def run_on_single_catalog(self, catalog_instance, catalog_name, output_dir):
prepared = self.prepare_galaxy_catalog(catalog_instance)
if prepared is None:
return TestResult(skipped=True)
if self.use_redmapper:
try:
redmapper = GCRCatalogs.load_catalog(catalog_name+'_redmapper')
except:
return TestResult(skipped=True)
redmapper = redmapper.get_quantities(['galaxy_id'])
absolute_magnitude_field, mag_fields, quantities_needed = prepared
bins = (self.z_bins, self.c_bins, self.mass_bins)
hist_cen = np.zeros((self.n_z_bins, self.n_c_bins, self.n_mass_bins, self.n_bands-1))
hist_sat = np.zeros_like(hist_cen)
hist_mem_cen = np.zeros_like(hist_cen)
hist_mem_sat = np.zeros_like(hist_cen)
print(absolute_magnitude_field)
cen_query = GCRQuery('is_central & ({} < -19)'.format(absolute_magnitude_field))
sat_query = GCRQuery('(~is_central) & ({} < -19)'.format(absolute_magnitude_field))
if 'r_host' in quantities_needed and 'r_vir' in quantities_needed:
sat_query &= GCRQuery('r_host < r_vir')
for data in catalog_instance.get_quantities(quantities_needed, return_iterator=True):
cen_mask = cen_query.mask(data)
sat_mask = sat_query.mask(data)
if self.use_redmapper:
mem_mask = np.in1d(data['galaxy_id'], redmapper['galaxy_id'])
for i in range(self.n_bands-1):
color = data[mag_fields[i]] - data[mag_fields[i+1]]
hdata = np.stack([data['redshift_true'], color, data['halo_mass']]).T
hist_cen[:,:,:,i] += np.histogramdd(hdata[cen_mask], bins)[0]
hist_sat[:,:,:,i] += np.histogramdd(hdata[sat_mask], bins)[0]
if self.use_redmapper:
hist_mem_cen[:,:,:,i] += np.histogramdd(hdata[mem_mask & cen_mask], bins)[0]
hist_mem_sat[:,:,:,i] += np.histogramdd(hdata[mem_mask & sat_mask], bins)[0]
data = cen_mask = sat_mask = mem_mask = None
rs_mean, rs_scat, red_frac_sat, red_frac_cen = self.compute_summary_statistics(hist_sat, hist_cen,
hist_mem_sat, hist_mem_cen)
red_seq = {'rs_mean':rs_mean,
'rs_scat':rs_scat,
'red_frac_sat':red_frac_sat,
'red_frac_cen':red_frac_cen}
self.make_plot(red_seq, hist_cen, hist_sat, hist_mem_cen, hist_mem_sat, catalog_name, os.path.join(output_dir, 'red_sequence.png'))
return TestResult(inspect_only=True)
def run_on_single_catalog(self, catalog_instance, catalog_name,
output_dir):
prepared = self.prepare_galaxy_catalog(catalog_instance)
if prepared is None:
return TestResult(skipped=True)
absolute_magnitude1_field, absolute_magnitude2_field, quantities_needed = prepared
colnames = [absolute_magnitude2_field, 'halo_mass', 'redshift_true']
bins = (self.magnitude_bins, self.mass_bins, self.z_bins)
hist_cen = np.zeros(
(self.n_magnitude_bins, self.n_mass_bins, self.n_z_bins))
hist_sat = np.zeros_like(hist_cen)
red_query = GCRQuery((self.color_cut, absolute_magnitude1_field,
absolute_magnitude2_field, 'redshift_true'))
cen_query = GCRQuery('is_central') & red_query
sat_query = ~GCRQuery('is_central') & red_query
if 'r_host' in quantities_needed and 'r_vir' in quantities_needed:
sat_query &= GCRQuery('r_host < r_vir')
for data in catalog_instance.get_quantities(quantities_needed,
return_iterator=True):
cen_mask = cen_query.mask(data)
sat_mask = sat_query.mask(data)
data = np.stack((data[k] for k in colnames)).T
hist_cen += np.histogramdd(data[cen_mask], bins)[0]
hist_sat += np.histogramdd(data[sat_mask], bins)[0]
data = cen_mask = sat_mask = None
halo_counts = hist_cen.sum(axis=0)
clf = dict()
clf['sat'] = hist_sat / halo_counts
clf['cen'] = hist_cen / halo_counts
clf['tot'] = clf['sat'] + clf['cen']
self.make_plot(clf, catalog_name, os.path.join(output_dir, 'clf.png'))
return TestResult(passed=True, score=0)
def run_on_single_catalog(self, catalog_instance, catalog_name,
output_dir):
prepared = self.prepare_galaxy_catalog(catalog_instance)
if prepared is None:
return TestResult(skipped=True)
absolute_magnitude1_field, absolute_magnitude2_field, quantities_needed = prepared
# find out color cut threshold
color = []
for data in catalog_instance.get_quantities(
[
absolute_magnitude1_field, absolute_magnitude2_field,
'redshift_true'
],
filters=['redshift_true < 0.2'],
return_iterator=True,
):
color.append(data[absolute_magnitude1_field] -
data[absolute_magnitude2_field])
color_cut_percentile_at = 100.0 * (1 - self.color_cut_fraction)
color_cut_thres = np.percentile(np.concatenate(color),
color_cut_percentile_at)
del color
colnames = [absolute_magnitude2_field, 'halo_mass', 'redshift_true']
bins = (self.magnitude_bins, self.mass_bins, self.z_bins)
hist_cen = np.zeros(
(self.n_magnitude_bins, self.n_mass_bins, self.n_z_bins))
hist_sat = np.zeros_like(hist_cen)
cen_query = GCRQuery('is_central')
sat_query = ~GCRQuery('is_central')
if 'r_host' in quantities_needed and 'r_vir' in quantities_needed:
sat_query &= GCRQuery('r_host < r_vir')
for data in catalog_instance.get_quantities(
quantities_needed,
filters=[
'{} - {} > {}'.format(absolute_magnitude1_field,
absolute_magnitude2_field,
color_cut_thres)
],
return_iterator=True,
):
cen_mask = cen_query.mask(data)
sat_mask = sat_query.mask(data)
data = np.stack((data[k] for k in colnames)).T
hist_cen += np.histogramdd(data[cen_mask], bins)[0]
hist_sat += np.histogramdd(data[sat_mask], bins)[0]
data = cen_mask = sat_mask = None
halo_counts = hist_cen.sum(axis=0)
clf = dict()
clf['sat'] = hist_sat / halo_counts
clf['cen'] = hist_cen / halo_counts
clf['tot'] = clf['sat'] + clf['cen']
self.make_plot(clf, catalog_name, os.path.join(output_dir, 'clf.png'))
return TestResult(inspect_only=True)