def loadTTreeTestChannel(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
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1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
test_chan = channelcollection.TestChannel2()
fname = os.path.join(MORPHOLOGIES_PATH_PREFIX, 'Tsovtree.swc')
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan, 50., -23.)
self.greenstree.fitLeakCurrent(v_eq, 10.)
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def 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
# test if channels storage is empty
assert len(tree.channel_storage) == 0
# test if computational root was removed
assert tree._computational_root is None
def loadTTreeTestChannelSoma(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.025
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
test_chan = channelcollection.TestChannel2()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(test_chan,
50.,
23.,
node_arg=[self.greenstree[1]])
self.greenstree.fitLeakCurrent(v_eq, 10.)
# for node in self.greenstree:
# print node.getGTot(channel_storage=self.greenstree.channel_storage)
# print node.currents
self.greenstree.setCompTree()
self.greenstree.setImpedance(self.ft.s)
# copy greenstree parameters into NEURON simulation tree
self.neurontree = NeuronSimTree(dt=self.dt,
t_calibrate=100.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def 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)
