# Source code for mars.tensor.base.ravel

```
#!/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.
import numpy as np
from ..datasource import tensor as astensor
[docs]def ravel(a, order="C"):
"""Return a contiguous flattened tensor.
A 1-D tensor, containing the elements of the input, is returned. A copy is
made only if needed.
Parameters
----------
a : array_like
Input tensor. The elements in `a` are packed as a 1-D tensor.
order : {'C','F', 'A', 'K'}, optional
The elements of `a` are read using this index order. 'C' means
to index the elements in row-major, C-style order,
with the last axis index changing fastest, back to the first
axis index changing slowest. 'F' means to index the elements
in column-major, Fortran-style order, with the
first index changing fastest, and the last index changing
slowest. Note that the 'C' and 'F' options take no account of
the memory layout of the underlying array, and only refer to
the order of axis indexing. 'A' means to read the elements in
Fortran-like index order if `a` is Fortran *contiguous* in
memory, C-like order otherwise. 'K' means to read the
elements in the order they occur in memory, except for
reversing the data when strides are negative. By default, 'C'
index order is used.
Returns
-------
y : array_like
If `a` is a matrix, y is a 1-D tensor, otherwise y is a tensor of
the same subtype as `a`. The shape of the returned array is
``(a.size,)``. Matrices are special cased for backward
compatibility.
See Also
--------
Tensor.flat : 1-D iterator over an array.
Tensor.flatten : 1-D array copy of the elements of an array
in row-major order.
Tensor.reshape : Change the shape of an array without changing its data.
Examples
--------
It is equivalent to ``reshape(-1)``.
>>> import mars.tensor as mt
>>> x = mt.array([[1, 2, 3], [4, 5, 6]])
>>> print(mt.ravel(x).execute())
[1 2 3 4 5 6]
>>> print(x.reshape(-1).execute())
[1 2 3 4 5 6]
>>> print(mt.ravel(x.T).execute())
[1 4 2 5 3 6]
>>> a = mt.arange(12).reshape(2,3,2).swapaxes(1,2); a.execute()
array([[[ 0, 2, 4],
[ 1, 3, 5]],
[[ 6, 8, 10],
[ 7, 9, 11]]])
>>> a.ravel().execute()
array([ 0, 2, 4, 1, 3, 5, 6, 8, 10, 7, 9, 11])
"""
a = astensor(a)
if a.ndim == 0:
return a[np.newaxis]
return a.reshape(-1, order=order)
```