def plot_spec(ccd1,
ccd2,
ccd3,
ccd4,
fuv,
offset,
ax_ccd,
ax_fuv,
fuv_offset=None):
if not fuv_offset:
fuv_offset = offset
tbdata1 = pyfits.getdata(ccd1, 1)
print tbdata1.names
wl1 = tbdata1['wavelength'].ravel()
net1 = tbdata1['net'].ravel()
net1_4000 = degrader(wl1, net1, 7000., 4000., quick=5)
tbdata2 = pyfits.getdata(ccd2, 1)
wl2 = tbdata2['wavelength'].ravel()
net2 = tbdata2['net'].ravel()
net2_4000 = degrader(wl2, net2, 7000., 4000., quick=5)
tbdata3 = pyfits.getdata(ccd3, 1)
wl3 = tbdata3['wavelength'].ravel()
net3 = tbdata3['net'].ravel()
net3_4000 = degrader(wl3, net3, 7000., 4000., quick=5)
tbdata4 = pyfits.getdata(ccd4, 1)
wl4 = tbdata4['wavelength'].ravel()
net4 = tbdata4['net'].ravel()
net4_4000 = degrader(wl4, net4, 7000., 4000., quick=5)
tbdata5 = pyfits.getdata(fuv, 1)
wl5 = tbdata5['wavelength'].ravel()
net5 = tbdata5['flux'].ravel()
ax_fuv.plot(wl5, net5 + fuv_offset, c='k')
ax_fuv.set_xlim(1150, 1700)
ax_fuv.set_ylim(-0.1, 2.3 + offset)
ax_ccd.plot(wl1, net1_4000 + offset, c='k')
ax_ccd.plot(wl2, net2_4000 + offset, c='b')
ax_ccd.plot(wl3, net3_4000 + offset, c='k')
ax_ccd.plot(wl4, net4_4000 + offset, c='b')
ax_ccd.set_xlim(3900, 4750)
ax_ccd.set_ylim(-0.1, 2.3 + offset)
return ax_ccd, ax_fuv
def rv(fsci, ftell, rsgmod, xrsg, atspec, hcorr):
"""
Calculate RV for a science spectrum extracted from brighest pixel
Initial guess comes from a cross-correlation for the entire region
This is then improved upon by using specific lines and regions within
the 1.17-1.22um region.
"""
scic = SciComb(pyfits.open(fsci))
# For cross-correlation to rest use telluric index:
it = np.where((scic.x > 1.12) & (scic.x < 1.15))[0]
xtell = scic.x[it]
scixtell = scic.bspec[it]
atxtell = atspec[it]
scixtell_, sr = cc.ccshift(atxtell, scixtell, xtell, quiet=False)
# Implement shift-to-rest (sr) in diagnostic region
xdiag = scic.x[scic.roi]
scidiag = scic.bspec[scic.roi]
atdiag = atspec[scic.roi]
print('[INFO] Shift science spectrum to rest using telluric features:')
scirest, s2 = cc.ccshift(atdiag, scidiag, xdiag, shift1=sr, quiet=False)
# Telluric correct:
print('[INFO] Telluric correct data once shifted onto rest wavelength:')
tellcube = TellCube(ftell)
tspec = tellcube.data[scic.ifu - 1].data
scitc = TellCor(scirest, tspec[scic.roi], scic.x[scic.roi])
# Prepare fake spectrum:
# Match sampling between fake spectrum and observations
# Degrade & resample:
fsam = resam(xrsg, rsgmod, scic.x)
fdeg = degrader(scic.x, fsam, 10000, 3000)
fdiag = fdeg[scic.roi]
print('[INFO] Calculate RV shift:')
scirv, rvs = cc.ccshift(fdiag, scitc.tcor, xdiag, quiet=False, width=20)
rvkms = lambda shift, delta, lam: shift * delta * C / lam
rvi = rvkms(rvs * -1, scic.delt, 1.2)
rvall = rvcorr(rvi, hcorr)
print('[INFO] RV derived using the whole region: {}'.format(rvall))
# Calculate errors:
slbl = linebyline(xdiag, scirv, fdiag) + rvs * -1
slbl = slbl[np.where(slbl != 0.0)]
rvlbl = rvkms(slbl, scic.delt, 1.2)
avrv = rvcorr(np.mean(rvlbl), hcorr)
erv = np.std(rvlbl) / np.sqrt(np.shape(rvlbl)[0])
print('[INFO] Average RV line by line: {} +/- {}'.format(avrv, erv))
print('[INFO] RVs: {}'.format(rvcorr(rvlbl, hcorr)))
return scic, scitc, avrv, erv, sr, rvcorr(rvlbl, hcorr)
def plot_spec(ccd1, ccd2, ccd3, ccd4, fuv, offset, ax_ccd, ax_fuv, fuv_offset = None):
if not fuv_offset:
fuv_offset = offset
tbdata1 = pyfits.getdata(ccd1, 1)
print tbdata1.names
wl1 = tbdata1['wavelength'].ravel()
net1 = tbdata1['net'].ravel()
net1_4000 = degrader(wl1, net1, 7000., 4000., quick = 5)
tbdata2 = pyfits.getdata(ccd2, 1)
wl2 = tbdata2['wavelength'].ravel()
net2 = tbdata2['net'].ravel()
net2_4000 = degrader(wl2, net2,7000., 4000., quick = 5)
tbdata3 = pyfits.getdata(ccd3, 1)
wl3 = tbdata3['wavelength'].ravel()
net3 = tbdata3['net'].ravel()
net3_4000 = degrader(wl3, net3, 7000., 4000., quick = 5)
tbdata4 = pyfits.getdata(ccd4, 1)
wl4 = tbdata4['wavelength'].ravel()
net4 = tbdata4['net'].ravel()
net4_4000 = degrader(wl4, net4, 7000., 4000., quick = 5)
tbdata5 = pyfits.getdata(fuv, 1)
wl5 = tbdata5['wavelength'].ravel()
net5 = tbdata5['flux'].ravel()
ax_fuv.plot(wl5, net5+fuv_offset, c = 'k')
ax_fuv.set_xlim(1150, 1700)
ax_fuv.set_ylim(-0.1, 2.3+offset)
ax_ccd.plot(wl1, net1_4000+offset, c = 'k')
ax_ccd.plot(wl2, net2_4000+offset, c = 'b')
ax_ccd.plot(wl3, net3_4000+offset, c = 'k')
ax_ccd.plot(wl4, net4_4000+offset, c = 'b')
ax_ccd.set_xlim(3900, 4750)
ax_ccd.set_ylim(-0.1, 2.3+offset)
return ax_ccd, ax_fuv
def wraprv(scipath, ftell, rsgmod, xrsg):
"""
Wrap the rv.rv routine by giving it a list of spectra
Usage:
rv.wraprv('path/to/sci_combined/files', '/path/telluric_YJYJYJ.fits')
"""
hcorr = raw_input('[INFO] Please enter heliocentric corr. for target:\n')
hcorr = float(hcorr)
scifiles = sorted(glob.glob(scipath + '*sci_combined_*'))
# Avoid repeating this procedure by assuming all spectra have same x-axis
tmpsci = SciComb(pyfits.open(scifiles[0]))
print('[INFO] Match sampling and degrade atmos. spec. to match observed')
atssam = resam(xat_hres, atspec_hres, tmpsci.x)
# Clean up edges -- taken from N6822_Vrad.v2.py -- is this necesssary?
atssam[np.where(atssam < 0.0)[0]] = 0
print('[INFO] Degrade atmos spec to match science:')
# From ESO's ISAAC decommissioned webpages R = 40000
atspec = degrader(tmpsci.x, atssam, 40000, 3000)
rvall = []
rvstd = []
name = []
tcspec = []
strest = []
rvwhole = []
for scifile in scifiles:
print('[INFO] Use rv.rv for ', scifile)
namei, tcspeci, rvi, erri, sr, rvwi = rv(scifile, ftell, rsgmod, xrsg,
atspec, hcorr)
rvall = np.append(rvall, rvi)
rvstd = np.append(rvstd, erri)
name = np.append(name, namei)
tcspec = np.append(tcspec, tcspeci)
strest = np.append(strest, sr)
rvwhole = np.append(rvwhole, rvwi)
print('--' * 30)
print('[INFO] Average RV for the sample: {} +/- {}'.format(
rvall.mean(), rvall.std()))
print('--' * 30)
return name, tcspec, rvall, rvstd, strest, rvwhole