def hTestTrial(iTrial, nPhotons, photonPulseFraction, pulseModel, pulseModelQueryPoints):
np.random.seed(int((time.time() + iTrial) * 1e6))
modelSampler = inverseTransformSampler(pdf=pulseModel, queryPoints=pulseModelQueryPoints)
nPulsePhotons = int(np.floor(photonPulseFraction * nPhotons))
nBackgroundPhotons = int(np.ceil((1.0 - photonPulseFraction) * nPhotons))
simPulsePhotons = modelSampler(nPulsePhotons)
# background photons come from a uniform distribution
simBackgroundPhotons = np.random.random(nBackgroundPhotons)
simPhases = np.append(simPulsePhotons, simBackgroundPhotons)
simHDict = h_test2(simPhases)
simH, simM, simPval, simFourierCoeffs = simHDict["H"], simHDict["M"], simHDict["fpp"], simHDict["cs"]
print "{} - H,M,fpp,sig:".format(iTrial), simH, simM, simPval
return {"H": simH, "M": simM, "fpp": simPval}
if bLoadFromPl:
photFile = tables.openFile(path, "r")
photTable = photFile.root.photons.photTable
phases = photTable.readWhere("(wvlStart < wavelength) & (wavelength < wvlEnd)")["phase"]
photFile.close()
print "cut wavelengths to range ({},{})".format(wvlStart, wvlEnd)
nPhotons = len(phases)
print nPhotons, "real photons read"
observedProfile, _ = np.histogram(phases, bins=phaseBinEdges)
observedProfile = 1.0 * observedProfile
observedProfileErrors = np.sqrt(observedProfile)
# Do H-test
hDict = h_test2(phases)
H, M, pval, fourierCoeffs = hDict["H"], hDict["M"], hDict["fpp"], hDict["cs"]
print "h-test on real data"
print "H,M,fpp:", H, M, pval
print nSigma(1 - pval), "sigmas"
# h_test2 calculates all fourierCoeffs out to 20, but for the fourier model, we only want the ones out to order M, which optimizes the Zm^2 metric
truncatedFourierCoeffs = fourierCoeffs[0:M]
print "fourier coeffs:", truncatedFourierCoeffs
# for the model, we want the negative modes as well as positve, so add them
modelFourierCoeffs = np.concatenate([truncatedFourierCoeffs[::-1], [1.0], np.conj(truncatedFourierCoeffs)])
# make array of mode numbers
modes = np.arange(-len(truncatedFourierCoeffs), len(truncatedFourierCoeffs) + 1)
