Python numeric.array方法代碼示例

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except Exception:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def any(self, axis=None, out=None):
        """
        Test whether any array element along a given axis evaluates to True.

        Refer to `numpy.any` for full documentation.

        Parameters
        ----------
        axis : int, optional
            Axis along which logical OR is performed
        out : ndarray, optional
            Output to existing array instead of creating new one, must have
            same shape as expected output

        Returns
        -------
            any : bool, ndarray
                Returns a single bool if `axis` is ``None``; otherwise,
                returns `ndarray`

        """
        return N.ndarray.any(self, axis, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def _make_along_axis_idx(arr_shape, indices, axis):
	# compute dimensions to iterate over
    if not _nx.issubdtype(indices.dtype, _nx.integer):
        raise IndexError('`indices` must be an integer array')
    if len(arr_shape) != indices.ndim:
        raise ValueError(
            "`indices` and `arr` must have the same number of dimensions")
    shape_ones = (1,) * indices.ndim
    dest_dims = list(range(axis)) + [None] + list(range(axis+1, indices.ndim))

    # build a fancy index, consisting of orthogonal aranges, with the
    # requested index inserted at the right location
    fancy_index = []
    for dim, n in zip(dest_dims, arr_shape):
        if dim is None:
            fancy_index.append(indices)
        else:
            ind_shape = shape_ones[:dim] + (-1,) + shape_ones[dim+1:]
            fancy_index.append(_nx.arange(n).reshape(ind_shape))

    return tuple(fancy_index) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def asscalar(a):
    """
    Convert an array of size 1 to its scalar equivalent.

    Parameters
    ----------
    a : ndarray
        Input array of size 1.

    Returns
    -------
    out : scalar
        Scalar representation of `a`. The output data type is the same type
        returned by the input's `item` method.

    Examples
    --------
    >>> np.asscalar(np.array([24]))
    24

    """
    return a.item()

#----------------------------------------------------------------------------- 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def __getitem__(self, index):
        self._getitem = True

        try:
            out = N.ndarray.__getitem__(self, index)
        finally:
            self._getitem = False

        if not isinstance(out, N.ndarray):
            return out

        if out.ndim == 0:
            return out[()]
        if out.ndim == 1:
            sh = out.shape[0]
            # Determine when we should have a column array
            try:
                n = len(index)
            except:
                n = 0
            if n > 1 and isscalar(index[1]):
                out.shape = (sh, 1)
            else:
                out.shape = (1, sh)
        return out 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def __init__(self, c_or_r, r=0, variable=None):
        if isinstance(c_or_r, poly1d):
            for key in c_or_r.__dict__.keys():
                self.__dict__[key] = c_or_r.__dict__[key]
            if variable is not None:
                self.__dict__['variable'] = variable
            return
        if r:
            c_or_r = poly(c_or_r)
        c_or_r = atleast_1d(c_or_r)
        if len(c_or_r.shape) > 1:
            raise ValueError("Polynomial must be 1d only.")
        c_or_r = trim_zeros(c_or_r, trim='f')
        if len(c_or_r) == 0:
            c_or_r = NX.array([0.])
        self.__dict__['coeffs'] = c_or_r
        self.__dict__['order'] = len(c_or_r) - 1
        if variable is None:
            variable = 'x'
        self.__dict__['variable'] = variable 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def asmatrix(data, dtype=None):
    """
    Interpret the input as a matrix.

    Unlike `matrix`, `asmatrix` does not make a copy if the input is already
    a matrix or an ndarray.  Equivalent to ``matrix(data, copy=False)``.

    Parameters
    ----------
    data : array_like
        Input data.
    dtype : data-type
       Data-type of the output matrix.

    Returns
    -------
    mat : matrix
        `data` interpreted as a matrix.

    Examples
    --------
    >>> x = np.array([[1, 2], [3, 4]])

    >>> m = np.asmatrix(x)

    >>> x[0,0] = 5

    >>> m
    matrix([[5, 2],
            [3, 4]])

    """
    return matrix(data, dtype=dtype, copy=False) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def sum(self, axis=None, dtype=None, out=None):
        """
        Returns the sum of the matrix elements, along the given axis.

        Refer to `numpy.sum` for full documentation.

        See Also
        --------
        numpy.sum

        Notes
        -----
        This is the same as `ndarray.sum`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix([[1, 2], [4, 3]])
        >>> x.sum()
        10
        >>> x.sum(axis=1)
        matrix([[3],
                [7]])
        >>> x.sum(axis=1, dtype='float')
        matrix([[ 3.],
                [ 7.]])
        >>> out = np.zeros((1, 2), dtype='float')
        >>> x.sum(axis=1, dtype='float', out=out)
        matrix([[ 3.],
                [ 7.]])

        """
        return N.ndarray.sum(self, axis, dtype, out, keepdims=True)._collapse(axis)


    # To update docstring from array to matrix... 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def std(self, axis=None, dtype=None, out=None, ddof=0):
        """
        Return the standard deviation of the array elements along the given axis.

        Refer to `numpy.std` for full documentation.

        See Also
        --------
        numpy.std

        Notes
        -----
        This is the same as `ndarray.std`, except that where an `ndarray` would
        be returned, a `matrix` object is returned instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3, 4)))
        >>> x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.std()
        3.4520525295346629
        >>> x.std(0)
        matrix([[ 3.26598632,  3.26598632,  3.26598632,  3.26598632]])
        >>> x.std(1)
        matrix([[ 1.11803399],
                [ 1.11803399],
                [ 1.11803399]])

        """
        return N.ndarray.std(self, axis, dtype, out, ddof, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def prod(self, axis=None, dtype=None, out=None):
        """
        Return the product of the array elements over the given axis.

        Refer to `prod` for full documentation.

        See Also
        --------
        prod, ndarray.prod

        Notes
        -----
        Same as `ndarray.prod`, except, where that returns an `ndarray`, this
        returns a `matrix` object instead.

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.prod()
        0
        >>> x.prod(0)
        matrix([[  0,  45, 120, 231]])
        >>> x.prod(1)
        matrix([[   0],
                [ 840],
                [7920]])

        """
        return N.ndarray.prod(self, axis, dtype, out, keepdims=True)._collapse(axis) 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def getA(self):
        """
        Return `self` as an `ndarray` object.

        Equivalent to ``np.asarray(self)``.

        Parameters
        ----------
        None

        Returns
        -------
        ret : ndarray
            `self` as an `ndarray`

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.getA()
        array([[ 0,  1,  2,  3],
               [ 4,  5,  6,  7],
               [ 8,  9, 10, 11]])

        """
        return self.__array__() 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def getA1(self):
        """
        Return `self` as a flattened `ndarray`.

        Equivalent to ``np.asarray(x).ravel()``

        Parameters
        ----------
        None

        Returns
        -------
        ret : ndarray
            `self`, 1-D, as an `ndarray`

        Examples
        --------
        >>> x = np.matrix(np.arange(12).reshape((3,4))); x
        matrix([[ 0,  1,  2,  3],
                [ 4,  5,  6,  7],
                [ 8,  9, 10, 11]])
        >>> x.getA1()
        array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11])

        """
        return self.__array__().ravel() 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def __init__(self, c_or_r, r=False, variable=None):
        if isinstance(c_or_r, poly1d):
            self._variable = c_or_r._variable
            self._coeffs = c_or_r._coeffs

            if set(c_or_r.__dict__) - set(self.__dict__):
                msg = ("In the future extra properties will not be copied "
                       "across when constructing one poly1d from another")
                warnings.warn(msg, FutureWarning, stacklevel=2)
                self.__dict__.update(c_or_r.__dict__)

            if variable is not None:
                self._variable = variable
            return
        if r:
            c_or_r = poly(c_or_r)
        c_or_r = atleast_1d(c_or_r)
        if c_or_r.ndim > 1:
            raise ValueError("Polynomial must be 1d only.")
        c_or_r = trim_zeros(c_or_r, trim='f')
        if len(c_or_r) == 0:
            c_or_r = NX.array([0.])
        self._coeffs = c_or_r
        if variable is None:
            variable = 'x'
        self._variable = variable 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def real(val):
    """
    Return the real part of the complex argument.

    Parameters
    ----------
    val : array_like
        Input array.

    Returns
    -------
    out : ndarray or scalar
        The real component of the complex argument. If `val` is real, the type
        of `val` is used for the output.  If `val` has complex elements, the
        returned type is float.

    See Also
    --------
    real_if_close, imag, angle

    Examples
    --------
    >>> a = np.array([1+2j, 3+4j, 5+6j])
    >>> a.real
    array([ 1.,  3.,  5.])
    >>> a.real = 9
    >>> a
    array([ 9.+2.j,  9.+4.j,  9.+6.j])
    >>> a.real = np.array([9, 8, 7])
    >>> a
    array([ 9.+2.j,  8.+4.j,  7.+6.j])
    >>> np.real(1 + 1j)
    1.0

    """
    try:
        return val.real
    except AttributeError:
        return asanyarray(val).real 

# 需要導入模塊: from numpy.core import numeric [as 別名]
# 或者: from numpy.core.numeric import array [as 別名]
def imag(val):
    """
    Return the imaginary part of the complex argument.

    Parameters
    ----------
    val : array_like
        Input array.

    Returns
    -------
    out : ndarray or scalar
        The imaginary component of the complex argument. If `val` is real,
        the type of `val` is used for the output.  If `val` has complex
        elements, the returned type is float.

    See Also
    --------
    real, angle, real_if_close

    Examples
    --------
    >>> a = np.array([1+2j, 3+4j, 5+6j])
    >>> a.imag
    array([ 2.,  4.,  6.])
    >>> a.imag = np.array([8, 10, 12])
    >>> a
    array([ 1. +8.j,  3.+10.j,  5.+12.j])
    >>> np.imag(1 + 1j)
    1.0

    """
    try:
        return val.imag
    except AttributeError:
        return asanyarray(val).imag