calibration_3rd_crossTalk.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Welcome to JupyROOT 6.28/06\n"
     ]
    }
   ],
   "source": [
    "import sys\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.ticker import ScalarFormatter  # Import ScalarFormatter\n",
    "import uproot\n",
    "sys.path.append(\"../\")\n",
    "import saq\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "26253\n",
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     ]
    }
   ],
   "source": [
    "# Define the list of file paths\n",
    "root_file_paths = [\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_08_04.root', #ch2\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_09_25.root', #ch13\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_10_34.root', #ch4\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_12_49.root', #ch8\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_14_21.root', #ch14\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_16_00.root', #ch6\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_17_20.root', #ch10\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_18_29.root', #ch3\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_20_22.root', #ch12\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_22_48.root', #ch5\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_23_52.root', #ch11\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_25_09.root', #ch7\n",
    "   '/home/wwz/saq_analysis/calibration/10_16_2023_5nA_random_order/10_16_2023_10_27_19.root'  #ch9\n",
    "   \n",
    "]\n",
    "\n",
    "# Initialize lists to store data for each file\n",
    "timestamps_list = []\n",
    "masks_list = []\n",
    "\n",
    "# Define constants\n",
    "clock_rate = 30.3e6\n",
    "nn = 0\n",
    "cutoff = (2**32) - 1\n",
    "loop_time = cutoff / clock_rate\n",
    "\n",
    "#real_channel =[2, 4, 8, 16, 32, 64, 128, 256, 512, 1024, 2048, 4096, 8192]\n",
    "real_channel =[2, 13, 4, 8, 14, 6, 10, 3, 12, 5, 11, 7, 9]\n",
    "\n",
    "# Iterate through each root file\n",
    "for j, root_file_path in enumerate(root_file_paths):\n",
    "    # Open the ROOT file\n",
    "    file = uproot.open(root_file_path)\n",
    "\n",
    "    # Access the tree from the ROOT file\n",
    "    tree = file[\"tt\"]  # 'tt' is the name of the data tree in the root file\n",
    "\n",
    "    # Convert the tree data into a dictionary\n",
    "    data = tree.arrays(library=\"np\")\n",
    "\n",
    "    # Get timestamps from the data\n",
    "    timestamps = data[\"Timestamp\"]\n",
    "    mask = data[\"ChMask\"]\n",
    "    print(len(timestamps))\n",
    "    #print(len(mask))\n",
    "\n",
    "    #print(timestamps[:10])\n",
    "    #print(mask[:10])\n",
    "    \n",
    "    #filtered_timestamps =[]\n",
    "    #for i in range(len(timestamps)):\n",
    "        #if mask[i] == 2**(ch-1):\n",
    "            #filtered_timestamps.append(timestamps[i]\n",
    "    \n",
    "    #This line does the same thing as the loop above, but in a more consice and Pythonic way\n",
    "    filtered_timestamps = [timestamps[i] for i in range(len(timestamps)) if mask[i] == 2**(real_channel[j]-1)]\n",
    "    #print(2**(real_channel[j]-1))\n",
    "    \n",
    "    print(len(filtered_timestamps))\n",
    "    #print(filtered_timestamps[:10])\n",
    "\n",
    "    # Initialize an array to store time in seconds\n",
    "    time_sec = np.zeros(len(filtered_timestamps))\n",
    "\n",
    "    # Convert the individual entries to show the time in seconds\n",
    "    for i in range(len(filtered_timestamps)):\n",
    "        time_sec[i] = filtered_timestamps[i] / clock_rate + (nn * loop_time)\n",
    "        if i > 0 and filtered_timestamps[i] < filtered_timestamps[i - 1]:\n",
    "            nn += 1\n",
    "            time_sec[i] = float(filtered_timestamps[i] / clock_rate) + float(nn * loop_time)\n",
    "\n",
    "    # Store the time_sec array in a list\n",
    "    timestamps_list.append(time_sec)\n",
    "    masks_list.append(data[\"ChMask\"])\n",
    "\n",
    "    nn=0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 2200x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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1YWeRymw2x/I0kTabhnRM1ItjTqu1/wpjHMvTRNpsGtglRbOuOtF93yIidN1rP2hxzl5VGOO4RtjH2L6PhRv36JZXVzldT+LKjsj22+G1n+OO90nrZsrslHqsfc8u0neb9zqdD47jeEUvjX9vo2PZm9iyo/rdca31+OW9dPdba2qdR0dj4nRCepKmX92n6lh4WO7K39eIwsJCJaen+zWeBXTSyIIFCzRkyJBa28eMGaMHH3xQWVlZLl83f/58nXXWWVq5cqVuv/12/fzzzzp69KiysrI0evRoTZw4sU4pIUPlHwoAEG5C5focDPEsVMYKAMJRqFyjiWcAAHdC6fpMPAMAuBMq1+dgiGWSf8ZrS36Rhj6+0O3z8yedVa8MEv62Jb9I2/aXKLN1orJSEkOm3TUt3LBHY2Yud/t8Q9p93Yxlji/066tvZkut3Haw1hf2A7ukaNbYfvWu15OCkjKNm7PKMRFAklomROtASZnj8aCuqZo+qrfGzVlVq4/29tmfr16PL69rrH6546q/9nYmJ0S7PI6RNpt6d2qhFdsOuK33/TsG6rHPNrqt19f9u2J//7VOjPG6D2/vzamXnqz+nVu7Pc89ta++x99bv+zXFV+f80VjxLOAThoJFqHyDwUACDdcn33HWAFA8OIa7TvGCgCCE9fnumG8ACA4cX2uG3+Nl7svqQPxhXpNB0tKNX7O6qCfCOCNq35U19B2e/ui3r6PPp1a6HB5hdbnFarSOD/nbVJCY0/Eyd1bXJWh47cv6Ws+9nWiUH1f19RqtlPyfhx9mdTjql5f91+Tu/ffpOHdtK+k1O1rfZnA5G1Ch7+Of136NX1UbxkZt8+5a6srjRHPovxSCwAAgBsNnTULAAAAAAAAIDS5+oW+/Zf7gTZ+zmqnZTokaXHOXo19ZbmuH5ipw2XlWr712ESHhrS7MT8jddWP6hra7g/W5nktV30fro73lad18DhpZOu+4kb97DgrxXncaz7etr/E4+vt7avv6xqDp3OqZjsl720dc0am4qN/9fhedVWvu/Z467e7958kjxOcXF1Talqcs1fj5qxyW4+/jr8r7vo1bs4qx//Xpa1NhUkjAACgUXiaUVuXWbMAAAAAAAAAQlNyQrRmje3nc4aCprIlv8jll84VxmjFtgOOCQ59M1rq+jMydUL75Hq1u7E/I3XXD7v/je2n33VNrXO93rKXeNqHq+O9Jb/IYx2ZrQN7TmS0SvD4vLv21fd1DVHfc8pbW09MT9assel1fq/Wpz2e3n+LNuUrd6/7yTbVrynfbdmnye/8UK96qvPXcfTWL1fq2tbGEhGwPQMAAEvzNqMWAAAAAAAAQHjISknUkO5pQTFhRPKeWcBu5faDemPFr/Vud2N/RuqtH+WV7pfw8MRb9hKpaumSQV1TXU5KqXm8O6c206CuqYq02VzWEejzor7tC0S/6ntO+drWur5X69MeXzJ7eJOVkqi2yXENrkfy33H09briiq9tbSxMGgEAAH5nn1Fbc13B6rNmAQAAAAAAACAQvGUWsGvI55lN8RlpY2S6cNfumuq67M30Ub01sEtKg+poTPVtX1P2q6HnlL/bWt/2+Ou89ef574+x8fW64kqgs+2wPA0AAKiloWtsBnItRwC+a8z1dAEAAAAAQPgK9s8c7JkFFufs9To5Qqrf55m+fkbakLFy149Im00Du6Q0yme7E87pqgt7ta9z3cG6VJFdfdvXlP1q6Ofu/m5rfc9xf523/jz//TE27toTIemE9CTFx0Rq5baDfnuv+hOTRgAAgIO/1tgMxFqOAHzX2OvpAgAAAACA8BRKnzlMH9Vb4+ascmqrO/X5PNPbZ6StEmJ03YxlDR4rV/1oSPYIb+2uz4SR6rJSgmuySE31bV9T9Mtfn7v7q60NOcf9dd76+/xv6Ni4ak+lpHV5hZKklgnROlBS5pe2+pPNGB+mz1lcYWGhkpOTVVBQoKSkpEA3BwDwG67PvvPXWF03Y5nbWbmzxvYLWF3+FOy/cqgLK/WlpmDrm7/aEyz9Ctb3p1URz3zHWAFAcOL6XDeMFwAEJ67PdVPf8QrFzxzsmQWenZ/jNgtAfdvuaTwk+XWs/JnpIhSPYzDz52eCwXZsGnqO++u8DbYMNrl7izVuzkqtzytUZbXZGJE2m/p0aqHbh3apd1sbI56RaQQAAEg6tv5gTdXXH6zLP2D8PcO3oULpVw7eWKkvNQVb3/zVnmDql7/f6wAAAAAAAFLofuZgzyzQp2NLv3+e6e4z0ruHd9NFzyyuVb4hY+XPTBfB9tluqGqMzwSD7dg09Bz313kbbBlsjDFat6Ow1vYKY7R824Ggmdxix6QRAAAgqeHrIdbk7/URGzobe/yc1Vqcs9dp2+KcvRo3Z1W9ZmAHMmNEsPbFH/UEW9/81R5/96sh/P1e97f6HrNgyeICAAAAAEC4CvbPHLzx9+eZnuqcv2GPx9cFeqwaYyzCUWN8JhhsxyZUz/HGFmrXQyaNAAAASf5bD7Gmhs7w9cdsbH/+yiHQGSOCsS/+qifY+uav9gTbr2wa673eUPU9ZoF+TwIAAAAAgCrB+plDXTVGxoKadYbKWAVb9oZQ0tifCQbbsQnVc7yxhFr/IwLdAAAAEBw6pzbToK6pirTZnLZH2mwa1DU1YP8A9TQb21e+zOptyvZUtyW/SPM37FHuXt/aEIx98Vc9wdY3f7XHn/2yq+t5U53V3uvuXjf2leX1HiMAAAAAAFB3vn7m0JDPNawiWD+fCXahdO744zPBUOpvTVY4x+sy/jXLhlr/yTQCAAAcgm09RH/NxvbXrN5gyIQRbH3x55gEW9/81R5/zir3V1YNq7zXPb1uxbYDumHmcklkHgEAAAAAoKl4+syBbKHOgu3zmWAWiudOQz4TDMX+uhKq53hdxt9T2VDqP5NGAACAgz/WQ9ySX6Rt+0v8spaiv9b9s8/qXZyzVxXGOLZH2mwa2CXF53b6cx3C+q5n6akvvTu1cMxQ99YOf/XFn2MSbMfJX+3xVz2S/9ZBDba1T+t7zLy9zq6uY1T9OmaM8ds1DQAAAAAAq/P0mcN1M5b55XMNqwi2z2eCmb8+E2tKDflMMBT764q7c3xLfpFW/nIgaM/5uoy/t7Kh8h5n0ggAAKilPushNsbsZ39maPDHrN5gyYThqi9J8VF1yqwQjFk0pOA6Tv5qj7/qaYx1UINl7dP6HjNvr7PzdYxcXceqC8VfdAAAAAAAEAg1P3NojM81rCJYPp8JVqF87tTnM8FQ7q879nP8YEmprpuxLKgzqNRl/H0tGwrvcSaNAAAAr3zJHlKf2c/e6vV1NnbNetxlCajrrF5X7TupfZLW5xWq8lhz6pwxYmnuPo/Pe8uEUXOG9rPzc7Ry20GnMt7G3l8ZS+pbj7tj72r2uTGmTjPP/ZHZo3r7/DEb3B+/HPFnVhd/8PS+q0s77K/rm9lSK7cdrNMxc3es3fE2Rq6uY9WF4i86AAAAAAAIBsH2uQZCRyifO/X5TDCU++tNKGRQqcv4W+lYMWkEAAC45Wv2kLrOfq5LVpK6roPaMiFaB0rKXPbHvg9v/1Cra72+ZozwlsXAztdMGFkpVRMqlm89UOs5X2ae+yNjSV3rMTI+HfuslES1TIiud/aa+mb28HRu+uMf+A2ZVe7vrC715cv7w5fj5Es9vhwzV8faHU9j5O46Vl0o/6IDAAAAAIBACpbPNRB6rHDu1OUzQSv015VQyaBSl/G30rFi0ggAAHDL3czfsa8sV/bQLo6Z0XWdUetLvZ4yhNgzT7jKsOFuYod9H77MWnbVvpr1Rkg6IT1J06/u43UdRnsmBVftra4+2TkaMpvZl4wl32zK1zUvfufoZ032Nj100YmO/XnKfGL//5rP1TynpIbNPK9vxhJf9+kpq4a7TDeexs9bPVkpiX7JoFJXrtrny/vD23njrp7Cw+Xqm9FSt9c4F7y1z9X5V5fsRJL391J1ofQrASDY1DcrEQAAAIDQFojPNWAN4XbuWLW/oZKVoy7jb6VjxaQRAACspLhYioysvT0yUoqLcy7nTkSEFB/vmPkbX3qkVpEfNx3W7Zt2qtJmU/8TOuju4d0kSXFlR2RzsUJEVpykkhIpIcFRb1zZEcUY1/Uam3Qk+lh7z85orif+0FMtW0Vr0psrnL5ojpF0OOZY2diyo4rwsEyF06zlI0ekigqn53Pzi7T8x19q11teqojKSqeym7ceUdG+g7rpzRX6YtshyWaTJA3NTNa0y06SkdGkt9bWbm90rKNsTHmZIiur2tAiIVo/btqp2zftlFSVZeHx606XibBp/JzVWvJznqJ+a+/ALil6/PJeyow1jmN0JDpGxhYhSYquKFNURUXV2Ls63nFxjnMlKylaKjJat3GnYlyM2fpfKzTksQVV2SP+cJKSo4wOlpTW6tvALikad96JjswnURXliq4odzy//MdfHGMgSaVR0aqIiFSFMVqVu1e3/+dYv+88u4vjOEhSWWSUyiOjVGGMFm/Ypa3b9ijT3T+6Y2Kk6KoMF1kt49TSlNY6b+zjl5ycUFVe0pZdBU77rG75j78oN6+rWrZI1Pg5q/X1xt2KKyt1qqv68S6PjFRZZFUbbKZScWWlx/aZEO00fhURkSqNiq4a36tOkSkpdjm2j1/eS9MvPUHj3llfNSvfGMWXHa167sJutY9zXd73NcoezD/gsg13nt1FS9f/qoroWMd2V9cIqer9ce6Uz9T/hA7Hso6UlEjGOL3P7Ozv++XbfpvYk2Bz2+aDh8s0/r2Njl8nxJYd1e+Oa63HL++lFy87QXe/tcap7X1P7OjITnT3K9/p2xoZaKq/l7y97yUde18lVjsHXVxPHDyNPRAm6pJlDAAAAIA11TczLBBu544V+xtKWTnqMv5WOVY2Y3xY/NviCgsLlZycrIKCAiUlJQW6OQCA33B99p1jrCS5HKnzz5c++ujY48TEqi9vXTh8xpmKX/y15m/YoxtmLtf3T12t1ocLXZZd07arLhkzTSekJyk+JlLT/nyJOhTucd3IE06QfvzRUe/nL96ubvu2uyz6a1KazrztJcfj91+ZoJ67Nrksuy8+SaeOf83x+PXX/qLTf1nnsmxJdKxOmPj/NPOGvhrSPU264ALp449dt1dS5p8/dPz/M+9O0QUbFrste/yEtx1fNj/20TT9Yd2Xbsv2GTdb+xOSJUkPf/6crlv1kduyE6fO1d6UdC3O2as/fTVD/7fsHbdlz7nxGW1KzZAkTfhmtu5cPMdt2R2fLdDGjOOrvpyf+az0pz+5LXvVqEf0XaeeirTZdP/2+Rrz2mNuyz54yz/1csuqrA9/+OELPfbxk27L3n7RX/RxjzMlSef//I2efW+q27KTzr9Lb588TJI0ZPNyzXz7IbdlN943RdHjx1VNDFqwQBoyxG3ZnD/er8g//UlZKYla8fZnOu3yc92Wzc2epAd6X67FOXvVec9WzXsp223Z//S7VFOG3ChJ6lCwW988P9Zt2Vm9L9D9w29ThKTTkyr12l8vdFtWY8ZIL7+s3L3F2v7LHg3u09lt0T0jRqr41TnHZrT/NlHJpd+uEfYMAAN6ZijOzWSQ7zqepKuuPnasvF0jLr3+SQ3sklKVqSUzU9q2zWXZja07afhNz0pS1Xv00iHS+vWu+9aqrQbcMsMxg/+9Vyaol5trREXr1orcWzWB5LoZy3T7Qzfp9F9+cFn2SEycTpz4/xz1vvTWgxq6ZYXLspKk6rdSl18uvf22y2KFkpIl4pkPiP3Wdd2MZW5/eRMs6xYDcI/rc90wXgAQnLg+101jjlf1zLCh9Et8BF64nTtW62+ofTZQl/FvymPVGNdnMo0AAAAna345qGdnLHNkD/GmUtK6vKovjCMiPHwp/RtvM4pd8fcM12CatezNsq379euBuv8C29uY3TZ7pda2K5IkTd28XVf5UGeFMcrJL/JYZtv+Eqmlb21sLP/7bpv+V1qVGeX+5kXq4qHsm8t/0Qu/ZVG5t43ncY6wyeW6m/5SKennXYc8ljl0pFzN9ds6qPFpHssu37pf2fYMMaN6K9lD2bKKSo2dsczRv/V+nFdefV3SDFO1tJM3rRJi9OvBw+rg5vnS8kqnm0tPIn+bLGPPcnS7h3dHpTFKio/yuMwVgLoLlXWLAQAAADSNrBRrfAGOphdu547V+htqWTnqMv6hfqzINCJmlwJAsOL67DvHWOXluR4rD8tU3PTKCn23eZ/jC9hKm03lMXEa2CVFkrRy/a9uv5yttNl0tNoyFYnlR9W7Q7JuHtxZGa0SnZcPsdm0pbhS2/aX6Nn5OVqfs0uVla7rrbk8jbclZ3xdnibSZlOfEzocm7V85IhydxVo+4ESZbRKlDFG2w+U6L+Ltmj19gIVVetbzWUqIm02dW/bTOt3Vn3J727JGZftrVY2vqJcJ6TFO+qpydWSM9U9P7qPBnermjyQW1ypcW+s1vq8QkWW1y5b3dGoaFVGVC1PE1dZrjMzW0iS07ngqmxURbkuPSlVH6zZ6bLe0qhoHd+xpX7KOyRbeZnT8jSuylb8Vm9kZYViyp2/qD8hvbk27CxShTGO5WkibTad2r6Zsgd2dHnMapY9sU2CNm3f57YN9mVkIm02ndqhuSoOH9bPOw+p+qkZabPp9ONa69rfHafrZ6+RdGzJGW/1uip7x9Dj9PRXmx2P7cvTSHIsOeNO9jnddUHfrKobEGN003OLXB4zSaqMiNDRqBhFSDohPUktTKljjGr2zUREaMG2Q47n3C05I9V+3/tadsI5XbXsx1+1ZttBl+01NqksJt5x7fn+p1/rdY2o/p5wSEzUnGXbNPmddV6vJ6Wx8erTqYVuH9pFWYmRUkWFtu0vrn1N+61eBw/L0xQWFio5PZ145gNivzXZs4y548gABiBocX2uG8YLAIIT1+e6YbwCx54J1irZJYCarJZBpamRaQQAAHiWmOj8Jaancqq6Aflie5FU7QtgSdJvv/x9/46Beky+Z1cojorVN7uO6G8d05y+XD1YUqrxc1Y71dMyqZnPv+Y/WrN9HiQku6/XnnHhWJvWuu1by+RmUrV6EpISneod1DVVdw/vpoueqb1kTdUEAN+yg/Tt0c5tPTWVRUY7JiLYdeqY5jiepqRI63ZUZX2pdFHWnSMRUfpie5Hev2OgSj/b6PF4l0dG6c2fDkjVJurU9MglJ+ux3+opj/Ttn5sVEZE6HBPptO2Bq/o56rFLio/Ssl8PadkbLpYtqXGeVBijtbuKPba1etllv/y2xEq0c/lBXVP1+Kje2ld8bDKHsUU4TVbypGbZk7u11+FvdrgubLN5rPexhdv02MJtjvPP5fu3hurZgFyV/WJ77ewxvvbNXrZlQrTX9/O0eZvctsFuUJeUY++HKN/f99WvEdXfE9Jv7/VqWVS8Xk+M0fJtB5TZ+tgkkcwMHxoR52HMPEzgAsJBKK1bDAAAAACB4uozVEcG2YS6ZyIGglWoZ+WwIiaNAAAQxrbtL/H4/L7iUs0a288x8/fZ+Tla6SZLQHVb9zmnmR8/Z7UW5+x1KlN4uFx9M1rq9qFdvNZrX9dQkss1D+1ZAewzk6vPVLa3p+asZVdtctc+V/Xa6xrUNbVWmzxx1V539UTabOrdqYWOlFdofV5hrewXA7ukOPXJ2/H0pvrxHjdnZa19+tK3gV1S1LNDC0c976/ZcWyygAtd05ppS36xy3Usq9dT/fyrq5PaJ+mnvEM+HyNJjuwc06/u4xjj5IToOh/v6uz9GtQttUH1SFXvg/3F7jOSNJUJ53TVhb3aO94f9Tlv7KZcerJG9euk+Rv21Kstrt4Tkvf3ujs1r2NAqAjGX6R1Tm3mNs65et/CtWA8tsGE8QEAAECoc/UZxuKcvRo3Z9WxzMkA0Ah8WVIcAABYlK+//M1KSdSQ7ml68bq+jskbvrxOqvoAf9Gm/FpfjldU+zW/t3rt6xpOH9W7VrmBXVL04piqtPb2Lwjs7bXPWK7+nKc2uWufq3rtXLXJE1ftdVdPUnyUVmw7oHU7an8J72qtR2/H05vqx3v22NN1ZpfUOr2+ZpuyUhI1sme6x9c8fkUvl8e0Zj0ZrRK0fOuBek2yeOSSk+t0jKQa2Tmqqevxrq56vxpSj1R1frpqX1OzTxiR6n/e2J3eubWk+p/Hrt4TvrzX3SHzAULNwZJSXTdjmYY+vlA3zFyuIY8t0HUzlqnAx6xejc1dDA/WdYuDSbAf20BjfAAAAGAFnj5DXbQpX7l7i928EgAajkwjAACEsbr+8jc5IdpjJor6ZL+w/5q/ekYJTxlCaparzy9J65KRw1u2AV/GxFVmEU/1uMuq4Sr7RXXujqc3ro5b9fZ4yxZSPduEr21yl03E3RjVJ4uKt4wlvoxRzeNf1+Ntr6Nmv3yt58xuKR7Hvj5ZVKrzJYvPYR8z3dTsl7fzxl1dvp7Hvry3vJ03njLd8Ct1hJpg/0VazThHNgjfBfuxDTTGBwAAAFbg62eoANAYyDQCAECYq88vf91lFKhP9ovqv+b3liHEVbn6qEsmA1+zDXgaE1eZRTzV4y6rhrvsF9W5Op4ta6x5WvOxp+PtS7YQdxNGPLXJVTYRT2NUn+wT7vbha8Ycyf3x9/V4e+uXt3q8jb2rLCo1j68nvmTx8fW9Xp0v542nunw5j315b3k7b3zJdAOEglD6RVpDY3i4CaVjGwjhMD5b8os0f8MeS/QFAAAA7tXlM1QA8DcyjQAAEObq+8tfX19X12wmTcGXTAb1aZ+/fkXdkF8WuGuDt8eeNPQY+mNcfD1mDcnqUte++et4e6onOSG6Xpla6pP5xlNf6tNPT+eNt+Pk63nckDbUJdMNEOz4RZp1cWw9s/L4HCwp1fg5q7VoU75j26CuqZo+qreS6zBBFAAAAKEhGD9DBRA+bMbUM5e1hRQWFio5OVkFBQVKSkoKdHMAAL/h+uy7YB+rgpIyjZuzKqg+9HbVpuoC2b4t+UUa+vhCt8/Pn3RWk98oBsMxbKxjFgx986S+7QuGftGGKsF+jQ4mTTlWW/KLtG1/iSUmCwVj3IB/NMax5dz3XmcwjM91M5a5/cIgEMvuEMvqhvECgODE9bluGK+mFwyfYQAIfo1xfSbTCAAAaHT+ysjQ2G2SFBTtC8ZfFgTDMWysYxYMffOksbMBNSbagGBkxV/vB2PcgH/489hy7nsWTONjX3anpurL7vC+BgAAsB4+wwAQKGQaEbMlASBYcX32HWNlPfyyALAOrtG+a4qxCrZf7/sLcaNpNWU2Cn8d22A59/09dvUZH1dtCJbxkaT5G/bohpnL3T4/84a+GtI9rQlbRCyrK8YLAIIT1+e6YbwAIDiRaQQAACBM8MsCAPA/K/96n7jRNAKRjcIfxzYYzv3GGru6jI+7Ntw9vFvAx6e6jFYJHp+3Z1sDAAAAAMAfIgLdAAAAALiXlZKoId3T+OIPAPxg2/4Sj8+/v2aHcvcWN1FrGoe3uLElv0jzN+wJ+X4Gyvg5q7U4Z6/TtsU5ezX2leWNPq4N+TeBt3N/677GPx/cjd24Oav8Ur8v4+OuDX+d+4PHuptifKqzL7sTabM5bY+02TSoayr/LgQAAAAA+BWZRgAAAAAAYcHbr/enzdukafM2WXJZl0BkyLAaT9k6Vmw74FhOJBjHNdCZK4Ih04mnNqzLK/T42kBk9pg+qnetZXcGdknR9FG9m7wtAAAAAABrI9MIAAAAACAsuPv1fk3+zH4QLBo7y4MV1czK4i1bh11dxrWxMr/UrDfQmSuCIdOJtzac1D7Jr+PT0GNrX3Zn/qSzNPOGvpo/6SzNGtsvqCYjAQAAAACsgUwjAAAAAICw4erX+zU1ZfaDphAMWR5CibusLHcP7+bT630Z18bK/OKp3kBmrgh0phNf2vDIJSfrsc82Nnh8/H1ss1ISeX8CAAAAABoVk0YAAAAAAGHD/uv93L3Fen/NDk2bt8lt2a37rDGZwpcsD1bop7+4y8oiVX35vzhnryqM8VqPp3H1lPll1th+9Wy593rt5/7WfcXKbN10kxHsmU5qjl2kzaaBXVKapB3e2tCzQwu/jE9jHVsAAAAAABoLy9MAAAAAAMJOVkqiRvZM91imKbIfNIVgyPIQKuxZWWpOCrFnD5k0opsGdknxqS534+ptH/VdzsTXerNSEjWke1qTTxSaPqp3rbFrqkwndWlDQ8ansY4tAAAAAACNiUwjAAAAAICwFAzZD5pCIPu5Jb9I2/aX1CtrQ0NeW1/esrLsKy51ykbx7Pwcrdx2sE7j6m0f76/ZoQt7ta9zn4M9o0z1LD/VM3lsyS/Syl8ONOpxrn4u1TWbSF3Ow8Y6tgAAAAAANCYmjQAAAAAAwtb0Ub01bs4qLdqU79jW1NkPmkJT9/NgSanGz1nttL9BXVM1fVRvJSdEN9prG8rXrCxZKVUTCPp0bFnncfW2j2nzNmnavE117nOoZJSxj93BklJdN2NZox5nT+eSt4kb9TkPG+vYAgAAAADQmJg0AgAAAAAIW+6yH/gqENkw6qOu/Wxov8bPWa3FOXudti3O2atxc1Zp1th+DX5tXdpX176c1D5J6/MKVVlthZFIm029O7XQ1n3HlniR6nf+uMv8UpOv4+Wt3rpklGnMca2pIedIU+yjPuehr8f2m035uubF7zT96j61xq4pjwEAAAAAABKTRgAAAAAAcGQ/8FUgs2E0hLd++qNfW/KLnF5vV2GMFm3KV+5e98ukeHvtml8O6vHPN/rUvrr0xVXZ6pLio7Ri2wHdMHO5y3rqev64yvzirs+exsuXen3JKNPQsWrKc8RXgToPfTm2lZLW5RVqyGMLHK8zMk16DAAAAAAAsIsI5M4XLVqkkSNHKj09XTabTe+++67T88YY3X///WrXrp3i4+M1bNgwbdq0yanM/v37dc011ygpKUktWrTQ2LFjVVRU1IS9AACEO+IZAMAKiGfOtuQXaf6GPcrdW+zyeU9ZCELVlvwiXTtjqb7Jcf6ye3HOXo19ZbnTeHgan237Szzux56twxVvr/3ruz/4PO6ujpE9w0PNdrsqGyHppPQk9c1sqcLD5T7t087b+WPPUDJ/0lmacE5Xt/VInsfLU70zb+ir+ZPO0qyx/bxOJKjL+eyPc78h50hT7KMh52Fdjm311zX1MbAy4hkAINQRywAATS2gk0aKi4vVq1cvPfPMMy6ff/TRR/XUU0/p+eef19KlS5WYmKgRI0boyJEjjjLXXHONfvzxR82bN08ffvihFi1apFtuuaWpugAAAPEMAGAJxLMqB0tKdd2MZRr6+ELdMHO5hjy2QNfNWKaCkjJHGXsWgprLT1TPYBBKqvd53Q7nZVmkqn7Zs2wMeWyBej/8ucfxyWiV4HF/ma3dZ5Dw9tp1Owp9Gnd3x6h6hgd7u72VXb71gM/H2pfzp7qslESN7Jnusc+exsudrJREDeme5vOSNL6ez/469xtyjvgq0OehL8e2+uua+hhYGfEMABDqiGUAgKYW0Ekj5513nv7+97/rkksuqfWcMUZPPvmk7r33Xl100UXq2bOnZs2apby8PMesyp9++kmffvqpXnzxRfXv319nnnmmpk+frtdff115eXlN3BsAQLgingEArMAq8cxThgdv2R8k337B3xRZEpqSqz57cqDGBIia49M5tZkGdU1VpM3mVC7SZtOgrqkelwTZtr9EfTNbunztSelJHttVfdy9HaPq7falrC/7lHw7f2qeh/Udr7rwV2aY+p77denzaRkttXVfsV8mP5zUPkkRzrvwaVw9ta8u56G7eurKH8cgnFglngEAwhexDADQ1AI6acST3Nxc7dq1S8OGDXNsS05OVv/+/bVkyRJJ0pIlS9SiRQuddtppjjLDhg1TRESEli5d6rbuo0ePqrCw0OkPAIDG0FjxjFgGAGhKoRDPPGV48DX7g6+/4G+KLAlNxV2f68JVhoPpo3prYJcUp3IDu6Ro+qjetV5f8/gs33pASfFRtV77j0tO9tiO6uPu7RhVb3dDvtCvvk9v58+aXw66PQ/rMl514cu5X5fzua7nvqf9u+pzUnyUU1YbT1la3PGWOcfXcXV3TOpyHrqrp64acgzgLBTiGQAAnvDdGQCgMUR5LxIYu3btkiS1adPGaXubNm0cz+3atUtpaWlOz0dFRalVq1aOMq5MmTJFDz30kJ9bDABAbY0Vz4hlAICmFArxzFuGB3fPzRrbz7HNl1/w27MTnNQ+SevznL+QjrTZNLBLil8yQzSVhmTZqKn6+CQnRGvW2H7K3VusrfuKldk60e24uDp2hYfL1TejpW4f2sXptYO6pmpxzl6niRmuxt2e4aFmWVcqjPFYrySf9ultLP/67g/6Ke+Q07bq56Gv41UXnt4X1c99X89nd+MaabOpd6cWjiwX9td423/1Pj87P0crtx302tb69DlC0gnpSZp+dZ9a42rPcFNzzD2dw76ehzXrGTdnpctx7t2phY6UVzTKMfDWz3ATCvEMAABP+O4MANAYgnbSSGOaPHmyJk6c6HhcWFiojh07BrBFAADUDbEMAGAFbuNZcbEUGVn7BZGRUlzcscfFVV+O5uYXafmPvyimWtFKm01Ho2O1aFO+JCm+9IhqWv7jL9q6LVOZac2l+HjHL/jjyo7I5mKeQYrKdNOzi/TF9mNf/FcvO7BLih6/sJujXbLZpIRqWQEOH5YqK90PSGJi/coeOSJVVNSrbGascRqbw9GxVe2WFFNepshK9/XWLJsVp2N9/01WvJTVIVGKjz+2sbRUKqvKHuHq2B2JjlGFIrR82wFlNo9WVvyxeqdf2E13v3XYMSngaFS0BnZvW5U5olq97spWRlSdV1EV5YquKK9qY1ztsqXV6y0r092zlztNRHA61rGxUlSUMlolKLKyQjHlrjNjbM49IltklBRZ9VGMvazjPExJPDZeUlVfoqOr/r+iourYuRMdLcXEOJV1NbaSVB4Z6ch88sSnP2vZTzskSbE1yg3skqLHLzvx2IbKSunw4VpjJUktEqK1dvMe3bDtgCRpUJcU/fF3HVzuX5K++ylPuXurJhlltU6Qioq0buNOl2WX/rTDUVZSrXOsutz9hx3vecn5fb956xHZioul307Fg0fKNf7dDU7XiIFdUvT45b2UnBDteF1WvJTVqbnTOTz9ou66+83Drs+JkhLn931JiWSMsuKl2VedrLvfWuN4nbFJ8cnNteK3cYstO6qIahNBal1TfnsvTx/VWxNfWaJvq/W1RUK0fty0U7dv2ilJ6ntiR00f1VtGRnfPWqpvN+5xrrd6P325nngYd3B/BgCwBuIZAISvoJ000rZtW0nS7t271a5dO8f23bt365RTTnGU2bNnj9PrysvLtX//fsfrXYmNjVVsbM2PQwAA8L/GimfEMgBAU2ryeJae7roh558vffTRscdpaVJJibIk/VSj6HcdT9JVV091PP7m+RvV+rCL9MrTJJ12mrR8ueMX/I9MHKkOhXtclv1TSid9MfZZx6b3X5mobvu2HyuTXa18Roa0deuxx4MGSStWuOxaRevWWrR4vSMLwOFhwxX/7Tcuy1YmJGjhylxH2ZKRFynhi89dlpUkVc+0MXq09Pbbjoc1x+74CW/rcEycIiQ9+/V/NGzZp26r7TNutvYnJCvSZtOzK19VZmbtNdcdcnOlzMyq///rX6XHHnO5f0k658ZntCk1o+rBI49IzzzmeC5Z0ovVyu74fKHan/NbFop//Uv605/clh016hEt6dSz6v/XfKq/zXu+6olpVf+pXnbX7LfV9urf6n15tl7MvqF2n+zH+s03pcsvV+fUZrrr0I8a9/w9LodAkiadf5fePrkqlfig3JWa+fZDTm1w8vTT2nLFGG3bX6IeP3+vdhef77befff/TWuvvqXqnMhdL/Xr53JsJenJgaP05JnX6K/v/qDyNev004zb3dar3ElV4ypp+6qf1Om0k2qNq92s3hfo/uG3SZJ++mGLTr75dJf7l6S3TzpbW28+o2oiSEmJsrLaui37UfeB2nrzwGOTRpo1c9vcpMHDpNPvcjz+/ulrlFB29FiBauO8s1sfLb70b47HjmtE9few3W/XCHu2jjPP6asXf9leu1y2VNq9h2J+rtabvn2l9esl1T4n97RqqwG3zHA8fvO1v6jXrk216pQkpaRI+VWTRJITojXjjfulhQtdNFYqiY7VyXe/48i0dMNjEzVjc41rT/V+erhGWA33ZwCAUMd3ZwCAxhAR6Aa4k5WVpbZt2+rLL790bCssLNTSpUs1YMAASdKAAQN08OBBff/9944yX331lSorK9W/f/8mbzMAADURzwAAVhBO8Wz6qN6KjXaR5cTO80onTvKLSlVQ4jrrRE0HS8p0w8zlGvLYAvV++HOt+eWg27JHyiqcyn63Zb/vjfLRmV1TNahrqk9lB3ZJ0e98LFtXLaplfHClfYtj2R8Ol5Z7LHty+xY+77dtcpz3Qi7cODCzXq9z5ZVvt2ro4wt1w8zlmvDGao9l/7Noi+OceOC9H32qf92OQq9L90jSwZJSXTdjma5+calP9Uryqd7M1r4vkeJr2fho3z/mKjhc6lM7Jam80ui6Gcscx2N3gfusL1v3lui6Gct8eu+Xllf63Ia6qjBGizbla9GmfDXSLkJSOMUzAIA1EcsAAI3BZkzgbh2LioqUk5MjSerdu7eeeOIJDRkyRK1atVKnTp30z3/+U1OnTtUrr7yirKws3XfffVq7dq3Wr1+vuN9SEp933nnavXu3nn/+eZWVlemGG27Qaaedptdee83ndhQWFio5OVkFBQVKSkpqlL4CAOouVK7PwRDPQmWsACAchco1OqjiWV6e67FyszyNJN30ygp9t3mf4wvYSptN5TFxGtglRZK0cv2vTl/ORtpsOv241npxzGlSRITz8iklJdqaX6Rt+4uV0SpRmSmJWrhxj27930oZm3Qk+lgb7MvTnJDeXBt2FjntIyLCplOP76BZY3/LWOFiyZnq7T4cc6ze6stU2NsqyXXZ8lJF/FavU79UtfzL1qM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      "text/plain": [
       "<Figure size 2200x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with a 3x5 grid of subplots\n",
    "fig, axes = plt.subplots(3, 5, figsize=(22, 15))\n",
    "\n",
    "# Create lists to store bin content from each histogram\n",
    "bin_contents = []\n",
    "bin_center=[]\n",
    "\n",
    "# Create lists to store averages for each channel\n",
    "averages = []\n",
    "\n",
    "# Define the desired range for each histogram\n",
    "histogram_ranges = [(0, 32), (0, 32),(0, 32), (142, 172),(0, 32), (0, 32),\n",
    "                    (0, 32), (0, 32), (0, 32),(0, 32), (0, 32), (0, 32), (142, 172)]\n",
    "\n",
    "# Loop through your data and create histograms\n",
    "for i in range(13):\n",
    "    row = i // 5\n",
    "    col = i % 5\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    # Create the histogram and plot for the \"timestamp\" data\n",
    "    hist, bins, _ = ax.hist(timestamps_list[i], range=histogram_ranges[i], bins=100, color='blue', alpha=1, edgecolor='blue')\n",
    "    ax.set_xlabel('Time (s)')\n",
    "    ax.set_ylabel('Counts')\n",
    "    #ax.set_title(f'Ch {i + 2}')\n",
    "\n",
    "  # Store the bin content in the bin_contents list\n",
    "    bin_contents.append(hist)\n",
    "    bin_center.append(bins)\n",
    "\n",
    "axes[0, 0].set_title('ch2')\n",
    "axes[0, 1].set_title('ch13')\n",
    "axes[0, 2].set_title('ch4')\n",
    "axes[0, 3].set_title('ch8')\n",
    "axes[0, 4].set_title('ch14')\n",
    "axes[1, 0].set_title('ch6')\n",
    "axes[1, 1].set_title('ch10')\n",
    "axes[1, 2].set_title('ch3')\n",
    "axes[1, 3].set_title('ch12')\n",
    "axes[1, 4].set_title('ch5')\n",
    "axes[2, 0].set_title('ch11')\n",
    "axes[2, 1].set_title('ch7')\n",
    "axes[2, 2].set_title('ch9')\n",
    "\n",
    "\n",
    "# Create a new 3x5 subplot grid to display the scatter plots of bin content\n",
    "fig2, axes2 = plt.subplots(3, 5, figsize=(22, 15))\n",
    "\n",
    "for i, content in enumerate(bin_contents):\n",
    "    # Calculate the center of each bin\n",
    "    bin_centers = (bin_center[i][:-1] + bin_center[i][1:]) / 2\n",
    "\n",
    "    # Filter out bins with zero bin content\n",
    "    non_zero_bins = content != 0\n",
    "    avg = np.mean(content[non_zero_bins])\n",
    "    averages.append(avg)\n",
    "\n",
    "    # Scatter plot of non-zero bin content in the new subplot grid with specified range\n",
    "    row = i // 5\n",
    "    col = i % 5\n",
    "    ax2 = axes2[row, col]\n",
    "    ax2.scatter(bin_centers[non_zero_bins][:-1], content[non_zero_bins][:-1], marker='o', s=20)\n",
    "\n",
    "    # Draw the average line on the scatter plot\n",
    "    ax2.axhline(y=averages[i], color='red', linestyle='--', label=f'Average: {averages[i]:.2f}')\n",
    "\n",
    "    ax2.set_xlabel('Time (s)')\n",
    "    ax2.set_ylabel('Counts')\n",
    "    ax2.set_ylim(50, 250)\n",
    "    #ax2.set_title(f'Ch {i + 2}')\n",
    "    ax2.legend()\n",
    "\n",
    "axes2[0, 0].set_title('ch2')\n",
    "#axes2[0, 0].set_ylim(50, 250)\n",
    "axes2[0, 1].set_title('ch13')\n",
    "axes2[0, 2].set_title('ch4')\n",
    "axes2[0, 3].set_title('ch8')\n",
    "axes2[0, 4].set_title('ch14')\n",
    "axes2[1, 0].set_title('ch6')\n",
    "axes2[1, 1].set_title('ch10')\n",
    "axes2[1, 2].set_title('ch3')\n",
    "axes2[1, 3].set_title('ch12')\n",
    "axes2[1, 4].set_title('ch5')\n",
    "axes2[2, 0].set_title('ch11')\n",
    "axes2[2, 1].set_title('ch7')\n",
    "axes2[2, 2].set_title('ch9')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2200x1500 with 15 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create a figure with a 3x5 grid of subplots\n",
    "fig, axes = plt.subplots(3, 5, figsize=(22, 15))\n",
    "\n",
    "# Loop through your data and create histograms\n",
    "for i in range(13):\n",
    "    row = i // 5\n",
    "    col = i % 5\n",
    "    ax = axes[row, col]\n",
    "\n",
    "    # Create the histogram and plot for the \"timestamp\" data\n",
    "    hist, bins, _ = ax.hist(masks_list[i], bins=100, color='blue', alpha=1, edgecolor='blue')\n",
    "    ax.set_xlabel('Channel Masks')\n",
    "    ax.set_ylabel('Counts')\n",
    "    ax.set_yscale('log')\n",
    "\n",
    "axes[0, 0].set_title('ch2')\n",
    "axes[0, 1].set_title('ch13')\n",
    "axes[0, 2].set_title('ch4')\n",
    "axes[0, 3].set_title('ch8')\n",
    "axes[0, 4].set_title('ch14')\n",
    "axes[1, 0].set_title('ch6')\n",
    "axes[1, 1].set_title('ch10')\n",
    "axes[1, 2].set_title('ch3')\n",
    "axes[1, 3].set_title('ch12')\n",
    "axes[1, 4].set_title('ch5')\n",
    "axes[2, 0].set_title('ch11')\n",
    "axes[2, 1].set_title('ch7')\n",
    "axes[2, 2].set_title('ch9')\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "nrta",
   "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
}