def fit_rv_shift(template, orders):
template_lines = []
order_lines = []
for t, o in zip(template, orders):
plt.plot(t.x, t.y/t.cont, 'k-', alpha=0.4)
plt.plot(o.x, o.y/o.cont, 'r-', alpha=0.5)
tlines = FittingUtilities.FindLines(t, tol=0.8)
olines = FittingUtilities.FindLines(o, tol=0.8)
if len(tlines) == 0 or len(tlines) != len(olines):
continue
template_lines.append(t.x[tlines])
order_lines.append(o.x[olines])
template_lines = np.hstack(template_lines)
order_lines = np.hstack(order_lines)
rv = (np.median(order_lines/template_lines) - 1.0)*c
#plt.scatter(template_lines, order_lines)
#plt.plot(plt.xlim(), plt.xlim(), 'r--')
plt.scatter(template_lines, order_lines - template_lines*(1.+rv/c))
plt.show()
return rv
order.cont = FittingUtilities.Continuum(order.x,
order.y,
fitorder=3,
lowreject=1.5,
highreject=10)
fitter.ImportData(order)
fitter.resolution_fit_mode = "gauss"
fitter.fit_source = False
fitter.fit_primary = False
model = fitter.GenerateModel(fitpars,
separate_source=False,
return_resolution=False)
# Find the best scale factor
model.cont = np.ones(model.size())
lines = FittingUtilities.FindLines(model, tol=0.95).astype(int)
if len(lines) > 5:
scale = np.median(
np.log(order.y[lines] / order.cont[lines]) /
np.log(model.y[lines]))
else:
scale = 1.0
print i, scale
h2o_scale.append(scale)
# Now, find the best O2 scale factor
o2_scale = []
for i in FindOrderNums(orders, [630, 690]):
print "\n***************************\nFitting order %i: " % (i)
order = orders[i]
fitter.AdjustValue({