1. Contact the Developers

To contact the ESPResSo developers, please write an email to the developers mailing list: espressomd-devel@nongnu.org to subscribe to the developers’ mailing list go to http://lists.nongnu.org/mailman/listinfo/espressomd-devel

1.1. Before you start a development project

Before you start a development project for ESPResSo, please always write to the developers mailing list and describe the project. This is to avoid that several people work on the same thing at the same time. Also, implementation details can be discussed in advance. In many cases, existing developers can point to re-usable code and simpler solutions.

2. Development Environment

2.1. Required Development Tools

  • First of all, please install the dependencies for compiling ESPResSo. See the section on “Getting, compiling and running” in the user guide.

  • To be able to access the development version of ESPResSo, you will need the distributed versioning control system git.

  • To build the sphinx documentation, you will need the Python packages listed in requirements.txt in the top-level source directory. To install them, issue:

    pip install --upgrade --user -r requirements.txt

    Note, that some distributions now use pip for Python3 and pip2 for Python 2.

  • To build the tutorials, you will need LaTeX.

  • To compile the Doxygen code documentation, you will need to have the tool doxygen.

All of these tools should be easy to install on most Unix operating systems.

2.2. Getting the Development Code

We use Github for storing the source code and its history, and for managing the development process. The repository is located at http://github.com/espressomd/espresso. To get the current development code, run:

git clone git://github.com/espressomd/espresso

This will create a directory named “espresso” which contains the code. The build process does not differ from the one for release versions described in the users’ guide.

2.3. Build System

The build system of ESPResSo is based on CMake.

The central source files of the build system are the following:

  • CMakeLists.txt
  • Contents of the cmake directory
  • The CMakeLists.txt files in the src/, doc/, and testsuite/ directories and their sub-directories

The most common reasons for editing these files are:

  • Adding new source files
  • Adding new external dependencies

2.3.1. Adding New Source Files

To add new files to ESPResSo (like C++ source files or header files) you need to look at the CMakeLists.txt in the directory where the file is located.

  • Please note that .hpp-header files usually do not have to be added to CMakeLists.txt

  • In some cases (e.g., src/core/CMakeLists.txt), the CMakeLists.txt contains a wild-card include like this:

    file(GLOB EspressoCore_SRC *.cpp)

    In this case, placing a file with that ending is enough.

  • In other cases, the files are explicitly included (e.g., testsuite/CMakeLists.txt):

    set(py_tests  bondedInteractions.py

    In that case, add the new file to the list.

2.4. Testsuite

  • New or significantly changed features will only be accepted, if they have a test case. This is to make sure, the feature is not broken by future changes to ESPResSo, and so other users can get an impression of what behavior is guaranteed to work.
  • There are two kinds of tests:
  • C++-unit tests, testing individual C++ functions and classes. They make use of the boost unit test framework and reside in src/core/unit_tests
  • Python integration tests, testing the Python interface and (physical) results of features. They reside in python
  • To execute the tests, run:

    make check

    in the top build directory.

3. Documentation

The documentation of ESPResSo consists of four parts:

  • The users’ guide and developers’ guide are located in doc/sphinx, and make use of the Sphinx Python package
  • In-code documentation for the Python interface is located in the various files in src/python/espressomd and also makes use of the Sphinx Python package. We make use of the napoleon extension and use the NumPy documentation style.
  • In-code documentation of the C++ core is located in the .cpp and .hpp files in /src/core and its sub-directories and makes use of Doxygen.

3.1. Doxygen Code Documentation

The documentation of each function should contain a short description, if necessary a more detailed description and a description for the return value and parameters.

Look at the documentation of existing files and functions to get a feeling how it should be!

Doxygen is able to understand simple LaTeX and HTML commands as well as some special command in order to give the documentation a nice structure and to make it more readable. In the following list you find a short description of the most common commands we need:

  • \anchor name description
    Create an anchor to which you can refer using the \ref command.
  • \ref name ["text"]
    Insert a link to another object in the documentation (e.g.an anchor).
  • <a href="http://www.your_url.html">title</a>
    Link to an external HTML source.
  • \file name description
    Special anchor for a file.
  • \image html image
    Include a picture. The picture file should reside in the subdir doc/doxygen/figs. Do not use the HTML <img>-tag to include pictures, as doxygen will not copy the pictures into the documentation.
  • <ul> <li>List entry 1</li> <li>List entry 2</li></ul>
    Creates a list in the documentation.
  • \param name description
    Document the parameter of a function.
  • \return description
    Document the return value of a function.

4. Programmer’s Guide

This chapter provides some hints on how to extend ESPResSo. It is not exhaustive, so for major changes the best documentation are the other developers.

4.1. Source code structure

The source tree has the following structure:

  • src: The actual source code
    • core: The C++ source code of the simulation core
    • python/espressomd: Source of the espressomd Python module and its submodules
    • script_interface: C++ source code of the script_interface component, which links Python classes to functionality in the simulation core
  • doc: Documentation
    • sphinx: The sphinx-based documentation, consisting of user and developer guide.
    • tutorials/python: Source and pdf files for the introductory tutorials
    • doxygen: Build directory for the C++ in-code documentation
  • testsuite/python: Python integration tests. Note that some C++ unit tests for individual core components are in src/core/unittests
  • samples/python: Some sample scripts
  • libs: External dependencies (at this point h5xx)
  • maintainer: Files used by the maintainers
    • configs: Collection of myconfig.hpp files which activate different sets of features for testing.
    • docker: Definitions of the docker images for various distributions used for continuous integration testing
    • CI: Support files for the continuous integration testing run on the Travis-CI service.
    • jenkins: Outdated support files for the Jenkins continuous integration testing

4.2. Flow control and communications architecture

Espresso uses two communication models, namely master-slave and synchronous.

  • When Espresso does not run an integration, it works in the master-slave mode, i.e. the head node (MPI rank 0) in a parallel simulation runs the Python script, whereas all other nodes are idle until they receive a command from the head node. Such commands include particle creation, changing of particle properties and changing global simulation parameters. When a Python command such as:

    system.part.add(pos=(1, 2, 3))

    is issued, the head node determines, which node is responsible for the given position, and then sends the node the command to place the particle.

  • When an integration is started in Python on the head node, a command to start the integration is sent to all nodes, in the master-slave framework described above. Then, Espresso switches into the synchronous mode, in which all nodes run the same code in the integration loop at the same time. The code of the main integration loop is in integrate.cpp:integrate_vv(). When writing code which is run during the main integration loop, no commands making use of the master-slave mechanism can be called. When code during the integration loop executes MPI communication, it has to be ensured, that the MPI call is executed on all nodes involved in the communication. If this is not done, a deadlock will result.

4.3. Adding calls to the master-slave framework

4.3.1. Using an instance of MpiCallback

  • Write the callback slave function, which will be executed on all nodes except the head node (0):

    void my_callback(int p1, int p2) {
      // Do something. The two int-parameters can be used for anything
  • On all nodes, the callback has to be registered:

    #include "MpiCallbacks.hpp"
    void register_my_callback() {

    You can, e.g., call your registration from initialize.cpp:on_program_start() Instead of a static function, from which a std::function<void(int,int)> can be constructed can be used. For example:

    #include "MpiCallbacks.hpp"
    void register_my_callback() {
      Communication::mpiCallbacks().add([](int, int){ /* Do something */ });

    can be used to add a lambda function as callback.

  • Then, you can use your callback from the head node:

    #include "MpiCallbacks.hpp"
    void call_my_callback() {
      Communication::mpiCallbacks.call(my_callback, param1, param2);

    This only works outside the integration loop. After the callback has been called, synchronous mpi communication can be done.

4.3.2. Legacy callbacks

Older code uses callbacks defined in the CALLBACK_LIST preprocessor macro in communications.cpp. They are called via mpi_call(). See communications.cpp:mpi_place_particle() for an example.

4.4. Adding New Bonded Interactions

To add a new bonded interaction, the following steps have to be taken

  • Simulation core:
    • Define a structure holding the parameters (prefactors, etc.) of the interaction
    • Write functions for calculating force and energy, respectively.
    • Write a setter function, which takes the parameters of the interactions and stores them in the bonded interactions data structure
    • Add calls to the force and energy calculation functions to the force calculation in the integration loop as well as to energy and pressure/stress tensor analysis
  • Python interface
    • Import the definition of the bond data structure from the simulation core
    • Implement a class for the bonded interaction derived from the BondedInteraction base class

4.4.1. Defining the data structure for the interaction

The data structures for bonded interactions reside in interaction_data.hpp.

  • Add your interaction to the enum BondedInteraction. This enumeration is used to identify different bonded interactions.

  • Add a typedef struct containing the parameters of the interaction. Use the one for the FENE interaction as template:

    typedef struct {
      double k;
    } Fene_bond_parameters;
  • Add a member to the typedef union Bond_parameters. For the FENE bond it looks like this:

    Fene_bond_parameters fene;

4.4.2. Functions for calculating force and energy, and for setting parameters

Every interaction resides in its own source .cpp and .hpp. A simple example for a bonded interaction is the FENE bond in src/core/fene.cpp and src/core/fene.hpp. Use these two files as templates for your interaction.


  • The names of function arguments mentioned below are taken from the FENE bond in src/core/fene.cpp and src/core/fene.hpp. It is recommended to use the same names for the corresponding functions for your interaction.

  • The recommended signatures of the force and energy functions are:

    inline int calc_fene_pair_force(Particle *p1, Particle *p2,
                                    Bonded_ia_parameters *iaparams,
                                    double dx[3], double force[3])
    inline int fene_pair_energy(Particle *p1, Particle *p2,
                                Bonded_ia_parameters *iaparams,
                                double dx[3], double *_energy)

    Here, fene needs to be replaced by the name of the new interaction.

  • The setter function gets a bond_type which is a numerical id identifying the number of the bond type in the simulation. It DOES NOT determine the type of the bond potential (harmonic vs FENE). The signature of the setter function has to contain the bond_type, the remaining parameters are specific to the interaction. For the FENE bond, e.g., we have:

    fene_set_params(int bond_type, double k, double drmax, double r0)

    A return value of ES_OK is returned on success, ES_ERR on error, e.g., when parameters are invalid.

  • The setter function must call make_bond_type_exists() with that bond type, to allocate the memory for storing the parameters.

  • Afterwards, the bond parameters can be stored in the global variable bonded_ia_params[bond_type]

    • bonded_ia_params[bond_type].num is the number of particles involved in the bond -1. I.e., 1 for a pairwise bonded potential such as the FENE bond.

    • The parameters for the individual bonded interaction go to the member of Bond_parameters for your interaction defined in the previous step. For the FENE bond, this would be:

  • At the end of the parameter setter function, do not forget the call to mpi_bcast_ia_params(), which will sync the parameters just set to other compute nodes in a parallel simulation.

  • The routines for calculating force and energy return an integer. A return value of 0 means OK, a value of 1 means that the particles are too far apart and the bond is broken. This will stop the integration with a runtime error.

  • The functions for calculating force and energy can make use of a pre-calculated distance vector (dx) pointing from particle 2 to particle 1.

  • The force on particle 1 has to be stored in the force vector (not added to it). The force on particle 2 will be obtained from Newton’s law.

  • The result of the energy calculation is placed in (NOT added to) the _energy argument of the energy calculation function.

4.4.3. Including the bonded interaction in the force calculation and the energy and pressure analysis

  • In src/core/interaction_data.cpp:

    1. Add a name for the interaction to get_name_of_bonded_ia().
    2. In calc_maximal_cutoff(), add a case for the new interaction which makes sure that max_cut is larger than the interaction range of the new interaction, typically the bond length. This is necessary to ensure that, in a parallel simulation, a compute node has access to both bond partners. This value is always used as calculated by calc_maximal_cutoff, therefore it is not strictly necessary that the maximal interaction range is stored explicitly.
    3. Besides this, you have enter the force respectively the energy calculation routines in add_bonded_force, add_bonded_energy, add_bonded_virials and pressure_calc. The pressure occurs ice, once for the parallelized isotropic pressure and once for the tensorial pressure calculation. For pair forces, the pressure is calculated using the virials, for many body interactions currently no pressure is calculated.
    4. Do not forget to include the header file of your interaction.
  • Force calculation: in forces_inline.hpp in the function add_bonded_force(), add your bond to the switch statement. For the FENE bond, e.g., the code looks like this:

    case BONDED_IA_FENE:
      bond_broken = calc_fene_pair_force(p1, p2, iaparams, dx, force);
  • Energy calculation: add similar code to add_bonded_energy() in energy_inline.hpp

  • Pressure, stress tensor and virial calculation: If your bonded interaction is a pair bond and does not modify the particles involved, add similar code as above to pressure.hpp:calc_bonded_pair_force(). Otherwise, you have to implement a custom solution for virial calculation.

4.4.4. Adding the bonded interaction in the Python interface

Please note that the following is Cython code (www.cython.org), rather than pure Python.

  • In src/python/espressomd/interactions.pxd:

    • import the parameter data structure from the C++ header file for your interaction. For the FENE bond, this looks like:

      cdef extern from "interaction_data.hpp":
          ctypedef struct Fene_bond_parameters:
              double k
              double drmax
              double r0
              double drmax2
              double drmax2i
    • Add your bonded interaction to the Cython copy of the BondedInteractions enum analogous to the one in the core:, described above:

      cdef enum enum_bonded_interaction "BondedInteraction":
          BONDED_IA_NONE = -1,

      The spelling has to match the one in the C++ enum exactly.

    • Adapt the Cython copy of the bond_parameters union analogous to the C++ core. The member name has to match the one in C++ exactly:

      ctypedef union bond_parameters "Bond_parameters":
          Fene_bond_parameters fene
          Oif_global_forces_bond_parameters oif_global_forces
          Oif_local_forces_bond_parameters oif_local_forces
          Harmonic_bond_parameters harmonic
    • Import the declaration of the setter function implemented in the core. For the FENE bond, this looks like:

      cdef extern from "fene.hpp":
          int fene_set_params(int bond_type, double k, double drmax, double r0)
  • In src/python/espressomd/interactions.pyx:

    • Implement the Cython class for the bonded interaction, using the one for the FENE bond as template. Please use pep8 naming convention:

      class FeneBond(BondedInteraction):
          def __init__(self, *args, **kwargs):
              FeneBond initializer. Used to instantiate a FeneBond identifier
              with a given set of parameters.
              k : float
                  Specifies the magnitude of the bond interaction.
              d_r_max : float
                        Specifies the maximum stretch and compression length of the
              r_0 : float, optional
                    Specifies the equilibrium length of the bond.
              super(FeneBond, self).__init__(*args, **kwargs)
          def type_number(self):
              return BONDED_IA_FENE
          def type_name(self):
              return "FENE"
          def valid_keys(self):
              return "k", "d_r_max", "r_0"
          def required_keys(self):
              return "k", "d_r_max"
          def set_default_params(self):
              self._params = {"r_0": 0.}
          def _get_params_from_es_core(self):
              return \
                  {"k": bonded_ia_params[self._bond_id].p.fene.k,
                   "d_r_max": bonded_ia_params[self._bond_id].p.fene.drmax,
                   "r_0": bonded_ia_params[self._bond_id].p.fene.r0}
          def _set_params_in_es_core(self):
                  self._bond_id, self._params["k"], self._params["d_r_max"], self._params["r_0"])
  • In testsuite/python/bondedInteractions.py:

    • Add a test case, which verifies that parameters set and gotten from the interaction are consistent:

      test_fene = generateTestForBondParams(
          0, FeneBond, {"r_0": 1.1, "k": 5.2, "d_r_max": 3.})