danger_methods.py

#Parsa il file, occhio che ritorna 5 valori, costs e' una matrice con tutti i costi
def parse_dat_file(dat_file):
	file_dat = np.genfromtxt(file, delimiter='\n', dtype=None)

	n = int(file_dat[1][11:]) #parse param n: dimension of array

	ALPHA = float(file_dat[3][15:]) #parse param alpha
	file_dat = file_dat[5:]

	raw_x = []
	raw_y = [] 
	raw_d = []
	costs = []
	#start split coord x in vector raw_x and idem for y
	for row in file_dat:
		if "coordX" in row:
			isX = True
			isY = False
			isD = False
		if "coordY" in row:
			isX = False
			isY = True
			isD = False
		if "d [*,*]" in row:
			isY = False
			isD = True

		if isX:
			raw_x.append(" ".join(row.split()))

		if isY:
			raw_y.append(" ".join(row.split()))

		if isD: 
			raw_d.append(" ".join(row.split()))


	
	#delete initial words and final semicolumn
	raw_x.pop(0)
	raw_x.pop(len(raw_x)-1)
	raw_y.pop(0)
	raw_y.pop(len(raw_y)-1)
	raw_d.pop(0)
	raw_d.pop(0)
	raw_d.pop(len(raw_d)-1)

	raw_d =' '.join(raw_d)
	raw_d = raw_d.split(' ')

	raw_x =' '.join(raw_x)
	raw_x = raw_x.split(' ')
	raw_y =' '.join(raw_y)
	raw_y = raw_y.split(' ')
	
	

	#transfer vector raw_x in a dictionary. key=index, value=coordX
	i=0
	for column in raw_x:
		if i%2 == 0:
			even = int(column)
		if i%2 != 0:
			coord_x[even] = int(column)

		i = i+1

	#transfer vector raw_y in a dictionary. key=index, value=coordY
	i=0
	for column in raw_y:
		if i%2 == 0:
			even = int(column)
		if i%2 != 0:
			coord_y[even] = int(column)

		i = i+1

	#transfer raw_d in a matrix
	row = []
	danger = []
	for i in range (0, len(raw_d)+1):
		if (i%(n+2)) != 0:
			row.append(float(raw_d[i]))
		else:
			if i != 0:
				danger.append(row)
			row = []
	
	costs = [costs[:] for costs in [[0] * (n + 1)] * (n + 1)]

	for i in range(0, (n+1)):
		for j in range(0, (n+1)):
			costs[i][j] = float("{0:.4f}".format(math.sqrt((coord_x[i]-coord_x[j])**2 + (coord_y[i]-coord_y[j])**2)))


	#possibile ottimizzare le fusione in un unico dizionario, anche piu sopra
	#merge the two dictionaries
	coord = defaultdict(list)
	for k, v in chain(coord_x.items(), coord_y.items()):
    		coord[k].append(v)
	
	return n, ALPHA, coord, danger, costs

#calcola il pericolo di un path
def compute_danger(my_path):
	path_danger = 0
	for i in range(0, len(my_path)-1):
		path_danger = path_danger + danger[my_path[i]][my_path[i+1]]

	return path_danger


#tra un vettori di path ritorna quello con meno dangerous
def min_dangerous(paths):
	min_danger = 9999
	min_danger_path = []
	for pat in paths:
		if compute_danger(pat) < min_danger:  
			min_danger = compute_danger(pat)
			min_danger_path = pat
	return min_danger_path

#calcola il danger da una soluzione completa
def compute_danger_sol(my_sol):
	total_danger = 0
	for s_path in my_sol:
		total_danger = total_danger + compute_danger(s_path)

	return total_danger