def ReadFile(fname, blaze=None):
try:
orders = HelperFunctions.ReadFits(fname)
except ValueError:
orders = HelperFunctions.ReadFits(fname, errors=2)
orders = orders[::-1] # Reverse order so the bluest order is first
#Need to blaze-correct the later data
if int(os.path.split(fname)[1][2:7]) > 50400 and blaze is not None:
print "\tBlaze correcting!"
try:
blaze_orders = HelperFunctions.ReadFits(blaze)
except ValueError:
blaze_orders = HelperFunctions.ReadFits(blaze, errors=2)
blaze_orders = blaze_orders[::
-1] # Reverse order so the bluest order is first
blazecorrect = True
else:
blazecorrect = False
for i, order in enumerate(orders):
if blazecorrect:
b = blaze_orders[i].y / blaze_orders[i].y.mean()
#plt.plot(order.x, b, 'g-', alpha=0.4)
order.y /= b
order.cont = FittingUtilities.Continuum(order.x,
order.y,
fitorder=2,
lowreject=2,
highreject=5)
#plt.plot(order.x, order.y, 'k-', alpha=0.4)
#plt.plot(order.x, order.cont, 'r-', alpha=0.4)
orders[i] = order.copy()
#plt.show()
return orders
print "\n***************************\nFitting order %i: " % (i)
order = orders[i]
fitter.AdjustValue({
"wavestart": order.x[0] - 20.0,
"waveend": order.x[-1] + 20.0,
"o2": 0.0,
"h2o": humidity,
"resolution": resolution
})
fitpars = [
fitter.const_pars[j] for j in range(len(fitter.parnames))
if fitter.fitting[j]
]
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(
def get_ccfs(T=4000,
vsini=5,
logg=4.5,
metal=0.5,
hdf_file='Cross_correlations/CCF.hdf5',
xgrid=np.arange(-400, 400, 1),
addmode='simple'):
"""
Get the cross-correlation functions for the given parameters, for all stars
"""
ccfs = []
filenames = []
rv_shift = {} if T > 6000 else pickle.load(open('rvs.pkl'))
with h5py.File(hdf_file) as f:
starname = 'psi1 Dra A'
date_list = f[starname].keys()
for date in date_list:
datasets = f[starname][date].keys()
for ds_name in datasets:
ds = f[starname][date][ds_name]
if (ds.attrs['T'] == T and ds.attrs['vsini'] == vsini
and ds.attrs['logg'] == logg
and ds.attrs['[Fe/H]'] == metal
and ds.attrs['addmode'] == addmode):
vel, corr = ds.value
#ccf = spline(vel[::-1]*-1, (1.0-corr[::-1]))
ccf = spline(vel[::-1] * -1, corr[::-1])
#ccf = spline(vel, corr)
fname = ds.attrs['fname']
vbary = get_rv_correction(fname)
#cont = FittingUtilities.Continuum(xgrid, ccf(xgrid-vbary), fitorder=2, lowreject=2.5, highreject=5)
#normed_ccf = ccf(xgrid-vbary)/cont
cont = FittingUtilities.Continuum(xgrid,
ccf(xgrid - vbary),
fitorder=2,
lowreject=5,
highreject=2.5)
normed_ccf = ccf(xgrid - vbary) - cont
if T <= 6000:
centroid = rv_shift[fname]
#centroid = xgrid[np.argmax(normed_ccf)]
#top = 1.0
#amp = 1.0 - min(normed_ccf)
top = 0.0
amp = max(normed_ccf)
#idx = np.argmin(np.abs(xgrid-centroid))
#amp = normed_ccfs[idx]
else:
gauss_pars = fit_gaussian(xgrid, normed_ccf)
centroid = gauss_pars[2]
amp = gauss_pars[1]
top = gauss_pars[0]
amp = 0.5
print(centroid, fname)
#cont = FittingUtilities.Continuum(xgrid, ccf(xgrid-vbary+centroid), fitorder=2, lowreject=2.5, highreject=5)
#normed_ccf = (ccf(xgrid-vbary+centroid) / cont - top) * 0.5/abs(amp) + top
cont = FittingUtilities.Continuum(xgrid,
ccf(xgrid - vbary +
centroid),
fitorder=2,
lowreject=5,
highreject=2.5)
normed_ccf = (ccf(xgrid - vbary + centroid) -
cont) * 0.5 / abs(amp)
filenames.append(fname)
ccfs.append(normed_ccf)
rv_shift[fname] = centroid
if T > 6000:
pickle.dump(rv_shift, open('rvs.pkl', 'w'))
return np.array(ccfs), filenames
