def set_state(self):
m, p, x = myokit.load(os.path.join(myotest.DIR_DATA, 'lr-1991.mmt'))
n = 4
s = myokit.Simulation1d(m, p, n)
sm = m.state()
ss = [s.state(x) for x in xrange(n)]
for si in ss:
self.assertEqual(sm, si)
# Test setting a single, global state
sx = [0] * 8
self.assertNotEqual(sm, sx)
s.set_state(sx)
for i in xrange(n):
self.assertEqual(sx, s.state(i))
self.assertEqual(sx * n, s.state())
s.set_state(sm)
self.assertEqual(sm * n, s.state())
# Test setting a single state
j = 1
s.set_state(sx, j)
for i in xrange(n):
if i == j:
self.assertEqual(s.state(i), sx)
else:
self.assertEqual(s.state(i), sm)
def test_with_progress_reporter(self):
# Test running with a progress reporter.
m, p, _ = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
# Test using a progress reporter
s = myokit.Simulation1d(m, p, ncells=5)
s.set_step_size(0.05)
with myokit.PyCapture() as c:
s.run(110, progress=myokit.ProgressPrinter())
c = c.text().splitlines()
self.assertTrue(len(c) > 0)
# Not a progress reporter
self.assertRaisesRegex(
ValueError, 'ProgressReporter', s.run, 5, progress=12)
# Cancel from reporter
self.assertRaises(
myokit.SimulationCancelledError, s.run, 1,
progress=CancellingReporter(0))
def test_set_default_state(self):
# Test :meth:`Simulation1d.set_default_state()`.
m, p, _ = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
n = 4
s = myokit.Simulation1d(m, p, n)
self.assertEqual(s.state(), m.state() * n)
# Test setting a full state
sx = [0] * 8 * n
self.assertNotEqual(sx, s.default_state())
s.set_default_state(sx)
self.assertEqual(sx, s.default_state())
# Test setting a single, global state
sx = [0] * 8
s.set_default_state(sx)
self.assertEqual(s.default_state(), sx * n)
s.set_default_state(m.state())
self.assertEqual(s.default_state(), m.state() * n)
# Test setting a single state
j = 1
s.set_default_state(sx, j)
for i in range(n):
if i == j:
self.assertEqual(s.default_state(i), sx)
else:
self.assertEqual(s.default_state(i), m.state())
# Invalid cell index
s.set_default_state(sx, 0)
self.assertRaises(ValueError, s.set_default_state, sx, -1)
self.assertRaises(ValueError, s.set_default_state, sx, n)
self.assertRaises(ValueError, s.default_state, -1)
self.assertRaises(ValueError, s.default_state, n)
def test_simple_run(self):
# Compare the SimulationOpenCL output with Simulation1d output
# Set up simulations
m = myokit.load_model(os.path.join(DIR_DATA, 'beeler-1977-model.mmt'))
m.get('membrane.i_stim.amplitude').set_rhs(-80)
p = myokit.pacing.blocktrain(period=1000, duration=2, offset=3)
n = 10
sa = myokit.Simulation1d(m, p, ncells=n)
sb = myokit.SimulationOpenCL(m,
p,
ncells=n,
precision=myokit.DOUBLE_PRECISION)
dt = 0.005
sa.set_step_size(dt)
sb.set_step_size(dt)
sa.set_paced_cells(2)
sb.set_paced_cells(2)
tmax = 15
logvars = [
'engine.time',
'membrane.V',
'engine.pace',
'isi.Isi',
'membrane.i_diff',
]
da = sa.run(tmax, log=logvars, log_interval=0.5).npview()
db = sb.run(tmax, log=logvars, log_interval=0.5).npview()
# Show results
if debug:
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(12, 9))
fig.subplots_adjust(0.08, 0.08, 0.98, 0.98, 0.5, 0.3)
ax = fig.add_subplot(2, 4, 1)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('V')
ax.plot(da.time(), da['membrane.V', 0], '-', lw=2, alpha=0.5)
ax.plot(da.time(), da['membrane.V', 9], '-', lw=2, alpha=0.5)
ax.plot(db.time(), db['membrane.V', 0], '--')
ax.plot(db.time(), db['membrane.V', 9], '--')
ax = fig.add_subplot(2, 4, 2)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Idiff')
ax.plot(da.time(), da['membrane.i_diff', 0], '-', lw=2, alpha=0.5)
ax.plot(da.time(), da['membrane.i_diff', 9], '-', lw=2, alpha=0.5)
ax.plot(db.time(), db['membrane.i_diff', 0], '--')
ax.plot(db.time(), db['membrane.i_diff', 9], '--')
ax = fig.add_subplot(2, 4, 3)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Isi')
ax.plot(da.time(), da['isi.Isi', 0], '-', lw=2, alpha=0.5)
ax.plot(da.time(), da['isi.Isi', 9], '-', lw=2, alpha=0.5)
ax.plot(db.time(), db['isi.Isi', 0], '--')
ax.plot(db.time(), db['isi.Isi', 9], '--')
ax = fig.add_subplot(2, 4, 4)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('pace')
ax.plot(da.time(), da['engine.pace'], '-', lw=2, alpha=0.5)
ax.plot(db.time(), db['engine.pace'], '--')
ax = fig.add_subplot(2, 4, 5)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('V error')
ax.plot(da.time(), da['membrane.V', 0] - db['membrane.V', 0])
ax.plot(da.time(), da['membrane.V', 9] - db['membrane.V', 9])
ax = fig.add_subplot(2, 4, 6)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Idiff error')
ax.plot(da.time(),
da['membrane.i_diff', 0] - db['membrane.i_diff', 0])
ax.plot(da.time(),
da['membrane.i_diff', 9] - db['membrane.i_diff', 9])
ax = fig.add_subplot(2, 4, 7)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('Isi error')
ax.plot(da.time(), da['isi.Isi', 0] - db['isi.Isi', 0])
ax.plot(da.time(), da['isi.Isi', 9] - db['isi.Isi', 9])
ax = fig.add_subplot(2, 4, 8)
ax.set_xlabel('Time (ms)')
ax.set_ylabel('pace error')
ax.plot(da.time(), da['engine.pace'] - db['engine.pace'])
plt.show()
e = np.abs(da.time() - db.time())
self.assertLess(np.max(e), 1e-17)
e = np.abs(da['engine.pace'] - db['engine.pace'])
self.assertLess(np.max(e), 1e-17)
e = np.abs(da['membrane.V', 0] - db['membrane.V', 0])
self.assertLess(np.max(e), 1e-13)
e = np.abs(da['membrane.V', 9] - db['membrane.V', 9])
self.assertLess(np.max(e), 1e-13)
e = np.abs(da['membrane.i_diff', 0] - db['membrane.i_diff', 0])
self.assertLess(np.max(e), 1e-13)
e = np.abs(da['membrane.i_diff', 9] - db['membrane.i_diff', 9])
self.assertLess(np.max(e), 1e-13)
e = np.abs(da['isi.Isi', 0] - db['isi.Isi', 0])
self.assertLess(np.max(e), 1e-13)
e = np.abs(da['isi.Isi', 9] - db['isi.Isi', 9])
self.assertLess(np.max(e), 1e-13)
def test_basic(self):
# Test basic usage.
# Load model
m, p, _ = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
# Run a simulation
ncells = 5
s = myokit.Simulation1d(m, p, ncells=ncells)
s.set_step_size(0.05)
self.assertEqual(s.time(), 0)
self.assertEqual(s.state(0), m.state())
self.assertEqual(s.default_state(0), m.state())
d = s.run(5, log_interval=1)
self.assertEqual(s.time(), 5)
self.assertNotEqual(s.state(0), m.state())
self.assertEqual(s.default_state(0), m.state())
# Test full state getting and reset
self.assertEqual(s.default_state(), m.state() * ncells)
self.assertNotEqual(s.state(), m.state() * ncells)
s.reset()
self.assertEqual(s.state(), s.default_state())
# Test pre updates the default state.
s.pre(1)
self.assertNotEqual(s.default_state(0), m.state())
# Test running without a protocol
s.set_protocol(None)
s.run(1)
# Simulation time can't be negative
self.assertRaises(ValueError, s.run, -1)
# Number of cells must be >0
self.assertRaisesRegex(
ValueError, 'number of cells', myokit.Simulation1d, m, p, 0)
# Model must have a membrane potential
v = m.get('membrane.V')
v.set_label(None)
self.assertRaisesRegex(
ValueError, 'membrane_potential', myokit.Simulation1d, m, p, 5)
v.set_label('membrane_potential')
# Model must have a diffusion current
d = m.binding('diffusion_current')
d.set_binding(None)
self.assertRaisesRegex(
ValueError, 'diffusion_current', myokit.Simulation1d, m, p, 5)
# Test setting conductance
s.set_conductance(10)
self.assertEqual(s.conductance(), 10)
self.assertRaises(ValueError, s.set_conductance, -1)
# Test setting paced cells
s.set_paced_cells(1)
self.assertEqual(s.paced_cells(), 1)
self.assertRaises(ValueError, s.set_paced_cells, -1)
# Test setting step size
s.set_step_size(0.01)
self.assertRaises(ValueError, s.set_step_size, 0)
# Test setting time
self.assertNotEqual(s.time(), 100)
s.set_time(100)
self.assertEqual(s.time(), 100)
def test_against_cvode(self):
# Compare the Simulation1d output with CVODE output
# Load model and event with appropriate level/duration
m, p, _ = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
e = p.head()
#
# Make protocol to compare t=0 event implementations
#
dt = 0.1 # Note: this is too big to be very accurate
tmax = 1300
p = myokit.Protocol()
p.schedule(level=e.level(), duration=e.duration(), period=600, start=0)
logvars = ['engine.time', 'membrane.V', 'engine.pace', 'ica.ICa']
s1 = myokit.Simulation1d(m, p, ncells=1, rl=True)
s1.set_step_size(dt)
d1 = s1.run(tmax, logvars, log_interval=dt).npview()
s2 = myokit.Simulation(m, p)
s2.set_tolerance(1e-8, 1e-8)
d2 = s2.run(tmax, logvars, log_interval=dt).npview()
# Check implementation of logging point selection
e0 = np.max(np.abs(d1.time() - d2.time()))
# Check implementation of pacing
r1 = d1['engine.pace', 0] - d2['engine.pace']
e1 = np.sum(r1**2)
# Check membrane potential (will have some error!)
# Using MRMS from Marsh, Ziaratgahi, Spiteri 2012
r2 = d1['membrane.V', 0] - d2['membrane.V']
r2 /= (1 + np.abs(d2['membrane.V']))
e2 = np.sqrt(np.sum(r2**2) / len(r2))
# Check logging of intermediary variables
r3 = d1['ica.ICa', 0] - d2['ica.ICa']
r3 /= (1 + np.abs(d2['ica.ICa']))
e3 = np.sqrt(np.sum(r3**2) / len(r3))
if debug:
import matplotlib.pyplot as plt
print('Event at t=0')
plt.figure()
plt.suptitle('Time points')
plt.plot(d1.time(), label='Euler')
plt.plot(d1.time(), label='CVODE')
plt.legend()
print(d1.time()[:7])
print(d2.time()[:7])
print(d1.time()[-7:])
print(d2.time()[-7:])
print(e0)
plt.figure()
plt.suptitle('Pacing signals')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['engine.pace', 0], label='Euler')
plt.plot(d2.time(), d2['engine.pace'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r1)
print(e1)
plt.figure()
plt.suptitle('Membrane potential')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['membrane.V', 0], label='Euler')
plt.plot(d2.time(), d2['membrane.V'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e2)
plt.figure()
plt.suptitle('Calcium current')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['ica.ICa', 0], label='Euler')
plt.plot(d2.time(), d2['ica.ICa'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e3)
plt.show()
self.assertLess(e0, 1e-14)
self.assertLess(e1, 1e-14)
self.assertLess(e2, 0.1) # Note: The step size is really too big here
self.assertLess(e3, 0.1)
#
# Make protocol to compare with event at step-size multiple
#
dt = 0.1 # Note: this is too big to be very accurate
tmax = 1300
p = myokit.Protocol()
p.schedule(
level=e.level(),
duration=e.duration(),
period=600,
start=10)
logvars = ['engine.time', 'membrane.V', 'engine.pace', 'ica.ICa']
s1.reset()
s1.set_protocol(p)
s1.set_step_size(dt)
d1 = s1.run(tmax, logvars, log_interval=dt).npview()
s2.reset()
s2.set_protocol(p)
s2.set_tolerance(1e-8, 1e-8)
d2 = s2.run(tmax, logvars, log_interval=dt).npview()
# Check implementation of logging point selection
e0 = np.max(np.abs(d1.time() - d2.time()))
# Check implementation of pacing
r1 = d1['engine.pace', 0] - d2['engine.pace']
e1 = np.sum(r1**2)
# Check membrane potential (will have some error!)
# Using MRMS from Marsh, Ziaratgahi, Spiteri 2012
r2 = d1['membrane.V', 0] - d2['membrane.V']
r2 /= (1 + np.abs(d2['membrane.V']))
e2 = np.sqrt(np.sum(r2**2) / len(r2))
# Check logging of intermediary variables
r3 = d1['ica.ICa', 0] - d2['ica.ICa']
r3 /= (1 + np.abs(d2['ica.ICa']))
e3 = np.sqrt(np.sum(r3**2) / len(r3))
if debug:
import matplotlib.pyplot as plt
print('Event at t=0')
plt.figure()
plt.suptitle('Time points')
plt.plot(d1.time(), label='Euler')
plt.plot(d1.time(), label='CVODE')
plt.legend()
print(d1.time()[:7])
print(d2.time()[:7])
print(d1.time()[-7:])
print(d2.time()[-7:])
print(e0)
plt.figure()
plt.suptitle('Pacing signals')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['engine.pace', 0], label='Euler')
plt.plot(d2.time(), d2['engine.pace'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r1)
print(e1)
plt.figure()
plt.suptitle('Membrane potential')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['membrane.V', 0], label='Euler')
plt.plot(d2.time(), d2['membrane.V'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e2)
plt.figure()
plt.suptitle('Calcium current')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['ica.ICa', 0], label='Euler')
plt.plot(d2.time(), d2['ica.ICa'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e3)
plt.show()
self.assertLess(e0, 1e-14)
self.assertLess(e1, 1e-14)
self.assertLess(e2, 0.1) # Note: The step size is really too big here
self.assertLess(e3, 0.1)
#
# Make protocol to compare with event NOT at step-size multiple
#
dt = 0.1 # Note: this is too big to be very accurate
tmax = 1300
p = myokit.Protocol()
p.schedule(
level=e.level(),
duration=e.duration(),
period=600,
start=1.05)
logvars = ['engine.time', 'membrane.V', 'engine.pace', 'ica.ICa']
s1.reset()
s1.set_protocol(p)
s1.set_step_size(dt)
d1 = s1.run(tmax, logvars, log_interval=dt).npview()
s2.reset()
s2.set_protocol(p)
s2.set_tolerance(1e-8, 1e-8)
d2 = s2.run(tmax, logvars, log_interval=dt).npview()
# Check implementation of logging point selection
e0 = np.max(np.abs(d1.time() - d2.time()))
# Check implementation of pacing
r1 = d1['engine.pace', 0] - d2['engine.pace']
e1 = np.sum(r1**2)
# Check membrane potential (will have some error!)
# Using MRMS from Marsh, Ziaratgahi, Spiteri 2012
r2 = d1['membrane.V', 0] - d2['membrane.V']
r2 /= (1 + np.abs(d2['membrane.V']))
e2 = np.sqrt(np.sum(r2**2) / len(r2))
# Check logging of intermediary variables
r3 = d1['ica.ICa', 0] - d2['ica.ICa']
r3 /= (1 + np.abs(d2['ica.ICa']))
e3 = np.sqrt(np.sum(r3**2) / len(r3))
if debug:
import matplotlib.pyplot as plt
print('Event at t=0')
plt.figure()
plt.suptitle('Time points')
plt.plot(d1.time(), label='Euler')
plt.plot(d1.time(), label='CVODE')
plt.legend()
print(d1.time()[:7])
print(d2.time()[:7])
print(d1.time()[-7:])
print(d2.time()[-7:])
print(e0)
plt.figure()
plt.suptitle('Pacing signals')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['engine.pace', 0], label='Euler')
plt.plot(d2.time(), d2['engine.pace'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r1)
print(e1)
plt.figure()
plt.suptitle('Membrane potential')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['membrane.V', 0], label='Euler')
plt.plot(d2.time(), d2['membrane.V'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e2)
plt.figure()
plt.suptitle('Calcium current')
plt.subplot(2, 1, 1)
plt.plot(d1.time(), d1['ica.ICa', 0], label='Euler')
plt.plot(d2.time(), d2['ica.ICa'], label='CVODE')
plt.legend()
plt.subplot(2, 1, 2)
plt.plot(d1.time(), r2)
print(e3)
plt.show()
self.assertLess(e0, 1e-14)
self.assertLess(e1, 1e-14)
self.assertLess(e2, 0.2) # Note: The step size is really too big here
self.assertLess(e3, 0.2)
#
# Test again, with event at step multiple, but now without Rush-Larsen
#
dt = 0.01 # Note: this is too big to be very accurate
tmax = 200
p = myokit.Protocol()
p.schedule(
level=e.level(),
duration=e.duration(),
period=600,
start=10)
logvars = ['engine.time', 'membrane.V', 'engine.pace', 'ica.ICa']
s1 = myokit.Simulation1d(m, p, ncells=1, rl=False)
s1.set_step_size(dt)
d1 = s1.run(tmax, logvars, log_interval=dt).npview()
s2.reset()
s2.set_protocol(p)
s2.set_tolerance(1e-8, 1e-8)
d2 = s2.run(tmax, logvars, log_interval=dt).npview()
# Check membrane potential (will have some error!)
# Using MRMS from Marsh, Ziaratgahi, Spiteri 2012
r2 = d1['membrane.V', 0] - d2['membrane.V']
r2 /= (1 + np.abs(d2['membrane.V']))
e2 = np.sqrt(np.sum(r2**2) / len(r2))
self.assertLess(e2, 0.05) # Note: The step size is really too big here
# Check logging of intermediary variables
r3 = d1['ica.ICa', 0] - d2['ica.ICa']
r3 /= (1 + np.abs(d2['ica.ICa']))
e3 = np.sqrt(np.sum(r3**2) / len(r3))
self.assertLess(e3, 0.05)
def test_periodic(self):
# Test periodic logging.
m, p, x = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
s = myokit.Simulation1d(m, p, ncells=1)
self.periodic(s)
def sim_for_periodic(self):
m, p, x = myokit.load(os.path.join(myotest.DIR_DATA, 'lr-1991.mmt'))
return myokit.Simulation1d(m, p, ncells=1)