2_compare_wiener&OF.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Applying ATLAS style settings...\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load style\n",
    "from ROOT import gROOT\n",
    "\n",
    "path = '/Users/yy/cernbox/'\n",
    "# path = '/eos/user/y/youyou/'\n",
    "gROOT.SetMacroPath(path+\"atlasstyle/\")\n",
    "gROOT.LoadMacro(\"AtlasLabels.C\")\n",
    "gROOT.LoadMacro(\"AtlasStyle.C\")\n",
    "gROOT.LoadMacro(\"AtlasUtils.C\")\n",
    "from ROOT import SetAtlasStyle, ATLASLabel, myText, myMarkerText\n",
    "SetAtlasStyle()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load lib\n",
    "import ROOT\n",
    "import numpy as np\n",
    "# import pandas as pd\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load data\n",
    "\n",
    "data_folder = './data/'\n",
    "pulse = np.loadtxt(data_folder + 'pule_shape.txt')  # pulse shape\n",
    "adc = np.loadtxt(data_folder + 'adc_out.txt')       # simulated adc output\n",
    "signal = np.loadtxt(data_folder + 'signal.txt')     # delta func signal\n",
    "pileup = np.loadtxt(data_folder + 'pileup.txt')     # pileup noise\n",
    "wiener = np.loadtxt(data_folder + 'wiener_out.txt') # wiener filter output\n",
    "of_coeff = np.loadtxt(data_folder + 'of_coeffs.txt')# optimal filter coefficients \n",
    "of_out = np.loadtxt(data_folder + 'of_out.txt')     # optimal filter output with above coeffs\n",
    "\n",
    "plot_folder = './plots/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# distribution of E_true\n",
    "\n",
    "below is not correct for E_true\n",
    "\n",
    "cuz i only plot distribution of delta func signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "signal_none_zero = [i for i in signal if i != 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean of the histogram:  24.968571164961713\n",
      "Standard devistion: 14.480601486996386\n",
      "The range containing 98% of entries: (0.45679009000000004, 49.504818889999996)\n",
      "Median of the range containing 98% of entries: 24.980804489999997\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# above is not correct for E_true\n",
    "# cuz i only plot distribution of delta func signal\n",
    "\n",
    "signal_none_zero = [i for i in signal if i != 0]\n",
    "\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "\n",
    "# Create a TH1D histogram object with variable bin sizes\n",
    "# if want to add 0 entries: bin_edges = [-3, 0.0001, 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.]\n",
    "# if want to add 0 entries, remember to use log in y-axis\n",
    "bin_edges = [0.0001, 5., 10., 15., 20., 25., 30., 35., 40., 45., 50.]\n",
    "\n",
    "num_bins = len(bin_edges) - 1\n",
    "hist = ROOT.TH1D(\"hist\", \"Energy Distribution\", num_bins, np.array(bin_edges, 'd'))\n",
    "\n",
    "for i in signal:\n",
    "    hist.Fill(i)\n",
    "\n",
    "hist.SetMinimum(0)\n",
    "\n",
    "hist.GetYaxis().SetTitle(\"count\")\n",
    "hist.GetXaxis().SetTitle(\"E_{true}\")\n",
    "hist.SetMinimum(1e3)\n",
    "\n",
    "hist.Draw('hist')\n",
    "\n",
    "canvas.SetLogy (0)\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "\n",
    "\n",
    "print('Mean of the histogram: ', hist.GetMean())\n",
    "print('Standard devistion:', hist.GetStdDev())\n",
    "quantiles = np.percentile(signal_none_zero, 1),  np.percentile(signal_none_zero, 99)\n",
    "print(\"The range containing 98% of entries:\", (quantiles[0] , quantiles[1]))\n",
    "print(\"Median of the range containing 98% of entries:\", (quantiles[0] + quantiles[1]) / 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "metadata": {},
   "outputs": [],
   "source": [
    "# there is a shift bwtn the given optimal filter output and adc\n",
    "# generate residual btwn of output and E_true\n",
    "\n",
    "of_new = of_out[5:]\n",
    "\n",
    "e_diff_of = [i-j for i,j in zip(of_new, signal)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## seperate into diff ranges of $E_{true}$ and analyze the distribution of $\\Delta E$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def find_position(x, arr):\n",
    "    for i in range(len(arr)):\n",
    "        if arr[i] > x:\n",
    "            return i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 225,
   "metadata": {},
   "outputs": [],
   "source": [
    "# plotter that plot distribution for delta E with data points of E_true in a given range [start, end) \n",
    "\n",
    "def plot_signal_range(start, end, bins = np.linspace(-15, 25, 21), e_diff = e_diff_of):\n",
    "    indexs = [i for i in range(len(signal)) if signal[i] > start and signal[i] <= end]\n",
    "    data = [e_diff[i] for i in indexs]\n",
    "\n",
    "    n_bins = len(bins)-1\n",
    "    hist = ROOT.TH1D(\"hist\", \"Energy Distribution\", n_bins, np.array(bins, 'd'))\n",
    "\n",
    "    for i in data:\n",
    "        hist.Fill(i)\n",
    "\n",
    "    hist.GetYaxis().SetTitle(\"count\")\n",
    "    hist.GetXaxis().SetTitle(\"E_{reco}-E_{true}\")\n",
    "\n",
    "    # begin\n",
    "\n",
    "    mean = hist.GetMean()\n",
    "    std_dev = hist.GetStdDev()\n",
    "    one_sigma_low = mean - std_dev\n",
    "    one_sigma_high = mean + std_dev\n",
    "    quantiles = np.percentile(data, 1),  np.percentile(data, 99)\n",
    "\n",
    "    # median of 98%\n",
    "    sorted_signal = np.sort(data)\n",
    "    quantiles = np.percentile(sorted_signal, 1), np.percentile(sorted_signal, 99)\n",
    "    filtered_signal = sorted_signal[(sorted_signal >= quantiles[0]) & (sorted_signal <= quantiles[1])]\n",
    "    median = np.median(filtered_signal)\n",
    "\n",
    "\n",
    "    line_arr = []\n",
    "    bin_content = [hist.GetBinContent(i) for i in range(n_bins+1)]\n",
    "\n",
    "    for i in [mean, one_sigma_low, one_sigma_high]:\n",
    "        l = ROOT.TLine(i, 0, i, bin_content[find_position(i, bins)])\n",
    "        l.SetLineStyle(ROOT.kDashed)\n",
    "        l.SetLineColor(ROOT.kRed)\n",
    "        line_arr += [l]\n",
    "    for  j in [median, quantiles[0], quantiles[1]]:\n",
    "        #print(j, '------')\n",
    "        l = ROOT.TLine(j, 0, j, bin_content[find_position(j, bins)])\n",
    "        l.SetLineStyle(ROOT.kDashed)\n",
    "        l.SetLineColor(ROOT.kBlue)\n",
    "        line_arr += [l]\n",
    "\n",
    "    height = find_position(j, bins) \n",
    "    label_one_sigma = ROOT.TLatex(mean, height*1.2, \"1 \\sigma\")\n",
    "    label_one_sigma.SetTextAlign(12)\n",
    "    label_one_sigma.SetTextSize(0.03)\n",
    "    arrow_one_sigma_high = ROOT.TArrow(one_sigma_low, height, one_sigma_high, height, 0.02, \"<>\")\n",
    "    arrow_one_sigma_high.SetLineColor(ROOT.kRed)\n",
    "\n",
    "    height = height / 2.5\n",
    "    label_98 = ROOT.TLatex(mean, height*1.2, \"98% range\")\n",
    "    label_98.SetTextAlign(12)\n",
    "    label_98.SetTextSize(0.03)\n",
    "    arrow_98_low = ROOT.TArrow(quantiles[1], height, quantiles[0], height, 0.02, \"<>\")\n",
    "    arrow_98_low.SetLineColor(ROOT.kBlue)\n",
    "    line_arr += [label_one_sigma, arrow_one_sigma_high, label_98, arrow_98_low]\n",
    "\n",
    "    print('==================================')\n",
    "    print('Mean of the histogram: ', mean)\n",
    "    print('Standard devistion:', std_dev)\n",
    "    print(\"The range containing 98% of entries:\", (quantiles[0] , quantiles[1]))\n",
    "    print(\"Median of the range containing 98% of entries:\", median)\n",
    "    print('==================================')\n",
    "\n",
    "    return hist, line_arr\n",
    "\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.1272604847220155\n",
      "Standard devistion: 0.8892799962137165\n",
      "The range containing 98% of entries: (-1.8537757200000002, 2.279504520000001)\n",
      "Median of the range containing 98% of entries: -0.21362799999999993\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function of E_true in [0, 10)\n",
    "\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(0, 10, bins = np.linspace(-5, 7, 21), e_diff = e_diff_of)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.2993976969651211\n",
      "Standard devistion: 0.8805443426984089\n",
      "The range containing 98% of entries: (-2.00572688, 2.136698279999999)\n",
      "Median of the range containing 98% of entries: -0.3848830000000003\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# call the function of E_true in [10, 20)\n",
    "\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(10, 20, bins = np.linspace(-5, 7, 21), e_diff = e_diff_of)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.4757204880216964\n",
      "Standard devistion: 0.8941084298130623\n",
      "The range containing 98% of entries: (-2.2051571200000017, 2.0636632799999983)\n",
      "Median of the range containing 98% of entries: -0.553253999999999\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(20, 30, bins = np.linspace(-5, 7, 21), e_diff = e_diff_of)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.6518695627196361\n",
      "Standard devistion: 0.8922462417612117\n",
      "The range containing 98% of entries: (-2.385264199999999, 1.837838319999999)\n",
      "Median of the range containing 98% of entries: -0.7427379999999992\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(30, 40, bins = np.linspace(-5, 7, 21), e_diff = e_diff_of)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.8301252328073849\n",
      "Standard devistion: 0.879111236960588\n",
      "The range containing 98% of entries: (-2.5138268999999984, 1.5949626299999964)\n",
      "Median of the range containing 98% of entries: -0.9156895000000027\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(40, 50, bins = np.linspace(-5, 7, 21), e_diff = e_diff_of)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## analyze the distribution of mean, std, 98% median within whole range of E_true"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "metadata": {},
   "outputs": [],
   "source": [
    "# calculate the mean, std, 98% median of the distributions\n",
    "\n",
    "\n",
    "bins = np.linspace(0, 50, 11)\n",
    "e_diff = e_diff_of\n",
    "x, mean, std, median, lower_98, higher_98, indice = [], [], [], [], [], [], []\n",
    "\n",
    "\n",
    "for j in range(len(bins)-1):\n",
    "    x += [(bins[j] + bins[j+1])/2]\n",
    "    indexs = [i for i in range(len(signal)) if signal[i] > bins[j] and signal[i] <= bins[j+1]]\n",
    "    data = [e_diff[i] for i in indexs]\n",
    "    indice += [len(indexs)]\n",
    "\n",
    "    mean += [np.mean(data)]\n",
    "    std += [np.std(data)]\n",
    "    \n",
    "    sorted_signal = np.sort(data)\n",
    "    quantiles = np.percentile(sorted_signal, 1), np.percentile(sorted_signal, 99)\n",
    "    filtered_signal = sorted_signal[(sorted_signal >= quantiles[0]) & (sorted_signal <= quantiles[1])]\n",
    "    median += [np.median(filtered_signal)]\n",
    "    lower_98 += [quantiles[0]]\n",
    "    higher_98 += [quantiles[1]]\n",
    "\n",
    "x_of, mean_of, std_of, median_of, lower_98_of, higher_98_of, indice_of = x, mean, std, median, lower_98, higher_98, indice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Graph Canvas\", 800, 600)\n",
    "\n",
    "x = x\n",
    "y = mean\n",
    "uncertainty_y = std\n",
    "\n",
    "graph = ROOT.TGraphErrors(len(x), np.array(x, 'd'), np.array(y, 'd'), np.zeros(len(x)), np.array(uncertainty_y, 'd'))\n",
    "\n",
    "# Customize the graph appearance\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "# Set the axis titles\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"mean\\ of \\overline{\\Delta E}\")\n",
    "graph.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "# Draw the graph\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "# Update the canvas\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Graph Canvas\", 800, 600)\n",
    "\n",
    "x = x\n",
    "y = np.array(std)\n",
    "n = np.array(indice)\n",
    "uncertainty_y = y / np.sqrt(2 * n-2)\n",
    "\n",
    "graph = ROOT.TGraphErrors(len(x), np.array(x, 'd'), np.array(y, 'd'), np.zeros(len(x)), np.array(uncertainty_y, 'd'))\n",
    "\n",
    "# Customize the graph appearance\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "# Set the axis titles\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"\\sigma_{\\Delta E}\")\n",
    "graph.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "# Draw the graph\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "# Update the canvas\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "\n",
    "\n",
    "x = np.array(x, 'd')\n",
    "y = np.array(median, 'd')\n",
    "lower_error = np.array(lower_98, 'd')  \n",
    "upper_error = np.array(higher_98, 'd')  \n",
    "\n",
    "\n",
    "graph = ROOT.TGraphAsymmErrors(len(x), x, y, np.zeros(len(x)), np.zeros(len(x)), y-lower_error, upper_error-y)\n",
    "\n",
    "\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"(\\Delta E)_{1/2} with\\ 98\\ percentage\")\n",
    "\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Repeat above with wiener filter output"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "wiener_new = wiener[4:]\n",
    "\n",
    "plt.plot(wiener_new[5000:5100])\n",
    "plt.plot(signal[5000:5100], alpha = 0.5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "# delatE of wiener filter\n",
    "\n",
    "e_diff_wf = [i-j for i,j in zip(wiener_new, signal)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.11590010314502878\n",
      "Standard devistion: 0.8924036015307285\n",
      "The range containing 98% of entries: (-1.98694096, 2.312772559999999)\n",
      "Median of the range containing 98% of entries: -0.1980959999999996\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(0, 10, bins = np.linspace(-5, 5, 21), e_diff=e_diff_wf)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.2838545455231775\n",
      "Standard devistion: 0.8825509114405391\n",
      "The range containing 98% of entries: (-2.164272100000002, 2.153048879999997)\n",
      "Median of the range containing 98% of entries: -0.3473600000000001\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(10, 20, bins = np.linspace(-6, 5, 21), e_diff=e_diff_wf)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.458366625433176\n",
      "Standard devistion: 0.8858874109124869\n",
      "The range containing 98% of entries: (-2.3403064799999984, 1.9366896000000027)\n",
      "Median of the range containing 98% of entries: -0.5164609999999996\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(20, 30, bins = np.linspace(-5, 5, 21), e_diff=e_diff_wf)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.6302430386554602\n",
      "Standard devistion: 0.8891289404409001\n",
      "The range containing 98% of entries: (-2.530898480000002, 1.70205904)\n",
      "Median of the range containing 98% of entries: -0.691905000000002\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(30, 40, bins = np.linspace(-5, 5, 21), e_diff=e_diff_wf)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==================================\n",
      "Mean of the histogram:  -0.806374899523668\n",
      "Standard devistion: 0.8703125742101065\n",
      "The range containing 98% of entries: (-2.6661448, 1.5670765599999972)\n",
      "Median of the range containing 98% of entries: -0.8751834999999986\n",
      "==================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "hist, arr = plot_signal_range(40, 50, bins = np.linspace(-5, 5, 21), e_diff=e_diff_wf)\n",
    "hist.Draw()\n",
    "for l in arr:\n",
    "    l.Draw()\n",
    "canvas.SetLogy (1)\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "metadata": {},
   "outputs": [],
   "source": [
    "bins = np.linspace(0, 50, 11)\n",
    "e_diff = e_diff_wf\n",
    "x, mean, std, median, lower_98, higher_98, indice = [], [], [], [], [], [], []\n",
    "\n",
    "\n",
    "for j in range(len(bins)-1):\n",
    "    x += [(bins[j] + bins[j+1])/2]\n",
    "    indexs = [i for i in range(len(signal)) if signal[i] > bins[j] and signal[i] <= bins[j+1]]\n",
    "    data = [e_diff[i] for i in indexs]\n",
    "    indice += [len(indexs)]\n",
    "\n",
    "    mean += [np.mean(data)]\n",
    "    std += [np.std(data)]\n",
    "    \n",
    "    sorted_signal = np.sort(data)\n",
    "    quantiles = np.percentile(sorted_signal, 1), np.percentile(sorted_signal, 99)\n",
    "    filtered_signal = sorted_signal[(sorted_signal >= quantiles[0]) & (sorted_signal <= quantiles[1])]\n",
    "    median += [np.median(filtered_signal)]\n",
    "    lower_98 += [quantiles[0]]\n",
    "    higher_98 += [quantiles[1]]\n",
    "\n",
    "x_wf, mean_wf, std_wf, median_wf, lower_98_wf, higher_98_wf, indice_wf = x, mean, std, median, lower_98, higher_98, indice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if add 0 entries\n",
    "\n",
    "'''e_diff = e_diff_of\n",
    "\n",
    "x_of += [0]\n",
    "indexs = [i for i in range(len(signal)) if signal[i] == 0]\n",
    "data = [e_diff[i] for i in indexs]\n",
    "indice_of += [len(indexs)]\n",
    "mean_of += [np.mean(data)]\n",
    "std_of += [np.std(data)]\n",
    "\n",
    "sorted_signal = np.sort(data)\n",
    "quantiles = np.percentile(sorted_signal, 1), np.percentile(sorted_signal, 99)\n",
    "filtered_signal = sorted_signal[(sorted_signal >= quantiles[0]) & (sorted_signal <= quantiles[1])]\n",
    "median_of += [np.median(filtered_signal)]\n",
    "lower_98_of += [quantiles[0]]\n",
    "higher_98_of += [quantiles[1]]'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "# if add 0 entries\n",
    "\n",
    "'''e_diff = e_diff_wf\n",
    "\n",
    "x_wf += [0]\n",
    "indexs = [i for i in range(len(signal)) if signal[i] == 0]\n",
    "data = [e_diff[i] for i in indexs]\n",
    "indice_wf += [len(indexs)]\n",
    "mean_wf += [np.mean(data)]\n",
    "std_wf += [np.std(data)]\n",
    "\n",
    "sorted_signal = np.sort(data)\n",
    "quantiles = np.percentile(sorted_signal, 1), np.percentile(sorted_signal, 99)\n",
    "filtered_signal = sorted_signal[(sorted_signal >= quantiles[0]) & (sorted_signal <= quantiles[1])]\n",
    "median_wf += [np.median(filtered_signal)]\n",
    "lower_98_wf += [quantiles[0]]\n",
    "higher_98_wf += [quantiles[1]]'''"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 227,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Graph Canvas\", 800, 600)\n",
    "\n",
    "x = x\n",
    "y = mean\n",
    "uncertainty_y = std\n",
    "\n",
    "graph = ROOT.TGraphErrors(len(x), np.array(x, 'd'), np.array(y, 'd'), np.zeros(len(x)), np.array(uncertainty_y, 'd'))\n",
    "\n",
    "# Customize the graph appearance\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "# Set the axis titles\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"mean\\ of \\overline{\\Delta E}\")\n",
    "graph.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "# Draw the graph\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "# Update the canvas\n",
    "canvas.Update()\n",
    "canvas.Draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Graph Canvas\", 800, 600)\n",
    "\n",
    "x = x\n",
    "y = np.array(std)\n",
    "n = np.array(indice)\n",
    "uncertainty_y = y / np.sqrt(2 * n-2)\n",
    "\n",
    "graph = ROOT.TGraphErrors(len(x), np.array(x, 'd'), np.array(y, 'd'), np.zeros(len(x)), np.array(uncertainty_y, 'd'))\n",
    "\n",
    "# Customize the graph appearance\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "# Set the axis titles\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"\\sigma_{\\Delta E}\")\n",
    "graph.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "# Draw the graph\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "# Update the canvas\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Histogram Canvas\", 800, 600)\n",
    "\n",
    "x = np.array(x, 'd')\n",
    "y = np.array(median, 'd')\n",
    "lower_error = np.array(lower_98, 'd')  \n",
    "upper_error = np.array(higher_98, 'd')  \n",
    "\n",
    "\n",
    "#graph = ROOT.TGraphErrors(len(x), x, y, np.zeros(len(x)), y_uncertainty)\n",
    "graph = ROOT.TGraphAsymmErrors(len(x), x, y, np.zeros(len(x)), np.zeros(len(x)), y-lower_error, upper_error-y)\n",
    "\n",
    "\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(1.5)\n",
    "graph.SetLineColor(ROOT.kBlue)\n",
    "graph.SetLineWidth(2)\n",
    "\n",
    "graph.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph.GetYaxis().SetTitle(\"(\\Delta E)_{1/2} with\\ 98\\ percentage\")\n",
    "\n",
    "graph.Draw(\"AP\")\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Comparison Canvas\", 800, 600)\n",
    "\n",
    "\n",
    "graph1 = ROOT.TGraphErrors(len(x_of), np.array(x_of, 'd')-0.25, np.array(mean_of, 'd'), np.zeros(len(x_of)), np.array(std_of, 'd'))\n",
    "graph1.SetMarkerStyle(22)\n",
    "graph1.SetMarkerSize(1.5)\n",
    "graph1.SetLineColorAlpha(ROOT.kRed, 0.8)\n",
    "graph1.SetLineWidth(2)\n",
    "graph1.SetMarkerColorAlpha(ROOT.kRed, 0.8)\n",
    "\n",
    "graph2 = ROOT.TGraphErrors(len(x_wf), np.array(x_wf, 'd')+0.25, np.array(mean_wf, 'd'), np.zeros(len(x_of)), np.array(std_wf, 'd'))\n",
    "graph2.SetMarkerStyle(20)\n",
    "graph2.SetMarkerSize(1.5)\n",
    "graph2.SetLineColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetMarkerColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetLineWidth(2)\n",
    "\n",
    "graph1.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph1.GetYaxis().SetTitle(\"mean\\ of \\overline{\\Delta E}\")\n",
    "\n",
    "\n",
    "legend = ROOT.TLegend(0.7, 0.8, 0.95, 0.95)\n",
    "legend.AddEntry(graph1, \"E_{reco} with OF\", \"lep\")\n",
    "legend.AddEntry(graph2, \"E_{reco} with WF\", \"lep\")\n",
    "\n",
    "graph1.Draw(\"APE\")\n",
    "graph2.Draw(\"SAMEPE\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Comparison Canvas\", 800, 600)\n",
    "\n",
    "y = np.array(std_of)\n",
    "n = np.array(indice_of)\n",
    "uncertainty_y = y / np.sqrt(2 * n-2)\n",
    "\n",
    "graph1 = ROOT.TGraphErrors(len(x_of), np.array(x_of, 'd')-0.25, np.array(y, 'd'), np.zeros(len(x_of)), np.array(uncertainty_y, 'd'))\n",
    "graph1.SetMarkerStyle(22)\n",
    "graph1.SetMarkerSize(1.5)\n",
    "graph1.SetLineColorAlpha(ROOT.kRed, 0.8)\n",
    "graph1.SetLineWidth(2)\n",
    "graph1.SetMarkerColorAlpha(ROOT.kRed, 0.8)\n",
    "\n",
    "\n",
    "\n",
    "y = np.array(std_wf)\n",
    "n = np.array(indice_wf)\n",
    "uncertainty_y = y / np.sqrt(2 * n-2)\n",
    "\n",
    "graph2 = ROOT.TGraphErrors(len(x_wf), np.array(x_wf, 'd')+0.25, np.array(y, 'd'), np.zeros(len(x_wf)), np.array(uncertainty_y, 'd'))\n",
    "graph2.SetMarkerStyle(20)\n",
    "graph2.SetMarkerSize(1.5)\n",
    "graph2.SetLineColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetMarkerColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetLineWidth(2)\n",
    "\n",
    "graph1.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph1.GetYaxis().SetTitle(\"\\sigma_{\\Delta E}\")\n",
    "#graph1.GetYaxis().SetRangeUser(0.85, 0.92)\n",
    "\n",
    "legend = ROOT.TLegend(0.7, 0.8, 0.95, 0.95)\n",
    "legend.AddEntry(graph1, \"E_{reco} with OF\", \"lep\")\n",
    "legend.AddEntry(graph2, \"E_{reco} with wF\", \"lep\")\n",
    "\n",
    "graph1.Draw(\"APE\")\n",
    "graph2.Draw(\"SAMEPE\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "canvas = ROOT.TCanvas(\"canvas\", \"Comparison Canvas\", 800, 600)\n",
    "\n",
    "\n",
    "x = np.array(x_of, 'd')-0.25\n",
    "y = np.array(median_of, 'd')\n",
    "lower_error = np.array(lower_98_of, 'd')  \n",
    "upper_error = np.array(higher_98_of, 'd')  \n",
    "\n",
    "graph1 = ROOT.TGraphAsymmErrors(len(x), x, y, np.zeros(len(x)), np.zeros(len(x)), y-lower_error, upper_error-y)\n",
    "\n",
    "graph1.SetMarkerStyle(22)\n",
    "graph1.SetMarkerSize(1.5)\n",
    "graph1.SetLineColorAlpha(ROOT.kRed, 0.8)\n",
    "graph1.SetLineWidth(2)\n",
    "graph1.SetMarkerColorAlpha(ROOT.kRed, 0.8)\n",
    "\n",
    "\n",
    "x = np.array(x_wf, 'd')+0.25\n",
    "y = np.array(median_wf, 'd')\n",
    "lower_error = np.array(lower_98_wf, 'd')  \n",
    "upper_error = np.array(higher_98_wf, 'd')  \n",
    "\n",
    "graph2 = ROOT.TGraphAsymmErrors(len(x), x, y, np.zeros(len(x)), np.zeros(len(x)), y-lower_error, upper_error-y)\n",
    "\n",
    "graph2.SetMarkerStyle(20)\n",
    "graph2.SetMarkerSize(1.5)\n",
    "graph2.SetLineColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetMarkerColorAlpha(ROOT.kBlue, 0.8)\n",
    "graph2.SetLineWidth(2)\n",
    "\n",
    "graph1.GetXaxis().SetTitle(\"E_{true}\")\n",
    "graph1.GetYaxis().SetTitle(\"(\\Delta E)_{1/2} with\\ 98\\ percentage\")\n",
    "graph1.GetYaxis().SetRangeUser(-22,18)\n",
    "\n",
    "legend = ROOT.TLegend(0.7, 0.8, 0.95, 0.95)\n",
    "legend.AddEntry(graph1, \"E_{reco} with OF\", \"lep\")\n",
    "legend.AddEntry(graph2, \"E_{reco} with wF\", \"lep\")\n",
    "legend.SetFillColorAlpha(ROOT.kWhite, 0.5)\n",
    "\n",
    "graph1.Draw(\"APE\")\n",
    "graph2.Draw(\"SAMEPE\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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