def task_runner(args):
pconf = projconf.ProjectConfiguration(sim_path = args["sim_path"],
config_file = projconf.norm_path(args["sim_path"],"full.cfg"))
item_list = parse_result_candidates(pconf.get_local_path("resultDensityFile"))
(fitness, candidates) = zip(*item_list)
if args["best_neurons"] is not None:
last = min(args["best_neurons"], len(candidates))
candidates = candidates[:last]
evals = evaluation.evaluate(pconf.get_logger("start_evaluation"), pconf, candidates, cleanup = args["cleanup"])
return {"mode" : pconf.get("mode", "Simulation"), "evals" : evals}
def __init__(self, config, temp):
self.config_file = projconf.norm_path(config)
self.temp = temp
self.pconf = projconf.ProjectConfiguration(config_file = self.config_file)
# Create and set sim_path.
if self.temp:
temp_path = tempfile.mkdtemp(prefix="neuron_sim-")
self.pconf.set_sim_path(temp_path)
else:
result_directory = self._mk_result_directory()
self.pconf.set_sim_path(result_directory)
# Copy configurations.
full_config = projconf.norm_path(self.pconf.sim_path, "full.cfg")
with open(full_config, "w") as sim_config_file:
self.pconf.cfg.write(sim_config_file)
shutil.copy(projconf.norm_path(projconf.DEFAULT_CONFIG), self.pconf.sim_path)
custom_config = projconf.norm_path(self.pconf.sim_path, "custom.cfg")
shutil.copyfile(self.config_file, custom_config)
self.pconf.set_config_file(custom_config)
# Populate sim folder
project_name = "Pyr_" + self.pconf.get("mode", "Simulation")
project_path = projconf.norm_path(self.pconf.get("resources"), project_name)
shutil.copytree(project_path, projconf.norm_path(self.pconf.sim_path, project_name))
def evaluate(logger, pconf, candidates, cleanup = False):
"""Returns a list containing the neuron itself and its evaluation results."""
#pconf.invoke_neurosim(logger, type = "current", candidates = candidates, prefix = EVAL_PREFIX)
try:
# Get parameters, ignoring the evaluator function.
parsed_kwargs = {"pconf" : pconf, "mode" : pconf.get("mode", "Simulation")}
evaluator_section = pconf.get("evaluator", "Simulation")
parsed_kwargs = callable.get_section_variables(pconf, logger, evaluator_section, parsed_kwargs)
return fitness.evaluate_channels(candidates, parsed_kwargs)
finally:
if cleanup:
dir = projconf.norm_path(pconf.get_sim_project_path(), "simulations")
if os.path.isdir(dir):
shutil.rmtree(dir)
def main():
parser = argparse.ArgumentParser(description="Plot a neuroConstruct neuron after simulation.")
parser.add_argument(metavar = "SIM_PATH", dest = "sim_path",
help = """Path to the simulation path containing the config files used and the neuroConstruct project.
This is also called simulation path and the result of calling "start_sim".""")
parser.add_argument("-s", "--simulation", required = True, action = "append",
help = """Just the name of the simulation as
found in the neuroConstruct's neuron simulation.
Example: Use "PySim_27" for "Pyr_RS/simulations/PySim_27" """)
parser.add_argument("-t", "--time", type = float, default = 1.0,
help = """The time in seconds that should be displayed.
Valid values depend on sampling size and are usually between 0 and 1.0.""")
parser.add_argument("-f", "--file", action = "store",
help = """Don't show image but save the plot in plots/FILE.
This name can have a correct file extension to specify the type (*.png or *.eps ..).""")
options = parser.parse_args()
configFile = projconf.norm_path(options.sim_path, "full.cfg")
pconf = projconf.ProjectConfiguration(configFile, options.sim_path)
if options.file is not None:
output_file = projconf.norm_path("plots", options.file)
plot(pconf, options.simulation, options.time, show_plot = False, output_file = output_file)
else:
plot(pconf, options.simulation, options.time, show_plot = True)
def plot(pconf, simulations, time = 1.0, show_plot = True, output_file = None):
dt = pconf.get_float("dt", "Simulation")
Fs = 1000 / dt
Ts = 1.0 / Fs; # sampling interval
duration = pconf.get_float("duration", "Simulation")
time = min(time, duration / 1000)
t = numpy.arange(0, time, Ts) # time vector
D = []
for simulation in simulations:
densityFile = projconf.norm_path(pconf.get_sim_project_path(),
"simulations", simulation, "CellGroup_1_0.dat")
with open(densityFile,'r') as fileDE:
density = numpy.zeros(len(t))
densities_list= fileDE.read().split('#\n')
densities = densities_list[0].split('\n')
for i in range(len(density)):
dens = densities[i].strip()
density[i] = float(dens)
fileDE.close()
y = density
D.append(y)
plt.plot(t,y)
xlabel('Zeit [s]')
ylabel('Membranpotenzial [mV]')
for z in range(len(simulations)):
subplot(len(simulations), 1, z + 1)
plt.plot(t, D[z])
if(z == len(simulations) // 2):
ylabel('Membranpotenzial [mV]')
xlabel('Zeit [s]')
if show_plot:
show()
if output_file is not None:
savefig(output_file)
def run(self, quiet = False):
"""Runs the neuron generation and simulation.
This may take some time, depending on the settings.
Returns 0 if no error is encountered.
"""
returncode = 0
# Initialize logging.
self.pconf.suppress_logging = quiet
logger_server = logserver.initFileLogServer(log_file = self.pconf.get_local_path("log_server_file", "Logging"),
port = self.pconf.get_int("log_server_port", "Logging"),
file_level = self.pconf.get_int("file_log_level", "Logging"),
logger_name = "")
logger_server.start()
logger = self.pconf.get_logger("simulation", logger_server.port)
try:
start_time = time.time()
logger.info(" =============================================")
logger.info(" =============================================")
logger.info(" ======= Starting new Simulation =======")
logger.info(" =============================================")
logger.info(" =============================================")
version = subprocess.check_output(["git", "describe", "--always"])
logger.info("Git project version: " + version.strip())
logger.info
logger.info("Configuration used:")
self.pconf.log_configuration(logger)
if self.config_file == projconf.norm_path(projconf.DEFAULT_CONFIG):
logger.warning("The specified configuration file is the default configuration."
+ " Please make any customization in a seperate file.")
if self.temp:
self._check_result_dir(logger)
self.pconf.write_project_data(logger_server.port)
# Start algorithm
algorithm = self.pconf.get("algorithm", "Simulation")
if algorithm == "annealing":
annealing.start(self.pconf)
elif algorithm == "genetic":
evolution.start(self.pconf)
elif algorithm == "gridsearch":
gridsearch.start(self.pconf)
else:
raise RuntimeError("Unknown algorithm selected: '" + algorithm + "'")
stop_time = time.time()
logger.info("Time passed: " + str(stop_time - start_time) + " seconds")
except (KeyboardInterrupt, SystemExit):
raise
except:
logger.exception("Exception encountered, aborting.")
returncode = 10
finally:
try:
if self.temp:
# Log messages after this are likely to be never saved.
self._move_to_output(logger)
except:
logger.exception("Failed to move simulation folder '"
+ self.pconf.sim_path + "'"
+ "to the ouput directory '"
+ projconf.norm_path(".", self.pconf.get("result_directory")))
returncode += 20
finally:
# Necessary to avoid hanging on open sockets
logger_server.stop()
return returncode
