def rotate(infile, outfile, angle, xcen=None, ycen=None):
# Open the FITS file using pyfits. A list of the HDUs in the FITS file is
# returned.
hdu_list = pyfits.open(infile)
# Create an object that will transfer FITS header cards between an AST
# FitsChan and the PyFITS header describing the primary HDU of the
# supplied FITS file.
adapter = Atl.PyFITSAdapter(hdu_list[0])
# Create a FitsChan and use the above adapter to copy all the header
# cards into it.
fitschan = Ast.FitsChan(adapter, adapter)
# Get the flavour of FITS-WCS used by the header cards currently in the
# FitsChan. This is so that we can use the same flavour when we write
# out the modified WCS.
encoding = fitschan.Encoding
# Read WCS information from the FitsChan. Additionally, this removes all
# WCS information from the FitsChan. The returned wcsinfo object
# is an AST FrameSet, in which the current Frame describes WCS coordinates
# and the base Frame describes pixel coodineates. The FrameSet includes a
# Mapping that specifies the transformation between the two Frames.
wcsinfo = fitschan.read()
# Check that the FITS header contained WCS in a form that can be
# understood by AST.
if wcsinfo == None:
print("Failed to read WCS information from {0}".format(infile))
# Rotation is restricted to 2D arrays, so check the FITS file has 2 pixel
# axes (given by Nin) and 2 WCS axes (given by Nout).
elif wcsinfo.Nin != 2 or wcsinfo.Nout != 2:
print("{0} is not 2-dimensional".format(infile))
# Proceed if the WCS information was read OK.
else:
# Get the lower and upper bounds of the input image in pixel indices.
# All FITS arrays by definition have lower pixel bounds of [1,1] (unlike
# NDFs). Note, unlike pyfits AST uses FITS ordering for storing pixel axis
# values in an array (i.e. NAXIS1 first, NAXIS2 second, etc).
lbnd_in = [1, 1]
ubnd_in = [fitschan["NAXIS1"], fitschan["NAXIS2"]]
# Get the rotation angle and convert from degrees to radians.
angle = float(angle) * Ast.DD2R
# Construct a MatrixMap that rotates by the required angle, converted to radians.
sinang = math.sin(angle)
cosang = math.cos(angle)
pixmap = Ast.MatrixMap([[cosang, sinang], [-sinang, cosang]])
# If supplied, get the centre of rotation.
if (xcen is not None) and (ycen is not None):
# Create aShiftMap to shift the origin and combine it with the
# MatrixMap, to give the desired centre of rotation (shift,
# rotate, then shift back again).
shiftmap = Ast.ShiftMap([-xcen, -ycen])
pixmap = Ast.CmpMap(shiftmap, pixmap)
shiftmap.invert()
pixmap = Ast.CmpMap(pixmap, shiftmap)
# The dimensions of the output array are the same as the input array.
lbnd_out = lbnd_in
ubnd_out = ubnd_in
# If no centre of rotation was specified, we use the MatrixMap
# unchanged, and determine the bounds of the smallest output image that
# will hold the entire rotated input image. These are with respect to
# the pixel coordinates of the input array, and so some corners may have
# negative pixel coordinates.
else:
(lb1, ub1, xl, xu) = pixmap.mapbox(lbnd_in, ubnd_in, 1)
(lb2, ub2, xl, xu) = pixmap.mapbox(lbnd_in, ubnd_in, 2)
lbnd_out = [int(lb1), int(lb2)]
ubnd_out = [int(ub1), int(ub2)]
# Get the value used to represent missing pixel values
if "BLANK" in fitschan:
badval = fitschan["BLANK"]
flags = Ast.USEBAD
else:
badval = 0
flags = 0
# Resample the data array using the above mapping.
(npix, out, out_var) = pixmap.resample(lbnd_in, ubnd_in,
hdu_list[0].data, None,
Ast.LINEAR, None, flags, 0.05,
1000, badval, lbnd_out,
ubnd_out, lbnd_out, ubnd_out)
# Store the rotated data in the HDU, and update the NAXISi keywords
# to hold the number of pixels along each edge of the rotated image.
hdu_list[0].data = out
fitschan["NAXIS1"] = ubnd_out[0] - lbnd_out[0] + 1
fitschan["NAXIS2"] = ubnd_out[1] - lbnd_out[1] + 1
# Move the pixel origin of the output from "lbnd_out" to (1,1). Create a
# suitable ShiftMap, and append it to the total "old pixel coord" to
# "new pixel coords" mapping.
shiftmap = Ast.ShiftMap([-lbnd_out[0], -lbnd_out[1]])
pixmap = Ast.CmpMap(pixmap, shiftmap)
# Re-map the base Frame (i.e. the pixel coordinates Frame) so that it
# refers to the new data grid instead of the old one.
wcsinfo.remapframe(Ast.BASE, pixmap)
# Attempt to write the modified WCS information to the primary HDU (i.e.
# convert the FrameSet to a set of FITS header cards stored in the
# FITS file). Indicate that we want to use original flavour of FITS-WCS.
fitschan.Encoding = encoding
fitschan.clear('Card')
if fitschan.write(wcsinfo) == 0:
print("Failed to convert the rotated WCS to Fits-WCS")
# If successfull, force the FitsCHan to copy its contents into the
# PyFITS header, then write the changed data and header to the output
# FITS file.
else:
fitschan.writefits()
hdu_list.writeto(outfile, clobber=True, output_verify='ignore')
def wcsalign(hdu_in, header, outname=None, clobber=False):
"""
Align one FITS image to a specified header
Requires pyast.
Parameters
----------
hdu_in : `~astropy.io.fits.PrimaryHDU`
The HDU to reproject (must have header & data)
header : `~astropy.io.fits.Header`
The target header to project to
outname : str (optional)
The filename to write to.
clobber : bool
Overwrite the file ``outname`` if it exists
Returns
-------
The reprojected fits.PrimaryHDU
Credits
-------
Written by David Berry and adapted to functional form by Adam Ginsburg
([email protected])
"""
try:
import starlink.Ast as Ast
import starlink.Atl as Atl
except ImportError:
raise ImportError("starlink could not be imported, wcsalign is not "
"available")
# Create objects that will transfer FITS header cards between an AST
# FitsChan and the fits header describing the primary HDU of the
# supplied FITS file.
adapter_in = Atl.PyFITSAdapter(hdu_in)
hdu_ref = pyfits.PrimaryHDU(header=header)
adapter_ref = Atl.PyFITSAdapter(hdu_ref)
# Create a FitsChan for each and use the above adapters to copy all the
# header cards into it.
fitschan_in = Ast.FitsChan(adapter_in, adapter_in)
fitschan_ref = Ast.FitsChan(adapter_ref, adapter_ref)
# Get the flavour of FITS-WCS used by the header cards currently in the
# input FITS file. This is so that we can use the same flavour when we
# write out the modified WCS.
encoding = fitschan_in.Encoding
# Read WCS information from the two FitsChans. Additionally, this removes
# all WCS information from each FitsChan. The returned wcsinfo object
# is an AST FrameSet, in which the current Frame describes WCS coordinates
# and the base Frame describes pixel coodineates. The FrameSet includes a
# Mapping that specifies the transformation between the two Frames.
wcsinfo_in = fitschan_in.read()
wcsinfo_ref = fitschan_ref.read()
# Check that the input FITS header contained WCS in a form that can be
# understood by AST.
if wcsinfo_in is None:
raise ValueError(
"Failed to read WCS information from {0}".format(hdu_in))
# This is restricted to 2D arrays, so check theinput FITS file has 2
# pixel axes (given by Nin) and 2 WCS axes (given by Nout).
elif wcsinfo_in.Nin != 2 or wcsinfo_in.Nout != 2:
raise ValueError("{0} is not 2-dimensional".format(hdu_in))
# Check the reference FITS file in the same way.
elif wcsinfo_ref is None:
raise ValueError(
"Failed to read WCS information from {0}".format(hdu_ref))
elif wcsinfo_ref.Nin != 2 or wcsinfo_ref.Nout != 2:
raise ValueError("{0} is not 2-dimensional".format(hdu_ref))
# Proceed if the WCS information was read OK.
# Attempt to get a mapping from pixel coords in the input FITS file to
# pixel coords in the reference fits file, with alignment occuring by
# preference in the current WCS frame. Since the pixel coordinate frame
# will be the base frame in each Frameset, we first invert the
# FrameSets. This is because the Convert method aligns current Frames,
# not base frames.
wcsinfo_in.invert()
wcsinfo_ref.invert()
alignment_fs = wcsinfo_in.convert(wcsinfo_ref)
# Invert them again to put them back to their original state (i.e.
# base frame = pixel coords, and current Frame = WCS coords).
wcsinfo_in.invert()
wcsinfo_ref.invert()
# Check alignment was possible.
if alignment_fs is None:
raise Exception("Cannot find a common coordinate system shared by "
"{0} and {1}".format(hdu_in, hdu_ref))
else:
# Get the lower and upper bounds of the input image in pixel
# indices. All FITS arrays by definition have lower pixel bounds of
# [1, 1] (unlike NDFs). Note, unlike fits AST uses FITS ordering for
# storing pixel axis values in an array (i.e. NAXIS1 first, NAXIS2
# second, etc).
lbnd_in = [1, 1]
ubnd_in = [fitschan_in["NAXIS1"], fitschan_in["NAXIS2"]]
# Find the pixel bounds of the input image within the pixel coordinate
# system of the reference fits file.
(lb1, ub1, xl, xu) = alignment_fs.mapbox(lbnd_in, ubnd_in, 1)
(lb2, ub2, xl, xu) = alignment_fs.mapbox(lbnd_in, ubnd_in, 2)
# Calculate the bounds of the output image.
lbnd_out = [int(lb1), int(lb2)]
ubnd_out = [int(ub1), int(ub2)]
# Unlike NDFs, FITS images cannot have an arbitrary pixel origin so
# we need to ensure that the bottom left corner of the input image
# gets mapped to pixel [1,1] in the output. To do this we, extract the
# mapping from the alignment FrameSet and add on a ShiftMap (a mapping
# that just applies a shift to each axis).
shift = [1 - lbnd_out[0], 1 - lbnd_out[1]]
alignment_mapping = alignment_fs.getmapping()
shiftmap = Ast.ShiftMap(shift)
total_map = Ast.CmpMap(alignment_mapping, shiftmap)
# Modify the pixel bounds of the output image to take account of this
# shift of origin.
lbnd_out[0] += shift[0]
lbnd_out[1] += shift[1]
ubnd_out[0] += shift[0]
ubnd_out[1] += shift[1]
# Get the value used to represent missing pixel values
if "BLANK" in fitschan_in:
badval = fitschan_in["BLANK"]
flags = Ast.USEBAD
else:
badval = 0
flags = 0
# Resample the data array using the above mapping.
# total_map was pixmap; is this right?
(npix, out, out_var) = total_map.resample(lbnd_in, ubnd_in,
hdu_in.data, None,
Ast.LINEAR, None, flags,
0.05, 1000, badval, lbnd_out,
ubnd_out, lbnd_out, ubnd_out)
# Store the aligned data in the primary HDU, and update the NAXISi
# keywords to hold the number of pixels along each edge of the
# rotated image.
hdu_in.data = out
fitschan_in["NAXIS1"] = ubnd_out[0] - lbnd_out[0] + 1
fitschan_in["NAXIS2"] = ubnd_out[1] - lbnd_out[1] + 1
# The WCS to store in the output is the same as the reference WCS
# except for the extra shift of origin. So use the above shiftmap to
# remap the pixel coordinate frame in the reference WCS FrameSet. We
# can then use this FrameSet as the output FrameSet.
wcsinfo_ref.remapframe(Ast.BASE, shiftmap)
# Attempt to write the modified WCS information to the primary HDU (i.e.
# convert the FrameSet to a set of FITS header cards stored in the
# FITS file). Indicate that we want to use original flavour of FITS-WCS.
fitschan_in.Encoding = encoding
fitschan_in.clear('Card')
if fitschan_in.write(wcsinfo_ref) == 0:
raise Exception("Failed to convert the aligned WCS to Fits-WCS")
# If successful, force the FitsChan to copy its contents into the
# fits header, then write the changed data and header to the output
# FITS file.
else:
fitschan_in.writefits()
if outname is not None:
hdu_in.writeto(outname, clobber=clobber)
return hdu_in
angle = float( sys.argv[3] )*Ast.DD2R
# Construct a MatrixMap that rotates by the required angle, converted to radians.
sinang = math.sin( angle )
cosang = math.cos( angle )
pixmap = Ast.MatrixMap( [ [cosang,sinang], [-sinang,cosang] ] )
# If supplied, get the centre of rotation.
if len(sys.argv) > 5:
x0 = float( sys.argv[4] )
y0 = float( sys.argv[5] )
# Create aShiftMap to shift the origin and combine it with the
# MatrixMap, to give the desired centre of rotation (shift,
# rotate, then shift back again).
shiftmap = Ast.ShiftMap( [ -x0, -y0 ] )
pixmap = Ast.CmpMap( shiftmap, pixmap );
shiftmap.invert()
pixmap = Ast.CmpMap( pixmap, shiftmap );
# The dimensions of the output array are the same as the input array.
lbnd_out = lbnd_in
ubnd_out = ubnd_in
# If no centre of rotation was specified, we use the MatrixMap
# unchanged, and determine the bounds of the smallest output image that
# will hold the entire rotated input image. These are with respect to
# the pixel coordinates of the input array, and so some corners may have
# negative pixel coordinates.
else:
( lb1, ub1, xl, xu ) = pixmap.mapbox( lbnd_in, ubnd_in, 1 )