def bfixpix(image_file, mask_file, outsuffix='_f', msksuffix='_s'):
"""
Inputs
---------
image_file : string
input image file to fix bad pixels on
mask_file : string
mask file (0 == good pixels, >0 == bad pixels
outsuffix : string
suffix for fixed image. default = '_f'
msksuffix : string
suffix for bad pixels significance mask. default = '_s'
"""
outf = image_file.replace('.fits', outsuffix + '.fits')
outm = image_file.replace('.fits', msksuffix + '.fits')
util.rmall([outf, outm])
print("bfixpix: {0} -> {1}".format(image_file, outf))
# fetch the image, fetch the mask
img, hdr = fits.getdata(image_file, header=True)
msk = fits.getdata(mask_file)
# median the image
medimg = ndimage.median_filter(img, 3, mode='nearest')
# generate the pixel files
outf_img = np.where(msk == 0, img, medimg)
outm_img = np.where(msk == 1, (img - medimg), 0)
fits.writeto(outf, outf_img, hdr)
fits.writeto(outm, outm_img, hdr)
def darPlusDistortion(inputFits, outputRoot, xgeoim=None, ygeoim=None):
"""
Create lookup tables (stored as FITS files) that can be used
to correct DAR. Optionally, the shifts due to DAR can be added
to existing NIRC2 distortion lookup tables if the xgeoim/ygeoim
input parameters are set.
Inputs:
inputFits - a NIRC2 image for which to determine the DAR correction
outputRoot - the root name for the output. This will be used as the
root name of two new images with names, <outputRoot>_x.fits and
<outputRoot>_y.fits.
Optional Inputs:
xgeoim/ygeoim - FITS images used in Drizzle distortion correction
(lookup tables) will be modified to incorporate the DAR correction.
The order of the correction is 1. distortion, 2. DAR.
"""
# Get the size of the image and the half-points
hdr = pyfits.getheader(inputFits)
imgsizeX = float(hdr['NAXIS1'])
imgsizeY = float(hdr['NAXIS2'])
halfX = round(imgsizeX / 2.0)
halfY = round(imgsizeY / 2.0)
# First get the coefficients
(pa, darCoeffL, darCoeffQ) = nirc2dar(inputFits)
#(a, b) = nirc2darPoly(inputFits)
# Create two 1024 arrays (or read in existing ones) for the
# X and Y lookup tables
if ((xgeoim == None) or (xgeoim == '')):
x = np.zeros((imgsizeY, imgsizeX), dtype=float)
else:
x = pyfits.getdata(xgeoim)
if ((ygeoim == None) or (ygeoim == '')):
y = np.zeros((imgsizeY, imgsizeX), dtype=float)
else:
y = pyfits.getdata(ygeoim)
# Get proper header info.
fits = pyfits.open(inputFits)
axisX = np.arange(imgsizeX, dtype=float) - halfX
axisY = np.arange(imgsizeY, dtype=float) - halfY
xcoo2d, ycoo2d = np.meshgrid(axisX, axisY)
xnew1 = xcoo2d + x
ynew1 = ycoo2d + y
# Rotate coordinates clockwise by PA so that zenith is along +ynew2
# PA = parallactic angle (angle from +y to zenith going CCW)
sina = math.sin(pa)
cosa = math.cos(pa)
xnew2 = xnew1 * cosa + ynew1 * sina
ynew2 = -xnew1 * sina + ynew1 * cosa
# Apply DAR correction along the y axis
xnew3 = xnew2
ynew3 = ynew2*(1 + darCoeffL) + ynew2*np.abs(ynew2)*darCoeffQ
# Rotate coordinates counter-clockwise by PA back to original
xnew4 = xnew3 * cosa - ynew3 * sina
ynew4 = xnew3 * sina + ynew3 * cosa
#xnew2 = a[0] + a[1]*xnew1 + a[2]*ynew1 + \
# a[3]*xnew1**2 + a[4]*xnew1*ynew1 + a[5]*ynew1**2
#ynew2 = b[0] + b[1]*xnew1 + b[2]*ynew1 + \
# b[3]*xnew1**2 + b[4]*xnew1*ynew1 + b[5]*ynew1**2
x = xnew4 - xcoo2d
y = ynew4 - ycoo2d
xout = outputRoot + '_x.fits'
yout = outputRoot + '_y.fits'
util.rmall([xout, yout])
fits[0].data = x
fits[0].writeto(xout, output_verify='silentfix')
fits[0].data = y
fits[0].writeto(yout, output_verify='silentfix')
return (xout, yout)
def red_dir(directory,
clean_dir,
sky_key='sky',
flat_key='Domeflat',
sci_keys=['Wd 2 pos 1', 'Wd 2 pos 2', 'Wd 2 pos 3', 'Wd 2 pos 4'],
frame_list=None):
'''
Note, must be ran from pyraf interavtive terminal
perform reduction on directory given
directory must be full path name
sky_key is header keywaord for sky frames
dome_key is header keyword for domes
sci_coadds is the minimum number of coadds required for an image to be considered a science image
'''
#os.chdir(directory)
print 'Reduction being performed in ', directory
if frame_list == None:
frame_list = glob.glob(directory + '*.fits')
dir_ap = ''
for i in range(len(frame_list)):
frame_list[i] = frame_list[i].replace('.fits', '')
else:
dir_ap = directory
#go through the fits files and make 3 lists, one of skies, one of domes one of science frames
sci_f = open('obj.lis', 'w')
dome_f = open('flat.lis', 'w')
all_f = open('all.lis', 'w')
sky_f = open('sky.lis', 'w')
dome_list = []
sci_l = []
for i in frame_list:
#import pdb; pdb.set_trace()
print >> all_f, dir_ap + i + '.fits'
head = fits.getheader(dir_ap + i + '.fits')
if head['OBJECT'] == sky_key:
print >> sky_f, dir_ap + 'g' + i + '.fits'
elif head['OBJECT'] == flat_key:
print >> dome_f, dir_ap + 'g' + i + '.fits'
dome_list.append(i)
else:
for j in sci_keys:
if head['OBJECT'].replace(' ', '') == j:
print >> sci_f, dir_ap + 'g' + i + '.fits'
sci_l.append(i + '.fits')
if len(sci_l) == 0:
print 'No science frames found in directory'
import pdb
pdb.set_trace()
sky_f.close()
sci_f.close()
dome_f.close()
all_f.close()
from pyraf.iraf import gemini
from pyraf.iraf import gsaoi
from pyraf import iraf
gemini.unlearn()
gsaoi.unlearn()
#raw_dir = util.getcwd()
#prep_dir = raw_dir+'g'
#print raw_dir
util.rmall(['gaprep.log'])
print 'Arguements for gaprepare', '@all.lis', directory + 'g'
gsaoi.gaprepare('@all.lis',
outpref=directory + 'g',
fl_vardq='yes',
logfile='gaprep.log')
gsaoi.gaflat('@flat.lis', outsufx='flat', fl_vardq='yes')
flat_name = 'g' + dome_list[0] + "_flat.fits"
shutil.move('g' + dome_list[0] + "_flat.fits",
directory + 'g' + dome_list[0] + "_flat.fits")
#print flat_name
#gsaoi.gareduce('@sky.lis', rawpath=directory, gaprep_pref = directory+'g',calpath=directory, fl_flat='yes', flatimg=flat_name)
gsaoi.gasky('@sky.lis',
outimages='sky.fits',
fl_vardq='yes',
fl_dqprop='yes',
flatimg=directory + flat_name)
shutil.move('sky.fits', directory + 'sky.fits')
gsaoi.gareduce('@obj.lis',
fl_vardq='yes',
fl_dqprop='yes',
fl_dark='no',
calpath=directory,
fl_sky='yes',
skyimg=directory + 'sky.fits',
fl_flat='yes',
flatimg=flat_name)
#util.rmall(['obj.lis','sky.lis','flat.lis', 'all.lis'])
for i in sci_l:
for k in range(4):
iraf.imcopy('rg' + i + '[' + str(k + 1) + '][inherit+]',
'rg' + i.replace('.fits',
str(k + 1) + '.fits'))
#add in line to reflect the x-axis --- note I do not correct the WCS at all!!!!
iraf.imcopy('rg' + i.replace('.fits',
str(k + 1) + '.fits[-*,*]'),
'rg' + i.replace('.fits',
str(k + 1) + '.fits'))
shutil.move(
'rg' + i.replace('.fits',
str(k + 1) + '.fits'), clean_dir + 'rg' +
i.replace('.fits', '_' + str(k + 1) + '.fits'))
shutil.copy('rg' + i, directory + 'rg' + i)
os.remove('rg' + i)
def __del__(self): rmall(self.tempdir)
def darPlusDistortion(inputFits, outputRoot, xgeoim=None, ygeoim=None):
"""
Create lookup tables (stored as FITS files) that can be used
to correct DAR. Optionally, the shifts due to DAR can be added
to existing NIRC2 distortion lookup tables if the xgeoim/ygeoim
input parameters are set.
Inputs:
inputFits - a NIRC2 image for which to determine the DAR correction
outputRoot - the root name for the output. This will be used as the
root name of two new images with names, <outputRoot>_x.fits and
<outputRoot>_y.fits.
Optional Inputs:
xgeoim/ygeoim - FITS images used in Drizzle distortion correction
(lookup tables) will be modified to incorporate the DAR correction.
The order of the correction is 1. distortion, 2. DAR.
"""
# Get the size of the image and the half-points
hdr = pyfits.getheader(inputFits)
imgsizeX = float(hdr['NAXIS1'])
imgsizeY = float(hdr['NAXIS2'])
halfX = round(imgsizeX / 2.0)
halfY = round(imgsizeY / 2.0)
# First get the coefficients
(pa, darCoeffL, darCoeffQ) = nirc2dar(inputFits)
#(a, b) = nirc2darPoly(inputFits)
# Create two 1024 arrays (or read in existing ones) for the
# X and Y lookup tables
if ((xgeoim == None) or (xgeoim == '')):
x = np.zeros((imgsizeY, imgsizeX), dtype=float)
else:
x = pyfits.getdata(xgeoim)
if ((ygeoim == None) or (ygeoim == '')):
y = np.zeros((imgsizeY, imgsizeX), dtype=float)
else:
y = pyfits.getdata(ygeoim)
# Get proper header info.
fits = pyfits.open(inputFits)
axisX = np.arange(imgsizeX, dtype=float) - halfX
axisY = np.arange(imgsizeY, dtype=float) - halfY
xcoo2d, ycoo2d = np.meshgrid(axisX, axisY)
xnew1 = xcoo2d + x
ynew1 = ycoo2d + y
# Rotate coordinates clockwise by PA so that zenith is along +ynew2
# PA = parallactic angle (angle from +y to zenith going CCW)
sina = math.sin(pa)
cosa = math.cos(pa)
xnew2 = xnew1 * cosa + ynew1 * sina
ynew2 = -xnew1 * sina + ynew1 * cosa
# Apply DAR correction along the y axis
xnew3 = xnew2
ynew3 = ynew2 * (1 + darCoeffL) + ynew2 * np.abs(ynew2) * darCoeffQ
# Rotate coordinates counter-clockwise by PA back to original
xnew4 = xnew3 * cosa - ynew3 * sina
ynew4 = xnew3 * sina + ynew3 * cosa
#xnew2 = a[0] + a[1]*xnew1 + a[2]*ynew1 + \
# a[3]*xnew1**2 + a[4]*xnew1*ynew1 + a[5]*ynew1**2
#ynew2 = b[0] + b[1]*xnew1 + b[2]*ynew1 + \
# b[3]*xnew1**2 + b[4]*xnew1*ynew1 + b[5]*ynew1**2
x = xnew4 - xcoo2d
y = ynew4 - ycoo2d
xout = outputRoot + '_x.fits'
yout = outputRoot + '_y.fits'
util.rmall([xout, yout])
fits[0].data = x
fits[0].writeto(xout, output_verify='silentfix')
fits[0].data = y
fits[0].writeto(yout, output_verify='silentfix')
return (xout, yout)
def red_dir(directory,clean_dir, sky_key='sky', flat_key='Domeflat', sci_keys= ['Wd 2 pos 1','Wd 2 pos 2', 'Wd 2 pos 3', 'Wd 2 pos 4'], frame_list = None):
'''
Note, must be ran from pyraf interavtive terminal
perform reduction on directory given
directory must be full path name
sky_key is header keywaord for sky frames
dome_key is header keyword for domes
sci_coadds is the minimum number of coadds required for an image to be considered a science image
'''
#os.chdir(directory)
print 'Reduction being performed in ', directory
if frame_list == None:
frame_list = glob.glob(directory+'*.fits')
dir_ap = ''
for i in range(len(frame_list)):
frame_list[i] = frame_list[i].replace('.fits','')
else:
dir_ap=directory
#go through the fits files and make 3 lists, one of skies, one of domes one of science frames
sci_f = open('obj.lis', 'w')
dome_f = open('flat.lis', 'w')
all_f = open('all.lis', 'w')
sky_f = open('sky.lis','w')
dome_list = []
sci_l = []
for i in frame_list:
#import pdb; pdb.set_trace()
print >> all_f, dir_ap+i+'.fits'
head = fits.getheader(dir_ap+i+'.fits')
if head['OBJECT'] == sky_key:
print >> sky_f, dir_ap+'g'+i+'.fits'
elif head['OBJECT']==flat_key:
print >> dome_f, dir_ap+'g'+i+'.fits'
dome_list.append(i)
else:
for j in sci_keys:
if head['OBJECT'].replace(' ','') == j:
print >> sci_f, dir_ap+'g'+i+'.fits'
sci_l.append(i+'.fits')
if len(sci_l)==0:
print 'No science frames found in directory'
import pdb; pdb.set_trace()
sky_f.close()
sci_f.close()
dome_f.close()
all_f.close()
from pyraf.iraf import gemini
from pyraf.iraf import gsaoi
from pyraf import iraf
gemini.unlearn()
gsaoi.unlearn()
#raw_dir = util.getcwd()
#prep_dir = raw_dir+'g'
#print raw_dir
util.rmall(['gaprep.log'])
print 'Arguements for gaprepare', '@all.lis', directory+'g'
gsaoi.gaprepare('@all.lis',outpref=directory+'g',fl_vardq='yes', logfile='gaprep.log')
gsaoi.gaflat('@flat.lis', outsufx='flat', fl_vardq='yes')
flat_name= 'g'+dome_list[0]+"_flat.fits"
shutil.move('g'+dome_list[0]+"_flat.fits", directory+'g'+dome_list[0]+"_flat.fits")
#print flat_name
#gsaoi.gareduce('@sky.lis', rawpath=directory, gaprep_pref = directory+'g',calpath=directory, fl_flat='yes', flatimg=flat_name)
gsaoi.gasky('@sky.lis', outimages='sky.fits', fl_vardq='yes', fl_dqprop='yes', flatimg=directory+flat_name)
shutil.move('sky.fits', directory+'sky.fits')
gsaoi.gareduce('@obj.lis',fl_vardq='yes', fl_dqprop='yes', fl_dark='no',calpath=directory, fl_sky='yes',skyimg=directory+'sky.fits', fl_flat='yes',flatimg=flat_name)
#util.rmall(['obj.lis','sky.lis','flat.lis', 'all.lis'])
for i in sci_l:
for k in range(4):
iraf.imcopy('rg'+i+'['+str(k+1)+'][inherit+]' , 'rg'+i.replace('.fits',str(k+1)+'.fits'))
#add in line to reflect the x-axis --- note I do not correct the WCS at all!!!!
iraf.imcopy('rg'+i.replace('.fits',str(k+1)+'.fits[-*,*]'), 'rg'+i.replace('.fits',str(k+1)+'.fits'))
shutil.move('rg'+i.replace('.fits',str(k+1)+'.fits'), clean_dir+'rg'+i.replace('.fits','_'+str(k+1)+'.fits'))
shutil.copy('rg'+i, directory+'rg'+i)
os.remove('rg'+i)
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 __del__(self): """Destructor. Cleans up as this object is destroyed.""" rmall(self.work_dir)
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()