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929 lines
27 KiB
Python
929 lines
27 KiB
Python
"""
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A place for internal 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 unicode
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from numpy.core.overrides import set_module
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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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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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|
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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.value
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|
class c_void_p(object):
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|
def __init__(self, ptr):
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|
self.value = ptr
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|
|
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class _unsafe_first_element_pointer(object):
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|
"""
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|
Helper to allow viewing an array as a ctypes pointer to the first element
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|
This avoids:
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|
* dealing with strides
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|
* `.view` rejecting object-containing arrays
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|
* `memoryview` not supporting overlapping fields
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|
"""
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|
def __init__(self, arr):
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|
self.base = arr
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|
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|
@property
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|
def __array_interface__(self):
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|
i = dict(
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|
shape=(),
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|
typestr='|V0',
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|
data=(self.base.__array_interface__['data'][0], False),
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|
strides=(),
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|
version=3,
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|
)
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|
return i
|
|
|
|
|
|
def _get_void_ptr(arr):
|
|
"""
|
|
Get a `ctypes.c_void_p` to arr.data, that keeps a reference to the array
|
|
"""
|
|
import numpy as np
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|
# convert to a 0d array that has a data pointer referrign to the start
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|
# of arr. This holds a reference to arr.
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|
simple_arr = np.asarray(_unsafe_first_element_pointer(arr))
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|
|
|
# create a `char[0]` using the same memory.
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|
c_arr = (ctypes.c_char * 0).from_buffer(simple_arr)
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|
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|
# finally cast to void*
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|
return ctypes.cast(ctypes.pointer(c_arr), ctypes.c_void_p)
|
|
|
|
|
|
class _ctypes(object):
|
|
def __init__(self, array, ptr=None):
|
|
self._arr = array
|
|
|
|
if ctypes:
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|
self._ctypes = ctypes
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|
# get a void pointer to the buffer, which keeps the array alive
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|
self._data = _get_void_ptr(array)
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|
assert self._data.value == ptr
|
|
else:
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|
# fake a pointer-like object that holds onto the reference
|
|
self._ctypes = _missing_ctypes()
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|
self._data = self._ctypes.c_void_p(ptr)
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|
self._data._objects = array
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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
|
|
|
|
def data_as(self, obj):
|
|
"""
|
|
Return the data pointer cast to a particular c-types object.
|
|
For example, calling ``self._as_parameter_`` is equivalent to
|
|
``self.data_as(ctypes.c_void_p)``. Perhaps you want to use the data as a
|
|
pointer to a ctypes array of floating-point data:
|
|
``self.data_as(ctypes.POINTER(ctypes.c_double))``.
|
|
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|
The returned pointer will keep a reference to the array.
|
|
"""
|
|
return self._ctypes.cast(self._data, obj)
|
|
|
|
def shape_as(self, obj):
|
|
"""
|
|
Return the shape tuple as an array of some other c-types
|
|
type. For example: ``self.shape_as(ctypes.c_short)``.
|
|
"""
|
|
if self._zerod:
|
|
return None
|
|
return (obj*self._arr.ndim)(*self._arr.shape)
|
|
|
|
def strides_as(self, obj):
|
|
"""
|
|
Return the strides tuple as an array of some other
|
|
c-types type. For example: ``self.strides_as(ctypes.c_longlong)``.
|
|
"""
|
|
if self._zerod:
|
|
return None
|
|
return (obj*self._arr.ndim)(*self._arr.strides)
|
|
|
|
@property
|
|
def data(self):
|
|
"""
|
|
A pointer to the memory area of the array as a Python integer.
|
|
This memory area may contain data that is not aligned, or not in correct
|
|
byte-order. The memory area may not even be writeable. The array
|
|
flags and data-type of this array should be respected when passing this
|
|
attribute to arbitrary C-code to avoid trouble that can include Python
|
|
crashing. User Beware! The value of this attribute is exactly the same
|
|
as ``self._array_interface_['data'][0]``.
|
|
|
|
Note that unlike `data_as`, a reference will not be kept to the array:
|
|
code like ``ctypes.c_void_p((a + b).ctypes.data)`` will result in a
|
|
pointer to a deallocated array, and should be spelt
|
|
``(a + b).ctypes.data_as(ctypes.c_void_p)``
|
|
"""
|
|
return self._data.value
|
|
|
|
@property
|
|
def shape(self):
|
|
"""
|
|
(c_intp*self.ndim): A ctypes array of length self.ndim where
|
|
the basetype is the C-integer corresponding to ``dtype('p')`` on this
|
|
platform. This base-type could be `ctypes.c_int`, `ctypes.c_long`, or
|
|
`ctypes.c_longlong` depending on the platform.
|
|
The c_intp type is defined accordingly in `numpy.ctypeslib`.
|
|
The ctypes array contains the shape of the underlying array.
|
|
"""
|
|
return self.shape_as(_getintp_ctype())
|
|
|
|
@property
|
|
def strides(self):
|
|
"""
|
|
(c_intp*self.ndim): A ctypes array of length self.ndim where
|
|
the basetype is the same as for the shape attribute. This ctypes array
|
|
contains the strides information from the underlying array. This strides
|
|
information is important for showing how many bytes must be jumped to
|
|
get to the next element in the array.
|
|
"""
|
|
return self.strides_as(_getintp_ctype())
|
|
|
|
@property
|
|
def _as_parameter_(self):
|
|
"""
|
|
Overrides the ctypes semi-magic method
|
|
|
|
Enables `c_func(some_array.ctypes)`
|
|
"""
|
|
return self._data
|
|
|
|
# kept for compatibility
|
|
get_data = data.fget
|
|
get_shape = shape.fget
|
|
get_strides = strides.fget
|
|
get_as_parameter = _as_parameter_.fget
|
|
|
|
|
|
def _newnames(datatype, order):
|
|
"""
|
|
Given a datatype and an order object, return a new names tuple, with the
|
|
order indicated
|
|
"""
|
|
oldnames = datatype.names
|
|
nameslist = list(oldnames)
|
|
if isinstance(order, (str, unicode)):
|
|
order = [order]
|
|
seen = set()
|
|
if isinstance(order, (list, tuple)):
|
|
for name in order:
|
|
try:
|
|
nameslist.remove(name)
|
|
except ValueError:
|
|
if name in seen:
|
|
raise ValueError("duplicate field name: %s" % (name,))
|
|
else:
|
|
raise ValueError("unknown field name: %s" % (name,))
|
|
seen.add(name)
|
|
return tuple(list(order) + nameslist)
|
|
raise ValueError("unsupported order value: %s" % (order,))
|
|
|
|
def _copy_fields(ary):
|
|
"""Return copy of structured array with padding between fields removed.
|
|
|
|
Parameters
|
|
----------
|
|
ary : ndarray
|
|
Structured array from which to remove padding bytes
|
|
|
|
Returns
|
|
-------
|
|
ary_copy : ndarray
|
|
Copy of ary with padding bytes removed
|
|
"""
|
|
dt = ary.dtype
|
|
copy_dtype = {'names': dt.names,
|
|
'formats': [dt.fields[name][0] for name in dt.names]}
|
|
return array(ary, dtype=copy_dtype, copy=True)
|
|
|
|
def _getfield_is_safe(oldtype, newtype, offset):
|
|
""" Checks safety of getfield for object arrays.
|
|
|
|
As in _view_is_safe, we need to check that memory containing objects is not
|
|
reinterpreted as a non-object datatype and vice versa.
|
|
|
|
Parameters
|
|
----------
|
|
oldtype : data-type
|
|
Data type of the original ndarray.
|
|
newtype : data-type
|
|
Data type of the field being accessed by ndarray.getfield
|
|
offset : int
|
|
Offset of the field being accessed by ndarray.getfield
|
|
|
|
Raises
|
|
------
|
|
TypeError
|
|
If the field access is invalid
|
|
|
|
"""
|
|
if newtype.hasobject or oldtype.hasobject:
|
|
if offset == 0 and newtype == oldtype:
|
|
return
|
|
if oldtype.names:
|
|
for name in oldtype.names:
|
|
if (oldtype.fields[name][1] == offset and
|
|
oldtype.fields[name][0] == newtype):
|
|
return
|
|
raise TypeError("Cannot get/set field of an object array")
|
|
return
|
|
|
|
def _view_is_safe(oldtype, newtype):
|
|
""" Checks safety of a view involving object arrays, for example when
|
|
doing::
|
|
|
|
np.zeros(10, dtype=oldtype).view(newtype)
|
|
|
|
Parameters
|
|
----------
|
|
oldtype : data-type
|
|
Data type of original ndarray
|
|
newtype : data-type
|
|
Data type of the view
|
|
|
|
Raises
|
|
------
|
|
TypeError
|
|
If the new type is incompatible with the old type.
|
|
|
|
"""
|
|
|
|
# if the types are equivalent, there is no problem.
|
|
# for example: dtype((np.record, 'i4,i4')) == dtype((np.void, 'i4,i4'))
|
|
if oldtype == newtype:
|
|
return
|
|
|
|
if newtype.hasobject or oldtype.hasobject:
|
|
raise TypeError("Cannot change data-type for object array.")
|
|
return
|
|
|
|
# Given a string containing a PEP 3118 format specifier,
|
|
# construct a NumPy dtype
|
|
|
|
_pep3118_native_map = {
|
|
'?': '?',
|
|
'c': 'S1',
|
|
'b': 'b',
|
|
'B': 'B',
|
|
'h': 'h',
|
|
'H': 'H',
|
|
'i': 'i',
|
|
'I': 'I',
|
|
'l': 'l',
|
|
'L': 'L',
|
|
'q': 'q',
|
|
'Q': 'Q',
|
|
'e': 'e',
|
|
'f': 'f',
|
|
'd': 'd',
|
|
'g': 'g',
|
|
'Zf': 'F',
|
|
'Zd': 'D',
|
|
'Zg': 'G',
|
|
's': 'S',
|
|
'w': 'U',
|
|
'O': 'O',
|
|
'x': 'V', # padding
|
|
}
|
|
_pep3118_native_typechars = ''.join(_pep3118_native_map.keys())
|
|
|
|
_pep3118_standard_map = {
|
|
'?': '?',
|
|
'c': 'S1',
|
|
'b': 'b',
|
|
'B': 'B',
|
|
'h': 'i2',
|
|
'H': 'u2',
|
|
'i': 'i4',
|
|
'I': 'u4',
|
|
'l': 'i4',
|
|
'L': 'u4',
|
|
'q': 'i8',
|
|
'Q': 'u8',
|
|
'e': 'f2',
|
|
'f': 'f',
|
|
'd': 'd',
|
|
'Zf': 'F',
|
|
'Zd': 'D',
|
|
's': 'S',
|
|
'w': 'U',
|
|
'O': 'O',
|
|
'x': 'V', # padding
|
|
}
|
|
_pep3118_standard_typechars = ''.join(_pep3118_standard_map.keys())
|
|
|
|
_pep3118_unsupported_map = {
|
|
'u': 'UCS-2 strings',
|
|
'&': 'pointers',
|
|
't': 'bitfields',
|
|
'X': 'function pointers',
|
|
}
|
|
|
|
class _Stream(object):
|
|
def __init__(self, s):
|
|
self.s = s
|
|
self.byteorder = '@'
|
|
|
|
def advance(self, n):
|
|
res = self.s[:n]
|
|
self.s = self.s[n:]
|
|
return res
|
|
|
|
def consume(self, c):
|
|
if self.s[:len(c)] == c:
|
|
self.advance(len(c))
|
|
return True
|
|
return False
|
|
|
|
def consume_until(self, c):
|
|
if callable(c):
|
|
i = 0
|
|
while i < len(self.s) and not c(self.s[i]):
|
|
i = i + 1
|
|
return self.advance(i)
|
|
else:
|
|
i = self.s.index(c)
|
|
res = self.advance(i)
|
|
self.advance(len(c))
|
|
return res
|
|
|
|
@property
|
|
def next(self):
|
|
return self.s[0]
|
|
|
|
def __bool__(self):
|
|
return bool(self.s)
|
|
__nonzero__ = __bool__
|
|
|
|
|
|
def _dtype_from_pep3118(spec):
|
|
stream = _Stream(spec)
|
|
dtype, align = __dtype_from_pep3118(stream, is_subdtype=False)
|
|
return dtype
|
|
|
|
def __dtype_from_pep3118(stream, is_subdtype):
|
|
field_spec = dict(
|
|
names=[],
|
|
formats=[],
|
|
offsets=[],
|
|
itemsize=0
|
|
)
|
|
offset = 0
|
|
common_alignment = 1
|
|
is_padding = False
|
|
|
|
# Parse spec
|
|
while stream:
|
|
value = None
|
|
|
|
# End of structure, bail out to upper level
|
|
if stream.consume('}'):
|
|
break
|
|
|
|
# Sub-arrays (1)
|
|
shape = None
|
|
if stream.consume('('):
|
|
shape = stream.consume_until(')')
|
|
shape = tuple(map(int, shape.split(',')))
|
|
|
|
# Byte order
|
|
if stream.next in ('@', '=', '<', '>', '^', '!'):
|
|
byteorder = stream.advance(1)
|
|
if byteorder == '!':
|
|
byteorder = '>'
|
|
stream.byteorder = byteorder
|
|
|
|
# Byte order characters also control native vs. standard type sizes
|
|
if stream.byteorder in ('@', '^'):
|
|
type_map = _pep3118_native_map
|
|
type_map_chars = _pep3118_native_typechars
|
|
else:
|
|
type_map = _pep3118_standard_map
|
|
type_map_chars = _pep3118_standard_typechars
|
|
|
|
# Item sizes
|
|
itemsize_str = stream.consume_until(lambda c: not c.isdigit())
|
|
if itemsize_str:
|
|
itemsize = int(itemsize_str)
|
|
else:
|
|
itemsize = 1
|
|
|
|
# Data types
|
|
is_padding = False
|
|
|
|
if stream.consume('T{'):
|
|
value, align = __dtype_from_pep3118(
|
|
stream, is_subdtype=True)
|
|
elif stream.next in type_map_chars:
|
|
if stream.next == 'Z':
|
|
typechar = stream.advance(2)
|
|
else:
|
|
typechar = stream.advance(1)
|
|
|
|
is_padding = (typechar == 'x')
|
|
dtypechar = type_map[typechar]
|
|
if dtypechar in 'USV':
|
|
dtypechar += '%d' % itemsize
|
|
itemsize = 1
|
|
numpy_byteorder = {'@': '=', '^': '='}.get(
|
|
stream.byteorder, stream.byteorder)
|
|
value = dtype(numpy_byteorder + dtypechar)
|
|
align = value.alignment
|
|
elif stream.next in _pep3118_unsupported_map:
|
|
desc = _pep3118_unsupported_map[stream.next]
|
|
raise NotImplementedError(
|
|
"Unrepresentable PEP 3118 data type {!r} ({})"
|
|
.format(stream.next, desc))
|
|
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()
|
|
@set_module('numpy')
|
|
class TooHardError(RuntimeError):
|
|
pass
|
|
|
|
@set_module('numpy')
|
|
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 array_function_errmsg_formatter(public_api, types):
|
|
""" Format the error message for when __array_ufunc__ gives up. """
|
|
func_name = '{}.{}'.format(public_api.__module__, public_api.__name__)
|
|
return ("no implementation found for '{}' on types that implement "
|
|
'__array_function__: {}'.format(func_name, list(types)))
|
|
|
|
|
|
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
|
|
)
|
|
|
|
|
|
def npy_ctypes_check(cls):
|
|
# determine if a class comes from ctypes, in order to work around
|
|
# a bug in the buffer protocol for those objects, bpo-10746
|
|
try:
|
|
# ctypes class are new-style, so have an __mro__. This probably fails
|
|
# for ctypes classes with multiple inheritance.
|
|
ctype_base = cls.__mro__[-2]
|
|
# right now, they're part of the _ctypes module
|
|
return 'ctypes' in ctype_base.__module__
|
|
except Exception:
|
|
return False
|
|
|
|
|
|
class recursive(object):
|
|
'''
|
|
A decorator class for recursive nested functions.
|
|
Naive recursive nested functions hold a reference to themselves:
|
|
|
|
def outer(*args):
|
|
def stringify_leaky(arg0, *arg1):
|
|
if len(arg1) > 0:
|
|
return stringify_leaky(*arg1) # <- HERE
|
|
return str(arg0)
|
|
stringify_leaky(*args)
|
|
|
|
This design pattern creates a reference cycle that is difficult for a
|
|
garbage collector to resolve. The decorator class prevents the
|
|
cycle by passing the nested function in as an argument `self`:
|
|
|
|
def outer(*args):
|
|
@recursive
|
|
def stringify(self, arg0, *arg1):
|
|
if len(arg1) > 0:
|
|
return self(*arg1)
|
|
return str(arg0)
|
|
stringify(*args)
|
|
|
|
'''
|
|
def __init__(self, func):
|
|
self.func = func
|
|
def __call__(self, *args, **kwargs):
|
|
return self.func(self, *args, **kwargs)
|
|
|