def aggre_records(self):
ards = []
tmp_rds = [self.rds[0]]
cent = self.rds[0]['gps_lonlat']
def ards_add():
tt = tmp_rds[len(tmp_rds) / 2]['time']
hh = tmp_rds[len(tmp_rds) / 2]['head']
ss = tmp_rds[len(tmp_rds) / 2]['speed']
avg_rd = {'time':tt, 'head':hh, 'speed':ss, 'gps_lonlat':cent}
ards.append(avg_rd)
for i,rd in enumerate(self.rds[1:]):
d = cg.lonlats2km(rd['gps_lonlat'], cent)
if d < 0.01:
tmp_rds.append(rd)
cent = ((cent[0] + rd['gps_lonlat'][0]) / 2, (cent[1] + rd['gps_lonlat'][1]) / 2)
else:
ards_add()
tmp_rds = [rd]
cent = rd['gps_lonlat']
ards_add()
return ards
def mm():
for tbname_suffix in tbname_suffixs:
gw = cg.new_gw()
cdb.create_path_occupied_crusing_table(tbname_suffix,method=METHOD)#函数调用的时候，如果第一个参数使用了关键字绑定，后面的参数也必须使用关键字绑定！
cdb.create_path_occupied_crusing_table_attr(tbname_suffix,method=METHOD)
trd = cdb.new_track_reader_for_purpose(tbname_suffix,purpose='mm')
pwd = cdb.new_path_writer_for_method(tbname_suffix,method=METHOD)
pawd = cdb.new_path_attr_writer_for_method(tbname_suffix,method=METHOD)
#p2tj = cj.new_pt2geojson(method=METHOD)
print ''
start_at = datetime.datetime.now()
print tbname_suffix + (' start at ' + str(start_at) + ' ').center(70, '-')
print 'preparing...',
alg.prepare(gw)
print 'end'
tracks_num = 0
paths_failed = 0
max_fetched = TRACKS_TO_ROW - TRACKS_FROM_ROW + 1
start_match_at = datetime.datetime.now()
print (' start matching at ' + str(start_match_at) + '').center(70, '-')
while TRACKS_TO_ROW < 0 or trd.fetched_num < max_fetched:
track = trd.fetch_one()
if trd.fetched_num % 1000 == 0:
print 'fetched', trd.fetched_num
if track is None:
break
d_max = 0
pre_lonlat = track.rds[0]['gps_lonlat']
for i in range(1,len(track.rds)):
cur_lonlat = track.rds[i]['gps_lonlat']
d = cg.lonlats2km(pre_lonlat, cur_lonlat)
if d > d_max:
d_max = d
pre_lonlat = cur_lonlat
if d_max > 3 or track.length() >= 80:
continue
else:
tracks_num += 1
print 'track', str(track.tid).ljust(10),' ',
if not match(pwd,pawd,gw,track):
paths_failed += 1
end_match_at = datetime.datetime.now()
print(' end matching at ' + str(end_match_at) + ' ').center(70, '-'),'elapsed time',str(end_match_at - start_match_at)
print 'fetched tracks: %s, paths failed: %s' % (trd.fetched_num, paths_failed)
print ''.center(70, '-')
#clear()
end_at = datetime.datetime.now()
print tbname_suffix + (' end at ' + str(end_at) + ' ').center(70, '-'),'elapsed time',str(end_at - start_at)
def length(self):
l = 0
pre_lonlat = self.rds[0]['gps_lonlat']
for i in range(1,len(self.rds)):
cur_lonlat = self.rds[i]['gps_lonlat']
d = cg.lonlats2km(pre_lonlat, cur_lonlat)
l = l + d
pre_lonlat = cur_lonlat
return l
def influence_dag(dag, wi, rds):
idag = dag.copy()
es = dag.edges()
for e in es:
i = e[0][0]
if i == wi:
continue
d_wi_i = cg.lonlats2km(rds[wi]['gps_lonlat'], rds[i]['gps_lonlat'])
idag[e[0]][e[1]]['weight'] = dag[e[0]][e[1]]['weight'] * (2**(-d_wi_i))
return idag
def influence_dag(dag, wi, rds):
idag = dag.copy()
es = dag.edges()
for e in es:
i = e[0][0]
if i == wi:
continue
d_wi_i = cg.lonlats2km( rds[wi]['gps_lonlat'], rds[i]['gps_lonlat'] )
idag[e[0]][e[1]]['weight'] = dag[e[0]][e[1]]['weight'] * ( 2**(-d_wi_i) )
return idag
def create_dag_rds(gw, track, k=5, r=0.1, sigma=0.02):
dag = nx.DiGraph()
rds = []
projss = []
# for every gps-record in track, find its valid projection candidates
for i in range(0, len(track.rds)):
rd = track.rds[i]
if i > 0:
if time.mktime(track.rds[i]['time']) - time.mktime(
track.rds[i - 1]['time']) <= 0:
continue
lonlat_rds = rd['gps_lonlat']
projs = gw.find_projs_within(lonlat_rds, r)
# max candidates of each gps-record is k
if len(projs) > k:
ordered_projs = list(projs)
ordered_projs.sort(key=operator.itemgetter('d_proj'))
projs = tuple(ordered_projs[0:k])
# if no projection candidates found, remove the gps-record from track
if len(projs) > 0:
rds.append(rd)
projss.append(projs)
# for every projection candidate of every gps-record, add vertex to DAG
for i in range(0, len(projss)):
projs = projss[i]
for ii in range(0, len(projs)):
proj = projs[ii]
# vertex in DAG represented by tuple (i,ii)
# 1) i is the index of gps-record in track
# 2) ii is the index of projection candidate in i gps-record
dag.add_node((i, ii), s = proj['s'], t = proj['t'], \
gps_lonlat = rds[i]['gps_lonlat'], \
proj_lonlat = proj['proj_lonlat'], \
l_s = proj['l_s'], \
l_t = proj['l_t'], \
d_proj = proj['d_proj'], \
weight = norm(0,sigma).pdf(proj['d_proj']))
# for every pair of contiguous gps-records
for i in range(0, len(projss) - 1):
j = i + 1
i_projs = projss[i]
j_projs = projss[j]
d_i_j = cg.lonlats2km(rds[i]['gps_lonlat'], rds[j]['gps_lonlat'])
time_i_j = (time.mktime(rds[j]['time']) -
time.mktime(rds[i]['time'])) / 3600.0 # in hour
if time_i_j <= 0:
time_i_j = 0.0001
# for every projection candidate ii of i gps-record
for ii in range(0, len(i_projs)):
ii_proj = i_projs[ii]
s_ii = ii_proj['s']
t_ii = ii_proj['t']
speed_ii = gw.G[s_ii][t_ii]['speed']
# for every projection candidate jj of j=i+1 gps-record
for jj in range(0, len(j_projs)):
jj_proj = j_projs[jj]
s_jj = jj_proj['s']
t_jj = jj_proj['t']
speed_jj = gw.G[s_jj][t_jj]['speed']
# calculate:
# 1) path on map between ii and jj
# 2) weight of path between ii and jj
w_tii_sjj = 0
if t_ii == s_jj:
p_tii_sjj = ()
ls_tii_sjj = [ii_proj['l_t'], jj_proj['l_s']]
vs_tii_sjj = [speed_ii, speed_jj]
elif t_ii == t_jj and s_ii == s_jj:
p_tii_sjj = ()
ls_tii_sjj = [
jj_proj['l_s'] - ii_proj['l_s'],
]
vs_tii_sjj = [
speed_ii,
]
if abs(ls_tii_sjj[0]) < 0.00000001:
ls_tii_sjj = [
d_i_j,
]
elif ls_tii_sjj[0] < 0:
p_tii_sjj = gw.shortest_path_from_to(
t_ii, s_jj, 'time')
if not len(p_tii_sjj) > 0:
w_tii_sjj = -INF
else:
ls_tii_sjj = [
ii_proj['l_t'],
] + list(gw.get_edges_attr(p_tii_sjj,
'length')) + [
jj_proj['l_s'],
]
vs_tii_sjj = [
speed_ii,
] + list(gw.get_edges_attr(p_tii_sjj, 'speed')) + [
speed_jj,
]
else:
p_tii_sjj = gw.shortest_path_from_to(t_ii, s_jj, 'time')
if not len(p_tii_sjj) > 0:
w_tii_sjj = -INF
else:
ls_tii_sjj = [
ii_proj['l_t'],
] + list(gw.get_edges_attr(p_tii_sjj, 'length')) + [
jj_proj['l_s'],
]
vs_tii_sjj = [
speed_ii,
] + list(gw.get_edges_attr(p_tii_sjj, 'speed')) + [
speed_jj,
]
if w_tii_sjj != 0:
dag.add_edge((i, ii), (j, jj),
path=p_tii_sjj,
weight=w_tii_sjj)
continue
# add edge between ii and jj to DAG
N_jj = norm(0, sigma).pdf(jj_proj['d_proj'])
#ts = []
#for a in range(0,len(ls_ii_jj)):
# ts.append(ls_ii_jj[a] / vs_ii_jj[a])
#V_ii_jj = sum(ts)
t_tii_sjj = 0
for ilv in range(0, len(ls_tii_sjj)):
t_tii_sjj = t_tii_sjj + ls_tii_sjj[ilv] / vs_tii_sjj[ilv]
l_tii_sjj = sum(ls_tii_sjj)
if l_tii_sjj <= 0:
V_tii_sjj = 1
else:
V_tii_sjj = d_i_j / l_tii_sjj
Fs_tii_sjj = V_tii_sjj * N_jj
avg_v = l_tii_sjj / time_i_j
Ft_tii_sjj = sum(vs_tii_sjj) / math.sqrt(
sum([v**2
for v in vs_tii_sjj])) / math.sqrt(len(vs_tii_sjj))
#Ft_tii_sjj = math.sqrt(sum([(v-avg_v)**2, for v in vs_tii_sjj]))
F_tii_sjj = Fs_tii_sjj * Ft_tii_sjj
dag.add_edge((i, ii), (j, jj),
path=p_tii_sjj,
weight=F_tii_sjj)
return {'dag': dag, 'rds': rds}
def mm():
for tbname_suffix in tbname_suffixs:
gw = cg.new_gw()
cdb.create_path_occupied_crusing_table(
tbname_suffix,
method=METHOD) #函数调用的时候，如果第一个参数使用了关键字绑定，后面的参数也必须使用关键字绑定！
cdb.create_path_occupied_crusing_table_attr(tbname_suffix,
method=METHOD)
trd = cdb.new_track_reader_for_purpose(tbname_suffix, purpose='mm')
pwd = cdb.new_path_writer_for_method(tbname_suffix, method=METHOD)
pawd = cdb.new_path_attr_writer_for_method(tbname_suffix,
method=METHOD)
#p2tj = cj.new_pt2geojson(method=METHOD)
print ''
start_at = datetime.datetime.now()
print tbname_suffix + (' start at ' + str(start_at) + ' ').center(
70, '-')
print 'preparing...',
alg.prepare(gw)
print 'end'
tracks_num = 0
paths_failed = 0
max_fetched = TRACKS_TO_ROW - TRACKS_FROM_ROW + 1
start_match_at = datetime.datetime.now()
print(' start matching at ' + str(start_match_at) + '').center(70, '-')
while TRACKS_TO_ROW < 0 or trd.fetched_num < max_fetched:
track = trd.fetch_one()
if trd.fetched_num % 1000 == 0:
print 'fetched', trd.fetched_num
if track is None:
break
d_max = 0
pre_lonlat = track.rds[0]['gps_lonlat']
for i in range(1, len(track.rds)):
cur_lonlat = track.rds[i]['gps_lonlat']
d = cg.lonlats2km(pre_lonlat, cur_lonlat)
if d > d_max:
d_max = d
pre_lonlat = cur_lonlat
if d_max > 3 or track.length() >= 80:
continue
else:
tracks_num += 1
print 'track', str(track.tid).ljust(10), ' ',
if not match(pwd, pawd, gw, track):
paths_failed += 1
end_match_at = datetime.datetime.now()
print(' end matching at ' + str(end_match_at) + ' ').center(
70, '-'), 'elapsed time', str(end_match_at - start_match_at)
print 'fetched tracks: %s, paths failed: %s' % (trd.fetched_num,
paths_failed)
print ''.center(70, '-')
#clear()
end_at = datetime.datetime.now()
print tbname_suffix + (' end at ' + str(end_at) + ' ').center(
70, '-'), 'elapsed time', str(end_at - start_at)
def create_dag_rds(gw, track, k=5, r=0.1, sigma=0.02):
dag = nx.DiGraph()
rds = []
projss = []
# for every gps-record in track, find its valid projection candidates
for i in range(0,len(track.rds)):
rd = track.rds[i]
if i > 0:
if time.mktime(track.rds[i]['time']) - time.mktime(track.rds[i-1]['time']) <= 0:
continue
lonlat_rds = rd['gps_lonlat']
projs = gw.find_projs_within(lonlat_rds, r)
# max candidates of each gps-record is k
if len(projs) > k:
ordered_projs = list(projs)
ordered_projs.sort(key = operator.itemgetter('d_proj'))
projs = tuple(ordered_projs[0:k])
# if no projection candidates found, remove the gps-record from track
if len(projs) > 0:
rds.append(rd)
projss.append(projs)
# for every projection candidate of every gps-record, add vertex to DAG
for i in range(0,len(projss)):
projs = projss[i]
for ii in range(0,len(projs)):
proj = projs[ii]
# vertex in DAG represented by tuple (i,ii)
# 1) i is the index of gps-record in track
# 2) ii is the index of projection candidate in i gps-record
dag.add_node((i, ii), s = proj['s'], t = proj['t'], \
gps_lonlat = rds[i]['gps_lonlat'], \
proj_lonlat = proj['proj_lonlat'], \
l_s = proj['l_s'], \
l_t = proj['l_t'], \
d_proj = proj['d_proj'], \
weight = norm(0,sigma).pdf(proj['d_proj']))
# for every pair of contiguous gps-records
for i in range(0,len(projss)-1):
j = i + 1
i_projs = projss[i]
j_projs = projss[j]
d_i_j = cg.lonlats2km(rds[i]['gps_lonlat'], rds[j]['gps_lonlat'])
time_i_j = (time.mktime(rds[j]['time']) - time.mktime(rds[i]['time'])) / 3600.0 # in hour
if time_i_j <= 0:
time_i_j = 0.0001
# for every projection candidate ii of i gps-record
for ii in range(0,len(i_projs)):
ii_proj = i_projs[ii]
s_ii = ii_proj['s']
t_ii = ii_proj['t']
speed_ii = gw.G[s_ii][t_ii]['speed']
# for every projection candidate jj of j=i+1 gps-record
for jj in range(0,len(j_projs)):
jj_proj = j_projs[jj]
s_jj = jj_proj['s']
t_jj = jj_proj['t']
speed_jj = gw.G[s_jj][t_jj]['speed']
# calculate:
# 1) path on map between ii and jj
# 2) weight of path between ii and jj
w_tii_sjj = 0
if t_ii == s_jj:
p_tii_sjj = ()
ls_tii_sjj = [ii_proj['l_t'], jj_proj['l_s']]
vs_tii_sjj = [speed_ii, speed_jj]
elif t_ii == t_jj and s_ii == s_jj:
p_tii_sjj = ()
ls_tii_sjj = [jj_proj['l_s'] - ii_proj['l_s'],]
vs_tii_sjj = [speed_ii, ]
if abs(ls_tii_sjj[0]) < 0.00000001:
ls_tii_sjj = [d_i_j,]
elif ls_tii_sjj[0] < 0:
p_tii_sjj = gw.shortest_path_from_to(t_ii, s_jj, 'time')
if not len(p_tii_sjj) > 0:
w_tii_sjj = -INF
else:
ls_tii_sjj = [ii_proj['l_t'],] + list(gw.get_edges_attr(p_tii_sjj, 'length')) + [jj_proj['l_s'],]
vs_tii_sjj = [speed_ii, ] + list(gw.get_edges_attr(p_tii_sjj, 'speed')) + [speed_jj, ]
else:
p_tii_sjj = gw.shortest_path_from_to(t_ii, s_jj, 'time')
if not len(p_tii_sjj) > 0:
w_tii_sjj = -INF
else:
ls_tii_sjj = [ii_proj['l_t'],] + list(gw.get_edges_attr(p_tii_sjj, 'length')) + [jj_proj['l_s'],]
vs_tii_sjj = [speed_ii, ] + list(gw.get_edges_attr(p_tii_sjj, 'speed')) + [speed_jj, ]
if w_tii_sjj != 0:
dag.add_edge((i, ii), (j, jj), path = p_tii_sjj, weight = w_tii_sjj)
continue
# add edge between ii and jj to DAG
N_jj = norm(0,sigma).pdf(jj_proj['d_proj'])
#ts = []
#for a in range(0,len(ls_ii_jj)):
# ts.append(ls_ii_jj[a] / vs_ii_jj[a])
#V_ii_jj = sum(ts)
t_tii_sjj = 0
for ilv in range(0,len(ls_tii_sjj)):
t_tii_sjj = t_tii_sjj + ls_tii_sjj[ilv] / vs_tii_sjj[ilv]
l_tii_sjj = sum(ls_tii_sjj)
if l_tii_sjj <= 0:
V_tii_sjj = 1
else:
V_tii_sjj = d_i_j / l_tii_sjj
Fs_tii_sjj = V_tii_sjj * N_jj
avg_v = l_tii_sjj / time_i_j
Ft_tii_sjj = sum(vs_tii_sjj) / math.sqrt(sum([v**2 for v in vs_tii_sjj])) / math.sqrt(len(vs_tii_sjj))
#Ft_tii_sjj = math.sqrt(sum([(v-avg_v)**2, for v in vs_tii_sjj]))
F_tii_sjj = Fs_tii_sjj * Ft_tii_sjj
dag.add_edge((i, ii), (j, jj), path = p_tii_sjj, weight = F_tii_sjj)
return {'dag':dag, 'rds':rds}
