def outFrac_3sigma68_1pz(z1, z2):
delta_z_1pz = bhz.delta_z_1pz(z1, z2)
return bhz.outFrac_3sigma68(delta_z_1pz)
def median_1pz(z1, z2):
delta_z_1pz = bhz.delta_z_1pz(z1, z2)
return np.median(delta_z_1pz)
def sigma_68_1pz(z1, z2):
delta_z_1pz = bhz.delta_z_1pz(z1, z2)
return bhz.sigma_68(delta_z_1pz)
res[ptype][f][test_name] = {}
#should we calculate an error on these metrics
error_function = pval.key_not_none(tst, 'error_function')
err_metric = {}
if error_function:
for ef in tst['error_function']:
#turn error function.string into a function
err_metric[ef.split('.')[-1]] = pval.get_function(ef)
for photoz in tst['predictions']:
res[ptype][f][test_name][photoz] = {}
diff = pval.delta_z(d[tst['truths']], d[photoz])
diff_1pz = pval.delta_z_1pz(d[tst['truths']], d[photoz])
points = {'delta_z': diff, 'diff_1pz': diff_1pz}
for metric in tst['metrics']:
#set all objects equal weight, unless defined
weights = get_weights(tst, 'weights', d)
res[ptype][f][test_name][photoz][metric] = {}
#turn string into function
metric_function = pval.get_function(metric)
#which residuals shall we employ?
for diffpp in points.keys():