goodness-of-fit 1.0.1

# 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](https://github.com/scipy/scipy), [Spotpy](https://github.com/thouska/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](https://github.com/numpy/numpy) for efficient computation on array.

### Installing

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

`bash 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 :

`bash python ./setup.py build_sphinx `

### Running the tests

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

`bash pytest `

## License

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