def __init__(self, snapnum, subhalos, mi, ci, RA_deg, DEC_deg, snapz,
galaxy_camera_posx, galaxy_camera_posy, galaxy_camera_posz,
centerz, galaxy_camera_velx, galaxy_camera_vely,
galaxy_camera_velz, cyl, gmag):
#fields=['SubhaloMass','SubhaloMassInMaxRad','SubhaloMassInRadType','SubhaloMassInMaxRadType','SubhaloPos','SubhaloSFR','SubhaloSFRinRad','SubhaloVel','SubhaloBHMass','SubhaloBHMdot','SubhaloStellarPhotometrics','SubhaloWindMass']
self.snapshot_number = snapnum + np.zeros_like(ci)
self.subhalo_index = subhalos['SubFindID'][mi[ci]]
self.RA_deg = RA_deg
self.DEC_deg = DEC_deg
self.snapz = snapz + np.zeros_like(RA_deg)
self.center_z = centerz + np.zeros_like(RA_deg)
self.cylinder_number = cyl + np.zeros_like(self.subhalo_index)
self.galaxy_comoving_x_mpc = galaxy_camera_posx[ci] / 1000.0
self.galaxy_comoving_y_mpc = galaxy_camera_posy[ci] / 1000.0
self.galaxy_comoving_z_mpc = galaxy_camera_posz[ci] / 1000.0
#self.galaxy_camera_posx = galaxy_camera_posx[ci]/1000.0
#self.galaxy_camera_posy = galaxy_camera_posy[ci]/1000.0
#self.galaxy_camera_posz = galaxy_camera_posz[ci]/1000.0
self.galaxy_camera_velx = galaxy_camera_velx[ci]
self.galaxy_camera_vely = galaxy_camera_vely[ci]
self.galaxy_camera_velz = galaxy_camera_velz[ci]
self.galaxy_peculiar_vr = 1.0 * self.galaxy_camera_velz
self.galaxy_peculiar_z = 1.0 * self.galaxy_peculiar_vr / (
astropy.constants.c.value / 1.0e3)
self.cosmological_redshift = np.zeros_like(self.RA_deg)
for i, index in enumerate(ci):
self.cosmological_redshift[i] = np.float64(
z_at_value(WMAP7.comoving_distance,
self.galaxy_comoving_z_mpc[i] * u.megaparsec,
ztol=1e-12,
maxfun=2000))
self.hubble_velocity = self.cosmological_redshift * astropy.constants.c.value / 1.0e3 #in km/s
self.galaxy_observed_z = 1.0 * self.cosmological_redshift + self.galaxy_peculiar_z
#self.galaxy_comoving_x_mpc = self.galaxy_camera_posx*(1.0 + self.cosmological_redshift)
#self.galaxy_comoving_y_mpc = self.galaxy_camera_posy*(1.0 + self.cosmological_redshift)
#self.galaxy_comoving_z_mpc = self.galaxy_camera_posz*(1.0 + self.cosmological_redshift)
self.galaxy_camera_posx = self.galaxy_comoving_x_mpc / (
1.0 + self.cosmological_redshift)
self.galaxy_camera_posy = self.galaxy_comoving_y_mpc / (
1.0 + self.cosmological_redshift)
self.galaxy_camera_posz = self.galaxy_comoving_z_mpc / (
1.0 + self.cosmological_redshift)
self.angdiam_mpc = np.asarray(
WMAP7.angular_diameter_distance(self.cosmological_redshift))
self.kpc_per_arcsec = np.asarray(
WMAP7.kpc_proper_per_arcmin(self.cosmological_redshift) / 60.0)
self.observed_angdiam_mpc = np.asarray(
WMAP7.angular_diameter_distance(self.galaxy_observed_z))
self.observed_comoving_mpc = np.asarray(
WMAP7.comoving_distance(self.galaxy_observed_z))
self.observed_kpc_per_arcsec = np.asarray(
WMAP7.kpc_proper_per_arcmin(self.galaxy_observed_z) / 60.0)
self.RA_kpc = self.RA_deg * 3600.0 * self.kpc_per_arcsec
self.DEC_kpc = self.DEC_deg * 3600.0 * self.kpc_per_arcsec
self.observed_RA_kpc = self.RA_deg * 3600.0 * self.observed_kpc_per_arcsec
self.observed_DEC_kpc = self.DEC_deg * 3600.0 * self.observed_kpc_per_arcsec
self.mstar_msun = subhalos['SubhaloMassInRadType'][self.subhalo_index,
4] * (1.0e10) / ilh
self.mgas_msun = subhalos['SubhaloMassInRadType'][
self.subhalo_index, 0] * (1.0e10) / ilh #includes wind mass
self.mbh_msun = subhalos['SubhaloMassInRadType'][self.subhalo_index,
5] * (1.0e10) / ilh
self.mhalo_msun = subhalos['SubhaloMass'][self.subhalo_index] * (
1.0e10) / ilh
self.baryonmass_msun = self.mstar_msun + self.mgas_msun + self.mbh_msun #within 2x stellar half mass radius... best?
self.xpos_ckh = subhalos['SubhaloPos'][self.subhalo_index,
0] #in cKpc/h of max bound part
self.ypos_ckh = subhalos['SubhaloPos'][self.subhalo_index, 1]
self.zpos_ckh = subhalos['SubhaloPos'][self.subhalo_index, 2]
self.xpos_pmpc = (self.xpos_ckh * 1.0 / (1.0 + snapz) / ilh) / 1.0e3
self.ypos_pmpc = (self.ypos_ckh * 1.0 / (1.0 + snapz) / ilh) / 1.0e3
self.zpos_pmpc = (self.zpos_ckh * 1.0 / (1.0 + snapz) / ilh) / 1.0e3
self.xvel_kms = subhalos['SubhaloVel'][self.subhalo_index, 0]
self.yvel_kms = subhalos['SubhaloVel'][self.subhalo_index, 1]
self.zvel_kms = subhalos['SubhaloVel'][self.subhalo_index, 2]
self.sfr = subhalos['SubhaloSFRinRad'][self.subhalo_index]
self.bhmdot = subhalos['SubhaloBHMdot'][self.subhalo_index]
self.gmag = subhalos['SubhaloStellarPhotometrics'][self.subhalo_index,
4]
self.rmag = subhalos['SubhaloStellarPhotometrics'][self.subhalo_index,
5]
self.imag = subhalos['SubhaloStellarPhotometrics'][self.subhalo_index,
6]
self.zmag = subhalos['SubhaloStellarPhotometrics'][self.subhalo_index,
7]
self.gmag_apparent = gmag[ci]
#self.total_redshift =
return
i = np.random.randint(len(objs[div]))
print(objs[div][i])
if i % tenpct == 0:
print(i, 'of', len(objs[div]))
#Loop over the simulation projections
for p in projection:
#Load data, has to be reloaded for reasons (IDK)
f = h5py.File(objs[div][i], 'r')
px = 1.0
#get redshift and determine effective resolution of EAGLE
if high_z:
while px / 3600 > 0.00011:
rand_idx = np.random.randint(len(table))
kpc_as = cosmo.kpc_proper_per_arcmin(
table[z_col][rand_idx]).to(u.kpc / u.arcsec)
px = (((60 * u.kpc) / kpc_as) /
256).value #calculate pixel size at redshift
else:
rand_idx = np.random.randint(len(table))
kpc_as = cosmo.kpc_proper_per_arcmin(
table[z_col][rand_idx]).to(u.kpc / u.arcsec)
px = (((60 * u.kpc) / kpc_as) /
256).value #calculate pixel size at redshift
wcs_egl.wcs.cdelt = np.array([px / 3600,
px / 3600]) #update eagle WCS
obj_name.append(objs[div][i][len(path) + len(div) + 1:-len(extn)] +
p)
obj_zsft.append(table[z_col][rand_idx])
def calculate_Pr_center2(fname):
cat = {}
hdulist = pyfits.open(fname)
tdata = hdulist[1].data
print(hdulist[1].columns)
# help(hdulist[1])
print(tdata)
cat['p_cen'] = tdata.field('p_cen')
cat['z_lambda'] = tdata.field('z_lambda')
cat['ra_cen'] = tdata.field('ra_cen')
cat['dec_cen'] = tdata.field('dec_cen')
print(cat['p_cen'])
probs = []
d_kpcs = []
non_center_avg = []
non_center_avg_prob = []
correct_prop = []
radii_to_save = []
print("progress")
for i in range(len(cat['z_lambda'])):
# for i in range(1000):
z = cat['z_lambda'][i]
a = 1.0/(1.0+z)
prob = cat['p_cen'][i]
ra = cat['ra_cen'][i]
dec = cat['dec_cen'][i]
d_r = rad_dist2(ra, dec, ra[0], dec[0])
d_kpc = cosmowmap7.kpc_proper_per_arcmin(z).value * (60.0*d_r)
probs += list(prob)
d_kpcs += list(d_kpc)
## Converting from physical kpc to comoving kpc/h
## 1.27323
non_center_avg.append(np.average(d_kpcs[1:])*0.7/a)
non_center_avg_prob.append(np.average(d_kpc[1:], weights=prob[1:])*0.7/a)
correct_prop.append(prob[0])
radii_to_save.append(select_radius(prob, d_kpc)*0.7/a)
if i%1000 == 0:
print('\t{:.3f}\r'.format(i/len(cat['z_lambda'])), end='')
print()
save_radii('data/Pr_center/distribution.hdf5', np.array(radii_to_save)/1000)
save_radii('data/Pr_center/distribution.0mpc.hdf5', np.zeros_like(radii_to_save))
save_radii('data/Pr_center/distribution.1mpc.hdf5', np.ones_like(radii_to_save))
probs = np.array(probs)
d_kpcs = np.array(d_kpcs)
# probs[d_kpcs>0.2] = 0
plt.figure()
plt.hist(probs, bins=np.linspace(0,1,100))
plt.yscale('log')
plt.figure()
plt.hist(d_kpcs/1000.0, bins=64, histtype='step', label='unweighted')
plt.hist(d_kpcs/1000.0, bins=64, weights=probs, histtype='step', label='weighted')
plt.ylabel('Counts')
plt.xlabel('Clustering Miscentering Distance\n[h$^{-1}$ Mpc Comoving]')
plt.legend(framealpha=0.0)
# plt.yscale('log')
plt.tight_layout()
num_clusters = len(cat['z_lambda'])
avg_displaced = np.sum((probs*d_kpcs))/num_clusters
print("Average Miscentering: {:2f} [comoving kpc/h]".format(avg_displaced))
print("Average Distance when Miscentered: {:.2f} [comoving kpc/h]".format(np.average(non_center_avg_prob)))
print("Miscentering Prob: {:.2f}".format(np.average(correct_prop)))
