def are_answers_correctly_set(trial_conf, trial_data):
df_force_answer = np.array(trial_data[list(FORCE_CLASS_COLS)].iloc[0])
df_string_force_answer = FORCE_CLASS_COLS[df_force_answer.argmax()]
conf_string_force_answer = gps.get_force_answer(trial_conf[0]["lf"])
df_mass_answer = np.array(trial_data[list(MASS_CLASS_COLS)].iloc[0])
df_string_mass_answer = MASS_CLASS_COLS[df_mass_answer.argmax()]
conf_string_mass_answer = gps.get_mass_answer(trial_conf[0]["mass"])
return (conf_string_force_answer
== df_string_force_answer) and (conf_string_mass_answer
== df_string_mass_answer)
def test_empty_list(self):
forces = []
with self.assertRaises(IndexError):
gps.get_force_answer(forces)
def test_list_too_short(self):
forces = [[0]]
with self.assertRaises(IndexError):
gps.get_force_answer(forces)
def test_same(self):
forces = np.zeros((4, 4))
self.assertEqual(gps.get_force_answer(forces), 'none')
def test_repel(self):
forces = np.random.rand(3, 3)
forces[0][1] *= -1
self.assertEqual(gps.get_force_answer(forces), 'repel')
def test_attract(self):
forces = np.random.rand(2, 2)
self.assertEqual(gps.get_force_answer(forces), 'attract')