def run_c302(config,
parameter_set,
prefix,
duration,
dt,
simulator,
save=False,
show_plot_already=True,
data_reader="SpreadsheetDataReader",
verbose=False,
plot_ca=True,
param_overrides={}):
print(
"********************\n\n Going to generate c302_%s_%s and run for %s on %s\n\n********************"
% (parameter_set, config, duration, simulator))
exec('from c302_%s import setup' % config)
cells, cells_to_stimulate, params, muscles = setup(
parameter_set,
data_reader=data_reader,
generate=True,
duration=duration,
dt=dt,
target_directory='examples',
verbose=verbose,
param_overrides=param_overrides)
os.chdir('examples')
lems_file = 'LEMS_c302_%s_%s.xml' % (parameter_set, config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file,
nogui=True,
load_saved_data=True,
verbose=verbose)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file,
nogui=True,
load_saved_data=True,
verbose=verbose)
c302.print_("Finished simulation of %s and have reloaded results" %
lems_file)
c302_utils.plot_c302_results(results,
config,
parameter_set,
directory=save_fig_dir,
save=save,
show_plot_already=show_plot_already,
data_reader=data_reader,
plot_ca=plot_ca)
os.chdir('..')
return cells, cells_to_stimulate, params, muscles
def main(config, parameter_set, prefix, duration, dt, simulator, save=False, show_plot_already=True):
exec('from c302_%s import setup'%config)
cells, cells_to_stimulate, params, muscles = setup(parameter_set,
generate=True,
duration = duration,
dt = dt,
target_directory='examples')
os.chdir('examples')
lems_file = 'LEMS_c302_%s_%s.xml'%(parameter_set,config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file, nogui=True, load_saved_data=True, verbose=True)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file, nogui=True, load_saved_data=True, verbose=True)
c302_utils.plot_c302_results(results, config, parameter_set, directory=save_fig_dir,save=save,show_plot_already=show_plot_already)
def main(config,
parameter_set,
prefix,
duration,
dt,
simulator,
save=False,
show_plot_already=True):
exec('from c302_%s import setup' % config)
cells, cells_to_stimulate, params, muscles = setup(
parameter_set,
generate=True,
duration=duration,
dt=dt,
target_directory='examples')
os.chdir('examples')
lems_file = 'LEMS_c302_%s_%s.xml' % (parameter_set, config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file,
nogui=True,
load_saved_data=True,
verbose=True)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file,
nogui=True,
load_saved_data=True,
verbose=True)
c302_utils.plot_c302_results(results,
config,
parameter_set,
directory=save_fig_dir,
save=save,
show_plot_already=show_plot_already)
def main(config, parameter_set, prefix, duration, dt, simulator, save=False, show_plot_already=True, data_reader="SpreadsheetDataReader"):
print("********************\n\n Going to generate c302_%s_%s and run for %s on %s\n\n********************"%(parameter_set,config,duration, simulator))
exec('from c302_%s import setup'%config)
cells, cells_to_stimulate, params, muscles = setup(parameter_set,
data_reader=data_reader,
generate=True,
duration = duration,
dt = dt,
target_directory='examples')
os.chdir('examples')
lems_file = 'LEMS_c302_%s_%s.xml'%(parameter_set,config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file, nogui=True, load_saved_data=True, verbose=True)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file, nogui=True, load_saved_data=True, verbose=True)
c302_utils.plot_c302_results(results, config, parameter_set, directory=save_fig_dir,save=save,show_plot_already=show_plot_already, data_reader=data_reader)
os.chdir('..')
def run_c302(config,
parameter_set,
prefix,
duration,
dt,
simulator,
save=False,
show_plot_already=True,
data_reader="SpreadsheetDataReader",
verbose=False,
plot_ca=True,
plot_connectivity=False,
param_overrides={},
config_param_overrides={},
config_package="",
target_directory='examples',
save_fig_to=None):
if save_fig_to:
global save_fig_dir
save_fig_dir = save_fig_to
c302.print_("********************\n\n Going to generate c302_%s_%s and run for %sms (dt: %sms) on %s\n\n********************"%(parameter_set,config,duration, dt, simulator))
if config_package:
exec ('from %s.c302_%s import setup' % (config_package, config), globals())
else:
exec ('from c302_%s import setup' % config, globals())
try:
os.makedirs(target_directory)
except OSError as e:
if e.errno != errno.EEXIST:
raise
cells, cells_to_stimulate, params, muscles, nml_doc = setup(parameter_set,
data_reader=data_reader,
generate=True,
duration = duration,
dt = dt,
target_directory=target_directory,
verbose=verbose,
param_overrides=param_overrides,
config_param_overrides=config_param_overrides)
orig_dir = os.getcwd()
os.chdir(target_directory)
try:
os.makedirs(save_fig_dir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
lems_file = 'LEMS_c302_%s_%s.xml'%(parameter_set,config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file, nogui=True, load_saved_data=True, verbose=verbose)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file, nogui=True, load_saved_data=True, verbose=verbose)
c302.print_("Finished simulation of %s and have reloaded results"%lems_file)
c302_utils.plot_c302_results(results,
config,
parameter_set,
directory=save_image_full_dir,
save=save,
show_plot_already=show_plot_already,
data_reader=data_reader,
plot_ca=plot_ca)
if plot_connectivity:
c302_utils.generate_conn_matrix(nml_doc, save_fig_dir=save_image_full_dir)
os.chdir(orig_dir)
return cells, cells_to_stimulate, params, muscles
def run_optimisation(prefix,
config,
level,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time=500,
dt=0.05,
analysis_start_time=0,
population_size=20,
max_evaluations=20,
num_selected=10,
num_offspring=20,
mutation_rate=0.9,
num_elites=1,
seed=12345,
simulator='jNeuroML',
nogui=False,
num_local_procesors_to_use=4):
print("Running optimisation...")
print("parameters: %s" % parameters)
print("max_constraints: %s" % max_constraints)
print("min_constraints: %s" % min_constraints)
print("simulator: %s" % simulator)
ref = prefix + config
run_dir = "NT_%s_%s" % (ref, time.ctime().replace(' ', '_').replace(
':', '.'))
os.mkdir(run_dir)
my_controller = C302Controller(
ref,
level,
config,
sim_time,
dt,
simulator=simulator,
generate_dir=run_dir,
num_local_procesors_to_use=num_local_procesors_to_use)
peak_threshold = -31 if level.startswith('A') or level.startswith(
'B') else (-40)
analysis_var = {
'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold
}
data = ref + '.dat'
sim_var = OrderedDict()
for i in range(len(parameters)):
sim_var[
parameters[i]] = max_constraints[i] / 2 + min_constraints[i] / 2
#make an evaluator, using automatic target evaluation:
my_evaluator = c302Evaluators.EnhancedNetworkEvaluator(
controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False,
seed=seed,
summary_dir=run_dir)
secs = time.time() - start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins total; %fs per eval)\n\n" % (
max_evaluations, population_size, secs, secs / 60.0,
(secs / max_evaluations))
report = "----------------------------------------------------\n\n" + info
reportj['comment'] = info
reportj['time'] = secs
for key, value in zip(parameters, best_candidate):
sim_var[key] = value
best_candidate_t, best_candidate_v = my_controller.run_individual(
sim_var, show=False)
best_candidate_results = my_controller.last_results
best_candidate_analysis = c302Analysis.Data_Analyser(
best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=analysis_start_time,
end_analysis=sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(target_data)
report += "---------- Best candidate ------------------------------------------\n"
report += pp.pformat(best_candidate) + "\n\n"
report += pp.pformat(best_cand_analysis_full) + "\n"
report += pp.pformat(best_cand_analysis) + "\n\n"
report += "FITNESS: %f\n\n" % fitness
print(report)
reportj['fitness'] = fitness
reportj['fittest vars'] = dict(sim_var)
reportj['best_cand_details'] = best_candidate
reportj['best_cand_analysis_full'] = best_cand_analysis_full
reportj['best_cand_analysis'] = best_cand_analysis
reportj['parameters'] = parameters
reportj['analysis_var'] = analysis_var
reportj['target_data'] = target_data
reportj['weights'] = weights
reportj['analysis_start_time'] = analysis_start_time
reportj['population_size'] = population_size
reportj['max_evaluations'] = max_evaluations
reportj['num_selected'] = num_selected
reportj['num_offspring'] = num_offspring
reportj['mutation_rate'] = mutation_rate
reportj['num_elites'] = num_elites
reportj['seed'] = seed
reportj['simulator'] = simulator
reportj['sim_time'] = sim_time
reportj['dt'] = dt
reportj['run_directory'] = run_dir
reportj['reference'] = ref
report_file = open("%s/report.json" % run_dir, 'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py" % run_dir, 'w')
plot_file.write(
'from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n' % parameters)
plot_file.write('\n')
plot_file.write(
"curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n"
)
plot_file.write(
"plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n"
)
plot_file.close()
if not nogui:
added = []
for wref in weights.keys():
ref = wref.split(':')[0]
if not ref in added and not "phase_offset" in ref:
added.append(ref)
best_candidate_plot = plt.plot(best_candidate_t,
best_candidate_v[ref],
label="%s - %i evaluations" %
(ref, max_evaluations))
plt.legend()
plt.ylim(-80.0, 80.0)
plt.xlim(0.0, sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
utils.plot_generation_evolution(
sim_var.keys(),
individuals_file_name='%s/ga_individuals.csv' % run_dir)
c302_utils.plot_c302_results(best_candidate_results,
config,
level,
directory=run_dir,
save=True)
def run(a=None, **kwargs):
try:
import neuroml
import pyneuroml
import xlrd
except Exception as e:
print_("Cannot import one of the required packages. Please install!\n"
"Exception: %s\n" % e)
try:
if os.environ.has_key('C302_HOME'):
os.environ['C302_HOME']
sys.path.append(os.environ['C302_HOME'])
print_('Python path now: %s' % sys.path)
import c302
import c302_utils
except Exception as e:
print_(
"Cannot import c302!\n"
"Exception: %s\n" % e +
"Please set environment variable C302_HOME to point to the directory: CElegansNeuroML/CElegans/pythonScripts/c302!\n"
)
exit()
a = build_namespace(a, **kwargs)
gen_start = time.time()
ref = a.reference
if not os.path.isdir('simulations'):
os.mkdir('simulations')
sim_ref = "%s_%s_%s" % (a.c302params, ref, time.ctime().replace(
' ', '_').replace(':', '.'))
sim_dir = "simulations/%s" % (sim_ref)
os.mkdir(sim_dir)
#exec('from %s import ParameterisedModel'%a.c302params)
#params = ParameterisedModel()
id = '%s_%s' % (a.c302params, ref)
exec('from c302_%s import setup' % ref)
setup(a.c302params,
generate=True,
duration=a.duration,
dt=a.dt,
target_directory=sim_dir)
lems_file0 = '%s/LEMS_c302_%s.xml' % (sim_dir, id)
lems_file = '%s/LEMS_c302.xml' % (sim_dir)
print_("Renaming %s -> %s" % (lems_file0, lems_file))
os.rename(lems_file0, lems_file)
announce("Generating NEURON files from: %s..." % lems_file)
pynml.run_lems_with_jneuroml_neuron(lems_file,
only_generate_scripts=True,
nogui=True,
load_saved_data=False,
verbose=True)
main_nrn_py = open('%s/LEMS_c302_nrn.py' % (sim_dir), 'r')
updated = ''
for line in main_nrn_py:
line = line.replace('GenericCell.hoc', '%s/GenericCell.hoc' % sim_dir)
line = line.replace('GenericNeuronCell.hoc',
'%s/GenericNeuronCell.hoc' % sim_dir)
line = line.replace('GenericMuscleCell.hoc',
'%s/GenericMuscleCell.hoc' % sim_dir)
line = line.replace("open('time.dat", "open('%s/time.dat" % sim_dir)
line = line.replace("open('c302_", "open('%s/c302_" % sim_dir)
updated += line
main_nrn_py.close()
main_nrn_py = open('%s/LEMS_c302_nrn.py' % (sim_dir), 'w')
main_nrn_py.write(updated)
main_nrn_py.close()
run_dir = '.'
command = 'nrnivmodl %s' % sim_dir
announce("Compiling NMODL files for NEURON...")
pynml.execute_command_in_dir(command, run_dir, prefix="nrnivmodl: ")
command = './Release/Sibernetic -c302 -f worm -no_g -l_to lpath=%s timelimit=%s timestep=%s' % (
sim_dir, a.duration / 1000.0, a.dt / 1000)
env = {"PYTHONPATH": "./src:./%s" % sim_dir}
sim_start = time.time()
announce(
"Executing main Sibernetic simulation of %sms using: \n\n %s \n\n in %s with %s"
% (a.duration, command, run_dir, env))
#pynml.execute_command_in_dir('env', run_dir, prefix="Sibernetic: ",env=env,verbose=True)
pynml.execute_command_in_dir(command,
run_dir,
prefix="Sibernetic: ",
env=env,
verbose=True)
sim_end = time.time()
reportj = {}
reportj['duration'] = '%s ms' % a.duration
reportj['dt'] = '%s ms' % a.dt
reportj['sim_ref'] = sim_ref
reportj['reference'] = a.reference
reportj['c302params'] = a.c302params
reportj['generation_time'] = '%s s' % (sim_start - gen_start)
reportj['run_time'] = '%s s' % (sim_end - sim_start)
reportj['command'] = '%s' % (command)
report_file = open("%s/report.json" % sim_dir, 'w')
report_file.write(pp.pformat(reportj))
report_file.close()
announce("Generating images for neuronal activity...")
results = pynml.reload_saved_data(lems_file,
plot=False,
show_plot_already=False,
simulator=None,
verbose=True)
c302_utils.plot_c302_results(results,
config=a.reference,
parameter_set=a.c302params,
directory=sim_dir,
save=True,
show_plot_already=False)
pos_file_name = os.path.abspath('%s/position_buffer.txt' % sim_dir)
announce("Plotting positions of worm body particles in %s..." %
pos_file_name)
from plot_positions import plot_positions
if not os.path.isfile(pos_file_name):
time.sleep(2)
plot_positions(pos_file_name, rate_to_plot=int(a.duration / 5))
announce("Finished in %s sec!\n\nSimulation saved in: %s\n\n"%((sim_end-sim_start),sim_dir) + \
"Report of simulation at: %s/report.json\n\n"%(sim_dir)+ \
"Rerun simulation with: ./Release/Sibernetic -l_from lpath=%s\n"%(sim_dir))
def run(a=None,**kwargs):
try:
import neuroml
import pyneuroml
import xlrd
except Exception as e:
print_("Cannot import one of the required packages. Please install!\n"
"Exception: %s\n"%e)
try:
if os.environ.has_key('C302_HOME'):
os.environ['C302_HOME']
sys.path.append(os.environ['C302_HOME'])
print_('Python path now: %s'%sys.path)
import c302
import c302_utils
except Exception as e:
print_("Cannot import c302!\n"
"Exception: %s\n"%e
+"Please set environment variable C302_HOME to point to the directory: CElegansNeuroML/CElegans/pythonScripts/c302!\n")
exit()
a = build_namespace(a,**kwargs)
gen_start = time.time()
ref = a.reference
if not os.path.isdir('simulations'):
os.mkdir('simulations')
sim_ref = "%s_%s_%s"%(a.c302params,ref, time.ctime().replace(' ','_' ).replace(':','.' ))
sim_dir = "simulations/%s"%(sim_ref)
os.mkdir(sim_dir)
#exec('from %s import ParameterisedModel'%a.c302params)
#params = ParameterisedModel()
id = '%s_%s'%(a.c302params,ref)
exec('from c302_%s import setup'%ref)
setup(a.c302params,
generate=True,
duration = a.duration,
dt = a.dt,
target_directory=sim_dir)
lems_file0 = '%s/LEMS_c302_%s.xml'%(sim_dir,id)
lems_file = '%s/LEMS_c302.xml'%(sim_dir)
print_("Renaming %s -> %s"%(lems_file0,lems_file))
os.rename(lems_file0,lems_file)
announce("Generating NEURON files from: %s..."%lems_file)
pynml.run_lems_with_jneuroml_neuron(lems_file,
only_generate_scripts=True,
nogui=True,
load_saved_data=False,
verbose=True)
main_nrn_py = open('%s/LEMS_c302_nrn.py'%(sim_dir),'r')
updated =''
for line in main_nrn_py:
line = line.replace('GenericCell.hoc','%s/GenericCell.hoc'%sim_dir)
line = line.replace('GenericNeuronCell.hoc','%s/GenericNeuronCell.hoc'%sim_dir)
line = line.replace('GenericMuscleCell.hoc','%s/GenericMuscleCell.hoc'%sim_dir)
line = line.replace("open('time.dat","open('%s/time.dat"%sim_dir)
line = line.replace("open('c302_","open('%s/c302_"%sim_dir)
updated += line
main_nrn_py.close()
main_nrn_py = open('%s/LEMS_c302_nrn.py'%(sim_dir),'w')
main_nrn_py.write(updated)
main_nrn_py.close()
run_dir = '.'
command = 'nrnivmodl %s'%sim_dir
announce("Compiling NMODL files for NEURON...")
pynml.execute_command_in_dir(command, run_dir, prefix="nrnivmodl: ")
command = './Release/Sibernetic -c302 -f worm -no_g -l_to lpath=%s timelimit=%s timestep=%s'%(sim_dir,a.duration/1000.0,a.dt/1000)
env={"PYTHONPATH":"./src:./%s"%sim_dir}
sim_start = time.time()
announce("Executing main Sibernetic simulation of %sms using: \n\n %s \n\n in %s with %s"%(a.duration, command, run_dir, env))
#pynml.execute_command_in_dir('env', run_dir, prefix="Sibernetic: ",env=env,verbose=True)
pynml.execute_command_in_dir(command, run_dir, prefix="Sibernetic: ",env=env,verbose=True)
sim_end = time.time()
reportj = {}
reportj['duration'] = '%s ms'%a.duration
reportj['dt'] = '%s ms'%a.dt
reportj['sim_ref'] = sim_ref
reportj['reference'] = a.reference
reportj['c302params'] = a.c302params
reportj['generation_time'] = '%s s'%(sim_start-gen_start)
reportj['run_time'] = '%s s'%(sim_end-sim_start)
reportj['command'] = '%s'%(command)
report_file = open("%s/report.json"%sim_dir,'w')
report_file.write(pp.pformat(reportj))
report_file.close()
announce("Generating images for neuronal activity...")
results = pynml.reload_saved_data(lems_file,
plot=False,
show_plot_already=False,
simulator=None,
verbose=True)
c302_utils.plot_c302_results(results,
config=a.reference,
parameter_set=a.c302params,
directory=sim_dir,
save=True,
show_plot_already=False)
pos_file_name = os.path.abspath('%s/position_buffer.txt'%sim_dir)
announce("Plotting positions of worm body particles in %s..."%pos_file_name)
from plot_positions import plot_positions
if not os.path.isfile(pos_file_name):
time.sleep(2)
plot_positions(pos_file_name,rate_to_plot = int(a.duration/5))
announce("Finished in %s sec!\n\nSimulation saved in: %s\n\n"%((sim_end-sim_start),sim_dir) + \
"Report of simulation at: %s/report.json\n\n"%(sim_dir)+ \
"Rerun simulation with: ./Release/Sibernetic -l_from lpath=%s\n"%(sim_dir))
def run_c302(config,
parameter_set,
prefix,
duration,
dt,
simulator,
save=False,
show_plot_already=True,
data_reader="SpreadsheetDataReader",
verbose=False,
plot_ca=True,
plot_connectivity=False,
param_overrides={},
config_param_overrides={},
config_package="",
target_directory='examples',
save_fig_to=None):
if save_fig_to:
global save_fig_dir
save_fig_dir = save_fig_to
print("********************\n\n Going to generate c302_%s_%s and run for %sms (dt: %sms) on %s\n\n********************"%(parameter_set,config,duration, dt, simulator))
if config_package:
exec ('from %s.c302_%s import setup' % (config_package, config), globals())
else:
exec ('from c302_%s import setup' % config, globals())
try:
os.makedirs(target_directory)
except OSError as e:
if e.errno != errno.EEXIST:
raise
cells, cells_to_stimulate, params, muscles, nml_doc = setup(parameter_set,
data_reader=data_reader,
generate=True,
duration = duration,
dt = dt,
target_directory=target_directory,
verbose=verbose,
param_overrides=param_overrides,
config_param_overrides=config_param_overrides)
orig_dir = os.getcwd()
os.chdir(target_directory)
try:
os.makedirs(save_fig_dir)
except OSError as e:
if e.errno != errno.EEXIST:
raise
lems_file = 'LEMS_c302_%s_%s.xml'%(parameter_set,config)
if simulator == 'jNeuroML':
results = pynml.run_lems_with_jneuroml(lems_file, nogui=True, load_saved_data=True, verbose=verbose)
elif simulator == 'jNeuroML_NEURON':
results = pynml.run_lems_with_jneuroml_neuron(lems_file, nogui=True, load_saved_data=True, verbose=verbose)
c302.print_("Finished simulation of %s and have reloaded results"%lems_file)
c302_utils.plot_c302_results(results,
config,
parameter_set,
directory=save_image_full_dir,
save=save,
show_plot_already=show_plot_already,
data_reader=data_reader,
plot_ca=plot_ca)
if plot_connectivity:
c302_utils.generate_conn_matrix(nml_doc, save_fig_dir=save_image_full_dir)
os.chdir(orig_dir)
return cells, cells_to_stimulate, params, muscles
def run_optimisation(prefix,
config,
level,
parameters,
max_constraints,
min_constraints,
weights,
target_data,
sim_time = 500,
dt = 0.05,
analysis_start_time = 0,
population_size = 20,
max_evaluations = 20,
num_selected = 10,
num_offspring = 20,
mutation_rate = 0.9,
num_elites = 1,
seed = 12345,
simulator = 'jNeuroML',
nogui = False,
num_local_procesors_to_use = 4):
print("Running optimisation...")
print("parameters: %s"%parameters)
print("max_constraints: %s"%max_constraints)
print("min_constraints: %s"%min_constraints)
print("simulator: %s"%simulator)
ref = prefix+config
run_dir = "NT_%s_%s"%(ref, time.ctime().replace(' ','_' ).replace(':','.' ))
os.mkdir(run_dir)
my_controller = C302Controller(ref,
level,
config,
sim_time,
dt,
simulator = simulator,
generate_dir=run_dir,
num_local_procesors_to_use = num_local_procesors_to_use)
peak_threshold = -31 if level.startswith('A') or level.startswith('B') else (-20)
analysis_var = {'peak_delta': 0,
'baseline': 0,
'dvdt_threshold': 0,
'peak_threshold': peak_threshold}
data = ref+'.dat'
sim_var = OrderedDict()
for i in range(len(parameters)):
sim_var[parameters[i]] = max_constraints[i]/2 + min_constraints[i]/2
#make an evaluator, using automatic target evaluation:
my_evaluator = c302Evaluators.EnhancedNetworkEvaluator(controller=my_controller,
analysis_start_time=analysis_start_time,
analysis_end_time=sim_time,
parameters=parameters,
analysis_var=analysis_var,
weights=weights,
targets=target_data)
#make an optimizer
my_optimizer = optimizers.CustomOptimizerA(max_constraints,
min_constraints,
my_evaluator,
population_size=population_size,
max_evaluations=max_evaluations,
num_selected=num_selected,
num_offspring=num_offspring,
num_elites=num_elites,
mutation_rate=mutation_rate,
seeds=None,
verbose=False)
start = time.time()
#run the optimizer
best_candidate, fitness = my_optimizer.optimize(do_plot=False,
seed=seed,
summary_dir = run_dir)
secs = time.time()-start
reportj = {}
info = "Ran %s evaluations (pop: %s) in %f seconds (%f mins total; %fs per eval)\n\n"%(max_evaluations, population_size, secs, secs/60.0, (secs/max_evaluations))
report = "----------------------------------------------------\n\n"+ info
reportj['comment'] = info
reportj['time'] = secs
for key,value in zip(parameters,best_candidate):
sim_var[key]=value
best_candidate_t, best_candidate_v = my_controller.run_individual(sim_var,show=False)
best_candidate_results = my_controller.last_results
best_candidate_analysis = c302Analysis.Data_Analyser(best_candidate_v,
best_candidate_t,
analysis_var,
start_analysis=analysis_start_time,
end_analysis=sim_time)
best_cand_analysis_full = best_candidate_analysis.analyse()
best_cand_analysis = best_candidate_analysis.analyse(target_data)
report+="---------- Best candidate ------------------------------------------\n"
report+=pp.pformat(best_candidate)+"\n\n"
report+=pp.pformat(best_cand_analysis_full)+"\n"
report+=pp.pformat(best_cand_analysis)+"\n\n"
report+="FITNESS: %f\n\n"%fitness
print(report)
reportj['fitness']=fitness
reportj['fittest vars']=dict(sim_var)
reportj['best_cand_details']=best_candidate
reportj['best_cand_analysis_full']=best_cand_analysis_full
reportj['best_cand_analysis']=best_cand_analysis
reportj['parameters']=parameters
reportj['analysis_var']=analysis_var
reportj['target_data']=target_data
reportj['weights']=weights
reportj['analysis_start_time']=analysis_start_time
reportj['population_size']=population_size
reportj['max_evaluations']=max_evaluations
reportj['num_selected']=num_selected
reportj['num_offspring']=num_offspring
reportj['mutation_rate']=mutation_rate
reportj['num_elites']=num_elites
reportj['seed']=seed
reportj['simulator']=simulator
reportj['sim_time']=sim_time
reportj['dt']=dt
reportj['run_directory'] = run_dir
reportj['reference'] = ref
report_file = open("%s/report.json"%run_dir,'w')
report_file.write(pp.pformat(reportj))
report_file.close()
plot_file = open("%s/plotgens.py"%run_dir,'w')
plot_file.write('from neurotune.utils import plot_generation_evolution\nimport os\n')
plot_file.write('\n')
plot_file.write('parameters = %s\n'%parameters)
plot_file.write('\n')
plot_file.write("curr_dir = os.path.dirname(__file__) if len(os.path.dirname(__file__))>0 else '.'\n")
plot_file.write("plot_generation_evolution(parameters, individuals_file_name = '%s/ga_individuals.csv'%curr_dir)\n")
plot_file.close()
if not nogui:
added =[]
for wref in weights.keys():
ref = wref.split(':')[0]
if not ref in added and not "phase_offset" in ref:
added.append(ref)
best_candidate_plot = plt.plot(best_candidate_t,best_candidate_v[ref], label="%s - %i evaluations"%(ref,max_evaluations))
plt.legend()
plt.ylim(-80.0,80.0)
plt.xlim(0.0,sim_time)
plt.title("Models")
plt.xlabel("Time (ms)")
plt.ylabel("Membrane potential(mV)")
utils.plot_generation_evolution(sim_var.keys(), individuals_file_name = '%s/ga_individuals.csv'%run_dir)
c302_utils.plot_c302_results(best_candidate_results, config, level, directory=run_dir,save=True)