def compCframe(plate,
frame,
apred='test',
ratio=True,
rows=range(300),
yr=None,
hdu=1):
load = apload.ApLoad(apred=apred)
mjd = 55562 + int(frame // 10000)
new = load.apCframe('M67', plate, mjd, frame)
old = {}
fig, ax = plots.multi(1, 3, hspace=0.001)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
old[chip] = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apCframe-{:s}-{:d}.fits'.
format(plate, mjd, chip, frame))
for row in rows:
if ratio:
plots.plotl(ax[ichip],
x,
new[chip][hdu].data[row, :] /
old[chip][hdu].data[row, :],
yr=[0, 1.5])
else:
plots.plotl(ax[ichip], x, new[chip][hdu].data[row, :], yr=yr)
plots.plotl(ax[ichip], x, old[chip][hdu].data[row, :], yr=yr)
plots.plotl(ax[ichip],
x,
new[chip][hdu].data[row, :] -
old[chip][hdu].data[row, :],
yr=yr)
def comp1d(frame, apred='test', rows=range(300)):
load = apload.ApLoad(apred=apred)
new = load.ap1D(frame)
old = {}
mjd = 55562 + int(frame // 10000)
fig, ax = plots.multi(1, 3, hspace=0.001)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
old[chip] = fits.open(
os.environ['APOGEE_REDUX'] +
'/r8/red/{:d}/ap1D-{:s}-{:d}.fits'.format(mjd, chip, frame))
for row in rows:
plots.plotl(ax[ichip],
x,
new[chip][1].data[row, :] / old[chip][1].data[row, :],
yr=[0, 1.5])
def dr_compare():
# load the DRs, select stars with SN>150
dr12load = apload.ApLoad(dr='dr12')
dr12 = dr12load.allStar()[1].data
gd = np.where(dr12['SNR'] > 150)[0]
dr12 = dr12[gd]
dr13load = apload.ApLoad(dr='dr13')
dr13 = dr13load.allStar()[1].data
gd = np.where(dr13['SNR'] > 150)[0]
dr13 = dr13[gd]
dr14load = apload.ApLoad(dr='dr14')
dr14 = dr14load.allStar()[1].data
gd = np.where(dr14['SNR'] > 150)[0]
dr14 = dr14[gd]
c = apload.allStar()[3].data
# match them
m1a, m2a = match.match(dr12['APOGEE_ID'], dr13['APOGEE_ID'])
m1b, m2b = match.match(dr12['APOGEE_ID'], dr14['APOGEE_ID'])
m1c, m2c = match.match(dr13['APOGEE_ID'], dr14['APOGEE_ID'])
# parameter figures
figu, axu = plots.multi(3, 7, hspace=0.001, wspace=0.001)
figc, axc = plots.multi(3, 7, hspace=0.001, wspace=0.001)
tit = [
r'T$_{\rm eff}$', 'log g', r'V$_{\rm micro}$', '[M/H]', '[C/M]',
'[N/M]', r'[$\alpha$/M]'
]
for iparam in range(7):
print(iparam)
for iy, param in enumerate(['FPARAM', 'PARAM']):
if iy == 0:
ax = axu
else:
ax = axc
yt = r'$\Delta$' + tit[iparam]
if iparam == 6: xt = r'T$_{\rm eff}$'
else: xt = None
if iparam == 0:
ax[iparam, 0].text(0.5,
1.0,
'DR13-DR12',
transform=ax[iparam, 0].transAxes,
ha='center',
va='bottom')
ax[iparam, 1].text(0.5,
1.0,
'DR14-DR12',
transform=ax[iparam, 1].transAxes,
ha='center',
va='bottom')
ax[iparam, 2].text(0.5,
1.0,
'DR14-DR13',
transform=ax[iparam, 2].transAxes,
ha='center',
va='bottom')
if iparam == 0:
yr = [-300, 300]
elif iparam == 1:
yr = [-0.5, 0.5]
else:
yr = [-0.3, 0.3]
xr = [3500, 6000]
axim = plots.plotc(ax[iparam, 0],
dr12['TEFF'][m1a],
dr13[param][m2a, iparam] -
dr12[param][m1a, iparam],
dr12[param][m1a, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
yt=yt,
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 0], xr, [0., 0.], ls=':')
plots.plotc(ax[iparam, 1],
dr12['TEFF'][m1b],
dr14[param][m2b, iparam] - dr12[param][m1b, iparam],
dr12[param][m1b, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 1], xr, [0., 0.], ls=':')
plots.plotc(ax[iparam, 2],
dr13['TEFF'][m1c],
dr14[param][m2c, iparam] - dr13[param][m1c, iparam],
dr13[param][m1c, 3],
size=1,
xr=xr,
yr=yr,
zr=[-1, 0.5],
xt=xt,
rasterized=True)
plots.plotl(ax[iparam, 2], xr, [0., 0.], ls=':')
for iax in range(3):
ax[iparam, iax].tick_params(axis='both', labelsize=8)
# add colorbar
for fig in [figu, figc]:
cbaxes = fig.add_axes([0.91, 0.1, 0.01, 0.8])
cb = plt.colorbar(axim, cax=cbaxes)
cb.set_label('[M/H]')
cbaxes.tick_params(axis='both', labelsize=8)
figu.savefig('drcomp_uncal.pdf')
figc.savefig('drcomp_cal.pdf')
plots.close()
# abundance figure
fig, ax = plots.multi(3, 14, hspace=0.001, wspace=0.001, figsize=(8, 32))
for ielem, elem in enumerate([
'C', 'N', 'O', 'Na', 'Mg', 'Al', 'Si', 'S', 'K', 'Ca', 'Ti', 'V',
'Mn', 'Ni'
]):
print(elem)
yt = r'$\Delta$' + elem
if ielem == 13: xt = r'T$_{\rm eff}$'
else: xt = None
if ielem == 0:
ax[ielem, 0].text(0.5,
1.0,
'DR13-DR12',
transform=ax[ielem, 0].transAxes,
ha='center',
va='bottom')
ax[ielem, 1].text(0.5,
1.0,
'DR14-DR12',
transform=ax[ielem, 1].transAxes,
ha='center',
va='bottom')
ax[ielem, 2].text(0.5,
1.0,
'DR14-DR13',
transform=ax[ielem, 2].transAxes,
ha='center',
va='bottom')
yr = [-0.5, 0.5]
dr12elem = dr12[elem.upper() + '_H'][m1a] - dr12['FE_H'][m1a]
dr13elem = dr13[elem.upper() + '_FE'][m2a]
gd = np.where((dr12elem > -99) & (dr13elem > -99))[0]
plots.plotc(ax[ielem, 0],
dr12['TEFF'][m1a[gd]],
dr13elem[gd] - dr12elem[gd],
dr12['PARAM'][m1a[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
yt=yt,
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 0], xr, [0., 0.], ls=':')
ax[ielem, 0].tick_params(axis='both', labelsize=8)
dr12elem = dr12[elem.upper() + '_H'][m1b] - dr12['FE_H'][m1b]
dr14elem = dr14[elem.upper() + '_FE'][m2b]
gd = np.where((dr12elem > -99) & (dr14elem > -99))[0]
plots.plotc(ax[ielem, 1],
dr12['TEFF'][m1b[gd]],
dr14elem[gd] - dr12elem[gd],
dr12['PARAM'][m1b[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 1], xr, [0., 0.], ls=':')
ax[ielem, 1].tick_params(axis='both', labelsize=8)
dr13elem = dr13[elem.upper() + '_FE'][m1c]
dr14elem = dr14[elem.upper() + '_FE'][m2c]
gd = np.where((dr13elem > -99) & (dr14elem > -99))[0]
plots.plotc(ax[ielem, 2],
dr13['TEFF'][m1c[gd]],
dr14elem[gd] - dr13elem[gd],
dr13['PARAM'][m1c[gd], 3],
size=1,
xr=[3500, 6000],
yr=yr,
zr=[-1, 0.5],
xt=xt,
nytick=5,
rasterized=True)
plots.plotl(ax[ielem, 2], xr, [0., 0.], ls=':')
ax[ielem, 2].tick_params(axis='both', labelsize=8)
cbaxes = fig.add_axes([0.91, 0.1, 0.01, 0.8])
cb = plt.colorbar(axim, cax=cbaxes)
cb.set_label('[M/H]')
cbaxes.tick_params(axis='both', labelsize=8)
for item in (cbaxes.get_xticklabels() + cbaxes.get_yticklabels()):
item.set_fontsize(8)
fig.savefig('drcomp_elem.pdf')
print('y6: ')
lsfsum(y6)
y7 = load.apLSF(25560065)[chip]
print('y7: ')
lsfsum(y7)
ii = 0
for ipar in range(26):
iy = ipar % 3
ix = ipar // 3
pax[iy, ix].plot(y1[0].data[ii, :])
ii += 1
for icol in range(4):
for fiber in [150]:
col = 500 + icol * 500
n = y1[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y1[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y2[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y2[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y3[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y3[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y4[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y4[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y5[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y5[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
n = y6[1].data.shape[0]
plots.plotl(ax[ichip, icol], np.arange(21),
y6[1].data[n / 2 - 10:n / 2 + 11, fiber, col])
def comp(plate=7267, mjd=56654, fiber=150, frame=10920059, field='M67'):
r11 = apload.ApLoad(apred='r11')
v = r11.apVisit(plate, mjd, fiber)
a = r11.ap1D(frame)
c = r11.apCframe(field, plate, mjd, frame)
v14 = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apVisit-r8-{:d}-{:d}-{:03d}.fits'.
format(plate, mjd, plate, mjd, fiber))
a14 = {}
c14 = {}
for chip in chips:
a14[chip] = fits.open(
os.environ['APOGEE_REDUX'] +
'/r8/red/{:d}/ap1D-{:s}-{:d}.fits'.format(mjd, chip, frame))
c14[chip] = fits.open(os.environ['APOGEE_REDUX'] +
'/r8/apo25m/{:d}/{:d}/apCframe-{:s}-{:08d}.fits'.
format(plate, mjd, chip, frame))
fig, ax = plots.multi(1, 3, hspace=0.01)
x = np.arange(4096)
pixmask = bitmask.PixelBitMask()
for ichip, chip in enumerate(chips):
y = v[1].data[ichip, :]
plots.plotl(ax[ichip], x, v[1].data[ichip, :] / v14[1].data[ichip, :])
bd = np.where(((v[3].data[ichip, :] & pixmask.badval()) > 0) | (
(v[3].data[ichip, :] & pixmask.getval('SIG_SKYLINE')) > 0))[0]
y[bd] = np.nan
plots.plotl(ax[ichip], x, y / v14[1].data[ichip, :])
fig, ax = plots.multi(3, 3, hspace=0.01)
x = np.arange(2048)
for ichip, chip in enumerate(chips):
plots.plotl(ax[ichip, 0], x, c[chip][1].data[300 - fiber, :])
plots.plotl(ax[ichip, 0], x, c14[chip][1].data[300 - fiber, :])
plots.plotl(
ax[ichip, 1], x,
c[chip][1].data[300 - fiber, :] / c14[chip][1].data[300 - fiber])
plots.plotl(
ax[ichip, 2], x,
a[chip][1].data[300 - fiber, :] / a14[chip][1].data[300 - fiber])
def visitcomb(allvisit,
starver,
load=None,
apred='r13',
telescope='apo25m',
nres=[5, 4.25, 3.5],
bconly=False,
plot=False,
write=True,
dorvfit=True,
apstar_vers='stars',
logger=None):
""" Combine multiple visits with individual RVs to rest frame sum
"""
if logger is None:
logger = dln.basiclogger()
if load is None: load = apload.ApLoad(apred=apred, telescope=telescope)
cspeed = 2.99792458e5 # speed of light in km/s
logger.info('Doing visitcomb for {:s} '.format(allvisit['apogee_id'][0]))
wnew = norm.apStarWave()
nwave = len(wnew)
nvisit = len(allvisit)
# initialize array for stack of interpolated spectra
zeros = np.zeros([nvisit, nwave])
izeros = np.zeros([nvisit, nwave], dtype=int)
stack = apload.ApSpec(zeros,
err=zeros.copy(),
bitmask=izeros,
cont=zeros.copy(),
sky=zeros.copy(),
skyerr=zeros.copy(),
telluric=zeros.copy(),
telerr=zeros.copy())
apogee_target1, apogee_target2, apogee_target3 = 0, 0, 0
apogee2_target1, apogee2_target2, apogee2_target3, apogee2_target4 = 0, 0, 0, 0
starflag, andflag = np.uint64(0), np.uint64(0)
starmask = bitmask.StarBitMask()
# Loop over each visit and interpolate to final wavelength grid
if plot: fig, ax = plots.multi(1, 2, hspace=0.001)
for i, visit in enumerate(allvisit):
if bconly: vrel = -visit['bc']
else: vrel = visit['vrel']
# Skip if we don't have an RV
if np.isfinite(vrel) is False: continue
# Load the visit
if load.telescope == 'apo1m':
apvisit = load.apVisit1m(visit['plate'],
visit['mjd'],
visit['apogee_id'],
load=True)
else:
apvisit = load.apVisit(int(visit['plate']),
visit['mjd'],
visit['fiberid'],
load=True)
pixelmask = bitmask.PixelBitMask()
# Rest-frame wavelengths transformed to this visit spectra
w = norm.apStarWave() * (1.0 + vrel / cspeed)
# Loop over the chips
for chip in range(3):
# Get the pixel values to interpolate to
pix = wave.wave2pix(w, apvisit.wave[chip, :])
gd, = np.where(np.isfinite(pix))
# Get a smoothed, filtered spectrum to use as replacement for bad values
cont = gaussian_filter(
median_filter(apvisit.flux[chip, :], [501], mode='reflect'),
100)
errcont = gaussian_filter(
median_filter(apvisit.flux[chip, :], [501], mode='reflect'),
100)
bd, = np.where(apvisit.bitmask[chip, :] & pixelmask.badval())
if len(bd) > 0:
apvisit.flux[chip, bd] = cont[bd]
apvisit.err[chip, bd] = errcont[bd]
# Load up quantity/error pairs for interpolation
raw = [[apvisit.flux[chip, :], apvisit.err[chip, :]**2],
[apvisit.sky[chip, :], apvisit.skyerr[chip, :]**2],
[apvisit.telluric[chip, :], apvisit.telerr[chip, :]**2]]
# Load up individual mask bits
for ibit, name in enumerate(pixelmask.name):
if name is not '' and len(
np.where(apvisit.bitmask[chip, :] & 2**ibit)[0]) > 0:
raw.append([
np.clip(apvisit.bitmask[chip, :] & 2**ibit, None, 1),
None
])
# Do the sinc interpolation
out = sincint.sincint(pix[gd], nres[chip], raw)
# From output flux, get continuum to remove, so that all spectra are
# on same scale. We'll later multiply in the median continuum
flux = out[0][0]
stack.cont[i, gd] = gaussian_filter(
median_filter(flux, [501], mode='reflect'), 100)
# Load interpolated spectra into output stack
stack.flux[i, gd] = out[0][0] / stack.cont[i, gd]
stack.err[i, gd] = out[0][1] / stack.cont[i, gd]
stack.sky[i, gd] = out[1][0]
stack.skyerr[i, gd] = out[1][1]
stack.telluric[i, gd] = out[2][0]
stack.telerr[i, gd] = out[2][1]
# For mask, set bits where interpolated value is above some threshold
# defined for each mask bit
iout = 3
for ibit, name in enumerate(pixelmask.name):
if name is not '' and len(
np.where(apvisit.bitmask[chip, :] & 2**ibit)[0]) > 0:
j = np.where(
np.abs(out[iout][0]) > pixelmask.maskcontrib[ibit])[0]
stack.bitmask[i, gd[j]] |= 2**ibit
iout += 1
# Increase uncertainties for persistence pixels
bd, = np.where(
(stack.bitmask[i, :] & pixelmask.getval('PERSIST_HIGH')) > 0)
if len(bd) > 0: stack.err[i, bd] *= np.sqrt(5)
bd, = np.where((
(stack.bitmask[i, :] & pixelmask.getval('PERSIST_HIGH')) == 0) & (
(stack.bitmask[i, :] & pixelmask.getval('PERSIST_MED')) > 0))
if len(bd) > 0: stack.err[i, bd] *= np.sqrt(4)
bd, = np.where(
((stack.bitmask[i, :] & pixelmask.getval('PERSIST_HIGH')) == 0)
& ((stack.bitmask[i, :] & pixelmask.getval('PERSIST_MED')) == 0)
& ((stack.bitmask[i, :] & pixelmask.getval('PERSIST_LOW')) > 0))
if len(bd) > 0: stack.err[i, bd] *= np.sqrt(3)
bd, = np.where(
(stack.bitmask[i, :] & pixelmask.getval('SIG_SKYLINE')) > 0)
if len(bd) > 0: stack.err[i, bd] *= np.sqrt(100)
if plot:
ax[0].plot(norm.apStarWave(), stack.flux[i, :])
ax[1].plot(norm.apStarWave(), stack.flux[i, :] / stack.err[i, :])
plt.draw()
pdb.set_trace()
# Accumulate for header of combined frame. Turn off visit specific RV flags first
visitflag = visit['starflag'] & ~starmask.getval(
'RV_REJECT') & ~starmask.getval('RV_SUSPECT')
starflag |= visitflag
andflag &= visitflag
if visit['survey'] == 'apogee':
apogee_target1 |= visit['apogee_target1']
apogee_target2 |= visit['apogee_target2']
apogee_target3 |= visit['apogee_target3']
elif visit['survey'].find('apogee2') >= 0:
apogee2_target1 |= visit['apogee_target1']
apogee2_target2 |= visit['apogee_target2']
apogee2_target3 |= visit['apogee_target3']
try:
apogee2_target4 |= visit['apogee_target4']
except:
pass
# MWM target flags?
# Create final spectrum
zeros = np.zeros([nvisit + 2, nwave])
izeros = np.zeros([nvisit + 2, nwave], dtype=int)
apstar = apload.ApSpec(zeros,
err=zeros.copy(),
bitmask=izeros,
wave=norm.apStarWave(),
sky=zeros.copy(),
skyerr=zeros.copy(),
telluric=zeros.copy(),
telerr=zeros.copy(),
cont=zeros.copy(),
template=zeros.copy())
apstar.header['CRVAL1'] = norm.logw0
apstar.header['CDELT1'] = norm.dlogw
apstar.header['CRPIX1'] = 1
apstar.header['CTYPE1'] = (
'LOG-LINEAR', 'Logarithmic wavelength scale in subsequent HDU')
apstar.header['DC-FLAG'] = 1
# Pixel-by-pixel weighted average
cont = np.median(stack.cont, axis=0)
apstar.flux[0, :] = np.sum(stack.flux / stack.err**2, axis=0) / np.sum(
1. / stack.err**2, axis=0) * cont
apstar.err[0, :] = np.sqrt(1. / np.sum(1. / stack.err**2, axis=0)) * cont
apstar.bitmask[0, :] = np.bitwise_and.reduce(stack.bitmask, 0)
apstar.cont[0, :] = cont
# Individual visits
apstar.flux[2:, :] = stack.flux * stack.cont
apstar.err[2:, :] = stack.err * stack.cont
apstar.bitmask[2:, :] = stack.bitmask
apstar.sky[2:, :] = stack.sky
apstar.skyerr[2:, :] = stack.skyerr
apstar.telluric[2:, :] = stack.telluric
apstar.telerr[2:, :] = stack.telerr
# Populate header
apstar.header['OBJID'] = (allvisit['apogee_id'][0], 'APOGEE object name')
apstar.header['APRED'] = (apred, 'APOGEE reduction version')
apstar.header['STARVER'] = (starver, 'apStar version')
apstar.header['HEALPIX'] = (apload.obj2healpix(allvisit['apogee_id'][0]),
'HEALPix location')
try:
apstar.header['SNR'] = (np.nanmedian(apstar.flux / apstar.err),
'Median S/N per apStar pixel')
except:
apstar.header['SNR'] = (0., 'Median S/N per apStar pixel')
apstar.header['RA'] = (allvisit['ra'].max(), 'right ascension, deg, J2000')
apstar.header['DEC'] = (allvisit['dec'].max(), 'declination, deg, J2000')
apstar.header['GLON'] = (allvisit['glon'].max(), 'Galactic longitude')
apstar.header['GLAT'] = (allvisit['glat'].max(), 'Galactic latitude')
apstar.header['J'] = (allvisit['j'].max(), '2MASS J magnitude')
apstar.header['J_ERR'] = (allvisit['j_err'].max(),
'2MASS J magnitude uncertainty')
apstar.header['H'] = (allvisit['h'].max(), '2MASS H magnitude')
apstar.header['H_ERR'] = (allvisit['h_err'].max(),
'2MASS H magnitude uncertainty')
apstar.header['K'] = (allvisit['k'].max(), '2MASS K magnitude')
apstar.header['K_ERR'] = (allvisit['k_err'].max(),
'2MASS K magnitude uncertainty')
try:
apstar.header['SRC_H'] = (allvisit['src_h'][0],
'source of H magnitude')
except KeyError:
pass
keys = [
'wash_m', 'wash_t2', 'ddo51', 'irac_3_6', 'irac_4_5', 'irac_5_8',
'wise_4_5', 'targ_4_5'
]
for key in keys:
try:
apstar.header[key] = allvisit[key].max()
except KeyError:
pass
apstar.header['AKTARG'] = (allvisit['ak_targ'].max(),
'Extinction used for targeting')
apstar.header['AKMETHOD'] = (allvisit['ak_targ_method'][0],
'Extinction method using for targeting')
apstar.header['AKWISE'] = (allvisit['ak_wise'].max(),
'WISE all-sky extinction')
apstar.header['SFD_EBV'] = (allvisit['sfd_ebv'].max(), 'SFD E(B-V)')
apstar.header['APTARG1'] = (apogee_target1,
'APOGEE_TARGET1 targeting flag')
apstar.header['APTARG2'] = (apogee_target2,
'APOGEE_TARGET2 targeting flag')
apstar.header['APTARG3'] = (apogee_target3,
'APOGEE_TARGET3 targeting flag')
apstar.header['AP2TARG1'] = (apogee2_target1,
'APOGEE2_TARGET1 targeting flag')
apstar.header['AP2TARG2'] = (apogee2_target2,
'APOGEE2_TARGET2 targeting flag')
apstar.header['AP2TARG3'] = (apogee2_target3,
'APOGEE2_TARGET3 targeting flag')
apstar.header['AP2TARG4'] = (apogee2_target4,
'APOGEE2_TARGET4 targeting flag')
apstar.header['NVISITS'] = (len(allvisit),
'Number of visit spectra combined flag')
apstar.header['STARFLAG'] = (starflag,
'bitwise OR of individual visit starflags')
apstar.header['ANDFLAG'] = (andflag,
'bitwise AND of individual visit starflags')
try:
apstar.header['N_COMP'] = (allvisit['n_components'].max(),
'Maximum number of components in RV CCFs')
except:
pass
apstar.header['VHBARY'] = (
(allvisit['vheliobary'] * allvisit['snr']).sum() /
allvisit['snr'].sum(), 'S/N weighted mean barycentric RV')
if len(allvisit) > 1:
apstar.header['vscatter'] = (allvisit['vheliobary'].std(ddof=1),
'standard deviation of visit RVs')
else:
apstar.header['VSCATTER'] = (0., 'standard deviation of visit RVs')
apstar.header['VERR'] = (0., 'unused')
apstar.header['RV_TEFF'] = (allvisit['rv_teff'].max(),
'Effective temperature from RV fit')
apstar.header['RV_LOGG'] = (allvisit['rv_logg'].max(),
'Surface gravity from RV fit')
apstar.header['RV_FEH'] = (allvisit['rv_feh'].max(),
'Metallicity from RV fit')
if len(allvisit) > 0:
meanfib = (allvisit['fiberid'] *
allvisit['snr']).sum() / allvisit['snr'].sum()
else:
meanfib = 999999.
if len(allvisit) > 1: sigfib = allvisit['fiberid'].std(ddof=1)
else: sigfib = 0.
apstar.header['MEANFIB'] = (meanfib, 'S/N weighted mean fiber number')
apstar.header['SIGFIB'] = (
sigfib, 'standard deviation (unweighted) of fiber number')
apstar.header['NRES'] = ('{:5.2f}{:5.2f}{:5.2f}'.format(*nres),
'number of pixels/resolution used for sinc')
# individual visit information in header
for i0, visit in enumerate(allvisit):
i = i0 + 1
apstar.header['SFILE{:d}'.format(i)] = (
visit['file'], ' Visit #{:d} spectrum file'.format(i))
apstar.header['DATE{:d}'.format(i)] = (
visit['dateobs'], 'DATE-OBS of visit {:d}'.format(i))
apstar.header['JD{:d}'.format(i)] = (
visit['jd'], 'Julian date of visit {:d}'.format(i))
# hjd = helio_jd(visitstr[i].jd-2400000.0,visitstr[i].ra,visitstr[i].dec)
#apstar.header['HJD{:d}'.format(i)] =
apstar.header['FIBER{:d}'.format(i)] = (visit['fiberid'],
' Fiber, visit {:d}'.format(i))
apstar.header['BC{:d}'.format(i)] = (
visit['bc'],
' Barycentric correction (km/s), visit {:d}'.format(i))
apstar.header['VRAD{:d}'.format(i)] = (
visit['vrel'], ' Doppler shift (km/s) of visit {:d}'.format(i))
#apstar.header['VERR%d'.format(i)] =
apstar.header['VHBARY{:d}'.format(i)] = (
visit['vheliobary'],
' Barycentric velocity (km/s), visit {:d}'.format(i))
apstar.header['SNRVIS{:d}'.format(i)] = (
visit['snr'], ' Signal/Noise ratio, visit {:d}'.format(i))
apstar.header['FLAG{:d}'.format(i)] = (
visit['starflag'], ' STARFLAG for visit {:d}'.format(i))
apstar.header.insert('SFILE{:d}'.format(i),
('COMMENT', 'VISIT {:d} INFORMATION'.format(i)))
# Do a RV fit just to get a template and normalized spectrum, for plotting
if dorvfit:
try:
apstar.setmask(pixelmask.badval())
spec = doppler.Spec1D(apstar.flux[0, :],
err=apstar.err[0, :],
bitmask=apstar.bitmask[0, :],
mask=apstar.mask[0, :],
wave=apstar.wave,
lsfpars=np.array([0]),
lsfsigma=apstar.wave / 22500 / 2.354,
instrument='APOGEE',
filename=apstar.filename)
out = doppler.rv.jointfit([spec],
verbose=False,
plot=False,
tweak=False,
maxvel=[-50, 50])
apstar.cont = out[3][0].flux
apstar.template = out[2][0].flux
except ValueError as err:
logger.error('Exception raised in visitcomb RV for: ',
apstar.header['FIELD'], apstar.header['OBJID'])
logger.error("ValueError: {0}".format(err))
except RuntimeError as err:
logger.error('Exception raised in visitcomb RV for: ',
apstar.header['FIELD'], apstar.header['OBJID'])
logger.error("Runtime error: {0}".format(err))
except:
logger.error('Exception raised in visitcomb RV fit for: ',
apstar.header['FIELD'], apstar.header['OBJID'])
# Write the spectrum to file
if write:
outfilenover = load.filename('Star', obj=apstar.header['OBJID'])
outdir = os.path.dirname(outfilenover)
outbase = os.path.splitext(os.path.basename(outfilenover))[0]
outbase += '-' + starver # add star version
outfile = outdir + '/' + outbase + '.fits'
if apstar_vers != 'stars':
outfile = outfile.replace('/stars/', '/' + apstar_vers + '/')
outdir = os.path.dirname(outfile)
try:
os.makedirs(os.path.dirname(outfile))
except:
pass
logger.info('Writing apStar file to ' + outfile)
apstar.write(outfile)
apstar.filename = outfile
mwm_root = os.environ['MWM_ROOT']
apstar.uri = outfile[len(mwm_root) + 1:]
# Create symlink no file with no version
if os.path.exists(outfilenover) or os.path.islink(outfilenover):
os.remove(outfilenover)
os.symlink(os.path.basename(outfile), outfilenover) # relative path
# Plot
gd, = np.where(
(apstar.bitmask[0, :]
& (pixelmask.badval() | pixelmask.getval('SIG_SKYLINE'))) == 0)
fig, ax = plots.multi(1, 3, hspace=0.001, figsize=(48, 6))
med = np.nanmedian(apstar.flux[0, :])
plots.plotl(ax[0],
norm.apStarWave(),
apstar.flux[0, :],
color='k',
yr=[0, 2 * med])
ax[0].plot(norm.apStarWave()[gd], apstar.flux[0, gd], color='g')
ax[0].set_ylabel('Flux')
try:
ax[1].plot(norm.apStarWave()[gd], apstar.cont[gd], color='g')
ax[1].set_ylabel('Normalized')
ax[1].plot(norm.apStarWave(), apstar.template, color='r')
except:
pass
plots.plotl(ax[2],
norm.apStarWave(),
apstar.flux[0, :] / apstar.err[0, :],
yt='S/N')
for i in range(3):
ax[i].set_xlim(15100, 17000)
ax[0].set_xlabel('Wavelength')
fig.savefig(outdir + '/plots/' + outbase + '.png')
# Plot
if plot:
ax[0].plot(norm.apStarWave(), apstar.flux, color='k')
ax[1].plot(norm.apStarWave(), apstar.flux / apstar.err, color='k')
plt.draw()
pdb.set_trace()
return apstar
def gauss_decomp(out,
phase='one',
alpha1=0.5,
alpha2=1.5,
thresh=[4, 4],
plot=None,
filt=False):
""" Do Gaussian decomposition of CCF using gausspy
Parameters:
out : list of dictionaries for each frame, giving x_ccf, ccf, and ccferr
phase : gausspy paramater
alpha1 : gausspy parameter
alpha2 : gausspy parameter for second set of gaussians if phase=='two'
thresh : gausspy parameter
plot (str) : if not None, do plot and use as root file name for plot
filt (bool) : if true, apply filtering to remove components judged to be insignificant
"""
g = gp.GaussianDecomposer()
g.set('phase', phase)
g.set('SNR_thresh', thresh)
g.set('alpha1', alpha1)
g.set('alpha2', alpha2)
gout = []
if plot is not None:
fig, ax = plots.multi(1, len(out), hspace=0.001, figsize=(6, 2 + n))
for i, final in enumerate(out):
gd, = np.where(np.isfinite(final['x_ccf']))
x = final['x_ccf'][gd]
y = final['ccf'][gd]
# high pass filter for better performance
if filt:
final['ccf'][gd] -= gaussian_filter(final['ccf'][gd],
50,
mode='nearest')
try:
decomp = g.decompose(x, final['ccf'][gd], final['ccferr'][gd])
n = decomp['N_components']
except:
print(
'Exception in Gaussian decomposition, setting to 0 components')
n = 0
decomp = None
if filt and n > 0:
# remove components if they are within width of brighter component, or <0.25 peak ,
# or more than twice as wide, or if primary component is wide
for j in range(1, n):
pars_j = decomp['best_fit_parameters'][j::n]
for k in range(j):
pars_k = decomp['best_fit_parameters'][k::n]
if (pars_j[0] > pars_k[0] and pars_k[0] > 0 and
(abs(pars_j[2] - pars_k[2]) < abs(pars_j[1]) or
pars_k[0] < 0.25 * pars_j[0] or abs(pars_j[1]) > 100
or np.abs(pars_k[1]) > 2 * np.abs(pars_j[1]))):
decomp['best_fit_parameters'][k] = 0
decomp['N_components'] -= 1
elif (pars_k[0] > pars_j[0] and pars_j[0] > 0
and (abs(pars_j[2] - pars_k[2]) < abs(pars_k[1])
or pars_j[0] < 0.25 * pars_k[0]
or abs(pars_k[1]) > 100
or np.abs(pars_j[1]) > 2 * np.abs(pars_k[1]))):
decomp['best_fit_parameters'][j] = 0
pars_j = decomp['best_fit_parameters'][j::n]
decomp['N_components'] -= 1
gout.append(decomp)
if plot is not None:
plots.plotl(ax[i], final['x_ccf'], final['ccf'])
ax[i].plot(final['x_ccf'], final['ccferr'], color='r')
for j in range(n):
pars = gout[i]['best_fit_parameters'][j::n]
ax[i].plot(x, gaussian(*pars)(x))
if pars[0] > 0: color = 'k'
else: color = 'r'
ax[i].text(0.1,
0.8 - j * 0.1,
'{:8.1f}{:8.1f}{:8.1f}'.format(*pars),
transform=ax[i].transAxes,
color=color)
fig.savefig(plot + '_ccf.png')
del g
return gout
def norm(w, coef, ichip):
x = w - 16000.
logflux = coef[0] * x**3 + coef[1] * x**2 + coef[2] * x
if ichip == 0: logflux += coef[3]
elif ichip == 2: logflux += coef[4]
return 10.**logflux
for row in rows[0:30:5]:
for ichip, chip in enumerate(chips):
x = b[chip][4].data[row, :]
plots.plotl(ax[0],
x,
b[chip][1].data[row, :],
color='k',
semilogy=True)
plots.plotl(ax[0],
x,
b[chip][1].data[row, :] / norm(x, w, ichip),
color=colors[ichip],
semilogy=True)
r10 = apload.ApLoad(apred='r10')
b = r10.ap1D(3190056)
for row in rows[0:30:5]:
plots.plotl(ax[1],
b['c'][4].data[row, :],
b['c'][1].data[row, :],
color='b',