def find_and_disconnect_axon(soma_ref):
'''Searching for an axon, it can be a child of the soma or a parent of the soma.'''
axon_section, axon_parent, soma_axon_x = [], False, None
for sec in soma_ref.child:
name = sec.hname().lower()
if 'axon' in name or 'hill' in name:
axon_section.append(sec)
# disconnect axon
soma_axon_x = sec.parentseg().x
sec.push()
h.disconnect()
h.define_shape()
if soma_ref.has_parent():
name = soma_ref.parent().sec.hname().lower()
if 'axon' in name or 'hill' in name:
axon_section.append(soma_ref.parent())
axon_parent = True
soma_axon_x = None
soma_ref.push()
h.disconnect()
else:
raise Exception('Soma has a parent which is not an axon')
if len(axon_section) > 1:
raise Exception('Soma has a two axons')
return axon_section, axon_parent, soma_axon_x
def fix_axon_allactive_granule(hobj):
"""Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
"""
# find the start and end diameter of the original axon, this is different from the perisomatic cell model
# where diameter == 1.
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
h.distance(sec=hobj.soma[0]) # need this to set all distances relative to soma (not sure if from center?)
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
for seg in sec:
if h.distance(seg.x) > 60:
axon_diams[1] = sec.diam
#if h.distance(0.5, sec) > 60:
# axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for index, sec in enumerate(hobj.axon):
sec.L = 30
sec.diam = axon_diams[index] # 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
h.define_shape()
def morph_per_root(root):
morph = []
h.define_shape()
for sec in secs_with_root(root):
n3d = int(h.n3d(sec=sec))
x = [h.x3d(i, sec=sec) for i in xrange(n3d)]
y = [h.y3d(i, sec=sec) for i in xrange(n3d)]
z = [h.z3d(i, sec=sec) for i in xrange(n3d)]
d = [h.diam3d(i, sec=sec) for i in xrange(n3d)]
arc = [h.arc3d(i, sec=sec) for i in xrange(n3d)]
length = sec.L
half_dx = 0.5 / sec.nseg
for seg in sec:
morph.append(get_pts_between(x, y, z, d, arc, (seg.x - half_dx) * length, (seg.x + half_dx) * length))
# add end points
for end_pt in [0, 1]:
for sec in secs_with_root(root):
n3d = int(h.n3d(sec=sec))
pt1 = [h.x3d(0, sec=sec), h.y3d(0, sec=sec), h.z3d(0, sec=sec), h.diam3d(0, sec=sec)]
pt2 = [h.x3d(n3d - 1, sec=sec), h.y3d(n3d - 1, sec=sec), h.z3d(n3d - 1, sec=sec), h.diam3d(n3d - 1, sec=sec)]
if h.section_orientation(sec=sec) == 0:
morph_to_append = [pt1] if end_pt == 0 else [pt2]
else:
morph_to_append = [pt2] if end_pt == 0 else [pt1]
round3(morph_to_append)
morph.append(morph_to_append)
return morph
def fix_axon(hobj):
'''
Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
'''
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for sec in hobj.axon:
sec.L = 30
sec.diam = 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def morphology_to_dict(sections, outfile=None):
section_map = {sec: i for i, sec in enumerate(sections)}
result = []
h.define_shape()
for sec in sections:
my_parent = parent(sec)
my_parent_loc = -1 if my_parent is None else parent_loc(sec, my_parent)
my_parent = -1 if my_parent is None else section_map[my_parent]
n3d = int(h.n3d(sec=sec))
result.append({
'section_orientation': h.section_orientation(sec=sec),
'parent': my_parent,
'parent_loc': my_parent_loc,
'x': [h.x3d(i, sec=sec) for i in xrange(n3d)],
'y': [h.y3d(i, sec=sec) for i in xrange(n3d)],
'z': [h.z3d(i, sec=sec) for i in xrange(n3d)],
'diam': [h.diam3d(i, sec=sec) for i in xrange(n3d)],
'name': sec.hname()
})
if outfile is not None:
with open(outfile, 'w') as f:
json.dump(result, f)
return result
def initialize_neuron(swc_path, file_paths):
h.load_file("stdgui.hoc")
h.load_file("import3d.hoc")
swc = h.Import3d_SWC_read()
swc.input(swc_path)
imprt = h.Import3d_GUI(swc, 0)
h("objref this")
imprt.instantiate(h.this)
print file_paths
for sec in h.allsec():
if sec.name().startswith("axon"):
h.delete_section(sec=sec)
axon = h.Section()
axon.L = 60
axon.diam = 1
axon.connect(h.soma[0], 0.5, 0)
h.define_shape()
for sec in h.allsec():
sec.insert('pas')
for seg in sec:
seg.pas.e = 0
for file_path in file_paths:
h.load_file(file_path.encode("ascii", "ignore"))
def fix_axon_allactive(hobj):
"""Replace reconstructed axon with a stub
Parameters
----------
hobj: instance of a Biophysical template
NEURON's cell object
"""
# find the start and end diameter of the original axon, this is different from the perisomatic cell model
# where diameter == 1.
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
for index, sec in enumerate(hobj.axon):
sec.L = 30
print axon_diams[index]
sec.diam = axon_diams[index] # 1
hobj.axonal.append(sec=sec)
hobj.all.append(sec=sec) # need to remove this comment
# original allen sdk: hobj.soma[0], 0.5, 0
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def fix_axon_peri(hobj):
"""Replace reconstructed axon with a stub
:param hobj: hoc object
"""
if hasattr(hobj, 'axon'):
for i, sec in enumerate(hobj.axon):
#h.delete_section(sec=sec)
hobj.axon[i] = None
for i,sec in enumerate(hobj.all):
if 'axon' in sec.name():
hobj.all[i] = None
hobj.all = [sec for sec in hobj.all if sec is not None]
hobj.axon = None
#h.execute('create axon[2]', hobj)
hobj.axon = [h.Section(name='axon[0]'), h.Section(name='axon[1]')]
hobj.axonal = []
for sec in hobj.axon:
sec.L = 30
sec.diam = 1
hobj.axonal.append(sec)
hobj.all.append(sec) # need to remove this comment
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1, 0)
h.define_shape()
def morphology_to_dict(sections, outfile=None):
section_map = {sec: i for i, sec in enumerate(sections)}
result = []
h.define_shape()
for sec in sections:
my_parent = parent(sec)
my_parent_loc = -1 if my_parent is None else parent_loc(sec, my_parent)
my_parent = -1 if my_parent is None else section_map[my_parent]
n3d = int(h.n3d(sec=sec))
result.append({
'section_orientation': h.section_orientation(sec=sec),
'parent': my_parent,
'parent_loc': my_parent_loc,
'x': [h.x3d(i, sec=sec) for i in xrange(n3d)],
'y': [h.y3d(i, sec=sec) for i in xrange(n3d)],
'z': [h.z3d(i, sec=sec) for i in xrange(n3d)],
'diam': [h.diam3d(i, sec=sec) for i in xrange(n3d)],
'name': sec.hname()
})
if outfile is not None:
with open(outfile, 'w') as f:
json.dump(result, f)
return result
def _fix_axon(self):
"""Removes and refixes axon"""
axon_diams = [self._hobj.axon[0].diam, self._hobj.axon[0].diam]
axon_indices = []
for i, sec in enumerate(self._hobj.all):
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
axon_indices.append(i)
for sec in self._hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', self._hobj)
for index, sec in enumerate(self._hobj.axon):
sec.L = 30
sec.diam = 1
self._hobj.axonal.append(sec=sec)
self._hobj.all.append(sec=sec) # need to remove this comment
self._hobj.axon[0].connect(self._hobj.soma[0], 1.0, 0)
self._hobj.axon[1].connect(self._hobj.axon[0], 1.0, 0)
h.define_shape()
def define_geometry(self):
"""Set the 3D geometry of the cell."""
self.soma.L = self.soma.diam = 12.6157 # microns
self.dend.L = 200 # microns
self.dend.diam = 1 # microns
self.dend.nseg = 5
h.define_shape() # Translate into 3D points.
def fix_axon_perisomatic_directed(hobj):
# io.log_info('Fixing Axon like perisomatic')
all_sec_names = []
for sec in hobj.all:
all_sec_names.append(sec.name().split(".")[1][:4])
if 'axon' not in all_sec_names:
io.log_exception('There is no axonal recostruction in swc file.')
else:
beg1, end1, beg2, end2 = get_axon_direction(hobj)
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
h.pt3dadd(beg1[0], beg1[1], beg1[2], 1, sec=hobj.axon[0])
h.pt3dadd(end1[0], end1[1], end1[2], 1, sec=hobj.axon[0])
hobj.all.append(sec=hobj.axon[0])
h.pt3dadd(beg2[0], beg2[1], beg2[2], 1, sec=hobj.axon[1])
h.pt3dadd(end2[0], end2[1], end2[2], 1, sec=hobj.axon[1])
hobj.all.append(sec=hobj.axon[1])
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
hobj.axon[0].L = 30.0
hobj.axon[1].L = 30.0
h.define_shape()
for sec in hobj.axon:
# print "sec.L:", sec.L
if np.abs(30 - sec.L) > 0.0001:
io.log_exception('Axon stub L is less than 30')
def geom(self):
'''
Adds length and diameter to sections
'''
for sec in self.node:
sec.L = self.nodelength # microns
sec.diam = self.nodeD # microns
sec.nseg = 1
for sec in self.MYSA:
sec.L = self.paralength1 # microns
sec.diam = self.fiberD # microns
sec.nseg = 1
for sec in self.FLUT:
sec.L = self.paralength2 # microns
sec.diam = self.fiberD # microns
sec.nseg = 1
for sec in self.STIN:
sec.L = self.interlength # microns
sec.diam = self.fiberD # microns
sec.nseg = 1
h.define_shape()
def move(self, offset=(0, 0, 0)):
ox, oy, oz = offset[0], offset[1], offset[2]
h.define_shape()
xlo = ylo = zlo = xhi = yhi = zhi = None
for sec in self.all:
sec.nseg = 1
n3d = sec.n3d()
xs = [sec.x3d(i) + ox for i in range(n3d)]
ys = [sec.y3d(i) + oy for i in range(n3d)]
zs = [sec.z3d(i) + oz for i in range(n3d)]
ds = [sec.diam3d(i) for i in range(n3d)]
sec.pt3dclear()
for x, y, z, d in zip(xs, ys, zs, ds):
sec.pt3dadd(x, y, z, d)
my_xlo, my_ylo, my_zlo = min(xs), min(ys), min(zs)
my_xhi, my_yhi, my_zhi = max(xs), max(ys), max(zs)
if xlo is None:
xlo, ylo, zlo = my_xlo, my_ylo, my_zlo
xhi, yhi, zhi = my_xhi, my_yhi, my_zhi
else:
xlo, ylo, zlo = min(xlo,
my_xlo), min(ylo,
my_ylo), min(zlo, my_zlo)
xhi, yhi, zhi = max(xhi,
my_xhi), max(yhi,
my_yhi), max(zhi, my_zhi)
return xlo, ylo, zlo, xhi, yhi, zhi
def generate_morphology(self, morph_filename):
"""Instantiate a morphology from an SWC file at `morph_filename`
Also deletes the axonal compartments from the SWC file and inserts an axonal
"stub" in its place.
"""
cell = self.cell
swc = self.h.Import3d_SWC_read()
swc.quiet = 1
swc.input(morph_filename.encode('ascii', 'ignore'))
imprt = self.h.Import3d_GUI(swc, 0)
imprt.instantiate(cell)
for seg in cell.soma[0]:
seg.area()
for sec in cell.all:
sec.nseg = 1 + 2 * int(sec.L / 40)
cell.simplify_axon()
for sec in cell.axonal:
sec.L = 30
sec.diam = 1
sec.nseg = 1 + 2 * int(sec.L / 40)
cell.axon[0].connect(cell.soma[0], 0.5, 0)
cell.axon[1].connect(cell.axon[0], 1, 0)
h.define_shape()
def setup_pcmorphology(kk, x, y, z):
print 'i value is:', kk
cellpc = Purkinje()
h.define_shape()
sections = h.SectionList()
sections.wholetree(cellpc.soma)
cellpc.soma.push()
n = h.n3d(sec=cellpc.soma)
xs = [h.x3d(i) for i in range(int(n))]
ys = [h.y3d(i) for i in range(int(n))]
zs = [h.z3d(i) for i in range(int(n))]
ds = [h.diam3d(i) for i in range(int(n))]
j = 0
#sec.push()
for a, b, c, d in zip(xs, ys, zs, ds):
#print 'sec here is:', sec
h.pt3dchange(j, a + x, b + y, c + z, d)
j += 1
h.define_shape()
h.pop_section()
pulist.append(cellpc)
#call another function to locate the local granule neurons
cellpc.soma.push()
getpurksecs = h.SectionList()
getpurksecs.wholetree(cellpc.soma)
func_local_grcneurons(kk, getpurksecs)
def get_coords_and_radii(self):
nrn_section = self.nrn_section
# Count 3D points
point_count = int(h.n3d(sec=nrn_section))
# Let NEURON create them if missing
if point_count == 0:
h.define_shape(sec=self.nrn_section)
point_count = int(h.n3d(sec=self.nrn_section))
# Collect the coordinates
coords = [None] * point_count * 3 # 3 for xy and z
radii = [None] * point_count
for c in range(point_count):
ci = c * 3
coords[ci] = h.x3d(c, sec=nrn_section)
coords[ci + 1] = h.y3d(c, sec=nrn_section)
coords[ci + 2] = h.z3d(c, sec=nrn_section)
radii[c] = h.diam3d(c, sec=nrn_section) / 2.0
self.nseg = int(nrn_section.nseg)
self.point_count = point_count
self.coords = coords
self.radii = radii
def insert(self):
h = self.h
if not hasattr(h, 'cvode'):
h.load_file('stdrun.hoc')
if not h.cvode.use_fast_imem():
h.cvode.use_fast_imem(1)
h.init()
if self.method == 'Point':
LfpClass = SectionLfpPointMethod
elif self.method == 'Line':
LfpClass = SectionLfpLineMethod
else: # self.method == 'RC':
LfpClass = SectionLfpRCMethod
for sec in self.sec_list: # h.allsec():
if self.is_lfp_section(sec.name()):
# Let NEURON create 3D points if missing
if h.n3d(sec=sec) <= 0:
h.define_shape(sec=sec)
# Keep track of sections being monitored
self.section_lfps[sec] = LfpClass(self, sec)
def __init__(self, gid, x, y, z, theta):
self._gid = gid
self._setup_morphology()
self._setup_biophysics()
self.x = self.y = self.z = 0
h.define_shape()
self._rotate_z(theta)
self._set_position(x, y, z)
def define_geometry(self, soma_d, soma_l, dend_d, dend_l, dend_nseg):
"""Set the 3D geometry of the cell."""
self.soma.L = soma_l # microns
self.soma.diam = soma_d # microns
self.dend.L = dend_l # microns
self.dend.diam = dend_d # microns
self.dend.nseg = dend_nseg # number of divisions for dendrite
h.define_shape() # Translate into 3D points.
def define_soma_at_root( self, neuron, rad = 2000, L = 2000):
root_section = self.find_section_from_nodeid( neuron.root )
self.sections.append( h.Section(name='soma', cell=self) )
root_section.connect( self.sections[-1](1) )
self.sections[-1].L = L * 0.001
self.sections[-1].diam = 2*rad * 0.001
self.soma = self.sections[-1]
h.define_shape()
def set_cell_position(self, x, y, z):
h.define_shape()
for sec in self.secs:
for i in range(sec.n3d()):
sec.pt3dchange(i,
x - sec.x3d(i),
y - sec.y3d(i),
z - sec.z3d(i),
sec.diam3d(i))
def __init__(self, c_id, x, y, z, theta):
self.c_id = c_id
self.x = self.y = self.z = 0
self._set_morphology()
self.all = self.soma.wholetree()
self._set_biophysics()
h.define_shape()
self._set_position(x, y, z)
self._rotate_z(theta)
def defineCellShapes(self):
from .. import sim
if sim.cfg.createNEURONObj:
sim.net.compartCells = [
c for c in sim.net.cells if type(c) is sim.CompartCell
]
h.define_shape()
for cell in sim.net.compartCells:
cell.updateShape()
def get_data(data_dir, simulate_new=True, run_original=True):
''' returns the data from the simulations, depending on the params, it either
runs new simulations or not.
run_original = True -> original Hallermann model,
False -> reduced Nav model'''
if run_original:
save_file = 'data_hallerman_raw_original.npz'
else:
save_file = 'data_hallerman_raw_redNav.npz'
save_file = os.path.join(data_dir, save_file)
if simulate_new:
# params
dt = 0.0125
sim_len = 60 # ms
stim_delay = 40 # ms
stim_dur = 1 # ms
postfix = ''
det = True
if run_original:
simulate_original_model(data_dir) # to simulate original model
else:
simulate_reduced_Nav_model(data_dir) # to simulate reduced Nav model
st = add_stimulation(st_delay=stim_delay,st_dur=stim_dur, stim_delay=stim_delay)
vecs_ais, vec_soma, vec_st = get_recording_vecs(st)
# simulate the cell
cell.initialize(dt=dt)
cell.h.finitialize(-85)
t, I, I_axial = cell.integrate(sim_len, i_axial=True)
# extract the data
v_soma = np.array(vec_soma)
h.define_shape()
seg_coords = cell.get_seg_coords()
#!!!! Need of current correction
areas = []
for sec in cell.h.allsec():
areas += [seg.area() for seg in sec]
surface = np.pi * coords['diam'] * coords['L']
currents_with_electrode = currents_with_electrode * areas / surface
#!!!!!
# save the data
time = (np.arange(len(v_soma))*dt)
np.savez(save_file, I=I, t=time, I_axial=I_axial, v_soma=v_soma, seg_coords=seg_coords,vecs_ais=vecs_ais,
dt=h.dt, stim_delay=stim_delay)
data = np.load(save_file)
return data
def _do_init1(self):
from . import rxd
# TODO: if a list of sections is passed in, make that one region
# _species_count is used to create a unique _real_name for the species
global _species_count
regions = self.regions
self._real_name = self._name
initial = self.initial
charge = self._charge
# invalidate any old initialization of external solvers
rxd._external_solver_initialized = False
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
name = self.name
if name is not None:
if not isinstance(name, str):
raise RxDException('Species name must be a string')
if name in _defined_species and _defined_species[name]() is not None:
raise RxDException('Species "%s" previously defined: %r' % (name, _defined_species[name]()))
else:
name = _species_count
self._id = _species_count
_species_count += 1
_defined_species[name] = weakref.ref(self)
if regions is None:
raise RxDException('Must specify region where species is present')
if hasattr(regions, '__len__'):
regions = list(regions)
else:
regions = list([regions])
# TODO: unite handling of _regions and _extracellular_regions
self._regions = [r for r in regions if not isinstance(r, region.Extracellular)]
self._extracellular_regions = [r for r in regions if isinstance(r, region.Extracellular)]
if not all(isinstance(r, region.Region) for r in self._regions):
raise RxDException('regions list must consist of Region and Extracellular objects only')
if self._extracellular_regions:
# make sure that the extracellular callbacks are configured, if necessary
_ensure_extracellular()
self._species = weakref.ref(self)
# at this point self._name is None if unnamed or a string == name if
# named
self._ion_register()
# TODO: remove this line when certain no longer need it (commented out 2013-04-17)
# self._real_secs = region._sort_secs(sum([r.secs for r in regions], []))
#Set the SpeciesOnRegion id
for sp in _species_on_regions:
s = sp()
if s and s._species() == self:
s._id = self._id
def define_geometry(self):
'''
Adds length and diameter to sections
'''
for sec in self.stimsec:
sec.L = self.L# microns
sec.diam = self.diam # microns
self.branch.L = self.L
self.branch.diam = self.diam
self.branch.nseg = 1
h.define_shape() # Translate into 3D points.
def define_geometry(self):
'''
Adds length and diameter to sections
'''
for sec in self.stimsec:
sec.L = self.L# microns
sec.diam = self.diam # microns
self.branch.L = self.L
self.branch.diam = self.diam
self.branch.nseg = 1
h.define_shape() # Translate into 3D points.
def _init():
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
section1d._purge_cptrs()
for sr in species._get_all_species().values():
s = sr()
if s is not None:
s._register_cptrs()
s._finitialize()
_setup_matrices()
def rotate_cell_z(self, theta):
h.define_shape()
"""Rotate the cell about the Z axis."""
for sec in self.secs:
for i in range(sec.n3d()):
x = sec.x3d(i)
y = sec.y3d(i)
c = h.cos(theta)
s = h.sin(theta)
xprime = x * c - y * s
yprime = x * s + y * c
sec.pt3dchange(i, xprime, yprime, sec.z3d(i), sec.diam3d(i))
def _init():
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
section1d._purge_cptrs()
for sr in species._get_all_species().values():
s = sr()
if s is not None:
s._register_cptrs()
s._finitialize()
_setup_matrices()
def define_geometry(self, dl, dd, sl, sd, hl, hd, ail, aid, axl, axd):
# Set the geometry of the neuron
self.dend1.L = self.dend1.nseg = dl # microns
self.dend1.diam = dd
self.soma.L = self.soma.nseg = sl
self.soma.diam = sd
self.hill.L = self.hill.nseg = hl
self.hill.diam = hd
self.ais.L = self.ais.nseg = ail
self.ais.diam = aid
self.axon.L = self.axon.nseg = axl
self.axon.diam = axd
h.define_shape() # translate into 3D points
def fix_axon_peri_v2(hobj):
"""Replace reconstructed axon with a stub
:param hobj: hoc object
"""
for i, sec in enumerate(hobj.axon):
if i < 2:
sec.L = 30
sec.diam = 1
else:
sec.L = 1e-6
sec.diam = 1
h.define_shape()
def _do_reset_geometry():
h.define_shape()
secs = list(h.allsec())
geo = []
for sec in secs:
geo += _segment_3d_pts(sec)
javascript_embedder(("set_neuron_section_data(%s);" % json.dumps(geo)))
for sp in _shape_plot_list:
sp = sp()
if sp is not None:
sp._reload_morphology() # could probably get rid of this, but slower response
sp._force_redraw = True
del secs
def draw_model(self):
"""Draw the model.
Params:
controls - the main gui obj."""
# Draw the new one
h.define_shape()
num_sections = 0
# Disable the render. Faster drawing.
self.mayavi.visualization.scene.disable_render = True
x,y,z,d = [], [], [], []
voltage = []
connections = []
for sec in h.allsec():
x_sec, y_sec, z_sec, d_sec = self.retrieve_coordinate(sec)
self.sec2coords[sec.name()] = [x_sec, y_sec, z_sec]
# Store the section. later.
radius = sec.diam/2.
sec_coords_bound = ((x_sec.min(), x_sec.max()),
(y_sec.min() - radius,
y_sec.max() + radius),
(z_sec.min() - radius,
z_sec.max() + radius))
self.cyl2sec[sec_coords_bound] = sec
self.sec2cyl[sec] = sec_coords_bound
for i,xi in enumerate(x_sec):
x.append(x_sec[i])
y.append(y_sec[i])
z.append(z_sec[i])
d.append(d_sec[i])
indx_geom_seg = len(x) -1
if len(x) > 1 and i > 0:
connections.append([indx_geom_seg, indx_geom_seg-1])
self.edges = connections
self.x = x
self.y = y
self.z = z
# Mayavi pipeline
d = np.array(d) # Transforming for easy division
self.draw_mayavi(x, y, z, d, self.edges)
def _do_updates():
global _last_diam_change_count, _last_structure_change_count
while True:
old_diam_changed = h.diam_changed
h.define_shape()
if old_diam_changed or h.diam_changed or _diam_change_count.value != _last_diam_change_count or _structure_change_count.value != _last_structure_change_count:
h.diam_changed = 0
_last_diam_change_count = _diam_change_count.value
_last_structure_change_count = _structure_change_count.value
_do_reset_geometry()
for obj in _gui_widgets:
obj = obj()
if obj is not None and obj._ready:
obj._update()
time.sleep(update_interval)
def _init():
initializer._do_init()
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
if species._has_1d:
section1d._purge_cptrs()
for sr in list(_species_get_all_species().values()):
s = sr()
if s is not None:
# TODO: are there issues with hybrid or 3D here? (I don't think so, but here's a bookmark just in case)
s._register_cptrs()
s._finitialize()
_setup_matrices()
def _load_geometry(self):
'''Load the morphology-file in NEURON'''
try:
h.sec_counted = 0
except LookupError:
h('sec_counted = 0')
#import the morphology, try and determine format
fileEnding = self.morphology.split('.')[-1]
if fileEnding == 'hoc' or fileEnding == 'HOC':
h.load_file(1, self.morphology)
elif fileEnding == 'py':
geom_func = imp.load_source('shape_3D', self.morphology)
geom_func.shape_3D(self)
else:
neuron.h('objref this')
if fileEnding == 'asc' or fileEnding == 'ASC':
Import = h.Import3d_Neurolucida3()
if not self.verbose:
Import.quiet = 1
elif fileEnding == 'swc' or fileEnding == 'SWC':
Import = h.Import3d_SWC_read()
elif fileEnding == 'xml' or fileEnding == 'XML':
Import = h.Import3d_MorphML()
else:
raise ValueError('%s is not a recognised morphology file format!'
).with_traceback(
'Should be either .hoc, .asc, .swc, .xml!' %self.morphology)
#assuming now that morphologies file is the correct format
try:
Import.input(self.morphology)
except:
if not hasattr(neuron, 'neuroml'):
raise Exception('Can not import, try and copy the ' + \
'nrn/share/lib/python/neuron/neuroml ' + \
'folder into %s' % neuron.__path__[0])
else:
raise Exception('something wrong with file, see output')
try:
imprt = neuron.h.Import3d_GUI(Import, 0)
except:
raise Exception('See output, try to correct the file')
imprt.instantiate(neuron.h.this)
h.define_shape()
self._create_sectionlists()
def re_init():
"""reinitializes all rxd concentrations to match HOC values, updates matrices"""
h.define_shape()
# update current pointers
section1d._purge_cptrs()
for sr in species._get_all_species().values():
s = sr()
if s is not None:
s._register_cptrs()
# update matrix equations
_setup_matrices()
for sr in species._get_all_species().values():
s = sr()
if s is not None: s.re_init()
_cvode_object.re_init()
def _save_geom(self, h5file_holder):
"""Store the NeuroML in the geometry table"""
# writing the NeuroML model
h.define_shape() # We need the 3D points
h.load_file('mview.hoc')
modelView = h.ModelView(0)
modelXml = h.ModelViewXML(modelView)
tmp_file = 'temp.xml'
modelXml.xportLevel1(tmp_file)
xml_data = ''
with open(tmp_file, 'r') as f:
xml_data = f.read()
geom_group = h5file_holder.createGroup('/', self.geometry_root)
h5file_holder.createArray(geom_group, self.geometry_node_name,
xml_data)
os.remove(tmp_file)
def generate_morphology(self, cell, morph_filename):
'''
This code is from the Allen Brain API examples.
This code is no longer executed.
morph4.hoc is executed instead.
'''
h = self.h
swc = self.h.Import3d_SWC_read()
swc.input(morph_filename)
imprt = self.h.Import3d_GUI(swc, 0)
h('execute("forall delete_section()",cell)')
imprt.instantiate(cell)
for seg in cell.soma[0]:
seg.area()
for sec in cell.allsec():
sec.nseg = 1 + 2 * int(sec.L / 40)
h.define_shape()
def _do_init1(self):
from . import rxd
# TODO: if a list of sections is passed in, make that one region
# _species_count is used to create a unique _real_name for the species
global _species_count
regions = self._regions
self._real_name = self._name
initial = self.initial
charge = self._charge
# invalidate any old initialization of external solvers
rxd._external_solver_initialized = False
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
name = self.name
if name is not None:
if not isinstance(name, str):
raise RxDException('Species name must be a string')
if name in _defined_species and _defined_species[name]() is not None:
raise RxDException('Species "%s" previously defined: %r' % (name, _defined_species[name]()))
else:
name = _species_count
self._id = _species_count
_species_count += 1
_defined_species[name] = weakref.ref(self)
if regions is None:
raise RxDException('Must specify region where species is present')
if hasattr(regions, '__len__'):
regions = list(regions)
else:
regions = list([regions])
self._regions = regions
if not all(isinstance(r, region.Region) for r in regions):
raise RxDException('regions list must consist of Region objects only')
self._species = weakref.ref(self)
# at this point self._name is None if unnamed or a string == name if
# named
self._ion_register()
def gcs(self,NCELL):
'''
Instantiate NEURON cell Objects in the Python variable space such
that all cells have unique identifiers.
'''
NCELL=self.NCELL
SIZE=self.SIZE
RANK=self.RANK
#from neuron import h
pc=h.ParallelContext()
h('objref nc, cells')
swcdict={}
NFILE = 3175
bothtrans=self.both_trans(self.prep_list())
self.names_list=[0 for x in xrange(0,len(bothtrans))]
os.chdir(os.getcwd() + '/swclist')
self.make_cells(bothtrans)
os.chdir(os.getcwd() + '/../')
h.define_shape()
h('forall{ for(x,0){ insert xtra }}')
h('forall{ for(x,0){ insert extracellular}}')
h('xopen("interpxyz.hoc")')
h('grindaway()')
def re_init():
"""reinitializes all rxd concentrations to match HOC values, updates matrices"""
global _external_solver_initialized
h.define_shape()
if not species._has_3d:
# TODO: if we do have 3D, make sure that we do the necessary parts of this
# update current pointers
section1d._purge_cptrs()
for sr in list(_species_get_all_species().values()):
s = sr()
if s is not None:
s._register_cptrs()
# update matrix equations
_setup_matrices()
for sr in list(_species_get_all_species().values()):
s = sr()
if s is not None: s.re_init()
# TODO: is this safe?
_cvode_object.re_init()
_external_solver_initialized = False
def constructive_neuronal_geometry(source, n_soma_step, dx):
objects = []
source_is_import3d = False
# TODO: come up with a better way of checking type
if hasattr(source, 'sections'):
source_is_import3d = True
cell = source
# probably an Import3D type
num_contours = sum(sec.iscontour_ for sec in cell.sections)
if num_contours > 1:
raise Exception('more than one contour is not currently supported')
if num_contours == 1:
# setup the soma
# CTNG:soma
branches = []
parent_sec_name = []
for sec in cell.sections:
if sec.iscontour_:
soma_sec = sec.hname()
x, y, z = [sec.raw.getrow(i).to_python() for i in xrange(3)]
# compute the center of the contour based on uniformly spaced points around the perimeter
center_vec = sec.contourcenter(sec.raw.getrow(0), sec.raw.getrow(1), sec.raw.getrow(2))
x0, y0, z0 = [center_vec.x[i] for i in xrange(3)]
somax, somay, somaz = x0, y0, z0
xshifted = [xx - x0 for xx in x]
yshifted = [yy - y0 for yy in y]
# this is a hack to pretend everything is on the same z level
zshifted = [0] * len(x)
# locate the major and minor axis, adapted from import3d_gui.hoc
m = h.Matrix(3, 3)
for i, p in enumerate([xshifted, yshifted, zshifted]):
for j, q in enumerate([xshifted, yshifted, zshifted]):
if j < i: continue
v = numpy.dot(p, q)
m.setval(i, j, v)
m.setval(j, i, v)
# CTNG:majoraxis
tobj = m.symmeig(m)
# major axis is the one with largest eigenvalue
major = m.getcol(tobj.max_ind())
# minor is normal and in xy plane
minor = m.getcol(3 - tobj.min_ind() - tobj.max_ind())
#minor.x[2] = 0
minor.div(minor.mag())
x1 = x0; y1 = y0
x2 = x1 + major.x[0]; y2 = y1 + major.x[1]
xs_loop = x + [x[0]]
ys_loop = y + [y[0]]
# locate the extrema of the major axis CTNG:somaextrema
# this is defined by the furthest points on it that lie on the minor axis
pts = []
pts_sources = {}
for x3, y3 in zip(x, y):
x4, y4 = x3 + minor.x[0], y3 + minor.x[1]
pt = seg_line_intersection(x1, y1, x2, y2, x3, y3, x4, y4, clip=False)
if pt is not None:
pts.append(pt)
if pt not in pts_sources:
pts_sources[pt] = []
pts_sources[pt].append((x3, y3))
major_p1, major_p2 = extreme_pts(pts)
extreme1 = pts_sources[major_p1]
extreme2 = pts_sources[major_p2]
major_p1, major_p2 = numpy.array(major_p1), numpy.array(major_p2)
del pts_sources
if len(extreme1) != 1 or len(extreme2) != 1:
raise Exception('multiple most extreme points')
extreme1 = extreme1[0]
extreme2 = extreme2[0]
major_length = linalg.norm(major_p1 - major_p2)
delta_x, delta_y = major_p2 - major_p1
delta_x /= n_soma_step
delta_y /= n_soma_step
f_pts = [major_p1]
f_diams = [0]
# CTNG:slicesoma
for i in xrange(1, n_soma_step):
x0, y0 = major_p1[0] + i * delta_x, major_p1[1] + i * delta_y
# slice in dir of minor axis
x1, y1 = x0 + minor.x[0], y0 + minor.x[1]
pts = []
for i in xrange(len(x)):
pt = seg_line_intersection(xs_loop[i], ys_loop[i], xs_loop[i + 1], ys_loop[i + 1], x0, y0, x1, y1, clip=True)
if pt is not None: pts.append(pt)
p1, p2 = extreme_pts(pts)
p1, p2 = numpy.array(p1), numpy.array(p2)
cx, cy = (p1 + p2) / 2.
f_pts.append((cx, cy))
f_diams.append(linalg.norm(p1 - p2))
f_pts.append(major_p2)
f_diams.append(0)
for i in xrange(len(f_pts) - 1):
pt1x, pt1y = f_pts[i]
pt2x, pt2y = f_pts[i + 1]
diam1 = f_diams[i]
diam2 = f_diams[i + 1]
objects.append(SkewCone(pt1x, pt1y, z0, diam1 * 0.5, pt1x + delta_x, pt1y + delta_y, z0, diam2 * 0.5, pt2x, pt2y, z0))
else:
parent_sec_name.append(sec.parentsec.hname())
branches.append(sec)
else:
h.define_shape()
soma_sec = None
branches = []
for sec in source:
branches.append(sec)
# this is ignored in this case, but needs to be same length
# so this way no extra memory except the pointer
parent_sec_name = branches
#####################################################################
#
# add the branches
#
#####################################################################
diam_corrections = {None: None}
while diam_corrections:
all_cones = []
pts_cones_db = {}
diam_db = {}
for branch, psec in zip(branches, parent_sec_name):
if source_is_import3d:
x, y, z = [branch.raw.getrow(i).to_python() for i in xrange(3)]
d = branch.d.to_python()
else:
x = [h.x3d(i, sec=branch) for i in xrange(int(h.n3d(sec=branch)))]
y = [h.y3d(i, sec=branch) for i in xrange(int(h.n3d(sec=branch)))]
z = [h.z3d(i, sec=branch) for i in xrange(int(h.n3d(sec=branch)))]
d = [h.diam3d(i, sec=branch) for i in xrange(int(h.n3d(sec=branch)))]
# make sure that all the ones that connect to the soma do in fact connect
# do this by connecting to local center axis
# CTNG:connectdends
if psec == soma_sec:
pt = (x[1], y[1], z[1])
cp = closest_pt(pt, f_pts, somaz)
# NEURON includes the wire point at the center; we want to connect
# to the closest place on the soma's axis instead with full diameter
x, y, z, d = [cp[0]] + [X for X in x[1 :]], [cp[1]] + [Y for Y in y[1:]], [somaz] + [Z for Z in z[1:]], [d[1]] + [D for D in d[1 :]]
for i in xrange(len(x) - 1):
d0, d1 = d[i : i + 2]
if (x[i] != x[i + 1] or y[i] != y[i + 1] or z[i] != z[i + 1]):
# short section check
#if linalg.norm((x[i + 1] - x[i], y[i + 1] - y[i], z[i + 1] - z[i])) < (d1 + d0) * 0.5:
# short_segs += 1
axisx, axisy, axisz, deltad = x[i + 1] - x[i], y[i + 1] - y[i], z[i + 1] - z[i], d1 - d0
axislength = (axisx ** 2 + axisy ** 2 + axisz ** 2) ** 0.5
axisx /= axislength; axisy /= axislength; axisz /= axislength; deltad /= axislength
x0, y0, z0 = x[i], y[i], z[i]
x1, y1, z1 = x[i + 1], y[i + 1], z[i + 1]
if (x0, y0, z0) in diam_corrections:
d0 = diam_corrections[(x0, y0, z0)]
if (x1, y1, z1) in diam_corrections:
d1 = diam_corrections[(x1, y1, z1)]
if d0 != d1:
all_cones.append(Cone(x0, y0, z0, d0 * 0.5, x1, y1, z1, d1 * 0.5))
else:
all_cones.append(Cylinder(x0, y0, z0, x1, y1, z1, d1 * 0.5))
register(pts_cones_db, (x0, y0, z0), all_cones[-1])
register(pts_cones_db, (x1, y1, z1), all_cones[-1])
register(diam_db, (x0, y0, z0), d0)
register(diam_db, (x1, y1, z1), d1)
# at join, should always be the size of the biggest branch
# this is different behavior than NEURON, which continues the size of the
# first point away from the join to the join
diam_corrections = {}
for pt in diam_db:
vals = diam_db[pt]
if max(vals) != min(vals):
diam_corrections[pt] = max(vals)
cone_clip_db = {cone: [] for cone in all_cones}
join_counts = {'2m': 0, '2s': 0, '3m': 0, '3s': 0, '4m': 0, '4s': 0, '0m': 0, '0s': 0, '1m': 0, '1s': 0}
for cone in all_cones:
x1, y1, z1, r1 = cone._x0, cone._y0, cone._z0, cone._r0
x2, y2, z2, r2 = cone._x1, cone._y1, cone._z1, cone._r1
pt1 = numpy.array([x1, y1, z1])
pt2 = numpy.array([x2, y2, z2])
axis = (pt2 - pt1) / linalg.norm(pt2 - pt1)
left_neighbors = list(pts_cones_db[(x1, y1, z1)])
right_neighbors = list(pts_cones_db[(x2, y2, z2)])
left_neighbors.remove(cone)
right_neighbors.remove(cone)
if not left_neighbors: left_neighbors = [None]
if not right_neighbors: right_neighbors = [None]
for neighbor_left, neighbor_right in itertools.product(left_neighbors, right_neighbors):
clips = []
# process the join on the "left" (end 1)
if neighbor_left is not None:
# any joins are created on the left pass; the right pass will only do clippings
x0, y0, z0, r0 = neighbor_left._x0, neighbor_left._y0, neighbor_left._z0, neighbor_left._r0
if x0 == x1 and y0 == y1 and z0 == z1:
x0, y0, z0, r0 = neighbor_left._x1, neighbor_left._y1, neighbor_left._z1, neighbor_left._r1
pt0 = numpy.array([x0, y0, z0])
naxis = (pt1 - pt0) / linalg.norm(pt1 - pt0)
# no need to clip if the cones are perfectly aligned
if any(axis != naxis):
if r0 == r1 == r2:
# simplest join: two cylinders (no need for all that nastiness below)
sp = Sphere(x1, y1, z1, r1)
sp.set_clip([Plane(x0, y0, z0, -naxis[0], -naxis[1], -naxis[2]), Plane(x2, y2, z2, axis[0], axis[1], axis[2])])
objects.append(sp)
else:
# is the turn sharp or not
# CTNG:joinangle
sharp_turn = numpy.dot(axis, naxis) < 0
# locate key vectors
plane_normal = numpy.cross(axis, naxis)
radial_vec = numpy.cross(plane_normal, axis)
nradial_vec = numpy.cross(plane_normal, naxis)
# normalize all of these
radial_vec /= linalg.norm(radial_vec)
nradial_vec /= linalg.norm(nradial_vec)
# count the corners that are inside the other cone (for both ways)
# CTNG:outsidecorners
my_corner_count = count_outside(neighbor_left, [pt1 + r1 * radial_vec, pt1 - r1 * radial_vec])
corner_count = my_corner_count + count_outside(cone, [pt1 + r1 * nradial_vec, pt1 - r1 * nradial_vec])
# if corner_count == 0, then probably all nan's from size 0 meeting size 0; ignore
# if is 1, probably parallel; no joins
# if corner_count not in (1, 2, 3, 4):
# print 'corner_count: ', corner_count, [pt1 + r1 * radial_vec, pt1 - r1 * radial_vec] + [pt1 + r1 * nradial_vec, pt1 - r1 * nradial_vec]
if corner_count == 2:
# CTNG:2outside
# add clipped sphere; same rule if sharp or mild turn
objects += join_outside(x0, y0, z0, r0, x1, y1, z1, r1, x2, y2, z2, r2, dx)
elif corner_count == 3:
sp = Sphere(x1, y1, z1, r1)
if sharp_turn:
# CTNG:3outobtuse
if my_corner_count == 1:
sp.set_clip([Plane(x1, y1, z1, -naxis[0], -naxis[1], -naxis[2])])
else:
sp.set_clip([Plane(x1, y1, z1, axis[0], axis[1], axis[2])])
objects.append(sp)
else:
# CTNG:3outacute
objects += join_outside(x0, y0, z0, r0, x1, y1, z1, r1, x2, y2, z2, r2, dx)
if my_corner_count == 1:
objects.append(tangent_sphere(neighbor_left, 1))
objects[-1].set_clip([Plane(x2, y2, z2, naxis[0], naxis[1], naxis[2])])
else:
objects.append(tangent_sphere(cone, 0))
objects[-1].set_clip([Plane(x0, y0, z0, -axis[0], -axis[1], -axis[2])])
elif corner_count == 4:
sp = Sphere(x1, y1, z1, r1)
if sharp_turn:
# CTNG:4outobtuse
# join with the portions of a sphere that are outside at least one of the planes
sp.set_clip([Union([
Plane(x1, y1, z1, axis[0], axis[1], axis[2]),
Plane(x1, y1, z1, -naxis[0], -naxis[1], -naxis[2])])])
objects.append(sp)
else:
# CTNG:4outacute (+ 1 more)
# join with the portions of a sphere that are outside both planes
objects += join_outside(x0, y0, z0, r0, x1, y1, z1, r1, x2, y2, z2, r2, dx)
# AND clip the cone to not extend pass the union of the neighbor's plane and the neighbor
if r0 == r1:
neighbor_copy = Cylinder(x0, y0, z0, x1, y1, z1, r0)
else:
neighbor_copy = Cone(x0, y0, z0, r0, x1, y1, z1, r1)
clips.append(Union([
Plane(x1, y1, z1, -naxis[0], -naxis[1], -naxis[2]),
neighbor_copy]))
join_type = '%d%s' % (corner_count, 's' if sharp_turn else 'm')
join_counts[join_type] += 1
if neighbor_right is not None:
# any joins are created on the left pass; the right pass will only do clippings
x3, y3, z3, r3 = neighbor_right._x0, neighbor_right._y0, neighbor_right._z0, neighbor_right._r0
if x2 == x3 and y2 == y3 and z2 == z3:
x3, y3, z3, r3 = neighbor_right._x1, neighbor_right._y1, neighbor_right._z1, neighbor_right._r1
pt3 = numpy.array([x3, y3, z3])
naxis = (pt3 - pt2) / linalg.norm(pt3 - pt2)
# no need to clip if the cones are perfectly aligned
if any(axis != naxis):
# locate key vectors
plane_normal = numpy.cross(axis, naxis)
radial_vec = numpy.cross(plane_normal, axis)
radial_vec_norm = linalg.norm(radial_vec)
# we check again because sometimes there are roundoff errors that this catches
if radial_vec_norm:
# is the turn sharp or not
sharp_turn = numpy.dot(axis, naxis) < 0
nradial_vec = numpy.cross(plane_normal, naxis)
# normalize all of these
radial_vec /= radial_vec_norm
nradial_vec /= linalg.norm(nradial_vec)
# count the corners that are inside the other cone (for both ways)
my_corner_count = count_outside(neighbor_right, [pt2 + r2 * radial_vec, pt2 - r2 * radial_vec])
corner_count = my_corner_count + count_outside(cone, [pt2 + r2 * nradial_vec, pt2 - r2 * nradial_vec])
if corner_count == 2:
# no clipping; already joined
pass
elif corner_count == 3:
pass
elif corner_count == 4:
# CTNG:4outacute (+ 1 more)
# already joined; just clip (only in mild turn case)
if not sharp_turn:
if r2 == r3:
neighbor_copy = Cylinder(x2, y2, z2, x3, y3, z3, r3)
else:
neighbor_copy = Cone(x2, y2, z2, r2, x3, y3, z3, r3)
#print 'cc=4: (%g, %g, %g; %g) (%g, %g, %g; %g) (%g, %g, %g; %g) ' % (x1, y1, z1, r1, x2, y2, z2, r2, x3, y3, z3, r3)
clips.append(Union([
Plane(x2, y2, z2, naxis[0], naxis[1], naxis[2]),
neighbor_copy]))
if clips:
cone_clip_db[cone].append(Intersection(clips))
#print 'join_counts:'
#print join_counts
for cone in all_cones:
clip = cone_clip_db[cone]
if clip:
cone.set_clip([Union(clip)])
#####################################################################
#
# add the clipped objects to the list
#
#####################################################################
objects += all_cones
return objects
def __init__(self, regions=None, d=0, name=None, charge=0, initial=None):
"""s = rxd.Species(regions, d = 0, name = None, charge = 0, initial = None)
Declare a species.
Parameters:
regions -- a Region or list of Region objects containing the species
d -- the diffusion constant of the species (optional; default is 0, i.e. non-diffusing)
name -- the name of the Species; used for syncing with HOC (optional; default is none)
charge -- the charge of the Species (optional; default is 0)
initial -- the initial concentration or None (if None, then imports from HOC if the species is defined at finitialize, else 0)
Note:
charge must match the charges specified in NMODL files for the same ion, if any."""
# TODO: if a list of sections is passed in, make that one region
# _species_count is used to create a unique _real_name for the species
global _species_count
# TODO: check about the 0<x<1 problem alluded to in the documentation
h.define_shape()
self._name = name
if name is not None:
if not isinstance(name, str):
raise Exception('Species name must be a string')
if name in _defined_species and _defined_species[name]() is not None:
raise Exception('Species "%s" previously defined: %r' % (name, _defined_species[name]()))
else:
name = _species_count
self._id = _species_count
_species_count += 1
_defined_species[name] = weakref.ref(self)
if regions is None:
raise Exception('Must specify region where species is present')
if hasattr(regions, '__len__'):
regions = list(regions)
else:
regions = list([regions])
if not all(isinstance(r, region.Region) for r in regions):
raise Exception('regions list must consist of Region objects only')
self._regions = regions
self._real_name = name
self.initial = initial
self._charge = charge
self._d = d
# at this point self._name is None if unnamed or a string == name if
# named
if self._name is not None:
ion_type = h.ion_register(name, charge)
if ion_type == -1:
raise Exception('Unable to register species: %s' % species)
# insert the species if not already present
for r in regions:
if r.nrn_region in ('i', 'o'):
for s in r.secs:
try:
ion_forms = [name + 'i', name + 'o', 'i' + name, 'e' + name]
for i in ion_forms:
# this throws an exception if one of the ion forms is missing
temp = s.__getattribute__(name + 'i')
except:
s.insert(name + '_ion')
# set to recalculate reversal potential automatically
# the last 1 says to set based on global initial concentrations
# e.g. nai0_na_ion, etc...
h.ion_style(name + '_ion', 3, 2, 1, 1, 1, sec=s)
self._real_secs = region._sort_secs(sum([r.secs for r in regions], []))
# TODO: at this point the sections are sorted within each region, but for
# tree solver (which not currently using) would need sorted across
# all regions if diffusion between multiple regions
self._secs = [Section1D(self, sec, d, r) for r in regions for sec in r.secs]
if self._secs:
self._offset = self._secs[0]._offset
else:
self._offset = 0
self._has_adjusted_offsets = False
self._assign_parents()
self._update_region_indices()
