def mk_mitrals(model):
''' Create all the mitrals specified by mitral_gids set.'''
model.mitrals = {}
for gid in model.mitral_gids:
m = mkmitral.mkmitral(gid)
model.mitrals.update({gid : m})
util.elapsed('%d mitrals created and connections to mitrals determined'%int(pc.allreduce(len(model.mitrals),1)))
def mk_mitrals(model):
''' Create all the mitrals specified by mitral_gids set.'''
model.mitrals = {}
for gid in model.mitral_gids:
m = mkmitral.mkmitral(gid)
model.mitrals.update({gid : m})
util.elapsed('%d mitrals created and connections to mitrals determined'%int(pc.allreduce(len(model.mitrals),1)))
def f(ii):
i = int(ii)
density = numpy.zeros(matrank)
for gid in range(i , params.Nmitral, nhost):
m = mkmitral.mkmitral(gid)
for sec in m.secdens:
accumulate_density(sec, density, domain)
print gid
return density
def msoma(mgid):
c = mgid2pieces(mgid)
if c and h.section_exists('soma', c):
return c.soma
return None
def mpriden(mgid):
c = mgid2pieces(mgid)
if c and h.section_exists('priden', c):
return c.priden
return None
def gsoma(ggid):
c = ggid2pieces(ggid)
if c and h.section_exists('soma', c):
return c.soma
return None
if __name__ == "__main__":
from mkmitral import mkmitral
gid = 259
mcell = mkmitral(gid) # according to mkmitral.py this has tertiary branches
print "mitral_complexity ", mitral_complexity(mcell)
print "cell_complexity = ", lb.cell_complexity(mcell)
pieces = secden_indices_connected_to_soma(mcell)
pieces.append(-1)
splitmitral(gid, mcell, pieces)
h.topology()
print "mgid2piece ", model.mgid2piece
def export(num_cells_to_export=5):
cells = []
for mgid in range(num_cells_to_export):
print mgid
cells.append(mkmitral(mgid))
nml_net_file = "../NeuroML2/MitralCells/Exported/PartialBulb_%iMTCells.net.nml" % num_cells_to_export
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
for i in range(num_cells_to_export):
print("Processing cell %i out of %i" % (i, num_cells_to_export))
nml_cell_file = "../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % i
nml_doc = pynml.read_neuroml2_file(nml_cell_file)
cell = nml_doc.cells[0]
soma_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_soma")
initial_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_initialseg")
hillock_seg = next(seg for seg in cell.morphology.segments
if seg.name == "Seg0_hillock")
# Ensure hillock parent is soma
hillock_seg.parent.segments = soma_seg.id
# Fix initial and hillock segs by moving them to the soma
hillock_seg.proximal = pointMovedByOffset(hillock_seg.proximal,
soma_seg.distal)
hillock_seg.distal = pointMovedByOffset(hillock_seg.distal,
soma_seg.distal)
initial_seg.proximal = pointMovedByOffset(initial_seg.proximal,
soma_seg.distal)
initial_seg.distal = pointMovedByOffset(initial_seg.distal,
soma_seg.distal)
# Set root to id=0 and increment others
exportHelper.resetRoot(cell)
# TODO: cell.position(x,y,z) used for cell positioning in networks does not work as expected
# See: https://github.com/NeuroML/jNeuroML/issues/55
# Skipping the translation for now
# # Move everything back to the origin
# originOffset = type("", (), dict(x = -soma_seg.proximal.x, y = -soma_seg.proximal.y, z = -soma_seg.proximal.z ))()
#
# for seg in cell.morphology.segments:
# seg.proximal = pointMovedByOffset(seg.proximal, originOffset)
# seg.distal = pointMovedByOffset(seg.distal, originOffset)
# Replace ModelViewParmSubset_N groups with all, axon, soma, dendrite groups
buildStandardSegmentGroups(cell)
# Add channel placeholders
nml_doc.includes.append(
neuroml.IncludeType(href="channelIncludesPLACEHOLDER"))
cell.biophysical_properties = neuroml.BiophysicalProperties(
id="biophysPLACEHOLDER")
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from MitralCell...xml files
replaceChannelPlaceholders(nml_cell_file)
print("COMPLETED: " + nml_cell_file)
print("DONE")
return MGRS(mgid, isec, xm, ggid, ipri, xg, slot)
return None
def multiple_cnt():
cnt = 0;
for mgrs in getmodel().mgrss.values():
if mgrs.slot > 0:
if mgrs.gd: cnt += 1
if mgrs.md: cnt += 1
return cnt
if __name__ == "__main__":
import mkmitral, split
h.load_file("granule.hoc")
m = mkmitral.mkmitral(1)
pieces = split.secden_indices_connected_to_soma(m)
pieces.append(-1)
split.splitmitral(1, m, pieces)
pc.set_gid2node(1, pc.id())
pc.cell(1, h.NetCon(m.soma(.5)._ref_v, None, sec=m.soma))
g = h.Granule()
pc.set_gid2node(10000, pc.id())
pc.cell(10000, h.NetCon(g.soma(.5)._ref_v, None, sec=g.soma))
mgrs = MGRS(1, 0, .8, 10000, 0, .1)
mgrs.pr()
mgrs2 = MGRS(1, 0, .8, 10000, 0, .1)
subtree = h.SectionList()
subtree.subtree(sec = cell.secden[i])
secden_cx.append(subset_complexity(subtree))
total_cx += secden_cx[-1]
return (total_cx, soma_etc_cx, secden_cx)
def msoma(mgid):
c = mgid2pieces(mgid)
if c and h.section_exists('soma', c):
return c.soma
return None
def gsoma(ggid):
c = ggid2pieces(ggid)
if c and h.section_exists('soma', c):
return c.soma
return None
if __name__ == "__main__":
from mkmitral import mkmitral
gid = 259
mcell = mkmitral(gid) # according to mkmitral.py this has tertiary branches
print "mitral_complexity ", mitral_complexity(mcell)
print "cell_complexity = ", lb.cell_complexity(mcell)
pieces = secden_indices_connected_to_soma(mcell)
pieces.append(-1)
splitmitral(gid, mcell, pieces)
h.topology()
print "mgid2piece ", model.mgid2piece
def __init__(self, mgid):
self.__cutted = False
self.weights_max=True
self.mgid = mgid
from getmitral import getmitral as genMitral
self.mitral = genMitral(mgid)
self.soma = None
self.apic = None
self.dend = []
self.tuft = []
self.soma_color = (250. / 255, 210. / 255, 51. / 255)
self.section_color = (1., 1., 1.)
self.__vtkconvert()
self.sel_color = (0., 1., 0.)
self.sel_all = False
import granules
self.conn_info = []
if flag_dict:
grans=set()
for gid in gd.mgid_dict[mgid]:
if gid >= params.gid_granule_begin+granules.Ngranule and gid%2 == 0:
self.conn_info.append(gd.gid_dict[gid])
grans.add(self.conn_info[-1][3])
print 'Mitral %d has %d synapses in %d granules'%(mgid, len(self.conn_info), len(grans))
even = set()
for gid in gd.mgid_dict[mgid]:
if gid >= params.gid_granule_begin+granules.Ngranule and gid%2 == 0:
isec,x = gd.gid_dict[gid][1:3]
iseg = int(x*len(self.dend[isec]))
if x >= 1: iseg = len(self.dend[isec])-1
if (isec, iseg) not in even:
self.dend_gids[isec][iseg].add(gid)
even.add((isec, iseg))
self.dend_gids[isec][iseg].add(gid-1)
#for gid in gd.mgid_dict[mgid]:
# if gid >= params.gid_granule_begin + granules.Ngranule:
# if gid % 2 == 0:
# self.conn_info.append(gd.gid_dict[gid])
#
#for gid in gd.mgid_dict[mgid]:
# if gid >= params.Nmitral + granules.Ngranule:
# if gid % 2 == 0:
# isec, x = gd.gid_dict[gid][1:3]
# iseg = int(x * len(self.dend[isec]))
#
# if x >= 1:
# iseg = len(self.dend[isec]) - 1
#
# self.dend_gids[isec][iseg].add(gid)
else:
from mkmitral import mkmitral
from m2g_connections import determine_mitral_connections
self.conn_info = determine_mitral_connections(mgid, mkmitral(mgid))
self.interpolate = self.__interpolate(self.dend_gids) # complete the colors info using interpolation
#self.gr1 = []
#self.gr2 = []
self.__show_weights = False
self.__show_freqs = False
def __main__():
num_cells_to_export = 1
cells = []
for mgid in range(num_cells_to_export):
print mgid
cells.append(mkmitral(mgid))
nml_net_file = "../NeuroML2/MitralCells/Exported/PartialBulb_%iMTCells.net.nml" % num_cells_to_export
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
for i in range(num_cells_to_export):
print("Processing cell %i out of %i"%(i, num_cells_to_export))
nml_cell_file = "../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % i
nml_doc = pynml.read_neuroml2_file(nml_cell_file)
cell = nml_doc.cells[0]
import pydevd
pydevd.settrace('10.211.55.3', port=4200, stdoutToServer=True, stderrToServer=True, suspend=True)
# Set root to id=0 and increment others
exportHelper.resetRoot(cell)
somaSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_soma"][0]
initialSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_initialseg"][0]
hillockSeg = [seg for seg in cell.morphology.segments if seg.name == "Seg0_hillock"][0]
# Fix initial and hillock segs by moving them to the soma
hillockSeg.proximal = pointMovedByOffset(hillockSeg.proximal, somaSeg.distal)
hillockSeg.distal = pointMovedByOffset(hillockSeg.distal, somaSeg.distal)
initialSeg.proximal = pointMovedByOffset(initialSeg.proximal, somaSeg.distal)
initialSeg.distal = pointMovedByOffset(initialSeg.distal, somaSeg.distal)
# Move everything back to the origin
originOffset = type("", (), dict(x = -somaSeg.proximal.x, y = -somaSeg.proximal.y, z = -somaSeg.proximal.z ))()
for seg in cell.morphology.segments:
seg.proximal = pointMovedByOffset(seg.proximal, originOffset)
seg.distal = pointMovedByOffset(seg.distal, originOffset)
# Replace ModelViewParmSubset_N groups with all, axon, soma, dendrite groups
buildStandardSegmentGroups(cell)
# Add channel placeholders
nml_doc.includes.append(neuroml.IncludeType(href="channelIncludesPLACEHOLDER"))
cell.biophysical_properties = neuroml.BiophysicalProperties(id="biophysPLACEHOLDER")
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from MitralCell...xml files
replaceChannelPlaceholders(nml_cell_file)
print("COMPLETED: " + nml_cell_file)
print("DONE")
return None
def multiple_cnt():
cnt = 0
for mgrs in getmodel().mgrss.values():
if mgrs.slot > 0:
if mgrs.gd: cnt += 1
if mgrs.md: cnt += 1
return cnt
if __name__ == "__main__":
import mkmitral, split
h.load_file("granule.hoc")
m = mkmitral.mkmitral(1)
pieces = split.secden_indices_connected_to_soma(m)
pieces.append(-1)
split.splitmitral(1, m, pieces)
pc.set_gid2node(1, pc.id())
pc.cell(1, h.NetCon(m.soma(.5)._ref_v, None, sec=m.soma))
g = h.Granule()
pc.set_gid2node(10000, pc.id())
pc.cell(10000, h.NetCon(g.soma(.5)._ref_v, None, sec=g.soma))
mgrs = MGRS(1, 0, .8, 10000, 0, .1)
mgrs.pr()
mgrs2 = MGRS(1, 0, .8, 10000, 0, .1)
