Efficient 2D mapping for astronomers

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Hello! Welcome to the documentation for astroQTpy (Astronomy Quad Trees in Python), a Python package for efficiently mapping 2D parameter space using quadtrees.

What is astroQTpy?

astroQTpy makes it easy to implement quadtree data structures in your astronomical research. Quadtrees are an efficient way to partition a two-dimensional space by recursively subdividing it into four equal quadrants (or nodes).

In practice, this can speed up your simulations or data analysis by focusing your computational resources on regions of parameter space where large gradients or inhomogeneities exist. In other words, you don’t have to waste time computing models where you know large areas of your parameter space yield similar results because astroQTpy quadtrees automatically subdivide where inhomogeneities exist. You can also use astroQTpy to simply create 2D histograms of your data.

As an added bonus, quadtrees look really cool when you plot them. Nice!

To get started using astroQTpy, check out the Quick Start guide or follow along with the Tutorials.

Note

astroQTpy is a living project that will be continually updated and upgraded over time. Check back for updates or feel free to give us suggestions by creating a new issue on GitHub. You can also define your own custom astroQTpy class – check out the tutorials for more info!

Why use quadtrees?

There are many potential applications of quadtrees in astronomical research! In general, quadtrees can be useful just about anywhere you need to plot some values as a function of two independent variables.

Just to name a few examples where you might use a quadtree:

• Grid-based approach to model optimization.

  • E.g., plotting \(\chi^2\) as a function of two model parameters.

• N-body simulation grids.

  • E.g., MEGNO as a function of two orbital elements.

• Pipeline sensitivity maps.

  • E.g., Exoplanet transit injection/recovery tests as a function of \(R_p/R_*\) and period.

• 2D histograms.

  • E.g., H-R Diagram for a large number of stars.

Attribution:

• If you use astroQTpy in your work, please cite our Zenodo:

@software{harada_astroQTpy_2023,
   author       = {Caleb K. Harada},
   title        = {astroQTpy},
   month        = jul,
   year         = 2023,
   publisher    = {Zenodo},
   version      = {latest},
   doi          = {10.5281/zenodo.8187916},
   url          = {https://doi.org/10.5281/zenodo.8187916}
}

• If you use REBOUND or any of dependencies, please also cite those. The simplest way to find the citations relevant to the specific setup of your REBOUND simulation is:

sim = rebound.Simulation()
-your setup-
sim.cite()

Note

The backbone of astroQTpy is based upon the quadtree algorithm described by Ment & Charbonneau (2023). We thank the authors for their insight and helpful discussions about quadtrees.

User Guide:

• Quick Start
  • Installing astroQTpy
  • Getting started
  • Learn more
• Installation
• Tutorials
  • Random Quadtree Tutorial
  • Linear Chi-Squared Tutorial
  • N-Body MEGNO Stability Map Tutorial
  • 2D Histogram Tutorial
  • N-Body Exomoon Stability Map Tutorial
  • Custom Quadtree Tutorial
• Detailed API Documentation
  • Quadtree
  • Quadtree Node
  • Quadtree Point
  • Basetree

Changelog:

0.2.0 (2023-08-17)

• Clean up 2D histogram feature, complete docs and tutorials (@CalebHarada)

0.1.7 (2023-08-16)

• Add 2D histogram feature (@CalebHarada)

0.1.6 (2023-08-16)

• Add overwrite option (@CalebHarada)

0.1.5 (2023-08-03)

• Minor bug fixes, update docs (@CalebHarada)

0.1.4 (2023-07-31)

• Complete documentation (@CalebHarada)

0.1.3 (2023-07-28)

• New tutorials, update docs, etc. (@CalebHarada)

0.1.2 (2023-07-27)

• Update docs, etc. (@CalebHarada)

0.1.1 (2023-07-26)

• First release on Zenodo (@CalebHarada)