def read(self, filename, hdu=None):
self.filename = filename
self.name = self.name or os.path.basename(filename)
# read FITS file
if not hdu:
dprint(3, "opening", filename)
hdu = pyfits.open(filename)[0]
hdu.verify('silentfix')
hdr = self.fits_header = hdu.header
dprint(3, "reading data")
data = hdu.data
# NB: all-data operations (such as getting global min/max or computing of histograms) are much faster
# (almost x2) when data is iterated
# over in the proper order. After a transpose(), data is in fortran order. Tell this to setData().
data = numpy.transpose(data)
# .copy()
dprint(3, "setting data")
self.setData(data, fortran_order=True)
dprint(3, "reading header")
ndim = hdr['NAXIS']
if ndim < 2:
raise ValueError, "Cannot load a one-dimensional FITS file"
# setup projection
# (strip out history from header, as big histories really slow down FITSWCS)
hdr1 = pyfits.Header(
filter(lambda x: not str(x).startswith('HISTORY'), hdr.cards))
proj = Projection.FITSWCS(hdr1)
nx = ny = None
# find X and Y axes
for iaxis in range(ndim):
axs = str(iaxis + 1)
npix = hdr['NAXIS' + axs]
name = hdr.get('CTYPE' + axs, axs).strip().upper()
# have we found the coordinate axes?
if FITSHeaders.isAxisTypeX(name):
nx = npix
iaxis_ra = iaxis
elif FITSHeaders.isAxisTypeY(name):
ny = npix
iaxis_dec = iaxis
# check that we have them
if nx is None or ny is None:
iaxis_ra, iaxis_dec = 0, 1
nx, ny = hdr.get('NAXIS1'), hdr.get('NAXIS2')
for iaxis in range(ndim):
axs = str(iaxis + 1)
# get axis description
npix = hdr['NAXIS' + axs]
crval = hdr.get('CRVAL' + axs, 0)
cdelt = hdr.get('CDELT' + axs, 1)
crpix = hdr.get('CRPIX' + axs, 1) - 1
name = hdr.get('CTYPE' + axs, axs).strip().upper()
unit = hdr.get('CUNIT' + axs)
# if this is not an X/Y axis, add it to the slicers
if iaxis not in (iaxis_ra, iaxis_dec):
# values becomes a list of axis values
values = list(crval + (numpy.arange(npix) - crpix) * cdelt)
unit = unit and unit.lower().capitalize()
# FITS knows of two enumerable axes: STOKES and COMPLEX. For these two, replace values with proper names
if name == "STOKES":
labels = [(self.StokesNames[int(i)] if i > 0
and i < len(self.StokesNames) else "%d" % i)
for i in values]
elif name == "COMPLEX":
labels = [(self.ComplexNames[int(i)] if i > 0
and i < len(self.ComplexNames) else "%d" % i)
for i in values]
else:
name = name.split("-")[0]
# if values are a simple sequence startying at 0 or 1, make simple labels
if cdelt == 1 and values[0] in (0., 1.):
labels = ["%d%s" % (val, unit) for val in values]
# else set labels to None: setExtraAxis() will figure it out
else:
labels = None
self.setExtraAxis(iaxis, name or ("axis " + axs), labels,
values, unit)
# check for beam parameters
psf = [hdr.get(x, None) for x in 'BMAJ', 'BMIN', 'BPA']
if all([x is not None for x in psf]):
self.setPsfSize(*[p / 180 * math.pi for p in psf])
self.setSkyAxis(0, iaxis_ra, nx, proj.ra0, -proj.xscale, proj.xpix0)
self.setSkyAxis(1, iaxis_dec, ny, proj.dec0, proj.yscale, proj.ypix0)
self.setDefaultProjection(proj)
dprint(3, "setting initial slice")
self._setupSlice()
def getImageCube (fitshdu,filename="",extra_axes=None):
"""Converts a FITS HDU (consisting of a header and data) into a 4+-dim numpy array where the
first two axes are x and y, the third is Stokes (possibly of length 1, if missing in the
original image), and the rest are either as found in the FITS header (if extra_axes=None),
or in the order specified by CTYPE in extra_axes (if present, else a dummy axis of size 1 is inserted),
with axes not present in extra_axes removed by taking the 0-th plane along each.
Returns tuple of
array,stokes_list,extra_axes_ctype_list,removed_axes_ctype_list
e.g. array,("I","Q"),("FREQ--FOO","TIME--BAR")
"""
hdr = fitshdu.header;
data = fitshdu.data;
# recognized axes
ix = iy = istokes = None;
naxis = len(data.shape);
# other axes which will be returned
other_axes = [];
other_axes_ctype = [];
remove_axes = [];
remove_axes_ctype = [];
# match axis ctype
# this makes FREQ equivalent to FELO*
def match_ctype (ctype,ctype_list):
for i,ct in enumerate(ctype_list):
if ct == ctype or ( ct == "FREQ" and ctype.startswith("FELO") ) or ( ctype == "FREQ" and ct.startswith("FELO") ):
return i;
return None;
# identify X, Y and stokes axes
for n in range(naxis):
iax = naxis-1-n;
axs = str(n+1);
ctype = hdr.get('CTYPE'+axs).strip().upper();
if ix is None and FITSHeaders.isAxisTypeX(ctype):
ix = iax; # in numpy order, axes are reversed
elif iy is None and FITSHeaders.isAxisTypeY(ctype):
iy = iax;
elif ctype == 'STOKES':
if istokes is not None:
raise ValueError,"duplicate STOKES axis in FITS file %s"%filename;
istokes = iax;
crval = hdr.get('CRVAL'+axs,0);
cdelt = hdr.get('CDELT'+axs,1);
crpix = hdr.get('CRPIX'+axs,1)-1;
values = map(int,list(crval + (numpy.arange(data.shape[iax]) - crpix)*cdelt));
stokes_names = [ (FITSHeaders.StokesNames[i]
if i>0 and i<len(FITSHeaders.StokesNames) else "%d"%i) for i in values ];
else:
other_axes.append(iax);
other_axes_ctype.append(ctype);
# not found?
if ix is None or iy is None:
raise ValueError,"FITS file %s does not appear to contain an X and/or Y axis"%filename;
# form up shape of resulting image, and order of axes for transpose
shape = [data.shape[ix],data.shape[iy]];
axes = [ix,iy];
# add stokes axis
if istokes is None:
shape.append(1);
stokes_labels = ("I",);
else:
shape.append(data.shape[istokes]);
axes.append(istokes);
if extra_axes:
# if a fixed order for the extra axes is specified, add the ones we found
for ctype in extra_axes:
i = match_ctype(ctype,other_axes_ctype);
if i is not None:
iax = other_axes[i];
axes.append(iax);
shape.append(data.shape[iax]);
else:
shape.append(1);
# add the ones that were not found into the remove list
for iaxis,ctype in zip(other_axes,other_axes_ctype):
if match_ctype(ctype,extra_axes) is None:
axes.append(iaxis);
remove_axes.append(iaxis);
remove_axes_ctype.append(ctype);
# return all extra axes found in header
else:
shape += [ data.shape[i] for i in other_axes ];
axes += other_axes;
extra_axes = other_axes_ctype;
# tranpose
data = data.transpose(axes);
# trim off axes which are to be removed, if we have any
if remove_axes:
data = data[[Ellipsis]+[0]*len(remove_axes)];
# reshape and return
return data.reshape(shape),stokes_names,extra_axes,remove_axes_ctype;
def getImageCube(fitshdu, filename="", extra_axes=None):
"""Converts a FITS HDU (consisting of a header and data) into a 4+-dim numpy array where the
first two axes are x and y, the third is Stokes (possibly of length 1, if missing in the
original image), and the rest are either as found in the FITS header (if extra_axes=None),
or in the order specified by CTYPE in extra_axes (if present, else a dummy axis of size 1 is inserted),
with axes not present in extra_axes removed by taking the 0-th plane along each.
Returns tuple of
array,stokes_list,extra_axes_ctype_list,removed_axes_ctype_list
e.g. array,("I","Q"),("FREQ--FOO","TIME--BAR")
"""
hdr = fitshdu.header
data = fitshdu.data
# recognized axes
ix = iy = istokes = None
naxis = len(data.shape)
# other axes which will be returned
other_axes = []
other_axes_ctype = []
remove_axes = []
remove_axes_ctype = []
# match axis ctype
# this makes FREQ equivalent to FELO*
def match_ctype(ctype, ctype_list):
for i, ct in enumerate(ctype_list):
if ct == ctype or (ct == "FREQ" and ctype.startswith("FELO")) or (
ctype == "FREQ" and ct.startswith("FELO")):
return i
return None
# identify X, Y and stokes axes
for n in range(naxis):
iax = naxis - 1 - n
axs = str(n + 1)
ctype = hdr.get('CTYPE' + axs).strip().upper()
if ix is None and FITSHeaders.isAxisTypeX(ctype):
ix = iax
# in numpy order, axes are reversed
elif iy is None and FITSHeaders.isAxisTypeY(ctype):
iy = iax
elif ctype == 'STOKES':
if istokes is not None:
raise ValueError, "duplicate STOKES axis in FITS file %s" % filename
istokes = iax
crval = hdr.get('CRVAL' + axs, 0)
cdelt = hdr.get('CDELT' + axs, 1)
crpix = hdr.get('CRPIX' + axs, 1) - 1
values = map(
int,
list(crval + (numpy.arange(data.shape[iax]) - crpix) * cdelt))
stokes_names = [
(FITSHeaders.StokesNames[i]
if i > 0 and i < len(FITSHeaders.StokesNames) else "%d" % i)
for i in values
]
else:
other_axes.append(iax)
other_axes_ctype.append(ctype)
# not found?
if ix is None or iy is None:
raise ValueError, "FITS file %s does not appear to contain an X and/or Y axis" % filename
# form up shape of resulting image, and order of axes for transpose
shape = [data.shape[ix], data.shape[iy]]
axes = [ix, iy]
# add stokes axis
if istokes is None:
shape.append(1)
stokes_names = ("I", )
else:
shape.append(data.shape[istokes])
axes.append(istokes)
if extra_axes:
# if a fixed order for the extra axes is specified, add the ones we found
for ctype in extra_axes:
i = match_ctype(ctype, other_axes_ctype)
if i is not None:
iax = other_axes[i]
axes.append(iax)
shape.append(data.shape[iax])
else:
shape.append(1)
# add the ones that were not found into the remove list
for iaxis, ctype in zip(other_axes, other_axes_ctype):
if match_ctype(ctype, extra_axes) is None:
axes.append(iaxis)
remove_axes.append(iaxis)
remove_axes_ctype.append(ctype)
# return all extra axes found in header
else:
shape += [data.shape[i] for i in other_axes]
axes += other_axes
extra_axes = other_axes_ctype
# tranpose
data = data.transpose(axes)
# trim off axes which are to be removed, if we have any
if remove_axes:
data = data[[Ellipsis] + [0] * len(remove_axes)]
# reshape and return
return data.reshape(shape), stokes_names, extra_axes, remove_axes_ctype
def read (self,filename,hdu=None):
self.filename = filename;
self.name = self.name or os.path.basename(filename);
# read FITS file
if not hdu:
dprint(3,"opening",filename);
hdu = pyfits.open(filename)[0];
hdu.verify('silentfix');
hdr = self.fits_header = hdu.header;
dprint(3,"reading data");
data = hdu.data;
# NB: all-data operations (such as getting global min/max or computing of histograms) are much faster
# (almost x2) when data is iterated
# over in the proper order. After a transpose(), data is in fortran order. Tell this to setData().
data = numpy.transpose(data); # .copy()
dprint(3,"setting data");
self.setData(data,fortran_order=True);
dprint(3,"reading header");
ndim = hdr['NAXIS'];
if ndim < 2:
raise ValueError,"Cannot load a one-dimensional FITS file";
# setup projection
# (strip out history from header, as big histories really slow down FITSWCS)
hdr1 = pyfits.Header(filter(lambda x:not str(x).startswith('HISTORY'),hdr.cards));
proj = Projection.FITSWCS(hdr1);
nx = ny = None;
# find X and Y axes
for iaxis in range(ndim):
axs = str(iaxis+1);
npix = hdr['NAXIS'+axs];
name = hdr.get('CTYPE'+axs,axs).strip().upper();
# have we found the coordinate axes?
if FITSHeaders.isAxisTypeX(name):
nx = npix;
iaxis_ra = iaxis;
elif FITSHeaders.isAxisTypeY(name):
ny = npix;
iaxis_dec = iaxis;
# check that we have them
if nx is None or ny is None:
iaxis_ra,iaxis_dec = 0,1;
nx,ny = hdr.get('NAXIS1'),hdr.get('NAXIS2');
for iaxis in range(ndim):
axs = str(iaxis+1);
# get axis description
npix = hdr['NAXIS'+axs];
crval = hdr.get('CRVAL'+axs,0 );
cdelt = hdr.get('CDELT'+axs,1) ;
crpix = hdr.get('CRPIX'+axs,1) -1;
name = hdr.get('CTYPE'+axs,axs).strip().upper();
unit = hdr.get('CUNIT'+axs);
# if this is not an X/Y axis, add it to the slicers
if iaxis not in (iaxis_ra,iaxis_dec):
# values becomes a list of axis values
values = list(crval + (numpy.arange(npix) - crpix)*cdelt);
unit = unit and unit.lower().capitalize();
# FITS knows of two enumerable axes: STOKES and COMPLEX. For these two, replace values with proper names
if name == "STOKES":
labels = [ (self.StokesNames[int(i)] if i>0 and i<len(self.StokesNames) else "%d"%i) for i in values ];
elif name == "COMPLEX":
labels = [ (self.ComplexNames[int(i)] if i>0 and i<len(self.ComplexNames) else "%d"%i) for i in values ];
else:
name = name.split("-")[0];
# if values are a simple sequence startying at 0 or 1, make simple labels
if cdelt == 1 and values[0] in (0.,1.):
labels = [ "%d%s"%(val,unit) for val in values ];
# else set labels to None: setExtraAxis() will figure it out
else:
labels = None;
self.setExtraAxis(iaxis,name or ("axis "+axs),labels,values,unit);
# check for beam parameters
psf = [ hdr.get(x,None) for x in 'BMAJ','BMIN','BPA' ];
if all([x is not None for x in psf]):
self.setPsfSize(*[ p/180*math.pi for p in psf ]);
self.setSkyAxis(0,iaxis_ra,nx,proj.ra0,-proj.xscale,proj.xpix0);
self.setSkyAxis(1,iaxis_dec,ny,proj.dec0,proj.yscale,proj.ypix0);
self.setDefaultProjection(proj);
dprint(3,"setting initial slice");
self._setupSlice();