Fix compact modeling examples

CMakeLists.txt

@@ -114,13 +114,13 @@ CPMFindPackage(
 
 CPMFindPackage(
   NAME ViennaLS
-  GIT_TAG viennacore
+  VERSION 4.0.0
   GIT_REPOSITORY "https://github.com/ViennaTools/ViennaLS"
   EXCLUDE_FROM_ALL ${VIENNAPS_BUILD_PYTHON})
 
 CPMFindPackage(
   NAME ViennaCS
-  GIT_TAG main
+  VERSION 1.0.0
   GIT_REPOSITORY "https://github.com/ViennaTools/ViennaCS"
   EXCLUDE_FROM_ALL ${VIENNAPS_BUILD_PYTHON})
 

examples/KDTreeBenchmark/KDTreeBenchmark.cpp

@@ -6,7 +6,8 @@
 #include <omp.h>
 #endif
 
-#include <compact/psKDTree.hpp>
+#include <vcKDTree.hpp>
+#include <vcSmartPointer.hpp>
 
 inline double getTime() {
 #ifdef _OPENMP
@@ -41,7 +42,7 @@ std::vector<std::vector<T>> generatePoints(unsigned N, unsigned D) {
   return data;
 }
 
-namespace ps = viennaps;
+using namespace viennacore;
 
 int main(int argc, char *argv[]) {
   using NumericType = double;
@@ -81,10 +82,10 @@ int main(int argc, char *argv[]) {
 
   {
     std::cout << "Growing Tree...\n";
-    ps::SmartPointer<ps::KDTree<NumericType>> tree = nullptr;
+    SmartPointer<KDTree<NumericType>> tree = nullptr;
     auto startTime = getTime();
     for (unsigned i = 0; i < repetitions; ++i) {
-      tree = ps::SmartPointer<ps::KDTree<NumericType>>::New(points);
+      tree = SmartPointer<KDTree<NumericType>>::New(points);
       tree->build();
     }
     auto endTime = getTime();

examples/interpolationDemo/interpolationDemo.cpp

@@ -19,6 +19,7 @@
 #include <compact/psDataScaler.hpp>
 
 namespace fs = std::filesystem;
+namespace ps = viennaps;
 
 template <typename DataSource, typename Estimator, typename SizeType>
 void doEstimation(DataSource &dataSource, Estimator &estimator,
@@ -58,7 +59,7 @@ int main(int argc, char *argv[]) {
   int numSamples = 40;
 
   {
-    psCSVDataSource<NumericType> dataSource(
+    ps::CSVDataSource<NumericType> dataSource(
         (dataPath / "griddata.csv").string());
 
     // Quick demo of the positional and named parameters feature
@@ -96,13 +97,13 @@ int main(int argc, char *argv[]) {
 
     // End of parameter demo
 
-    psRectilinearGridInterpolation<NumericType> estimator;
+    ps::RectilinearGridInterpolation<NumericType> estimator;
     estimator.setDataDimensions(InputDim, OutputDim);
 
     auto data = dataSource.getData();
     estimator.setData(data);
 
-    psCSVDataSource<NumericType> writer("grid_output.csv");
+    ps::CSVDataSource<NumericType> writer("grid_output.csv");
     writer.setHeader(
         "x, y, z, data\nData generated by rectilinear interpolation "
         "on rectilinear grid");
@@ -114,36 +115,36 @@ int main(int argc, char *argv[]) {
   int OutputDim = 1;
 
   {
-    psCSVDataSource<NumericType> dataSource(
+    ps::CSVDataSource<NumericType> dataSource(
         (dataPath / "scatterdata.csv").string());
 
     int numberOfNeighbors = 5;
     NumericType distanceExponent = 2.;
 
-    psNearestNeighborsInterpolation<NumericType> estimator;
+    ps::NearestNeighborsInterpolation<NumericType> estimator;
     estimator.setDistanceExponent(distanceExponent);
     estimator.setNumberOfNeighbors(numberOfNeighbors);
     estimator.setDataDimensions(InputDim, OutputDim);
 
     auto data = dataSource.getData();
     estimator.setData(data);
 
-    psCSVDataSource<NumericType> writer("nn_std_output.csv");
+    ps::CSVDataSource<NumericType> writer("nn_std_output.csv");
     writer.setHeader("x, y, z, data\nData generated by nearest neighbors "
                      "interpolation using standard deviation scaling");
 
     doEstimation(writer, estimator, numSamples, InputDim);
   }
 
   {
-    psCSVDataSource<NumericType> dataSource(
+    ps::CSVDataSource<NumericType> dataSource(
         (dataPath / "scatterdata.csv").string());
 
     int numberOfNeighbors = 5;
     NumericType distanceExponent = 1. / 5.;
 
-    psNearestNeighborsInterpolation<NumericType,
-                                    psMedianDistanceScaler<NumericType>>
+    ps::NearestNeighborsInterpolation<NumericType,
+                                      ps::MedianDistanceScaler<NumericType>>
         estimator;
     estimator.setDistanceExponent(distanceExponent);
     estimator.setNumberOfNeighbors(numberOfNeighbors);
@@ -152,7 +153,7 @@ int main(int argc, char *argv[]) {
     auto data = dataSource.getData();
     estimator.setData(data);
 
-    psCSVDataSource<NumericType> writer("nn_median_output.csv");
+    ps::CSVDataSource<NumericType> writer("nn_median_output.csv");
     writer.setHeader("x, y, z, data\nData generated by nearest neighbors "
                      "interpolation using median distance scaling.");
 

include/viennaps/compact/psDataScaler.hpp

@@ -5,8 +5,12 @@
 #include <numeric>
 #include <vector>
 
+namespace viennaps {
+
+using namespace viennacore;
+
 // Base class for data scalers
-template <typename NumericType> class psDataScaler {
+template <typename NumericType> class DataScaler {
 protected:
   using ItemType = std::vector<NumericType>;
   using ItemVectorType = std::vector<ItemType>;
@@ -21,8 +25,8 @@ template <typename NumericType> class psDataScaler {
 
 // Class that calculates scaling factors based on standard deviation
 template <typename NumericType>
-class psStandardScaler : public psDataScaler<NumericType> {
-  using Parent = psDataScaler<NumericType>;
+class StandardScaler : public DataScaler<NumericType> {
+  using Parent = DataScaler<NumericType>;
 
   using typename Parent::ItemType;
   using typename Parent::ItemVectorType;
@@ -32,7 +36,7 @@ class psStandardScaler : public psDataScaler<NumericType> {
   const ItemVectorType &data;
 
 public:
-  psStandardScaler(const ItemVectorType &passedData) : data(passedData) {}
+  StandardScaler(const ItemVectorType &passedData) : data(passedData) {}
 
   void apply() override {
     if (data.empty())
@@ -75,8 +79,8 @@ class psStandardScaler : public psDataScaler<NumericType> {
 
 // Class that calculates scaling factors based on median distances
 template <typename NumericType>
-class psMedianDistanceScaler : public psDataScaler<NumericType> {
-  using Parent = psDataScaler<NumericType>;
+class MedianDistanceScaler : public DataScaler<NumericType> {
+  using Parent = DataScaler<NumericType>;
 
   using typename Parent::ItemType;
   using typename Parent::ItemVectorType;
@@ -86,7 +90,7 @@ class psMedianDistanceScaler : public psDataScaler<NumericType> {
   const ItemVectorType &data;
 
 public:
-  psMedianDistanceScaler(const ItemVectorType &passedData) : data(passedData) {}
+  MedianDistanceScaler(const ItemVectorType &passedData) : data(passedData) {}
 
   void apply() override {
     if (data.empty())
@@ -122,3 +126,5 @@ class psMedianDistanceScaler : public psDataScaler<NumericType> {
     }
   }
 };
+
+} // namespace viennaps