def _buildStaticDQUserMask(img, ext, sky_bits, use_static, umask, umaskext,
in_memory):
# creates a temporary mask by combining 'static' mask,
# DQ image, and user-supplied mask.
def merge_masks(m1, m2):
if m1 is None: return m2
if m2 is None: return m1
return np.logical_and(m1, m2).astype(np.uint8)
mask = None
# build DQ mask
if sky_bits is not None:
mask = img.buildMask(img[ext]._chip, bits=sky_bits)
# get correct static mask mask filenames/objects
staticMaskName = img[ext].outputNames['staticMask']
smask = None
if use_static:
if img.inmemory:
if staticMaskName in img.virtualOutputs:
smask = img.virtualOutputs[staticMaskName].data
else:
if staticMaskName is not None and os.path.isfile(staticMaskName):
sm, dq = openImageEx(staticMaskName,
mode='readonly',
memmap=False,
saveAsMEF=False,
clobber=False,
imageOnly=True,
openImageHDU=True,
openDQHDU=False,
preferMEF=False,
verbose=False)
if sm.hdu is not None:
smask = sm.hdu[0].data
sm.release()
else:
log.warning("Static mask for file \'{}\', ext={} NOT FOUND." \
.format(img._filename, ext))
# combine DQ and static masks:
mask = merge_masks(mask, smask)
# combine user mask with the previously computed mask:
if umask is not None and not umask.closed:
if mask is None:
# return user-supplied mask:
umask.hold()
return (umask, umaskext)
else:
# combine user mask with the previously computed mask:
dtm = umask.hdu[umaskext].data
mask = merge_masks(mask, dtm)
if mask is None:
return (None, None)
elif mask.sum() == 0:
log.warning("All pixels masked out when applying DQ, " \
"static, and user masks!")
# save mask to a temporary file:
(root, suffix, fext) = file_name_components(img._filename)
if in_memory:
tmpmask = in_memory_mask(mask)
strext = ext2str(ext, compact=True, default_extver=None)
tmpmask.original_fname = "{1:s}{0:s}{2:s}{0:s}{3:s}" \
.format('_', root, suffix, 'in-memory_skymatch_mask')
else:
(tmpfname, tmpmask) = temp_mask_file(mask,
root,
prefix='',
suffix='skymatch_mask',
ext=ext,
randomize_prefix=False)
img[ext].outputNames['skyMatchMask'] = tmpfname
return (tmpmask, 0)
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 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}({:s}) started at: {:s}\n"
.format(__version__,__version_date__,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)
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 k in wnames:
if wnames[k] == wcsname:
wcskey = k
break
else:
wcskey = wkeys[-1]
final_wcs = wcsutil.HSTWCS(drzfile, ext=sciext, wcskey=wkeys[-1])
if not util.is_blank(origwcs):
for k in wnames:
if wnames[k] == origwcs:
orig_wcskey = k
orig_wcsname = origwcs
break
else:
orig_wcsname,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]
# insure that input PRIMARY WCS has been archived before overwriting
# with new solution
wcsutil.altwcs.archiveWCS(imhdulist, extlist, reusekey=True)
# 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 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 _buildStaticDQUserMask(img, ext, sky_bits, use_static, umask,
umaskext, in_memory):
# creates a temporary mask by combining 'static' mask,
# DQ image, and user-supplied mask.
def merge_masks(m1, m2):
if m1 is None: return m2
if m2 is None: return m1
return np.logical_and(m1, m2).astype(np.uint8)
mask = None
# build DQ mask
if sky_bits is not None:
mask = img.buildMask(img[ext]._chip,bits=sky_bits)
# get correct static mask mask filenames/objects
staticMaskName = img[ext].outputNames['staticMask']
smask = None
if use_static:
if img.inmemory:
if staticMaskName in img.virtualOutputs:
smask = img.virtualOutputs[staticMaskName].data
else:
if staticMaskName is not None and os.path.isfile(staticMaskName):
sm, dq = openImageEx(staticMaskName, mode='readonly',
saveAsMEF=False, clobber=False,
imageOnly=True, openImageHDU=True, openDQHDU=False,
preferMEF=False, verbose=False)
if sm.hdu is not None:
smask = sm.hdu[0].data
sm.release()
else:
log.warning("Static mask for file \'{}\', ext={} NOT FOUND." \
.format(img._filename, ext))
# combine DQ and static masks:
mask = merge_masks(mask, smask)
# combine user mask with the previously computed mask:
if umask is not None and not umask.closed:
if mask is None:
# return user-supplied mask:
umask.hold()
return (umask, umaskext)
else:
# combine user mask with the previously computed mask:
dtm = umask.hdu[umaskext].data
mask = merge_masks(mask, dtm)
if mask is None:
return (None, None)
elif mask.sum() == 0:
log.warning("All pixels masked out when applying DQ, " \
"static, and user masks!")
# save mask to a temporary file:
(root,suffix,fext) = file_name_components(img._filename)
if in_memory:
tmpmask = in_memory_mask(mask)
strext = ext2str(ext, compact=True, default_extver=None)
tmpmask.original_fname = "{1:s}{0:s}{2:s}{0:s}{3:s}" \
.format('_', root, suffix, 'in-memory_skymatch_mask')
else:
(tmpfname, tmpmask) = temp_mask_file(mask, root,
prefix='', suffix='skymatch_mask', ext=ext,
randomize_prefix=False)
img[ext].outputNames['skyMatchMask'] = tmpfname
return (tmpmask, 0)
def photeq(files='*_flt.fits', sciext='SCI', errext='ERR',
ref_phot=None, ref_phot_ext=None,
phot_kwd='PHOTFLAM', aux_phot_kwd='PHOTFNU',
search_primary=True,
readonly=True, clobber=False, logfile='photeq.log'):
"""
Adjust data values of images by equalizing each chip's PHOTFLAM value
to a single common value so that all chips can be treated equally
by `AstroDrizzle`.
Parameters
----------
files : str (Default = ``'*_flt.fits'``)
A string containing one of the following:
* a comma-separated list of valid science image file names,
e.g.: ``'j1234567q_flt.fits, j1234568q_flt.fits'``;
* an @-file name, e.g., ``'@files_to_match.txt'``. See notes
section for details on the format of the @-files.
.. note::
**Valid science image file names** are:
* file names of existing FITS, GEIS, or WAIVER FITS files;
* partial file names containing wildcard characters, e.g.,
``'*_flt.fits'``;
* Association (ASN) tables (must have ``_asn``, or ``_asc``
suffix), e.g., ``'j12345670_asn.fits'``.
sciext : str (Default = 'SCI')
Extension *name* of extensions whose data and/or headers should
be corrected.
errext : str (Default = 'ERR')
Extension *name* of the extensions containing corresponding error
arrays. Error arrays are corrected in the same way as science data.
ref_phot : float, None (Default = None)
A number indicating the new value of PHOTFLAM or PHOTFNU
(set by 'phot_kwd') to which the data should be adjusted.
ref_phot_ext : int, str, tuple, None (Default = None)
Extension from which the `photeq` should get the reference photometric
value specified by the `phot_kwd` parameter. This parameter is ignored
if `ref_phot` **is not** `None`. When `ref_phot_ext` is `None`, then
the reference inverse sensitivity value will be picked from the
first `sciext` of the first input image containing `phot_kwd`.
phot_kwd : str (Default = 'PHOTFLAM')
Specifies the primary keyword which contains inverse sensitivity
(e.g., PHOTFLAM). It is used to compute conversion factors by
which data should be rescaled.
aux_phot_kwd : str, None, list of str (Default = 'PHOTFNU')
Same as `phot_kwd` but describes *other* photometric keyword(s)
that should be corrected by inverse of the scale factor used to correct
data. These keywords are *not* used to compute conversion factors.
Multiple keywords can be specified as a Python list of strings:
``['PHOTFNU', 'PHOTOHMY']``.
.. note::
If specifying multiple secondary photometric keywords in the TEAL
interface, use a comma-separated list of keywords.
search_primary : bool (Default = True)
Specifies whether to first search the primary header for the
presence of `phot_kwd` keyword and compute conversion factor based on
that value. This is (partially) ignored when `ref_phot` is not `None` in
the sense that the value specified by `ref_phot` will be used as the
reference *but* in all images primary will be searched for `phot_kwd`
and `aux_phot_kwd` and those values will be corrected
(if ``search_primary=True``).
readonly : bool (Default = True)
If `True`, `photeq` will not modify input files (nevertheless, it will
convert input GEIS or WAVERED FITS files to MEF and could overwrite
existing MEF files if `clobber` is set to `True`).
The (console or log file) output however will be identical to the case
when ``readonly=False`` and it can be examined before applying these
changes to input files.
clobber : bool (Default = False)
Overwrite existing MEF files when converting input WAVERED FITS or GEIS
to MEF.
logfile : str, None (Default = 'photeq.log')
File name of the log file.
Notes
-----
By default, `photeq` will search for the first inverse sensitivity
value (given by the header keyword specified by the `phot_kwd` parameter,
e.g., PHOTFLAM or PHOTFNU) found in the input images and it will equalize
all other images to this reference value.
It is possible to tell `photeq` to look for the reference inverse
sensitivity value only in a specific extension of input images, e.g.: 3,
('sci',3), etc. This can be done by setting `ref_phot_ext` to a specific
extension. This may be useful, for example, for WFPC2 images: WF3 chip was
one of the better calibrated chips, and so, if one prefers to have
inverse sensitivities equalized to the inverse sensitivity of the WF3 chip,
one can set ``ref_phot_ext=3``.
Alternatively, one can provide their own reference inverse sensitivity
value to which all other images should be "equalized" through the
parameter `ref_phot`.
.. note::
Default parameter values (except for `files`, `readonly`, and `clobber`)
should be acceptable for most HST images.
.. warning::
If images are intended to be used with `AstroDrizzle`, it is recommended
that sky background measurement be performed on "equalized" images as
the `photeq` is not aware of sky user keyword in the image headers and
thus it cannot correct sky values already recorded in the headers.
Examples
--------
#. In most cases the default parameters should suffice:
>>> from drizzlepac import photeq
>>> photeq.photeq(files='*_flt.fits', readonly=False)
#. If the re-calibration needs to be done on PHOTFNU rather than
PHOTFLAM, then:
>>> photeq.photeq(files='*_flt.fits', ref_phot='PHOTFNU',
... aux_phot_kwd='PHOTFLAM')
#. If for WFPC2 data one desires that PHOTFLAM from WF3 be used as the
reference in WFPC2 images, then:
>>> photeq.photeq(files='*_flt.fits', ref_phot_ext=3) # or ('sci',3)
"""
# Time it
runtime_begin = datetime.now()
# check that input file name is a string:
if not isinstance(files, str):
raise TypeError("Argument 'files' must be a comma-separated list of "
" file names")
# Set-up log files:
if isinstance(logfile, str):
# first, in case there are any "leftover" file handlers,
# close and remove them:
for h in _log.handlers:
if h is not _sh_log and isinstance(h, logging.FileHandler):
h.close()
_log.removeHandler(h)
# create file handler:
log_formatter = logging.Formatter('[%(levelname)s:] %(message)s')
log_file_handler = logging.FileHandler(logfile)
log_file_handler.setFormatter(log_formatter)
# add log_file_handler to logger
_log.addHandler(log_file_handler)
elif logfile is not None:
raise TypeError("Unsupported 'logfile' type")
# BEGIN:
_mlinfo("***** {0} started on {1}".format(__taskname__, runtime_begin))
_mlinfo(" Version {0} ({1})".format(__version__, __vdate__))
# check that extension names are strings (or None for error ext):
if sciext is None:
sci_ext4parse = '*'
ext2get = None
else:
if not isinstance(sciext, str):
raise TypeError("Argument 'sciext' must be a string or None")
sciext = sciext.strip()
if sciext.upper() == 'PRIMARY':
sciext = sciext.upper()
ext2get = (sciext, 1)
else:
ext2get = (sciext, '*')
sci_ext4parse = ext2get
if errext is not None and not isinstance(errext, str):
raise TypeError("Argument 'errext' must be a string or None")
# check that phot_kwd is supported:
if not isinstance(phot_kwd, str):
raise TypeError("Argument 'phot_kwd' must be a string")
phot_kwd = phot_kwd.strip().upper()
# check that ref_phot_ext has correct type:
if ref_phot_ext is not None and not \
(isinstance(ref_phot_ext, int) or isinstance(ref_phot_ext, str) \
or (isinstance(ref_phot_ext, tuple) and len(ref_phot_ext) == 2 \
and isinstance(ref_phot_ext[0], str) and \
isinstance(ref_phot_ext[1], int))):
raise TypeError("Unsupported 'ref_phot_ext' type")
if isinstance(ref_phot_ext, str):
ref_phot_ext = (ref_phot_ext, 1)
if aux_phot_kwd is None:
aux_phot_kwd = []
elif isinstance(aux_phot_kwd, str):
aux_phot_kwd = [aux_phot_kwd.strip().upper()]
if phot_kwd == aux_phot_kwd:
raise ValueError("Auxiliary photometric keyword must be different "
"from the main photometric keyword 'phot_kwd'.")
elif hasattr(aux_phot_kwd, '__iter__'):
if not all([isinstance(phot, str) for phot in aux_phot_kwd]):
raise TypeError("Argument 'aux_phot_kwd' must be a string, list of "
"strings, or None")
aux_phot_kwd = [phot.strip().upper() for phot in aux_phot_kwd]
if ref_phot in aux_phot_kwd:
raise ValueError("Auxiliary photometric keyword(s) must be "
"different from the main photometric keyword "
"'phot_kwd'.")
else:
raise TypeError("Argument 'aux_phot_kwd' must be a string, list of "
"strings, or None")
# read input file list:
fl = parseat.parse_cs_line(csline=files, default_ext=sci_ext4parse,
im_fmode='readonly' if readonly else 'update',
clobber=clobber, fnamesOnly=True,
doNotOpenDQ=True)
# check if user supplied file extensions, set them to the sciext,
# and warn that they will be ignored:
for f in fl:
if f.count > 1 or f.fext[0] != sci_ext4parse:
_mlwarn("WARNING: Extension specifications for file {:s} "
"will be ignored. Using all {:s} extensions instead."
.format(f.image, 'image-like' if sciext is None else \
"{:s}".format(utils.ext2str(sciext,
default_extver=None))))
# find the reference PHOTFLAM/PHOTNU:
flc = fl[:]
ref_hdu = None
ref_ext = None
ref_user = True
if ref_phot is None:
ref_user = False
for f in flc:
f.convert2ImageRef()
# get primary hdu:
pri_hdu = f.image.hdu[0]
# find all valid extensions:
if ref_phot_ext is None:
if sciext == 'PRIMARY':
extnum = [0]
else:
extnum = utils.get_ext_list(f.image, sciext)
is_pri_hdu = [f.image.hdu[ext] is pri_hdu for ext in extnum]
# if necessary, add primary header to the hdu list:
if search_primary:
try:
pri_index = is_pri_hdu.index(True)
extnum.insert(0, extnum.pop(pri_index))
except ValueError:
extnum.insert(0, 0)
else:
extnum = [ref_phot_ext]
for ext in extnum:
hdu = f.image.hdu[ext]
if phot_kwd in hdu.header:
ref_phot = hdu.header[phot_kwd]
ref_ext = ext
ref_hdu = hdu
break
if ref_phot is None:
_mlwarn("WARNING: Could not find specified inverse "
" sensitivity keyword '{:s}'\n"
" in any of the {} extensions of file '{}'.\n"
" This input file will be ignored."
.format(phot_kwd, 'image-like' if sciext is None else \
"{:s}".format(utils.ext2str(sciext,
default_extver=None)),
os.path.basename(f.image.original_fname)))
f.release_all_images()
fl.remove(f)
else:
break
if ref_phot is None:
raise RuntimeError("Could not find the inverse sensitivity keyword "
"'{:s}' in the specified headers of "
"the input image(s).\nCannot continue."
.format(phot_kwd))
aux_phot_kwd_list = ','.join(aux_phot_kwd)
_mlinfo("\nPRIMARY PHOTOMETRIC KEYWORD: {:s}".format(phot_kwd))
_mlinfo("SECONDARY PHOTOMETRIC KEYWORD(S): {:s}"
.format(aux_phot_kwd_list if aux_phot_kwd_list else 'None'))
if ref_user:
_mlinfo("REFERENCE VALUE PROVIDED BY USER: '******'={}\n"
.format(phot_kwd, ref_phot))
else:
_mlinfo("REFERENCE VALUE FROM FILE: '{:s}[{:s}]'\n"
.format(os.path.basename(f.image.original_fname),
utils.ext2str(ref_ext)))
_mlinfo("REFERENCE '{:s}' VALUE IS: {}".format(phot_kwd, ref_phot))
# equalize PHOTFLAM/PHOTNU
for f in fl:
# open the file if necessary:
if f.fnamesOnly:
_mlinfo("\nProcessing file '{:s}'".format(f.image))
f.convert2ImageRef()
else:
_mlinfo("\nProcessing file '{:s}'".format(f.image.original_fname))
# first, see if photflam is in the primary header and save this value:
pri_conv = None
if search_primary:
whdu = f.image.hdu[0]
if phot_kwd in whdu.header:
_mlinfo(" * Primary header:")
if whdu is ref_hdu:
pri_conv = 1.0
_mlinfo(" - '{}' = {} found in the primary header."
.format(phot_kwd, whdu.header[phot_kwd]))
_mlinfo(" - Data conversion factor based on primary "
"header: {}".format(pri_conv))
else:
_mlinfo(" - '{}' found in the primary header."
.format(phot_kwd))
pri_conv = whdu.header[phot_kwd] / ref_phot
_mlinfo(" - Setting {:s} in the primary header to {} "
"(old value was {})"
.format(phot_kwd, ref_phot, whdu.header[phot_kwd]))
_mlinfo(" - Data conversion factor based on primary "
"header: {}".format(pri_conv))
whdu.header[phot_kwd] = ref_phot
# correct the "other" photometric keyword, if present:
if pri_conv is not None and whdu is not ref_hdu:
for aux_kwd in aux_phot_kwd:
if aux_kwd in whdu.header:
old_aux_phot = whdu.header[aux_kwd]
new_aux_phot = old_aux_phot / pri_conv
whdu.header[aux_kwd] = new_aux_phot
_mlinfo(" - Setting {:s} in the primary header "
"to {} (old value was {})"
.format(aux_kwd, new_aux_phot, old_aux_phot))
# process data and error arrays when 'sciext' was specifically set to
# 'PRIMARY':
if sciext == 'PRIMARY' and pri_conv is not None:
has_data = (hasattr(whdu, 'data') and
whdu.data is not None)
# correct data:
if has_data:
if np.issubdtype(whdu.data.dtype, np.float):
whdu.data *= pri_conv
_mlinfo(" - Data have been multiplied by {}"
.format(pri_conv))
else:
_mlwarn("WARNING: Data not converted because it is of "
"non-floating point type.")
# correct error array:
if errext is not None:
eext = (errext, 1)
try:
whdu = f.image.hdu[eext]
except KeyError:
_mlwarn(" - WARNING: Error extension {:s} not found."
.format(utils.ext2str(eext)))
f.release_all_images()
continue
if hasattr(whdu, 'data') and whdu.data is not None:
if np.issubdtype(whdu.data.dtype, np.float):
whdu.data *= pri_conv
_mlinfo(" - Error array (ext={}) has been "
"multiplied by {}".format(eext, pri_conv))
else:
_mlinfo(" - Error array in extension {:s} "
"contains non-floating point data.\n"
" Skipping this extension"
.format(utils.ext2str(ext)))
f.release_all_images()
continue
# find all valid extensions:
extnum = utils.get_ext_list(f.image, sciext)
for ext in extnum:
whdu = f.image.hdu[ext]
conv = None
if whdu is ref_hdu:
_mlinfo(" * EXT: {} - This is the \"reference\" extension.\n"
" Nothing to do. Skipping this extension..."
.format(ext))
continue
has_data = (hasattr(whdu, 'data') and
whdu.data is not None)
if has_data and not np.issubdtype(whdu.data.dtype, np.float):
_mlinfo(" * EXT: {} contains non-floating point data. "
"Skipping this extension".format(ext))
# find all auxiliary photometric keywords present in the header:
paux = [aux_kwd for aux_kwd in aux_phot_kwd if aux_kwd \
in whdu.header]
if phot_kwd in whdu.header:
_mlinfo(" * EXT: {}".format(ext))
old_phot = whdu.header[phot_kwd]
conv = old_phot / ref_phot
_mlinfo(" - Setting {:s} to {} (old value was {})"
.format(phot_kwd, ref_phot, old_phot))
whdu.header[phot_kwd] = ref_phot
_mlinfo(" - Computed conversion factor for data: {}"
.format(conv))
elif pri_conv is None:
_mlinfo(" * EXT: {}".format(ext))
_mlinfo(" - '{:s} not found. Skipping this extension..."
.format(phot_kwd))
continue
else:
_mlinfo(" * EXT: {}".format(ext))
# if paux:
# print("ERROR: Primary photometric keyword ('{:s}') is "
# "missing but\n the secondary keywords ('{:s}') "
# "are present. This extension cannot be processed."
# .format(phot_kwd, ','.join(paux)))
# continue
_mlinfo(" - '{:s} not found. Using conversion factor "
"based\n on the primary header: {}"
.format(phot_kwd, pri_conv))
conv = pri_conv
# correct the "other" photometric keyword, if present:
if conv is not None:
for aux_kwd in paux:
old_aux_phot = whdu.header[aux_kwd]
new_aux_phot = old_aux_phot / conv
whdu.header[aux_kwd] = new_aux_phot
_mlinfo(" - Setting {:s} to {} (old value was {})"
.format(aux_kwd, new_aux_phot, old_aux_phot))
# correct data:
if has_data:
if conv is None:
_mlinfo(" * EXT: {}".format(ext))
if np.issubdtype(whdu.data.dtype, np.float):
whdu.data *= conv
_mlinfo(" - Data have been multiplied by {}"
.format(conv))
else:
_mlinfo("WARNING: Non-floating point data. Data cannot "
"be re-scaled.")
# correct error array:
if errext is not None and isinstance(ext, tuple) and len(ext) == 2:
eext = (errext, ext[1])
try:
whdu = f.image.hdu[eext]
except KeyError:
continue
if hasattr(whdu, 'data') and whdu.data is not None:
if np.issubdtype(whdu.data.dtype, np.float):
whdu.data *= conv
_mlinfo(" - Error array (ext={}) has been "
"multiplied by {}".format(eext, conv))
else:
_mlinfo(" - Error array in extension {:s} "
"contains non-floating point data.\n"
" Skipping this extension"
.format(utils.ext2str(ext)))
f.release_all_images()
_mlinfo("\nDone.")
if readonly:
_mlinfo("\nNOTE: '{:s}' was run in READONLY mode\n"
" and input image(s)' content WAS NOT MODIFIED."
.format(__taskname__))
# close all log file handlers:
for h in _log.handlers:
if h is not _sh_log and isinstance(h, logging.FileHandler):
h.close()
_log.removeHandler(h)
def photeq(files='*_flt.fits',
sciext='SCI',
errext='ERR',
ref_phot=None,
ref_phot_ext=None,
phot_kwd='PHOTFLAM',
aux_phot_kwd='PHOTFNU',
search_primary=True,
readonly=True,
clobber=False,
logfile='photeq.log'):
"""
Adjust data values of images by equalizing each chip's PHOTFLAM value
to a single common value so that all chips can be treated equally
by ``AstroDrizzle``.
Parameters
----------
files : str (Default = ``'*_flt.fits'``)
A string containing one of the following:
* a comma-separated list of valid science image file names,
e.g.: ``'j1234567q_flt.fits, j1234568q_flt.fits'``;
* an @-file name, e.g., ``'@files_to_match.txt'``. See notes
section for details on the format of the @-files.
.. note::
**Valid science image file names** are:
* file names of existing FITS, GEIS, or WAIVER FITS files;
* partial file names containing wildcard characters, e.g.,
``'*_flt.fits'``;
* Association (ASN) tables (must have ``_asn``, or ``_asc``
suffix), e.g., ``'j12345670_asn.fits'``.
sciext : str (Default = 'SCI')
Extension *name* of extensions whose data and/or headers should
be corrected.
errext : str (Default = 'ERR')
Extension *name* of the extensions containing corresponding error
arrays. Error arrays are corrected in the same way as science data.
ref_phot : float, None (Default = None)
A number indicating the new value of PHOTFLAM or PHOTFNU
(set by 'phot_kwd') to which the data should be adjusted.
ref_phot_ext : int, str, tuple, None (Default = None)
Extension from which the `photeq` should get the reference photometric
value specified by the `phot_kwd` parameter. This parameter is ignored
if `ref_phot` **is not** `None`. When `ref_phot_ext` is `None`, then
the reference inverse sensitivity value will be picked from the
first `sciext` of the first input image containing `phot_kwd`.
phot_kwd : str (Default = 'PHOTFLAM')
Specifies the primary keyword which contains inverse sensitivity
(e.g., PHOTFLAM). It is used to compute conversion factors by
which data should be rescaled.
aux_phot_kwd : str, None, list of str (Default = 'PHOTFNU')
Same as `phot_kwd` but describes *other* photometric keyword(s)
that should be corrected by inverse of the scale factor used to correct
data. These keywords are *not* used to compute conversion factors.
Multiple keywords can be specified as a Python list of strings:
``['PHOTFNU', 'PHOTOHMY']``.
.. note::
If specifying multiple secondary photometric keywords in the TEAL
interface, use a comma-separated list of keywords.
search_primary : bool (Default = True)
Specifies whether to first search the primary header for the
presence of `phot_kwd` keyword and compute conversion factor based on
that value. This is (partially) ignored when `ref_phot` is not `None` in
the sense that the value specified by `ref_phot` will be used as the
reference *but* in all images primary will be searched for `phot_kwd`
and `aux_phot_kwd` and those values will be corrected
(if ``search_primary=True``).
readonly : bool (Default = True)
If `True`, `photeq` will not modify input files (nevertheless, it will
convert input GEIS or WAVERED FITS files to MEF and could overwrite
existing MEF files if `clobber` is set to `True`).
The (console or log file) output however will be identical to the case
when ``readonly=False`` and it can be examined before applying these
changes to input files.
clobber : bool (Default = False)
Overwrite existing MEF files when converting input WAVERED FITS or GEIS
to MEF.
logfile : str, None (Default = 'photeq.log')
File name of the log file.
Notes
-----
By default, `photeq` will search for the first inverse sensitivity
value (given by the header keyword specified by the `phot_kwd` parameter,
e.g., PHOTFLAM or PHOTFNU) found in the input images and it will equalize
all other images to this reference value.
It is possible to tell `photeq` to look for the reference inverse
sensitivity value only in a specific extension of input images, e.g.: 3,
('sci',3), etc. This can be done by setting `ref_phot_ext` to a specific
extension. This may be useful, for example, for WFPC2 images: WF3 chip was
one of the better calibrated chips, and so, if one prefers to have
inverse sensitivities equalized to the inverse sensitivity of the WF3 chip,
one can set ``ref_phot_ext=3``.
Alternatively, one can provide their own reference inverse sensitivity
value to which all other images should be "equalized" through the
parameter `ref_phot`.
.. note::
Default parameter values (except for `files`, `readonly`, and `clobber`)
should be acceptable for most HST images.
.. warning::
If images are intended to be used with ``AstroDrizzle``, it is
recommended that sky background measurement be performed on "equalized"
images as the `photeq` is not aware of sky user keyword in the image
headers and thus it cannot correct sky values already recorded in the
headers.
Examples
--------
#. In most cases the default parameters should suffice:
>>> from drizzlepac import photeq
>>> photeq.photeq(files='*_flt.fits', readonly=False)
#. If the re-calibration needs to be done on PHOTFNU rather than
PHOTFLAM, then:
>>> photeq.photeq(files='*_flt.fits', ref_phot='PHOTFNU',
... aux_phot_kwd='PHOTFLAM')
#. If for WFPC2 data one desires that PHOTFLAM from WF3 be used as the
reference in WFPC2 images, then:
>>> photeq.photeq(files='*_flt.fits', ref_phot_ext=3) # or ('sci',3)
"""
# Time it
runtime_begin = datetime.now()
# check that input file name is a string:
if not isinstance(files, str):
raise TypeError("Argument 'files' must be a comma-separated list of "
" file names")
# Set-up log files:
if isinstance(logfile, str):
# first, in case there are any "leftover" file handlers,
# close and remove them:
for h in _log.handlers:
if h is not _sh_log and isinstance(h, logging.FileHandler):
h.close()
_log.removeHandler(h)
# create file handler:
log_formatter = logging.Formatter('[%(levelname)s:] %(message)s')
log_file_handler = logging.FileHandler(logfile)
log_file_handler.setFormatter(log_formatter)
# add log_file_handler to logger
_log.addHandler(log_file_handler)
elif logfile is not None:
raise TypeError("Unsupported 'logfile' type")
# BEGIN:
_mlinfo("***** {0} started on {1}".format(__taskname__, runtime_begin))
_mlinfo(" Version {0} ({1})".format(__version__, __version_date__))
# check that extension names are strings (or None for error ext):
if sciext is None:
sci_ext4parse = '*'
ext2get = None
else:
if not isinstance(sciext, str):
raise TypeError("Argument 'sciext' must be a string or None")
sciext = sciext.strip()
if sciext.upper() == 'PRIMARY':
sciext = sciext.upper()
ext2get = (sciext, 1)
else:
ext2get = (sciext, '*')
sci_ext4parse = ext2get
if errext is not None and not isinstance(errext, str):
raise TypeError("Argument 'errext' must be a string or None")
# check that phot_kwd is supported:
if not isinstance(phot_kwd, str):
raise TypeError("Argument 'phot_kwd' must be a string")
phot_kwd = phot_kwd.strip().upper()
# check that ref_phot_ext has correct type:
if ref_phot_ext is not None and not \
(isinstance(ref_phot_ext, int) or isinstance(ref_phot_ext, str) \
or (isinstance(ref_phot_ext, tuple) and len(ref_phot_ext) == 2 \
and isinstance(ref_phot_ext[0], str) and \
isinstance(ref_phot_ext[1], int))):
raise TypeError("Unsupported 'ref_phot_ext' type")
if isinstance(ref_phot_ext, str):
ref_phot_ext = (ref_phot_ext, 1)
if aux_phot_kwd is None:
aux_phot_kwd = []
elif isinstance(aux_phot_kwd, str):
aux_phot_kwd = [aux_phot_kwd.strip().upper()]
if phot_kwd == aux_phot_kwd:
raise ValueError("Auxiliary photometric keyword must be different "
"from the main photometric keyword 'phot_kwd'.")
elif hasattr(aux_phot_kwd, '__iter__'):
if not all([isinstance(phot, str) for phot in aux_phot_kwd]):
raise TypeError(
"Argument 'aux_phot_kwd' must be a string, list of "
"strings, or None")
aux_phot_kwd = [phot.strip().upper() for phot in aux_phot_kwd]
if ref_phot in aux_phot_kwd:
raise ValueError("Auxiliary photometric keyword(s) must be "
"different from the main photometric keyword "
"'phot_kwd'.")
else:
raise TypeError("Argument 'aux_phot_kwd' must be a string, list of "
"strings, or None")
# read input file list:
fl = parseat.parse_cs_line(csline=files,
default_ext=sci_ext4parse,
im_fmode='readonly' if readonly else 'update',
clobber=clobber,
fnamesOnly=True,
doNotOpenDQ=True)
# check if user supplied file extensions, set them to the sciext,
# and warn that they will be ignored:
for f in fl:
if f.count > 1 or f.fext[0] != sci_ext4parse:
_mlwarn("WARNING: Extension specifications for file {:s} "
"will be ignored. Using all {:s} extensions instead."
.format(f.image, 'image-like' if sciext is None else \
"{:s}".format(utils.ext2str(sciext,
default_extver=None))))
# find the reference PHOTFLAM/PHOTNU:
flc = fl[:]
ref_hdu = None
ref_ext = None
ref_user = True
if ref_phot is None:
ref_user = False
for f in flc:
f.convert2ImageRef()
# get primary hdu:
pri_hdu = f.image.hdu[0]
# find all valid extensions:
if ref_phot_ext is None:
if sciext == 'PRIMARY':
extnum = [0]
else:
extnum = utils.get_ext_list(f.image, sciext)
is_pri_hdu = [f.image.hdu[ext] is pri_hdu for ext in extnum]
# if necessary, add primary header to the hdu list:
if search_primary:
try:
pri_index = is_pri_hdu.index(True)
extnum.insert(0, extnum.pop(pri_index))
except ValueError:
extnum.insert(0, 0)
else:
extnum = [ref_phot_ext]
for ext in extnum:
hdu = f.image.hdu[ext]
if phot_kwd in hdu.header:
ref_phot = hdu.header[phot_kwd]
ref_ext = ext
ref_hdu = hdu
break
if ref_phot is None:
_mlwarn("WARNING: Could not find specified inverse "
" sensitivity keyword '{:s}'\n"
" in any of the {} extensions of file '{}'.\n"
" This input file will be ignored."
.format(phot_kwd, 'image-like' if sciext is None else \
"{:s}".format(utils.ext2str(sciext,
default_extver=None)),
os.path.basename(f.image.original_fname)))
f.release_all_images()
fl.remove(f)
else:
break
if ref_phot is None:
raise RuntimeError(
"Could not find the inverse sensitivity keyword "
"'{:s}' in the specified headers of "
"the input image(s).\nCannot continue.".format(phot_kwd))
aux_phot_kwd_list = ','.join(aux_phot_kwd)
_mlinfo("\nPRIMARY PHOTOMETRIC KEYWORD: {:s}".format(phot_kwd))
_mlinfo("SECONDARY PHOTOMETRIC KEYWORD(S): {:s}".format(
aux_phot_kwd_list if aux_phot_kwd_list else 'None'))
if ref_user:
_mlinfo("REFERENCE VALUE PROVIDED BY USER: '******'={}\n".format(
phot_kwd, ref_phot))
else:
_mlinfo("REFERENCE VALUE FROM FILE: '{:s}[{:s}]'\n".format(
os.path.basename(f.image.original_fname), utils.ext2str(ref_ext)))
_mlinfo("REFERENCE '{:s}' VALUE IS: {}".format(phot_kwd, ref_phot))
# equalize PHOTFLAM/PHOTNU
for f in fl:
# open the file if necessary:
if f.fnamesOnly:
_mlinfo("\nProcessing file '{:s}'".format(f.image))
f.convert2ImageRef()
else:
_mlinfo("\nProcessing file '{:s}'".format(f.image.original_fname))
# first, see if photflam is in the primary header and save this value:
pri_conv = None
if search_primary:
whdu = f.image.hdu[0]
if phot_kwd in whdu.header:
_mlinfo(" * Primary header:")
if whdu is ref_hdu:
pri_conv = 1.0
_mlinfo(
" - '{}' = {} found in the primary header.".format(
phot_kwd, whdu.header[phot_kwd]))
_mlinfo(" - Data conversion factor based on primary "
"header: {}".format(pri_conv))
else:
_mlinfo(" - '{}' found in the primary header.".format(
phot_kwd))
pri_conv = whdu.header[phot_kwd] / ref_phot
_mlinfo(" - Setting {:s} in the primary header to {} "
"(old value was {})".format(
phot_kwd, ref_phot, whdu.header[phot_kwd]))
_mlinfo(" - Data conversion factor based on primary "
"header: {}".format(pri_conv))
whdu.header[phot_kwd] = ref_phot
# correct the "other" photometric keyword, if present:
if pri_conv is not None and whdu is not ref_hdu:
for aux_kwd in aux_phot_kwd:
if aux_kwd in whdu.header:
old_aux_phot = whdu.header[aux_kwd]
new_aux_phot = old_aux_phot / pri_conv
whdu.header[aux_kwd] = new_aux_phot
_mlinfo(" - Setting {:s} in the primary header "
"to {} (old value was {})".format(
aux_kwd, new_aux_phot, old_aux_phot))
# process data and error arrays when 'sciext' was specifically set to
# 'PRIMARY':
if sciext == 'PRIMARY' and pri_conv is not None:
has_data = (hasattr(whdu, 'data') and whdu.data is not None)
# correct data:
if has_data:
if np.issubdtype(whdu.data.dtype, np.floating):
whdu.data *= pri_conv
_mlinfo(
" - Data have been multiplied by {}".format(
pri_conv))
else:
_mlwarn("WARNING: Data not converted because it is of "
"non-floating point type.")
# correct error array:
if errext is not None:
eext = (errext, 1)
try:
whdu = f.image.hdu[eext]
except KeyError:
_mlwarn(
" - WARNING: Error extension {:s} not found.".
format(utils.ext2str(eext)))
f.release_all_images()
continue
if hasattr(whdu, 'data') and whdu.data is not None:
if np.issubdtype(whdu.data.dtype, np.floating):
whdu.data *= pri_conv
_mlinfo(" - Error array (ext={}) has been "
"multiplied by {}".format(eext, pri_conv))
else:
_mlinfo(" - Error array in extension {:s} "
"contains non-floating point data.\n"
" Skipping this extension".format(
utils.ext2str(ext)))
f.release_all_images()
continue
# find all valid extensions:
extnum = utils.get_ext_list(f.image, sciext)
for ext in extnum:
whdu = f.image.hdu[ext]
conv = None
if whdu is ref_hdu:
_mlinfo(" * EXT: {} - This is the \"reference\" extension.\n"
" Nothing to do. Skipping this extension...".
format(ext))
continue
has_data = (hasattr(whdu, 'data') and whdu.data is not None)
if has_data and not np.issubdtype(whdu.data.dtype, np.floating):
_mlinfo(" * EXT: {} contains non-floating point data. "
"Skipping this extension".format(ext))
# find all auxiliary photometric keywords present in the header:
paux = [aux_kwd for aux_kwd in aux_phot_kwd if aux_kwd \
in whdu.header]
if phot_kwd in whdu.header:
_mlinfo(" * EXT: {}".format(ext))
old_phot = whdu.header[phot_kwd]
conv = old_phot / ref_phot
_mlinfo(" - Setting {:s} to {} (old value was {})".format(
phot_kwd, ref_phot, old_phot))
whdu.header[phot_kwd] = ref_phot
_mlinfo(
" - Computed conversion factor for data: {}".format(
conv))
elif pri_conv is None:
_mlinfo(" * EXT: {}".format(ext))
_mlinfo(" - '{:s} not found. Skipping this extension...".
format(phot_kwd))
continue
else:
_mlinfo(" * EXT: {}".format(ext))
# if paux:
# print("ERROR: Primary photometric keyword ('{:s}') is "
# "missing but\n the secondary keywords ('{:s}') "
# "are present. This extension cannot be processed."
# .format(phot_kwd, ','.join(paux)))
# continue
_mlinfo(" - '{:s} not found. Using conversion factor "
"based\n on the primary header: {}".format(
phot_kwd, pri_conv))
conv = pri_conv
# correct the "other" photometric keyword, if present:
if conv is not None:
for aux_kwd in paux:
old_aux_phot = whdu.header[aux_kwd]
new_aux_phot = old_aux_phot / conv
whdu.header[aux_kwd] = new_aux_phot
_mlinfo(
" - Setting {:s} to {} (old value was {})".format(
aux_kwd, new_aux_phot, old_aux_phot))
# correct data:
if has_data:
if conv is None:
_mlinfo(" * EXT: {}".format(ext))
if np.issubdtype(whdu.data.dtype, np.floating):
whdu.data *= conv
_mlinfo(
" - Data have been multiplied by {}".format(conv))
else:
_mlinfo("WARNING: Non-floating point data. Data cannot "
"be re-scaled.")
# correct error array:
if errext is not None and isinstance(ext, tuple) and len(ext) == 2:
eext = (errext, ext[1])
try:
whdu = f.image.hdu[eext]
except KeyError:
continue
if hasattr(whdu, 'data') and whdu.data is not None:
if np.issubdtype(whdu.data.dtype, np.floating):
whdu.data *= conv
_mlinfo(" - Error array (ext={}) has been "
"multiplied by {}".format(eext, conv))
else:
_mlinfo(" - Error array in extension {:s} "
"contains non-floating point data.\n"
" Skipping this extension".format(
utils.ext2str(ext)))
f.release_all_images()
_mlinfo("\nDone.")
if readonly:
_mlinfo("\nNOTE: '{:s}' was run in READONLY mode\n"
" and input image(s)' content WAS NOT MODIFIED.".format(
__taskname__))
# close all log file handlers:
for h in _log.handlers:
if h is not _sh_log and isinstance(h, logging.FileHandler):
h.close()
_log.removeHandler(h)
