test: add the unit test of sph_bessel_recursive.h

range: source/module_base/test

Author: pxlxingliang

source/module_base/test/CMakeLists.txt

@@ -42,4 +42,8 @@ AddTest(
   TARGET base_matrix
   LIBS ${math_libs}
   SOURCES matrix_test.cpp ../matrix.cpp
+)
+AddTest(
+  TARGET base_sph_bessel_recursive
+  SOURCES sph_bessel_recursive_test.cpp ../sph_bessel_recursive-d1.cpp ../sph_bessel_recursive-d2.cpp
 )

source/module_base/test/sph_bessel_recursive_test.cpp

@@ -0,0 +1,84 @@
+#include"../sph_bessel_recursive.h"
+#include"gtest/gtest.h"
+
+#define threshold 1e-12
+
+/************************************************
+*  unit test of class Sph_Bessel_Recursive
+***********************************************/
+
+/**
+ * Note: this unit test try to ensure the invariance
+ * of the spherical Bessel produced by class Sph_Bessel_Recursive,
+ * and the reference results are produced by ModuleBase::Sph_Bessel_Recursive
+ * at 2022-1-25.
+ * 
+ */
+
+double mean(std::vector<double> &vect)
+{
+    double meanv = 0.0;
+
+    int totN = vect.size();
+    for (int i=0; i< totN; ++i) {meanv += vect[i]/totN;}
+
+    return meanv;
+}
+
+TEST(SphBessel,D1)
+{
+    int lmax = 7;
+    int rmesh = 700;
+    double dx = 0.01;
+
+    ModuleBase::Sph_Bessel_Recursive::D1 sphbesseld1;
+    sphbesseld1.set_dx(dx);
+    sphbesseld1.cal_jlx(lmax,rmesh);   
+    std::vector<std::vector<double>> jlx = sphbesseld1.get_jlx();
+
+    ASSERT_EQ(jlx.size(),static_cast<size_t>(lmax + 1));
+    EXPECT_NEAR( mean(jlx[0])/0.2084468748396,    1.0, threshold);
+    EXPECT_NEAR( mean(jlx[1])/0.12951635180384,   1.0, threshold);
+    EXPECT_NEAR( mean(jlx[2])/0.124201140093879,  1.0, threshold);
+    EXPECT_NEAR( mean(jlx[3])/0.118268654505568,  1.0, threshold);
+    EXPECT_NEAR( mean(jlx[4])/0.0933871035384385, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[5])/0.0603800487910689, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[6])/0.0327117051555907, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[7])/0.0152155566653926, 1.0, threshold);
+}
+
+
+TEST(SphBessel,D2)
+{
+    int lmax = 7;
+    int rmesh = 700;
+    int kmesh = 800;
+    double dx = 0.0001;
+
+    ModuleBase::Sph_Bessel_Recursive::D2 sphbesseld2;
+    sphbesseld2.set_dx(dx);
+    sphbesseld2.cal_jlx(lmax,rmesh,kmesh);   
+    std::vector<std::vector<std::vector<double>>> jlxd2 = sphbesseld2.get_jlx();
+    std::vector<std::vector<double>> jlx(lmax+1);
+
+    ASSERT_EQ(jlxd2.size(),static_cast<size_t>(lmax + 1));
+
+    //calculate the mean of jlxd2[i][j] and assign to jlx[i][j]
+    for(int i=0; i<jlxd2.size(); ++i)
+    {
+        jlx[i].resize(jlxd2[i].size());
+        for(int j=0; j< jlxd2[i].size(); ++j)
+        {
+            jlx[i][j] = mean(jlxd2[i][j]);
+        }
+    }  
+    
+    EXPECT_NEAR( mean(jlx[0])/0.130406547960426,  1.0, threshold);
+    EXPECT_NEAR( mean(jlx[1])/0.0643093491554227, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[2])/0.0434912807857165, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[3])/0.0329463027246214, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[4])/0.0264877891341284, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[5])/0.0220804766801247, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[6])/0.0188550846449362, 1.0, threshold);
+    EXPECT_NEAR( mean(jlx[7])/0.0163891245775448, 1.0, threshold);
+}