def generate(morphologies=[16,17,18,19]):
for m in morphologies:
# try:
db = Database(
db_name='postgresql://hal08.g-node.pri/morphjongleur',
exec_role='morphjokey_admin',
exec_path='mitsubachi')
# must be mapped before Object is created
#mapping = morphjongleur.orm.experiment.Mapper( db.engine )
mapping = morphjongleur.orm.morphology.Mapper( db.engine )
mapping.orm_map()
morphology = db.load_morphology( m )
print morphology
iclamp = morphjongleur.model.clamp.IClamp(morphology.biggest)
r = morphjongleur.model.experiment.RecordingPoint(morphology.compartments[len(morphology.compartments)-1])
experiment = morphjongleur.model.experiment.Experiment(
morphology,
clamps=[iclamp], recording_points=r,
neuron_passive_parameter=Neuron_passive_parameter(Ra=35.4,g=0.001)
)
db.store( experiment )
print experiment
experiment.run_simulation()
voltage_trace = experiment.get_voltage_trace()
db.store( voltage_trace )
print voltage_trace
def sinus_maxima(morphologies, rounds=3, frequency=265, xlim=None, ylim=None, picture_file='/tmp/', picture_formats=['png']):
import numpy
maxima = {}
minima = {}
titles = {}
for morphology in morphologies:
voltage_traces = {}
cs = [morphology.biggest]
cs.extend( morphology.leafs )
print len(cs)
print cs
leafs = {}
for c in cs:
leafs[c.radius] = c
cs = leafs.values()#TODO: remove pseudo representative choice
print len(cs)
print cs
maxima[morphology] = []
minima[morphology] = []
titles[morphology] = morphology.name
for c in cs:
recording_point = morphjongleur.model.experiment.RecordingPoint(compartment=morphology.biggest)
print str(cs.index(c)+1)+"/"+str(len(cs))
iclamp = morphjongleur.util.pattern_generator.SinusClamp(compartment=c,
frequency=frequency, duration=rounds/float(frequency)
)
neuron_passive_parameter = Neuron_passive_parameter(Ra=35.4,g=0.001)
experiment = morphjongleur.model.experiment.Experiment(
morphology,
clamps=[iclamp], recording_points=[recording_point],
neuron_passive_parameter=neuron_passive_parameter,
duration=rounds/float(frequency),
dt=numpy.power(10, - (numpy.ceil(numpy.log10(265))+1) ),#one magnitue greater: e.g. 265 -> 1e-4,
description = u"%f µm @ %i" % (c.radius, c.compartment_id)
)
#print experiment
experiment.run_simulation()
voltage_trace = recording_point.get_voltage_trace()
maxima[morphology].append(voltage_trace.v_max)
minima[morphology].append(voltage_trace.v_min)
#print voltage_trace
voltage_traces[c] = voltage_trace
voltage_traces[morphology.biggest].plot_ensemble(voltage_traces.values(), title="Voltagetrace in %s for sinusoidal inputsignal at %i Hz" % (morphology.name, frequency), ylim=ylim, picture_file=picture_file+'voltage_trace_f-'+str(frequency)+'_'+str(morphology.name), picture_formats=picture_formats)
plot_daempfung(maxima.values(),titles=titles.values(), picture_file=picture_file+'/daempfung_maxima_f-265_'+str(morphology.name), picture_formats=picture_formats)
plot_daempfung(minima.values(),titles=titles.values(), picture_file=picture_file+'/daempfung_minima_f-265_'+str(morphology.name), picture_formats=picture_formats)
def sinusclamp(morphology, rounds=3, frequency=265, picture_file='/tmp/voltage_trace_f-265_sinusclamp_', picture_formats=['png']):
iclamp = morphjongleur.util.pattern_generator.SinusClamp(compartment=morphology.biggest,
frequency=frequency, duration=rounds/float(frequency)
)
r = morphjongleur.model.experiment.RecordingPoint(morphology.compartments[len(morphology.compartments)-1])
experiment = morphjongleur.model.experiment.Experiment(
morphology=morphology,
clamps=[iclamp], recording_points=r,
neuron_passive_parameter = Neuron_passive_parameter(Ra=35.4,g=0.001),
duration=rounds/float(frequency)
)
print experiment
experiment.run_simulation()
voltage_trace = r.get_voltage_trace()
print voltage_trace
voltage_trace.plot(picture_file=picture_file+str(morphology.name), picture_formats=picture_formats)
def taus(morphology):
recordingpoints = [morphjongleur.model.experiment.RecordingPoint(compartment=recordingpoint) for recordingpoint in morphology.compartments]
iclamp = morphjongleur.model.clamp.IClamp(compartment=morphology.root,
amplitude=-1e-9, delay=0e-3, duration=3e-3
)
neuron_passive_parameter = morphjongleur.model.neuron_passive.Neuron_passive_parameter(Ra=35.4,g=0.001)
experiment = morphjongleur.model.experiment.Experiment(
morphology=morphology,
recordingpoints=recordingpoints, clamps=[iclamp],
neuron_passive_parameter=neuron_passive_parameter,
duration=5e-3, dt=1e-4,
description = "TauExperiment @ %i\t%s " % (morphology.root.compartment_id, morphology.name),
)
experiment.run_simulation()
taus = [recordingpoint.get_tau_fit(iclamp) for recordingpoint in recordingpoints]
return taus
def amplitudes(morphology):
recordingpoints = [morphjongleur.model.experiment.RecordingPoint(compartment=recordingpoint) for recordingpoint in morphology.compartments]
iclamp = morphjongleur.model.clamp.IClamp(compartment=morphology.root,
amplitude=-1e-9, delay=0e-3, duration=3e-3
)
neuron_passive_parameter = morphjongleur.model.neuron_passive.Neuron_passive_parameter(Ra=35.4,g=0.001)
experiment = morphjongleur.model.experiment.Experiment(
morphology=morphology,
recordingpoints=recordingpoints, clamps=[iclamp],
neuron_passive_parameter=neuron_passive_parameter,
duration=5e-3, dt=1e-4,
description = "TauExperiment @ %i\t%s " % (morphology.root.compartment_id, morphology.name),
)
experiment.run_simulation()
#voltage_trace = experiment.get_voltage_trace(delay=2./frequency)# nach Einschwinphase
return recordingpoints
def experiment(morphology, compartment, frequency, amplitude=-1e-9):#, amplitude
recordingpoints = [morphjongleur.model.experiment.RecordingPoint(compartment=recordingpoint) for recordingpoint in morphology.terminal_tips]
iclamp = morphjongleur.util.pattern_generator.SinusClamp(compartment=compartment,
amplitude=amplitude,
frequency=frequency, duration=3./frequency
)
neuron_passive_parameter = morphjongleur.model.neuron_passive.Neuron_passive_parameter(Ra=35.4,g=0.001)
experiment = morphjongleur.model.experiment.Experiment(
morphology=morphology,
recordingpoints=recordingpoints, clamps=[iclamp],
neuron_passive_parameter=neuron_passive_parameter,
duration=5e-3, dt=1e-4,
description = "TauExperiment @ %i\t%s " % (morphology.root.compartment_id, morphology.name),
)
experiment.run_simulation()
return recordingpoints
def experiment(morphology):#, amplitude
recordingpoint = morphjongleur.model.experiment.RecordingPoint(compartment=morphology.root)
iclamp = morphjongleur.model.clamp.IClamp(compartment=morphology.root,
amplitude=-1e-9, delay=0e-3, duration=3e-3
)
neuron_passive_parameter = morphjongleur.model.neuron_passive.Neuron_passive_parameter(Ra=35.4,g=0.001)
experiment = morphjongleur.model.experiment.Experiment(
morphology=morphology,
recordingpoints=[recordingpoint], clamps=[iclamp],
neuron_passive_parameter=neuron_passive_parameter,
duration=5e-3, dt=1e-4,
description = "TauExperiment @ %i\t%s " % (morphology.root.compartment_id, morphology.name),
)
experiment.run_simulation()
tau_fit = recordingpoint.get_tau_fit(iclamp)
print "%f %f %f" % (tau_fit.get_R_in(), tau_fit.get_tau_eff(), tau_fit.tau_lin_fit())
return tau_fit
