Comments (7)
@rgerkin Can we set up a time in a couple weeks to discuss this?
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@slarson Yes, most mid-day times will work for me.
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@slarson
In progress.
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Currently neuronunit is giving each processor its own copy of the root NML/LEMS file so that it may rewrite it as needed for certain tasks. This is important when simulations are done via jNeuroML since jNeuroML takes a path to a file which then represents the structure of the model which cannot be changed in memory.
c302 NML files contain local include directives to other c302 NML files in same directory. In order to copy the root c302 NML file elsewhere, I need jNeuroML to know to look in a list of include directories that includes the original root c302 NML file directory, so that it can find these include files. Padraig implemented this in NeuroML/jNeuroML@a714ffa.
However, in order to use this development branch, I have to get it to compile, which is currently failing as described in NeuroML/jNeuroML#52.
When that issue is resolved I can continue making progress on this one.
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The above issues are now solved and I have a prototype shown here. This just demonstrates wrapping the model with a SciUnit Model class and getting the c302 simulation to run. The next step is to actually do the testing, but that should be the easy part.
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@slarson Is the data in the figure in the "Figures are here" link in your first comment on this issue available in digitized form anywhere? Panel C is easy enough to do by hand, but A, D, and E probably require the raw values or a digitization of the figure in order to test. Does muscle_model have its own version of channelworm-django?
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@slarson Here I have the muscle model being tested (against a suite of two tests) in SciUnit. I can add more tests from the table in panel C of the figure later. Showing an overlaid comparison probably requires getting a digitized figure.
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Related Issues (20)
- Draft workflow outline for validation tests
- Write doctests for muscle_model HOT 16
- Write Pytest tests for muscle_model HOT 1
- Write SciUnit validation tests for muscle_model HOT 10
- Document muscle_model testing framework
- Digitize the figure mentioned in #31 HOT 7
- Write a script that takes the digitized figure and the simulation output and calculates the error/difference
- Write a script that generates an overlaid image of the simulated and digitized data HOT 1
- Turn the error-generating script into a SciUnit test
- Integrate the test in the test suite being run on TravisCI
- Add jneuroML to documentation here HOT 3
- Replace jnml in scripts with pynml? HOT 2
- The same conductance for different channels in nml files HOT 6
- Check for performance regressions in testing suite HOT 1
- Create tests to catch performance regressions in specific functions
- Write PyTest tests for basic command line runs HOT 12
- Delete or archive unused branches HOT 1
- Muscle Model Explorer - Error while loading channel files HOT 2
- Add to the readme to create a well cited and reasoned case for the ideal set of ion channels that should be in the muscle model HOT 8
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