def simulate_fire(grid_size, prob_tree, prob_burning, prob_lightning,
prob_immune, t):
grids = []
grid = init_grid(grid_size, prob_tree, prob_burning)
grids.append(grid)
for i in range(t):
new_grid = np.zeros_like(grid)
ndimage.generic_filter(grids[-1], spread, size=3, mode="constant",
output=new_grid,
# these are passed to spread
extra_arguments=(prob_immune,
prob_lightning))
grids.append(new_grid.copy())
return grids
return padded_grid
if __name__ == "__main__":
grid_size = 50
prob_tree = .8
prob_burning = .0005
prob_immune = .25
prob_lightning = .00001
# Optionally, set a *seed* to make the random numbers reproducible.
np.random.seed(123)
grid = init_grid(grid_size, prob_tree, prob_burning)
# Run the simulation
t = 50
grids = [grid.copy()]
for i in range(t):
padded_grid = pad_grid(grids[-1], grid_size)
new_grid = np.zeros_like(grid)
for j in range(1, grid_size + 1):
for k in range(1, grid_size + 1):
new_grid[j-1, k-1] = spread(padded_grid[j-1:j+2,
k-1:k+2].flatten(),
prob_immune,
prob_lightning)
