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Hierarchical Graph Analysis

Project description

# Higra: Hierarchical Graph Analysis

[![Build Status](https://travis-ci.org/higra/Higra.svg?branch=master)](https://travis-ci.org/higra/Higra) [![Build status](https://ci.appveyor.com/api/projects/status/oo0v2uepcxihvwno?svg=true)](https://ci.appveyor.com/project/PerretB/higra-21ed3) [![codecov](https://codecov.io/gh/higra/Higra/branch/master/graph/badge.svg)](https://codecov.io/gh/higra/Higra) [![Documentation Status](https://readthedocs.org/projects/higra/badge/?version=latest)](https://higra.readthedocs.io/en/latest/?badge=latest)

Higra is a C++/Python library for efficient sparse graph analysis with a special focus on hierarchical methods. Some of the main features are:

  • efficient methods and data structures to handle the dual representations of hierarchical clustering: trees (dendrograms) and saliency maps (ultrametric distances);
  • hierarchical clusterings: quasi-flat zone hierarchy, hierarchical watersheds, agglomerative clustering (single-linkage, average-linkage, complete-linkage, exponential-linkage, Ward, or custom rule), constrained connectivity hierarchy;
  • component trees: min and max trees;
  • manipulate and explore hierarchies: simplification, accumulators, cluster extraction, various attributes (size, volume, dynamics, perimeter, compactness, moments, etc.), horizontal and non-horizontal cuts, hierarchies alignment;
  • optimization on hierarchies: optimal cuts, energy hierarchies;
  • algorithms on graphs: accumulators, vertices and clusters dissimilarities, region adjacency graphs, minimum spanning trees and forests, watershed cuts;
  • assessment: supervised assessment of graph clusterings and hierarchical clusterings;
  • image toolbox: special methods for grid graphs, tree of shapes, hierarchical clustering methods dedicated to image analysis, optimization of Mumford-Shah energy.

Higra is thought for modularity, performance and seamless integration with classical data analysis pipelines. The data structures (graphs and trees) are decoupled from data (vertex and edge weights ) which are simply arrays ([xtensor](https://github.com/QuantStack/xtensor) arrays in C++ and [numpy](https://github.com/numpy/numpy) arrays in Python).

## Installation

### Python

The Python package can be installed with Pypi:

`bash pip install higra `

Supported systems:

  • Python 3.4, 3.5, 3.6, 3.7
  • Linux 64 bits, macOS, Windows 64 bits

### C++ backend

The C++ backend is an header only library. No facilities for system wide installation is currently provided: just copy/past where you need it!

## Documentation

[https://higra.readthedocs.io/](https://higra.readthedocs.io/)

## Demonstration and tutorials

Check the dedicated repository [Higra-Notebooks](https://github.com/higra/Higra-Notebooks) for a collection of Jupyter Notebooks dedicated to Higra.

## Build

### With cmake

Requires:

  • cmake
  • Python + Numpy

Commands:

`bash git clone https://github.com/higra/Higra.git mkdir build cd build cmake ../Higra/ make `

Sometimes, cmake gets confused when several Python versions are installed on the system. You can specify which version to use with -DPYTHON_EXECUTABLE:FILEPATH=/PATH-TO-PYTHON/python, e.g.

` cmake -DPYTHON_EXECUTABLE:FILEPATH=/anaconda3/bin/python ../Higra/ `

The python package is build in the directory

` build/higra/ `

### With setup.py

The file setup.py is a thin wrapper around the cmake script to provide compatibility with python setuptools.

` pip install cmake python setup.py bdist_wheel cd dist pip install higra*.whl `

## Developing extensions

While Higra provides many vectorized operators to implement algorithms efficiently in Python, it is possible that some operations cannot be done efficiently in Python. In such case, the [Higra-cppextension-cookiecutter](https://github.com/higra/Higra-cppextension-cookiecutter) enables to easily setup and generate c++ extension using Higra with Python bindings.

## Third-party libraries

Higra bundles several third-party libraries (inside the lib folder):

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