#EDIT PRM STAR PARAM
args = parser.parse_args()
args.log_dir = './output/max_nodes-' + args.map_type + str(args.max_nodes) + "-obs-thres" + str(args.obstacle_threshold) +\
"-k_nearest-" + \
str(args.k_nearest) + "-connection_radius-" + str(args.connection_radius) + "-date-" + \
datetime.datetime.now().strftime("%Y-%m-%d-%H-%M-%S") + '/'
if not os.path.exists(args.log_dir):
os.makedirs(args.log_dir)
tic = time.time()
# load map
map_data, resolution = load_hilbert_map(map_type=args.map_type)
#resolution = 0.2
#with open("freiburg_ground_map_q_resolution_final.pickle", 'rb') as tf:
# map_data = pickle.load(tf)
map_array = convert_map_dict_to_array(map_data, resolution)
map_data["yq"] = 1.0 * (map_data["yq"] > args.obstacle_threshold)
# get samples from hilbert maps
sample_list = hilbert_samples(map_data.copy(), args.exp_factor, args.obstacle_threshold, num_samples=args.number_of_samples)
# take unique samples
sample_list = [list(t) for t in set(tuple(element) for element in sample_list)]
# truncated based on max nodes
sample_list = sample_list[:args.max_nodes]
# find k nearest neighbor
nbrs = NearestNeighbors(n_neighbors=args.k_nearest, algorithm='ball_tree').fit(sample_list)
distances, indices = nbrs.kneighbors(sample_list)
# create gragh
prm_graph = nx.Graph()
# add graph nodes
for indx, s in enumerate(sample_list):
prm_graph.add_node(indx, pos=(s[0], s[1]))
def get_top_n_persistence_node_location(n,
map_type,
obs_threshold,
location_type="death",
feature_type=0):
"""
:param feature_type: 0 for connected components, 1 for loops
:param n: top number of persistence
:param map_type: intel or drive
:param location_type: string representing birth or death
:return: returns the birth or death persistence node
"""
if location_type == "death":
location_type_index = 1
elif location_type == "birth":
location_type_index = 0
else:
raise ValueError("Invalid location type")
map_data, resolution = load_hilbert_map(map_type=map_type)
map_array = convert_map_dict_to_array(map_data, resolution)
fc = FreudenthalComplex(map_array)
st = fc.init_freudenthal_2d()
print_complex_attributes(st)
if st.make_filtration_non_decreasing():
print("modified filtration value")
st.initialize_filtration()
if len(st.persistence()) <= 10:
for i in st.persistence():
print(i)
first_persistence = st.persistence_intervals_in_dimension(feature_type)
if feature_type == 1:
remove_indices = []
for i in range(len(first_persistence)):
if first_persistence[i][0] > obs_threshold:
remove_indices.append(i)
first_persistence = np.delete(first_persistence, remove_indices, 0)
if feature_type == 0:
remove_indices = []
for i in range(len(first_persistence)):
if first_persistence[i][1] > obs_threshold:
remove_indices.append(i)
first_persistence = np.delete(first_persistence, remove_indices, 0)
# remove feature ending after 0.4
life_span = first_persistence[:, 1] - first_persistence[:, 0]
winner_index = life_span.argsort()[-n:][::-1]
print("len winner index ", len(winner_index))
#print(life_span)
winner_persistence = first_persistence[winner_index]
print(winner_persistence, "winner_persistence")
top_persistence_node = []
for indx, intensity in enumerate(map_data['yq']):
for j in range(n):
p = winner_persistence[j]
# if np.isclose(intensity, p[1]):
# top_persistence_node.append(map_data["Xq"][indx])
if np.isclose(intensity,
p[location_type_index],
rtol=1e-10,
atol=1e-13):
top_persistence_node.append(map_data["Xq"][indx])
print(j, intensity)
#return winner_persistence, life_span[winner_index]
return top_persistence_node, life_span[winner_index]