Demo Notebooks

New to PyBird? Start with the Correlator Tutorial - it showcases the basic usage and key features of PyBird.

All demo notebooks are located in the demo/ directory. You can run them locally or view them on GitHub.

Correlator Tutorial (Start Here!)

The main PyBird tutorial covering:

  • Basic correlator setup and configuration

  • Computing power spectra and correlation functions

  • JAX acceleration and neural network emulator

A quick note on the backend control:

Regardless of installation mode, you control which backend PyBird uses:

from pybird.config import set_jax_enabled

# Use NumPy backend (works with both installation modes)
set_jax_enabled(False)

# Use JAX backend (works if JAX dependencies are available)
set_jax_enabled(True)

View: correlator.ipynb on GitHub | NBViewer

Likelihood Tutorial

Data analysis with PyBird:

  • Loading BOSS data and configuring likelihoods

  • Parameter estimation and χ² evaluation

  • Survey effects and observational systematics

View: likelihood.ipynb on GitHub | NBViewer

Complete Parameter Inference

High-level PyBird workflow:

  • Parameter inference with the Run class

  • MCMC sampling and optimization

  • GetDist integration and posterior analysis

View: run.ipynb on GitHub | NBViewer

BOSS DR12 Analysis

Full inference pipeline with BOSS DR12 LRG data:

  • Loading and configuring BOSS likelihood

  • Running MCMC with real data

  • Posterior analysis and visualization

View: run_boss.ipynb on GitHub | NBViewer

Abacus Simulations

Running PyBird with Abacus mock data:

  • Configuring Abacus likelihood

  • ELG, LRG, and QSO tracers

  • Validation against simulations

View: run_abacus.ipynb on GitHub | NBViewer

JAX Benchmarking

Speed optimization and JAX features:

  • Performance comparison: Standard vs JAX vs Emulator

  • Up to 1000x speedup demonstration

  • Vectorized batch processing

View: jaxbird_benchmarking.ipynb on GitHub | NBViewer

Fake Data Generation

Generate synthetic datasets:

  • Creating realistic mock catalogs

  • Survey geometry simulation

  • Statistical validation

View: fake.ipynb on GitHub | NBViewer

Advanced Inference

Low-level inference control:

  • Custom sampler configuration

  • Fisher matrix calculations

  • Taylor expansion methods

View: inference.ipynb on GitHub | NBViewer

Running Locally

Quick Setup:

# Clone and install
git clone https://github.com/pierrexyz/pybird.git
cd pybird
pip install jupyter matplotlib getdist

# Launch notebooks
jupyter notebook demo/

Learning Path

  1. Start: correlator.ipynb - Learn PyBird basics and JAX features

  2. Data Analysis: likelihood.ipynb - Understand data fitting

  3. Complete Workflow: run.ipynb - Master parameter inference

  4. Real Data: run_boss.ipynb - BOSS DR12 analysis

  5. Simulations: run_abacus.ipynb - Abacus mock data

  6. Custom Analysis: inference.ipynb + fake.ipynb - Build custom solutions

  7. Performance (optional): jaxbird_benchmarking.ipynb - Test the speed of PyBird on your local machine

Getting Help