#! /usr/bin/env python
# -*- coding: utf-8 -*-
# Victor Calderon
# Created : 2018-05-08
# Last Modified: 2018-05-08
from __future__ import absolute_import, division, print_function
__author__ = ['Victor Calderon']
__copyright__ = ["Copyright 2018 Victor Calderon"]
__email__ = ['victor.calderon@vanderbilt.edu']
__maintainer__ = ['Victor Calderon']
__all__ = [ "catl_keys",
"catl_keys_prop",
"catl_sdss_dir",
"extract_catls",
"sdss_catl_clean",
"sdss_catl_clean_nmin",
"catl_sdss_merge"]
## Import modules
import os
from collections import Counter
import numpy as np
import pandas as pd
from cosmo_utils.utils import file_utils as fd
from cosmo_utils.utils import work_paths as wp
from cosmo_utils.utils import file_readers as fr
from cosmo_utils.custom_exceptions import LSSUtils_Error
## Functions
## Catalogue Keys - Main
[docs]def catl_keys(catl_kind, perf_opt=False, return_type='list'):
"""
Dictionary keys for the different types of catalogues
Parameters
----------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
perf_opt : `bool`, optional
Option for using a `perfect` mock catalogue.
return_type : {'list', 'dict'} str, optional
Type of output to the be returned. This variable is set to `list`
by default.
Options:
- 'list' : Returns the values as part of a list
- 'dict' : Returns the values as part of a python dictionary
Returns
----------
catl_keys : python dictionary or array_like
Dictionary/array with the proper keys for the catalogue(s).
Order : 1) `gm_key`, 2) `id_key`, 3) `galtype_key`
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
Examples
----------
>>> catl_keys('data', perf_opt=False, return_type='list')
['M_h', 'groupid', 'galtype']
>>> catl_keys('mocks', perf_opt=True, return_type='list')
['M_h', 'haloid', 'galtype']
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
# `catl_kind`
if not (catl_kind in ['data', 'mocks']):
msg = '{0} `catl_kind` ({1}) is not a valid input parameter!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `return_type`
if not (return_type in ['list', 'dict']):
msg = '{0} `return_type` ({1}) is not a valid input parameter'.format(
file_msg, return_type)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) must be a boolean object!'.format(
file_msg, type(perf_opt))
raise LSSUtils_Error(msg)
##
## Perfect Catalogue
if catl_kind == 'data':
perf_opt = False
##
## Property keys
if catl_kind == 'data':
gm_key, id_key, galtype_key = ['M_h', 'groupid', 'galtype']
elif catl_kind == 'mocks':
if perf_opt:
gm_key, id_key, galtype_key = ['M_h', 'haloid', 'galtype']
else:
gm_key, id_key, galtype_key = ['M_group', 'groupid', 'g_galtype']
##
## Saving values
if return_type == 'dict':
catl_objs = { 'gm_key' : gm_key,
'id_key' : id_key,
'galtype_key' : galtype_key}
elif return_type == 'list':
catl_objs = [ gm_key, id_key, galtype_key]
return catl_objs
## Catalogue Keys - Properties
[docs]def catl_keys_prop(catl_kind, catl_info='members', return_type='list'):
"""
Dictionary keys for the diffeent galaxy and group properties of
catalogues.
Parameters
------------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
return_type : {'list', 'dict'} str, optional
Type of output to the be returned. This variable is set to `list`
by default.
Options:
- 'list' : Returns the values as part of a list
- 'dict' : Returns the values as part of a python dictionary
Return
------------
catl_objs : python dictionary or array_like
Dictionary/array with the proper keys for the catalogue(s).
Order : 1) `ssfr_key`, 2) `mstar_key`
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
Examples
------------
>>> catl_keys_prop('data')
['logssfr', 'logMstar_JHU']
>>> catl_keys_prop('mocks', catl_info='groups', return_type='list')
['logssfr', 'logMstar']
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_kind_valid = ['data' , 'mocks' ]
catl_info_valid = ['members', 'groups']
return_type_valid = ['list' , 'dict' ]
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(
file_msg, catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(
file_msg, catl_info)
raise LSSUtils_Error(msg)
# `return_type`
if not (return_type in return_type_valid):
msg = '{0} `return_type` ({1}) is not a valid input!'.format(
file_msg, return_type)
raise LSSUtils_Error(msg)
#
# Property keys
#
# Data
if (catl_kind == 'data'):
# Members
if catl_info == 'members':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar_JHU']
# Groups
if catl_info == 'groups':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr_tot', 'logMstar_tot']
#
# Mocks
if (catl_kind == 'mocks'):
# Members
if catl_info == 'members':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar']
# Groups
if catl_info == 'groups':
# SSFR and Stellar mass
logssfr_key, logmstar_key = ['logssfr', 'logMstar']
#
# Saving values
if return_type == 'dict':
catl_objs = { 'logssfr_key' : logssfr_key,
'logmstar_key': logmstar_key}
elif return_type == 'list':
catl_objs = [ logssfr_key, logmstar_key]
return catl_objs
## Extracting path of synthetic catalogues
[docs]def catl_sdss_dir(catl_kind='data', catl_type='mr', sample_s='19',
catl_info='members', halotype='fof', clf_method=3, hod_n=0, clf_seed=1235,
dv=1.0, sigma_clf_c=0.1417, perf_opt=False, print_filedir=True):
"""
Extracts the path to the synthetic catalogues.
Parameters
-----------
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_type : {'mr', 'mstar'} str, optional
Type of catalogue to use. It shows which abundance matching method
was used for the CLF when assigning halo masses. This variable is
set to 'mr' by default.
Options:
- `mr` : Uses r-band absolute magnitude
- `mstar` : Uses stellar masses
sample_s : {'19', '20', '21'} str, optional
Volume-limited sample to use. This variable is set to '19' by default.
Options:
- '19' : Uses the Mr19 volume-limited sample, i.e. 'Consuelo'
- '20' : Uses the Mr20 volume-limited sample, i.e. 'Esmeralda'
- '21' : Uses the Mr21 volume-limited sample, i.e. 'Carmen'
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
halotype : {'fof', 'so'} str, optional
Type of the dark matter halo of the simulation used to create the
synthetic catalogues. This variable is set to `fof` by default.
Options:
- 'fof': Friends-of-Friends halos.
- 'so' : Spherical overdensity halos.
clf_method : {1, 2, 3} int, optional
Method for assigning galaxy properties to mock galaxies.
This variable is set to `3` by default.
Options:
- `1` : Independent assigment of (g-r) color, sersic, and log(ssfr)
- `2` : (g-r) decides active/passive designation and draw values
independently.
- `3` : (g-r) decides active/passive designations, and
assigns other galaxy properties for that given galaxy.
hod_n : {0, 1} int, optional
HOD model to use. Only relevant when `catl_kind == mocks`.
clf_seed : int, optional
Seed used for the `CLF` random seed. This variable is set to `1235`
by default.
dv : float, optional
Difference between galaxy and mass velocity profiles
(v_g-v_c)/(v_m-v_c). This value is set to `1.0` by default.
sigma_clf_c : `float`, optional
Value of the scatter in log(L) for central galaxies in the CLF.
This variable is set to ``0.1417`` by default.
perf_opt : `bool`, optional
If True, it chooses to analyze the `perfect` set of synthetic
catalogues. This variable is set to `False` by default.
print_filedir : `bool`, optional
If True, the output directory is printed onto the screen.
Returns
-----------
catls_path : str
Path to the desired set of synthetic catalogues.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_type_valid = ['mr', 'mstar']
sample_s_valid = ['19', '20', '21']
catl_info_valid = ['members', 'groups']
halotype_valid = ['fof', 'so']
clf_method_valid = [1, 2, 3]
hod_n_valid = np.arange(0, 20)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(file_msg,
catl_kind)
raise LSSUtils_Error(msg)
# `catl_type`
if not (catl_type in catl_type_valid):
msg = '{0} `catl_type` ({1}) is not a valid input!'.format(file_msg,
catl_type)
raise LSSUtils_Error(msg)
# `sample_s`
if not (sample_s in sample_s_valid):
msg = '{0} `sample_s` ({1}) is not a valid input!'.format(file_msg,
sample_s)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(file_msg,
catl_info)
raise LSSUtils_Error(msg)
# `halotype`
if not (halotype in halotype_valid):
msg = '{0} `halotype` ({1}) is not a valid input!'.format(file_msg,
halotype)
raise LSSUtils_Error(msg)
# `clf_method`
if not (clf_method in clf_method_valid):
msg = '{0} `clf_method` ({1}) is not a valid input!'.format(file_msg,
clf_method)
raise LSSUtils_Error(msg)
# `hod_n`
if not (hod_n in hod_n_valid):
msg = '{0} `hod_n` ({1}) is not a valid input!'.format(file_msg,
hod_n)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(file_msg,
type(perf_opt))
raise LSSUtils_Error(msg)
# `print_filedir`
if not (isinstance(print_filedir, bool)):
msg = '{0} `print_filedir` ({1}) is not a valid type!'.format(file_msg,
type(print_filedir))
raise LSSUtils_Error(msg)
# `dv`
if not (dv > 0):
msg = '{0} `dv` ({1}) must be larger than 0!'.format(file_msg, dv)
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Type
if not (isinstance(sigma_clf_c, float)):
msg = '{0} `sigma_clf_c` ({1}) is not a valid input type!'
msg = msg.format(file_msg, type(sigma_clf_c))
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Value
if not (sigma_clf_c >= 0.):
msg = '{0} `sigma_clf_c` ({1}) must be larger than 0!'
msg = msg.format(file_msg, sigma_clf_c)
raise LSSUtils_Error(msg)
#
# Type of catalogue
if catl_info == 'members':
catl_info_str = 'member_galaxy_catalogues'
elif catl_info == 'groups':
catl_info_str = 'group_galaxy_catalogues'
#
# Perfect catalogue
if perf_opt:
# Data
if catl_kind == 'data':
msg = '{0} Invalid `catl_kind` ({1}) for when `perf_opt == True'
msg = msg.format(file_msg, catl_kind)
raise LSSUtils_Error(msg)
# Mocks
catl_info_perf_str = 'perfect_{0}'.format(catl_info_str)
else:
# Mocks
catl_info_perf_str = catl_info_str
#
# Extracting path of the files
# Data
if catl_kind == 'data':
# Joining paths
filedir = os.path.join( wp.get_output_path(),
'SDSS',
catl_kind,
catl_type,
'Mr' + sample_s,
catl_info_perf_str)
# Mocks
if catl_kind == 'mocks':
# Joining paths
filedir = os.path.join( wp.get_output_path(),
'SDSS',
catl_kind,
'halos_{0}'.format(halotype),
'dv_{0}'.format(dv),
'hod_model_{0}'.format(hod_n),
'clf_seed_{0}'.format(clf_seed),
'clf_method_{0}'.format(clf_method),
'sigma_c_{0}'.format(sigma_clf_c),
catl_type,
'Mr' + sample_s,
catl_info_perf_str)
#
# Making sure `filedir` exists
if not (os.path.exists(filedir)):
msg = '{0} `filedir` ({1}) does NOT exist! Check input variables'
msg = msg.format(file_msg, filedir)
raise LSSUtils_Error(msg)
#
# Printing out paths
if print_filedir:
print('{0} `filedir`: {1}'.format(file_msg, filedir))
return filedir
# Extracting list of synthetic catalogues given input parameters
# Cleaning the catalogue removing `failed` values
[docs]def sdss_catl_clean(catl_pd, catl_kind, catl_info='members', reindex=True):
"""
Cleans the catalogue by removing `failed` values.
Parameters
-----------
catl_pd : `pandas.DataFrame`
Dataset with the catalogue information.
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
reindex : `bool`, optional
If True, the output catalogue is re-indexed.
Return
-----------
catl_pd_clean : `pandas.DataFrame`
Cleaned version of `catl_pd`, after having removed `failed` values.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_info_valid = ['members', 'groups']
# `catl_pd`
if not (isinstance(catl_pd, pd.DataFrame)):
msg = '{0} `catl_pd` ({1}) is not a valid type!'.format(file_msg,
catl_pd)
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(file_msg,
catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(file_msg,
catl_info)
raise LSSUtils_Error(msg)
# `reindex
if not (isinstance(reindex, bool)):
msg = '{0} `reindex` ({1}) is not a valid type!'.format(file_msg,
type(reindex))
raise LSSUtils_Error(msg)
#
# Defining `failed` values
ssfr_fail_arr = [0, -99, -999, np.nan]
mstar_fail_arr = [-1, 0, np.nan]
#
# Getting keys for catalogues
( logssfr_key ,
logmstar_key ) = catl_keys_prop( catl_kind=catl_kind,
catl_info=catl_info,
return_type='list')
#
# Cleaning catalogue entries
#
# Data
if catl_kind == 'data':
# Clean version
catl_pd_clean = catl_pd[~catl_pd[logssfr_key].isin(ssfr_fail_arr) & \
~catl_pd[logmstar_key].isin(mstar_fail_arr)]
# Mocks
if catl_kind == 'mocks':
# Clean version
catl_pd_clean = catl_pd[~catl_pd[logssfr_key].isin(ssfr_fail_arr)]
#
# Reindexing
if reindex:
catl_pd_clean.reset_index(inplace=True, drop=True)
return catl_pd_clean
# Cleans dataset and includes only groups above a number of galaxy threshold
[docs]def sdss_catl_clean_nmin(catl_pd, catl_kind, catl_info='members', nmin=1,
perf_opt=False):
"""
Cleans the catalogue removing `failed` values, and only includes
galaxies that are in groups/halos above a `nmin` threshold.
Parameters
-----------
catl_pd : `pandas.DataFrame`
Dataset with the catalogue information.
catl_kind : {'data', 'mocks'} str, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_info : {'members', 'groups'} str, optional
Option for which kind of catalogues to use.
Options:
- `members` : Member galaxies of group catalogues
- `groups` : Catalogues with `group` information.
nmin : int, optional
Minimum group richness to have in the (galaxy) group catalogue.
This variable is set to `1` by default.
perf_opt : `bool`, optional
Option for using a `perfect` mock catalogue.
Return
-----------
catl_pd_clean : `pandas.DataFrame`
Cleaned version of `catl_pd` after having removed `failed` values,
and having choosen only galaxies within groups above a group richness
threshold of `nmin`.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
# Checking input parameters
catl_kind_valid = ['data', 'mocks']
catl_info_valid = ['members', 'groups']
# `catl_pd`
if not (isinstance(catl_pd, pd.DataFrame)):
msg = '{0} `catl_pd` ({1}) is not a valid type!'.format(file_msg,
catl_pd)
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(file_msg,
catl_kind)
raise LSSUtils_Error(msg)
# `catl_info`
if not (catl_info in catl_info_valid):
msg = '{0} `catl_info` ({1}) is not a valid input!'.format(file_msg,
catl_info)
raise LSSUtils_Error(msg)
# `nmin`
if not ((nmin > 0) and (isinstance(nmin, int))):
msg = '{0} `nmin` must be an integer and have a value above `0`'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(file_msg,
type(perf_opt))
raise LSSUtils_Error(msg)
#
# Types of galaxies
cens = int(1)
nmin = int(nmin)
#
# Getting keys for catalogue
( gm_key,
id_key,
galtype_key) = catl_keys( catl_kind,
return_type='list',
perf_opt=perf_opt)
# Cleaning catalogue entries
catl_pd_clean_all = sdss_catl_clean( catl_pd,
catl_kind=catl_kind,
catl_info=catl_info,
reindex=True)
# Choosing only galaxies in groups of richness >= `nmin`
# Member galaxies
if catl_info == 'members':
# Centrals
catl_pd_cens = catl_pd_clean_all.loc[(catl_pd_clean_all[galtype_key] == cens), id_key]
catl_pd_cl = catl_pd_clean_all[(catl_pd_clean_all[id_key].isin(catl_pd_cens))]
# Group counts
group_counts = Counter(catl_pd_cl[id_key])
group_ngals = np.array([xx for xx in group_counts.keys() if
group_counts[xx] >= nmin])
# Cleaned version
catl_pd_clean = catl_pd_cl[catl_pd_cl[id_key].isin(group_ngals)]
catl_pd_clean.reset_index(inplace=True, drop=True)
# Group catalogue
if catl_info == 'groups':
if ('ngals' in catl_pd_clean_all.columns.tolist()):
catl_pd_clean = catl_pd_clean_all.loc[catl_pd_clean_all['ngals'] >= nmin]
catl_pd_clean.reset_index(inplace=True, drop=True)
else:
msg = '{0} Key `ngals` not found in DataFrame ... Exiting!'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
return catl_pd_clean
# Merges the member and group catalogues for a given set of input parameters
[docs]def catl_sdss_merge(catl_pd_ii, catl_kind='data', catl_type='mr',
sample_s='19', halotype='fof', clf_method=3, hod_n=0, clf_seed=1235,
dv=1.0, sigma_clf_c=0.1417, perf_opt=False, return_memb_group=False, print_filedir=False):
"""
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_pd_ii : `int`
Index of the catalogue to match,
from :func:`~cosmo_utils.mock_catalogues.catls_utils.extract_catls`
function.
catl_kind : {'data', 'mocks'} `str`, optional
Type of catalogue to use. This variable is set to `data` by default.
Options:
- `data` : catalogues come from SDSS `real` catalogue
- `mocks` : catalogue come from SDSS `mock` catalogues
catl_type : {'mr', 'mstar'} `str`, optional
Type of catalogue to use. It shows which abundance matching method
was used for the CLF when assigning halo masses. This variable is
set to 'mr' by default.
Options:
- `mr` : Uses r-band absolute magnitude
- `mstar` : Uses stellar masses
sample_s : {'19', '20', '21'} str, optional
Volume-limited sample to use. This variable is set to '19' by default.
Options:
- '19' : Uses the Mr19 volume-limited sample, i.e. 'Consuelo'
- '20' : Uses the Mr20 volume-limited sample, i.e. 'Esmeralda'
- '21' : Uses the Mr21 volume-limited sample, i.e. 'Carmen'
halotype : {'fof', 'so'} str, optional
Type of the dark matter halo of the simulation used to create the
synthetic catalogues. This variable is set to `fof` by default.
Options:
- 'fof': Friends-of-Friends halos.
- 'so' : Spherical overdensity halos.
clf_method : {1, 2, 3} int, optional
Method for assigning galaxy properties to mock galaxies.
This variable is set to `3` by default.
Options:
- `1` : Independent assigment of (g-r) color, sersic, and log(ssfr)
- `2` : (g-r) decides active/passive designation and draw values
independently.
- `3` : (g-r) decides active/passive designations, and
assigns other galaxy properties for that given galaxy.
hod_n : {0, 1} int, optional
HOD model to use. Only relevant when `catl_kind == mocks`.
clf_seed : int, optional
Seed used for the `CLF` random seed. This variable is set to `1235`
by default.
dv : float, optional
Difference between galaxy and mass velocity profiles
(v_g-v_c)/(v_m-v_c). This value is set to `1.0` by default.
sigma_clf_c : `float`, optional
Value of the scatter in log(L) for central galaxies in the CLF.
This variable is set to ``0.1417`` by default.
perf_opt : `bool`, optional
If True, it chooses to analyze the `perfect` set of synthetic
catalogues. This variable is set to `False` by default.
return_memb_group : `bool`, optional
If True, the function returns the member and group catalogues,
along with the merged catalogue.
It returns ``<memb_group_pd, memb_pd, group_pd>``
print_filedir : `bool`, optional
If True, the output directory is printed onto the screen.
Return
------------
memb_group_pd : `pandas.DataFrame`
Combined version of the i-th member and group catalogues.
It contains both galaxy and group information.
memb_pd : `pandas.DataFrame`
Catalogue of the member galaxies of the i-th catalogue.
This catalogue contains information of the `member galaxies`.
group_pd : `pandas.DataFrame`
Catalogue of the groups of the i-th catalogue.
This catalogue contains information of the `galaxy groups`.
Raises
------------
LSSUtils_Error : Exception from `LSSUtils_Error`
Program exception if input parameters are accepted.
"""
file_msg = fd.Program_Msg(__file__)
## Checking input parameters
catl_pd_ii_valid = (float, int, np.int64, np.int32, np.float32, np.float64)
catl_kind_valid = ['data', 'mocks']
catl_type_valid = ['mr', 'mstar']
sample_s_valid = ['19', '20', '21']
halotype_valid = ['fof', 'so']
clf_method_valid = [1, 2, 3]
hod_n_valid = np.arange(0, 20)
# `catl_pd_ii`
if (isinstance(catl_pd_ii, catl_pd_ii_valid)):
catl_pd_ii = int(catl_pd_ii)
else:
msg = '{0} `catl_pd_ii` ({1}) is not a valid input!'.format(file_msg,
type(catl_pd_ii))
raise LSSUtils_Error(msg)
# `catl_kind`
if not (catl_kind in catl_kind_valid):
msg = '{0} `catl_kind` ({1}) is not a valid input!'.format(file_msg,
catl_kind)
raise LSSUtils_Error(msg)
# `catl_type`
if not (catl_type in catl_type_valid):
msg = '{0} `catl_type` ({1}) is not a valid input!'.format(file_msg,
catl_type)
raise LSSUtils_Error(msg)
# `sample_s`
if not (sample_s in sample_s_valid):
msg = '{0} `sample_s` ({1}) is not a valid input!'.format(file_msg,
sample_s)
raise LSSUtils_Error(msg)
# `halotype`
if not (halotype in halotype_valid):
msg = '{0} `halotype` ({1}) is not a valid input!'.format(file_msg,
halotype)
raise LSSUtils_Error(msg)
# `clf_method`
if not (clf_method in clf_method_valid):
msg = '{0} `clf_method` ({1}) is not a valid input!'.format(file_msg,
clf_method)
raise LSSUtils_Error(msg)
# `dv`
if not (dv > 0):
msg = '{0} `dv` ({1}) must be larger than 0!'.format(file_msg, dv)
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Type
if not (isinstance(sigma_clf_c, float)):
msg = '{0} `sigma_clf_c` ({1}) is not a valid input type!'
msg = msg.format(file_msg, type(sigma_clf_c))
raise LSSUtils_Error(msg)
# `sigma_clf_c` - Value
if not (sigma_clf_c >= 0.):
msg = '{0} `sigma_clf_c` ({1}) must be larger than 0!'
msg = msg.format(file_msg, sigma_clf_c)
raise LSSUtils_Error(msg)
# `hod_n`
if not (hod_n in hod_n_valid):
msg = '{0} `hod_n` ({1}) is not a valid input!'.format(file_msg,
hod_n)
raise LSSUtils_Error(msg)
# `perf_opt`
if not (isinstance(perf_opt, bool)):
msg = '{0} `perf_opt` ({1}) is not a valid type!'.format(file_msg,
type(perf_opt))
raise LSSUtils_Error(msg)
# `return_memb_group`
if not (isinstance(return_memb_group, bool)):
msg = '{0} `return_memb_group` ({1}) is not a valid type!'.format(file_msg,
type(return_memb_group))
raise LSSUtils_Error(msg)
# `print_filedir`
if not (isinstance(print_filedir, bool)):
msg = '{0} `print_filedir` ({1}) is not a valid type!'.format(file_msg,
type(print_filedir))
raise LSSUtils_Error(msg)
#
# Extracting catalogues given input parameters
( memb_arr,
memb_len) = extract_catls( catl_kind=catl_kind,
catl_type=catl_type,
sample_s=sample_s,
halotype=halotype,
clf_method=clf_method,
hod_n=hod_n,
clf_seed=clf_seed,
dv=dv,
sigma_clf_c=sigma_clf_c,
perf_opt=perf_opt,
catl_info='members',
return_len=True,
print_filedir=print_filedir)
# Checking number of catalogues
if catl_pd_ii > (memb_len - 1):
msg = '{0} `catl_pd_ii` ({1}) is OUT of range ({2})!'.format(
file_msg, catl_pd_ii, memb_len)
raise LSSUtils_Error(msg)
#
# Extracting group catalogue
# i-th Galaxy catalogue
memb_path = memb_arr[catl_pd_ii]
# i-th Galaxy Group catalogue
group_path = catl_sdss_dir( catl_kind=catl_kind,
catl_type=catl_type,
sample_s=sample_s,
halotype=halotype,
clf_method=clf_method,
dv=dv,
sigma_clf_c=sigma_clf_c,
hod_n=hod_n,
clf_seed=clf_seed,
perf_opt=perf_opt,
catl_info='groups',
print_filedir=print_filedir)
#
# Paths to catalogue
# Mocks
if catl_kind == 'mocks':
group_path = os.path.join(group_path,
os.path.basename(memb_path).replace('memb', 'group'))
# Data
if catl_kind == 'data':
group_path = os.path.join(group_path,
os.path.basename(memb_path).replace('Gals', 'Group'))
# Checking that file exists
fd.File_Exists(group_path)
##
## Reading in Catalogues
memb_pd = fr.read_hdf5_file_to_pandas_DF(memb_path )
group_pd = fr.read_hdf5_file_to_pandas_DF(group_path)
## Keys for the catalogues
( gm_key,
id_key,
galtype_key) = catl_keys( catl_kind,
perf_opt=perf_opt,
return_type='list')
## Matching keys from Group catalogue
if len(np.unique(memb_pd[id_key])) == len(np.unique(group_pd[id_key])):
# Group column names
group_colnames = np.sort(group_pd.columns.values)
## Sorting `memb_pd` by `id_key`
# Member catalogue
memb_pd.sort_values(by=id_key, inplace=True)
memb_pd.reset_index(inplace=True, drop=True)
# Group catalogue
group_pd.sort_values(by=id_key, inplace=True)
group_pd.reset_index(inplace=True, drop=True)
## Renaming columns
g_colnames_dict = {ii: 'GG_' + ii for ii in group_colnames}
group_pd.rename(columns=g_colnames_dict, inplace=True)
group_pd.rename(columns={'GG_' + id_key: id_key}, inplace=True)
##
## Merging the 2 DataFrames
memb_group_pd = pd.merge( left=memb_pd ,
right=group_pd ,
how='left' ,
left_on=id_key ,
right_on=id_key)
else:
msg = '{0} Lengths of the 2 DataFrames (`memb_pd`, `group_pd`) '
msg += 'do not match!'
msg = msg.format(file_msg)
raise LSSUtils_Error(msg)
##
## Returning DataFrames
if return_memb_group:
return_obj = (memb_group_pd, memb_pd, group_pd)
else:
return_obj = memb_group_pd
return return_obj