#
# Simulation class
#
from __future__ import annotations
import pickle
import pybamm
import numpy as np
import hashlib
import warnings
import sys
from functools import lru_cache
from datetime import timedelta
from pybamm.util import import_optional_dependency
from pybamm.expression_tree.operations.serialise import Serialise
def is_notebook():
try:
shell = get_ipython().__class__.__name__
if shell == "ZMQInteractiveShell": # pragma: no cover
# Jupyter notebook or qtconsole
cfg = get_ipython().config
nb = len(cfg["InteractiveShell"].keys()) == 0
return nb
elif shell == "TerminalInteractiveShell": # pragma: no cover
return False # Terminal running IPython
elif shell == "Shell": # pragma: no cover
return True # Google Colab notebook
else: # pragma: no cover
return False # Other type (?)
except NameError:
return False # Probably standard Python interpreter
[docs]
class Simulation:
"""A Simulation class for easy building and running of PyBaMM simulations.
Parameters
----------
model : :class:`pybamm.BaseModel`
The model to be simulated
experiment : :class:`pybamm.Experiment` or string or list (optional)
The experimental conditions under which to solve the model. If a string is
passed, the experiment is constructed as `pybamm.Experiment([experiment])`. If
a list is passed, the experiment is constructed as
`pybamm.Experiment(experiment)`.
geometry: :class:`pybamm.Geometry` (optional)
The geometry upon which to solve the model
parameter_values: :class:`pybamm.ParameterValues` (optional)
Parameters and their corresponding numerical values.
submesh_types: dict (optional)
A dictionary of the types of submesh to use on each subdomain
var_pts: dict (optional)
A dictionary of the number of points used by each spatial variable
spatial_methods: dict (optional)
A dictionary of the types of spatial method to use on each
domain (e.g. pybamm.FiniteVolume)
solver: :class:`pybamm.BaseSolver` (optional)
The solver to use to solve the model.
output_variables: list (optional)
A list of variables to plot automatically
C_rate: float (optional)
The C-rate at which you would like to run a constant current (dis)charge.
"""
def __init__(
self,
model,
experiment=None,
geometry=None,
parameter_values=None,
submesh_types=None,
var_pts=None,
spatial_methods=None,
solver=None,
output_variables=None,
C_rate=None,
):
self._parameter_values = parameter_values or model.default_parameter_values
self._unprocessed_parameter_values = self._parameter_values
if isinstance(model, pybamm.lithium_ion.BasicDFNHalfCell):
if experiment is not None:
raise NotImplementedError(
"BasicDFNHalfCell is not compatible with experiment simulations."
)
if experiment is None:
# Check to see if the current is provided as data (i.e. drive cycle)
current = self._parameter_values.get("Current function [A]")
if isinstance(current, pybamm.Interpolant):
self.operating_mode = "drive cycle"
else:
self.operating_mode = "without experiment"
if C_rate:
self.C_rate = C_rate
self._parameter_values.update(
{
"Current function [A]": self.C_rate
* self._parameter_values["Nominal cell capacity [A.h]"]
}
)
else:
if isinstance(experiment, (str, pybamm.step.BaseStep)):
experiment = pybamm.Experiment([experiment])
elif isinstance(experiment, list):
experiment = pybamm.Experiment(experiment)
elif not isinstance(experiment, pybamm.Experiment):
raise TypeError(
"experiment must be a pybamm `Experiment` instance, a single "
"experiment step, or a list of experiment steps"
)
self.operating_mode = "with experiment"
# Save the experiment
self.experiment = experiment.copy()
self._unprocessed_model = model
self._model = model
self._geometry = geometry or self._model.default_geometry
self._submesh_types = submesh_types or self._model.default_submesh_types
self._var_pts = var_pts or self._model.default_var_pts
self._spatial_methods = spatial_methods or self._model.default_spatial_methods
self._solver = solver or self._model.default_solver
self._output_variables = output_variables
# Initialize empty built states
self._model_with_set_params = None
self._built_model = None
self._built_initial_soc = None
self.steps_to_built_models = None
self.steps_to_built_solvers = None
self._mesh = None
self._disc = None
self._solution = None
self.quick_plot = None
# Initialise instances of Simulation class with the same random seed
self._set_random_seed()
# ignore runtime warnings in notebooks
if is_notebook(): # pragma: no cover
import warnings
warnings.filterwarnings("ignore")
self.get_esoh_solver = lru_cache()(self._get_esoh_solver)
def __getstate__(self):
"""
Return dictionary of picklable items
"""
result = self.__dict__.copy()
result["get_esoh_solver"] = None # Exclude LRU cache
return result
def __setstate__(self, state):
"""
Unpickle, restoring unpicklable relationships
"""
self.__dict__ = state
self.get_esoh_solver = lru_cache()(self._get_esoh_solver)
# If the solver being used is CasadiSolver or its variants, set a fixed
# random seed during class initialization to the SHA-256 hash of the class
# name for purposes of reproducibility.
def _set_random_seed(self):
if isinstance(self._solver, pybamm.CasadiSolver) or isinstance(
self._solver, pybamm.CasadiAlgebraicSolver
):
np.random.seed(
int(hashlib.sha256(self.__class__.__name__.encode()).hexdigest(), 16)
% (2**32)
)
[docs]
def set_up_and_parameterise_experiment(self):
"""
Create and parameterise the models for each step in the experiment.
This increases set-up time since several models to be processed, but
reduces simulation time since the model formulation is efficient.
"""
parameter_values = self._parameter_values.copy()
# Set the initial temperature to be the temperature of the first step
# We can set this globally for all steps since any subsequent steps will either
# start at the temperature at the end of the previous step (if non-isothermal
# model), or will use the "Ambient temperature" input (if isothermal model).
# In either case, the initial temperature is not used for any steps except
# the first.
init_temp = self.experiment.steps[0].temperature
if init_temp is not None:
parameter_values["Initial temperature [K]"] = init_temp
# Process each step
self.experiment_unique_steps_to_model = {}
for step in self.experiment.unique_steps:
parameterised_model = step.process_model(self._model, parameter_values)
self.experiment_unique_steps_to_model[step.basic_repr()] = (
parameterised_model
)
# Set up rest model if experiment has start times
if self.experiment.initial_start_time:
# duration doesn't matter, we just need the model
rest_step = pybamm.step.rest(duration=1)
# Change ambient temperature to be an input, which will be changed at
# solve time
parameter_values["Ambient temperature [K]"] = "[input]"
parameterised_model = rest_step.process_model(self._model, parameter_values)
self.experiment_unique_steps_to_model["Rest for padding"] = (
parameterised_model
)
[docs]
def set_parameters(self):
"""
A method to set the parameters in the model and the associated geometry.
"""
if self.model_with_set_params:
return
self._model_with_set_params = self._parameter_values.process_model(
self._unprocessed_model, inplace=False
)
self._parameter_values.process_geometry(self._geometry)
self._model = self._model_with_set_params
def set_initial_soc(self, initial_soc, inputs=None):
if self._built_initial_soc != initial_soc:
# reset
self._model_with_set_params = None
self._built_model = None
self.steps_to_built_models = None
self.steps_to_built_solvers = None
options = self.model.options
param = self._model.param
if options["open-circuit potential"] == "MSMR":
self._parameter_values = (
self._unprocessed_parameter_values.set_initial_ocps(
initial_soc, param=param, inplace=False, options=options
)
)
elif options["working electrode"] == "positive":
self._parameter_values = (
self._unprocessed_parameter_values.set_initial_stoichiometry_half_cell(
initial_soc,
param=param,
inplace=False,
options=options,
inputs=inputs,
)
)
else:
self._parameter_values = (
self._unprocessed_parameter_values.set_initial_stoichiometries(
initial_soc,
param=param,
inplace=False,
options=options,
inputs=inputs,
)
)
# Save solved initial SOC in case we need to re-build the model
self._built_initial_soc = initial_soc
[docs]
def build(self, check_model=True, initial_soc=None, inputs=None):
"""
A method to build the model into a system of matrices and vectors suitable for
performing numerical computations. If the model has already been built or
solved then this function will have no effect.
This method will automatically set the parameters
if they have not already been set.
Parameters
----------
check_model : bool, optional
If True, model checks are performed after discretisation (see
:meth:`pybamm.Discretisation.process_model`). Default is True.
initial_soc : float, optional
Initial State of Charge (SOC) for the simulation. Must be between 0 and 1.
If given, overwrites the initial concentrations provided in the parameter
set.
"""
if initial_soc is not None:
self.set_initial_soc(initial_soc, inputs=inputs)
if self.built_model:
return
elif self._model.is_discretised:
self._model_with_set_params = self._model
self._built_model = self._model
else:
self.set_parameters()
self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts)
self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods)
self._built_model = self._disc.process_model(
self._model_with_set_params, inplace=False, check_model=check_model
)
# rebuilt model so clear solver setup
self._solver._model_set_up = {}
[docs]
def build_for_experiment(self, check_model=True, initial_soc=None, inputs=None):
"""
Similar to :meth:`Simulation.build`, but for the case of simulating an
experiment, where there may be several models and solvers to build.
"""
if initial_soc is not None:
self.set_initial_soc(initial_soc, inputs)
if self.steps_to_built_models:
return
else:
self.set_up_and_parameterise_experiment()
# Can process geometry with default parameter values (only electrical
# parameters change between parameter values)
self._parameter_values.process_geometry(self._geometry)
# Only needs to set up mesh and discretisation once
self._mesh = pybamm.Mesh(self._geometry, self._submesh_types, self._var_pts)
self._disc = pybamm.Discretisation(self._mesh, self._spatial_methods)
# Process all the different models
self.steps_to_built_models = {}
self.steps_to_built_solvers = {}
for (
step,
model_with_set_params,
) in self.experiment_unique_steps_to_model.items():
# It's ok to modify the model with set parameters in place as it's
# not returned anywhere
built_model = self._disc.process_model(
model_with_set_params, inplace=True, check_model=check_model
)
solver = self._solver.copy()
self.steps_to_built_solvers[step] = solver
self.steps_to_built_models[step] = built_model
[docs]
def solve(
self,
t_eval=None,
solver=None,
check_model=True,
save_at_cycles=None,
calc_esoh=True,
starting_solution=None,
initial_soc=None,
callbacks=None,
showprogress=False,
inputs=None,
**kwargs,
):
"""
A method to solve the model. This method will automatically build
and set the model parameters if not already done so.
Parameters
----------
t_eval : numeric type, optional
The times (in seconds) at which to compute the solution. Can be
provided as an array of times at which to return the solution, or as a
list `[t0, tf]` where `t0` is the initial time and `tf` is the final time.
If provided as a list the solution is returned at 100 points within the
interval `[t0, tf]`.
If not using an experiment or running a drive cycle simulation (current
provided as data) `t_eval` *must* be provided.
If running an experiment the values in `t_eval` are ignored, and the
solution times are specified by the experiment.
If None and the parameter "Current function [A]" is read from data
(i.e. drive cycle simulation) the model will be solved at the times
provided in the data.
solver : :class:`pybamm.BaseSolver`, optional
The solver to use to solve the model. If None, Simulation.solver is used
check_model : bool, optional
If True, model checks are performed after discretisation (see
:meth:`pybamm.Discretisation.process_model`). Default is True.
save_at_cycles : int or list of ints, optional
Which cycles to save the full sub-solutions for. If None, all cycles are
saved. If int, every multiple of save_at_cycles is saved. If list, every
cycle in the list is saved. The first cycle (cycle 1) is always saved.
calc_esoh : bool, optional
Whether to include eSOH variables in the summary variables. If `False`
then only summary variables that do not require the eSOH calculation
are calculated. Default is True.
starting_solution : :class:`pybamm.Solution`
The solution to start stepping from. If None (default), then self._solution
is used. Must be None if not using an experiment.
initial_soc : float, optional
Initial State of Charge (SOC) for the simulation. Must be between 0 and 1.
If given, overwrites the initial concentrations provided in the parameter
set.
callbacks : list of callbacks, optional
A list of callbacks to be called at each time step. Each callback must
implement all the methods defined in :class:`pybamm.callbacks.BaseCallback`.
showprogress : bool, optional
Whether to show a progress bar for cycling. If true, shows a progress bar
for cycles. Has no effect when not used with an experiment.
Default is False.
**kwargs
Additional key-word arguments passed to `solver.solve`.
See :meth:`pybamm.BaseSolver.solve`.
"""
# Setup
if solver is None:
solver = self._solver
callbacks = pybamm.callbacks.setup_callbacks(callbacks)
logs = {}
inputs = inputs or {}
if self.operating_mode in ["without experiment", "drive cycle"]:
self.build(check_model=check_model, initial_soc=initial_soc, inputs=inputs)
if save_at_cycles is not None:
raise ValueError(
"'save_at_cycles' option can only be used if simulating an "
"Experiment "
)
if starting_solution is not None:
raise ValueError(
"starting_solution can only be provided if simulating an Experiment"
)
if (
self.operating_mode == "without experiment"
or "ElectrodeSOH" in self._model.name
):
if t_eval is None:
raise pybamm.SolverError(
"'t_eval' must be provided if not using an experiment or "
"simulating a drive cycle. 't_eval' can be provided as an "
"array of times at which to return the solution, or as a "
"list [t0, tf] where t0 is the initial time and tf is the "
"final time. "
"For a constant current (dis)charge the suggested 't_eval' "
"is [0, 3700/C] where C is the C-rate. "
"For example, run\n\n"
"\tsim.solve([0, 3700])\n\n"
"for a 1C discharge."
)
elif self.operating_mode == "drive cycle":
# For drive cycles (current provided as data) we perform additional
# tests on t_eval (if provided) to ensure the returned solution
# captures the input.
time_data = self._parameter_values["Current function [A]"].x[0]
# If no t_eval is provided, we use the times provided in the data.
if t_eval is None:
pybamm.logger.info("Setting t_eval as specified by the data")
t_eval = time_data
# If t_eval is provided we first check if it contains all of the
# times in the data to within 10-12. If it doesn't, we then check
# that the largest gap in t_eval is smaller than the smallest gap in
# the time data (to ensure the resolution of t_eval is fine enough).
# We only raise a warning here as users may genuinely only want
# the solution returned at some specified points.
elif (
set(np.round(time_data, 12)).issubset(set(np.round(t_eval, 12)))
) is False:
warnings.warn(
"""
t_eval does not contain all of the time points in the data
set. Note: passing t_eval = None automatically sets t_eval
to be the points in the data.
""",
pybamm.SolverWarning,
stacklevel=2,
)
dt_data_min = np.min(np.diff(time_data))
dt_eval_max = np.max(np.diff(t_eval))
if dt_eval_max > dt_data_min + sys.float_info.epsilon:
warnings.warn(
f"""
The largest timestep in t_eval ({dt_eval_max}) is larger than
the smallest timestep in the data ({dt_data_min}). The returned
solution may not have the correct resolution to accurately
capture the input. Try refining t_eval. Alternatively,
passing t_eval = None automatically sets t_eval to be the
points in the data.
""",
pybamm.SolverWarning,
stacklevel=2,
)
self._solution = solver.solve(
self.built_model, t_eval, inputs=inputs, **kwargs
)
elif self.operating_mode == "with experiment":
callbacks.on_experiment_start(logs)
self.build_for_experiment(
check_model=check_model, initial_soc=initial_soc, inputs=inputs
)
if t_eval is not None:
pybamm.logger.warning(
"Ignoring t_eval as solution times are specified by the experiment"
)
# Re-initialize solution, e.g. for solving multiple times with different
# inputs without having to build the simulation again
self._solution = starting_solution
# Step through all experimental conditions
user_inputs = inputs
timer = pybamm.Timer()
# Set up eSOH solver (for summary variables)
esoh_solver = self.get_esoh_solver(calc_esoh)
if starting_solution is None:
starting_solution_cycles = []
starting_solution_summary_variables = []
starting_solution_first_states = []
elif not hasattr(starting_solution, "all_summary_variables"):
(
cycle_solution,
cycle_sum_vars,
cycle_first_state,
) = pybamm.make_cycle_solution(
[starting_solution],
esoh_solver=esoh_solver,
save_this_cycle=True,
inputs=user_inputs,
)
starting_solution_cycles = [cycle_solution]
starting_solution_summary_variables = [cycle_sum_vars]
starting_solution_first_states = [cycle_first_state]
else:
starting_solution_cycles = starting_solution.cycles.copy()
starting_solution_summary_variables = (
starting_solution.all_summary_variables.copy()
)
starting_solution_first_states = (
starting_solution.all_first_states.copy()
)
# set simulation initial_start_time
if starting_solution is None:
initial_start_time = self.experiment.initial_start_time
else:
initial_start_time = starting_solution.initial_start_time
if (
initial_start_time is None
and self.experiment.initial_start_time is not None
):
raise ValueError(
"When using experiments with `start_time`, the starting_solution "
"must have a `start_time` too."
)
cycle_offset = len(starting_solution_cycles)
all_cycle_solutions = starting_solution_cycles
all_summary_variables = starting_solution_summary_variables
all_first_states = starting_solution_first_states
current_solution = starting_solution or pybamm.EmptySolution()
voltage_stop = self.experiment.termination.get("voltage")
logs["stopping conditions"] = {"voltage": voltage_stop}
idx = 0
num_cycles = len(self.experiment.cycle_lengths)
feasible = True # simulation will stop if experiment is infeasible
# Add initial padding rest if current time is earlier than first start time
# This could be the case when using a starting solution
if starting_solution is not None:
step = self.experiment.steps[0]
if step.start_time is not None:
rest_time = (
step.start_time
- (
initial_start_time
+ timedelta(seconds=float(current_solution.t[-1]))
)
).total_seconds()
if rest_time > pybamm.settings.step_start_offset:
# logs["step operating conditions"] = "Initial rest for padding"
# callbacks.on_step_start(logs)
inputs = {
**user_inputs,
"Ambient temperature [K]": (
step.temperature
or self._parameter_values["Ambient temperature [K]"]
),
"start time": current_solution.t[-1],
}
steps = current_solution.cycles[-1].steps
step_solution = current_solution.cycles[-1].steps[-1]
step_solution_with_rest = self.run_padding_rest(
kwargs, rest_time, step_solution, inputs
)
steps[-1] = step_solution + step_solution_with_rest
cycle_solution, _, _ = pybamm.make_cycle_solution(
steps, esoh_solver=esoh_solver, save_this_cycle=True
)
old_cycles = current_solution.cycles.copy()
old_cycles[-1] = cycle_solution
current_solution += step_solution_with_rest
current_solution.cycles = old_cycles
# Update _solution
self._solution = current_solution
# check if a user has tqdm installed
if showprogress:
tqdm = import_optional_dependency("tqdm")
cycle_lengths = tqdm.tqdm(
self.experiment.cycle_lengths,
desc="Cycling",
)
else:
cycle_lengths = self.experiment.cycle_lengths
for cycle_num, cycle_length in enumerate(
cycle_lengths,
start=1,
):
logs["cycle number"] = (
cycle_num + cycle_offset,
num_cycles + cycle_offset,
)
logs["elapsed time"] = timer.time()
callbacks.on_cycle_start(logs)
steps = []
cycle_solution = None
# Decide whether we should save this cycle
save_this_cycle = (
# always save cycle 1
cycle_num == 1
# always save last cycle
or cycle_num == num_cycles
# None: save all cycles
or save_at_cycles is None
# list: save all cycles in the list
or (
isinstance(save_at_cycles, list)
and cycle_num + cycle_offset in save_at_cycles
)
# int: save all multiples
or (
isinstance(save_at_cycles, int)
and (cycle_num + cycle_offset) % save_at_cycles == 0
)
)
for step_num in range(1, cycle_length + 1):
# Use 1-indexing for printing cycle number as it is more
# human-intuitive
step = self.experiment.steps[idx]
start_time = current_solution.t[-1]
# If step has an end time, dt must take that into account
if step.end_time is not None:
dt = min(
step.duration,
(
step.end_time
- (
initial_start_time
+ timedelta(seconds=float(start_time))
)
).total_seconds(),
)
else:
dt = step.duration
step_str = str(step)
model = self.steps_to_built_models[step.basic_repr()]
solver = self.steps_to_built_solvers[step.basic_repr()]
logs["step number"] = (step_num, cycle_length)
logs["step operating conditions"] = step_str
callbacks.on_step_start(logs)
inputs = {
**user_inputs,
"start time": start_time,
}
# Make sure we take at least 2 timesteps
npts = max(int(round(dt / step.period)) + 1, 2)
try:
step_solution = solver.step(
current_solution,
model,
dt,
t_eval=np.linspace(0, dt, npts),
save=False,
inputs=inputs,
**kwargs,
)
except pybamm.SolverError as error:
if (
"non-positive at initial conditions" in error.message
and "[experiment]" in error.message
):
step_solution = pybamm.EmptySolution(
"Event exceeded in initial conditions", t=start_time
)
else:
logs["error"] = error
callbacks.on_experiment_error(logs)
feasible = False
# If none of the cycles worked, raise an error
if cycle_num == 1 and step_num == 1:
raise error
# Otherwise, just stop this cycle
break
step_termination = step_solution.termination
# Add a padding rest step if necessary
if step.next_start_time is not None:
rest_time = (
step.next_start_time
- (
initial_start_time
+ timedelta(seconds=float(step_solution.t[-1]))
)
).total_seconds()
if rest_time > pybamm.settings.step_start_offset:
logs["step number"] = (step_num, cycle_length)
logs["step operating conditions"] = "Rest for padding"
callbacks.on_step_start(logs)
inputs = {
**user_inputs,
"Ambient temperature [K]": (
step.temperature
or self._parameter_values["Ambient temperature [K]"]
),
"start time": step_solution.t[-1],
}
step_solution_with_rest = self.run_padding_rest(
kwargs, rest_time, step_solution, inputs=inputs
)
step_solution += step_solution_with_rest
steps.append(step_solution)
# If there haven't been any successful steps yet in this cycle, then
# carry the solution over from the previous cycle (but
# `step_solution` should still be an EmptySolution so that in the
# list of returned step solutions we can see which steps were
# skipped)
if (
cycle_solution is None
and isinstance(step_solution, pybamm.EmptySolution)
and not isinstance(current_solution, pybamm.EmptySolution)
):
cycle_solution = current_solution.last_state
else:
cycle_solution = cycle_solution + step_solution
current_solution = cycle_solution
callbacks.on_step_end(logs)
logs["termination"] = step_solution.termination
# Only allow events specified by experiment
if not (
isinstance(step_solution, pybamm.EmptySolution)
or step_termination == "final time"
or "[experiment]" in step_termination
):
callbacks.on_experiment_infeasible(logs)
feasible = False
break
# Increment index for next iteration
idx += 1
if save_this_cycle or feasible is False:
self._solution = self._solution + cycle_solution
# At the final step of the inner loop we save the cycle
if len(steps) > 0:
# Check for EmptySolution
if all(isinstance(step, pybamm.EmptySolution) for step in steps):
if len(steps) == 1:
raise pybamm.SolverError(
f"Step '{step_str}' is infeasible "
"due to exceeded bounds at initial conditions. "
"If this step is part of a longer cycle, "
"round brackets should be used to indicate this, "
"e.g.:\n pybamm.Experiment([(\n"
"\tDischarge at C/5 for 10 hours or until 3.3 V,\n"
"\tCharge at 1 A until 4.1 V,\n"
"\tHold at 4.1 V until 10 mA\n"
"])"
)
else:
this_cycle = self.experiment.cycles[cycle_num - 1]
raise pybamm.SolverError(
f"All steps in the cycle {this_cycle} are infeasible "
"due to exceeded bounds at initial conditions."
)
cycle_sol = pybamm.make_cycle_solution(
steps,
esoh_solver=esoh_solver,
save_this_cycle=save_this_cycle,
inputs=user_inputs,
)
cycle_solution, cycle_sum_vars, cycle_first_state = cycle_sol
all_cycle_solutions.append(cycle_solution)
all_summary_variables.append(cycle_sum_vars)
all_first_states.append(cycle_first_state)
logs["summary variables"] = cycle_sum_vars
# Calculate capacity_start using the first cycle
if cycle_num == 1:
# Note capacity_start could be defined as
# self._parameter_values["Nominal cell capacity [A.h]"] instead
if "capacity" in self.experiment.termination:
capacity_start = all_summary_variables[0]["Capacity [A.h]"]
logs["start capacity"] = capacity_start
value, typ = self.experiment.termination["capacity"]
if typ == "Ah":
capacity_stop = value
elif typ == "%":
capacity_stop = value / 100 * capacity_start
else:
capacity_stop = None
logs["stopping conditions"]["capacity"] = capacity_stop
logs["elapsed time"] = timer.time()
callbacks.on_cycle_end(logs)
# Break if stopping conditions are met
# Logging is done in the callbacks
if capacity_stop is not None:
capacity_now = cycle_sum_vars["Capacity [A.h]"]
if not np.isnan(capacity_now) and capacity_now <= capacity_stop:
break
if voltage_stop is not None:
min_voltage = cycle_sum_vars["Minimum voltage [V]"]
if min_voltage <= voltage_stop[0]:
break
# Break if the experiment is infeasible (or errored)
if feasible is False:
break
if self.solution is not None and len(all_cycle_solutions) > 0:
self.solution.cycles = all_cycle_solutions
self.solution.set_summary_variables(all_summary_variables)
self.solution.all_first_states = all_first_states
callbacks.on_experiment_end(logs)
# record initial_start_time of the solution
self.solution.initial_start_time = initial_start_time
return self.solution
def run_padding_rest(self, kwargs, rest_time, step_solution, inputs):
model = self.steps_to_built_models["Rest for padding"]
solver = self.steps_to_built_solvers["Rest for padding"]
# Make sure we take at least 2 timesteps. The period is hardcoded to 10
# minutes,the user can always override it by adding a rest step
npts = max(int(round(rest_time / 600)) + 1, 2)
step_solution_with_rest = solver.step(
step_solution,
model,
rest_time,
t_eval=np.linspace(0, rest_time, npts),
save=False,
inputs=inputs,
**kwargs,
)
return step_solution_with_rest
[docs]
def step(
self,
dt,
solver=None,
t_eval=None,
save=True,
starting_solution=None,
inputs=None,
**kwargs,
):
"""
A method to step the model forward one timestep. This method will
automatically build and set the model parameters if not already done so.
Parameters
----------
dt : numeric type
The timestep over which to step the solution
solver : :class:`pybamm.BaseSolver`
The solver to use to solve the model.
t_eval : list or numpy.ndarray, optional
An array of times at which to return the solution during the step
(Note: t_eval is the time measured from the start of the step, so should start at 0 and end at dt).
By default, the solution is returned at t0 and t0 + dt.
save : bool
Turn on to store the solution of all previous timesteps
starting_solution : :class:`pybamm.Solution`
The solution to start stepping from. If None (default), then self._solution
is used
**kwargs
Additional key-word arguments passed to `solver.solve`.
See :meth:`pybamm.BaseSolver.step`.
"""
if self.operating_mode in ["without experiment", "drive cycle"]:
self.build()
if solver is None:
solver = self._solver
if starting_solution is None:
starting_solution = self._solution
self._solution = solver.step(
starting_solution,
self.built_model,
dt,
t_eval=t_eval,
save=save,
inputs=inputs,
**kwargs,
)
return self.solution
def _get_esoh_solver(self, calc_esoh):
if (
calc_esoh is False
or isinstance(self._model, pybamm.lead_acid.BaseModel)
or isinstance(self._model, pybamm.equivalent_circuit.Thevenin)
or self._model.options["working electrode"] != "both"
):
return None
return pybamm.lithium_ion.ElectrodeSOHSolver(
self._parameter_values, self._model.param, options=self._model.options
)
[docs]
def plot(self, output_variables=None, **kwargs):
"""
A method to quickly plot the outputs of the simulation. Creates a
:class:`pybamm.QuickPlot` object (with keyword arguments 'kwargs') and
then calls :meth:`pybamm.QuickPlot.dynamic_plot`.
Parameters
----------
output_variables: list, optional
A list of the variables to plot.
**kwargs
Additional keyword arguments passed to
:meth:`pybamm.QuickPlot.dynamic_plot`.
For a list of all possible keyword arguments see :class:`pybamm.QuickPlot`.
"""
if self._solution is None:
raise ValueError(
"Model has not been solved, please solve the model before plotting."
)
if output_variables is None:
output_variables = self._output_variables
self.quick_plot = pybamm.dynamic_plot(
self._solution, output_variables=output_variables, **kwargs
)
return self.quick_plot
[docs]
def create_gif(self, number_of_images=80, duration=0.1, output_filename="plot.gif"):
"""
Generates x plots over a time span of t_eval and compiles them to create
a GIF. For more information see :meth:`pybamm.QuickPlot.create_gif`
Parameters
----------
number_of_images : int (optional)
Number of images/plots to be compiled for a GIF.
duration : float (optional)
Duration of visibility of a single image/plot in the created GIF.
output_filename : str (optional)
Name of the generated GIF file.
"""
if self.solution is None:
raise ValueError("The simulation has not been solved yet.")
if self.quick_plot is None:
self.quick_plot = pybamm.QuickPlot(self._solution)
self.quick_plot.create_gif(
number_of_images=number_of_images,
duration=duration,
output_filename=output_filename,
)
@property
def model(self):
return self._model
@property
def model_with_set_params(self):
return self._model_with_set_params
@property
def built_model(self):
return self._built_model
@property
def geometry(self):
return self._geometry
@property
def parameter_values(self):
return self._parameter_values
@property
def submesh_types(self):
return self._submesh_types
@property
def mesh(self):
return self._mesh
@property
def var_pts(self):
return self._var_pts
@property
def spatial_methods(self):
return self._spatial_methods
@property
def solver(self):
return self._solver
@property
def output_variables(self):
return self._output_variables
@property
def solution(self):
return self._solution
[docs]
def save(self, filename):
"""Save simulation using pickle module.
Parameters
----------
filename : str
The file extension can be arbitrary, but it is common to use ".pkl" or ".pickle"
"""
if self._model.convert_to_format == "python":
# We currently cannot save models in the 'python' format
raise NotImplementedError(
"""
Cannot save simulation if model format is python.
Set model.convert_to_format = 'casadi' instead.
"""
)
# Clear solver problem (not pickle-able, will automatically be recomputed)
if (
isinstance(self._solver, pybamm.CasadiSolver)
and self._solver.integrator_specs != {}
):
self._solver.integrator_specs = {}
if self.steps_to_built_solvers is not None:
for solver in self.steps_to_built_solvers.values():
if (
isinstance(solver, pybamm.CasadiSolver)
and solver.integrator_specs != {}
):
solver.integrator_specs = {}
with open(filename, "wb") as f:
pickle.dump(self, f, pickle.HIGHEST_PROTOCOL)
[docs]
def save_model(
self,
filename: str | None = None,
mesh: bool = False,
variables: bool = False,
):
"""
Write out a discretised model to a JSON file
Parameters
----------
mesh: bool
The mesh used to discretise the model. If false, plotting tools will not
be available when the model is read back in and solved.
variables: bool
The discretised variables. Not required to solve a model, but if false
tools will not be availble. Will automatically save meshes as well, required
for plotting tools.
filename: str, optional
The desired name of the JSON file. If no name is provided, one will be
created based on the model name, and the current datetime.
"""
mesh = self.mesh if (mesh or variables) else None
variables = self.built_model.variables if variables else None
if self.operating_mode == "with experiment":
raise NotImplementedError(
"""
Serialising models coupled to experiments is not yet supported.
"""
)
if self.built_model:
Serialise().save_model(
self.built_model, filename=filename, mesh=mesh, variables=variables
)
else:
raise NotImplementedError(
"""
PyBaMM can only serialise a discretised model.
Ensure the model has been built (e.g. run `build()`) before saving.
"""
)
[docs]
def plot_voltage_components(
self,
ax=None,
show_legend=True,
split_by_electrode=False,
show_plot=True,
**kwargs_fill,
):
"""
Generate a plot showing the component overpotentials that make up the voltage
Parameters
----------
ax : matplotlib Axis, optional
The axis on which to put the plot. If None, a new figure and axis is created.
show_legend : bool, optional
Whether to display the legend. Default is True.
split_by_electrode : bool, optional
Whether to show the overpotentials for the negative and positive electrodes
separately. Default is False.
show_plot : bool, optional
Whether to show the plots. Default is True. Set to False if you want to
only display the plot after plt.show() has been called.
kwargs_fill
Keyword arguments, passed to ax.fill_between.
"""
if self.solution is None:
raise ValueError("The simulation has not been solved yet.")
return pybamm.plot_voltage_components(
self.solution,
ax=ax,
show_legend=show_legend,
split_by_electrode=split_by_electrode,
show_plot=show_plot,
**kwargs_fill,
)
def load_sim(filename):
"""Load a saved simulation"""
return pybamm.load(filename)
