Osdag Screening Task – Xarray Data Visualization using PyPlot

Overview

This project demonstrates how to load, process, and visualize a scientific dataset using Xarray and Matplotlib (PyPlot).

It was developed as part of the screening task for the FOSSEE Osdag Spring Semester Long Internship – 2026 under the Software Development category.

The program performs the following steps automatically:

1. Generates a synthetic temperature dataset in NetCDF format
2. Loads the dataset using Xarray
3. Produces multiple plots using PyPlot
4. Saves the plots as image files

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Tools and Technologies Used

• Python
• Xarray
• Matplotlib (PyPlot)
• NumPy
• Pandas
• NetCDF4

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Project Structure

osdag-xarray-pyplot/

├── data/ │ └── dataset_generator.py ├── plots/ │ ├── line_plot.png │ ├── histogram.png │ ├── scatter_plot.png │ └── rolling_avg_plot.png ├── plot_xarray.py ├── main.py ├── requirements.txt └── README.md

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Installation

Install all required dependencies using:

pip install -r requirements.txt

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Run the Project

Execute the complete workflow using a single command:

python main.py

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Sample Outputs

Monthly Temperature Line Plot

Temperature Distribution (Histogram)

Temperature Scatter Plot

Temperature Trend with Rolling Average

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Relevance to Osdag

This visualization workflow can be integrated into Osdag for post-processing engineering datasets such as load histories, stress variations, or time-dependent structural responses.

Automated Xarray-based plotting helps engineers quickly analyze simulation results, identify trends, and support data-driven design decisions.

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Conclusion

This project demonstrates a clean and reproducible workflow for scientific data visualization using Xarray and PyPlot. The modular structure and automated plotting approach align well with the goals of engineering software systems like Osdag.