Tirsvad
790860c465
Refs #17
⏱️ Speed Calc Decorator
A Python decorator that measures how many seconds a function takes to run. It is my solution to a lecture exercise from Udemy's 100 Days of Code: The Complete Python Pro Bootcamp, written for fellow course participants and for anyone browsing for ideas. It has no runtime dependencies.
📚 Table of Contents
- Overview
- Requirements
- Setup
- Run
- Lecture steps
- Tests
- Documentation
- License
- Links
- Appendix: Project documents
🧭 Overview
The assignment: write speed_calc_decorator and use it to print how long
fast_function and slow_function take. Expected output has this shape:
1695050908.1985211
fast_function run speed: 0.33974480628967285s
slow_function run speed: 2.9590742588043213s
The numbers differ on every machine. The repository is laid out as:
| Folder | Purpose |
|---|---|
src/speed_calc/ |
The package: the decorator, the two demo functions, constants.py |
tests/ |
pytest tests; they use a fake clock, so they never depend on real time |
docs/ |
Project documents (Business Case, plan, milestones), screenshots and the wiki |
📋 Requirements
- Python 3.13 or newer (
python --version). git, to clone the repository.- Optional: Doxygen to build the API documentation.
There are no runtime dependencies. pytest is needed only to run the tests.
🛠️ Setup
Create a local virtual environment named .venv in the project folder and
activate it. Windows (PowerShell):
python -m venv .venv
.venv\Scripts\Activate.ps1
Linux and macOS:
python3 -m venv .venv
source .venv/bin/activate
Upgrade pip, then install the project with the test tools:
python -m pip install --upgrade pip
python -m pip install -e ".[dev]"
Leave the environment with deactivate. Delete the .venv folder to start over.
The .venv folder is ignored by git.
▶️ Run
With the environment active:
python -m speed_calc
It prints the current time, then the run time of fast_function and
slow_function. The values depend on your machine.
🪜 Lecture steps
Each step runs on its own with python -m speed_calc.lecture_steps N (N is 1 to
4); step 4 is the same as python -m speed_calc. The screenshots are rendered
from the real output of each command by
docs/screenshots/generate_screenshots.py.
Step 1: the current time
time.time() returns the seconds since January 1st, 1970.
Step 2: the decorator skeleton
A decorator takes a function and returns a wrapper that calls it. functools.wraps
keeps the original name.
Step 3: timing the call
The wrapper reads the clock before and after the call and prints the difference.
Step 4: fast against slow
@speed_calc_decorator above fast_function and slow_function gives the
expected output.
🧪 Tests
python -m pytest
All tests pass; they use a fake clock, so they do not depend on how fast your machine is.
📖 Documentation
The source uses Doxygen comments. Build the HTML documentation into
docs/doxygen/:
doxygen Doxyfile
📄 License
GNU Affero General Public License v3.0, see LICENSE.
🔗 Links
📎 Appendix: Project documents
| Document | What it tells you |
|---|---|
| AGENTS.md | The rules AI agents follow in this repository |
| Business Case | Why the project exists, its scope and success criteria |
| Stakeholder Analysis | Who is involved; the stakeholder IDs used for owners and reviewers |
| Project Plan | The gateways and their schedule |
| Milestones | One document per gateway: Go/No-Go criteria and tasks |
| Artifact registry | Where each artifact type lives, the next version, the PO language and domain |
| Traceability matrix | How the artifacts link and when each was last reviewed |
| Framework README | The framework this project uses |
| Framework changelog | What changed in each framework release |