1_familiar_with_data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.26/08\n",
      "\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()\n",
    "#gROOT.SetStyle(\"ATLAS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load lib\n",
    "import ROOT\n",
    "import os, sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "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": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code from brigitte\n",
    "# first sight of the deconvolution and wiener output\n",
    "\n",
    "pulse_pad=0*adc\n",
    "pulse_pad[:len(pulse)]=pulse\n",
    "\n",
    "pulseft=np.fft.rfft(pulse_pad)\n",
    "adcft=np.fft.rfft(adc)\n",
    "thresh=0.0  #if the pulse FT goes to zero, you can use this to condition the deconvolution\n",
    "mask=np.abs(adcft)<thresh\n",
    "if np.sum(mask)>0:\n",
    "    tmp=pulseft.copy()\n",
    "    tmp[mask]=tmp[mask]+thresh\n",
    "    filt=1/tmp\n",
    "else:\n",
    "    filt=1/pulseft  #this would be a standard deconvolution\n",
    "\n",
    "deconv=np.fft.irfft(adcft*filt)\n",
    "#deconv=deconv#-np.median(deconv)\n",
    "i1=100000\n",
    "i2=100200\n",
    "plt.clf()\n",
    "\n",
    "truth=signal+pileup\n",
    "\n",
    "plt.figure(1)\n",
    "plt.clf()\n",
    "demedian=True\n",
    "if demedian:\n",
    "    plt.plot(truth[:i2]-np.median(truth),'*')\n",
    "    plt.plot(wiener[4:i2]-np.median(wiener),'g.')\n",
    "    plt.plot(deconv[:i2]-np.median(deconv),'r.')\n",
    "else:\n",
    "    plt.plot(truth[:i2],'*')\n",
    "    plt.plot(wiener[4:i2],'go')\n",
    "    plt.plot(deconv[:i2],'r.')\n",
    "\n",
    "\n",
    "plt.xlim(i1,i2)\n",
    "plt.legend(['Truth','Wiener','Deconv'])\n",
    "plt.show()\n",
    "# plt.savefig('reconstruction.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "rms/median deconvolution err:  0.38331823905332807 0.2534017597912934\n",
      "rms/median wiener err:  3.033477392194672 0.4623109999999999\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# code from brigitte\n",
    "# continue above\n",
    "\n",
    "err=deconv[:len(signal)]-(signal+pileup)\n",
    "err=err-np.median(err)\n",
    "print('rms/median deconvolution err: ',np.std(err[1000:-1000]),np.median(np.abs(err[1000:-1000])))\n",
    "werr=wiener[4:len(signal)]#-(signal+pileup)\n",
    "werr=werr-(signal+pileup)[:len(werr)]\n",
    "werr=werr-np.median(werr)\n",
    "print('rms/median wiener err: ',np.std(werr[1000:-1000]),np.median(np.abs(werr[1000:-1000])))\n",
    "\n",
    "plt.figure(2)\n",
    "plt.clf()\n",
    "plt.plot(werr[4:i2]-np.median(werr),'g.')\n",
    "plt.plot(err[:i2]-np.median(err),'r.')\n",
    "plt.xlim(i1,i2)\n",
    "plt.legend(['Wiener error','Deconv error'])\n",
    "plt.show()\n",
    "#plt.savefig('reconstruction_errors.png')"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pulse shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Info in <TCanvas::Print>: pdf file ./plots/pulse_shape.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TGraph object\n",
    "n_points = len(pulse)\n",
    "graph = ROOT.TGraph(n_points)\n",
    "\n",
    "# Fill TGraph object with pulse data\n",
    "for i in range(n_points):\n",
    "    graph.SetPoint(i, i, pulse[i])\n",
    "\n",
    "# Set x and y axis titles\n",
    "graph.GetXaxis().SetTitle(\"BC\")\n",
    "graph.GetYaxis().SetTitle(\"Arbitrary Units\")\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(0.5)\n",
    "\n",
    "# Draw the graph\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Pulse Graph\", 500, 500)\n",
    "graph.SetTitle(\"Pulse Shape\")\n",
    "graph.Draw(\"APL\")\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"pulse_shape.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ploting functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_curve(signal=signal, x_min=0, x_max=len(signal)):    \n",
    "    # Create TGraph object\n",
    "    n_points = len(signal)\n",
    "    graph = ROOT.TGraph(n_points)\n",
    "\n",
    "    # Fill TGraph object with signal data\n",
    "    for i in range(n_points):\n",
    "        graph.SetPoint(i, i, signal[i])\n",
    "\n",
    "    # Set x and y axis titles\n",
    "    graph.GetXaxis().SetTitle(\"BC\")\n",
    "    graph.GetYaxis().SetTitle(\"ADC count\")\n",
    "    graph.GetYaxis().SetTitleOffset(0.8)\n",
    "    graph.GetXaxis().SetLimits(x_min, x_max)\n",
    "    graph.SetTitle('signal')\n",
    "\n",
    "    return graph\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_hist(signal, x_min=0, x_max=500):\n",
    "    n_bins = x_max-x_min\n",
    "\n",
    "    # Create TH1F histogram object\n",
    "    histogram = ROOT.TH1F(str(signal), str(signal), n_bins, x_min, x_max)\n",
    "\n",
    "    # Set histogram content to pileup data\n",
    "    histogram.SetContent(signal[x_min: x_max])\n",
    "    histogram.SetLineWidth(1)\n",
    "\n",
    "    # Set x and y axis titles\n",
    "    histogram.GetYaxis().SetTitle(\"ADC count\")\n",
    "    histogram.GetXaxis().SetTitle(\"BC\")\n",
    "    histogram.GetYaxis().SetTitleOffset(0.8)\n",
    "\n",
    "    return histogram"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## signal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "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": [
    "h = plot_hist(signal,0,len(signal))\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [0. 0. 0. ... 0. 0. 0.] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n"
     ]
    },
    {
     "data": {
      "image/png": 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wytMD6LnLJQlVJ5OcAGCD9YHqZ1Tk9B8AQAxCFQBABEIVAEAEQhUAQARCFQBABEIVAEAEQhUAQARCFQBABEIVAEAEQhUAycnz/Nz1RnjKi7LOMjUns/YfAGxg7T/mJCcA2MDafwAA9yRUAQBEIFQBAEQgVAEARCBUAQBEIFQBAEQgVAEARCBUAQBEIFQBcG3WtCERZlQ/mWVqAGADy9QwJzkBwAaWqQEAuCehCgAgAqEKgI0MD4cpoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIALL1JzM2n8AsIG1/5iTnABgA2v/AQDck1AFABCBUAUAEIFQBQAQgVAFABCBUBVB3/dPLzQoyzLP8zzPy7I8vCgA4FCmVIigqqrlxrIsh2Go6zrLsrZtx+s8AYBb0lO1XeigetpH1fd9SFRN0zRNE+JU0zRHlwgAHEWo2q4sy7quQ1/UTN/32X9TVFEUYSMAcEtO/30lxKbQLzXd/jQ/ze4DQDaZBTv6neFgeqp2scxPr8aq51vt/hwA4FThYPfqILiy8ayC9VTtpSiKd+7mKxcAPPV4PF71TT7dbu2/eyqKwsk+APgpQtVBjFIHgHsTqnYxDmAftwzD8OYJQQDgioSqXYRh6eOUCuEH81QBkDJXQX3JQPW9dF1XVdX4Bu26zmI1AHBjJvzYVzgDuDKfgvYHTjf9LPrz5/Bdcfz5oz/caS+bS/qoEXZ94tMynu7llW3PJfoTf/qA71y49+dz3Hz13ymHVz1V+9I7BQA/wpgqAOAC0h/yJVQBAETg9N/JVnK34VYA8MrTA+i5vVlC1ckkJwDYwDI1AAD3JFQBAEQgVAEARCBUAcBvSX9ugosSqgAAIhCqAAAiEKoAACIQqgAAIhCqAAAiEKoAACKwTM3JrP0HABtY+485yQkANrD2HwDAPQlVAMC+fmQOd6EKAC7vR1JL4oQqAIAIhCoASJ2OqEsQqgAAIhCqAAAiEKoAgJPleX6DU5xCFQBABEIVAEAElqk5mbX/AGADa/8xJzkBnGVcKo4rsvYfAHA3NxhjHoVQBQAQgVAFABCBUAUAEIFQBQAQgVAFACTkusPehSoAgAiEKgCACIQqAIAIzKh+MsvUAGBu9w0sU8Oc/0UAsMG4Is14JLVMDQDAHQhVbHTdS14zxZ/kupVnij+P4rkQoQoArkRWS5ZQBQAQgVAFABCBULWjvu/zf5qmObscAGBHQtVe+r6vqqooiq7r6rpu23Zbrnrz3HnEu0U8W398VYqPvkfFf+T4qtIsPtbu4jq+Sd90+xfx0m+bj5htbC9lWQ7DMDbv7Nfgndne3pwRLuLdbv9Qx+9R8dEf6vg9pvlQX+5xOqnP4/GY/rv8w/HW8OvKFEGvfp5tXH+Q8Q7Te/555/Uylvf589E27GW5x3daZvrI3zTv+y/iR4/8qqQ/X8T1Jt31RTwl3uip2sswDEVRjL+Gbqq+78+qBwDYlVC1ixCeyrIct4SfhSoAuCvL1OxoGqpeSXMExu0f6vg9Kj76Qx2/xzQf6ss9jtvDD9N/l3/49OfNd/7oQaKU9PQ+Hz3yp3+YbWrSzeV99CJGKenPB4z+xJ/eZ+XOBxOqDjXrqTKgDQBuw+m/Xbzqo3qn7woAuCKhakdGUAHA7xCqdjQNVeHqP1OAAsBdCVV7qet6GIYxV7VtO86wkPhM69PyRtM7lGUZNi7PZp741MKul9s3V7vyh9E9LX4s4GmRpxffNM1YwHIviTf7SvGJN/t68es1JF58+i0/Cs/i/RoSLz7xlt/vkLRL8Q92U9f1sqm7rsuybJxpPcuyuq5PLXMuVFj/13hriIZh4+wtNH1q490OK/vp+3lzteHOT//wsOJDedNXoeu6RIoPjzx9G0/3knizrxefcrOPxYeqxicy3nqJln9VfOItPwrFzPaSeMuvF594y+90SNqpeKFqd13XjW/Qx793wKtfUxDeYU9vGt/c45bpr0+PrDsWOilpFKXa8Iezj5Xpi3hA8bNqlzedW/zscDitNv1mXyn+kXazPxbFT/+3Xq7lZx81ibf8rJjlwTvlll8p/pF8y+9xSNqv+LQO579g9rFywP+iT62EoeWbuyiK8enM3tyHPbVXXzU2V7tsgeVheO/iw05ftd7pxS8/hce9pN/sK8U/0m72ZXnTGi7R8q+KX946+8PTiw/GDo/pTtNv+ZXiH8m3/B6HpP2KF6oOtYzVj9VvCacI77bi3wiwZUfr8s6PFxHqyKe2Uttyy3q1y/9de/e6LR9/fKuEVyH0YE9vSqf4WQEXavZlDZdr9uleLtfyy56DxFt+rGR2LL9Ey78qPv2W3+OQtF/xBqqfIPHZqoZhCD/UdV0URdu246jA8aZRys/lWtXOhEsc2rYNXVnDMFRVlewkHeEdEsaBXq7Zp8VfpdnH4bfTZdqv0vJPi79Ey1dVVdf1slUv0fKvik+/5a91SDKjehKSegeH/3jjW7Pv+6qqmqYJR53x68IlXKvaqdD+4+Uq4YKdqqrGg1AimqZp2zbLsm4yPuwqzb4s/irNXpZlXdd93w/DUJbl+AFyiZZ/Wnz6LZ/neVEUry5qTrzlV4pPv+WvdUjSU3WoS8y03jTNq6Wgi6JYfjNI1rWqnSnLcvYJOPbPJyL0N4QvuI/HY3zPXKLZXxWffrOPmqbp+77rumEYxqNL+i0fLItPvOXHiQb7f7IsG39IvOXXi0+85bOrHZKEqhMk1S/1pfG5TN/o05uSyotvVvv0P2o634dSKD58WQyDEv6ckCy1Zv+o+FEKxfd9Pyt4/f9XUi3/afGzu53+tsmyrPon7DH8/PSeSbV88H7xo3SKf9/pLS9UnWD6Mqc203r4Br+MfdMu4umtwzBM34hJharN1T4dM3HwE1nORzd7Uc4tPoSSp18P0m/2leITb/Ysy9q2ffWtLP2WXyk+8ZZvmmY6GDn05YRB0FnyLb9efOItv9Mhacfivx/rzkemb+jHzhfQbhPeGGOFs2sipgXPnsv01+7frGuHlf302o3N1U5/PeBKqOUu6v9ODPv017OKH/dY/9fTd3Vqzb5efMrNPu4le33dWcotv158+i0/VT+blSDllp+aFZ9+y+90SNqpeKHqBLMz1meXMzcdcTx7Ny9vnV2wOv2KcHBYfPq/YnO16394TPGzvuj6v5NTnFj88h0yKyPlZv+z+GSb/VX9Uf57plB84i0/tQxVibf8evGJt/y13vP5I5kR/r/m9LNj62Znpp/e+qr41J7a5mpTeCLXLf66lf9Zw+nF7/TfU/FfUvx+rvK2EaoAACIwUB0AIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAggv87uwAAgP/p+77v+/HXsizHf5eaphnv9uo+h8kfj8e5FQAAjMqyHIZhub3rumlsapqmbdvZfc5NNU7/AQDJeUx0XZdlWVVV460hUdV1PbtPnuenVSxUAQCJK8uyKIrplrZti6IYz/2F+9R1nWXZ9NThwYypAgCuJGSpZXiaZqxTCFUAQLrCuPVhGEJHVLbaF3VurhKqAIDkzEZHTU/2DcMwOxuYCKEKAEhOGHge9H3ftm2e/2/KgqeXB55OqAIAkjOdPSHMQVVVVdM0TdMURfEqVIXerLNOArr6DwBIXchYYTTV9OeZtm1PvPpPqAIAUjeNU6+6o87tpsrMqA4AJCXMqD5e6xeEydPH0DJOpz5Osx62FEVxYk+VUAUAJOTpMjXLtNT3/XSO9af3OZhQBQBc1ZiiTl9NOROqAACiMFAdACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAgAqEKACACoQoAIAKhCgAggv8PnvmHyica3ZgAAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = plot_hist(signal,0,5000)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [0. 0. 0. ... 0. 0. 0.] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/signal_partition.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = plot_hist(signal,5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"signal_partition.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [0. 0. 0. ... 0. 0. 0.] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/signal_partition.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = plot_hist(signal,5000,5500)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"signal_partition.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pile up"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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": [
    "h = plot_hist(pileup,0,len(pileup))\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"pileup Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [2.675229 0.977085 1.117406 ... 1.573923 0.793955 1.936442] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/pileup_partition.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = plot_hist(pileup,5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"pileup Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"pileup_partition.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ADC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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": [
    "h = plot_hist(adc,0,len(adc))\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"pileup Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -3.959317 -5.719013 -6.268919] (Potential memory leak).\n",
      "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": [
    "h = plot_hist(adc,0,5000)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"pileup Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/adc_partition.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = plot_hist(adc,5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"pileup Graph\", 500, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"adc_partition.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OF coeff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/of_coefficient.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TGraph object\n",
    "n_points = len(of_coeff)\n",
    "graph = ROOT.TGraph(n_points)\n",
    "\n",
    "# Fill TGraph object with pulse data\n",
    "for i in range(n_points):\n",
    "    graph.SetPoint(i, i+1, of_coeff[i])\n",
    "\n",
    "# Set x and y axis titles\n",
    "graph.GetXaxis().SetTitle(\"OF Coefficient\")\n",
    "graph.GetYaxis().SetTitle(\"OF coefficient value\")\n",
    "graph.SetMarkerStyle(20)\n",
    "graph.SetMarkerSize(0.5)\n",
    "\n",
    "graph.GetXaxis().SetNdivisions(n_points)\n",
    "graph.GetXaxis().SetLimits(0.5, n_points+0.5)\n",
    "\n",
    "line = ROOT.TLine(0.5, 0, n_points+0.5, 0)\n",
    "line.SetLineColorAlpha(ROOT.kBlack, 0.5)\n",
    "line.SetLineWidth(1)\n",
    "\n",
    "# Draw the graph\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"of Graph\", 500, 500)\n",
    "graph.SetTitle(\"of coefficient\")\n",
    "graph.Draw(\"AP\")\n",
    "line.Draw()\n",
    "\n",
    "canvas.cd()\n",
    "canvas.SetTopMargin(0.1)\n",
    "title = ROOT.TPaveText(5, 3.7, 0.9, 0.95, \"NB\")\n",
    "title.SetFillColor(0)\n",
    "title.SetFillStyle(0)\n",
    "title.SetBorderSize(0)\n",
    "title.SetTextFont(42)\n",
    "title.SetTextSize(0.05)\n",
    "title.AddText(\"Distribution of Optimal Filtering Coefficients\")\n",
    "title.Draw()\n",
    "\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"of_coefficient.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OF out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": [
    "h = plot_hist(of_out,0,5000)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -4.674194 -6.818824 -4.34425 ] (Potential memory leak).\n",
      "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": [
    "h = plot_hist(of_out,5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h.Draw()\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## OF & ADC"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -3.959317 -5.719013 -6.268919] (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -4.674194 -6.818824 -4.34425 ] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/adc_of.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1 = plot_hist(adc[:],5000,5100)\n",
    "h2 = plot_hist(of_out[:],5000,5100)\n",
    "# h2 = plot_hist(of_out[2:],5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 500, 400)\n",
    "\n",
    "h2.SetLineColorAlpha(ROOT.kRed, 1)\n",
    "h1.SetLineColorAlpha(ROOT.kBlue, 1)\n",
    "'''h1.SetFillColorAlpha(ROOT.kRed, 0.3)\n",
    "h2.SetFillColorAlpha(ROOT.kBlue, 0.3)'''\n",
    "\n",
    "h1.Draw()\n",
    "h2.Draw('same')\n",
    "\n",
    "legend = ROOT.TLegend(0.45, 0.7, 0.65, 0.9)  # x1, y1, x2, y2\n",
    "legend.AddEntry(h1, \"ADC output\", \"l\")\n",
    "legend.AddEntry(h2, \"OF output\", \"l\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"adc_of.pdf\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## apply OF coeff to adc out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# i wrote the convolution myself... this is same as numpy.convolve()\n",
    "\n",
    "def apply_of(adc=adc, of_coeff=of_coeff):\n",
    "    result = []\n",
    "    n = len(of_coeff)\n",
    "    adc = np.array(adc)\n",
    "    #result = np.array(result, dtype=np.float64)\n",
    "\n",
    "    for i in range(n,len(adc)):\n",
    "        sum_i = np.sum([adc[i-j] * of_coeff[j] for j in range(n)])\n",
    "        result += [sum_i]\n",
    "\n",
    "    return np.array(result)\n",
    "\n",
    "\n",
    "\n",
    "adc_of = apply_of(of_coeff=[0.216309, -0.940796, 1.77594, -0.391205, -0.300415])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.          0.          0.         ... -4.64827397 -6.79374032\n",
      " -4.33795972] (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -4.674194 -6.818824 -4.34425 ] (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/apply_of.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see if the apply_of func is correct,\n",
    "# if the output is the same as of_out\n",
    "\n",
    "h1 = plot_hist(adc_of,35000,35100)\n",
    "h2 = plot_hist(of_out[5:],35000,35100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "\n",
    "h1.SetFillColorAlpha(ROOT.kBlue, 0.7)\n",
    "h1.SetFillStyle(3005)                   \n",
    "h2.SetFillColorAlpha(ROOT.kRed, 0.2)      \n",
    "h2.SetFillStyle(1001)                      \n",
    "\n",
    "h1.Draw()\n",
    "h2.Draw('same')\n",
    "\n",
    "legend = ROOT.TLegend(0.45, 0.7, 0.65, 0.9)\n",
    "legend.AddEntry(h1, \"ADC after OF\", \"fl\")\n",
    "legend.AddEntry(h2, \"OF output\", \"fl\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"apply_of.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: pdf file ./plots/diff_part.pdf has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# to see if outpout(of_out) is the same as of_out(adc_of)\n",
    "# plot their residual\n",
    "\n",
    "diff = [(j-i)/1 for i,j in zip(adc_of, of_out[5:])]\n",
    "diff = [i if not np.isnan(i) else 0 for i in diff]\n",
    "diff = [i if not i == -np.inf else 0 for i in diff]\n",
    "diff = [i if not i == np.inf else 0 for i in diff]\n",
    "\n",
    "diff = diff[200000:200500]\n",
    "\n",
    "g = plot_curve(diff, 0, len(diff))\n",
    "g.GetXaxis().SetRangeUser(200000, 200500)\n",
    "g.GetYaxis().SetTitle(\"Relative Percentage Difference\")\n",
    "g.GetYaxis().SetDecimals(True)\n",
    "g.GetYaxis().SetTitleOffset(1)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "g.SetMarkerSize(0.3)\n",
    "g.Draw(\"AP\")\n",
    "\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"diff_part.pdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## distribution of residual"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: hist (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: png file ./plots/diff_cut.png has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h1 = ROOT.TH1F(\"hist\", \"Difference Histogram\", 60, -0.04, 0.04)\n",
    "h2 = ROOT.TH1F(\"hist\", \"Difference Histogram\", 60, -0.04, 0.04)\n",
    "\n",
    "# Fill the histogram with the values from the diff array\n",
    "for i in range(len(diff)):\n",
    "    if signal[i]+pileup[i] > 10:\n",
    "        h1.Fill(diff[i])\n",
    "        h2.Fill(diff[i])\n",
    "    elif signal[i]+pileup[i] > 5:\n",
    "        h1.Fill(diff[i])\n",
    "\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Difference Histogram\", 800, 400)\n",
    "\n",
    "# Set the x and y axis titles\n",
    "h1.GetXaxis().SetTitle(\"Relative difference\")\n",
    "h1.GetYaxis().SetTitle(\"count\")\n",
    "h1.SetMarkerStyle(0)\n",
    "h1.GetYaxis().SetTitleOffset(0.8)\n",
    "h1.GetYaxis().SetTitleSize(0.06)\n",
    "h1.GetXaxis().SetTitleSize(0.06)\n",
    "\n",
    "h2.SetLineColorAlpha(ROOT.kRed, 0.7)\n",
    "h1.SetLineColorAlpha(ROOT.kBlue, 0.7)\n",
    "\n",
    "h1.Draw()\n",
    "h2.Draw('same')\n",
    "\n",
    "legend = ROOT.TLegend(0.55, 0.75, 0.9, 0.9)  # x1, y1, x2, y2\n",
    "legend.AddEntry(h1, \"ADC output above 5 GeV\", \"l\")\n",
    "legend.AddEntry(h2, \"ADC output above 10 GeV\", \"l\")\n",
    "#legend.Draw()\n",
    "\n",
    "canvas.SetLogy ( 1 )\n",
    "canvas.Draw()\n",
    "canvas.Print(plot_folder+\"diff_cut.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: 2d_hist (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: png file ./plots/relative_diff_corre.png has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TH2F histogram object\n",
    "x_min = 0\n",
    "x_max = 50\n",
    "y_min = -0.6\n",
    "y_max = 1.1\n",
    "histogram = ROOT.TH2F(\"2d_hist\", \"2D Histogram\", 250, x_min, x_max, 340, y_min, y_max)\n",
    "\n",
    "# Fill the histogram with data\n",
    "for i in range(len(signal)):\n",
    "    x = signal[i] + pileup[i]\n",
    "    y = diff[i]\n",
    "    histogram.Fill(x, y)\n",
    "\n",
    "# Set x and y axis titles\n",
    "histogram.GetXaxis().SetTitle(\"E_{true} [GeV]\")\n",
    "histogram.GetYaxis().SetTitle(\" (E_{OF} - E_{ADCxOFC}) / E_{OF} \")\n",
    "histogram.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "#ROOT.gStyle.SetPalette(ROOT.kBird)\n",
    "\n",
    "# Draw the histogram\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"2D Histogram\", 800, 600)\n",
    "histogram.Draw(\"colz\")\n",
    "canvas.SetRightMargin(0.2)\n",
    "\n",
    "\n",
    "'''canvas.SetTopMargin(0.1)\n",
    "title = ROOT.TPaveText(45, 11, 15, 0.95, \"NB\")\n",
    "title.SetFillColor(0)\n",
    "title.SetFillStyle(0)\n",
    "title.SetBorderSize(0)\n",
    "title.SetTextFont(42)\n",
    "title.SetTextSize(0.05)\n",
    "title.AddText(\"correlation between (E_{reconstructed} - E_{true}) and E_{true}\")\n",
    "title.Draw()'''\n",
    "\n",
    "\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"relative_diff_corre.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: 2d_hist (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: png file ./plots/relative_diff_corre.png has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TH2F histogram object\n",
    "x_min = -5\n",
    "x_max = 55\n",
    "y_min = -15\n",
    "y_max = 55\n",
    "histogram = ROOT.TH2F(\"2d_hist\", \"2D Histogram\", 250, x_min, x_max, 340, y_min, y_max)\n",
    "\n",
    "# Fill the histogram with data\n",
    "for i in range(len(signal)):\n",
    "    x = signal[i] \n",
    "    y = adc_of[i]\n",
    "    histogram.Fill(x, y)\n",
    "\n",
    "# Set x and y axis titles\n",
    "histogram.GetXaxis().SetTitle(\"E_{true} [GeV]\")\n",
    "histogram.GetYaxis().SetTitle(\" ADC * OFC \")\n",
    "histogram.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "#ROOT.gStyle.SetPalette(ROOT.kBird)\n",
    "\n",
    "# Draw the histogram\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"2D Histogram\", 800, 600)\n",
    "histogram.Draw(\"colz\")\n",
    "canvas.SetRightMargin(0.2)\n",
    "\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"relative_diff_corre.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: 2d_hist (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: png file ./plots/relative_diff_corre.png has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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RdahaIzso7ZZRJCf1fc9DfADOiWf9gBws/nmDXUMhneHSX+37fu0BwIf3gWLKIYAdMC09ii38EEWouuHmwN+TyEYA6kScSnj4o5s0dmxNDv/tRn77h2HYZOCP0UMAAA6MStUqd7TOfQjRHpeD+o23O42L9RQANIoaFZCPUHXbWlqyx92Nk2Uf5Wh7KlUAmsCcdOAxhKpVWmu7f5/Llp3kq15UCkNVzmoLAFAD5lEBzyBUpUSf17O7JntfXXvEj+WpAAA4g4YnqtcWVux2NN4ELElga5vbAEAN2IIGeF7VG/rWuYaTrVSFr2L3VLYrhdp5V9FTYkNlAJWQREWWejU2VD42hv+2JBOq5jdysO/72opqAGAxLf3k3DvU2txfY8zajcx9VCvsVm6LN6cUS/+ZLfO73V/VlZKHs0jZH7R5Y1dbWGtJpQpAcUxO31NVlSo78OIahsGdxCLSA0dhz2H76/UavRuG5xA9AaWUMUaW406/dFnc1EsiVAEoiDi1v3pClZtm5GF2O8ASxppEqApHY2xjr9swV9k5M+rtYa+1Pt1E5XVb1W2Um3pJhCoARRCnSqknVNktQ9z8ZFOOF4Ckcc4Ny/bgNrYxyz1oc5J7DjbqRU8g2m1Vc2zae/rPGDO+qefn+LBuXelTA3BwPOX3IvV/sLshxj1u76ru7fWuW63NZO7B6P650XMYx1EKUdGNTKLdVrVnSTOhSqYodV13uVymN5fLRX5No+OvTVjWlT41AMAj6v9gl5gSXeDabRD+Oc3ei8NhPq+BegtD4XpD0tiNSmtraEe7LauNUKW1vlwuiTQ6TVPXdQcoXAHAS7nrUeG0tNZr88FtA+9IIoFZiawmB8NKWHgO9ojXOKfb4hpYUsGtlw7D4G20Z1fXVEpdLpe15wsA4OSYR1Wbf/vKz+73Yn/8E/dva3EqWhNyD7rVjTCWyVejd2G73tB9p/3Kbl+h9kqV/SEOw7AsyziO7o9Vaz2O47Isdpw1fN4SAE6O1dKRJtOUZYKNeqtf2K9KZAmzi4wRZb7E2q64CfXUn/JVXakyxtgx1/SIqdb6er3Kb4PWusV3AgCAtF/41X/evE93wEclb7jRx/R2u+c2MQxVdaiSN6/v+5w5aDJCPE1TPWVAAKgHBaoD+L9//R8f+8ZEGpO7pwSjeZ7lOTB3Tr19HM+NNXJflnuuPEn22Ik9SWsdrl9aUNXDfxKP8mf1h7PbAODMmJaOHLJEkTHGzqXx5lRFY1MN99zabvdVhypxV/6t7UEAACiCeVR4gFStVPbiT/n33Ofvy03c2esNVY/9+CSBNfGjBwCgiPQGyQ93m5+xcl4lp83NBbd2Vm+oeoz8fJuYzgYAr0aNClGXy+VyuWRWmOTZwEQbe88N1+10+1FO+nGXRoq2dNvId0XnTiVWWyii3lD12M+IWeoATo55VMgUDVXhTsYydT3ag5dpElEpnCQdbkcj7DNq3pGEekJV1Rv6Rrd7TLC7M9Z8US42VAawIZb3rJ/7sf+P3/253V73F371n93bzdq+xdFNjtf2Lc7fOzna0r6Wew7Rg2vnEH2tsqq+qa+962sq/PmmpZdNa+UqAFSCUFWJzM/2gqFKOSfZ972djmxHe6IJzDZ2W67FMvW2r59tHN7Kbdjyug0DnO3Wnq09pap2Uqk6VCnnXb/5U7PvTVU/3zQqVQA2QZxqRSWVKuFtOyPCQKM+m6tc0VuYm6ustVtzeA7RE7i321Jqv6m7P8S1H7T7ZuePFdaAUAVgExKqiFP1qypUqc8+BuhurRt1V2P3Xpy+L5s3+s0m3RbRwE09Gk5lCpsXb9tKVIpQBeBp1KjaUluowraq3qZGaK2XZfEqhGHFsrYaIAAAOJUGQpWQkqMspa+UmudZilU51UIAOB5qVEBtGH4qieE/AA8gTrWL4b9ja6ZSBQBwEaeA2tS7ojoAAEBDqFQBQDPYfwaoGaEKAGrnZSkG/oA6EaoAoF7EKaAhzKkCAADYAJWqwhL7bvL4KwBBgaot6Q2VcWCEqsJITgCimJPersQHO3nr2AhVAFAX1vYEGsWcKgAAgA1QqQKAGlGjAppDqAKAWjCPCmgaoQoAymMeFXAAzKkCAADYAJUqACiJGhVwGIQqACiPOAUcAKEKAMpgWjpwMMypAgAA2ACVKgDYG/OogEMiVBXGhsrAqRCnzoAN/k6LUFUYyQk4IeLUsbGh8mkRqgAAKODfvvKzpU8BGyNU5TLG2D9rrW82NsZorW+2BHASPOsHHF7H8NNN4zhO0+QdHIZhHMecxn3fu4HM1XX8/IHjYx4VLPdj/wfqg91e90vdP3C72QGVqhvckNT3vVJqnmellBz0cpXXWFrO86y1XstVAE6COIU6vWgcRrrNb/yKbvdHpeQGmVTo1aW01hKYrterfVONMZfLRX22NGVjltvS7ZyfP3B4UqkiVEFVVql66TiMdyR6E4x2u3YC9ibrqu0eyk09xb7Z4U8pDFs2aXmN5Xj0l49QBRwbcQqeekLV2jiMisWa6DiMWslVNlF53Ya5yt43b3brJiqv26puo6yoniJvqrx/iQZC3uBhGLw28qtp334AZ9B1HzMzHTWTkDQMw7IsMvq2LIvc76Zpcu9uxhhp3Pe9NF6WRW528zx76cfGJrdbOeLVmYwx9r55s1v7vWG3VQ0CEqpStNZrdUjbIP8I06qAM3Dj1LK8o0yFCtn7mneDs/cp94Zl23gHJYF5Pdi5Me5B76+Jc4h2a1832m1VNQtCVco4juM4hjnJvsFhYEpEZkIVAKAGLxqHcefDuC3tX92otNatNHa7XbvDRrsti1B1B8lYXddJHXIYhqqqjgDqQY0KNXvROEwiq8nBsBIWnkNYGMvvtjiWVMhlB5WF9+uYfkdl/l20zcNbFlQ1NQ+AYnnPM2l9t5m1OPXwOIxbXoq2lDnpjw3VvajbV6BSlUtyfd/3dh7fJv9RLY96/qUBbIV5VGdzsI/uHcZhvA5zakv11J/yUam6g1uTlN+8u1b1ZKwQAPCML3X/sHmfLxqH2VwT91AqVY+QqpVyZtKl3+x6KpMAtkWNCnv62+U/Pfa/RJ8vGofZR21Ji1CVIothRL+09gxCIrDX9t4D2ApxCk0bx1Hud3bNgrtuWAXvbrUNERKqUi6Xy+Vyues9CxvnzPID0ByW98TxvG4c5vn0U1t+iiJU3RZ9I8ODaw92NvF7AAA4jxeNw+QvcJBTZchpc3PBrZ0RqlIksHsL9itnWp+7apldBs2b4he2BNA05lGhdS8ahwnX7fQa2/STyGpht3ayV9htYrWFIghVKTYeXS4XrbVdYN1uQuTmJ621feNt42hLAI0iTuFINh+HSUQlST/urTC6y4094hafbt5A6wlVXbXLZtTD3Ubbim7NHW281lJ9drtyAPWTREWWwsPcj/0fqA92e90vdf/g3m7GcZTCz/V6dROJXTDIXVgh/6BylkV1X87eGd2Dtgf3HKIHbbfe/TT6WmVxU8/iDj9rrdOh2LzRb9ZaEqqAthCq8KRKQpVyEknf93KfMsbYooDX2KYi29gOxnktbSpSb/NebLdeTgq7tS3DYoTtNjyBsNuCuKmXRKgCWuE+6EeowsPqCVXqZeMwbq6y1qLPi7othZt6SYQqoH7EKWyoqlClXjYOoz47ESo9KepF3RbBTb2k9Kq1vDVAWcQpPCbzs72GUIVtsfdfYfyWA8DBJD7YG9oBBg8gVAFACjUqAJkIVQDgY/8ZAA8gVAHATzGPCsDDWFEdAABgA1SqAMAf76NGBeABhCoA+CniFICHEaoAnBpz0gFshTlVAAAAG6BSBeCkeNAPwLYIVQBOjTgFYCuEKgCnwzwqAK9AqCossQ8U2wICm2PIDztgg7/TIlQVRnICgIPJ3FD52z/+aJfTEd/c8bXOi1AF4BSoUQF4NUIVgBMhTgF4HUIVgINjWjqAfbD4JwAAwAaoVAE4LOZRAdgToQrAARGnAOyPUAXgsIhTAPbEnCoAAIANUKkCcCg86wegFEIVgINgHhWAsghVAJpHnAJQA0JVYWyoDAAHw4bKp0WoKozkBGyFGhUqkbmhMo6HUAWgYUxLB55kjJH/11prrdfaSLPQ2nfldOv1n9kyv9v9dVRKCuo6fv7Ag5hHhRa5H/t/8uPf3e11v/b5b4a3G2PM5XLxDvZ9H+andIEt7Dlsf71eoxloHMdpmtwjwzCM4xi2jJ5tbfdQ1qkCAOB0vIzS9738YZ7nu8Yo7Tda9tv7vrdfvVwuYVbTWttEZVtO0xTGL/ds3W5rG04lVAFo2LK8o0wFPMBmlGVZlmUxxizLcr1e5WC0qrTEeFHJfqN8Sbr1XlEYY+Z5VkoNw2BPYBgGpdQ8z1637tl63VY1CEioAtCYrvuYqVTAM2xk8YbPtNY21oSNc8g32nAmvL8KO8bnDvaN4yhVKPegPYFot+6pFkeoAtAMN05RowIelshJtvBj2+SHKpuEvOqR/asblWyZKnoC0VSX021ZhCoAAM5FYko4HUqtj6ZFG+d3KwfDoBbmIXvEa5zTbXGEKgANoEYFbEjmJEWzSFhtcgtFnSPMQ1JeisaysP6U70XdvgKhCkAziFPASxlj5HE8tywkkWWeZy+7TNOU//CdF4lyakv11J/ysfgngKoxJx2wvvb5b76uc3fJqGigcVeQso211vukn6qe8ltDqAIAoA1f/3//7bFvTKcxb80q75FA+zieG2skXU3TJMsflEo87kpXNWD4r7BuXelTAwpjHhUa1dYHu9baJipZMsprYPeQ8Y6HM8r3V9sQIaGqsOhaaqL0qQHFEKfQtFY+2I0xXde5K3DeuzZB/sN3z6ef2vJTFKEKQKWIU8Dr2CG/vu8fiFNR+RkrZ7gwp01itYUiCFUAAJyOTVTpDGSMGccxHbls+kkscOCln3C9Bq+l20a+Kzp3KrHaQhGEqlwyqDyOY2adM7MlABdb0AA7yJ8OJYssrE0G9zJNIipJSzechdvRuOfmFp9uVtHqCVVdbUO8FfIeixBr6d59JDXdUinVdfz8gffcLMWoH47K/dj/kx//7m6v+7XPf9O93ciU+b7vE3nFJhXbONw7WaJS2LN3MNrS3luv16ubxsKDa+cQfa2yuKnf4CWqvu/dwqb303MTldtyLVcRqgBFnMKZVBWq0mx779YmC1PZG5yXftybpuzrZxt7LZUTtrxuw5umOwNMOrGnFHZbEDf1G6JB2H133c2JwoP2dzH6rhOqAEWowpm0GKpUbBAmbGNFh3fWoo/NVdZaGeKubkvhpp5i38Lwp2R/w+yXouVNezz6W0KoAtRbqCJO4QwqCVWPkbnF8metdTrNuAOL6UlR5o1+s0m3RXBTTwmTkxWO+0rwd1fx91qGnRCqcHLUqHA2TYcq3MQ2NSmJBTDWonR43J1/V1WVEiiIOAXgeAhVKYlnTW05SnJSuK5GtDdCFQAAR0WoeoSs26FqWsUVaBE1KgBHQqi6m/sQhPvoX+JbZHmFtVUVHjsNRsfRKNb2xAFssjXyt//lo+c7yfbNHV/rvAhVd/Ce59wk1pCNcB7Mo8JhPPzRvUkaQ7XYpiaX1tpd0OyB/6KYUAUAwIERqm4zxnRdJ2tQSZwK18ZIB6bo7pLASch2frZMtSzvKFMBOCSG/26IrpMeylk3gUoVTo4sBeDYqFTdkJOoXGGznNUWgENyC1QAcHiEqhQ7zJeTqGR5hUSoAgAAB8bwX4pdjCpn4G8cx8vlMs/zOI5uGpNOZLNu4CR40A/ACbH3XMq9+3jbPZX7vpewZVe0iv6c2fsPx0OcAhLcj/1f/+G3dnvd73zxN7jd7ICbesq9oUo5ucpKFLoIVTgeCVXEKSCKUHVsDP+lPPAraBz6zQtODQAA1IVQtT2CFM6JB/0AnByhCsCzmEcFAIpQVVxi2hbj36gfcQoIscHfaRGqCiM5AcDBJD7YyVvHRqgC8CxqVACgCFUAHsO0dADwEKoA3Id5VAAQxd5/AAAAG6BSBeAR1KgAwEOoAog1IVAAAB/RSURBVJCLeVQAkECoAnAb86gA4CbmVAEAAGyAShWAFGpUAJCJUAXgNuIUcFTGGPl/rbXW+mbj/JbFu91fxzYpBXUdP3/UixoVsDn3Y//Xf/it3V73O1/8jfB2Y4y5XC7ewb7vJbh4xnGcpimnpYrtxnO9XqMZKOx2GIZxHMOW0bOt7R7KnKrCunWlTw0A8IgmPti9jNL3vfxhnufwPN3o47aM5iT77X3f28aXyyVMYFrrsNtpmsJu3bN1u63qR6oIVcUt60qfGs6r6z62ZapleUeZCrhLEx/sNqPIiRljlmW5Xq9y0I01xhiJPn3f25bDMCil5nn2opL9Rmkpjb1XtN3O86yUGobhZrfu2XrdVjUIyPBTSQz/oTYM+QEvVcnwny38hPcgW5RyU4ukH6+xHPcGAaV05A32RV8uv1v77TndlkWlCoCP6hRwbGtzoZRT+LFtbD3Jaykzn+Sr7hEVVI/sX93JUmvdSmO3W3smOd2WRagCAOBcJKbYmUmutdG08HgYvxLdykGvpYrlIXvkgW6LI1QBUOqz86gAHJvMSVp7yk/+IJlprUrk9SZ/kPJStGVYf8r3om5fgVAFnB3T0gEId076tj17kSintlRP/Skfi38C58W0dKAt3/nib7yuc3fdhHCcLqrv+/BJvRep6im/NYQqAADa8Gs/+J+PfWM6jXlrVtXzMN1N7kpXNWD4Dzg7hvyAM9Na20QlS0Y90MPG55SttiFCQhVwRkxLB2CM6brOXYEzfBYvHZjyZ4g/n35qy09RhCrgXJiWDkA5Q36yTvraUk/hugmJNvkLHOQUt3LaJFZbKII5VQAAFPCdf/mo4KvbRJVZATLGrD3BZ4/LYujR8pWXftysdrNbmQ4/TVOY/BKrLRRBpaqwJvbdxDFQowL2Uf8He7jAZsJa/Sm6QfLalyT9uKlIul1b/NMtPt1cML2eUMXecyWx9x/2JImKLAUU5H7sd9/f8YV/5TO3G4l3fd8n8oqbkKSsNQyDm8bCg7ZnlbHNX3RHv7Vt/uwJh/sMqpoeV+SmXhKhCvtgPSqgElWFqrRoKur7XrKOXcjAu4u5qzPIvn7GGPleLyeF3dqW4aCkOwPMO4Gw24K4qZdEqMI+CFVAJRoNVcoJQNbafCxv1SuxFn1e1G0p3NRLIlTh1YhTQFUqCVWPMW/0m0Rjd0wwPSnqRd0WwU29JEIVXoc4BVSo6VCFm1hSATgy4hQA7IYlFQAAADZApQo4GvafAYAiCFXAcTCPCgAKIlQBR0OcAoAimFMFAACwASpVQPOYRAUANSBUFZZY1pY1RXCTF6cY+ANqUM/GydgZoaowkhMew5x0oFqJD3by1rExpwoAAGADVKqAhlGjAoB6UKm6jzEmZwdHaRbdZxt4Utd9zMx0AKgQOyzeR4bDEz+0cRynaXKP9H2/lq7YUBl3YR4V0Do2VD42KlV3uFl5chNV3/fyh3metdavPC8AAFAeoSrXOI6XyyXRwBgjiarv+2VZjDHLsgzDoJSa55mhQDzDHfJblneUqQCgQtQDbzDGhFkq+kPTWs/zHH5VjkcHARn+QyZJVGQpoHUM/x0blarNSKKS0pRLJrbLV4F7MS0dAFpBqLpBa728uV6vOe3XjjACCADAgbFO1TZsYErMSTfGMGMd+XjWDwDaQqgCqkOcAoAWEaq2kR7a6/t+7QHAh/eBYsrh4RGngGqxhR+iCFWFkY3gYk460ISHP7pJY8fGRPX9MKEKAIADI1RtIx2YWE8BN7G8JwC0jlC1jZx1E6hU4SbiFAC0i1C1sTBU5ay2gDNjeU8AOAZC1WZkB+VEqAIAAAfG03+bkR2X53kex1G2plHOLsvh9jUA61EBqIQxxhhjb17Rr0a/pLWOjsNIe1n1+uZAjfSf2TK/2/2xw+Id7ObKaz80u6dy3/fyZkuiWvsWNlQ+LeIUcE7Vbqgsaz2stUmvBBF+V9j+er1GM9A4jvZGKYZhiGY7ewtOv3RZ3NTvcDNUKSdXWX3frwV8QtVpSagiTgFnU2eounl3S4Sq8B5nG8usGHtPDHOVe8eUVbLX+nQTlddtVbdRburbM2/0m7WWhKrTIlQB5/SZUPXnf7ffC//2F9ZuN26tKB2qcm5YNie5jW3Mcg/anOSWpuzJeAks2oMcTFQu9sdNvSRC1Qkx8AecWT2hKn80LWeUxpKg40WiaA/R+GWPu1HJfntOt2Xx9B+wE5b3BNCi/DqQLTiFw3xeA/U2fhc+xSWN3Yk0aysTRbsti1BVWLeu9KlhM8Qp4FSa+GDXWi9vrtfrzfYykylN0k+0pbfqkP1DmIfcx+fv7bY4QlVhy7rSpwYAeMTBPtjdQpEbEMM8JOWl6EzisP6U70XdvgKhCtgJNSoALZLIMs+zl12macqvvXmRKKe2VE/9KR+LfwIvxP4zALb0218o+OLRx/S01vuknwqX+gwRqoCX4Ck/ANv77z988BufSGMyb2kcRzfWSLqapmmeZ1lC6OH+n6G19tYOLYvhPwAAsMquvOgdD2eU76+2IUJCFbAxnvUDcBL5D989n35qy09RhCrgJYhTAM4mP2PlDBfmtEmstlAEoQrYjFujAoADMMaM45heXdOmn8QCB176sd8SJrBwqU/5rujcqcRqC0UQqgAAQJwxZpqmtcngXqZJRCVp6YYzO//daylH3OLTzQXT6wlV7D1XEnv/HQbP+gHIUc/ef670Jnpr+xbn750cbRnd0W9tm7/oOURfqyyWVAA2Q5wCcDzDMMjSCV3X9X0vC1PZMT5vi5vr9SqpqOs62dfPNvZaaq37vp/n+XK5eN3KX8Nu53nWWsuXbPEsZ4+d3VApKSm9Fi1vTROoUQHwZH62t1KpUs5Sn55oe9uby6s8WbaIZYUlsQe6LYVQVRLDfwdAqAKQr87hv0yyYJX82VaM1rgTodKToswb/WaTbovgpl4SoappxCkA92o6VOEm5lQBdyNOAQBChCrgQcQpAICLdaoAAAA2QKUKuAMLpgMA1hCqgCzMowIApBGqgBSvNEWcAgCsYU4VAADABqhUAVmoUQEA0ghVQBxz0gEAdyFUAT7mpAMAHkCoKiyx7yZbCgBAi9IbKuPACFWFkZyqQo0KwPMSH+zkrWMjVAE+4hQA4AGEKkAppqUDAJ7GOlUAAAAboFKFs2MeFQBgE4QqnBdxCgCwIUIVzo44BQDYBHOqAAAANkClCmfEs34AgM0RqnAuzKMCUItP/770GWBjhCqcxfs49eUPlFLL936z8NkAAA6HOVUAAAAboFJVGBsq74caFYBdsMHfaRGqCiM57aD78BOlmL4AYCdsqHxahCoc2ft5VL/zXxQ1KgDAizGnCgAAYANUql7FGGOM0VprrUufy4n9zTul1PLLpU8DAHACHXN6Nqe1nufZPXK9XqPRquv4+b9K932llFK/8rFiPSoA1XA/9ruvzunGW/oznb7dSCFgHMd0N/n1AmOM/H9m41d0uz9u6htzJyH2fW/TVTRXEapewf2cWv60L3gmAOCpNlTJzSvRZhzHaZrcI33fS8RZ6821VlwIux2GIZrtjDGXy8U7WNs9lDlVW7K/MdfrdVkWY4x9v8NfBQAAarCWjSw3+vT9+3+szvO8NghjW9rGl8slfBWtddjtNE1ht26icrut7WlKQtWWpC41DIP7C2Fz1c3fWjyp+/CT7sNPvv5Hf/n1P/rL5U97ylQAcNM4jul/9htjJPr0fW/rBcMwKKXmefZubfb2Jy0TxQVjjL1p3uzWfm/YbVWDgAw/bcbm6PBHKlE6LGky/LetH6gPlFJfVKxHBaBS9Qz/5Y+m2YnC3lfluDcIKPc7b7Aven/M79Z+e063ZVGp2kyiECXR2xs2xoZ+oD74gfrgS1/95Etf/aT0uQDAodh6kndcygTug1m2cOBVj+xf3crCWrfS2O3W3l5zui2LULUZedftQK+rquIkAABa6+XN9XrNab92xC0pqZX7oBz0WqpYHrJHHui2OELVZiRWk5921n1fdd9Xv/fDb/zeD7/BPCoA2NZalSjaJnEfDOtP+V7U7SsQqgrrHlX6xMvruo+77mPzUWc+6r79hY++/YWPSp8RgLPgo/teXiTKqS3VU3/Kx4rqhdUzva455hf/QCnV/x9+gAD29vBH97O56s/0U99+v3SykeUY90k/TQwEEaoAAGjE75gHv3H3NLYPd6WrGhCqNrNnYD85WTpBf2tRSlGnAoCyCtaQarvnMqdqD7W96+3qvjp3X50/992ffO67P1l+mZ2SAeC10oEpf4b48/fBJu6khKrNJJ5BSDwOiruYv9Lmr/TPf+Wffv4r/1T6XADg+MJ1ExJt8hc4yClu5bSp7fZKqNpMuLSGxWoLAICmhbe2cLWF/OJCIquF3cp3RedO1XZ7JVRtSd54b+H/Cpd8bdG//v7n/vX3P6d/9If6R39Y+lwA4ETW6k/RDZLXviTpx70PSrdri3+6xaebd896QhV7z23MPi4rS+/bDSO9HYtsY37+N/36D7+llPrOv3ykFJOoALStnr3/XOlN9OxX3R1soweVcxO8uc1fdEe/tW3+pNvoPoNrp10EN/WNRbeoDLdSFoSqtO7DT5RSX//r/62U+r3/8I3SpwMAz2oxVCknFfV9L1nHDsYl9mm+WVzwurUtvfDkdhueQLRmUQo39ZcwxiQ2NrIIVWmEKgAH02ioUk4AssLo4/XmWos+L+q2FG7qJRGq0v7xuz+nlOJBPwCHUWeoymTe6DeJxm5BIT0p6kXdFsFNvSRC1Zo/+fHvKqW+9vlfVEoty7vSpwMA22g6VOEmVlQvLLEP1Dn/A+j+/O+UUurDbyilTvkDANC8M2+cfHKEqsLOmZwA4MASH+zkrWMjVKEWXfexUurXfvArSqlvf+E/lz0ZAADuRahCLf52+Qul1BfV35c+EQAAHkGoQmHd99/+9F8/Vkot3yt4LgAAPI5tagAAADZApQrFvH+c+MsfyF+X7/1mybMBAOA5hCoUIHPS1d+8U2znBwA4CkIVSiBOAQAOh1AFAEAJ3+Nh56MhVGFX7+dRyUcJk6gAAAdCqMJOug8/UUqpDz9QzEkHABwRoQp7kTj1p33p8wAA4CUIVYWxoTIAHAwb/J0WoaqwMySn9/Oo/ux/KaUUlSoAR8eGyqdFqMILvV+PSiml1LK8K3gmAAC8GqEKL/E+Tn2ZaekAgLNg7z8AAIANUKnCa1CjAgCcDKEKG3u/HtWnrBQMADgXQhU2w7R0AMCZMacKAABgA1SqsAFqVAAAEKqwGeIUAODMCFV4ilujAgDgzJhTBQAAsAEqVYW1u6Ey86gAIIoN/k6LUFVY5ckpijgFAAmtbKg8juO9XzLGGGO01lrrdOfGGPn/zMaZLSvXtXhTP4yua/LnL6GKOAUA93I/9t8vlbyPT3/Lu90YYy6Xy1rzYRi8XDWO4zRN7pG+7yU5hcLseL1eo4Ep7DZ86YYwpwp36LqPmZkOAAewloeEF4Dc6NP3vfxhnudoTrKJqu972/hyuYSvqLUOu52mqd16FaEKAICT6vt+iXFjjTFGoo80NsYsyzIMg1JqnmcvKtlvlJbSWI54hTFjzDzPSqlhGG522wpCFbK4NaplecfYHwA0TVJLTk3IDsa5QWccR6kteUN1kpOu16t70Pur163bw1q3rSBU4QbiFAAcj6SfnFBl60necck98lX3SNit/asblda6lcZutw0hVCELcQoAziyMX/aIrWDJH+zsKJcc9FqqWEUqWhhrBaEKAIDz6hxaay/K2L8malq2TaL61XT9KR+hCqt41g8ADsnGIG/y+DzPl8vlFfOZvKSVU4VqsVLF4p+IYHlPAKjRp7+1STc2r/R9P46jJB67cpUsamAPJvrp+77dJ/VegVCFVcQpAKjLl//Hg9/42TQmw3xaa7copbVelkVWmbpcLi2uTV0coQoAgHMJ505Z1+s1sdL6Wm/Pn9IxMKeqsG5dofNhHhUAPKW2D/a7eM/0pQNT/sTzkwwREqoKiy5lK3Y+E9ajAoBN1PPB/rxw3YREG2/dhJxun2xTG0IViFMAcC7jOK494re2hkIYlcKWiXUTvCWsElktZxGHahGqAAA4l2ma1vYtDlPOWv0pukHy2pckablJbm07GjkSXUG0foSq+xhjchbwkGZtDSFTowKAk1jbt9junezu1me3o3Fvf7alu8+MDVXeVHevlOV1656D3WW50b3/uhaHeAuSaYaJH9o4jvJ7ZvV9v5auuq7wz5/1qABgT+7HfvfhJ/u98Ke/5d1u7Kz5vu/tklQSaMLbltbafkka2zud161d7Eq95S3b7fV6DfcEdLtNnEArCFV3sL8raz80N1HJkmj2z9Hfj4KhijgFAPurJ1QpJ9O4hmGIVonCxmu3NjdXWWGiurfbJhCqcrmBKfpDs79G7i+E/a7o7xOhCgBO5TOhatf1a/5g7c7lTgxPzw03b/SbRGM3maXH8u7qtnKEqhuiiTv6Q7Nx2/uqHI9G7yKhijgFAKXUFqqwLSaqb0YSlTtlT9i5ePufUgLT0gEA2Bah6gbZC0m4T0Mk2q8dKT5IzGrpAAC8DqFqGzmLlRUPVQAA4HXYUPkUmEcFAMCrEaq2ka5CyfIKa6sqPPaKOVMOvcE+4hQAbKKJrZGxP0JVYfs8jkGcAoANPfzRTRo7tnOFqvytY14x/6nptTcAAEDauUKVylva4IF9HLXW3u40977otnjKDwCAnbH45x3S29RIUXdt5fTol16x+Cdz0gGgWiz+eWynq1S9miy07x2RP7x6+I84BQBAQaxTtRkZNAwnY7E8FQAAZ0Co2ozdjsbdOdIYI3Otwu1rXoT9ZwAAKII5VXdIz6lSzp7Kfd/LYJ+dvb42DWuTnz8DfwDQBOZUHRtzqrYkE6rmN3Kw7/vXjQASpwAAqASh6g6yuXK6jXHoN7ucHQAAKIlQtb39gxQ1KgAAiiNUtYrlPQEAqAqhqrDEPlBrQ43MowKAmrHB32kRqgrjcQwAOJjEBzt569gIVS2hRgUAQLUIVe0hTgEAUCFCVRuYlg4AQOXYpgYAAGADVKpqxzwqAACaQKiqHUEKAIAmMPx3Fgd+jpdLa9FRL+2o16W4NCADoQoAAGADhCoAAE7NGDOOozGm9Ik0r2NF74K6br+f/56vtTMurUVHvbSjXpfi0l7wWvuulfMH0WvUWs/z7B65Xq9a651O6nCoVAEAcEZd19lE1fe9/OFyuVCyehihqrBuXea3v+isNm/5um7vctRLq+FsubR71XC2R720stf15Af7Pmw56nq9LstijLGlrMvlUuy0GkeoKmwJ2IOlTw0A8Ajvw9z7hK+E1KiGYXAH++wZUqx6DKEKAIBzsZlpHMd0A9yFUAUAwLkkMtMwDEqpaZr2O5sDIVQBAHAuEqrs5HQXj/49g1AFAMC5yIQq8tPm2PuvsOjDIDU8DnPUbms4B7qt5BzotpJzOG23D2ztWtXzgwgRqkqq6kkQAEDluGtUjuE/AACADRCqAAA4F5mizroJmyNUAQCA90hazyBUAQBwLvLcn7eVskistoCbCFUAAJyLXUg9rEux2sIzOh4lOANjjDFmbTsCr5nWuqH/nFo859Ah3yD5sM484YYuzb0JHem6POM4pk+7lUtL/GeV2KGliUt7ktZa8pMbA6IHcYdwu0ccz833WvYlcPV9v9fZPSisTste6y062Bt0vV7Dj5q1E27o0sJTVUoNw5DZuNrrCtn/uKJfbejSor+KifeuoUvbhPujGIbBvu/tfpYWR6g6PvuxstbA/Rxxk0rNnybep579c4ufBQd7g7zbmJd9vcYNXZp3qu7Zpu/NlV9XyH0Hw6+2dWnRHLz2cdHWpW0iGjrX/p2AHISqg3M/JqIN7H9U7geH/a46M0r0n1Ppy6zW8d6g6OVEr6KtS4veb6K/im1dV8i9v3pfau7S5NxyUlFzl7ah6/U6vCl9Ls1r7A6ETNF/f0RbrtX55Xid/0Rb++dUQx9/R32DElU3e3+yRxq6tPDkrfC3saHrCqUri81dmpxYTlZo7tJQJ0LVMeXfs9cCys0xqVISJ7Z2LRU66huUCB/2hG3qbejSEndWOVX3Sw1dl8fWddbex+YuzfuVu9myoUtDnVhS4Zi01vY9Tk/VtO3XjtS2EFzifOROME3TfmfzqKO+QYkVbtYepGri0rTWwzAkniNLXEV4pJ7rchlj5L+dm6fX3KXlO/ClYR+EqlOzHxPpB6d3OZdcD9y229XcG2SMWZYleko2kci1tHVp4zjKEgPe8fAq2rou1+VyUUolIn67l6aU6hxaa+88m740VIVQhcawMF2LbBXkGMs0S8bquk6CyDAMrf9Cyvn3fd/6hbhsDJK3yZrn+XK53FwWDngAoerU0v/2YsfN4o7xBo3jaO9q9mzbvTQJiHaU2RsWbPG6jDHyb5X0iTV3afZk+r6306psKW6apgP8NqI2hCoAr2KM6brO5o/lEGs0y/wqu1TVNE1d15U+qafcHPhrlNZaJt3L8uj2oP099CpYwPN+pvQJIMs4jpn/Ttr8n1NHGg44pGrfILvfhQrKOfk9bHtKW3H3TZMbczhNJ6Gq65JEuNUIZlWXlnhTrtfrvYmqqktDtQhVzYhuJ+65d8KK1jrxrFzOK+6v7/t5nk9Sim/xDVJO1FDrcarRS/NI1WqaJnvCbV2X+99RdBBTDuo3DV1amvtgwcEuDYXtuX4DikivsyJfiq7jkvhSQYkVgxKLJNXsYG9QdGXqqIYu7Xq9rp1M+Pa1dV05twm7elNDl3aTd8JHujQUxJwqKBUbNMx5xrgIOZ/ovx0Tqy20rqE3SGpUfd8/PGBd4aVdLpfL5XJXfbSJ65JZRyHbQP7qnXATl6beHtKMfmnthFu5NFSLUHV2aw+2VDu+5k5n8b50yNUW2nqDEu9OqK1LUysnFh5s67pMjC36yl/tf1NtXZo8pBn9QGj9XUO9SpfK8HLp0SX7VXd/hujBekR36Vrbuqt+R3qD5KzkCfY1tnFDl7a2sW7+JdR5XWvWRtLburSb71qjv42oWbcc4iFnJNhZw2vvtX1Ky9b5638G3j7ELp+bdqGd6/XaXKXqSG9QzuIC7jm3eGn2VO1vnQrOtqHrihrHUU44PNu2Li3xroUj1G1dGipVJsthRzkbgobzkCrflT06wbbRf00e6Q164DOnlUtbYqeaONuGriuUfuajrUuLvmtrnxVtXRoqRKUK79npFPbx6dJndJucsPz58JtOtPgGZWro0txfuZun2tB13autS+Ndw24IVQAAABvg6T8AAIANEKoAAAA2QKgCAADYAKEKAABgA4QqAACADRCqAAAANkCoAgAA2AChCgAAYAOEKgAAgA0QqgAAADZAqAIAANgAoQoAAGADhCoAAIANEKoAAAA2QKgCAADYAKEKAABgA4QqAACADRCqAAAANkCoAgAA2AChCgAAYAOEKgAAgA0QqgAAADZAqAIAANgAoQoAcDTGmNKngDMiVAHA2Y3j2GXQWhc8yZtnYozRWkuDy+ViG4/juMlL3+zHPTfvR/fkCaAVhCoAQPO01pfLZZ7n8EvTNHVd90ztqu976SfRxvb/fIZDu36m9AkAAGpxvV4TXy1bqRLLsoQH3VLQ9Xp1z3McRwlDl8vF+1K+cRwvl4t6K4attZE/SAM5T2OMfCNOglAFAHivhth0L3vOwzCEVaJxHKWIpZS6XC7RTJb/EuM4rlW8pEgmNS2cFsN/AIBWGWNsmlkbd9Na2wrcw4OAkpaiw4uKsT+8IVQBAFplQ0w6LWmtJRVFm3nz9KPBKP1C3tgfTovhPwBAq6R0NAzDzZZrqSt8NG+apmmavIHC9Ahg/mng2KhUAQBOyiaq6/W6LMuyLDYYhTUn+VI4AmgzFmUqEKoAAO+l16mqbUXNJ9OMHbNblsUtRMkErHmeveu1bbzjjP3BIlQBANoWTTNr0dA2kKUWwjE7OwHLm1zlBi/3OGN/sJhTBQB4r/51qqISy0fdFP1GrfU8z+FI3zAM0zS5x3nuDy5CFQDgvWpj0wO8meZr63DetTinXUrUxrjahkRRFsN/AIAmrU1yivLaPLlgla1LrY0h4pwIVQCAtqV35YuygWxJWvtGGQFk7A8eQhUA4D6yWmbps1DKKRHdjDVrwevekpV9ocSWNTgtQhUAoFXeMNya6Fyx6CN+tv3a0ur2G40xjP3BQ6gCANzBGCMVGluqcf8gQcS2Cb/LPaK11lo/OXZmn1iMxiBjTNd10T37pPE8z953jeMo7dem7dtvdP8KKHVrOBkAcHj5tRav8TAMy7Iop1rT9/2yLH3fyx8sqe54f73rZpRo5q0E0b/x+g97sG36vh+GYRgGe0QuLX0y9nrX2BO7eXU4Bt5pADi7u0KVbW+/PYwX6VAl3253hpHkkU4nSzJUuS8RsvHIC3aJa08nKve10i0JVWfTLbGnGwAAWCPLNdnbh0xad+8m4RpOspymtOm6ru9796teh1Hhq6ydm3LGFjNX3rKjk/nfktmtrIPFrfYkWPwTAPCstSpRgjsVacPH6B6b4bRtlsJpEaoAAPuR/BTdBOaZ3WaAGhCqAAAvZyOUxKZhGB4rKY3jKD1UHr8kO7KQ1dkQqgAA24uuYmB5acOdcZU2TdM0Td6UrArdtaUgDoNQBQB4xM3ROtvAazYMwzRNdlErWRfq5hOIba2x2dbZYis8/QcAuJs8iycVo/BpPttAyDR2txYlj/u5DSqvPAE5CFUAgEfI9KZEscouUvBwA6AthCoAAIANsPcfAADABghVAAAAGyBUAQAAbIBQBQAAsAFCFQAAwAYIVQAAABsgVAEAAGyAUAUAALABQhUAAMAGCFUAAAAbIFQBAABsgFAFAACwgf8PqLlNd/cTlK0AAAAASUVORK5CYII=",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TH2F histogram object\n",
    "x_min = -15\n",
    "x_max = 55\n",
    "y_min = -15\n",
    "y_max = 55\n",
    "histogram = ROOT.TH2F(\"2d_hist\", \"2D Histogram\", 250, x_min, x_max, 340, y_min, y_max)\n",
    "of_new = of_out[5:]\n",
    "\n",
    "# Fill the histogram with data\n",
    "for i in range(len(signal)):\n",
    "    x = adc_of[i] \n",
    "    y = of_new[i]\n",
    "    histogram.Fill(x, y)\n",
    "\n",
    "# Set x and y axis titles\n",
    "histogram.GetXaxis().SetTitle(\"E_{true} [GeV]\")\n",
    "histogram.GetYaxis().SetTitle(\" OF output \")\n",
    "histogram.GetYaxis().SetTitleOffset(1)\n",
    "\n",
    "#ROOT.gStyle.SetPalette(ROOT.kBird)\n",
    "\n",
    "# Draw the histogram\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"2D Histogram\", 800, 600)\n",
    "histogram.Draw(\"colz\")\n",
    "canvas.SetRightMargin(0.2)\n",
    "\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"relative_diff_corre.png\")"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2D correlation of residual and $E_{true}$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: [0. 0. 0. ... 0. 0. 0.] (Potential memory leak).\n",
      "Warning in <TROOT::Append>: Replacing existing TH1: [ 0.        0.        0.       ... -3.959317 -5.719013 -6.268919] (Potential memory leak).\n",
      "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": [
    "h1 = plot_hist(signal[:],5000,5100)\n",
    "h2 = plot_hist(adc[3:],5000,5100)\n",
    "# h2 = plot_hist(of_out[2:],5000,5100)\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"Signal Graph\", 800, 400)\n",
    "\n",
    "h2.SetLineColorAlpha(ROOT.kRed, 1)\n",
    "h1.SetLineColorAlpha(ROOT.kBlue, 1)\n",
    "'''h1.SetFillColorAlpha(ROOT.kRed, 0.3)\n",
    "h2.SetFillColorAlpha(ROOT.kBlue, 0.3)'''\n",
    "\n",
    "h1.Draw()\n",
    "h2.Draw('same')\n",
    "\n",
    "legend = ROOT.TLegend(0.45, 0.7, 0.65, 0.9)  # x1, y1, x2, y2\n",
    "legend.AddEntry(h1, \"sig output\", \"l\")\n",
    "legend.AddEntry(h2, \"OF output\", \"l\")\n",
    "legend.Draw()\n",
    "\n",
    "canvas.Update()\n",
    "canvas.Draw()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define my own color palette\n",
    "\n",
    "from array import array\n",
    "\n",
    "def set_palette(name='palette', ncontours=12):\n",
    "    \"\"\"Set a color palette from a given RGB list\n",
    "    stops, red, green and blue should all be lists of the same length\n",
    "    see set_decent_colors for an example\"\"\"\n",
    "\n",
    "    if name == \"gray\" or name == \"grayscale\":\n",
    "        stops = [0.00, 0.34, 0.61, 0.84, 1.00]\n",
    "        red   = [1.00, 0.84, 0.61, 0.34, 0.00]\n",
    "        green = [1.00, 0.84, 0.61, 0.34, 0.00]\n",
    "        blue  = [1.00, 0.84, 0.61, 0.34, 0.00]\n",
    "    else:\n",
    "        # default palette, looks cool\n",
    "        stops = [0.00, 0.34, 0.61, 0.84, 1.00]\n",
    "        red   = [0.00, 0.00, 0.87, 1.00, 0.51]\n",
    "        green = [0.00, 0.81, 1.00, 0.20, 0.00]\n",
    "        blue  = [0.51, 1.00, 0.12, 0.00, 0.00]\n",
    "\n",
    "        \n",
    "\n",
    "\n",
    "    s = array('d', stops)\n",
    "    r = array('d', red)\n",
    "    g = array('d', green)\n",
    "    b = array('d', blue)\n",
    "\n",
    "    npoints = len(s)\n",
    "    ROOT.TColor.CreateGradientColorTable(npoints, s, r, g, b, ncontours)\n",
    "    ROOT.gStyle.SetNumberContours(ncontours)\n",
    "\n",
    "set_palette()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning in <TROOT::Append>: Replacing existing TH1: 2d_hist (Potential memory leak).\n",
      "Warning in <TCanvas::Constructor>: Deleting canvas with same name: canvas\n",
      "Info in <TCanvas::Print>: png file ./plots/correlation.png has been created\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create TH2F histogram object\n",
    "x_min = 5\n",
    "x_max = 50\n",
    "y_min = -5\n",
    "y_max = 5\n",
    "histogram = ROOT.TH2F(\"2d_hist\", \"2D Histogram\", 250, x_min, x_max, 500, y_min, y_max)\n",
    "of_new = of_out[5:]\n",
    "\n",
    "# Fill the histogram with data\n",
    "for i in range(len(signal)):\n",
    "    x = signal[i]\n",
    "    y = of_new[i] - signal[i]\n",
    "    histogram.Fill(x, y)\n",
    "\n",
    "# Set x and y axis titles\n",
    "histogram.GetXaxis().SetTitle(\"E_{true} [GeV]\")\n",
    "histogram.GetYaxis().SetTitle(\"(E_{reco}-E_{true}) [GeV]\")\n",
    "histogram.GetYaxis().SetTitleOffset(0.8)\n",
    "\n",
    "#ROOT.gStyle.SetPalette(ROOT.kBird)\n",
    "\n",
    "# Draw the histogram\n",
    "canvas = ROOT.TCanvas(\"canvas\", \"2D Histogram\", 800, 600)\n",
    "histogram.Draw(\"colz\")\n",
    "canvas.SetRightMargin(0.15)\n",
    "\n",
    "\n",
    "canvas.SetTopMargin(0.1)\n",
    "title = ROOT.TPaveText(45, 11, 15, 0.95, \"NB\")\n",
    "title.SetFillColor(0)\n",
    "title.SetFillStyle(0)\n",
    "title.SetBorderSize(0)\n",
    "title.SetTextFont(42)\n",
    "title.SetTextSize(0.05)\n",
    "title.AddText(\"correlation between (E_{reco} - E_{true}) and E_{true}\")\n",
    "title.Draw()\n",
    "\n",
    "\n",
    "canvas.Draw()\n",
    "\n",
    "canvas.Print(plot_folder+\"correlation.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cern2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.6"
  },
  "orig_nbformat": 4
 },
 "nbformat": 4,
 "nbformat_minor": 2
}