def runSORN(c, src):
# Initialize experiment (source and statictics)
(source, stats_single, _) = experiment.start(src)
# Initialize SORN network, has simulation() and step() function
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all statistics
# that should be tracked (and later plotted)
stats = StatsCollection(sorn)
stats.methods = stats_single
sorn.stats = stats
# Datalog is used to store all results and parameters
#stats.dlog.set_handler('*',utils.StoreToH5,
# utils.logfilename("result.h5"))
#stats.dlog.append('c', utils.unbunchify(c))
#stats.dlog.set_handler('*',utils.TextPrinter)
# Final experimental preparations
experiment.reset(sorn)
# Start stats
sorn.stats.start()
sorn.stats.clear()
# Run experiment once
pickle_objects = experiment.run(sorn)
# Save sources etc
for key in pickle_objects:
filename = os.path.join(c.logfilepath, "%s.pickle" % key)
topickle = pickle_objects[key]
pickle.dump(topickle, gzip.open(filename, "wb"),
pickle.HIGHEST_PROTOCOL)
# Control: Firing-rate model: Substitute spikes by drawing random spikes
# according to firing rate for each inputindex
if sorn.c.stats.control_rates:
experiment.control_rates(sorn)
# Report stats and close
stats.single_report()
stats.disable = True
#stats.dlog.close()
sorn.quicksave(filename=os.path.join(c.logfilepath, 'net.pickle'))
# 1. load param file
try:
exp_dir = import_module(sys.argv[1])
except:
raise ValueError('Please specify a valid experiment name as first argument.')
params = exp_dir.param
# 2. add experiment specific parameters
params.get_par()
params.get_aux()
params.aux.display = True
try:
tag = sys.argv[2]
except:
raise ValueError('Please specify a valid experiment tag.')
params.aux.experiment_tag = '_{}'.format(tag)
# 3. initialize experiment, sorn, and stats objects
# the experiment class keeps a copy of the initial sorn main parameters
experiment = exp_dir.experiment.Experiment(params)
experiment.files_tosave = ['params', 'stats', 'scripts']
sorn = Sorn(params, experiment.inputsource)
stats = Stats(experiment.stats_cache, params)
# 4. run one experiment once
experiment.run(sorn, stats)
# 5. save initial sorn parameters and stats objects
backup_pickle(experiment, stats)
elif c.cluster.vary_param == 'source.control':
pass
else:
experiment_param = c.cluster.vary_param
# Prepare experiment
experiment = getattr(experiment_module,
experiment_name)(param,
cluster_param=experiment_param)
# Save current parameter for logging
c.cluster.current_param = current_param
# Start experiment
(source, _, stats_cluster) = experiment.start()
sorn = Sorn(c, source)
# Create a StatsCollection and fill it with methods for all
# statistics that should be tracked (and later plotted)
stats = StatsCollection(sorn, dlog)
stats.methods = stats_cluster
sorn.stats = stats
# Run experiments
experiment.reset(sorn)
sorn.stats.start()
dlog.progress("Start inner loop")
for n in range(c.N_iterations):
experiment.reset(sorn)
stats.clear()
