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https://github.com/ahojukka5/judycon.jl

Judycon.jl implements dynamic connectivity algorithms for Julia programming language. In computing and graph theory, a dynamic connectivity structure is a data structure that dynamically maintains information about the connected components of a graph.
https://github.com/ahojukka5/judycon.jl

dynamic-connectivity quickfind

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Judycon.jl implements dynamic connectivity algorithms for Julia programming language. In computing and graph theory, a dynamic connectivity structure is a data structure that dynamically maintains information about the connected components of a graph.

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README 

        
![Judycon.jl](demos/percolation.gif)



[![Build Status][travis-img]][travis-url]

[![Coverage Status][coveralls-img]][coveralls-url]

[![Documentation (stable)][docs-stable-img]][docs-stable-url]

[![Documentation (development)][docs-dev-img]][docs-dev-url]



Package author: Jukka Aho (@ahojukka5)



Judycon.jl implements dynamic connectivity algorithms for Julia programming

language. In computing and graph theory, a [dynamic connectivity structure][1]

is a data structure that dynamically maintains information about the connected

components of a graph. Dynamic connectivity has a lot of applications. For

example, dynamic connetivity [can be used][2] to determine functional

connectivity change points in fMRI data. In the top of this readme, you see a

percolation model which is solved using the functions provided this package. For

more information about the model and the package, see the documentation. Project

documentation is found from url .



## Usage



The following two algoritms are implemented:



- QuickFind

- QuickUnion



Both of the algorithms have same API, but the internal data structure is

different. Typical use case is:



```julia

using Judycon: QuickFind, QuickUnion, connect!, isconnected



wuf = QuickUnion(10)

connect!(wuf, 1, 2)

connect!(wuf, 2, 3)

connect!(wuf, 3, 4)

isconnected(wuf, 1, 4)



# output



true

```



For further information about the implementation details and usage, please see

the [documentation][docs-dev-url].



QuickFind is a simple data structure making it possible to very fast query, does

points p and q belong to the same connected component, but connecting the points

is slow, up to ~ N^2 in the worst case.



QuickUnion makes it fast to connect points. Finding points is not that fast than

with QuickFind, but with some common modifications, i.e. weighting and path

compression, it gives good a performance.



Weighted quick union with path compression makes it possible to solve problems

that could not otherwise be addressed. In case of doubt which suits for your

need, use that.



The performance of the algorithms (M union-find operations on a set of N object)

is given below.



| algorithm                      | worst-case time |

| ------------------------------ | --------------- |

| quick-find                     | M N             |

| quick-union                    | M N             |

| weighted QU                    | M + N log N     |

| QU + path compression          | M + N log N     |

| weighted QU + path compression | N + M lg N      |



## Dynamic connectivity application: percolation



Source: 



In physics, chemistry and materials science, percolation (from Latin percōlāre,

"to filter" or "trickle through") refers to the movement and filtering of fluids

through porous materials. It is described by Darcy's law. Broader applications

have since been developed that cover connectivity of many systems modeled as

lattices or graphs, analogous to connectivity of lattice components in the

filtration problem that modulates capacity for percolation.



During the last decades, percolation theory, the mathematical study of

percolation, has brought new understanding and techniques to a broad range of

topics in physics, materials science, complex networks, epidemiology, and other

fields. For example, in geology, percolation refers to filtration of water

through soil and permeable rocks. The water flows to recharge the groundwater in

the water table and aquifers. In places where infiltration basins or septic

drain fields are planned to dispose of substantial amounts of water, a

percolation test is needed beforehand to determine whether the intended

structure is likely to succeed or fail.



Percolation typically exhibits universality. Statistical physics concepts such

as scaling theory, renormalization, phase transition, critical phenomena and

fractals are used to characterize percolation properties. Percolation is the

downward movement of water through pores and other spaces in the soil due to

gravity. Combinatorics is commonly employed to study percolation thresholds.



Due to the complexity involved in obtaining exact results from analytical models

of percolation, computer simulations are typically used. The current fastest

algorithm for percolation was published in 2000 by Mark Newman and Robert

Ziff.



### Use cases of percolation model



- Coffee percolation, where the solvent is water, the permeable substance is

  the coffee grounds, and the soluble constituents are the chemical compounds

  that give coffee its color, taste, and aroma.

- Movement of weathered material down on a slope under the earth's surface.

- Cracking of trees with the presence of two conditions, sunlight and under

  the influence of pressure.

- Collapse and robustness of biological virus shells to random subunit removal

  (experimentally verified fragmentation and disassembly of viruses).

- Robustness of networks to random and targeted attacks.

- Transport in porous media.

- Epidemic spreading.

- Surface roughening.

- Dental percolation, increase rate of decay under crowns because of a

  conducive environment for strep mutants and lactobacillus

- Potential sites for septic systems are tested by the "perk test".

  Example/theory: A hole (usually 6–10 inches in diameter) is dug in the ground

  surface (usually 12–24" deep). Water is filled in to the hole, and the time is

  measured for a drop of one inch in the water surface. If the water surface

  quickly drops, as usually seen in poorly-graded sands, then it is a potentially

  good place for a septic "leach field". If the hydraulic conductivity of the

  site is low (usually in clayey and loamy soils), then the site is undesirable.

- Traffic percolation.



From `demos`, you find a percolation model implemented using Judycon.jl The

development of system from initial state to percolation is animated in the top

of this file.



[1]: https://en.wikipedia.org/wiki/Dynamic_connectivity

[2]: https://www.frontiersin.org/articles/10.3389/fnins.2015.00285/full

[travis-img]: https://travis-ci.org/ahojukka5/Judycon.jl.svg?branch=master

[travis-url]: https://travis-ci.org/ahojukka5/Judycon.jl

[coveralls-img]: https://coveralls.io/repos/github/ahojukka5/Judycon.jl/badge.svg?branch=master

[coveralls-url]: https://coveralls.io/github/ahojukka5/Judycon.jl?branch=master

[docs-dev-img]: https://img.shields.io/badge/docs-dev-blue.svg

[docs-dev-url]: https://ahojukka5.github.io/Judycon.jl/dev

[docs-stable-img]: https://img.shields.io/badge/docs-stable-blue.svg

[docs-stable-url]: https://ahojukka5.github.io/Judycon.jl/stable