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- bugfix: loop counter of vectorized loop now correctly stored as SIMD vector with entries i, i+1, i+2, ...
- basis for in-kernel boundary handling

For sympy, oo s a number. So pystencils prints a type infinity a double
INFINITY as INFINITY.0

Pystencils will print two const when printing an assignment to a const numpy.dtype.
With the new type interference, this case can now occur more often

There is now a superior implementation in pystencils_autodiff.

This is actually a AST Node for wrapper code. pystencils_autodiff has
now a AST printer for framework integration code. So framework AST nodes
are now there and kernel AST nodes remain here.

headers and global_declarations are generated by methods that return
sets. So even with the same inputs it is not guaranteed that the same
source code is generated since sets do not guarantee a specific order
when iterating over them.

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

- for example: sqrt(cast_func(3, double)) previously printed "sqrt(3)"
  instead of "sqrt(3.0)" leading to errors in CUDA code

`instruction_set`

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

functions like absolute values could not be used in CUDA backend since
known_functions was replaced instead of updated

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`

```