1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
|
# 
[](https://github.com/xtensor-stack/xtensor-blas/actions/workflows/linux.yml)
[](https://github.com/xtensor-stack/xtensor-blas/actions/workflows/osx.yml)
[](https://github.com/xtensor-stack/xtensor-blas/actions/workflows/windows.yml)
[](https://xtensor-blas.readthedocs.io/en/latest/?badge=latest)
[](https://mybinder.org/v2/gh/xtensor-stack/xtensor/stable?filepath=notebooks%2Fxtensor.ipynb)
[](https://gitter.im/QuantStack/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
## Introduction
`xtensor-blas` is an extension to the xtensor library, offering bindings to BLAS and LAPACK libraries through cxxblas and cxxlapack from the [FLENS](https://github.com/michael-lehn/FLENS) project.
`xtensor-blas` currently provides non-broadcasting `dot`, `norm` (1- and 2-norm for vectors), `inverse`, `solve`,
`eig`, `cross`, `det`, `slogdet`, `matrix_rank`, `inv`, `cholesky`, `qr`, `svd` in the `xt::linalg` namespace (check the corresponding `xlinalg.hpp` header for the function signatures). The functions, and signatures, are trying to be 1-to-1 equivalent to NumPy.
Low-level functions to interface with BLAS or LAPACK with xtensor containers are also offered in the `blas` and `lapack` namespace.
`xtensor` and `xtensor-blas` require a modern C++ compiler supporting C++14. The following C++ compilers are supported:
- On Windows platforms, Visual C++ 2015 Update 2, or more recent
- On Unix platforms, gcc 4.9 or a recent version of Clang
## Installation
xtensor-blas is a header-only library. We provide a package for the mamba (or conda) package manager.
```
mamba install -c conda-forge xtensor-blas
```
which will also install the core `xtensor` package.
Or you can directly install it from the sources:
```
cmake -D CMAKE_INSTALL_PREFIX=your_install_prefix
make install
```
To build the tests or actually use `xtensor-blas`, you will need binaries for
- `openblas`
- `lapack`
which are also available on conda-forge.
## Trying it online
You can play with `xtensor` interactively in a Jupyter notebook right now! Just click on the binder link below:
[](https://mybinder.org/v2/gh/xtensor-stack/xtensor/stable?filepath=notebooks%2Fxtensor.ipynb)
The C++ support in Jupyter is powered by the [xeus-cling](https://github.com/xtensor-stack/xeus-cling) C++ kernel. Together with xeus-cling, xtensor enables a similar workflow to that of NumPy with the IPython Jupyter kernel.
## Documentation
For more information on using `xtensor`, check out the reference documentation
http://xtensor-blas.readthedocs.io/
## Dependency on `xtensor`
`xtensor-blas` depends on the `xtensor` package
| `xtensor-blas` | `xtensor` |
|-----------------|-----------|
| master | ^0.25.0 |
| 0.21.0 | ^0.25.0 |
| 0.20.0 | ^0.24.0 |
| 0.19.2 | ^0.23.3 |
| 0.19.1 | ^0.23.3 |
| 0.19.0 | ^0.23.0 |
| 0.18.0 | ^0.22.0 |
| 0.17.2 | ^0.21.4 |
| 0.17.1 | ^0.21.2 |
| 0.17.0 | ^0.21.1 |
| 0.16.1 | ^0.20.4 |
| 0.16.0 | ^0.20.0 |
## License
We use a shared copyright model that enables all contributors to maintain the
copyright on their contributions.
This software is licensed under the BSD-3-Clause license. See the [LICENSE](LICENSE) file for details.
|
