def setUpClass(cls):
# Load model, simulation etc. only once
fname = os.path.join(DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
# Extract the Markov model of INa
parameters = ['ina.p1', 'ina.p2']
cls._boundaries = [[1e-3, 10], [1e-3, 10]]
markov_model = markov.LinearModel.from_component(
model.get('ina'),
parameters=parameters,
current='ina.i',
)
# Create an analytical markov model simulation
cls._sim = markov.AnalyticalSimulation(markov_model)
# Voltages to test at
r = 10
cls._voltages = np.arange(-70, -10 + r, r)
# Times to evaluate at
times = np.concatenate((np.linspace(0, 4, 100), np.linspace(4, 8, 20)))
# Generate reference traces
cls._references = []
for v in cls._voltages:
cls._sim.set_membrane_potential(v)
x, i = cls._sim.solve(times)
cls._references.append(i)
# Give a hint
# p1 = 0.1027 / 3.802 ~ 0.0270
# p2 = 0.20 / 3.802 ~ 0.0526
cls._hint = [0.0269, 0.052]
def create_manual(self):
"""
Tests manual creation of a Markov model
"""
# Load model
fname = os.path.join(myotest.DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
# Select a number of states and parameters
states = [
'ina.C3',
'ina.C2',
'ina.C1',
'ina.IF',
'ina.IS',
'ina.O',
]
parameters = [
'ina.p1',
'ina.p2',
'ina.p3',
'ina.p4',
]
current = 'ina.i'
# Create a markov model
m = markov.LinearModel(model, states, parameters, current)
# Create a simulation
s = markov.AnalyticalSimulation(m)
# Times to evaluate at
times = np.linspace(0, 100, 5)
# Voltages to test at
voltages = np.arange(-70, 0, 30)
# Generate traces
for v in voltages:
s.set_membrane_potential(v)
x, i = s.solve(times)
def test_analytical_simulation_properties(self):
# Test basic get/set methods of analytical simulation.
# Create a simulation
fname = os.path.join(DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
m = markov.LinearModel.from_component(model.get('ina'))
s = markov.AnalyticalSimulation(m)
# membrane potential
self.assertEqual(s.membrane_potential(),
model.get('membrane.V').eval())
s.set_membrane_potential(10)
self.assertEqual(s.membrane_potential(), 10)
# Parameter values
p = list(range(len(s.parameters())))
self.assertNotEqual(p, s.parameters())
s.set_parameters(p)
self.assertEqual(p, s.parameters())
self.assertRaises(ValueError, s.set_parameters, p[:-1])
# Change parameter with set_constant
p[3] += 1
self.assertNotEqual(p, s.parameters())
s.set_constant(m.parameters()[3], p[3])
self.assertEqual(p, s.parameters())
# State
state = np.zeros(len(s.state()))
state[0] = 0.5
state[1] = 0.5
self.assertNotEqual(list(state), list(s.state()))
s.set_state(state)
self.assertEqual(list(state), list(s.state()))
self.assertRaisesRegex(ValueError, 'Wrong size', s.set_state,
state[:-1])
state[0] += 0.1
self.assertRaisesRegex(ValueError, 'sum to 1', s.set_state, state)
state[0] = -.1
state[1] = 1.1
self.assertRaisesRegex(ValueError, 'negative', s.set_state, state)
# Default state
dstate = np.zeros(len(s.default_state()))
dstate[0] = 0.5
dstate[1] = 0.5
self.assertNotEqual(list(dstate), list(s.default_state()))
s.set_default_state(dstate)
self.assertEqual(list(dstate), list(s.default_state()))
self.assertRaisesRegex(ValueError, 'Wrong size', s.set_default_state,
dstate[:-1])
dstate[0] += 0.1
self.assertRaisesRegex(ValueError, 'sum to 1', s.set_default_state,
dstate)
dstate[0] = -.1
dstate[1] = 1.1
self.assertRaisesRegex(ValueError, 'negative', s.set_default_state,
dstate)
def __init__(self, name):
super(FittingTest, self).__init__(name)
# Load model, simulation etc. only once
fname = os.path.join(myotest.DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
# Extract the Markov model of INa
parameters = ['ina.p1', 'ina.p2']
self._boundaries = [[1e-3, 10], [1e-3, 10]]
markov_model = markov.LinearModel.from_component(
model.get('ina'),
parameters=parameters,
current='ina.i',
)
# Create an analytical markov model simulation
self._sim = markov.AnalyticalSimulation(markov_model)
# Voltages to test at
r = 10
self._voltages = np.arange(-70, -10 + r, r)
# Times to evaluate at
times = np.concatenate((np.linspace(0, 4, 100), np.linspace(4, 8, 20)))
# Generate reference traces
self._references = []
for v in self._voltages:
self._sim.set_membrane_potential(v)
x, i = self._sim.solve(times)
self._references.append(i)
# Define function to optimize
def score(guess):
try:
error = 0
self._sim.set_parameters(guess)
for k, v in enumerate(self._voltages):
self._sim.set_membrane_potential(v)
x, i = self._sim.solve(times)
r = self._references[k]
rmax = np.max(np.abs(r))
error += np.sqrt(np.sum(((i - r) / rmax) ** 2))
return error / len(self._voltages)
except Exception:
return float('inf')
self._score = score
# Give a hint
self._hint = [0.03, 0.05]
def test_against_cvode(self):
# Validate against a cvode sim.
# Get a model
fname = os.path.join(DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
# Create a protocol
vs = [-30, -20, -10]
p = myokit.pacing.steptrain(
vsteps=vs,
vhold=-120,
tpre=8,
tstep=2,
tpost=0)
t = p.characteristic_time()
# Run an analytical simulation
dt = 0.01
m = markov.LinearModel.from_component(model.get('ina'))
s1 = markov.AnalyticalSimulation(m, p)
d1 = s1.run(t, log_interval=dt).npview()
s2 = myokit.Simulation(model, p)
s2.set_tolerance(1e-8, 1e-8)
d2 = s2.run(t, log_interval=dt).npview()
# Test protocol output is the same
e = np.abs(d1['membrane.V'] - d2['membrane.V'])
if False:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(d1['membrane.V'])
plt.plot(d2['membrane.V'])
plt.show()
self.assertEqual(np.max(e), 0)
# Test current output is very similar
e = np.abs(d1['ina.i'] - d2['ina.i'])
if False:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(d1['ina.i'])
plt.plot(d2['ina.i'])
plt.figure()
plt.plot(d1['ina.i'] - d2['ina.i'])
plt.show()
self.assertLess(np.max(e), 2e-4)
def create_automatic(self):
# Load model
fname = os.path.join(myotest.DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
# Create a markov model
m = markov.LinearModel.from_component(model.get('ina'))
# Create a simulation
s = markov.AnalyticalSimulation(m)
# Times to evaluate at
times = np.linspace(0, 100, 5)
# Voltages to test at
voltages = np.arange(-70, 0, 30)
# Generate traces
for v in voltages:
s.set_membrane_potential(v)
x, i = s.solve(times)
def test_create_and_run(self):
# Test basics of analytical simulation
# Create a simulation
fname = os.path.join(DIR_DATA, 'clancy-1999-fitting.mmt')
model = myokit.load_model(fname)
m = markov.LinearModel.from_component(model.get('ina'))
# Bad constructors
self.assertRaisesRegex(ValueError, 'LinearModel',
markov.AnalyticalSimulation, 1)
self.assertRaisesRegex(ValueError, 'Protocol',
markov.AnalyticalSimulation, m, 1)
# Create properly
s = markov.AnalyticalSimulation(m)
# Times to evaluate at
times = np.linspace(0, 100, 5)
# Voltages to test at
voltages = np.arange(-70, 0, 30)
# Generate traces with "solve" method
state = s.state()
dstate = s.default_state()
for v in voltages:
s.set_membrane_potential(v)
x, i = s.solve(times)
# Solve shouldn't change the state
self.assertEqual(state, s.state())
self.assertEqual(dstate, s.default_state())
# Run for a bit
self.assertIsInstance(s.run(10), myokit.DataLog)
# Calculate current for a particular state
self.assertIsInstance(s.current(s.state()), float)
# No current variable? Then current can't be calculated
model2 = model.clone()
model2.get('ina').remove_variable(model2.get('ina.i'))
m2 = markov.LinearModel.from_component(model2.get('ina'))
s2 = markov.AnalyticalSimulation(m2)
self.assertRaisesRegex(Exception, 'did not specify a current',
s2.current, s2.state())
# But simulation still works
self.assertIsInstance(s2.run(10), myokit.DataLog)
del (model2, m2, s2)
# Create protocol
# Protocol times: prep, step, post, full
tprep = 2800
tstep = 15
tpost = 0
# Step voltages
vhold = -80
vlo = -140
vhi = 100
res = 50
v = np.arange(vlo, vhi + res, res)
p = myokit.pacing.steptrain(v, vhold, tprep, tstep, tpost)
t = p.characteristic_time()
# Create simulation with protocol (set_protocol is not supported)
s = markov.AnalyticalSimulation(m, p)
# Membrane potential and protocol can't be used simultaneously
self.assertRaisesRegex(Exception, 'cannot be set if',
s.set_membrane_potential, -80)
# Pre should change the state and default state
state = s.state()
dstate = s.default_state()
s.pre(tprep + tstep)
self.assertNotEqual(state, s.state())
self.assertNotEqual(dstate, s.default_state())
self.assertRaises(ValueError, s.pre, -1)
# Run should change the state, not the default state
state = s.state()
dstate = s.default_state()
d = s.run(t)
self.assertNotEqual(state, s.state())
self.assertEqual(dstate, s.default_state())
self.assertRaisesRegex(ValueError, 'Duration', s.run, -1)
self.assertRaisesRegex(ValueError,
'Log interval',
s.run,
1,
log_interval=-1)
d['hello'] = [1, 2, 3]
self.assertRaisesRegex(ValueError, 'extra keys', s.run, 1, log=d)
del (d['hello'])
del (d[next(iter(d.keys()))])
self.assertRaisesRegex(ValueError, 'missing', s.run, 1, log=d)
# Reset should reset the state
s.reset()
self.assertEqual(state, s.state())
# Run can append to log
d = s.run(10)
n = len(d['engine.time'])
e = s.run(1, log=d)
self.assertIs(d, e)
self.assertTrue(len(d['engine.time']) > n)
