connorferster/handcalcs

Python library for converting Python calculations into rendered latex.

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Summary Information

Updated 37 minutes ago
Added to GitGenius on September 11th, 2026
Created on February 19th, 2020
Open Issues & Pull Requests: 106 (+0)
GitHub issues: Enabled
Number of forks: 450
Total Stargazers: 5,801 (+0)
Total Subscribers: 87 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 10.9 days
Mean response time: 117.5 days
90th percentile: 490.1 days
Tracked items: 34

How this project is maintained

Around half of the issues opened in the past year never receive a reply. 96% of open issues come from outside the core team, so the backlog reflects real-world use rather than internal planning. Only 3% of issues opened in the past year have been closed. Three people close 50% of everything that gets resolved.

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Issue Activity (beta)

Open issues: 23
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 769 days
Stale 30+ days: 23
Stale 90+ days: 21

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 0
Events in 7 days: 0

Top labels

  • enhancement (1)

Most active issues this week

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Detailed Description

handcalcs is a Python library that converts calculations written in code into rendered LaTeX that mimics hand-written mathematical work.

The library solves the problem of making numerical calculations transparent and verifiable by rendering Python code as formatted mathematical expressions. It displays the symbolic formula, followed by the numeric substitution of values, and then the final result, mirroring how an engineer or scientist would write calculations by hand on paper. This approach makes calculations significantly easier to check and verify because all intermediate substitutions are visible.

The tool is designed for engineers and scientists working in Jupyter Notebook or Jupyter Lab who need to document calculations in a clear, auditable format. It works as either a cell magic for quick rendering or as a decorator on functions for more structured use. The library supports PDF printing directly from Jupyter, making it suitable for generating formal calculation reports. Users can also chain multiple notebooks together so that results from one calculation become inputs for the next, enabling the creation of larger engineering automation workflows.

Almost all open issues are raised by outside users rather than the core team, indicating a substantial base of adopters reporting real-world use. A first response to issues or pull requests typically takes one to two weeks. Work in the issue tracker is dominated by enhancement requests.