def field(self, key):
"""
A view of a `Column`'s data as an array.
"""
indx = _get_index(self.names, key)
recformat = self._coldefs._recformats[indx]
# If field's base is a FITS_rec, we can run into trouble because it
# contains a reference to the ._coldefs object of the original data;
# this can lead to a circular reference; see ticket #49
base = self
while isinstance(base, FITS_rec) and \
isinstance(base.base, np.recarray):
base = base.base
# base could still be a FITS_rec in some cases, so take care to
# use rec.recarray.field to avoid a potential infinite
# recursion
field = np.recarray.field(base, indx)
if self._convert[indx] is None:
# for X format
if isinstance(recformat, _FormatX):
_nx = recformat._nx
dummy = np.zeros(self.shape + (_nx,), dtype=np.bool_)
_unwrapx(field, dummy, _nx)
self._convert[indx] = dummy
return self._convert[indx]
(_str, _bool, _number, _scale, _zero, bscale, bzero, dim) = \
self._get_scale_factors(indx)
# for P format
buff = None
if isinstance(recformat, _FormatP):
dummy = _VLF([None] * len(self), dtype=recformat.dtype)
for i in range(len(self)):
_offset = field[i, 1] + self._heapoffset
if self._file is not None:
self._file.seek(_offset)
def get_pdata(dtype, count):
return _array_from_file(self._file, dtype=dtype,
count=count, sep='')
else: # There must be a _buffer or something is wrong
# Sometimes the buffer is already a Numpy array; in
# particular this can occur in compressed HDUs.
# Hypothetically other cases as well.
if buff is None:
buff = self._buffer
if not isinstance(buff, np.ndarray):
# Go ahead and great a single ndarray from the
# buffer if it is not already one; we will then
# take slices from it. This is more efficient than
# the previous approach that created separate
# arrays for each VLA.
buff = np.fromstring(buff, dtype=np.uint8)
def get_pdata(dtype, count):
dtype = np.dtype(dtype)
nbytes = count * dtype.itemsize
slc = slice(_offset, _offset + nbytes)
return buff[slc].view(dtype=dtype)
if recformat.dtype == 'a':
count = field[i, 0]
dt = recformat.dtype + str(1)
da = get_pdata(dt, count)
dummy[i] = np.char.array(da, itemsize=count)
dummy[i] = decode_ascii(dummy[i])
else:
count = field[i, 0]
dt = recformat.dtype
dummy[i] = get_pdata(dt, count)
dummy[i].dtype = dummy[i].dtype.newbyteorder('>')
# scale by TSCAL and TZERO
if _scale or _zero:
for i in range(len(self)):
dummy[i][:] = dummy[i] * bscale + bzero
# Boolean (logical) column
if recformat.dtype == FITS2NUMPY['L']:
for i in range(len(self)):
dummy[i] = np.equal(dummy[i], ord('T'))
self._convert[indx] = dummy
return self._convert[indx]
# ASCII table, convert strings to numbers
if not _str and self._coldefs._tbtype == 'TableHDU':
_fmap = {'I': np.int32, 'F': np.float32, 'E': np.float32,
'D': np.float64}
_type = _fmap[self._coldefs.formats[indx][0]]
# if the string = TNULL, return ASCIITNULL
nullval = self._coldefs.nulls[indx].strip().encode('ascii')
dummy = field.replace('D'.encode('ascii'),
'E'.encode('ascii'))
dummy = np.where(dummy.strip() == nullval, str(ASCIITNULL),
dummy)
dummy = np.array(dummy, dtype=_type)
self._convert[indx] = dummy
else:
dummy = field
# Test that the dimensions given in dim are sensible; otherwise
# display a warning and ignore them
if dim:
# See if the dimensions already match, if not, make sure the
# number items will fit in the specified dimensions
if dummy.ndim > 1:
actual_shape = dummy[0].shape
if _str:
actual_shape = (dummy[0].itemsize,) + actual_shape
else:
actual_shape = len(dummy[0])
if dim == actual_shape:
# The array already has the correct dimensions, so we
# ignore dim and don't convert
dim = None
else:
nitems = reduce(operator.mul, dim)
if _str:
actual_nitems = dummy.itemsize
else:
actual_nitems = dummy.shape[1]
if nitems != actual_nitems:
warnings.warn(
'TDIM%d value %s does not fit with the size of '
'the array items (%d). TDIM%d will be ignored.'
% (indx + 1, self._coldefs.dims[indx],
actual_nitems, indx + 1))
dim = None
# further conversion for both ASCII and binary tables
if _number and (_scale or _zero):
# only do the scaling the first time and store it in _convert
self._convert[indx] = np.array(dummy, dtype=np.float64)
if _scale:
np.multiply(self._convert[indx], bscale,
self._convert[indx])
if _zero:
self._convert[indx] += bzero
elif _bool:
self._convert[indx] = np.equal(dummy, ord('T'))
elif _str:
try:
self._convert[indx] = decode_ascii(dummy)
except UnicodeDecodeError:
pass
if dim:
if self._convert[indx] is None:
self._convert[indx] = dummy
if _str:
fmt = self._convert[indx].dtype.char
dtype = ('|%s%d' % (fmt, dim[-1]), dim[:-1])
self._convert[indx].dtype = dtype
else:
self._convert[indx].shape = (dummy.shape[0],) + dim
if self._convert[indx] is not None:
return self._convert[indx]
else:
return dummy
