def transform(images, out_images, coord_fits, data_path='.', params={}):
"""
Run iraf transform to rectify 2D spectrum.
Parameters
----------
images : str
Images to transform.
out_images : str
Output image file names.
coord_fits:
Name of coordinate fits in database.
data_path : str
Directory that contains the data.
params : dict
Iraf fitcoords parameters.
"""
_check_working_dir(data_path)
logger.info('running iraf transform')
iraf.transform.unlearn()
iraf.transform.x1 = params.pop('x1', 'INDEF')
iraf.transform.x2 = params.pop('x2', 'INDEF')
iraf.transform.dx = params.pop('dx', 'INDEF')
iraf.transform.nx = params.pop('nx', 'INDEF')
iraf.transform.flux = params.pop('flux', 'yes')
iraf.transform.interptype = params.pop('interptype', 'linear')
iraf.transform(input=images, output=out_images, fitnames=coord_fits)
def transform(lst):
f = open(lst)
l = f.readlines()
f.close()
namelst = ['ftbo' + i for i in l]
outputlst = ['wftbo' + i for i in l]
# namelst = [i.split('.')[0] + 'otbf.fits' for i in l]
# outputlst = [i.split('.')[0] + 'otbfw.fits' for i in l]
f = open("temp1.txt", 'w')
for i in namelst:
f.write(i + '\n')
f.close()
f = open("temp2.txt", 'w')
for i in outputlst:
f.write(i + '\n')
f.close()
iraf.twodspec()
iraf.longslit(dispaxis=2)
# for i in namelst:
# print '#' * 30, i, '===>', i.split('.')[0] + 'w.fits'
# iraf.transform(input = i, output = i.split('.')[0] + 'w.fits',
# minput = '', moutput = '', fitnames = 'LampLamp',
# database = 'database', interptype = 'spline3',
# flux = 'yes')
iraf.transform(input='@temp1.txt',
output='@temp2.txt',
minput='',
moutput='',
fitnames='LampLamp',
database='database',
interptype='spline3',
flux='yes')
def transform(lst):
f = open(lst)
l = f.readlines()
f.close()
namelst = ['ftb' + i for i in l]
outputlst = ['wftb' + i for i in l]
f = open("temp1.txt", 'w')
for i in namelst:
f.write(i + '\n')
f.close()
f = open("temp2.txt", 'w')
for i in outputlst:
f.write(i + '\n')
f.close()
iraf.twodspec()
iraf.longslit(dispaxis=2)
#
iraf.transform(input='@temp1.txt',
output='@temp2.txt',
minput='',
moutput='',
fitnames='LampLamp',
database='database',
interptype='spline3',
flux='yes')
def wal(lstfile):
iraf.noao()
iraf.twodspec()
iraf.longslit()
iraf.identify(images = 'Lamp'
, section = 'middle column', database = 'database'
, coordlist = 'linelists$idhenear.dat', units = '', nsum = 10
, match = -3.0, maxfeatures = 50, zwidth = 100.0
, ftype = 'emission', fwidth = 20.0, cradius = 5.0
, threshold = 0.0, minsep = 2.0, function = 'chebyshev'
, order = 6, sample = '*', niterate = 0
, low_reject = 3.0, high_reject = 3.0, grow = 0.0
, autowrite = False, graphics = 'stdgraph', cursor = ''
, crval = '', cdelt = '')
iraf.reidentify(reference = 'Lamp'
, images = 'Lamp', interactive = 'no', section = 'column'
, newaps = True, override = True, refit = True, trace = False
, step = 10, nsum = 10, shift = 0.0, search = 0.0, nlost = 5
, cradius = 7.0, threshold = 0.0, addfeatures = False
, coordlist = 'linelists$idhenear.dat', match = -3.0, maxfeatures = 50
, minsep = 2.0, database = 'database', logfiles = 'logfile'
, plotfile = '', verbose = False, graphics = 'stdgraph', cursor = ''
, answer = 'yes', crval = '', cdelt = '', mode = 'al')
iraf.fitcoords(images = 'Lamp'
, fitname = 'Lamp', interactive = True, combine = False, database = 'database'
, deletions = 'deletions.db', function = 'chebyshev', xorder = 6
, yorder = 6, logfiles = 'STDOUT,logfile', plotfile = 'plotfile'
, graphics = 'stdgraph', cursor = '', mode = 'al')
iraf.longslit(dispaxis = 2)
iraf.transform(input = '%ftbo%ftbo%@' + lstfile
, output = '%wftbo%wftbo%@' + lstfile, minput = '', moutput = ''
, fitnames = 'LampLamp', database = 'database', interptype = 'spline3'
, flux = True)
def transform(science_list,object_match,arc_fc_dict,arc_coords,fcstar='star'):
'''Transforms filename to t+filename using noao>twodspec>longslit>transform'''
irf_prm.set_transform(iraf.transform)
for obj in science_list:
fcarc = arc_fc_dict[object_match[obj]]
dx_param = arc_coords[object_match[obj]]
#These should be the same for each observing mode
#Because then they're all mapped together for
#Accurate flux calibration and imcombining
iraf.transform.x1 = dx_param[0]
iraf.transform.x2 = dx_param[1]
iraf.transform.dx = dx_param[2]
iraf.transform(input=obj,output='t'+obj,fitnames=fcstar+','+fcarc)
return
def reduce2d(self, obj, arc, do_error=None):
images = obj.name
do_error = do_error if do_error is not None else self.do_error
hduorig= fits.open(images)
os.remove(images)
hduimg = fits.PrimaryHDU(data=hduorig[0].data,header=hduorig[0].header)
hduimg.writeto(images)
hduvarimg = fits.PrimaryHDU(data=hduorig[1].data,header=hduorig[0].header)
rt.rmexist([obj.var])
hduvarimg.writeto(obj.var)
rt.prepare_image(images, do_error=do_error)
self.rssidentify(arc)
nxpix = obj.header['NAXIS1']
rt.loadparam(self.rssconfig, ['iraf.transform', 'iraf.fit1d'])
config= self.rssconfig
if self.do_error:
iraf.errorpars.errtype='uniform'
iraf.errorpars.resample='no'
iraf.samplepars.axis=1
iraf.samplepars.setParam('naverage', config['iraf.fit1d']['naverage'])
iraf.samplepars.setParam('low_reject', config['iraf.fit1d']['low_reject'])
iraf.samplepars.setParam('high_reject', config['iraf.fit1d']['high_reject'])
iraf.samplepars.setParam('niterate', config['iraf.fit1d']['niterate'])
iraf.samplepars.setParam('grow', config['iraf.fit1d']['grow'])
rt.rmexist([obj.wave, obj.bkg, obj.wave_var, obj.bkg_var])
# Wavelength calibration
iraf.transform(obj.noext, obj.wave, fitnames = arc.noext, logfiles = self.logfile)
iraf.fit1d(obj.wave, obj.sky, "fit", axis=2)
iraf.imarith(obj.wave, "-", obj.sky, obj.bkg)
# Calibrated error file
if do_error:
iraf.transform(obj.var, obj.wave_var, fitnames = arc.noext, logfiles = self.logfile)
iraf.gfit1d(obj.wave, 'tmptab.tab', function=config['iraf.fit1d']['function'],
order=config['iraf.fit1d']['order'], xmin=1, xmax=nxpix, interactive='no')
iraf.tabpar('tmptab.tab',"rms", 1, Stdout=self.logfile)
rmsval = float(iraf.tabpar.value)
tmpval=rmsval*rmsval
print('RMS of the fit (for VAR plane propagation) = %s ' %rmsval)
iraf.imexpr("a+b",obj.bkg_var, a=obj.wave_var, b=tmpval)
print("**** Done with image %s ****" % (images))
def wal(lstfile, lampname):
iraf.noao()
iraf.twodspec()
iraf.longslit()
# iraf.identify(images = 'Lamp.fits', section = 'middle column',
# database = 'database', coordlist = 'linelists$idhenear.dat',
# nsum = 10, match = -3.0, maxfeatures = 50, zwidth = 100.0,
# ftype = 'emission', fwidth = 20.0, cradius = 7.0, threshold = 0.0,
# minsep = 2.0, function = 'chebyshev', order = 6, sample = '*',
# niterate = 0, low_reject = 3.0, high_reject = 3.0, grow = 0.0,
# autowrite = 'no')
# iraf.reidentify(reference = 'Lamp', images = 'Lamp', interactive = 'no',
# section = 'column', newaps = 'yes', override = 'yes', refit = 'yes',
# trace = 'no', step = 10, nsum = 10, shift = 0.0, search = 0.0,
# nlost = 5, cradius = 7.0, threshold = 0.0, addfeatures = 'no',
# coordlist = 'linelists$idhenear.dat', match = -3.0,
# maxfeatures = 50, minsep = 2.0, database = 'database')
iraf.identify(images=lampname,
section='middle column',
database='database',
coordlist='linelists$idhenear.dat',
units='',
nsum=10,
match=-3.0,
maxfeatures=50,
zwidth=100.0,
ftype='emission',
fwidth=20.0,
cradius=7.0,
threshold=0.0,
minsep=2.0,
function='chebyshev',
order=6,
sample='*',
niterate=0,
low_reject=3.0,
high_reject=3.0,
grow=0.0,
autowrite=False,
graphics='stdgraph',
cursor='',
crval='',
cdelt='')
iraf.reidentify(reference=lampname,
images=lampname,
interactive='no',
section='column',
newaps=True,
override=True,
refit=True,
trace=False,
step=10,
nsum=10,
shift=0.0,
search=0.0,
nlost=5,
cradius=7.0,
threshold=0.0,
addfeatures=False,
coordlist='linelists$idhenear.dat',
match=-3.0,
maxfeatures=50,
minsep=2.0,
database='database',
logfiles='logfile',
plotfile='',
verbose=False,
graphics='stdgraph',
cursor='',
answer='yes',
crval='',
cdelt='',
mode='al')
iraf.fitcoords(images=lampname,
fitname=lampname,
interactive=True,
combine=False,
database='database',
deletions='deletions.db',
function='chebyshev',
xorder=6,
yorder=6,
logfiles='STDOUT,logfile',
plotfile='plotfile',
graphics='stdgraph',
cursor='',
mode='al')
iraf.longslit(dispaxis=2)
iraf.transform(input='%ftbo%ftbo%@' + lstfile,
output='%wftbo%wftbo%@' + lstfile,
minput='',
moutput='',
fitnames=lampname + lampname,
database='database',
interptype='spline3',
flux=True)
def transform(basename, waveref, spatialref, overwrite=False):
# input: input fits file basename to be corrected.
# wavefef: wavelength reference basename.
# spatialref referrence: spatial referrence basename.
print('\n#############################')
print('Transforming')
hdl = fits.open(basename + '.ov.fits')
if not fi.check_version(hdl):
return False
if not fi.check_version_f(waveref + '.ov.fits'):
return False
if not fi.check_version_f(spatialref + '.fcmb.fits'):
return False
hdr = hdl[0].header
binfct1 = hdr['BIN-FCT1']
disperser = hdr['DISPERSR']
filter1 = hdr['FILTER01']
filter2 = hdr['FILTER02']
filter3 = hdr['FILTER03']
hdl.close()
# entering the channel image directory.
print('\t Entering the channel image directory, \"' + fi.chimagedir +
'\".')
os.chdir(fi.chimagedir)
ny = 5500
# 300R + SO58
if disperser == 'SCFCGRMR01' and filter1 == 'NONE' and \
filter2 == 'SCFCFLSO58' and filter3 == 'NONE':
y1 = 5600.
dy = 1.34
ny = 3800
# 300R + L600
if disperser == 'SCFCGRMR01' and filter1 == 'SCFCFLL600' and \
filter2 == 'NONE' and filter3 == 'NONE':
y1 = 2800.
dy = 0.636
ny = 4000
# 300B+SY47
if disperser == 'SCFCGRMB01' and filter1 == 'NONE' and \
filter2 == 'SCFCFLSY47' and filter3 == 'NONE':
y1 = 4500.
dy = 1.34
ny = 4000
# 300B + NONE
if disperser == 'SCFCGRMB01' and filter1 == 'NONE' and \
filter2 == 'NONE' and filter3 == 'NONE':
y1 = 3500.
dy = 1.34
ny = 3500
# VPH850 + SO58
if disperser == 'SCFCGRHD85' and filter1 == 'NONE' and \
filter2 == 'SCFCFLSO58' and filter3 == 'NONE':
y1 = 5600.
dy = 1.17
ny = 4600
# VPH520
if disperser == 'SCFCGRHD52' and filter1 == 'NONE' and \
filter2 == 'NONE' and filter3 == 'NONE':
y1 = 4100.0
dy = 0.39
# VPH650+SY47
if disperser == 'SCFCGRHD65' and filter1 == 'NONE' and \
filter2 == 'SCFCFLSY47' and filter3 == 'NONE':
y1 = 5200.0
dy = 0.60
# VPH900 + SO58
if disperser == 'SCFCGRHD90' and filter1 == 'NONE' and \
filter2 == 'SCFCFLSO58' and filter3 == 'NONE':
y1 = 7400.0
dy = 0.74
# Not to display items in IRAF packages
sys.stdout = open('/dev/null', 'w')
iraf.noao()
iraf.twodspec()
iraf.longslit()
sys.stdout = sys.__stdout__ # Back to the stadard output
for i in range(1, 25):
checkfile = basename + '.ch%02d.wc.fits' % i
if os.path.isfile(checkfile) and not overwrite:
print('\t ' + checkfile + ' already exits. Skiped.')
else:
if overwrite:
try:
os.remove(basename + '.ch%02d.wc.fits' % i)
except:
pass
iraf.transform(basename+'.ch%02d'%i, basename+'.ch%02d.wc'%i,\
waveref+'.ch%02d,'%i+spatialref+'.ch%02dedge'%i,\
interpt='linear', database='database',\
x1=1.0, x2='INDEF', dx=1.0, nx=int(200.0/binfct1), xlog='no',\
y1=y1, y2='INDEF', dy=dy, ny=ny,ylog='no',\
flux='no', blank=0, logfile='transform.log')
hdl = fits.open(basename + '.ch%02d.wc.fits' % i, mode='update')
#hdl.info()
hdl[0].header['COMPBASE']=(waveref, \
'Frame ID of the comparison frame.')
hdl[0].header['DISTBASE']=(spatialref, \
'Frame ID used for distortion correction.')
hdl[0].header['CUNIT2'] = ('Angstrom')
print('\t ' + basename + '.ch%02d.wc.fits was created.' % i)
print('\t Going back to the original directory.')
os.chdir('..')
return True
print "transforming images " + image_to_reduce
### Add the parameter dispaxis to the image header
### else iraf.transform would query for it
### 1 indicates that the x axis is the dispersion axis
iraf.hedit(images = image_to_reduce,fields="DISPAXIS",value="1",add=1,addonly=0,delete=0,verify=0,show=1,update=1)
iraf.transform(
input = image_to_reduce,\
output = output_images,\
minput = "",\
moutput = "",\
fitnames = im_slice + "_" + string.split(arc_name,".")[0],\
database = "database",\
interptype = "spline3",\
x1 = "INDEF",\
x2 = "INDEF",\
dx = "INDEF",\
nx = "INDEF",\
xlog = 0,\
y1 = "INDEF",\
y2 = "INDEF",\
dy = "INDEF",\
ny = "INDEF",\
ylog = 0,\
flux = 1,\
blank = "INDEF",\
logfile = "STDOUT,logfile",\
mode = "al")
file = item.split('\n')[0]
shutil.copy(file, 'spec' + str(i) + '.fits')
i += 1
#assign 2d dispersion solution - from now IRAF
print('Applying wavelength solution...')
print('.')
print('.')
iraf.noao.twodspec.longslit
for file in os.listdir(os.getcwd()):
if file.startswith('spec'):
iraf.transform(file,
output='w' + file,
database="wavelength",
fitnames="He_Ne_ThAr_bsHe_Ne_ThAr_bs",
flux='yes')
#minor tweak to wavelength solution
iraf.hedit('wspec*.fits',
fields='CRVAL2',
value='2573.5979',
verify='no',
show='no')
#
#FROM NOW -> iraf
print('.')
print('.')
