def produce_restingVm(self, **kwargs):
"""
kwargs = { "parameters": dictionary with keys,
"stimparameters": dictionary with keys "type" and "stimlist",
"onmodel": instantiated model }
"""
print("Sim produce_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")
#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")
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_Vm_pre_epoch( timestamps = timestamps_over_epochs,
datavalues = data_over_epochs )
#print("Signal Processing Done.")
setattr(model, "prediction", baseVms)
print("Simulation produce_restingVm Done.")
return model
def produce_inputR(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 + "_inputR 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",
self._compute_inputR(baseVms, kwargs["stimparameters"]))
print("Simulation produce_" + roi + "_inputR 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")
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
setattr(model, "prediction", peakVms[0] - baseVm[0])
print("Simulation produce_soma_spikeheight Done.")
return model
def test_2_transform_signal_to_spikes(self):
os.chdir("..") # move up to load the model
# pick the model
parameters = {"dt": 0.1, "celsius": 30, "tstop": 10, "v_init": 65}
currparameters = {
"type": ["current", "IClamp"],
"stimlist": [{
'amp': 0.5,
'dur': 10.0,
'delay': 5.0
}, {
'amp': 1.0,
'dur': 20.0,
'delay': 5.0 + 10.0
}]
}
sm.prepare_model_NEURON(parameters=parameters,
chosenmodel=self.chosenmodel)
stimuli_list = sm.stimulate_model_NEURON(
stimparameters=currparameters,
modelsite=self.chosenmodel.cell.soma)
self.rec["time"], self.rec["response"], self.rec["stimulus"] = \
rm.prepare_recording_NEURON(self.chosenmodel)
sm.engage_NEURON()
spikes = sp.transform_signal(chosenmodel=self.chosenmodel,
recordings=self.rec,
tosignal='spikes')
ans = \
len( range(
int(spikes[self.regionslist_str[0]].t_start.magnitude),
int(spikes[self.regionslist_str[0]].t_stop.magnitude/parameters["dt"])
) ) + 1 # for the additional dt step
self.assertEqual(ans, len(self.rec["time"]))
os.chdir(
pwd
) # reset to the location of this managerSignalProcessingTest.py