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A minimal ODE model

This example will cover the basic setup to setup a new model in SUND. It will:

  1. Install and import the model
  2. Build the model and simulation objects
  3. Simulate using the simulation object

Python Code

The code uses this model file: small ode model.

The code requires that the following packages are installed

uv add numpy matplotlib

pip install numpy matplotlib

Following the python code is attached, and it can also be downloaded from here: here. 

#%% Import packages
import matplotlib.pyplot
import numpy

import sund

#%% Print the model to a file called 'minimal_ode.txt'
MODEL_NAME = "minimal_ode"

#%% Install the model by using the SUND function 'install_model'
sund.install_model(f"modelfiles/{MODEL_NAME}.txt")

#%% Import the model by using the SUND function 'import_model'
model = sund.load_model(f"{MODEL_NAME}")

#%% The built in model objects functions can now be used 
model.state_values = [2, 2]
print(model.state_values)
model.reset_states()
print(model.state_values)

#%% A simulation object can be constructed from the model template 
sim1 = sund.Simulation(models = model, time_unit = model.time_unit)
print(sim1.state_values)

# Note that the simulation object will be crated from the current instance of the model object 
model.state_values = [2, 2]
sim2 = sund.Simulation(models = model, time_unit = model.time_unit)
print(sim2.state_values)

#%% Now we can simulate the model using the simulation object
sim1.simulate(time_vector = numpy.linspace(0, 10, 11))

fig = matplotlib.pyplot.figure()
feature_index = sim1.feature_names.index('yA')
matplotlib.pyplot.plot(sim1.time_vector, sim1.feature_data[:, feature_index])
matplotlib.pyplot.xlabel(sim1.time_unit)
matplotlib.pyplot.ylabel('A.U.')
matplotlib.pyplot.title("Simulation result")

#%% Simulating the object again will start the simulation from the last result
sim1.simulate(time_vector = numpy.linspace(0, 10, 11))

fig = matplotlib.pyplot.figure()
feature_index = sim1.feature_names.index('yA')
matplotlib.pyplot.plot(sim1.time_vector, sim1.feature_data[:, feature_index])
matplotlib.pyplot.xlabel(sim1.time_unit)
matplotlib.pyplot.ylabel('A.U.')
matplotlib.pyplot.title("Simulation result")

#%% to recreate the original results we need to reset the simulation object
sim1.reset_states()
sim1.simulate(time_vector = numpy.linspace(0, 10, 11))

fig = matplotlib.pyplot.figure()
feature_index = sim1.feature_names.index('yA')
matplotlib.pyplot.plot(sim1.time_vector, sim1.feature_data[:, feature_index])
matplotlib.pyplot.xlabel(sim1.time_unit)
matplotlib.pyplot.ylabel('A.U.')
matplotlib.pyplot.title("Simulation result") 

matplotlib.pyplot.show()