CatlUtils

class sdss_catl_utils.models.catl_models.CatlUtils(**kwargs)[source] [edit on github]

Bases: sdss_catl_utils.models.catl_models_template.CatlClassTemplate

Class used to handle the galaxy/group galaxy/group catalogues, after the catalogues have been downloaded. This class has functions to read, modify, and analyze the galaxy/group catalogues.

For a list of available pre-processed galaxy- and group-galaxy catalogues provided by sdss_catl_utils, see …

Parameters:
catl_kind : {data, mocks} str

Kind of catalogues to download. This variable is set to mocks by default.

Options:
  • data: Downloads the SDSS DR7 real catalogues.
  • mocks: Downloads the synthetic catalogues of SDSS DR7.
hod_n : int, optional

Number of the HOD model to use. This value is set to 0 by default.

halotype : {‘so’, ‘fof’}, str, optional

Type of dark matter definition to use. This value is set to so by default.

Options:
  • so: Spherical Overdensity halo definition.
  • fof: Friends-of-Friends halo definition.
clf_method : {1, 2, 3}, int, optional

Method for assigning galaxy properties to mock galaxies. This variable dictates how galaxies are assigned luminosities or stellar masses based on their galaxy type and host halo’s mass. This variable is set to 1 by default.

Options:
  • 1: Independent assignment of (g-r) colour, sersic, and specific star formation rate (logssfr)
  • 2: (g-r) colour dictates active/passive designation and draws values independently.
  • 3: (g-r) colour dictates active/passive designation, and assigns other galaxy properties for that given galaxy.
clf_seed : int, optional

Value of the random seed used for the conditional luminosity function. This variable is set to 1235 default.

dv : float, optional

Value for the velocity bias parameter. It is the difference between the galaxy and matter velocity profiles.

\[\begin{split}dv = \\frac{v_{g} - v_{c}}{v_{m} - v_{c}}\end{split}\]

where \(v_g\) is the galaxy’s velocity; \(v_m\), the matter velocity.

sigma_clf_c : float, optional

Value of the scatter in log(L) for central galaxies, when being assigned during the conditional luminosity function (CLF). This variable is set to 0.1417 by default.

sample : {‘18’, ‘19’, ‘20’, ‘21’}, str, optional

Luminosity of the SDSS volume-limited sample to analyze. This variable is set to '19' by default.

Options:
  • '18': \(M_r = 18\) volume-limited sample
  • '19': \(M_r = 19\) volume-limited sample
  • '20': \(M_r = 20\) volume-limited sample
  • '21': \(M_r = 21\) volume-limited sample
type_am : {‘mr’, ‘mstar’}, str, optional

Type of Abundance matching used in the catalogue. This variable is set to 'mr' by default.

Options:
  • 'mr': Luminosity-based abundance matching used
  • 'mstar': Stellar-mass-based abundance matching used.
cosmo_choice : { 'LasDamas', 'Planck'} str, optional

Choice of cosmology to use. This variable is set to LasDamas by default.

Options:
  • LasDamas : Uses the cosmological parameters from the
    LasDamas simulations.
  • Planck : Uses the Planck 2015 cosmology.
perf_opt : bool, optional

If True, it chooses to analyze the perfect version of the synthetic galaxy/group galaxy catalogues. Otherwise, it downloads the catalogues with group-finding errors included. This variable is set to False by default.

environ_name : str

Name of the environment variable to assign to outdir. This variable is set to the default environ_name from mocks_default

Notes

There are many combinations of parameters that could be performed. However, note that not all variations exist and would not be available for download/analysis!

Examples

>>> from sdss_catl_utils.models.catl_models import CatlUtils

If one wants to initialize an catalogue object with the default input parameters, one could write:

>>> catl_obj = CatlUtils() # Initialzing catalogue object

However, if one wants to have a modified version of catl_obj, one can pass a dictionary with the proper set of input parameters:

>>> params_d = {'catl_kind': 'mocks', 'clf_method': 3, 'clf_seed': 123}
>>> catl_obj = CatlUtils(**params_d)

This will create an catalogue object that corresponds to mocks catalogues with clf_method = 3 and clf_seed = 123.

Methods Summary

catl_arr_extract([catl_type, catl_kind, …]) Extracts the list of galaxy/group catalogues.
catl_merge([catl_idx, return_memb_group, …]) Merges the member and group catalogues for a given set of input parameters, and returns a modified version of the galaxy group catalogues with added info about the galaxy groups.
catls_dir([catl_type, catl_kind, print_filedir]) Location of the group and group galaxy catalogues with the specified parameters.
get_params_dict() Gets the dictionary of input parameters for the given model.
main_dir() Path to the main directory, in which all catalogues are stored.

Methods Documentation

catl_arr_extract(catl_type='memb', catl_kind='mocks', ext='hdf5', print_filedir=False, return_len=False)[source] [edit on github]

Extracts the list of galaxy/group catalogues.

Parameters:
catl_type : {gal, memb, group}, str

Option for which kind of catalogue is being analyzed. This variable is set to memb by default.

Options:
  • 'gal' : Galaxy catalogue
  • 'memb' : Group Member galaxy catalogue
  • 'group' : Group galaxy catalogue
catl_kind : {data, mocks} str

Kind of catalogues to download. This variable is set to mocks by default.

Options:
  • data: Downloads the SDSS DR7 real catalogues.
  • mocks: Downloads the synthetic catalogues of SDSS DR7.
ext : {‘hdf5’} str

File extension used for the catalogues. This variable is set to hdf5 by default.

print_filedir : bool, optional

If True, the path of the catalogue directory is printed onto the screen. This variable is set to False by default.
Returns:
catls_arr : numpy.ndarray, shape (N,)

Array of the paths of the galaxy/group catalogues with specified parameters. The shape of the variable is (N,), where N, is the number of catalogues in the directory.

Notes

If no files are present or exist in the given directory, it will return an empty array of files.

Examples

This function is able to return the path to individual kinds of galaxy and group catalogues that meet some criteria based on the different combinations of input parameters.

For example, if one wants to get the paths to the mocks member galaxy catalogues with a halotype = 'fof' prescription, and following a clf_method = 2 methodology, one could retrieve them by:

>>> params_dict = {'catl_kind': 'mocks', 'halotype': 'fof', 'clf_method': 2}
>>> catl_obj = CatlUtils(**params_dict) # doctest: +SKIP
>>> catl_paths = catl_arr_extract(catl_type='memb', catl_kind='mocks', halotype='fof') # doctest: +SKIP

This will return the paths to the member galaxy catalogues that meet those prescribed parameters.

catl_merge(catl_idx=0, return_memb_group=False, print_filedir=False)[source] [edit on github]

Merges the member and group catalogues for a given set of input parameters, and returns a modified version of the galaxy group catalogues with added info about the galaxy groups.

Parameters:
catl_idx : int, optional

Index of the catalogue to match. The index must be smaller than the number of files returned by function catl_arr_extract for the given combination of parameters. This variable is set to 0 by default.

return_memb_group : bool, optional

If True, the function returns the member and group catalogues along with the merged catalogue. This variable is set to False by default.

print_filedir : bool, optional

If True, the output directory is printed onto the screen. This variable is set to False by default.
Returns:
merged_pd : pandas.DataFrame

Combined DataFrame containing both galaxy and group information for the given combination of parameters and the catl_idx-th catalogue(s).

memb_pd : pandas.DataFrame

Member galaxy catalogue of the catl_idx-th catalogue. This catalogue contains the information about the member galaxies. This object is only returned if return_memb_group == True,

group_pd : pandas.DataFrame

Group galaxy catalogue of the catl_idx-th catalogue. This catalogue contains the information about the galaxy groups This object is only returned if return_memb_group == True,
Raises:
SDSSCatlUtils_Error : Exception from SDSSCatlUtils_Error

Program exception if input parameters are not accepted.

Examples

This function will merge the member and group catalogues into a single pandas.DataFrame object, and it can be used to merge two existing catalogues. For example, to merge the member and group catalogues using the default parameters, one can easily write:

>>> merged_pd = catl_merge() # doctest: +SKIP

The resulting object will consist of a merge between the member and group catalogues, with the columns pertaining to group properties having a GG_ attached to their names.

However, if one wants to merge two mock catalogues with a clf_method = 2 and halotype = 'fof' prescription, one could easily write this:

>>> params_dict = {'catl_kind': 'mocks', 'halotype': 'fof', 'clf_method': 2}
>>> catl_obj = CatlUtils(**params_dict) # doctest: +SKIP
>>> merged_pd = catl_obj.catl_merge() # doctest: +SKIP

Additionally, one could recover the member and group catalogues as well:

>>> merged_pd, memb_pd, group_pd = catl_obj.catl_merge(return_memb_group=True) # doctest: +SKIP

For more information and examples, please refer to Getting started with SDSS_Catl_Utils.

catls_dir(catl_type='memb', catl_kind='mocks', print_filedir=False)[source] [edit on github]

Location of the group and group galaxy catalogues with the specified parameters.

Parameters:
catl_type : {‘gal’, ‘memb’, ‘group’}, str

Option for which kind of catalogue is being analyzed. This variable is set to memb by default.

Options:
  • 'gal' : Galaxy catalogue
  • 'memb' : Group Member galaxy catalogue
  • 'group' : Group galaxy catalogue
catl_kind : {data, mocks} str

Kind of catalogues to download. This variable is set to mocks by default.

Options:
  • data: Downloads the SDSS DR7 real catalogues.
  • mocks: Downloads the synthetic catalogues of SDSS DR7.
print_filedir : bool, optional

If True, the path of the catalogue directory is printed onto the screen. This variable is set to False by default.
Returns:
catls_dirpath : str

Path to the location of the group and galaxy catalogues with the specified parameters.

Notes

This method can be used to access the different kinds of catalogues, i.e. galaxy-, member-, and group-catalogues for different combinations of input parameters.

Examples

catls_dir can be used to explore the directories that host different kinds of catalogues, i.e. galaxy-, member-, and group-catalogues for various combinations of input parameters.

For example, if one wants to read the member galaxy catalogue, i.e. a catalogue with both galaxy and group information, for mocks catalogues, one could easily write:

>>> memb_dir = catls_dir(catl_type='memb', catl_kind='mocks') # doctest: +SKIP

This will return the path to the member galaxy catalogues directory with the synthetic catalogues in it. This method only returns the path of the directory, and not the path to the actual files in the directory. To accomplish this, one would need to use the function catl_arr_extract.

get_params_dict()[source] [edit on github]

Gets the dictionary of input parameters for the given model.

Returns:
param_dict : dict

Dictionary of input parameters for the chosen combination of parameters.

Examples

After having initializing an CatlUtils object, one can easily recover the set of input parameters used.

>>> from sdss_catl_utils.models.catl_models import CatlUtils
>>> catl_params_dict = {'catl_kind': 'mocks', 'clf_seed': 3} # Catalogue parameters
>>> catl_obj = CatlUtils(**catl_params_dict) # Initialing object

The dictionary of parameters is saved as self.param_dict for the class object. It can be easily accessed by:

>>> catl_obj.param_dict # doctest: +SKIP

Or, it can also be accessed as:

>>> catl_obj.get_params_dict() # doctest: +SKIP

For example, in order to print out the list of input parameters, one can do the following:

>>> param_dict = catl_obj.param_dict # doctest: +SKIP
>>> param_dict # doctest: +SKIP
{'catl_kind': 'mocks',
 'hod_n': 0,
 'halotype': 'fof',
 'clf_method': 1,
 'clf_seed': 3,
 'dv': 1.0,
 'sigma_clf_c': 0.1417,
 'sample': '19',
 'type_am': 'mr',
 'cosmo_choice': 'LasDamas',
 'perf_opt': False,
 'remove_files': False,
 'environ_name': 'sdss_catl_path',
 'sample_Mr': 'Mr19',
 'sample_s': '19'}

This dictionary can now be used in other parts of one’s analysis, or as a reference of the parameters used.

main_dir()[source] [edit on github]

Path to the main directory, in which all catalogues are stored. This directory depends on your installation of SDSS_Catl_Utils and on your current working space.

Returns:
main_dirpath : str

Path to the main directory, in which all catalogues are stored. It uses the environment variable environ_name from the class.

Examples

The main directory of the catalogues can be easily accessed after having created an object for the combination of input parameters for the catalogues.

>>> from sdss_catl_utils.models.catl_models import CatlUtils
>>> catl_params_dict = {'catl_kind': 'mocks', 'clf_seed': 3} # Catalogue parameters
>>> catl_obj = CatlUtils(**catl_params_dict) # Initialing object

One can access the directory, under which the galaxy- and group-catalogues are saved, by typing:

>>> catl_obj.main_dir() # doctest: +SKIP

This will return the path of the directory.