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1093 lines
37 KiB
1093 lines
37 KiB
"""
|
|
Record Arrays
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|
=============
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|
Record arrays expose the fields of structured arrays as properties.
|
|
|
|
Most commonly, ndarrays contain elements of a single type, e.g. floats,
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|
integers, bools etc. However, it is possible for elements to be combinations
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|
of these using structured types, such as::
|
|
|
|
>>> a = np.array([(1, 2.0), (1, 2.0)], dtype=[('x', np.int64), ('y', np.float64)])
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>>> a
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array([(1, 2.), (1, 2.)], dtype=[('x', '<i8'), ('y', '<f8')])
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|
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Here, each element consists of two fields: x (and int), and y (a float).
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This is known as a structured array. The different fields are analogous
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|
to columns in a spread-sheet. The different fields can be accessed as
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one would a dictionary::
|
|
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|
>>> a['x']
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array([1, 1])
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>>> a['y']
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array([2., 2.])
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|
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Record arrays allow us to access fields as properties::
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|
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>>> ar = np.rec.array(a)
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|
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|
>>> ar.x
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array([1, 1])
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>>> ar.y
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array([2., 2.])
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|
"""
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import os
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|
import warnings
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from collections import Counter
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from contextlib import nullcontext
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|
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from . import numeric as sb
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from . import numerictypes as nt
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from numpy.compat import os_fspath
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from numpy.core.overrides import set_module
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from .arrayprint import get_printoptions
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# All of the functions allow formats to be a dtype
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__all__ = [
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'record', 'recarray', 'format_parser',
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'fromarrays', 'fromrecords', 'fromstring', 'fromfile', 'array',
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]
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|
|
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ndarray = sb.ndarray
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|
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_byteorderconv = {'b':'>',
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'l':'<',
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'n':'=',
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'B':'>',
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'L':'<',
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'N':'=',
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'S':'s',
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's':'s',
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'>':'>',
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'<':'<',
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'=':'=',
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'|':'|',
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'I':'|',
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'i':'|'}
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# formats regular expression
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# allows multidimension spec with a tuple syntax in front
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# of the letter code '(2,3)f4' and ' ( 2 , 3 ) f4 '
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# are equally allowed
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|
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numfmt = nt.sctypeDict
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|
|
|
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def find_duplicate(list):
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|
"""Find duplication in a list, return a list of duplicated elements"""
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|
return [
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item
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for item, counts in Counter(list).items()
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if counts > 1
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]
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|
|
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|
@set_module('numpy')
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class format_parser:
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|
"""
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Class to convert formats, names, titles description to a dtype.
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|
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After constructing the format_parser object, the dtype attribute is
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|
the converted data-type:
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``dtype = format_parser(formats, names, titles).dtype``
|
|
|
|
Attributes
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----------
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dtype : dtype
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The converted data-type.
|
|
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Parameters
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----------
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formats : str or list of str
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The format description, either specified as a string with
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comma-separated format descriptions in the form ``'f8, i4, a5'``, or
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a list of format description strings in the form
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``['f8', 'i4', 'a5']``.
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names : str or list/tuple of str
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The field names, either specified as a comma-separated string in the
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form ``'col1, col2, col3'``, or as a list or tuple of strings in the
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|
form ``['col1', 'col2', 'col3']``.
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An empty list can be used, in that case default field names
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('f0', 'f1', ...) are used.
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titles : sequence
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|
Sequence of title strings. An empty list can be used to leave titles
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out.
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aligned : bool, optional
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If True, align the fields by padding as the C-compiler would.
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Default is False.
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byteorder : str, optional
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If specified, all the fields will be changed to the
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provided byte-order. Otherwise, the default byte-order is
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used. For all available string specifiers, see `dtype.newbyteorder`.
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|
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|
See Also
|
|
--------
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|
dtype, typename, sctype2char
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|
|
|
Examples
|
|
--------
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|
>>> np.format_parser(['<f8', '<i4', '<a5'], ['col1', 'col2', 'col3'],
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... ['T1', 'T2', 'T3']).dtype
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dtype([(('T1', 'col1'), '<f8'), (('T2', 'col2'), '<i4'), (('T3', 'col3'), 'S5')])
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|
|
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`names` and/or `titles` can be empty lists. If `titles` is an empty list,
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titles will simply not appear. If `names` is empty, default field names
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will be used.
|
|
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>>> np.format_parser(['f8', 'i4', 'a5'], ['col1', 'col2', 'col3'],
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... []).dtype
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dtype([('col1', '<f8'), ('col2', '<i4'), ('col3', '<S5')])
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>>> np.format_parser(['<f8', '<i4', '<a5'], [], []).dtype
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dtype([('f0', '<f8'), ('f1', '<i4'), ('f2', 'S5')])
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|
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|
"""
|
|
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|
def __init__(self, formats, names, titles, aligned=False, byteorder=None):
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self._parseFormats(formats, aligned)
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self._setfieldnames(names, titles)
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self._createdtype(byteorder)
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|
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|
def _parseFormats(self, formats, aligned=False):
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|
""" Parse the field formats """
|
|
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|
if formats is None:
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raise ValueError("Need formats argument")
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|
if isinstance(formats, list):
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dtype = sb.dtype(
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[('f{}'.format(i), format_) for i, format_ in enumerate(formats)],
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aligned,
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)
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|
else:
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dtype = sb.dtype(formats, aligned)
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fields = dtype.fields
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|
if fields is None:
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|
dtype = sb.dtype([('f1', dtype)], aligned)
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fields = dtype.fields
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keys = dtype.names
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|
self._f_formats = [fields[key][0] for key in keys]
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self._offsets = [fields[key][1] for key in keys]
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self._nfields = len(keys)
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def _setfieldnames(self, names, titles):
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|
"""convert input field names into a list and assign to the _names
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|
attribute """
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|
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|
if names:
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|
if type(names) in [list, tuple]:
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|
pass
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|
elif isinstance(names, str):
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|
names = names.split(',')
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|
else:
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raise NameError("illegal input names %s" % repr(names))
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|
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|
self._names = [n.strip() for n in names[:self._nfields]]
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else:
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self._names = []
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|
# if the names are not specified, they will be assigned as
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# "f0, f1, f2,..."
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# if not enough names are specified, they will be assigned as "f[n],
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# f[n+1],..." etc. where n is the number of specified names..."
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self._names += ['f%d' % i for i in range(len(self._names),
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self._nfields)]
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# check for redundant names
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_dup = find_duplicate(self._names)
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if _dup:
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raise ValueError("Duplicate field names: %s" % _dup)
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if titles:
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self._titles = [n.strip() for n in titles[:self._nfields]]
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else:
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self._titles = []
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titles = []
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if self._nfields > len(titles):
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self._titles += [None] * (self._nfields - len(titles))
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|
|
|
def _createdtype(self, byteorder):
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dtype = sb.dtype({
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'names': self._names,
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'formats': self._f_formats,
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'offsets': self._offsets,
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|
'titles': self._titles,
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})
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if byteorder is not None:
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byteorder = _byteorderconv[byteorder[0]]
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dtype = dtype.newbyteorder(byteorder)
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|
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|
self.dtype = dtype
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|
|
|
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|
class record(nt.void):
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|
"""A data-type scalar that allows field access as attribute lookup.
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|
"""
|
|
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|
# manually set name and module so that this class's type shows up
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|
# as numpy.record when printed
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|
__name__ = 'record'
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|
__module__ = 'numpy'
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|
|
|
def __repr__(self):
|
|
if get_printoptions()['legacy'] == '1.13':
|
|
return self.__str__()
|
|
return super().__repr__()
|
|
|
|
def __str__(self):
|
|
if get_printoptions()['legacy'] == '1.13':
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|
return str(self.item())
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|
return super().__str__()
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|
|
|
def __getattribute__(self, attr):
|
|
if attr in ('setfield', 'getfield', 'dtype'):
|
|
return nt.void.__getattribute__(self, attr)
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|
try:
|
|
return nt.void.__getattribute__(self, attr)
|
|
except AttributeError:
|
|
pass
|
|
fielddict = nt.void.__getattribute__(self, 'dtype').fields
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|
res = fielddict.get(attr, None)
|
|
if res:
|
|
obj = self.getfield(*res[:2])
|
|
# if it has fields return a record,
|
|
# otherwise return the object
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|
try:
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|
dt = obj.dtype
|
|
except AttributeError:
|
|
#happens if field is Object type
|
|
return obj
|
|
if dt.names is not None:
|
|
return obj.view((self.__class__, obj.dtype))
|
|
return obj
|
|
else:
|
|
raise AttributeError("'record' object has no "
|
|
"attribute '%s'" % attr)
|
|
|
|
def __setattr__(self, attr, val):
|
|
if attr in ('setfield', 'getfield', 'dtype'):
|
|
raise AttributeError("Cannot set '%s' attribute" % attr)
|
|
fielddict = nt.void.__getattribute__(self, 'dtype').fields
|
|
res = fielddict.get(attr, None)
|
|
if res:
|
|
return self.setfield(val, *res[:2])
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|
else:
|
|
if getattr(self, attr, None):
|
|
return nt.void.__setattr__(self, attr, val)
|
|
else:
|
|
raise AttributeError("'record' object has no "
|
|
"attribute '%s'" % attr)
|
|
|
|
def __getitem__(self, indx):
|
|
obj = nt.void.__getitem__(self, indx)
|
|
|
|
# copy behavior of record.__getattribute__,
|
|
if isinstance(obj, nt.void) and obj.dtype.names is not None:
|
|
return obj.view((self.__class__, obj.dtype))
|
|
else:
|
|
# return a single element
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|
return obj
|
|
|
|
def pprint(self):
|
|
"""Pretty-print all fields."""
|
|
# pretty-print all fields
|
|
names = self.dtype.names
|
|
maxlen = max(len(name) for name in names)
|
|
fmt = '%% %ds: %%s' % maxlen
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|
rows = [fmt % (name, getattr(self, name)) for name in names]
|
|
return "\n".join(rows)
|
|
|
|
# The recarray is almost identical to a standard array (which supports
|
|
# named fields already) The biggest difference is that it can use
|
|
# attribute-lookup to find the fields and it is constructed using
|
|
# a record.
|
|
|
|
# If byteorder is given it forces a particular byteorder on all
|
|
# the fields (and any subfields)
|
|
|
|
class recarray(ndarray):
|
|
"""Construct an ndarray that allows field access using attributes.
|
|
|
|
Arrays may have a data-types containing fields, analogous
|
|
to columns in a spread sheet. An example is ``[(x, int), (y, float)]``,
|
|
where each entry in the array is a pair of ``(int, float)``. Normally,
|
|
these attributes are accessed using dictionary lookups such as ``arr['x']``
|
|
and ``arr['y']``. Record arrays allow the fields to be accessed as members
|
|
of the array, using ``arr.x`` and ``arr.y``.
|
|
|
|
Parameters
|
|
----------
|
|
shape : tuple
|
|
Shape of output array.
|
|
dtype : data-type, optional
|
|
The desired data-type. By default, the data-type is determined
|
|
from `formats`, `names`, `titles`, `aligned` and `byteorder`.
|
|
formats : list of data-types, optional
|
|
A list containing the data-types for the different columns, e.g.
|
|
``['i4', 'f8', 'i4']``. `formats` does *not* support the new
|
|
convention of using types directly, i.e. ``(int, float, int)``.
|
|
Note that `formats` must be a list, not a tuple.
|
|
Given that `formats` is somewhat limited, we recommend specifying
|
|
`dtype` instead.
|
|
names : tuple of str, optional
|
|
The name of each column, e.g. ``('x', 'y', 'z')``.
|
|
buf : buffer, optional
|
|
By default, a new array is created of the given shape and data-type.
|
|
If `buf` is specified and is an object exposing the buffer interface,
|
|
the array will use the memory from the existing buffer. In this case,
|
|
the `offset` and `strides` keywords are available.
|
|
|
|
Other Parameters
|
|
----------------
|
|
titles : tuple of str, optional
|
|
Aliases for column names. For example, if `names` were
|
|
``('x', 'y', 'z')`` and `titles` is
|
|
``('x_coordinate', 'y_coordinate', 'z_coordinate')``, then
|
|
``arr['x']`` is equivalent to both ``arr.x`` and ``arr.x_coordinate``.
|
|
byteorder : {'<', '>', '='}, optional
|
|
Byte-order for all fields.
|
|
aligned : bool, optional
|
|
Align the fields in memory as the C-compiler would.
|
|
strides : tuple of ints, optional
|
|
Buffer (`buf`) is interpreted according to these strides (strides
|
|
define how many bytes each array element, row, column, etc.
|
|
occupy in memory).
|
|
offset : int, optional
|
|
Start reading buffer (`buf`) from this offset onwards.
|
|
order : {'C', 'F'}, optional
|
|
Row-major (C-style) or column-major (Fortran-style) order.
|
|
|
|
Returns
|
|
-------
|
|
rec : recarray
|
|
Empty array of the given shape and type.
|
|
|
|
See Also
|
|
--------
|
|
core.records.fromrecords : Construct a record array from data.
|
|
record : fundamental data-type for `recarray`.
|
|
format_parser : determine a data-type from formats, names, titles.
|
|
|
|
Notes
|
|
-----
|
|
This constructor can be compared to ``empty``: it creates a new record
|
|
array but does not fill it with data. To create a record array from data,
|
|
use one of the following methods:
|
|
|
|
1. Create a standard ndarray and convert it to a record array,
|
|
using ``arr.view(np.recarray)``
|
|
2. Use the `buf` keyword.
|
|
3. Use `np.rec.fromrecords`.
|
|
|
|
Examples
|
|
--------
|
|
Create an array with two fields, ``x`` and ``y``:
|
|
|
|
>>> x = np.array([(1.0, 2), (3.0, 4)], dtype=[('x', '<f8'), ('y', '<i8')])
|
|
>>> x
|
|
array([(1., 2), (3., 4)], dtype=[('x', '<f8'), ('y', '<i8')])
|
|
|
|
>>> x['x']
|
|
array([1., 3.])
|
|
|
|
View the array as a record array:
|
|
|
|
>>> x = x.view(np.recarray)
|
|
|
|
>>> x.x
|
|
array([1., 3.])
|
|
|
|
>>> x.y
|
|
array([2, 4])
|
|
|
|
Create a new, empty record array:
|
|
|
|
>>> np.recarray((2,),
|
|
... dtype=[('x', int), ('y', float), ('z', int)]) #doctest: +SKIP
|
|
rec.array([(-1073741821, 1.2249118382103472e-301, 24547520),
|
|
(3471280, 1.2134086255804012e-316, 0)],
|
|
dtype=[('x', '<i4'), ('y', '<f8'), ('z', '<i4')])
|
|
|
|
"""
|
|
|
|
# manually set name and module so that this class's type shows
|
|
# up as "numpy.recarray" when printed
|
|
__name__ = 'recarray'
|
|
__module__ = 'numpy'
|
|
|
|
def __new__(subtype, shape, dtype=None, buf=None, offset=0, strides=None,
|
|
formats=None, names=None, titles=None,
|
|
byteorder=None, aligned=False, order='C'):
|
|
|
|
if dtype is not None:
|
|
descr = sb.dtype(dtype)
|
|
else:
|
|
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
|
|
|
|
if buf is None:
|
|
self = ndarray.__new__(subtype, shape, (record, descr), order=order)
|
|
else:
|
|
self = ndarray.__new__(subtype, shape, (record, descr),
|
|
buffer=buf, offset=offset,
|
|
strides=strides, order=order)
|
|
return self
|
|
|
|
def __array_finalize__(self, obj):
|
|
if self.dtype.type is not record and self.dtype.names is not None:
|
|
# if self.dtype is not np.record, invoke __setattr__ which will
|
|
# convert it to a record if it is a void dtype.
|
|
self.dtype = self.dtype
|
|
|
|
def __getattribute__(self, attr):
|
|
# See if ndarray has this attr, and return it if so. (note that this
|
|
# means a field with the same name as an ndarray attr cannot be
|
|
# accessed by attribute).
|
|
try:
|
|
return object.__getattribute__(self, attr)
|
|
except AttributeError: # attr must be a fieldname
|
|
pass
|
|
|
|
# look for a field with this name
|
|
fielddict = ndarray.__getattribute__(self, 'dtype').fields
|
|
try:
|
|
res = fielddict[attr][:2]
|
|
except (TypeError, KeyError) as e:
|
|
raise AttributeError("recarray has no attribute %s" % attr) from e
|
|
obj = self.getfield(*res)
|
|
|
|
# At this point obj will always be a recarray, since (see
|
|
# PyArray_GetField) the type of obj is inherited. Next, if obj.dtype is
|
|
# non-structured, convert it to an ndarray. Then if obj is structured
|
|
# with void type convert it to the same dtype.type (eg to preserve
|
|
# numpy.record type if present), since nested structured fields do not
|
|
# inherit type. Don't do this for non-void structures though.
|
|
if obj.dtype.names is not None:
|
|
if issubclass(obj.dtype.type, nt.void):
|
|
return obj.view(dtype=(self.dtype.type, obj.dtype))
|
|
return obj
|
|
else:
|
|
return obj.view(ndarray)
|
|
|
|
# Save the dictionary.
|
|
# If the attr is a field name and not in the saved dictionary
|
|
# Undo any "setting" of the attribute and do a setfield
|
|
# Thus, you can't create attributes on-the-fly that are field names.
|
|
def __setattr__(self, attr, val):
|
|
|
|
# Automatically convert (void) structured types to records
|
|
# (but not non-void structures, subarrays, or non-structured voids)
|
|
if attr == 'dtype' and issubclass(val.type, nt.void) and val.names is not None:
|
|
val = sb.dtype((record, val))
|
|
|
|
newattr = attr not in self.__dict__
|
|
try:
|
|
ret = object.__setattr__(self, attr, val)
|
|
except Exception:
|
|
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
|
|
if attr not in fielddict:
|
|
raise
|
|
else:
|
|
fielddict = ndarray.__getattribute__(self, 'dtype').fields or {}
|
|
if attr not in fielddict:
|
|
return ret
|
|
if newattr:
|
|
# We just added this one or this setattr worked on an
|
|
# internal attribute.
|
|
try:
|
|
object.__delattr__(self, attr)
|
|
except Exception:
|
|
return ret
|
|
try:
|
|
res = fielddict[attr][:2]
|
|
except (TypeError, KeyError) as e:
|
|
raise AttributeError(
|
|
"record array has no attribute %s" % attr
|
|
) from e
|
|
return self.setfield(val, *res)
|
|
|
|
def __getitem__(self, indx):
|
|
obj = super().__getitem__(indx)
|
|
|
|
# copy behavior of getattr, except that here
|
|
# we might also be returning a single element
|
|
if isinstance(obj, ndarray):
|
|
if obj.dtype.names is not None:
|
|
obj = obj.view(type(self))
|
|
if issubclass(obj.dtype.type, nt.void):
|
|
return obj.view(dtype=(self.dtype.type, obj.dtype))
|
|
return obj
|
|
else:
|
|
return obj.view(type=ndarray)
|
|
else:
|
|
# return a single element
|
|
return obj
|
|
|
|
def __repr__(self):
|
|
|
|
repr_dtype = self.dtype
|
|
if self.dtype.type is record or not issubclass(self.dtype.type, nt.void):
|
|
# If this is a full record array (has numpy.record dtype),
|
|
# or if it has a scalar (non-void) dtype with no records,
|
|
# represent it using the rec.array function. Since rec.array
|
|
# converts dtype to a numpy.record for us, convert back
|
|
# to non-record before printing
|
|
if repr_dtype.type is record:
|
|
repr_dtype = sb.dtype((nt.void, repr_dtype))
|
|
prefix = "rec.array("
|
|
fmt = 'rec.array(%s,%sdtype=%s)'
|
|
else:
|
|
# otherwise represent it using np.array plus a view
|
|
# This should only happen if the user is playing
|
|
# strange games with dtypes.
|
|
prefix = "array("
|
|
fmt = 'array(%s,%sdtype=%s).view(numpy.recarray)'
|
|
|
|
# get data/shape string. logic taken from numeric.array_repr
|
|
if self.size > 0 or self.shape == (0,):
|
|
lst = sb.array2string(
|
|
self, separator=', ', prefix=prefix, suffix=',')
|
|
else:
|
|
# show zero-length shape unless it is (0,)
|
|
lst = "[], shape=%s" % (repr(self.shape),)
|
|
|
|
lf = '\n'+' '*len(prefix)
|
|
if get_printoptions()['legacy'] == '1.13':
|
|
lf = ' ' + lf # trailing space
|
|
return fmt % (lst, lf, repr_dtype)
|
|
|
|
def field(self, attr, val=None):
|
|
if isinstance(attr, int):
|
|
names = ndarray.__getattribute__(self, 'dtype').names
|
|
attr = names[attr]
|
|
|
|
fielddict = ndarray.__getattribute__(self, 'dtype').fields
|
|
|
|
res = fielddict[attr][:2]
|
|
|
|
if val is None:
|
|
obj = self.getfield(*res)
|
|
if obj.dtype.names is not None:
|
|
return obj
|
|
return obj.view(ndarray)
|
|
else:
|
|
return self.setfield(val, *res)
|
|
|
|
|
|
def _deprecate_shape_0_as_None(shape):
|
|
if shape == 0:
|
|
warnings.warn(
|
|
"Passing `shape=0` to have the shape be inferred is deprecated, "
|
|
"and in future will be equivalent to `shape=(0,)`. To infer "
|
|
"the shape and suppress this warning, pass `shape=None` instead.",
|
|
FutureWarning, stacklevel=3)
|
|
return None
|
|
else:
|
|
return shape
|
|
|
|
|
|
def fromarrays(arrayList, dtype=None, shape=None, formats=None,
|
|
names=None, titles=None, aligned=False, byteorder=None):
|
|
"""Create a record array from a (flat) list of arrays
|
|
|
|
Parameters
|
|
----------
|
|
arrayList : list or tuple
|
|
List of array-like objects (such as lists, tuples,
|
|
and ndarrays).
|
|
dtype : data-type, optional
|
|
valid dtype for all arrays
|
|
shape : int or tuple of ints, optional
|
|
Shape of the resulting array. If not provided, inferred from
|
|
``arrayList[0]``.
|
|
formats, names, titles, aligned, byteorder :
|
|
If `dtype` is ``None``, these arguments are passed to
|
|
`numpy.format_parser` to construct a dtype. See that function for
|
|
detailed documentation.
|
|
|
|
Returns
|
|
-------
|
|
np.recarray
|
|
Record array consisting of given arrayList columns.
|
|
|
|
Examples
|
|
--------
|
|
>>> x1=np.array([1,2,3,4])
|
|
>>> x2=np.array(['a','dd','xyz','12'])
|
|
>>> x3=np.array([1.1,2,3,4])
|
|
>>> r = np.core.records.fromarrays([x1,x2,x3],names='a,b,c')
|
|
>>> print(r[1])
|
|
(2, 'dd', 2.0) # may vary
|
|
>>> x1[1]=34
|
|
>>> r.a
|
|
array([1, 2, 3, 4])
|
|
|
|
>>> x1 = np.array([1, 2, 3, 4])
|
|
>>> x2 = np.array(['a', 'dd', 'xyz', '12'])
|
|
>>> x3 = np.array([1.1, 2, 3,4])
|
|
>>> r = np.core.records.fromarrays(
|
|
... [x1, x2, x3],
|
|
... dtype=np.dtype([('a', np.int32), ('b', 'S3'), ('c', np.float32)]))
|
|
>>> r
|
|
rec.array([(1, b'a', 1.1), (2, b'dd', 2. ), (3, b'xyz', 3. ),
|
|
(4, b'12', 4. )],
|
|
dtype=[('a', '<i4'), ('b', 'S3'), ('c', '<f4')])
|
|
"""
|
|
|
|
arrayList = [sb.asarray(x) for x in arrayList]
|
|
|
|
# NumPy 1.19.0, 2020-01-01
|
|
shape = _deprecate_shape_0_as_None(shape)
|
|
|
|
if shape is None:
|
|
shape = arrayList[0].shape
|
|
elif isinstance(shape, int):
|
|
shape = (shape,)
|
|
|
|
if formats is None and dtype is None:
|
|
# go through each object in the list to see if it is an ndarray
|
|
# and determine the formats.
|
|
formats = [obj.dtype for obj in arrayList]
|
|
|
|
if dtype is not None:
|
|
descr = sb.dtype(dtype)
|
|
else:
|
|
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
|
|
_names = descr.names
|
|
|
|
# Determine shape from data-type.
|
|
if len(descr) != len(arrayList):
|
|
raise ValueError("mismatch between the number of fields "
|
|
"and the number of arrays")
|
|
|
|
d0 = descr[0].shape
|
|
nn = len(d0)
|
|
if nn > 0:
|
|
shape = shape[:-nn]
|
|
|
|
for k, obj in enumerate(arrayList):
|
|
nn = descr[k].ndim
|
|
testshape = obj.shape[:obj.ndim - nn]
|
|
if testshape != shape:
|
|
raise ValueError("array-shape mismatch in array %d" % k)
|
|
|
|
_array = recarray(shape, descr)
|
|
|
|
# populate the record array (makes a copy)
|
|
for i in range(len(arrayList)):
|
|
_array[_names[i]] = arrayList[i]
|
|
|
|
return _array
|
|
|
|
def fromrecords(recList, dtype=None, shape=None, formats=None, names=None,
|
|
titles=None, aligned=False, byteorder=None):
|
|
"""Create a recarray from a list of records in text form.
|
|
|
|
Parameters
|
|
----------
|
|
recList : sequence
|
|
data in the same field may be heterogeneous - they will be promoted
|
|
to the highest data type.
|
|
dtype : data-type, optional
|
|
valid dtype for all arrays
|
|
shape : int or tuple of ints, optional
|
|
shape of each array.
|
|
formats, names, titles, aligned, byteorder :
|
|
If `dtype` is ``None``, these arguments are passed to
|
|
`numpy.format_parser` to construct a dtype. See that function for
|
|
detailed documentation.
|
|
|
|
If both `formats` and `dtype` are None, then this will auto-detect
|
|
formats. Use list of tuples rather than list of lists for faster
|
|
processing.
|
|
|
|
Returns
|
|
-------
|
|
np.recarray
|
|
record array consisting of given recList rows.
|
|
|
|
Examples
|
|
--------
|
|
>>> r=np.core.records.fromrecords([(456,'dbe',1.2),(2,'de',1.3)],
|
|
... names='col1,col2,col3')
|
|
>>> print(r[0])
|
|
(456, 'dbe', 1.2)
|
|
>>> r.col1
|
|
array([456, 2])
|
|
>>> r.col2
|
|
array(['dbe', 'de'], dtype='<U3')
|
|
>>> import pickle
|
|
>>> pickle.loads(pickle.dumps(r))
|
|
rec.array([(456, 'dbe', 1.2), ( 2, 'de', 1.3)],
|
|
dtype=[('col1', '<i8'), ('col2', '<U3'), ('col3', '<f8')])
|
|
"""
|
|
|
|
if formats is None and dtype is None: # slower
|
|
obj = sb.array(recList, dtype=object)
|
|
arrlist = [sb.array(obj[..., i].tolist()) for i in range(obj.shape[-1])]
|
|
return fromarrays(arrlist, formats=formats, shape=shape, names=names,
|
|
titles=titles, aligned=aligned, byteorder=byteorder)
|
|
|
|
if dtype is not None:
|
|
descr = sb.dtype((record, dtype))
|
|
else:
|
|
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
|
|
|
|
try:
|
|
retval = sb.array(recList, dtype=descr)
|
|
except (TypeError, ValueError):
|
|
# NumPy 1.19.0, 2020-01-01
|
|
shape = _deprecate_shape_0_as_None(shape)
|
|
if shape is None:
|
|
shape = len(recList)
|
|
if isinstance(shape, int):
|
|
shape = (shape,)
|
|
if len(shape) > 1:
|
|
raise ValueError("Can only deal with 1-d array.")
|
|
_array = recarray(shape, descr)
|
|
for k in range(_array.size):
|
|
_array[k] = tuple(recList[k])
|
|
# list of lists instead of list of tuples ?
|
|
# 2018-02-07, 1.14.1
|
|
warnings.warn(
|
|
"fromrecords expected a list of tuples, may have received a list "
|
|
"of lists instead. In the future that will raise an error",
|
|
FutureWarning, stacklevel=2)
|
|
return _array
|
|
else:
|
|
if shape is not None and retval.shape != shape:
|
|
retval.shape = shape
|
|
|
|
res = retval.view(recarray)
|
|
|
|
return res
|
|
|
|
|
|
def fromstring(datastring, dtype=None, shape=None, offset=0, formats=None,
|
|
names=None, titles=None, aligned=False, byteorder=None):
|
|
r"""Create a record array from binary data
|
|
|
|
Note that despite the name of this function it does not accept `str`
|
|
instances.
|
|
|
|
Parameters
|
|
----------
|
|
datastring : bytes-like
|
|
Buffer of binary data
|
|
dtype : data-type, optional
|
|
Valid dtype for all arrays
|
|
shape : int or tuple of ints, optional
|
|
Shape of each array.
|
|
offset : int, optional
|
|
Position in the buffer to start reading from.
|
|
formats, names, titles, aligned, byteorder :
|
|
If `dtype` is ``None``, these arguments are passed to
|
|
`numpy.format_parser` to construct a dtype. See that function for
|
|
detailed documentation.
|
|
|
|
|
|
Returns
|
|
-------
|
|
np.recarray
|
|
Record array view into the data in datastring. This will be readonly
|
|
if `datastring` is readonly.
|
|
|
|
See Also
|
|
--------
|
|
numpy.frombuffer
|
|
|
|
Examples
|
|
--------
|
|
>>> a = b'\x01\x02\x03abc'
|
|
>>> np.core.records.fromstring(a, dtype='u1,u1,u1,S3')
|
|
rec.array([(1, 2, 3, b'abc')],
|
|
dtype=[('f0', 'u1'), ('f1', 'u1'), ('f2', 'u1'), ('f3', 'S3')])
|
|
|
|
>>> grades_dtype = [('Name', (np.str_, 10)), ('Marks', np.float64),
|
|
... ('GradeLevel', np.int32)]
|
|
>>> grades_array = np.array([('Sam', 33.3, 3), ('Mike', 44.4, 5),
|
|
... ('Aadi', 66.6, 6)], dtype=grades_dtype)
|
|
>>> np.core.records.fromstring(grades_array.tobytes(), dtype=grades_dtype)
|
|
rec.array([('Sam', 33.3, 3), ('Mike', 44.4, 5), ('Aadi', 66.6, 6)],
|
|
dtype=[('Name', '<U10'), ('Marks', '<f8'), ('GradeLevel', '<i4')])
|
|
|
|
>>> s = '\x01\x02\x03abc'
|
|
>>> np.core.records.fromstring(s, dtype='u1,u1,u1,S3')
|
|
Traceback (most recent call last)
|
|
...
|
|
TypeError: a bytes-like object is required, not 'str'
|
|
"""
|
|
|
|
if dtype is None and formats is None:
|
|
raise TypeError("fromstring() needs a 'dtype' or 'formats' argument")
|
|
|
|
if dtype is not None:
|
|
descr = sb.dtype(dtype)
|
|
else:
|
|
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
|
|
|
|
itemsize = descr.itemsize
|
|
|
|
# NumPy 1.19.0, 2020-01-01
|
|
shape = _deprecate_shape_0_as_None(shape)
|
|
|
|
if shape in (None, -1):
|
|
shape = (len(datastring) - offset) // itemsize
|
|
|
|
_array = recarray(shape, descr, buf=datastring, offset=offset)
|
|
return _array
|
|
|
|
def get_remaining_size(fd):
|
|
pos = fd.tell()
|
|
try:
|
|
fd.seek(0, 2)
|
|
return fd.tell() - pos
|
|
finally:
|
|
fd.seek(pos, 0)
|
|
|
|
def fromfile(fd, dtype=None, shape=None, offset=0, formats=None,
|
|
names=None, titles=None, aligned=False, byteorder=None):
|
|
"""Create an array from binary file data
|
|
|
|
Parameters
|
|
----------
|
|
fd : str or file type
|
|
If file is a string or a path-like object then that file is opened,
|
|
else it is assumed to be a file object. The file object must
|
|
support random access (i.e. it must have tell and seek methods).
|
|
dtype : data-type, optional
|
|
valid dtype for all arrays
|
|
shape : int or tuple of ints, optional
|
|
shape of each array.
|
|
offset : int, optional
|
|
Position in the file to start reading from.
|
|
formats, names, titles, aligned, byteorder :
|
|
If `dtype` is ``None``, these arguments are passed to
|
|
`numpy.format_parser` to construct a dtype. See that function for
|
|
detailed documentation
|
|
|
|
Returns
|
|
-------
|
|
np.recarray
|
|
record array consisting of data enclosed in file.
|
|
|
|
Examples
|
|
--------
|
|
>>> from tempfile import TemporaryFile
|
|
>>> a = np.empty(10,dtype='f8,i4,a5')
|
|
>>> a[5] = (0.5,10,'abcde')
|
|
>>>
|
|
>>> fd=TemporaryFile()
|
|
>>> a = a.newbyteorder('<')
|
|
>>> a.tofile(fd)
|
|
>>>
|
|
>>> _ = fd.seek(0)
|
|
>>> r=np.core.records.fromfile(fd, formats='f8,i4,a5', shape=10,
|
|
... byteorder='<')
|
|
>>> print(r[5])
|
|
(0.5, 10, 'abcde')
|
|
>>> r.shape
|
|
(10,)
|
|
"""
|
|
|
|
if dtype is None and formats is None:
|
|
raise TypeError("fromfile() needs a 'dtype' or 'formats' argument")
|
|
|
|
# NumPy 1.19.0, 2020-01-01
|
|
shape = _deprecate_shape_0_as_None(shape)
|
|
|
|
if shape is None:
|
|
shape = (-1,)
|
|
elif isinstance(shape, int):
|
|
shape = (shape,)
|
|
|
|
if hasattr(fd, 'readinto'):
|
|
# GH issue 2504. fd supports io.RawIOBase or io.BufferedIOBase interface.
|
|
# Example of fd: gzip, BytesIO, BufferedReader
|
|
# file already opened
|
|
ctx = nullcontext(fd)
|
|
else:
|
|
# open file
|
|
ctx = open(os_fspath(fd), 'rb')
|
|
|
|
with ctx as fd:
|
|
if offset > 0:
|
|
fd.seek(offset, 1)
|
|
size = get_remaining_size(fd)
|
|
|
|
if dtype is not None:
|
|
descr = sb.dtype(dtype)
|
|
else:
|
|
descr = format_parser(formats, names, titles, aligned, byteorder).dtype
|
|
|
|
itemsize = descr.itemsize
|
|
|
|
shapeprod = sb.array(shape).prod(dtype=nt.intp)
|
|
shapesize = shapeprod * itemsize
|
|
if shapesize < 0:
|
|
shape = list(shape)
|
|
shape[shape.index(-1)] = size // -shapesize
|
|
shape = tuple(shape)
|
|
shapeprod = sb.array(shape).prod(dtype=nt.intp)
|
|
|
|
nbytes = shapeprod * itemsize
|
|
|
|
if nbytes > size:
|
|
raise ValueError(
|
|
"Not enough bytes left in file for specified shape and type")
|
|
|
|
# create the array
|
|
_array = recarray(shape, descr)
|
|
nbytesread = fd.readinto(_array.data)
|
|
if nbytesread != nbytes:
|
|
raise IOError("Didn't read as many bytes as expected")
|
|
|
|
return _array
|
|
|
|
def array(obj, dtype=None, shape=None, offset=0, strides=None, formats=None,
|
|
names=None, titles=None, aligned=False, byteorder=None, copy=True):
|
|
"""
|
|
Construct a record array from a wide-variety of objects.
|
|
|
|
A general-purpose record array constructor that dispatches to the
|
|
appropriate `recarray` creation function based on the inputs (see Notes).
|
|
|
|
Parameters
|
|
----------
|
|
obj : any
|
|
Input object. See Notes for details on how various input types are
|
|
treated.
|
|
dtype : data-type, optional
|
|
Valid dtype for array.
|
|
shape : int or tuple of ints, optional
|
|
Shape of each array.
|
|
offset : int, optional
|
|
Position in the file or buffer to start reading from.
|
|
strides : tuple of ints, optional
|
|
Buffer (`buf`) is interpreted according to these strides (strides
|
|
define how many bytes each array element, row, column, etc.
|
|
occupy in memory).
|
|
formats, names, titles, aligned, byteorder :
|
|
If `dtype` is ``None``, these arguments are passed to
|
|
`numpy.format_parser` to construct a dtype. See that function for
|
|
detailed documentation.
|
|
copy : bool, optional
|
|
Whether to copy the input object (True), or to use a reference instead.
|
|
This option only applies when the input is an ndarray or recarray.
|
|
Defaults to True.
|
|
|
|
Returns
|
|
-------
|
|
np.recarray
|
|
Record array created from the specified object.
|
|
|
|
Notes
|
|
-----
|
|
If `obj` is ``None``, then call the `~numpy.recarray` constructor. If
|
|
`obj` is a string, then call the `fromstring` constructor. If `obj` is a
|
|
list or a tuple, then if the first object is an `~numpy.ndarray`, call
|
|
`fromarrays`, otherwise call `fromrecords`. If `obj` is a
|
|
`~numpy.recarray`, then make a copy of the data in the recarray
|
|
(if ``copy=True``) and use the new formats, names, and titles. If `obj`
|
|
is a file, then call `fromfile`. Finally, if obj is an `ndarray`, then
|
|
return ``obj.view(recarray)``, making a copy of the data if ``copy=True``.
|
|
|
|
Examples
|
|
--------
|
|
>>> a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
|
|
array([[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]])
|
|
|
|
>>> np.core.records.array(a)
|
|
rec.array([[1, 2, 3],
|
|
[4, 5, 6],
|
|
[7, 8, 9]],
|
|
dtype=int32)
|
|
|
|
>>> b = [(1, 1), (2, 4), (3, 9)]
|
|
>>> c = np.core.records.array(b, formats = ['i2', 'f2'], names = ('x', 'y'))
|
|
>>> c
|
|
rec.array([(1, 1.0), (2, 4.0), (3, 9.0)],
|
|
dtype=[('x', '<i2'), ('y', '<f2')])
|
|
|
|
>>> c.x
|
|
rec.array([1, 2, 3], dtype=int16)
|
|
|
|
>>> c.y
|
|
rec.array([ 1.0, 4.0, 9.0], dtype=float16)
|
|
|
|
>>> r = np.rec.array(['abc','def'], names=['col1','col2'])
|
|
>>> print(r.col1)
|
|
abc
|
|
|
|
>>> r.col1
|
|
array('abc', dtype='<U3')
|
|
|
|
>>> r.col2
|
|
array('def', dtype='<U3')
|
|
"""
|
|
|
|
if ((isinstance(obj, (type(None), str)) or hasattr(obj, 'readinto')) and
|
|
formats is None and dtype is None):
|
|
raise ValueError("Must define formats (or dtype) if object is "
|
|
"None, string, or an open file")
|
|
|
|
kwds = {}
|
|
if dtype is not None:
|
|
dtype = sb.dtype(dtype)
|
|
elif formats is not None:
|
|
dtype = format_parser(formats, names, titles,
|
|
aligned, byteorder).dtype
|
|
else:
|
|
kwds = {'formats': formats,
|
|
'names': names,
|
|
'titles': titles,
|
|
'aligned': aligned,
|
|
'byteorder': byteorder
|
|
}
|
|
|
|
if obj is None:
|
|
if shape is None:
|
|
raise ValueError("Must define a shape if obj is None")
|
|
return recarray(shape, dtype, buf=obj, offset=offset, strides=strides)
|
|
|
|
elif isinstance(obj, bytes):
|
|
return fromstring(obj, dtype, shape=shape, offset=offset, **kwds)
|
|
|
|
elif isinstance(obj, (list, tuple)):
|
|
if isinstance(obj[0], (tuple, list)):
|
|
return fromrecords(obj, dtype=dtype, shape=shape, **kwds)
|
|
else:
|
|
return fromarrays(obj, dtype=dtype, shape=shape, **kwds)
|
|
|
|
elif isinstance(obj, recarray):
|
|
if dtype is not None and (obj.dtype != dtype):
|
|
new = obj.view(dtype)
|
|
else:
|
|
new = obj
|
|
if copy:
|
|
new = new.copy()
|
|
return new
|
|
|
|
elif hasattr(obj, 'readinto'):
|
|
return fromfile(obj, dtype=dtype, shape=shape, offset=offset)
|
|
|
|
elif isinstance(obj, ndarray):
|
|
if dtype is not None and (obj.dtype != dtype):
|
|
new = obj.view(dtype)
|
|
else:
|
|
new = obj
|
|
if copy:
|
|
new = new.copy()
|
|
return new.view(recarray)
|
|
|
|
else:
|
|
interface = getattr(obj, "__array_interface__", None)
|
|
if interface is None or not isinstance(interface, dict):
|
|
raise ValueError("Unknown input type")
|
|
obj = sb.array(obj)
|
|
if dtype is not None and (obj.dtype != dtype):
|
|
obj = obj.view(dtype)
|
|
return obj.view(recarray)
|