def relog(_log, _dt):
# Get first occurence of error
ifirst, kfirst = find_error_position(_log)
if kfirst is None:
raise myokit.FindNanError('Error condition not found in log.')
if ifirst == 0:
raise myokit.FindNanError(
'Unable to work with simulation logs where the error'
' condition is met in the very first data point.')
# Position to start deep search at
istart = ifirst - 1
# Get last logged state before error
state = []
for dims in myokit.dimco(*self._dims):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._model.states():
state.append(_log[pre + s.qname()][istart])
# Get last time before error
time = _log[time_var][istart]
# Save current state & time
old_state = self._state
old_time = self._time
self._state = state
self._time = time
# Run until next time point, log every step
duration = _log[time_var][ifirst] - time
_log = self.run(duration,
log=myokit.LOG_BOUND + myokit.LOG_STATE,
log_interval=_dt,
report_nan=False)
# Reset simulation to original state
self._state = old_state
self._time = old_time
# Return new log
return _log
def relog(_logf, _logt, _dt):
# Get first occurence of error
ifirstf, kfirstf = find_error_position(_logf)
ifirstt, kfirstt = find_error_position(_logt)
if kfirstf is None and kfirstt is None:
raise myokit.FindNanError('Error condition not found in logs.')
elif kfirstf is None:
ifirst = ifirstt
elif kfirstt is None:
ifirst = ifirstf
elif ifirstf == 0 or ifirstt == 0:
raise myokit.FindNanError(
'Unable to work with simulation logs where the error'
' condition is met in the very first data point.')
else:
ifirst = min(ifirstf, ifirstt)
# Position to start deep search at
istart = ifirst - 1
# Get last logged states before error
statef = []
statet = []
for dims in myokit._dimco(*self._ncellsf):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._modelf.states():
statef.append(_logf[pre + s.qname()][istart])
for dims in myokit._dimco(*self._ncellst):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._modelt.states():
statet.append(_logt[pre + s.qname()][istart])
# Get last time before error
time = _logf[time_varf][istart]
# Save current state & time
old_statef = self._statef
old_statet = self._statet
old_time = self._time
self._statef = statef
self._statet = statet
self._time = time
# Run until next time point, log every step
duration = _logf[time_varf][ifirst] - time
log = myokit.LOG_BOUND + myokit.LOG_STATE
_logf, _logt = self.run(duration,
logf=log,
logt=log,
log_interval=_dt,
report_nan=False)
# Reset simulation to original state
self._statef = old_statef
self._statet = old_statet
self._time = old_time
# Return new logs
return _logf, _logt
def find_nan(self, logf, logt):
"""
Searches for the origin of a ``NaN`` (or ``inf``) in a set of
simulation logs generated by this Simulation.
The logs must contain the state of each cell and all bound variables.
The NaN can occur at any point in time except the first.
Returns a tuple ``(part, time, icell, variable, value, states, bound)``
where ``time`` is the time the first ``NaN`` was found and ``icell`` is
the index of the cell in which it happened. The entry ``part`` is a
string containing either "fiber" or "tissue", indicating which part of
the simulation triggered the error. The offending variable's name is
given as ``variable`` and its (illegal) value as ``value``. The current
state and, if available, any previous states are given in the list
``states``. Here, ``states[0]`` points to the current state in the
simulation part causing the error, ``state[1]`` is the previous state
and so on. Similarly the values of the error causing model's bound
variables is given in ``bound``.
"""
import numpy as np
# Check if logs contain all states and bound variables
lt = []
lf = []
for label in self._global:
v = self._modelf.binding(label)
if v is not None:
lf.append(v.qname())
v = self._modelt.binding(label)
if v is not None:
lt.append(v.qname())
lf = myokit.prepare_log(myokit.LOG_STATE + myokit.LOG_BOUND,
self._modelf,
dims=self._ncellsf,
global_vars=lf)
lt = myokit.prepare_log(myokit.LOG_STATE + myokit.LOG_BOUND,
self._modelt,
dims=self._ncellst,
global_vars=lt)
for key in lf:
if key not in logf:
raise myokit.FindNanError(
'Method requires a simulation log from the fiber model'
' containing all states and bound variables. Missing'
' variable <' + key + '>.')
for key in lt:
if key not in logt:
raise myokit.FindNanError(
'Method requires a simulation log from the tissue model'
' containing all states and bound variables. Missing at'
' least variable <' + key + '>.')
del (lf)
del (lt)
# Error criterium: NaN/inf detection
def bisect(ar, lo, hi):
if not np.isfinite(ar[lo]):
return lo
md = lo + int(np.ceil(0.5 * (hi - lo)))
if md == hi:
return hi
if not np.isfinite(ar[md]):
return bisect(ar, lo, md)
else:
return bisect(ar, md, hi)
# Search for first occurrence of propagating NaN in the log
def find_error_position(log):
ifirst = None # Log list index
kfirst = None # Log key
for key, ar in log.items():
if ifirst is None:
if not np.isfinite(ar[-1]):
# First NaN found
kfirst = key
ifirst = bisect(ar, 0, len(ar) - 1)
if ifirst == 0:
break
elif not np.isfinite(ar[ifirst - 1]):
# Earlier NaN found
kfirst = key
ifirst = bisect(ar, 0, ifirst)
if ifirst == 0:
break
return ifirst, kfirst
# Get the name of a time variable in the fiber model
time_varf = self._modelf.time().qname()
# Deep searching function
def relog(_logf, _logt, _dt):
# Get first occurence of error
ifirstf, kfirstf = find_error_position(_logf)
ifirstt, kfirstt = find_error_position(_logt)
if kfirstf is None and kfirstt is None:
raise myokit.FindNanError('Error condition not found in logs.')
elif kfirstf is None:
ifirst = ifirstt
elif kfirstt is None:
ifirst = ifirstf
elif ifirstf == 0 or ifirstt == 0:
raise myokit.FindNanError(
'Unable to work with simulation logs where the error'
' condition is met in the very first data point.')
else:
ifirst = min(ifirstf, ifirstt)
# Position to start deep search at
istart = ifirst - 1
# Get last logged states before error
statef = []
statet = []
for dims in myokit._dimco(*self._ncellsf):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._modelf.states():
statef.append(_logf[pre + s.qname()][istart])
for dims in myokit._dimco(*self._ncellst):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._modelt.states():
statet.append(_logt[pre + s.qname()][istart])
# Get last time before error
time = _logf[time_varf][istart]
# Save current state & time
old_statef = self._statef
old_statet = self._statet
old_time = self._time
self._statef = statef
self._statet = statet
self._time = time
# Run until next time point, log every step
duration = _logf[time_varf][ifirst] - time
log = myokit.LOG_BOUND + myokit.LOG_STATE
_logf, _logt = self.run(duration,
logf=log,
logt=log,
log_interval=_dt,
report_nan=False)
# Reset simulation to original state
self._statef = old_statef
self._statet = old_statet
self._time = old_time
# Return new logs
return _logf, _logt
# Get time step
dt = logf[time_varf][1] - logf[time_varf][0]
# Search with successively fine log interval
while dt > 0:
dt *= 0.1
if dt < 0.5:
dt = 0
logf, logt = relog(logf, logt, dt)
# Search for first occurrence of error in the detailed log
ifirstf, kfirstf = find_error_position(logf)
ifirstt, kfirstt = find_error_position(logt)
if kfirstt is None or (kfirstf is not None and kfirstf < kfirstt):
part = 'fiber'
ifirst = ifirstf
kfirst = kfirstf
model = self._modelf
log = logf
else:
part = 'tissue'
ifirst = ifirstt
kfirst = kfirstt
model = self._modelt
log = logt
# Get indices of cell in state vector
icell = [int(x) for x in kfirst.split('.')[0:2]]
# Get state & bound before, during and after error
def state(index, icell):
s = []
b = {}
for var in model.states():
s.append(log[var.qname(), icell][index])
for var in model.variables(bound=True):
if var.binding() in self._global:
b[var.qname()] = log[var.qname()][index]
else:
b[var.qname()] = log[var.qname(), icell][index]
return s, b
# Get error cell's states before, during and after
states = []
bound = []
max_states = 3
for k in range(ifirst, ifirst - max_states - 1, -1):
if k < 0:
break
s, b = state(k, icell)
states.append(s)
bound.append(b)
# Get variable causing error
var = model.get('.'.join(kfirst.split('.')[2:]))
# Get value causing error
value = states[1][var.indice()]
var = var.qname()
# Get time error occurred
time = logf[time_varf][ifirst]
# Return part, time, icell, variable, value, states, bound
return part, time, icell, var, value, states, bound
def find_nan(self, log, watch_var=None, safe_range=None):
"""
Searches for the origin of a ``NaN`` (or ``inf``) in a simulation log
generated by this Simulation.
The log must contain the state of each cell and all bound variables.
The NaN can occur at any point in time except the first.
Returns a tuple ``(time, icell, variable, value, states, bound)`` where
``time`` is the time the first ``NaN`` was found and ``icell`` is the
index of the cell in which it happened. The variable's name is given as
``variable`` and its (illegal) value as ``value``. The current state
and, if available, any previous states are given in the list
``states``. Here, ``states[0]`` points to the current state,
``state[1]`` is the previous state and so on. Similarly the values of
the model's bound variables is given in ``bound``.
To aid in diagnosis, a variable can be selected as ``watch_var`` and a
``safe_range`` can be specified. With this option, the function will
find and report either the first ``NaN`` or the first time the watched
variable left the safe range, whatever came first. The safe range
should be specified as ``(lower, upper)`` where both bounds are assumed
to be in the safe range. The watched variable must be a state variable.
"""
import numpy as np
# Test if log contains all states and bound variables
t = []
for label in self._global:
var = self._model.binding(label)
if var is not None:
t.append(var.qname())
t = myokit.prepare_log(myokit.LOG_STATE + myokit.LOG_BOUND,
self._model,
dims=self._dims,
global_vars=t)
for key in t:
if key not in log:
raise myokit.FindNanError(
'Method requires a simulation log containing all states'
' and bound variables. Missing variable <' + key + '>.')
del (t)
# Error criterium
if watch_var is None:
# NaN/inf detection
def bisect(ar, lo, hi):
if not np.isfinite(ar[lo]):
return lo
md = lo + int(np.ceil(0.5 * (hi - lo)))
if md == hi:
return hi
if not np.isfinite(ar[md]):
return bisect(ar, lo, md)
else:
return bisect(ar, md, hi)
def find_error_position(log):
# Search for first occurrence of propagating NaN in the log
ifirst = None
kfirst = None
for key, ar in log.iteritems():
if ifirst is None:
if not np.isfinite(ar[-1]):
# First NaN found
kfirst = key
ifirst = bisect(ar, 0, len(ar) - 1)
if ifirst == 0:
break
elif not np.isfinite(ar[ifirst - 1]):
# Earlier NaN found
kfirst = key
ifirst = bisect(ar, 0, ifirst)
if ifirst == 0:
break
return ifirst, kfirst
else:
# Variable out of bounds detection
try:
watch_var = self._model.get(watch_var)
except KeyError:
raise myokit.FindNanError('Variable <' + str(watch_var) +
'> not found.')
if not watch_var.is_state():
raise myokit.FindNanError(
'The watched variable must be a state.')
try:
lo, hi = safe_range
except Exception:
raise myokit.FindNanError(
'A safe range must be specified for the watched variable'
' as a tuple (lower, upper).')
if lo >= hi:
raise myokit.FindNanError(
'The safe range must have a lower bound that is lower than'
' the upper bound.')
def find_error_position(_log):
# Find first occurence of out-of-bounds error
ifirst = None
kfirst = None
post = '.' + watch_var.qname()
lower, upper = safe_range
for dims in myokit.dimco(*self._dims):
key = '.'.join([str(x) for x in dims]) + post
ar = np.array(_log[key], copy=False)
i = np.where((ar < lower)
| (ar > upper)
| np.isnan(ar)
| np.isinf(ar))[0]
if len(i) > 0:
i = i[0]
if ifirst is None:
kfirst = key
ifirst = i
elif i < ifirst:
kfirst = key
ifirst = i
if i == 0:
break
return ifirst, kfirst
# Get the name of a time variable
time_var = self._model.time().qname()
# Deep searching function
def relog(_log, _dt):
# Get first occurence of error
ifirst, kfirst = find_error_position(_log)
if kfirst is None:
raise myokit.FindNanError('Error condition not found in log.')
if ifirst == 0:
raise myokit.FindNanError(
'Unable to work with simulation logs where the error'
' condition is met in the very first data point.')
# Position to start deep search at
istart = ifirst - 1
# Get last logged state before error
state = []
for dims in myokit.dimco(*self._dims):
pre = '.'.join([str(x) for x in dims]) + '.'
for s in self._model.states():
state.append(_log[pre + s.qname()][istart])
# Get last time before error
time = _log[time_var][istart]
# Save current state & time
old_state = self._state
old_time = self._time
self._state = state
self._time = time
# Run until next time point, log every step
duration = _log[time_var][ifirst] - time
_log = self.run(duration,
log=myokit.LOG_BOUND + myokit.LOG_STATE,
log_interval=_dt,
report_nan=False)
# Reset simulation to original state
self._state = old_state
self._time = old_time
# Return new log
return _log
# Get time step
try:
dt = log[time_var][1] - log[time_var][0]
except IndexError:
# Unable to guess dt!
# So... Nan occurs before the first log interval is reached
# That probably means dt was relatively large, so guess it was
# large! Assuming milliseconds, start off with dt=5ms
dt = 5
# Search with successively fine log interval
while dt > 0:
dt *= 0.1
if dt < 0.5:
dt = 0
log = relog(log, dt)
# Search for first occurrence of error in the detailed log
ifirst, kfirst = find_error_position(log)
# Get indices of cell in state vector
ndims = len(self._dims)
icell = [int(x) for x in kfirst.split('.')[0:ndims]]
# Get state & bound before, during and after error
def state(index, icell):
s = []
b = {}
for var in self._model.states():
s.append(log[var.qname(), icell][index])
for var in self._model.variables(bound=True):
if var.binding() in self._global:
b[var.qname()] = log[var.qname()][index]
else:
b[var.qname()] = log[var.qname(), icell][index]
return s, b
# Get error cell's states before, during and after
states = []
bound = []
max_states = 3
for k in xrange(ifirst, ifirst - max_states - 1, -1):
if k < 0:
break
s, b = state(k, icell)
states.append(s)
bound.append(b)
# Get variable causing error
var = self._model.get('.'.join(kfirst.split('.')[ndims:]))
# Get value causing error
value = states[1][var.indice()]
var = var.qname()
# Get time error occurred
time = log[time_var][ifirst]
# Return time, icell, variable, value, states, bound
return time, icell, var, value, states, bound
