def getL23Pyramid():
"""
Return a model of the L2/3 pyramidal cell with somatic and basal Na-, K- and
Ca-channels
"""
cm = 1. # uF / cm^2
rm = 10000. # Ohm * cm^2
ri = 150. # Ohm * cm
el = -75. # mV
tadj = 3.21
phys_tree = PhysTree('models/morphologies/L23PyrBranco.swc')
phys_tree.setPhysiology(
cm, # Cm [uF/cm^2]
ri / 1e6, # Ra[MOhm*cm]
)
# channels
Na = channels_branco.Na()
K_v = channels_branco.K_v()
K_m = channels_branco.K_m()
K_ca = channels_branco.K_ca()
Ca_H = channels_branco.Ca_H()
Ca_T = channels_branco.Ca_T()
# somatic channels
phys_tree.addCurrent(Na, tadj * 1500. * 1e2, 60.,
node_arg=[phys_tree[1]]) # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_v, tadj * 200. * 1e2, -90.,
node_arg=[phys_tree[1]]) # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_m, tadj * 2.2 * 1e2, -90.,
node_arg=[phys_tree[1]]) # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_ca, tadj * 2.5 * 1e2, -90.,
node_arg=[phys_tree[1]]) # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(Ca_H, tadj * 0.5 * 1e2, 140.,
node_arg=[phys_tree[1]]) # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(Ca_T, 0.0003 * 1e6, 60.,
node_arg=[phys_tree[1]]) # mho/cm^2 -> uS/cm^2
phys_tree.setLeakCurrent(1. / rm * 1e6, el, node_arg=[phys_tree[1]])
# basal channels
phys_tree.addCurrent(Na, tadj * 40. * 1e2, 60.,
node_arg='basal') # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_v, tadj * 30. * 1e2, -90.,
node_arg='basal') # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_m, tadj * 0.05 * 1e2, -90.,
node_arg='basal') # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(K_ca, tadj * 2.5 * 1e2, -90.,
node_arg='basal') # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(Ca_H, tadj * 0.5 * 1e2, 140.,
node_arg='basal') # pS/um^2 -> uS/cm^2
phys_tree.addCurrent(Ca_T, 0.0006 * 1e6, 60.,
node_arg='basal') # mho/cm^2 -> mho/cm^2
phys_tree.setLeakCurrent(1. / rm * 1e6, el, node_arg='basal')
# passive apical dendrite
phys_tree.setLeakCurrent(1. / rm * 1e6, el, node_arg='apical')
return phys_tree
def getL23PyramidPas():
"""
Return a passive model of the L2/3 pyramidal cell
"""
cm = 1. # uF / cm^2
rm = 10000. # Ohm * cm^2
ri = 150. # Ohm * cm
el = -75. # mV
phys_tree = PhysTree('models/morphologies/L23PyrBranco.swc',
types=[1, 2, 3, 4])
# set specific membrane capacitance and axial resistance
phys_tree.setPhysiology(
cm, # Cm [uF/cm^2]
ri / 1e6, # Ra[MOhm*cm]
)
# passive membrane conductance
phys_tree.setLeakCurrent(1e6 / 10000., el)
phys_tree.setLeakCurrent(0.02 * 1e6, el, node_arg='axonal')
return phys_tree
def getL5Pyramid():
"""
Return a minimal model of the L5 pyramid for BAC-firing
"""
# load the morphology
phys_tree = PhysTree('models/morphologies/cell1_simplified.swc')
# set specific membrane capacitance and axial resistance
phys_tree.setPhysiology(1., # Cm [uF/cm^2]
100./1e6, # Ra[MOhm*cm]
node_arg=[phys_tree[1]])
# set specific membrane capacitance and axial resistance
phys_tree.setPhysiology(2., # Cm [uF/cm^2]
100./1e6, # Ra[MOhm*cm]
node_arg=[n for n in phys_tree if not phys_tree.isRoot(n)])
# channels present in tree
Kv3_1 = channels_hay.Kv3_1()
Na_Ta = channels_hay.Na_Ta()
Ca_LVA = channels_hay.Ca_LVA()
Ca_HVA = channels_hay.Ca_HVA()
h_HAY = channels_hay.h_HAY()
# soma ion channels [uS/cm^2]
phys_tree.addCurrent(Kv3_1, 0.766 *1e6, -85., node_arg=[phys_tree[1]])
phys_tree.addCurrent(Na_Ta, 1.71 *1e6, 50., node_arg=[phys_tree[1]])
phys_tree.addCurrent(Ca_LVA, 0.00432 *1e6, 50., node_arg=[phys_tree[1]])
phys_tree.addCurrent(Ca_HVA, 0.000567 *1e6, 50., node_arg=[phys_tree[1]])
phys_tree.addCurrent(h_HAY, 0.0002 *1e6, -45., node_arg=[phys_tree[1]])
phys_tree.setLeakCurrent(0.0000344 *1e6, -90., node_arg=[phys_tree[1]])
# basal ion channels [uS/cm^2]
phys_tree.addCurrent(h_HAY, 0.0002 *1e6, -45., node_arg='basal')
phys_tree.setLeakCurrent(0.0000535 *1e6, -90., node_arg='basal')
# apical ion channels [uS/cm^2]
phys_tree.addCurrent(Kv3_1, 0.000298 *1e6, -85., node_arg='apical')
phys_tree.addCurrent(Na_Ta, 0.0211 *1e6, 50., node_arg='apical')
phys_tree.addCurrent(Ca_LVA, lambda x: 0.0198*1e6 if (x>685. and x<885.) else 0.0198*1e-2*1e6, 50., node_arg='apical')
phys_tree.addCurrent(Ca_HVA, lambda x: 0.000437*1e6 if (x>685. and x<885.) else 0.000437*1e-1*1e6, 50., node_arg='apical')
phys_tree.addCurrent(h_HAY, lambda x: 0.0002*1e6 * (-0.8696 + 2.0870 * np.exp(x/323.)), -45., node_arg='apical')
phys_tree.setLeakCurrent(0.0000447*1e6, -90., node_arg='apical')
return phys_tree
class TestPhysTree():
def loadTree(self, reinitialize=0, segments=False):
"""
Load the T-tree morphology in memory
6--5--4--7--8
|
|
1
"""
if not hasattr(self, 'tree') or reinitialize:
print('>>> loading T-tree <<<')
fname = 'Ttree_segments.swc' if segments else 'Ttree.swc'
self.tree = PhysTree('test_morphologies/' + fname, types=[1, 3, 4])
def testLeakDistr(self):
self.loadTree(reinitialize=1)
with pytest.raises(AssertionError):
self.tree.fitLeakCurrent(-75., -10.)
# test simple distribution
self.tree.fitLeakCurrent(-75., 10.)
for node in self.tree:
assert np.abs(node.c_m - 1.0) < 1e-9
assert np.abs(node.currents['L'][0] - 1. / (10. * 1e-3)) < 1e-9
assert np.abs(node.e_eq + 75.) < 1e-9
# create complex distribution
tau_distr = lambda x: x + 100.
for node in self.tree:
d2s = self.tree.pathLength({
'node': node.index,
'x': 1.
}, (1., 0.5))
node.fitLeakCurrent(self.tree.channel_storage,
e_eq_target=-75.,
tau_m_target=tau_distr(d2s))
assert np.abs(node.c_m - 1.0) < 1e-9
assert np.abs(node.currents['L'][0] - 1. / (tau_distr(d2s)*1e-3)) < \
1e-9
assert np.abs(node.e_eq + 75.) < 1e-9
def testPhysiologySetting(self):
self.loadTree(reinitialize=1)
d2s = {1: 0., 4: 50., 5: 125., 6: 175., 7: 125., 8: 175.}
# passive parameters as float
c_m = 1.
r_a = 100. * 1e-6
self.tree.setPhysiology(c_m, r_a)
for node in self.tree:
assert np.abs(node.c_m - c_m) < 1e-10
assert np.abs(node.r_a - r_a) < 1e-10
# passive parameters as function
c_m = lambda x: .5 * x + 1.
r_a = lambda x: np.exp(0.01 * x) * 100 * 1e-6
self.tree.setPhysiology(c_m, r_a)
for node in self.tree:
assert np.abs(node.c_m - c_m(d2s[node.index])) < 1e-10
assert np.abs(node.r_a - r_a(d2s[node.index])) < 1e-10
# passive parameters as incomplete dict
r_a = 100. * 1e-6
c_m = {1: 1., 4: 1.2}
with pytest.raises(KeyError):
self.tree.setPhysiology(c_m, r_a)
# passive parameters as complete dict
c_m.update({5: 1.1, 6: 0.9, 7: 0.8, 8: 1.})
self.tree.setPhysiology(c_m, r_a)
for node in self.tree:
assert np.abs(node.c_m - c_m[node.index]) < 1e-10
# equilibrium potential as float
e_eq = -75.
self.tree.setEEq(e_eq)
for node in self.tree:
assert np.abs(node.e_eq - e_eq) < 1e-10
# equilibrium potential as dict
e_eq = {1: -75., 4: -74., 5: -73., 6: -72., 7: -71., 8: -70.}
self.tree.setEEq(e_eq)
for node in self.tree:
assert np.abs(node.e_eq - e_eq[node.index]) < 1e-10
# equilibrium potential as function
e_eq = lambda x: -70. + 0.1 * x
self.tree.setEEq(e_eq)
for node in self.tree:
assert np.abs(node.e_eq - e_eq(d2s[node.index])) < 1e-10
# as wrong type
with pytest.raises(TypeError):
self.tree.setEEq([])
self.tree.setPhysiology([], [])
# leak as float
g_l, e_l = 100., -75.
self.tree.setLeakCurrent(g_l, e_l)
for node in self.tree:
g, e = node.currents['L']
assert np.abs(g - g_l) < 1e-10
assert np.abs(e - e_l) < 1e-10
# equilibrium potential as dict
g_l = {1: 101., 4: 103., 5: 105., 6: 107., 7: 108., 8: 109.}
e_l = {1: -75., 4: -74., 5: -73., 6: -72., 7: -71., 8: -70.}
self.tree.setLeakCurrent(g_l, e_l)
for node in self.tree:
g, e = node.currents['L']
assert np.abs(g - g_l[node.index]) < 1e-10
assert np.abs(e - e_l[node.index]) < 1e-10
# equilibrium potential as function
g_l = lambda x: 100. + 0.05 * x
e_l = lambda x: -70. + 0.05 * x
self.tree.setLeakCurrent(g_l, e_l)
for node in self.tree:
g, e = node.currents['L']
assert np.abs(g - g_l(d2s[node.index])) < 1e-10
assert np.abs(e - e_l(d2s[node.index])) < 1e-10
# as wrong type
with pytest.raises(TypeError):
self.tree.setLeakCurrent([])
# gmax as potential as float
e_rev = 100.
g_max = 100.
channel = channelcollection.TestChannel2()
self.tree.addCurrent(channel, g_max, e_rev)
for node in self.tree:
g_m = node.currents['TestChannel2'][0]
assert np.abs(g_m - g_max) < 1e-10
# equilibrium potential as dict
g_max = {1: 101., 4: 103., 5: 104., 6: 106., 7: 107., 8: 110.}
self.tree.addCurrent(channel, g_max, e_rev)
for node in self.tree:
g_m = node.currents['TestChannel2'][0]
assert np.abs(g_m - g_max[node.index]) < 1e-10
# equilibrium potential as function
g_max = lambda x: 100. + 0.005 * x**2
self.tree.addCurrent(channel, g_max, e_rev)
for node in self.tree:
g_m = node.currents['TestChannel2'][0]
assert np.abs(g_m - g_max(d2s[node.index])) < 1e-10
# test is channel is stored
assert isinstance(
self.tree.channel_storage[channel.__class__.__name__],
channelcollection.TestChannel2)
# check if error is thrown if an ionchannel is not give
with pytest.raises(IOError):
self.tree.addCurrent('TestChannel2', g_max, e_rev)
def testMembraneFunctions(self):
self.loadTree(reinitialize=1)
self.tree.setPhysiology(1., 100 * 1e-6)
# passive parameters
c_m = 1.
r_a = 100. * 1e-6
e_eq = -75.
self.tree.setPhysiology(c_m, r_a)
self.tree.setEEq(e_eq)
# channel
p_open = .9 * .3**3 * .5**2 + .1 * .4**2 * .6**1 # TestChannel2
g_chan, e_chan = 100., 100.
channel = channelcollection.TestChannel2()
self.tree.addCurrent(channel, g_chan, e_chan)
# fit the leak current
self.tree.fitLeakCurrent(-30., 10.)
# test if fit was correct
for node in self.tree:
tau_mem = c_m / (node.currents['L'][0] + g_chan * p_open) * 1e3
assert np.abs(tau_mem - 10.) < 1e-10
e_eq = (node.currents['L'][0]*node.currents['L'][1] + \
g_chan*p_open*e_chan) / (node.currents['L'][0] + g_chan*p_open)
assert np.abs(e_eq - (-30.)) < 1e-10
# test if warning is raised for impossible to reach time scale
with pytest.warns(UserWarning):
tree = copy.deepcopy(self.tree)
tree.fitLeakCurrent(-30., 100000.)
# total membrane conductance
g_pas = self.tree[1].currents['L'][0] + g_chan * p_open
# make passive membrane
tree = copy.deepcopy(self.tree)
tree.asPassiveMembrane()
# test if fit was correct
for node in tree:
assert np.abs(node.currents['L'][0] - g_pas) < 1e-10
def testCompTree(self):
self.loadTree(reinitialize=1, segments=True)
# capacitance axial resistance constant
c_m = 1.
r_a = 100. * 1e-6
self.tree.setPhysiology(c_m, r_a)
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 8, 10, 12]
# capacitance and axial resistance change
c_m = lambda x: 1. if x < 200. else 1.6
r_a = lambda x: 1. if x < 300. else 1.6
self.tree.setPhysiology(c_m, r_a)
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 5, 6, 8, 10, 12]
# leak current changes
g_l = lambda x: 100. if x < 400. else 160.
self.tree.setLeakCurrent(g_l, -75.)
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 5, 6, 7, 8, 10, 12]
# leak current & reversal change
g_l = 100.
e_l = {ind: -75. for ind in [1, 4, 5, 6, 7, 8, 11, 12]}
e_l.update({ind: -55. for ind in [9, 10]})
self.tree.setLeakCurrent(g_l, e_l)
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 5, 6, 8, 10, 12]
# leak current & reversal change
g_l = 100.
e_l = {ind: -75. for ind in [1, 4, 5, 6, 7, 8, 10, 11, 12]}
e_l.update({9: -55.})
self.tree.setLeakCurrent(g_l, e_l)
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 5, 6, 8, 9, 10, 12]
# shunt
self.tree.treetype = 'original'
self.tree[7].g_shunt = 1.
self.tree.setCompTree()
self.tree.treetype = 'computational'
assert [n.index for n in self.tree] == [1, 5, 6, 7, 8, 9, 10, 12]
class TestCompartmentFitter():
def loadTTree(self):
'''
Load the T-tree model
6--5--4--7--8
|
|
1
'''
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.tree = PhysTree(fname, types=[1, 3, 4])
self.tree.setPhysiology(0.8, 100. / 1e6)
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
def loadBallAndStick(self):
'''
Load the ball and stick model
1--4
'''
self.tree = PhysTree(file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX,
'ball_and_stick.swc'))
self.tree.setPhysiology(0.8, 100. / 1e6)
self.tree.setLeakCurrent(100., -75.)
self.tree.setCompTree()
def loadBall(self):
'''
Load point neuron model
'''
self.tree = PhysTree(
file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc'))
# capacitance and axial resistance
self.tree.setPhysiology(0.8, 100. / 1e6)
# ion channels
k_chan = channelcollection.Kv3_1()
self.tree.addCurrent(k_chan, 0.766 * 1e6, -85.)
na_chan = channelcollection.Na_Ta()
self.tree.addCurrent(na_chan, 1.71 * 1e6, 50.)
# fit leak current
self.tree.fitLeakCurrent(-75., 10.)
# set equilibirum potententials
self.tree.setEEq(-75.)
# set computational tree
self.tree.setCompTree()
def loadTSegmentTree(self):
'''
Load point neuron model
'''
self.tree = PhysTree(file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX,
'Ttree_segments.swc'))
# self.tree = PhysTree(file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'L23PyrBranco.swc'))
# capacitance and axial resistance
self.tree.setPhysiology(0.8, 100. / 1e6)
# ion channels
k_chan = channelcollection.Kv3_1()
g_k = {1: 0.766 * 1e6}
g_k.update({n.index: 0.034*1e6 / self.tree.pathLength((1,.5), (n.index,.5)) \
for n in self.tree if n.index != 1})
self.tree.addCurrent(k_chan, g_k, -85.)
na_chan = channelcollection.Na_Ta()
self.tree.addCurrent(na_chan, 1.71 * 1e6, 50., node_arg=[self.tree[1]])
# fit leak current
self.tree.fitLeakCurrent(-75., 10.)
# set equilibirum potententials
self.tree.setEEq(-75.)
# set computational tree
self.tree.setCompTree()
def testTreeStructure(self):
self.loadTTree()
cm = CompartmentFitter(self.tree)
# set of locations
fit_locs1 = [(1, .5), (4, .5), (5, .5)] # no bifurcations
fit_locs2 = [(1, .5), (4, .5), (5, .5),
(8, .5)] # w bifurcation, should be added
fit_locs3 = [(1, .5), (4, 1.), (5, .5),
(8, .5)] # w bifurcation, already added
# test fit_locs1, no bifurcation are added
# input paradigm 1
cm.setCTree(fit_locs1, extend_w_bifurc=True)
# fl1_a = cm.tree.getLocs('fit locs')
# with pytest.warns(UserWarning):
# cm.setCTree(fit_locs1, extend_w_bifurc=False)
# fl1_b = cm.tree.getLocs('fit locs')
# assert len(fl1_a) == len(fl1_b)
# for fla, flb in zip(fl1_a, fl1_b): assert fla == flb
# # input paradigm 2
# cm.tree.storeLocs(fit_locs1, 'fl1')
# cm.setCTree('fl1', extend_w_bifurc=True)
# fl1_a = cm.tree.getLocs('fit locs')
# assert len(fl1_a) == len(fl1_b)
# for fla, flb in zip(fl1_a, fl1_b): assert fla == flb
# # test tree structure
# assert len(cm.ctree) == 3
# for cn in cm.ctree: assert len(cn.child_nodes) <= 1
# # test fit_locs2, a bifurcation should be added
# with pytest.warns(UserWarning):
# cm.setCTree(fit_locs2, extend_w_bifurc=False)
# fl2_b = cm.tree.getLocs('fit locs')
# cm.setCTree(fit_locs2, extend_w_bifurc=True)
# fl2_a = cm.tree.getLocs('fit locs')
# assert len(fl2_a) == len(fl2_b) + 1
# for fla, flb in zip(fl2_a, fl2_b): assert fla == flb
# assert fl2_a[-1] == (4,1.)
# # test tree structure
# assert len(cm.ctree) == 5
# for cn in cm.ctree:
# assert len(cn.child_nodes) <= 1 if cn.loc_ind != 4 else \
# len(cn.child_nodes) == 2
# # test fit_locs2, no bifurcation should be added as it is already present
# cm.setCTree(fit_locs3, extend_w_bifurc=True)
# fl3 = cm.tree.getLocs('fit locs')
# for fl_, fl3 in zip(fit_locs3, fl3): assert fl_ == fl3
# # test tree structure
# assert len(cm.ctree) == 4
# for cn in cm.ctree:
# assert len(cn.child_nodes) <= 1 if cn.loc_ind != 1 else \
# len(cn.child_nodes) == 2
def _checkChannels(self, tree, channel_names):
assert isinstance(tree, compartmentfitter.FitTreeGF)
assert set(tree.channel_storage.keys()) == set(channel_names)
for node in tree:
assert set(node.currents.keys()) == set(channel_names + ['L'])
def testCreateTreeGF(self):
self.loadBall()
cm = CompartmentFitter(self.tree)
# create tree with only 'L'
tree_pas = cm.createTreeGF()
self._checkChannels(tree_pas, [])
# create tree with only 'Na_Ta'
tree_na = cm.createTreeGF(['Na_Ta'])
self._checkChannels(tree_na, ['Na_Ta'])
# create tree with only 'Kv3_1'
tree_k = cm.createTreeGF(['Kv3_1'])
self._checkChannels(tree_k, ['Kv3_1'])
# create tree with all channels
tree_all = cm.createTreeGF(['Na_Ta', 'Kv3_1'])
self._checkChannels(tree_all, ['Na_Ta', 'Kv3_1'])
def reduceExplicit(self):
self.loadBall()
freqs = np.array([0.])
locs = [(1, 0.5)]
e_eqs = [-75., -55., -35., -15.]
# create compartment tree
ctree = self.tree.createCompartmentTree(locs)
ctree.addCurrent(channelcollection.Na_Ta(), 50.)
ctree.addCurrent(channelcollection.Kv3_1(), -85.)
# create tree with only leak
greens_tree_pas = self.tree.__copy__(new_tree=GreensTree())
greens_tree_pas[1].currents = {'L': greens_tree_pas[1].currents['L']}
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(freqs)
# compute the passive impedance matrix
z_mat_pas = greens_tree_pas.calcImpedanceMatrix(locs)[0]
# create tree with only potassium
greens_tree_k = self.tree.__copy__(new_tree=GreensTree())
greens_tree_k[1].currents = {key: val for key, val in greens_tree_k[1].currents.items() \
if key != 'Na_Ta'}
# compute potassium impedance matrices
z_mats_k = []
for e_eq in e_eqs:
greens_tree_k.setEEq(e_eq)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(freqs)
z_mats_k.append(greens_tree_k.calcImpedanceMatrix(locs))
# create tree with only sodium
greens_tree_na = self.tree.__copy__(new_tree=GreensTree())
greens_tree_na[1].currents = {key: val for key, val in greens_tree_na[1].currents.items() \
if key != 'Kv3_1'}
# create state variable expansion points
svs = []
e_eqs_ = []
na_chan = greens_tree_na.channel_storage['Na_Ta']
for e_eq1 in e_eqs:
sv1 = na_chan.computeVarinf(e_eq1)
for e_eq2 in e_eqs:
e_eqs_.append(e_eq2)
sv2 = na_chan.computeVarinf(e_eq2)
svs.append({'m': sv2['m'], 'h': sv1['h']})
# compute sodium impedance matrices
z_mats_na = []
for sv, eh in zip(svs, e_eqs_):
greens_tree_na.setEEq(eh)
greens_tree_na[1].setExpansionPoint('Na_Ta', sv)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(freqs)
z_mats_na.append(greens_tree_na.calcImpedanceMatrix(locs))
# passive fit
ctree.computeGMC(z_mat_pas)
# potassium channel fit matrices
fit_mats_k = []
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
mf, vt = ctree.computeGSingleChanFromImpedance(
'Kv3_1',
z_mat_k,
e_eq,
freqs,
other_channel_names=['L'],
action='return')
fit_mats_k.append([mf, vt])
# sodium channel fit matrices
fit_mats_na = []
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
mf, vt = ctree.computeGSingleChanFromImpedance(
'Na_Ta',
z_mat_na,
e_eq,
freqs,
sv=sv,
other_channel_names=['L'],
action='return')
fit_mats_na.append([mf, vt])
return fit_mats_na, fit_mats_k
def testChannelFitMats(self):
self.loadBall()
cm = CompartmentFitter(self.tree)
cm.setCTree([(1, .5)])
# check if reversals are correct
for key in set(cm.ctree[0].currents) - {'L'}:
assert np.abs(cm.ctree[0].currents[key][1] - \
self.tree[1].currents[key][1]) < 1e-10
# fit the passive model
cm.fitPassive(use_all_channels=False)
fit_mats_cm_na = cm.evalChannel('Na_Ta', parallel=False)
fit_mats_cm_k = cm.evalChannel('Kv3_1', parallel=False)
fit_mats_control_na, fit_mats_control_k = self.reduceExplicit()
# test whether potassium fit matrices agree
for fm_cm, fm_control in zip(fit_mats_cm_k, fit_mats_control_k):
assert np.allclose(np.sum(fm_cm[0]),
fm_control[0][0, 0]) # feature matrices
assert np.allclose(fm_cm[1], fm_control[1]) # target vectors
# test whether sodium fit matrices agree
for fm_cm, fm_control in zip(fit_mats_cm_na[4:], fit_mats_control_na):
assert np.allclose(np.sum(fm_cm[0]),
fm_control[0][0, 0]) # feature matrices
assert np.allclose(fm_cm[1], fm_control[1]) # target vectors
def _checkPasCondProps(self, ctree1, ctree2):
assert len(ctree1) == len(ctree2)
for n1, n2 in zip(ctree1, ctree2):
assert np.allclose(n1.currents['L'][0], n2.currents['L'][0])
assert np.allclose(n1.g_c, n2.g_c)
def _checkPasCaProps(self, ctree1, ctree2):
assert len(ctree1) == len(ctree2)
for n1, n2 in zip(ctree1, ctree2):
assert np.allclose(n1.ca, n2.ca)
def _checkAllCurrProps(self, ctree1, ctree2):
assert len(ctree1) == len(ctree2)
assert ctree1.channel_storage.keys() == ctree2.channel_storage.keys()
for n1, n2 in zip(ctree1, ctree2):
assert np.allclose(n1.g_c, n2.g_c)
for key in n1.currents:
assert np.allclose(n1.currents[key][0], n2.currents[key][0])
assert np.allclose(n1.currents[key][1], n2.currents[key][1])
def _checkPhysTrees(self, tree1, tree2):
assert len(tree1) == len(tree2)
assert tree1.channel_storage.keys() == tree2.channel_storage.keys()
for n1, n2 in zip(tree1, tree2):
assert np.allclose(n1.r_a, n2.r_a)
assert np.allclose(n1.c_m, n2.c_m)
for key in n1.currents:
assert np.allclose(n1.currents[key][0], n2.currents[key][0])
assert np.allclose(n1.currents[key][1], n2.currents[key][1])
def _checkEL(self, ctree, e_l):
for n in ctree:
assert np.allclose(n.currents['L'][1], e_l)
def testPassiveFit(self):
self.loadTTree()
fit_locs = [(1, .5), (4, 1.), (5, .5), (8, .5)]
# fit a tree directly from CompartmentTree
greens_tree = self.tree.__copy__(new_tree=GreensTree())
greens_tree.setCompTree()
freqs = np.array([0.])
greens_tree.setImpedance(freqs)
z_mat = greens_tree.calcImpedanceMatrix(fit_locs)[0].real
ctree = greens_tree.createCompartmentTree(fit_locs)
ctree.computeGMC(z_mat)
sov_tree = self.tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
alphas, phimat = sov_tree.getImportantModes(locarg=fit_locs)
ctree.computeC(-alphas[0:1].real * 1e3, phimat[0:1, :].real)
# fit a tree with compartment fitter
cm = CompartmentFitter(self.tree)
cm.setCTree(fit_locs)
cm.fitPassive()
cm.fitCapacitance()
cm.fitEEq()
# check whether both trees are the same
self._checkPasCondProps(ctree, cm.ctree)
self._checkPasCaProps(ctree, cm.ctree)
self._checkEL(cm.ctree, -75.)
# test whether all channels are used correctly for passive fit
self.loadBall()
fit_locs = [(1, .5)]
# fit ball model with only leak
greens_tree = self.tree.__copy__(new_tree=GreensTree())
greens_tree.channel_storage = {}
for n in greens_tree:
n.currents = {'L': n.currents['L']}
greens_tree.setCompTree()
freqs = np.array([0.])
greens_tree.setImpedance(freqs)
z_mat = greens_tree.calcImpedanceMatrix(fit_locs)[0].real
ctree_leak = greens_tree.createCompartmentTree(fit_locs)
ctree_leak.computeGMC(z_mat)
sov_tree = greens_tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
alphas, phimat = sov_tree.getImportantModes(locarg=fit_locs)
ctree_leak.computeC(-alphas[0:1].real * 1e3, phimat[0:1, :].real)
# make ball model with leak based on all channels
tree = self.tree.__copy__()
tree.asPassiveMembrane()
greens_tree = tree.__copy__(new_tree=GreensTree())
greens_tree.setCompTree()
freqs = np.array([0.])
greens_tree.setImpedance(freqs)
z_mat = greens_tree.calcImpedanceMatrix(fit_locs)[0].real
ctree_all = greens_tree.createCompartmentTree(fit_locs)
ctree_all.computeGMC(z_mat)
sov_tree = tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
alphas, phimat = sov_tree.getImportantModes(locarg=fit_locs)
ctree_all.computeC(-alphas[0:1].real * 1e3, phimat[0:1, :].real)
# new compartment fitter
cm = CompartmentFitter(self.tree)
cm.setCTree(fit_locs)
# test fitting
cm.fitPassive(use_all_channels=False)
cm.fitCapacitance()
cm.fitEEq()
self._checkPasCondProps(ctree_leak, cm.ctree)
self._checkPasCaProps(ctree_leak, cm.ctree)
with pytest.raises(AssertionError):
self._checkEL(cm.ctree, self.tree[1].currents['L'][1])
cm.fitPassive(use_all_channels=True)
cm.fitCapacitance()
cm.fitEEq()
self._checkPasCondProps(ctree_all, cm.ctree)
self._checkPasCaProps(ctree_all, cm.ctree)
self._checkEL(cm.ctree, greens_tree[1].currents['L'][1])
with pytest.raises(AssertionError):
self._checkEL(cm.ctree, self.tree[1].currents['L'][1])
with pytest.raises(AssertionError):
self._checkPasCondProps(ctree_leak, ctree_all)
def testRecalcImpedanceMatrix(self, g_inp=np.linspace(0., 0.01, 20)):
self.loadBall()
fit_locs = [(1, .5)]
cm = CompartmentFitter(self.tree)
cm.setCTree(fit_locs)
# test only leak
# compute impedances explicitly
greens_tree = cm.createTreeGF(channel_names=[])
greens_tree.setEEq(-75.)
greens_tree.setImpedancesInTree()
z_mat = greens_tree.calcImpedanceMatrix(fit_locs,
explicit_method=False)[0]
z_test = z_mat[:, :,
None] / (1. + z_mat[:, :, None] * g_inp[None, None, :])
# compute impedances with compartmentfitter function
z_calc = np.array([ \
cm.recalcImpedanceMatrix('fit locs', [g_i], \
channel_names=[]
) \
for g_i in g_inp \
])
z_calc = np.swapaxes(z_calc, 0, 2)
assert np.allclose(z_calc, z_test)
# test with z based on all channels (passive)
# compute impedances explicitly
greens_tree = cm.createTreeGF(
channel_names=list(cm.tree.channel_storage.keys()))
greens_tree.setEEq(-75.)
greens_tree.setImpedancesInTree()
z_mat = greens_tree.calcImpedanceMatrix(fit_locs,
explicit_method=False)[0]
z_test = z_mat[:, :,
None] / (1. + z_mat[:, :, None] * g_inp[None, None, :])
# compute impedances with compartmentfitter function
z_calc = np.array([ \
cm.recalcImpedanceMatrix('fit locs', [g_i], \
channel_names=list(cm.tree.channel_storage.keys())) \
for g_i in g_inp \
])
z_calc = np.swapaxes(z_calc, 0, 2)
assert np.allclose(z_calc, z_test)
def testSynRescale(self, g_inp=np.linspace(0., 0.01, 20)):
e_rev, v_eq = 0., -75.
self.loadBallAndStick()
fit_locs = [(4, .7)]
syn_locs = [(4, 1.)]
cm = CompartmentFitter(self.tree)
cm.setCTree(fit_locs)
# compute impedance matrix
greens_tree = cm.createTreeGF(channel_names=[])
greens_tree.setEEq(-75.)
greens_tree.setImpedancesInTree()
z_mat = greens_tree.calcImpedanceMatrix(fit_locs + syn_locs)[0]
# analytical synapse scale factors
beta_calc = 1. / (1. + (z_mat[1, 1] - z_mat[0, 0]) * g_inp)
beta_full = z_mat[0,1] / z_mat[0,0] * (e_rev - v_eq) / \
((1. + (z_mat[1,1] - z_mat[0,0]) * g_inp ) * (e_rev - v_eq))
# synapse scale factors from compartment fitter
beta_cm = np.array([cm.fitSynRescale(fit_locs, syn_locs, [0], [g_i], e_revs=[0.])[0] \
for g_i in g_inp])
assert np.allclose(beta_calc, beta_cm, atol=.020)
assert np.allclose(beta_full, beta_cm, atol=.015)
def fitBall(self):
self.loadBall()
freqs = np.array([0.])
locs = [(1, 0.5)]
e_eqs = [-75., -55., -35., -15.]
# create compartment tree
ctree = self.tree.createCompartmentTree(locs)
ctree.addCurrent(channelcollection.Na_Ta(), 50.)
ctree.addCurrent(channelcollection.Kv3_1(), -85.)
# create tree with only leak
greens_tree_pas = self.tree.__copy__(new_tree=GreensTree())
greens_tree_pas[1].currents = {'L': greens_tree_pas[1].currents['L']}
greens_tree_pas.setCompTree()
greens_tree_pas.setImpedance(freqs)
# compute the passive impedance matrix
z_mat_pas = greens_tree_pas.calcImpedanceMatrix(locs)[0]
# create tree with only potassium
greens_tree_k = self.tree.__copy__(new_tree=GreensTree())
greens_tree_k[1].currents = {key: val for key, val in greens_tree_k[1].currents.items() \
if key != 'Na_Ta'}
# compute potassium impedance matrices
z_mats_k = []
for e_eq in e_eqs:
greens_tree_k.setEEq(e_eq)
greens_tree_k.setCompTree()
greens_tree_k.setImpedance(freqs)
z_mats_k.append(greens_tree_k.calcImpedanceMatrix(locs))
# create tree with only sodium
greens_tree_na = self.tree.__copy__(new_tree=GreensTree())
greens_tree_na[1].currents = {key: val for key, val in greens_tree_na[1].currents.items() \
if key != 'Kv3_1'}
# create state variable expansion points
svs = []
e_eqs_ = []
na_chan = greens_tree_na.channel_storage['Na_Ta']
for e_eq1 in e_eqs:
sv1 = na_chan.computeVarinf(e_eq1)
for e_eq2 in e_eqs:
e_eqs_.append(e_eq2)
sv2 = na_chan.computeVarinf(e_eq2)
svs.append({'m': sv2['m'], 'h': sv1['h']})
# compute sodium impedance matrices
z_mats_na = []
for ii, sv in enumerate(svs):
greens_tree_na.setEEq(e_eqs[ii % len(e_eqs)])
greens_tree_na[1].setExpansionPoint('Na_Ta', sv)
greens_tree_na.setCompTree()
greens_tree_na.setImpedance(freqs)
z_mats_na.append(greens_tree_na.calcImpedanceMatrix(locs))
# passive fit
ctree.computeGMC(z_mat_pas)
# get SOV constants for capacitance fit
sov_tree = greens_tree_pas.__copy__(new_tree=SOVTree())
sov_tree.setCompTree()
sov_tree.calcSOVEquations()
alphas, phimat, importance = sov_tree.getImportantModes(
locarg=locs,
sort_type='importance',
eps=1e-12,
return_importance=True)
# fit the capacitances from SOV time-scales
ctree.computeC(-alphas[0:1].real * 1e3,
phimat[0:1, :].real,
weights=importance[0:1])
# potassium channel fit
for z_mat_k, e_eq in zip(z_mats_k, e_eqs):
ctree.computeGSingleChanFromImpedance('Kv3_1',
z_mat_k,
e_eq,
freqs,
other_channel_names=['L'])
ctree.runFit()
# sodium channel fit
for z_mat_na, e_eq, sv in zip(z_mats_na, e_eqs_, svs):
ctree.computeGSingleChanFromImpedance('Na_Ta',
z_mat_na,
e_eq,
freqs,
sv=sv,
other_channel_names=['L'])
ctree.runFit()
ctree.setEEq(-75.)
ctree.removeExpansionPoints()
ctree.fitEL()
self.ctree = ctree
def testFitModel(self):
self.loadTTree()
fit_locs = [(1, .5), (4, 1.), (5, .5), (8, .5)]
# fit a tree directly from CompartmentTree
greens_tree = self.tree.__copy__(new_tree=GreensTree())
greens_tree.setCompTree()
freqs = np.array([0.])
greens_tree.setImpedance(freqs)
z_mat = greens_tree.calcImpedanceMatrix(fit_locs)[0]
ctree = greens_tree.createCompartmentTree(fit_locs)
ctree.computeGMC(z_mat)
sov_tree = self.tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
alphas, phimat = sov_tree.getImportantModes(locarg=fit_locs)
ctree.computeC(-alphas[0:1].real * 1e3, phimat[0:1, :].real)
# fit a tree with compartmentfitter
cm = CompartmentFitter(self.tree)
ctree_cm = cm.fitModel(fit_locs)
# compare the two trees
self._checkPasCondProps(ctree_cm, ctree)
self._checkPasCaProps(ctree_cm, ctree)
self._checkEL(ctree_cm, -75.)
# check active channel
self.fitBall()
locs = [(1, 0.5)]
cm = CompartmentFitter(self.tree)
ctree_cm_1 = cm.fitModel(locs,
parallel=False,
use_all_channels_for_passive=False)
ctree_cm_2 = cm.fitModel(locs,
parallel=False,
use_all_channels_for_passive=True)
self._checkAllCurrProps(self.ctree, ctree_cm_1)
self._checkAllCurrProps(self.ctree, ctree_cm_2)
def testPickling(self):
self.loadBall()
# of PhysTree
ss = pickle.dumps(self.tree)
pt_ = pickle.loads(ss)
self._checkPhysTrees(self.tree, pt_)
# of GreensTree
greens_tree = self.tree.__copy__(new_tree=GreensTree())
greens_tree.setCompTree()
freqs = np.array([0.])
greens_tree.setImpedance(freqs)
ss = dill.dumps(greens_tree)
gt_ = dill.loads(ss)
self._checkPhysTrees(greens_tree, gt_)
# of SOVTree
sov_tree = self.tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
# fails with pickle (lambda functions)
with pytest.raises(AttributeError):
ss = pickle.dumps(sov_tree)
# works with dill
ss = dill.dumps(sov_tree)
st_ = dill.loads(ss)
self._checkPhysTrees(sov_tree, st_)
def testParallel(self, w_benchmark=False):
self.loadTSegmentTree()
locs = [(nn.index, 0.5) for nn in self.tree.nodes[:30]]
cm = CompartmentFitter(self.tree)
ctree_cm = cm.fitModel(locs,
parallel=False,
use_all_channels_for_passive=True)
if w_benchmark:
from timeit import default_timer as timer
t0 = timer()
cm.fitChannels(recompute=False, pprint=False, parallel=False)
t1 = timer()
print('Not parallel: %.8f s' % (t1 - t0))
t0 = timer()
cm.fitChannels(recompute=False, pprint=False, parallel=True)
t1 = timer()
print('Parallel: %.8f s' % (t1 - t0))