def test_get_multi_dipole_potential00(self):
neuron.h('forall delete_section()')
soma = neuron.h.Section(name='soma')
dend1 = neuron.h.Section(name='dend1')
dend2 = neuron.h.Section(name='dend2')
dend3 = neuron.h.Section(name='dend3')
dend4 = neuron.h.Section(name='dend4')
dend5 = neuron.h.Section(name='dend5')
dend1.connect(soma(0.5), 0)
dend2.connect(dend1(1.0), 0)
dend3.connect(dend2(1.0), 0)
dend4.connect(dend3(1.0), 0)
dend5.connect(dend4(1.0), 0)
morphology = neuron.h.SectionList()
morphology.wholetree()
electrode_locs = np.array([[0., 0., 10000.]])
cell, electrode = cell_w_synapse_from_sections_w_electrode(morphology, electrode_locs)
sigma = 0.3
t_point = 0
MD_inf = LFPy.InfiniteVolumeConductor(sigma)
pot_MD = MD_inf.get_multi_dipole_potential(cell, electrode_locs)
pot_cb = electrode.LFP
np.testing.assert_almost_equal(pot_MD, pot_cb)
np.testing.assert_allclose(pot_MD, pot_cb, rtol=1E-4)
def initModel(h,par,vecpar,recpar, verbose):
'''
Initializes the model.
Creates the axon and so on.
'''
passValuesToNeuron(par,verb=verbose)
h('{load_file("MRGnodeHFS.hoc")}')
passValuesToNeuron(vecpar,[a for a in vecpar.keys()],verb=verbose)
#h('{load_file("nrngui.hoc")}')
#h('{load_proc("nrnmainmenu")}')
h('{buildModel()}')
if verbose:
print("Passed parameters and built model.")
# insert recorders and record action potential timestamps!
segmentsToRecordV = [];
segmentsNames = [int(h.intrinsicNode), int(h.HFSreferenceNode), int(h.axonnodes-1)] #segments to record
for ii in segmentsNames:
segmentsToRecordV.append(h.node[ii](0.5))
rec = None
for ii in range(0,len(segmentsToRecordV)):
rec = insertRecorders(segmentsToRecordV[ii],{'node'+str(segmentsNames[ii]):'_ref_v'},rec)
h.node[int(h.axonnodes-1)].push()
apc = h.APCount(h.node[int(h.axonnodes-1)](0.5))
apc.thresh = 0
rec['apc'] = h.Vector()
apc.record(rec['apc'])
#rec['electrodeWaveform'] = h.rec_electrode
if verbose:
print("Inserted recorders and APCount. Returning recorders.")
##########################################################################
return rec
def passive_soma(quad, show=False):
"""
Creates the model with basic pyramidal passive properties.
"""
# Load the hoc into neuron
h('xopen(%s)' %quad.hocfile)
h.load_file('stdrun.hoc')
seclist = list(h.allsec())
for sec in seclist:
sec.insert('pas')
sec.Ra = 200.
# Current injection into soma or tip
soma_sec = return_soma_seg(quad, h)
stim_loc = 0.
stim = h.IClamp(stim_loc, sec=soma_sec)
stim.delay = 1 # ms
stim.dur = 1 # ms
stim.amp = 20 # nA
# Run sim and record data
(v, labels) = ez_record(h) # PyNeuron to record all compartments
t, I = h.Vector(), h.Vector()
t.record(h._ref_t)
I.record(stim._ref_i)
h.init()
h.tstop = 10 # s?
h.run()
v = ez_convert(v) # Convert v to numpy 2D array
# If show, plot, else just return v
if show:
def setup_sections(self):
start = h.startsw()
###################################################
# set up sections
self.sections = []
# old style, but it is need for section_name in hoc
h(self.section_def_template % (self.name, len(self.tree)))
for sec in h.allsec():
self.sections.append(sec)
###################################################
# connect sections
for i,sec in enumerate(self.sections):
parent = self.tree[i]
#print "%d to %d" % (i, tree[i])
if(parent != 0):
sec.connect(self.sections[parent-1], 1, 0)
self.num_compartment = 0
for sec in h.allsec():
self.num_compartment += 1
self.setup_time += h.startsw() - start
def alloc_synapse_ff(self,r,post_syn,cellind,k,gidn,i):
NCELL=self.NCELL
SIZE=self.SIZE
COMM = self.COMM
RANK=self.RANK
#from neuron import h
h=self.h
pc=h.ParallelContext()
polarity = 0
polarity=int(h.Cell[int(cellind)].polarity)
if polarity==1:
#TODO pickle load the graphs here instead of making them manually.
self.ecm[i][gidn] = self.ecm[i][gidn] + 1
self.ecg.add_edge(i,gidn,weight=r/0.4)
assert np.sum(self.ecm)!=0
else:
self.icm[i][gidn] = self.icm[i][gidn] + 1
self.icg.add_edge(i,gidn,weight=r/0.4)
assert np.sum(self.icm)!=0
#TODO Add other edge attributes like secnames etc.
print post_syn
h('objref syn_')
h(post_syn)
syn_=h.syn_
h.syn_.cid=i
h.Cell[cellind].ampalist.append(h.syn_)
h.Cell[cellind].div.append(k['gid'])
h.Cell[cellind].gvpre.append(k['gid'])
nc=pc.gid_connect(k['gid'],syn_)
nc.threshold = -20
nc.delay=1+r/0.4
nc.weight[0]=r/0.4
self.nclist.append(nc)
def test_current_balance_synapse_at_section_end():
h('synapse.loc(0)')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I))<1e-6).all()
def test_current_balance_synapse_in_segment():
h('synapse.loc(0.1)')
h('soma {insert pas}')
cell.initialize(dt=0.025)
t, I = cell.integrate(1)
coord = cell.get_seg_coords()
assert (np.abs(total_current(coord, I))<1e-6).all()
def useSTDP(self, mechanism, parameters, ddf):
"""
Set this connection to use spike-timing-dependent plasticity.
`mechanism` -- the name of an NMODL mechanism that modifies synaptic
weights based on the times of pre- and post-synaptic spikes.
`parameters` -- a dictionary containing the parameters of the weight-
adjuster mechanism.
`ddf` -- dendritic delay fraction. If ddf=1, the synaptic delay
`d` is considered to occur entirely in the post-synaptic
dendrite, i.e., the weight adjuster receives the pre-
synaptic spike at the time of emission, and the post-
synaptic spike a time `d` after emission. If ddf=0, the
synaptic delay is considered to occur entirely in the
pre-synaptic axon.
"""
self.ddf = ddf
self.weight_adjuster = getattr(h, mechanism)(0.5)
self.pre2wa = state.parallel_context.gid_connect(int(self.source), self.weight_adjuster)
self.pre2wa.threshold = self.nc.threshold
self.pre2wa.delay = self.nc.delay * (1-ddf)
self.pre2wa.weight[0] = 1
# directly create NetCon as wa is on the same machine as the post-synaptic cell
self.post2wa = h.NetCon(self.target._cell.source, self.weight_adjuster,
sec=self.target._cell.source_section)
self.post2wa.threshold = 1
self.post2wa.delay = self.nc.delay * ddf
self.post2wa.weight[0] = -1
for name, value in parameters.items():
setattr(self.weight_adjuster, name, value)
# setpointer
i = len(h.plastic_connections)
h.plastic_connections.append(self)
h('setpointer plastic_connections._[%d].weight_adjuster.wsyn, plastic_connections._[%d].nc.weight' % (i,i))
def test_calc_potential_from_multi_dipoles01(self):
neuron.h('forall delete_section()')
soma = neuron.h.Section(name='soma')
dend1 = neuron.h.Section(name='dend1')
dend2 = neuron.h.Section(name='dend2')
dend1.connect(soma(0.5), 0)
dend2.connect(dend1(1.0), 0)
morphology = neuron.h.SectionList()
morphology.wholetree()
radii = [300, 400, 500, 600]
sigmas = [0.3, 1.5, 0.015, 0.3]
electrode_locs = np.array([[0., 0., 290.],
[10., 90., 300.],
[-90, 50., 400.],
[110.3, -100., 500.]])
cell = cell_w_synapse_from_sections(morphology)
t_point = -1
MD_4s = LFPy.FourSphereVolumeConductor(radii, sigmas, electrode_locs)
dipoles, dipole_locs = cell.get_multi_current_dipole_moments()
p = dipoles[:,t_point,:]
Np = p.shape[0]
Nt = 1
Ne = electrode_locs.shape[0]
pot_MD = MD_4s.calc_potential_from_multi_dipoles(cell)[:,t_point]
pot_sum = np.zeros((Ne, Nt))
for i in range(Np):
dip = np.array([p[i]])
dip_loc = dipole_locs[i]
fs = LFPy.FourSphereVolumeConductor(radii, sigmas, electrode_locs)
pot = fs.calc_potential(dip, dip_loc)
pot_sum += pot
pot_sum = pot_sum.reshape(4)
np.testing.assert_almost_equal(pot_MD, pot_sum)
np.testing.assert_allclose(pot_MD, pot_sum, rtol=1E-4)
def test_point_process_coord():
h('synapse.loc(0.15)')
h('access soma')
x, y, z = cell.get_pp_coord(h.synapse)
assert x == 1.5
assert y == 0
assert z == 0
def test_get_point_processes():
h('synapse.loc(0.15)')
h('access soma')
pps = cell.get_point_processes()
assert len(pps)==1
assert pps[0][0].same(h.synapse)
assert pps[0][1] == 1.5
def load_morphology(filename):
swc = h.Import3d_SWC_read()
swc.input(filename)
imprt = h.Import3d_GUI(swc, 0)
h("objref this")
imprt.instantiate(h.this)
def _do_callback(self):
if callable(self._callable):
self._callable()
else:
h(self._callable)
if self._thread is not None:
self.start()
def create_head(self, neck, head_vol, big_spine):
"""Create the head of the spine and populate it with the right channels"""
name_sec = self.id + "_head"
h("create " + name_sec)
head = getattr(h, name_sec)
if big_spine:
head.L = 1
head.diam = 1.175
r = head.diam/2.
self.head_vol = math.pi * r * r * head.L
else:
head.L = 0.5
head.diam = math.sqrt(head_vol / (head.L * math.pi) ) * 2
self.Ra = 150.0
head.nseg = 1
head.connect(neck)
#head.insert("pas")
head.insert("kir")
head.insert("can")
head.insert("caq")
head.insert("car")
head.insert("skkca")
h.factors_caltrack()
head.insert("caltrack")
h.factors_catrack()
head.insert("catrack")
return head
def alloc_synapse(self,r,h,sec,seg,cellind,secnames,k,i,gidn):
'''
Allocate a synaptic cleft from exhuastive collision detection.
'''
NCELL=self.NCELL
SIZE=self.SIZE
COMM = self.COMM
RANK=self.RANK
from neuron import h
pc=h.ParallelContext()
h=self.h
self.visited[i][gidn] = self.visited[i][gidn] + 1
if r < 2.5: #2.5 micro metres.
polarity = 0
polarity=int(h.Cell[int(cellind)].polarity)
h('objref syn_')
if int(polarity) == int(0):
post_syn = secnames + ' ' + 'syn_ = new FastInhib(' + str(seg.x) + ')'
#post_syn = secnames + ' ' + 'syn_ = new GABAa(' + str(seg.x) + ')'
self.icm[i][gidn] = self.icm[i][gidn] + 1
self.icg.add_edge(i,gidn,weight=r/0.4)
self.icg[i][gidn]['post_loc']=secnames
self.icg[i][gidn]['pre_loc']=k['secnames']
assert np.sum(self.icm)!=0
else:
if (k['gid']%2==0):
#TODO Find standard open source brain affiliated code for NMDA synapse
post_syn = secnames + ' ' + 'syn_ = new AmpaNmda(' + str(seg.x) + ')'
self.ecm[i][gidn] = self.ecm[i][gidn] + 1
self.ecg.add_edge(i,gidn,weight=r/0.4)
self.ecg[i][gidn]['post_loc']=secnames
self.ecg[i][gidn]['pre_loc']=k['secnames']
self.seclists.append(secnames)
assert np.sum(self.ecm)!=0
else:
#TODO Find standard open source brain affiliated code for NMDA synapse
post_syn = secnames + ' ' + 'syn_ = new ExpSid(' + str(seg.x) + ')'
self.ecm[i][gidn] = self.ecm[i][gidn] + 1
self.ecg.add_edge(i,gidn,weight=r/0.4)
self.ecg[i][gidn]['post_loc']=secnames
self.ecg[i][gidn]['pre_loc']=k['secnames']
self.seclists.append(secnames)
assert np.sum(self.ecm)!=0
h(post_syn)
print post_syn
self.synapse_list.append((r,post_syn,cellind,k,gidn,i))
syn_=h.syn_
h.syn_.cid=i
h.Cell[cellind].ampalist.append(h.syn_)
h.Cell[cellind].div.append(k['gid'])
h.Cell[cellind].gvpre.append(k['gid'])
nc=pc.gid_connect(k['gid'],syn_)
nc.threshold = -20
nc.delay=1+r/0.4
nc.weight[0]=r/0.4
self.nclist.append(nc)
def measure_fi (gid, pop_name, v_init, env, cell_dict={}):
biophys_cell = init_biophys_cell(env, pop_name, gid, register_cell=False, cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
soma = list(hoc_cell.soma)[0]
h.dt = 0.025
prelength = 1000.0
mainlength = 2000.0
tstop = prelength+mainlength
stimdur = 1000.0
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = 0.2
h('objref tlog, Vlog, spikelog')
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (soma(0.5)._ref_v)
h.spikelog = h.Vector()
nc = biophys_cell.spike_detector
nc.record(h.spikelog)
h.tstop = tstop
frs = []
stim_amps = [stim1.amp]
for it in range(1, 9):
neuron_utils.simulate(v_init, prelength, mainlength)
logger.info("fi_test: stim1.amp = %g spikelog.size = %d\n" % (stim1.amp, h.spikelog.size()))
stim1.amp = stim1.amp + 0.1
stim_amps.append(stim1.amp)
frs.append(h.spikelog.size())
h.spikelog.clear()
h.tlog.clear()
h.Vlog.clear()
results = {'FI_curve_amplitude': np.asarray(stim_amps, dtype=np.float32),
'FI_curve_frequency': np.asarray(frs, dtype=np.float32) }
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def test_Network_04(self):
cellParameters = dict(
morphology=os.path.join(LFPy.__path__[0], 'test', 'ball_and_sticks_w_lists.hoc'),
templatefile=os.path.join(LFPy.__path__[0], 'test', 'ball_and_stick_template.hoc'),
templatename='ball_and_stick_template',
templateargs=None,
passive=True,
dt=2**-3,
tstop=100,
delete_sections=False,
)
synapseParameters = dict(idx=0, syntype='Exp2Syn', weight=0.002,
tau1=0.1, tau2=0.1, e=0)
populationParameters = dict(
CWD=None,
CELLPATH=None,
Cell=LFPy.NetworkCell,
cell_args = cellParameters,
pop_args = dict(
radius=100,
loc=0.,
scale=20.),
rotation_args = dict(x=0, y=0),
POP_SIZE = 1,
name = 'test',
)
networkParameters = dict(
dt=2**-3,
tstart=0.,
tstop=100.,
v_init=-70.,
celsius=6.3,
OUTPUTPATH='tmp_testNetworkPopulation'
)
# set up
network = LFPy.Network(**networkParameters)
network.create_population(**populationParameters)
cell = network.populations['test'].cells[0]
# create synapses
synlist = []
numsynapses = 2
for i in range(numsynapses):
synlist.append(LFPy.Synapse(cell=cell, **synapseParameters))
synlist[-1].set_spike_times(np.array([10+(i*10)]))
network.simulate()
# test that the input results in the correct amount of PSPs
np.testing.assert_equal(ss.argrelextrema(cell.somav, np.greater)[0].size, numsynapses)
# clean up
network.pc.gid_clear()
os.system('rm -r tmp_testNetworkPopulation')
neuron.h('forall delete_section()')
def conduct_parallel_NEURON(population_name, last_point, N_index_glob, N_index,
Ampl_scale, t_steps, n_segments, dt, tstop,
n_pulse, v_init, output):
os.chdir("..")
# nodes=[]
# for point_inx in range(n_segments):
# nodes_point_in_time=np.load(os.environ['PATIENTDIR']+'/Points_in_time/Signal_t_conv'+str(point_inx+N_index*n_segments+last_point)+'.npy') #get solution for each compartment in time for one neuron
# nodes.append(nodes_point_in_time*(1000)*Ampl_scale) #convert to mV
# nodes=np.asarray(nodes)
# nodes = nodes.ravel()
#
# V_art=np.zeros((n_segments,t_steps),float)
#
# for i in range(n_segments):
# V_art[i,:]=nodes[(i*t_steps):((i*t_steps)+t_steps)]
#to distinguish axons in different populations, we indexed them with the global index of the last compartment
axon_in_time = np.load(os.environ['PATIENTDIR'] +
'/Axons_in_time/Signal_t_conv' +
str(n_segments - 1 + N_index * n_segments +
last_point) + '.npy')
V_art = np.zeros((n_segments, t_steps), float)
for i in range(n_segments):
V_art[i, :] = axon_in_time[i, :] * (-1000) * Ampl_scale #convert to mV
#only if we want to save potential in time on axons
#np.save('Field_on_axons_in_time/'+str(population_name)+'axon_'+str(N_index_glob), V_art)
os.chdir("Axon_files/")
n.h('{load_file("axon4pyfull.hoc")}')
n.h.deletenodes()
n.h.createnodes()
n.h.dependent_var()
n.h.initialize()
n.h.setupAPWatcher_0() # 'left' end of axon
n.h.setupAPWatcher_1() # 'right' end of axon
n.h.dt = dt
n.h.tstop = tstop
n.h.n_pulse = n_pulse
n.h.v_init = v_init
for i in range(0, V_art.shape[0]):
n.h.wf[i] = n.h.Vector(V_art[
i, :]) # feed the potential in time for compartment i to NEURON
n.h.stimul()
n.h.run()
spike = n.h.stoprun - 0.5
if spike == 0.5:
return output.put([N_index_glob, N_index])
else:
return output.put([N_index_glob, -1])
def _setup_plasticity(self, synapse_type, parameters):
"""
Set this connection to use spike-timing-dependent plasticity.
`mechanism` -- the name of an NMODL mechanism that modifies synaptic
weights based on the times of pre- and post-synaptic spikes.
`parameters` -- a dictionary containing the parameters of the weight-
adjuster mechanism.
"""
mechanism = synapse_type.model
self.weight_adjuster = getattr(h, mechanism)(0.5)
if synapse_type.postsynaptic_variable == 'spikes':
parameters['allow_update_on_post'] = int(
False) # for compatibility with NEST
self.ddf = parameters.pop('dendritic_delay_fraction')
# If ddf=1, the synaptic delay
# `d` is considered to occur entirely in the post-synaptic
# dendrite, i.e., the weight adjuster receives the pre-
# synaptic spike at the time of emission, and the post-
# synaptic spike a time `d` after emission. If ddf=0, the
# synaptic delay is considered to occur entirely in the
# pre-synaptic axon.
elif synapse_type.postsynaptic_variable is None:
self.ddf = 0
else:
raise NotImplementedError(
"Only post-synaptic-spike-dependent mechanisms available for now."
)
self.pre2wa = state.parallel_context.gid_connect(
int(self.presynaptic_cell), self.weight_adjuster)
self.pre2wa.threshold = self.nc.threshold
self.pre2wa.delay = self.nc.delay * (1 - self.ddf)
if self.pre2wa.delay > 1e-9:
self.pre2wa.delay -= 1e-9 # we subtract a small value so that the synaptic weight gets updated before it is used.
if synapse_type.postsynaptic_variable == 'spikes':
# directly create NetCon as wa is on the same machine as the post-synaptic cell
self.post2wa = h.NetCon(
self.postsynaptic_cell._cell.source,
self.weight_adjuster,
sec=self.postsynaptic_cell._cell.source_section)
self.post2wa.threshold = 1
self.post2wa.delay = self.nc.delay * self.ddf
self.post2wa.weight[0] = -1
self.pre2wa.weight[0] = 1
else:
self.pre2wa.weight[0] = self.nc.weight[0]
parameters.pop('x', None) # for the Tsodyks-Markram model
parameters.pop(
'y', None) # would be better to actually use these initial values
for name, value in parameters.items():
setattr(self.weight_adjuster, name, value)
if mechanism == 'TsodyksMarkramWA': # or could assume that any weight_adjuster parameter called "tau_syn" should be set like this
self.weight_adjuster.tau_syn = self.nc.syn().tau
# setpointer
i = len(h.plastic_connections)
h.plastic_connections.append(self)
h('setpointer plastic_connections._[%d].weight_adjuster.wsyn, plastic_connections._[%d].nc.weight'
% (i, i))
def _setup_plasticity(self, synapse_type, parameters):
"""
Set this connection to use spike-timing-dependent plasticity.
`mechanism` -- the name of an NMODL mechanism that modifies synaptic
weights based on the times of pre- and post-synaptic spikes.
`parameters` -- a dictionary containing the parameters of the weight-
adjuster mechanism.
"""
mechanism = synapse_type.model
self.weight_adjuster = getattr(h, mechanism)(0.5)
if synapse_type.postsynaptic_variable == "spikes":
parameters["allow_update_on_post"] = int(False) # for compatibility with NEST
self.ddf = parameters.pop("dendritic_delay_fraction")
# If ddf=1, the synaptic delay
# `d` is considered to occur entirely in the post-synaptic
# dendrite, i.e., the weight adjuster receives the pre-
# synaptic spike at the time of emission, and the post-
# synaptic spike a time `d` after emission. If ddf=0, the
# synaptic delay is considered to occur entirely in the
# pre-synaptic axon.
elif synapse_type.postsynaptic_variable is None:
self.ddf = 0
else:
raise NotImplementedError("Only post-synaptic-spike-dependent mechanisms available for now.")
self.pre2wa = state.parallel_context.gid_connect(int(self.presynaptic_cell), self.weight_adjuster)
self.pre2wa.threshold = self.nc.threshold
self.pre2wa.delay = self.nc.delay * (1 - self.ddf)
if self.pre2wa.delay > 1e-9:
self.pre2wa.delay -= (
1e-9
) # we subtract a small value so that the synaptic weight gets updated before it is used.
if synapse_type.postsynaptic_variable == "spikes":
# directly create NetCon as wa is on the same machine as the post-synaptic cell
self.post2wa = h.NetCon(
self.postsynaptic_cell._cell.source,
self.weight_adjuster,
sec=self.postsynaptic_cell._cell.source_section,
)
self.post2wa.threshold = 1
self.post2wa.delay = self.nc.delay * self.ddf
self.post2wa.weight[0] = -1
self.pre2wa.weight[0] = 1
else:
self.pre2wa.weight[0] = self.nc.weight[0]
parameters.pop("x", None) # for the Tsodyks-Markram model
parameters.pop("y", None) # would be better to actually use these initial values
for name, value in parameters.items():
setattr(self.weight_adjuster, name, value)
if (
mechanism == "TsodyksMarkramWA"
): # or could assume that any weight_adjuster parameter called "tau_syn" should be set like this
self.weight_adjuster.tau_syn = self.nc.syn().tau
# setpointer
i = len(h.plastic_connections)
h.plastic_connections.append(self)
h("setpointer plastic_connections._[%d].weight_adjuster.wsyn, plastic_connections._[%d].nc.weight" % (i, i))
def __init__(self):
"""
Create an `FinitializeHandler` object in Hoc, which will call the
`_initialize()` method when NEURON is initialized.
"""
h('objref initializer')
h.initializer = self
self.fih = h.FInitializeHandler(1, "initializer._initialize()")
self.clear()
def record_spiketimes(sec, pos=0.5, threshold=-20.0):
if not hasattr(h, 'nil'):
h('objref nil')
sec.push()
nc = h.NetCon(sec(0.5)._ref_v, h.nil, threshold, 0.0, 1.0)
h.pop_section()
vec = h.Vector()
nc.record(vec)
return nc, vec
def hoc_execute_quiet(arg):
with open(os.devnull, 'wb') as null:
fd = sys.stdout.fileno()
keep = os.dup(fd)
sys.stdout.flush()
os.dup2(null.fileno(), fd)
h(arg)
sys.stdout.flush()
os.dup2(keep, fd)
def destroy(self):
"""
Takes care that neuron objects and python objects reffering to the cell
are destroyed.
At this point it actually destroys all the sections.
"""
neuron.h('objref tuft')
super(self.__class__, self).destroy()
def __init__(self):
"""
Create an `FinitializeHandler` object in Hoc, which will call the
`_initialize()` method when NEURON is initialized.
"""
h('objref initializer')
h.initializer = self
self.fih = h.FInitializeHandler(1, "initializer._initialize()")
self.clear()
def reinit_diam(sec, diam_bounds):
"""
For a node associated with a hoc section that is a tapered cylinder, every time the spatial resolution
of the section (nseg) is changed, the section diameters must be reinitialized. This method checks the
node's content dictionary for diameter boundaries and recalibrates the hoc section associated with this node.
"""
if diam_bounds is not None:
diam1, diam2 = diam_bounds
h(f'diam(0:1)={diam1}:{diam2}', sec=sec)
def connectgjs(env):
rank = int(env.pc.id())
nhosts = int(env.pc.nhost())
datasetPath = os.path.join(env.datasetPrefix, env.datasetName)
gapjunctions = env.gapjunctions
if env.gapjunctionsFile is None:
gapjunctionsFilePath = None
else:
gapjunctionsFilePath = os.path.join(datasetPath, env.gapjunctionsFile)
if gapjunctions is not None:
h('objref gjlist')
h.gjlist = h.List()
if env.verbose:
if env.pc.id() == 0:
print gapjunctions
datasetPath = os.path.join(env.datasetPrefix, env.datasetName)
(graph, a) = bcast_graph(env.comm, gapjunctionsFilePath, attributes=True)
ggid = 2e6
for name in gapjunctions.keys():
if env.verbose:
if env.pc.id() == 0:
print "*** Creating gap junctions %s" % name
prj = graph[name]
attrmap = a[name]
weight_attr_idx = attrmap['Weight'] + 1
dstbranch_attr_idx = attrmap['Destination Branch'] + 1
dstsec_attr_idx = attrmap['Destination Section'] + 1
srcbranch_attr_idx = attrmap['Source Branch'] + 1
srcsec_attr_idx = attrmap['Source Section'] + 1
for destination in sorted(prj.keys()):
edges = prj[destination]
sources = edges[0]
weights = edges[weight_attr_idx]
dstbranches = edges[dstbranch_attr_idx]
dstsecs = edges[dstsec_attr_idx]
srcbranches = edges[srcbranch_attr_idx]
srcsecs = edges[srcsec_attr_idx]
for i in range(0, len(sources)):
source = sources[i]
srcbranch = srcbranches[i]
srcsec = srcsecs[i]
dstbranch = dstbranches[i]
dstsec = dstsecs[i]
weight = weights[i]
if env.pc.gid_exists(source):
h.mkgap(env.pc, h.gjlist, source, srcbranch, srcsec, ggid, ggid + 1, weight)
if env.pc.gid_exists(destination):
h.mkgap(env.pc, h.gjlist, destination, dstbranch, dstsec, ggid + 1, ggid, weight)
ggid = ggid + 2
del graph[name]
def test_ste():
m1 = model()
# one state ste with two self transitions
var = m1["s"](0.5)._ref_v
thresh1 = h.ref(-50)
thresh2 = h.ref(-10)
result = []
ste = h.StateTransitionEvent(1)
ste.transition(0, 0, var, thresh1, (act, (1, m1, thresh1, result)))
ste.transition(0, 0, var, thresh2, (act, (2, m1, thresh2, result)))
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
print("final v=%g" % m1["s"](0.5).v)
chk_result(2, result, m1)
h.cvode_active(1)
run(20)
chk_result(2, result, m1)
h.cvode_active(1)
h.cvode.condition_order(2)
run(5)
chk_result(2, result, m1)
h.cvode.condition_order(1)
h.cvode_active(0)
# ste associated with point process
del fih, ste
ste = h.StateTransitionEvent(2, m1["ic"])
fih = h.FInitializeHandler((on_finit, (ste, result)))
run(5)
# transition with hoc callback
h("""proc foo() { printf("foo called at t=%g\\n", t) }""")
thresh3 = h.ref(-30)
ste.transition(0, 0, var, thresh3, "foo()")
run(5)
# transition with hoc callback in hoc object
h("""
begintemplate FooSTEtest
objref this
proc foo() { printf("foo in %s called at t=%g\\n", this, t) }
endtemplate FooSTEtest
""")
thresh4 = h.ref(-20)
obj = h.FooSTEtest()
ste.transition(0, 0, var, thresh4, "foo()", obj)
run(5)
del ste, fih
assert h.List("StateTransitionEvent").count() == 0
assert h.List("FInitializeHandler").count() == 0
def test_Network_01(self):
cellParameters = dict(
morphology=os.path.join(LFPy.__path__[0], 'test',
'ball_and_sticks_w_lists.hoc'),
templatefile=os.path.join(LFPy.__path__[0], 'test',
'ball_and_stick_template.hoc'),
templatename='ball_and_stick_template',
templateargs=None,
passive=False,
dt=2**-3,
tstop=100,
delete_sections=False,
)
populationParameters = dict(
CWD=None,
CELLPATH=None,
Cell=LFPy.NetworkCell,
cell_args=cellParameters,
pop_args=dict(radius=100, loc=0., scale=20.),
rotation_args=dict(x=0, y=0),
POP_SIZE=4,
name='test',
)
networkParameters = dict(dt=2**-3,
tstart=0.,
tstop=100.,
v_init=-65.,
celsius=6.3,
OUTPUTPATH='tmp_testNetworkPopulation')
# set up
network = LFPy.Network(**networkParameters)
network.create_population(**populationParameters)
connectivity = network.get_connectivity_rand(pre='test',
post='test',
connprob=0.5)
# connect and run sim
network.connect(pre='test', post='test', connectivity=connectivity)
SPIKES, LFP, P = network.simulate(rec_current_dipole_moment=True)
# test output
for population in network.populations.values():
for cell in population.cells:
self.assertTrue(np.all(cell.somav == network.v_init))
self.assertTrue(np.all(P['test'] == 0.))
self.assertTrue(P.shape[0] == cell.somav.size)
self.assertTrue(len(LFP) == 0)
network.pc.gid_clear()
os.system('rm -r tmp_testNetworkPopulation')
for population in network.populations.values():
for cell in population.cells:
cell.strip_hoc_objects()
neuron.h('forall delete_section()')
def run_single_test(cell_params, input_array, input_idx, hold_potential,
use_channels, sim_idx):
cell_params.update({
'v_init':
hold_potential,
'custom_fun': [active_declarations], # will execute this function
'custom_fun_args': [{
'use_channels': use_channels,
'hold_potential': hold_potential
}]
})
neuron.h('forall delete_section()')
cell = LFPy.Cell(**cell_params)
noiseVec = neuron.h.Vector(input_array)
i = 0
syn = None
for sec in cell.allseclist:
for seg in sec:
if i == input_idx:
print "Input i" \
"nserted in ", sec.name()
syn = neuron.h.ISyn(seg.x, sec=sec)
i += 1
if type(syn) == type(None):
raise RuntimeError("Wrong stimuli index")
syn.dur = 1E9
syn.delay = 0
noiseVec.play(syn._ref_amp, cell.timeres_NEURON)
figfolder = 'verifications'
if not os.path.isdir(figfolder): os.mkdir(figfolder)
sim_params = {'rec_vmem': True, 'rec_imem': True, 'rec_variables': []}
cell.simulate(**sim_params)
simfolder = 'simresults'
if not os.path.isdir(simfolder): os.mkdir(simfolder)
simname = join(simfolder, '%d' % input_idx)
if len(use_channels) == 0:
simname += '_passive'
else:
for ion in use_channels:
simname += '_%s' % ion
simname += '_%+d' % hold_potential
simname += '_sim_%d' % sim_idx
savedata(cell, simname, input_idx)
# recreate_Hu_figs(cell, input_idx, idx_list, cell_params, figfolder)
plot_resonances(cell, input_idx, 0.01, [0, 460, 565, 451, 728, 303],
cell_params, figfolder)
del cell
del noiseVec
del syn
def insert_glutamate_stim(cell, section = 'apic[63]', site = 0.5):
gmaxS=20
neuron.h('access %s' %section)
glut_syn = neuron.h.glutamate(site)
glut_syn.delay = 50
glut_syn.ntar = 1
glut_syn.gmax = gmaxS
glut_syn.Nspike=2
glut_syn.Tspike=20
return glut_syn
def create_graphs():
h('objref py')
h.py = h.PythonObject() # lets Hoc access Python
h.nrnmainmenu()
h.nrncontrolmenu()
h.steps_per_ms = 1.0/DT
# adding graphs
for id in NEURONS_TO_PLOT:
addgraph(-80, 40, "py.neurons[%d]._cell.seg.v" % id, 4)
def insert_glutamate_stim(cell, section="apic[63]", site=0.5):
gmaxS = 10
neuron.h("access %s" % section)
glut_syn = neuron.h.glutamate(site)
glut_syn.delay = 50
glut_syn.ntar = 1.3
glut_syn.gmax = gmaxS
glut_syn.Nspike = 3
glut_syn.Tspike = 20
return glut_syn
def compare_diam(filename, param_list=None):
"""Change distal dendrite's diameter"""
diam_list = param_list
if not diam_list:
diam_list = [0.5, 1, 1.5, 2]
print("diam_list:{}".format(diam_list))
load_file(filename)
for diam in diam_list:
h("""ldend.diam = {}""".format(diam))
save_run("{}_{}".format(filename, diam))
def hoc_setup():
with open(os.devnull, 'wb') as null:
fd = sys.stdout.fileno()
keep = os.dup(fd)
sys.stdout.flush()
os.dup2(null.fileno(), fd)
neuron.h('load_file("stdrun.hoc")')
sys.stdout.flush()
os.dup2(keep, fd)
def test_soma():
h("""create soma""")
assert h.soma is not None
h.soma.L = 5.6419
h.soma.diam = 5.6419
assert h.soma.L == 5.6419
assert h.soma.diam == 5.6419
def query_neuron(lst, model_file): # MOCKUP
h.load_file(model_file)
values = []
aa = 0
for item in lst:
h('aa = ' + item)
val = float(h.aa)
values.append(val)
print(f"par is {item} values is {val}")
return values
def getOrig(fileName):
nrn.h.paramsFile = fileName
nrn.h.psize = 1
nrn.h("pmat = new Matrix(psize, nparams)")
nrn.h("readMatrix(paramsFile, pmat)")
nrn.h("runMatrix(pmat,stims)")
nrn.h("print matOut")
nrn.h("matOut = matOut.to_vector()")
orig_volts = nrn.h.matOut.to_python()
return orig_volts
def setpointers(self):
self.caller.interpCoordinates.getSecRefs()
print("hello")
for sec in h.allsec():
if h.ismembrane("xtra", sec=sec) and h.ismembrane("extracellular", sec=sec):
# print(sec)
# should be a way to do this without resortin to this ugly str coding but fine for now
neuron.h("for (x,0){ setpointer ex_xtra(x), e_extracellular(x) }")
def __init__(self):
############################# MORPHOLOGY ##############################
h("GJ = 1") # 0 for no gap-junctions between GoCs
# 0 or 1, GJ must be set prior to loading network.hoc
h.xopen("network.hoc") # Within it is the function Goc() which
# below are the components set as attribute to this python class
self.GrC = h.GrC # len(h.GrC) -> 8100
self.GoC = h.GoC # len(h.GoC) -> 225
self.MF = h.fiber # len(h.fiber) -> 900
def get_ggn(fname):
with h5.File(fname, 'r') as fd:
ggn_model = fd['/celltemplate'].value.decode('utf-8')
cellname = [line for line in ggn_model.split('\n')
if 'begintemplate' in line]
rest = cellname[0].partition('begintemplate')[-1]
cellname = rest.split()[0]
if not hasattr(h, cellname):
h(ggn_model)
return eval('h.{}()'.format(cellname))
def test_soma():
h('''create soma''')
assert h.soma is not None
h.soma.L = 5.6419
h.soma.diam = 5.6419
assert h.soma.L == 5.6419
assert h.soma.diam == 5.6419
def __init__(self):
self.x = self.y = self.z = 0
hoc_file = "..\\mn_geometries\\burke_mn_2 - Copy"
hoc_command = "{xopen(" + hoc_command + ")}"
h(hoc_command)
self.build_topology()
self.build_subsets()
self.define_geometry()
self.define_biophysics()
def show_plot(quad):
"""
Plot the thing.
"""
h('xopen(quad.hocfile)')
plt.figure(figsize=(7,7))
shapeax = plt.subplot(111, projection='3d')
shapeax.view_init(75,66)
shapeplot(h,shapeax)
plt.show()
return
def test_Network_01(self):
cellParameters = dict(
morphology=os.path.join(LFPy.__path__[0], 'test', 'ball_and_sticks_w_lists.hoc'),
templatefile=os.path.join(LFPy.__path__[0], 'test', 'ball_and_stick_template.hoc'),
templatename='ball_and_stick_template',
templateargs=None,
passive=False,
dt=2**-3,
tstop=100,
delete_sections=False,
)
populationParameters = dict(
CWD=None,
CELLPATH=None,
Cell=LFPy.NetworkCell,
cell_args = cellParameters,
pop_args = dict(
radius=100,
loc=0.,
scale=20.),
rotation_args = dict(x=0, y=0),
POP_SIZE = 4,
name = 'test',
)
networkParameters = dict(
dt=2**-3,
tstart=0.,
tstop=100.,
v_init=-65.,
celsius=6.3,
OUTPUTPATH='tmp_testNetworkPopulation'
)
# set up
network = LFPy.Network(**networkParameters)
network.create_population(**populationParameters)
connectivity = network.get_connectivity_rand(pre='test', post='test', connprob=0.5)
# connect and run sim
network.connect(pre='test', post='test', connectivity=connectivity)
SPIKES, LFP, P = network.simulate(rec_current_dipole_moment=True)
# test output
for population in network.populations.values():
for cell in population.cells:
self.assertTrue(np.all(cell.somav == network.v_init))
self.assertTrue(np.all(P['test'] == 0.))
self.assertTrue(P.shape[0] == cell.somav.size)
self.assertTrue(len(LFP) == 0)
network.pc.gid_clear()
os.system('rm -r tmp_testNetworkPopulation')
neuron.h('forall delete_section()')
def insertchans():
h.tstop = 1e3
for vals in thalDict.values():
for ce in vals['cel']:
ce.soma[0].insert('hh2nafjr')
ce.soma[0].gnabar_hh2nafjr = 0.0
for ce in thalDict['RE']['cel']:
sec = ce.soma[0]
for mech in it2l:
ce.soma[0].insert(mech)
h('%s.gcabar_%s = 0.0' % (str(sec), mech))
def measure_passive (gid, pop_name, v_init, env, prelength=1000.0, mainlength=2000.0, stimdur=500.0, cell_dict={}):
biophys_cell = init_biophys_cell(env, pop_name, gid, register_cell=False, cell_dict=cell_dict)
hoc_cell = biophys_cell.hoc_cell
h.dt = env.dt
tstop = prelength+mainlength
soma = list(hoc_cell.soma)[0]
stim1 = h.IClamp(soma(0.5))
stim1.delay = prelength
stim1.dur = stimdur
stim1.amp = -0.1
h('objref tlog, Vlog')
h.tlog = h.Vector()
h.tlog.record (h._ref_t)
h.Vlog = h.Vector()
h.Vlog.record (soma(0.5)._ref_v)
h.tstop = tstop
Rin = h.rn(hoc_cell)
neuron_utils.simulate(v_init, prelength, mainlength)
## compute membrane time constant
vrest = h.Vlog.x[int(h.tlog.indwhere(">=",prelength-1))]
vmin = h.Vlog.min()
vmax = vrest
## the time it takes the system's step response to reach 1-1/e (or
## 63.2%) of the peak value
amp23 = 0.632 * abs (vmax - vmin)
vtau0 = vrest - amp23
tau0 = h.tlog.x[int(h.Vlog.indwhere ("<=", vtau0))] - prelength
results = {'Rin': np.asarray([Rin], dtype=np.float32),
'vmin': np.asarray([vmin], dtype=np.float32),
'vmax': np.asarray([vmax], dtype=np.float32),
'vtau0': np.asarray([vtau0], dtype=np.float32),
'tau0': np.asarray([tau0], dtype=np.float32)
}
env.synapse_attributes.del_syn_id_attr_dict(gid)
if gid in env.biophys_cells[pop_name]:
del env.biophys_cells[pop_name][gid]
return results
def __init__(self):
h("objref cvode") # initialize multiple order variable time step
h("cvode = new CVode()"
) # integration method as it is called in hoc file
h.xopen("start.hoc")
# nsyn1 = 4 at line 11
# ncells = 1 at line 79
# nmossy = 4 at line 80
# Notice that one should try to have nmossy = nsyn1 =/= NumSyn (synapse/MF)
self.cell = h.GrCell[0] # len(h.GrCell) -> 1
self.mossy = h.Mossy # len(h.Mossy) -> 4
def setparams(params):
params_copy = params.copy()
keys = params.keys()
for ikey in range(0, len(keys)):
params_copy[keys[ikey]] = params[keys[ikey]] * defVals[keys[ikey]]
print "setparams: params_copy = " + str(params_copy)
for ikey in range(0, len(keys)):
key = keys[ikey]
print(key + " = " + str(params_copy[key]))
h(key + " = " + str(params_copy[key]))
h("tfunk()")
def get_tempdata_address(double_escaped=0):
h('systype = unix_mac_pc()')
if h.systype == 3:
if double_escaped:
tempdata_address = '..\\\\..\\\\tempdata\\\\'
else:
tempdata_address = '..\\..\\tempdata\\'
else:
tempdata_address = "../tempdata/"
return tempdata_address
def make_cells(self,cell_list):
'''
Distribute cells across the hosts in a
Round robin distribution (circular dealing of cells)
https://en.wikipedia.org/wiki/Round-robin
'''
#import neuron
#from neuron import h
coords = [0 for i in xrange(0,3)]#define list as a local variable.
h('objref py')
h('py = new PythonObject()')
NCELL=self.NCELL
SIZE=self.SIZE
RANK=self.RANK
pc=h.ParallelContext()
h('objref tvec, gidvec')
h('gidvec = new Vector()')
h('tvec = new Vector()')
assert len(cell_list)!=0
d = { x: y for x,y in enumerate(cell_list)}
itergids = iter( (d[i][3],i) for i in range(RANK, NCELL, SIZE) )
#Create a dictionary, where keys are soma centre coordinates to check for two cells occupying the same position.
#since dictionary keys have to be unique should throw error once two cell somas occupy exactly the same coordinates.
#TODO keep rank0 free of cells, such that all the memory associated with that CPU is free for graph theory related objects.
#This would require an iterator such as the following.
for (j,i) in itergids:
has_cells=1#RANK specific attribute simplifies later code.
cell = h.mkcell(j)
self.names_list[i]=j
print cell, j,i
cell.geom_nseg()
cell.gid1=i
cell.name=j
# Populate the dictionary with appropriate keys for the sanity check.
#excitatory neuron.
#self.test_cell(d[i])
if 'pyramid' in d[i]:
cell.pyr()
cell.polarity=1
#inhibitory neuron.
else:
cell.basket()
cell.polarity=0
#8 lines of trailing code is drawn from.
#http://neuron.yale.edu/neuron/static/docs/neuronpython/ballandstick5.html
pc.set_gid2node(i,RANK)
nc = cell.connect2target(None)
pc.cell(i, nc) # Associate the cell with this host and gid
#### Record spikes of this cell
pc.spike_record(i, self.tvec, self.gidvec)
assert None!=pc.gid2cell(i)
self.celldict[i]=cell
self.cells.append(cell)
def create_reduced_cell(soma_cable,
has_apical,
original_cell,
model_obj_name,
new_cable_properties,
new_cables_nsegs,
subtrees_xs):
h("objref reduced_cell")
h("reduced_cell = new " + model_obj_name + "()")
create_sections_in_hoc("soma", 1, "reduced_cell")
soma = original_cell.soma[0] if original_cell.soma.hname()[-1] == ']' else original_cell.soma
append_to_section_lists("soma[0]", "somatic", "reduced_cell")
if has_apical: # creates reduced apical cable if apical subtree existed
create_sections_in_hoc("apic", 1, "reduced_cell")
apic = h.reduced_cell.apic[0]
num_of_basal_subtrees = len(new_cable_properties) - 1
cable_params = new_cable_properties[0]
nseg = new_cables_nsegs[0]
apply_params_to_section("apic[0]", "apical", "reduced_cell",
apic, cable_params, nseg)
apic.connect(soma, subtrees_xs[0], 0)
else:
apic = None
num_of_basal_subtrees = len(new_cable_properties)
# creates reduced basal cables
create_sections_in_hoc("dend", num_of_basal_subtrees, "reduced_cell")
basals = [h.reduced_cell.dend[i] for i in range(num_of_basal_subtrees)]
for i in range(num_of_basal_subtrees):
if has_apical:
index_in_reduced_cables_dimensions = i + 1
else:
index_in_reduced_cables_dimensions = i
cable_params = new_cable_properties[index_in_reduced_cables_dimensions]
nseg = new_cables_nsegs[index_in_reduced_cables_dimensions]
apply_params_to_section("dend[" + str(i) + "]", "basal", "reduced_cell",
basals[i], cable_params, nseg)
basals[i].connect(soma, subtrees_xs[index_in_reduced_cables_dimensions], 0)
# create cell python template
cell = Neuron(h.reduced_cell)
cell.soma = original_cell.soma
cell.apic = apic
return cell, basals
def get_mn_geom_address(double_escaped = 0):
h('systype = unix_mac_pc()')
if h.systype == 3:
if double_escaped:
mn_geom_address = '..\\\\mn_geometries\\\\'
else:
mn_geom_address = '..\\mn_geometries\\'
else:
mn_geom_address = "mn_geometries/"
return mn_geom_address
def get_tempdata_address(double_escaped = 0):
h('systype = unix_mac_pc()')
if h.systype == 3:
if double_escaped:
tempdata_address = '..\\\\..\\\\tempdata\\\\'
else:
tempdata_address = '..\\..\\tempdata\\'
else:
tempdata_address = "../tempdata/"
return tempdata_address
def get_mn_geom_address(double_escaped=0):
h('systype = unix_mac_pc()')
if h.systype == 3:
if double_escaped:
mn_geom_address = '..\\\\mn_geometries\\\\'
else:
mn_geom_address = '..\\mn_geometries\\'
else:
mn_geom_address = "mn_geometries/"
return mn_geom_address
def set_params_all_active(hobj, params_file_name):
"""Configure a neuron after the cell morphology has been loaded"""
with open(params_file_name) as biophys_params_file:
biophys_params = json.load(biophys_params_file)
passive = biophys_params['passive'][0]
genome = biophys_params['genome']
conditions = biophys_params['conditions'][0]
# Set fixed passive properties
for sec in hobj.all:
sec.Ra = passive['ra']
sec.insert('pas')
# Insert channels and set parameters
for p in genome:
section_array = p["section"]
mechanism = p["mechanism"]
param_name = p["name"]
param_value = float(p["value"])
if section_array == "glob":
h(p["name"] + " = %g " % p["value"])
else:
if hasattr(hobj, section_array):
if mechanism != "":
print 'Adding mechanism %s to %s' % (mechanism,
section_array)
for section in getattr(hobj, section_array):
if h.ismembrane(str(mechanism), sec=section) != 1:
section.insert(mechanism)
print 'Setting %s to %.6g in %s' % (param_name, param_value,
section_array)
for section in getattr(hobj, section_array):
setattr(section, param_name, param_value)
# Set reversal potentials
for erev in conditions['erev']:
erev_section_array = erev["section"]
ek = float(erev["ek"])
ena = float(erev["ena"])
print 'Setting ek to %.6g and ena to %.6g in %s' % (ek, ena,
erev_section_array)
if hasattr(hobj, erev_section_array):
for section in getattr(hobj, erev_section_array):
if h.ismembrane("k_ion", sec=section) == 1:
setattr(section, 'ek', ek)
if h.ismembrane("na_ion", sec=section) == 1:
setattr(section, 'ena', ena)
else:
print "Warning: can't set erev for %s, section array doesn't exist" % erev_section_array
def plot_m_types(ax, PSET, colors, section=['dend', 'apic'], spacing=300, linewidths=0.05):
'''draw comparison plot of each individual morphology'''
CWD=PSET.CWD
CELLPATH=PSET.CELLPATH
n_segs = []
areas = []
for i, data in enumerate(PSET.populationParameters):
NRN = data["me_type"]
os.chdir(os.path.join(CWD, CELLPATH, NRN))
cell = NetworkCell(**PSET.cellParameters[NRN])
cell.set_pos(x=i*spacing, y=0, z=data['pop_args']['loc'])
cell.set_rotation(x=np.pi/2)
print(NRN, cell.zstart.min(), cell.zmid.min(), cell.zend.min(), cell.zstart.max(), cell.zmid.max(), cell.zend.max())
n_segs += [cell.totnsegs]
areas += [cell.area[cell.get_idx(section)].sum()]
zips = []
for x, z in cell.get_idx_polygons(projection=('x', 'z')):
zips.append(list(zip(x, z)))
polycol = PolyCollection(zips,
edgecolors=colors[i],
linewidths=linewidths,
facecolors=colors[i],
label=NRN,
)
ax.add_collection(polycol)
os.chdir(CWD)
axis = ax.axis(ax.axis('tight'))
# draw lines showing the layer boundaries
ax.hlines(np.r_[0., -PSET.layer_data['thickness'].cumsum()][:4], axis[0], axis[1]-300, 'k', lw=0.5)
ax.hlines(np.r_[0., -PSET.layer_data['thickness'].cumsum()][4:], axis[0], axis[1], 'k', lw=0.5)
# annotate hlines with values
for z in np.r_[0., -PSET.layer_data['thickness'].cumsum()]:
ax.text(axis[0], z, r'$z={}$'.format(int(z)) + '$\mu$m', ha='right', va='center')
ax.set_yticks(PSET.layer_data['center'])
ax.set_yticklabels(PSET.layer_data['layer'])
ax.set_xticks(np.arange(PSET.populationParameters.size)*spacing)
ax.set_xticklabels(PSET.populationParameters['m_type'], rotation='vertical')
ax.axis(ax.axis('equal'))
ax.set_title('m-types')
neuron.h("forall delete_section()")
return n_segs, areas
def test_axial_currents():
h('soma {insert pas}')
isim = h.IClamp(1, sec=h.cable)
isim.delay = 0
isim.dur = 1
isim.amp = 2 #nA
h.cable.Ra = 0.1
cell.initialize(0.1)
t, imem_density, iax_density = cell.integrate(0.2, i_axial=True)
coord = cell.get_seg_coords()
iax = iax_density*coord['diam'][None,:]**2*1e-8/4.*h.PI #mA
assert np.abs(iax[-1,1]*1e6 - isim.amp)<0.1*isim.amp
def insert(pat, chan):
"""Insert the pattern into segments """
global nchan
chanName, expr = chan
for sec in topology.nodes():
secName = sec.hname()
if pat.match(secName):
expr = expr.replace('r', str(topology.node[sec]['r']))
g = eval(expr)
print("|- Inserting {} into {} with conductance: {} uS".format(
chanName, secName, g)
)
chan = sec.insert(chanName)
h('gmax=%s' % (g))
nchan += 1
def main():
h.load_file("multisplit.hoc")
h('{nrn_load_dll("../specials/x86_64/.libs/libnrnmech.so")}')
tstop = 1000
pc = h.ParallelContext()
start = h.startsw()
######################################################
# load model
cell = load_model("./1056.swc")
######################################################
# generate mcomplex.dat
lb = h.MyLoadBalance()
lb.ExperimentalMechComplex()
#save_mcomplex(lb)
pc.barrier()
######################################################
# set up multi split
cplx = h.multisplit(lb)
pc.multisplit()
pc.set_maxstep(10)
######################################################
# set stim and record of v
stim = set_iclamp("CellSwc[0].Dend[2000]", pc)
vec_t = h.Vector()
vec_t.record(h._ref_t)
vec_v = set_vec_v("CellSwc[0].Dend[100]", pc)
setup_time = h.startsw() - start
######################################################
# run simulation
start = h.startsw()
h.stdinit()
pc.psolve(tstop)
pc.barrier()
calc_time = h.startsw() - start
######################################################
# disp info
show_result(pc, setup_time, calc_time, cplx)
#show_section()
#if(vec_v != 0):
# show_plot(vec_t, vec_v)
pc.done()
