from data2 import trials, fancy
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):
    trial = trials[trialno]
    size = trial.size[0]
    pits = len(trial.pits)

    nnsim = []
    for j in range(1000):
        curpits = []
        k = 0
        while k < pits:
            add = (random()*size,random()*size)
            if trial.method == 'obstacles':
                for obs in trial.fake:
                    if obs.contains(add):
                        continue
            if trial.method == 'pits':
                for pit in trial.fake:
                    if distance(add,pit.loc) < pit.diam/2:
                        continue
            curpits.append(add)
            k += 1
        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

sizes = [[12,12],[24,24]]
meths = ['obstacles','pits','trails']
pvals = [['']*len(sizes) for x in range(len(meths))]
for n in range(len(trials)):
    trial = trials[n]
    pvals[meths.index(trial.method)][sizes.index(trial.size)] = '%.4f' % calcpval(n)

print('\\tabrule &$12\\times12$&$24\\times24$\\cr\\tabrule')
for row in range(len(meths)):
    print(fancy[meths[row]],end='')
    for col in range(len(sizes)):
        print('&'+pvals[row][col],end='')
    print('\\cr\\tabrule')