def disterr(self):
"""
Distance error (given WMAP7 cosmology/H0)
"""
from astropy.cosmology import WMAP7
dist = WMAP7.luminosity_distance(self.zdist)
dp = abs(dist - WMAP7.luminosity_distance(self.zdist + self.zdisterr))
dm = abs(dist - WMAP7.luminosity_distance(self.zdist - self.zdisterr))
return (dp + dm) / 2
def dist(self):
"""
Distance to host (given WMAP7 cosmology/H0)
"""
from astropy.cosmology import WMAP7
return WMAP7.luminosity_distance(self.zdist)
def getAbsMag(self, z, appmag, Lbol, Filter): # Calculate value of fractions in K-correction tempflux, zeroflux = self.getFluxes(z, Lbol, Filter)[2:] frac1 = 10**(-0.4*appmag) frac2 = zeroflux/tempflux # Calculate K-correction KQR = -2.5 * np.log10(frac1*frac2) # Calculate distance modulus DLpc = WMAP7.luminosity_distance(z).to_value(u.pc) DM = 5 * np.log10(0.1 * DLpc) # Calculate apparent magnitude based on the apparent-absolute magnitude relationship absMag = appmag - DM - KQR return absMag
def getFluxes(self, z, Lbol, Filter): # Redshift to object's z DLcm = WMAP7.luminosity_distance(z).to_value(u.cm) waveZ = self.QSOtemp['Wave']*(1.+z) filtZ = np.interp(waveZ, Filter['lambda'], Filter['trans']) # Normalise spectral densities L5100 = Lbol / (9.*5100.) lum = L5100 * self.QSOtemp['FluxD']/self.QSOtemp['FluxD'][4300] flux = lum / (4*np.pi*DLcm*DLcm * (1.+z)) # Compute flux through filter tempflux = igrt.trapz(waveZ * flux * filtZ, x=waveZ) zeroflux = igrt.trapz(self.c/waveZ * self.AB * filtZ, x=waveZ) return waveZ, lum, tempflux, zeroflux
def getQSOmag(self, Lbol, z, Filter):
# Return apparent magnitude of QSO at redshift z in filter
# Redshift the QSO to z
waveZ = self.QSOTemplate['Wave'] * (1.0+z)
# Normalise spectrum from relative to absolute flux units
# Index 4300 is 5100.5 Angstroms
L5100 = Lbol/(5100.0*9.0)
lumZ = self.QSOTemplate['FluxD'] * L5100 / self.QSOTemplate['FluxD'][4300]
# Calculate fluxes based on luminosity distance DL in cm
DL = WMAP7.luminosity_distance(z) * 1.0e6 * 3.0857e18 # cm
fluxZ = lumZ / (4*np.pi*DL.value**2) # ergs/s/cm^2
# Convolve with Filter and report flux in AB mag
return self.QSOConv(waveZ, fluxZ, Filter)
def _init_code(self):
"""Compute the age of the Universe at a given redshift
"""
self.redshift = float(self.parameters["redshift"])
# Raise an error when applying a negative redshift. This module is
# not for blue-shifting.
if self.redshift < 0.:
raise Exception("The redshift provided is negative <{}>."
.format(self.redshift))
self.universe_age = cosmology.age(self.redshift).value * 1000.
if self.redshift == 0.:
self.luminosity_distance = 10. * parsec
else:
self.luminosity_distance = (
cosmology.luminosity_distance(self.redshift).value * 1e6 *
parsec)
# We do not define the values of the IGM attenuation component yet.
# This is because we need the wavelength grid for that first. This
# will be assigned on the first call.
self.igm_attenuation = {}
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as clr
import h5py
import dtk
import sys
from astropy.cosmology import WMAP7 as cosmo
import time
from scipy.interpolate import interp1d
h = 0.702
stepz = dtk.StepZ(200, 0, 500)
zs = np.linspace(0, 1.5, 1000)
z_to_dl = interp1d(zs, cosmo.luminosity_distance(zs))
step2nan_str_z = {}
param = dtk.Param(sys.argv[1])
output = param.get_string('output')
output_mod = output.replace('.hdf5', '_mod.hdf5')
hfile = h5py.File(output_mod, 'a')
redshift = hfile['galaxyProperties/redshiftHubble'].value
dl = z_to_dl(redshift)
print "done getting the luminosity distance..."
adjust = -2.5 * np.log10(1 + redshift) + 5 * np.log10(dl) + 25.0
def recal_obs(hgroup):
def dLum(z):
return cosmo.luminosity_distance(z).value * h #in Mpc/h
g = np.array(f[ch[1] + p])
b = np.array(f[ch[2] + p])
if plot:
print('Origional')
print(np.max(np.dstack((r, g, b))))
plt.imshow(
np.dstack(
(r[96:160, 96:160] / np.max(r[96:160, 96:160]),
g[96:160, 96:160] / np.max(g[96:160, 96:160]),
b[96:160, 96:160] / np.max(b[96:160, 96:160]))))
plt.show()
print('Pixel size of', round(px, 3), ' arcsec at z =',
table[z_col][rand_idx])
#correct brightness for distance
d_redsh = cosmo.luminosity_distance(
table[z_col][rand_idx]).value
r *= (d_eagle * d_eagle) / (d_redsh * d_redsh)
g *= (d_eagle * d_eagle) / (d_redsh * d_redsh)
b *= (d_eagle * d_eagle) / (d_redsh * d_redsh)
#Convert from maggies to mJy (do now to prevent overflow)
r *= 3.631e6
g *= 3.631e6
b *= 3.631e6
#Scale to match g-band observations
r *= scale
g *= scale
b *= scale
else:
r = np.array(f[ch[0] + p]) #Load data
g = np.array(f[ch[1] + p])
b = np.array(f[ch[2] + p])
from astropy.cosmology import WMAP7 as cosmo
from astroML.stats import binned_statistic_2d
import matplotlib as mpl
plt.rc('xtick', labelsize=16)
plt.rc('ytick', labelsize=16)
plt.rc('xtick', direction='inout')
plt.rc('ytick', direction='inout')
plt.rc('axes', linewidth=1.5)
plt.rc('font', family='sans-serif')
plt.rc('font', size=16)
f1 = fits.open(
'/Users/jarmijo/Documents/Mocks/mocks_MBlue_SDSSphot_mi23cut.fits')
data1 = f1[1].data
dL = cosmo.luminosity_distance(data1['Z'])
Ns = 9
zmin = 0.11
zmax = 0.9
M_min = -16.
M_max = -23.5
b = 30 #N of bins LF
dM = abs(M_max - M_min) / b
zbins = np.linspace(0.11, 0.9, Ns, endpoint=True)
# =============================================================================
# f,ax = plt.subplots(1,1,figsize=(7,6))
# ax.hist(data1['M_I'],bins=b,color='r',histtype='step',label='Mock')
# ax.set_yscale('log')
# ax.vlines(M_min,1,len(data1['M_I']),linestyles='--',colors='k')
F_in[c] *= ((dx2 - abs(Ix[c] - xi)) / dx)
elif (0 < (xi - Ix[c]) < dx2):
F_in[c] *= ((dx2 + abs(Ix[c] - xi)) / dx)
if c == len(Ix) - 1:
if (0 < (xf - Ix[c]) < dx2):
F_in[c] *= ((dx2 - abs(Ix[c] - xf)) / dx)
elif (0 < (Ix[c] - xf) < dx2):
F_in[c] *= ((dx2 + abs(Ix[c] - xf)) / dx)
P_B.append(np.sum(F_in * dx) / dx0)
PB = np.array(P_B)
PAUS_BLUE = PB[(table[:, 2] < 0.9) & (table[:, 2] > 0.11)]
new_table = table[(table[:, 2] < 0.9) & (table[:, 2] > 0.11)]
mB = -2.5 * np.log10(
PAUS_BLUE
) - 48 # from flux to PAUS magnitud e
dL = cosmo.luminosity_distance(new_table[:, 2]).value * cosmo.h
MB = mB - 25 - 5 * np.log10(dL)
new_table = np.vstack([new_table.T, MB]).T
t = Table(
new_table,
names=[
'RA', 'DEC', 'Z', 'SDSS_g', 'SDSS_i', 'SDSS_r', 'SDSS_u', 'SDSS_G',
'SDSS_I', 'SDSS_R', 'SDSS_U', 'SFR Mdot', 'MB'
],
)
print "saving data from " + str(len(MB)) + " of " + str(
i
) + "galaxies in fits format... at /cosma/home/durham/jarmijo/PAU_test/catalogs/mocks_MBlue_SDSSphot.fits\n"
t.write(
'/cosma/home/dp004/dc-armi2/PAU_test/catalogs/mocks_radecz_MIMB_SFRHaplha_'
+ icut + 'cut.fits',
