portelli/LatAnalyze
1
0
Fork 0
mirror of https://github.com/aportelli/LatAnalyze.git synced 2024-11-10 00:45:36 +00:00
Code Releases Activity

Added conditionals+appropriate limits for linear model; fi still doesn't work

Browse Source
This commit is contained in:
Andrew Zhen Ning Yong 2019-01-04 15:48:51 +00:00
parent 8a229ec0b0
commit 32a8495026
1 changed files with 79 additions and 8 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

87
physics/2pt-fit.cpp
View File

@ -13,6 +13,8 @@ using namespace Latan;
int main(int argc, char *argv[])
{
cout << "Version edited by: Andrew Yong, 4/01/19\n" << endl;
// parse arguments /////////////////////////////////////////////////////////
OptParser opt;
bool parsed, doPlot, doHeatmap, doCorr, fold;
@ -30,7 +32,7 @@ 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|<interpreter code>)", "cosh");
"fit model (exp|exp2|exp3|cosh|cosh2|cosh3|linear|<interpreter code>)", "cosh");
opt.addOption("" , "nPar" , OptParser::OptType::value , true,
"number of model parameters for custom models "
"(-1 if irrelevant)", "-1");
@ -118,6 +120,8 @@ int main(int argc, char *argv[])
// make model //////////////////////////////////////////////////////////////
DoubleModel mod;
bool coshModel = false;
bool linearModel = false;
if ((model == "exp") or (model == "exp1"))
{
@ -174,6 +178,16 @@ int main(int argc, char *argv[])
+ p[5]*(exp(-p[2]*x[0])+exp(-p[4]*(nt-x[0])));
}, 1, nPar);
}
else if (model == "linear")
{
linearModel = true;
nPar = 2;
mod.setFunction([](const double *x, const double *p)
{
return p[1]-p[0]*x[0];
}, 1, nPar);
}
else
{
if (nPar > 0)
@ -207,23 +221,78 @@ int main(int argc, char *argv[])
data.setUnidimData(tvec, corr);
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));
if((model == "cosh") or (model =="cosh1") or (model == "cosh2") or (model == "cosh3"))
{
mod.parName().setName(p, "E_" + strFrom(p/2));
mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
}
else if(model == "linear")
{
mod.parName().setName(p, "dm");
mod.parName().setName(p + 1, "dA/A_0");
}
else // to edit when necessary
{
mod.parName().setName(p, "E_" + strFrom(p/2));
mod.parName().setName(p + 1, "Z_" + strFrom(p/2));
}
}
init(0) = log(data.y(nt/4, 0)[central]/data.y(nt/4 + 1, 0)[central]);
init(1) = data.y(nt/4, 0)[central]/(exp(-init(0)*nt/4));
for (Index p = 2; p < nPar; p += 2)
// initial values & limits//////////////////////////////////////////////////////////
if(model == "linear")
{
init(0) = data.y(nt/4,0)[central] - data.y(nt/4 + 1,0)[central] ;
init(1) = data.y(nt/4,0)[central] + init(0)*nt/4;
cout << "init(0) = " << init(0) << "\tinit(1) = " << init(1) << endl;
for (Index p = 2; p < nPar; p += 2)
{
init(p) = 2*init(p - 2);
init(p + 1) = init(p - 1)/2.;
}
double standard = 10;
for (Index p = 0; p < nPar; p += 2)
{
// allows us to vary the gradient without flipping sign(ie slope direction)
if(init(p)>0) // positive gradient
{
globMin.setLowLimit(p, 0);
globMin.setHighLimit(p, init(p)*standard);
}
else // negative gradient (or flat)
{
globMin.setLowLimit(p, init(p)*standard);
globMin.setHighLimit(p, 0);
}
globMin.setLowLimit(p + 1, init(p + 1)-standard);
globMin.setHighLimit(p + 1, init(p + 1)+standard);
locMin.setLowLimit(p, init(p)/standard);
}
}
for (Index p = 0; p < nPar; p += 2)
else
{
globMin.setLowLimit(p, 0.);
init(0) = log(data.y(nt/4, 0)[central]/data.y(nt/4 + 1, 0)[central]);
init(1) = data.y(nt/4, 0)[central]/(exp(-init(0)*nt/4));
cout << "init(0) = " << init(0) << "\tinit(1) = " << init(1) << endl;
for (Index p = 2; p < nPar; p += 2)
{
init(p) = 2*init(p - 2);
init(p + 1) = init(p - 1)/2.;
}
for (Index p = 0; p < nPar; p += 2)
{
globMin.setLowLimit(p, 0.);
globMin.setHighLimit(p, 10.*init(p));
globMin.setLowLimit(p + 1, -10.*init(p + 1));
globMin.setHighLimit(p + 1, 10.*init(p + 1));
locMin.setLowLimit(p, 0.);
}
}
globMin.setPrecision(0.001);
globMin.setMaxIteration(100000);
@ -263,6 +332,7 @@ int main(int argc, char *argv[])
Index maxT = (coshModel) ? (nt - 2) : (nt - 1);
double e0, e0Err;
p << Title("Correlated Fit");
p << PlotRange(Axis::x, 0, nt - 1);
p << LogScale(Axis::y);
p << Color("rgb 'blue'") << PlotPredBand(fit.getModel(_), 0, nt - 1);
@ -296,6 +366,7 @@ int main(int argc, char *argv[])
}
}
p.reset();
p << Title("Uncorrelated Fit");
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);