def plot(self, output_variables=None, quick_plot_vars=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.
quick_plot_vars: list, optional
A list of the variables to plot. Deprecated, use output_variables instead.
**kwargs
Additional keyword arguments passed to
:meth:`pybamm.QuickPlot.dynamic_plot`.
For a list of all possible keyword arguments see :class:`pybamm.QuickPlot`.
"""
if quick_plot_vars is not None:
raise NotImplementedError(
"'quick_plot_vars' has been deprecated. Use 'output_variables' instead."
)
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)
def plot(self, output_variables=None, **kwargs):
"""
For more information on the parameters used in the plot,
See :meth:`pybamm.Simulation.plot`
"""
self.quick_plot = pybamm.dynamic_plot(
self.sims, output_variables=output_variables, **kwargs)
return self.quick_plot
def plot(self, output_variables=None, **kwargs):
"""
A method to quickly plot the outputs of the solution. 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`.
"""
return pybamm.dynamic_plot(self, output_variables=output_variables, **kwargs)
def plot(self, quick_plot_vars=None, testing=False):
"""
A method to quickly plot the outputs of the simulation.
Parameters
----------
quick_plot_vars: list, optional
A list of the variables to plot.
testing, bool, optional
If False the plot will not be displayed
"""
if self._solution is None:
raise ValueError(
"Model has not been solved, please solve the model before plotting."
)
if quick_plot_vars is None:
quick_plot_vars = self.quick_plot_vars
self.quick_plot = pybamm.dynamic_plot(
self._solution, output_variables=quick_plot_vars, testing=testing
)
if model.name == "user":
# add the user supplied parameters
param.update(
{
"Negative electrode surface area to volume ratio [m-1]":
170000,
"Positive electrode surface area to volume ratio [m-1]":
200000,
},
check_already_exists=False,
)
sim = pybamm.Simulation(model, parameter_values=param)
solution = sim.solve(t_eval)
solutions.append(solution)
# plot solutions
pybamm.dynamic_plot(
solutions,
[
"Negative particle surface concentration [mol.m-3]",
"Positive particle surface concentration [mol.m-3]",
"Negative electrode interfacial current density [A.m-2]",
"Positive electrode interfacial current density [A.m-2]",
"Negative electrode potential [V]",
"Electrolyte potential [V]",
"Positive electrode potential [V]",
"Terminal voltage [V]",
],
)
seconds = minutes * 60
t_eval = np.linspace(0, seconds, 100)
sim.solve(t_eval=t_eval, solver=solver)
sims.append(sim)
pb.dynamic_plot(
sims,
[
"Terminal voltage [V]",
"Negative particle surface concentration",
"X-averaged negative particle surface concentration",
"Electrolyte concentration [mol.m-3]",
"Total negative electrode sei thickness [m]",
"X-averaged total negative electrode sei thickness [m]",
"X-averaged total negative electrode sei thickness",
"X-averaged negative electrode sei concentration [mol.m-3]",
"Loss of lithium to negative electrode sei [mol]",
[
"Negative electrode sei interfacial current density [A.m-2]",
"Negative electrode interfacial current density [A.m-2]",
],
[
"X-averaged negative electrode sei interfacial current density [A.m-2]",
"X-averaged negative electrode interfacial current density [A.m-2]",
],
"Sum of x-averaged negative electrode interfacial current densities",
"X-averaged electrolyte concentration",
],
)
#
# Compare lead-acid battery models
#
import pybamm
pybamm.set_logging_level("INFO")
# load models
models = [
pybamm.lead_acid.LOQS(),
pybamm.lead_acid.FOQS(),
pybamm.lead_acid.Composite(),
pybamm.lead_acid.Full(),
]
# create and run simulations
sims = []
for model in models:
model.convert_to_format = None
sim = pybamm.Simulation(model)
sim.solve([0, 3600 * 17])
sims.append(sim)
# plot
pybamm.dynamic_plot(sims)
@property
def default_solver(self):
return pybamm.CasadiAlgebraicSolver()
if __name__ == "__main__":
pybamm.set_logging_level("INFO")
true_model = RHTModel()
true_model.name = "True RHT"
parameter_values = true_model.default_parameter_values
parameter_values["T_inf"] = 45
sim_true = pybamm.Simulation(true_model, parameter_values=parameter_values)
base_model = RHTModel()
base_model.name = "Base RHT"
parameter_values = base_model.default_parameter_values
parameter_values["T_inf"] = 45
parameter_values["beta"] = 1
sim_base = pybamm.Simulation(base_model, parameter_values=parameter_values)
t_eval = np.array([0, 1])
sims = []
for sim in [sim_true, sim_base]:
sim.solve(t_eval)
sims.append(sim)
pybamm.dynamic_plot(sims, ["Temperature", "beta", "beta_decoupled"],
spatial_unit="m")
param = pybamm.ParameterValues(chemistry=pybamm.parameter_sets.Chen2020)
param = set_thermal_parameters(param, 20, 2.85e6, temperature)
mesh_factors = [1, 2, 4, 8]
solutions = []
var = pybamm.standard_spatial_vars
for factor in mesh_factors:
var_pts = {
var.x_n: 20 * factor,
var.x_s: 20 * factor,
var.x_p: 20 * factor,
var.r_n: 30 * factor,
var.r_p: 30 * factor,
var.y: 10,
var.z: 10,
}
sim = pybamm.Simulation(
model,
parameter_values=param,
var_pts=var_pts,
C_rate=Crate,
)
sim.model.name
sim.solve([0, 3600])
sim.solution.model.name += " x{} mesh".format(factor)
solutions.append(sim.solution)
pybamm.dynamic_plot(solutions)
years = 30
days = years * 365
hours = days * 24
minutes = hours * 60
seconds = minutes * 60
t_eval = np.linspace(0, seconds, 100)
sim.solve(t_eval=t_eval, solver=solver)
sims.append(sim)
pb.dynamic_plot(
sims,
[
"Terminal voltage [V]",
"Negative particle surface concentration",
"X-averaged negative particle surface concentration",
"Electrolyte concentration [mol.m-3]",
"Total negative electrode SEI thickness [m]",
"X-averaged total negative electrode SEI thickness [m]",
"X-averaged total negative electrode SEI thickness",
"X-averaged negative electrode SEI concentration [mol.m-3]",
"Loss of lithium to negative electrode SEI [mol]",
"Sum of x-averaged negative electrode interfacial current densities",
"Loss of lithium inventory [%]",
[
"Total lithium lost [mol]",
"Loss of lithium to negative electrode SEI [mol]"
],
],
)
