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 mcalcat_write(cat, path):
from os import remove
if exists(path):
remove(path)
fits = FITS(path, 'rw')
for c in cat:
fits.write(c.reset_index().to_records(index=False))
fits.close()
def write_fit(data, names, filename):
import fitsio
from fitsio import FITS, FITSHDR
import os.path
if not os.path.isfile(filename):
fits = FITS(filename, 'rw')
fits.write(data, names=names, clobber=False)
print("Writing file: ", filename)
else:
fits = FITS(filename, 'rw')
fits[-1].append(data)
print("Apending File: ", filename)
def random_preprocess():
randoms = fitsio.read('flagship_randoms_v2.fits')
ra, dec, jk_label = randoms["RA"], randoms["DEC"], randoms["JK_LABEL"]
centers = np.loadtxt("flagship_jk_centers_v2.txt")
for jk in range(len(centers)):
jk_mask = jk_label != jk
random_jk = {"RA": ra[jk_mask], "DEC": dec[jk_mask]}
fits = FITS('data/random_jk_' + str(jk) + '.fits', 'rw')
fits.write(random_jk, names=["RA", "DEC"])
fits.close()
return None
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
def write_maps(self, maps, fname):
f = FITS(fname, 'rw', clobber=True)
f.write([self.goodpix], names=['pixels'])
names = ['map_%d' % i for i in range(len(maps))]
f.write(list(maps), names=names)
f.close()
def write_maps(self, maps, fname):
f = FITS(fname, 'rw', clobber=True)
f.write(self.goodpix)
f.write(maps)
f.close()
