def test_place_pwlin_id(self):
# Single branch, discontiguous segments.
s0p = A.mpoint(0, 0, 0, 10)
s0d = A.mpoint(1, 0, 0, 10)
s1p = A.mpoint(3, 0, 0, 10)
s1d = A.mpoint(4, 0, 0, 10)
tree = A.segment_tree()
i = tree.append(A.mnpos, s0p, s0d, 1)
tree.append(i, s1p, s1d, 2)
m = A.morphology(tree)
place = A.place_pwlin(m)
L0 = place.at(A.location(0, 0))
L0s = place.all_at(A.location(0, 0))
self.assertEqual(s0p, L0)
self.assertEqual([s0p], L0s)
Lhalf = place.at(A.location(0, 0.5))
Lhalfs = place.all_at(A.location(0, 0.5))
self.assertTrue(s0d == Lhalf or s1p == Lhalf)
self.assertTrue([s0d, s1p] == Lhalfs)
Chalf = [(s.prox, s.dist)
for s in place.segments([A.cable(0, 0., 0.5)])]
self.assertEqual([(s0p, s0d)], Chalf)
Chalf_all = [(s.prox, s.dist)
for s in place.all_segments([A.cable(0, 0., 0.5)])]
self.assertEqual([(s0p, s0d), (s1p, s1p)], Chalf_all)
def __init__(self):
A.recipe.__init__(self)
st = A.segment_tree()
i = st.append(A.mnpos, (0, 0, 0, 10), (1, 0, 0, 10), 1)
st.append(i, (1, 3, 0, 5), 1)
st.append(i, (1, -4, 0, 3), 1)
self.the_morphology = A.morphology(st)
self.the_cat = A.default_catalogue()
self.the_props = A.neuron_cable_properties()
self.the_props.register(self.the_cat)
def create_individual(self):
fit, swc, ref = self.fns
import pandas as pd
segment_tree = arb.load_swc_allen(swc, no_gaps=False)
self.morphology = arb.morphology(segment_tree)
self.labels = arb.label_dict({
'soma': '(tag 1)',
'axon': '(tag 2)',
'dend': '(tag 3)',
'apic': '(tag 4)',
'center': '(location 0 0.5)'
})
self.reference = pd.read_csv(ref)['U/mV'].values * 1000.0
self.defaults, self.regions, self.ions, mechs = load_allen_fit(fit)
# randomise mechanisms
result = {}
for r, m, vs in mechs:
for k, _ in vs.items():
result[f"{r}.{m}.{k}"] = rand(-1.0, +1.0)
return result
def test_place_pwlin_isometry(self):
# Single branch, discontiguous segments.
s0p = A.mpoint(0, 0, 0, 10)
s0d = A.mpoint(1, 0, 0, 10)
s1p = A.mpoint(3, 0, 0, 10)
s1d = A.mpoint(4, 0, 0, 10)
tree = A.segment_tree()
i = tree.append(A.mnpos, s0p, s0d, 1)
tree.append(i, s1p, s1d, 2)
m = A.morphology(tree)
iso = A.isometry.translate(2, 3, 4)
place = A.place_pwlin(m, iso)
x0p = iso(s0p)
x0d = iso(s0d)
x1p = iso(s1p)
x1d = iso(s1d)
L0 = place.at(A.location(0, 0))
L0s = place.all_at(A.location(0, 0))
self.assertEqual(x0p, L0)
self.assertEqual([x0p], L0s)
Lhalf = place.at(A.location(0, 0.5))
Lhalfs = place.all_at(A.location(0, 0.5))
self.assertTrue(x0d == Lhalf or x1p == Lhalf)
self.assertTrue([x0d, x1p] == Lhalfs)
Chalf = [(s.prox, s.dist)
for s in place.segments([A.cable(0, 0., 0.5)])]
self.assertEqual([(x0p, x0d)], Chalf)
Chalf_all = [(s.prox, s.dist)
for s in place.all_segments([A.cable(0, 0., 0.5)])]
self.assertEqual([(x0p, x0d), (x1p, x1p)], Chalf_all)
# The morphology used for all of the region/locset illustrations label_tree = arbor.segment_tree() label_tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint( 4, 0.0, 0, 2.0), tag=1) label_tree.append(0, mpoint(4, 0.0, 0, 0.8), mpoint( 8, 0.0, 0, 0.8), tag=3) label_tree.append(1, mpoint(8, 0.0, 0, 0.8), mpoint(12, -0.5, 0, 0.8), tag=3) label_tree.append(2, mpoint(12, -0.5, 0, 0.8), mpoint(20, 4.0, 0, 0.4), tag=3) label_tree.append(3, mpoint(20, 4.0, 0, 0.4), mpoint(26, 6.0, 0, 0.2), tag=3) label_tree.append(2, mpoint(12, -0.5, 0, 0.5), mpoint(19, -3.0, 0, 0.5), tag=3) label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(24, -7.0, 0, 0.2), tag=3) label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(23, -1.0, 0, 0.2), tag=3) label_tree.append(7, mpoint(23, -1.0, 0, 0.2), mpoint(26, -2.0, 0, 0.2), tag=3) label_tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint(-7, 0.0, 0, 0.4), tag=2) label_tree.append(9, mpoint(-7, 0.0, 0, 0.4), mpoint(-10, 0.0, 0, 0.4), tag=2) label_morph = arbor.morphology(label_tree) # The label morphology with some gaps (at start of dendritic tree and remove the axon hillock) label_tree = arbor.segment_tree() label_tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint( 4, 0.0, 0, 2.0), tag=1) label_tree.append(0, mpoint(5, 0.0, 0, 0.8), mpoint( 8, 0.0, 0, 0.8), tag=3) label_tree.append(1, mpoint(8, 0.0, 0, 0.8), mpoint(12, -0.5, 0, 0.8), tag=3) label_tree.append(2, mpoint(12, -0.5, 0, 0.8), mpoint(20, 4.0, 0, 0.4), tag=3) label_tree.append(3, mpoint(20, 4.0, 0, 0.4), mpoint(26, 6.0, 0, 0.2), tag=3) label_tree.append(2, mpoint(12, -0.5, 0, 0.5), mpoint(19, -3.0, 0, 0.5), tag=3) label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(24, -7.0, 0, 0.2), tag=3) label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(23, -1.0, 0, 0.2), tag=3) label_tree.append(7, mpoint(23, -1.0, 0, 0.2), mpoint(26, -2.0, 0, 0.2), tag=3) label_tree.append(mnpos, mpoint(-2, 0.0, 0, 0.4), mpoint(-10, 0.0, 0, 0.4), tag=2) detached_morph = arbor.morphology(label_tree)
new.append(parent=p,
x=s.loc.x,
y=s.loc.y,
z=s.loc.z,
radius=s.loc.radius,
tag=s.tag)
else:
if p_axon is None:
p_axon = p
x0, y0, z0 = s.loc.x, s.loc.y, s.loc.z
x1, y1, z1 = s.loc.x, s.loc.y, s.loc.z
axon = new.append(p_axon, x0, y0, z0, 1, 2)
new.append(axon, x0 + 60 + tree.samples[0].loc.radius, y0, z0, 1, 2)
morph = arbor.morphology(tree, spherical_root=True)
# Label regions
labels = arbor.label_dict({
'soma': '(tag 1)',
'axon': '(tag 2)',
'dend': '(tag 3)',
'apic': '(tag 4)',
'center': '(location 0 0.5)'
})
# Build cell and attach Clamp and Detector
cell = arbor.cable_cell(tree, labels)
cell.place('center', arbor.iclamp(200, 1200, i))
cell.place('center', arbor.spike_detector(-40))
def run_arb(fit, swc, current, t_start, t_stop):
tree = arbor.load_swc_allen(swc, no_gaps=False)
# Load mechanism data
with open(fit) as fd:
fit = json.load(fd)
## collect parameters in dict
mechs = defaultdict(dict)
### Passive parameters
ra = float(fit['passive'][0]['ra'])
### Remaining parameters
for block in fit['genome']:
mech = block['mechanism'] or 'pas'
region = block['section']
name = block['name']
if name.endswith('_' + mech):
name = name[:-(len(mech) + 1)]
mechs[(mech, region)][name] = float(block['value'])
# Label regions
labels = arbor.label_dict({
'soma': '(tag 1)',
'axon': '(tag 2)',
'dend': '(tag 3)',
'apic': '(tag 4)',
'center': '(location 0 0.5)'
})
properties = fit['conditions'][0]
T = properties['celsius'] + 273.15
Vm = properties['v_init']
# Run simulation
morph = arbor.morphology(tree)
# Build cell and attach Clamp and Detector
cell = arbor.cable_cell(morph, labels)
cell.place('center', arbor.iclamp(t_start, t_stop - t_start, current))
cell.place('center', arbor.spike_detector(-40))
cell.compartments_length(20)
# read json file and proceed to set parameters and mechanisms
# set global values
print('Setting global parameters')
print(f" * T = {T}K = {T - 273.15}C")
print(f" * Vm = {Vm}mV")
cell.set_properties(tempK=T, Vm=Vm, rL=ra)
# Set reversal potentials
print("Setting reversal potential for")
for kv in properties['erev']:
region = kv['section']
for k, v in kv.items():
if k == 'section':
continue
ion = k[1:]
print(f' * region {region:6} species {ion:5}: {v:10}')
cell.paint(region, arbor.ion(ion, rev_pot=float(v)))
cell.set_ion('ca',
int_con=5e-5,
ext_con=2.0,
method=arbor.mechanism('default_nernst/x=ca'))
# Setup mechanisms and parameters
print('Setting up mechanisms')
## Now paint the cell using the dict
for (mech, region), vs in mechs.items():
print(f" * {region:10} -> {mech:10}: {str(vs):>60}", end=' ')
try:
if mech != 'pas':
m = arbor.mechanism(mech, vs)
cell.paint(region, m)
else:
m = arbor.mechanism('default_pas', {
'e': vs['e'],
'g': vs['g']
})
cell.paint(region, m)
cell.paint(region, cm=vs["cm"] / 100, rL=vs["Ra"])
print("OK")
except Exception as e:
print("ERROR")
print("When trying to set", mech, vs)
print(" ->", e)
exit()
# Run the simulation, collecting voltages
print('Simulation', end=' ')
default = arbor.default_catalogue()
catalogue = arbor.allen_catalogue()
catalogue.extend(default, 'default_')
model = arbor.single_cell_model(cell)
model.properties.catalogue = catalogue
model.probe('voltage', 'center', frequency=200000)
model.run(tfinal=t_start + t_stop, dt=1000 / 200000)
print('DONE')
for t in model.traces:
ts = t.time[:]
vs = t.value[:]
break
spikes = np.array(model.spikes)
count = len(spikes)
print('Counted spikes', count)
return np.array(ts), np.array(vs) + 14
# describe neurite_3 (tag 3) attached to axon # naming of segments neur3_x_y with neur3 = neurite 3, x = number of neurite segment, y = neuroML segment ID neur3_0_22 = tree.append(a14_21, arbor.mpoint(-5.000000e-01, -2.321750e+02, 4.900000e+01, 0.5836462148750231 / 2), arbor.mpoint(-7.000000e-01, -2.325000e+02, 4.910000e+01, 0.5836462148750231 / 2), tag=3) neur3_1_23 = tree.append(neur3_0_22, arbor.mpoint(-7.000000e-01, -2.363500e+02, 5.060000e+01, 0.5836462148750231 / 2), tag=3) neur3_2_24 = tree.append(neur3_1_23, arbor.mpoint(-7.000000e-01, -2.377500e+02, 5.105000e+01, 0.5836462148750231 / 2), tag=3) # describe neurite_4 (tag 4) attached to axon # naming of segments neur4_x_y with neur4 = neurite 4, x = number of neurite segment, y = neuroML segment ID neur4_0_25 = tree.append(a14_21, arbor.mpoint(-5.000000e-01, -2.321750e+02, 4.900000e+01, 0.5747685124121756 / 2), arbor.mpoint(-7.000000e-01, -2.318500e+02, 4.890000e+01, 0.5747685124121756 / 2), tag=4) neur4_1_26 = tree.append(neur4_0_25, arbor.mpoint(-7.000000e-01, -2.107500e+02, 4.070000e+01, 0.5747685124121756 / 2), tag=4) neur4_2_27 = tree.append(neur4_1_26, arbor.mpoint(-7.000000e-01, -2.059500e+02, 3.770000e+01, 0.5747685124121756 / 2), tag=4) neur4_3_28 = tree.append(neur4_2_27, arbor.mpoint(-7.000000e-01, -1.762500e+02, 1.840000e+01, 0.5747685124121756 / 2), tag=4) neur4_4_29 = tree.append(neur4_3_28, arbor.mpoint(-7.000000e-01, -1.740500e+02, 1.700000e+01, 0.5747685124121756 / 2), tag=4) # (1.2) Create cell morphology from segment tree. morph = arbor.morphology(tree) # (2) Create and populate the label dictionary. labels = arbor.label_dict() # Regions labels['soma'] = '(tag 0)' labels['axon'] = '(tag 1)' labels['dend_2'] = '(tag 2)' labels['neur_3'] = '(tag 3)' labels['neur_4'] = '(tag 4)' labels['center'] = '(location 0 0.5)' labels['all'] = '(all)'
import utils, arbor as arb
# !\circled{1}! read in geometry
segment_tree = arb.load_swc_allen('cell.swc', no_gaps=False)
morphology = arb.morphology(segment_tree)
# !\circled{2}! assign names to regions defined by SWC and center of soma
labels = arb.label_dict({'soma': '(tag 1)', 'axon': '(tag 2)',
'dend': '(tag 3)', 'apic': '(tag 4)',
'center': '(location 0 0.5)'})
cell = arb.cable_cell(morphology, labels) # see !\circled{3}!
cell.compartments_length(20) # discretisation strategy: max compartment length
# !\circled{4}! load and assign electro-physical parameters
defaults, regions, ions, mechanisms = utils.load_allen_fit('fit.json')
# set defaults and override by region
cell.set_properties(tempK=defaults.tempK, Vm=defaults.Vm,
cm=defaults.cm, rL=defaults.rL)
for region, vs in regions:
cell.paint('"'+region+'"', tempK=vs.tempK, Vm=vs.Vm, cm=vs.cm, rL=vs.rL)
# set reversal potentials
for region, ion, e in ions:
cell.paint('"'+region+'"', ion, rev_pot=e)
cell.set_ion('ca', int_con=5e-5, ext_con=2.0, method=arb.mechanism('nernst/x=ca'))
# assign ion dynamics
for region, mech, values in mechanisms:
cell.paint('"'+region+'"', arb.mechanism(mech, values))
print(mech)
# !\circled{5}! attach stimulus and spike detector
cell.place('"center"', arb.iclamp(200, 1000, 0.15))
cell.place('"center"', arb.spike_detector(-40))
# !\circled{6}! set up runnable simulation
model = arb.single_cell_model(cell)
label_tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint(4, 0.0, 0, 2.0), tag=1)
label_tree.append(0, mpoint(4, 0.0, 0, 0.8), mpoint(8, 0.0, 0, 0.8), tag=3)
label_tree.append(1, mpoint(8, 0.0, 0, 0.8), mpoint(12, -0.5, 0, 0.8), tag=3)
label_tree.append(2, mpoint(12, -0.5, 0, 0.8), mpoint(20, 4.0, 0, 0.4), tag=3)
label_tree.append(3, mpoint(20, 4.0, 0, 0.4), mpoint(26, 6.0, 0, 0.2), tag=3)
label_tree.append(2, mpoint(12, -0.5, 0, 0.5), mpoint(19, -3.0, 0, 0.5), tag=3)
label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(24, -7.0, 0, 0.2), tag=3)
label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(23, -1.0, 0, 0.2), tag=3)
label_tree.append(7, mpoint(23, -1.0, 0, 0.2), mpoint(26, -2.0, 0, 0.2), tag=3)
label_tree.append(mnpos,
mpoint(0, 0.0, 0, 2.0),
mpoint(-7, 0.0, 0, 0.4),
tag=2)
label_tree.append(9, mpoint(-7, 0.0, 0, 0.4), mpoint(-10, 0.0, 0, 0.4), tag=2)
label_morph = arbor.morphology(label_tree)
# The label morphology with some gaps (at start of dendritic tree and remove the axon hillock)
label_tree = arbor.segment_tree()
label_tree.append(mnpos, mpoint(0, 0.0, 0, 2.0), mpoint(4, 0.0, 0, 2.0), tag=1)
label_tree.append(0, mpoint(5, 0.0, 0, 0.8), mpoint(8, 0.0, 0, 0.8), tag=3)
label_tree.append(1, mpoint(8, 0.0, 0, 0.8), mpoint(12, -0.5, 0, 0.8), tag=3)
label_tree.append(2, mpoint(12, -0.5, 0, 0.8), mpoint(20, 4.0, 0, 0.4), tag=3)
label_tree.append(3, mpoint(20, 4.0, 0, 0.4), mpoint(26, 6.0, 0, 0.2), tag=3)
label_tree.append(2, mpoint(12, -0.5, 0, 0.5), mpoint(19, -3.0, 0, 0.5), tag=3)
label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(24, -7.0, 0, 0.2), tag=3)
label_tree.append(5, mpoint(19, -3.0, 0, 0.5), mpoint(23, -1.0, 0, 0.2), tag=3)
label_tree.append(7, mpoint(23, -1.0, 0, 0.2), mpoint(26, -2.0, 0, 0.2), tag=3)
label_tree.append(mnpos,
mpoint(-2, 0.0, 0, 0.4),
mpoint(-10, 0.0, 0, 0.4),
