def disp(fitres, idsurv, cut=3):
headn = linef.linef(fitres, 'zCMB')
#ids,mures = np.loadtxt(fitres, usecols=(3,41), unpack=True, dtype='string', skiprows=45)
data1 = np.genfromtxt(fitres, skip_header=headn, names=True, comments='#')
zCMB = data1['zCMB'].astype(float)
MU = data1['MU'].astype(float)
MU_MODEL = data1['MUMODEL'].astype(float)
mures = MU - MU_MODEL
mures = mures.astype(float)
#print mures
#if ((idsurv=='15')|(idsurv=='1')|(idsurv=='4')):
# xx=(ids==idsurv)
#if ((idsurv!='15')&(idsurv!='1')&(idsurv!='4')):
# xx=((ids!='15')&(ids!='1')&(ids!='4'))
if (idsurv == 99):
xx = (np.absolute(mures) > (-1000))
#print xx
#print mures[xx]
mures = mures[zCMB < cut]
#print idsurv, 1.48*np.median(np.absolute(mures[xx])), len(mures[xx])
#return 1.48*np.median(np.absolute(mures[xx]))
return np.sqrt(np.mean(np.square(mures)))
rede1 = []
rede2 = []
for num in range(0, len(red2) - 1):
red = np.append(red, (red2[num] + red2[num + 1]) / 2.0)
rede1 = np.append(
rede1, np.absolute((red2[num] + red2[num + 1]) / 2.0 - red2[num]))
rede2 = np.append(
rede2, np.absolute((red2[num] + red2[num + 1]) / 2.0 - red2[num + 1]))
#xerr=[rede1,rede2]
print len(rede1)
print rede1
print rede2
print red
#stop
num = linef.linef('../DATA/SALT2mu/SALT2mu_fitoptg0bin.M0DIF', 'VARNAME')
z1, mures, murese = np.loadtxt('../DATA/SALT2mu/SALT2mu_fitoptg0bin.M0DIF',
usecols=(4, 5, 6),
unpack=True,
dtype='string',
skiprows=num + 1)
z1 = z1.astype(float)
mures = mures.astype(float)
murese = murese.astype(float)
muresebin = murese
print z1, red
#stop
red = z1
for x in range(0, 10):
for xl in range(0, 2):
singx = sing1
for ii in range(0, 2):
betas = 3.1
#print '../SIMS//PS1_GRIDu_sys/DS_smear'+scat[xl]+'_PS1_GRIDs-0'+str(i)+'/FITOPT000.FITRES'
if (xl > 0):
sing1 = sing2
betas = 3.8
singx = sing2
#a = plt.axes([0.0, -.03, .08, .03])
#simalpha,simbeta,z,simx11,simc1,simmb,mb,x11,c1,simdlmag,pke,fitprob,mbe,x11e,c1e = np.loadtxt(sing1[ii]+'/FITOPT000.FITRES', usecols=(42,43,6,40,41,45,23,19,21,38,18,32,24,20,22), unpack=True, dtype='string', skiprows=12)
if (os.path.isfile(singx[ii] + '/FITOPT000.FITRES') == False):
os.system('gunzip ' + singx[ii] + '/FITOPT000.FITRES.gz')
headn = linef.linef(singx[ii] + '/FITOPT000.FITRES', 'zCMB')
data1 = np.genfromtxt(singx[ii] + '/FITOPT000.FITRES',
skip_header=headn,
names=True,
comments='#')
cid = np.genfromtxt(singx[ii] + '/FITOPT000.FITRES',
skip_header=headn,
usecols=(1),
comments='#',
dtype='str')[1:]
z = data1['zCMB'].astype(float)
SNRMAX11 = data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
c1 = data1['c'].astype(float)
NDOF1 = data1['NDOF'].astype(float)
fitprob = data1['FITPROB'].astype(float)
plotsetup.halfpaperfig()
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
gs1 = gridspec.GridSpec(2, 1)
gs1.update(hspace=0.04,bottom=0.13,top=0.92)
ax2= plt.subplot(gs1[1])
ax1= plt.subplot(gs1[0])
ax=[ax1,ax2]
pos=[]
for i in range(1,2999):
pos.append(i/1000.0)
headn=linef.linef('../DATA/SALT2mu/SALT2mu_fitoptg0.fitres','zCMB')
data1=np.genfromtxt('../DATA/SALT2mu/SALT2mu_fitoptg0.fitres',skip_header=headn,names=True,comments='#')
cid=np.genfromtxt('../DATA/SALT2mu/SALT2mu_fitoptg0.fitres',skip_header=headn,usecols=(1),comments='#',dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
mu1=data1['MU'].astype(float)
mu1e = data1['MUERR'].astype(float)
idsurvey=data1['IDSURVEY'].astype(float)
x=cosmo.luminosity_distance(pos).value
y=5.0*(np.log10(x))+25.0
return [(x[3]), x[5]]
def prec(x):
return '(' + "%.3f" % float(x[0]) + '\pm' + "%.3f" % float(x[1]) + ')'
cosmo = FlatLambdaCDM(H0=70, Om0=0.3)
#SALT2mu/SALT2mu_fitoptgb0.fitres
# -0.081421771 0.081707301 0.031227249 0.054284053
# -0.081480861 0.076136832 0.025882917 0.045994964
#../DATA/SALT2mu/SALT2mu_fitoptgm0.fitres
#list1, idsurvey,z1, mass1,x11,c1,mu1, mu1e = np.loadtxt('../DATA/SALT2mu_fitopt.fitres', usecols=(0,3, 7,13,18,20,37,39), unpack=True, dtype='string', skiprows=12)
headn = linef.linef(
'../DATA/SALT2mu_SNLS+SDSS+LOWZ+PS1_Scolnic2+HST/DS17/SALT2mu_FITOPT000_MUOPT000.FITRES',
'zCMB')
data1 = np.genfromtxt(
'../DATA/SALT2mu_SNLS+SDSS+LOWZ+PS1_Scolnic2+HST/DS17/SALT2mu_FITOPT000_MUOPT000.FITRES',
skip_header=headn,
names=True,
comments='#')
list1 = np.genfromtxt(
'../DATA/SALT2mu_SNLS+SDSS+LOWZ+PS1_Scolnic2+HST/DS17/SALT2mu_FITOPT000_MUOPT000.FITRES',
skip_header=headn,
usecols=(1),
comments='#',
dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
SNRMAX11 = data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
gs1.update(bottom=0.5, top=0.95, hspace=0.0)
ax1 = plt.subplot(gs1[0])
ax2 = plt.subplot(gs1[1])
gs1 = gridspec.GridSpec(1, 1)
gs1.update(bottom=0.1, top=0.36, hspace=0.0)
ax3 = plt.subplot(gs1[0])
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('wc0a.fitres', delimiter=' ', usecols=(0,3, 6,17,19,35,37), unpack=True, dtype='string', skiprows=12)
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('wc0a.fitres', delimiter=' ', usecols=(0,3, 6,16,17,31,33), unpack=True, dtype='string', skiprows=12)
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('wc0a.fitres', usecols=(0,3, 7,17,19,34,35), unpack=True, dtype='string', skiprows=12)
#list1, idsurvey,z1, mass1,x11,c1,mu1, mu1e,mures1 = np.loadtxt('../DATA/SALT2mu_fitopt.fitres', usecols=(0,3, 7,13,20,22,37,39,41), unpack=True, dtype='string', skiprows=12)
name1 = '../DATA/SALT2mu/SALT2mu_fitoptg0.fitres'
name1 = '../DATA/SALT2mu_SNLS+SDSS+LOWZ+PS1_Scolnic2+HST/DS17/SALT2mu_FITOPT000_MUOPT000.FITRES'
headn = linef.linef(name1, 'zCMB')
header1 = headn
data1 = np.genfromtxt(name1, skip_header=header1, names=True, comments='#')
cid1 = np.genfromtxt(name1,
skip_header=header1,
usecols=(1),
comments='#',
dtype='str')[1:]
z1 = data1['zHD'].astype(float)
SNRMAX11 = data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
c1 = data1['c'].astype(float)
mb1 = data1['mB'].astype(float)
NDOF1 = data1['NDOF'].astype(float)
#TGAPMAX2=data1['TGAPMAX'].astype(float)
FITPROB1 = data1['FITPROB'].astype(float)
]
#names1[1]='/project/rkessler/djones/NN_forDan/empiricalsim/biascor/SIMGEN_LOWZ/SIMGEN_LOWZ/SIMGEN_LOWZ_G10/FITOPT000.FITRES'
#/project/rkessler/djones/NN_forDan/empiricalsim/biascor/SIMGEN_LOWZ/SIMGEN_LOWZ/SIMGEN_LOWZ_G10/FITOPT000.FITRES
files = [
'ps1', 'lowz', 'sdss', 'snls', 'hst', 'csp', 'cfa1', 'cfa2', 'cfa3',
'cfa3k', 'cfa41', 'cfa42'
]
header1d = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
header1s = [5, 6, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6]
zm1 = [0, 0, 0.05, 0.1, 0.8, 0, 0, 0, 0, 0, 0, 0]
zm2 = [0.69, 0.065, 0.49, 0.99, 2.29, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09, 0.09]
zt = [0.2, 0.02, 0.1, 0.3, 0.5, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02, 0.02]
for ii in range(0, 2):
print named[0]
if (ii != 40):
headn = linef.linef(named[0], 'zCMB')
print headn
#stop
data1 = np.genfromtxt(named[0],
skip_header=headn,
names=True,
comments='#')
cid = np.genfromtxt(named[0],
skip_header=headn,
usecols=(1),
comments='#',
dtype='str')[1:]
#if (ii==4):
# print 'ok'
# data1=np.genfromtxt('/project/rkessler/dscolnic/HST_analysis/CANDELS/CANDELs/FITOPT000.FITRES',skip_header=10,names=True,comments='#')
# cid=np.genfromtxt('/project/rkessler/dscolnic/HST_analysis/CANDELS/CANDELs/FITOPT000.FITRES',skip_header=10,usecols=(1),comments='#',dtype='str')[1:]
for i in range(0, 4):
if (i == 0):
name1 = '../SIMS/PS1_SINGLEu2/DS_smearG10_PS1_GRIDu_single/FITOPT000.FITRES'
betas = 3.1
if (i == 1):
name1 = '../SIMS/PS1_SINGLEu2/DS_smearC11_PS1_GRIDu_single/FITOPT000.FITRES'
betas = 3.8
if (i == 2):
name1 = '../SIMS/PS1_SINGLEu2/DS_smearG10_PS1_GRIDu_sysing/FITOPT000.FITRES'
betas = 3.1
if (i == 3):
name1 = '../SIMS/PS1_SINGLEu2/DS_smearC11_PS1_GRIDu_sysing/FITOPT000.FITRES'
betas = 3.8
header1 = 5
header1 = linef.linef(name1, 'zCMB')
data1 = np.genfromtxt(name1, skip_header=header1, names=True, comments='#')
cid1 = np.genfromtxt(name1,
skip_header=header1,
usecols=(1),
comments='#',
dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
SNRMAX11 = data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
c1 = data1['c'].astype(float)
mb1 = data1['mB'].astype(float)
NDOF1 = data1['NDOF'].astype(float)
#TGAPMAX2=data1['TGAPMAX'].astype(float)
FITPROB1 = data1['FITPROB'].astype(float)
PKMJD1 = data1['PKMJD'].astype(float)
from astropy import cosmology as cosmo
import wmom
import matplotlib
import plotsetup
from matplotlib import gridspec
import matplotlib.ticker as ticker
import linef
plotsetup.halfpaperfig()
#list1, idsurvey,z1, mass1,x11,c1,mu1, mu1e = np.loadtxt('wc0a.fitres', usecols=(0,3, 6,10,17,19,34,35), unpack=True, dtype='string', skiprows=12)
#VARNAMES: CID CIDint IDSURVEY TYPE FIELD CUTFLAG_SNANA zCMB zCMBERR zHD zHDERR VPEC VPEC_ERR HOST_LOGMASS HOST_LOGMASS_ERR SNRMAX1 SNRMAX2 SNRMAX3 PKMJD PKMJDERR x1 x1ERR c cERR mB mBERR x0 x0ERR \
# COV_x1_c COV_x1_x0 COV_c_x0 NDOF FITCHI2 FITPROB RA DECL TGAPMAX MU MUMODEL MUERR MUERR_RAW MURES MUPULL ERRCODE biasCor_mu biasCorErr_mu biasCor_mB biasCor_x1 biasCor_c biasScale_muCOV IDSAM#PLE
name1 = '../DATA/SALT2mu/SALT2mu_fitoptg0.fitres'
header1 = linef.linef(name1, 'zCMB')
data1 = np.genfromtxt(name1, skip_header=header1, names=True, comments='#')
cid1 = np.genfromtxt(name1,
skip_header=header1,
usecols=(1),
comments='#',
dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
SNRMAX11 = data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
c1 = data1['c'].astype(float)
mb1 = data1['mB'].astype(float)
NDOF1 = data1['NDOF'].astype(float)
#TGAPMAX2=data1['TGAPMAX'].astype(float)
FITPROB1 = data1['FITPROB'].astype(float)
PKMJD1 = data1['PKMJD'].astype(float)
import linef
from matplotlib import gridspec
import matplotlib.ticker as ticker
plotsetup.halfpaperfig()
xcid, xred, xtype, xfield,xmjd = np.loadtxt('PS1_Noah2.txt', delimiter=' ', usecols=(0, 1,2,3,4), unpack=True, dtype='string', skiprows=2)
print 'mjd', xred
#stop
#xred=xred[0]
xmjd = xmjd.astype(int)
xred = xred.astype(float)
print 'xr', xred
#stop
#list1, idsurvey, z1,x11,c1,mb1,mu1, mu1e = np.loadtxt('PS1_Scolnic/NewDan100f/FITOPT000+SALT2mu.FITRES', usecols=(1, 2,6,17,19,21,36,37), unpack=True, dtype='string', skiprows=15)
headn=linef.linef("../DATA/sntable_dump_SNANA.fitres",'z')
data1=np.genfromtxt("../DATA/sntable_dump_SNANA.fitres",skip_header=headn,names=True,comments='#')
cid=np.genfromtxt("../DATA/sntable_dump_SNANA.fitres",skip_header=headn,usecols=(1),comments='#',dtype='str')[1:]
z1 = data1['z'].astype(float)
#SNRMAX11=data1['SNRMAX1'].astype(float)
#x11 = data1['x1'].astype(float)
#c1 = data1['c'].astype(float)
#NDOF1=data1['NDOF'].astype(float)
#TGAPMAX1=data1['TGAPMAX'].astype(float)
#FITPROB=data1['FITPROB'].astype(float)
PKMJD=data1['PKMJDINI'].astype(float)
RA=data1['RA'].astype(float)
DECL=data1['DECL'].astype(float)
idsurvey=data1['IDSURVEY'].astype(float)
headn=linef.linef("../DATA/SALT2mu/SALT2mu_fitoptg0.fitres",'zCMB')
data1=np.genfromtxt("../DATA/SALT2mu/SALT2mu_fitoptg0.fitres",skip_header=headn,names=True,comments='#')
#if (num<0): return r"$\bar{\mathrm{c}}=\textrm{--}$"+"{:10.2f}".format(np.absolute(num))
if (num<0): return r"$\bar{\mathrm{c}}=\textrm{--}$"+"{:10.2f}".format(np.absolute(num))
def fformx(num):
if (num>0): return r"$\bar{\mathrm{x_1}}=$"+"{:10.2f}".format(num)
if (num<0): return r"$\bar{\mathrm{x_1}}=\textrm{--}$"+"{:10.2f}".format(np.absolute(num))
#candels_hubble_62a.fitres
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('candels_hubble_0a.fitres', delimiter=' ', usecols=(0, 2,5,16,18,31,33), unpack=True, dtype='string', skiprows=2)
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('candels_hubble_0a.fitres', delimiter=' ', usecols=(0,3, 6,17,19,35,37), unpack=True, dtype='string', skiprows=2)
#list1, idsurvey, z1,x11,c1,mu1, mu1e = np.loadtxt('wc0a.fitres', delimiter=' ', usecols=(0,3, 6,17,19,31,33), unpack=True, dtype='string', skiprows=12)
list1, idsurvey, z1,mass,x11,c1,mb1,mu1, mu1e,mures = np.loadtxt('../DATA/SALT2mu/SALT2mu_fitoptg0.fitres', usecols=(1,3, 7,13,20,22,24,37,39,41), unpack=True, dtype='string', skiprows=15)
name1='../DATA/SALT2mu/SALT2mu_fitoptg0.fitres'
header1=linef.linef('../DATA/SALT2mu/SALT2mu_fitoptg0.fitres','zCMB')
data1=np.genfromtxt(name1,skip_header=header1,names=True,comments='#')
cid1=np.genfromtxt(name1,skip_header=header1,usecols=(1),comments='#',dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
SNRMAX11=data1['SNRMAX1'].astype(float)
x11 = data1['x1'].astype(float)
c1 = data1['c'].astype(float)
mb1= data1['mB'].astype(float)
NDOF1=data1['NDOF'].astype(float)
#TGAPMAX2=data1['TGAPMAX'].astype(float)
FITPROB1=data1['FITPROB'].astype(float)
PKMJD1=data1['PKMJD'].astype(float)
#RA2=data1['RA'].astype(float)
#DEC2=data1['DECL'].astype(float)
idsurvey1=data1['IDSURVEY'].astype(float)
NDOF1=data1['NDOF'].astype(float)
import plotsetup
from matplotlib import gridspec
import matplotlib.ticker as ticker
plotsetup.halfpaperfig()
#malmquist_tree12z.txt
#Restetal.fitres
#SALT2mu_PS1.M0DIF
#SALT2mu_PS1_conv.M0DIF
#SALT2mu_Rest_pre.M0DIF
#SALT2mu/SALT2mu_fitoptgs_ps1
#SALT2mu/SALT2mu_fitoptgos_ps1
headn = linef.linef('../DATA/SALT2mu/SALT2mu_fitoptgos_ps1.M0DIF', 'VARN')
z1, muerr1, mures1 = np.loadtxt('../DATA/SALT2mu/SALT2mu_fitoptgos2_ps1.M0DIF',
usecols=(4, 6, 5),
unpack=True,
dtype='string',
skiprows=headn + 1)
z1b, muerr1b, mures1b = np.loadtxt(
'../DATA/SALT2mu/SALT2mu_fitoptgs2_ps1.M0DIF',
usecols=(4, 6, 5),
unpack=True,
dtype='string',
skiprows=headn + 1)
headn = linef.linef('SALT2mu/SALT2mu_Rest_pre.M0DIF', 'VARN')
z2, muerr2, mures2 = np.loadtxt('SALT2mu/SALT2mu_Rest_pre.M0DIF',
usecols=(2, 4, 3),
z=[]
err=[]
err2=[]
cntr=[]
offset=[]
offsetp=[]
cntrlines=open('allcntr.dat','r').readlines()
for x in cntrlines:
cntr=np.append(cntr,x[68:74])
offset=np.append(offset,float(x[131:137]))
file1='../DATA/DS17_PS1_Scolnic2/PS1_Spec_DS17/FITOPT000.FITRES'
if (os.path.isfile(file1)==False): os.system('gunzip '+file1+'.gz')
#name1,z1,SNRMAX11,x11,c1 ,NDOF1 ,TGAPMAX1 = np.loadtxt('../DATA/DS16_PS1_Scolnic2/NewDan101f_DS16/FITOPT000.FITRES', usecols=(1,7,12,17,19,28,33), unpack=True, dtype='string', skiprows=16)
import linef
headn=linef.linef(file1,'zCMB')
data1=np.genfromtxt(file1,skip_header=headn,names=True,comments='#')
name1=np.genfromtxt(file1,skip_header=headn,usecols=(1),comments='#',dtype='str')[1:]
z1 = data1['zCMB'].astype(float)
z=[]
err2=[]
print 'cntr', cntr
for i in range(0,len(name1)):
xxc=(name1[i]==cntr)
print 'xxc', xxc
if (len(cntr[xxc])>0):
z=np.append(z,float(z1[i]))
offsetp=np.append(offsetp,float(offset[xxc][0]))
print 'z', len(z)