### Before we calculated the pdfs, we want to know what the over densities
### are in each of the regions calculated on the area we consider.
print("Calculating region densities...")
pdf_maker.compute_region_densities(z_bin_edge_array, args.z_max)
if args.output_region_pickle_file is not None:
pdf_maker.write_region_densities(args.output_region_pickle_file, args)
### Now that we've "collapsed" the estimate around the target object we need
### to bin up the results in redshift and create our final PDF.
print("Calculating pdf...")
if args.bootstrap_samples is None:
pdf_maker.compute_pdf_bootstrap(args.n_bootstrap)
else:
bootstrap_region_array = np.loadtxt(args.bootstrap_samples,
dtype = np.int_)
pdf_maker._compute_pdf_bootstrap(bootstrap_region_array)
### Write individual bootstraps to file.
if args.output_bootstraps_file is not None:
pdf_maker.write_bootstrap_samples_to_ascii(args.output_bootstraps_file,
args)
### Now that we have the results. We just need to write them to file and we
### are done.
print("Writing...")
output_file = _core_utils.create_ascii_file(args.output_pdf_file_name, args)
pdf_maker.write_pdf_to_ascii(output_file)
output_file.close()
### TODO:
### Include bootstrapping and writing of the raw boostrap samples.
print("Done!")
# finder
pair_finder = _pair_maker_utils.RawPairFinder(unknown_itree, target_vector,
target_ids, stomp_map)
# We need to tell the pair finder what scale we would like to run over
# before we begin.
min_scale_list = args.min_scale.split(',')
max_scale_list = args.max_scale.split(',')
print(min_scale_list)
print(max_scale_list)
if len(min_scale_list) != len(max_scale_list):
print("Number of min scales requested does not match number of max"
"sales. Exitting.")
sys.exit()
over_density_list = []
# Loop over the min/max scales requested.
output_file = _core_utils.create_ascii_file(args.output_pair_hdf5_file,
args)
output_file.writelines('#type0 = redshift\n')
for scale_idx, min_scale, max_scale in enumerate(zip(min_scale_list,
max_scale_list)):
output_file.writelines('#type%i = kpc%st%s\n' %
(scale_idx + 1, min_scale, max_scale))
print("Running scale: %s to %s" % (min_scale, max_scale))
# Pair finder does what it says. It also computes the areas, unmasked
# fractions for target object.
pair_finder.find_pairs(np.float_(min_scale), np.float_(max_scale))
# This is an optional part of the pair finder. It takes as an argument
# the a stomp tree map containing uniform random points as generated
# by the stomp. For non-uniform randoms it is recommened that the
# user run this software with said randoms as the unknown sample.
if args.n_randoms > 0:
pair_finder.random_loop(np.float_(min_scale), np.float_(max_scale),
