def ymodel(wa, pars):
"""Generate the model normalised flux array for every region.
Note this needs the global variables `lines_vary` and `regions` to
be defined.
"""
linepars = pars
dz = 0
if options['wa_vary']:
linepars, regpars = pars[:-len(regions)], pars[-len(regions):]
lines = copy_par_to_lines(linepars, lines_vary)
fl = []
for ireg, (wa, tau0, sp, (i, j),tname) in enumerate(regions):
tau = tau0.copy()
for l in lines:
if options['wa_vary']:
dz = regpars[ireg] / wa[len(wa)//2] * (l.z + 1)
trans = atom[l.name.strip()]
tau += calc_iontau(wa, trans, l.z+1 + dz, l.logN, l.b)
dwpix = wa[1] - wa[0]
flmodel0 = convolve_with_COS_FOS(np.exp(-tau), wa, dwpix)
flmodel = np.interp(sp.wa[i:j], wa, flmodel0)
fl.extend(flmodel)
return fl
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 find_tau(wa, lines):
""" generate a tau array given a list of transitions and a
wavelength array.
The transition list is in a vp.lines format
"""
tau = np.zeros_like(wa)
for l in lines:
trans = atom[l.name.strip()]
tau += calc_iontau(wa, trans, l.z + 1, l.logN, l.b, maxdv=None)
return 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)
def plot_model(pars):
"""
`regions`, `x` must be defined in the model.py module namespace
"""
from run_emcee.plot_mcmc import get_fig_axes, get_nrows_ncols
linepars = pars
dz = 0
if options['wa_vary']:
linepars, regpars = pars[:-len(regions)], pars[-len(regions):]
lines = copy_par_to_lines(linepars, lines_vary)
nrows, ncols = get_nrows_ncols(len(regions))
fig, axes = get_fig_axes(nrows, ncols, len(regions), width=8.2)
fig.subplots_adjust(wspace=1e-6, hspace=1e-6, right=1, top=1,
left=0.07, bottom=0.06)
z0 = lines.z[1]
zp1 = lines.z.mean()
colours = dict(J0='purple',J1='g',J2='r',J3='b')
for ind, (wa, tau0, sp, (i, j), tname) in enumerate(regions):
tau = tau0.copy()
for l in lines:
if options['wa_vary']:
#import pdb; pdb.set_trace()
dz = regpars[ind] / wa[len(wa)//2] * (l.z + 1)
#print l.z, dz, l.logN
trans = atom[l.name.strip()]
tau += calc_iontau(wa, trans, l.z+1 + dz, l.logN, l.b)
dwpix = wa[1] - wa[0]
flmodel0 = convolve_with_COS_FOS(np.exp(-tau), wa, dwpix)
ymod = np.interp(sp.wa[i:j], wa, flmodel0)
ax = axes[ind]
i1 = max(i-50, 0)
j1 = min(j+50, len(sp.wa)-1)
nfl = sp.fl[i1:j1] / sp.co[i1:j1]
ner = sp.er[i1:j1] / sp.co[i1:j1]
tstr, t = findtrans(tname, atom)
obswa = t.wa * (1 + z0)
dv = c_kms * (sp.wa[i:j] / obswa - 1)
dv1 = c_kms * (sp.wa[i1:j1] / obswa - 1)
tstr = tstr[2:]
ax.axhline(0, color='0.5',lw=0.5)
ax.axhline(1, color='k', lw=0.5, ls='--')
ax.fill_between([-150, 150], -0.35, -0.15, color='0.9')
ax.axhline(-0.25, color='k', lw=0.25)
ax.plot(dv1, nfl, color='0.7', lw=0.5, drawstyle='steps-mid')
ax.plot(dv, sp.fl[i:j]/sp.co[i:j], 'k', lw=0.5, drawstyle='steps-mid')
ax.plot(dv, ymod, color='0.3')
#ax.axvline(0, color='0.5', lw=0.5)
ax.plot([-23]*2, [1.1, 1.4], '0.5')
ax.plot([0]*2, [1.1, 1.4], '0.5')
puttext(0.95, 0.95, tstr, ax, fontsize=9, va='top', ha='right',
color=colours[tstr[:2]])
resid = (sp.fl[i:j]/sp.co[i:j] - ymod) / sp.er[i:j] * sp.co[i:j]
ax.plot(dv, -0.25 + resid*0.1, '.', color='0.3', ms=2)
ax.set_ylim(-0.5, 1.9)
ax.set_xlim(-120, 120)
ax.set_yticks([0, 0.5, 1, 1.5])
ax.set_yticklabels(['0.0', '0.5', '1.0', '1.5'])
ax.set_xticks([-100, -50, 0, 50, 100])
ax.set_xticklabels(['', '-50', '0', '50', ''])
if ind+1 < (ncols*(nrows-1)):
ax.set_xticklabels([])
if ind % ncols:
ax.set_yticklabels([])
fig.text(0.5, 0.00, 'Velocity offset (km/s)', fontsize=12, ha='center',va='bottom')
fig.text(0.015, 0.5, 'Transmission', fontsize=12, rotation=90,va='center')
ndf = len(ydata) - len(pars)
print (((ydata - ymodel(x, pars))/ ysigma) **2).sum() / ndf
pl.savefig('fig/model.pdf')
pl.savefig('fig/model.png',dpi=300)
return fig
def model(*par):
tau = np.zeros_like(X)
for i in xrange(len(par) // 3):
z, logN, b = par[3 * i:3 * i + 3]
tau += calc_iontau(X, trans, z + 1, logN, b)
return np.exp(-tau)
def model(*par):
tau = np.zeros_like(X)
for i in xrange(len(par)//3):
z,logN,b = par[3*i:3*i+3]
tau += calc_iontau(X, trans, z+1, logN, b)
return np.exp(-tau)
