def show3Dimage(self):
"""Generate 3D view button has been pressed"""
print_v("image3D button was clicked.")
self.update_net_sim()
self.tabs.setCurrentWidget(self.all_tabs[self.IMAGE_3D_TAB])
from neuromllite.NetworkGenerator import generate_neuroml2_from_network
nml_file_name, nml_doc = generate_neuroml2_from_network(self.network,
print_summary=True,
format='xml',
base_dir=None,
copy_included_elements=False,
target_dir=None,
validate=False,
simulation=self.simulation)
post_args = "-png"
from pyneuroml.pynml import run_jneuroml
run_jneuroml("",
nml_file_name,
post_args,
verbose = True)
nml_view_file = nml_file_name.replace('.nml','.png')
self.add_image(nml_view_file, self.IMAGE_3D_TAB)
def generateNeuroML2(self):
"""Generate NeuroML 2 representation of network"""
print_v("Generate NeuroML 2 button was clicked.")
self.update_net_sim()
from neuromllite.NetworkGenerator import generate_neuroml2_from_network
nml_file_name, nml_doc = generate_neuroml2_from_network(
self.network,
print_summary=True,
format="xml",
base_dir=None,
copy_included_elements=False,
target_dir=None,
validate=False,
simulation=self.simulation,
)
with open(nml_file_name, "r") as reader:
nml_txt = reader.read()
self.nml2Text.clear()
self.nml2Text.insertPlainText(nml_txt)
self.tabs.setCurrentWidget(self.nml2Tab)
Input(
id="stim_%s" % pop,
input_source=input_source.id,
population=pop,
percentage=80,
))
new_file = net.to_json_file("%s.json" % net.id)
################################################################################
### Builds a NeuroML 2 representation, save as XML
format_ = "xml"
nml_file_name, nml_doc = generate_neuroml2_from_network(
net,
nml_file_name="%s.net.nml%s" %
(net.id, ".h5" if format_ == "hdf5" else ""),
format=format_,
)
from neuromllite import Simulation
record_traces = {"all": "*"}
record_spikes = {"all": "*"}
sim = Simulation(
id="SimExample5",
network=new_file,
duration=500,
dt=0.025,
record_traces=record_traces,
record_spikes=record_spikes,
for pop in ['L4_PC']:
net.inputs.append(Input(id='stim_%s'%pop,
input_source=input_source.id,
population=pop,
percentage=80))
new_file = net.to_json_file('%s.json'%net.id)
################################################################################
### Builds a NeuroML 2 representation, save as XML
format_='xml'
generate_neuroml2_from_network(net,
nml_file_name='%s.net.nml%s'%(net.id, '.h5' if format_=='hdf5' else ''),
format=format_)
from neuromllite import Simulation
sim = Simulation(id='SimExample5',
network=new_file,
duration='500',
dt='0.025',
recordTraces={'all':'*'})
################################################################################
### Run in some simulators
