# Source code for mars.tensor.merge.vstack

```
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# Copyright 1999-2021 Alibaba Group Holding Ltd.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ..base import atleast_2d
from .concatenate import concatenate
[docs]def vstack(tup):
"""
Stack tensors in sequence vertically (row wise).
This is equivalent to concatenation along the first axis after 1-D tensors
of shape `(N,)` have been reshaped to `(1,N)`. Rebuilds tensors divided by
`vsplit`.
This function makes most sense for tensors 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 tensors
The tensors must have the same shape along all but the first axis.
1-D tensors must have the same length.
Returns
-------
stacked : Tensor
The tensor formed by stacking the given tensors, will be at least 2-D.
See Also
--------
stack : Join a sequence of tensors along a new axis.
hstack : Stack tensors in sequence horizontally (column wise).
dstack : Stack tensors in sequence depth wise (along third dimension).
concatenate : Join a sequence of tensors along an existing axis.
vsplit : Split tensor into a list of multiple sub-arrays vertically.
block : Assemble tensors from blocks.
Examples
--------
>>> import mars.tensor as mt
>>> a = mt.array([1, 2, 3])
>>> b = mt.array([2, 3, 4])
>>> mt.vstack((a,b)).execute()
array([[1, 2, 3],
[2, 3, 4]])
>>> a = mt.array([[1], [2], [3]])
>>> b = mt.array([[2], [3], [4]])
>>> mt.vstack((a,b)).execute()
array([[1],
[2],
[3],
[2],
[3],
[4]])
"""
return concatenate([atleast_2d(t) for t in tup], axis=0)
```