759 lines
21 KiB
Python
759 lines
21 KiB
Python
"""
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A place for code to be called from core C-code.
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Some things are more easily handled Python.
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"""
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from __future__ import division, absolute_import, print_function
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import re
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import sys
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from numpy.compat import basestring
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from .multiarray import dtype, array, ndarray
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try:
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import ctypes
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except ImportError:
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ctypes = None
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from .numerictypes import object_
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if (sys.byteorder == 'little'):
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_nbo = b'<'
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else:
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_nbo = b'>'
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def _makenames_list(adict, align):
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allfields = []
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fnames = list(adict.keys())
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for fname in fnames:
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obj = adict[fname]
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n = len(obj)
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if not isinstance(obj, tuple) or n not in [2, 3]:
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raise ValueError("entry not a 2- or 3- tuple")
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if (n > 2) and (obj[2] == fname):
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continue
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num = int(obj[1])
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if (num < 0):
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raise ValueError("invalid offset.")
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format = dtype(obj[0], align=align)
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if (n > 2):
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title = obj[2]
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else:
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title = None
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allfields.append((fname, format, num, title))
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# sort by offsets
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allfields.sort(key=lambda x: x[2])
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names = [x[0] for x in allfields]
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formats = [x[1] for x in allfields]
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offsets = [x[2] for x in allfields]
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titles = [x[3] for x in allfields]
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return names, formats, offsets, titles
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# Called in PyArray_DescrConverter function when
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# a dictionary without "names" and "formats"
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# fields is used as a data-type descriptor.
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def _usefields(adict, align):
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try:
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names = adict[-1]
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except KeyError:
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names = None
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if names is None:
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names, formats, offsets, titles = _makenames_list(adict, align)
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else:
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formats = []
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offsets = []
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titles = []
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for name in names:
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res = adict[name]
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formats.append(res[0])
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offsets.append(res[1])
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if (len(res) > 2):
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titles.append(res[2])
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else:
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titles.append(None)
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return dtype({"names": names,
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"formats": formats,
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"offsets": offsets,
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"titles": titles}, align)
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# construct an array_protocol descriptor list
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# from the fields attribute of a descriptor
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# This calls itself recursively but should eventually hit
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# a descriptor that has no fields and then return
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# a simple typestring
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def _array_descr(descriptor):
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fields = descriptor.fields
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if fields is None:
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subdtype = descriptor.subdtype
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if subdtype is None:
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if descriptor.metadata is None:
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return descriptor.str
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else:
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new = descriptor.metadata.copy()
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if new:
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return (descriptor.str, new)
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else:
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return descriptor.str
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else:
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return (_array_descr(subdtype[0]), subdtype[1])
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names = descriptor.names
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ordered_fields = [fields[x] + (x,) for x in names]
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result = []
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offset = 0
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for field in ordered_fields:
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if field[1] > offset:
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num = field[1] - offset
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result.append(('', '|V%d' % num))
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offset += num
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elif field[1] < offset:
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raise ValueError(
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"dtype.descr is not defined for types with overlapping or "
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"out-of-order fields")
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if len(field) > 3:
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name = (field[2], field[3])
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else:
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name = field[2]
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if field[0].subdtype:
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tup = (name, _array_descr(field[0].subdtype[0]),
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field[0].subdtype[1])
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else:
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tup = (name, _array_descr(field[0]))
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offset += field[0].itemsize
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result.append(tup)
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if descriptor.itemsize > offset:
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num = descriptor.itemsize - offset
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result.append(('', '|V%d' % num))
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return result
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# Build a new array from the information in a pickle.
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# Note that the name numpy.core._internal._reconstruct is embedded in
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# pickles of ndarrays made with NumPy before release 1.0
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# so don't remove the name here, or you'll
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# break backward compatibility.
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def _reconstruct(subtype, shape, dtype):
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return ndarray.__new__(subtype, shape, dtype)
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# format_re was originally from numarray by J. Todd Miller
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format_re = re.compile(br'(?P<order1>[<>|=]?)'
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br'(?P<repeats> *[(]?[ ,0-9L]*[)]? *)'
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br'(?P<order2>[<>|=]?)'
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br'(?P<dtype>[A-Za-z0-9.?]*(?:\[[a-zA-Z0-9,.]+\])?)')
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sep_re = re.compile(br'\s*,\s*')
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space_re = re.compile(br'\s+$')
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# astr is a string (perhaps comma separated)
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_convorder = {b'=': _nbo}
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def _commastring(astr):
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startindex = 0
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result = []
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while startindex < len(astr):
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mo = format_re.match(astr, pos=startindex)
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try:
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(order1, repeats, order2, dtype) = mo.groups()
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except (TypeError, AttributeError):
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raise ValueError('format number %d of "%s" is not recognized' %
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(len(result)+1, astr))
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startindex = mo.end()
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# Separator or ending padding
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if startindex < len(astr):
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if space_re.match(astr, pos=startindex):
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startindex = len(astr)
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else:
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mo = sep_re.match(astr, pos=startindex)
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if not mo:
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raise ValueError(
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'format number %d of "%s" is not recognized' %
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(len(result)+1, astr))
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startindex = mo.end()
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if order2 == b'':
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order = order1
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elif order1 == b'':
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order = order2
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else:
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order1 = _convorder.get(order1, order1)
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order2 = _convorder.get(order2, order2)
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if (order1 != order2):
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raise ValueError(
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'inconsistent byte-order specification %s and %s' %
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(order1, order2))
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order = order1
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if order in [b'|', b'=', _nbo]:
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order = b''
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dtype = order + dtype
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if (repeats == b''):
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newitem = dtype
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else:
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newitem = (dtype, eval(repeats))
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result.append(newitem)
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return result
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class dummy_ctype(object):
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def __init__(self, cls):
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self._cls = cls
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def __mul__(self, other):
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return self
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def __call__(self, *other):
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return self._cls(other)
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def __eq__(self, other):
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return self._cls == other._cls
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def __ne__(self, other):
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return self._cls != other._cls
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def _getintp_ctype():
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val = _getintp_ctype.cache
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if val is not None:
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return val
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if ctypes is None:
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import numpy as np
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val = dummy_ctype(np.intp)
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else:
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char = dtype('p').char
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if (char == 'i'):
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val = ctypes.c_int
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elif char == 'l':
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val = ctypes.c_long
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elif char == 'q':
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val = ctypes.c_longlong
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else:
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val = ctypes.c_long
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_getintp_ctype.cache = val
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return val
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_getintp_ctype.cache = None
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# Used for .ctypes attribute of ndarray
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class _missing_ctypes(object):
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def cast(self, num, obj):
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return num
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def c_void_p(self, num):
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return num
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class _ctypes(object):
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def __init__(self, array, ptr=None):
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if ctypes:
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self._ctypes = ctypes
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else:
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self._ctypes = _missing_ctypes()
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self._arr = array
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self._data = ptr
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if self._arr.ndim == 0:
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self._zerod = True
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else:
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self._zerod = False
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def data_as(self, obj):
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return self._ctypes.cast(self._data, obj)
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def shape_as(self, obj):
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if self._zerod:
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return None
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return (obj*self._arr.ndim)(*self._arr.shape)
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def strides_as(self, obj):
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if self._zerod:
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return None
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return (obj*self._arr.ndim)(*self._arr.strides)
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def get_data(self):
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return self._data
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def get_shape(self):
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return self.shape_as(_getintp_ctype())
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def get_strides(self):
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return self.strides_as(_getintp_ctype())
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def get_as_parameter(self):
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return self._ctypes.c_void_p(self._data)
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data = property(get_data, None, doc="c-types data")
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shape = property(get_shape, None, doc="c-types shape")
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strides = property(get_strides, None, doc="c-types strides")
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_as_parameter_ = property(get_as_parameter, None, doc="_as parameter_")
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def _newnames(datatype, order):
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"""
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Given a datatype and an order object, return a new names tuple, with the
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order indicated
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"""
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oldnames = datatype.names
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nameslist = list(oldnames)
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if isinstance(order, str):
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order = [order]
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seen = set()
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if isinstance(order, (list, tuple)):
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for name in order:
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try:
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nameslist.remove(name)
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except ValueError:
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if name in seen:
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raise ValueError("duplicate field name: %s" % (name,))
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else:
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raise ValueError("unknown field name: %s" % (name,))
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seen.add(name)
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return tuple(list(order) + nameslist)
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raise ValueError("unsupported order value: %s" % (order,))
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def _copy_fields(ary):
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"""Return copy of structured array with padding between fields removed.
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Parameters
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----------
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ary : ndarray
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Structured array from which to remove padding bytes
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Returns
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-------
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ary_copy : ndarray
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Copy of ary with padding bytes removed
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"""
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dt = ary.dtype
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copy_dtype = {'names': dt.names,
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'formats': [dt.fields[name][0] for name in dt.names]}
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return array(ary, dtype=copy_dtype, copy=True)
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def _getfield_is_safe(oldtype, newtype, offset):
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""" Checks safety of getfield for object arrays.
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As in _view_is_safe, we need to check that memory containing objects is not
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reinterpreted as a non-object datatype and vice versa.
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Parameters
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----------
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oldtype : data-type
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Data type of the original ndarray.
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newtype : data-type
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Data type of the field being accessed by ndarray.getfield
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offset : int
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Offset of the field being accessed by ndarray.getfield
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Raises
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------
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TypeError
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If the field access is invalid
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"""
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if newtype.hasobject or oldtype.hasobject:
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if offset == 0 and newtype == oldtype:
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return
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if oldtype.names:
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for name in oldtype.names:
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if (oldtype.fields[name][1] == offset and
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oldtype.fields[name][0] == newtype):
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return
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raise TypeError("Cannot get/set field of an object array")
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return
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def _view_is_safe(oldtype, newtype):
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""" Checks safety of a view involving object arrays, for example when
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doing::
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np.zeros(10, dtype=oldtype).view(newtype)
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Parameters
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----------
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oldtype : data-type
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Data type of original ndarray
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newtype : data-type
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Data type of the view
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Raises
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------
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TypeError
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If the new type is incompatible with the old type.
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"""
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# if the types are equivalent, there is no problem.
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# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
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if oldtype == newtype:
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return
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if newtype.hasobject or oldtype.hasobject:
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raise TypeError("Cannot change data-type for object array.")
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return
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# Given a string containing a PEP 3118 format specifier,
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# construct a NumPy dtype
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_pep3118_native_map = {
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'?': '?',
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'c': 'S1',
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'b': 'b',
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'B': 'B',
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'h': 'h',
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'H': 'H',
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'i': 'i',
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'I': 'I',
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'l': 'l',
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'L': 'L',
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'q': 'q',
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'Q': 'Q',
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'e': 'e',
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'f': 'f',
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'd': 'd',
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'g': 'g',
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'Zf': 'F',
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'Zd': 'D',
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'Zg': 'G',
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's': 'S',
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'w': 'U',
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'O': 'O',
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'x': 'V', # padding
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}
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_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
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_pep3118_standard_map = {
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'?': '?',
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'c': 'S1',
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'b': 'b',
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'B': 'B',
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'h': 'i2',
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'H': 'u2',
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'i': 'i4',
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'I': 'u4',
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'l': 'i4',
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'L': 'u4',
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'q': 'i8',
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'Q': 'u8',
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'e': 'f2',
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'f': 'f',
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'd': 'd',
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'Zf': 'F',
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'Zd': 'D',
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's': 'S',
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'w': 'U',
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'O': 'O',
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'x': 'V', # padding
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}
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_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
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def _dtype_from_pep3118(spec):
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class Stream(object):
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def __init__(self, s):
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self.s = s
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self.byteorder = '@'
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def advance(self, n):
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res = self.s[:n]
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self.s = self.s[n:]
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return res
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def consume(self, c):
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if self.s[:len(c)] == c:
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self.advance(len(c))
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return True
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return False
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def consume_until(self, c):
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if callable(c):
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i = 0
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while i < len(self.s) and not c(self.s[i]):
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i = i + 1
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return self.advance(i)
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else:
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i = self.s.index(c)
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res = self.advance(i)
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self.advance(len(c))
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return res
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@property
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def next(self):
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return self.s[0]
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def __bool__(self):
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return bool(self.s)
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__nonzero__ = __bool__
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stream = Stream(spec)
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dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
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return dtype
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def __dtype_from_pep3118(stream, is_subdtype):
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field_spec = dict(
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names=[],
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formats=[],
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offsets=[],
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itemsize=0
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)
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offset = 0
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common_alignment = 1
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is_padding = False
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# Parse spec
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while stream:
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value = None
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# End of structure, bail out to upper level
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if stream.consume('}'):
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break
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# Sub-arrays (1)
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shape = None
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if stream.consume('('):
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shape = stream.consume_until(')')
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shape = tuple(map(int, shape.split(',')))
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# Byte order
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if stream.next in ('@', '=', '<', '>', '^', '!'):
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byteorder = stream.advance(1)
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if byteorder == '!':
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byteorder = '>'
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stream.byteorder = byteorder
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# Byte order characters also control native vs. standard type sizes
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if stream.byteorder in ('@', '^'):
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type_map = _pep3118_native_map
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type_map_chars = _pep3118_native_typechars
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else:
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type_map = _pep3118_standard_map
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type_map_chars = _pep3118_standard_typechars
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# Item sizes
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itemsize_str = stream.consume_until(lambda c: not c.isdigit())
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if itemsize_str:
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itemsize = int(itemsize_str)
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else:
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itemsize = 1
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# Data types
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is_padding = False
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if stream.consume('T{'):
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value, align = __dtype_from_pep3118(
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stream, is_subdtype=True)
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elif stream.next in type_map_chars:
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if stream.next == 'Z':
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typechar = stream.advance(2)
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else:
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typechar = stream.advance(1)
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is_padding = (typechar == 'x')
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dtypechar = type_map[typechar]
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if dtypechar in 'USV':
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dtypechar += '%d' % itemsize
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itemsize = 1
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numpy_byteorder = {'@': '=', '^': '='}.get(
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stream.byteorder, stream.byteorder)
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value = dtype(numpy_byteorder + dtypechar)
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align = value.alignment
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else:
|
|
raise ValueError("Unknown PEP 3118 data type specifier %r" % stream.s)
|
|
|
|
#
|
|
# Native alignment may require padding
|
|
#
|
|
# Here we assume that the presence of a '@' character implicitly implies
|
|
# that the start of the array is *already* aligned.
|
|
#
|
|
extra_offset = 0
|
|
if stream.byteorder == '@':
|
|
start_padding = (-offset) % align
|
|
intra_padding = (-value.itemsize) % align
|
|
|
|
offset += start_padding
|
|
|
|
if intra_padding != 0:
|
|
if itemsize > 1 or (shape is not None and _prod(shape) > 1):
|
|
# Inject internal padding to the end of the sub-item
|
|
value = _add_trailing_padding(value, intra_padding)
|
|
else:
|
|
# We can postpone the injection of internal padding,
|
|
# as the item appears at most once
|
|
extra_offset += intra_padding
|
|
|
|
# Update common alignment
|
|
common_alignment = _lcm(align, common_alignment)
|
|
|
|
# Convert itemsize to sub-array
|
|
if itemsize != 1:
|
|
value = dtype((value, (itemsize,)))
|
|
|
|
# Sub-arrays (2)
|
|
if shape is not None:
|
|
value = dtype((value, shape))
|
|
|
|
# Field name
|
|
if stream.consume(':'):
|
|
name = stream.consume_until(':')
|
|
else:
|
|
name = None
|
|
|
|
if not (is_padding and name is None):
|
|
if name is not None and name in field_spec['names']:
|
|
raise RuntimeError("Duplicate field name '%s' in PEP3118 format"
|
|
% name)
|
|
field_spec['names'].append(name)
|
|
field_spec['formats'].append(value)
|
|
field_spec['offsets'].append(offset)
|
|
|
|
offset += value.itemsize
|
|
offset += extra_offset
|
|
|
|
field_spec['itemsize'] = offset
|
|
|
|
# extra final padding for aligned types
|
|
if stream.byteorder == '@':
|
|
field_spec['itemsize'] += (-offset) % common_alignment
|
|
|
|
# Check if this was a simple 1-item type, and unwrap it
|
|
if (field_spec['names'] == [None]
|
|
and field_spec['offsets'][0] == 0
|
|
and field_spec['itemsize'] == field_spec['formats'][0].itemsize
|
|
and not is_subdtype):
|
|
ret = field_spec['formats'][0]
|
|
else:
|
|
_fix_names(field_spec)
|
|
ret = dtype(field_spec)
|
|
|
|
# Finished
|
|
return ret, common_alignment
|
|
|
|
def _fix_names(field_spec):
|
|
""" Replace names which are None with the next unused f%d name """
|
|
names = field_spec['names']
|
|
for i, name in enumerate(names):
|
|
if name is not None:
|
|
continue
|
|
|
|
j = 0
|
|
while True:
|
|
name = 'f{}'.format(j)
|
|
if name not in names:
|
|
break
|
|
j = j + 1
|
|
names[i] = name
|
|
|
|
def _add_trailing_padding(value, padding):
|
|
"""Inject the specified number of padding bytes at the end of a dtype"""
|
|
if value.fields is None:
|
|
field_spec = dict(
|
|
names=['f0'],
|
|
formats=[value],
|
|
offsets=[0],
|
|
itemsize=value.itemsize
|
|
)
|
|
else:
|
|
fields = value.fields
|
|
names = value.names
|
|
field_spec = dict(
|
|
names=names,
|
|
formats=[fields[name][0] for name in names],
|
|
offsets=[fields[name][1] for name in names],
|
|
itemsize=value.itemsize
|
|
)
|
|
|
|
field_spec['itemsize'] += padding
|
|
return dtype(field_spec)
|
|
|
|
def _prod(a):
|
|
p = 1
|
|
for x in a:
|
|
p *= x
|
|
return p
|
|
|
|
def _gcd(a, b):
|
|
"""Calculate the greatest common divisor of a and b"""
|
|
while b:
|
|
a, b = b, a % b
|
|
return a
|
|
|
|
def _lcm(a, b):
|
|
return a // _gcd(a, b) * b
|
|
|
|
# Exception used in shares_memory()
|
|
class TooHardError(RuntimeError):
|
|
pass
|
|
|
|
class AxisError(ValueError, IndexError):
|
|
""" Axis supplied was invalid. """
|
|
def __init__(self, axis, ndim=None, msg_prefix=None):
|
|
# single-argument form just delegates to base class
|
|
if ndim is None and msg_prefix is None:
|
|
msg = axis
|
|
|
|
# do the string formatting here, to save work in the C code
|
|
else:
|
|
msg = ("axis {} is out of bounds for array of dimension {}"
|
|
.format(axis, ndim))
|
|
if msg_prefix is not None:
|
|
msg = "{}: {}".format(msg_prefix, msg)
|
|
|
|
super(AxisError, self).__init__(msg)
|
|
|
|
|
|
def array_ufunc_errmsg_formatter(dummy, ufunc, method, *inputs, **kwargs):
|
|
""" Format the error message for when __array_ufunc__ gives up. """
|
|
args_string = ', '.join(['{!r}'.format(arg) for arg in inputs] +
|
|
['{}={!r}'.format(k, v)
|
|
for k, v in kwargs.items()])
|
|
args = inputs + kwargs.get('out', ())
|
|
types_string = ', '.join(repr(type(arg).__name__) for arg in args)
|
|
return ('operand type(s) all returned NotImplemented from '
|
|
'__array_ufunc__({!r}, {!r}, {}): {}'
|
|
.format(ufunc, method, args_string, types_string))
|
|
|
|
|
|
def _ufunc_doc_signature_formatter(ufunc):
|
|
"""
|
|
Builds a signature string which resembles PEP 457
|
|
|
|
This is used to construct the first line of the docstring
|
|
"""
|
|
|
|
# input arguments are simple
|
|
if ufunc.nin == 1:
|
|
in_args = 'x'
|
|
else:
|
|
in_args = ', '.join('x{}'.format(i+1) for i in range(ufunc.nin))
|
|
|
|
# output arguments are both keyword or positional
|
|
if ufunc.nout == 0:
|
|
out_args = ', /, out=()'
|
|
elif ufunc.nout == 1:
|
|
out_args = ', /, out=None'
|
|
else:
|
|
out_args = '[, {positional}], / [, out={default}]'.format(
|
|
positional=', '.join(
|
|
'out{}'.format(i+1) for i in range(ufunc.nout)),
|
|
default=repr((None,)*ufunc.nout)
|
|
)
|
|
|
|
# keyword only args depend on whether this is a gufunc
|
|
kwargs = (
|
|
", casting='same_kind'"
|
|
", order='K'"
|
|
", dtype=None"
|
|
", subok=True"
|
|
"[, signature"
|
|
", extobj]"
|
|
)
|
|
if ufunc.signature is None:
|
|
kwargs = ", where=True" + kwargs
|
|
|
|
# join all the parts together
|
|
return '{name}({in_args}{out_args}, *{kwargs})'.format(
|
|
name=ufunc.__name__,
|
|
in_args=in_args,
|
|
out_args=out_args,
|
|
kwargs=kwargs
|
|
)
|