fully implemented AvSweepPowerSpectra

cpp_src/dsp/lasp_avpowerspectra.cpp

@@ -158,7 +158,8 @@ ccube AvPowerSpectra::get_est() const
 
 AvSweepPowerSpectra::AvSweepPowerSpectra(const us nfft, const Window::WindowType w,
                                          const d overlap_percentage,
-                                         const d time_constant_times_fs)
+                                         const d time_constant_times_fs,
+                                         const vd *A)
     : _nfft(nfft), _ps(nfft, w)
 {
 
@@ -167,6 +168,14 @@ AvSweepPowerSpectra::AvSweepPowerSpectra(const us nfft, const Window::WindowType
   {
     throw rte("Overlap percentage should be >= 0 and < 100");
   }
+  if(A == nullptr) {
+    _A = vd(nfft, arma::fill::ones);
+  } else {
+    if(A->n_elem != (nfft / 2 + 1)) {
+      throw rte("Invalid length given for amplitude coefficients");
+    }
+    _A = arma::square(*A);
+  }
 
   _overlap_keep = (nfft * overlap_percentage) / 100;
   DEBUGTRACE_PRINT(_overlap_keep);
@@ -235,25 +244,24 @@ ccube AvSweepPowerSpectra::compute(const dmat &timedata)
         _n_averages = vd(nfreq, arma::fill::zeros);
       }
             
-      // d threshold = arma::mean(arma::real(temp.slice(0).col(0)));
+      vd corr_pow = arma::real(temp.slice(0).col(0)) / _A;
-      d threshold = 1e-13;
+
+      d threshold = pow(10.0, log10(arma::max(corr_pow)*arma::median(corr_pow))/2.0);
+      // d threshold = 1e-13;
       
       for (us f=0; f < nfreq; f++)
       {
-        if (real(temp(f, 0, 0)) > threshold)
+        if (real(temp(f, 0, 0)) / _A(f) > threshold)
         {
           _n_averages[f]++;
           if (_n_averages[f] == 1)
           {
             _est.row(f) = temp.row(f);
-            // _est.subcube(f, 0, 0, f, ncols - 1, ncols - 1) = temp.subcube(f, 0, 0, f, ncols - 1, ncols - 1);
           }
           else
           {
             _est.row(f) = (static_cast<d>(_n_averages[f] - 1) / _n_averages[f]) * _est.row(f) +
                                                              temp.row(f) / _n_averages[f];
-            // _est.subcube(f, 0, 0, f, ncols - 1, ncols - 1) = (static_cast<d>(_n_averages[f] - 1) / _n_averages[f]) * _est.subcube(f, 0, 0, f, ncols - 1, ncols - 1) +
-                                                            //  temp.subcube(f, 0, 0, f, ncols - 1, ncols - 1) / _n_averages[f];
           }
         }
       }
@@ -284,3 +292,8 @@ ccube AvSweepPowerSpectra::get_est() const
 {
   return _est;
 }
+
+vd AvSweepPowerSpectra::get_A() const
+{
+  return _A;
+}

cpp_src/dsp/lasp_avpowerspectra.h

@@ -186,6 +186,7 @@ class AvSweepPowerSpectra {
   d _alpha;          // Only valid in case of 'Leaking'
   us _nfft;
   vd _n_averages { 1, arma::fill::zeros}; // Only valid in case of 'Averaging'
+  vd _A; // squared amplitude envelope
 
   PowerSpectra _ps;
   /**
@@ -225,10 +226,12 @@ public:
   AvSweepPowerSpectra(const us nfft = 2048,
                  const Window::WindowType w = Window::WindowType::Hann,
                  const d overlap_percentage = 50.,
-                 const d fs_tau = -1);
+                 const d fs_tau = -1,
+                 const vd *A = nullptr); // amplitude envelope
                  
-  AvSweepPowerSpectra(const AvSweepPowerSpectra &) = delete;
+  AvSweepPowerSpectra(const AvSweepPowerSpectra &) = default;
-  AvSweepPowerSpectra &operator=(const AvSweepPowerSpectra &) = delete;
+  AvSweepPowerSpectra &operator=(const AvSweepPowerSpectra &) = default;
+  AvSweepPowerSpectra (AvSweepPowerSpectra&& ) = default;
 
   /**
    * @brief Reset to empty state. Clears the time buffer and sets estimator to
@@ -260,6 +263,8 @@ public:
    * */
   ccube get_est() const;
 
+  vd get_A() const;
+
   /**
    * @brief The overlap is rounded to a certain amount of time samples. This
    * function returns that value.

cpp_src/pybind11/lasp_dsp_pybind.cpp

@@ -29,27 +29,28 @@ using rte = std::runtime_error;
  * @param m The Python module to add classes and methods to
  */
 
-void init_dsp(py::module &m) {
+void init_dsp(py::module &m)
+{
   py::class_<Fft> fft(m, "Fft");
   fft.def(py::init<us>());
-  fft.def("fft", [](Fft &f, dpyarray dat) {
+  fft.def("fft", [](Fft &f, dpyarray dat)
+          {
     if (dat.ndim() == 1) {
       return ColToNpy<c>(f.fft(NpyToCol<d, false>(dat)));
     } else if (dat.ndim() == 2) {
       return MatToNpy<c>(f.fft(NpyToMat<d, false>(dat)));
     } else {
       throw rte("Invalid dimensions of array");
-    }
+    } });
-  });
+  fft.def("ifft", [](Fft &f, cpyarray dat)
-  fft.def("ifft", [](Fft &f, cpyarray dat) {
+          {
     if (dat.ndim() == 1) {
       return ColToNpy<d>(f.ifft(NpyToCol<c, false>(dat)));
     } else if (dat.ndim() == 2) {
       return MatToNpy<d>(f.ifft(NpyToMat<c, false>(dat)));
     } else {
       throw rte("Invalid dimensions of array");
-    }
+    } });
-  });
 
   fft.def_static("load_fft_wisdom", &Fft::load_fft_wisdom);
   fft.def_static("store_fft_wisdom", &Fft::store_fft_wisdom);
@@ -71,41 +72,39 @@ void init_dsp(py::module &m) {
   /// SeriesBiquad
   py::class_<SeriesBiquad, std::shared_ptr<SeriesBiquad>> sbq(m, "SeriesBiquad",
                                                               filter);
-  sbq.def(py::init([](dpyarray filter) {
+  sbq.def(py::init([](dpyarray filter)
-    return std::make_shared<SeriesBiquad>(NpyToCol<d, false>(filter));
+                   { return std::make_shared<SeriesBiquad>(NpyToCol<d, false>(filter)); }));
-  }));
+  sbq.def("filter", [](SeriesBiquad &s, dpyarray input)
-  sbq.def("filter", [](SeriesBiquad &s, dpyarray input) {
+          {
     vd res = NpyToCol<d, true>(input);
     s.filter(res);
-    return ColToNpy<d>(res);
+    return ColToNpy<d>(res); });
-  });
 
   /// BiquadBank
   py::class_<BiquadBank, Filter, std::shared_ptr<BiquadBank>> bqb(m,
                                                                   "BiquadBank");
-  bqb.def(py::init([](dpyarray coefs) {
+  bqb.def(py::init([](dpyarray coefs)
-    return std::make_shared<BiquadBank>(NpyToMat<d, false>(coefs));
+                   { return std::make_shared<BiquadBank>(NpyToMat<d, false>(coefs)); }));
-  }));
+  bqb.def(py::init([](dpyarray coefs, dpyarray gains)
-  bqb.def(py::init([](dpyarray coefs, dpyarray gains) {
+                   {
     vd gains_arma = NpyToMat<d, false>(gains);
-    return std::make_shared<BiquadBank>(NpyToMat<d, false>(coefs), &gains_arma);
+    return std::make_shared<BiquadBank>(NpyToMat<d, false>(coefs), &gains_arma); }));
-  }));
 
   bqb.def("setGains",
-          [](BiquadBank &b, dpyarray gains) { b.setGains(NpyToCol(gains)); });
+          [](BiquadBank &b, dpyarray gains)
-  bqb.def("filter", [](BiquadBank &b, dpyarray input) {
+          { b.setGains(NpyToCol(gains)); });
+  bqb.def("filter", [](BiquadBank &b, dpyarray input)
+          {
     // Yes, a copy here
     vd inout = NpyToCol<d, true>(input);
     b.filter(inout);
-    return ColToNpy(inout);
+    return ColToNpy(inout); });
-  });
 
   /// PowerSpectra
   py::class_<PowerSpectra> ps(m, "PowerSpectra");
   ps.def(py::init<const us, const Window::WindowType>());
-  ps.def("compute", [](PowerSpectra &ps, dpyarray input) {
+  ps.def("compute", [](PowerSpectra &ps, dpyarray input)
-    return CubeToNpy<c>(ps.compute(NpyToMat<d, false>(input)));
+         { return CubeToNpy<c>(ps.compute(NpyToMat<d, false>(input))); });
-  });
 
   /// AvPowerSpectra
   py::class_<AvPowerSpectra> aps(m, "AvPowerSpectra");
@@ -114,7 +113,8 @@ void init_dsp(py::module &m) {
           py::arg("windowType") = Window::WindowType::Hann,
           py::arg("overlap_percentage") = 50.0, py::arg("time_constant") = -1);
 
-  aps.def("compute", [](AvPowerSpectra &aps, dpyarray timedata) {
+  aps.def("compute", [](AvPowerSpectra &aps, dpyarray timedata)
+          {
     std::optional<ccube> res;
     dmat timedata_mat = NpyToMat<d, false>(timedata);
     {
@@ -122,22 +122,36 @@ void init_dsp(py::module &m) {
       res = aps.compute(timedata_mat);
     }
 
-    return CubeToNpy<c>(res.value_or(ccube(0, 0, 0)));
+    return CubeToNpy<c>(res.value_or(ccube(0, 0, 0))); });
-  });
+  aps.def("get_est", [](const AvPowerSpectra &ps)
-  aps.def("get_est", [](const AvPowerSpectra &ps) {
+          {
     ccube est = ps.get_est();
-    return CubeToNpy<c>(est);
+    return CubeToNpy<c>(est); });
-  });
 
   /// AvSweepPowerSpectra
-  py::class_<AvSweepPowerSpectra> asps(m, "AvSweepPowerSpectra");
+  py::class_<AvSweepPowerSpectra, std::unique_ptr<AvSweepPowerSpectra>> asps(m, "AvSweepPowerSpectra");
-  asps.def(py::init<const us, const Window::WindowType, const d, const d>(),
+  asps.def(py::init([](const us nfft, 
-          py::arg("nfft") = 2048,
+                       const Window::WindowType windowtype, 
-          py::arg("windowType") = Window::WindowType::Hann,
+                       const d overlap_percentage, 
-          py::arg("overlap_percentage") = 50.0,
+                       const d time_constant, 
-          py::arg("time_constant") = -1);
+                       const dpyarray A)
+                       {
+    std::unique_ptr<vd> theA = std::make_unique<vd>(NpyToCol<d, false>(A));
 
-  asps.def("compute", [](AvSweepPowerSpectra &asps, dpyarray timedata) {
+    return std::make_unique<AvSweepPowerSpectra>(
+        nfft,
+        windowtype,
+        overlap_percentage,
+        time_constant,
+        theA.get()); 
+
+                         }
+
+  ));
+
+
+  asps.def("compute", [](AvSweepPowerSpectra &asps, dpyarray timedata)
+           {
     std::optional<ccube> res;
     dmat timedata_mat = NpyToMat<d, false>(timedata);
     {
@@ -145,44 +159,60 @@ void init_dsp(py::module &m) {
       res = asps.compute(timedata_mat);
     }
 
-    return CubeToNpy<c>(res.value_or(ccube(0, 0, 0)));
+    return CubeToNpy<c>(res.value_or(ccube(0, 0, 0))); });
-  });
+  asps.def("get_est", [](const AvSweepPowerSpectra &sps)
-  asps.def("get_est", [](const AvSweepPowerSpectra &sps) {
+           {
     ccube est = sps.get_est();
-    return CubeToNpy<c>(est);
+    return CubeToNpy<c>(est); });
-  });
+
+  asps.def("get_A", [](const AvSweepPowerSpectra &sps)
+          {
+  vd A = sps.get_A();
+  return ColToNpy<d>(A); });
 
   py::class_<SLM> slm(m, "cppSLM");
 
   slm.def_static("fromBiquads", [](const d fs, const d Lref, const us ds,
-                                   const d tau, dpyarray bandpass) {
+                                   const d tau, dpyarray bandpass)
-    return SLM::fromBiquads(fs, Lref, ds, tau, NpyToMat<d, false>(bandpass));
+                 {
-  });
+    return SLM::fromBiquads(fs, Lref, ds, tau, NpyToMat<d, false>(bandpass)); });
 
   slm.def_static("fromBiquads", [](const d fs, const d Lref, const us ds,
                                    const d tau, dpyarray prefilter,
-                                   py::array_t<d> bandpass) {
+                                   py::array_t<d> bandpass)
+                 {
     return SLM::fromBiquads(fs, Lref, ds, tau, NpyToCol<d, false>(prefilter),
-                            NpyToMat<d, false>(bandpass));
+                            NpyToMat<d, false>(bandpass)); });
-  });
 
-  slm.def("run", [](SLM &slm, dpyarray in) {
+  slm.def("run", [](SLM &slm, dpyarray in)
-    return MatToNpy<d>(slm.run(NpyToCol<d, false>(in)));
+          {
-  });
+    return MatToNpy<d>(slm.run(NpyToCol<d, false>(in))); });
-  slm.def("Pm", [](const SLM &slm) { return ColToNpy<d>(slm.Pm); });
+  slm.def("Pm", [](const SLM &slm)
-  slm.def("Pmax", [](const SLM &slm) { return ColToNpy<d>(slm.Pmax); });
+          {
-  slm.def("Ppeak", [](const SLM &slm) { return ColToNpy<d>(slm.Ppeak); });
+    return ColToNpy<d>(slm.Pm); });
+  slm.def("Pmax", [](const SLM &slm)
+          {
+    return ColToNpy<d>(slm.Pmax); });
+  slm.def("Ppeak", [](const SLM &slm)
+          {
+    return ColToNpy<d>(slm.Ppeak); });
 
-  slm.def("Leq", [](const SLM &slm) { return ColToNpy<d>(slm.Leq()); });
+  slm.def("Leq", [](const SLM &slm)
-  slm.def("Lmax", [](const SLM &slm) { return ColToNpy<d>(slm.Lmax()); });
+          {
-  slm.def("Lpeak", [](const SLM &slm) { return ColToNpy<d>(slm.Lpeak()); });
+    return ColToNpy<d>(slm.Leq()); });
+  slm.def("Lmax", [](const SLM &slm)
+          {
+    return ColToNpy<d>(slm.Lmax()); });
+  slm.def("Lpeak", [](const SLM &slm)
+          {
+    return ColToNpy<d>(slm.Lpeak()); });
   slm.def_static("suggestedDownSamplingFac", &SLM::suggestedDownSamplingFac);
 
   // Frequency smoother
-  m.def("freqSmooth", [](dpyarray freq, dpyarray X, unsigned w) {
+  m.def("freqSmooth", [](dpyarray freq, dpyarray X, unsigned w)
+        {
     vd freqa = NpyToCol<d, false>(freq);
     vd Xa = NpyToCol<d, false>(X);
-    return ColToNpy(freqSmooth(freqa, Xa, w));
+    return ColToNpy(freqSmooth(freqa, Xa, w)); });
-  });
 }
 /** @} */

python_src/lasp/lasp_measurement.py

@@ -19,6 +19,9 @@ from .lasp_cpp import Window, DaqChannel, AvPowerSpectra, AvSweepPowerSpectra
 from typing import List
 from functools import lru_cache
 from .lasp_config import ones
+
+from scipy.interpolate import CubicSpline
+
 """!
 Author: J.A. de Jong - ASCEE
 
@@ -842,10 +845,15 @@ class Measurement:
 
         nchannels = len(channels)
 
-        # aps = AvPowerSpectra(nfft, window, overlap)
-        aps = AvSweepPowerSpectra(nfft, window, overlap)
         freq = getFreq(self.samplerate, nfft)
-        
+
+        if self._siggen['Type'] == 'Sweep':
+            iA = CubicSpline(self._siggen['Data']['fA'], self._siggen['Data']['A'])
+
+            aps = AvSweepPowerSpectra(nfft, window, overlap, -1, iA(freq))
+        else:
+            aps = AvPowerSpectra(nfft, window, overlap)
+
         for data in self.iterData(channels, **kwargs):
             CS = aps.compute(data)