Python for Engineering Calculations: An Overview
When developing CalcTree, our objective was to design a platform for engineers that would maximize efficiency and quality control by building components and workflows intentionally structured for engineering.
A key part of the design was to enable users to create calculations that are reliable and transparent. At the core of every calculation is a math engine that must be auditable, unit-aware, and trusted across a wide range of disciplines.
Rather than building a proprietary engine from scratch inherent with risks, CalcTree leverages two mature and widely adopted technologies: Python and MathJS. This decision ensures that our users benefit from advanced features such as automation, accurate analysis, and streamlined workflows, as well as the same rigour, community testing, and reproducibility that power scientific research, structural design, and numerical simulations worldwide.
Why Python for Engineering Analysis?
Python is the backbone of modern scientific and engineering computing. It is used extensively for:
- Finite element analysis and structural mechanics
- Matrix-based calculations and response simulations
- Code-based checks and load path evaluations
- Advanced data processing, analysis and visualisation by using libraries such MatPlotLib, Pandas, Numpy, and SciPy
- Integration with open-source structural libraries (e.g., sectionproperties, structural codes etc.)
In CalcTree, Python enables engineers to work efficiently and automate repetitive tasks in their workflow using powerful tools and modules. Calculations involving thousands of data points—such as a large matrix multiplication—execute at comparable speeds to local desktop environments.
Common use cases of Python in CalcTree:
- Automation of repetitive engineering calculations and modelling tasks
- Efficiently replacing traditional spreadsheets for engineering calculations
- Integrating with Excel for advanced data processing and analysis
- Analyzing structural models and engineering data for capacity assessment and verification
Python is also the perfect tool to help users automate workflows and other components of their work in CalcTree, like reporting.
Why MathJS for Calculations?
MathJS powers the live math interface on every CalcTree page. It enables real-time, symbolic, and unit-aware calculations directly in the browser, forming the foundation of CalcTree’s front-end editor. This robust JavaScript library is widely adopted across technical domains and offers:
- Symbolic math support (e.g., sqrt, sum, symbolic derivatives)
- Unit handling, critical for structural and civil workflows
- Parsing of structured, user-defined expressions
- A strong open-source governance model and contributor base
MathJS enables dynamic calculations on the front end, while Python handles back-end computation for advanced tasks. This dual-engine approach ensures performance, accuracy, and flexibility.
What This Means for Engineering Calculations & Analysis in CalcTree
- Every calculation is transparent and auditable. There are no black boxes.
- Our engine is continuously validated by a global developer and research community. Millions of users a day are using, testing and optimising these calculation libraries.
- Integration with Python allows adoption of industry-standard libraries and design code logic.
- CalcTree enhances collaboration among engineering teams, enabling synchronised work and efficient sharing of calculation data.
- CalcTree helps engineers deliver clear and professional presentations to clients and peers, improving the communication of results and design intent.
- CalcTree supports engineering practice by streamlining routine calculations and optimising daily workflows.
- CalcTree can be applied to a wide range of engineering projects, from small-scale designs to large, multidisciplinary initiatives.
While the collaborative nature of the platform can occasionally surface UI issues (such as rendering or syncing delays), the mathematical results are as robust and validated as the underlying libraries allow.
CalcTree does not require a custom QA process for its engines because the open-source community effectively provides one at scale.
