def prun(tstop, restore=False):
pc.set_maxstep(10 * ms)
h.finitialize(-65 * mV)
if restore == "SaveState":
ns = h.SaveState()
sf = h.File("state%d.bin" % pc.id())
ns.fread(sf)
ns.restore(0) # event queue restored
sf.close()
elif restore == "BBSaveState":
cp_out_to_in() # prepare for restore.
bbss = h.BBSaveState()
bbss.restore_test()
else:
pc.psolve(tstop / 2)
# SaveState save
ss = h.SaveState()
ss.save()
sf = h.File("state%d.bin" % pc.id())
ss.fwrite(sf)
sf.close()
# BBSaveState Save
cnt = h.List("PythonObject").count()
for i in range(1):
bbss = h.BBSaveState()
bbss.save_test()
bbss = None
assert h.List("PythonObject").count() == cnt
pc.psolve(tstop)
def restore_state(self, state_file='state.bin', keep_events=False):
from neuron import h
ns = h.SaveState()
sf = h.File(os.path.join(self.get_permanent_model_directory(), state_file))
ns.fread(sf)
h.stdinit()
if keep_events:
ns.restore(1)
# Workaround - without the fixed step cycle, NEURON crashes with same error as in:
# https://www.neuron.yale.edu/phpBB/viewtopic.php?f=2&t=3845&p=16542#p16542
# Only happens when there is a vector.play added after a state restore
# Running one cycle using the fixed integration method addresses the problem
h.cvode_active(0)
prev_dt = h.dt
h.dt = 0.000001
h.steprun()
h.dt = prev_dt
# END Workaround
else:
ns.restore()
h.cvode_active(self.config.cvode_active)
def savehocstate(filestr):
'''saves the current hoc states (not rxd) of the simulation into file with name filestr.dat
filestr: a string of file path (without file extension)'''
myss = h.SaveState()
#h.initnrn() # to set h.t to zero
myss.save()
myfile = h.File('./data/' + filestr + '_hoc_.dat')
myss.fwrite(myfile)
myfile.close()
def read_state(conf):
state_dir = conf["output"]["state_dir"]
state = h.SaveState()
f = h.File('{}/state_rank-{}'.format(state_dir, int(pc.id())))
# f = h.File(state_dir+'/state_rank-%d' % (int(pc.id())))
state.fread(f, 0)
state.restore()
rlist = h.List('Random')
for r_tmp in rlist:
r_tmp.seq(f.scanvar())
f.close()
def save_state(conf):
state = h.SaveState()
state.save()
state_dir = conf["output"]["state_dir"]
f = h.File('{}/state_rank-{}'.format(state_dir, int(pc.id())))
# f = h.File(state_dir + '/state_rank-%d' % (int(pc.id())))
state.fwrite(f, 0)
rlist = h.List('Random')
for r_tmp in rlist:
f.printf('%g\n', r_tmp.seq())
f.close()
def run_to_steady_state(self, tstop):
self._update_current_sources(tstop)
self._pre_run()
self.parallel_context.set_maxstep(self.default_maxstep)
self.tstop = tstop
#logger.info("Running the simulation until steady state: %g ms" % tstop)
if self.tstop > self.t:
self.parallel_context.psolve(self.tstop)
# Make object to save the model state
svstate = h.SaveState()
# Save the model state and write it to file
svstate.save()
f = h.File("steady_state.bin")
svstate.fwrite(f)
def run_from_steady_state(self, tstop):
self._update_current_sources(tstop)
self._pre_run()
self.parallel_context.set_maxstep(self.default_maxstep)
self.tstop = tstop
h.stdinit()
ns = h.SaveState()
sf = h.File('steady_state.bin')
ns.fread(sf)
#print("Time before restore = %g ms" % h.t)
ns.restore(0)
h.cvode_active(0)
#print("Time after restore = %g ms" % h.t)
#logger.info("Running the simulation until %g ms" % tstop)
if self.tstop > self.t:
self.parallel_context.psolve(self.tstop)
def loadhocstate(filestr):
'''loads hoc states (not rxd states) of simulation from file filestr.dat
filestr: a string of file path (without file extension).
For loading to happen appropriately, this statement has to be called after any initialization (eg h.finitalize)'''
global tstart, tstop
myfile = h.File()
myfile.ropen('./data/' + filestr + '_hoc_.dat')
myss = h.SaveState()
if myfile.isopen():
myss.fread(myfile)
myfile.close()
myss.restore(1)
print 'loaded hoc states from:', myfile.getname()
tstart = h.t
tstop = tstart + simdur # stopping time of simulation
h.tstop = tstop
#for ns in netStimlist: ns.start = nstimStart + tstart
else:
print "file cannot be open to read"
def restoreSS():
svst = h.SaveState()
f = h.File(os.path.join(restoredir, 'save_test_'+str(pcid) + '.dat'))
svst.fread(f)
svst.restore()
def runSS():
svst = h.SaveState()
svst.save()
f = h.File(os.path.join(outdir,'save_test_' + str(pcid) + '.dat'))
svst.fwrite(f)
def restorestate():
s = 'svst.{pcid}'.format(pcid=int(pc.id()))
f = h.File(s)
ss = h.SaveState()
ss.fread(f)
ss.restore()
def savestate():
s = 'svst.{pcid}'.format(pcid=int(pc.id()))
f = h.File(s)
ss = h.SaveState()
ss.save()
ss.fwrite(f)
pnm.nc_append(i, (i + 1) % ncell, -1, 1.1, 2)
# stimulate
if pnm.gid_exists(4):
stim = h.NetStim(.5)
ncstim = h.NetCon(stim, pnm.pc.gid2obj(4))
ncstim.weight[0] = 1.1
ncstim.delay = 1
stim.number = 1
stim.start = 1
pnm.set_maxstep(100) # will end up being 2
pnm.want_all_spikes()
ss = h.SaveState()
def savestate():
s = 'svst.{pcid}'.format(pcid=int(pc.id()))
f = h.File(s)
ss = h.SaveState()
ss.save()
ss.fwrite(f)
def restorestate():
s = 'svst.{pcid}'.format(pcid=int(pc.id()))
f = h.File(s)
ss = h.SaveState()
ss.fread(f)
def flowJac(noiseTimes):
cvodewrap.states(x)
t0 = h.t
print 't0'
print t0
ss = [None] * (len(noiseTimes) + 1)
k = 0
ss[0] = h.SaveState()
ss[0].save()
cvodewrap.re_init()
while k < len(noiseTimes):
print noiseTimes[k]
h.continuerun(noiseTimes[k])
ss[k + 1] = h.SaveState()
ss[k + 1].save()
cvodewrap.re_init()
k += 1
cvodewrap.states(value)
# Derivative with respect to state
DFx = pylab.zeros((len(value), len(x)))
k = 0
while k < len(x):
ss[0].restore(1)
cvodewrap.re_init()
cvodewrap.states(x)
temp = x[k]
if abs(temp) > 1:
dx = sqrtEps * abs(temp)
else:
dx = sqrtEps
x.x[k] = temp + dx
cvodewrap.yscatter(x)
cvodewrap.re_init()
dx = x[k] - temp # trick to reduce finite precision error
h.continuerun(noiseTimes[len(noiseTimes) - 1])
cvodewrap.states(df)
df.sub(value)
df.div(dx)
DFx[:, k] = numpy.array(df)
k += 1
# Derivative with respect to noise
DFn = pylab.zeros((len(value), len(noiseTimes)))
noiseTimes.insert(0, t0)
k = 0
while k < len(noiseTimes) - 1:
ss[k + 1].restore(1)
cvodewrap.re_init()
if k < len(noiseTimes) - 2:
dx = sqrtEps
cvodewrap.states(x)
x.x[0] += sigp * math.sqrt(noiseTimes[k + 1] - noiseTimes[k]) * dx
cvodewrap.yscatter(x)
cvodewrap.re_init()
h.continuerun(noiseTimes[len(noiseTimes) - 1])
cvodewrap.states(df)
df.sub(value)
df.div(dx)
DFn[:, k] = numpy.array(df)
else:
DFn[:, k] = numpy.array(
[sigp * math.sqrt(noiseTimes[k + 1] - noiseTimes[k]), 0, 0, 0])
k += 1
return [numpy.matrix(value).T, numpy.matrix(DFx), numpy.matrix(DFn)]
def save_state(self, state_file='state.bin'):
from neuron import h
ns = h.SaveState()
sf = h.File(os.path.join(self.get_permanent_model_directory(), state_file))
ns.save()
ns.fwrite(sf)
def __init__(self):
self.ss = h.SaveState()
self.ss.save()
self.t0 = h.t
