ananke.Ananke module#

Contains the Ananke class definition

Please note that this module is private. The Ananke class is available in the main ananke namespace - use that instead.

class ananke.Ananke.Ananke(particles: Dict[str, ndarray[Any, dtype[_ScalarType_co]]], name: str, ngb: int = 64, caching: bool = False, append_hash: bool | None = None, k_params: Dict[str, Any] = {}, e_params: Dict[str, Any] = {}, err_params: Dict[str, Any] = {}, il_params: Dict[str, Any] = {}, **kwargs: Dict[str, Any])[source]#

Bases: object

Represents a single ananke pipeline.

__init__(particles: Dict[str, ndarray[Any, dtype[_ScalarType_co]]], name: str, ngb: int = 64, caching: bool = False, append_hash: bool | None = None, k_params: Dict[str, Any] = {}, e_params: Dict[str, Any] = {}, err_params: Dict[str, Any] = {}, il_params: Dict[str, Any] = {}, **kwargs: Dict[str, Any]) → None[source]#
Parameters:

  • particles (dict) – A dictionary of same-length arrays representing particles data of a stellar population - see notes for formatting

  • name (str) – Name for the pipeline

  • ngb (int) – Number of neighbours to use in kernel density estimation

  • caching (bool) – EXPERIMENTAL: activate caching mode at every steps to resume work where it was left at from a previous python instance if needed. Default to True.

  • append_hash (bool) – TODO

  • k_params (dict) – Parameters to configure the kernel sizes estimation. Use class method display_kernel_docs to find what parameters can be defined

  • e_params (dict) – Parameters to configure the extinction pipeline. Use class method display_extinction_docs to find what parameters can be defined

  • err_params (dict) – Parameters to configure the error model pipeline. Use class method display_errormodel_docs to find what parameters can be defined

  • il_params (dict) – Parameters to configure the integrated light pipeline. Use class method display_integratedlight_docs to find what parameters can be defined

  • observer (array-like shape (3,) or dict of array-like shape (3,)) – Coordinates for the observer in phase space. Position and velocity quantities must respectively be given in kpc and km/s. To only specify position, an array-like object of shape (3,) is enough. If specifying both position and velocity, please provide a dictionary containing both coordinates as array-like objects of shape (3,), respectively denoting the position and velocity coordinates with keys pos3 and vel3. Position coordinates default to:

    [-8.12197337e+00 -1.47294445e-11  2.08000000e-02]

    and velocity coordinates default to:

    [ 12.9  245.6    7.78]

  • rshell (array-like shape (2,)) – Range in kpc of distances from the observer position of the particles that are to be considered. Default to:

    [  0 500]

  • photo_sys (string or list) – Name(s) of the photometric system(s) Galaxia should use to generate the survey. Default to ['padova/GAIA__DR2']. Available photometric systems can be queried with the class method display_available_photometric_systems.

  • cmd_magnames (string or dict) – Names of the filters Galaxia should use for the color-magnitude diagram box selection. The input can be given as string in which case it must meet the following format:

    "band1,band2-band3"

    where band1 is the magnitude filter and (band2, band3) are the filters that define the band2-band3 color index. Alternatively, a dictionary can be passed with the following format:

    dict('magnitude': band1,
     'color_minuend': band2,
     'color_subtrahend': band3)

    The filter names must correspond to filters that are part of the first chosen photometric system in photo_sys. Default to 'G,Gbp-Grp'.

  • fsample (float) – Sampling rate from 0 to 1 for the resulting synthetic star survey. 1 returns a full sample while any value under returns partial surveys. Default to 1.

  • app_mag_lim_lo, app_mag_lim_hi, abs_mag_lim_lo, abs_mag_lim_hi, color_lim_lo, color_lim_hi (float) – These allow to specify the limits of the chosen color-magnitude diagram box selection (lo for lower and hi for upper). app_mag, abs_mag and color represent respectively limits in apparent magnitudes, absolute magnitudes and color index. Default values follow those set in the dictionary:

    {'abs_mag': [-1000, 20],
 'app_mag': [-1000, 1000],
 'color': [-1000, 1000]}

Notes

The input particles must include same-length arrays for every key of the list of keys return by property required_particles_keys. Particular attention should be given to arrays of keys 'pos3' and 'vel3' that must be shaped as (Nx3) arrays of, respectively, position and velocity vectors. Use the class method make_dummy_particles_input to generate a dummy example of such input dictionary.

run(caching: bool | None = None, append_hash: bool | None = None, no_post_processing: bool | None = False, **kwargs) → Output[source]#

Method to run the pipeline

Parameters:

  • caching (bool) – EXPERIMENTAL: activate caching mode at every steps to resume work where it was left at from a previous python instance if needed. Default to existing caching given to object at construction.

  • append_hash (bool) – Only relevant if caching is active. When True, ananke automatically adds truncated hashes to all files it produces, to uniquely identify them. Default to True.

  • input_dir, output_dir, i_o_dir (string) – Optional arguments to specify paths for the directories where ananke should generate input and output data. If the i_o_dir keyword argument is provided, it overrides any path given to the input_dir and output_dir keyword arguments.

  • no_post_processing (bool) – If True, ignore the post-processing pipeline following the galaxia_ananke run. This post-processing pipeline involves the replacement of absolute magnitude quantities by their apparent ones, followed by the extinction estimation pipeline and by the error model application. Default to False.

  • k_factor (float) – Scaling factor applied to the kernels lengths to adjust all the kernels sizes uniformly. Lower values reduces the kernels extents, while higher values increases them. Default to 1 (no adjustment).

  • surveyname (string) – Optional name Galaxia should use for the output files. Default to ‘survey’.

  • input_sorter (array_like) – TODO

  • n_gens, n_jobs (int or iterable of int) – Number of independent catalog generations ran in parallel. Can also receive an iterable containing each generation number to run in parallel. Default to 1. Usage of n_jobs is deprecated and will be removed.

  • max_gen_workers (int) – CURRENTLY NOT PROPERLY IMPLEMENTED Maximum number of workers to parallelize the initial catalog generations. Default to the number of independent generations in n_gens.

  • max_pp_workers (int) – Maximum number of workers to parallelize the post-processing pipelines after the initial catalog generation. Default to 1.

  • pp_auto_flush (bool) – TODO

  • verbose (bool) – Verbose boolean flag to allow pipeline to print what it’s doing to stdout. Default to True.

  • partitioning_rule (TODO) – TODO

  • rand_seed (int) – Seed to be used by Galaxia’s pseudorandom number generator. Default to 17052

  • nstart (int) – Index at which to start indexing synthetic stars. Default to 0

  • longitude, latitude (float) –

    Currently not implemented. Respectively default to:

    76.273 & 13.4725

  • star_type (int) – Currently not implemented. Default to 0

  • geometry_opt (int) – Currently not implemented. Default to 0

  • survey_area (float) – Currently not implemented. Default to 207.455

  • pop_id (int) – Currently not implemented. Default to 10

  • warp_flare_on (int) – Currently not implemented. Default to 0

  • photo_error (int) – Currently not implemented. Default to 0

Returns:

galaxia_output – Handler with utilities to utilize the output survey and its data.

Return type:

Galaxia.Output

Notes

An Output object almost behaves as a vaex DataFrame, also please consult vaex online tutorials for more hands-on information:

The DataFrame represents the catalogue with columns corresponding to properties of the stars from the synthetic stellar population it simulates.

Warning

When generated directly by galaxia_ananke, the catalogue properties reflect directly the quantities as computed by Galaxia. However the catalogue can be modified/amended by applying post-processing routines using the method apply_post_process_pipeline_and_flush. Also if such Output object was generated by other software than galaxia_ananke, post-processing may have been applied: also please refer to that software documentation for a more complete overview of the catalogue.

The catalogue properties include the photometric magnitudes per filter, with each filter identified by a key in the following lowercase format:

photosys_filtername

where

  • photo_sys corresponds to the chosen photometric system

  • filtername corresponds to a filter name of that system

As an example, the photometry in filters gbp, grp & g of the Gaia DR2 system identified as GAIA__DR2 are respectively under keys gaia__dr2_gbp, gaia__dr2_grp & gaia__dr2_g.

With those are also always included the following properties:

  • Distance modulus in magnitude units via key dmod

  • Stellar metallicity \([Fe/H]\) in \(dex\) relative to solar via key feh

  • Radial velocity in \(km/s\) via key vr

  • Parallax in milliarcseconds via key pi

  • Velocity coordinates in \(km/s\) via keys (vx, vy, vz)

  • Equatorial proper motions in milliarcseconds per year via keys (mura, mudec)

  • Galactic proper motions in milliarcseconds per year via keys (mul, mub)

  • Tip of the Red Giant Branch stellar mass in solar masses via key mtip

  • Surface temperature in Kelvin and decimal logarithmic scale via key teff

  • Celestial galactic coordinates in \(degrees\) via keys (glon, glat)

  • Index of the data partition that contains the particle via key partitionid

  • Stellar ages in years and decimal logarithmic scale via key age

  • Flag = 1 if star not at center of its parent particle via key partid

  • Distance in \(kpc\) via key rad

  • Stellar luminosity in solar luminosities and decimal logarithmic scale via key lum

  • Zero Age Main Sequence stellar mass in solar masses via key smass

  • Position coordinates in \(kpc\) via keys (px, py, pz)

  • Surface gravity in CGS units and decimal logarithmic scale via key grav

  • Celestial equatorial coordinates in \(degrees\) via keys (ra, dec)

  • Index of parent particle via key parentid

  • Current stellar mass in solar masses via key mact

Additionally, depending on what optional properties were provided with the input particle data, the output can also include the following properties:

  • Neon abundance \([Ne/H]\) in \(dex\) via key neon

  • Carbon abundance \([C/H]\) in \(dex\) via key carbon

  • Nitrogen abundance \([N/H]\) in \(dex\) via key nitrogen

  • Silicon abundance \([Si/H]\) in \(dex\) via key silicon

  • Magnesium abundance \([Mg/H]\) in \(dex\) via key magnesium

  • Helium abundance \([He/H]\) in \(dex\) via key helium

  • Oxygen abundance \([O/H]\) in \(dex\) via key oxygen

  • Calcium abundance \([Ca/H]\) in \(dex\) via key calcium

  • Formation distance of parent particle in kpc via key dform

  • Alpha abundance \([Mg/Fe]\) in \(dex\) via key alpha

  • Index of parent particle population via key satid

  • Sulphur abundance \([S/H]\) in \(dex\) via key sulphur

Ananke complements this set of properties with those that are generated from its various post-processing subroutines. As a result the photosys_filtername-formatted columns contain the apparent photometry, computed with addition of extinction and instrument error. Each component contributing to this final apparent photometry are stored in other columns with the photosys_filtername format with relevant prefixing/suffixing as listed below:

  • The intrinsic photometry are stored in the suffixed _Intrinsic keys

  • The extinction values are stored in the prefixed A_ keys

  • The properties’ standard error are stored in the suffixed _Sig keys

  • The properties’ actually drawn gaussian error are stored in the suffixed _Err keys

Note that because the error model generally also affect astrometry, the latter 2 suffixing rules also apply to the astrometric properties.

The extinction post-processing routine also add 3 properties:

  • The line-of-sight hydrogen column density in \(cm^{-2}\) and decimal logarithmic scale via key log10_NH

  • The reddening index via key E(B-V)

  • The reference extinction (which extinction coefficients are based on) via key A_0

property particles: Dict[str, ndarray[Any, dtype[_ScalarType_co]]]#
property particle_masses: ndarray[Any, dtype[_ScalarType_co]]#
property particle_positions: ndarray[Any, dtype[_ScalarType_co]]#
property particle_velocities: ndarray[Any, dtype[_ScalarType_co]]#
property particle_metallicities: ndarray[Any, dtype[_ScalarType_co]]#
property particle_ages: ndarray[Any, dtype[_ScalarType_co]]#
property particle_parentids: ndarray[Any, dtype[_ScalarType_co]]#
property name: str#
property ngb: int#
property caching: bool#
property append_hash: bool#
property universe: Universe#
property universe_rshell: ndarray[Any, dtype[_ScalarType_co]]#
property observer: Observer#
property observer_position: ndarray[Any, dtype[_ScalarType_co]]#
property observer_velocity: ndarray[Any, dtype[_ScalarType_co]]#
property particle_observed_positions: ndarray[Any, dtype[_ScalarType_co]]#
property particle_observed_velocities: ndarray[Any, dtype[_ScalarType_co]]#
property particle_observed_distances: ndarray[Any, dtype[_ScalarType_co]]#
property particle_input_kernels: ndarray[Any, dtype[_ScalarType_co]]#
property particle_input_position_kernels: ndarray[Any, dtype[_ScalarType_co]]#
property particle_input_velocity_kernels: ndarray[Any, dtype[_ScalarType_co]]#
property particle_nearest_observed_distances: ndarray[Any, dtype[_ScalarType_co]]#
property particle_nearest_observed_distmod: ndarray[Any, dtype[_ScalarType_co]]#
property extinctions#
property errors#
property residuals#
property parameters: Dict[str, Any]#
property photo_sys: str#
property galaxia_photosystems: List[PhotoSystem]#
property galaxia_isochrones#
property galaxia_catalogue_mag_names: Tuple[str]#
property intrinsic_catalogue_mag_names: Tuple[str]#
property galaxia_catalogue_mag_and_astrometrics: Tuple[str]#
property galaxia_catalogue_keys: Tuple[str]#
property photosystems_zeropoints: Quantity#
property photosystems_zeropoints_dict: Dict[str, Quantity]#
classmethod make_dummy_dictionary_description() → str[source]#
classmethod make_dummy_particles_input(n_parts=100000, with_kernels=False) → Dict[str, ndarray[Any, dtype[_ScalarType_co]]][source]#

Generate an example dummy input particles dictionary for Ananke made of randomly generated arrays.

Parameters:

  • n_parts (int) – Number of particles the example include. Default to 10**5.

  • with_kernels (bool) – Flag to include dummy kernels estimates in the returned dictionary. Default to False

Returns:

p – Dummy example input particles dictionary for Ananke.

Return type:

dict

Notes

The particle dictionary includes the following properties with corresponding keys:

  • Position coordinates in \(kpc\) (Nx3) via key pos3

  • Stellar ages in years and decimal logarithmic scale via key age

  • Velocity coordinates in \(km/s\) (Nx3) via key vel3

  • Stellar masses in solar masses via key mass

  • Stellar metallicity \([Fe/H]\) in dex relative to solar via key feh

Additionally, Galaxia can optionally receive particle properties that will be carried over to the generated synthetic star, those include the following:

  • Neon abundance \([Ne/H]\) in \(dex\) via key neon

  • Carbon abundance \([C/H]\) in \(dex\) via key carbon

  • Nitrogen abundance \([N/H]\) in \(dex\) via key nitrogen

  • Magnesium abundance \([Mg/H]\) in \(dex\) via key magnesium

  • Silicon abundance \([Si/H]\) in \(dex\) via key silicon

  • Index of the data partition that contains the particle via key partitionid

  • Helium abundance \([He/H]\) in \(dex\) via key helium

  • Oxygen abundance \([O/H]\) in \(dex\) via key oxygen

  • Formation distance of parent particle in kpc via key dform

  • Calcium abundance \([Ca/H]\) in \(dex\) via key calcium

  • Alpha abundance \([Mg/Fe]\) in \(dex\) via key alpha

  • Index of parent particle population via key id

  • Sulphur abundance \([S/H]\) in \(dex\) via key sulphur

  • Index of parent particle via key parentid

Ananke compute the phase space densities that are used to determine particle kernel lengths, but the dictionary can include pre-computed kernels with the following entries:

  • Phase-space kernel radii in \(kpc\) and \(km/s\) (Nx2) via key kernels

classmethod display_available_photometric_systems()[source]#

Return a nested dictionary of all photometric systems that are available in Galaxia.

Returns:

available_photo_systems – Dictionary of dictionaries of Isochrone objects.

Return type:

dict

classmethod display_kernels_docs() → None[source]#

Print the KernelsDriver constructor docstring

classmethod display_EnBiD_docs() → None[source]#

Print the EnBiD.run_enbid method docstring

classmethod display_extinction_docs() → None[source]#

Print the ExtinctionDriver constructor docstring

classmethod display_errormodel_docs() → None[source]#

Print the ErrorModelDriver constructor docstring

classmethod display_integratedlight_docs() → None[source]#

Print the IntegratedLightDriver constructor docstring

classmethod display_galaxia_makesurvey_docs() → None[source]#

Print the Galaxia.Survey.make_survey method docstring