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}
pulseModelQueryPoints = np.linspace(1.0 / nSmoothPlotPoints, 1, nSmoothPlotPoints)
def modelProfile(thetas):
return np.sum(modelFourierCoeffs * np.exp(2.0j * np.pi * modes * thetas[:, np.newaxis]), axis=1)
lightCurveModel = np.abs(modelProfile(pulseModelQueryPoints))
# for this test we only want the model to be the pulsed component. We will add a DC offset later
pulseModel = lightCurveModel - np.min(lightCurveModel)
# initialPhotonPulseFraction = 1.*np.sum(pulseModel) / np.sum(lightCurveModel)
photonPulseFraction = 15400.0 / nPhotons # skip to previously determined answer
print "photon fraction", photonPulseFraction
# get samples with distribution of the modelProfile
# modelSampler = inverseTransformSampler(pdf=lightCurveModel,queryPoints=pulseModelQueryPoints)
modelSampler = inverseTransformSampler(pdf=pulseModel, queryPoints=pulseModelQueryPoints)
nTrials = 1
# for each trial run the h test on a set of photon phases with our model profile, and with the pulse fraction specified
# we want to make a distribution of H values for this pulse fraction, model, and number of photons
# make a function that only takes the trial number (as an identifier)
mappableHTestTrial = functools.partial(
hTestTrial,
pulseModel=pulseModel,
pulseModelQueryPoints=pulseModelQueryPoints,
nPhotons=nPhotons,
photonPulseFraction=photonPulseFraction,
)
pool = multiprocessing.Pool(processes=multiprocessing.cpu_count() - 3) # leave a few processors for other people
outDicts = pool.map(mappableHTestTrial, np.arange(nTrials))
