function GrauLeguiaPRE2019example(N,resets,seed,epsilon,rho,noise,homo,varargin)
% Inferring directed networks using a rank-based connectivity measure.
% PRE, 99.1 (2019) 012319.
% For further information, please refer to it, and to GrauLeguiaPRE2019Readme.pdf.
%How you call it
% N: number of nodes
% resets: number of dynamical resets which we average.
% seed: Seed for the generation of Adjacency matrix
% epsilon: coupling strength
% rho: link density of the Adjacency matrix
%Homo: if 1 homogeneous Lorenz with b=28, if 0 heterogeneous Lorenz with b\in[28,48]if nargin < 1
% load dataPaper
% Parameters as in the paper [1]:
if nargin==0
N = 16;
rho = 0.1;
resets = 1;
seed = 1;
epsilon =0.1;
noise=0;
homo=1;
end
%nš of resets
llavor=resets;
%seed value
llavor2=seed;
%Matrix we will save of L, Croscorrelation,stand deviation of
%signals
Lmeantot=zeros(N,N,llavor);
%Llavor is number of resets we want to make for the inference
for seed=1:llavor
% for all nš of llavors resets, we keep A the same.
rng(llavor2);
pd = makedist('Normal','mu',epsilon,'sigma',0);
problink=rho;
%Random Adj matrix
A=rand(N,N);
A=A