from astropy.io import fits
from astropy.table import Table
from astropy import stats
import os, sys, shutil
from kai.reduce import util
#import util
import numpy as np
from pyraf import iraf as ir
from kai import instruments
from datetime import datetime
import pdb
import astropy
from pkg_resources import parse_version
[docs]def makesky(files, nite,
wave, skyscale=True,
raw_dir=None,
instrument=instruments.default_inst):
"""
Make short wavelength (not L-band or longer) skies.
Parameters
----------
files : list of int
Integer list of the files. Does not require padded zeros.
nite : str
Name for night of observation (e.g.: "nite1"), used as suffix
inside the reduce sub-directories.
wave : str
Name for the observation passband (e.g.: "kp")
skyscale : bool, default=True
Whether or not to scale the sky files to the common median.
Turn on for scaling skies before subtraction.
raw_dir : str, optional
Directory where raw files are stored. By default,
assumes that raw files are stored in '../raw'
instrument : instruments object, optional
Instrument of data. Default is `instruments.default_inst`
"""
# Make new directory for the current passband and switch into it
util.mkdir(wave)
os.chdir(wave)
# Determine directory locatons
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
# Set location of raw data
rawDir = rootDir + 'raw/'
# Check if user has specified a specific raw directory
if raw_dir is not None:
rawDir = util.trimdir(os.path.abspath(raw_dir) + '/')
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 = instrument.make_filenames(files, rootDir=skyDir)
nsc = instrument.make_filenames(files, rootDir=skyDir, prefix='scale')
skies = instrument.make_filenames(files, rootDir=rawDir)
for ii in range(len(nn)):
if os.path.exists(nn[ii]): os.remove(nn[ii])
if os.path.exists(nsc[ii]): os.remove(nsc[ii])
shutil.copy(skies[ii], nn[ii])
# Write out the sources of the sky files
data_sources_file = open(redDir + 'data_sources.txt', 'a')
data_sources_file.write('---\n# Sky Files ({0})\n'.format(wave))
for cur_file in skies:
out_line = '{0} ({1})\n'.format(cur_file, datetime.now())
data_sources_file.write(out_line)
data_sources_file.close()
# 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)):
# Get the sigma-clipped mean and stddev on the dark
img_sky = fits.getdata(nn[i], ignore_missing_end=True)
if parse_version(astropy.__version__) < parse_version('3.0'):
sky_stats = stats.sigma_clipped_stats(img_sky,
sigma=3,
iters=4)
else:
sky_stats = stats.sigma_clipped_stats(img_sky,
sigma=10,
maxiters=4)
sky_mean[i] = sky_stats[0]
sky_all = sky_mean.mean()
sky_scale = sky_all/sky_mean
for i in range(len(skies)):
_nn = fits.open(nn[i], ignore_missing_end=True)
_nn[0].data = _nn[0].data * sky_scale[i]
_nn[0].writeto(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'
if os.path.exists(output): os.remove(output)
ir.unlearn('imcombine')
ir.imcombine.combine = 'median'
ir.imcombine.reject = 'none'
ir.imcombine.nlow = 1
ir.imcombine.nhigh = 1
ir.imcombine('@' + skylist, output)
# Change back to original directory
os.chdir('../')
[docs]def makesky_lp(files, nite, wave, number=3, rejectHsigma=None,
raw_dir=None,
instrument=instruments.default_inst):
"""
Make L' skies by carefully treating the ROTPPOSN angle
of the K-mirror. Uses 3 skies combined (set by number keyword).
Parameters
----------
files : list of int
Integer list of the files. Does not require padded zeros.
nite : str
Name for night of observation (e.g.: "nite1"), used as suffix
inside the reduce sub-directories.
wave : str
Name for the observation passband (e.g.: "lp")
number : int, default=3
Number of skies to be combined
rejectHsigma : int, default:None
Flag to pass for rejectHsigma for IRAF imcombine
By default, no flags are passed
raw_dir : str, optional
Directory where raw files are stored. By default,
assumes that raw files are stored in '../raw'
instrument : instruments object, optional
Instrument of data. Default is `instruments.default_inst`
"""
# Make new directory for the current passband and switch into it
util.mkdir(wave)
os.chdir(wave)
# Determine directory locatons
waveDir = os.getcwd() + '/'
redDir = util.trimdir(os.path.abspath(waveDir + '../') + '/')
rootDir = util.trimdir(os.path.abspath(redDir + '../') + '/')
skyDir = waveDir + 'sky_' + nite + '/'
rawDir = rootDir + 'raw/'
# Check if user has specified a specific raw directory
if raw_dir is not None:
rawDir = util.trimdir(os.path.abspath(raw_dir) + '/')
util.mkdir(skyDir)
print('sky dir: ',skyDir)
print('wave dir: ',waveDir)
raw = instrument.make_filenames(files, rootDir=rawDir)
skies = instrument.make_filenames(files, rootDir=skyDir)
# Write out the sources of the sky files
data_sources_file = open(redDir + 'data_sources.txt', 'a')
data_sources_file.write('---\n# Sky Files ({0})\n'.format(wave))
for cur_file in skies:
out_line = '{0} ({1})\n'.format(cur_file, datetime.now())
data_sources_file.write(out_line)
data_sources_file.close()
_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
files, angles = read_sky_rot_file(_skyRot)
# 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()
# Change back to original directory
os.chdir('../')
[docs]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 = instrument.make_filenames(files, rootDir=rawDir)
skies = instrument.make_filenames(files, rootDir=skyDir)
_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
files, angles = read_sky_rot_file(_skyRot)
# 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()
#ir.imdelete('@' + _nlis)
[docs]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 = instrument.make_filenames(files, rootDir=skyDir)
nsc = instrument.make_filenames(files, rootDir=skyDir, prefix='scale')
skies = instrument.make_filenames(files, rootDir=rawDir)
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)
for ii in range(len(nn)):
img_sky = fits.getdata(nn[i], ignore_missing_end=True)
if parse_version(astropy.__version__) < parse_version('3.0'):
sky_stats = stats.sigma_clipped_stats(img_sky,
sigma_lower=10, sigma_upper=3,
iters=10)
else:
sky_stats = stats.sigma_clipped_stats(img_sky,
sigma_lower=10, sigma_upper=3,
maxiters=10)
sky_mean[ii] = sky_stats[0]
sky_std[ii] = sky_stats[2]
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)
[docs]def makesky_lp_fromsci(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 = instrument.make_filenames(files, rootDir=rawDir)
skies = instrument.make_filenames(files, rootDir=skyDir)
flatDir = redDir + 'calib/flats/'
flat = flatDir + 'flat_' + wave + '.fits'
if not os.access(flat, os.F_OK):
flat = flatDir + 'flat.fits'
_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.imarith('@'+_rawlis, '/', flat, '@'+_nlis)
#ir.imcopy('@' + _rawlis, '@' + _nlis, verbose='no')
ir.hselect('@' + _nlis, "$I,ROTPPOSN", 'yes', Stdout=_skyRot)
# Read in the list of files and rotation angles
files, angles = read_sky_rot_file(_skyRot)
# 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])
skyFitsTmp = skyDir + sky + '_tmp.fits'
skyFits = skyDir + sky + '.fits'
util.rmall([skyFitsTmp, 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), skyFitsTmp)
ir.imarith(skyFitsTmp, '*', flat, 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()
[docs]def read_sky_rot_file(sky_rot_file):
"""Read in the list of files and rotation angles."""
rotTab = Table.read(sky_rot_file, format='ascii', header_start=None)
cols = list(rotTab.columns.keys())
files = rotTab[cols[0]]
angles = rotTab[cols[1]]
return files, angles