def export(MCs=1, GCsPerMC=10):
import os
import sys
import re
import copy
import exportHelper
numGranulesTotal = MCs * GCsPerMC
# Nav to neuron folder where compiled MOD files are present
os.chdir("../../NEURON")
from neuron import h, gui
# Build the network with desired number of GCs - including spines
print("Building GC network...")
sys.path.append(os.getcwd())
import customsim
import modeldata
customsim.setup(MCs, GCsPerMC)
model = modeldata.getmodel()
print("GC network built.")
result = exportToNML(model.granules)
print("Exported the following cell files:")
for file in result:
print(" " + file)
def export(MCs=2):
print("Exporting odor input for " + ` MCs ` + " MCs...")
# Build the network
import os, sys
sys.path.append(os.path.abspath("../../NEURON"))
os.chdir("../../NEURON")
from neuron import h, gui
import customsim
import modeldata
customsim.setup(MCs, 0)
model = modeldata.getmodel()
# Initialize default odor input sequence
from odorstim import OdorSequence
import custom_params
import params
odseq = OdorSequence(params.odor_sequence)
odstim = odseq[0]
next_time = odstim.init_ev(params.odor_sequence[0][1])
# Simulate h.run() to obtain NetCon events
sectionTimes = {}
loop = True
while (loop):
h.t = next_time
event = odstim.ev(next_time)
for sec in event["weights"]:
secName = sec["label"]
if secName not in sectionTimes:
sectionTimes[secName] = {'times': [], 'weights': []}
sectionTimes[secName]['times'].append(event['time'])
sectionTimes[secName]['weights'].append(sec['weight'])
loop = 'next_time' in event
if loop:
next_time = event['next_time']
import json, sys, os
fileName = "odor-events.json"
with open(fileName, "w") as file:
json.dump(sectionTimes, file, indent=4)
print("Odor stim times written to " + os.path.abspath(fileName))
def __main__():
import customsim
import modeldata
MCs = 1
GCsPerMC = 1
networkTemplate = FileTemplate("../NeuroML2/Networks/NetworkTemplate.xml")
includeTemplate = FileTemplate("../NeuroML2/Networks/IncludeTemplate.xml")
populationTemplate = FileTemplate("../NeuroML2/Networks/PopulationTemplate.xml")
projectionTemplate = FileTemplate("../NeuroML2/Networks/ProjectionTemplate.xml")
customsim.setup(MCs, GCsPerMC)
model = modeldata.getmodel()
netFile = "../NeuroML2/Networks/Bulb_%iMC_%iGC.net.nml" % (len(model.mitral_gids), len(model.granule_gids))
includes = ""
populations = ""
projections = ""
mcNMLs = {}
gcNMLs = {}
#import pydevd
#pydevd.settrace('10.211.55.3', port=4200, stdoutToServer=True, stderrToServer=True)
# Make MC includes and populations
for mcgid in model.mitral_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`)\
.replace("[X]", `model.mitrals[mcgid].x`)\
.replace("[Y]", `model.mitrals[mcgid].y`)\
.replace("[Z]", `model.mitrals[mcgid].z`)
# Retain mitral cell NML
mcNML = pynml\
.read_neuroml2_file("../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % mcgid)\
.cells[0]
mcNMLs.update({mcgid:mcNML})
# Make GC includes and populations
import granules
from neuroml.nml.nml import NeuroMLDocument
for gcgid in model.granule_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)\
.replace("[X]", `granules.gid2pos[gcgid][0]`)\
.replace("[Y]", `granules.gid2pos[gcgid][1]`)\
.replace("[Z]", `granules.gid2pos[gcgid][2]`)
# Retain granule cell NML
gcNML = pynml\
.read_neuroml2_file("../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml" % gcgid)\
gcNMLs.update({gcgid:gcNML})
# Add a projection for each synapse
synCount = len(model.mgrss.keys())
curSyn = 0
for sgid in model.mgrss.keys():
print('Building synapse %i of %i' % (curSyn+1,synCount))
synapse = model.mgrss[sgid]
nsecden = model.mitrals[synapse.mgid].secden[synapse.isec].nseg
secdenIndex = min(nsecden-1, int(synapse.xm * nsecden))
postSegmentId = [seg.id\
for seg in mcNMLs[synapse.mgid].morphology.segments\
if seg.name == "Seg%i_secden_%i"%(secdenIndex,synapse.isec)\
][0]
gcNML = gcNMLs[synapse.ggid].cells[0]
# Position the spine along the GC priden
import exportHelper
exportHelper.splitSegmentAlongFraction(gcNML,"Seg0_priden2_0","priden2_0",synapse.xg,'Seg0_neck')
pynml.write_neuroml2_file(gcNMLs[synapse.ggid], "../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml" % synapse.ggid)
# Add Dendro-dendritic synapses
# GC -> MC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_G2M')\
.replace("[PreCellType]", "Granule")\
.replace("[PreGID]", `synapse.ggid`)\
.replace("[PreSegment]", `4`)\
.replace("[PreAlong]", `0.5`)\
.replace("[PostCellType]", "Mitral")\
.replace("[PostGID]", `synapse.mgid`)\
.replace("[PostSegment]", `postSegmentId`)\
.replace("[PostAlong]", "0.5")\
.replace("[Synapse]", "FIsyn")\
# MC -> GC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_M2G')\
.replace("[PreCellType]", "Mitral")\
.replace("[PreGID]", `synapse.mgid`)\
.replace("[PreSegment]", `postSegmentId`)\
.replace("[PreAlong]", `0.5`)\
.replace("[PostCellType]", "Granule")\
.replace("[PostGID]", `synapse.ggid`)\
.replace("[PostSegment]", `4`)\
.replace("[PostAlong]", "0.5")\
.replace("[Synapse]", "AmpaNmdaSyn")\
curSyn += 1
network = networkTemplate.text\
.replace("[IncludesPlaceholder]", includes)\
.replace("[PopulationsPlaceholder]", populations)\
.replace("[ProjectionsPlaceholder]", projections)
with open(netFile, "w") as file:
file.write(network)
print('Net file saved to: ' + netFile)
piece exists on this cpu. For other existence questions, use
mpiece_exists, mgid2pieces, and msecden.
Although granules are not currently split, we have their equivalents:
gpiece_exists, ggid2pieces, and gpriden.
'''
from neuron import h
pc = h.ParallelContext()
try:
from loadbalutil import lb
except ImportError:
pass
#dictionary for mgid, mcell (whats left of it)
#model.mgid2piece
from modeldata import getmodel
model = getmodel()
def mpiece_exists(mgid):
return model.mgid2piece.has_key(mgid)
def mgid2pieces(mgid):
''' return cell with existing pieces for mgid; None if does not exist.'''
if mpiece_exists(mgid):
return model.mgid2piece[mgid]
return None
def msecden(mgid, i):
''' return secondary dendrite if it exists, otherwise None.'''
c = mgid2pieces(mgid)
def export(MCs = 60, GCsPerMC = 5, useOdorInput = True, odorInputMaxTime = 200, export_cells = True):
import subprocess
if export_cells:
# Export cells first - in their own NEURON instances
subprocess.Popen("python -c 'import export_mitral; export_mitral.export("+`MCs`+");'", shell=True).wait()
subprocess.Popen("python -c 'import export_granule; export_granule.export(" + `MCs` + ","+`GCsPerMC`+");'", shell=True).wait()
# Export the odor input
if useOdorInput:
subprocess.Popen("python -c 'import export_input; export_input.export(" + `MCs` + ");'", shell=True).wait()
import os, neuroml, sys
from math import ceil
os.chdir('../../NEURON')
sys.path.append(os.path.abspath(os.getcwd()))
from neuron import h, gui
from pyneuroml import pynml
from pyneuroml.neuron import export_to_neuroml2
import customsim
import modeldata
networkTemplate = FileTemplate("../NeuroML2/Networks/NetworkTemplate.xml")
includeTemplate = FileTemplate("../NeuroML2/Networks/IncludeTemplate.xml")
glomsTemplate = FileTemplate("../NeuroML2/Networks/GlomeruliTemplate.xml")
populationTemplate = FileTemplate("../NeuroML2/Networks/PopulationTemplate.xml")
projectionTemplate = FileTemplate("../NeuroML2/Networks/ProjectionTemplate.xml")
odorInputListTemplate = FileTemplate("../NeuroML2/Networks/OdorInputListTemplate.xml")
odorSynapseTemplate = FileTemplate("../NeuroML2/Networks/OdorSynapseTemplate.xml")
customsim.setup(MCs, GCsPerMC)
model = modeldata.getmodel()
# Exp2Syns are used for odor input
if useOdorInput:
section2synapse = {syn.get_segment().sec.name(): syn for syn in h.Exp2Syn}
netFile = "../NeuroML2/Networks/Bulb_%iMC_%iGC%s.net.nml" % (len(model.mitral_gids), len(model.granule_gids),"_OdorIn" if useOdorInput else "")
includes = ""
populations = ""
projections = ""
gloms = ""
odorInputLists = ""
odorSyns = ""
mcNMLs = {}
gcNMLs = {}
# Make MC includes and populations
for mcgid in model.mitral_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Mitral")\
.replace("[GID]", `mcgid`) \
.replace("[X]", `0`) \
.replace("[Y]", `0`) \
.replace("[Z]", `0`)
# TODO: restore these when this is resolved: https://github.com/NeuroML/jNeuroML/issues/55
# .replace("[X]", `model.mitrals[mcgid].x`)\
# .replace("[Y]", `model.mitrals[mcgid].y`)\
# .replace("[Z]", `model.mitrals[mcgid].z`)
# Retain mitral cell NML for later
mcNML = pynml\
.read_neuroml2_file("../NeuroML2/MitralCells/Exported/Mitral_0_%i.cell.nml" % mcgid)\
.cells[0]
mcNMLs.update({mcgid:mcNML})
# Each glom consists of 5 mcs -- using the highest mc id, generate the required gloms
num_gloms = int(ceil((max(model.mitral_gids)+1)/5.0))
with open('realgloms.txt') as f:
for g in range(num_gloms):
g_x, g_y, g_z = map(float, f.readline().split(' '))
gloms += glomsTemplate.text \
.replace("[GlomID]", `g`) \
.replace("[X]", `g_x`) \
.replace("[Y]", `g_y`) \
.replace("[Z]", `g_z`)
# Make GC includes and populations
import granules
from neuroml.nml.nml import NeuroMLDocument
for gcgid in model.granule_gids:
includes += includeTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)
populations += populationTemplate.text\
.replace("[CellType]", "Granule")\
.replace("[GID]", `gcgid`)\
.replace("[X]", `granules.gid2pos[gcgid][0]`)\
.replace("[Y]", `granules.gid2pos[gcgid][1]`)\
.replace("[Z]", `granules.gid2pos[gcgid][2]`)
# Retain granule cell NML
gcNML = pynml\
.read_neuroml2_file("../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml" % gcgid)\
.cells[0]
gcNMLs.update({gcgid:gcNML})
# Add a projection for each synapse
synCount = len(model.mgrss.keys())
curSyn = 0
for sgid in model.mgrss.keys():
print('Building synapse %i of %i' % (curSyn+1,synCount))
synapse = model.mgrss[sgid]
# Compute the segment on the MC to which the synapse will be connected
alongSegment = getFractionAlongSegment(model.mitrals[synapse.mgid].secden[synapse.isec], synapse.xm)
mcSegmentIndex = alongSegment["segment_index"]
alongMcSegment = alongSegment["along_segment"]
# Get the NML ID of the MC segment
mcSegmentID = next(seg.id for seg in mcNMLs[synapse.mgid].morphology.segments if seg.name == "Seg%i_secden_%i"%(mcSegmentIndex,synapse.isec))
# Get the NML ID of the MC spine head, which all syns are attached (at 0.5 position)
gcSpineHeadSegmentID = next(seg.id for seg in gcNMLs[synapse.ggid].morphology.segments if seg.name == "head_seg")
# Add Dendro-dendritic synapses
# GC -> MC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_G2M')\
.replace("[PreCellType]", "Granule")\
.replace("[PreGID]", `synapse.ggid`)\
.replace("[PreSegment]", `gcSpineHeadSegmentID`)\
.replace("[PreAlong]", `0.5`)\
.replace("[PostCellType]", "Mitral")\
.replace("[PostGID]", `synapse.mgid`)\
.replace("[PostSegment]", `mcSegmentID`)\
.replace("[PostAlong]", `alongMcSegment`)\
.replace("[Synapse]", "FIsyn")\
# MC -> GC part
projections += projectionTemplate.text\
.replace("[ProjectionID]", `sgid`+'_M2G')\
.replace("[PreCellType]", "Mitral")\
.replace("[PreGID]", `synapse.mgid`)\
.replace("[PreSegment]", `mcSegmentID`)\
.replace("[PreAlong]", `alongMcSegment`)\
.replace("[PostCellType]", "Granule")\
.replace("[PostGID]", `synapse.ggid`)\
.replace("[PostSegment]", `gcSpineHeadSegmentID`)\
.replace("[PostAlong]", "0.5")\
.replace("[Synapse]", "AmpaNmdaSynapse")\
curSyn += 1
# Add odor inputs
if useOdorInput:
import re, json
synId = 0
with open('odor-events.json') as r:
odorTimes = json.load(r)
for mcid in model.mitral_gids:
mc = model.mitrals[mcid]
nmlmc = mcNMLs[mcid]
for tuftden in mc.tuftden:
# Translate NRN name into NML name
tuftdenIndex = re.compile('tuftden\[(.*)\]').search(tuftden.name()).groups(1)[0]
nmlTuftDenName = 'tuftden_'+tuftdenIndex
nmlTuftDenSegs = [seg for seg in nmlmc.morphology.segments if seg.name.endswith(nmlTuftDenName)]
alongSegment = getFractionAlongSegment(tuftden, 0.1) # Syns are placed at 0.1
nmlTuftDenSeg = nmlTuftDenSegs[alongSegment['segment_index']]
nmlAlongTuftDenSeg = alongSegment['along_segment']
times = odorTimes[tuftden.name()]
for i, time in enumerate(times['times']):
if time > odorInputMaxTime:
break
# Add inputList
odorInputLists += odorInputListTemplate.text\
.replace("[ID]", `synId`)\
.replace("[SynInputID]", "OdorSynTimes"+`synId`)\
.replace("[McID]", `mcid`)\
.replace("[SegmentID]", `nmlTuftDenSeg.id`)\
.replace("[FractionAlong]", `nmlAlongTuftDenSeg`)
# Add synapse and timed input
odorSyns += odorSynapseTemplate.text\
.replace("[ID]", `synId`)\
.replace("[Weight]", `times['weights'][i]`)\
.replace("[Time]", `time`)
synId += 1
network = networkTemplate.text\
.replace("[NumGloms]", `num_gloms`) \
.replace("[GlomeruliPlaceholder]", gloms) \
.replace("[IncludesPlaceholder]", includes)\
.replace("[PopulationsPlaceholder]", populations)\
.replace("[ProjectionsPlaceholder]", projections)\
.replace("[OdorInputLists]", odorInputLists)\
.replace("[OdorInputSynapses]", odorSyns)
with open(netFile, "w") as file:
file.write(network)
print('Net file saved to: ' + netFile)
piece exists on this cpu. For other existence questions, use
mpiece_exists, mgid2pieces, and msecden.
Although granules are not currently split, we have their equivalents:
gpiece_exists, ggid2pieces, and gpriden.
'''
from neuron import h
pc = h.ParallelContext()
try:
from loadbalutil import lb
except ImportError:
pass
#dictionary for mgid, mcell (whats left of it)
#model.mgid2piece
from modeldata import getmodel
model = getmodel()
def mpiece_exists(mgid):
return model.mgid2piece.has_key(mgid)
def mgid2pieces(mgid):
''' return cell with existing pieces for mgid; None if does not exist.'''
if mpiece_exists(mgid):
return model.mgid2piece[mgid]
return None
def msecden(mgid, i):
''' return secondary dendrite if it exists, otherwise None.'''
c = mgid2pieces(mgid)
if c and h.section_exists("secden", i, c):
return c.secden[i]
def chk_gran_conn():
model = modeldata.getmodel()
ggids = model.granule_gids
mgids = model.mitral_gids
mgrss = model.mgrss
# for each granule, list of all mgid it connects to and vice versa
g2m = {}
m2g = {}
for mgrs in mgrss.values():
ggid = mgrs.ggid
mgid = mgrs.mgid
if mgrs.gd: # granule exists on this rank
if ggid not in g2m:
g2m[ggid] = []
g2m[ggid].append(mgid)
if mgrs.md: # mitral or mtufted exists on this rank
if mgid not in m2g:
m2g[mgid] = []
m2g[mgid].append(ggid)
a = 'Number of granules that go to mitral and mtufted (regardless of glomerulus)'
na = 0
for ms in g2m.values():
nmit = 0
nmtuft = 0
for mgid in ms:
nmit += 1 if gidfunc.ismitral(mgid) else 0
nmtuft += 1 if gidfunc.ismtufted(mgid) else 0
na += 1 if nmit > 0 and nmtuft > 0 else 0
na = int(pc.allreduce(na, 1))
if pc.id() == 0:
print("%d %s" % (na, a))
a = '[total,imin,imax,iavg,xmin,xmax,xavg] granule connections to mitral'
b = '[total,imin,imax,iavg,xmin,xmax,xavg] granule connections to mtuft'
nab = [[[], [], [], []], [[], [], [], []]]
for ms in g2m.values():
# assume if first is mitral then all are mitrals otherwise mtuft
dat = nab[0] if gidfunc.ismitral(ms[0]) else nab[1]
# want to distinguish intra mitral, distinct mitral, and any mitral
# generate map of distinct:count for that distinct instance
distinct = {}
for d in set(ms):
distinct[d] = 0
for m in ms:
distinct[m] += 1
n_any = len(ms)
n_distinct_m = len(distinct)
n_intra_m_min = min(distinct.values())
n_intra_m_max = max(distinct.values())
dat[0].append(n_any)
dat[1].append(n_distinct_m)
dat[2].append(n_intra_m_min)
dat[3].append(n_intra_m_max)
def nhost_len(v):
return int(pc.allreduce(len(v), 1))
def nhost_sum(v):
return int(pc.allreduce(sum(v), 1))
def nhost_max(v):
return int(pc.allreduce(max(v), 2))
def nhost_min(v):
return int(pc.allreduce(min(v), 3))
def pr0(s):
if pc.id() == 0:
print(s)
def pr(title, dat):
i = 0
pr0(title)
totg = nhost_len(dat[i])
pr0("%d granules of this type" % totg)
pr0("%d total connections" % nhost_sum(dat[i]))
if totg:
pr0("%d min ; %d max ; %g avg" % (nhost_min(
dat[i]), nhost_max(dat[i]), float(nhost_sum(dat[i])) / totg))
pr(a, nab[0])
pr(b, nab[1])
