def __nml2_doc(cls,file_name,optimized=False):
import sys
import logging
logging.basicConfig(level=logging.INFO, format="%(name)-19s %(levelname)-5s - %(message)s")
from neuroml.hdf5.NeuroMLHdf5Parser import NeuroMLHdf5Parser
if optimized:
currParser = NeuroMLHdf5Parser(None,optimized=True)
currParser.parse(file_name)
return currParser.get_nml_doc()
else:
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
nmlHandler = NetworkBuilder()
currParser = NeuroMLHdf5Parser(nmlHandler)
currParser.parse(file_name)
nml2_doc = nmlHandler.get_nml_doc()
if currParser.nml_doc_extra_elements:
add_all_to_document(currParser.nml_doc_extra_elements,nml2_doc)
return nml2_doc
def __nml2_doc(cls,file_name):
import sys
import logging
logging.basicConfig(level=logging.INFO, format="%(name)-19s %(levelname)-5s - %(message)s")
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
from neuroml.hdf5.NeuroMLHdf5Parser import NeuroMLHdf5Parser
try:
nmlHandler = NetworkBuilder()
currParser = NeuroMLHdf5Parser(nmlHandler)
currParser.parse(file_name)
nml2_doc = nmlHandler.get_nml_doc()
except Exception:
print("Not a valid NeuroML 2 doc: %s" % str(sys.exc_info()))
return None
return nml2_doc
def __nml2_doc(cls, file_name):
import sys
import logging
logging.basicConfig(level=logging.INFO,
format="%(name)-19s %(levelname)-5s - %(message)s")
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
from neuroml.hdf5.NeuroMLHdf5Parser import NeuroMLHdf5Parser
try:
nmlHandler = NetworkBuilder()
currParser = NeuroMLHdf5Parser(nmlHandler)
currParser.parse(file_name)
nml2_doc = nmlHandler.get_nml_doc()
except Exception:
print("Not a valid NeuroML 2 doc: %s" % str(sys.exc_info()))
return None
return nml2_doc
def test_parse(self):
base_dir = os.path.dirname(__file__)
#base_dir = '.'
logging.basicConfig(level=logging.INFO, format="%(name)-19s %(levelname)-5s - %(message)s")
for f in ['simplenet.nml','testh5.nml','pyr_4_sym.cell.nml','MediumNet.net.nml','complete.nml']:
file_name = base_dir+'/../examples/test_files/'+f
nml_doc0 = loaders.read_neuroml2_file(file_name,
include_includes=True,
verbose=True)
summary0 = nml_doc0.summary(show_includes=False,show_non_network=False)
print('\n'+summary0)
from neuroml.hdf5.DefaultNetworkHandler import DefaultNetworkHandler
nmlHandler = DefaultNetworkHandler()
currParser = NeuroMLXMLParser(nmlHandler)
currParser.parse(file_name)
print('-------------------------------\n\n')
nmlHandler = NetworkBuilder()
currParser = NeuroMLXMLParser(nmlHandler)
currParser.parse(file_name)
nml_doc = nmlHandler.get_nml_doc()
summary = nml_doc.summary(show_includes=False,show_non_network=False)
print(summary)
compare(summary,summary0)
nml_file = base_dir+'/../examples/tmp/EXP_'+f
import neuroml.writers as writers
writers.NeuroMLWriter.write(nml_doc, nml_file)
print("Written network file to: "+nml_file)
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)
percentage_cells_per_pop = 1
bbp = BBPConnectomeReader(
filename=filename,
percentage_cells_per_pop=percentage_cells_per_pop,
DEFAULT_CELL_ID='hhcell')
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
from neuroml.hdf5.DefaultNetworkHandler import DefaultNetworkHandler
nmlHandler = DefaultNetworkHandler()
currParser = NeuroMLHdf5Parser(
nmlHandler
) # The HDF5 handler knows of the structure of NetworkML and calls appropriate functions in NetworkHandler
currParser.parse(file_name)
print('-------------------------------\n\n')
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
nmlHandler = NetworkBuilder()
currParser = NeuroMLHdf5Parser(nmlHandler)
currParser.parse(file_name)
nml_doc = currParser.get_nml_doc()
summary0 = nml_doc.summary()
print(summary0)
exit()
print('-------------------------------\n\n')
currParser = NeuroMLHdf5Parser(None, optimized=True)
currParser.parse(file_name)
]
test_files = [
"../../neuroConstruct/osb/showcase/PsyNeuLinkShowcase/NeuroML2/FN.bids-mdf.json"
]
for filename in test_files:
pnl_parser = PsyNeuLinkReader(filename=filename,
DEFAULT_CELL_ID="hhcell")
from neuromllite.DefaultNetworkHandler import DefaultNetworkHandler
def_handler = DefaultNetworkHandler()
pnl_parser.parse(def_handler)
from neuroml.hdf5.NetworkBuilder import NetworkBuilder
neuroml_handler = NetworkBuilder()
pnl_parser = PsyNeuLinkReader(filename=filename,
DEFAULT_CELL_ID="hhcell")
pnl_parser.parse(neuroml_handler)
from neuroml.writers import NeuroMLWriter
nml_file_name = "%s.net.nml" % neuroml_handler.get_nml_doc().id
NeuroMLWriter.write(neuroml_handler.get_nml_doc(), nml_file_name)
print("Written NeuroML file to: %s" % nml_file_name)
