def export_to_neuroml2(hoc_or_python_file, nml2_file_name, includeBiophysicalProperties=True, separateCellFiles=False, validate=True):
from neuron import *
from nrn import *
if hoc_or_python_file is not None:
if hoc_or_python_file.endswith(".py"):
print("Importing Python scripts not yet implemented...")
else:
h.load_file(hoc_or_python_file)
print "Loaded NEURON file: %s"%hoc_or_python_file
h.load_file("mview.hoc")
h('objref mv')
h('mv = new ModelView()')
h.load_file("%s/mview_neuroml2.hoc"%(os.path.dirname(__file__)))
h('objref mvnml')
h('mvnml = new ModelViewNeuroML2(mv)')
nml2_level = 2 if includeBiophysicalProperties else 1
h.mvnml.exportNeuroML2(nml2_file_name, nml2_level, int(separateCellFiles))
if validate:
validate_neuroml2(nml2_file_name)
def export_to_neuroml2(hoc_or_python_file,
nml2_file_name,
includeBiophysicalProperties=True,
separateCellFiles=False,
known_rev_potentials={},
validate=True):
from neuron import *
from nrn import *
if hoc_or_python_file is not None:
if hoc_or_python_file.endswith(".py"):
print_comment_v(
"***************\nImporting Python scripts not yet implemented...\n***************"
)
else:
if not os.path.isfile(hoc_or_python_file):
print_comment_v(
"***************\nProblem importing file %s (%s)..\n***************"
%
(hoc_or_python_file, os.path.abspath(hoc_or_python_file)))
h.load_file(
1, hoc_or_python_file
) # Using 1 to force loading of the file, in case file with same name was loaded before...
else:
print_comment_v(
"hoc_or_python_file variable is None; exporting what's currently in memory..."
)
for ion in known_rev_potentials.keys():
set_erev_for_mechanism(ion, known_rev_potentials[ion])
print_comment_v("Loaded NEURON file: %s" % hoc_or_python_file)
h.load_file("mview.hoc")
h('objref mv')
h('mv = new ModelView(0)')
h.load_file("%s/mview_neuroml2.hoc" % (os.path.dirname(__file__)))
h('objref mvnml')
h('mvnml = new ModelViewNeuroML2(mv)')
nml2_level = 2 if includeBiophysicalProperties else 1
h.mvnml.exportNeuroML2(nml2_file_name, nml2_level, int(separateCellFiles))
if validate:
validate_neuroml2(nml2_file_name)
h('mv.destroy()')
def save_network(nml_doc, nml_file_name, validate=True, comment=True, format='xml'):
info = "\n\nThis NeuroML 2 file was generated by OpenCortex v%s using: \n"%(opencortex.__version__)
info += " libNeuroML v%s\n"%(neuroml.__version__)
info += " pyNeuroML v%s\n\n "%(pyneuroml.__version__)
if nml_doc.notes:
nml_doc.notes += info
else:
nml_doc.notes = info
if format == 'xml':
writers.NeuroMLWriter.write(nml_doc, nml_file_name)
elif format == 'hdf5':
writers.NeuroMLHdf5Writer.write(nml_doc, nml_file_name)
opencortex.print_comment_v("Saved NeuroML with id: %s to %s"%(nml_doc.id, nml_file_name))
if validate:
from pyneuroml.pynml import validate_neuroml2
passed = validate_neuroml2(nml_file_name)
if passed:
opencortex.print_comment_v("Generated NeuroML file is valid")
else:
opencortex.print_comment_v("Generated NeuroML file is NOT valid!")
def export_to_neuroml2(hoc_or_python_file,
nml2_file_name,
includeBiophysicalProperties=True,
separateCellFiles=False,
known_rev_potentials={},
validate=True):
from neuron import *
from nrn import *
if hoc_or_python_file is not None:
if hoc_or_python_file.endswith(".py"):
print_comment_v("***************\nImporting Python scripts not yet implemented...\n***************")
else:
if not os.path.isfile(hoc_or_python_file):
print_comment_v("***************\nProblem importing file %s (%s)..\n***************"%(hoc_or_python_file, os.path.abspath(hoc_or_python_file)))
h.load_file(1, hoc_or_python_file) # Using 1 to force loading of the file, in case file with same name was loaded before...
else:
print_comment_v("hoc_or_python_file variable is None; exporting what's currently in memory...")
for ion in known_rev_potentials.keys():
set_erev_for_mechanism(ion,known_rev_potentials[ion])
print_comment_v("Loaded NEURON file: %s"%hoc_or_python_file)
h.load_file("mview.hoc")
h('objref mv')
h('mv = new ModelView(0)')
h.load_file("%s/mview_neuroml2.hoc"%(os.path.dirname(__file__)))
h('objref mvnml')
h('mvnml = new ModelViewNeuroML2(mv)')
nml2_level = 2 if includeBiophysicalProperties else 1
h.mvnml.exportNeuroML2(nml2_file_name, nml2_level, int(separateCellFiles))
if validate:
validate_neuroml2(nml2_file_name)
h('mv.destroy()')
def export_to_neuroml2(
hoc_or_python_file, nml2_file_name, includeBiophysicalProperties=True, separateCellFiles=False, validate=True
):
from neuron import *
from nrn import *
if hoc_or_python_file is not None:
if hoc_or_python_file.endswith(".py"):
print_comment_v("Importing Python scripts not yet implemented...")
else:
h.load_file(
1, hoc_or_python_file
) # Using 1 to force loading of the file, in case file with same name was loaded before...
else:
print_comment_v("hoc_or_python_file variable is None; exporting what's currently in memory...")
print_comment_v("Loaded NEURON file: %s" % hoc_or_python_file)
h.load_file("mview.hoc")
h("objref mv")
h("mv = new ModelView(0)")
h.load_file("%s/mview_neuroml2.hoc" % (os.path.dirname(__file__)))
h("objref mvnml")
h("mvnml = new ModelViewNeuroML2(mv)")
nml2_level = 2 if includeBiophysicalProperties else 1
h.mvnml.exportNeuroML2(nml2_file_name, nml2_level, int(separateCellFiles))
if validate:
validate_neuroml2(nml2_file_name)
h("mv.destroy()")
def save_network(nml_doc,
nml_file_name,
validate=True,
format='xml',
max_memory=None,
target_dir='./',
use_subfolder=True):
"""
Save the contents of the built NeuroML document, including the network to the file specified by `nml_file_name`, optionally specifying the `target_dir`
"""
oc_build._finalise_copy_to_dir_for_model(nml_doc,
target_dir,
use_subfolder=use_subfolder)
info = "\n\nThis NeuroML 2 file was generated by OpenCortex v%s using: \n" % (
opencortex.__version__)
info += " libNeuroML v%s\n" % (neuroml.__version__)
info += " pyNeuroML v%s\n\n " % (pyneuroml.__version__)
if nml_doc.notes:
nml_doc.notes += info
else:
nml_doc.notes = info
nml_full_file_name = target_dir + '/' + nml_file_name
if format == 'xml':
writers.NeuroMLWriter.write(nml_doc, nml_full_file_name)
elif format == 'xml_hdf5':
writers.NeuroMLHdf5Writer.write_xml_and_hdf5(
nml_doc, nml_full_file_name, '%s.h5' % nml_full_file_name)
elif format == 'hdf5':
writers.NeuroMLHdf5Writer.write(nml_doc, nml_full_file_name)
opencortex.print_comment_v("Saved NeuroML with id: %s to %s" %
(nml_doc.id, nml_full_file_name))
if validate:
from pyneuroml.pynml import validate_neuroml2
passed = validate_neuroml2(nml_full_file_name, max_memory=max_memory)
if passed:
opencortex.print_comment_v("Generated NeuroML file is valid")
else:
opencortex.print_comment_v("Generated NeuroML file is NOT valid!")
def generate_neuroml2_from_network(nl_model,
nml_file_name=None,
print_summary=True,
seed=1234,
format='xml',
base_dir=None,
copy_included_elements=False,
target_dir=None,
validate=False):
"""
Generate and save NeuroML2 file (in either XML or HDF5 format) from the
NeuroMLlite description
"""
print_v("Generating NeuroML2 for %s%s..." %
(nl_model.id, ' (base dir: %s; target dir: %s)' %
(base_dir, target_dir) if base_dir or target_dir else ''))
import neuroml
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
neuroml_handler = NetworkBuilder()
generate_network(nl_model, neuroml_handler, seed=seed, base_dir=base_dir)
nml_doc = neuroml_handler.get_nml_doc()
for i in nl_model.input_sources:
if nml_doc.get_by_id(i.id) == None:
if i.neuroml2_source_file:
incl = neuroml.IncludeType(
_locate_file(i.neuroml2_source_file, base_dir))
if not incl in nml_doc.includes:
nml_doc.includes.append(incl)
if i.neuroml2_input:
input_params = i.parameters if i.parameters else {}
# TODO make more generic...
if i.neuroml2_input.lower() == 'pulsegenerator':
input = neuroml.PulseGenerator(id=i.id)
nml_doc.pulse_generators.append(input)
elif i.neuroml2_input.lower() == 'pulsegeneratordl':
input = neuroml.PulseGeneratorDL(id=i.id)
nml_doc.pulse_generator_dls.append(input)
elif i.neuroml2_input.lower() == 'poissonfiringsynapse':
input = neuroml.PoissonFiringSynapse(id=i.id)
nml_doc.poisson_firing_synapses.append(input)
for p in input_params:
exec('input.%s = "%s"' %
(p, evaluate(input_params[p], nl_model.parameters)))
for c in nl_model.cells:
if c.neuroml2_source_file:
incl = neuroml.IncludeType(
_locate_file(c.neuroml2_source_file, base_dir))
found_cell = False
for cell in nml_doc.cells:
if cell.id == c.id:
nml_doc.cells.remove(
cell
) # Better to use imported cell file; will have channels, etc.
nml_doc.includes.append(incl)
found_cell = True
if not found_cell:
for p in nl_model.populations:
if p.component == c.id:
pass
if not incl in nml_doc.includes:
nml_doc.includes.append(incl)
''' Needed???
if c.lems_source_file:
incl = neuroml.IncludeType(_locate_file(c.lems_source_file, base_dir))
if not incl in nml_doc.includes:
nml_doc.includes.append(incl)'''
if c.neuroml2_cell:
cell_params = c.parameters if c.parameters else {}
# TODO make more generic...
if c.neuroml2_cell.lower() == 'spikegenerator':
cell = neuroml.SpikeGenerator(id=c.id)
nml_doc.spike_generators.append(cell)
elif c.neuroml2_cell.lower() == 'spikegeneratorpoisson':
cell = neuroml.SpikeGeneratorPoisson(id=c.id)
nml_doc.spike_generator_poissons.append(cell)
elif c.neuroml2_cell.lower() == 'spikegeneratorrefpoisson':
cell = neuroml.SpikeGeneratorRefPoisson(id=c.id)
nml_doc.spike_generator_ref_poissons.append(cell)
else:
raise Exception(
'The neuroml2_cell: %s is not yet supported...' %
c.neuroml2_cell)
for p in cell_params:
exec('cell.%s = "%s"' %
(p, evaluate(cell_params[p], nl_model.parameters)))
for s in nl_model.synapses:
if nml_doc.get_by_id(s.id) == None:
if s.neuroml2_source_file:
incl = neuroml.IncludeType(
_locate_file(s.neuroml2_source_file, base_dir))
if not incl in nml_doc.includes:
nml_doc.includes.append(incl)
# Look for and add the PyNN based elements to the NeuroMLDocument
_extract_pynn_components_to_neuroml(nl_model, nml_doc)
if print_summary:
# Print info
print_v(nml_doc.summary())
# Save to file
if target_dir == None:
target_dir = base_dir
if format == 'xml':
if not nml_file_name:
nml_file_name = _locate_file('%s.net.nml' % nml_doc.id, target_dir)
from neuroml.writers import NeuroMLWriter
NeuroMLWriter.write(nml_doc, nml_file_name)
if format == 'hdf5':
if not nml_file_name:
nml_file_name = _locate_file('%s.net.nml.h5' % nml_doc.id,
target_dir)
from neuroml.writers import NeuroMLHdf5Writer
NeuroMLHdf5Writer.write(nml_doc, nml_file_name)
print_v("Written NeuroML to %s" % nml_file_name)
if validate and format == 'xml':
from pyneuroml import pynml
success = pynml.validate_neuroml2(nml_file_name,
verbose_validate=False)
if success:
print_v('Generated file is valid NeuroML2!')
else:
print_v('Generated file is NOT valid NeuroML2!')
return nml_file_name, nml_doc
channel_id = 'Channelpedia_%s_%s' % (root.attrib['ModelName'].replace(
"/", "_").replace(" ", "_").replace(".",
"_"), root.attrib['ModelID'])
print("Channel id: %s" % channel_id)
file_out = open("test/%s.xml" % channel_id, 'w')
file_out.write(cpd_xml)
file_out.close()
nml2_file_name = "%s.channel.nml" % channel_id
nml2_file_path = "test/" + nml2_file_name
unknowns = channelpedia_xml_to_neuroml2(cpd_xml, nml2_file_path,
unknowns)
pynml.validate_neuroml2(nml2_file_path)
doc = loaders.NeuroMLLoader.load(nml2_file_path)
#print dir(doc)
gates = []
for ic in doc.ion_channel_hhs:
if ic.id == channel_id:
for g in ic.gates:
gates.append(g.id)
new_lems_file = "test/LEMS_Test_%s.xml" % channel_id
lems_helper = generate(nml2_file_name,
channel_id,
gates,
temperature,
net.explicit_inputs.append(exp_input)
for post in range(0, size1):
if random.random() <= prob_connection:
syn = Connection(id=count,
pre_cell_id="../%s[%i]" % (pop0.id, pre),
synapse=syn0.id,
post_cell_id="../%s[%i]" % (pop1.id, post))
proj.connections.append(syn)
count += 1
nml_file = 'izhikevich2007_network.nml'
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: " + nml_file)
pynml.validate_neuroml2(nml_file)
simulation_id = "example_izhikevich2007network_sim"
simulation = LEMSSimulation(sim_id=simulation_id,
duration=1000,
dt=0.1,
simulation_seed=123)
simulation.assign_simulation_target(net.id)
simulation.include_neuroml2_file(nml_file)
simulation.create_event_output_file("pop0",
"%s.0.spikes.dat" % simulation_id,
format='ID_TIME')
for pre in range(0, size0):
simulation.add_selection_to_event_output_file("pop0", pre,
'IzPop0[{}]'.format(pre),
root = ET.fromstring(cpd_xml)
channel_id='Channelpedia_%s_%s'%(root.attrib['ModelName'].replace("/","_").replace(" ","_").replace(".","_"), root.attrib['ModelID'])
print("Channel id: %s"%channel_id)
file_out = open("test/%s.xml"%channel_id, 'w')
file_out.write(cpd_xml)
file_out.close()
nml2_file_name = "%s.channel.nml"%channel_id
nml2_file_path = "test/"+nml2_file_name
unknowns = channelpedia_xml_to_neuroml2(cpd_xml, nml2_file_path, unknowns)
pynml.validate_neuroml2(nml2_file_path)
doc = loaders.NeuroMLLoader.load(nml2_file_path)
#print dir(doc)
gates = []
for ic in doc.ion_channel_hhs:
if ic.id == channel_id:
for g in ic.gates:
gates.append(g.id)
new_lems_file = "LEMS_Test_%s.xml"%channel_id
lems_helper = generate(nml2_file_path, channel_id, gates, temperature, ion=doc.ion_channel_hhs[0].species)
file_out = open(new_lems_file, 'w')
file_out.write(lems_helper)
