""" save the label features table as .fits file
"""
path_dir = os.path.dirname(path)
if not os.path.exists(path_dir):
os.makedirs(path_dir)
ft.write(path, table, clobber=True)
if __name__ == '__main__':
from argparse import ArgumentParser
ap = ArgumentParser(description='Prepare eBOSS data for NN regression')
ap.add_argument('-d', '--data_path', type=str, required=True)
ap.add_argument('-r', '--randoms_path', type=str, required=True)
ap.add_argument('-s', '--templates_path', type=str, required=True)
ap.add_argument('-o', '--output_path', type=str, required=True)
ap.add_argument('--label', type=str, default='ngal')
ap.add_argument('-n', '--nside', type=int, default=512)
ap.add_argument('-sl',
'--slices',
type=str,
nargs='*',
default=['main', 'highz'])
config = ap.parse_args()
setup_logging('info')
prepare_table(config)
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)
if __name__ == '__main__':
setup_logging("info") # turn on logging to screen
comm = CurrentMPIComm.get()
if comm.rank == 0:
print(f'hi from {comm.rank}')
from argparse import ArgumentParser
ap = ArgumentParser(description='Angular Clustering')
ap.add_argument('-d', '--data_path', required=True)
ap.add_argument('-o', '--output_path', required=True)
ap.add_argument('-s', '--selection', default=None)
ns = ap.parse_args()
for (key, value) in ns.__dict__.items():
print(f'{key:15s} : {value}')
else:
import sys
sys.path.append('/Users/mehdi/github/lssutils')
from lssutils.lab import *
from lssutils import setup_logging
setup_logging("info")
biases = [1.0, 2.0, 3.0, 4.0]
# initialize the task manager to run the tasks
with TaskManager(cpus_per_task=1, use_all_cpus=True) as tm:
# set up the linear power spectrum
# iterate through the bias values
for bias in tm.iterate(biases):
print(2 * bias)
def main(mockid, my_cols=cols_dr8_rand):
setup_logging('info')
logger = logging.getLogger('RegressionPrep')
# --- input parameters
nside = 256
dataframe = '/home/mehdi/data/templates/pixweight-dr8-0.31.1.h5'
random = f'/B/Shared/mehdi/mocksys/FA_EZmock_desi_ELG_v0_rand_00to2.hp{nside}.fits'
output_dir = '/B/Shared/mehdi/mocksys/regression/'
zcuts = {'low': [0.7, 1.0], 'high': [1.0, 1.5], 'all': [0.7, 1.5]}
#---
# start
#---
logger.info(f'ouput : {output_dir}')
# --- templates
df = pd.read_hdf(dataframe, key='templates')
logger.info(f'read {dataframe}')
# --- random
hprandom = hp.read_map(random, verbose=False)
logger.info(f'read {random}')
# --- data
data = ft.read(
f'/B/Shared/Shadab/FA_LSS/FA_EZmock_desi_ELG_v0_{mockid}.fits')
mask = ft.read(
f'/B/Shared/Shadab/FA_LSS/EZmock_desi_v0.0_{mockid}/bool_index.fits'
)['bool_index']
data = data[mask]
z_rsd = data['Z_COSMO'] + data['DZ_RSD']
logger.info(f'read mock-{mockid}')
for i, key_i in enumerate(zcuts):
logger.info('split based on {}'.format(zcuts[key_i]))
# --- prepare the names for the output files
hpcat = None #output_dir + f'/galmap_{mockid}_{key_i}_{nside}.hp.fits'
hpmask = None #output_dir + f'/mask_{mockid}_{key_i}_{nside}.hp.fits'
fracgood = None #output_dir + f'/frac_{mockid}_{key_i}_{nside}.hp.fits'
fitname = None #output_dir + f'/ngal_features_{mockid}_{key_i}_{nside}.fits'
fitkfold = output_dir + f'ngal_features_{mockid}_{key_i}_{nside}.5r.npy'
good = (z_rsd >= zcuts[key_i][0]) & (z_rsd < zcuts[key_i][1])
logger.info(f'total # : {good.sum()}')
hpdata = hpixsum(nside, data[good]['RA'], data[good]['DEC'])
# --- append the galaxy and random density
dataframe_i = df.copy()
dataframe_i['ngal'] = hpdata
dataframe_i['nran'] = hprandom
dataframe_i['nran'][hprandom == 0] = np.nan
dataframe_i.replace()
dataframe_i.replace([np.inf, -np.inf], value=np.nan,
inplace=True) # replace inf
dataframe_i.dropna(inplace=True)
logger.info('df shape : {}'.format(dataframe_i.shape))
logger.info('columns : {}'.format(my_cols))
for column in dataframe_i.columns:
logger.info(
f'{column}: {np.percentile(dataframe_i[column], [0,1,99, 100])}'
)
# --- write
hd5_2_fits(dataframe_i,
my_cols,
fitname,
hpmask,
fracgood,
fitkfold,
res=nside,
k=5)
default=[i for i in range(18)])
ap.add_argument('--nbin', default=8, type=int)
ap.add_argument('--njack', default=20, type=int)
ap.add_argument('--nside', default=256, type=int)
ap.add_argument('--lmax', default=512, type=int)
ap.add_argument('--smooth', action='store_true')
ap.add_argument('--verbose', action='store_true')
ns = ap.parse_args()
if not os.path.exists(ns.oudir):os.makedirs(ns.oudir)
logfile = ''.join([ns.oudir, ns.log]) if ns.log!='none' else None
if logfile is not None:print(f'log in {logfile}')
setup_logging('info', logfile=logfile)
else:
ns = None
ns = comm.bcast(ns, root=0)
#--- run
engine = PhotData(ns)
engine.read()
if ns.nnbar != 'none':
engine.run_nnbar() # mean density
if ns.clfile != 'none':
