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Wildlife","monthly_downloads":208,"total_dependent_repos":0,"total_dependent_packages":2,"readme":"# Circuitscape\n\n[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://docs.circuitscape.org/Circuitscape.jl/latest/)\n[![Build Status](https://github.com/Circuitscape/Circuitscape.jl/workflows/CI/badge.svg)](https://github.com/Circuitscape/Circuitscape.jl/actions?query=workflow%3ACI) \n[![codecov.io](http://codecov.io/github/Circuitscape/Circuitscape.jl/coverage.svg?branch=master)](http://codecov.io/github/Circuitscape/Circuitscape.jl?branch=master)\n\n\u003e [!NOTE]  \n\u003e Please note that to run the latest version of Circuitscape.jl, you need to install [Julia v1.11](https://julialang.org/downloads/) or up. \n\nCircuitscape is an open-source program that uses circuit theory to model connectivity \nin heterogeneous landscapes. Its most common applications include modeling movement and gene flow \nof plants and animals, as well as identifying areas important for connectivity conservation. \n\nCircuitscape has now been rewritten in [Julia](https://julialang.org) for better performance and scalability. \nThis work is based on the Python implemention in [Circuitscape.py](https://github.com/Circuitscape/Circuitscape.py).\n\n## The New Circuitscape - Modern, Fast and Flexible\n\nThe new Circuitscape is built entirely in the Julia language, a new\nprogramming language for technical computing. Julia is built from the\nground up to be [fast](http://julialang.org/benchmarks). As such, this offers a\nnumber of advantages over the previous version, and these are detailed below.\n\n### Faster and More Scalable\n\nWe benchmarked `Circuitscape.jl` (v0.1.0) with the Python version (v4.0.5) to obtain the\nfollowing results. We started up Circuitscape with 16 parallel processes,\nand used benchmark problems from the standard Circuitscape \n[benchmark suite.](https://github.com/Circuitscape/BigTests)\n\n\u003cimg src=\"docs/src/benchmark/benchmark.png\" width=650 height=450\u003e\n\nThese benchmarks were run on a Linux (Ubuntu) server machine with the following specs: \n* Name: Intel(R) Xeon(R) Silver 4114 CPU \n* Clock Speed: 2.20GHz\n* Number of cores: 20  \n* RAM: 384 GB\n\nFrom the benchmark, we see that the new version is upto *4x faster*\non 16 processes. However, the best performing bar in the chart is \n_Julia-CHOLMOD_, which is a new feature introduced.\n\n### New Solver Mode - CHOLMOD\n\nJulia-CHOLMOD is a new solver mode used in the new Circuitscape. It performs a [cholesky\ndecomposition](https://en.wikipedia.org/wiki/Cholesky_decomposition) on the graph \nconstructed, and performs a batched back substitution\nto compute the voltages. It plugs into the \n[CHOLMOD](http://faculty.cse.tamu.edu/davis/suitesparse.html) library, \nwhich is part of the SuiteSparse collection of high performance sparse \nmatrix algorithms.\n\nTo use the this new mode, include a line in your Circuitscape \nINI file:\n```\nsolver = cholmod\n```\n\nThe cholesky decomposition is a direct solver method, unlike the algebraic\nmultigrid method used by default in both the old and the new version.\nThe advantage with this new direct method is that it can be much faster than\nthe iterative solution, within a particular problem size. \n\n*Word of caution*: The cholesky decomposition is not practical\nto use beyond a certain problem size because of phenomenon called\n[fill-in](https://algowiki-project.org/en/Cholesky_method#Reordering_to_reduce_the_number_of_fill-in_elements), which results in loss of sparsity and large memory consumption.\n\n### Parallel, everywhere \n\nThe old Circuitscape had limited support for parallelism, which worked on Mac and\nLinux, but didn't work on Windows. \n\nJulia as a programming language is built from the ground up to be parallel,\nand as a result the new Circuitscape natively supports parallelism on all three\nplatforms.\n\n### Single Precision (Experimental)\n\nThe new Circuitscape introduces the ability to run problems in\nsingle precision as opposed to the standard double precision.\n\nSingle precision usually takes much less memory, but trades off\nagainst solution accuracy. \n\nUse this new feature by including a line in your config file:\n```\nprecision = single\n```\n\n## Installation \n\n1. You will need to [install](https://julialang.org/downloads/) Julia on your system first.  \n\n2. Once you start Julia, install Circuitscape by: \n\n```julia\njulia\u003e using Pkg\njulia\u003e Pkg.add(\"Circuitscape\")\n```\n\nIf you want the latest development version, you can additionally do: \n\n```julia\njulia\u003e Pkg.add(PackageSpec(name=\"Circuitscape\", rev=\"master\"))\n```\n\nCheck if all the tests are passing by doing the following:\n\n```julia\njulia\u003e Pkg.test(\"Circuitscape\")\n```\n\n## Usage\n\nThe current interface to Circuitscape is through the Julia terminal. \n\n```julia\njulia\u003e using Circuitscape # loads the package into your environment\njulia\u003e compute(\"path/to/config/file.ini\")\n```\n\n## Contributing\n\nIf you have encounter any issues or would like to ask a question, please file \na report [here](https://github.com/ranjanan/Circuitscape.jl/issues).\nContributions in the form of \n[pull requests](https://github.com/ranjanan/Circuitscape.jl/pulls) are also welcome! \n\n## Notes on INI files \n\nCircuitscape takes as input INI files, which contain paths to the raster map, sources, grounds,\nand other inputs, as well as flags for each run. If you're using the [GUI](https://circuitscape.org/downloads/)\nthe INI file will automatically be generated for you and then fed into Circuitscape. But if you're \nusing the Julia prompt, then you must write one yourself. The easiest way to do this is to copy \nan INI file [from the tests](https://github.com/Circuitscape/Circuitscape.jl/tree/master/test/input) and then modify it depending on your problem. \n\n## Citation\n\nA preprint is available here: https://proceedings.juliacon.org/papers/10.21105/jcon.00058. You can also use the following BibTeX entry to cite this package: \n```bibtex\n@article{Anantharaman2020,\n  doi = {10.21105/jcon.00058},\n  url = {https://doi.org/10.21105/jcon.00058},\n  year = {2020},\n  publisher = {The Open Journal},\n  volume = {1},\n  number = {1},\n  pages = {58},\n  author = {Ranjan Anantharaman and Kimberly Hall and Viral B. Shah and Alan Edelman},\n  title = {Circuitscape in Julia: High Performance Connectivity Modelling to Support Conservation Decisions},\n  journal = {Proceedings of the JuliaCon Conferences}\n}\n\n```\n","funding_links":[],"readme_doi_urls":["https://doi.org/10.21105/jcon.00058"],"works":{},"citation_counts":{},"total_citations":0,"keywords_from_contributors":["geo","ecology","gdal","geospatial-data","arrays","connectivity","julia-package","circuitscape","circuit-theory","unconstrained-optimization"],"project_url":"https://ost.ecosyste.ms/api/v1/projects/4262","html_url":"https://ost.ecosyste.ms/projects/4262"}