popsynth package

Subpackages

• popsynth.aux_samplers package
  • Submodules
    • popsynth.aux_samplers.delta_aux_sampler module
    • popsynth.aux_samplers.lognormal_aux_sampler module
    • popsynth.aux_samplers.normal_aux_sampler module
    • popsynth.aux_samplers.plaw_aux_sampler module
    • popsynth.aux_samplers.sky_sampler module
    • popsynth.aux_samplers.trunc_normal_aux_sampler module
    • popsynth.aux_samplers.viewing_angle_sampler module
  • Module contents
• popsynth.distributions package
  • Submodules
    • popsynth.distributions.bpl_distribution module
    • popsynth.distributions.cosmological_distribution module
    • popsynth.distributions.delta_distribution module
    • popsynth.distributions.flatland_distribution module
    • popsynth.distributions.log10_normal_distribution module
    • popsynth.distributions.log_normal_distribution module
    • popsynth.distributions.pareto_distribution module
    • popsynth.distributions.schechter_distribution module
    • popsynth.distributions.spherical_distribution module
    • popsynth.distributions.spiral_galaxy_distribution module
  • Module contents
• popsynth.populations package
  • Submodules
    • popsynth.populations.bpl_population module
    • popsynth.populations.lognormal_population module
    • popsynth.populations.pareto_populations module
    • popsynth.populations.schechter_populations module
    • popsynth.populations.spatial_populations module
  • Module contents
• popsynth.selection_probability package
  • Submodules
    • popsynth.selection_probability.flux_selectors module
    • popsynth.selection_probability.generic_selectors module
    • popsynth.selection_probability.selection_probability module
    • popsynth.selection_probability.spatial_selection module
  • Module contents
• popsynth.utils package
  • Submodules
    • popsynth.utils.array_to_cmap module
    • popsynth.utils.configuration module
    • popsynth.utils.cosmology module
    • popsynth.utils.hdf5_utils module
    • popsynth.utils.logging module
    • popsynth.utils.meta module
    • popsynth.utils.package_data module
    • popsynth.utils.progress_bar module
    • popsynth.utils.registry module
    • popsynth.utils.rejection_sample module
    • popsynth.utils.spherical_geometry module
  • Module contents

Submodules

• popsynth.auxiliary_sampler module
• popsynth.distribution module
• popsynth.population module
• popsynth.population_synth module

Module contents

class popsynth.AuxiliarySampler(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False)

Bases: object

__init__(name: str, observed: bool = True, uses_distance: bool = False, uses_luminosity: bool = False, uses_sky_position: bool = False) → None

Base class for auxiliary samplers.

Parameters:

• name (str) – Name of the sampler
• observed (bool) – True if the property is observed, False if it is latent. Defaults to True
• uses_distance (bool) – True if sampler uses distance values
• uses_luminosity (bool) – True if sampler uses luminosities
• uses_sky_position (bool) – True if sampler uses sky positions

display()

draw(size: int = 1)

Draw the primary and secondary samplers. This is the main call.

Parameters:size (int) – The number of samples to draw

get_secondary_objects(recursive_secondaries: Optional[Dict[str, Any]] = None) → Dict[str, Any]

Get secondary objects.

Parameters:recursive_secondaries – Recursive dict of secondaries Returns:Dict of objects Return type:Dict[str, Any]

get_secondary_properties(graph=None, primary=None, spatial_distribution=None) → popsynth.auxiliary_sampler.SecondaryStorage

Get properties of secondary samplers.

Parameters:

• graph – Graph
• primary – Primary sampler
• spatial_distribution – Spatial Distribution

Returns:

Dict of samplers

Return type:

SamplerDict

has_secondary

if this sampler has a secondary :returns:

is_secondary

If another sampler depends on this

Returns:

luminosity_distance

luminosity distance if needed.

make_secondary(parent_name: str) → None

sets this sampler as secondary for book keeping

Parameters:parent_name (str) – Returns:

name

The name of the sampler

Returns:

obs_name

obs_values

The values obscured by measurement error.

Returns:

observation_sampler(size: int = 1) → numpy.ndarray

observed

if this sampler is observed

Returns:

parents

The parents of this sampler

reset()

Reset all the selections.

secondary_samplers

Secondary samplers. :returns: Dict of secondary samplers :rtype: SamplerDict

selection

The selection booleans on the values

Returns:

selector

The selection probability object

Returns:

set_luminosity(luminosity: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]]) → None

Set the luminosity values.

Parameters:luminosity (ArrayLike) – Luminosity

set_secondary_sampler(sampler: popsynth.auxiliary_sampler.AuxiliarySampler) → None

Add a secondary sampler upon which this sampler will depend. The sampled values can be accessed via an internal dictionary with the samplers ‘name’

self._secondary_sampler[‘name’].true_values self._secondary_sampler[‘name’].obs_values

Parameters:sampler ("AuxiliarySampler") – An auxiliary sampler Returns:

set_selection_probability(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None

Set a selection probabilty for this sampler.

Parameters:selector (SelectionProbability) – A selection probability oobject Returns:

set_spatial_values(value: popsynth.distribution.SpatialContainer) → None

Set the spatial values.

Parameters:value (SpatialContainer) – Spatial values

true_sampler(size: int = 1)

true_values

The true or latent values

Returns:

truth

A dictionary containing true paramters used to simulate the distribution

uses_distance

If this uses distance

Returns:

uses_luminosity

If this uses luminosity

Returns:

uses_sky_position

If this uses sky position

Returns:

class popsynth.DerivedLumAuxSampler(name: str, uses_distance: bool = False)

Bases: popsynth.auxiliary_sampler.AuxiliarySampler

__init__(name: str, uses_distance: bool = False)

Base class for generating luminosity from other properties.

Parameters:

• name (str) – Name of the sampler
• uses_distance (bool) – True if sampler uses distance values

compute_luminosity()

class popsynth.Population(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None)

Bases: object

__init__(luminosities: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distances: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], known_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], unknown_distance_idx: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], fluxes: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_obs: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], selection: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]]], flux_sigma: float, r_max: float, n_model: int, lf_params: Dict[str, Any], spatial_params: Optional[Dict[str, Any]] = None, model_spaces: Union[Sequence[Sequence[Sequence[Sequence[Sequence[Any]]]]], numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype], Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]], Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]], Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]], Sequence[Sequence[Sequence[Sequence[numpy.typing._array_like._SupportsArray[numpy.dtype][numpy.dtype]]]]], bool, int, float, complex, str, bytes, Sequence[Union[bool, int, float, complex, str, bytes]], Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]], Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]], Sequence[Sequence[Sequence[Sequence[Union[bool, int, float, complex, str, bytes]]]]], None] = None, seed: int = 1234, name: Optional[str] = None, spatial_form: Optional[Dict[str, Any]] = None, lf_form: Optional[Dict[str, Any]] = None, auxiliary_quantities: Optional[popsynth.auxiliary_sampler.SecondaryStorage] = None, truth: Dict[str, float] = {}, graph: Optional[Dict[str, Any]] = None, theta=None, phi=None, pop_synth: Optional[Dict[str, Any]] = None) → None

A population container for all the properties of the population.

Parameters:

• luminosities (ArrayLike) – The luminosities
• distances (ArrayLike) – The distances
• known_distances (ArrayLike) – The known distances
• known_distance_idx (ArrayLike) – The index of the known distances
• unknown_distance_idx (ArrayLike) – The index of the unknown distances
• fluxes (ArrayLike) – The latent fluxes
• flux_obs (ArrayLike) – The observed fluxes
• selection (ArrayLike) – The selection vector
• flux_sigma (float) – The uncertainty on the observed flux
• r_max (float) – The maximum distance of the survey
• n_model (int) – Number of models
• lf_params (Dict[str, Any]) – Luminosity function parameters
• spatial_params (Optional[Dict[str, Any]]) – Spatial distribution parameters
• model_spaces (ArrayLike) – Model spaces
• seed (int) – Random seed
• name (str) – Name of the population
• spatial_form (Optional[Dict[str, Any]]) – Form of the spatial distribution
• lf_form (Optional[Dict[str, Any]]) – Form of the luminosity function
• auxiliary_quantities (Optional[Dict[str, Any]]) – Dict of auxiliary quantities
• truth (Dict[str, float]) – Dict of true values
• graph (Optional[Dict[str, Any]]) – Graph of generative model
• theta – Theta
• phi – Phi
• pop_synth (Optional[Dict[str, Any]]) – Population synth

addto(file_name: str, group_name: str) → None

Write population to a group in an existing HDF5 file.

Parameters:

• file_name (str) – Name of the file
• group_name (str) – Name of the group

dec

The declination of the objects

display()

Display the simulation parameters.

display_distances(ax=None)

Display the distances

Parameters:ax – Axis on which to plot

display_flux_sphere(seen_cmap='Reds', unseen_cmap='Blues', distance_transform=None, use_log=False, background_color='white', **kwargs)

display_fluxes(ax=None, true_color='#dec3c3', obs_color='#8F2727', arrow_color='k', with_arrows=True, **kwargs)

Display the fluxes.

Parameters:

• ax – Axis on which to plot
• true_color – Color of true values
• obs_color – Color of obs values
• arrow_color – Color of arrows
• with_arrows – If True, display arrows

display_hidden_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)

display_luminosities(ax=None, true_color='#dec3c3', obs_color='#8F2727', **kwargs)

Display the luminosities

Parameters:

• ax – Axis on which to plot
• true_color – Color of true values
• obs_color – Color of obs values

display_obs_fluxes(ax=None, flux_color='#8F2727', **kwargs)

Display the observed fluxes.

Parameters:

• ax – Axis on which to plot
• flux_color – Color of fluxes

display_obs_fluxes_sphere(cmap='magma', distance_transform=None, use_log=False, background_color='white', show=True, **kwargs)

display_true_fluxes(ax=None, flux_color='#8F2727', **kwargs)

Display the fluxes.

Parameters:

• ax – Axis on which to plot
• flux_color – Color of fluxes

distance_probability

distances

The distances to the objects

flux_sigma

The simulated flux sigma

fluxes_latent

The latent fluxes of the objects

fluxes_observed

All of the observed fluxes, i.e., scattered with error

classmethod from_file(file_name: str)

Load a population from a file.

Parameters:file_name (str) – Name of the file

classmethod from_group(file_name: str, group_name: str)

Load a population from a group in a file.

Parameters:

• file_name (str) – Name of the file
• group_name (str) – Name of the group

graph

The networkx graph of the population

Returns:

hard_cut

has_detections

If the population has detections

hidden_distances

The distances that are hidden by the selection

hidden_fluxes_latent

The latent fluxes that are hidden by the selection

hidden_fluxes_observed

The observed fluxes that are hidden by the selection

known_distances

The observed distances

luminosities_latent

The true luminosities of the objects. These are always latent as one cannot directly observe them.

luminosity_parameters

n_detections

The number of DETECTED objects in the population

n_non_detections

The number of NON-DETECTED objects in the population

n_objects

The number of objects in the population

phi

The phi angle of the objects

pop_synth

Dictionary population synth used to create this population

ra

The right ascension of the objects

selected_distances

The selected distances. Note, this is different than the KNOWN distances.

selected_fluxes_latent

The selected latent fluxes

selected_fluxes_observed

The selected obs fluxes

selection

The selection vector

spatial_parameters

spatial parameters

Returns:

theta

The polar angle of the objects

to_stan_data() → dict

Create Stan input

to_sub_population(observed: bool = True) → popsynth.population.Population

Create a population that is down selected from either the observed or unobserved population

Parameters:observed (bool) – Extract the observed or unobserved object Returns:A new population object Return type:Population

truth

The simulated truth parameters

writeto(file_name: str) → None

Write population to an HDF5 file

Parameters:file_name (str) – Name of the file

class popsynth.PopulationSynth(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)

Bases: object

__init__(spatial_distribution: popsynth.distribution.SpatialDistribution, luminosity_distribution: Optional[popsynth.distribution.LuminosityDistribution] = None, seed: int = 1234)

Basic and generic population synth. One specifies the spatial and luminosity distribution OR derived luminosity distribution and everything is setup.

Parameters:

• spatial_distribution (SpatialDistribution) – The spatial distribution to sample locations from
• luminosity_distribution (LuminosityDistribution) – The optional luminosity distribution
• seed (int) – Random seed

add_auxiliary_sampler(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])

Add an auxiliary sampler or derived luminosity sampler to the population synth.

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler

add_model_space(name, start, stop, log=True)

Add a model space for stan generated quantities

Parameters:

• name – Name that Stan will use
• start – Start of the grid
• stop – Stop of the grid
• log – Use log10 or not

add_observed_quantity(auxiliary_sampler: Union[popsynth.auxiliary_sampler.DerivedLumAuxSampler, popsynth.auxiliary_sampler.AuxiliarySampler])

Add an auxiliary sampler or derived luminosity sampler to the population synth

Parameters:auxiliary_sampler (Union[DerivedLumAuxSampler, AuxiliarySampler]) – The auxiliary_sampler

add_spatial_selector(spatial_selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None

Add a spatial selector into the mix

Parameters:spatial_selector (SelectionProbability) – The spatial selector

clean(reset: bool = False)

Clean the auxiliary samplers, selections, etc from the population synth

Parameters:reset (bool) – If True, reset any attached distributions and samplers

display() → None

Display the simulation parameters.

draw_log10_fobs(f, f_sigma, size=1) → numpy.ndarray

Draw the log10 of the the fluxes.

draw_log_fobs(f, f_sigma, size=1) → numpy.ndarray

Draw the log10 of the the fluxes.

draw_survey(flux_sigma: Optional[float] = None, log10_flux_draw: bool = True) → popsynth.population.Population

Draw the total survey and return a Population object.

This will sample all attached distributions and apply selection functions.

If a value of flux_sigma is given, the log10 observed fluxes are sampled with measurement error.

Parameters:

• flux_sigma (Optional[float]) – The homoskedastic sigma for the flux in log10 space
• log10_flux_draw (bool) – if True, fluxes are drawn in log space

Returns:

a Population object

Return type:

Population

classmethod from_dict(input: Dict[str, Any]) → popsynth.population_synth.PopulationSynth

Build a PopulationSynth object from a dictionary

Parameters:input (Dict[str, Any]) – the dictionary from which to build Returns:Popsynth object Return type:PopulationSynth

classmethod from_file(file_name: str) → popsynth.population_synth.PopulationSynth

read the population in from a yaml file

Parameters:file_name – the file name of the population synth

graph

luminosity_distribution

name

set_distance_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None

Set the selection type for the distance.

Parameters:selector (SelectionProbability) – The selector

set_flux_selection(selector: popsynth.selection_probability.selection_probability.SelectionProbability) → None

Set the selection type for the flux

Parameters:selector (SelectionProbability) – The selector

spatial_distribution

to_dict() → Dict[str, Any]

Convert the population synth to a dictionary

Returns:Popsynth dict Return type:Dict[str, Any]

write_to(file_name: str) → None

Write the population synth to a YAML file.

Parameters:file_name (str) – the file name of the output YAML