# !usr/bin/env python
# -*- coding: utf-8 -*-
#
# Licensed under a 3-clause BSD license.
#
# @Author: Brian Cherinka
# @Date: 2018-10-11 17:51:43
# @Last modified by: Brian Cherinka
# @Last Modified time: 2018-11-29 17:23:15
from __future__ import print_function, division, absolute_import
from astropy.io import fits
import marvin.tools
from marvin import log
from .base import VACMixIn
[docs]class GEMAVAC(VACMixIn):
"""Provides access to the MaNGA-GEMA VAC.
VAC name: GEMA
URL: https://www.sdss.org/dr17/data_access/value-added-catalogs/?vac_id=gema-vac:-galaxy-environment-for-manga-value-added-catalog
Description: The GEMA VAC contains many different quantifications of the local and the large-scale environments for MaNGA galaxies. Please visit the DATAMODEL at https://data.sdss.org/datamodel/files/MANGA_GEMA/GEMA_VER to see the description of each table composing the catalogue.
Authors: Maria Argudo-Fernandez, Daniel Goddard, Daniel Thomas, Zheng Zheng, Lihwai Lin, Ting Xiao, Fangting Yuan, Jianhui Lian, et al
"""
# Required parameters
name = 'gema'
description = 'Returns GEMA table data'
version = {'MPL-7': '1.0.1', 'DR15': '1.0.1', 'DR16': '1.0.1', 'DR17': '2.0.2',
'MPL-11': '2.0.2'}
display_name = 'GEMA'
url = 'https://www.sdss.org/dr17/data_access/value-added-catalogs/?vac_id=gema-vac:-galaxy-environment-for-manga-value-added-catalog'
# optional Marvin Tools to attach your vac to
include = (marvin.tools.cube.Cube, marvin.tools.maps.Maps, marvin.tools.modelcube.ModelCube)
# Required method
[docs] def set_summary_file(self, release):
''' Sets the path to the GEMA summary file '''
# define the variables to build a unique path to your VAC file
self.path_params = {"ver": self.version[release]}
# get_path returns False if the files do not exist locally
self.summary_file = self.get_path('mangagema', path_params=self.path_params)
# Required method
[docs] def get_target(self, parent_object):
''' Accesses VAC data for a specific target from a Marvin Tool object '''
# Create a dictionary for GEMA VAC data
gemadata = {
"completeness": "",
"LSS_1_all": "",
"LSS_1_002": "",
"LSS_1_006": "",
"LSS_1_010": "",
"LSS_1_015": "",
"LSS_5_all": "",
"LSS_5_002": "",
"LSS_5_006": "",
"LSS_5_010": "",
"LSS_5_015": "",
"pairs": "",
"groups": "",
"overdensity": "",
"LSS_tensor": "",
}
# get any parameters you need from the parent object
mangaid = parent_object.mangaid
# download the vac from the SAS if it does not already exist locally
if not self.file_exists(self.summary_file):
self.summary_file = self.download_vac('mangagema', path_params=self.path_params)
# opening tables in VAC file
gemafile = fits.open(self.summary_file)
# Return selected line(s) for a mangaid galaxy
# LSS parameters
indata = mangaid in gemafile[1].data['mangaid']
if not indata:
log.warning("No LSS data exists for {0}".format(mangaid))
else:
log.info("LSS data exists for {0}".format(mangaid))
log.warning(
"Warning: Do not use LSS parameters defined in volume with z < z_manga_galaxy"
)
indexLSS = gemafile[1].data['mangaid'] == mangaid
gemadata["completeness"] = gemafile[1].data[indexLSS]
gemadata["LSS_1_all"] = gemafile[2].data[indexLSS]
gemadata["LSS_1_002"] = gemafile[3].data[indexLSS]
gemadata["LSS_1_006"] = gemafile[4].data[indexLSS]
gemadata["LSS_1_010"] = gemafile[5].data[indexLSS]
gemadata["LSS_1_015"] = gemafile[6].data[indexLSS]
gemadata["LSS_5_all"] = gemafile[7].data[indexLSS]
gemadata["LSS_5_002"] = gemafile[8].data[indexLSS]
gemadata["LSS_5_006"] = gemafile[9].data[indexLSS]
gemadata["LSS_5_010"] = gemafile[10].data[indexLSS]
gemadata["LSS_5_015"] = gemafile[11].data[indexLSS]
# Local environment: close pair galaxies
indata = mangaid in gemafile[12].data['mangaid']
if not indata:
log.warning("No pair data exists for {0}".format(mangaid))
else:
log.info("Pair data exists for {0}".format(mangaid))
indexpair = gemafile[12].data['mangaid'] == mangaid
gemadata["pairs"] = gemafile[12].data[indexpair]
# Local environment: group galaxies
indata = mangaid in gemafile[13].data['mangaid']
if not indata:
log.warning("No group data exists for {0}".format(mangaid))
else:
log.info("Group data exists for {0}".format(mangaid))
indexgroup = gemafile[13].data['mangaid'] == mangaid
gemadata["groups"] = gemafile[13].data[indexgroup]
# LSS overdensity-corrected local density
indata = mangaid in gemafile[14].data['mangaid']
if not indata:
log.warning("No overdensity data exists for {0}".format(mangaid))
else:
log.info("Overdensity data exists for {0}".format(mangaid))
indexover = gemafile[14].data['mangaid'] == mangaid
gemadata["overdensity"] = gemafile[14].data[indexover]
# LSS structure
indata = mangaid in gemafile[15].data['mangaid']
if not indata:
log.warning("No structure data exists for {0}".format(mangaid))
else:
log.info("Structure data exists for {0}".format(mangaid))
indextensor = gemafile[15].data['mangaid'] == mangaid
gemadata["LSS_tensor"] = gemafile[15].data[indextensor]
gema_data = gemadata
# closing the FITS file
gemafile.close()
return gema_data