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swarm-variable-speed – simulation code for agent-based collective movement with variable speed

described and applied in arxiv

(Python-wrapper package calling c++ running)

Github

General Notes

Swarm-variable-speed is a C++ code which runs numerical simulations of the agent based stochastic differential equations. The motivation of this code are group living animals, e.g. fish shoals in shallow water(2 dimensions). Importantly, fundamental kinematics as inertia and rotational friction are hard coded. In principle the code can be easily extended to three dimensions. However, the higher the dimension the smaller the volume if the number of agents stays constant. For a full mathematical description of the stochastic differential equations and the social forces (repulsion, attraction and alignment) see ARXIV-MISSING.

Required C++ libraries:

LINUX

next to standard C libraries the following libraries need to be installed via apt-get install'

  • libhdf5-serial-dev
  • libgsl-dev
  • libcgal-dev

MAC

the needed libraries can be installed via homebrew:

  • brew install hdf5
  • brew install gsl
  • brew install cgal

Required python packages

The code runs in the anaconda environment specified in environment.yml which can be created from the projects root directory via

conda env create -f environment.yml

which creates the environment animateSwarm (name specified in environment.yml) which you activate by

conda activate animateSwarm

In case you want to update your python packages to enable the code to run the most important packages are:

  • numpy
  • h5py
  • matplotlib
  • pathlib

Compilation(MacOS/Linux):

On MacOS/Linux system: The included Makefile is for compilation of the source code (see tutorial: http://mrbook.org/blog/tutorials/make/). Just run

make

in the terminal in the directory of this repository.

Possible compilation problem

If you are using anaconda and h5py is installed the linking to the libhdf5-serial-dev library might not work. A simple work around is to comment in your ~/.bashrc (or ~/.shrc or ~/.zshrc depending on the shell you are using) the anaconda initialization. Than you start a new terminal and retry the compilation (run make). You can check that not the anaconda python is used via which python. If the compilation was succesfull you can uncomment the anaconda initialization again and run the python-scripts.

LINUX-cgal linking problem

in older distributions of ubuntu (e.g. 16.04) CGAL needs to be explicitly linked in the Makefile (however, in current version this links hinders succesfull linking during compilation). If you are in an older distribution try to uncomment -lCGAL in the Makefile.

Running

After successful compilation the executable file swarmdyn will be generated. The executable is meant to be called via running:

python RunSingle.py
  • RunSingle.py - starting a single simulation of swarmdyn and defining parameters.

RunSingle.py will also call a visualization of the simulations by calling:

  • AnimateRun.py - animating the results using pythons matplotlib

User Agreement

By downloading swarm-variable-speed you agree with the following points: swarm-variable-speed is provided without any warranty or conditions of any kind. We assume no responsibility for errors or omissions in the results and interpretations following from application of swarm-variable-speed.

License

Copyright (C) 2016-2021 Pascal Klamser, Pawel Romanczuk

This program is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version.

This program is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details.

You should have received a copy of the GNU General Public License along with this program. If not, see http://www.gnu.org/licenses/.

Implementation details

The code consists of a c++ part (costly numerical simulations) which is accessed via python-wrapper scripts.

C++ simulation

The c++ code is in the src-folder and split in multiple logic (more or less) parts. The splitting in different files and headers is usefull for developing, because it strongly reduces compilation time if only part of the code is changed. The following parts exists (sorted according their "conceptual importance"):

  • main (main loop, output):
    • swarmdyn.cpp
  • agent- and system-parameter: definitions, initialization and parsing:
    • agents.cpp/.h (definition)
    • settings.cpp (initialization)
    • input_output.cpp (parsing)
  • agents: individual dynamics, interactions(network), social forces, operations
    • agents_dynamics.cpp
    • agents_interact.cpp
    • social_forces.cpp
    • agents_operation.cpp
  • tools: hdf5 writing, maths
    • h5tools.cpp
    • mathtools.cpp

Hard coded peculiarities

  • if fileID = 'xx' the hdf5-file out_xx.h5 is created by the c++ code in the current directory.
    • otherwise the c++ code expects that an hdf5-file exist with the name out_fileID.h5
  • if a anything in the header src/agents.h is changed, the whole code needs a recompilation
    • do rm -f obj/* (removes all obj and forces a recompilation)
    • run make
    • Todo: maybe place the class definitions in agents.cpp to prevent this

Python wrapper

Single Run (RunSingle.py)

Animation (AnimateRun.py)

  • The code can be animated if the dataset part (for partiticles) exists via python AnimateRun.py assuming that the file out_xx.h5 exists (or specify another file).
    • the script AnimateRun.py is based on the github gitrepository animateswarm

Parameter Scans (Scan2D.py)

This script can call mutliple 2D-scans sequentially.

  • Each scan is parallelized by running each point in parameter-space on a seperate process.
    • each process saves the output to a seperate hdf5-file (with the parameter-values in its name)
    • for each parameter-pair multiple samples can be computed and the scan can get extended
      • to extend: run the Scan2D.py with the same parameters but a larger value for runs
      • Problem: if para_values0 or para_values0 is changed the code checks only if already runs exist for the first parameter-pair -> if they exist, the total number of runs is reduced by the number of existing runs (no parameter)
    • if the number of parameter-pairs is smaller than the number of available processors: the samples are split and later reunited

Evaluation of Scans (EvaluateScan.py)

The script evaluates each parameter-point and returns the output (mean or std or more specific processing) in an array which whose rows and columns correspond to the respective paremter-points. These arrays and a dictionary which links keywords to the entry in the last dimension of the array is saved in Mean.pkl.