def test_combine_fitsheader():
headers = [
create_fitsheader((1, 1), cdelt=3., crval=(0, 0)),
create_fitsheader((3, 3), cdelt=1., crval=(1, 1)),
create_fitsheader((5, 5), cdelt=1., crval=(3, 3)),
create_fitsheader((2, 2), cdelt=1., crval=(5, 5)),
]
header0 = combine_fitsheader(headers)
proj0 = WCS(header0)
epsilon = 1.e-10
def func(iheader, header):
nx = header['NAXIS1']
ny = header['NAXIS2']
x = header['CRPIX1']
y = header['CRPIX2']
edges = (np.array(3*(0.5, x, nx+0.5)),
np.array((0.5, 0.5, 0.5, y, y, y, ny+0.5, ny+0.5, ny+0.5)))
a, d = WCS(header).wcs_pix2world(edges[0], edges[1], 1)
x0, y0 = proj0.wcs_world2pix(a, d, 1)
assert np.all(x0 > 0.5-epsilon)
assert np.all(y0 > 0.5-epsilon)
assert np.all(x0 < header0['NAXIS1'] + 0.5 + epsilon)
assert np.all(y0 < header0['NAXIS2'] + 0.5 + epsilon)
if iheader == 0:
assert y0[0] <= 1.5 and x0[-1] >= header0['NAXIS1']-0.5
if iheader == len(headers)-1:
assert x0[0] <= 1.5 and y0[-1] >= header0['NAXIS2']-0.5
for iheader, header in enumerate(headers):
yield func, iheader, header
def test_get_cdelt_pa2():
cdelt = (-1.5, 3)
pa = -25.
header = create_fitsheader((10, 10), cdelt=cdelt, pa=pa)
cdelt_, pa_ = get_cdelt_pa(header)
assert_eq(cdelt, cdelt_)
assert_eq(pa, pa_)
def func(a):
h1 = create_fitsheader(a.shape[::-1],
dtype=float if a.ndim > 0 else np.int32)
h2 = create_fitsheader_for(a if a.ndim > 0 else None)
assert h1 == h2
assert h1['NAXIS'] == a.ndim
for i, s in enumerate(a.shape[::-1]):
assert h1['NAXIS'+str(i+1)] == s
def test_has_wcs():
header = create_fitsheader([2, 4])
assert not has_wcs(header)
header = create_fitsheader([2, 4], cdelt=1)
assert has_wcs(header)
def test_get_cdelt_pa1():
header = create_fitsheader((10, 10), cdelt=(-1.2, 3))
cdelt, pa = get_cdelt_pa(header)
assert_eq(cdelt, (-1.2, 3))
assert_eq(pa, 0)
def func(naxes):
header = create_fitsheader(naxes)
assert_eq(fitsheader2shape(header), naxes[::-1])
def test_create_fitsheader3():
header = create_fitsheader((10, 3), cdelt=1)
assert_eq(header['RADESYS'], 'ICRS')
assert 'EQUINOX' not in header
def write_fits(filename, data, header, extension, extname, comm):
"""
Collectively write local arrays into a single FITS file.
Parameters
----------
filename : str
The FITS file name.
data : ndarray
The array to be written.
header : pyfits.Header
The data FITS header. None can be set, in which case a minimal FITS
header will be inferred from the data.
extension : boolean
If True, the data will be written as an extension to an already
existing FITS file.
extname : str
The FITS extension name. Use None to write the primary HDU.
comm : mpi4py.Comm
The MPI communicator of the local arrays. Use MPI.COMM_SELF if the data
are not meant to be combined into a global array. Make sure that the MPI
processes are not executing this routine with the same file name.
"""
# check if the file name is the same for all MPI jobs
files = comm.allgather(filename+str(extname))
all_equal = all(f == files[0] for f in files)
if comm.size > 1 and not all_equal:
raise ValueError('The file name is not the same for all MPI jobs.')
ndims = comm.allgather(data.ndim)
if any(n != ndims[0] for n in ndims):
raise ValueError("The arrays have an incompatible number of dimensions:"
" '{0}'.".format(', '.join(str(n) for n in ndims)))
ndim = ndims[0]
shapes = comm.allgather(data.shape)
if any(s[1:] != shapes[0][1:] for s in shapes):
raise ValueError("The arrays have incompatible shapes: '{0}'.".format(
strshape(shapes)))
# get header
if header is None:
header = create_fitsheader(fromdata=data, extname=extname)
else:
header = header.copy()
if extname is not None:
header.update('extname', extname)
# we remove the file first to avoid an annoying pyfits informative message
if not extension:
if comm.rank == 0:
try:
os.remove(filename)
except OSError:
pass
# case without MPI communication
if comm.size == 1:
if not extension:
hdu = pyfits.PrimaryHDU(data, header)
hdu.writeto(filename, clobber=True)
else:
pyfits.append(filename, data, header)
return
# get global/local parameters
nglobal = sum(s[0] for s in shapes)
s = distribute_slice(nglobal)
nlocal = s.stop - s.start
if data.shape[0] != nlocal:
raise ValueError("On rank {}, the local array shape '{}' is invalid. Th"
"e first dimension does not match the expected local number '{}' gi"
"ven the global number '{}'.{}".format(comm.rank, data.shape,
nlocal, nglobal, '' if comm.rank > 0 else ' Shapes are: {}.'.format(
shapes)))
# write FITS header
if comm.rank == 0:
header['NAXIS' + str(ndim)] = nglobal
shdu = pyfits.StreamingHDU(filename, header)
data_loc = shdu._datLoc
shdu.close()
else:
data_loc = None
data_loc = comm.bcast(data_loc)
# get a communicator excluding the processes which have no work to do
# (Create_subarray does not allow 0-sized subarrays)
chunk = product(data.shape[1:])
rank_nowork = min(comm.size, nglobal)
group = comm.Get_group()
group.Incl(range(rank_nowork))
newcomm = comm.Create(group)
# collectively write data
if comm.rank < rank_nowork:
# mpi4py 1.2.2: pb with viewing data as big endian KeyError '>d'
if sys.byteorder == 'little' and data.dtype.byteorder == '=' or \
data.dtype.byteorder == '<':
data = data.byteswap()
data = data.newbyteorder('=')
mtype = DTYPE_MAP[data.dtype]
ftype = mtype.Create_subarray([nglobal*chunk], [nlocal*chunk],
[s.start*chunk])
ftype.Commit()
f = MPI.File.Open(newcomm, filename, amode=MPI.MODE_APPEND |
MPI.MODE_WRONLY | MPI.MODE_CREATE)
f.Set_view(data_loc, mtype, ftype, 'native', MPI.INFO_NULL)
f.Write_all(data)
f.Close()
ftype.Free()
newcomm.Free()
# pad FITS file with zeros
if comm.rank == 0:
datasize = nglobal * chunk * data.dtype.itemsize
BLOCK_SIZE = 2880
padding = BLOCK_SIZE - (datasize % BLOCK_SIZE)
with open(filename, 'a') as f:
if f.tell() - data_loc != datasize:
raise RuntimeError('Unexpected file size.')
f.write(padding * '\0')
comm.Barrier()
def write_fits(filename, data, header, extension, extname, comm):
"""
Collectively write local arrays into a single FITS file.
Parameters
----------
filename : str
The FITS file name.
data : ndarray
The array to be written.
header : pyfits.Header
The data FITS header. None can be set, in which case a minimal FITS
header will be inferred from the data.
extension : boolean
If True, the data will be written as an extension to an already
existing FITS file.
extname : str
The FITS extension name. Use None to write the primary HDU.
comm : mpi4py.Comm
The MPI communicator of the local arrays. Use MPI.COMM_SELF if the data
are not meant to be combined into a global array. Make sure that the MPI
processes are not executing this routine with the same file name.
"""
# check if the file name is the same for all MPI jobs
files = comm.allgather(filename + str(extname))
all_equal = all(f == files[0] for f in files)
if comm.size > 1 and not all_equal:
raise ValueError('The file name is not the same for all MPI jobs.')
ndims = comm.allgather(data.ndim)
if any(n != ndims[0] for n in ndims):
raise ValueError(
"The arrays have an incompatible number of dimensions:"
" '{0}'.".format(', '.join(str(n) for n in ndims)))
ndim = ndims[0]
shapes = comm.allgather(data.shape)
if any(s[1:] != shapes[0][1:] for s in shapes):
raise ValueError("The arrays have incompatible shapes: '{0}'.".format(
strshape(shapes)))
# get header
if header is None:
header = create_fitsheader(fromdata=data, extname=extname)
else:
header = header.copy()
if extname is not None:
header.update('extname', extname)
# we remove the file first to avoid an annoying pyfits informative message
if not extension:
if comm.rank == 0:
try:
os.remove(filename)
except OSError:
pass
# case without MPI communication
if comm.size == 1:
if not extension:
hdu = pyfits.PrimaryHDU(data, header)
hdu.writeto(filename, clobber=True)
else:
pyfits.append(filename, data, header)
return
# get global/local parameters
nglobal = sum(s[0] for s in shapes)
s = distribute_slice(nglobal)
nlocal = s.stop - s.start
if data.shape[0] != nlocal:
raise ValueError(
"On rank {}, the local array shape '{}' is invalid. Th"
"e first dimension does not match the expected local number '{}' gi"
"ven the global number '{}'.{}".format(
comm.rank, data.shape, nlocal, nglobal,
'' if comm.rank > 0 else ' Shapes are: {}.'.format(shapes)))
# write FITS header
if comm.rank == 0:
header['NAXIS' + str(ndim)] = nglobal
shdu = pyfits.StreamingHDU(filename, header)
data_loc = shdu._datLoc
shdu.close()
else:
data_loc = None
data_loc = comm.bcast(data_loc)
# get a communicator excluding the processes which have no work to do
# (Create_subarray does not allow 0-sized subarrays)
chunk = product(data.shape[1:])
rank_nowork = min(comm.size, nglobal)
group = comm.Get_group()
group.Incl(range(rank_nowork))
newcomm = comm.Create(group)
# collectively write data
if comm.rank < rank_nowork:
# mpi4py 1.2.2: pb with viewing data as big endian KeyError '>d'
if sys.byteorder == 'little' and data.dtype.byteorder == '=' or \
data.dtype.byteorder == '<':
data = data.byteswap()
data = data.newbyteorder('=')
mtype = DTYPE_MAP[data.dtype]
ftype = mtype.Create_subarray([nglobal * chunk], [nlocal * chunk],
[s.start * chunk])
ftype.Commit()
f = MPI.File.Open(newcomm,
filename,
amode=MPI.MODE_APPEND | MPI.MODE_WRONLY
| MPI.MODE_CREATE)
f.Set_view(data_loc, mtype, ftype, 'native', MPI.INFO_NULL)
f.Write_all(data)
f.Close()
ftype.Free()
newcomm.Free()
# pad FITS file with zeros
if comm.rank == 0:
datasize = nglobal * chunk * data.dtype.itemsize
BLOCK_SIZE = 2880
padding = BLOCK_SIZE - (datasize % BLOCK_SIZE)
with open(filename, 'a') as f:
if f.tell() - data_loc != datasize:
raise RuntimeError('Unexpected file size.')
f.write(padding * '\0')
comm.Barrier()