Python ndarray.__new__方法代碼示例

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import __new__ [as 別名]
def __new__(self, data, mask=nomask, dtype=None, fill_value=None,
                hardmask=False, copy=False, subok=True):
        _data = np.array(data, copy=copy, subok=subok, dtype=dtype)
        _data = _data.view(self)
        _data._hardmask = hardmask
        if mask is not nomask:
            if isinstance(mask, np.void):
                _data._mask = mask
            else:
                try:
                    # Mask is already a 0D array
                    _data._mask = np.void(mask)
                except TypeError:
                    # Transform the mask to a void
                    mdtype = make_mask_descr(dtype)
                    _data._mask = np.array(mask, dtype=mdtype)[()]
        if fill_value is not None:
            _data.fill_value = fill_value
        return _data 

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import __new__ [as 別名]
def __deepcopy__(self, memo=None):
        from copy import deepcopy
        copied = MaskedArray.__new__(type(self), self, copy=True)
        if memo is None:
            memo = {}
        memo[id(self)] = copied
        for (k, v) in self.__dict__.items():
            copied.__dict__[k] = deepcopy(v, memo)
        return copied 

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import __new__ [as 別名]
def _mareconstruct(subtype, baseclass, baseshape, basetype,):
    """Internal function that builds a new MaskedArray from the
    information stored in a pickle.

    """
    _data = ndarray.__new__(baseclass, baseshape, basetype)
    _mask = ndarray.__new__(ndarray, baseshape, make_mask_descr(basetype))
    return subtype.__new__(subtype, _data, mask=_mask, dtype=basetype,) 

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import __new__ [as 別名]
def __new__(cls,
                values,
                missing_value,
                categories=None,
                sort=True):

        # Numpy's fixed-width string types aren't very efficient. Working with
        # object arrays is faster than bytes or unicode arrays in almost all
        # cases.
        if not is_object(values):
            values = values.astype(object)

        if categories is None:
            codes, categories, reverse_categories = factorize_strings(
                values.ravel(),
                missing_value=missing_value,
                sort=sort,
            )
        else:
            codes, categories, reverse_categories = (
                factorize_strings_known_categories(
                    values.ravel(),
                    categories=categories,
                    missing_value=missing_value,
                    sort=sort,
                )
            )
        categories.setflags(write=False)

        return cls.from_codes_and_metadata(
            codes=codes.reshape(values.shape),
            categories=categories,
            reverse_categories=reverse_categories,
            missing_value=missing_value,
        ) 

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import __new__ [as 別名]
def __array_finalize__(self, obj):
        """
        Called by Numpy after array construction.

        There are three cases where this can happen:

        1. Someone tries to directly construct a new array by doing::

            >>> ndarray.__new__(LabelArray, ...)  # doctest: +SKIP

           In this case, obj will be None.  We treat this as an error case and
           fail.

        2. Someone (most likely our own __new__) does::

           >>> other_array.view(type=LabelArray)  # doctest: +SKIP

           In this case, `self` will be the new LabelArray instance, and
           ``obj` will be the array on which ``view`` is being called.

           The caller of ``obj.view`` is responsible for setting category
           metadata on ``self`` after we exit.

        3. Someone creates a new LabelArray by slicing an existing one.

           In this case, ``obj`` will be the original LabelArray.  We're
           responsible for copying over the parent array's category metadata.
        """
        if obj is None:
            raise TypeError(
                "Direct construction of LabelArrays is not supported."
            )

        # See docstring for an explanation of when these will or will not be
        # set.
        self._categories = getattr(obj, 'categories', None)
        self._reverse_categories = getattr(obj, 'reverse_categories', None)
        self._missing_value = getattr(obj, 'missing_value', None)