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LinkedUBs.py
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LinkedUBs.py
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# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2019 ISIS Rutherford Appleton Laboratory UKRI,
# NScD Oak Ridge National Laboratory, European Spallation Source,
# Institut Laue - Langevin & CSNS, Institute of High Energy Physics, CAS
# SPDX - License - Identifier: GPL - 3.0 +
from mantid.api import (DataProcessorAlgorithm, mtd, AlgorithmFactory,
WorkspaceProperty,
PropertyMode, ITableWorkspaceProperty)
from mantid.simpleapi import (CalculateUMatrix, PredictPeaks, FilterPeaks,
DeleteWorkspace, CreatePeaksWorkspace)
from mantid.kernel import (Direction,
FloatBoundedValidator, IntBoundedValidator,
StringMandatoryValidator, StringListValidator, V3D)
import numpy as np
class LinkedUBs(DataProcessorAlgorithm):
_qtol = None
_qdecrement = None
_dtol = None
_num_peaks = None
_peak_increment = None
_iterations = None
_a = None
_b = None
_c = None
_alpha = None
_beta = None
_gamma = None
_wavelength_min = None
_wavelength_max = None
_min_dspacing = None
_max_dspacing = None
_reflection_condition = None
_workspace = None
_observed_peaks = None
_predicted_peaks = None
_linked_peaks = None
_linked_predicted_peaks = None
_delete_ws = None
def category(self):
return "Diffraction\\Reduction"
def seeAlso(self):
return ["SetGoniometer", "CalculateUMatrix"]
def name(self):
return "LinkedUBs"
def summary(self):
return "Links the indexing of a given run to the UB of a reference\
run. PredictedPeaks should be calculated via goniometer rotation of\
the reference UB. Use of this algorithm will result in a seperate\
(linked) UB for each goniometer setting considered."
def PyInit(self):
# Refinement parameters
self.declareProperty(
name="QTolerance",
defaultValue=0.5,
direction=Direction.Input,
validator=FloatBoundedValidator(
lower=0.0),
doc="Radius of isotropic q envelope to search within.")
self.declareProperty(
name="QDecrement",
defaultValue=0.95,
validator=FloatBoundedValidator(
lower=0.0,
upper=1.0),
direction=Direction.Input,
doc="Multiplicative factor by which to decrement q envelope\
on each iteration.")
self.declareProperty(
name="DTolerance",
defaultValue=0.01,
direction=Direction.Input,
validator=FloatBoundedValidator(
lower=0.0),
doc="Observed peak is linked if\
abs(dSpacing) < dPredicted + dTolerance.")
self.declareProperty(
name="NumPeaks",
defaultValue=15,
direction=Direction.Input,
validator=IntBoundedValidator(
lower=0),
doc="Number of peaks, ordered from highest to lowest \
dSpacing to consider.")
self.declareProperty(
name="PeakIncrement",
defaultValue=10,
validator=IntBoundedValidator(
lower=0),
direction=Direction.Input,
doc="Number of peaks to add to numPeaks on each iteration.")
self.declareProperty(name="Iterations",
defaultValue=10,
validator=IntBoundedValidator(lower=1),
direction=Direction.Input,
doc="Number of cycles of refinement.")
# lattice
self.declareProperty(name="a",
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter a.")
self.declareProperty(name="b",
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter b.")
self.declareProperty(name="c",
defaultValue=1.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter c.")
self.declareProperty(name="alpha",
defaultValue=90.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter alpha.")
self.declareProperty(name="beta",
defaultValue=90.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter beta.")
self.declareProperty(name="gamma",
defaultValue=90.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Lattice parameter gamma.")
# linked predicted peaks parameters
self.declareProperty(
name="MinWavelength",
defaultValue=0.8,
validator=FloatBoundedValidator(
lower=0.0),
direction=Direction.Input,
doc="Minimum wavelength for LinkedPredictedPeaks.")
self.declareProperty(
name="MaxWavelength",
defaultValue=9.3,
validator=FloatBoundedValidator(
lower=0.0),
direction=Direction.Input,
doc="Maximum wavelength for LinkedPredictedPeaks.")
self.declareProperty(name="MinDSpacing",
defaultValue=0.6,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Minimum dSpacing for LinkedPredictedPeaks.")
self.declareProperty(name="MaxDSpacing",
defaultValue=20.0,
validator=FloatBoundedValidator(lower=0.0),
direction=Direction.Input,
doc="Maximum dSpacing for LinkedPredictedPeaks.")
self.declareProperty(name="ReflectionCondition",
defaultValue="Primitive",
direction=Direction.Input,
validator=StringListValidator(
["Primitive",
"C-face centred",
"A-face centred",
"B-face centred",
"Body centred",
"All-face centred",
"Rhombohedrally centred, obverse",
"Rhombohedrally centred, reverse",
"Hexagonally centred, reverse"]),
doc="Reflection condition \
for LinkedPredictedPeaks.")
# input workspaces
self.declareProperty(
WorkspaceProperty(
name="Workspace",
defaultValue="",
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc="Instrument workspace on which observed peaks are defined.")
self.declareProperty(
ITableWorkspaceProperty(
name="ObservedPeaks",
defaultValue="",
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc="FindPeaks table to which PredictedPeaks are compared.")
self.declareProperty(
ITableWorkspaceProperty(
name="PredictedPeaks",
defaultValue="",
optional=PropertyMode.Mandatory,
direction=Direction.Input),
doc="PredictedPeaks table to which ObservedPeaks are compared.")
# output workspaces
self.declareProperty(
ITableWorkspaceProperty(
name="LinkedPeaks",
defaultValue="",
validator=StringMandatoryValidator(),
direction=Direction.Output),
doc="Linked peaks: UB matrix consistent with that of \
PredictedPeaks.")
self.declareProperty(
ITableWorkspaceProperty(
name="LinkedPredictedPeaks",
defaultValue="",
validator=StringMandatoryValidator(),
direction=Direction.Output),
doc="LinkedPredictedPeaks: UB matrix consistent with \
PredictedPeaks.")
self.declareProperty(
"DeleteWorkspace",
defaultValue=False,
direction=Direction.Input,
doc="Delete workspace after execution for memory management.")
# groupings
self.setPropertyGroup("QTolerance", "Refinement parameters")
self.setPropertyGroup("QDecrement", "Refinement parameters")
self.setPropertyGroup("DTolerance", "Refinement parameters")
self.setPropertyGroup("NumPeaks", "Refinement parameters")
self.setPropertyGroup("PeakIncrement", "Refinement parameters")
self.setPropertyGroup("Iterations", "Refinement parameters")
self.setPropertyGroup("a", "Lattice")
self.setPropertyGroup("b", "Lattice")
self.setPropertyGroup("c", "Lattice")
self.setPropertyGroup("alpha", "Lattice")
self.setPropertyGroup("beta", "Lattice")
self.setPropertyGroup("gamma", "Lattice")
self.setPropertyGroup("MinWavelength", "PredictPeaksParameters")
self.setPropertyGroup("MaxWavelength", "PredictPeaksParameters")
self.setPropertyGroup("MinDSpacing", "PredictPeaksParameters")
self.setPropertyGroup("MaxDSpacing", "PredictPeaksParameters")
self.setPropertyGroup("ReflectionCondition", "PredictPeaksParameters")
self.setPropertyGroup("Workspace", "Input")
self.setPropertyGroup("ObservedPeaks", "Input")
self.setPropertyGroup("PredictedPeaks", "Input")
self.setPropertyGroup("LinkedPeaks", "Output")
self.setPropertyGroup("LinkedPredictedPeaks", "Output")
self.setPropertyGroup("DeleteWorkspace", "Output")
def validateInputs(self):
self._get_properties()
issues = dict()
return issues
def _get_properties(self):
self._qtol = self.getProperty("QTolerance").value
self._qdecrement = self.getProperty("QDecrement").value
self._dtol = self.getProperty("DTolerance").value
self._num_peaks = self.getProperty("NumPeaks").value
self._peak_increment = self.getProperty("PeakIncrement").value
self._iterations = self.getProperty("Iterations").value
self._a = self.getProperty("a").value
self._b = self.getProperty("b").value
self._c = self.getProperty("c").value
self._alpha = self.getProperty("alpha").value
self._beta = self.getProperty("beta").value
self._gamma = self.getProperty("gamma").value
self._wavelength_min = self.getProperty("MinWavelength").value
self._wavelength_max = self.getProperty("MaxWavelength").value
self._min_dspacing = self.getProperty("MinDSpacing").value
self._max_dspacing = self.getProperty("MaxDSpacing").value
self._reflection_condition = self.getProperty(
"ReflectionCondition").value
self._workspace = self.getProperty("Workspace").value
self._observed_peaks = self.getProperty("ObservedPeaks").value
self._predicted_peaks = self.getProperty("PredictedPeaks").value
self._linked_peaks = self.getPropertyValue("LinkedPeaks")
self._linked_predicted_peaks = self.getPropertyValue(
"LinkedPredictedPeaks")
self._delete_ws = self.getProperty("DeleteWorkspace").value
def PyExec(self):
# create peaks workspace to store linked peaks
linked_peaks = CreatePeaksWorkspace(
InstrumentWorkspace=self._workspace,
NumberOfPeaks=0,
StoreInADS=False)
# create peaks table to store linked predicted peaks
linked_peaks_predicted = CreatePeaksWorkspace(
InstrumentWorkspace=self._workspace,
NumberOfPeaks=0,
StoreInADS=False)
for m in range(0, self._iterations):
if m == 0:
predictor = self._predicted_peaks
if m > 0:
predictor = linked_peaks_predicted
qtol_var = self._qtol * self._qdecrement**m
num_peaks_var = self._num_peaks + self._peak_increment * m
# add q_lab and dpsacing values of found peaks to a list
qlabs_observed = np.array(self._observed_peaks.column("QLab"))
dspacings_observed = np.array(self._observed_peaks.column("DSpacing"))
# sort the predicted peaks from largest to smallest dspacing
qlabs_predicted = np.array(predictor.column("QLab"))
dspacings_predicted = np.array(predictor.column("DSpacing"))
# get the indexing list that sorts dspacing from largest to
# smallest
hkls = np.array([[p.getH(), p.getK(), p.getL()] for p in predictor])
idx = dspacings_predicted.argsort()[::-1]
HKL_predicted = hkls[idx, :]
# sort q, d and h, k, l by this indexing
qlabs_predicted = qlabs_predicted[idx]
dspacings_predicted = dspacings_predicted[idx]
q_ordered = qlabs_predicted[:num_peaks_var]
d_ordered = dspacings_predicted[:num_peaks_var]
HKL_ordered = HKL_predicted[:num_peaks_var]
# loop through the ordered find peaks, compare q and d to each
# predicted peak if the q and d values of a found peak match a
# predicted peak within tolerance, the found peak inherits
# the HKL of the predicted peak
for i in range(len(qlabs_observed)):
qx_obs, qy_obs, qz_obs = qlabs_observed[i]
q_obs = V3D(qx_obs, qy_obs, qz_obs)
p_obs = linked_peaks.createPeak(q_obs)
d_obs = dspacings_observed[i]
for j in range(len(q_ordered)):
qx_pred, qy_pred, qz_pred = q_ordered[j]
d_pred = d_ordered[j]
if (qx_pred - qtol_var <= qx_obs <= qx_pred
+ qtol_var and qy_pred - qtol_var <= qy_obs <= qy_pred
+ qtol_var and qz_pred - qtol_var <= qz_obs <= qz_pred
+ qtol_var and d_pred - self._dtol <= d_obs <= d_pred + self._dtol):
h, k, l = HKL_ordered[j]
p_obs.setHKL(h, k, l)
linked_peaks.addPeak(p_obs)
# Clean up peaks where H == K == L == 0
linked_peaks = FilterPeaks(linked_peaks,
FilterVariable="h^2+k^2+l^2",
Operator="!=",
FilterValue="0")
# force UB on linked_peaks using known lattice parameters
CalculateUMatrix(PeaksWorkspace=linked_peaks,
a=self._a,
b=self._b,
c=self._c,
alpha=self._alpha,
beta=self._beta,
gamma=self._gamma,
StoreInADS=False)
# new linked predicted peaks
linked_peaks_predicted = PredictPeaks(
InputWorkspace=linked_peaks,
WavelengthMin=self._wavelength_min,
WavelengthMax=self._wavelength_max,
MinDSpacing=self._min_dspacing,
MaxDSpacing=self._max_dspacing,
ReflectionCondition=self._reflection_condition,
StoreInADS=False)
# clean up
self.setProperty("LinkedPeaks", linked_peaks)
self.setProperty("LinkedPredictedPeaks", linked_peaks_predicted)
if mtd.doesExist("linked_peaks"):
DeleteWorkspace(linked_peaks)
if mtd.doesExist("linked_peaks_predicted"):
DeleteWorkspace(linked_peaks_predicted)
if self._delete_ws:
DeleteWorkspace(self._workspace)
# register algorithm with mantid
AlgorithmFactory.subscribe(LinkedUBs)