PyBaMM provides several plotting utilities built on Matplotlib. Matplotlib is an optional dependency — it is imported lazily inside plot methods so that PyBaMM can run on headless systems without a display.
Install Matplotlib with: pip install pybamm[plot]
Quick plotting with sim.plot()
After solving, call sim.plot() to open an interactive slider plot of the default output variables:
import pybamm
model = pybamm.lithium_ion.SPM()
sim = pybamm.Simulation(model)
sim.solve([0, 3600])
sim.plot()
pybamm.QuickPlot and calls dynamic_plot(), which renders a matplotlib figure with a time slider.
Choosing variables to plot
sim.plot([
"Battery voltage [V]",
"Current [A]",
"Cell temperature [K]",
"Negative particle surface concentration [mol.m-3]",
])
QuickPlot
pybamm.QuickPlot gives you full control over the plot configuration. It accepts solutions or simulation objects.
fig, axes = pybamm.QuickPlot(
sim,
output_variables=[
"Battery voltage [V]",
"Electrolyte concentration [mol.m-3]",
"Negative particle surface concentration [mol.m-3]",
"Positive particle surface concentration [mol.m-3]",
],
labels=["SPM"],
time_unit="hours",
spatial_unit="um",
figsize=(12, 8),
n_rows=2,
)
Constructor options
| Parameter | Description | Default |
|---|
solutions | Solution(s) or Simulation(s) to plot | required |
output_variables | List of variable names | model defaults |
labels | Legend labels for each solution | model names |
colors | Color cycle | ["r", "b", "k", "g", "m", "c"] |
linestyles | Linestyle cycle | ["-", ":", "--", "-."] |
figsize | Figure size (width, height) | auto |
n_rows | Number of subplot rows | auto (square layout) |
time_unit | "hours", "minutes", or "seconds" | auto |
spatial_unit | "m", "mm", or "um" | "um" |
variable_limits | Axis limits: "fixed", "tight", or a dict | "fixed" |
Comparing multiple solutions
Pass a list of solutions or simulations to overlay them:
model_spm = pybamm.lithium_ion.SPM()
model_dfn = pybamm.lithium_ion.DFN()
param = pybamm.ParameterValues("Chen2020")
sim_spm = pybamm.Simulation(model_spm, parameter_values=param)
sim_dfn = pybamm.Simulation(model_dfn, parameter_values=param)
sim_spm.solve([0, 3600])
sim_dfn.solve([0, 3600])
pybamm.dynamic_plot(
[sim_spm, sim_dfn],
output_variables=["Battery voltage [V]", "Current [A]"],
labels=["SPM", "DFN"],
)
Dynamic (interactive) plot
Call dynamic_plot() directly for the interactive slider:
quick_plot = pybamm.QuickPlot(sim)
quick_plot.dynamic_plot()
Static plot at a fixed time
Use plot(t) to render the figure at a single time point (in the solution’s time unit):
quick_plot = pybamm.QuickPlot(sim)
quick_plot.plot(t=1800) # plot at t = 1800 s
Voltage components
pybamm.plot_voltage_components breaks down the terminal voltage into its contributing overpotentials:
pybamm.plot_voltage_components(sim)
Solution object:
pybamm.plot_voltage_components(sim.solution)
pybamm.plot_voltage_components(
sim,
show_legend=True,
split_by_electrode=True, # separate negative and positive contributions
electrode_phases=("primary", "primary"),
)
Simulation object too:
sim.plot_voltage_components()
Summary variables
For multi-cycle experiments, pybamm.plot_summary_variables plots cycle-level degradation metrics:
# After a long cycling experiment
pybamm.plot_summary_variables(sol)
Capacity [A.h]Loss of lithium inventory [%]Total capacity lost to side reactions [A.h]Loss of active material in negative electrode [%]Loss of active material in positive electrode [%]- Stoichiometric limits
x_100, x_0, y_100, y_0
Custom variables:
pybamm.plot_summary_variables(
sol,
output_variables=["Capacity [A.h]", "Loss of lithium inventory [%]"],
labels=["Simulation 1"],
)
pybamm.plot_summary_variables([sol1, sol2], labels=["Cell A", "Cell B"])
2D plots
For spatially-resolved models (dimensionality > 0), use pybamm.plot2D to visualise a variable as a function of two spatial coordinates:
# Evaluate variable at a given time
t_plot = 1800 # seconds
x = sol["x [m]"](t=t_plot)
y = sol["y [m]"](t=t_plot)
z = sol["Electrolyte concentration [mol.m-3]"](t=t_plot)
pybamm.plot2D(x, y, z)
x, y, and z arguments must be pybamm.Array objects. plot2D calls matplotlib.pyplot.contourf under the hood.
pybamm.plot2D(
x, y, z,
ax=my_axis, # optional: plot on an existing axis
show_plot=False, # suppress plt.show() call
)
Creating a GIF
Generate an animated GIF of the solution evolving over time:
sim.create_gif(
number_of_images=80,
duration=0.1, # seconds per frame
output_filename="discharge.gif",
)
QuickPlot object:
qp = pybamm.QuickPlot(sim)
qp.create_gif(number_of_images=60, duration=0.15, output_filename="animation.gif")
Working with Matplotlib directly
You can access the underlying Matplotlib figure and axes from a QuickPlot:
import matplotlib.pyplot as plt
qp = pybamm.QuickPlot(sim, output_variables=["Battery voltage [V]"])
qp.plot(t=0) # render once
fig = qp.fig
ax = qp.axes[0][0]
ax.set_title("DFN Discharge")
ax.set_xlabel("Time (s)")
fig.savefig("voltage.png", dpi=150, bbox_inches="tight")
plt.show()
Set show_plot=False in plot_voltage_components, plot_summary_variables, or plot2D when you want to combine multiple figures before calling plt.show() once at the end.
