mirror of
https://github.com/Richard-Sti/csiborgtools_public.git
synced 2025-06-08 09:51:12 +00:00
SDSS galaxies and catalogs rework (#15)
* add SDSS skeleton * rm nb * update gitignore * add docs * add survey volume * add masking * add SDSS mask * add SDSS comments * add reference * rm * move planck to fits clusters * Rm comment * update masking routines * rm masks * Update docs * rm data * make MCXC fits surv * change MCXC name * Move to extrasapce * Update paths
This commit is contained in:
Richard Stiskalek
GitHub
parent
91beb4df50
commit
1bb4255874
10 changed files with 549 additions and 78062 deletions
|
|
@ -15,5 +15,6 @@
|
|||
|
||||
from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, get_positions) # noqa
|
||||
from .make_cat import (HaloCatalogue, CombinedHaloCatalogue) # noqa
|
||||
from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, TwoMPPGroups) # noqa
|
||||
from .readobs import (PlanckClusters, MCXCClusters, TwoMPPGalaxies, # noqa
|
||||
TwoMPPGroups, SDSS) # noqa
|
||||
from .outsim import (dump_split, combine_splits, make_ascii_powmes) # noqa
|
||||
|
|
|
|||
|
|
@ -17,18 +17,26 @@ Scripts to read in observation.
|
|||
"""
|
||||
|
||||
import numpy
|
||||
from abc import ABC, abstractproperty
|
||||
from os.path import join
|
||||
from astropy.io import fits
|
||||
from astropy.coordinates import SkyCoord
|
||||
from astropy.cosmology import FlatLambdaCDM
|
||||
from astropy import units as u
|
||||
from ..utils import (add_columns, cols_to_structured)
|
||||
from astropy import units
|
||||
from scipy import constants
|
||||
from warnings import warn
|
||||
from ..utils import (cols_to_structured)
|
||||
|
||||
F64 = numpy.float64
|
||||
|
||||
|
||||
class BaseSurvey:
|
||||
###############################################################################
|
||||
# Text survey base class #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class TextSurvey:
|
||||
"""
|
||||
Base survey class with some methods that are common to all survey classes.
|
||||
Base survey class for extracting data from text files.
|
||||
"""
|
||||
_data = None
|
||||
_cosmo = None
|
||||
|
|
@ -63,21 +71,341 @@ class BaseSurvey:
|
|||
return self._data[key]
|
||||
|
||||
|
||||
class PlanckClusters(BaseSurvey):
|
||||
r"""
|
||||
Planck 2nd Sunyaev-Zeldovich source catalogue [1]. Automatically removes
|
||||
clusters without a redshift estimate.
|
||||
###############################################################################
|
||||
# 2M++ galaxies #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class TwoMPPGalaxies(TextSurvey):
|
||||
"""
|
||||
The 2M++ galaxy redshift catalogue [1], with the catalogue at [2].
|
||||
Removes fake galaxies used to fill the zone of avoidance. Note that the
|
||||
stated redshift is in the CMB frame.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str
|
||||
Path to the source catalogue.
|
||||
cosmo : `astropy.cosmology` object, optional
|
||||
Cosmology to convert masses (particularly :math:`H_0`). By default
|
||||
`FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)`.
|
||||
max_redshift: float, optional
|
||||
Maximum cluster redshift. By default `None` and no selection is
|
||||
performed.
|
||||
fpath : str, optional.
|
||||
File path to the catalogue. By default
|
||||
`/mnt/extraspace/rstiskalek/catalogs/2M++_galaxy_catalog.dat`.
|
||||
|
||||
References
|
||||
----------
|
||||
[1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J.
|
||||
[2] https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/416/2840#/article
|
||||
[3] Improving NASA/IPAC Extragalactic Database Redshift Calculations
|
||||
(2021); Anthony Carr and Tamara Davis
|
||||
"""
|
||||
|
||||
def __init__(self, fpath=None):
|
||||
if fpath is None:
|
||||
fpath = join("/mnt/extraspace/rstiskalek/catalogs/"
|
||||
"2M++_galaxy_catalog.dat")
|
||||
self._set_data(fpath)
|
||||
|
||||
def _set_data(self, fpath):
|
||||
"""
|
||||
Set the catalogue
|
||||
"""
|
||||
from scipy.constants import c
|
||||
# Read the catalogue and select non-fake galaxies
|
||||
cat = numpy.genfromtxt(fpath, delimiter="|", )
|
||||
cat = cat[cat[:, 12] == 0, :]
|
||||
# Pre=allocate array and fillt it
|
||||
cols = [("RA", F64), ("DEC", F64), ("Ksmag", F64), ("ZCMB", F64),
|
||||
("DIST", F64)]
|
||||
data = cols_to_structured(cat.shape[0], cols)
|
||||
data["RA"] = cat[:, 1]
|
||||
data["DEC"] = cat[:, 2]
|
||||
data["Ksmag"] = cat[:, 5]
|
||||
data["ZCMB"] = cat[:, 7] / (c * 1e-3)
|
||||
self._data = data
|
||||
|
||||
|
||||
###############################################################################
|
||||
# 2M++ groups #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class TwoMPPGroups(TextSurvey):
|
||||
"""
|
||||
The 2M++ galaxy group catalogue [1], with the catalogue at [2].
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str, optional
|
||||
File path to the catalogue. By default
|
||||
`/mnt/extraspace/rstiskalek/catalogs/2M++_group_catalog.dat`
|
||||
|
||||
References
|
||||
----------
|
||||
[1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J.
|
||||
[2] https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/416/2840#/article
|
||||
[3] Improving NASA/IPAC Extragalactic Database Redshift Calculations
|
||||
(2021); Anthony Carr and Tamara Davis
|
||||
"""
|
||||
|
||||
def __init__(self, fpath):
|
||||
if fpath is None:
|
||||
fpath = join("/mnt/extraspace/rstiskalek/catalogs",
|
||||
"2M++_group_catalog.dat")
|
||||
self._set_data(fpath)
|
||||
|
||||
def _set_data(self, fpath):
|
||||
"""
|
||||
Set the catalogue
|
||||
"""
|
||||
cat = numpy.genfromtxt(fpath, delimiter="|", )
|
||||
# Pre-allocate and fill the array
|
||||
cols = [("RA", F64), ("DEC", F64), ("K2mag", F64),
|
||||
("Rich", numpy.int64), ("sigma", F64)]
|
||||
data = cols_to_structured(cat.shape[0], cols)
|
||||
data["K2mag"] = cat[:, 3]
|
||||
data["Rich"] = cat[:, 4]
|
||||
data["sigma"] = cat[:, 7]
|
||||
|
||||
# Convert galactic coordinates to RA, dec
|
||||
glon = data[:, 1]
|
||||
glat = data[:, 2]
|
||||
coords = SkyCoord(l=glon*units.degree, b=glat*units.degree,
|
||||
frame='galactic')
|
||||
coords = coords.transform_to("icrs")
|
||||
data["RA"] = coords.ra
|
||||
data["DEC"] = coords.dec
|
||||
self._data = data
|
||||
|
||||
|
||||
###############################################################################
|
||||
# FITS base class #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class FitsSurvey(ABC):
|
||||
"""
|
||||
Base class for extracting data from FITS files. Contains two sets of
|
||||
keys: `routine_keys` and `fits_keys`. The former are user-defined
|
||||
properties calculated from the FITS file data. Both are accesible via
|
||||
`self[key]`.
|
||||
"""
|
||||
_file = None
|
||||
_h = None
|
||||
_routines = None
|
||||
_selection_mask = None
|
||||
|
||||
@property
|
||||
def file(self):
|
||||
"""
|
||||
The survey FITS file.
|
||||
|
||||
Returns
|
||||
-------
|
||||
file : py:class:`astropy.io.fits.hdu.hdulist.HDUList`
|
||||
"""
|
||||
if self._file is None:
|
||||
raise ValueError("`file` is not set!")
|
||||
return self._file
|
||||
|
||||
@property
|
||||
def h(self):
|
||||
"""
|
||||
Little h.
|
||||
|
||||
Returns
|
||||
-------
|
||||
h : float
|
||||
"""
|
||||
return self._h
|
||||
|
||||
@h.setter
|
||||
def h(self, h):
|
||||
"""Sets the little h."""
|
||||
self._h = h
|
||||
|
||||
@staticmethod
|
||||
def _check_in_list(member, members, kind):
|
||||
"""
|
||||
Checks that `member` is a member of a list `members`, `kind` is a
|
||||
member type name.
|
||||
"""
|
||||
if member not in members:
|
||||
raise ValueError("Unknown {} `{}`, must be one of `{}`."
|
||||
.format(kind, member, members))
|
||||
|
||||
@property
|
||||
def routines(self):
|
||||
"""
|
||||
Processing routines.
|
||||
|
||||
Returns
|
||||
-------
|
||||
routines : dict
|
||||
Dictionary of routines. Keys are functions and values are their
|
||||
arguments.
|
||||
"""
|
||||
return self._routines
|
||||
|
||||
@abstractproperty
|
||||
def size(self):
|
||||
"""
|
||||
Number of samples in the catalogue.
|
||||
|
||||
Returns
|
||||
-------
|
||||
size : int
|
||||
"""
|
||||
pass
|
||||
|
||||
@property
|
||||
def masked_size(self):
|
||||
if self.selection_mask is None:
|
||||
return self.size
|
||||
return numpy.sum(self.selection_mask)
|
||||
|
||||
@property
|
||||
def selection_mask(self):
|
||||
"""
|
||||
Selection mask, generated with `fmask` when initialised.
|
||||
|
||||
Returns
|
||||
-------
|
||||
mask : 1-dimensional boolean array
|
||||
"""
|
||||
return self._selection_mask
|
||||
|
||||
@selection_mask.setter
|
||||
def selection_mask(self, mask):
|
||||
"""Sets the selection mask."""
|
||||
if not (isinstance(mask, numpy.ndarray)
|
||||
and mask.ndim == 1
|
||||
and mask.dtype == bool):
|
||||
raise TypeError("`selection_mask` must be a 1-dimensional boolean "
|
||||
"array. Check output of `fmask`.")
|
||||
self._selection_mask = mask
|
||||
|
||||
@property
|
||||
def fits_keys(self):
|
||||
"""
|
||||
Keys of the FITS file `self.file`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
keys : list of str
|
||||
"""
|
||||
return self.file[1].data.columns.names
|
||||
|
||||
@property
|
||||
def routine_keys(self):
|
||||
"""
|
||||
Routine keys.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
keys : list of str
|
||||
"""
|
||||
return list(self.routines.keys())
|
||||
|
||||
def get_fitsitem(self, key):
|
||||
"""
|
||||
Get a column `key` from the FITS file `self.file`.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
key : str
|
||||
|
||||
Returns
|
||||
-------
|
||||
col : 1-dimensional array
|
||||
"""
|
||||
return self.file[1].data[key]
|
||||
|
||||
@property
|
||||
def keys(self):
|
||||
"""
|
||||
Routine and FITS keys.
|
||||
|
||||
Returns
|
||||
-------
|
||||
keys : list of str
|
||||
"""
|
||||
return self.routine_keys + self.fits_keys
|
||||
|
||||
def make_mask(self, steps):
|
||||
"""
|
||||
Make a survey mask from a series of steps. Expected to look e.g. like
|
||||
|
||||
```
|
||||
def steps(cls):
|
||||
return [(lambda x: cls[x], ("IN_DR7_LSS",)),
|
||||
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
|
||||
]
|
||||
```
|
||||
|
||||
Parameters
|
||||
----------
|
||||
steps : list of steps
|
||||
|
||||
Returns
|
||||
-------
|
||||
mask : 1-dimensional boolean array
|
||||
"""
|
||||
out = None
|
||||
steps = steps(self)
|
||||
for i, step in enumerate(steps):
|
||||
func, args = step
|
||||
if i == 0:
|
||||
out = func(*args)
|
||||
else:
|
||||
out = out & func(*args)
|
||||
return out
|
||||
|
||||
def __getitem__(self, key):
|
||||
"""
|
||||
Return values for this `key`. If in both return from `routine_keys`.
|
||||
"""
|
||||
# Check duplicates
|
||||
if key in self.routine_keys and key in self.fits_keys:
|
||||
warn("Key `{}` found in both `routine_keys` and `fits_keys`. "
|
||||
"Returning `routine_keys` value.".format(key), UserWarning)
|
||||
|
||||
if key in self.routine_keys:
|
||||
func, args = self.routines[key]
|
||||
out = func(*args)
|
||||
elif key in self.fits_keys:
|
||||
warn("Returning a FITS property. Be careful about little h!",
|
||||
UserWarning)
|
||||
out = self.get_fitsitem(key)
|
||||
else:
|
||||
raise KeyError("Unrecognised key `{}`.".format(key))
|
||||
|
||||
if self.selection_mask is None:
|
||||
return out
|
||||
return out[self.selection_mask]
|
||||
|
||||
|
||||
###############################################################################
|
||||
# Planck clusters #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class PlanckClusters(FitsSurvey):
|
||||
r"""
|
||||
Planck 2nd Sunyaev-Zeldovich source catalogue [1].
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str, optional
|
||||
Path to the FITS file. By default
|
||||
`/mnt/extraspace/rstiskalek/catalogs/HFI_PCCS_SZ-union_R2.08.fits`.
|
||||
h : float, optional
|
||||
Little h. By default `h = 0.7`. The catalogue assumes this value.
|
||||
The routine properties should take care of little h conversion.
|
||||
sel_steps : py:function:
|
||||
Steps to mask the survey. Expected to look for example like
|
||||
```
|
||||
def steps(cls):
|
||||
return [(lambda x: cls[x], ("IN_DR7_LSS",)),
|
||||
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
|
||||
]
|
||||
```
|
||||
|
||||
References
|
||||
----------
|
||||
|
|
@ -85,32 +413,29 @@ class PlanckClusters(BaseSurvey):
|
|||
"""
|
||||
_hdata = 0.7 # little h value of the data
|
||||
|
||||
def __init__(self, fpath, cosmo=None, max_redshift=None):
|
||||
if cosmo is None:
|
||||
self._cosmo = FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)
|
||||
else:
|
||||
self._cosmo = cosmo
|
||||
self.set_data(fpath, max_redshift)
|
||||
def __init__(self, fpath=None, h=0.7, sel_steps=None):
|
||||
if fpath is None:
|
||||
fpath = join("/mnt/extraspace/rstiskalek/catalogs/",
|
||||
"HFI_PCCS_SZ-union_R2.08.fits")
|
||||
self._file = fits.open(fpath, memmap=False)
|
||||
self.h = h
|
||||
|
||||
def set_data(self, fpath, max_redshift=None):
|
||||
"""
|
||||
Set the catalogue, loads it and applies a maximum redshift cut.
|
||||
"""
|
||||
cat = fits.open(fpath)[1].data
|
||||
# Convert FITS to a structured array
|
||||
data = numpy.full(cat.size, numpy.nan, dtype=cat.dtype.descr)
|
||||
for name in cat.dtype.names:
|
||||
data[name] = cat[name]
|
||||
# Take only clusters with redshifts
|
||||
data = data[data["REDSHIFT"] >= 0]
|
||||
# Convert masses
|
||||
for par in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
|
||||
data[par] *= 1e14
|
||||
data[par] *= (self._hdata / self.cosmo.h)**2
|
||||
# Redshift cut
|
||||
if max_redshift is not None:
|
||||
data = data["REDSHIFT" <= max_redshift]
|
||||
self._data = data
|
||||
self._routines = {}
|
||||
# Set MSZ routines
|
||||
for key in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
|
||||
self._routines.update({key: (self._mass, (key,))})
|
||||
|
||||
# Add masking. Do this at the end!
|
||||
if sel_steps is not None:
|
||||
self.selection_mask = self.make_mask(sel_steps)
|
||||
|
||||
@property
|
||||
def size(self):
|
||||
return self.get_fitsitem("MSZ").size
|
||||
|
||||
def _mass(self, key):
|
||||
"""Get mass. Puts in units of 1e14 and converts little h."""
|
||||
return self.get_fitsitem(key) * 1e14 * (self._hdata / self.h)**2
|
||||
|
||||
def match_to_mcxc(self, mcxc):
|
||||
"""
|
||||
|
|
@ -135,7 +460,7 @@ class PlanckClusters(BaseSurvey):
|
|||
|
||||
# Planck MCXC need to be decoded to str
|
||||
planck_names = [name.decode() for name in self["MCXC"]]
|
||||
mcxc_names = [name for name in mcxc["name"]]
|
||||
mcxc_names = [name for name in mcxc["MCXC"]]
|
||||
|
||||
indxs = [numpy.nan] * len(planck_names)
|
||||
for i, name in enumerate(planck_names):
|
||||
|
|
@ -149,7 +474,12 @@ class PlanckClusters(BaseSurvey):
|
|||
return indxs
|
||||
|
||||
|
||||
class MCXCClusters(BaseSurvey):
|
||||
###############################################################################
|
||||
# MCXC Clusters #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class MCXCClusters(FitsSurvey):
|
||||
r"""
|
||||
MCXC Meta-Catalog of X-Ray Detected Clusters of Galaxies catalogue [1],
|
||||
with data description at [2] and download at [3].
|
||||
|
|
@ -161,15 +491,20 @@ class MCXCClusters(BaseSurvey):
|
|||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str
|
||||
fpath : str, optional
|
||||
Path to the source catalogue obtained from [3]. Expected to be the fits
|
||||
file.
|
||||
cosmo : `astropy.cosmology` object, optional
|
||||
The cosmology to to convert cluster masses (to first order). By default
|
||||
`FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)`.
|
||||
max_redshift: float, optional
|
||||
Maximum cluster redshift. By default `None` and no selection is
|
||||
performed.
|
||||
file. By default `/mnt/extraspace/rstiskalek/catalogs/mcxc.fits`.
|
||||
h : float, optional
|
||||
Little h. By default `h = 0.7`. The catalogue assumes this value.
|
||||
The routine properties should take care of little h conversion.
|
||||
sel_steps : py:function:
|
||||
Steps to mask the survey. Expected to look for example like
|
||||
```
|
||||
steps = [(lambda x: cls[x], ("IN_DR7_LSS",)),
|
||||
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
|
||||
]
|
||||
```.
|
||||
|
||||
|
||||
References
|
||||
----------
|
||||
|
|
@ -181,120 +516,184 @@ class MCXCClusters(BaseSurvey):
|
|||
"""
|
||||
_hdata = 0.7 # Little h of the catalogue
|
||||
|
||||
def __init__(self, fpath, cosmo=None, max_redshift=None):
|
||||
if cosmo is None:
|
||||
self._cosmo = FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)
|
||||
else:
|
||||
self._cosmo = cosmo
|
||||
self._set_data(fpath, max_redshift)
|
||||
def __init__(self, fpath=None, h=0.7, sel_steps=None):
|
||||
if fpath is None:
|
||||
fpath = "/mnt/extraspace/rstiskalek/catalogs/mcxc.fits"
|
||||
self._file = fits.open(fpath, memmap=False)
|
||||
self.h = h
|
||||
# Set mass and luminosity routines
|
||||
self._routines = {}
|
||||
self._routines.update({"M500": (self._mass, ("M500",))})
|
||||
self._routines.update({"L500": (self._lum, ("L500",))})
|
||||
|
||||
def _set_data(self, fpath, max_redshift):
|
||||
"""
|
||||
Set the catalogue, loads it and applies a maximum redshift cut.
|
||||
"""
|
||||
cat = fits.open(fpath)[1].data
|
||||
# Pre-allocate array and extract selected variables
|
||||
cols = [("RAdeg", F64), ("DEdeg", F64), ("z", F64),
|
||||
("L500", F64), ("M500", F64), ("R500", F64)]
|
||||
data = cols_to_structured(cat.size, cols)
|
||||
for col in cols:
|
||||
par = col[0]
|
||||
data[par] = cat[par]
|
||||
# Add the cluster names
|
||||
data = add_columns(data, cat["MCXC"], "name")
|
||||
if sel_steps is not None:
|
||||
self.selection_mask = self.make_mask(sel_steps)
|
||||
|
||||
# Get little h units to match the cosmology
|
||||
data["L500"] *= (self._hdata / self.cosmo.h)**2
|
||||
data["M500"] *= (self._hdata / self.cosmo.h)**2
|
||||
# Get the 10s back in
|
||||
data["L500"] *= 1e44 # ergs/s
|
||||
data["M500"] *= 1e14 # Msun
|
||||
@property
|
||||
def size(self):
|
||||
return self.get_fitsitem("M500").size
|
||||
|
||||
if max_redshift is not None:
|
||||
data = data["z" <= max_redshift]
|
||||
def _mass(self, key):
|
||||
"""Get mass. Put in units of 1e14 Msun back and convert little h."""
|
||||
return self.get_fitsitem(key) * 1e14 * (self._hdata / self.h)**2
|
||||
|
||||
self._data = data
|
||||
def _lum(self, key):
|
||||
"""Get luminosity. Puts back units to be in ergs/s"""
|
||||
return self.get_fitsitem(key) * 1e44 * (self._hdata / self.h)**2
|
||||
|
||||
###############################################################################
|
||||
# SDSS galaxies #
|
||||
###############################################################################
|
||||
|
||||
|
||||
class TwoMPPGalaxies(BaseSurvey):
|
||||
class SDSS(FitsSurvey):
|
||||
"""
|
||||
The 2M++ galaxy redshift catalogue [1], with the catalogue at [2].
|
||||
Removes fake galaxies used to fill the zone of avoidance. Note that the
|
||||
stated redshift is in the CMB frame.
|
||||
SDSS data manipulations. Data obtained from [1]. Carries routines for
|
||||
ABSMAG, APPMAG, COL, DIST, MTOL.
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str
|
||||
File path to the catalogue.
|
||||
fpath : str, optional
|
||||
Path to the FITS file. By default
|
||||
`/mnt/extraspace/rstiskalek/catalogs/nsa_v1_0_1.fits`.
|
||||
h : float, optional
|
||||
Little h. By default `h = 1`. The catalogue assumes this value.
|
||||
The routine properties should take care of little h conversion.
|
||||
sel_steps : py:function:
|
||||
Steps to mask the survey. Expected to look for example like
|
||||
```
|
||||
steps = [(lambda x: cls[x], ("IN_DR7_LSS",)),
|
||||
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
|
||||
]
|
||||
```.
|
||||
|
||||
References
|
||||
----------
|
||||
[1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J.
|
||||
[2] https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/416/2840#/article
|
||||
[3] Improving NASA/IPAC Extragalactic Database Redshift Calculations
|
||||
(2021); Anthony Carr and Tamara Davis
|
||||
[1] https://www.sdss.org/dr13/manga/manga-target-selection/nsa/
|
||||
"""
|
||||
|
||||
def __init__(self, fpath):
|
||||
self._set_data(fpath)
|
||||
def __init__(self, fpath=None, h=1, sel_steps=None):
|
||||
if fpath is None:
|
||||
fpath = "/mnt/extraspace/rstiskalek/catalogs/nsa_v1_0_1.fits"
|
||||
self._file = fits.open(fpath, memmap=False)
|
||||
self.h = h
|
||||
|
||||
def _set_data(self, fpath):
|
||||
# Survey bands and photometries
|
||||
self._bands = ['F', 'N', 'u', 'g', 'r', 'i', 'z']
|
||||
self._photos = ["SERSIC", "ELPETRO"]
|
||||
|
||||
self._routines = {}
|
||||
# Set ABSMAGroutines
|
||||
for photo in self._photos:
|
||||
for band in self._bands:
|
||||
# ABSMAG
|
||||
key = "{}_ABSMAG_{}".format(photo, band)
|
||||
val = (self._absmag, (photo, band))
|
||||
self.routines.update({key: val})
|
||||
# Set APPMAG routines
|
||||
for photo in self._photos:
|
||||
for band in self._bands:
|
||||
key = "{}_APPMAG_{}".format(photo, band)
|
||||
val = (self._appmag, (photo, band))
|
||||
self.routines.update({key: val})
|
||||
# Set COL routines
|
||||
for photo in self._photos:
|
||||
for band1 in self._bands:
|
||||
for band2 in self._bands:
|
||||
key = "{}_COL_{}{}".format(photo, band1, band2)
|
||||
val = (self._colour, (photo, band1, band2))
|
||||
self.routines.update({key: val})
|
||||
# Set DIST routine
|
||||
self.routines.update({"DIST": (self._dist, ())})
|
||||
# Set MASS routines
|
||||
for photo in self._photos:
|
||||
key = "{}_MASS".format(photo)
|
||||
val = (self._solmass, (photo,))
|
||||
self.routines.update({key: val})
|
||||
# Set MTOL
|
||||
for photo in self._photos:
|
||||
for band in self._bands:
|
||||
key = "{}_MTOL_{}".format(photo, band)
|
||||
val = (self._mtol, (photo, band))
|
||||
self.routines.update({key: val})
|
||||
# Set IN_DR7_LSS
|
||||
self.routines.update({"IN_DR7_LSS": (self._in_dr7_lss, ())})
|
||||
|
||||
# Add masking. Do this at the end!
|
||||
if sel_steps is not None:
|
||||
self.selection_mask = self.make_mask(sel_steps)
|
||||
|
||||
@property
|
||||
def size(self):
|
||||
# Here pick some property that is in the catalogue..
|
||||
return self.get_fitsitem("ZDIST").size
|
||||
|
||||
def _absmag(self, photo, band):
|
||||
"""
|
||||
Set the catalogue
|
||||
Get absolute magnitude of a given photometry and band. Converts to
|
||||
the right little h.
|
||||
"""
|
||||
from scipy.constants import c
|
||||
# Read the catalogue and select non-fake galaxies
|
||||
cat = numpy.genfromtxt(fpath, delimiter="|", )
|
||||
cat = cat[cat[:, 12] == 0, :]
|
||||
# Pre=allocate array and fillt it
|
||||
cols = [("RA", F64), ("DEC", F64), ("Ksmag", F64), ("ZCMB", F64),
|
||||
("DIST", F64)]
|
||||
data = cols_to_structured(cat.shape[0], cols)
|
||||
data["RA"] = cat[:, 1]
|
||||
data["DEC"] = cat[:, 2]
|
||||
data["Ksmag"] = cat[:, 5]
|
||||
data["ZCMB"] = cat[:, 7] / (c * 1e-3)
|
||||
self._data = data
|
||||
self._check_in_list(photo, self._photos, "photometry")
|
||||
self._check_in_list(band, self._bands, "band")
|
||||
k = self._bands.index(band)
|
||||
mag = self.get_fitsitem("{}_ABSMAG".format(photo))[:, k]
|
||||
return mag + 5 * numpy.log10(self.h)
|
||||
|
||||
|
||||
class TwoMPPGroups(BaseSurvey):
|
||||
"""
|
||||
The 2M++ galaxy group catalogue [1], with the catalogue at [2].
|
||||
|
||||
Parameters
|
||||
----------
|
||||
fpath : str
|
||||
File path to the catalogue.
|
||||
|
||||
References
|
||||
----------
|
||||
[1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J.
|
||||
[2] https://cdsarc.cds.unistra.fr/viz-bin/cat/J/MNRAS/416/2840#/article
|
||||
[3] Improving NASA/IPAC Extragalactic Database Redshift Calculations
|
||||
(2021); Anthony Carr and Tamara Davis
|
||||
"""
|
||||
|
||||
def __init__(self, fpath):
|
||||
self._set_data(fpath)
|
||||
|
||||
def _set_data(self, fpath):
|
||||
def _kcorr(self, photo, band):
|
||||
"""
|
||||
Set the catalogue
|
||||
Get K-correction of a given photometry and band.
|
||||
"""
|
||||
cat = numpy.genfromtxt(fpath, delimiter="|", )
|
||||
# Pre-allocate and fill the array
|
||||
cols = [("RA", F64), ("DEC", F64), ("K2mag", F64),
|
||||
("Rich", numpy.int64), ("sigma", F64)]
|
||||
data = cols_to_structured(cat.shape[0], cols)
|
||||
data["K2mag"] = cat[:, 3]
|
||||
data["Rich"] = cat[:, 4]
|
||||
data["sigma"] = cat[:, 7]
|
||||
self._check_in_list(photo, self._photos, "photometry")
|
||||
self._check_in_list(band, self._bands, "band")
|
||||
k = self._bands.index(band)
|
||||
return self.get_fitsitem("{}_KCORRECT".format(photo))[:, k]
|
||||
|
||||
# Convert galactic coordinates to RA, dec
|
||||
glon = data[:, 1]
|
||||
glat = data[:, 2]
|
||||
coords = SkyCoord(l=glon*u.degree, b=glat*u.degree, frame='galactic')
|
||||
coords = coords.transform_to("icrs")
|
||||
data["RA"] = coords.ra
|
||||
data["DEC"] = coords.dec
|
||||
self._data = data
|
||||
def _appmag(self, photo, band):
|
||||
"""
|
||||
Get apparent magnitude of a given photometry and band.
|
||||
"""
|
||||
lumdist = (1 + self.get_fitsitem("ZDIST")) * self._dist()
|
||||
absmag = self._absmag(photo, band)
|
||||
kcorr = self._kcorr(photo, band)
|
||||
return absmag + 25 + 5 * numpy.log10(lumdist) + kcorr
|
||||
|
||||
def _colour(self, photo, band1, band2):
|
||||
"""
|
||||
Get colour of a given photometry, i.e. `band1` - `band2` absolute
|
||||
magnitude.
|
||||
"""
|
||||
return self._absmag(photo, band1) - self._absmag(photo, band2)
|
||||
|
||||
def _dist(self):
|
||||
"""
|
||||
Get the corresponding distance estimate from `ZDIST`, which is defined
|
||||
as:
|
||||
"Distance estimate using pecular velocity model of Willick et al.
|
||||
(1997), expressed as a redshift equivalent; multiply by c/H0 for
|
||||
Mpc"
|
||||
|
||||
Converts little h.
|
||||
"""
|
||||
return self.get_fitsitem("ZDIST") * constants.c * 1e-3 / (100 * self.h)
|
||||
|
||||
def _solmass(self, photo):
|
||||
"""
|
||||
Get solar mass of a given photometry. Converts little h.
|
||||
"""
|
||||
self._check_in_list(photo, self._photos, "photometry")
|
||||
return self.get_fitsitem("{}_MASS".format(photo)) / self.h**2
|
||||
|
||||
def _mtol(self, photo, band):
|
||||
"""
|
||||
Get mass-to-light ratio of a given photometry. Converts little h.
|
||||
"""
|
||||
self._check_in_list(photo, self._photos, "photometry")
|
||||
self._check_in_list(band, self._bands, "band")
|
||||
k = self._bands.index(band)
|
||||
return self.get_fitsitem("{}_MTOL".format(photo))[:, k] / self.h**2
|
||||
|
||||
def _in_dr7_lss(self):
|
||||
"""
|
||||
Get `IN_DR7_LSS` and turn to a boolean array.
|
||||
"""
|
||||
return self.get_fitsitem("IN_DR7_LSS").astype(bool)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue