def Sim_spec(self, metal, age, tau):
import pysynphot as S
if self.delayed_tau == True:
model = '../../../fsps_models_for_fit/fsps_spec/m%s_a%s_dt%s_spec.npy' % (metal, age, tau)
else:
model = '../../../fsps_models_for_fit/fsps_spec/m%s_a%s_t%s_spec.npy' % (metal, age, tau)
wave, fl = np.load(model)
spec = S.ArraySpectrum(wave, fl, fluxunits='flam')
spec = spec.redshift(self.redshift).renorm(1., 'flam', S.ObsBandpass('wfc3,ir,f105w'))
spec.convert('flam')
## Compute the models
self.beam.compute_model(spectrum_1d=[spec.wave, spec.flux])
## Extractions the model (error array here is meaningless)
w, f, e = self.beam.beam.optimal_extract(self.beam.model, bin=0)
ifl = interp1d(w, f)(self.gal_wv)
## Get sensitivity function
fwv, ffl = [self.beam.beam.lam, self.beam.beam.sensitivity / np.max(self.beam.beam.sensitivity)]
filt = interp1d(fwv, ffl)(self.gal_wv)
adj_ifl = self.atten * ifl /filt
C = Scale_model(self.gal_fl, self.gal_er, adj_ifl)
self.fl = C * adj_ifl
def Full_fit(spec, Gmfl, Pmfl):
Gchi = 0
for i in range(len(wvs2)):
scale = Scale_model(flxs2[i], errs2[i], Gmfl[i])
Gchi = Gchi + np.sum(((((flxs2[i] / scale) - Gmfl[i]) / (errs2[i] / scale))**2))
Pchi = np.sum((((spec.SPflx - Pmfl) / spec.SPerr)**2))
return Gchi, Pchi
ew=np.zeros(len(age))
ewgr=np.zeros(len(age))
for i in range(len(age)):
wv, fl = np.array(Readfile('../../../fsps_models_for_fit/fsps_spec/m0.015_a%s_t8.72_spec.dat' % age[i], 1))
ew[i]= D4000(wv,fl)
wvgr,flgr,ergr= np.array(Readfile('../../../fsps_models_for_fit/models/m0.015_a%s_t8.72_z1.5_model.dat' % age[i], 1))
wvgr/=2.5
ewgr[i]=D4000(wvgr,flgr)
M=D4000(swv,sfl)
print M
print wvgr[0],wvgr[-1]
print swv[0],swv[-1]
C1=Scale_model(sfl,serr,interp1d(wv,fl)(swv))
# C2=Scale_model(sfl,serr,interp1d(wvgr,flgr)(swv))
plt.plot(swv,sfl)
plt.plot(wv,fl*C1)
# plt.plot(wvgr,flgr*C2)
plt.xlim(3000,5500)
plt.show()
# plt.plot(age,ew,label='Normal resolution')
# plt.plot(age,ewgr,label='Grism resolution')
# plt.hlines(M,min(age),max(age))
# plt.ylabel("D4000",size=20)
# plt.xlabel('Age (Gyrs)',size=20)
# plt.tick_params(axis='both', which='major', labelsize=17)
# plt.gcf().subplots_adjust(bottom=0.16)
# plt.legend(loc=2,fontsize=12)
def Full_scale(spec, Pmfl):
return Scale_model(spec.SPflx, spec.SPerr, Pmfl)
def Best_fit_scale(wv, fl, er, mfl, p1):
cal = Calibrate_grism([wv, fl, er], mfl, p1)
scale = Scale_model(fl / cal, er / cal, mfl)
FL = fl / cal / scale
ER = er / cal / scale
return FL, ER
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)
C = Scale_model(fl, er, Mfl)
plt.plot(w, fl, label='%s' % zsmax[IDS])
plt.plot(w, C * Mfl)
plt.axvspan(3910, 3979, alpha=.2)
plt.axvspan(3981, 4030, alpha=.2)
plt.axvspan(4082, 4122, alpha=.2)
plt.axvspan(4250, 4400, alpha=.2)
plt.axvspan(4830, 4930, alpha=.2)
plt.axvspan(4990, 5030, alpha=.2)
plt.axvspan(5109, 5250, alpha=.2)
plt.title('%s' % speclist[IDS][18:24])
plt.xlim(3600, 5500)
plt.legend()
# plt.show()
plt.savefig('../research_plots/zsmax.png')