Various for fixes from main (#39257)

* Several Cppcheck fixes

* [pre-commit.ci] pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.11.6 → v0.11.7](astral-sh/ruff-pre-commit@v0.11.6...v0.11.7)

* specify python install to fix linux standalone launch

change pystack message to be warning not error

simplify startup test now that workbench calls launch_mantidworkbench

switch to specifying python install using sys.executable

* fix error on sample log plot resize

add release note

---------

Co-authored-by: James Clarke <james.clarke@stfc.ac.uk>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Jonathan Haigh <jonathan.haigh@stfc.ac.uk>
Co-authored-by: MialLewis <95620982+MialLewis@users.noreply.github.com>

Author: 5 people

.pre-commit-config.yaml

@@ -59,7 +59,7 @@ repos:
           )$
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.11.4
+    rev: v0.11.7
     # ruff must appear before black in the list of hooks
     hooks:
       - id: ruff

Framework/CurveFitting/src/Algorithms/EstimateFitParameters.cpp

@@ -211,7 +211,7 @@ void runCrossEntropy(CostFunctions::CostFuncFitting &costFunction, const std::ve
   // Initialise the normal distribution parameters (mean and sigma for each
   // function parameter).
   std::vector<std::pair<double, double>> distributionParams;
-  for (auto &range : ranges) {
+  for (const auto &range : ranges) {
     auto mean = (range.first + range.second) / 2;
     auto sigma = std::fabs(range.first - range.second) / 2;
     distributionParams.emplace_back(mean, sigma);
@@ -257,7 +257,7 @@ void runCrossEntropy(CostFunctions::CostFuncFitting &costFunction, const std::ve
     EigenVector means(nParams);
     EigenVector variances(nParams);
     for (size_t isam = 0; isam < nSelection; ++isam) {
-      auto &paramSet = sampleSets[isam];
+      const auto &paramSet = sampleSets[isam];
       for (size_t i = 0; i < nParams; ++i) {
         auto p = paramSet.second[i];
         means[i] += p;
@@ -359,7 +359,7 @@ void EstimateFitParameters::execConcrete() {
       func->fix(i);
       continue;
     }
-    auto boundary = dynamic_cast<Constraints::BoundaryConstraint *>(constraint);
+    const auto *boundary = dynamic_cast<Constraints::BoundaryConstraint *>(constraint);
     if (!boundary) {
       throw std::runtime_error("Parameter " + func->parameterName(i) + " must have a boundary constraint. ");
     }
@@ -386,7 +386,7 @@ void EstimateFitParameters::execConcrete() {
 
   if (getPropertyValue("Type") == "Monte Carlo") {
     int nOutput = getProperty("NOutputs");
-    auto outputWorkspaceProp = getPointerToProperty("OutputWorkspace");
+    const auto *outputWorkspaceProp = getPointerToProperty("OutputWorkspace");
     if (outputWorkspaceProp->isDefault() || nOutput <= 0) {
       nOutput = 1;
     }
@@ -405,7 +405,7 @@ void EstimateFitParameters::execConcrete() {
         if (m_function->isActive(i)) {
           TableRow row = table->appendRow();
           row << m_function->parameterName(i);
-          for (auto &j : output) {
+          for (const auto &j : output) {
             row << j[ia];
           }
           ++ia;

Framework/CurveFitting/src/Functions/BivariateNormal.cpp

@@ -87,8 +87,8 @@ void BivariateNormal::function1D(double *out, const double *xValues, const size_
       out[i] = 0.0;
     return;
   }
-  double coefNorm, expCoeffx2, expCoeffy2, expCoeffxy, Varxx, Varxy, Varyy;
-  int NCells;
+  double coefficientNorm, exponentialCoeffx2, exponentialCoeffy2, exponentialCoeffxy, Varxx, Varxy, Varyy;
+  int numberOfCells;
   bool isNaNs;
 
   API::MatrixWorkspace_const_sptr ws = getMatrixWorkspace();
@@ -103,7 +103,8 @@ void BivariateNormal::function1D(double *out, const double *xValues, const size_
 
   getConstraint(IBACK)->setPenaltyFactor(K * 3000);
 
-  double badParams = initCoeff(D, X, Y, coefNorm, expCoeffx2, expCoeffy2, expCoeffxy, NCells, Varxx, Varxy, Varyy);
+  double badParams = initCoeff(D, X, Y, coefficientNorm, exponentialCoeffx2, exponentialCoeffy2, exponentialCoeffxy,
+                               numberOfCells, Varxx, Varxy, Varyy);
 
   std::ostringstream inf;
   inf << "F Parameters=";
@@ -113,7 +114,7 @@ void BivariateNormal::function1D(double *out, const double *xValues, const size_
     inf << "," << Varxx << "," << Varyy << "," << Varxy;
   inf << '\n';
 
-  NCells = std::min<int>(static_cast<int>(nData), NCells);
+  numberOfCells = std::min<int>(static_cast<int>(nData), numberOfCells);
 
   double Background = getParameter(IBACK);
   double Intensity = getParameter(ITINTENS);
@@ -127,7 +128,7 @@ void BivariateNormal::function1D(double *out, const double *xValues, const size_
 
   // double penalty =0;
 
-  for (int i = 0; i < NCells; i++) {
+  for (int i = 0; i < numberOfCells; i++) {
     // double pen =0;
     if (badParams > 0)
       out[x] = badParams;
@@ -136,8 +137,9 @@ void BivariateNormal::function1D(double *out, const double *xValues, const size_
     else {
       double dx = X[i] - Xmean;
       double dy = Y[i] - Ymean;
-      out[x] =
-          Background + coefNorm * Intensity * exp(expCoeffx2 * dx * dx + expCoeffxy * dx * dy + expCoeffy2 * dy * dy);
+      out[x] = Background +
+               coefficientNorm * Intensity *
+                   exp(exponentialCoeffx2 * dx * dx + exponentialCoeffxy * dx * dy + exponentialCoeffy2 * dy * dy);
       out[x] = out[x] + DDD;
 
       if (out[x] != out[x]) {
@@ -177,7 +179,7 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
   std::vector<double> outf(nData, 0.0);
   function1D(outf.data(), xValues, nData);
 
-  double uu = LastParams[IVXX] * LastParams[IVYY] - LastParams[IVXY] * LastParams[IVXY];
+  double paramUU = LastParams[IVXX] * LastParams[IVYY] - LastParams[IVXY] * LastParams[IVXY];
   /*  if( uu <=0)
   {
     penDeriv = -2*uu;
@@ -206,8 +208,8 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
 
     double coefExp = coefNorm * LastParams[ITINTENS];
 
-    double coefxy = LastParams[IVXY] / uu;
-    double coefx2 = -LastParams[IVYY] / 2 / uu;
+    double coefxy = LastParams[IVXY] / paramUU;
+    double coefx2 = -LastParams[IVYY] / 2 / paramUU;
 
     if (penaltyDeriv <= 0)
       out->set(x, IXMEAN,
@@ -220,7 +222,7 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
 
     coefExp = coefNorm * LastParams[ITINTENS];
 
-    double coefy2 = -LastParams[IVXX] / 2 / uu;
+    double coefy2 = -LastParams[IVXX] / 2 / paramUU;
 
     if (penaltyDeriv <= 0)
       out->set(x, IYMEAN,
@@ -236,17 +238,17 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
       M = 10;
     coefExp = coefNorm * LastParams[ITINTENS];
 
-    double C = -LastParams[IVYY] / 2 / uu;
+    double C = -LastParams[IVYY] / 2 / paramUU;
 
     double SIVXX;
     if (penaltyDeriv <= 0)
       SIVXX = coefExp * expVals[x] *
               (C +
-               -LastParams[IVYY] / uu *
+               -LastParams[IVYY] / paramUU *
                    (coefx2 * (c - LastParams[IXMEAN]) * (c - LastParams[IXMEAN]) +
                     coefxy * (r - LastParams[IYMEAN]) * (c - LastParams[IXMEAN]) +
                     coefy2 * (r - LastParams[IYMEAN]) * (r - LastParams[IYMEAN])) -
-               (r - LastParams[IYMEAN]) * (r - LastParams[IYMEAN]) / 2 / uu);
+               (r - LastParams[IYMEAN]) * (r - LastParams[IYMEAN]) / 2 / paramUU);
     else if (LastParams[IVXX] < .01)
       SIVXX = -M;
     else
@@ -258,17 +260,17 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
       SIVXX = 0;
     coefExp = coefNorm * LastParams[ITINTENS];
 
-    C = -LastParams[IVXX] / 2 / uu;
+    C = -LastParams[IVXX] / 2 / paramUU;
 
     double SIVYY;
     if (penaltyDeriv <= 0)
       SIVYY = coefExp * expVals[x] *
               (C +
-               -LastParams[IVXX] / uu *
+               -LastParams[IVXX] / paramUU *
                    (coefx2 * (c - LastParams[IXMEAN]) * (c - LastParams[IXMEAN]) +
                     coefxy * (r - LastParams[IYMEAN]) * (c - LastParams[IXMEAN]) +
                     coefy2 * (r - LastParams[IYMEAN]) * (r - LastParams[IYMEAN])) -
-               (c - LastParams[IXMEAN]) * (c - LastParams[IXMEAN]) / 2 / uu);
+               (c - LastParams[IXMEAN]) * (c - LastParams[IXMEAN]) / 2 / paramUU);
 
     else if (LastParams[IVYY] < .01)
       SIVYY = -M;
@@ -282,19 +284,19 @@ void BivariateNormal::functionDeriv1D(API::Jacobian *out, const double *xValues,
       SIVYY = 0;
     coefExp = coefNorm * LastParams[ITINTENS];
 
-    C = LastParams[IVXY] / uu;
+    C = LastParams[IVXY] / paramUU;
 
     double SIVXY;
     if (penaltyDeriv <= 0)
       SIVXY = coefExp * expVals[x] *
               (C +
-               2 * LastParams[IVXY] / uu *
+               2 * LastParams[IVXY] / paramUU *
                    (coefx2 * (c - LastParams[IXMEAN]) * (c - LastParams[IXMEAN]) +
                     coefxy * (r - LastParams[IYMEAN]) * (c - LastParams[IXMEAN]) +
                     coefy2 * (r - LastParams[IYMEAN]) * (r - LastParams[IYMEAN])) +
-               (r - LastParams[IYMEAN]) * (c - LastParams[IXMEAN]) / uu);
+               (r - LastParams[IYMEAN]) * (c - LastParams[IXMEAN]) / paramUU);
 
-    else if (uu < 0)
+    else if (paramUU < 0)
       SIVXY = 2 * LastParams[IVXY];
     else
       SIVXY = 0;
@@ -468,16 +470,15 @@ double BivariateNormal::initCommon() {
       addConstraint(std::make_unique<BoundaryConstraint>(this, "Intensity", 0, maxIntensity));
     }
 
-    double minMeany = MinY * .9 + .1 * MaxY;
-    double maxMeany = MinY * .1 + .9 * MaxY;
-
     if (getConstraint(3) == nullptr) {
+      double minMeany = MinY * .9 + .1 * MaxY;
+      double maxMeany = MinY * .1 + .9 * MaxY;
       addConstraint(std::make_unique<BoundaryConstraint>(this, "Mrow", minMeany, maxMeany));
     }
 
-    double minMeanx = MinX * .9 + .1 * MaxX;
-    double maxMeanx = MinX * .1 + .9 * MaxX;
     if (getConstraint(2) == nullptr) {
+      double minMeanx = MinX * .9 + .1 * MaxX;
+      double maxMeanx = MinX * .1 + .9 * MaxX;
       addConstraint(std::make_unique<BoundaryConstraint>(this, "Mcol", minMeanx, maxMeanx));
     }
 
@@ -609,18 +610,19 @@ double BivariateNormal::initCoeff(const HistogramY &D, const HistogramY &X, cons
     Varxy = getParameter(IVXY);
   }
 
-  double uu = Varxx * Varyy - Varxy * Varxy;
+  double paramUU = Varxx * Varyy - Varxy * Varxy;
   double penalty;
   if (zeroDenom)
     penalty = 200;
   else
-    penalty = std::max<double>(0, -Varxx + .01) + std::max<double>(0, -Varyy + .01) + std::max<double>(0, -uu + .01);
+    penalty =
+        std::max<double>(0, -Varxx + .01) + std::max<double>(0, -Varyy + .01) + std::max<double>(0, -paramUU + .01);
 
-  if (fabs(uu) < .01) {
-    if (uu < 0)
-      uu = -.01;
+  if (fabs(paramUU) < .01) {
+    if (paramUU < 0)
+      paramUU = -.01;
     else
-      uu = .01;
+      paramUU = .01;
   }
 
   NCells = static_cast<int>(std::min<size_t>(D.size(), std::min<size_t>(X.size(), Y.size())));
@@ -631,11 +633,11 @@ double BivariateNormal::initCoeff(const HistogramY &D, const HistogramY &X, cons
     Varxy = 0;
     return penalty;
   }
-  coefNorm = .5 / M_PI / sqrt(fabs(uu));
+  coefNorm = .5 / M_PI / sqrt(fabs(paramUU));
 
-  expCoeffx2 = -fabs(Varyy) / 2 / fabs(uu);
-  expCoeffxy = Varxy / uu;
-  expCoeffy2 = -fabs(Varxx) / 2 / fabs(uu);
+  expCoeffx2 = -fabs(Varyy) / 2 / fabs(paramUU);
+  expCoeffxy = Varxy / paramUU;
+  expCoeffy2 = -fabs(Varxx) / 2 / fabs(paramUU);
 
   if (nParams() < 5)
     penalty *= 10;

Framework/DataHandling/inc/MantidDataHandling/CheckMantidVersion.h

@@ -17,7 +17,6 @@ namespace DataHandling {
 */
 class MANTID_DATAHANDLING_DLL CheckMantidVersion : public API::Algorithm {
 public:
-  virtual ~CheckMantidVersion() = default;
   const std::string name() const override;
   int version() const override;
   const std::string category() const override;

Framework/DataHandling/inc/MantidDataHandling/CreatePolarizationEfficienciesBase.h

@@ -27,7 +27,6 @@ class MANTID_DATAHANDLING_DLL CreatePolarizationEfficienciesBase : public API::A
   const std::string category() const override;
 
 protected:
-  ~CreatePolarizationEfficienciesBase() = default;
   void initOutputWorkspace();
   std::vector<std::string> getNonDefaultProperties(std::vector<std::string> const &props) const;
 

Framework/DataHandling/inc/MantidDataHandling/DownloadInstrument.h

@@ -23,8 +23,6 @@ namespace DataHandling {
 class MANTID_DATAHANDLING_DLL DownloadInstrument : public API::Algorithm {
 public:
   DownloadInstrument();
-  virtual ~DownloadInstrument() = default;
-
   const std::string name() const override;
   int version() const override;
   const std::vector<std::string> seeAlso() const override { return {"LoadInstrument", "UpdateScriptRepository"}; }

Framework/DataHandling/inc/MantidDataHandling/FindDetectorsPar.h

@@ -121,7 +121,6 @@ class DetParameters;
 
 class MANTID_DATAHANDLING_DLL FindDetectorsPar : public API::Algorithm {
 public:
-  virtual ~FindDetectorsPar() = default;
   /// Algorithm's name for identification overriding a virtual method
   const std::string name() const override { return "FindDetectorsPar"; };
   /// Summary of algorithms purpose

Framework/DataHandling/inc/MantidDataHandling/ISISJournal.h

@@ -11,6 +11,7 @@
 #include "MantidKernel/InternetHelper.h"
 
 #include <Poco/AutoPtr.h>
+#include <Poco/DOM/Document.h>
 #include <map>
 #include <memory>
 #include <string>
@@ -32,8 +33,6 @@ class MANTID_DATAHANDLING_DLL ISISJournal : public API::IJournal {
 public:
   ISISJournal(std::string const &instrument, std::string const &cycle,
               std::unique_ptr<Kernel::InternetHelper> internetHelper = std::make_unique<Kernel::InternetHelper>());
-  virtual ~ISISJournal();
-
   ISISJournal(ISISJournal const &rhs) = delete;
   ISISJournal(ISISJournal &&rhs);
   ISISJournal const &operator=(ISISJournal const &rhs) = delete;

Framework/DataHandling/inc/MantidDataHandling/LoadDNSEvent.h

@@ -41,7 +41,6 @@ class MANTID_DATAHANDLING_DLL LoadDNSEvent : public API::IFileLoader<Kernel::Fil
 
   // Algorithm's version for identification
   int version() const override { return 1; }
-  const std::vector<std::string> seeAlso() const override { return {}; }
   // Algorithm's category for identification
   const std::string category() const override { return "DataHandling"; }
   // Returns a confidence value that this algorithm can load a file

Framework/DataHandling/src/ISISJournal.cpp

@@ -243,8 +243,6 @@ ISISJournal::ISISJournal(std::string const &instrument, std::string const &cycle
     : m_internetHelper(std::move(internetHelper)), m_runsFileURL(constructURL(instrument, cycle)),
       m_indexFileURL(constructURL(instrument, INDEX_FILE_NAME)) {}
 
-ISISJournal::~ISISJournal() = default;
-
 ISISJournal::ISISJournal(ISISJournal &&rhs) = default;
 
 ISISJournal &ISISJournal::operator=(ISISJournal &&rhs) = default;