def test_initialisation_errors(self, read_csv_mock):
"""
Tests the errors in the initialisation
"""
test_array = np.array( [[1,2,3,4], [5,6,7,8]] )
read_csv_mock.return_value = pd.DataFrame( test_array )
data_file_paths = ['', '']
# Tests the error when the length of data_file_labels is not equal
# to the length of the data_file_paths
with self.assertRaises(ValueError):
data_labels = ['beautiful model']
dose_types = ['sc', 'iv']
pk.Visualisation(data_file_paths,
data_labels,
dose_types)
# Tests the error when the length of dose_types is not equal to the
# length of the data_file_paths
with self.assertRaises(ValueError):
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc']
pk.Visualisation(data_file_paths,
data_labels,
dose_types)
# Tests the error when the dose_types elements are not a subset of
# set(['sc', 'iv'])
with self.assertRaises(ValueError):
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc', 'illegal dosing type']
pk.Visualisation(data_file_paths,
data_labels,
dose_types)
def test_initialisation(self, read_csv_mock):
"""
Tests the initialisation of the public and
private attributes
"""
test_array = np.array( [[1,2,3,4], [5,6,7,8]] )
read_csv_mock.return_value = pd.DataFrame( test_array )
data_file_paths = ['', '']
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc', 'iv']
compartment_num = np.shape(test_array)[1] - 1
plots = pk.Visualisation(data_file_paths,
data_labels,
dose_types)
for k, label in enumerate(data_labels):
self.assertEqual(plots._data_labels[k], label )
self.assertEqual(plots._compartment_num[label], compartment_num )
for (i,j), element in np.ndenumerate(test_array):
if (j==0):
self.assertEqual(plots._time[label][i], element )
else:
self.assertEqual(plots._quantity[label][i,j-1], element )
if dose_types[k] == 'sc':
self.assertEqual(plots.plot_labels[label][0], label + ' $q_0$')
self.assertEqual(plots.plot_labels[label][1], label + ' $q_c$')
[self.assertEqual(plots.plot_labels[label][1+i],
label + ' $q_{p%d}$' %(i))
for i in range(1, compartment_num - 1) ]
elif dose_types[k] == 'iv':
self.assertEqual(plots.plot_labels[label][0], label + ' $q_c$')
[self.assertEqual(plots.plot_labels[label][i],
label + ' $q_{p%d}$' %(i))
for i in range(1, compartment_num) ]
def test_visualise_errors(self, read_csv_mock):
"""
Tests the errors in the visualise method
"""
test_array = np.array( [[1,2,3,4], [5,6,7,8]] )
read_csv_mock.return_value = pd.DataFrame( test_array )
data_file_paths = ['', '']
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc', 'iv']
plots = pk.Visualisation(data_file_paths,
data_labels,
dose_types)
# Tests the error when the elements of labels_of_data_to_visualise
# are not elements of the data_labels list from the class
# initialisation
with self.assertRaises(ValueError):
plots.visualise(['beautiful model', 'terrible model'])
def test_visualise_save_file(self, read_csv_mock):
"""
Tests whether or not the visualise method saves a plot
(More specifically whether plt.savefig is successfully called)
"""
test_array = np.array( [[1,2,3,4], [5,6,7,8]] )
read_csv_mock.return_value = pd.DataFrame( test_array )
data_file_paths = ['', '']
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc', 'iv']
plots = pk.Visualisation(data_file_paths,
data_labels,
dose_types)
with patch("matplotlib.pyplot.savefig") as savefig_mock:
plots.visualise(['beautiful model', 'fantastic model'],
save_file_path='/path/to/file.pdf')
assert savefig_mock.called
def test_visualise_plot(self, read_csv_mock):
"""
Tests whether or not the visualise method produces a plot
(More specifically whether plt.show is successfully called
which occurs at the very end of the file)
"""
test_array = np.array( [[1,2,3,4], [5,6,7,8]] )
read_csv_mock.return_value = pd.DataFrame( test_array )
data_file_paths = ['', '']
data_labels = ['beautiful model', 'fantastic model']
dose_types = ['sc', 'iv']
plots = pk.Visualisation(data_file_paths,
data_labels,
dose_types)
with patch("matplotlib.pyplot.show") as show_mock:
plots.visualise(['beautiful model', 'fantastic model'])
assert show_mock.called
'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'))