def main():
"""Main"""
nml2_cell_dir = '../NeuroML2/'
net_ref = "ManyCells"
net_doc = neuroml.NeuroMLDocument(id=net_ref)
net = neuroml.Network(id=net_ref)
net_doc.networks.append(net)
cell_dirs = [
f
for f in os.listdir
('.')
if(os.path.isdir(f) and os.path.isfile(f + '/.provenance.json'))]
clear_neuron()
inputs_list = []
for index, cell_dir in enumerate(cell_dirs):
inputs_list.append((index, cell_dir, nml2_cell_dir, len(cell_dirs)))
# Parallelise the generation of the files using multiprocessing if the
# -parallel option is specified
if parallel:
import multiprocessing
pool = multiprocessing.Pool(maxtasksperchild=1) # pylint: disable=E1123
nml_cell_files = pool.map(process_celldir, inputs_list, chunksize=1)
else:
nml_cell_files = map(process_celldir, inputs_list)
for nml_cell_file, pop in nml_cell_files:
net.populations.append(pop)
net_doc.includes.append(neuroml.IncludeType(nml_cell_file))
count = len(cell_dirs)
if not make_zips:
net_file = '%s/%s.net.nml' % (nml2_cell_dir, net_ref)
neuroml.writers.NeuroMLWriter.write(net_doc, net_file)
print(
"Written network with %i cells in network to: %s" %
(count, net_file))
pynml.nml2_to_svg(net_file)
def main():
"""Main"""
nml2_cell_dir = '../NeuroML2/'
net_ref = "ManyCells"
net_doc = neuroml.NeuroMLDocument(id=net_ref)
net = neuroml.Network(id=net_ref)
net_doc.networks.append(net)
cell_dirs = [
f for f in os.listdir('.')
if (os.path.isdir(f) and os.path.isfile(f + '/.provenance.json'))
]
clear_neuron()
inputs_list = []
for index, cell_dir in enumerate(cell_dirs):
inputs_list.append((index, cell_dir, nml2_cell_dir, len(cell_dirs)))
# Parallelise the generation of the files using multiprocessing if the
# -parallel option is specified
if parallel:
import multiprocessing
pool = multiprocessing.Pool(maxtasksperchild=1) # pylint: disable=E1123
nml_cell_files = pool.map(process_celldir, inputs_list, chunksize=1)
else:
nml_cell_files = map(process_celldir, inputs_list)
for nml_cell_file, pop in nml_cell_files:
net.populations.append(pop)
net_doc.includes.append(neuroml.IncludeType(nml_cell_file))
count = len(cell_dirs)
if not make_zips:
net_file = '%s/%s.net.nml' % (nml2_cell_dir, net_ref)
neuroml.writers.NeuroMLWriter.write(net_doc, net_file)
print("Written network with %i cells in network to: %s" %
(count, net_file))
pynml.nml2_to_svg(net_file)
intracellular_properties = neuroml.IntracellularProperties(resistivities=resistivities, species=species)
biophysical_properties = neuroml.BiophysicalProperties(id="biophys",
intracellular_properties=intracellular_properties,
membrane_properties=membrane_properties)
cell.biophysical_properties = biophysical_properties
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
pynml.nml2_to_svg(nml_cell_loc)
pref_duration_ms = 2500
pref_dt_ms = 0.005 # used in Allen Neuron runs
new_nml_file_name = "Network_%s.net.nml"%model_id
new_net_loc = "%s/%s"%(nml2_cell_dir, new_nml_file_name)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net = new_net_doc.networks[0]
new_net_doc.notes = notes
new_net.properties.append(neuroml.Property('recommended_duration_ms',pref_duration_ms))
new_net.properties.append(neuroml.Property('recommended_dt_ms',pref_dt_ms))
input_list.input.append(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,
nml2_cell_dir,
copy_neuroml = False,
seed=1234)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
net_doc.includes.append(IncludeType(nml_cell_file))
pop = Population(id="Pop_%s"%bbp_ref, component=bbp_ref+'_0_0', type="populationList")
net.populations.append(pop)
inst = Instance(id="0")
pop.instances.append(inst)
width = 6
X = count%width
Z = (count -X) / width
input_list.input.append(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,
nml2_cell_dir,
copy_neuroml=False,
seed=1234)
pynml.nml2_to_svg(nml_net_loc)
clear_neuron()
net_doc.includes.append(IncludeType(nml_cell_file))
pop = Population(id="Pop_%s" % bbp_ref,
component=bbp_ref + '_0_0',
type="populationList")
net.populations.append(pop)
inst = Instance(id="0")
pop.instances.append(inst)
width = 6
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
inst.location = Location(x=X, y=Y, z=Z)
count+=1
net_file = '%s.net.nml'%(net_ref)
writers.NeuroMLWriter.write(net_doc, net_file)
print("Written network with %i cells in network to: %s"%(count,net_file))
from neuroml.utils import validate_neuroml2
validate_neuroml2(net_file)
generate_lems_file_for_neuroml("Sim_"+net_ref,
net_file,
net_ref,
50,
0.025,
"LEMS_%s.xml"%net_ref,
".",
gen_plots_for_all_v = True,
plot_all_segments = True,
gen_saves_for_all_v = True,
save_all_segments = True,
copy_neuroml = False,
seed = 1234)
pynml.nml2_to_svg(net_file)
Y = ys[layer][0] + random.random() * (ys[layer][1] - ys[layer][0])
inst.location = Location(x=X, y=Y, z=Z)
count += 1
net_file = '%s.net.nml' % (net_ref)
writers.NeuroMLWriter.write(net_doc, net_file)
print("Written network with %i cells in network to: %s" % (count, net_file))
from neuroml.utils import validate_neuroml2
validate_neuroml2(net_file)
generate_lems_file_for_neuroml("Sim_" + net_ref,
net_file,
net_ref,
50,
0.025,
"LEMS_%s.xml" % net_ref,
".",
gen_plots_for_all_v=True,
plot_all_segments=True,
gen_saves_for_all_v=True,
save_all_segments=True,
copy_neuroml=False,
seed=1234)
pynml.nml2_to_svg(net_file)
for layer in ['L23','L4','L5','L6']:
for cell in cells[layer]:
new_ref, new_cell_file = get_oriented_cell(cell)
net_doc.includes.append(IncludeType(new_cell_file))
pop = Population(id="Pop_%s"%new_ref, component=new_ref, type="populationList")
net.populations.append(pop)
for i in range(population_nums[layer]):
inst = Instance(id=i)
pop.instances.append(inst)
X=xs[0]+random.random()*(xs[1]-xs[0])
Z=zs[0]+random.random()*(zs[1]-zs[0])
Y = ys[layer][0]+random.random()*(ys[layer][1]-ys[layer][0])
inst.location = Location(x=X, y=Y, z=Z)
count+=1
net_file = '%s.net.nml'%(net_ref)
writers.NeuroMLWriter.write(net_doc, net_file)
print("Written network with %i cells in network to: %s"%(count,net_file))
pynml.nml2_to_svg(net_file, max_memory="2G")
intracellular_properties = neuroml.IntracellularProperties(resistivities=resistivities, species=species)
biophysical_properties = neuroml.BiophysicalProperties(id="biophys",
intracellular_properties=intracellular_properties,
membrane_properties=membrane_properties)
cell.biophysical_properties = biophysical_properties
pynml.write_neuroml2_file(nml_doc, nml_cell_loc)
pynml.nml2_to_svg(nml_cell_loc)
new_nml_file_name = "Network_%s.net.nml"%model_id
new_net_loc = "%s/%s"%(nml2_cell_dir, new_nml_file_name)
new_net_doc = pynml.read_neuroml2_file(nml_net_loc)
new_net = new_net_doc.networks[0]
new_net_doc.notes = notes
for k in metadata_info.keys():
if k.startswith("AIBS:"):
p = neuroml.Property(tag=k, value=metadata_info[k])
new_net_doc.properties.append(p)
new_ref, new_cell_file = get_oriented_cell(cell)
net_doc.includes.append(IncludeType(new_cell_file))
pop = Population(id="Pop_%s" % new_ref,
component=new_ref,
type="populationList")
net.populations.append(pop)
for i in range(population_nums[layer]):
inst = Instance(id=i)
pop.instances.append(inst)
X = xs[0] + random.random() * (xs[1] - xs[0])
Z = zs[0] + random.random() * (zs[1] - zs[0])
Y = ys[layer][0] + random.random() * (ys[layer][1] - ys[layer][0])
inst.location = Location(x=X, y=Y, z=Z)
count += 1
net_file = '%s.net.nml' % (net_ref)
writers.NeuroMLWriter.write(net_doc, net_file)
print("Written network with %i cells in network to: %s" % (count, net_file))
pynml.nml2_to_svg(net_file, max_memory="2G")
