Pre-Made Systems and Parameters
There will be a list of all pre-made systems and parameters here.
MCBB.DEParameters
— TypeDEParameters
Abstract type for all parameter types. If you define your own systems the parameters have to have this as a supertype.
MCBB.logistic
— Functionlogistic(u_next, u, p::logistic_parameters, t)
Logistic Map: $x_{n+1} = rx_n (1 - x_n)$
MCBB.logistic_parameters
— TypeMCBB.henon
— Functionhenon(u_next, u, p::henon_parameters, t)
Henon map. $x_{n+1} = 1 - a x_{n}^2 + y_{n}$ $y_{n+1} = b x_{n}$
MCBB.henon_parameters
— TypeMCBB.kuramoto
— Functionkuramoto(du, u, p::kuramoto_parameters, t)
First order Kuramoto system with all-to-all coupling. $\dot{\theta}_i = w_i + K/N \sum_{i} \sin(\theta_j - \theta_i)$
MCBB.kuramoto_parameters
— Typekuramoto_parameters
Parameters of a first order Kuramoto system with all-to-all coupling. Fields:
K::Number
: Coupling Strengthw::Array{Float64}
: EigenfrequenciesN::Int
: Number of Oscillators
MCBB.kuramoto_network
— Functionkuramoto_network(du, u, p::kuramoto_parameters, t)
First order Kuramoto system on a network. $\dot{\theta}_i = w_i + K/N \sum_{i} A_{ij}\sin(\theta_j - \theta_i)$
MCBB.kuramoto_network_parameters
— Typekuramoto_network_parameters
Parameters of a first order Kuramoto system on a network. Fields:
K::Number
: Coupling Strengthw::Array{Float64}
: EigenfrequenciesN::Int
: Number of OscillatorsA
: Adjacency matrix
MCBB.second_order_kuramoto
— Functionsecond_order_kuramoto(du, u, p::second_order_kuramoto_parameters, t)
Second order Kuramoto system on the adjacency matrix $A_{ij} = E'_{ie} E_{ej}$.
$\dot{\theta}_i = w_i$ $\dot{\omega} = \Omega_i - \alpha\omega + \lambda\sum_{j=1}^N A_{ij} sin(\theta_j - \theta_i)$
MCBB.second_order_kuramoto_parameters
— Typesecond_order_kuramoto_parameters
Fields:
systemsize::Int
, number of oscillatorsdamping::Float64
, Damping, also referred to as $\alpha$coupling::Float64
, Coupling strength, also referred to as $\lambda$incidence
, oriented incidence matix of the network, also referred to as $\E_{ei}$
indicating if a vertex $i$ belongs to an edge $e$. Following the conventions of LightGraphs
drive::Array{Float64}
, external driving, also referred to as $\Omega$
MCBB.second_order_kuramoto_chain
— Functionsecond_order_kuramoto_chain(du, u, p::kuramoto_parameters, t)
Second order Kuramoto system on a chain.
$\dot{\theta}_i = w_i$ $\dot{\omega} = \Omega_i - \alpha\omega + \lambda\sum_{j=1}^N A_{ij} sin(\theta_j - \theta_i)$
$A_{ij} = 1$ if and only if $|i-j|=1$, otherwise $A_{ij}=0$
MCBB.second_order_kuramoto_chain_parameters
— Typesecond_order_kuramoto_chain_parameters
Fields:
systemsize::Int
, number of oscillatorsdamping::Float64
, Damping, also referred to as $\alpha$coupling::Float64
, Coupling strength, also referred to as $\lambda$drive::Array{Float64}
, external driving, also referred to as $\Omega$
MCBB.non_local_kuramoto_ring
— Functionnon_local_kuramoto_ring(du, u, p::non_local_kuramoto_ring_parameters, t)
First order Kuramoto oscillators on a ring with non-local coupling.
$\frac{\theta_k}{dt} = \omega_0 - \sum_{j=1}^N G_1\left(\frac{2\pi}{N}(k-j)\right)\sin\left(\theta_k(t) - \theta_j(k) + \alpha\right)$
MCBB.non_local_kuramoto_ring_parameters
— Typenon_local_kuramoto_ring_parameters <: DEParameters
N::Integer
: Number of Oscillatorsfak::Float64
: $\frac{2\pi}{n}$omega_0::Number
: eigenfrequency of all oscillatorsphase_delay
: Phase Delay $\alpha$coupling_function
: Coupling function $G(x)$, signature (x::Number)-> value::Number
MCBB.order_parameter
— Functionorder_parameter(u::AbstractArray, N::Int)
Order Parameter of a Kuramoto System
MCBB.roessler_parameters
— Typeroessler_parameters <: DEParameters
Parameters of a Roessler network
a::Array{Float64}
:a
Parameters of all oscillators.b::Array{Float64}
:b
Parameters of all oscillators.c::Array{Float64}
:c
Parameters of all oscillators.K::Float64
: Coupling StrengthL::Array{Float64}
: Laplacian matrix of the networkN::Int
: Number of nodes/oscilattorsroessler_parameters(a, b, c, K, k::Int64, p::Float64, N)
Generates a set of roessler_parameters with a Watts Strogatz random Network with mean degreee k
and rewiring probability p
MCBB.roessler_network
— Functionroessler_network(du, u, p::roessler_parameters, t)
N Roessler Parameters coupled on their x-component