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Baby steps with KNIME

I am trying to explore documenting some of the things I try at work a little better. Rather than just write things into a notebook and not find it ever again I thought I’d give making a small video a go. The story behind this little project, is that I am trying build workflows for biomolecular simulations. However, there are a great many ( or at least one ) workflow tools out there already, so I am trying to explore how easily adaptable such a workflow tool might be for the purpose of running a biomolecular simulation. Until about a month ago I had never heard about KNIME, until today I had absolutely no idea what it did. So I took my first baby steps, downloaded it (, and tried to run a first workflow. Running a simple k-means clustering on a typical benchmark clustering dataset (, was really quite easy and everything was very intuitive and didn’t take much longer than half an hour to get the hang of things. I made a little video of how to do this:

The video certainly has a lot of room for improvement, but it also happens to be my first ever screen capture video I attempted to make, so bare with me as I learn to improve on these skills. (Yes, I am also aware that apparently I don’t know the difference between 250 and 2500, but redoing the video just because of that seemed not all that useful)

Next I’ll try and actually write my own KNIME node that will execute a simple python script, since being able to execute workflow elements mostly run in python is really what I am interested in.

PyTRAM – a free energy python package

Just before the weekend a colleague and I finally managed to add a new release alpha of the pytram package.
It is now on version 0.1.5 and can either be found on github:
Or the python package index:

New features are an additional free energy estimator. The package now contains dTRAM and xTRAM [1,2]. But probably the best enhancement to the previous release is the existence of a short documentation for this version. It gives a brief overview over supported input file structure, how to use the API etc. Also there are two ipython notebooks available that illustrate very basic usage examples of both estimators.

More features will follow soon.

[1] Statistically optimal analysis of state-discretized trajectory data from multiple thermodynamic states H. Wu, A.S.J.S. Mey, E. Rosta, Frank Noé, J. Chem. Phys., 141 214106 (2014).

[2] xTRAM: Estimating equilibrium expectations from time-correlated simulation data at multiple thermodynamic states,  A.S.J.S. Mey,   H. Wu, and F. Noé, Phys. Rev. X, 4 041018 (2014)