def save_all_summary_statistics(basename, ta, tr, ai, ri, iv):
"""
Saves summary statistics to files.
Arguments ``ta`` etc. must be tuples (v, stats).
"""
d = myokit.DataLog()
d['v'] = ta[0]
d['ta'] = ta[1]
d.save_csv(basename + '-ta.csv')
d = myokit.DataLog()
d['v'] = tr[0]
d['tr'] = tr[1]
d.save_csv(basename + '-tr.csv')
d = myokit.DataLog()
d['v'] = ai[0]
d['ai'] = ai[1]
d.save_csv(basename + '-ai.csv')
d = myokit.DataLog()
d['v'] = ri[0]
d['ri'] = ri[1]
d.save_csv(basename + '-ri.csv')
d = myokit.DataLog()
d['v'] = iv[0]
d['iv'] = iv[1]
d.save_csv(basename + '-iv.csv')
def test_from_data_log(self):
# Test some edge cases of `DataBlock2d.from_log`.
# Test valid construction
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = [1, 2, 3]
d['x', 0, 0] = [0, 0, 0]
d['x', 1, 0] = [1, 1, 2]
d['x', 0, 1] = [1, 2, 2]
d['x', 1, 1] = [3, 4, 5]
d['x', 0, 2] = [6, 6, 6]
d['x', 1, 2] = [7, 7, 2]
myokit.DataBlock2d.from_log(d)
# Deprecated alias
with WarningCollector() as wc:
myokit.DataBlock2d.from_DataLog(d)
self.assertIn('deprecated', wc.text())
# No time key
d.set_time_key(None)
self.assertRaises(ValueError, myokit.DataBlock2d.from_log, d)
# Bad time key
d.set_time_key('z')
# Multi-dimensional time key
d.set_time_key('0.0.x')
self.assertRaises(ValueError, myokit.DataBlock2d.from_log, d)
# 1-dimensional stuff
d.set_time_key('time')
myokit.DataBlock2d.from_log(d)
d['y', 0] = [10, 10, 10]
d['y', 1] = [20, 20, 20]
self.assertRaises(ValueError, myokit.DataBlock2d.from_log, d)
# Mismatched dimensions
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = [1, 2, 3]
d['x', 0, 0] = [0, 0, 0]
d['x', 1, 0] = [1, 1, 2]
d['x', 0, 1] = [1, 2, 2]
d['x', 1, 1] = [3, 4, 5]
d['x', 0, 2] = [6, 6, 6]
d['x', 1, 2] = [7, 7, 2]
d['y', 0, 0] = [0, 0, 0]
d['y', 1, 0] = [1, 1, 2]
d['y', 0, 1] = [1, 2, 2]
d['y', 1, 1] = [3, 4, 5]
d['y', 0, 2] = [6, 6, 6]
d['y', 1, 2] = [7, 7, 2]
myokit.DataBlock2d.from_log(d)
del (d['0.2.y'])
del (d['1.2.y'])
self.assertRaises(ValueError, myokit.DataBlock2d.from_log, d)
def test_cv(self):
# Test the CV method.
b = os.path.join(DIR_DATA, 'cv1d.zip')
b = myokit.DataBlock1d.load(b)
self.assertAlmostEqual(b.cv('membrane.V'), 5.95272837350686004e+01)
# Invalid border argument
# Negative
self.assertRaises(ValueError, b.cv, 'membrane.V', border=-1)
# Too large
self.assertRaises(ValueError, b.cv, 'membrane.V', border=1000)
# No upstroke
time = np.linspace(0, 1, 100)
down = np.zeros(100) - 85
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = time
d['x', 0] = down
d['x', 1] = down
d['x', 2] = down
d['x', 3] = down
d['x', 4] = down
b = myokit.DataBlock1d.from_log(d)
self.assertEqual(b.cv('x'), 0)
# No propagation: Single-cell stimulus
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = time
d['x', 0] = np.array(down, copy=True)
d['x', 1] = np.array(down, copy=True)
d['x', 2] = np.array(down, copy=True)
d['x', 3] = np.array(down, copy=True)
d['x', 4] = np.array(down, copy=True)
d['x', 1][10:] = 40
b = myokit.DataBlock1d.from_log(d)
self.assertEqual(b.cv('x'), 0)
# No propagation: Multi-cell stimulus
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = time
d['x', 0] = np.array(down, copy=True)
d['x', 1] = np.array(down, copy=True)
d['x', 2] = np.array(down, copy=True)
d['x', 3] = np.array(down, copy=True)
d['x', 4] = np.array(down, copy=True)
d['x', 1][10:] = 40
d['x', 2][10:] = 40
d['x', 3][10:] = 40
b = myokit.DataBlock1d.from_log(d)
self.assertEqual(b.cv('x'), 0)
def ratio(self):
"""
Returns the ratios of the peak conductances (p1 / p2) for step 1 and
step 2.
The returned value is a :class:`myokit.DataLog` with entries
corresponding to the given variable names.
"""
# Times to wait
twaits = np.exp(
np.linspace(np.log(self._tmin), np.log(self._tmax), self._nt))
# Variables to log
log_vars = [x.qname() for x in self._vars]
# Run simulations
self._vvar.set_rhs(self._vhold) # Make V a constant
s = myokit.Simulation(self._model)
log = myokit.DataLog()
gvars = [x.qname() for x in self._vars]
for g in gvars:
log[g] = []
log[self._tvar.qname()] = list(twaits)
for twait in twaits:
s.set_constant(self._vvar, self._vhold)
s.run(self._thold, log=myokit.LOG_NONE)
s.set_constant(self._vvar, self._vstep)
d1 = s.run(self._tstep1, log=log_vars)
s.set_constant(self._vvar, self._vhold)
s.run(twait, log=myokit.LOG_NONE)
s.set_constant(self._vvar, self._vstep)
d2 = s.run(self._tstep2, log=log_vars)
for g in gvars:
ratio = np.max(d1[g])
ratio = np.nan if ratio == 0 else np.max(d2[g]) / ratio
log[g].append(ratio)
return log
def test_simple(self):
# Test basic functionality.
# Create test model
m = myokit.Model('test')
c = m.add_component('c')
t = c.add_variable('time')
t.set_rhs('0')
t.set_binding('time')
v = c.add_variable('V')
v.set_rhs('0')
v.promote(-80.1)
x = c.add_variable('x')
x.set_rhs('exp(V)')
m.validate()
# Create simulation log
log = myokit.DataLog()
log['c.time'] = np.zeros(10)
log['c.V'] = np.linspace(-80.0, 50.0, 10)
# Number of repeats
repeats = 10
# Run
x.set_rhs('1 / (7 * exp((V + 12) / 35) + 9 * exp(-(V + 77) / 6))')
b = myokit.RhsBenchmarker(m, [x])
t = b.bench_full(log, repeats)
t = b.bench_part(log, repeats)
# No errors = pass
# Get mean and std after outlier removal
mean = b.mean(t)
mean, std = b.mean_std(t)
def log_for_interval(protocol, a, b):
"""
Copied from PacingSystem. This is useful for testing!
"""
# Test the input
a, b = float(a), float(b)
if b < a:
raise ValueError('The argument `b` cannot be smaller than `a`')
# Create a simulation log
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = time = []
log['pace'] = pace = []
# Create a pacing system
p = AnsicEventBasedPacing(protocol)
# Fill in points
t = a
v = p.advance(t)
time.append(t)
pace.append(v)
while t < b:
t = min(p.next_time(), b)
v = p.advance(t)
time.append(t)
pace.append(v)
return log
def create_log_for_times(self, times):
"""
Creates a :class:`myokit.DataLog` containing the entries `time`
and `pace` representing the value of the pacing stimulus at each point.
The time entries ``times`` must be an non-descreasing series of
non-negative points.
"""
# Create empty log
log = myokit.DataLog()
log.set_time_key('time')
# Times empty? Then return empty log
if len(times) == 0:
log['time'] = []
log['pace'] = []
return log
# Test times
times = np.asarray(times)
dt = times[1:] - times[:-1]
if np.any(dt < 0):
raise ValueError('The argument `times` must contain a'
' non-decreasing sequence of time points.')
if times[0] < 0:
raise ValueError('Times cannot be negative.')
# Create a pacing system and calculate the values
p = PacingSystem(self)
# Return log
log['time'] = list(times)
log['pace'] = [p.advance(t) for t in times]
return log
def test_from_log(self):
# Test some edge cases of `DataBlock1d.from_log`.
# Test valid construction
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = [1, 2, 3]
d['x', 0] = [0, 0, 0]
d['x', 1] = [1, 1, 2]
myokit.DataBlock1d.from_log(d)
# Deprecated alias
with WarningCollector() as wc:
myokit.DataBlock1d.from_DataLog(d)
self.assertIn('deprecated', wc.text())
# No time key
d = myokit.DataLog()
d['time'] = [1, 2, 3]
d['x', 0] = [0, 0, 0]
d['x', 1] = [1, 1, 2]
self.assertRaises(ValueError, myokit.DataBlock1d.from_log, d)
# Multi-dimensional time key
d.set_time_key('0.x')
self.assertRaises(ValueError, myokit.DataBlock1d.from_log, d)
# 2-dimensional stuff
d.set_time_key('time')
myokit.DataBlock1d.from_log(d)
d['y', 0, 0] = [10, 10, 10]
self.assertRaises(ValueError, myokit.DataBlock1d.from_log, d)
# Mismatched dimensions
d = myokit.DataLog()
d.set_time_key('time')
d['time'] = [1, 2, 3]
d['x', 0] = [0, 0, 0]
d['x', 1] = [1, 1, 2]
d['y', 0] = [2, 0, 0]
d['y', 1] = [3, 1, 2]
d['y', 2] = [0, 4, 5]
self.assertRaises(ValueError, myokit.DataBlock1d.from_log, d)
def myokit_log(self):
"""
Returns this file's time series data as a :class:`myokit.DataLog`.
"""
import myokit
log = myokit.DataLog()
if len(self._data) > 0:
log.set_time_key(next(iter(self._data.keys())))
for k, v in self._data.items():
log[k] = v
return log
def test_accessors(self):
# Test various accessor methods of :class:`AtfFile`.
with TemporaryDirectory() as d:
# Create data log
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = np.arange(10)
log['sint'] = np.sin(log['time'])
log['cost'] = np.cos(log['time'])
# Write atf file
path = d.path('test.atf')
axon.save_atf(log, path)
# Read atf file
atf = myokit.formats.axon.AtfFile(path)
# Test filename()
self.assertEqual(atf.filename(), path)
# Test iter and getitem
self.assertEqual(len(list(iter(atf))), 3)
self.assertTrue(np.all(atf['time'] == log['time']))
self.assertTrue(np.all(atf['sint'] == log['sint']))
self.assertTrue(np.all(atf['cost'] == log['cost']))
# Test items()
items = list(atf.items())
self.assertEqual(items[0][0], 'time')
self.assertEqual(items[1][0], 'sint')
self.assertEqual(items[2][0], 'cost')
self.assertTrue(np.all(items[0][1] == log['time']))
self.assertTrue(np.all(items[1][1] == log['sint']))
self.assertTrue(np.all(items[2][1] == log['cost']))
# Test keys()
self.assertEqual(list(atf.keys()), ['time', 'sint', 'cost'])
# Test values()
values = list(atf.values())
self.assertTrue(np.all(values[0] == log['time']))
self.assertTrue(np.all(values[1] == log['sint']))
self.assertTrue(np.all(values[2] == log['cost']))
# Test len
self.assertEqual(len(atf), 3)
# Test info
self.assertIn('myokit', atf.info())
# Test version
self.assertEqual(atf.version(), '1.0')
def log_for_times(self, times):
"""
Returns a :class:`myokit.DataLog` containing the entries ``time`` and
``pace`` representing the value of the pacing stimulus at each point.
The time entries ``times`` must be an non-descreasing series of
non-negative points.
"""
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = times
log['pace'] = self.value_at_times(times)
return log
def test_bad_log(self):
""" Test error handling when unsuitable log is used. """
# Create test model
m = myokit.Model('test')
c = m.add_component('c')
t = c.add_variable('time')
t.set_rhs('0')
t.set_binding('time')
v = c.add_variable('V')
v.set_rhs('0')
v.promote(-80.1)
w = c.add_variable('W')
w.set_rhs(0)
w.promote(10)
x = c.add_variable('x')
x.set_rhs('exp(V) + W')
m.validate()
# Create benchmarker
b = myokit.RhsBenchmarker(m, [x])
# Missing state variable
d = myokit.DataLog()
d['c.time'] = np.zeros(10)
#d['c.V'] = np.linspace(-80.0, 50.0, 10)
d['c.W'] = np.linspace(0.0, 10.0, 10)
self.assertRaisesRegex(
ValueError, 'State variable <c.V> not found', b.bench_full, d, 1)
# Different size log entries
d = myokit.DataLog()
d['c.time'] = np.zeros(1000)
d['c.V'] = np.linspace(-80.0, 50.0, 10)
d['c.W'] = np.linspace(0.0, 10.0, 11)
self.assertRaisesRegex(
ValueError, 'same length', b.bench_full, d, 1)
def log(self):
"""
Returns a myokit DataLog containing the data currently displayed in the
graph area.
"""
d = myokit.DataLog()
if self._data:
d['engine.time'] = self._data.time()
for index in self._frozen.iterkeys():
x, y, variable = index
d[variable, x, y] = self._data.trace(variable, x, y)
if self._temp_index:
x, y, variable = self._temp_index
d[variable, x, y] = self._data.trace(variable, x, y)
return d
def log_for_interval(self, a, b, for_drawing=False):
"""
Returns a :class:`myokit.DataLog` containing the entries ``time`` and
``pace``, representing the value of the pacing stimulus at each point
on the interval ``[a, b]``.
The time points in the log will be ``a`` and ``b``, and any time in
between at which the pacing value changes.
If ``for_drawing`` is set to ``True`` each time value where the
protocol changes will be listed twice, so that a vertical line can be
drawn from the old to the new pacing value.
Note that the points returned are from ``a`` to ``b`` inclusive (the
interval ``[a, b]``), and so if ``b`` coincides with the end of the
protocol a point ``(b, 0)`` will be included in the output (protocol
steps are defined as half-open, so include their starting point but not
their end point).
"""
# Test the input
a, b = float(a), float(b)
if b < a:
raise ValueError('The argument `b` cannot be smaller than `a`')
# Create a simulation log
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = time = []
log['pace'] = pace = []
# Create a pacing system
p = PacingSystem(self)
# Fill in points
t = a
v = p.advance(t)
time.append(t)
pace.append(v)
while t < b:
t = min(p.next_time(), b)
w = p.advance(t)
if for_drawing and v != w:
time.append(t)
pace.append(v)
v = w
time.append(t)
pace.append(v)
return log
def myokit_log(self):
"""
Creates and returns a:class:`myokit.DataLog` containing all the
data from this file.
Each channel is stored under its own name, with an indice indicating
the record it was from. Time is stored under ``time``.
"""
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = np.array(self._time)
for i, record in enumerate(self._records):
for j, data in enumerate(record):
name = self._channel_names[j]
log[name, i] = np.array(data)
return log
def simulate(self, parameters):
# Transform parameters back to model space
parameters = self.transformation.detransform(parameters)
# Run all simulations
logs = [0] * 4
for i, s in enumerate(self.simulations):
# Reset simulation
s.reset()
# Update simulation parameters
s.set_parameters(parameters)
# Run simulation
t = self.times[i]
try:
d = s.run(t[-1] + 0.1, log_times=t).npview()
except myokit.SimulationError:
return float('inf')
# Store in same format as experimental data
e = myokit.DataLog()
e.set_time_key('time')
e['time'] = d['engine.time']
e['current'] = d['ikr.IKr']
logs[i] = e
# Calculate summary statistics
try:
stats = sumstat.all_summary_statistics(self.cell,
pr2_log=logs[0],
pr3_log=logs[1],
pr4_log=logs[2],
pr5_log=logs[3])
except Exception:
import traceback
e = traceback.format_exc()
if 'Optimal parameters not found' not in e:
print(e)
return None
return stats[0][1], stats[1][1], stats[2][1], stats[3][1], stats[4][1]
def run(self):
"""
Returns a :class:`DataLog` containing the tested cycle lengths as
``cl`` and the measured action potential durations as ``apd``. The
diastolic intervals are given as ``di``.
Each cycle length is repeated ``beats`` number of times, where
``beats`` is the number of beats specified in the constructor.
"""
# Run
if self._data is None:
self._run()
# Get data
cl, apd = self._data
d = myokit.DataLog()
d['cl'] = list(cl)
d['apd'] = list(apd)
d['di'] = list(np.array(cl, copy=False) - np.array(apd, copy=False))
return d
def peaks(self, normalize=False):
"""
Returns a :class:`myokit.DataLog` containing the tested step
voltages and the peak values of the logged variable(s).
The names used in the simulation log correspond to those used in the
model. For example, when doing an experiment on a Sodium channel the
simulation log might have entries ``membrane.v`` and ``ina.g`` where
``membrane.v`` contains the used voltage steps while ``ina.g`` contains
the peak values measured at those voltages.
If ``normalize`` is set to ``True``, the peak data returned will be
normalized by dividing all values by the largest (most positive) peak
in the list. If no positive, non-zero values are found no normalization
will be applied.
If any conversion factor was specified the data will be converted
before normalization.
"""
# Run simulation if needed
if self._logs is None:
self._run()
# Create a copy of the voltage steps
v = np.array(self._steps, copy=True)
# Create a simulation log
d = myokit.DataLog()
d[self._vvar] = v
# Factor
factor = self._factor if self._factor is not None else 1
# Find the peaks
for var in self._vars:
peaks = np.zeros(len(v))
for k, log in enumerate(self._logs):
peaks[k] = log[var][np.argmax(np.abs(log[var]))]
d[var] = peaks * factor
# Normalize (only if log contains positive values)
if normalize:
for var in self._vars:
x = d[var]
m = np.max(x)
if m > 0:
d[var] = x / m
return d
def load_base_aps(force=False):
"""
Loads the baseline APs from the optical mapping data.
"""
cachefile = os.path.join(methods.RESULTS, 'exp-aps-base.csv')
if not force and os.path.isfile(cachefile):
print('Using cached baseline optical APs')
log = myokit.DataLog.load_csv(cachefile).npview()
time = log.time()
aps = [log['ap', i] for i in xrange(len(log) - 1)]
return time, np.array(aps)
print('Collecting baseline optical APs!')
files = glob.glob('optical/Group */Well*.csv')
aps = []
log = myokit.DataLog()
log.set_time_key('time')
for i, filename in enumerate(files):
data = np.loadtxt(filename, delimiter=',')
# Split into time and voltage
t = data[:, 0] * 1000
v = data[:, 1]
# Normalise
filename = os.path.join(AP_FIGURES, 'exp-base-' + str(i + 1) + '.png')
t, v = normalise(t, v, filename=filename, correct_time=True)
# Select bit that's present in all traces
imin = np.where(t >= 0)[0][0]
imax = np.where(t >= 575)[0][0]
t = t[imin:imax]
v = v[imin:imax]
# Store
aps.append(v)
log['ap', i] = v
# Store and return
log['time'] = t
log.save_csv(cachefile)
return t, np.array(aps)
def times(self):
"""
Returns a :class:`myokit.DataLog` containing the time-to-peak for
each logged variable at each voltage step.
"""
# Run simulation if needed
if self._logs is None:
self._run()
# Create a copy of the voltage steps
v = np.array(self._steps, copy=True)
# Create a simulation log
d = myokit.DataLog()
d[self._vvar] = v
# Find the peaks
for var in self._vars:
times = np.zeros(len(v))
for k, log in enumerate(self._logs):
times[k] = log[self._tvar][np.argmax(np.abs(log[var]))]
d[var] = times
return d
def create_log_for_interval(self, a, b, for_drawing=False):
"""
Creates a :class:`myokit.DataLog` containing the entries `time`
and `pace` representing the value of the pacing stimulus at each point.
The time points in the log will be on the interval ``[a, b]``, such
that every time at which the pacing value changes is present in the
log.
If ``for_drawing`` is set to ``True`` each time value between ``a`` and
``b`` will be listed twice, so that a vertical line can be drawn from
the old to the new pacing value.
"""
# Test the input
a, b = float(a), float(b)
if b < a:
raise ValueError('The argument `b` cannot be smaller than `a`')
# Create a simulation log
log = myokit.DataLog()
log.set_time_key('time')
log['time'] = time = []
log['pace'] = pace = []
# Create a pacing system
p = PacingSystem(self)
# Fill in points
t = a
v = p.advance(t, max_time=b)
time.append(t)
pace.append(v)
while t < b:
t = p.next_time()
if for_drawing:
if t != b:
time.append(t)
pace.append(v)
v = p.advance(t, max_time=b)
time.append(t)
pace.append(v)
return log
def calculate_base_apds(time, aps, pt=0.1, force=False):
"""
Calculates APDs for baseline optical experiments.
"""
cachefile = os.path.join(methods.RESULTS,
'exp-apds-base-' + str(pt) + '.csv')
if not force and os.path.isfile(cachefile):
print('Using cached baseline optical APDs')
log = myokit.DataLog.load_csv(cachefile).npview()
return log['apds']
print('Collecting baseline optical APDs!')
apds = []
for i, ap in enumerate(aps):
filename = os.path.join(APD_FIGURES, 'exp-base-' + str(i) + '.png')
apds.append(calculate_apd(time, ap, pt, filename=filename)[0])
apds = np.array(apds)
# Store and return
log = myokit.DataLog()
log['apds'] = apds
log.save_csv(cachefile)
return apds
def traces(self):
"""
Returns the logged traces for each variable as an ordered list of
tuples ``(v, DataLog)``.
If any conversion factor was specified the data will be converted
before returning.
"""
if self._logs is None:
self._run()
data = []
steps = iter(self._steps)
factor = self._factor
if factor is None:
factor = np.ones(len(self._steps))
for k, log in enumerate(self._logs):
v = steps.next()
d = myokit.DataLog()
for var in self._vars:
d[var] = np.array(log[var]) * factor[k]
d[self._tvar] = log[self._tvar]
data.append((v, d))
return data
def test_block(self):
# Test :meth:`PSimulation.block()`.
m, p, x = myokit.load(os.path.join(DIR_DATA, 'lr-1991.mmt'))
with WarningCollector() as c:
s = myokit.PSimulation(m,
p,
variables=['membrane.V'],
parameters=['ina.gNa', 'ica.gCa'])
s.set_step_size(0.002)
d, dp = s.run(10, log_interval=2)
b = s.block(d, dp)
self.assertIsInstance(b, myokit.DataBlock2d)
self.assertEqual(b.len0d(), len(d) - 1)
self.assertTrue(np.all(b.time() == d.time()))
# Log without time
e = myokit.DataLog(d)
del (e[e.time_key()])
self.assertRaisesRegex(ValueError, 'must contain', s.block, e, dp)
# Wrong size derivatives array
self.assertRaisesRegex(ValueError, 'shape', s.block, d, dp[:, :-1])
def load_outward_peaks(force=False):
"""
Loads the peak outward current per cell.
"""
# Return cached version
cachefile = os.path.join(methods.RESULTS, 'exp-peaks.csv')
if not force and os.path.isfile(cachefile):
print('Using cached peak outward current data!')
log = myokit.DataLog.load_csv(cachefile).npview()
return log['peaks']
print('Gathering peak outward current data!')
peaks = []
for i, long_id in enumerate(ORDER):
t, v = load(i)
peaks.append(np.max(v))
# Store
log = myokit.DataLog()
log['peaks'] = peaks
log.save_csv(cachefile)
# Return
return peaks
def calculate_tailored_apds(time, aps, pt=0.1, force=False):
"""
Calculates the tailored simulation aps using the results from
:meth:`normalise_tailored_simulations`.
"""
cachefile = os.path.join(methods.RESULTS,
'sim-base-apds-' + str(pt) + '.csv')
if not force and os.path.isfile(cachefile):
print('Using cached baseline tailored APDs')
log = myokit.DataLog.load_csv(cachefile).npview()
return log['apds']
print('Collecting baseline tailored APDs!')
apds = []
for i, long_id in enumerate(outward.ORDER):
filename = os.path.join(APD_FIGURES, 'sim-base-' + long_id + '.png')
apds.append(calculate_apd(time, aps[i], pt, filename=filename)[0])
apds = np.array(apds)
# Store and return
log = myokit.DataLog()
log['apds'] = apds
log.save_csv(cachefile)
return apds
def run_simple(self):
# Create test model
m = myokit.Model('test')
c = m.add_component('c')
t = c.add_variable('time')
t.set_rhs('0')
t.set_binding('time')
v = c.add_variable('V')
v.set_rhs('0')
v.promote(-80.1)
x = c.add_variable('x')
x.set_rhs('exp(V)')
m.validate()
# Create simulation log
log = myokit.DataLog()
log['c.time'] = np.zeros(1000)
log['c.V'] = np.linspace(-80.0, 50.0, 10)
# Number of repeats
repeats = 10
# Run
x.set_rhs('1 / (7 * exp((V + 12) / 35) + 9 * exp(-(V + 77) / 6))')
b = myokit.RhsBenchmarker(m, [x])
t = b.bench_full(log, repeats)
t = b.bench_part(log, repeats)
def current(self, parameter, voltage, times):
"""
Generate current of voltage clamp using the given scalings.
- Not fitting on this, so voltage resolution is not as important.
- Not running this often, so can setup everything here...
"""
parameter = np.array(parameter)
# Update model parameters
if self.transform is not None:
parameter = self.transform(parameter)
model = myokit.load_model(self._model_file)
# Simulate with modified model
for i, name in enumerate(self._conductance):
'''
# normal way of doing it...
self.simulation.set_constant(name,
parameter[i] * self.original[i])
'''
# try to set conductance for non-literal...
model.get(name).set_rhs(
parameter[i] * self.original[i]
#'''
)
# Get current names of output
current = []
m_cur = model_current[self._model_file_name]
for name in self.parameters:
current.append(m_cur[name])
# Set up voltage clamp
#for ion_var, ion_conc in model_ion[self._model_file_name]:
# self._fix_concentration(model, ion_var, ion_conc)
# Detach voltage for voltage clamp(?)
model_v = model.get('membrane.V')
model_v.demote()
tmp_vhold = vhold
model_v.set_rhs(tmp_vhold)
model.get('engine.pace').set_binding(None)
model_v.set_binding('pace')
# Create pre-pacing protocol
protocol = pacing.constant(tmp_vhold)
# Create pre-pacing simulation
simulation1 = myokit.Simulation(model, protocol)
simulation2 = myokit.Simulation(model)
simulation2.set_fixed_form_protocol(
times,
voltage
)
simulation2.set_tolerance(1e-8, 1e-10)
simulation2.set_max_step_size(1e-2) # ms
# Run
simulation1.reset()
simulation2.reset()
simulation1.set_state(self.original_state)
simulation1.pre(100)
simulation2.set_state(simulation1.state())
# Log some beats
d = simulation2.run(np.max(times)+0.02,
log_times = times,
log = current,
).npview()
# rename output names
d_out = myokit.DataLog()
for s, c in m_cur.iteritems():
if s in self.parameters:
d_out[s[:-2]] = d[c]
del(d)
return d_out
def _run(self, duration, log, log_interval, apd_threshold, progress, msg):
# Reset error state
self._error_state = None
# Simulation times
if duration < 0:
raise Exception('Simulation time can\'t be negative.')
tmin = self._time
tmax = tmin + duration
# Parse log argument
log = myokit.prepare_log(log, self._model, if_empty=myokit.LOG_ALL)
# Logging period (0 = disabled)
log_interval = 0 if log_interval is None else float(log_interval)
if log_interval < 0:
log_interval = 0
# Threshold for APD measurement
root_list = None
root_threshold = 0
if apd_threshold is not None:
if self._apd_var is None:
raise ValueError('Threshold given but Simulation object was'
' created without apd_var argument.')
else:
root_list = []
root_threshold = float(apd_threshold)
# Get progress indication function (if any)
if progress is None:
progress = myokit._Simulation_progress
if progress:
if not isinstance(progress, myokit.ProgressReporter):
raise ValueError(
'The argument "progress" must be either a'
' subclass of myokit.ProgressReporter or None.')
# Determine benchmarking mode, create time() function if needed
if self._model.binding('realtime') is not None:
import timeit
bench = timeit.default_timer
else:
bench = None
# Run simulation
with myokit.SubCapture():
arithmetic_error = None
if duration > 0:
# Initialize
state = [0] * len(self._state)
bound = [0, 0, 0, 0] # time, pace, realtime, evaluations
self._sim.sim_init(
tmin,
tmax,
list(self._state),
state,
bound,
self._protocol,
self._fixed_form_protocol,
log,
log_interval,
root_list,
root_threshold,
bench,
)
t = tmin
try:
if progress:
# Loop with feedback
with progress.job(msg):
r = 1.0 / duration if duration != 0 else 1
while t < tmax:
t = self._sim.sim_step()
if not progress.update(min((t - tmin) * r, 1)):
raise myokit.SimulationCancelledError()
else:
# Loop without feedback
while t < tmax:
t = self._sim.sim_step()
except ArithmeticError as ea:
self._error_state = list(state)
txt = [
'A numerical error occurred during simulation at'
' t = ' + str(t) + '.', 'Last reached state: '
]
txt.extend([
' ' + x
for x in self._model.format_state(state).splitlines()
])
txt.append('Inputs for binding: ')
txt.append(' time = ' + myokit.strfloat(bound[0]))
txt.append(' pace = ' + myokit.strfloat(bound[1]))
txt.append(' realtime = ' + myokit.strfloat(bound[2]))
txt.append(' evaluations = ' + myokit.strfloat(bound[3]))
try:
self._model.eval_state_derivatives(state)
except myokit.NumericalError as en:
txt.append(en.message)
raise myokit.SimulationError('\n'.join(txt))
except Exception as e:
# Store error state
self._error_state = list(state)
# Check for zero-step error
if e.message[0:9] == 'ZERO_STEP':
time = float(e.message[10:])
raise myokit.SimulationError('Too many failed steps at'
' t=' + str(time))
# Unhandled exception: re-raise!
raise
finally:
# Clean even after KeyboardInterrupt or other Exception
self._sim.sim_clean()
# Update internal state
self._state = state
# Return
if root_list is not None:
# Calculate apds and return (log, apds)
st = []
dr = []
if root_list:
roots = iter(root_list)
time, direction = roots.next()
tlast = time if direction > 0 else None
for time, direction in roots:
if direction > 0:
tlast = time
else:
st.append(tlast)
dr.append(time - tlast)
apds = myokit.DataLog()
apds['start'] = st
apds['duration'] = dr
return log, apds
else:
# Return log
return log
def run(self, duration, log=None, log_interval=0.01, log_times=None):
"""
Runs a simulation for ``duration`` time units.
After the simulation:
- The simulation time will be increased by ``duration`` time units.
- The simulation state will be updated to the last reached state.
Arguments:
``duration``
The number of time units to simulate.
``log``
A log from a previous run can be passed in, in which case the
results will be appended to this one.
``log_interval``
The time between logged points.
``log_times``
A pre-defined sequence of times to log at. If set, ``log_interval``
will be ignored.
Returns a :class:`myokit.DataLog` with the simulation results.
"""
# Check arguments
duration = float(duration)
if duration < 0:
raise ValueError('Duration must be non-negative.')
log_interval = float(log_interval)
if log_interval <= 0:
raise ValueError('Log interval must be greater than zero.')
# Check log_times
if log_times is None:
log_times = self._time + np.arange(0, duration, log_interval)
# Set up logging
if log is None:
# Create new log
log = myokit.DataLog()
log.set_time_key(self._time_key)
for key in self._log_keys:
log[key] = np.zeros(log_times.shape)
offset = 0
else:
# Check existing log
if len(log.keys()) > len(self._log_keys):
raise ValueError('Invalid log: contains extra keys.')
try:
key = self._time_key # Note: error msg below uses `key`
offset = len(log[key])
for key in self._log_keys:
log[key] = np.concatenate(
(log[key], np.zeros(log_times.shape)))
except KeyError:
raise ValueError('Invalid log: missing entry for <' +
str(key) + '>.')
# Run simulation
if self._protocol is None:
# User defined membrane potential
self._run(log, log_times, self._time + duration, offset)
else:
# Voltage clamp
tfinal = self._time + duration
while self._time < tfinal:
# Run simulation
tnext = min(tfinal, self._pacing.next_time())
times = log_times[np.logical_and(log_times >= self._time,
log_times < tnext)]
self._run(log, times, tnext, offset)
offset += len(times)
# Update pacing
self._membrane_potential = self._pacing.advance(tnext)
# Return
return log