def test_plotting(self):
"""
Tests Solution for the ability to create separate plots for solutions.
"""
mock_model = Mock()
mock_model.size = 2
mock_model.Vc = 1
mock_model.CL = 1
mock_model.Qps = [1]
mock_model.Vps = [1]
mock_protocol = Mock()
mock_protocol.subcutaneous = False
mock_protocol.dose_time_function.return_value = 1
solution = pk.Solution(model=mock_model, protocol=mock_protocol)
sol_fig = solution.generate_plot()
self.assertIsInstance(sol_fig, matplotlib.figure.Figure)
def test_intravenous(self):
"""
Tests Solution creation for intravenous protocol.
"""
mock_model = Mock()
mock_model.size = 2
mock_model.Vc = 1
mock_model.CL = 1
mock_model.Qps = [1]
mock_model.Vps = [1]
mock_protocol = Mock()
mock_protocol.subcutaneous = False
mock_protocol.dose_time_function.return_value = 1
solution = pk.Solution(model=mock_model, protocol=mock_protocol)
self.assertEqual(solution.sol.y.shape[0], solution.model.size)
self.assertEqual(solution.sol.y.shape[1], solution.t_eval.shape[0])
self.assertIsInstance(solution.t_eval, np.ndarray)
self.assertIsInstance(solution.y0, np.ndarray)
def test_analyse_models(self, mock_class):
"""
Test creation of a list containing a solution of each model.
"""
# Create a Solution object
model1 = mock_class
model2 = mock_class
mock_class.configure_mock(components=2)
mock_class.configure_mock(model_args={'name': 'test_model'})
model_list = [model1, model2]
t_eval = [0, 1, 2, 3, 4, 5]
y0 = [[0, 0], [1, 1]]
solution = pk.Solution(model_list, t_eval, y0)
# Set mock functions and properties
solution._integrate = Mock(return_value=mock_class)
t = PropertyMock(return_value='t')
type(mock_class).t = t
solution._save_to_csv = Mock(return_value='save_csv')
sol_list = solution.analyse_models()
self.assertEqual(sol_list, [mock_class, mock_class])
def test_plot_pipeline(self):
"""
Tests the whole pipeline;
- make model
- make protocol
- make solution
- plot solution
for an intravenous dosing protocol.
"""
model = pk.Model(Vc=2., Vps=[], Qps=[], CL=3.)
dosing = pk.Protocol(dose_amount=10,
subcutaneous=True,
k_a=0.3,
continuous=True,
continuous_period=[0.2, 0.6],
instantaneous=True,
dose_times=[0, .1, .2, .3])
solution = pk.Solution(model=model, protocol=dosing)
sol_fig = solution.generate_plot()
self.assertIsInstance(sol_fig, matplotlib.figure.Figure)
def test_all_zeros(self):
"""
Tests the whole pipeline;
- make model
- make protocol
- make solution
for an intravenous dosing protocol.
"""
model = pk.Model(Vc=2., Vps=[], Qps=[], CL=3.)
dosing = pk.Protocol(dose_amount=0,
subcutaneous=True,
k_a=0.3,
continuous=False,
continuous_period=[],
instantaneous=True,
dose_times=[])
solution = pk.Solution(model=model, protocol=dosing)
sol = solution.sol
assert (not ((sol.y != 0).any()))
def test_integrate(self, mock_class):
"""
Test integration of a given model.
"""
def test_solution(t):
"""
Create the analytical solution of the test_rhs model.
"""
y_sol = -1 * math.exp(-3 * t / 2)
return y_sol
def test_rhs(t, y, args):
"""
A test model for the ODE solver.
"""
x, z = y
dx_dt = -x + 1 / 4 * z
dz_dt = x - z
return [dx_dt, dz_dt]
# Create a Solution object
y_0 = [[-1, 2]]
test_model = mock_class
test_model.configure_mock(components=2)
t_eval = np.linspace(0, 100, 1000)
testsol = pk.Solution([test_model], t_eval, y_0)
# Set the behaviour of the mock rhs method to the test model
test_model.rhs.side_effect = test_rhs
args = [-1, 1 / 4]
sol = testsol._integrate(test_model, args, np.array(y_0[0]))
# Obtain y values for the analytical solution
y_sol = np.array([test_solution(x) for x in t_eval])
self.assertIsNone(
np.testing.assert_almost_equal(sol.y[0], y_sol, decimal=3))
def test_build_sc(self):
"""
Tests the whole pipeline can be built;
- make model
- make protocol
- make solution
for an subcataneous dosing protocol.
"""
model = pk.Model(Vc=2., Vps=[1, 2], Qps=[3, 4], CL=3.)
dosing = pk.Protocol(dose_amount=10,
subcutaneous=True,
k_a=0.3,
continuous=True,
continuous_period=[0.2, 0.6],
instantaneous=True,
dose_times=[0, .1, .2, .3])
solution = pk.Solution(model=model, protocol=dosing)
self.assertEqual(solution.sol.y.shape[0], solution.model.size + 1)
self.assertEqual(solution.sol.y.shape[1], solution.t_eval.shape[0])
self.assertIsInstance(solution.t_eval, np.ndarray)
self.assertIsInstance(solution.y0, np.ndarray)
def test_create(self):
"""
Tests Solution creation.
"""
model = pk.Solution()
self.assertEqual(model.value, 44)
'V_p1': 0.10,
'CL': 2.0
}
model2_args = {'name': 'iv-2 comp', 'V_c': 1.0, 'CL': 1.0, 'k_a': 5}
# Protocol
trial_protocol = pk.Protocol(quantity=2, t_start=t_0, t_end=t_end, n=precison)
X = trial_protocol.linear_dose()
# Model
trial1 = pk.Model(2, model1_args, 'iv', X)
trial2 = pk.Model(2, model2_args, 'sc', X)
models = [trial1, trial2]
# Solution
trial_sol = pk.Solution(models,
t_eval=np.linspace(t_0, t_end, precison),
y0=[[0, 0], [0, 0]])
trial_sol.analyse_models()
# Visualisation
labels = [model1_args['name'], model2_args['name']]
path = []
for model in models:
path.append(os.path.join('data', model.model_args['name']))
trial_vis = pk.Visualisation(path, labels,
[trial1.dose_type, trial2.dose_type])
trial_vis.visualise(labels, os.path.join('data/plots', 'testplot.png'))