Author	Krishna Naidoo
Version	2.0.0
Homepage	https://github.com/knaidoo29/mistree
Documentation	https://knaidoo29.github.io/mistreedoc/

Introduction

The Minimum Spanning Tree (MST) has been used in a broad range of scientific research including computer science, epidemiology, social sciences, particle physics, astronomy and cosmology. Its success in these field has been driven by its sensitivity to the spatial distribution of points and the patterns within. MiSTree, a public Python package, allows a user to construct the MST in a variety of coordinates systems, including Celestial coordinates used in astronomy. The package enables the MST to be constructed quickly by initially using a k-nearest neighbour graph (rather than a matrix of pairwise distances) which is then fed to Kruskal’s algorithm to construct the MST. MiSTree enables a user to measure the statistics of the MST and provides classes for binning the MST statistics (into histograms) and plotting the distributions. Including the MST in parameter estimation studies in cosmology will enable the inclusion of high-order statistics information from the cosmic web. This information has traditionally been unexploited due to the computational cost of calculating N-point statistics.

Dependencies

MiSTree will work on Python >= 3.7 and requires the following Python modules:

• numpy

• numba

• matplotlib

• scipy

• scikit-learn

For testing you will require nose or pytest .

Installation

MiSTree can be installed as follows:

pip install mistree [--user]

The --user is optional and only required if you don’t have write permission. If you are using a windows machine this may not work, in this case (or as an alternative to pip) clone the repository:

git clone https://github.com/knaidoo29/mistree.git
cd mistree

and install by either running:

pip install . [--user]

or:

python setup.py build
python setup.py install

Similarly, if you would like to work and edit mistree you can clone the repository and install an editable version:

git clone https://github.com/knaidoo29/mistree.git
cd mistree
pip install -e . [--user]

From the mistree directory you can then test the install using nose:

python setup.py test

or using pytest:

python -m pytest

Once this is done you should be able to call MiSTree from python:

import mistree as mist

Citing

You can cite MiSTree using the following BibTex:

@ARTICLE{Naidoo2019,
         author = {{Naidoo}, Krishna},
         title = "{MiSTree: a Python package for constructing and analysing Minimum Spanning Trees}",
         journal = {The Journal of Open Source Software},
         year = "2019",
         month = "Oct",
         volume = {4},
         number = {42},
         eid = {1721},
         pages = {1721},
         doi = {10.21105/joss.01721},
         adsurl = {https://ui.adsabs.harvard.edu/abs/2019JOSS....4.1721N}
}

Support

If you have any issues with the code or want to suggest ways to improve it please open a new issue (here) or (if you don’t have a github account) email krishna.naidoo.11@ucl.ac.uk.

Contents

• Lévy Flight
  • Tutorial
    • 2D vs 3D
    • Periodic boundary
    • Random Walk Models
  • Functions
    • get_random_flight()
    • get_levy_flight()
    • get_adjusted_levy_flight()
• Minimum Spanning Tree
  • Tutorials
    • Basic Usage
    • Binning and Plotting
    • Advanced Usage
  • Functions
    • GetMST
    • HistMST
    • PlotHistMST
    • coordinate_utility
    • binning
    • branches
    • construct
    • density
    • graph
    • scale_cut
    • stats
    • tomo
    • plotting

Version History

Version 1.0:

• Constructing the MST of an input data set:

  • 2D, 3D, tomographic or spherical polar coordinates.

  • Apply scale cuts.

• Analysis routines for the MST:

  • Measures the degree, edge length, branch length and branch shape of the constructed MST.

• Constructs random walk distribution with :

  • Lévy flight.

  • Adjusted Lévy flight.

  • User defined random walk distribution.

  • In 2D/3D and with/without periodic boundary conditions.

Version 1.1:

• Added binning (HistMST) and plotting (PlotHistMST) classes for handling the MST statistics.

Version 1.2:

• Added automated testing routines which can be executed using nose or pytest.

Version 2.0

• Completely rewritten fortran subroutines in numba for easier installation without compromise in speed.