def lp():
for perc in np.linspace(0.0, 0.8, 9): # now is percent block
h.gnablock_ina2005 = perc # h.perc_ina2005
sim.runSim()
sim.gatherData()
sim.saveData(include=['simData'],
filename='data/19dec06blkA_%02d' % (100 * perc))
def run_sim():
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = "../../../"
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
netParams = sim.loadNetParams("./netParams.json", None, False)
simConfig = sim.loadSimCfg("./simConfig.json", None, False)
sim.createSimulate(netParams, simConfig)
sim.saveData()
def simulateNetPyNEModelInGeppetto(self, args):
try:
with redirect_stdout(sys.__stdout__):
# TODO mpi is not finding libmpi.dylib.. set LD_LIBRARY_PATH to openmpi bin folder, but nothing
if args['parallelSimulation']:
logging.debug('Running parallel simulation')
if not 'usePrevInst' in args or not args['usePrevInst']:
self.netParams.save("netParams.json")
self.simConfig.saveJson = True
self.simConfig.save("simParams.json")
template = os.path.join(os.path.dirname(__file__),
'template.py')
else:
sim.cfg.saveJson = True
oldName = sim.cfg.filename
sim.cfg.filename = 'model_output'
sim.saveData()
sim.cfg.filename = oldName
template = os.path.join(os.path.dirname(__file__),
'template2.py')
copyfile(template, './init.py')
cp = subprocess.run([
"mpiexec", "-n", args['cores'], "nrniv", "-mpi",
"-python", "init.py"
],
capture_output=True)
print(cp.stdout.decode() + cp.stderr.decode())
if cp.returncode != 0:
return utils.getJSONError(
"Error while simulating the NetPyNE model",
cp.stderr.decode())
sim.load('model_output.json')
self.geppetto_model = self.model_interpreter.getGeppettoModel(
sim)
netpyne_model = sim
else:
logging.info("Starting simulation")
if not args.get('usePrevInst', False):
logging.debug('Instantiating single thread simulation')
netpyne_model = self.instantiateNetPyNEModel()
self.geppetto_model = self.model_interpreter.getGeppettoModel(
netpyne_model)
logging.debug('Running single thread simulation')
netpyne_model = self.simulateNetPyNEModel()
return json.loads(
GeppettoModelSerializer.serialize(self.geppetto_model))
except:
return utils.getJSONError(
"Error while simulating the NetPyNE model", sys.exc_info())
def run():
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = "../../../"
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
# read cfg and netParams from command line arguments if available; otherwise use default
simConfig, netParams = sim.readCmdLineArgs(simConfigDefault="cfg.py",
netParamsDefault="netParams.py")
# Create network and run simulation
sim.createSimulate(netParams=netParams, simConfig=simConfig)
sim.saveData()
def simulateModel(self):
"""
Simulates model
Returns
-------
SimConfig
generated SimConfig
"""
sim.setupRecording()
sim.simulate()
sim.saveData()
return sim
def exportModel(self, args):
try:
with redirect_stdout(sys.__stdout__):
if not args['netCells']:
sim.initialize(netParams=self.netParams,
simConfig=self.simConfig)
sim.cfg.filename = args['fileName']
include = [
el for el in specs.SimConfig().saveDataInclude
if el in args.keys() and args[el]
]
if args['netCells']: include += ['netPops']
sim.cfg.saveJson = True
sim.saveData(include)
sim.cfg.saveJson = False
return utils.getJSONReply()
except:
return utils.getJSONError(
"Error while exporting the NetPyNE model", sys.exc_info())
def exportModel(self, args):
try:
with redirect_stdout(sys.__stdout__):
if not args['netCells']:
sim.initialize(netParams=self.netParams,
simConfig=self.simConfig)
sim.cfg.filename = args['fileName']
include = [
el for el in specs.SimConfig().saveDataInclude
if el in args.keys() and args[el]
]
if args['netCells']: include += ['netPops']
sim.cfg.saveJson = True
sim.saveData(include)
sim.cfg.saveJson = False
with open(f"{sim.cfg.filename}_data.json") as json_file:
data = json.load(json_file)
return data
except Exception:
message = "Error while exporting the NetPyNE model"
logging.exception(message)
return utils.getJSONError(message, sys.exc_info())
Usage: python init.py # Run simulation, optionally plot a raster
MPI usage: mpiexec -n 4 nrniv -python -mpi init.py
"""
from netpyne import sim
cfg, netParams = sim.readCmdLineArgs(simConfigDefault='cfg.py',
netParamsDefault='netParams.py')
# sim.createSimulateAnalyze(netParams, cfg)
#
sim.initialize(
simConfig=cfg,
netParams=netParams) # create network object and set cfg and net params
sim.net.createPops() # instantiate network populations
sim.net.createCells() # instantiate network cells based on defined populations
sim.net.connectCells() # create connections between cells based on params
sim.net.addStims() # add network stimulation
sim.setupRecording(
) # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
# distributed saving (to avoid errors with large output data)
#sim.saveDataInNodes()
#sim.gatherDataFromFiles(saveMerged=True)
sim.saveData() # gather spiking data and cell info from each node
sim.analysis.plotData()
def simulateNetPyNEModel(self):
with redirect_stdout(sys.__stdout__):
sim.setupRecording()
sim.simulate()
sim.saveData()
return sim
#import matplotlib; matplotlib.use('Agg') # to avoid graphics error in servers
from netpyne import sim
cfg, netParams = sim.readCmdLineArgs()
sim.initialize(
simConfig=cfg,
netParams=netParams) # create network object and set cfg and net params
sim.net.createPops() # instantiate network populations
sim.net.createCells() # instantiate network cells based on defined populations
sim.net.connectCells() # create connections between cells based on params
sim.net.addStims() # add network stimulation
sim.setupRecording() # setup variables to record (spikes, V traces, etc)
# sim.runSim() # run parallel Neuron simulation
# sim.gatherData() # gather spiking data and cell info from each node
sim.saveData() # save params, cell info and sim output to file
#sim.analysis.plotData() # plot spike raster etc
# connDict = {}
# for pop in sim.net.pops.keys():
# print("Population: ", pop)
# connDict[pop] = {}
# popGids = sim.net.pops[pop].cellGids
# for cellGid in popGids:
# cell = sim.net.cells[cellGid]
# allConns = cell.conns
# create network object and set cfg and net params sim.initialize(simConfig = cfg, netParams = netParams) # instantiate network populations sim.net.createPops() # instantiate network cells based on defined populations sim.net.createCells() # create connections between cells based on params sim.net.connectCells() # add network stimulation sim.net.addStims() # setup variables to record for each cell (spikes, V traces, etc) sim.setupRecording() # run parallel Neuron simulation sim.runSim() # gather spiking data and cell info from each node sim.gatherData() # save params, cell info and sim output to file (pickle,mat,txt,etc) sim.saveData() # plot spike raster sim.analysis.plotData()
for x in range(0, len(sim.net.cells)):
if 'inInh' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frinh[i]
i += 1
if 'inL23exc' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frl23[j]
j += 1
if 'inL5exc' in sim.net.cells[x].tags['pop']:
sim.net.cells[x].params['interval'] = 1000 / frl5[k]
k += 1
# %%
sim.setupRecording(
) # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData(
) # save params, cell info and sim output to file (pickle,mat,txt,etc)
sim.analysis.plotData() # plot spike raster
#%% Save raster for each population
sim.analysis.plotRaster(include=[('L23exc'), ('inL23exc')],
spikeHist='overlay',
spikeHistBin=10,
saveData='raster_TMSall_single_L23.pkl')
sim.analysis.plotRaster(include=[('L5exc'), ('inL5exc')],
spikeHist='overlay',
spikeHistBin=10,
saveData='raster_TMSall_single_L5.pkl')
sim.analysis.plotRaster(include=[('Inh'), ('inInh')],
spikeHist='overlay',
spikeHistBin=10,
saveData='raster_TMSall_single_Inh.pkl')
#sim.analysis.plotTraces(include = [('L23exc'),('L5exc'),('Inh')],saveData = 'gsyn_TMS.pkl')
def gatherDataFromFiles(gatherLFP=True,
saveFolder=None,
simLabel=None,
sim=None,
fileType='pkl',
saveMerged=False):
"""
Function to gather data from multiple files (from distributed or interval saving)
Parameters
----------
gatherLFP : bool
Whether or not to gather LFP data.
**Default:** ``True`` gathers LFP data if available.
**Options:** ``False`` does not gather LFP data.
saveFolder : str
Name of the directory where data files are located.
**Default:** ``None`` attempts to auto-locate the data directory.
"""
import os
if not sim:
from netpyne import sim
if getattr(sim, 'rank', None) is None:
sim.initialize()
sim.timing('start', 'gatherTime')
if sim.rank == 0:
fileType = fileType.lower()
if fileType not in ['pkl', 'json']:
print(
f"Could not gather data from '.{fileType}' files. Only .pkl and .json are supported so far."
)
return False
if not simLabel:
simLabel = sim.cfg.simLabel
if not saveFolder:
saveFolder = sim.cfg.saveFolder
nodeDataDir = os.path.join(saveFolder, simLabel + '_node_data')
print(f"\nSearching for .{fileType} node files in {nodeDataDir} ...")
simLabels = [
f.replace(f'_node_0.{fileType}', '')
for f in os.listdir(nodeDataDir)
if f.endswith(f'_node_0.{fileType}')
]
if len(simLabels) == 0:
print(f"Could not gather data from files. No node files found.")
return False
mergedFiles = []
for simLabel in simLabels:
allSimData = Dict()
allCells = []
allPops = ODict()
print('\nGathering data from files for simulation: %s ...' %
(simLabel))
simDataVecs = ['spkt', 'spkid', 'stims'] + list(
sim.cfg.recordTraces.keys())
singleNodeVecs = ['t']
if sim.cfg.recordDipolesHNN:
_aggregateDipoles()
simDataVecs.append('dipole')
fileData = {'simData': sim.simData}
fileList = sorted([
f for f in os.listdir(nodeDataDir)
if (f.startswith(simLabel +
'_node') and f.endswith(f'.{fileType}'))
])
for ifile, file in enumerate(fileList):
print(' Merging data file: %s' % (file))
with open(os.path.join(nodeDataDir, file), 'rb') as openFile:
if fileType == 'pkl':
data = pickle.load(openFile)
elif fileType == 'json':
import json
data = json.load(openFile)
if 'cells' in data.keys():
if fileType == 'pkl':
allCells.extend([
cell.__getstate__() for cell in data['cells']
])
else:
allCells.extend(data['cells'])
if 'pops' in data.keys():
loadedPops = data['pops']
if fileType == 'pkl':
for popLabel, pop in loadedPops.items():
allPops[popLabel] = pop['tags']
elif fileType == 'json':
# if populations order is not preserved (which is inherently the case for JSON), need to sort them again
loadedPops = list(loadedPops.items())
def sort(popKeyAndValue):
# the assumption while sorting is that populations order corresponds to cell gids in this population
cellGids = popKeyAndValue[1]['cellGids']
if len(cellGids) > 0:
return cellGids[0]
else:
return -1
loadedPops.sort(key=sort)
for popLabel, pop in loadedPops:
allPops[popLabel] = pop['tags']
if 'simConfig' in data.keys():
setup.setSimCfg(data['simConfig'])
if 'net' in data and gatherLFP:
if 'recXElectrode' in data['net']:
xElectrode = data['net']['recXElectrode']
if False == isinstance(xElectrode, RecXElectrode):
xElectrode = RecXElectrode.fromJSON(xElectrode)
sim.net.recXElectrode = xElectrode
nodePopsCellGids = {
popLabel: list(pop['cellGids'])
for popLabel, pop in data['pops'].items()
}
if ifile == 0 and gatherLFP and 'LFP' in data['simData']:
lfpData = data['simData']['LFP']
if False == isinstance(lfpData, np.ndarray):
lfpData = np.array(lfpData)
data['simData']['LFP'] = lfpData
allSimData['LFP'] = np.zeros(lfpData.shape)
if 'LFPPops' in data['simData']:
allSimData['LFPPops'] = {
p: np.zeros(lfpData.shape)
for p in data['simData']['LFPPops'].keys()
}
for key, value in data['simData'].items():
if key in simDataVecs:
if isinstance(value, dict):
for key2, value2 in value.items():
if isinstance(value2, dict):
allSimData[key].update(
Dict({key2: Dict()}))
for stim, value3 in value2.items():
allSimData[key][key2].update(
{stim: list(value3)})
elif key == 'dipole':
allSimData[key][key2] = np.add(
allSimData[key][key2],
value2.as_numpy())
else:
allSimData[key].update(
{key2: list(value2)})
else:
allSimData[key] = list(
allSimData[key]) + list(value)
elif gatherLFP and key == 'LFP':
allSimData['LFP'] += np.array(value)
elif gatherLFP and key == 'LFPPops':
for p in value:
allSimData['LFPPops'][p] += np.array(value[p])
elif key == 'dipoleSum':
if key not in allSimData.keys():
allSimData[key] = value
else:
allSimData[key] += value
elif key not in singleNodeVecs:
allSimData[key].update(value)
if file == fileList[0]:
for key in singleNodeVecs:
allSimData[key] = list(fileData['simData'][key])
allPopsCellGids = {
popLabel: []
for popLabel in nodePopsCellGids
}
else:
for popLabel, popCellGids in nodePopsCellGids.items():
allPopsCellGids[popLabel].extend(popCellGids)
mergedFiles.append(file)
if len(allSimData['spkt']) > 0:
allSimData['spkt'], allSimData['spkid'] = zip(
*sorted(zip(allSimData['spkt'], allSimData['spkid'])))
allSimData['spkt'], allSimData['spkid'] = list(
allSimData['spkt']), list(allSimData['spkid'])
sim.allSimData = allSimData
sim.net.allCells = sorted(allCells, key=lambda k: k['gid'])
for popLabel, pop in allPops.items():
pop['cellGids'] = sorted(allPopsCellGids[popLabel])
sim.net.allPops = allPops
## Print statistics
sim.pc.barrier()
if sim.rank != 0:
sim.pc.barrier()
else:
sim.timing('stop', 'gatherTime')
if sim.cfg.timing:
print((' Done; gather time = %0.2f s.' %
sim.timingData['gatherTime']))
if saveMerged:
print('\nSaving merged data into single file ...')
saved = sim.saveData()
if len(saved) > 0:
# if single file saved successfully, clean up node data
for file in mergedFiles:
path = os.path.join(nodeDataDir, file)
os.remove(path)
print('\nAnalyzing...')
sim.totalSpikes = len(sim.allSimData['spkt'])
sim.totalSynapses = sum(
[len(cell['conns']) for cell in sim.net.allCells])
if sim.cfg.createPyStruct:
if sim.cfg.compactConnFormat:
preGidIndex = sim.cfg.compactConnFormat.index(
'preGid') if 'preGid' in sim.cfg.compactConnFormat else 0
sim.totalConnections = sum([
len(set([conn[preGidIndex] for conn in cell['conns']]))
for cell in sim.net.allCells
])
else:
sim.totalConnections = sum([
len(set([conn['preGid'] for conn in cell['conns']]))
for cell in sim.net.allCells
])
else:
sim.totalConnections = sim.totalSynapses
sim.numCells = len(sim.net.allCells)
if sim.totalSpikes > 0:
sim.firingRate = float(
sim.totalSpikes) / sim.numCells / sim.cfg.duration * 1e3
else:
sim.firingRate = 0
if sim.numCells > 0:
sim.connsPerCell = sim.totalConnections / float(sim.numCells)
sim.synsPerCell = sim.totalSynapses / float(sim.numCells)
else:
sim.connsPerCell = 0
sim.synsPerCell = 0
print((' Cells: %i' % (sim.numCells)))
print((' Connections: %i (%0.2f per cell)' %
(sim.totalConnections, sim.connsPerCell)))
if sim.totalSynapses != sim.totalConnections:
print((' Synaptic contacts: %i (%0.2f per cell)' %
(sim.totalSynapses, sim.synsPerCell)))
print((' Spikes: %i (%0.2f Hz)' % (sim.totalSpikes, sim.firingRate)))
if 'runTime' in sim.timingData:
print((' Simulated time: %0.1f s; %i workers' %
(sim.cfg.duration / 1e3, sim.nhosts)))
print((' Run time: %0.2f s' % (sim.timingData['runTime'])))
if sim.cfg.printPopAvgRates and not sim.cfg.gatherOnlySimData:
trange = sim.cfg.printPopAvgRates if isinstance(
sim.cfg.printPopAvgRates, list) else None
sim.allSimData['popRates'] = sim.analysis.popAvgRates(
tranges=trange)
if 'plotfI' in sim.cfg.analysis:
sim.analysis.calculatefI()
sim.allSimData['avgRate'] = sim.firingRate
del sim.net.pops['external_virtual_100']
# remove axon
for k in sim.net.params.cellParams:
try:
del sim.net.params.cellParams[k]['secs']['axon_0']
del sim.net.params.cellParams[k]['secs']['axon_1']
for c in sim.net.cells:
del c.secs['axon_0']
del c.secs['axon_1']
except:
pass
# remove conns
for c in sim.net.cells:
c.conns = []
# save
sim.saveData(filename='sonata_300cells')
# save json with psection
if saveJsonPsection:
import json
data = {}
remove = ['cell', 'regions','species', 'point_processes', 'hoc_internal_name', 'name']#, 'morphology']
removeMorph = ['parent', 'trueparent']
for icell, c in enumerate(sim.net.cells):
try:
data[icell] = {}
for isec, sec in enumerate(c.secs.values()):
name = str(sec['hObj'].name()).split('.')[-1]
data[icell][name] = sec['hObj'].psection()
for x in remove:
def _prepare_simulation_files(self,
experiment: model.Experiment = None,
use_prev_inst: bool = False) -> str:
"""Prepares template files and netpyne model files for a single simulation """
exp = copy.deepcopy(experiment)
# Remove parameter & trials for single run
exp.params = []
exp.trials = []
save_folder_path = os.path.join(constants.NETPYNE_WORKDIR_PATH,
constants.EXPERIMENTS_FOLDER, exp.name)
try:
os.makedirs(save_folder_path)
except OSError:
raise
if use_prev_inst:
sim.cfg.saveJson = True
oldName = sim.cfg.filename
sim.cfg.filename = constants.MODEL_OUTPUT_FILENAME
# workaround for issue with empty LFP dict when calling saveData()
if 'LFP' in sim.allSimData:
del sim.allSimData['LFP']
# TODO: store in experiments folder!
sim.saveData()
sim.cfg.filename = oldName
template_name = constants.TEMPLATE_FILENAME_SINGLE_RUN_INSTANTIATED
else:
# Create netParams and SimConfig
self.netParams.save(
os.path.join(save_folder_path, experiments.NET_PARAMS_FILE))
simCfg = copy.copy(self.simConfig)
# filename and simLabel must be set to define the output filename
simCfg.saveJson = True
simCfg.filename = 'model_output'
simCfg.simLabel = 'model_output'
simCfg.saveDataInclude = [
"simData", "simConfig", "netParams", "net"
]
simCfg.save(
os.path.join(save_folder_path, experiments.SIM_CONFIG_FILE))
template_name = constants.TEMPLATE_FILENAME_SINGLE_RUN
# Create Experiment Config
config_dict = dataclasses.asdict(exp)
config_dict["runCfg"] = dataclasses.asdict(self.run_config)
experiment_config = os.path.join(save_folder_path,
experiments.EXPERIMENT_FILE)
json.dump(config_dict,
open(experiment_config, 'w'),
default=str,
sort_keys=True,
indent=4)
# Copy Template
template = os.path.join(os.path.dirname(__file__), "templates",
template_name)
copyfile(
template,
os.path.join(save_folder_path, constants.SIMULATION_SCRIPT_NAME))
return save_folder_path
def generate_and_run(simulation,
simulator,
network=None,
return_results=False,
base_dir=None,
target_dir=None,
num_processors=1):
"""
Generates the network in the specified simulator and runs, if appropriate
"""
if network == None:
network = load_network_json(simulation.network)
print_v("Generating network %s and running in simulator: %s..." %
(network.id, simulator))
if simulator == 'NEURON':
_generate_neuron_files_from_neuroml(network,
dir_for_mod_files=target_dir)
from neuromllite.NeuronHandler import NeuronHandler
nrn_handler = NeuronHandler()
for c in network.cells:
if c.neuroml2_source_file:
src_dir = os.path.dirname(
os.path.abspath(c.neuroml2_source_file))
nrn_handler.executeHoc('load_file("%s/%s.hoc")' %
(src_dir, c.id))
generate_network(network, nrn_handler, generate_network, base_dir)
if return_results:
raise NotImplementedError(
"Reloading results not supported in Neuron yet...")
elif simulator.lower() == 'sonata': # Will not "run" obviously...
from neuromllite.SonataHandler import SonataHandler
sonata_handler = SonataHandler()
generate_network(network,
sonata_handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with Sonata...")
elif simulator.lower().startswith('graph'): # Will not "run" obviously...
from neuromllite.GraphVizHandler import GraphVizHandler, engines
try:
if simulator[-1].isalpha():
engine = engines[simulator[-1]]
level = int(simulator[5:-1])
else:
engine = 'dot'
level = int(simulator[5:])
except Exception as e:
print_v("Error parsing: %s: %s" % (simulator, e))
print_v(
"Graphs of the network structure can be generated at many levels of detail (1-6, required) and laid out using GraphViz engines (d - dot (default); c - circo; n - neato; f - fdp), so use: -graph3c, -graph2, -graph4f etc."
)
return
handler = GraphVizHandler(level, engine=engine, nl_network=network)
generate_network(network,
handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with GraphViz...")
elif simulator.lower().startswith('matrix'): # Will not "run" obviously...
from neuromllite.MatrixHandler import MatrixHandler
try:
level = int(simulator[6:])
except:
print_v("Error parsing: %s" % simulator)
print_v(
"Matrices of the network structure can be generated at many levels of detail (1-n, required), so use: -matrix1, -matrix2, etc."
)
return
handler = MatrixHandler(level, nl_network=network)
generate_network(network,
handler,
always_include_props=True,
base_dir=base_dir)
print_v("Done with MatrixHandler...")
elif simulator.startswith('PyNN'):
#_generate_neuron_files_from_neuroml(network)
simulator_name = simulator.split('_')[1].lower()
from neuromllite.PyNNHandler import PyNNHandler
pynn_handler = PyNNHandler(simulator_name, simulation.dt, network.id)
syn_cell_params = {}
for proj in network.projections:
synapse = network.get_child(proj.synapse, 'synapses')
post_pop = network.get_child(proj.postsynaptic, 'populations')
if not post_pop.component in syn_cell_params:
syn_cell_params[post_pop.component] = {}
for p in synapse.parameters:
post = ''
if synapse.pynn_receptor_type == "excitatory":
post = '_E'
elif synapse.pynn_receptor_type == "inhibitory":
post = '_I'
syn_cell_params[post_pop.component][
'%s%s' % (p, post)] = synapse.parameters[p]
cells = {}
for c in network.cells:
if c.pynn_cell:
cell_params = {}
if c.parameters:
for p in c.parameters:
cell_params[p] = evaluate(c.parameters[p],
network.parameters)
dont_set_here = [
'tau_syn_E', 'e_rev_E', 'tau_syn_I', 'e_rev_I'
]
for d in dont_set_here:
if d in c.parameters:
raise Exception(
'Synaptic parameters like %s should be set ' +
'in individual synapses, not in the list of parameters associated with the cell'
% d)
if c.id in syn_cell_params:
cell_params.update(syn_cell_params[c.id])
print_v("Creating cell with params: %s" % cell_params)
exec('cells["%s"] = pynn_handler.sim.%s(**cell_params)' %
(c.id, c.pynn_cell))
if c.pynn_cell != 'SpikeSourcePoisson':
exec(
"cells['%s'].default_initial_values['v'] = cells['%s'].parameter_space['v_rest'].base_value"
% (c.id, c.id))
pynn_handler.set_cells(cells)
receptor_types = {}
for s in network.synapses:
if s.pynn_receptor_type:
receptor_types[s.id] = s.pynn_receptor_type
pynn_handler.set_receptor_types(receptor_types)
for input_source in network.input_sources:
if input_source.pynn_input:
pynn_handler.add_input_source(input_source)
generate_network(network,
pynn_handler,
always_include_props=True,
base_dir=base_dir)
for pid in pynn_handler.populations:
pop = pynn_handler.populations[pid]
if 'all' in simulation.recordTraces or pop.label in simulation.recordTraces:
if pop.can_record('v'):
pop.record('v')
pynn_handler.sim.run(simulation.duration)
pynn_handler.sim.end()
traces = {}
events = {}
if not 'NeuroML' in simulator:
from neo.io import PyNNTextIO
for pid in pynn_handler.populations:
pop = pynn_handler.populations[pid]
if 'all' in simulation.recordTraces or pop.label in simulation.recordTraces:
filename = "%s.%s.v.dat" % (simulation.id, pop.label)
all_columns = []
print_v("Writing data for %s to %s" %
(pop.label, filename))
for i in range(len(pop)):
if pop.can_record('v'):
ref = '%s[%i]' % (pop.label, i)
traces[ref] = []
data = pop.get_data('v', gather=False)
for segment in data.segments:
vm = segment.analogsignals[0].transpose()[i]
if len(all_columns) == 0:
tt = np.array([
t * simulation.dt / 1000.
for t in range(len(vm))
])
all_columns.append(tt)
vm_si = [float(v / 1000.) for v in vm]
traces[ref] = vm_si
all_columns.append(vm_si)
times_vm = np.array(all_columns).transpose()
np.savetxt(filename, times_vm, delimiter='\t', fmt='%s')
if return_results:
_print_result_info(traces, events)
return traces, events
elif simulator == 'NetPyNE':
if target_dir == None:
target_dir = './'
_generate_neuron_files_from_neuroml(network,
dir_for_mod_files=target_dir)
from netpyne import specs
from netpyne import sim
# Note NetPyNE from this branch is required: https://github.com/Neurosim-lab/netpyne/tree/neuroml_updates
from netpyne.conversion.neuromlFormat import NetPyNEBuilder
import pprint
pp = pprint.PrettyPrinter(depth=6)
netParams = specs.NetParams()
simConfig = specs.SimConfig()
netpyne_handler = NetPyNEBuilder(netParams,
simConfig=simConfig,
verbose=True)
generate_network(network, netpyne_handler, base_dir=base_dir)
netpyne_handler.finalise()
simConfig = specs.SimConfig()
simConfig.tstop = simulation.duration
simConfig.duration = simulation.duration
simConfig.dt = simulation.dt
simConfig.seed = simulation.seed
simConfig.recordStep = simulation.dt
simConfig.recordCells = ['all']
simConfig.recordTraces = {}
for pop in netpyne_handler.popParams.values():
if 'all' in simulation.recordTraces or pop.id in simulation.recordTraces:
for i in pop['cellsList']:
id = pop['pop']
index = i['cellLabel']
simConfig.recordTraces['v_%s_%s' % (id, index)] = {
'sec': 'soma',
'loc': 0.5,
'var': 'v',
'conds': {
'pop': id,
'cellLabel': index
}
}
simConfig.saveDat = True
print_v("NetPyNE netParams: ")
pp.pprint(netParams.todict())
#print_v("NetPyNE simConfig: ")
#pp.pprint(simConfig.todict())
sim.initialize(
netParams,
simConfig) # create network object and set cfg and net params
sim.net.createPops()
cells = sim.net.createCells(
) # instantiate network cells based on defined populations
for proj_id in netpyne_handler.projection_infos.keys():
projName, prePop, postPop, synapse, ptype = netpyne_handler.projection_infos[
proj_id]
print_v("Creating connections for %s (%s): %s->%s via %s" %
(projName, ptype, prePop, postPop, synapse))
preComp = netpyne_handler.pop_ids_vs_components[prePop]
for conn in netpyne_handler.connections[projName]:
pre_id, pre_seg, pre_fract, post_id, post_seg, post_fract, delay, weight = conn
#connParam = {'delay':delay,'weight':weight,'synsPerConn':1, 'sec':post_seg, 'loc':post_fract, 'threshold':threshold}
connParam = {
'delay': delay,
'weight': weight,
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract
}
if ptype == 'electricalProjection':
if weight != 1:
raise Exception(
'Cannot yet support inputs where weight !=1!')
connParam = {
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract,
'gapJunction': True,
'weight': weight
}
else:
connParam = {
'delay': delay,
'weight': weight,
'synsPerConn': 1,
'sec': post_seg,
'loc': post_fract
}
#'threshold': threshold}
connParam['synMech'] = synapse
if post_id in sim.net.gid2lid: # check if postsyn is in this node's list of gids
sim.net._addCellConn(connParam, pre_id, post_id)
stims = sim.net.addStims(
) # add external stimulation to cells (IClamps etc)
simData = sim.setupRecording(
) # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData(
) # save params, cell info and sim output to file (pickle,mat,txt,etc)
if return_results:
raise NotImplementedError(
"Reloading results not supported in NetPyNE yet...")
elif simulator == 'jNeuroML' or simulator == 'jNeuroML_NEURON' or simulator == 'jNeuroML_NetPyNE':
from pyneuroml.lems import generate_lems_file_for_neuroml
from pyneuroml import pynml
lems_file_name = 'LEMS_%s.xml' % simulation.id
nml_file_name, nml_doc = generate_neuroml2_from_network(
network, base_dir=base_dir, target_dir=target_dir)
included_files = ['PyNN.xml']
for c in network.cells:
if c.lems_source_file:
included_files.append(c.lems_source_file)
'''
if network.cells:
for c in network.cells:
included_files.append(c.neuroml2_source_file)
'''
if network.synapses:
for s in network.synapses:
if s.lems_source_file:
included_files.append(s.lems_source_file)
print_v("Generating LEMS file prior to running in %s" % simulator)
pops_plot_save = []
pops_spike_save = []
gen_plots_for_quantities = {}
gen_saves_for_quantities = {}
for p in network.populations:
if simulation.recordTraces and ('all' in simulation.recordTraces or
p.id in simulation.recordTraces):
pops_plot_save.append(p.id)
if simulation.recordSpikes and ('all' in simulation.recordSpikes or
p.id in simulation.recordSpikes):
pops_spike_save.append(p.id)
if simulation.recordRates and ('all' in simulation.recordRates
or p.id in simulation.recordRates):
size = evaluate(p.size, network.parameters)
for i in range(size):
quantity = '%s/%i/%s/r' % (p.id, i, p.component)
gen_plots_for_quantities['%s_%i_r' %
(p.id, i)] = [quantity]
gen_saves_for_quantities['%s_%i.r.dat' %
(p.id, i)] = [quantity]
if simulation.recordVariables:
for var in simulation.recordVariables:
to_rec = simulation.recordVariables[var]
if ('all' in to_rec or p.id in to_rec):
size = evaluate(p.size, network.parameters)
for i in range(size):
quantity = '%s/%i/%s/%s' % (p.id, i, p.component,
var)
gen_plots_for_quantities['%s_%i_%s' %
(p.id, i, var)] = [
quantity
]
gen_saves_for_quantities['%s_%i.%s.dat' %
(p.id, i, var)] = [
quantity
]
generate_lems_file_for_neuroml(
simulation.id,
nml_file_name,
network.id,
simulation.duration,
simulation.dt,
lems_file_name,
target_dir=target_dir if target_dir else '.',
nml_doc=
nml_doc, # Use this if the nml doc has already been loaded (to avoid delay in reload)
include_extra_files=included_files,
gen_plots_for_all_v=False,
plot_all_segments=False,
gen_plots_for_quantities=
gen_plots_for_quantities, # Dict with displays vs lists of quantity paths
gen_plots_for_only_populations=
pops_plot_save, # List of populations, all pops if = []
gen_saves_for_all_v=False,
save_all_segments=False,
gen_saves_for_only_populations=
pops_plot_save, # List of populations, all pops if = []
gen_saves_for_quantities=
gen_saves_for_quantities, # Dict with file names vs lists of quantity paths
gen_spike_saves_for_all_somas=False,
gen_spike_saves_for_only_populations=
pops_spike_save, # List of populations, all pops if = []
gen_spike_saves_for_cells=
{}, # Dict with file names vs lists of quantity paths
spike_time_format='ID_TIME',
copy_neuroml=True,
lems_file_generate_seed=12345,
report_file_name='report.%s.txt' % simulation.id,
simulation_seed=simulation.seed if simulation.seed else 12345,
verbose=True)
lems_file_name = _locate_file(lems_file_name, target_dir)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(
lems_file_name,
nogui=True,
load_saved_data=return_results,
reload_events=return_results)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(
lems_file_name,
nogui=True,
load_saved_data=return_results,
reload_events=return_results)
elif simulator == 'jNeuroML_NetPyNE':
results = pynml.run_lems_with_jneuroml_netpyne(
lems_file_name,
nogui=True,
verbose=True,
load_saved_data=return_results,
reload_events=return_results,
num_processors=num_processors)
print_v("Finished running LEMS file %s in %s (returning results: %s)" %
(lems_file_name, simulator, return_results))
if return_results:
traces, events = results
_print_result_info(traces, events)
return results # traces, events =
###############################################################################
# EXECUTION CODE (via netpyne)
###############################################################################
from netpyne import sim
# Create network and run simulation
sim.initialize( # create network object and set cfg and net params
simConfig = simConfig, # pass simulation config and network params as arguments
netParams = netParams)
sim.net.createPops() # instantiate network populations
sim.net.createCells() # instantiate network cells based on defined populations
sim.net.connectCells() # create connections between cells based on params
sim.setupRecording() # setup variables to record for each cell (spikes, V traces, etc)
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData() # save params, cell info and sim output to file (pickle,mat,txt,etc)
sim.analysis.plotData() # plot spike raster
# ###############################################################################
# # INTERACTING WITH INSTANTIATED NETWORK
# ###############################################################################
# modify conn weights
sim.net.modifyConns({'conds': {'label': 'hop->hop'}, 'weight': 0.5})
sim.runSim() # run parallel Neuron simulation
sim.gatherData() # gather spiking data and cell info from each node
sim.saveData() # save params, cell info and sim output to file (pickle,mat,txt,etc)
sim.analysis.plotData() # plot spike raster
Example of saving different network components to file
"""
from netpyne import sim
from netParams import netParams
from cfg import cfg
sim.createSimulateAnalyze(netParams, cfg)
# Saving different network components to file
sim.cfg.saveJson = True
# save network params (rules)
sim.saveData(include=['netParams'], filename='out_netParams')
# save network instance
sim.saveData(include=['net'], filename='out_netInstance')
# save network params and instance together
sim.saveData(include=['netParams', 'net'],
filename='out_netParams_netInstance')
# save sim config
sim.saveData(include=['simConfig'], filename='out_simConfig')
# save sim output data
sim.saveData(include=['simData'], filename='out_simData')
# save network instance with compact conn format (list instead of dict)
def _run_in_same_process(self):
logging.debug('Running single core simulation')
sim.setupRecording()
sim.simulate()
sim.saveData()
