dQ = np.abs(ICCFT.getDQFracHKL(UBMatrix, frac=0.5))
dQ[0,:] = 0.05088
dQ[dQ>0.2]=0.2
nX = 32; nY = 32; nZ = 32
#qMask = pickle.load(open('/data/ml_peak_sets/peaks_tf_mltoolstest_limitedNoise_0p025_cutoff_0p5MaxNoise/qMask.pkl', 'rb'))
qMask = pickle.load(open(baseDirectory+'qMask.pkl', 'rb'))
cX, cY, cZ = np.array(qMask.shape)//2
dX, dY, dZ = nX//2, nY//2, nZ//2
qMaskSimulated = qMask[cX-dX:cX+dX, cY-dY:cY+dY, cZ-dZ:cZ+dZ]
peak = peaks_ws.getPeak(peakToGet)
MDdata = mltools.getMDData(peak, nxsTemplate, DetCalFile, None, q_frame)
box = ICCFT.getBoxFracHKL(peak, peaks_ws, MDdata, UBMatrix, peakToGet, dQ, fracHKL=0.5, dQPixel=dQPixel, q_frame=q_frame);
n_events_cropped, image = mltools.getImageFromBox(box, UBMatrix, peak, rebinToHKL=trainedOnHKL, qMaskSimulated=qMaskSimulated)
peakMask, testim, blobs = mltools.getPeakMask(image, model, thresh=0.15)
mltools.makeShowPredictedPeakFigure(image, peakMask, peakToGet)
#Integration
countsInPeak = np.sum(n_events_cropped[peakMask])
neigh_length_m = 3
convBox = 1.0*np.ones([neigh_length_m, neigh_length_m,neigh_length_m]) / neigh_length_m**3
conv_n_events_cropped = convolve(n_events_cropped,convBox)
if not trainedOnHKL:
bgIDX = reduce(np.logical_and, [~peakMask, qMaskSimulated, conv_n_events_cropped > 0])
else:
bgIDX = np.logical_and(~peakMask, conv_n_events_cropped > 0)
meanBG = n_events_cropped[bgIDX].mean()
bgCountsInPeak = meanBG*np.sum(peakMask)
intensity = countsInPeak - meanBG*np.sum(peakMask)
edgeConvBox = np.ones([3, 3, 3])
newMask = convolve(~np.isfinite(norm), edgeConvBox).copy()
normData[newMask] = 0.
mtd['dataMD'].setSignalArray(data)
mtd['dataMD'].setErrorSquaredArray(errorsq)
mtd['result'].setSignalArray(normData)
normErrorSq = 1. * errorsq / norm / norm
normErrorSq[newMask] = 0.
mtd['result'].setErrorSquaredArray(normErrorSq)
box = mtd['dataMD']
n_events_cropped, n_errorsq_cropped, image = mltools.getImageFromBox(
box,
UBMatrix,
peak,
rebinToHKL=trainedOnHKL,
qMaskSimulated=qMaskSimulated,
returnErrorSq=True)
peakMask, testim, blobs = mltools.getPeakMask(image,
model,
thresh=thresh)
box = mtd['result']
n_events_cropped, n_errorsq_cropped, image2 = mltools.getImageFromBox(
box,
UBMatrix,
peak,
rebinToHKL=trainedOnHKL,
qMaskSimulated=qMaskSimulated,
returnErrorSq=True)