def run(self, initialConditions, step_n):
pinit = np.zeros(
self.nx * self.ny *
self.nz) #TODO: how to map from the ABM domain to this form
if initialConditions == None:
for i in range(self.nx * self.ny * self.nz):
pinit[i] = float(
decimal.Decimal(random.randrange(8, 50)) /
1000000) #ng/mm3
else:
pass
print("Initial values:")
print(pinit)
phi = CellVariable(name="Concentration (ng/ml)",
mesh=self.mesh,
value=pinit)
t = Variable(0.)
for step in range(step_n):
print("Time = {}".format(t.value))
self.eq.solve(var=phi, dt=self.timeStepDuration)
t.setValue(t.value + self.timeStepDuration)
return phi.value
class ThawSlump(object): # 1D
# time_initial only works when forcing is provided
def __init__(self,
tsmesh,
time_step_module=None,
output_step_module=None,
forcing_module=None,
thermal_properties=None,
time_initial=None):
self.mesh = tsmesh
self.variables = {}
self.variables_store = []
self.diagnostic_modules = {}
self.diagnostic_update_order = []
self.eq = None
self.boundary_condition_collection = None
self._time = Variable(value=0)
self._time_step_module = time_step_module
self._timeref = None # will generally be set by forcing_module; otherwise manually
if forcing_module is not None:
self.initializeForcing(forcing_module)
if time_initial is not None:
self.time = time_initial
if thermal_properties is not None:
self.initializeThermalProperties(thermal_properties)
self._output_step_module = output_step_module
self._output_module = SlumpOutput()
if output_step_module is None:
self._output_step_module = OutputStep()
@property
def time(self):
return float(self._time.value)
@time.setter
def time(self, t): # can also handle date objects
try:
self.date = t
except:
self._time.setValue(t)
@property
def timeStep(self):
return self._time_step_module.calculate(self)
@property
def date(self):
return self._internal_time_to_date(self.time)
def _internal_time_to_date(self, internal_time):
return self._timeref + datetime.timedelta(seconds=internal_time)
@date.setter
def date(self, d):
dtsec = self._date_to_internal_time(d)
self._time.setValue(dtsec)
def _date_to_internal_time(self, d):
dt = d - self._timeref
dtsec = dt.days * 24 * 3600 + dt.seconds + dt.microseconds * 1e-6
return dtsec
def initializeTimeReference(self, timeref):
# timeref is a datetime object
self._timeref = timeref
def initializePDE(self, tseq=None):
self.eq = tseq
def initializeTimeStepModule(self, time_step_module):
self._time_step_module = time_step_module
def _initializeSourcesZero(self, source_name='S'):
self.variables[source_name] = CellVariable(name=source_name,
mesh=self.mesh.mesh,
value=0.0)
def initializeDiagnostic(self,
variable,
funpointer,
default=0.0,
face_variable=False,
output_variable=True):
if not face_variable:
self.variables[variable] = CellVariable(name=variable,
mesh=self.mesh.mesh,
value=default)
else:
self.variables[variable] = FaceVariable(name=variable,
mesh=self.mesh.mesh,
value=default)
self.diagnostic_modules[variable] = DiagnosticModule(funpointer, self)
if output_variable:
self.variables_store.append(variable)
self.diagnostic_update_order.append(variable)
def initializeOutputStepModule(self, output_step_module):
self._output_step_module = output_step_module
def initializeThermalProperties(self, thermal_properties):
self.thermal_properties = thermal_properties
self.thermal_properties.initializeVariables(self)
self.initializeTright()
def initializeForcing(self, forcing_module):
self.forcing_module = forcing_module
for varj in self.forcing_module.variables:
assert varj not in self.variables
self.variables[varj] = self.forcing_module.variables[varj]
self.initializeTimeReference(self.forcing_module._timeref)
def initializeEnthalpyTemperature(self,
T_initial,
proportion_frozen=None,
time=None):
# time can be internal time or also a datetime object
pf = 0.0 if proportion_frozen is None else proportion_frozen
assert pf >= 0.0 and pf <= 1.0
self.variables['T'].setValue(T_initial)
self.variables['h'].setValue(
self.thermal_properties.enthalpyFromTemperature(
self, T=T_initial, proportion_frozen=pf))
self.updateDiagnostics()
if time is not None:
self.time = time
def updateDiagnostic(self, variable):
self.variables[variable].setValue(
self.diagnostic_modules[variable].evaluate())
def updateDiagnostics(self, variables=None):
if variables is not None:
variablesorder = variables
else:
variablesorder = self.diagnostic_update_order
for variable in variablesorder:
self.updateDiagnostic(variable)
def specifyBoundaryConditions(self, boundary_condition_collection):
self.boundary_condition_collection = boundary_condition_collection
self.updateGeometryBoundaryConditions()
self.invokeBoundaryConditions()
self.initializePDE()
def updateGeometryBoundaryConditions(self):
self.boundary_condition_collection.updateGeometry(self)
def updateBoundaryConditions(self, bc_data, invoke=True):
self.boundary_condition_collection.update(bc_data)
if invoke:
self.invokeBoundaryConditions()
def invokeBoundaryConditions(self):
self.boundary_condition_collection.invoke(self)
def updateGeometry(self):
self.boundary_condition_collection.updateGeometry(self)
def nextOutput(self):
return self._output_step_module.next(self)
def updateOutput(self, datanew={}):
for v in self.variables_store:
datanew[v] = np.copy(self.variables[v].value)
# boundary condition outputs:
# separate routine: total source, source components, or for basic b.c. just value)
datanew.update(self.boundary_condition_collection.output())
self._output_module.update(self.date, datanew)
def exportOutput(self, fn):
self._output_module.export(fn)
def addStoredVariable(self, varname):
# varname can also be list
if isinstance(varname, str):
if varname not in self.variables_store:
self.variables_store.append(varname)
else: # tuple/list,etc.
for varnamej in varname:
self.addStoredVariable(varnamej)
if sweep == 0:
res_heat1 = res_heat
res_phase1 = res_phase
print
print 'sweep',sweep
print res_heat
print res_phase
solid_area = (np.array(phase.globalValue)>0).sum()*dx*dy # initial size of solid nucleus
if parallelComm.procID==0:
print
print 'dt',dt.value
print 'current_step',current_step
print 'res_heat',res_heat0, res_heat
print 'res_phase',res_phase0, res_phase
print 'solid area', solid_area
if (res_heat < res_heat1 * tolerance) and (res_phase < res_phase1 * tolerance):
elapsed_time += dt.value
current_step += 1
dt.setValue(dt.value * 1.1)
if current_step % 10 == 0:
glu = uu.globalValue
glp = phase.globalValue
if parallelComm.procID==0:
np.savez_compressed('data-test/dump{0}.npz'.format(current_step), uu=np.array(glu), phase=np.array(glp), elapsed_time=np.array(elapsed_time))
else:
dt.setValue(dt.value * 0.8)
uu[:] == uu.old
phase[:] = phase.old
class Simulation(CreateMixin):
"""
This class enables the process of repeatedly solving the model's
equations for a (small) time step to a certain numerical accuracy,
and then incrementing the model clock. During the evolution of the
simulation, the state of the model as well as the simulation is yielded
repeatedly.
Numerically approximating the solution to a set of partial differential
equations requires that the solver system has a reasonable target accuracy
("residual") and enough attempts ("sweeps") to reach both a stable and
accurate approximation for a time step. This class attempts to abstract out
these optimizations for the user, by performing adaptive time-stepping. The
user needs to specify a worst-case residual ( :attr:`.max_residual`),
maximum number of sweeps per time-step ( :attr:`.max_sweeps`) and the range
of time-step values to explore during evolution (:attr:`.simtime_lims`).
During the evolution of the simulation, the time-step is penalized if the
max residual is overshot or max sweeps reached. If not, the reward is a bump
up in the time-step duration, allowing for faster evolution of the
simulation.
See Also:
The scheme of simulation :meth:`.evolution`.
The adaptive scheme to :meth:`.update_time_step`.
"""
schema_key = 'simulation'
FIPY_SOLVERS = ('scipy', 'pyAMG', 'trilinos', 'pysparse')
def __init__(
self,
simtime_total=6,
simtime_days=None,
simtime_lims=(0.1, 120),
snapshot_interval=60,
fipy_solver='scipy',
max_sweeps=100,
max_residual=1e-12,
):
"""
Args:
simtime_total (float, PhysicalField): The number of hours for the
simulation to run
simtime_days (float): The number of days (in terms of the
model's irradiance cycle) the simulation should run for. Note
that specifying this
will override the given :attr:`.simtime_total` when the
:attr:`.model` is supplied.
simtime_lims (float, PhysicalField): The minimum and maximum
limits for the
:attr:`simtime_step` for adaptive time-stepping. This should
be supplied as a
pair of values, which are assumed to be in seconds and cast
into PhysicalField
internally. (default: 0.01, 240)
max_sweeps (int): Maximum number of sweeps to attempt per
timestep (default: 50)
max_residual (float): Maximum residual value for the solver at a
timestep (default:
1e-14)
snapshot_interval (int, float, :class:`PhysicalField`): the
duration in seconds
of the model clock between yielding snapshots of the model
state for exporters
(default: 60)
fipy_solver (str): Name of the fipy solver to use. One of
``('scipy', 'pyAMG', 'trilinos','pysparse')`` (default: "scipy")
"""
super(Simulation, self).__init__()
# the __init__ call is deliberately empty. will implement
# cooeperative inheritance only
# when necessary
self.logger = logging.getLogger(__name__)
self._started = False
self._solver = None
self._fipy_solver = None
self.fipy_solver = fipy_solver
self._simtime_lims = None
self._simtime_total = None
self._simtime_step = None
#: Numer of days to simulate in terms of the model's irradiance source
self.simtime_days = None
if simtime_days:
simtime_days = float(simtime_days)
if simtime_days <= 0:
raise ValueError(
'simtime_days should be >0, not {:.2f}'.format(
simtime_days))
self.simtime_days = simtime_days
self.simtime_lims = simtime_lims
self.simtime_total = simtime_total
self.simtime_step = self.simtime_lims[0]
self.snapshot_interval = PhysicalField(snapshot_interval, 's')
self.max_residual = float(max_residual)
if not (0 < self.max_residual < 1e-3):
raise ValueError('Max residual should be a small positive number, '
'not {:.3g}'.format(self.max_residual))
self._residualQ = deque([], maxlen=10)
self._max_sweeps = None
self.max_sweeps = max_sweeps
self._sweepsQ = deque([], maxlen=5)
self._model = None
@property
def started(self):
"""
Returns:
bool: Flag for if the sim evolution has started
"""
return self._started
@property
def fipy_solver(self):
return self._fipy_solver
@fipy_solver.setter
def fipy_solver(self, val):
if val not in self.FIPY_SOLVERS:
raise ValueError('Solver {!r} not in {}'.format(
val, self.FIPY_SOLVERS))
if self.started:
raise RuntimeError('Fipy solver cannot be changed after started')
self._fipy_solver = val
@property
def simtime_total(self):
"""
The number of hours of the model clock the simulation should be
evolved for.
The supplied value must be larger than the time-steps allowed. Also, it
may be over-ridden by supplying :attr:`.simtime_days`.
Returns:
PhysicalField: duration in hours
"""
return self._simtime_total
@simtime_total.setter
def simtime_total(self, val):
try:
val = PhysicalField(val, 'h')
except TypeError:
raise ValueError(
'simtime_total {!r} not compatible with time units'.format(
val))
if val <= 0:
raise ValueError('simtime_total should be > 0')
if self.simtime_step is not None:
if val <= self.simtime_lims[0]:
raise ValueError('simtime_total {} should be > step {}'.format(
val, self.simtime_lims[0]))
self._simtime_total = val
@property
def simtime_step(self):
"""
The current time-step duration. While setting, the supplied value will
be clipped to within :attr:`simtime_lims`.
Returns:
PhysicalField: in seconds
"""
return self._simtime_step
@simtime_step.setter
def simtime_step(self, val):
try:
val = PhysicalField(val, 's')
except TypeError:
raise ValueError(
'simtime_step {!r} not compatible with time units'.format(val))
dtMin, dtMax = self.simtime_lims
# val = min(max(val, dtMin), dtMax)
val = min(val, dtMax)
assert hasattr(val, 'unit')
if self.simtime_total is not None:
if self.simtime_total <= val:
raise ValueError('simtime_total {} should be > step {}'.format(
self.simtime_total, val))
self._simtime_step = val
@property
def simtime_lims(self):
"""
The limits for the time-step duration allowed during evolution.
This parameter determines the degree to which the simulation evolution
can be speeded up. In phases of the model evolution where the numerical
solution is reached within a few sweeps, the clock would run at the max
limit, whereas when a large number of sweeps are required, it would be
penalized towards the min limit.
A high max value enables faster evolution, but can also lead to
numerical inaccuracy ( higher residual) or solution breakdown (numerical
error) during :meth:`.run_timestep`. A small enough min value allows
recovery, but turning back the clock to the previous time step and
restarting with the min timestep and allowing subsequent relaxation.
Args:
vals (float, PhysicalField): the (min, max) durations in seconds
Returns:
lims (tuple): The (min, max) limits of :attr:`simtime_step` each
as a :class:`.PhysicalField`
"""
return self._simtime_lims
@simtime_lims.setter
def simtime_lims(self, vals):
if vals is None:
lmin = PhysicalField(0.1, 's')
# lmax = (self.simtime_total / 25.0).inUnitsOf('s').floor()
lmax = PhysicalField(120, 's')
else:
lmin, lmax = [PhysicalField(float(_), 's') for _ in vals]
if not (0 < lmin < lmax):
raise ValueError(
'simtime_lims ({}, {}) are not positive and in order'.format(
lmin, lmax))
self._simtime_lims = (lmin, lmax)
self.logger.debug('simtime_lims set: {}'.format(self._simtime_lims))
@property
def max_sweeps(self):
"""
The maximum number of sweeps allowed for a timestep
Args:
val (int): should be > 0
Returns:
int
"""
return self._max_sweeps
@max_sweeps.setter
def max_sweeps(self, val):
try:
val = int(val)
assert val > 0
self._max_sweeps = val
except:
raise ValueError('max_sweeps {} should be > 0'.format(val))
@property
def model(self) -> MicroBenthosModel:
"""
The model to run the simulation on. This is typically an instance of
:class:`~microbenthos.MicroBenthosModel` or its subclasses. The
interface it must
provide is:
* a method :meth:`create_full_equation()`
* an attribute :attr:`full_eqn` created by above method, which is a
:class:`~fipy.terms.binaryTerm._BinaryTerm` that has a
:meth:`sweep()` method.
* method :meth:`model.update_vars()` which is called before each
timestep
* method :meth:`model.clock.increment_time(dt)` which is called
after each timestep
Additionally, if :attr:`.simtime_days` is set, then setting the model
will try to find the ``"env.irradiance:`` object and use its
:attr:`.hours_total` attribute to set the :attr:`.simtime_total`.
Args:
model (:class:`~microbenthos.MicroBenthosModel`): model instance
Returns:
:class:`~microbenthos.MicroBenthosModel`
Raises:
RuntimeError: if model has already been set
ValueError: if modes interface does not match
ValueError: if model :attr:`.model.full_eqn` does not get created
even after :meth:`.model.create_full_equation` is called.
"""
return self._model
@model.setter
def model(self, m):
if self.model:
raise RuntimeError('Model already set')
full_eqn = getattr(m, 'full_eqn', None)
if full_eqn is None:
if hasattr(m, 'create_full_equation'):
m.create_full_equation()
full_eqn = getattr(m, 'full_eqn', None)
if full_eqn is None:
raise ValueError(
'Model {!r} (type={}) does not have a valid equation'.format(
m, type(m)))
def recursive_hasattr(obj, path, is_callable=False):
parts = path.split('.')
S = obj
FOUND = False
for p in parts:
if hasattr(S, p):
S = getattr(S, p)
FOUND = True
else:
FOUND = False
break
if not FOUND:
return False
else:
if is_callable:
return callable(S)
else:
return True
expected_attrs = ['clock', 'full_eqn']
expected_callables = [
'full_eqn.sweep', 'update_vars', 'clock.increment_time'
]
failed_attrs = tuple(
filter(lambda x: not recursive_hasattr(m, x), expected_attrs))
failed_callables = tuple(
filter(lambda x: not recursive_hasattr(m, x, is_callable=True),
expected_callables))
if failed_attrs:
self.logger.error(
'Model is missing required attributes: {}'.format(
failed_attrs))
if failed_callables:
self.logger.error('Model is missing required callables: {}'.format(
failed_callables))
if failed_callables or failed_attrs:
raise ValueError('Model interface is missing: {}'.format(
set(failed_attrs + failed_callables)))
self._model = m
# if simtime_days is given, override the simtime_total with it
if self.simtime_days is not None:
I = self.model.get_object('env.irradiance')
simtime_total = self.simtime_days * I.hours_total
self.logger.info(
'Setting simtime_total={} for {} days of simtime'.format(
simtime_total, self.simtime_days))
self.simtime_total = simtime_total
def _create_solver(self):
"""
Create the fipy solver to be used
"""
solver_module = importlib.import_module('fipy.solvers.{}'.format(
self.fipy_solver))
Solver = getattr(solver_module, 'DefaultSolver')
self._solver = Solver()
self.logger.debug('Created fipy {} solver: {}'.format(
self.fipy_solver, self._solver))
def run_timestep(self):
"""
Evolve the model through a single timestep
"""
if not self.started:
raise RuntimeError(
'Simulation timestep cannot be run since started=False')
if self.model is None:
raise RuntimeError('Simulation model is None, cannot run timestep')
dt = self.simtime_step
self.logger.info('Running timestep {} + {}'.format(
self.model.clock, dt))
num_sweeps = 0
EQN = self.model.full_eqn
retry = True
res = 100.0
while (res > self.max_residual) and (num_sweeps < self.max_sweeps) \
and retry:
try:
res = EQN.sweep(solver=self._solver, dt=float(dt.numericValue))
num_sweeps += 1
res = float(res)
self.logger.debug('Sweeps: {} residual: {:.2g}'.format(
num_sweeps, res))
except (TypeError, RuntimeError):
self.logger.warning(f'Error with simulation timestep dt={dt}')
res = self.max_residual * 100
break
return res, num_sweeps
def evolution(self):
"""
Evolves the model clock through the time steps for the simulation, i.e.
by calling :meth:`.run_timestep` and :meth:`.model.clock.increment_time`
repeatedly while ``model.clock() <= self.simtime_total``.
This is a generator that yields the step number, and the state of the
evolution after each time step. If :meth:`snapshot_due` is true, then
also the model snapshot is included in the state.
Yields:
`(step, state)` tuple of step number and simulation state
Raises:
RuntimeError: if :attr:`.started` is already True
"""
if self.started:
raise RuntimeError(
'Simulation already started. Cannot run parallel evolutions!')
self.logger.debug(
'simtime_total={o.simtime_total} simtime_step={o.simtime_step}, '
'max_sweeps={o.max_sweeps} max_residual={o.max_residual}'.format(
o=self))
self.logger.debug('Solving: {}'.format(self.model.full_eqn))
self._create_solver()
self._started = True
self.logger.info('Simulation evolution starting')
self.model.update_vars()
self._prev_snapshot = Variable(self.model.clock.copy(),
name='prev_snapshot')
step = 0
self.simtime_step = self.simtime_lims[0]
while self.model.clock() <= self.simtime_total:
self.logger.debug('Running step #{} {}'.format(
step, self.model.clock))
dt = self.simtime_step
tic = time.time()
residual, num_sweeps = self.run_timestep()
toc = time.time()
self.logger.debug(
f'For dt={dt} residual={residual:.4g} with sweeps={num_sweeps}'
)
# if residual == 0:
# raise RuntimeError(f'Residual perfect 0 for dt={dt}. Problem in domain!')
self._sweepsQ.appendleft(num_sweeps)
self._residualQ.appendleft(residual)
if residual >= self.max_residual:
self.logger.info(
f'Ignoring dt={dt}: res={residual:.4g} > {self.max_residual:.4g}'
)
self.update_simtime_step(residual, num_sweeps)
self.model.revert_vars()
# just go back to while loop start, now that dt has been made smaller
continue
else:
self.model.update_vars()
self.model.update_equations(dt)
step += 1
self.update_simtime_step(residual, num_sweeps)
calc_time = 1000 * (toc - tic)
self.logger.debug('Time step {} done in {:.2f} msec'.format(
self.simtime_step, calc_time))
if self.snapshot_due():
self.logger.debug('Snapshot in step #{}'.format(step))
state = self.get_state(state=self.model.snapshot(),
calc_time=calc_time,
residual=residual,
num_sweeps=num_sweeps)
yield (step, state)
# now set the prev_snapshot so that snapshot_due() will
# remain true for processing
self._prev_snapshot.setValue(self.model.clock.copy())
self.logger.debug('Prev snapshot set: {}'.format(
self._prev_snapshot))
else:
# create a minimal state
# this is the model clock and current residual
state = self.get_state(calc_time=calc_time,
residual=residual,
num_sweeps=num_sweeps)
yield (step, state)
self.model.clock.increment_time(dt)
self.logger.info('Simulation evolution completed')
self._started = False
def get_state(self, state=None, metrics=None, **kwargs):
"""
Get the state of the simulation evolution
Args:
state (None, dict):
If state is given (from ``model.snapshot()``), then that is used.
If None, then just the time info is created by using
:attr:`.model.clock`.
metrics (None, dict):
a dict to get the simulation metrics from,
else from `kwargs`
**kwargs:
parameters to build metrics dict. Currently the keys
`"calc_time"`, `"residual"` and `"num_sweeps"` are used,
if available.
Returns:
dict: the simulation state
"""
if state is None:
state = dict(time=dict(data=snapshot_var(self.model.clock)))
if metrics is None:
metrics = dict(
calc_time=dict(data=(kwargs.get('calc_time', 0.0),
dict(unit='ms'))),
residual=dict(data=(kwargs.get('residual', 0.0), None)),
num_sweeps=dict(data=(kwargs.get('num_sweeps', 0), None)),
)
state['metrics'] = metrics
return state
def snapshot_due(self):
"""
Returns:
bool: If the current model clock time has exceeded
:attr:`.snapshot_interval` since the last snapshot time
"""
return self.model.clock() - self._prev_snapshot() >= \
self.snapshot_interval
def update_simtime_step(self, residual, num_sweeps):
"""
Update the :attr:`.simtime_step` to be adaptive to the current
residual and sweeps.
A multiplicative factor for the time-step is determined based on the
number of sweeps and residual. If the `residual` is more than
:attr:`.max_residual`, then the time-step is quartered. If not, it is
boosted by up to double, depending on the `num_sweeps` and
:attr:`.max_sweeps`. Once a new timestep is determined, it is limited to
the time left in the model simulation.
Args:
residual (float): the residual from the last equation step
num_sweeps (int): the number of sweeps from the last equation step
"""
self.logger.info(
'Updating step {} after {}/{} sweeps and {:.3g}/{:.3g} '
'residual'.format(self.simtime_step, num_sweeps, self.max_sweeps,
residual, self.max_residual))
old_step = self.simtime_step
mult = 1.0
if residual >= self.max_residual:
mult = 0.5
else:
# if the last N simtime steps have produced residuals lesser than max, then boost the
# timestep
if all([r < self.max_residual for r in list(self._residualQ)]):
mult = 1.25
else:
mult = 1.0
new_step = self.simtime_step * max(0.01, mult)
self.simtime_step = min(new_step,
self.simtime_total - self.model.clock())
self.logger.info(f'Residual={residual} max={self.max_residual}')
self.logger.info('Time-step update {} x {:.2g} = {}'.format(
old_step, mult, self.simtime_step))
