def run(self):
if self.parser.options is None:
self.parser.parse_args()
o = self.parser.options
for noise_sigma in self.parser.noise_sigmas:
p = self._dp.copy()
p['noise_sigma'] = noise_sigma # pA
p['time'] = 10e3 if o.time is None else o.time # ms
p['nthreads'] = 1
p['ntrials'] = o.ntrials
p['verbosity'] = o.verbosity
p.update(self._up)
# Submitting
ac = ArgumentCreator(p, printout=True)
simLabel = '{0}pA'.format(int(p['noise_sigma']))
numRepeat = 1
submitter = SubmitterFactory.getSubmitter(ac,
self._app_name,
envType=o.env,
rtLimit=o.rtLimit,
output_dir=o.where,
label=simLabel,
blocking=True,
timePrefix=False,
numCPU=o.nCPU)
ac.setOption('output_dir', submitter.outputDir())
startJobNum = 0
submitter.submitAll(startJobNum, numRepeat, dry_run=o.dry_run)
def submitNoiseSweep(p,
gEp,
gIp,
noisep,
ENV,
simRootDir,
simLabel,
appName,
rtLimit,
numCPU,
blocking,
timePrefix,
numRepeat,
dry_run,
extraIterparams=None):
'''
Submit a parameter sweep with an extra dimension: noise_sigma.
'''
if extraIterparams is None:
extraIterparams = {}
noise_sigma_arr = []
g_AMPA_total_arr = []
g_GABA_total_arr = []
for n in noisep.values():
for E_coupling in gEp.values():
for I_coupling in gIp.values():
noise_sigma_arr.append(n)
g_AMPA_total_arr.append(E_coupling)
g_GABA_total_arr.append(I_coupling)
iterparams = {
'noise_sigma': np.array(noise_sigma_arr),
'g_AMPA_total': np.array(g_AMPA_total_arr),
'g_GABA_total': np.array(g_GABA_total_arr),
#'noise_sigma' : [150],
#'g_AMPA_total' : [1020],
#'g_GABA_total' : [3060]
}
iterparams.update(extraIterparams)
ac = ArgumentCreator(p, printout=True)
ac.insertDict(iterparams, mult=False)
###############################################################################
submitter = SubmitterFactory.getSubmitter(ac,
appName,
envType=ENV,
rtLimit=rtLimit,
output_dir=simRootDir,
label=simLabel,
blocking=blocking,
timePrefix=timePrefix,
numCPU=numCPU)
ac.setOption('output_dir', submitter.outputDir())
startJobNum = 0
submitter.submitAll(startJobNum, numRepeat, dry_run=dry_run)
submitter.saveIterParams(iterparams, None, None, dry_run=dry_run)
def run(self):
'''Run the parameter sweep.'''
if self.parser.options is None:
self.parser.parse_args()
o = self.parser.options
for noise_sigma in self.parser.noise_sigmas:
p = self._dp.copy()
p['noise_sigma'] = noise_sigma # pA
p['time'] = 10e3 if o.time is None else o.time # ms
p['nthreads'] = 1
p['ntrials'] = o.ntrials
p['verbosity'] = o.verbosity
p.update(self._up)
# Submitting
iterparams = self.parser.iter_params
if self._slope_selector is not None:
iterparams.update({
'bumpCurrentSlope':
self._slope_selector.get_slopes(noise_sigma)
})
ac = ArgumentCreator(p, printout=True)
ac.insertDict(iterparams, mult=False)
###################################################################
simLabel = '{0}pA'.format(int(p['noise_sigma']))
numRepeat = 1
submitter = SubmitterFactory.getSubmitter(ac,
self._app_name,
envType=o.env,
rtLimit=o.rtLimit,
output_dir=o.where,
label=simLabel,
blocking=True,
timePrefix=False,
numCPU=o.nCPU)
ac.setOption('output_dir', submitter.outputDir())
startJobNum = 0
submitter.submitAll(startJobNum,
numRepeat,
dry_run=o.dry_run,
filter=self.parser.flat_filter)
submitter.saveIterParams(iterparams,
self.parser.options.param_list,
self.parser.dimensions,
dry_run=o.dry_run)
p['master_seed'] = 123456
p['time'] = 0.1 # unused
p['nthreads'] = 1
p['ntrials'] = 1
p['verbosity'] = o.verbosity
p['probabilistic_synapses'] = o.probabilistic_synapses
# Range of E/I synaptic conductances
Nvals = 31 # Number of values (these will be equivalent to go through the
# diagonal of the parameter space
startG = 0.0 # nS
endG = 6120.0 # nS
###############################################################################
ac = ArgumentCreator(p, printout=True)
GArr = np.linspace(startG, endG, Nvals)
g_AMPA_total_arr = []
g_GABA_total_arr = []
for coupling in GArr:
g_AMPA_total_arr.append(coupling)
g_GABA_total_arr.append(coupling)
iterparams = {
'g_AMPA_total': np.array(g_AMPA_total_arr),
'g_GABA_total': np.array(g_GABA_total_arr),
}
ac.insertDict(iterparams, mult=False)
###############################################################################
def submitParamSweep(p,
startG,
endG,
Nvals,
ENV,
simRootDir,
simLabel,
appName,
rtLimit,
numCPU,
blocking,
timePrefix,
numRepeat,
dry_run,
extraIterparams=None,
rc=None,
**kwargs):
'''Submit and save metadata for the gE vs gI parameter sweep.'''
printout = kwargs.pop('printout', True)
if extraIterparams is None:
extraIterparams = {}
ac = ArgumentCreator(p, printout=printout)
GArr = np.linspace(startG, endG, Nvals)
print(GArr)
g_AMPA_total_arr = []
g_GABA_total_arr = []
for E_coupling in GArr:
for I_coupling in GArr:
g_AMPA_total_arr.append(E_coupling)
g_GABA_total_arr.append(I_coupling)
iterparams = {
'g_AMPA_total': np.array(g_AMPA_total_arr),
'g_GABA_total': np.array(g_GABA_total_arr),
}
dimension_labels = ['g_AMPA_total', 'g_GABA_total']
dimensions = [Nvals, Nvals]
iterparams.update(extraIterparams)
ac.insertDict(iterparams, mult=False)
###############################################################################
submitter = SubmitterFactory.getSubmitter(ac,
appName,
envType=ENV,
rtLimit=rtLimit,
output_dir=simRootDir,
label=simLabel,
blocking=blocking,
timePrefix=timePrefix,
numCPU=numCPU,
**kwargs)
ac.setOption('output_dir', submitter.outputDir())
startJobNum = 0
filter = rc[0] * len(GArr) + rc[1] if rc is not None else None
submitter.submitAll(startJobNum, numRepeat, dry_run=dry_run, filter=filter)
submitter.saveIterParams(iterparams,
dimension_labels,
dimensions,
dry_run=dry_run)
p['ntrials'] = o.ntrials
p['verbosity'] = o.verbosity
p['gammaNSample'] = 0
# Range of E/I synaptic conductances and pAMPA_sigma
Nvals_G = 61 # Number of values for g_AMPA_total
startG = 0.0 # nS
endG = 3060.0 # nS
Nvals_sigma = 11 # Number of values for pAMPA_sigma
startSigma = .5 / 6
endSigma = .175
###############################################################################
ac = ArgumentCreator(p, printout=o.printout)
GArr = np.linspace(startG, endG, Nvals_G)
sigmaArr = np.linspace(startSigma, endSigma, Nvals_sigma)
print(GArr)
print(sigmaArr)
g_AMPA_total_arr = []
pAMPA_sigma_arr = []
for E_coupling in GArr:
for pAMPA_sigma in sigmaArr:
g_AMPA_total_arr.append(E_coupling)
pAMPA_sigma_arr.append(pAMPA_sigma)
iterparams = {
'g_AMPA_total': np.array(g_AMPA_total_arr),
