def select_gfas(infiles,
maglim=18,
numproc=4,
tilesfile=None,
cmx=False,
mindec=-30,
mingalb=10,
addurat=True):
"""Create a set of GFA locations using Gaia and matching to sweeps.
Parameters
----------
infiles : :class:`list` or `str`
A list of input filenames (sweep files) OR a single filename.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
numproc : :class:`int`, optional, defaults to 4
The number of parallel processes to use.
tilesfile : :class:`str`, optional, defaults to ``None``
Name of tiles file to load. For full details, see
:func:`~desimodel.io.load_tiles`.
cmx : :class:`bool`, defaults to ``False``
If ``True``, do not limit output to DESI tiling footprint.
Used for selecting wider-ranging commissioning targets.
mindec : :class:`float`, optional, defaults to -30
Minimum declination (o) for output sources that do NOT match
an object in the passed `infiles`.
mingalb : :class:`float`, optional, defaults to 10
Closest latitude to Galactic plane for output sources that
do NOT match an object in the passed `infiles` (e.g. send
10 to limit to regions beyond -10o <= b < 10o)".
addurat : :class:`bool`, optional, defaults to ``True``
If ``True`` then substitute proper motions from the URAT
catalog where Gaia is missing proper motions. Requires that
the :envvar:`URAT_DIR` is set and points to data downloaded and
formatted by, e.g., :func:`~desitarget.uratmatch.make_urat_files`.
Returns
-------
:class:`~numpy.ndarray`
GFA objects from Gaia with the passed geometric constraints
limited to the passed maglim and matched to the passed input
files, formatted according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- If numproc==1, use the serial code instead of parallel code.
- If numproc > 4, then numproc=4 is enforced for (just those)
parts of the code that are I/O limited.
- The tiles loaded from `tilesfile` will only be those in DESI.
So, for custom tilings, set IN_DESI==1 in your tiles file.
"""
# ADM force to no more than numproc=4 for I/O limited processes.
numproc4 = numproc
if numproc4 > 4:
log.info('Forcing numproc to 4 for I/O limited parts of code')
numproc4 = 4
# ADM convert a single file, if passed to a list of files.
if isinstance(infiles, str):
infiles = [
infiles,
]
nfiles = len(infiles)
# ADM check that files exist before proceeding.
for filename in infiles:
if not os.path.exists(filename):
msg = "{} doesn't exist".format(filename)
log.critical(msg)
raise ValueError(msg)
# ADM load the tiles file.
tiles = desimodel.io.load_tiles(tilesfile=tilesfile)
# ADM check some files loaded.
if len(tiles) == 0:
msg = "no tiles found in {}".format(tilesfile)
log.critical(msg)
raise ValueError(msg)
# ADM the critical function to run on every file.
def _get_gfas(fn):
'''wrapper on gaia_gfas_from_sweep() given a file name'''
return gaia_gfas_from_sweep(fn, maglim=maglim)
# ADM this is just to count sweeps files in _update_status.
t0 = time()
nfile = np.zeros((), dtype='i8')
def _update_status(result):
"""wrapper function for the critical reduction operation,
that occurs on the main parallel process"""
if nfile % 50 == 0 and nfile > 0:
elapsed = (time() - t0) / 60.
rate = nfile / elapsed / 60.
log.info('{}/{} files; {:.1f} files/sec...t = {:.1f} mins'.format(
nfile, nfiles, rate, elapsed))
nfile[...] += 1 # this is an in-place modification.
return result
# - Parallel process input files.
if numproc4 > 1:
pool = sharedmem.MapReduce(np=numproc4)
with pool:
gfas = pool.map(_get_gfas, infiles, reduce=_update_status)
else:
gfas = list()
for file in infiles:
gfas.append(_update_status(_get_gfas(file)))
gfas = np.concatenate(gfas)
# ADM resolve any duplicates between imaging data releases.
gfas = resolve(gfas)
# ADM retrieve Gaia objects in the DESI footprint or passed tiles.
log.info('Retrieving additional Gaia objects...t = {:.1f} mins'.format(
(time() - t0) / 60))
gaia = all_gaia_in_tiles(maglim=maglim,
numproc=numproc4,
allsky=cmx,
tiles=tiles,
mindec=mindec,
mingalb=mingalb)
# ADM remove any duplicates. Order is important here, as np.unique
# ADM keeps the first occurence, and we want to retain sweeps
# ADM information as much as possible.
gfas = np.concatenate([gfas, gaia])
_, ind = np.unique(gfas["REF_ID"], return_index=True)
gfas = gfas[ind]
# ADM for zero/NaN proper motion objects, add in URAT proper motions.
if addurat:
ii = ((np.isnan(gfas["PMRA"]) | (gfas["PMRA"] == 0)) &
(np.isnan(gfas["PMDEC"]) | (gfas["PMDEC"] == 0)))
log.info(
'Adding URAT for {} objects with no PMs...t = {:.1f} mins'.format(
np.sum(ii), (time() - t0) / 60))
urat = add_urat_pms(gfas[ii], numproc=numproc)
log.info(
'Found an additional {} URAT objects...t = {:.1f} mins'.format(
np.sum(urat["URAT_ID"] != -1), (time() - t0) / 60))
for col in "PMRA", "PMDEC", "URAT_ID", "URAT_SEP":
gfas[col][ii] = urat[col]
# ADM a final clean-up to remove columns that are NaN (from
# ADM Gaia-matching) or that are exactly 0 (in the sweeps).
ii = ((np.isnan(gfas["PMRA"]) | (gfas["PMRA"] == 0)) &
(np.isnan(gfas["PMDEC"]) | (gfas["PMDEC"] == 0)))
gfas = gfas[~ii]
# ADM limit to DESI footprint or passed tiles, if not cmx'ing.
if not cmx:
ii = is_point_in_desi(tiles, gfas["RA"], gfas["DEC"])
gfas = gfas[ii]
return gfas
def select_gfas(infiles,
maglim=18,
numproc=4,
nside=None,
pixlist=None,
bundlefiles=None,
extra=None,
mindec=-30,
mingalb=10,
addurat=True):
"""Create a set of GFA locations using Gaia and matching to sweeps.
Parameters
----------
infiles : :class:`list` or `str`
A list of input filenames (sweep files) OR a single filename.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
numproc : :class:`int`, optional, defaults to 4
The number of parallel processes to use.
nside : :class:`int`, optional, defaults to `None`
(NESTED) HEALPix `nside` to use with `pixlist` and `bundlefiles`.
pixlist : :class:`list` or `int`, optional, defaults to `None`
Only return targets in a set of (NESTED) HEALpixels at the
supplied `nside`. Useful for parallelizing.
bundlefiles : :class:`int`, defaults to `None`
If not `None`, then, instead of selecting gfas, print the slurm
script to run in pixels at `nside`. Is an integer rather than
a boolean for historical reasons.
extra : :class:`str`, optional
Extra command line flags to be passed to the executable lines in
the output slurm script. Used in conjunction with `bundlefiles`.
mindec : :class:`float`, optional, defaults to -30
Minimum declination (o) for output sources that do NOT match
an object in the passed `infiles`.
mingalb : :class:`float`, optional, defaults to 10
Closest latitude to Galactic plane for output sources that
do NOT match an object in the passed `infiles` (e.g. send
10 to limit to regions beyond -10o <= b < 10o)".
addurat : :class:`bool`, optional, defaults to ``True``
If ``True`` then substitute proper motions from the URAT
catalog where Gaia is missing proper motions. Requires that
the :envvar:`URAT_DIR` is set and points to data downloaded and
formatted by, e.g., :func:`~desitarget.uratmatch.make_urat_files`.
Returns
-------
:class:`~numpy.ndarray`
GFA objects from Gaia with the passed geometric constraints
limited to the passed maglim and matched to the passed input
files, formatted according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- If numproc==1, use the serial code instead of parallel code.
- If numproc > 4, then numproc=4 is enforced for (just those)
parts of the code that are I/O limited.
"""
# ADM the code can have memory issues for nside=2 with large numproc.
if nside is not None and nside < 4 and numproc > 8:
msg = 'Memory may be an issue near Plane for nside < 4 and numproc > 8'
log.warning(msg)
# ADM force to no more than numproc=4 for I/O limited processes.
numproc4 = numproc
if numproc4 > 4:
log.info('Forcing numproc to 4 for I/O limited parts of code')
numproc4 = 4
# ADM convert a single file, if passed to a list of files.
if isinstance(infiles, str):
infiles = [
infiles,
]
# ADM check that files exist before proceeding.
for filename in infiles:
if not os.path.exists(filename):
msg = "{} doesn't exist".format(filename)
log.critical(msg)
raise ValueError(msg)
# ADM if the pixlist option was sent, we'll need to
# ADM know which HEALPixels touch each file.
if pixlist is not None:
filesperpixel, _, _ = sweep_files_touch_hp(nside, pixlist, infiles)
# ADM if the bundlefiles option was sent, call the packing code.
if bundlefiles is not None:
# ADM were files from one or two input directories passed?
surveydirs = list(set([os.path.dirname(fn) for fn in infiles]))
bundle_bricks([0],
bundlefiles,
nside,
gather=False,
prefix='gfas',
surveydirs=surveydirs,
extra=extra)
return
# ADM restrict to input files in a set of HEALPixels, if requested.
if pixlist is not None:
infiles = list(set(np.hstack([filesperpixel[pix] for pix in pixlist])))
if len(infiles) == 0:
log.info('ZERO sweep files in passed pixel list!!!')
log.info("Processing files in (nside={}, pixel numbers={}) HEALPixels".
format(nside, pixlist))
nfiles = len(infiles)
# ADM a little more information if we're slurming across nodes.
if os.getenv('SLURMD_NODENAME') is not None:
log.info('Running on Node {}'.format(os.getenv('SLURMD_NODENAME')))
# ADM the critical function to run on every file.
def _get_gfas(fn):
'''wrapper on gaia_gfas_from_sweep() given a file name'''
return gaia_gfas_from_sweep(fn, maglim=maglim)
# ADM this is just to count sweeps files in _update_status.
t0 = time()
nfile = np.zeros((), dtype='i8')
def _update_status(result):
"""wrapper function for the critical reduction operation,
that occurs on the main parallel process"""
if nfile % 20 == 0 and nfile > 0:
elapsed = (time() - t0) / 60.
rate = nfile / elapsed / 60.
log.info('{}/{} files; {:.1f} files/sec...t = {:.1f} mins'.format(
nfile, nfiles, rate, elapsed))
nfile[...] += 1 # this is an in-place modification.
return result
# - Parallel process input files.
if len(infiles) > 0:
if numproc4 > 1:
pool = sharedmem.MapReduce(np=numproc4)
with pool:
gfas = pool.map(_get_gfas, infiles, reduce=_update_status)
else:
gfas = list()
for file in infiles:
gfas.append(_update_status(_get_gfas(file)))
gfas = np.concatenate(gfas)
# ADM resolve any duplicates between imaging data releases.
gfas = resolve(gfas)
# ADM retrieve Gaia objects in the DESI footprint or passed tiles.
log.info('Retrieving additional Gaia objects...t = {:.1f} mins'.format(
(time() - t0) / 60))
gaia = all_gaia_in_tiles(maglim=maglim,
numproc=numproc4,
allsky=True,
mindec=mindec,
mingalb=mingalb,
nside=nside,
pixlist=pixlist)
# ADM remove any duplicates. Order is important here, as np.unique
# ADM keeps the first occurence, and we want to retain sweeps
# ADM information as much as possible.
if len(infiles) > 0:
gfas = np.concatenate([gfas, gaia])
_, ind = np.unique(gfas["REF_ID"], return_index=True)
gfas = gfas[ind]
else:
gfas = gaia
# ADM for zero/NaN proper motion objects, add URAT proper motions.
if addurat:
ii = ((np.isnan(gfas["PMRA"]) | (gfas["PMRA"] == 0)) &
(np.isnan(gfas["PMDEC"]) | (gfas["PMDEC"] == 0)))
log.info(
'Adding URAT for {} objects with no PMs...t = {:.1f} mins'.format(
np.sum(ii), (time() - t0) / 60))
urat = add_urat_pms(gfas[ii], numproc=numproc)
log.info(
'Found an additional {} URAT objects...t = {:.1f} mins'.format(
np.sum(urat["URAT_ID"] != -1), (time() - t0) / 60))
for col in "PMRA", "PMDEC", "URAT_ID", "URAT_SEP":
gfas[col][ii] = urat[col]
# ADM restrict to only GFAs in a set of HEALPixels, if requested.
if pixlist is not None:
ii = is_in_hp(gfas, nside, pixlist)
gfas = gfas[ii]
return gfas
def select_randoms(drdir,
density=100000,
numproc=32,
nside=4,
pixlist=None,
bundlebricks=None,
brickspersec=2.5,
dustdir=None):
"""NOBS, GALDEPTH, PSFDEPTH (per-band) for random points in a DR of the Legacy Surveys
Parameters
----------
drdir : :class:`str`
The root directory pointing to a Data Release from the Legacy Surveys
e.g. /global/project/projectdirs/cosmo/data/legacysurvey/dr7.
density : :class:`int`, optional, defaults to 100,000
The number of random points to return per sq. deg. As a typical brick is
~0.25 x 0.25 sq. deg. about (0.0625*density) points will be returned
numproc : :class:`int`, optional, defaults to 32
The number of processes over which to parallelize
nside : :class:`int`, optional, defaults to nside=4 (214.86 sq. deg.)
The (NESTED) HEALPixel nside to be used with the `pixlist` and `bundlebricks` input.
pixlist : :class:`list` or `int`, optional, defaults to None
Bricks will only be processed if the CENTER of the brick lies within the bounds of
pixels that are in this list of integers, at the supplied HEALPixel `nside`.
Uses the HEALPix NESTED scheme. Useful for parallelizing. If pixlist is None
then all bricks in the passed `survey` will be processed.
bundlebricks : :class:`int`, defaults to None
If not None, then instead of selecting the skies, print, to screen, the slurm
script that will approximately balance the brick distribution at `bundlebricks`
bricks per node. So, for instance, if bundlebricks is 14000 (which as of
the latest git push works well to fit on the interactive nodes on Cori and run
in about an hour), then commands would be returned with the correct pixlist values
to pass to the code to pack at about 14000 bricks per node across all of the bricks
in `survey`.
brickspersec : :class:`float`, optional, defaults to 2.5
The rough number of bricks processed per second by the code (parallelized across
a chosen number of nodes). Used in conjunction with `bundlebricks` for the code
to estimate time to completion when parallelizing across pixels.
dustdir : :class:`str`, optional, defaults to $DUST_DIR+'maps'
The root directory pointing to SFD dust maps. If not
sent the code will try to use $DUST_DIR+'maps')
before failing.
Returns
-------
:class:`~numpy.ndarray`
a numpy structured array with the following columns:
RA: Right Ascension of a random point
DEC: Declination of a random point
BRICKNAME: Passed brick name
NOBS_G: Number of observations at this location in the g-band
NOBS_R: Number of observations at this location in the r-band
NOBS_Z: Number of observations at this location in the z-band
PSFDEPTH_G: PSF depth at this location in the g-band
PSFDEPTH_R: PSF depth at this location in the r-band
PSFDEPTH_Z: PSF depth at this location in the z-band
GALDEPTH_G: Galaxy depth at this location in the g-band
GALDEPTH_R: Galaxy depth at this location in the r-band
GALDEPTH_Z: Galaxy depth at this location in the z-band
MASKBITS: Extra mask bits info as stored in the header of e.g.,
dr7dir + 'coadd/111/1116p210/legacysurvey-1116p210-maskbits.fits.gz'
EBV: E(B-V) at this location from the SFD dust maps
"""
# ADM read in the survey bricks file, which lists the bricks of interest for this DR.
# ADM if this is pre-or-post-DR8 we need to find the correct directory or directories.
drdirs = _pre_or_post_dr8(drdir)
bricknames = []
brickinfo = []
for dd in drdirs:
sbfile = glob(dd + '/*bricks-dr*')
if len(sbfile) > 0:
sbfile = sbfile[0]
hdu = fits.open(sbfile)
brickinfo.append(hdu[1].data)
bricknames.append(hdu[1].data['BRICKNAME'])
else:
# ADM this is a hack for test bricks where we didn't always generate the
# ADM bricks file. It's probably safe to remove it at some point.
from desitarget.io import brickname_from_filename
fns = glob(os.path.join(dd, 'tractor', '*', '*fits'))
bricknames.append([brickname_from_filename(fn) for fn in fns])
brickinfo.append([])
if pixlist is not None or bundlebricks is not None:
msg = 'DR-specific bricks file not found'
msg += 'and pixlist or bundlebricks passed!!!'
log.critical(msg)
raise ValueError(msg)
bricknames = np.concatenate(bricknames)
brickinfo = np.concatenate(brickinfo)
# ADM if the pixlist or bundlebricks option was sent, we'll need the HEALPixel
# ADM information for each brick.
if pixlist is not None or bundlebricks is not None:
theta, phi = np.radians(90 - brickinfo["dec"]), np.radians(
brickinfo["ra"])
pixnum = hp.ang2pix(nside, theta, phi, nest=True)
# ADM if the bundlebricks option was sent, call the packing code.
if bundlebricks is not None:
bundle_bricks(pixnum,
bundlebricks,
nside,
brickspersec=brickspersec,
prefix='randoms',
surveydir=drdir)
return
# ADM restrict to only bricks in a set of HEALPixels, if requested.
if pixlist is not None:
# ADM if an integer was passed, turn it into a list.
if isinstance(pixlist, int):
pixlist = [pixlist]
wbricks = np.where([pix in pixlist for pix in pixnum])[0]
bricknames = bricknames[wbricks]
if len(wbricks) == 0:
log.warning('ZERO bricks in passed pixel list!!!')
log.info(
"Processing bricks in (nside={}, pixel numbers={}) HEALPixels".
format(nside, pixlist))
nbricks = len(bricknames)
log.info(
'Processing {} bricks from DR at {} at density {:.1e} per sq. deg...t = {:.1f}s'
.format(nbricks, drdir, density,
time() - start))
# ADM a little more information if we're slurming across nodes.
if os.getenv('SLURMD_NODENAME') is not None:
log.info('Running on Node {}'.format(os.getenv('SLURMD_NODENAME')))
# ADM initialize the bricks class, and retrieve the brick information look-up table
# ADM so it can be used in a common fashion.
from desiutil import brick
bricktable = brick.Bricks(bricksize=0.25).to_table()
# ADM the critical function to run on every brick.
def _get_quantities(brickname):
'''wrapper on nobs_positions_in_a_brick_from_edges() given a brick name'''
# ADM retrieve the edges for the brick that we're working on
wbrick = np.where(bricktable["BRICKNAME"] == brickname)[0]
ramin, ramax, decmin, decmax = np.array(bricktable[wbrick]["RA1",
"RA2",
"DEC1",
"DEC2"])[0]
# ADM populate the brick with random points, and retrieve the quantities
# ADM of interest at those points.
return get_quantities_in_a_brick(ramin,
ramax,
decmin,
decmax,
brickname,
drdir,
density=density,
dustdir=dustdir)
# ADM this is just to count bricks in _update_status
nbrick = np.zeros((), dtype='i8')
t0 = time()
def _update_status(result):
''' wrapper function for the critical reduction operation,
that occurs on the main parallel process '''
if nbrick % 50 == 0 and nbrick > 0:
rate = nbrick / (time() - t0)
log.info('{}/{} bricks; {:.1f} bricks/sec'.format(
nbrick, nbricks, rate))
# ADM if we're going to exceed 4 hours, warn the user
if nbricks / rate > 4 * 3600.:
log.error(
"May take > 4 hours to run. Try running with bundlebricks instead."
)
nbrick[...] += 1 # this is an in-place modification
return result
# - Parallel process input files
if numproc > 1:
pool = sharedmem.MapReduce(np=numproc)
with pool:
qinfo = pool.map(_get_quantities,
bricknames,
reduce=_update_status)
else:
qinfo = list()
for brickname in bricknames:
qinfo.append(_update_status(_get_quantities(brickname)))
# ADM concatenate the randoms into a single long list and resolve whether
# ADM they are officially in the north or the south.
qinfo = np.concatenate(qinfo)
qinfo = resolve(qinfo)
# ADM one last shuffle to randomize across brick boundaries.
np.random.seed(616)
np.random.shuffle(qinfo)
return qinfo
def select_gfas(infiles, maglim=18, numproc=4, tilesfile=None, cmx=False):
"""Create a set of GFA locations using Gaia.
Parameters
----------
infiles : :class:`list` or `str`
A list of input filenames (sweep files) OR a single filename.
maglim : :class:`float`, optional, defaults to 18
Magnitude limit for GFAs in Gaia G-band.
numproc : :class:`int`, optional, defaults to 4
The number of parallel processes to use.
tilesfile : :class:`str`, optional, defaults to ``None``
Name of tiles file to load. For full details, see
:func:`~desimodel.io.load_tiles`.
cmx : :class:`bool`, defaults to ``False``
If ``True``, do not limit output to DESI tiling footprint.
Used for selecting wider-ranging commissioning targets.
Returns
-------
:class:`~numpy.ndarray`
GFA objects from Gaia across all of the passed input files, formatted
according to `desitarget.gfa.gfadatamodel`.
Notes
-----
- If numproc==1, use the serial code instead of the parallel code.
- The tiles loaded from `tilesfile` will only be those in DESI.
So, for custom tilings, set IN_DESI==1 in your tiles file.
"""
# ADM convert a single file, if passed to a list of files.
if isinstance(infiles, str):
infiles = [infiles, ]
nfiles = len(infiles)
# ADM check that files exist before proceeding.
for filename in infiles:
if not os.path.exists(filename):
msg = "{} doesn't exist".format(filename)
log.critical(msg)
raise ValueError(msg)
# ADM load the tiles file.
tiles = desimodel.io.load_tiles(tilesfile=tilesfile)
# ADM check some files loaded.
if len(tiles) == 0:
msg = "no tiles found in {}".format(tilesfile)
log.critical(msg)
raise ValueError(msg)
# ADM the critical function to run on every file.
def _get_gfas(fn):
'''wrapper on gaia_gfas_from_sweep() given a file name'''
return gaia_gfas_from_sweep(fn, maglim=maglim)
# ADM this is just to count sweeps files in _update_status.
nfile = np.zeros((), dtype='i8')
t0 = time()
def _update_status(result):
"""wrapper function for the critical reduction operation,
that occurs on the main parallel process"""
if nfile % 50 == 0 and nfile > 0:
elapsed = (time()-t0)/60.
rate = nfile/elapsed/60.
log.info('{}/{} files; {:.1f} files/sec...t = {:.1f} mins'
.format(nfile, nfiles, rate, elapsed))
nfile[...] += 1 # this is an in-place modification.
return result
# - Parallel process input files.
if numproc > 1:
pool = sharedmem.MapReduce(np=numproc)
with pool:
gfas = pool.map(_get_gfas, infiles, reduce=_update_status)
else:
gfas = list()
for file in infiles:
gfas.append(_update_status(_get_gfas(file)))
gfas = np.concatenate(gfas)
# ADM resolve any duplicates between imaging data releases.
gfas = resolve(gfas)
# ADM retrieve Gaia objects in the DESI footprint or passed tiles.
log.info('Retrieving additional Gaia objects...t = {:.1f} mins'
.format((time()-t0)/60))
gaia = all_gaia_in_tiles(maglim=maglim, numproc=numproc, allsky=cmx,
tiles=tiles)
# ADM and limit them to just any missing bricks...
brickids = set(gfas['BRICKID'])
ii = [gbrickid not in brickids for gbrickid in gaia["BRICKID"]]
gaia = gaia[ii]
gfas = np.concatenate([gfas, gaia])
# ADM limit to DESI footprint or passed tiles, if not cmx'ing.
if not cmx:
ii = is_point_in_desi(tiles, gfas["RA"], gfas["DEC"])
gfas = gfas[ii]
return gfas