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Python ndarray.ravel方法代碼示例

本文整理匯總了Python中numpy.ndarray.ravel方法的典型用法代碼示例。如果您正苦於以下問題:Python ndarray.ravel方法的具體用法?Python ndarray.ravel怎麽用?Python ndarray.ravel使用的例子?那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在numpy.ndarray的用法示例。


在下文中一共展示了ndarray.ravel方法的8個代碼示例,這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚,您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: reduce

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
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 
開發者ID:amoose136,項目名稱:radar,代碼行數:25,代碼來源:core.py

示例2: __getitem__

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
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 
開發者ID:amoose136,項目名稱:radar,代碼行數:17,代碼來源:core.py

示例3: _set_flat

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def _set_flat(self, value):
        "Set a flattened version of self to value."
        y = self.ravel()
        y[:] = value 
開發者ID:amoose136,項目名稱:radar,代碼行數:6,代碼來源:core.py

示例4: compressed

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def compressed(self):
        """
        Return all the non-masked data as a 1-D array.

        Returns
        -------
        data : ndarray
            A new `ndarray` holding the non-masked data is returned.

        Notes
        -----
        The result is **not** a MaskedArray!

        Examples
        --------
        >>> x = np.ma.array(np.arange(5), mask=[0]*2 + [1]*3)
        >>> x.compressed()
        array([0, 1])
        >>> type(x.compressed())
        <type 'numpy.ndarray'>

        """
        data = ndarray.ravel(self._data)
        if self._mask is not nomask:
            data = data.compress(np.logical_not(ndarray.ravel(self._mask)))
        return data 
開發者ID:amoose136,項目名稱:radar,代碼行數:28,代碼來源:core.py

示例5: outer

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def outer(a, b):
    "maskedarray version of the numpy function."
    fa = filled(a, 0).ravel()
    fb = filled(b, 0).ravel()
    d = np.outer(fa, fb)
    ma = getmask(a)
    mb = getmask(b)
    if ma is nomask and mb is nomask:
        return masked_array(d)
    ma = getmaskarray(a)
    mb = getmaskarray(b)
    m = make_mask(1 - np.outer(1 - ma, 1 - mb), copy=0)
    return masked_array(d, mask=m) 
開發者ID:amoose136,項目名稱:radar,代碼行數:15,代碼來源:core.py

示例6: flatten_structured_array

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def flatten_structured_array(a):
    """
    Flatten a structured array.

    The data type of the output is chosen such that it can represent all of the
    (nested) fields.

    Parameters
    ----------
    a : structured array

    Returns
    -------
    output : masked array or ndarray
        A flattened masked array if the input is a masked array, otherwise a
        standard ndarray.

    Examples
    --------
    >>> ndtype = [('a', int), ('b', float)]
    >>> a = np.array([(1, 1), (2, 2)], dtype=ndtype)
    >>> flatten_structured_array(a)
    array([[1., 1.],
           [2., 2.]])

    """

    def flatten_sequence(iterable):
        """
        Flattens a compound of nested iterables.

        """
        for elm in iter(iterable):
            if hasattr(elm, '__iter__'):
                for f in flatten_sequence(elm):
                    yield f
            else:
                yield elm

    a = np.asanyarray(a)
    inishape = a.shape
    a = a.ravel()
    if isinstance(a, MaskedArray):
        out = np.array([tuple(flatten_sequence(d.item())) for d in a._data])
        out = out.view(MaskedArray)
        out._mask = np.array([tuple(flatten_sequence(d.item()))
                              for d in getmaskarray(a)])
    else:
        out = np.array([tuple(flatten_sequence(d.item())) for d in a])
    if len(inishape) > 1:
        newshape = list(out.shape)
        newshape[0] = inishape
        out.shape = tuple(flatten_sequence(newshape))
    return out 
開發者ID:amoose136,項目名稱:radar,代碼行數:56,代碼來源:core.py

示例7: ravel

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def ravel(self, order='C'):
        """
        Returns a 1D version of self, as a view.

        Parameters
        ----------
        order : {'C', 'F', 'A', 'K'}, optional
            The elements of `a` are read using this index order. 'C' means to
            index the elements in C-like order, with the last axis index
            changing fastest, back to the first axis index changing slowest.
            'F' means to index the elements in Fortran-like index order, with
            the first index changing fastest, and the last index changing
            slowest. Note that the 'C' and 'F' options take no account of the
            memory layout of the underlying array, and only refer to the order
            of axis indexing.  'A' means to read the elements in Fortran-like
            index order if `m` is Fortran *contiguous* in memory, C-like order
            otherwise.  'K' means to read the elements in the order they occur
            in memory, except for reversing the data when strides are negative.
            By default, 'C' index order is used.

        Returns
        -------
        MaskedArray
            Output view is of shape ``(self.size,)`` (or
            ``(np.ma.product(self.shape),)``).

        Examples
        --------
        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
        >>> print(x)
        [[1 -- 3]
         [-- 5 --]
         [7 -- 9]]
        >>> print(x.ravel())
        [1 -- 3 -- 5 -- 7 -- 9]

        """
        r = ndarray.ravel(self._data, order=order).view(type(self))
        r._update_from(self)
        if self._mask is not nomask:
            r._mask = ndarray.ravel(self._mask, order=order).reshape(r.shape)
        else:
            r._mask = nomask
        return r 
開發者ID:amoose136,項目名稱:radar,代碼行數:46,代碼來源:core.py

示例8: ravel

# 需要導入模塊: from numpy import ndarray [as 別名]
# 或者: from numpy.ndarray import ravel [as 別名]
def ravel(self, order='C'):
        """
        Returns a 1D version of self, as a view.

        Parameters
        ----------
        order : {'C', 'F', 'A', 'K'}, optional
            The elements of `a` are read using this index order. 'C' means to
            index the elements in C-like order, with the last axis index
            changing fastest, back to the first axis index changing slowest.
            'F' means to index the elements in Fortran-like index order, with
            the first index changing fastest, and the last index changing
            slowest. Note that the 'C' and 'F' options take no account of the
            memory layout of the underlying array, and only refer to the order
            of axis indexing.  'A' means to read the elements in Fortran-like
            index order if `m` is Fortran *contiguous* in memory, C-like order
            otherwise.  'K' means to read the elements in the order they occur
            in memory, except for reversing the data when strides are negative.
            By default, 'C' index order is used.

        Returns
        -------
        MaskedArray
            Output view is of shape ``(self.size,)`` (or
            ``(np.ma.product(self.shape),)``).

        Examples
        --------
        >>> x = np.ma.array([[1,2,3],[4,5,6],[7,8,9]], mask=[0] + [1,0]*4)
        >>> print x
        [[1 -- 3]
         [-- 5 --]
         [7 -- 9]]
        >>> print x.ravel()
        [1 -- 3 -- 5 -- 7 -- 9]

        """
        r = ndarray.ravel(self._data, order=order).view(type(self))
        r._update_from(self)
        if self._mask is not nomask:
            r._mask = ndarray.ravel(self._mask, order=order).reshape(r.shape)
        else:
            r._mask = nomask
        return r 
開發者ID:SignalMedia,項目名稱:PyDataLondon29-EmbarrassinglyParallelDAWithAWSLambda,代碼行數:46,代碼來源:core.py


注:本文中的numpy.ndarray.ravel方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台,相關代碼片段篩選自各路編程大神貢獻的開源項目,源碼版權歸原作者所有,傳播和使用請參考對應項目的License;未經允許,請勿轉載。