def findMaxLLR(caselist, poplist, regionmatrix):
#print sum(caselist), sum(poplist)
popsum = sum(poplist)
llr = likelihoodratio.likelihoodratio(sum(caselist), popsum)
maxLLR = -1
maxllrlevel = -1
maxllrcase = 0
maxllrpop = 0
for level in xrange(len(regionmatrix[1,:])):
regid = regionmatrix[:,level]
unique_regid = np.unique(regid)
numreg = len(unique_regid)
if numreg > 1: # and numreg < numUnit:
regcasepop = np.zeros((numreg,3)) ## [[case, pop, count], ...]
regid_dict = {}
for i in xrange(numreg):
regid_dict[unique_regid[i]] = i
for i in xrange(len(regid)):
regcasepop[regid_dict[regid[i]],0] += caselist[i]
regcasepop[regid_dict[regid[i]],1] += poplist[i]
regcasepop[regid_dict[regid[i]],2] += 1
for item in regcasepop:
if item[2] > 1 and item[1] < popsum * 0.5:
#if item[1] < popsum * 0.5:
temp = llr.llr(item[0],item[1])
if temp > maxLLR:
maxLLR = temp
#/-----------debug
maxllrlevel = level
maxllrcase = item[0]
maxllrpop = item[1]
print 'maxllr_level', maxllrlevel, 'maxllr_case =', maxllrcase, 'maxllr_pop =', maxllrpop, 'maxllr =', maxLLR
#debug---------------/
return maxLLR
pop = data[:,1]
randomLLRcsv = filePath + 'LLR_buildonly/Random/maxllr.csv'
r = readcsv.readcsv(randomLLRcsv)
randomLLR = r.getData()
randomLLR = randomLLR[:,0]
randomCount = len(randomLLR) + 1
repeatTime = len(data[0,:])-2
#repeatTime = 100
numUnit = len(pop)
maxPvaluelist = []
popsum = sum(pop)
llr = likelihoodratio.likelihoodratio(sum(data[:,2]), popsum)
printstep = 100
for repeat in xrange(repeatTime):
if repeat%printstep == 0:
print repeat, str(datetime.now())
case = data[:,2+repeat]
#print case[0]
regionCSV = filePath + 'LLR_buildonly/' + clustertype + '/LLR_EBS_' + str(repeat) + ".csv"
r = readcsv.readcsv(regionCSV,'int')
regiondata = r.getData()
#temp = findMaxLLR(case, pop, regiondata[:,1:])
#print temp
#maxPvaluelist.append(temp)
llr_weight = 1 # seems doesn't have impact on the results
repeatTime = 100
numUnit = 255
sigma = []
s = measureSigma.SSS_sigma(range(numUnit), hot_16)
popsum = sum(pop)
halfpop = popsum/2
minPvaluelist = []
for repeat in xrange(repeatTime):
#repeat = 299
maxLLR = -1
scale = 1
case = data[:,2+repeat]
llr = likelihoodratio.likelihoodratio(sum(case), popsum)
halfcase = sum(case)/2
imagedata = case/pop
#imagedata = smoothdata[:,repeat]
indexList = range(len(imagedata))#imagedata[:,0].tolist()
#intensityList = data_stretch(imagedata, 'min_max')#imagedata #imagedata[:,1].tolist()
intensityList = data_stretch(imagedata)#imagedata #imagedata[:,1].tolist()
intensityList = imagedata
#plotshapefile(shpfile, np.array(intensityList)/255, 'Set3')
scale_amg_weight = []
if llr_weight > 0:
cur_pixels_weight, cur_adjWeight, cur_prop = find_weight_for_pixels_llr(indexList, case, pop)
seeds_generation_llr(cur_pixels_weight, cur_adjWeight, cur_prop, np.ones(len(imagedata)))
else:
cur_pixels_weight = find_weight_for_pixels(indexList)
