if __name__ == '__main__':
showPlots = not (len(sys.argv) == 2 and sys.argv[1] == '-nogui')
sim_vars = {'amp': 65,
'period': 250,
'offset': -10}
min_constraints = [60, 150, -20]
max_constraints = [100, 300, 20]
swc = SineWaveController(1000, 0.1)
times, volts = swc.run_individual(sim_vars, showPlots, False)
weights={'value_200': 1.0,
'value_400': 1.0,
'value_812': 1.0}
data_analysis = evaluators.PointBasedAnalysis(volts, times)
targets = data_analysis.analyse(weights.keys())
print("Target data: %s"%targets)
#make an evaluator
my_evaluator=evaluators.PointValueEvaluator(controller=swc,
parameters=sim_vars.keys(),
# Implementation of SineWaveController
# moved to: https://github.com/pgleeson/neurotune/blob/master/neurotune/controllers.py
from neurotune.controllers import SineWaveController
if __name__ == '__main__':
sim_vars = {'amp': 65,
'period': 250,
'offset': -10}
swc = SineWaveController(1000, 0.1)
swc.run_individual(sim_vars, True, True)
from neurotune.controllers import SineWaveController
if __name__ == '__main__':
sim_vars = {'amp': 65,
'period': 250,
'offset': -10}
min_constraints = [60, 150, -50]
max_constraints = [100, 300, 50]
swc = SineWaveController(1000, 0.1)
swc.run_individual(sim_vars, True, False)
times, volts = swc.run_individual(sim_vars, False)
analysis_var={'peak_delta':0,'baseline':0,'dvdt_threshold':0, 'peak_threshold':0}
surrogate_analysis=analysis.IClampAnalysis(volts,
times,
analysis_var,
start_analysis=0,
end_analysis=1000,
smooth_data=False,
show_smoothed_data=False)
# The output of the analysis will serve as the basis for model optimization:
surrogate_targets = surrogate_analysis.analyse()