length scales and SparseEfficiencyWarning
Posted: Fri Nov 08, 2019 7:02 pm
Hi. I have two likely related questions. The first is, I sometimes get the following warning:
I get this a bunch of times, through line 174 of section1d.py (the first line - 155- is in the setup for the diffusion matrix), and again in species.py, and again in rxd.py. Sometimes it seg faults - if so, I rerun the exact same code and it runs. Which is why it has taken me so long to get to the bottom of this. Any idea what exactly is going on here? can I change the tolerance somewhere? I know "rate_r" and "rate_l" are related to section.L and section.nseg, which is why I think this is related to the below question.
The second related question is with respect to length scales (the diffusion matrix is an issue with large nsegs/small compartments, which seems to make sense). I have been setting my segment lengths according to the d_lambda rule for most sections, but that only takes electrical properties into account. The diffusion of voltage is way higher than it is for calcium (I am using D_Ca = 0.5 um^2/ms), where my segments should be much smaller with respect to calcium diffusion. Is there any way to separate these length scales?
Note: as you can see, I am still using Neuron 7.6 - apologies if this was addressed in 7.7! I haven't made the switch yet.
Code: Select all
/Applications/NEURON-7.6/nrn/lib/python/neuron/rxd/section1d.py:155: SparseEfficiencyWarning: Changing the sparsity structure of a csr_matrix is expensive. lil_matrix is more efficient.
g[io, io] += rate_r + rate_l
The second related question is with respect to length scales (the diffusion matrix is an issue with large nsegs/small compartments, which seems to make sense). I have been setting my segment lengths according to the d_lambda rule for most sections, but that only takes electrical properties into account. The diffusion of voltage is way higher than it is for calcium (I am using D_Ca = 0.5 um^2/ms), where my segments should be much smaller with respect to calcium diffusion. Is there any way to separate these length scales?
Note: as you can see, I am still using Neuron 7.6 - apologies if this was addressed in 7.7! I haven't made the switch yet.