def test_blockav_psd(self):
out = xu.blockAveragePSD(self.seq2d, self.n)
self.assertAlmostEqual(out[0, 0], numpy.average(self.seq2d[0,
0:self.n]))
self.assertEqual(out.shape,
(self.seq2d.shape[0],
numpy.ceil(self.seq2d.shape[1] / float(self.n))))
def test_blockav_psd(self):
out = xu.blockAveragePSD(self.seq2d, self.n)
self.assertAlmostEqual(out[0, 0],
numpy.average(self.seq2d[0, 0:self.n]))
self.assertEqual(
out.shape,
(self.seq2d.shape[0],
numpy.ceil(self.seq2d.shape[1] / float(self.n))))
#################################
# InP (333) reciprocal space map
omalign = 43.0529 # experimental aligned values
ttalign = 86.0733
[omnominal, dummy, dummy,
ttnominal] = hxrd.Q2Ang(InP.Q(3, 3,
3)) # nominal values of the substrate peak
# read the data from the HDF5 file (scan number:36, names of motors in
# spec file: omega= sample rocking, gamma = twotheta)
[om, tt], MAP = xu.io.geth5_scan(h5file, 36, 'omega', 'gamma')
# normalize the intensity values (absorber and count time corrections)
psdraw = normalizer_detcorr(MAP)
# remove unusable detector channels/regions (no averaging of detector channels)
psd = xu.blockAveragePSD(psdraw, 1, roi=roi)
# determine offset of substrate peak from experimental values (optional)
omalign, ttalign, p, cov = xu.analysis.fit_bragg_peak(om,
tt,
psd,
omalign,
ttalign,
hxrd,
plot=False)
# convert angular coordinates to reciprocal space + correct for offsets
[qx, qy,
qz] = hxrd.Ang2Q.linear(om,
tt,
delta=[omalign - omnominal, ttalign - ttnominal])
#################################
# InP (333) reciprocal space map
omalign = 43.0529 # experimental aligned values
ttalign = 86.0733
[omnominal, dummy, dummy, ttnominal] = hxrd.Q2Ang(
InP.Q(3, 3, 3)) # nominal values of the substrate peak
# read the data from the HDF5 file
# scan number:36, names of motors in spec file: omega= sample rocking, gamma =
# twotheta
[om, tt], MAP = xu.io.geth5_scan(h5file, 36, 'omega', 'gamma')
# normalize the intensity values (absorber and count time corrections)
psdraw = normalizer_detcorr(MAP)
# remove unusable detector channels/regions (no averaging of detector channels)
psd = xu.blockAveragePSD(psdraw, 1, roi=roi)
# convert angular coordinates to reciprocal space + correct for offsets
[qx, qy, qz] = hxrd.Ang2Q.linear(
om, tt,
delta=[omalign - omnominal, ttalign - ttnominal])
# calculate data on a regular grid of 200x201 points
gridder = xu.Gridder2D(200, 201)
gridder(qy, qz, psd)
# maplog function limits the shown dynamic range to 8 orders of magnitude
# from the maxium
INT = xu.maplog(gridder.data.T, 8., 0)
# plot the intensity as contour plot using matplotlib
plt.figure()