def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module=importlib.import_module(setf)
globals().update(set_module.__dict__)
expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n'*5
print len(expt.parameters)
ncols = len(expt.parameters)
summf=os.path.join(outdir,'1-summary.txt')
summary=numpy.genfromtxt(summf)[-1,:]
drawmap=summary[-(ncols+2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap=ana.get_best_fit()['parameters']
if dataset=='sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf)==20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][4:]))
print datafiles[0][4:]
elif dataset=='cosmos':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf)==24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][8:]))
print datafiles[0][8:]
elif 'sim' in dataset:
d=numpy.genfromtxt(os.path.join(outdir,'sim.txt'))
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0,29.2,0.4)
bins3 = numpy.arange(18.,29.2,0.1)
print len(expt.parameters)
print 'this is 1st num ', num
#sys.exit()
params = drawmap# [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
#novak 2017
L_n =[21.77,22.15,22.46,22.77,23.09,23.34]
rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]
mcalpine=openlit('mcalpine-2013-tot.txt')
padovani=openlit('Padovani_2015_RL_AGN')[1:]
mcalpine_panels=[0,1,2,3,4,4,5,6,6]
padovani_panels=[0,1,0,2,2,3,3,5,6,6]
#print padovani
print padovani[0][:,0],padovani[0][:,1],padovani[0][:,2],padovani[0][:,3]
#sys.exit()
#novak 2018
L_n2 =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
[22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
[22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
[22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
[23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
[23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
[23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
[23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
[24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
[24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]
L_ner_u2 =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
[0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
[0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
[0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
[0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
[0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
[0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
[0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
[0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
[0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]
L_ner_d2 =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
[0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
[0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
[0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
[0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
[0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
[0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
[0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
[0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
[0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]
rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
[-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
[-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
[-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
[-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
[-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
[-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
[-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
[-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
[-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]
rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
[0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
[0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
[0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
[0.045,0.019,0.033,0.072,0.15, 0.17,0.45,0.25],\
[0.040,0.022,0.033,0.055,0.13, 0.22,0.17,0.45],\
[0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
[0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
[0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
[0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]
rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
[0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
[0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
[0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
[0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
[0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
[0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
[0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
[0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
[0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]
fig = plt.figure()
pre_chain='12'
#pre_chain='01'
chains=['12a','12b','12c','12d','12e','12f','12g','12h','12i']#chains_191062
chains=['22a','22b','22c','22d','22e','22f','22g','22h','22i']
chains=['32a','32b','32c','32d','32e','32f','32g','32h','32i']
chains=['42a','42b','42c','42d','42e','42f','42g','42h','42i']
chains=['62a_2','62b_2','62c_2','62d_2','62e_2','62f_2','62g_2','62h_2','62i_2'] #chains_191062
chains=['01a_3','01b_3','01c_3','01d_3','01e','01f_3','01g_3','01h_3','01i_3']#chains_191101
chains=['01a_4','01b_4','01c_4','01d_4','01e_4','01f_4','01g_4','01h_4','01i_4_2']#chains_191101 7x7 grid
chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_5','02h_4','02i_4']#chains_191101 7x7 grid lognorm
#chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5']#chains_191101 3x3 grid scaled
#chains=['01a_6','01b_6_3','01c_6','01d_6_2','01e_6_2','01f_6','01g_6_2','01h_6_2','01i_6_2']#chains_191101 11x11 grid
#chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i'] #chains_1912 #includeing maskregion
chains=['01a_2_1','01b_2_1','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2'] #1912 exclding peterinclde match
#chains=['01a_2','01b_2','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2'] #chains_1912 excluding peter
#chains=['01a_3','01b_3','01c_3','01d_3','01e_3','01f_3_1','01g','01h','01i'] #chains_1912 including peter
#chains=['01a_4_1','01b_4_1','01c_4','01d_4','01e_4','01f_4_1','01g_4','01h_4','01i_4'] #chains_1912 no mass selection
chains=['02a','02b_2','02c','02d','02e','02f','02g','02h','02i'] #lgnorm #chains_1912 excluding peter
chains=['01a_1','01b','01c','01d','01e','01f_1','01g','01h','01i'] #chains_2001 DR4
chains4=['01a_4','01b_2','01c_2','01d_2','01e_2','01f_2','01g','01h_2','01i_2'] #chains_2001 DR4 remove phi2
#chains=['01a_9','01b_9','01c_9','01d_9','01e_9','01f_9','01g_9','01h_9','01i_9','01j_9'] #chains_2001 DR4 dpl_dpl no mass selection
chains=['01a_7','01b_7','01c_7','01d_7','01e_7','01f_7','01g_7','01h_7','01i_7','01j_7'] #chains_2001 DR4 dpl_dpl
chains4=['01a_6','01b_6','01c_6','01d_6','01e_6_3','01f_6','01g_6','01h_6','01i_6','01j_6'] #chains_2001 DR4 dpl_dpl Lmin=15
#chains=['01a_6_3','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=21
chains2=['02a_z_6x','02b_z_6x','02c_z_6x','02d_z_6x','02e_z_6x','02f_z_6x','02g_z_6x','02h_z_6x','02i_z_6x','02j_z_6x']#Novak individual model
chains2=['02a_z_6x_1','02a_z_6x_2','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']#Novak individual model newz
#chains=['02a_4','02b_3','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3'] ##chains_2001 DR4 logn_dpl Lmin=19
#chains=['01a_9_2','01b_9_2','01c_9_2','01d_9_2','01e_9_2_1','01f_9_2','01g_9_2_2','01h_9_2','01i_9_2_1','01j_9_2_1'] #chains_2001 DR4 dpl_dpl no mass selection fixed zmean
#chains=['01a_6_1','01a_8_2','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=19 newz
#chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3'] ##chains_2001 DR4 logn_dpl Lmin=21 newz
#chains=['02a_z_6x','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']#Novak individual model newz
chains=['01a_1','01b_1','01c_1','01d_1','01e_1','01f_1','01g_1','01h_1','01i_1','01j_1'] #95% mass sample
chains=['02a_z_9x','02b_z_9x','02c_z_9x','02d_z_9x','02e_z_9x','02f_z_9x','02g_z_9x','02h_z_9x','02i_z_9x','02j_z_9x']#95 mass limit
chains=['01a_2','01b_2','01c_2','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2','01j_2'] #50% mass sample
chains=['02a_z_9x_1','02b_z_9x_1','02c_z_9x_1','02d_z_9x_1','02e_z_9x_1','02f_z_9x_1','02g_z_9x_1','02h_z_9x_1','02i_z_9x_1','02j_z_9x_1']#50 mass limi1
chains2=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #stellar mass>10.2 dpl
chains2=['02a_5','02b_5','02c_5','02d_5','02e_5','02f_5','02g_5','02h_5','02i_5','02j_5'] #stellar mass>10.2 dpl
chains=['02a_z_7x','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x'] #stellar mass>10.2
chains=['01a_8','01b_8','01c_8','01d_8','01e_8','01f_8','01g_8','01h_8','01i_8','01j'] #stellar mass<10. dpl
chains4=['02a_3','02b_3','02c_3','02d_3','02e_3','02f_3','02g_3','02h_3','02i_3','02j_3'] #stellar mass<10. logn
chains3=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x','02j_z_5x']#stellar mass limit mass<10
#chains3=['02a_z_5f','02b_z_5f','02c_z_5f','02d_z_5f','02e_z_5f','02f_z_5f','02g_z_5f','02h_z_5f','02i_z_5f','02j_z_8x']#stellar mass limit mass<10
#chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
chains=['02a','02b','02c','02d','02e','02f','02g','02h','02i','02j'] #lgnorm #chains_2001 DR4 newz new_area
chains=['02a_z_8x','02b_z_8x','02c_z_8x','02d_z_8x','02e_z_8x','02f_z_8x','02g_z_8x','02h_z_8x','02i_z_8x','02j_z_8x']#fixed model newz area
chains=['02a_z_8x_6','02b_z_8x_6','02c_z_8x_6','02d_z_8x_6','02e_z_8x_6','02f_z_8x_6','02g_z_8x_6','02h_z_8x_6','02i_z_8x_6','02j_z_8x_6']#fixed cut data more bins _el lmin lose
chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
#chains3='02z_31_3' #Novak evolution fit
#chains3='02z_31_5' #Fixed evolution newz, area
#chains3='02z_31_6' #Fixed evolution newz, area, 10.2 mass lim
#z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
#z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
print expt.parameters
n,m=3,3
#print xrecon
#print yrecon
#sys.exit()
z_new=True
Replot=False
z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
if z_new:
z =[0.1,0.3,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]
z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
#chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_201111
else:
z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
#ana3=pymultinest.analyse.Analyzer(ncols-1,\
# outputfiles_basename=os.path.join('chains_2002%s'%chains3,outstem))
#drawmap3=ana3.get_best_fit()['parameters']
#params3 = drawmap3
#parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
#parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN', 'LMAX']
#parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN_1', 'LMIN_2', 'LMIN_3', 'LMIN_4', 'LMIN_5', 'LMIN_6', 'LMIN_7', 'LMIN_8', 'LMIN_9', 'LMIN', 'LMAX']
#parameters4=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
parameters3= ['noise', 'LMIN', 'LMAX', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
parameters4= ['noise', 'LMIN', 'LMAX', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSIGMA'] #lognorm
parameters2=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
parameters3= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']#fixed
parameters2=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSIGMA']
pass1=False
for num in range(1,11):
#num = 1.
print num
if num==2:pass1=False
print 'new num ',num
z_min,z_max, z_m = get_z(num)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min,z_max)
dsdl = get_dsdl(z_m,dl)
#z_m = (z_min + z_max)/2
dl = get_dl(z_m)
print z_min,z_max
print expt.kind
area = SURVEY_AREA*sqDeg2sr
area1 = SURVEY_AREA*sqDeg2sr
print SURVEY_AREA
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
drawmap=ana.get_best_fit()['parameters']
drawmid=ana.get_stats()['marginals']
params = drawmap
ana2=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%('11',chains2[num-1]),outstem))
drawmap2=ana2.get_best_fit()['parameters']
params2 = drawmap2
ana3=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains3[num-1]),outstem))
drawmap3=ana3.get_best_fit()['parameters']
params3 = drawmap3
ana4=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%('11',chains4[num-1]),outstem))
drawmap4=ana4.get_best_fit()['parameters']
params4 = drawmap4
settingf='chains_20%s%s/bayestack_settings.py'%(pre_chain,chains[num-1])
f = open(settingf, 'r')
mod = f.readlines()[31].split()[0]
print mod
if 'logn' in mod:
if 'dpl' in mod:
model='LFlognorm_dpl'
elif '_el' in mod:
model='LFevol_logn_el'
elif 'pl_' in mod:
model='LFpl_lognorm'
elif 'phi' in mod:
model='LFevol_phi_logn_mat'
elif 'mat' in mod:
model='LFevol_logn_mat'
else:
model='LFlognorm'
elif 'LFdpl' in mod or 'LFpl' in mod:
if 'LFdpl_dpl' in mod:
model='LFdpl_dpl'
elif '_pl' in mod:
model='LFdpl_pl'
elif 'LFpl_' in mod:
model='LFpl_dpl'
elif 'LFpl' in mod:
model='LFpl'
else:
model = 'LFdpl'
with open('chains_20%s%s/params.tex'%(pre_chain,chains[num -1])) as f:
parameters=[line.split(None,1)[0] for line in f]
#model='LFdpl_pl'
print parameters
print model
print 'LSIGMA' in parameters
'''
if model in ['LFsch','LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl','LFpl_lognorm']:
Lnorm=params[parameters.index('LNORM')]
Lstar=params[parameters.index('LSTAR')]
Lslope=params[parameters.index('LSLOPE')]
#Lzevol=params[parameters.index('LZEVOL')]
if model in ['LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
Lslope2=params[parameters.index('LSLOPE2')]
if model in ['LFlognorm','LFpl', 'LFdpl', 'LFdpl_pl','LFpl_dpl', 'LFlognorm_dpl','LFpl_lognorm','LFdpl_dpl']:
Lnorm_2=params[parameters.index('LNORM_2')]
Lstar_2=params[parameters.index('LSTAR_2')]
Lslope_2=params[parameters.index('LSLOPE_2')]
if model in ['LFdpl_dpl','LFdpl']:
Lslope2_2=params[parameters.index('LSLOPE2_2')]
if model in ['LFlognorm','LFlognorm_dpl','LFpl_lognorm']:
Lsigma = params[parameters.index('LSIGMA')]
'''
Lmin=params[parameters.index('LMIN')]
Lmax=params[parameters.index('LMAX')]
#Lmin=params[expt.parameters.index('LMIN')]
#Lmax=params[expt.parameters.index('LMAX')]
SMIN = get_sbins(numpy.power(10,Lmin),z_m,dl)*1e6
SMAX = get_sbins(numpy.power(10,Lmax),z_m,dl)*1e6
sigma,fsigma,Lmin = numpy.log10(get_Lbins([SURVEY_NOISE,18.9, Lmin],z_m,dl,'muJy')*(1.4/3)**(-.7))
sigma2,fsigma2 = numpy.log10(get_Lbins([450,2400],z_m,dl,'muJy'))
print z_m,dl, sigma,fsigma
print SURVEY_NOISE,SURVEY_NOISE*5,SURVEY_AREA
s=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains[num -1]))
s2=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%('11',chains2[num -1]))
s3=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains3[num -1]))
s4=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%('11',chains4[num -1]))
xrecon=s[:-1,0]; yrecon=s[:-1,1]
yrecon_d=s[:-1,2]; yrecon_u=s[:-1,3]
yrecon_rms = s[:-1,4]
yrecon_avr = s[:-1,5]
yrecon_rms_down = yrecon_rms
xreco = get_Lbins(xrecon,z_m,dl,'muJy')#*(1.4/3)**(-.7)
yreco = yrecon
lin_yrecon = numpy.log10(yreco)
lin_xrecon = numpy.log10(xreco)
xrecon2=s2[:-1,0]; yrecon2=s2[:-1,1]
yrecon_d2=s2[:-1,2]; yrecon_u2=s2[:-1,3]
lin_xrecon2 = numpy.log10(get_Lbins(xrecon2,z_m,dl,'muJy'))
xrecon3=s3[:-1,0]; yrecon3=s3[:-1,1]
yrecon_d3=s3[:-1,2]; yrecon_u3=s3[:-1,3]
lin_xrecon3 = numpy.log10(get_Lbins(xrecon3,z_m,dl,'muJy'))
xrecon4=s4[:-1,0]; yrecon4=s4[:-1,1]
yrecon_d4=s4[:-1,2]; yrecon_u4=s4[:-1,3]
lin_xrecon4 = numpy.log10(get_Lbins(xrecon4,z_m,dl,'muJy'))
bins2 = numpy.arange(15.,25.,0.4)
bins = numpy.arange(fsigma-0.4,29.2,0.4)#bin +0.2
if not os.path.isfile('cos_data/cos_s%s_LF.el'%num) and z_new or not os.path.isfile('cos_data/cos_s%s_LF.el'%num) and not z_new or Replot:
print 'doing all calculations'
#l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
#l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
if z_new:
L,z_L = open_anytype('cos_data/cos_s%s.txt'%num,(2,3), F='uJy',L=True,getz=True,band='S')
print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
else:
L,z_L = open_anytype('cos_data/cos_s%s.txt'%num,(2,3), F='uJy',L=True,getz=True,band='S')
print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
L_c,z_c=open_anytype('cosmos_counter_parts_II.txt',(3,4),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
z_c2,S_c2,L_c2 =numpy.loadtxt('cosmos_counter_parts_II.txt',unpack=True, usecols=(3,4,5))
L_c2,z_c2=open_anytype('cosmos_comp_vla.el',(2,3),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
L_c3,z_c3=open_anytype('cosmos_vla.el',(2,3),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
L_c4,z_c4=open_anytype('/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',(-4,-1),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
L_c5,z_c5=open_anytype('/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',(3,-1),[z_min,z_max], F='uJy',L=True,getz=True, band='S')
#L_c2=L_c2[(z_c2<z_max)&(z_c2 >z_min)]
#S_c2=S_c2[(z_c2<z_max)&(z_c2 >z_min)]
#z_c2=z_c2[(z_c2<z_max)&(z_c2 >z_min)]
#rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
#rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
rho_3,er3,b3,n3 = calc_Vmax(calcu_zeff(z_L,L,1.15e-31),L,bins,z_max,z_min,area,table=True)
#rho_4,er4 = calc_Vmax(99.,L,bins2,z_max,z_min,area)
print 'COS15 VLA'
rho_5,er5,b5,n5 = calc_Vmax(calcu_zeff(z_c,L_c,1.15e-31),L_c,bins,z_max,z_min,1.77*sqDeg2sr,table=True)
print 'mass lim DR4-vla'
rho_6,er6,b6,n6 = calc_Vmax(calcu_zeff(z_c2,L_c2,1.15e-31),L_c2,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
print 'DR4-vla'
rho_7,er7,b7,n7 = calc_Vmax(calcu_zeff(z_c3,L_c3,1.15e-31),L_c3,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
print 'cos-DR4-vla DR4 photo-z'
rho_8,er8,b8,n8 = calc_Vmax(calcu_zeff(z_c4,L_c4,1.15e-31),L_c4,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
print 'cos-DR4-vla COS15 photo-z'
rho_9,er9,b9,n9 = calc_Vmax(calcu_zeff(z_c5,L_c5,1.15e-31),L_c5,bins,z_max,z_min,1.5*sqDeg2sr,table=True)
for i in range(len(b5)):
print '%5.2f %7d %10d %13d %17d %11d'%(b5[i],n3[i],n7[i],n8[i],n9[i],n5[i])
if z_new:
f = open('cos_data/cos_s%s_LF_4.el'%num,'w') #I used LF_2 for the final results, _5 for stellar-mass 10.2
print 'saving data for future use in cos_data/cos_s%s_LF_4.el'%num
else:
f = open('cos_data/cos_s%s_LF_old.el'%num,'w')
print 'saving data for future use in cos_data/cos_s%s_LF.el'%num
for i in range(len(bins)):
f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n'%(bins[i], rho_3[i],er3[i],rho_5[i],er5[i],rho_6[i],er6[i],rho_7[i],er7[i],rho_8[i],er8[i],rho_9[i],er9[i]) )
f.close()
dl_c=get_dl(z_c2)
L_c3=numpy.log10(get_Lbins(S_c2,z_c2,numpy.array(dl_c),'muJy')*(1.4/3.)**(-.7))
else:
if z_new:
bins,rho_3,er3,rho_5,er5,rho_6,er6 ,rho_7,er7 ,rho_8,er8 ,rho_9,er9 = numpy.loadtxt('cos_data/cos_s%s_LF_2.el'%num, unpack=True)
print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
else:
bins,rho_3,er3,rho_5,er5,rho_6,er6 = numpy.loadtxt('cos_data/cos_s%s_LF_old.el'%num, unpack=True)
print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'
#ssd = numpy.loadtxt('%s/data_cos_s%s.txt'%(outdir,num))
ssd = numpy.loadtxt('cos_data/cos_s%s.txt'%(num), unpack=True, usecols=(-1,2))
ssbin2 = ssd[:,0] #2
ssbin1 = ssd[:,2] #0
Nbin2 = ssd[:,3]
zbin3 = ssd[:,-1]
dl1 = get_dl(z_min)
dl2 = get_dl(z_max)
#dl3 = get_dl(zbin3)
Ncount = sum(Nbin2[fsigma>ssbin1])
Llmin = numpy.log10(get_Lbins([fsigma/numpy.sqrt(Ncount)],z_min,get_dl(z_min),'muJy'))[0]
Llmin*=(1.4/3.)**(-.7)
print Llmin
#sys.exit()
if z_new:
try:pos=get_changed_multiplot(n,m,num)
except:print 'changed pos doesnt work for ',num
if num==4:
pos = 10
elif num==8:
pos=11
elif num==10:
pos=11
elif num==11:
pos=11
if num>4 and num!=8:
n_s = 1
if num>8:
n_s+=1
pos=get_changed_multiplot(n,m,num-n_s)
ax=fig.add_subplot(n+1,m,pos)
print 'plot change', n, m ,pos
else:
ax=fig.add_subplot(n,m,get_changed_multiplot(n,m,num))
plt.rc('lines', linewidth=2)
#plt.errorbar(bins,rho_1 ,yerr=er1,fmt='*r',label='detected extracted',markersize=11) #bin
'''
if model in ['LFdpl','LFdpl_dpl']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
elif model in ['LFpl','LFdpl_pl']:
faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
elif model in ['LFpl_dpl']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
elif model =='LFevol_logn_mat':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=parameters,\
family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
else:
faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
#phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
'''
#phi_dpl
#dpl =
dm =0.4#05
hm=0.
#if num==1:hm=-0.25
##if num>1:num-=1
L_1 = numpy.power(10,bins3)/(1 + z_m)**(2.86-z_m*0.7)
L_2 = numpy.power(10,bins3)/(1 + z_m)**(2.95-z_m*0.29)
print z_m, z_nov[num-1]
Novak17=lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3/2.5))
Novak17_2=lumfuncUtils.lognormpl(numpy.power(10,bins3)/(1 + z_nov[num-1])**(3.16-z_nov[num-1]*0.32),\
numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3/2.5))
phi_agn_n=lumfuncUtils.doublepowerlaw(L_1,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
phi_tot_n = phi_agn_n+Novak17
phi_tot_n2 = phi_agn_n+Novak17_2
#phi_2 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params2,paramsList=parameters2,\
# family='LFdpl_dpl',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
#phi_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
# family='LFlognorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
phi = [lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=parameters,\
family=model,inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
#phi_2 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params2,paramsList=parameters2,\
# family='LFlognorm_dpl',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
phi_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
#faint_3 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params3,paramsList=parameters3,\
# family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
phi_4 =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params4,paramsList=parameters4,\
family='LFlognorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0) for lL in bins3]
#sys.exit()
##plt.plot(bins3+hm,numpy.log10(faint)-dm,'--k',linewidth=4 ,label='SF MAP Indiv. Novak 2018 fit')
#plt.plot(numpy.log10(10**bins3),numpy.log10(phi_dpl)-dm,'--c', label='agn')
#plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7))+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')
#plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP No mass-limit',linewidth=1.8)
plt.errorbar(bins[2:],rho_3[2:] ,yerr=er3[2:],fmt='hb', fillstyle= 'none', markeredgewidth=3 ,label=r'$\rm{1/V_{max}}$',markersize=12) #bin
#plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP Model A $M/M_\odot > 10$',linewidth=1.8)
#plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP $\rm{Model}$ $\rm{C}$ Individual',linewidth=1.8)
#plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-b', label='MAP $\rm{Model}$ $\rm{C}$ 50% mass limit',linewidth=1.8)
##plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-k', label='MAP Fixed Individual fit',linewidth=2.)
##plt.errorbar(lin_xrecon+hm,lin_yrecon-dm ,fmt='-k', label=r'$\rm{Total}$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$',linewidth=3)
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi) -0.4 ,fmt='-k', label=r'$\rm{Total}$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$',linewidth=3)
#plt.plot(bins3+hm,numpy.log10(faint)-dm,'-.b' ,label='SF MAP $\rm{Model}$ $\rm{C}$ Individual $M > 10 M/M_\odot$')
#plt.plot(bins3+hm,numpy.log10(faint_3)-dm,'-g' ,label='SF MAP $\rm{Model}$ $\rm{C}$ PLE $M > 10 M/M_\odot$')
#plt.plot(bins3+hm,numpy.log10(faint)-dm,'-.b' ,label='SF MAP $\rm{Model}$ $\rm{B}$')
#if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--',color='orange',linewidth=3,label=r'$8.5<\log_{10}(M/M_\odot) <10$ $\rm{MAP}$ $\rm{Model}$ $\rm{B}$ ')
#if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_3) -0.4,fmt='.',color='purple',linewidth=3,label=r'$8.5<\log_{10}(M/M_\odot) <10$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$ ')
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_2) -0.4,fmt='-.r',linewidth=5.,label=r'$\log_{10}(M/M_\odot) > 10$ $\rm{Model}$ $\rm{B}$')
#phi_5=numpy.array(phi_2)+numpy.array(phi_3)
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_5) -0.4,fmt='-.b',linewidth=3.,label=r'$\rm{Total}$ $\rm{Model C+B}$')
#if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--',color='orange',linewidth=3,label=r'$10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{MAP}$ $\rm{Model}$ $\rm{B}$ ')
#if num!=10:plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_3) -0.4,fmt='.',color='purple',linewidth=3,label=r'$10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{MAP}$ $\rm{Model}$ $\rm{C}$ ')
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_2) -0.4,fmt='-.r',linewidth=5.,label=r'$M > 10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$')
#plt.errorbar(lin_xrecon+hm,numpy.log10(yrecon_avr)-dm,fmt='-c', label = 'Average')
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_4) -0.4,fmt='--k',linewidth=3,label='MAP with mass limit')
##plt.errorbar(bins,rho_5 ,yerr=er5,fmt='^k',label='VLA-COSMOS2015',markersize=10) #bin
##plt.errorbar(bins,rho_8 ,yerr=er8,fmt='sc',label='VLA-COSMOS2015 in DR4 using DR4 z',markersize=10) #bin
##plt.errorbar(bins,rho_9 ,yerr=er9,fmt='*m',label='VLA-COSMOS2015 in DR4 using COS15z',markersize=14) #bin
##plt.errorbar(bins,rho_6 ,yerr=er6,fmt='ok',label='DR4 detected',fillstyle='none',markersize=14) #bin
##plt.errorbar(bins,rho_7 ,yerr=er6,fmt='dg',label='DR4 detected - Full NIR sample',markersize=12) #bin
#if not pass1:plt.errorbar(numpy.array(L_n2[num-1]),numpy.array(rho_n2[num-1]) -0.4,xerr=[L_ner_d2[num-1],L_ner_u2[num-1]],yerr=[rho_ner_d2[num-1],rho_ner_u2[num-1]], fmt='sr',fillstyle='none',ecolor='k',markeredgewidth=1.5, label='Total Novak+2018')
#plt.errorbar(numpy.log10(10**bins3), numpy.log10(phi_tot_n),fmt='--k',linewidth=3,label='Total Novak+2018')
#plt.errorbar(bins3, numpy.log10(Novak17_2),fmt='.r',linewidth=0.4,label='SF Novak + 2017')
#if num <9:
#plt.errorbar(mcalpine[mcalpine_panels[num] -1][:,0] ,mcalpine[mcalpine_panels[num] -1][:,1]-0.4 ,yerr=mcalpine[mcalpine_panels[num] -1][:,2],fmt='>c', fillstyle= 'none', markeredgewidth=2 ,label='Total McAlpine+2013',markersize=12)
#print padovani[num-1]
#if num <9 and num!=2:
#plt.errorbar(padovani[padovani_panels[num] -1][:,0],padovani[padovani_panels[num] -1][:,1] -(9-numpy.log10(10)),fmt='dk', fillstyle= 'none', markeredgewidth=1 ,label='padovani+2015 RL AGN',markersize=13)
#plt.errorbar(bins3, numpy.log10(Novak17)-0.4,fmt='--b',label='SF galaxies Novak + 2017')
#plt.plot(bins3,numpy.log10(phi_agn_n),'--g', label='local agn nov')
##if num>1:num+=1
#plt.plot(L_s, numpy.log10(rho_1), '*m',label='bins LF',markersize=8)
#plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=10) #bin
#plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
#plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label='95% Model C Individaul' ,alpha=0.2)
#plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label='95% Model A $M/M_\odot > 10$' ,alpha=0.2)
plt.fill_between(lin_xrecon+hm,numpy.log10((yrecon_d))-dm,numpy.log10(yrecon_u) -dm, color='k',label=r'$\rm{95%}$ $\rm{Total}$ $\rm{Model}$ $\rm{C}$' ,alpha=0.5)
plt.fill_between(lin_xrecon2+hm,numpy.log10((yrecon_d2))-dm,numpy.log10(yrecon_u2) -dm, color='r',label=r'$\rm{95%}$ $\log_{10}(M/M_\odot) > 10$ $\rm{Model}$ $\rm{B}$' ,alpha=0.5)
plt.fill_between(lin_xrecon4+hm,numpy.log10((yrecon_d4))-dm,numpy.log10(yrecon_u4) -dm, color='orange',label=r'$\rm{95%}$ $8.5<\log_{10}(M/M_\odot) <10$ $\rm{Model}$ $\rm{B}$ ' ,alpha=0.7)
plt.fill_between(lin_xrecon3+hm,numpy.log10((yrecon_d3))-dm,numpy.log10(yrecon_u3) -dm, color='purple',label=r'$\rm{95%}$ $8.5<\log_{10}(M/M_\odot) <10$ $\rm{Model}$ $\rm{C}$ ' ,alpha=0.7)
#plt.errorbar(bins[1:],rho_3[1:] ,yerr=er3[1:],fmt='h', fillstyle= 'none', markeredgewidth=3 ,label='extracted fluxes (No mass limit)',markersize=12) #bin
#plt.fill_between(lin_xrecon2+hm,numpy.log10((yrecon_d2))-dm,numpy.log10(yrecon_u2) -dm, color='r',label=r'$\rm{95%}$ $M > 10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$' ,alpha=0.5)
#if num!=10:plt.fill_between(lin_xrecon4+hm,numpy.log10((yrecon_d4))-dm,numpy.log10(yrecon_u4) -dm, color='orange',label=r'$\rm{95%}$ $10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{Model}$ $\rm{B}$ ' ,alpha=0.7)
#if num!=10:plt.fill_between(lin_xrecon3+hm,numpy.log10((yrecon_d3))-dm,numpy.log10(yrecon_u3) -dm, color='purple',label=r'$\rm{95%}$ $10^{8.5} M_\odot<M <10^{10} M_\odot$ $\rm{Model}$ $\rm{C}$ ' ,alpha=0.7)
plt.ylim(-8.,-1.6)
plt.axvline(fsigma,color='g',linestyle='dashed',label=r'$\rm{5\sigma}$')
#plt.axvline(sigma,color='g')
#plt.axvline(Lmin,color='r',linewidth=2)
##z_new=True
if num<2 or num<6 and num>3 or num>6 and num<9:
plt.xticks( visible=False)
plt.ylim(-8.,-1.6)
if num<5:
plt.xlim(18.6,25.7)
plt.text(18.8,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
elif num<9:
plt.xlim(20.,27.2)
plt.text(20.2,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
else:
plt.xlim(20.6,27.8)
plt.text(21.,-7.,r'$\rm{%.1f < z < %.1f}$ %s$\rm{z_{MED} = %.2f}$'%(z_min,z_max,'\n',z_m),fontsize = 28 ) # \n $M_i$ < -22
plt.tick_params(axis='both',which = 'minor',width=1)
plt.tick_params(axis='both',which = 'major', width=1.5)
ax.xaxis.set_minor_locator(AutoMinorLocator())
#ax.yaxis.set_minor_locator(AutoMinorLocator())
#plt.axes().xaxis.set_minor_locator(AutoMinorLocator())
#plt.axes().yaxis.set_minor_locator(AutoMinorLocator())
#plt.minorticks_on()
if num==4:
plt.xticks([19,20,21,22,23,24,25],fontsize=30)
plt.xticks( visible=True)
handles,labels=plt.gca().get_legend_handles_labels()
print labels
print len(labels)
#order=[0, 5, 6,1, 2 ,3 ,4, 7, 8, 9]#model_C
order=[0,5,1,4,2,3]
#plt.legend(frameon=False).draggable()
plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],frameon=False).draggable()
if num==8:
plt.xticks([21,22,23,24,25,26,27],fontsize=30)
plt.xticks( visible=True)
if num == 10:
plt.xticks([21,22,23,24,25,26,27],fontsize=30)
plt.xticks( visible=True)
if num==1 or num ==2 or num==3 or num==4:
plt.yticks([-2.,-3,-4,-5, -6, -7], fontsize=30)
if num==4:
plt.yticks([-2.,-3,-4,-5, -6, -7, -8], fontsize=30)
continue
plt.yticks(visible=False)
'''#z_new=False
print 'Is this still num ',num
if num<2 or num<6 and num>3 or num>6 and num<9:
plt.xticks( visible=False)
plt.ylim(-7.6,-1.6)
if num<4:
plt.xlim(19,25.8)
plt.text(19.2,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 32 ) # \n $M_i$ < -22
elif num<7:
plt.xlim(20.8,27.2)
plt.text(21.,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 33 ) # \n $M_i$ < -22
else:
plt.xlim(21.3,27.8)
plt.text(21.5,-7.,r'$\rm{%.1f < z < %.1f}$'%(z_min,z_max),fontsize = 32 ) # \n $M_i$ < -22
if num==3:
plt.xticks([19,20,21,22,23,24,25],fontsize=22)
if num==6:
plt.xticks([21,22,23,24,25,26,27],fontsize=22)
if num == 9:
plt.xticks([22,23,24,25,26,27],fontsize=22)
plt.legend(frameon=False).draggable()
if num==1 or num ==2 or num==3 or num==10:
plt.yticks([-2.,-3,-4,-5, -6], fontsize=22)
if num==3:
plt.yticks([-2.,-3,-4,-5, -6, -7], fontsize=22)
continue
plt.yticks(visible=False)
'''
fig.text(0.06,0.5,r'$\rm{log_{10}[\Phi /(Mpc^{-3}mag^{-1})]}$',fontsize=32, ha='center',va='center', rotation='vertical')
fig.text(0.5,0.04,r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',fontsize = 32, ha='center',va='center')
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
#plt.tick_params(axis='both',which = 'major', labelsize=20,width =2)
#plt.tick_params(axis='both',which = 'minor', labelsize=10, width=2)
plt.subplots_adjust(hspace=0,wspace=0)
#plt.xtick(fontsize=15)
#plt.ytick(fontsize=15)
#plt.ylim(-10.2,-5.8)
#plt.ylim(-11,-5.5)
#ax.xaxis.set_minor_locator(AutoMinorLocator())
#ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plotf='%s/LF_recon_s1.pdf' % (outdir)
plt.show()
return 0
def simulate(family,params,paramsList,bins,\
seed=None,N=None,noise=None,output=None,\
dump=None,version=2,verbose=False,area=None,\
skadsf=None,pole_posns=None,simarrayf=None,\
simdocatnoise=True):
"""
Based on lumfunc.simtable()
Specify family + parameters
Specify number of sources
Build CDF (or set up function)
Draw deviates
Sample CDF given deviates
Add noise (None or some value)
Bin
Write out
Return
Look at simulate.ipynb for an example run
Need to add normalization capability
Families:
========
skads:
-----
r=countUtils.simulate('skads',[0.01,85.0],['S0','S1'],numpy.linspace(-60.0,100.0,26),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
ppl:
---
r=countUtils.simulate('ppl',[1000.0,5.0,75.0,-1.6],['C','S0','S1','a0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,75.0,-1.6,-2.5],['C','S0','S1','S2','a0','a1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,-1.6,-2.5,-1.0],['C','S0','S1','S2','S3','a0','a1','a2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,90.0,-1.6,-2.5,-1.0,2.0],['C','S0','S1','S2','S3','S4','a0','a1','a2','a3'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
poly:
----
r=countUtils.simulate('poly',[5.0,75.0,1.0],['S0','S1','p0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0],['S0','S1','p0','p1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0,5.0],['S0','S1','p0','p1','p2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
bins:
----
test:
----
array:
-----
"""
# Initialize seed for variates AND any noise
if seed is not None:
numpy.random.seed(seed=SEED_SIM)
if family == 'ppl':
C = alpha = Smin = Smax = beta = S0 = gamma = S1 = delta = S2 = -99.0
nlaws = int(0.5 * len(paramsList) - 1)
C = params[paramsList.index('C')]
Smin = params[paramsList.index('S0')]
alpha = params[paramsList.index('a0')]
if nlaws > 1:
beta = params[paramsList.index('a1')]
S0 = params[paramsList.index('S1')]
if nlaws > 2:
gamma = params[paramsList.index('a2')]
S1 = params[paramsList.index('S2')]
if nlaws > 3:
delta = params[paramsList.index('a3')]
S2 = params[paramsList.index('S3')]
iSmax = int([i for i in paramsList if i.startswith('S')][-1][-1])
Smax = params[paramsList.index('S%i' % iSmax)]
function = lambda S:powerLawFuncWrap(nlaws,S,C,alpha,-99.0,beta,\
Smin/1e6,Smax/1e6,S0/1e6,gamma,S1/1e6,delta,S2/1e6,1.0)
elif family == 'test':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = lambda S: S**2
elif family == 'poly':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('p')
]
S_1 = 1.0
function = lambda S: polyFunc(S, S_1, Smin, Smax, coeffs)
elif family == 'bins':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('b')
]
if pole_posns is None:
pole_posns = numpy.logspace(numpy.log10(Smin), numpy.log10(Smax),
len(coeffs) + 1)
assert (len(coeffs) == len(pole_posns) -
1), '***Mismatch in number of poles!!'
Smin = pole_posns[0]
Smax = pole_posns[-1]
function = lambda S: polesFunc(S, pole_posns, Smin, Smax, coeffs)
elif family == 'array':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
assert (simarrayf
is not None), '***Need to specify an input simulation!'
print 'Reading %s...' % simarrayf
dataMatrix = numpy.genfromtxt(simarrayf)
dndsInArr = dataMatrix[:, 4]
binsDogleg = numpy.concatenate((dataMatrix[:, 0], [dataMatrix[-1, 1]]))
binsMedian = dataMatrix[:, 2]
assert ((
medianArray(binsDogleg) == binsMedian).all()), '***bin mismatch!'
Smin = binsDogleg[0]
Smax = binsDogleg[-1]
if not simdocatnoise:
Smin = -5.01 #-2.01 # binsMedian[0]
print dndsInArr
function = lambda S: arrayFunc(S, binsMedian, dndsInArr, Smin, Smax)
#function2=lambda S:arrayFunc(S,binsMedian,dndsInArr,Smin,Smax)
#for x in numpy.linspace(-10.0,100.0,500):
# print x,function(x),function2(x)
#sys.exit(0)
elif family == 'skads':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = None
assert (skadsf is not None), '***Need to specify input SKADS file!'
print 'Reading %s...' % skadsf
R = Jy2muJy * 10**numpy.genfromtxt(skadsf)
numpy.ndarray.sort(R)
iRmin, Rmin = find_nearest(R, Smin)
iRmax, Rmax = find_nearest(R, Smax)
F = R[iRmin:iRmax]
print '%i/%i sources ingested after Smin/Smax cuts' % (len(F), len(R))
if N is not None:
F = numpy.random.choice(F, size=N, replace=False)
N = len(F)
print 'NSKADS = %i' % N
elif family == 'Lrad':
Smin = params[paramsList.index('LoptMIN')]
Smax = params[paramsList.index('LoptMAX')]
A = params[paramsList.index('A')]
B = params[paramsList.index('B')]
sigma_Lrad = params[paramsList.index('sigma_Lrad')]
#print Loptmin,Loptmax
print 'Doing LF simulation'
inta = None
#intg = integrate.quad(lambda Lopt:Lopt2Lrad(Lopt,A=A,B=B,flux=False),Loptmin,Loptmax,epsabs=0.)[0]
function = lambda Lopt: Lopt2Lrad(Lopt, A=A, B=B, flux=False)
elif family in ['LFsch', 'LFdpl']:
redshift = 0.325
z_min = 0.2
z_max = 0.45
Lmin = params[paramsList.index('LMIN')]
Lmax = params[paramsList.index('LMAX')]
[Smin, Smax] = SMIN_SIM, SMAX_SIM
print Smin, Smax
[Smin, Smax] = get_sbins([10**Lmin, 10**Lmax], redshift, dl) * 1e6
print Smin, Smax, Lmin, Lmax
print 'Doing LF simulation'
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(redshift, dl)
inta = None
intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
print intg * Vmax
print Vmax
area = N / (Vmax * intg)
area1 = area
print N, area
function = lambda S:dNdS_LF(S,z_min,redshift,z_max,dl,params=params,paramsList=paramsList,\
area=area,family=family)
if family != 'skads':
# Set up the 'rough' array
gridlength = 10000 # Good enough to prevent bleeding at the edges
Ss = numpy.linspace(Smin, Smax, gridlength)
print Smin, Smax
print 'checking for one sample'
kl = function(20 / 1e6)
print kl
#sys.exit()
values = numpy.array([function(ix / 1e6) for ix in Ss])
print values[:10]
# Build the CDF
CDF = buildCDF(values)
plt.plot(CDF, Ss)
#plt.xscale('log')
#plt.yscale('log')
plt.ylabel('Flux')
plt.xlabel('CDF')
plt.show()
print CDF.max()
# Create the interpolant object
sampler = interp1d(CDF, Ss)
plt.plot(Ss, values, '.')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Flux')
plt.ylabel('LF')
plt.show()
x = numpy.linspace(0., 1., 10000)
z = numpy.logspace(0, 1, 1000) / 10.
f = sampler(z)
y = sampler(x)
plt.yscale('log')
#plt.xscale('log')
plt.axhline(Smin)
plt.axhline(Smax)
plt.xlabel('R')
plt.ylabel('Sampler(R) [flux]')
#plt.plot(x,y)
plt.plot(z, f)
plt.show()
#sys.exit()
# Test that the sampler extrema match
print Smin, sampler(0.0), 'you know wa mean'
print Smax, sampler(0.99999)
# assert(numpy.isclose(sampler(0.0),Smin)[0])
# assert(numpy.isclose(sampler(0.99999),Smax,atol=1.0e-3)[0])
# Draw the random deviates
R = numpy.random.rand(N)
print len(R)
F = sampler(R)
Nt = 0.
for f in F:
if f < 1.:
Nt += 1.
F = F[F > 1.]
print N, Nt
print len(F)
Nt = len(F)
#sys.exit()
# Normalize here - this is N2C
# EITHER N is specified explicitly
# BOTH N2C and C2N are useful
# Integrate the original function
#intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
# inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
#print intg*Vmax
#print Vmax
#area = Nt/(Vmax*intg)
#print N,area,area1
#plt.show()
#sys.exit()
A = integrate.quad(function, Smin, Smax)[0]
# print A,N
# Bin the random samples
bbins = numpy.linspace(Smin, Smax, 100)
E = numpy.histogram(F, bins=bbins)[0]
# And calculate their area
G = integrate.trapz(E, x=medianArray(bbins))
# print G
# print G/A
# Gunpowder, treason and....
if False:
plt.xlim(0.0, 100.0)
plt.xlabel('S / $\mu$Jy')
plt.hist(F, bins=bbins)
plt.plot(Ss, values * G / A, 'r')
plt.savefig('N2C.pdf')
plt.close()
# Want: C given N, to compare to original C
numbins = 1000
if family == 'ppl':
C_calc = N / N2C(function, F, Smin, Smax, numbins)
#print N2C(function,F,Smin,Smax,numbins),C
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, C)
elif family == 'poly':
C_calc = log10(N / N2C(function, F, Smin, Smax, numbins))
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, coeffs[0])
# Dump noiseless fluxes to file
puredumpf = dump
idl_style = False
numpy.savetxt(puredumpf, F)
print 'Draws (noiseless) are in %s' % puredumpf
writeCountsFile(output[1],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print output[1]
# Now add noise if requested
if simdocatnoise:
numpy.random.seed(seed=SEED_SIM)
poln = False
if poln:
F += rice.rvs(F / noise, size=N)
else:
F += numpy.random.normal(0.0, noise, Nt)
# Dump noisy fluxes to file
if dump is not None:
noisydumpf = '%s_noisy.txt' % puredumpf.split('.')[0]
numpy.savetxt(noisydumpf, F)
print 'Draws (noisy) are in %s' % noisydumpf
print 'Minimum flux in catalogue = %f' % F.min()
print 'Maximum flux in catalogue = %f' % F.max()
# Write counts file
print output[0]
writeCountsFile(output[0],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print N, area #,area1
return F
def main():
"""
"""
# Import the settings variables
print 'Settings file is %s' % param_file
# Import the settings variables
set_module = importlib.import_module(settingsf)
globals().update(set_module.__dict__)
# Set up the experiment
#bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins
bins = numpy.logspace(0, 3.01, 10)
bins = list(bins)
bins.append(2e3)
bins1 = numpy.arange(17.2, 28.2, 0.1)
print 'run expt'
nbins = len(bins)
z_m = redshifts[0]
dl = get_dl(z_m)
sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
expt = countModel(modelFamily,
nlaws,
settingsf, [dataset],
floatNoise,
doRedshiftSlices=True,
mybins=sbin1)
#expt=countModel(modelFamily,nlaws,settingsf,dataset,floatNoise,\
# doRedshiftSlices=True)#numpy.logspace(0,3.0,20))
#print 'these are my beens', expt.bins
#sys.exit()
f = '%spost_equal_weights.dat' % outstem
#f='%sev.dat'% outstem
f = os.path.join(outdir, f)
# Fetch best-fit parameters and calculate best-fit line
plotTruth = dict((name, -99.0) for name in expt.parameters)
# Load equally-weighted posterior samples
x = numpy.genfromtxt(f)
nsamp = x.shape[0]
ncols = x.shape[1] # The fifth [seventh] column is the posterior value
# There must be a better way, but:
#ncols = 14
z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
z[:, :-(expt.nbins - 1)] = x
# Shift posterior values to end
z[:, -1] = z[:, ncols - 1] # Copy...
z[:, ncols - 1] = 0.0 # ...and blank
# Fetch best-fit parameters and calculate best-fit line
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
#print 'These is MAP',drawml
#sys.exit()
#summf=os.path.join(outdir,'1-summary.txt')
#summary=numpy.genfromtxt(summf)[-1,:]
#drawmap=summary[-(ncols+1):-2]
print 'Now this is MAP', drawmap
#drawmap = [ 1.76792100e+02, 5.24202324e+18, 7.67284256e+24, 2.52157587e+1, 9.63136831e+22, 3.30233162e+00]
if True:
print '--> Calculating *ML* reconstruction'
drawmap = drawmap
# Convert drawmap into correct units etc.
power = 2.5
#drawmap[6]-=0.6
#ymap=expt.evaluate(expt.convertPosterior(drawmap,power))
ymap = expt.evaluate(drawmap)[0] #,expt.binsMedian)
#ymap=expt.realise(drawmap)
#print 'ymap'
#print ymap
#print 'yy',['%e'%y for y in ymap]
#sys.exit(0)
for isamp in xrange(nsamp):
#z[isamp,ncols-1:]=expt.evaluate(expt.convertPosterior(z[isamp,:],power))
#z[isamp,:][6]-=0.6
z[isamp,
ncols - 1:] = expt.evaluate(z[isamp, :])[0] #,expt.binsMedian)
#print z[isamp,:]
#sys.exit()
# Blanking, 0.0 -> NaN
z[numpy.where(z == 0.0)] = 'NaN'
#sys.exit()
# Save the raw reconstructions
reconf = 'recon_raw.txt'
reconf = os.path.join(outdir, reconf)
recons = z[:, ncols - 1:]
#numpy.savetxt(reconf,recons)
# Generate stats here...
s = numpy.zeros((len(expt.binsMedian), 8))
s[:, 0] = expt.binsMedian
#print '# ibin flux fit low high dlower dupper skew kurtosis'
#print '%6s %12s %12s %12s %12s %12s %12s %12s %12s'%('bin', 'lower phi_rms','lower phi', 'l_err','ymap','u_err', 'upper phi','u_phi_rms', '2*rms')
for ibin in xrange(len(expt.binsMedian)):
x = recons[:, ibin]
# Remove NaNs from stats vectors
# http://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
x = x[~numpy.isnan(x)]
#ss=stats.bayes_mvs(x,alpha=0.68)[0]
#x*=numpy.power(s[ibin,0]/1.0e6,2.5)
#print bins1[ibin]
#plt.hist(numpy.log10(x),bins=15)
#plt.xlabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$',fontsize = 20)
#plt.show()
#tt=peak_confidence(x,bins=10)
try:
ss = numpy.zeros(3)
ss[0], dlow, dhigh, ss[1], ss[2] = calculate_confidence(
x, alpha=0.95, ret_all=True)
#ss[0],dlow,dhigh,ss[1],ss[2]=calculate_confidence2(x,alpha=0.68,ret_all=True,\
# value_central=ymap[ibin],\
# truncate_edges=True)
except:
ss = numpy.nan * numpy.ones(3)
print "didn't work for ", x
continue
#sys.exit()
#tt=peak_confidence(x,bins=10)
#ss*=numpy.power(s[ibin,0]/1.0e6,2.5)
#print ss[0],tt
# s[ibin,1]=ss[0] # median
# s[ibin,1]=tt # peak
s[ibin, 1] = ymap[ibin] # MAP
#print ymap
s[ibin, 2] = ss[1] # lower
s[ibin, 3] = ss[2] # upper
s[ibin, 4] = 2 * numpy.std(
x) # (s[ibin,3]- s[ibin,2])/(2*numpy.std(x))# skewness
s[ibin, 5] = ss[0]
#print '%6.1f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f'%(bins1[ibin], numpy.log10((ymap[ibin] - s[ibin,4])), numpy.log10(ss[1]),numpy.log10(s[ibin,2]),numpy.log10(ymap[ibin]),numpy.log10(s[ibin,3]) ,numpy.log10(ss[2]), numpy.log10(s[ibin,4]+ ymap[ibin]), numpy.log10(s[ibin,4]) )
#print ibin,s[ibin,0],s[ibin,1],dlow,dhigh,ss[1],ss[2],s[ibin,4],s[ibin,5]#,stats.skewtest(x)
#sys.exit()
# ...and output to file
#sys.exit()
rstatsf = 'recon_stats.txt'
rstatsf = os.path.join(outdir, rstatsf)
hdr = '# median_flux_uJy dnds_2p5_Jy1p5srm1 delta_dnds_2p5_lower_Jy1p5srm1 delta_dnds_2p5_upper_Jy1p5srm1 skewness kurtosis'
fid = open(rstatsf, 'w')
#print hdr
#print s
print '-> Writing stats (i.e. dnds2p5+/- v. S/uJy) to %s' % rstatsf
fid.write('%s\n' % hdr)
numpy.savetxt(fid, s)
fid.close()
print 'Finished.'
return 0
def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module = importlib.import_module(setf)
globals().update(set_module.__dict__)
expt = countModel(modelFamily,
nlaws,
setf, [dataset],
floatNoise,
doRedshiftSlices=True)
#recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n' * 5
print len(expt.parameters)
ncols = len(expt.parameters)
summf = os.path.join(outdir, '1-summary.txt')
summary = numpy.genfromtxt(summf)[-1, :]
drawmap = summary[-(ncols + 2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
if dataset == 'sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
print datafiles[0][4:]
elif dataset == 'cosmos':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
print datafiles[0][8:]
elif 'sim' in dataset:
d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0, 29.2, 0.4)
bins3 = numpy.arange(18., 29.2, 0.01)
print len(expt.parameters)
print 'this is 1st num ', num
#sys.exit()
params = drawmap # [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
#novak 2017
L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]
#novak 2018
L_n2 =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
[22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
[22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
[22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
[23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
[23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
[23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
[23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
[24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
[24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]
L_ner_u2 =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
[0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
[0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
[0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
[0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
[0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
[0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
[0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
[0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
[0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]
L_ner_d2 =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
[0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
[0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
[0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
[0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
[0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
[0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
[0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
[0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
[0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]
rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
[-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
[-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
[-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
[-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
[-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
[-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
[-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
[-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
[-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]
rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
[0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
[0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
[0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
[0.045,0.019,0.033,0.072,0.15, 0.17,0.45,0.25],\
[0.040,0.022,0.033,0.055,0.13, 0.22,0.17,0.45],\
[0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
[0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
[0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
[0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]
rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
[0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
[0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
[0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
[0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
[0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
[0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
[0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
[0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
[0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]
fig = plt.figure()
pre_chain = 12
pre_chain = '02'
chains = [
'01a_1', '01b', '01c', '01d', '01e', '01f_1', '01g', '01h', '01i'
] #chains_2001 DR4
chains = [
'01a_4', '01b_2', '01c_2', '01d_2', '01e_2', '01f_2', '01g', '01h_2',
'01i_2'
] #chains_2001 DR4 remove phi2
chains = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']
#z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
#z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
print expt.parameters
n, m = 3, 3
z_new = True
Replot = False
z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
if z_new:
z = [0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
#chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_201111
else:
z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0] #oldest ;)
model = modelFamily
if model in [
'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
'LFlognorm_dpl', 'LFpl_lognorm'
]:
Lnorm = params[expt.parameters.index('LNORM')]
Lstar = params[expt.parameters.index('LSTAR')]
Lslope = params[expt.parameters.index('LSLOPE')]
#Lzevol=params[expt.parameters.index('LZEVOL')]
if model in [
'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFlognorm_dpl'
]:
Lslope2 = params[expt.parameters.index('LSLOPE2')]
if model in [
'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
'LFlognorm_dpl', 'LFpl_lognorm', 'LFdpl_dpl', 'LFdpl_dpl_z'
]:
Lnorm_2 = params[expt.parameters.index('LNORM_2')]
Lstar_2 = params[expt.parameters.index('LSTAR_2')]
Lslope_2 = params[expt.parameters.index('LSLOPE_2')]
if model in ['LFdpl_dpl', 'LFdpl', 'LFdpl_dpl_z']:
Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]
if model in ['LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm']:
Lsigma = params[expt.parameters.index('LSIGMA')]
if modelFamily in [
'LFdpl_dpl_z', 'LFevol', 'LFevol_dpl', 'LFevol_logn',
'LFevol_logn_L'
]:
alpha_agn = params[expt.parameters.index('A_agn')]
alpha_SF = params[expt.parameters.index('A_SF')]
beta_agn = params[expt.parameters.index('B_agn')]
beta_SF = params[expt.parameters.index('B_SF')]
if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
alpha_SF = params[expt.parameters.index('A_SF')]
beta_SF = params[expt.parameters.index('B_SF')]
if modelFamily in ['LFevol_dpl_a', 'LFevol_logn_a']:
alpha_agn = params[expt.parameters.index('A_agn')]
beta_agn = params[expt.parameters.index('B_agn')]
Lmin = params[expt.parameters.index('LMIN')]
LMIN = params[expt.parameters.index('LMIN')]
Lmax = params[expt.parameters.index('LMAX')]
loglike = 0.
loglike_nov = 0.
print model
bins3 = numpy.arange(15., 30.2, 0.2)
#xbins = numpy.arange(fsigma,26.2,0.4)#bin +0.2
sbin4 = get_sbins(10**bins3, z[0], get_dl(z[0])) * 1e6
expt2 = countModel(modelFamily,
nlaws,
setf, [dataset],
floatNoise,
doRedshiftSlices=True,
mybins=sbin4)
sbin5 = medianArray(sbin4)
phi_tot3 = expt2.evaluate(params)
#print phi_tot3
#print len(phi_tot3[0]), len(bins3)
for num in range(1, 10):
#num = 1.
print num
print 'new num ', num
z_min, z_max, z_m = get_z(num, False)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(z_m, dl)
#z_m = (z_min + z_max)/2
dl = get_dl(z_m)
print z_min, z_max, 'lmin ',
SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
sigma, fsigma, Lmin = numpy.log10(
get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5, LMIN], z_m, dl, 'muJy')
* (1.4 / 3)**(-.7))
sigma2, fsigma2 = numpy.log10(get_Lbins([450, 2400], z_m, dl, 'muJy'))
print Lmin
print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA
print num, '%s/recon_stats_%s.txt' % (outdir, chains[num - 1])
s = numpy.loadtxt('%s/recon_stats_%s.txt' % (outdir, chains[num - 1]))
print chains[num - 1]
xrecon = s[:-1, 0]
yrecon = s[:-1, 1]
yrecon_d = s[:-1, 2]
yrecon_u = s[:-1, 3]
yrecon_rms = s[:-1, 4]
yrecon_avr = s[:-1, 5]
yrecon_rms_down = yrecon_rms
#area = 6672*sqDeg2sr #143165.15 49996.49 59876.8861135
sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
#sbin4 = get_sbins(10**bins3,z_m,dl)*1e6
xreco = get_Lbins(xrecon, z_m, dl, 'muJy') #*(1.4/3)**(-.7)
yreco = yrecon
print SMIN, SMAX
#sys.exit()
lin_yrecon = numpy.log10(yreco)
#print xrecon
lin_xrecon = numpy.log10(xreco)
#bins3 = numpy.arange(15.,30.,0.4)
bins3 = numpy.log10(get_Lbins(sbin4, z_m, dl,
'muJy')) #*(1.4/3)**(-.7))
#bins = numpy.arange(fsigma,26.2,0.4)#bin +0.2
#sbin4 = get_sbins(10**bins3,z_m,dl)*1e6
if not os.path.isfile('cos_data/cos_s%s_LF.el' %
num) and z_new or not os.path.isfile(
'cos_data/cos_s%s_LF_old.el' %
num) and not z_new or Replot:
print 'doing all calculations'
#l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
#l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
if z_new:
L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
else:
L, z_L = open_anytype('cos_data/cos_s%s_old.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
[z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
z_c2, S_c2, L_c2 = numpy.loadtxt('cosmos_counter_parts_II.txt',
unpack=True,
usecols=(3, 4, 5))
L_c2 = L_c2[(z_c2 < z_max) & (z_c2 > z_min)]
S_c2 = S_c2[(z_c2 < z_max) & (z_c2 > z_min)]
z_c2 = z_c2[(z_c2 < z_max) & (z_c2 > z_min)]
#rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
#rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
rho_3, er3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31), L, bins,
z_max, z_min, area)
#rho_4,er4 = calc_Vmax(99.,L,bins2,z_max,z_min,area)
rho_5, er5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31), L_c, bins,
z_max, z_min, area)
rho_6, er6 = calc_Vmax(calcu_zeff(z_c2, 10**L_c2, 1.15e-31),
10**L_c2, bins, z_max, z_min, area)
if z_new:
f = open('cos_data/cos_s%s_LF.el' % num, 'w')
else:
f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
for i in range(len(bins)):
f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n' %
(bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i],
er6[i]))
f.close()
dl_c = get_dl(z_c2)
L_c3 = numpy.log10(
get_Lbins(S_c2, z_c2, numpy.array(dl_c), 'muJy') *
(1.4 / 3.)**(-.7))
else:
if z_new:
#bins,rho_3,er3,rho_5,er5,rho_6,er6 = numpy.loadtxt('cos_data/cos_s%s_LF.el'%num, unpack=True)
bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
'cos_data/cos_s%s_LF_2.el' % num, unpack=True)
print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
else:
bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'
ssd = numpy.loadtxt('%s/data_cos_s%s.txt' % (outdir, num))
#ssd = numpy.loadtxt('cos_data/data_cos_s%s.txt'%(num))
ssbin2 = ssd[:, 0] #2
ssbin1 = ssd[:, 2] #0
Nbin2 = ssd[:, 3]
zbin3 = ssd[:, -1]
dl1 = get_dl(z_min)
dl2 = get_dl(z_max)
#dl3 = get_dl(zbin3)
Ncount = sum(Nbin2[fsigma > ssbin1])
Llmin = numpy.log10(
get_Lbins([fsigma / numpy.sqrt(Ncount)], z_min, get_dl(z_min),
'muJy'))[0]
Llmin *= (1.4 / 3.)**(-.7)
print Llmin
dn = expt.realise(params)
nov_real = expt.realise([2.3, 2.95, 2.86, -0.29, -0.7, 19.5, 26.0])
#sys.exit()
fig.add_subplot(n, m, get_changed_multiplot(n, m, num))
plt.rc('lines', linewidth=2)
#plt.errorbar(bins,rho_1 ,yerr=er1,fmt='*r',label='detected extracted',markersize=11) #bin
plt.errorbar(bins[1:],
rho_3[1:],
yerr=er3[1:],
fmt='ob',
label='extracted',
markersize=8) #bin
'''
if model in ['LFdpl','LFdpl_dpl']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
elif model in ['LFpl','LFdpl_pl']:
faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
elif model in ['LFpl_dpl']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
elif model in ['LFdpl_dpl_z']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
else:
faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
'''
#phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
#phi_dpl
#dpl =
dm = 0.4 #05
hm = 0.
Novak17 = lumfuncUtils.lognormpl(
numpy.power(10, bins3) /
(1 + z_nov[num - 1])**(3.16 - z_nov[num - 1] * 0.32),
numpy.log10(1.85e21), 1.22, 0.63, numpy.log10(3.55e-3))
Novak18=[lumfuncUtils.LF(S,z_m,0,0,dl,[2.3,2.86, 2.95, -0.70, -0.29,0.01,28.],\
['noise','A_agn','A_SF','B_agn','B_SF','LMIN','LMAX'],area=SURVEY_AREA,\
family='LFevol_logn') for S in sbin4/1.0e6]
phi_tot2 = [lumfuncUtils.LF(S,z_m,0,0,dl,params,expt.parameters,area=expt.survey.SURVEY_AREA,\
family=modelFamily) for S in sbin5/1.0e6]
L_1 = numpy.power(10, bins3) / (1 + z_m)**(2.86 - z_m * 0.7)
L_2 = numpy.power(10, bins3) / (1 + z_m)**(2.95 - z_m * 0.29)
L_4 = numpy.power(10, bins3) / (1 + z_m)**(alpha_SF + z_m * beta_SF)
#L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn+z_m*beta_agn)
print z_m
#print L_4
#print L_3
#sys.exit()
phi_agn_n = lumfuncUtils.doublepowerlaw(L_1, 24.59, 1.27, 0.49,
numpy.log10(10**(-5.5)))
phi_sf_n = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22,
0.63, numpy.log10(3.55e-3))
phi_exp =[lumfuncUtils.LF(S,z_m,0,0,dl,[2.3,3, 5.58, -0.9, -1.99,0.01,28.],\
['noise','A_agn','A_SF','B_agn','B_SF','LMIN','LMAX'],area=SURVEY_AREA,\
family='LFevol_logn') for S in sbin4/1.0e6]
#Novak18= phi_agn_n+phi_sf_n
#print 'this is Lminz', numpy.log10(10**18/(1 + z_m)**(alpha_SF+z_m*beta_SF))
#phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(2.5*10**(-5.5)))
phi_agn = 0
phi_sf = lumfuncUtils.doublepowerlaw(L_4, 22.41, 2.4, 0.43,
-3.50) #0.33 2.97
phi_sf = lumfuncUtils.lognormpl(L_4, numpy.log10(1.85e21), 1.22, 0.63,
numpy.log10(3.55e-3))
phi_tot = phi_agn + phi_sf
#for k in range(len(phi_sf)):
# print bins3[k],numpy.power(10,bins3[k]), numpy.log10(L_4[k]), numpy.log10(L_3[k]), numpy.log10(phi_sf[k]), numpy.log10(phi_agn[k]),numpy.log10(phi_tot[k])
#sys.ek
#phi_sf =lumfuncUtils.lognormpl(L_4,numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3))
#plt.plot(bins3+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')
#plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7))+hm,numpy.log10(faint)-dm,'--b' ,label='faint-end')
#plt.plot(numpy.log10(10**bins3),numpy.log10(phi_sf)-dm,'--c', label=' SF')
#plt.plot(bins3,numpy.log10(phi_agn)-dm,'--r', label='AGN')
#plt.plot(numpy.log10(10**bins3),numpy.log10(phi_tot)-dm,'--g',linewidth=3, label='Tot')
#plt.plot(numpy.log10(10**bins3[:-1]),numpy.log10(phi_tot2)-dm,'-k',linewidth=4, label='Tot2')
#plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(phi_exp)-dm,'-k',linewidth=4, label='Expected')
plt.errorbar(lin_xrecon + hm,
lin_yrecon - 0.4,
fmt='-b',
label='MAP',
linewidth=3)
#plt.plot(numpy.log10(10**bins3[:-1] *(1.4/3)**(-.7)),numpy.log10(phi_tot3[num-1])-dm,'-r',linewidth=3, label='Tot3')
plt.errorbar(lin_xrecon + hm,
numpy.log10(yrecon_avr) - dm,
fmt='-c',
label='Average')
plt.errorbar(numpy.array(L_n2[num - 1]),
numpy.array(rho_n2[num - 1]) - 0.4,
xerr=[L_ner_d2[num - 1], L_ner_u2[num - 1]],
yerr=[rho_ner_d2[num - 1], rho_ner_u2[num - 1]],
fmt='sk',
label='Total RLF Novak et al 2018')
#plt.errorbar(bins3, numpy.log10(Novak17)-0.4,fmt='--b',label='Local SF Novak et al 2017')
#plt.plot(bins3,numpy.log10(phi_agn_n),'--g', label='Local AGN Smolcic et al. 2017')
plt.errorbar(numpy.log10(10**bins3),
numpy.log10(Novak18) - 0.4,
fmt='--k',
label='Total RLF Novak et al 2018')
#plt.plot(L_s, numpy.log10(rho_1), '*m',label='bins LF',markersize=8)
#plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=10) #bin
#plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
plt.fill_between(lin_xrecon + hm,
numpy.log10((yrecon_d)) - dm,
numpy.log10(yrecon_u) - dm,
color='k',
alpha=0.2)
#plt.axvline(Lmin,color='r')
print 'This is the dn'
print z_m
#print dn.keys()
'''
for key in dn.keys():
if round(key,2)==round(z_m,2):z_m=key
dn=dn[z_m]
nov_real=nov_real[z_m]
#print nov_real
#dn=dn[round(z_m,2)]
for i in range(len(ssbin1)):
loglike_i= Nbin2[i]*numpy.log(dn[i]) + Nbin2[i] - (Nbin2[i] + 0.5)*numpy.log(Nbin2[i]) - 0.5*numpy.log(2*numpy.pi) - dn[i]
loglike_nov_i= Nbin2[i]*numpy.log(nov_real[i]) + Nbin2[i] - (Nbin2[i] + 0.5)*numpy.log(Nbin2[i]) - 0.5*numpy.log(2*numpy.pi) - nov_real[i]
if nov_real[i]>0:loglike_nov+=loglike_nov_i
if dn[i]>0:loglike+=loglike_i
print '%10.5f %8.2d %8.2f %8.2f %8.2f %8.2f %8.2f %8.2f'%(ssbin1[i], Nbin2[i], dn[i],nov_real[i],loglike_i,loglike_nov_i,loglike, loglike_nov)
'''
plt.ylim(-7.6, -1.6)
plt.axvline(fsigma, color='k', linestyle='dashed')
plt.axvline(sigma, color='g')
#plt.axvline(Llmin,color='r',linewidth=5)
print 'Is this still num ', num
if num < 2 or num < 6 and num > 3 or num > 6 and num < 9:
plt.xticks(visible=False)
plt.ylim(-7.6, -1.6)
if num < 4:
plt.xlim(19, 25.8)
plt.text(19.2,
-7.,
'%.1f < z < %.1f' % (z_min, z_max),
fontsize=19) # \n $M_i$ < -22
elif num < 7:
plt.xlim(20.8, 27.2)
plt.text(21., -7., '%.1f < z < %.1f' % (z_min, z_max),
fontsize=19) # \n $M_i$ < -22
else:
plt.xlim(21.3, 27.8)
plt.text(21.5,
-7.,
'%.1f < z < %.1f' % (z_min, z_max),
fontsize=19) # \n $M_i$ < -22
if num == 3:
plt.xticks([19, 20, 21, 22, 23, 24, 25], fontsize=22)
if num == 6:
plt.xticks([21, 22, 23, 24, 25, 26, 27], fontsize=22)
if num == 9:
plt.xticks([22, 23, 24, 25, 26, 27], fontsize=22)
plt.legend(frameon=False).draggable()
if num == 1 or num == 2 or num == 3 or num == 10:
plt.yticks([-2., -3, -4, -5, -6], fontsize=22)
if num == 3:
plt.yticks([-2., -3, -4, -5, -6, -7], fontsize=22)
continue
plt.yticks(visible=False)
fig.text(0.06,
0.5,
r'$\rm{log_{10}[\Phi /(Mpc^{-3}mag^{-1})]}$',
fontsize=30,
ha='center',
va='center',
rotation='vertical')
fig.text(0.5,
0.04,
r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',
fontsize=30,
ha='center',
va='center')
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both', which='major', labelsize=20, width=3)
plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
plt.subplots_adjust(hspace=0, wspace=0)
#plt.xtick(fontsize=15)
#plt.ytick(fontsize=15)
#plt.ylim(-10.2,-5.8)
#plt.ylim(-11,-5.5)
#ax.xaxis.set_minor_locator(AutoMinorLocator())
#ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plotf = '%s/LF_recon_s1.pdf' % (outdir)
plt.show()
return 0
def main():
"""
"""
# Import the settings variables
print 'Settings file is %s' % param_file
# Import the settings variables
set_module = importlib.import_module(settingsf)
globals().update(set_module.__dict__)
# Set up the experiment
#bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins
chains = [
'01a_3', '01b_3', '01c_3', '01d_3', '01e', '01f', '01g', '01h', '01i',
'01j'
] #chains_1912 including peter
chains = [
'01a_3', '01b_2', '01c_2', '01d_2', '01e_2', '01f_2', '01g', '01h_2',
'01i_2', '01j_3'
] #chains_2001 DR4 remove phi2
chains = [
'01a_8', '01b_8', '01c_8', '01d_8', '01e_8', '01f_8', '01g_7', '01h_8',
'01i_8', '01j_7'
] #chains_2001 DR4 dpl_dpl
chains = [
'01a_9', '01b_9', '01c_9', '01d_9', '01e_9', '01f_9', '01g_9', '01h_9',
'01i_9', '01j_9'
] #chains_2001 DR4 dpl_dpl no mass selection
chains = [
'02a_4_1', '02a_4_2', '02b_4', '02c_4', '02d_4', '02e_4', '02f_4',
'02g_4', '02h_4', '02i_4', '02j_3'
]
chains = [
'02a_z_6x_1', '02a_z_6x_2', '02b_z_7x', '02c_z_7x', '02d_z_7x',
'02e_z_7x', '02f_z_7x', '02g_z_7x', '02h_z_7x', '02i_z_7x', '02j_z_7x'
]
chains = [
'02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3',
'02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3', '02z_31_3'
]
chains = [
'02a_z_8x', '02b_z_8x', '02c_z_8x', '02d_z_8x', '02e_z_8x', '02f_z_8x',
'02g_z_8x', '02h_z_8x', '02i_z_8x', '02j_z_8x'
] #fixed model newz area
chains = [
'02a', '02b', '02c', '02d', '02e', '02f', '02g', '02h', '02i', '02j'
] #lgnorm #chains_2001 DR4 newz new_area
#chains=['02a_3','02b_3','02c_3','02d_3','02e_3','02f_3','02g_3','02h_3','02i_3','02j_3'] #mass <10
#chains=['02a_z_6e','02b_z_6e','02c_z_6e','02d_z_6e','02e_z_6e','02e_z_6e','02g_z_6e','02h_z_6e','02i_z_6e','02j_z_6e']# New fixed 6e (using a)
#chains=['02a_z_6f','02b_z_6f','02c_z_6f','02d_z_6f','02e_z_6f','02e_z_6f','02g_z_6f','02h_z_6f','02i_z_6f','02j_z_6f']# New fixed 6f (using a)
#chains=['02a_z_6g','02b_z_6g','02c_z_6g','02d_z_6g','02e_z_6g','02e_z_6g','02g_z_6g','02h_z_6g','02i_z_6g','02j_z_6g']# New fixed 6f (using a)
#chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #stellar mass>10.2
chains = [
'02a_5', '02b_5', '02c_5', '02d_5', '02e_5', '02f_5', '02g_5', '02h_5',
'02i_5', '02j'
] #stellar mass>10
chains = [
'02a_3', '02b_3', '02c_3', '02d_3', '02e_3', '02f_3', '02g_3', '02h_3',
'02i_3', '02j_3'
] #stellar mass<10. logn
#chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
#chains=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x','02j_z_5x']#stellar mass limit mass<10
#chains=['02a_z_5f','02b_z_5f','02c_z_5f','02d_z_5f','02e_z_5f','02f_z_5f','02g_z_5f','02h_z_5f','02i_z_5f','02j_z_5x']#stellar mass limit mass<10
#chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_10','02i_z_8x_6_10','02j_z_8x_6_10']#fixed cut data more bins _el lmin lose
#chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_1','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_4']
#chains='02z_31_5_15'
pre_chain = '11'
bins = numpy.logspace(0, 3.01, 10)
bins = list(bins)
bins.append(2e3)
bins1 = numpy.arange(15.2, 30.2, 0.1)
nbins = len(bins)
z_m = redshifts[0]
dl = get_dl(z_m)
sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
expt = countModel(modelFamily,
nlaws,
settingsf, [dataset],
floatNoise,
doRedshiftSlices=True,
mybins=sbin1)
Lmin = 21 #21.5# 19. 21.5
Lmax = 26.
z_med = []
SFRD, SFRD_l, SFRD_h = [], [], []
sfrd_map = []
nov_z, nov_z_u, nov_z_l, nov_sfrd, nov_sfrd_u, nov_sfrd_l, Lower, lw_up, lw_lw = numpy.loadtxt(
'novak_2017.txt', usecols=(0, 1, 2, 3, 4, 5, 6, 7, 8), unpack=True)
print modelFamily
evaluations = []
evaluate = True
for num in range(1, 11):
print num
z_min, z_max, z_m = get_z(num, z_new=False)
z_med.append(z_m)
print z_m
#f='%sev.dat'% outstem
f = '1-post_equal_weights.dat'
f = os.path.join('chains_20%s%s' % (pre_chain, chains[num - 1]), f)
#f=os.path.join('chains_20%s%s'%(pre_chain,chains),f)
print 'os.pathf', f
#sfrd_map
# Load equally-weighted posterior samples
x = numpy.genfromtxt(f)
nsamp = x.shape[0]
ncols = x.shape[1] # The fifth [seventh] column is the posterior value
# There must be a better way, but:
#ncols = 14
z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
z[:, :-(expt.nbins - 1)] = x
# Shift posterior values to end
z[:, -1] = z[:, ncols - 1] # Copy...
z[:, ncols - 1] = 0.0 # ...and blank
print 'outstem', outstem
# Fetch best-fit parameters and calculate best-fit line
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
drawmap = ana.get_best_fit()['parameters']
#ana=pymultinest.analyse.Analyzer(ncols-1,\
# outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains),outstem))
#drawmap=ana.get_best_fit()['parameters']
if True:
print '--> Calculating *ML* reconstruction'
drawmap = drawmap
#ymap=expt.evaluate(drawmap)[0]#,expt.binsMedian)
q_l, q, q_h = get_q(z_m)
yrecon, lin_xrecon2, str_q_l = 0, 0, 0
sfrd = []
sfrd_map.append(
get_sfrd_z(yrecon, lin_xrecon2, str_q_l, drawmap, expt.parameters,
modelFamily, q_h, 26., Lmin, z_m)[0])
print drawmap
for isamp in xrange(nsamp):
#z[isamp,ncols-1:]=expt.evaluate(z[isamp,:])[0]#,expt.binsMedian)
#Lmin =z[isamp,:][expt.parameters.index('LMIN')]
Lmax = z[isamp, :][expt.parameters.index('LMAX')]
#sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,z[isamp,:],expt.parameters,modelFamily,q_l ,Lmax,Lmin,z_m)[0] #lower limit
#sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,z[isamp,:],expt.parameters,modelFamily,q_h ,Lmax,Lmin,z_m)[0] #upper limit
sfrd_z = get_sfrd_z(yrecon, lin_xrecon2, str_q_l, z[isamp, :],
expt.parameters, modelFamily, q, Lmax, Lmin,
z_m)[0] #26, 21.5
#plt.plot(z_m,numpy.log10(sfrd_z),'.', alpha=0.02)
#plt.plot(z_m,numpy.log10(sfrd_z_l),'.', alpha=0.01)
#plt.plot(z_m,numpy.log10(sfrd_z_h),'.', alpha=0.01)
#sfrd.append(sfrd_z_l)
sfrd.append(sfrd_z)
#sfrd.append(sfrd_z_h)
#plt.show()
# Blanking, 0.0 -> NaN
#sys.exit()
s_med, dlow, dhigh, s_low, s_high = calculate_confidence(sfrd,
alpha=0.95,
ret_all=True)
SFRD.append(s_med)
SFRD_l.append(s_low)
SFRD_h.append(s_high)
'''
plt.hist(numpy.log10(sfrd),20)
plt.axvline(numpy.log10(s_med),color='r')
plt.axvline(numpy.log10(s_low),color='k')
plt.axvline(numpy.log10(s_high), color='k')
plt.xlabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$',fontsize=20)
plt.show()
'''
sfrdf = 'sfrd_chains_20%s%s_Lmin_%s' % (pre_chain, chains[0], '21_5')
#sfrdf='sfrd_chains_20%s%s_Lmin_%s'%(pre_chain,chains,'21_5')
fils = open(sfrdf, 'w')
hdr = '# median redshift sfrd_median sfrd_low sfrd_high'
fils.write('%s\n' % hdr)
for i in range(len(SFRD)):
fils.write('%f %f %f %f \n' %
(z_med[i], SFRD[i], SFRD_l[i], SFRD_h[i]))
fils.close()
z_b = numpy.arange(0, 60) / 10.
Ilbert_h = sfrd_Behroozi(z_b,
z0=0.95 - 0.410,
A=-1.,
B=0.194 - 0.082,
C=0.111 + 0.04)
Ilbert_l = sfrd_Behroozi(z_b,
z0=0.95 + 0.343,
A=-1.,
B=0.194 + 0.128,
C=0.111 - 0.029)
G_x, Gruppioni = numpy.loadtxt('Gruppioni_2013',
unpack=True,
delimiter=',')
G_x1, Gruppioni_yup = numpy.loadtxt('Gruppioni_2013_y_up',
unpack=True,
delimiter=',')
G_x2, Gruppioni_ydown = numpy.loadtxt('Gruppioni_2013_y_down',
unpack=True,
delimiter=',')
Burg_z, Burg_FIR, Burg_FIR_er, Burg_tot, Burg_tot_er = numpy.loadtxt(
'Burgarella_2013', unpack=True)
plt.fill_between(z_med,
numpy.log10((SFRD_l)),
numpy.log10(SFRD_h),
color='b',
alpha=0.2)
plt.errorbar(z_med,
numpy.log10(SFRD),
fmt='*',
markersize=14,
label='Faint LF function')
#plt.errorbar(z_med,numpy.log10(sfrd_map),fmt='pr',markersize=14, label='Faint LF function')
plt.errorbar(z_b,
numpy.log10(sfrd_Behroozi(z_b)),
fmt='--',
markersize=12,
label='Behroozi et al. 2013')
plt.errorbar(z_b,
numpy.log10(
sfrd_Behroozi(z_b, z0=0.95, A=-1., B=0.194, C=0.111)),
fmt=':',
markersize=12,
label='IIlbert et al. 2013 (UltraVista DR1)')
plt.errorbar(z_b,
numpy.log10(sfrd_Madau(z_b)),
fmt='.-',
markersize=12,
label='Madau&Dickinson 2014')
try:
plt.errorbar(numpy.array(nov_z),
numpy.array(nov_sfrd),
xerr=[nov_z_l, nov_z_u],
yerr=[nov_sfrd_l, nov_sfrd_u],
fmt='sk',
label='Total SFRD Novak et al 2017')
except:
print ''
plt.ylabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$', fontsize=30)
plt.xlabel(r'$\rm{z} $', fontsize=30)
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both', which='major', labelsize=20, width=3)
plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
plt.show()
return 0
def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module=importlib.import_module(setf)
globals().update(set_module.__dict__)
expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
recon_expt=countModel(modelFamily,nlaws,setf,[dataset],floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n'*5
print len(expt.parameters)
ncols = len(expt.parameters)
summf=os.path.join(outdir,'1-summary.txt')
summary=numpy.genfromtxt(summf)[-1,:]
drawmap=summary[-(ncols+2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap=ana.get_best_fit()['parameters']
if dataset=='sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf)==20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][4:]))
print datafiles[0][4:]
elif dataset=='cosmos':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf)==24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d=numpy.genfromtxt('%s/%s'%(outdir,datafiles[0][8:]))
print datafiles[0][8:]
elif 'sim' in dataset:
d=numpy.genfromtxt(os.path.join(outdir,'sim.txt'))
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0,29.2,0.4)
bins3 = numpy.arange(18.,29.2,0.01)
print len(expt.parameters)
print 'this is 1st num ', num
#sys.exit()
params = drawmap# [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
#novak 2017
L_n =[21.77,22.15,22.46,22.77,23.09,23.34]
rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]
#novak 2018
nov_z,nov_z_u, nov_z_l, nov_sfrd, nov_sfrd_u, nov_sfrd_l, Lower, lw_up, lw_lw, nov_phi_L, nov_phi_L2 = numpy.loadtxt('novak_2017.txt',usecols=(0,1,2, 3,4,5, 6,7,8,-2,-1),unpack=True)
#fig,ax = subplots()
pre_chain='12'
chains=['01a_4','01b_4','01c_4','01d_4','01e_4','01f_4','01g_4','01h_4','01i_4_2', '01j_4_2']#chains_191101 7x7 grid
chains=['02a_4','02b_4','02c_4','02d_4','02e_4','02f_4','02g_5','02h_4','02i_4','02j_4']#chains_191101 7x7 grid lognorm
#chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5']#chains_191101 3x3 grid scaled
#chains=['01a_6','01b_6','01c_6','01d_6','01e_6','01f_6','01g_6','01h_6','01i_6']#chains_191101 11x11 grid
#chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i'] #chains_1912 #includeing maskregion
chains=['01a_3','01b_3','01c_3','01d_3','01e','01f','01g','01h','01i','01j'] #chains_1912 including peter
#chains=['01a_2_1','01b_2_1','01c_2_1','01d_2','01e_2','01f_2','01g_2','01h_2','01i_2','01j_2']
#chains=['01a_4_1','01b_4_1','01c_4','01d_4','01e_4','01f_4_1','01g_4','01h_4','01i_4','01j_4'] #chains_1912 no mass selection
#chains=['01a_3','01b_2','01c_2','01d_2','01e_2','01f_2','01g','01h_2','01i_2','01j_3'] #chains_2001 DR4 remove phi2
chains=['01a_5','01b_5','01c_5','01d_5','01e_5','01f_5','01g_5','01h_5','01i_5','01j_5'] #chains_2001 DR4 remove phi2 new changes
chains4=['01a_8','01b_8','01c_8','01d_8','01e_8','01f_8','01g_7','01h_8','01i_8','01j_7'] #chains_2001 DR4 dpl_dpl
chains=['01a_9','01b_9','01c_9','01d_9','01e_9','01f_9','01g_9','01h_9','01i_9','01j_9'] #chains_2001 DR4 dpl_dpl no mass selection
#chains=['02a_z_9_6','02b_z_9_6','02c_z_9_6','02d_z_9_6','02e_z_9_6','02f_z_9_6','02g_z_9_6','02h_z_9_6','02i_z_9_6','02j_z_9_6']
chains=['01a_6_1','01a_8_2','01b_6_3','01c_6_3','01d_6_2','01e_6_2','01f_6_2','01g_6_2','01h_6_3','01i_6_3','01j_6_3'] #chains_2001 DR4 dpl_dpl Lmin=19 newz
chains=['02a_4_1','02a_4_2','02b_4','02c_4','02d_4','02e_4','02f_4','02g_4','02h_4','02i_4','02j_3']
chains2=['02a_z_5x','02b_z_5x','02c_z_5x','02d_z_5x','02e_z_5x','02f_z_5x','02g_z_5x','02h_z_5x','02i_z_5x_2','02j_z_5x']
chains2=['02a_z_6x_1','02a_z_6x_2','02b_z_7x','02c_z_7x','02d_z_7x','02e_z_7x','02f_z_7x','02g_z_7x','02h_z_7x','02i_z_7x','02j_z_7x']
#chains=['01a','01b','01c','01d','01e','01f','01g','01h','01i','01j'] #dpl #chains_2001 DR4 newz new_area
chains4=['02a','02b','02c','02d','02e','02f','02g','02h','02i','02j'] #lgnorm #chains_2001 DR4 newz new_area
chains2=['02a_z_8x','02b_z_8x','02c_z_8x','02d_z_8x','02e_z_8x','02f_z_8x','02g_z_8x','02h_z_8x','02i_z_8x','02j_z_8x']#fixed model newz area
chains=['02a_z_8x_4','02b_z_8x_4','02c_z_8x_4','02d_z_8x_4','02e_z_8x_4','02f_z_8x_4','02g_z_8x_4','02h_z_8x_4','02i_z_8x_4','02j_z_8x_4']#fixed model newz area
#chains=['02a_z_8x_6','02b_z_8x_6','02c_z_8x_6','02d_z_8x_6','02e_z_8x_6','02f_z_8x_6','02g_z_8x_6','02h_z_8x_6','02i_z_8x_6','02j_z_8x_6']#fixed cut data more bins wrong noise
chains=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
chains2=['02a_z_8x_6_1','02b_z_8x_6_1','02c_z_8x_6_1','02d_z_8x_6_1','02e_z_8x_6_1','02f_z_8x_6_1','02g_z_8x_6_3','02h_z_8x_6_3','02i_z_8x_6_3','02j_z_8x_6_3']#fixed cut data more bins _el lmin lose
chains3='02z_31_5_15'
print expt.parameters
z_new=True
Replot=False
z_nov = [0.31, 0.5, 0.69, 0.9, 1.16, 1.44, 1.81, 2.18, 2.81, 3.71, 4.83]
if z_new:
z =[0.1,0.3,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]
#chains=['11a','11b','11c_3','11d_3','11e_3','11f_3','11g_3','11h','11i','11j', '11k', '11l' ,'11m', '11n']#chains_191111
else:
z=[0.1,0.4,0.6,0.8,1.0,1.3,1.6,2.0,2.5,3.2,4.0]#oldest ;)
sfrd, sfrd_l,sfrd_h,z_med = numpy.zeros(len(z)+1),numpy.zeros(len(z)+1) ,\
numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1)
sfrd_nov, sfrd_nov_l,sfrd_nov_h, sfrd_2, sfrd_3, sfrd_4, sfrd_2_l, sfrd_3_l, sfrd_2_h, sfrd_3_h = numpy.zeros(len(z)+1),\
numpy.zeros(len(z)+1), numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1),numpy.zeros(len(z)+1),\
numpy.zeros(len(z)+1), numpy.zeros(len(z)+1) ,numpy.zeros(len(z)+1),numpy.zeros(len(z)+1) ,\
numpy.zeros(len(z)+1)
ana3=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_2012%s'%chains3,outstem))
drawmap3=ana3.get_best_fit()['parameters']
params3 = drawmap3
print 'chains_2002%s'%chains3
print params3
parameters2= ['noise', 'A_SF', 'A_agn', 'LMIN', 'LMAX']
parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN', 'LMAX']
parameters3= ['noise', 'A_agn', 'A_SF', 'B_agn', 'B_SF', 'LMIN_1', 'LMIN_2', 'LMIN_3', 'LMIN_4', 'LMIN_5', 'LMIN_6', 'LMIN_7', 'LMIN_8', 'LMIN_9', 'LMIN', 'LMAX']
parameters4=['noise', 'LMIN', 'LMAX2', 'LMIN2', 'LMAX', 'LNORM', 'LSTAR', 'LSLOPE', 'LSLOPE2', 'LNORM_2', 'LSTAR_2', 'LSLOPE_2', 'LSLOPE2_2']
for num in range(1,11):
#num = 1.
print num
z_min,z_max, z_m = get_z(num,False)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min,z_max)
dsdl = get_dsdl(z_m,dl)
dl = get_dl(z_m)
print z_min,z_m,z_max
print expt.kind
area = SURVEY_AREA*sqDeg2sr
area1 = SURVEY_AREA*sqDeg2sr
print SURVEY_AREA
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains[num-1]),outstem))
drawmap=ana.get_best_fit()['parameters']
drawmid=ana.get_stats()['marginals']
params = drawmap
print params
ana2=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join('chains_2012%s'%(chains2[num-1]),outstem))
drawmap2=ana2.get_best_fit()['parameters']
params2 = drawmap2
#ana4=pymultinest.analyse.Analyzer(ncols-1,\
# outputfiles_basename=os.path.join('chains_20%s%s'%(pre_chain,chains4[num-1]),outstem))
#drawmap4=ana4.get_best_fit()['parameters']
#params4 = drawmap4
print 'parameter 1 ', params
print 'parameters 2 ', params2
print 'parameters 3 ', params3
settingf='chains_20%s%s/bayestack_settings.py'%(pre_chain,chains[num-1])
f = open(settingf, 'r')
mod = f.readlines()[31].split()[0]
print mod
if 'logn' in mod:
if 'dpl' in mod:
model='LFlognorm_dpl'
elif '_el' in mod:
model='LFevol_logn_el'
elif 'pl_' in mod:
model='LFpl_lognorm'
else:
model='LFlognorm'
elif 'LFdpl' in mod or 'LFpl' in mod:
if 'LFdpl_dpl' in mod:
model='LFdpl_dpl'
elif '_pl' in mod:
model='LFdpl_pl'
elif 'LFpl_' in mod:
model='LFpl_dpl'
elif 'LFpl' in mod:
model='LFpl'
else:
model = 'LFdpl'
with open('chains_20%s%s/params.tex'%(pre_chain,chains[num -1])) as f:
parameters=[line.split(None,1)[0] for line in f]
#model='LFdpl_pl'
print parameters
print model
print 'LSIGMA' in parameters
if model in ['LFsch','LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
Lnorm=params[parameters.index('LNORM')]
Lstar=params[parameters.index('LSTAR')]
Lslope=params[parameters.index('LSLOPE')]
#Lzevol=params[parameters.index('LZEVOL')]
if model in ['LFdpl_pl','LFpl_dpl','LFdpl_dpl','LFlognorm_dpl']:
Lslope2=params[parameters.index('LSLOPE2')]
if model in ['LFlognorm','LFpl', 'LFdpl', 'LFdpl_pl','LFpl_dpl', 'LFlognorm_dpl','LFdpl_dpl']:
Lnorm_2=params[parameters.index('LNORM_2')]
Lstar_2=params[parameters.index('LSTAR_2')]
Lslope_2=params[parameters.index('LSLOPE_2')]
if model in ['LFdpl_dpl','LFdpl']:
Lslope2_2=params[parameters.index('LSLOPE2_2')]
if model in ['LFlognorm','LFlognorm_dpl']:
Lsigma = params[parameters.index('LSIGMA')]
Lmin=params[parameters.index('LMIN')]
Lmax=params[parameters.index('LMAX')]
#Lmax2=params[parameters.index('LMAX2')]
Lmax2=Lmax
SMIN = get_sbins(numpy.power(10,Lmin),z_m,dl)*1e6
SMAX = get_sbins(numpy.power(10,Lmax),z_m,dl)*1e6
sigma,fsigma = numpy.log10(get_Lbins([SURVEY_NOISE,SURVEY_NOISE*5],z_m,dl,'muJy')*(1.4/3)**(-.7))
sigma2,fsigma2 = numpy.log10(get_Lbins([450,2400],z_m,dl,'muJy'))
print z_m,dl, sigma,fsigma
print SURVEY_NOISE,SURVEY_NOISE*5,SURVEY_AREA
s=numpy.loadtxt('chains_20%s%s/recon_stats.txt'%(pre_chain,chains[num -1]))
xrecon=s[:-1,0]; yrecon=s[:-1,1]
yrecon_d=s[:-1,2]; yrecon_u=s[:-1,3]
yrecon_rms = s[:-1,4]
yrecon_avr = s[:-1,5]
yrecon_rms_down = yrecon_rms
yrec_u = yrecon_u - yrecon
yrec_d = yrecon - yrecon_d
#print yrecon_d
#print yrecon
#print yrecon_u
#sys.exit()
#y_err_u = sqrt(yrec)
xreco = get_Lbins(xrecon,z_m,dl,'muJy')#*(1.4/3)**(-.7)
xreco2 = get_Lbins(xrecon,z_m,dl,'muJy')
yreco = yrecon
lin_yrecon = numpy.log10(yreco)
lin_xrecon = numpy.log10(xreco)
lin_xrecon2 = numpy.log10(xreco2)
q_l,q,q_h = get_q(z_m)
#print z_nov[num -1]
try:
yrecon_nov = lumfuncUtils.lognormpl(get_Lbins(xrecon,z_m,dl,'muJy')/(1 + z_nov[num-1])**(3.16-z_nov[num-1]*0.32), numpy.log10(1.85e21),1.22,0.63, numpy.log10(3.55e-3))
except:
print ''
str_q_l = get_sfr_q(1.,q_l,xreco2)
str_q_h = get_sfr_q(1.,q_h,xreco2)
str_q = get_sfr_q(1.,q,xreco2)
#sys.exit()
lmin = 21.5#. 21.5
Lmax=26.
sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,params,parameters,model,q_l ,Lmax2,lmin,z_m)[0] #lower limit
sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_h,params,parameters,model,q_h ,Lmax2,lmin,z_m)[0] #upper limit
sfrd_z =get_sfrd_z(yrecon,lin_xrecon2,str_q ,params,parameters,model,q ,Lmax2,lmin,z_m)[0] #26, 21.5
sfrd_2_z =get_sfrd_z(yrecon,lin_xrecon2,str_q ,params2,parameters2,'LFevol_logn_mat',q ,Lmax2,lmin,z_m)[0]
sfrd_3_z =get_sfrd_z(yrecon,lin_xrecon2,str_q ,params3,parameters3,'LFevol_logn_L',q ,Lmax2,lmin,z_m)[0]
#sfrd_4_z =get_sfrd_z(yrecon,lin_xrecon2,str_q ,params4,parameters4,'LFdpl_dpl',q ,Lmax2,lmin,z_m)[0]
sfrd_2_z_l =get_sfrd_z(yrecon,lin_xrecon2,str_q_l ,params2,parameters2,'LFevol_logn_mat',q_l ,Lmax2,lmin,z_m)[0]
sfrd_3_z_l =get_sfrd_z(yrecon,lin_xrecon2,str_q_l ,params3,parameters3,'LFevol_logn_L',q_l ,Lmax2,lmin,z_m)[0]
sfrd_2_z_h =get_sfrd_z(yrecon,lin_xrecon2,str_q_h ,params2,parameters2,'LFevol_logn_mat',q_h ,Lmax2,lmin,z_m)[0]
sfrd_3_z_h =get_sfrd_z(yrecon,lin_xrecon2,str_q_h ,params3,parameters3,'LFevol_logn_L',q_h ,Lmax2,lmin,z_m)[0]
#sfrd_z_l=get_sfrd_z(yrecon,lin_xrecon2,str_q_l,params,parameters,model,q_l ,Lmax2,Lmin)[0] #lower limit
#sfrd_z_h=get_sfrd_z(yrecon,lin_xrecon2,str_q_h,params,parameters,model,q_h ,Lmax2,Lmin)[0] #upper limit
#sfrd_z =get_sfrd_z(yrecon,lin_xrecon2,str_q ,params,parameters,model,q ,Lmax2,Lmin)[0] #26, 21.5
#sys.exit()
#sfrd_nov_z =get_sfrd_z(yrecon_nov,lin_xrecon,str_q ,params,parameters,'novak',get_q(z_nov[num-1])[1] ,26,lmin, z=z_nov[num-1])[0]
#print 'This is nov z ', z_nov[num-1]
#sys.exit()
#sfrd_z =get_sfrd_z(yrecon,lin_xrecon,str_q ,params,parameters,model,q ,Lmax2,Lmin)[0]
#sfrd_z,sfrd_zerr= get_sfrd_z(yrecon,lin_xrecon,str_q,numpy.inf,0.)
sfrd[num-1] = sfrd_z
sfrd_l[num-1] = sfrd_z_l
sfrd_h[num-1] = sfrd_z_h
z_med[num-1] = z_m
#sfrd_nov[num-1] =sfrd_nov_z
sfrd_2[num-1] = sfrd_2_z
sfrd_3[num-1] = sfrd_3_z
#sfrd_4[num-1] = sfrd_4_z
sfrd_2_l[num-1] = sfrd_2_z_l
sfrd_3_l[num-1] = sfrd_3_z_l
sfrd_2_h[num-1] = sfrd_2_z_h
sfrd_3_h[num-1] = sfrd_3_z_h
#plt.plot(lin_xrecon,numpy.log10(str_q),'*',markersize=8)
try:
zrecon, srecon, srecon_l, srecon_h =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains[0],'21_5'),unpack=True)
except:
print 'Please note that the 95 percent region has to be run for chains_2001%s ... python reconstruct_lf_plot.py'%chains[0]
print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
zrecon, srecon, srecon_l, srecon_h=0,0,0,0
try:
zrecon, srecon2, srecon_l2, srecon_h2 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains[0],'19'),unpack=True)
except:
print 'Please note that the 95 percent region has to be run for chains_2001%s ... python reconstruct_lf_plot.py'%chains[0]
print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
zrecon2, srecon2, srecon_l2, srecon_h2=0,0,0,0
try:
zrecon_2, srecon_2, srecon_l_2, srecon_h_2 =numpy.loadtxt('sfrd_chains_2011%s_Lmin_%s'%(chains4[0],'21_5'),unpack=True)
except:
print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%(chains2[0])
print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
zrecon_2, srecon_2, srecon_l_2, srecon_h_2=0,0,0,0
try:
zrecon_3, srecon_3, srecon_l_3, srecon_h_3 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains3,'21_5'),unpack=True)
except:
print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%chains3[0]
print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
zrecon_3, srecon_3, srecon_l_3, srecon_h_3=0,0,0,0
try:
zrecon_3_2, srecon_3_2, srecon_l_3_2, srecon_h_3_2 =numpy.loadtxt('sfrd_chains_2012%s_Lmin_%s'%(chains3,'21_5'),unpack=True)
except:
print 'Please note that the 95 percent region has to be run for chains_2002%s ... python reconstruct_lf_plot.py'%chains3[0]
print 'Ensure that chains in reconstruct_lf_plot.py cooresponds to chains in this file!'
zrecon_3_2, srecon_3_2, srecon_l_3_2, srecon_h_3_2=0,0,0,0
#plt.rc('lines', linewidth=2)
#plt.xlabel(r'$\rm{log_{10}[L_{1.4}/(W}$ $\rm{Hz^{-1})]}$',fontsize = 30)
#plt.ylabel(r'$\rm{log_{10}[SFR_{IR}(M_\odot yr^{-1})]}$',fontsize = 30)
#plt.tick_params(axis='both',which = 'major', labelsize=20,width =3)
#plt.tick_params(axis='both',which = 'minor', labelsize=10, width=2)
#plt.show()
#fig = plt.figure()
#print z_med
#print sfrd_l
#print sfrd
#print sfrd_h
#print len(z),len(sfrd)
#Salpeter to Chabrier * 0.63
print zrecon_2,srecon_2, srecon_l_2, srecon_h_2
srecon_2_err=2*0.434*(srecon_h_2)/numpy.log10(srecon_2)
z_b=numpy.arange(0,60)/10.
Ilbert_h = sfrd_Behroozi(z_b,z0=0.95-0.410,A=-1.,B=0.194-0.082,C=0.111+0.04)
Ilbert_l = sfrd_Behroozi(z_b,z0=0.95+0.343,A=-1.,B=0.194+0.128,C=0.111-0.029)
parasa = parsa_z(z_b,a=0.11,b=1.48,c=2.75)
Koprowski= parsa_z(z_b,a=0.18,b=1.04,c=1.77)
#plt.errorbar(z_med,numpy.log10(sfrd),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$')
#plt.errorbar(z_med,numpy.log10(sfrd_3),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$')
plt.errorbar(zrecon_2,numpy.log10(srecon_2),yerr=srecon_2_err,fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{B}$')
#plt.errorbar(z_med,numpy.log10(sfrd),fmt='h',fillstyle='none',markeredgewidth=3,markersize=18, label=r'$\rm{Model}$ $\rm{C}$')
#plt.errorbar(z_med,numpy.log10(sfrd_2),fmt='h',fillstyle='none',markeredgewidth=3,markersize=15, label=r'$\rm{Model}$ $\rm{C}$')
#plt.errorbar(z_med,numpy.log10(sfrd_3),fmt='-k',linewidth=5, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$')
#plt.errorbar(zrecon_3_2,numpy.log10(srecon_3_2),fmt='-m',linewidth=5, label=r'$\rm{Model}$ $\rm{C}$ $\rm{PLE}$ $\rm{q(z=0)}$')
#plt.errorbar(zrecon_2,numpy.log10(srecon_2),fmt='.-k',linewidth=5, label=r'$\rm{Model}$ $\rm{B}$')
G_x , Gruppioni = numpy.loadtxt('Gruppioni_2013',unpack=True,delimiter=',')
G_x1, Gruppioni_yup = numpy.loadtxt('Gruppioni_2013_y_up',unpack=True,delimiter=',')
G_x2, Gruppioni_ydown = numpy.loadtxt('Gruppioni_2013_y_down',unpack=True,delimiter=',')
Burg_z, Burg_FIR, Burg_FIR_er, Burg_tot, Burg_tot_er = numpy.loadtxt('Burgarella_2013',unpack=True)
Lzl,Lzh,Lphi_tot,Lphi_ir,Ler = numpy.loadtxt('Liu_2018',unpack=True)
print Lzl,Lzh
Lz = numpy.mean(numpy.array([Lzl,Lzh]),axis=0)
print Lz
#plt.errorbar(z_med,numpy.log10(sfrd_4),fmt='--k',linewidth=7, label='No mass-limit')
plt.errorbar(Lz,Lphi_tot,yerr=Ler,fmt='*g',markeredgewidth=1.8,markersize=16, fillstyle='none', label=r'$\rm{Liu+2018}$ $\rm{FIR+UV}$')
try:
plt.errorbar(numpy.array(nov_z),numpy.array(nov_sfrd),xerr=[nov_z_l,nov_z_u],yerr=[nov_sfrd_l,nov_sfrd_u], fmt='sr',fillstyle='none',ecolor='k',markeredgewidth=1.5, markersize=10, label=r'$\rm{Novak+2017}$ $\rm{PLE}$ $\rm{radio}$')
except:
print ''
plt.errorbar(z_b,numpy.log10(Koprowski),fmt='--k',linewidth=3, label=r'$\rm{Koprowski+2017}$ $\rm{IR}$')
#plt.errorbar(z_b,numpy.log10(parasa),fmt='.g',linewidth=3, label=r'$\rm{Parsa}$ + $\rm{2016}$ $\rm{UV}$')
plt.errorbar(z_b,numpy.log10(sfrd_Madau(z_b)),fmt='o-c',linewidth=3,fillstyle='none', label=r'$\rm{Madau&Dickinson}$ $\rm{2014}$')
#plt.errorbar(z_med,numpy.log10(sfrd_l)+0.4,fmt='--',markersize=8,label='low')
#plt.errorbar(z_med,numpy.log10(sfrd_h)+0.4,fmt='--',markersize=8,label='high')
#plt.errorbar(numpy.array(nov_z),numpy.array(Lower),xerr=[nov_z_l,nov_z_u],yerr=[lw_up, lw_lw], fmt='*g', label='lower limit SFRD Novak et al 2017')
#plt.yscale('log')
#plt.fill_between(z_med,numpy.log10((sfrd_l)),numpy.log10(sfrd_h), color='b',alpha=0.5)
#plt.fill_between(z_med,numpy.log10((sfrd_2_l)),numpy.log10(sfrd_2_h), color='c',alpha=0.7)
#plt.fill_between(z_med,numpy.log10((sfrd_3_l)),numpy.log10(sfrd_3_h), color='g',alpha=0.5)
#plt.fill_between(zrecon,numpy.log10((srecon_l)),numpy.log10(srecon_h), label='95% Free Model',color='b',alpha=0.3)
#plt.fill_between(z_b,numpy.log10((Ilbert_l)),numpy.log10(Ilbert_h), color='orange',alpha=0.2)
#plt.fill_between(zrecon,numpy.log10((srecon_l_2)),numpy.log10(srecon_h_2), label='95% Model B',color='g',alpha=0.6)
#plt.fill_between(zrecon,numpy.log10(srecon_l_3_2),numpy.log10(srecon_h_3_2),edgecolor="k",linewidth=5, label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{PLE}$ $\rm{q_{TIR}(z=0)}$',color='m',alpha=0.6)
plt.fill_between(zrecon,numpy.log10((srecon_l_3)),numpy.log10(srecon_h_3), label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{PLE}$',color='m',alpha=0.6)
plt.fill_between(zrecon,numpy.log10((srecon_l)),numpy.log10(srecon_h), label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{Individual}$',color='b',alpha=0.4)
plt.fill_between(zrecon,numpy.log10((srecon_l2)),numpy.log10(srecon_h2), edgecolor="k", linewidth=4, label=r'$\rm{95\%}$ $\rm{Model}$ $\rm{C}$ $\rm{Individual}$ $\rm{q_{TIR}(z=0)}$',color='b',alpha=0.4)
plt.fill_between(G_x,numpy.log10(Gruppioni_ydown),numpy.log10(Gruppioni_yup), color='k',alpha=0.3,label=r'$\rm{Gruppioni+2013}$ $\rm{IR}$')
#plt.fill_between(nov_z,nov_phi_L, nov_phi_L2, color='r',alpha=0.4,label='Novak+2017 $\Phi$ and L Evolution')
#plt.fill_between(Burg_z,numpy.log10(0.63*0.01*(Burg_FIR - Burg_FIR_er)),numpy.log10(0.63*0.01*(Burg_FIR + Burg_FIR_er)), color='r',alpha=0.2,label='Burgarella-2013 FIR')
#plt.fill_between(Burg_z,numpy.log10(0.63*0.01*(Burg_tot - Burg_tot_er)),numpy.log10(0.63*0.01*(Burg_tot + Burg_tot_er)), color='g',alpha=0.2,label='Burgarella-2013 Total')
plt.ylabel(r'$\rm{\log10(SFRD[M_\odot yr^{-1} Mpc^{-3}])}$',fontsize=32)
plt.xlabel(r'$\rm{z} $',fontsize = 35)
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both',which = 'major', labelsize=20,width =1)
plt.tick_params(axis='both',which = 'minor', labelsize=10, width=1)
plt.subplots_adjust(hspace=0,wspace=0)
#plt.xtick(fontsize=15)
#plt.ytick(fontsize=15)
#plt.ylim(-10.2,-5.8)
##plt.ylim(-2.7,0)
plt.ylim(-2.2,-0.5)
plt.xlim(0,3.8)
#plt.ylim(-2.3,-0.3)
#ax.xaxis.set_minor_locator(AutoMinorLocator())
#ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plotf='%s/LF_recon_s1.pdf' % (outdir)
handles,labels=plt.gca().get_legend_handles_labels()
print labels
print len(labels)
order=[4, 1,2, 0,3,5, 6, 7,8]
plt.legend([handles[idx] for idx in order],[labels[idx] for idx in order],frameon=False,ncol=2, prop={'size':24}).draggable()
plt.show()
return 0
def main():
"""
"""
# Import the settings variables
print 'Settings file is %s' % param_file
# Import the settings variables
set_module = importlib.import_module(settingsf)
globals().update(set_module.__dict__)
# Set up the experiment
#bins=[12.679592, 31.84969509, 80.00281696, 200.9579904, 504.78364938, 1010, 3267.95919972]# 0.4 bins
bins = numpy.logspace(0, 3.01, 10)
bins = list(bins)
bins.append(2e3)
bins2 = numpy.arange(18., 27., 0.2)
bins1 = numpy.arange(18.2, 27.2, 0.2)
print redshifts
nbins = len(bins)
z_m = redshifts[0]
dl = get_dl(z_m)
sbin1 = get_sbins(10**bins1, z_m, dl) * 1e6
sbin2 = get_sbins(10**bins2, z_m, dl) * 1e6
#L = 10**bins1*(1.4/3.)**(.7)
#L =get_Lbins(sbin1,z_m,dl)*(1.4/3.)**(-.7)
expt = countModel(modelFamily,
nlaws,
settingsf, [dataset],
floatNoise,
doRedshiftSlices=True,
mybins=sbin2)
f = '%spost_equal_weights.dat' % outstem
f = os.path.join(outdir, f)
print 'os.pathf', f
chain = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k']
# Load equally-weighted posterior samples
x = numpy.genfromtxt(f)
nsamp = x.shape[0]
ncols = x.shape[1] # The fifth [seventh] column is the posterior value
# There must be a better way, but:
#ncols = 14
z = numpy.zeros((nsamp, ncols - 1 + expt.nbins))
z[:, :-(expt.nbins - 1)] = x
# Shift posterior values to end
z[:, -1] = z[:, ncols - 1] # Copy...
z[:, ncols - 1] = 0.0 # ...and blank
print 'outstem', outstem
# Fetch best-fit parameters and calculate best-fit line
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
if True:
print '--> Calculating *ML* reconstruction'
drawmap = drawmap
ymap = expt.evaluate(drawmap) #,expt.binsMedian)
#print ymap
evaluations = []
evaluate = True
for num in range(1, 12):
print num
z_min, z_max, z_m = get_z(num, False)
# for k in range(len(phi_sf)):
# print sbin1[k],bins1[k],numpy.power(10,bins1[k]),numpy.log10(L_1[k]),numpy.log10(L_2[k]), numpy.log10(phi_sf[k]), numpy.log10(phi_agn[k]),numpy.log10(ymap[k]),numpy.log10(ymap2[k]),numpy.log10(ymap_ev[num-1][k])
#sys.ek
#print ymap
for isamp in xrange(nsamp):
if evaluate:
evaluations.append(expt.evaluate(z[isamp, :]))
z[isamp, ncols - 1:] = evaluations[isamp][num - 1]
#[LF(S,z_m,0,0,dl,z[isamp,:],\
#expt.parameters,area=expt.survey.SURVEY_AREA,\
#family=modelFamily) for S in expt.binsMedian/1.0e6]
#print [LF(S,z_m,0,0,dl,z[isamp,:],\
# expt.parameters,area=expt.survey.SURVEY_AREA,\
# family=modelFamily) for S in expt.binsMedian/1.0e6]
#sys.exit()
#sys.exit()
evaluate = False
z[numpy.where(z == 0.0)] = 'NaN'
s = numpy.zeros((len(expt.binsMedian), 8))
s[:, 0] = expt.binsMedian
recons = z[:, ncols - 1:]
#print '# ibin flux fit low high dlower dupper skew kurtosis'
print '%6s %12s %12s %12s %12s %12s %12s %12s %12s' % (
'bin', 'lower phi_rms', 'lower phi', 'l_err', 'ymap', 'u_err',
'upper phi', 'u_phi_rms', '2*rms')
for ibin in xrange(len(expt.binsMedian)):
x = recons[:, ibin]
# Remove NaNs from stats vectors
# http://stackoverflow.com/questions/11620914/removing-nan-values-from-an-array
x = x[~numpy.isnan(x)]
#ss=stats.bayes_mvs(x,alpha=0.68)[0]
#x*=numpy.power(s[ibin,0]/1.0e6,2.5)
#print bins1[ibin]
#plt.hist(numpy.log10(x),bins=15)
#plt.xlabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$',fontsize = 20)
#plt.show()
tt = peak_confidence(x, bins=10)
try:
ss = numpy.zeros(3)
ss[0], dlow, dhigh, ss[1], ss[2] = calculate_confidence(
x, alpha=0.95, ret_all=True)
#ss[0],dlow,dhigh,ss[1],ss[2]=calculate_confidence2(x,alpha=0.68,ret_all=True,\
# value_central=ymap[ibin],\
# truncate_edges=True)
except:
ss = numpy.nan * numpy.ones(3)
print "didn't work for ", x
continue
#sys.exit()
#tt=peak_confidence(x,bins=10)
#ss*=numpy.power(s[ibin,0]/1.0e6,2.5)
#print ss[0],tt
# s[ibin,1]=ss[0] # median
# s[ibin,1]=tt # peak
s[ibin, 1] = ymap[num - 1][ibin] # MAP
#print ymap
s[ibin, 2] = ss[1] # lower
s[ibin, 3] = ss[2] # upper
s[ibin, 4] = 2 * numpy.std(
x) # (s[ibin,3]- s[ibin,2])/(2*numpy.std(x))# skewness
s[ibin, 5] = ss[0]
#print '%6.1f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f %12.3f'%(bins1[ibin], numpy.log10((ymap[ibin] - s[ibin,4])), numpy.log10(ss[1]),numpy.log10(s[ibin,2]),numpy.log10(ymap[ibin]),numpy.log10(s[ibin,3]) ,numpy.log10(ss[2]), numpy.log10(s[ibin,4]+ ymap[ibin]), numpy.log10(s[ibin,4]) )
#print ibin,s[ibin,0],s[ibin,1],dlow,dhigh,ss[1],ss[2],s[ibin,4],s[ibin,5]#,stats.skewtest(x)
#sys.exit()
# ...and output to file
#sys.exit()
rstatsf = 'recon_stats_%s.txt' % (chain[num - 1])
rstatsf = os.path.join(outdir, rstatsf)
hdr = '# median_flux_uJy dnds_2p5_Jy1p5srm1 delta_dnds_2p5_lower_Jy1p5srm1 delta_dnds_2p5_upper_Jy1p5srm1 skewness kurtosis'
fid = open(rstatsf, 'w')
print hdr
print s
print '-> Writing stats (i.e. dnds2p5+/- v. S/uJy) to %s' % rstatsf
fid.write('%s\n' % hdr)
numpy.savetxt(fid, s)
fid.close()
print 'Finished.'
return 0
def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module = importlib.import_module(setf)
globals().update(set_module.__dict__)
expt = countModel(modelFamily,
nlaws,
setf, [dataset],
floatNoise,
doRedshiftSlices=True)
recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n' * 5
print len(expt.parameters)
ncols = len(expt.parameters)
summf = os.path.join(outdir, '1-summary.txt')
summary = numpy.genfromtxt(summf)[-1, :]
drawmap = summary[-(ncols + 2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
if dataset == 'sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
print datafiles[0][4:]
elif dataset == 'cosmos':
#print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
#print '%s%s'%(outdir,datafiles[0][8:])
print datafiles[0][8:]
elif 'sim' in dataset:
d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))
ssbin = d[:, 2]
Nbin = d[:, 3]
dnds = d[:, 4]
sbin2 = ssbin
dnds2 = dnds
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0, 29.2, 0.4)
bins3 = numpy.arange(18., 29.2, 0.01)
print len(expt.parameters)
print 'this is 1st num ', num
#sys.exit()
params = drawmap # [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
if modelFamily in [
'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
'LFlognorm_dpl', 'LFpl_lognorm'
]:
Lnorm = params[expt.parameters.index('LNORM')]
Lstar = params[expt.parameters.index('LSTAR')]
Lslope = params[expt.parameters.index('LSLOPE')]
#Lzevol=params[expt.parameters.index('LZEVOL')]
if modelFamily in ['LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFlognorm_dpl']:
Lslope2 = params[expt.parameters.index('LSLOPE2')]
if modelFamily in [
'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
'LFlognorm_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFpl_lognorm'
]:
Lnorm_2 = params[expt.parameters.index('LNORM_2')]
Lstar_2 = params[expt.parameters.index('LSTAR_2')]
Lslope_2 = params[expt.parameters.index('LSLOPE_2')]
if modelFamily in ['LFdpl_dpl', 'LFdpl', 'LFdpl_dpl_z']:
Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]
if modelFamily in [
'LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm', 'LFevol_logn_sigma'
]:
Lsigma = params[expt.parameters.index('LSIGMA')]
if modelFamily in [
'LFdpl_dpl_z', 'LFevol_dpl', 'LFevol_logn', 'LFevol_logn_all',
'LFevol_logn_all_L'
]:
alpha_agn = params[expt.parameters.index('A_agn')]
alpha_SF = params[expt.parameters.index('A_SF')]
beta_agn = params[expt.parameters.index('B_agn')]
beta_SF = params[expt.parameters.index('B_SF')]
if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
alpha_SF = params[expt.parameters.index('A_SF')]
beta_SF = params[expt.parameters.index('B_SF')]
if modelFamily in [
'LFevol_logn_lnorm', 'LFevol_logn_all', 'LFevol_logn_all_L'
]:
Lnorm = params[expt.parameters.index('LNORM')]
Lstar = params[expt.parameters.index('LSTAR')]
if modelFamily in [
'LFevol_logn_mat', 'LFevol_phi_logn_mat', 'LFevol_logn_el',
'LFevol_logn_sigma', 'LFevol_logn_lmin', 'LFevol_logn_lnorm'
]:
alpha_SF = params[expt.parameters.index('A_SF')]
alpha_agn = params[expt.parameters.index('A_agn')]
if modelFamily in [
'LFevol_logn_slope', 'LFevol_logn_all', 'LFevol_logn_all_L'
]:
alpha_SF = params[expt.parameters.index('A_SF')]
alpha_agn = params[expt.parameters.index('A_agn')]
Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]
Lmin = params[expt.parameters.index('LMIN')]
Lmax = params[expt.parameters.index('LMAX')]
truth = {
'noise': 150,
'LMIN': 22.,
'LMAX': 24.5,
'LNORM': numpy.log10(1e-7),
'LSTAR': 23.3,
'LSLOPE': 3.,
'LSLOPE2': 1.
}
#z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
#z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
print expt.parameters
s = pylab.loadtxt('%s/recon_stats.txt' % outdir)
xrecon = s[:-1, 0]
yrecon = s[:-1, 1]
yrecon_d = s[:-1, 2]
yrecon_u = s[:-1, 3]
yrecon_rms = s[:-1, 4]
yrecon_avr = s[:-1, 5]
yrecon_rms_down = yrecon_rms
#novak 2018
L_n2 =[[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56],\
[22.30,22.61,22.96,23.38,23.80,24.10, 24.55,25.14],\
[22.61,22.86,23.14,23.45,23.82,24.14, 24.40,24.71],\
[22.85,23.16,23.69,24.24,24.80,25.31, 25.96,26.69],\
[23.10,23.38,23.86,24.36,24.86,25.35, 25.94,26.36],\
[23.32,23.57,23.94,24.32,24.67,25.06, 25.47,25.96],\
[23.54,23.75,24.13,24.45,24.90,25.27, 25.74,26.10],\
[23.73,23.99,24.57,25.10,25.68,26.18, 26.83,27.51],\
[24.01,24.26,24.76,25.26,25.91,26.19, 27.45],\
[24.30,24.56,24.80,25.13,25.37,25.80, 25.97,26.49]]
L_ner_u2 =[[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03],\
[0.11,0.20, 0.26, 0.25, 0.24, 0.35, 0.31, 0.15],\
[0.08,0.15, 0.20, 0.22, 0.17, 0.18, 0.24, 0.28],\
[0.072,0.33,0.38, 0.40, 0.48, 0.42, 0.41, 0.28],\
[0.080,0.30,0.32, 0.32, 0.32, 0.33, 0.24, 0.34],\
[0.068,0.22,0.24, 0.25, 0.29, 0.30, 0.28, 0.21],\
[0.067,0.22,0.21, 0.26, 0.18, 0.17,0.075, 0.11],\
[0.093,0.36,0.31, 0.30, 0.25, 0.28, 0.16,0.028],\
[0.076,0.34,0.36, 0.38, 0.24, 0.47, 0.27],\
[0.097,0.14,0.20, 0.16, 0.23, 0.092,0.22,0.026]]
L_ner_d2 =[[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50],\
[0.29, 0.21, 0.15, 0.16, 0.17, 0.06, 0.099,0.29],\
[0.24, 0.17, 0.12, 0.10, 0.15, 0.15, 0.08,0.074],\
[0.16, 0.24, 0.19, 0.17, 0.15, 0.09, 0.16, 0.33],\
[0.30, 0.19, 0.18, 0.18, 0.18, 0.17, 0.26, 0.18],\
[0.14, 0.18, 0.15, 0.14, 0.10, 0.095,0.11, 0.21],\
[0.19, 0.14, 0.15, 0.11, 0.19, 0.19, 0.29, 0.28],\
[0.40, 0.17, 0.21, 0.23, 0.27, 0.25, 0.37, 0.53],\
[0.20, 0.17, 0.16, 0.14, 0.27, 0.042, 0.27],\
[0.22, 0.16, 0.10, 0.14,0.072, 0.21, 0.08, 0.30]]
rho_n2 =[[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07],\
[-2.95,-3.17,-3.46,-4.24,-4.76,-5.41, -5.23,-5.44],\
[-2.95,-3.11,-3.44,-3.81,-4.37,-4.48, -4.90,-5.14],\
[-2.99,-3.24,-3.89,-4.54,-5.27,-5.33, -5.69,-5.89],\
[-3.32,-3.51,-4.06,-4.74,-5.28,-5.43, -6.08,-5.70],\
[-3.29,-3.54,-4.02,-4.45,-5.11,-5.56, -5.37,-6.07],\
[-3.43,-3.61,-4.19,-4.56,-5.09,-5.34, -5.70,-5.75],\
[-3.88,-3.98,-4.64,-5.33,-5.73,-6.27, -6.14,-6.77],\
[-3.85,-4.28,-5.05,-5.60,-5.68,-6.44, -6.59],\
[-4.43,-4.91,-5.46,-5.58,-5.89,-6.23, -6.26,-6.91]]
rho_ner_u2=[[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34],\
[0.048,0.027,0.036,0.089,0.17,0.45, 0.34, 0.45],\
[0.062,0.024,0.032,0.048,0.096, 0.11,0.18,0.25],\
[0.042,0.018,0.035,0.073,0.180, 0.20,0.34,0.45],\
[0.045,0.019,0.033,0.072,0.15, 0.17,0.45,0.25],\
[0.040,0.022,0.033,0.055,0.13, 0.22,0.17,0.45],\
[0.061,0.020,0.035,0.055,0.099, 0.14,0.22,0.25],\
[0.044,0.022,0.044,0.11, 0.16, 0.34, 0.28,0.76],\
[0.048,0.025,0.058,0.13, 0.24, 0.34, 0.45],\
[0.084,0.058,0.11 ,0.24, 0.20, 0.28, 0.28,0.76]]
rho_ner_d2=[[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30],\
[0.043,0.026,0.033,0.074,0.16,0.37, 0.30, 0.37],\
[0.054,0.023,0.030,0.043,0.079,0.086,0.17,0.22],\
[0.038,0.017,0.032,0.062,0.17,0.19, 0.30, 0.37],\
[0.041,0.019,0.031,0.062,0.11,0.16, 0.37, 0.22],\
[0.037,0.021,0.031,0.049,0.098,0.20,0.16, 0.37],\
[0.054,0.019,0.032,0.049,0.081,0.11,0.20, 0.22],\
[0.040,0.021,0.040,0.085,0.15, 0.30, 0.25,0.52],\
[0.043,0.024,0.051,0.10, 0.15, 0.30, 0.37],\
[0.070,0.051,0.087,0.16,0.19, 0.25, 0.25, 0.52]]
#print xrecon
#print yrecon
#sys.exit()
z_new = True
Replot = False
loglike = 0.
loglike_nov = 0.
lmin3 = [19.2, 19.8, 20.5, 20.8, 21.6, 22.2, 22.5, 23., 22.9, 23.]
lmin3 = [19.7, 20.3, 21., 21.3, 22.1, 22.7, 23., 23.5, 23.4, 23.5]
lmin3 = [20.2, 20.8, 21.5, 21.8, 22.6, 23.2, 23.5, 24., 23.9, 24.]
lmin3 = [20.7, 21.3, 22., 22.3, 23.1, 23.7, 24., 24.5, 24.4, 24.5]
lmin3 = [21.7, 22.3, 23., 23.3, 24.1, 24.7, 25., 25.5, 25.4, 25.5]
lmin3 = [21.2, 21.8, 22.5, 22.8, 23.6, 24.2, 24.5, 25., 24.9, 25.]
sm = [
0.05162624, 0.0651364, 0.17144366, 0.19570955, 0.73934002, 1.58958387
]
sm = [
0.16325651, 0.20597938, 0.54215247, 0.61888793, 2.33799844, 5.02670556
]
sm = [
0.51626243, 0.651364, 1.71443663, 1.95709547, 7.39340024, 15.89583871
]
sm = [
1.63256513, 2.05979383, 5.42152466, 6.18887928, 23.37998442,
50.26705564
]
sm = [
16.32565134, 20.5979383, 54.21524659, 61.88879277, 233.79984425,
502.67055637
]
sm = [5.16, 6.51, 17.14, 19.57, 73.93, 158.95, 206.55]
if not z_new:
z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0]
else:
z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0] #oldest ;)
num = int(num)
#num = 1.
print num
z_min, z_max, z_m = get_z(num, z_new=False)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(z_m, dl)
#z_m = (z_min + z_max)/2
dl = get_dl(z_m)
print z_min, z_max
print expt.kind
area = SURVEY_AREA * sqDeg2sr
area1 = SURVEY_AREA * sqDeg2sr
print SURVEY_AREA
SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
sigma, fsigma = numpy.log10(
get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl, 'muJy') *
(1.4 / 3)**(-.7))
print z_m, dl, sigma, fsigma
print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA
#area = 6672*sqDeg2sr #143165.15 49996.49 59876.8861135
sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
#L_s = numpy.log10(get_Lbins(ssbin,z_m,dl,'muJy'))
#print expt.binsMedian
#sys.exit()
xreco = get_Lbins(xrecon, z_m, dl, 'muJy') #*(1.4/3)**(-.7)
yreco = yrecon
print SMIN, SMAX
#sys.exit()
lin_yrecon = numpy.log10(yreco)
lin_xrecon = numpy.log10(xreco)
bins2 = numpy.arange(15., 25., 0.4)
bins = numpy.arange(fsigma - 0.2, 29.2, 0.4) #bin +0.2
#bins =[23.6,23.9,24.1,24.3,24.9,25.6,26.,26.7,28] #2< z<3.2
#bins =[23.6, 23.9,24.1,24.4,24.6,24.9,25.2,25.6,26.,26.4,27.,27.5,28] #3.2< z<4
bins = numpy.array(bins)
if not os.path.isfile(
'cos_data/cos_s%s_LF.el' % num) and z_new or not os.path.isfile(
'cos_data/cos_s%s_LF_old.el' % num) and not z_new or Replot:
print 'Reploting the RLF and storing them for next time.'
#l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
#l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
if z_new:
L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
else:
L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
[z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
L_c2, z_c2 = open_anytype('cosmos_comp_vla.el', (2, 3), [z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
L_c3, z_c3 = open_anytype('cosmos_vla.el', (2, 3), [z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
L_c4, z_c4 = open_anytype(
'/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',
(-4, -1), [z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
L_c5, z_c5 = open_anytype(
'/home/eliab/Documents/OC/Project/cosmos/Cosmos/dr4_cos2015_vla.tx',
(3, -1), [z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
#z_c2,S_c2,L_c2 =numpy.loadtxt('cosmos_counter_parts_II.txt',unpack=True, usecols=(3,4,5))
#L_c2=L_c2[(z_c2<z_max)&(z_c2 >z_min)]
#S_c2=S_c2[(z_c2<z_max)&(z_c2 >z_min)]
#z_c2=z_c2[(z_c2<z_max)&(z_c2 >z_min)]
#rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
#rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
#print numpy.log10(L)
#print (1.4/3)**(-.7)
#print numpy.log10(L*(1.4/3)**(-.7))
#sys.exit()
rho_3, er3, b3, n3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31),
L,
bins,
z_max,
z_min,
area,
table=True)
#rho_4,er4 = calc_Vmax(99.,L,bins,z_max,z_min,area)
print 'COS15 VLA'
rho_5, er5, b5, n5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31),
L_c,
bins,
z_max,
z_min,
1.77 * sqDeg2sr,
table=True)
print 'mass lim DR4-vla'
rho_6, er6, b6, n6 = calc_Vmax(calcu_zeff(z_c2, L_c2, 1.15e-31),
L_c2,
bins,
z_max,
z_min,
1.5 * sqDeg2sr,
table=True)
print 'DR4-vla'
rho_7, er7, b7, n7 = calc_Vmax(calcu_zeff(z_c3, L_c3, 1.15e-31),
L_c3,
bins,
z_max,
z_min,
1.5 * sqDeg2sr,
table=True)
print 'cos-DR4-vla DR4 photo-z'
rho_8, er8, b8, n8 = calc_Vmax(calcu_zeff(z_c4, L_c4, 1.15e-31),
L_c4,
bins,
z_max,
z_min,
1.5 * sqDeg2sr,
table=True)
print 'cos-DR4-vla COS15 photo-z'
rho_9, er9, b9, n9 = calc_Vmax(calcu_zeff(z_c5, L_c5, 1.15e-31),
L_c5,
bins,
z_max,
z_min,
1.5 * sqDeg2sr,
table=True)
for i in range(len(b3)):
print '%5.2f %7d %10d %13d %17d %11d' % (b3[i], n3[i], n7[i],
n8[i], n9[i], n5[i])
if z_new:
f = open('cos_data/cos_s%s_LF_5.el' % num, 'w')
#_2 paper _5 stellar mass 10.2
print 'saving data for future use in cos_data/cos_s%s_LF_5.el' % num
else:
f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
print 'saving data for future use in cos_data/cos_s%s_LF_old.el' % num
for i in range(len(bins)):
f.write(
'%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n'
%
(bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i], er6[i],
rho_7[i], er7[i], rho_8[i], er8[i], rho_9[i], er9[i]))
f.close()
else:
if z_new:
bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
'cos_data/cos_s%s_LF_2.el' % num, unpack=True)
print 'Using stored RLF for the new z (The one with more z bins ) from cos_data/cos_s%s_LF.el. If you want to calculate the plot the set "Replot=True"' % num
else:
bins, rho_3, er3, rho_5, er5, rho_6, er6, rho_7, er7, rho_8, er8, rho_9, er9 = numpy.loadtxt(
'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
print 'Using stored RLF for the is old z bins in fr cos_data/cos_s%s_LF.el. If you want to calculate the plot the set "Replot=True"' % num
#novak 2017
'''
L_n =[21.77,22.15,22.46,22.77,23.09,23.34]
rho_n =[-2.85,-2.88,-3.12,-3.55,-4.05,-4.63]
L_ner_u =[0.23,0.18,0.19, 0.2, 0.21, 0.28]
L_ner_d =[1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner=[0.09, 0.03,0.0355,0.059,0.1,0.234]
#novak 2018
L_n2 =[21.77,22.24,22.68,23.16,23.69,24.34, 24.74,25.56]
rho_n2 =[-2.84,-2.90,-3.34,-4.00,-4.92,-4.92, -5.22,-5.07]
L_ner_u2 =[0.23,0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
L_ner_d2 =[1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
rho_ner_u2=[0.08,0.024,0.038,0.083,0.28,0.28 , 0.45, 0.34]
rho_ner_d2=[0.07,0.023,0.035,0.070,0.25,0.25 , 0.37, 0.30]
#l*= (1.4/3)**(-.7)
#l2*= (1.4/3)**(-.7)
#L*= (1.4/3)**(-.7)
'''
#Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
print z_min, z_max
#bins = numpy.arange(fsigma -4.2,30,0.4) #bin
#s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))
s, s_z = numpy.loadtxt('cos_data/cos_s%s.txt' % (num),
unpack=True,
usecols=(-1, 2))
#s=s*1e6
print fsigma
L_s = (get_Lbins(ssbin, z_m, dl, 'muJy')) * (1.4 / 3.)**(-.7)
L_s = numpy.log10(L_s)
rho_1 = lumfuncs(
Nbin, L_s,
calc_Vmax(calcu_zeff(z_m, 10**L_s),
L_s,
L_s,
z_max,
z_min,
area,
getLF=False))
#L_s = numpy.log10(10**L_s*(1.4/3.)**(-.7))
#sys.exit()
rho_11 = dntoLF(L_s, Nbin, dsdl, Vmax)
#dn = expt.realise(params)
#nov_real=expt.realise([2.3,2.95,2.86,-0.29,-0.7,19.5,26.0])
fig, ax = plt.subplots()
plt.rc('lines', linewidth=2)
if modelFamily in ['LFdpl', 'LFdpl_dpl', 'LFdpl_dpl_z']:
faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lslope2_2, Lnorm_2)
elif modelFamily in ['LFpl', 'LFdpl_pl']:
faint = lumfuncUtils.powerlaw(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lnorm_2)
elif modelFamily in ['LFpl_dpl', 'LFdpl_dpl_z']:
faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar,
Lslope, Lslope2, Lnorm)
elif modelFamily == 'LFevol_logn_mat':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_phi_logn_mat':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_phi_logn_mat',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_el':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_el',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_phi_logn':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_phi_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_slope':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_slope',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_sigma':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_sigma',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_lstar':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_lstar',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_lmin':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_lmin',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_lnorm':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_lnorm',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_all':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_all',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
elif modelFamily == 'LFevol_logn_all_L':
faint =[lumfuncUtils.LF(L=numpy.power(10,lL),params=params,paramsList=expt.parameters,\
family='LFevol_logn_all_L',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True,SF=True) for lL in bins3]
else:
faint = lumfuncUtils.lognormpl(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lsigma, Lnorm_2)
#phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
L_1 = numpy.power(10, bins3) / (1 + z_m)**(2.86 - z_m * 0.7)
L_2 = numpy.power(10, bins3) / (1 + z_m)**(2.95 - z_m * 0.29)
#L_4 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF)
#L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn)
#L_4 = numpy.power(10,bins3)/(10)**(alpha_SF)
#L_3 = numpy.power(10,bins3)/(10)**(1)#alpha_agn)
#for i in range(len(L_3)):
#print bins3[i],numpy.log10(L_3[i])
#L_4 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF+z_m*beta_SF)
#L_3 = numpy.power(10,bins3)/(1 + z_m)**(alpha_agn+z_m*beta_agn)
phi_agn_n = lumfuncUtils.doublepowerlaw(L_1, 24.59, 1.27, 0.49,
numpy.log10(10**(-5.5)))
phi_sf_n = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
numpy.log10(3.55e-3 / 2.5)) #0.33 2.97
#phi_sf =lumfuncUtils.lognormpl(L_4, 20.56,1.22,0.63,-2.26)#numpy.log10(3.55e-3/2.5))#0.33 2.97
phi_sf2 = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
numpy.log10(3.55e-3 / 2.5)) #0.33 2.97
#phi_sf =lumfuncUtils.lognormpl(L_4, Lstar,1.22,0.63,Lnorm)#numpy.log10(3.55e-3/2.5))#0.33 2.97
#phi_sf =lumfuncUtils.lognormpl(L_4, numpy.log10(1.85e21),1.22,0.63,numpy.log10(3.55e-3/2.5))#0.33 2.97
nov_Tot_n = phi_agn_n + phi_sf_n
#phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
#print 'this is Lminz', numpy.log10(10**18/(1 + z_m)**(alpha_SF+z_m*beta_SF))
#phi_agn=lumfuncUtils.doublepowerlaw(L_3,24.59,1.27,0.49,numpy.log10(10**(-5.5)))
phi_1 = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), 23.68, 3.68,
0.8, -3.36) #0.33 2.97
phi_2 = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), 24.5, 1.37,
0.5, -4.)
#tot = phi_1+phi_2
dm = 0.4 #02
hm = 0.
#Map =expt.evaluate(params)[0]
bins_map = get_Lbins(expt.binsMedian, z_m, dl, 'muJy')
bins1_4 = numpy.log10(10**bins * (1.4 / 3.)**(-.7))
#fix=[lumfuncUtils.LF(L=numpy.power(10,lL),params=[3.75,2.55,4.74,0,0,20.3,26.65],paramsList=expt.parameters,\
# family='LFevol_logn',inta=None,area=0,S=0, z=z_m, dlds=0,Vmax=0,dl=0,bypassLim=True) for lL in bins3]
#plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(faint)-dm,'--b' ,label='faint-end')
#plt.plot(numpy.log10(bins_map),numpy.log10(Map), label='my MAP')
#plt.plot(bins3,numpy.log10(fix)-dm,'--c', label='Fix')
#plt.plot(bins3,numpy.log10(phi_agn),'-.c', label='local agn')
#plt.plot(bins3,numpy.log10(phi_sf),'--r', label='local sf')
#plt.plot(bins3,numpy.log10(phi_sf2),'--c', label='local sf2')
#plt.plot(numpy.log10(10**bins3),numpy.log10(phi_dpl)-dm,'--c', label='agn')
#plt.plot(numpy.log10(10**bins3*(1.4/3.)**(.7)),numpy.log10(tot)-0.4,'-.g',linewidth=5, label='Tot')
#plt.plot(bins3,numpy.log10(phi_agn),'--r', label='local agn nov')
plt.errorbar(lin_xrecon + hm,
lin_yrecon - dm,
fmt='-b',
label='This work Total',
linewidth=2)
plt.plot(numpy.log10(10**bins3),
numpy.log10(faint) - dm,
'--b',
label='This work SFG')
#plt.errorbar(lin_xrecon+hm,numpy.log10(yrecon_avr)-dm,fmt='-g', label = 'Average')
#plt.plot(L_s2+.2, numpy.log10(rho_10), '*c',label='average LF',markersize=13)#bin + 0.2
plt.errorbar(bins,
rho_3,
yerr=er3,
fmt='ob',
label='This work 1/Vmax',
markersize=8) #bin
#plt.errorbar(bins2[17:],rho_3[17:] ,yerr=er3[17:],fmt='ob',label='extracted',markersize=8) #bin
#plt.errorbar(bins,rho_5 ,yerr=er5,fmt='^k',label='VLA-COSMOS2015',markersize=10) #bin
#plt.errorbar(bins,rho_8 ,yerr=er8,fmt='sc',label='VLA-COSMOS2015 in DR4 using DR4 z',markersize=10) #bin
#plt.errorbar(bins,rho_9 ,yerr=er9,fmt='*m',label='VLA-COSMOS2015 in DR4 using COS15z',markersize=14) #bin
#plt.errorbar(bins,rho_6 ,yerr=er6,fmt='ok',label='DR4 detected',fillstyle='none',markersize=14) #bin
#plt.errorbar(bins,rho_7 ,yerr=er6,fmt='dg',label='DR4 detected - Full NIR sample',markersize=12) #bin
#plt.plot(L_s, numpy.log10(rho_1), '*m',label='low LF',markersize=10)
#plt.plot(L_s, numpy.log10(rho_11), 'sc',label='reconstruct LF',markersize=10)
#plt.errorbar(numpy.array(L_n2[num-1]),numpy.array(rho_n2[num-1]) -0.4,xerr=[L_ner_d2[num-1],L_ner_u2[num-1]],yerr=[rho_ner_d2[num-1],rho_ner_u2[num-1]], fmt='sr',fillstyle='none',ecolor='r',markeredgewidth=1.5, label='Total RLF Novak+2018')
plt.errorbar(numpy.log10(10**bins3),
numpy.log10(nov_Tot_n),
fmt='-r',
label='Total Novak + 2018')
plt.plot(bins3, numpy.log10(phi_sf_n), '-.r', label='SFG Novak + 2017')
'''
print 'This is the dn'
print dn
for key in dn.keys():
if round(key,2)==round(z_m,2):z_m=key
dn=dn[z_m]
nov_real=nov_real[z_m]
for i in range(len(L_s)):
loglike_i= Nbin[i]*numpy.log(dn[i]) + Nbin[i] - (Nbin[i] + 0.5)*numpy.log(Nbin[i]) - 0.5*numpy.log(2*numpy.pi) - dn[i]
loglike_nov_i= Nbin[i]*numpy.log(nov_real[i]) + Nbin[i] - (Nbin[i] + 0.5)*numpy.log(Nbin[i]) - 0.5*numpy.log(2*numpy.pi) - nov_real[i]
if nov_real[i]>0:loglike_nov+=loglike_nov_i
if dn[i]>0:loglike+=loglike_i
print '%10.5f %8.2d %8.2f %8.2f %8.2f %8.2f %8.2f %8.2f'%(ssbin[i], Nbin[i], dn[i],nov_real[i],loglike_i,loglike_nov_i,loglike, loglike_nov)
'''
#plt.errorbar(bins[1:],rho_2[1:] ,yerr=er2[1:],fmt='ok',label='dectected catalog',markersize=10) #bin
#plt.errorbar(bins2[1:]-0.2,rho_3[1:],yerr=er3[1:],fmt='sr',label='noisy',markersize=10) #bin
#plt.errorbar(bins2[1:]-0.2,rho_4[1:],yerr=er4[1:],fmt='vm',label='noisy lim',markersize=10) #bin
#plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
#plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')
#plt.plot(bins3+0.4,numpy.log10(logn)-0.4 ,'--k', label='faint-end')
#plt.plot(bins3,numpy.log10(logn) ,'--', label='lognorm')
#plt.plot(bins3,numpy.log10(logn2)-dm,'--r', label='lognorm2')
#
#plt.plot(bins3,numpy.log10(phi_dpl2), label='dpl bright-end2')
#plt.plot(Lbins4[(Lbins4<23.6) &(Lbins4>22.12)],numpy.log10(phi_dpl)[(Lbins4<23.6) &(Lbins4>22.12)], '-c',linewidth=5 ,label = r'$\rm{ MAP dpl < 5\sigma}$')
#plt.plot(bins2,numpy.log10(logn_2),label='starforming_2')
#plt.plot(bins2,numpy.log10(logn_3),label='starforming_3')
#plt.plot(bins2,numpy.log10(logn_4),label='starforming_4')
#plt.fill_between(xrecon,numpy.log10(yrecon - yrecon_down2 ) ,numpy.log10(yrecon+yrecon_up2), color='c',alpha=0.2)
#plt.errorbar(lin_xrecon+0.4,lin_yrecon -0.4,fmt='-b', label='MAP',linewidth=2)
#plt.errorbar(lin_xrecon+0.4,lin_yrecon- 0.4,yerr=numpy.log10(yrecon_rms),fmt='r',label='rms')
#plt.plot(L_s, numpy.log10(rho_1), '*c',label='average LF',markersize=10)
#plt.plot(L_s5, numpy.log10(rho_14)-0.2, '*r',label='average LF',markersize=13)
#plt.plot(L_s6+0.0, numpy.log10(rho_16)-0.2, '*b',label='average LF(bin)',markersize=13)
#plt.plot(bins2[:-1], numpy.log10(rho_15), '*g',label='LF average',markersize=13)
#print len(rho_11),len(rho_10),len(rho_12)
#plt.fill_betweenx(numpy.log10(rho_10) ,L_s3,L_s4, color='red',alpha=0.2)
#plt.plot(L_s6, numpy.log10(rho_13), '*b',label='average LF(bin+0.2)',markersize=13)#bin + 0.2
#plt.plot(L_s1, numpy.log10(rho_1), 'oc',label='average LF(bin)',markersize=13)#bin
#plt.plot(Lbins4,lin_phi ,'--b',label='MAP')
#plt.fill_between(lin_xrecon+.0,numpy.log10((yrecon_avr -yrecon_rms))-dm,numpy.log10(yrecon_avr +yrecon_rms)-dm , color='r',alpha=0.3)
#plt.fill_between(lin_xrecon[1:-1]+0.2,numpy.log10(yreco[1:-1] - yrecon_down[1:-1] )-dm,numpy.log10(yreco[1:-1]+yrecon_up[1:-1])-dm , color='b',alpha=0.2)
plt.fill_between(lin_xrecon + hm,
numpy.log10((yrecon_d)) - dm,
numpy.log10(yrecon_u) - dm,
color='k',
alpha=0.2)
#plt.errorbar(bins[2:],rho_1[2:],yerr=er1[2:],fmt = 'ob',label='Detected')
#plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='ob',label='Detected') #working one
#plt.plot(Lbins2,rho,'ob',label='simulation')
#plt.plot(Lbins3,rho_,label='Noisy fluxes')
#rho_2
#plt.errorbar(bins,rho_2 -0.2,yerr=er2,fmt='ok',label='data',markersize=10) #bin +0.2
#plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='vm',label='Noisy NVSS')
#plt.errorbar(bins2,rho_7-0.2,yerr=er6,fmt='xc',label='NVSS Detected')
#for i in range(len(bins)):
# print bins[i], rho_2[i]
#sys.exit()
#plt.errorbar(Lbins2,rho, err,fmt = 'ob',label='simulation')
#plt.errorbar(Lbins2,rho_1,err_1,fmt = 'or',label='simulation_noisy')
#plt.errorbar(L2,rho2,color='g',label='Analytic')
#plt.errorbar(L3,rho3,color='g',label='Analytic II')
#plt.axvspan(22.55 ,23.51,color='r',alpha=0.2)
#plt.axvline(Lmin,color='r',linestyle='dashed')
plt.axvline(fsigma, color='k', linestyle='dashed')
#plt.axvline(sigma,color='g')
#plt.axvline(Llmin,color='r',linewidth=5)
#plt.axvline(sigma2,color='m')
#plt.axvline(fsigma2,color='m')
#plt.axvline(23.89,color='b')
#plt.axvline(23.71,color='c')
#plt.axvline(23.52,color='r')
#plt.axvline(truth['LMIN'],color='r', linestyle='dashed', label = 'True params')
#plt.axvline(truth['LMAX'],color='r', linestyle='dashed')
#plt.axvline(truth['LSTAR'],color='b')
#plt.text(truth['LSTAR']-0.2,-9., '$L_*$',fontsize=18)
#plt.axvline(Lmax,color='r', label= 'MAP params')
#plt.axvline(Lmin,color='b')
#plt.axvline(Lmax, color='r',linestyle='dashed')
#plt.text(21,-1, '$L_*$',fontsize=18)
#plt.text(fsigma+0.1,-6.7, '$5\sigma$',fontsize=25)
#plt.text(sigma+0.1,-6.7, '$\sigma$',fontsize=25)
plt.xlabel(r'$\rm{log_{10}[L_{1.4 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
lmin3_14 = 10**lmin3[num - 1] * (1.4 / 3.)**(-.7)
#plt.text(22,-7.7,'%.1f < z < %.1f %s Lmin=%6.2f %s Lmin_cut=%6.2f %s Smin_cut=%6.2f $\mu$Jy'%(z_min,z_max,'\n',Lmin,'\n',numpy.log10(lmin3_14),'\n',sm[num-1]),fontsize = 30,alpha=0.6 ) # \n $M_i$ < -22
#plt.text(22,-7.7,'%.1f < z < %.1f %s Lmin=%6.2f'%(z_min,z_max,'\n',Lmin),fontsize = 30,alpha=0.6 ) # \n $M_i$ < -22
plt.text(22,
-7.7,
'%.1f < z < %.1f' % (z_min, z_max),
fontsize=30,
alpha=0.6) # \n $M_i$ < -22
#plt.text(22.9,-8.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both', which='major', labelsize=20, width=3)
plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
#plt.tight_layout()
#plt.xtick(fontsize=15)
#plt.ytick(fontsize=15)
plt.xlim(20, 28.)
plt.ylim(-9, -2)
#plt.ylim(-11,-5.5)
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plt.legend(frameon=False).draggable()
plotf = '%s/LF_recon_s1.pdf' % (outdir)
plt.show()
return 0
def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module = importlib.import_module(setf)
globals().update(set_module.__dict__)
expt = countModel(modelFamily,
nlaws,
setf, [dataset],
floatNoise,
doRedshiftSlices=True)
recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n' * 5
print len(expt.parameters)
ncols = len(expt.parameters)
summf = os.path.join(outdir, '1-summary.txt')
summary = numpy.genfromtxt(summf)[-1, :]
drawmap = summary[-(ncols + 2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
chains = ['a', 'a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']
if dataset == 'sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
print datafiles[0][4:]
elif dataset == 'cosmos':
#print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
#print '%s%s'%(outdir,datafiles[0][8:])
print datafiles[0][8:]
elif 'sim' in dataset:
d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))
ssbin = d[:, 2]
Nbin = d[:, 3]
dnds = d[:, 4]
sbin2 = ssbin
dnds2 = dnds
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0, 29.2, 0.4)
bins3 = numpy.arange(18., 29.2, 0.01)
print len(expt.parameters)
num = 5
print 'this is 1st num ', num
#sys.exit()
params = drawmap # [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
if modelFamily in [
'LFsch', 'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z',
'LFlognorm_dpl', 'LFpl_lognorm'
]:
Lnorm = params[expt.parameters.index('LNORM')]
Lstar = params[expt.parameters.index('LSTAR')]
Lslope = params[expt.parameters.index('LSLOPE')]
#Lzevol=params[expt.parameters.index('LZEVOL')]
if modelFamily in [
'LFdpl_pl', 'LFpl_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFlognorm_dpl'
]:
Lslope2 = params[expt.parameters.index('LSLOPE2')]
if modelFamily in [
'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFpl_dpl',
'LFlognorm_dpl', 'LFdpl_dpl', 'LFdpl_dpl_z', 'LFpl_lognorm'
]:
Lnorm_2 = params[expt.parameters.index('LNORM_2')]
Lstar_2 = params[expt.parameters.index('LSTAR_2')]
Lslope_2 = params[expt.parameters.index('LSLOPE_2')]
if modelFamily in ['LFdpl_dpl', 'LFdpl_dpl_z', 'LFdpl']:
Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]
if modelFamily in ['LFlognorm', 'LFlognorm_dpl', 'LFpl_lognorm']:
Lsigma = params[expt.parameters.index('LSIGMA')]
if modelFamily in ['LFdpl_dpl_z', 'LFevol']:
alpha_agn = params[expt.parameters.index('A_agn')]
alpha_SF = params[expt.parameters.index('A_SF')]
beta_agn = params[expt.parameters.index('B_agn')]
beta_SF = params[expt.parameters.index('B_SF')]
if modelFamily in ['LFevol_dpl_s', 'LFevol_logn_s']:
alpha_SF = params[expt.parameters.index('A_SF')]
beta_SF = params[expt.parameters.index('B_SF')]
Lmin = params[expt.parameters.index('LMIN')]
Lmax = params[expt.parameters.index('LMAX')]
truth = {
'noise': 150,
'LMIN': 22.,
'LMAX': 24.5,
'LNORM': numpy.log10(1e-7),
'LSTAR': 23.3,
'LSLOPE': 3.,
'LSLOPE2': 1.
}
#z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
#z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
print expt.parameters
s = pylab.loadtxt('%s/recon_stats_%s.txt' % (outdir, chains[num - 1]))
xrecon = s[:-1, 0]
yrecon = s[:-1, 1]
yrecon_d = s[:-1, 2]
yrecon_u = s[:-1, 3]
yrecon_rms = s[:-1, 4]
yrecon_avr = s[:-1, 5]
yrecon_rms_down = yrecon_rms
for i in range(len(yrecon_rms)):
if yrecon_avr[i] < yrecon_rms[i]:
yrecon_rms_down[i] = 10**(numpy.log10(yrecon_avr[i]) - 0.01)
#print xrecon
#print yrecon
#sys.exit()
z_new = False
Replot = False
if z_new:
z = [
0.1, 0.3, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 1.9, 2.2, 2.5, 2.8, 3.2,
3.6, 4.0
]
else:
z = [0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.5, 3.2, 4.0] #oldest ;)
num = int(num)
#num = 1.
print num
z_min, z_max, z_m = get_z(num, z_new=z_new)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(z_m, dl)
z_m = (z_min + z_max) / 2
dl = get_dl(z_m)
print z_min, z_max
print expt.kind
area = SURVEY_AREA * sqDeg2sr
area1 = SURVEY_AREA * sqDeg2sr
print SURVEY_AREA
SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
sigma, fsigma = numpy.log10(
get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl, 'muJy') *
(1.4 / 3)**(-.7))
print z_m, dl, sigma, fsigma
print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA
#area = 6672*sqDeg2sr #143165.15 49996.49 59876.8861135
sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
#L_s = numpy.log10(get_Lbins(ssbin,z_m,dl,'muJy'))
#print expt.binsMedian
#sys.exit()
xreco = get_Lbins(xrecon, z_m, dl, 'muJy') #*(1.4/3)**(-.7)
yreco = yrecon
print SMIN, SMAX
#sys.exit()
lin_yrecon = numpy.log10(yreco)
lin_xrecon = numpy.log10(xreco)
bins2 = numpy.arange(15., 25., 0.4)
bins = numpy.arange(fsigma, 26.2, 0.4) #bin +0.2
if not os.path.isfile(
'cos_data/cos_s%s_LF.el' % num) and z_new or not os.path.isfile(
'cos_data/cos_s%s_LF_old.el' % num) and not z_new or Replot:
print 'Reploting the RLF and storing them for next time.'
#l,z_l = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,21), F='uJy',L=True,getz=True)
#l2,z_l2 = open_anytype('cos_data/cosmos_d1_0_8arc.txt',(12,24), F='uJy',L=True,getz=True)
if z_new:
L, z_L = open_anytype('cos_data/cos_s%s.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the new z bins (please check if lumfuncUtils and cos_manifest are using the right z)'
else:
L, z_L = open_anytype('cos_data/cos_s%s_old.txt' % num, (2, 3),
F='uJy',
L=True,
getz=True,
band='S')
print 'this is the old z binning style. (please check if lumfuncUtils and cos_manifest are using the right z)'
L_c, z_c = open_anytype('cosmos_counter_parts_II.txt', (3, 4),
[z_min, z_max],
F='uJy',
L=True,
getz=True,
band='S')
z_c2, S_c2, L_c2 = numpy.loadtxt('cosmos_counter_parts_II.txt',
unpack=True,
usecols=(3, 4, 5))
L_c2 = L_c2[(z_c2 < z_max) & (z_c2 > z_min)]
S_c2 = S_c2[(z_c2 < z_max) & (z_c2 > z_min)]
z_c2 = z_c2[(z_c2 < z_max) & (z_c2 > z_min)]
#rho_1,er1 = calc_Vmax(calcu_zeff(z_l,l,11.5e-32),l,bins,z_max,z_min,area)
#rho_2,er2 = calc_Vmax(calcu_zeff(z_l2,l2,11.5e-32),l2,bins,z_max,z_min,area)
#print numpy.log10(L)
#print (1.4/3)**(-.7)
#print numpy.log10(L*(1.4/3)**(-.7))
#sys.exit()
rho_3, er3 = calc_Vmax(calcu_zeff(z_L, L, 1.15e-31), L, bins, z_max,
z_min, area)
rho_4, er4 = calc_Vmax(99., L, bins, z_max, z_min, area)
rho_5, er5 = calc_Vmax(calcu_zeff(z_c, L_c, 1.15e-31), L_c, bins,
z_max, z_min, area)
rho_6, er6 = calc_Vmax(calcu_zeff(z_c2, 10**L_c2, 1.15e-31), 10**L_c2,
bins, z_max, z_min, area)
if z_new:
f = open('cos_data/cos_s%s_LF.el' % num, 'w')
else:
f = open('cos_data/cos_s%s_LF_old.el' % num, 'w')
for i in range(len(bins)):
f.write('%5.1f %5.3f %5.3f %5.3f %5.3f %5.3f %5.3f\n' %
(bins[i], rho_3[i], er3[i], rho_5[i], er5[i], rho_6[i],
er6[i]))
f.close()
dl_c = get_dl(z_c2)
L_c3 = numpy.log10(
get_Lbins(S_c2, z_c2, numpy.array(dl_c), 'muJy') *
(1.4 / 3.)**(-.7))
else:
if z_new:
bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
'cos_data/cos_s%s_LF.el' % num, unpack=True)
print 'Using stored RLF for the new z (The one with more z bins). If you want to calculate the plot the set "Replot=True"'
else:
bins, rho_3, er3, rho_5, er5, rho_6, er6 = numpy.loadtxt(
'cos_data/cos_s%s_LF_old.el' % num, unpack=True)
print 'Using stored RLF for the is old z bins. If you want to calculate the plot the set "Replot=True"'
#novak 2017
L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]
#novak 2018
L_n2 = [21.77, 22.24, 22.68, 23.16, 23.69, 24.34, 24.74, 25.56]
rho_n2 = [-2.84, -2.90, -3.34, -4.00, -4.92, -4.92, -5.22, -5.07]
L_ner_u2 = [0.23, 0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
L_ner_d2 = [1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
rho_ner_u2 = [0.08, 0.024, 0.038, 0.083, 0.28, 0.28, 0.45, 0.34]
rho_ner_d2 = [0.07, 0.023, 0.035, 0.070, 0.25, 0.25, 0.37, 0.30]
#l*= (1.4/3)**(-.7)
#l2*= (1.4/3)**(-.7)
#L*= (1.4/3)**(-.7)
#Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
print z_min, z_max
#bins = numpy.arange(fsigma -4.2,30,0.4) #bin
#s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))
s, s_z = numpy.loadtxt('cos_data/cos_s%s.txt' % (num),
unpack=True,
usecols=(-1, 2))
#s=s*1e6
print fsigma
#sys.exit()
Ncount = sum(Nbin[fsigma > ssbin])
Llmin = numpy.log10(
get_Lbins([fsigma / numpy.sqrt(Ncount)], z_min, get_dl(z_min),
'muJy'))[0]
Llmin *= (1.4 / 3.)**(-.7)
print Llmin
L_s = (get_Lbins(ssbin, z_m, dl, 'muJy'))
L_s *= (1.4 / 3.)**(-.7)
L_s = numpy.log10(L_s)
rho_1 = lumfuncs(
Nbin, L_s,
calc_Vmax(calcu_zeff(z_m, 10**L_s),
L_s,
L_s,
z_max,
z_min,
area,
getLF=False))
#sys.exit()
fig, ax = plt.subplots()
plt.rc('lines', linewidth=2)
'''
if modelFamily in ['LFdpl','LFdpl_dpl']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lslope2_2,Lnorm_2)
elif modelFamily in ['LFpl','LFdpl_pl']:
faint =lumfuncUtils.powerlaw(numpy.power(10,bins3), Lstar_2,Lslope_2,Lnorm_2)
elif modelFamily in ['LFpl_dpl','LFdpl_dpl_z']:
faint =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar,Lslope,Lslope2,Lnorm)
else:
faint = lumfuncUtils.lognormpl(numpy.power(10,bins3), Lstar_2,Lslope_2,Lsigma,Lnorm_2)
'''
#phi_dpl =lumfuncUtils.doublepowerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lslope2,Lnorm)
#L_2 = numpy.power(10,bins3)/(1 + z_m)**(alpha_SF+z_m*beta_SF)
L_2 = numpy.power(
10, bins3) * (1.4 / 3.)**(-.7) / (1 + z_m)**(2.95 - z_m * 0.29)
L_1 = numpy.power(
10, bins3) * (1.4 / 3.)**(-.7) / (1 + z_m)**(2.86 - z_m * 0.7)
phi_agn = lumfuncUtils.doublepowerlaw(L_1, 24.29, 1.27, 0.49,
numpy.log10(2.5 * 10**(-5.5)))
phi_sf = lumfuncUtils.doublepowerlaw(L_2, 22.11, 2.4, 0.43,
-3.10) #0.33 2.97
phi_logn = lumfuncUtils.lognormpl(L_2, numpy.log10(1.85e21), 1.22, 0.63,
numpy.log10(3.55e-3))
#phi_pl =lumfuncUtils.powerlaw(numpy.power(10,bins3),Lstar ,Lslope,Lnorm)
dm = 0. #05
hm = 0.
Map = expt.evaluate(params)[0]
bins_map = get_Lbins(expt.binsMedian, z_m, dl, 'muJy')
#plt.plot(numpy.log10(10**bins3 *(1.4/3)**(-.7)),numpy.log10(faint)-dm,'--b' ,label='faint-end')
plt.plot(bins3, numpy.log10(phi_sf) - dm, '--c', label='local sf')
plt.plot(bins3, numpy.log10(phi_agn) - dm, '--c', label='local agn')
plt.plot(bins3, numpy.log10(phi_logn) - dm, '--r', label='local lognorm')
#plt.plot(numpy.log10(bins_map),numpy.log10(Map), label='my MAP')
plt.errorbar(lin_xrecon + hm,
lin_yrecon - dm,
fmt='-k',
label='MAP',
linewidth=2)
plt.errorbar(lin_xrecon + hm,
numpy.log10(yrecon_avr) - dm,
fmt='-g',
label='Average')
#plt.plot(L_s2+.2, numpy.log10(rho_10), '*c',label='average LF',markersize=13)#bin + 0.2
plt.errorbar(bins,
rho_3,
yerr=er3,
fmt='ob',
label='extracted',
markersize=8) #bin
#plt.errorbar(bins2[17:],rho_3[17:] ,yerr=er3[17:],fmt='ob',label='extracted',markersize=8) #bin
#plt.errorbar(bins,rho_5 ,yerr=er5,fmt='*r',label='Total',markersize=8) #bin
#plt.errorbar(bins,rho_6 ,yerr=er6,fmt='>g',label='Total LF',markersize=10) #bin
plt.plot(L_s, numpy.log10(rho_1), '*m', label='low LF', markersize=10)
#plt.errorbar(bins[1:],rho_2[1:] ,yerr=er2[1:],fmt='ok',label='dectected catalog',markersize=10) #bin
#plt.errorbar(bins2[1:]-0.2,rho_3[1:],yerr=er3[1:],fmt='sr',label='noisy',markersize=10) #bin
#plt.errorbar(bins2[1:]-0.2,rho_4[1:],yerr=er4[1:],fmt='vm',label='noisy lim',markersize=10) #bin
#plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
#plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')
#plt.plot(bins3+0.4,numpy.log10(logn)-0.4 ,'--k', label='faint-end')
#plt.plot(bins3,numpy.log10(logn) ,'--', label='lognorm')
#plt.plot(bins3,numpy.log10(logn2)-dm,'--r', label='lognorm2')
#
#plt.plot(bins3,numpy.log10(phi_dpl2), label='dpl bright-end2')
#plt.plot(Lbins4[(Lbins4<23.6) &(Lbins4>22.12)],numpy.log10(phi_dpl)[(Lbins4<23.6) &(Lbins4>22.12)], '-c',linewidth=5 ,label = r'$\rm{ MAP dpl < 5\sigma}$')
#plt.plot(bins2,numpy.log10(logn_2),label='starforming_2')
#plt.plot(bins2,numpy.log10(logn_3),label='starforming_3')
#plt.plot(bins2,numpy.log10(logn_4),label='starforming_4')
#plt.fill_between(xrecon,numpy.log10(yrecon - yrecon_down2 ) ,numpy.log10(yrecon+yrecon_up2), color='c',alpha=0.2)
#plt.errorbar(lin_xrecon+0.4,lin_yrecon -0.4,fmt='-b', label='MAP',linewidth=2)
#plt.errorbar(lin_xrecon+0.4,lin_yrecon- 0.4,yerr=numpy.log10(yrecon_rms),fmt='r',label='rms')
#plt.plot(L_s, numpy.log10(rho_1), '*c',label='average LF',markersize=10)
#plt.plot(L_s5, numpy.log10(rho_14)-0.2, '*r',label='average LF',markersize=13)
#plt.plot(L_s6+0.0, numpy.log10(rho_16)-0.2, '*b',label='average LF(bin)',markersize=13)
#plt.plot(bins2[:-1], numpy.log10(rho_15), '*g',label='LF average',markersize=13)
#print len(rho_11),len(rho_10),len(rho_12)
#plt.fill_betweenx(numpy.log10(rho_10) ,L_s3,L_s4, color='red',alpha=0.2)
#plt.plot(L_s6, numpy.log10(rho_13), '*b',label='average LF(bin+0.2)',markersize=13)#bin + 0.2
#plt.plot(L_s1, numpy.log10(rho_1), 'oc',label='average LF(bin)',markersize=13)#bin
#plt.plot(Lbins4,lin_phi ,'--b',label='MAP')
#plt.fill_between(lin_xrecon+.0,numpy.log10((yrecon_avr -yrecon_rms))-dm,numpy.log10(yrecon_avr +yrecon_rms)-dm , color='r',alpha=0.3)
#plt.fill_between(lin_xrecon[1:-1]+0.2,numpy.log10(yreco[1:-1] - yrecon_down[1:-1] )-dm,numpy.log10(yreco[1:-1]+yrecon_up[1:-1])-dm , color='b',alpha=0.2)
plt.fill_between(lin_xrecon + hm,
numpy.log10((yrecon_d)),
numpy.log10(yrecon_u) - dm,
color='k',
alpha=0.2)
#plt.errorbar(bins[2:],rho_1[2:],yerr=er1[2:],fmt = 'ob',label='Detected')
#plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='ob',label='Detected') #working one
#plt.plot(Lbins2,rho,'ob',label='simulation')
#plt.plot(Lbins3,rho_,label='Noisy fluxes')
#rho_2
#plt.errorbar(bins,rho_2 -0.2,yerr=er2,fmt='ok',label='data',markersize=10) #bin +0.2
#plt.errorbar(bins2,rho_6-0.2,yerr=er6,fmt='vm',label='Noisy NVSS')
#plt.errorbar(bins2,rho_7-0.2,yerr=er6,fmt='xc',label='NVSS Detected')
#for i in range(len(bins)):
# print bins[i], rho_2[i]
#sys.exit()
#plt.errorbar(Lbins2,rho, err,fmt = 'ob',label='simulation')
#plt.errorbar(Lbins2,rho_1,err_1,fmt = 'or',label='simulation_noisy')
#plt.errorbar(L2,rho2,color='g',label='Analytic')
#plt.errorbar(L3,rho3,color='g',label='Analytic II')
#plt.axvspan(22.55 ,23.51,color='r',alpha=0.2)
#plt.axvline(Lmin,color='r',linestyle='dashed')
plt.axvline(fsigma, color='k', linestyle='dashed')
#plt.axvline(sigma,color='g')
plt.axvline(Llmin, color='r', linewidth=5)
#plt.axvline(sigma2,color='m')
#plt.axvline(fsigma2,color='m')
#plt.axvline(23.89,color='b')
#plt.axvline(23.71,color='c')
#plt.axvline(23.52,color='r')
#plt.axvline(truth['LMIN'],color='r', linestyle='dashed', label = 'True params')
#plt.axvline(truth['LMAX'],color='r', linestyle='dashed')
#plt.axvline(truth['LSTAR'],color='b')
#plt.text(truth['LSTAR']-0.2,-9., '$L_*$',fontsize=18)
#plt.axvline(Lmax,color='r', label= 'MAP params')
#plt.axvline(Lstar,color='b',linestyle='dashed')
#plt.axvline(Lmax, color='r',linestyle='dashed')
#plt.text(Lstar-0.12,-10.5, '$L_*$',fontsize=18)
#plt.text(fsigma+0.1,-6.7, '$5\sigma$',fontsize=25)
#plt.text(sigma+0.1,-6.7, '$\sigma$',fontsize=25)
plt.xlabel(r'$\rm{log_{10}[L_{1.4 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
plt.text(19.9,
-7.7,
'%.1f < z < %.1f' % (z_min, z_max),
fontsize=30,
alpha=0.6) # \n $M_i$ < -22
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both', which='major', labelsize=20, width=3)
plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
#plt.tight_layout()
#plt.xtick(fontsize=15)
#plt.ytick(fontsize=15)
plt.xlim(18, 27.)
plt.ylim(-11.2, 0.)
#plt.ylim(-11,-5.5)
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plt.legend(frameon=False).draggable()
plotf = '%s/LF_recon_s1.pdf' % (outdir)
plt.show()
return 0
def main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module = importlib.import_module(setf)
globals().update(set_module.__dict__)
expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
recon_expt = countModel(modelFamily, nlaws, setf, [dataset], floatNoise)
#print expt.data
#print expt.bins
# Get MAP parameters
print '\n' * 5
print len(expt.parameters)
ncols = len(expt.parameters)
summf = os.path.join(outdir, '1-summary.txt')
summary = numpy.genfromtxt(summf)[-1, :]
drawmap = summary[-(ncols + 2):-2]
ana=pymultinest.analyse.Analyzer(ncols-1,\
outputfiles_basename=os.path.join(outdir,outstem))
drawmap = ana.get_best_fit()['parameters']
if dataset == 'sdss':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 20:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][4:]))
print datafiles[0][4:]
elif dataset == 'cosmos':
print datafiles, len(datafiles[0])
dataf = datafiles[0]
if len(dataf) == 24:
num = dataf[-5]
print num
else:
num = dataf[-6:-4]
d = numpy.genfromtxt('%s/%s' % (outdir, datafiles[0][8:]))
print datafiles[0][8:]
elif 'sim' in dataset:
d = numpy.genfromtxt(os.path.join(outdir, 'sim.txt'))
ssbin = d[:, 2]
Nbin = d[:, 3]
dnds = d[:, 4]
sbin2 = ssbin
dnds2 = dnds
#bins2 = numpy.arange(21.4,29.2,0.4)
bins2 = numpy.arange(18.0, 29.2, 0.4)
bins3 = numpy.arange(18., 29.2, 0.01)
print len(expt.parameters)
print 'this is 1st num ', num
#sys.exit()
params = drawmap # [0]
#for i in drawmap:
# params.append(i)
print params
print expt.parameters
if modelFamily in ['LFsch', 'LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
Lnorm = params[expt.parameters.index('LNORM')]
Lstar = params[expt.parameters.index('LSTAR')]
Lslope = params[expt.parameters.index('LSLOPE')]
#Lzevol=params[expt.parameters.index('LZEVOL')]
if modelFamily in ['LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
Lslope2 = params[expt.parameters.index('LSLOPE2')]
if modelFamily in [
'LFlognorm', 'LFpl', 'LFdpl', 'LFdpl_pl', 'LFlognorm_dpl',
'LFdpl_dpl'
]:
Lnorm_2 = params[expt.parameters.index('LNORM_2')]
Lstar_2 = params[expt.parameters.index('LSTAR_2')]
Lslope_2 = params[expt.parameters.index('LSLOPE_2')]
if modelFamily in ['LFdpl_dpl', 'LFdpl']:
Lslope2_2 = params[expt.parameters.index('LSLOPE2_2')]
if modelFamily in ['LFlognorm', 'LFlognorm_dpl']:
Lsigma = params[expt.parameters.index('LSIGMA')]
Lmin = params[expt.parameters.index('LMIN')]
Lmax = params[expt.parameters.index('LMAX')]
truth = {
'noise': 150,
'LMIN': 22.,
'LMAX': 24.5,
'LNORM': numpy.log10(1e-7),
'LSTAR': 23.3,
'LSLOPE': 3.,
'LSLOPE2': 1.
}
#z = [0, 0.5, 1 , 1.5, 2, 2.3, 2.6, 3, 3.5, 4] #old
#z = [ 0.7, 1 , 1.35, 1.7, 2, 2.3, 2.6, 3, 3.5, 4]# older
print expt.parameters
s = pylab.loadtxt('%s/recon_stats.txt' % outdir)
xrecon = s[:-1, 0]
yrecon = s[:-1, 1]
yrecon_d = s[:-1, 2]
yrecon_u = s[:-1, 3]
yrecon_rms = s[:-1, 4]
yrecon_avr = s[:-1, 5]
yrecon_rms_down = yrecon_rms
for i in range(len(yrecon_rms)):
if yrecon_avr[i] < yrecon_rms[i]:
yrecon_rms_down[i] = 10**(numpy.log10(yrecon_avr[i]) - 0.01)
#print xrecon
#print yrecon
#sys.exit()
z = [
0.1, 0.4, 0.6, 0.8, 1.0, 1.3, 1.6, 2.0, 2.15, 2.35, 2.55, 2.85, 3.15,
3.5
] #oldest ;)
num = int(num)
#num = 1.
print num
z_min, z_max, z_m = get_z(num)
dl = get_dl(z_m)
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(z_m, dl)
z_m = (z_min + z_max) / 2
dl = get_dl(z_m)
print z_min, z_max
print expt.kind
area = SURVEY_AREA * sqDeg2sr
area1 = SURVEY_AREA * sqDeg2sr
print SURVEY_AREA
SMIN = get_sbins(numpy.power(10, Lmin), z_m, dl) * 1e6
SMAX = get_sbins(numpy.power(10, Lmax), z_m, dl) * 1e6
sigma, fsigma = numpy.log10(
get_Lbins([SURVEY_NOISE, SURVEY_NOISE * 5], z_m, dl,
'muJy')) #*(1.4/3)**(-.7))
sigma2, fsigma2 = numpy.log10(get_Lbins([450, 2400], z_m, dl, 'muJy'))
print z_m, dl, sigma, fsigma
print SURVEY_NOISE, SURVEY_NOISE * 5, SURVEY_AREA
#area = 6672*sqDeg2sr #143165.15 49996.49 59876.8861135
sbin3 = get_sbins(10**bins2, z_m, dl) * 1e6
sbin4 = get_sbins(10**bins3, z_m, dl) * 1e6
L_s = numpy.log10(get_Lbins(ssbin, z_m, dl, 'muJy'))
#print expt.binsMedian
#sys.exit()
xreco = get_Lbins(xrecon, z_m, dl, 'muJy') #*(1.4/3)**(-.7)
yreco = yrecon
print SMIN, SMAX
#sys.exit()
lin_yrecon = numpy.log10(yreco)
print xrecon
lin_xrecon = numpy.log10(xreco)
bins2 = numpy.arange(15., 25., 0.4)
bins5, rho_5, er5, rho_6, er6 = numpy.loadtxt('cos_data/skads_s1_3_LF.el',
unpack=True)
#novak 2017
L_n = [21.77, 22.15, 22.46, 22.77, 23.09, 23.34]
rho_n = [-2.85, -2.88, -3.12, -3.55, -4.05, -4.63]
L_ner_u = [0.23, 0.18, 0.19, 0.2, 0.21, 0.28]
L_ner_d = [1.1, 0.15, 0.14, 0.12, 0.12, 0.048]
rho_ner = [0.09, 0.03, 0.0355, 0.059, 0.1, 0.234]
#novak 2018
L_n2 = [21.77, 22.24, 22.68, 23.16, 23.69, 24.34, 24.74, 25.56]
rho_n2 = [-2.84, -2.90, -3.34, -4.00, -4.92, -4.92, -5.22, -5.07]
L_ner_u2 = [0.23, 0.27, 0.34, 0.37, 0.35, 0.21, 0.31, 0.03]
L_ner_d2 = [1.0, 0.24, 0.17, 0.14, 0.16, 0.30, 0.20, 0.50]
rho_ner_u2 = [0.08, 0.024, 0.038, 0.083, 0.28, 0.28, 0.45, 0.34]
rho_ner_d2 = [0.07, 0.023, 0.035, 0.070, 0.25, 0.25, 0.37, 0.30]
#l*= (1.4/3)**(-.7)
#l2*= (1.4/3)**(-.7)
#L*= (1.4/3)**(-.7)
#Lbins2 = numpy.log10(lf.get_Lbins(sbin2[sbin2>0],z_m,dl))
print z_min, z_max
bins = numpy.arange(fsigma - 0., 26.2, 0.4) #bin +0.2
#bins = numpy.arange(fsigma -4.2,30,0.4) #bin
#s,s_z = numpy.loadtxt('sdss/dr12s_s1_vol.txt', unpack=True, usecols=(-1,2))
fig, ax = plt.subplots()
plt.rc('lines', linewidth=2)
if modelFamily in ['LFdpl', 'LFdpl_dpl']:
faint = lumfuncUtils.doublepowerlaw(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lslope2_2, Lnorm_2)
elif modelFamily in ['LFpl', 'LFdpl_pl']:
faint = lumfuncUtils.powerlaw(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lnorm_2)
else:
faint = lumfuncUtils.lognormpl(numpy.power(10, bins3), Lstar_2,
Lslope_2, Lsigma, Lnorm_2)
dm = 0. #05
hm = 0.
plt.errorbar(bins5,
rho_6,
yerr=er6,
fmt='sr',
label=r'$\rm{25 >\log_{10}(L) > %s}$' % (20.8),
markersize=10)
plt.errorbar(bins5,
rho_5,
yerr=er5,
fmt='*b',
label=r'$\rm{25 >\log_{10}(L) > %s}$' % (20.8),
markersize=10)
plt.plot(bins3 + hm, numpy.log10(faint) - dm, '--b', label='faint-end')
plt.errorbar(lin_xrecon + hm,
lin_yrecon - dm,
fmt='-k',
label='MAP',
linewidth=2)
plt.errorbar(lin_xrecon + hm,
numpy.log10(yrecon_avr) - dm,
fmt='-g',
label='Average')
#plt.errorbar(numpy.array(L_n),numpy.array(rho_n) -0.4,xerr=[L_ner_d,L_ner_u],yerr=rho_ner, fmt='^c', label='Novak et al 2017')
#plt.errorbar(numpy.array(L_n2),numpy.array(rho_n2) -0.4,xerr=[L_ner_d2,L_ner_u2],yerr=[rho_ner_d2,rho_ner_u2], fmt='sk', label='Total RLF Novak et al 2018')
plt.fill_between(lin_xrecon + hm,
numpy.log10((yrecon_d)),
numpy.log10(yrecon_u) - dm,
color='k',
alpha=0.2)
plt.axvline(fsigma, color='k', linestyle='dashed')
#plt.axvline(sigma,color='g')
#plt.axvline(Llmin,color='r',linewidth=5)
plt.xlabel(r'$\rm{log_{10}[L_{3 GHz}/(W}$ $\rm{Hz^{-1})]}$', fontsize=30)
plt.ylabel(r'$\rm{log_{10}[\rho_m(Mpc^{-3} mag^{-1})]}$', fontsize=30)
plt.text(19.9,
-7.7,
'%.1f < z < %.1f' % (0.1, 0.4),
fontsize=30,
alpha=0.6) # \n $M_i$ < -22
#plt.text(23.9,-9.7,'%s'%outdir,fontsize = 16 ) # \n $M_i$ < -22
plt.tick_params(axis='both', which='major', labelsize=20, width=3)
plt.tick_params(axis='both', which='minor', labelsize=10, width=2)
plt.xlim(18, 26.)
plt.ylim(-7, -1)
ax.xaxis.set_minor_locator(AutoMinorLocator())
ax.yaxis.set_minor_locator(AutoMinorLocator())
#print truth['LMIN']
plt.legend(frameon=False).draggable()
plotf = '%s/LF_recon_s1.pdf' % (outdir)
plt.show()
return 0
