def save_to_xml(self, file_name):
from neuroml.writers import NeuroMLWriter
NeuroMLWriter.write(self.nml_doc, file_name)
print("Written to %s" % file_name)
def write_izhikevich(filename="./tmp/SingleIzhikevich_test.nml"):
nml_doc = NeuroMLDocument(id="SingleIzhikevich")
nml_filename = filename
iz0 = IzhikevichCell(id="iz0", v0="-70mV", thresh="30mV", a="0.02", b="0.2", c="-65.0", d="6")
nml_doc.izhikevich_cells.append(iz0)
NeuroMLWriter.write(nml_doc, nml_filename)
validate_neuroml2(nml_filename)
def test_convert_morphology(self):
morph = converter.convert_morphology(self.neuron, positions='auto')
cell = neuroml.Cell()
cell.name = self.neuron.name
cell.id = cell.name
cell.morphology = morph
doc = neuroml.NeuroMLDocument()
doc.cells.append(cell)
doc.id = 'TestNeuroMLDocument'
fname = os.path.join(outdir, 'test_morphology_conversion.nml')
NeuroMLWriter.write(doc, fname)
print('Wrote', fname)
def test_convert_morphology(self):
morph = converter.convert_morphology(self.neuron, positions='auto')
cell = neuroml.Cell()
cell.name = self.neuron.name
cell.id = cell.name
cell.morphology = morph
doc = neuroml.NeuroMLDocument()
doc.cells.append(cell)
doc.id = 'TestNeuroMLDocument'
fname = os.path.join(outdir, 'test_morphology_conversion.nml')
NeuroMLWriter.write(doc, fname)
print 'Wrote', fname
def write_izhikevich(filename="./tmp/SingleIzhikevich_test.nml"):
nml_doc = NeuroMLDocument(id="SingleIzhikevich")
nml_filename = filename
iz0 = IzhikevichCell(id="iz0",
v0="-70mV",
thresh="30mV",
a="0.02",
b="0.2",
c="-65.0",
d="6")
nml_doc.izhikevich_cells.append(iz0)
NeuroMLWriter.write(nml_doc, nml_filename)
validate_neuroml2(nml_filename)
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
net.populations[1].random_layout.append(
RandomLayout(id='test2', x=1000, y=1000, z=1000))
print net.to_json()
### Use a handler which just prints info on positions, etc.
def_handler = DefaultNetworkHandler()
generate_network(net, def_handler)
### Use a handler which builds a NeuroML 2 representation
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
neuroml_handler = NetworkBuilder()
generate_network(net, neuroml_handler)
nml_doc = neuroml_handler.get_nml_doc()
# Print info
print(nml_doc.summary())
# Save to file
file_name = '%s.net.nml' % nml_doc.id
from neuroml.writers import NeuroMLWriter
NeuroMLWriter.write(nml_doc, file_name)
print("Written NeuroML to %s" % file_name)
from neuromllite.DefaultNetworkHandler import DefaultNetworkHandler
def_handler = DefaultNetworkHandler()
bbp.parse(def_handler)
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
neuroml_handler = NetworkBuilder()
bbp = BBPConnectomeReader(
filename=filename,
percentage_cells_per_pop=percentage_cells_per_pop,
DEFAULT_CELL_ID='hhcell')
bbp.parse(neuroml_handler)
nml_file_name = 'BBP.net.nml'
from neuroml.writers import NeuroMLWriter
NeuroMLWriter.write(neuroml_handler.get_nml_doc(), nml_file_name)
'''
from neuromllite.NeuronHandler import NeuronHandler
nrn_handler = NeuronHandler()
nrn_handler.executeHoc('load_file("hhcell.hoc")')
bbp = BBPConnectomeReader()
bbp.parse(file_name, nrn_handler)
#bbp.save_to_hdf5(nml_h5_file_name)'''
