def PyExec(self):
k = self.getProperty("NumOfQs").value
peak_radius = self.getProperty("PeakRadius").value
background_radii = (self.getProperty("BackgroundInnerRadius").value,
self.getProperty("BackgroundOuterRadius").value)
I_over_sigma = self.getProperty("IOverSigma").value
cluster_threshold = self.getProperty("ClusterThreshold").value
# if user did not specify the number of qs then
# set the k value to None
if k == -1:
k = None
md = self.getProperty("MDWorkspace").value
nuclear = self.getProperty("NuclearPeaks").value
sats = self.getProperty("SatellitePeaks").value
nuclear_hkls = indexing.get_hkls(nuclear)
sats_hkls = indexing.get_hkls(sats)
qs = indexing.find_q_vectors(nuclear_hkls, sats_hkls)
clusters, k = indexing.cluster_qs(qs, threshold=cluster_threshold, k=k)
qs = indexing.average_clusters(qs, clusters)
predicted_satellites = self.create_fractional_peaks_workspace(
qs, nuclear)
centroid_satellites = CentroidPeaksMD(
InputWorkspace=md,
PeaksWorkspace=predicted_satellites,
PeakRadius=peak_radius,
StoreInADS=False)
satellites_int_spherical = IntegratePeaksMD(
InputWorkspace=md,
PeaksWorkspace=centroid_satellites,
PeakRadius=peak_radius,
BackgroundInnerRadius=background_radii[0],
BackgroundOuterRadius=background_radii[1],
IntegrateIfOnEdge=True,
StoreInADS=False)
satellites_int_spherical = FilterPeaks(satellites_int_spherical,
FilterVariable="Intensity",
FilterValue=0,
Operator=">",
StoreInADS=False)
satellites_int_spherical = FilterPeaks(satellites_int_spherical,
FilterVariable="Signal/Noise",
FilterValue=I_over_sigma,
Operator=">",
StoreInADS=False)
self.log().notice("Q vectors are: \n{}".format(qs))
self.setProperty("OutputWorkspace", satellites_int_spherical)
def test_cluster_qs_with_auto_k(self):
qs = np.array([
[0, .1, .1],
[0, .1, .1],
[0, .0, .1],
[0, .0, .1],
[0, .1, .1],
])
qs += np.random.random(qs.shape) * 0.01
clusters, k = indexing.cluster_qs(qs, threshold=0.01)
self.assertEqual(k, 2)
npt.assert_array_equal(clusters, np.array([2, 2, 1, 1, 2]))
def test_cluster_qs_with_auto_k(self):
qs = np.array([
[0, .1, .1],
[0, .1, .1],
[0, .0, .1],
[0, .0, .1],
[0, .1, .1],
])
qs += np.random.random(qs.shape) * 0.01
clusters, k = indexing.cluster_qs(qs, threshold=0.01)
self.assertEqual(k, 2)
npt.assert_array_equal(clusters, np.array([2, 2, 1, 1, 2]))
def test_cluster_qs_with_fixed_k(self):
qs = np.array([
[0, .1, .1],
[0, .1, .1],
[0, .0, .1],
[0, .0, .1],
[0, .1, .1],
])
qs += np.random.random(qs.shape) * 0.01
k = 2
clusters, k = indexing.cluster_qs(qs, k)
self.assertEqual(k, 2)
npt.assert_array_equal(clusters, np.array([0, 0, 1, 1, 0]))
def test_cluster_qs_with_fixed_k(self):
qs = np.array([
[0, .1, .1],
[0, .1, .1],
[0, .0, .1],
[0, .0, .1],
[0, .1, .1],
])
qs += np.random.random(qs.shape) * 0.01
k = 2
clusters, k = indexing.cluster_qs(qs, k)
self.assertEqual(k, 2)
npt.assert_array_equal(clusters, np.array([0, 0, 1, 1, 0]))
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)