def fitcoords(images, data_path='.', params={}):
"""
Run iraf fitcoords to fit 2D function to wavelength as a function of
column and line number.
Parameters
----------
images : str
Image(s) to be reidentified.
data_path : str
Directory that contains the data.
params : dict
Iraf fitcoords parameters.
"""
_check_working_dir(data_path)
logger.info('running iraf fitcoords')
iraf.fitcoords.unlearn()
iraf.fitcoords.function = params.pop('function', 'legendre')
iraf.fitcoords.xorder = params.pop('xorder', 5)
iraf.fitcoords.yorder = params.pop('yorder', 3)
iraf.fitcoords.interactive = params.pop('interactive', 'yes')
images = images.replace('.fits', '')
iraf.fitcoords(images=images)
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 make_lambda_solution(arc_list,fcnamedict):
irf_prm.set_identify_calibration(iraf.identify)
irf_prm.set_reidentify_calibration(iraf.reidentify)
irf_prm.set_fitcoords_calibration(iraf.fitcoords)
for arc in arc_list:
iraf.identify(images=arc)
iraf.reidentify(reference=arc,images=arc)
iraf.fitcoords(images=arc[:-5],fitname=fcnamedict[arc])
return
def fitcoords():
iraf.twodspec()
iraf.longslit()
iraf.fitcoords(images='Lamp',
fitname='Lamp',
interactive='yes',
combine='no',
database='database',
deletions='deletions.db',
function='chebyshev',
xorder=6,
yorder=6)
def standard_trace(standard_list,supplement_list,outname='star'):
irf_prm.set_identify_standard(iraf.identify)
irf_prm.set_reidentify_standard(iraf.reidentify)
irf_prm.set_fitcoords_standard(iraf.fitcoords)
all_standards = ''
for standrd in standard_list:
iraf.identify(images=standrd)
iraf.reidentify(reference=standrd,images=standrd)
all_standards += standrd[:-5]+','
for supplement in supplement_list:
iraf.identify(images=supplement)
iraf.reidentify(reference=supplement,images=supplement)
all_standards += supplement[:-5]+','
iraf.fitcoords(images=all_standards,fitname=outname)
return
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)
# graphics = "stdgraph",\
# cursor = "",\
# answer = "yes",\
# crval = "",\
# cdelt = "",\
# aidpars = "")
### Run fitcoords to fit coordinate and get ready for transformation
### xorder and yorder specifies the fitting orders
iraf.fitcoords(
images = im_slice + "_" + string.split(arc_name,".")[0],\
fitname = "",\
interactive = 0,\
combine = 0,\
database = "database",\
deletions = "deletions.db",\
function = "chebyshev",\
xorder = 6,\
yorder = 2,\
logfiles = "STDOUT,logfile",\
plotfile = "plotfile",\
graphics = "stdgraph")
### Run transform according to the results of fitcoords
### This is the final step
### The product is a nicely straightened fits file
os.system("rm -f t" + im_slice + "_" + file_name)
for i in arc_list:
os.system("rm -f t" + im_slice + "_" + i)
image_to_reduce = im_slice + "_" + file_name + ","