void
TestLikeHigh::runSubTest(unsigned int i, double& res, double& expected, std::string& subTestName)
{
using namespace Math;
check(i >= 0 && i < 1, "invalid index " << i);
const long nSide = 2048;
const int lMaxSim = 2 * int(nSide);
const int lMin = 31;
const int lMax = 2000;
const double fwhm = double(5) / 60.0;
const int n = 5000;
const double h = 0.6704;
const double omBH2 = 0.022032;
const double omCH2 = 0.12038;
const double tau = 0.0925;
const double ns = 0.9619;
const double as = 2.2154e-9;
const double pivot = 0.05;
LambdaCDMParams paramsLCDM(omBH2, omCH2, h, tau, ns, as, pivot);
CMB cmb;
std::vector<double> clTT;
output_screen1("Calculating Cl..." << std::endl);
cmb.preInitialize(lMaxSim + 1000, false, true, false);
cmb.initialize(paramsLCDM, true, false, true, false);
cmb.getLensedCl(&clTT);
output_screen1("OK" << std::endl);
double nl = 0.005;
std::vector<double> beam(lMaxSim + 1);
Utils::readPixelWindowFunction(beam, nSide, lMaxSim, fwhm);
Healpix_Map<double> uniformMask;
uniformMask.SetNside(nSide, RING);
for(unsigned long i = 0; i < uniformMask.Npix(); ++i)
uniformMask[i] = 1;
Healpix_Map<double>& mask = uniformMask;
const double omegaPixel = 4 * Math::pi / mask.Npix();
const double w = omegaPixel / nl;
// mask out some stuff
std::stringstream maskFileName;
maskFileName << "slow_test_files/test_highl_mask_" << i << ".fits";
std::stringstream maskFileName1;
maskFileName1 << "slow_test_files/test_highl_mask_ap_" << i << ".fits";
Healpix_Map<double> apodizedMask;
if(!fileExists(maskFileName.str().c_str()))
{
output_screen1("Masking out some regions..." << std::endl);
int nRegions = 25;
time_t seed1 = 1000000;
Math::UniformRealGenerator thetaGen(seed1, pi / 50, pi - pi / 50), phiGen(seed1 + 1, 0, 2 * pi), angleGen(seed1 + 2, pi / 60, pi / 40);
std::vector<double> maskTheta(nRegions), maskPhi(nRegions), maskAngle(nRegions);
for(int i = 0; i < nRegions; ++i)
{
maskTheta[i] = thetaGen.generate();
maskPhi[i] = phiGen.generate();
maskAngle[i] = angleGen.generate();
}
Utils::maskRegions(mask, maskTheta, maskPhi, maskAngle);
for(unsigned long i = 0; i < mask.Npix(); ++i)
{
double theta, phi;
pix2ang_ring(nSide, i, &theta, &phi);
if(theta < Math::pi / 2 + pi / 20 && theta > pi / 2 - pi / 20)
mask[i] = 0;
}
output_screen1("OK" << std::endl);
fitshandle outMaskHandle;
outMaskHandle.create(maskFileName.str().c_str());
write_Healpix_map_to_fits(outMaskHandle, mask, PLANCK_FLOAT64);
outMaskHandle.close();
}
else
read_Healpix_map_from_fits(maskFileName.str(), mask);
if(!fileExists(maskFileName1.str().c_str()))
{
output_screen1("Apodizing the mask..." << std::endl);
MaskApodizer ap(mask);
const double apAngle = 30.0 / 60.0 / 180.0 * pi;
ap.apodize(MaskApodizer::COSINE_APODIZATION, apAngle, apodizedMask);
output_screen1("OK" << std::endl);
fitshandle outMaskHandle1;
outMaskHandle1.create(maskFileName1.str().c_str());
write_Healpix_map_to_fits(outMaskHandle1, apodizedMask, PLANCK_FLOAT64);
outMaskHandle1.close();
}
else
read_Healpix_map_from_fits(maskFileName1.str(), apodizedMask);
check(apodizedMask.Nside() == nSide, "");
Healpix_Map<double> noiseWeight, noiseInvMat;
noiseWeight.SetNside(nSide, RING);
noiseInvMat.SetNside(nSide, RING);
double nwAvg = 0;
for(unsigned long i = 0; i < noiseWeight.Npix(); ++i)
{
noiseInvMat[i] = w;
nwAvg += 1.0 / noiseInvMat[i];
noiseWeight[i] = noiseInvMat[i] * apodizedMask[i];
}
nwAvg /= noiseWeight.Npix();
check(nwAvg > 0, "");
nl = omegaPixel * nwAvg;
std::vector<double> nlTT(lMaxSim + 1, nl);
std::ofstream outCl("slow_test_files/test_like_high_cl.txt");
for(int l = 0; l <= lMax; ++l)
{
const double factor = l * (l + 1) / (2 * Math::pi);
outCl << l << '\t' << clTT[l] * factor << '\t' << nlTT[l] * factor << std::endl;
}
outCl.close();
std::stringstream couplingKernelFileName;
couplingKernelFileName << "slow_test_files/" << "test_highl_mask_" << i << "_cc.txt";
std::stringstream noiseCouplingKernelFileName;
noiseCouplingKernelFileName << "slow_test_files/" << "test_highl_noise_" << i << "_cc.txt";
output_screen1("Calculating mask coupling kernel..." << std::endl);
Master::calculateCouplingKernel(apodizedMask, lMax + 500, couplingKernelFileName.str().c_str());
output_screen1("OK" << std::endl);
output_screen1("Calculating noise coupling kernel..." << std::endl);
Master::calculateCouplingKernel(noiseWeight, lMax + 500, noiseCouplingKernelFileName.str().c_str());
output_screen1("OK" << std::endl);
Master master(apodizedMask, couplingKernelFileName.str().c_str(), beam, NULL, lMax + 500);
output_screen1("Starting simulations..." << std::endl);
time_t seed = std::time(0);
StandardException exc;
std::ofstream outLike("slow_test_files/test_highl_likes.txt");
if(!outLike)
{
std::string exceptionStr = "Cannot write into file slow_test_files/test_highl_likes.txt";
exc.set(exceptionStr);
throw exc;
}
Math::Histogram<double> chi2Hist;
Healpix_Map<double> noiseMap;
noiseMap.SetNside(nSide, RING);
ProgressMeter meter(n);
for(int i = 0; i < n; ++i)
{
Alm<xcomplex<double> > alm, noiseAlm;
Simulate::simulateAlm(clTT, alm, lMaxSim, seed);
++seed;
Math::GaussianGenerator noiseGen(seed, 0.0, 1.0);
for(unsigned long j = 0; j < noiseMap.Npix(); ++j)
{
check(noiseInvMat[j] > 0, "");
const double r = noiseGen.generate();
noiseMap[j] = r * std::sqrt(1.0 / noiseInvMat[j]);
}
++seed;
noiseAlm.Set(lMaxSim, lMaxSim);
arr<double> weight(2 * noiseMap.Nside(), 1);
map2alm(noiseMap, noiseAlm, weight);
for(int l = 0; l <= lMaxSim; ++l)
{
for(int m = 0; m <= l; ++m)
{
alm(l, m) += noiseAlm(l, m);
alm(l, m) *= beam[l];
}
}
Healpix_Map<double> map;
map.SetNside(nSide, RING);
alm2map(alm, map);
master.calculate(map);
std::vector<double> clSim(lMax + 1);
for(int l = 0; l <= lMax; ++l)
{
std::map<double, double> ps = master.powerSpectrum();
check(ps.find(double(l)) != ps.end(), "");
const double lFactor = (l == 0 ? 1 : 2.0 * Math::pi / double(l * (l + 1)));
clSim[l] = ps[double(l)] * lFactor - nlTT[l];
}
LikelihoodHigh likelihood(clSim, nlTT, couplingKernelFileName.str().c_str(), lMin, lMax, noiseCouplingKernelFileName.str().c_str());
double like;
like = likelihood.calculate(clTT);
outLike << like << std::endl;
chi2Hist.addData(like);
meter.advance();
}
outLike.close();
output_screen1("OK" << std::endl);
chi2Hist.createHistogram(chi2Hist.min(), chi2Hist.max());
typedef Math::Histogram<double>::HistogramType HistogramType;
const HistogramType& chi2Histogram = chi2Hist.getHistogram();
output_screen1("A total of " << chi2Hist.getDataSize() << " elements in the histogram." << std::endl);
output_screen1("Calculating the chi squared distribution..." << std::endl);
std::ofstream outDist("slow_test_files/test_highl_chi2.txt");
if(!outDist)
{
std::string exceptionStr = "Cannot write into file slow_test_files/test_highl_chi2.txt.";
exc.set(exceptionStr);
throw exc;
}
const double min = chi2Hist.min(), max = chi2Hist.max();
const int dof = lMax - lMin;
Math::ChiSquared chi2(dof);
int num = 10000;
const double step = (max - min) / num;
for(int i = 0; i < num; ++i)
{
const double x = min + step * i;
const double y = chi2.evaluate(x);
outDist << x << ' ' << y << std::endl;
}
outDist.close();
output_screen1("OK" << std::endl);
std::ofstream outChi2("slow_test_files/test_highl_chi2_histogram.txt");
if(!outChi2)
{
std::string exceptionStr = "Cannot write into file slow_test_files/test_highl_chi2_histogram.txt.";
exc.set(exceptionStr);
throw exc;
}
double normalization = chi2Hist.getDataSize() * (chi2Hist.max() - chi2Hist.min()) / chi2Histogram.size();
const double deltaX = (chi2Hist.max() - chi2Hist.min()) / chi2Histogram.size();
double myChi2 = 0;
int myDof = 0;
for(HistogramType::const_iterator it = chi2Histogram.begin(); it != chi2Histogram.end(); ++it)
{
++myDof;
const double x = (*it).first + deltaX / 2;
const double y = (double)(*it).second / normalization;
outChi2 << x << ' ' << y << std::endl;
const double expectedY = chi2.evaluate(x);
const double diff = y - expectedY;
const double e = std::sqrt(expectedY / normalization);
myChi2 += diff * diff / (e * e);
}
output_screen("Chi^2 = " << myChi2 << " per " << myDof - 1 << " degrees of freedom." << std::endl);
outChi2.close();
res = 1;
expected = 1;
const double chi2PerDof = myChi2 / (myDof - 1);
const double chi2Sigma = std::sqrt(2.0 * (myDof - 1));
if(std::abs(myChi2 - myDof +1) > 5 * chi2Sigma)
{
output_screen("FAIL: Not a good fit!" << std::endl);
res = 0;
}
subTestName = "highl";
}
