def quicksave(self, filename=None):
"""
Saves this sorn object
:param filename: str, Default: "net.pickle"
"""
if filename == None:
filename = utils.logfilename("sorn.pickle")
print("\nThe RM-SORN network are saved to:", filename)
pickle.dump(self, gzip.open(filename, "wb"), pickle.HIGHEST_PROTOCOL)
def quicksave(self, filename=None):
"""
Saves this object
Parameters:
filename: string
Filename to save in. Default: "net.pickle"
"""
if filename == None:
filename = utils.logfilename("net.pickle")
temp = self.stats
self.stats = 0 # pickle cannot deal with running stats
pickle.dump(self, gzip.open(filename, "wb"), pickle.HIGHEST_PROTOCOL)
self.stats = temp
def quicksave(self, filename=None):
"""
Saves this object
Parameters:
filename: string
Filename to save in. Default: "net.pickle"
"""
if filename == None:
filename = utils.logfilename("net.pickle")
temp = self.stats
self.stats = 0 # pickle cannot deal with running stats
pickle.dump( self, gzip.open( filename, "wb" ), \
pickle.HIGHEST_PROTOCOL )
self.stats = temp
import ipdb
ipdb.set_trace()
# ~ np.seterrcall(debugger)
# ~ np.seterr(all='call')
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
experiment = getattr(experiment_module, experiment_name)(param)
c = param.c
c.logfilepath = utils.logfilename("") + "/"
(source, stats_single, _) = 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)
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)
print 'In debugger!'
#~ import ipdb
#~ ipdb.set_trace()
np.seterrcall(debugger)
np.seterr(all='call')
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
c = param.c
if rank == 0:
logfilepath = utils.logfilename('') + '/'
else:
logfilepath = None
c.logfilepath = comm.bcast(logfilepath, root=0)
c.display = False # Keep logfile small.
dlog = utils.DataLog()
dlog.progress("Code Backed up")
# Create one logger for all simulations
dlog.progress("NUMBER_OF_SIMS = %d" % c.cluster.NUMBER_OF_SIMS)
result_path = c.logfilepath + 'result.h5'
dlog.set_handler("*", utils.StoreToH5, result_path)
dlog.append('c', utils.unbunchify(c))
# Loop over all simulations on this core
# Start debugging mode when an error is raised
def debugger(type,flag):
print 'In debugger!'
#~ import ipdb
#~ ipdb.set_trace()
np.seterrcall(debugger)
np.seterr(all='call')
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
c = param.c
if rank==0:
logfilepath = utils.logfilename('')+'/'
else:
logfilepath = None
c.logfilepath = comm.bcast(logfilepath, root=0)
c.display = False # Keep logfile small.
dlog = utils.DataLog()
dlog.progress("Code Backed up")
# Create one logger for all simulations
dlog.progress("NUMBER_OF_SIMS = %d"%c.cluster.NUMBER_OF_SIMS)
result_path = c.logfilepath+'result.h5'
dlog.set_handler("*", utils.StoreToH5, result_path)
dlog.append('c', utils.unbunchify(c))
# Loop over all simulations on this core
print 'In debugger! (test_single.py)'
import ipdb
ipdb.set_trace()
#~ np.seterrcall(debugger)
#~ np.seterr(all='call')
# Parameters are read from the second command line argument
param = import_module(utils.param_file())
experiment_module = import_module(param.c.experiment.module)
experiment_name = param.c.experiment.name
experiment = getattr(experiment_module, experiment_name)(param)
c = param.c
c.logfilepath = utils.logfilename('') + '/'
(source, stats_single, _) = 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)
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)