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 update_model(self):
lines = lines_from_f26(self.opt.f26)
wa = self.wa
if self.opt.wadiv is not None:
wa = self.wa1
# first remove models that no longer present in lines
for l in set(self.taus).difference(lines):
del self.taus[l]
del self.models[l]
del self.ticks[l]
for tr_id in self.artists['models']:
self.artists['models'][tr_id][l].remove()
del self.artists['models'][tr_id][l]
dtype = np.dtype([('name', 'S10'), ('wa', 'f8'), ('z', 'f8'),
('wa0', 'f8'), ('ind', 'i4')])
# now add the new models from lines
new_lines = set(lines).difference(self.taus)
print len(new_lines), 'new lines'
for l in new_lines:
#print 'z, logN, b', z, logN, b
ion, z, b, logN = l
if ion in ('__', '<>', '<<', '>>'):
continue
maxdv = 20000 if logN > 18 else 500
t, tick = calc_iontau(wa,
self.opt.atom[ion],
z + 1,
logN,
b,
ticks=True,
maxdv=maxdv,
logNthresh_LL=self.opt.logNthresh_LL)
self.taus[l] = t
self.models[l] = np.exp(-t)
temp = np.empty(0, dtype=dtype)
if tick:
ions = [ion] * len(tick)
temp = np.rec.fromarrays([ions] + zip(*tick), dtype=dtype)
self.ticks[l] = temp
self.allticks = []
if len(self.ticks) > 0:
self.allticks = np.concatenate([self.ticks[l] for l in self.ticks
]).view(np.recarray)
self.allticks.sort(order='wa')
tau = np.zeros_like(wa)
for line in self.taus:
tau += self.taus[line]
self.tau = tau
self.model = np.exp(-tau)
def update_model(self):
lines = lines_from_f26(self.opt.f26)
wa = self.wa
if self.opt.wadiv is not None:
wa = self.wa1
# first remove models that no longer present in lines
for l in set(self.taus).difference(lines):
del self.taus[l]
del self.models[l]
del self.ticks[l]
for tr_id in self.artists['models']:
self.artists['models'][tr_id][l].remove()
del self.artists['models'][tr_id][l]
dtype = np.dtype([('name', 'S10'), ('wa', 'f8'), ('z', 'f8'),
('wa0', 'f8'), ('ind', 'i4')])
# now add the new models from lines
new_lines = set(lines).difference(self.taus)
print len(new_lines), 'new lines'
for l in new_lines:
#print 'z, logN, b', z, logN, b
ion, z, b, logN = l
if ion in ('__', '<>', '<<', '>>'):
continue
maxdv = 20000 if logN > 18 else 500
t, tick = calc_iontau(wa, self.opt.atom[ion], z + 1, logN, b,
ticks=True, maxdv=maxdv,
logNthresh_LL=self.opt.logNthresh_LL)
self.taus[l] = t
self.models[l] = np.exp(-t)
temp = np.empty(0, dtype=dtype)
if tick:
ions = [ion] * len(tick)
temp = np.rec.fromarrays([ions] + zip(*tick), dtype=dtype)
self.ticks[l] = temp
self.allticks = []
if len(self.ticks) > 0:
self.allticks = np.concatenate(
[self.ticks[l] for l in self.ticks]).view(np.recarray)
self.allticks.sort(order='wa')
tau = np.zeros_like(wa)
for line in self.taus:
tau += self.taus[line]
self.tau = tau
self.model = np.exp(-tau)
