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15 Commits

Author SHA1 Message Date
b919e845c7 git: add submodule libnpy 2026-03-15 23:09:02 +01:00
d88ac81345 feat: StepDetector
move StepDetector from lockstep android to libpasada
2026-03-15 23:07:05 +01:00
ba923c53bd feat: RQF: filter() style interface RunningQuality 2026-03-12 22:10:49 +01:00
13eecdb706 debug: print lockedAt 2026-03-12 21:39:03 +01:00
716a54e76e refactor: disable debug prints by a switch in RunningQuality 2026-03-12 21:35:49 +01:00
bfb3c99184 fix: RunningQuality must add ssf, not add beat 2026-03-12 21:28:50 +01:00
ee77180994 fix: resample(): adjust trailing i 2026-03-12 20:59:26 +01:00
9aaec182a8 refactor: move test_helpers, RunningQuality 2026-03-12 20:34:54 +01:00
90f8943930 feat: iterate on SsfStepDetector
* use SSF signal instead of accelerometer signal
* use higher BEAT_CORR_THR_{12} for SSF signal
* add absolute SSF_THRESHOLD to ignore small accelero bumps
* compute ssf_threshold according to detected SSF peaks, not the mean (more robust vs. noise)
2026-03-11 20:47:53 +01:00
95d1fee44d feat: RunningQuality - running SQI 2026-03-09 18:38:21 +01:00
fe300dabd3 feat: pd_signal: resample, linspace 2026-03-04 16:27:00 +01:00
0fb4c4d6c1 feat: pd_signal::interp() 2026-03-04 15:34:46 +01:00
40e10c1718 feat: SSF step detector 2026-03-03 00:33:03 +01:00
c5acce5699 test: iir filter works but is not exact -- over 0.01 abs error 2026-03-02 22:33:46 +01:00
6ff7db4d93 refactor: move iir_filter to separate compilation unit 2026-03-02 17:23:14 +01:00
27 changed files with 1299 additions and 71 deletions

3
.gitmodules vendored Normal file
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@@ -0,0 +1,3 @@
[submodule "google-tests/libnpy"]
path = google-tests/libnpy
url = https://github.com/llohse/libnpy.git

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@@ -8,9 +8,26 @@ include_directories(${gtest_SOURCE_DIR}/include ${gtest_SOURCE_DIR} libnpy/inclu
# 'Google_Tests_run' is the target name
# 'test1.cpp test2.cpp' are source files with tests
add_executable(Google_Tests_run test1.cpp)
add_executable(Google_Tests_run
test_helpers.cpp
test1.cpp
test2.cpp
test3.cpp
test4.cpp
)
file(COPY test1/data1.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
file(COPY test1/iir_t1_a.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
file(COPY test1/iir_t1_b.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
file(COPY test1/iir_t1_x.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
file(COPY test1/iir_t1_y.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test1)
file(COPY test2/ssf_t2_acc.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test2)
file(COPY test2/ssf_t2_y_ref.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test2)
file(COPY test3/ssf_t3_acc.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test3)
file(COPY test4/step_150a.npy DESTINATION ${CMAKE_CURRENT_BINARY_DIR}/test4)
target_link_libraries(Google_Tests_run pasada)
#target_include_directories(Google_Tests_run PRIVATE "${CMAKE_CURRENT_SOURCE_DIR}/pasada-lib/include")

1
google-tests/libnpy Submodule

Submodule google-tests/libnpy added at 471fe480d5

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@@ -3,6 +3,7 @@
//
#include <gtest/gtest.h>
#include "library.h"
#include "iir_filter.h"
#include "npy.hpp"
#include <utility>
#include <vector>
@@ -51,76 +52,6 @@ TEST(HelloTest, Save_npy_matrix) {
npy::write_npy(path, d);
}
/** Shift register implemented as a circular buffer. */
class Buf {
protected:
std::vector<double> data;
size_t size;
size_t n;
public:
Buf(size_t N): size(N), n(0) {
data.resize(N);
data.assign(N, 0.0);
}
void push(double val) {
data[n] = val;
n = (n+1) % size;
}
};
/** Running filter base. */
class Filt : Buf {
protected:
std::vector<double> taps;
size_t shift;
size_t offset;
public:
Filt(size_t N, size_t shift, size_t offset, std::vector<double> taps): Buf(N), shift(shift), offset(offset), taps(taps) {
if (taps.size() != N) throw std::invalid_argument("taps.size() != N");
}
double filter(double val) {
this->push(val);
return this->peek();
}
double peek() {
double sum = 0;
for (size_t i = offset; i < this->size; i++) {
//size_t n = (this->n - i + shift - 1) % this->size; // unsigned % size ... bad if u is negative
size_t n = (this->size + this->n - i + shift - 1) % this->size;
//std::cout << " t[" << i << "] * v[" << n << "]" << std::endl;
sum += this->data[n] * this->taps[i];
}
return sum;
}
void push(double val) {
Buf::push(val);
}
};
class IirFilter {
protected:
Filt y;
Filt x;
public:
IirFilter(std::vector<double> b, std::vector<double> a) : x(b.size(), 0, 0, b), y(a.size(), 1, 1, a) {
if (b.size() != a.size()) throw std::invalid_argument("b.size() != a.size()");
}
double filter(double val) {
//std::cout << "x.filter(" << val << ")" << std::endl;
double xv = x.filter(val);
//std::cout << "xv=" << xv << std::endl;
//std::cout << "y.peek()" << std::endl;
double yv = y.peek();
//std::cout << "yv=" << yv << std::endl;
//std::cout << "---" << std::endl;
double yo = xv - yv;
y.push(yo);
return yo;
}
};
// TODO: copy npy files from CMake output folder
// TODO: add npy files to CMakeLists.txt so they are copied into output folder
TEST(HelloTest, Test_IIR_1_Apply_IIR) {
npy::npy_data x = npy::read_npy<double>("test1/iir_t1_x.npy");
npy::npy_data y_e = npy::read_npy<double>("test1/iir_t1_y.npy");

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172
google-tests/test2.cpp Normal file
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//
// Created by david on 02.03.2026.
//
#include <gtest/gtest.h>
#include "npy.hpp"
#include <vector>
#include "iir_filter.h"
#include "ssf_filter.h"
#include "test_helpers.h"
#include <cmath>
#include <limits>
#define FPS 60
#define MAX_BPM 300
TEST(HelloTest, Zong_SSF_Stage1) {
npy::npy_data acc = npy::read_npy<double>("test2/ssf_t2_acc.npy");
npy::npy_data y_ref = npy::read_npy<double>("test2/ssf_t2_y_ref.npy");
std::vector<double> signal = fetch_y_axis(acc);
const size_t N = signal.size();
ASSERT_NEAR(1.7, signal[0], 1e-5);
ASSERT_NEAR(3.6, signal[1], 1e-5);
ASSERT_NEAR(4.3, signal[2], 1e-5);
// # de-trending using a high-pass has helped the SSF be less noisy!
// # but it adds delay...
// # <- we try reducing that to 100 ms delay.
#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);
//
// apply high-pass filter
//
std::vector<double> y;
y.resize(N);
for (int i = 0; i < N; i++) {
y[i] = filter.filter(signal[i]);
}
// see: http://localhost:8888/notebooks/2026-02-25%20Accelero1/2026-03-01%20SSF3.ipynb
// check results
for (int i = 0; i < N; i++) {
//double rel_error = std::abs((y[i] - y_ref.data[i]) / y_ref.data[i]);
//ASSERT_NEAR(0, rel_error, 1e-2 + ((double) i) * 1e-9); // hack: put in the index into error msg
double abs_error = (y[i] - y_ref.data[i]);
//ASSERT_NEAR(0, abs_error, 1e-2 + ((double) i) * 1e-9); // hack: put in the index into error msg
ASSERT_NEAR(0, abs_error, 0.1 + ((double) i) * 1e-9); // hack: put in the index into error msg
}
npy::npy_data_ptr<double> d;
d.data_ptr = y.data();
d.shape = {(unsigned long) N};
const std::string path{"test2/ssf_t2_y_out.npy"};
npy::write_npy(path, d);
}
TEST(HelloTest, Filter_Delta_U) {
Filt f_delta_u(2, 0, 0, std::vector {1.0, -1.0});
std::vector x { 1.0, 3.0, 2.0 };
std::vector y_ref { 1.0, 2.0, -1.0 };
std::vector<double> y;
y.resize(x.size());
for (int i = 0; i < x.size(); i++) {
y[i] = f_delta_u.filter(x[i]);
}
for (int i = 0; i < x.size(); i++) {
ASSERT_NEAR(y_ref[i], y[i], 1e-5);
}
}
// NOTE: later SSF must be fed -u, not u
TEST(HelloTest, Filter_SSF) {
SsfFilter f_ssf(3);
std::vector x { 1.0, 3.0, 2.0, 5.0, 1.0, 1.5 };
// du { 1.0, 2.0, -1.0, 3.0, -4.0, 0.5 }
// duc { 1.0, 2.0, 0.0, 3.0, 0.0, 0.5 }
// ssf { 1.0, 3.0, 3.0, 5.0, 3.0, 3.5 }
std::vector ssf_ref { 1.0, 3.0, 3.0, 5.0, 3.0, 3.5 };
std::vector<double> ssf;
ssf.resize(x.size());
for (int i = 0; i < x.size(); i++) {
ssf[i] = f_ssf.filter(x[i]);
}
for (int i = 0; i < x.size(); i++) {
ASSERT_NEAR(ssf_ref[i], ssf[i], 1e-5);
}
}
TEST(HelloTest, Zong_SSF_Stage2) {
npy::npy_data acc = npy::read_npy<double>("test2/ssf_t2_acc.npy");
std::vector<double> signal = fetch_y_axis(acc);
#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);
// Stage 1: high-pass
auto y = apply_filter(filter, signal);
Filt f_neg(1, 0, 0, std::vector {-1.0});
auto y_neg = apply_filter(f_neg, y);
// Stage 2: sum slope function
const size_t upslope_width = 4;
SsfFilter f_ssf(upslope_width);
auto ssf = apply_filter(f_ssf, y_neg);
npy_save("test2/ssf_t2_ssf.npy", ssf);
}
TEST(HelloTest, Zong_SSF_Stage3) {
npy::npy_data acc = npy::read_npy<double>("test2/ssf_t2_acc.npy");
std::vector<double> signal = fetch_y_axis(acc);
#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);
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("test2/ssf_t2_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("test2/ssf_t2_steps.npy", steps);
}

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228
google-tests/test3.cpp Normal file
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//
// Created by david on 04.03.2026.
//
#include <gtest/gtest.h>
#include "npy.hpp"
//#include <utility>
#include <deque>
#include <iomanip>
#include "pd_signal.h"
#include "ssf_filter.h"
#include "test_helpers.h"
using namespace pd_signal;
TEST(SignalTest, interp_t1) {
//EXPECT_EQ();
std::vector<double> xp { 1.0, 2.0, 4.0, 5.0, 5.01, 6.0 };
std::vector<double> fp { 1.0, 2.0, 4.0, 5.0, 5.01, 7.0 };
std::vector<double> x { 0.9, 1.0, 1.5, 2.0, 3.9, 4.1, 5.0, 5.5, 6.0, 6.1 };
std::vector<double>y_e{ 1.0, 1.0, 1.5, 2.0, 3.9, 4.1, 5.0, 5.99494949495, 7.0, 7.0 };
size_t N = x.size();
// 5.99494949495 = (5.5-5.01)/0.99*(7-5.01)+5.01
std::vector<double> y;
interp(y, x, xp, fp);
// assert y == y_e, nb. upto 5 digits
double abs_err = 1e-5;
for (size_t i = 0; i < N; i++) {
ASSERT_NEAR(y_e[i], y[i], abs_err + 1e-9 * i);
}
}
TEST(SignalTest, cross_corr_t1) {
std::vector<double> sig {0.9, 1.5, 2.0, 3.0, 5.0, 4.0, 1.0, 0.5, 0.3, 0.2};
double corr = pd_signal::crossCorr(sig, sig);
ASSERT_NEAR(1.0, corr, 1e-7);
}
TEST(SignalTest, cross_corr_t2) {
std::vector<double> x {0.9, 1.5, 2.0, 3.0, 5.0, 4.0, 1.0, 0.5, 0.3, 0.2};
std::vector<double> y {0.4, 0.7, 0.9, 1.5, 2.5, 2.0, 0.5, 0.25, 0.15, 0.1};
double corr = pd_signal::crossCorr(x, y);
ASSERT_NEAR(0.999, corr, 1e-3);
}
TEST(SignalTest, resample_t1) {
std::vector<double> x {0.9, 1.5, 2.0, 3.0, 5.0, 4.0, 1.0, 0.5, 0.3, 0.2};
std::vector<double> y_e {0.9, 1.2, 1.5, 1.75, 2.0, 2.5, 3.0, 4.0, 5.0, 4.5, 4.0, 2.5, 1.0, 0.75, 0.5, 0.4, 0.3, 0.25, 0.2};
std::vector<double> t;
linspace(t, 0, (double) x.size()-1, y_e.size(), false);
// interp t=0.000 0.500 1.000 1.500 2.000 2.500 3.000 3.500 4.000 4.500 5.000 5.500 6.000 6.500 7.000 7.500 8.000 8.500 9.000
/*
std::cout << "interp t=";
for (size_t n = 0; n < t.size(); n++) {
std::cout << std::fixed << std::setw(5) << std::setprecision(3) << t[n] << " ";
}
std::cout << std::endl;
*/
std::vector<double> y;
pd_signal::resample(y, x, y_e.size());
double corr = pd_signal::crossCorr(y, y_e);
ASSERT_NEAR(1.0, corr, 1e-3);
}
TEST(SignalTest, ranges) {
const double abs_error = 1e-5;
std::vector<double> i;
size_t N = 3;
linspace(i, 0, (int) (N-1), (int) N, false);
ASSERT_NEAR(0.0, i[0], abs_error);
ASSERT_NEAR(1.0, i[1], abs_error);
ASSERT_NEAR(2.0, i[2], abs_error);
}
class DebugRunningQuality : public RunningQuality {
protected:
virtual void dispatchLocked() { locked = true; }
virtual void dispatchBeat(int idx, bool good, double posCorr) {
if (locked && lockedAt == -1)
lockedAt = idx;
goods.push_back(good);
corrs.push_back(posCorr);
}
int lockedAt;
bool locked;
std::vector<double> corrs;
std::vector<bool> goods;
public:
DebugRunningQuality(): lockedAt(-1), locked(false) {}
explicit DebugRunningQuality(bool disableSsf): RunningQuality(disableSsf), locked(false) {}
virtual ~DebugRunningQuality() {}
bool isLocked() { return locked; }
std::vector<double> getCorrs() { return corrs; }
std::vector<bool> getGoods() { return goods; }
int getLockedAt() { return lockedAt; }
std::vector<double> getBeatTemplate() { return this->beatTemplate; }
};
/*
TEST(SignalTest, resample_same_len) {
std::vector<double> rawBeat {0.0, 0.3, 0.9, 1.0, 0.7, 0.5, 0.1};
std::vector<double> beat;
resample(beat, rawBeat, 7);
// TODO
ASSERT_NEAR(0.3, beat[1], 1e-6);
}
*/
/*
TEST(SignalTest, resample_same_len) {
std::vector<double> rawBeat {0.0, 0.3, 0.9, 1.0, 0.7, 0.5, 0.1};
std::vector<double> beat;
resample(beat, rawBeat, 7);
// TODO
//ASSERT_NEAR(0.3, beat[1], 1e-6);
for (int i = 0; i < 7; i++)
std::cout << "b[" << i << "]=" << beat[i] << std::endl;
}
*/
TEST(SignalTest, RunningQuality_t1) {
DebugRunningQuality sqi(true);
std::vector a {0.0, 0.3, 0.9, 1.0, 0.7, 0.5, 0.1};
std::vector b {0.0, 0.3, 0.9, 1.0, 0.5, 0.5, 0.1};
std::vector c {0.0, 0.3, 0.9, 1.0, 0.9, 0.5, 0.1};
std::vector d {0.0, 0.3, 0.9, 1.0, 0.7, 0.4, 0.1};
sqi.append(a, a);
sqi.append(b, b);
sqi.append(c, c);
EXPECT_FALSE(sqi.isLocked());
sqi.append(d, d);
EXPECT_TRUE(sqi.isLocked());
ASSERT_EQ(1, sqi.getCorrs().size());
double norm = sqrt((0.3*0.3 + 0.9*0.9 + 1.0 + 0.7*0.7 + 0.5*0.5 + 0.1*0.1) // \sum x_i^2
* (0.3*0.3 + 0.9*0.9 + 1.0 + 0.7*0.7 + 0.4*0.4 + 0.1*0.1)); // \sum y_i^2
double num = (0.3*0.3 + 0.9*0.9 + 1.0 + 0.7*0.7 + 0.5*0.4 + 0.1*0.1); // \sum x_i * y_i
//ASSERT_NEAR(0.3, sqi.getBeatTemplate()[1], 1e-6);
//ASSERT_NEAR(0.7, sqi.getBeatTemplate()[4], 1e-6); // nb. resampled!
ASSERT_NEAR(num/norm, sqi.getCorrs()[0], 1e-3);
}
TEST(SignalTest, RunningQuality_t2) {
npy::npy_data acc = npy::read_npy<double>("test3/ssf_t3_acc.npy");
std::vector<double> signal = fetch_y_axis(acc);
#if (FPS != 60)
#error "FPS must currently be 60, as highpass taps are pre-computed for that value"
#endif
// TODO: SQI: cehck input file
// TODO: SQI: print debug values corr,idx, checkedSsf
// 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);
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("test2/ssf_t3_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("test2/ssf_t3_steps.npy", steps);
// Debug SQI
DebugRunningQuality sqi;
std::vector<double> beat_buf;
std::vector<double> ssf_buf;
// y, ssf
size_t N = y.size();
for (size_t i = 0; i < N; i++) {
if (steps[i] == 1.0) {
sqi.append(beat_buf, ssf_buf);
beat_buf.clear();
ssf_buf.clear();
}
beat_buf.push_back(y[i]);
ssf_buf.push_back(ssf[i]);
}
EXPECT_TRUE(sqi.isLocked());
EXPECT_TRUE(sqi.getCorrs().size() > 50);
EXPECT_TRUE(sqi.getLockedAt() < 10);
std::cout << "lockedAt=" << sqi.getLockedAt() << std::endl;
std::vector<double> corrs(sqi.getCorrs());
npy_save("test3/ssf_t3_sqi_corrs.npy", corrs);
std::vector<bool> goods(sqi.getGoods());
npy_save("test3/ssf_t3_sqi_goods.npy", goods);
}

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//
// Created by david on 15.03.2026.
//
#include <gtest/gtest.h>
#include "step_detector.h"
#include "npy.hpp"
#include "test_helpers.h"
TEST(StepDetector, t1_sub_sample_resolution) {
npy::npy_data s = npy::read_npy<double>("test4/step_150a.npy");
std::vector<double> signal = fetch_y_axis(s);
const size_t N = signal.size();
const size_t N_INIT = SsfStepDetector::initial_samples();
StepDetector det(nullptr, true);
// initialize: feed for priming the filters
det.primeFilters(signal);
// feed for actual test
for (size_t i = 0; i < N; i++) {
const auto a_i = static_cast<float>(signal[i]);
det.filter(std::vector<float> {0.0f, a_i, 0.0f});
}
std::vector<double> ssd = det.getBufSsd(); // raw SsfStepDetector
std::vector<double> sqi = det.getBufSqi(); // SQI - RunningQuality beat correlations
std::vector<double> out = det.getBufOut(); // steps where SQI is OK
npy_save("test4/t1_ssd.npy", ssd);
npy_save("test4/t1_sqi.npy", sqi);
npy_save("test4/t1_out.npy", out);
// http://localhost:8888/notebooks/2026-03-10%20step%20interpolate%2F2026-03-15%20synth.ipynb
}

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//
// Created by david on 11.03.2026.
//
#include "test_helpers.h"
void npy_save(std::string path, std::vector<double>& x) {
npy::npy_data_ptr<double> d;
d.data_ptr = x.data();
d.shape = {(unsigned long) x.size()};
npy::write_npy(path, d);
}
void npy_save(std::string path, std::vector<bool>& x) {
npy::npy_data_ptr<int> d;
std::vector<int> xx(x.begin(), x.end());
d.data_ptr = xx.data();
d.shape = {(unsigned long) x.size()};
npy::write_npy(path, d);
}
std::vector<double> fetch_y_axis(npy::npy_data<double>& acc) {
// TODO: later on, we should use a vector projection towards gravity
std::vector<double> signal;
const size_t rows_real = acc.shape[0];
#if DEBUG_IIR == 1
const size_t rows = 5;
#else
const size_t rows = acc.shape[0];
#endif
int stride = 3;
int offset = 1; // [x,y,z] per row - fetch y
signal.resize(rows);
if (acc.fortran_order) {
stride = 1;
offset = (int) rows_real;
}
/*
std::cout << "is_fortran=" << acc.fortran_order << std::endl;
for (size_t i = 0; i < 10; i++) {
std::cout << "acc.data[" << i << "]=" << acc.data[i] << std::endl;
}
*/
for (int i = 0; i < rows; i++) {
signal[i] = acc.data[i * stride + offset];
}
return signal;
}
DebugSsfStepDetectorThreshold::DebugSsfStepDetectorThreshold(size_t len_refr) : SsfStepDetector(len_refr) {}
double DebugSsfStepDetectorThreshold::filter(double val) {
this->SsfStepDetector::filter(val);
return peek_threshold();
}

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//
// Created by david on 11.03.2026.
//
#ifndef PASADASUPERPROJECT_TEST_HELPERS_H
#define PASADASUPERPROJECT_TEST_HELPERS_H
#include "npy.hpp"
#include "ssf_filter.h"
#include <string>
#include <vector>
template <typename T> static std::vector<double> apply_filter(T& filter, std::vector<double>& x) {
std::vector<double> y;
y.resize(x.size());
for (int i = 0; i < x.size(); i++) {
y[i] = filter.filter(x[i]);
}
return y;
}
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);
/** Returns the ssf_threshold as the filter output for debugging. */
class DebugSsfStepDetectorThreshold : public SsfStepDetector {
public:
DebugSsfStepDetectorThreshold(size_t len_refr);
double filter(double val);
};
#endif //PASADASUPERPROJECT_TEST_HELPERS_H

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@@ -2,6 +2,10 @@ project(Pasada_Lib)
SET(PASADA_SRC
library.cpp
iir_filter.cpp
ssf_filter.cpp
pd_signal.cpp
step_detector.cpp
)
if(PASADA_BUILD_TESTS)

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//
// Created by david on 02.03.2026.
//
#include "iir_filter.h"
#include <iostream>
#ifndef DEBUG_IIR
#define DEBUG_IIR 0
#endif
#if (DEBUG_IIR == 1)
#define DEBUG_PRINT(expr) do { expr; } while (0)
#else
#define DEBUG_PRINT(expr) while(0) { expr; }
#endif
Buf::Buf(size_t N): N(N), n(0) {
data.resize(N);
data.assign(N, 0.0);
}
void Buf::push(double val) {
data[n] = val;
n = (n+1) % N;
}
Filt::Filt(size_t N, size_t shift, size_t offset, std::vector<double> taps): Buf(N), shift(shift), offset(offset), taps(taps) {
if (taps.size() != N) throw std::invalid_argument("taps.size() != N");
}
double Filt::filter(double val) {
this->push(val);
return this->peek();
}
double Filt::peek() {
double sum = 0;
for (size_t i = offset; i < this->N; i++) {
//size_t n = (this->n - i + shift - 1) % this->size; // unsigned % size ... bad if u is negative
size_t n = (this->N + this->n - i + shift - 1) % this->N;
DEBUG_PRINT(std::cout << " t[" << i << "] * v[" << n << "]" << std::endl);
sum += this->data[n] * this->taps[i];
}
return sum;
}
void Filt::push(double val) {
Buf::push(val);
}
void Filt::prime(double val) {
data.assign(this->N, val);
}
size_t Filt::size() {
return this->N;
}
IirFilter::IirFilter(std::vector<double> b, std::vector<double> a) : x(b.size(), 0, 0, b), y(a.size(), 1, 1, a) {
if (b.size() != a.size()) throw std::invalid_argument("b.size() != a.size()");
}
double IirFilter::filter(double val) {
DEBUG_PRINT(std::cout << "x.filter(" << val << ")" << std::endl);
double xv = x.filter(val);
DEBUG_PRINT(std::cout << "xv=" << xv << std::endl);
DEBUG_PRINT(std::cout << "y.peek()" << std::endl);
double yv = y.peek();
DEBUG_PRINT(std::cout << "yv=" << yv << std::endl);
DEBUG_PRINT(std::cout << "---" << std::endl);
double yo = xv - yv;
y.push(yo);
return yo;
}

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//
// Created by david on 02.03.2026.
//
#ifndef PASADASUPERPROJECT_IIR_FILTER_H
#define PASADASUPERPROJECT_IIR_FILTER_H
#include <utility>
#include <vector>
#include <stdexcept>
/** Shift register implemented as a circular buffer. */
class Buf {
protected:
std::vector<double> data;
size_t N;
size_t n;
public:
Buf(size_t N);
void push(double val);
};
/** Running filter base. */
class Filt : public Buf {
protected:
std::vector<double> taps;
size_t shift;
size_t offset;
public:
Filt(size_t N, size_t shift, size_t offset, std::vector<double> taps);
double filter(double val);
double peek();
void push(double val);
/** prime the filter by overwriting the entire buffer with 'val' */
void prime(double val);
size_t size();
};
/** Running IIR filter. */
class IirFilter {
protected:
Filt y;
Filt x;
public:
IirFilter(std::vector<double> b, std::vector<double> a);
double filter(double val);
};
#endif //PASADASUPERPROJECT_IIR_FILTER_H

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//
// Created by david on 04.03.2026.
//
#ifndef PASADASUPERPROJECT_SIGNAL_H
#define PASADASUPERPROJECT_SIGNAL_H
#include <vector>
#include <deque>
namespace pd_signal {
/** `num` evenly spaced numbers over interval [start,stop] */
void linspace(std::vector<double>& data, double start, double stop, int num);
/** `num` evenly spaced numbers over interval [start,stop] with endpoint=true or [start,stop) with endpoint=false */
void linspace(std::vector<double>& data, double start, double stop, int num, bool endpoint);
/**
* Evaluate at points x the function given by the samples fp[xp[n]].
* Returned in y.
*/
void interp(std::vector<double>& y, std::vector<double>& x, std::vector<double>& xp, std::vector<double>& fp);
/** resample to BEAT_LEN */
void resample(std::vector<double> &out, std::vector<double> &x, int beat_len);
/**
* normalized cross-correlation of the two signals of same length.
* normalization factor is <c>1 / sqrt(\sum_i x_i^2 * \sum_i y_i^2)</c>
*/
double crossCorr(std::vector<double> &x, std::vector<double> &y);
/** clip 'val' to between 'a_min' and 'a_max'. */
double clip(double val, double a_min, double a_max);
/** two-dimensional mean of a collection of signals */
void mean(std::vector<double> &out, std::vector<std::vector<double> >& m);
/** two-dimensional mean of a collection of signals */
void mean(std::vector<double> &out, std::deque<std::vector<double> >& m);
}
#endif //PASADASUPERPROJECT_SIGNAL_H

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//
// Created by david on 03.03.2026.
//
#ifndef PASADASUPERPROJECT_SSF_FILTER_H
#define PASADASUPERPROJECT_SSF_FILTER_H
#include "iir_filter.h"
#include <deque>
#define FPS 60
#define MAX_BPM 300
/**
* Sum-Slope Function filter.
*
* See Zong et al, 2003: An open-source algorithm to detect onset of arterial blood pressure pulses.
*/
class SsfFilter {
protected:
size_t sw;
Filt f_delta_u;
Filt f_window;
public:
SsfFilter(size_t upslope_width);
double filter(double val);
};
/**
* Provides dirac pulses upon steps, based on SSF input.
*
* Settling time is LEN_INIT (currently 3.0 sec),
* no steps are detected before.
*/
class SsfStepDetector {
protected:
const size_t LEN_INIT;
const size_t LEN_TH_WIN;
size_t num_samples;
double ssf_threshold;
double ssf_threshold_nm1;
Filt f_ssf_threshold_smoothing;
size_t len_refr;
size_t n_refr;
bool is_refr;
double ssf_nm1;
Filt f_ssf_mean;
public:
/**
* @param len_refr duration of refractory period, in samples
*/
SsfStepDetector(size_t len_refr);
double filter(double val);
double peek_threshold();
static size_t initial_samples();
};
/**
* Running signal quality indicator.
*/
class RunningQuality {
protected:
// TODO: make it a filter (output proper samples)
/** template beat is resampled to this #samples */
const int BEAT_LEN = 120 /* 2*FPS for 30 bpm lower end */;
/** threshold for accepting initial beats */
const double BEAT_CORR_THR_1 = 0.9;
/** threshold for accepting subsequent beats */
const double BEAT_CORR_THR_2 = 0.8;
/** absolute SSF threshold for accepting any beat */
const double SSF_THRESHOLD = 5.0;
/** number of recent beats to use for beat template. must be even (alternating feet have different patterns; make it symmetric) */
const int NUM_BEATS = 4;
std::deque<std::vector<double> > beatTemplates;
std::vector<double> beatTemplate;
//std::vector<std::pair<int, int> > badBeatRanges;
double beatCorrThr2;
bool justLocked;
int idx;
/** for debugging only - disable SSF_THRESHOLD */
bool disableSsf;
void addTemplate(std::vector<double>& x);
void replaceTemplate(std::vector<double>& x);
virtual void dispatchLocked();
virtual void dispatchBeat(int idx, bool good, double posCorr);
public:
RunningQuality();
explicit RunningQuality(bool disableSsf);
virtual ~RunningQuality();
// note: arg should be an iterator really, but can do later
/**
* @param beat individual beat accelero signal
* @return true if it is good beat
*/
bool append(std::vector<double> &rawBeat, std::vector<double> &rawSsf);
};
/**
* Signal quality indicator.
*/
class RunningQualityFilter {
protected:
RunningQuality f_sqi;
std::vector<double> beat_buf;
std::vector<double> ssf_buf;
double sqi;
public:
RunningQualityFilter(size_t upslope_width);
double filter(double y, double ssf, double step);
};
#endif //PASADASUPERPROJECT_SSF_FILTER_H

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//
// Created by david on 15.03.2026.
//
#ifndef PASADASUPERPROJECT_STEP_DETECTOR_H
#define PASADASUPERPROJECT_STEP_DETECTOR_H
#include "iir_filter.h"
#include "ssf_filter.h"
#include <vector>
class StepListener {
public:
virtual ~StepListener() {}
virtual void playBeat() = 0;
};
/**
* Step detector from accelerometer signal.
*
* Settling time is 3.0 sec (defined in SsfStepDetector.LEN_INIT),
* no steps are detected before.
*/
class StepDetector {
protected:
StepListener *listener;
IirFilter f_highpass;
Filt f_neg;
SsfFilter f_ssf;
SsfStepDetector f_ssd;
RunningQualityFilter f_sqi;
bool debug;
std::vector<double> buf_ssd;
std::vector<double> buf_sqi;
std::vector<double> buf_out;
public:
StepDetector(StepListener *listener, bool debug = false);
void filter(std::vector<float> values);
std::vector<double> getBufSsd();
std::vector<double> getBufSqi();
std::vector<double> getBufOut();
/**
* Prime the filters using the given input signal.
* Used for debugging (non-realtime processing) to align the signal.
*/
void primeFilters(std::vector<double> sig);
};
#endif //PASADASUPERPROJECT_STEP_DETECTOR_H

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//
// Created by david on 04.03.2026.
//
#include "include/pd_signal.h"
#include <stdexcept>
#include <algorithm>
#include <iostream>
namespace pd_signal {
static void throwIfNotAscending(std::vector<double>& xp) {
size_t N = xp.size();
for(int i = 0; i < N - 1; i++)
if((xp[i+1] - xp[i]) <= 0.0)
throw std::invalid_argument("xp is not strictly monotonically ascending");
}
static int binarySearch(std::vector<double>& data, double x) {
auto p = std::equal_range(data.begin(), data.end(), x);
if(p.first == data.end()) return -((int) data.size()) - 1; // out of range on upper end
int insertion_point = (int) std::distance(data.begin(), p.first);
if(p.first != p.second) return insertion_point; // element found
return -(insertion_point) - 1; // no element found directly
}
void linspace(std::vector<double>& data, double start, double stop, int num) {
linspace(data, start, stop, num, true);
}
// `num` evenly spaced numbers over interval [start,stop] with endpoint=true or [start,stop) with endpoint=false
void linspace(std::vector<double>& data, double start, double stop, int num, bool endpoint) {
if(num < 0) throw std::invalid_argument("num must be >= 0");
int end = endpoint ? num : (num-1);
double step = (stop - start) / (double) end;
double d = start;
data.resize(num);
for(int i = 0; i < num; i++) {
data[i] = d;
d += step;
}
}
// Evaluate at points x the function given by the samples fp[xp[n]].
void interp(std::vector<double>& y, std::vector<double>& x, std::vector<double>& xp, std::vector<double>& fp) {
if (xp.size() != fp.size()) throw std::invalid_argument("xp.size() != fp.size()");
size_t N = xp.size();
size_t M = x.size();
if (N < 2) throw std::invalid_argument("N < 2");
throwIfNotAscending(xp);
std::vector<double> slope, intercept;
y.resize(M);
slope.resize(N-1);
intercept.resize(N-1);
for(int i = 0; i < N-1; i++) {
double dxp = xp[i+1] - xp[i];
double dfp = fp[i+1] - fp[i];
slope[i] = dfp / dxp;
intercept[i] = fp[i] - slope[i] * xp[i];
}
for(int i = 0; i < M; i++) {
if(x[i] < xp[0]) {
y[i] = fp[0];
} else if(x[i] > xp[N-1]) {
y[i] = fp[N-1];
} else {
int idx = binarySearch(xp, x[i]);
if (idx <= -1) {
// not found precisely. interpolate
idx = -idx - 1; // convert back to array index (insertion point)
// idx==0 or idx==M cannot happen, handled above.
//assert(idx > 0 && idx < N);
idx = idx - 1; // one below insertion point (left point)
y[i] = slope[idx] * x[i] + intercept[idx];
} else {
// found precisely
y[i] = fp[idx];
}
}
}
}
// resample to BEAT_LEN
void resample(std::vector<double> &out, std::vector<double> &x, int beat_len) {
std::vector<double> t;
std::vector<double> i;
linspace(t, 0, (double) (x.size()-1), beat_len, false);
linspace(i, 0, (int) (x.size()-1), (int) x.size(), false);
interp(out, t, i, x);
}
// normalized cross-correlation of the two signals of same length
double crossCorr(std::vector<double> &x, std::vector<double> &y) {
if (x.size() != y.size()) throw std::invalid_argument("x.size() != y.size()");
double xs = 0.0, ys = 0.0, cs = 0.0;
for (size_t i = 0; i < x.size(); i++) {
xs += x[i] * x[i];
ys += y[i] * y[i];
cs += x[i] * y[i];
}
return cs / sqrt(xs * ys);
}
// clip 'val' to between 'a_min' and 'a_max'.
double clip(double val, double a_min, double a_max) {
return std::min(std::max(val, a_min), a_max);
}
// two-dimensional mean of a collection of signals
template<class T> void mean_tpl(std::vector<double> &out, T& m) {
if (m.empty()) {
out.resize(0);
return;
}
const size_t sz = m[0].size();
out.resize(sz);
out.assign(sz, 0.0);
const size_t N = m.size();
for (size_t i = 0; i < N; i++) {
for (size_t j = 0; j < sz; j++) {
out[j] += m[i][j];
}
}
for (size_t j = 0; j < sz; j++) {
out[j] /= static_cast<double>(N);
}
}
void mean(std::vector<double> &out, std::vector<std::vector<double> >& m) {
mean_tpl(out, m);
}
void mean(std::vector<double> &out, std::deque<std::vector<double> >& m) {
mean_tpl(out, m);
}
}

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//
// Created by david on 03.03.2026.
//
#include "ssf_filter.h"
#include "pd_signal.h"
#include <limits>
#include <cassert>
#include <iomanip>
#include <iostream>
#ifndef DEBUG_SSF
#define DEBUG_SSF 0
#endif
#if (DEBUG_SSF == 1)
#define DEBUG_PRINT(expr) do { expr; } while (0)
#else
#define DEBUG_PRINT(expr) while(0) { expr; }
#endif
static std::vector<double> make_ones(size_t sw) {
std::vector<double> ones;
ones.resize(sw);
ones.assign(sw, 1.0);
return ones;
}
SsfFilter::SsfFilter(size_t upslope_width) :
sw(upslope_width),
// Filt(N, shift, offset, taps)
f_delta_u(2, 0, 0, std::vector<double> {1.0, -1.0}),
f_window(upslope_width, 0, 0, make_ones(upslope_width))
{}
double SsfFilter::filter(double val) {
double du = f_delta_u.filter(val);
double duc = std::max(0.0, du);
double ssf = f_window.filter(duc);
return ssf;
}
size_t SsfStepDetector::initial_samples() { return (size_t) (3.0 * FPS); }
SsfStepDetector::SsfStepDetector(size_t len_refr) :
// note: also change above, in initial_samples()
LEN_INIT((size_t) (3.0 * FPS)), // initial window length for ssf_threshold
LEN_TH_WIN((size_t) (3.0 * FPS)), // subsequent window length for ssf_threshold
num_samples(0),
ssf_threshold(std::numeric_limits<double>::infinity()),
ssf_threshold_nm1(std::numeric_limits<double>::infinity()),
f_ssf_threshold_smoothing(6, 0, 0, make_ones(6)),
len_refr(len_refr), n_refr(0), is_refr(false),
ssf_nm1(0.0),
f_ssf_mean(LEN_TH_WIN, 0, 0, make_ones(LEN_TH_WIN))
{
assert (LEN_INIT >= LEN_TH_WIN && "LEN_INIT < LEN_TH_WIN, check normalization of initial ssf_threshold");
}
double SsfStepDetector::filter(double ssf) {
double ssf_mean = f_ssf_mean.filter(ssf) / ((double) LEN_TH_WIN);
double rv = 0.0;
if (num_samples >= LEN_INIT) {
// initial and subsequent threshold setting.
ssf_threshold = 3.0 * ssf_mean * 0.99; // see Zong 2003 for the magic numbers
}
// threshold crossing detection
// 'is_prev_lower' fixes a glitch where a falling threshold leads to undetected crossings
bool is_prev_lower = ssf_nm1 < ssf_threshold || ssf_nm1 < ssf_threshold_nm1;
bool is_cur_higher = ssf >= ssf_threshold;
bool is_txing = is_prev_lower && is_cur_higher;
// refractory period reset
if (num_samples - n_refr >= len_refr) is_refr = false;
// transition and not in refractory period? detected a step.
if (is_txing && !is_refr) {
rv = 1.0;
is_refr = true;
n_refr = num_samples;
}
if (num_samples == LEN_INIT) {
// initial threshold setting
ssf_threshold = 3.0 * ssf_mean * 0.99; // see Zong 2003 for the magic numbers
//DEBUG_PRINT(std::cerr << "before prime()" << std::endl);
f_ssf_threshold_smoothing.prime(ssf_threshold);
} else if (num_samples > LEN_TH_WIN) {
//DEBUG_PRINT(std::cerr << "adaptive threshold setting" << std::endl);
// adaptive threshold setting
// +2 is half the window size
// TODO: param upon SsfFilter.upslope_width/2 instead of hardcoding -- also f_ssf_threshold_smoothing(), nb. should be even number
if (num_samples == n_refr + 2) {
//DEBUG_PRINT(std::cerr << "setting adaptive threshold setting" << std::endl);
ssf_threshold_nm1 = ssf_threshold;
// the ssf peak comes 3 samples (half-window + 1 sample) after the crossing
ssf_threshold = f_ssf_threshold_smoothing.filter(ssf) / ((double) f_ssf_threshold_smoothing.size()) * 0.6;
}
}
ssf_nm1 = ssf;
num_samples++;
return rv;
}
double SsfStepDetector::peek_threshold() {
return ssf_threshold;
}
void RunningQuality::addTemplate(std::vector<double>& x) {
beatTemplates.emplace_back(x);
while (beatTemplates.size() > NUM_BEATS) {
// sliding window on 'beat_templates', do not use all history
beatTemplates.pop_front();
}
pd_signal::mean(beatTemplate, beatTemplates);
}
void RunningQuality::replaceTemplate(std::vector<double>& x) {
beatTemplates.clear();
beatTemplates.emplace_back(x);
// essentially just a copy
pd_signal::mean(beatTemplate, beatTemplates);
}
void RunningQuality::dispatchLocked() { /* implement me, add Listener etc. */ }
void RunningQuality::dispatchBeat(int idx, bool good, double posCorr) { /* implement me, add Listener etc. */ }
RunningQuality::RunningQuality(): beatCorrThr2(BEAT_CORR_THR_2), justLocked(false), idx(0), disableSsf(false) {}
RunningQuality::RunningQuality(bool disableSsf): beatCorrThr2(BEAT_CORR_THR_2), justLocked(false), idx(0), disableSsf(disableSsf) {}
RunningQuality::~RunningQuality() {}
// note: arg should be an iterator really, but can do later
bool RunningQuality::append(std::vector<double> &rawBeat, std::vector<double> &rawSsf) {
// TODO: should ignore crazy-long and very short beats here. (filter up on beat detector)
std::vector<double> beat;
std::vector<double> ssf;
pd_signal::resample(beat, rawBeat, BEAT_LEN);
pd_signal::resample(ssf, rawSsf, BEAT_LEN);
//std::ranges::copy(rawBeat, std::back_inserter(beat));
// check ssf at sample 2 (mid-slope of 4 window of ssf)
// TODO: param upon SsfFilter.upslope_width/2 instead of hardcoding
double checkedSsf = ssf[(int) (2*((double)beat.size())/((double)rawBeat.size()))];
double corr = std::numeric_limits<double>::quiet_NaN();
double posCorr = std::numeric_limits<double>::quiet_NaN();
bool goodBeat = false;
if (beatTemplates.size() > 0) {
corr = pd_signal::crossCorr(ssf, beatTemplate);
posCorr = pd_signal::clip(corr, 0.0, 1.0);
double corrThreshold = (beatTemplates.size() > 2) ? beatCorrThr2 : BEAT_CORR_THR_1;
goodBeat = (corr > corrThreshold) && (checkedSsf > SSF_THRESHOLD || disableSsf);
}
if (beatTemplates.size() == 0) {
// cannot correlate the first beat, no template yet
DEBUG_PRINT(std::cerr << "(0) first beat -> addTemplate()" << std::endl);
addTemplate(ssf);
justLocked = false;
} else if (beatTemplates.size() <= 2) {
// restart if there is no clear correlation between beats
if (goodBeat) {
DEBUG_PRINT(std::cerr << "(2) good initial beat -> addTemplate()" << std::endl);
addTemplate(ssf);
if (beatTemplates.size() > 2)
justLocked = true;
//DEBUG_PRINT(std::cerr << " (2) beatTemplates.size()=" << beatTemplates.size() << " justLocked=" << ((int) justLocked) << std::endl);
} else {
DEBUG_PRINT(std::cerr << "(2) bad initial beat idx=" << idx << " -> replaceTemplate() corr=" << std::fixed << std::setw(7) << std::setprecision(4) << corr << " checkedSsf=" << checkedSsf << std::endl);
replaceTemplate(ssf);
//badBeatRanges.clear();
justLocked = false;
}
} else {
// running mode: collect bad beats, but may be OK not to restart immediately
DEBUG_PRINT(std::cerr << "(3) running mode, good=" << ((int) goodBeat) << " justLocked=" << ((int) justLocked) << std::endl);
if (goodBeat) {
addTemplate(ssf);
} else {
// badBeatRanges.add(s, e)
// numNoisy++
}
// runningCorrs.add(posCorr)
if (justLocked) { dispatchLocked(); justLocked = false; }
dispatchBeat(idx, goodBeat, posCorr);
}
idx++;
if (!goodBeat) return 0.0;
return posCorr;
}
RunningQualityFilter::RunningQualityFilter(size_t upslope_width) : sqi(0.0) {}
double RunningQualityFilter::filter(double y, double ssf, double step) {
if (step == 1.0) {
sqi = f_sqi.append(beat_buf, ssf_buf);
beat_buf.clear();
ssf_buf.clear();
}
beat_buf.push_back(y);
ssf_buf.push_back(ssf);
return sqi;
}

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@@ -0,0 +1,70 @@
//
// Created by david on 15.03.2026.
//
#include "step_detector.h"
// TODO: we are hardcoding filter coefficients for 60 Hz
// TODO: this is tolerable for 50 Hz
// TODO: check if we can do with floats instead of doubles
// (check how much the [already bad] accuracy of filtering suffers)
// TODO: in Java, check if delta timestamps effectively match FPS
// TODO: FPS constant should be passed as argument to C++ (but we keep an FPS define to validate the coefficients)
// Butterworth filter: order=5, fc=0.5, fs=60, btype='highpass'
static std::vector<double> hpf_taps_b {0.91875845, -4.59379227, 9.18758454, -9.18758454, 4.59379227, -0.91875845};
static std::vector<double> hpf_taps_a {1. , -4.83056552, 9.33652742, -9.02545247, 4.36360803, -0.8441171};
static size_t upslope_width = 4;
const size_t len_refr = (size_t) (FPS / (MAX_BPM / 60));
StepDetector::StepDetector(StepListener *listener, bool debug) :
listener(listener),
f_highpass(hpf_taps_b, hpf_taps_a),
f_neg(1, 0, 0, std::vector<double> {-1.0}),
f_ssf(upslope_width),
f_ssd(len_refr),
f_sqi(upslope_width),
debug(debug)
{}
#if (FPS != 60)
#error "FPS must currently be 60, as highpass taps are pre-computed for that value"
#endif
void StepDetector::filter(std::vector<float> values) {
// TODO: later on, we should use a vector projection towards gravity
auto s0 = (double) values[1]; // take y-axis value for now
auto s1 = f_highpass.filter(s0);
auto s2 = f_neg.filter(s1);
auto s3 = f_ssf.filter(s2);
auto s4 = f_ssd.filter(s3);
auto q5 = f_sqi.filter(s2, s3, s4);
if (debug) {
buf_ssd.push_back(s4);
buf_sqi.push_back(q5);
buf_out.push_back(s4 * (q5 > 0.0 ? 1.0 : 0.0));
}
// is step, step quality is OK, and we have a listener?
if(s4 > 0.0 && q5 > 0.0 && listener != nullptr) {
listener->playBeat();
}
}
std::vector<double> StepDetector::getBufSsd() { return buf_ssd; }
std::vector<double> StepDetector::getBufSqi() { return buf_sqi; }
std::vector<double> StepDetector::getBufOut() { return buf_out; }
void StepDetector::primeFilters(std::vector<double> sig) {
const size_t N_INIT = SsfStepDetector::initial_samples();
// initialize: feed for priming the filters
for (size_t i = 0; i < N_INIT; i++) {
const auto a_i = static_cast<float>(sig[i]);
filter(std::vector<float> {0.0f, a_i, 0.0f});
}
// clear debug buffers
buf_ssd.clear();
buf_sqi.clear();
buf_out.clear();
}