Reporting module

Simulation module

The tutorial has been moved to: https://gitlab.version.fz-juelich.de/hpc4ns/dl_on_supercomputers

Visualization module

Goal of the project is to provide several algorithms to smooth the trajectories of persons a) to correct data uncertainty b) to remove swaying of heads and get paths of the real main direction of movement

Framework for simulating pedestrians with state of the art models.

A library of Python utilities developed at the Simulation and Data Laboratory Neuroscience, and the High Performance Computing in Neuroscience (HPCNS) division of the Jülich Supercomputing Center.

Samples and documentation for the "Getting started with Deep Learning on Supercomputers" tutorial.

High-level Fortran and C wrappers for D.E.Shaw Research's random123

method to define and switch between multiple desktop profiles

Portable "minimal" Emacs configuration. Works on Linux and Mac.