def __init__(self, header=None, wcs=None):
super(WCSCoordinates, self).__init__()
from astropy.wcs import WCS
if header is None and wcs is None:
raise ValueError('Must provide either FITS header or WCS or both')
if header is None:
header = wcs.to_header()
self._header = header
try:
naxis = header['NAXIS']
except (KeyError, TypeError):
naxis = None
wcs = wcs or WCS(header, naxis=naxis)
# update WCS interface if using old API
mapping = {
'wcs_pix2world': 'wcs_pix2sky',
'wcs_world2pix': 'wcs_sky2pix',
'all_pix2world': 'all_pix2sky'
}
for k, v in mapping.items():
if not hasattr(wcs, k):
setattr(wcs, k, getattr(wcs, v))
self._wcs = wcs
# Pre-compute dependent axes. The matrix returned by
# axis_correlation_matrix is (n_world, n_pixel) but we want to know
# which pixel coordinates are linked to which other pixel coordinates.
# So to do this we take a column from the matrix and find if there are
# any entries in common with all other columns in the matrix.
matrix = axis_correlation_matrix(wcs)[::-1, ::-1]
self._dependent_axes = []
for axis in range(wcs.naxis):
world_dep = matrix[:, axis:axis + 1]
dependent = tuple(np.nonzero((world_dep & matrix).any(axis=0))[0])
self._dependent_axes.append(dependent)
def __init__(self, header=None, wcs=None):
super(WCSCoordinates, self).__init__()
from astropy.wcs import WCS
if header is None and wcs is None:
raise ValueError('Must provide either FITS header or WCS or both')
if header is None:
header = wcs.to_header()
self._header = header
try:
naxis = header['NAXIS']
except (KeyError, TypeError):
naxis = None
wcs = wcs or WCS(header, naxis=naxis)
# update WCS interface if using old API
mapping = {'wcs_pix2world': 'wcs_pix2sky',
'wcs_world2pix': 'wcs_sky2pix',
'all_pix2world': 'all_pix2sky'}
for k, v in mapping.items():
if not hasattr(wcs, k):
setattr(wcs, k, getattr(wcs, v))
self._wcs = wcs
# Pre-compute dependent axes. The matrix returned by
# axis_correlation_matrix is (n_world, n_pixel) but we want to know
# which pixel coordinates are linked to which other pixel coordinates.
# So to do this we take a column from the matrix and find if there are
# any entries in common with all other columns in the matrix.
matrix = axis_correlation_matrix(wcs)[::-1, ::-1]
self._dependent_axes = []
for axis in range(wcs.naxis):
world_dep = matrix[:, axis:axis + 1]
dependent = tuple(np.nonzero((world_dep & matrix).any(axis=0))[0])
self._dependent_axes.append(dependent)