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https://github.com/aportelli/LatAnalyze.git
synced 2025-06-22 08:52:01 +01:00
tuning fixes and update comparing with Andrew
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@ -6,17 +6,18 @@ if CXX_INTEL
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endif
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endif
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bin_PROGRAMS = \
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latan-plot \
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latan-sample-combine \
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latan-sample-dwt \
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latan-sample-element \
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latan-sample-fake \
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latan-sample-ft \
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latan-sample-merge \
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latan-sample-plot \
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latan-sample-plot-corr\
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latan-sample-read \
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bin_PROGRAMS = \
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latan-plot \
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latan-sample-combine \
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latan-sample-dwt \
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latan-sample-element \
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latan-sample-fake \
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latan-sample-ft \
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latan-sample-merge \
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latan-sample-noise-analysis\
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latan-sample-plot \
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latan-sample-plot-corr \
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latan-sample-read \
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latan-resample
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latan_plot_SOURCES = plot.cpp
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@ -47,6 +48,10 @@ latan_sample_merge_SOURCES = sample-merge.cpp
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latan_sample_merge_CXXFLAGS = $(COM_CXXFLAGS)
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latan_sample_merge_LDFLAGS = -L../lib/.libs -lLatAnalyze
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latan_sample_noise_analysis_SOURCES = sample-noise-analysis.cpp
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latan_sample_noise_analysis_CXXFLAGS = $(COM_CXXFLAGS)
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latan_sample_noise_analysis_LDFLAGS = -L../lib/.libs -lLatAnalyze
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latan_sample_plot_corr_SOURCES = sample-plot-corr.cpp
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latan_sample_plot_corr_CXXFLAGS = $(COM_CXXFLAGS)
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latan_sample_plot_corr_LDFLAGS = -L../lib/.libs -lLatAnalyze
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193
utils/sample-noise-analysis.cpp
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193
utils/sample-noise-analysis.cpp
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@ -0,0 +1,193 @@
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/*
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* sample-noise-analysis.cpp, part of LatAnalyze 3
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*
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* Copyright (C) 2013 - 2020 Antonin Portelli
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*
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* LatAnalyze 3 is free software: you can redistribute it and/or modify
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* it under the terms of the GNU General Public License as published by
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* the Free Software Foundation, either version 3 of the License, or
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* (at your option) any later version.
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*
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* LatAnalyze 3 is distributed in the hope that it will be useful,
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* but WITHOUT ANY WARRANTY; without even the implied warranty of
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* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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* GNU General Public License for more details.
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*
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* You should have received a copy of the GNU General Public License
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* along with LatAnalyze 3. If not, see <http://www.gnu.org/licenses/>.
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*/
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#include <LatAnalyze/Core/OptParser.hpp>
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#include <LatAnalyze/Io/Io.hpp>
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#include <LatAnalyze/Core/Math.hpp>
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#include <LatAnalyze/Core/Plot.hpp>
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#include <LatAnalyze/Numerical/GslFFT.hpp>
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#include <LatAnalyze/Numerical/MinuitMinimizer.hpp>
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#include <LatAnalyze/Statistics/XYSampleData.hpp>
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using namespace std;
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using namespace Latan;
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using namespace Math;
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int main(int argc, char *argv[])
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{
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// argument parsing ////////////////////////////////////////////////////////
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OptParser opt;
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bool parsed;
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string filename;
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opt.addOption("" , "help" , OptParser::OptType::trigger, true,
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"show this help message and exit");
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parsed = opt.parse(argc, argv);
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if (!parsed or (opt.getArgs().size() != 1) or opt.gotOption("help"))
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{
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cerr << "usage: " << argv[0];
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cerr << "<options> <sample file>" << endl;
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cerr << endl << "Possible options:" << endl << opt << endl;
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return EXIT_FAILURE;
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}
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filename = opt.getArgs()[0];
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// load data ///////////////////////////////////////////////////////////////
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DMatSample sample;
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cout << "-- load data" << endl;
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sample = Io::load<DMatSample>(filename);
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// compute power spectrum //////////////////////////////////////////////////
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DMat av, err;
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double l0;
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Index nSample = sample.size(), n = sample[central].rows();
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DMatSample noise(nSample), pow(nSample, n, 1);
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CMatSample ftBuf(nSample, n, 1);
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GslFFT fft(n);
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cout << "-- compute power spectrum" << endl;
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FOR_STAT_ARRAY(sample, s)
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{
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sample[s].conservativeResize(n, 1);
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}
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av = sample.mean();
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err = sample.variance().cwiseSqrt();
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FOR_STAT_ARRAY(sample, s)
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{
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noise[s] = sample[s] - av;
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ftBuf[s].real() = noise[s];
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ftBuf[s].imag().fill(0.);
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fft(ftBuf[s]);
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pow[s] = ftBuf[s].cwiseAbs2().unaryExpr([](const double x){return 10.*log10(x);});
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//pow[s] = ftBuf[s].cwiseAbs2();
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pow[s].conservativeResize(n/2, 1);
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}
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pow[central] = pow.mean();
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// {
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// Plot p;
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// DVec x;
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// x.setLinSpaced(n/2, 0., n/2 - 1.);
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// p << LogScale(Axis::x);
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// p << PlotData(x, pow);
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// p.display();
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// }
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// l0 = pow.mean()(1);
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// FOR_STAT_ARRAY(sample, s)
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// {
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// pow[s] = pow[s].unaryExpr([l0](const double x){return x - l0;});
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// }
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// fit decay ///////////////////////////////////////////////////////////////
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DVec x, init(2);
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DMat fitErr;
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DMatSample xs(nSample, n/2, 1);
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DSample beta(nSample);
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XYSampleData data(nSample);
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MinuitMinimizer min;
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DoubleModel lin([](const double *x, const double *p){return x[0]*p[0] + p[1];}, 1, 2);
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cout << "-- fit decay" << endl;
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x.setLinSpaced(n/2, 0., n/2 - 1.);
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FOR_VEC(x, i)
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{
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x(i) = log2(x(i));
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}
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xs.fill(x);
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data.addXDim(n/2, "f", true);
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data.addYDim("pow");
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data.setUnidimData(xs, pow);
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data.assumeYYCorrelated(true, 0, 0);
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for (unsigned int i = 0; i < n/2; ++i)
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{
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data.fitPoint((x(i) >= 2.) and (x(i) <= log2(n/2) - 0.5), i);
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}
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init(0) = -0.1; init(1) = -0.1;
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auto fit = data.fit(min, init, lin);
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fitErr = fit.variance().cwiseSqrt();
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FOR_STAT_ARRAY(beta, s)
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{
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beta[s] = fit[s](0)/(10.*log10(2.));
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}
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printf("chi^2/dof = %.1e/%d= %.2e -- chi^2 CCDF = %.2e -- p-value = %.2e -- CDR = %.1f dB\n",
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fit.getChi2(), static_cast<int>(fit.getNDof()), fit.getChi2PerDof(),
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fit.getCcdf(), fit.getPValue(), fit.getCorrRangeDb());
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printf(" decay = %.2f +/- %.2f dB/oct\n", fit[central](0), fitErr(0));
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printf(" exponent = %.2f +/- %.2f\n", beta[central], sqrt(beta.variance()));
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Plot p;
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p << Caption("noise power spectrum");
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p << PlotRange(Axis::x, -0.5, log2(n/2) + 0.5)
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<< Label("frequency (oct)", Axis::x) << Label("power (dB)", Axis::y);
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p << Color("1") << PlotPredBand(fit.getModel(_), 0., log2(n/2) + 0.5);
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p << Color("1") << PlotFunction(fit.getModel(), 0., log2(n/2) + 0.5);
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p << Color("2") << PlotData(x, pow);
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p.display();
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// p.reset();
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// p << PlotCorrMatrix(data.getFitCorrMat());
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// p.display();
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// filter correlator ///////////////////////////////////////////////////////
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DVec filter(n);
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DMatSample fsample(nSample, n, 1);
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FOR_VEC(filter, i)
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{
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filter(i) = -std::pow(2.*sin(pi/n*i), 2);//-beta[central]*.5);
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}
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FOR_STAT_ARRAY(sample, s)
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{
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ftBuf[s].real() = sample[s].col(0);
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ftBuf[s].imag().fill(0.);
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fft(ftBuf[s], FFT::Forward);
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ftBuf[s] = ftBuf[s].cwiseProduct(filter);
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fft(ftBuf[s], FFT::Backward);
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fsample[s] = ftBuf[s].real();
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}
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// p.reset();
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x.setLinSpaced(n, 0., n - 1.);
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// p << PlotData(x, sample);
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// p << PlotData(x, fsample);
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// p.display();
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p.reset();
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FOR_VEC(x, i)
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{
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x(i) = log2(x(i));
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}
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p << PlotRange(Axis::x, -0.5, log2(n/2) + 0.5);
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p << PlotPoints(x, -filter);
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p.display();
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p.reset();
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p << PlotCorrMatrix(sample.correlationMatrix());
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p.display();
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p.reset();
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p << PlotCorrMatrix(fsample.correlationMatrix());
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p.display();
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Io::save(fsample, "test.h5");
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return EXIT_SUCCESS;
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}
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