- Feature parity with ArrayFire v3.6. Refer to the[release notes](https://github.com/arrayfire/arrayfire/blob/master/docs/pages/release_notes.md) for more information regarding upstream library improvements in v3.6.

-`anisotropic_diffusion()`: Anisotropic diffusion filter.

-`topk()`: Returns top-K elements given an array.

- Bug fixes:

- Fixed`sift()` and`gloh()`, which were improperly calling the library.

- Enhancements:

- Added`len()` method, which returns`array.elements()`.

- Documentation:

- Documented statistics API.

- Corrected`sign()` documentation.

- Modified`helloworld` example to match C++ lib.

- Bug fixes when using v3.5 of arrayfire libs + graphics

[ArrayFire](https://github.com/arrayfire/arrayfire) is a high performance library for parallel computing with an easy-to-use API. It enables users to write scientific computing code that is portable across CUDA, OpenCL and CPU devices. This project provides Python bindings for the ArrayFire library.

| OS| Tests|

|:-------:|:-------:|

| Linux|[](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-linux/)|

| Windows|[](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-windows/)|

| OSX|[](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-osx/)|

| Linux on ARM|[](http://ci.arrayfire.org/view/All/job/arrayfire-wrappers/job/python-tegrak1/)|

Documentation for this project can be found[over here](http://arrayfire.org/arrayfire-python/).

author="Pavan Yalamanchili",

author_email="contact@pavanky.com",

name="arrayfire",

version="3.6.0",

version="3.6.20181017",

description="Python bindings for ArrayFire",

license="BSD",

url="http://arrayfire.com",