def dfunc(pos, t):
'''
in a in-progress graph dfunc is used to dynamically
compute a distance equivalent to parameter 't' at position 'pos'
estimated given k nearest sampled points
'''
[dist, idx] = posvtree.query(pos, k=123)
v = [posv['v'][i] for i in idx]
v = np.median(v)
return v * t
posv = {'pos': list(EV.apply(mid, axis=1)), 'v': list(EV.apply(speed, axis=1))}
posvtree = spatial.cKDTree(posv['pos'])
g = mlaterationgraph(dfunc=dfunc)
for i, v in V.iterrows():
g.add_position(v.numr_cell, [v.lon, v.lat])
if (i % 1000 == 0):
print 'load positions to graph : ', i, '/', len(V)
for i, e in E.iterrows():
g.add_edge(e.numr_cell, e.nnumr_cell, e.dt)
if (i % 1000 == 0):
print 'load edges to graph : ', i, '/', len(E)
g.step = 100
g.solve(complete=True)
with open(tsv('ngaude_cell_graph_computing_vertex'), 'a') as f:
def dfunc(pos,t):
'''
in a in-progress graph dfunc is used to dynamically
compute a distance equivalent to parameter 't' at position 'pos'
estimated given k nearest sampled points
'''
[dist,idx] = posvtree.query(pos, k = 123)
v = [posv['v'][i] for i in idx]
v = np.median(v)
return v*t
posv = { 'pos' : list(EV.apply(mid, axis = 1)), 'v' : list(EV.apply(speed, axis = 1)) }
posvtree = spatial.cKDTree(posv['pos'])
g = mlaterationgraph(dfunc = dfunc)
for i,v in V.iterrows():
g.add_position(v.numr_cell, [v.lon,v.lat])
if (i%1000 == 0):
print 'load positions to graph : ',i,'/',len(V)
for i,e in E.iterrows():
g.add_edge(e.numr_cell, e.nnumr_cell, e.dt)
if (i%1000 == 0):
print 'load edges to graph : ',i,'/',len(E)
g.step = 100
g.solve(complete=True)
with open(tsv('ngaude_cell_graph_computing_vertex'), 'a') as f:
# -*- coding: utf-8 -*-
"""
Created on Thu Dec 18 21:04:43 2014
@author: ngaude
"""
from mlateration import mlaterationgraph
import math
import random
import matplotlib.pyplot as plt
random.seed(123456)
g = mlaterationgraph()
for i in range(40):
while True:
x = random.randint(1, 10)
y = random.randint(1, 10)
u = (x, y)
if not u in g.pos:
break
g.add_position(u, [x, y])
for i in range(500):
noise = 0.005
while True:
xy = [random.randint(1, 10) for i in range(4)]
d = math.sqrt(math.pow(xy[0] - xy[2], 2) + math.pow(xy[1] - xy[3], 2))
d = d * (1.0 + random.uniform(-noise, noise))
Created on Thu Dec 18 21:04:43 2014
@author: ngaude
"""
from mlateration import mlaterationgraph
import math
import random
import matplotlib.pyplot as plt
random.seed(123456)
g = mlaterationgraph()
for i in range(40):
while True:
x = random.randint(1, 10)
y = random.randint(1, 10)
u = (x, y)
if not u in g.pos:
break
g.add_position(u, [x, y])
for i in range(500):
noise = 0.005
while True:
xy = [random.randint(1, 10) for i in range(4)]
d = math.sqrt(math.pow(xy[0] - xy[2], 2) + math.pow(xy[1] - xy[3], 2))
d = d * (1.0 + random.uniform(-noise, noise))
