F. Paul Spitzner

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I recently joined the group of Viola Priesemann at the Max Planck Institute for Dynamics and Self-Organization in Göttingen. Most of the time I work on the Mr. Estimator Python toolbox. The aim is to give the neuroscience community easy access to the multistep regression estimator.

Over the past years, I studied physics at Leipzig University. During my studies I have spent quite some time implementing Monte Carlo techniques - especially for my master thesis (about the condensation of a Lennard-Jones fluid), when I simulated a particle gas in canoncical and grand canonical ensembles. So far, my simulations were written in C++ relying on MPI for parallelization. On my part-time job I have worked on a GPU version of an atmospheric model in OpenCL. The time integration is done with multirate infinitesimal step methods, allowing efficient computation without divergence damping. Then again, this needs more memory than an rk3.

In my spare time, I tend to tinker around with hardware projects and macOS but some time ago I discovered an interest in web development. Even though I like the integrity of the toolkit offered by Apple through Xcode and Swift, I appreciate the appeal of platform independent code. Whenever an opportunity opens up, I play around with photography and artwork, where the latter actually incorporates non-digital aspects for a change.


Journal Articles



Mr. Estimator

Mister Estimator is a python toolbox to measure the branching parameter in neurological data by fitting autocorrelation functions. It relies on popular libraries such as matplotlib and numpy and aims to offer an easy to use (high level) interface. Fine control is also possible since we are consistent with the underlying libraries and try to integrate them seamlessly. See the repository on github for more details.

Mr. Estimator

AtmoCL and AtmoWEB

AtmoCL is an OpenCL port of the All Scale Atmospheric Model (ASAM). The code was initially based on the OpenGL derivative ASAMgpu. While OpenGL as a base for the initial GPU model was the intuitive choice, the more recent OpenCL offers some neat advantages. Apart from allowing the same code to run on a variety of hosts including heterogeneous environments of GPUs, CPUs and accelerators, we can profit from the 3D image class. The mapping from 3D volume to 2D textures - that are the favourable memory format for GPUs - is done by the driver. Further, one can directly access any point of the volume through integer indices instead of the more cumbersome float coordinates, inherent to OpenGL.

One main idea is to export the model state as images where the volume is mapped to 2D cutplanes and state variables are presented in RGB. To animate the pictures as a moving sequence, I developed a lightweight webinterface using bootstrap. It also plots vertical profiles and time series with highcharts. Checkout the demo.

Mr. Estimator

New Site

My reworked site has been around offline for a while and I find less and less bugs every day. Url based navigation does not work yet and I plan to implement a hashbased one. Still, I felt it is time for the move and registered a domain. Engage!