def loadTSegmentTree(self):
'''
Load point neuron model
'''
self.tree = PhysTree(file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX,
'Ttree_segments.swc'))
# self.tree = PhysTree(file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'L23PyrBranco.swc'))
# capacitance and axial resistance
self.tree.setPhysiology(0.8, 100. / 1e6)
# ion channels
k_chan = channelcollection.Kv3_1()
g_k = {1: 0.766 * 1e6}
g_k.update({n.index: 0.034*1e6 / self.tree.pathLength((1,.5), (n.index,.5)) \
for n in self.tree if n.index != 1})
self.tree.addCurrent(k_chan, g_k, -85.)
na_chan = channelcollection.Na_Ta()
self.tree.addCurrent(na_chan, 1.71 * 1e6, 50., node_arg=[self.tree[1]])
# fit leak current
self.tree.fitLeakCurrent(-75., 10.)
# set equilibirum potententials
self.tree.setEEq(-75.)
# set computational tree
self.tree.setCompTree()
def test_pickling():
# pickle and restore
na_ta_channel = channelcollection.Na_Ta()
s = pickle.dumps(na_ta_channel)
new_na_ta_channel = pickle.loads(s)
# multiple pickles
s = pickle.dumps(na_ta_channel)
s = pickle.dumps(na_ta_channel)
new_na_ta_channel = pickle.loads(s)
assert True # reaching this means we didn't encounter an error
def loadBall(self):
'''
Load point neuron model
'''
self.tree = PhysTree(file_n='test_morphologies/ball.swc')
# capacitance and axial resistance
self.tree.setPhysiology(0.8, 100. / 1e6)
# ion channels
k_chan = channelcollection.Kv3_1()
self.tree.addCurrent(k_chan, 0.766 * 1e6, -85.)
na_chan = channelcollection.Na_Ta()
self.tree.addCurrent(na_chan, 1.71 * 1e6, 50.)
# fit leak current
self.tree.fitLeakCurrent(-75., 10.)
# set equilibirum potententials
self.tree.setEEq(-75.)
# set computational tree
self.tree.setCompTree()
def test_ionchannel_simplified(remove=True):
if not os.path.exists('mech/'):
os.mkdir('mech/')
na = channelcollection.Na_Ta()
p_o = na.computePOpen(-35.)
assert np.allclose(p_o, 0.002009216860105564)
l_s = na.computeLinSum(-35., 0., 50.)
assert np.allclose(l_s, -0.00534261017220376)
na.writeModFile('mech/')
sk = channelcollection.SK()
sk.writeModFile('mech/')
if remove:
shutil.rmtree('mech/')
def loadBall(self):
self.greens_tree = GreensTree(
file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc'))
# capacitance and axial resistance
self.greens_tree.setPhysiology(0.8, 100. / 1e6)
# ion channels
k_chan = channelcollection.Kv3_1()
self.greens_tree.addCurrent(k_chan, 0.766 * 1e6, -85.)
na_chan = channelcollection.Na_Ta()
self.greens_tree.addCurrent(na_chan, 1.71 * 1e6, 50.)
# fit leak current
self.greens_tree.fitLeakCurrent(-75., 10.)
# set computational tree
self.greens_tree.setCompTree()
# set the impedances
self.freqs = np.array([0.])
self.greens_tree.setImpedance(self.freqs)
# create sov tree
self.sov_tree = self.greens_tree.__copy__(new_tree=SOVTree())
self.sov_tree.calcSOVEquations(maxspace_freq=100.)
def test_expansionpoints():
kv3_1 = channelcollection.Kv3_1()
na_ta = channelcollection.Na_Ta()
e_hs = np.array([-75., -15.])
# test expansion point for channel with 1 state variable
sv_hs = compartmentfitter.getExpansionPoints(e_hs, kv3_1)
for svar, f_inf in kv3_1.f_varinf.items():
assert np.allclose(f_inf(e_hs), sv_hs[svar])
assert np.allclose(sv_hs['v'], e_hs)
# test expansion point for channel with 2 state variables
sv_hs = compartmentfitter.getExpansionPoints(e_hs, na_ta)
v_act = np.array([-75., -15., -75., -75., -15., -15.])
v_inact = np.array([-75., -15., -75., -15., -75., -15.])
assert np.allclose(na_ta.f_varinf['m'](v_act), sv_hs['m'])
assert np.allclose(na_ta.f_varinf['h'](v_inact), sv_hs['h'])
assert not np.allclose(na_ta.f_varinf['h'](v_act), sv_hs['h'])
assert np.allclose(v_act, sv_hs['v'])
def test_broadcasting():
na_ta = channelcollection.Na_Ta()
v = np.array([-73.234, -50.325, -25.459])
s = np.array([0., 10., 20., 40.]) * 1j
# error must be raised if arguments are not broadcastable
with pytest.raises(ValueError):
na_ta.computeLinSum(v, s)
# check if broadcasting rules are applied correctly for voltage and frequency
ll = na_ta.computeLinSum(v[:, None], s[None, :])
l1 = na_ta.computeLinear(v[:, None], s[None, :])
l2 = na_ta.computePOpen(v[:, None])
assert ll.shape == (3, 4)
assert l1.shape == (3, 4)
assert l2.shape == (3, 1)
assert np.allclose(ll, (na_ta._getReversal(None) - v[:, None]) * l1 - l2)
# check if broadcasting rules are applied correctly for state variables
sv = {'m': .2, 'h': .4}
ll = na_ta.computeLinSum(v[:, None], s[None, :], **sv)
assert ll.shape == (3, 4)
sv = {'m': np.array([0.1, 0.2, 0.3]), 'h': np.array([0.9, 0.6, 0.3])}
with pytest.raises(ValueError):
ll = na_ta.computeLinSum(v[:, None], s[None, :], **sv)
sv_ = {'m': sv['m'][:, None], 'h': sv['h'][:, None]}
ll = na_ta.computeLinSum(v[:, None], s[None, :], **sv_)
assert ll.shape == (3, 4)
sv__ = {'m': sv['m'][:, None, None], 'h': sv['h'][None, None, :]}
l_ = na_ta.computeLinSum(v[:, None, None], s[None, :, None], **sv__)
assert l_.shape == (3, 4, 3)
for ii in range(4):
assert np.allclose([ll[0, ii], ll[1, ii], ll[2, ii]],
[l_[0, ii, 0], l_[1, ii, 1], l_[2, ii, 2]])
def testChannelFit(self):
self.loadBall()
locs = [(1, 0.5)]
e_eqs = [-75., -55., -35., -15.]
# create compartment tree
ctree = self.greens_tree.createCompartmentTree(locs)
ctree.addCurrent(channelcollection.Na_Ta(), 50.)
ctree.addCurrent(channelcollection.Kv3_1(), -85.)
# create tree with only leak
greens_tree_pas = self.greens_tree.__copy__()
greens_tree_pas[1].currents = {'L': greens_tree_pas[1].currents['L']}
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(self.freqs)
# compute the passive impedance matrix
z_mat_pas = greens_tree_pas.calcImpedanceMatrix(locs)[0]
# create tree with only potassium
greens_tree_k = self.greens_tree.__copy__()
greens_tree_k[1].currents = {key: val for key, val in greens_tree_k[1].currents.items() \
if key != 'Na_Ta'}
# compute potassium impedance matrices
z_mats_k = []
for e_eq in e_eqs:
greens_tree_k.setEEq(e_eq)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(self.freqs)
z_mats_k.append(greens_tree_k.calcImpedanceMatrix(locs))
# create tree with only sodium
greens_tree_na = self.greens_tree.__copy__()
greens_tree_na[1].currents = {key: val for key, val in greens_tree_na[1].currents.items() \
if key != 'Kv3_1'}
# create state variable expansion points
svs = []
e_eqs_ = []
na_chan = greens_tree_na.channel_storage['Na_Ta']
for e_eq1 in e_eqs:
sv1 = na_chan.computeVarinf(e_eq1)
for e_eq2 in e_eqs:
e_eqs_.append(e_eq2)
sv2 = na_chan.computeVarinf(e_eq2)
svs.append({'m': sv2['m'], 'h': sv1['h']})
# compute sodium impedance matrices
z_mats_na = []
for ii, sv in enumerate(svs):
greens_tree_na.setEEq(e_eqs[ii % len(e_eqs)])
greens_tree_na[1].setExpansionPoint('Na_Ta', sv)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(self.freqs)
z_mats_na.append(greens_tree_na.calcImpedanceMatrix(locs))
# compute combined impedance matrices
z_mats_comb = []
for e_eq in e_eqs:
self.greens_tree.setEEq(e_eq)
self.greens_tree.setCompTree()
self.greens_tree.setImpedance(self.freqs)
z_mats_comb.append(self.greens_tree.calcImpedanceMatrix(locs))
# passive fit
ctree.computeGMC(z_mat_pas)
# get SOV constants for capacitance fit
sov_tree = greens_tree_pas.__copy__(new_tree=SOVTree())
sov_tree.setCompTree()
sov_tree.calcSOVEquations()
alphas, phimat, importance = sov_tree.getImportantModes(
locarg=locs,
sort_type='importance',
eps=1e-12,
return_importance=True)
# fit the capacitances from SOV time-scales
ctree.computeC(-alphas[0:1].real * 1e3,
phimat[0:1, :].real,
weights=importance[0:1])
ctree1 = copy.deepcopy(ctree)
ctree2 = copy.deepcopy(ctree)
ctree3 = copy.deepcopy(ctree)
ctree4 = copy.deepcopy(ctree)
# fit paradigm 1 --> separate impedance matrices and separate fits
# potassium channel fit
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
ctree1.computeGSingleChanFromImpedance('Kv3_1',
z_mat_k,
e_eq,
self.freqs,
other_channel_names=['L'])
ctree1.runFit()
# sodium channel fit
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
ctree1.computeGSingleChanFromImpedance('Na_Ta',
z_mat_na,
e_eq,
self.freqs,
sv=sv,
other_channel_names=['L'])
ctree1.runFit()
# fit paradigm 2 --> separate impedance matrices, same fit
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
ctree2.computeGSingleChanFromImpedance(
'Kv3_1',
z_mat_k,
e_eq,
self.freqs,
all_channel_names=['Kv3_1', 'Na_Ta'])
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
ctree2.computeGSingleChanFromImpedance(
'Na_Ta',
z_mat_na,
e_eq,
self.freqs,
sv=sv,
all_channel_names=['Kv3_1', 'Na_Ta'])
ctree2.runFit()
# fit paradigm 3 --> same impedance matrices
for z_mat_comb, e_eq in zip(z_mats_comb, e_eqs):
ctree3.computeGChanFromImpedance(['Kv3_1', 'Na_Ta'], z_mat_comb,
e_eq, self.freqs)
ctree3.runFit()
# fit paradigm 4 --> fit incrementally
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
ctree4.computeGSingleChanFromImpedance('Na_Ta',
z_mat_na,
e_eq,
self.freqs,
sv=sv)
ctree4.runFit()
for z_mat_comb, e_eq in zip(z_mats_comb, e_eqs):
ctree4.computeGSingleChanFromImpedance(
'Kv3_1',
z_mat_comb,
e_eq,
self.freqs,
other_channel_names=['Na_Ta', 'L'])
ctree4.runFit()
# test if correct
keys = ['L', 'Na_Ta', 'Kv3_1']
# soma surface (cm) for total conductance calculation
a_soma = 4. * np.pi * (self.greens_tree[1].R * 1e-4)**2
conds = np.array(
[self.greens_tree[1].currents[key][0] * a_soma for key in keys])
# compartment models conductances
cconds1 = np.array([ctree1[0].currents[key][0] for key in keys])
cconds2 = np.array([ctree2[0].currents[key][0] for key in keys])
cconds3 = np.array([ctree3[0].currents[key][0] for key in keys])
cconds4 = np.array([ctree4[0].currents[key][0] for key in keys])
assert np.allclose(conds, cconds1)
assert np.allclose(conds, cconds2)
assert np.allclose(conds, cconds3)
assert np.allclose(conds, cconds4)
# rename for further testing
ctree = ctree1
# frequency array
ft = ke.FourrierTools(np.linspace(0., 50., 100))
freqs = ft.s
# compute impedance matrix
v_h = -42.
# original
self.greens_tree.setEEq(v_h)
self.greens_tree.setCompTree()
self.greens_tree.setImpedance(freqs)
z_mat_orig = self.greens_tree.calcImpedanceMatrix([(1., .5)])
# potassium
greens_tree_k.setEEq(v_h)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(freqs)
z_mat_k = greens_tree_k.calcImpedanceMatrix([(1, .5)])
# sodium
greens_tree_na.removeExpansionPoints()
greens_tree_na.setEEq(v_h)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(freqs)
z_mat_na = greens_tree_na.calcImpedanceMatrix([(1, .5)])
# passive
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(freqs)
z_mat_pas = greens_tree_pas.calcImpedanceMatrix([(1, .5)])
# reduced impedance matrices
ctree.removeExpansionPoints()
ctree.setEEq(v_h)
z_mat_fit = ctree.calcImpedanceMatrix(freqs=freqs)
z_mat_fit_k = ctree.calcImpedanceMatrix(channel_names=['L', 'Kv3_1'],
freqs=freqs)
z_mat_fit_na = ctree.calcImpedanceMatrix(channel_names=['L', 'Na_Ta'],
freqs=freqs)
z_mat_fit_pas = ctree.calcImpedanceMatrix(channel_names=['L'],
freqs=freqs)
assert np.allclose(z_mat_orig, z_mat_fit)
assert np.allclose(z_mat_k, z_mat_fit_k)
assert np.allclose(z_mat_na, z_mat_fit_na)
assert np.allclose(z_mat_pas, z_mat_fit_pas)
# test total current, conductance
sv = svs[-1]
p_open = sv['m']**3 * sv['h']
# with p_open given
g1 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'],
p_open_channels={'Na_Ta': p_open})
i1 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'],
p_open_channels={'Na_Ta': p_open})
# with expansion point given
ctree.setExpansionPoints({'Na_Ta': sv})
g2 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'])
i2 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'])
# with e_eq given
g3 = ctree[0].getGTot(ctree.channel_storage,
v=e_eqs[-1],
channel_names=['L', 'Na_Ta'])
i3 = ctree[0].getGTot(ctree.channel_storage,
v=e_eqs[-1],
channel_names=['L', 'Na_Ta'])
# with e_eq stored
ctree.setEEq(e_eqs[-1])
g4 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'])
i4 = ctree[0].getGTot(ctree.channel_storage,
channel_names=['L', 'Na_Ta'])
# check if correct
assert np.abs(g1 - g2) < 1e-10
assert np.abs(g1 - g3) < 1e-10
assert np.abs(g1 - g4) < 1e-10
assert np.abs(i1 - i2) < 1e-10
assert np.abs(i1 - i3) < 1e-10
assert np.abs(i1 - i4) < 1e-10
# compare current, conductance
g_ = ctree[0].getGTot(ctree.channel_storage, channel_names=['Na_Ta'])
i_ = ctree[0].getITot(ctree.channel_storage, channel_names=['Na_Ta'])
assert np.abs(g_ *
(e_eqs[-1] - ctree[0].currents['Na_Ta'][1]) - i_) < 1e-10
# test leak fitting
self.greens_tree.setEEq(-75.)
self.greens_tree.setCompTree()
ctree.setEEq(-75.)
ctree.removeExpansionPoints()
ctree.fitEL()
assert np.abs(ctree[0].currents['L'][1] -
self.greens_tree[1].currents['L'][1]) < 1e-10
def fitBall(self):
self.loadBall()
freqs = np.array([0.])
locs = [(1, 0.5)]
e_eqs = [-75., -55., -35., -15.]
# create compartment tree
ctree = self.tree.createCompartmentTree(locs)
ctree.addCurrent(channelcollection.Na_Ta(), 50.)
ctree.addCurrent(channelcollection.Kv3_1(), -85.)
# create tree with only leak
greens_tree_pas = self.tree.__copy__(new_tree=GreensTree())
greens_tree_pas[1].currents = {'L': greens_tree_pas[1].currents['L']}
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(freqs)
# compute the passive impedance matrix
z_mat_pas = greens_tree_pas.calcImpedanceMatrix(locs)[0]
# create tree with only potassium
greens_tree_k = self.tree.__copy__(new_tree=GreensTree())
greens_tree_k[1].currents = {key: val for key, val in greens_tree_k[1].currents.items() \
if key != 'Na_Ta'}
# compute potassium impedance matrices
z_mats_k = []
for e_eq in e_eqs:
greens_tree_k.setEEq(e_eq)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(freqs)
z_mats_k.append(greens_tree_k.calcImpedanceMatrix(locs))
# create tree with only sodium
greens_tree_na = self.tree.__copy__(new_tree=GreensTree())
greens_tree_na[1].currents = {key: val for key, val in greens_tree_na[1].currents.items() \
if key != 'Kv3_1'}
# create state variable expansion points
svs = []
e_eqs_ = []
na_chan = greens_tree_na.channel_storage['Na_Ta']
for e_eq1 in e_eqs:
sv1 = na_chan.computeVarinf(e_eq1)
for e_eq2 in e_eqs:
e_eqs_.append(e_eq2)
sv2 = na_chan.computeVarinf(e_eq2)
svs.append({'m': sv2['m'], 'h': sv1['h']})
# compute sodium impedance matrices
z_mats_na = []
for ii, sv in enumerate(svs):
greens_tree_na.setEEq(e_eqs[ii % len(e_eqs)])
greens_tree_na[1].setExpansionPoint('Na_Ta', sv)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(freqs)
z_mats_na.append(greens_tree_na.calcImpedanceMatrix(locs))
# passive fit
ctree.computeGMC(z_mat_pas)
# get SOV constants for capacitance fit
sov_tree = greens_tree_pas.__copy__(new_tree=SOVTree())
sov_tree.setCompTree()
sov_tree.calcSOVEquations()
alphas, phimat, importance = sov_tree.getImportantModes(
locarg=locs,
sort_type='importance',
eps=1e-12,
return_importance=True)
# fit the capacitances from SOV time-scales
ctree.computeC(-alphas[0:1].real * 1e3,
phimat[0:1, :].real,
weights=importance[0:1])
# potassium channel fit
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
ctree.computeGSingleChanFromImpedance('Kv3_1',
z_mat_k,
e_eq,
freqs,
other_channel_names=['L'])
ctree.runFit()
# sodium channel fit
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
ctree.computeGSingleChanFromImpedance('Na_Ta',
z_mat_na,
e_eq,
freqs,
sv=sv,
other_channel_names=['L'])
ctree.runFit()
ctree.setEEq(-75.)
ctree.removeExpansionPoints()
ctree.fitEL()
self.ctree = ctree
def reduceExplicit(self):
self.loadBall()
freqs = np.array([0.])
locs = [(1, 0.5)]
e_eqs = [-75., -55., -35., -15.]
# create compartment tree
ctree = self.tree.createCompartmentTree(locs)
ctree.addCurrent(channelcollection.Na_Ta(), 50.)
ctree.addCurrent(channelcollection.Kv3_1(), -85.)
# create tree with only leak
greens_tree_pas = self.tree.__copy__(new_tree=GreensTree())
greens_tree_pas[1].currents = {'L': greens_tree_pas[1].currents['L']}
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(freqs)
# compute the passive impedance matrix
z_mat_pas = greens_tree_pas.calcImpedanceMatrix(locs)[0]
# create tree with only potassium
greens_tree_k = self.tree.__copy__(new_tree=GreensTree())
greens_tree_k[1].currents = {key: val for key, val in greens_tree_k[1].currents.items() \
if key != 'Na_Ta'}
# compute potassium impedance matrices
z_mats_k = []
for e_eq in e_eqs:
greens_tree_k.setEEq(e_eq)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(freqs)
z_mats_k.append(greens_tree_k.calcImpedanceMatrix(locs))
# create tree with only sodium
greens_tree_na = self.tree.__copy__(new_tree=GreensTree())
greens_tree_na[1].currents = {key: val for key, val in greens_tree_na[1].currents.items() \
if key != 'Kv3_1'}
# create state variable expansion points
svs = []
e_eqs_ = []
na_chan = greens_tree_na.channel_storage['Na_Ta']
for e_eq1 in e_eqs:
sv1 = na_chan.computeVarinf(e_eq1)
for e_eq2 in e_eqs:
e_eqs_.append(e_eq2)
sv2 = na_chan.computeVarinf(e_eq2)
svs.append({'m': sv2['m'], 'h': sv1['h']})
# compute sodium impedance matrices
z_mats_na = []
for sv, eh in zip(svs, e_eqs_):
greens_tree_na.setEEq(eh)
greens_tree_na[1].setExpansionPoint('Na_Ta', sv)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(freqs)
z_mats_na.append(greens_tree_na.calcImpedanceMatrix(locs))
# passive fit
ctree.computeGMC(z_mat_pas)
# potassium channel fit matrices
fit_mats_k = []
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
mf, vt = ctree.computeGSingleChanFromImpedance(
'Kv3_1',
z_mat_k,
e_eq,
freqs,
other_channel_names=['L'],
action='return')
fit_mats_k.append([mf, vt])
# sodium channel fit matrices
fit_mats_na = []
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
mf, vt = ctree.computeGSingleChanFromImpedance(
'Na_Ta',
z_mat_na,
e_eq,
freqs,
sv=sv,
other_channel_names=['L'],
action='return')
fit_mats_na.append([mf, vt])
return fit_mats_na, fit_mats_k
def testLinSum(self):
na = channelcollection.Na_Ta()
l_s_1 = na.computeLinSum(-35., 0., 50.)
l_s_2 = self.computeLinSum(-35., 0., 50.)
assert np.allclose(l_s_1, l_s_2)
def testPOpen(self):
na = channelcollection.Na_Ta()
p_o_1 = na.computePOpen(-35.)
p_o_2 = self.computePOpen(-35.)
assert np.allclose(p_o_1, p_o_2)
