lasp_dsp_pybind.cpp

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#include "arma_npy.h"
#include "lasp_avpowerspectra.h"
#include "lasp_biquadbank.h"
#include "lasp_fft.h"
#include "lasp_filter.h"
#include "lasp_slm.h"
#include "lasp_streammgr.h"
#include "lasp_window.h"
#include <iostream>
#include <pybind11/pybind11.h>
 
using std::cerr;
using std::endl;
namespace py = pybind11;
using rte = std::runtime_error;
 
void init_dsp(py::module &m) {
 
  py::class_<Fft> fft(m, "Fft");
  fft.def(py::init<us>());
  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) {
    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);
 
  py::class_<Window> w(m, "Window");
 
  py::enum_<Window::WindowType>(w, "WindowType")
      .value("Hann", Window::WindowType::Hann)
      .value("Hamming", Window::WindowType::Hamming)
      .value("Bartlett", Window::WindowType::Bartlett)
      .value("Blackman", Window::WindowType::Bartlett)
      .value("Rectangular", Window::WindowType::Rectangular);
 
  w.def_static("toTxt", &Window::toText);
 
  py::class_<Filter, std::shared_ptr<Filter>> filter(m, "Filter");
 
  py::class_<SeriesBiquad, std::shared_ptr<SeriesBiquad>> sbq(m, "SeriesBiquad",
                                                              filter);
  sbq.def(py::init([](dpyarray filter) {
    return std::make_shared<SeriesBiquad>(NpyToCol<d, false>(filter));
  }));
  sbq.def("filter", [](SeriesBiquad &s, dpyarray input) {
    vd res = NpyToCol<d, true>(input);
    s.filter(res);
    return ColToNpy<d>(res);
  });
 
  py::class_<BiquadBank, Filter, std::shared_ptr<BiquadBank>> bqb(m,
                                                                  "BiquadBank");
  bqb.def(py::init([](dpyarray coefs) {
    return std::make_shared<BiquadBank>(NpyToMat<d, false>(coefs));
  }));
  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);
  }));
 
  bqb.def("setGains",
          [](BiquadBank &b, dpyarray gains) { 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);
  });
 
  py::class_<PowerSpectra> ps(m, "PowerSpectra");
  ps.def(py::init<const us, const Window::WindowType>());
  ps.def("compute", [](PowerSpectra &ps, dpyarray input) {
    return CubeToNpy<c>(ps.compute(NpyToMat<d, false>(input)));
  });
 
  py::class_<AvPowerSpectra> aps(m, "AvPowerSpectra");
  aps.def(py::init<const us, const Window::WindowType, const d, const d>(),
          py::arg("nfft") = 2048,
          py::arg("windowType") = Window::WindowType::Hann,
          py::arg("overlap_percentage") = 50.0, py::arg("time_constant") = -1);
 
  aps.def("compute", [](AvPowerSpectra &aps, dpyarray timedata) {
    std::optional<ccube> res;
    {
      py::gil_scoped_release release;
      res = aps.compute(NpyToMat<d, false>(timedata));
    }
 
    return CubeToNpy<c>(res.value_or(ccube(0, 0, 0)));
  });
  aps.def("get_est", [](const AvPowerSpectra &ps) {
    ccube est = ps.get_est();
    return CubeToNpy<c>(est);
  });
 
  py::class_<SLM> slm(m, "cppSLM");
 
  slm.def_static("fromBiquads", [](const d fs, const d Lref, const us ds,
                                   const d tau, dpyarray 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) {
    return SLM::fromBiquads(fs, Lref, ds, tau, NpyToCol<d, false>(prefilter),
                            NpyToMat<d, false>(bandpass));
  });
 
  slm.def("run", [](SLM &slm, dpyarray in) {
    return MatToNpy<d>(slm.run(NpyToCol<d, false>(in)));
  });
  slm.def("Pm", [](const SLM &slm) { 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("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);
}