Diff exp dev

.github/workflows/omv-ci.yml

@@ -30,10 +30,10 @@ jobs:
 
 
     steps:
-    - uses: actions/checkout@v3
+    - uses: actions/checkout@v4
 
     - name: Set up Python  ${{ matrix.python-version }}
-      uses: actions/setup-python@v4
+      uses: actions/setup-python@v5
       with:
         python-version:  ${{ matrix.python-version }}
 

.gitignore

@@ -30,3 +30,9 @@ NEST_SLI/*.gdf
 /PyNN/*.mod
 /PyNN/*.net.nml
 /PyNN/tests.log
+/PyNN/data/2024*
+/p0/*
+/NeuroML2/*.dat
+/PyNN/LEMS_Sim_Microcircuit*.xml
+/NeuroML2/*.spikes
+/NeuroML2/Microcircuit*.gv

NeuroML2/.test.validate.omt

@@ -1,6 +1,4 @@
-# Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation 
-# Still in development, subject to change without notice!!
-
-# This test will validate all of the NeuroML 2 files in the current directory using: jnml -validate *.nml
-target: "*.nml"
+# Script for running automated tests on OSB, see https://github.com/OpenSourceBrain/osb-model-validation
+
+target: "*.nml" 
 engine: jNeuroML_validate

NeuroML2/regenerate.sh

@@ -0,0 +1,7 @@
+#!/bin/bash
+set -ex
+
+cd ../PyNN
+python test_neuroml.py
+
+cp *.nml LEMS*ml ../NeuroML2

PyNN/.test.pynn.mep

@@ -4,25 +4,25 @@ experiments:
 
     spike_rate_l23e:
         expected:
-            spike rate: 1.6693811074918568 # Note: this reflects only rate of cells that spiked at least once...
+            spike rate: 1.4385775862068966 # Note: this reflects only rate of cells that spiked at least once...
     spike_rate_l23i:
         expected:
-            spike rate: 3.643617021276596
+            spike rate: 3.44954128440367
     spike_rate_l4e:
         expected:
-            spike rate: 4.9837255883825735
+            spike rate: 4.850835322195704
     spike_rate_l4i:
         expected:
-            spike rate: 7.290489642184557
+            spike rate: 7.12312734082397
     spike_rate_l5e:
         expected:
-            spike rate: 7.753623188405797
+            spike rate: 7.869022869022869
     spike_rate_l5i:
         expected:
-            spike rate: 8.016826923076923
+            spike rate: 7.85377358490566
     spike_rate_l6e:
         expected:
-            spike rate: 2.094361334867664
+            spike rate: 1.8208245243128964
     spike_rate_l6i:
         expected:
-            spike rate: 8.274828767123287
+            spike rate: 8.225255972696246

PyNN/.test.pynnnest.comparison.omt

@@ -1,6 +1,6 @@
 # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
 
-target: microcircuit.py
+target: test.py
 engine: PyNN_Nest
 mep: ../PyNEST/.test.pynest.mep
 experiments:

PyNN/.test.pynnnest.omt

@@ -1,6 +1,6 @@
 # Script for running automated tests on OSB using Travis-CI, see https://github.com/OpenSourceBrain/osb-model-validation
 
-target: microcircuit.py
+target: test.py 
 engine: PyNN_Nest
 mep: .test.pynn.mep
 experiments:

PyNN/example.py

@@ -0,0 +1,49 @@
+# -*- coding: utf-8 -*-
+"""
+PyNN microcircuit example
+---------------------------
+
+Example file to run the microcircuit.
+
+Based on original PyNEST version by Hendrik Rothe, Hannah Bos, Sacha van Albada; May 2016
+Adapted for PyNN by Andrew Davison, December 2017
+
+"""
+
+import time
+import numpy as np
+import network
+from network_params import net_dict
+from sim_params import sim_dict
+from stimulus_params import stim_dict
+
+
+# Initialize the network and pass parameters to it.
+tic = time.time()
+net = network.Network(sim_dict, net_dict, stim_dict)
+toc = time.time() - tic
+print("Time to initialize the network: %.2f s" % toc)
+# Connect all nodes.
+tic = time.time()
+net.setup()
+toc = time.time() - tic
+print("Time to create the connections: %.2f s" % toc)
+# Simulate.
+tic = time.time()
+net.simulate()
+toc = time.time() - tic
+print("Time to simulate: %.2f s" % toc)
+tic = time.time()
+net.write_data()
+toc = time.time() - tic
+print("Time to write data: %.2f s" % toc)
+# Plot a raster plot of the spikes of the simulated neurons and the average
+# spike rate of all populations. For visual purposes only spikes 100 ms
+# before and 100 ms after the thalamic stimulus time are plotted here by
+# default. The computation of spike rates discards the first 500 ms of
+# the simulation to exclude initialization artifacts.
+raster_plot_time_idx = np.array(
+    [stim_dict['th_start'] - 100.0, stim_dict['th_start'] + 100.0]
+    )
+fire_rate_time_idx = np.array([500.0, sim_dict['t_sim']])
+net.evaluate(raster_plot_time_idx, fire_rate_time_idx)