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 simulate_single_model(self,
experiment: model.Experiment = None,
use_prev_inst: bool = False):
if experiment:
working_directory = self._prepare_simulation_files(
experiment, use_prev_inst)
simulations.run(
parallel=self.run_config.parallel,
cores=self.run_config.cores,
asynchronous=self.run_config.asynchronous,
method=simulations.MPI_BULLETIN,
working_directory=working_directory,
)
if self.run_config.asynchronous:
message = "Experiment is pending! " \
f"Results will be stored in your workspace at ./{os.path.join(constants.EXPERIMENTS_FOLDER, experiment.name)}"
return utils.getJSONError(message, "")
else:
sim.load(f'{constants.MODEL_OUTPUT_FILENAME}.json')
self.geppetto_model = self.model_interpreter.getGeppettoModel(
sim)
response = json.loads(
GeppettoModelSerializer.serialize(self.geppetto_model))
return response
else:
# Run in same process
if not use_prev_inst:
logging.debug('Instantiating single thread simulation')
netpyne_model = self.instantiateNetPyNEModel()
self.geppetto_model = self.model_interpreter.getGeppettoModel(
netpyne_model)
simulations.run()
if self.geppetto_model:
response = json.loads(
GeppettoModelSerializer.serialize(self.geppetto_model))
return response
def plotBatchConn(batchPath='batch_data',
batchLabel='v01_batch48',
feature='all'):
batchPath = os.path.join(batchPath, batchLabel)
batchPathFiles = os.listdir(batchPath)
simFiles = [file for file in batchPathFiles if file.endswith('.json')]
simFiles = [file for file in simFiles if not file.endswith('cfg.json')]
simFiles = [file for file in simFiles if not file.endswith('batch.json')]
simFiles.sort()
includePre = ['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5']
includePost = ['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5']
features = [
'numConns', 'divergence', 'convergence', 'weight', 'probability',
'strength'
]
groupBy = 'pop'
orderBy = 'gid'
EEmean = {}
EImean = {}
IEmean = {}
IImean = {}
EEstd = {}
EIstd = {}
IEstd = {}
IIstd = {}
if feature == 'all':
for feature in features:
EEmean[feature] = []
EImean[feature] = []
IEmean[feature] = []
IImean[feature] = []
EEstd[feature] = []
EIstd[feature] = []
IEstd[feature] = []
IIstd[feature] = []
for simFile in simFiles:
simPath = os.path.join('batch_data', batchLabel, simFile)
sim.load(simPath, createNEURONObj=False)
for feature in features:
(fig, output) = sim.analysis.plotConn(includePre=includePre,
includePost=includePost,
feature=feature,
groupBy=groupBy,
orderBy=orderBy,
showFig=False)
connMatrix = output['connMatrix']
EEmean[feature].append(np.mean(connMatrix[0:4, 0:4]))
EImean[feature].append(np.mean(connMatrix[0:4, 4:5]))
IEmean[feature].append(np.mean(connMatrix[4:5, 0:4]))
IImean[feature].append(np.mean(connMatrix[4:5, 4:5]))
EEstd[feature].append(np.std(connMatrix[0:4, 0:4]))
EIstd[feature].append(np.std(connMatrix[0:4, 4:5]))
IEstd[feature].append(np.std(connMatrix[4:5, 0:4]))
IIstd[feature].append(np.std(connMatrix[4:5, 4:5]))
for feature in features:
labels = ['10', '100', '500', '1000', '5000']
x = np.arange(len(labels)) # the label locations
width = 0.2 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width * (3 / 2),
EEmean[feature],
yerr=EEstd[feature],
width=width,
label='E->E')
rects2 = ax.bar(x - width / (2),
EImean[feature],
yerr=EIstd[feature],
width=width,
label='E->I')
rects3 = ax.bar(x + width / (2),
IEmean[feature],
yerr=IEstd[feature],
width=width,
label='I->E')
rects4 = ax.bar(x + width * (3 / 2),
IImean[feature],
yerr=IIstd[feature],
width=width,
label='I->I')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Connectivity ' + feature)
ax.set_title('Connectivity ' + feature)
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.set_xlabel('Number of Cells')
ax.legend()
fig.tight_layout()
return
def plotSimSync(simPath='data',
simLabel='eee_net_25',
showFigs=False,
batchLabel=None):
if not batchLabel:
simPath = os.path.join(simPath, simLabel, simLabel + '.json')
sim.load(simPath, createNEURONObj=False)
else:
simPath = os.path.join(simPath, batchLabel, simLabel + '.json')
sim.load(simPath, createNEURONObj=False)
#sim.analysis.plotTraces()
rasterFig, rasterData = sim.analysis.plotRaster(
include=['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5'],
orderInverse=False)
syncFigPre, syncDataPre = sim.analysis.plotSpikeStats(
include=['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5'],
timeRange=[200, 400],
graphType='boxplot',
stats=['sync'],
figSize=(6, 8),
saveData=None,
saveFig=None,
showFig=showFigs)
syncFigPlat, syncDataPlat = sim.analysis.plotSpikeStats(
include=['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5'],
timeRange=[800, 1000],
graphType='boxplot',
stats=['sync'],
figSize=(6, 8),
saveData=None,
saveFig=None,
showFig=showFigs)
syncFigInput, syncDataInput = sim.analysis.plotSpikeStats(
include=['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5'],
timeRange=[1400, 1600],
graphType='boxplot',
stats=['sync'],
figSize=(6, 8),
saveData=None,
saveFig=None,
showFig=showFigs)
syncFigPlatInput, syncDataPlatInput = sim.analysis.plotSpikeStats(
include=['PT5_1', 'PT5_2', 'PT5_3', 'PT5_4', 'PV5'],
timeRange=[2000, 2200],
graphType='boxplot',
stats=['sync'],
figSize=(6, 8),
saveData=None,
saveFig=None,
showFig=showFigs)
# Bar plot of synchrony
labels = ['Control', 'Plats', 'Inputs', 'Plats+Inputs']
PT51_sync = []
PT51_sync.append(syncDataPre['statData'][0][0])
PT51_sync.append(syncDataPlat['statData'][0][0])
PT51_sync.append(syncDataInput['statData'][0][0])
PT51_sync.append(syncDataPlatInput['statData'][0][0])
PT52_sync = []
PT52_sync.append(syncDataPre['statData'][1][0])
PT52_sync.append(syncDataPlat['statData'][1][0])
PT52_sync.append(syncDataInput['statData'][1][0])
PT52_sync.append(syncDataPlatInput['statData'][1][0])
PT53_sync = []
PT53_sync.append(syncDataPre['statData'][2][0])
PT53_sync.append(syncDataPlat['statData'][2][0])
PT53_sync.append(syncDataInput['statData'][2][0])
PT53_sync.append(syncDataPlatInput['statData'][2][0])
PT54_sync = []
PT54_sync.append(syncDataPre['statData'][3][0])
PT54_sync.append(syncDataPlat['statData'][3][0])
PT54_sync.append(syncDataInput['statData'][3][0])
PT54_sync.append(syncDataPlatInput['statData'][3][0])
x = np.arange(len(labels)) # the label locations
width = 0.2 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width * (3 / 2),
PT51_sync,
width,
label='PT5_1 (+P+I)')
rects2 = ax.bar(x - width / (2), PT52_sync, width, label='PT5_2 (+P-I)')
rects3 = ax.bar(x + width / (2), PT53_sync, width, label='PT5_3 (-P+I)')
rects4 = ax.bar(x + width * (3 / 2),
PT54_sync,
width,
label='PT5_4 (-P-I)')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Synchrony')
ax.set_title('Synchrony by population and condition')
ax.set_xticks(x)
ax.set_xticklabels(labels)
ax.legend()
fig.tight_layout()
outputData = [PT51_sync, PT52_sync, PT53_sync, PT54_sync]
return outputData
import neuron
from netpyne import sim
# Folder that contains x86_64 folder
NETPYNE_WORKDIR_PATH = '../../../'
neuron.load_mechanisms(NETPYNE_WORKDIR_PATH)
sim.load("./model_output.json")
sim.createSimulate()
sim.saveData()
