def PyExec(self):
tolerance = self.getProperty("Tolerance").value
k = int(self.getProperty("NumOfQs").value)
nuclear = self.getProperty("NuclearPeaks").value
satellites = self.getProperty("SatellitePeaks").value
cluster_threshold = self.getProperty("ClusterThreshold").value
n_trunc_decimals = int(np.ceil(abs(np.log10(tolerance))))
if nuclear.getNumberPeaks() == 0:
raise RuntimeError(
"The NuclearPeaks parameter must have at least one peak")
if satellites.getNumberPeaks() == 0:
raise RuntimeError(
"The SatellitePeaks parameter must have at least one peak")
nuclear_hkls = indexing.get_hkls(nuclear)
sats_hkls = indexing.get_hkls(satellites)
qs = indexing.find_q_vectors(nuclear_hkls, sats_hkls)
self.log().notice("K value is {}".format(k))
k = None if k == -1 else k
clusters, k = indexing.cluster_qs(qs, k=k, threshold=cluster_threshold)
qs = indexing.average_clusters(qs, clusters)
qs = indexing.trunc_decimals(qs, n_trunc_decimals)
qs = indexing.sort_vectors_by_norm(qs)
self.log().notice("Q vectors are: \n{}".format(qs))
indices = indexing.index_q_vectors(qs, tolerance)
ndim = indices.shape[1] + 3
hkls = indexing.find_nearest_integer_peaks(nuclear_hkls, sats_hkls)
hklm = np.zeros((hkls.shape[0], ndim))
hklm[:, :3] = np.round(hkls)
raw_qs = hkls - sats_hkls
peak_map = KDTree(qs)
for i, q in enumerate(raw_qs):
distance, index = peak_map.query(q, k=1)
hklm[i, 3:] = indices[index]
indexed = self.create_indexed_peaksworkspace(satellites, qs, hklm)
self.setProperty("OutputWorkspace", indexed)
def PyExec(self):
tolerance = self.getProperty("Tolerance").value
k = int(self.getProperty("NumOfQs").value)
nuclear = self.getProperty("NuclearPeaks").value
satellites = self.getProperty("SatellitePeaks").value
cluster_threshold = self.getProperty("ClusterThreshold").value
n_trunc_decimals = int(np.ceil(abs(np.log10(tolerance))))
if nuclear.getNumberPeaks() == 0:
raise RuntimeError("The NuclearPeaks parameter must have at least one peak")
if satellites.getNumberPeaks() == 0:
raise RuntimeError("The SatellitePeaks parameter must have at least one peak")
nuclear_hkls = indexing.get_hkls(nuclear)
sats_hkls = indexing.get_hkls(satellites)
qs = indexing.find_q_vectors(nuclear_hkls, sats_hkls)
self.log().notice("K value is {}".format(k))
k = None if k == -1 else k
clusters, k = indexing.cluster_qs(qs, k=k, threshold=cluster_threshold)
qs = indexing.average_clusters(qs, clusters)
qs = indexing.trunc_decimals(qs, n_trunc_decimals)
qs = indexing.sort_vectors_by_norm(qs)
self.log().notice("Q vectors are: \n{}".format(qs))
indices = indexing.index_q_vectors(qs, tolerance)
ndim = indices.shape[1] + 3
hkls = indexing.find_nearest_integer_peaks(nuclear_hkls, sats_hkls)
hklm = np.zeros((hkls.shape[0], ndim))
hklm[:, :3] = np.round(hkls)
raw_qs = hkls - sats_hkls
peak_map = KDTree(qs)
for i, q in enumerate(raw_qs):
distance, index = peak_map.query(q, k=1)
hklm[i, 3:] = indices[index]
indexed = self.create_indexed_workspace(satellites, ndim, hklm)
self.setProperty("OutputWorkspace", indexed)
def test_find_indexing_with_non_orthogonal_cell(self):
cell = UnitCell(1, 1, 1, 90, 95, 103)
cart_cell = cell.getB()
qs = np.array([
[.5, 0, 0],
[0, .5, 0],
[0, 0, .5],
[0, 0, .25],
])
qs = np.dot(qs, cart_cell)
indices = indexing.index_q_vectors(qs)
expected_indexing = np.array([[0, 0, 1], [0, 1, 0], [2, 0, 0],
[1, 0, 0]])
npt.assert_equal(indices,
expected_indexing,
err_msg="Indexing does not match expected.")
def test_index_with_modulation(self):
qs = np.array([
[0, .1, .1],
[0, -.1, 0],
[0, 0, .1],
[0, 0, -.1],
[0, .1, .2],
[0, 0, .45],
])
expected_indexing = np.array([
[-1, 1, 0],
[ 1, 0, 0],
[ 0, 1, 0],
[ 0, -1, 0],
[-1, 2, 0],
[ 0, 0, 1]
])
actual_indexing = indexing.index_q_vectors(qs, tolerance=.03)
npt.assert_array_equal(actual_indexing, expected_indexing)
def test_find_indexing_with_non_orthogonal_cell(self):
cell = UnitCell(1, 1, 1, 90, 95, 103)
cart_cell = cell.getB()
qs = np.array([
[.5, 0, 0],
[0, .5, 0],
[0, 0, .5],
[0, 0, .25],
])
qs = np.dot(qs, cart_cell)
indices = indexing.index_q_vectors(qs)
expected_indexing = np.array([
[0, 0, 1],
[0, 1, 0],
[2, 0, 0],
[1, 0, 0]
])
npt.assert_equal(indices, expected_indexing, err_msg="Indexing does not match expected.")
