def init_map_match(map_con, infile_path, outfile_path):
matcher = DistanceMatcher(
map_con,
max_dist=45, # meter
min_prob_norm=0.1,
obs_noise=25, # meter
dist_noise=25, # meter
non_emitting_states=True)
df = pd.read_csv(infile_path)
nodes = []
prev_nodes = []
all_nodes = []
for _, row in df.iterrows():
polyline = ast.literal_eval(row['POLYLINE'])
prev_nodes = nodes
for i in range(len(polyline)):
polyline[i] = tuple(polyline[i][::-1])
try:
matcher.match(polyline)
nodes = matcher.path_pred_onlynodes
if set(nodes) == set(prev_nodes):
continue
all_nodes.append(nodes)
except:
continue
with open(f'{outfile_path}', 'w') as outfile:
json.dump(all_nodes, outfile, indent=2)
print(f'Finished file: {infile_path}, found {len(all_nodes)} trips')
def test_path2_dist():
mapdb, path1, path2, path_sol = setup_map()
matcher = DistanceMatcher(mapdb,
max_dist_init=1,
min_prob_norm=0.5,
obs_noise=0.5,
dist_noise=0.5,
non_emitting_states=True)
matcher.match(path2, unique=True)
path_pred = matcher.path_pred_onlynodes
if directory:
from leuvenmapmatching import visualization as mmviz
matcher.print_lattice_stats()
matcher.print_lattice()
# with (directory / 'lattice_path2.gv').open('w') as ofile:
# matcher.lattice_dot(file=ofile)
mmviz.plot_map(mapdb,
matcher=matcher,
show_labels=True,
show_matching=True,
filename=str(directory /
"test_nonemitting_test_path2_dist.png"))
assert path_pred == path_sol, "Nodes not equal:\n{}\n{}".format(
path_pred, path_sol)
def test_path3_dist():
path = [(3.0, 3.2), (3.1, 3.8), (3.0, 4.0), (3.1, 4.3), (3.1, 4.6),
(3.0, 4.9)]
path_sol = ['E', 'F']
mapdb = InMemMap("map",
graph={
"E": ((3, 3), ["F"]),
"F": ((3, 5), ["E"]),
},
use_latlon=False)
matcher = DistanceMatcher(mapdb,
max_dist=None,
min_prob_norm=0.0001,
max_dist_init=1,
obs_noise=0.25,
obs_noise_ne=10,
non_emitting_states=True)
matcher.match(path, unique=True)
path_pred = matcher.path_pred_onlynodes
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(mapdb,
matcher=matcher,
show_labels=True,
show_matching=True,
filename=str(directory / "test_path3_dist.png"))
print("Path through lattice:\n" +
"\n".join(m.label for m in matcher.lattice_best))
assert path_pred == path_sol, "Nodes not equal:\n{}\n{}".format(
path_pred, path_sol)
def test_path_outlier_dist():
path = [(0.8, 0.7), (0.9, 0.7), (1.1, 1.0), (1.2, 1.5), (1.2, 1.6), (1.1, 2.0),
(1.1, 2.3), (1.3, 2.9), (1.2, 3.1), (1.5, 3.2), (1.8, 3.5), (2.0, 3.7),
(2.1, 3.3), (2.4, 3.2), (2.6, 3.1), (2.9, 3.1), (3.0, 3.2), (3.1, 3.8),
(3.0, 4.0), (3.1, 4.3), (3.1, 4.6), (3.0, 4.9)]
path_sol = ['A', 'B', 'D', 'C', 'E', 'F']
path.insert(13, (2.3, 1.8))
mapdb = InMemMap("map", graph={
"A": ((1, 1), ["B", "C", "X"]),
"B": ((1, 3), ["A", "C", "D", "K"]),
"C": ((2, 2), ["A", "B", "D", "E", "X", "Y"]),
"D": ((2, 4), ["B", "C", "F", "E", "K", "L"]),
"E": ((3, 3), ["C", "D", "F", "Y"]),
"F": ((3, 5), ["D", "E", "L"]),
"X": ((2, 0), ["A", "C", "Y"]),
"Y": ((3, 1), ["X", "C", "E"]),
"K": ((1, 5), ["B", "D", "L"]),
"L": ((2, 6), ["K", "D", "F"])
}, use_latlon=False)
matcher = DistanceMatcher(mapdb, max_dist=None, min_prob_norm=0.0001,
max_dist_init=1, obs_noise=0.5, obs_noise_ne=10,
non_emitting_states=True)
matcher.match(path)
path_pred = matcher.path_pred_onlynodes
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(mapdb, matcher=matcher,
show_labels=True, show_matching=True, show_graph=True,
filename=str(directory / "test_path_outlier_dist.png"))
# TODO: Smoothing the observation distances could eliminate the outlier
assert path_pred == path_sol, "Nodes not equal:\n{}\n{}".format(path_pred, path_sol)
def test_path1_dist():
path = [(0.8, 0.7), (0.9, 0.7), (1.1, 1.0), (1.2, 1.5), (1.2, 1.6),
(1.1, 2.0), (1.1, 2.3), (1.3, 2.9), (1.2, 3.1), (1.5, 3.2),
(1.8, 3.5), (2.0, 3.7), (2.1, 3.3), (2.4, 3.2), (2.6, 3.1),
(2.9, 3.1), (3.0, 3.2), (3.1, 3.8), (3.0, 4.0), (3.1, 4.3),
(3.1, 4.6), (3.0, 4.9)]
# path_sol = ['A', ('A', 'B'), 'B', ('B', 'D'), 'D', ('D', 'E'), 'E', ('E', 'F')]
path_sol_nodes = ['A', 'B', 'D', 'E', 'F']
mapdb = InMemMap("map",
graph={
"A": ((1, 1), ["B", "C"]),
"B": ((1, 3), ["A", "C", "D"]),
"C": ((2, 2), ["A", "B", "D", "E"]),
"D": ((2, 4), ["B", "C", "D", "E"]),
"E": ((3, 3), ["C", "D", "F"]),
"F": ((3, 5), ["D", "E"])
},
use_latlon=False)
matcher = DistanceMatcher(mapdb,
max_dist=None,
min_prob_norm=None,
obs_noise=0.5,
non_emitting_states=False)
matcher.match(path)
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(mapdb,
matcher=matcher,
show_labels=True,
show_matching=True,
filename=str(directory / "test_path1_dist.png"))
nodes_pred = matcher.path_pred_onlynodes
assert nodes_pred == path_sol_nodes, f"Nodes not equal:\n{nodes_pred}\n{path_sol_nodes}"
def test_route_slice1():
if directory:
import matplotlib.pyplot as plt
# load path, map and route from database
nodes, map_con, route = load_data()
# zoom_path = True
# get a slice of route
route_slice = route[2657:2662]
# init matcher object
matcher = DistanceMatcher(map_con,
min_prob_norm=0.001,
max_dist=200,
dist_noise=6,
dist_noise_ne=12,
obs_noise=30,
obs_noise_ne=150,
non_emitting_states=True)
# matching
matcher.match(route_slice)
path_pred = matcher.path_pred_onlynodes
path_sol = [
172815, 172816, 172817, 172818, 172819, 172820, 172821, 172822, 172823,
172824, 172825, 172826, 172827, 172828, 172829, 172830, 884148100261,
172835, 172836, 172837, 884148100254, 172806, 884148100255, 172807
] # Can change when building db
assert len(path_pred) == len(path_sol)
def test_path4_dist_inc():
map_con = InMemMap("mymap",
graph={
"A": ((1, 1), ["B", "C", "X"]),
"B": ((1, 3), ["A", "C", "D", "K"]),
"C": ((2, 2), ["A", "B", "D", "E", "X", "Y"]),
"D": ((2, 4), ["B", "C", "D", "E", "K", "L"]),
"E": ((3, 3), ["C", "D", "F", "Y"]),
"F": ((3, 5), ["D", "E", "L"]),
"X": ((2, 0), ["A", "C", "Y"]),
"Y": ((3, 1), ["X", "C", "E"]),
"K": ((1, 5), ["B", "D", "L"]),
"L": ((2, 6), ["K", "D", "F"])
},
use_latlon=False)
path = [(0.8, 0.7), (0.9, 0.7), (1.1, 1.0), (1.2, 1.5), (1.2, 1.6),
(1.1, 2.0), (1.1, 2.3), (1.3, 2.9), (1.2, 3.1), (1.5, 3.2),
(1.8, 3.5), (2.0, 3.7), (2.3, 3.5), (2.4, 3.2), (2.6, 3.1),
(2.9, 3.1), (3.0, 3.2), (3.1, 3.8), (3.0, 4.0), (3.1, 4.3),
(3.1, 4.6), (3.0, 4.9)]
matcher = DistanceMatcher(map_con,
max_dist=2,
obs_noise=1,
min_prob_norm=0.5)
matcher.match(path[:5])
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(map_con,
matcher=matcher,
show_labels=True,
show_matching=True,
show_graph=True,
filename=str(directory / "test_path4_dist_inc_1.png"))
matcher.match(path, expand=True)
nodes = matcher.path_pred_onlynodes
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(map_con,
matcher=matcher,
show_labels=True,
show_matching=True,
show_graph=True,
filename=str(directory / "test_path4_dist_inc_2.png"))
nodes_sol = ['X', 'A', 'B', 'D', 'E', 'F']
assert nodes == nodes_sol, "Nodes not equal:\n{}\n{}".format(
nodes, nodes_sol)
def test_path1():
prepare_files()
track = gpx_to_path(track_fn)
track = [loc[:2] for loc in track]
track = track[:5]
track_int = interpolate_path(track, 5)
map_con = create_map_from_xml(osm_fn)
matcher = DistanceMatcher(map_con,
max_dist=50,
obs_noise=50,
min_prob_norm=0.1)
states, last_idx = matcher.match(track_int)
if directory:
# matcher.print_lattice_stats()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
zoom_path=True,
show_graph=True,
filename=str(directory / "test_path_latlon_path1.png"))
assert len(states) == len(track_int), f"Path ({len(track_int)}) not fully matched by best path ({len(states)}), " + \
f"last index = {last_idx}"
states_sol = [(2963305939, 249348325), (2963305939, 249348325),
(2963305939, 249348325), (2963305939, 249348325),
(2963305939, 249348325), (2963305939, 249348325),
(249348325, 1545679243), (249348325, 1545679243),
(1545679243, 3663115134), (1545679243, 3663115134),
(1545679243, 3663115134), (3663115134, 1545679251),
(1545679251, 20910628), (1545679251, 20910628),
(1545679251, 20910628), (1545679251, 20910628),
(20910628, 3663115130)]
assert states == states_sol, f"Got states: {states}"
def test_path3_dist():
path = [(0, 1), (0.65, 7.5), (1.9, 10.1)]
path_sol = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I']
mapdb = InMemMap("map", graph={
"A": ((0.00, 1), ["B"]),
"B": ((0.00, 3), ["A", "C"]),
"C": ((0.70, 3), ["B", "D"]),
"D": ((1.00, 5), ["C", "E"]),
"E": ((1.00, 6), ["D", "F"]),
"F": ((0.70, 7), ["E", "G"]),
"G": ((0.00, 8), ["F", "H"]),
"H": ((0.0, 10), ["G", "I"]),
"I": ((2.0, 10), ["H"])
}, use_latlon=False)
matcher = DistanceMatcher(mapdb, max_dist_init=0.2,
obs_noise=0.5, obs_noise_ne=2, dist_noise=0.5,
non_emitting_states=True)
states, lastidx = matcher.match(path)
path_pred = matcher.path_pred_onlynodes
if directory:
from leuvenmapmatching import visualization as mmviz
mmviz.plot_map(mapdb, matcher=matcher, show_labels=True, show_matching=True, linewidth=2,
filename=str(directory / "test_path_3_dist.png"))
assert path_pred == path_sol, f"Nodes not equal:\n{path_pred}\n{path_sol}"
for obs_idx, m in enumerate(matcher.lattice_best): # type: Tuple[int, DistanceMatching]
state = m.shortkey # tuple indicating edge
ne_str = "e" if m.is_emitting() else "ne" # state is emitting or not
p1_str = "{:>5.2f}-{:<5.2f}".format(*m.edge_m.pi) # best matching location on graph
p2_str = "{:>5.2f}-{:<5.2f}".format(*m.edge_o.pi) # best matching location on track
print(f"{obs_idx:<2} | {state} | {ne_str:<2} | {p1_str} | {p2_str}")
def test_path1_full():
prepare_files()
track = gpx_to_path(track_fn)
track = [loc[:2] for loc in track]
track_int = interpolate_path(track, 5)
map_con = create_map_from_xml(osm_fn,
include_footways=True,
include_parking=True)
matcher = DistanceMatcher(map_con,
max_dist=50,
obs_noise=50,
min_prob_norm=0.1)
states, last_idx = matcher.match(track_int)
if directory:
# matcher.print_lattice_stats()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
zoom_path=True,
show_graph=True,
filename=str(directory / "test_path_latlon_path1.png"))
assert len(states) == len(track_int), f"Path ({len(track_int)}) not fully matched by best path ({len(states)}), " + \
f"last index = {last_idx}"
def example1():
map_con = InMemMap("mymap",
graph={
"A": ((1, 1), ["B", "C", "X"]),
"B": ((1, 3), ["A", "C", "D", "K"]),
"C": ((2, 2), ["A", "B", "D", "E", "X", "Y"]),
"D": ((2, 4), ["B", "C", "F", "E", "K", "L"]),
"E": ((3, 3), ["C", "D", "F", "Y"]),
"F": ((3, 5), ["D", "E", "L"]),
"X": ((2, 0), ["A", "C", "Y"]),
"Y": ((3, 1), ["X", "C", "E"]),
"K": ((1, 5), ["B", "D", "L"]),
"L": ((2, 6), ["K", "D", "F"])
},
use_latlon=False)
path = [(0.8, 0.7), (0.9, 0.7), (1.1, 1.0), (1.2, 1.5), (1.2, 1.6),
(1.1, 2.0), (1.1, 2.3), (1.3, 2.9), (1.2, 3.1), (1.5, 3.2),
(1.8, 3.5), (2.0, 3.7), (2.3, 3.5), (2.4, 3.2), (2.6, 3.1),
(2.9, 3.1), (3.0, 3.2), (3.1, 3.8), (3.0, 4.0), (3.1, 4.3),
(3.1, 4.6), (3.0, 4.9)]
matcher = DistanceMatcher(map_con,
max_dist=2,
obs_noise=1,
min_prob_norm=0.5)
states, _ = matcher.match(path)
nodes = matcher.path_pred_onlynodes
print("States\n------")
print(states)
print("Nodes\n------")
print(nodes)
print("")
matcher.print_lattice_stats()
def test_path_outlier2():
path = [(0.8, 0.7), (0.9, 0.7), (1.1, 1.0), (1.2, 1.5), (1.2, 1.6), (1.1, 2.0),
(1.1, 2.3), (1.3, 2.9), (1.2, 3.1), (1.5, 3.2), (1.8, 3.5), (2.0, 3.7),
(2.1, 3.3), (2.4, 3.2), (2.6, 3.1), (2.9, 3.1), (3.0, 3.2), (3.1, 3.8),
(3.0, 4.0), (3.1, 4.3), (3.1, 4.6), (3.0, 4.9)]
path.insert(13, (2.3, -3.0))
mapdb = InMemMap("map", graph={
"A": ((1, 1), ["B", "C", "X"]),
"B": ((1, 3), ["A", "C", "D", "K"]),
"C": ((2, 2), ["A", "B", "D", "E", "X", "Y"]),
"D": ((2, 4), ["B", "C", "F", "E", "K", "L"]),
"E": ((3, 3), ["C", "D", "F", "Y"]),
"F": ((3, 5), ["D", "E", "L"]),
"X": ((2, 0), ["A", "C", "Y"]),
"Y": ((3, 1), ["X", "C", "E"]),
"K": ((1, 5), ["B", "D", "L"]),
"L": ((2, 6), ["K", "D", "F"])
}, use_latlon=False)
matcher = DistanceMatcher(mapdb, max_dist=None, min_prob_norm=0.1,
max_dist_init=1, obs_noise=0.25, obs_noise_ne=1,
non_emitting_states=True)
_, last_idx = matcher.match(path, unique=True)
if directory:
# matcher.print_lattice_stats()
# matcher.print_lattice()
from leuvenmapmatching import visualization as mmviz
# with (directory / 'lattice.gv').open('w') as ofile:
# matcher.lattice_dot(file=ofile)
mmviz.plot_map(mapdb, matcher=matcher, show_labels=True, show_matching=True,
filename=str(directory / "test_path_outlier2.png"))
assert last_idx == 12
def test_bug2():
this_path = Path(os.path.realpath(__file__)).parent / "rsrc" / "bug2"
edges_fn = this_path / "edgesrl.csv"
nodes_fn = this_path / "nodesrl.csv"
path_fn = this_path / "path.csv"
logger.debug(f"Reading map ...")
mmap = SqliteMap("road_network", use_latlon=True, dir=this_path)
path = []
with path_fn.open("r") as path_f:
reader = csv.reader(path_f, delimiter=',')
for row in reader:
lat, lon = [float(coord) for coord in row]
path.append((lat, lon))
node_cnt = 0
with nodes_fn.open("r") as nodes_f:
reader = csv.reader(nodes_f, delimiter=',')
for row in reader:
nid, lonlat, _ = row
nid = int(nid)
lon, lat = [float(coord) for coord in lonlat[1:-1].split(",")]
mmap.add_node(nid, (lat, lon), ignore_doubles=True, no_index=True, no_commit=True)
node_cnt += 1
edge_cnt = 0
with edges_fn.open("r") as edges_f:
reader = csv.reader(edges_f, delimiter=',')
for row in reader:
_eid, nid1, nid2, pid = [int(val) for val in row]
mmap.add_edge(nid1, nid2, edge_type=0, path=pid, no_index=True, no_commit=True)
edge_cnt += 1
logger.debug(f"... done: {node_cnt} nodes and {edge_cnt} edges")
logger.debug("Indexing ...")
mmap.reindex_nodes()
mmap.reindex_edges()
logger.debug("... done")
matcher = DistanceMatcher(mmap, min_prob_norm=0.001,
max_dist=200, obs_noise=4.07,
non_emitting_states=True)
# path = path[:2]
nodes, idx = matcher.match(path, unique=True)
path_pred = matcher.path_pred
if directory:
import matplotlib.pyplot as plt
matcher.print_lattice_stats()
logger.debug("Plotting post map ...")
fig = plt.figure(figsize=(100, 100))
ax = fig.get_axes()
mm_viz.plot_map(mmap, matcher=matcher, use_osm=True, ax=ax,
show_lattice=False, show_labels=True, show_graph=False, zoom_path=True,
show_matching=True)
plt.savefig(str(directory / "test_bug1.png"))
plt.close(fig)
logger.debug("... done")
def test_bug1():
map_con = SqliteMap("map", use_latlon=True)
map_con.add_nodes([(1, (47.590439915657, -122.238368690014)),
(2, (47.5910192728043, -122.239519357681)),
(3, (47.5913706421852, -122.240168452263))])
map_con.add_edges([(1, 2), (2, 3)])
path = [
# (47.59043333, -122.2384167),
(47.59058333, -122.2387),
(47.59071667, -122.2389833),
(47.59086667, -122.2392667),
(47.59101667, -122.23955),
(47.59115, -122.2398333)
]
path_sol = [(1, 2), (2, 3)]
matcher = DistanceMatcher(map_con,
min_prob_norm=0.001,
max_dist=200,
obs_noise=4.07,
non_emitting_states=True)
matcher.match(path, unique=True)
path_pred = matcher.path_pred
if directory:
import matplotlib.pyplot as plt
matcher.print_lattice_stats()
logger.debug("Plotting post map ...")
fig = plt.figure(figsize=(100, 100))
ax = fig.get_axes()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
ax=ax,
show_lattice=False,
show_labels=True,
show_graph=True,
zoom_path=True,
show_matching=True)
plt.savefig(str(directory / "test_newson_bug1.png"))
plt.close(fig)
logger.debug("... done")
assert path_pred == path_sol, f"Edges not equal:\n{path_pred}\n{path_sol}"
def map_matching_osm():
track = gpx_to_path("mytrack.gpx")
matcher = DistanceMatcher(
map_con,
max_dist=100,
max_dist_init=25, # meter
min_prob_norm=0.001,
non_emitting_length_factor=0.75,
obs_noise=50,
obs_noise_ne=75, # meter
dist_noise=50, # meter
non_emitting_states=True)
states, lastidx = matcher.match(track)
return
def dis_matcher(map_con, track):
matcher = DistanceMatcher(map_con,
max_dist=200,
min_prob_norm=0.0001,
non_emitting_length_factor=0.5,
obs_noise=10,
obs_noise_ne=10,
dist_noise=100,
max_lattice_width=5,
avoid_goingback=True,
non_emitting_states=True)
states, last_idx = matcher.match(track)
nodes = matcher.path_pred_onlynodes
return matcher, nodes
def test_path2_proj():
prepare_files()
map_con_latlon = create_map_from_xml(osm2_fn)
map_con = map_con_latlon.to_xy()
track = [map_con.latlon2yx(p[0], p[1]) for p in gpx_to_path(track2_fn)]
matcher = DistanceMatcher(map_con,
max_dist=300,
max_dist_init=25,
min_prob_norm=0.0001,
non_emitting_length_factor=0.95,
obs_noise=50,
obs_noise_ne=50,
dist_noise=50,
max_lattice_width=5,
non_emitting_states=True)
states, last_idx = matcher.match(track, unique=False)
nodes = matcher.path_pred_onlynodes
if directory:
matcher.print_lattice_stats()
mm_viz.plot_map(map_con,
matcher=matcher,
path=track,
use_osm=False,
show_graph=True,
show_matching=True,
show_labels=5,
filename=str(directory /
"test_path_latlon_path2_proj.png"))
nodes_sol = [
2634474831, 1096512242, 3051083902, 1096512239, 1096512241, 1096512240,
1096508366, 1096508372, 16483861, 1096508360, 159656075, 1096508382,
16483862, 3051083898, 16526535, 3060597381, 3060515059, 16526534,
16526532, 1274158119, 16526540, 3060597377, 16526541, 16424220,
1233373340, 613125597, 1076057753
]
nodes_sol2 = [
1096512242, 3051083902, 1096512239, 1096512241, 1096512240, 159654664,
1096508373, 1096508381, 16483859, 1096508369, 159654663, 1096508363,
16483862, 3051083898, 16526535, 3060597381, 3060515059, 16526534,
16526532, 611867918, 3060725817, 16483866, 3060725817, 611867918,
16526532, 1274158119, 16526540, 3060597377, 16526541, 16424220,
1233373340, 613125597, 1076057753
]
assert (nodes
== nodes_sol) or (nodes
== nodes_sol2), f"Nodes do not match: {nodes}"
def test_path2():
prepare_files()
map_con = create_map(osm2_fn)
track = [(p[0], p[1]) for p in gpx_to_path(track2_fn)]
matcher = DistanceMatcher(map_con, max_dist=300, max_dist_init=25, min_prob_norm=0.0001,
non_emitting_length_factor=0.75,
obs_noise=50, obs_noise_ne=75,
dist_noise=30,
max_lattice_width=5,
non_emitting_states=True)
states, last_idx = matcher.match(track, unique=False)
nodes = matcher.path_pred_onlynodes
if directory:
mm_viz.plot_map(map_con, matcher=matcher, nodes=nodes, path=track, z=17, use_osm=True,
show_graph=True, show_matching=True,
filename=str(directory / "test_path_latlon_path2.png"))
nodes_sol = [2634474831, 1096512242, 3051083902, 1096512239, 1096512241, 1096512240, 1096508366, 1096508372,
16483861, 3051083900, 16483864, 16483865, 3060515058, 16526534, 16526532, 1274158119, 16526540,
3060597377, 16526541, 16424220, 1233373340, 613125597, 1076057753]
assert nodes == nodes_sol, f"Nodes do not match: {nodes}"
def map_matching(map_con, df, s=0, n=3):
trj_ids = df.trj_id.unique()
for i in range(s, n):
df_path = df[df['trj_id'] == trj_ids[i]]
df_trj = df_path[['rawlat', 'rawlng']]
track = []
max_dist = 2
for j in range(len(df_trj)):
track.append((df_trj.iloc[j, 0], df_trj.iloc[j, 1]))
while True:
try:
matcher = DistanceMatcher(
map_con,
max_dist=max_dist,
max_dist_init=1000000, # meter
min_prob_norm=0.001,
non_emitting_length_factor=0.75,
obs_noise=50,
obs_noise_ne=75, # meter
dist_noise=50, # meter
non_emitting_states=True)
states, lastidx = matcher.match(track)
nodes = matcher.path_pred_onlynodes
a = states[-1]
break
except:
max_dist += max_dist * 0.5
print(max_dist)
# print('current trajectory ID: {}'.format(trj_ids[i]))
with open("file.txt", "a+") as output:
for j in range(len(nodes)):
trj_data = [
trj_ids[i], map_con.graph[nodes[j]][0][0],
map_con.graph[nodes[j]][0][1]
]
output.write('{}\n'.format(str(trj_data)))
print(i)
def example2():
path = [(1, 0), (7.5, 0.65), (10.1, 1.9)]
mapdb = InMemMap("mymap",
graph={
"A": ((1, 0.00), ["B"]),
"B": ((3, 0.00), ["A", "C"]),
"C": ((4, 0.70), ["B", "D"]),
"D": ((5, 1.00), ["C", "E"]),
"E": ((6, 1.00), ["D", "F"]),
"F": ((7, 0.70), ["E", "G"]),
"G": ((8, 0.00), ["F", "H"]),
"H": ((10, 0.0), ["G", "I"]),
"I": ((10, 2.0), ["H"])
},
use_latlon=False)
matcher = DistanceMatcher(mapdb,
max_dist_init=0.2,
obs_noise=1,
obs_noise_ne=10,
non_emitting_states=True,
only_edges=True)
states, _ = matcher.match(path)
nodes = matcher.path_pred_onlynodes
print("States\n------")
print(states)
print("Nodes\n------")
print(nodes)
print("")
matcher.print_lattice_stats()
mmviz.plot_map(mapdb,
matcher=matcher,
show_labels=True,
show_matching=True,
filename="output.png")
def match(track,
graph,
streetmap,
matcher_alg='DistanceMatcher',
max_dist=200,
max_dist_init=100,
min_prob_norm=0.001,
non_emitting_length_factor=0.75,
obs_noise=50,
obs_noise_ne=75,
dist_noise=50,
non_emitting_edgeid=False):
'''
This function match the floating car track.
Parameters:
___________
track: np.array of tuples (x,y)
Array of the floating car track coordinates.
graph: OSMNX road network
Osmnx road network of the area of study
streetmap: InMem map
LeuvenMapMatching InMem map of the graph
mather_alg: string
Name of the matcher algorithm of choice
max_dist: int
maximum distance from the track
max_dist_init: int
initial maximum distance
min_prob_norm: float
Minmum normalized probability
non_emitting_length_factor: float
The factor no emmiting state that it takes
obs_noise: int
Standard Deviation of noise
obs_noise_ne: int
Standard Deviation of noise in non emitting state
dist_noise: int
distance of the noise
non_emitting_edgeid: boolean
If allow on-emitting states
Returns:
__________
edge_id: np.array of tuples
An array consisting tuples, (start node id, end node id)
last_idx: int
Last index of the point mapped
track_corr: np.array
array of map matched coordinates
route: np.array
array of computed route of the map matched track
'''
# get leuvenmapmatching InMem mam from the road network
#streetmap = get_InMemMap(graph)
# select the matcher of choise
if (matcher_alg == 'DistanceMatcher'):
matcher = DistanceMatcher(
streetmap,
max_dist=max_dist,
max_dist_init=max_dist_init,
min_prob_norm=min_prob_norm,
non_emitting_length_factor=non_emitting_length_factor,
obs_noise=obs_noise,
obs_noise_ne=obs_noise_ne,
dist_noise=dist_noise,
non_emitting_edgeid=non_emitting_edgeid)
elif (matcher_alg == 'NewsonKrummMatcher'):
matcher = NewsonKrummMatcher(
streetmap,
max_dist=max_dist,
max_dist_init=max_dist_init,
min_prob_norm=min_prob_norm,
non_emitting_length_factor=non_emitting_length_factor,
obs_noise=obs_noise,
obs_noise_ne=obs_noise_ne,
dist_noise=dist_noise,
non_emitting_edgeid=non_emitting_edgeid)
elif (matcher_alg == 'SimpleMatcher'):
matcher = SimpleMatcher(
streetmap,
max_dist=max_dist,
max_dist_init=max_dist_init,
min_prob_norm=min_prob_norm,
non_emitting_length_factor=non_emitting_length_factor,
obs_noise=obs_noise,
obs_noise_ne=obs_noise_ne,
dist_noise=dist_noise,
non_emitting_edgeid=non_emitting_edgeid)
else:
print('No matcher selected')
return
# Perform the mapmatching
edge_ids, last_idx = matcher.match(track)
# Reference ellipsoid for distance
geod = Geod(ellps='WGS84')
proj_dist = np.zeros(len(track))
# edgeid refers to edges id (node1_id, node2_id) where the GPS point is projected
lat_corr, lon_corr = [], []
lat_nodes = matcher.lattice_best
for idx, m in enumerate(lat_nodes):
if (idx == len(track)):
break
lat, lon = m.edge_m.pi[:2]
lat_corr.append(lat)
lon_corr.append(lon)
# print(idx)
_, _, distance = geod.inv(track[idx][1], track[idx][0], lon, lat)
proj_dist[idx] += distance
# construct array of cordinates
track_corr = np.column_stack((lat_corr, lon_corr))
# get the route from the projected cordinates
route = compute_route(streetmap, track_corr, edge_ids)
# returns
return edge_ids, last_idx, track_corr, route
def test_route():
if directory:
import matplotlib.pyplot as plt
else:
plt = None
paths, map_con, route = load_data()
route = [(lat, lon) for lat, lon in route]
zoom_path = True
# zoom_path = slice(2645, 2665)
slice_route = None
# slice_route = slice(650, 750)
# slice_route = slice(2657, 2662) # First location where some observations are missing
# slice_route = slice(2770, 2800) # Observations are missing
# slice_route = slice(2910, 2950) # Interesting point
# slice_route = slice(2910, 2929) # Interesting point
# slice_route = slice(6825, 6833) # Outlier observation
slice_route = slice(6300, )
# if directory is not None:
# logger.debug("Plotting pre map ...")
# mm_viz.plot_map(map_con_latlon, path=route_latlon, use_osm=True,
# show_lattice=False, show_labels=False, show_graph=False, zoom_path=zoom_path,
# filename=str(directory / "test_newson_route.png"))
# logger.debug("... done")
matcher = DistanceMatcher(map_con,
min_prob_norm=0.0001,
max_dist=200,
dist_noise=15,
dist_noise_ne=30,
obs_noise=30,
obs_noise_ne=150,
non_emitting_states=True)
if slice_route is None:
pkl_fn = this_path / "nodes_pred.pkl"
if pkl_fn.exists():
with pkl_fn.open("rb") as pkl_file:
logger.debug(f"Reading predicted nodes from pkl file")
route_nodes = pickle.load(pkl_file)
else:
matcher.match(route)
route_nodes = matcher.path_pred_onlynodes
with pkl_fn.open("wb") as pkl_file:
pickle.dump(route_nodes, pkl_file)
from leuvenmapmatching.util.evaluation import route_mismatch_factor
print(route_nodes[:10])
# route_edges = map_con.nodes_to_paths(route_nodes)
# print(route_edges[:10])
grnd_paths, _ = zip(*paths)
print(grnd_paths[:10])
route_paths = map_con.nodes_to_paths(route_nodes)
print(route_paths[:10])
logger.debug(f"Compute route mismatch factor")
factor, cnt_matches, cnt_mismatches, total_length, mismatches, _, _ = \
route_mismatch_factor(map_con, route_paths, grnd_paths,window=None, keep_mismatches=True)
logger.debug(
f"factor = {factor}, "
f"cnt_matches = {cnt_matches}/{cnt_mismatches} of {len(grnd_paths)}/{len(route_paths)}, "
f"total_length = {total_length}\n"
f"mismatches = " + " | ".join(str(v) for v in mismatches))
else:
_, last_idx = matcher.match(route[slice_route])
logger.debug(f"Last index = {last_idx}")
# matcher.match(route[2657:2662]) # First location where some observations are missing
# matcher.match(route[2770:2800]) # Observations are missing
# matcher.match(route[2910:2950]) # Interesting point
# matcher.match(route[2910:2929]) # Interesting point
# matcher.match(route[6000:])
path_pred = matcher.path_pred_onlynodes
if directory:
matcher.print_lattice_stats()
logger.debug("Plotting post map ...")
fig = plt.figure(figsize=(200, 200))
ax = fig.get_axes()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
ax=ax,
show_lattice=False,
show_labels=True,
zoom_path=zoom_path,
show_matching=True,
show_graph=False)
plt.savefig(str(directory / "test_newson_route_matched.png"))
plt.close(fig)
logger.debug("... done")
logger.debug("Best path:")
for m in matcher.lattice_best:
logger.debug(m)
print(path_pred)
def test_bug2():
from leuvenmapmatching.util.openstreetmap import locations_to_map
map_con = SqliteMap("map", use_latlon=True, dir=directory)
path = [(50.87205, 4.66089), (50.874550000000006, 4.672980000000001),
(50.87538000000001, 4.67698),
(50.875800000000005, 4.6787600000000005),
(50.876520000000006, 4.6818),
(50.87688000000001, 4.683280000000001), (50.87814, 4.68733),
(50.87832, 4.68778), (50.87879, 4.68851),
(50.87903000000001, 4.68895),
(50.879560000000005, 4.689170000000001),
(50.87946, 4.6900900000000005),
(50.879290000000005, 4.6909600000000005),
(50.87906, 4.6921800000000005), (50.87935, 4.6924),
(50.879720000000006, 4.69275), (50.88002, 4.6930700000000005),
(50.880430000000004, 4.693440000000001),
(50.880660000000006, 4.69357),
(50.880660000000006, 4.6936100000000005),
(50.88058, 4.694640000000001), (50.88055000000001, 4.69491),
(50.88036, 4.696160000000001), (50.88009, 4.697550000000001),
(50.87986, 4.6982800000000005),
(50.879720000000006, 4.698790000000001),
(50.87948, 4.699730000000001),
(50.87914000000001, 4.6996400000000005),
(50.87894000000001, 4.6995000000000005),
(50.878800000000005, 4.699350000000001),
(50.8785, 4.6991000000000005), (50.87841, 4.6990300000000005)]
locations_to_map(path, map_con, filename=directory / "osm.xml")
path_sol = [(5777282112, 2633552218), (2633552218, 5777282111),
(5777282111, 5777282110), (5777282110, 1642021707),
(1642021707, 71361087), (71361087, 71364203),
(71364203, 1151697757), (1151697757, 1647339017),
(1647339017, 1647339030), (1647339030, 2058510349),
(2058510349, 2633552212), (2633552212, 1380538577),
(1380538577, 1439572271), (1439572271, 836434313),
(836434313, 2633771041), (2633771041, 5042874484),
(5042874484, 5042874485), (5042874485, 2518922583),
(2518922583, 2659762546), (2659762546, 5777282063),
(5777282063, 2633771037), (2633771037, 2633771035),
(2633771035, 2633771033), (2633771033, 1151668705),
(1151668705, 2633771094), (2633771094, 1151668722),
(1151668722, 1151668724), (1151668724, 5543948222),
(5543948222, 2058481517), (2058481517, 16933576),
(16933576, 5543948221), (5543948221, 2518923620),
(2518923620, 5543948020), (5543948020, 5543948019),
(5543948019, 18635886), (18635886, 18635887),
(18635887, 1036909153), (1036909153, 2658942230),
(2658942230, 1001099975), (1001099975, 16933574),
(16933574, 1125604152), (1125604152, 5543948238),
(5543948238, 1125604150), (1125604150, 1125604148),
(1125604148, 2634195334), (2634195334, 2087854243),
(2087854243, 5543948237), (5543948237, 160226603),
(160226603, 180130266), (180130266, 5543948227),
(5543948227, 5543948226), (5543948226, 1195681902),
(1195681902, 101135392), (101135392, 2606704673),
(2606704673, 18635977), (18635977, 1026111708),
(1026111708, 1026111631), (1026111631, 16571375),
(16571375, 2000680621), (2000680621, 999580042),
(999580042, 16571370), (16571370, 2000680620),
(2000680620, 5078692402), (5078692402, 5543948008),
(5543948008, 16571371), (16571371, 999579936),
(999579936, 2639836143), (2639836143, 5543948014),
(5543948014, 5222992316), (5222992316, 30251323),
(30251323, 159701080), (159701080, 3173217124),
(3173217124, 1165209673), (1165209673, 1380538689),
(1380538689, 2878334668), (2878334668, 2871137399),
(2871137399, 2876902981), (2876902981, 2873624508),
(2873624508, 2873624509), (2873624509, 2899666507),
(2899666507, 2899666518), (2899666518, 2899666513),
(2899666513, 2903073945), (2903073945, 2903073951),
(2903073951, 1380538681), (1380538681, 2914810627),
(2914810627, 2914810618), (2914810618, 2914810607),
(2914810607, 2914810604), (2914810604, 2914810483),
(2914810483, 2914810462), (2914810462, 2914810464),
(2914810464, 1312433523), (1312433523, 20918594),
(20918594, 2634267817), (2634267817, 2967425445),
(2967425445, 3201523879), (3201523879, 157217466),
(157217466, 2963305939), (2963305939, 3201523877),
(3201523877, 3889275909), (3889275909, 3889275897),
(3889275897, 157255077), (157255077, 30251882),
(30251882, 157245624), (157245624, 1150903673),
(1150903673, 4504936404)]
matcher = DistanceMatcher(map_con,
min_prob_norm=0.001,
max_dist=200,
obs_noise=4.07,
non_emitting_states=True)
nodes, idx = matcher.match(path, unique=True)
path_pred = matcher.path_pred
if directory:
import matplotlib.pyplot as plt
matcher.print_lattice_stats()
logger.debug("Plotting post map ...")
fig = plt.figure(figsize=(100, 100))
ax = fig.get_axes()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
ax=ax,
show_lattice=False,
show_labels=True,
show_graph=False,
zoom_path=True,
show_matching=True)
plt.savefig(str(directory / "test_newson_bug1.png"))
plt.close(fig)
logger.debug("... done")
assert path_pred == path_sol, f"Edges not equal:\n{path_pred}\n{path_sol}"
def test_bug2():
this_path = Path(os.path.realpath(__file__)).parent / "rsrc" / "bug2"
edges_fn = this_path / "edgesrl.csv"
nodes_fn = this_path / "nodesrl.csv"
path_fn = this_path / "path.csv"
zip_fn = this_path / "leuvenmapmatching_testdata.zip"
if not (edges_fn.exists() and nodes_fn.exists() and path_fn.exists()):
import requests
url = 'https://people.cs.kuleuven.be/wannes.meert/leuvenmapmatching/leuvenmapmatching_testdata.zip'
logger.debug("Download testfiles from kuleuven.be")
r = requests.get(url, stream=True)
with zip_fn.open('wb') as ofile:
for chunk in r.iter_content(chunk_size=1024):
if chunk:
ofile.write(chunk)
import zipfile
logger.debug("Unzipping leuvenmapmatching_testdata.zip")
with zipfile.ZipFile(str(zip_fn), "r") as zip_ref:
zip_ref.extractall(str(zip_fn.parent))
logger.debug(f"Reading map ...")
mmap = SqliteMap("road_network", use_latlon=True, dir=this_path)
path = []
with path_fn.open("r") as path_f:
reader = csv.reader(path_f, delimiter=',')
for row in reader:
lat, lon = [float(coord) for coord in row]
path.append((lat, lon))
node_cnt = 0
with nodes_fn.open("r") as nodes_f:
reader = csv.reader(nodes_f, delimiter=',')
for row in reader:
nid, lonlat, _ = row
nid = int(nid)
lon, lat = [float(coord) for coord in lonlat[1:-1].split(",")]
mmap.add_node(nid, (lat, lon), ignore_doubles=True, no_index=True, no_commit=True)
node_cnt += 1
edge_cnt = 0
with edges_fn.open("r") as edges_f:
reader = csv.reader(edges_f, delimiter=',')
for row in reader:
_eid, nid1, nid2, pid = [int(val) for val in row]
mmap.add_edge(nid1, nid2, edge_type=0, path=pid, no_index=True, no_commit=True)
edge_cnt += 1
logger.debug(f"... done: {node_cnt} nodes and {edge_cnt} edges")
logger.debug("Indexing ...")
mmap.reindex_nodes()
mmap.reindex_edges()
logger.debug("... done")
matcher = DistanceMatcher(mmap, min_prob_norm=0.001,
max_dist=200, obs_noise=4.07,
non_emitting_states=True)
# path = path[:2]
nodes, idx = matcher.match(path, unique=True)
path_pred = matcher.path_pred
if directory:
import matplotlib.pyplot as plt
matcher.print_lattice_stats()
logger.debug("Plotting post map ...")
fig = plt.figure(figsize=(100, 100))
ax = fig.get_axes()
mm_viz.plot_map(mmap, matcher=matcher, use_osm=True, ax=ax,
show_lattice=False, show_labels=True, show_graph=False, zoom_path=True,
show_matching=True)
plt.savefig(str(directory / "test_bug1.png"))
plt.close(fig)
logger.debug("... done")
from leuvenmapmatching.matcher.distance import DistanceMatcher
from leuvenmapmatching.map.inmem import InMemMap
from leuvenmapmatching import visualization as mmviz
path = [(1, 0), (7.5, 0.65), (10.1, 1.9)]
mapdb = InMemMap("mymap", graph={
"A": ((1, 0.00), ["B"]),
"B": ((3, 0.00), ["A", "C"]),
"C": ((4, 0.70), ["B", "D"]),
"D": ((5, 1.00), ["C", "E"]),
"E": ((6, 1.00), ["D", "F"]),
"F": ((7, 0.70), ["E", "G"]),
"G": ((8, 0.00), ["F", "H"]),
"H": ((10, 0.0), ["G", "I"]),
"I": ((10, 2.0), ["H"])
}, use_latlon=False)
matcher = DistanceMatcher(mapdb, max_dist_init=0.2, obs_noise=1, obs_noise_ne=10,
non_emitting_states=True, only_edges=True)
states, _ = matcher.match(path)
nodes = matcher.path_pred_onlynodes
print("States\n------")
print(states)
print("Nodes\n------")
print(nodes)
print("")
matcher.print_lattice_stats()
mmviz.plot_map(mapdb, matcher=matcher,
show_labels=True, show_matching=True
filename="output.png")
def map_track_to_roads(cls,
track_positions,
map_matching_graph,
stepsize=5,
obs_noise=5,
obs_noise_ne=5,
max_dist_init=1000,
max_dist=5,
min_prob_norm=0.5,
non_emitting_states=False,
non_emitting_length_factor=0.75,
max_lattice_width=10,
dist_noise=5,
dist_noise_ne=5,
restrained_ne=True,
avoid_goingback=True):
"""Map a series of track_positions to a road network using a map_matching_graph
:param track_positions: a numpy array of [x, y, timestamp]
where x and y are assumed to be in meters
:param map_matching_graph: The map matching graph for a road network
:return: An array of road edges and corresponding timestamps.
When a road mapping cannot be made for a given track position,
a 'None' is inserted
"""
track_arc_lengths_m = np.hstack([
0,
np.cumsum(
np.sqrt(np.sum(np.diff(track_positions[:, :2], axis=0)**2, 1)))
])
total_track_length_m = track_arc_lengths_m[-1]
if total_track_length_m == 0.0:
print(f"Total track length must be > 0.0, skipping!")
return [], []
elif total_track_length_m > 100000:
print(f"Total track length is abnormally large "
f"({total_track_length_m} meters), skipping!")
return [], []
# We need to remove positions with duplicate x, y to be able
# to interpolate (always keeping the latter of the two; we
# never throw away the last point)
ind = np.hstack([np.diff(track_arc_lengths_m), 1]) != 0
interp_f = interp1d(track_arc_lengths_m[ind],
track_positions[ind],
axis=0)
steps = np.linspace(0, total_track_length_m,
int(np.ceil(total_track_length_m / stepsize)) + 1)
interp_positions = interp_f(steps)
node_path = []
k = 0
matcher = DistanceMatcher(
map_matching_graph,
obs_noise=obs_noise,
obs_noise_ne=obs_noise_ne,
max_dist_init=max_dist_init,
max_dist=max_dist,
min_prob_norm=min_prob_norm,
non_emitting_states=non_emitting_states,
non_emitting_length_factor=non_emitting_length_factor,
max_lattice_width=max_lattice_width,
dist_noise=dist_noise,
dist_noise_ne=dist_noise_ne,
restrained_ne=restrained_ne,
avoid_goingback=avoid_goingback)
while k < len(interp_positions):
node_pathi, ind = matcher.match(interp_positions[k:, :2])
if len(node_pathi) == 0:
node_path.append(None)
k += 1
else:
node_path.extend(node_pathi)
k += ind + 1
return node_path, interp_positions[:, 2]
def test_path3():
prepare_files()
track = [(50.87881, 4.698930000000001), (50.87899, 4.69836),
(50.87905000000001, 4.698110000000001),
(50.879000000000005, 4.69793), (50.87903000000001, 4.69766),
(50.87906, 4.697500000000001), (50.87908, 4.6973),
(50.879110000000004, 4.69665), (50.87854, 4.696420000000001),
(50.878440000000005, 4.696330000000001),
(50.878370000000004, 4.696140000000001), (50.8783, 4.69578),
(50.87832, 4.69543), (50.87767, 4.695530000000001),
(50.87763, 4.695080000000001), (50.87758, 4.6948300000000005),
(50.877480000000006, 4.69395),
(50.877500000000005, 4.693700000000001),
(50.877520000000004, 4.69343),
(50.877610000000004, 4.692670000000001),
(50.87776, 4.6917800000000005), (50.87783, 4.69141),
(50.87744000000001, 4.6908900000000004),
(50.87736, 4.690790000000001), (50.877300000000005, 4.69078),
(50.876650000000005, 4.6907000000000005), (50.87597, 4.69066),
(50.875820000000004, 4.69068), (50.87561, 4.6907700000000006),
(50.874430000000004, 4.69136),
(50.874210000000005, 4.691490000000001), (50.87413, 4.69151),
(50.87406000000001, 4.69151), (50.87397000000001, 4.69148),
(50.87346, 4.6913800000000005), (50.87279, 4.691260000000001),
(50.872490000000006, 4.69115), (50.87259, 4.6908900000000004),
(50.87225, 4.690650000000001),
(50.872080000000004, 4.6904900000000005),
(50.871550000000006, 4.69125), (50.87097000000001, 4.69216),
(50.87033, 4.69324), (50.87017, 4.6935400000000005),
(50.87012000000001, 4.69373), (50.86997, 4.69406),
(50.86981, 4.694520000000001), (50.86943, 4.69585),
(50.868970000000004, 4.697500000000001),
(50.868770000000005, 4.698130000000001), (50.86863, 4.6985),
(50.86844000000001, 4.69899), (50.868140000000004, 4.69977),
(50.86802, 4.70023), (50.867920000000005, 4.70078),
(50.86787, 4.701180000000001), (50.86784, 4.70195),
(50.86786000000001, 4.702310000000001),
(50.86791, 4.702870000000001), (50.86836, 4.7052700000000005),
(50.86863, 4.7064900000000005),
(50.86880000000001, 4.707210000000001),
(50.869220000000006, 4.708410000000001),
(50.869400000000006, 4.70891), (50.86959, 4.709350000000001),
(50.86995, 4.71004), (50.87006, 4.71021),
(50.870900000000006, 4.7112300000000005),
(50.872260000000004, 4.712890000000001), (50.87308, 4.71389),
(50.873430000000006, 4.714300000000001),
(50.873560000000005, 4.71441),
(50.873740000000005, 4.714530000000001),
(50.874280000000006, 4.714740000000001),
(50.876250000000006, 4.71544),
(50.876490000000004, 4.7155700000000005),
(50.876900000000006, 4.7158500000000005), (50.87709, 4.71598),
(50.877190000000006, 4.716010000000001),
(50.87751, 4.7160400000000005),
(50.87782000000001, 4.7160400000000005), (50.87832, 4.71591),
(50.87894000000001, 4.71567), (50.87975, 4.71536),
(50.88004, 4.71525), (50.8804, 4.715070000000001),
(50.88163, 4.71452), (50.881750000000004, 4.71447),
(50.8819, 4.714390000000001), (50.882200000000005, 4.71415),
(50.882470000000005, 4.7138800000000005),
(50.883480000000006, 4.7127300000000005),
(50.88552000000001, 4.710470000000001), (50.88624, 4.70966),
(50.88635000000001, 4.7096100000000005),
(50.886520000000004, 4.709580000000001),
(50.88664000000001, 4.7095400000000005),
(50.886750000000006, 4.709280000000001),
(50.88684000000001, 4.70906), (50.886970000000005, 4.70898),
(50.88705, 4.70887), (50.88714, 4.70868), (50.88743, 4.7079),
(50.887840000000004, 4.7069), (50.88776000000001, 4.70687),
(50.88765, 4.706790000000001), (50.887100000000004, 4.70627),
(50.88702000000001, 4.70619),
(50.886950000000006, 4.706040000000001),
(50.886950000000006, 4.7058800000000005),
(50.886970000000005, 4.705620000000001),
(50.88711000000001, 4.70417), (50.88720000000001, 4.70324),
(50.88723, 4.7027600000000005), (50.88709000000001, 4.70253),
(50.886480000000006, 4.70148), (50.88636, 4.70131),
(50.886050000000004, 4.70101), (50.88593, 4.70092),
(50.885810000000006, 4.700880000000001),
(50.88539, 4.7008600000000005), (50.88497, 4.70082),
(50.88436, 4.70089), (50.88398, 4.70094),
(50.883250000000004, 4.7010700000000005),
(50.88271, 4.701160000000001), (50.88136, 4.70159),
(50.881130000000006, 4.701790000000001),
(50.880930000000006, 4.7020100000000005), (50.88078, 4.70223),
(50.88046000000001, 4.70146), (50.88015000000001, 4.70101),
(50.880030000000005, 4.700880000000001),
(50.87997000000001, 4.70078), (50.879900000000006, 4.70061),
(50.87984, 4.70052), (50.879960000000004, 4.70026)]
track = track[:30]
map_con = create_map_from_xml(osm3_fn)
matcher = DistanceMatcher(map_con,
max_dist_init=30,
max_dist=50,
min_prob_norm=0.1,
obs_noise=10,
obs_noise_ne=20,
dist_noise=10,
non_emitting_states=True)
states, last_idx = matcher.match(track)
if directory:
# matcher.print_lattice_stats()
mm_viz.plot_map(map_con,
matcher=matcher,
use_osm=True,
zoom_path=True,
show_graph=False,
show_matching=True,
filename=str(directory / "test_path_latlon_path3.png"))
nodes = matcher.path_pred_onlynodes
nodes_sol = [
3906576303, 1150903750, 4506996820, 4506996819, 4506996798, 3906576457,
130147477, 3906576346, 231974072, 231974123, 1180606706, 19792164,
19792172, 1180606683, 1180606709, 5236409057, 19792169, 5236409056,
180241961, 180241975, 4506996259, 19792156, 5236409048, 180241625,
180241638, 231953030, 241928030, 241928031, 83796665, 231953028,
1125556965, 1380538625, 1824115892, 4909655515, 16571387, 16737662,
16571388, 179425214, 3705540990, 4567021046
]
assert nodes == nodes_sol, f"Nodes do not match: {nodes}"