def archive_prefix_OPUS_WCS(fobj, extname='SCI'):
""" Identifies WCS keywords which were generated by OPUS and archived
using a prefix of 'O' for all 'SCI' extensions in the file
Parameters
----------
fobj : str or `astropy.io.fits.HDUList`
Filename or fits object of a file
"""
if stwcs is None:
print('=====================')
print(
'The STWCS package is needed to convert an old-style OPUS WCS to an alternate WCS'
)
print('=====================')
raise ImportError
closefits = False
if isinstance(fobj, str):
# A filename was provided as input
fobj = fits.open(fobj, mode='update')
closefits = True
# Define the header
ext = ('sci', 1)
hdr = fobj[ext].header
numextn = fileutil.countExtn(fobj)
extlist = []
for e in range(1, numextn + 1):
extlist.append(('sci', e))
# Insure that the 'O' alternate WCS is present
if 'O' not in wcsutil.wcskeys(hdr):
# if not, archive the Primary WCS as the default OPUS WCS
wcsutil.archiveWCS(fobj, extlist, wcskey='O', wcsname='OPUS')
# find out how many SCI extensions are in the image
numextn = fileutil.countExtn(fobj, extname=extname)
if numextn == 0:
extname = 'PRIMARY'
# create HSTWCS object from PRIMARY WCS
wcsobj = wcsutil.HSTWCS(fobj, ext=ext, wcskey='O')
# get list of WCS keywords
wcskeys = list(wcsobj.wcs2header().keys())
# For each SCI extension...
for e in range(1, numextn + 1):
# Now, look for any WCS keywords with a prefix of 'O'
for key in wcskeys:
okey = 'O' + key[:7]
hdr = fobj[(extname, e)].header
if okey in hdr:
# Update alternate WCS keyword with prefix-O OPUS keyword value
hdr[key] = hdr[okey]
if closefits:
fobj.close()
def archive_prefix_OPUS_WCS(fobj,extname='SCI'):
""" Identifies WCS keywords which were generated by OPUS and archived
using a prefix of 'O' for all 'SCI' extensions in the file
Parameters
----------
fobj : str or `astropy.io.fits.HDUList`
Filename or fits object of a file
"""
if stwcs is None:
print('=====================')
print('The STWCS package is needed to convert an old-style OPUS WCS to an alternate WCS')
print('=====================')
raise ImportError
closefits = False
if isinstance(fobj,str):
# A filename was provided as input
fobj = fits.open(fobj,mode='update')
closefits=True
# Define the header
ext = ('sci',1)
hdr = fobj[ext].header
numextn = fileutil.countExtn(fobj)
extlist = []
for e in range(1,numextn+1):
extlist.append(('sci',e))
# Insure that the 'O' alternate WCS is present
if 'O' not in wcsutil.wcskeys(hdr):
# if not, archive the Primary WCS as the default OPUS WCS
wcsutil.archiveWCS(fobj,extlist, wcskey='O', wcsname='OPUS')
# find out how many SCI extensions are in the image
numextn = fileutil.countExtn(fobj,extname=extname)
if numextn == 0:
extname = 'PRIMARY'
# create HSTWCS object from PRIMARY WCS
wcsobj = wcsutil.HSTWCS(fobj,ext=ext,wcskey='O')
# get list of WCS keywords
wcskeys = list(wcsobj.wcs2header().keys())
# For each SCI extension...
for e in range(1,numextn+1):
# Now, look for any WCS keywords with a prefix of 'O'
for key in wcskeys:
okey = 'O'+key[:7]
hdr = fobj[(extname,e)].header
if okey in hdr:
# Update alternate WCS keyword with prefix-O OPUS keyword value
hdr[key] = hdr[okey]
if closefits:
fobj.close()
def build_npolname(fobj, npolfile=None):
"""
Build a NPOLNAME from NPOLFILE
Parameters
----------
fobj : `astropy.io.fits.HDUList`
file object
npolfile : string
user supplied NPOLFILE keyword
Returns
-------
npolname, npolfile
"""
if not npolfile:
try:
npolfile = fobj[0].header["NPOLFILE"]
except KeyError:
npolfile = ' '
if fileutil.countExtn(fobj, 'WCSDVARR'):
npolname = 'UNKNOWN'
else:
npolname = 'NOMODEL'
npolname = extract_rootname(npolfile, suffix='_npl')
if npolname == 'NONE':
npolname = 'NOMODEL'
else:
npolname = extract_rootname(npolfile, suffix='_npl')
if npolname == 'NONE':
npolname = 'NOMODEL'
return npolname, npolfile
def build_d2imname(fobj, d2imfile=None):
"""
Build a D2IMNAME from D2IMFILE
Parameters
----------
fobj : `astropy.io.fits.HDUList`
file object
d2imfile : string
user supplied NPOLFILE keyword
Returns
-------
d2imname, d2imfile
"""
if not d2imfile:
try:
d2imfile = fobj[0].header["D2IMFILE"]
except KeyError:
d2imfile = 'N/A'
if fileutil.countExtn(fobj, 'D2IMARR'):
d2imname = 'UNKNOWN'
else:
d2imname = 'NOMODEL'
d2imname = extract_rootname(d2imfile, suffix='_d2i')
if d2imname == 'NONE':
d2imname = 'NOMODEL'
else:
d2imname = extract_rootname(d2imfile, suffix='_d2i')
if d2imname == 'NONE':
d2imname = 'NOMODEL'
return d2imname, d2imfile
def verify_sci_hdrname(filename):
"""Insures that HDRNAME keyword is populated in SCI extensions.
This function checks to make sure the HDRNAME keyword in the SCI
extension of the science image `filename` is populated with a valid
non-empty string.
"""
fhdu, closefits = proc_utils._process_input(filename)
# Find all extensions to be updated
numext = countExtn(fhdu, extname='SCI')
for ext in range(1, numext + 1):
sciext = ('sci', ext)
scihdr = fhdu[sciext].header
if 'hdrname' not in scihdr or scihdr['hdrname'].rstrip() == '':
# We need to create a valid value for the keyword
# Define new HDRNAME value in case it is needed.
# Same value for all SCI extensions, so just precompute it and be ready.
# This code came from 'stwcs.updatewcs.astrometry_utils'
hdrname = "{}_{}".format(filename.replace('.fits', ''), scihdr['wcsname'])
# Create full filename for headerlet:
hfilename = "{}_hlet.fits".format(hdrname)
# Update the header with the new value, inserting after WCSNAME
scihdr.set('hdrname', hfilename, 'Name of headerlet file', after='wcsname')
if closefits:
fhdu.close()
del fhdu
def build_npolname(fobj, npolfile=None):
"""
Build a NPOLNAME from NPOLFILE
Parameters
----------
fobj : `astropy.io.fits.HDUList`
file object
npolfile : string
user supplied NPOLFILE keyword
Returns
-------
npolname, npolfile
"""
if not npolfile:
try:
npolfile = fobj[0].header["NPOLFILE"]
except KeyError:
npolfile = ' '
if fileutil.countExtn(fobj, 'WCSDVARR'):
npolname = 'UNKNOWN'
else:
npolname = 'NOMODEL'
npolname = extract_rootname(npolfile, suffix='_npl')
if npolname == 'NONE':
npolname = 'NOMODEL'
else:
npolname = extract_rootname(npolfile, suffix='_npl')
if npolname == 'NONE':
npolname = 'NOMODEL'
return npolname, npolfile
def build_d2imname(fobj, d2imfile=None):
"""
Build a D2IMNAME from D2IMFILE
Parameters
----------
fobj : `astropy.io.fits.HDUList`
file object
d2imfile : string
user supplied NPOLFILE keyword
Returns
-------
d2imname, d2imfile
"""
if not d2imfile:
try:
d2imfile = fobj[0].header["D2IMFILE"]
except KeyError:
d2imfile = 'N/A'
if fileutil.countExtn(fobj, 'D2IMARR'):
d2imname = 'UNKNOWN'
else:
d2imname = 'NOMODEL'
d2imname = extract_rootname(d2imfile, suffix='_d2i')
if d2imname == 'NONE':
d2imname = 'NOMODEL'
else:
d2imname = extract_rootname(d2imfile, suffix='_d2i')
if d2imname == 'NONE':
d2imname = 'NOMODEL'
return d2imname, d2imfile
def compute_sregion(image, extname='SCI'):
"""Compute the S_REGION keyword for a given WCS.
Parameters
-----------
image : Astropy io.fits HDUList object
Image to update with the S_REGION keyword in each of the SCI extensions.
extname : str, optional
EXTNAME value for extension containing the WCS(s) to be updated
"""
# This function could, conceivably, be called directly...
hdu, closefits = _process_input(image)
# Find all extensions to be updated
numext = countExtn(hdu, extname=extname)
for extnum in range(1, numext + 1):
sregion_str = 'POLYGON ICRS '
sciext = (extname, extnum)
extwcs = wcsutil.HSTWCS(hdu, ext=sciext)
footprint = extwcs.calc_footprint(center=True)
for corner in footprint:
sregion_str += '{} {} '.format(corner[0], corner[1])
hdu[sciext].header['s_region'] = sregion_str
# close file if opened by this functions
if closefits:
hdu.close()
def create_image_footprint(image, refwcs, border=0.):
""" Create the footprint of the image in the reference WCS frame.
Parameters
----------
image : `astropy.io.fits.HDUList` or str
Image to extract sources for matching to
the external astrometric catalog.
refwcs : `stwcs.wcsutil.HSTWCS`
Reference WCS for coordinate frame of image.
border : float
Buffer (in arcseconds) around edge of image to exclude astrometric
sources.
"""
# Interpret input image to generate initial source catalog and WCS
if isinstance(image, str):
image = pf.open(image)
numSci = countExtn(image, extname='SCI')
ref_x = refwcs._naxis1
ref_y = refwcs._naxis2
# convert border value into pixels
border_pixels = int(border / refwcs.pscale)
mask_arr = np.zeros((ref_y, ref_x), dtype=int)
for chip in range(numSci):
chip += 1
# Build arrays of pixel positions for all edges of chip
chip_y, chip_x = image['sci', chip].data.shape
chipwcs = wcsutil.HSTWCS(image, ext=('sci', chip))
xpix = np.arange(chip_x) + 1
ypix = np.arange(chip_y) + 1
edge_x = np.hstack([[1] * chip_y, xpix, [chip_x] * chip_y, xpix])
edge_y = np.hstack([ypix, [1] * chip_x, ypix, [chip_y] * chip_x])
edge_ra, edge_dec = chipwcs.all_pix2world(edge_x, edge_y, 1)
edge_x_out, edge_y_out = refwcs.all_world2pix(edge_ra, edge_dec, 0)
edge_x_out = np.clip(edge_x_out.astype(np.int32), 0, ref_x - 1)
edge_y_out = np.clip(edge_y_out.astype(np.int32), 0, ref_y - 1)
mask_arr[edge_y_out, edge_x_out] = 1
# Fill in outline of each chip
mask_arr = ndimage.binary_fill_holes(ndimage.binary_dilation(mask_arr, iterations=2))
if border > 0.:
mask_arr = ndimage.binary_erosion(mask_arr, iterations=border_pixels)
return mask_arr
def compute_sregion(image, extname='SCI'):
"""Compute the S_REGION keyword for a given WCS.
Parameters
-----------
image : Astropy io.fits HDUList object
Image to update with the S_REGION keyword in each of the SCI extensions.
extname : str, optional
EXTNAME value for extension containing the WCS(s) to be updated
"""
# This function could, conceivably, be called directly...
hdu, closefits = _process_input(image)
# Find all extensions to be updated
numext = countExtn(hdu, extname=extname)
for extnum in range(1, numext + 1):
sciext = (extname, extnum)
if 'd001data' not in hdu[0].header:
sregion_str = 'POLYGON ICRS '
# Working with FLT/FLC file, so simply use
# the array corners directly
extwcs = wcsutil.HSTWCS(hdu, ext=sciext)
footprint = extwcs.calc_footprint(center=True)
for corner in footprint:
sregion_str += '{} {} '.format(corner[0], corner[1])
else:
if hdu[(extname,
extnum)].data.min() == 0 and hdu[(extname,
extnum)].data.max() == 0:
continue
# Working with a drizzled image, so we need to
# get all the corners from each of the input files
footprint = find_footprint(hdu, extname=extname, extnum=extnum)
sregion_str = ''
for region in footprint.corners:
# S_REGION string should contain a separate POLYGON
# for each region or chip in the SCI array
sregion_str += 'POLYGON ICRS '
for corner in region:
sregion_str += '{} {} '.format(corner[0], corner[1])
hdu[sciext].header['s_region'] = sregion_str
# close file if opened by this functions
if closefits:
hdu.close()
def restore_file_from_wcscorr(image, id='OPUS', wcskey=''):
""" Copies the values of the WCS from the WCSCORR based on ID specified by user.
The default will be to restore the original OPUS-derived values to the Primary WCS.
If wcskey is specified, the WCS with that key will be updated instead.
"""
if not isinstance(image, fits.HDUList):
fimg = fits.open(image, mode='update')
close_image = True
else:
fimg = image
close_image = False
numsci = fileutil.countExtn(fimg)
wcs_table = fimg['WCSCORR']
orig_rows = (wcs_table.data.field('WCS_ID') == 'OPUS')
# create an HSTWCS object to figure out what WCS keywords need to be updated
wcsobj = stwcs.wcsutil.HSTWCS(fimg, ext=('sci', 1))
wcshdr = wcsobj.wcs2header()
for extn in range(1, numsci + 1):
# find corresponding row from table
ext_rows = (wcs_table.data.field('EXTVER') == extn)
erow = np.where(np.logical_and(ext_rows, orig_rows))[0][0]
for key in wcshdr:
if key in wcs_table.data.names: # insure that keyword is column in table
tkey = key
if 'orient' in key.lower():
key = 'ORIENT'
if wcskey == '':
skey = key
else:
skey = key[:7] + wcskey
fimg['sci',
extn].header[skey] = wcs_table.data.field(tkey)[erow]
for key in DEFAULT_PRI_KEYS:
if key in wcs_table.data.names:
if wcskey == '':
pkey = key
else:
pkey = key[:7] + wcskey
fimg[0].header[pkey] = wcs_table.data.field(key)[erow]
utils.updateNEXTENDKw(fimg)
# close the image now that the update has been completed.
if close_image:
fimg.close()
def interpret_sregion(image, extname='SCI'):
"""Interpret the S_REGION keyword as a list of RA/Dec points"""
# This function could, conceivably, be called directly...
hdu, closefits = _process_input(image)
# Find all extensions to be updated
numext = countExtn(hdu, extname=extname)
sregions = []
for extnum in range(1, numext + 1):
sregions.append(fits.getval(image, 's_region', ext=(extname, extnum)))
coords = []
for region_str in sregions:
radec_str = np.array(region_str.split(' ')[2:], dtype=np.float64)
coords.append(radec_str.reshape((radec_str.shape[0] // 2, 2)))
return coords
def restore_file_from_wcscorr(image, id='OPUS', wcskey=''):
""" Copies the values of the WCS from the WCSCORR based on ID specified by user.
The default will be to restore the original OPUS-derived values to the Primary WCS.
If wcskey is specified, the WCS with that key will be updated instead.
"""
if not isinstance(image, fits.HDUList):
fimg = fits.open(image, mode='update')
close_image = True
else:
fimg = image
close_image = False
numsci = fileutil.countExtn(fimg)
wcs_table = fimg['WCSCORR']
orig_rows = (wcs_table.data.field('WCS_ID') == 'OPUS')
# create an HSTWCS object to figure out what WCS keywords need to be updated
wcsobj = stwcs.wcsutil.HSTWCS(fimg,ext=('sci',1))
wcshdr = wcsobj.wcs2header()
for extn in range(1,numsci+1):
# find corresponding row from table
ext_rows = (wcs_table.data.field('EXTVER') == extn)
erow = np.where(np.logical_and(ext_rows,orig_rows))[0][0]
for key in wcshdr:
if key in wcs_table.data.names: # insure that keyword is column in table
tkey = key
if 'orient' in key.lower():
key = 'ORIENT'
if wcskey == '':
skey = key
else:
skey = key[:7]+wcskey
fimg['sci',extn].header[skey] = wcs_table.data.field(tkey)[erow]
for key in DEFAULT_PRI_KEYS:
if key in wcs_table.data.names:
if wcskey == '':
pkey = key
else:
pkey = key[:7]+wcskey
fimg[0].header[pkey] = wcs_table.data.field(key)[erow]
utils.updateNEXTENDKw(fimg)
# close the image now that the update has been completed.
if close_image:
fimg.close()
def create_prefix_OPUS_WCS(fobj, extname='SCI'):
""" Creates alternate WCS with a prefix of 'O' for OPUS generated WCS values
to work with old MultiDrizzle.
Parameters
----------
fobj : str or `astropy.io.fits.HDUList`
Filename or fits object of a file
Raises
------
IOError:
if input FITS object was not opened in 'update' mode
"""
# List of O-prefix keywords to create
owcskeys = OPUS_WCSKEYS
closefits = False
if isinstance(fobj, str):
# A filename was provided as input
fobj = fits.open(fobj, mode='update')
closefits = True
else:
# check to make sure this FITS obj has been opened in update mode
if fobj.fileinfo(0)['filemode'] != 'update':
print('File not opened with "mode=update". Quitting...')
raise IOError
# check for existance of O-prefix WCS
if owcskeys[0] not in fobj['sci', 1].header:
# find out how many SCI extensions are in the image
numextn = fileutil.countExtn(fobj, extname=extname)
if numextn == 0:
extname = ''
for extn in range(1, numextn + 1):
hdr = fobj[(extname, extn)].header
for okey in owcskeys:
hdr[okey] = hdr[okey[1: ] + 'O']
# Close FITS image if we had to open it...
if closefits:
fobj.close()
def _create_input_psf(psf_name, calimg, total_flux):
# Create copy of input science image based on input psf filename
psf_root = os.path.basename(psf_name)
lib_psf_arr = fits.getdata(psf_name)
lib_psf_arr *= total_flux
lib_size = [lib_psf_arr.shape[0] // 2, lib_psf_arr.shape[1] // 2]
# create hamming 2d filter to avoid edge effects
h = ss.hamming(lib_psf_arr.shape[0])
h2d = np.sqrt(np.outer(h, h))
lib_psf_arr *= h2d
# This will be the name of the new file containing the library PSF that will be drizzled to
# match the input image `drzimg`
psf_flt_name = psf_root.replace('.fits', '_psf_flt.fits')
# create version of PSF that will be drizzled
psf_base = fits.getdata(calimg, ext=1) * 0.0
# Copy library PSF into this array
out_cen = [psf_base.shape[0] // 2, psf_base.shape[1] // 2]
edge = (lib_psf_arr.shape[0] % 2, lib_psf_arr.shape[1] % 2)
psf_base[out_cen[0] - lib_size[0]:out_cen[0] + lib_size[0] + edge[0],
out_cen[1] - lib_size[1]:out_cen[1] + lib_size[1] +
edge[1]] = lib_psf_arr
# Write out library PSF FLT file now
psf_flt = shutil.copy(calimg, psf_flt_name)
# Update file with library PSF
flt_hdu = fits.open(psf_flt, mode='update')
flt_hdu[('sci', 1)].data = psf_base
flt_hdu[('sci', 1)].header['psf_nx'] = psf_base.shape[1]
flt_hdu[('sci', 1)].header['psf_ny'] = psf_base.shape[0]
num_sci = fu.countExtn(calimg)
# Also zero out all other science data in this 'PSF' file.
if num_sci > 1:
for extn in range(2, num_sci + 1):
flt_hdu[('sci', extn)].data *= 0.0
flt_hdu.close()
del flt_hdu, lib_psf_arr
return psf_flt_name
def build_reference_wcs(input, sciname='sci'):
"""Create the reference WCS based on all the inputs for a field
Parameters
-----------
input : str or `astropy.io.fits.HDUList` object or list
Full filename or `fits.HDUList` object
of the observation to use in building a tangent plane WCS.
If a list of filenames or HDUList objects are provided, then all
of them will be used to generate the reference WCS for the entire
field of view.
sciname : str
EXTNAME of extensions which have WCS information for the observation
"""
# Insure that input is a list at all times.
# If a single filename (str) or single HDUList is provided, wrap it as a list.
if not isinstance(input, list) or isinstance(input, fits.HDUList):
input = [input]
# Create a composite field-of-view for all inputs
wcslist = []
for img in input:
nsci = fileutil.countExtn(img)
for num in range(nsci):
extname = (sciname, num + 1)
if sciname == 'sci':
extwcs = HSTWCS(img, ext=extname)
else:
# Working with HDRLET as input and do the best we can...
extwcs = read_hlet_wcs(img, ext=extname)
wcslist.append(extwcs)
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
outwcs = utils.output_wcs(wcslist)
return outwcs
def build_reference_wcs(inputs, sciname='sci'):
"""Create the reference WCS based on all the inputs for a field"""
# start by creating a composite field-of-view for all inputs
wcslist = []
for img in inputs:
nsci = countExtn(img)
for num in range(nsci):
extname = (sciname, num + 1)
if sciname == 'sci':
extwcs = wcsutil.HSTWCS(img, ext=extname)
else:
# Working with HDRLET as input and do the best we can...
extwcs = read_hlet_wcs(img, ext=extname)
wcslist.append(extwcs)
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
outwcs = utils.output_wcs(wcslist)
return outwcs
def build_reference_wcs(inputs, sciname='sci'):
"""Create the reference WCS based on all the inputs for a field"""
# start by creating a composite field-of-view for all inputs
wcslist = []
for img in inputs:
nsci = countExtn(img)
for num in range(nsci):
extname = (sciname, num + 1)
if sciname == 'sci':
extwcs = wcsutil.HSTWCS(img, ext=extname)
else:
# Working with HDRLET as input and do the best we can...
extwcs = read_hlet_wcs(img, ext=extname)
wcslist.append(extwcs)
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
outwcs = utils.output_wcs(wcslist)
return outwcs
def _process_input_wcs_single(fname, wcskey, updatewcs):
"""
See docs for _process_input_wcs.
This is separated to be spawned in parallel.
"""
if wcskey in ['', ' ', 'INDEF', None]:
if updatewcs:
uw.updatewcs(fname, checkfiles=False)
else:
numext = fileutil.countExtn(fname)
extlist = []
for extn in range(1, numext + 1):
extlist.append(('SCI', extn))
if wcskey in string.ascii_uppercase:
wkey = wcskey
wname = ' '
else:
wname = wcskey
wkey = ' '
altwcs.restoreWCS(fname, extlist, wcskey=wkey, wcsname=wname)
# make an asn table at the end
# Make sure there is a WCSCORR table for each input image
if wcskey not in ['', ' ', 'INDEF', None] or updatewcs:
wcscorr.init_wcscorr(fname)
def _process_input_wcs_single(fname, wcskey, updatewcs):
"""
See docs for _process_input_wcs.
This is separated to be spawned in parallel.
"""
if wcskey in ['', ' ', 'INDEF', None]:
if updatewcs:
uw.updatewcs(fname, checkfiles=False)
else:
numext = fileutil.countExtn(fname)
extlist = []
for extn in range(1, numext + 1):
extlist.append(('SCI', extn))
if wcskey in string.ascii_uppercase:
wkey = wcskey
wname = ' '
else:
wname = wcskey
wkey = ' '
altwcs.restoreWCS(fname, extlist, wcskey=wkey, wcsname=wname)
# make an asn table at the end
# Make sure there is a WCSCORR table for each input image
if wcskey not in ['', ' ', 'INDEF', None] or updatewcs:
wcscorr.init_wcscorr(fname)
def generate_sky_catalog(image, refwcs, **kwargs):
"""Build source catalog from input image using photutils.
This script borrows heavily from build_source_catalog.
The catalog returned by this function includes sources found in all chips
of the input image with the positions translated to the coordinate frame
defined by the reference WCS `refwcs`. The sources will be
- identified using photutils segmentation-based source finding code
- ignore any input pixel which has been flagged as 'bad' in the DQ
array, should a DQ array be found in the input HDUList.
- classified as probable cosmic-rays (if enabled) using central_moments
properties of each source, with these sources being removed from the
catalog.
Parameters
----------
image : ~astropy.io.fits.HDUList`
Input image.
refwcs : `~stwcs.wcsutils.HSTWCS`
Definition of the reference frame WCS.
dqname : str
EXTNAME for the DQ array, if present, in the input image.
output : bool
Specify whether or not to write out a separate catalog file for all the
sources found in each chip. Default: None (False)
threshold : float, optional
This parameter controls the S/N threshold used for identifying sources in
the image relative to the background RMS in much the same way that
the 'threshold' parameter in 'tweakreg' works.
fwhm : float, optional
FWHM (in pixels) of the expected sources from the image, comparable to the
'conv_width' parameter from 'tweakreg'. Objects with FWHM closest to
this value will be identified as sources in the catalog.
Returns
--------
master_cat : `~astropy.table.Table`
Source catalog for all 'valid' sources identified from all chips of the
input image with positions translated to the reference WCS coordinate
frame.
"""
# Extract source catalogs for each chip
source_cats = generate_source_catalog(image, **kwargs)
# Build source catalog for entire image
master_cat = None
numSci = countExtn(image, extname='SCI')
# if no refwcs specified, build one now...
if refwcs is None:
refwcs = build_reference_wcs([image])
for chip in range(numSci):
chip += 1
# work with sources identified from this specific chip
seg_tab_phot = source_cats[chip]
if seg_tab_phot is None:
continue
# Convert pixel coordinates from this chip to sky coordinates
chip_wcs = wcsutil.HSTWCS(image, ext=('sci', chip))
seg_ra, seg_dec = chip_wcs.all_pix2world(seg_tab_phot['xcentroid'], seg_tab_phot['ycentroid'], 1)
# Convert sky positions to pixel positions in the reference WCS frame
seg_xy_out = refwcs.all_world2pix(seg_ra, seg_dec, 1)
seg_tab_phot['xcentroid'] = seg_xy_out[0]
seg_tab_phot['ycentroid'] = seg_xy_out[1]
if master_cat is None:
master_cat = seg_tab_phot
else:
master_cat = vstack([master_cat, seg_tab_phot])
return master_cat
def wfpc2_to_flt(imgname):
"""Convert separate GEIS-based FITS files into single FLT file
Parameters
----------
imgname : str
Filename of calibrated WFPC2 SCI (*_c0m.fits) image
Returns
-------
flt_filename : str
Filename of WFPC2 MEF _*flt.fits file that was written out
"""
is_mef = 'c0m' in imgname
if not is_mef:
raise TypeError("MEF C0M file needed as input.")
dq_file = imgname.replace('c0m', 'c1m')
is_dq = os.path.exists(dq_file)
flt_filename = imgname.replace('c0m', 'flt')
# Read in input SCI file
in_sci = fits.open(imgname)
# Add keywords to be more compatible with ACS and WFC3 data
num_sci = fileutil.countExtn(imgname)
det_name = 'PC' if in_sci[('sci', 1)].header['detector'] == 1 else 'WF'
in_sci[0].header['DETECTOR'] = det_name
if is_dq:
# Read in existing input DQ file
in_dq = fits.open(dq_file)
dq_extns = [extn for extn in in_dq[1:]]
else:
# Could not find a DQ file, so create empty DQ arrays
# based on SCI arrays
dq_extns = [extn for extn in copy.deepcopy(in_sci[1:])]
for extn in dq_extns:
extn.data = np.zeros(extn.data.shape, dtype=np.int32)
# Update EXTNAME to be consistent with ACS and WFC3 DQ extname
for i, extn in enumerate(dq_extns):
extn.header['extname'] = 'DQ'
extn.header['extver'] = i + 1
# Now create ERR arrays as well...
err_extns = [extn for extn in copy.deepcopy(in_sci[1:])]
for i, extn in enumerate(err_extns):
# Initialize using Poisson error estimate
extn.data = np.sqrt(extn.data)
extn.header['extname'] = 'ERR'
extn.header['extver'] = i + 1
# Create output FLT file now to avoid having astropy
# create a tmp* file that doesn't always get cleaned up...
out_hdu = copy.deepcopy(in_sci)
fname_kw = out_hdu[0].header['filename']
out_hdu[0].header['filename'] = f"{fname_kw[:-8]}flt.fits"
for dq_extn, err_extn in zip(dq_extns, err_extns):
out_hdu.append(dq_extn)
out_hdu.append(err_extn)
print(f"Writing out {flt_filename}")
out_file = open(flt_filename, 'wb')
out_hdu.writeto(out_file, overwrite=True)
in_sci.close()
del in_sci
if is_dq:
in_dq.close()
return flt_filename
def restore_from_to(f, fromext=None, toext=None, wcskey=" ", wcsname=" "):
"""
Copy an alternate WCS from one extension as a primary WCS of another extension
Reads in a WCS defined with wcskey and saves it as the primary WCS.
Goes sequentially through the list of extensions in ext.
Alternatively uses 'fromext' and 'toext'.
Parameters
----------
f: string or `astropy.io.fits.HDUList`
a file name or a file object
fromext: string
extname from which to read in the alternate WCS, for example 'SCI'
toext: string or python list
extname or a list of extnames to which the WCS will be copied as
primary, for example ['SCI', 'ERR', 'DQ']
wcskey: a charater
"A"-"Z" - Used for one of 26 alternate WCS definitions.
or " " - find a key from WCSNAMe value
wcsname: string (optional)
if given and wcskey is " ", will try to restore by WCSNAME value
See Also
--------
archiveWCS - copy the primary WCS as an alternate WCS
restoreWCS - Copy a WCS with key "WCSKEY" to the primary WCS
"""
if isinstance(f, str):
fobj = fits.open(f, mode='update')
else:
fobj = f
if not _parpasscheck(fobj, ext=None, wcskey=wcskey, fromext=fromext, toext=toext):
closefobj(f, fobj)
raise ValueError("Input parameters problem")
# Interpret input 'ext' value to get list of extensions to process
# ext = _buildExtlist(fobj, ext)
if isinstance(toext, str):
toext = [toext]
# the case of an HDUList object in memory without an associated file
# if fobj.filename() is not None:
# name = fobj.filename()
simplefits = fu.isFits(fobj)[1] is 'simple'
if simplefits:
wcskeyext = 0
else:
wcskeyext = 1
if wcskey == " ":
if wcsname.strip():
wkey = getKeyFromName(fobj[wcskeyext].header, wcsname)
if not wkey:
closefobj(f, fobj)
raise KeyError("Could not get a key from wcsname %s ." % wcsname)
else:
if wcskey not in wcskeys(fobj, ext=wcskeyext):
print("Could not find alternate WCS with key %s in this file" % wcskey)
closefobj(f, fobj)
return
wkey = wcskey
countext = fu.countExtn(fobj, fromext)
if not countext:
raise KeyError("File does not have extension with extname %s", fromext)
else:
for i in range(1, countext + 1):
for toe in toext:
_restore(fobj, fromextnum=i, fromextnam=fromext, toextnum=i, toextnam=toe, ukey=wkey)
if fobj.filename() is not None:
# fobj.writeto(name)
closefobj(f, fobj)
def determine_alignment_residuals(input, files, max_srcs=2000):
"""Determine the relative alignment between members of an association.
Parameters
-----------
input : string
Original pipeline input filename. This filename will be used to
define the output analysis results filename.
files : list
Set of files on which to actually perform comparison. The original
pipeline can work on both CTE-corrected and non-CTE-corrected files,
but this comparison will only be performed on CTE-corrected
products when available.
Returns
--------
results : string
Name of JSON file containing all the extracted results from the comparisons
being performed.
"""
# Open all files as HDUList objects
hdus = [fits.open(f) for f in files]
# Determine sources from each chip
src_cats = []
num_srcs = []
for hdu in hdus:
numsci = countExtn(hdu)
nums = 0
img_cats = {}
for chip in range(numsci):
chip += 1
img_cats[chip] = amutils.extract_point_sources(hdu[("SCI",
chip)].data,
nbright=max_srcs)
nums += len(img_cats[chip])
num_srcs.append(nums)
src_cats.append(img_cats)
if len(num_srcs) == 0 or (len(num_srcs) > 0 and max(num_srcs) <= 3):
return None
# src_cats = [amutils.generate_source_catalog(hdu) for hdu in hdus]
# Combine WCS from HDULists and source catalogs into tweakwcs-compatible input
imglist = []
for i, (f, cat) in enumerate(zip(files, src_cats)):
imglist += amutils.build_wcscat(f, i, cat)
# Setup matching algorithm using parameters tuned to well-aligned images
match = tweakwcs.TPMatch(searchrad=5,
separation=1.0,
tolerance=4.0,
use2dhist=True)
try:
# perform relative fitting
matchlist = tweakwcs.align_wcs(imglist,
None,
match=match,
expand_refcat=False)
del matchlist
except Exception:
return None
# Check to see whether there were any successful fits...
align_success = False
for img in imglist:
if img.meta['fit_info']['status'] == 'SUCCESS':
align_success = True
break
if align_success:
# extract results in the style of 'tweakreg'
resids = extract_residuals(imglist)
if resids is None:
resids_file = None
else:
# Define name for output JSON file...
resids_file = "{}_astrometry_resids.json".format(input[:9])
# Remove any previously computed results
if os.path.exists(resids_file):
os.remove(resids_file)
# Dump the results to a JSON file now...
with open(resids_file, 'w') as jfile:
json.dump(resids, jfile)
else:
resids_file = None
return resids_file
def build_wcscat(image, group_id, source_catalog):
""" Return a list of `~tweakwcs.tpwcs.FITSWCS` objects for all chips in an image.
Parameters
----------
image : str, `~astropy.io.fits.HDUList`
Either filename or HDUList of a single HST observation.
group_id : int
Integer ID for group this image should be associated with; primarily
used when separate chips are in separate files to treat them all as one
exposure.
source_catalog : dict
If provided, these catalogs will be attached as `catalog`
entries in each chip's ``FITSWCS`` object. It should be provided as a
dict of astropy Tables identified by chip number with
each table containing sources from image extension ``('sci', chip)`` as
generated by `generate_source_catalog()`.
Returns
-------
wcs_catalogs : list of `~tweakwcs.tpwcs.FITSWCS`
List of `~tweakwcs.tpwcs.FITSWCS` objects defined for all chips in input image.
"""
open_file = False
if isinstance(image, str):
hdulist = fits.open(image)
open_file = True
elif isinstance(image, fits.HDUList):
hdulist = image
else:
log.info("Wrong type of input, {}, for build_wcscat...".format(
type(image)))
raise ValueError
wcs_catalogs = []
numsci = countExtn(hdulist)
for chip in range(1, numsci + 1):
w = wcsutil.HSTWCS(hdulist, ('SCI', chip))
imcat = source_catalog[chip]
# rename xcentroid/ycentroid columns, if necessary, to be consistent with tweakwcs
if 'xcentroid' in imcat.colnames:
imcat.rename_column('xcentroid', 'x')
imcat.rename_column('ycentroid', 'y')
wcscat = FITSWCS(w,
meta={
'chip': chip,
'group_id': group_id,
'filename': image,
'catalog': imcat,
'name': image
})
wcs_catalogs.append(wcscat)
if open_file:
hdulist.close()
return wcs_catalogs
def generate_sky_catalog(image, refwcs, dqname="DQ", output=False):
"""Build source catalog from input image using photutils.
This script borrows heavily from build_source_catalog.
The catalog returned by this function includes sources found in all chips
of the input image with the positions translated to the coordinate frame
defined by the reference WCS `refwcs`. The sources will be
- identified using photutils segmentation-based source finding code
- ignore any input pixel which has been flagged as 'bad' in the DQ
array, should a DQ array be found in the input HDUList.
- classified as probable cosmic-rays (if enabled) using central_moments
properties of each source, with these sources being removed from the
catalog.
Parameters
----------
image : `~astropy.io.fits.HDUList`
Input image.
refwcs : `~stwcs.wcsutil.HSTWCS`
Definition of the reference frame WCS.
dqname : str, optional
EXTNAME for the DQ array, if present, in the input image.
output : bool, optional
Specify whether or not to write out a separate catalog file for all the
sources found in each chip.
Returns
--------
master_cat : `~astropy.table.Table`
Source catalog for all 'valid' sources identified from all chips of the
input image with positions translated to the reference WCS coordinate
frame.
"""
# Extract source catalogs for each chip
source_cats = generate_source_catalog(image, dqname=dqname, output=output)
# Build source catalog for entire image
master_cat = None
numSci = countExtn(image, extname='SCI')
# if no refwcs specified, build one now...
if refwcs is None:
refwcs = build_reference_wcs([image])
for chip in range(numSci):
chip += 1
# work with sources identified from this specific chip
seg_tab_phot = source_cats[chip]
if seg_tab_phot is None:
continue
# Convert pixel coordinates from this chip to sky coordinates
chip_wcs = wcsutil.HSTWCS(image, ext=('sci', chip))
seg_ra, seg_dec = chip_wcs.all_pix2world(seg_tab_phot['xcentroid'],
seg_tab_phot['ycentroid'], 1)
# Convert sky positions to pixel positions in the reference WCS frame
seg_xy_out = refwcs.all_world2pix(seg_ra, seg_dec, 1)
seg_tab_phot['xcentroid'] = seg_xy_out[0]
seg_tab_phot['ycentroid'] = seg_xy_out[1]
if master_cat is None:
master_cat = seg_tab_phot
else:
master_cat = vstack([master_cat, seg_tab_phot])
return master_cat
def update_wcscorr(dest, source=None, extname='SCI', wcs_id=None, active=True):
"""
Update WCSCORR table with a new row or rows for this extension header. It
copies the current set of WCS keywords as a new row of the table based on
keyed WCSs as per Paper I Multiple WCS standard).
Parameters
----------
dest : HDUList
The HDU list whose WCSCORR table should be appended to (the WCSCORR HDU
must already exist)
source : HDUList, optional
The HDU list containing the extension from which to extract the WCS
keywords to add to the WCSCORR table. If None, the dest is also used
as the source.
extname : str, optional
The extension name from which to take new WCS keywords. If there are
multiple extensions with that name, rows are added for each extension
version.
wcs_id : str, optional
The name of the WCS to add, as in the WCSNAMEa keyword. If
unspecified, all the WCSs in the specified extensions are added.
active: bool, optional
When True, indicates that the update should reflect an update of the
active WCS information, not just appending the WCS to the file as a
headerlet
"""
if not isinstance(dest, fits.HDUList):
dest = fits.open(dest,mode='update')
fname = dest.filename()
if source is None:
source = dest
if extname == 'PRIMARY':
return
numext = fileutil.countExtn(source, extname)
if numext == 0:
raise ValueError('No %s extensions found in the source HDU list.'
% extname)
# Initialize the WCSCORR table extension in dest if not already present
init_wcscorr(dest)
try:
dest.index_of('WCSCORR')
except KeyError:
return
# check to see whether or not this is an up-to-date table
# replace with newly initialized table with current format
old_table = dest['WCSCORR']
wcscorr_cols = ['WCS_ID','EXTVER', 'SIPNAME',
'HDRNAME', 'NPOLNAME', 'D2IMNAME']
for colname in wcscorr_cols:
if colname not in old_table.data.columns.names:
print("WARNING: Replacing outdated WCSCORR table...")
outdated_table = old_table.copy()
del dest['WCSCORR']
init_wcscorr(dest)
old_table = dest['WCSCORR']
break
# Current implementation assumes the same WCS keywords are in each
# extension version; if this should not be assumed then this can be
# modified...
wcs_keys = altwcs.wcskeys(source[(extname, 1)].header)
wcs_keys = [kk for kk in wcs_keys if kk]
if ' ' not in wcs_keys: wcs_keys.append(' ') # Insure that primary WCS gets used
# apply logic for only updating WCSCORR table with specified keywords
# corresponding to the WCS with WCSNAME=wcs_id
if wcs_id is not None:
wnames = altwcs.wcsnames(source[(extname, 1)].header)
wkeys = []
for letter in wnames:
if wnames[letter] == wcs_id:
wkeys.append(letter)
if len(wkeys) > 1 and ' ' in wkeys:
wkeys.remove(' ')
wcs_keys = wkeys
wcshdr = stwcs.wcsutil.HSTWCS(source, ext=(extname, 1)).wcs2header()
wcs_keywords = list(wcshdr.keys())
if 'O' in wcs_keys:
wcs_keys.remove('O') # 'O' is reserved for original OPUS WCS
# create new table for hdr and populate it with the newly updated values
new_table = create_wcscorr(descrip=True,numrows=0, padding=len(wcs_keys)*numext)
prihdr = source[0].header
# Get headerlet related keywords here
sipname, idctab = utils.build_sipname(source, fname, "None")
npolname, npolfile = utils.build_npolname(source, None)
d2imname, d2imfile = utils.build_d2imname(source, None)
if 'hdrname' in prihdr:
hdrname = prihdr['hdrname']
else:
hdrname = ''
idx = -1
for wcs_key in wcs_keys:
for extver in range(1, numext + 1):
extn = (extname, extver)
if 'SIPWCS' in extname and not active:
tab_extver = 0 # Since it has not been added to the SCI header yet
else:
tab_extver = extver
hdr = source[extn].header
if 'WCSNAME'+wcs_key in hdr:
wcsname = hdr['WCSNAME' + wcs_key]
else:
wcsname = utils.build_default_wcsname(hdr['idctab'])
selection = {'WCS_ID': wcsname, 'EXTVER': tab_extver,
'SIPNAME':sipname, 'HDRNAME': hdrname,
'NPOLNAME': npolname, 'D2IMNAME':d2imname
}
# Ensure that an entry for this WCS is not already in the dest
# table; if so just skip it
rowind = find_wcscorr_row(old_table.data, selection)
if np.any(rowind):
continue
idx += 1
wcs = stwcs.wcsutil.HSTWCS(source, ext=extn, wcskey=wcs_key)
wcshdr = wcs.wcs2header()
# Update selection column values
for key, val in selection.items():
if key in new_table.data.names:
new_table.data.field(key)[idx] = val
for key in wcs_keywords:
if key in new_table.data.names:
new_table.data.field(key)[idx] = wcshdr[key + wcs_key]
for key in DEFAULT_PRI_KEYS:
if key in new_table.data.names and key in prihdr:
new_table.data.field(key)[idx] = prihdr[key]
# Now look for additional, non-WCS-keyword table column data
for key in COL_FITSKW_DICT:
fitkw = COL_FITSKW_DICT[key]
# Interpret any 'pri.hdrname' or
# 'sci.crpix1' formatted keyword names
if '.' in fitkw:
srchdr,fitkw = fitkw.split('.')
if 'pri' in srchdr.lower(): srchdr = prihdr
else: srchdr = source[extn].header
else:
srchdr = source[extn].header
if fitkw+wcs_key in srchdr:
new_table.data.field(key)[idx] = srchdr[fitkw+wcs_key]
# If idx was never incremented, no rows were added, so there's nothing else
# to do...
if idx < 0:
return
# Now, we need to merge this into the existing table
rowind = find_wcscorr_row(old_table.data, {'wcs_id':['','0.0']})
old_nrows = np.where(rowind)[0][0]
new_nrows = new_table.data.shape[0]
# check to see if there is room for the new row
if (old_nrows + new_nrows) > old_table.data.shape[0]-1:
pad_rows = 2 * new_nrows
# if not, create a new table with 'pad_rows' new empty rows
upd_table = fits.new_table(old_table.columns,header=old_table.header,
nrows=old_table.data.shape[0]+pad_rows)
else:
upd_table = old_table
pad_rows = 0
# Now, add
for name in old_table.columns.names:
if name in new_table.data.names:
# reset the default values to ones specific to the row definitions
for i in range(pad_rows):
upd_table.data.field(name)[old_nrows+i] = old_table.data.field(name)[-1]
# Now populate with values from new table
upd_table.data.field(name)[old_nrows:old_nrows + new_nrows] = \
new_table.data.field(name)
upd_table.header['TROWS'] = old_nrows + new_nrows
# replace old extension with newly updated table extension
dest['WCSCORR'] = upd_table
def tran(inimage,
outimage,
direction='forward',
x=None,
y=None,
coords=None,
coordfile=None,
colnames=None,
separator=None,
precision=6,
output=None,
verbose=True):
""" Primary interface to perform coordinate transformations in pixel
coordinates between 2 images using STWCS and full distortion models
read from each image's header.
"""
single_coord = False
# Only use value provided in `coords` if nothing has been specified for coordfile
if coords is not None and coordfile is None:
coordfile = coords
warnings.simplefilter('always', DeprecationWarning)
warnings.warn(
"Please update calling code to pass in `coordfile` instead of `coords`.",
category=DeprecationWarning)
warnings.simplefilter('default', DeprecationWarning)
if coordfile is not None:
if colnames in util.blank_list:
colnames = ['c1', 'c2']
# Determine columns which contain pixel positions
cols = util.parse_colnames(colnames, coordfile)
# read in columns from input coordinates file
xyvals = np.loadtxt(coordfile, usecols=cols, delimiter=separator)
if xyvals.ndim == 1: # only 1 entry in coordfile
xlist = [xyvals[0].copy()]
ylist = [xyvals[1].copy()]
else:
xlist = xyvals[:, 0].copy()
ylist = xyvals[:, 1].copy()
del xyvals
else:
if isinstance(x, np.ndarray):
xlist = x.tolist()
ylist = y.tolist()
elif not isinstance(x, list):
xlist = [x]
ylist = [y]
single_coord = True
else:
xlist = x
ylist = y
# start by reading in WCS+distortion info for each image
im1wcs = wcsutil.HSTWCS(inimage)
if im1wcs.wcs.is_unity():
print(
"####\nNo valid input WCS found in {}.\n Results may be invalid.\n####\n"
.format(inimage))
if util.is_blank(outimage):
fname, fextn = fileutil.parseFilename(inimage)
numsci = fileutil.countExtn(fname)
chips = []
for e in range(1, numsci + 1):
chips.append(wcsutil.HSTWCS(fname, ext=('sci', e)))
if len(chips) == 0:
chips = [im1wcs]
im2wcs = distortion.utils.output_wcs(chips)
else:
im2wcs = wcsutil.HSTWCS(outimage)
if im2wcs.wcs.is_unity():
print(
"####\nNo valid output WCS found in {}.\n Results may be invalid.\n####\n"
.format(outimage))
# Setup the transformation
p2p = wcs_functions.WCSMap(im1wcs, im2wcs)
if direction[0].lower() == 'f':
outx, outy = p2p.forward(xlist, ylist)
else:
outx, outy = p2p.backward(xlist, ylist)
if isinstance(outx, np.ndarray):
outx = outx.tolist()
outy = outy.tolist()
# add formatting based on precision here...
xstr = []
ystr = []
fmt = "%." + repr(precision) + "f"
for ox, oy in zip(outx, outy):
xstr.append(fmt % ox)
ystr.append(fmt % oy)
if verbose or (not verbose and util.is_blank(output)):
print('# Coordinate transformations for ', inimage)
print('# X(in) Y(in) X(out) Y(out)\n')
for xs, ys, a, b in zip(xlist, ylist, xstr, ystr):
print("%.4f %.4f %s %s" % (xs, ys, a, b))
# Create output file, if specified
if output:
f = open(output, mode='w')
f.write("# Coordinates converted from %s\n" % inimage)
for xs, ys in zip(xstr, ystr):
f.write('%s %s\n' % (xs, ys))
f.close()
print('Wrote out results to: ', output)
if single_coord:
outx = outx[0]
outy = outy[0]
return outx, outy
def build_wcscat(image, group_id, source_catalog):
""" Return a list of `~tweakwcs.tpwcs.FITSWCS` objects for all chips in an image.
Parameters
----------
image : str, ~astropy.io.fits.HDUList`
Either filename or HDUList of a single HST observation.
group_id : int
Integer ID for group this image should be associated with; primarily
used when separate chips are in separate files to treat them all as one
exposure.
source_catalog : dict
If provided, these catalogs will be attached as `catalog`
entries in each chip's ``FITSWCS`` object. It should be provided as a
dict of astropy Tables identified by chip number with
each table containing sources from image extension ``('sci', chip)`` as
generated by `generate_source_catalog()`.
Returns
-------
wcs_catalogs : list of `~tweakwcs.tpwcs.FITSWCS`
List of `~tweakwcs.tpwcs.FITSWCS` objects defined for all chips in input image.
"""
open_file = False
if isinstance(image, str):
hdulist = pf.open(image)
open_file = True
elif isinstance(image, pf.HDUList):
hdulist = image
else:
log.info("Wrong type of input, {}, for build_wcscat...".format(type(image)))
raise ValueError
wcs_catalogs = []
numsci = countExtn(hdulist)
for chip in range(1, numsci + 1):
w = wcsutil.HSTWCS(hdulist, ('SCI', chip))
imcat = source_catalog[chip]
# rename xcentroid/ycentroid columns, if necessary, to be consistent with tweakwcs
if 'xcentroid' in imcat.colnames:
imcat.rename_column('xcentroid', 'x')
imcat.rename_column('ycentroid', 'y')
wcscat = FITSWCS(
w,
meta={
'chip': chip,
'group_id': group_id,
'filename': image,
'catalog': imcat,
'name': image
}
)
wcs_catalogs.append(wcscat)
if open_file:
hdulist.close()
return wcs_catalogs
def init_wcscorr(input, force=False):
"""
This function will initialize the WCSCORR table if it is not already present,
and look for WCS keywords with a prefix of 'O' as the original OPUS
generated WCS as the initial row for the table or use the current WCS
keywords as initial row if no 'O' prefix keywords are found.
This function will NOT overwrite any rows already present.
This function works on all SCI extensions at one time.
"""
# TODO: Create some sort of decorator or (for Python2.5) context for
# opening a FITS file and closing it when done, if necessary
if not isinstance(input, fits.HDUList):
# input must be a filename, so open as `astropy.io.fits.HDUList` object
fimg = fits.open(input, mode='update')
need_to_close = True
else:
fimg = input
need_to_close = False
# Do not try to generate a WCSCORR table for a simple FITS file
numsci = fileutil.countExtn(fimg)
if len(fimg) == 1 or numsci == 0:
return
enames = []
for e in fimg:
enames.append(e.name)
if 'WCSCORR' in enames:
if not force:
return
else:
del fimg['wcscorr']
print('Initializing new WCSCORR table for ', fimg.filename())
used_wcskeys = altwcs.wcskeys(fimg['SCI', 1].header)
# define the primary columns of the WCSEXT table with initial rows for each
# SCI extension for the original OPUS solution
numwcs = len(used_wcskeys)
if numwcs == 0: numwcs = 1
# create new table with more rows than needed initially to make it easier to
# add new rows later
wcsext = create_wcscorr(descrip=True,
numrows=numsci,
padding=(numsci * numwcs) + numsci * 4)
# Assign the correct EXTNAME value to this table extension
wcsext.header['TROWS'] = (numsci * 2, 'Number of updated rows in table')
wcsext.header['EXTNAME'] = ('WCSCORR', 'Table with WCS Update history')
wcsext.header['EXTVER'] = 1
# define set of WCS keywords which need to be managed and copied to the table
wcs1 = stwcs.wcsutil.HSTWCS(fimg, ext=('SCI', 1))
idc2header = True
if wcs1.idcscale is None:
idc2header = False
wcs_keywords = list(wcs1.wcs2header(idc2hdr=idc2header).keys())
prihdr = fimg[0].header
prihdr_keys = DEFAULT_PRI_KEYS
pri_funcs = {
'SIPNAME': stwcs.updatewcs.utils.build_sipname,
'NPOLNAME': stwcs.updatewcs.utils.build_npolname,
'D2IMNAME': stwcs.updatewcs.utils.build_d2imname
}
# Now copy original OPUS values into table
for extver in range(1, numsci + 1):
rowind = find_wcscorr_row(wcsext.data, {
'WCS_ID': 'OPUS',
'EXTVER': extver,
'WCS_key': 'O'
})
# There should only EVER be a single row for each extension with OPUS values
rownum = np.where(rowind)[0][0]
#print 'Archiving OPUS WCS in row number ',rownum,' in WCSCORR table for SCI,',extver
hdr = fimg['SCI', extver].header
# define set of WCS keywords which need to be managed and copied to the table
if used_wcskeys is None:
used_wcskeys = altwcs.wcskeys(hdr)
# Check to see whether or not there is an OPUS alternate WCS present,
# if so, get its values directly, otherwise, archive the PRIMARY WCS
# as the OPUS values in the WCSCORR table
if 'O' not in used_wcskeys:
altwcs.archiveWCS(fimg, ('SCI', extver),
wcskey='O',
wcsname='OPUS')
wkey = 'O'
wcs = stwcs.wcsutil.HSTWCS(fimg, ext=('SCI', extver), wcskey=wkey)
wcshdr = wcs.wcs2header(idc2hdr=idc2header)
if wcsext.data.field('CRVAL1')[rownum] != 0:
# If we find values for these keywords already in the table, do not
# overwrite them again
print('WCS keywords already updated...')
break
for key in wcs_keywords:
if key in wcsext.data.names:
wcsext.data.field(key)[rownum] = wcshdr[(key + wkey)[:8]]
# Now get any keywords from PRIMARY header needed for WCS updates
for key in prihdr_keys:
if key in prihdr:
val = prihdr[key]
else:
val = ''
wcsext.data.field(key)[rownum] = val
# Now that we have archived the OPUS alternate WCS, remove it from the list
# of used_wcskeys
if 'O' in used_wcskeys:
used_wcskeys.remove('O')
# Now copy remaining alternate WCSs into table
# TODO: Much of this appears to be redundant with update_wcscorr; consider
# merging them...
for uwkey in used_wcskeys:
for extver in range(1, numsci + 1):
hdr = fimg['SCI', extver].header
wcs = stwcs.wcsutil.HSTWCS(fimg, ext=('SCI', extver), wcskey=uwkey)
wcshdr = wcs.wcs2header()
if 'WCSNAME' + uwkey not in wcshdr:
wcsid = utils.build_default_wcsname(fimg[0].header['idctab'])
else:
wcsid = wcshdr['WCSNAME' + uwkey]
# identify next empty row
rowind = find_wcscorr_row(wcsext.data,
selections={'wcs_id': ['', '0.0']})
rows = np.where(rowind)
if len(rows[0]) > 0:
rownum = np.where(rowind)[0][0]
else:
print('No available rows found for updating. ')
# Update selection columns for this row with relevant values
wcsext.data.field('WCS_ID')[rownum] = wcsid
wcsext.data.field('EXTVER')[rownum] = extver
wcsext.data.field('WCS_key')[rownum] = uwkey
# Look for standard WCS keyword values
for key in wcs_keywords:
if key in wcsext.data.names:
wcsext.data.field(key)[rownum] = wcshdr[key + uwkey]
# Now get any keywords from PRIMARY header needed for WCS updates
for key in prihdr_keys:
if key in pri_funcs:
val = pri_funcs[key](fimg)[0]
else:
if key in prihdr:
val = prihdr[key]
else:
val = ''
wcsext.data.field(key)[rownum] = val
# Append this table to the image FITS file
fimg.append(wcsext)
# force an update now
# set the verify flag to 'warn' so that it will always succeed, but still
# tell the user if PyFITS detects any problems with the file as a whole
utils.updateNEXTENDKw(fimg)
fimg.flush('warn')
if need_to_close:
fimg.close()
def update_wcscorr(dest, source=None, extname='SCI', wcs_id=None, active=True):
"""
Update WCSCORR table with a new row or rows for this extension header. It
copies the current set of WCS keywords as a new row of the table based on
keyed WCSs as per Paper I Multiple WCS standard).
Parameters
----------
dest : HDUList
The HDU list whose WCSCORR table should be appended to (the WCSCORR HDU
must already exist)
source : HDUList, optional
The HDU list containing the extension from which to extract the WCS
keywords to add to the WCSCORR table. If None, the dest is also used
as the source.
extname : str, optional
The extension name from which to take new WCS keywords. If there are
multiple extensions with that name, rows are added for each extension
version.
wcs_id : str, optional
The name of the WCS to add, as in the WCSNAMEa keyword. If
unspecified, all the WCSs in the specified extensions are added.
active: bool, optional
When True, indicates that the update should reflect an update of the
active WCS information, not just appending the WCS to the file as a
headerlet
"""
if not isinstance(dest, fits.HDUList):
dest = fits.open(dest, mode='update')
fname = dest.filename()
if source is None:
source = dest
if extname == 'PRIMARY':
return
numext = fileutil.countExtn(source, extname)
if numext == 0:
raise ValueError('No %s extensions found in the source HDU list.' %
extname)
# Initialize the WCSCORR table extension in dest if not already present
init_wcscorr(dest)
try:
dest.index_of('WCSCORR')
except KeyError:
return
# check to see whether or not this is an up-to-date table
# replace with newly initialized table with current format
old_table = dest['WCSCORR']
wcscorr_cols = [
'WCS_ID', 'EXTVER', 'SIPNAME', 'HDRNAME', 'NPOLNAME', 'D2IMNAME'
]
for colname in wcscorr_cols:
if colname not in old_table.data.columns.names:
print("WARNING: Replacing outdated WCSCORR table...")
outdated_table = old_table.copy()
del dest['WCSCORR']
init_wcscorr(dest)
old_table = dest['WCSCORR']
break
# Current implementation assumes the same WCS keywords are in each
# extension version; if this should not be assumed then this can be
# modified...
wcs_keys = altwcs.wcskeys(source[(extname, 1)].header)
wcs_keys = [kk for kk in wcs_keys if kk]
if ' ' not in wcs_keys:
wcs_keys.append(' ') # Insure that primary WCS gets used
# apply logic for only updating WCSCORR table with specified keywords
# corresponding to the WCS with WCSNAME=wcs_id
if wcs_id is not None:
wnames = altwcs.wcsnames(source[(extname, 1)].header)
wkeys = []
for letter in wnames:
if wnames[letter] == wcs_id:
wkeys.append(letter)
if len(wkeys) > 1 and ' ' in wkeys:
wkeys.remove(' ')
wcs_keys = wkeys
wcshdr = stwcs.wcsutil.HSTWCS(source, ext=(extname, 1)).wcs2header()
wcs_keywords = list(wcshdr.keys())
if 'O' in wcs_keys:
wcs_keys.remove('O') # 'O' is reserved for original OPUS WCS
# create new table for hdr and populate it with the newly updated values
new_table = create_wcscorr(descrip=True,
numrows=0,
padding=len(wcs_keys) * numext)
prihdr = source[0].header
# Get headerlet related keywords here
sipname, idctab = utils.build_sipname(source, fname, "None")
npolname, npolfile = utils.build_npolname(source, None)
d2imname, d2imfile = utils.build_d2imname(source, None)
if 'hdrname' in prihdr:
hdrname = prihdr['hdrname']
else:
hdrname = ''
idx = -1
for wcs_key in wcs_keys:
for extver in range(1, numext + 1):
extn = (extname, extver)
if 'SIPWCS' in extname and not active:
tab_extver = 0 # Since it has not been added to the SCI header yet
else:
tab_extver = extver
hdr = source[extn].header
if 'WCSNAME' + wcs_key in hdr:
wcsname = hdr['WCSNAME' + wcs_key]
else:
wcsname = utils.build_default_wcsname(hdr['idctab'])
selection = {
'WCS_ID': wcsname,
'EXTVER': tab_extver,
'SIPNAME': sipname,
'HDRNAME': hdrname,
'NPOLNAME': npolname,
'D2IMNAME': d2imname
}
# Ensure that an entry for this WCS is not already in the dest
# table; if so just skip it
rowind = find_wcscorr_row(old_table.data, selection)
if np.any(rowind):
continue
idx += 1
wcs = stwcs.wcsutil.HSTWCS(source, ext=extn, wcskey=wcs_key)
wcshdr = wcs.wcs2header()
# Update selection column values
for key, val in selection.items():
if key in new_table.data.names:
new_table.data.field(key)[idx] = val
for key in wcs_keywords:
if key in new_table.data.names:
new_table.data.field(key)[idx] = wcshdr[key + wcs_key]
for key in DEFAULT_PRI_KEYS:
if key in new_table.data.names and key in prihdr:
new_table.data.field(key)[idx] = prihdr[key]
# Now look for additional, non-WCS-keyword table column data
for key in COL_FITSKW_DICT:
fitkw = COL_FITSKW_DICT[key]
# Interpret any 'pri.hdrname' or
# 'sci.crpix1' formatted keyword names
if '.' in fitkw:
srchdr, fitkw = fitkw.split('.')
if 'pri' in srchdr.lower(): srchdr = prihdr
else: srchdr = source[extn].header
else:
srchdr = source[extn].header
if fitkw + wcs_key in srchdr:
new_table.data.field(key)[idx] = srchdr[fitkw + wcs_key]
# If idx was never incremented, no rows were added, so there's nothing else
# to do...
if idx < 0:
return
# Now, we need to merge this into the existing table
rowind = find_wcscorr_row(old_table.data, {'wcs_id': ['', '0.0']})
old_nrows = np.where(rowind)[0][0]
new_nrows = new_table.data.shape[0]
# check to see if there is room for the new row
if (old_nrows + new_nrows) > old_table.data.shape[0] - 1:
pad_rows = 2 * new_nrows
# if not, create a new table with 'pad_rows' new empty rows
upd_table = fits.new_table(old_table.columns,
header=old_table.header,
nrows=old_table.data.shape[0] + pad_rows)
else:
upd_table = old_table
pad_rows = 0
# Now, add
for name in old_table.columns.names:
if name in new_table.data.names:
# reset the default values to ones specific to the row definitions
for i in range(pad_rows):
upd_table.data.field(name)[old_nrows +
i] = old_table.data.field(name)[-1]
# Now populate with values from new table
upd_table.data.field(name)[old_nrows:old_nrows + new_nrows] = \
new_table.data.field(name)
upd_table.header['TROWS'] = old_nrows + new_nrows
# replace old extension with newly updated table extension
dest['WCSCORR'] = upd_table
def determine_alignment_residuals(input,
files,
catalogs=None,
max_srcs=1000,
json_timestamp=None,
json_time_since_epoch=None,
log_level=logutil.logging.INFO):
"""Determine the relative alignment between members of an association.
Parameters
-----------
input : string
Original pipeline input filename. This filename will be used to
define the output analysis results filename.
files : list
Set of files on which to actually perform comparison. The original
pipeline can work on both CTE-corrected and non-CTE-corrected files,
but this comparison will only be performed on CTE-corrected
products when available.
catalogs : list, optional
List of dictionaries containing the source catalogs for each input chip.
The list NEEDS to be in the same order as the filenames given in `files`.
Each dictionary for each file will need to have numerical (integer) keys
for each 'sci' extension. If left as `None`, this function will create
it's own set of catalogs using `astrometric_utils.extract_point_sources`.
json_timestamp: str, optional
Universal .json file generation date and time (local timezone) that will be used in the instantiation
of the HapDiagnostic object. Format: MM/DD/YYYYTHH:MM:SS (Example: 05/04/2020T13:46:35). If not
specified, default value is logical 'None'
json_time_since_epoch : float
Universal .json file generation time that will be used in the instantiation of the HapDiagnostic
object. Format: Time (in seconds) elapsed since January 1, 1970, 00:00:00 (UTC). If not specified,
default value is logical 'None'
log_level : int, optional
The desired level of verboseness in the log statements displayed on the screen and written to the
.log file. Default value is 'NOTSET'.
Returns
--------
resids_files : list of string
Name of JSON files containing all the extracted results from the comparisons
being performed.
"""
log.setLevel(log_level)
if catalogs is None:
# Open all files as HDUList objects
hdus = [fits.open(f) for f in files]
# Determine sources from each chip
src_cats = []
num_srcs = []
for hdu in hdus:
numsci = countExtn(hdu)
nums = 0
img_cats = {}
if hdu[("SCI", 1)].data.max() == 0.0:
log.info(
"SKIPPING point-source finding for blank image: {}".format(
hdu.filename()))
continue
log.info("Determining point-sources for {}".format(hdu.filename()))
for chip in range(numsci):
chip += 1
img_cats[chip] = amutils.extract_point_sources(
hdu[("SCI", chip)].data, nbright=max_srcs)
nums += len(img_cats[chip])
log.info("Identified {} point-sources from {}".format(
nums, hdu.filename()))
num_srcs.append(nums)
src_cats.append(img_cats)
else:
src_cats = catalogs
num_srcs = []
for img in src_cats:
num_img = 0
for chip in img:
num_img += len(img[chip])
num_srcs.append(num_img)
if len(num_srcs) == 0 or (len(num_srcs) > 0 and max(num_srcs) <= 3):
log.warning(
"Not enough sources identified in input images for comparison")
return []
# Combine WCS from HDULists and source catalogs into tweakwcs-compatible input
imglist = []
for i, (f, cat) in enumerate(zip(files, src_cats)):
imglist += amutils.build_wcscat(f, i, cat)
# Setup matching algorithm using parameters tuned to well-aligned images
match = tweakwcs.TPMatch(searchrad=5,
separation=4.0,
tolerance=1.0,
use2dhist=True)
try:
# perform relative fitting
matchlist = tweakwcs.align_wcs(imglist,
None,
minobj=6,
match=match,
expand_refcat=False)
del matchlist
except Exception:
try:
# Try without 2dHist use to see whether we can get any matches at all
match = tweakwcs.TPMatch(searchrad=5,
separation=4.0,
tolerance=1.0,
use2dhist=False)
matchlist = tweakwcs.align_wcs(imglist,
None,
minobj=6,
match=match,
expand_refcat=False)
del matchlist
except Exception:
log.warning("Problem encountered during matching of sources")
return []
# Check to see whether there were any successful fits...
align_success = False
for img in imglist:
wcsname = fits.getval(img.meta['filename'], 'wcsname', ext=("sci", 1))
img.meta['wcsname'] = wcsname
img.meta['fit_info']['aligned_to'] = imglist[0].meta['filename']
img.meta['reference_catalog'] = None
for img in imglist:
if img.meta['fit_info']['status'] == 'SUCCESS' and '-FIT' in wcsname:
align_success = True
break
resids_files = []
if align_success:
# extract results in the style of 'tweakreg'
resids = extract_residuals(imglist)
if resids is not None:
resids_files = generate_output_files(
resids,
json_timestamp=json_timestamp,
json_time_since_epoch=json_time_since_epoch,
exclude_fields=['group_id'])
return resids_files
def generate_source_catalog(image, **kwargs):
""" Build source catalogs for each chip using photutils.
The catalog returned by this function includes sources found in all chips
of the input image with the positions translated to the coordinate frame
defined by the reference WCS `refwcs`. The sources will be
- identified using photutils segmentation-based source finding code
- ignore any input pixel which has been flagged as 'bad' in the DQ
array, should a DQ array be found in the input HDUList.
- classified as probable cosmic-rays (if enabled) using central_moments
properties of each source, with these sources being removed from the
catalog.
Parameters
----------
image : `~astropy.io.fits.HDUList`
Input image as an astropy.io.fits HDUList.
dqname : str
EXTNAME for the DQ array, if present, in the input image HDUList.
output : bool
Specify whether or not to write out a separate catalog file for all the
sources found in each chip. Default: None (False)
threshold : float, optional
This parameter controls the threshold used for identifying sources in
the image relative to the background RMS.
If None, compute a default value of (background+3*rms(background)).
If threshold < 0.0, use absolute value as scaling factor for default value.
fwhm : float, optional
FWHM (in pixels) of the expected sources from the image, comparable to the
'conv_width' parameter from 'tweakreg'. Objects with FWHM closest to
this value will be identified as sources in the catalog.
Returns
-------
source_cats : dict
Dict of astropy Tables identified by chip number with
each table containing sources from image extension ``('sci', chip)``.
"""
if not isinstance(image, pf.HDUList):
raise ValueError("Input {} not fits.HDUList object".format(image))
dqname = kwargs.get('dqname', 'DQ')
output = kwargs.get('output', None)
# Build source catalog for entire image
source_cats = {}
numSci = countExtn(image, extname='SCI')
for chip in range(numSci):
chip += 1
# find sources in image
if output:
rootname = image[0].header['rootname']
outroot = '{}_sci{}_src'.format(rootname, chip)
kwargs['output'] = outroot
imgarr = image['sci', chip].data
# apply any DQ array, if available
dqmask = None
if image.index_of(dqname):
dqarr = image[dqname, chip].data
# "grow out" regions in DQ mask flagged as saturated by several
# pixels in every direction to prevent the
# source match algorithm from trying to match multiple sources
# from one image to a single source in the
# other or vice-versa.
# Create temp DQ mask containing all pixels flagged with any value EXCEPT 256
non_sat_mask = bitfield_to_boolean_mask(dqarr, ignore_flags=256)
# Create temp DQ mask containing saturated pixels ONLY
sat_mask = bitfield_to_boolean_mask(dqarr, ignore_flags=~256)
# Grow out saturated pixels by a few pixels in every direction
grown_sat_mask = ndimage.binary_dilation(sat_mask, iterations=5)
# combine the two temporary DQ masks into a single composite DQ mask.
dqmask = np.bitwise_or(non_sat_mask, grown_sat_mask)
# dqmask = bitfield_to_boolean_mask(dqarr, good_mask_value=False)
# TODO: <---Remove this old no-sat bit grow line once this
# thing works
seg_tab, segmap = extract_sources(imgarr, dqmask=dqmask, **kwargs)
seg_tab_phot = seg_tab
source_cats[chip] = seg_tab_phot
return source_cats
def create_astrometric_catalog(inputs, **pars):
"""Create an astrometric catalog that covers the inputs' field-of-view.
Parameters
===========
input : str
Filenames of images to be aligned to astrometric catalog
catalog : str, optional
Name of catalog to extract astrometric positions for sources in the
input images' field-of-view. Default: GSC241. Options available are
documented on the catalog web page.
output : str, optional
Filename to give to the astrometric catalog read in from the master
catalog web service. If 'None', no file will be written out.
Default: ref.cat
gaia_only : bool, optional
Specify whether or not to only use sources from GAIA in output catalog
Default: False
note ::
This function will point to astrometric catalog web service defined
through the use of the ASTROMETRIC_CATALOG_URL environment variable.
Returns
=======
ref_table : object
Astropy Table object of the catalog
"""
# interpret input parameters
catalog = pars.get("catalog", 'GSC241')
output = pars.get("output", 'ref_cat.ecsv')
gaia_only = pars.get("gaia_only", False)
table_format = pars.get("table_format", 'ascii.ecsv')
inputs, _ = parseinput.parseinput(inputs)
# start by creating a composite field-of-view for all inputs
wcslist = []
for img in inputs:
nsci = fu.countExtn(img)
for num in range(nsci):
extname = '{}[sci,{}]'.format(img, num+1)
wcslist.append(stwcs.wcsutil.HSTWCS(extname))
# This default output WCS will have the same plate-scale and orientation
# as the first chip in the list, which for WFPC2 data means the PC.
# Fortunately, for alignment, this doesn't matter since no resampling of
# data will be performed
outwcs = utils.output_wcs(wcslist)
radius = compute_radius(outwcs)
ra, dec = outwcs.wcs.crval
# perform query for this field-of-view
ref_dict = get_catalog(ra, dec, sr=radius, catalog=catalog)
colnames = ('ra','dec', 'mag', 'objID', 'GaiaID')
col_types = ('f8', 'f8', 'f4', 'U25', 'U25')
ref_table = Table(names = colnames, dtype=col_types)
# extract just the columns we want...
num_sources = 0
for source in ref_dict:
if 'GAIAsourceID' in source:
g = source['GAIAsourceID']
if gaia_only and g.strip() is '':
continue
else:
g = -1 # indicator for no source ID extracted
r = float(source['ra'])
d = float(source['dec'])
m = float(source['mag'])
o = source['objID']
num_sources += 1
ref_table.add_row((r,d,m,o,g))
# Write out table to a file, if specified
if output:
ref_table.write(output, format=table_format)
print("Created catalog '{}' with {} sources".format(output, num_sources))
return ref_table
def tran(inimage,
outimage,
direction='forward',
x=None,
y=None,
coordfile=None,
colnames=None,
separator=None,
precision=6,
output=None,
verbose=True):
""" Primary interface to perform coordinate transformations in pixel
coordinates between 2 images using STWCS and full distortion models
read from each image's header.
"""
single_coord = False
if coordfile is None:
if isinstance(x, np.ndarray):
xlist = x.tolist()
ylist = y.tolist()
elif not isinstance(x, list):
xlist = [x]
ylist = [y]
single_coord = True
else:
xlist = x
ylist = y
else:
if colnames in util.blank_list:
colnames = ['c1', 'c2']
# Determine columns which contain pixel positions
cols = util.parse_colnames(colnames, coordfile)
# read in columns from input coordinates file
xyvals = np.loadtxt(coordfile, usecols=cols, delimiter=separator)
if xyvals.ndim == 1: # only 1 entry in coordfile
xlist = [xyvals[0].copy()]
ylist = [xyvals[1].copy()]
else:
xlist = xyvals[:, 0].copy()
ylist = xyvals[:, 1].copy()
# start by reading in WCS+distortion info for each image
im1wcs = wcsutil.HSTWCS(inimage)
if im1wcs.wcs.is_unity():
print("####\nNo valid input WCS found in '{}'."
"\n Results may be invalid.\n####\n".format(inimage))
if util.is_blank(outimage):
fname, fextn = fileutil.parseFilename(inimage)
numsci = fileutil.countExtn(fname)
chips = [
wcsutil.HSTWCS(fname, ext=('sci', e + 1)) for e in range(numsci)
]
if len(chips) == 0:
chips = [im1wcs]
im2wcs = distortion.utils.output_wcs(chips)
else:
im2wcs = wcsutil.HSTWCS(outimage)
if im2wcs.wcs.is_unity():
print("####\nNo valid output WCS found in '{}'."
"\n Results may be invalid.\n####\n".format(outimage))
# Setup the transformation
p2p = wcs_functions.WCSMap(im1wcs, im2wcs)
if direction[0].lower() == 'f':
outx, outy = p2p.forward(xlist, ylist)
else:
outx, outy = p2p.backward(xlist, ylist)
if isinstance(outx, np.ndarray):
outx = outx.tolist()
outy = outy.tolist()
# add formatting based on precision here...
xstr = []
ystr = []
for ox, oy in zip(outx, outy):
xstr.append('{0:.{1}f}'.format(ox, precision))
ystr.append('{0:.{1}f}'.format(oy, precision))
if verbose:
print('# Coordinate transformations for ', inimage)
print('# X(in) Y(in) X(out) Y(out)\n')
for xs, ys, a, b in zip(xlist, ylist, xstr, ystr):
print("%.4f %.4f %s %s" % (xs, ys, a, b))
# Create output file, if specified
if output:
with open(output, mode='w') as f:
f.write("# Coordinates converted from %s\n" % inimage)
for xs, ys in zip(xstr, ystr):
f.write('%s %s\n' % (xs, ys))
print('Wrote out results to: ', output)
if single_coord:
outx = outx[0]
outy = outy[0]
return outx, outy
def tran(inimage,outimage,direction='forward',x=None,y=None,
coords=None, coordfile=None,colnames=None,separator=None,
precision=6, output=None,verbose=True):
""" Primary interface to perform coordinate transformations in pixel
coordinates between 2 images using STWCS and full distortion models
read from each image's header.
"""
single_coord = False
# Only use value provided in `coords` if nothing has been specified for coordfile
if coords is not None and coordfile is None:
coordfile = coords
warnings.simplefilter('always',DeprecationWarning)
warnings.warn("Please update calling code to pass in `coordfile` instead of `coords`.",
category=DeprecationWarning)
warnings.simplefilter('default',DeprecationWarning)
if coordfile is not None:
if colnames in util.blank_list:
colnames = ['c1','c2']
# Determine columns which contain pixel positions
cols = util.parse_colnames(colnames,coordfile)
# read in columns from input coordinates file
xyvals = np.loadtxt(coordfile,usecols=cols,delimiter=separator)
if xyvals.ndim == 1: # only 1 entry in coordfile
xlist = [xyvals[0].copy()]
ylist = [xyvals[1].copy()]
else:
xlist = xyvals[:,0].copy()
ylist = xyvals[:,1].copy()
del xyvals
else:
if isinstance(x,np.ndarray):
xlist = x.tolist()
ylist = y.tolist()
elif not isinstance(x,list):
xlist = [x]
ylist = [y]
single_coord = True
else:
xlist = x
ylist = y
# start by reading in WCS+distortion info for each image
im1wcs = wcsutil.HSTWCS(inimage)
if im1wcs.wcs.is_unity():
print("####\nNo valid input WCS found in {}.\n Results may be invalid.\n####\n".format(inimage))
if util.is_blank(outimage):
fname,fextn = fileutil.parseFilename(inimage)
numsci = fileutil.countExtn(fname)
chips = []
for e in range(1,numsci+1):
chips.append(wcsutil.HSTWCS(fname,ext=('sci',e)))
if len(chips) == 0:
chips = [im1wcs]
im2wcs = distortion.utils.output_wcs(chips)
else:
im2wcs = wcsutil.HSTWCS(outimage)
if im2wcs.wcs.is_unity():
print("####\nNo valid output WCS found in {}.\n Results may be invalid.\n####\n".format(outimage))
# Setup the transformation
p2p = wcs_functions.WCSMap(im1wcs,im2wcs)
if direction[0].lower() == 'f':
outx,outy = p2p.forward(xlist,ylist)
else:
outx,outy = p2p.backward(xlist,ylist)
if isinstance(outx,np.ndarray):
outx = outx.tolist()
outy = outy.tolist()
# add formatting based on precision here...
xstr = []
ystr = []
fmt = "%."+repr(precision)+"f"
for ox,oy in zip(outx,outy):
xstr.append(fmt%ox)
ystr.append(fmt%oy)
if verbose or (not verbose and util.is_blank(output)):
print('# Coordinate transformations for ',inimage)
print('# X(in) Y(in) X(out) Y(out)\n')
for xs,ys,a,b in zip(xlist,ylist,xstr,ystr):
print("%.4f %.4f %s %s"%(xs,ys,a,b))
# Create output file, if specified
if output:
f = open(output,mode='w')
f.write("# Coordinates converted from %s\n"%inimage)
for xs,ys in zip(xstr,ystr):
f.write('%s %s\n'%(xs,ys))
f.close()
print('Wrote out results to: ',output)
if single_coord:
outx = outx[0]
outy = outy[0]
return outx,outy
def init_wcscorr(input, force=False):
"""
This function will initialize the WCSCORR table if it is not already present,
and look for WCS keywords with a prefix of 'O' as the original OPUS
generated WCS as the initial row for the table or use the current WCS
keywords as initial row if no 'O' prefix keywords are found.
This function will NOT overwrite any rows already present.
This function works on all SCI extensions at one time.
"""
# TODO: Create some sort of decorator or (for Python2.5) context for
# opening a FITS file and closing it when done, if necessary
if not isinstance(input, fits.HDUList):
# input must be a filename, so open as `astropy.io.fits.HDUList` object
fimg = fits.open(input, mode='update')
need_to_close = True
else:
fimg = input
need_to_close = False
# Do not try to generate a WCSCORR table for a simple FITS file
numsci = fileutil.countExtn(fimg)
if len(fimg) == 1 or numsci == 0:
return
enames = []
for e in fimg: enames.append(e.name)
if 'WCSCORR' in enames:
if not force:
return
else:
del fimg['wcscorr']
print('Initializing new WCSCORR table for ',fimg.filename())
used_wcskeys = altwcs.wcskeys(fimg['SCI', 1].header)
# define the primary columns of the WCSEXT table with initial rows for each
# SCI extension for the original OPUS solution
numwcs = len(used_wcskeys)
if numwcs == 0: numwcs = 1
# create new table with more rows than needed initially to make it easier to
# add new rows later
wcsext = create_wcscorr(descrip=True,numrows=numsci, padding=(numsci*numwcs) + numsci * 4)
# Assign the correct EXTNAME value to this table extension
wcsext.header['TROWS'] = (numsci * 2, 'Number of updated rows in table')
wcsext.header['EXTNAME'] = ('WCSCORR', 'Table with WCS Update history')
wcsext.header['EXTVER'] = 1
# define set of WCS keywords which need to be managed and copied to the table
wcs1 = stwcs.wcsutil.HSTWCS(fimg,ext=('SCI',1))
idc2header = True
if wcs1.idcscale is None:
idc2header = False
wcs_keywords = list(wcs1.wcs2header(idc2hdr=idc2header).keys())
prihdr = fimg[0].header
prihdr_keys = DEFAULT_PRI_KEYS
pri_funcs = {'SIPNAME':stwcs.updatewcs.utils.build_sipname,
'NPOLNAME':stwcs.updatewcs.utils.build_npolname,
'D2IMNAME':stwcs.updatewcs.utils.build_d2imname}
# Now copy original OPUS values into table
for extver in range(1, numsci + 1):
rowind = find_wcscorr_row(wcsext.data,
{'WCS_ID': 'OPUS', 'EXTVER': extver,
'WCS_key':'O'})
# There should only EVER be a single row for each extension with OPUS values
rownum = np.where(rowind)[0][0]
#print 'Archiving OPUS WCS in row number ',rownum,' in WCSCORR table for SCI,',extver
hdr = fimg['SCI', extver].header
# define set of WCS keywords which need to be managed and copied to the table
if used_wcskeys is None:
used_wcskeys = altwcs.wcskeys(hdr)
# Check to see whether or not there is an OPUS alternate WCS present,
# if so, get its values directly, otherwise, archive the PRIMARY WCS
# as the OPUS values in the WCSCORR table
if 'O' not in used_wcskeys:
altwcs.archiveWCS(fimg,('SCI',extver),wcskey='O', wcsname='OPUS')
wkey = 'O'
wcs = stwcs.wcsutil.HSTWCS(fimg, ext=('SCI', extver), wcskey=wkey)
wcshdr = wcs.wcs2header(idc2hdr=idc2header)
if wcsext.data.field('CRVAL1')[rownum] != 0:
# If we find values for these keywords already in the table, do not
# overwrite them again
print('WCS keywords already updated...')
break
for key in wcs_keywords:
if key in wcsext.data.names:
wcsext.data.field(key)[rownum] = wcshdr[(key+wkey)[:8]]
# Now get any keywords from PRIMARY header needed for WCS updates
for key in prihdr_keys:
if key in prihdr:
val = prihdr[key]
else:
val = ''
wcsext.data.field(key)[rownum] = val
# Now that we have archived the OPUS alternate WCS, remove it from the list
# of used_wcskeys
if 'O' in used_wcskeys:
used_wcskeys.remove('O')
# Now copy remaining alternate WCSs into table
# TODO: Much of this appears to be redundant with update_wcscorr; consider
# merging them...
for uwkey in used_wcskeys:
for extver in range(1, numsci + 1):
hdr = fimg['SCI', extver].header
wcs = stwcs.wcsutil.HSTWCS(fimg, ext=('SCI', extver),
wcskey=uwkey)
wcshdr = wcs.wcs2header()
if 'WCSNAME' + uwkey not in wcshdr:
wcsid = utils.build_default_wcsname(fimg[0].header['idctab'])
else:
wcsid = wcshdr['WCSNAME' + uwkey]
# identify next empty row
rowind = find_wcscorr_row(wcsext.data,
selections={'wcs_id':['','0.0']})
rows = np.where(rowind)
if len(rows[0]) > 0:
rownum = np.where(rowind)[0][0]
else:
print('No available rows found for updating. ')
# Update selection columns for this row with relevant values
wcsext.data.field('WCS_ID')[rownum] = wcsid
wcsext.data.field('EXTVER')[rownum] = extver
wcsext.data.field('WCS_key')[rownum] = uwkey
# Look for standard WCS keyword values
for key in wcs_keywords:
if key in wcsext.data.names:
wcsext.data.field(key)[rownum] = wcshdr[key + uwkey]
# Now get any keywords from PRIMARY header needed for WCS updates
for key in prihdr_keys:
if key in pri_funcs:
val = pri_funcs[key](fimg)[0]
else:
if key in prihdr:
val = prihdr[key]
else:
val = ''
wcsext.data.field(key)[rownum] = val
# Append this table to the image FITS file
fimg.append(wcsext)
# force an update now
# set the verify flag to 'warn' so that it will always succeed, but still
# tell the user if PyFITS detects any problems with the file as a whole
utils.updateNEXTENDKw(fimg)
fimg.flush('warn')
if need_to_close:
fimg.close()
def generate_source_catalog(image,
dqname="DQ",
output=False,
fwhm=3.0,
**detector_pars):
""" Build source catalogs for each chip using photutils.
The catalog returned by this function includes sources found in all chips
of the input image with the positions translated to the coordinate frame
defined by the reference WCS `refwcs`. The sources will be
- identified using photutils segmentation-based source finding code
- ignore any input pixel which has been flagged as 'bad' in the DQ
array, should a DQ array be found in the input HDUList.
- classified as probable cosmic-rays (if enabled) using central_moments
properties of each source, with these sources being removed from the
catalog.
Parameters
----------
image : `~astropy.io.fits.HDUList`
Input image as an astropy.io.fits HDUList.
dqname : str
EXTNAME for the DQ array, if present, in
the input image HDUList.
output : bool
Specify whether or not to write out a separate catalog file for all the
sources found in each chip.
fwhm : float
Full-width half-maximum (fwhm) of the PSF in pixels.
Returns
-------
source_cats : dict
Dict of astropy Tables identified by chip number with
each table containing sources from image extension ``('sci', chip)``.
"""
if not isinstance(image, fits.HDUList):
raise ValueError("Input {} not fits.HDUList object".format(image))
# remove parameters that are not needed by subsequent functions
del detector_pars['fwhmpsf']
# Build source catalog for entire image
source_cats = {}
numSci = countExtn(image, extname='SCI')
outroot = None
for chip in range(numSci):
chip += 1
# find sources in image
if output:
rootname = image[0].header['rootname']
outroot = '{}_sci{}_src'.format(rootname, chip)
imgarr = image['sci', chip].data
# apply any DQ array, if available
dqmask = None
if image.index_of(dqname):
dqarr = image[dqname, chip].data
# "grow out" regions in DQ mask flagged as saturated by several
# pixels in every direction to prevent the
# source match algorithm from trying to match multiple sources
# from one image to a single source in the
# other or vice-versa.
# Create temp DQ mask containing all pixels flagged with any value EXCEPT 256
non_sat_mask = bitfield_to_boolean_mask(dqarr, ignore_flags=256)
# Create temp DQ mask containing saturated pixels ONLY
sat_mask = bitfield_to_boolean_mask(dqarr, ignore_flags=~256)
# Grow out saturated pixels by a few pixels in every direction
grown_sat_mask = ndimage.binary_dilation(sat_mask, iterations=5)
# combine the two temporary DQ masks into a single composite DQ mask.
dqmask = np.bitwise_or(non_sat_mask, grown_sat_mask)
# dqmask = bitfield_to_boolean_mask(dqarr, good_mask_value=False)
# TODO: <---Remove this old no-sat bit grow line once this
# thing works
seg_tab, segmap = extract_sources(imgarr,
dqmask=dqmask,
outroot=outroot,
fwhm=fwhm,
**detector_pars)
seg_tab_phot = seg_tab
source_cats[chip] = seg_tab_phot
return source_cats
