- build-catalogue
  - now installed by pip
  - enable GPU-backends
  - isolate from being installed in a non-default dir
  - rename to arbor-build-catalogue
  - allow hand-written C++ mechanisms to be linked in the same catalogue.
- CMake
  - enable PIC on arbor always to allow linking to shared objects
  - use relative paths when configuring a-b-c, to make relocation less of a problem
- Python
    - ensure a-b-c is installed by pip, along with headers and libarbor.a
    - throw errors if tools (cmake scripts, arbor package, modcc) missing
    - transition setup.py setuptools -> skbuild.
- Wheels
    - Add NML2 support to wheels
    - scripts/build-wheels.sh builds wheels, in principle valid for submission to PyPI, on your own hardware. This should be kept in sync with .github/workflows/ciwheel.yml
    - scripts/patchwheel.py corrects the rpath in the libraries in the wheels, working around a bad interplay between auditwheel and skbuild, see pypa/auditwheel#363
    - Python Wheels are tested as part of the Github Action 
    - Add nml and bundled status to config().

The duplication arose from slightly more complicated composition than usual but could be resolved by proper MRO, it took me a few passes from its original form as well before I figured out the situation is not as complicated as it seems.

`super()` is equal to `super(cls, self)` where `cls` is obtained from the current function's class scope. If arg 2  is an object it will start an MRO search of the object, but skipping classes before the 1st arg type is encountered. In our case with the following base classes: `(_command_template, base_command_class)` this means we can define our base logic in `_command_template` and use `super()` calls there to traverse the MRO in `(base_command_class,)`, so by simply defining `class install_command(_command_template, install)` and `bdist_wheel_command(_command_template, bdist_wheel)` our MRO issue is solved and the duplication removed.

Don't pass any architecture by default in setup.py to allow for building binary wheels for targets like x86_64.

Build Python package with 'none' arch by default.
Bump version to 0.5.2.

* Update URLs to arbor-sim.org
* update badge links

* Auto-generate binary Python wheels, triggered by git tags 'v*'.
  * Wheels are generated for Python versions 3.6-3.9, for Linux using the [PyPA manylinux-2014 image](https://github.com/pypa/manylinux) and MacOS using macos-latest (10.15 at time of writing).
  * The Action generates `sdist` for any other platforms.
  * The cibuildwheel action automatically prepares the wheels using [auditwheel](https://cibuildwheel.readthedocs.io/en/stable/options/#repair-wheel-command), which means bundling of external deps (e.g. libxml2 for NeuroML).
  * The output is all that's needed for publication of a new version to (Test)PyPI.
* `setup.py` defaults `neuroml` to on.
* A small change to `CMakeLists.txt` was required to build the wheels: based on this [Pybind hint](https://pybind11.readthedocs.io/en/stable/compiling.html#findpython-mode) `Development.Module`is now required, and not `Development` when CMake searches for Python. I confirm this change is needed to make Arbor build in the manylinux docker images, but I do not understand what the change implies, other than that we now don't link to `libpython`.

* Update `README.md`: moved citation to `doc/index.rst`.
* Advertise Py3.9 compat PyPI in setup.py
* Fix typos.

* Move contents of `arbornml` to to `arborio` 
* `arborio` will be built with NeuroML2 morphology support if `ARB_CMAKE_NEUROML` is set. 
* Wrap `arborio::neuroml` and `arborio::morphology_data` for pyarb.
* Add documentation.
* add `neuroml` flag to `setup.py`

Fixes #1256
Fixes #1234

Numpy is now explicitly required for use in the Python probes interface.

* Add the ability to use json and pybind11 libraries installed on the system instead of the
  vesrsions of those libraries bundled as submodules in the Arbor repository
  * Turned on by default
  * Toggled using the new `ARB_USE_BUNDLED_LIBS` CMake flag
* Update documentation for installation
* Update CI and pip workflows to opt in to the bundled libraries

Revert a change introduced in #1282 that required the python module for arbor to build the documentation.

This breaks the readthedocs build, where arbor is not available.

* Remove arbor dependence from `doc/conf.py`, reverting to the original "out of core" approach
* Fix other recently introduced issues that lead to CMake errors when configuring the documentation
  when the Python target is not configured.
  * The CMake variable `PYTHON_EXECUTABLE` wasn't set, but was required to make calls to the
     Python interpreter.
  * Instead of relying on pybind11 to find Python, explicitly detect it using `find_package(Python3 ...)`
  * Make Python3 a hard requirement when building the Python wrapper
  * And a soft requirement otherwise, that will fail only when the docs are built.

- Add option to compile Arbor for HIP/CUDA backend using Clang. 
- Add new CMake option `ARB_GPU_COMPILE_TYPE` to distinguish between three possible gpu platforms/builds: `cuda-nvcc`; `cuda-clang`; `hip-clang`
- Add gpu wrapper functions in `arbor/backends/gpu/gpu_api.hpp` and `arborenv/gpu_api.hpp` which call HIP/CUDA functions depending on the platform
- Rename functions and files: cuda -> gpu
- Add downgraded warp primitives for HIP
- Implement `uuid` workaround for HIP
- Set correct alignment and warp size for AMD gpus
- Update installation guide and docs.
- Update pip/setuptools to support new gpu targets

Fixes #833

Work around issue caused by some versions of pip skipping the `install` stage when no additional command line args are passed via `--install-option`.

Keep settings that can be modified via install option in a singleton that initializes them to their default.

* add MANIFEST file that describes all paths required in source distribution so that pip can compile Arbor.
* fixes to `setup.py` to help it work across supported Python versions.

Tested with:
* setup tools and pip
* python 3.7 and 3.8
* pip 19 and pip 20
* Linux and OS X

Add a `setup.py` for installing Arbor directly with pip/setuptools.

Implement a setuptools extension for CMake in `setup.py`. To be honest, I don't understand entirely how it works, but setuptools is obtuse enough that I don't feel at all guilty about this.

Define additional flags for optionally enabling GPUs, MPI, Vectorization and micro-architecture targets, for more adventurous users.

The documentation is updated with a "howto pip" for more casual users who don't want anything to do with CMake.

Fixes #958 .