def test_die(self, mocker):
"""
Tests the die_animal method and death_prob in two conditions:
1. animal with fitness zero (herb_1).
2. a fixed return value for random number with the use of mock
(carn_1).
Explanation of code:
First, generating animals population for one of the landscapes types
(i.e. jungle).
Generating herb_1 with weight 0 to get the fitness zero and gain
death_prob = True.
Also, generates carn_1 with some default value just to have fixed
values for its fitness.
Then, use mock for numpy random number and set the return_value to
it in the way taht it always fulfill death_prob = True for carn_1
Finally, run the die_animal method for all of the animals of jungle
instance and assert that the 2 animals are died and only the other 2
are in the animal list.
"""
herb_1 = Herbivore(6, 0)
herb_2 = Herbivore()
carn_1 = Carnivore(1, 5)
carn_2 = Carnivore()
params = {'f_max': 10.0}
j = Jungle(params)
j.add_animal(carn_1)
j.add_animal(carn_2)
j.add_animal(herb_1)
j.add_animal(herb_2)
herb_1.calculate_fitness()
assert herb_1.death_prob
mocker.patch('numpy.random.random', return_value=0.0)
j.die_animals()
h_count = len(j.in_cell_fauna['Herbivore'])
c_count = len(j.in_cell_fauna['Carnivore'])
fauna_count = h_count + c_count
assert fauna_count == 2
def test_calculate_fitness_and_formula():
"""Test if the method 'calculate_fitness()' correctly communicates to
the method 'fit_formula()' and returns the correct fitness of the
animal (pop_object)'"""
herbivore = Herbivore(10, 20)
carnivore = Carnivore(15, 30)
assert pytest.approx(
herbivore.calculate_fitness(herbivore.age, herbivore.weight,
herbivore.parameters), 0.7292)
assert pytest.approx(
carnivore.calculate_fitness(carnivore.age, carnivore.weight,
carnivore.parameters), 0.999969)