Welcome to La Forge’s documentation!

La Forge

Pulsar Timing Array Bayesian Data Visualization

Graphic Credit: Stewart Vernon, via Deviant Art

Python package for conveniently plotting results from pulsar timing array bayesian analyses. Many of the functions are best used with enterprise outputs.

La Forge is available on PyPI:

pip install la-forge

• Free software: MIT license

• Documentation: https://la-forge.readthedocs.io.

Features

• Sweep up Bayesian analysis MCMC chains along with sampling info.

• Allow easy retrieval of various samples from chains.

• Support for saving chains as HDF5 files.

• Call chains with parameter names.

• Plot posteriors easily.

• Reconstruct Gaussian process realizations using posterior chains.

• Plot red noise power spectral density.

• Separate consituent models of a hypermodel analysis.

This package was created with Cookiecutter and the audreyr/cookiecutter-pypackage project template.

Contents:

• Installation
  • Stable release
  • From sources
• Tutorial #1 Basic Core Usage
  • Loading a Chain Directory
  • Access Parameters as Keywords
  • Parameter Statistics
  • Jump Proposal Acceptance
  • Runtime Information
  • Parameter Covariance Matrix
• Tutorial #2 Plotting Posteriors
• Tutorial #3 Plotting Red Noise Spectra
• Tutorial #4 HyperModel Cores
  • Loading Chains
• Tutorial #5 Visualizing Gaussian Process Realizations
  • Load Chains
• Gaussian Process Realizations
  • The reconstruct_signal method
• la_forge
  • la_forge package
• Contributing
  • Types of Contributions
  • Get Started!
  • Pull Request Guidelines
  • Tips
  • Deploying
• Credits
  • Development Leads
  • Contributors
• History
  • 0.1.0 (2018-09-21)*

Indices and tables

• Index

• Module Index

• Search Page