Sum and Product have a indexing variable which is a Atom but not a free
symbol

This commit avoid dependency of core pystencils on jinja2.
However this could make the printing of some AST-nodes less elegant.

Stephan Seitz authored Jul 18, 2019 and Martin Bauer committed Jul 18, 2019

- rename header Field.h is not a unique name in waLBerla context
- add PyStencilsField.h
- bindings were lacking data type

- 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`

```

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

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

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

- was not used consistently before
- symbol names are expected to be valid C identifiers
- for complicated field names, the latex_name of field should be used

- 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

- 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

- flags can be force when setting boundaries, this is helpful for:
- setting up quantities (e.g. relaxation rate..) dependent on current
  flag configuration
- bit operation fix when pickling: function has to have the same name
  as the python object

- new kernel creation function for kernels that write to staggered fields
- simplification of conditionals with integers uses ISL library now
  via islpy bindings

- removed warnings
- added flake8 as CI target

- test run again
- notebooks not yet

- assignment collection
- sympyextensions

- switched to google style docstrings
- removed dead code
- started to annotate types

- single function to create kernel for specified target
- data type creation from string - reuse numpy functionality
- bugfixes in dot display

- loop cutting
- simplification of conditionals inside loop

Testcases test_mu_equivalence, test_phi_equivalence are passing.

Added LLVM: CodePrinter and a compiler
Updated data_types
Added tests
Added jupyter notebooks
Fixed bugs
Restructured transformation functions