def get_kernel(self, data, subkernel=True):
indep, dep, kshape, lo, hi = self._get_kernel_data(data, subkernel)
# Use kernel data set WCS if available
eqpos = getattr(self.kernel, 'eqpos', None)
sky = getattr(self.kernel, 'sky', None)
# If kernel is a model, use WCS from data if available
if callable(self.kernel):
eqpos = getattr(data, 'eqpos', None)
sky = getattr(data, 'sky', None)
dataset = None
ndim = len(kshape)
if ndim == 1:
dataset = Data1D('kernel', indep[0], dep)
elif ndim == 2:
# Edit WCS to reflect the subkernel extraction in
# physical coordinates.
if (subkernel and sky is not None and lo is not None
and hi is not None):
if (WCS is not None):
sky = WCS(sky.name, sky.type, sky.crval, sky.crpix - lo,
sky.cdelt, sky.crota, sky.epoch, sky.equinox)
# FIXME: Support for WCS only (non-Chandra) coordinate
# transformations?
dataset = DataIMG('kernel',
indep[0],
indep[1],
dep,
kshape[::-1],
sky=sky,
eqpos=eqpos)
else:
raise PSFErr('ndim')
return dataset
def get_image_data(arg, make_copy=True, fix_type=True):
"""
get_image_data ( filename [, make_copy=True, fix_type=True ])
get_image_data ( IMAGECrate [, make_copy=True, fix_type=True ])
"""
filename = ''
close_dataset = False
if type(arg) == str:
img = open_crate(arg)
if not isinstance(img, pycrates.IMAGECrate):
# ??????????????????????????????????######## dtn
close_crate_dataset(img.get_dataset())
# ??????????????????????????????????######## dtn
raise IOErr('badfile', arg, "IMAGECrate obj")
filename = arg
close_dataset = True
elif isinstance(arg, pycrates.IMAGECrate):
img = arg
filename = arg.get_filename()
make_copy = False
else:
raise IOErr('badfile', arg, "IMAGECrate obj")
data = {}
data['y'] = _require_image(img, make_copy, fix_type)
sky = None
skynames = ['SKY', 'sky', 'pos', 'POS']
names = img.get_axisnames()
# find the SKY name using the set intersection
inter = list(set(names) & set(skynames))
if inter:
sky = img.get_transform(inter[0])
wcs = None
if 'EQPOS' in names:
wcs = img.get_transform('EQPOS')
if sky is not None and transformstatus:
linear = pycrates.WCSTANTransform()
linear.set_name("LINEAR")
linear.set_transform_matrix(sky.get_transform_matrix())
cdelt = numpy.array(linear.get_parameter_value('CDELT'))
crpix = numpy.array(linear.get_parameter_value('CRPIX'))
crval = numpy.array(linear.get_parameter_value('CRVAL'))
data['sky'] = WCS('physical', 'LINEAR', crval, crpix, cdelt)
if wcs is not None and transformstatus:
cdelt = numpy.array(wcs.get_parameter_value('CDELT'))
crpix = numpy.array(wcs.get_parameter_value('CRPIX'))
crval = numpy.array(wcs.get_parameter_value('CRVAL'))
crota = SherpaFloat(wcs.get_parameter_value('CROTA'))
equin = SherpaFloat(wcs.get_parameter_value('EQUINOX'))
epoch = SherpaFloat(wcs.get_parameter_value('EPOCH'))
data['eqpos'] = WCS('world', 'WCS', crval, crpix, cdelt, crota, epoch,
equin)
data['header'] = _get_meta_data(img)
keys = [
'MTYPE1', 'MFORM1', 'CTYPE1P', 'CTYPE2P', 'WCSNAMEP', 'CDELT1P',
'CDELT2P', 'CRPIX1P', 'CRPIX2P', 'CRVAL1P', 'CRVAL2P', 'MTYPE2',
'MFORM2', 'CTYPE1', 'CTYPE2', 'CDELT1', 'CDELT2', 'CRPIX1', 'CRPIX2',
'CRVAL1', 'CRVAL2', 'CUNIT1', 'CUNIT2', 'EQUINOX'
]
# 'WCSTY1P', 'WCSTY2P']
for key in keys:
try:
data['header'].pop(key)
except KeyError:
pass
if close_dataset:
close_crate_dataset(img.get_dataset())
return data, filename
def get_image_data(arg, make_copy=False):
"""
arg is a filename or a HDUList object
"""
hdu, filename = _get_file_contents(arg)
# FITS uses logical-to-world where we use physical-to-world.
# For all transforms, update their physical-to-world
# values from their logical-to-world values.
# Find the matching physical transform
# (same axis no, but sub = 'P' )
# and use it for the update.
# Physical tfms themselves do not get updated.
#
# Fill the physical-to-world transform given the
# logical-to-world and the associated logical-to-physical.
# W = wv + wd * ( P - wp )
# P = pv + pd * ( L - pp )
# W = lv + ld * ( L - lp )
# Then
# L = pp + ( P - pv ) / pd
# so W = lv + ld * ( pp + (P-pv)/pd - lp )
# = lv + ( ld / pd ) * ( P - [ pv + (lp-pp)*pd ] )
# Hence
# wv = lv
# wd = ld / pd
# wp = pv + ( lp - pp ) * pd
# EG suppose phys-to-world is
# W = 1000 + 2.0 * ( P - 4.0 )
# and we bin and scale to generate a logical-to-phys of
# P = 20 + 4.0 * ( L - 10 )
# Then
# W = 1000 + 2.0 * ( (20-4) - 4 * 10 ) + 2 * 4 $
#
try:
data = {}
img = hdu[0]
if hdu[0].data is None:
img = hdu[1]
if hdu[1].data is None:
raise IOErr('badimg', '')
data['y'] = numpy.asarray(img.data)
cdeltp = _get_wcs_key(img, 'CDELT1P', 'CDELT2P')
crpixp = _get_wcs_key(img, 'CRPIX1P', 'CRPIX2P')
crvalp = _get_wcs_key(img, 'CRVAL1P', 'CRVAL2P')
cdeltw = _get_wcs_key(img, 'CDELT1', 'CDELT2')
crpixw = _get_wcs_key(img, 'CRPIX1', 'CRPIX2')
crvalw = _get_wcs_key(img, 'CRVAL1', 'CRVAL2')
# proper calculation of cdelt wrt PHYSICAL coords
if (isinstance(cdeltw, numpy.ndarray)
and isinstance(cdeltp, numpy.ndarray)):
cdeltw = cdeltw / cdeltp
# proper calculation of crpix wrt PHYSICAL coords
if (isinstance(crpixw, numpy.ndarray)
and isinstance(crvalp, numpy.ndarray)
and isinstance(cdeltp, numpy.ndarray)
and isinstance(crpixp, numpy.ndarray)):
crpixw = crvalp + (crpixw - crpixp) * cdeltp
sky = None
if (transformstatus and isinstance(cdeltp, numpy.ndarray)
and isinstance(crpixp, numpy.ndarray)
and isinstance(crvalp, numpy.ndarray)):
sky = WCS('physical', 'LINEAR', crvalp, crpixp, cdeltp)
eqpos = None
if (transformstatus and isinstance(cdeltw, numpy.ndarray)
and isinstance(crpixw, numpy.ndarray)
and isinstance(crvalw, numpy.ndarray)):
eqpos = WCS('world', 'WCS', crvalw, crpixw, cdeltw)
data['sky'] = sky
data['eqpos'] = eqpos
data['header'] = _get_meta_data(img)
keys = [
'MTYPE1', 'MFORM1', 'CTYPE1P', 'CTYPE2P', 'WCSNAMEP', 'CDELT1P',
'CDELT2P', 'CRPIX1P', 'CRPIX2P', 'CRVAL1P', 'CRVAL2P', 'MTYPE2',
'MFORM2', 'CTYPE1', 'CTYPE2', 'CDELT1', 'CDELT2', 'CRPIX1',
'CRPIX2', 'CRVAL1', 'CRVAL2', 'CUNIT1', 'CUNIT2', 'EQUINOX'
]
for key in keys:
try:
data['header'].pop(key)
except KeyError:
pass
finally:
hdu.close()
return data, filename
def get_image_data(arg, make_copy=False):
"""
get_image_data( filename [, make_copy=False ])
get_image_data( [PrimaryHDU] [, make_copy=False ])
"""
filename = ''
if type(arg) == str and is_binary_file(arg):
hdu = pyfits.open(arg)
filename = arg
elif ( (type(arg) is pyfits.HDUList) and
(len(arg) > 0 ) and
(arg[0].__class__ is pyfits.PrimaryHDU) ):
hdu = arg
filename = hdu[0]._file.name
else:
raise IOErr('badfile', arg, "a binary FITS file or a PyFITS.PrimaryHDU list")
# FITS uses logical-to-world where we use physical-to-world.
# For all transforms, update their physical-to-world
# values from their logical-to-world values.
# Find the matching physical transform
# (same axis no, but sub = 'P' )
# and use it for the update.
# Physical tfms themselves do not get updated.
#
# Fill the physical-to-world transform given the
# logical-to-world and the associated logical-to-physical.
# W = wv + wd * ( P - wp )
# P = pv + pd * ( L - pp )
# W = lv + ld * ( L - lp )
# Then
# L = pp + ( P - pv ) / pd
# so W = lv + ld * ( pp + (P-pv)/pd - lp )
# = lv + ( ld / pd ) * ( P - [ pv + (lp-pp)*pd ] )
# Hence
# wv = lv
# wd = ld / pd
# wp = pv + ( lp - pp ) * pd
# EG suppose phys-to-world is
# W = 1000 + 2.0 * ( P - 4.0 )
# and we bin and scale to generate a logical-to-phys of
# P = 20 + 4.0 * ( L - 10 )
# Then
# W = 1000 + 2.0 * ( (20-4) - 4 * 10 ) + 2 * 4 $
#
try:
data = {}
img = hdu[0]
if hdu[0].data is None:
img = hdu[1]
if hdu[1].data is None:
raise IOErr('badimg', '')
data['y'] = numpy.asarray(img.data)
cdeltp = _get_wcs_key(img, 'CDELT1P', 'CDELT2P')
crpixp = _get_wcs_key(img, 'CRPIX1P', 'CRPIX2P')
crvalp = _get_wcs_key(img, 'CRVAL1P', 'CRVAL2P')
cdeltw = _get_wcs_key(img, 'CDELT1', 'CDELT2')
crpixw = _get_wcs_key(img, 'CRPIX1', 'CRPIX2')
crvalw = _get_wcs_key(img, 'CRVAL1', 'CRVAL2')
# proper calculation of cdelt wrt PHYSICAL coords
if (( cdeltw != () ) and ( cdeltp != () ) ):
cdeltw = cdeltw/cdeltp
# proper calculation of crpix wrt PHYSICAL coords
if (( crpixw != () ) and ( crvalp != () ) and
( cdeltp != () ) and ( crpixp != () ) ):
crpixw = crvalp + ( crpixw - crpixp ) * cdeltp
sky = None
if(cdeltp != () and crpixp != () and crvalp != () and transformstatus):
sky = WCS('physical', 'LINEAR', crvalp, crpixp, cdeltp)
eqpos = None
if(cdeltw != () and crpixw != () and crvalw != () and transformstatus):
eqpos = WCS('world', 'WCS', crvalw, crpixw, cdeltw)
data['sky'] = sky
data['eqpos'] = eqpos
data['header'] = _get_meta_data(img)
keys = ['MTYPE1','MFORM1','CTYPE1P','CTYPE2P','WCSNAMEP','CDELT1P',
'CDELT2P','CRPIX1P','CRPIX2P','CRVAL1P','CRVAL2P',
'MTYPE2','MFORM2','CTYPE1','CTYPE2','CDELT1','CDELT2','CRPIX1',
'CRPIX2','CRVAL1','CRVAL2','CUNIT1','CUNIT2','EQUINOX']
for key in keys:
try:
data['header'].pop(key)
except KeyError:
pass
finally:
hdu.close()
return data, filename
