- backend, target and compile are now normal members of the
  KernelFunction node and populated in constructor

DestructuringBindingsForFieldClass defines all field-related variables
in its subordinated block.
However, it leaves a TypedSymbol of type 'Field' for each field
undefined.
By that trick we can generate kernels that accept structs as
kernelparameters.
Either to include a pystencils specific Field struct of the following
definition:

```cpp
template<DTYPE_T, DIMENSION>
struct Field
{
    DTYPE_T* data;
    std::array<DTYPE_T, DIMENSION> shape;
    std::array<DTYPE_T, DIMENSION> stride;
}

or to be able to destructure user defined types like `pybind11::array`,
`at::Tensor`, `tensorflow::Tensor`

```

This enables astnodes.Nodes to have a member required_global_declarations
by which they can specify a global declaration required for their usage.

- previous method did not work with kernels generated for walberla where
  block size changes are made at runtime
- device query does not always work, since the compile system may have
  no GPU or not the same GPU
-> max block size is passed as parameter and only optionally determined
   by a device query

Block.subs method tried to be too smart:
a = field[..]
b = a + b

was "simplified" incorrectly to
b = field[...] + b

- stencil plotting & transformation now in ps.stencil
- additional documentation & notebooks

When two loops have assignments to the same symbol with different
rhs and both are pulled before the loops, one of them is now renamed.
Previously one of them was left inside the loop.

Fixes #27

- turned on restrict keyword by default (makes large difference on GPUs)
- smarter block indexing: changing block size depending on domain size
  Example: previously there where (1,1,1) blocks when requested
  block size was (64, 1, 1) and domain size (1, 512, 512), now the
  block size is changed automatically to (1, 64, 1) in this case
- added __lauch_bounds__ to kernels to allow better optimizations from
  the CUDA compiler

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