#include <TParameter.h>
#include <TMacro.h>
#include <TPaveText.h>
+#include <TVirtualFitter.h>
// --- AliRoot header files ---
#include "AliLog.h"
func->SetParLimits(1, 0, xmax);
func->SetParLimits(2, 0, xmax);
- func->SetParLimits(3, 0.01, 0.4);
+ func->SetParLimits(3, 0.01, 1);
if (sigmaN < 0) func->FixParameter(4, 0);
else func->SetParLimits(4, 0, xmax);
fELossMinEntries(1000),
fELossMaxEntries(-1),
fELossGoodParError(0.1),
+ fELossMinSharing(0.1),
fROErrorsBad(0.3),
- fROErrorsFkup(0.5)
+ fROErrorsFkup(0.5),
+ fMaxNProblem(10),
+ fMaxNBad(10),
+ fNoFits(false)
{
}
ProcessExternalParam("ELossGoodParError", fELossGoodParError);
ProcessExternalParam("ROErrorsBad", fROErrorsBad);
ProcessExternalParam("ROErrorsFkup", fROErrorsFkup);
+ ProcessExternalParam("ELossMinSharing", fELossMinSharing);
Double_t tmp = 0;
- ProcessExternalParam("CommonScale", tmp);
- fDoScale = tmp > 0;
- ProcessExternalParam("ELossMinEntries", tmp);
- fELossMinEntries = tmp;
- ProcessExternalParam("ELossMaxEntries", tmp);
- fELossMaxEntries = tmp;
+ ProcessExternalParam("CommonScale", tmp);
+ fDoScale = tmp > 0; tmp = fELossMinEntries;
+ ProcessExternalParam("ELossMinEntries", tmp);
+ fELossMinEntries = tmp; tmp = fELossMaxEntries;
+ ProcessExternalParam("ELossMaxEntries", tmp);
+ fELossMaxEntries = tmp; tmp = fMaxNProblem;
+ ProcessExternalParam("MaxNProblem", tmp);
+ fMaxNProblem = tmp; tmp = 0;
+ fELossMaxEntries = tmp; tmp = fMaxNBad;
+ ProcessExternalParam("MaxNBad", tmp);
+ fMaxNBad = tmp; tmp = 0;
+ ProcessExternalParam("NoFits", tmp);
+ fNoFits = tmp > 0; tmp = 0;
GetThresholds();
else if (name.EqualTo("ELossGoodParError")) fELossGoodParError = val;
else if (name.EqualTo("ROErrorsBad")) fROErrorsBad = val;
else if (name.EqualTo("ROErrorsFkup")) fROErrorsFkup = val;
+ else if (name.EqualTo("MaxNProblem")) fMaxNProblem = val;
+ else if (name.EqualTo("MaxNBad")) fMaxNBad = val;
AliDebugF(3, "Threshold %s=%f", name.Data(), val);
}
}
{
return BasicCheck(hist);
}
+namespace {
+ Double_t Chi2Scale(TH1* h, Double_t base=10000)
+ {
+ return 1. / TMath::Max(1., h->GetEntries() / base);
+ }
+ void AddLine(TObjArray* lines,
+ Double_t x1, Double_t x2, Double_t x3,
+ Double_t y, Double_t dy,
+ const char* name, Double_t val, Double_t lim,
+ Bool_t ok, Int_t color)
+ {
+ TString n; n.Form("%s:", name);
+ TLatex* ltx = new TLatex(x1, y, n);
+ ltx->SetNDC(true);
+ ltx->SetTextSize(dy-0.01);
+ ltx->SetTextColor(color);
+ lines->Add(ltx);
+
+ n.Form("%7.3f", val);
+ ltx = new TLatex(x2, y, n);
+ ltx->SetNDC(true);
+ ltx->SetTextSize(dy-0.01);
+ ltx->SetTextColor(color);
+ lines->Add(ltx);
+
+ if (lim < 0) n = "(ignored)";
+ else n.Form("%c %4.2f", ok ? '<' : '>', lim);
+ ltx = new TLatex(x3, y, n);
+ ltx->SetNDC(true);
+ ltx->SetTextSize(dy-0.01);
+ ltx->SetTextColor(color);
+ lines->Add(ltx);
+ }
+}
+
//__________________________________________________________________
UShort_t
AliFMDQAChecker::CheckFit(TH1* hist, const TFitResultPtr& res,
{
color = kGreen+4;
+ // Check if there's indeed a result - if not, flag as OK
+ if (!res.Get()) return 0;
+
UShort_t ret = 0;
Int_t nPar = res->NPar();
Double_t dy = .06;
Double_t x = .2;
+ Double_t x2 = .3;
+ Double_t x3 = .4;
Double_t y = .9-dy;
Double_t chi2 = res->Chi2();
Int_t nu = res->Ndf();
+ Double_t s = Chi2Scale(hist,fELossMinEntries);
Double_t red = (nu == 0 ? fELossFkupChi2Nu : chi2 / nu);
TObjArray* lines = 0;
// TLatex* lRed = 0;
TLatex* ltx = 0;
Int_t chi2Check = 0;
Double_t chi2Lim = fELossBadChi2Nu;
+ if (AliDebugLevel() > 0)
+ printf("FIT: %s, 1, %d, %f, %f\n", hist->GetName(),
+ Int_t(hist->GetEntries()), red, s * red);
+ red *= s;
if (red > fELossBadChi2Nu) { // || res->Prob() < .01) {
// AliWarningF("Fit gave chi^2/nu=%f/%d=%f>%f (%f)",
// res->Chi2(), res->Ndf(), red, fELossBadChi2Nu,
lines = new TObjArray(nPar+3);
lines->SetName("lines");
lines->SetOwner(true);
-
- ltx = new TLatex(x, y, Form("#chi^{2}/#nu: %7.3f %c %6.2f",
- red, chi2Check < 1 ? '<' : '>',
- chi2Lim));
- ltx->SetNDC(true);
- ltx->SetTextColor(chi2Check < 1 ? color :
- chi2Check < 2 ? kOrange+2 : kRed+2);
- // ltx->SetTextColor(color);
- ltx->SetTextSize(dy-.01);
- lines->Add(ltx);
- // lRed = ltx;
+
+ AddLine(lines, x, x2, x3, y, dy, "#chi^{2}/#nu", red, chi2Lim,
+ chi2Check < 1, chi2Check < 1 ? color :
+ chi2Check < 2 ? kOrange+2 : kRed+2);
Double_t x1 = .85;
Double_t y1 = .5;
-#if 0
- ltx = new TLatex(x1, y1, Form("[thresholds: %6.2f, %6.2f]",
- fELossBadChi2Nu, fELossFkupChi2Nu));
- ltx->SetTextColor(kGray+3);
- ltx->SetTextSize(dy-.01);
- ltx->SetNDC(true);
- ltx->SetTextAlign(31);
- // lines->Add(ltx);
-#endif
// y1 -= dy;
ltx = new TLatex(x1, y1, Form("Fit range: [%6.2f,%6.2f]", low, high));
ltx->SetTextAlign(31);
ltx->SetNDC(true);
lines->Add(ltx);
+
+ y1 -= dy;
+ ltx = new TLatex(x1, y1, Form("%s: %f #pm %f",
+ res->ParName(1).c_str(),
+ res->Parameter(1),
+ res->ParError(1)));
+ ltx->SetTextColor(kGray+3);
+ ltx->SetTextSize(dy-.01);
+ ltx->SetTextAlign(31);
+ ltx->SetNDC(true);
+ lines->Add(ltx);
}
// Now check the relative error on the fit parameters
Bool_t ok = (i == 3) || (rel < thr);
if (lines) {
y -= dy;
- TString txt(Form("#delta%s/%s: %7.3f ",
- res->ParName(i).c_str(),
- res->ParName(i).c_str(),
- /*pv, pe,*/ rel));
- if (i != 3) txt.Append(Form("%c %4.2f", ok ? '<' : '>', thr));
- else txt.Append("(ignored)");
- ltx = new TLatex(x, y, txt);
- ltx->SetNDC(true);
- ltx->SetTextSize(dy-.01);
- ltx->SetTextColor(ok ? color : kOrange+2);
- lines->Add(ltx);
+ AddLine(lines, x, x2, x3, y, dy,Form("#delta%s/%s",
+ res->ParName(i).c_str(),
+ res->ParName(i).c_str()),
+ rel, (i == 3 ? -1 : thr), ok, ok ? color : kOrange+2);
}
if (i == 3) continue; // Skip sigma
if (ok) parsOk++;
if (ret > 1) color = kRed+2;
if (ret > 0) color = kOrange+2;
- TList* lf = hist->GetListOfFunctions();
- TObject* old = lf->FindObject(lines->GetName());
- if (old) {
- lf->Remove(old);
- delete old;
+ if (lines) {
+ TList* lf = hist->GetListOfFunctions();
+ TObject* old = lf->FindObject(lines->GetName());
+ if (old) {
+ lf->Remove(old);
+ delete old;
+ }
+ lf->Add(lines);
}
- lf->Add(lines);
hist->SetStats(false);
return ret;
}
-//__________________________________________________________________
-void
-AliFMDQAChecker::AddFitResults(TH1* hist, const TFitResultPtr& res,
- Int_t color, Double_t low, Double_t high) const
-{
- // Obsolete - not used
- if (!fShowFitResults) return;
-
- Int_t nPar = res->NPar();
- TObjArray* lines = new TObjArray(nPar+1);
- lines->SetOwner(kTRUE);
- lines->SetName("fitResults");
-
- Double_t dy = .06;
- Double_t x = .2;
- Double_t y = .9-dy;
- Double_t chi2 = res->Chi2();
- Int_t nu = res->Ndf();
- Double_t red = (nu == 0 ? fELossFkupChi2Nu : chi2 / nu);
-
- TLatex* ltx = new TLatex(x, y, Form("#chi^{2}/#nu: %7.3f",red));
- ltx->SetNDC(true);
- ltx->SetTextColor(color);
- ltx->SetTextSize(dy-.01);
- lines->Add(ltx);
-
- y -= dy;
- ltx = new TLatex(x, y, Form("[thresholds: %6.2f, %6.2f]",
- fELossBadChi2Nu, fELossFkupChi2Nu));
- ltx->SetTextColor(kGray+3);
- ltx->SetTextSize(dy-.01);
- ltx->SetNDC(true);
- lines->Add(ltx);
-
- y -= dy;
- ltx = new TLatex(x, y, Form("Fit range: [%6.2f,%6.2f]", low, high));
- ltx->SetTextColor(kGray+3);
- ltx->SetTextSize(dy-.01);
- ltx->SetNDC(true);
- lines->Add(ltx);
-
- for (Int_t i = 0; i < nPar; i++) {
- if (res->IsParameterFixed(i)) continue;
- y -= dy;
- Double_t pv = res->Parameter(i);
- Double_t pe = res->ParError(i);
- Double_t rel = (pv == 0 ? 100 : pe / pv);
- ltx = new TLatex(x, y, Form("#delta%s/%s: %7.3f",
- res->ParName(i).c_str(),
- res->ParName(i).c_str(),
- /*pv, pe,*/ rel));
- ltx->SetNDC(true);
- ltx->SetTextColor(color);
- ltx->SetTextSize(dy-.01);
- lines->Add(ltx);
- }
- TList* lf = hist->GetListOfFunctions();
- TObject* old = lf->FindObject(lines->GetName());
- if (old) {
- lf->Remove(old);
- delete old;
- }
- lf->Add(lines);
- hist->SetStats(false);
-}
-
//__________________________________________________________________
UShort_t
AliFMDQAChecker::CheckRaw(AliRecoParam::EventSpecie_t specie,
sum += n * roErrors->GetYaxis()->GetBinCenter(j);
cnt += n;
}
- Double_t mean = sum / cnt;
+ Double_t mean = (cnt <= 0 ? 0 : sum / cnt);
Double_t x = ((i-.5) * (1-0.1-0.1) / 3 + 0.1);
ltx = new TLatex(x, kROErrorsLabelY, Form("Mean: %6.3f", mean));
}
}
else if (name.Contains("eloss",TString::kIgnoreCase)) {
- // Try to fit a function to the histogram
- if (hist->GetEntries() < 1000) return ret;
+ // If we' asked to not fit the data, return immediately
+ if (fNoFits) return ret;
+ // Do not fit cosmic or calibration data
if (specie == AliRecoParam::kCosmic ||
specie == AliRecoParam::kCalib) return ret;
+ // Do not fit `expert' histograms
+ if (hist->TestBit(AliQAv1::GetExpertBit())) return ret;
+ // Do not fit histograms with too little data
+ if (hist->GetEntries() < fELossMinEntries) return ret;
+ // Try to fit a function to the histogram
Double_t xMin = hist->GetXaxis()->GetXmin();
Double_t xMax = hist->GetXaxis()->GetXmax();
func->SetLineColor(kGreen+4);
// func->SetLineStyle(2);
Double_t high = xMax; // xMaxY+fELossNRMS*rms;
+ if (fELossNRMS > 0) high = xMaxY+fELossNRMS*rms;
+
+ // Check we don't have an empty fit range
+ if (low >= high) return ret;
- TFitResultPtr res = hist->Fit(func, "QS", "", low, high);
+ // Check that we have enough counts in the fit range
+ Int_t bLow = hist->FindBin(low);
+ Int_t bHigh = hist->FindBin(high);
+ if (bLow >= bHigh || hist->Integral(bLow, bHigh) < fELossMinEntries)
+ return ret;
- Int_t color = func->GetLineColor();
- UShort_t qual = CheckFit(hist, res, low, high, color);
+ // Set our fit function
+ TString fitOpt("QS");
+ TFitResultPtr res = hist->Fit(func, fitOpt, "", low, high);
+ Int_t color = func->GetLineColor();
+ UShort_t qual = CheckFit(hist, res, low, high, color);
// Make sure we save the function in the full range of the histogram
func = hist->GetFunction("landauGaus");
- func->SetRange(xMin, xMax);
+ if (fELossNRMS <= 0) func->SetRange(xMin, xMax);
// func->SetParent(hist);
func->Save(xMin, xMax, 0, 0, 0, 0);
func->SetLineColor(color);
+ fitOpt.Append("+");
+ res = hist->Fit("pol2", fitOpt, "", fELossMinSharing, low-0.05);
+ func = hist->GetFunction("pol2");
+ Double_t s = Chi2Scale(hist,fELossMinEntries*100);
+ Double_t chi2 = (!res.Get() ? 0 : res->Chi2());
+ Int_t nu = (!res.Get() ? 1 : res->Ndf());
+ Double_t red = s * (nu == 0 ? fELossFkupChi2Nu : chi2 / nu);
+ if (AliDebugLevel())
+ printf("FIT: %s, 2, %d, %f, %f\n", hist->GetName(),
+ Int_t(hist->GetEntries()), red, s * red);
+ red *= s;
+ if (red > fELossFkupChi2Nu) func->SetLineColor(kRed);
+ else func->SetLineColor(kGreen+4);
+
// Now check if this histogram should be cleared or not
if (fELossMaxEntries > 0 && hist->GetEntries() > fELossMaxEntries)
- hist->SetBit(BIT(23));
+ hist->SetBit(AliFMDQADataMakerRec::kResetBit);
if (qual > 0) {
func->SetLineWidth(3);
func->SetLineStyle(1);
AliDebugF(3, "Combined sum is %d -> %f", ret, rv[specie]);
if (status) {
- Int_t qual = 0;
+ Int_t nProblem = 0;
+ Int_t nBad = 0;
for (Int_t i = 1; i < status->GetXaxis()->GetNbins(); i++) {
- if (status->GetBinContent(i, 3) > 1) qual++;
- if (status->GetBinContent(i, 4) > 0) qual++;
+ nProblem += status->GetBinContent(i, 3);
+ nBad += status->GetBinContent(i, 4);
}
+ Int_t qual = 0;
+ if (nProblem > fMaxNProblem) qual++;
+ if (nBad > fMaxNBad) qual += 2;
status->SetUniqueID(Quality2Bit(qual));
AddStatusPave(status, qual);
}
hist->DrawCopy(opt);
// Special cases
- if (name.Contains("readouterrors", TString::kIgnoreCase)) {
-#if 0
- for (Int_t kk = 1; kk <= 3; kk++) {
- TH1* proj = static_cast<TH2*>(hist)->ProjectionY("",kk,kk);
- Double_t m = proj->GetMean();
- TLatex* l = new TLatex(kk, 30, Form("Mean: %f", m));
- l->SetTextAngle(90);
- l->SetTextColor(m > 10 ? kRed+1 : m > .7 ? kOrange+2 :kGreen+2);
- l->Draw();
- }
-#endif
- }
- else {
+ if (!name.Contains("readouterrors", TString::kIgnoreCase)) {
gStyle->SetOptTitle(0);
TPad* insert = new TPad("insert", "Zoom",
.5,.5, .99, .95, 0, 0, 0);
}
// Print to a post-script file
fImage[specie]->Print(outName, "ps");
- if (AliDebugLevel() > 1)
+ if (AliDebugLevel() > 0)
fImage[specie]->Print(Form("%s_%d.png",
AliRecoParam::GetEventSpecieName(specie),
AliQAChecker::Instance()->GetRunNumber()));
git://git.uio.no / u / mrichter / AliRoot.git / blobdiff