Example2.py

from neuromllite import (
    RandomLayout,
    Cell,
    Synapse,
    InputSource,
    Input,
    RectangularRegion,
)
from neuromllite.NetworkGenerator import generate_network
from neuromllite.utils import load_network_json
from neuromllite.DefaultNetworkHandler import DefaultNetworkHandler

################################################################################
###   Reuse network from Example1

net = load_network_json("Example1_TestNetwork.json")
net.id = "Example2_TestNetwork"

net.notes = (
    "A simple network with 2 populations & projection between them. "
    + "Cells are specified to be NeuroML 2 HH cell models & pre population "
    "is given a spiking input."
)


################################################################################
###   Add some elements to the network & save new JSON

r1 = RectangularRegion(id="region1", x=0, y=0, z=0, width=1000, height=100, depth=1000)
r2 = RectangularRegion(
    id="region2", x=0, y=200, z=0, width=1000, height=100, depth=1000
)
net.regions.append(r1)
net.regions.append(r2)

net.populations[0].random_layout = RandomLayout(region=r1.id)
net.populations[1].random_layout = RandomLayout(region=r2.id)

net.populations[0].component = "hhcell"
net.populations[1].component = "hhcell"

net.cells.append(Cell(id="hhcell", neuroml2_source_file="test_files/hhcell.cell.nml"))
net.synapses.append(
    Synapse(id="ampa", neuroml2_source_file="test_files/ampa.synapse.nml")
)

input_source = InputSource(
    id="poissonFiringSyn", neuroml2_source_file="test_files/inputs.nml"
)
net.input_sources.append(input_source)

net.inputs.append(
    Input(
        id="stim_%s" % net.populations[0].id,
        input_source=input_source.id,
        population=net.populations[0].id,
        percentage=80,
    )
)

print(net.to_json())
new_file = net.to_json_file("%s.json" % net.id)


################################################################################
###   Use a handler which just prints info on positions, etc.

def_handler = DefaultNetworkHandler()

generate_network(net, def_handler)


################################################################################
###   Export to some formats, e.g. try:
###        python Example2.py -graph2

from neuromllite.NetworkGenerator import check_to_generate_or_run
from neuromllite import Simulation
import sys

check_to_generate_or_run(sys.argv, Simulation(id="SimExample2", network=new_file))