Smif

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- Implemented continuous deployment to PyPi using Travis CI
- Uses numpy arrays for passing data between scenarios and models
- Refactored space-time convertor functions
- Read ModelSet convergence settings from model configuration data
- Added units to model metadata class and require as well as spatial and
temporal resolutions
- Added UML class diagrams to documentation
- Refactored to create discrete model objects which inherit from an
abstractclass
- Complete restructuring of package

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- Fast, more compact YAML
- Input, output and pre-specified planning files can now be empty
- State is passed between successive time steps
- Interdependencies (cycles in dependencies) are now supported,
models are run in cycles stopping at convergence or timeout
- Non-unique time interval definitions are supported

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- Basic conversion of time intervals (aggregation, disaggregation, remapping) and regions (aggregation, disaggregation)
- Results are written out in a yaml dump with the ``-o`` flag e.g. ``smif run -o results.yaml model.yaml``
- Single one-way dependencies with spatio-temporal conversion are supported
- Simplified and harmonised implementation of model inputs and outputs

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- Run a single simulation model for a single timestep
- Provide a model with scenario data and planned interventions
- Configure a model with sets of regions and sets of time intervals for within-
timestep simulation