Graphics¶

class do_mpc.graphics.Graphics(data)[source]¶

Graphics module to present the results of do-mpc. The module is independent of all other modules and can be used optionally. The module can also be used with pickled result files in post-processing for flexible and custom graphics.

The graphics module is based on Matplotlib and allows for fully customizable, publication ready graphics and animations.

The Graphics module is initialized with an do_mpc.data.Data or do_mpc.data.MPCData module and will showcase this data.

User defined graphics are configured prior to plotting results, e.g.:

mpc = do_mpc.controller.MPC(model)
...

# Initialize graphic:
graphics = do_mpc.graphics.Graphics(mpc.data)

# Create figure with arbitrary Matplotlib method
fig, ax = plt.subplots(5, sharex=True)
# Configure plot (pass the previously obtained ax objects):
graphics.add_line(var_type='_x', var_name='C_a', axis=ax[0])
graphics.add_line(var_type='_x', var_name='C_b', axis=ax[0])
graphics.add_line(var_type='_x', var_name='T_R', axis=ax[1])
graphics.add_line(var_type='_x', var_name='T_K', axis=ax[1])
graphics.add_line(var_type='_aux', var_name='T_dif', axis=ax[2])
graphics.add_line(var_type='_u', var_name='Q_dot', axis=ax[3])
graphics.add_line(var_type='_u', var_name='F', axis=ax[4])
# Optional configuration of the plot(s) with matplotlib:
ax[0].set_ylabel('c [mol/l]')
ax[1].set_ylabel('Temperature [K]')
ax[2].set_ylabel('\Delta T [K]')
ax[3].set_ylabel('Q_heat [kW]')
ax[4].set_ylabel('Flow [l/h]')

fig.align_ylabels()

After initializing the Graphics module, the Graphics.add_line() method is used to define which results are to be plotted on which existing axes object. The method created (empty) line objects for each plotted variable. The graphic is updated with the most recent data with Graphics.plot_results(). Furthermore, the module contains the Graphics.plot_predictions() method which is applicable only for do_mpc.data.MPCData, and can be used to show the predicted trajectories.

Note

A high-level API for obtaining a configured Graphics module is the default_plot() function. Use this function and the obtained Graphics module in the developement process.

Animations can be setup with the follwing loop:

for k in range(50):
    u0 = mpc.make_step(x0)
    y_next = simulator.make_step(u0)
    x0 = estimator.make_step(y_next)

    graphics.plot_results()
    graphics.plot_predictions()
    graphics.reset_axes()
    plt.show()
    plt.pause(0.01)

Parameters:	data (`do_mpc.data.Data` or `do_mpc.data.MPCData`) – Data object from the do-mpc modules (simulator, estimator, controller)

Attributes

`Graphics.pred_lines`	Structure that holds the prediction line objects.
`Graphics.result_lines`	Structure that holds the result line objects.

Methods

`Graphics.add_line`	`add_line` is called during setting up the `Graphics` class.
`Graphics.clear`	Clears all data from lines.
`Graphics.plot_predictions`	Plots the predicted trajectories for the plot configuration.
`Graphics.plot_results`	Plots the results stored in the data object.
`Graphics.reset_axes`	Relimits and scales all axes.
`Graphics.reset_prop_cycle`	Resets the property cycle for all axes which were passed with `Graphics.add_line()`.

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