def CYCLECOST( cycle, demands, roadnet, length_attr='length' ) :
addr = lambda p : ROAD.RoadAddress( *p )
carry = [ ROAD.distance( roadnet, addr(dem.pick), addr(dem.delv), length_attr ) for dem in demands ]
edges = zip( cycle, cycle[1:] + cycle[:1] )
#print edges
edges = [ ( demands[i], demands[j] ) for i,j in edges ]
empty = [ ROAD.distance( roadnet, addr(dem1.delv), addr(dem2.pick), length_attr ) for dem1,dem2 in edges ]
return sum( carry ) + sum( empty )
def _options_roads_neq( roadnet, road1, road2, length_attr ) :
edge1, data1 = ROAD.obtain_edge( roadnet, road1, data_flag=True )
u1,v1,_ = edge1 ; roadlen1 = data1.get( length_attr, np.inf )
edge2, data2 = ROAD.obtain_edge( roadnet, road2, data_flag=True )
u2,v2,_ = edge2 ; roadlen2 = data2.get( length_attr, np.inf )
x = GLOBAL.x ; y = GLOBAL.y
cost_dict = {
'<-s' : x,
's->' : roadlen1 - x,
'->t' : y,
't<-' : roadlen2 - y
}
options = []
# paths through endpoints
WAYP1 = [ ( 's->', v1 ) ]
if not data1.get( 'oneway', False ) : WAYP1.append( ( '<-s', u1 ) )
WAYP2 = [ ( '->t', u2 ) ]
if not data2.get( 'oneway', False ) : WAYP2.append( ( 't<-', v2 ) )
for s,u in WAYP1 :
p = ROAD.roadify( roadnet, u, length_attr )
for t,v in WAYP2 :
q = ROAD.roadify( roadnet, v, length_attr )
dst = ROAD.distance( roadnet, p, q, length_attr )
cost = cost_dict[s] + dst + cost_dict[t]
options.append( cost )
return options
def MATCHCOSTS(match, P, Q, roadnet):
costs = []
for i, j in match:
p = ROAD.RoadAddress(*P[i])
q = ROAD.RoadAddress(*Q[j])
d = ROAD.distance(roadnet, p, q, 'length')
costs.append(d)
return costs
def MATCHCOSTS( match, P, Q, roadnet ) :
costs = []
for i, j in match :
p = ROAD.RoadAddress( *P[i] )
q = ROAD.RoadAddress( *Q[j] )
d = ROAD.distance( roadnet, p, q, 'length' )
costs.append( d )
return costs
def _options_roads_eq_yleqx( roadnet, road, length_attr ) :
edge, data = ROAD.obtain_edge( roadnet, road, data_flag=True )
u,v,_ = edge ; roadlen = data.get( length_attr, np.inf )
p = ROAD.roadify( roadnet, u, length_attr )
q = ROAD.roadify( roadnet, v, length_attr )
x = GLOBAL.x ; y = GLOBAL.y
sojourn_cost = roadlen - x + ROAD.distance( roadnet, q, p, length_attr ) + y # a path using the rest of the network
options = [ sojourn_cost ]
if not data.get( 'oneway', False ) :
options.append( x - y )
return options
def balance_cost( pairs, roadmap ) :
distfunc = lambda p, q : ROAD.distance( roadmap, p, q, 'length' )
graph = nx.Graph()
N = len( pairs )
for i in xrange(N) :
for j in xrange(N) :
_, t = pairs[i]
s, _ = pairs[j]
dist = distfunc(t,s)
graph.add_edge( (i,0), (j,1), weight = -dist ) # about to do MAX_weight_matching
""" obvious TODO: use the N log N matching algorithm here, instead! """
mate = nx.matching.max_weight_matching( graph, maxcardinality=True )
def cost(i) :
first = (i,0)
second = mate[first]
return -graph.get_edge_data( first, second ).get('weight')
balances = [ cost(i) for i in xrange(N) ]
total_balance = sum( balances )
return total_balance
def distance(self, p, q ) :
return ROAD.distance( self.roadnet, p, q, 'length' )
if True and NUMPOINT <= 50:
PR = [ROAD.RoadAddress(road, y) for road, y in PP]
QR = [ROAD.RoadAddress(road, y) for road, y in QQ]
class pointnode():
def __init__(self, point, idx):
self.point = point
self.idx = idx
RED = [pointnode(p, i) for i, p in enumerate(PR)]
BLU = [pointnode(q, j) for j, q in enumerate(QR)]
graph = nx.Graph()
match_mat = np.zeros((NUMPOINT, NUMPOINT))
for (i, r), (j, b) in itertools.product(enumerate(RED),
enumerate(BLU)):
w = ROAD.distance(roadnet, r.point, b.point, 'length')
graph.add_edge(r, b, weight=-w)
match_mat[i, j] = w
match_dict = nx.max_weight_matching(graph, True)
match_brute = [(r.idx, match_dict[r].idx)
for r in RED] # match pruning
# matchstats = [ ( r.point, b.point, ROAD.distance( roadnet, r.point, b.point, 'length' ) )
# for r,b in match ]
costs_brute = MATCHCOSTS(match_brute, PP, QQ, roadnet)
cost_brute = ROADMATCHCOST(match_brute, PP, QQ, roadnet)
print match_brute
print costs_brute
print cost_brute
#print 'optimal match has cost: %f' % matchcost
import setiptah.roadgeometry.probability as roadprob
if True :
roadmap = roadprob.sampleroadnet()
p = roadprob.sampleaddress( roadmap )
q = roadprob.sampleaddress( roadmap )
# give me a summary of the road network
for u,v,road, data in roadmap.edges_iter( keys=True, data=True ) :
print '%s: %s -> %s ; length=%f' % ( road, repr(u), repr(v), data.get('length',1) )
print '...going from %s to %s' % ( repr(p), repr(q) )
frwd = minpath( p, q, roadmap )
frwdL = pathLength( frwd )
frwdLRef = distance( roadmap, p, q, 'length' )
print frwd
print 'FRWD LENGTH: %f; survey says: %f' % ( frwdL, frwdLRef )
bkwd = minpath( q, p, roadmap )
bkwdL = pathLength( bkwd )
bkwdLRef = distance( roadmap, q, p, 'length' )
print 'BKWD LENGTH: %f; survey says: %f' % ( bkwdL, bkwdLRef )
if np.abs( frwdL - frwdLRef ) > 10**-10 or np.abs( bkwdL - bkwdLRef ) > 10**-10 :
print 'ALERT!!! NOT AGREE WITH REF'
else :
print 'cool as a cucumber'
# compare to optimal matching
if True and NUMPOINT <= 50 :
PR = [ ROAD.RoadAddress( road, y ) for road, y in PP ]
QR = [ ROAD.RoadAddress( road, y ) for road, y in QQ ]
class pointnode() :
def __init__(self, point, idx ) :
self.point = point
self.idx = idx
RED = [ pointnode(p,i) for i,p in enumerate(PR) ]
BLU = [ pointnode(q,j) for j,q in enumerate(QR) ]
graph = nx.Graph()
match_mat = np.zeros((NUMPOINT,NUMPOINT))
for (i,r), (j,b) in itertools.product( enumerate(RED), enumerate(BLU) ) :
w = ROAD.distance( roadnet, r.point, b.point, 'length' )
graph.add_edge( r, b, weight=-w )
match_mat[i,j] = w
match_dict = nx.max_weight_matching( graph, True )
match_brute = [ (r.idx,match_dict[r].idx) for r in RED ] # match pruning
# matchstats = [ ( r.point, b.point, ROAD.distance( roadnet, r.point, b.point, 'length' ) )
# for r,b in match ]
costs_brute = MATCHCOSTS( match_brute, PP, QQ, roadnet )
cost_brute = ROADMATCHCOST( match_brute, PP, QQ, roadnet )
print match_brute
print costs_brute
print cost_brute
#print 'optimal match has cost: %f' % matchcost
label, length = road_iter.next()
roadnet.add_edge( u, v, label, length=length, oneway=True )
if True :
distr = {}
distr[('W','E')] = 1./5
distr[('N','E')] = 1./5
distr[('W','S')] = 3./5
Ed = roadEd( roadnet, distr, length_attr='length' )
print 'Ed computed %f' % Ed
pairs = [ roadprob.samplepair( roadnet, distr ) for i in range(20000) ]
dst = [ ROAD.distance( roadnet, p, q, 'length' ) for p,q in pairs ]
Ed_emp = np.mean( dst )
print 'Ed empirical %f' % Ed_emp
else :
ROADS = ['N', 'S', 'E', 'W' ]
#PAIRS = itertools.product( ROADS, ROADS )
#PAIRS = [ ('E','E') ]
edges = [ name for _,__,name in roadnet.edges( keys=True ) ]
#PAIRS = [ ( random.choice(edges), random.choice(edges) ) for i in range(5) ]
for road1, road2 in PAIRS :
Ed_cond = roadEd_conditional( roadnet, road1, road2 )
#
pairs = [ sample( roadnet, { (road1, road2) : 1.} ) for i in range(20000) ]
dst = [ ROAD.distance( roadnet, p, q, 'length' ) for p,q in pairs ]
def distance(self, x, y ) :
return ROAD.distance( self.roadmap, x, y, weight=self.length_attr )
def run(self) :
""" setup """
sim = Simulation()
self.sim = sim
clock = PoissonClock( self.rate )
clock.join_sim( sim )
# prepare geometry queries
if True :
ORIGIN = np.zeros(2)
def samplepoint() :
return np.random.rand(2)
planner = EuclideanPlanner
#scheduler = RoundRobinScheduler()
# Euclidean instantiation
getTail = lambda dem : dem.origin
getHead = lambda dem : dem.destination
distance = lambda x, y : np.linalg.norm( y - x )
scheduler = kCraneScheduler( getTail, getHead, distance )
else :
import setiptah.roadgeometry.roadmap_basic as ROAD
import setiptah.roadgeometry.probability as roadprob
from setiptah.roadgeometry.roadmap_paths import RoadmapPlanner
roadmap = roadprob.sampleroadnet()
U = roadprob.UniformDist( roadmap )
samplepoint = U.sample
ORIGIN = samplepoint()
distance = lambda x, y : ROAD.distance( roadmap,
x, y, length='length' )
planner = RoadmapPlanner( roadmap )
if True :
scheduler = RoundRobinScheduler()
else :
getTail = lambda dem : dem.origin
getHead = lambda dem : dem.destination
scheduler = kCraneScheduler( getTail, getHead, distance )
# instantiate the gate
gate = GatedTaxiDispatch()
gate.setScheduler( scheduler )
# instantiate the fleet
TAXI = []
for i in range( self.numveh ) :
taxi = Taxi()
TAXI.append( taxi )
taxi.setPlanner( planner )
taxi.setLocation( ORIGIN )
taxi.setSpeed( self.vehspeed )
taxi.join_sim( sim )
gateIF = gate.newInterface()
gate.add( gateIF )
gateIF.output.connect( taxi.appendDemands )
taxi.signalWakeup.connect( gateIF.input )
taxi.signalIdle.connect( gateIF.input )
gate.join_sim( sim )
def tick() :
print 'tick, %g' % sim.get_time()
SIMULATION.DEMANDS = []
def myarrival() :
x = samplepoint()
y = samplepoint()
#print x, y
time = sim.get_time()
demand = Taxi.Demand( x, y, time )
print 'demand generated ', x, y, time
SIMULATION.DEMANDS.append( demand )
# send the demand to the gate, not any one taxi
gate.queueDemand( demand )
EVER = dumbcounter()
clock.source().connect( myarrival )
clock.source().connect( EVER.increment )
RESULTS.ever_tape = []
RESULTS.alive_tape = []
def record() :
RESULTS.ever_tape.append( EVER.value() )
total = len( gate._demandQ )
for taxi in TAXI :
total += len( taxi._demandQ )
RESULTS.alive_tape.append( total )
probe = UniformClock(.1) # why not
probe.join_sim( sim )
probe.source().connect( record )
""" run """
if False :
while sim.get_time() <= self.horizon :
callback = sim.get_next_action()
callback()
frac = sim.get_time() / self.horizon
perc = int( 100. * frac )
#print perc
self.timeElapsed.emit( perc ) # emit the custom signal?
self.alarm()
else :
T0 = 100.
alpha = 2.
beta = 1.1
gamma = 1.
XTHRESH = 250
# phase I --- simulate base time
while sim.get_time() <= T0 :
callback = sim.get_next_action()
callback()
self.alarm()
# phase II
T = [ T0 ]
xmax = max( RESULTS.alive_tape )
Tk = T0
while True :
Tk = alpha*Tk
epoch = sim.get_time()
while sim.get_time() - epoch <= Tk :
callback = sim.get_next_action()
callback()
T.append( Tk )
self.alarm()
newmax = max( RESULTS.alive_tape )
if newmax > XTHRESH :
print 'TOO MUCH! BAILING!'
return
if newmax <= beta * xmax : break
xmax = newmax
# phase III
Tf = gamma * sum( T )
epoch = sim.get_time()
while sim.get_time() - epoch <= Tf :
callback = sim.get_next_action()
callback()
RESULTS.finalT = Tf
self.alarm()
print 'SIMULATION DONE'
def enroute_cost( pairs, roadmap ) :
distfunc = lambda p, q : ROAD.distance( roadmap, p, q, 'length' )
enroutes = [ distfunc(s,t) for s,t in pairs ]
total_enroute = sum( enroutes )
return total_enroute
def cost( demidx ) :
x.init( *demands[demidx].pick )
return ROAD.distance( roadmap, agentLoc, x, 'length' )
def distance(self, p, q ) :
return ROAD.distance( self.roadmap, p, q, weight='length' )
def run(self) :
""" setup """
sim = Simulation()
clock = PoissonClock( self.rate )
clock.join_sim( sim )
# prepare geometry queries
if True :
ORIGIN = np.zeros(2)
def samplepoint() :
return np.random.rand(2)
planner = EuclideanPlanner
#scheduler = RoundRobinScheduler()
# Euclidean instantiation
getTail = lambda dem : dem.origin
getHead = lambda dem : dem.destination
distance = lambda x, y : np.linalg.norm( y - x )
scheduler = kCraneScheduler( getTail, getHead, distance )
else :
import setiptah.roadgeometry.roadmap_basic as ROAD
import setiptah.roadgeometry.probability as roadprob
from setiptah.roadgeometry.roadmap_paths import RoadmapPlanner
roadmap = roadprob.sampleroadnet()
U = roadprob.UniformDist( roadmap )
samplepoint = U.sample
ORIGIN = samplepoint()
distance = lambda x, y : ROAD.distance( roadmap,
x, y, length='length' )
planner = RoadmapPlanner( roadmap )
if True :
scheduler = RoundRobinScheduler()
else :
getTail = lambda dem : dem.origin
getHead = lambda dem : dem.destination
scheduler = kCraneScheduler( getTail, getHead, distance )
# instantiate the gate
gate = GatedTaxiDispatch()
gate.setScheduler( scheduler )
# instantiate the fleet
TAXI = []
for i in range( self.numveh ) :
taxi = Taxi()
TAXI.append( taxi )
taxi.setPlanner( planner )
taxi.setLocation( ORIGIN )
taxi.setSpeed( self.vehspeed )
taxi.join_sim( sim )
gateIF = gate.newInterface()
gate.add( gateIF )
gateIF.output.connect( taxi.appendDemands )
taxi.signalWakeup.connect( gateIF.input )
taxi.signalIdle.connect( gateIF.input )
gate.join_sim( sim )
def tick() :
print 'tick, %g' % sim.get_time()
def myarrival() :
x = samplepoint()
y = samplepoint()
#print x, y
time = sim.get_time()
demand = Taxi.Demand( x, y, time )
print 'demand generated ', x, y, time
# send the demand to the gate, not any one taxi
gate.queueDemand( demand )
EVER = dumbcounter()
clock.source().connect( myarrival )
clock.source().connect( EVER.increment )
RESULTS.ever_tape = []
RESULTS.alive_tape = []
def record() :
RESULTS.ever_tape.append( EVER.value() )
total = len( gate._demandQ )
for taxi in TAXI :
total += len( taxi._demandQ )
RESULTS.alive_tape.append( total )
probe = UniformClock(.1) # why not
probe.join_sim( sim )
probe.source().connect( record )
""" run """
#T = 50.
while sim.get_time() <= self.horizon :
callback = sim.get_next_action()
callback()
frac = sim.get_time() / self.horizon
perc = int( 100. * frac )
#print perc
self.timeElapsed.emit( perc ) # emit the custom signal?
self.simulateDone.emit()