def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
nml_doc = simulator._get_nml_doc()
import neuroml.writers as writers
if save_format == 'xml':
nml_file = '%s.net.nml' % nml_doc.id
writers.NeuroMLWriter.write(nml_doc, nml_file)
elif save_format == 'hdf5':
nml_file = '%s.net.nml.h5' % nml_doc.id
writers.NeuroMLHdf5Writer.write(nml_doc, nml_file)
logger.info("Written NeuroML 2 file out to: " + nml_file)
lems_sim = simulator._get_lems_sim()
lems_sim.include_neuroml2_file("PyNN.xml", include_included=False)
lems_sim.include_neuroml2_file(nml_file)
lems_file = lems_sim.save_to_file()
logger.info("Written LEMS file (to simulate NeuroML file) to: " +
lems_file)
def setup(timestep=DEFAULT_TIMESTEP,
min_delay=DEFAULT_MIN_DELAY,
**extra_params):
""" Set up for saving cell models and network structure to NeuroML """
common.setup(timestep, min_delay, **extra_params)
simulator.state.clear()
simulator.state.dt = timestep # move to common.setup?
simulator.state.min_delay = min_delay
simulator.state.max_delay = extra_params.get('max_delay',
DEFAULT_MAX_DELAY)
simulator.state.mpi_rank = extra_params.get('rank', 0)
simulator.state.num_processes = extra_params.get('num_processes', 1)
logger.debug("Creating network in NeuroML document to store structure")
nml_doc = simulator._get_nml_doc(extra_params.get('reference',
"PyNN_NeuroML2_Export"),
reset=True)
global save_format
save_format = extra_params.get('save_format', "xml")
# Create network
net = neuroml.Network(id=nml_doc.id)
nml_doc.networks.append(net)
lems_sim = simulator._get_lems_sim(reset=True)
lems_sim.dt = '%s' % timestep
return rank()
def add_to_nml_doc(self, nml_doc, cells):
ci = neuroml.CompoundInput(id=self.get_id_for_nml(cells))
num_steps = len(self.amplitudes)
for i in range(num_steps):
next_time = 1e9 if i==num_steps-1 else self.times[i+1]
ci.pulse_generators.append(neuroml.PulseGenerator(id='step_%s'%i,delay='%sms'%self.times[i],duration='%sms'%(next_time-self.times[i]),amplitude='%snA'%self.amplitudes[i]))
self.nml_doc = _get_nml_doc()
self.nml_doc.compound_inputs.append(ci)
return ci.id
def add_to_nml_doc(self, nml_doc, cells):
id=self.get_id_for_nml(cells)
pg = neuroml.PulseGenerator(id=id,
delay='%sms'%self.start,
duration='%sms'%(self.stop-self.start),
amplitude='%snA'%self.amplitude)
self.nml_doc = _get_nml_doc()
found = False
for pg in self.nml_doc.pulse_generators:
if pg.id==id: found = True
if not found:
self.nml_doc.pulse_generators.append(pg)
return pg.id
def __init__(self, **parameters):
super(StandardCurrentSource, self).__init__(**parameters)
global current_sources
self.cell_list = []
self.indices = []
self.ind = len(current_sources) # Todo use self.indices instead...
current_sources.append(self)
parameter_space = ParameterSpace(self.default_parameters,
self.get_schema(),
shape=(1, ))
parameter_space.update(**parameters)
parameter_space = self.translate(parameter_space)
self.set_native_parameters(parameter_space)
self.nml_doc = _get_nml_doc()
self.network = _get_main_network()
def inject_into(self, cells):
__doc__ = StandardCurrentSource.inject_into.__doc__
logger.debug("%s injecting into: %s"%(self.__class__.__name__, cells))
self.nml_doc = _get_nml_doc()
id = self.add_to_nml_doc(self.nml_doc, cells)
for cell in cells:
pop_id = cell.parent.label
index = cell.parent.id_to_index(cell)
celltype = cell.parent.celltype.__class__.__name__
logger.debug("Injecting: %s to %s (%s[%s])"%(id, cell, pop_id, index))
input_list = self._get_input_list(id, pop_id)
input = neuroml.Input(id=len(input_list.input),
target="../%s/%i/%s_%s"%(pop_id, index, celltype, pop_id),
destination="synapses")
input_list.input.append(input)
def add_to_nml_doc(self, nml_doc, cells):
ci = neuroml.CompoundInput(id=self.get_id_for_nml(cells))
sg = neuroml.SineGenerator(id='SG_'+self.get_id_for_nml(cells),
delay='%sms'%self.start,
duration='%sms'%(self.stop-self.start),
amplitude='%snA'%self.amplitude,
period='%s s'%(1/float(self.frequency)),
phase=(3.14159265 * self.phase/180))
pg = neuroml.PulseGenerator(id='PG_'+self.get_id_for_nml(cells),
delay='%sms'%self.start,
duration='%sms'%(self.stop-self.start),
amplitude='%snA'%self.offset)
ci.sine_generators.append(sg)
ci.pulse_generators.append(pg)
self.nml_doc = _get_nml_doc()
self.nml_doc.compound_inputs.append(ci)
return ci.id
def end(compatible_output=True):
"""Do any necessary cleaning up before exiting."""
for (population, variables, filename) in simulator.state.write_on_end:
io = get_io(filename)
population.write_data(io, variables)
simulator.state.write_on_end = []
nml_doc = simulator._get_nml_doc()
import neuroml.writers as writers
if save_format == 'xml':
nml_file = '%s.net.nml'%nml_doc.id
writers.NeuroMLWriter.write(nml_doc, nml_file)
elif save_format == 'hdf5':
nml_file = '%s.net.nml.h5'%nml_doc.id
writers.NeuroMLHdf5Writer.write(nml_doc, nml_file)
logger.info("Written NeuroML 2 file out to: "+nml_file)
lems_sim = simulator._get_lems_sim()
lems_sim.include_neuroml2_file("PyNN.xml", include_included=False)
lems_sim.include_neuroml2_file(nml_file)
lems_file = lems_sim.save_to_file()
logger.info("Written LEMS file (to simulate NeuroML file) to: "+lems_file)
def setup(timestep=DEFAULT_TIMESTEP, min_delay=DEFAULT_MIN_DELAY,
**extra_params):
""" Set up for saving cell models and network structure to NeuroML """
common.setup(timestep, min_delay, **extra_params)
simulator.state.clear()
simulator.state.dt = timestep # move to common.setup?
simulator.state.min_delay = min_delay
simulator.state.max_delay = extra_params.get('max_delay', DEFAULT_MAX_DELAY)
simulator.state.mpi_rank = extra_params.get('rank', 0)
simulator.state.num_processes = extra_params.get('num_processes', 1)
logger.debug("Creating network in NeuroML document to store structure")
nml_doc = simulator._get_nml_doc(extra_params.get('reference', "PyNN_NeuroML2_Export"),reset=True)
global save_format
save_format = extra_params.get('save_format', "xml")
# Create network
net = neuroml.Network(id="network")
nml_doc.networks.append(net)
lems_sim = simulator._get_lems_sim(reset=True)
lems_sim.dt = '%s'%timestep
return rank()