本文整理汇总了Python中numpy.core.atleast_3d方法的典型用法代码示例。如果您正苦于以下问题:Python core.atleast_3d方法的具体用法?Python core.atleast_3d怎么用?Python core.atleast_3d使用的例子?那么, 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
的用法示例。
在下文中一共展示了core.atleast_3d方法的7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: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of arrays
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
_warn_for_nonsequence(tup)
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
示例2: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
This is equivalent to concatenation along the third axis after 2-D arrays
of shape `(M,N)` have been reshaped to `(M,N,1)` and 1-D arrays of shape
`(N,)` have been reshaped to `(1,N,1)`. Rebuilds arrays divided by
`dsplit`.
This function makes most sense for arrays with up to 3 dimensions. For
instance, for pixel-data with a height (first axis), width (second axis),
and r/g/b channels (third axis). The functions `concatenate`, `stack` and
`block` provide more general stacking and concatenation operations.
Parameters
----------
tup : sequence of arrays
The arrays must have the same shape along all but the third axis.
1-D or 2-D arrays must have the same shape.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays, will be at least 3-D.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
示例3: expand_dims
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def expand_dims(a, axis):
"""
Expand the shape of an array.
Insert a new axis, corresponding to a given position in the array shape.
Parameters
----------
a : array_like
Input array.
axis : int
Position (amongst axes) where new axis is to be inserted.
Returns
-------
res : ndarray
Output array. The number of dimensions is one greater than that of
the input array.
See Also
--------
doc.indexing, atleast_1d, atleast_2d, atleast_3d
Examples
--------
>>> x = np.array([1,2])
>>> x.shape
(2,)
The following is equivalent to ``x[np.newaxis,:]`` or ``x[np.newaxis]``:
>>> y = np.expand_dims(x, axis=0)
>>> y
array([[1, 2]])
>>> y.shape
(1, 2)
>>> y = np.expand_dims(x, axis=1) # Equivalent to x[:,newaxis]
>>> y
array([[1],
[2]])
>>> y.shape
(2, 1)
Note that some examples may use ``None`` instead of ``np.newaxis``. These
are the same objects:
>>> np.newaxis is None
True
"""
a = asarray(a)
shape = a.shape
if axis < 0:
axis = axis + len(shape) + 1
return a.reshape(shape[:axis] + (1,) + shape[axis:])
示例4: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
示例5: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
示例6: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join arrays.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)``.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)
示例7: dstack
# 需要导入模块: from numpy import core [as 别名]
# 或者: from numpy.core import atleast_3d [as 别名]
def dstack(tup):
"""
Stack arrays in sequence depth wise (along third axis).
Takes a sequence of arrays and stack them along the third axis
to make a single array. Rebuilds arrays divided by `dsplit`.
This is a simple way to stack 2D arrays (images) into a single
3D array for processing.
This function continues to be supported for backward compatibility, but
you should prefer ``np.concatenate`` or ``np.stack``. The ``np.stack``
function was added in NumPy 1.10.
Parameters
----------
tup : sequence of arrays
Arrays to stack. All of them must have the same shape along all
but the third axis.
Returns
-------
stacked : ndarray
The array formed by stacking the given arrays.
See Also
--------
stack : Join a sequence of arrays along a new axis.
vstack : Stack along first axis.
hstack : Stack along second axis.
concatenate : Join a sequence of arrays along an existing axis.
dsplit : Split array along third axis.
Notes
-----
Equivalent to ``np.concatenate(tup, axis=2)`` if `tup` contains arrays that
are at least 3-dimensional.
Examples
--------
>>> a = np.array((1,2,3))
>>> b = np.array((2,3,4))
>>> np.dstack((a,b))
array([[[1, 2],
[2, 3],
[3, 4]]])
>>> a = np.array([[1],[2],[3]])
>>> b = np.array([[2],[3],[4]])
>>> np.dstack((a,b))
array([[[1, 2]],
[[2, 3]],
[[3, 4]]])
"""
return _nx.concatenate([atleast_3d(_m) for _m in tup], 2)