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 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 run_cl(self):
if self.comm.rank==0:
self.logger.info('C_ell vs systematics')
self.logger.info(f'{self.df.shape}')
cl_obs = get_cl(self.galmap, self.ranmap, self.mask,
systematics=self.df, njack=self.args['njack'])
if self.comm.rank==0:
ouname = ''.join([self.args['oudir'], self.args['clfile']])
np.save(ouname, cl_obs)
self.logger.info(f'write cl in {ouname}')
def angular_power(args, columns=maps_eboss_v7p2):
zmin, zmax = args.zlim
nside = args.nside
use_systot = args.use_systot
data_path = args.data_path
randoms_path = args.randoms_path
templates_path = args.templates_path
output_path = args.output_path
# read data, randoms, and templates
data = EbossCat(data_path, kind='data', zmin=zmin, zmax=zmax)
randoms = EbossCat(randoms_path, kind='randoms', zmin=zmin, zmax=zmax)
templates = pd.read_hdf(templates_path, key='templates')
sysm = templates[columns].values
if nside is None:
nside = npix2nside(sysm.shape[0])
# project to HEALPix
if use_systot:
ngal = data.to_hp(nside, zmin, zmax, raw=2)
nran = randoms.to_hp(nside, zmin, zmax, raw=2)
else:
ngal = data.to_hp(nside, zmin, zmax, raw=1)
nran = randoms.to_hp(nside, zmin, zmax, raw=1)
# construct the mask
mask_nran = nran > 0
mask_ngal = np.isfinite(ngal)
mask_sysm = (~np.isfinite(sysm)).sum(axis=1) < 1
if npix2nside(sysm.shape[0]) != nside:
mask_sysm = ud_grade(mask_sysm, nside_out=nside)
mask = mask_sysm & mask_nran & mask_ngal
nran_bar = nside2pixarea(nside, degrees=True)*5000.
cls_list = get_cl(ngal, nran, mask, systematics=None, njack=0, nran_bar=nran_bar) # no systematics
output_dir = os.path.dirname(output_path)
if not os.path.exists(output_dir):
print(f'creating {output_dir}')
os.makedirs(output_dir)
np.save(output_path, cls_list)
# In[7]:
assert np.array_equal(np.sort(wnn_old['hpind']), np.sort(wnn['hpix']))
# In[8]:
# compute C_ells
kw = dict(njack=0, nran_bar=nran_mean)
cl_old = get_cl(nobs_w, nran_w, mask, selection_fn=wnn_old_hp, **kw) # old NN
cl_def = get_cl(nobs_def, nran_def, mask, **kw) # default systot
cl_avg = {}
cl_avg[-1] = get_cl(nobs_w, nran_w, mask, selection_fn=None, **kw) # wo correction
# average wNN over chains -> get C_ell
for idx in range(1, wnn['weight'].shape[1]+1):
wsys = wnn['weight'][:, :idx].mean(axis=1)
wsys_hp = make_hp(hpix, wsys)
wsys_normed = normalize(wsys_hp, nobs_w, mask)
cl_avg[idx] = get_cl(nobs_w, nran_w, mask, selection_fn=wsys_normed, **kw)
print('.', end='')
def angular_power_wcross(args, columns=maps_eboss_v7p2, comm=None):
if comm.rank == 0:
# --- only rank 0
zmin, zmax = args.zlim
nside = args.nside
use_systot = args.use_systot
data_path = args.data_path
randoms_path = args.randoms_path
templates_path = args.templates_path
output_path = args.output_path
# read data, randoms, and templates
data = EbossCat(data_path, kind='data', zmin=zmin, zmax=zmax)
randoms = EbossCat(randoms_path, kind='randoms', zmin=zmin, zmax=zmax)
templates = pd.read_hdf(templates_path, key='templates')
sysm = templates[columns].values
if nside is not None:
assert sysm.shape[0] == 12*nside*nside, 'templates do not match with nside'
else:
nside = npix2nside(sysm.shape[0])
# project to HEALPix
if use_systot:
ngal = data.to_hp(nside, zmin, zmax, raw=2)
nran = randoms.to_hp(nside, zmin, zmax, raw=2)
else:
ngal = data.to_hp(nside, zmin, zmax, raw=1)
nran = randoms.to_hp(nside, zmin, zmax, raw=1)
# construct the mask
mask_nran = nran > 0
mask_ngal = np.isfinite(ngal)
mask_sysm = (~np.isfinite(sysm)).sum(axis=1) < 1
mask = mask_sysm & mask_nran & mask_ngal
nran_bar = nside2pixarea(nside, degrees=True)*5000.
else:
ngal = None
nran = None
mask = None
sysm = None
nran_bar = 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)
nran_bar = comm.bcast(nran_bar, root=0)
cls_list = get_cl(ngal, nran, mask, systematics=sysm, njack=0, nran_bar=nran_bar, cross_only=args.cross_only)
if comm.rank == 0:
output_dir = os.path.dirname(output_path)
if not os.path.exists(output_dir):
print(f'creating {output_dir}')
os.makedirs(output_dir)
np.save(output_path, cls_list)