Taylor Diagram

A Taylor Diagram is a graphical representation used to evaluate how well models reproduce the observed data. It displays three statistical measures: correlation coefficient, root mean square deviation (RMSD), and the standard deviation of both observed and model data. This repository contains the code for generating Taylor Diagrams to visualize the performance of various models based on multiple evaluation metrics. The Taylor Diagrams are created from a CSV file containing the observed values and model predictions.

Features

• Generates Taylor Diagrams for multiple models from a CSV file.
• Customizable markers and marker sizes.
• Interactive plots with standard deviation and correlation contours.

Getting Started

Prerequisites

Ensure you have the following Python packages installed:

• numpy
• matplotlib
• pandas

You can install these packages using pip:

pip install numpy matplotlib pandas

Repository Structure

• TaylorDiagram.py: Contains the TaylorDiagram class and script to generate Taylor Diagrams.
• data.csv: Sample CSV file with observed values and model predictions.

Usage

1. Clone the repository:

   git clone https://github.com/EbrahimAlwajih/Taylor-Diagram.git
   cd Taylor-Diagram

2. Prepare your CSV file:

   Ensure your CSV file (data.csv) has the following structure:

   model,observed,Model 1,Model 2,Model 3,Model 4,Model 5
   value1,observed_value,Model 1_value,Model 2_value,Model 3_value,Model 4_value,Model 5_value
   value2,observed_value,Model 1_value,Model 2_value,Model 3_value,Model 4_value,Model 5_value
   ...
   

3. Run the script:

   Execute the script to generate the Taylor Diagrams:

   python TaylorDiagram.py

   The Taylor Diagrams will be displayed in a new window.

Customization

• Markers: You can customize the markers for the radar charts by modifying the markers parameter in the TaylorDiagram class.

  markers = ['*', 's', '^', 'D', 'x']

• Marker Size: You can adjust the size of the markers by changing the marker_size parameter.

  marker_size = 10

License

This code is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Contact

If you have any questions, feel free to reach out to the repository owner.