def fix_axon_perisomatic_directed(hobj):
# io.log_info('Fixing Axon like perisomatic')
all_sec_names = []
for sec in hobj.all:
all_sec_names.append(sec.name().split(".")[1][:4])
if 'axon' not in all_sec_names:
io.log_exception('There is no axonal recostruction in swc file.')
else:
beg1, end1, beg2, end2 = get_axon_direction(hobj)
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
h.pt3dadd(beg1[0], beg1[1], beg1[2], 1, sec=hobj.axon[0])
h.pt3dadd(end1[0], end1[1], end1[2], 1, sec=hobj.axon[0])
hobj.all.append(sec=hobj.axon[0])
h.pt3dadd(beg2[0], beg2[1], beg2[2], 1, sec=hobj.axon[1])
h.pt3dadd(end2[0], end2[1], end2[2], 1, sec=hobj.axon[1])
hobj.all.append(sec=hobj.axon[1])
hobj.axon[0].connect(hobj.soma[0], 0.5, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
hobj.axon[0].L = 30.0
hobj.axon[1].L = 30.0
h.define_shape()
for sec in hobj.axon:
# print "sec.L:", sec.L
if np.abs(30 - sec.L) > 0.0001:
io.log_exception('Axon stub L is less than 30')
def __init__(self, network_dir, single_file=False, **params):
self._network_dir = network_dir
self._virt_lookup = {}
self._gid_lookup = {}
self._sec_lookup = {}
if not os.path.exists(network_dir):
os.makedirs(network_dir)
if N_HOSTS > 1:
io.log_exception(
'save_synapses module is not current supported with mpi')
self._syn_writer = ConnectionWriter(network_dir)
def set_transfer_resistance(self, gid, seg_coords):
rho = 300.0 # ohm cm
r05 = seg_coords['p05']
nseg = r05.shape[1]
cell_map = np.zeros((self.elnsites, nseg))
for el in six.moves.range(self.elnsites):
mesh_size = self.el_mesh_size[el]
for k in range(mesh_size):
rel = np.expand_dims(self.el_mesh[el][:, k], axis=1)
rel_05 = rel - r05
r2 = np.einsum('ij,ij->j', rel_05, rel_05)
r = np.sqrt(r2)
if not all(i >= 10 for i in r):
io.log_exception('External electrode is too close')
cell_map[el, :] += 1. / r
cell_map *= (rho / (4 * math.pi)) * 0.01
self.trans_X[gid] = cell_map
def set_stim(self, stim_prop, spike_train):
"""Gets the spike trains for each individual cell."""
spikes = spike_train.get_times(node_id=self.node_id)
if spikes is None:
spikes = []
if np.any(np.array(spikes) < 0.0):
# NRN will fail if VecStim contains negative spike-time, throw an exception and log info for user
io.log_exception(
'spike train {} contains negative number, unable to run virtual cell in NEURON'
.format(spikes))
spikes = np.sort(
spikes
) # sort the spikes for NEURON, will throw a segfault if not sorted
self._train_vec = h.Vector(spikes)
vecstim = h.VecStim()
vecstim.play(self._train_vec)
self._hobj = vecstim
def fix_axon_allactive_directed(hobj):
all_sec_names = []
for sec in hobj.all:
all_sec_names.append(sec.name().split(".")[1][:4])
if 'axon' not in all_sec_names:
io.log_exception('There is no axonal recostruction in swc file.')
else:
beg1, end1, beg2, end2 = get_axon_direction(hobj)
axon_diams = [hobj.axon[0].diam, hobj.axon[0].diam]
for sec in hobj.all:
section_name = sec.name().split(".")[1][:4]
if section_name == 'axon':
axon_diams[1] = sec.diam
for sec in hobj.axon:
h.delete_section(sec=sec)
h.execute('create axon[2]', hobj)
hobj.axon[0].connect(hobj.soma[0], 1.0, 0)
hobj.axon[1].connect(hobj.axon[0], 1.0, 0)
h.pt3dadd(beg1[0], beg1[1], beg1[2], axon_diams[0], sec=hobj.axon[0])
h.pt3dadd(end1[0], end1[1], end1[2], axon_diams[0], sec=hobj.axon[0])
hobj.all.append(sec=hobj.axon[0])
h.pt3dadd(beg2[0], beg2[1], beg2[2], axon_diams[1], sec=hobj.axon[1])
h.pt3dadd(end2[0], end2[1], end2[2], axon_diams[1], sec=hobj.axon[1])
hobj.all.append(sec=hobj.axon[1])
hobj.axon[0].L = 30.0
hobj.axon[1].L = 30.0
h.define_shape()
for sec in hobj.axon:
# io.log_info('sec.L: {}'.format(sec.L))
if np.abs(30 - sec.L) > 0.0001:
io.log_exception('Axon stub L is less than 30')
def from_config(cls, config, network, set_recordings=True):
# TODO: convert from json to sonata config if necessary
sim = cls(network=network,
dt=config.dt,
tstop=config.tstop,
v_init=config.v_init,
celsius=config.celsius,
cao0=config.cao0,
optocell=config.optocell,
nsteps_block=config.block_step)
network.io.log_info('Building cells.')
network.build_nodes()
network.io.log_info('Building recurrent connections')
network.build_recurrent_edges()
# TODO: Need to create a gid selector
for sim_input in inputs.from_config(config):
node_set = network.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes':
spikes = spike_trains.SpikesInput.load(
name=sim_input.name,
module=sim_input.module,
input_type=sim_input.input_type,
params=sim_input.params)
io.log_info('Build virtual cell stimulations for {}'.format(
sim_input.name))
network.add_spike_trains(spikes, node_set)
elif sim_input.module == 'IClamp':
# TODO: Parse from csv file
amplitude = sim_input.params['amp']
delay = sim_input.params['delay']
duration = sim_input.params['duration']
gids = sim_input.params['node_set']
sim.attach_current_clamp(amplitude, delay, duration, node_set)
elif sim_input.module == 'xstim':
sim.add_mod(mods.XStimMod(**sim_input.params))
else:
io.log_exception('Can not parse input format {}'.format(
sim_input.name))
if config.calc_ecp:
for gid, cell in network.cell_type_maps('biophysical').items():
cell.setup_ecp()
sim.h.cvode.use_fast_imem(1)
# Parse the "reports" section of the config and load an associated output module for each report
sim_reports = reports.from_config(config)
for report in sim_reports:
if isinstance(report, reports.SpikesReport):
mod = mods.SpikesMod(**report.params)
elif isinstance(report, reports.SectionReport):
mod = mods.SectionReport(**report.params)
elif isinstance(report, reports.MembraneReport):
if report.params['sections'] == 'soma':
mod = mods.SomaReport(**report.params)
else:
mod = mods.MembraneReport(**report.params)
elif isinstance(report, reports.ECPReport):
assert config.calc_ecp
mod = mods.EcpMod(**report.params)
# Set up the ability for ecp on all relevant cells
# TODO: According to spec we need to allow a different subset other than only biophysical cells
# for gid, cell in network.cell_type_maps('biophysical').items():
# cell.setup_ecp()
elif report.module == 'save_synapses':
mod = mods.SaveSynapses(**report.params)
else:
# TODO: Allow users to register customized modules using pymodules
io.log_warning('Unrecognized module {}, skipping.'.format(
report.module))
continue
sim.add_mod(mod)
return sim
def from_config(cls, config, network, set_recordings=True):
# TODO: convert from json to sonata config if necessary
#The network must be built before initializing the simulator because
#gap junctions must be set up before the simulation is initialized.
network.io.log_info('Building cells.')
network.build_nodes()
network.io.log_info('Building recurrent connections')
network.build_recurrent_edges()
sim = cls(network=network,
dt=config.dt,
tstop=config.tstop,
v_init=config.v_init,
celsius=config.celsius,
nsteps_block=config.block_step)
# TODO: Need to create a gid selector
for sim_input in inputs.from_config(config):
try:
network.get_node_set(sim_input.node_set)
except:
print(
"Parameter node_set must be given in inputs module of simulation_config file. If unsure of what node_set should be, set it to 'all'."
)
node_set = network.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes':
io.log_info('Building virtual cell stimulations for {}'.format(
sim_input.name))
path = sim_input.params['input_file']
spikes = SpikeTrains.load(path=path,
file_type=sim_input.module,
**sim_input.params)
network.add_spike_trains(spikes, node_set)
elif sim_input.module == "FileIClamp":
sim.attach_file_current_clamp(sim_input.params["input_file"])
elif sim_input.module == 'IClamp':
# TODO: Parse from csv file
try:
len(sim_input.params['amp'])
except:
sim_input.params['amp'] = [float(sim_input.params['amp'])]
if len(sim_input.params['amp']) > 1:
sim_input.params['amp'] = [
float(i) for i in sim_input.params['amp']
]
try:
len(sim_input.params['delay'])
except:
sim_input.params['delay'] = [
float(sim_input.params['delay'])
]
if len(sim_input.params['delay']) > 1:
sim_input.params['delay'] = [
float(i) for i in sim_input.params['delay']
]
try:
len(sim_input.params['duration'])
except:
sim_input.params['duration'] = [
float(sim_input.params['duration'])
]
if len(sim_input.params['duration']) > 1:
sim_input.params['duration'] = [
float(i) for i in sim_input.params['duration']
]
amplitude = sim_input.params['amp']
delay = sim_input.params['delay']
duration = sim_input.params['duration']
try:
sim_input.params['gids']
except:
sim_input.params['gids'] = None
if sim_input.params['gids'] is not None:
gids = sim_input.params['gids']
else:
gids = list(node_set.gids())
sim.attach_current_clamp(amplitude, delay, duration, gids)
elif sim_input.module == "SEClamp":
try:
len(sim_input.params['amps'])
except:
sim_input.params['amps'] = [
float(sim_input.params['amps'])
]
try:
len(sim_input.params['durations'])
except:
sim_input.params['durations'] = [
float(sim_input.params['durations'])
]
amplitudes = sim_input.params['amps']
durations = sim_input.params['durations']
rs = None
if "rs" in sim_input.params.keys():
try:
len(sim_input.params['rs'])
except:
sim_input.params['rs'] = [
float(sim_input.params['rs'])
]
if len(sim_input.params['rs']) > 1:
sim_input.params['rs'] = [
float(i) for i in sim_input.params['rs']
]
rs = sim_input.params["rs"]
try:
sim_input.params['gids']
except:
sim_input.params['gids'] = None
if sim_input.params['gids'] is not None:
gids = sim_input.params['gids']
else:
gids = list(node_set.gids())
sim.attach_se_voltage_clamp(amplitudes, durations, gids, rs)
elif sim_input.module == 'xstim':
sim.add_mod(mods.XStimMod(**sim_input.params))
else:
io.log_exception('Can not parse input format {}'.format(
sim_input.name))
# Parse the "reports" section of the config and load an associated output module for each report
sim_reports = reports.from_config(config)
for report in sim_reports:
if isinstance(report, reports.SpikesReport):
mod = mods.SpikesMod(**report.params)
elif report.module == 'netcon_report':
mod = mods.NetconReport(**report.params)
elif isinstance(report, reports.MembraneReport):
if report.params['sections'] == 'soma':
mod = mods.SomaReport(**report.params)
else:
mod = mods.MembraneReport(**report.params)
elif isinstance(report, reports.ClampReport):
mod = mods.ClampReport(**report.params)
elif isinstance(report, reports.ECPReport):
mod = mods.EcpMod(**report.params)
# Set up the ability for ecp on all relevant cells
# TODO: According to spec we need to allow a different subset other than only biophysical cells
for gid, cell in network.cell_type_maps('biophysical').items():
cell.setup_ecp()
elif report.module == 'save_synapses':
mod = mods.SaveSynapses(**report.params)
else:
# TODO: Allow users to register customized modules using pymodules
io.log_warning('Unrecognized module {}, skipping.'.format(
report.module))
continue
sim.add_mod(mod)
return sim
def from_config(cls, config, network, set_recordings=True):
simulation_inputs = inputs.from_config(config)
# Special case for setting synapses to spontaneously (for a given set of pre-synaptic cell-types). Using this
# input will change the way the network builds cells/connections and thus needs to be set first.
for sim_input in simulation_inputs:
if sim_input.input_type == 'syn_activity':
network.set_spont_syn_activity(
precell_filter=sim_input.params['precell_filter'],
timestamps=sim_input.params['timestamps']
)
# The network must be built before initializing the simulator because
# gap junctions must be set up before the simulation is initialized.
network.io.log_info('Building cells.')
network.build_nodes()
network.io.log_info('Building recurrent connections')
network.build_recurrent_edges()
sim = cls(network=network,
dt=config.dt,
tstop=config.tstop,
v_init=config.v_init,
celsius=config.celsius,
nsteps_block=config.block_step)
# TODO: Need to create a gid selector
for sim_input in inputs.from_config(config):
try:
network.get_node_set(sim_input.node_set)
except:
print("Parameter node_set must be given in inputs module of simulation_config file. If unsure of what node_set should be, set it to 'all'.")
node_set = network.get_node_set(sim_input.node_set)
if sim_input.input_type == 'spikes':
io.log_info('Building virtual cell stimulations for {}'.format(sim_input.name))
path = sim_input.params['input_file']
spikes = SpikeTrains.load(path=path, file_type=sim_input.module, **sim_input.params)
network.add_spike_trains(spikes, node_set)
elif sim_input.module == "FileIClamp":
sim.attach_file_current_clamp(sim_input.params["input_file"])
elif sim_input.module == 'IClamp':
# TODO: Parse from csv file
try:
len(sim_input.params['amp'])
except:
sim_input.params['amp']=[float(sim_input.params['amp'])]
if len(sim_input.params['amp'])>1:
sim_input.params['amp']=[float(i) for i in sim_input.params['amp']]
try:
len(sim_input.params['delay'])
except:
sim_input.params['delay']=[float(sim_input.params['delay'])]
if len(sim_input.params['delay'])>1:
sim_input.params['delay']=[float(i) for i in sim_input.params['delay']]
try:
len(sim_input.params['duration'])
except:
sim_input.params['duration']=[float(sim_input.params['duration'])]
if len(sim_input.params['duration'])>1:
sim_input.params['duration']=[float(i) for i in sim_input.params['duration']]
amplitude = sim_input.params['amp']
delay = sim_input.params['delay']
duration = sim_input.params['duration']
# specificed for location to place iclamp hobj.<section_name>[<section_index>](<section_dist>). The
# default is hobj.soma[0](0.5), the center of the soma
section_name = sim_input.params.get('section_name', 'soma')
section_index = sim_input.params.get('section_index', 0)
section_dist = sim_input.params.get('section_dist', 0.5)
# section_name = section_name if isinstance(section_name, (list, tuple)) else [section_name]
# section_index = section_index if isinstance(section_index, (list, tuple)) else [section_index]
# section_dist = section_dist if isinstance(section_dist, (list, tuple)) else [section_dist]
try:
sim_input.params['gids']
except:
sim_input.params['gids'] = None
if sim_input.params['gids'] is not None:
gids = sim_input.params['gids']
else:
gids = list(node_set.gids())
sim.attach_current_clamp(amplitude, delay, duration, gids, section_name, section_index, section_dist)
elif sim_input.module == "SEClamp":
try:
len(sim_input.params['amps'])
except:
sim_input.params['amps']=[float(sim_input.params['amps'])]
try:
len(sim_input.params['durations'])
except:
sim_input.params['durations']=[float(sim_input.params['durations'])]
amplitudes = sim_input.params['amps']
durations = sim_input.params['durations']
rs = None
if "rs" in sim_input.params.keys():
try:
len(sim_input.params['rs'])
except:
sim_input.params['rs']=[float(sim_input.params['rs'])]
if len(sim_input.params['rs'])>1:
sim_input.params['rs']=[float(i) for i in sim_input.params['rs']]
rs = sim_input.params["rs"]
try:
sim_input.params['gids']
except:
sim_input.params['gids'] = None
if sim_input.params['gids'] is not None:
gids = sim_input.params['gids']
else:
gids = list(node_set.gids())
sim.attach_se_voltage_clamp(amplitudes, durations, gids, rs)
elif sim_input.module == 'xstim':
sim.add_mod(mods.XStimMod(**sim_input.params))
elif sim_input.module == 'syn_activity':
pass
else:
io.log_exception('Can not parse input format {}'.format(sim_input.name))
# Parse the "reports" section of the config and load an associated output module for each report
sim_reports = reports.from_config(config)
for report in sim_reports:
if isinstance(report, reports.SpikesReport):
mod = mods.SpikesMod(**report.params)
elif report.module == 'netcon_report':
mod = mods.NetconReport(**report.params)
elif isinstance(report, reports.MembraneReport):
if report.params['sections'] == 'soma':
mod = mods.SomaReport(**report.params)
else:
mod = mods.MembraneReport(**report.params)
elif isinstance(report, reports.ClampReport):
mod = mods.ClampReport(**report.params)
elif isinstance(report, reports.ECPReport):
mod = mods.EcpMod(**report.params)
# Set up the ability for ecp on all relevant cells
# TODO: According to spec we need to allow a different subset other than only biophysical cells
for gid, cell in network.cell_type_maps('biophysical').items():
cell.setup_ecp()
elif report.module == 'save_synapses':
mod = mods.SaveSynapses(**report.params)
else:
# TODO: Allow users to register customized modules using pymodules
io.log_warning('Unrecognized module {}, skipping.'.format(report.module))
continue
sim.add_mod(mod)
return sim
