class TestSOVTree():
def loadTTree(self):
'''
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
'''
print '>>> loading T-tree <<<'
fname = 'test_morphologies/Tsovtree.swc'
self.tree = SOVTree(fname, types=[1, 3, 4])
self.tree.fitLeakCurrent(e_eq_target=-75., tau_m_target=10.)
self.tree.setCompTree()
def loadValidationTree(self):
'''
Load the T-tree morphology in memory
5---1---4
'''
print '>>> loading validation tree <<<'
fname = 'test_morphologies/sovvalidationtree.swc'
self.tree = SOVTree(fname, types=[1, 3, 4])
self.tree.fitLeakCurrent(e_eq_target=-75., tau_m_target=10.)
self.tree.setCompTree()
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
# import matplotlib.pyplot as pl
# pl.plot(ft.s.imag, z_mat_ft[:,2,4].real, 'b')
# pl.plot(ft.s.imag, z_mat_ft[:,2,4].imag, 'r')
# pl.show()
# self.tree.distributeLocsUniform(dx=4., name='NET_eval')
# print [str(loc) for loc in self.tree.getLocs(name='NET_eval')]
# z_m = self.tree.calcImpedanceMatrix(name='NET_eval', eps=1e-10)
# pl.imshow(z_m, origin='lower', interpolation='none')
# 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.show()
# import morphologyReader as morphR
def testNETDerivation(self):
# initialize
self.loadValidationTree()
self.tree.calcSOVEquations()
# construct the NET
net = self.tree.constructNET()
# print str(net)
# initialize
self.loadTTree()
self.tree.calcSOVEquations()
# construct the NET
net = self.tree.constructNET(dz=20.)
# print str(net)
# contruct the NET with linear terms
net, lin_terms = self.tree.constructNET(dz=20., add_lin_terms=True)
# check if correct
alphas, gammas = self.tree.getImportantModes(name='NET_eval',
eps=1e-4,
sort_type='timescale')
for ii, lin_term in enumerate(lin_terms):
z_k_trans = net.getReducedTree([0, ii
]).getRoot().z_kernel + lin_term
assert np.abs(z_k_trans.k_bar - Kernel(
(alphas, gammas[:, 0] * gammas[:, ii])).k_bar) < 1e-8
class TestGreensTree():
def loadTTree(self):
"""
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.tree = GreensTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
def loadValidationTree(self):
"""
Load the T-tree morphology in memory
5---1---4
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'sovvalidationtree.swc')
self.tree = GreensTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
def loadSOVTTree(self):
"""
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.sovtree = SOVTree(fname, types=[1,3,4])
self.sovtree.fitLeakCurrent(-75., 10.)
self.sovtree.setCompTree()
self.sovtree.calcSOVEquations()
def loadSOVValidationTree(self):
"""
Load the T-tree morphology in memory
5---1---4
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'sovvalidationtree.swc')
self.sovtree = SOVTree(fname, types=[1,3,4])
self.sovtree.fitLeakCurrent(-75., 10.)
self.sovtree.setCompTree()
self.sovtree.calcSOVEquations()
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)]
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 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)
# 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
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)
class TestCompartmentTree():
def loadTTree(self):
'''
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
'''
print '>>> loading T-tree <<<'
fname = 'test_morphologies/Tsovtree.swc'
self.tree = SOVTree(fname, types=[1, 3, 4])
self.tree.fitLeakCurrent(e_eq_target=-75., tau_m_target=10.)
self.tree.setCompTree()
# do SOV calculation
self.tree.calcSOVEquations()
def testTreeDerivation(self):
self.loadTTree()
# locations
locs_soma = [(1, 0.5)]
locs_prox = [(4, 0.2)]
locs_bifur = [(4, 1.0)]
locs_dist_nobifur = [(6., 0.5), (8., 0.5)]
locs_dist_bifur = [(4, 1.0), (6., 0.5), (8., 0.5)]
locs_dist_nroot = [(4, 1.0), (4, 0.5), (6., 0.5), (8., 0.5)]
# test structures
with pytest.raises(KeyError):
self.tree.createCompartmentTree('set0')
# test root (is soma) in set
self.tree.storeLocs(locs_dist_bifur + locs_soma, 'set0')
ctree = self.tree.createCompartmentTree('set0')
assert ctree[0].loc_ind == 3
assert ctree[1].loc_ind == 0
cloc_inds = [cn.loc_ind for cn in ctree[1].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test soma not in set (but common root)
self.tree.storeLocs(locs_dist_bifur, 'set1')
ctree = self.tree.createCompartmentTree('set1')
assert ctree[0].loc_ind == 0
cloc_inds = [cn.loc_ind for cn in ctree[0].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test soma not in set and no common root
self.tree.storeLocs(locs_dist_nobifur, 'set2')
with pytest.warns(UserWarning):
ctree = self.tree.createCompartmentTree('set2')
assert self.tree.getLocs('set2')[0] == (4, 1.)
cloc_inds = [cn.loc_ind for cn in ctree[0].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test 2 locs on common root
self.tree.storeLocs(locs_dist_nroot, 'set3')
ctree = self.tree.createCompartmentTree('set3')
assert ctree[0].loc_ind == 1
assert ctree[1].loc_ind == 0
def testFitting(self):
self.loadTTree()
# locations
locs_soma = [(1, 0.5)]
locs_prox = [(4, 0.2)]
locs_bifur = [(4, 1.0)]
locs_dist_nobifur = [(6., 0.5), (8., 0.5)]
locs_dist_bifur = [(4, 1.0), (6., 0.5), (8., 0.5)]
# store the locations
self.tree.storeLocs(locs_soma + locs_prox, 'prox')
self.tree.storeLocs(locs_soma + locs_bifur, 'bifur')
self.tree.storeLocs(locs_soma + locs_dist_nobifur, 'dist_nobifur')
self.tree.storeLocs(locs_soma + locs_dist_bifur, 'dist_bifur')
# derive steady state impedance matrices
z_mat_prox = self.tree.calcImpedanceMatrix(name='prox')
z_mat_bifur = self.tree.calcImpedanceMatrix(name='bifur')
z_mat_dist_nobifur = self.tree.calcImpedanceMatrix(name='dist_nobifur')
z_mat_dist_bifur = self.tree.calcImpedanceMatrix(name='dist_bifur')
# create the tree structures
ctree_prox = self.tree.createCompartmentTree('prox')
ctree_bifur = self.tree.createCompartmentTree('bifur')
ctree_dist_nobifur = self.tree.createCompartmentTree('dist_nobifur')
ctree_dist_bifur = self.tree.createCompartmentTree('dist_bifur')
# test the tree structures
assert len(ctree_prox) == len(locs_prox) + 1
assert len(ctree_bifur) == len(locs_bifur) + 1
assert len(ctree_dist_nobifur) == len(locs_dist_nobifur) + 1
assert len(ctree_dist_bifur) == len(locs_dist_bifur) + 1
# fit the steady state models
ctree_prox.computeGMC(z_mat_prox)
ctree_bifur.computeGMC(z_mat_bifur)
ctree_dist_nobifur.computeGMC(z_mat_dist_nobifur)
ctree_dist_bifur.computeGMC(z_mat_dist_bifur)
# compute the fitted impedance matrices
z_fit_prox = ctree_prox.calcImpedanceMatrix()
z_fit_bifur = ctree_bifur.calcImpedanceMatrix()
z_fit_dist_nobifur = ctree_dist_nobifur.calcImpedanceMatrix()
z_fit_dist_bifur = ctree_dist_bifur.calcImpedanceMatrix()
# test correctness
assert np.allclose(z_fit_prox, z_mat_prox, atol=0.5)
assert np.allclose(z_fit_bifur, z_mat_bifur, atol=0.5)
assert not np.allclose(
z_fit_dist_nobifur, z_mat_dist_nobifur, atol=0.5)
assert np.allclose(z_fit_dist_bifur, z_mat_dist_bifur, atol=0.5)
# TODO: test with capacitances
def testReordering(self):
self.loadTTree()
# test reordering
locs_dist_badorder = [(1., 0.5), (8., 0.5), (4, 1.0)]
self.tree.storeLocs(locs_dist_badorder, 'badorder')
z_mat_badorder = self.tree.calcImpedanceMatrix(name='badorder')
ctree_badorder = self.tree.createCompartmentTree('badorder')
# check if location indices are assigned correctly
assert [node.loc_ind for node in ctree_badorder] == [0, 2, 1]
# check if reordering works
z_mat_reordered = ctree_badorder._preprocessZMatArg(z_mat_badorder)
assert np.allclose(z_mat_reordered,
z_mat_badorder[:, [0, 2, 1]][[0, 2, 1], :])
# check if fitting is correct
ctree_badorder.computeGMC(z_mat_badorder)
z_fit_badorder = ctree_badorder.calcImpedanceMatrix()
assert np.allclose(z_mat_badorder, z_fit_badorder, atol=0.5)
assert not np.allclose(z_mat_reordered, z_fit_badorder)
# test if equivalent locs are returned correctly
locs_equiv = ctree_badorder.getEquivalentLocs()
assert all([
loc == loc_
for loc, loc_ in zip(locs_equiv, [(0, .5), (2, .5), (1, .5)])
])
def loadBallAndStick(self):
self.greens_tree = GreensTree(
file_n='test_morphologies/ball_and_stick.swc')
for node in self.greens_tree:
node.setPhysiology(
0.8, # Cm [uF/cm^2]
100. / 1e6, # Ra [MOhm*cm]
)
node.addCurrent(
'L', # leak current
100., # g_max [uS/cm^2]
-75., # e_rev [mV]
)
self.greens_tree.setCompTree()
# set the impedances
self.freqs = np.array([0., 1., 10., 100., 1000]) * 1j
self.greens_tree.setImpedance(self.freqs)
def testLocationMapping(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
locs_1 = [(1, 0.5)] + [(4, x) for x in xvals]
locs_2 = [(1, 0.5)] + [(4, x) for x in xvals][::-1]
locs_3 = [(4, x) for x in xvals] + [(1, 0.5)]
# create compartment trees
ctree_1 = self.greens_tree.createCompartmentTree(locs_1)
ctree_2 = self.greens_tree.createCompartmentTree(locs_2)
ctree_3 = self.greens_tree.createCompartmentTree(locs_3)
# test location indices
locinds_1 = np.array([node.loc_ind for node in ctree_1])
locinds_2 = np.array([node.loc_ind for node in ctree_2])
locinds_3 = np.array([node.loc_ind for node in ctree_3])
# check consecutive
assert np.allclose(locinds_1[:-1], locinds_1[1:] - 1)
# check permutation
assert np.allclose(locinds_1[1:], locinds_2[1:][::-1])
assert np.allclose(locinds_1[:-1], locinds_3[1:])
def testGCFit(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
locs_1 = [(1, 0.5)] + [(4, x) for x in xvals]
locs_2 = [(1, 0.5)] + [(4, x) for x in xvals][::-1]
locs_3 = [(4, x) for x in xvals] + [(1, 0.5)]
locs_4 = random.sample(locs_1, k=len(locs_1))
# calculate impedance matrices
z_mat_1 = self.greens_tree.calcImpedanceMatrix(locs_1)
z_mat_2 = self.greens_tree.calcImpedanceMatrix(locs_2)
z_mat_3 = self.greens_tree.calcImpedanceMatrix(locs_3)
z_mat_4 = self.greens_tree.calcImpedanceMatrix(locs_4)
# create compartment trees
ctree_1 = self.greens_tree.createCompartmentTree(locs_1)
ctree_2 = self.greens_tree.createCompartmentTree(locs_2)
ctree_3 = self.greens_tree.createCompartmentTree(locs_3)
ctree_4 = self.greens_tree.createCompartmentTree(locs_4)
# fit g_m and g_c
ctree_1.computeGMC(z_mat_1[0, :, :], channel_names=['L'])
ctree_2.computeGMC(z_mat_2[0, :, :], channel_names=['L'])
ctree_3.computeGMC(z_mat_3[0, :, :], channel_names=['L'])
ctree_4.computeGMC(z_mat_4[0, :, :], channel_names=['L'])
# fit c_m
ctree_1.computeC(self.freqs, z_mat_1)
ctree_2.computeC(self.freqs, z_mat_2)
ctree_3.computeC(self.freqs, z_mat_3)
ctree_4.computeC(self.freqs, z_mat_4)
# compare both models
assert str(ctree_1) == str(ctree_2)
assert str(ctree_1) == str(ctree_3)
assert str(ctree_1) == str(ctree_4)
# compare impedance matrices
z_fit_1 = ctree_1.calcImpedanceMatrix(self.freqs)
z_fit_2 = ctree_2.calcImpedanceMatrix(self.freqs)
z_fit_3 = ctree_3.calcImpedanceMatrix(self.freqs)
z_fit_4 = ctree_4.calcImpedanceMatrix(self.freqs)
assert np.allclose(z_fit_1, z_mat_1, atol=0.1)
assert np.allclose(z_fit_2, z_mat_2, atol=0.1)
assert np.allclose(z_fit_3, z_mat_3, atol=0.1)
assert np.allclose(z_fit_4, z_mat_4, atol=0.1)
assert np.allclose(
z_fit_1, ctree_2.calcImpedanceMatrix(self.freqs, indexing='tree'))
assert np.allclose(
z_fit_1, ctree_3.calcImpedanceMatrix(self.freqs, indexing='tree'))
assert np.allclose(
z_fit_1, ctree_4.calcImpedanceMatrix(self.freqs, indexing='tree'))
class TestCompartmentTree():
def loadTTree(self):
"""
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.tree = SOVTree(fname, types=[1, 3, 4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
# do SOV calculation
self.tree.calcSOVEquations()
def testTreeDerivation(self):
self.loadTTree()
# locations
locs_soma = [(1, 0.5)]
locs_prox = [(4, 0.2)]
locs_bifur = [(4, 1.0)]
locs_dist_nobifur = [(6., 0.5), (8., 0.5)]
locs_dist_bifur = [(4, 1.0), (6., 0.5), (8., 0.5)]
locs_dist_nroot = [(4, 1.0), (4, 0.5), (6., 0.5), (8., 0.5)]
# test structures
with pytest.raises(KeyError):
self.tree.createCompartmentTree('set0')
# test root (is soma) in set
self.tree.storeLocs(locs_dist_bifur + locs_soma, 'set0')
ctree = self.tree.createCompartmentTree('set0')
assert ctree[0].loc_ind == 3
assert ctree[1].loc_ind == 0
cloc_inds = [cn.loc_ind for cn in ctree[1].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test soma not in set (but common root)
self.tree.storeLocs(locs_dist_bifur, 'set1')
ctree = self.tree.createCompartmentTree('set1')
assert ctree[0].loc_ind == 0
cloc_inds = [cn.loc_ind for cn in ctree[0].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test soma not in set and no common root
self.tree.storeLocs(locs_dist_nobifur, 'set2')
with pytest.warns(UserWarning):
ctree = self.tree.createCompartmentTree('set2')
assert self.tree.getLocs('set2')[0] == (4, 1.)
cloc_inds = [cn.loc_ind for cn in ctree[0].child_nodes]
assert 1 in cloc_inds and 2 in cloc_inds
# test 2 locs on common root
self.tree.storeLocs(locs_dist_nroot, 'set3')
ctree = self.tree.createCompartmentTree('set3')
assert ctree[0].loc_ind == 1
assert ctree[1].loc_ind == 0
def testFitting(self):
self.loadTTree()
# locations
locs_soma = [(1, 0.5)]
locs_prox = [(4, 0.2)]
locs_bifur = [(4, 1.0)]
locs_dist_nobifur = [(6., 0.5), (8., 0.5)]
locs_dist_bifur = [(4, 1.0), (6., 0.5), (8., 0.5)]
# store the locations
self.tree.storeLocs(locs_soma + locs_prox, 'prox')
self.tree.storeLocs(locs_soma + locs_bifur, 'bifur')
self.tree.storeLocs(locs_soma + locs_dist_nobifur, 'dist_nobifur')
self.tree.storeLocs(locs_soma + locs_dist_bifur, 'dist_bifur')
# derive steady state impedance matrices
z_mat_prox = self.tree.calcImpedanceMatrix(locarg='prox')
z_mat_bifur = self.tree.calcImpedanceMatrix(locarg='bifur')
z_mat_dist_nobifur = self.tree.calcImpedanceMatrix(
locarg='dist_nobifur')
z_mat_dist_bifur = self.tree.calcImpedanceMatrix(locarg='dist_bifur')
# create the tree structures
ctree_prox = self.tree.createCompartmentTree('prox')
ctree_bifur = self.tree.createCompartmentTree('bifur')
ctree_dist_nobifur = self.tree.createCompartmentTree('dist_nobifur')
ctree_dist_bifur = self.tree.createCompartmentTree('dist_bifur')
# test the tree structures
assert len(ctree_prox) == len(locs_prox) + 1
assert len(ctree_bifur) == len(locs_bifur) + 1
assert len(ctree_dist_nobifur) == len(locs_dist_nobifur) + 1
assert len(ctree_dist_bifur) == len(locs_dist_bifur) + 1
# fit the steady state models
ctree_prox.computeGMC(z_mat_prox)
ctree_bifur.computeGMC(z_mat_bifur)
ctree_dist_nobifur.computeGMC(z_mat_dist_nobifur)
ctree_dist_bifur.computeGMC(z_mat_dist_bifur)
# compute the fitted impedance matrices
z_fit_prox = ctree_prox.calcImpedanceMatrix()
z_fit_bifur = ctree_bifur.calcImpedanceMatrix()
z_fit_dist_nobifur = ctree_dist_nobifur.calcImpedanceMatrix()
z_fit_dist_bifur = ctree_dist_bifur.calcImpedanceMatrix()
# test correctness
assert np.allclose(z_fit_prox, z_mat_prox, atol=0.5)
assert np.allclose(z_fit_bifur, z_mat_bifur, atol=0.5)
assert not np.allclose(
z_fit_dist_nobifur, z_mat_dist_nobifur, atol=0.5)
assert np.allclose(z_fit_dist_bifur, z_mat_dist_bifur, atol=0.5)
def testReordering(self):
self.loadTTree()
# test reordering
locs_dist_badorder = [(1., 0.5), (8., 0.5), (4, 1.0)]
self.tree.storeLocs(locs_dist_badorder, 'badorder')
z_mat_badorder = self.tree.calcImpedanceMatrix(locarg='badorder')
ctree_badorder = self.tree.createCompartmentTree('badorder')
# check if location indices are assigned correctly
assert [node.loc_ind for node in ctree_badorder] == [0, 2, 1]
# check if reordering works
z_mat_reordered = ctree_badorder._preprocessZMatArg(z_mat_badorder)
assert np.allclose(z_mat_reordered,
z_mat_badorder[:, [0, 2, 1]][[0, 2, 1], :])
# check if fitting is correct
ctree_badorder.computeGMC(z_mat_badorder)
z_fit_badorder = ctree_badorder.calcImpedanceMatrix()
assert np.allclose(z_mat_badorder, z_fit_badorder, atol=0.5)
assert not np.allclose(z_mat_reordered, z_fit_badorder)
# test if equivalent locs are returned correctly
locs_equiv = ctree_badorder.getEquivalentLocs()
assert all([
loc == loc_
for loc, loc_ in zip(locs_equiv, [(0, .5), (2, .5), (1, .5)])
])
def loadBallAndStick(self):
self.greens_tree = GreensTree(file_n=os.path.join(
MORPHOLOGIES_PATH_PREFIX, 'ball_and_stick.swc'))
self.greens_tree.setPhysiology(0.8, 100. / 1e6)
self.greens_tree.setLeakCurrent(100., -75.)
self.greens_tree.setCompTree()
# set the impedances
self.freqs = np.array([0.]) * 1j
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=50.)
def testLocationMapping(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
locs_1 = [(1, 0.5)] + [(4, x) for x in xvals]
locs_2 = [(1, 0.5)] + [(4, x) for x in xvals][::-1]
locs_3 = [(4, x) for x in xvals] + [(1, 0.5)]
# create compartment trees
ctree_1 = self.greens_tree.createCompartmentTree(locs_1)
ctree_2 = self.greens_tree.createCompartmentTree(locs_2)
ctree_3 = self.greens_tree.createCompartmentTree(locs_3)
# test location indices
locinds_1 = np.array([node.loc_ind for node in ctree_1])
locinds_2 = np.array([node.loc_ind for node in ctree_2])
locinds_3 = np.array([node.loc_ind for node in ctree_3])
# check consecutive
assert np.allclose(locinds_1[:-1], locinds_1[1:] - 1)
# check permutation
assert np.allclose(locinds_1[1:], locinds_2[1:][::-1])
assert np.allclose(locinds_1[:-1], locinds_3[1:])
def testGSSFit(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
locs_1 = [(1, 0.5)] + [(4, x) for x in xvals]
locs_2 = [(1, 0.5)] + [(4, x) for x in xvals][::-1]
locs_3 = [(4, x) for x in xvals] + [(1, 0.5)]
locs_4 = random.sample(locs_1, k=len(locs_1))
# calculate impedance matrices
z_mat_1 = self.greens_tree.calcImpedanceMatrix(locs_1)[0].real
z_mat_2 = self.greens_tree.calcImpedanceMatrix(locs_2)[0].real
z_mat_3 = self.greens_tree.calcImpedanceMatrix(locs_3)[0].real
z_mat_4 = self.greens_tree.calcImpedanceMatrix(locs_4)[0].real
# create compartment trees
ctree_1 = self.greens_tree.createCompartmentTree(locs_1)
ctree_2 = self.greens_tree.createCompartmentTree(locs_2)
ctree_3 = self.greens_tree.createCompartmentTree(locs_3)
ctree_4 = self.greens_tree.createCompartmentTree(locs_4)
# fit g_m and g_c
ctree_1.computeGMC(z_mat_1, channel_names=['L'])
ctree_2.computeGMC(z_mat_2, channel_names=['L'])
ctree_3.computeGMC(z_mat_3, channel_names=['L'])
ctree_4.computeGMC(z_mat_4, channel_names=['L'])
# compare both models
assert str(ctree_1) == str(ctree_2)
assert str(ctree_1) == str(ctree_3)
assert str(ctree_1) == str(ctree_4)
# compare impedance matrices
z_fit_1 = ctree_1.calcImpedanceMatrix(self.freqs)
z_fit_2 = ctree_2.calcImpedanceMatrix(self.freqs)
z_fit_3 = ctree_3.calcImpedanceMatrix(self.freqs)
z_fit_4 = ctree_4.calcImpedanceMatrix(self.freqs)
assert np.allclose(z_fit_1, z_mat_1, atol=1e-8)
assert np.allclose(z_fit_2, z_mat_2, atol=1e-8)
assert np.allclose(z_fit_3, z_mat_3, atol=1e-8)
assert np.allclose(z_fit_4, z_mat_4, atol=1e-8)
assert np.allclose(z_fit_1,
ctree_2.calcImpedanceMatrix(indexing='tree'))
assert np.allclose(z_fit_1,
ctree_3.calcImpedanceMatrix(indexing='tree'))
assert np.allclose(z_fit_1,
ctree_4.calcImpedanceMatrix(indexing='tree'))
def testCFit(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
locs = [(1, 0.5)] + [(4, x) for x in xvals]
# create compartment tree
ctree = self.greens_tree.createCompartmentTree(locs)
# steady state fit
z_mat = self.greens_tree.calcImpedanceMatrix(locs)[0].real
ctree.computeGMC(z_mat)
# get SOV constants for capacitance fit
alphas, phimat, importance = self.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])
# check if equal to membrane time scale
nds = [self.greens_tree[loc[0]] for loc in locs]
taus_orig = np.array([n.c_m / n.currents['L'][0] for n in nds])
taus_fit = np.array([n.ca / n.currents['L'][0] for n in ctree])
assert np.allclose(taus_orig, taus_fit)
# fit capacitances with experimental vector fit
for n in ctree:
n.ca = 1.
self.greens_tree.setImpedance(freqs=ke.create_logspace_freqarray())
z_mat = self.greens_tree.calcImpedanceMatrix(locs)
# run the vector fit
ctree.computeCVF(self.greens_tree.freqs, z_mat)
taus_fit2 = np.array([n.ca / n.currents['L'][0] for n in ctree])
assert np.allclose(taus_orig, taus_fit2, atol=.3)
def fitBallAndStick(self, n_loc=20):
self.loadBallAndStick()
# define locations
xvals = np.linspace(0., 1., n_loc + 1)[1:]
np.random.shuffle(xvals)
locs = [(1, 0.5)] + [(4, x) for x in xvals]
# create compartment tree
ctree = self.greens_tree.createCompartmentTree(locs)
# steady state fit
z_mat = self.greens_tree.calcImpedanceMatrix(locs)[0].real
ctree.computeGMC(z_mat)
# get SOV constants for capacitance fit
alphas, phimat, importance = self.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])
self.ctree = ctree
def testPasFunctionality(self, n_loc=10):
self.fitBallAndStick(n_loc=n_loc)
# test equilibrium potential setting
e_eq = -75. + np.random.randint(10, size=n_loc + 1)
# with tree indexing
self.ctree.setEEq(e_eq, indexing='tree')
assert np.allclose(e_eq, np.array([n.e_eq for n in self.ctree]))
assert np.allclose(e_eq, self.ctree.getEEq(indexing='tree'))
assert not np.allclose(e_eq, self.ctree.getEEq(indexing='locs'))
# with loc indexing
self.ctree.setEEq(e_eq, indexing='locs')
assert not np.allclose(e_eq, np.array([n.e_eq for n in self.ctree]))
assert not np.allclose(e_eq, self.ctree.getEEq(indexing='tree'))
assert np.allclose(e_eq, self.ctree.getEEq(indexing='locs'))
# conductance matrices
gm1 = self.ctree.calcConductanceMatrix(indexing='locs')
gm2 = self.ctree.calcSystemMatrix(indexing='locs',
channel_names=['L'],
with_ca=True,
use_conc=False)
gm3 = self.ctree.calcSystemMatrix(indexing='locs',
channel_names=['L'],
with_ca=False,
use_conc=False)
gm4 = self.ctree.calcSystemMatrix(indexing='locs',
channel_names=['L'],
with_ca=False,
use_conc=True)
gm5 = self.ctree.calcSystemMatrix(indexing='locs',
with_ca=False,
use_conc=True)
gm6 = self.ctree.calcSystemMatrix(indexing='tree',
with_ca=False,
use_conc=True)
assert np.allclose(gm1, gm2)
assert np.allclose(gm1, gm3)
assert np.allclose(gm1, gm4)
assert np.allclose(gm1, gm5)
assert not np.allclose(gm1, gm6)
# eigenvalues
alphas, phimat, phimat_inv = self.ctree.calcEigenvalues()
ca_vec = np.array([1. / node.ca for node in self.ctree]) * 1e-3
assert np.allclose(np.dot(phimat, phimat_inv), np.diag(ca_vec))
assert np.allclose(
np.array([n.ca / n.currents['L'][0] for n in self.ctree]),
np.ones(len(self.ctree)) * np.max(1e-3 / np.abs(alphas)))
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 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
class TestSOVTree():
def loadTTree(self):
"""
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.tree = SOVTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
def loadValidationTree(self):
"""
Load the T-tree morphology in memory
5---1---4
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'sovvalidationtree.swc')
self.tree = SOVTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
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
## 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(locarg=locs_0)
imp_b = self.tree.getModeImportance(locarg='0')
imp_c = self.tree.getModeImportance(
sov_data=self.tree.getSOVMatrices(locarg=locs_0))
imp_d = self.tree.getModeImportance(
sov_data=self.tree.getSOVMatrices(locarg='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(locarg='2')
assert not np.allclose(imp_a, imp_2)
# test impedance matrix
z_mat_a = self.tree.calcImpedanceMatrix(
sov_data=self.tree.getImportantModes(locarg='1', eps=1e-10))
z_mat_b = self.tree.calcImpedanceMatrix(locarg='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(locarg='1', eps=1e-10, freqs=ft.s)
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 loadBall(self):
"""
Load point neuron model
"""
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc')
self.btree = SOVTree(fname, types=[1,3,4])
self.btree.fitLeakCurrent(-75., 10.)
self.btree.setCompTree()
def testSingleCompartment(self):
self.loadBall()
# for validation
greenstree = self.btree.__copy__(new_tree=GreensTree())
greenstree.setCompTree()
greenstree.setImpedance(np.array([0.]))
z_inp = greenstree.calcImpedanceMatrix([(1.,0.5)])
self.btree.calcSOVEquations(maxspace_freq=500)
alphas, gammas = self.btree.getSOVMatrices(locarg=[(1.,.5)])
z_inp_sov = self.btree.calcImpedanceMatrix(locarg=[(1.,.5)])
assert alphas.shape[0] == 1
assert gammas.shape == (1,1)
assert np.abs(1./np.abs(alphas[0]) - 10.) < 1e-10
g_m = self.btree[1].getGTot(self.btree.channel_storage)
g_s = g_m * 4.*np.pi*(self.btree[1].R*1e-4)**2
assert np.abs(gammas[0,0]**2/np.abs(alphas[0]) - 1./g_s) < 1e-10
assert np.abs(z_inp_sov - 1./g_s) < 1e-10
def testNETDerivation(self):
# initialize
self.loadValidationTree()
self.tree.calcSOVEquations()
# construct the NET
net = self.tree.constructNET()
# initialize
self.loadTTree()
self.tree.calcSOVEquations()
# construct the NET
net = self.tree.constructNET(dz=20.)
# contruct the NET with linear terms
net, lin_terms = self.tree.constructNET(dz=20., add_lin_terms=True)
# check if correct
alphas, gammas = self.tree.getImportantModes(locarg='net eval',
eps=1e-4, sort_type='timescale')
for ii, lin_term in lin_terms.items():
z_k_trans = net.getReducedTree([0,ii]).getRoot().z_kernel + lin_term
assert np.abs(z_k_trans.k_bar - Kernel((alphas, gammas[:,0]*gammas[:,ii])).k_bar) < 1e-8