def S_non(self):
xm = medianArray(self.nu_arr)
z, L0, a = self.red_g, self.L_g, self.index_g
y = np.ones(len(xm) - 1)
for i, xi in enumerate(xm[:-1]):
#y[i]=Int_S(self.nu_arr[i],self.nu_arr[i+1],8.0,23.0,z_g)
y[i] = Int_S_N(xm[i], xm[i + 1], a, z, L0)
return y
def S_P(self, z, L0, a):
xm = medianArray(self.nu_arr) #defualt is los 4: 1st try 2,3,noda()
Sn011 = np.array([
Int_S_EoR(xm[i], xm[i + 1], a, z, L0, self.xi_z, self.d_z,
self.Ts_z, i) for i in range(len(xm[:-1]))
])
Sn1_av = (Sn011)
return Sn1_av
def writeCountsFile(output,bins,fluxes,area,idl_style=None,\
version=2,verbose=None,corrs=None):
"""
Write an array of fluxes to a binned counts file
"""
# Test version
if version < 2: return '***Unsupported!!'
# Bin up the fluxes
counts=numpy.histogram(fluxes,bins=bins)[0]
N=len(fluxes)
print '-> %i/%i objects observed in total (after binning)\n' % (counts.sum(),N)
# Calculate differential counts
idl_style=False
dn_by_ds,dn_by_ds_eucl,dn_by_ds_errs,dn_by_ds_b,dn_by_ds_b_errs=\
calculateDnByDs(1.0e-6*bins,counts,idl_style=idl_style,return_all=True)
median_bins=medianArray(bins) # uJy
NB=len(median_bins)
# Set up the corrections
if corrs is None:
corrs=numpy.ones(NB)
if output is not None:
outputf=output
s=open(outputf,'w')
if version < 2:
header='# bin_median ksRaw ksNoisy'
else:
header='# bin_low_uJy bin_high_uJy bin_median_uJy Ns_tot_obs dnds_srm1Jym1 dnds_eucl_srm1Jy1p5 delta_dnds_eucl_lower_srm1Jy1p5 delta_dnds_eucl_upper_srm1Jy1p5 corr Ncgts_degm2 dNcgts_lower_degm2 dNcgts_upper_degm2'
s.write('%s\n'%header)
if verbose: print header
for ibin in range(NB):
if version < 2:
line='%f %i %i' % (median_bins[ibin],-99.0,counts[ibin])
else:
line='%f %f %f %i %e %e %e %e %f %i %i %i' % \
(bins[ibin],bins[ibin+1],median_bins[ibin],round(counts[ibin]),\
dn_by_ds[ibin]/(sqDeg2sr*area),\
dn_by_ds_eucl[ibin]/(sqDeg2sr*area),\
dn_by_ds_errs[ibin]/(sqDeg2sr*area),\
dn_by_ds_errs[ibin]/(sqDeg2sr*area),\
corrs[ibin],\
round(counts[ibin:].sum()*1.00/area),\
round(numpy.sqrt(counts[ibin:].sum()*1.00/area)),\
round(numpy.sqrt(counts[ibin:].sum()*1.00/area)))
s.write('%s\n'%line)
if verbose: print line
print counts.sum()
s.close()
print 'Look in %s' % outputf
return
def Con(self):
z, L0, a = self.red_g, self.L_g, self.index_g
t, Dnu = self.t_int, self.Dnu
xm = medianArray(self.nu_arr)
ssg = np.array([sig_s(jj, Dnu, t) for jj in xm[:-1]])
Sn014 = np.array([
Int_S_EoR(xm[i], xm[i + 1], a, z, L0, self.xi_z4, self.d_z4,
self.Ts_z4, i) for i in range(len(xm[:-1]))
])
#Sn014 = np.array( [ S_n1(xm[i],z,L0,a)*A(z,(1420.4/xm[i])-1.0,self.xi_z4,self.d_z4,self.Ts_z4,i) for i in range(len(xm[:-1])) ] )
return (self.nu_arr), Sn014, ssg
def Mod(self, A, B, z, L0, a):
t, Dnu = self.t_int, self.Dnu
xm = medianArray(self.nu_arr)
ssg = np.array([sig_s(jj, Dnu, t) for jj in xm[:-1]])
#Sn014 = np.array( [ S_n1(xm[i],z,L0,a)*Snu_Model_A(xm[i],A,B,z) for i in range(len(xm[:-1])) ] )
#Sn014 = np.array( [ Int_S(xm[i],xm[i+1],A,B,z,a,L0) for i in range(len(xm[:-1])) ] )
Sn014 = np.array([
Int_S(xm[i], xm[i + 1], A, B, z, a, L0)
for i in range(len(xm[:-1]))
])
return Sn014
def N2C(function, deviates, Smin, Smax, numbins):
"""
Since C is a function of the number of deviates N drawn from the
function, calculate what the ratio is and return it
"""
A = integrate.quad(function, Smin, Smax)[0]
# print A,N
# Bin the random samples
bbins = numpy.linspace(Smin, Smax, numbins)
E = numpy.histogram(deviates, bins=bbins)[0]
# And calculate their area
G = integrate.trapz(E, x=medianArray(bbins))
return numbins * G / A
def calculateDnByDs(bins,counts,eucl=False,verbose=False,idl_style=True,
errors=False,bright=False,bright_errors=False,
return_all=False):
"""
This function expects bins to be in Jy
The output units are Jy^-1, or Jy^1.5 if eucl=True
The function is area-agnostic: sr^-1 -> sr^-1, deg^-2 -> deg^-2 etc.
Be careful with units:
e.g. if bins are in uJy, dn_by_ds will be uJy^-1 = 10^6 Jy
dn_ny_ds_eucl in uJy^1.5 = 10^-9 Jy
etc.
"""
dn=counts
Smed=medianArray(bins)
ds=numpy.absolute(numpy.gradient(Smed))
if idl_style:
# i.e. the way DERIV.PRO does it
ds[0] = abs(-3.0*Smed[0] + 4.0*Smed[1] - Smed[2] / 2.0)
ds[-1] = abs(3.0*Smed[-1] - 4.0*Smed[-2] + Smed[-3] / 2.0)
if verbose:
print 'dn',dn
print 'ds',ds
if return_all:
return dn/ds, (Smed**2.5) * dn/ds,\
(Smed**2.5) * numpy.sqrt(dn)/ds,\
(Smed**2.0) * dn/ds,\
(Smed**2.0) * numpy.sqrt(dn)/ds
if eucl:
dn_by_ds = (Smed**2.5) * dn/ds
elif errors:
dn_by_ds = (Smed**2.5) * numpy.sqrt(dn)/ds
elif bright:
dn_by_ds = (Smed**2.0) * dn/ds
elif bright_errors:
dn_by_ds = (Smed**2.0) * numpy.sqrt(dn)/ds
else:
dn_by_ds = dn/ds
return dn_by_ds
def myloglike(cube, ndim, nparams):
global yi, x, xm, y, notInited, sg, bb, cc
if notInited:
zin, Li, ai = float(sys.argv[-4]), float(sys.argv[-3]), float(
sys.argv[-2])
F = AB.FLUX(zin, Li, ai)
bb, cc = zip(*AB.Memoize(r).memo.items())
y = F.Result()[1] #
x = F.Result()[0] #
xm = medianArray(x)
sg = F.Result()[
2] #np.array( [ sig_s(i,dnu,T_int) for i in xm[:-1] ] )
np.random.seed(1234)
yi = y + np.random.normal(0.0, sg) #input data + random.noramal noise
notInited = False
loglike = 0.0
for idatum in range(len(xm[:-1])):
data = yi[idatum]
zfit = cube[0]
B = cube[1]
K = cube[2]
amp = cube[3]
a = cube[5]
model = Int_S(xm[idatum], xm[idatum + 1], B, K, zfit, a, amp)
if model < 0.0:
print '+',
return -1.0e99
#print model
sigma = cube[4] * sg[idatum]
chisq = 0.5 * ((data - model) / sigma)**2.0
prefactor = 0.5 * log(2.0 * pi * sigma**2.0)
loglike -= prefactor + chisq
return loglike
def simulate(family,params,paramsList,bins,\
seed=None,N=None,noise=None,output=None,\
dump=None,version=2,verbose=False,area=None,\
skadsf=None,pole_posns=None,simarrayf=None,\
simdocatnoise=True):
"""
Based on lumfunc.simtable()
Specify family + parameters
Specify number of sources
Build CDF (or set up function)
Draw deviates
Sample CDF given deviates
Add noise (None or some value)
Bin
Write out
Return
Look at simulate.ipynb for an example run
Need to add normalization capability
Families:
========
skads:
-----
r=countUtils.simulate('skads',[0.01,85.0],['S0','S1'],numpy.linspace(-60.0,100.0,26),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
ppl:
---
r=countUtils.simulate('ppl',[1000.0,5.0,75.0,-1.6],['C','S0','S1','a0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,75.0,-1.6,-2.5],['C','S0','S1','S2','a0','a1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,-1.6,-2.5,-1.0],['C','S0','S1','S2','S3','a0','a1','a2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('ppl',[1000.0,5.0,25.0,40.0,75.0,90.0,-1.6,-2.5,-1.0,2.0],['C','S0','S1','S2','S3','S4','a0','a1','a2','a3'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
poly:
----
r=countUtils.simulate('poly',[5.0,75.0,1.0],['S0','S1','p0'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0],['S0','S1','p0','p1'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
r=countUtils.simulate('poly',[5.0,75.0,1.0,-1.0,5.0],['S0','S1','p0','p1','p2'],numpy.linspace(-20.0,100.0,22),seed=1234,N=40000,noise=17.0,dump='R.txt',output='dummy.txt',verbose=True)
bins:
----
test:
----
array:
-----
"""
# Initialize seed for variates AND any noise
if seed is not None:
numpy.random.seed(seed=SEED_SIM)
if family == 'ppl':
C = alpha = Smin = Smax = beta = S0 = gamma = S1 = delta = S2 = -99.0
nlaws = int(0.5 * len(paramsList) - 1)
C = params[paramsList.index('C')]
Smin = params[paramsList.index('S0')]
alpha = params[paramsList.index('a0')]
if nlaws > 1:
beta = params[paramsList.index('a1')]
S0 = params[paramsList.index('S1')]
if nlaws > 2:
gamma = params[paramsList.index('a2')]
S1 = params[paramsList.index('S2')]
if nlaws > 3:
delta = params[paramsList.index('a3')]
S2 = params[paramsList.index('S3')]
iSmax = int([i for i in paramsList if i.startswith('S')][-1][-1])
Smax = params[paramsList.index('S%i' % iSmax)]
function = lambda S:powerLawFuncWrap(nlaws,S,C,alpha,-99.0,beta,\
Smin/1e6,Smax/1e6,S0/1e6,gamma,S1/1e6,delta,S2/1e6,1.0)
elif family == 'test':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = lambda S: S**2
elif family == 'poly':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('p')
]
S_1 = 1.0
function = lambda S: polyFunc(S, S_1, Smin, Smax, coeffs)
elif family == 'bins':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
coeffs = [
params[paramsList.index(p)] for p in paramsList
if p.startswith('b')
]
if pole_posns is None:
pole_posns = numpy.logspace(numpy.log10(Smin), numpy.log10(Smax),
len(coeffs) + 1)
assert (len(coeffs) == len(pole_posns) -
1), '***Mismatch in number of poles!!'
Smin = pole_posns[0]
Smax = pole_posns[-1]
function = lambda S: polesFunc(S, pole_posns, Smin, Smax, coeffs)
elif family == 'array':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
assert (simarrayf
is not None), '***Need to specify an input simulation!'
print 'Reading %s...' % simarrayf
dataMatrix = numpy.genfromtxt(simarrayf)
dndsInArr = dataMatrix[:, 4]
binsDogleg = numpy.concatenate((dataMatrix[:, 0], [dataMatrix[-1, 1]]))
binsMedian = dataMatrix[:, 2]
assert ((
medianArray(binsDogleg) == binsMedian).all()), '***bin mismatch!'
Smin = binsDogleg[0]
Smax = binsDogleg[-1]
if not simdocatnoise:
Smin = -5.01 #-2.01 # binsMedian[0]
print dndsInArr
function = lambda S: arrayFunc(S, binsMedian, dndsInArr, Smin, Smax)
#function2=lambda S:arrayFunc(S,binsMedian,dndsInArr,Smin,Smax)
#for x in numpy.linspace(-10.0,100.0,500):
# print x,function(x),function2(x)
#sys.exit(0)
elif family == 'skads':
Smin = params[paramsList.index('S0')]
Smax = params[paramsList.index('S1')]
function = None
assert (skadsf is not None), '***Need to specify input SKADS file!'
print 'Reading %s...' % skadsf
R = Jy2muJy * 10**numpy.genfromtxt(skadsf)
numpy.ndarray.sort(R)
iRmin, Rmin = find_nearest(R, Smin)
iRmax, Rmax = find_nearest(R, Smax)
F = R[iRmin:iRmax]
print '%i/%i sources ingested after Smin/Smax cuts' % (len(F), len(R))
if N is not None:
F = numpy.random.choice(F, size=N, replace=False)
N = len(F)
print 'NSKADS = %i' % N
elif family == 'Lrad':
Smin = params[paramsList.index('LoptMIN')]
Smax = params[paramsList.index('LoptMAX')]
A = params[paramsList.index('A')]
B = params[paramsList.index('B')]
sigma_Lrad = params[paramsList.index('sigma_Lrad')]
#print Loptmin,Loptmax
print 'Doing LF simulation'
inta = None
#intg = integrate.quad(lambda Lopt:Lopt2Lrad(Lopt,A=A,B=B,flux=False),Loptmin,Loptmax,epsabs=0.)[0]
function = lambda Lopt: Lopt2Lrad(Lopt, A=A, B=B, flux=False)
elif family in ['LFsch', 'LFdpl']:
redshift = 0.325
z_min = 0.2
z_max = 0.45
Lmin = params[paramsList.index('LMIN')]
Lmax = params[paramsList.index('LMAX')]
[Smin, Smax] = SMIN_SIM, SMAX_SIM
print Smin, Smax
[Smin, Smax] = get_sbins([10**Lmin, 10**Lmax], redshift, dl) * 1e6
print Smin, Smax, Lmin, Lmax
print 'Doing LF simulation'
Vmax = get_Vmax(z_min, z_max)
dsdl = get_dsdl(redshift, dl)
inta = None
intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
print intg * Vmax
print Vmax
area = N / (Vmax * intg)
area1 = area
print N, area
function = lambda S:dNdS_LF(S,z_min,redshift,z_max,dl,params=params,paramsList=paramsList,\
area=area,family=family)
if family != 'skads':
# Set up the 'rough' array
gridlength = 10000 # Good enough to prevent bleeding at the edges
Ss = numpy.linspace(Smin, Smax, gridlength)
print Smin, Smax
print 'checking for one sample'
kl = function(20 / 1e6)
print kl
#sys.exit()
values = numpy.array([function(ix / 1e6) for ix in Ss])
print values[:10]
# Build the CDF
CDF = buildCDF(values)
plt.plot(CDF, Ss)
#plt.xscale('log')
#plt.yscale('log')
plt.ylabel('Flux')
plt.xlabel('CDF')
plt.show()
print CDF.max()
# Create the interpolant object
sampler = interp1d(CDF, Ss)
plt.plot(Ss, values, '.')
plt.xscale('log')
plt.yscale('log')
plt.xlabel('Flux')
plt.ylabel('LF')
plt.show()
x = numpy.linspace(0., 1., 10000)
z = numpy.logspace(0, 1, 1000) / 10.
f = sampler(z)
y = sampler(x)
plt.yscale('log')
#plt.xscale('log')
plt.axhline(Smin)
plt.axhline(Smax)
plt.xlabel('R')
plt.ylabel('Sampler(R) [flux]')
#plt.plot(x,y)
plt.plot(z, f)
plt.show()
#sys.exit()
# Test that the sampler extrema match
print Smin, sampler(0.0), 'you know wa mean'
print Smax, sampler(0.99999)
# assert(numpy.isclose(sampler(0.0),Smin)[0])
# assert(numpy.isclose(sampler(0.99999),Smax,atol=1.0e-3)[0])
# Draw the random deviates
R = numpy.random.rand(N)
print len(R)
F = sampler(R)
Nt = 0.
for f in F:
if f < 1.:
Nt += 1.
F = F[F > 1.]
print N, Nt
print len(F)
Nt = len(F)
#sys.exit()
# Normalize here - this is N2C
# EITHER N is specified explicitly
# BOTH N2C and C2N are useful
# Integrate the original function
#intg = integrate.quad(lambda S:LF(S,redshift,dsdl,Vmax,dl,params=params,paramsList=paramsList,\
# inta=inta,area=area,family=family),Smin*1e-6,Smax*1e-6,epsabs=0.)[0]
#print intg*Vmax
#print Vmax
#area = Nt/(Vmax*intg)
#print N,area,area1
#plt.show()
#sys.exit()
A = integrate.quad(function, Smin, Smax)[0]
# print A,N
# Bin the random samples
bbins = numpy.linspace(Smin, Smax, 100)
E = numpy.histogram(F, bins=bbins)[0]
# And calculate their area
G = integrate.trapz(E, x=medianArray(bbins))
# print G
# print G/A
# Gunpowder, treason and....
if False:
plt.xlim(0.0, 100.0)
plt.xlabel('S / $\mu$Jy')
plt.hist(F, bins=bbins)
plt.plot(Ss, values * G / A, 'r')
plt.savefig('N2C.pdf')
plt.close()
# Want: C given N, to compare to original C
numbins = 1000
if family == 'ppl':
C_calc = N / N2C(function, F, Smin, Smax, numbins)
#print N2C(function,F,Smin,Smax,numbins),C
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, C)
elif family == 'poly':
C_calc = log10(N / N2C(function, F, Smin, Smax, numbins))
print 'For %i sources, C is %e (should be %e)' % (N, C_calc, coeffs[0])
# Dump noiseless fluxes to file
puredumpf = dump
idl_style = False
numpy.savetxt(puredumpf, F)
print 'Draws (noiseless) are in %s' % puredumpf
writeCountsFile(output[1],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print output[1]
# Now add noise if requested
if simdocatnoise:
numpy.random.seed(seed=SEED_SIM)
poln = False
if poln:
F += rice.rvs(F / noise, size=N)
else:
F += numpy.random.normal(0.0, noise, Nt)
# Dump noisy fluxes to file
if dump is not None:
noisydumpf = '%s_noisy.txt' % puredumpf.split('.')[0]
numpy.savetxt(noisydumpf, F)
print 'Draws (noisy) are in %s' % noisydumpf
print 'Minimum flux in catalogue = %f' % F.min()
print 'Maximum flux in catalogue = %f' % F.max()
# Write counts file
print output[0]
writeCountsFile(output[0],
bins,
F,
area,
idl_style=idl_style,
verbose=verbose)
print N, area #,area1
return F
def __init__(self,kind,order,settingsf,whichSurvey,floatNoise,doPoln=False,\
doRayleigh=False,doRedshiftSlices=False,mybins=None):
# Import settings
print 'Settings file is %s' % settingsf
set_module = importlib.import_module(settingsf)
globals().update(set_module.__dict__)
# Set up model
self.kind = kind
self.name = kind
self.order = order
self.nlaws = order
self.model = model(self.kind)
self.floatNoise = floatNoise
self.doPoln = doPoln
self.doRayleigh = doRayleigh
self.doRedshiftSlices = doRedshiftSlices
if self.doRedshiftSlices:
self.zDataObject = dataSetup('sdss',
zmanifestf,
redshiftSlices=self.doRedshiftSlices)
self.redshifts = redshifts
# Set up parameters for this model
# --> This defines the order of the parameters:
self.paramsAvail=\
OrderedDict([('extra',['noise']),\
('evol',['A_agn','A_SF','B_agn','B_SF']),\
('evol_SF',['A_SF','B_SF']),\
('evol_mat',['A_SF','A_agn']),\
('evol_el',['A_SF','A_agn']),\
('evol_sl',['A_SF','A_agn','LSLOPE2_2']),\
('evol_sg',['A_SF','A_agn','LSIGMA']),\
('evol_ls',['A_SF','A_agn','LMIN2']),\
('evol_ph',['A_SF','A_agn','LNORM','LSTAR']),\
('evol_all',['LNORM','LSTAR','LSLOPE2_2','LSIGMA']),\
('evol_phi',['A_D','B_D']),\
('breaks',['S%i'%ic for ic in xrange(self.nlaws+1)]),\
('amp',['C']),\
('coeffs',['p%i'%ic for ic in xrange(self.nlaws)]),\
('limits',['S%i'%ic for ic in xrange(2)]),\
('Llimits',['LMIN_%i'%ic for ic in xrange(1,10)]),\
#('Llimits',['LMIN_%i'%ic for ic in xrange(1,5)]),\
('llimits',['LMIN','LMAX']),\
('poles',['b%i'%ic for ic in xrange(self.nlaws)]),\
('slopes',['a%i'%ic for ic in xrange(self.nlaws)]),\
#('schechter',['LMIN','LMAX','LNORM','LSTAR','LSLOPE','LZEVOL']),\
('schechter',['LMIN','LMAX','LNORM','LSTAR','LSLOPE']),\
#('doublepl',['LMIN','LMAX','LNORM_2','LSTAR_2','LSLOPE_2','LSLOPE2_2'),\
('powerlaw',['LMIN','LMAX','LNORM_2','LSTAR_2','LSLOPE_2']),\
('doublepl',['LMIN','LMAX','LNORM_2','LSTAR_2','LSLOPE_2','LSLOPE2_2']),\
('dpl_pl',['LMIN','LMAX2','LMIN2','LMAX','LNORM','LSTAR','LSLOPE','LSLOPE2','LNORM_2','LSTAR_2','LSLOPE_2']),\
('pl_dpl',['LMIN','LMAX2','LMIN2','LMAX','LNORM','LSTAR','LSLOPE','LSLOPE2','LNORM_2','LSTAR_2','LSLOPE_2']),\
('dpl_dpl',['LMIN','LMAX2','LMIN2','LMAX','LNORM','LSTAR','LSLOPE','LSLOPE2','LNORM_2','LSTAR_2','LSLOPE_2','LSLOPE2_2']),\
('lognorm',['LMIN','LMAX','LNORM_2','LSTAR_2','LSLOPE_2','LSIGMA']),\
('lognorm_dpl',['LMIN','LMAX2','LMIN2','LMAX','LNORM','LSTAR','LSLOPE','LSLOPE2','LNORM_2','LSTAR_2','LSLOPE_2','LSIGMA']),\
('pl_lognorm',['LMIN','LMAX2','LMIN2','LMAX','LNORM','LSTAR','LSLOPE','LNORM_2','LSTAR_2','LSLOPE_2','LSIGMA']),\
('schechterM',['MMIN','MMAX','MNORM','MSTAR','MSLOPE','MZEVOL']),\
('doubleplM',['MMIN','MMAX','MNORM','MSTAR','MSLOPE','MSLOPE2','MZEVOL'])])
familyMap={'ppl':['breaks','slopes','amp','extra'],\
'poly':['limits','coeffs','extra'],\
'bins':['poles','extra'],\
'LFsch':['schechter','extra'],\
'LFpl':['powerlaw','extra'],\
'LFdpl':['doublepl','extra'],\
'LFdpl_dpl':['dpl_dpl','extra'],\
'LFdpl_pl':['dpl_pl','extra'],\
'LFdpl_dpl_z':['dpl_dpl','evol','extra'],\
'LFevol' :['evol','llimits','extra'],\
'LFevol_dpl' :['evol','llimits','extra'],\
'LFevol_dpl_s' :['evol_SF','llimits','extra'],\
'LFevol_dpl_L':['Llimits','evol','llimits','extra'],
'LFevol_logn':['evol','llimits','extra'],\
'LFevol_logn_mat':['evol_mat','llimits','extra'],\
'LFevol_logn_el':['evol_el','llimits','extra'],\
'LFevol_logn_slope':['evol_sl','llimits','extra'],\
'LFevol_logn_sigma':['evol_sg','llimits','extra'],\
'LFevol_logn_lmin':['evol_ls','llimits','extra'],\
'LFevol_logn_lnorm':['evol_ph','llimits','extra'],\
'LFevol_logn_all':['evol','evol_all','llimits','extra'],\
'LFevol_logn_all_L':['Llimits','evol','evol_all','llimits','extra'],\
'LFevol_logn_s':['evol_SF','llimits','extra'],\
'LFevol_logn_L':['Llimits','evol','llimits','extra'],
'LFevol_phi_logn':['evol','evol_phi','llimits','extra'],\
'LFevol_phi_logn_mat':['evol_mat','evol_phi','llimits','extra'],\
'LFpl_dpl':['pl_dpl','extra'],\
'LFlognorm_dpl':['lognorm_dpl','extra'],\
'LFlognorm':['lognorm','extra'],\
'LFpl_lognorm':['pl_lognorm','extra'],\
'HIsch':['schechterM','extra'],\
'HIdpl':['doubleplM','extra']}
self.paramsStruct=\
[self.paramsAvail[p] for p in self.paramsAvail if p in familyMap[kind]]
self.parameters = list(itertools.chain(*self.paramsStruct))
self.nparams = len(self.parameters)
#print self.nparams,self.parameters
self.currentPhysParams = -99.0 * numpy.ones(self.nparams)
# Set up priors
self.priors = Priors()
self.priorsDict = self.parsePriors(self.parameters, self.floatNoise)
# Load the data and derive the bins
self.survey = surveySetup(whichSurvey, [datafiles], [SURVEY_AREA],
[SURVEY_NOISE])
if doRedshiftSlices:
# And load any multiple data sets
self.fdata = {}
self.fbins = {}
self.fnbins = {}
self.fbinsMedian = {}
for df in self.survey.datafiles[0]:
self.fdata[df], self.fbins[df] = self.loadData(df)
#print df
#print self.fdata[df]
#print self.fbins[df]
self.fnbins[df] = len(self.fbins[df]) - 1
self.fbinsMedian[df] = medianArray(self.fbins[df])
self.binsMedian = self.fbinsMedian[df]
self.bins = self.fbins[df]
self.data = self.fdata[df]
self.nbins = self.fnbins[df]
else:
print self.survey.datafile
self.data, self.bins = self.loadData(self.survey.datafile[0])
print 'different?', self.data, self.survey.datafile
self.nbins = len(self.bins) - 1
self.binsMedian = medianArray(self.bins)
self.nsrc = int(self.data.sum())
if mybins is not None:
self.bins = mybins
self.binsMedian = medianArray(self.bins)
self.nbins = len(self.bins) - 1
return
def S_N(self, z, L0, a, Dnu, t_int):
xm = medianArray(self.nu_arr)
ssg = np.array([sig_s(jj, Dnu, t_int) for jj in xm[:-1]])
#tt = np.array( [ Int_ST(xm[i],xm[i+1],a,z,L0,self.xi_z2,self.d_z2,self.Ts_z2,i) for i in range(len(xm[:-1])) ] )
return (self.nu_arr), self.S_P(z, L0, a), ssg #,tt
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