# Licensed under a 3-clause BSD style license - see PYFITS.rst
import copy
import operator
import warnings
import weakref
from functools import reduce
import numpy as np
from numpy import char as chararray
from .column import (ASCIITNULL, FITS2NUMPY, ASCII2NUMPY, ASCII2STR, ColDefs,
_AsciiColDefs, _FormatX, _FormatP, _VLF, _get_index,
_wrapx, _unwrapx, _makep, Delayed)
from .util import decode_ascii, encode_ascii
from ...extern.six import string_types
from ...extern.six.moves import xrange
from ...utils import lazyproperty
from ...utils.compat import ignored
from ...utils.exceptions import AstropyDeprecationWarning, AstropyUserWarning
[docs]class FITS_record(object):
"""
FITS record class.
`FITS_record` is used to access records of the `FITS_rec` object.
This will allow us to deal with scaled columns. It also handles
conversion/scaling of columns in ASCII tables. The `FITS_record`
class expects a `FITS_rec` object as input.
"""
def __init__(self, input, row=0, start=None, end=None, step=None,
base=None, **kwargs):
"""
Parameters
----------
input : array
The array to wrap.
row : int, optional
The starting logical row of the array.
start : int, optional
The starting column in the row associated with this object.
Used for subsetting the columns of the `FITS_rec` object.
end : int, optional
The ending column in the row associated with this object.
Used for subsetting the columns of the `FITS_rec` object.
"""
# For backward compatibility...
for arg in [('startColumn', 'start'), ('endColumn', 'end')]:
if arg[0] in kwargs:
warnings.warn('The %s argument to FITS_record is deprecated; '
'use %s instead' % arg, AstropyDeprecationWarning)
if arg[0] == 'startColumn':
start = kwargs[arg[0]]
elif arg[0] == 'endColumn':
end = kwargs[arg[0]]
self.array = input
self.row = row
if base:
width = len(base)
else:
width = self.array._nfields
s = slice(start, end, step).indices(width)
self.start, self.end, self.step = s
self.base = base
def __getitem__(self, key):
if isinstance(key, string_types):
indx = _get_index(self.array.names, key)
if indx < self.start or indx > self.end - 1:
raise KeyError("Key '%s' does not exist." % key)
elif isinstance(key, slice):
return type(self)(self.array, self.row, key.start, key.stop,
key.step, self)
else:
indx = self._get_index(key)
if indx > self.array._nfields - 1:
raise IndexError('Index out of bounds')
return self.array.field(indx)[self.row]
def __setitem__(self, key, value):
if isinstance(key, string_types):
indx = _get_index(self.array._coldefs.names, key)
if indx < self.start or indx > self.end - 1:
raise KeyError("Key '%s' does not exist." % key)
elif isinstance(key, slice):
for indx in xrange(slice.start, slice.stop, slice.step):
indx = self._get_indx(indx)
self.array.field(indx)[self.row] = value
else:
indx = self._get_index(key)
if indx > self.array._nfields - 1:
raise IndexError('Index out of bounds')
self.array.field(indx)[self.row] = value
def __getslice__(self, start, end):
return self[slice(start, end)]
def __len__(self):
return len(xrange(self.start, self.end, self.step))
def __repr__(self):
"""
Display a single row.
"""
outlist = []
for idx in xrange(len(self)):
outlist.append(repr(self[idx]))
return '(%s)' % ', '.join(outlist)
[docs] def field(self, field):
"""
Get the field data of the record.
"""
return self.__getitem__(field)
[docs] def setfield(self, field, value):
"""
Set the field data of the record.
"""
self.__setitem__(field, value)
@lazyproperty
def _bases(self):
bases = [weakref.proxy(self)]
base = self.base
while base:
bases.append(base)
base = base.base
return bases
def _get_index(self, indx):
indices = np.ogrid[:self.array._nfields]
for base in reversed(self._bases):
if base.step < 1:
s = slice(base.start, None, base.step)
else:
s = slice(base.start, base.end, base.step)
indices = indices[s]
return indices[indx]
[docs]class FITS_rec(np.recarray):
"""
FITS record array class.
`FITS_rec` is the data part of a table HDU's data part. This is a layer
over the `~numpy.recarray`, so we can deal with scaled columns.
It inherits all of the standard methods from `numpy.ndarray`.
"""
_record_type = FITS_record
def __new__(subtype, input):
"""
Construct a FITS record array from a recarray.
"""
# input should be a record array
if input.dtype.subdtype is None:
self = np.recarray.__new__(subtype, input.shape, input.dtype,
buf=input.data)
else:
self = np.recarray.__new__(subtype, input.shape, input.dtype,
buf=input.data, strides=input.strides)
self._nfields = len(self.dtype.names)
self._converted = {}
self._heapoffset = 0
self._heapsize = 0
self._coldefs = None
self._gap = 0
self._uint = False
return self
def __setstate__(self, state):
meta = state[-1]
column_state = state[-2]
state = state[:-2]
super(FITS_rec, self).__setstate__(state)
for attr, value in zip(meta, column_state):
setattr(self, attr, value)
def __reduce__(self):
"""
Return a 3-tuple for pickling a FITS_rec. Use the super-class
functionality but then add in a tuple of FITS_rec-specific
values that get used in __setstate__.
"""
reconst_func, reconst_func_args, state = \
super(FITS_rec, self).__reduce__()
# Define FITS_rec-specific attrs that get added to state
column_state = []
meta = []
for attrs in ['_converted', '_heapoffset', '_heapsize', '_nfields',
'_gap', '_uint', 'parnames', '_coldefs']:
with ignored(AttributeError):
# _coldefs can be Delayed, and file objects cannot be
# picked, it needs to be deepcopied first
if attrs == '_coldefs':
column_state.append(self._coldefs.__deepcopy__(None))
else:
column_state.append(getattr(self, attrs))
meta.append(attrs)
state = state + (column_state, meta)
return reconst_func, reconst_func_args, state
def __array_finalize__(self, obj):
if obj is None:
return
if isinstance(obj, FITS_rec):
self._converted = obj._converted
self._heapoffset = obj._heapoffset
self._heapsize = obj._heapsize
self._coldefs = obj._coldefs
self._nfields = obj._nfields
self._gap = obj._gap
self._uint = obj._uint
else:
# This will allow regular ndarrays with fields, rather than
# just other FITS_rec objects
self._nfields = len(obj.dtype.names)
self._converted = {}
self._heapoffset = getattr(obj, '_heapoffset', 0)
self._heapsize = getattr(obj, '_heapsize', 0)
self._coldefs = None
self._gap = getattr(obj, '_gap', 0)
self._uint = getattr(obj, '_uint', False)
attrs = ['_converted', '_coldefs', '_gap']
for attr in attrs:
if hasattr(obj, attr):
value = getattr(obj, attr, None)
if value is None:
warnings.warn('Setting attribute %s as None' % attr,
AstropyUserWarning)
setattr(self, attr, value)
if self._coldefs is None:
self._coldefs = ColDefs(self)
@classmethod
[docs] def from_columns(cls, columns, nrows=0, fill=False):
"""
Given a `ColDefs` object of unknown origin, initialize a new `FITS_rec`
object.
.. note::
This was originally part of the `new_table` function in the table
module but was moved into a class method since most of its
functionality always had more to do with initializing a `FITS_rec`
object than anything else, and much of it also overlapped with
``FITS_rec._scale_back``.
Parameters
----------
columns : sequence of `Column` or a `ColDefs`
The columns from which to create the table data. If these
columns have data arrays attached that data may be used in
initializing the new table. Otherwise the input columns
will be used as a template for a new table with the requested
number of rows.
nrows : int
Number of rows in the new table. If the input columns have data
associated with them, the size of the largest input column is used.
Otherwise the default is 0.
fill : bool
If `True`, will fill all cells with zeros or blanks. If
`False`, copy the data from input, undefined cells will still
be filled with zeros/blanks.
"""
if not isinstance(columns, ColDefs):
columns = ColDefs(columns)
# read the delayed data
for column in columns:
arr = column.array
if isinstance(arr, Delayed):
if arr.hdu.data is None:
column.array = None
else:
column.array = _get_recarray_field(arr.hdu.data,
arr.field)
# Reset columns._arrays (which we may want to just do away with
# altogether
del columns._arrays
# use the largest column shape as the shape of the record
if nrows == 0:
for arr in columns._arrays:
if arr is not None:
dim = arr.shape[0]
else:
dim = 0
if dim > nrows:
nrows = dim
raw_data = np.empty(columns.dtype.itemsize * nrows, dtype=np.uint8)
raw_data.fill(ord(columns._padding_byte))
data = np.recarray(nrows, dtype=columns.dtype, buf=raw_data).view(cls)
# Make sure the data is a listener for changes to the columns
columns._add_listener(data)
# Previously this assignment was made from hdu.columns, but that's a
# bug since if a _TableBaseHDU has a FITS_rec in its .data attribute
# the _TableBaseHDU.columns property is actually returned from
# .data._coldefs, so this assignment was circular! Don't make that
# mistake again.
# All of this is an artifact of the fragility of the FITS_rec class,
# and that it can't just be initialized by columns...
data._coldefs = columns
# If fill is True we don't copy anything from the column arrays. We're
# just using them as a template, and returning a table filled with
# zeros/blanks
if fill:
return data
# Otherwise we have to fill the recarray with data from the input
# columns
for idx, column in enumerate(columns):
# For each column in the ColDef object, determine the number of
# rows in that column. This will be either the number of rows in
# the ndarray associated with the column, or the number of rows
# given in the call to this function, which ever is smaller. If
# the input FILL argument is true, the number of rows is set to
# zero so that no data is copied from the original input data.
arr = column.array
if arr is None:
array_size = 0
else:
array_size = len(arr)
n = min(array_size, nrows)
# TODO: At least *some* of this logic is mostly redundant with the
# _convert_foo methods in this class; see if we can eliminate some
# of that duplication.
if not n:
# The input column had an empty array, so just use the fill
# value
continue
field = _get_recarray_field(data, idx)
name = column.name
fitsformat = column.format
recformat = fitsformat.recformat
outarr = field[:n]
inarr = arr[:n]
if isinstance(recformat, _FormatX):
# Data is a bit array
if inarr.shape[-1] == recformat.repeat:
_wrapx(inarr, outarr, recformat.repeat)
continue
elif isinstance(recformat, _FormatP):
data._converted[name] = _makep(inarr, field, recformat,
nrows=nrows)
continue
# TODO: Find a better way of determining that the column is meant
# to be FITS L formatted
elif recformat[-2:] == FITS2NUMPY['L'] and inarr.dtype == bool:
# column is boolean
# The raw data field should be filled with either 'T' or 'F'
# (not 0). Use 'F' as a default
field[:] = ord('F')
# Also save the original boolean array in data._converted so
# that it doesn't have to be re-converted
data._converted[name] = np.zeros(field.shape, dtype=bool)
data._converted[name][:n] = inarr
# TODO: Maybe this step isn't necessary at all if _scale_back
# will handle it?
inarr = np.where(inarr == False, ord('F'), ord('T'))
elif (columns[idx]._physical_values and
columns[idx]._pseudo_unsigned_ints):
# Temporary hack...
bzero = column.bzero
data._converted[name] = np.zeros(field.shape,
dtype=inarr.dtype)
data._converted[name][:n] = inarr
if n < nrows:
# Pre-scale rows below the input data
field[n:] = -bzero
inarr = inarr - bzero
elif isinstance(columns, _AsciiColDefs):
# Regardless whether the format is character or numeric, if the
# input array contains characters then it's already in the raw
# format for ASCII tables
if fitsformat._pseudo_logical:
# Hack to support converting from 8-bit T/F characters
# Normally the column array is a chararray of 1 character
# strings, but we need to view it as a normal ndarray of
# 8-bit ints to fill it with ASCII codes for 'T' and 'F'
outarr = field.view(np.uint8, np.ndarray)[:n]
elif not isinstance(arr, chararray.chararray):
# Fill with the appropriate blanks for the column format
data._converted[name] = np.zeros(nrows, dtype=arr.dtype)
outarr = data._converted[name][:n]
outarr[:] = inarr
continue
if inarr.shape != outarr.shape:
if (inarr.dtype.kind == outarr.dtype.kind and
inarr.dtype.kind in ('U', 'S') and
inarr.dtype != outarr.dtype):
inarr = inarr.view(outarr.dtype)
# This is a special case to handle input arrays with
# non-trivial TDIMn.
# By design each row of the outarray is 1-D, while each row of
# the input array may be n-D
if outarr.ndim > 1:
# The normal case where the first dimension is the rows
inarr_rowsize = inarr[0].size
inarr = inarr.reshape((n, inarr_rowsize))
outarr[:, :inarr_rowsize] = inarr
else:
# Special case for strings where the out array only has one
# dimension (the second dimension is rolled up into the
# strings
outarr[:n] = inarr.ravel()
else:
outarr[:] = inarr
# Now replace the original column array references with the new
# fields
# This is required to prevent the issue reported in
# https://github.com/spacetelescope/PyFITS/issues/99
for idx in range(len(columns)):
columns._arrays[idx] = data.field(idx)
return data
def __repr__(self):
# Force use of the normal ndarray repr (rather than the new
# one added for recarray in Numpy 1.10) for backwards compat
return np.ndarray.__repr__(self)
def __getitem__(self, key):
if isinstance(key, string_types):
return self.field(key)
elif isinstance(key, (slice, np.ndarray, tuple, list)):
# Have to view as a recarray then back as a FITS_rec, otherwise the
# circular reference fix/hack in FITS_rec.field() won't preserve
# the slice
subtype = type(self)
out = self.view(np.recarray).__getitem__(key).view(subtype)
out._coldefs = ColDefs(self._coldefs)
arrays = []
out._converted = {}
for idx, name in enumerate(self._coldefs.names):
#
# Store the new arrays for the _coldefs object
#
arrays.append(self._coldefs._arrays[idx][key])
# touch all fields to expand the original ._converted dict
# so the sliced FITS_rec will view the same scaled columns as
# the original
dummy = self.field(idx)
if name in self._converted:
out._converted[name] = \
np.ndarray.__getitem__(self._converted[name], key)
del dummy
out._coldefs._arrays = arrays
return out
# if not a slice, do this because Record has no __getstate__.
# also more efficient.
else:
if isinstance(key, int) and key >= len(self):
raise IndexError("Index out of bounds")
newrecord = self._record_type(self, key)
return newrecord
def __setitem__(self, key, value):
if isinstance(key, string_types):
self[key][:] = value
return
if isinstance(key, slice):
end = min(len(self), key.stop or len(self))
end = max(0, end)
start = max(0, key.start or 0)
end = min(end, start + len(value))
for idx in xrange(start, end):
self.__setitem__(idx, value[idx - start])
return
if isinstance(value, FITS_record):
for idx in range(self._nfields):
self.field(self.names[idx])[key] = value.field(self.names[idx])
elif isinstance(value, (tuple, list, np.void)):
if self._nfields == len(value):
for idx in range(self._nfields):
self.field(idx)[key] = value[idx]
else:
raise ValueError('Input tuple or list required to have %s '
'elements.' % self._nfields)
else:
raise TypeError('Assignment requires a FITS_record, tuple, or '
'list as input.')
def __getslice__(self, start, end):
return self[slice(start, end)]
def __setslice__(self, start, end, value):
self[slice(start, end)] = value
[docs] def copy(self, order='C'):
"""
The Numpy documentation lies; `numpy.ndarray.copy` is not equivalent to
`numpy.copy`. Differences include that it re-views the copied array as
self's ndarray subclass, as though it were taking a slice; this means
``__array_finalize__`` is called and the copy shares all the array
attributes (including ``._converted``!). So we need to make a deep
copy of all those attributes so that the two arrays truly do not share
any data.
"""
new = super(FITS_rec, self).copy(order=order)
new.__dict__ = copy.deepcopy(self.__dict__)
return new
@property
def columns(self):
"""
A user-visible accessor for the coldefs.
See https://aeon.stsci.edu/ssb/trac/pyfits/ticket/44
"""
return self._coldefs
@property
def names(self):
"""List of column names."""
if hasattr(self, '_coldefs') and self._coldefs is not None:
return self._coldefs.names
else:
return list(self.dtype.names)
@property
def formats(self):
"""List of column FITS foramts."""
return self._coldefs.formats
[docs] def field(self, key):
"""
A view of a `Column`'s data as an array.
"""
# NOTE: The *column* index may not be the same as the field index in
# the recarray, if the column is a phantom column
column = self.columns[key]
name = column.name
format = column.format
if format.dtype.itemsize == 0:
warnings.warn(
'Field %r has a repeat count of 0 in its format code, '
'indicating an empty field.' % key)
return np.array([], dtype=format.dtype)
# 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 = _get_recarray_field(base, name)
if name not in self._converted:
recformat = format.recformat
# TODO: If we're now passing the column to these subroutines, do we
# really need to pass them the recformat?
if isinstance(recformat, _FormatP):
# for P format
converted = self._convert_p(column, field, recformat)
else:
# Handle all other column data types which are fixed-width
# fields
converted = self._convert_other(column, field, recformat)
self._converted[name] = converted
return converted
return self._converted[name]
def _update_column_attribute_changed(self, column, idx, attr, old_value,
new_value):
"""
Update how the data is formatted depending on changes to column
attributes initiated by the user through the `Column` interface.
Dispatches column attribute change notifications to individual methods
for each attribute ``_update_column_<attr>``
"""
method_name = '_update_column_{0}'.format(attr)
if hasattr(self, method_name):
# Right now this is so we can be lazy and not implement updaters
# for every attribute yet--some we may not need at all, TBD
getattr(self, method_name)(column, idx, old_value, new_value)
def _update_column_name(self, column, idx, old_name, name):
"""Update the dtype field names when a column name is changed."""
dtype = self.dtype
# Updating the names on the dtype should suffice
dtype.names = dtype.names[:idx] + (name,) + dtype.names[idx + 1:]
def _convert_x(self, field, recformat):
"""Convert a raw table column to a bit array as specified by the
FITS X format.
"""
dummy = np.zeros(self.shape + (recformat.repeat,), dtype=np.bool_)
_unwrapx(field, dummy, recformat.repeat)
return dummy
def _convert_p(self, column, field, recformat):
"""Convert a raw table column of FITS P or Q format descriptors
to a VLA column with the array data returned from the heap.
"""
dummy = _VLF([None] * len(self), dtype=recformat.dtype)
raw_data = self._get_raw_data()
if raw_data is None:
raise IOError(
"Could not find heap data for the %r variable-length "
"array column." % column.name)
for idx in range(len(self)):
offset = field[idx, 1] + self._heapoffset
count = field[idx, 0]
if recformat.dtype == 'a':
dt = np.dtype(recformat.dtype + str(1))
arr_len = count * dt.itemsize
da = raw_data[offset:offset + arr_len].view(dt)
da = np.char.array(da.view(dtype=dt), itemsize=count)
dummy[idx] = decode_ascii(da)
else:
dt = np.dtype(recformat.dtype)
arr_len = count * dt.itemsize
dummy[idx] = raw_data[offset:offset + arr_len].view(dt)
dummy[idx].dtype = dummy[idx].dtype.newbyteorder('>')
# Each array in the field may now require additional
# scaling depending on the other scaling parameters
# TODO: The same scaling parameters apply to every
# array in the column so this is currently very slow; we
# really only need to check once whether any scaling will
# be necessary and skip this step if not
# TODO: Test that this works for X format; I don't think
# that it does--the recformat variable only applies to the P
# format not the X format
dummy[idx] = self._convert_other(column, dummy[idx],
recformat)
return dummy
def _convert_ascii(self, column, field):
"""
Special handling for ASCII table columns to convert columns containing
numeric types to actual numeric arrays from the string representation.
"""
format = column.format
recformat = ASCII2NUMPY[format[0]]
# if the string = TNULL, return ASCIITNULL
nullval = str(column.null).strip().encode('ascii')
if len(nullval) > format.width:
nullval = nullval[:format.width]
# Before using .replace make sure that any trailing bytes in each
# column are filled with spaces, and *not*, say, nulls; this causes
# functions like replace to potentially leave gibberish bytes in the
# array buffer.
dummy = np.char.ljust(field, format.width)
dummy = np.char.replace(dummy, encode_ascii('D'), encode_ascii('E'))
null_fill = encode_ascii(str(ASCIITNULL).rjust(format.width))
dummy = np.where(np.char.strip(dummy) == nullval, null_fill, dummy)
try:
dummy = np.array(dummy, dtype=recformat)
except ValueError as exc:
indx = self._coldefs.names.index(column.name)
raise ValueError(
'%s; the header may be missing the necessary TNULL%d '
'keyword or the table contains invalid data' %
(exc, indx + 1))
return dummy
def _convert_other(self, column, field, recformat):
"""Perform conversions on any other fixed-width column data types.
This may not perform any conversion at all if it's not necessary, in
which case the original column array is returned.
"""
if isinstance(recformat, _FormatX):
# special handling for the X format
return self._convert_x(field, recformat)
(_str, _bool, _number, _scale, _zero, bscale, bzero, dim) = \
self._get_scale_factors(column)
indx = self._coldefs.names.index(column.name)
# ASCII table, convert strings to numbers
# TODO:
# For now, check that these are ASCII columns by checking the coldefs
# type; in the future all columns (for binary tables, ASCII tables, or
# otherwise) should "know" what type they are already and how to handle
# converting their data from FITS format to native format and vice
# versa...
if not _str and isinstance(self._coldefs, _AsciiColDefs):
field = self._convert_ascii(column, 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 field.ndim > 1:
actual_shape = field.shape[1:]
if _str:
actual_shape = (field.itemsize,) + actual_shape
else:
actual_shape = field.shape[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 = field.itemsize
elif len(field.shape) == 1: # No repeat count in TFORMn, equivalent to 1
actual_nitems = 1
else:
actual_nitems = field.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
# For now we've made columns responsible for *knowing* whether their
# data has been scaled, but we make the FITS_rec class responsible for
# actually doing the scaling
# TODO: This also needs to be fixed in the effort to make Columns
# responsible for scaling their arrays to/from FITS native values
if not column.ascii and column.format.p_format:
format_code = column.format.p_format
else:
# TODO: Rather than having this if/else it might be nice if the
# ColumnFormat class had an attribute guaranteed to give the format
# of actual values in a column regardless of whether the true
# format is something like P or Q
format_code = column.format.format
if (_number and (_scale or _zero) and not column._physical_values):
# This is to handle pseudo unsigned ints in table columns
# TODO: For now this only really works correctly for binary tables
# Should it work for ASCII tables as well?
if self._uint:
if bzero == 2**15 and format_code == 'I':
field = np.array(field, dtype=np.uint16)
elif bzero == 2**31 and format_code == 'J':
field = np.array(field, dtype=np.uint32)
elif bzero == 2**63 and format_code == 'K':
field = np.array(field, dtype=np.uint64)
bzero64 = np.uint64(2 ** 63)
else:
field = np.array(field, dtype=np.float64)
else:
field = np.array(field, dtype=np.float64)
if _scale:
np.multiply(field, bscale, field)
if _zero:
if self._uint and format_code == 'K':
# There is a chance of overflow, so be careful
test_overflow = field.copy()
try:
test_overflow += bzero64
except OverflowError:
warnings.warn(
"Overflow detected while applying TZERO{0:d}. "
"Returning unscaled data.".format(indx + 1))
else:
field = test_overflow
else:
field += bzero
elif _bool and field.dtype != bool:
field = np.equal(field, ord('T'))
elif _str:
with ignored(UnicodeDecodeError):
field = decode_ascii(field)
if dim:
# Apply the new field item dimensions
nitems = reduce(operator.mul, dim)
if field.ndim > 1:
field = field[:, :nitems]
if _str:
fmt = field.dtype.char
dtype = ('|%s%d' % (fmt, dim[-1]), dim[:-1])
field.dtype = dtype
else:
field.shape = (field.shape[0],) + dim
return field
def _get_heap_data(self):
"""
Returns a pointer into the table's raw data to its heap (if present).
This is returned as a numpy byte array.
"""
if self._heapsize:
raw_data = self._get_raw_data().view(np.ubyte)
heap_end = self._heapoffset + self._heapsize
return raw_data[self._heapoffset:heap_end]
else:
return np.array([], dtype=np.ubyte)
def _get_raw_data(self):
"""
Returns the base array of self that "raw data array" that is the
array in the format that it was first read from a file before it was
sliced or viewed as a different type in any way.
This is determined by walking through the bases until finding one that
has at least the same number of bytes as self, plus the heapsize. This
may be the immediate .base but is not always. This is used primarily
for variable-length array support which needs to be able to find the
heap (the raw data *may* be larger than nbytes + heapsize if it
contains a gap or padding).
May return ``None`` if no array resembling the "raw data" according to
the stated criteria can be found.
"""
raw_data_bytes = self.nbytes + self._heapsize
base = self
while hasattr(base, 'base') and base.base is not None:
base = base.base
if hasattr(base, 'nbytes') and base.nbytes >= raw_data_bytes:
return base
def _get_scale_factors(self, column):
"""Get all the scaling flags and factors for one column."""
# TODO: Maybe this should be a method/property on Column? Or maybe
# it's not really needed at all...
_str = column.format.format == 'A'
_bool = column.format.format == 'L'
_number = not (_bool or _str)
bscale = column.bscale
bzero = column.bzero
_scale = bscale not in ('', None, 1)
_zero = bzero not in ('', None, 0)
# ensure bscale/bzero are numbers
if not _scale:
bscale = 1
if not _zero:
bzero = 0
# column._dims gives a tuple, rather than column.dim which returns the
# original string format code from the FITS header...
dim = column._dims
return (_str, _bool, _number, _scale, _zero, bscale, bzero, dim)
def _scale_back(self, update_heap_pointers=True):
"""
Update the parent array, using the (latest) scaled array.
If ``update_heap_pointers`` is `False`, this will leave all the heap
pointers in P/Q columns as they are verbatim--it only makes sense to do
this if there is already data on the heap and it can be guaranteed that
that data has not been modified, and there is not new data to add to
the heap. Currently this is only used as an optimization for
CompImageHDU that does its own handling of the heap.
"""
# Running total for the new heap size
heapsize = 0
for indx, name in enumerate(self.dtype.names):
column = self._coldefs[indx]
recformat = column.format.recformat
field = _get_recarray_field(self, indx)
# add the location offset of the heap area for each
# variable length column
if isinstance(recformat, _FormatP):
# Irritatingly, this can return a different dtype than just
# doing np.dtype(recformat.dtype); but this returns the results
# that we want. For example if recformat.dtype is 'a' we want
# an array of characters.
dtype = np.array([], dtype=recformat.dtype).dtype
if update_heap_pointers and name in self._converted:
# The VLA has potentially been updated, so we need to
# update the array descriptors
field[:] = 0 # reset
npts = [len(arr) for arr in self._converted[name]]
field[:len(npts), 0] = npts
field[1:, 1] = (np.add.accumulate(field[:-1, 0]) *
dtype.itemsize)
field[:, 1][:] += heapsize
heapsize += field[:, 0].sum() * dtype.itemsize
# Even if this VLA has not been read or updated, we need to
# include the size of its constituent arrays in the heap size
# total
if name not in self._converted:
continue
if isinstance(recformat, _FormatX):
_wrapx(self._converted[name], field, recformat.repeat)
continue
_str, _bool, _number, _scale, _zero, bscale, bzero, _ = \
self._get_scale_factors(column)
# conversion for both ASCII and binary tables
if _number or _str:
if _number and (_scale or _zero) and column._physical_values:
dummy = self._converted[name].copy()
if _zero:
dummy -= bzero
if _scale:
dummy /= bscale
# This will set the raw values in the recarray back to
# their non-physical storage values, so the column should
# be mark is not scaled
column._physical_values = False
elif _str:
dummy = self._converted[name]
elif isinstance(self._coldefs, _AsciiColDefs):
dummy = self._converted[name]
else:
continue
# ASCII table, convert numbers to strings
if isinstance(self._coldefs, _AsciiColDefs):
self._scale_back_ascii(indx, dummy, field)
# binary table
else:
if len(field) and isinstance(field[0], np.integer):
dummy = np.around(dummy)
elif isinstance(field, np.chararray):
# Ensure that blanks at the end of each string are
# converted to nulls instead of spaces, see Trac #15
# and #111
itemsize = dummy.itemsize
if dummy.dtype.kind == 'U':
pad = self._coldefs._padding_byte
else:
pad = self._coldefs._padding_byte.encode('ascii')
for idx in range(len(dummy)):
val = dummy[idx]
dummy[idx] = val + (pad * (itemsize - len(val)))
# Encode *after* handling the padding byte or else
# Numpy will complain about trying to append bytes to
# an array
if dummy.dtype.kind == 'U':
dummy = dummy.encode('ascii')
if field.shape == dummy.shape:
field[:] = dummy
else:
# Reshaping the data is necessary in cases where the
# TDIMn keyword was used to shape a column's entries
# into arrays
field[:] = dummy.ravel().view(field.dtype)
del dummy
# ASCII table does not have Boolean type
elif _bool:
field[:] = np.choose(self._converted[name],
(np.array([ord('F')], dtype=np.int8)[0],
np.array([ord('T')], dtype=np.int8)[0]))
# Store the updated heapsize
self._heapsize = heapsize
def _scale_back_ascii(self, col_idx, input_field, output_field):
"""
Convert internal array values back to ASCII table representation.
The ``input_field`` is the internal representation of the values, and
the ``output_field`` is the character array representing the ASCII
output that will be written.
"""
starts = self._coldefs.starts[:]
spans = self._coldefs.spans
format = self._coldefs.formats[col_idx]
# The the index of the "end" column of the record, beyond
# which we can't write
end = super(FITS_rec, self).field(-1).itemsize
starts.append(end + starts[-1])
if col_idx > 0:
lead = starts[col_idx] - starts[col_idx - 1] - spans[col_idx - 1]
else:
lead = 0
if lead < 0:
warnings.warn('Column %r starting point overlaps the previous '
'column.' % (col_idx + 1))
trail = starts[col_idx + 1] - starts[col_idx] - spans[col_idx]
if trail < 0:
warnings.warn('Column %r ending point overlaps the next '
'column.' % (col_idx + 1))
# TODO: It would be nice if these string column formatting
# details were left to a specialized class, as is the case
# with FormatX and FormatP
if 'A' in format:
_pc = '%-'
else:
_pc = '%'
fmt = ''.join([_pc, format[1:], ASCII2STR[format[0]],
(' ' * trail)])
# Even if the format precision is 0, we should output a decimal point
# as long as there is space to do so--not including a decimal point in
# a float value is discouraged by the FITS Standard
trailing_decimal = (format.precision == 0 and
format.format in ('F', 'E', 'D'))
# not using numarray.strings's num2char because the
# result is not allowed to expand (as C/Python does).
for jdx, value in enumerate(input_field):
value = fmt % value
if len(value) > starts[col_idx + 1] - starts[col_idx]:
raise ValueError(
"Value %r does not fit into the output's itemsize of "
"%s." % (value, spans[col_idx]))
if trailing_decimal and value[0] == ' ':
# We have some extra space in the field for the trailing
# decimal point
value = value[1:] + '.'
output_field[jdx] = value
# Replace exponent separator in floating point numbers
if 'D' in format:
output_field.replace(encode_ascii('E'), encode_ascii('D'))
def _get_recarray_field(array, key):
"""
Compatibility function for using the recarray base class's field method.
This incorporates the legacy functionality of returning string arrays as
Numeric-style chararray objects.
"""
# Numpy >= 1.10.dev recarray no longer returns chararrays for strings
# This is currently needed for backwards-compatibility and for
# automatic truncation of trailing whitespace
field = np.recarray.field(array, key)
if field.dtype.char in ('S', 'U') and not isinstance(field, np.chararray):
field = field.view(np.chararray)
return field