From 0512df425cedcc3f5301a04333b3fc7272c44006 Mon Sep 17 00:00:00 2001 From: "Documenter.jl" Date: Tue, 17 Oct 2023 13:33:44 +0000 Subject: [PATCH] build based on 34f1837 --- dev/index.html | 2 +- dev/library/library/index.html | 2 +- dev/search/index.html | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/dev/index.html b/dev/index.html index 591159a..679be71 100644 --- a/dev/index.html +++ b/dev/index.html @@ -1,3 +1,3 @@ ParticleCorrelations.jl · ParticleCorrelations
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diff --git a/dev/library/library/index.html b/dev/library/library/index.html index 7db294a..319f01a 100644 --- a/dev/library/library/index.html +++ b/dev/library/library/index.html @@ -1,2 +1,2 @@ -Library · ParticleCorrelations
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Base

All types

ParticleCorrelations.gls_pair_radial_fun — Method
gls_pair_radial_fun(pair_corr_distance::Function, a12::T; polynomial_order::Int, mesh_size::Int)

Return a function gls_fun. For any radial distances r1 and r2 we have gls = gls_fun(r1,r2) where gls is an array of the Legendre coefficients for the pair correlation.

Using mathematics, we have that such that $g(r_1,r_2,\cos \theta_{12}) = \sum_{\ell_1 =0} \frac{2\ell_1 + 1}{4\pi} gls[\ells+1] P_{\ell_1}(\cos \theta_{12})$, where $g(r_1,r_2,\cos \theta_{12})$ is the radially symmetric pair-correlation, so it depends only on the radial distances $r_1$ and $r_2$, and the angle between two position vectors $\theta_{12}$.

The function gls_fun is calculated from the function pair_corr_distance, where pair_corr_distance(sqrt(r1^2 + r2^2 - 2r1 * r2 * cos(θ12))) gives the pair correlation.

source
ParticleCorrelations.number_density — Method
number_density(s::Specie)

Gives the number of particles per unit volume. Note this is given exactly by N / V where V is the volume of the region containing the origins of all particles. For consistency, volume_fraction is given by N * volume(s) / V.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s::Specie, pairtype::PairCorrelationType, distances::AbstractVector)

Generates a DiscretePairCorrelation for the specie s by using the type of paircorrelation pairtype provided, and for the radial distances provided.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s1::Specie, s2::Specie, pairtype::PairCorrelationType; kws...)

currently provides an approximation for the pair-correlation for two different types of particles s1 and s2.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(particles::Vector{AbstractParticle}, distances::AbstractVector)

Calculates the isotropic pair correlation from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(particle_centres::Vector, distances::AbstractVector)

Calculates the isotropic pair correlation from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s::Specie, pairtype::PairCorrelationType)

Generates a DiscretePairCorrelation for the specie s by using the type of paircorrelation pairtype provided. The distances where the pair correlation is sampled is calculated from the properties of pairtype.

source
ParticleCorrelations.pair_radial_fun — Method
pair_radial_fun(pair_corr::Function, a12::T; polynomial_order::Int, mesh_size::Int)

Return a function pair_radial such that pair_radial(r1,r2, cos(θ12)) gives the pair correlation particles at the radial distances r1 and r2, with and angle of θ12 between them.

The function pair_radial is calculated from the function pair_corr_distance, where pair_corr_distance(sqrt(r1^2 + r2^2 - 2r1 * r2 * cos(θ12))) gives the pair correlation.

source
ParticleCorrelations.structure_factor — Function

structurefactor(particlecentres::Vector, distances::AbstractVector)

Calculates the isotropic structure_factor from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.DiscretePairCorrelation — Type
DiscretePairCorrelation

Represents the pair correlation between two types of particles. The particles could be the same or different species.

This struct has the field r, which represent the radial distance, and the field g is the value of the pair correlation. For radial distances < r[1] we assume that the pair correlation is zero. For radial distances > r[end] we assume the pair correlation is 1.

source
ParticleCorrelations.HardMedium — Type
HardMedium

Represents a solid type of material. Solid particles can not overlap. Currently the package has no soft medium, but this can easily be added.

source
ParticleCorrelations.Specie — Type

Specie

Represents a set of particles which are all the same. The type of particle is given by Specie.particle and the volume fraction this specie occupies is given by Specie.volume_fraction.

We can use Specie.numberofparticles to specify the number of particles, otherwise for an infinite Specie.numberofparticles = Inf.

The minimum distance between any two particles will equal outer_radius(Specie) * Specie.separation_ratio.

source
+Library · ParticleCorrelations
Edit on GitHub

Base

All types

ParticleCorrelations.gls_pair_radial_fun — Method
gls_pair_radial_fun(pair_corr_distance::Function, a12::T; polynomial_order::Int, mesh_size::Int)

Return a function gls_fun. For any radial distances r1 and r2 we have gls = gls_fun(r1,r2) where gls is an array of the Legendre coefficients for the pair correlation.

Using mathematics, we have that such that $g(r_1,r_2,\cos \theta_{12}) = \sum_{\ell_1 =0} \frac{2\ell_1 + 1}{4\pi} gls[\ells+1] P_{\ell_1}(\cos \theta_{12})$, where $g(r_1,r_2,\cos \theta_{12})$ is the radially symmetric pair-correlation, so it depends only on the radial distances $r_1$ and $r_2$, and the angle between two position vectors $\theta_{12}$.

The function gls_fun is calculated from the function pair_corr_distance, where pair_corr_distance(sqrt(r1^2 + r2^2 - 2r1 * r2 * cos(θ12))) gives the pair correlation.

source
ParticleCorrelations.number_density — Method
number_density(s::Specie)

Gives the number of particles per unit volume. Note this is given exactly by N / V where V is the volume of the region containing the origins of all particles. For consistency, volume_fraction is given by N * volume(s) / V.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s::Specie, pairtype::PairCorrelationType, distances::AbstractVector)

Generates a DiscretePairCorrelation for the specie s by using the type of paircorrelation pairtype provided, and for the radial distances provided.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s1::Specie, s2::Specie, pairtype::PairCorrelationType; kws...)

currently provides an approximation for the pair-correlation for two different types of particles s1 and s2.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(particles::Vector{AbstractParticle}, distances::AbstractVector)

Calculates the isotropic pair correlation from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(particle_centres::Vector, distances::AbstractVector)

Calculates the isotropic pair correlation from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.pair_correlation — Method
pair_correlation(s::Specie, pairtype::PairCorrelationType)

Generates a DiscretePairCorrelation for the specie s by using the type of paircorrelation pairtype provided. The distances where the pair correlation is sampled is calculated from the properties of pairtype.

source
ParticleCorrelations.pair_radial_fun — Method
pair_radial_fun(pair_corr::Function, a12::T; polynomial_order::Int, mesh_size::Int)

Return a function pair_radial such that pair_radial(r1,r2, cos(θ12)) gives the pair correlation particles at the radial distances r1 and r2, with and angle of θ12 between them.

The function pair_radial is calculated from the function pair_corr_distance, where pair_corr_distance(sqrt(r1^2 + r2^2 - 2r1 * r2 * cos(θ12))) gives the pair correlation.

source
ParticleCorrelations.structure_factor — Function

structurefactor(particlecentres::Vector, distances::AbstractVector)

Calculates the isotropic structure_factor from one configuration of particles. To use many configurations of particles, call this function for each, then take the average of the pair-correlation.

source
ParticleCorrelations.DiscretePairCorrelation — Type
DiscretePairCorrelation

Represents the pair correlation between two types of particles. The particles could be the same or different species.

This struct has the field r, which represent the radial distance, and the field g is the value of the pair correlation. For radial distances < r[1] we assume that the pair correlation is zero. For radial distances > r[end] we assume the pair correlation is 1.

source
ParticleCorrelations.HardMedium — Type
HardMedium

Represents a solid type of material. Solid particles can not overlap. Currently the package has no soft medium, but this can easily be added.

source
ParticleCorrelations.Specie — Type

Specie

Represents a set of particles which are all the same. The type of particle is given by Specie.particle and the volume fraction this specie occupies is given by Specie.volume_fraction.

We can use Specie.numberofparticles to specify the number of particles, otherwise for an infinite Specie.numberofparticles = Inf.

The minimum distance between any two particles will equal outer_radius(Specie) * Specie.separation_ratio.

source
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