GitLab

This restructuring allows for easier separation of modules into
separate repositories later. Also, now pip install with repo url can be
used.

The setup.py files have also been updated to correctly reference each
other. Module versions are not extracted from git state

- counter-based philox RNG: counter/key is filled with cell coordinate
  and optional external parameters like block position and time step
- works on CPU and GPU - on CPU only for non-vectorized versions

- introduced more flexible "CustomCodeNode" that can inject
  backend-specific hand-written code

- small (length < 5) arrays with shape and stride information had to be
  memcpy'd to the GPU before every kernel call
- instead of passing the information as arrays, the single elements are
  passed
- leads to more function arguments, but simplifies GPU kernel calls

-> changes in all backends required

- defined ordering of headers
- lbm split kernels produced different ordering of assignments due to
  usage of a set

- new time loop caches all kernel functions with their argument dict
  -> inner loop just calls C functions with cached kwargs

- not generic enough - does not work if there a multiple blocks

- better latex display for indirect accesses
- new field type 'custom': only custom fields can be accessed indirectly
  no static bounds check possible for custom fields

- support aligned load/stores
- nontemporal stores
- aligned memory allocation for arrays and temporary buffers

- vectorization for loops with ranges that are not a multiple of vector width
- vectorization for variable sized loops if special transformation
  replace_inner_stride_with_one is run

- when running multiple pystencils instances, sometimes errors happened
  because one process might have partially written a cached file, which
  is already read before writing was finished
-> switched to "atomic write" (only on linux yet) that uses os.rename
   which is guaranteed to be atomic

- removed warnings
- added flake8 as CI target

- test run again
- notebooks not yet

- previously all objects where cached by id()
- for waLBerla simulations in each time step a new np.array view
  is created from the waLBerla field. Each of these views has a
  different id -> caching did not work for waLBerla setups
- changed hash for numpy arrays: instead of id, a tuple of
  (dataPtr, strides, shapes) is used as hash input

- scaling interface width eta instead of surface tensions tau to correct
  interface profile & surface tensions