def main():
settingsf = param_file.split('.')[-2]
expt = countModel(modelFamily,
nlaws,
settingsf, [dataset],
floatNoise,
doRedshiftSlices=doRedshiftSlices)
# Set up MPI
world = MPI.COMM_WORLD
rank = world.rank
size = world.size
master = rank == 0
if master:
set_module = importlib.import_module(settingsf)
globals().update(set_module.__dict__)
note = 'MPI processors checked in: rank/size = (%i/%i)' % (rank, size)
print note
if master:
try:
os.mkdir(outdir)
# Fix permissions
os.chmod(outdir, 0755)
except OSError:
pass
logf = os.path.join(outdir, logfile)
if master and os.path.exists(logf): os.remove(logf)
log = open(logf, 'w')
remark(log, note)
# Wait here after check-in...
world.Barrier()
if master: print 'All %i processors checked in...' % size
# Broadcast global settings variables
if master:
set_dict = set_module.__dict__
else:
set_dict = None
set_dict = world.bcast(set_dict, root=0)
if not master:
globals().update(set_dict)
#print globals()
# Wait here after broadcast...
world.Barrier()
if master: print 'All %i processors received OK...\n' % size
# Write settings variables to file
if master:
variablesf = os.path.join(outdir, variablesfile)
dump_variable_values(set_module, variablesf, verbose=False)
startTime = time.strftime('%X %x %Z')
shutil.copy(param_file, os.path.join(outdir, 'bayestack_settings.py'))
if doRedshiftSlices:
for datafile in datafiles:
shutil.copy(datafile, outdir)
else:
shutil.copy(datafile, outdir)
shutil.copy('bayestackClasses.py', outdir)
shutil.copy('bayestack.py', outdir)
shutil.copy('lumfuncUtils.py', outdir)
notes=['Time now is %s' % startTime,\
'Settings file: %s' % param_file,\
'Data file: %s' % datafile]
remarks(log, notes)
# This is to allow import of settings from outdir
# i.e. from outdir import * [or whatever]
init_file = '__init__.py'
initf = os.path.join(outdir, init_file)
touch(initf)
notes=['Bins taken from %s' % datafile,\
'# Bin occupancies [i uJy uJy field^-1]:']
remarks(log, notes)
if doRedshiftSlices:
print 'Multi data'
for df in expt.survey.datafiles[0]:
print df
for ibin in xrange(expt.fnbins[df]):
try:
line = '%i %f %f %f' % (ibin + 1, expt.fbins[df][ibin],
expt.fbins[df][ibin + 1],
expt.fdata[df][ibin])
except IndexError:
print "Probably your binstyle doesn't match the datafile bins"
sys.exit(0)
remark(log, line)
else:
for ibin in xrange(expt.nbins):
try:
line = '%i %f %f %f' % (ibin + 1, expt.bins[ibin],
expt.bins[ibin + 1],
expt.data[ibin])
except IndexError:
print "Probably your binstyle doesn't match the datafile bins"
sys.exit(0)
remark(log, line)
# Run MultiNest
if master:
t0 = time.time()
print outdir
try:
pymultinest.run(expt.loglike,expt.logprior,expt.nparams,\
resume=RESUME,verbose=True,\
multimodal=multimodal,max_modes=max_modes,write_output=True,\
n_live_points=n_live_points,\
evidence_tolerance=evidence_tolerance,\
# mode_tolerance=-1e90 bugfix for earlier versions
# of PyMultiNest
mode_tolerance=-1e90,seed=SEED_SAMP,max_iter=max_iter,\
importance_nested_sampling=do_INS,\
sampling_efficiency=sampling_efficiency,\
outputfiles_basename=os.path.join(outdir,outstem),\
# NB MPI is already init'ed by mpi4py (crashes otherwise)
init_MPI=False)
except:
return 1
if master:
stopTime = time.strftime('%X %x %Z')
t1 = time.time()
dt = t1 - t0
# Touch the output dir so Dropbox picks it up
touch(outdir)
notes=['Time then was %s' % startTime,\
'Time now is %s' % stopTime,\
'Execution took %6.4f sec (~ %i hours %i min) with %i cores' % \
(dt,divmod(dt,3600)[0],int(divmod(dt,3600)[1]/60),size),\
'Arguments: %s' % ' '.join(sys.argv),\
'Model used: %s'%modelFamily,\
'INS = %s' % do_INS,\
'nlive = %i' % n_live_points,\
'Run comment: %s' % comment,\
'Now execute:',\
'\n./plot.py %s' % outdir,\
'and\n./reconstruct.py %s' % outdir]
remarks(log, notes)
log.close()
print 'Parameters were:', expt.parameters
# Copy the stats file so it's legible on my iPhone, Google, email etc.
stats_dotdat = '%(od)s/%(os)sstats.dat' % {'od': outdir, 'os': outstem}
stats_dottxt = '%(od)s/%(os)sstats.txt' % {'od': outdir, 'os': outstem}
shutil.copy(stats_dotdat, stats_dottxt)
# Now make all the files world readable
globlist = glob.glob(os.path.join(outdir, '*'))
[os.chmod(f, 0644) for f in globlist]
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 main():
"""
"""
print 'Settings file is %s' % setf
# Import the settings variables
set_module = importlib.import_module(setf)
globals().update(set_module.__dict__)
# Load prior ranges etc.
expt = countModel(modelFamily, nlaws, setf, dataset, floatNoise)
# Insert hacks here
#plotRanges['C']=[0,200]
print modelFamily
if modelFamily == 'LFsch':
labelDict= {'noise': '$\sigma$','LMIN':r'$\log_{10}[L_\rm{min}]$','LMAX':r'$\log_{10}[L_\rm{max}]$',\
'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha$'}#,'LZEVOL':'$Z_{evol}$'}
elif modelFamily == 'LFpl':
print 'this..'
labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSLOPE2_2':r'$\alpha_1$'}
elif modelFamily == 'LFdpl':
print 'this..'
labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSLOPE2_2':r'$\beta_1$','LZEVOL':'$Z_{evol}$'}
elif modelFamily == 'LFlognorm':
labelDict= {'LMIN':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_1$','LSIGMA':r'$\sigma_{LF}$','LZEVOL':'$Z_{evol}$'}
elif modelFamily == 'LFlognorm_dpl':
print 'this is the right model'
labelDict= {'LMIN':r'$\log_{10}[L_{\rm{min}_1}]$','LMAX2':r'$\log_{10}[L_{\rm{max}_1}]$','LMIN2':r'$\log_{10}[L_{\rm{min}_2}]$','LMAX':r'$\log_{10}[L_{\rm{max}_2}]$',\
'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha_1$',\
'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\log_{10}[\Phi_2^*]$','LSTAR_2': '$\log_{10}[L_2^*]$','LSLOPE_2':r'$\alpha_2$','LSIGMA':r'$\sigma_{LF}$'}
print labelDict
elif modelFamily == 'LFdpl_pl':
labelDict= {'noise': '$\sigma$','LMIN':r'$\log_{10}[L_{\rm{min}_1}]$','LMAX2': r'$\log_{10}[L_{\rm{max}_1}]$','LMIN2':r'$\log_{10}[L_{\rm{min}_2}]$','LMAX':r'$\log_{10}[L_{\rm{max}_2]$',\
'LNORM':'$\log_{10}[\Phi_1^*]$','LSTAR': '$\log_{10}[L_1^*]$','LSLOPE':r'$\alpha_1$',\
'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\log_{10}[\Phi_2^*]$','LSTAR_2': '$\log_{10}[L_2^*]$','LSLOPE_2':r'$\alpha_2$','LSLOPE2_2':r'$\beta_2$'}
elif modelFamily == 'LFdpl_dpl':
labelDict= {'noise': '$\sigma$','LMIN': r'$L_{\rm{min}_1}$','LMAX2': r'$L_{\rm{max}_1}$','LMIN2':r'$L_{\rm{min}_2}$','LMAX':r'$L_{\rm{max}_2}$',\
'LNORM':'$\Phi_1^*$','LSTAR': '$L_1^*$','LSLOPE':r'$\alpha_1$',\
'LSLOPE2': r'$\beta_1$', 'LNORM_2':'$\Phi_2^*$','LSTAR_2': '$L_2^*$','LSLOPE_2':r'$\alpha_2$','LSLOPE2_2':r'$\beta_2$'}
elif modelFamily == 'LFevol_logn_L':
labelDict= {'noise': '$\sigma$','LMIN':'$L_{min}$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$', 'LMIN_1':'$L_{min1}$', 'LMIN_2':'$L_{min2}$' ,'LMIN_3':'$L_{min3}$' ,'LMIN_4':'$L_{min4}$' ,'LMIN_5':'$L_{min5}$' ,'LMIN_6':'$L_{min6}$' ,'LMIN_7':'$L_{min7}$' ,'LMIN_8':'$L_{min8}$' ,'LMIN_9':'$L_{min9}$'}
elif modelFamily == 'LFevol':
labelDict= {'noise': '$\sigma$','LMIN':'$L_{min}$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
elif modelFamily == 'LFevol_dpl':
labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
elif modelFamily == 'LFevol_logn':
labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
'B_SF':r'$\beta_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
elif modelFamily == 'LFevol_phi_logn':
labelDict= {'noise': '$\sigma$','LMIN':'$S_{min} (\mu Jy)$','LMAX':'$L_{max}$','A_SF':r'$\alpha_{SF}$','A_B':r'$\alpha^D_{SF}$', 'A_agn':r'$\alpha_{AGN}$',\
'B_SF':r'$\beta_{SF}$','B_D':r'$\beta^D_{SF}$', 'B_agn':r'$\beta_{AGN}$'}
elif modelFamily == 'LFevol_logn_mat' or modelFamily == 'LFevol_logn_el':
labelDict = {
'noise': '$\sigma$',
'LMIN': r'$\log_{10}[L_{\rm{min}}]$',
'LMAX': r'$\log_{10}[L_{\rm{max}}]$',
'A_agn': r'$\alpha_{AGN}$',
'A_SF': r'$\alpha_{SF}$'
}
elif modelFamily == 'LFevol_dpl_s':
labelDict = {
'noise': '$\sigma$',
'LMIN': '$S_{min} (\mu Jy)$',
'LMAX': '$L_{max}$',
'A_SF': r'$\alpha_{SF}$',
'B_SF': r'$\beta_{SF}$'
}
elif modelFamily == 'LFevol_logn_s':
labelDict = {
'noise': '$\sigma$',
'LMIN': '$S_{min} (\mu Jy)$',
'LMAX': '$L_{max}$',
'A_SF': r'$\alpha_{SF}$',
'B_SF': r'$\beta_{SF}$'
}
elif modelFamily == 'LFevol_logn_mat':
labelDict = {
'noise': '$\sigma$',
'LMIN': '$L_{min}$',
'LMAX': '$L_{max}$',
'A_agn': r'$L_{agn}$',
'A_SF': r'$\L_{SF}$'
}
else:
labelDict = dict((name, lables) for name in expt.parameters)
plotTruth = dict((name, -99.0) for name in expt.parameters)
if modelFamily in ['LFsch', 'LFdpl_pl', 'LFdpl_dpl', 'LFlognorm_dpl']:
Lnorm = expt.parameters[expt.parameters.index('LNORM')]
Lstar = expt.parameters[expt.parameters.index('LSTAR')]
Lslope = expt.parameters[expt.parameters.index('LSLOPE')]
#Lzevol=expt.parameters[expt.parameters.index('LZEVOL')]
if modelFamily in ['LFdpl_dpl', 'LFlognorm_dpl']:
Lslope2 = expt.parameters[expt.parameters.index('LSLOPE2')]
if modelFamily in [
'LFlognorm', 'LFdpl', 'LFpl', 'LFdpl_pl', 'LFlognorm_dpl',
'LFdpl_dpl'
]:
Lnorm = expt.parameters[expt.parameters.index('LNORM_2')]
Lstar = expt.parameters[expt.parameters.index('LSTAR_2')]
Lslope = expt.parameters[expt.parameters.index('LSLOPE_2')]
if modelFamily in ['LFdpl_dpl']:
Lslope2_2 = expt.parameters[expt.parameters.index('LSLOPE2_2')]
noise = expt.parameters[expt.parameters.index('noise')]
plotRanges = dict((k, v[-2:]) for (k, v) in expt.priorsDict.items())
'''
furniture={'TRUNCATE_phi':False,'TRUNCATE_phi_limit':2.0e7,\
'Lstar':Lnorm ,'FONTSIZE':4,\
'ROTATION':60.0,'FIGSIZE':(8.27,11.69), 'DPI':400,\
'AXIS_LABEL_OFFSET':-0.3,'LOG_BINS':Lnorm,\
'PADDING':0.05}
'''
furniture={'TRUNCATE_Lstar':False,'TRUNCATE_Lstar_limit':2.0e7,\
'FONTSIZE':4,\
'ROTATION':60.0,'FIGSIZE':(8.27,11.69), 'DPI':400,\
'AXIS_LABEL_OFFSET':-0.3}
#furniture={}
# Load the data
chain = numpy.loadtxt('%s/1-post_equal_weights.dat' % outdir) #
stats = fetchStats(outdir, expt.parameters, plotTruth)
print 'start'
#print chain[0][1:]
chain = numpy.array([c[1:] for c in chain])
#chain=numpy.delete(chain,-1,1)
print expt.parameters
#print expt.parameters.keys()
remove = ['noise'] #,'LMAX2', 'LMAX']
#parameters= [ele for ele in expt.parameters if ele not in remove]
#chain =numpy.delete(chain,[1],1)
#print chain
#print parameters
#print chains
#print chain2
#sys.exit()
'''
print chain[0]
#sys.exit()
#chain=pylab.loadtxt('%s/1-ev.dat'%outdir)
print expt.parameters,'oka'
print modelFamily
labels=[labelDict[param] for param in expt.parameters[1:] ]
rc('xtick',labelsize=24)
rc('ytick',labelsize=24)
if corner_tri:
figure= corner.corner(chains,bins=50,labels=labels,color='#0057f6',label_kwargs={'fontsize':32},plot_contours=True,fill_contours=True,contour_kwargs={'linewidth':100})#contour_kwargs={'levels',0.6})
show()
sys.exit()
plotRanges={'noise': [148., 160],'LMIN': [20, 26],'LMAX':[23,30] ,'LMIN2': [20, 28],'LMAX2':[23,30],\
'LNORM':[-10,2],'LSTAR': [24,30],'LSLOPE':[-5,5],'LSLOPE2':[-5,5], 'LNORM_2':[-10,2],\
'LSTAR_2': [20,26],'LSLOPE_2':[-2,2],'LSLOPE2_2':[-5,1]}
# Local plot
line=False
print labelDict
autoscale=True
print 'this is noise',noise
title=''#r'$\rm{%s \mu Jy}$'%noise#%s - %s'%(outdir,dataset)
truth=plotTruth
bundle=contour_plot.contourTri(chain,\
line=line,\
labels=expt.parameters[1:],\
ranges=plotRanges,truth=truth,\
autoscale=autoscale,title=title,\
X_LABEL_OFFSET=-0.8,Y_LABEL_OFFSET=-0.7 ,\
labelDict=labelDict\
,furniture=furniture)
'''
# Plot for publication
print 'latex stuff'
printLaTeX(expt.parameters, stats, dump=outdir)
line = False
autoscale = True
title = ''
truth = {
'noise': 150,
'LMIN': 21.7,
'LMAX': 24.6,
'LNORM_2': numpy.log10(1e-7),
'LSTAR_2': 23.1,
'LSLOPE_2': 1.,
'LSLOPE2_2': 1.
}
truth = {par: stats[par][0] for par in expt.parameters}
print 'This is the truth\n \n'
print truth
#plotRanges={'noise': [148., 160],'LMIN': [20, 26],'LMAX':[26,30] ,'LMIN2': [20, 30],'LMAX2':[20,30],\
# 'LNORM':[-10,-2],'LSTAR': [24,30],'LSLOPE':[-5,5],'LSLOPE2':[-5,5], 'LNORM_2':[-10,-2],\
# 'LSTAR_2': [20,26],'LSLOPE_2':[-5,5],'LSLOPE2_2':[-5,5]}
extn = 'png'
plotRanges = {}
bundle=contour_plot.contourTri(chain,\
line=line,\
outfile='%s/triangle_%s.%s'%(outdir,outdir[-3:],extn),\
labels=expt.parameters[1:],\
ranges=plotRanges,truth=truth,\
autoscale=autoscale,title=title,\
X_LABEL_OFFSET=-0.55,Y_LABEL_OFFSET=-0.45,FONTSIZE=24.,\
binsize=50,labelDict=labelDict)
#X_LABEL_OFFSET=-0.45,Y_LABEL_OFFSET=-0.35,FONTSIZE=15.,\ dpl_dpl
#X_LABEL_OFFSET=-0.45,Y_LABEL_OFFSET=-0.35,FONTSIZE=20.,\ model C
stats = fetchStats(outdir, expt.parameters, plotTruth)
print stats
printLaTeX(expt.parameters, stats, dump=outdir)
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)
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