The general goal of the project is to create a Python package that can
- trigger the Dynare Preprocessor
- apply Python algorithms to Python representation of a Dynare model
- call Julia to leverage DynareJulia algorithms and recover results as Python objects
- call Matlab/Octave to leverage Dynare Matlab/Octave algorithms and recover results as Python objects
## Development tasks
### Add Python output to the Dynare Preprocessor
Either
- as *.pi file
- as \*.py file
- calling the bytecode library generated by the preprosessor
#### *.py files
#### \*.py files
- Adapt the Julia specific code for Python in
*`ModelTree.hh`
*`DynamicModel.cc` and `DynamicModel.hh`
*`StaticModel.cc` and `StaticModel.hh`
*`ExtendedPreprocessorTypes.hh`
*`ExprNode.hh`
*`ModelEquationBlock.cc` and `ModelEquationsBlock.hh`
* add language Python as language option in `DynareMain.cc` (see Julia option)
* the main differences between Python code and Julia code relevant here are
-`def` instead of `function`
- array indexing starts with 0 instead of 1
- Python functions don't terminate with `end`
- maybe it is possible to generalize the existing code for Julia
* in `ModelTree.hh` creates `dynamic_set_auxiliary_series()` that manipulates time series with `lag` and `lead` functions. We need to choose a time series representation in Python.
#### calling the bytecode library generated by the preprocessor
- the code for `bytecode``mex` file needs to separated between a `mex` wrapper and a standard `C`library
### Decide about the distribution of Dynare Preprocessor for Python (should be transparent to the Python user who installs the Dynare Python package)
### Use [JuliaCall](https://juliapy.github.io/PythonCall.jl/stable/juliacall/) to call DynareJulia
