sanjayankur31
commented
1 year ago This one is also ready for review. It adds an interactive vispy based viewer which is fairly performant. Screencast for a good sized model:
$ pynml-summary Balanced_610cells_0conns.net.nml
pyNeuroML >>> INFO - Loading NeuroML2 file: Balanced_610cells_0conns.net.nml
*******************************************************
* NeuroMLDocument: Balanced_610cells_0conns
*
* ExpTwoSynapse: ['synAmpa1', 'synGaba1']
* PoissonFiringSynapse: ['psf1']
* Property: ['Network seed = 1234']
*
* Network: Balanced_610cells_0conns (temperature: 32degC)
*
* 610 cells in 3 populations
* Population: popBBP with 10 components of type cADpyr229_L23_PC_5ecbf9b163_0_0
* Locations: [(-135.6341, 183.0145, 367.6131), ...]
* Properties: color=0 .8 0;
* Population: popExc with 400 components of type HH_477127614
* Locations: [(573.1628, 70.3663, 104.4949), ...]
* Properties: color=.8 0 0;
* Population: popInh with 200 components of type HH_476686112
* Locations: [(273.6470, 115.2328, 162.9573), ...]
* Properties: color=0 0 .8;
*
* 0 connections in 0 projections
*
* 400 inputs in 1 input lists
* Input list: Stim0 to popExc, component psf1
* 400 inputs: [(Input 0: 0:0(0.500000)), ...]
*
*******************************************************pynml-plotmorph-interactive3d.webm
Things we still need to do:
- plot inputs in both 2d and 3d plots: this requires some more thought since we'll need to parse the target strings, which can be provided in multiple formats, in the input lists to get the target segments and then get the locations from the segments
Adds more morphology related plots