Python multiarray.array方法代碼示例

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def _create_J_without_numba(Ybus, V, pvpq, pq):
    # create Jacobian with standard pypower implementation.
    dS_dVm, dS_dVa = dSbus_dV(Ybus, V)

    ## evaluate Jacobian
    J11 = dS_dVa[array([pvpq]).T, pvpq].real
    J12 = dS_dVm[array([pvpq]).T, pq].real
    if len(pq) > 0:
        J21 = dS_dVa[array([pq]).T, pvpq].imag
        J22 = dS_dVm[array([pq]).T, pq].imag
        J = vstack([
            hstack([J11, J12]),
            hstack([J21, J22])
        ], format="csr")
    else:
        J = vstack([
            hstack([J11, J12])
        ], format="csr")
    return J 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def draw_pz(self, tfcn):
        """Draw pzmap"""
        self.f_pzmap.clf()
        # Make adaptive window size, with min [-10, 10] in range,
        # always atleast 25% extra space outside poles/zeros
        tmp = list(self.zeros)+list(self.poles)+[8]
        val = 1.25*max(abs(array(tmp)))
        plt.figure(self.f_pzmap.number)
        control.matlab.pzmap(tfcn)
        plt.suptitle('Pole-Zero Diagram')

        plt.axis([-val, val, -val, val]) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def ascontiguousarray(a, dtype=None):
    """
    Return a contiguous array in memory (C order).

    Parameters
    ----------
    a : array_like
        Input array.
    dtype : str or dtype object, optional
        Data-type of returned array.

    Returns
    -------
    out : ndarray
        Contiguous array of same shape and content as `a`, with type `dtype`
        if specified.

    See Also
    --------
    asfortranarray : Convert input to an ndarray with column-major
                     memory order.
    require : Return an ndarray that satisfies requirements.
    ndarray.flags : Information about the memory layout of the array.

    Examples
    --------
    >>> x = np.arange(6).reshape(2,3)
    >>> np.ascontiguousarray(x, dtype=np.float32)
    array([[ 0.,  1.,  2.],
           [ 3.,  4.,  5.]], dtype=float32)
    >>> x.flags['C_CONTIGUOUS']
    True

    """
    return array(a, dtype, copy=False, order='C', ndmin=1) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def asfortranarray(a, dtype=None):
    """
    Return an array laid out in Fortran order in memory.

    Parameters
    ----------
    a : array_like
        Input array.
    dtype : str or dtype object, optional
        By default, the data-type is inferred from the input data.

    Returns
    -------
    out : ndarray
        The input `a` in Fortran, or column-major, order.

    See Also
    --------
    ascontiguousarray : Convert input to a contiguous (C order) array.
    asanyarray : Convert input to an ndarray with either row or
        column-major memory order.
    require : Return an ndarray that satisfies requirements.
    ndarray.flags : Information about the memory layout of the array.

    Examples
    --------
    >>> x = np.arange(6).reshape(2,3)
    >>> y = np.asfortranarray(x)
    >>> x.flags['F_CONTIGUOUS']
    False
    >>> y.flags['F_CONTIGUOUS']
    True

    """
    return array(a, dtype, copy=False, order='F', ndmin=1) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def argwhere(a):
    """
    Find the indices of array elements that are non-zero, grouped by element.

    Parameters
    ----------
    a : array_like
        Input data.

    Returns
    -------
    index_array : ndarray
        Indices of elements that are non-zero. Indices are grouped by element.

    See Also
    --------
    where, nonzero

    Notes
    -----
    ``np.argwhere(a)`` is the same as ``np.transpose(np.nonzero(a))``.

    The output of ``argwhere`` is not suitable for indexing arrays.
    For this purpose use ``where(a)`` instead.

    Examples
    --------
    >>> x = np.arange(6).reshape(2,3)
    >>> x
    array([[0, 1, 2],
           [3, 4, 5]])
    >>> np.argwhere(x>1)
    array([[0, 2],
           [1, 0],
           [1, 1],
           [1, 2]])

    """
    return transpose(nonzero(a)) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def flatnonzero(a):
    """
    Return indices that are non-zero in the flattened version of a.

    This is equivalent to a.ravel().nonzero()[0].

    Parameters
    ----------
    a : ndarray
        Input array.

    Returns
    -------
    res : ndarray
        Output array, containing the indices of the elements of `a.ravel()`
        that are non-zero.

    See Also
    --------
    nonzero : Return the indices of the non-zero elements of the input array.
    ravel : Return a 1-D array containing the elements of the input array.

    Examples
    --------
    >>> x = np.arange(-2, 3)
    >>> x
    array([-2, -1,  0,  1,  2])
    >>> np.flatnonzero(x)
    array([0, 1, 3, 4])

    Use the indices of the non-zero elements as an index array to extract
    these elements:

    >>> x.ravel()[np.flatnonzero(x)]
    array([-2, -1,  1,  2])

    """
    return a.ravel().nonzero()[0] 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def _validate_axis(axis, ndim, argname):
    try:
        axis = [operator.index(axis)]
    except TypeError:
        axis = list(axis)
    axis = [a + ndim if a < 0 else a for a in axis]
    if not builtins.all(0 <= a < ndim for a in axis):
        raise ValueError('invalid axis for this array in `%s` argument' %
                         argname)
    if len(set(axis)) != len(axis):
        raise ValueError('repeated axis in `%s` argument' % argname)
    return axis 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def array_str(a, max_line_width=None, precision=None, suppress_small=None):
    """
    Return a string representation of the data in an array.

    The data in the array is returned as a single string.  This function is
    similar to `array_repr`, the difference being that `array_repr` also
    returns information on the kind of array and its data type.

    Parameters
    ----------
    a : ndarray
        Input array.
    max_line_width : int, optional
        Inserts newlines if text is longer than `max_line_width`.  The
        default is, indirectly, 75.
    precision : int, optional
        Floating point precision.  Default is the current printing precision
        (usually 8), which can be altered using `set_printoptions`.
    suppress_small : bool, optional
        Represent numbers "very close" to zero as zero; default is False.
        Very close is defined by precision: if the precision is 8, e.g.,
        numbers smaller (in absolute value) than 5e-9 are represented as
        zero.

    See Also
    --------
    array2string, array_repr, set_printoptions

    Examples
    --------
    >>> np.array_str(np.arange(3))
    '[0 1 2]'

    """
    return array2string(a, max_line_width, precision, suppress_small, ' ', "", str) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def identity(n, dtype=None):
    """
    Return the identity array.

    The identity array is a square array with ones on
    the main diagonal.

    Parameters
    ----------
    n : int
        Number of rows (and columns) in `n` x `n` output.
    dtype : data-type, optional
        Data-type of the output.  Defaults to ``float``.

    Returns
    -------
    out : ndarray
        `n` x `n` array with its main diagonal set to one,
        and all other elements 0.

    Examples
    --------
    >>> np.identity(3)
    array([[ 1.,  0.,  0.],
           [ 0.,  1.,  0.],
           [ 0.,  0.,  1.]])

    """
    from numpy import eye
    return eye(n, dtype=dtype) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_var_corrected_two_pass(self):
        assert_allclose(np.array(var(self.f)).reshape(4,), np.array([2, 2.25, 0.666667, 2]), rtol=1e-02)
        assert_allclose(np.array(var(self.f, 'corrected two pass')).reshape(4,),
                                   np.array([2, 2.25, 0.666667, 2]), rtol=1e-02)

        assert_allclose(var(self.h).reshape(4,), np.array([32, 9, 3.666667, 17]), rtol=1e-02) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_var_textbook_one_pass(self):
        assert_allclose(np.array(var(self.f, 'textbook one pass')).reshape(4,),
                                   np.array([2, 2.25, 0.666667, 2]), rtol=1e-02)

        assert_allclose(np.array(var(self.h, 'textbook one pass')).reshape(4,),
                                   np.array([32, 9, 3.666667, 17]), rtol=1e-02)

        assert_almost_equal(var(self.fa[:, 2], 'textbook one pass'), 0.66666666666666663) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_var_standard_two_pass(self):
        assert_allclose(np.array(var(self.f, 'standard two pass')).reshape(4,),
                                   np.array([2, 2.25, 0.666667, 2]), rtol=1e-02)

        assert_allclose(np.array(var(self.h, 'standard two pass')).reshape(4,),
                                   np.array([32, 9, 3.666667, 17]), rtol=1e-02)

        assert_equal(var(self.fa[:, 1], 'standard two pass'), 2.25) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_var_youngs_cramer(self):
        assert_allclose(np.array(var(self.f, 'youngs cramer')).reshape(4,),
                                   np.array([2, 2.25, 0.666667, 2]), rtol=1e-02)

        assert_allclose(np.array(var(self.h, 'youngs cramer')).reshape(4,),
                                   np.array([32, 9, 3.666667, 17]), rtol=1e-02)

        assert_equal(var(self.fa[:, 1], 'youngs cramer'), 2.25) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_stddev(self):
        assert_equal(std_dev(self.fa[:, 1]), 1.5)
        assert_allclose(std_dev(self.fa), array([ 1.41421356,  1.5       ,  0.81649658,  1.41421356])) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import array [as 別名]
def test_errors(self):
        with pytest.raises(ValueError):
            var(self.f, 'NA')

        ff = np.array([np.array(self.f), np.array(self.f)])

        with pytest.raises(ValueError):
            var(ff)