# Source code for mars.tensor.base.atleast_3d

```#!/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
#
#
# 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.

import numpy as np

from ...core import ExecutableTuple
from ..datasource import tensor as astensor

[docs]def atleast_3d(*tensors):
"""
View inputs as tensors with at least three dimensions.

Parameters
----------
tensors1, tensors2, ... : array_like
One or more tensor-like sequences.  Non-tensor inputs are converted to
tensors.  Tensors that already have three or more dimensions are
preserved.

Returns
-------
res1, res2, ... : Tensor
A tensor, or list of tensors, each with ``a.ndim >= 3``.  Copies are
avoided where possible, and views with three or more dimensions are
returned.  For example, a 1-D tensor of shape ``(N,)`` becomes a view
of shape ``(1, N, 1)``, and a 2-D tensor of shape ``(M, N)`` becomes a
view of shape ``(M, N, 1)``.

--------
atleast_1d, atleast_2d

Examples
--------
>>> import mars.tensor as mt

>>> mt.atleast_3d(3.0).execute()
array([[[ 3.]]])

>>> x = mt.arange(3.0)
>>> mt.atleast_3d(x).shape
(1, 3, 1)

>>> x = mt.arange(12.0).reshape(4,3)
>>> mt.atleast_3d(x).shape
(4, 3, 1)

>>> for arr in mt.atleast_3d([1, 2], [[1, 2]], [[[1, 2]]]).execute():
...     print(arr, arr.shape)
...
[[[1]
[2]]] (1, 2, 1)
[[[1]
[2]]] (1, 2, 1)
[[[1 2]]] (1, 1, 2)

"""
new_tensors = []
for x in tensors:
x = astensor(x)
if x.ndim == 0:
x = x[np.newaxis, np.newaxis, np.newaxis]
elif x.ndim == 1:
x = x[np.newaxis, :, np.newaxis]
elif x.ndim == 2:
x = x[:, :, None]

new_tensors.append(x)

if len(new_tensors) == 1:
return new_tensors[0]
return ExecutableTuple(new_tensors)
```