phases = photTable.readWhere('(wvlStart < wavelength) & (wavelength < wvlEnd)')['phase']
photFile.close()
simDs = np.load(trialsPath)['dMetrics']
bUseSimulationFpp=False
nPhotons = len(phases)
print nPhotons,'real photons read'
D,pval = kuiper(phases)
simFpp = 1.*np.sum(simDs>=D) / len(simDs)
print 'kuiper test'
print 'D,fpp:',D,pval
print 'simulated fpp:',simFpp
print nSigma(1-pval),'sigmas'
print nSigma(1-simFpp),'sigmas (sim)'
nSigmaThreshold = 5.
nPhaseBins = 20
phaseBinEdges = np.linspace(0.,1.,nPhaseBins+1)
profile,_ = np.histogram(phases,bins=phaseBinEdges)
profileErrors = np.sqrt(profile)
magG = 17.93
photonIncrement = 1000
#Inject Sine first
nSigmaSig = 0.
nFakePhotons = 0
while nSigmaSig < nSigmaThreshold:
print 'sorted phases'
i = 0
print 'sorted:',issorted(allPhotons['phase'])
for iWvlStart,wvlStart in enumerate(wvlLimits[:-1]):
for iWvlEnd,wvlEnd in enumerate(wvlLimits[iWvlStart+1:]):
print 'cut wavelengths to range ({},{})'.format(wvlStart,wvlEnd)
mask = numexpr.evaluate('(wvlStart < allWavelengths) & (allWavelengths < wvlEnd)')
phases = allPhotons['phase'][mask]
nPhotons = len(phases)
print nPhotons,'photons in range'
D,pval = kuiper(phases,assumeSorted=True)
sig = nSigma(1-pval)
print 'kuiper test'
print 'D,fpp:',D,pval
print sig,'sigmas'
metrics[i]['wvlStart'] = wvlStart
metrics[i]['wvlEnd'] = wvlEnd
metrics[i]['D'] = D
metrics[i]['fpp'] = pval
metricImg[iWvlStart,iWvlEnd+iWvlStart+1] = D
fppImg[iWvlStart,iWvlEnd+iWvlStart+1] = pval
sigmaImg[iWvlStart,iWvlEnd+iWvlStart+1] = sig
del phases
i+=1
nWvlStarts = len(wvlLimits) - 1
nWvlLimits = len(wvlLimits)
iWvlPair = 0
for iWvlSet in xrange(len(data)//nWvlPairs):
metricImg = np.zeros((nWvlLimits,nWvlLimits))
fppImg = np.zeros((nWvlLimits,nWvlLimits))
sigmaImg = np.zeros((nWvlLimits,nWvlLimits))
for iWvlStart,wvlStart in enumerate(wvlLimits[:-1]):
for iWvlEnd,wvlEnd in enumerate(wvlLimits[iWvlStart+1:]):
assert wvlStart == wvlStartList[iWvlPair]
assert wvlEnd == wvlStopList[iWvlPair]
h = hMetrics[iWvlPair]
pval = h_fpp(h)
sig = nSigma(1-pval)
metricImg[iWvlStart,iWvlEnd+iWvlStart+1] = h
fppImg[iWvlStart,iWvlEnd+iWvlStart+1] = pval
sigmaImg[iWvlStart,iWvlEnd+iWvlStart+1] = sig
iWvlPair += 1
metricImgs.append(metricImg)
fppImgs.append(fppImg)
sigmaImgs.append(sigmaImg)
metricImgs = np.array(metricImgs)
fppImgs = np.array(fppImgs)
sigmaImgs = np.array(sigmaImgs)
metricImgs = metricImgs[0:1000] #trim to just 1000 trials
nTrials = len(metricImgs)
