Python multiarray.ndarray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 ndarray [as 別名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)


    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = um.add.reduce(arr, axis=axis, dtype=dtype, out=out, keepdims=keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    else:
        ret = ret.dtype.type(ret / rcount)

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning)


    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None and issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
        dtype = mu.dtype('f8')

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def _std(array, epsilon=1.0, bounds=None, axis=None, dtype=None, keepdims=np._NoValue, accountant=None, nan=False):
    ret = _var(array, epsilon=epsilon, bounds=bounds, axis=axis, dtype=dtype, keepdims=keepdims, accountant=accountant,
               nan=nan)

    if isinstance(ret, mu.ndarray):
        ret = um.sqrt(ret)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(um.sqrt(ret))
    else:
        ret = um.sqrt(ret)

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def _mean(a, axis=None, dtype=None, out=None, keepdims=False):
    arr = asanyarray(a)

    is_float16_result = False
    rcount = _count_reduce_items(arr, axis)
    # Make this warning show up first
    if rcount == 0:
        warnings.warn("Mean of empty slice.", RuntimeWarning, stacklevel=2)

    # Cast bool, unsigned int, and int to float64 by default
    if dtype is None:
        if issubclass(arr.dtype.type, (nt.integer, nt.bool_)):
            dtype = mu.dtype('f8')
        elif issubclass(arr.dtype.type, nt.float16):
            dtype = mu.dtype('f4')
            is_float16_result = True

    ret = umr_sum(arr, axis, dtype, out, keepdims)
    if isinstance(ret, mu.ndarray):
        ret = um.true_divide(
                ret, rcount, out=ret, casting='unsafe', subok=False)
        if is_float16_result and out is None:
            ret = arr.dtype.type(ret)
    elif hasattr(ret, 'dtype'):
        if is_float16_result:
            ret = arr.dtype.type(ret / rcount)
        else:
            ret = ret.dtype.type(ret / rcount)
    else:
        ret = ret / rcount

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
               keepdims=keepdims)

    if isinstance(ret, mu.ndarray):
        ret = um.sqrt(ret, out=ret)
    elif hasattr(ret, 'dtype'):
        ret = ret.dtype.type(um.sqrt(ret))
    else:
        ret = um.sqrt(ret)

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [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 ndarray [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 ndarray [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 ndarray [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 ndarray [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 ndarray [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 ndarray [as 別名]
def _std(a, axis=None, dtype=None, out=None, ddof=0, keepdims=False):
    ret = _var(a, axis=axis, dtype=dtype, out=out, ddof=ddof,
               keepdims=keepdims)

    if isinstance(ret, mu.ndarray):
        ret = um.sqrt(ret, out=ret)
    else:
        ret = ret.dtype.type(um.sqrt(ret))

    return ret 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def open(path: str) -> ndarray:
        return io.imread(path) 

# 需要導入模塊: from numpy.core import multiarray [as 別名]
# 或者: from numpy.core.multiarray import ndarray [as 別名]
def save_file(processed_image: ndarray, folder_path: str, file_prefix: str):
        FileUtil.create_folder(folder_path)
        full_destination = FileUtil.generate_next_file_path(folder_path, file_prefix)
        io.imsave(full_destination, processed_image)