def match_coor(x):
res = []
# with Network(SparkFiles.get("my_connected_path4.shp")) as network:
# graph = NetworkGraph(netwk.value)
# model = STMATCH(netwk.value, graph)
network = Network("./data/my_connected_path4.shp")
graph = NetworkGraph(network)
model = STMATCH(network, graph)
config = STMATCHConfig()
config.k = 4
config.gps_error = 0.5
# config.radius = 0.4
config.radius = 100
config.vmax = 60
config.factor = 1.5
for traj in x:
tmp = []
for item in traj[1]:
y,x = float(item[4]), float(item[3])
if x>180 or x<-180 or y>80 or y<-80:
continue
trans_coor = utm.from_latlon(y,x, force_zone_number=49)
tmp.append(str(trans_coor[0])+" "+str(trans_coor[1]))
if len(tmp) == 0:
continue
wkt = "LINESTRING("+",".join(tmp)+")"
result = model.match_wkt(wkt,config)
opath = list(result.opath)
x2 = list(zip(traj[1],opath))
res.append(x2)
return iter(res)
from fmm import Network, NetworkGraph, STMATCH, STMATCHConfig
network = Network("../data/edges.shp")
graph = NetworkGraph(network)
print graph.get_num_vertices()
model = STMATCH(network, graph)
wkt = "LINESTRING(0.200812146892656 2.14088983050848,1.44262005649717 2.14879943502825,3.06408898305084 2.16066384180791,3.06408898305084 2.7103813559322,3.70872175141242 2.97930790960452,4.11606638418078 2.62337570621469)"
config = STMATCHConfig()
config.k = 4
config.gps_error = 0.5
config.radius = 0.4
config.vmax = 30
config.factor = 1.5
result = model.match_wkt(wkt, config)
print type(result)
print "Opath ", list(result.opath)
print "Cpath ", list(result.cpath)
print "WKT ", result.mgeom.export_wkt()
train1000 = []
with open("../../data/train-1000.csv", "r") as csvfile:
reader = csv.reader(csvfile)
for line in reader:
train1000.append(line[8]) #POLYLINES
results = []
for t_number in range(1, 1001):
gps_points = eval(train1000[t_number])
wkt = 'LINESTRING(' + ','.join(
[' '.join([str(j) for j in i]) for i in gps_points]) + ')'
result = fmm_model.match_wkt(wkt, fmm_config)
if list(result.cpath) == []:
print('stmatching')
result = stmatch_model.match_wkt(wkt, stmatch_config)
candidates = list(result.candidates)
results.append(
dict(cpath=str(list(result.cpath)),
mgeom=result.mgeom.export_wkt(),
opath=str(list(result.opath)),
offset=str([c.offset for c in candidates]),
length=str([c.length for c in candidates]),
spdist=str([c.spdist for c in candidates])))
# cpath, opath, offset, length, spdist, mgeom
with open("../../data/match_result.csv", "w") as csvfile:
writer = csv.writer(csvfile)
writer.writerow(
["t_number", "cpath", "opath", "offset", "length", "spdist", "mgeom"])