def real_eg1():
sig = 5
# Algorithm parameters
n = 100 # number of samples
K = 100 # minimum number of candidate paths
vmean = 10. # mean velocity
vvar = 4. # velocity variance
boundingboxbuffer_ratio = 2.0
z,obstimes,extent, endID = testdb.ecourier_vertex_data(8,boundingboxbuffer_ratio)
print "measurements:"
print z
print "observation times"
print obstimes
G=testdb.london_roadmap(extent)
#plot
# position={}
# for node in G:
# position[node]=G.node[node]['pos']
# nx.draw(G,pos=position,node_size=50)
# for point in z:
# plt.plot(point[0],point[1],'kx',markersize=10.0,markeredgewidth=2)
# plt.show()
t0 = time.time()
[pp,sep,wp] = calcpostprobs(G,z,obstimes,n,K,vmean,vvar,sig*sig)
et = time.time()-t0
print et, "secs, done algorithm"
print endID
return pp, sep, wp, endID
def test_UTM():
boundingboxbuffer_ratio = 2.0
z,obstimes,extent = testdb.ecourier_data(8,2,boundingboxbuffer_ratio)
G=testdb.london_roadmap(extent)
x0,y0 = z[0]
dis=[]
tmpx=G.node[315161]['pos'][0]
tmpy=G.node[315161]['pos'][1]
dis.append(math.sqrt((x0-tmpx)**2+(y0-tmpy)**2))
print 'Min distance:',min(dis)
def real_eg1():
# Example to demonstrate interaction detection
boundingboxratio = 0.1
z1,obstimes1,z2,obstimes2,extent=testdb.ecourier_pair_data((72,'motorbike'),(44,'motorbike'),boundingboxratio)
print "Found measurements."
print obstimes1,obstimes2
print z1
print z2
G=testdb.london_roadmap(extent)
print "Graph generated. "
# Environment parameters
obstimebottom = max([obstimes1[0],obstimes2[0]])
obstimetop = min([obstimes1[1],obstimes2[1]])
print "path 1 length:", math.sqrt((z1[0][0]-z1[1][0])**2+(z1[1][1]-z1[0][1])**2)
print "path 2 length:", math.sqrt((z2[0][0]-z2[1][0])**2+(z2[1][1]-z2[0][1])**2)
print "obstimes1:",obstimes1
print "obstimes2:",obstimes2
# Measurement parameters
sig = 5.
# Algorithm parameters
n = 100 # Sample size for path posterior
n2 = 100 # sample size for position probability approximation
K = 50 # Number of candidate paths
# Movement statistics
vmean = 36/3.6
vvar = 2.*2.
# Times at which interaction is considered
ntpts = int(obstimetop-obstimebottom)-1
tax = numpy.linspace(obstimebottom+1,obstimetop-1,ntpts)
print "tax:",tax
t0 = time.time()
[H,P1,P2,s1,eidx1] = calcinter(G,z1,obstimes1,z2,obstimes2,n,n2,K,tax,1000,vmean,vvar,sig*sig)
et = time.time()-t0
print "Algorithm took "+str(et)+" seconds."
return H,tax
def case1_vehicle(vehicleid):
boundingboxratio=3.0
sig = 5
# Algorithm parameters
n = 100 # number of samples
K = 100 # minimum number of candidate paths
vmean = 10. # mean velocity
vvar = 4. # velocity variance
conn = psycopg2.connect("dbname=gis user=postgres password=peach")
cur = conn.cursor()
pointsqlx ="ST_X(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
pointsqly ="ST_Y(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
cur.execute("select id,"+pointsqlx+","+pointsqly+", timestamp from ecourier_oneday a where vehicleid=%s and exists (select * from hh_2po_4pgr where st_dwithin(source_geom,a.geom,0.00014) or st_dwithin(target_geom,a.geom,0.00014)) order by timestamp",(vehicleid,))
rows = cur.fetchall()
mesnum = len(rows)
results = []
for i in range(mesnum-1):
pid,x,y,timex=rows[i]
startid = pid
startpoint = numpy.array([x,y])
starttime = timex
pid,x,y,timex=rows[i+1]
endid = pid
endpoint = numpy.array([x,y])
endtime = timex
if 20<(endtime-starttime).total_seconds()<100:
IDlist = [startid,endid]
z=[startpoint,endpoint]
t=[0.0,(endtime-starttime).total_seconds()]
minx = startpoint[0] if startpoint[0]<endpoint[0] else endpoint[0]
maxx = startpoint[0] if startpoint[0]>endpoint[0] else endpoint[0]
miny = startpoint[1] if startpoint[1]<endpoint[1] else endpoint[1]
maxy = startpoint[1] if startpoint[1]>endpoint[1] else endpoint[1]
width=maxx-minx
height=maxy-miny
minx=minx-width*boundingboxratio/2.0
maxx=maxx+width*boundingboxratio/2.0
miny=miny-height*boundingboxratio/2.0
maxy=maxy+height*boundingboxratio/2.0
G=testdb.london_roadmap((minx,miny,maxx,maxy))
if G.nodes():
startvertex = nearest_vertex(G,startpoint)
endvertex = nearest_vertex(G,endpoint)
print z
print t
if len(G.nodes())>10 and nx.has_path(G,startvertex,endvertex) and startvertex!=endvertex and reasonable_distance(z[0],z[1]):
# position={}
# for node in G:
# position[node]=G.node[node]['pos']
# nx.draw(G,pos=position,node_size=50)
# for point in z:
# plt.plot(point[0],point[1],'kx',markersize=10.0,markeredgewidth=2)
# plt.show()
# try:
t0 = time.time()
[pp,sep,wp] = case1.calcpostprobs(G,z,t,n,K,vmean,vvar,sig*sig)
et = time.time()-t0
print et, "secs, done algorithm"
possibleroutes = len(pp)
result=[]
for j in range(possibleroutes):
edge = get_edges(pp[j],cur)
allmeasurements = get_allmeasurements(IDlist,vehicleid,cur)
result.append({'route':pp[j],'possibility':wp[j],'rmse':RSME(edge,allmeasurements,cur),'rmse_temporal':temporal_RSME(edge,allmeasurements,pp[j][0],cur),'period':t[-1]})
results.append(result)
# except:
# pass
return results
def case1_vehicle(vehicleid,interval):
boundingboxratio=0.5
sig = 5
# Algorithm parameters
n = 200 # number of samples
K = 50 # minimum number of candidate paths
vmean = 6. # mean velocity
vvar = 4. # velocity variance
includetruepath=False# without inputting true path
conn = psycopg2.connect("dbname=gis user=postgres password=peach")
cur = conn.cursor()
pointsqlx ="ST_X(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
pointsqly ="ST_Y(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
cur.execute("select id, timestamp,"+pointsqlx+","+pointsqly+" from near_vertex_measurement where vehicleid=%s and type=%s order by timestamp",vehicleid)
vehicle_records = MeasureList([measurement for measurement in cur],interval)
zl,tl,idl=vehicle_records.next()
# mesnum = len(rows)
results = []
while zl:
starttime=tl[0]
t=[(item-starttime).total_seconds() for item in tl]
allmeasurements = get_allmeasurements(idl,vehicleid,cur)
allmeasurements_id = [measurement[0] for measurement in allmeasurements]
addrow_query = "INSERT INTO testcases3(time_slot, start_num, end_num, measurements) VALUES (%s, %s, %s, %s) RETURNING test_id;"
measurementsstr = "{%s}" % (','.join([str(m_id) for m_id in allmeasurements_id]),)
cur.execute(addrow_query,(t[-1],idl[0],idl[-1],measurementsstr))
testid = cur.fetchone()[0]
conn.commit()
minx, maxx, miny, maxy = cal_min_bbox(allmeasurements,cur)
width=maxx-minx
height=maxy-miny
minx=minx-width*boundingboxratio/2.0
maxx=maxx+width*boundingboxratio/2.0
miny=miny-height*boundingboxratio/2.0
maxy=maxy+height*boundingboxratio/2.0
G=testdb.london_roadmap((minx,miny,maxx,maxy))
if G.nodes():
startvertex = nearest_vertex(G,zl[0])
endvertex = nearest_vertex(G,zl[-1])
print zl
print t
# large graph take too long time to process
if nx.has_path(G,startvertex,endvertex) and startvertex!=endvertex:
try:
t0 = time.time()
tedge,tpath = cal_route(allmeasurements_id,testid,cur)
updateroute = "{%s}" % (','.join(tedge),)
updatevertex = "{%s}" % (','.join([str(x) for x in tpath]),)
cur.execute('''update testcases3 set gt_mm_edges=%s where test_id = %s''',(updateroute,testid))
cur.execute('''update testcases3 set gt_mm_vertex=%s where test_id = %s''',(updatevertex,testid))
conn.commit()
[pp,sep,wp] = case1.calcpostprobs(G,zl,t,n,K,vmean,vvar,sig*sig,tpath if includetruepath else None)
et = time.time()-t0
print et, "secs, done algorithm"
possibleroutes = len(pp)
# print pp
if allmeasurements and allmeasurements[0][0]==idl[0] and allmeasurements[-1][0]==idl[-1]:
result=[]
for j in range(possibleroutes):
# print "modeled path:",pp[j]
edge = get_edges(pp[j],cur)
sarea = surrounding_area(edge,pp[j][0],cur,testid)
temproute = {'route':pp[j],
'possibility':wp[j],
'RMSE':RSME(edge,allmeasurements,cur),
'temporal_RMSE':temporal_RSME(edge,allmeasurements,pp[j][0],cur,allmeasurements_id,testid),
'common_length':share_distance(edge,pp[j][0],cur,testid),
'common_edges':common_edge(tedge,edge),
'area':sarea,
'edgeIDList':edge
}
result.append(temproute)
newtest = Test(result)
newtest.test_para = {'period':t[-1],
'measurements':allmeasurements_id,
"tpath":tpath,
"tedge":tedge,
"vehicleid":vehicleid,
"test_id":testid,
'v':len(G.nodes()),
'e':len(G.edges())}
results.append(newtest)
# conn.commit()
if len(results)>30:
return results
else:
print "Success.************************", len(results)
else:
print "vertx edge table error",idl,allmeasurements
except:
conn.rollback()
print "FAIL............................."
zl,tl,idl=vehicle_records.next()
return results
def case1_vehicle(interval):
sig = 5
# Algorithm parameters
n = 100 # number of samples
K = 100 # minimum number of candidate paths
vmean = 10. # mean velocity
vvar = 4. # velocity variance
conn = psycopg2.connect("dbname=gis user=postgres password=peach")
cur = conn.cursor()
cur.execute('''select measurements, gt_manul_edges, test_id
from testcases
where time_slot=%s and gt_manul_edges != '{}'
''', (interval,))
tests = cur.fetchall()
results = []
for test in tests:
allmeasurements_id = test[0]
manul_route = test[1]
locations,timestamp = get_exact_record(allmeasurements_id,cur)
zl = [locations[0],locations[-1]]
t = [timestamp[0],timestamp[-1]]
allmeasurements = get_allmeasurements(allmeasurements_id,cur)
G= testdb.london_roadmap(allmeasurements_id)
if G.nodes():
startvertex = nearest_vertex(G,zl[0])
endvertex = nearest_vertex(G,zl[-1])
print zl
print t
if len(G.edges())!=501 and nx.has_path(G,startvertex,endvertex) and startvertex!=endvertex:
try:
t0 = time.time()
[pp,sep,wp] = case1.calcpostprobs(G,zl,t,n,K,vmean,vvar,sig*sig)
et = time.time()-t0
print et, "secs, done algorithm"
possibleroutes = len(pp)
if allmeasurements:
# tpath = real_path(G,allmeasurements,startvertex,endvertex,cur)
# print "TRUE PATH", tpath
# tedge = get_edges(tpath,cur)
tedge = manul_route
result=[]
for j in range(possibleroutes):
# print "modeled path:",pp[j]
edge = get_edges(pp[j],cur)
try:
area = surrounding_area(edge,pp[j][0],tedge,startvertex,cur)
except:
conn.rollback()
area = surrounding_area(edge,pp[j][-1],tedge,startvertex,cur)
result.append({'route':pp[j],
'possibility':wp[j],
'RMSE':RSME(edge,allmeasurements,cur),
'temporal_RMSE':temporal_RSME(edge,allmeasurements,pp[j][0],cur,tedge),
'area':area,
'common_length':share_distance(edge,pp[j][0],tedge,startvertex,cur),
'edgeIDList':edge
})
newtest = Test(result)
newtest.test_para = {'period':t[-1],"test_id":test[2]}
results.append(newtest)
print "Success.************************", len(results)
else:
print "vertx edge table error",idl,allmeasurements
except:
conn.rollback()
print "FAIL............................."
return results
def case1_vehicle(vehicleid,interval):
boundingboxratio=0.6
sig = 5
# Algorithm parameters
n = 100 # number of samples
K = 100 # minimum number of candidate paths
vmean = 10. # mean velocity
vvar = 4. # velocity variance
conn = psycopg2.connect("dbname=gis user=postgres password=peach")
cur = conn.cursor()
pointsqlx ="ST_X(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
pointsqly ="ST_Y(ST_transform(ST_setsrid(ST_makepoint(longitude,latitude),4326),32630))"
cur.execute("select id, timestamp,"+pointsqlx+","+pointsqly+" from near_vertex_measurement where vehicleid=%s and type=%s order by timestamp",vehicleid)
vehicle_records = MeasureList([measurement for measurement in cur],interval)
zl,tl,idl=vehicle_records.next()
# mesnum = len(rows)
results = []
while zl:
starttime=tl[0]
t=[(item-starttime).total_seconds() for item in tl]
allmeasurements = get_allmeasurements(idl,vehicleid,cur)
minx, maxx, miny, maxy = cal_min_bbox(allmeasurements,cur)
width=maxx-minx
height=maxy-miny
minx=minx-width*boundingboxratio/2.0
maxx=maxx+width*boundingboxratio/2.0
miny=miny-height*boundingboxratio/2.0
maxy=maxy+height*boundingboxratio/2.0
G=testdb.london_roadmap((minx,miny,maxx,maxy))
if G.nodes():
startvertex = nearest_vertex(G,zl[0])
endvertex = nearest_vertex(G,zl[-1])
print zl
print t
# large graph take too long time to process
if 5000>len(G.nodes())>10 and nx.has_path(G,startvertex,endvertex) and startvertex!=endvertex:
try:
t0 = time.time()
[pp,sep,wp] = case1.calcpostprobs(G,zl,t,n,K,vmean,vvar,sig*sig)
et = time.time()-t0
print et, "secs, done algorithm"
possibleroutes = len(pp)
if allmeasurements and allmeasurements[0][0]==idl[0] and allmeasurements[-1][0]==idl[-1]:
tpath = real_path(G,allmeasurements,startvertex,endvertex,cur)
# print "TRUE PATH", tpath
tedge = get_edges(tpath,cur)
result=[]
for j in range(possibleroutes):
# print "modeled path:",pp[j]
edge = get_edges(pp[j],cur)
try:
area = surrounding_area(edge,pp[j][0],tedge,startvertex,cur)
except:
conn.rollback()
area = surrounding_area(edge,pp[j][-1],tedge,startvertex,cur)
result.append({'route':pp[j],
'possibility':wp[j],
'RMSE':RSME(edge,allmeasurements,cur),
'temporal_RMSE':temporal_RSME(edge,allmeasurements,pp[j][0],cur),
'area':area,
'common_length':share_distance(edge,pp[j][0],tedge,startvertex,cur),
'edgeIDList':edge
})
newtest = Test(result)
newtest.test_para = {'period':t[-1],'measurements':[x[0] for x in allmeasurements],"tpath":tpath,"tedge":tedge,"vehicleid":vehicleid}
results.append(newtest)
if len(results)>30:
return results
else:
print "Success.************************", len(results)
else:
print "vertx edge table error",idl,allmeasurements
except:
conn.rollback()
print "FAIL............................."
zl,tl,idl=vehicle_records.next()
return results