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 test_ste():
m1 = model()
# one state ste with two self transitions
var = m1["s"](0.5)._ref_v
thresh1 = h.ref(-50)
thresh2 = h.ref(-10)
result = []
ste = h.StateTransitionEvent(1)
ste.transition(0, 0, var, thresh1, (act, (1, m1, thresh1, result)))
ste.transition(0, 0, var, thresh2, (act, (2, m1, thresh2, result)))
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
print("final v=%g" % m1["s"](0.5).v)
chk_result(2, result, m1)
h.cvode_active(1)
run(20)
chk_result(2, result, m1)
h.cvode_active(1)
h.cvode.condition_order(2)
run(5)
chk_result(2, result, m1)
h.cvode.condition_order(1)
h.cvode_active(0)
# ste associated with point process
del fih, ste
ste = h.StateTransitionEvent(2, m1["ic"])
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
# transition with hoc callback
h("""proc foo() { printf("foo called at t=%g\\n", t) }""")
thresh3 = h.ref(-30)
ste.transition(0, 0, var, thresh3, "foo()")
run(5)
# transition with hoc callback in hoc object
h("""
begintemplate FooSTEtest
objref this
proc foo() { printf("foo in %s called at t=%g\\n", this, t) }
endtemplate FooSTEtest
""")
thresh4 = h.ref(-20)
obj = h.FooSTEtest()
ste.transition(0, 0, var, thresh4, "foo()", obj)
run(5)
del ste, fih
assert h.List("StateTransitionEvent").count() == 0
assert h.List("FInitializeHandler").count() == 0
def test_newobj_err(self):
'''Test deletion of incompletely constructed objects'''
print() # Error message not on above line
h.load_file("stdlib.hoc") # need hoc String
h('''
begintemplate Foo
endtemplate Foo
begintemplate NewObj
objref this, ob, foo1, foo2
proc init() {localobj s
foo1 = new Foo() // Constructed before error, even partial constructions fill this field.
if ($1 == 0) {
execerror("generate an error") // All NewObj instances undergoing construction
} else if ($1 == $2) {
// This and all NewObj instances prior to this will construct successfully.
// All after this will be partially constructed.
// The execerror should cause only the partially constructed NewObj to
// be destroyed.
s = new String()
sprint(s.s, "ob = new NewObj(%d, %d)", $1-1, $2)
execute1(s.s, this)
} else {
ob = new NewObj($1-1, $2)
}
foo2 = new Foo() // Only instances prior to execute1 reach here.
}
endtemplate NewObj
''')
# arg[0] recursion depth
# arg[0] - arg[1] + 1 should be successfully constructed
# arg[1] should be partially constructed and destroyed.
args = (4, 2)
a = h.NewObj(*args)
b = h.List("NewObj")
c = h.List("Foo")
print("#NewObj and #Foo in existence", b.count(), c.count())
z = args[0] - args[1] + 1
assert (b.count() == z)
assert (c.count() == 2 * z)
del a
del b
del c
b = h.List("NewObj")
c = h.List("Foo")
print("after del a #NewObj and #Foo in existence", b.count(),
c.count())
assert (b.count() == 0)
assert (c.count() == 0)
return 1
def test_fastimem():
cells = [Cell(id, 10) for id in range(2)]
# h.topology()
cvode = h.CVode()
ics = h.List("IClamp")
syns = h.List("ExpSyn")
cvode.use_fast_imem(1)
h.finitialize(-65)
run(1.0, ics, 1e-13)
total_syn_g(syns)
h.cvode_active(1)
run(1.0, ics, 1e-12)
cvode.use_fast_imem(0)
h.cvode_active(0)
def add_spines_on_segments(list_of_segments, seg_to_num_of_syn):
HCell.delete_spine()
total_synapses = 0
Xs_vec = h.Vector()
secs_sref_list = h.List()
for seg in list_of_segments:
sec = seg.sec
num_of_synapses = seg_to_num_of_syn[seg]
assert num_of_synapses != 0, "segment on %s has 0 synapses" % sec.hname(
)
Lsec = sec.L
sref = h.SectionRef(sec=sec)
if Lsec > CLUSTER_LENGTH: # distributes the spines on CLUSTER_LENGTH um on the section
min_x = (Lsec - CLUSTER_LENGTH) / float(seg.x)
else: # in cases where the section is shorter than CLUSTER_LENGTH
mix_x = 0
for ix in range(num_of_synapses):
secs_sref_list.append(sref)
x_syn = min_x + ix * CLUSTER_LENGTH / float(num_of_synapses - 1)
Xs_vec.append(min(x_syn / float(Lsec), 1))
total_synapses += num_of_synapses
HCell.add_few_spines(secs_sref_list, Xs_vec, 0.25, 1.35, 2.8,
HCell.soma[0].Ra)
HCell.soma[0].push()
return total_synapses
def synapse_vclamp_test(label, syntype, cell, w, v_holding, v_init, indexes,
section):
vv = h.Vector()
vv.append(0, 0, 0, 0, 0, 0)
se = h.SEClamp(cell.sections[section](0.5))
h('objref synlst')
h.synlst = h.List()
for i in indexes:
h.synlst.append(cell.syns.o(i))
if syntype == syn_type_excitatory:
v = cell.syntest_exc(h.synlst, se, w, v_holding, v_init)
else:
v = cell.syntest_inh(h.synlst, se, w, v_holding, v_init)
vv = vv.add(v)
amp = vv.x[0]
t_10_90 = vv.x[1]
t_20_80 = vv.x[2]
t_all = vv.x[3]
t_50 = vv.x[4]
t_decay = vv.x[5]
print("%s synapse: \n" % label)
print(" Amplitude %f\n" % amp)
print(" 10-90 Rise Time %f\n" % t_10_90)
print(" 20-80 Rise Time %f\n" % t_20_80)
print(" Decay Time Constant %f\n" % t_decay)
def __init__(self, n, seed, modelses, datagenhoc):
self.n = n
h.load_file(modelses)
mrflist = h.List("MulRunFitter")
mrf = mrflist.o(int(mrflist.count()) - 1)
self.N = mrf.p.pf.generatorlist.o(0).gen.po
h('objref nb')
h.nb = mrf.p.pf.generatorlist.o(0).gen.po
cvodewrap.fs.panel()
self.trueParm = self.N.getParm()
self.modelses = modelses
self.seed = seed
if n == 0:
G = noisegen.Gen(self.N)
G.reseed(seed)
self.seed = seed
self.Data = G.datasim()
else:
h.load_file(datagenhoc)
tvec = h.Vector(self.N.Eve.collectionTimes)
vec = h.ch3ssdata(n, seed, tvec, self.trueParm,
self.N.rf.fitnesslist.o(0))
self.Data = []
for i in range(len(vec)):
self.Data.append(numpy.matrix(vec[i]))
if fitglobals.verbose: h.topology()
ss = h.Vector()
cvodewrap.states(ss)
if fitglobals.verbose: ss.printf()
self.N.overwrite(self.Data)
self.H = numpy.matrix(self.Hessian())
def zeroselfs():
selfconns = [
nc for nc in h.List('NetCon') if nc.precell() == nc.postcell()
]
for nc in selfconns:
for x in range(int(nc.wcnt())):
nc.weight[x] = 0.0
def getParmFitness(self):
# the ParmFitness instance that owns me.
# there are probably not many so we can work forward from ParmFitness
pfl = h.List('ParmFitness')
for pf in pfl:
for gi in pf.generatorlist:
if gi.gen.hocobjptr() == self.rf.hocobjptr():
return pf
def checkMemory():
from .. import sim
# print memory diagnostic info
if sim.rank == 0: # and checkMemory:
import resource
print('\nMEMORY -----------------------')
print('Sections: ')
print(h.topology())
print('NetCons: ')
print(len(h.List("NetCon")))
print('NetStims:')
print(len(h.List("NetStim")))
print('\n Memory usage: %s \n' %
resource.getrusage(resource.RUSAGE_SELF).ru_maxrss)
# import objgraph
# objgraph.show_most_common_types()
print('--------------------------------\n')
def connectgjs(env):
rank = int(env.pc.id())
nhosts = int(env.pc.nhost())
datasetPath = os.path.join(env.datasetPrefix, env.datasetName)
gapjunctions = env.gapjunctions
if env.gapjunctionsFile is None:
gapjunctionsFilePath = None
else:
gapjunctionsFilePath = os.path.join(datasetPath, env.gapjunctionsFile)
if gapjunctions is not None:
h('objref gjlist')
h.gjlist = h.List()
if env.verbose:
if env.pc.id() == 0:
print gapjunctions
datasetPath = os.path.join(env.datasetPrefix, env.datasetName)
(graph, a) = bcast_graph(env.comm, gapjunctionsFilePath, attributes=True)
ggid = 2e6
for name in gapjunctions.keys():
if env.verbose:
if env.pc.id() == 0:
print "*** Creating gap junctions %s" % name
prj = graph[name]
attrmap = a[name]
weight_attr_idx = attrmap['Weight'] + 1
dstbranch_attr_idx = attrmap['Destination Branch'] + 1
dstsec_attr_idx = attrmap['Destination Section'] + 1
srcbranch_attr_idx = attrmap['Source Branch'] + 1
srcsec_attr_idx = attrmap['Source Section'] + 1
for destination in sorted(prj.keys()):
edges = prj[destination]
sources = edges[0]
weights = edges[weight_attr_idx]
dstbranches = edges[dstbranch_attr_idx]
dstsecs = edges[dstsec_attr_idx]
srcbranches = edges[srcbranch_attr_idx]
srcsecs = edges[srcsec_attr_idx]
for i in range(0, len(sources)):
source = sources[i]
srcbranch = srcbranches[i]
srcsec = srcsecs[i]
dstbranch = dstbranches[i]
dstsec = dstsecs[i]
weight = weights[i]
if env.pc.gid_exists(source):
h.mkgap(env.pc, h.gjlist, source, srcbranch, srcsec, ggid, ggid + 1, weight)
if env.pc.gid_exists(destination):
h.mkgap(env.pc, h.gjlist, destination, dstbranch, dstsec, ggid + 1, ggid, weight)
ggid = ggid + 2
del graph[name]
def printGetEventQueueinfo():
'''will print the size of tvec, flagvec and targetlist obtained from h.cvode.event_queue_info'''
tvec = h.Vector()
flagvec = h.Vector()
targetlist = h.List()
h.cvode.event_queue_info(3, tvec, flagvec, targetlist)
print 'tvec:', tvec.printf()
print 'flagvec:', flagvec.printf()
print 'targetlist count:', targetlist.count()
return tvec, flagvec, targetlist
def fill(self, channels, seed, n_trajectory):
self.seed = seed
self.channels = channels
xmd = self.fitfun.xdat.c()
ymd = h.List()
for i in range(n_trajectory):
seed2 = i
ymd.append(self.datagen(self.channels, self.seed, seed2, xmd))
self.fitfun.set_data(xmd, ymd)
h.execute("setxy()", self.fitfun)
def add_spines(num_of_synapses, secs, Xs):
secs_sref_list = h.List()
Xs_vec = h.Vector()
for ix in range(num_of_synapses): #create Neuron's list of section refs
sref = h.SectionRef(sec=HCell.apic[secs[ix]])
secs_sref_list.append(sref)
Xs_vec.append(Xs[ix])
HCell.add_few_spines(secs_sref_list, Xs_vec, 0.25, 1.35, 2.8,
HCell.soma[0].Ra)
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 add_Spines(num_of_spines, syn_trees, syn_secs, syn_segs):
hocL = h.List()
for i in range(num_of_spines):
if syn_trees[i] == APIC:
cell.apic[syn_secs[i]].push()
else:
cell.dend[syn_secs[i]].push()
hocL.append(h.SectionRef())
h.pop_section()
hocVxs = h.Vector(syn_segs)
cell.add_few_spines(hocL, hocVxs, SPINE_NECK_DIAM, SPINE_NECK_L,
SPINE_HEAD_AREA, SPINE_NECK_RA)
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 add_spines_on_segments(HCell, list_of_segments):
Xs_vec = h.Vector()
secs_sref_list = h.List()
for seg in list_of_segments:
sec = seg.sec
sref = h.SectionRef(sec=sec)
secs_sref_list.append(sref)
Xs_vec.append(min(seg.x, 1))
HCell.add_few_spines(secs_sref_list, Xs_vec, 0.25, 1.35, 2.8,
HCell.soma[0].Ra)
def testHClass(self):
"""Test subclass of hoc class."""
from ._subclass import A1
a = A1(5)
assert a.x == 5.0
assert a.p() == 6.0
b = A1(4)
a.s = 'one'
b.s = 'two'
assert a.s == 'one'
assert b.s == 'two'
assert h.A[0].s == 'one'
assert a.p() == 7.0
assert b.p() == 5.0
a.a = 2
b.a = 3
assert a.a == 2
assert b.a == 3
assert h.List('A').count() == 2
a = 1
b = 1
assert h.List('A').count() == 0
def add_spines(num_of_synapses, trees, secs, Xs):
HCell.delete_spine()
secs_sref_list = h.List()
Xs_vec = h.Vector()
for ix in range(num_of_synapses): #creates NEURON's list of section refs
if trees[ix] == 'apic':
sref = h.SectionRef(sec=HCell.apic[secs[ix]])
else:
sref = h.SectionRef(sec=HCell.dend[secs[ix]])
secs_sref_list.append(sref)
Xs_vec.append(Xs[ix])
HCell.add_few_spines(secs_sref_list, Xs_vec, 0.25, 1.35, 2.8,
HCell.soma[0].Ra)
def run():
h.finitialize(-65)
for sgid in sgids:
if pc.gid_exists(sgid) == 3:
sec = pc.gid2cell(sgid).soma
sec(.5).nai = float(sgid)/100. + .001
tars = h.List("NaTrans")
for tar in tars:
tar.napre = .0001 # correct values don't carryover from previous sim
pc.psolve(tstop)
for tar in tars:
x = (tar.sgid/100. + .001) if tar.sgid >= 0.0 else 0.0
differ = ("differ") if abs(tar.napre - x) > 1e-10 else ""
if differ != "":
print("%d %s %g %g %g %s"%(rank, tar.hname(), tar.sgid, tar.napre, x, differ))
assert(differ == "")
def add_data(self, mname, d):
instances = h.List(mname)
if instances.count() == 0:
return
names = []
inst = instances.o(0)
for name in dir(inst):
if '__' not in name:
try:
if type(getattr(inst, name)) == float:
names.append(name)
except:
pass
for inst in instances:
for name in names:
d.append(getattr(inst, name))
def add_spines_on_seg(num_of_synapses,sec,x): Lsec = sec.L sref = h.SectionRef(sec=sec) xL = float(x)*Lsec maxX = xL+CLUSTER_LENGTH/2.0 if xL+10>Lsec: maxX = Lsec minX = xL-10 if xL-10<0: minX = 0 local_vec_x = h.Vector() list_sref = h.List() for ix in range(num_of_synapses): list_sref.append(sref) local_vec_x.append(np.random.uniform(minX/Lsec,maxX/Lsec)) HCell.add_few_spines(list_sref,local_vec_x,0.25,1.35,2.8,HCell.soma[0].Ra)
def set_voltage_recorders(self):
# Record voltage for all segments
self.vreclist = h.List()
for sec in self.allseclist:
# address the problem of the high number of segments necessary to compute the accurate AP propagation
# in the unmyelinated axon case by limiting the number of monitored segments.
if sec.nseg > self.numberOfSavedSegments:
for i in range(1, self.numberOfSavedSegments + 1):
vrec = h.Vector(int(h.tstop / h.dt + 1))
vrec.record(
sec(float(i) / self.numberOfSavedSegments)._ref_v)
self.vreclist.append(vrec)
else:
for seg in sec:
vrec = h.Vector(int(h.tstop / h.dt + 1))
vrec.record(seg._ref_v)
self.vreclist.append(vrec)
def testABI(self):
"""Test use of some Py_LIMITED_API for python3."""
# Py_nb_bool
assert True if h else False
assert False if h.List else True
l = h.List()
assert True if h.List else False
assert False if l else True
v = h.Vector(1)
l.append(v)
assert True if l else False
# Py_sq_length
assert len(l) == 1
# Py_sq_item
assert l[0] == v
# Py_sq_ass_item
v.x[0] = 5
assert v.x[0] == 5
def synapse_iclamp_test(label, syntype, cell, w, v_init, indexes):
h('objref synlst')
h.synlst = h.List()
for i in indexes:
h.synlst.append(cell.syns.o(i))
if syntype == syn_type_excitatory:
v = cell.syn_iclamp_exc(h.synlst, w, v_init)
else:
v = cell.syn_iclamp_inh(h.synlst, w, v_init)
v_amp = v.x[0]
v_peak = v.x[1]
v_pre = v.x[2]
t_peak = v.x[3]
t_pre = v.x[4]
print("%s synapse: \n" % label)
print(" V Amplitude %f\n" % v_amp)
print(" V Peak %f\n" % v_peak)
print(" V Pre %f\n" % v_pre)
return v_amp
def main(config, template_path, output_path, forest_path, populations, io_size,
chunk_size, value_chunk_size, cache_size, verbose):
"""
:param config:
:param template_path:
:param forest_path:
:param populations:
:param io_size:
:param chunk_size:
:param value_chunk_size:
:param cache_size:
"""
utils.config_logging(verbose)
logger = utils.get_script_logger(script_name)
comm = MPI.COMM_WORLD
rank = comm.rank
env = Env(comm=MPI.COMM_WORLD,
config_file=config,
template_paths=template_path)
h('objref nil, pc, templatePaths')
h.load_file("nrngui.hoc")
h.load_file("./templates/Value.hoc")
h.xopen("./lib.hoc")
h.pc = h.ParallelContext()
if io_size == -1:
io_size = comm.size
if rank == 0:
logger.info('%i ranks have been allocated' % comm.size)
h.templatePaths = h.List()
for path in env.templatePaths:
h.templatePaths.append(h.Value(1, path))
if output_path is None:
output_path = forest_path
if rank == 0:
if not os.path.isfile(output_path):
input_file = h5py.File(forest_path, 'r')
output_file = h5py.File(output_path, 'w')
input_file.copy('/H5Types', output_file)
input_file.close()
output_file.close()
comm.barrier()
(pop_ranges, _) = read_population_ranges(forest_path, comm=comm)
start_time = time.time()
for population in populations:
logger.info('Rank %i population: %s' % (rank, population))
count = 0
(population_start, _) = pop_ranges[population]
template_name = env.celltypes[population]['template']
h.find_template(h.pc, h.templatePaths, template_name)
template_class = eval('h.%s' % template_name)
measures_dict = {}
for gid, morph_dict in NeuroH5TreeGen(forest_path,
population,
io_size=io_size,
comm=comm,
topology=True):
if gid is not None:
logger.info('Rank %i gid: %i' % (rank, gid))
cell = cells.make_neurotree_cell(template_class,
neurotree_dict=morph_dict,
gid=gid)
secnodes_dict = morph_dict['section_topology']['nodes']
apicalidx = set(cell.apicalidx)
basalidx = set(cell.basalidx)
dendrite_area_dict = {k + 1: 0.0 for k in range(0, 4)}
dendrite_length_dict = {k + 1: 0.0 for k in range(0, 4)}
for (i, sec) in enumerate(cell.sections):
if (i in apicalidx) or (i in basalidx):
secnodes = secnodes_dict[i]
prev_layer = None
for seg in sec.allseg():
L = seg.sec.L
nseg = seg.sec.nseg
seg_l = old_div(L, nseg)
seg_area = h.area(seg.x)
layer = cells.get_node_attribute(
'layer', morph_dict, seg.sec, secnodes, seg.x)
layer = layer if layer > 0 else (
prev_layer if prev_layer is not None else 1)
prev_layer = layer
dendrite_length_dict[layer] += seg_l
dendrite_area_dict[layer] += seg_area
measures_dict[gid] = { 'dendrite_area': np.asarray([ dendrite_area_dict[k] for k in sorted(dendrite_area_dict.keys()) ], dtype=np.float32), \
'dendrite_length': np.asarray([ dendrite_length_dict[k] for k in sorted(dendrite_length_dict.keys()) ], dtype=np.float32) }
del cell
count += 1
else:
logger.info('Rank %i gid is None' % rank)
append_cell_attributes(output_path,
population,
measures_dict,
namespace='Tree Measurements',
comm=comm,
io_size=io_size,
chunk_size=chunk_size,
value_chunk_size=value_chunk_size,
cache_size=cache_size)
MPI.Finalize()
def MulRunFitHandle():
mrflist = h.List("MulRunFitter")
mrf = mrflist.o(int(mrflist.count()) - 1)
return mrf
arc = [h.arc3d(i, sec=sec) for i in xrange(n)]
f = seg.x * sec.L
return (numpy.interp(f, arc, x), numpy.interp(f, arc, y), numpy.interp(f, arc, z))
pointprocess_locs_by_root = {}
pointprocess_mouseovers_by_root = {}
for name in pointprocess_names:
pointprocess_locs_by_root[name] = {}
pointprocess_mouseovers_by_root[name] = {}
for root in root_sections:
pointprocess_locs_by_root[name][root] = []
pointprocess_mouseovers_by_root[name][root] = []
#pointprocess_locs_by_root[name] = {root: [] for root in root_sections}
#pointprocess_mouseovers_by_root[name] = {root: [] for root in root_sections}
ell = h.List(name)
for i in xrange(int(ell.count())):
obj = ell.o(i)
if obj.has_loc():
seg = obj.get_segment()
pt = list(pt_from_seg(seg))
pointprocess_locs_by_root[name][get_root(seg.sec)].append(pt)
pointprocess_mouseovers_by_root[name][get_root(seg.sec)].append('%s at %s(%g)<br/>(%g, %g, %g)' % (name, seg.sec.name(), seg.x, pt[0], pt[1], pt[2]))
if 'children' in point_processes:
base = point_processes['children']
if len(base):
text = base[0]['text'].split()
if len(text) == 3 and text[1] == 'Point' and text[2] == 'Processes' and 'children' in base[0]:
base = base[0]['children']
for child in base:
from neuron import h
import fitglobals
fitglobals.debugoff()
h.load_file('mulfit.hoc')
h.load_file('eonerunmlf.hoc')
import nrnbfilt
h.load_file('ch3_11p.ses')
h('objref nb')
h.nb = h.List("PythonObject").o(0)
