This document discusses using Neuronvisio and Sumatra together to run, store, search, locate, and reload neural simulation results. Neuronvisio is a graphical interface that can visualize simulation results from NEURON models and save results to HDF files. Sumatra is a tool that tracks parameters, dependencies, and details of simulations run from the command line to archive and allow searching simulations. It suggests using these tools together so that simulation results computed in NEURON can be efficiently stored, organized, and reloaded for further analysis or visualization in Neuronvisio.

1. Neuronvisio and Sumatra Run, Store, Search, Locate and Reload simulations' results Michele Mattioni -- [email_address]

3. Complexity increase

4. Tons of Jobs executed http://www.flickr.com/photos/mmayo/6340736/

5. Two main areas: Computed Results Simulations Details

6. My current framework to handle this + Neuronvisio Sumatra

7. Neuronvisio -- background Graphical User Interface helper for NEURON

8. 3D model visualization using Mayavi

9. plotting of any vectors/variables

10. Save/Reload of simulation results http://mattions.github.com/neuronvisio/

11. Integrate with your existing model easily if in Python if in Hoc

12. Pyramidal cell in Model Db

13. Neuronvisio -- saving simulation results + Geometry Computed Array NeuroML HDF format

14. Why HDF (Hierarchical Data Format) store any kind of python list/numpy array (using PyTable)

15. extremely optimized for numerical data

16. extremely good with enourmous amount of data

17. hierarchic structure easy to expand

18. sqlite approach didn't work...

19. Neuronvisio HDF file structure

20. The tree in details - geometry geometry leaf: NeuroML rep of the model

21. The tree in details -- vecRef Final leaf is an array with computed data.

22. Every variable has its own array, with the name

23. Every GenericRef has an indipendent var

24. Loaded the results of a simulation.

25. Second area to tackle Neuronvisio Computed Results Simulations Details

26. Simulations details: Model specific: Parameters used/ Initial conditions

27. Stimulations applied: where, when, how?

28. Which kind of geometry?

29. ... Simulation specific: Reason: why we ran it?

30. Outcome: how did it go?

31. ... Code specific: which version of the software did I use?

32. which dependencies have I used?

33. ...

34. Slide from Andrew Davison (main developer), http://neuralensemble.org/meetings/talks/CodeJam3_Davison_Sumatra.pdf http://neuralensemble.org/trac/sumatra

35. Code under version control Accepted version control: Mercurial (hg)

36. Subversion (svn)

37. Git (git) (I patched it! Power of the opensource..) Scripts organized as Main class and parameter_file Adding git to smt supported vcs

38. How to run the simulation with Sumatra smt run ­­simulator=python ­­main=main.py default.param After Configuration smt run default.param

39. A part of my param file

40. Sumatra web interface

41. Retrieving the simulation details

42. Accessing the plot from the link

43. Summing up To run the simulation: smt run default.param To browse ans search within the simulations smtweb To load them in Neuronvisio: run nrnvisio Data/DD-MM-YYYY/Sim_N parameters & deps automatically tracked everything is archived and searchable all the data are accessible: further analysis/modifying the plot/explore something else

44. Acknowledgement Nicolas Le Novère

45. The Computational Systems Neurobiology group @EBI

46. Gael Varoquaux

47. Andrew Davison

48. CodeJam4 organizers

49. The end Neuronvisio Sumatra Thanks. Computed Results Simulations Details