IndirectILLReductionQENS.py

from __future__ import (absolute_import, division, print_function)

from mantid.simpleapi import *  # noqa
from mantid.kernel import *  # noqa
from mantid.api import *  # noqa
from mantid import mtd
import numpy


class IndirectILLReductionQENS(PythonAlgorithm):

    _sample_files = None
    _alignment_files = None
    _background_files = None
    _calibration_files = None
    _sum_all_runs = None
    _unmirror_option = None
    _back_scaling = None
    _criteria = None
    _progress = None
    _red_ws = None
    _common_args = {}
    _peak_range = []
    _runs = None

    def category(self):
        return "Workflow\\MIDAS;Workflow\\Inelastic;Inelastic\\Indirect;Inelastic\\Reduction"

    def summary(self):
        return 'Performs quasi-elastic neutron scattering (QENS) multiple file reduction ' \
               'for ILL indirect geometry data, instrument IN16B.'

    def name(self):
        return "IndirectILLReductionQENS"

    def PyInit(self):

        self.declareProperty(MultipleFileProperty('Run', extensions=['nxs']),
                             doc='Run number(s) of sample run(s).')

        self.declareProperty(MultipleFileProperty('BackgroundRun',
                                                  action=FileAction.OptionalLoad,
                                                  extensions=['nxs']),
                             doc='Run number(s) of background (empty can) run(s).')

        self.declareProperty(MultipleFileProperty('CalibrationRun',
                                                  action=FileAction.OptionalLoad,
                                                  extensions=['nxs']),
                             doc='Run number(s) of vanadium calibration run(s).')

        self.declareProperty(MultipleFileProperty('AlignmentRun',
                                                  action=FileAction.OptionalLoad,
                                                  extensions=['nxs']),
                             doc='Run number(s) of vanadium run(s) used for '
                                 'peak alignment for UnmirrorOption=[5, 7]')

        self.declareProperty(name='SumRuns',
                             defaultValue=False,
                             doc='Whether to sum all the input runs.')

        self.declareProperty(name='CropDeadMonitorChannels', defaultValue=False,
                             doc='Whether or not to exclude the first and last few channels '
                                 'with 0 monitor count in the energy transfer formula.')

        self.declareProperty(name='UnmirrorOption', defaultValue=6,
                             validator=IntBoundedValidator(lower=0, upper=7),
                             doc='Unmirroring options : \n'
                                 '0 no unmirroring\n'
                                 '1 sum of left and right\n'
                                 '2 left\n'
                                 '3 right\n'
                                 '4 shift right according to left and sum\n'
                                 '5 like 4, but use alignment run for peak positions\n'
                                 '6 center both left and right at zero and sum\n'
                                 '7 like 6, but use alignment run for peak positions')

        self.declareProperty(name='BackgroundScalingFactor', defaultValue=1.,
                             validator=FloatBoundedValidator(lower=0),
                             doc='Scaling factor for background subtraction')

        self.declareProperty(name='CalibrationPeakRange', defaultValue=[-0.003,0.003],
                             validator=FloatArrayMandatoryValidator(),
                             doc='Peak range for integration over calibration file peak (in mev)')

        self.declareProperty(FileProperty('MapFile', '',
                                          action=FileAction.OptionalLoad,
                                          extensions=['map','xml']),
                             doc='Filename of the detector grouping map file to use. \n'
                                 'By default all the pixels will be summed per each tube. \n'
                                 'Use .map or .xml file (see GroupDetectors documentation) '
                                 'only if different range is needed for each tube.')

        self.declareProperty(name='ManualPSDIntegrationRange',defaultValue=[1,128],
                             doc='Integration range of vertical pixels in each PSD tube. \n'
                                 'By default all the pixels will be summed per each tube. \n'
                                 'Use this option if the same range (other than default) '
                                 'is needed for all the tubes.')

        self.declareProperty(name='Analyser',
                             defaultValue='silicon',
                             validator=StringListValidator(['silicon']),
                             doc='Analyser crystal.')

        self.declareProperty(name='Reflection',
                             defaultValue='111',
                             validator=StringListValidator(['111', '311']),
                             doc='Analyser reflection.')

        self.declareProperty(WorkspaceGroupProperty('OutputWorkspace', '',
                                                    direction=Direction.Output),
                             doc='Group name for the reduced workspace(s).')

    def validateInputs(self):

        issues = dict()

        uo = self.getProperty('UnmirrorOption').value

        if (uo == 5 or uo == 7) and not self.getPropertyValue('AlignmentRun'):
            issues['AlignmentRun'] = 'Given UnmirrorOption requires alignment run to be set'

        if self.getPropertyValue('CalibrationRun'):
            range = self.getProperty('CalibrationPeakRange').value
            if len(range) != 2:
                issues['CalibrationPeakRange'] = 'Please provide valid calibration range ' \
                                                 '(comma separated 2 energy values).'
            elif range[0] >= range[1]:
                issues['CalibrationPeakRange'] = 'Please provide valid calibration range. ' \
                                                 'Start energy is bigger than end energy.'

        return issues

    def setUp(self):

        self._sample_file = self.getPropertyValue('Run')
        self._alignment_file = self.getPropertyValue('AlignmentRun').replace(',', '+') # automatic summing
        self._background_file = self.getPropertyValue('BackgroundRun').replace(',', '+') # automatic summing
        self._calibration_file = self.getPropertyValue('CalibrationRun').replace(',', '+') # automatic summing
        self._sum_all_runs = self.getProperty('SumRuns').value
        self._unmirror_option = self.getProperty('UnmirrorOption').value
        self._back_scaling = self.getProperty('BackgroundScalingFactor').value
        self._peak_range = self.getProperty('CalibrationPeakRange').value

        self._red_ws = self.getPropertyValue('OutputWorkspace') + '_red'

        # arguments to pass to IndirectILLEnergyTransfer
        self._common_args['MapFile'] = self.getPropertyValue('MapFile')
        self._common_args['Analyser'] = self.getPropertyValue('Analyser')
        self._common_args['Reflection'] = self.getPropertyValue('Reflection')
        self._common_args['ManualPSDIntegrationRange'] = self.getProperty('ManualPSDIntegrationRange').value
        self._common_args['CropDeadMonitorChannels'] = self.getProperty('CropDeadMonitorChannels').value

        if self._sum_all_runs is True:
            self.log().notice('All the sample runs will be summed')
            self._sample_file = self._sample_file.replace(',', '+')

        # Nexus metadata criteria for QENS type of data
        self._criteria = '$/entry0/instrument/Doppler/maximum_delta_energy$ != 0. and ' \
                         '$/entry0/instrument/Doppler/velocity_profile$ == 0'

        # empty list to store all final workspaces to group
        self._ws_list = []

    def _mask(self, ws, xstart, xend):
        """
        Masks the first and last bins
        @param   ws           :: input workspace name
        @param   xstart       :: MaskBins between x[0] and x[xstart]
        @param   xend         :: MaskBins between x[xend] and x[-1]
        """
        x_values = mtd[ws].readX(0)

        if xstart > 0:
            self.log().debug('Mask bins smaller than {0}'.format(xstart))
            MaskBins(InputWorkspace=ws, OutputWorkspace=ws, XMin=x_values[0], XMax=x_values[xstart])

        if xend < len(x_values) - 1:
            self.log().debug('Mask bins larger than {0}'.format(xend))
            MaskBins(InputWorkspace=ws, OutputWorkspace=ws, XMin=x_values[xend + 1], XMax=x_values[-1])

    def _filter_files(self, files, label):
        '''
        Filters the given list of files according to nexus criteria
        @param  files :: list of input files (i.e. , and + separated string)
        @param  label :: label of error message if nothing left after filtering
        @throws RuntimeError :: when nothing left after filtering
        @return :: the list of input files that passsed the criteria
        '''

        files = SelectNexusFilesByMetadata(files, self._criteria)

        if not files:
            raise RuntimeError('None of the {0} runs are of QENS type.'
                               'Check the files or reduction type.'.format(label))
        else:
            self.log().information('Filtered {0} runs are: {0} \\n'.format(label,files.replace(',','\\n')))

        return files

    def _filter_all_input_files(self):
        '''
        Filters all the lists of input files needed for the reduction.
        '''

        self._sample_file = self._filter_files(self._sample_file,'sample')

        if self._background_file:
            self._background_file = self._filter_files(self._background_file, 'background')

        if self._calibration_file:
            self._calibration_file = self._filter_files(self._calibration_file, 'calibration')

        if self._alignment_file:
            self._alignment_file = self._filter_files(self._alignment_file, 'alignment')

    def _warn_negative_integral(self, ws, message):
        '''
        Raises an error if an integral of the given workspace is <= 0
        @param ws :: input workspace name
        @param message :: message suffix for the error
        @throws RuntimeError :: on non-positive integral found
        '''

        tmp_int = '__tmp_int'+ws
        Integration(InputWorkspace=ws,OutputWorkspace=tmp_int)

        for item in mtd[tmp_int]:
            for index in range(item.getNumberHistograms()):
                if item.readY(index)[0] <= 0:
                    raise RuntimeError('Negative or 0 integral in spectrum #{0} {1}'.format(index,message))

        DeleteWorkspace(tmp_int)

    def PyExec(self):

        self.setUp()

        self._filter_all_input_files()

        if self._background_file:
            background = '__background_'+self._red_ws
            IndirectILLEnergyTransfer(Run = self._background_file, OutputWorkspace = background, **self._common_args)
            Scale(InputWorkspace=background ,Factor=self._back_scaling,OutputWorkspace=background)

        if self._calibration_file:
            calibration = '__calibration_'+self._red_ws
            IndirectILLEnergyTransfer(Run = self._calibration_file, OutputWorkspace = calibration, **self._common_args)
            MatchPeaks(InputWorkspace=calibration,OutputWorkspace=calibration,MaskBins=True)
            Integration(InputWorkspace=calibration,RangeLower=self._peak_range[0],RangeUpper=self._peak_range[1],
                        OutputWorkspace=calibration)
            self._warn_negative_integral(calibration,'in calibration run.')

        if self._unmirror_option == 5 or self._unmirror_option == 7:
            alignment = '__alignment_'+self._red_ws
            IndirectILLEnergyTransfer(Run = self._alignment_file, OutputWorkspace = alignment, **self._common_args)

        runs = self._sample_file.split(',')

        self._progress = Progress(self, start=0.0, end=1.0, nreports=len(runs))

        for run in runs:
            self._reduce_run(run)

        if self._background_file:
            DeleteWorkspace(background)

        if self._calibration_file:
            DeleteWorkspace(calibration)

        if self._unmirror_option == 5 or self._unmirror_option == 7:
            DeleteWorkspace(alignment)

        GroupWorkspaces(InputWorkspaces=self._ws_list,OutputWorkspace=self._red_ws)

        # unhide the final workspaces, i.e. remove __ prefix
        for ws in mtd[self._red_ws]:
            RenameWorkspace(InputWorkspace=ws,OutputWorkspace=ws.getName()[2:])

        self.setProperty('OutputWorkspace',self._red_ws)

    def _reduce_run(self,run):
        '''
        Reduces the given (single or summed multiple) run
        @param run :: run path
        '''

        runs_list = run.split('+')

        runnumber = os.path.basename(runs_list[0]).split('.')[0]

        self._progress.report("Reducing run #" + runnumber)

        ws = '__' + runnumber

        if (len(runs_list) > 1):
            ws += '_multiple'

        ws += '_' + self._red_ws

        back_ws = '__background_'+self._red_ws

        calib_ws = '__calibration_'+self._red_ws

        IndirectILLEnergyTransfer(Run = run, OutputWorkspace = ws, **self._common_args)

        wings = mtd[ws].getNumberOfEntries()

        if self._background_file:
            if wings == mtd[back_ws].getNumberOfEntries():
                Minus(LHSWorkspace=ws, RHSWorkspace=back_ws, OutputWorkspace=ws)
                self._warn_negative_integral(ws,'after background subtraction.')
            else:
                raise RuntimeError('Inconsistent mirror sense in background run. Unable to perform subtraction.')

        if self._calibration_file:
            if wings == mtd[calib_ws].getNumberOfEntries():
                Divide(LHSWorkspace=ws, RHSWorkspace=calib_ws, OutputWorkspace=ws)
                self._scale_calibration(ws, calib_ws)
            else:
                raise RuntimeError('Inconsistent mirror sense in calibration run. Unable to perform calibration.')

        self._perform_unmirror(ws,runnumber)

        # register to reduced runs list
        self._ws_list.append(ws)

    def _scale_calibration(self, ws, calib_ws):
        '''
        Scales the wings of calibrated sample ws with the maximum
        of the integrated intensities in each wing of calib ws
        @param ws       :: calibrated sample workspace
        @param calib_ws :: calibration workspace
        '''

        # number of wings are checked to be the same in ws and calib_ws here already

        for wing in range(mtd[ws].getNumberOfEntries()):
            sample = mtd[ws].getItem(wing).getName()
            integral = mtd[calib_ws].getItem(wing).getName()
            scale = numpy.max(mtd[integral].extractY()[:,0])
            self.log().information("Wing {0} will be scaled up with {1} after calibration"
                                   .format(wing,scale))
            Scale(InputWorkspace=sample,Factor=scale,OutputWorkspace=sample,Operation='Multiply')

    def _perform_unmirror(self, ws, run):
        '''
        Performs unmirroring, i.e. summing of left and right wings
        for two-wing data or centering the one wing data
        @param ws  :: workspace
        @param run :: runnumber
        '''

        outname = ws + '_tmp'

        wings = mtd[ws].getNumberOfEntries()

        self.log().information('Unmirroring workspace {0} with option {1}'
                               .format(ws,self._unmirror_option))

        alignment = '__alignment_'+self._red_ws

        # make sure the sample and alignment runs have the same mirror sense for unmirror 5,7
        if self._unmirror_option == 5 or self._unmirror_option == 7:
            if wings != mtd[alignment].getNumberOfEntries():
                raise RuntimeError('Inconsistent mirror sense in alignment run. Unable to perform unmirror.')

        if wings == 1:   # one wing

            name = mtd[ws].getItem(0).getName()

            if self._unmirror_option < 6:  # do unmirror 0, i.e. nothing
                CloneWorkspace(InputWorkspace = name, OutputWorkspace = outname)
            elif self._unmirror_option == 6:
                MatchPeaks(InputWorkspace = name, OutputWorkspace = outname, MaskBins = True)
            elif self._unmirror_option == 7:
                MatchPeaks(InputWorkspace = name, InputWorkspace2 = mtd[alignment].getItem(0).getName(),
                           MatchInput2ToCenter = True, OutputWorkspace = outname, MaskBins = True)

        elif wings == 2:  # two wing

            left = mtd[ws].getItem(0).getName()
            right = mtd[ws].getItem(1).getName()

            mask_min = 0
            mask_max = mtd[left].blocksize()

            if self._unmirror_option == 0:
                left_out = '__'+run+'_'+self._red_ws+'_left'
                right_out = '__'+run+'_'+self._red_ws+'_right'
                CloneWorkspace(InputWorkspace=left, OutputWorkspace=left_out)
                CloneWorkspace(InputWorkspace=right, OutputWorkspace=right_out)
                GroupWorkspaces(InputWorkspaces=[left_out,right_out],OutputWorkspace=outname)
            elif self._unmirror_option == 1:
                Plus(LHSWorkspace=left, RHSWorkspace=right, OutputWorkspace=outname)
                Scale(InputWorkspace=outname, OutputWorkspace=outname, Factor=0.5)
            elif self._unmirror_option == 2:
                CloneWorkspace(InputWorkspace=left, OutputWorkspace=outname)
            elif self._unmirror_option == 3:
                CloneWorkspace(InputWorkspace=right, OutputWorkspace=outname)
            elif self._unmirror_option == 4:
                bin_range_table = '__um4_'+right
                MatchPeaks(InputWorkspace=right, InputWorkspace2=left, OutputWorkspace=right,
                           MaskBins = True, BinRangeTable = bin_range_table)
                mask_min = mtd[bin_range_table].row(0)['MinBin']
                mask_max = mtd[bin_range_table].row(0)['MaxBin']
                DeleteWorkspace(bin_range_table)
            elif self._unmirror_option == 5:
                bin_range_table = '__um5_' + right
                MatchPeaks(InputWorkspace=right, InputWorkspace2=mtd[alignment].getItem(0).getName(),
                           InputWorkspace3=mtd[alignment].getItem(1).getName(), OutputWorkspace=right,
                           MaskBins = True, BinRangeTable = bin_range_table)
                mask_min = mtd[bin_range_table].row(0)['MinBin']
                mask_max = mtd[bin_range_table].row(0)['MaxBin']
                DeleteWorkspace(bin_range_table)
            elif self._unmirror_option == 6:
                bin_range_table_left = '__um6_' + left
                bin_range_table_right = '__um6_' + right
                MatchPeaks(InputWorkspace=left, OutputWorkspace=left, MaskBins = True,
                           BinRangeTable = bin_range_table_left)
                MatchPeaks(InputWorkspace=right, OutputWorkspace=right, MaskBins = True,
                           BinRangeTable=bin_range_table_right)
                mask_min = max(mtd[bin_range_table_left].row(0)['MinBin'],mtd[bin_range_table_right].row(0)['MinBin'])
                mask_max = min(mtd[bin_range_table_left].row(0)['MaxBin'],mtd[bin_range_table_right].row(0)['MaxBin'])
                DeleteWorkspace(bin_range_table_left)
                DeleteWorkspace(bin_range_table_right)
            elif self._unmirror_option == 7:
                bin_range_table_left = '__um7_' + left
                bin_range_table_right = '__um7_' + right
                MatchPeaks(InputWorkspace=left, InputWorkspace2=mtd[alignment].getItem(0).getName(),
                           OutputWorkspace=left,MatchInput2ToCenter=True,
                           MaskBins = True, BinRangeTable=bin_range_table_left)
                MatchPeaks(InputWorkspace=right, InputWorkspace2=mtd[alignment].getItem(1).getName(),
                           OutputWorkspace=right, MatchInput2ToCenter=True,
                           MaskBins = True, BinRangeTable=bin_range_table_right)
                mask_min = max(mtd[bin_range_table_left].row(0)['MinBin'], mtd[bin_range_table_right].row(0)['MinBin'])
                mask_max = min(mtd[bin_range_table_left].row(0)['MaxBin'], mtd[bin_range_table_right].row(0)['MaxBin'])
                DeleteWorkspace(bin_range_table_left)
                DeleteWorkspace(bin_range_table_right)

            if self._unmirror_option > 3:
                Plus(LHSWorkspace=left, RHSWorkspace=right, OutputWorkspace=outname)
                Scale(InputWorkspace=outname, OutputWorkspace=outname, Factor=0.5)
                self._mask(outname, mask_min, mask_max)

        DeleteWorkspace(ws)
        RenameWorkspace(InputWorkspace=outname,OutputWorkspace=ws)

# Register algorithm with Mantid
AlgorithmFactory.subscribe(IndirectILLReductionQENS)