def testValues(self):
# load trees
self.loadTTree()
self.loadSOVTTree()
# test Fourrier impedance matrix
ft = ke.FourrierTools(np.arange(0.,100.,0.1))
# set the impedances
self.tree.setImpedance(ft.s)
print('!!! ', ft.ind_0s)
# sets of location
# sets of location
locs = [(1, .5), (4, .5), (4, 1.), (5, .5), (6, .5), (7, .5), (8, .5)]
self.tree.storeLocs(locs, 'locs')
self.sovtree.storeLocs(locs, 'locs')
# compute impedance matrices with both methods
z_sov = self.sovtree.calcImpedanceMatrix(locarg='locs', eps=1e-10)
z_gf = self.tree.calcImpedanceMatrix('locs')[ft.ind_0s].real
assert np.allclose(z_gf, z_sov, atol=5e-1)
z_gf2 = self.tree.calcImpedanceMatrix('locs', explicit_method=False)[ft.ind_0s].real
print('> z_gf =\n', z_gf)
print('> z_gf2 =\n', z_gf2)
assert np.allclose(z_gf2, z_gf, atol=5e-6)
zf_sov = self.sovtree.calcImpedanceMatrix(locarg='locs', eps=1e-10, freqs=ft.s)
zf_gf = self.tree.calcImpedanceMatrix('locs')
assert np.allclose(zf_gf, zf_sov, atol=5e-1)
zf_gf2 = self.tree.calcImpedanceMatrix('locs', explicit_method=False)
assert np.allclose(zf_gf2, zf_gf, atol=5e-6)
# load trees
self.loadValidationTree()
self.loadSOVValidationTree()
# test Fourrier impedance matrix
ft = ke.FourrierTools(np.arange(0.,100.,0.1))
# set the impedances
self.tree.setImpedance(ft.s)
# set of locations
locs = [(1, .5), (4, .5), (4, 1.), (5, .5), (5, 1.)]
self.tree.storeLocs(locs, 'locs')
self.sovtree.storeLocs(locs, 'locs')
# compute impedance matrices with both methods
z_sov = self.sovtree.calcImpedanceMatrix(locarg='locs', eps=1e-10)
z_gf = self.tree.calcImpedanceMatrix('locs')[ft.ind_0s].real
assert np.allclose(z_gf, z_sov, atol=5e-1)
z_gf2 = self.tree.calcImpedanceMatrix('locs', explicit_method=False)[ft.ind_0s].real
assert np.allclose(z_gf2, z_gf, atol=5e-6)
zf_sov = self.sovtree.calcImpedanceMatrix(locarg='locs', eps=1e-10, freqs=ft.s)
zf_gf = self.tree.calcImpedanceMatrix('locs')
assert np.allclose(zf_gf, zf_sov, atol=5e-1)
zf_gf2 = self.tree.calcImpedanceMatrix('locs', explicit_method=False)
assert np.allclose(zf_gf2, zf_gf, atol=5e-6)
def loadTTreeTestChannel(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
test_chan = channelcollection.TestChannel2()
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan, 50., -23.)
self.greenstree.fitLeakCurrent(v_eq, 10.)
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def loadTTreeActive(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.1
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
h_chan = channelcollection.h()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(h_chan, 50., -43.)
self.greenstree.fitLeakCurrent(v_eq, 10.)
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=10.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def loadTTreeActive(self):
'''
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
'''
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print '>>> loading T-tree <<<'
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1,3,4])
self.greenstree.addCurrent('h', 50., -43.)
self.greenstree.fitLeakCurrent(e_eq_target=v_eq, tau_m_target=10.)
# for node in self.greenstree:
# print node.getGTot(channel_storage=self.greenstree.channel_storage)
# print node.currents
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = neurm.NeuronSimTree(dt=self.dt, t_calibrate=10., v_eq=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def testBasicProperties(self):
self.loadTTree()
# test Fourrier impedance matrix
ft = ke.FourrierTools(np.arange(0.,100.,0.1))
# set the impedances
self.tree.setImpedance(ft.s)
# sets of location
locs_0 = [(6, .5), (8, .5)]
zf = self.tree.calcZF(*locs_0)
locs_1 = [(1, .5), (4, .5), (4, 1.), (5, .5), (6, .5), (7, .5), (8, .5)]
locs_2 = [(7, .5), (8, .5)]
self.tree.storeLocs(locs_0, '0')
self.tree.storeLocs(locs_1, '1')
self.tree.storeLocs(locs_2, '2')
# compute impedance matrices
z_mat_0 = self.tree.calcImpedanceMatrix('0')[ft.ind_0s]
z_mat_1 = self.tree.calcImpedanceMatrix('1')[ft.ind_0s]
z_mat_2 = self.tree.calcImpedanceMatrix('2')[ft.ind_0s]
# check complex steady state component zero
assert np.allclose(z_mat_0.imag, np.zeros_like(z_mat_0.imag))
assert np.allclose(z_mat_1.imag, np.zeros_like(z_mat_1.imag))
assert np.allclose(z_mat_2.imag, np.zeros_like(z_mat_2.imag))
# check symmetry
assert np.allclose(z_mat_0, z_mat_0.T)
assert np.allclose(z_mat_1, z_mat_1.T)
assert np.allclose(z_mat_2, z_mat_2.T)
# check symmetry directly
assert np.allclose(self.tree.calcZF(locs_0[0], locs_0[1]),
self.tree.calcZF(locs_0[1], locs_0[0]))
assert np.allclose(self.tree.calcZF(locs_1[0], locs_1[3]),
self.tree.calcZF(locs_1[3], locs_1[0]))
assert np.allclose(self.tree.calcZF(locs_1[2], locs_1[5]),
self.tree.calcZF(locs_1[5], locs_1[2]))
# check transitivity
z_14_ = self.tree.calcZF(locs_1[1], locs_1[3]) * \
self.tree.calcZF(locs_1[3], locs_1[4]) / \
self.tree.calcZF(locs_1[3], locs_1[3])
z_14 = self.tree.calcZF(locs_1[1], locs_1[4])
assert np.allclose(z_14, z_14_)
z_06_ = self.tree.calcZF(locs_1[0], locs_1[5]) * \
self.tree.calcZF(locs_1[5], locs_1[6]) / \
self.tree.calcZF(locs_1[5], locs_1[5])
z_06 = self.tree.calcZF(locs_1[0], locs_1[6])
assert np.allclose(z_06, z_06_)
z_46_ = self.tree.calcZF(locs_1[4], locs_1[2]) * \
self.tree.calcZF(locs_1[2], locs_1[6]) / \
self.tree.calcZF(locs_1[2], locs_1[2])
z_46 = self.tree.calcZF(locs_1[4], locs_1[6])
assert np.allclose(z_46, z_46_)
z_n15_ = self.tree.calcZF(locs_1[1], locs_1[3]) * \
self.tree.calcZF(locs_1[3], locs_1[5]) / \
self.tree.calcZF(locs_1[3], locs_1[3])
z_15 = self.tree.calcZF(locs_1[1], locs_1[5])
assert not np.allclose(z_15, z_n15_)
def loadTTreeTestChannelSoma(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
test_chan = channelcollection.TestChannel2()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan,
50.,
23.,
node_arg=[self.greenstree[1]])
self.greenstree.fitLeakCurrent(v_eq, 10.)
# for node in self.greenstree:
# print node.getGTot(channel_storage=self.greenstree.channel_storage)
# print node.currents
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def testSOVCalculation(self):
# validate the calculation on analytical model
self.loadValidationTree()
# do SOV calculation
self.tree.calcSOVEquations()
alphas, gammas = self.tree.getSOVMatrices([(1, 0.5)])
# compute time scales analytically
self.tree.treetype = 'computational'
lambda_m_test = np.sqrt(self.tree[4].R_sov / \
(2.*self.tree[4].g_m*self.tree[4].r_a))
tau_m_test = self.tree[4].c_m / self.tree[4].g_m * 1e3
alphas_test = \
(1. + \
(np.pi * np.arange(20) * lambda_m_test / \
(self.tree[4].L_sov + self.tree[5].L_sov))**2) / \
tau_m_test
# compare analytical and computed time scales
assert np.allclose(alphas[:20], alphas_test)
# compute the spatial mode functions analytically
# import matplotlib.pyplot as pl
# self.tree.distributeLocsUniform(dx=4., name='NET_eval')
# alphas, gammas = self.tree.getSOVMatrices(self.tree.getLocs(name='NET_eval'))
# for kk in range(5):
# print 'tau_' + str(kk) + ' =', -1./alphas[kk].real
# pl.plot(range(gammas.shape[1]), gammas[kk,:])
# pl.plot(range(gammas.shape[1]), g)
# pl.show()
## TODO
# test basic identities
self.loadTTree()
self.tree.calcSOVEquations(maxspace_freq=500)
# sets of location
locs_0 = [(6, .5), (8, .5)]
locs_1 = [(1, .5), (4, .5), (4, 1.), (5, .5), (6, .5), (7, .5),
(8, .5)]
locs_2 = [(7, .5), (8, .5)]
self.tree.storeLocs(locs_0, '0')
self.tree.storeLocs(locs_1, '1')
self.tree.storeLocs(locs_2, '2')
# test mode importance
imp_a = self.tree.getModeImportance(locs=locs_0)
imp_b = self.tree.getModeImportance(name='0')
imp_c = self.tree.getModeImportance(sov_data=self.tree.getSOVMatrices(
locs=locs_0))
imp_d = self.tree.getModeImportance(sov_data=self.tree.getSOVMatrices(
name='0'))
assert np.allclose(imp_a, imp_b)
assert np.allclose(imp_a, imp_c)
assert np.allclose(imp_a, imp_d)
assert np.abs(1. - np.max(imp_a)) < 1e-12
with pytest.raises(IOError):
self.tree.getModeImportance()
# test important modes
imp_2 = self.tree.getModeImportance(name='2')
assert not np.allclose(imp_a, imp_2)
# test impedance matrix
z_mat_a = self.tree.calcImpedanceMatrix(
sov_data=self.tree.getImportantModes(name='1', eps=1e-10))
z_mat_b = self.tree.calcImpedanceMatrix(name='1', eps=1e-10)
assert np.allclose(z_mat_a, z_mat_b)
assert np.allclose(z_mat_a - z_mat_a.T, np.zeros(z_mat_a.shape))
for ii, z_row in enumerate(z_mat_a):
assert np.argmax(z_row) == ii
# test Fourrier impedance matrix
ft = ke.FourrierTools(np.arange(0., 100., 0.1))
z_mat_ft = self.tree.calcImpedanceMatrix(name='1',
eps=1e-10,
freqs=ft.s)
print z_mat_ft[ft.ind_0s, :, :]
print z_mat_a
assert np.allclose(z_mat_ft[ft.ind_0s,:,:].real, \
z_mat_a, atol=1e-1) # check steady state
assert np.allclose(z_mat_ft - np.transpose(z_mat_ft, axes=(0,2,1)), \
np.zeros(z_mat_ft.shape)) # check symmetry
assert np.allclose(z_mat_ft[:ft.ind_0s,:,:].real, \
z_mat_ft[ft.ind_0s+1:,:,:][::-1,:,:].real) # check real part even
assert np.allclose(z_mat_ft[:ft.ind_0s,:,:].imag, \
-z_mat_ft[ft.ind_0s+1:,:,:][::-1,:,:].imag) # check imaginary part odd
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
