def ascii_barrier(id_vent, propagation_method, edges_list):
utm_filename = "ASCII_grids/" + propagation_method + "_" + str(
id_vent) + "_barrier.txt"
# Creazione Matrice da stampare
M = np.zeros((ROWS, COLS), dtype=int)
G = utility.load_graph()
for u, v in edges_list:
coords = G.nodes[u]["coord_regions"].split("|")[0]
coord_u_x, coord_u_y = conversion.cast_coord_attr(coords)
M[coord_u_x][coord_u_y] = 1
coords = G.nodes[v]["coord_regions"].split("|")[0]
coord_v_x, coord_v_y = conversion.cast_coord_attr(coords)
M[coord_v_x][coord_v_y] = 1
# Scrittura del file
with open(utm_filename, 'w') as utmfile:
# Scrittura dell'header del file (northing...)
for i in range(0, len(header)):
utmfile.write(header[i] + "\n")
# Scrittura valori nel DEM
for x in range(0, ROWS):
for y in range(0, COLS):
if M[x][y] == 0:
utmfile.write("0" + " ")
else:
utmfile.write(str(M[x][y]) + " ")
utmfile.write("\n")
def vect_sim(id_vent):
#matrice 91*75
M = np.zeros((91, 75), dtype=int)
# carico la lista dei file delle simulazioni
filelist = glob("Data/simulations/NotN_vent_" + str(id_vent) + "_" + "*" + ".txt")
if len(filelist) == 0:
return
print("vent: ", id_vent)
for vent_file in filelist:
#apre ogni singolo file
with open(vent_file, 'r') as in_file:
# legge ogni riga del file
for line in in_file:
row, col = line.split(" ")
row = int(row)
col = int(col)
#per ogni riga del file inserisce 1 nella matrice
M[int(row / 25)][int(col / 25)] = 1
#carica il grafo
G = load_graph()
#istanzia il vettore di output
vect = np.zeros((len(G.nodes)), dtype= int)
#scorre i nodi del grafo
for u in G.nodes():
# si ottiene una lista di coordinate
coords = G.nodes[u]['coord_regions'].split("|")
for coord in coords:
# ottiene il valore di riga e colonna
row, col = conversion.cast_coord_attr(coord)
# si riempie il vettore in posizione u con il valore di M(row, col)
vect[int(u)] = M[row][col]
filename = "Data/real_vectors/" + str(id_vent)
# esporta il vettore
np.save(filename, vect)
def get_height(scaled_hmap, coord_regions):
coord_regions = coord_regions.split("|")
median_list = []
for coord in coord_regions:
coord = conversion.cast_coord_attr(coord)
median_list.append(scaled_hmap[coord[0]][coord[1]])
med = median(median_list)
return med
def create_sparse(self, vect):
M = np.zeros((91, 75), dtype=float)
for u in self.G.nodes():
regions = self.G.nodes[u]["coord_regions"].split("|")
for reg in regions:
reg_row, reg_col = conversion.cast_coord_attr(reg)
M[reg_row][reg_col] = vect[int(u)]
sparse_M = sparse.csr_matrix(M)
return sparse_M
def add_cities(G):
# Estrapola dal file gml le coordinate e i nomi degli edificati
city, names = mc.create_city_map()
# Per ogni nodo, si va a prendere dalla matrice il corrispondente paese
for u, data in G.nodes(data=True):
for coords in data["coord_regions"].split("|"):
coord_x, coord_y = conversion.cast_coord_attr(coords)
G.node[u]['is_city'] = city[coord_x][coord_y]
# Controllo fatto perché la matrice di default ha valore 0
if not names[coord_x][coord_y] == 0:
G.node[u]['city_names'] += str(names[coord_x][coord_y])
return G
def graph_to_UTM(G, filename):
utm_map = np.zeros((ROWS, COLS), dtype=float)
for u, data in G.nodes(data=True):
for coords in data["coord_regions"].split("|"):
coord_x, coord_y = conversion.cast_coord_attr(coords)
utm_map[coord_x][coord_y] = data["priority"]
# scrittura header
with open(filename, 'w') as utmfile:
for i in range(0, len(header)):
utmfile.write(header[i] + "\n")
for x in range(0, ROWS):
for y in range(0, COLS):
utmfile.write(str(utm_map[x][y]) + " ")
utmfile.write("\n")
def plot_3d_cmd(metric: str, id_vent: int, size: int, step: int):
# Load dei grafi
original_graph = utility.load_graph()
immunological_graph = utility.load_graph(
gexf_filename="immunological_graph.gexf")
if metric == "precision":
filename = "plot/" + str(id_vent) + "_" + str(size) + "_" + str(
step) + "_precision3d.plt"
if os.path.isfile(filename):
x_coords, y_coords, original_list, trained_list = visualize.load_plot3D_from_file(
filename)
else:
# Lista vents/nodes del sottografo
id_nodes, id_vents = utility.get_node_vent_chessboard(
id_vent, size, step)
# Coords
x_coords = []
y_coords = []
for id_node in id_nodes:
x, y = conversion.cast_coord_attr(
original_graph.nodes[id_node]['coord_regions'])
x_coords.append(x)
y_coords.append(y)
# Lista precision su grafo originale
original_list = metrics.get_ppv_list(original_graph, id_vents)
# Lista precision su grafo trained
trained_list = metrics.get_ppv_list(immunological_graph, id_vents)
# Salvataggio dei risultati su file
visualize.save_plot3D_on_file(
x_coords, y_coords, original_list, trained_list,
"plot/" + str(id_vent) + "_" + str(size) + "_" + str(step) +
"_precision3d.plt")
elif metric == "recall":
filename = "plot/" + str(id_vent) + "_" + str(size) + "_" + str(
step) + "_recall3d.plt"
if os.path.isfile(filename):
x_coords, y_coords, original_list, trained_list = visualize.load_plot3D_from_file(
filename)
else:
# Lista vents/nodes del sottografo
id_nodes, id_vents = utility.get_node_vent_chessboard(
id_vent, size, step)
# Coords
x_coords = []
y_coords = []
for id_node in id_nodes:
x, y = conversion.cast_coord_attr(
original_graph.nodes[id_node]['coord_regions'])
x_coords.append(x)
y_coords.append(y)
# Lista recall su grafo originale
original_list = metrics.get_tpr_list(original_graph, id_vents)
# Lista recall su grafo trained
trained_list = metrics.get_tpr_list(immunological_graph, id_vents)
# Salvataggio dei risultati su file
visualize.save_plot3D_on_file(
x_coords, y_coords, original_list, trained_list,
"plot/" + str(id_vent) + "_" + str(size) + "_" + str(step) +
"_recall3d.plt")
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
ax.scatter(x_coords,
y_coords,
original_list,
color='red',
marker='X',
s=50)
ax.scatter(x_coords,
y_coords,
trained_list,
color='blue',
marker='D',
s=50)
for i in range(len(trained_list)):
if trained_list[i] > original_list[i]:
color = 'blue'
else:
color = 'red'
ax.plot([x_coords[i], x_coords[i]], [y_coords[i], y_coords[i]],
[original_list[i], trained_list[i]],
color=color)
plt.show()