Correct CalculatePlaczekSelfScattering and POLARIS script

CalculatePlaczekSelfScattering outputs non-distrebution workspace
correction to CalculatePlaczekSelfScattering algorithm and POLARIS
_generate_grouped_ts_pdf function makes self scattering correction
to the correct order of magnetude after processing

Framework/Algorithms/src/CalculatePlaczekSelfScattering.cpp

@@ -110,7 +110,7 @@ void CalculatePlaczekSelfScattering::exec() {
   std::vector<double> eps1;
   const double lambdaD = 1.44;
   for (size_t i = 0; i < xLambda.size() - 1; i++) {
-    double xTerm = (xLambda[i] + dx) / lambdaD;
+    double xTerm = -(xLambda[i] + dx) / lambdaD;
     eps1.push_back(xTerm * exp(xTerm) / (1.0 - exp(xTerm)));
   }
   /* Placzek
@@ -160,7 +160,7 @@ void CalculatePlaczekSelfScattering::exec() {
             2.0 * (term1 + term2 - 3) * sinThetaBy2 * sinThetaBy2 *
             summationTerm;
         x[xIndex] = wavelength.singleToTOF(xLambda[xIndex]);
-        y[xIndex] = inelasticPlaczekSelfCorrection;
+        y[xIndex] = 1 + inelasticPlaczekSelfCorrection;
       }
       x.back() = wavelength.singleToTOF(xLambda.back());
     } else {
@@ -173,6 +173,7 @@ void CalculatePlaczekSelfScattering::exec() {
   }
   auto incidentUnit = inWS->getAxis(0)->unit();
   outputWS->getAxis(0)->unit() = incidentUnit;
+  outputWS->setDistribution(FALSE);
   setProperty("OutputWorkspace", outputWS);
 }
 

scripts/Diffraction/isis_powder/polaris_routines/polaris_algs.py

@@ -148,16 +148,14 @@ def _generate_grouped_ts_pdf(focused_ws, q_lims):
     width_x = (max_x-min_x)/x_data.size
     fit_spectra = mantid.FitIncidentSpectrum(InputWorkspace=monitor,
                                              BinningForCalc=[min_x, 1*width_x, max_x],
-                                             BinningForFit=[min_x, 1*width_x, max_x],
+                                             BinningForFit=[min_x, 10*width_x, max_x],
                                              FitSpectrumWith="CubicSpline")
     placzek = mantid.CalculatePlaczekSelfScattering(InputWorkspace=raw_ws, IncidentSpecta=fit_spectra)
-    mantid.ConvertFromDistribution(Workspace=placzek)
     cal_workspace = mantid.LoadCalFile(InputWorkspace=placzek,
                                        CalFileName=cal_file_name,
                                        Workspacename='cal_workspace',
                                        MakeOffsetsWorkspace=False,
                                        MakeMaskWorkspace=False)
-    placzek = mantid.AlignDetectors(InputWorkspace=placzek, CalibrationFile=cal_file_name)
     placzek = mantid.DiffractionFocussing(InputWorkspace=placzek, GroupingFilename=cal_file_name)
     n_pixel = np.zeros(placzek.getNumberHistograms())
     for i in range(cal_workspace.getNumberHistograms()):
@@ -166,8 +164,10 @@ def _generate_grouped_ts_pdf(focused_ws, q_lims):
             n_pixel[int(grouping[0]-1)] += 1
     correction_ws = mantid.CreateWorkspace(DataY=n_pixel, DataX=[0, 1], NSpec=placzek.getNumberHistograms())
     placzek = mantid.Divide(LHSWorkspace=placzek, RHSWorkspace=correction_ws)
+    mantid.ConvertToDistribution(Workspace=placzek)
     placzek = mantid.ConvertUnits(InputWorkspace=placzek, Target="MomentumTransfer", EMode='Elastic')
     placzek = mantid.RebinToWorkspace(WorkspaceToRebin=placzek, WorkspaceToMatch=focused_data_combined)
+    mantid.ConvertFromDistribution(Workspace=placzek)
     mantid.Subtract(LHSWorkspace=focused_data_combined,
                     RHSWorkspace=placzek,
                     OutputWorkspace=focused_data_combined)
@@ -198,10 +198,10 @@ def _generate_grouped_ts_pdf(focused_ws, q_lims):
         q_min[i] = pdf_x_array[tmp2]
         q_max[i] = pdf_x_array[np.amax(np.where(pdf_x_array <= q_max[i]))]
         bin_width = min(pdf_x_array[1] - pdf_x_array[0], bin_width)
-    focused_data_combined = mantid.CropWorkspaceRagged(InputWorkspace=focused_data_combined, XMin=q_min, XMax=q_max)
     mantid.MatchSpectra(InputWorkspace=focused_data_combined,
                         OutputWorkspace=focused_data_combined,
                         ReferenceSpectrum=1)
+    focused_data_combined = mantid.CropWorkspaceRagged(InputWorkspace=focused_data_combined, XMin=q_min, XMax=q_max)
     focused_data_combined = mantid.Rebin(InputWorkspace=focused_data_combined,
                                          Params=[min(q_min), bin_width, max(q_max)])
     focused_data_combined = mantid.SumSpectra(InputWorkspace=focused_data_combined,