Chapter 5: Software Engineering for Scientific Python¶

Part I — Python for Data Science

Learning Objectives¶

By the end of this chapter you will be able to:

• Use Git and GitHub for version-controlled research
• Write and run tests with pytest
• Document code for long-term reproducibility

{note}
Link to dissmodel test suite as a real example of pytest in research.

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# Standard imports — add chapter-specific imports below
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

# Standard imports — add chapter-specific imports below import numpy as np import pandas as pd import matplotlib.pyplot as plt

Version Control with Git¶

TODO: write content.

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# Code for section: Version Control with Git

# Code for section: Version Control with Git

GitHub Workflows for Research¶

TODO: write content.

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# Code for section: GitHub Workflows for Research

# Code for section: GitHub Workflows for Research

Writing Tests with pytest¶

TODO: write content.

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# Code for section: Writing Tests with pytest

# Code for section: Writing Tests with pytest

Reproducible Environments¶

TODO: write content.

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# Code for section: Reproducible Environments

# Code for section: Reproducible Environments

Software Design Principles for Scientists¶

TODO: write content.

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# Code for section: Software Design Principles for Scientists

# Code for section: Software Design Principles for Scientists

Exercises¶

TODO: write content.

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# Code for section: Exercises

# Code for section: Exercises

Summary¶

TODO: summarise key takeaways.

Key concepts introduced¶

• TODO
• TODO
• TODO

Further Reading¶

• TODO: add references.