times = []
vs = []
verrs = []
for spectra in data.data:
dat = np.random.normal(spectra.flux, spectra.ferr)
vel = doppler.CKMS*(spectra.wave-model.data[0].wave[0])/model.data[0].wave[0]-model.data[0].gamma[0]
wcen = model.data[0].wave
gam = model.data[0].gamma
for f,e,t,w in zip(dat,spectra.ferr,spectra.time,spectra.wave):
xcen, xerr = subs.centroid((len(f)-1)/2., fwhm, f, True, e)
icen = int(round(xcen))
if icen >=0 and icen < len(w):
dw = (w[-1]-w[0])/(len(w)-1)
v = doppler.CKMS*(w[icen] + (xcen-icen)*dw - wcen)/wcen - gam
dv = doppler.CKMS*xerr*dw/wcen
vs.append(v)
verrs.append(dv)
times.append(t)
l = len(vs)
vs = np.array(vs).reshape((l))
verrs = np.array(verrs).reshape((l))
times = np.array(times).reshape((l))
# compute periodogram on same set of frequencies as
p = pgram.wmls(times, vs, verrs, fs)
# output best frequency
fout.write(str(fs[p.argmax()]) + '\n')
# add noise and accumulate V/R data
times = []
ratios = []
rerrs = []
for spectra in data.data:
dat = np.random.normal(spectra.flux, spectra.ferr)
vel = doppler.CKMS*(spectra.wave-model.data[0].wave[0])/model.data[0].wave[0]-model.data[0].gamma[0]
for f,e,v,t in zip(dat,spectra.ferr,vel,spectra.time):
bpix = (v > -vmax) & (v < 0)
blue = f[bpix].sum()
bvar = (e[bpix]**2).sum()
rpix = (v < vmax) & (v >= 0)
red = f[rpix].sum()
rvar = (e[rpix]**2).sum()
rat = blue/red
rerr = np.abs(rat)*np.sqrt(bvar/blue**2+rvar/red**2)
ratios.append(rat)
rerrs.append(rerr)
times.append(t)
ratios = np.array(ratios)
rerrs = np.array(rerrs)
times = np.array(times)
# compute periodogram on same set of frequencies as
p = pgram.wmls(times, ratios, rerrs, fs)
# output best frequency
fout.write(str(fs[p.argmax()]) + '\n')
