/
tuneGrc.py
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/
tuneGrc.py
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'''
Still under development!!
Subject to change without notice!!
'''
from pyneuroml.tune.NeuroMLTuner import run_optimisation
from pyneuroml.tune.NeuroMLController import NeuroMLController
import sys
from collections import OrderedDict
from pyelectro import utils
def plot_baseline_data(targets = None):
baseline = {}
file_name = 'TestGranNet.v.basedat'
cols = ['t', 'Gran/0/Granule_98/v', 'Gran/1/Granule_98/v', 'Gran/2/Granule_98/v']
for c in cols:
baseline[c] = []
for line in open(file_name):
values = line.split()
for vi in range(len(values)):
baseline[cols[vi]].append(float(values[vi])*1000)
volts = {}
for k in baseline.keys():
if k!='t':
volts[k]=baseline[k]
utils.simple_network_analysis(volts,
baseline['t'],
targets = targets,
plot=True,
show_plot_already=False)
if __name__ == '__main__':
sim_time = 600
dt = 0.01
prefix = 'TestGran'
neuroml_file = 'models/GranuleCell.net.nml'
target = 'network_GranuleCell'
simulator = 'jNeuroML_NEURON'
#simulator = 'jNeuroML'
quick = True
#quick = False
if quick:
population_size = 5
max_evaluations = 20
num_selected = 10
num_offspring = 8
else:
population_size = 50
max_evaluations = 300
num_selected = 50
num_offspring = 30
mutation_rate = 0.5
num_elites = 1
seed = 1234
verbose = False
parameters = ['cell:Granule_98/channelDensity:Gran_NaF_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:Gran_KDr_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:Gran_H_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:GranPassiveCond_all/mS_per_cm2',
'cell:Granule_98/specificCapacitance:all/uF_per_cm2']
#above parameters will not be modified outside these bounds:
min_constraints = [50, 6, 0, 0, 0.5]
max_constraints = [60, 15, 0.05, 0.1, 1.5]
ref0 = 'Gran/0/Granule_98/v:'
ref1 = 'Gran/1/Granule_98/v:'
ref2 = 'Gran/2/Granule_98/v:'
average_maximum0 = ref0+'average_maximum'
average_minimum0 = ref0+'average_minimum'
mean_spike_frequency0 = ref0+'mean_spike_frequency'
first_spike_time0 = ref0+'first_spike_time'
weights = {average_maximum0: 1,
average_minimum0: 1,
mean_spike_frequency0: 1,
first_spike_time0: 1}
target_data = {average_maximum0: 15.3011,
average_minimum0: -70.066,
mean_spike_frequency0: 38.339,
first_spike_time0: 108.34}
minimum_pas1 = ref1+'minimum'
value_110_pas1 = ref1+'value_110'
minimum_pas2 = ref2+'minimum'
value_110_pas2 = ref2+'value_110'
value_400_pas2 = ref2+'value_400'
target_data_pas = {minimum_pas1: -71.39,
value_110_pas1: -67.22,
minimum_pas2: -84.97147,
value_110_pas2: -74.36766,
value_400_pas2: -80.15374}
weights_pas = {minimum_pas1: 1,
value_110_pas1: 1,
minimum_pas2: 1,
value_110_pas2: 1,
value_400_pas2: 1}
nogui = '-nogui' in sys.argv
vars = {'cell:Granule_98/channelDensity:GranPassiveCond_all/mS_per_cm2': 0.03337471902862413,
'cell:Granule_98/channelDensity:Gran_H_98_all/mS_per_cm2': 0.03559929171361169,
'cell:Granule_98/channelDensity:Gran_KDr_98_all/mS_per_cm2': 7.016013314649269,
'cell:Granule_98/channelDensity:Gran_NaF_98_all/mS_per_cm2': 55.74495636439219,
'cell:Granule_98/specificCapacitance:all/uF_per_cm2': 1.0808061342038033}
sim_vars = OrderedDict(vars)
#sim_vars = OrderedDict([])
if '-one' in sys.argv:
simulator = 'jNeuroML_NEURON'
#simulator = 'jNeuroML'
cont = NeuroMLController('TestGran',
'models/GranuleCell.net.nml',
'network_GranuleCell',
sim_time,
dt,
simulator)
t, v = cont.run_individual(sim_vars, show=False)
analysis = utils.simple_iclamp_analysis(v['Gran/0/Granule_98/v'], t, plot=True)
elif '-mone' in sys.argv:
simulator = 'jNeuroML_NEURON'
#simulator = 'jNeuroML'
sim_time = 700
cont = NeuroMLController('TestGranNet',
'models/GranuleCellMulti.net.nml',
'network_GranuleCell_multi',
sim_time,
dt,
simulator)
t, v = cont.run_individual(sim_vars, show=False)
plot_baseline_data(targets = target_data_pas.keys())
analysis = utils.simple_network_analysis(v,
t,
plot=True,
targets = target_data_pas.keys())
elif '-mtune' in sys.argv:
simulator = 'jNeuroML'
simulator = 'jNeuroML_NEURON'
prefix = 'TestGranNet'
neuroml_file = 'models/GranuleCellMulti.net.nml'
target = 'network_GranuleCell_multi'
sim_time = 700
population_size = 20
max_evaluations = 20
num_selected = 5
num_offspring = 5
parameters = ['cell:Granule_98/channelDensity:Gran_NaF_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:Gran_KDr_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:Gran_H_98_all/mS_per_cm2',
'cell:Granule_98/channelDensity:GranPassiveCond_all/mS_per_cm2',
'cell:Granule_98/specificCapacitance:all/uF_per_cm2']
#above parameters will not be modified outside these bounds:
min_constraints_pas = [0, 0, 0, 0.005, 0.5]
max_constraints_pas = [0, 0, 0.1, 0.1, 2]
known_target_values = {'cell:Granule_98/channelDensity:Gran_NaF_98_all/mS_per_cm2':55.7227,
'cell:Granule_98/channelDensity:Gran_KDr_98_all/mS_per_cm2':8.89691,
'cell:Granule_98/channelDensity:Gran_H_98_all/mS_per_cm2': 0.03090506,
'cell:Granule_98/channelDensity:GranPassiveCond_all/mS_per_cm2': 0.0330033,
'cell:Granule_98/specificCapacitance:all/uF_per_cm2': 1}
report = run_optimisation(prefix = 'Pas_'+prefix,
neuroml_file = neuroml_file,
target = target,
parameters = parameters,
max_constraints = max_constraints_pas,
min_constraints = min_constraints_pas,
weights = weights_pas,
target_data = target_data_pas,
sim_time = sim_time,
dt = dt,
population_size = population_size,
max_evaluations = max_evaluations,
num_selected = num_selected,
num_offspring = num_offspring,
mutation_rate = mutation_rate,
num_elites = num_elites,
simulator = simulator,
nogui = nogui,
show_plot_already = False,
seed = seed,
known_target_values = known_target_values)
plot_baseline_data()
eps = 0.01
gh = report['fittest vars']['cell:Granule_98/channelDensity:Gran_H_98_all/mS_per_cm2']
gpas = report['fittest vars']['cell:Granule_98/channelDensity:GranPassiveCond_all/mS_per_cm2']
sc = report['fittest vars']['cell:Granule_98/specificCapacitance:all/uF_per_cm2']
min_constraints = [40, 3, gh*(1-eps), gpas*(1-eps), sc*(1-eps)]
max_constraints = [80, 30, gh*(1+eps), gpas*(1+eps), sc*(1+eps)]
report = run_optimisation(prefix = prefix,
neuroml_file = neuroml_file,
target = target,
parameters = parameters,
max_constraints = max_constraints,
min_constraints = min_constraints,
weights = weights,
target_data = target_data,
sim_time = sim_time,
dt = dt,
population_size = population_size,
max_evaluations = max_evaluations,
num_selected = num_selected,
num_offspring = num_offspring,
mutation_rate = mutation_rate,
num_elites = num_elites,
simulator = simulator,
nogui = nogui,
show_plot_already = True,
seed = seed,
known_target_values = known_target_values)
else:
if not '-remote' in sys.argv:
run_optimisation(prefix = prefix,
neuroml_file = neuroml_file,
target = target,
parameters = parameters,
max_constraints = max_constraints,
min_constraints = min_constraints,
weights = weights,
target_data = target_data,
sim_time = sim_time,
dt = dt,
population_size = population_size,
max_evaluations = max_evaluations,
num_selected = num_selected,
num_offspring = num_offspring,
mutation_rate = mutation_rate,
num_elites = num_elites,
simulator = simulator,
nogui = nogui,
seed = seed)
else:
from RemoteRun import run_remotely, to_command_line_arg
prefix_ = 'Grc_%s_e%s_m%s_s%s_%s'%(population_size,max_evaluations,mutation_rate,seed,prefix)
command = 'pynml-tune %s %s %s %s %s %s %s %s -simTime %s -dt %s -populationSize %s -maxEvaluations %s -numSelected %s -numOffspring %s -mutationRate %s -numElites %s -seed %s -simulator %s %s %s'%(prefix_,
neuroml_file,
target,
to_command_line_arg(parameters),
to_command_line_arg(max_constraints),
to_command_line_arg(min_constraints),
to_command_line_arg(target_data),
to_command_line_arg(weights),
sim_time,
dt,
population_size,
max_evaluations,
num_selected,
num_offspring,
mutation_rate,
num_elites,
seed,
simulator,
'-verbose' if verbose else '',
'-nogui')
print('-----------------------------------------')
print(command)
run_remotely(command, prefix_, '/home/ucgbpgl/git/testtune', runtime='03:00:00')