本文整理汇总了Python中numpy.core.numeric.normalize_axis_tuple方法的典型用法代码示例。如果您正苦于以下问题：Python numeric.normalize_axis_tuple方法的具体用法？Python numeric.normalize_axis_tuple怎么用？Python numeric.normalize_axis_tuple使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类numpy.core.numeric的用法示例。
在下文中一共展示了numeric.normalize_axis_tuple方法的4个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: compress_nd
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import normalize_axis_tuple [as 别名]
def compress_nd(x, axis=None):
"""Suppress slices from multiple dimensions which contain masked values.
Parameters
----------
x : array_like, MaskedArray
The array to operate on. If not a MaskedArray instance (or if no array
elements are masked, `x` is interpreted as a MaskedArray with `mask`
set to `nomask`.
axis : tuple of ints or int, optional
Which dimensions to suppress slices from can be configured with this
parameter.
- If axis is a tuple of ints, those are the axes to suppress slices from.
- If axis is an int, then that is the only axis to suppress slices from.
- If axis is None, all axis are selected.
Returns
-------
compress_array : ndarray
The compressed array.
"""
x = asarray(x)
m = getmask(x)
# Set axis to tuple of ints
if axis is None:
axis = tuple(range(x.ndim))
else:
axis = normalize_axis_tuple(axis, x.ndim)
# Nothing is masked: return x
if m is nomask or not m.any():
return x._data
# All is masked: return empty
if m.all():
return nxarray([])
# Filter elements through boolean indexing
data = x._data
for ax in axis:
axes = tuple(list(range(ax)) + list(range(ax + 1, x.ndim)))
data = data[(slice(None),)*ax + (~m.any(axis=axes),)]
return data 示例2: _ureduce
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import normalize_axis_tuple [as 别名]
def _ureduce(a, func, **kwargs):
"""
Internal Function.
Call `func` with `a` as first argument swapping the axes to use extended
axis on functions that don't support it natively.
Returns result and a.shape with axis dims set to 1.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
func : callable
Reduction function capable of receiving a single axis argument.
It is called with `a` as first argument followed by `kwargs`.
kwargs : keyword arguments
additional keyword arguments to pass to `func`.
Returns
-------
result : tuple
Result of func(a, **kwargs) and a.shape with axis dims set to 1
which can be used to reshape the result to the same shape a ufunc with
keepdims=True would produce.
"""
a = np.asanyarray(a)
axis = kwargs.get('axis', None)
if axis is not None:
keepdim = list(a.shape)
nd = a.ndim
axis = _nx.normalize_axis_tuple(axis, nd)
for ax in axis:
keepdim[ax] = 1
if len(axis) == 1:
kwargs['axis'] = axis[0]
else:
keep = set(range(nd)) - set(axis)
nkeep = len(keep)
# swap axis that should not be reduced to front
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
# merge reduced axis
a = a.reshape(a.shape[:nkeep] + (-1,))
kwargs['axis'] = -1
keepdim = tuple(keepdim)
else:
keepdim = (1,) * a.ndim
r = func(a, **kwargs)
return r, keepdim 示例3: _ureduce
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import normalize_axis_tuple [as 别名]
def _ureduce(a, func, **kwargs):
"""
Internal Function.
Call `func` with `a` as first argument swapping the axes to use extended
axis on functions that don't support it natively.
Returns result and a.shape with axis dims set to 1.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
func : callable
Reduction function capable of receiving a single axis argument.
It is is called with `a` as first argument followed by `kwargs`.
kwargs : keyword arguments
additional keyword arguments to pass to `func`.
Returns
-------
result : tuple
Result of func(a, **kwargs) and a.shape with axis dims set to 1
which can be used to reshape the result to the same shape a ufunc with
keepdims=True would produce.
"""
a = np.asanyarray(a)
axis = kwargs.get('axis', None)
if axis is not None:
keepdim = list(a.shape)
nd = a.ndim
axis = _nx.normalize_axis_tuple(axis, nd)
for ax in axis:
keepdim[ax] = 1
if len(axis) == 1:
kwargs['axis'] = axis[0]
else:
keep = set(range(nd)) - set(axis)
nkeep = len(keep)
# swap axis that should not be reduced to front
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
# merge reduced axis
a = a.reshape(a.shape[:nkeep] + (-1,))
kwargs['axis'] = -1
keepdim = tuple(keepdim)
else:
keepdim = (1,) * a.ndim
r = func(a, **kwargs)
return r, keepdim 示例4: _ureduce
# 需要导入模块: from numpy.core import numeric [as 别名]
# 或者: from numpy.core.numeric import normalize_axis_tuple [as 别名]
def _ureduce(a, func, **kwargs):
"""
Internal Function.
Call `func` with `a` as first argument swapping the axes to use extended
axis on functions that don't support it natively.
Returns result and a.shape with axis dims set to 1.
Parameters
----------
a : array_like
Input array or object that can be converted to an array.
func : callable
Reduction function capable of receiving a single axis argument.
It is is called with `a` as first argument followed by `kwargs`.
kwargs : keyword arguments
additional keyword arguments to pass to `func`.
Returns
-------
result : tuple
Result of func(a, **kwargs) and a.shape with axis dims set to 1
which can be used to reshape the result to the same shape a ufunc with
keepdims=True would produce.
"""
a = np.asanyarray(a)
axis = kwargs.get('axis', None)
if axis is not None:
keepdim = list(a.shape)
nd = a.ndim
axis = _nx.normalize_axis_tuple(axis, nd)
for ax in axis:
keepdim[ax] = 1
if len(axis) == 1:
kwargs['axis'] = axis[0]
else:
keep = set(range(nd)) - set(axis)
nkeep = len(keep)
# swap axis that should not be reduced to front
for i, s in enumerate(sorted(keep)):
a = a.swapaxes(i, s)
# merge reduced axis
a = a.reshape(a.shape[:nkeep] + (-1,))
kwargs['axis'] = -1
else:
keepdim = [1] * a.ndim
r = func(a, **kwargs)
return r, keepdim