def calc_model(self):
lines = lines_from_f26(self.opt.f26)
wa = self.spec[self.i].wa
dw = np.median(np.diff(wa))
print 'finding tau'
if self.opt.wadiv is not None:
dw1 = dw / self.opt.wadiv
wa1 = np.arange(wa[0], wa[-1] + 0.5 * dw1, dw1)
tau, ticks = find_tau(wa1, lines, self.opt.atom,
logNthresh_LL=self.opt.logNthresh_LL)
else:
tau, ticks = find_tau(wa, lines, self.opt.atom,
logNthresh_LL=self.opt.logNthresh_LL)
model = np.exp(-tau)
# if we want to calculate the optical depth per line,
# do it here.
if self.opt.wadiv is not None:
model, _ = process_Rfwhm(self.opt.Rfwhm, wa1, model, [])
else:
model, _ = process_Rfwhm(self.opt.Rfwhm, wa, model, [])
if self.opt.wadiv is not None:
model = np.interp(wa, wa1, model)
self.models[self.i] = model
self.ticks = ticks
self.model = model
def calc_model(self):
lines = lines_from_f26(self.opt.f26)
lines = [l for l in lines if l[0] not in ('__', '<>')]
wa = self.spec[self.i].wa
dw = np.median(np.diff(wa))
print('finding tau')
if self.opt.wadiv is not None:
dw1 = dw / self.opt.wadiv
wa1 = np.arange(wa[0], wa[-1] + 0.5 * dw1, dw1)
tau, ticks = find_tau(wa1, lines, self.opt.atom,
logNthresh_LL=self.opt.logNthresh_LL)
else:
tau, ticks = find_tau(wa, lines, self.opt.atom,
logNthresh_LL=self.opt.logNthresh_LL)
model = np.exp(-tau)
# if we want to calculate the optical depth per line,
# do it here.
if self.opt.wadiv is not None:
model, _ = process_Rfwhm(self.opt.Rfwhm, wa1, model, [])
else:
model, _ = process_Rfwhm(self.opt.Rfwhm, wa, model, [])
if self.opt.wadiv is not None:
model = np.interp(wa, wa1, model)
#self.apply_zero_offsets()
self.models[self.i] = model
self.ticks = ticks
self.model = model
def convolve_LSF(self):
print 'convolving'
if self.opt.wadiv is not None:
self.model, models = process_Rfwhm(self.opt.Rfwhm, self.wa1,
self.model, [])
else:
self.model, models = process_Rfwhm(self.opt.Rfwhm, self.wa,
self.model, [])
if self.opt.wadiv is not None:
self.model = np.interp(self.wa, self.wa1, self.model)
#self.models = [np.interp(wa, self.wa1, m) for m in self.models]
print 'done!'
def convolve_LSF(self):
print 'convolving'
if self.opt.wadiv is not None:
self.model, _ = process_Rfwhm(
self.opt.Rfwhm, self.wa1, self.model, [])
else:
self.model, _ = process_Rfwhm(
self.opt.Rfwhm, self.wa, self.model, [])
if self.opt.wadiv is not None:
self.model = np.interp(self.wa, self.wa1, self.model)
#self.models = [np.interp(wa, self.wa1, m) for m in self.models]
print 'done!'