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# Basically want to test that net_move statement doesn't get
# mixed up with other instances.
from neuron.tests.utils.strtobool import strtobool
import os
from neuron import h
h.load_file("stdrun.hoc")
pc = h.ParallelContext()
h.steps_per_ms = 8
h.dt = 1.0 / h.steps_per_ms
class Cell:
def __init__(self, gid):
self.soma = h.Section(name="soma", cell=self)
if gid % 2 == 0:
# CoreNEURON permutation not the identity if cell topology not homogeneous
self.dend = h.Section(name="dend", cell=self)
self.dend.connect(self.soma(0.5))
self.gid = gid
pc.set_gid2node(gid, pc.id())
self.r = h.Random()
self.r.Random123(gid, 0, 0)
self.syn = h.Bounce(self.soma(0.5))
pc.cell(gid, h.NetCon(self.soma(0.5)._ref_v, None, sec=self.soma))
self.syn.noiseFromRandom123(gid, 0, 1)
self.t1vec = h.Vector()
self.t1vec.record(self.syn._ref_t1, sec=self.soma)
self.xvec = h.Vector()
self.xvec.record(self.syn._ref_x, sec=self.soma)
self.rvec = h.Vector()
self.rvec.record(self.syn._ref_r, sec=self.soma)
def result(self):
return (
self.syn.n_high,
self.syn.n_mid,
self.syn.n_low,
self.t1vec.c(),
self.xvec.c(),
self.rvec.c(),
)
def test_watchrange():
from neuron import coreneuron
coreneuron.enable = False
ncell = 10
gids = range(pc.id(), ncell, pc.nhost()) # round robin
cells = [Cell(gid) for gid in gids]
# complete the coverage of netcvode.cpp static void steer_val
# Just so happens that Bounce declares an x var that does not get
# mirrored by NetCon.x
nc = h.NetCon(cells[2].syn, None)
cells[2].syn.x = 0.1
nc.x = 2.0
assert nc.x == 0.0
assert cells[2].syn.x == 0.1
del nc
# @olupton changed from 20 to trigger assert(datum==2) failure.
tstop = 1.0
def run(tstop, mode):
pc.set_maxstep(10)
h.finitialize(-65)
if mode == 0:
pc.psolve(tstop)
elif mode == 1:
while h.t < tstop:
pc.psolve(h.t + h.dt)
else:
while h.t < tstop:
h.continuerun(h.t + h.dt)
pc.psolve(h.t + h.dt)
tvec = h.Vector()
tvec.record(h._ref_t, sec=cells[0].soma)
run(tstop, 0) # NEURON run
tvec = tvec.c() # don't record again but save.
stdlist = [cell.result() for cell in cells]
print("CoreNEURON run")
h.CVode().cache_efficient(1)
coreneuron.enable = True
coreneuron.verbose = 0
coreneuron.gpu = bool(strtobool(os.environ.get("CORENRN_ENABLE_GPU", "false")))
def runassert(mode):
run(tstop, mode)
hml = ["invalid", "low", " mid", " high"]
for i, cell in enumerate(cells):
result = cell.result()
std = stdlist[i]
# Organised this way so we get a better overview of what went wrong
# when something fails.
success = all(std[j] == result[j] for j in range(3)) and all(
std[j].eq(result[j]) for j in range(3, 6)
)
if not success:
print("mode=" + str(mode))
for j in range(3):
if std[j] != result[j]:
print(
"cell=%d %s:(nrn: %d cnrn: %d)"
% (i, ("high", "mid", "low")[j], std[j], result[j])
)
# Look at the first place the flag value differs
k = int(std[4].c().sub(result[4]).indwhere("!=", 0))
print(
"first difference at %d (%g, %s, r=%g) vs (%g, %s, r=%g)"
% (
k,
std[3][k],
hml[int(std[4][k])],
std[5][k],
result[3][k],
hml[int(result[4][k])],
result[5][k],
)
)
for ik in range(k + 1):
print(
" %d %g nrn(%g, %s, r=%g) vs cnrn(%g, %s, r=%g)"
% (
ik,
tvec[ik],
std[3][ik],
hml[int(std[4][ik])],
std[5][ik],
result[3][ik],
hml[int(result[4][ik])],
result[5][ik],
)
)
assert success
for mode in [0, 1, 2]:
runassert(mode)
coreneuron.enable = False
# teardown
pc.gid_clear()
return stdlist, tvec
if __name__ == "__main__":
from neuron import gui
stdlist, tvec = test_watchrange()
g = h.Graph()
print("n_high n_mid n_low")
for i, result in enumerate(stdlist):
print(result[0], result[1], result[2])
result[4].line(g, tvec, i, 2)
g.exec_menu("View = plot")
h.quit()
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