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 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 get_hstwcs(filename, hdulist, extnum):
""" Return the HSTWCS object for a given chip. """
hdrwcs = wcsutil.HSTWCS(hdulist, ext=extnum)
hdrwcs.filename = filename
hdrwcs.expname = hdulist[extnum].header['expname']
hdrwcs.extver = hdulist[extnum].header['extver']
return hdrwcs
def get_hstwcs(filename, extnum):
""" Return the HSTWCS object for a given chip. """
hdrwcs = wcsutil.HSTWCS(filename, ext=extnum)
hdrwcs.filename = filename
hdrwcs.expname = pyfits.getval(filename, 'expname', ext=extnum)
hdrwcs.extver = pyfits.getval(filename, 'extver', ext=extnum)
return hdrwcs
def findWCSExtn(filename):
""" Return new filename with extension that points to an extension with a
valid WCS.
Returns
=======
extnum : str, None
Value of extension name as a string either as provided by the user
or based on the extension number for the first extension which
contains a valid HSTWCS object. Returns None if no extension can be
found with a valid WCS.
Notes
=====
The return value from this function can be used as input to
create another HSTWCS with the syntax::
`HSTWCS('{}[{}]'.format(filename,extnum))
"""
rootname, extroot = fileutil.parseFilename(filename)
extnum = None
if extroot is None:
fimg = fits.open(rootname, memmap=False)
for i, extn in enumerate(fimg):
if 'crval1' in extn.header:
refwcs = wcsutil.HSTWCS('{}[{}]'.format(rootname, i))
if refwcs.wcs.has_cd():
extnum = '{}'.format(i)
break
fimg.close()
else:
try:
refwcs = wcsutil.HSTWCS(filename)
if refwcs.wcs.has_cd():
extnum = extroot
except:
extnum = None
return extnum
def read_hlet_wcs(filename, ext):
"""Insure HSTWCS includes all attributes of a full image WCS.
For headerlets, the WCS does not contain information about the size of the
image, as the image array is not present in the headerlet.
"""
hstwcs = wcsutil.HSTWCS(filename, ext=ext)
if hstwcs.naxis1 is None:
hstwcs.naxis1 = int(hstwcs.wcs.crpix[0] *
2.) # Assume crpix is center of chip
hstwcs.naxis2 = int(hstwcs.wcs.crpix[1] * 2.)
return hstwcs
def create_image_footprint(image, refwcs, border=0.):
""" Create the footprint of the image in the reference WCS frame
Parameters
----------
image : HDUList or filename
Image to extract sources for matching to
the external astrometric catalog
refwcs : object
Reference WCS for coordinate frame of image
border : float
Buffer (in arcseconds) around edge of image to exclude astrometric
sources. Default: 0.
"""
# 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 build_hstwcs(crval1, crval2, crpix1, crpix2, naxis1, naxis2, pscale, orientat):
""" Create an HSTWCS object for a default instrument without distortion
based on user provided parameter values.
"""
wcsout = wcsutil.HSTWCS()
wcsout.wcs.crval = np.array([crval1, crval2])
wcsout.wcs.crpix = np.array([crpix1, crpix2])
wcsout.naxis1 = naxis1
wcsout.naxis2 = naxis2
wcsout.wcs.cd = fileutil.buildRotMatrix(orientat) * [-1, 1] * pscale / 3600.0
# Synchronize updates with astropy.wcs/WCSLIB objects
wcsout.wcs.set()
wcsout.setPscale()
wcsout.setOrient()
wcsout.wcs.ctype = ['RA---TAN', 'DEC--TAN']
return wcsout
def build_hstwcs(crval, crpix, naxis1, naxis2, pscale, orientat):
""" Create an `~stwcs.wcsutil.HSTWCS` object for a default instrument without
distortion based on user provided parameter values.
"""
wcsout = wcsutil.HSTWCS()
wcsout.wcs.crval = crval.copy()
wcsout.wcs.crpix = crpix.copy()
wcsout.naxis1 = naxis1
wcsout.naxis2 = naxis2
wcsout.wcs.cd = fu.buildRotMatrix(orientat) * [-1, 1] * pscale / 3600.0
# Synchronize updates with astropy.wcs objects
wcsout.wcs.set()
wcsout.setPscale()
wcsout.setOrient()
wcsout.wcs.ctype = ['RA---TAN', 'DEC--TAN']
return wcsout
def verifyRefimage(refimage):
"""
Verify that the value of refimage specified by the user points to an
extension with a proper WCS defined. It starts by making sure an extension gets
specified by the user when using a MEF file. The final check comes by looking
for a CD matrix in the WCS object itself. If either test fails, it returns
a value of False.
"""
valid = True
# start by trying to see whether the code can even find the file
if is_blank(refimage):
return True
if isinstance(refimage, astropy.wcs.WCS):
return True
refroot, extroot = fileutil.parseFilename(refimage)
if not os.path.exists(refroot):
return False
# if a MEF has been specified, make sure extension contains a valid WCS
if valid:
if extroot is None:
extn = findWCSExtn(refimage)
if extn is None:
valid = False
else:
valid = True
else:
# check for CD matrix in WCS object
refwcs = wcsutil.HSTWCS(refimage)
if not refwcs.wcs.has_cd():
valid = False
else:
valid = True
del refwcs
return valid
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 find_footprint(hdu, extname='SCI', extnum=1):
"""Extract the footprints from each input file
Determine the composite of all the input chip's corners
as the footprint for the entire set of overlapping images
that went into creating this drizzled image.
Parameters
===========
hdu : str or `fits.HDUList`
Filename or HDUList for a drizzled image
extname : str, optional
Name of science array extension (extname value)
extnum : int, optional
EXTVER value for science array extension
Returns
========
footprint : ndarray
Array of RA/Dec for the 4 corners that comprise the
footprint on the sky for this mosaic. Values were
determined from northern-most corner counter-clockwise
to the rest.
"""
# extract WCS from this product
meta_wcs = wcsutil.HSTWCS(hdu, ext=(extname, extnum))
# create SkyFootprint object for all input_files to determine footprint
footprint = SkyFootprint(meta_wcs=meta_wcs)
# create mask of all input chips as they overlap on the product WCS
footprint.extract_mask(hdu.filename())
# Now, find the corners from this mask
footprint.find_corners()
return footprint
def run_sourcelist_flagging(filter_product_obj,
filter_product_catalogs,
log_level,
diagnostic_mode=False):
"""
Super-basic and profoundly inelegant interface to hla_flag_filter.py.
Execute haputils.hla_flag_filter.run_source_list_flaging() to populate the "Flags" column in the catalog tables
generated by HAPcatalogs.measure().
Parameters
----------
filter_product_obj : drizzlepac.hlautils.product.FilterProduct object
object containing all the relevant info for the drizzled filter product
filter_product_catalogs : drizzlepac.hlautils.catalog_utils.HAPCatalogs object
drizzled filter product catalog object
log_level : int
The desired level of verboseness in the log statements displayed on the screen and written to the .log file.
diagnostic_mode : Boolean, optional.
create intermediate diagnostic files? Default value is False.
Returns
-------
filter_product_catalogs : drizzlepac.hlautils.catalog_utils.HAPCatalogs object
updated version of filter_product_catalogs object with fully populated source flags
"""
drizzled_image = filter_product_obj.drizzle_filename
flt_list = []
for edp_obj in filter_product_obj.edp_list:
flt_list.append(edp_obj.full_filename)
param_dict = filter_product_obj.configobj_pars.as_single_giant_dict()
plate_scale = wcsutil.HSTWCS(drizzled_image, ext=('sci', 1)).pscale
median_sky = filter_product_catalogs.image.bkg_median
# Create mask array that will be used by hla_flag_filter.hla_nexp_flags() for both point and segment catalogs.
if not hasattr(filter_product_obj, 'hla_flag_msk'):
filter_product_obj.hla_flag_msk = hla_flag_filter.make_mask_array(
drizzled_image)
if filter_product_obj.configobj_pars.use_defaults:
ci_lookup_file_path = "default_parameters/any"
else:
ci_lookup_file_path = "user_parameters/any"
output_custom_pars_file = filter_product_obj.configobj_pars.output_custom_pars_file
for cat_type in filter_product_catalogs.catalogs.keys():
exptime = filter_product_catalogs.catalogs[cat_type].image.imghdu[
0].header[
'exptime'] # TODO: This works for ACS. Make sure that it also works for WFC3. Look at "TEXPTIME"
catalog_name = filter_product_catalogs.catalogs[
cat_type].sourcelist_filename
catalog_data = filter_product_catalogs.catalogs[cat_type].source_cat
drz_root_dir = os.getcwd()
log.info("Run source list flagging on catalog file {}.".format(
catalog_name))
filter_product_catalogs.catalogs[
cat_type].source_cat = hla_flag_filter.run_source_list_flagging(
drizzled_image, flt_list, param_dict, exptime, plate_scale,
median_sky, catalog_name, catalog_data, cat_type, drz_root_dir,
filter_product_obj.hla_flag_msk, ci_lookup_file_path,
output_custom_pars_file, log_level, diagnostic_mode)
return filter_product_catalogs
def rd2xy(input,
ra=None,
dec=None,
coordfile=None,
colnames=None,
precision=6,
output=None,
verbose=True):
""" Primary interface to perform coordinate transformations from
pixel to sky coordinates using STWCS and full distortion models
read from the input image header.
"""
single_coord = False
if coordfile is not None:
if colnames in blank_list:
colnames = ['c1', 'c2']
elif isinstance(colnames, type('a')):
colnames = colnames.split(',')
# convert input file coordinates to lists of decimal degrees values
xlist, ylist = tweakutils.readcols(coordfile, cols=colnames)
else:
if isinstance(ra, np.ndarray):
ralist = ra.tolist()
declist = dec.tolist()
elif not isinstance(ra, list):
ralist = [ra]
declist = [dec]
else:
ralist = ra
declist = dec
xlist = [0] * len(ralist)
ylist = [0] * len(ralist)
if len(xlist) == 1:
single_coord = True
for i, (r, d) in enumerate(zip(ralist, declist)):
# convert input value into decimal degrees value
xval, yval = tweakutils.parse_skypos(r, d)
xlist[i] = xval
ylist[i] = yval
# start by reading in WCS+distortion info for input image
inwcs = wcsutil.HSTWCS(input)
if inwcs.wcs.is_unity():
print(
"####\nNo valid WCS found in {}.\n Results may be invalid.\n####\n"
.format(input))
# Now, convert pixel coordinates into sky coordinates
try:
outx, outy = inwcs.all_world2pix(xlist, ylist, 1)
except RuntimeError:
outx, outy = inwcs.wcs_world2pix(xlist, ylist, 1)
# add formatting based on precision here...
xstr = []
ystr = []
fmt = "%." + repr(precision) + "f"
for x, y in zip(outx, outy):
xstr.append(fmt % x)
ystr.append(fmt % y)
if verbose or (not verbose and util.is_blank(output)):
print('# Coordinate transformations for ', input)
print('# X Y RA Dec\n')
for x, y, r, d in zip(xstr, ystr, xlist, ylist):
print("%s %s %s %s" % (x, y, r, d))
# Create output file, if specified
if output:
f = open(output, mode='w')
f.write("# Coordinates converted from %s\n" % input)
for x, y in zip(xstr, ystr):
f.write('%s %s\n' % (x, y))
f.close()
print('Wrote out results to: ', output)
if single_coord:
outx = outx[0]
outy = outy[0]
return outx, outy
""" Convert colnames input into list of column numbers
"""
cols = []
if not isinstance(colnames, list):
colnames = colnames.split(',')
# parse column names from coords file and match to input values
if coordfile is not None and fileutil.isFits(coordfile)[0]:
# Open FITS file with table
ftab = fits.open(coordfile)
# determine which extension has the table
for extn in ftab:
if isinstance(extn, fits.BinTableHDU):
# parse column names from table and match to inputs
cnames = extn.columns.names
if colnames is not None:
for c in colnames:
for name, i in zip(cnames, list(range(len(cnames)))):
if c == name.lower(): cols.append(i)
if len(cols) < len(colnames):
errmsg = "Not all input columns found in table..."
ftab.close()
raise ValueError(errmsg)
else:
cols = cnames[:2]
break
ftab.close()
else:
for c in colnames:
if isinstance(c, str):
if c[0].lower() == 'c':
cols.append(int(c[1:]) - 1)
else:
cols.append(int(c))
else:
if isinstance(c, int):
cols.append(c)
else:
errmsg = "Unsupported column names..."
raise ValueError(errmsg)
return cols
def create_product_page(prodname,
zoom_size=128,
wcsname="",
gcolor='magenta',
fsize=8):
"""Create a matplotlib Figure() object which summarizes this product FITS file."""
# obtain image data to display
with fits.open(prodname) as prod:
data = prod[1].data
phdr = prod[0].header
if 'texptime' not in phdr:
return None, None
targname = phdr['targname']
inst = phdr['instrume']
det = phdr['detector']
texptime = phdr['texptime']
inexp = phdr['d001data'].split('[')[0]
wcstype = prod[1].header['wcstype']
wcs = wcsutil.HSTWCS(prod, ext=1)
hdrtab = prod['hdrtab'].data
filters = ';'.join([phdr[f] for f in phdr['filter*']])
dateobs = phdr['date-obs'] # human-readable date
expstart = phdr['expstart'] # MJD float value
asnid = phdr.get('asn_id', '')
center = (data.shape[0] // 2, data.shape[1] // 2)
prod_path = os.path.split(prodname)[0]
data = np.nan_to_num(data, 0.0)
# Get GAIA catalog
refcat = amutils.create_astrometric_catalog(prodname,
existing_wcs=wcs,
output=None)
refx, refy = wcs.all_world2pix(refcat['RA'], refcat['DEC'], 0)
# Remove points outside the full-size image area
rx = []
ry = []
zx = []
zy = []
for x, y in zip(refx, refy):
if 0 < x < data.shape[1] and 0 < y < data.shape[0]:
rx.append(x)
ry.append(y)
if -zoom_size < x - center[1] < zoom_size and -zoom_size < y - center[
0] < zoom_size:
zx.append(x - center[1] + zoom_size)
zy.append(y - center[0] + zoom_size)
# Define subplot regions on page
fig = plt.figure(constrained_layout=True, figsize=(8.5, 11))
gs = fig.add_gridspec(ncols=4, nrows=5)
# title plots
rootname = os.path.basename(prodname)
img_title = "{} image of {} with WCSNAME={}".format(
rootname, targname, wcsname)
fig.suptitle(img_title, ha='center', va='top', fontsize=fsize)
# Define image display
fig_img = fig.add_subplot(gs[:3, :])
fig_zoom = fig.add_subplot(gs[3:, 2:])
fig_summary = fig.add_subplot(gs[3:, :2])
# Compute display range
dmax = (data.max() // 10) if data.max() <= 1000. else 100
dscaled = np.log10(np.clip(data, -0.1, dmax) + 0.10001)
# identify zoom region around center of data
zoom = dscaled[center[0] - zoom_size:center[0] + zoom_size,
center[1] - zoom_size:center[1] + zoom_size]
# display full image
fig_img.imshow(dscaled, cmap='gray', origin='lower')
# display zoomed section
fig_zoom.imshow(zoom, cmap='gray', origin='lower')
# define markerstyle
mstyle = markers.MarkerStyle(marker='o')
mstyle.set_fillstyle('none')
# plot GAIA sources onto full size image
fig_img.scatter(rx, ry, marker=mstyle, alpha=0.35, c=gcolor, s=3)
fig_zoom.scatter(zx, zy, marker=mstyle, alpha=0.35, c=gcolor)
# Print summary info
pname = os.path.split(prodname)[1]
fig_summary.text(0.01,
0.95,
"Summary for {}".format(pname),
fontsize=fsize)
fig_summary.text(0.01, 0.9, "WCSNAME: {}".format(wcsname), fontsize=fsize)
fig_summary.text(0.01, 0.85, "TARGET: {}".format(targname), fontsize=fsize)
fig_summary.text(0.01,
0.8,
"Instrument: {}/{}".format(inst, det),
fontsize=fsize)
fig_summary.text(0.01, 0.75, "Filters: {}".format(filters), fontsize=fsize)
fig_summary.text(0.01,
0.7,
"Total Exptime: {}".format(texptime),
fontsize=fsize)
fig_summary.text(0.01, 0.65, "WCSTYPE: {}".format(wcstype), fontsize=fsize)
fig_summary.text(0.01,
0.5,
"Total # of GAIA sources: {}".format(len(refx)),
fontsize=fsize)
fig_summary.text(0.01,
0.45,
"# of GAIA matches: {}".format(len(rx)),
fontsize=fsize)
# Get extended information about observation
hdrtab_cols = hdrtab.columns.names
mtflag = get_col_val(hdrtab, 'mtflag', default="")
gyromode = get_col_val(hdrtab, 'gyromode', default='N/A')
# populate JSON summary info
summary = dict(wcsname=wcsname,
targname=targname,
asnid=asnid,
dateobs=dateobs,
expstart=expstart,
instrument=(inst, det),
exptime=texptime,
wcstype=wcstype,
num_gaia=len(refx),
filters=filters,
rms_ra=-1,
rms_dec=-1,
nmatch=-1,
catalog="")
obs_kws = [
'gyromode', 'fgslock', 'aperture', 'mtflag', 'subarray', 'obstype',
'obsmode', 'scan_typ', 'photmode'
]
for kw in obs_kws:
summary[kw] = get_col_val(hdrtab, kw, default="")
if 'FIT' in wcsname:
# Look for FIT RMS and other stats from headerlet
exp = fits.open(os.path.join(prod_path, inexp))
for ext in exp:
if 'extname' in ext.header and ext.header['extname'] == 'HDRLET' \
and ext.header['wcsname'] == wcsname:
hdrlet = ext.headerlet
rms_ra = hdrlet[0].header['rms_ra']
rms_dec = hdrlet[0].header['rms_dec']
nmatch = hdrlet[0].header['nmatch']
catalog = hdrlet[0].header['catalog']
fit_vals = dict(rms_ra=rms_ra,
rms_dec=rms_dec,
nmatch=nmatch,
catalog=catalog)
summary.update(fit_vals)
break
exp.close()
try:
fig_summary.text(0.01,
0.4,
"RMS: RA={:0.3f}mas, DEC={:0.3f}mas".format(
rms_ra, rms_dec),
fontsize=fsize)
fig_summary.text(0.01,
0.35,
"# matches: {}".format(nmatch),
fontsize=fsize)
fig_summary.text(0.01,
0.3,
"Matched to {} catalog".format(catalog),
fontsize=fsize)
except:
fig_summary.text(0.01, 0.35, "No MATCH to GAIA")
print("Data without a match to GAIA: {},{}".format(inexp, wcsname))
return fig, summary
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 : HDUList object
Input image as an astropy.io.fits HDUList object
refwcs : HSTWCS object
Definition of the reference frame WCS.
dqname : string
EXTNAME for the DQ array, if present, in the input image HDUList.
output : boolean
Specify whether or not to write out a separate catalog file for all the
sources found in each chip. Default: None (False)
Optional Parameters
--------------------
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.
Default: 1000.
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. Default: 3.0.
Returns
--------
master_cat : astropy.Table object
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]
# 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 rd2xy(input,
ra=None,
dec=None,
coordfile=None,
colnames=None,
precision=6,
output=None,
verbose=True):
""" Primary interface to perform coordinate transformations from
pixel to sky coordinates using STWCS and full distortion models
read from the input image header.
"""
single_coord = False
if coordfile is not None:
if colnames in blank_list:
colnames = ['c1', 'c2']
elif isinstance(colnames, type('a')):
colnames = colnames.split(',')
# convert input file coordinates to lists of decimal degrees values
xlist, ylist = tweakutils.readcols(coordfile, cols=colnames)
else:
if isinstance(ra, np.ndarray):
ralist = ra.tolist()
declist = dec.tolist()
elif not isinstance(ra, list):
ralist = [ra]
declist = [dec]
else:
ralist = ra
declist = dec
xlist = [0] * len(ralist)
ylist = [0] * len(ralist)
if len(xlist) == 1:
single_coord = True
for i, (r, d) in enumerate(zip(ralist, declist)):
# convert input value into decimal degrees value
xval, yval = tweakutils.parse_skypos(r, d)
xlist[i] = xval
ylist[i] = yval
# start by reading in WCS+distortion info for input image
inwcs = wcsutil.HSTWCS(input)
if inwcs.wcs.is_unity():
print(
"####\nNo valid WCS found in {}.\n Results may be invalid.\n####\n"
.format(input))
# Now, convert pixel coordinates into sky coordinates
try:
outx, outy = inwcs.all_world2pix(xlist, ylist, 1)
except RuntimeError:
outx, outy = inwcs.wcs_world2pix(xlist, ylist, 1)
# add formatting based on precision here...
xstr = []
ystr = []
fmt = "%." + repr(precision) + "f"
for x, y in zip(outx, outy):
xstr.append(fmt % x)
ystr.append(fmt % y)
if verbose or (not verbose and util.is_blank(output)):
print('# Coordinate transformations for ', input)
print('# X Y RA Dec\n')
for x, y, r, d in zip(xstr, ystr, xlist, ylist):
print("%s %s %s %s" % (x, y, r, d))
# Create output file, if specified
if output:
f = open(output, mode='w')
f.write("# Coordinates converted from %s\n" % input)
for x, y in zip(xstr, ystr):
f.write('%s %s\n' % (x, y))
f.close()
print('Wrote out results to: ', output)
if single_coord:
outx = outx[0]
outy = outy[0]
return outx, outy
def build(outname,
wcsname,
refimage,
undistort=False,
applycoeffs=False,
coeffsfile=None,
**wcspars):
""" Core functionality to create a WCS instance from a reference image WCS,
user supplied parameters or user adjusted reference WCS.
The distortion information can either be read in as part of the reference
image WCS or given in 'coeffsfile'.
Parameters
----------
outname : string
filename of output WCS
wcsname : string
WCSNAME ID for generated WCS
refimage : string
filename of image with source WCS used as basis for output WCS
undistort : bool
Create an undistorted WCS?
applycoeffs : bool
Apply coefficients from refimage to generate undistorted WCS?
coeffsfile : string
If specified, read distortion coeffs from separate file
"""
# Insure that the User WCS parameters have values for all the parameters,
# even if that value is 'None'
user_wcs_pars = convert_user_pars(wcspars)
userwcs = wcspars['userwcs']
"""
Use cases to document the logic required to interpret the parameters
WCS generation based on refimage/userwcs parameters
-------------------------------------------------------------
refimage == None, userwcs == False:
*NO WCS specified*
=> print a WARNING message and return without doing anything
refimage == None, userwcs == True:
=> Create WCS without a distortion model entirely from user parameters*
refimage != None, userwcs == False:
=> No user WCS parameters specified
=> Simply use refimage WCS as specified
refimage != None, userwcs == True:
=> Update refimage WCS with user specified values*
Apply distortion and generate final headerlet using processed WCS
-----------------------------------------------------------------
refimage == None, userwcs == True:
*Output WCS generated entirely from user supplied parameters*
Case 1: applycoeffs == False, undistort == True/False (ignored)
=> no distortion model to interpret
=> generate undistorted headerlet with no distortion model
Case 2: applycoeffs == True/False, undistort == True
=> ignore any user specified distortion model
=> generate undistorted headerlet with no distortion model
Case 3: applycoeffs == True, undistort == False
=> WCS from scratch combined with distortion model from another image
=> generate headerlet with distortion model
refimage != None, userwcs == True/False:
*Output WCS generated from reference image possibly modified by user parameters*
Case 4: applycoeffs == False, undistort == True
=> If refimage has distortion, remove it
=> generate undistorted headerlet with no distortion model
Case 5: applycoeffs == False, undistort == False
=> Leave refimage distortion model (if any) unmodified
=> generate a headerlet using same distortion model (if any) as refimage
Case 6: applycoeffs == True, undistort == False
=> Update refimage with distortion model with user-specified model
=> generate a headerlet with a distortion model
Case 7: applycoeffs == True, undistort == True
=> ignore user specified distortion model and undistort WCS
=> generate a headerlet without a distortion model
"""
### Build WCS from refimage and/or user pars
if util.is_blank(refimage) and not userwcs:
print('WARNING: No WCS specified... No WCS created!')
return
customwcs = None
if util.is_blank(refimage) and userwcs:
# create HSTWCS object from user parameters
complete_wcs = True
for key in user_wcs_pars:
if util.is_blank(user_wcs_pars[key]):
complete_wcs = False
break
if complete_wcs:
customwcs = wcs_functions.build_hstwcs(
user_wcs_pars['crval1'], user_wcs_pars['crval2'],
user_wcs_pars['crpix1'], user_wcs_pars['crpix2'],
user_wcs_pars['naxis1'], user_wcs_pars['naxis2'],
user_wcs_pars['pscale'], user_wcs_pars['orientat'])
else:
print('WARNING: Not enough WCS information provided by user!')
raise ValueError
if not util.is_blank(refimage):
refwcs = stwcs.wcsutil.HSTWCS(refimage)
else:
refwcs = customwcs
### Apply distortion model (if any) to update WCS
if applycoeffs and not util.is_blank(coeffsfile):
if not util.is_blank(refimage):
replace_model(refwcs, coeffsfile)
else:
if not undistort:
add_model(refwcs, coeffsfile)
# Only working with custom WCS from user, no distortion
# so apply model to WCS, including modifying the CD matrix
apply_model(refwcs)
### Create undistorted WCS, if requested
if undistort:
outwcs = undistortWCS(refwcs)
else:
outwcs = refwcs
if userwcs:
# replace (some/all?) WCS values from refimage with user WCS values
# by running 'updatewcs' functions on input WCS
outwcs = mergewcs(outwcs, customwcs, user_wcs_pars)
### Create the final headerlet and write it out, if specified
if not util.is_blank(refimage):
template = refimage
elif not util.is_blank(coeffsfile):
template = coeffsfile
else:
template = None
# create default WCSNAME if None was given
wcsname = create_WCSname(wcsname)
print('Creating final headerlet with name ', wcsname, ' using template ',
template)
outhdr = generate_headerlet(outwcs, template, wcsname, outname=outname)
# synchronize this new WCS with the rest of the chips in the image
for ext in outhdr:
if 'extname' in ext.header and ext.header['extname'] == 'SIPWCS':
ext_wcs = wcsutil.HSTWCS(ext)
stwcs.updatewcs.makewcs.MakeWCS.updateWCS(ext_wcs, outwcs)
return outwcs
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_gaia_catalog(hap_obj, columns_to_remove=None):
"""Uses astrometric_utils.create_astrometric_catalog() to create a catalog of all GAIA sources in the
image footprint. This catalog contains right ascension, declination, and magnitude values, and is sorted
in descending order by brightness.
Parameters
----------
hap_obj : drizzlepac.hlautils.Product.TotalProduct, drizzlepac.hlautils.Product.FilterProduct, or
drizzlepac.hlautils.Product.ExposureProduct, depending on input.
hap product object to process
Returns
-------
gaia_table : astropy table
table containing right ascension, declination, and magnitude of all GAIA sources identified in the
image footprint, sorted in descending order by brightness.
"""
# Gather list of input flc/flt images
img_list = []
log.debug("GAIA catalog will be created using the following input images:")
# Create a list of the input flc.fits/flt.fits that were drizzled to create the final HAP product being
# processed here. edp_item.info and hap_obj.info are both structured as follows:
# <proposal id>_<visit #>_<instrument>_<detector>_<input filename>_<filter>_<drizzled product
# image filetype>
# Example: '10265_01_acs_wfc_j92c01b9q_flc.fits_f606w_drc'
# what is being extracted here is just the input filename, which in this case is 'j92c01b9q_flc.fits'.
if hasattr(hap_obj, "edp_list"): # for total and filter product objects
for edp_item in hap_obj.edp_list:
parse_info = edp_item.info.split("_")
imgname = "{}_{}".format(parse_info[4], parse_info[5])
log.debug(imgname)
img_list.append(imgname)
else: # For single-exposure product objects
parse_info = hap_obj.info.split("_")
imgname = "{}_{}".format(parse_info[4], parse_info[5])
log.debug(imgname)
img_list.append(imgname)
# generate catalog of GAIA sources
gaia_table = au.create_astrometric_catalog(img_list,
gaia_only=True,
use_footprint=True)
# trim off specified columns
if columns_to_remove:
gaia_table.remove_columns(columns_to_remove)
# remove sources outside image footprint
outwcs = wcsutil.HSTWCS(hap_obj.drizzle_filename, ext=1)
x, y = outwcs.all_world2pix(gaia_table['RA'], gaia_table['DEC'], 1)
imghdu = fits.open(hap_obj.drizzle_filename)
in_img_data = imghdu['WHT'].data.copy()
in_img_data = np.where(in_img_data == 0, np.nan, in_img_data)
mask = au.within_footprint(in_img_data, outwcs, x, y)
gaia_table = gaia_table[mask]
# Report results to log
if len(gaia_table) == 0:
log.warning("No GAIA sources were found!")
elif len(gaia_table) == 1:
log.info("1 GAIA source was found.")
else:
log.info("{} GAIA sources were found.".format(len(gaia_table)))
return gaia_table
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 create_product_page(prodname, zoom_size=128, wcsname=""):
# obtain image data to display
with fits.open(prodname) as prod:
data = prod[1].data
phdr = prod[0].header
targname = phdr['targname']
inst = phdr['instrume']
det = phdr['detector']
texptime = phdr['texptime']
inexp = phdr['d001data'].split('[')[0]
wcstype = prod[1].header['wcstype']
wcs = wcsutil.HSTWCS(prod, ext=1)
center = (data.shape[0] // 2, data.shape[1] // 2)
prod_path = os.path.split(prodname)[0]
data = np.nan_to_num(data, 0.0)
# Get GAIA catalog
refcat = amutils.create_astrometric_catalog(prodname,
existing_wcs=wcs,
output=None)
refx, refy = wcs.all_world2pix(refcat['RA'], refcat['DEC'], 0)
# Remove points outside the full-size image area
rx = []
ry = []
zx = []
zy = []
for x, y in zip(refx, refy):
if 0 < x < data.shape[1] and 0 < y < data.shape[0]:
rx.append(x)
ry.append(y)
if -zoom_size < x - center[1] < zoom_size and -zoom_size < y - center[
0] < zoom_size:
zx.append(x - center[1] + zoom_size)
zy.append(y - center[0] + zoom_size)
# Define subplot regions on page
fig = plt.figure(constrained_layout=True, figsize=(7, 10))
gs = fig.add_gridspec(ncols=4, nrows=5)
# title plots
img_title = "{} image of {} with WCSNAME={}".format(
prodname, targname, wcsname)
plt.title(img_title, loc='center', fontsize=8)
# Define image display
fig_img = fig.add_subplot(gs[:3, :])
fig_zoom = fig.add_subplot(gs[3:, 2:])
fig_summary = fig.add_subplot(gs[3:, :2])
# Compute display range
dmax = (data.max() // 10)
dscaled = np.log10(np.clip(data, -0.9, dmax) + 1)
# identify zoom region around center of data
zoom = dscaled[center[0] - zoom_size:center[0] + zoom_size,
center[1] - zoom_size:center[1] + zoom_size]
# display full image
fig_img.imshow(dscaled, cmap='gray', origin='lower')
# display zoomed section
fig_zoom.imshow(zoom, cmap='gray', origin='lower')
# define markerstyle
mstyle = markers.MarkerStyle(marker='o')
mstyle.set_fillstyle('none')
# plot GAIA sources onto full size image
fig_img.scatter(rx, ry, marker=mstyle, alpha=0.25, c='cyan', s=3)
fig_zoom.scatter(zx, zy, marker=mstyle, alpha=0.25, c='cyan')
# Print summary info
fsize = 8
pname = os.path.split(prodname)[1]
fig_summary.text(0.01,
0.95,
"Summary for {}".format(pname),
fontsize=fsize)
fig_summary.text(0.01, 0.9, "WCSNAME: {}".format(wcsname), fontsize=fsize)
fig_summary.text(0.01, 0.85, "TARGET: {}".format(targname), fontsize=fsize)
fig_summary.text(0.01,
0.8,
"Instrument: {}/{}".format(inst, det),
fontsize=fsize)
fig_summary.text(0.01,
0.7,
"Total Exptime: {}".format(texptime),
fontsize=fsize)
fig_summary.text(0.01, 0.65, "WCSTYPE: {}".format(wcstype), fontsize=fsize)
fig_summary.text(0.01,
0.5,
"# of GAIA sources: {}".format(len(rx)),
fontsize=fsize)
if 'FIT' in wcsname:
# Look for FIT RMS and other stats from headerlet
exp = fits.open(os.path.join(prod_path, inexp))
for ext in exp:
if 'extname' in ext.header and ext.header['extname'] == 'HDRLET' \
and ext.header['wcsname'] == wcsname:
hdrlet = ext.headerlet
rms_ra = hdrlet[0].header['rms_ra']
rms_dec = hdrlet[0].header['rms_dec']
nmatch = hdrlet[0].header['nmatch']
catalog = hdrlet[0].header['catalog']
break
exp.close()
fig_summary.text(0.01,
0.4,
"RMS: RA={:0.3}mas, DEC={:0.3}mas".format(
rms_ra, rms_dec),
fontsize=fsize)
fig_summary.text(0.01,
0.35,
"# matches: {}".format(nmatch),
fontsize=fsize)
fig_summary.text(0.01,
0.3,
"Matched to {} catalog".format(catalog),
fontsize=fsize)
return fig
def make_outputwcs(imageObjectList, output, configObj=None, perfect=False):
""" Computes the full output WCS based on the set of input imageObjects
provided as input, along with the pre-determined output name from
process_input. The user specified output parameters are then used to
modify the default WCS to produce the final desired output frame.
The input imageObjectList has the outputValues dictionary
updated with the information from the computed output WCS.
It then returns this WCS as a WCSObject(imageObject)
instance.
"""
if not isinstance(imageObjectList, list):
imageObjectList = [imageObjectList]
# Compute default output WCS, replace later if user specifies a refimage
hstwcs_list = []
undistort = True
for img in imageObjectList:
chip_wcs = copy.deepcopy(img.getKeywordList('wcs'))
# IF the user turned off use of coeffs (coeffs==False)
if not configObj['coeffs']:
for cw in chip_wcs:
# Turn off distortion model for each input
cw.sip = None
cw.cpdis1 = None
cw.cpdis2 = None
cw.det2im = None
undistort = False
hstwcs_list += chip_wcs
if not undistort and len(hstwcs_list) == 1:
default_wcs = hstwcs_list[0].deepcopy()
else:
default_wcs = utils.output_wcs(hstwcs_list, undistort=undistort)
if perfect:
default_wcs.wcs.cd = make_perfect_cd(default_wcs)
# Turn WCS instances into WCSObject instances
outwcs = createWCSObject(output, default_wcs, imageObjectList)
# Merge in user-specified attributes for the output WCS
# as recorded in the input configObj object.
final_pars = DEFAULT_WCS_PARS.copy()
# More interpretation of the configObj needs to be done here to translate
# the input parameter names to those understood by 'mergeWCS' as defined
# by the DEFAULT_WCS_PARS dictionary.
single_step = configObj[util.getSectionName(configObj, 3)]
singleParDict = configObj[util.getSectionName(configObj, '3a')].copy()
if single_step['driz_separate'] and singleParDict['driz_sep_wcs']:
single_pars = DEFAULT_WCS_PARS.copy()
del singleParDict['driz_sep_wcs']
keyname = 'driz_sep_'
for key in singleParDict:
k = key[len(keyname):]
if k != 'refimage':
single_pars[k] = singleParDict[key]
# Now, account for any user-specified reference image
def_wcs = default_wcs.deepcopy()
single_ref = singleParDict[keyname + 'refimage']
if single_ref:
if isinstance(single_ref, wcs.WCS):
default_wcs = single_ref
else:
default_wcs = wcsutil.HSTWCS(singleParDict[keyname + 'refimage'])
# ## Create single_wcs instance based on user parameters
outwcs.single_wcs = mergeWCS(default_wcs, single_pars)
# restore global default WCS to original value so single_drizzle WCS does not
# influence final_drizzle WCS
default_wcs = def_wcs.deepcopy()
final_step = configObj[util.getSectionName(configObj, 7)]
finalParDict = configObj[util.getSectionName(configObj, '7a')].copy()
if final_step['driz_combine'] and finalParDict['final_wcs']:
del finalParDict['final_wcs']
keyname = 'final_'
for key in finalParDict:
k = key[len(keyname):]
if k != 'refimage':
final_pars[k] = finalParDict[key]
# Now, account for any user-specified reference image
final_ref = finalParDict[keyname + 'refimage']
if final_ref:
if isinstance(final_ref, wcs.WCS):
default_wcs = final_ref
if hasattr(final_ref, 'filename'):
rootname = final_ref.filename
else:
rootname = ""
print('Creating OUTPUT WCS from WCS object based on {}'.format(rootname))
else:
rootname, extnum = fileutil.parseFilename(finalParDict[keyname + 'refimage'])
extnum = util.findWCSExtn(finalParDict[keyname + 'refimage'])
print('Creating OUTPUT WCS from {}[{}]'.format(rootname, extnum))
default_wcs = wcsutil.HSTWCS('{}[{}]'.format(rootname, extnum))
# ## Create single_wcs instance based on user parameters
outwcs.final_wcs = mergeWCS(default_wcs, final_pars)
outwcs.wcs = outwcs.final_wcs.copy()
# Apply user settings to create custom output_wcs instances
# for each drizzle step
updateImageWCS(imageObjectList, outwcs)
return outwcs
def tweakback(drzfile,
input=None,
origwcs=None,
newname=None,
wcsname=None,
extname='SCI',
force=False,
verbose=False):
"""
Apply WCS solution recorded in drizzled file to distorted input images
(``_flt.fits`` files) used to create the drizzled file. This task relies on
the original WCS and updated WCS to be recorded in the drizzled image's
header as the last 2 alternate WCSs.
Parameters
----------
drzfile : str (Default = '')
filename of undistorted image which contains the new WCS
and WCS prior to being updated
newname : str (Default = None)
Value of ``WCSNAME`` to be used to label the updated solution in the
output (eq., ``_flt.fits``) files. If left blank or None, it will
default to using the current ``WCSNAME`` value from the input drzfile.
input : str (Default = '')
filenames of distorted images to be updated using new WCS
from 'drzfile'. These can be provided either as an ``@-file``,
a comma-separated list of filenames or using wildcards.
.. note:: A blank value will indicate that the task should derive the
filenames from the 'drzfile' itself, if possible. The filenames will be
derived from the ``D*DATA`` keywords written out by
``AstroDrizzle``. If they can not be found, the task will quit.
origwcs : str (Default = None)
Value of ``WCSNAME`` keyword prior to the drzfile image being updated
by ``TweakReg``. If left blank or None, it will default to using the
second to last ``WCSNAME*`` keyword value found in the header.
wcsname : str (Default = None)
Value of WCSNAME for updated solution written out by ``TweakReg`` as
specified by the `wcsname` parameter from ``TweakReg``. If this is
left blank or `None`, it will default to the current ``WCSNAME``
value from the input drzfile.
extname : str (Default = 'SCI')
Name of extension in `input` files to be updated with new WCS
force : bool (Default = False)
This parameters specified whether or not to force an update of the WCS
even though WCS already exists with this solution or `wcsname`?
verbose : bool (Default = False)
This parameter specifies whether or not to print out additional
messages during processing.
Notes
-----
The algorithm used by this function is based on linearization of
the exact compound operator that converts input image coordinates
to the coordinates (in the input image) that would result in
alignment with the new drizzled image WCS.
If no input distorted files are specified as input, this task will attempt
to generate the list of filenames from the drizzled input file's own
header.
EXAMPLES
--------
An image named ``acswfc_mos2_drz.fits`` was created from 4 images using
astrodrizzle. This drizzled image was then aligned to another image using
tweakreg and the header was updated using the ``WCSNAME`` = ``TWEAK_DRZ``.
The new WCS can then be used to update each of the 4 images that were
combined to make up this drizzled image using:
>>> from drizzlepac import tweakback
>>> tweakback.tweakback('acswfc_mos2_drz.fits')
If the same WCS should be applied to a specific set of images, those images
can be updated using:
>>> tweakback.tweakback('acswfc_mos2_drz.fits',
... input='img_mos2a_flt.fits,img_mos2e_flt.fits')
See Also
--------
stwcs.wcsutil.altwcs: Alternate WCS implementation
"""
print("TweakBack Version {:s} started at: {:s}\n".format(
__version__,
util._ptime()[0]))
# Interpret input list/string into list of filename(s)
fltfiles = parseinput.parseinput(input)[0]
if fltfiles is None or len(fltfiles) == 0:
# try to extract the filenames from the drizzled file's header
fltfiles = extract_input_filenames(drzfile)
if fltfiles is None:
print('*' * 60)
print('*')
print('* ERROR:')
print('* No input filenames found! ')
print(
'* Please specify "fltfiles" or insure that input drizzled')
print('* image contains D*DATA keywords. ')
print('*')
print('*' * 60)
raise ValueError
if not isinstance(fltfiles, list):
fltfiles = [fltfiles]
sciext = determine_extnum(drzfile, extname='SCI')
scihdr = fits.getheader(drzfile, ext=sciext, memmap=False)
### Step 1: Read in updated and original WCS solutions
# determine keys for all alternate WCS solutions in drizzled image header
wkeys = wcsutil.altwcs.wcskeys(drzfile, ext=sciext)
if len(wkeys) < 2:
raise ValueError(
f"'{drzfile}' must contain at least two valid WCS: original and updated."
)
wnames = wcsutil.altwcs.wcsnames(drzfile, ext=sciext)
if not util.is_blank(newname):
final_name = newname
else:
final_name = wnames[wkeys[-1]]
# Read in HSTWCS objects for final,updated WCS and previous WCS from
# from drizzled image header
# The final solution also serves as reference WCS when using updatehdr
if not util.is_blank(wcsname):
for wkey, wname in wnames.items():
if wname == wcsname:
wcskey = wkey
break
else:
raise ValueError(
f"WCS with name '{wcsname}' not found in '{drzfile}'")
else:
wcskey = wkeys[-1]
final_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=wcskey)
if not util.is_blank(origwcs):
for wkey, wname in wnames.items():
if wname == origwcs:
orig_wcskey = wkey
break
else:
raise ValueError(
f"WCS with name '{origwcs}' not found in '{drzfile}'")
else:
_, orig_wcskey = determine_orig_wcsname(scihdr, wnames, wkeys)
orig_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=orig_wcskey)
# read in RMS values reported for new solution
crderr1kw = 'CRDER1' + wkeys[-1]
crderr2kw = 'CRDER2' + wkeys[-1]
if crderr1kw in scihdr:
crderr1 = fits.getval(drzfile, crderr1kw, ext=sciext, memmap=False)
else:
crderr1 = 0.0
if crderr2kw in scihdr:
crderr2 = fits.getval(drzfile, crderr2kw, ext=sciext, memmap=False)
else:
crderr2 = 0.0
del scihdr
### Step 2: Apply solution to input file headers
for fname in fltfiles:
logstr = "....Updating header for {:s}...".format(fname)
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
# reset header WCS keywords to original (OPUS generated) values
imhdulist = fits.open(fname, mode='update', memmap=False)
extlist = get_ext_list(imhdulist, extname='SCI')
if not extlist:
extlist = [0]
# Process MEF images...
for ext in extlist:
logstr = "Processing {:s}[{:s}]".format(imhdulist.filename(),
ext2str(ext))
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
chip_wcs = wcsutil.HSTWCS(imhdulist, ext=ext)
update_chip_wcs(chip_wcs,
orig_wcs,
final_wcs,
xrms=crderr1,
yrms=crderr2)
# Update FITS file with newly updated WCS for this chip
extnum = imhdulist.index(imhdulist[ext])
updatehdr.update_wcs(imhdulist,
extnum,
chip_wcs,
wcsname=final_name,
reusename=False,
verbose=verbose)
imhdulist.close()
def build_nddata(image, group_id, source_catalog):
""" Return a list of NDData objects for all chips in an image.
Parameters
===========
image : filename, 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, optional
If provided (default:None), these catalogs will be attached as `catalog`
entries in each chip's NDData.meta. 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
========
ndlist : list
List of astropy NDData 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:
print("Wrong type of input, {}, for build_nddata...".format(
type(image)))
raise ValueError
images = []
numsci = countExtn(hdulist)
for chip in range(1, numsci + 1):
im_data = hdulist[('SCI', chip)].data
dq_data = hdulist[('DQ', chip)].data
w = wcsutil.HSTWCS(hdulist, ('SCI', chip))
# Below, simply consider non-zero DQ data as invalid.
# A more sophisticated approach would use bitmask module.
# Also, here we set group ID to a different number for each image,
# but for ACS images, for example, we likely would assign
# the same group ID to the images corresponding to different
# SCI extensions *of the same FITS file* so that they can be
# aligned together.
img = NDData(data=im_data,
mask=dq_data != 0,
wcs=w,
meta={
'chip': chip,
'group_id': group_id
})
# append source catalog, if provided
if source_catalog:
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')
imcat.meta['name'] = 'im{:d} sources'.format(group_id)
img.meta['catalog'] = imcat
images.append(img)
if open_file:
hdulist.close()
return images
def __init__(self,
image,
ext,
dq_bits=0,
dqimage=None,
dqext=None,
usermask=None,
usermask_ext=None):
"""
Parameters
----------
image : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file
ext : tuple, int, str
Extension specification in the `image` the `SkyLineMember`
object will be associated with.
An int `ext` specifies extension number. A tuple in the form
(str, int) specifies extension name and number. A string `ext`
specifies extension name and the extension version is assumed
to be 1. See documentation for `astropy.io.fits.getData`
for examples.
dq_bits : int, None (Default = 0)
Integer sum of all the DQ bit values from the
input `image`'s DQ array that should be considered "good"
when building masks for sky computations. For example,
if pixels in the DQ array can be combinations of 1, 2, 4,
and 8 flags and one wants to consider DQ "defects" having
flags 2 and 4 as being acceptable for sky computations,
then `dq_bits` should be set to 2+4=6. Then a DQ pixel
having values 2,4, or 6 will be considered a good pixel,
while a DQ pixel with a value, e.g., 1+2=3, 4+8=12, etc.
will be flagged as a "bad" pixel.
| Default value (0) will make *all* non-zero
pixels in the DQ mask to be considered "bad" pixels,
and the corresponding image pixels will not be used
for sky computations.
| Set `dq_bits` to `None` to turn off the use of
image's DQ array for sky computations.
.. note::
DQ masks (if used), *will be* combined with user masks
specified by the `usermask` parameter.
dqimage : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file that has DQ data of the input `image`.
.. note::
When DQ data are located in the same FITS file as the
science image data (e.g., HST/ACS, HST/WFC3, etc.),
`dqimage` may point to the
same :py:class:`~stsci.skypac.utils.ImageRef` object.
In this case the reference count of the
\ :py:class:`~stsci.skypac.utils.ImageRef` object must be
increased adequately.
dqext : tuple, int, str
Extension specification of the `dqimage` that contains
`image`'s DQ information. See help for `ext` for more
details on acceptable formats for this parameter.
usermask : ImageRef
An :py:class:`~stsci.skypac.utils.ImageRef` object that refers
to an open FITS file that has user mask data that indicate
what pixels in the input `image` should be used for sky
computations (``1``) and which pixels should **not** be used
for sky computations (``0``).
usermask_ext : tuple, int, str
Extension specification of the `usermask` mask file that
contains user's mask data that should be associated with
the input `image` and `ext`. See help for `ext` for more
details on acceptable formats for this parameter.
"""
assert(hasattr(self.__class__, '_initialized') and \
self.__class__._initialized)
self._reset()
# check that input images and extensions are valid --
# either integers or tuples of strings and integers, e.g., ('sci',1):
_check_valid_imgext(image, 'image', ext, 'ext', can_img_be_None=False)
if dq_bits is not None:
if dqimage is None:
dq_bits = 0
else:
_check_valid_imgext(dqimage, 'dqimage', dqext, 'dqext')
_check_valid_imgext(usermask, 'usermask', usermask_ext, 'usermask_ext')
# get telescope, instrument, and detector info:
self.telescope, self.instrument, self.detector = get_instrument_info(
image, ext)
# check dq_bits:
if dq_bits is not None and not isinstance(dq_bits, int):
if image: dqimage.release()
if usermask: usermask.release()
if dqimage: dqimage.release()
raise TypeError(
"Argument 'dq_bits' must be either an integer or None.")
# buld mask:
self._buildMask(image.original_fname, ext, dq_bits, dqimage, dqext,
usermask, usermask_ext)
if dqimage: dqimage.release()
if usermask: usermask.release()
# save file, user mask, and DQ extension info:
self._fname = image.original_fname
self._basefname = basename(self._fname)
self._image = image
self._ext = ext
self._can_free_image = image.can_reload_data and self.optimize != 'speed'
# check extension and create a string representation:
try:
extstr = ext2str(ext)
except ValueError:
raise ValueError("Unexpected extension type \'{}\' for file {}.".\
format(ext,self._basefname))
self._id = "{:s}[{:s}]".format(self._basefname, extstr)
# extract WCS for bounding-box computation
try:
if hasattr(image.hdu[ext], 'wcs'):
self._wcs = image.hdu[ext].wcs
else:
if self.telescope in supported_telescopes:
self._wcs = wcsutil.HSTWCS(image.hdu, ext)
else:
self._wcs = pywcs.WCS(image.hdu[ext].header, image.hdu)
if self._wcs is None:
raise Exception("Invalid WCS.")
except:
msg = "Unable to obtain WCS information for the file {:s}." \
.format(self._id)
self._ml.error(msg)
self._ml.flush()
self._release_all()
raise
# determine pixel scale:
self._get_pixel_scale()
# see if image data are in counts or count-rate
# and compute count(-rate) to flux (per arcsec^2) conversion factor:
self._brightness_conv_from_hdu(image.hdu, self._idcscale)
# process Sky user's keyword and its value:
self._init_skyuser(image.hdu[ext].header)
# Set polygon to be the bounding box of the chip:
self._polygon = SphericalPolygon.from_wcs(self.wcs, steps=1)
def updatewcs_with_shift(image,reference,wcsname=None, reusename=False,
fitgeom='rscale',
rot=0.0,scale=1.0,xsh=0.0,ysh=0.0,fit=None,
xrms=None, yrms = None,
verbose=False,force=False,sciext='SCI'):
"""
Update the SCI headers in 'image' based on the fit provided as determined
in the WCS specified by 'reference'. The fit should be a 2-D matrix as
generated for use with 'make_vector_plot()'.
Notes
-----
The algorithm used to apply the provided fit solution to the image
involves applying the following steps to the WCS of each of the
input image's chips:
1. compute RA/Dec with full distortion correction for
reference point as (Rc_i,Dc_i)
2. find the Xc,Yc for each Rc_i,Dc_i and get the difference from the
CRPIX position for the reference WCS as (dXc_i,dYc_i)
3. apply fit (rot&scale) to (dXc_i,dYc_i) then apply shift, then add
CRPIX back to get new (Xcs_i,Ycs_i) position
4. compute (Rcs_i,Dcs_i) as the sky coordinates for (Xcs_i,Ycs_i)
5. compute delta of (Rcs_i-Rc_i, Dcs_i-Dcs_i) as (dRcs_i,dDcs_i)
6. apply the fit to the chip's undistorted CD matrix, the apply linear
distortion terms back in to create a new CD matrix
7. add (dRcs_i,dDcs_i) to CRVAL of the reference chip's WCS
8. update header with new WCS values
Parameters
----------
image : str or PyFITS.HDUList object
Filename, or PyFITS object, of image with WCS to be updated.
All extensions with EXTNAME matches the value of the 'sciext'
parameter value (by default, all 'SCI' extensions) will be updated.
reference : str
Filename of image/headerlet (FITS file) which contains the WCS
used to define the tangent plane in which all the fit parameters
(shift, rot, scale) were measured.
wcsname : str
Label to give to new WCS solution being created by this fit. If
a value of None is given, it will automatically use 'TWEAK' as the
label. If a WCS has a name with this specific value, the code will
automatically append a version ID using the format '_n', such as
'TWEAK_1', 'TWEAK_2',or 'TWEAK_update_1'.
[Default =None]
reusename : bool
User can specify whether or not to over-write WCS with same name.
[Default: False]
rot : float
Amount of rotation measured in fit to be applied.
[Default=0.0]
scale : float
Amount of scale change measured in fit to be applied.
[Default=1.0]
xsh : float
Offset in X pixels from defined tangent plane to be applied to image.
[Default=0.0]
ysh : float
Offset in Y pixels from defined tangent plane to be applied to image.
[Default=0.0]
fit : arr
Linear coefficients for fit
[Default = None]
xrms : float
RMS of fit in RA (in decimal degrees) that will be recorded as
CRDER1 in WCS and header
[Default = None]
yrms : float
RMS of fit in Dec (in decimal degrees) that will be recorded as
CRDER2 in WCS and header
[Default = None]
verbose : bool
Print extra messages during processing? [Default=False]
force : bool
Update header even though WCS already exists with this solution or
wcsname? [Default=False]
sciext : string
Value of FITS EXTNAME keyword for extensions with WCS headers to
be updated with the fit values. [Default='SCI']
"""
# if input reference is a ref_wcs file from tweakshifts, use it
if isinstance(reference, wcsutil.HSTWCS) or isinstance(reference, pywcs.WCS):
wref = reference
else:
refimg = fits.open(reference, memmap=False)
wref = None
for extn in refimg:
if 'extname' in extn.header and extn.header['extname'] == 'WCS':
wref = pywcs.WCS(refimg['wcs'].header)
break
refimg.close()
# else, we have presumably been provided a full undistorted image
# as a reference, so use it with HSTWCS instead
if wref is None:
wref = wcsutil.HSTWCS(reference)
if isinstance(image, fits.HDUList):
open_image = False
filename = image.filename()
if image.fileinfo(0)['filemode'] is 'update':
image_update = True
else:
image_update = False
else:
open_image = True
filename = image
image_update = None
# Now that we are sure we have a good reference WCS to use,
# continue with the update
logstr = "....Updating header for {:s}...".format(filename)
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
# reset header WCS keywords to original (OPUS generated) values
extlist = get_ext_list(image, extname='SCI')
if extlist:
if image_update:
# Create initial WCSCORR extension
wcscorr.init_wcscorr(image,force=force)
else:
extlist = [0]
# insure that input PRIMARY WCS has been archived before overwriting
# with new solution
if open_image:
fimg = fits.open(image, mode='update', memmap=False)
image_update = True
else:
fimg = image
if image_update:
wcsutil.altwcs.archiveWCS(fimg,extlist,reusekey=True)
# Process MEF images...
for ext in extlist:
logstr = "Processing {:s}[{:s}]".format(fimg.filename(),
ext2str(ext))
if verbose:
print("\n{:s}\n".format(logstr))
else:
log.info(logstr)
chip_wcs = wcsutil.HSTWCS(fimg,ext=ext)
update_refchip_with_shift(chip_wcs, wref, fitgeom=fitgeom,
rot=rot, scale=scale, xsh=xsh, ysh=ysh,
fit=fit, xrms=xrms, yrms=yrms)
#if util.is_blank(wcsname):
#wcsname = 'TWEAK'
# Update FITS file with newly updated WCS for this chip
extnum = fimg.index(fimg[ext])
update_wcs(fimg, extnum, chip_wcs, wcsname=wcsname,
reusename=reusename, verbose=verbose)
if open_image:
fimg.close()
def xy2rd(input,
x=None,
y=None,
coords=None,
coordfile=None,
colnames=None,
separator=None,
hms=True,
precision=6,
output=None,
verbose=True):
""" Primary interface to perform coordinate transformations from
pixel to sky coordinates using STWCS and full distortion models
read from the input image 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 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 input image
inwcs = wcsutil.HSTWCS(input)
if inwcs.wcs.is_unity():
print(
"####\nNo valid WCS found in {}.\n Results may be invalid.\n####\n"
.format(input))
# Now, convert pixel coordinates into sky coordinates
dra, ddec = inwcs.all_pix2world(xlist, ylist, 1)
# convert to HH:MM:SS.S format, if specified
if hms:
ra, dec = wcs_functions.ddtohms(dra, ddec, precision=precision)
rastr = ra
decstr = dec
else:
# add formatting based on precision here...
rastr = []
decstr = []
fmt = "%." + repr(precision) + "f"
for r, d in zip(dra, ddec):
rastr.append(fmt % r)
decstr.append(fmt % d)
ra = dra
dec = ddec
if verbose or (not verbose and util.is_blank(output)):
print('# Coordinate transformations for ', input)
print('# X Y RA Dec\n')
for x, y, r, d in zip(xlist, ylist, rastr, decstr):
print("%.4f %.4f %s %s" % (x, y, r, d))
# Create output file, if specified
if output:
f = open(output, mode='w')
f.write("# Coordinates converted from %s\n" % input)
for r, d in zip(rastr, decstr):
f.write('%s %s\n' % (r, d))
f.close()
print('Wrote out results to: ', output)
if single_coord:
ra = ra[0]
dec = dec[0]
return ra, dec
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
