add test5

google-tests/CMakeLists.txt

@@ -14,6 +14,7 @@ add_executable(Google_Tests_run
         test2.cpp
         test3.cpp
         test4.cpp
+        test5.cpp
 )
 
 file(COPY test1/data1.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
@@ -29,6 +30,8 @@ file(COPY test3/ssf_t3_acc.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test3)
 
 file(COPY test4/step_150a.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test4)
 
+file(COPY test5/acc_1.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test5)
+
 target_link_libraries(Google_Tests_run pasada)
 #target_include_directories(Google_Tests_run PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/pasada-lib/include")
 

google-tests/test5.cpp

@@ -0,0 +1,66 @@
+//
+// Created by david on 10.05.2026.
+//
+
+#include <gtest/gtest.h>
+
+#include "step_detector.h"
+#include "npy.hpp"
+#include "test_helpers.h"
+
+/**
+ * These test a sweep over running speed (6.0 - 18.0 km/h with - normally - 20 sec steps).
+ * - 'acc_1' is with phone oscillations in a loose pocket
+ * - 'acc_2' is with phone fixed in front, in a side orientation (TODO: getting y axis does not work here)
+ * Both are 4-dim vectors with (ts, x, y, z) entries.
+ */
+TEST(HelloTest, Zong_SSF_Test5_a1) {
+    npy::npy_data acc = npy::read_npy<double>("test5/acc_1.npy");
+
+    std::vector<double> signal = fetch_y_axis(acc, 2); // (ts, x, y, z) entries -> fetch 'y'
+
+#if (FPS != 60)
+#error "FPS must currently be 60, as highpass taps are pre-computed for that value"
+#endif
+
+    // Butterworth filter: order=5, fc=0.5, fs=60, btype='highpass'
+    std::vector b {0.91875845, -4.59379227,  9.18758454, -9.18758454,  4.59379227, -0.91875845};
+    std::vector a {1.        , -4.83056552,  9.33652742, -9.02545247,  4.36360803, -0.8441171};
+    IirFilter filter(b, a);
+
+    //std::cerr << "before stage 1" << std::endl;
+
+    // Stage 1: high-pass
+    auto y = apply_filter(filter, signal);
+    //auto y = signal;
+
+    Filt f_neg(1, 0, 0, std::vector {-1.0});
+    auto y_neg = apply_filter(f_neg, y);
+
+    //std::cerr << "before stage 2" << std::endl;
+
+    // Stage 2: sum slope function
+    const size_t upslope_width = 4;
+    SsfFilter f_ssf(upslope_width);
+    auto ssf = apply_filter(f_ssf, y_neg);
+
+    //std::cerr << "before stage 3" << std::endl;
+
+    // Stage 3: threshold detection
+    const size_t len_refr = (size_t) (FPS / (MAX_BPM / 60));
+    DebugSsfStepDetectorThreshold f_ssd_thr(len_refr);
+    auto ssf_threshold = apply_filter(f_ssd_thr, ssf);
+
+    //std::cerr << "before writing results 1 and doing step detection" << std::endl;
+
+    npy_save("test5/ssf_a1_y.npy", y);
+    npy_save("test5/ssf_a1_ssf.npy", ssf);
+    npy_save("test5/ssf_a1_ssf_threshold.npy", ssf_threshold);
+
+    SsfStepDetector f_ssd(len_refr);
+    auto steps = apply_filter(f_ssd, ssf);
+
+    //std::cerr << "before writing results 2" << std::endl;
+
+    npy_save("test5/ssf_a1_steps.npy", steps);
+}

google-tests/test_helpers.cpp

@@ -18,7 +18,7 @@ void npy_save(std::string path, std::vector<bool>& x) {
     npy::write_npy(path, d);
 }
 
-std::vector<double> fetch_y_axis(npy::npy_data<double>& acc) {
+std::vector<double> fetch_y_axis(npy::npy_data<double>& acc, int dim) {
     // TODO: later on, we should use a vector projection towards gravity
     std::vector<double> signal;
     const size_t rows_real = acc.shape[0];
@@ -27,12 +27,12 @@ std::vector<double> fetch_y_axis(npy::npy_data<double>& acc) {
 #else
     const size_t rows = acc.shape[0];
 #endif
-    int stride = 3;
-    int offset = 1; // [x,y,z] per row - fetch y
+    int stride = (int) acc.shape[1];
+    int offset = dim; // [x,y,z] per row - fetch y by default
     signal.resize(rows);
     if (acc.fortran_order) {
         stride = 1;
-        offset = (int) rows_real;
+        offset = (int) rows_real * offset;
     }
     /*
     std::cout << "is_fortran=" << acc.fortran_order << std::endl;

google-tests/test_helpers.h

@@ -22,7 +22,7 @@ template <typename T> static std::vector<double> apply_filter(T& filter, std::ve
 void npy_save(std::string path, std::vector<double>& x);
 void npy_save(std::string path, std::vector<bool>& x);
 
-std::vector<double> fetch_y_axis(npy::npy_data<double>& acc);
+std::vector<double> fetch_y_axis(npy::npy_data<double>& acc, int dim = 1);
 
 /** Returns the ssf_threshold as the filter output for debugging. */
 class DebugSsfStepDetectorThreshold : public SsfStepDetector {

pasada-lib/include/iir_filter.h

@@ -42,7 +42,18 @@ protected:
     Filt y;
     Filt x;
 public:
+    /**
+     * Create IIR filter from coefficients 'b' and 'a' (numerator and denominator polynomial coefficients).
+     */
     IirFilter(std::vector<double> b, std::vector<double> a);
+
+    /**
+     * Create Butterworth lowpass filter.
+     * @param N order of Butterworth filter
+     * @param fc cutoff frequency in Hz
+     * @param fs sampling rate in Hz
+     */
+    IirFilter(int N, double fc, double fs);
     double filter(double val);
 };