Merge pull request #20 from OpenSourceBrain/test_pynn3

Add export of new PyNN to NeuroML

.gitignore

@@ -32,3 +32,7 @@ NEST_SLI/*.gdf
 /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/network.py

@@ -18,6 +18,8 @@
 from helpers import boxplot
 from helpers import compute_DC
 from pyNN.random import RandomDistribution
+from pyNN.space import RandomStructure, Cuboid
+import math
 
 
 class Network:
@@ -52,7 +54,7 @@ def __init__(self, sim_dict, net_dict, stim_dict=None):
         self.data_path = sim_dict['data_path']
 
 
-    def setup_pyNN(self):
+    def setup_pyNN(self, extra_setup_params):
         """ Reset and configure the simulator.
 
         Where the simulator is NEST,
@@ -73,7 +75,8 @@ def setup_pyNN(self):
         self.sim.setup(timestep=self.sim_resolution,
                        threads=self.sim_dict['local_num_threads'],
                        grng_seed=grng_seed,
-                       rng_seeds=rng_seeds)
+                       rng_seeds=rng_seeds,
+                       **extra_setup_params)
         if self.sim.rank() == 0:
             print('Master seed: %i ' % master_seed)
             print('Number of total processes: %i' % N_tp)
@@ -158,12 +161,65 @@ def create_populations(self):
                                     [self.net_dict['neuron_params']['V0_mean'],
                                      self.net_dict['neuron_params']['V0_sd']],
                                    )  # todo: specify rng
+        layer_structures = {}
+
+        x_dim_scaled = self.net_dict['x_dimension'] * math.sqrt(self.N_scaling)
+        z_dim_scaled = self.net_dict['z_dimension'] * math.sqrt(self.N_scaling)
+
+        default_cell_radius = 10 # for visualisation 
+        default_input_radius = 5 # for visualisation 
+        layer_thicknesses = self.net_dict["layer_thicknesses"]
+
+
         for i, pop in enumerate(self.net_dict['populations']):
+            layer = pop[:-1]
+
+            y_offset = 0
+            if layer == 'L6': y_offset = layer_thicknesses['L6']/2
+            elif layer == 'L5': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']/2
+            elif layer == 'L4': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']/2
+            elif layer == 'L23': y_offset = layer_thicknesses['L6']+layer_thicknesses['L5']+layer_thicknesses['L4']+layer_thicknesses['L23']/2
+            else:
+                raise Exception("Problem with %s"%layer)
+
+            layer_volume = Cuboid(x_dim_scaled,layer_thicknesses[layer],z_dim_scaled)
+            layer_structures[layer] = RandomStructure(layer_volume, origin=(0,y_offset,0))
+
             parameters['i_offset'] = self.DC_amp_e[i] * 0.001   # pA --> nA
             population = self.sim.Population(int(self.nr_neurons[i]),
                                              neuron_model(**parameters),
+                                             structure=layer_structures[layer], 
                                              label=pop)
             population.initialize(v=v_init)
+            # Store whether population is inhibitory or excitatory
+            population.annotate(type=pop[-1:])
+
+            population.annotate(radius=default_cell_radius)
+            population.annotate(structure=str(layer_structures[layer]))
+
+
+            try:
+                import opencortex.utils.color as occ
+                print('Adding color for %s'%pop)
+                if 'L23' in pop:
+                    if 'E' in pop: color = occ.L23_PRINCIPAL_CELL
+                    if 'I' in pop: color = occ.L23_INTERNEURON
+                if 'L4' in pop:
+                    if 'E' in pop: color = occ.L4_PRINCIPAL_CELL
+                    if 'I' in pop: color = occ.L4_INTERNEURON
+                if 'L5' in pop:
+                    if 'E' in pop: color = occ.L5_PRINCIPAL_CELL
+                    if 'I' in pop: color = occ.L5_INTERNEURON
+                if 'L6' in pop:
+                    if 'E' in pop: color = occ.L6_PRINCIPAL_CELL
+                    if 'I' in pop: color = occ.L6_INTERNEURON
+
+                population.annotate(color=color)
+            except Exception as e:
+                print(e)
+                # Don't worry about it, it's just metadata
+                pass
+
             self.pops.append(population)
 
     def create_devices(self):
@@ -230,7 +286,7 @@ def create_poisson(self):
             for i, target_pop in enumerate(self.pops):
                 poisson = self.sim.Population(target_pop.size,
                                               self.sim.SpikeSourcePoisson(rate=rate_ext[i]),
-                                              label="Input to {}".format(target_pop.label))
+                                              label="Input_to_{}".format(target_pop.label))
                 self.poisson.append(poisson)
 
     def create_dc_generator(self):
@@ -369,7 +425,7 @@ def connect_dc_generator(self):
             if self.stim_dict['dc_input']:
                 self.dc[i].inject_into(target_pop)
 
-    def setup(self):
+    def setup(self, extra_setup_params = {}):
         """ 
         Execute subfunctions of the network.
 
@@ -378,7 +434,7 @@ def setup(self):
         each other and with devices and input nodes.
 
         """
-        self.setup_pyNN()
+        self.setup_pyNN(extra_setup_params)
         self.create_populations()
         self.create_devices()
         self.create_thalamic_input()

PyNN/network_params.py

@@ -224,3 +224,26 @@ def get_std_PSP_matrix(PSP_rel, number_of_pop):
 
 
 net_dict.update(updated_dict)
+
+total_cortical_thickness = 1500.0
+N_full = net_dict['N_full']
+N_E_total = N_full[0]+N_full[2]+N_full[4]+N_full[6]
+
+dimensions_3D = {
+    'x_dimension': 1000,
+    'z_dimension': 1000,
+    #thalamus_offset = -300
+
+    'total_cortical_thickness': total_cortical_thickness,
+
+# Have the thicknesses proportional to the numbers of E cells in each layer
+    'layer_thicknesses': {
+    'L23': total_cortical_thickness*N_full[0]/N_E_total,
+    'L4' : total_cortical_thickness*N_full[2]/N_E_total,
+    'L5' : total_cortical_thickness*N_full[4]/N_E_total,
+    'L6' : total_cortical_thickness*N_full[6]/N_E_total,
+    'thalamus' : 100
+    }
+}
+
+net_dict.update(dimensions_3D)

PyNN/test.py

@@ -3,10 +3,7 @@
 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
+Test file for the microcircuit.
 
 """
 
@@ -18,29 +15,46 @@
 from stimulus_params import stim_dict
 import os
 
-
-# Initialize the network and pass parameters to it.
-tic = time.time()
-
-net_dict['N_scaling'] = 0.1
-net_dict['to_record'] = ['spikes', 'v']
-sim_dict['data_path'] = os.path.join(os.getcwd(), 'results')
-
-
-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)
+def setup(simulator='nest', N_scaling=0.1):
+    # Initialize the network and pass parameters to it.
+    tic = time.time()
+
+    net_dict['N_scaling'] = N_scaling
+
+    net_dict['to_record'] = ['spikes', 'v']
+    sim_dict['data_path'] = os.path.join(os.getcwd(), 'results')
+
+    sim_dict['simulator'] = simulator
+
+    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()
+    extra_setup_params = {}
+    if simulator=='neuroml':
+        extra_setup_params['reference']='Microcircuit_%spcnt'%(str(N_scaling*100).replace('.','_'))
+
+    net.setup(extra_setup_params)
+    toc = time.time() - tic
+    print("Time to create the connections: %.2f s" % toc)
+    return net, net_dict, sim_dict
+
+
+def run(net):
+
+    # 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)
+
+
+if __name__ == "__main__":
+    net, net_dict, sim_dict = setup(N_scaling=0.1)
+    run(net)

PyNN/test_neuroml.py

@@ -0,0 +1,35 @@
+# -*- coding: utf-8 -*-
+"""
+PyNN microcircuit example
+---------------------------
+
+Export to NeuroML of the microcircuit.
+
+"""
+
+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
+import os
+
+from test import setup
+
+def export(net):
+
+    # Export.
+    tic = time.time()
+    print("Exporting...")
+    net.simulate()
+    net.sim.end()
+    toc = time.time() - tic
+    print("Time to export: %.2f s" % toc)
+
+
+
+if __name__ == "__main__":
+    net, net_dict, sim_dict = setup(simulator='neuroml', 
+                N_scaling=0.002)
+    export(net)