def test_push_section():
h("""create hCable1, hCable2""")
h.push_section("hCable1")
assert h.secname() == "hCable1"
h.pop_section()
h.push_section("hCable2")
assert h.secname() == "hCable2"
h.pop_section()
h.delete_section(sec=h.hCable1)
h.delete_section(sec=h.hCable2)
sections = [h.Section(name="pCable%d" % i) for i in range(2)]
sections.append(h.Section()) # Anonymous in the past
for sec in sections:
name_in_hoc = h.secname(sec=sec)
h.push_section(name_in_hoc)
assert h.secname() == name_in_hoc
h.pop_section()
s = sections[-1]
h.delete_section(sec=s) # but not yet freed (though the name is now "")
# not [no longer] a section pointer
expect_err(
"s.hoc_internal_name()"
) # this is what is generating the error in the next statement.
expect_err('h.push_section(int(s.hoc_internal_name().replace("__nrnsec_", ""), 0))')
# not a sectionname
expect_hocerr(h.push_section, ("not_a_sectionname",))
def test_push_section():
h('''create hCable1, hCable2''')
h.push_section("hCable1")
assert (h.secname() == "hCable1")
h.pop_section()
h.push_section("hCable2")
assert (h.secname() == "hCable2")
h.pop_section()
h.delete_section(sec=h.hCable1)
h.delete_section(sec=h.hCable2)
sections = [h.Section(name="pCable%d" % i) for i in range(2)]
sections.append(h.Section()) # Anonymous in the past
for sec in sections:
name_in_hoc = h.secname(sec=sec)
h.push_section(name_in_hoc)
assert (h.secname() == name_in_hoc)
h.pop_section()
s = sections[-1]
h.delete_section(sec=s) # but not yet freed (though the name is now "")
# not [no longer] a section pointer
expect_hocerr(h.push_section,
(int(s.hoc_internal_name().replace("__nrnsec_", ""), 0), ))
# not a sectionname
expect_hocerr(h.push_section, ("not_a_sectionname", ))
def get_sec_list():
sec_list = []
sec_dends = []
sec_axons = []
h.load_file('mosinit.hoc')
h('''
objref root
root = new SectionRef()
if (root.has_parent()){
root = root.root()
}
''')
h('objref sec_list,axonal,dendritic')
h('sec_list = new SectionList()')
h('axonal = new SectionList()')
h('dendritic = new SectionList()')
h('forsec "dend"{dendritic.append()}')
h('forsec "apic"{dendritic.append()}')
h('forsec "axon"{axonal.append()}')
h('access root.sec')
h('distance()')
h('sec_list.wholetree()')
for curr_sec in h.sec_list:
curr_name = h.secname(sec=curr_sec)
sec_list.append(curr_name)
for curr_sec in h.dendritic:
curr_name = h.secname(sec=curr_sec)
sec_dends.append(curr_name)
for curr_sec in h.axonal:
curr_name = h.secname(sec=curr_sec)
sec_axons.append(curr_name)
return sec_list, sec_dends, sec_axons
def initialise(self):
save_stdout = sys.stdout # To supress hoc output from Jupyter notebook
# sys.stdout=open("trash","w")
sys.stdout = open('stdout.txt', 'w') # rather print it to the console
self.load_mod_files()
if self.hocpath is None:
raise Exception(
"Please give the path to the hoc file (eg. model.modelpath = \"/home/models/CA1_pyr/CA1_pyr_model.hoc\")"
)
h.load_file("stdrun.hoc")
h.load_file(str(self.hocpath))
if self.soma is None and self.SomaSecList_name is None:
raise Exception(
"Please give the name of the soma (eg. model.soma=\"soma[0]\"), or the name of the somatic section list (eg. model.SomaSecList_name=\"somatic\")"
)
try:
if self.template_name is not None and self.SomaSecList_name is not None:
h('objref testcell')
h('testcell = new ' + self.template_name)
exec('self.soma_ = h.testcell.' + self.SomaSecList_name)
for s in self.soma_:
self.soma = h.secname()
elif self.template_name is not None and self.SomaSecList_name is None:
h('objref testcell')
h('testcell = new ' + self.template_name)
# in this case self.soma is set in the jupyter notebook
elif self.template_name is None and self.SomaSecList_name is not None:
exec('self.soma_ = h.' + self.SomaSecList_name)
for s in self.soma_:
self.soma = h.secname()
# if both is None, the model is loaded, self.soma will be used
except AttributeError:
print(
"The provided model template is not accurate. Please verify!")
except Exception:
print(
"If a model template is used, please give the name of the template to be instantiated (with parameters, if any). Eg. model.template_name=CCell(\"morph_path\")"
)
raise
sys.stdout = save_stdout # setting output back to normal
def writeDistance(cell1, srlist1, cell2, srlist2, filename):
f = open(filename, 'w')
cnt = 0
tmpdist = 0
for sr1 in srlist1:
cell1n = h.secname(sec=cell1.Dend[sr1])
#print cell1.Type[sr1]
for sr2 in srlist2:
#print cell2.Type[sr2]
cell2n = h.secname(sec=cell2.Dend[sr2])
# print cell1n, cell2n
dist = CalcDistance(cell1.Dend[sr1], cell2.Dend[sr2])
s = "%s,%s,%f\n"%(cell1n, cell2n, dist)
f.write(s)
f.close()
def writeDistance(cell1, srlist1, cell2, srlist2, filename):
f = open(filename, 'w')
cnt = 0
tmpdist = 0
for sr1 in srlist1:
cell1n = h.secname(sec=cell1.Dend[sr1])
#print cell1.Type[sr1]
for sr2 in srlist2:
#print cell2.Type[sr2]
cell2n = h.secname(sec=cell2.Dend[sr2])
# print cell1n, cell2n
dist = CalcDistance(cell1.Dend[sr1], cell2.Dend[sr2])
s = "%s,%s,%f\n" % (cell1n, cell2n, dist)
f.write(s)
f.close()
def __init__(self,origin,delay=1,duration=5,initial_amplitude=38.0,distance=1000,pulses=1,frequency=1):
from numpy import pi
self.origin=h.secname(sec=origin)
self.sec=h.Section(name=str(self))
self.sec.L=1000
self.sec.diam=200 # um # Aravanis: 200 um # Gradinaru: 400 um
self.stim=h.ostim(0.5,sec=self.sec)
self.delay=delay
self.pulses=pulses
self.frequency=frequency
self.duration=duration
self.amplitude=initial_amplitude
h.setpointer(h._ref_source_irradiance_chanrhod, 'irradiance',self.stim)
h.setpointer(h._ref_source_photons_chanrhod, 'photons',self.stim)
h.setpointer(h._ref_source_flux_chanrhod, 'flux',self.stim)
h.setpointer(h._ref_tstimon_chanrhod, 'tstimon',self.stim)
h.setpointer(h._ref_tstimoff_chanrhod, 'tstimoff',self.stim)
self.stim.radius=self.sec.diam/2.0
self.stim.pulses=self.pulses
self.stim.isi = 1000 / self.frequency - self.duration #in ms
self.stim.amp=initial_amplitude
self.absorbance_coefficient = 0.1249 # (1/mm) # Range: (0.05233, 0.1975)
self.scatter_coefficient = 7.37 # (1/mm) # Range: (6.679, 8.062)
self.n = 1.36 # index of refraction of gray matter
self.NA = 0.37 # numerical aperture of the optical fiber
#self.NA = 0.48
self.set_distance(origin, distance)
def __init__(self,model_path,special_path,base=os.getcwd()):
#print "init"
self.base_directory=base
self.special=special_path
self.model=model_path
os.chdir(self.special)
from neuron import h
from nrn import *
self.hoc_obj=h
self.hoc_obj.load_file(str(self.model))
self.hoc_obj.load_file("stdrun.hoc")
self.vec=self.hoc_obj.Vector()
os.chdir(self.base_directory)
self.stimulus=None
self.record=[]
self.spike_times=None
self.sections={}
for n in h.allsec():
self.sections[str(h.secname())]=n
self.channels={}
optimizerHandler.setmods(self.hoc_obj,self.sections)
for sec in h.allsec():
for seg in sec:
for mech in seg:
self.channels[str(mech.name())]=mech
def find_synapse_loc(synapse_or_segment, mapping_sections_to_subtree_index):
''' Returns the location of the given synapse object'''
if not isinstance(synapse_or_segment, neuron.nrn.Segment):
synapse_or_segment = synapse_or_segment.get_segment()
x = synapse_or_segment.x
with push_section(synapse_or_segment.sec):
# extracts the section type ("soma", "apic", "dend") and the section number
# out of the section name
full_sec_name = h.secname()
sec_name_as_list = full_sec_name.split(".")
short_sec_name = sec_name_as_list[len(sec_name_as_list) - 1]
section_type = short_sec_name.split("[")[0]
section_num = re.findall(r'\d+', short_sec_name)[0]
# finds the index of the subtree that this synapse belongs to using the
# given mapping_sections_to_subtree_index which maps sections to the
# subtree indexes that they belong to
if section_type == "apic":
subtree_index = mapping_sections_to_subtree_index[("apic", section_num)]
elif section_type == "dend":
subtree_index = mapping_sections_to_subtree_index[("basal", section_num)]
else: # somatic synapse
subtree_index, section_num, x = SOMA_LABEL, 0, 0
return SynapseLocation(subtree_index, int(section_num), x)
def __info__(self):
info=str(self)
info+="\n-Section: %s" % self.origin
xyz0,xyz1=self.xyz
info+="\n-Base: %g,%g,%g" % (xyz0[0],xyz0[1],xyz0[2])
info+="\n-Tip: %g,%g,%g" % (xyz1[0],xyz1[1],xyz1[2])
info+="\n-Delay: %s" % self.delay
info+="\n-Duration: %s" % self.duration
info+="\n-Amplitude: %s" % self.amplitude
info+="\n-Length: %g" % self.length
info+="\n-Diameter: %g" % self.diameter
info+="\n-closest_section: %s" % h.secname(sec=self.closest_section)
return info
def __init__(self,model_path,special_path,base=os.getcwd()):
#print "init"
self.base_directory=base
self.special=special_path
self.model=model_path
os.chdir(self.special)
from neuron import h
from nrn import *
self.hoc_obj=h
self.hoc_obj.load_file(str(self.model))
self.hoc_obj.load_file("stdrun.hoc")
self.vec=self.hoc_obj.Vector()
os.chdir(self.base_directory)
self.stimulus=None
self.record=[]
self.spike_times=None
self.sections={}
for n in h.allsec():
self.sections[str(h.secname())]=n
self.channels={}
for sec in h.allsec():
for seg in sec:
for mech in seg:
self.channels[str(mech.name())]=mech
soma = h.Section(name="soma")
dend = h.Section(name="dend")
dend.connect(soma(1))
aden = [h.Section(name="aden[%d]" % i) for i in range(4)]
axon = h.Section()
axon.connect(soma(0))
h('create apical')
h.apical.connect(soma(.5))
s = h.ref("")
for sec in h.allsec():
h.sectionname(s)
print((sec.name(), s[0], h.secname(1), sec.hoc_internal_name()))
h('''forall print secname(), " ", secname(1)''')
names = [h.secname(1) for _ in h.allsec()]
for s in names:
h('%s print "hoc ", secname(1)' % s)
if False: # direct Python access to h._pysec.name does not work
for s in names:
print(("h.%s" % s + ".name()"))
print(("python %s %s" % (s, eval("h.%s" % s + ".name()"))))
h.load_file("pysecname.ses")
def section_index(self, section):
return int(h.secname(sec=section).split('_')[-1])
apicalDend5[0].insert('pas')
apicalDend6[0].insert('pas')
apicalDend7[0].insert('pas')
apicalDend8[0].insert('pas')
apicalDend1[0].nseg = 10
apicalDend2[0].nseg = 10
apicalDend3[0].nseg = 10
apicalDend4[0].nseg = 10
apicalDend5[0].nseg = 10
apicalDend6[0].nseg = 10
apicalDend7[0].nseg = 10
apicalDend8[0].nseg = 10
names = []
for sec in h.allsec():
names.append(h.secname())
print names
# Connect neurons in network...
"""
syn = []
nc = []
for i in range(0,l):
tempSectionName = "soma" + connections[i][9]
print tempSectionName
print soma8.name
syn.append(h.ExpSyn(float(connections[i][8]), sec = tempSectionName))
"""
syn1 = h.ExpSyn(0.5, sec=soma1)
syn2 = h.ExpSyn(0.5, sec=soma2)
def __info__(self):
info = str(self)
info += "\n-Axon Root: %s" % h.secname(sec=self.root)
info += "\n-Axon Root XYZ: %s" % str(
self.retrieve_coordinates(sec=self.root))
return info
def compute_nseg(self):
for sec in h.allsec():
sec.nseg = int((sec.L/(0.1*h.lambda_f(100,sec=sec))+0.9)/2)*2 + 1
if DEBUG:
print('%s has %d segments.' % (h.secname(sec=sec),sec.nseg))
import numpy
import types
h("objref p")
h("p = new PythonObject()")
try:
import pylab
from pylab import plot, arange, figure
my_pylab_loaded = True
except ImportError:
print "Pylab not imported"
my_pylab_loaded = False
def htype (obj): st=obj.hname(); sv=st.split('['); return sv[0]
def secname (obj): obj.push(); print h.secname() ; h.pop_section()
def psection (obj): obj.push(); print h.psection() ; h.pop_section()
allsecs=None #global list containing all NEURON sections, initialized via mkallsecs
# still need to generate a full allsecs
def mkallsecs ():
""" mkallsecs - make the global allsecs variable, containing
all the NEURON sections.
"""
global allsecs
allsecs=h.SectionList() # no .clear() command
roots=h.SectionList()
roots.allroots()
for s in roots:
s.push()
def find_section_number(section):
''' extracts and returns the section number from the given section object '''
sec_name = h.secname(sec=section)
ints_in_name = re.findall(r'\d+', sec_name)
sec_num = ints_in_name[len(ints_in_name) - 1] # extracts section number
return sec_num
