Metadata-Version: 2.1
Name: mistree
Version: 1.1.3
Summary: A python package for constructing and analysing the minimum spanning tree
Home-page: https://knaidoo29.github.io/mistreedoc/
Author: Krishna Naidoo
Author-email: krishna.naidoo.11@ucl.ac.uk
License: MIT
Description: # MiSTree
        
        Author:         Krishna Naidoo                          
        Version:        1.1.3                               
        Homepage:       https://github.com/knaidoo29/mistree    
        Documentation:  https://knaidoo29.github.io/mistreedoc/
        
        [![Build Status](https://travis-ci.org/knaidoo29/mistree.svg?branch=master)](https://travis-ci.org/knaidoo29/mistree) [![codecov](https://codecov.io/gh/knaidoo29/mistree/branch/master/graph/badge.svg)](https://codecov.io/gh/knaidoo29/mistree) [![PyPI version](https://badge.fury.io/py/mistree.svg)](https://badge.fury.io/py/mistree) [![status](https://joss.theoj.org/papers/461d79e9e5faf21029c0a7b1c928be28/status.svg)](https://joss.theoj.org/papers/461d79e9e5faf21029c0a7b1c928be28) [![DOI](https://zenodo.org/badge/170473458.svg)](https://zenodo.org/badge/latestdoi/170473458) [![License: MIT](https://img.shields.io/badge/License-MIT-blue.svg)](https://opensource.org/licenses/MIT) [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/knaidoo29/mistree/master?filepath=tutorials%2Fnotebooks%2F)
        
        ## 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 cosmological parameter estimation studies 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
        
        * Python 2.7 or 3.4+
        * `numpy`
        * `matplotlib`
        * `scipy`
        * `scikit-learn`
        * `f2py` (should be installed with numpy)
        
        ## 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
        want to work on the Github version you can clone the repository and install an editable version:
        
        ```
        git clone https://github.com/knaidoo29/mistree.git
        cd mistree
        pip install -e . [--user]
        ```
        
        You should now be able to import the module:
        
        ```python
        import mistree as mist
        ```
        
        ## Documentation
        
        In depth documentation and tutorials are provided [here](https://knaidoo29.github.io/mistreedoc/).
        
        ## Tutorials
        
        The tutorials in the documentation are supplied as ipython notebooks which can be downloaded from [here](https://github.com/knaidoo29/mistree/tree/master/tutorials/notebooks) or can be run online using [binder](https://mybinder.org/v2/gh/knaidoo29/mistree/master?filepath=tutorials%2Fnotebooks%2F).
        
        ## Support
        
        If you have any issues with the code or want to suggest ways to improve it please open a new issue ([here](https://github.com/knaidoo29/mistree/issues))
        or (if you don't have a github account) email _krishna.naidoo.11@ucl.ac.uk_.
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: Natural Language :: English
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Fortran
Classifier: Programming Language :: Python :: 2.7
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Topic :: Scientific/Engineering
Classifier: Topic :: Scientific/Engineering :: Astronomy
Classifier: Topic :: Scientific/Engineering :: Physics
Classifier: Topic :: Scientific/Engineering :: Visualization
Classifier: Topic :: Scientific/Engineering :: Information Analysis
Classifier: Topic :: Scientific/Engineering :: Mathematics
Requires-Python: >=2.7
Description-Content-Type: text/markdown
