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simetenn / uncertainpy

Uncertainpy: a Python toolbox for uncertainty quantification and sensitivity analysis, tailored towards computational neuroscience.
http://uncertainpy.readthedocs.io
GNU General Public License v3.0
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isnan for empty entries in a spike train #14

Closed maciej-jedynak closed 6 years ago

maciej-jedynak commented 6 years ago

Spike train [[19.700000000000003, 22.0, 24.7, 28.200000000000003, 30.500000000000004, 32.4, 34.2, 36.1, 38.0, 49.9], [12.5, 14.8, 17.5, 21.0, 23.1, 25.7, 27.6, 29.5, 31.400000000000002, 47.300000000000004, 48.9], [15.5, 17.9, 20.6, 24.0, 30.3], [], [], [28.200000000000003, 30.500000000000004, 33.2, 36.7, 42.800000000000004], [8.1, 10.200000000000001, 12.0, 13.7, 15.4, 17.6, 19.5, 21.400000000000002, 23.700000000000003, 26.3, 29.400000000000002, 33.9, 36.2, 38.3, 40.199999999999996, 42.1], [20.3, 22.3, 24.099999999999998, 25.8, 27.6, 29.5, 31.400000000000002, 39.199999999999996, 41.5, 43.4, 45.3, 47.199999999999996, 49.2], [13.5, 15.8, 18.6, 22.1], [8.1, 10.100000000000001, 12.0, 13.7, 15.4, 17.6, 19.5, 21.400000000000002, 23.700000000000003, 26.3, 29.400000000000002, 33.9, 36.2, 38.3, 40.199999999999996, 42.1], [], [25.7, 28.0, 30.7, 34.2, 40.400000000000006], [7.800000000000001, 10.0, 11.9, 13.600000000000001, 19.3, 34.9, 37.1, 39.300000000000004, 41.2, 43.1, 45.00000000000001, 46.8, 48.6], [20.0, 22.1, 24.0, 26.0, 28.200000000000003, 30.0, 31.900000000000006, 33.800000000000004, 35.6, 41.6, 43.9, 46.3, 49.1], [7.5, 9.9, 11.799999999999999, 13.9, 16.400000000000002, 19.200000000000003, 23.0], [28.5, 30.6, 32.5, 34.5, 36.7, 38.5, 40.699999999999996, 42.9, 44.8, 47.199999999999996, 49.800000000000004]] Running model: 100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22/22 [02:07<00:00, 5.81s/it] ERROR Brian 2 encountered an unexpected error. If you think this is bug in Brian 2, please report this issue either to the mailing list at http://groups.google.com/group/brian-development/, or to the issue tracker at https://github.com/brian-team/brian2/issues. Please include this file with debug information in your report: /tmp/brian_debug_sRAD4V.log Additionally, you can also include a copy of the script that was run, available at: /tmp/brian_script_LTQpab.py Thanks! [brian2] Traceback (most recent call last): File "ntwk_sim_demo_un_NetworkFeatures.py", line 307, in data = UQ.quantify() File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/uncertainty.py", line 372, in quantify custom_kwargs) File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/uncertainty.py", line 636, in polynomial_chaos custom_kwargs File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/uncertainty_calculations.py", line 1219, in polynomial_chaos allow_incomplete=allow_incomplete) File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/uncertainty_calculations.py", line 518, in create_PCE_collocation data = self.runmodel.run(nodes, uncertain_parameters) File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/run_model.py", line 498, in run data = self.results_to_data(results) File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/run_model.py", line 221, in results_to_data results = self.regularize_nan_results(results) File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/run_model.py", line 590, in regularize_nan_results results = regularize(results, "values") File "/home/maciek/anaconda3/envs/HH_MF/lib/python2.7/site-packages/uncertainpy/core/run_model.py", line 577, in regularize if not np.all(np.isnan(values)): TypeError: ufunc 'isnan' not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ''safe''

simetenn commented 6 years ago

This happens when returning a list of spiketrains (and using ignore=True otherwise the code will raise an error elsewhere), and when not using the NestModel class.

The model evaluations (the spiketrains) are then not regular, and are are therefore a numpy object array. numpy.isnan does not work for such object arrays, nor can such arrays be saved as hdf5 files.

You can try to remove the model evaluations before saving by:

data = UQ.quantify(save=False)
data["name_of_your_model"].evaluations = np.nan 
data.save("filename")

I will figure out a proper fix for how to either properly ignore the model evaluations, or creating a regular result of a list of general spiketrains (from for example Brian)

simetenn commented 6 years ago

This is fixed in v0.9.3, along with proper handling of model.ignore and ability to save irregular and empty results.