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from neuron.tests.utils.strtobool import strtobool
import os
from neuron import h, gui
pc = h.ParallelContext()
def model():
pc.gid_clear()
for s in h.allsec():
h.delete_section(sec=s)
s = h.Section()
s.L = 10
s.diam = 10
s.insert("hh")
ic = h.IClamp(s(0.5))
ic.delay = 0.1
ic.dur = 0.1
ic.amp = 0.5 * 0
syn = h.ExpSyn(s(0.5))
nc = h.NetCon(None, syn)
nc.weight[0] = 0.001
return {"s": s, "ic": ic, "syn": syn, "nc": nc}
def test_psolve():
# sequence of psolve with only beginning initialization
m = model()
vvec = h.Vector()
h.tstop = 5
vvec.record(m["s"](0.5)._ref_v, sec=m["s"])
def run(tstop):
pc.set_maxstep(10)
h.finitialize(-65)
m["nc"].event(3.5)
m["nc"].event(2.6)
h.continuerun(1) # Classic NEURON so psolve starts at t>0
while h.t < tstop:
pc.psolve(h.t + 1)
run(h.tstop)
vvec_std = vvec.c() # standard result
from neuron import coreneuron
coreneuron.enable = True
coreneuron.verbose = 0
coreneuron.gpu = bool(strtobool(os.environ.get("CORENRN_ENABLE_GPU", "false")))
h.CVode().cache_efficient(True)
run(h.tstop)
if vvec_std.eq(vvec) == 0:
for i, x in enumerate(vvec_std):
print("%.3f %g %g %g" % (i * h.dt, x, vvec[i], x - vvec[i]))
assert vvec_std.eq(vvec)
assert vvec_std.size() == vvec.size()
coreneuron.enable = False
def test_NetStim_noise():
# Can use noiseFromRandom
use_noiseFromRandom123 = False
cells = {gid: (h.NetStim(), h.Random()) for gid in range(pc.id(), 5, pc.nhost())}
for gid, cell in cells.items():
pc.set_gid2node(gid, pc.id())
pc.cell(gid, h.NetCon(cell[0], None))
cell[1].Random123(gid, 2, 3)
cell[1].negexp(1)
if use_noiseFromRandom123:
cell[0].noiseFromRandom123(gid, 2, 3)
else:
cell[0].noiseFromRandom(cell[1])
cell[0].interval = gid + 1
cell[0].number = 100
cell[0].start = 0
cell[0].noise = 1
spiketime = h.Vector()
spikegid = h.Vector()
pc.spike_record(-1, spiketime, spikegid)
pc.set_maxstep(10)
tstop = 100
for cell in cells.values():
cell[1].seq(0) # only _ran_compat==2 initializes the streams
h.finitialize()
pc.psolve(tstop)
spiketime_std = spiketime.c()
spikegid_std = spikegid.c()
print("spiketime_std.size %d" % spiketime_std.size())
spiketime.resize(0)
spikegid.resize(0)
from neuron import coreneuron
h.CVode().cache_efficient(True)
coreneuron.verbose = 0
coreneuron.enable = True
for cell in cells.values():
cell[1].seq(0)
h.finitialize()
while h.t < tstop:
h.continuerun(h.t + 5)
pc.psolve(h.t + 5)
print("spiketime.size %d" % spiketime.size())
assert spiketime.eq(spiketime_std)
assert spikegid.eq(spikegid_std)
pc.gid_clear()
if __name__ == "__main__":
test_psolve()
test_NetStim_noise()
for i in range(0):
test_NetStim_noise() # for checking memory leak
h.quit()
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