portelli/LatAnalyze
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Merge branch 'master' into tmp-merge

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This commit is contained in:
Andrew Zhen Ning Yong
2019-03-28 11:28:55 +00:00
parent cb2f6bf0aa 4a95bc39c8
commit c49da2ec2d
5 changed files with 470 additions and 46 deletions
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133
physics/2pt-fit.cpp
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@ -30,7 +30,7 @@ int main(int argc, char *argv[])
opt.addOption("s", "shift" , OptParser::OptType::value , true,
"time variable shift", "0");
opt.addOption("m", "model" , OptParser::OptType::value , true,
"fit model (exp|exp2|exp3|cosh|cosh2|cosh3|explin|<interpreter code>)", "cosh");
"fit model (exp|exp2|exp3|cosh|cosh2|cosh3|explin|const|<interpreter code>)", "cosh");
opt.addOption("" , "nPar" , OptParser::OptType::value , true,
"number of model parameters for custom models "
"(-1 if irrelevant)", "-1");
@ -117,7 +117,7 @@ int main(int argc, char *argv[])
// make model //////////////////////////////////////////////////////////////
DoubleModel mod;
bool coshModel = false, linearModel = false;
bool coshModel = false, linearModel = false, constModel = false;
if ((model == "exp") or (model == "exp1"))
{
@ -183,6 +183,15 @@ int main(int argc, char *argv[])
return p[1] - p[0]*x[0];
}, 1, nPar);
}
else if (model == "const")
{
constModel = true;
nPar = 1;
mod.setFunction([](const double *x __dumb, const double *p)
{
return p[0];
}, 1, nPar);
}
else
{
if (nPar > 0)
@ -214,16 +223,30 @@ int main(int argc, char *argv[])
data.addXDim(nt, "t/a", true);
data.addYDim("C(t)");
data.setUnidimData(tvec, corr);
for (Index p = 0; p < nPar; p += 2)
// set parameter name /////////////
if(constModel)
{
mod.parName().setName(p, "E_" + strFrom(p/2));
mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
mod.parName().setName(0, "const");
}
else
{
for (Index p = 0; p < nPar; p += 2)
{
mod.parName().setName(p, "E_" + strFrom(p/2));
mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
}
}
//set initial values ////////////////
if (linearModel)
{
init(0) = data.y(nt/4, 0)[central] - data.y(nt/4 + 1, 0)[central];
init(1) = data.y(nt/4, 0)[central] + nt/4*init(0);
}
else if(constModel)
{
init(0) = data.y(nt/4, 0)[central];
}
else
{
init(0) = log(data.y(nt/4, 0)[central]/data.y(nt/4 + 1, 0)[central]);
@ -234,6 +257,7 @@ int main(int argc, char *argv[])
init(p) = 2*init(p - 2);
init(p + 1) = init(p - 1)/2.;
}
// set limits for minimiser //////////////
for (Index p = 0; p < nPar; p += 2)
{
if (linearModel)
@ -241,20 +265,32 @@ int main(int argc, char *argv[])
globMin.setLowLimit(p, -10.*fabs(init(p)));
globMin.setHighLimit(p, 10.*fabs(init(p)));
}
else if(constModel)
{
globMin.setLowLimit(p, -10*fabs(init(0)));
locMin.setLowLimit(p, -10*fabs(init(0)));
// cout << "Suppressing low limits" << endl;
globMin.setHighLimit(p, 10*fabs(init(0)));
}
else
{
globMin.setLowLimit(p, 0.);
locMin.setLowLimit(p, 0.);
globMin.setHighLimit(p, 10.*init(p));
}
globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
if(!constModel)
{
globMin.setLowLimit(p + 1, -10.*fabs(init(p + 1)));
globMin.setHighLimit(p + 1, 10.*fabs(init(p + 1)));
}
}
globMin.setPrecision(0.001);
globMin.setMaxIteration(100000);
globMin.setVerbosity(verbosity);
locMin.setMaxIteration(1000000);
locMin.setVerbosity(verbosity);
// fit /////////////////////////////////
for (Index t = 0; t < nt; ++t)
{
data.fitPoint((t >= ti) and (t <= tf)
@ -278,69 +314,75 @@ int main(int argc, char *argv[])
fit = data.fit(locMin, init, mod);
fit.print();
}
// plots ///////////////////////////////////////////////////////////////////
if (doPlot)
{
Plot p;
DMatSample effMass(nSample);
DVec effMassT, fitErr;
Index maxT = (coshModel) ? (nt - 2) : (nt - 1);
double e0, e0Err;
p << PlotRange(Axis::x, 0, nt - 1);
if (!linearModel)
if (!linearModel and !constModel)
{
p << LogScale(Axis::y);
}
p << Color("rgb 'blue'") << PlotPredBand(fit.getModel(_), 0, nt - 1);
p << Color("rgb 'blue'") << PlotFunction(fit.getModel(), 0, nt - 1);
p << Color("rgb 'red'") << PlotData(data.getData());
p << Color("rgb 'red'") << PlotData(data.getData());
p.display();
effMass.resizeMat(maxT, 1);
effMassT.setLinSpaced(maxT, 1, maxT);
fitErr = fit.variance().cwiseSqrt();
e0 = fit[central](0);
e0Err = fitErr(0);
if (coshModel)
// effective mass plot //////////////////////////////////////////////////////
if (!constModel)
{
FOR_STAT_ARRAY(effMass, s)
DMatSample effMass(nSample);
DVec effMassT, fitErr;
Index maxT = (coshModel) ? (nt - 2) : (nt - 1);
double e0, e0Err;
effMass.resizeMat(maxT, 1);
effMassT.setLinSpaced(maxT, 0, maxT-1);
fitErr = fit.variance().cwiseSqrt();
e0 = fit[central](0);
e0Err = fitErr(0);
if (coshModel)
{
for (Index t = 1; t < nt - 1; ++t)
FOR_STAT_ARRAY(effMass, s)
{
effMass[s](t - 1) = acosh((corr[s](t-1) + corr[s](t+1))
/(2.*corr[s](t)));
for (Index t = 1; t < nt - 1; ++t)
{
effMass[s](t - 1) = acosh((corr[s](t-1) + corr[s](t+1))
/(2.*corr[s](t)));
}
}
}
}
else if (linearModel)
{
FOR_STAT_ARRAY(effMass, s)
else if (linearModel)
{
for (Index t = 0; t < nt - 1; ++t)
FOR_STAT_ARRAY(effMass, s)
{
effMass[s](t) = corr[s](t) - corr[s](t+1);
for (Index t = 0; t < nt - 1; ++t)
{
effMass[s](t) = corr[s](t) - corr[s](t+1);
}
}
}
}
else
{
FOR_STAT_ARRAY(effMass, s)
else
{
for (Index t = 1; t < nt; ++t)
FOR_STAT_ARRAY(effMass, s)
{
effMass[s](t - 1) = log(corr[s](t-1)/corr[s](t));
for (Index t = 1; t < nt; ++t)
{
effMass[s](t - 1) = log(corr[s](t-1)/corr[s](t));
}
}
}
p.reset();
p << PlotRange(Axis::x, 0, maxT);
p << PlotRange(Axis::y, e0 - 20.*e0Err, e0 + 20.*e0Err);
p << Color("rgb 'blue'") << PlotBand(0, maxT, e0 - e0Err, e0 + e0Err);
p << Color("rgb 'blue'") << PlotHLine(e0);
p << Color("rgb 'red'") << PlotData(effMassT, effMass);
p << Caption("Effective Mass");
p.display();
}
p.reset();
p << PlotRange(Axis::x, 1, maxT);
p << PlotRange(Axis::y, e0 - 20.*e0Err, e0 + 20.*e0Err);
p << Color("rgb 'blue'") << PlotBand(0, maxT, e0 - e0Err, e0 + e0Err);
p << Color("rgb 'blue'") << PlotHLine(e0);
p << Color("rgb 'red'") << PlotData(effMassT, effMass);
p.display();
}
}
if (doHeatmap)
{
Plot p;
@ -359,6 +401,7 @@ int main(int argc, char *argv[])
}
}
// output //////////////////////////////////////////////////////////////////
if (!outFileName.empty())
{