def lens_rand_jk(zmin, zmax, jk):
lens_file = h5py.File('LRG_lum_jk.h5')
lens_z = lens_file["redshift"][:]
lens_ra = lens_file["RA"][:]
lens_dec = lens_file["DEC"][:]
coord_lens_jk = np.vstack([lens_ra, lens_dec]).T
centers = np.loadtxt("centers.txt")
labels_lens = kmeans_radec.find_nearest(coord_lens_jk, centers)
label_mask = labels_lens == jk
lens_mask = (lens_z > zmin) & (lens_z < zmax) & (~label_mask)
lens_z = lens_z[lens_mask]
lens_ra = lens_ra[lens_mask]
lens_dec = lens_dec[lens_mask]
nlens = len(lens_z)
lens_input = np.vstack(
[np.arange(nlens), lens_ra, lens_dec, lens_z,
np.ones((nlens))]).T
random_file = h5py.File("randoms_jk.h5")
random_coord = random_file["random_jk"][:]
random_ra = random_coord[:, 0]
random_dec = random_coord[:, 1]
centers = np.loadtxt("centers.txt")
labels_random = kmeans_radec.find_nearest(random_coord, centers)
label_mask = labels_random == jk
random_ra = random_ra[~label_mask]
random_dec = random_dec[~label_mask]
random_file.close()
nrands = len(random_ra)
shuffle = np.random.choice(nrands, 10 * nlens)
random_ra = random_ra[shuffle]
random_dec = random_dec[shuffle]
nrands = len(random_ra)
print "nrands/nlens", nrands * 1. / nlens
zdist = lens_z
hist, bins = np.histogram(zdist, bins=100)
bin_midpoints = bins[:-1] + np.diff(bins) / 2
cdf = np.cumsum(hist)
cdf = cdf / float(cdf[-1])
values = np.random.rand(nrands)
value_bins = np.searchsorted(cdf, values)
random_z = bin_midpoints[value_bins]
rands_input = np.stack([
np.arange(nrands), random_ra, random_dec, random_z,
np.ones((nrands))
]).T
return lens_input, rands_input
def incomplete_shear_preprocess(zmin, zmax):
shear = fitsio.read("source_v2.fits",
columns=[
"ra_gal", "dec_gal", "observed_redshift_gal",
"gamma1", "gamma2"
])
ra, dec, z = shear["ra_gal"], shear["dec_gal"], shear[
"observed_redshift_gal"]
g1, g2 = shear["gamma1"], shear["gamma2"]
dz = 0.05 * (1 + z) #redshift uncertainties
z = np.random.normal(z, dz) #perturbed redshifts
mask = (z > zmax + 0.1)
z = z[mask]
ra, dec = ra[mask], dec[mask]
g1, g2 = g1[mask], g2[mask]
print("length of the catalog after applying the cut", len(ra))
coord = np.vstack([ra, dec]).T
centers = np.loadtxt("flagship_jk_centers_v2.txt")
NJK = centers.shape[0]
print("Segmentation begins!")
gal_labels_jk = kmeans_radec.find_nearest(coord, centers)
print("Done with assigning jacknife labels to galaxies")
gals = {
"RA": ra,
"DEC": dec,
"gamma1": g1,
"gamma2": g2,
"redshift": z,
"JK_LABEL": gal_labels_jk
}
fits = FITS('data/incomplete_shear_zmax_' + str(round(zmax, 1)) + '.fits',
'rw')
fits.write(gals,
names=["RA", "DEC", "gamma1", "gamma2", "redshift", "JK_LABEL"])
fits.close()
for jk in range(len(centers)):
gal_jk_mask = gals["JK_LABEL"] != jk
gal_jk = {
"RA": ra[gal_jk_mask],
"DEC": dec[gal_jk_mask],
"gamma1": g1[gal_jk_mask],
"gamma2": g2[gal_jk_mask],
"redshift": z[gal_jk_mask]
}
fits = FITS(
'data/incomplete_shear_zmax_' + str(round(zmax, 1)) + '_jk_' +
str(jk) + '.fits', 'rw')
fits.write(gal_jk, names=["RA", "DEC", "gamma1", "gamma2", "redshift"])
fits.close()
return None
def segment_data(RA, DEC):
coord = np.vstack([RA, DEC]).T
#centers of the jacknife regions
centers = np.loadtxt("new_DR4_9bandnoAwr_uniform_centers.txt")
#number of jacknife regions
NJK = centers.shape[0]
print("Segmentation begins!")
labels_jk = kmeans_radec.find_nearest(coord, centers)
print("Done with assigning jacknife labels to galaxies")
#final_cat = {"RA": RA,
# "DEC": DEC,
# "JK_LABEL": labels_jk}
return labels_jk
def find_kmeans_labels(points, centers):
import numpy as np
import kmeans_radec as kmrd
import sys
# Make sure ra, dec, and centers have correct shapes
assert points.shape[0] == 2 or points.shape[
1] == 2, "Points must have RA and Dec"
if points.shape[0] == 2:
points.T
assert centers.shape[0] == 2 or centers.shape[
1] == 2, "Centers must have RA and Dec"
if centers.shape[0] == 2:
centers.T
# Run find_nearest
return (kmrd.find_nearest(points, centers))
def incomplete_gal_preprocess(zmin, zmax):
lens = fitsio.read("lens.fits",
columns=["ra_gal", "dec_gal", "observed_redshift_gal"])
ra, dec, z = lens["ra_gal"], lens["dec_gal"], lens["observed_redshift_gal"]
mask = (z > zmin) & (z < zmax)
ra, dec, z = ra[mask], dec[mask], z[mask]
random_choice = np.random.choice(np.arange(len(ra)),
int(0.45 * len(ra)),
replace=False)
ra, dec, z = ra[random_choice], dec[random_choice], z[random_choice]
print("length of the catalog after applying the cut", len(ra))
coord = np.vstack([ra, dec]).T
centers = np.loadtxt("flagship_jk_centers_v2.txt")
NJK = centers.shape[0]
print("Segmentation begins!")
gal_labels_jk = kmeans_radec.find_nearest(coord, centers)
print("Done with assigning jacknife labels to galaxies")
gals = {"RA": ra, "DEC": dec, "redshift": z, "JK_LABEL": gal_labels_jk}
fits = FITS('data/incomplete_gal_zmax_' + str(round(zmax, 1)) + '.fits',
'rw')
fits.write(gals, names=["RA", "DEC", "redshift", "JK_LABEL"])
fits.close()
for jk in range(len(centers)):
gal_jk_mask = gals["JK_LABEL"] != jk
gal_jk = {
"RA": ra[gal_jk_mask],
"DEC": dec[gal_jk_mask],
"redshift": z[gal_jk_mask]
}
fits = FITS(
'data/incomplete_gal_zmax_' + str(round(zmax, 1)) + '_jk_' +
str(jk) + '.fits', 'rw')
fits.write(gal_jk, names=["RA", "DEC", "redshift"])
fits.close()
return None
import seaborn as sns
from kmeans_radec import KMeans, kmeans_sample
random_file = h5py.File("randoms_jk.h5")
random_coord = random_file["random_jk"][:]
random_ra = random_coord[:, 0]
random_dec = random_coord[:, 1]
random_file.close()
random_file_jk = h5py.File("randoms_jk.h5")
coord_rand_jk = random_file_jk["random_jk"][:]
random_ra_jk = coord_rand_jk[:, 0]
random_dec_jk = coord_rand_jk[:, 1]
random_file_jk.close()
centers = np.loadtxt("centers.txt")
labels_random_jk = kmeans_radec.find_nearest(coord_rand_jk, centers)
lens_file_jk = h5py.File("LRG_dense_jk.h5")
dense_jk_mask = h5py.File("dense_jk_mask.h5")
dense_mask = dense_jk_mask["mask"][:]
dense_jk_mask.close()
lens_z_jk = lens_file_jk["redshift"][dense_mask]
lens_ra_jk = lens_file_jk["RA"][dense_mask]
lens_dec_jk = lens_file_jk["DEC"][dense_mask]
lens_file_jk.close()
coord_lens_jk = np.vstack([lens_ra_jk, lens_dec_jk]).T
centers = np.loadtxt("centers.txt")
labels_lens_jk = kmeans_radec.find_nearest(coord_lens_jk, centers)
random_ra = random_coord[:, 0]
random_dec = random_coord[:, 1]
def get_center_labels(x, cent):
return kmrd.find_nearest(x.T, cent)
