def identify_slits(slit_img, order=3, section=3, width=25, sigma=2.5, thres=6):
"""determine the x and y positions of the slits from identification in the image"""
# split the image into sections determined from sect
inc = mt.divide_image(slit_img, section)
# determine where the slits are located, if option = True then only return the
# edges, if option = False then return all the additional info.
slits = mt.locate_slits(slit_img, inc, width, sigma, thres, False)
# Check the detected slits for any variations in the amount of slits detected
# for each image section and that the same amount of edges for left and right
# have been detected for each image section.
slit_positions = mt.get_slits(slits)
return order, slit_positions
def identify_slits(slit_img, order=3, section=3, width=25, sigma=2.5, thres=6):
"""determine the x and y positions of the slits from identification in the image"""
# split the image into sections determined from sect
inc = mt.divide_image(slit_img, section)
# determine where the slits are located, if option = True then only return the
# edges, if option = False then return all the additional info.
slits = mt.locate_slits(slit_img, inc, width, sigma, thres, False)
# Check the detected slits for any variations in the amount of slits detected
# for each image section and that the same amount of edges for left and right
# have been detected for each image section.
slit_positions = mt.get_slits(slits)
return order, slit_positions
def specslit(image, outimage, outpref, exttype='auto', slitfile='', outputslitfile='',
regprefix='', sections=3, width=25, sigma=2.2, thres=6, order=3, padding=5, yoffset=0,
inter=False, clobber=True, logfile='salt.log', verbose=True):
with logging(logfile, debug) as log:
# check all the input and make sure that all the input needed is provided
# by the user
# read the image or image list and check if each in the list exist
infiles = saltio.argunpack('Input', image)
# unpack the outfiles
outfiles = saltio.listparse(
'Outimages',
outimage,
outpref,
infiles,
'')
# from the extraction type, check whether the input file is specified.
# if the slitfile parameter is specified then use the slit files for
# the extraction. if the extraction type is auto then use image for the
# detection and the slit extraction
if exttype == 'rsmt' or exttype == 'fits' or exttype == 'ascii' or exttype == 'ds9':
slitfiles = saltio.argunpack('Slitfile', slitfile)
if len(slitfiles) == 1:
slitfiles = slitfiles * len(infiles)
saltio.comparelists(infiles, slitfiles, 'image', 'slitfile')
elif exttype == 'auto':
slitfiles = infiles
log.message(
'Extraction type is AUTO. Slit detection will be done from image')
# read in if an optional ascii file is requested
if len(outputslitfile) > 0:
outslitfiles = saltio.argunpack('Outslitfiles', outputslitfile)
saltio.comparelists(
infiles,
outslitfiles,
'image',
'outputslitfile')
else:
outslitfiles = [''] * len(infiles)
# check if the width and sigma parameters were specified.
# default is 25 and 2.2
if width < 10.:
msg = 'The width parameter needs be a value larger than 10'
raise SALTSpecError(msg)
if sigma < 0.0:
msg = 'Sigma must be greater than zero'
raise SaltSpecError(msg)
# check the treshold parameter. this needs to be specified by the user
if thres <= 0.0:
msg = 'Threshold must be greater than zero'
raise SaltSpecError(msg)
# check to make sure that the sections are greater than the order
if sections <= order:
msg = 'Number of sections must be greater than the order for the spline fit'
raise SaltSpecError(msg)
# run through each of the images and extract the slits
for img, oimg, sfile, oslit in zip(
infiles, outfiles, slitfiles, outslitfiles):
log.message('Proccessing image %s' % img)
# open the image
struct = saltio.openfits(img)
ylen, xlen = struct[1].data.shape
xbin, ybin = saltkey.ccdbin(struct[0], img)
# setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', struct[1]):
varext = saltkey.get('VAREXT', struct[1])
varlist = []
else:
varext = None
# setup the BPM frames
if saltkey.found('BPMEXT', struct[1]):
bpmext = saltkey.get('BPMEXT', struct[1])
bpmlist = []
else:
bpmext = None
# open the slit definition file or identify the slits in the image
slitmask = None
ycheck = False
if exttype == 'rsmt':
log.message('Using slits from %s' % sfile)
if yoffset is None:
yoffset = 0
ycheck = True
slitmask = mt.read_slitmask_from_xml(sfile)
xpos = -0.3066
ypos = 0.0117
cx = int(xlen / 2.0)
cy = int(ylen / 2.0) + ypos / 0.015 / ybin + yoffset
order, slit_positions = mt.convert_slits_from_mask(
slitmask, order=1, xbin=xbin, ybin=ybin, pix_scale=0.1267, cx=cx, cy=cy)
sections = 1
elif exttype == 'fits':
log.message('Using slits from %s' % sfile)
order, slit_positions = read_slits_from_fits(sfile)
elif exttype == 'ascii':
log.message('Using slits from %s' % sfile)
order, slit_positions = mt.read_slits_from_ascii(sfile)
elif exttype == 'ds9':
log.message('Using slits from %s' % sfile)
order, slit_positions, slitmask = mt.read_slits_from_ds9(
sfile, order=order)
slitmask = None
sections = 1
elif exttype == 'auto':
log.message('Identifying slits in %s' % img)
# identify the slits in the image
order, slit_positions = identify_slits(
struct[1].data, order, sections, width, sigma, thres)
# write out the slit identifications if ofile has been supplied
if oslit:
log.message('Writing slit positions to %s' % oslit)
mt.write_outputslitfile(slit_positions, oslit, order)
if ycheck:
slit_positions, dy = check_ypos(slit_positions, struct[1].data)
log.message('Using an offset of {}'.format(dy))
# extract the slits
spline_x = mt.divide_image(struct[1].data, sections)
spline_x = 0.5 * (np.array(spline_x[:-1]) + np.array(spline_x[1:]))
extracted_spectra, spline_positions = mt.extract_slits(slit_positions,
spline_x, struct[1].data, order=order, padding=padding)
if varext:
extracted_var, var_positions = mt.extract_slits(slit_positions,
spline_x, struct[varext].data, order=order, padding=padding)
if bpmext:
extracted_bpm, bpm_positions = mt.extract_slits(slit_positions,
spline_x, struct[bpmext].data, order=order, padding=padding)
# write out the data to the new array
# create the new file
hdulist = fits.HDUList([struct[0]])
# log the extracted spectra if needed
log.message('', with_stdout=verbose)
# setup output ds9 file
if regprefix:
regout = open(
regprefix +
os.path.basename(img).strip('.fits') +
'.reg',
'w')
regout.write('# Region file format: DS9 version 4.1\n')
regout.write('# Filename: %s\n' % img)
regout.write(
'global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1\nphysical\n')
# add each
imglist = []
nslits = len(spline_positions)
for i in range(nslits):
y1 = spline_positions[i][0].min()
y2 = spline_positions[i][1].max()
msg = 'Extracted Spectra %i between %i to %i' % (i + 1, y1, y2)
# log.message(msg, with_header=False, with_stdout=verbose)
sdu = fits.ImageHDU(
extracted_spectra[i],
header=struct[1].header)
if varext:
vdu = fits.ImageHDU(
extracted_var[i],
header=struct[varext].header)
sdu.header['VAREXT'] = i + nslits + 1
varlist.append(vdu)
if bpmext:
bdu = fits.ImageHDU(
extracted_bpm[i],
header=struct[bpmext].header)
sdu.header['BPMEXT']= i + 2 * nslits + 1
bpmlist.append(bdu)
imglist.append(sdu)
# add in some additional keywords
imglist[i].header['MINY'] = (y1,
'Lower Y value in original image')
imglist[i].header['MAXY'] = (y2,
'Upper Y value in original image')
if regprefix:
xsize = struct[1].data.shape[1]
xsize = int(0.5 * xsize)
rtext = ''
if slitmask:
# rtext='%s, %8.7f, %8.7f, %3.2f' % (slitmask.slitlets.data[i]['name'], slitmask.slitlets.data[i]['targ_ra'], slitmask.slitlets.data[i]['targ_dec'], slitmask.slitlets.data[i]['slit_width'])
pass
regout.write('box(%i,%i, %i, %i) #text={%s}\n' % (
xsize, 0.5 * (y1 + y2), 2 * xsize, y2 - y1, rtext))
# add slit information
if slitmask:
imglist[i].header['SLITNAME'] = (slitmask.slitlets.data[i]['name'],
'Slit Name')
imglist[i].header['SLIT_RA'] = (slitmask.slitlets.data[i]['targ_ra'],
'Slit RA')
imglist[i].header['SLIT_DEC'] = (slitmask.slitlets.data[i]['targ_dec'],
'Slit DEC')
imglist[i].header['SLIT'] = (slitmask.slitlets.data[i]['slit_width'],
'Slit Width')
# add to the hdulist
hdulist += imglist
if varext:
hdulist += varlist
if bpmext:
hdulist += bpmlist
# write the slit positions to the header
# create the binary table HDU that contains the split positions
tbhdu = mt.slits_HDUtable(slit_positions, order)
bintable_hdr = tbhdu.header
# add the extname parameter to the extension
tbhdu.header['EXTNAME'] = 'BINTABLE'
# add the extname parameter to the extension
hdulist[0].header['SLITEXT'] = len(hdulist)
hdulist.append(tbhdu)
# add addition header information about the mask
if slitmask:
hdulist[0].header['MASKNAME'] = (slitmask.mask_name, 'SlitMask Name')
hdulist[0].header['MASK_RA'] = (slitmask.center_ra,
'SlitMask RA')
hdulist[0].header['MASK_DEC'] = ( slitmask.center_dec,
'SlitMask DEC')
hdulist[0].header['MASK_PA'] = ( slitmask.position_angle,
'SlitMask Position Angle')
# write out the image
saltio.writefits(hdulist, oimg, clobber)
def specslit(image,
outimage,
outpref,
exttype='auto',
slitfile='',
outputslitfile='',
regprefix='',
sections=3,
width=25,
sigma=2.2,
thres=6,
order=3,
padding=5,
yoffset=0,
inter=False,
clobber=True,
logfile='salt.log',
verbose=True):
with logging(logfile, debug) as log:
# check all the input and make sure that all the input needed is provided
# by the user
# read the image or image list and check if each in the list exist
infiles = saltio.argunpack('Input', image)
# unpack the outfiles
outfiles = saltio.listparse('Outimages', outimage, outpref, infiles,
'')
# from the extraction type, check whether the input file is specified.
# if the slitfile parameter is specified then use the slit files for
# the extraction. if the extraction type is auto then use image for the
# detection and the slit extraction
if exttype == 'rsmt' or exttype == 'fits' or exttype == 'ascii' or exttype == 'ds9':
slitfiles = saltio.argunpack('Slitfile', slitfile)
if len(slitfiles) == 1:
slitfiles = slitfiles * len(infiles)
saltio.comparelists(infiles, slitfiles, 'image', 'slitfile')
elif exttype == 'auto':
slitfiles = infiles
log.message(
'Extraction type is AUTO. Slit detection will be done from image'
)
# read in if an optional ascii file is requested
if len(outputslitfile) > 0:
outslitfiles = saltio.argunpack('Outslitfiles', outputslitfile)
saltio.comparelists(infiles, outslitfiles, 'image',
'outputslitfile')
else:
outslitfiles = [''] * len(infiles)
# check if the width and sigma parameters were specified.
# default is 25 and 2.2
if width < 10.:
msg = 'The width parameter needs be a value larger than 10'
raise SALTSpecError(msg)
if sigma < 0.0:
msg = 'Sigma must be greater than zero'
raise SaltSpecError(msg)
# check the treshold parameter. this needs to be specified by the user
if thres <= 0.0:
msg = 'Threshold must be greater than zero'
raise SaltSpecError(msg)
# check to make sure that the sections are greater than the order
if sections <= order:
msg = 'Number of sections must be greater than the order for the spline fit'
raise SaltSpecError(msg)
# run through each of the images and extract the slits
for img, oimg, sfile, oslit in zip(infiles, outfiles, slitfiles,
outslitfiles):
log.message('Proccessing image %s' % img)
# open the image
struct = saltio.openfits(img)
ylen, xlen = struct[1].data.shape
xbin, ybin = saltkey.ccdbin(struct[0], img)
# setup the VARIANCE and BPM frames
if saltkey.found('VAREXT', struct[1]):
varext = saltkey.get('VAREXT', struct[1])
varlist = []
else:
varext = None
# setup the BPM frames
if saltkey.found('BPMEXT', struct[1]):
bpmext = saltkey.get('BPMEXT', struct[1])
bpmlist = []
else:
bpmext = None
# open the slit definition file or identify the slits in the image
slitmask = None
ycheck = False
if exttype == 'rsmt':
log.message('Using slits from %s' % sfile)
if yoffset is None:
yoffset = 0
ycheck = True
slitmask = mt.read_slitmask_from_xml(sfile)
xpos = -0.3066
ypos = 0.0117
cx = int(xlen / 2.0)
cy = int(ylen / 2.0) + ypos / 0.015 / ybin + yoffset
order, slit_positions = mt.convert_slits_from_mask(
slitmask,
order=1,
xbin=xbin,
ybin=ybin,
pix_scale=0.1267,
cx=cx,
cy=cy)
sections = 1
elif exttype == 'fits':
log.message('Using slits from %s' % sfile)
order, slit_positions = read_slits_from_fits(sfile)
elif exttype == 'ascii':
log.message('Using slits from %s' % sfile)
order, slit_positions = mt.read_slits_from_ascii(sfile)
elif exttype == 'ds9':
log.message('Using slits from %s' % sfile)
order, slit_positions, slitmask = mt.read_slits_from_ds9(
sfile, order=order)
slitmask = None
sections = 1
elif exttype == 'auto':
log.message('Identifying slits in %s' % img)
# identify the slits in the image
order, slit_positions = identify_slits(struct[1].data, order,
sections, width, sigma,
thres)
# write out the slit identifications if ofile has been supplied
if oslit:
log.message('Writing slit positions to %s' % oslit)
mt.write_outputslitfile(slit_positions, oslit, order)
if ycheck:
slit_positions, dy = check_ypos(slit_positions, struct[1].data)
log.message('Using an offset of {}'.format(dy))
# extract the slits
spline_x = mt.divide_image(struct[1].data, sections)
spline_x = 0.5 * (np.array(spline_x[:-1]) + np.array(spline_x[1:]))
extracted_spectra, spline_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[1].data,
order=order,
padding=padding)
if varext:
extracted_var, var_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[varext].data,
order=order,
padding=padding)
if bpmext:
extracted_bpm, bpm_positions = mt.extract_slits(
slit_positions,
spline_x,
struct[bpmext].data,
order=order,
padding=padding)
# write out the data to the new array
# create the new file
hdulist = fits.HDUList([struct[0]])
# log the extracted spectra if needed
log.message('', with_stdout=verbose)
# setup output ds9 file
if regprefix:
regout = open(
regprefix + os.path.basename(img).strip('.fits') + '.reg',
'w')
regout.write('# Region file format: DS9 version 4.1\n')
regout.write('# Filename: %s\n' % img)
regout.write(
'global color=green dashlist=8 3 width=1 font="helvetica 10 normal roman" select=1 highlite=1 dash=0 fixed=0 edit=1 move=1 delete=1 include=1 source=1\nphysical\n'
)
# add each
imglist = []
nslits = len(spline_positions)
for i in range(nslits):
y1 = spline_positions[i][0].min()
y2 = spline_positions[i][1].max()
msg = 'Extracted Spectra %i between %i to %i' % (i + 1, y1, y2)
# log.message(msg, with_header=False, with_stdout=verbose)
sdu = fits.ImageHDU(extracted_spectra[i],
header=struct[1].header)
if varext:
vdu = fits.ImageHDU(extracted_var[i],
header=struct[varext].header)
sdu.header['VAREXT'] = i + nslits + 1
varlist.append(vdu)
if bpmext:
bdu = fits.ImageHDU(extracted_bpm[i],
header=struct[bpmext].header)
sdu.header['BPMEXT'] = i + 2 * nslits + 1
bpmlist.append(bdu)
imglist.append(sdu)
# add in some additional keywords
imglist[i].header['MINY'] = (y1,
'Lower Y value in original image')
imglist[i].header['MAXY'] = (y2,
'Upper Y value in original image')
if regprefix:
xsize = struct[1].data.shape[1]
xsize = int(0.5 * xsize)
rtext = ''
if slitmask:
# rtext='%s, %8.7f, %8.7f, %3.2f' % (slitmask.slitlets.data[i]['name'], slitmask.slitlets.data[i]['targ_ra'], slitmask.slitlets.data[i]['targ_dec'], slitmask.slitlets.data[i]['slit_width'])
pass
regout.write('box(%i,%i, %i, %i) #text={%s}\n' %
(xsize, 0.5 *
(y1 + y2), 2 * xsize, y2 - y1, rtext))
# add slit information
if slitmask:
imglist[i].header['SLITNAME'] = (
slitmask.slitlets.data[i]['name'], 'Slit Name')
imglist[i].header['SLIT_RA'] = (
slitmask.slitlets.data[i]['targ_ra'], 'Slit RA')
imglist[i].header['SLIT_DEC'] = (
slitmask.slitlets.data[i]['targ_dec'], 'Slit DEC')
imglist[i].header['SLIT'] = (
slitmask.slitlets.data[i]['slit_width'], 'Slit Width')
# add to the hdulist
hdulist += imglist
if varext:
hdulist += varlist
if bpmext:
hdulist += bpmlist
# write the slit positions to the header
# create the binary table HDU that contains the split positions
tbhdu = mt.slits_HDUtable(slit_positions, order)
bintable_hdr = tbhdu.header
# add the extname parameter to the extension
tbhdu.header['EXTNAME'] = 'BINTABLE'
# add the extname parameter to the extension
hdulist[0].header['SLITEXT'] = len(hdulist)
hdulist.append(tbhdu)
# add addition header information about the mask
if slitmask:
hdulist[0].header['MASKNAME'] = (slitmask.mask_name,
'SlitMask Name')
hdulist[0].header['MASK_RA'] = (slitmask.center_ra,
'SlitMask RA')
hdulist[0].header['MASK_DEC'] = (slitmask.center_dec,
'SlitMask DEC')
hdulist[0].header['MASK_PA'] = (slitmask.position_angle,
'SlitMask Position Angle')
# write out the image
saltio.writefits(hdulist, oimg, clobber)
