def BOSS_mask(sample):
''' read mangle polygon for specified sample
'''
if sample == 'cmass-north':
boss_poly = pymangle.Mangle(
os.path.join(os.environ('BOSSSBI_DIR'), 'boss',
'mask_DR12v5_CMASS_North.ply'))
else:
boss_poly = pymangle.Mangle(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dat',
'boss_geometry_2014_05_28.ply'))
return boss_poly
def mask(inl, maskl, md='veto', upper=True):
if upper:
RA = 'RA'
DEC = 'DEC'
else:
RA = 'ra'
DEC = 'dec'
if md == 'foot':
keep = np.zeros(
inl.size,
dtype='bool') #object is outside of footprint unless it is found
if md == 'veto':
keep = np.ones(
inl.size,
dtype='bool') #object is outside of veto mask unless it is found
for mask in maskl:
mng = pymangle.Mangle(mask)
polyid = mng.polyid(inl[RA], inl[DEC])
if md == 'foot':
keep[polyid != -1] = True #keep the object if a polyid is found
if md == 'veto':
keep[polyid !=
-1] = False #do not keep the object if a polyid is found
print(mask + ' done')
return keep
def mask(inl,maskl,md='veto',upper = False):
'''
input: inl--data, mask--file names of the mask list
output: list of True/False for whether a point should be masked
'''
if upper:
RA = 'RA'
DEC = 'DEC'
else:
RA = 'ra'
DEC = 'dec'
if md == 'foot':
keep = np.zeros(inl.size,dtype='bool') #object is outside of footprint unless it is found
if md == 'veto':
keep = np.ones(inl.size,dtype='bool') #object is outside of veto mask unless it is found
for mask in maskl:
mng = pymangle.Mangle(mask)
polyid = mng.polyid(inl[RA],inl[DEC])
if md == 'foot':
keep[polyid!=-1] = True #keep the object if a polyid is found
if md == 'veto':
keep[polyid!=-1] = False #do not keep the object if a polyid is found
print(mask+' done')
return keep
def dr8_imaging_randoms(nrandoms, writetable=False):
dr8_mask = pymangle.Mangle('footprints/sdss_dr8_footprint_dipomp.ply')
ra_rand, dec_rand = dr8_mask.genrand(nrandoms)
ra_rand, dec_rand = remove_bright_star_regions(ra_rand, dec_rand)
ra_rand, dec_rand = remove_bad_field_regions(ra_rand, dec_rand)
map4stack = hp.read_map('maps/smoothed_masked_planck.fits',
dtype=np.single)
ls, bs = stacking.equatorial_to_galactic(ra_rand, dec_rand)
outsidemaskbool = (map4stack[hp.ang2pix(2048, ls, bs, lonlat=True)] !=
hp.UNSEEN)
planckidxs = np.where(outsidemaskbool)
ra_rand, dec_rand = ra_rand[planckidxs], dec_rand[planckidxs]
if writetable:
t = Table()
t['RA'] = np.array(ra_rand, dtype=np.float)
t['DEC'] = np.array(dec_rand, dtype=np.float)
t.write('random_catalogs/dr8rands.fits', format='fits', overwrite=True)
return ra_rand, dec_rand
def createBooleanColumn(name, ra, dec, dict_mask=dict_mask): #print dict_mask[name] mng = mangle.Mangle(dict_mask[name]) polyid = mng.polyid(ra,dec) tmp = (polyid!=-1) col = fits.Column(name=name, format="L", array=tmp) return col
def construct_random_files(spec_file, geometry_file, output_cat, FACTOR=40):
print os.path.isfile(spec_file), spec_file
print os.path.isfile(geometry_file), geometry_file
hd_s = fits.open(spec_file)[1].data
Nspec = len(hd_s)
print "N targeted in chunk = ", Nspec
mng = mangle.Mangle(geometry_file)
raR, decR = mng.genrand(Nspec * FACTOR)
polyid_randoms = mng.polyid(raR, decR)
ids_with_data = n.array(
list(set(hd_s['tiling_polyid'][(hd_s['tiling_polyid'] > 0)])))
mask = n.in1d(polyid_randoms, ids_with_data)
print len(raR[mask]) * 1. / Nspec
ra_col = fits.Column(name="RA", format='D', array=raR[mask])
dec_col = fits.Column(name="DEC", format='D', array=decR[mask])
polyid_col_r = fits.Column(name="tiling_polyid",
format='I',
array=polyid_randoms[mask])
hd_r_columns = fits.ColDefs([ra_col, dec_col, polyid_col_r])
hdu = fits.BinTableHDU.from_columns(hd_r_columns)
if os.path.isfile(output_cat):
os.remove(output_cat)
hdu.writeto(output_cat)
def mask_creator(ra_ctr, dec_ctr, fov_x, fov_y, pixSize, mangle_fn):
# Define the number of pixels of the output mask (AMICO style)
nx = int(math.floor(fov_x / pixSize + 1))
ny = int(math.floor(fov_y / pixSize + 1))
# Set WCS system
w = wcs.WCS(naxis=2)
w.wcs.crpix = [int(nx / 2.0), int(ny / 2.0)]
w.wcs.crval = [ra_ctr, dec_ctr]
w.wcs.cdelt = np.array([pixSize, pixSize])
w.wcs.ctype = ["RA---TAN", "DEC--TAN"]
# Read the mangle polygons describing regions to be masked using pymangle
mask = pymangle.Mangle(mangle_fn)
# Loop over the mask image
m = np.arange(nx * ny).reshape(ny, nx)
for i in range(0, nx):
for j in range(0, ny):
# Transform to celestial coordinates
this_ra, this_dec = w.wcs_pix2world(i, j, 0)
m[j, i] = int(1 - mask.contains(this_ra, this_dec) + 0.5)
return m, w.to_header()
def __init__(self, mask=None, path=None, rng=None, seed=None):
self.mask = mask
if path is not None:
import pymangle
self.logger.info('Loading geometry file: {}.'.format(path))
self.mask = pymangle.Mangle(path)
self.rng = rng
if rng is None: self.rng = scipy.random.RandomState(seed=seed)
def remove_bad_field_regions(ras, decs):
bad_field_mask = pymangle.Mangle(
'footprints/badfield_mask_unphot_seeing_extinction_pixs8_dr12.ply')
in_mask = bad_field_mask.contains(ras, decs)
ras = ras[np.logical_not(in_mask)]
decs = decs[np.logical_not((in_mask))]
return ras, decs
def createBooleanColumn(name, ra, dec, dict_mask=dict_mask):
#print dict_mask[name]
mng = mangle.Mangle(dict_mask[name])
polyid, w = mng.polyid_and_weight(ra, dec)
#print polyid, w
tmp = (polyid != -1)
col = fits.Column(name=name, format="L", array=tmp)
colw = fits.Column(name=name + "_w", format="D", array=w)
return col, colw
def remove_bright_star_regions(ras, decs):
bright_star_mask = pymangle.Mangle(
'footprints/allsky_bright_star_mask_pix.ply')
in_mask = bright_star_mask.contains(ras, decs)
ras = ras[np.logical_not(in_mask)]
decs = decs[np.logical_not((in_mask))]
return ras, decs
def apply_mangle(self, lon=None, lat=None):
try:
m = mangle.Mangle(self.filename)
except:
raise Exception(f"Unable to open mangle mask file: {self.filename}")
m_mask = m.contains(lon, lat)
return m_mask
def BOSS_mask(sample):
''' read mangle polygon for specified sample
'''
if sample == 'lowz-south':
f_poly = os.path.join(os.path.dirname(os.path.realpath(__file__)),
'dat', 'mask_DR12v5_LOWZ_South.ply')
else:
raise NotImplementedError
boss_poly = pymangle.Mangle(f_poly)
return boss_poly
def read_data(target=None,
cap=None,
data_name=None,
rand_name=None,
survey_comp=None,
survey_geometry=None,
nbar_name=None):
#-- Read real data
if data_name is None:
data_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}_full_{cap}_v7.dat.fits'
rand_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}_full_{cap}_v7.ran.fits'
nbar_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/nbar_eBOSS_{target}_{cap}_v7.dat'
survey_comp = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}geometry_v7.fits'
survey_geometry = '/mnt/lustre/eboss/DR16_geometry/eboss_geometry_eboss0_eboss27'
print('Reading data from ', data_name)
data = Table.read(data_name)
print('Reading randoms from ', rand_name)
rand = Table.read(rand_name)
#-- Cut on COMP_BOSS > 0.5 (there's no mocks outside)
wd = (data['COMP_BOSS'] > 0.5) & (data['sector_SSR'] > 0.5)
data = data[wd]
wr = (rand['COMP_BOSS'] > 0.5) & (rand['sector_SSR'] > 0.5)
rand = rand[wr]
#-- Removing NaN (just to avoid annoying warning messages)
w = np.isnan(data['Z'])
data['Z'][w] = -1
#-- Read mask files
print('Reading completeness info from ', survey_comp)
survey_comp = Table.read(survey_comp)
sec_comp = unique(survey_comp['SECTOR', 'COMP_BOSS'])
geo_sector = sec_comp['SECTOR']
geo_comp = sec_comp['COMP_BOSS']
print('Reading survey geometry from ', survey_geometry)
mask_fits = Table.read(survey_geometry + '.fits')
mask_ply = pymangle.Mangle((survey_geometry + '.ply'))
mask_dict = {
'geo_sector': geo_sector,
'geo_comp': geo_comp,
'mask_ply': mask_ply,
'mask_fits': mask_fits
}
nbar = read_nbar(nbar_name)
return data, rand, mask_dict, nbar
def random_veto(sample='lowz-south'):
''' construct random catalog of RA, Dec with veto mask applied
'''
fvetos = [
'badfield_mask_postprocess_pixs8.ply',
'badfield_mask_unphot_seeing_extinction_pixs8_dr12.ply',
'allsky_bright_star_mask_pix.ply', 'bright_object_mask_rykoff_pix.ply',
'centerpost_mask_dr12.ply', 'collision_priority_mask_dr12.ply'
]
veto_dir = os.path.join(os.environ['QUIJOTE_DIR'], 'chang', 'simbig')
for i, fveto in enumerate(fvetos):
if i == 0:
if sample == 'lowz-south':
frand = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_LOWZ_South.hdf5'), 'r')
elif sample == 'cmass-south':
frand = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_CMASS_South.hdf5'), 'r')
else:
if sample == 'lowz-south':
frand = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_LOWZ_South.veto.hdf5'), 'r')
elif sample == 'cmass-south':
frand = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_CMASS_South.veto.hdf5'), 'r')
rand_ra = frand['ra'][...]
rand_dec = frand['dec'][...]
frand.close()
veto = pymangle.Mangle(os.path.join(veto_dir, fveto))
w_veto = veto.weight(rand_ra, rand_dec)
in_veto = (w_veto > 0.)
# save points outside of veto mask to hdf5 file
if sample == 'lowz-south':
f = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_LOWZ_South.veto.hdf5'), 'w')
elif sample == 'cmass-south':
f = h5py.File(
os.path.join(os.environ['QUIJOTE_DIR'], 'chang',
'random_DR12v5_CMASS_South.veto.hdf5'), 'w')
f.create_dataset('ra', data=rand_ra[~in_veto])
f.create_dataset('dec', data=rand_dec[~in_veto])
f.close()
return None
def eBOSS_randoms(nrandoms):
eBOSS_mask = pymangle.Mangle(
'footprints/eBOSS_QSOandLRG_fullfootprintgeometry_noveto.ply')
ra_rand, dec_rand = eBOSS_mask.genrand(nrandoms)
centerpost_mask = pymangle.Mangle(
'footprints/centerpost_mask_eboss_DR16_new.ply')
in_centerpost_mask = centerpost_mask.contains(ra_rand, dec_rand)
# remove randoms inside centerpost mask
ra_rand = ra_rand[np.logical_not(in_centerpost_mask)]
dec_rand = dec_rand[np.logical_not(in_centerpost_mask)]
ra_rand, dec_rand = remove_bright_star_regions(ra_rand, dec_rand)
t = Table()
t['RA'] = np.array(ra_rand, dtype=np.float)
t['DEC'] = np.array(dec_rand, dtype=np.float)
t.write('random_catalogs/ebossrands.fits', format='fits', overwrite=True)
return ra_rand, dec_rand
def BOSS_randoms(nrandoms, writetable=False):
BOSS_mask = pymangle.Mangle('footprints/sdss_boss_dipomp.ply')
ra_rand, dec_rand = BOSS_mask.genrand(nrandoms)
if writetable:
t = Table()
t['RA'] = np.array(ra_rand, dtype=np.float)
t['DEC'] = np.array(dec_rand, dtype=np.float)
t.write('random_catalogs/bossrands.fits',
format='fits',
overwrite=True)
return ra_rand, dec_rand
def prep_indes(nside=256, fname=None):
if fname == None:
fname = os.getenv(
'CSCRATCH'
) + '/BGS/SV-ASSIGN/des/des.ply' ## '/global/cscratch1/sd/raichoor/desits/des.ply'
##
nside = np.int(nside)
npix = hp.nside2npix(nside)
# checking hp pixels
mng = pymangle.Mangle(fname)
theta, phi = hp.pix2ang(nside, np.arange(0, npix, 1), nest=False)
hpra, hpdec = 180. / np.pi * phi, 90. - 180. / np.pi * theta
allhp = mng.polyid(hpra, hpdec)
hpindes = (allhp != -1).astype(int)
# pixels with all neighbours in des.
hpindes_secure = np.array([
i for i in range(npix)
if hpindes[i] + hpindes[hp.get_all_neighbours(nside, i)].sum() == 9
])
# pixels with all neighbours outside des.
hpoutdes_secure = np.array([
i for i in range(npix)
if hpindes[i] + hpindes[hp.get_all_neighbours(nside, i)].sum() == 0
])
# hpind to be checked
tmp = np.ones(npix, dtype=bool)
tmp[hpindes_secure] = False
tmp[hpoutdes_secure] = False
hp_tbc = np.array(range(npix))[tmp]
np.savetxt(os.getenv('CSCRATCH') + '/BGS/SV-ASSIGN/des/hpindes_secure.txt',
hpindes_secure,
fmt='%d')
np.savetxt(os.getenv('CSCRATCH') + '/BGS/SV-ASSIGN/des/hp_tbc.txt',
hp_tbc,
fmt='%d')
def BOSS_veto(ra, dec):
''' given RA and Dec, find the objects that fall within one of the veto
masks of BOSS. At the moment it checks through the veto masks one by one.
'''
in_veto = np.zeros(len(ra)).astype(bool)
fvetos = [
'badfield_mask_postprocess_pixs8.ply',
'badfield_mask_unphot_seeing_extinction_pixs8_dr12.ply',
'allsky_bright_star_mask_pix.ply', 'bright_object_mask_rykoff_pix.ply',
'centerpost_mask_dr12.ply', 'collision_priority_mask_dr12.ply'
]
for fveto in fvetos:
veto = pymangle.Mangle(
os.path.join(os.path.dirname(os.path.realpath(__file__)), 'dat',
fveto))
w_veto = veto.weight(ra, dec)
in_veto = in_veto | (w_veto > 0.)
return in_veto
def wise_mask(ra, dec, mangle_files=wise_mangles):
"""
Takes a list of RA and DEC and returns the indices which are permissible
Uses equatorial coordinates, converts them to galactic, does the thing, and
returns the indices.
Dependencies: numpy, astropy.coordinates.SkyCoord, mangle
performance: medium (probably max)
@params
ra_list, dec_list
The coordinate pairs to be compared to the footprint
mangle_files = wise_mangles
The iterable of full filepaths to the .ply files to be used for
masking
galactic = True
@returns indices
The indices of ra and dec which are not masked
"""
coordinates = SkyCoord(ra=np.array(ra, dtype=float) * u.degree,
dec=np.array(dec, dtype=float) * u.degree,
frame='icrs')
coordinates = coordinates.transform_to("galactic")
ga_ra_list = coordinates.l.deg
ga_dec_list = coordinates.b.deg
m = []
for path in mangle_files:
m.append(pymangle.Mangle(path))
ins = []
for j in range(0, len(m)):
ins.append(m[j].contains(ga_ra_list, ga_dec_list))
ins_true = ins[0]
for j in range(1, len(m)):
ins_true = np.logical_or(ins_true, ins[j])
return np.logical_not(ins_true)
def hp_from_mangle(weight_ply_files,
nside=None,
veto_ply_files=None,
hp_coords='equ',
verbose=False,
coords=None,
return_coords=False):
"""
Rasterize a set of mangle ply files to a healpix map.
Parameters
----------
weight_ply_files : list of strings
List of filenames of mangle ply files from which weights for the output mask will be
extracted. The weights from different files will be summed into the same output map,
so if a pixel is masked in one file, it need not be masked in the final map if a
different file contains a non-zero weight for it.
nside : int, optional
Healpix map nside parameter. If coords is provided, this is optional.
veto_ply_files : list of strings, optional
List of filenames of mangle ply files containing regions that need to be masked
or vetoed. A region only needs to exist in at least one of the provided files
in order to be masked.
hp_coords : string, optional
The coordinate system of the output healpix map. Use 'fk5','j2000','equatorial' or 'equ'
for Equatorial, which is the default. Use 'galactic' or 'gal' for Galactic.
verbose : bool, optinal
Whether to print more information. Defaults to False.
coords : optional, (2,npix) array
Pre-calculated ra,dec values corresponding to each pixel in the output healpix map.
If not provided, nside has to be provided and the coords will be calculated
based on the coordinate system hp_coords.
return_coords : bool
Whether to return the ra,dec values corresponding to each pixel in the output healpix map.
Defaults to False.
Returns
-------
output : 1d numpy array
The resulting healpix map.
coords : optional, (2,npix) array
The ra,dec values corresponding to each pixel in the output healpix map.
Only returned if return_coords is True.
"""
import pymangle
if coords is None:
npix = hp.nside2npix(nside)
pixs = np.arange(npix, dtype=np.int)
if verbose: print("Converting healpix pixels to coordinates.")
ra, dec = hp.pix2ang(nside, pixs, lonlat=True)
eq_coords = ['fk5', 'j2000', 'equatorial', 'equ']
gal_coords = ['galactic', 'gal']
if hp_coords in gal_coords:
if verbose: print("Transforming coords...")
from astropy.coordinates import SkyCoord
import astropy.units as u
gc = SkyCoord(ra * u.degree, dec * u.degree, frame='galactic')
equ = gc.transform_to('fk5')
ra = equ.ra.deg
dec = equ.dec.deg
else:
ra, dec = coords
output = 0
for filename in weight_ply_files:
if verbose: print(f"Reading weight file {filename}...")
m = pymangle.Mangle(filename)
output = output + m.weight(ra, dec)
if veto_ply_files is None: veto_ply_files = []
for veto in veto_ply_files:
if verbose: print(f"Reading veto file {veto}...")
m = pymangle.Mangle(veto)
bad = m.contains(ra, dec)
output[bad] = 0
if return_coords:
return output, np.asarray((ra, dec))
else:
return output
cm_ratio = (cm_nrand / cm_ndata)
return eb_ratio, cm_ratio
eb_ratio, cm_ratio = get_ratios(eb_data, eb_rand, cm_data, cm_rand)
print(f' ratios before: {eb_ratio:.2f} {cm_ratio:.2f}')
np.random.seed(imock)
if eb_ratio > cm_ratio:
w = np.random.rand(len(eb_rand)) < cm_ratio / eb_ratio
eb_rand = eb_rand[w]
else:
w = np.random.rand(len(cm_rand)) < eb_ratio / cm_ratio
cm_rand = cm_rand[w]
eb_ratio, cm_ratio = get_ratios(eb_data, eb_rand, cm_data, cm_rand)
print(f' ratios after: {eb_ratio:.2f} {cm_ratio:.2f}')
m = pymangle.Mangle(mply)
geo_sector = Table.read(mfits, format='fits', hdu=1)['SECTOR']
try:
mask = Table.read(eboss_comp, format='fits',
hdu=1)['SECTOR', 'COMP_BOSS', 'sector_SSR']
except:
mask = Table.read(eboss_comp, format='fits',
hdu=1)['SECTOR', 'COMP_BOSS', 'SPEC_SUCCESS_RATE']
mask.rename_column('SPEC_SUCCESS_RATE', 'sector_SSR')
mask = unique(mask)
for cm, eb in zip([cm_data, cm_rand], [eb_data, eb_rand]):
print('> Getting eBOSS sectors for CMASS data')
ids = m.polyid(cm['RA'], cm['DEC'])
sectors = geo_sector[ids] * (ids != -1) - 1 * (ids == -1)
6199270,6371066,6547876,6551972,6645991,6711673,6735965,6744444,6744445, \
6748540,6752636,6769023,6773119,6781133])
conversion = constants.degree
theta_rad = catalog['DEC'] * conversion
phi_rad = catalog['RA'] * conversion
theta_rad = constants.pi / 2. - theta_rad
phi_rad %= 2. * constants.pi
pix = healpy.ang2pix(nside, theta_rad, phi_rad, nest=nest)
mask = in1d(pix, mask_pixel)
mskbit += mask * 2**8
# adding 2**9 for centerpost masking
mask_centerpost = '{}/ELG_centerpost.ply'.format(mdir)
mask_mng = pymangle.Mangle(mask_centerpost)
mask = mask_mng.polyid(catalog['RA'], catalog['DEC']) != -1
mskbit += mask * 2**9
# adding 2**10 for tdss_fes masking
mask_tdss = '{}/ELG_TDSSFES_62arcsec.pix.snap.balk.ply'.format(mdir)
mask_mng = pymangle.Mangle(mask_tdss)
mask = mask_mng.polyid(catalog['RA'], catalog['DEC']) != -1
mskbit += mask * 2**10
# adding 2**11 for bad photometric calibration
mask_badexp = '{}/ebosselg_badphot.26Aug2019.ply'.format(mdir)
mask_mng = pymangle.Mangle(mask_badexp)
mask = mask_mng.polyid(catalog['RA'], catalog['DEC']) != -1
mskbit += mask * 2**11
def read_data(target=None, cap=None, data_name=None, rand_name=None,
survey_comp=None, survey_geometry=None, nbar_name=None):
#-- Read real data
if data_name is None:
data_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}_full_{cap}_v7.dat.fits'
rand_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}_full_{cap}_v7.ran.fits'
nbar_name = f'/mnt/lustre/eboss/DR16_{target}_data/v7/nbar_eBOSS_{target}_{cap}_v7.dat'
survey_comp = f'/mnt/lustre/eboss/DR16_{target}_data/v7/eBOSS_{target}geometry_v7.fits'
survey_geometry = '/mnt/lustre/eboss/DR16_geometry/eboss_geometry_eboss0_eboss27'
print('Reading data from ', data_name)
data = Table.read(data_name)
print('Reading randoms from ', rand_name)
rand = Table.read(rand_name)
#-- Cut on COMP_BOSS > 0.5 (there's no mocks outside)
wd = (data['COMP_BOSS'] >0.5)
print(f' Cutting data COMP_BOSS > 0.5: {np.sum(wd)}/{wd.size}')
wd &= (data['sector_SSR']>0.5)
print(f' Cutting data sector_SSR > 0.5: {np.sum(wd)}/{wd.size}')
data = data[wd]
wr = (rand['COMP_BOSS'] >0.5)
print(f' Cutting rand COMP_BOSS > 0.5: {np.sum(wr)}/{wr.size}')
wr &= (rand['sector_SSR']>0.5)
print(f' Cutting rand sector_SSR > 0.5: {np.sum(wr)}/{wr.size}')
rand = rand[wr]
#-- Removing NaN (just to avoid annoying warning messages)
w = np.isnan(data['Z'])
data['Z'][w] = -1
#-- Read mask files
print('Reading completeness info from ', survey_comp)
survey_comp = Table.read(survey_comp)
sec_comp = unique(survey_comp['SECTOR', 'COMP_BOSS', 'SECTORAREA'])
print('Reading survey geometry from ', survey_geometry)
mask_fits = Table.read(survey_geometry+'.fits')
mask_ply = pymangle.Mangle((survey_geometry+'.ply'))
mask_fits['AREA'] = mask_ply.areas*1.
usect = np.unique(mask_fits['SECTOR'])
for sec in usect:
w = mask_fits['SECTOR'] == sec
area = np.sum(mask_fits['AREA'][w])
mask_fits['SECTORAREA'][w] = area*1.
for i in range(len(sec_comp)):
sec = sec_comp['SECTOR'][i]
w = mask_fits['SECTOR'] == sec
sec_comp['SECTORAREA'][i] = mask_fits['SECTORAREA'][w][0]
mask_dict = {'sector': sec_comp['SECTOR'],
'comp_boss': sec_comp['COMP_BOSS'],
'sector_area': sec_comp['SECTORAREA'],
'mask_ply': mask_ply,
'mask_fits': mask_fits}
nbar = read_nbar(nbar_name)
return data, rand, mask_dict, nbar
def eBOSS_ELG_mask(ra, dec, mskdir, mskbit=None):
'''
Assign extra masks to eBOSS ELGs.
Parameters
----------
ra: array_like
Right Ascension of the catalogue.
dec: array_like
Declination of the catalogue.
mskdir: str
Directory containing the following mask files:
* ELG_centerpost.ply
* ELG_TDSSFES_62arcsec.pix.snap.balk.ply
* ebosselg_badphot.26Aug2019.ply
These files are available at:
https://data.sdss.org/sas/dr16/eboss/lss/catalogs/DR16/ELGmasks
mskbit: array_like, optional
Maskbit codes assigned by the `brickmask` program.
See https://github.com/cheng-zhao/brickmask
Returns
-------
mask: array
Maskbit codes with extra masks.
References
----------
* https://doi.org/10.1093/mnras/staa3336 (https://arxiv.org/abs/2007.09007)
* https://doi.org/10.1093/mnras/stab510 (https://arxiv.org/abs/2007.08997)
'''
# Validate inputs
ra = np.atleast_1d(ra)
dec = np.atleast_1d(dec)
if mskbit is None:
mask = np.zeros_like(ra, dtype='int16')
else:
mask = copy.copy(np.atleast_1d(mskbit)).astype('int16')
if len(ra) != len(dec) or len(ra) != len(mask):
raise ValueError(
'ra, dec, and mskbit (if set) must have the same length')
# Add 2**8 for discrepancy between mskbit and anymask
mask_pixel = [2981667,3464728,3514005,3645255,4546075,4685432,5867869, \
5933353,6031493,6072514,6080368,6092477,6301369,6408277,6834661, \
2907700,3583785,3587880,4067035,4669088,6007074,6186688,6190785, \
6199270,6371066,6547876,6551972,6645991,6711673,6735965,6744444, \
6744445,6748540,6752636,6769023,6773119,6781133]
theta = np.radians(90 - dec)
phi = np.radians(360 - ra)
pix = hp.pixelfunc.ang2pix(1024, theta, phi, nest=False, lonlat=True)
bit = in1d(pix, mask_pixel)
mask += bit * 2**8
# Add bit codes for polygon masks
plys = ['ELG_centerpost.ply', 'ELG_TDSSFES_62arcsec.pix.snap.balk.ply', \
'ebosselg_badphot.26Aug2019.ply']
ply_codes = [9, 10, 11] # Bit code for the polygon masks
for ply, code in zip(plys, ply_codes):
iply = '{}/{}'.format(mskdir, ply)
m = pymangle.Mangle(iply)
bit = m.polyid(ra, dec) != -1
mask += bit * 2**code
return mask
def fourmost_get_survbit_indiv(ra,dec,bname): # # Galactic l,b c = SkyCoord(ra=ra, dec=dec, frame='fk5') l,b = c.galactic.l.value,c.galactic.b.value lon,lat = c.barycentrictrueecliptic.lon.degree,c.barycentrictrueecliptic.lat.degree # 4most/s8 , s8elg if (bname in ['s8elg','s8']): iss8,iss8elg = fourmost_get_s8foot(ra,dec) if (bname=='s8elg'): keep = iss8elg else: # s8 keep = iss8 # desi: no ply file for the moment... elif (bname=='desi'): # desi sgc polyra = np.array([0 ,-25,-35,-50,-54,-45,10,10, 60, 70, 70,53,42,42,38,0]) polydec= np.array([33,33, 25, 8, -8, -15,-15,-20,-20,-15,0, 0, 10,20,33,33]) sgcpoly = Path(np.concatenate( (polyra. reshape((len(polyra),1)), polydec.reshape((len(polyra),1))), axis=1)) # desi ngc polyra = np.array([275,107,115,130,230,230,230,255,265,275]) polydec= np.array([33, 33, 12, -10,-10, -2, -2, -2, 13, 33]) ngcpoly = Path(np.concatenate( (polyra. reshape((len(polyra),1)), polydec.reshape((len(polyra),1))), axis=1)) # tmpradec = np.transpose(np.array([ra,dec])) tmp = (ra>300) tmpradec[tmp,0] -= 360. keep = np.zeros(len(ra),dtype=bool) for poly in [sgcpoly,ngcpoly]: keep[poly.contains_points(tmpradec)] = True elif (bname=='erosita'): # johan email 30/07/2018 14:12 keep = (abs(b)>15) & (l>180) elif (bname=='waveswide'): # https://wavesurvey.org/project/survey-design/ keep = (((ra>155) & (ra<240) & (dec>-5) & (dec<5)) | (((ra>330) | (ra<50)) & (dec>-36) & (dec<-26))) elif (bname=='euclid'): keep = (np.abs(b)>=30) & (np.abs(lat)>5.) else: if (bname[:3]=='vhs'): ## -70<dec<0 mng = pymangle.Mangle(MASKDIR+'/vhsdec.ply') polyid = mng.polyid(ra,dec) keepdec= (polyid!=-1) # |b|>bmin mng = pymangle.Mangle(MASKDIR+'/'+bname[3:6]+'.ply') polyid = mng.polyid(l,b) keepb = (polyid!=-1) ## keep = (keepdec) & (keepb) else: mng = pymangle.Mangle(MASKDIR+'/'+bname+'.ply') polyid = mng.polyid(ra,dec) keep = (polyid!=-1) if (bname=='vhsb20clean'): ## Jext<0.1 and low nstar selection [both VHS and DES] ra60 = (ra>55) & (ra<65) & (dec>-5) & (dec<0) ra70 = (ra>67) & (ra<72) & (dec>-16) & (dec<-13) ra100 = (ra>50) & (ra<180) & (dec>-20) & (dec<0) & (b>-23) & (b<0) ra120 = (ra>100) & (ra<180) & (b>-15) & (b<0) ra230 = (ra>228) & (ra<270) & (dec>-40) & (dec<-20) & (b>0) ra250 = (ra>235) & (ra<270) & (dec>-20) & (dec<0) & (b>0) ra300 = (ra>180) & (ra<360) & (dec>-70) & (dec<0) & (b>-25) & (b<0) LMC = (ra>70) & (ra<90) & (dec>-70) & (dec<-65) keep = ((keep) & (~ra60) & (~ra70) & (~ra100) & (~ra120) & (~ra230) & (~ra250) & (~ra300) & (~LMC)) print( bname, len(ra[keep])) return keep
def construct_summary_files_data(spec_file,
phot_file,
geometry_file,
output_phot_cat,
rmax=0.5 / 3600.):
# spectroscopy
print os.path.isfile(spec_file), spec_file
print os.path.isfile(phot_file), phot_file
print os.path.isfile(geometry_file), geometry_file
tiling_mask = mangle.Mangle(geometry_file)
hd_p = fits.open(phot_file)[1].data
hd_s_i = fits.open(spec_file)[1].data
hd_s = hd_s_i[hd_s_i['ZWARNING'] == 0]
treeP = t.cKDTree(n.transpose([hd_p['ra'], hd_p['dec']]))
treeS = t.cKDTree(n.transpose([hd_s['plug_ra'], hd_s['plug_dec']]))
Ntarg = len(hd_p)
Nspec = len(hd_s)
print "N targeted in chunk = ", Ntarg
# Adding columns to the complete photometry file
polyid_val_p = tiling_mask.polyid(hd_p['ra'], hd_p['dec'])
polyid_val_s = tiling_mask.polyid(hd_s['plug_ra'], hd_s['plug_dec'])
polyid_col_p = fits.Column(name="tiling_polyid",
format='I',
array=polyid_val_p)
polyid_col_s = fits.Column(name="tiling_polyid",
format='I',
array=polyid_val_s)
#number of observations in 0.5 arcsec
# 0.5 arcsecond
nobs_val_p = n.zeros_like(hd_p['ra'])
nobs_val_s = n.zeros_like(hd_s['plug_ra'])
id_ps = treeP.query_ball_tree(treeS, rmax)
nobs_val_p = n.array([len(el) for el in id_ps])
id_ss = treeS.query_ball_tree(treeS, rmax)
nobs_val_s = n.array([len(el) for el in id_ss])
nobs_col_p = fits.Column(name="nobs", format='I', array=nobs_val_p)
nobs_col_s = fits.Column(name="nobs", format='I', array=nobs_val_s)
# assign redshift and redshift error to photometric catalog
z_val = n.ones_like(hd_p['ra']) * -1.
zerr_val = n.ones_like(hd_p['ra']) * -1.
print "merges redshift measurements with photometry"
for ii, el in enumerate(id_ps):
if len(el) == 0:
z_val[ii] = -9.
zerr_val[ii] = -9.
elif len(el) == 1:
z_val[ii] = hd_s['Z'][el[0]]
zerr_val[ii] = hd_s['Z_ERR'][el[0]]
print z_val[ii], zerr_val[ii]
elif len(el) >= 2:
# gets the first guess
z_0 = hd_s['Z'][el[0]]
zerr_0 = hd_s['Z_ERR'][el[0]]
# gets the second guess
z_1 = hd_s['Z'][el[1]]
zerr_1 = hd_s['Z_ERR'][el[1]]
print "observed many times:", len(el), z_0, zerr_0, z_1, zerr_1
# measurments agree
if abs(z_0 - z_1) < 0.05:
z_val[ii] = z_0
zerr_val[ii] = zerr_0
else:
id_zq = n.argmin([zerr_0, zerr_1])
z_val[ii] = [z_0, z_1][id_zq]
zerr_val[ii] = [zerr_0, zerr_1][id_zq]
z_col_p = fits.Column(name="Z", format='D', array=z_val)
zerr_col_p = fits.Column(name="Z_ERR", format='D', array=zerr_val)
hd_p_columns = hd_p.columns + nobs_col_p + polyid_col_p + z_col_p + zerr_col_p
hdu = fits.BinTableHDU.from_columns(hd_p_columns)
if os.path.isfile(output_phot_cat):
os.remove(output_phot_cat)
hdu.writeto(output_phot_cat)
imageinPixscale = float(imageinHDR['HIERARCH OAJ INS PIXSCALE']) imageinPSF = settings['psf_image'] imageinWeight = settings['weight_map_file'] dataSeg = fits.open(settings['segmentation_map_file'])[0].data dataSex = settings['sex_file'] modelsFolder = settings['models_path'] if modelsFolder[-1] == "/": modelsFolder = modelsFolder[:-1] magZeroPoint = float(settings['mag_zero_point']) MODELS_PER_MAG = int(settings['models_per_mag']) iterations = int(settings['iter']) modelsType = settings['model_type'] # right now, 'STAR' (from psf) # or 'GALAXY' chef galaxies #### # I read the mask to avoid bad regions. By default it reads a full sky mask. maskfile = settings['mask'] mng = pymangle.Mangle(maskfile) #### modelsSexTable = [] modelsInsertedTable = [] imageoutData = imageinData.copy() modelInsertedID = 0 imageinNy = N.shape(imageinData)[0] imageinNx = N.shape(imageinData)[1] """ Models handling. NOTE: This version is for CHEFS galaxy models models. """ i = 0 if (modelsType == 'GALAXY'):
def generate_random_points_from_mangle(manglename, npoints):
manglemask = pymangle.Mangle('mangle/%s_footprint.ply' % manglename)
ra_rand, dec_rand = manglemask.genrand(npoints)
return ra_rand, dec_rand
#alternative catalogs
path_2_target_AGN_RASS_eboss = os.path.join(
target_dir, "SPIDERS_RASS_AGN_targets_for_EBOSS_TDSS_SPIDERS.fits.gz")
path_2_target_AGN_RASS_sequels = os.path.join(
target_dir, "SPIDERS_RASS_AGN_targets_for_SEQUELS.fits.gz")
path_2_target_AGN_XMMSL_eboss = os.path.join(
target_dir, "SPIDERS_XMMSL_AGN_targets_for_EBOSS_TDSS_SPIDERS.fits.gz")
path_2_specObjAll = os.path.join(os.environ['SDSS_DATA_DIR'], "dr14",
"specObj-BOSS-dr14.fits")
# loads the DR14 mask
#path_2_mask_fits = os.path.join(os.environ['SDSS_DATA_DIR'], 'LSS', 'mask-lrg-N-eboss_v1.8_IRt.fits')
path_2_mask_ply = os.path.join(os.environ['SDSS_DATA_DIR'], 'LSS', 'LRG',
'v1.8', 'mask-lrg-N-eboss_v1.8_IRt.ply')
mng = mangle.Mangle(path_2_mask_ply)
def construct_summary_files_data(spec_file,
phot_file,
geometry_file,
output_phot_cat,
rmax=0.5 / 3600.):
# spectroscopy
print os.path.isfile(spec_file), spec_file
print os.path.isfile(phot_file), phot_file
print os.path.isfile(geometry_file), geometry_file
tiling_mask = mangle.Mangle(geometry_file)
hd_p = fits.open(phot_file)[1].data
hd_s_i = fits.open(spec_file)[1].data
