rows, cols = 10, 10

densities = (0.2, 0.1)

ca = CA(rows, cols, densities, neighborhood="moore", params={}, cell_params={}, seed=42)

assert ca.grid.shape == (rows, cols)

assert ca.n_species == len(densities)

total_cells = rows * cols

# expected counts use the same rounding as CA.__init__

expected_counts = [int(round(total_cells * d)) for d in densities]

# verify actual counts equal expected

for i, exp in enumerate(expected_counts, start=1):

# invalid rows/cols

with pytest.raises(AssertionError):

CA(0, 5, (0.1,), "moore", {}, {}, seed=1)

with pytest.raises(AssertionError):

CA(5, -1, (0.1,), "moore", {}, {}, seed=1)

# densities must be non-empty tuple

with pytest.raises(AssertionError):

CA(5, 5, (), "moore", {}, {}, seed=1)

# densities sum > 1

with pytest.raises(AssertionError):

CA(5, 5, (0.8, 0.8), "moore", {}, {}, seed=1)

# invalid neighborhood

with pytest.raises(AssertionError):

CA(5, 5, (0.1,), "invalid", {}, {}, seed=1)

# PP: params must be a dict or None

with pytest.raises(TypeError):

# set up a small grid with a single prey in the center and check neighbor counts

ca = CA(3, 3, (0.0,), neighborhood="moore", params={}, cell_params={}, seed=1)

ca.grid[:] = 0

ca.grid[1, 1] = 1

counts = ca.count_neighbors()

# counts is a tuple with one array (state 1)

neigh = counts[0]

# all 8 neighbors of center should have count 1

expected_positions = [(0, 0), (0, 1), (0, 2), (1, 0), (1, 2), (2, 0), (2, 1), (2, 2)]

for r in range(3):

for c in range(3):

if (r, c) in expected_positions:

assert neigh[r, c] == 1

else:

# center has 0 neighbors of same state

# create a PP and enable evolution for a parameter

pp = PP(rows=5, cols=5, densities=(0.2, 0.1), neighborhood="moore", params=None, cell_params=None, seed=2)

pp.evolve("prey_death", sd=0.01, min_val=0.0, max_val=1.0)

# wrong shape should raise informative ValueError via validate()

pp.cell_params["prey_death"] = np.zeros((1, 1))

with pytest.raises(ValueError) as excinfo:

pp.validate()

assert "shape equal to grid" in str(excinfo.value)

# now create a same-shaped array but with non-NaN positions that don't match prey positions

arr = np.zeros(pp.grid.shape, dtype=float) # filled with non-NaN everywhere

pp.cell_params["prey_death"] = arr

with pytest.raises(ValueError) as excinfo2:

pp.validate()

# when both death rates are 1 all individuals should die in one step

params = {

"prey_death": 1.0,

"predator_death": 1.0,

"prey_birth": 0.0,

"predator_birth": 0.0,

}

pp = PP(rows=10, cols=10, densities=(0.2, 0.1), neighborhood="moore", params=params, cell_params=None, seed=3)

pp.run(1)

# no prey or predators should remain

params = {

"prey_death": 0.0,

"predator_death": 0.0,

"prey_birth": 1.0,

"predator_birth": 1.0,

}

pp = PP(rows=10, cols=10, densities=(0.1, 0.05), neighborhood="moore", params=params, cell_params=None, seed=4)

# run longer to allow predators to consume prey; expect prey extinction

pp.run(200)

after_prey = int(np.count_nonzero(pp.grid == 1))

after_pred = int(np.count_nonzero(pp.grid == 2))

# after sufficient time, prey should go extinct and predators remain

assert after_prey == 0