disp = vsini
else:
disp = 3.
else:
known_sigma = True
mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c / 1.0e3)
ml_v = av_m - mask_hw_wide
mh_v = av_m + mask_hw_wide
print '\t\t\tComputing the CCF...'
cond = True
while (cond):
# first rough correlation to find the minimum
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(spec, ml_v, mh_v, weight, 0, lbary_ltopo, vel_width=velw,vel_step=velsh,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=velw)
xc_av = GLOBALutils.Average_CCF(xc_full,
sn,
sn_min=3.,
Simple=True,
W=W_ccf)
yy = scipy.signal.medfilt(xc_av, 11)
pred = lowess(yy, vels, frac=0.4, it=10, return_sorted=False)
tck1 = scipy.interpolate.splrep(vels, pred, k=1)
xc_av_orig = xc_av.copy()
xc_av /= pred
vel0_xc = vels[np.argmin(xc_av)]
rvels, rxc_av, rpred, rxc_av_orig, rvel0_xc = vels.copy(
), xc_av.copy(), pred.copy(), xc_av_orig.copy(), vel0_xc
xc_av_rough = xc_av
for i in range(nspec.shape[1]):
npix = np.arange(nspec.shape[2])
wav = np.polyval(coefs[i][1:][::-1], npix) * 10
nspec[0, i, :] = wav
spl = interpolate.splrep(np.arange(wav.shape[0]), wav, k=3)
dlambda_dx = interpolate.splev(np.arange(wav.shape[0]), spl, der=1)
NN = np.average(dlambda_dx)
dlambda_dx /= NN
nspec[9, i, :] = nspec[5, i, :] * (dlambda_dx**1)
nspec[8, i, :] = 100.
ml_v = tlines - 0.01
mh_v = tlines + 0.01
weight = np.ones(len(tlines))
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(nspec, ml_v, mh_v, weight, 0, 1., vel_width=300,vel_step=3.,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3., Simple=True, W=W_ccf)
#plot(vels,xc_av)
#show()
j = np.median(xc_av)
im = np.argmax(xc_av)
rv = vels[im]
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(nspec, ml_v, mh_v, weight, rv, 1., vel_width=20,vel_step=0.3,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3., Simple=True, W=W_ccf)
cent = np.sum(xc_av * vels) / np.sum(xc_av)
print cent
#axvline(cent)
#plot(vels,xc_av-j)
disp = 3.
else:
known_sigma = True
if disp < 10:
disp = 5
mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c / 1.0e3)
ml_v = av_m - mask_hw_wide
mh_v = av_m + mask_hw_wide
print '\t\t\tComputing the CCF...'
cond = True
while (cond):
# first rough correlation to find the minimum
vels, xc_full, sn, nlines_ccf, W_ccf = \
GLOBALutils.XCor(spec, ml_v, mh_v, weight, 0, 1., vel_width=300,vel_step=1.,\
spec_order=9,iv_order=10,sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full,
sn,
sn_min=0.0,
Simple=True,
W=W_ccf)
#print W_ccf
# Normalize the continuum of the CCF robustly with lowess
yy = scipy.signal.medfilt(xc_av, 11)
pred = lowess(yy, vels, frac=0.4, it=10, return_sorted=False)
tck1 = scipy.interpolate.splrep(vels, pred, k=1)
xc_av_orig = xc_av.copy()
xc_av /= pred
vel0_xc = vels[np.argmin(xc_av)]
rvels, rxc_av, rpred, rxc_av_orig, rvel0_xc = vels.copy(), \
xc_av.copy(), pred.copy(), xc_av_orig.copy(), vel0_xc
disp = vsini
else:
disp = 3.
else:
known_sigma = True
mask_hw_wide = av_m * disp / (GLOBALutils.Constants.c/1.0e3)
ml_v = av_m - mask_hw_wide
mh_v = av_m + mask_hw_wide
print '\t\t\tComputing the CCF...'
cond = True
while (cond):
#first rough correlation to find the minimum
vels, xc_full, sn, nlines_ccf, W_ccf = GLOBALutils.XCor(spec1, ml_v, mh_v,\
weight, 0, lbary_ltopo, vel_width=300, vel_step=3, start_order=0,\
spec_order=9, iv_order=10, sn_order=8,max_vel_rough=300)
xc_av = GLOBALutils.Average_CCF(xc_full, sn, sn_min=3, Simple=True, start_order=0, W=W_ccf)
#Normalize the continuum of the CCF robustly with R
yy = scipy.signal.medfilt(xc_av,11)
pred = lowess(yy, vels,frac=0.4,it=10,return_sorted=False)
tck1 = scipy.interpolate.splrep(vels,pred,k=1)
xc_av_orig = xc_av.copy()
xc_av /= pred
pred_rough = pred.copy()
vel0_xc = vels[ np.argmin( xc_av ) ]
rvels, rxc_av, rpred, rxc_av_orig, rvel0_xc = vels.copy(), \
xc_av.copy(), pred.copy(), xc_av_orig.copy(), vel0_xc
xc_av_rough = xc_av