def test__init__(self):
start = 0
end = 10
with self.assertRaises(TypeError):
se.SimulationController(se.SimulationController, start, end)
se.SimulationController('1', start, end)
def add_problem(self, data, model):
# Check model is ForwardModel
if not issubclass(model, se.ForwardModel):
raise TypeError(
'Model has to be a subclass of seirmo.ForwardModel.')
time_key = 'Time'
inc_key = 'Incidence Number'
# Visualise data
self.data = data
self._subplot_fig.add_data(self.data, time_key, inc_key)
self.simulate = se.SimulationController(model, self.simulation_start,
self.simulation_end)
initialise_data = pd.DataFrame({
'Time': [0],
'Incidence Number': [0],
'Susceptible': [0],
'Exposed': [0],
'Infectious': [0],
'Recovered': [0],
})
self._subplot_fig.add_simulation(initialise_data)
# Create model
model = model()
model = se.SEIRModel()
model.set_outputs(['Incidence'])
self.model = se.ReducedModel(model)
def add_model(self, model, parameters_name):
"""
Plot subplots of simulation for the given model.
Parameters
----------
model
A subclass of seirmo.ForwardModel class, which specifies
the model of simulation.
parameters_name
Name of parameters of model in list of strings.
"""
parameters_name = list(parameters_name)
init_parameters = []
for model_parameter in parameters_name:
model_parameter = str(model_parameter)
self._slider_component.add_slider(slider_id=model_parameter,
min_value=0,
max_value=1,
initial_value=0.5)
init_parameters.append(
self._slider_component._sliders[model_parameter].children[1].
value) # noqa
self._slider_component.group_sliders(parameters_name,
'Sliders of parameters')
self.simulate = se.SimulationController(model, self.simulation_start,
self.simulation_end)
data = self.simulate.run(init_parameters)
data = pd.DataFrame({
'Time': list(self.simulate._simulation_times),
'Incidence Number': data[:, -1],
'Susceptible': data[:, 0],
'Exposed': data[:, 1],
'Infectious': data[:, 2],
'Recovered': data[:, 3],
})
self._subplot_fig.add_simulation(data)
def test_run(self):
start = 0
end = 10
model = se.SEIRModel
simulation = se.SimulationController(model, start, end)
initial_values = [0.9, 0, 0.1, 0]
constants = [1, 1, 1]
test_parameters = initial_values + constants
output = simulation.run(test_parameters, ['S', 'E', 'I', 'R'])
# Check output shape
self.assertEqual(output.shape, (10, 4))
# Check that sum of states is one at all times
output = simulation.run(test_parameters)
total = np.sum(output, axis=1)
expected = np.ones(shape=10)
np.testing.assert_almost_equal(total, expected)