Feature overview

The AMEP Python library provides a unified framework for handling both particle-based and continuum simulation data. It is made for the analysis of molecular-dynamics (MD), Brownian-dynamics (BD), and continuum simulation data of condensed matter systems and active matter systems in particular. AMEP provides a huge variety of analysis methods for both data types that allow to evaluate various dynamic and static observables based on the trajectories of the particles or the time evolution of continuum fields. For fast and efficient data handling, AMEP provides a unified framework for loading and storing simulation data and analysis results in a compressed, HDF5-based data format. AMEP is written purely in Python and uses powerful libraries such as NumPy, SciPy, Matplotlib, and scikit-image commonly used in computational physics. Therefore, understanding, modifying, and building up on the provided framework is comparatively easy. All evaluation functions are optimized to run efficiently on HPC hardware to provide fast computations. To plot and visualize simulation data and analysis results, AMEP provides an optimized plotting framework based on the Matplotlib Python library, which allows to easily plot and animate particles, fields, and lines. Compared to other analysis libraries, the huge variety of analysis methods combined with the possibility to handle both most common data types used in soft-matter physics and in the active matter community in particular, enables the analysis of a much broader class of simulation data including not only classical molecular-dynamics or Brownian-dynamics simulations but also any kind of numerical solutions of partial differential equations. The following table gives an overview on the observables provided by AMEP and on their capability of processing particle-based and continuum simulation data.

We try to keep the following table up to date, but please check the API Reference for the full documentation and all features of AMEP

Observable	Particles	Fields
Spatial Correlation Functions:
RDF (radial pair distribution function)	✔	➖
PCF2d (2d pair correlation function)	✔	➖
PCFangle (angular pair correlation function)	✔	➖
SFiso (isotropic static structure factor)	✔	✔
SF2d (2d static structure factor)	✔	✔
SpatialVelCor (spatial velocity correlation function)	✔	➖
PosOrderCor (positional order correlation function)	✔	➖
HexOrderCor (hexagonal order correlation function)	✔	➖
Local Order:
Voronoi tesselation	✔	➖
Local density	✔	✔
Local packing fraction	✔	➖
k-atic bond order parameter	✔	➖
Next/nearest neighbor search	✔	➖
Time Correlation Functions:
MSD (mean square displacement)	✔	➖
VACF (velocity autocorrelation function)	✔	➖
OACF (orientation autocorrelation function)	✔	➖
Cluster Analysis:
Clustersize distribution	✔	✔
Cluster growth	✔	✔
Radius of gyration	✔	✔
Linear extension	✔	✔
Center of mass	✔	✔
Gyration tensor	✔	✔
Inertia tensor	✔	✔
Miscellaneous:
Translational/rotational kinetic energy	✔	➖
Kinetic temperature	✔	➖