High priority features, bugs, and other elements of active effort are listed on the github issue tracker. To get involved see guidelines.
- Improve running speed using numpy.arrays instead of xarray.DataArrays
- Adding unit and full tests for Xmile translation
- Improve model execution speed using cython, theano, numba, or another package
- Import model component documentation in a way that enables doctest, to enable writing unit tests within the modeling environment
- Handle simulating over timeseries
- Implement run memoization to improve speed of larger analyses
- Implement an interface for running the model over a range of conditions, build in intelligent parallelization.
- Model Construction
- Outputting models to XMILE or other formats
- SD-lint checker (units, modeling conventions, bounds/limits, etc)
- Contribution to external Data Science tools to make them more appropriate for dynamic assistant
- Basic XMILE and Vensim parser
- Established library structure and data formats
- Simulation using existing Python integration tools
- Integration with basic Python Data Science functionality
- Run-at-a-time parameter modification
- Time-variant exogenous inputs
- Extended backends for storing parameters and output values
- Demonstration of integration with Machine Learning/Monte Carlo/Statistical Methods
- Python methods for programmatically manipulating SD model structure
- Turn off and on 'traces' or records of the values of variables