def test_vector_get_indexed_diffraction_vectors_warn():
match_results = VectorMatchingResults(np.array([[1], [2]]))
match_results.hkls = [0, 0, 1]
vectors = DiffractionVectors(np.array([[1], [2]]))
vectors.hkls = [0, 0, 0]
with pytest.warns(Warning):
match_results.get_indexed_diffraction_vectors(vectors)
np.testing.assert_allclose(vectors.hkls, [0, 0, 0])
def test_vector_get_indexed_diffraction_vectors(overwrite, result_hkl,
current_hkl, expected_hkl):
match_results = VectorMatchingResults(np.array([[1], [2]]))
match_results.hkls = result_hkl
vectors = DiffractionVectors(np.array([[1], [2]]))
vectors.hkls = current_hkl
match_results.get_indexed_diffraction_vectors(vectors, overwrite)
np.testing.assert_allclose(vectors.hkls, expected_hkl)
def dp_vector_match_result():
res = np.empty(4, dtype="object")
res = res.reshape(2, 2)
res[0, 0] = OrientationResult(
0,
euler2mat(*np.deg2rad([90, 0, 0]), "rzxz"),
0.6,
np.array([0.1, 0.10, 0.2]),
0.3,
1.0,
0,
0,
)
res[0, 1] = OrientationResult(
0,
euler2mat(*np.deg2rad([0, 10, 20]), "rzxz"),
0.5,
np.array([0.1, 0.05, 0.2]),
0.4,
1.0,
0,
0,
)
res[1, 0] = OrientationResult(
1,
euler2mat(*np.deg2rad([0, 45, 45]), "rzxz"),
0.8,
np.array([0.1, 0.30, 0.2]),
0.1,
1.0,
0,
0,
)
res[1, 1] = OrientationResult(
1,
euler2mat(*np.deg2rad([0, 0, 90]), "rzxz"),
0.7,
np.array([0.1, 0.05, 0.1]),
0.2,
1.0,
0,
0,
)
return VectorMatchingResults(res)
def sp_vector_match_result():
# We require (total_error of row_1 > correlation row_2)
res = np.empty(2, dtype="object")
res[0] = OrientationResult(
0,
euler2mat(*np.deg2rad([0, 0, 90]), "rzxz"),
0.5,
np.array([0.1, 0.05, 0.2]),
0.1,
1.0,
0,
0,
)
res[1] = OrientationResult(
0,
euler2mat(*np.deg2rad([0, 0, 90]), "rzxz"),
0.6,
np.array([0.1, 0.10, 0.2]),
0.2,
1.0,
0,
0,
)
return VectorMatchingResults(res)
def refine_n_best_orientations(
self,
orientations,
accelarating_voltage,
camera_length,
n_best=0,
rank=0,
index_error_tol=0.2,
vary_angles=True,
vary_center=False,
vary_scale=False,
method="leastsq",
):
"""Refines the best orientation and assigns hkl indices to diffraction vectors.
Parameters
----------
orientations : VectorMatchingResults
List of orientations to refine, must be an instance of `VectorMatchingResults`.
accelerating_voltage : float
The acceleration voltage with which the data was acquired.
camera_length : float
The camera length in meters.
n_best : int
Refine the best `n` orientations starting from `rank`.
With `n_best=0` (default), all orientations are refined.
rank : int
The rank of the solution to start from.
index_error_tol : float
Max allowed error in peak indexation for classifying it as indexed,
calculated as :math:`|hkl_calculated - round(hkl_calculated)|`.
method : str
Minimization algorithm to use, choose from:
'leastsq', 'nelder', 'powell', 'cobyla', 'least-squares'.
See `lmfit` documentation (https://lmfit.github.io/lmfit-py/fitting.html)
for more information.
vary_angles : bool,
Free the euler angles (rotation matrix) during the refinement.
vary_center : bool
Free the center of the diffraction pattern (beam center) during the refinement.
vary_scale : bool
Free the scale (i.e. pixel size) of the diffraction vectors during refinement.
Returns
-------
indexation_results : VectorMatchingResults
Navigation axes of the diffraction vectors signal containing vector
indexation results for each probe position.
"""
vectors = self.vectors
library = self.library
matched = orientations.map(
_refine_best_orientations,
vectors=vectors,
library=library,
accelarating_voltage=accelarating_voltage,
camera_length=camera_length,
n_best=n_best,
rank=rank,
method="leastsq",
verbose=False,
vary_angles=vary_angles,
vary_center=vary_center,
vary_scale=vary_scale,
inplace=False,
parallel=False,
)
indexation = matched.isig[0]
rhkls = matched.isig[1].data
indexation_results = VectorMatchingResults(indexation)
indexation_results.vectors = vectors
indexation_results.hkls = rhkls
indexation_results = transfer_navigation_axes(indexation_results,
vectors.cartesian)
return indexation_results
def index_vectors(
self,
mag_tol,
angle_tol,
index_error_tol,
n_peaks_to_index,
n_best,
*args,
**kwargs,
):
"""Assigns hkl indices to diffraction vectors.
Parameters
----------
mag_tol : float
The maximum absolute error in diffraction vector magnitude, in units
of reciprocal Angstroms, allowed for indexation.
angle_tol : float
The maximum absolute error in inter-vector angle, in units of
degrees, allowed for indexation.
index_error_tol : float
Max allowed error in peak indexation for classifying it as indexed,
calculated as :math:`|hkl_calculated - round(hkl_calculated)|`.
n_peaks_to_index : int
The maximum number of peak to index.
n_best : int
The maximum number of good solutions to be retained.
*args : arguments
Arguments passed to the map() function.
**kwargs : arguments
Keyword arguments passed to the map() function.
Returns
-------
indexation_results : VectorMatchingResults
Navigation axes of the diffraction vectors signal containing vector
indexation results for each probe position.
"""
vectors = self.vectors
library = self.library
matched = vectors.cartesian.map(
match_vectors,
library=library,
mag_tol=mag_tol,
angle_tol=np.deg2rad(angle_tol),
index_error_tol=index_error_tol,
n_peaks_to_index=n_peaks_to_index,
n_best=n_best,
inplace=False,
*args,
**kwargs,
)
indexation = matched.isig[0]
rhkls = matched.isig[1].data
indexation_results = VectorMatchingResults(indexation)
indexation_results.vectors = vectors
indexation_results.hkls = rhkls
indexation_results = transfer_navigation_axes(indexation_results,
vectors.cartesian)
vectors.hkls = rhkls
return indexation_results