def createTree3(self, reinitialize=1, add_lin=True, v_eq=-75.): """ Create simple NET structure 6 4 5 | | | | | | | 2 ---3--- | | | | ---1--- | | | 0------ | """ self.v_eq = v_eq # kernel constants alphas = 1. / np.array([1.]) gammas = np.array([1.]) # nodes node_0 = NETNode(0, [0, 1, 2, 3], [], z_kernel=(alphas, gammas)) node_1 = NETNode(1, [0, 1, 2], [], z_kernel=(alphas, gammas)) node_2 = NETNode(2, [0], [0], z_kernel=(alphas, gammas)) node_3 = NETNode(3, [1, 2], [], z_kernel=(alphas, gammas)) node_4 = NETNode(4, [1], [1], z_kernel=(alphas, gammas)) node_5 = NETNode(5, [2], [2], z_kernel=(alphas, gammas)) node_6 = NETNode(6, [3], [3], z_kernel=(alphas, gammas)) # add nodes to tree net_py = NET() net_py.setRoot(node_0) net_py.addNodeWithParent(node_1, node_0) net_py.addNodeWithParent(node_2, node_1) net_py.addNodeWithParent(node_3, node_1) net_py.addNodeWithParent(node_4, node_3) net_py.addNodeWithParent(node_5, node_3) net_py.addNodeWithParent(node_6, node_0) # linear terms alphas = 1. / np.array([1.]) gammas = np.array([1.]) self.lin_terms = { 1: Kernel((alphas, gammas)), 2: Kernel((alphas, gammas)), 3: Kernel((alphas, gammas)) } if add_lin else {} # store self.net_py = net_py self.cnet = netsim.NETSim(net_py, lin_terms=self.lin_terms)
def testKernels(self): # kernel 1 a1 = np.array([1., 10.]) c1 = np.array([2., 20.]) k1 = Kernel((a1, c1)) # kernel 2 a2 = np.array([1., 10.]) c2 = np.array([4., 40.]) k2 = Kernel({'a': a2, 'c': c2}) # kernel 3 k3 = Kernel(1.) # kbar assert np.abs(k1.k_bar - 4.) < 1e-12 assert np.abs(k2.k_bar - 8.) < 1e-12 assert np.abs(k3.k_bar - 1.) < 1e-12 # temporal kernel t_arr = np.array([0., np.infty]) assert np.allclose(k1(t_arr), np.array([22., 0.])) assert np.allclose(k2(t_arr), np.array([44., 0.])) assert np.allclose(k3(t_arr), np.array([1., 0.])) # frequency kernel s_arr = np.array([0. * 1j, np.infty * 1j]) assert np.allclose(k1.ft(s_arr), np.array([4. + 0j, np.nan * 1j]), equal_nan=True) assert np.allclose(k2.ft(s_arr), np.array([8. + 0j, np.nan * 1j]), equal_nan=True) assert np.allclose(k3.ft(s_arr), np.array([1. + 0j, np.nan * 1j]), equal_nan=True) # test addition k4 = k1 + k2 assert np.abs(k4.k_bar - 12.) < 1e-12 assert len(k4.a) == 2 k5 = k1 + k3 assert np.abs(k5.k_bar - 5.) < 1e-12 assert len(k5.a) == 3 # test subtraction k6 = k2 - k1 assert len(k6.a) == 2 assert np.allclose(k6.c, np.array([2., 20])) assert np.abs(k6.k_bar - 4.) < 1e-12 k7 = k1 - k3 assert len(k7.a) == 3 assert np.allclose(k7.c, np.array([2., 20., -1.])) assert np.abs(k7.k_bar - 3.) < 1e-12
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
def createTree2(self, reinitialize=1, add_lin=True, v_eq=-75.): ''' Create simple NET structure 3 4 | | | | ---2--- 1 | | | ---0--- | ''' self.v_eq = v_eq loc_ind = np.array([0, 1, 2]) # import btstructs # import morphologyReader as morphR # net_py = btstructs.STree() # # node 0 # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # self.z_0 = - np.sum(gammas / alphas) # node_0 = btstructs.SNode(0) # node_0.set_content({'layerdata': morphR.layerData(loc_ind, alphas, gammas)}) # dat = node_0.get_content()['layerdata'] # dat.set_ninds(np.array([])) # net_py.set_root(node_0) # # node 1 # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # self.z_1 = - np.sum(gammas / alphas) # node_1 = btstructs.SNode(1) # node_1.set_content({'layerdata': morphR.layerData(loc_ind[0:1], alphas, gammas)}) # dat = node_1.get_content()['layerdata'] # dat.set_ninds(np.array([0])) # net_py.add_node_with_parent(node_1, node_0) # # node 2 # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # self.z_2 = - np.sum(gammas / alphas) # node_2 = btstructs.SNode(2) # node_2.set_content({'layerdata': morphR.layerData(loc_ind[1:], alphas, gammas)}) # dat = node_2.get_content()['layerdata'] # dat.set_ninds(np.array([])) # net_py.add_node_with_parent(node_2, node_0) # # node 3 # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # self.z_3 = - np.sum(gammas / alphas) # node_3 = btstructs.SNode(3) # node_3.set_content({'layerdata': morphR.layerData(loc_ind[1:2], alphas, gammas)}) # dat = node_3.get_content()['layerdata'] # dat.set_ninds(np.array([loc_ind[1]])) # net_py.add_node_with_parent(node_3, node_2) # # node 4 # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # node_4 = btstructs.SNode(4) # self.z_4 = - np.sum(gammas / alphas) # node_4.set_content({'layerdata': morphR.layerData(loc_ind[2:], alphas, gammas)}) # dat = node_4.get_content()['layerdata'] # dat.set_ninds(np.array([loc_ind[2]])) # net_py.add_node_with_parent(node_4, node_2) # # linear terms # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # lin3 = morphR.linearLayerData([1], alphas, gammas) # alphas = -1. / np.array([1.]) # gammas = np.array([1.]) # lin4 = morphR.linearLayerData([2], alphas, gammas) # self.lin_terms = [lin3, lin4] if add_lin else [] # kernel constants alphas = 1. / np.array([1.]) gammas = np.array([1.]) # nodes node_0 = NETNode(0, [0, 1, 2], [], z_kernel=(alphas, gammas)) node_1 = NETNode(1, [0], [0], z_kernel=(alphas, gammas)) node_2 = NETNode(2, [1, 2], [], z_kernel=(alphas, gammas)) node_3 = NETNode(3, [1], [1], z_kernel=(alphas, gammas)) node_4 = NETNode(4, [2], [2], z_kernel=(alphas, gammas)) # add nodes to tree net_py = NET() net_py.setRoot(node_0) net_py.addNodeWithParent(node_1, node_0) net_py.addNodeWithParent(node_2, node_0) net_py.addNodeWithParent(node_3, node_2) net_py.addNodeWithParent(node_4, node_2) # linear terms alphas = 1. / np.array([1.]) gammas = np.array([1.]) self.lin_terms = { 1: Kernel((alphas, gammas)), 2: Kernel((alphas, gammas)) } if add_lin else {} # store self.net_py = net_py self.cnet = netsim.NETSim(net_py, lin_terms=self.lin_terms, v_eq=self.v_eq)