def test_run(self):
br_pro_model = bp.BranchProModel(2, np.array([1, 2, 3, 2, 1]))
simulationController = bp.SimulationController(br_pro_model, 2, 7)
one_run_of_simulator = simulationController.run(1)
self.assertEqual(one_run_of_simulator.shape, (6, ))
def test_get_regime(self):
br_pro_model = bp.BranchProModel(2, np.array([1, 2, 3, 2, 1]))
simulationController = bp.SimulationController(br_pro_model, 2, 7)
regime = simulationController.get_regime()
npt.assert_array_equal(regime, np.arange(2, 8).astype(int))
def test_init(self):
with self.assertRaises(TypeError):
bp.SimulationController('1', 2, 5)
def test_get_time_bounds(self):
br_pro_model = bp.BranchProModel(2, np.array([1, 2, 3, 2, 1]))
simulationController = bp.SimulationController(br_pro_model, 2, 7)
bounds = simulationController.get_time_bounds()
self.assertEqual(bounds, (2, 7))
def update_simulation(self, new_init_cond, new_r0, new_r1, new_t1,
new_epsilon):
"""Run a simulation of the branchpro model at the given slider values.
Parameters
----------
new_init_cond
(int) updated position on the slider for the number of initial
cases for the Branch Pro model in the simulator.
new_r0
(float) updated position on the slider for the initial reproduction
number for the Branch Pro model in the simulator.
new_r1
(float) updated position on the slider for the second reproduction
number for the Branch Pro model in the simulator.
new_t1
(float) updated position on the slider for the time change in
reproduction numbers for the Branch Pro model in the simulator.
new_epsilon
(float) updated position on the slider for the constant of
proportionality between local and imported cases for the Branch Pro
model in the posterior.
Returns
-------
pandas.DataFrame
Simulations storage dataframe
"""
data = self.session_data.get('data_storage')
serial_interval = self.session_data.get(
'interval_storage').iloc[:, 0].values
if data is None:
raise dash.exceptions.PreventUpdate()
time_label, inc_label = (data.columns[0], 'Incidence Number')
times = data[time_label]
# Make a new dataframe to save the simulation result
simulations = data[[time_label]]
# Add the correct R profile to the branchpro model
if 'Imported Cases' in data.columns:
br_pro_model = bp.LocImpBranchProModel(new_r0, serial_interval,
new_epsilon)
br_pro_model.set_imported_cases(
times, data.loc[:, ['Imported Cases']].squeeze().tolist())
else:
br_pro_model = bp.BranchProModel(new_r0, serial_interval)
br_pro_model.set_r_profile([new_r0, new_r1], [0, new_t1])
# Generate one simulation trajectory from this model
simulation_controller = bp.SimulationController(
br_pro_model, min(times), max(times))
try:
sim_data = simulation_controller.run(new_init_cond)
except ValueError:
sim_data = -np.ones(max(times))
# Add data to simulations storage
sim_times = simulation_controller.get_regime()
simulations = pd.DataFrame({
time_label: sim_times,
inc_label: sim_data
})
return simulations