def load_unknown_sample(sample_file_name, stomp_map, args):
""" Method for loading a set of objects with unknown redshifts into
The-wiZZ. This function maskes the data and returns a STOMP.iTreeMap object
which is a searchable quad tree where each object stored has a unique index.
If a name of an index column is not specified a simple counting index from
thestart of the file is stored.
----------------------------------------------------------------------------
Args:
sample_file_name: string name specifying the file containing the unknown
sample. Assumed file type is FITS.
stomp_map: STOMP.Map object specifying the geomometry of the area
considered.
args: ArgumentParser.parse_args object returned from input_flags.
Returns:
a STOMP::iTree map object
"""
print("Loading unknown sample...")
# TODO: This is the main bottle neck of the code. Need to make loading,
# masking, and creating the quadtree much faster. This may require
# creating a python wrapped C++ function for loading and creating
# a STOMP iTreeMap.
sample_data = _core_utils.file_checker_loader(sample_file_name)
unknown_itree_map = stomp.IndexedTreeMap(stomp_map.RegionResolution(), 200)
for idx, obj in enumerate(sample_data):
tmp_iang = stomp.IndexedAngularCoordinate(
obj[args.unknown_ra_name], obj[args.unknown_dec_name], idx, stomp.AngularCoordinate.Equatorial
)
if args.unknown_index_name is not None:
tmp_iang.SetIndex(int(obj[args.unknown_index_name]))
if stomp_map.Contains(tmp_iang):
unknown_itree_map.AddPoint(tmp_iang)
print("\tLoaded %i / %i target galaxies..." % (unknown_itree_map.NPoints(), sample_data.shape[0]))
return unknown_itree_map
def load_target_sample(sample_file_name, stomp_map, args):
"""Method for loading the targert object sample with known redshifts. The
objects are masked against the requested geomometry and stored with their
redshifts and into a STOMP.CosmoVector object. The code also returns an
array of the indices of the target objects from the columns requested in
input_flags or simply counts.
----------------------------------------------------------------------------
Args:
sample_file_name: string name of the file containing the target, known
redshift objects. Currently only FITS is supported.
stomp_map: STOMP.Map object specifying the geomometry of the area
considered.
args: ArgumentParser.parse_args object returned from input_flags.
Returns:
tuple: STOMP::CosmosVector, int array
"""
print("Loading target sample...")
sample_data = _core_utils.file_checker_loader(sample_file_name)
target_vect = stomp.CosmoVector()
target_idx_array = np.ones(sample_data.shape[0]) * -99
for idx, obj in enumerate(sample_data):
if obj[args.target_redshift_name] < args.z_min or obj[args.target_redshift_name] >= args.z_max:
# Continue if the target object redshift is out of range.
continue
tmp_cang = stomp.CosmoCoordinate(
np.double(obj[args.target_ra_name]),
np.double(obj[args.target_dec_name]),
np.double(obj[args.target_redshift_name]),
1.0,
stomp.AngularCoordinate.Equatorial,
)
if stomp_map.Contains(tmp_cang):
target_vect.push_back(tmp_cang)
if args.target_index_name is None:
target_idx_array[idx] = idx
else:
target_idx_array[idx] = obj[args.target_index_name]
print("\tLoaded %i / %i target galaxies..." % (target_vect.size(), sample_data.shape[0]))
return target_vect, target_idx_array[target_idx_array > -99]
args = input_flags.parse_input_pdf_single_galaxy_args()
print("")
print("The-wiZZ has begun conjuring: running pair maker for single "
"galaxies...")
input_flags.print_args(args)
### Create the HDF5 file we will write out to store the PDFs.
output_pdf_hdf5_file = _core_utils.create_hdf5_file(
args.output_pdf_hdf5_file, args)
### Load the file containing all matched pairs of spectroscopic and
### photometric objects.
print("Loading files...")
hdf5_pair_file = _core_utils.file_checker_loader(args.input_pair_hdf5_file)
unknown_data = _core_utils.file_checker_loader(args.unknown_sample_file)
match_data = _core_utils.file_checker_loader(args.match_sample_file)
### Load the spectroscopic data from the HDF5 data file.
print("Preloading target data...")
pdf_maker = _pdf_maker_utils.PDFMaker(hdf5_pair_file[args.pair_scale_name],
args)
target_pair_data = _pdf_maker_utils._load_pair_data(
hdf5_pair_file[args.pair_scale_name], 0,
pdf_maker.target_redshift_array.shape[0])
if pdf_maker.target_redshift_array.max() < args.z_max:
print("WARNING: requested z_max is greater than available target "
"redshifts.")
### Now we figure out what kind of redshift binning we would like to have.
### This will be one of the largest impacts on the signal to noise of the
### measurement. Some rules of thumb are:
