Pb_detector_Cs137.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "cbf802a2",
   "metadata": {},
   "source": [
    "# Pemodelan melibatkan udara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fd188543",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import openmc as mc\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.animation as animation\n",
    "import time as tm"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "62d0bd4c",
   "metadata": {},
   "source": [
    "## Takrifan bahan"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eebdabb3",
   "metadata": {},
   "source": [
    "sumber = Cs-137\n",
    "(1 Ci = 3.7×10$^{10}$ Bq atau 37 GBq)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f41779b8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 100 uCi\n",
    "sourceStrength = 100e-6*(3.7e10)\n",
    "sourceStrength\n",
    "tenaga_foton = 661.7e3# eV - Cs137"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c10b2d7",
   "metadata": {},
   "source": [
    "Penyediaan nama komposis udara: N$_2$, O$_2$, Ar, CO$_2$"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "92d6522f",
   "metadata": {},
   "source": [
    "Pemasukan elemen bagi setiap bahan dan ketumpatan masing-masing dalam udara:  \n",
    "ketumpatan udara pada 30&deg;C diambil sebagai 1.1644 g/cm$^3$:  \n",
    "  \n",
    "| Molekul |  mol%  |  wt%  |\n",
    "|---------|:------:|:-----:|\n",
    "| N      | 78.084 | 75.52 |\n",
    "| O      | 20.946 | 23.14 |\n",
    "| Ar      | 0.934  | 1.29  |\n",
    "| CO$_2$      | 0.033  | 0.051  |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4928a3b6",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Molecular weight of elements/compounds in air\n",
    "mw = {'N':14.007,'O':15.999,'Ar':39.948,'C':12.011}\n",
    "airMolRatio = {'N2':0.78084,'O2':0.20946+(0.00033),'Ar':0.00934,'C':0.00033}\n",
    "airMolRatioAt = {'N':2*0.78084,'O':2*(0.20946+(0.00033)),'Ar':1*0.00934,'C':1*0.00033}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8484c1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.20979"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "airMolRatio['O2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "538c8827",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N \t 14.007\n",
      "O \t 15.999\n",
      "Ar \t 39.948\n",
      "C \t 12.011\n"
     ]
    }
   ],
   "source": [
    "# for one mole\n",
    "for i,j in enumerate(mw):\n",
    "    print (j,'\\t',mw[j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6e253b2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "N \t 1.56168\n",
      "O \t 0.41958\n",
      "Ar \t 0.00934\n",
      "C \t 0.00033\n",
      "total atomic no:  1.9909299999999999 \n",
      "\n",
      "atomic wt%:\n",
      "N \t 0.7843972414901579\n",
      "O \t 0.21074573189413995\n",
      "Ar \t 0.004691274931815785\n",
      "C \t 0.00016575168388642495\n"
     ]
    }
   ],
   "source": [
    "# element weight in one mole\n",
    "totalAtom = 0\n",
    "for i,j in enumerate(airMolRatioAt):\n",
    "    print (j,'\\t',airMolRatioAt[j])\n",
    "    totalAtom = totalAtom+airMolRatioAt[j]\n",
    "    \n",
    "print ('total atomic no: ', totalAtom,'\\n')\n",
    "\n",
    "print ('atomic wt%:')\n",
    "for i,j in enumerate(airMolRatioAt):\n",
    "    print (j,'\\t',airMolRatioAt[j]/totalAtom)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1147bd0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material\n",
       "\tID             =\t1\n",
       "\tName           =\tkomposisi udara\n",
       "\tTemperature    =\t300.0\n",
       "\tDensity        =\t0.0011644 [g/cm3]\n",
       "\tS(a,b) Tables  \n",
       "\tNuclides       \n",
       "\tN14            =\t0.7815237537890001 [ao]\n",
       "\tN15            =\t0.002873246211 [ao]\n",
       "\tO16            =\t0.21066612726599998 [ao]\n",
       "\tO17            =\t7.987273399999999e-05 [ao]\n",
       "\tAr36           =\t1.5649176e-05 [ao]\n",
       "\tAr38           =\t2.9506389999999998e-06 [ao]\n",
       "\tAr40           =\t0.004672400185 [ao]\n",
       "\tC0             =\t0.000166     [ao]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# udara luar\n",
    "udara=mc.Material(material_id=1,name='komposisi udara')\n",
    "\n",
    "udara.add_element(element='N',percent=0.784397,percent_type='ao')\n",
    "udara.add_element(element='O',percent=0.210746,percent_type='ao')\n",
    "udara.add_element(element='Ar',percent=0.004691,percent_type='ao')\n",
    "udara.add_element(element='C',percent=0.000166,percent_type='ao')\n",
    "\n",
    "udara.set_density('g/cm3',0.0011644) \n",
    "udara.temperature=300.0\n",
    "udara"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "eda5c977",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Material\n",
       "\tID             =\t2\n",
       "\tName           =\tPb\n",
       "\tTemperature    =\t300\n",
       "\tDensity        =\t11.29 [g/cm3]\n",
       "\tS(a,b) Tables  \n",
       "\tNuclides       \n",
       "\tPb204          =\t0.014        [ao]\n",
       "\tPb206          =\t0.241        [ao]\n",
       "\tPb207          =\t0.221        [ao]\n",
       "\tPb208          =\t0.524        [ao]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pb = mc.Material(material_id=2,name=\"Pb\") # (id,nama)\n",
    "pb.add_element('Pb',percent=1.0,percent_type='ao')\n",
    "pb.set_density('g/cm3', 11.29)\n",
    "pb.temperature=300\n",
    "pb"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abb26997",
   "metadata": {},
   "source": [
    "Pewakilan bahan-bahan kepada fail materials.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "49e72a50",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf-8'?>\r\n",
      "<materials>\r\n",
      "  <material id=\"1\" name=\"komposisi udara\" temperature=\"300.0\">\r\n",
      "    <density units=\"g/cm3\" value=\"0.0011644\" />\r\n",
      "    <nuclide ao=\"0.7815237537890001\" name=\"N14\" />\r\n",
      "    <nuclide ao=\"0.002873246211\" name=\"N15\" />\r\n",
      "    <nuclide ao=\"0.21066612726599998\" name=\"O16\" />\r\n",
      "    <nuclide ao=\"7.987273399999999e-05\" name=\"O17\" />\r\n",
      "    <nuclide ao=\"1.5649176e-05\" name=\"Ar36\" />\r\n",
      "    <nuclide ao=\"2.9506389999999998e-06\" name=\"Ar38\" />\r\n",
      "    <nuclide ao=\"0.004672400185\" name=\"Ar40\" />\r\n",
      "    <nuclide ao=\"0.000166\" name=\"C0\" />\r\n",
      "  </material>\r\n",
      "  <material id=\"2\" name=\"Pb\" temperature=\"300\">\r\n",
      "    <density units=\"g/cm3\" value=\"11.29\" />\r\n",
      "    <nuclide ao=\"0.014\" name=\"Pb204\" />\r\n",
      "    <nuclide ao=\"0.241\" name=\"Pb206\" />\r\n",
      "    <nuclide ao=\"0.221\" name=\"Pb207\" />\r\n",
      "    <nuclide ao=\"0.524\" name=\"Pb208\" />\r\n",
      "  </material>\r\n",
      "</materials>\r\n"
     ]
    }
   ],
   "source": [
    "materials = mc.Materials([udara, pb])\n",
    "materials.export_to_xml()\n",
    "!cat materials.xml"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f4e5b58",
   "metadata": {},
   "source": [
    "## Takrifan geometri"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5b2bb716",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Struktur kotak (xmin, xmax, ymin, ymax, zmin, zmax)\n",
    "# saiz trajektori (5 cm x 5 cm x 50 cm)\n",
    "boxPb = mc.model.RectangularParallelepiped(-0.4,0.4,\n",
    "                                         -0.4,0.4,\n",
    "                                         -1.5,1.5, boundary_type='transmission')\n",
    "boxUdara = mc.model.RectangularParallelepiped(-0.5,0.5,\n",
    "                                         -0.5,0.5,\n",
    "                                         -2.5,2.5, boundary_type='vacuum')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "f4a24176",
   "metadata": {},
   "outputs": [],
   "source": [
    "wilayahPb = -boxPb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4720daf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "wilayahUdara = -boxUdara & +boxPb"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d804e5f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.4, -0.4, -1.5]), array([0.4, 0.4, 1.5]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilayahPb.bounding_box"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "dd3310f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([-0.5, -0.5, -2.5]), array([0.5, 0.5, 2.5]))"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wilayahUdara.bounding_box"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "679d1465",
   "metadata": {},
   "source": [
    "## Takrif kandungan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "3c140598",
   "metadata": {},
   "outputs": [],
   "source": [
    "kandunganUdara= mc.Cell(fill=udara, region=wilayahUdara) # Silikon sebagai medium\n",
    "kandunganPb = mc.Cell(fill=pb, region=wilayahPb) # Silikon sebagai medium"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "acde01c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf-8'?>\r\n",
      "<geometry>\r\n",
      "  <cell id=\"1\" material=\"1\" region=\"7 -8 9 -10 11 -12 (-1 | 2 | -3 | 4 | -5 | 6)\" universe=\"1\" />\r\n",
      "  <cell id=\"2\" material=\"2\" region=\"1 -2 3 -4 5 -6\" universe=\"1\" />\r\n",
      "  <surface coeffs=\"-0.4\" id=\"1\" type=\"x-plane\" />\r\n",
      "  <surface coeffs=\"0.4\" id=\"2\" type=\"x-plane\" />\r\n",
      "  <surface coeffs=\"-0.4\" id=\"3\" type=\"y-plane\" />\r\n",
      "  <surface coeffs=\"0.4\" id=\"4\" type=\"y-plane\" />\r\n",
      "  <surface coeffs=\"-1.5\" id=\"5\" type=\"z-plane\" />\r\n",
      "  <surface coeffs=\"1.5\" id=\"6\" type=\"z-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"-0.5\" id=\"7\" type=\"x-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"0.5\" id=\"8\" type=\"x-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"-0.5\" id=\"9\" type=\"y-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"0.5\" id=\"10\" type=\"y-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"-2.5\" id=\"11\" type=\"z-plane\" />\r\n",
      "  <surface boundary=\"vacuum\" coeffs=\"2.5\" id=\"12\" type=\"z-plane\" />\r\n",
      "</geometry>\r\n"
     ]
    }
   ],
   "source": [
    "# masukkan kandungan ke dalam Universe iaitu ruang yang merangkumi semua cell\n",
    "alamsemesta = mc.Universe(cells=[kandunganUdara, kandunganPb])\n",
    "geom=mc.Geometry(alamsemesta)\n",
    "geom.export_to_xml()\n",
    "!cat geometry.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "6823bde5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5fe2e29270>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lihat rupabentuk\n",
    "alamsemesta.plot(origin=(0,0,0.0), width=(2,7), basis='yz', color_by='cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "1135a96a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5fdad36140>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAARUAAAD8CAYAAABZ0jAcAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAOpUlEQVR4nO3df6xkZX3H8fenKH+4JaXy2x9rrSHimhQKN4ilqWwVIptaStMmkAaMkWxoIKmmbUpCQv2zaq2JVbGrJZWkYtrIKtFFYE0bag2WXQLLUkAWhLjuhgWkUEujpf32jzkXh8ude2fuPHd+3LxfyeSec57nmXnODPthzsw5801VIUmt/Ny0JyBpYzFUJDVlqEhqylCR1JShIqkpQ0VSU01CJckNSY4k2T+gPUk+leRAkn1Jzuxre2+Sh7u2a1rMR9L0tHqn8nfAe1dovxA4tbttB64HSHIU8JmufQtwaZItjeYkaQqahEpV3Qn8aIUuFwE3Vs9dwLFJTgHOBg5U1WNV9VPgy11fSXPqVRN6nNcDP+hbP9htW277O5a7gyTb6b3LYdOmTWeddtpp6zNTSezdu/fpqjphLWMnFSpZZlutsP2VG6t2ADsAFhYWas+ePe1mJ+llkjyx1rGTCpWDwBv71t8AHAKOHrBd0pya1FfKtwCXd98CnQM8V1WHgbuBU5O8OcnRwCVdX0lzqsk7lSQ3AecBxyc5CPw58GqAqvocsAvYBhwAXgA+0LW9mORq4DbgKOCGqnqgxZwkTUeTUKmqS1dpL+CqAW276IWOpA3AM2olNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGqqVdnTFUuXJvnTJPd2t/1J/jfJa7u2x5Pc37VZd0Oac2P/Rm1f6dLz6ZXiuDvJLVX174t9qurjwMe7/u8DPlxV/RUNt1bV0+PORdL0tXinMmrp0kuBmxo8rqQZ1CJUBpU0fYUkr6FXyP0rfZsLuD3J3q60qaQ51qJEx9ClS4H3Af+65NDn3Ko6lORE4I4kD3UF31/+IH21lDdv3jzunCWtkxbvVAaVNF3OJSw59KmqQ93fI8BOeodTr1BVO6pqoaoWTjhhTXWjJU1Ai1AZqnRpkl8A3gV8rW/bpiTHLC4DFwD7G8xJ0pSMffgzqHRpkiu79s91XS8Gbq+q/+obfhKwM8niXL5UVd8cd06Spie9iqTzZWFhofbs8ZQWab0k2VtVC2sZ6xm1kpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIaspQkdSUoSKpKUNFUlOGiqSmDBVJTRkqkpoyVCQ1ZahIampStZTPS/JcXz3l64YdK2m+TKSWcudfquq31jhW0pxoUaHwpVrKAEkWaykPEwzjjN1w/mT7G6Y9BY3oL3ccnPYUZs4kaym/M8l9SW5N8vYRx5Jke5I9SfY89dRTDaYtaT20CJVhainfA7ypqk4H/hr46ghjexsteyrNhYnUUq6q56vqx93yLuDVSY4fZqyk+TKRWspJTk5X2zTJ2d3jPjPMWEnzZVK1lH8P+MMkLwL/DVxSvXqry44dd06SpqfFtz+LhzS7lmz7XN/yp4FPDztW0vzyjFpJTRkqkpoyVCQ1ZahIaqrJB7WavPd84c+mPYUNbfcVH532FOaW71QkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJTkyp7+gdJ9nW37yQ5va/t8ST3d+VQ97SYj6TpmVTZ0+8D76qqZ5NcCOwA3tHXvrWqnh53LpKmr8U7lZdKl1bVT4HF0qUvqarvVNWz3epd9Or7SNqAJln2dNEHgVv71gu4PcneJNsHDbLsqTQfWvzy29ClS5NspRcqv963+dyqOpTkROCOJA9V1Z2vuMOqHfQOm1hYWFj2/iVN30TKngIk+RXgC8BFVfXM4vaqOtT9PQLspHc4JWlOTars6WbgZuCyqvpe3/ZNSY5ZXAYuAPY3mJOkKZlU2dPrgOOAz3YllV+sqgXgJGBnt+1VwJeq6pvjzknS9Eyq7OkVwBXLjHsMOH3pdknzyzNqJTVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqalJlT5PkU137viRnDjtW0nwZO1T6yp5eCGwBLk2yZUm3C4FTu9t24PoRxkqaIxMpe9qt31g9dwHHJjllyLGS5sikyp4O6jN0yVTLnkrzoUWoDFP2dFCfoUumVtWOqlqoqoUTTjhhxClKmpQWdX+GKXs6qM/RQ4yVNEcmUva0W7+8+xboHOC5qjo85FhJc2RSZU93AduAA8ALwAdWGjvunCRNz6TKnhZw1bBjJc0vz6iV1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqmpsUIlyWuT3JHkke7vLy7T541J/inJg0keSPJHfW0fSfLDJPd2t23jzEfS9I37TuUa4FtVdSrwrW59qReBP66qtwHnAFctKW36yao6o7v5W7XSnBs3VC4CvtgtfxH4naUdqupwVd3TLf8n8CADqhBKmn/jhspJXf0eur8nrtQ5yS8Bvwp8t2/z1Un2JblhucOnvrGWPZXmwKqhkmR3kv3L3EYqpJ7k54GvAB+qque7zdcDbwHOAA4Dnxg03rKn0nxYte5PVb1nUFuSJ5OcUlWHk5wCHBnQ79X0AuXvq+rmvvt+sq/P54GvjzJ5SbNn3MOfW4D3d8vvB762tEOSAH8LPFhVf7Wk7ZS+1YuB/WPOR9KUjRsqfwGcn+QR4PxunSSvS7L4Tc65wGXAby7z1fHHktyfZB+wFfjwmPORNGVjlT2tqmeAdy+z/RC92slU1beBDBh/2TiPL2n2eEatpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmhrr2h9Nz+4rPjrtKUjL8p2KpKYMFUlNGSqSmjJUJDVlqEhqylCR1NS6lz3t+j3e/RbtvUn2jDpe0vyYRNnTRVu70qYLaxwvaQ6se9nTdR4vacakqtY+OPmPqjq2b/3ZqnrFIUyS7wPPAgX8TVXtGGV817Yd2A6wefPms5544ok1z1vSypLsXXJUMbRVT9NPshs4eZmma0d4nHOr6lCSE4E7kjxUVXeOMJ4uiHYALCwsrD0JJa2riZQ97eoAUVVHkuwEzgbuBIYaL2l+TKLs6aYkxywuAxfws/Kmq46XNF8mUfb0JODbSe4D/g34RlV9c6XxkubXJMqePgacPsp4SfPLM2olNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHUlKEiqSlDRVJThoqkpgwVSU0ZKpKaMlQkNWWoSGpq3cueJnlrV+508fZ8kg91bR9J8sO+tm3jzEfS9K172dOqergrd3oGcBbwArCzr8snF9uratfS8ZLmy6TLnr4beLSqLC8obVDjhspJVXUYoPt74ir9LwFuWrLt6iT7ktyw3OGTpPmyaqgk2Z1k/zK3i0Z5oCRHA78N/GPf5uuBtwBnAIeBT6wwfnuSPUn2PPXUU6M8tKQJmkjZ086FwD1V9WTffb+0nOTzwNdXmIe1lKU5sO5lT/tcypJDny6IFl3Mz8qhSppTkyh7SpLXdO03Lxn/sST3J9kHbAU+POZ8JE3Zupc97dZfAI5bpt9l4zy+pNnjGbWSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqylCR1JShIqkpQ0VSU4aKpKYMFUlNGSqSmjJUJDVlqEhqatxayr+f5IEk/5dkYYV+703ycJIDSa7p275qLWZJ82Xcdyr7gd8F7hzUIclRwGfo1f3ZAlyaZEvXvGotZknzZaxQqaoHq+rhVbqdDRyoqseq6qfAl+nVYIbRazFLmnFjlegY0uuBH/StHwTe0S2/rBZzkoG1mJNsB7Z3qz9JshELjx0PPD3tSayTjbpvG3W/3rrWgauGSpLdwMnLNF1bVStVJHzpLpbZNnLZ0v6yp0n2VNXAz3Dm1UbdL9i4+7aR92utY8eqpTykg8Ab+9bfABzqlkepxSxpDkziK+W7gVOTvDnJ0cAl9Goww2i1mCXNgXG/Ur44yUHgncA3ktzWbX+plnJVvQhcDdwGPAj8Q1U90N3FsrWYh7BjnHnPsI26X7Bx9839WiJVI3+8IUkDeUatpKYMFUlNzUWojHs5wKwa9jKFJI8nuT/JveN81bfeVnv+0/Oprn1fkjOnMc+1GGLfzkvyXPca3ZvkumnMc1RJbkhyZNB5X2t6zapq5m/A2+idjPPPwMKAPkcBjwK/DBwN3AdsmfbcV9mvjwHXdMvXAB8d0O9x4Phpz3eVfVn1+Qe2AbfSO3fpHOC70553w307D/j6tOe6hn37DeBMYP+A9pFfs7l4p1LjXw4wqzbSZQrDPP8XATdWz13Asd35SbNuHv/bGkpV3Qn8aIUuI79mcxEqQ1rucoDXT2kuw3rZZQrAoMsUCrg9yd7ucoVZNMzzP4+vEQw/73cmuS/JrUnePpmprbuRX7NJXPszlFm5HKC1lfZrhLs5t6oOdddG3ZHkoe7/MLNkmOd/Jl+jIQwz73uAN1XVj5NsA74KnLreE5uAkV+zmQmVWt/LAaZmpf1KMtRlClV1qPt7JMlOem/HZy1Uhnn+Z/I1GsKq866q5/uWdyX5bJLjq2reLzYc+TXbSIc/K10OMKtWvUwhyaYkxywuAxfQ+x2bWTPM838LcHn3jcI5wHOLh38zbtV9S3JyknTLZ9P7t/XMxGfa3uiv2bQ/fR7yE+qL6SXmT4Angdu67a8Ddi35pPp79D6pv3ba8x5iv46j9+NUj3R/X7t0v+h943Bfd3tglvdruecfuBK4slsOvR/sehS4nwHf5M3ibYh9u7p7fe4D7gJ+bdpzHnK/bgIOA//T/Rv74LivmafpS2pqIx3+SJoBhoqkpgwVSU0ZKpKaMlQkNWWoSGrKUJHU1P8DRlvIuS4sR30AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lihat rupabentuk\n",
    "alamsemesta.plot(origin=(0,0,0.0), width=(2,2), basis='xy', color_by='cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcbb3dec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5fdadae5c0>"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# lihat rupabentuk\n",
    "alamsemesta.plot(origin=(0,0,0.0), width=(2,7), basis='xz', color_by='cell')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d1297437",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf-8'?>\r\n",
      "<settings>\r\n",
      "  <run_mode>fixed source</run_mode>\r\n",
      "  <particles>100000</particles>\r\n",
      "  <batches>1000</batches>\r\n",
      "  <inactive>100</inactive>\r\n",
      "  <source particle=\"photon\" strength=\"3700000.0\">\r\n",
      "    <space type=\"point\">\r\n",
      "      <parameters>0.0 0.0 -1.6</parameters>\r\n",
      "    </space>\r\n",
      "    <energy type=\"discrete\">\r\n",
      "      <parameters>661700.0 1.0</parameters>\r\n",
      "    </energy>\r\n",
      "  </source>\r\n",
      "</settings>\r\n"
     ]
    }
   ],
   "source": [
    "## Sumber radiasi satu titik\n",
    "#tetapan = mc.Settings()\n",
    "\n",
    "titik_radiasi = (0.0,0.0,-1.6)\n",
    "taburan_tenaga = mc.stats.Discrete([tenaga_foton],[1.0])\n",
    "taburan_ruang = mc.stats.Point(xyz=titik_radiasi)\n",
    "punca = mc.Source(space=taburan_ruang,energy=taburan_tenaga,\n",
    "                  particle='photon',strength=sourceStrength)\n",
    "\n",
    "tetapan = mc.Settings()\n",
    "tetapan.run_mode = 'fixed source'\n",
    "\n",
    "tetapan.source = punca\n",
    "tetapan.batches = 1000\n",
    "tetapan.inactive = 100\n",
    "tetapan.particles = 100000\n",
    "\n",
    "tetapan.export_to_xml()\n",
    "! cat settings.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7d28cd1d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0.,  10000.,  20000.,  30000.,  40000.,  50000.,  60000.,\n",
       "        70000.,  80000.,  90000., 100000., 110000., 120000., 130000.,\n",
       "       140000., 150000., 160000., 170000., 180000., 190000., 200000.,\n",
       "       210000., 220000., 230000., 240000., 250000., 260000., 270000.,\n",
       "       280000., 290000., 300000., 310000., 320000., 330000., 340000.,\n",
       "       350000., 360000., 370000., 380000., 390000., 400000., 410000.,\n",
       "       420000., 430000., 440000., 450000., 460000., 470000., 480000.,\n",
       "       490000., 500000., 510000., 520000., 530000., 540000., 550000.,\n",
       "       560000., 570000., 580000., 590000., 600000., 610000., 620000.,\n",
       "       630000., 640000., 650000., 660000., 670000.])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bin_initiate = np.linspace(0,67,68)*10000\n",
    "bin_initiate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "058cb5bc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<?xml version='1.0' encoding='utf-8'?>\r\n",
      "<settings>\r\n",
      "  <run_mode>fixed source</run_mode>\r\n",
      "  <particles>100000</particles>\r\n",
      "  <batches>1000</batches>\r\n",
      "  <inactive>100</inactive>\r\n",
      "  <source particle=\"photon\" strength=\"3700000.0\">\r\n",
      "    <space type=\"point\">\r\n",
      "      <parameters>0.0 0.0 -1.6</parameters>\r\n",
      "    </space>\r\n",
      "    <energy type=\"discrete\">\r\n",
      "      <parameters>661700.0 1.0</parameters>\r\n",
      "    </energy>\r\n",
      "  </source>\r\n",
      "</settings>\r\n"
     ]
    }
   ],
   "source": [
    "# Tallies\n",
    "tf=mc.Tallies()\n",
    "\n",
    "# mesh filters\n",
    "jaring=mc.RegularMesh()\n",
    "jaring.dimension=[10,10,50]\n",
    "\n",
    "jaring.lower_left=[-0.5,-0.5,-2.5]\n",
    "jaring.upper_right=[0.5,0.5,2.5]\n",
    "\n",
    "tapis_jaring=mc.MeshFilter(jaring)\n",
    "\n",
    "# tapis tenaga\n",
    "'''bin_tenaga=np.array([0.0,2.0,4.0,6.0,8.0,\n",
    "            10.0,12.0,14.0,16.0,18.0,\n",
    "            20.0,22.0,24.0,26.0,28.0,\n",
    "            30.0,32.0,34.0,36.0,38.0,\n",
    "            40.0,42.0,44.0,46.0,48.0,\n",
    "            50.0,52.0,54.0,56.0,58.0,\n",
    "            60.0,62.0,64.0,66.0,68.0])*10000'''\n",
    "\n",
    "#bin_tenaga = np.linspace(0,68,35)*10000\n",
    "bin_tenaga = np.linspace(0,67,68)*10000\n",
    "\n",
    "\n",
    "tapis_tenaga=mc.EnergyFilter(bin_tenaga)\n",
    "\n",
    "tally1=mc.Tally(name=\"flux mesh\")\n",
    "tally1.filters=[tapis_jaring,tapis_tenaga]\n",
    "\n",
    "tally1.scores=['flux']\n",
    "tf.append(tally1)\n",
    "\n",
    "'''\n",
    "bin_tenagaPeak=np.array([66.169,66.171])*10000\n",
    "\n",
    "tapis_tenagaPeak=mc.EnergyFilter(bin_tenagaPeak)\n",
    "\n",
    "tally2=mc.Tally(name=\"flux mesh\")\n",
    "tally2.filters=[tapis_jaring,tapis_tenagaPeak]\n",
    "\n",
    "tally2.scores=['flux']\n",
    "tf.append(tally2)'''\n",
    "\n",
    "tf.export_to_xml()\n",
    "! cat settings.xml"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "33ee8e09",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                %%%%%%%%%%%%%%%\n",
      "                           %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                        %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                      %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                    %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                   %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                    %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                                     %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                 ###############      %%%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ##################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ###################     %%%%%%%%%%%%%%%%%%%%%%%\n",
      "                ####################     %%%%%%%%%%%%%%%%%%%%%%\n",
      "                #####################     %%%%%%%%%%%%%%%%%%%%%\n",
      "                ######################     %%%%%%%%%%%%%%%%%%%%\n",
      "                #######################     %%%%%%%%%%%%%%%%%%\n",
      "                 #######################     %%%%%%%%%%%%%%%%%\n",
      "                 ######################     %%%%%%%%%%%%%%%%%\n",
      "                  ####################     %%%%%%%%%%%%%%%%%\n",
      "                    #################     %%%%%%%%%%%%%%%%%\n",
      "                     ###############     %%%%%%%%%%%%%%%%\n",
      "                       ############     %%%%%%%%%%%%%%%\n",
      "                          ########     %%%%%%%%%%%%%%\n",
      "                                      %%%%%%%%%%%\n",
      "\n",
      "                 | The OpenMC Monte Carlo Code\n",
      "       Copyright | 2011-2022 MIT, UChicago Argonne LLC, and contributors\n",
      "         License | https://docs.openmc.org/en/latest/license.html\n",
      "         Version | 0.13.0\n",
      "        Git SHA1 | d9478c1304a37590453eabe3a99e97abb7f92013\n",
      "       Date/Time | 2022-09-27 22:05:19\n",
      "   MPI Processes | 1\n",
      "  OpenMP Threads | 4\n",
      "\n",
      " Reading settings XML file...\n",
      " Reading cross sections XML file...\n",
      " Reading materials XML file...\n",
      " Reading geometry XML file...\n",
      " Reading N14 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/N14.h5\n",
      " Reading N from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/photon/N.h5 \n",
      " Reading N15 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/N15.h5\n",
      " Reading O16 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/O16.h5\n",
      " Reading O from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/photon/O.h5 \n",
      " Reading O17 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/O17.h5\n",
      " Reading Ar36 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Ar36.h5\n",
      " WARNING: Negative value(s) found on probability table for nuclide Ar36 at 294K\n",
      " Reading Ar from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/photon/Ar.h5 \n",
      " Reading Ar38 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Ar38.h5\n",
      " Reading Ar40 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Ar40.h5\n",
      " Reading C0 from /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/C0.h5\n",
      " Reading C from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/photon/C.h5 \n",
      " Reading Pb204 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Pb204.h5\n",
      " Reading Pb from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/photon/Pb.h5 \n",
      " Reading Pb206 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Pb206.h5\n",
      " Reading Pb207 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Pb207.h5\n",
      " Reading Pb208 from\n",
      " /home/megatharun/Workspace/MSI/Algorithm/MC/endfb71_hdf5/Pb208.h5\n",
      " Minimum neutron data temperature: 294 K\n",
      " Maximum neutron data temperature: 294 K\n",
      " Reading tallies XML file...\n",
      " WARNING: Particle filter is not used with photon transport on and flux score.\n",
      " Preparing distributed cell instances...\n",
      " Reading plot XML file...\n",
      " Writing summary.h5 file...\n",
      " Maximum neutron transport energy: 20000000 eV for N15\n",
      "\n",
      " ===============>     FIXED SOURCE TRANSPORT SIMULATION     <===============\n",
      "\n",
      " Simulating batch 1\n",
      " Simulating batch 2\n",
      " Simulating batch 3\n",
      " Simulating batch 4\n",
      " Simulating batch 5\n",
      " Simulating batch 6\n",
      " Simulating batch 7\n",
      " Simulating batch 8\n",
      " Simulating batch 9\n",
      " Simulating batch 10\n",
      " Simulating batch 11\n",
      " Simulating batch 12\n",
      " Simulating batch 13\n",
      " Simulating batch 14\n",
      " Simulating batch 15\n",
      " Simulating batch 16\n",
      " Simulating batch 17\n",
      " Simulating batch 18\n",
      " Simulating batch 19\n",
      " Simulating batch 20\n",
      " Simulating batch 21\n",
      " Simulating batch 22\n",
      " Simulating batch 23\n",
      " Simulating batch 24\n",
      " Simulating batch 25\n",
      " Simulating batch 26\n",
      " Simulating batch 27\n",
      " Simulating batch 28\n",
      " Simulating batch 29\n",
      " Simulating batch 30\n",
      " Simulating batch 31\n",
      " Simulating batch 32\n",
      " Simulating batch 33\n",
      " Simulating batch 34\n",
      " Simulating batch 35\n",
      " Simulating batch 36\n",
      " Simulating batch 37\n",
      " Simulating batch 38\n",
      " Simulating batch 39\n",
      " Simulating batch 40\n",
      " Simulating batch 41\n",
      " Simulating batch 42\n",
      " Simulating batch 43\n",
      " Simulating batch 44\n",
      " Simulating batch 45\n",
      " Simulating batch 46\n",
      " Simulating batch 47\n",
      " Simulating batch 48\n",
      " Simulating batch 49\n",
      " Simulating batch 50\n",
      " Simulating batch 51\n",
      " Simulating batch 52\n",
      " Simulating batch 53\n",
      " Simulating batch 54\n",
      " Simulating batch 55\n",
      " Simulating batch 56\n",
      " Simulating batch 57\n",
      " Simulating batch 58\n",
      " Simulating batch 59\n",
      " Simulating batch 60\n",
      " Simulating batch 61\n",
      " Simulating batch 62\n",
      " Simulating batch 63\n",
      " Simulating batch 64\n",
      " Simulating batch 65\n",
      " Simulating batch 66\n",
      " Simulating batch 67\n",
      " Simulating batch 68\n",
      " Simulating batch 69\n",
      " Simulating batch 70\n",
      " Simulating batch 71\n",
      " Simulating batch 72\n",
      " Simulating batch 73\n",
      " Simulating batch 74\n",
      " Simulating batch 75\n",
      " Simulating batch 76\n",
      " Simulating batch 77\n",
      " Simulating batch 78\n",
      " Simulating batch 79\n",
      " Simulating batch 80\n",
      " Simulating batch 81\n",
      " Simulating batch 82\n",
      " Simulating batch 83\n",
      " Simulating batch 84\n",
      " Simulating batch 85\n",
      " Simulating batch 86\n",
      " Simulating batch 87\n",
      " Simulating batch 88\n",
      " Simulating batch 89\n",
      " Simulating batch 90\n",
      " Simulating batch 91\n",
      " Simulating batch 92\n",
      " Simulating batch 93\n",
      " Simulating batch 94\n",
      " Simulating batch 95\n",
      " Simulating batch 96\n",
      " Simulating batch 97\n",
      " Simulating batch 98\n",
      " Simulating batch 99\n",
      " Simulating batch 100\n",
      " Simulating batch 101\n",
      " Simulating batch 102\n",
      " Simulating batch 103\n",
      " Simulating batch 104\n",
      " Simulating batch 105\n",
      " Simulating batch 106\n",
      " Simulating batch 107\n",
      " Simulating batch 108\n",
      " Simulating batch 109\n",
      " Simulating batch 110\n",
      " Simulating batch 111\n",
      " Simulating batch 112\n",
      " Simulating batch 113\n",
      " Simulating batch 114\n",
      " Simulating batch 115\n",
      " Simulating batch 116\n",
      " Simulating batch 117\n",
      " Simulating batch 118\n",
      " Simulating batch 119\n",
      " Simulating batch 120\n",
      " Simulating batch 121\n",
      " Simulating batch 122\n",
      " Simulating batch 123\n",
      " Simulating batch 124\n",
      " Simulating batch 125\n",
      " Simulating batch 126\n",
      " Simulating batch 127\n",
      " Simulating batch 128\n",
      " Simulating batch 129\n",
      " Simulating batch 130\n",
      " Simulating batch 131\n",
      " Simulating batch 132\n",
      " Simulating batch 133\n",
      " Simulating batch 134\n",
      " Simulating batch 135\n",
      " Simulating batch 136\n",
      " Simulating batch 137\n",
      " Simulating batch 138\n",
      " Simulating batch 139\n",
      " Simulating batch 140\n",
      " Simulating batch 141\n",
      " Simulating batch 142\n",
      " Simulating batch 143\n",
      " Simulating batch 144\n",
      " Simulating batch 145\n",
      " Simulating batch 146\n",
      " Simulating batch 147\n",
      " Simulating batch 148\n",
      " Simulating batch 149\n",
      " Simulating batch 150\n",
      " Simulating batch 151\n",
      " Simulating batch 152\n",
      " Simulating batch 153\n",
      " Simulating batch 154\n",
      " Simulating batch 155\n",
      " Simulating batch 156\n",
      " Simulating batch 157\n",
      " Simulating batch 158\n",
      " Simulating batch 159\n",
      " Simulating batch 160\n",
      " Simulating batch 161\n",
      " Simulating batch 162\n",
      " Simulating batch 163\n",
      " Simulating batch 164\n",
      " Simulating batch 165\n",
      " Simulating batch 166\n",
      " Simulating batch 167\n",
      " Simulating batch 168\n",
      " Simulating batch 169\n",
      " Simulating batch 170\n",
      " Simulating batch 171\n",
      " Simulating batch 172\n",
      " Simulating batch 173\n",
      " Simulating batch 174\n",
      " Simulating batch 175\n",
      " Simulating batch 176\n",
      " Simulating batch 177\n",
      " Simulating batch 178\n",
      " Simulating batch 179\n",
      " Simulating batch 180\n",
      " Simulating batch 181\n",
      " Simulating batch 182\n",
      " Simulating batch 183\n",
      " Simulating batch 184\n",
      " Simulating batch 185\n",
      " Simulating batch 186\n",
      " Simulating batch 187\n",
      " Simulating batch 188\n",
      " Simulating batch 189\n",
      " Simulating batch 190\n",
      " Simulating batch 191\n",
      " Simulating batch 192\n",
      " Simulating batch 193\n",
      " Simulating batch 194\n",
      " Simulating batch 195\n",
      " Simulating batch 196\n",
      " Simulating batch 197\n",
      " Simulating batch 198\n",
      " Simulating batch 199\n",
      " Simulating batch 200\n",
      " Simulating batch 201\n",
      " Simulating batch 202\n",
      " Simulating batch 203\n",
      " Simulating batch 204\n",
      " Simulating batch 205\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Simulating batch 206\n",
      " Simulating batch 207\n",
      " Simulating batch 208\n",
      " Simulating batch 209\n",
      " Simulating batch 210\n",
      " Simulating batch 211\n",
      " Simulating batch 212\n",
      " Simulating batch 213\n",
      " Simulating batch 214\n",
      " Simulating batch 215\n",
      " Simulating batch 216\n",
      " Simulating batch 217\n",
      " Simulating batch 218\n",
      " Simulating batch 219\n",
      " Simulating batch 220\n",
      " Simulating batch 221\n",
      " Simulating batch 222\n",
      " Simulating batch 223\n",
      " Simulating batch 224\n",
      " Simulating batch 225\n",
      " Simulating batch 226\n",
      " Simulating batch 227\n",
      " Simulating batch 228\n",
      " Simulating batch 229\n",
      " Simulating batch 230\n",
      " Simulating batch 231\n",
      " Simulating batch 232\n",
      " Simulating batch 233\n",
      " Simulating batch 234\n",
      " Simulating batch 235\n",
      " Simulating batch 236\n",
      " Simulating batch 237\n",
      " Simulating batch 238\n",
      " Simulating batch 239\n",
      " Simulating batch 240\n",
      " Simulating batch 241\n",
      " Simulating batch 242\n",
      " Simulating batch 243\n",
      " Simulating batch 244\n",
      " Simulating batch 245\n",
      " Simulating batch 246\n",
      " Simulating batch 247\n",
      " Simulating batch 248\n",
      " Simulating batch 249\n",
      " Simulating batch 250\n",
      " Simulating batch 251\n",
      " Simulating batch 252\n",
      " Simulating batch 253\n",
      " Simulating batch 254\n",
      " Simulating batch 255\n",
      " Simulating batch 256\n",
      " Simulating batch 257\n",
      " Simulating batch 258\n",
      " Simulating batch 259\n",
      " Simulating batch 260\n",
      " Simulating batch 261\n",
      " Simulating batch 262\n",
      " Simulating batch 263\n",
      " Simulating batch 264\n",
      " Simulating batch 265\n",
      " Simulating batch 266\n",
      " Simulating batch 267\n",
      " Simulating batch 268\n",
      " Simulating batch 269\n",
      " Simulating batch 270\n",
      " Simulating batch 271\n",
      " Simulating batch 272\n",
      " Simulating batch 273\n",
      " Simulating batch 274\n",
      " Simulating batch 275\n",
      " Simulating batch 276\n",
      " Simulating batch 277\n",
      " Simulating batch 278\n",
      " Simulating batch 279\n",
      " Simulating batch 280\n",
      " Simulating batch 281\n",
      " Simulating batch 282\n",
      " Simulating batch 283\n",
      " Simulating batch 284\n",
      " Simulating batch 285\n",
      " Simulating batch 286\n",
      " Simulating batch 287\n",
      " Simulating batch 288\n",
      " Simulating batch 289\n",
      " Simulating batch 290\n",
      " Simulating batch 291\n",
      " Simulating batch 292\n",
      " Simulating batch 293\n",
      " Simulating batch 294\n",
      " Simulating batch 295\n",
      " Simulating batch 296\n",
      " Simulating batch 297\n",
      " Simulating batch 298\n",
      " Simulating batch 299\n",
      " Simulating batch 300\n",
      " Simulating batch 301\n",
      " Simulating batch 302\n",
      " Simulating batch 303\n",
      " Simulating batch 304\n",
      " Simulating batch 305\n",
      " Simulating batch 306\n",
      " Simulating batch 307\n",
      " Simulating batch 308\n",
      " Simulating batch 309\n",
      " Simulating batch 310\n",
      " Simulating batch 311\n",
      " Simulating batch 312\n",
      " Simulating batch 313\n",
      " Simulating batch 314\n",
      " Simulating batch 315\n",
      " Simulating batch 316\n",
      " Simulating batch 317\n",
      " Simulating batch 318\n",
      " Simulating batch 319\n",
      " Simulating batch 320\n",
      " Simulating batch 321\n",
      " Simulating batch 322\n",
      " Simulating batch 323\n",
      " Simulating batch 324\n",
      " Simulating batch 325\n",
      " Simulating batch 326\n",
      " Simulating batch 327\n",
      " Simulating batch 328\n",
      " Simulating batch 329\n",
      " Simulating batch 330\n",
      " Simulating batch 331\n",
      " Simulating batch 332\n",
      " Simulating batch 333\n",
      " Simulating batch 334\n",
      " Simulating batch 335\n",
      " Simulating batch 336\n",
      " Simulating batch 337\n",
      " Simulating batch 338\n",
      " Simulating batch 339\n",
      " Simulating batch 340\n",
      " Simulating batch 341\n",
      " Simulating batch 342\n",
      " Simulating batch 343\n",
      " Simulating batch 344\n",
      " Simulating batch 345\n",
      " Simulating batch 346\n",
      " Simulating batch 347\n",
      " Simulating batch 348\n",
      " Simulating batch 349\n",
      " Simulating batch 350\n",
      " Simulating batch 351\n",
      " Simulating batch 352\n",
      " Simulating batch 353\n",
      " Simulating batch 354\n",
      " Simulating batch 355\n",
      " Simulating batch 356\n",
      " Simulating batch 357\n",
      " Simulating batch 358\n",
      " Simulating batch 359\n",
      " Simulating batch 360\n",
      " Simulating batch 361\n",
      " Simulating batch 362\n",
      " Simulating batch 363\n",
      " Simulating batch 364\n",
      " Simulating batch 365\n",
      " Simulating batch 366\n",
      " Simulating batch 367\n",
      " Simulating batch 368\n",
      " Simulating batch 369\n",
      " Simulating batch 370\n",
      " Simulating batch 371\n",
      " Simulating batch 372\n",
      " Simulating batch 373\n",
      " Simulating batch 374\n",
      " Simulating batch 375\n",
      " Simulating batch 376\n",
      " Simulating batch 377\n",
      " Simulating batch 378\n",
      " Simulating batch 379\n",
      " Simulating batch 380\n",
      " Simulating batch 381\n",
      " Simulating batch 382\n",
      " Simulating batch 383\n",
      " Simulating batch 384\n",
      " Simulating batch 385\n",
      " Simulating batch 386\n",
      " Simulating batch 387\n",
      " Simulating batch 388\n",
      " Simulating batch 389\n",
      " Simulating batch 390\n",
      " Simulating batch 391\n",
      " Simulating batch 392\n",
      " Simulating batch 393\n",
      " Simulating batch 394\n",
      " Simulating batch 395\n",
      " Simulating batch 396\n",
      " Simulating batch 397\n",
      " Simulating batch 398\n",
      " Simulating batch 399\n",
      " Simulating batch 400\n",
      " Simulating batch 401\n",
      " Simulating batch 402\n",
      " Simulating batch 403\n",
      " Simulating batch 404\n",
      " Simulating batch 405\n",
      " Simulating batch 406\n",
      " Simulating batch 407\n",
      " Simulating batch 408\n",
      " Simulating batch 409\n",
      " Simulating batch 410\n",
      " Simulating batch 411\n",
      " Simulating batch 412\n",
      " Simulating batch 413\n",
      " Simulating batch 414\n",
      " Simulating batch 415\n",
      " Simulating batch 416\n",
      " Simulating batch 417\n",
      " Simulating batch 418\n",
      " Simulating batch 419\n",
      " Simulating batch 420\n",
      " Simulating batch 421\n",
      " Simulating batch 422\n",
      " Simulating batch 423\n",
      " Simulating batch 424\n",
      " Simulating batch 425\n",
      " Simulating batch 426\n",
      " Simulating batch 427\n",
      " Simulating batch 428\n",
      " Simulating batch 429\n",
      " Simulating batch 430\n",
      " Simulating batch 431\n",
      " Simulating batch 432\n",
      " Simulating batch 433\n",
      " Simulating batch 434\n",
      " Simulating batch 435\n",
      " Simulating batch 436\n",
      " Simulating batch 437\n",
      " Simulating batch 438\n",
      " Simulating batch 439\n",
      " Simulating batch 440\n",
      " Simulating batch 441\n",
      " Simulating batch 442\n",
      " Simulating batch 443\n",
      " Simulating batch 444\n",
      " Simulating batch 445\n",
      " Simulating batch 446\n",
      " Simulating batch 447\n",
      " Simulating batch 448\n",
      " Simulating batch 449\n",
      " Simulating batch 450\n",
      " Simulating batch 451\n",
      " Simulating batch 452\n",
      " Simulating batch 453\n",
      " Simulating batch 454\n",
      " Simulating batch 455\n",
      " Simulating batch 456\n",
      " Simulating batch 457\n",
      " Simulating batch 458\n",
      " Simulating batch 459\n",
      " Simulating batch 460\n",
      " Simulating batch 461\n",
      " Simulating batch 462\n",
      " Simulating batch 463\n",
      " Simulating batch 464\n",
      " Simulating batch 465\n",
      " Simulating batch 466\n",
      " Simulating batch 467\n",
      " Simulating batch 468\n",
      " Simulating batch 469\n",
      " Simulating batch 470\n",
      " Simulating batch 471\n",
      " Simulating batch 472\n",
      " Simulating batch 473\n",
      " Simulating batch 474\n",
      " Simulating batch 475\n",
      " Simulating batch 476\n",
      " Simulating batch 477\n",
      " Simulating batch 478\n",
      " Simulating batch 479\n",
      " Simulating batch 480\n",
      " Simulating batch 481\n",
      " Simulating batch 482\n",
      " Simulating batch 483\n",
      " Simulating batch 484\n",
      " Simulating batch 485\n",
      " Simulating batch 486\n",
      " Simulating batch 487\n",
      " Simulating batch 488\n",
      " Simulating batch 489\n",
      " Simulating batch 490\n",
      " Simulating batch 491\n",
      " Simulating batch 492\n",
      " Simulating batch 493\n",
      " Simulating batch 494\n",
      " Simulating batch 495\n",
      " Simulating batch 496\n",
      " Simulating batch 497\n",
      " Simulating batch 498\n",
      " Simulating batch 499\n",
      " Simulating batch 500\n",
      " Simulating batch 501\n",
      " Simulating batch 502\n",
      " Simulating batch 503\n",
      " Simulating batch 504\n",
      " Simulating batch 505\n",
      " Simulating batch 506\n",
      " Simulating batch 507\n",
      " Simulating batch 508\n",
      " Simulating batch 509\n",
      " Simulating batch 510\n",
      " Simulating batch 511\n",
      " Simulating batch 512\n",
      " Simulating batch 513\n",
      " Simulating batch 514\n",
      " Simulating batch 515\n",
      " Simulating batch 516\n",
      " Simulating batch 517\n",
      " Simulating batch 518\n",
      " Simulating batch 519\n",
      " Simulating batch 520\n",
      " Simulating batch 521\n",
      " Simulating batch 522\n",
      " Simulating batch 523\n",
      " Simulating batch 524\n",
      " Simulating batch 525\n",
      " Simulating batch 526\n",
      " Simulating batch 527\n",
      " Simulating batch 528\n",
      " Simulating batch 529\n",
      " Simulating batch 530\n",
      " Simulating batch 531\n",
      " Simulating batch 532\n",
      " Simulating batch 533\n",
      " Simulating batch 534\n",
      " Simulating batch 535\n",
      " Simulating batch 536\n",
      " Simulating batch 537\n",
      " Simulating batch 538\n",
      " Simulating batch 539\n",
      " Simulating batch 540\n",
      " Simulating batch 541\n",
      " Simulating batch 542\n",
      " Simulating batch 543\n",
      " Simulating batch 544\n",
      " Simulating batch 545\n",
      " Simulating batch 546\n",
      " Simulating batch 547\n",
      " Simulating batch 548\n",
      " Simulating batch 549\n",
      " Simulating batch 550\n",
      " Simulating batch 551\n",
      " Simulating batch 552\n",
      " Simulating batch 553\n",
      " Simulating batch 554\n",
      " Simulating batch 555\n",
      " Simulating batch 556\n",
      " Simulating batch 557\n",
      " Simulating batch 558\n",
      " Simulating batch 559\n",
      " Simulating batch 560\n",
      " Simulating batch 561\n",
      " Simulating batch 562\n",
      " Simulating batch 563\n",
      " Simulating batch 564\n",
      " Simulating batch 565\n",
      " Simulating batch 566\n",
      " Simulating batch 567\n",
      " Simulating batch 568\n",
      " Simulating batch 569\n",
      " Simulating batch 570\n",
      " Simulating batch 571\n",
      " Simulating batch 572\n",
      " Simulating batch 573\n",
      " Simulating batch 574\n",
      " Simulating batch 575\n",
      " Simulating batch 576\n",
      " Simulating batch 577\n",
      " Simulating batch 578\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Simulating batch 579\n",
      " Simulating batch 580\n",
      " Simulating batch 581\n",
      " Simulating batch 582\n",
      " Simulating batch 583\n",
      " Simulating batch 584\n",
      " Simulating batch 585\n",
      " Simulating batch 586\n",
      " Simulating batch 587\n",
      " Simulating batch 588\n",
      " Simulating batch 589\n",
      " Simulating batch 590\n",
      " Simulating batch 591\n",
      " Simulating batch 592\n",
      " Simulating batch 593\n",
      " Simulating batch 594\n",
      " Simulating batch 595\n",
      " Simulating batch 596\n",
      " Simulating batch 597\n",
      " Simulating batch 598\n",
      " Simulating batch 599\n",
      " Simulating batch 600\n",
      " Simulating batch 601\n",
      " Simulating batch 602\n",
      " Simulating batch 603\n",
      " Simulating batch 604\n",
      " Simulating batch 605\n",
      " Simulating batch 606\n",
      " Simulating batch 607\n",
      " Simulating batch 608\n",
      " Simulating batch 609\n",
      " Simulating batch 610\n",
      " Simulating batch 611\n",
      " Simulating batch 612\n",
      " Simulating batch 613\n",
      " Simulating batch 614\n",
      " Simulating batch 615\n",
      " Simulating batch 616\n",
      " Simulating batch 617\n",
      " Simulating batch 618\n",
      " Simulating batch 619\n",
      " Simulating batch 620\n",
      " Simulating batch 621\n",
      " Simulating batch 622\n",
      " Simulating batch 623\n",
      " Simulating batch 624\n",
      " Simulating batch 625\n",
      " Simulating batch 626\n",
      " Simulating batch 627\n",
      " Simulating batch 628\n",
      " Simulating batch 629\n",
      " Simulating batch 630\n",
      " Simulating batch 631\n",
      " Simulating batch 632\n",
      " Simulating batch 633\n",
      " Simulating batch 634\n",
      " Simulating batch 635\n",
      " Simulating batch 636\n",
      " Simulating batch 637\n",
      " Simulating batch 638\n",
      " Simulating batch 639\n",
      " Simulating batch 640\n",
      " Simulating batch 641\n",
      " Simulating batch 642\n",
      " Simulating batch 643\n",
      " Simulating batch 644\n",
      " Simulating batch 645\n",
      " Simulating batch 646\n",
      " Simulating batch 647\n",
      " Simulating batch 648\n",
      " Simulating batch 649\n",
      " Simulating batch 650\n",
      " Simulating batch 651\n",
      " Simulating batch 652\n",
      " Simulating batch 653\n",
      " Simulating batch 654\n",
      " Simulating batch 655\n",
      " Simulating batch 656\n",
      " Simulating batch 657\n",
      " Simulating batch 658\n",
      " Simulating batch 659\n",
      " Simulating batch 660\n",
      " Simulating batch 661\n",
      " Simulating batch 662\n",
      " Simulating batch 663\n",
      " Simulating batch 664\n",
      " Simulating batch 665\n",
      " Simulating batch 666\n",
      " Simulating batch 667\n",
      " Simulating batch 668\n",
      " Simulating batch 669\n",
      " Simulating batch 670\n",
      " Simulating batch 671\n",
      " Simulating batch 672\n",
      " Simulating batch 673\n",
      " Simulating batch 674\n",
      " Simulating batch 675\n",
      " Simulating batch 676\n",
      " Simulating batch 677\n",
      " Simulating batch 678\n",
      " Simulating batch 679\n",
      " Simulating batch 680\n",
      " Simulating batch 681\n",
      " Simulating batch 682\n",
      " Simulating batch 683\n",
      " Simulating batch 684\n",
      " Simulating batch 685\n",
      " Simulating batch 686\n",
      " Simulating batch 687\n",
      " Simulating batch 688\n",
      " Simulating batch 689\n",
      " Simulating batch 690\n",
      " Simulating batch 691\n",
      " Simulating batch 692\n",
      " Simulating batch 693\n",
      " Simulating batch 694\n",
      " Simulating batch 695\n",
      " Simulating batch 696\n",
      " Simulating batch 697\n",
      " Simulating batch 698\n",
      " Simulating batch 699\n",
      " Simulating batch 700\n",
      " Simulating batch 701\n",
      " Simulating batch 702\n",
      " Simulating batch 703\n",
      " Simulating batch 704\n",
      " Simulating batch 705\n",
      " Simulating batch 706\n",
      " Simulating batch 707\n",
      " Simulating batch 708\n",
      " Simulating batch 709\n",
      " Simulating batch 710\n",
      " Simulating batch 711\n",
      " Simulating batch 712\n",
      " Simulating batch 713\n",
      " Simulating batch 714\n",
      " Simulating batch 715\n",
      " Simulating batch 716\n",
      " Simulating batch 717\n",
      " Simulating batch 718\n",
      " Simulating batch 719\n",
      " Simulating batch 720\n",
      " Simulating batch 721\n",
      " Simulating batch 722\n",
      " Simulating batch 723\n",
      " Simulating batch 724\n",
      " Simulating batch 725\n",
      " Simulating batch 726\n",
      " Simulating batch 727\n",
      " Simulating batch 728\n",
      " Simulating batch 729\n",
      " Simulating batch 730\n",
      " Simulating batch 731\n",
      " Simulating batch 732\n",
      " Simulating batch 733\n",
      " Simulating batch 734\n",
      " Simulating batch 735\n",
      " Simulating batch 736\n",
      " Simulating batch 737\n",
      " Simulating batch 738\n",
      " Simulating batch 739\n",
      " Simulating batch 740\n",
      " Simulating batch 741\n",
      " Simulating batch 742\n",
      " Simulating batch 743\n",
      " Simulating batch 744\n",
      " Simulating batch 745\n",
      " Simulating batch 746\n",
      " Simulating batch 747\n",
      " Simulating batch 748\n",
      " Simulating batch 749\n",
      " Simulating batch 750\n",
      " Simulating batch 751\n",
      " Simulating batch 752\n",
      " Simulating batch 753\n",
      " Simulating batch 754\n",
      " Simulating batch 755\n",
      " Simulating batch 756\n",
      " Simulating batch 757\n",
      " Simulating batch 758\n",
      " Simulating batch 759\n",
      " Simulating batch 760\n",
      " Simulating batch 761\n",
      " Simulating batch 762\n",
      " Simulating batch 763\n",
      " Simulating batch 764\n",
      " Simulating batch 765\n",
      " Simulating batch 766\n",
      " Simulating batch 767\n",
      " Simulating batch 768\n",
      " Simulating batch 769\n",
      " Simulating batch 770\n",
      " Simulating batch 771\n",
      " Simulating batch 772\n",
      " Simulating batch 773\n",
      " Simulating batch 774\n",
      " Simulating batch 775\n",
      " Simulating batch 776\n",
      " Simulating batch 777\n",
      " Simulating batch 778\n",
      " Simulating batch 779\n",
      " Simulating batch 780\n",
      " Simulating batch 781\n",
      " Simulating batch 782\n",
      " Simulating batch 783\n",
      " Simulating batch 784\n",
      " Simulating batch 785\n",
      " Simulating batch 786\n",
      " Simulating batch 787\n",
      " Simulating batch 788\n",
      " Simulating batch 789\n",
      " Simulating batch 790\n",
      " Simulating batch 791\n",
      " Simulating batch 792\n",
      " Simulating batch 793\n",
      " Simulating batch 794\n",
      " Simulating batch 795\n",
      " Simulating batch 796\n",
      " Simulating batch 797\n",
      " Simulating batch 798\n",
      " Simulating batch 799\n",
      " Simulating batch 800\n",
      " Simulating batch 801\n",
      " Simulating batch 802\n",
      " Simulating batch 803\n",
      " Simulating batch 804\n",
      " Simulating batch 805\n",
      " Simulating batch 806\n",
      " Simulating batch 807\n",
      " Simulating batch 808\n",
      " Simulating batch 809\n",
      " Simulating batch 810\n",
      " Simulating batch 811\n",
      " Simulating batch 812\n",
      " Simulating batch 813\n",
      " Simulating batch 814\n",
      " Simulating batch 815\n",
      " Simulating batch 816\n",
      " Simulating batch 817\n",
      " Simulating batch 818\n",
      " Simulating batch 819\n",
      " Simulating batch 820\n",
      " Simulating batch 821\n",
      " Simulating batch 822\n",
      " Simulating batch 823\n",
      " Simulating batch 824\n",
      " Simulating batch 825\n",
      " Simulating batch 826\n",
      " Simulating batch 827\n",
      " Simulating batch 828\n",
      " Simulating batch 829\n",
      " Simulating batch 830\n",
      " Simulating batch 831\n",
      " Simulating batch 832\n",
      " Simulating batch 833\n",
      " Simulating batch 834\n",
      " Simulating batch 835\n",
      " Simulating batch 836\n",
      " Simulating batch 837\n",
      " Simulating batch 838\n",
      " Simulating batch 839\n",
      " Simulating batch 840\n",
      " Simulating batch 841\n",
      " Simulating batch 842\n",
      " Simulating batch 843\n",
      " Simulating batch 844\n",
      " Simulating batch 845\n",
      " Simulating batch 846\n",
      " Simulating batch 847\n",
      " Simulating batch 848\n",
      " Simulating batch 849\n",
      " Simulating batch 850\n",
      " Simulating batch 851\n",
      " Simulating batch 852\n",
      " Simulating batch 853\n",
      " Simulating batch 854\n",
      " Simulating batch 855\n",
      " Simulating batch 856\n",
      " Simulating batch 857\n",
      " Simulating batch 858\n",
      " Simulating batch 859\n",
      " Simulating batch 860\n",
      " Simulating batch 861\n",
      " Simulating batch 862\n",
      " Simulating batch 863\n",
      " Simulating batch 864\n",
      " Simulating batch 865\n",
      " Simulating batch 866\n",
      " Simulating batch 867\n",
      " Simulating batch 868\n",
      " Simulating batch 869\n",
      " Simulating batch 870\n",
      " Simulating batch 871\n",
      " Simulating batch 872\n",
      " Simulating batch 873\n",
      " Simulating batch 874\n",
      " Simulating batch 875\n",
      " Simulating batch 876\n",
      " Simulating batch 877\n",
      " Simulating batch 878\n",
      " Simulating batch 879\n",
      " Simulating batch 880\n",
      " Simulating batch 881\n",
      " Simulating batch 882\n",
      " Simulating batch 883\n",
      " Simulating batch 884\n",
      " Simulating batch 885\n",
      " Simulating batch 886\n",
      " Simulating batch 887\n",
      " Simulating batch 888\n",
      " Simulating batch 889\n",
      " Simulating batch 890\n",
      " Simulating batch 891\n",
      " Simulating batch 892\n",
      " Simulating batch 893\n",
      " Simulating batch 894\n",
      " Simulating batch 895\n",
      " Simulating batch 896\n",
      " Simulating batch 897\n",
      " Simulating batch 898\n",
      " Simulating batch 899\n",
      " Simulating batch 900\n",
      " Simulating batch 901\n",
      " Simulating batch 902\n",
      " Simulating batch 903\n",
      " Simulating batch 904\n",
      " Simulating batch 905\n",
      " Simulating batch 906\n",
      " Simulating batch 907\n",
      " Simulating batch 908\n",
      " Simulating batch 909\n",
      " Simulating batch 910\n",
      " Simulating batch 911\n",
      " Simulating batch 912\n",
      " Simulating batch 913\n",
      " Simulating batch 914\n",
      " Simulating batch 915\n",
      " Simulating batch 916\n",
      " Simulating batch 917\n",
      " Simulating batch 918\n",
      " Simulating batch 919\n",
      " Simulating batch 920\n",
      " Simulating batch 921\n",
      " Simulating batch 922\n",
      " Simulating batch 923\n",
      " Simulating batch 924\n",
      " Simulating batch 925\n",
      " Simulating batch 926\n",
      " Simulating batch 927\n",
      " Simulating batch 928\n",
      " Simulating batch 929\n",
      " Simulating batch 930\n",
      " Simulating batch 931\n",
      " Simulating batch 932\n",
      " Simulating batch 933\n",
      " Simulating batch 934\n",
      " Simulating batch 935\n",
      " Simulating batch 936\n",
      " Simulating batch 937\n",
      " Simulating batch 938\n",
      " Simulating batch 939\n",
      " Simulating batch 940\n",
      " Simulating batch 941\n",
      " Simulating batch 942\n",
      " Simulating batch 943\n",
      " Simulating batch 944\n",
      " Simulating batch 945\n",
      " Simulating batch 946\n",
      " Simulating batch 947\n",
      " Simulating batch 948\n",
      " Simulating batch 949\n",
      " Simulating batch 950\n",
      " Simulating batch 951\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " Simulating batch 952\n",
      " Simulating batch 953\n",
      " Simulating batch 954\n",
      " Simulating batch 955\n",
      " Simulating batch 956\n",
      " Simulating batch 957\n",
      " Simulating batch 958\n",
      " Simulating batch 959\n",
      " Simulating batch 960\n",
      " Simulating batch 961\n",
      " Simulating batch 962\n",
      " Simulating batch 963\n",
      " Simulating batch 964\n",
      " Simulating batch 965\n",
      " Simulating batch 966\n",
      " Simulating batch 967\n",
      " Simulating batch 968\n",
      " Simulating batch 969\n",
      " Simulating batch 970\n",
      " Simulating batch 971\n",
      " Simulating batch 972\n",
      " Simulating batch 973\n",
      " Simulating batch 974\n",
      " Simulating batch 975\n",
      " Simulating batch 976\n",
      " Simulating batch 977\n",
      " Simulating batch 978\n",
      " Simulating batch 979\n",
      " Simulating batch 980\n",
      " Simulating batch 981\n",
      " Simulating batch 982\n",
      " Simulating batch 983\n",
      " Simulating batch 984\n",
      " Simulating batch 985\n",
      " Simulating batch 986\n",
      " Simulating batch 987\n",
      " Simulating batch 988\n",
      " Simulating batch 989\n",
      " Simulating batch 990\n",
      " Simulating batch 991\n",
      " Simulating batch 992\n",
      " Simulating batch 993\n",
      " Simulating batch 994\n",
      " Simulating batch 995\n",
      " Simulating batch 996\n",
      " Simulating batch 997\n",
      " Simulating batch 998\n",
      " Simulating batch 999\n",
      " Simulating batch 1000\n",
      " Creating state point statepoint.1000.h5...\n",
      "\n",
      " =======================>     TIMING STATISTICS     <=======================\n",
      "\n",
      " Total time for initialization     = 1.0130e+00 seconds\n",
      "   Reading cross sections          = 9.9524e-01 seconds\n",
      " Total time in simulation          = 5.8533e+02 seconds\n",
      "   Time in transport only          = 5.8387e+02 seconds\n",
      "   Time in active batches          = 5.8533e+02 seconds\n",
      "   Time accumulating tallies       = 1.1585e+00 seconds\n",
      "   Time writing statepoints        = 1.5738e-02 seconds\n",
      " Total time for finalization       = 6.4065e-01 seconds\n",
      " Total time elapsed                = 5.8699e+02 seconds\n",
      " Calculation Rate (active)         = 170844 particles/second\n",
      "\n",
      " ============================>     RESULTS     <============================\n",
      "\n",
      " Leakage Fraction            = 0.89405 +/- 0.00003\n",
      "\n",
      "time :  9.786638581752777  min\n"
     ]
    }
   ],
   "source": [
    "start = tm.time()\n",
    "runData = mc.run()\n",
    "stop = tm.time()\n",
    "print ('time : ', (stop-start)/60, ' min')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "f3d65bfc",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash\n",
    "cp statepoint.1000.h5 Pb_detector_cs_137.h5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "82a0ca42",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Load the statepoint file\n",
    "sp = mc.StatePoint('Pb_detector_cs_137.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "744ae013",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Tally\n",
       "\tID             =\t1\n",
       "\tName           =\tflux mesh\n",
       "\tFilters        =\tMeshFilter, EnergyFilter\n",
       "\tNuclides       =\ttotal\n",
       "\tScores         =\t['flux']\n",
       "\tEstimator      =\ttracklength"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ekorCs137 = sp.get_tally()\n",
    "ekorCs137"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "6f0733ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>energy low [eV]</th>\n",
       "      <th>energy high [eV]</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.057899</td>\n",
       "      <td>0.013692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>20000.0</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.003611</td>\n",
       "      <td>0.003611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>30000.0</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>40000.0</td>\n",
       "      <td>50000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334995</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>620000.0</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.007162</td>\n",
       "      <td>0.004445</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334996</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>630000.0</td>\n",
       "      <td>640000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.026053</td>\n",
       "      <td>0.009623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334997</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>640000.0</td>\n",
       "      <td>650000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.017885</td>\n",
       "      <td>0.007831</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334998</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>650000.0</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.022854</td>\n",
       "      <td>0.008220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334999</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.530041</td>\n",
       "      <td>0.042277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>335000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mesh 1         energy low [eV] energy high [eV] nuclide score     mean  \\\n",
       "            x   y   z                                                           \n",
       "0           1   1   1        0.00e+00         1.00e+04   total  flux 0.00e+00   \n",
       "1           1   1   1        1.00e+04         2.00e+04   total  flux 5.79e-02   \n",
       "2           1   1   1        2.00e+04         3.00e+04   total  flux 3.61e-03   \n",
       "3           1   1   1        3.00e+04         4.00e+04   total  flux 0.00e+00   \n",
       "4           1   1   1        4.00e+04         5.00e+04   total  flux 0.00e+00   \n",
       "...       ...  ..  ..             ...              ...     ...   ...      ...   \n",
       "334995     10  10  50        6.20e+05         6.30e+05   total  flux 7.16e-03   \n",
       "334996     10  10  50        6.30e+05         6.40e+05   total  flux 2.61e-02   \n",
       "334997     10  10  50        6.40e+05         6.50e+05   total  flux 1.79e-02   \n",
       "334998     10  10  50        6.50e+05         6.60e+05   total  flux 2.29e-02   \n",
       "334999     10  10  50        6.60e+05         6.70e+05   total  flux 5.30e-01   \n",
       "\n",
       "       std. dev.  \n",
       "                  \n",
       "0       0.00e+00  \n",
       "1       1.37e-02  \n",
       "2       3.61e-03  \n",
       "3       0.00e+00  \n",
       "4       0.00e+00  \n",
       "...          ...  \n",
       "334995  4.45e-03  \n",
       "334996  9.62e-03  \n",
       "334997  7.83e-03  \n",
       "334998  8.22e-03  \n",
       "334999  4.23e-02  \n",
       "\n",
       "[335000 rows x 9 columns]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfCs137 = ekorCs137.get_pandas_dataframe()\n",
    "dfCs137"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e928ede3",
   "metadata": {},
   "outputs": [],
   "source": [
    "chName = np.linspace(1,67,67).astype('str')\n",
    "\n",
    "chDataFrame = dict.fromkeys(chName,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "8e0a6a3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'1.0': 0,\n",
       " '2.0': 0,\n",
       " '3.0': 0,\n",
       " '4.0': 0,\n",
       " '5.0': 0,\n",
       " '6.0': 0,\n",
       " '7.0': 0,\n",
       " '8.0': 0,\n",
       " '9.0': 0,\n",
       " '10.0': 0,\n",
       " '11.0': 0,\n",
       " '12.0': 0,\n",
       " '13.0': 0,\n",
       " '14.0': 0,\n",
       " '15.0': 0,\n",
       " '16.0': 0,\n",
       " '17.0': 0,\n",
       " '18.0': 0,\n",
       " '19.0': 0,\n",
       " '20.0': 0,\n",
       " '21.0': 0,\n",
       " '22.0': 0,\n",
       " '23.0': 0,\n",
       " '24.0': 0,\n",
       " '25.0': 0,\n",
       " '26.0': 0,\n",
       " '27.0': 0,\n",
       " '28.0': 0,\n",
       " '29.0': 0,\n",
       " '30.0': 0,\n",
       " '31.0': 0,\n",
       " '32.0': 0,\n",
       " '33.0': 0,\n",
       " '34.0': 0,\n",
       " '35.0': 0,\n",
       " '36.0': 0,\n",
       " '37.0': 0,\n",
       " '38.0': 0,\n",
       " '39.0': 0,\n",
       " '40.0': 0,\n",
       " '41.0': 0,\n",
       " '42.0': 0,\n",
       " '43.0': 0,\n",
       " '44.0': 0,\n",
       " '45.0': 0,\n",
       " '46.0': 0,\n",
       " '47.0': 0,\n",
       " '48.0': 0,\n",
       " '49.0': 0,\n",
       " '50.0': 0,\n",
       " '51.0': 0,\n",
       " '52.0': 0,\n",
       " '53.0': 0,\n",
       " '54.0': 0,\n",
       " '55.0': 0,\n",
       " '56.0': 0,\n",
       " '57.0': 0,\n",
       " '58.0': 0,\n",
       " '59.0': 0,\n",
       " '60.0': 0,\n",
       " '61.0': 0,\n",
       " '62.0': 0,\n",
       " '63.0': 0,\n",
       " '64.0': 0,\n",
       " '65.0': 0,\n",
       " '66.0': 0,\n",
       " '67.0': 0}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chDataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7aee7bbb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0 0.0 10000.0\n",
      "2.0 10000.0 20000.0\n",
      "3.0 20000.0 30000.0\n",
      "4.0 30000.0 40000.0\n",
      "5.0 40000.0 50000.0\n",
      "6.0 50000.0 60000.0\n",
      "7.0 60000.0 70000.0\n",
      "8.0 70000.0 80000.0\n",
      "9.0 80000.0 90000.0\n",
      "10.0 90000.0 100000.0\n",
      "11.0 100000.0 110000.0\n",
      "12.0 110000.0 120000.0\n",
      "13.0 120000.0 130000.0\n",
      "14.0 130000.0 140000.0\n",
      "15.0 140000.0 150000.0\n",
      "16.0 150000.0 160000.0\n",
      "17.0 160000.0 170000.0\n",
      "18.0 170000.0 180000.0\n",
      "19.0 180000.0 190000.0\n",
      "20.0 190000.0 200000.0\n",
      "21.0 200000.0 210000.0\n",
      "22.0 210000.0 220000.0\n",
      "23.0 220000.0 230000.0\n",
      "24.0 230000.0 240000.0\n",
      "25.0 240000.0 250000.0\n",
      "26.0 250000.0 260000.0\n",
      "27.0 260000.0 270000.0\n",
      "28.0 270000.0 280000.0\n",
      "29.0 280000.0 290000.0\n",
      "30.0 290000.0 300000.0\n",
      "31.0 300000.0 310000.0\n",
      "32.0 310000.0 320000.0\n",
      "33.0 320000.0 330000.0\n",
      "34.0 330000.0 340000.0\n",
      "35.0 340000.0 350000.0\n",
      "36.0 350000.0 360000.0\n",
      "37.0 360000.0 370000.0\n",
      "38.0 370000.0 380000.0\n",
      "39.0 380000.0 390000.0\n",
      "40.0 390000.0 400000.0\n",
      "41.0 400000.0 410000.0\n",
      "42.0 410000.0 420000.0\n",
      "43.0 420000.0 430000.0\n",
      "44.0 430000.0 440000.0\n",
      "45.0 440000.0 450000.0\n",
      "46.0 450000.0 460000.0\n",
      "47.0 460000.0 470000.0\n",
      "48.0 470000.0 480000.0\n",
      "49.0 480000.0 490000.0\n",
      "50.0 490000.0 500000.0\n",
      "51.0 500000.0 510000.0\n",
      "52.0 510000.0 520000.0\n",
      "53.0 520000.0 530000.0\n",
      "54.0 530000.0 540000.0\n",
      "55.0 540000.0 550000.0\n",
      "56.0 550000.0 560000.0\n",
      "57.0 560000.0 570000.0\n",
      "58.0 570000.0 580000.0\n",
      "59.0 580000.0 590000.0\n",
      "60.0 590000.0 600000.0\n",
      "61.0 600000.0 610000.0\n",
      "62.0 610000.0 620000.0\n",
      "63.0 620000.0 630000.0\n",
      "64.0 630000.0 640000.0\n",
      "65.0 640000.0 650000.0\n",
      "66.0 650000.0 660000.0\n",
      "67.0 660000.0 670000.0\n"
     ]
    }
   ],
   "source": [
    "incrLV = 0.0\n",
    "incrUP = 10000.0 # eV\n",
    "\n",
    "for i, j in enumerate(chDataFrame):\n",
    "    print (j,incrLV,incrUP)\n",
    "    chDataFrame[j]=dfCs137.loc[(dfCs137['energy low [eV]'] == incrLV) & (dfCs137['energy high [eV]'] == incrUP)]\n",
    "    incrLV = incrLV+10000.0\n",
    "    incrUP = incrUP+10000.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "442519fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"3\" halign=\"left\">mesh 1</th>\n",
       "      <th>energy low [eV]</th>\n",
       "      <th>energy high [eV]</th>\n",
       "      <th>nuclide</th>\n",
       "      <th>score</th>\n",
       "      <th>mean</th>\n",
       "      <th>std. dev.</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>260.197153</td>\n",
       "      <td>0.923592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>280.755341</td>\n",
       "      <td>0.935666</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>299.165797</td>\n",
       "      <td>1.044236</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>267</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>309.692421</td>\n",
       "      <td>1.011779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>319.066234</td>\n",
       "      <td>1.058774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334731</th>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.474120</td>\n",
       "      <td>0.043379</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334798</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.550195</td>\n",
       "      <td>0.044914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334865</th>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.616594</td>\n",
       "      <td>0.046523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334932</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.499733</td>\n",
       "      <td>0.042226</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334999</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>50</td>\n",
       "      <td>660000.0</td>\n",
       "      <td>670000.0</td>\n",
       "      <td>total</td>\n",
       "      <td>flux</td>\n",
       "      <td>0.530041</td>\n",
       "      <td>0.042277</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5000 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       mesh 1         energy low [eV] energy high [eV] nuclide score  \\\n",
       "            x   y   z                                                  \n",
       "66          1   1   1        660000.0         670000.0   total  flux   \n",
       "133         2   1   1        660000.0         670000.0   total  flux   \n",
       "200         3   1   1        660000.0         670000.0   total  flux   \n",
       "267         4   1   1        660000.0         670000.0   total  flux   \n",
       "334         5   1   1        660000.0         670000.0   total  flux   \n",
       "...       ...  ..  ..             ...              ...     ...   ...   \n",
       "334731      6  10  50        660000.0         670000.0   total  flux   \n",
       "334798      7  10  50        660000.0         670000.0   total  flux   \n",
       "334865      8  10  50        660000.0         670000.0   total  flux   \n",
       "334932      9  10  50        660000.0         670000.0   total  flux   \n",
       "334999     10  10  50        660000.0         670000.0   total  flux   \n",
       "\n",
       "              mean std. dev.  \n",
       "                              \n",
       "66      260.197153  0.923592  \n",
       "133     280.755341  0.935666  \n",
       "200     299.165797  1.044236  \n",
       "267     309.692421  1.011779  \n",
       "334     319.066234  1.058774  \n",
       "...            ...       ...  \n",
       "334731    0.474120  0.043379  \n",
       "334798    0.550195  0.044914  \n",
       "334865    0.616594  0.046523  \n",
       "334932    0.499733  0.042226  \n",
       "334999    0.530041  0.042277  \n",
       "\n",
       "[5000 rows x 9 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chDataFrame['67.0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a7c45034",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Creating new dictionary template for spectrum values for each individual mesh\n",
    "chDataFrameMean = dict.fromkeys(chName,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "b135d84b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 (50, 10, 10)\n",
      "1 (50, 10, 10)\n",
      "2 (50, 10, 10)\n",
      "3 (50, 10, 10)\n",
      "4 (50, 10, 10)\n",
      "5 (50, 10, 10)\n",
      "6 (50, 10, 10)\n",
      "7 (50, 10, 10)\n",
      "8 (50, 10, 10)\n",
      "9 (50, 10, 10)\n",
      "10 (50, 10, 10)\n",
      "11 (50, 10, 10)\n",
      "12 (50, 10, 10)\n",
      "13 (50, 10, 10)\n",
      "14 (50, 10, 10)\n",
      "15 (50, 10, 10)\n",
      "16 (50, 10, 10)\n",
      "17 (50, 10, 10)\n",
      "18 (50, 10, 10)\n",
      "19 (50, 10, 10)\n",
      "20 (50, 10, 10)\n",
      "21 (50, 10, 10)\n",
      "22 (50, 10, 10)\n",
      "23 (50, 10, 10)\n",
      "24 (50, 10, 10)\n",
      "25 (50, 10, 10)\n",
      "26 (50, 10, 10)\n",
      "27 (50, 10, 10)\n",
      "28 (50, 10, 10)\n",
      "29 (50, 10, 10)\n",
      "30 (50, 10, 10)\n",
      "31 (50, 10, 10)\n",
      "32 (50, 10, 10)\n",
      "33 (50, 10, 10)\n",
      "34 (50, 10, 10)\n",
      "35 (50, 10, 10)\n",
      "36 (50, 10, 10)\n",
      "37 (50, 10, 10)\n",
      "38 (50, 10, 10)\n",
      "39 (50, 10, 10)\n",
      "40 (50, 10, 10)\n",
      "41 (50, 10, 10)\n",
      "42 (50, 10, 10)\n",
      "43 (50, 10, 10)\n",
      "44 (50, 10, 10)\n",
      "45 (50, 10, 10)\n",
      "46 (50, 10, 10)\n",
      "47 (50, 10, 10)\n",
      "48 (50, 10, 10)\n",
      "49 (50, 10, 10)\n",
      "50 (50, 10, 10)\n",
      "51 (50, 10, 10)\n",
      "52 (50, 10, 10)\n",
      "53 (50, 10, 10)\n",
      "54 (50, 10, 10)\n",
      "55 (50, 10, 10)\n",
      "56 (50, 10, 10)\n",
      "57 (50, 10, 10)\n",
      "58 (50, 10, 10)\n",
      "59 (50, 10, 10)\n",
      "60 (50, 10, 10)\n",
      "61 (50, 10, 10)\n",
      "62 (50, 10, 10)\n",
      "63 (50, 10, 10)\n",
      "64 (50, 10, 10)\n",
      "65 (50, 10, 10)\n",
      "66 (50, 10, 10)\n"
     ]
    }
   ],
   "source": [
    "# Inserting spectrum\n",
    "for i, j in enumerate(chDataFrameMean):\n",
    "        chDataFrameMean[j]=chDataFrame[j]['mean'].values.reshape((50,10,10))\n",
    "        print (i,chDataFrameMean[j].shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "5751b166",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         2.02800462e-05, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [1.22563628e-05, 3.73611365e-03, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 4.19575757e-03],\n",
       "        [3.65589769e-04, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 4.26853016e-03],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 2.04740537e-03, 1.21687677e-03]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 4.11395978e-03, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 1.02154640e-03, 7.45935868e-03],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 2.00157949e-04],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 4.39909045e-03, 0.00000000e+00]],\n",
       "\n",
       "       [[0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 3.50341546e-03, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 0.00000000e+00, 0.00000000e+00],\n",
       "        ...,\n",
       "        [0.00000000e+00, 2.75246246e-03, 0.00000000e+00, ...,\n",
       "         0.00000000e+00, 7.73004558e-03, 0.00000000e+00],\n",
       "        [0.00000000e+00, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         2.91703875e-05, 3.20286362e-03, 9.42593600e-04],\n",
       "        [1.48679831e-03, 0.00000000e+00, 0.00000000e+00, ...,\n",
       "         7.55827321e-04, 1.16705645e-03, 7.13055543e-04]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[1.68638889e-02, 9.20832615e-03, 2.81091879e-02, ...,\n",
       "         1.35538769e-02, 8.57554104e-03, 1.40087593e-02],\n",
       "        [1.30328001e-02, 7.32159953e-03, 1.98640489e-02, ...,\n",
       "         1.41113434e-02, 1.14396675e-02, 6.97467863e-03],\n",
       "        [2.11205019e-02, 1.19055594e-02, 1.12976359e-02, ...,\n",
       "         1.05346837e-02, 3.04618876e-03, 1.26411595e-02],\n",
       "        ...,\n",
       "        [1.02563938e-02, 9.86471814e-03, 8.83255560e-03, ...,\n",
       "         3.87446151e-03, 4.87224069e-03, 7.57702405e-03],\n",
       "        [2.43406920e-02, 1.52240772e-02, 2.12559682e-02, ...,\n",
       "         1.18651616e-02, 4.61612253e-03, 9.67017014e-03],\n",
       "        [1.15324568e-02, 1.76127837e-02, 2.62345257e-02, ...,\n",
       "         1.88107119e-02, 7.81938170e-03, 1.63753706e-02]],\n",
       "\n",
       "       [[5.30141009e-03, 1.01818177e-02, 2.59819453e-02, ...,\n",
       "         1.39875225e-02, 5.95806323e-03, 1.24301755e-02],\n",
       "        [1.86989360e-02, 7.49724496e-03, 1.76875130e-02, ...,\n",
       "         1.70344615e-02, 9.47903462e-03, 7.77303821e-03],\n",
       "        [2.13573416e-02, 3.88038500e-03, 1.36172627e-02, ...,\n",
       "         7.86001216e-03, 3.97953974e-03, 9.21959398e-03],\n",
       "        ...,\n",
       "        [7.49218629e-03, 1.44808231e-02, 8.98753111e-03, ...,\n",
       "         2.40392540e-03, 1.95676573e-03, 5.88964852e-03],\n",
       "        [1.20523991e-02, 1.03280744e-02, 2.48669668e-02, ...,\n",
       "         7.27679722e-03, 6.37235888e-03, 1.00898035e-02],\n",
       "        [1.55702381e-02, 1.71848225e-02, 2.26751183e-02, ...,\n",
       "         1.04292305e-02, 8.05918214e-03, 1.12019157e-02]],\n",
       "\n",
       "       [[2.43802393e-03, 1.07270865e-02, 1.95873242e-02, ...,\n",
       "         1.22279508e-02, 6.64596999e-03, 9.19161582e-03],\n",
       "        [1.62308875e-02, 9.15067522e-03, 1.87138798e-02, ...,\n",
       "         2.22323145e-02, 5.74643904e-03, 5.97240159e-03],\n",
       "        [1.55138964e-02, 6.15480242e-03, 1.51881877e-02, ...,\n",
       "         8.72382503e-03, 5.35483748e-03, 7.75587061e-03],\n",
       "        ...,\n",
       "        [8.87686345e-03, 2.22033887e-02, 1.07126542e-02, ...,\n",
       "         3.27841697e-03, 3.45106535e-03, 3.70733787e-03],\n",
       "        [1.58270980e-02, 1.06391828e-02, 1.91796522e-02, ...,\n",
       "         7.65232474e-03, 7.86368045e-03, 5.49097932e-03],\n",
       "        [1.85688681e-02, 1.94846189e-02, 1.73287754e-02, ...,\n",
       "         1.09280877e-02, 7.65717869e-03, 9.67200413e-03]]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chDataFrameMean['60.0']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "114595e0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7f5fd3807fa0>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAIAAAAD6CAYAAABph09sAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8qNh9FAAAACXBIWXMAAAsTAAALEwEAmpwYAAAS9klEQVR4nO2dfawl5V3HP99z7929+8Yu67JIF+wSpSixpdYtoGhtUdoFjatRY0FLS9psTFpTjYmgf2hM/8E0Nk1TW7KpBIgvpKnUIsEixSI2LciLuLwVuqUUFsiuC+z7y733nJ9/zNy9Z17uneecM3PO3DO/TzLZ88x55pnZOb/7PL/5Pb/nOzIznObSGvUFOKPFDaDhuAE0HDeAhuMG0HDcABrOQAYgabuk5yTtkXRjWRflDA/1GweQNAE8D1wJ7AUeAa4xs2cWO2Zy1RpbsW7j6XJnKlvHJlI7WjnXp1S5k24kXSGniXbOvq52Zg+9wdzxY8UNLcEH3rfGXn8j50Q5PLb71L1mtn2Q8/XD5ADHXgLsMbMXACTdAewAFjWAFes2cuFv/fHp8tHzsnVm1yd/TZtO/7qgFcmbaieS/w2dynZsSjWz4lC2zuTxhc8v3PaZ7MX1yIE32jx877lBdafO+f6mgU/YB4MYwBbg5a7yXuDSwS5n3DDaljXgOjGIAeR1j5n+WtJOYCfA1NozBzjd8sOATvaW1IpBDGAv0N2Jnwu8mq5kZruAXQDTbznPTmxasJvOZPHN2fijhzL7Dh9ZnShvPvtAorzvqc2ZY9prkn+JOaMErbydA9LJOCj1YhADeAS4QNL5wCvAB4FrS7mqMcEwZsd1CDCzOUmfAO4FJoBbzOzp0q5sDDCgPcZDAGZ2D3BPSdcyloyzD+AUYEC75vkWQzUAm4CZMxfGxM509uas2Hw8UV6zYjZTZ3rj4UT5ojP3JcoHzlubOaZzYFXyWlZmx+YTb1m4nrwgVT/U2wPwHqBSDBtvH8BZGjOYrffv7wZQLaKdGy+rD0M1ABlMnFq4Ie312YmSDWtPJMrvOXtPps5rJ9cnylOtZDvv2fr9zDGPr96SKB9KBZMApp5b2Ke5zNc9Y0DHe4Bm4z1Ag4kCQW4AjcWAWat30tVoDWAy+5S8df0bifLq1kymzs+vT/oFW6beTJS/eeSnMsfMzCX/q+2T2f/6RPfkVAl/uIZo1zzrznuAiukEZCeNEjeACnEfoPGItvsAzSXKCHIDOI3aMHVkoUucaWe7x1UTycmfiXQ2J/D26ZcT5UtWJmduvnU0G2B6++bXEuUnOSdTx57bsHCtJczimImZTJpzvfAeoGI67gM0l8gJ9CGgwbgTmMAmYGZ9V7BlLntzHt771kS5vSXbhW5b/UKifLyTnED6ubXfyxyz59hZifKxw9OZOhMbuxJCShi63Ql0aHsgqLkYYtbqfYvrfXXLnOXgBNb76pY5hmhb2FaEpFsk7Zf01CLfS9Ln4qX6uyW9K+Qah9oDTJyC9V3+2cG3ZT2tExPJ7N2N5x/P1Hm9ncz6vevYGYnyg4cvzBzz8JM/kShPvZk995pXFn6IiWwycl+U6ATeCnweuH2R768CLoi3S4EvErBY14eACjGjtMdAM3tQ0tYlquwAbrdI8OEhSRsknWNmry1xjBtAlUROYPDz5CZJj3aVd8ULa0PJW66/BXADGCU9OIEHzGzbAKcKWq6fZqgG0DplnPHDhQyf1tyKTJ2j5yYndu5Zd1GmzkNnbE2UDx5N+g2njq7MHDN1MPmXuObl7P1as29hEqlVgg9gaJgJIUHL9dP4U0DFtGkFbSVwF3Bd/DRwGXCoaPwHHwIqJVoXUM7fmKR/At5L5CvsBf4SmAIws5uJVmlfDewBjgPXh7TrBlAp5a0MMrNrCr434OO9tjtcH2C2zcq9C5IvK1/O+ihrtiZ1hA7vW5Opc2pVMg6wKvX9GUez7a7+v+RSn1bOor2pg6cWvp8ZPCMkSgv3hJDGYqbShoCqcAOoGM8HaDBRPkC9p4MLzTNvEkLSRkn3Sfpe/G+zBACDiTKCQrZREdID3Ep2EuJG4H4zuykWib4RuKGwpdk52P/66aLOyEq5TO9Nyr9MHkm7eDCzIRlAUsAa7BVvnkqUJw6dyNTR8ZMLn2cHXx8ePQYu8x7AzB4E3kjt3gHcFn++DfiNci9rPJifCwjZRkW/PsDZ81EmM3tNUlaaM6ZbKna6lf2LH3canxPYLRW7fvKsmutllEs0HVzvIaBfA9g3P9cs6Rxgf9BRE63kuD+R7fpmN6akW5S9gUqZUXtV8q9s4mTWzubWpvyGHFOcOHkqu3NAlr0PsAh3AR+OP38Y+Fo5lzNeRLOBraBtVBT2AItMQtwEfFnSR4GXgN+p8iKXK2OhELLEJMQvl3wtY4iHgpN0OtixhSRPrco+40+9fixRnt2cI/s6mRxX05Ju7ZXZcXfqcGoy6OCxTB3rigOUpe9W90igh4IrZJyfApxAfAhoMEPOCewLN4AKMWDOe4Au2h3s6ILzlecEkpqE0Ww2M2d6f3IixyaTN3lubVbsPz1hpLmcFzqu7AoW5QSg+sGHgCZjPgQ0muWQEOIGUDHeA3QjQWthTLScyRdbnwz8tGZyxurUi5jSdTSX9RtaJ1NLfWayS3/sZFcgqIT3/S2HhBDvASrEEHMddwIbjfsATcZ8CMjSnQTSyt6c1pHUJM3RnBvYSnarlpKKbeW8rLF19GRyRyc7xid8khImg9wHcGpvAPX2UJY5hmh3WkFbCJK2S3ouFoK6Mef79ZL+VdL/SnpaUuEKYTeAiumgoK0ISRPA3xKJQV0EXCMprZ7xceAZM7uYKIvrbyRlVTi6cAOoEIudwJAtgEuAPWb2gpnNAHcQrc9InBJYJ0nAWqL1HEuucBmuD2AGs10BmFNZ+7O51GTQVM5bnKezEjCJY9JBH8g4fXYiuzKIdldAqaS3flu4D1AkEpUnApWWgfs8UcLuq8A64HfNlo5ouRNYKT1NBhWJRIWIQH0AeAK4Avhx4D5J/2Vmh9MHzuNDQMWYKWgLIEQE6nrgTovYA/wA+MmlGnUDqBAzaHcUtAXwCHCBpPNjx+6DRN19Ny8RZ2tLOhu4EHiBJajfEJBelTud1fVPB4J0MvVyyZzx2w4eSu7QcGy/rFCwmc1J+gRwLzAB3GJmT0v6g/j7m4FPAbdKepJoyLjBzA4s1W79DGCMMHpyAovbM7uHSA2se9/NXZ9fBd7fS5tuAJXiGUGNp6SnycoYaUJI7ji8Kvncn0jSmCeVzKHVST+he/XRaVYmYwd25GimirUXHpnL+t3KHAKqwHuAComeAur9oOUGUDE+BDQcHwIajBEc5RsZw58M6pqUyZuQSd+u9OQQgCaT/Wr3aiPIzzbO9MV5K39yMpQGpeYjgPcAlWJgYWHekeEGUDF1HwJCpGLPk/RNSc/GaUafjPe7XGwAZmHbqAjpAeaAPzGzxyWtAx6TdB/wEXqUizUsMaZrMnt6m0lO7OT5AOnkDk0F/DdSknTp8wDJhJASRu+y5wKqIEQq9jUzezz+fAR4lig7xeViizDAFLaNiJ58gPjFhT8DPEwPcrFNZmwCQZLWAv8M/JGZHVaggEJCK5jVBbXHDdX+KSAoUC1piujH/wczuzPevS+WiWUpuVgz22Vm28xs25SWTuYcSyxwGxEhSqEC/g541sw+0/XVvFzsTQTLxSoxA9g9+9ZVI0k7Z3l4qvfJ3L+8Y9LkLP2y7n1l/ChWfycwZAi4HPgQ8KSkJ+J9f47LxYax3H0AM/sW+SnJ4HKxASz/HsAZhMGFRipl6JNBNteVzZOXEZRayJIXLMq8ZyA15udOIKVXGJUgAVPIfBygxngPUDFjEwdw+sQNoOH4EJCia9zPncQJGM9Jxw/S43meb5GODeS8r4i58t8ZlPduojrhPUCVmKDmoWA3gKrxHqDh1NwA6r1qYRwocTKoSCQqrvNeSU/E2Vv/WdTm8HuAboctYNLGcieDiu5YzjHpjN+iSaaSJoPKegroEom6kkgs4hFJd5nZM111NgBfALab2UshORreA1SMLGwLIEQk6loihZCXAMys8I2ubgBVEz4EbJL0aNe2M9VSnkjUllSdtwFnSnpA0mOSriu6PHcCK6aHOEAZIlGTwM8SzdKuAr4j6SEze36xRkfgAyxcc974rlSARpM5MnGZNosndiz9LqKcVUCJc5e2Pry0OECISNReIkM6BhyT9CBwMbCoAfgQUCWh3X+YsYWIRH0N+EVJk5JWE+kIPrtUoz4EVE1JPUmISJSZPSvp68BuokyEL5nZU0u16wZQMSox7aBIJCoufxr4dGibozWAvMnyEPm21JifGysoamKu4E+zNB+gpHYqwnuACunhGX9kuAFUjecDNBzvAZqNDwE9ksgaBrQi+8ILS79MMu045gWGRpGdaeU+BVRB7Qxg7PAeoOG4ATQb9wGWopWTmdtJZQWn3x+QRz+rfALO3QS8B6ga7wEajD8FON4DLEXemJvWHgoZ30PiAOkxfwjjvXAn0HEDaDA+G+jUXSEkRCt4WtJ/d72S/K/i/a4VHECJ6wIqISQp9BRwRfxK8ncC2yVdRqQNfL+ZXQDcH5d7Q8puoerKia2T2gLq5J27CmquExiiFWxmNv+Kral4M1wruJhys4IrIVQpdCLWCNwP3GdmGa1gIHcdmqSd86tdZilfgKHujMMQgJm1zeydRIsRLpH006EnSEjF4lKxdesBenoKMLODkh4AthNrBcdK4YtqBRc02PMhQE6wKKCdkHcGVUDdQ8EhTwFnxcuOkbQK+BXguyxoBUOwVnDDWAY+QEgPcA5wW7w+vQV82czulvQdXCt4SUTdhWLDtIJ3E70kIr3/dVwruBiPBDYbDwXXhLTmcD/Lyfqi5gbgy8OrJE4ICdlCCBGJiuu9W1Jb0m8XtekGUDUlPQV0iURdBVwEXCPpokXq/TXRMvJC3AAqZsgiUQB/SPR+p6C4TP19gLyATR8BpFzN4WEQfqmbJD3aVd5lZru6ynkiUZd2NyBpC/CbwBXAu0NOWn8DWOYMWSTqs8ANZtYOfa2fG0CVGGUmhISIRG0D7oh//E3A1ZLmzOxfFmvUDaBCSk4KPS0SBbxCJBJ1bXcFMzv/9LmlW4G7l/rxYTkYQMh438/k0LBWBpVkACEiUf20W38DWOaoxGXpISJRXfs/EtKmG0CVjHimLwQ3gIrxuYCGU/eEkOYawDBeHAk+BDSaESd8huAGUDVuAM3FVweXQchkUD/P2rk6xSW/MwhQp94WUH8DWM54HMDxx8Cm4z3AgAxT4rWCc7kT2GSM4RpwH7gBVIz7AA3G4wBNZ16dpMa4AVSM9wBNxw2g2XgP0GQMaNfbAppjAP1kDpdx2nr//g0ygFHhTwHNpu49QPDq4Fgr8H8k3R2XXSq2iGUgEtXL8vBPknwX/eBSscMkLR07BASobUHbqAhVCj0X+FXgS127d+BSsYXILGgbFaE9wGeBPyW51tWlYosYhyFA0q8B+83ssX5O0GypWMsOPYttIyKkB7gc+HVJLxLJklwh6e+JpWIB+paKbQBlikUXiURJ+j1Ju+Pt25IuLmozRC7+z8zsXDPbSrQm/T/M7PdxqdgwSuoBAkWifgD8kpm9A/gUsIsCBokD3IRLxS6NUaaHf1okCkDSvEjUM6dPZ/btrvoPEamILEmvauEPAA/En10qNoQhikSl+Cjwb0Un9UhgxfTwiFeGSFRUUXofkQH8QtFJ3QCqpjwPP0QkCknvIIrXXBX30kviQpFVMq8SFrIVc1okStIKIof8ru4Kkn4MuBP4kJk9H9Ko9wAVIsqL8gWKRP0F8CPAF2KpuLmCYcUNoHI65eWFF4lEmdnHgI/10qYbQJWUKxRZCW4AFTPKiZ4Q3ACqxg2gyfjCkGbjWcGO+wBNxw2gwRjgIlFNxp1Axw2gwRjQrnco0A2gUmx4otR94gZQNT4ENBh/CnC8B2g6bgANxgyG9Zr6PnEDqBrvARqOG0CTMX8KaDQG5oGghuOh4AZjVmpaeBW4AVSNO4HNxrwHaDKeENJslsFkkK8OrhADrN0O2kII0AiSpM/F3++W9K6iNt0AqsTihJCQrYBAjaCrgAvibSfwxaJ23QAqxjoWtAVwWiPIzGaIFNt2pOrsAG63iIeADfNKbovhBlA1JfUA5GsEbemjToKhOoFHePPAN+wrPwQ2AQcqOEWZ7b510AaO8Oa937CvbAqsPl0gEhWiERSsIzTPUA3AzM4CkPRokXJFP1TVbr+Y2fYSmwvRCArSEerGh4DlQ6FGUFy+Ln4auAw4NK/nvBgeB1gmBGoE3QNcDewBjgPXF7UrG0GkStLO1PhW63bHmZEYgFMf3AdoOEM3gKJwZp9tvijpSUlPpB6lnAKGOgTE4czngSuJHlkeAa4xs2eWPLC43ReBbWZWRWxhrBl2DxASznSGyLANoOdQZSAG/LukxyTtLKG9xjDsOEDPocpALjezVyVtBu6T9F0ze7CEdseeYfcAPYcqQzCzV+N/9wNfJRpqnACGbQAh4cyekLRG0rr5z8D7gacGvtKGMOzJoNxw5oDNng18NZZHnwT+0cy+PmCbjcEjgQ3HI4ENxw2g4bgBNBw3gIbjBtBw3AAajhtAw3EDaDj/DwktsBu90CuEAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# parameters: energy 1000eV and postion 0 on z-axis\n",
    "plt.imshow(chDataFrameMean['20.0'][:,9,:]);\n",
    "plt.colorbar()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "50080b96",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28.009307728017333\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 7 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "backvmax = chDataFrameMean['20.0'][:,:,4].max()\n",
    "print (backvmax)\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "#fig, ax = plt.subplots(1, 4)\n",
    "\n",
    "ax0 = fig.add_subplot(1, 5, 1)\n",
    "ax0.title.set_text('Source at 662 keV')\n",
    "source = ax0.imshow((chDataFrameMean['67.0'][:,:,4]),\n",
    "                      cmap='gist_ncar_r',interpolation='bessel');\n",
    "ax0.plot([1,8],[9,9],'g')\n",
    "ax0.plot([1,8],[39,39],'g')\n",
    "ax0.plot([1,1],[9,39],'g')\n",
    "ax0.plot([8,8],[9,39],'g')\n",
    "ax0.plot([4.5],[8.5],'rx',ms=10,markeredgewidth=3)\n",
    "fig.colorbar(source,ax=ax0)\n",
    "\n",
    "text0 = fig.add_subplot(1, 5, 2)\n",
    "\n",
    "text0.xaxis.set_visible(False)\n",
    "text0.yaxis.set_visible(False)\n",
    "for spine in ['top', 'right', 'left', 'bottom']:\n",
    "    text0.spines[spine].set_visible(False)\n",
    "\n",
    "text0.text(0.1,0.4,'The green\\nsquare contains\\nlead', fontsize=18)\n",
    "\n",
    "ax1 = fig.add_subplot(1, 5, 3)\n",
    "ax1.title.set_text('Backscatter 200keV\\nat middle plane')\n",
    "backscatter = ax1.imshow((chDataFrameMean['20.0'][:,:,4]),vmax = backvmax,\n",
    "                           cmap='gist_ncar_r',interpolation='bessel');\n",
    "ax1.plot([1,8],[9,9],'g')\n",
    "ax1.plot([1,8],[39,39],'g')\n",
    "ax1.plot([1,1],[9,39],'g')\n",
    "ax1.plot([8,8],[9,39],'g')\n",
    "ax1.plot([4.5],[8.5],'rx',ms=10,markeredgewidth=3)\n",
    "#fig.colorbar(backscatter,ax=ax1)\n",
    "\n",
    "\n",
    "\n",
    "ax2 = fig.add_subplot(1, 5, 4)\n",
    "ax2.title.set_text('Backscatter 200keV\\nat quarter plane')\n",
    "backscatter = ax2.imshow((chDataFrameMean['20.0'][:,:,2]),vmax = backvmax,\n",
    "                           cmap='gist_ncar_r',interpolation='bessel');\n",
    "ax2.plot([1,8],[9,9],'g')\n",
    "ax2.plot([1,8],[39,39],'g')\n",
    "ax2.plot([1,1],[9,39],'g')\n",
    "ax2.plot([8,8],[9,39],'g')\n",
    "ax2.plot([4.5],[8.5],'rx',ms=10,markeredgewidth=3)\n",
    "#fig.colorbar(backscatter,ax=ax[2])\n",
    "\n",
    "ax3 = fig.add_subplot(1, 5, 5)\n",
    "ax3.title.set_text('Backscatter 200keV\\nat lowest plane')\n",
    "backscatter = ax3.imshow((chDataFrameMean['20.0'][:,:,0]),vmax = backvmax,\n",
    "                           cmap='gist_ncar_r',interpolation='bessel');\n",
    "ax3.plot([1,8],[9,9],'g')\n",
    "ax3.plot([1,8],[39,39],'g')\n",
    "ax3.plot([1,1],[9,39],'g')\n",
    "ax3.plot([8,8],[9,39],'g')\n",
    "ax3.plot([4.5],[8.5],'rx',ms=10,markeredgewidth=3)\n",
    "fig.colorbar(backscatter,ax=ax3)\n",
    "\n",
    "plt.tight_layout()\n",
    "plt.savefig('backscatter_Pb.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "a9c6c141",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{0: 0,\n",
       " 1: 0,\n",
       " 2: 0,\n",
       " 3: 0,\n",
       " 4: 0,\n",
       " 5: 0,\n",
       " 6: 0,\n",
       " 7: 0,\n",
       " 8: 0,\n",
       " 9: 0,\n",
       " 10: 0,\n",
       " 11: 0,\n",
       " 12: 0,\n",
       " 13: 0,\n",
       " 14: 0,\n",
       " 15: 0,\n",
       " 16: 0,\n",
       " 17: 0,\n",
       " 18: 0,\n",
       " 19: 0,\n",
       " 20: 0,\n",
       " 21: 0,\n",
       " 22: 0,\n",
       " 23: 0,\n",
       " 24: 0,\n",
       " 25: 0,\n",
       " 26: 0,\n",
       " 27: 0,\n",
       " 28: 0,\n",
       " 29: 0,\n",
       " 30: 0,\n",
       " 31: 0,\n",
       " 32: 0,\n",
       " 33: 0,\n",
       " 34: 0,\n",
       " 35: 0,\n",
       " 36: 0,\n",
       " 37: 0,\n",
       " 38: 0,\n",
       " 39: 0,\n",
       " 40: 0,\n",
       " 41: 0,\n",
       " 42: 0,\n",
       " 43: 0,\n",
       " 44: 0,\n",
       " 45: 0,\n",
       " 46: 0,\n",
       " 47: 0,\n",
       " 48: 0,\n",
       " 49: 0}"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating new dictionary (layerSpectr) template for sum of spectrum values for each slice in z-axis\n",
    "layer = np.arange(50)\n",
    "layerSpectr = dict.fromkeys(layer,0)\n",
    "layerSpectr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "60af6375",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Inserting spectrum in layerSpectr\n",
    "layer = np.arange(50)\n",
    "layerSpectr = dict.fromkeys(layer,0)\n",
    "\n",
    "for i,j in enumerate(layer):\n",
    "    spectr = []\n",
    "    for m,n in enumerate(chDataFrameMean):\n",
    "\n",
    "        spectr = spectr+[np.sum(chDataFrameMean[n][j,1:9,1:9])]\n",
    "        \n",
    "    layerSpectr[j]=spectr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "05f64ed6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([     0.,      0.,  10000.,  10000.,  20000.,  20000.,  30000.,\n",
       "        30000.,  40000.,  40000.,  50000.,  50000.,  60000.,  60000.,\n",
       "        70000.,  70000.,  80000.,  80000.,  90000.,  90000., 100000.,\n",
       "       100000., 110000., 110000., 120000., 120000., 130000., 130000.,\n",
       "       140000., 140000., 150000., 150000., 160000., 160000., 170000.,\n",
       "       170000., 180000., 180000., 190000., 190000., 200000., 200000.,\n",
       "       210000., 210000., 220000., 220000., 230000., 230000., 240000.,\n",
       "       240000., 250000., 250000., 260000., 260000., 270000., 270000.,\n",
       "       280000., 280000., 290000., 290000., 300000., 300000., 310000.,\n",
       "       310000., 320000., 320000., 330000., 330000., 340000., 340000.,\n",
       "       350000., 350000., 360000., 360000., 370000., 370000., 380000.,\n",
       "       380000., 390000., 390000., 400000., 400000., 410000., 410000.,\n",
       "       420000., 420000., 430000., 430000., 440000., 440000., 450000.,\n",
       "       450000., 460000., 460000., 470000., 470000., 480000., 480000.,\n",
       "       490000., 490000., 500000., 500000., 510000., 510000., 520000.,\n",
       "       520000., 530000., 530000., 540000., 540000., 550000., 550000.,\n",
       "       560000., 560000., 570000., 570000., 580000., 580000., 590000.,\n",
       "       590000., 600000., 600000., 610000., 610000., 620000., 620000.,\n",
       "       630000., 630000., 640000., 640000., 650000., 650000., 660000.,\n",
       "       660000., 670000., 670000.])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Example building x-axis array for bar chart\n",
    "xEnergy = np.repeat(np.linspace(0,670000,68),2)\n",
    "xEnergy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "c7ad4a85",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.23229657891099584,\n",
       " 4.641784956468201,\n",
       " 0.11194237589904577,\n",
       " 0.2376604727131999,\n",
       " 0.3398452727799568,\n",
       " 0.8032984973699759,\n",
       " 2.5956642889605717,\n",
       " 156.7649971202199,\n",
       " 57.43067901439992,\n",
       " 0.2449850617254833,\n",
       " 0.3502692868385072,\n",
       " 0.44037840423593505,\n",
       " 0.6074608621107196,\n",
       " 1.0917916981471434,\n",
       " 1.907543831499225,\n",
       " 3.1695612840303635,\n",
       " 5.490090565151599,\n",
       " 10.265620045440604,\n",
       " 31.523460759313952,\n",
       " 53.751586853284465,\n",
       " 54.40180053447773,\n",
       " 44.442232706256746,\n",
       " 31.95414858332838,\n",
       " 21.172948610773595,\n",
       " 12.965446293157118,\n",
       " 7.132987341600977,\n",
       " 4.4942655819206125,\n",
       " 2.9470050268806407,\n",
       " 1.9945740162238514,\n",
       " 1.331594259985916,\n",
       " 1.2139437897243948,\n",
       " 0.921984081218845,\n",
       " 0.6150852610268871,\n",
       " 0.5228770348919884,\n",
       " 0.5375242458168704,\n",
       " 0.46996285471042687,\n",
       " 0.3825794573158319,\n",
       " 0.35995400026650376,\n",
       " 0.29210674694429856,\n",
       " 0.3182204045607018,\n",
       " 0.23891100153718653,\n",
       " 0.11990466616542172,\n",
       " 0.15015498237780292,\n",
       " 0.12410084433128236,\n",
       " 0.11336925044111558,\n",
       " 0.10229866779820725,\n",
       " 0.1086300217676637,\n",
       " 0.08054612118205465,\n",
       " 0.04910157171498612,\n",
       " 0.06496931593701488,\n",
       " 0.07643319731469303,\n",
       " 0.05602408129920054,\n",
       " 0.06129224319530323,\n",
       " 0.06262429605431227,\n",
       " 0.0342279171683358,\n",
       " 0.06662638410605268,\n",
       " 0.05433438481282758,\n",
       " 0.07116263965758524,\n",
       " 0.058140395028645805,\n",
       " 0.036774862752209286,\n",
       " 0.06267990810246436,\n",
       " 0.040644908969132934,\n",
       " 0.043450947724383335,\n",
       " 0.042869600972546995,\n",
       " 0.023792072532352264,\n",
       " 0.03560005890014815,\n",
       " 22935.349280627448]"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layerSpectr[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "4ed8e385",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1=np.array(np.repeat(layerSpectr[0],2))\n",
    "y2=np.array(np.repeat(layerSpectr[8],2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9c2ccf91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(134,)"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "f526bb84",
   "metadata": {},
   "outputs": [],
   "source": [
    "y1 = np.insert(y1, 0, 0., axis=0) #insert the value zero as first element\n",
    "y1 = np.concatenate([y1, np.zeros(1)]) #add the value zero as last element\n",
    "y2 = np.insert(y2, 0, 0., axis=0)\n",
    "y2 = np.concatenate([y2, np.zeros(1)])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5ae43b8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.00000000e+00, 2.32296579e-01, 2.32296579e-01, 4.64178496e+00,\n",
       "       4.64178496e+00, 1.11942376e-01, 1.11942376e-01, 2.37660473e-01,\n",
       "       2.37660473e-01, 3.39845273e-01, 3.39845273e-01, 8.03298497e-01,\n",
       "       8.03298497e-01, 2.59566429e+00, 2.59566429e+00, 1.56764997e+02,\n",
       "       1.56764997e+02, 5.74306790e+01, 5.74306790e+01, 2.44985062e-01,\n",
       "       2.44985062e-01, 3.50269287e-01, 3.50269287e-01, 4.40378404e-01,\n",
       "       4.40378404e-01, 6.07460862e-01, 6.07460862e-01, 1.09179170e+00,\n",
       "       1.09179170e+00, 1.90754383e+00, 1.90754383e+00, 3.16956128e+00,\n",
       "       3.16956128e+00, 5.49009057e+00, 5.49009057e+00, 1.02656200e+01,\n",
       "       1.02656200e+01, 3.15234608e+01, 3.15234608e+01, 5.37515869e+01,\n",
       "       5.37515869e+01, 5.44018005e+01, 5.44018005e+01, 4.44422327e+01,\n",
       "       4.44422327e+01, 3.19541486e+01, 3.19541486e+01, 2.11729486e+01,\n",
       "       2.11729486e+01, 1.29654463e+01, 1.29654463e+01, 7.13298734e+00,\n",
       "       7.13298734e+00, 4.49426558e+00, 4.49426558e+00, 2.94700503e+00,\n",
       "       2.94700503e+00, 1.99457402e+00, 1.99457402e+00, 1.33159426e+00,\n",
       "       1.33159426e+00, 1.21394379e+00, 1.21394379e+00, 9.21984081e-01,\n",
       "       9.21984081e-01, 6.15085261e-01, 6.15085261e-01, 5.22877035e-01,\n",
       "       5.22877035e-01, 5.37524246e-01, 5.37524246e-01, 4.69962855e-01,\n",
       "       4.69962855e-01, 3.82579457e-01, 3.82579457e-01, 3.59954000e-01,\n",
       "       3.59954000e-01, 2.92106747e-01, 2.92106747e-01, 3.18220405e-01,\n",
       "       3.18220405e-01, 2.38911002e-01, 2.38911002e-01, 1.19904666e-01,\n",
       "       1.19904666e-01, 1.50154982e-01, 1.50154982e-01, 1.24100844e-01,\n",
       "       1.24100844e-01, 1.13369250e-01, 1.13369250e-01, 1.02298668e-01,\n",
       "       1.02298668e-01, 1.08630022e-01, 1.08630022e-01, 8.05461212e-02,\n",
       "       8.05461212e-02, 4.91015717e-02, 4.91015717e-02, 6.49693159e-02,\n",
       "       6.49693159e-02, 7.64331973e-02, 7.64331973e-02, 5.60240813e-02,\n",
       "       5.60240813e-02, 6.12922432e-02, 6.12922432e-02, 6.26242961e-02,\n",
       "       6.26242961e-02, 3.42279172e-02, 3.42279172e-02, 6.66263841e-02,\n",
       "       6.66263841e-02, 5.43343848e-02, 5.43343848e-02, 7.11626397e-02,\n",
       "       7.11626397e-02, 5.81403950e-02, 5.81403950e-02, 3.67748628e-02,\n",
       "       3.67748628e-02, 6.26799081e-02, 6.26799081e-02, 4.06449090e-02,\n",
       "       4.06449090e-02, 4.34509477e-02, 4.34509477e-02, 4.28696010e-02,\n",
       "       4.28696010e-02, 2.37920725e-02, 2.37920725e-02, 3.56000589e-02,\n",
       "       3.56000589e-02, 2.29353493e+04, 2.29353493e+04, 0.00000000e+00])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "51c8a2a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(xEnergy,y1,'b')\n",
    "plt.plot(xEnergy,y2,'g')\n",
    "plt.plot([184000,184000],[0,10000],'r')\n",
    "plt.plot([478000,478000],[0,10000],'r')\n",
    "plt.plot([75000,75000],[0,10000],'r')\n",
    "plt.plot([32200,32200],[0,10000],'r')\n",
    "plt.text(184000,10500,\"Backscatter\")\n",
    "plt.text(478000,10500,\"Compton's edge\")\n",
    "plt.text(75000,10500,\"K X-rays\")\n",
    "plt.yscale('log')\n",
    "plt.xlabel('eV')\n",
    "plt.ylabel('flux')\n",
    "#plt.ylim(0,1000)\n",
    "plt.savefig('backscatter_Si_Cs137_200keV.jpeg')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "1cdaadfa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames = 50  # number of frames in the animation equal number of mesh layer in z-axis\n",
    "y1=0\n",
    "\n",
    "fig, ax = plt.subplots()\n",
    "\n",
    "y1 = np.array(np.repeat(layerSpectr[0],2))\n",
    "y1 = np.insert(y1, 0, 0., axis=0) # add zero to front\n",
    "y1 = np.concatenate([y1, np.zeros(1)]) # add zero at back\n",
    "\n",
    "line, = ax.plot(xEnergy,y1)\n",
    "ax.plot([184000,184000],[0,10000],'r')\n",
    "ax.plot([478000,478000],[0,10000],'r')\n",
    "ax.plot([75000,75000],[0,10000],'r')\n",
    "\n",
    "ax.text(184000,10500,\"Backscatter\")\n",
    "ax.text(478000,10500,\"Compton's edge\")\n",
    "ax.text(75000,10500,\"K X-rays\")\n",
    "\n",
    "\n",
    "label = ax.text(3.5e5, 2e7, str(0.0)+' cm', fontsize=10)\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim(0.001,1e7)\n",
    "ax.set_xlabel('ev')\n",
    "ax.set_ylabel('flux')\n",
    "\n",
    "y1=0\n",
    "\n",
    "def animate(i):\n",
    "        \n",
    "    y1 = np.array(np.repeat(layerSpectr[i],2))\n",
    "    y1 = np.insert(y1, 0, 0., axis=0)\n",
    "    y1 = np.concatenate([y1, np.zeros(1)])\n",
    "    \n",
    "    line.set_ydata(y1)  # update the data.\n",
    "    label.set_text(str((i+1)/10)+' cm')\n",
    "    \n",
    "    y1=0\n",
    "\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=frames,interval=500)#, blit=True)\n",
    "anim.save('Cs137_Pbspectrum.gif', writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "31b226ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "frames = 50  # number of frames in the animation equal number of mesh layer in z-axis\n",
    "y1=0\n",
    "\n",
    "fig = plt.figure(figsize=(12, 6))\n",
    "\n",
    "#fig, ax = plt.subplots(1, 4)\n",
    "\n",
    "ax = fig.add_subplot(1, 2, 1)\n",
    "\n",
    "y1 = np.array(np.repeat(layerSpectr[0],2))\n",
    "y1 = np.insert(y1, 0, 0., axis=0) # add zero to front\n",
    "y1 = np.concatenate([y1, np.zeros(1)]) # add zero at back\n",
    "\n",
    "line, = ax.plot(xEnergy,y1)\n",
    "ax.plot([184000,184000],[0,10000],'r')\n",
    "ax.plot([478000,478000],[0,10000],'r')\n",
    "ax.plot([75000,75000],[0,10000],'r')\n",
    "\n",
    "ax.text(184000,10500,\"Backscatter\")\n",
    "ax.text(478000,10500,\"Compton's edge\")\n",
    "ax.text(75000,10500,\"K X-rays\")\n",
    "\n",
    "label = ax.text(3.5e5, 2e7, str(0.0)+' cm', fontsize=10)\n",
    "ax.set_yscale('log')\n",
    "ax.set_ylim(0.001,1e7)\n",
    "ax.set_xlabel('ev')\n",
    "ax.set_ylabel('flux')\n",
    "\n",
    "#--------------------geometry--------------------\n",
    "ax0 = fig.add_subplot(1, 2, 2)\n",
    "\n",
    "ax0.title.set_text('Source at 662 keV')\n",
    "source = ax0.imshow((chDataFrameMean['67.0'][:,:,4]),\n",
    "                      cmap='gist_ncar_r',interpolation='bessel');\n",
    "ax0.plot([1,8],[9,9],'g')\n",
    "ax0.plot([1,8],[39,39],'g')\n",
    "ax0.plot([1,1],[9,39],'g', alpha=0.75)\n",
    "ax0.plot([8,8],[9,39],'g', alpha=0.75)\n",
    "\n",
    "ax0.plot([0,9],[0,0],'b')\n",
    "ax0.plot([0,9],[49,49],'b')\n",
    "ax0.plot([0,0],[0,49],'b')\n",
    "ax0.plot([9,9],[0,49],'b')\n",
    "\n",
    "ax0.plot([1,1],[0,49],'g--')\n",
    "ax0.plot([8,8],[0,49],'g--')\n",
    "\n",
    "ax0.plot([4.5],[8.5],'rx',ms=10,markeredgewidth=3)\n",
    "\n",
    "ax0.text(4,26,\"Pb\")\n",
    "ax0.text(4,6,\"Air\")\n",
    "ax0.text(4,46,\"Air\")\n",
    "\n",
    "line2, = ax0.plot([-1,10],[0,0],'r')\n",
    "\n",
    "\n",
    "y1=0\n",
    "\n",
    "def animate(i):\n",
    "        \n",
    "    y1 = np.array(np.repeat(layerSpectr[i],2))\n",
    "    y1 = np.insert(y1, 0, 0., axis=0)\n",
    "    y1 = np.concatenate([y1, np.zeros(1)])\n",
    "    \n",
    "    line.set_ydata(y1)  # update the data.\n",
    "    line2.set_ydata([i,i])\n",
    "    label.set_text(str((i+1)/10)+' cm')\n",
    "    \n",
    "    y1=0\n",
    "\n",
    "\n",
    "anim = animation.FuncAnimation(fig, animate, frames=frames,interval=500)#, blit=True)\n",
    "anim.save('Cs137_Pb_Geo_spectrum.gif', writer='imagemagick')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d6ac5fd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dict_keys([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "layerSpectr.keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "302564ea",
   "metadata": {},
   "source": [
    "Ref: https://physicsopenlab.org/2016/02/04/compton-scattering-2/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "687852c6",
   "metadata": {},
   "outputs": [],
   "source": [
    "Cs68 = []\n",
    "for i,j in enumerate(layerSpectr):\n",
    "    # print (layerSpectr[j][-1])\n",
    "    Cs68 = Cs68 + [layerSpectr[j][-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "565d1986",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5233b3c6a0>]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(np.log(Cs68))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e2d825d",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}