def pre_process(self):
"""
This method is called after the plugin has been created by the
pipeline framework as a pre-processing step
:param parameters: A dictionary of the parameters for this plugin, or
None if no customisation is required
:type parameters: dict
"""
in_dataset, out_datasets = self.get_datasets()
mData = self.get_in_meta_data()[0]
in_d1 = in_dataset[0]
ai = AzimuthalIntegrator() # get me an integrator object
### prep the goemtry
bc = [mData.get_meta_data("beam_center_x")[...],
mData.get_meta_data("beam_center_y")[...]]
distance = mData.get_meta_data('distance')[...]
wl = mData.get_meta_data('incident_wavelength')[...]
px = mData.get_meta_data('x_pixel_size')[...]
orien = mData.get_meta_data(
'detector_orientation')[...].reshape((3, 3))
#Transform
yaw = math.degrees(-math.atan2(orien[2, 0], orien[2, 2]))
roll = math.degrees(-math.atan2(orien[0, 1], orien[1, 1]))
ai.setFit2D(distance, bc[0], bc[1], -yaw, roll, px, px, None)
ai.set_wavelength(wl)
self.sh = in_d1.get_shape()[-2:]
self.npts = self.get_parameters('num_bins')
foo = units.to_unit(units.TTH) # preallocate
pretenddata = np.zeros(self.sh)
pretendfit = ai.xrpd(data=pretenddata, npt=self.npts)
twotheta=ai.__getattribute__(foo.center)(ai.detector.max_shape) # get the detector array of Q # preallocate
twotheta *= 180.0/np.pi # preallocate
twotheta_flat = twotheta.ravel() #pre allocate
self.params = [self.npts, mData, ai, twotheta_flat]
