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 make_models(opt, path=None):
if opt.f26name is not None:
n = (path + opt.f26name if path is not None else opt.f26name)
f26 = readf26(n)
n = (path + opt.trfilename if path is not None else opt.trfilename)
transitions = read_transitions(n, atomdat)
ntrans = len(transitions)
if isinstance(opt.spfilename, basestring):
n = (path + opt.spfilename if path is not None else opt.spfilename)
sp = barak.spec.read(n)
else:
sp = opt.spfilename
wa = sp.wa
edges = barak.spec.find_bin_edges(wa) # pixel edges
dwa = edges[1:] - edges[:-1] # width per pixel
ticks = []
model = None
models = []
if opt.f26name is not None:
dw = np.median(dwa)
lines = []
for l in f26.lines:
lines.append((l['name'].replace(' ',''),l['z'],l['b'],l['logN']))
if opt.wadiv is not None:
dw1 = dw / opt.wadiv
wa1 = np.arange(wa[0], wa[-1]+0.5*dw1, dw1)
tau, ticks = find_tau(wa1, lines, atomdat)
else:
tau, ticks = find_tau(wa, lines, atomdat)
model = np.exp(-tau)
models = []
#models = [np.exp(-t) for t in taus]
if opt.wadiv is not None:
model, models = process_Rfwhm(opt.Rfwhm, wa1, model, models)
else:
model, models = process_Rfwhm(opt.Rfwhm, wa, model, models)
if opt.wadiv is not None:
model = np.interp(wa, wa1, model)
models = [np.interp(wa, wa1, m) for m in models]
return sp, transitions, model, models, ticks
'OVI 1037': [(-500,-290), (-100, +45), (70,200)],
}
cfg = parse_config(config)
transitions = read_transitions(cfg.trfilename, ATOMDAT)
if 1:
sp = barak.spec.read(cfg.spfilename)
ndiv = 4.
wa_dv = make_constant_dv_wa_scale(sp.wa[0], sp.wa[-1], cfg.Rfwhm / ndiv)
expand_cont_adjustment = 5
vp = readf26(cfg.f26name)
lines = vp.lines[vp.lines.name != '<>']
tau, ticks, alltau = find_tau(sp.wa, lines, ATOMDAT, per_trans=1)
model = convolve_constant_dv(sp.wa, np.exp(-tau), wa_dv, ndiv)
models = [convolve_constant_dv(sp.wa, np.exp(-t), wa_dv, ndiv) for t in
alltau]
adjust = [l for l in vp.lines if l['name'].strip() == '<>']
print 'Nadjust', adjust
if len(adjust) > 0:
regions = vp.regions
isort = regions.wmin.argsort()
print 'Applying continuum adjustments for', len(adjust), 'regions'
for val in adjust:
wav0 = ATOMDAT['<>'].wa[0] * (1 + val['z'])
i0 = regions.wmin[isort].searchsorted(wav0)
i1 = regions.wmax[isort].searchsorted(wav0)
#print i0, i1
#import pdb; pdb.set_trace()
if 1:
linelist = read_transitions(trfilename)
ntrans = len(linelist)
sp = barak.spec.read(spfilename)
wa = sp.wa
nfl = sp.fl / sp.co
ner = sp.fl / sp.co
edges = barak.spec.find_wa_edges(wa) # pixel edges
dwa = edges[1:] - edges[:-1] # width per pixel
dw = np.median(dwa)
if wadiv is not None:
dw1 = dw / wadiv
wa1 = np.arange(wa[0], wa[-1]+0.5*dw1, dw1)
tau, ticks = find_tau(wa1, f26.lines, atomdat)
else:
tau, ticks = find_tau(wa, f26.lines, atomdat)
model = np.exp(-tau)
models = []
#models = [np.exp(-t) for t in taus]
if wadiv is not None:
model, models = process_Rfwhm(Rfwhm, wa1, model, models)
else:
model, models = process_Rfwhm(Rfwhm, wa, model, models)
if wadiv is not None:
model = np.interp(wa, wa1, model)
'OVI 1037': [(-500, -290), (-100, +45), (70, 200)],
}
cfg = parse_config(config)
transitions = read_transitions(cfg.trfilename, ATOMDAT)
if 1:
sp = barak.spec.read(cfg.spfilename)
ndiv = 4.
wa_dv = make_constant_dv_wa_scale(sp.wa[0], sp.wa[-1], cfg.Rfwhm / ndiv)
expand_cont_adjustment = 5
vp = readf26(cfg.f26name)
lines = vp.lines[vp.lines.name != '<>']
tau, ticks, alltau = find_tau(sp.wa, lines, ATOMDAT, per_trans=1)
model = convolve_constant_dv(sp.wa, np.exp(-tau), wa_dv, ndiv)
models = [
convolve_constant_dv(sp.wa, np.exp(-t), wa_dv, ndiv) for t in alltau
]
adjust = [l for l in vp.lines if l['name'].strip() == '<>']
print 'Nadjust', adjust
if len(adjust) > 0:
regions = vp.regions
isort = regions.wmin.argsort()
print 'Applying continuum adjustments for', len(adjust), 'regions'
for val in adjust:
wav0 = ATOMDAT['<>'].wa[0] * (1 + val['z'])
i0 = regions.wmin[isort].searchsorted(wav0)
i1 = regions.wmax[isort].searchsorted(wav0)
#print i0, i1
pl.rc('font', size=12.5)
if 1:
sp = barak.spec.read('fos_colo2.txt')
sp.fl *= 1e16
sp.co *= 1e16
sp.er *= 1e16
#sp = barak.spec.read('../vpfit_COS/qsoc_G190H.txt')
wa = sp.wa + 0.2
dw = np.median(np.diff(sp.wa))
# resolution seems to be 0.8 expected value? Or sign of lower N
# comoponents in wings?
npix = 1.39 / dw * 0.8
tau, ticks = find_tau(wa, lines, atom)
taulo, ticks = find_tau(wa, lineslo, atom)
tauhi, ticks = find_tau(wa, lineshi, atom)
fig = pl.figure(figsize=(4.3, 3))
fig.subplots_adjust(bottom=0.16, left=0.16, right=0.98, top=0.96)
ax = fig.add_subplot(111)
ax.plot(wa, sp.fl,color='0.3', lw=0.5,drawstyle='steps-mid')
ax.plot(wa, sp.co, '0.7', ls='dashed')
ax.plot(wa, sp.co*convolve_psf(np.exp(-taulo), npix), 'r', lw=0.3)
ax.plot(wa, sp.co*convolve_psf(np.exp(-tau), npix), 'r')
ax.plot(wa, sp.co*convolve_psf(np.exp(-tauhi), npix), 'r', lw=0.3)
ax.axhline(0, color='k', lw=0.3)