(~np.isinf(mag_app)))
gals_filt = len(id_filter_i[0])
if gals_filt == 0:
continue
z_min = p10p90W[j,0]/L_line - 1
z_max = p10p90W[j,1]/L_line - 1
d1 = cosmo.comoving_distance(z_max)
d0 = cosmo.comoving_distance(z_min)
Vol = 4./3*np.pi*(d1.value**3 - d0.value**3)/8.0
mag_sel = np.zeros(len(id_filter_i[0]))
for i in range(len(id_filter_i[0])): mag_sel[i] = mag_app[id_filter_i[0][i]]
# mag_sel = mag_app[0][id_filter_i
# allmag_lf[j] = get_lf(mag_app[0][id_filter_i],
allmag_lf[j] = get_lf(list(mag_sel),
Vol = Vol, binsize=lfbin,minsample = lfmin,maxsample=lfmax)
print 'LF computed for filter ',j
nfilters += 1
if Make_Figure:
pl.figure()
pl.subplots_adjust(hspace=0,wspace=0)
# xtloc = [21,23,25,27]
xtloc = [15,27]
for ip in np.arange(nfilters):
ax = pl.subplot(3,np.ceil(nfilters/3.),ip+1,xlim=[15,27],ylim=[-11,-2],aspect=1.75)
if (ip ==0) or (ip == np.ceil(nfilters/3)+1) or (ip == 2*(np.ceil(nfilters/3)+1)):
pl.setp(ax.get_yticklabels(), visible=True)
pl.ylabel(r'$\log({\rm d}n/{\rm d}m \ (\Delta m)^{-1} [{\rm (Mpc/h)^{-3}}])$',fontsize=5)
else:
for i in range(nLfs):
data = oiidata('comparat', zrArr['zmean'][i])
nbb = len(data['Lum'])
d1 = cosmo.comoving_distance(zmaxlist[i])
d0 = cosmo.comoving_distance(zminlist[i])
Vol_z = 4. / 3 * np.pi * (d1.value**3 - d0.value**3) / 8.0
izr = np.where((GalArr['redshift'] < zmaxlist[i])
& (GalArr['redshift'] >= zminlist[i]))
loii_i = loii[0][izr]
print 'computing mock LF for redshift range ', zrArr['zmean'][i]
lf0 = get_lf(loii_i + 40.0, Vol=Vol_z, binsize=0.1, minsample=42.0)
pl.subplot(240 + (i + 1), xlim=[40, 45.9], ylim=[-10, -1])
pl.plot(data['Lum'] + facl,
np.log10(data['dn_dL']) + facn,
'-o',
markersize=3,
label='Comparat+14, z=' + np.str(zrArr['zmean'][i]),
color='0.75')
errhi = np.log10(data['err'] + data['dn_dL']) - np.log10(data['dn_dL'])
errlo = -np.log10(-data['err'] + data['dn_dL']) + np.log10(data['dn_dL'])
pl.errorbar(data['Lum'] + facl,
np.log10(data['dn_dL']) + facn, [errlo, errhi],
for i in range(nLfs):
data = oiidata('comparat',zrArr['zmean'][i])
nbb = len(data['Lum'])
d1 = cosmo.comoving_distance(zmaxlist[i])
d0 = cosmo.comoving_distance(zminlist[i])
Vol_z = 4./3*np.pi*(d1.value**3 - d0.value**3)/8.0
izr = np.where((GalArr['redshift'] < zmaxlist[i]) &
(GalArr['redshift'] >= zminlist[i]))
loii_i = loii[0][izr]
print 'computing mock LF for redshift range ',zrArr['zmean'][i]
lf0 = get_lf(loii_i+40.0,Vol=Vol_z,binsize=0.1,
minsample=42.0)
pl.subplot(240 + (i+1),xlim=[40,45.9],ylim=[-10,-1])
pl.plot(data['Lum']+facl,np.log10(data['dn_dL'])+facn,'-o',markersize=3,
label='Comparat+14, z='+np.str(zrArr['zmean'][i]),color='0.75')
errhi = np.log10(data['err'] + data['dn_dL']) - np.log10(data['dn_dL'])
errlo = - np.log10( - data['err'] + data['dn_dL']) + np.log10(data['dn_dL'])
pl.errorbar(data['Lum']+facl,np.log10(data['dn_dL'])+facn,[errlo,errhi],
color='0.75',ecolor='0.75')
#pdata=pl.errorbar(xc+facl,yc+facn,yerr = (yc - (np.log10(10**yc-ec)),(np.log10(ec + 10**yc)-yc)),
#ecolor='0.75',linewidth=2,fmt=None)
