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MIVF - Medical Imaging and Visualization Factory,
is a framework for medical applications. It supplies a platform, in which image processing and 3D visualization algorithms can be employed as reusable components (functional modules or plugins).
Pypes is a framework which allows users to break complex dataprocessing logic down into a series of smaller less complex tasks. These tasks, referred to as components, can then be connected so that the output of one becomes the input to another.
MBDyn_sim_suite is a collection of free pre&post-processing tools and simulation models for the open-source multi-body analysis software MBDyn forming a general purpose simulation environment for structural dynamics with an emphasis on wind turbines.
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Quick reference for switching between mathematical computation environments for computer algebra, numeric processing and data visualisation. Examples are Matlab, IDL, SPlus, and their open-source counterparts Octave, Scilab, Python+NumPy and R.
Sed.py is a python module to provide a easy way to do text stream processing. Just like the name of module, it likes to do the work that sed can do. But not in sed's way, it's in Python's way. To use this module, the knowledge of regexp is necessary.
Signal Processing and Classification Environment in Python using YAML
pySPACE is a modular software for processing of large data streams that has been specifically designed to enable distributed execution and empirical evaluation of signal processing chains. Various signal processing algorithms (so called nodes) are available within the software, from finite impulse response filters over data-dependent spatial filters (e.g. CSP, xDAWN) to established classifiers (e.g.
Open Worlds VR is a general, visual and interactive simulation platform, designed to support thousands of independent actors (objects). It also implements a strong peer-to-peer networking algorithm to allow multi-computer processing of a single large simu
StreamMine is a distributed event processing (streaming) infrastructure.
You can create low-latency, fault-tolerant stream processing functionality with any stream-oriented operators that can be implemented in Python.