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 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_sort_vectors_by_norm(self):
vecs = np.array([
[0, 0, 3],
[0, 2, 0],
[1, 0, 0],
[0, 5, 5],
[0, 4, 4],
])
expected_output = np.array([
[1, 0, 0],
[0, 2, 0],
[0, 0, 3],
[0, 4, 4],
[0, 5, 5],
])
vecs_sorted = indexing.sort_vectors_by_norm(vecs)
npt.assert_allclose(vecs_sorted, expected_output)
def test_sort_vectors_by_norm(self):
vecs = np.array([
[0, 0, 3],
[0, 2, 0],
[1, 0, 0],
[0, 5, 5],
[0, 4, 4],
])
expected_output = np.array([
[1, 0, 0],
[0, 2, 0],
[0, 0, 3],
[0, 4, 4],
[0, 5, 5],
])
vecs_sorted = indexing.sort_vectors_by_norm(vecs)
npt.assert_allclose(vecs_sorted, expected_output)
