import numpy as np

CELL_IS_ALIVE = 0b1
CELL_WILL_BE_ALIVE = 0b10
CELL_WILL_BE_DEAD = 0b100

def is_alive(model, i, j):
	return model[i][j] & CELL_IS_ALIVE

def get_cells_around(model, i, j):
	# Most common case. Handle this first. 
	# Fortunately the most common case is also the fastets to handle.
	if(i > 2 and j > 2 and i < model.shape[0] and j < model.shape[1]):
		return model[i - 2: i + 2, j - 2: j + 2]

	# Neither negative indices nor indices that overflow 
	# are handled properly by numpy. So basically I have to do that manually:
	cells_around = np.zeros([3, 3], np.int8)
	for k, m in enumerate(range(i - 1, i + 2)):
		for l, n in enumerate(range(j - 1, j + 2)):
			real_m = m
			if(m >= model.shape[0]):
				real_m = m - model.shape[0]
			if(m < 0):
				real_m = m + model.shape[0]
			real_n = n
			if(n >= model.shape[1]):
				real_n = n - model.shape[1]
			if(n < 0):
				real_n = n + model.shape[1]
			cells_around[k][l] = model[real_m][real_n]
	return cells_around

def count_living_cells_around(model, i, j):
	cells_around = get_cells_around(model, i, j)
	living_cells_around = (cells_around & CELL_IS_ALIVE) == 1
	living_cells_around[1][1] = False
	unique, counts = np.unique(living_cells_around, return_counts=True)
	if(True not in unique):
		return 0
	return counts[np.where(unique == True)[0]]



def handle_cell(model, i, j):
	living_cells_around = count_living_cells_around(model, i, j)
	if(not is_alive(model, i, j)):
		if(living_cells_around == 3):
			model[i][j] = CELL_WILL_BE_ALIVE
		return

	if(living_cells_around > 3 or living_cells_around < 2):
		model[i][j] = CELL_WILL_BE_DEAD

def after_tick_finished(model):
	# kill the cells that have died
	model[(model & CELL_WILL_BE_DEAD) != 0] = 0

	# create the new cells
	model[(model & CELL_WILL_BE_ALIVE) != 0] = 1

def total_living_cells(model):
	living_cells = (model & CELL_IS_ALIVE) == 1
	unique, counts = np.unique(living_cells, return_counts=True)
	if(True not in unique):
		return 0
	return counts[np.where(unique == True)[0]]