Note
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Multi Phase Orientation Mapping#
You can also calculate the orientation of the grains for multiple phases using the
pyxem.signals.PolarSignal2D.get_orientation() method. This requires that you
simulate the entire S2 space for the phase and then compare to the simulated diffraction.
For more information on the orientation mapping process see [CCAAnes+22]
import pyxem as pxm
from pyxem.data import fe_multi_phase_grains, fe_bcc_phase, fe_fcc_phase
from diffsims.generators.simulation_generator import SimulationGenerator
from orix.quaternion import Rotation
from orix.sampling import get_sample_reduced_fundamental
mulit_phase = fe_multi_phase_grains()
First we center the diffraction patterns and get a polar signal Increasing the number of npt_azim with give better polar sampling but will take longer to compute the orientation map The mean=True argument will return the mean pixel value in each bin rather than the sum this makes the high k values more visible
mulit_phase.calibration.center = None
polar_multi = mulit_phase.get_azimuthal_integral2d(
npt=100, npt_azim=360, inplace=False, mean=True
)
polar_multi.plot()
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Now we can get make a simulation. In this case we want to set a minimum_intensity which removes the low intensity reflections. we also sample the S2 space using the :func`orix.sampling.get_sample_reduced_fundamental` We have two phases here so we can make a simulation object with both of the phases.
bcc = fe_bcc_phase()
fcc = fe_fcc_phase()
bcc.name = "BCC Phase"
fcc.name = "FCC Phase"
fcc.color = "red"
bcc.color = "blue"
generator = SimulationGenerator(200, minimum_intensity=0.05)
rotations_bcc = get_sample_reduced_fundamental(
resolution=1, point_group=bcc.point_group
)
rotations_fcc = get_sample_reduced_fundamental(
resolution=1, point_group=fcc.point_group
)
sim = generator.calculate_diffraction2d(
[bcc, fcc],
rotation=[rotations_bcc, rotations_fcc],
max_excitation_error=0.1,
reciprocal_radius=2,
with_direct_beam=False,
)
orientation_map = polar_multi.get_orientation(sim)
cmap = orientation_map.to_crystal_map()
cmap.plot()
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