SkillMetrics 1.2.4

A Python library for calculating and displaying the skill of model predictions against observations.

Skill Metrics Project

This package contains a collection of functions for calculating the skill of model predictions against observations. It includes metrics such as root-mean-square-error (RMSE) difference, centered root-mean-square (RMS) difference, and skill score (SS), as well as a collection of functions for producing target and Taylor diagrams. The more valuable feature of the package are the plotting functions for target and Taylor diagrams and the ability to easily customize the diagrams.

Features

• Statistical metrics such as root-mean-square-error (RMSE) difference, centered root-mean-square (RMS) difference, and skill score (SS)
• Functions to calculate statistical metrics for target & Taylor diagrams
• Target Diagrams
• Taylor Diagrams
• Options to control plot features such as color of labels and lines, width of lines, choice of markers, etc.
• Output of graphics to any supported matplotlib format (default PNG).

Installation

To install the package simply use the pip3 command:

% pip3 install SkillMetrics

If you are upgrading the package then include the upgrade option:

% pip3 install SkillMetrics --upgrade

Note that the SkillMetrics package now only supports Python 3 because Python 2 has been depricated. Use of pip may not successfully install the latest version of the package.

Example Scripts

A primer on Taylor diagrams is provided as well as a 6-page description of target and Taylor diagrams as visual tools to aid in the analysis of model predictive skill. The figures used in the latter were generated with the SkillMetrics package. There is also an "Examples" folder that contains a collection of example Python scripts showing how to produce target and Taylor diagrams in a variety of formats on the Wiki Home page. There are multiple examples for target and Taylor diagrams that successively progress from very simple to more customized figures. These series of examples provide an easy tutorial on how to use the various options of the target_diagram and taylor_diagram functions. They also provide a quick reference in future for how to produce the diagrams with specific features.

There is also a simple program all_stats.py available via the Wiki that provides examples of how to calculate the various skill metrics used or available in the package. All the calculated skill metrics are written to a spreadsheet file for easy viewing and manipulation: Excel for a Windows operating system, Comma Separated Value (CSV) for a Macintosh operating system (MacOS). The Python code is kept to a minimum.

Example Diagrams

The diagrams produced by the example scripts are in Portable Network Graphics (PNG) format and have the same file name as the script with a .png suffix. The PNG files created can be viewed by following the links shown below. This is a useful starting point for users looking to identify the best example from which to begin creating a diagram for their specific need by modifying the accompanying Python script.

Target Diagrams

Taylor Diagrams

Here is a sample of the target and Taylor diagrams you'll find in the above examples:

target diagram	Taylor diagram

FAQ

A list of Frequently Asked Questions (FAQ) is maintained on the Wiki. Users are encouraged to look there for solutions to problems they may encounter when using the package.

Available Metrics

Here is a list of currently supported metrics. Examples of how to obtain them can be found in the all_stats.py program. A far more extensive list of statistical metrics can be calculated using the SeqMetrics package.

Metric	Description
bias	Mean error
BS	Brier score
BSS	Brier skill score
CRMSD	centered root-mean-square error deviation
KGE09	Kling-Gupta efficiency 2009
KGE12	Kling-Gupta efficiency 2012
NSE	Nash-Sutcliffe efficiency
r	Correlation coefficient
RMSD	root-mean-square error deviation
SDEV	standard deviation
SS	Murphy's skill score

How to cite SkillMetrics

Peter A. Rochford (2016) SkillMetrics: A Python package for calculating the skill of model predictions against observations, http://github.com/PeterRochford/SkillMetrics

  @misc{rochfordskillmetrics, 
    title={SkillMetrics: A Python package for calculating the skill of model predictions against observations}, 
    author={Peter A. Rochford}, 
    year={2016}, 
    url={http://github.com/PeterRochford/SkillMetrics}, 

Guidelines to contribute

1. In the description of your Pull Request (PR) explain clearly what it implements/fixes and your changes. Possibly give an example in the description of the PR.
2. Give your pull request a helpful title that summarises what your contribution does.
3. Write unit tests for your code and make sure the existing backward compatibility tests pass.
4. Make sure your code is properly commented and documented. Each public method needs to be documented as the existing ones.