def Stack_spec_normwmean(spec,redshifts, wv):
flgrid=np.zeros([len(spec),len(wv)])
errgrid=np.zeros([len(spec),len(wv)])
for i in range(len(spec)):
wave, flux, error = np.array(Readfile(spec[i], 1))
wave /= (1 + redshifts[i])
mask = np.array([wave[0] < U < wave[-1] for U in wv])
ifl=interp1d(wave,flux)
ier=interp1d(wave,error)
reg = np.arange(4000, 4210, 1)
Cr = np.trapz(ifl(reg), reg)
flgrid[i][mask] = ifl(wv[mask]) / Cr
errgrid[i][mask] = ier(wv[mask]) / Cr
################
flgrid=np.transpose(flgrid)
errgrid=np.transpose(errgrid)
weigrid=errgrid**(-2)
infmask=np.isinf(weigrid)
weigrid[infmask]=0
################
stack,err=np.zeros([2,len(wv)])
for i in range(len(wv)):
stack[i]=np.sum(flgrid[i]*weigrid[[i]])/np.sum(weigrid[i])
err[i]=1/np.sqrt(np.sum(weigrid[i]))
################
###take out nans
IDX=[U for U in range(len(wv)) if stack[U] > 0]
return wv[IDX], stack[IDX], err[IDX]
def Analyze_JWST_LH(
chifits,
specz,
metal,
age,
tau,
age_conv='/fdata/scratch/vestrada78840/data/tau_scale_nirspec.dat'):
####### Get maximum age
max_age = Oldest_galaxy(specz)
####### Read in file
chi = np.load(chifits).T
chi[:, len(age[age <= max_age]):, :] = 1E5
####### Get scaling factor for tau reshaping
ultau = np.append(0, np.power(10, np.array(tau)[1:] - 9))
scale = Readfile(age_conv)
overhead = np.zeros(len(scale)).astype(int)
for i in range(len(scale)):
amt = []
for ii in range(len(age)):
if age[ii] > scale[i][-1]:
amt.append(1)
overhead[i] = sum(amt)
######## Reshape likelihood to get average age instead of age when marginalized
newchi = np.zeros(chi.shape)
for i in range(len(chi)):
if i == 0:
newchi[i] = chi[i]
else:
frame = interp2d(metal, scale[i], chi[i])(metal,
age[:-overhead[i]])
newchi[i] = np.append(frame,
np.repeat([np.repeat(1E5, len(metal))],
overhead[i],
axis=0),
axis=0)
####### Create normalize probablity marginalized over tau
P = np.exp(-newchi.T.astype(np.float128) / 2)
prob = np.trapz(P, ultau, axis=2)
C = np.trapz(np.trapz(prob, age, axis=1), metal)
prob /= C
#### Get Z and t posteriors
PZ = np.trapz(prob, age, axis=1)
Pt = np.trapz(prob.T, metal, axis=1)
return prob.T, PZ, Pt
infmask=np.isinf(weigrid)
weigrid[infmask]=0
################
stack,err=np.zeros([2,len(wv)])
for i in range(len(wv)):
stack[i]=np.sum(flgrid[i]*weigrid[[i]])/np.sum(weigrid[i])
err[i]=1/np.sqrt(np.sum(weigrid[i]))
################
###take out nans
IDX=[U for U in range(len(wv)) if stack[U] > 0]
return wv[IDX], stack[IDX], err[IDX]
ids,lmass,rshift=np.array(Readfile('masslist_sep28.dat',1,is_float=False))
lmass,rshift=np.array([lmass,rshift]).astype(float)
nlist=glob('spec_stacks/*')
IDS=[]
for i in range(len(ids)):
if 10.87<lmass[i] and 1<rshift[i]<1.75:
IDS.append(i)
speclist=[]
for i in range(len(ids[IDS])):
for ii in range(len(nlist)):
if ids[IDS][i]==nlist[ii][12:18]:
speclist.append(nlist[ii])
stack, err = np.zeros([2, len(wv)])
for i in range(len(wv)):
stack[i] = np.sum(flgrid[i] * weigrid[[i]]) / np.sum(weigrid[i])
err[i] = 1 / np.sqrt(np.sum(weigrid[i]))
################
###take out nans
# IDX = [U for U in range(len(wv)) if stack[U] > 0]
return wv, stack, err
"""get galaxies"""
###get list of spectra
ids, speclist, lmass, rshift = np.array(
Readfile('masslist_dec8.dat', 1, is_float=False))
lmass, rshift = np.array([lmass, rshift]).astype(float)
IDS = []
for i in range(len(ids)):
if 1 <= rshift[i] <= 1.75:
IDS.append(i)
metal = np.array([
0.0020, 0.0025, 0.0031, 0.0039, 0.0049, 0.0061, 0.0068, 0.0077, 0.0085,
0.0096, 0.0106, 0.012, 0.0132, 0.014, 0.0150, 0.0164, 0.018, 0.019, 0.021,
0.024, 0.027, 0.03
])
age = [
0.5, 0.57, 0.65, 0.74, 0.84, 0.96, 1.1, 1.25, 1.42, 1.62, 1.85, 2.11, 2.4,
def Mag(band):
magnitude=25-2.5*np.log10(band)
return magnitude
cat_data=Table.read('../../../Clear_data/goodsn_3dhst.v4.3.cat', format='ascii')
IDS=np.array(cat_data.field('id'))
ra=np.array(cat_data.field('ra'))
dec=np.array(cat_data.field('dec'))
J=np.array(cat_data.field('f_F125W'))
star=np.array(cat_data.field('class_star'))
fast_data=fits.open('../../../Desktop/catalogs_for_CLEAR/goodsn_3dhst.v4.1.cats/Fast/goodsn_3dhst.v4.1.fout.FITS')[1].data
lmass=np.array(fast_data.field('lmass'))
RF_dat=Readfile('../../../Desktop/catalogs_for_CLEAR/goodsn_3dhst.v4.1.cats/RF_colors/goodsn_3dhst.v4.1.master.RF',27)
ids=np.array(RF_dat[0])
z=np.array(RF_dat[1])
u=np.array(RF_dat[3])
v=np.array(RF_dat[7])
j=np.array(RF_dat[9])
######Get colors
uv=Mag(u)-Mag(v)
vj=Mag(v)-Mag(j)
print len(IDS)-len(ids)
INDEX1=[]
import numpy as np
from vtl.Readfile import Readfile
from scipy.interpolate import interp1d
from glob import glob
import matplotlib.pyplot as plt
import seaborn as sea
from spec_id import Scale_model
sea.set(style='white')
sea.set(style='ticks')
sea.set_style({"xtick.direction": "in", "ytick.direction": "in"})
"""read in data"""
###read in data
speclist,zsmax,zschi,zs,zsl,zsh,zpsmax,zpschi,\
zps,zpsl,zpsh=np.array(Readfile('stack_redshifts_10-6.dat',is_float=False))
zsmax, zschi, zs, zsl, zsh, zpsmax, zpschi, zps, zpsl, zpsh = np.array(
[zsmax, zschi, zs, zsl, zsh, zpsmax, zpschi, zps, zpsl,
zpsh]).astype(float)
# print speclist[0][18:24]
### set index and file path to models
IDS = 45
fp = '/Users/Vince.ec/fsps_models_for_fit/fsps_spec/'
print speclist[IDS][18:24]
### read data and models
mwv, mfl = np.array(Readfile(fp + 'm0.015_a1.5_t8.0_spec.dat'))
wv, fl, er = np.array(Readfile(speclist[IDS]))
"""plotting"""
### zps
w = wv / (1 + zsmax[IDS])
Mfl = interp1d(mwv, mfl)(w)
def MC_fit(galaxy,
metal,
age,
tau,
sim_m,
sim_a,
sim_t,
specz,
name,
repeats=1000,
delayed_tau=True,
age_conv='/fdata/scratch/vestrada78840/data/tau_scale_ntau.dat'):
mlist = np.zeros(repeats)
alist = np.zeros(repeats)
ultau = np.append(0, np.power(10, np.array(tau[1:]) - 9))
spec = Gen_sim(galaxy, specz, sim_m, sim_a, sim_t, delayed_tau=delayed_tau)
###############Get model list
mfl = np.zeros([len(metal) * len(age) * len(tau), len(spec.gal_wv_rf)])
for i in range(len(metal)):
for ii in range(len(age)):
for iii in range(len(tau)):
spec.Sim_spec(metal[i], age[ii], tau[iii])
mfl[i * len(age) * len(tau) + ii * len(tau) + iii] = spec.mfl
convtau = np.array([
0, 8.0, 8.3, 8.48, 8.6, 8.7, 8.78, 8.85, 8.9, 8.95, 9.0, 9.04, 9.08,
9.11, 9.15, 9.18, 9.2, 9.23, 9.26, 9.28, 9.3, 9.32, 9.34, 9.36, 9.38,
9.4, 9.41, 9.43, 9.45, 9.46, 9.48
])
convage = np.arange(.5, 6.1, .1)
mt = [U for U in range(len(convtau)) if convtau[U] in tau]
ma = [
U for U in range(len(convage))
if np.round(convage[U], 1) in np.round(age, 1)
]
convtable = Readfile(age_conv)
scale = convtable[mt[0]:mt[-1] + 1, ma[0]:ma[-1] + 1]
overhead = np.zeros(len(scale)).astype(int)
for i in range(len(scale)):
amt = []
for ii in range(len(age)):
if age[ii] > scale[i][-1]:
amt.append(1)
overhead[i] = sum(amt)
for xx in range(repeats):
spec.Perturb_flux()
chi = np.sum(((spec.flx_err - mfl) / spec.gal_er)**2,
axis=1).reshape([len(metal),
len(age),
len(tau)]).astype(np.float128).T
######## Reshape likelihood to get average age instead of age when marginalized
newchi = np.zeros(chi.shape)
for i in range(len(chi)):
if i == 0:
newchi[i] = chi[i]
else:
frame = interp2d(metal, scale[i], chi[i])(metal,
age[:-overhead[i]])
newchi[i] = np.append(frame,
np.repeat([np.repeat(1E5, len(metal))],
overhead[i],
axis=0),
axis=0)
####### Create normalize probablity marginalized over tau
prob = np.exp(-newchi.T.astype(np.float128) / 2)
P = np.trapz(prob, ultau, axis=2)
C = np.trapz(np.trapz(P, age, axis=1), metal)
#### Get Z and t posteriors
PZ = np.trapz(P / C, age, axis=1)
Pt = np.trapz(P.T / C, metal, axis=1)
mlist[xx], ml, mh = Median_w_Error_cont(PZ, metal)
alist[xx], ml, mh = Median_w_Error_cont(Pt, age)
np.save('/home/vestrada78840/mcerr/' + name, [mlist, alist])
return
def Analyze_LH_cont_feat(
contfits,
featfits,
specz,
metal,
age,
tau,
age_conv='/fdata/scratch/vestrada78840/data/tau_scale_ntau.dat'):
####### Get maximum age
max_age = Oldest_galaxy(specz)
####### Read in file
Cchi = np.load(contfits).T
Fchi = np.load(featfits).T
Fchi[:, len(age[age <= max_age]):, :] = 1E5
Cchi[:, len(age[age <= max_age]):, :] = 1E5
####### Get scaling factor for tau reshaping
ultau = np.append(0, np.power(10, np.array(tau)[1:] - 9))
convtau = np.array([
0, 8.0, 8.3, 8.48, 8.6, 8.7, 8.78, 8.85, 8.9, 8.95, 9.0, 9.04, 9.08,
9.11, 9.15, 9.18, 9.2, 9.23, 9.26, 9.28, 9.3, 9.32, 9.34, 9.36, 9.38,
9.4, 9.41, 9.43, 9.45, 9.46, 9.48
])
convage = np.arange(.5, 6.1, .1)
mt = [U for U in range(len(convtau)) if convtau[U] in tau]
ma = [
U for U in range(len(convage))
if np.round(convage[U], 1) in np.round(age, 1)
]
convtable = Readfile(age_conv)
scale = convtable[mt[0]:mt[-1] + 1, ma[0]:ma[-1] + 1]
overhead = np.zeros(len(scale)).astype(int)
for i in range(len(scale)):
amt = []
for ii in range(len(age)):
if age[ii] > scale[i][-1]:
amt.append(1)
overhead[i] = sum(amt)
######## Reshape likelihood to get average age instead of age when marginalized
newCchi = np.zeros(Cchi.shape)
newFchi = np.zeros(Fchi.shape)
for i in range(len(Cchi)):
if i == 0:
newCchi[i] = Cchi[i]
newFchi[i] = Fchi[i]
else:
cframe = interp2d(metal, scale[i], Cchi[i])(metal,
age[:-overhead[i]])
newCchi[i] = np.append(cframe,
np.repeat([np.repeat(1E5, len(metal))],
overhead[i],
axis=0),
axis=0)
fframe = interp2d(metal, scale[i], Fchi[i])(metal,
age[:-overhead[i]])
newFchi[i] = np.append(fframe,
np.repeat([np.repeat(1E5, len(metal))],
overhead[i],
axis=0),
axis=0)
####### Create normalize probablity marginalized over tau
cprob = np.exp(-newCchi.T.astype(np.float128) / 2)
Pc = np.trapz(cprob, ultau, axis=2)
Cc = np.trapz(np.trapz(Pc, age, axis=1), metal)
fprob = np.exp(-newFchi.T.astype(np.float128) / 2)
Pf = np.trapz(fprob, ultau, axis=2)
Cf = np.trapz(np.trapz(Pf, age, axis=1), metal)
comb_prob = cprob / Cc * fprob / Cf
prob = np.trapz(comb_prob, ultau, axis=2)
C0 = np.trapz(np.trapz(prob, age, axis=1), metal)
prob /= C0
#### Get Z and t posteriors
PZ = np.trapz(prob, age, axis=1)
Pt = np.trapz(prob.T, metal, axis=1)
return prob.T, PZ, Pt
age = [0.5, 0.57, 0.65, 0.74, 0.84, 0.96, 1.1, 1.25, 1.42,
1.62, 1.85, 2.11, 2.4, 2.74, 3.12, 3.56, 4.05, 4.62, 5.26, 6.0]
modeldat=[]
filepath = '../../../fsps_models_for_fit/models/'
for i in range(len(age)):
modeldat.append(filepath + 'm0.019_a%s_z1.2_model.dat' % age[i])
d4=np.zeros(len(modeldat))
d4nc=np.zeros(len(modeldat))
for i in range(len(modeldat)):
w,f,e=np.array(Readfile(modeldat[i],1))
w/=2.2
d4[i]=D4000(w,f)
# plt.plot(w, f)
mr = [[3910, 3990], [4082, 4122], [4250, 4330], [4830, 4890], [4990, 5030]]
Mask = np.zeros(len(w))
for ii in range(len(mr)):
for iii in range(len(w)):
if mr[ii][0] <= w[iii] <= mr[ii][1]:
Mask[iii] = 1
maskw = np.ma.masked_array(w, Mask)
x3, x2, x1, x0 = np.ma.polyfit(maskw, f, 3, w=1 / e ** 2)
C0 = x3 * w ** 3 + x2 * w ** 2 + x1 * w + x0