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from data2 import trials
from scipy.stats import cramervonmises
from bisect import bisect_left
from random import random
def distance(pit1, pit2):
out = ((pit2[1]-pit1[1])**2 + (pit2[0]-pit1[0])**2)**(1/2)
if (out == 0):
return 100
else:
return out
def cdf(a,nnsim,tup):
return [bisect_left(nnsim, x)/len(nnsim) for x in a]
def calcpval(trialno):
size = trials[trialno].size[0]
pits = len(trials[trialno].pits)
nnsim = []
for j in range(10000):
curpits = []
for k in range(pits):
curpits.append([random()*size,random()*size])
for pit in curpits:
l = size
for pit2 in curpits:
l = min(l,distance(pit,pit2))
nnsim.append(l)
nnsim.sort()
nnreal = trials[trialno].nearest_neighbor()
stat = cramervonmises(nnreal,cdf,(nnsim,()))
return stat.pvalue
for n in range(len(trials)):
print(calcpval(n))
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