mc-lab-edu 0.2.5

Educational implementations of core Monte Carlo method algorithms

mc-lab

Educational implementations of core Monte Carlo method algorithms. The goal is learning, clarity, and nu## Collaboration

Open to collaboration and contributions. If you're interested:

• Open an issue to discuss ideas or report bugs.
• Submit small, focused PRs with tests when public behavior changes.
• For larger changes, start with a brief design proposal in an issue.

Publishing new versions to PyPI

This package is published to PyPI as mc-lab-edu. To publish a new version:

1. Update the version

Edit pyproject.toml and increment the version number:

[project]
name = "mc-lab-edu"
version = "0.1.1"  # or 0.2.0 for larger changes

2. Build and upload

# Clean previous builds
rm -rf dist/

# Build the package
uv run python -m build

# Check the package for issues
uv run twine check dist/*

# Upload to PyPI (requires API token in ~/.pypirc)
uv run twine upload dist/*

3. Setup PyPI authentication (one-time setup)

If you haven't set up authentication yet:

1. Get an API token from https://pypi.org/manage/account/token/
2. Create ~/.pypirc:

[distutils]
index-servers = pypi

[pypi]
repository = https://upload.pypi.org/legacy/
username = __token__
password = pypi-your-actual-token-here

Note: Never commit API tokens to version control. The .pypirc file should remain in your home directory only.

Course contextorrectness over raw speed or micro-optimizations. Expect straightforward NumPy/SciPy-based code with helpful tests and a couple of demo notebooks.

What’s inside

• Basic importance sampling
• Rejection sampling and a few more advance rejection sampling methods
• Normal sampling via Box–Muller (classic and polar) — src/mc_lab/box_mueller.py
• Inverse transform sampling (analytical, numerical interpolation/root-finding, adaptive; plus alias method for discrete) — src/mc_lab/inverse_transform.py
• Multivariate Gaussian sampling with Cholesky/eigendecomposition fallback — src/mc_lab/multivariate_gaussian.py
• Tests in tests/ and a demo notebook in notebooks/

Installation

Install from PyPI:

pip install mc-lab-edu

Google Colab Compatibility

Version 0.2.2+ has been specifically tested to work with Google Colab's package environment. The numpy version is constrained to >=1.24.0,<2.0.0 to avoid compatibility issues with pre-installed packages and scipy. This resolves the _center import errors that occurred with numpy 2.x versions.

Or for local development:

Clone the repository

git clone https://github.com/carsten-j/mc-lab.git
cd mc-lab

Setup for local development

Recommended (uses uv to manage a local .venv and sync dependencies):

# If you don’t have uv yet, see https://docs.astral.sh/uv/ for install options
uv sync
source .venv/bin/activate

Alternative (standard venv + pip):

python3 -m venv .venv
source .venv/bin/activate
pip install -U pip
pip install -e .
# Dev tools (optional but recommended)
pip install pytest ruff ipykernel pre-commit

Verify the install:

python -c "import mc_lab; print(mc_lab.hello())"

Run tests and linting

With uv:

uv run pytest
# Format & lint (either use the Makefile below or direct commands)
uv run ruff format .
uv run ruff check --select I --fix
uv run ruff check --fix

With a plain venv:

pytest
ruff format .
ruff check --select I --fix
ruff check --fix
# Or via the Makefile at the repo root:
make format-fix
make lint-fix
make perftest   # run only tests marked with @pytest.mark.performance (prints output)

Quick usage example

import numpy as np
from mc_lab.multivariate_gaussian import sample_multivariate_gaussian

mu = np.array([0.0, 1.0])
Sigma = np.array([[1.0, 0.5], [0.5, 2.0]])
X = sample_multivariate_gaussian(mu, Sigma, n=1000, random_state=42)
print(X.shape)  # (1000, 2)

Notebooks

Demo notebooks live in notebooks/. If you want the environment available as a Jupyter kernel:

python -m ipykernel install --user --name mc-lab

---

Note: This is an educational project; APIs and implementations may evolve for clarity. If you need production-grade performance, consider specialized libraries or contribute optimizations guarded by tests.

Numba

The Box-Muller implementation can be used with numba for performance improvenments. See the installation notes for numba on how to set it up on your hardware or simply try

uv pip install numba

Collaboration

Open to collaboration and contributions. If you’re interested:

• Open an issue to discuss ideas or report bugs.
• Submit small, focused PRs with tests when public behavior changes.
• For larger changes, start with a brief design proposal in an issue.

Course context

This project was initiated and developed while following the course “NMAK24010U Topics in Statistics” at the University of Copenhagen (UCPH) fall 2025.