def roads_bipartite_match():
data = {key: value for key, value in request.args.iteritems()}
query_json = data['query']
query = json.loads(query_json)
inst = roadbm_json.RoadMatchInstance.from_json(query)
S = inst.source_points()
T = inst.target_points()
roadmap = inst.get_roadmap()
match = bm.ROADSBIPARTITEMATCH(S, T, roadmap)
return jsonify(match)
def euclidean_roads_bipartite_match_svg():
data = {key: value for key, value in request.args.iteritems()}
query_json = data['query']
query = json.loads(query_json)
width = int(data.get('width'))
height = int(data.get('height'))
inst = roadbm_json.RoadMatchInstanceEuclidean.from_json(query)
S = inst.source_points()
T = inst.target_points()
roadmap = inst.get_roadmap()
layout = inst.get_layout()
layout = {u: np.array(p) for u, p in layout.iteritems()}
roadmap_ = euclidean_roadmap.euclidean_roadmap(roadmap, layout)
match = bm.ROADSBIPARTITEMATCH(S, T, roadmap_)
print roadmap_.edge
interval_graph, layout_ = matchvis_util.INTERVAL_GRAPH(
match, S, T, roadmap_, layout)
print layout_
# turn it into an SVG!
if False:
graph_repr = [
'%s -> %s' % (repr(u), repr(v))
for u, v in interval_graph.edges_iter()
]
other_pos_repr = {repr(u): repr(p) for u, p in layout_.iteritems()}
return jsonify((graph_repr, other_pos_repr))
else:
return draw_interval_graph(interval_graph, layout_, roadmap, layout,
width, height)
('road 8', .05),
('road 10', .05),
]
TT = [
('road 6', .1),
('road 6', .15),
('road 8', .1),
('road 10', .1),
('road 10', .15),
('road 12', .1),
('road 13', .1),
]
""" obtain the optimal matching """
import setiptah.roadbm.bm as roadbm
opt_match = roadbm.ROADSBIPARTITEMATCH(SS, TT, roadmap)
""" obtain an interval graph from the matching """
I_graph, I_pos = matchvis_util.INTERVAL_GRAPH(opt_match, SS, TT, roadmap,
pos)
# sometimes, there's still a cycle?
""" make tikz animation """
def writeslide(k, mystr):
f = open('slides/slide%d.tex' % k, 'w')
f.write(mystr)
f.close()
f = open('slides/construction_animation.tex', 'w')
SANITIZE(I_graph)
INITIALIZE_BAGS(I_graph)
match = []
roadnet.add_edge(1, 2, 'E', length=1.)
roadnet.add_edge(2, 3, 'S', length=1.)
roadnet.add_edge(3, 0, 'W', length=1.)
sampler = roadbm.UniformDist(roadnet)
Nmin, Nmax = 10, 10000
N = [int(n) for n in np.logspace(np.log10(Nmin), np.log10(Nmax), 10)]
trials = 5
nt = []
cost = []
for n, t in itertools.product(N, range(trials)):
PP = [sampler.sample() for i in xrange(n)]
QQ = [sampler.sample() for i in xrange(n)]
match = roadbm.ROADSBIPARTITEMATCH(PP, QQ, roadnet)
#costs = MATCHCOSTS( match, PP, QQ, roadnet )
c = roadbm.ROADMATCHCOST(match, PP, QQ, roadnet)
nt.append(n)
cost.append(c)
basis = [lambda n: n, lambda n: np.power(n, .5)]
[alpha, kappa] = fit(nt, cost, basis)
plt.scatter(nt, cost)
interp = np.array([alpha * basis[0](n) + kappa * basis[1](n) for n in N])
plt.plot(N, interp, '--')
plt.show()