def main(args, comm=None):
if comm.rank == 0:
import healpy as hp
# --- only rank 0
# read data, randoms, and templates
data = ft.read(args.data_path)
nside = 1024
ngal = hp.read_map(args.hpmap_path)
nran = make_hp(nside, data['hpix'],
1.0) # mocks do not have completeness
mask = make_hp(nside, data['hpix'], 1.0) > 0.5
sysm = np.zeros((12 * nside * nside, data['features'].shape[1]))
print(sysm.shape)
sysm[data['hpix'], :] = data['features']
if args.selection is not None:
s_ = ft.read(args.selection)
selection_fn = make_hp(nside, s_['hpix'],
np.median(s_['weight'],
axis=1)) #.mean(axis=1))
print(np.percentile(selection_fn[mask], [0, 1, 99, 100]))
else:
selection_fn = None
else:
ngal = None
nran = None
mask = None
sysm = None
selection_fn = None
ngal = comm.bcast(ngal, root=0)
nran = comm.bcast(nran, root=0)
mask = comm.bcast(mask, root=0)
sysm = comm.bcast(sysm, root=0)
selection_fn = comm.bcast(selection_fn, root=0)
cls_list = get_cl(
ngal,
nran,
mask,
selection_fn=selection_fn,
systematics=sysm,
njack=0,
cross_only=False,
is_delta=True) # don't compute Css for mocks & mocks are delta
if comm.rank == 0:
output_dir = os.path.dirname(args.output_path)
if not os.path.exists(output_dir):
print(f'creating {output_dir}')
os.makedirs(output_dir)
np.save(args.output_path, cls_list)
def d2dl(d):
nside = 1024
ng = make_hp(nside, d['hpix'], d['label'])
fr = make_hp(nside, d['hpix'], d['fracgood'])
ms = fr > 0.0
dl = make_overdensity(ng, fr, ms)
return fr, dl
def main(args, comm=None):
if comm.rank == 0:
# --- only rank 0
# read data, randoms, and templates
data = ft.read(args.data_path)
nside = 256
ngal = make_hp(nside, data['hpix'], data['label'])
nran = make_hp(nside, data['hpix'], data['fracgood'])
mask = make_hp(nside, data['hpix'], 1.0) > 0.5
sysm = np.zeros((12 * nside * nside, data['features'].shape[1]))
print(sysm.shape)
sysm[data['hpix'], :] = data['features']
if args.selection is not None:
s_ = ft.read(args.selection)
selection_fn = make_hp(nside, s_['hpix'],
np.median(s_['weight'],
axis=1)) #.mean(axis=1))
print(np.percentile(selection_fn[mask], [0, 1, 99, 100]))
else:
selection_fn = None
else:
ngal = None
nran = None
mask = None
sysm = None
selection_fn = None
ngal = comm.bcast(ngal, root=0)
nran = comm.bcast(nran, root=0)
mask = comm.bcast(mask, root=0)
sysm = comm.bcast(sysm, root=0)
selection_fn = comm.bcast(selection_fn, root=0)
cls_list = get_cl(ngal,
nran,
mask,
selection_fn=selection_fn,
systematics=sysm,
njack=0)
if comm.rank == 0:
output_dir = os.path.dirname(args.output_path)
if not os.path.exists(output_dir):
print(f'creating {output_dir}')
os.makedirs(output_dir)
np.save(args.output_path, cls_list)
def main(ns, comm=None):
if comm.rank == 0:
for (key, value) in ns.__dict__.items():
print(f'{key:15s} : {value}')
# get list of windows
windows = glob(os.path.join(ns.hpmap_dir, '*.fits'))
print(f'# windows: {len(windows)}')
# read imaging maps
data = ft.read(ns.data_path)
frac = make_hp(nside, data['hpix'], data['fracgood'])
mask = make_hp(nside, data['hpix'], 1.0) > 0.5
else:
windows = None
frac = None
mask = None
windows = comm.bcast(windows, root=0)
frac = comm.bcast(frac, root=0)
mask = comm.bcast(mask, root=0)
start, end = split_NtoM(len(windows), comm.size, comm.rank)
cl = []
for i in range(start, end + 1):
t0 = time()
w_i = hp.read_map(windows[i], verbose=False, dtype=np.float64)
#w_i = w_i / np.mean(w_i[mask])
#w_i = w_i.clip(0.5, 2.0)
w_i = (w_i / w_i[mask].mean()) - 1.0
cl_i = anafast(w_i, frac, mask)['cl']
t1 = time()
if comm.rank == 0:
print(f'rank-{comm.rank} finished {i} in {t1-t0} sec')
cl.append(cl_i)
comm.Barrier()
cl = comm.gather(cl, root=0)
if comm.rank == 0:
cl = np.array(cl)
print(cl.shape)
lmax = cl.shape[-1]
cl = np.concatenate(cl.reshape(-1, lmax).flatten())
np.save(ns.output_path, cl, allow_pickle=False)
def main(args, comm=None):
if comm.rank == 0:
import healpy as hp
# --- only rank 0
# read data, randoms, and templates
data = ft.read(args.data_path)
nside = 256
#ngal = data['label']
#nran = data['fracgood']
nran = np.ones(data['hpix'].size)
mask = np.ones(data['hpix'].size, '?')
sysm = data['features']
ngal_ = hp.read_map(args.hpmap_path, verbose=False, dtype=np.float64)
ngal = ngal_[data['hpix']]
if args.selection is not None:
s_ = ft.read(args.selection)
selection_fn = make_hp(nside, s_['hpix'],
np.median(s_['weight'],
axis=1)) #.mean(axis=1))
selection_fn = selection_fn[data['hpix']]
print(np.percentile(selection_fn[mask], [0, 1, 99, 100]))
else:
selection_fn = None
else:
ngal = None
nran = None
mask = None
sysm = None
selection_fn = None
ngal = comm.bcast(ngal, root=0)
nran = comm.bcast(nran, root=0)
mask = comm.bcast(mask, root=0)
sysm = comm.bcast(sysm, root=0)
selection_fn = comm.bcast(selection_fn, root=0)
nnbar_list = get_meandensity(ngal,
nran,
mask,
sysm,
columns=columns,
selection_fn=selection_fn,
binning='simple',
percentiles=[1, 99],
global_nbar=True)
if comm.rank == 0:
output_dir = os.path.dirname(args.output_path)
if not os.path.exists(output_dir):
print(f'creating {output_dir}')
os.makedirs(output_dir)
np.save(args.output_path, nnbar_list)
def plot_ngmoll():
desi = ft.read(
'/fs/ess/PHS0336/data/rongpu/imaging_sys/tables/0.57.0/nlrg_features_desi_256.fits'
)
ng = make_hp(
256, desi['hpix'],
desi['label'] / (desi['fracgood'] * hp.nside2pixarea(256, True)),
np.inf)
print(np.mean(ng[desi['hpix']]))
mollview(ng,
400,
1200,
r'Observed Galaxy Density [deg$^{-2}$]',
cmap='YlOrRd_r',
colorbar=True,
galaxy=True)
plt.savefig('figs/nlrg.pdf', bbox_inches='tight')
def read_mask(region):
import fitsio as ft
import healpy as hp
from lssutils.utils import make_hp
if region in ['bmzls', 'ndecals', 'sdecals', 'ngc', 'desi']:
# read survey geometry
data_path = '/fs/ess/PHS0336/data/'
dt = ft.read(
f'{data_path}/rongpu/imaging_sys/tables/0.57.0/nlrg_features_{region}_256.fits'
)
mask_ = make_hp(256, dt['hpix'], 1.0) > 0.5
mask = hp.ud_grade(mask_, 1024)
else:
# full sky
mask = np.ones(12 * 1024 * 1024, '?')
return mask * 1.0, mask
data_ng = ft.read(f'{path}/density_maps/{tag_d}/resolve/density_map_sv3_{kind}_{cap}_nside_{nside}_minobs_1_maskbits_{mb}.fits')
elif tag_d=='1.0.0':
data_ng = ft.read(f'{path}/density_maps/{tag_d}/resolve/density_map_{kind}_{cap}_nside_{nside}_minobs_1_maskbits_{mb}.fits')
data_tmp = ft.read(f'{path}/randoms_stats/{tag_r}/resolve/combined/pixmap_{cap}_nside_{nside}_minobs_1_maskbits_{mb}.fits')
# split south into sdecals and ndecals
if region in ['ndecals', 'sdecals']:
is_region = ut.select_region(data_ng['RA'], data_ng['DEC'], region)
data_ng = data_ng[is_region]
is_region = ut.select_region(data_tmp['RA'], data_tmp['DEC'], region)
data_tmp = data_tmp[is_region]
ngal = ut.make_hp(nside, data_ng['HPXPIXEL'], data_ng['n_targets'])
hpix = data_tmp['HPXPIXEL']
fracgood = data_tmp['FRACAREA']
label = ngal[hpix]
features = []
for col in ut.maps_dr9:
feat_ = data_tmp[col]
features.append(feat_)
features = np.array(features).T
data_nn = ut.to_numpy(label, features, fracgood, hpix)
for name in data_nn.dtype.names:
assert (~np.isfinite(data_nn[name])).sum() == 0, f'{name} is bad'
if region=='ndecals':
def read_wnn(path2file):
w_ = ft.read(path2file)
w_nn = w_['weight'] / np.median(w_['weight'])
w_nn = w_nn.clip(0.5, 2.0)
return ut.make_hp(1024, w_['hpix'], w_nn, True)
