cogwheel.gw_prior.pn.PNCoordinatesPrior

class cogwheel.gw_prior.pn.PNCoordinatesPrior(eigvecs, f_ref, par_dic_0, mchirp_range, dmu=2.0, q_min=0.05, **kwargs)

Bases: Prior

Implement the coordinates for intrinsic parameters of [Lee, Morisaki & Tagoshi 2203.05216].

These are quite similar except we normalize the eigenvectors of the Fisher matrix by the square root of their eigenvalue, so the Fisher errorbars in (mu1, mu2) are 1/snr.

Parameters:

eigvecsfloat array of shape (3, 2)

Fisher matrix eigenvectors, see .eigvecs_from_reference_waveform_finder().

f_reffloat

Reference frequency (Hz).

q_minfloat

Minimum mass ratio

**kwargs

Passed to super().__init__()

Methods

eigvecs_from_reference_waveform_finder	Return a float array of shape (3, 2) with the two main eigenvectors of the Fisher matrix in the space of the first 3 coefficients of the post-Newtonian expansion.
generate_random_samples	Sample the prior using rejection sampling.
get_fast_sampled_params	Return a list of parameter names that map to given "fast" standard parameters.
get_init_dict	Return keyword arguments to reproduce the class instance.
get_module_name	Name of the module that defines the instance's class.
init_parameters	Return parameters accepted by the class constructor.
inverse_transform	Standard parameters to sampled parameters.
inverse_transform_samples	Add columns in-place for self.sampled_params to samples.
ln_jacobian_determinant	Natural log Jacobian determinant of the inverse transform.
lnprior	Natural logarithm of the prior probability for (mu1, mu2, lnq, s2z) under a prior flat in detector-frame masses and volumetric in component spins.
lnprior_and_transform	Return log prior and standard parameters.
reinstantiate	Return a new instance of the class, possibly updating init_kwargs.
standard_lnprior	Log prior density in standard coordinates.
to_json	Write class instance to json file.
transform	Sampled parameters to standard parameters.
transform_samples	Add columns in-place for self.standard_params to samples.
unfold_apply	Return a function that unfolds its parameters and applies func to each unfolding.

Attributes

conditioned_on
folded_params
folded_reflected_params
folded_shifted_params
max_lnprior	Maximum log prior density, useful for rejection sampling.
periodic_params
range_dic
reflective_params
sampled_params
standard_params
subclass_registry

static eigvecs_from_reference_waveform_finder(reference_waveform_finder)

Return a float array of shape (3, 2) with the two main eigenvectors of the Fisher matrix in the space of the first 3 coefficients of the post-Newtonian expansion.

These are the first 2 columns of U.T in the notation of [2203.05216], except that we normalize each eigenvector to have norm sqrt(eigenvalue). This can be used as input to .__init__().

generate_random_samples(n_samples, seed=None, return_lnz=False)

Sample the prior using rejection sampling.

This is more efficient for more uniform priors.

Parameters:

n_samplesint

How many samples to generate.

seed

Passed to numpy.default_rng, for reproducibility.

Returns:

samplespd.DataFrame

Columns are .sampled_params + .standard_params, and rows are samples distributed according to the prior.

lnz, dlnzfloat, float

Log of the integral of the prior over the bounds, and estimate of the 1-sigma uncertainty. Only returned if return_lnz is set to True. Useful for estimating the normalization constant of the prior. Note: This differs from the lnz that nested samplers (e.g. PyMultiNest or Nautilus) would compute, in that it has the phase-space volume differential applied. I.e. if lnprior == 0, lnz == log(prod(self.cubesize)) while nested samplers would return lnz == 0.

classmethod get_fast_sampled_params(fast_standard_params)

Return a list of parameter names that map to given “fast” standard parameters.

Useful for sampling fast-slow parameters. Updating fast sampling parameters is guaranteed to only change fast standard parameters.

get_init_dict(**kwargs)

Return keyword arguments to reproduce the class instance.

get_module_name()

Name of the module that defines the instance’s class.

classmethod init_parameters(include_optional=True)

Return parameters accepted by the class constructor.

The parameters are sorted by parameter kind (i.e. positional arguments first, keyword arguments last). The self parameter is excluded.

Parameters:

include_optionalbool

Whether to include parameters with defaults in the returned list.

Returns:

list of inspect.Parameter objects

inverse_transform(m1, m2, s1z, s2z)

Standard parameters to sampled parameters.

inverse_transform_samples(samples: DataFrame)

Add columns in-place for self.sampled_params to samples.

Parameters:

samplespandas.Dataframe

Must include columns for .standard_params and .conditioned_on.

Raises:

ValueError

If samples.index is not a simple range.

ln_jacobian_determinant(m1, m2, s1z, s2z)

Natural log Jacobian determinant of the inverse transform.

Returns:

floatlog|∂{mu1, mu2, lnq, s2z} / ∂{m1, m2, s1z, s2z}|

lnprior(mu1, mu2, lnq, s2z)

Natural logarithm of the prior probability for (mu1, mu2, lnq, s2z) under a prior flat in detector-frame masses and volumetric in component spins.

lnprior_and_transform(*par_vals, **par_dic)

Return log prior and standard parameters.

The reason for this function is that for CombinedPrior it is already necessary to evaluate self.transform() in order to evaluate self.lnprior(). CombinedPrior overwrites this function so the user can get both lnprior and transform without evaluating transform twice.

Returns:

float

Log prior, output of .lnprior.

dict

Standard parameters, output of .transform.

property max_lnprior

Maximum log prior density, useful for rejection sampling.

reinstantiate(**new_init_kwargs)

Return a new instance of the class, possibly updating init_kwargs.

Values not passed will be taken from the current instance.

standard_lnprior(**parameters)

Log prior density in standard coordinates.

Parameters:

**parameters

Values for .standard_params and .conditioned_on parameters.

to_json(dirname, basename=None, *, dir_permissions=493, file_permissions=420, overwrite=False)

Write class instance to json file.

It can then be loaded with read_json.

transform(mu1, mu2, lnq, s2z)

Sampled parameters to standard parameters.

transform_samples(samples: DataFrame)

Add columns in-place for self.standard_params to samples.

Parameters:

samplespandas.Dataframe

Must include columns for .sampled_params and .conditioned_on.

Raises:

ValueError

If samples.index is not a simple range.

unfold_apply(func, otypes=(<class 'float'>, ))

Return a function that unfolds its parameters and applies func to each unfolding.

The returned function returns a list of length 2**n_folds.

Parameters:

funccallable

A python function or method.

otypesstr or list of dtypes

Output type, passed to np.vectorize. You can pass None to decide automatically, but then an extra function call will be made.

Returns:

callable

Unfolding function.