section, name) (or name only; default section is ‘catalog’) in data_block where to find the data catalog(s) or, if containing / (or ), a path to a catalog on disk A tuple of list of two strings can be provided for cross-correlations.
randoms_load	type	string list tuple
	default	randoms
	description	same as load_data, for the randoms catalog(s)
save	type	string
	default	None
	description	if not None, file to save the power spectrum measurement
saveroot	type	string
	default	_data/power
	description	if save is None, beginning of file to save the power spectrum measurement (completed with box size, etc information)
use_existing	type	bool
	default	None
	description	if save file exists, just read and return
zmin	type	float
	default	0.0
	description	minimum galaxy redshift
zmax	type	float
	default	10.0
	description	maximum galaxy redshift
execute input	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	fiducial cosmology, used if position or nbar are not provided
execute output	data	data_vector	type	cosmopipe.lib.data.DataVector
			description	power spectrum measurement

Description of module estimators.power_spectrum.BoxPowerSpectrum:

name	BoxPowerSpectrum
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Estimate periodic box power spectrum using nbodykit
url	https://github.com/bccp/nbodykit
licence	GNUv3
bibtex	arXiv:1712.05834v1 arXiv:1704.02357
requirements	nbodykit
long description	Algorithm to compute 2d power spectrum and/or multipoles in a periodic box, using a Fast Fourier Transform (FFT). This computes the power spectrum as the square of the Fourier modes of the density field, which are computed via a FFT.
options	Nmesh	type	int list
		default	512
		description	mesh size, i.e. number of mesh nodes along each axis
	BoxSize	type	float list
		description	box size, i.e. physical extent in \(\mathrm{Mpc}/h\) of the cartesian box along each axis
	resampler	type	string
		default	tsc
		choices	tsc cic pcs
		description	name of the resampler to use when interpolating the particles to the mesh
	interlaced	type	bool
		default	True
		description	whether to use interlacing to reduce aliasing when painting the particles on the mesh
	edges	type	dict
		default	{}
		description	“dictionary options for k-edges: min: the edge of the first wavenumber bin (default: 0); max: the edge of the last wavenumber bin (default: Nyquist frequency \(\pi \mathrm{Nmesh} / \mathrm{BoxSize}\)); step, the spacing in wavenumber (\(h/\mathrm{Mpc}\)) (default: the fundamental mode \(2 \pi / \mathrm{BoxSize}\) of the box)”
	muwedges	type	int
		default	3
		description	\(\mu\)-wedges to infer from multipole measurements
	ells	type	list tuple
		default	0 2 4
		description	a list of integer multipole numbers \(\ell\) to compute
	los	type	string list tuple
		default	x
		description	the direction to use as the line-of-sight, either an axis (‘x’, ‘y’, ‘z’) or a unit 3-vector.
	position	type	string
		default	None
		description	position column (in \(\mathrm{Mpc}/h\)) in the input catalog(s). If not provided, cartesian positions are computed from z, ra, dec and the input fiducial cosmology
	data_load	type	string list tuple
		default	data
		description	either (section, name) (or name only; default section is ‘catalog’) in data_block where to find the data catalog(s) or, if containing / (or ), a path to a catalog on disk A tuple of list of two strings can be provided for cross-correlations.
	save	type	string
		default	None
		description	if not None, path where to save the power spectrum measurement
execute output	data	data_vector	type	cosmopipe.lib.data.DataVector
			description	power spectrum measurement

Description of module estimators.window_function.FFTWindowFunction:

name	FFTWindowFunction
version	0.0.1
date	01/08/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Estimate survey window function
bibtex	arXiv:2106.06324
requirements
long description	Algorithm to compute window function using FFTs for a data survey with non-trivial geometry. The user should specify the module used to compute survey power spectrum through the keyword $modules. You’d better use a very large BoxSize (typically \(500000 \; \\mathrm{Mpc}/h\)) and a large Nmesh in the power spectrum estimation of the window function.
options	wa_orders	type	int list
		default	0
		description	compute window functions for these wide-angle orders
	ells	type	list
		default	0 2 4
		description	a list of integer multipole numbers \(\ell\) to compute, or such a list for each \(wa_orders\)
	swin	type	dict float_array
		default	None
		description	if provide, further take Hankel transforms of the Fourier-space window
	z	type	string
		default	Z
		description	redshift column in the input catalog(s)
	ra	type	string
		default	RA
		description	right ascension column (in degree) in the input catalog(s)
	dec	type	string
		default	DEC
		description	declination column (in degree) in the input catalog(s)
	position	type	string
		default	None
		description	position column (in \(\mathrm{Mpc}/h\)) in the input catalog(s). If not provided, cartesian positions are computed from z, ra, dec and the input fiducial cosmology
	weight_comp	type	string
		default	None
		description	column of completeness weights in the input catalog(s); can be specified with operations on columns, e.g. WEIGHT_PHOTO * WEIGHT_NOZ. If None provided, defaults to 1
	nbar	type	string dict float
		default	NZ
		description	redshift density (in \((h \ \mathrm{Mpc})^{3}\)) in the input catalog(s), or a dictionary holding fsky the sky fraction and bins (either ‘scott’ to be defined according to Scott’s rule, or an int for the the number of bins, or a list of edges). float value is just used for all nbar
	weight_fkp	type	string
		default	None
		description	column of FKP weights in the input catalog(s); if None, defaults to \(1/(1 + \overline{n} P_{0})\)
	P0_fkp	type	float
		default	0.0
		description	reference power for FKP weights
	data_load	type	string list tuple
		default	data
		description	either (section, name) (or name only; default section is ‘catalog’) in data_block where to find the data catalog(s) or, if containing / (or ), a path to a catalog on disk A tuple of list of two strings can be provided for cross-correlations.
	randoms_load	type	string list tuple
		default	randoms
		description	same as load_data, for the randoms catalog(s)
	save	type	string
		default	None
		description	if not None, path where to save the power spectrum measurement
	saveroot	type	string
		default	_data/window
		description	if save is None, beginning of file to save the window (completed with box size, etc information)
	use_existing	type	bool
		default	None
		description	if save file exists, just read and return
	zmin	type	float
		default	0.0
		description	minimum galaxy redshift
	zmax	type	float
		default	10.0
		description	maximum galaxy redshift
execute input	fiducial_cosmology	cosmo	type	cosmoprimo.Cosmology
			description	fiducial cosmology, used if position or nbar are not provided
execute output	survey_selection	window	type	cosmopipe.lib.survey_selection.WindowFunction
			description	Fourier-space window function

parameters

Description of module parameters.Parameterization:

name	Parameterization
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Set up parameters, with default values, priors and reference distributions
options	base_parameters	type	dict
		default	{}
		description	common parameters, to be shared between all modules
	update_parameters	type	dict
		default	{}
		description	update base parameters
setup output	parameters	list	type	cosmopipe.lib.ParameterCollection
			description	list of parameters

data_vector

Description of module data_vector.DataVector:

name	DataVector
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Load a data vector
options	xlim	type	dict list
		default	None
		description	dictionary holding projection names and corresponding tuple of limits (e.g. {‘ell_0’:(0.,0.2),’ell_2’:(0.,0.1)}) or list of tuples corresponding to the data projections
	projs_attrs	type	dict list
		default	{}
		description	dictionary holding attributes to add to projections (e.g., space?), which can be selected with select keywords. e.g.:: projs_attrs: select: {‘mode’:’multipole’} space: power will add the attribute space = power to multipole projections. One can also provide a list of such updates.
	data_load	type	string
		default	data_vector
		description	either (section, name) in data_block where to find the data vector or, if containing / (or ), a path to a data vector on disk. If path ends with .npy, none of the arguments below apply.
	comments	type	string
		default	#
		description	the characters used to indicate the start of a comment
	usecols	type	list
		default	None
		description	which columns to read, with 0 being the first. If None, reads all columns.
	skip_rows	type	int
		default	0
		description	skip the first skip_rows lines, including comments
	max_rows	type	int
		default	None
		description	read max_rows lines of content after skip_rows lines. The default is to read all the lines
	mapping_header	type	dict
		default	None
		description	dictionary holding keyword:regex mapping or (regex, type) to provide the type. The corresponding values will be saved in the attrs dictionary
	columns	type	list
		default	None
		description	column names corresponding to usecols. Columns ‘x’ and ‘y’ are used as x- and y-coordinates.
	mapping_proj	type	dict list
		default	None
		description	dictionary holding a mapping from column name to projection specifier (e.g. ‘ell_0’, [‘muwedge’, [0.0,0.2]], or with a name, e.g.: ‘ELG_ell_0’, [‘ELG’,’muwedge’,[0.0,0.2]]). It can also be a list corresponding to input columns (skipping the first - x).
	attrs	type	dict
		default	{}
		description	global attributes for data vector
setup output	data	data_vector	type	cosmopipe.lib.data.DataVector
			description	Data vector
		y	type	float_array
			description	array view of the y-coordinate of the data vector

Description of module data_vector.DataVectorPlotting:

name	DataVectorPlotting
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Plot data vector(s)
options	covariance_load	type	bool string
		default	False
		description	if True or (section, name), loads a covariance matrix from corresponding data_block entry
	data_load	type	string list
		default	data_vector
		description	list of (section, name) in data_block where to find the data vector(s) (defaults to standard location) or, if containing / (or ), a path to data vectors on disk
	$others	options for DataPlotStyle

Description of module data_vector.CovarianceMatrixPlotting:

name	CovarianceMatrixPlotting
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Plot covariance matrices
options	covariance_load	type	string list
		default	covariance_matrix
		description	list of (section, name) in data_block where to find the covariance matrix (defaults to standard location)
	style	type	string
		default	corr
		choices	corr cov
		description	plot correlation matrix or covariance matrix?
	$others	options for dataCovariancePlotStyle

Description of module data_vector.MockCovarianceMatrix:

name	MockCovarianceMatrix
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Build covariance matrix from mock observations
options	xlim	type	dict list
		default	None
		description	dictionary holding projection names and corresponding tuple of limits (e.g. {‘ell_0’:(0.,0.2),’ell_2’:(0.,0.1)}) or list of tuples corresponding to the data projections
	projs_attrs	type	dict list
		default	{}
		description	dictionary holding attributes to add to projections (e.g., space?), which can be selected with select keywords. e.g.:: projs_attrs: select: {‘mode’:’multipole’} space: power will add the attribute space = power to multipole projections. One can also provide a list of such updates.
	data_load	type	list
		description	either (section, name) in data_block where to find the data vectors or, if containing / (or ), a path to a catalog on disk. If path ends with .npy, none of the arguments below apply.
	comments	type	string
		default	#
		description	the characters used to indicate the start of a comment
	usecols	type	list
		default	None
		description	which columns to read, with 0 being the first. If None, reads all columns.
	skip_rows	type	int
		default	0
		description	skip the first skip_rows lines, including comments
	max_rows	type	int
		default	None
		description	read max_rows lines of content after skip_rows lines. The default is to read all the lines
	mapping_header	type	dict
		default	None
		description	dictionary holding keyword:regex mapping or (regex, type) to provide the type. The corresponding values will be saved in the attrs dictionary
	columns	type	list
		default	None
		description	column names corresponding to usecols. Columns ‘x’ and ‘y’ are used as x- and y-coordinates.
	mapping_proj	type	dict list
		default	None
		description	dictionary holding a mapping from column name to projection specifier (e.g. ‘ell_0’, [‘muwedge’, [0.0,0.2]], or with a name, e.g.: ‘ELG_ell_0’, [‘ELG’,’muwedge’,[0.0,0.2]]). It can also be a list corresponding to input columns (skipping the first - x).
	attrs	type	dict
		default	{}
		description	global attributes for data vector
	save	type	string
		default	None
		description	if not None, path where to save the covariance matrix measurement
setup output	covariance	covariance_matrix	type	cosmopipe.lib.data.CovarianceMatrix
			description	Covariance matrix
		cov	type	float_array
			description	array view of the covariance matrix
		invcov	type	float_array
			description	array view of the inverse covariance matrix
		nobs	type	int
			default	None
			description	number of observations (mocks) used to compute the covariance matrix (if from mocks)

Description of module data_vector.SyntheticDataVector:

name	SyntheticDataVector
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Generate a Gaussian mock data vector provided a covariance matrix
options	xlim	type	dict
		default	None
		description	dictionary holding projection names and corresponding tuple of limits (e.g. {‘ell_0’:(0.,0.2),’ell_2’:(0.,0.1)}) or list of tuples corresponding to the data projections
	data_load	type	bool string
		default	False
		description	if True or (section, name), starts from a data_vector from corresponding data_block entry
	projs	type	list
		default	None
		description	list of projections (e.g. [‘ell_0’,’mu_1/3_2/3’]). If None, the projections of the current data vector are used
	projs_attrs	type	dict list
		default	{}
		description	dictionary holding attributes to add to projections (e.g., space?), which can be selected with select keywords. e.g.:: projs_attrs: select: {‘mode’:’multipole’} space: power will add the attribute space = power to multipole projections. One can also provide a list of such updates.
	x	type	dict list float_array
		default	None
		description	an array, a list of arrays (for the different projections), a dictionary or a list of dictionary, to define the x-axis sampling. If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ or ‘step’ (optionally ‘scale’: ‘lin’ or ‘log’). If None, the x-coordinates of the data vector are used
	y	type	dict list float_array
		default	None
		description	an array, a list of arrays (for the different projections), a dictionary or a list of dictionary, to define the y-axis sampling. If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ or ‘step’ (optionally ‘scale’: ‘lin’ or ‘log’). If None, the y-coordinates of the data vector are used
	edges	type	dict list float_array
		default	None
		description	an array, a list of arrays (for the different projections), a dictionary or a list of dictionary, to define the edges. If a dictionary is provided, should contain ‘min’, ‘max’, ‘nbins’ or ‘step’ (optionally ‘scale’: ‘lin’ or ‘log’). If None, the edges of the data vector are used
	save	type	string
		default	None
		description	if not None, path to save generated data vector
setup output	data	data_vector	type	cosmopipe.lib.data.DataVector
			description	Data vector
		y	type	float_array
			description	array view of the y-coordinate of the data vector

Description of module data_vector.MockDataVector:

name	MockDataVector
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Generate a Gaussian mock data vector provided a covariance matrix
options	xlim	type	dict
		default	None
		description	dictionary holding projection names and corresponding tuple of limits (e.g. {‘ell_0’:(0.,0.2),’ell_2’:(0.,0.1)}) or list of tuples corresponding to the data projections
	seed	type	int
		default	None
		description	random seed to use (MPI-insensitive), ignore in case mean is True
	mean	type	bool
		default	None
		description	do not add Gaussian noise and set to mean provided with the covariance matrix?
	mean_load	type	bool string
		default	False
		description	if True or (section, name), take the mean from corresponding data_block entry (instead of using covariance matrix mean)
	save	type	string
		default	None
		description	if not None, path to save generated data vector
setup input	covariance	covariance_matrix	type	cosmopipe.lib.data.CovarianceMatrix
			description	Covariance matrix
setup output	data	data_vector	type	cosmopipe.lib.data.DataVector
			description	Data vector
		y	type	float_array
			description	array view of the y-coordinate of the data vector

Description of module data_vector.CovarianceMatrix:

name	CovarianceMatrix
version	0.0.1
date	01/06/2021
author	Arnaud de Mattia
maintainer	Arnaud de Mattia
description	Load a covariance matrix
options	projs_attrs	type	dict list
		default	{}
		description	dictionary holding attributes to add to projections (e.g., space?), which can be selected with select keywords. e.g.:: projs_attrs: select: {‘mode’:’multipole’} space: power will add the attribute space = power to multipole projections. One can also provide a list of such updates.
	covariance_load	type	string
		description	either (section, name) in data_block where to find the covariance matrix or, if containing / (or ), a path to a covariance matrix on disk. If path ends with .npy, none of the arguments below apply.
	comments	type	string
		default	#
		description	the characters used to indicate the start of a comment
	usecols	type	list
		default	None
		description	which columns to read, with 0 being the first. If None, reads all columns. Last column must be the covariance value.
	skip_rows	type	int
		default	0
		description	skip the first skip_rows lines, including comments
	max_rows	type	int
		default	None
		description	read max_rows lines of content after skip_rows lines. The default is to read all the lines
	mapping_header	type	dict
		default	None
		description	dictionary holding keyword:regex mapping or (regex, type) to provide the type. The corresponding values will be saved in the attrs dictionary
	columns	type	list tuple
		default	None
		description	column names corresponding to usecols. Can be a tuple of column lists for two different data vectors. Columns ‘x’ and ‘y’ are used as x- and y-coordinates for each data vector.
	mapping_proj	type	dict list
		default	None
		description	list of projection names (considered of the same size), or dictionary holding a mapping from projection specifier (e.g. ‘ell_0’) to the number of points for this projection (e.g. {'ell_0':10, 'ell_2':4} for a matrix of total size 14 x 14).
	data	type	dict
		default	None
		description	dictionary to load the data vector matching the provided covariance matrix, in which case ‘x’ columns of the covariance file are expected to be indices of the data vector.
	attrs	type	dict
		default	{}
		description	global attributes for CovarianceMatrix
setup output	covariance	covariance_matrix	type	cosmopipe.lib.data.CovarianceMatrix
			description	Covariance matrix
		cov	type	float_array
			description	array view of the covariance matrix
		invcov	type	float_array
			description	array view of the inverse covariance matrix
		nobs	type	int
			default	None
			description	number of observations (mocks) used to compute the covariance matrix (if from mocks)