From 555b182c48e18c19ef57e1833e6b684834c8cb56 Mon Sep 17 00:00:00 2001
From: Philipp Klaus <klaus@physik.uni-frankfurt.de>
Date: Tue, 19 Aug 2014 16:19:03 +0200
Subject: [PATCH] s-curves: view_ scripts added to analyze ROOT script
---
s-curves/README.txt | 3 +-
s-curves/view_all_averages.c | 231 ++++++++++++++++++++++++
s-curves/view_rootfile.c | 328 +++++++++++++++++++++++++++++++++++
3 files changed, 561 insertions(+), 1 deletion(-)
create mode 100644 s-curves/view_all_averages.c
create mode 100644 s-curves/view_rootfile.c
diff --git a/s-curves/README.txt b/s-curves/README.txt
index 99f4373..0e36b24 100644
--- a/s-curves/README.txt
+++ b/s-curves/README.txt
@@ -8,7 +8,8 @@ Sensors running in the S-Curve test mode create a custom output stream.
The data is is received from the event builder and written to HLD files.
Those .hld files then have to be processed by the C++ program
`process_rootmulti_4xbanks` which organizes the data as a TTree in a ROOT file.
-The ROOT data file can then be further analyzed with a simple ROOT macro.
+The ROOT data file can then be further analyzed with a simple ROOT macro,
+such as `view_all_averages.c` and `view_rootfile.c`.
Authors
-------
diff --git a/s-curves/view_all_averages.c b/s-curves/view_all_averages.c
new file mode 100644
index 0000000..16aff4f
--- /dev/null
+++ b/s-curves/view_all_averages.c
@@ -0,0 +1,231 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <time.h>
+#include <cmath>
+
+#include <TTree.h>
+#include <TFile.h>
+#include "TROOT.h"
+#include "TObject.h"
+#include "TBrowser.h"
+#include "TH1F.h"
+#include "TMath.h"
+#include "TF1.h"
+#include "TCanvas.h"
+#include "TRandom3.h"
+#include "TApplication.h"
+
+
+int view_all_averages() {
+
+ cout << "Starting ROOT File analysis\n" << endl;
+
+ //TFile *rfile1 = new TFile("dis_discri_test4.root","READ");
+ //TFile *rfile1 = new TFile("from_Samir/output.root","READ");
+ TFile *rfile1 = new TFile("./output.root","READ");
+ //TFile *rfile1 = new TFile("../hld/sensor_blackbox1/test_10.root","READ");
+ //TFile *rfile1 = new TFile("../hld/sensor_blackbox1/final_multi_50.root","READ");
+ if ( rfile1->IsOpen() ) printf("file opened successfully\n");
+
+ UShort_t threshold;
+ //Float_t* pixelprob;
+ Float_t pixelprob[576*1152];
+ int A_best_1e7 = 0;
+ int A_best_1e6 = 0;
+ int A_best_1e5 = 0;
+ Float_t A_best_1e7f = 1.;
+ Float_t A_best_1e6f = 1.;
+ Float_t A_best_1e5f = 1.;
+ int B_best_1e7 = 0;
+ int B_best_1e6 = 0;
+ int B_best_1e5 = 0;
+ Float_t B_best_1e7f = 1.;
+ Float_t B_best_1e6f = 1.;
+ Float_t B_best_1e5f = 1.;
+ int C_best_1e7 = 0;
+ int C_best_1e6 = 0;
+ int C_best_1e5 = 0;
+ Float_t C_best_1e7f = 1.;
+ Float_t C_best_1e6f = 1.;
+ Float_t C_best_1e5f = 1.;
+ int D_best_1e7 = 0;
+ int D_best_1e6 = 0;
+ int D_best_1e5 = 0;
+ Float_t D_best_1e7f = 1.;
+ Float_t D_best_1e6f = 1.;
+ Float_t D_best_1e5f = 1.;
+
+ canv = new TCanvas("canv","canv",600,400);
+ canv->cd(1);
+ hist1 = new TH1F( "scurve1", "S-Curve" , 255,0.,255.);
+ hist2 = new TH1F( "scurve2", "S-Curve" , 255,0.,255.);
+ hist3 = new TH1F( "scurve3", "S-Curve" , 255,0.,255.);
+ hist4 = new TH1F( "scurve4", "S-Curve" , 255,0.,255.);
+ hist1->SetLineColor( 1);
+ hist2->SetLineColor( 2);
+ hist3->SetLineColor( 3);
+ hist4->SetLineColor( 4);
+
+ TTree *scurveTree = (TTree *)rfile1->Get("scurves0");
+ scurveTree->SetBranchAddress( "threshold" , &threshold);
+ scurveTree->SetBranchAddress( "pixelprob" , pixelprob);
+
+ int entries = (int) scurveTree->GetEntries();
+ int rest = 256 - (256 / entries) * entries;
+ int step = (256 - rest) / (entries-1);
+ int scalefactor = step;
+ cout << "Found " << entries << " entries (thresholds), using stepsize " << scalefactor << endl;
+ for(int i=0;i<entries;i++){
+ scurveTree->GetEntry(i);
+ Float_t pix_avg = 0.;
+ for (int pix_id = 0; pix_id < 576*1152; pix_id++){
+ pix_avg += pixelprob[pix_id];
+ }
+ pix_avg = pix_avg/(576*1152/4);
+ //printf(">>> %f\n",abs((pix_avg-0.00001)));
+ if ( abs((pix_avg-0.00001)) < abs((A_best_1e5f-0.00001)) ){
+ A_best_1e5f = pix_avg;
+ A_best_1e5 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.000001)) < abs((A_best_1e6f-0.000001)) ){
+ A_best_1e6f = pix_avg;
+ A_best_1e6 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.0000001)) < abs((A_best_1e7f-0.0000001)) ){
+ A_best_1e7f = pix_avg;
+ A_best_1e7 = i*scalefactor;
+ }
+
+ hist1->Fill(i*scalefactor, pix_avg);
+ }
+
+ TTree *scurveTree = (TTree *)rfile1->Get("scurves1");
+ scurveTree->SetBranchAddress( "threshold" , &threshold);
+ scurveTree->SetBranchAddress( "pixelprob" , pixelprob);
+
+ int entries = (int) scurveTree->GetEntries();
+ rest = 256 - (256 / entries) * entries;
+ step = (256 - rest) / (entries-1);
+ scalefactor = step;
+ cout << "Found " << entries << " entries (thresholds), using stepsize " << scalefactor << endl;
+ for(int i=0;i<entries;i++){
+ scurveTree->GetEntry(i);
+ pix_avg = 0.;
+ for (int pix_id = 0; pix_id < 576*1152; pix_id++){
+ pix_avg += pixelprob[pix_id];
+ }
+ pix_avg = pix_avg/(576*1152/4);
+ if ( abs((pix_avg-0.00001)) < abs((B_best_1e5f-0.00001)) ){
+ B_best_1e5f = pix_avg;
+ B_best_1e5 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.000001)) < abs((B_best_1e6f-0.000001)) ){
+ B_best_1e6f = pix_avg;
+ B_best_1e6 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.0000001)) < abs((B_best_1e7f-0.0000001)) ){
+ B_best_1e7f = pix_avg;
+ B_best_1e7 = i*scalefactor;
+ }
+
+ hist2->Fill(i*scalefactor, pix_avg);
+ }
+
+ TTree *scurveTree = (TTree *)rfile1->Get("scurves2");
+ scurveTree->SetBranchAddress( "threshold" , &threshold);
+ scurveTree->SetBranchAddress( "pixelprob" , pixelprob);
+
+ int entries = (int) scurveTree->GetEntries();
+ rest = 256 - (256 / entries) * entries;
+ step = (256 - rest) / (entries-1);
+ scalefactor = step;
+ cout << "Found " << entries << " entries (thresholds), using stepsize " << scalefactor << endl;
+ for(int i=0;i<entries;i++){
+ scurveTree->GetEntry(i);
+ pix_avg = 0.;
+ for (int pix_id = 0; pix_id < 576*1152; pix_id++){
+ pix_avg += pixelprob[pix_id];
+ }
+ pix_avg = pix_avg/(576*1152/4);
+ if ( abs((pix_avg-0.00001)) < abs((C_best_1e5f-0.00001)) ){
+ C_best_1e5f = pix_avg;
+ C_best_1e5 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.000001)) < abs((C_best_1e6f-0.000001)) ){
+ C_best_1e6f = pix_avg;
+ C_best_1e6 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.0000001)) < abs((C_best_1e7f-0.0000001)) ){
+ C_best_1e7f = pix_avg;
+ C_best_1e7 = i*scalefactor;
+ }
+
+ hist3->Fill(i*scalefactor, pix_avg);
+ }
+
+ TTree *scurveTree = (TTree *)rfile1->Get("scurves3");
+ scurveTree->SetBranchAddress( "threshold" , &threshold);
+ scurveTree->SetBranchAddress( "pixelprob" , pixelprob);
+
+ int entries = (int) scurveTree->GetEntries();
+ rest = 256 - (256 / entries) * entries;
+ step = (256 - rest) / (entries-1);
+ scalefactor = step;
+ cout << "Found " << entries << " entries (thresholds), using stepsize " << scalefactor << endl;
+ for(int i=0;i<entries;i++){
+ scurveTree->GetEntry(i);
+ pix_avg = 0.;
+ for (int pix_id = 0; pix_id < 576*1152; pix_id++){
+ pix_avg += pixelprob[pix_id];
+ }
+ pix_avg = pix_avg/(576*1152/4);
+ if ( abs((pix_avg-0.00001)) < abs((D_best_1e5f-0.00001)) ){
+ D_best_1e5f = pix_avg;
+ D_best_1e5 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.000001)) < abs((D_best_1e6f-0.000001)) ){
+ D_best_1e6f = pix_avg;
+ D_best_1e6 = i*scalefactor;
+ }
+ if ( abs((pix_avg-0.0000001)) < abs((D_best_1e7f-0.0000001)) ){
+ D_best_1e7f = pix_avg;
+ D_best_1e7 = i*scalefactor;
+ }
+
+ hist4->Fill(i*scalefactor, pix_avg);
+ }
+
+ hist1->RebinX(scalefactor);
+ hist1->Draw("");
+ hist2->RebinX(scalefactor);
+ hist2->Draw("same");
+ hist3->RebinX(scalefactor);
+ hist3->Draw("same");
+ hist4->RebinX(scalefactor);
+ hist4->Draw("same");
+
+ leg = new TLegend(0.8,0.1,0.94,0.7);
+ leg->SetHeader("Average");
+ leg->AddEntry(hist1,"A","l");
+ leg->AddEntry(hist2,"B","l");
+ leg->AddEntry(hist3,"C","l");
+ leg->AddEntry(hist4,"D","l");
+
+ leg->Draw();
+
+ printf("FHR analysis: 1e-7 1e-6 1e-5\n");
+ printf(" A %03d %03d %03d\n", A_best_1e7, A_best_1e6, A_best_1e5);
+ printf(" B %03d %03d %03d\n", B_best_1e7, B_best_1e6, B_best_1e5);
+ printf(" C %03d %03d %03d\n", C_best_1e7, C_best_1e6, C_best_1e5);
+ printf(" D %03d %03d %03d\n", D_best_1e7, D_best_1e6, D_best_1e5);
+
+ //gApplication->Terminate();
+ return 1;
+
+}
+
+
+
+
+
diff --git a/s-curves/view_rootfile.c b/s-curves/view_rootfile.c
new file mode 100644
index 0000000..327234b
--- /dev/null
+++ b/s-curves/view_rootfile.c
@@ -0,0 +1,328 @@
+#include <stdio.h>
+#include <stdlib.h>
+#include <string.h>
+#include <time.h>
+
+#include <TTree.h>
+#include <TFile.h>
+#include "TROOT.h"
+#include "TObject.h"
+#include "TBrowser.h"
+#include "TH1F.h"
+#include "TMath.h"
+#include "TF1.h"
+#include "TCanvas.h"
+#include "TRandom3.h"
+#include "TApplication.h"
+
+
+int view_rootfile() {
+
+ cout << "Starting ROOT File analysis\n" << endl;
+
+ //TFile *rfile1 = new TFile("dis_discri_test4.root","READ");
+ TFile *rfile1 = new TFile("from_Samir/output.root","READ");
+ //TFile *rfile1 = new TFile("../hld/sensor_blackbox1/test_10.root","READ");
+ //TFile *rfile1 = new TFile("../hld/sensor_blackbox1/final_multi_50.root","READ");
+ if ( rfile1->IsOpen() ) printf("file opened successfully\n");
+
+ TTree *scurveTree = (TTree *)rfile1->Get("scurves3");
+
+ UShort_t threshold;
+ //Float_t* pixelprob;
+ Float_t pixelprob[576*1152];
+
+// // Tests
+// Float_t number[] = { 31., 28., 31., 30., 31., 30., 31., 31., 30., 31., 30., 31. };
+// printf("> %f\n",number[0]);
+// printf("> %f\n",number[1]);
+// pixelprob = number;
+// printf("> %f\n",*pixelprob++);
+// printf("> %f\n",*pixelprob);
+// return 0;
+
+ scurveTree->SetBranchAddress( "threshold" , &threshold);
+ scurveTree->SetBranchAddress( "pixelprob" , pixelprob);
+
+ canv = new TCanvas("canv","canv",600,400);
+ canv->cd(1);
+ hist1 = new TH1F( "scurve1", "S-Curve" , 255,0.,255.);
+ hist2 = new TH1F( "scurve2", "S-Curve" , 255,0.,255.);
+ hist3 = new TH1F( "scurve3", "S-Curve" , 255,0.,255.);
+ hist4 = new TH1F( "scurve4", "S-Curve" , 255,0.,255.);
+ hist5 = new TH1F( "scurve5", "S-Curve" , 255,0.,255.);
+ hist6 = new TH1F( "scurve6", "S-Curve" , 255,0.,255.);
+ hist7 = new TH1F( "scurve7", "S-Curve" , 255,0.,255.);
+ hist8 = new TH1F( "scurve8", "S-Curve" , 255,0.,255.);
+ hist9 = new TH1F( "scurve9", "S-Curve" , 255,0.,255.);
+ hist10 = new TH1F( "scurve10", "S-Curve" , 255,0.,255.);
+ hist11 = new TH1F( "scurve11", "S-Curve" , 255,0.,255.);
+ hist12 = new TH1F( "scurve12", "S-Curve" , 255,0.,255.);
+ hist13 = new TH1F( "scurve13", "S-Curve" , 255,0.,255.);
+ hist1->SetLineColor( 1);
+ hist2->SetLineColor( 2);
+ hist3->SetLineColor( 3);
+ hist4->SetLineColor( 4);
+ hist5->SetLineColor( 5);
+ hist6->SetLineColor( 6);
+ hist7->SetLineColor( 7);
+ hist8->SetLineColor( 8);
+ hist9->SetLineColor( 9);
+ hist10->SetLineColor(11);
+ hist11->SetLineColor(12);
+ hist12->SetLineColor(13);
+ hist13->SetLineColor(14);
+
+
+ int entries = (int) scurveTree->GetEntries();
+ cout << "Found " << entries << " entries (thresholds)" << endl;
+ for(int i=0;i<entries;i++){
+ scurveTree->GetEntry(i);
+// Block A
+// Row 500
+// hist1->Fill(i, pixelprob[500*1152+0]);
+// hist2->Fill(i, pixelprob[500*1152+23]);
+// hist3->Fill(i, pixelprob[500*1152+47]);
+// hist4->Fill(i, pixelprob[500*1152+71]);
+// hist5->Fill(i, pixelprob[500*1152+95]);
+// hist6->Fill(i, pixelprob[500*1152+119]);
+// hist7->Fill(i, pixelprob[500*1152+143]);
+// hist8->Fill(i, pixelprob[500*1152+167]);
+// hist9->Fill(i, pixelprob[500*1152+191]);
+// hist10->Fill(i,pixelprob[500*1152+215]);
+// hist11->Fill(i,pixelprob[500*1152+239]);
+// hist12->Fill(i,pixelprob[500*1152+263]);
+// hist13->Fill(i,pixelprob[500*1152+287]);
+// Row 5
+// hist1->Fill(i, pixelprob[5*1152+0]);
+// hist2->Fill(i, pixelprob[5*1152+23]);
+// hist3->Fill(i, pixelprob[5*1152+47]);
+// hist4->Fill(i, pixelprob[5*1152+71]);
+// hist5->Fill(i, pixelprob[5*1152+95]);
+// hist6->Fill(i, pixelprob[5*1152+119]);
+// hist7->Fill(i, pixelprob[5*1152+143]);
+// hist8->Fill(i, pixelprob[5*1152+167]);
+// hist9->Fill(i, pixelprob[5*1152+191]);
+// hist10->Fill(i,pixelprob[5*1152+215]);
+// hist11->Fill(i,pixelprob[5*1152+239]);
+// hist12->Fill(i,pixelprob[5*1152+263]);
+// hist13->Fill(i,pixelprob[5*1152+287]);
+
+// Block B
+// Row 5
+// hist1->Fill(i, pixelprob[5*1152+288+0]);
+// hist2->Fill(i, pixelprob[5*1152+288+23]);
+// hist3->Fill(i, pixelprob[5*1152+288+47]);
+// hist4->Fill(i, pixelprob[5*1152+288+71]);
+// hist5->Fill(i, pixelprob[5*1152+288+95]);
+// hist6->Fill(i, pixelprob[5*1152+288+119]);
+// hist7->Fill(i, pixelprob[5*1152+288+143]);
+// hist8->Fill(i, pixelprob[5*1152+288+167]);
+// hist9->Fill(i, pixelprob[5*1152+288+191]);
+// hist10->Fill(i,pixelprob[5*1152+288+215]);
+// hist11->Fill(i,pixelprob[5*1152+288+239]);
+// hist12->Fill(i,pixelprob[5*1152+288+263]);
+// hist13->Fill(i,pixelprob[5*1152+288+287]);
+// Row 500
+// hist1->Fill(i, pixelprob[500*1152+288+0]);
+// hist2->Fill(i, pixelprob[500*1152+288+23]);
+// hist3->Fill(i, pixelprob[500*1152+288+47]);
+// hist4->Fill(i, pixelprob[500*1152+288+71]);
+// hist5->Fill(i, pixelprob[500*1152+288+95]);
+// hist6->Fill(i, pixelprob[500*1152+288+119]);
+// hist7->Fill(i, pixelprob[500*1152+288+143]);
+// hist8->Fill(i, pixelprob[500*1152+288+167]);
+// hist9->Fill(i, pixelprob[500*1152+288+191]);
+// hist10->Fill(i,pixelprob[500*1152+288+215]);
+// hist11->Fill(i,pixelprob[500*1152+288+239]);
+// hist12->Fill(i,pixelprob[500*1152+288+263]);
+// hist13->Fill(i,pixelprob[500*1152+288+287]);
+
+// Block C
+// Row 5
+// hist1->Fill(i, pixelprob[5*1152+576+0]);
+// hist2->Fill(i, pixelprob[5*1152+576+23]);
+// hist3->Fill(i, pixelprob[5*1152+576+47]);
+// hist4->Fill(i, pixelprob[5*1152+576+71]);
+// hist5->Fill(i, pixelprob[5*1152+576+95]);
+// hist6->Fill(i, pixelprob[5*1152+576+119]);
+// hist7->Fill(i, pixelprob[5*1152+576+143]);
+// hist8->Fill(i, pixelprob[5*1152+576+167]);
+// hist9->Fill(i, pixelprob[5*1152+576+191]);
+// hist10->Fill(i,pixelprob[5*1152+576+215]);
+// hist11->Fill(i,pixelprob[5*1152+576+239]);
+// hist12->Fill(i,pixelprob[5*1152+576+263]);
+// hist13->Fill(i,pixelprob[5*1152+576+287]);
+// Row 500
+// hist1->Fill(i, pixelprob[500*1152+576+0]);
+// hist2->Fill(i, pixelprob[500*1152+576+23]);
+// hist3->Fill(i, pixelprob[500*1152+576+47]);
+// hist4->Fill(i, pixelprob[500*1152+576+71]);
+// hist5->Fill(i, pixelprob[500*1152+576+95]);
+// hist6->Fill(i, pixelprob[500*1152+576+119]);
+// hist7->Fill(i, pixelprob[500*1152+576+143]);
+// hist8->Fill(i, pixelprob[500*1152+576+167]);
+// hist9->Fill(i, pixelprob[500*1152+576+191]);
+// hist10->Fill(i,pixelprob[500*1152+576+215]);
+// hist11->Fill(i,pixelprob[500*1152+576+239]);
+// hist12->Fill(i,pixelprob[500*1152+576+263]);
+// hist13->Fill(i,pixelprob[500*1152+576+287]);
+
+// Block D
+// Row 5
+// hist1->Fill(i, pixelprob[5*1152+864+0]);
+// hist2->Fill(i, pixelprob[5*1152+864+23]);
+// hist3->Fill(i, pixelprob[5*1152+864+47]);
+// hist4->Fill(i, pixelprob[5*1152+864+71]);
+// hist5->Fill(i, pixelprob[5*1152+864+95]);
+// hist6->Fill(i, pixelprob[5*1152+864+119]);
+// hist7->Fill(i, pixelprob[5*1152+864+143]);
+// hist8->Fill(i, pixelprob[5*1152+864+167]);
+// hist9->Fill(i, pixelprob[5*1152+864+191]);
+// hist10->Fill(i,pixelprob[5*1152+864+215]);
+// hist11->Fill(i,pixelprob[5*1152+864+239]);
+// hist12->Fill(i,pixelprob[5*1152+864+263]);
+// hist13->Fill(i,pixelprob[5*1152+864+287]);
+// Row 500
+// hist1->Fill(i, pixelprob[500*1152+864+0]);
+// hist2->Fill(i, pixelprob[500*1152+864+23]);
+// hist3->Fill(i, pixelprob[500*1152+864+47]);
+// hist4->Fill(i, pixelprob[500*1152+864+71]);
+// hist5->Fill(i, pixelprob[500*1152+864+95]);
+// hist6->Fill(i, pixelprob[500*1152+864+119]);
+// hist7->Fill(i, pixelprob[500*1152+864+143]);
+// hist8->Fill(i, pixelprob[500*1152+864+167]);
+// hist9->Fill(i, pixelprob[500*1152+864+191]);
+// hist10->Fill(i,pixelprob[500*1152+864+215]);
+// hist11->Fill(i,pixelprob[500*1152+864+239]);
+// hist12->Fill(i,pixelprob[500*1152+864+263]);
+// hist13->Fill(i,pixelprob[500*1152+864+287]);
+
+
+// Mid Column A
+// hist1->Fill(i, pixelprob[0*1152+144]);
+// hist2->Fill(i, pixelprob[47*1152+144]);
+// hist3->Fill(i, pixelprob[95*1152+144]);
+// hist4->Fill(i, pixelprob[143*1152+144]);
+// hist5->Fill(i, pixelprob[191*1152+144]);
+// hist6->Fill(i, pixelprob[239*1152+144]);
+// hist7->Fill(i, pixelprob[287*1152+144]);
+// hist8->Fill(i, pixelprob[335*1152+144]);
+// hist9->Fill(i, pixelprob[383*1152+144]);
+// hist10->Fill(i,pixelprob[431*1152+144]);
+// hist11->Fill(i,pixelprob[479*1152+144]);
+// hist12->Fill(i,pixelprob[527*1152+144]);
+// hist13->Fill(i,pixelprob[574*1152+144]);
+// Mid Column B
+// hist1->Fill(i, pixelprob[0*1152+288+144]);
+// hist2->Fill(i, pixelprob[47*1152+288+144]);
+// hist3->Fill(i, pixelprob[95*1152+288+144]);
+// hist4->Fill(i, pixelprob[143*1152+288+144]);
+// hist5->Fill(i, pixelprob[191*1152+288+144]);
+// hist6->Fill(i, pixelprob[239*1152+288+144]);
+// hist7->Fill(i, pixelprob[287*1152+288+144]);
+// hist8->Fill(i, pixelprob[335*1152+288+144]);
+// hist9->Fill(i, pixelprob[383*1152+288+144]);
+// hist10->Fill(i,pixelprob[431*1152+288+144]);
+// hist11->Fill(i,pixelprob[479*1152+288+144]);
+// hist12->Fill(i,pixelprob[527*1152+288+144]);
+// hist13->Fill(i,pixelprob[574*1152+288+144]);
+// Mid Column C
+// hist1->Fill(i, pixelprob[0*1152+576+144]);
+// hist2->Fill(i, pixelprob[47*1152+576+144]);
+// hist3->Fill(i, pixelprob[95*1152+576+144]);
+// hist4->Fill(i, pixelprob[143*1152+576+144]);
+// hist5->Fill(i, pixelprob[191*1152+576+144]);
+// hist6->Fill(i, pixelprob[239*1152+576+144]);
+// hist7->Fill(i, pixelprob[287*1152+576+144]);
+// hist8->Fill(i, pixelprob[335*1152+576+144]);
+// hist9->Fill(i, pixelprob[383*1152+576+144]);
+// hist10->Fill(i,pixelprob[431*1152+576+144]);
+// hist11->Fill(i,pixelprob[479*1152+576+144]);
+// hist12->Fill(i,pixelprob[527*1152+576+144]);
+// hist13->Fill(i,pixelprob[574*1152+576+144]);
+// Mid Column D
+ hist1->Fill(i, pixelprob[0*1152+864+144]);
+ hist2->Fill(i, pixelprob[47*1152+864+144]);
+ hist3->Fill(i, pixelprob[95*1152+864+144]);
+ hist4->Fill(i, pixelprob[143*1152+864+144]);
+ hist5->Fill(i, pixelprob[191*1152+864+144]);
+ hist6->Fill(i, pixelprob[239*1152+864+144]);
+ hist7->Fill(i, pixelprob[287*1152+864+144]);
+ hist8->Fill(i, pixelprob[335*1152+864+144]);
+ hist9->Fill(i, pixelprob[383*1152+864+144]);
+ hist10->Fill(i,pixelprob[431*1152+864+144]);
+ hist11->Fill(i,pixelprob[479*1152+864+144]);
+ hist12->Fill(i,pixelprob[527*1152+864+144]);
+ hist13->Fill(i,pixelprob[574*1152+864+144]);
+
+// Column from Block A
+// int doit = 0;
+// for (int j=0; j<288;j++){
+// if (pixelprob[47*1152+j] != 0){
+// doit = 1;
+// }
+// }
+// if (doit){
+// printf(">>> ");
+// for (int j=0; j<288;j++){
+// if (pixelprob[47*1152+j] == 0){
+// printf("%d ",j);
+// }
+// }
+// printf("\n------------------\n",j);
+// }
+//
+//
+// hist1->Fill(i, pixelprob[0*1152+144]);
+// hist2->Fill(i, pixelprob[47*1152+144]);
+// hist3->Fill(i, pixelprob[95*1152+144]);
+// hist4->Fill(i, pixelprob[143*1152+144]);
+// hist5->Fill(i, pixelprob[191*1152+144]);
+// hist6->Fill(i, pixelprob[239*1152+144]);
+// hist7->Fill(i, pixelprob[287*1152+144]);
+// hist8->Fill(i, pixelprob[335*1152+144]);
+// hist9->Fill(i, pixelprob[383*1152+144]);
+// hist10->Fill(i,pixelprob[431*1152+144]);
+// hist11->Fill(i,pixelprob[479*1152+144]);
+// hist12->Fill(i,pixelprob[527*1152+144]);
+// hist13->Fill(i,pixelprob[574*1152+144]);
+
+ }
+ hist1->Draw("");
+ hist2->Draw("same");
+ hist3->Draw("same");
+ hist4->Draw("same");
+ hist5->Draw("same");
+ hist6->Draw("same");
+ hist7->Draw("same");
+ hist8->Draw("same");
+ hist9->Draw("same");
+ hist10->Draw("same");
+ hist11->Draw("same");
+ hist12->Draw("same");
+ hist13->Draw("same");
+
+ leg = new TLegend(0.8,0.1,0.94,0.7);
+ leg->SetHeader("Rows");
+ leg->AddEntry(hist1,"1","l");
+ leg->AddEntry(hist2,"48","l");
+ leg->AddEntry(hist3,"96","l");
+ leg->AddEntry(hist4,"144","l");
+ leg->AddEntry(hist5,"192","l");
+ leg->AddEntry(hist6,"240","l");
+ leg->AddEntry(hist7,"288","l");
+ leg->AddEntry(hist8,"336","l");
+ leg->AddEntry(hist9,"384","l");
+ leg->AddEntry(hist10,"432","l");
+ leg->AddEntry(hist11,"480","l");
+ leg->AddEntry(hist12,"528","l");
+ leg->AddEntry(hist13,"575","l");
+
+ leg->Draw();
+
+ return 1;
+
+}
+
+
--
2.43.0