Apply second batch of clang-tidy suggestions

Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

cmd/dwirecon.cpp

@@ -266,7 +266,7 @@ void run() {
   y.setZero();
   ssize_t j = 0;
   for (auto lv = Loop("loading image data", {0, 1, 2, 3})(dwisub); lv; lv++, j++) {
-    float const w = Wsub(size_t(dwisub.index(2)), size_t(dwisub.index(3))) * Wvox[j];
+    float const w = Wsub(dwisub.index(2), dwisub.index(3)) * Wvox[j];
     y[j] = std::sqrt(w) * dwisub.value();
   }
 
@@ -361,7 +361,7 @@ void run() {
   }
 
   // Output source prediction
-  bool complete = !get_options("complete").empty();
+  bool const complete = !get_options("complete").empty();
   opt = get_options("spred");
   if (!opt.empty()) {
     srchdr.size(3) = (complete) ? dwi.size(3) : dwisub.size(3);

cmd/dwisliceoutliergmm.cpp

@@ -76,11 +76,11 @@ class RMSErrorFunctor {
   }
 
   void operator()(Image<value_type> &data, Image<value_type> &pred) {
-    size_t v = data.get_index(3);
-    size_t z = data.get_index(2);
+    ssize_t const v = data.get_index(3);
+    ssize_t const z = data.get_index(2);
     // Get transformation for masking. Note that the MB-factor of the motion table and the OR settings can be different.
-    size_t ne_mot = motion.rows() / nv;
-    transform_type T{DWI::SVR::se3exp(motion.row(v * ne_mot + z % ne_mot)).cast<double>()};
+    ssize_t const ne_mot = motion.rows() / nv;
+    transform_type const T{DWI::SVR::se3exp(motion.row(v * ne_mot + z % ne_mot)).cast<double>()};
     // Calculate slice error
     value_type e = 0.0;
     int n = 0;
@@ -92,7 +92,7 @@ class RMSErrorFunctor {
         if (!mask.value())
           continue;
       }
-      value_type d = data.value() - pred.value();
+      value_type const d = data.value() - pred.value();
       e += d * d;
       n++;
     }
@@ -105,16 +105,16 @@ class RMSErrorFunctor {
     Emb.setZero();
     Eigen::MatrixXi Nmb(ne, nv);
     Nmb.setZero();
-    for (size_t b = 0; b < nz / ne; b++) {
+    for (ssize_t b = 0; b < nz / ne; b++) {
       Emb += E->block(b * ne, 0, ne, nv);
       Nmb += N->block(b * ne, 0, ne, nv);
     }
-    Eigen::MatrixXf R = (Nmb.array() > 0).select(Emb.cwiseQuotient(Nmb.cast<float>()), Eigen::MatrixXf::Zero(ne, nv));
+    Eigen::MatrixXf const R = (Nmb.array() > 0).select(Emb.cwiseQuotient(Nmb.cast<float>()), Eigen::MatrixXf::Zero(ne, nv));
     return R.cwiseSqrt();
   }
 
 private:
-  const size_t nv, nz, ne;
+  const ssize_t nv, nz, ne;
   const Transform T0;
   Interp::Nearest<Image<bool>> mask;
   const Eigen::MatrixXf motion;
@@ -140,7 +140,8 @@ template <typename T> class GMModel {
   void fit(const VecType &x) {
     // initialise
     init(x);
-    float_t ll, ll0 = -std::numeric_limits<float_t>::infinity();
+    float_t ll;
+    float_t ll0 = -std::numeric_limits<float_t>::infinity();
     // Expectation-Maximization
     for (int n = 0; n < niter; n++) {
       ll = e_step(x);