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SDSS galaxies and catalogs rework (#15)

Browse Source 
* 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
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Richard Stiskalek 2022-12-05 11:36:41 +00:00 committed by GitHub
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2
.gitignore vendored
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@ -15,5 +15,5 @@ build/*
.eggs/* .eggs/*
csiborgtools.egg-info/* csiborgtools.egg-info/*
scripts/playground_* scripts/playground_*
scripts/playground.ipynb scripts/*.ipynb
Pylians3/* Pylians3/*

3
csiborgtools/read/__init__.py
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@ -15,5 +15,6 @@
from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, get_positions) # noqa from .readsim import (CSiBORGPaths, ParticleReader, read_mmain, get_positions) # noqa
from .make_cat import (HaloCatalogue, CombinedHaloCatalogue) # 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 from .outsim import (dump_split, combine_splits, make_ascii_powmes) # noqa

693
csiborgtools/read/readobs.py
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@ -17,18 +17,26 @@ Scripts to read in observation.
""" """
import numpy import numpy
from abc import ABC, abstractproperty
from os.path import join
from astropy.io import fits from astropy.io import fits
from astropy.coordinates import SkyCoord from astropy.coordinates import SkyCoord
from astropy.cosmology import FlatLambdaCDM from astropy import units
from astropy import units as u from scipy import constants
from ..utils import (add_columns, cols_to_structured) from warnings import warn
from ..utils import (cols_to_structured)
F64 = numpy.float64 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 _data = None
_cosmo = None _cosmo = None
@ -63,21 +71,341 @@ class BaseSurvey:
return self._data[key] return self._data[key]
class PlanckClusters(BaseSurvey): ###############################################################################
r""" # 2M++ galaxies #
Planck 2nd Sunyaev-Zeldovich source catalogue [1]. Automatically removes ###############################################################################
clusters without a redshift estimate.
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 Parameters
---------- ----------
fpath : str fpath : str, optional.
Path to the source catalogue. File path to the catalogue. By default
cosmo : `astropy.cosmology` object, optional `/mnt/extraspace/rstiskalek/catalogs/2M++_galaxy_catalog.dat`.
Cosmology to convert masses (particularly :math:`H_0`). By default
`FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)`. References
max_redshift: float, optional ----------
Maximum cluster redshift. By default `None` and no selection is [1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J.
performed. [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 References
---------- ----------
@ -85,32 +413,29 @@ class PlanckClusters(BaseSurvey):
""" """
_hdata = 0.7 # little h value of the data _hdata = 0.7 # little h value of the data
def __init__(self, fpath, cosmo=None, max_redshift=None): def __init__(self, fpath=None, h=0.7, sel_steps=None):
if cosmo is None: if fpath is None:
self._cosmo = FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728) fpath = join("/mnt/extraspace/rstiskalek/catalogs/",
else: "HFI_PCCS_SZ-union_R2.08.fits")
self._cosmo = cosmo self._file = fits.open(fpath, memmap=False)
self.set_data(fpath, max_redshift) self.h = h
def set_data(self, fpath, max_redshift=None): self._routines = {}
""" # Set MSZ routines
Set the catalogue, loads it and applies a maximum redshift cut. for key in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
""" self._routines.update({key: (self._mass, (key,))})
cat = fits.open(fpath)[1].data
# Convert FITS to a structured array # Add masking. Do this at the end!
data = numpy.full(cat.size, numpy.nan, dtype=cat.dtype.descr) if sel_steps is not None:
for name in cat.dtype.names: self.selection_mask = self.make_mask(sel_steps)
data[name] = cat[name]
# Take only clusters with redshifts @property
data = data[data["REDSHIFT"] >= 0] def size(self):
# Convert masses return self.get_fitsitem("MSZ").size
for par in ("MSZ", "MSZ_ERR_UP", "MSZ_ERR_LOW"):
data[par] *= 1e14 def _mass(self, key):
data[par] *= (self._hdata / self.cosmo.h)**2 """Get mass. Puts in units of 1e14 and converts little h."""
# Redshift cut return self.get_fitsitem(key) * 1e14 * (self._hdata / self.h)**2
if max_redshift is not None:
data = data["REDSHIFT" <= max_redshift]
self._data = data
def match_to_mcxc(self, mcxc): def match_to_mcxc(self, mcxc):
""" """
@ -135,7 +460,7 @@ class PlanckClusters(BaseSurvey):
# Planck MCXC need to be decoded to str # Planck MCXC need to be decoded to str
planck_names = [name.decode() for name in self["MCXC"]] 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) indxs = [numpy.nan] * len(planck_names)
for i, name in enumerate(planck_names): for i, name in enumerate(planck_names):
@ -149,7 +474,12 @@ class PlanckClusters(BaseSurvey):
return indxs return indxs
class MCXCClusters(BaseSurvey): ###############################################################################
# MCXC Clusters #
###############################################################################
class MCXCClusters(FitsSurvey):
r""" r"""
MCXC Meta-Catalog of X-Ray Detected Clusters of Galaxies catalogue [1], MCXC Meta-Catalog of X-Ray Detected Clusters of Galaxies catalogue [1],
with data description at [2] and download at [3]. with data description at [2] and download at [3].
@ -161,15 +491,20 @@ class MCXCClusters(BaseSurvey):
Parameters Parameters
---------- ----------
fpath : str fpath : str, optional
Path to the source catalogue obtained from [3]. Expected to be the fits Path to the source catalogue obtained from [3]. Expected to be the fits
file. file. By default `/mnt/extraspace/rstiskalek/catalogs/mcxc.fits`.
cosmo : `astropy.cosmology` object, optional h : float, optional
The cosmology to to convert cluster masses (to first order). By default Little h. By default `h = 0.7`. The catalogue assumes this value.
`FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728)`. The routine properties should take care of little h conversion.
max_redshift: float, optional sel_steps : py:function:
Maximum cluster redshift. By default `None` and no selection is Steps to mask the survey. Expected to look for example like
performed. ```
steps = [(lambda x: cls[x], ("IN_DR7_LSS",)),
(lambda x: cls[x] < 17.6, ("ELPETRO_APPMAG_r", )),
]
```.
References References
---------- ----------
@ -181,120 +516,184 @@ class MCXCClusters(BaseSurvey):
""" """
_hdata = 0.7 # Little h of the catalogue _hdata = 0.7 # Little h of the catalogue
def __init__(self, fpath, cosmo=None, max_redshift=None): def __init__(self, fpath=None, h=0.7, sel_steps=None):
if cosmo is None: if fpath is None:
self._cosmo = FlatLambdaCDM(H0=70.5, Om0=0.307, Tcmb0=2.728) fpath = "/mnt/extraspace/rstiskalek/catalogs/mcxc.fits"
else: self._file = fits.open(fpath, memmap=False)
self._cosmo = cosmo self.h = h
self._set_data(fpath, max_redshift) # 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): if sel_steps is not None:
""" self.selection_mask = self.make_mask(sel_steps)
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")
# Get little h units to match the cosmology @property
data["L500"] *= (self._hdata / self.cosmo.h)**2 def size(self):
data["M500"] *= (self._hdata / self.cosmo.h)**2 return self.get_fitsitem("M500").size
# Get the 10s back in
data["L500"] *= 1e44 # ergs/s
data["M500"] *= 1e14 # Msun
if max_redshift is not None: def _mass(self, key):
data = data["z" <= max_redshift] """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]. SDSS data manipulations. Data obtained from [1]. Carries routines for
Removes fake galaxies used to fill the zone of avoidance. Note that the ABSMAG, APPMAG, COL, DIST, MTOL.
stated redshift is in the CMB frame.
Parameters Parameters
---------- ----------
fpath : str fpath : str, optional
File path to the catalogue. 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 References
---------- ----------
[1] The 2M++ galaxy redshift catalogue; Lavaux, Guilhem, Hudson, Michael J. [1] https://www.sdss.org/dr13/manga/manga-target-selection/nsa/
[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): def __init__(self, fpath=None, h=1, sel_steps=None):
self._set_data(fpath) 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 self._check_in_list(photo, self._photos, "photometry")
# Read the catalogue and select non-fake galaxies self._check_in_list(band, self._bands, "band")
cat = numpy.genfromtxt(fpath, delimiter="|", ) k = self._bands.index(band)
cat = cat[cat[:, 12] == 0, :] mag = self.get_fitsitem("{}_ABSMAG".format(photo))[:, k]
# Pre=allocate array and fillt it return mag + 5 * numpy.log10(self.h)
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
def _kcorr(self, photo, band):
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):
""" """
Set the catalogue Get K-correction of a given photometry and band.
""" """
cat = numpy.genfromtxt(fpath, delimiter="|", ) self._check_in_list(photo, self._photos, "photometry")
# Pre-allocate and fill the array self._check_in_list(band, self._bands, "band")
cols = [("RA", F64), ("DEC", F64), ("K2mag", F64), k = self._bands.index(band)
("Rich", numpy.int64), ("sigma", F64)] return self.get_fitsitem("{}_KCORRECT".format(photo))[:, k]
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 def _appmag(self, photo, band):
glon = data[:, 1] """
glat = data[:, 2] Get apparent magnitude of a given photometry and band.
coords = SkyCoord(l=glon*u.degree, b=glat*u.degree, frame='galactic') """
coords = coords.transform_to("icrs") lumdist = (1 + self.get_fitsitem("ZDIST")) * self._dist()
data["RA"] = coords.ra absmag = self._absmag(photo, band)
data["DEC"] = coords.dec kcorr = self._kcorr(photo, band)
self._data = data 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)

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J/MNRAS/416/2840 The 2M++ galaxy redshift catalogue (Lavaux+, 2011)
================================================================================
The 2M++ galaxy redshift catalogue.
Lavaux G., Hudson M.J.
<Mon. Not. R. Astron. Soc., 416, 2840-2856 (2011)>
=2011MNRAS.416.2840L
================================================================================
ADC_Keywords: Galaxy catalogs ; Infrared sources ; Redshifts
Keywords: methods: data analysis - methods: numerical - methods: observational -
catalogues - galaxies: luminosity function, mass function -
large-scale structure of Universe
Abstract:
Peculiar velocities arise from gravitational instability, and thus
are linked to the surrounding distribution of matter. In order to
understand the motion of the Local Group with respect to the cosmic
microwave background, a deep all-sky map of the galaxy distribution
is required. Here we present a new redshift compilation of 69160
galaxies, dubbed 2M++, to map large-scale structures of the local
Universe over nearly the whole sky, and reaching depths of K<=12.5,
or 200h^-1^Mpc. The target catalogue is based on the Two-Micron
All-Sky Survey Extended Source Catalog (2MASS-XSC). The primary
sources of redshifts are the 2MASS Redshift Survey, the 6dF galaxy
redshift survey and the Sloan Digital Sky Survey (Data Release 7).
We assess redshift completeness in each region and compute the weights
required to correct for redshift incompleteness and apparent magnitude
limits, and discuss corrections for incompleteness in the zone of
avoidance. We present the density field for this survey, and discuss
the importance of large-scale structures such as the Shapley
Concentration.
File Summary:
--------------------------------------------------------------------------------
FileName Lrecl Records Explanations
--------------------------------------------------------------------------------
ReadMe 80 . This file
catalog.dat 140 72973 *The 2M++ catalogue
group.dat 64 4002 The 2M++ group catalogue
--------------------------------------------------------------------------------
Note on catalog.dat: Number of real galaxies = 69160;
Number of fake galaxies in ZoA = 3813; Number of groups = 4002.
--------------------------------------------------------------------------------
See also:
VII/233 : The 2MASS Extended sources (IPAC/UMass, 2003-2006)
VII/249 : 6dF-DR2 Galaxy Survey (Jones+, 2005)
II/294 : The SDSS Photometric Catalog, Release 7 (Adelman-McCarthy+, 2009)
VII/260 : The SDSS-DR7 quasar catalog (Schneider+, 2010)
http://www.sdss.org : SDSS Home Page
Byte-by-byte Description of file: catalog.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
3- 18 A16 --- Name Name of the galaxy as given in the 2MASS-XSC
database (Cat. VII/233), or ZOA fake galaxy
20- 25 F6.2 deg RAdeg Right Ascension in decimal degrees (J2000)
27- 32 F6.2 deg DEdeg Declination in decimal degrees (J2000)
34- 39 F6.2 deg GLON [0/360] Galactic longitude
41- 46 F6.2 deg GLAT Galactic latitude
48- 52 F5.2 mag Ksmag Apparent magnitude in band K_S as defined in
Section 2.2.
55- 59 I5 km/s HV Heliocentric total apparent velocity
62- 66 I5 km/s Vcmb Total apparent velocity in CMB rest frame (G1)
68- 73 I6 km/s e_HV ?=0 Total apparent velocity error
(equal to zero if not measured)
77- 80 I4 --- GID ? Unique group identifier obtained from the
algorithm of Section 4.
84- 87 F4.2 --- c11.5 Redshift incompleteness at magnitude
K2M++<=11.5
91- 94 F4.2 --- c12.5 ? Redshift incompleteness at magnitude
KM2++<=12.5 (2)
99 I1 --- ZoA [0/1] Flag to indicate whether this is is a
fake galaxy to fill the zone of avoidance
following the algorithm of Section 3.
104 I1 --- Cln [0/1] Flag to indicate if the redshift has
been obtained by the cloning procedure
of Section 2.3.
109 I1 --- M0 [0/1] Flag to indicate whether this galaxy
lies in the exclusive region covered by the
2MRS target mask (2Mx6S region)
114 I1 --- M1 [0/1] Flag to indicate whether this galaxy
lies in the non-exclusion region covered by
the SDSS
119 I1 --- M2 [0/1] Flag to indicate whether this galaxy
lies in the non-exclusion region covered
by the 6dFGRS
122-140 A19 --- Ref Reference bibcode ("zoa" for fake galaxies
in the Zone of Avoidance)
--------------------------------------------------------------------------------
Note (2): It may be empty in that case the catalogue is limited to K2M++<=11.5
in the portion of the sky holding the galaxy.
--------------------------------------------------------------------------------
Byte-by-byte Description of file: group.dat
--------------------------------------------------------------------------------
Bytes Format Units Label Explanations
--------------------------------------------------------------------------------
7- 10 I4 --- GID Group identifier in the catalogue
12- 17 F6.2 deg GLON Galactic longitude
19- 24 F6.2 deg GLAT Galactic latitude
27- 31 F5.2 mag K2mag Apparent magnitude K2M++ (1)
40- 42 I3 --- Rich Richness uncorrected for incompleteness effect
45- 49 I5 km/s HV Heliocentric total apparent velocity
52- 56 I5 km/s Vcmb Total apparent velocity in CMB rest frame (G1)
62- 64 I3 km/s sigma Velocity dispersion in the group
--------------------------------------------------------------------------------
Note (1): We define as K2M++ (K2mag) the magnitude of a galaxy measured in
the K_S_ band, within the circular isophote at 20mag/arcsec^2^, after
various corrections described in Section 2.2. The magnitude is derived
from the 2M++ galaxies. This is a magnitude uncorrected for
incompleteness effect.
--------------------------------------------------------------------------------
Global notes:
Note (G1): using relation from Kogut et al. (1993ApJ...419....1K) and
Tully et al. (2008, Cat. J/ApJ/676/184)
--------------------------------------------------------------------------------
History:
From electronic version of the journal
================================================================================
(End) Patricia Vannier [CDS] 17-Apr-2012

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Notebook utility functions. Notebook utility functions.
""" """
# import numpy
# from os.path import join # from os.path import join
# try: # try: