"""
Single Phase Orientation Mapping
================================
You can also calculate the orientation of the grains in a single phase sample using the
:meth:`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 :cite:`pyxemorientationmapping2022`
"""

from pyxem.data import si_phase, si_grains
from diffsims.generators.simulation_generator import SimulationGenerator
from orix.sampling import get_sample_reduced_fundamental

simulated_si = si_grains()

# %%
# Pre-Processing
# --------------
# 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

simulated_si.calibration.center = None
polar_si = simulated_si.get_azimuthal_integral2d(
    npt=100, npt_azim=360, inplace=False, mean=True
)
polar_si.plot()

# %%
# Building a Simulation
# ---------------------
# 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`.  Make sure that you set
# ``with_direct_beam=False`` or the orientation mapping will be unduely affected by the center beam.
phase = si_phase()
generator = SimulationGenerator(200, minimum_intensity=0.05)
rotations = get_sample_reduced_fundamental(resolution=1, point_group=phase.point_group)
sim = generator.calculate_diffraction2d(
    phase,
    rotation=rotations,
    max_excitation_error=0.1,
    reciprocal_radius=2,
    with_direct_beam=False,
)  # Make sure that with_direct_beam ==False

# %%
# Getting the Orientation
# -----------------------
# By default the `get_orientation` function uses a gamma correction equilivent to polar_si**0.5.  For noisy datasets
# it might be a good idea to reduce the noise (Maybe by averaging neighboring patterns?) or simple background subtraction,
# otherwise the gamma correction will increase the effects of noise on the data. This tries to focus on  "Is the Bragg vector
# there?" rather than "Is the Bragg vector the right intensity?" patially because the intensity of the Bragg vector might have
# many different effects.

polar_si = polar_si**0.5  # gamma correction.
orientation_map = polar_si.get_orientation(sim)
orientation_map.plot_over_signal(simulated_si, vmax="96th")

# %%
# sphinx_gallery_thumbnail_number = 4