def produce_restingVm(self, roi, **kwargs):
"""
roi, region of interest is a string, i.e, 1 key in chosenmodel.regions
kwargs = { "parameters": dictionary with keys,
"stimparameters": dictionary with keys "type" and "stimlist",
"onmodel": instantiated model }
"""
print("Sim produce_" + roi + "_restingVm starting ...")
ec = ExecutiveControl() # only works when in ~/cerebmodels
model = ec.launch_model(parameters=kwargs["parameters"],
stimparameters=kwargs["stimparameters"],
stimloc=kwargs["stimloc"],
onmodel=kwargs["onmodel"],
capabilities={
"model": "produce_voltage_response",
"vtest": ProducesElectricalResponse
},
mode="capability")
nwbfile = rm.load_nwbfile(model.fullfilename)
orderedepochs = rm.order_all_epochs_for_region(nwbfile=nwbfile,
region=roi)
timestamps_over_epochs = [
rm.timestamps_for_epoch(orderedepochs[i])
for i in range(len(orderedepochs))
]
data_over_epochs = [
rm.data_for_epoch(orderedepochs[i])
for i in range(len(orderedepochs))
]
baseVms = spm.distill_baseVm_pre_epoch(
timestamps=timestamps_over_epochs, datavalues=data_over_epochs)
setattr(model, "prediction", baseVms)
print("Simulation produce_" + roi + "_restingVm Done.")
return model
def produce_soma_spikeheight(self, **kwargs):
"""
kwargs = { "parameters": dictionary with keys,
"stimparameters": dictionary with keys "type" and "stimlist",
"onmodel": instantiated model }
"""
print("Sim produce_soma_spikeheight starting ...")
ec = ExecutiveControl() # only works when in ~/cerebmodels
model = ec.launch_model(parameters=kwargs["parameters"],
stimparameters=kwargs["stimparameters"],
stimloc=kwargs["stimloc"],
onmodel=kwargs["onmodel"],
capabilities={
"model": "produce_voltage_response",
"vtest": ProducesElectricalResponse
},
mode="capability")
#self.fullfilename # already saved by invoking produce_voltage_response above
#print("Signal Processing ...")
nwbfile = rm.load_nwbfile(model.fullfilename)
orderedepochs = rm.order_all_epochs_for_region(nwbfile=nwbfile,
region="soma v")
timestamps_over_epochs = [
rm.timestamps_for_epoch(orderedepochs[i])
for i in range(len(orderedepochs))
]
data_over_epochs = [
rm.data_for_epoch(orderedepochs[i])
for i in range(len(orderedepochs))
]
baseVm = spm.distill_baseVm_pre_epoch(
timestamps=timestamps_over_epochs, datavalues=data_over_epochs)
try:
peakVms = spm.distill_peakVm_from_spikes(
timestamps=timestamps_over_epochs, datavalues=data_over_epochs)
except:
peakVms = baseVm
#print("Signal Processing Done.")
setattr(model, "prediction", peakVms[0] - baseVm[0])
print("Simulation produce_soma_spikeheight Done.")
return model