Beispiel #1
0
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
Beispiel #2
0
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)
Beispiel #3
0
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'))
Beispiel #4
0
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
Beispiel #5
0
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