PyForecastTools documentation¶
A Python module to provide model validation and forecast verification tools, including a set of convenient plot functions. A selection of capabilites provided by PyForecastTools includes:
- Accuracy and bias metrics for continuous predictands
Unscaled/absolute measures
Relative measures
Scaled measures
- 2x2 and NxN contingency table classes
Wide range of contingency table metrics and scores
Multiple methods of calculating confidence intervals on scores
- Convenient plotting for visually comparing models and data
Quantile-Quantile plots
Taylor diagrams
ROC curves
Reliability diagrams
The module builds on the scientific Python stack (Python, Numpy, MatPlotLib) and uses the dmarray class from SpacePy’s datamodel.
SpacePy is available through the Python Package Index, MacPorts, and is under version control at [github.com/spacepy/spacepy](https://github.com/spacepy/spacepy) If SpacePy is not available a reduced functionality implementation of the class is provided with this package.
PyForecastTools is available through the Python Package Index and can be installed simply with
> pip install PyForecastTools –user
To install (local user), run
> python setup.py install –user
After installation, the module can then be imported (within a Python script or interpreter) by
> import verify
For help, please see the docstrings for each function and/or class.
..module::verify