Python numeric.swapaxes方法代码示例

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def swapaxes (a, axis1, axis2):
    m = getmask(a)
    d = masked_array(a).data
    if m is nomask:
        return masked_array(data=numeric.swapaxes(d, axis1, axis2))
    else:
        return masked_array(data=numeric.swapaxes(d, axis1, axis2),
                            mask=numeric.swapaxes(m, axis1, axis2),) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def dot(a, b):
    """dot(a,b) returns matrix-multiplication between a and b.  The product-sum
    is over the last dimension of a and the second-to-last dimension of b.
    Masked values are replaced by zeros. See also innerproduct.
    """
    return innerproduct(filled(a, 0), numeric.swapaxes(filled(b, 0), -1, -2)) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def _swapaxes(self, axis1, axis2):
    return MaskedArray(data = self.data.swapaxes(axis1, axis2),
                       mask = self.mask.swapaxes(axis1, axis2)) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def _var(self,axis=None,dtype=None, out=None):
    if axis is None:
        return numeric.asarray(self.compressed()).var()
    a = self.swapaxes(axis, 0)
    a = a - a.mean(axis=0)
    a *= a
    a /= a.count(axis=0)
    return a.swapaxes(0, axis).sum(axis) 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def array_split(ary, indices_or_sections, axis=0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis. For an array of length l that should be split
    into n sections, it returns l % n sub-arrays of size l//n + 1
    and the rest of size l//n.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    >>> x = np.arange(7.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try:
        # handle array case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError:
        # indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = ([0] +
                         extras * [Neach_section+1] +
                         (Nsections-extras) * [Neach_section])
        div_points = _nx.array(section_sizes, dtype=_nx.intp).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]
        end = div_points[i + 1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    return sub_arys 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def array_split(ary, indices_or_sections, axis=0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis. For an array of length l that should be split
    into n sections, it returns l % n sub-arrays of size l//n + 1
    and the rest of size l//n.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    >>> x = np.arange(7.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.]), array([ 5.,  6.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try:
        # handle scalar case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError:
        # indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = ([0] +
                         extras * [Neach_section+1] +
                         (Nsections-extras) * [Neach_section])
        div_points = _nx.array(section_sizes).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]
        end = div_points[i + 1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    return sub_arys 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def array_split(ary, indices_or_sections, axis=0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try:
        # handle scalar case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError:
        # indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = ([0] +
                         extras * [Neach_section+1] +
                         (Nsections-extras) * [Neach_section])
        div_points = _nx.array(section_sizes).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]
        end = div_points[i + 1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    return sub_arys 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def array_split(ary,indices_or_sections,axis = 0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try: # handle scalar case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError: #indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = [0] + \
                        extras * [Neach_section+1] + \
                        (Nsections-extras) * [Neach_section]
        div_points = _nx.array(section_sizes).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]; end = div_points[i+1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    # there is a weird issue with array slicing that allows
    # 0x10 arrays and other such things. The following kludge is needed
    # to get around this issue.
    sub_arys = _replace_zero_by_x_arrays(sub_arys)
    # end kludge.

    return sub_arys 

# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import swapaxes [as 别名]
def array_split(ary, indices_or_sections, axis=0):
    """
    Split an array into multiple sub-arrays.

    Please refer to the ``split`` documentation.  The only difference
    between these functions is that ``array_split`` allows
    `indices_or_sections` to be an integer that does *not* equally
    divide the axis.

    See Also
    --------
    split : Split array into multiple sub-arrays of equal size.

    Examples
    --------
    >>> x = np.arange(8.0)
    >>> np.array_split(x, 3)
        [array([ 0.,  1.,  2.]), array([ 3.,  4.,  5.]), array([ 6.,  7.])]

    """
    try:
        Ntotal = ary.shape[axis]
    except AttributeError:
        Ntotal = len(ary)
    try:
        # handle scalar case.
        Nsections = len(indices_or_sections) + 1
        div_points = [0] + list(indices_or_sections) + [Ntotal]
    except TypeError:
        # indices_or_sections is a scalar, not an array.
        Nsections = int(indices_or_sections)
        if Nsections <= 0:
            raise ValueError('number sections must be larger than 0.')
        Neach_section, extras = divmod(Ntotal, Nsections)
        section_sizes = ([0] +
                         extras * [Neach_section+1] +
                         (Nsections-extras) * [Neach_section])
        div_points = _nx.array(section_sizes).cumsum()

    sub_arys = []
    sary = _nx.swapaxes(ary, axis, 0)
    for i in range(Nsections):
        st = div_points[i]
        end = div_points[i + 1]
        sub_arys.append(_nx.swapaxes(sary[st:end], axis, 0))

    # This "kludge" was introduced here to replace arrays shaped (0, 10)
    # or similar with an array shaped (0,).
    # There seems no need for this, so give a FutureWarning to remove later.
    if sub_arys[-1].size == 0 and sub_arys[-1].ndim != 1:
        warnings.warn("in the future np.array_split will retain the shape of "
                      "arrays with a zero size, instead of replacing them by "
                      "`array([])`, which always has a shape of (0,).",
                      FutureWarning)
        sub_arys = _replace_zero_by_x_arrays(sub_arys)

    return sub_arys