def on_image(self, exposureanalyzer, prefix, fsn, matrix, params, mask):
im = Exposure(matrix, header=Header(params), mask=mask)
try:
sample = im.header.title
except KeyError:
sample = "unknown sample"
legend = "FSN #{:d}, {} at {:.2f} mm".format(im.header.fsn, sample, float(im.header.distance))
if self.builder.get_object("plotimage_check").get_active():
if self.builder.get_object("reuseimage_check").get_active():
imgwin = PlotImageWindow.get_latest_window()
else:
imgwin = PlotImageWindow()
imgwin.set_image(im.intensity)
imgwin.set_mask(im.mask)
imgwin.set_distance(im.header.distance)
imgwin.set_beampos(im.header.beamcenterx, im.header.beamcentery)
assert im.header.pixelsizex == im.header.pixelsizey
imgwin.set_pixelsize(im.header.pixelsizex)
imgwin.set_wavelength(im.header.wavelength)
imgwin.set_title(legend)
if self.builder.get_object("plotradial_check").get_active():
if self.builder.get_object("reuseradial_check").get_active():
curvewin = PlotCurveWindow.get_latest_window()
else:
curvewin = PlotCurveWindow()
curve = im.radial_average()
assert im.header.pixelsizex == im.header.pixelsizey
curvewin.addcurve(
curve.q,
curve.Intensity,
curve.qError,
curve.Error,
legend,
"q",
im.header.pixelsizex,
im.header.distance,
im.header.wavelength,
)
self._images_done += 1
if self._images_done >= self._nimages:
self.instrument.services["exposureanalyzer"].disconnect(self._expanalyzerconnection)
self._expanalyzerconnection = None
def absolutescaling(self, im: Exposure, datared: Dict):
if im.header.title == self.config['datareduction']['absintrefname']:
self._logger.debug('History: {}'.format('\n'.join([h for h in datared['history']])))
dataset = np.loadtxt(self.config['datareduction']['absintrefdata'])
self._logger.debug('Q-range of absint dataset: {:g} to {:g}, {:d} points.'.format(
dataset[:, 0].min(), dataset[:, 0].max(), len(dataset[:, 0])))
testradavg = im.radial_average()
self._logger.debug('Auto-Q-range of the measured dataset: {:g} to {:g}, {:d} points.'.format(
testradavg.q.min(), testradavg.q.max(), len(testradavg)))
# noinspection PyPep8Naming,PyPep8Naming
q, dq, I, dI, area = im.radial_average(qrange=dataset[:, 0], raw_result=True)
dataset = dataset[area > 0, :]
# noinspection PyPep8Naming
I = I[area > 0]
# noinspection PyPep8Naming
dI = dI[area > 0]
q = q[area > 0]
self._logger.debug('Common q-range: {:g} to {:g}, {:d} points.'.format(q.min(), q.max(), len(q)))
scalingfactor, stat = nonlinear_odr(I, dataset[:, 1], dI, dataset[:, 2], lambda x, a: a * x, [1])
datared['absintscaling'] = {'q': q, 'area': area, 'Imeas': I, 'dImeas': dI,
'Iref': dataset[:, 1], 'dIref': dataset[:, 2],
'factor.val': scalingfactor.val,
'factor.err': scalingfactor.err, 'stat': stat}
self._logger.debug('Scaling factor: ' + str(scalingfactor))
self._logger.debug('Chi2: {:f}'.format(stat['Chi2_reduced']))
self._lastabsintref = im
self._absintscalingfactor = scalingfactor
self._absintstat = stat
self._absintqrange = q
datared['history'].append(
'This is an absolute intensity reference measurement. '
'Determined absolute intensity scaling factor: {}. Reduced Chi2: {:f}. DoF: {:d}. '
'This corresponds to beam flux {} photons*eta/sec'.format(
self._absintscalingfactor, self._absintstat['Chi2_reduced'], self._absintstat['DoF'],
1 / self._absintscalingfactor))
if ((im.header.distance - self._lastabsintref.header.distance).abs() <
self.config['datareduction']['distancetolerance']):
im *= self._absintscalingfactor
datared['statistics']['08_absolutescaling'] = im.get_statistics()
datared['history'].append(
'Using absolute intensity factor {} from measurement FSN #{:d} '
'for absolute intensity calibration.'.format(
self._absintscalingfactor, self._lastabsintref.header.fsn))
datared['history'].append('Absint factor was determined with Chi2 {:f} (DoF {:d})'.format(
self._absintstat['Chi2_reduced'], self._absintstat['DoF']))
datared['history'].append('Estimated flux: {} photons*eta/sec'.format(
self._absintscalingfactor.__reciprocal__()))
datared['absintrefFSN'] = self._lastabsintref.header.fsn
datared['flux'] = self._absintscalingfactor.__reciprocal__().val
datared['flux.err'] = self._absintscalingfactor.__reciprocal__().err
datared['absintchi2'] = self._absintstat['Chi2_reduced']
datared['absintdof'] = self._absintstat['DoF']
datared['absintfactor'] = self._absintscalingfactor.val
datared['absintfactor.err'] = self._absintscalingfactor.err
datared['absintqmin'] = self._absintqrange.min()
datared['absintqmax'] = self._absintqrange.max()
else:
raise ServiceError(
'S-D distance of the last seen absolute intensity reference measurement '
'does not match the exposure under reduction.')
self._logger.debug('Done absint FSN ' + str(im.header.fsn))
return im, datared
