def mi_star(z, cosmo=(0.3, 0.7, 0.7)):
# Set the cosmology
c = cosmopy.set(cosmo)
dlum = c.dlum(z)[0]
Mb_star = -19.43 - 1.01 * z
Mi_star = cosmology.reobs('El_Benitez2003',
m=Mb_star,
oldfilter="B_Johnson",
newfilter="i_MOSAICII")
return Mi_star + 5.0 * math.log10(dlum) + 25
def mr_star(z, h=0.7, cosmo=(0.3, 0.7, 0.7)):
#dlum = self.c.dlum(z)[0]
dlum = cosmology.dl(z, cosmology=cosmo)
# Red galaxies fit from Brown et al
Mb_star = -19.43 - 1.01 * z
Mr_star = cosmology.reobs('El_Benitez2003',
m=Mb_star,
oldfilter="B_Johnson",
newfilter="r_SDSS")
# Alternative -- Paolillo et al. (2001) LF for clusters
#Mr_star = -21.53 + self.evf['r'](z)[0]
#Mi_star = cosmology.reobs('El_Benitez2003',m=Mr_star, oldfilter="R_Cousins", newfilter="i_MOSAICII")
# Blanton M*
Mi_star = -21.22 - 5 * math.log10(h) #+ self.evf['i'](z)[0]
Mr_star = cosmology.reobs('El_Benitez2003',
m=Mi_star,
oldfilter="i_SDSS",
newfilter="r_SDSS")
return Mr_star + 5.0 * numpy.log10(dlum) + 25
def get_BCG_candidates(self, Mr_limit=-22.71, p_lim=1e-4):
t0 = time.time()
sout.write("# Computing p_BCG probabilities... ")
# The Abs mag limit @ z=0.1 in the i-band
Mi_limit = cosmology.reobs('El_Benitez2003',
m=Mr_limit,
oldfilter="r_MOSAICII",
newfilter="i_MOSAICII")
# Evaluate the genertic mask for BCG only onece
if not self.BCG_probs:
# We get the limit at the z_ph of each candidate, corrected by z=0.1
Mr_BCG_limit = Mr_limit + self.ev_r - self.evf['r'](
0.1) #+ self.DM_factor
Mi_BCG_limit = Mi_limit + self.ev_i - self.evf['i'](
0.1) #+ self.DM_factor
# Evaluate the BCG Probability function, we get the limit for each object
self.p = p_BCG(self.Mr, Mr_BCG_limit)
self.BCG_probs = True
i_lim = 25.0
star_lim = 0.5
mask_p = np.where(self.p >= p_lim, 1, 0)
mask_g = np.where(self.g < i_lim + 5, 1, 0)
mask_r = np.where(self.r < i_lim + 5, 1, 0)
mask_i = np.where(self.i < i_lim, 1, 0)
mask_t = np.where(self.type < 2.0, 1, 0)
# Avoid freakishly bright objects, 2.5 mags brighter than the M_BCG_limit
mask_br = np.where(self.Mr > Mr_BCG_limit - 3.5, 1, 0)
mask_bi = np.where(self.Mi > Mi_BCG_limit - 3.5, 1, 0)
# Put a more strict cut in class_star for bcg candidates
sout.write("# Avoiding CLASS_STAR > %s in BGCs\n" % star_lim)
mask_star = np.where(self.class_star <= star_lim, 1, 0)
# Construct the final mask now
self.mask_BCG = mask_t * mask_g * mask_r * mask_i * mask_br * mask_bi * mask_p
self.BCG_masked = True
sout.write(" \t Done: %s\n" % extras.elapsed_time_str(t0))
# Select the candidates now
idx = np.where(self.mask_BCG == 1)
# And pass up to to class
# The index number
self.idx_BCG = idx
self.ra_BCG = self.ra[idx]
self.dec_BCG = self.dec[idx]
self.p_BCG = self.p[idx]
self.z_BCG = self.z_ph[idx]
self.t_BCG = self.type[idx]
self.N_BCG = len(idx[0])
self.Mi_BCG = self.Mi[idx]
self.Mr_BCG = self.Mr[idx]
self.DM_BCG = self.DM[idx] # distance modulus
self.dang_BCG = self.dang[idx] # distance modulus
self.zml_BCG = self.z_ml[idx]
self.tml_BCG = self.t_ml[idx]
self.zb_BCG = self.z_b[idx]
self.tb_BCG = self.t_b[idx]
self.class_BCG = self.class_star[idx]
self.a_BCG = self.a_image[idx]
self.b_BCG = self.b_image[idx]
self.theta_BCG = self.theta[idx]
self.x_BCG = self.x_image[idx]
self.x_BCG = self.x_image[idx]
# r,i-band stuff
self.r_BCG = self.r[idx]
self.i_BCG = self.i[idx]
# Get the 1-sigma intervals
self.z1_BCG = self.z1[idx]
self.z2_BCG = self.z2[idx]
# The r-band Luminosity of the BCGs
self.LBCG = self.Lr[idx]
# The distance to the candidate's position for each BCG, in arcmin
sout.write("# Found %s BCG candidates\n" % self.N_BCG)
return
def get_BCG_candidates(self, Mr_limit=-22.71, p_lim=1e-4):
t0 = time.time()
sout.write("# Computing p_BCG probabilities...\n")
# The Abs mag limit @ z=0.1 in the i-band
Mi_limit = cosmology.reobs(
'El_Benitez2003',
m=Mr_limit,
oldfilter="r_MOSAICII",
newfilter="i_MOSAICII")
# Evaluate the genertic mask for BCG only onece
if not self.BCG_probs:
# We get the limit at the z_ph of each candidate, corrected by z=0.1
Mr_BCG_limit = Mr_limit + self.ev_r - self.evf['r'](
0.1) # + self.DM_factor
Mi_BCG_limit = Mi_limit + self.ev_i - self.evf['i'](
0.1) # + self.DM_factor
# Evaluate the BCG Probability function, we
# get the limit for each object
self.p = p_BCG(self.Mr, Mr_BCG_limit)
self.BCG_probs = True
i_lim = 25.0
star_lim = self.starlim
p_lim = max(self.p) * 0.8
sout.write("\tAvoiding BCG_prob < %.3f in BGCs\n" % p_lim)
mask_p = numpy.where(self.p >= p_lim, 1, 0)
mask_g = numpy.where(self.g < i_lim + 5, 1, 0)
mask_r = numpy.where(self.r < i_lim + 2, 1, 0)
mask_i = numpy.where(self.i < i_lim, 1, 0)
mask_z = numpy.where(self.z < i_lim + 1, 1, 0)
mask_t = numpy.where(self.type <= 2.0, 1, 0)
# Avoid freakishly bright objects, 2.5 mags brighter than the
# M_BCG_limit
mask_br = numpy.where(self.Mr > Mr_BCG_limit - 2.5, 1, 0)
mask_bi = numpy.where(self.Mi > Mi_BCG_limit - 2.5, 1, 0)
# Put a more strict cut in class_star for bcg candidates
sout.write("\tAvoiding CLASS_STAR > %s in BGCs\n" % star_lim)
mask_star = numpy.where(self.class_star <= star_lim, 1, 0)
# Construct the final mask now
self.mask_BCG = (mask_t * mask_g * mask_r * mask_i * mask_z *
mask_br * mask_bi * mask_p * mask_star)
self.BCG_masked = True
# Model color only once
#self.zx = numpy.arange(0.01, self.zlim, 0.01)
self.gr_model = cosmology.color_z(
sed='El_Benitez2003',
filter_new='g_MOSAICII',
filter_old='r_MOSAICII',
z=self.zx,
calibration='AB')
self.ri_model = cosmology.color_z(
sed='El_Benitez2003',
filter_new='r_MOSAICII',
filter_old='i_MOSAICII',
z=self.zx,
calibration='AB')
self.iz_model = cosmology.color_z(
sed='El_Benitez2003',
filter_new='i_MOSAICII',
filter_old='z_MOSAICII',
z=self.zx,
calibration='AB')
sout.write(" \tDone: %s\n" % extras.elapsed_time_str(t0))
# Select the candidates now
idx = numpy.where(self.mask_BCG == 1)
# And pass up to to class
self.idx_BCG = idx
self.id_BCG = self.id[idx]
self.ra_BCG = self.ra[idx]
self.dec_BCG = self.dec[idx]
self.p_BCG = self.p[idx]
self.z_BCG = self.z_ph[idx]
self.t_BCG = self.type[idx]
self.N_BCG = len(idx[0])
self.Mi_BCG = self.Mi[idx]
self.Mr_BCG = self.Mr[idx]
self.DM_BCG = self.DM[idx] # distance modulus
self.dang_BCG = self.dang[idx] # distance modulus
self.zml_BCG = self.z_ml[idx]
self.tml_BCG = self.t_ml[idx]
self.zb_BCG = self.z_b[idx]
self.tb_BCG = self.t_b[idx]
self.class_BCG = self.class_star[idx]
self.a_BCG = self.a_image[idx]
self.b_BCG = self.b_image[idx]
self.theta_BCG = self.theta[idx]
# r,i-band stuff
self.r_BCG = self.r[idx]
self.i_BCG = self.i[idx]
# Get the 1-sigma intervals
self.z1_BCG = self.z1[idx]
self.z2_BCG = self.z2[idx]
# The r-band Luminosity of the BCGs
self.LBCG = self.Lr[idx]
# The distance to the candidate's position for each BCG, in arcmin
if self.N_BCG:
sout.write(color("\tFound %s BCG candidates\n" % self.N_BCG, 36, 1))
else:
sout.write(color("\tFound %s BCG candidates\n" % self.N_BCG, 31, 5))
return