def process_celldir(inputs):
"""Process cell directory"""
count, cell_dir, nml2_cell_dir, total_count = inputs
local_nml2_cell_dir = os.path.join("..", nml2_cell_dir)
print(
'\n\n************************************************************\n\n'
'Parsing %s (cell %i/%i)\n' % (cell_dir, count, total_count))
if os.path.isdir(cell_dir):
old_cwd = os.getcwd()
os.chdir(cell_dir)
else:
old_cwd = os.getcwd()
os.chdir('../' + cell_dir)
if make_zips:
nml2_cell_dir = '%s/%s' % (zips_dir, cell_dir)
if not os.path.isdir(nml2_cell_dir):
os.mkdir(nml2_cell_dir)
print("Generating into %s" % nml2_cell_dir)
bbp_ref = None
template_file = open('template.hoc', 'r')
for line in template_file:
if line.startswith('begintemplate '):
bbp_ref = line.split(' ')[1].strip()
print(
' > Assuming cell in directory %s is in a template named %s' %
(cell_dir, bbp_ref))
load_cell_file = 'loadcell.hoc'
variables = {}
variables['cell'] = bbp_ref
variables['groups_info_file'] = groups_info_file
template = """
///////////////////////////////////////////////////////////////////////////////
//
// NOTE: This file is not part of the original BBP cell model distribution
// It has been generated by ../ParseAll.py to facilitate loading of the cell
// into NEURON for exporting the model morphology to NeuroML2
//
//////////////////////////////////////////////////////////////////////////////
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
//======================== settings ===================================
v_init = -80
hyp_amp = -0.062866
step_amp = 0.3112968
tstop = 3000
//=================== creating cell object ===========================
load_file("import3d.hoc")
objref cell
// Using 1 to force loading of the file, in case file with same name was loaded
// before...
load_file(1, "constants.hoc")
load_file(1, "morphology.hoc")
load_file(1, "biophysics.hoc")
print "Loaded morphology and biophysics..."
load_file(1, "synapses/synapses.hoc")
load_file(1, "template.hoc")
print "Loaded template..."
load_file(1, "createsimulation.hoc")
create_cell(0)
print "Created new cell using loadcell.hoc: {{ cell }}"
define_shape()
wopen("{{ groups_info_file }}")
fprint("//Saving information on groups in this cell...\\n")
fprint("- somatic\\n")
forsec {{ cell }}[0].somatic {
fprint("%s\\n",secname())
}
fprint("- basal\\n")
forsec {{ cell }}[0].basal {
fprint("%s\\n",secname())
}
fprint("- axonal\\n")
forsec {{ cell }}[0].axonal {
fprint("%s\\n",secname())
}
fprint("- apical\\n")
forsec {{ cell }}[0].apical {
fprint("%s\\n",secname())
}
wopen()
"""
t = Template(template)
contents = t.render(variables)
load_cell = open(load_cell_file, 'w')
load_cell.write(contents)
load_cell.close()
print(' > Written %s' % load_cell_file)
if os.path.isfile(load_cell_file):
cell_info = parse_cell_info_file(cell_dir)
nml_file_name = "%s.net.nml" % bbp_ref
nml_net_loc = "%s/%s" % (local_nml2_cell_dir, nml_file_name)
nml_cell_file = "%s_0_0.cell.nml" % bbp_ref
nml_cell_loc = "%s/%s" % (local_nml2_cell_dir, nml_cell_file)
print(' > Loading %s and exporting to %s' %
(load_cell_file, nml_net_loc))
export_to_neuroml2(load_cell_file,
nml_net_loc,
separateCellFiles=True,
includeBiophysicalProperties=False)
print(' > Exported to: %s and %s using %s' %
(nml_net_loc, nml_cell_loc, load_cell_file))
nml_doc = pynml.read_neuroml2_file(nml_cell_loc)
cell = nml_doc.cells[0]
print(' > Adding groups from: %s' % groups_info_file)
groups = {}
current_group = None
for line in open(groups_info_file):
if not line.startswith('//'):
if line.startswith('- '):
current_group = line[2:-1]
print(' > Adding group: [%s]' % current_group)
groups[current_group] = []
else:
section = line.split('.')[1].strip()
segment_group = section.replace('[', '_').replace(']', '')
groups[current_group].append(segment_group)
for g in groups.keys():
new_seg_group = neuroml.SegmentGroup(id=g)
cell.morphology.segment_groups.append(new_seg_group)
for sg in groups[g]:
new_seg_group.includes.append(neuroml.Include(sg))
if g in ['basal', 'apical']:
new_seg_group.inhomogeneous_parameters.append(
neuroml.InhomogeneousParameter(
id="PathLengthOver_" + g,
variable="p",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(
translation_start="0")))
ignore_chans = [
'Ih', 'Ca_HVA', 'Ca_LVAst', 'Ca', "SKv3_1", "SK_E2",
"CaDynamics_E2", "Nap_Et2", "Im", "K_Tst", "NaTa_t", "K_Pst",
"NaTs2_t"
]
# ignore_chans=['StochKv','StochKv_deterministic']
ignore_chans = []
bp, incl_chans = get_biophysical_properties(
cell_info['e-type'],
ignore_chans=ignore_chans,
templates_json="../templates.json")
cell.biophysical_properties = bp
print("Set biophysical properties")
notes = ''
notes += \
"\n\nExport of a cell model obtained from the BBP Neocortical" \
"Microcircuit Collaboration Portal into NeuroML2" \
"\n\n******************************************************\n*" \
" This export to NeuroML2 has not yet been fully validated!!" \
"\n* Use with caution!!\n***********************************" \
"*******************\n\n"
if len(ignore_chans) > 0:
notes += "Ignored channels = %s\n\n" % ignore_chans
notes += "For more information on this cell model see: " \
"https://bbp.epfl.ch/nmc-portal/microcircuit#/metype/%s/" \
"details\n\n" % cell_info['me-type']
cell.notes = notes
for channel in incl_chans:
nml_doc.includes.append(neuroml.IncludeType(href="%s" % channel))
if make_zips:
print("Copying %s to zip folder" % channel)
shutil.copyfile('../../NeuroML2/%s' % channel,
'%s/%s' % (local_nml2_cell_dir, channel))
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
stim_ref = 'stepcurrent3'
stim_ref_hyp = '%s_hyp' % stim_ref
stim_sim_duration = 3000
stim_hyp_amp, stim_amp = get_stimulus_amplitudes(bbp_ref)
stim_del = '700ms'
stim_dur = '2000ms'
new_net_loc = "%s/%s.%s.net.nml" % (local_nml2_cell_dir, bbp_ref,
stim_ref)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net_doc.notes = notes
stim_hyp = neuroml.PulseGenerator(id=stim_ref_hyp,
delay="0ms",
duration="%sms" % stim_sim_duration,
amplitude=stim_hyp_amp)
new_net_doc.pulse_generators.append(stim_hyp)
stim = neuroml.PulseGenerator(id=stim_ref,
delay=stim_del,
duration=stim_dur,
amplitude=stim_amp)
new_net_doc.pulse_generators.append(stim)
new_net = new_net_doc.networks[0]
pop_id = new_net.populations[0].id
pop_comp = new_net.populations[0].component
input_list = neuroml.InputList(id="%s_input" % stim_ref_hyp,
component=stim_ref_hyp,
populations=pop_id)
syn_input = neuroml.Input(id=0,
target="../%s/0/%s" % (pop_id, pop_comp),
destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
input_list = neuroml.InputList(id="%s_input" % stim_ref,
component=stim_ref,
populations=pop_id)
syn_input = neuroml.Input(id=0,
target="../%s/0/%s" % (pop_id, pop_comp),
destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
pynml.write_neuroml2_file(new_net_doc, new_net_loc)
generate_lems_file_for_neuroml(cell_dir,
new_net_loc,
"network",
stim_sim_duration,
0.025,
"LEMS_%s.xml" % cell_dir,
local_nml2_cell_dir,
copy_neuroml=False,
seed=1234)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
pop = neuroml.Population(id="Pop_%s" % bbp_ref,
component=bbp_ref + '_0_0',
type="populationList")
inst = neuroml.Instance(id="0")
pop.instances.append(inst)
width = 6
X = count % width
Z = (count - X) / width
inst.location = neuroml.Location(x=300 * X, y=0, z=300 * Z)
count += 1
if make_zips:
zip_file = "%s/%s.zip" % (zips_dir, cell_dir)
print("Creating zip file: %s" % zip_file)
with zipfile.ZipFile(zip_file, 'w') as myzip:
for next_file in os.listdir(local_nml2_cell_dir):
next_file = '%s/%s' % (local_nml2_cell_dir, next_file)
arcname = next_file[len(zips_dir):]
print("Adding : %s as %s" % (next_file, arcname))
myzip.write(next_file, arcname)
os.chdir(old_cwd)
return nml_cell_file, pop
def process_celldir(inputs):
"""Process cell directory"""
count, cell_dir, nml2_cell_dir, total_count = inputs
local_nml2_cell_dir = os.path.join("..", nml2_cell_dir)
print(
"\n\n************************************************************\n\n"
"Parsing %s (cell %i/%i)\n" % (cell_dir, count, total_count)
)
if os.path.isdir(cell_dir):
old_cwd = os.getcwd()
os.chdir(cell_dir)
else:
old_cwd = os.getcwd()
os.chdir("../" + cell_dir)
if make_zips:
nml2_cell_dir = "%s/%s" % (zips_dir, cell_dir)
if not os.path.isdir(nml2_cell_dir):
os.mkdir(nml2_cell_dir)
print("Generating into %s" % nml2_cell_dir)
bbp_ref = None
template_file = open("template.hoc", "r")
for line in template_file:
if line.startswith("begintemplate "):
bbp_ref = line.split(" ")[1].strip()
print(" > Assuming cell in directory %s is in a template named %s" % (cell_dir, bbp_ref))
load_cell_file = "loadcell.hoc"
variables = {}
variables["cell"] = bbp_ref
variables["groups_info_file"] = groups_info_file
template = """
///////////////////////////////////////////////////////////////////////////////
//
// NOTE: This file is not part of the original BBP cell model distribution
// It has been generated by ../ParseAll.py to facilitate loading of the cell
// into NEURON for exporting the model morphology to NeuroML2
//
//////////////////////////////////////////////////////////////////////////////
load_file("stdrun.hoc")
objref cvode
cvode = new CVode()
cvode.active(1)
//======================== settings ===================================
v_init = -80
hyp_amp = -0.062866
step_amp = 0.3112968
tstop = 3000
//=================== creating cell object ===========================
load_file("import3d.hoc")
objref cell
// Using 1 to force loading of the file, in case file with same name was loaded
// before...
load_file(1, "constants.hoc")
load_file(1, "morphology.hoc")
load_file(1, "biophysics.hoc")
print "Loaded morphology and biophysics..."
load_file(1, "synapses/synapses.hoc")
load_file(1, "template.hoc")
print "Loaded template..."
load_file(1, "createsimulation.hoc")
create_cell(0)
print "Created new cell using loadcell.hoc: {{ cell }}"
define_shape()
wopen("{{ groups_info_file }}")
fprint("//Saving information on groups in this cell...\\n")
fprint("- somatic\\n")
forsec {{ cell }}[0].somatic {
fprint("%s\\n",secname())
}
fprint("- basal\\n")
forsec {{ cell }}[0].basal {
fprint("%s\\n",secname())
}
fprint("- axonal\\n")
forsec {{ cell }}[0].axonal {
fprint("%s\\n",secname())
}
fprint("- apical\\n")
forsec {{ cell }}[0].apical {
fprint("%s\\n",secname())
}
wopen()
"""
t = Template(template)
contents = t.render(variables)
load_cell = open(load_cell_file, "w")
load_cell.write(contents)
load_cell.close()
print(" > Written %s" % load_cell_file)
if os.path.isfile(load_cell_file):
cell_info = parse_cell_info_file(cell_dir)
nml_file_name = "%s.net.nml" % bbp_ref
nml_net_loc = "%s/%s" % (local_nml2_cell_dir, nml_file_name)
nml_cell_file = "%s_0_0.cell.nml" % bbp_ref
nml_cell_loc = "%s/%s" % (local_nml2_cell_dir, nml_cell_file)
print(" > Loading %s and exporting to %s" % (load_cell_file, nml_net_loc))
export_to_neuroml2(load_cell_file, nml_net_loc, separateCellFiles=True, includeBiophysicalProperties=False)
print(" > Exported to: %s and %s using %s" % (nml_net_loc, nml_cell_loc, load_cell_file))
nml_doc = pynml.read_neuroml2_file(nml_cell_loc)
cell = nml_doc.cells[0]
print(" > Adding groups from: %s" % groups_info_file)
groups = {}
current_group = None
for line in open(groups_info_file):
if not line.startswith("//"):
if line.startswith("- "):
current_group = line[2:-1]
print(" > Adding group: [%s]" % current_group)
groups[current_group] = []
else:
section = line.split(".")[1].strip()
segment_group = section.replace("[", "_").replace("]", "")
groups[current_group].append(segment_group)
for g in groups.keys():
new_seg_group = neuroml.SegmentGroup(id=g)
cell.morphology.segment_groups.append(new_seg_group)
for sg in groups[g]:
new_seg_group.includes.append(neuroml.Include(sg))
if g in ["basal", "apical"]:
new_seg_group.inhomogeneous_parameters.append(
neuroml.InhomogeneousParameter(
id="PathLengthOver_" + g,
variable="p",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(translation_start="0"),
)
)
ignore_chans = [
"Ih",
"Ca_HVA",
"Ca_LVAst",
"Ca",
"SKv3_1",
"SK_E2",
"CaDynamics_E2",
"Nap_Et2",
"Im",
"K_Tst",
"NaTa_t",
"K_Pst",
"NaTs2_t",
]
# ignore_chans=['StochKv','StochKv_deterministic']
ignore_chans = []
bp, incl_chans = get_biophysical_properties(
cell_info["e-type"], ignore_chans=ignore_chans, templates_json="../templates.json"
)
cell.biophysical_properties = bp
print("Set biophysical properties")
notes = ""
notes += (
"\n\nExport of a cell model obtained from the BBP Neocortical"
"Microcircuit Collaboration Portal into NeuroML2"
"\n\n******************************************************\n*"
" This export to NeuroML2 has not yet been fully validated!!"
"\n* Use with caution!!\n***********************************"
"*******************\n\n"
)
if len(ignore_chans) > 0:
notes += "Ignored channels = %s\n\n" % ignore_chans
notes += (
"For more information on this cell model see: "
"https://bbp.epfl.ch/nmc-portal/microcircuit#/metype/%s/"
"details\n\n" % cell_info["me-type"]
)
cell.notes = notes
for channel in incl_chans:
nml_doc.includes.append(neuroml.IncludeType(href="%s" % channel))
if make_zips:
print("Copying %s to zip folder" % channel)
shutil.copyfile("../../NeuroML2/%s" % channel, "%s/%s" % (local_nml2_cell_dir, channel))
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
stim_ref = "stepcurrent3"
stim_ref_hyp = "%s_hyp" % stim_ref
stim_sim_duration = 3000
stim_hyp_amp, stim_amp = get_stimulus_amplitudes(bbp_ref)
stim_del = "700ms"
stim_dur = "2000ms"
new_net_loc = "%s/%s.%s.net.nml" % (local_nml2_cell_dir, bbp_ref, stim_ref)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net_doc.notes = notes
stim_hyp = neuroml.PulseGenerator(
id=stim_ref_hyp, delay="0ms", duration="%sms" % stim_sim_duration, amplitude=stim_hyp_amp
)
new_net_doc.pulse_generators.append(stim_hyp)
stim = neuroml.PulseGenerator(id=stim_ref, delay=stim_del, duration=stim_dur, amplitude=stim_amp)
new_net_doc.pulse_generators.append(stim)
new_net = new_net_doc.networks[0]
pop_id = new_net.populations[0].id
pop_comp = new_net.populations[0].component
input_list = neuroml.InputList(id="%s_input" % stim_ref_hyp, component=stim_ref_hyp, populations=pop_id)
syn_input = neuroml.Input(id=0, target="../%s/0/%s" % (pop_id, pop_comp), destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
input_list = neuroml.InputList(id="%s_input" % stim_ref, component=stim_ref, populations=pop_id)
syn_input = neuroml.Input(id=0, target="../%s/0/%s" % (pop_id, pop_comp), destination="synapses")
input_list.input.append(syn_input)
new_net.input_lists.append(input_list)
pynml.write_neuroml2_file(new_net_doc, new_net_loc)
generate_lems_file_for_neuroml(
cell_dir,
new_net_loc,
"network",
stim_sim_duration,
0.025,
"LEMS_%s.xml" % cell_dir,
local_nml2_cell_dir,
copy_neuroml=False,
seed=1234,
)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
pop = neuroml.Population(id="Pop_%s" % bbp_ref, component=bbp_ref + "_0_0", type="populationList")
inst = neuroml.Instance(id="0")
pop.instances.append(inst)
width = 6
X = count % width
Z = (count - X) / width
inst.location = neuroml.Location(x=300 * X, y=0, z=300 * Z)
count += 1
if make_zips:
zip_file = "%s/%s.zip" % (zips_dir, cell_dir)
print("Creating zip file: %s" % zip_file)
with zipfile.ZipFile(zip_file, "w") as myzip:
for next_file in os.listdir(local_nml2_cell_dir):
next_file = "%s/%s" % (local_nml2_cell_dir, next_file)
arcname = next_file[len(zips_dir) :]
print("Adding : %s as %s" % (next_file, arcname))
myzip.write(next_file, arcname)
os.chdir(old_cwd)
return nml_cell_file, pop
from pyneuroml.neuron import export_to_neuroml2
export_to_neuroml2("test.hoc", "test.morphonly.cell.nml", includeBiophysicalProperties=False)
export_to_neuroml2("test.hoc", "test.biophys.cell.nml", includeBiophysicalProperties=True)
from pyneuroml.neuron import export_to_neuroml2
# from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/tester.hoc",
"test.cell.nml",
includeBiophysicalProperties=True,
known_rev_potentials={'Ih':-32.9, 'cal':0, 'cat':0,
'kca':-95, 'Ika':-95, 'IM':-95, 'Ikdrf':-95, 'Ikdrs':-95, 'passsd':-70,
'Ikdrfaxon':-95, 'Ikdrsaxon':-95, 'passaxon':-70})
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")
from pyneuroml.neuron import export_to_neuroml2
export_to_neuroml2("test.hoc", "test.morphonly.nml", includeBiophysicalProperties=False)
#export_to_neuroml2("test.hoc", "test.biophys.nml", includeBiophysicalProperties=True)
from pyneuroml.neuron import export_to_neuroml2
# from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/test/test_Poirazi2003.hoc",
"poirazi.cell.nml",
includeBiophysicalProperties=True,
known_rev_potentials={'cal':-1, 'calH':140, 'can':140, 'car':140, 'cat':-1})
from pyneuroml.neuron import export_to_neuroml2
cells = ['axoaxonic', 'bistratified', 'cck', 'cutsuridis', 'ivy', 'ngf', 'olm', 'poolosyn', 'pvbasket', 'sca']
for cell in cells:
export_to_neuroml2("%s.hoc"%cell,
"cells/%s.cell.nml"%cell,
includeBiophysicalProperties=True,
known_rev_potentials={'ch_CavL':-1, 'ch_CavN':-1, 'ch_HCNolm':-30})
print "##### %s done #####"%cell
from pyneuroml.neuron import export_to_neuroml2
# from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/test/test_Poirazi2003.hoc",
"poirazi.cell.nml",
includeBiophysicalProperties=True,
known_rev_potentials={
'cal': -1,
'calH': 140,
'can': 140,
'car': 140,
'cat': -1
})
from pyneuroml.neuron import export_to_neuroml2
# from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/test_granulecell.hoc",
"granule.cell.nml",
includeBiophysicalProperties=True,
known_rev_potentials={'ichan2':0})
from pyneuroml.neuron import export_to_neuroml2
from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/tester_Case2.hoc",
"SaragaOLM_Case2.cell.nml",
includeBiophysicalProperties=True,
known_rev_potentials={'IA':-100, 'Ih':-32.9})
def exportToNML(cells):
nml_net_file = "../NeuroML2/GranuleCells/Exported/GCnet%iG.net.nml" % len(cells)
export_to_neuroml2(None,
nml_net_file,
includeBiophysicalProperties=False,
separateCellFiles=True)
# Rename files so their cell GIDs are preserved
for gcid in cells.keys():
fileId = cells[gcid]['index']
oldFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml' % fileId
newFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml_TEMP' % gcid
# Using TEMP files to avoid naming conflicts
os.rename(oldFile, newFile)
# Remove temp files after all have been renamed
for gcid in cells.keys():
oldFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml_TEMP' % gcid
newFile = '../NeuroML2/GranuleCells/Exported/Granule_0_%i.cell.nml' % gcid
os.rename(oldFile, newFile)
for gcid in cells.keys():
cell, nml_doc, nml_cell_file = readGCnml(gcid)
print("Loaded GC cell %i with %i segments"%(gcid, len(cell.morphology.segments)))
# Change cell ID to preserve GCID
cell.id = "Granule_0_%i" % gcid
# Change segment ids to start at 0 and increment
exportHelper.resetRoot(cell)
# 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")
cell.morphology.segments.append(neuroml.Segment(id="spinePLACEHOLDER"))
# Save the new NML
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
# Replace placeholders with contents from GranuleCell...xml files
replaceChannelPlaceholders(nml_cell_file)
cell, nml_doc, nml_cell_file = readGCnml(gcid)
# Fix the fractionAlong parent segment bug ( https://github.com/NeuroML/org.neuroml.export/issues/46 )
exportHelper.splitSegmentAlongFraction(cell, "Seg0_priden", "priden", 0.8, "Seg0_priden2_0")
# Orient cell along the versor
versor = granules.granule_position_orientation(gcid)[1]
for seg in cell.morphology.segments:
segLength = seg.length
if seg.parent is not None:
parentDistal = [parent for parent in cell.morphology.segments if parent.id == seg.parent.segments][0].distal
seg.proximal.x = parentDistal.x
seg.proximal.y = parentDistal.y
seg.proximal.z = parentDistal.z
seg.distal = setAlongVersor(seg.distal, versor, seg.proximal, segLength)
# Make sure spine is in the all group
[group for group in cell.morphology.segment_groups if group.id == 'all'][0]\
.includes\
.append(neuroml.Include(segment_groups='spine_group'))\
# and Dendrite group
[group for group in cell.morphology.segment_groups if group.id == 'dendrite_group'][0]\
.includes\
.append(neuroml.Include(segment_groups='spine_group'))
# Save orientation
pynml.write_neuroml2_file(nml_doc, nml_cell_file)
print(nml_cell_file)
from pyneuroml.neuron import export_to_neuroml2
#export_to_neuroml2("load_bc6.hoc", "BC6.cell.nml", includeBiophysicalProperties=False)
export_to_neuroml2("load_bc2.hoc", "BC2.cell.nml", includeBiophysicalProperties=False)
if os.path.isfile(load_cell_file):
cell_info = parse_cell_info_file(cell_dir)
nml_file_name = "%s.net.nml"%bbp_ref
nml_net_loc = "%s/%s"%(nml2_cell_dir,nml_file_name)
nml_cell_file = "%s_0_0.cell.nml"%bbp_ref
nml_cell_loc = "%s/%s"%(nml2_cell_dir,nml_cell_file)
print(' > Loading %s and exporting to %s'%(load_cell_file,nml_net_loc))
export_to_neuroml2(load_cell_file,
nml_net_loc,
separateCellFiles=True,
includeBiophysicalProperties=False)
print(' > Exported to: %s and %s using %s'%(nml_net_loc, nml_cell_loc, load_cell_file))
nml_doc = pynml.read_neuroml2_file(nml_cell_loc)
cell = nml_doc.cells[0]
print(' > Adding groups from: %s'%groups_info_file)
groups = {}
current_group = None
for line in open(groups_info_file):
if not line.startswith('//'):
if line.startswith('- '):
current_group = line[2:-1]
from pyneuroml.neuron import export_to_neuroml2
export_to_neuroml2("091008A2.hoc",
"../NeuroML2/dLGN.cell.nml",
includeBiophysicalProperties=False)
#export_to_neuroml2("test.hoc", "test.biophys.nml", includeBiophysicalProperties=True)
h.finitialize()
h.psection()
nml_file_name = "%s.net.nml"%model_id
nml_net_loc = "%s/%s"%(nml2_cell_dir,nml_file_name)
nml_cell_file0 = "Cell0.cell.nml"
nml_cell_loc0 = "%s/%s"%(nml2_cell_dir,nml_cell_file0)
nml_cell_file = "Cell_%s.cell.nml"%model_id
nml_cell_loc = "%s/%s"%(nml2_cell_dir,nml_cell_file)
print(' > Exporting to %s'%(nml_net_loc))
export_to_neuroml2(None,
nml_net_loc,
separateCellFiles=True,
includeBiophysicalProperties=False)
print(' > Exported to: %s and %s'%(nml_net_loc, nml_cell_loc))
clear_neuron()
nml_doc = pynml.read_neuroml2_file(nml_cell_loc0)
cell = nml_doc.cells[0]
cell.id = 'Cell_%s'%model_id
notes = ''
notes+="\n\nExport of a cell model (%s) obtained from the Allen Institute Cell Types Database into NeuroML2"%model_id + \
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")
from pyneuroml.neuron import export_to_neuroml2
import sys
import os
os.chdir("../NEURON/test")
sys.path.append(".")
export_to_neuroml2("load_model2.hoc",
"../../NeuroML2/L5_model2.cell.nml",
includeBiophysicalProperties=False,
known_rev_potentials={
"na": 60,
"k": -90,
"ca": 140
})
from pyneuroml.neuron import export_to_neuroml2
from pyneuroml.neuron.nrn_export_utils import clear_neuron
export_to_neuroml2("../NEURON/initFig9BCase2", "OLM_Fig9BCase2_morph.cell.nml", includeBiophysicalProperties=False)
'''
clear_neuron()
os.chdir('../NeuroML2')
export_to_neuroml2("create_cell.hoc", "../NeuroML2/OLM_Fig9BCase2.cell.nml", includeBiophysicalProperties=True)
'''
