OptimizeCrystalPlacementByRun.py

# Mantid Repository : https://github.com/mantidproject/mantid
#
# Copyright © 2018 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 +
#pylint: disable=no-init

from mantid.api import PythonAlgorithm, AlgorithmFactory, ITableWorkspaceProperty
from mantid.kernel import Direction
from mantid.simpleapi import *
from mantid import mtd

# Create an empty table workspace to be populated by a python script.


class OptimizeCrystalPlacementByRun(PythonAlgorithm):

    def summary(self):
        return "Optimizes the sample position for each run in a peaks workspace."

    def category(self):
        return 'Crystal\\Corrections'

    def seeAlso(self):
        return [ "OptimizeCrystalPlacement" ]

    def PyInit(self):
        # Declare properties
        self.declareProperty(ITableWorkspaceProperty("InputWorkspace", "", Direction.Input),
                             "The name of the peaks workspace that will be optimized.")
        self.declareProperty("Tolerance", 0.15, "Tolerance of indexing of peaks.")
        self.declareProperty("OutputWorkspace", "",
                             "The name of the peaks workspace that will be created.")

    def PyExec(self):
        ws = self.getProperty("InputWorkspace").value
        ws_append = self.getProperty("OutputWorkspace").value
        ws_group = 'ws_group'
        tolerance = self.getProperty("Tolerance").value
        if not ws.sample().hasOrientedLattice():
            FindUBUsingIndexedPeaks(PeaksWorkspace=ws,Tolerance=tolerance)
        result=IndexPeaks(PeaksWorkspace=ws,Tolerance=tolerance)
        logger.notice('Initial Number indexed: %s error: %s\n'%(result[0], result[1]))
        stats = StatisticsOfTableWorkspace(InputWorkspace=ws)
        stat_col = stats.column('Statistic')
        minR = int(stats.column('RunNumber')[stat_col.index('Minimum')])
        maxR = int(stats.column('RunNumber')[stat_col.index('Maximum')]) + 1
        AnalysisDataService.remove(stats.name())
        group = []
        for run in range(minR, maxR):
            FilterPeaks(InputWorkspace=ws, OutputWorkspace=str(run), FilterVariable='RunNumber',
                        FilterValue=run, Operator='=')
            run = mtd[str(run)]
            peaks = run.getNumberPeaks()
            if peaks == 0:
                AnalysisDataService.remove(str(run))
            else:
                group.append(str(run))
        GroupWorkspaces(InputWorkspaces=group, OutputWorkspace=ws_group)
        OptimizeCrystalPlacement(PeaksWorkspace=ws_group, ModifiedPeaksWorkspace=ws_group, AdjustSampleOffsets=True,
                                 MaxSamplePositionChangeMeters=0.005,MaxIndexingError=tolerance)
        RenameWorkspace(InputWorkspace=str(minR),OutputWorkspace=ws_append)
        for run in range(minR+1, maxR):
            if AnalysisDataService.doesExist(str(run)):
                CombinePeaksWorkspaces(LHSWorkspace=ws_append, RHSWorkspace=str(run),OutputWorkspace=ws_append)
                logger.notice('Optimized %s sample position: %s\n'%(str(run),mtd[str(run)].getPeak(0).getSamplePos()))
                AnalysisDataService.remove( str(run))
        result=IndexPeaks(PeaksWorkspace=ws_append,Tolerance=tolerance)
        logger.notice('After Optimization Number indexed: %s error: %s\n'%(result[0], result[1]))
        AnalysisDataService.remove(ws_group)
        self.setProperty("OutputWorkspace", ws_append)


# Register algorithm with Mantid
AlgorithmFactory.subscribe(OptimizeCrystalPlacementByRun)