def _morphology(self):
"""Return the morphology of the cell. Currently this is restricted to
`Neuron <#neuron>`_ objects.
"""
morph_name = "morphology_" + str(next(self.name()))
# Query for segments
query = segment_query.substitute(morph_name=morph_name)
qres = self.rdf.query(query, initNs=ns)
morph = neuroml.Morphology(id=morph_name)
for r in qres:
par = False
if r['par_id']:
par = neuroml.SegmentParent(segments=str(r['par_id']))
s = neuroml.Segment(name=str(r['seg_name']),
id=str(r['seg_id']),
parent=par)
else:
s = neuroml.Segment(name=str(r['seg_name']),
id=str(r['seg_id']))
if r['x_prox']:
loop_prox = neuroml.Point3DWithDiam(
*(r[x] for x in ['x_prox', 'y_prox', 'z_prox', 'd_prox']))
s.proximal = loop_prox
loop = neuroml.Point3DWithDiam(*(r[x]
for x in ['x', 'y', 'z', 'd']))
s.distal = loop
morph.segments.append(s)
# Query for segment groups
query = segment_group_query.substitute(morph_name=morph_name)
qres = self.rdf.query(query, initNs=ns)
for r in qres:
s = neuroml.SegmentGroup(id=r['gid'])
if r['member']:
m = neuroml.Member()
m.segments = str(r['member'])
s.members.append(m)
elif r['include']:
i = neuroml.Include()
i.segment_groups = str(r['include'])
s.includes.append(i)
morph.segment_groups.append(s)
return morph
def morphology(self):
morph_name = "morphology_" + str(self.name())
# Query for segments
query = segment_query.substitute(morph_name=morph_name)
qres = self['semantic_net'].query(query, initNs=ns)
morph = neuroml.Morphology(id=morph_name)
for r in qres:
par = False
if r['par_id']:
par = neuroml.SegmentParent(segments=str(r['par_id']))
s = neuroml.Segment(name=str(r['seg_name']),
id=str(r['seg_id']),
parent=par)
else:
s = neuroml.Segment(name=str(r['seg_name']),
id=str(r['seg_id']))
if r['x_prox']:
loop_prox = neuroml.Point3DWithDiam(
*(r[x] for x in ['x_prox', 'y_prox', 'z_prox', 'd_prox']))
s.proximal = loop_prox
loop = neuroml.Point3DWithDiam(*(r[x]
for x in ['x', 'y', 'z', 'd']))
s.distal = loop
morph.segments.append(s)
# Query for segment groups
query = segment_group_query.substitute(morph_name=morph_name)
qres = self['semantic_net'].query(query, initNs=ns)
for r in qres:
s = neuroml.SegmentGroup(id=r['gid'])
if r['member']:
m = neuroml.Member()
m.segments = str(r['member'])
s.members.append(m)
elif r['include']:
i = neuroml.Include()
i.segment_groups = str(r['include'])
s.includes.append(i)
morph.segment_groups.append(s)
return morph
if (sg.id.startswith('ModelViewParm')) and len(sg.members)==0:
replace = {}
replace['soma_'] = 'soma'
replace['axon_'] = 'axon'
replace['apic_'] = 'apic'
replace['dend_'] = 'dend'
for prefix in replace.keys():
all_match = True
for inc in sg.includes:
#print inc
all_match = all_match and inc.segment_groups.startswith(prefix)
if all_match:
print("Replacing group named %s with %s"%(sg.id,replace[prefix]))
sg.id = replace[prefix]
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="soma_group", includes=[neuroml.Include("soma")]))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="axon_group", includes=[neuroml.Include("axon")]))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="dendrite_group", includes=[neuroml.Include("dend")]))
with open(manifest_info['biophys'][0]["model_file"][1], "r") as json_file:
cell_info = json.load(json_file)
membrane_properties = neuroml.MembraneProperties()
for sc in cell_info['passive'][0]['cm']:
membrane_properties.specific_capacitances.append(neuroml.SpecificCapacitance(value='%s uF_per_cm2'%sc['cm'],
segment_groups=sc['section']))
for chan in cell_info['genome']:
chan_name = chan['mechanism']
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"
]
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 parse_templates_json(templates_json="templates.json",
ignore_chans = [],
save_example_files=False,
verbose=False):
with open(templates_json, "r") as templates_json_file:
json_cells = json.load(templates_json_file)
concentrationModels = ''
for firing_type_u in json_cells:
if verbose: print("\n --------------- %s "%(firing_type_u))
firing_type = str(firing_type_u)
cell_dict = json_cells[firing_type]
nml_doc = neuroml.NeuroMLDocument(id=firing_type)
# Membrane properties
#
included_channels[firing_type] = []
channel_densities = []
channel_density_nernsts = []
channel_density_non_uniform_nernsts = []
channel_density_non_uniforms = []
species = []
for section_list in cell_dict['forsecs']:
for parameter_name in cell_dict['forsecs'][section_list]:
value = cell_dict['forsecs'][section_list][parameter_name]
if verbose: print(" --- %s, %s: %s "%(section_list,parameter_name,value))
if parameter_name == 'g_pas':
channel = 'pas'
arguments = {}
cond_density = "%s S_per_cm2" % value
if verbose: print(' - Adding %s with %s'%(channel, cond_density))
channel_nml2_file = "%s.channel.nml"%channel
if channel_nml2_file not in included_channels[firing_type]:
nml_doc.includes.append(
neuroml.IncludeType(
href="../../NeuroML2/%s" %
channel_nml2_file))
included_channels[firing_type].append(channel_nml2_file)
erev = cell_dict['forsecs'][section_list]['e_pas']
erev = "%s mV" % erev
arguments["cond_density"] = cond_density
arguments['ion_channel'] = channel
arguments["ion"] = "non_specific"
arguments["erev"] = erev
arguments["id"] = "%s_%s" % (section_list, parameter_name)
channel_class = 'ChannelDensity'
density = getattr(neuroml, channel_class)(**arguments)
channel_densities.append(density)
for section_list in cell_dict['parameters']:
for parameter_name in cell_dict['parameters'][section_list]:
if parameter_name != 'e_pas' and 'CaDynamics_E2' not in parameter_name:
parameter_dict = cell_dict['parameters'][section_list][parameter_name]
if verbose: print(" --- %s, %s: %s "%(section_list,parameter_name,parameter_dict))
channel = parameter_dict['channel']
if channel not in ignore_chans:
arguments = {}
cond_density = None
variable_parameters = None
if parameter_dict['distribution']['disttype'] == "uniform":
value = float(parameter_dict['distribution']['value'])
if channel in density_scales:
value = value * density_scales[channel]
cond_density = "%s S_per_cm2" % value
else:
new_expr = '1e4 * (%s)'%parameter_dict['distribution']['value'].replace('x','p').replace('epp','exp')
iv = neuroml.InhomogeneousValue(inhomogeneous_parameters="PathLengthOver_%s"%section_list,
value=new_expr)
variable_parameters = [
neuroml.VariableParameter(
segment_groups=section_list,
parameter='condDensity',
inhomogeneous_value=iv)]
channel_name = channel
if channel_substitutes.has_key(channel):
channel_name = channel_substitutes[channel]
channel_nml2_file = "%s.channel.nml"%channel_name
if channel_nml2_file not in included_channels[firing_type]:
nml_doc.includes.append(
neuroml.IncludeType(
href="../../NeuroML2/%s" %
channel_nml2_file))
included_channels[firing_type].append(channel_nml2_file)
arguments['ion'] = channel_ions[channel]
erev = ion_erevs[arguments["ion"]]
channel_class = 'ChannelDensity'
if erev == "nernst":
erev = None
channel_class = 'ChannelDensityNernst'
elif erev == "pas":
erev = cell_dict['parameters'] \
[section_list]['e_pas']['distribution']\
['value']
erev = "%s mV" % erev
arguments["ion"] = "non_specific"
if variable_parameters is not None:
channel_class += 'NonUniform'
else:
arguments["segment_groups"] = section_list
if erev is not None:
arguments["erev"] = erev
arguments["id"] = "%s_%s" % (section_list, parameter_name)
if cond_density is not None:
arguments["cond_density"] = cond_density
arguments['ion_channel'] = channel_name
if variable_parameters is not None:
arguments['variable_parameters'] = variable_parameters
density = getattr(neuroml, channel_class)(**arguments)
if channel_class == "ChannelDensityNernst":
channel_density_nernsts.append(density)
elif channel_class == "ChannelDensityNernstNonUniform":
channel_density_non_uniform_nernsts.append(density)
elif channel_class == "ChannelDensityNonUniform":
channel_density_non_uniforms.append(density)
else:
channel_densities.append(density)
elif 'gamma_CaDynamics_E2' in parameter_name:
parameter_dict = cell_dict['parameters'][section_list][parameter_name]
model = 'CaDynamics_E2_NML2__%s_%s'%(firing_type,section_list)
value = parameter_dict['distribution']['value']
concentrationModels+='<concentrationModel id="%s" ion="ca" '%model +\
'type="concentrationModelHayEtAl" minCai="1e-4 mM" ' +\
'gamma="%s" '%value
elif 'decay_CaDynamics_E2' in parameter_name:
# calcium_model = \
# neuroml.DecayingPoolConcentrationModel(ion='ca')
model = 'CaDynamics_E2_NML2__%s_%s'%(firing_type,section_list)
species.append(neuroml.Species(
id='ca',
ion='ca',
initial_concentration='5.0E-11 mol_per_cm3',
initial_ext_concentration='2.0E-6 mol_per_cm3',
concentration_model=model,
segment_groups=section_list))
channel_nml2_file = 'CaDynamics_E2_NML2.nml'
if channel_nml2_file not in included_channels[firing_type]:
included_channels[firing_type].append(channel_nml2_file)
parameter_dict = cell_dict['parameters'][section_list][parameter_name]
value = parameter_dict['distribution']['value']
concentrationModels+='decay="%s ms" depth="0.1 um"/> <!-- For group %s in %s-->\n\n'%(value,section_list,firing_type)
capacitance_overwrites = {}
for section_list in cell_dict['forsecs']:
for parameter_name in cell_dict['forsecs'][section_list]:
if parameter_name == "cm" and section_list != 'all':
value = cell_dict['forsecs'][section_list][parameter_name]
capacitance_overwrites[
section_list] = "%s uF_per_cm2" % value
specific_capacitances = []
for section_list in default_capacitances:
if section_list in capacitance_overwrites:
capacitance = capacitance_overwrites[section_list]
else:
capacitance = default_capacitances[section_list]
specific_capacitances.append(
neuroml.SpecificCapacitance(value=capacitance,
segment_groups=section_list))
init_memb_potentials = [neuroml.InitMembPotential(
value="-80 mV", segment_groups='all')]
membrane_properties = neuroml.MembraneProperties(
channel_densities=channel_densities,
channel_density_nernsts=channel_density_nernsts,
channel_density_non_uniform_nernsts=channel_density_non_uniform_nernsts,
channel_density_non_uniforms=channel_density_non_uniforms,
specific_capacitances=specific_capacitances,
init_memb_potentials=init_memb_potentials)
# Intracellular Properties
#
resistivities = []
resistivities.append(neuroml.Resistivity(
value="100 ohm_cm", segment_groups='all'))
intracellular_properties = neuroml.IntracellularProperties(
resistivities=resistivities,
species=species)
# Cell construction
#
biophysical_properties = \
neuroml.BiophysicalProperties(id="biophys",
intracellular_properties=
intracellular_properties,
membrane_properties=
membrane_properties)
biophysical_properties_vs_types[firing_type] = biophysical_properties
if save_example_files:
cell = neuroml.Cell(id=firing_type,
notes="\n*************************\nThis is not a physiologically constrained cell model!!\n"+\
"It is only for testing formatting of the biophysicalProperties extracted from templates.json\n*************************\n",
biophysical_properties=biophysical_properties)
nml_doc.cells.append(cell)
cell.morphology = neuroml.Morphology(id="morph")
cell.morphology.segments.append(neuroml.Segment(id='0',
name='soma',
proximal=neuroml.Point3DWithDiam(x=0,y=0,z=0,diameter=10),
distal=neuroml.Point3DWithDiam(x=0,y=20,z=0,diameter=10)))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="soma",
neuro_lex_id="sao864921383",
members=[neuroml.Member("0")]))
cell.morphology.segments.append(neuroml.Segment(id='1',
name='axon',
parent=neuroml.SegmentParent(segments='0',fraction_along="0"),
proximal=neuroml.Point3DWithDiam(x=0,y=0,z=0,diameter=2),
distal=neuroml.Point3DWithDiam(x=0,y=-50,z=0,diameter=2)))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="axon",
neuro_lex_id="sao864921383",
members=[neuroml.Member("1")]))
cell.morphology.segments.append(neuroml.Segment(id='2',
name='basal_dend',
parent=neuroml.SegmentParent(segments='0'),
proximal=neuroml.Point3DWithDiam(x=0,y=20,z=0,diameter=3),
distal=neuroml.Point3DWithDiam(x=50,y=20,z=0,diameter=3)))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="basal_dend",
neuro_lex_id="sao864921383",
members=[neuroml.Member("2")]))
cell.morphology.segments.append(neuroml.Segment(id='3',
name='apical_dend1',
parent=neuroml.SegmentParent(segments='0'),
proximal=neuroml.Point3DWithDiam(x=0,y=20,z=0,diameter=3),
distal=neuroml.Point3DWithDiam(x=0,y=120,z=0,diameter=3)))
cell.morphology.segments.append(neuroml.Segment(id='4',
name='apical_dend2',
parent=neuroml.SegmentParent(segments='0'),
proximal=neuroml.Point3DWithDiam(x=0,y=120,z=0,diameter=3),
distal=neuroml.Point3DWithDiam(x=0,y=220,z=0,diameter=3)))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="apical_dend",
neuro_lex_id="sao864921383",
members=[neuroml.Member("3"),neuroml.Member("4")]))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="somatic",includes=[neuroml.Include("soma")]))
cell.morphology.segment_groups.append(neuroml.SegmentGroup(id="axonal", includes=[neuroml.Include("axon")]))
sg = neuroml.SegmentGroup(id="basal", includes=[neuroml.Include("basal_dend")])
sg.inhomogeneous_parameters.append(neuroml.InhomogeneousParameter(id="PathLengthOver_"+"basal",
variable="x",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(translation_start="0")))
cell.morphology.segment_groups.append(sg)
sg = neuroml.SegmentGroup(id="apical", includes=[neuroml.Include("apical_dend")])
sg.inhomogeneous_parameters.append(neuroml.InhomogeneousParameter(id="PathLengthOver_"+"apical",
variable="x",
metric="Path Length from root",
proximal=neuroml.ProximalDetails(translation_start="0")))
cell.morphology.segment_groups.append(sg)
nml_filename = 'test/%s.cell.nml' % firing_type
neuroml.writers.NeuroMLWriter.write(nml_doc, nml_filename)
logging.debug("Written cell file to: %s", nml_filename)
neuroml.utils.validate_neuroml2(nml_filename)
conc_mod_file = open('test/concentrationModel.txt','w')
conc_mod_file.write(concentrationModels)
conc_mod_file.close()
dend_seg_group = neuroml.SegmentGroup(id="dendrite_group",
neuro_lex_id="GO:0030425")
inhomogeneous_parameter = neuroml.InhomogeneousParameter(
id="PathLengthOverDendrites", variable="p", metric="Path Length from root")
dend_seg_group.inhomogeneous_parameters.append(inhomogeneous_parameter)
apic_dend_seg_group = neuroml.SegmentGroup(id="apic_dendrite_group")
included_sections = []
for seg in cell.morphology.segments:
neuron_section_name = seg.name[seg.name.index('_') + 1:]
if not neuron_section_name in included_sections:
if 'axon' in seg.name:
axon_seg_group.includes.append(
neuroml.Include(segment_groups=neuron_section_name))
elif 'soma' in seg.name:
soma_seg_group.includes.append(
neuroml.Include(segment_groups=neuron_section_name))
elif 'dend' in seg.name:
dend_seg_group.includes.append(
neuroml.Include(segment_groups=neuron_section_name))
elif 'apic' in seg.name:
dend_seg_group.includes.append(
neuroml.Include(segment_groups=neuron_section_name))
apic_dend_seg_group.includes.append(
neuroml.Include(segment_groups=neuron_section_name))
else:
raise Exception("Segment: %s is not axon, dend or soma!" % seg)
included_sections.append(neuron_section_name)
replace['apic_'] = 'apic'
replace['dend_'] = 'dend'
for prefix in replace.keys():
all_match = True
for inc in sg.includes:
#print inc
all_match = all_match and inc.segment_groups.startswith(
prefix)
if all_match:
print("Replacing group named %s with %s" %
(sg.id, replace[prefix]))
sg.id = replace[prefix]
cell.morphology.segment_groups.append(
neuroml.SegmentGroup(id="soma_group",
includes=[neuroml.Include("soma")]))
cell.morphology.segment_groups.append(
neuroml.SegmentGroup(id="axon_group",
includes=[neuroml.Include("axon")]))
cell.morphology.segment_groups.append(
neuroml.SegmentGroup(id="dendrite_group",
includes=[neuroml.Include("dend")]))
with open(manifest_info['biophys'][0]["model_file"][1], "r") as json_file:
cell_info = json.load(json_file)
membrane_properties = neuroml.MembraneProperties()
for sc in cell_info['passive'][0]['cm']:
membrane_properties.specific_capacitances.append(
neuroml.SpecificCapacitance(value='%s uF_per_cm2' % sc['cm'],