Plotting¶

To observe the simulation results and investigate the model behaviour, one typically plots the results using graphs. To generate plots we need to utilize a visualisation package, such as matplotlib.

In the example below, it is shown how to generate this figure using the simulation object.

Plotting figure

You find the model used in this example in this model file.

# %% import packages
import os

import matplotlib.pyplot as plt
import numpy
from matplotlib import rcParams

import sund

# %% install and load model
MODEL_NAME = "minimal_ode"
sund.install_model(f"extras/{MODEL_NAME}.txt")
model = sund.load_model(MODEL_NAME)

# %% create a simulation object
sim = sund.Simulation(models=model, time_vector=numpy.linspace(0, 10, 100))

# %% simulate
sim.simulate()

# %% formatting settings for all plots 
rcParams['font.family'] = 'arial'
rcParams['font.size'] = 12
rcParams['axes.titlesize'] = 14
rcParams['legend.fontsize'] = 14


# %% create a figure
plt.figure('Figure 1', figsize=(10, 10))

# %% create axes objects
ax1 = plt.subplot(1, 2, 1) # 1 row, 2 columns, first subplot
ax2 = plt.subplot(1, 2, 2) # 1 row, 2 columns, second subplot

# %% plot the results
# find the position of the features in the feature_names list
idxyA = model.feature_names.index('yA')
idxyB = model.feature_names.index('yB')

# lw - line width, ls - line style, color - color of the line
ax1.plot(sim.time_vector, sim.feature_data[:, idxyA], label='Simulation', lw=2, ls='-', color='black')
ax2.plot(sim.time_vector, sim.feature_data[:, idxyB], label='Simulation', lw=2, ls='-', color='blue')

# %% format the figure
ax1.set_title('yA')
ax1.set_xlabel(f"Time ({model.time_unit})")           # use the stored time unit in the model object
ax1.set_xlim(sim.time_vector[0], sim.time_vector[-1]) # set x-axis limits using the first an last time point
ax1.set_ylabel(f"yA ({model.feature_units[idxyA]})")  # use the stored feature unit in the model object
ax1.set_ylim(1, 1.10)                                 # set y-axis limits
ax1.legend(loc='upper right', edgecolor='None', facecolor='None') # show the legend in the upper right corner

# turn of the top and right spines (borders) of the plot
ax1.spines['top'].set_visible(False)
ax1.spines['right'].set_visible(False)

ax2.set_title('yB')
ax2.set_xlabel(f"Time ({model.time_unit})")           # use the stored time unit in the model object
ax2.set_xlim(sim.time_vector[0], sim.time_vector[-1]) # set x-axis limits using the first an last time point
ax2.set_ylabel(f"yB ({model.feature_units[idxyB]})")  # use the stored feature unit in the model object
ax2.set_ylim(0, 0.2)                                  # set y-axis limits
ax2.legend(loc='upper left', edgecolor='None', facecolor='None') # show the legend in the upper left corner

# turn of the top and right spines (borders) of the plot
ax2.spines['top'].set_visible(False)
ax2.spines['right'].set_visible(False)

plt.tight_layout()

# %% save the figure
os.makedirs("extras", exist_ok=True) # create the extras directory if it does not exist
plt.savefig("extras/plotting.png", dpi=300)

# %% show the figure
plt.show()