Asymmetric Error Bars Root
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Functions | Protected Attributes | List of all members TGraphAsymmErrors Class ReferenceHistogram Library TGraph with asymmetric error bars. The TGraphAsymmErrors asymmetric error bars matlab painting is performed thanks to the TGraphPainter class. All asymmetric error bars matplotlib details about the various painting options are given in this class. The picture below asymmetric error bars sigmaplot gives an example: { c1 = new TCanvas("c1","A Simple Graph with asymmetric error bars",200,10,700,500); c1->SetFillColor(42); c1->SetGrid(); c1->GetFrame()->SetFillColor(21); c1->GetFrame()->SetBorderSize(12); const Int_t n = 10;
Gnuplot Asymmetric Error Bars
Double_t x[n] = {-0.22, 0.05, 0.25, 0.35, 0.5, 0.61,0.7,0.85,0.89,0.95}; Double_t y[n] = {1,2.9,5.6,7.4,9,9.6,8.7,6.3,4.5,1}; Double_t exl[n] = {.05,.1,.07,.07,.04,.05,.06,.07,.08,.05}; Double_t eyl[n] = {.8,.7,.6,.5,.4,.4,.5,.6,.7,.8}; Double_t exh[n] = {.02,.08,.05,.05,.03,.03,.04,.05,.06,.03}; Double_t eyh[n] = {.6,.5,.4,.3,.2,.2,.3,.4,.5,.6}; gr = new TGraphAsymmErrors(n,x,y,exl,exh,eyl,eyh); gr->SetTitle("TGraphAsymmErrors Example"); gr->SetMarkerColor(4); gr->SetMarkerStyle(21); gr->Draw("ALP"); return c1;} Definition at line 28 of file TGraphAsymmErrors.h. python asymmetric error bars Public Member Functions TGraphAsymmErrors () TGraphAsymmErrors default constructor. More... TGraphAsymmErrors (Int_t n) TGraphAsymmErrors normal constructor. More... TGraphAsymmErrors (Int_t n, const Float_t *x, const Float_t *y, const Float_t *exl=0, const Float_t *exh=0, const Float_t *eyl=0, const Float_t *eyh=0) TGraphAsymmErrors normal constructor. More... TGraphAsymmErrors (Int_t n, const Double_t *x, const Double_t *y, const Double_t *exl=0, const Double_t *exh=0, const Double_t *eyl=0, const Double_t *eyh=0) TGraphAsymmErrors normal constructor. More... TGraphAsymmErrors (const TVectorF &vx, const TVectorF &vy, const TVectorF &vexl, const TVectorF &vexh, const TVectorF &veyl, const TVectorF &veyh) Constructor with six vectors of floats in input A grapherrors is built with the X coordinates taken from vx and Y coord from vy and the errors from vectors vexl/h and veyl/h. More... TGraphAsymmErrors (const TVectorD &vx, const TVectorD &vy, const TVectorD &vexl, const TVectorD &vexh, const TV
running ROOT here. Please post bug reports in Jira. Moderator: rootdev Post Reply Search Advanced search First unread post • 10 posts • root histogram error bars Page 1 of 1 aapresya Posts: 25 Joined: Thu Dec 01,
Tgraphasymmerrors Draw Options
2005 5:32 Asymmetric Error bars Quote Unread postby aapresya » Thu Feb 15, 2007 18:18 Hi forum,
Tgrapherrors
I am making an efficiency turn-on plot, and I want to see the error bars. I am using the following code Code: Select all
TH1F *h1= new https://root.cern.ch/doc/master/classTGraphAsymmErrors.html TH1F ("h1", "h1", 125, 0, 250);
TH1F *h2= new TH1F ("h2", "h2", 125, 0, 250);
h1->Sumw2();
h2->Sumw2();
Tree->Draw("met.metcorr>>h1", trigger&&offline);
Tree->Draw("met.metcorr>>h2", offline);
gr = new TGraphAsymmErrors();
gr->BayesDivide(h1,h2,"w");
gr->SetMarkerColor(4);
gr->SetMarkerStyle(7);
gr->Fit(fcurve,"R");
The problem is that even for points where the ratio of the two is 1, the https://root.cern.ch/phpBB3/viewtopic.php?t=4542 error bars go above 1.0. What would the procedure be to get the errors right (the efficiency cannot be more than 1.0)? I add an example *ps file to show what I mean. Thank you! Attachments l1-turnon.ps (1.66 MiB) Downloaded 89 times Top brun Posts: 5831 Joined: Wed Aug 27, 2003 10:49 Location: CERN Quote Unread postby brun » Fri Feb 16, 2007 14:11 Could you send a small ROOT file containing your two histograms? Rene Top aapresya Posts: 25 Joined: Thu Dec 01, 2005 5:32 Quote Unread postby aapresya » Sat Feb 17, 2007 5:40 Hi Rene, EDIT: Sorry I first attached the tree file. Now I attached the histos... So, h1 would be the numerator, and h2 the denominator... Thanks a lot! Artur Attachments histos.root (5.69 KiB) Downloaded 87 times Top brun Posts: 5831 Joined: Wed Aug 27, 2003 10:49 Location: CERN Quote Unread postby brun » Sat Feb 17, 2007 9:25 In the CVS head, I have modified TGraphAsymmErrors such that the high error + e
a quick overview of the site Help Center Detailed answers to any questions you might have Meta Discuss the workings and policies of http://physics.stackexchange.com/questions/151444/error-propagation-with-different-plus-and-min-errors-and-data-fitting this site About Us Learn more about Stack Overflow the company Business Learn more about hiring developers or posting ads with us Physics Questions Tags Users Badges Unanswered Ask Question _ Physics Stack Exchange is a question and answer site for active researchers, academics and students of physics. Join them; it only takes a minute: Sign up Here's how it works: Anybody error bars can ask a question Anybody can answer The best answers are voted up and rise to the top error propagation with different plus and min errors and data fitting up vote 3 down vote favorite 1 I am refreshing my memory on error propagation and data fitting (Levenberg-Marquadt). You have the absolute (measurement) error, the relative (measurement) error, the population/sample standard asymmetric error bars deviation and the population/sample standard error. These are typically written down as: $$a \pm b = a (+b, -b)$$ But suppose you have different errors in the plus and min direction? $$a (+b, -c)$$ What are the rules here again and how are the formula for error propagation modified? Also, how is this taken into account in data fitting? Any good resource on this is welcome. error-analysis share|cite|improve this question edited Dec 9 '14 at 20:43 John M 4971519 asked Dec 9 '14 at 18:47 user965972 1162 Interesting question. Asymmetric errors seem terribly complicated compared to our familiar errors, see page 28: phas.ubc.ca/~oser/p509/Lec_10.pdf . –jinawee Dec 9 '14 at 19:15 "Asymmetric errors: Quite honestly, the typical physicist doesn't have a clue." That's me alright. :( –user965972 Dec 9 '14 at 20:31 @jinawee The resource you posted is interesting, I had not come across the Barlow method. However, at least for the examples given, the results are extremely similar to propagating the positive and negative uncertainties separately. Are the differences worth worrying about unless you are very certain
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