Python ndarray.view方法代码示例

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def outer(self, a, b):
        """
        Return the function applied to the outer product of a and b.

        """
        (da, db) = (getdata(a), getdata(b))
        d = self.f.outer(da, db)
        ma = getmask(a)
        mb = getmask(b)
        if ma is nomask and mb is nomask:
            m = nomask
        else:
            ma = getmaskarray(a)
            mb = getmaskarray(b)
            m = umath.logical_or.outer(ma, mb)
        if (not m.ndim) and m:
            return masked
        if m is not nomask:
            np.copyto(d, da, where=m)
        if not d.shape:
            return d
        masked_d = d.view(get_masked_subclass(a, b))
        masked_d._mask = m
        return masked_d 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def round(self, decimals=0, out=None):
        """
        Return an array rounded a to the given number of decimals.

        Refer to `numpy.around` for full documentation.

        See Also
        --------
        numpy.around : equivalent function

        """
        result = self._data.round(decimals=decimals, out=out).view(type(self))
        if result.ndim > 0:
            result._mask = self._mask
            result._update_from(self)
        elif self._mask:
            # Return masked when the scalar is masked
            result = masked
        # No explicit output: we're done
        if out is None:
            return result
        if isinstance(out, MaskedArray):
            out.__setmask__(self._mask)
        return out 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def take(self, indices, axis=None, out=None, mode='raise'):
        """
        """
        (_data, _mask) = (self._data, self._mask)
        cls = type(self)
        # Make sure the indices are not masked
        maskindices = getattr(indices, '_mask', nomask)
        if maskindices is not nomask:
            indices = indices.filled(0)
        # Get the data
        if out is None:
            out = _data.take(indices, axis=axis, mode=mode).view(cls)
        else:
            np.take(_data, indices, axis=axis, mode=mode, out=out)
        # Get the mask
        if isinstance(out, MaskedArray):
            if _mask is nomask:
                outmask = maskindices
            else:
                outmask = _mask.take(indices, axis=axis, mode=mode)
                outmask |= maskindices
            out.__setmask__(outmask)
        return out

    # Array methods 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def reduce(self, target, axis=None):
        "Reduce target along the given axis."
        target = narray(target, copy=False, subok=True)
        m = getmask(target)
        if axis is not None:
            kargs = {'axis': axis}
        else:
            kargs = {}
            target = target.ravel()
            if not (m is nomask):
                m = m.ravel()
        if m is nomask:
            t = self.ufunc.reduce(target, **kargs)
        else:
            target = target.filled(
                self.fill_value_func(target)).view(type(target))
            t = self.ufunc.reduce(target, **kargs)
            m = umath.logical_and.reduce(m, **kargs)
            if hasattr(t, '_mask'):
                t._mask = m
            elif m:
                t = masked
        return t 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def diag(v, k=0):
    """
    Extract a diagonal or construct a diagonal array.

    This function is the equivalent of `numpy.diag` that takes masked
    values into account, see `numpy.diag` for details.

    See Also
    --------
    numpy.diag : Equivalent function for ndarrays.

    """
    output = np.diag(v, k).view(MaskedArray)
    if getmask(v) is not nomask:
        output._mask = np.diag(v._mask, k)
    return output 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def reshape(a, new_shape, order='C'):
    """
    Returns an array containing the same data with a new shape.

    Refer to `MaskedArray.reshape` for full documentation.

    See Also
    --------
    MaskedArray.reshape : equivalent function

    """
    # We can't use 'frommethod', it whine about some parameters. Dmmit.
    try:
        return a.reshape(new_shape, order=order)
    except AttributeError:
        _tmp = narray(a, copy=False).reshape(new_shape, order=order)
        return _tmp.view(MaskedArray) 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def from_codes_and_metadata(cls,
                                codes,
                                categories,
                                reverse_categories,
                                missing_value):
        """
        Rehydrate a LabelArray from the codes and metadata.

        Parameters
        ----------
        codes : np.ndarray[integral]
            The codes for the label array.
        categories : np.ndarray[object]
            The unique string categories.
        reverse_categories : dict[str, int]
            The mapping from category to its code-index.
        missing_value : any
            The value used to represent missing data.
        """
        ret = codes.view(type=cls, dtype=np.void)
        ret._categories = categories
        ret._reverse_categories = reverse_categories
        ret._missing_value = missing_value
        return ret 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def view(self, dtype=_NotPassed, type=_NotPassed):
        if type is _NotPassed and dtype not in (_NotPassed, self.dtype):
            raise TypeError("Can't view LabelArray as another dtype.")

        # The text signature on ndarray.view makes it look like the default
        # values for dtype and type are `None`, but passing None explicitly has
        # different semantics than not passing an arg at all, so we reconstruct
        # the kwargs dict here to simulate the args not being passed at all.
        kwargs = {}
        if dtype is not _NotPassed:
            kwargs['dtype'] = dtype
        if type is not _NotPassed:
            kwargs['type'] = type
        return super(LabelArray, self).view(**kwargs)

    # In general, we support resizing, slicing, and reshaping methods, but not
    # numeric methods. 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def outer(self, a, b):
        """
        Return the function applied to the outer product of a and b.

        """
        (da, db) = (getdata(a), getdata(b))
        d = self.f.outer(da, db)
        ma = getmask(a)
        mb = getmask(b)
        if ma is nomask and mb is nomask:
            m = nomask
        else:
            ma = getmaskarray(a)
            mb = getmaskarray(b)
            m = umath.logical_or.outer(ma, mb)
        if (not m.ndim) and m:
            return masked
        if m is not nomask:
            np.copyto(d, da, where=m)
        if not d.shape:
            return d
        masked_d = d.view(get_masked_subclass(a, b))
        masked_d._mask = m
        masked_d._update_from(d)
        return masked_d 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def __call__(self, *args, **params):
        methodname = self.__name__
        instance = self.obj
        # Fallback : if the instance has not been initialized, use the first
        # arg
        if instance is None:
            args = list(args)
            instance = args.pop(0)
        data = instance._data
        mask = instance._mask
        cls = type(instance)
        result = getattr(data, methodname)(*args, **params).view(cls)
        result._update_from(instance)
        if result.ndim:
            if not self._onmask:
                result.__setmask__(mask)
            elif mask is not nomask:
                result.__setmask__(getattr(mask, methodname)(*args, **params))
        else:
            if mask.ndim and (not mask.dtype.names and mask.all()):
                return masked
        return result 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
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 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def inner(a, b):
    """
    Returns the inner product of a and b for arrays of floating point types.

    Like the generic NumPy equivalent the product sum is over the last dimension
    of a and b.

    Notes
    -----
    The first argument is not conjugated.

    """
    fa = filled(a, 0)
    fb = filled(b, 0)
    if len(fa.shape) == 0:
        fa.shape = (1,)
    if len(fb.shape) == 0:
        fb.shape = (1,)
    return np.inner(fa, fb).view(MaskedArray) 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def round(self, decimals=0, out=None):
        """
        Return each element rounded to the given number of decimals.

        Refer to `numpy.around` for full documentation.

        See Also
        --------
        ndarray.around : corresponding function for ndarrays
        numpy.around : equivalent function
        """
        result = self._data.round(decimals=decimals, out=out).view(type(self))
        if result.ndim > 0:
            result._mask = self._mask
            result._update_from(self)
        elif self._mask:
            # Return masked when the scalar is masked
            result = masked
        # No explicit output: we're done
        if out is None:
            return result
        if isinstance(out, MaskedArray):
            out.__setmask__(self._mask)
        return out 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def __call__(self, a, b, *args, **kwargs):
        "Execute the call behavior."
        # Get the data
        (da, db) = (getdata(a), getdata(b))
        # Get the result
        with np.errstate(divide='ignore', invalid='ignore'):
            result = self.f(da, db, *args, **kwargs)
        # Get the mask as a combination of the source masks and invalid
        m = ~umath.isfinite(result)
        m |= getmask(a)
        m |= getmask(b)
        # Apply the domain
        domain = ufunc_domain.get(self.f, None)
        if domain is not None:
            m |= filled(domain(da, db), True)
        # Take care of the scalar case first
        if (not m.ndim):
            if m:
                return masked
            else:
                return result
        # When the mask is True, put back da if possible
        # any errors, just abort; impossible to guarantee masked values
        try:
            np.copyto(result, 0, casting='unsafe', where=m)
            # avoid using "*" since this may be overlaid
            masked_da = umath.multiply(m, da)
            # only add back if it can be cast safely
            if np.can_cast(masked_da.dtype, result.dtype, casting='safe'):
                result += masked_da
        except:
            pass

        # Transforms to a (subclass of) MaskedArray
        masked_result = result.view(get_masked_subclass(a, b))
        masked_result._mask = m
        if isinstance(a, MaskedArray):
            masked_result._update_from(a)
        elif isinstance(b, MaskedArray):
            masked_result._update_from(b)
        return masked_result 

# 需要导入模块: from numpy import ndarray [as 别名]
# 或者: from numpy.ndarray import view [as 别名]
def __getitem__(self, indx):
        result = self.dataiter.__getitem__(indx).view(type(self.ma))
        if self.maskiter is not None:
            _mask = self.maskiter.__getitem__(indx)
            if isinstance(_mask, ndarray):
                # set shape to match that of data; this is needed for matrices
                _mask.shape = result.shape
                result._mask = _mask
            elif isinstance(_mask, np.void):
                return mvoid(result, mask=_mask, hardmask=self.ma._hardmask)
            elif _mask:  # Just a scalar, masked
                return masked
        return result

    # This won't work if ravel makes a copy