def makesky_lp(files, nite, wave, number=3, rejectHsigma=None):
"""Make L' skies by carefully treating the ROTPPOSN angle
of the K-mirror. Uses 3 skies combined (set by number keyword)."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
raw = [rawDir + 'n' + str(i).zfill(4) for i in files]
skies = [skyDir + 'n' + str(i).zfill(4) for i in files]
_rawlis = skyDir + 'raw.lis'
_nlis = skyDir + 'n.lis'
_skyRot = skyDir + 'skyRot.txt'
_txt = skyDir + 'rotpposn.txt'
_out = skyDir + 'sky'
_log = _out + '.log'
util.rmall([_rawlis, _nlis, _skyRot, _txt, _out, _log])
util.rmall([sky + '.fits' for sky in skies])
open(_rawlis, 'w').write('\n'.join(raw) + '\n')
open(_nlis, 'w').write('\n'.join(skies) + '\n')
print 'makesky_lp: Getting raw files'
ir.imcopy('@' + _rawlis, '@' + _nlis, verbose='no')
ir.hselect('@' + _nlis, "$I,ROTPPOSN", 'yes', Stdout=_skyRot)
# Read in the list of files and rotation angles
rotTab = asciidata.open(_skyRot)
files = rotTab[0].tonumpy()
angles = rotTab[1].tonumpy()
# Fix angles to be between -180 and 180
angles[angles > 180] -= 360.0
angles[angles < -180] += 360.0
sidx = np.argsort(angles)
# Make sorted numarrays
angles = angles[sidx]
files = files[sidx]
f_log = open(_log, 'w')
f_txt = open(_txt, 'w')
# Skip the first and last since we are going to
# average every NN files.
print 'makesky_lp: Combining to make skies.'
startIdx = number / 2
stopIdx = len(sidx) - (number / 2)
for i in range(startIdx, stopIdx):
sky = 'sky%.1f' % (angles[i])
skyFits = skyDir + sky + '.fits'
util.rmall([skyFits])
# Take NN images
start = i - (number / 2)
stop = start + number
list = [file for file in files[start:stop]]
short = [file for file in files[start:stop]]
angleTmp = angles[start:stop]
# Make short names
for j in range(len(list)):
tmp = (short[j]).rsplit('/', 1)
short[j] = tmp[len(tmp) - 1]
print '%s: %s' % (sky, " ".join(short))
f_log.write('%s:' % sky)
for j in range(len(short)):
f_log.write(' %s' % short[j])
for j in range(len(angleTmp)):
f_log.write(' %6.1f' % angleTmp[j])
f_log.write('\n')
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
if (rejectHsigma == None):
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
else:
ir.imcombine.reject = 'sigclip'
ir.imcombine.lsigma = 100
ir.imcombine.hsigma = rejectHsigma
ir.imcombine.zero = 'median'
ir.imcombine.logfile = ''
ir.imcombine(','.join(list), skyFits)
ir.hedit(skyFits,
'SKYCOMB',
'%s: %s' % (sky, ' '.join(short)),
add='yes',
show='no',
verify='no')
f_txt.write('%13s %8.3f\n' % (sky, angles[i]))
f_txt.close()
f_log.close()
def makesky_fromsci(files, nite, wave):
"""Make short wavelength (not L-band or longer) skies."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
print 'sky dir: ', skyDir
print 'wave dir: ', waveDir
skylist = skyDir + 'skies_to_combine.lis'
output = skyDir + 'sky_' + wave + '.fits'
util.rmall([skylist, output])
nn = [skyDir + 'n' + str(i).zfill(4) for i in files]
nsc = [skyDir + 'scale' + str(i).zfill(4) for i in files]
skies = [rawDir + 'n' + str(i).zfill(4) for i in files]
for ii in range(len(nn)):
ir.imdelete(nn[ii])
ir.imdelete(nsc[ii])
ir.imcopy(skies[ii], nn[ii], verbose="no")
# Make list for combinng. Reset the skyDir to an IRAF variable.
ir.set(skydir=skyDir)
f_on = open(skylist, 'w')
for ii in range(len(nn)):
nn_new = nn[ii].replace(skyDir, "skydir$")
f_on.write(nn_new + '\n')
f_on.close()
# Calculate some sky statistics, but reject high (star-like) pixels
sky_mean = np.zeros([len(skies)], dtype=float)
sky_std = np.zeros([len(skies)], dtype=float)
text = ir.imstat("@" + skylist,
fields='midpt,stddev',
nclip=10,
lsigma=10,
usigma=3,
format=0,
Stdout=1)
for ii in range(len(nn)):
fields = text[ii].split()
sky_mean[ii] = float(fields[0])
sky_std[ii] = float(fields[1])
sky_mean_all = sky_mean.mean()
sky_std_all = sky_std.mean()
# Upper threshold above which we will ignore pixels when combining.
hthreshold = sky_mean_all + 3.0 * sky_std_all
ir.imdelete(output)
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'sigclip'
ir.imcombine.mclip = 'yes'
ir.imcombine.hsigma = 2
ir.imcombine.lsigma = 10
ir.imcombine.hthreshold = hthreshold
ir.imcombine('@' + skylist, output)
def makesky(files, nite, wave, skyscale=1):
"""Make short wavelength (not L-band or longer) skies."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
print 'sky dir: ', skyDir
print 'wave dir: ', waveDir
skylist = skyDir + 'skies_to_combine.lis'
output = skyDir + 'sky_' + wave + '.fits'
util.rmall([skylist, output])
nn = [skyDir + 'n' + str(i).zfill(4) for i in files]
nsc = [skyDir + 'scale' + str(i).zfill(4) for i in files]
skies = [rawDir + 'n' + str(i).zfill(4) for i in files]
for ii in range(len(nn)):
ir.imdelete(nn[ii])
ir.imdelete(nsc[ii])
ir.imcopy(skies[ii], nn[ii], verbose="no")
# scale skies to common median
if skyscale:
_skylog = skyDir + 'sky_scale.log'
util.rmall([_skylog])
f_skylog = open(_skylog, 'w')
sky_mean = np.zeros([len(skies)], dtype=float)
for i in range(len(skies)):
text = ir.imstat(nn[i],
fields='mean',
nclip=4,
lsigma=10,
usigma=10,
format=0,
Stdout=1)
sky_mean[i] = float(text[0])
sky_all = sky_mean.mean()
sky_scale = sky_all / sky_mean
for i in range(len(skies)):
ir.imarith(nn[i], '*', sky_scale[i], nsc[i])
skyf = nn[i].split('/')
print('%s skymean=%10.2f skyscale=%10.2f' %
(skyf[len(skyf) - 1], sky_mean[i], sky_scale[i]))
f_skylog.write('%s %10.2f %10.2f\n' %
(nn[i], sky_mean[i], sky_scale[i]))
# Make list for combinng
f_on = open(skylist, 'w')
f_on.write('\n'.join(nsc) + '\n')
f_on.close()
#skylist = skyDir + 'scale????.fits'
f_skylog.close()
else:
# Make list for combinng
f_on = open(skylist, 'w')
f_on.write('\n'.join(nn) + '\n')
f_on.close()
#skylist = skyDir + 'n????.fits'
ir.imdelete(output)
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine('@' + skylist, output)
def makesky_lp2(files, nite, wave):
"""Make L' skies by carefully treating the ROTPPOSN angle
of the K-mirror. Uses only 2 skies combined."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
raw = [rawDir + 'n' + str(i).zfill(4) for i in files]
skies = [skyDir + 'n' + str(i).zfill(4) for i in files]
_rawlis = skyDir + 'raw.lis'
_nlis = skyDir + 'n.lis'
_skyRot = skyDir + 'skyRot.txt'
_txt = skyDir + 'rotpposn.txt'
_out = skyDir + 'sky'
_log = _out + '.log'
util.rmall([_rawlis, _nlis, _skyRot, _txt, _out, _log])
util.rmall([sky + '.fits' for sky in skies])
open(_rawlis, 'w').write('\n'.join(raw) + '\n')
open(_nlis, 'w').write('\n'.join(skies) + '\n')
print 'makesky_lp: Getting raw files'
ir.imcopy('@' + _rawlis, '@' + _nlis, verbose='no')
ir.hselect('@' + _nlis, "$I,ROTPPOSN", 'yes', Stdout=_skyRot)
# Read in the list of files and rotation angles
rotTab = asciidata.open(_skyRot)
files = rotTab[0].tonumpy()
angles = rotTab[1].tonumpy()
# Fix angles to be between -180 and 180
angles[angles > 180] -= 360.0
angles[angles < -180] += 360.0
sidx = np.argsort(angles)
# Make sorted numarrays
angles = angles[sidx]
files = files[sidx]
f_log = open(_log, 'w')
f_txt = open(_txt, 'w')
# Skip the first and last since we are going to
# average every 3 files.
print 'makesky_lp: Combining to make skies.'
for i in range(1, len(sidx)):
angav = (angles[i] + angles[i - 1]) / 2.
sky = 'sky%.1f' % (angav)
skyFits = skyDir + sky + '.fits'
util.rmall([skyFits])
# Average 2 images
list = [file for file in files[i - 1:i + 1]]
short = [file for file in files[i - 1:i + 1]]
# Make short names
for j in range(len(list)):
tmp = (short[j]).rsplit('/', 1)
short[j] = tmp[len(tmp) - 1]
print '%s: %s %s' % (sky, short[0], short[1])
f_log.write('%s: %s %s %6.1f %6.1f\n' %
(sky, short[0], short[1], angles[i - 1], angles[i]))
ir.unlearn('imcombine')
ir.imcombine.combine = 'average'
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine.logfile = ''
ir.imcombine(list[1] + ',' + list[0], skyFits)
ir.hedit(skyFits,
'SKYCOMB',
'%s: %s %s' % (sky, short[0], short[1]),
add='yes',
show='no',
verify='no')
f_txt.write('%13s %8.3f\n' % (sky, angav))
f_txt.close()
f_log.close()
def makesky_lp(files, nite, wave, number=3, rejectHsigma=None):
"""Make L' skies by carefully treating the ROTPPOSN angle
of the K-mirror. Uses 3 skies combined (set by number keyword)."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
raw = [rawDir + 'n' + str(i).zfill(4) for i in files]
skies = [skyDir + 'n' + str(i).zfill(4) for i in files]
_rawlis = skyDir + 'raw.lis'
_nlis = skyDir + 'n.lis'
_skyRot = skyDir + 'skyRot.txt'
_txt = skyDir + 'rotpposn.txt'
_out = skyDir + 'sky'
_log = _out + '.log'
util.rmall([_rawlis, _nlis, _skyRot, _txt, _out, _log])
util.rmall([sky + '.fits' for sky in skies])
open(_rawlis, 'w').write('\n'.join(raw)+'\n')
open(_nlis, 'w').write('\n'.join(skies)+'\n')
print 'makesky_lp: Getting raw files'
ir.imcopy('@' + _rawlis, '@' + _nlis, verbose='no')
ir.hselect('@' + _nlis, "$I,ROTPPOSN", 'yes', Stdout=_skyRot)
# Read in the list of files and rotation angles
rotTab = asciidata.open(_skyRot)
files = rotTab[0].tonumpy()
angles = rotTab[1].tonumpy()
# Fix angles to be between -180 and 180
angles[angles > 180] -= 360.0
angles[angles < -180] += 360.0
sidx = np.argsort(angles)
# Make sorted numarrays
angles = angles[sidx]
files = files[sidx]
f_log = open(_log, 'w')
f_txt = open(_txt, 'w')
# Skip the first and last since we are going to
# average every NN files.
print 'makesky_lp: Combining to make skies.'
startIdx = number / 2
stopIdx = len(sidx) - (number / 2)
for i in range(startIdx, stopIdx):
sky = 'sky%.1f' % (angles[i])
skyFits = skyDir + sky + '.fits'
util.rmall([skyFits])
# Take NN images
start = i - (number/2)
stop = start + number
list = [file for file in files[start:stop]]
short = [file for file in files[start:stop]]
angleTmp = angles[start:stop]
# Make short names
for j in range(len(list)):
tmp = (short[j]).rsplit('/', 1)
short[j] = tmp[len(tmp)-1]
print '%s: %s' % (sky, " ".join(short))
f_log.write('%s:' % sky)
for j in range(len(short)):
f_log.write(' %s' % short[j])
for j in range(len(angleTmp)):
f_log.write(' %6.1f' % angleTmp[j])
f_log.write('\n')
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
if (rejectHsigma == None):
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
else:
ir.imcombine.reject = 'sigclip'
ir.imcombine.lsigma = 100
ir.imcombine.hsigma = rejectHsigma
ir.imcombine.zero = 'median'
ir.imcombine.logfile = ''
ir.imcombine(','.join(list), skyFits)
ir.hedit(skyFits, 'SKYCOMB',
'%s: %s' % (sky, ' '.join(short)),
add='yes', show='no', verify='no')
f_txt.write('%13s %8.3f\n' % (sky, angles[i]))
f_txt.close()
f_log.close()
def makesky(files, nite, wave, skyscale=1):
"""Make short wavelength (not L-band or longer) skies."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
print 'sky dir: ',skyDir
print 'wave dir: ',waveDir
skylist = skyDir + 'skies_to_combine.lis'
output = skyDir + 'sky_' + wave + '.fits'
util.rmall([skylist, output])
nn = [skyDir + 'n' + str(i).zfill(4) for i in files]
nsc = [skyDir + 'scale' + str(i).zfill(4) for i in files]
skies = [rawDir + 'n' + str(i).zfill(4) for i in files]
for ii in range(len(nn)):
ir.imdelete(nn[ii])
ir.imdelete(nsc[ii])
ir.imcopy(skies[ii], nn[ii], verbose="no")
# scale skies to common median
if skyscale:
_skylog = skyDir + 'sky_scale.log'
util.rmall([_skylog])
f_skylog = open(_skylog, 'w')
sky_mean = np.zeros([len(skies)], dtype=float)
for i in range(len(skies)):
text = ir.imstat(nn[i], fields='mean', nclip=4,
lsigma=10, usigma=10, format=0, Stdout=1)
sky_mean[i] = float(text[0])
sky_all = sky_mean.mean()
sky_scale = sky_all/sky_mean
for i in range(len(skies)):
ir.imarith(nn[i], '*', sky_scale[i], nsc[i])
skyf = nn[i].split('/')
print('%s skymean=%10.2f skyscale=%10.2f' %
(skyf[len(skyf)-1], sky_mean[i],sky_scale[i]))
f_skylog.write('%s %10.2f %10.2f\n' %
(nn[i], sky_mean[i], sky_scale[i]))
# Make list for combinng
f_on = open(skylist, 'w')
f_on.write('\n'.join(nsc) + '\n')
f_on.close()
#skylist = skyDir + 'scale????.fits'
f_skylog.close()
else:
# Make list for combinng
f_on = open(skylist, 'w')
f_on.write('\n'.join(nn) + '\n')
f_on.close()
#skylist = skyDir + 'n????.fits'
ir.imdelete(output)
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine('@' + skylist, output)
def makesky_fromsci(files, nite, wave):
"""Make short wavelength (not L-band or longer) skies."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
print 'sky dir: ',skyDir
print 'wave dir: ',waveDir
skylist = skyDir + 'skies_to_combine.lis'
output = skyDir + 'sky_' + wave + '.fits'
util.rmall([skylist, output])
nn = [skyDir + 'n' + str(i).zfill(4) for i in files]
nsc = [skyDir + 'scale' + str(i).zfill(4) for i in files]
skies = [rawDir + 'n' + str(i).zfill(4) for i in files]
for ii in range(len(nn)):
ir.imdelete(nn[ii])
ir.imdelete(nsc[ii])
ir.imcopy(skies[ii], nn[ii], verbose="no")
# Make list for combinng. Reset the skyDir to an IRAF variable.
ir.set(skydir=skyDir)
f_on = open(skylist, 'w')
for ii in range(len(nn)):
nn_new = nn[ii].replace(skyDir, "skydir$")
f_on.write(nn_new + '\n')
f_on.close()
# Calculate some sky statistics, but reject high (star-like) pixels
sky_mean = np.zeros([len(skies)], dtype=float)
sky_std = np.zeros([len(skies)], dtype=float)
text = ir.imstat("@" + skylist, fields='midpt,stddev', nclip=10,
lsigma=10, usigma=3, format=0, Stdout=1)
for ii in range(len(nn)):
fields = text[ii].split()
sky_mean[ii] = float(fields[0])
sky_std[ii] = float(fields[1])
sky_mean_all = sky_mean.mean()
sky_std_all = sky_std.mean()
# Upper threshold above which we will ignore pixels when combining.
hthreshold = sky_mean_all + 3.0 * sky_std_all
ir.imdelete(output)
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'sigclip'
ir.imcombine.mclip = 'yes'
ir.imcombine.hsigma = 2
ir.imcombine.lsigma = 10
ir.imcombine.hthreshold = hthreshold
ir.imcombine('@' + skylist, output)
def makesky_lp2(files, nite, wave):
"""Make L' skies by carefully treating the ROTPPOSN angle
of the K-mirror. Uses only 2 skies combined."""
# Start out in something like '06maylgs1/reduce/kp/'
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
util.mkdir(skyDir)
raw = [rawDir + 'n' + str(i).zfill(4) for i in files]
skies = [skyDir + 'n' + str(i).zfill(4) for i in files]
_rawlis = skyDir + 'raw.lis'
_nlis = skyDir + 'n.lis'
_skyRot = skyDir + 'skyRot.txt'
_txt = skyDir + 'rotpposn.txt'
_out = skyDir + 'sky'
_log = _out + '.log'
util.rmall([_rawlis, _nlis, _skyRot, _txt, _out, _log])
util.rmall([sky + '.fits' for sky in skies])
open(_rawlis, 'w').write('\n'.join(raw)+'\n')
open(_nlis, 'w').write('\n'.join(skies)+'\n')
print 'makesky_lp: Getting raw files'
ir.imcopy('@' + _rawlis, '@' + _nlis, verbose='no')
ir.hselect('@' + _nlis, "$I,ROTPPOSN", 'yes', Stdout=_skyRot)
# Read in the list of files and rotation angles
rotTab = asciidata.open(_skyRot)
files = rotTab[0].tonumpy()
angles = rotTab[1].tonumpy()
# Fix angles to be between -180 and 180
angles[angles > 180] -= 360.0
angles[angles < -180] += 360.0
sidx = np.argsort(angles)
# Make sorted numarrays
angles = angles[sidx]
files = files[sidx]
f_log = open(_log, 'w')
f_txt = open(_txt, 'w')
# Skip the first and last since we are going to
# average every 3 files.
print 'makesky_lp: Combining to make skies.'
for i in range(1, len(sidx)):
angav = (angles[i] + angles[i-1])/2.
sky = 'sky%.1f' % (angav)
skyFits = skyDir + sky + '.fits'
util.rmall([skyFits])
# Average 2 images
list = [file for file in files[i-1:i+1]]
short = [file for file in files[i-1:i+1]]
# Make short names
for j in range(len(list)):
tmp = (short[j]).rsplit('/', 1)
short[j] = tmp[len(tmp)-1]
print '%s: %s %s' % (sky, short[0], short[1])
f_log.write('%s: %s %s %6.1f %6.1f\n' %
(sky, short[0], short[1],
angles[i-1], angles[i]))
ir.unlearn('imcombine')
ir.imcombine.combine = 'average'
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine.logfile = ''
ir.imcombine(list[1]+','+list[0], skyFits)
ir.hedit(skyFits, 'SKYCOMB',
'%s: %s %s' % (sky, short[0], short[1]),
add='yes', show='no', verify='no')
f_txt.write('%13s %8.3f\n' % (sky, angav))
f_txt.close()
f_log.close()
