Bugfix for OpenMP combinded with FFT. That one could not run in parallel in its current form.
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@ -1,8 +1,8 @@
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/* #define DEBUGTRACE_ENABLED */
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#include "debugtrace.hpp"
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#include <optional>
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#include "lasp_avpowerspectra.h"
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#include "debugtrace.hpp"
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#include <cmath>
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#include <optional>
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using rte = std::runtime_error;
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using std::cerr;
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@ -17,11 +17,15 @@ PowerSpectra::PowerSpectra(const vd &window)
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d win_pow = arma::sum(window % window) / window.size();
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/* Scale fft such that power is easily computed */
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_scale_fac = 2 / (win_pow * nfft * nfft);
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_scale_fac = 2.0 / (win_pow * (d)nfft * (d)nfft);
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}
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arma::Cube<c> PowerSpectra::compute(const dmat &input) {
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/// Run very often. Silence this one.
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/* DEBUGTRACE_ENTER; */
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dmat input_tmp = input;
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// Multiply each column of the inputs element-wise with the window.
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@ -30,8 +34,10 @@ arma::Cube<c> PowerSpectra::compute(const dmat &input) {
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cmat rfft = _fft.fft(input_tmp);
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arma::cx_cube output(rfft.n_rows, input.n_cols, input.n_cols);
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#pragma omp parallel for
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for (us i = 0; i < input.n_cols; i++) {
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/// This one can be run in parallel without any problem. Note that it is
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/// the inner loop that is run in parallel.
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#pragma omp parallel for
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for (us j = 0; j < input.n_cols; j++) {
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output.slice(j).col(i) =
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_scale_fac * (rfft.col(i) % arma::conj(rfft.col(j)));
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@ -69,7 +75,8 @@ AvPowerSpectra::AvPowerSpectra(const us nfft, const Window::WindowType w,
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}
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std::optional<arma::cx_cube> AvPowerSpectra::compute(const dmat &timedata) {
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DEBUGTRACE_ENTER;
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/* DEBUGTRACE_ENTER; */
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_timeBuf.push(timedata);
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@ -101,7 +108,7 @@ std::optional<arma::cx_cube> AvPowerSpectra::compute(const dmat &timedata) {
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} // end switch mode
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i++;
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}
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if(i>0) {
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if (i > 0) {
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return _est;
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}
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return std::nullopt;
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@ -33,7 +33,7 @@ private:
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Fft _fft;
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vd _window;
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c _scale_fac;
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d _scale_fac;
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public:
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/**
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@ -131,7 +131,10 @@ cmat Fft::fft(const dmat &freqdata) {
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DEBUGTRACE_ENTER;
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assert(_impl);
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cmat res(_impl->nfft/2+1, freqdata.n_cols);
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#pragma omp parallel for
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/// * WARNING *. This was source of a serious bug. It is not possible to run
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/// FFT's and IFFT's on the same _impl, as it overwrites the same memory.
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/// Uncommenting the line below results in faulty results.
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/// #pragma omp parallel for
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for (us colno = 0; colno < freqdata.n_cols; colno++) {
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res.col(colno) = _impl->fft(freqdata.col(colno));
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}
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@ -145,7 +148,11 @@ vd Fft::ifft(const vc &freqdata) {
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}
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dmat Fft::ifft(const cmat &freqdata) {
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dmat res(_impl->nfft, freqdata.n_cols);
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#pragma omp parallel for
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/// * WARNING *. This was source of a serious bug. It is not possible to run
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/// FFT's and IFFT's on the same _impl, as it overwrites the same memory.
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/// Uncommenting the line below results in faulty results.
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/// #pragma omp parallel for
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for (us colno = 0; colno < freqdata.n_cols; colno++) {
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res.col(colno) = _impl->ifft(freqdata.col(colno));
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}
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153
test/test_ps.py
153
test/test_ps.py
@ -1,145 +1,38 @@
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#!/usr/bin/env python3
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# -*- coding: utf-8 -*-
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"""
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Created on Mon Jan 15 19:45:33 2018
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@author: anne
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Testing code for power spectra
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"""
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import numpy as np
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from lasp import PowerSpectra, Window
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import matplotlib.pyplot as plt
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plt.close('all')
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# def test_ps():
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# import matplotlib.pyplot as plt
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# plt.close('all')
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nfft = 8
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t = np.linspace(0, 1.0, nfft, endpoint=False)
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def test_ps():
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"""
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Check Parsevall for single-sided power spectra
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"""
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nfft = 2048
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t = np.linspace(0, 1.0, nfft, endpoint=False)
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ps = PowerSpectra(nfft, Window.Rectangular)
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ps = PowerSpectra(nfft, Window.WindowType.Rectangular)
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sig = np.random.randn(nfft)
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freq = 4
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omg = 2*np.pi*freq
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# sig = 8*np.cos(omg*t)
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sig = np.random.randn(nfft)
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freq = 4
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omg = 2*np.pi*freq
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# sig = 8*np.cos(omg*t)
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cps = ps.compute(sig)
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cps = ps.compute(sig)
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pow1 = np.sum(sig**2)/sig.size
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pow2 = np.sum((cps[:,0,0]).real)
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pow1 = np.sum(sig**2)/sig.size
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pow2 = np.sum((cps[:,0,0]).real)
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# print(pow1)
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# print(pow2)
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plt.plot(cps[:,0,0])
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assert np.isclose(pow2 - pow1,0, atol=1e-1)
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# test_ps()
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# plt.plot(res_lasp.real-res_npy.real)
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# plt.plot(res_lasp.imag-res_npy.imag)
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# plt.plot(res_npy.real)
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# plt.plot(res_npy.imag)
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# plt.plot(t, sig)
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# print('nfft:',nfft)
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# #print(nfft)
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# nchannels = 2
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# print(pow1)
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# print(pow2)
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# plt.plot(cps[:,0,0])
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assert np.isclose(pow2 - pow1,0, atol=1e-1)
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# t = np.linspace(0,1,nfft+1)
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# # print(t)
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# x1 = (np.cos(4*np.pi*t[:-1])+3.2*np.sin(6*np.pi*t[:-1]))[:,np.newaxis]+10
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# x = np.vstack([x1.T]*nchannels).T
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# # Using transpose to get the strides right
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# x = np.random.randn(nchannels,nfft).T
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# print("strides: ",x.strides)
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# # x.strides = (8,nfft*8)x
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# # print("signal:",x)
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if __name__ == '__main__':
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test_ps()
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# xms = np.sum(x**2,axis=0)/nfft
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# print('Total signal power time domain: ', xms)
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# X = np.fft.rfft(x,axis=0)
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# # X =np.fft.fft(x)
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# #X =np.fft.rfft(x)
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# # print(X)
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# Xs = 2*X/nfft
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# Xs[np.where(np.abs(Xs) < 1e-10)] = 0
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# Xs[0] /= np.sqrt(2)
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# Xs[-1] /= np.sqrt(2)
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# # print('single sided amplitude spectrum:\n',Xs)
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# power = Xs*np.conj(Xs)/2
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# # print('Frequency domain signal power\n', power)
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# print('Total signal power', np.sum(power,axis=0).real)
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# pstest = PowerSpectra(nfft,nchannels)
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# ps = pstest.compute(x)
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# fft = Fft(nfft,nchannels)
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# fft.fft(x)
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# ps[np.where(np.abs(ps) < 1e-10)] = 0+0j
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# print('our ps: \n' , ps)
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# print('Our total signal power: ',np.sum(ps,axis=0).real)
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# if __name__ == '__main__':
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# nfft=2048
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# fs = 2048
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# ps = PowerSpectra(nfft, Window.Rectangular)
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# t = np.linspace(0, 1.0, nfft, endpoint=False)
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# freq = 10
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# omg = 2*np.pi*freq
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# sig = np.sin(omg*t)
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# res = ps.compute(sig)
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# plt.plot(res[:,0,0])
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# # plt.plot(t, sig)
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# print('nfft:',nfft)
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# #print(nfft)
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# nchannels = 2
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# t = np.linspace(0,1,nfft+1)
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# # print(t)
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# x1 = (np.cos(4*np.pi*t[:-1])+3.2*np.sin(6*np.pi*t[:-1]))[:,np.newaxis]+10
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# x = np.vstack([x1.T]*nchannels).T
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# # Using transpose to get the strides right
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# x = np.random.randn(nchannels,nfft).T
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# print("strides: ",x.strides)
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# # x.strides = (8,nfft*8)x
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# # print("signal:",x)
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# xms = np.sum(x**2,axis=0)/nfft
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# print('Total signal power time domain: ', xms)
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# X = np.fft.rfft(x,axis=0)
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# # X =np.fft.fft(x)
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# #X =np.fft.rfft(x)
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# # print(X)
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# Xs = 2*X/nfft
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# Xs[np.where(np.abs(Xs) < 1e-10)] = 0
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# Xs[0] /= np.sqrt(2)
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# Xs[-1] /= np.sqrt(2)
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# # print('single sided amplitude spectrum:\n',Xs)
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# power = Xs*np.conj(Xs)/2
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# # print('Frequency domain signal power\n', power)
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# print('Total signal power', np.sum(power,axis=0).real)
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# pstest = PowerSpectra(nfft,nchannels)
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# ps = pstest.compute(x)
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# fft = Fft(nfft,nchannels)
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# fft.fft(x)
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# ps[np.where(np.abs(ps) < 1e-10)] = 0+0j
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# print('our ps: \n' , ps)
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# print('Our total signal power: ',np.sum(ps,axis=0).real)
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