def _createGreensTrees(self):
"""
_1 -> decrease in s2d attenuation in control compared to zd
_2 -> increase in s2d attenuation in control compared to zd
"""
sim_tree_zd = self.model_evaluator.getTreeWithParams()
sim_tree_wh_1 = self.att_evaluator_1.getTreeWithParams()
sim_tree_wh_2 = self.att_evaluator_2.getTreeWithParams()
self.greens_tree_zd = sim_tree_zd.__copy__(new_tree=GreensTree())
self.greens_tree_wh_1 = sim_tree_wh_1.__copy__(new_tree=GreensTree())
self.greens_tree_wh_2 = sim_tree_wh_2.__copy__(new_tree=GreensTree())
# for n1, n2, n3 in zip(self.greens_tree_zd, self.greens_tree_wh_1, self.greens_tree_wh_2): print(n1.e_eq, n2.e_eq, n3.e_eq)
# self.greens_tree_zd.setEEq(-60.)
# self.greens_tree_wh_1.setEEq(-60.)
# self.greens_tree_wh_2.setEEq(-60.)
self.greens_tree_zd.setCompTree()
self.greens_tree_wh_1.setCompTree()
self.greens_tree_wh_2.setCompTree()
self.greens_tree_zd.setImpedance(np.array([0.]))
self.greens_tree_wh_1.setImpedance(np.array([0.]))
self.greens_tree_wh_2.setImpedance(np.array([0.]))
def createPointNeurons(self, v_eq=-75.):
self.v_eq = v_eq
self.dt = .025
gh, eh = 50., -43.
h_chan = channelcollection.h()
self.greens_tree = GreensTree(
file_n=os.path.join(MORPHOLOGIES_PATH_PREFIX, 'ball.swc'))
self.greens_tree.setPhysiology(1., 100. / 1e6)
self.greens_tree.addCurrent(h_chan, gh, eh)
self.greens_tree.fitLeakCurrent(v_eq, 10.)
self.greens_tree.setEEq(v_eq)
self.greens_tree_pas = self.greens_tree.__copy__(new_tree=GreensTree())
self.greens_tree_pas.asPassiveMembrane()
self.sim_tree = self.greens_tree.__copy__(new_tree=NeuronSimTree())
# set the impedances
self.greens_tree_pas.setCompTree()
self.freqs = np.array([0.])
self.greens_tree_pas.setImpedance(self.freqs)
# create sov tree
self.sov_tree = self.greens_tree_pas.__copy__(new_tree=SOVTree())
self.sov_tree.calcSOVEquations(maxspace_freq=50.)
z_inp = self.greens_tree_pas.calcZF((1, .5), (1, .5))[0]
alphas, gammas = self.sov_tree.getSOVMatrices(locarg=[(1., .5)])
# create NET
node_0 = NETNode(0, [0], [0], z_kernel=(alphas, gammas[:, 0]**2))
net_py = NET()
net_py.setRoot(node_0)
# check if correct
assert np.abs(gammas[0, 0]**2 / np.abs(alphas[0]) - z_inp) < 1e-10
assert np.abs(node_0.z_bar - z_inp) < 1e-10
# to initialize neuron tree
self.sim_tree.initModel(dt=self.dt)
# add ion channel to NET simulator
a_soma = 4. * np.pi * (self.sim_tree[1].R * 1e-4)**2
self.cnet = netsim.NETSim(net_py, v_eq=self.v_eq)
hchan = channelcollection.h()
self.cnet.addChannel(hchan, 0, gh * a_soma, eh)
# add the synapse
# to neuron tree
self.sim_tree.addDoubleExpSynapse((1, .5), .2, 3., 0.)
self.sim_tree.setSpikeTrain(0, 0.001, [5.])
# to net sim
self.cnet.addSynapse(0, {
'tau_r': .2,
'tau_d': 3.,
'e_r': 0.
},
g_max=0.001)
self.cnet.setSpikeTimes(0, [5. + self.dt])
def loadTTreeTestChannel(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
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 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 = pickle.dumps(greens_tree)
gt_ = pickle.loads(ss)
self._checkPhysTrees(greens_tree, gt_)
# of SOVTree
sov_tree = self.tree.__copy__(new_tree=SOVTree())
sov_tree.calcSOVEquations()
# works with pickle
ss = pickle.dumps(sov_tree)
st_ = pickle.loads(ss)
self._checkPhysTrees(sov_tree, st_)
def loadTTreeActive(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 <<<'
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1,3,4])
self.greenstree.addCurrent('h', 50., -43.)
self.greenstree.fitLeakCurrent(e_eq_target=v_eq, tau_m_target=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 = neurm.NeuronSimTree(dt=self.dt, t_calibrate=10., v_eq=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
def loadTTreeActive(self):
"""
Load the T-tree morphology in memory with h-current
6--5--4--7--8
|
|
1
"""
v_eq = -75.
self.dt = 0.1
self.tmax = 100.
# for frequency derivation
self.ft = ke.FourrierTools(np.arange(0., self.tmax, self.dt))
# load the morphology
print('>>> loading T-tree <<<')
h_chan = channelcollection.h()
fname = 'test_morphologies/Tsovtree.swc'
self.greenstree = GreensTree(fname, types=[1, 3, 4])
self.greenstree.addCurrent(h_chan, 50., -43.)
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=10.,
v_init=v_eq,
factor_lambda=25.)
self.greenstree.__copy__(self.neurontree)
self.neurontree.treetype = 'computational'
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_chans_for_passive=False)
ctree_cm_2 = cm.fitModel(locs, parallel=False, use_all_chans_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 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 loadValidationTree(self):
'''
Load the T-tree morphology in memory
5---1---4
'''
print '>>> loading validation tree <<<'
fname = 'test_morphologies/sovvalidationtree.swc'
self.tree = GreensTree(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 = GreensTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
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 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 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 = GreensTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(-75., 10.)
self.tree.setCompTree()
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 = GreensTree(fname, types=[1,3,4])
self.tree.fitLeakCurrent(e_eq_target=-75., tau_m_target=10.)
self.tree.setCompTree()
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 getGreensTree(morph_name,
physiology_type='Pas',
recompute=False,
freqs=None):
if freqs is None:
freqs = np.array([0.])
if morph_name[-4:] == '.swc':
morph_name = morph_name[:-4]
# load a greenstree
greens_tree = GreensTree(file_n='morph/' + morph_name + '.swc')
# set the physiological parameters
eval('setPhysiology' + physiology_type + '(greens_tree)')
# set the computational tree
greens_tree.setCompTree(eps=1.)
# set the impedance
greens_tree.setImpedance(freqs)
return greens_tree
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 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 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 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 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 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)
