def local_k(network, events, refs, scale_set, cache=None):
"""
Computes local K function
network: an undirected network data to which reference points are injected
refs: a set of reference points on the given network
points unprojected into the network
events: a set of event points on the given network
points projected into the network
scale_set: a tuple defining spatial scales to be examined
(min, max, interval)
"""
node2localK = {}
net_distances = {}
if cache: net_distances = cache
for node in refs:
node = node[1][0]
a_dest = network[node].keys()[0]
node_proj = (node, a_dest, 0, network[node][a_dest])
if node not in net_distances:
net_distances[node] = pynet.dijkstras(network, node, scale_set[1])
if a_dest not in net_distances:
net_distances[a_dest] = pynet.dijkstras(network, node,
scale_set[1])
distances = pynet.proj_distances_undirected(
network, node_proj, events, scale_set[1],
cache=net_distances).values()
node2localK[node] = pykfuncs.kt_values(scale_set, distances, 1)
return node2localK, net_distances
def local_k(network, events, refs, scale_set, cache=None):
"""
Computes local K function
network: an undirected network data to which reference points are injected
refs: a set of reference points on the given network
points unprojected into the network
events: a set of event points on the given network
points projected into the network
scale_set: a tuple defining spatial scales to be examined
(min, max, interval)
"""
node2localK = {}
net_distances = {}
if cache: net_distances = cache
for node in refs:
node = node[1][0]
a_dest = network[node].keys()[0]
node_proj = (node, a_dest, 0, network[node][a_dest])
if node not in net_distances:
net_distances[node] = pynet.dijkstras(network, node, scale_set[1])
if a_dest not in net_distances:
net_distances[a_dest] = pynet.dijkstras(network, node, scale_set[1])
distances = pynet.proj_distances_undirected(network, node_proj, events, scale_set[1], cache=net_distances).values()
node2localK[node] = pykfuncs.kt_values(scale_set, distances, 1)
return node2localK, net_distances
def simulate_local_k_02(args):
sims = args[0]
n = args[1]
refs = args[2]
scale_set = args[3]
cache = args[4]
#print 'simulated_local_k_02'
sims_outcomes = []
sampler = RandomSampleGenerator(refs, n).next
for sim in xrange(sims):
sim_events = sampler()
sim_localk = {}
for node in refs:
all_distances = cache[node[1][0]]
distances = []
for event in sim_events:
event = event[1][0]
if event in all_distances:
distances.append(all_distances[event])
sim_localk[node[1][0]] = pykfuncs.kt_values(
scale_set, distances, 1)
sims_outcomes.append(sim_localk)
return sims_outcomes
def simulate_local_k_02(args):
sims = args[0]
n = args[1]
refs = args[2]
scale_set = args[3]
cache = args[4]
#print 'simulated_local_k_02'
sims_outcomes = []
sampler = RandomSampleGenerator(refs, n).next
for sim in xrange(sims):
sim_events = sampler()
sim_localk = {}
for node in refs:
all_distances = cache[node[1][0]]
distances = []
for event in sim_events:
event = event[1][0]
if event in all_distances:
distances.append(all_distances[event])
sim_localk[node[1][0]] = pykfuncs.kt_values(scale_set, distances, 1)
sims_outcomes.append(sim_localk)
return sims_outcomes
