Cluster configurations of the Hegselmann-Krause model on network ensembles

This is the raw data underlying the results of the preprint [arxiv:2102.10910](https://arxiv.org/abs/2102.10910).

## Data

For each measured combination of the confidence and system size, there is one gzipped
file. For different ensembles, we collected data in different ranges and quality.
The paramters are:

* Number of samples `m` per parameter combination
* Range `r` of confidences epsilon
* Distances `d` between values of epsilon (basically the resolution of the data)
* Largest size `N_max`

The single files follow a naming scheme of `n{N}_e{epsilon}.cluster.dat.gz`, where
`{N}` signals the system size of the simulation and `{epsilon}` is the confidence
value of the simulation (without a decimal point, i.e., `0050` corresponds to `epsilon = 0.050`).
The sizes `N` are usually powers of two (or for the lattices, perfect squares close to powers of two).

We present the data for each ensemble in one archive.

* Fully connected `full.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 262144`
* Barabasi Albert with a mean degree of 4 `BA4.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.002`, `N_max = 32768`
* Barabasi Albert with a mean degree of 10 `BA10.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`
* Square lattice with first nearest neighbors `lat1.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`
* Square lattice with second nearest neighbors `lat2.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 16384`
* Square lattice with third nearest neighbors `lat3.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`
* Square lattice with fourth nearest neighbors `lat4.tar`
    * `m = 1000`, `r = [0.0, 0.6]`, `d = 0.001`, `N_max = 65536`
* Square lattice with third nearest neighbors and 1% rewired edges `lat3_ws.tar`
    * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.001`, `N_max = 16384`
* connected Erdos Renyi with mean degree of 10 `ER10.tar`
    * `m = 1000`, `r = [0.0, 0.3]`, `d = 0.002`, `N_max = 32768`

## Data format

Each final state is encoded as three lines:

* The convergence time is a single integer with a line prefix '# sweeps: '
* The positions of all clusters in opinion space with a line prefix '# ' (unsorted)
* The number of agents in each of the clusters without a line prefix

## Python example for reading the format

An example script, which visualizes the S vs eps graph for the largest size of the fully connected
case, with a function to read this format is given in `example.py`.