Goodness of fit

goodness_of_fit is a python language software package that provide a set of function for goodness of fit measure between two signals.

While most of these functions are available in packages such as Scipy, Spotpy, etc... this package brings together all these functions and provides a unified interface for their use.

Content of the package

The package provides the following functions :

• Mean Error
• Mean Absolute Error
• Root Mean Square Error
• Normalized Root Mean Square Error
• Pearson product-moment correlation coefficient
• Coefficient of Determination
• Index of Agreement
• Modified Index of Agreement
• Relative Index of Agreement
• Ratio of Standard Deviations
• Nash-sutcliffe Efficiency
• Modified Nash-sutcliffe Efficiency
• Relative Nash-sutcliffe Efficiency
• Kling Gupta Efficiency
• Deviation of gain
• Standard deviation of residual

Getting Started

These instructions will get you a copy of the project up and running on your local machine for development and testing purposes. See deployment for notes on how to deploy the project on a live system.

Prerequisites

goodness_of_fit requires :

• Python 3
• Numpy for efficient computation on array.

Installing

To install the package, clone or download the repository and use the setup.py :

git clone https://github.com/SimonDelmas/goodness_of_fit.git
cd goodness_of_fit
python ./setup.py install

Building the documentation

The documentation could be generated using the command :

python ./setup.py build_sphinx

Running the tests

After installation, you can launch the test suite with pytest :

pytest

License

This project is licensed under the GLP-2.0 License - see the LICENSE.md file for details.