{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 8x8 BWR assembly at Clab\n",
    "\n",
    "This example is basically the same as the previous one, the only difference is that an 8x8 BWR assembly is being measured at the Clab storage facility in Sweden. There will be one detector with absorbers in front of it.\n",
    "\n",
    "We compare a case when the source locations are the center of the pins and an other case when 10 source locations are selected randomly in each pin."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from feign.geometry import *\n",
    "from feign.blocks import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Materials\n",
    "\n",
    "The fuel pins are made of uranium-dioxide, in zirconium cladding. The pool around the assembly is filled with water. Outside the pool air is considered (in reality there is ofc concrete walls, but the direct gamma ray won't pass through that, so no need to model those).\n",
    "\n",
    "In front of the detector, three absorber sheets are placed made of lead, aluminium and copper."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "uo2=Material('1')\n",
    "uo2.set_density(10.5)\n",
    "uo2.set_path(('/data/UO2.dat',1))\n",
    "\n",
    "he=Material('2')\n",
    "he.set_density(0.00561781)\n",
    "he.set_path(('/data/He.dat',1))\n",
    "\n",
    "zr=Material('3')\n",
    "zr.set_density(6.52)\n",
    "zr.set_path(('/data/Zr.dat',1))\n",
    "\n",
    "h2o=Material('4')\n",
    "h2o.set_density(1.0)\n",
    "h2o.set_path(('/data/H2O.dat',1))\n",
    "\n",
    "ss=Material('5')\n",
    "ss.set_density(8.02)\n",
    "ss.set_path(('/data/SS.dat',1))\n",
    "\n",
    "air=Material('6')\n",
    "air.set_density(0.001225)\n",
    "air.set_path(('/data/Air.dat',1))\n",
    "\n",
    "lead=Material('7')\n",
    "lead.set_density(11.34)\n",
    "lead.set_path(('/data/Pb.dat',1))\n",
    "\n",
    "copper=Material('8')\n",
    "copper.set_density(8.96)\n",
    "copper.set_path(('/data/Cu.dat',1))\n",
    "\n",
    "alu=Material('9')\n",
    "alu.set_density(2.7)\n",
    "alu.set_path(('/data/Al.dat',1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pins\n",
    "\n",
    "There are two types of pins in the assembly:\n",
    "\n",
    "- fuel pins\n",
    "- empty water channel"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel=Pin('1')\n",
    "fuel.add_region(uo2,0.52)\n",
    "fuel.add_region(he,0.53)\n",
    "fuel.add_region(zr,0.615)\n",
    "\n",
    "waterchannel=Pin('3')\n",
    "waterchannel.add_region(h2o,0.53)\n",
    "waterchannel.add_region(zr,0.615)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assembly\n",
    "\n",
    "While defining the assembly we set the pitch in cm, the coolant, create a pool around the assembly, and define the PWR fuelmap.\n",
    "\n",
    "The corner of the assembly is facing the pool, and the distance from the center of the assembly to the pool wall is 55 cm.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "bwrOrig=Assembly(8,8)\n",
    "bwrOrig.set_pitch(1.62)\n",
    "bwrOrig.set_source(uo2)\n",
    "bwrOrig.set_coolant(h2o)\n",
    "bwrOrig.set_surrounding(air)\n",
    "bwrOrig.set_pins(fuel,waterchannel)\n",
    "\n",
    "fuelmap= [['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '3', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1'], \n",
    "          ['1', '1', '1', '1', '1', '1', '1', '1']]\n",
    "\n",
    "bwrOrig.set_fuelmap(fuelmap)\n",
    "pool=Rectangle(Point(55, 55),Point(55, -55),Point(-55, -55),Point(-55, 55)).rotate(45)\n",
    "bwrOrig.set_pool(pool)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detector\n",
    "\n",
    "The detector point is facing the corner, and the distance of the detector from the center of the assembly is 247.1 cm.\n",
    "\n",
    "There is a collimator between the assembly and the detector. The back is 125cm from the center and has a 23.2 cm wide slit, and the front is 243cm from the center and has an 8.2 cm wide slit.\n",
    "\n",
    "An other detector point is facing the opposite corner."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "det=Detector('D')\n",
    "det.set_location(Point(174.726, 174.726))\n",
    "\n",
    "coll=Collimator('collD')\n",
    "coll.set_back(Segment(Point(125.0,-11.6),Point(125.0,11.6)).rotate(45))\n",
    "coll.set_front(Segment(Point(243.0,-4.1),Point(243.0,4.1)).rotate(45))\n",
    "det.set_collimator(coll)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Absorber\n",
    "\n",
    "We can define an absorber with a Rectangle or a Circle object. We have to set the material of the absorber and the material which is accommodating the absorber. There are 4 absorber sheets in front of the detector: 1mm of copper, 21mm of stainless steel, 3mm of aluminium and 8mm of lead\n",
    "\n",
    "There is a thin steel of 5mm on the pool side as well, to keep out the water from the collimator hole."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "steelwindow=Absorber('steelwindow')\n",
    "steelwindow.set_form(Rectangle(Point(55.0,-30),Point(55.0,30),Point(55.5,30),Point(55.5,-30)).rotate(45))\n",
    "steelwindow.set_material(ss)\n",
    "steelwindow.set_accommat(air)\n",
    "\n",
    "copper1mm=Absorber('copper1mm')\n",
    "copper1mm.set_form(Rectangle(Point(243.7, -40.0),Point(243.7, 40.0),Point(243.8,40),Point(243.8,-40)).rotate(45))\n",
    "copper1mm.set_material(copper)\n",
    "copper1mm.set_accommat(air)\n",
    "\n",
    "steel21mm=Absorber('steel21mm')\n",
    "steel21mm.set_form(Rectangle(Point(243.8,-40),Point(243.8,40),Point(245.9,40),Point(245.9,-40)).rotate(45))\n",
    "steel21mm.set_material(ss)\n",
    "steel21mm.set_accommat(air)\n",
    "\n",
    "alu3mm=Absorber('alu3mm')\n",
    "alu3mm.set_form(Rectangle(Point(245.9,-40),Point(245.9,40),Point(246.2,40),Point(246.2,-40)).rotate(45))\n",
    "alu3mm.set_material(alu)\n",
    "alu3mm.set_accommat(air)\n",
    "\n",
    "lead8mm=Absorber('lead8mm')\n",
    "lead8mm.set_form(Rectangle(Point(246.2,-40),Point(246.2,40),Point(247.0,40),Point(247.0,-40)).rotate(45))\n",
    "lead8mm.set_material(lead)\n",
    "lead8mm.set_accommat(air)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment\n",
    "\n",
    "Finally we re ready to perform define our experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex4=Experiment()\n",
    "ex4.set_assembly(bwrOrig)\n",
    "ex4.set_detectors(det)\n",
    "ex4.set_materials(uo2,he,zr,h2o,air,lead,alu,copper,ss)\n",
    "ex4.set_absorbers(steelwindow,lead8mm,steel21mm,alu3mm,copper1mm)\n",
    "\n",
    "elines=['0.4971',\n",
    " '0.563',\n",
    " '0.569',\n",
    " '0.6006',\n",
    " '0.604',\n",
    " '0.6103',\n",
    " '0.621',\n",
    " '0.635',\n",
    " '0.662',\n",
    " '0.723',\n",
    " '0.724',\n",
    " '0.756',\n",
    " '0.757',\n",
    " '0.765',\n",
    " '0.795',\n",
    " '0.801',\n",
    " '0.873',\n",
    " '0.996',\n",
    " '1.004',\n",
    " '1.038',\n",
    " '1.05',\n",
    " '1.167',\n",
    " '1.205',\n",
    " '1.246',\n",
    " '1.274',\n",
    " '1.365',\n",
    " '1.494',\n",
    " '1.562',\n",
    " '1.596',\n",
    " '1.766',\n",
    " '1.797',\n",
    " '1.988',\n",
    " '2.112',\n",
    " '2.185']\n",
    "ex4.set_elines(elines)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the geometry with the Plot() method. If the color attribute is set previously for the materials, then it is used for the plot, otherwise, the method will randomly assign colours for each material (the detector is set to white and the collimator is set to gray)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "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": [
    "uo2.set_color(\"#0f0e0e\")\n",
    "he.set_color(\"#474545\")\n",
    "zr.set_color(\"#635f5f\")\n",
    "h2o.set_color(\"#96deeb\")\n",
    "air.set_color(\"#ceedf2\")\n",
    "lead.set_color(\"#4e4869\")\n",
    "alu.set_color(\"#281b63\")\n",
    "copper.set_color(\"#5240a1\")\n",
    "ss.set_color(\"#3c3266\")\n",
    "ex4.Plot(xl=[-200.5,200.5],yl=[-200.5,200.5],detectorSize=0.4)\n",
    "ex4.Plot(xl=[170.5,200],yl=[170.5,200],detectorSize=0.4)\n",
    "ex4.Plot(xl=[-10,10],yl=[-10,10],detectorSize=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we run the experiment, we do not get any warnings, because everything is set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n"
     ]
    }
   ],
   "source": [
    "ex4.Run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the results\n",
    "\n",
    "Plot the distance travelled in some material and the pinwise contribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.52      , 0.52      , 0.52      , 0.52      , 0.52      ,\n",
       "        0.52      , 0.52      , 0.52      ],\n",
       "       [1.55223954, 1.55456556, 1.55649208, 1.55800944, 1.55910734,\n",
       "        1.55977478, 1.56      , 0.52      ],\n",
       "       [2.57315813, 2.58271374, 2.59020885, 2.59561506, 2.5988946 ,\n",
       "        2.6       , 1.55977478, 0.52      ],\n",
       "       [3.59219693, 3.61299665, 3.62792997, 3.63696088, 3.64      ,\n",
       "        2.5988946 , 1.55910734, 0.52      ],\n",
       "       [4.62293561, 4.65455761, 4.67360363, 4.68      , 0.        ,\n",
       "        2.59561506, 1.55800944, 0.52      ],\n",
       "       [5.67408899, 5.70847911, 5.72      , 3.63381661, 3.62792997,\n",
       "        2.59020885, 1.55649208, 0.52      ],\n",
       "       [6.7412677 , 6.76      , 4.66931583, 4.65455761, 3.61299665,\n",
       "        2.58271374, 1.55456556, 0.52      ],\n",
       "       [7.8       , 5.7031174 , 5.67408899, 4.62293561, 3.59219693,\n",
       "        2.57315813, 1.55223954, 0.52      ]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex4.dTmap['D']['1'] #to detector D, through matID '1'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 12 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(321)\n",
    "plt.imshow(ex4.dTmap['D']['1'],cmap='jet')\n",
    "plt.title('uo2')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(322)\n",
    "plt.imshow(ex4.dTmap['D']['2'],cmap='jet')\n",
    "plt.title('he')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(323)\n",
    "plt.imshow(ex4.dTmap['D']['3'],cmap='jet')\n",
    "plt.title('zr')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(324)\n",
    "plt.imshow(ex4.dTmap['D']['4'],cmap='jet')\n",
    "plt.title('h2o')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(325)\n",
    "plt.imshow(ex4.dTmap['D']['5'],cmap='jet')\n",
    "plt.title('ss')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(326)\n",
    "plt.imshow(ex4.dTmap['D']['6'],cmap='jet')\n",
    "plt.title('air')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplots_adjust(bottom=0.1, right=1.4, top=2.1)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Geometric efficiency and pinwise contribution\n",
    "\n",
    "We can see that high energy gamma photons emitted from central pins can contribute to the detector signal, whereas low energy photons cannot escape from central regions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "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.figure()\n",
    "plt.plot(ex4.elines,ex4.geomEffAve)\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.ylabel('Efficiency (1/particle)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAApwAAAHfCAYAAAD9bwj6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4zLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvnQurowAAIABJREFUeJzs3Xu8HVV9///XOxdIQoAIQcEkEC2UqoiAKah4QbwUkIJtsYIFRMGoP7Hwrb2IbUVp9ffDVqp+UTACBhBBGkEjBoFWKaKChIvcIhYByYEghHALt5Dk8/tj5oSdzT575pw9s2edc97Px2MeOXvP7DVrn5y932tmrVmjiMDMzMzMrC4Tmq6AmZmZmY1tbnCamZmZWa3c4DQzMzOzWrnBaWZmZma1coPTzMzMzGrlBqeZmZmZ1coNTjMzMzOr1bhtcEq6R9LTkla3LKc2Xa+RUuZkSQ/nyxckqcv220j6tqRHJT0i6by29W+XdIOkJyUtl/SX+fN/KOn7kh6StErSZZJ27rKfhZJC0kFtz38pf/6oEu/t15I+2OH54yQtLXq9mdlIjOecyLf9R0n3Snpc0gWStmhZPyvPglWSBiR9pO31EyX9q6T7JT0h6UZJM4bYl3NiHBi3Dc7cn0bE9Jbl2Kp3IGlS1WUOYT7wbuA1wK7AgcCHu2x/EfAAsAPwYuDfB1dIeiXwbeAfgS2B3YDr89UzgMXAzsBLgF8C3y+o22+A97eUPwl4D/DbUu8MzgaO7PD8Efk6M7O6jNecOJLsO3Zv4KXAVOD/tqz/FnA3WQ68C/i8pLe2rP8s8Abg9cAWeVnPdKmbc2KMG+8Nzo4kHSXpakn/np/9u1vS/i3rt5R0pqQVku7Lj+Imtrz2Z5L+Q9Iq4DP5kd4XJa3Myzo2P2qbJOk9kq5v2/8nJH1vmNV+P/DFiBiIiPuALwJHDfH+3gnMAf4uIh6LiOci4saWTf4J+HpEXBoRayPi4Yj4LUBE/DIizoyIVRHxHPAfwM6Stu5Stx8Ae0t6Uf54P+BmsgZva70+KGlZ/ju/TNIO+apzgTe2PEbSK8i+MM8v8bsxM6vUWM8J4E+BMyNieUSsBk4G3itpmqTpwD7A5/L8+BWwCPhgXrcXAccDH4qI30Xm1ojo1uB0ToxxbnAObS/gDmAm8AXgzJauh7OBtcCOwO7AO4Fj2l57F9mZw88BHwL2JztTuAfZEeagxcDL8g/GoMPJPjxI+qSybu+OS8trXgX8quXxr/LnOnld/t7OVtatcp2kt7StR9It+ZfltyRtNURZbwYeiIiHh1gP2VHtYuDQ/PGRwDmtG0h6N/Ap4M+BbYCfkn9JRMQA8BOyI1VaylgSESu77NfMrE5jOSeUL62PNwV2anm+ff0u+c+vzt/7IZIekPQbSR8bYj+DnBNjXUSMywW4B1gNPNqyfChfdxRwZ8u204AAtiXrPngWmNqy/jDgJy2vvbdtXz8GPtzy+O15eZPyx6eRHSlC9uF/BNh0mO9nHfBHLY93yvehDtsuyNcdDUwm+4A/CszM16/Jfz9/CEwHvguc16Gc2cB9wGFd6rUQ+FfgjcAvyLrof0/WPXM1cFS+3aXA0S2vmwA8BeyQPz4cuKNl3b3AnzX9d9T2Xs8CHgRurai8H+X/L5e0Pf8y4Frgf4HvAJs0/d69eBmLyzjPiWPIurnn5t/bi/NtX5+vv5qsi30KWQN5Vct39Pvybc/Mv+t3BR4C3jFEvcZNThT8/1SdIScDt+bLe5t+f+P9DOe7I2JGy/KNlnUbTuNHxFP5j9PJxjxOBla0HD1+newoddDytv28tO259vVnA+/Lj4yPAC6MiGeH+V5Wk42TGbQFsDryv7o2TwP3RNY1/lxEXJDXae+W9d+MiN9E1pXyeeCA1gIkbQNcDnwtIgq7KyLiarIj0n8ia0A93bbJDsCXW36nq8iOmGfl6y8CtpP0OrKunGnAD4v222cLybqBqvJvbHy0Puhk4D8iYiey0Dm6wn2a2cbGa06cRXb28ErgNrKzhwAD+b9/RXbwu5ysMXxey7rB7/eTIuLpiLgZuIC2HGk3TnKim4VUlCGS3kV2ILAb2dn0v1PLRV9NGO8NzpFYTnbkOrPlC2iLiGjtlmj/8K4gOxs4aE7ryoi4huys4pvIjgzPHVwn6VPa+ArJjZaWYm4jGwg+6DX5c53c3KGOpdfnY2wuBxZHxOe6lNPuW8AnaOsmyS0nO7pv/WKfGhE/hw1f5ot4fiD7BRGxZhj7rl1EXEX2BbiBpD+Q9CNJ10v6qaQ/GkZ5/w080VaegH3JfheQhdC7MbOUjPqciIj1EXFiRMyNiNn5dvflC5GNzTwwIraJiL2ArckuIoUsQzq9xzLGdE50U3GGvBL4n8iuw3iSbPhElSdEhs0NzmGKiBVkja0vStpC0oT8D+ItXV52IXCcsmkkZgD/0GGbc4BTgbX5Ud7g/j4fG18hudHS9vq/yffxUrIP7MIh6nMx8CJJ788Hqh9CdoT4s3z9N4EPSHq5pGl5fS8ByI+QLgN+FhGf7PKeO/kK8A7gqg7rTgdOkPSqfD9bSnpP2zZnA+8F/oLRc9XhAuDjEfFa4G+Br/VY3tbAoxGxNn88wPNH92aWgLGQE5K2yussZTOXnEJ2xnJ9vv4VkjaXtImkw8nGqJ6S1+e3ZOMr/1HSpvnY0/eS50iB8ZgT3Yw0Q34F7K/sIq+ZwFtpO4jpt/He4PxB25HgxSVfdySwCXA7WZfmImC7Ltt/g+zL52bgRmAJ2YDqdS3bnEs24PrcF7y6nK+TXeV3C9l4jR/mzwGQv783AUTEKuAgsj/ex4BPAgdHPrA6Is4i+2K6Fvgd2ZH6X+dF/Rnwx2QN0tbf3fZFFYzsyvb/7tR9ExEXk3UVXyDp8fw97N+22VV5fe+LiOtK/E4apexKzjcA/ynpJrL/j+3ydX8u6dYOy2VFxXZ4biRnEcysnHGZE2QXQi0BniQbO3lWRCxoKetPyC56egT4CLBfRDzUsv4wsi7wh/P9/HPea9PVeMuJbnrJkIi4nOz/7+dkQyN+Qfb31Bh1HrphdVI2dcbpEdE6fcNUssHCe0TE/zZWOeuJpLlkY492yc8G3xER3UKmqLx9gL+NiAPzxyIbfL9tRKyV9HrgMxHxJz1X3syS4ZwYn6rOkJZyvw18KyKW9FrWSI33M5x9IWmqpAOUzac2CziRrFu71UeB6/wlMnZExOPA3YNdPnnX1GsKXlZUZpAN3j8kf+r9FE+8b2aJc05Yu14yJB8ut3X+865kMwVcXltly9TJZzjrl4+D/B/gj8iu3vshcFz+x4Ske8i6St8dG0/AbqOIpPPJroycSTalx4lkU52cRtYNMplsEPtJJcv7KdnfzHSybqmjI+IySS8nu+JzK7Kut8NHcLWqmSXEOWFVZoikKcAN+cPHgY9ExE01VLs0NzjNzMzMrFbuUjczMzOzWrnBaWZmZma1mlRHoVNmTo/pc7euo+gNptJ+A4JqTePJWssHmL7+qeKNeqDHay0+82jxJr1a/0i95T+wvt7yHwWeiug0nVEpO0pR5i9lBVwWEY1O7GtW1vSZU2KruZvXuo+6c6Lu8gEmPVXzF9QTxZv0rA9ZtKbmffTjZuwrYGVEbDPS16eeFbU0OKfP3ZoDlw53TvDh2ZVbai3/tVxfa/kAez35y+KNejClH9ejLa5/F0/9Z73lf6HmY4sFxZt09TTwsRLb/VM20NxsVNhq7ub8/dKDa93HK7m91vJfXXMOAWyzdHXxRr34n3qLB/pybfRAzfs4o97iAfhsNu/1iKWeFbU0OM2sOiK7NNHMzGwoqWeFG5xmiZsATG26EmZmlrTUs8INTrPECX9Qzcysu9SzIuW6mRnpd5OYmVnzUs8KNzjNEpd6N4mZmTUv9axwg9Mscal3k5iZWfNSz4pSE79L2k/SHZLulFTvfEdmtpHBbpKixaxpzgqz5qSeFYWNYUkTga8C7wAGgOskLY6Ieic4MzMg/XE5ZuCsMGta6llR5uzrnsCdEXEXgKQLgIOh5hl1zQzIvkRSHpdjlnNWmDUo9awo0+CcBSxveTwA7FVPdcysXerjcsxyzgqzBqWeFWXq1uke0PGCjaT5wHyAzbbfqsdqmdmg1LtJzHKFWdGaEy/afrN+1Mls3Eg9K8o0OAeAOS2PZwP3t28UEQvIbxs9c94OL2iQmtnIpD7VhVmuMCtac2L7eds4J8wqlHpWlLlK/TpgJ0kvk7QJcCiwuN5qmdmgwW6SoqVUWdL/kXSbpFslnS9pSh11tnHJWWHWoIqzYqKkGyVd0mHdppK+k89Gca2kuWXKLGxwRsRa4FjgMmAZcGFE3FayzmbWo6qmupA0C/hrYF5E7AJMJGsUmPXMWWHWrIqnRTqO7HPcydHAIxGxI/AfwMllCizV2I2IJcCSMtuaWbUq7iaZBEyV9BwwjQ7DY8xGyllh1pyqskLSbOBdwOeAv+mwycHAZ/KfFwGnSlJEdB0mk/IFTWZGdQPBI+I+Sf8O3As8DVweEZdXULSZmTWswouGvgT8PbD5EOs3zEgREWslPQZsDazsVmipOw2ZWbNKjsuZKWlpyzK/tQxJLyI7Mn0Z8FJgM0mH9+s9mJlZvXrNCkkHAg9GxPVddlNq9qJOdTOzhE0QTN20xIbPsDIi5nXZ4u3A3RHxEICki4A3AN+qoJpmZtagirJib+AgSQcAU4AtJH0rIlpPTgzOSDEgaRKwJbCqsH4lqmZmDZJg8sTipYR7gddJmiZJwNsYelC4mZmNIlVkRUScEBGzI2Iu2UWlP25rbEI2+8T7858PybfxGU6z0U6CSRV8UiPiWkmLgBuAtcCN5HMimpnZ6FZVVnQuWycBSyNiMXAmcK6kO8nObJaa7cQNTrPECZhc0Sc1Ik4ETqymNDMzS0WVWQEQEVcCV+Y/f7rl+WeA9wy3PDc4zVI3ASgzLsfMzMavxLOilgbnw9dvw9n6SB1Ft+h0kVSFptdbPAAzai5/Zs3l92sff1pv8U/+oN6/pcVP91jA4O0jzMaQ5XftwMcPO6PendT9/bRtzeX3Yx+71Fw+wH7172L63IdqLX/Z9BfXWj7AZ3stIPGsSLhqZraBP6lmZlYk4axIuGpmBiTfTWJmZglIPCvc4DRLXeLdJGZmloDEsyLhqpnZBuXm2TQzs/Es4axwg9MsdRPI7vdgZmY2lMSzwg1Os9Eg4aNWMzNLRMJZ4QanWeoSH5djZmYJSDwrEq6amQHJf4mYmVkCEs+KCUUbSDpL0oOSbu1HhcysjcimuihazBrkrDBrWOJZUdjgBBbSl/sEmFlHg0etRYtZsxbirDBrTuJZUbjriLhK0tz6q2JmHSXeTWIGzgqzxiWeFWXOcJpZkwbvHpFoN4mZmSWgoqyQNEXSLyX9StJtkl5wm3dJR0l6SNJN+XJMUbmVtYUlzQfmZ4+2r6pYM4NKPqmSdga+0/LUy4FPR8SXei/drNhGOTHNOWFWuWpadc8C+0bEakmTgaslXRoR17Rt952IOLa/VQMiYgGwAECaF1WVazbuiUrmVouIO4DdACRNBO4DLu69ZLNyNsqJrZwTZpWqLisCWJ0/nJwvPX9e3aVulrrBu0cULcPzNuC3EfG7yuppZmbNqTArJE2UdBPwIHBFRFzbYbO/kHSzpEWS5pSpXtFOzwd+AewsaUDS0eWqa2aVKXfl4UxJS1uW+V1KPBQ4v9Y627jirDBLQEVZERHrImI3YDawp6Rd2jb5ATA3InYF/gs4u0zVuoqIw4q2MbMale8mWRkR8wqLkzYBDgJO6K1iZs9zVpg1rOKsAIiIRyVdSTbl2a0tzz/cstk3gJOLykr4AnozA57vJqnO/sANEfH7Sks1M7PmVJQVkrYBnssbm1OBt9PWoJS0XUSsyB8eBCwrKtcNTrPRoIKB4C0Ow93pZmZjTzVZsR1wdn5x6QTgwoi4RNJJwNKIWAz8taSDgLXAKuCookLd4DRLXYWT+UqaBrwD+HA1JZqZWRIqyoqIuBnYvcPzn275+QSGOSzLDU6z1FXY4IyIp4CtqynNzMySkfidhhKumpkB2ZeI7yRkZmbdJJ4VbnCapS7xo1YzM0tA4llRU9UepsSUTGlb3Yf/tdWT6y1/oB9/eTW/B4C3H1Rr8dPeU2vxTPhBjwUk/iViNiKPrIELBmreydRRXn4/9qGay6fqWTY6Wr10m1rLn/3OWovPXN7j6xPPioSrZmZA8t0kZmaWgMSzwg1Os9QlftRqZmYJSDwrEq6amQHDuXuEmZmNV4lnhRucZqkTfRkDZWZmo1jiWeEGp1nqEu8mMTOzBCSeFQlXzcyA5LtJzMwsAYlnhRucZqlL/KjVzMwSkHhWJFw1MwOSn+rCzMwSkHhWuMFplrrEj1rNzCwBiWfFhKINJM2R9BNJyyTdJum4flTMzHKDXyJFi1mDnBVmDUs8K8rsei3wiYi4QdLmwPWSroiI22uum5lBpd0kkmYAZwC7AAF8MCJ+UU3pNs45K8yaVFFWSJoCXJWXNglYFBEntm2zKXAO8Fqy+5m/NyLu6VZuYYMzIlYAK/Kfn5C0DJgF+EvErB+q7Sb5MvCjiDhE0ibAtMpKtnHNWWHWsOqy4llg34hYLWkycLWkSyPimpZtjgYeiYgdJR0KnAy8t1uhw6qapLnA7sC1HdbNB+Znj7YeTrFm1k1FXyKStgDeDBwFEBFrgDW9l2y2saGyYuOcmNXnWpmNcRVlRUQEsDp/ODlfom2zg4HP5D8vAk6VpPy1HRWO4RwkaTrwXeD4iHi8QwUXRMS8iJgHm5ct1syKDHaTFC3FXg48BHxT0o2SzpC0WR1VtvGrW1ZsnBNbNVNBs7GqfFbMlLS0ZZn/gqKkiZJuAh4EroiI9hONs4DlABGxFniMgrONpRqc+SnV7wLnRcRFZV5jZhUpPxC86EtkErAHcFpE7A48CXyyL+/BxgVnhVmDymfFysEDv3xZ0F5URKyLiN2A2cCeknbpsLcXvKxb9QpPvkoScCawLCJOKdrezGpQ7u4RK7MzR0MaAAZajlQX4QanVcRZYZaAiu80FBGPSroS2A+4tWXVADAHGJA0CdgSWNWtrDJnOPcGjgD2lXRTvhwwopqb2fBNAKaUWApExAPAckk750+9DV/QYdVxVpg1qaKskLRNPqMJkqYCbwd+3bbZYuD9+c+HAD/uNn4Tyl2lfjWdT52aWT9Ue5X6x4Hz8ivU7wI+UFnJNq45K8waVl1WbAecLWkiWTP2woi4RNJJwNKIWEzWm3GupDvJzmweWlSop4s2GwWiom6SiLgJ6NbtbmZmo1QVWRERN5PNMtH+/Kdbfn4GeM9wynWD0yxxIVjnT6qZmXWRelYkXDUzA4gJ8OymZYZbr6+9LmZmlqbUs8INTrPEhcS6SWU+qp7D3cxsvEo9K9zgNBsF1k2seK4LMzMbc1LOCjc4zRK3ngk8yyYltny69rqYmVmaUs+Kmhqca4D76im6b9wWL2fH+nexsuby/5+ay/9p70Ws89+jjTlPAj9ruhI96sfncvIoLx94Zv/69zFQc/nvrLl8gMt7LyLlrEi3ZmYGQCDWVX37CDMzG1NSzwo3OM0SF4g1pbpJzMxsvEo9K9zgNEtcINYmfNRqZmbNSz0r3OA0S1zWTeKPqpmZDS31rEi3Zma2QcrjcszMLA0pZ4UbnGaJW49KTnVhZmbjVepZ4QanWfLS7iYxM7MUpJ0V6dbMzIBqp7qQdA/wBLAOWBsR8yop2MzMGlVVVkiaA5wDbEt24/UFEfHltm32Ab4P3J0/dVFEnNSt3MIGp6QpwFXApvn2iyLixOG+ATMbmRqmunhrRNQ9nb6NM84Ks2ZVmBVrgU9ExA2SNgeul3RFRNzett1PI+LAsoWWOcP5LLBvRKyWNBm4WtKlEXFN+bqb2UilPtWFWc5ZYdagqrIiIlYAK/Kfn5C0DJgFtDc4h2VCiR1HRKzOH07Ol+hlp2Y2POuYVLiUFMDlkq6XNL/GKts446wwa16FWQGApLnA7sC1HVa/XtKvJF0q6VVFZZXas6SJwPVkN87+akR02rGZ1WA9E8p2k8yUtLTl8YKIWNC2zd4Rcb+kFwNXSPp1RFxVWWVtXHNWmDWn4qxA0nTgu8DxEfF42+obgB3yHo0DgO8BO3XbaakGZ0SsA3aTNAO4WNIuEXFrW8XmA/kZky3LFGtmJZXsJllZdBFQRNyf//ugpIuBPcnG3Zn1rCgrNs6JmY3U0Wwsqyor8mEx3wXOi4iL2te3NkAjYomkr0ma2e36gMIu9bYdPApcCezXYd2CiJiXvYnNhlOsmXUxePeIXrtJJG2WDwBH0mbAO4Fbu7/KbPiGyoqNc2KLRupmNlZVmBUCzgSWRcQpQ2yzbb4dkvYka08+3K3cMlepbwM8FxGPSpoKvB04ubDGZlaJCqdFegnZWSfIPvvfjogfVVGwmbPCrFkVZsXewBHALZJuyp/7FLA9QEScDhwCfFTSWuBp4NCI6Dpmu0yX+nbA2fnYnAnAhRFxycjeg5kNV1R094iIuAt4Te81MuvIWWHWoAqz4mpABducCpw6nHILG5wRcTPZFUpm1oBI/O4RZuCsMGta6lmRbs3MbIOq7jRkZmZjV8pZ4QanWeKGMdWFmZmNU6lnhRucZqOA7zRkZmZFUs4KNzjNEpf6uBwzM2te6lmRbs3MDMi+RFLuJjEzs+alnhVucJolLlDS3SRmZta81LOipgZnAM/VU3TfjPb698va+ncx5I2yqvHMO+stP/6lx9cn3k1iNjJrgPuarkSPxsLnclUf9vGCmxNWb6DrtJG9e0u9xVch9axIt2ZmBqTfTWJmZs1LPSvc4DQbBVKeW83MzNKQcla4wWmWuNTH5ZiZWfNSzwo3OM0Sl/q4HDMza17qWZFuzcwMSH9cjpmZNS/1rJjQdAXMrLvBbpKipSxJEyXdKOmSGqttZmZ9VFVWSJoj6SeSlkm6TdJxHbaRpK9IulPSzZL2KCrXZzjNRoGKu0mOA5YBW1RZqJmZNauirFgLfCIibpC0OXC9pCsi4vaWbfYHdsqXvYDT8n+H5AanWeLWM6GybhJJs4F3AZ8D/qaSQs3MrHFVZUVErABW5D8/IWkZMAtobXAeDJwTEQFcI2mGpO3y13bkBqfZKFDhVBdfAv4e2LyqAs3MLA1VT4skaS6wO3Bt26pZwPKWxwP5c703OCVNBJYC90XEgWVfZ2a9GcZUFzMlLW15vCAiFgw+kHQg8GBEXC9pn4qraeacMGtQVVkxSNJ04LvA8RHxePvqjlXoYjhnOD3uy6wB2ZWHm5bZdGVEzOuyfm/gIEkHAFOALSR9KyIOr6KeZjgnzBpTYVYgaTJZY/O8iLiowyYDwJyWx7OB+7uVWeoq9ZZxX2eU2d7MqpPNrTaxcCksJ+KEiJgdEXOBQ4Efu7FpVXFOmDWrqqyQJOBMYFlEnDLEZouBI/Or1V8HPNZt/CaUP8NZOO5L0nxgfvZoy5LFmlmR1O8eYZYbZk68qC+VMhsvKsyKvYEjgFsk3ZQ/9ylge4CIOB1YAhwA3Ak8BXygqNDCBmfZcV95//+C7DUv7dqPb2bDU/XdIyLiSuDKSgu1cWtkOTHHOWFWsSqyIiKupvMYzdZtAvjYcMotUzOP+zJrUJXTIpnVxDlh1rDUs6JwDKfHfZk1r8o7DZlVzTlhloaUs8LzcJolLhsI7o+qmZkNLfWsGFbNPO7LrP+yqS7S7SYxa+WcMGtG6lmRblPYzIDnp7owMzMbSupZ4QanWeICPEbTzMy6Sj0r3OA0S1wwoezdI8zMbJxKPSvc4DQbBVLuJjEzszSknBVucJolzncaMjOzIqlnhRucZonLrjxMt5vEzMyal3pWuMHZqMk1l9+P/97n6t/Fo/UWf+1me9Za/uoJt/b0+tSvPDQbmfXA001XYhTYsebyZ9VcPvTl//mBabUW/9DR02stP7O6p1ennhVucJolLhDr1qf7JWJmZs1LPSvc4DRLXcDatel+iZiZWQISzwo3OM0St379BNY8k+64HDMza17qWeEGp1nqAtZVcNQqaQpwFbAp2Wd/UUSc2HPBZmbWvOqy4izgQODBiNilw/p9gO8Dd+dPXRQRJxWV6wanWeIixNrnKukmeRbYNyJWS5oMXC3p0oi4porCzcysORVmxULgVOCcLtv8NCIOHE6hbnCapS7E+md77yaJiOD5yyAn50v0XLCZmTWvuqy4StLcngtq4wanWepCUK6bZKakpS2PF0TEgtYNJE0Erieba+WrEXFtdRU1M7PGVJgVJbxe0q+A+4G/jYjbil7gBqdZ6gJYqzJbroyIeV2LilgH7CZpBnCxpF0ioreJQs3MrHkVZkWBG4Ad8uFZBwDfA3YqelGpBqeke4AngHXA2h4rambDEcAzFRcZ8aikK4H9ADc4rRLOCrMG1ZAVHXcT8XjLz0skfU3SzIhY2e11wznD+daiwsysBgGs7b0YSdsAz+WNzanA24GTey/ZbCPOCrMmVJQVRSRtC/w+IkLSnsAE4OGi17lL3Sx1QVV3EN0OODsfxzkBuDAiLqmkZDMza1ZFWSHpfGAfsrGeA8CJ5PfijojTgUOAj0paS3bf0kPzi1K7KtvgDOBySQF8fQSDS81spIKsg7LXYiJuBnbvvSSzITkrzJpSXVYcVrD+VLJpk4albINz74i4X9KLgSsk/ToirmrdQNJ8YH72aMvh1sPMhtKncTlmFeiaFc4JsxolnhUTymwUEffn/z4IXAzs2WGbBRExLxskPq3aWpqNZ4PjcooWs4YVZcXGObFZE1U0G7sSz4rCBqekzSRtPvgz8E58VatZ/yT+JWIGzgqzxiWeFWW61F9CNl/f4Pbfjogf1VorM3te4t0kZjlnhVmTEs+KwgZnRNwFvKYPdTGzTqq7St2sNs4Ks4YlnhWeFsksdRVdeWhmZmNY4lnhBqdZ6taTdDeJmZklIPGscIPTLHV9unuEmZmNYolnhRucZqNBwl8iZmaWiISzwg1Os9QlftRqZmYJSDwr3OA0S916srvVmpmZDSXxrHCD0yx1iV95aGZmCUg8K9zgbFTCE2aV1ofz96vrLf56Xltr+U9xd++FJNxNYjYyiU+apkYGAAAgAElEQVQaWMoWfdjHfTWXP6vm8qEvp90eqPeW2rfw6lrLz/yi9yISzopS91I3swYNTnVRtBSQNEfSTyQtk3SbpONqq7OZmfVXdVlxlqQHJXW8Na0yX5F0p6SbJe1RpnpucJqlbvBEUNFSbC3wiYh4BfA64GOSXllDjc3MrN+qy4qFwH5d1u8P7JQv84HTyhTqBqdZ6gbH5RQtRcVErIiIG/KfnwCW0Z/+NDMzq1t1WXEVsKrLJgcD50TmGmCGpO2KyvUYTrPUBZXfPULSXGB34NpqSzYzs0bUkBVDmAUsb3k8kD+3otuL3OA0S135aytmSlra8nhBRCxo30jSdOC7wPER8XgldTQzs2ZVnBVdaIi9d+UGp1nqyk91sTIi5nXbQNJkssbmeRFxUe+VMzOzJFSYFQUGgDktj2cD9xe9yA1Os9RV1E0iScCZwLKIOKX3Es3MLBn961JfDBwr6QJgL+CxiOjanQ5ucJqlr7rpCvcGjgBukXRT/tynImJJJaWbmVlzKsoKSecD+5B1vQ8AJwKTASLidGAJcABwJ/AU8IEy5ZZqcEqaAZwB7EL2lj4YERXMUGpmhSq6e0REXE3nsTdmlXBWmDWouqw4rGB9AB8bbrllz3B+GfhRRBwiaROg3in9zex5QdJ3jzBr4awwa0riWVHY4JS0BfBm4CiAiFgDrKm3Wma2wXr6cmc4s144K8walnhWlJn4/eXAQ8A3Jd0o6QxJm7VvJGm+pKXZpfZPVV5Rs3Gtgsl8zWpWmBXOCbOaJZwVZRqck4A9gNMiYnfgSeCT7RtFxIKImJddau9eFLPKDHaTFC1mzSrMCueEWY0Sz4oyYzgHgIGIGLwjySI6NDjNrCaJd5OY5ZwVZk1KPCsKz3BGxAPAckk750+9Dbi91lqZ2fMquj+uWZ2cFWYNSzwryl6l/nHgvPyqw7soOeeSmVXEXeY2OjgrzJqUcFaUanBGxE1AL7dBMrORSrybxGyQs8KsQYlnhe80ZJa6iibzNTOzMSzxrHCD0yx1iU/ma2ZmCUg8K9zgNBsNEv4SMTOzRCScFW5wmqVuPfBM05UwM7OkJZ4VbnCapS7xbhIzM0tA4llR5k5DZtakiu4eIeksSQ9KurW2upqZWTMqvNOQpP0k3SHpTkkvuIGDpKMkPSTppnw5pqhMn+G0USBqLf1mXl1r+U/zw94KCKrqJlkInAqcU0lpZsmbXHP5/ZiDZmof9lG3PvyeVtZb/O28st4dAPCL3l5eUVZImgh8FXgH2R3ErpO0OCLab+TwnYg4tmy5PsNpNhpEiaWoiIirgFW11dHMzJpVQVYAewJ3RsRdEbEGuAA4uNequcFpZmZmZoNmActbHg/kz7X7C0k3S1okaU5RoW5wmo0dMyUtbVnmN10hMzNLTlFWqMNr2s+N/gCYGxG7Av8FnF20U4/hNEteAM+V2XBlRPi2gmZm41JlWTEAtJ6xnA3cv9GeIh5uefgN4OSinbrBaZa8xOe6MDOzBFSWFdcBO0l6GXAfcCjwvtYNJG0XESvyhwcBy4oKdYPTLHnrqeIqT0nnA/uQdacMACdGxJk9F2xmZgmoJisiYq2kY4HLgInAWRFxm6STgKURsRj4a0kHkbVwVwFHFZXrBqdZ8kp3k3QvJeKw3utiZmZpqiYrACJiCbCk7blPt/x8AnDCcMp0g9MsedV9iZiZ2ViVdlYUXqUuaeeWmeRvkvS4pOP7UTkzG1TR7SPMauKsMEtBullReIYzIu4AdoMNs8/fB1xcc73MbINqxuWY1clZYda0tLNiuF3qbwN+GxG/q6MyZtZJ2t0kZh04K8z6Lu2sGG6D81Dg/DoqYmZD8bRINuo4K8z6Lu2sKN3glLQJ2VxLHa9Kymeqz2er37KCqplZJu1uErNW3bLCOWFWp7SzYjhnOPcHboiI33daGRELgAUA0kvL3R7ezEpIu5vErM2QWeGcMKtT2lkxnAbnYbiLxKwBaXeTmLVxVpg1Iu2sKNXglDQNeAfw4XqrY2YvFKTcTWI2yFlh1qS0s6JUgzMingK2rrkuZtZR2t0kZoOcFWZNSjsrfKchs+Sl/SViZmYpSDsr3OA0S17a43LMzCwFaWeFG5xmyUt7qgszM0tB2lnhBqdZ8tLuJjEzsxSknRUTmq6AmRUZ7CYpWopJ2k/SHZLulPTJeuprZmb917+skLSppO/k66+VNLeoTJ/hNEteNd0kkiYCXyWbtmYAuE7S4oi4vefCzcysYX3NiqOBRyJiR0mHAicD7+1Wrs9wmiVvsJukaCm0J3BnRNwVEWuAC4CD66ixmZn1W1+z4mDg7PznRcDbJKlboYqo/u5ikh4CfjeMl8wEVlZekf7ye0hDiu9hh4jYZqQvlvQjsvdVZArwTMvjBfmtBAfLOQTYLyKOyR8fAewVEceOtG5mIzWCnIA0P9/D5feQhhTfw6jJCkm35tsM5I9/m28z5O+0li714f7CJC2NiHl11KVf/B7SMBbeQ7uI2K+iojodffp+1taIkQTrWPh8+z2kYSy8h3Z9zoph54m71M3GjwFgTsvj2cD9DdXFzMzSVCYrNmwjaRKwJbCqW6FucJqNH9cBO0l6maRNgEOBxQ3XyczM0lImKxYD789/PgT4cRSM0UzlKvUFxZskz+8hDWPhPdQiItZKOha4DJgInBURtzVcLbPhGAufb7+HNIyF91CLobJC0knA0ohYDJwJnCvpTrIzm4cWlVvLRUNmZmZmZoPcpW5mZmZmtXKD08zMzMxq1WiDcyzcZk/SHEk/kbRM0m2Sjmu6TiMhaaKkGyVd0nRdRkrSDEmLJP06//94fdN1MrPejfasGCs5AaM/K5wTzWlsDGd+66Tf0HLrJOCw0XabPUnbAdtFxA2SNgeuB949Ct/H3wDzgC0i4sCm6zMSks4GfhoRZ+RX1k2LiEebrpeZjdxYyIqxkhMw+rPCOdGcJs9wjonb7EXEioi4If/5CWAZMKvZWg2PpNnAu4Azmq7LSEnaAngz2ZVzRMQaf4mYjQmjPivGQk7A6M8K50SzmmxwzgKWtzweYBR+AFtJmgvsDlzbbE2G7UvA3wPrm65ID14OPAR8M+/uOUPSZk1Xysx6NqayYhTnBIz+rHBONKjJBueYus2epOnAd4HjI+LxputTlqQDgQcj4vqm69KjScAewGkRsTvwJDDqxnqZ2QuMmawYrTkBYyYrnBMNarLBOWZusydpMtmXyHkRcVHT9RmmvYGDJN1D1lW1r6RvNVulERkABiJi8KzBIrIvFjMb3cZEVozynICxkRXOiQY12eAcE7fZkySy8SDLIuKUpuszXBFxQkTMjoi5ZP8HP46Iwxuu1rBFxAPAckk750+9DRh1A/LN7AVGfVaM9pyAsZEVzolmNXZryzF0m729gSOAWyTdlD/3qYhY0mCdxquPA+floXQX8IGG62NmPRojWeGcSIdzoiG+taWZmZmZ1cp3GjIzMzOzWrnBaWZmZma1coPTzMzMzGrlBqeZmZmZ1coNTjMzMzOrlRucZmZmZlYrNzjNzMzMrFZucJqZmZlZrcZtg1PSPZKelrS6ZTm16XqNlKS3SvqJpMfye91223aupGh77//csn6WpO9LWiVpQNJH2l4/UdK/Srpf0hOSbpQ0Y4h9Lcz3dVDb81/Knz+qxHv7taQPdnj+OElLi15vZjYSYzAn/k7Srfn39t2S/q7LtptIWpT/DkLSPm3rh8wcSS+WdH6eEY9J+pmkvbrs6zP5Pv667fnj8+c/U+K9XSbppA7PHyzpAUmN3VnRMuO2wZn704iY3rIcW/UO+vhH/iRwFjDkF0gHM1re+7+0PP8t4G7gJcC7gM9LemvL+s8CbwBeD2xBdsu2Z7rs5zfA+wcf5L+T9wC/LVnPs4EjOzx/RL7OzKwuYyknRPZd+iJgP+BYSYd22f5q4HDggQ7rumXOdOA64LXAVmTf0z+UNL3LvjbKidyR+fNlLASOyO9b3+oI4LyIWFuyHKvJeG9wdiTpKElXS/p3SY/kR4L7t6zfUtKZklZIui8/2zex5bU/k/QfklYBn8nPCH5R0sq8rGPzo7ZJkt4j6fq2/X9C0veGU+eI+GVEnEt2b9he3vt0YB/gcxHxXET8ClgEfDBf/yLgeOBDEfG7yNwaEd0anD8A9s5fC9kX3c20fYlJ+qCkZfnv/DJJO+SrzgXe2PIYSa8AdgXO7+X9mpmNxCjNiS9ExA0RsTYi7gC+T3af907bromIL0XE1cC6DuuHzJyIuCsiTomIFRGxLiIWAJsAO3ep3nXANEmvyt/fq4Cp+fMbSDpQ0k2SHpX0c0m75qu+R9a4fVPLti8CDgTO6bJf6xM3OIe2F3AHMBP4AnBmy5HT2cBaYEdgd+CdwDFtr70LeDHwOeBDwP7AbsAewLtbtl0MvCxvQA06nKyRhaRP5h+sjkuP7/F3yrrMvylpZv6c2v4d/HmX/OdXk733Q/Juit9I+ljBfp4he5+DR9JH0vYFIOndwKeAPwe2AX5K3piMiAHgJ2RHqrSUsSQiVpZ6p2Zm1Ru1OZHX803AbSN+9yVJ2o2swXlnwabn8nxv1vt5YU7sQXZW9cPA1sDXgcWSNo2Ip4EL2bg37C+BX+cnTqxpETEuF+AeYDXwaMvyoXzdUcCdLdtOAwLYlqyb+Vlgasv6w4CftLz23rZ9/Rj4cMvjt+flTcofn0Z2RhHgVcAjwKYjfF9vB+4p2GY6MA+YlL+fRcBlLeuvBv4vMIXsi28VcEe+7n153c8kO/rcFXgIeMcQ+1oI/CvwRuAXwJbA7/PXXg0clW93KXB0y+smAE8BO+SPD2+pwwTgXuDPmv47Kvl/chbwIHBrReV9gSwklgFfAdT0e/TiZSwuYzUn8jI+C/yqTBnAALDPEOu6Zg7ZsKtbgBO6bPMZsqFc2+ff7ZPzf+fkz3+m5XfwL22vvQN4S/7zG4HHBn/vwM+A/9P039Ew/k/GdFaM9zOc746IGS3LN1rWbejujYin8h+nAzuQfRhWtBw9fp3sKHXQ8rb9vLTtufb1ZwPvy484jwAujIhnR/yuCkTE6ohYGlm3yu+BY4F3Stoi3+SvgJfl9TwNOI/sCwfg6fzfkyLi6Yi4GbgAOKBgn1eTnbn8J+CSyI5GW+0AfLnld7qK7MzqrHz9RcB2kl5H1uU/Dfjh8N99IxaSDSPomaQ3kHWB7Up21vmPgbdUUbaZdTTmckLSsWRnAt9VZ9ZImko2pOqaiPh/i7aPiHvJzoJ+HvjfiGj/HewAfKLt7O0cst/dYM48BBws6eVk34/fruwN1W8hYzgrfNXW8C0nO3KdGUMPQo62xyuA2S2P52y0ccQ1ktaQdW+8L18AkPQpsq7mzjuK6DYIu6zB+iov83dk414G6/Bt4Jf5w5vbXjMc3wI+Dby1w7rlZEfv53WsYMRTkhaRfUlOBS6IiDUjqEPfRcRVkua2PifpD4CvkjXCnyI7a/LrMsWRnXnehOz/azLZGWMzS0eyOaFsxo9PAm+ObLhSLSRtSjau8j6yLvCyziE70/eBDusGc+JzBa8/kmy86OX5SZVRYaxnxXg/wzlsEbECuBz4oqQtJE2Q9AeSuh05XAgcp2y6oRnAP3TY5hzgVGBtfpQ2uL/Px8ZXSG60DG6X12MK2R+VJE2RtEmnykjaS9LO+Wu2JjvVfmVEPJavf4WkzZVNi3E42dijU/L6/JZsfOU/Sto0H1P0XuCSEr++rwDvAK7qsO504ISWAeNbSnpP2zZn5/v6C0b/1ekLgI9HxGuBvwW+VuZFEfELsvGsK/LlsohYVlstzWzYEs6JvyI7e/iOiCi8wDT/jp+SP9wkzxXl64bMHEmTyYZqPQ0cGRHri/bV4jtkmXNhh3XfAD6SZ5gkbSbpXZI2b9nmHLJu/g8x+nMCxlBWjPcG5w+08fxqF5d83ZFkRw23k42jWQRs12X7b5B9+dwM3AgsIRtM3nrl37lkp73PHd5b2ODNZB/uJWTjYJ7O9wmApNvyLxuAlwM/Ap4AbiU7Ej+spaw/IRvM/gjwEWC/iHioZf1hZF0bD5N1a/9zRPx3UQUjYlVE/Hfkg0va1l0MnAxcIOnxvF77t212Fdn4nPsi4jpGKWUzAbwB+E9JN5F1tW2Xr/tzZfPktS+X5et3BF5BdiZkFrCvpDc3807MxoWxlBP/SnaxzXUt7+f0wZVtOQHZ+Minyb5rLst/HpwtpFvmvIGsl+ydwKMt+3oTBfKhWv/VYdgVEbGUrCF5Ktnv9E6y8bCt29wD/BzYjOxiq1FrrGWFOmS/1UzZ1BmnR0TrND9TyQYL7xER/9tY5awWeTfJJRGxSz5W9o6I6BY+Q5Xzd8CUyOdNlfRp4JmI+EKV9TWzZjknxqexnBXj/QxnX0iaKukAZfOpzQJOBNqPkj8KXOcvkbEvIh4H7h4cMpB3Db2m5MvvBd6S/y1NJhsE7i51s1HOOWHtxlpW+AxnH0iaBvwP8Edk3Q4/BI7L/5hQdlswkV0NeWNT9bR6SDqf7Mr6mWSDtk8kmwLlNLLukclkF0G94LZsHcqaSDaG581kg8J/FBF/U0/NzaxfnBM21rPCDU4zMzMzq5W71M3MzMysVrXMwzlj5qTYbu7kOoreYAq1zVULwMSn+nDm96niTXryZM3lQ3YPjpo93e0u7RV4pN7ieRR4KkKFGw5hRynK/KmsyKa9qGTSYLO6bT1TMWduvec8Jj07nNl4RuAF11GPwn3UnUPQlyx68rl6y3+s3uIBWAErI2Kbkb4+9ayopcG53dzJnLN0xzqK3mDndXfUWv4WN9b81wtwU83lX1tz+ZDdnLJmN5eZ4rYHF9VbPAt6fP1TlJs1+TPZuB+zUWHO3AlcsXRarfvY5n9rPiK+td7igeymkHWqO4cAlta/i2vb70lUsUvrLR6Az8Lvenl96lnhOw2ZJW4C2a2VeiXpLLK58R6MiF2G2GYf4Etkg9NXRoRvm2lmNgpUlRV1cYPTLHGisg/qQrIJk8/puJ/s7iZfI5vo/15JL+60nZmZpafCrKhFynUzM56/CW6vOt2nt837gIsi4t58+wcr2K2ZmfVBVVlRFzc4zRI3jKPWmZJaR0stiIjhDCH9Q2CypCuBzYEvR0THs6FmZpYWn+E0s54MY1zOyoiY18OuJgGvBd6W7/IXkq6JiN/0UKaZmfWBx3CaWU/62E0yQNZofRJ4UtJVwGsANzjNzBKXepe6J343S9xgN0nRUoHvA2/K7707DdgL36fdzGxU6GNWjEipfUvaD/gyMBE4IyL+v1prZWYbTACqmK2w9T69kgbI7tM7GSAiTo+IZZJ+BNwMrCf7rPdjpkEbI5wVZs2pKivqUtjgzG8A/1XgHWRdbtdJWhwRt9ddOTPLVHFUGhGHldjm34B/q2B3Ns44K8yal/I4yTJ12xO4MyLuApB0AXAw4C8Rsz5IfVyOWc5ZYdag1LOiTINzFtB606gBsrFdZtYHqV95aJZzVpg1KPWsKNPgVIfn4gUbSfOB+QDbbp9yG9tsdEl9bjWzXGFWtObE7O07bW5mI5V6VpS5Sn0AmNPyeDZwf/tGEbEgIuZFxLwZ20ysqn5m495gN0nRYtawwqxozYmtt3GD06xKqWdFmcbwdcBOkl4G3AccSnYLPDPrg9SPWs1yzgqzBqWeFYV1i4i1ko4FLiOb6uKsiLit9pqZGZD+uBwzcFaYNS31rCjVGI6IJcCSmutiZh2kfuWh2SBnhVlzUs+KlM++mhnpf4mYmVnzUs8KNzjNEidgaplP6tq6a2JmZqlKPSvc4DRLnASTEv4SMTOz5qWeFW5wmiVOgsmeaczMzLpIPSvc4DRL3ATB1CklNnyy9qqYmVmiUs8KNzjNUieySWbMzMyGknhWuMFplrrUZ/M1M7PmJZ4VtVTt10+8ij3/Z2kdRT9v9gtu516p6a9YWWv5AC+e92Ct5b/0mBfcgbT6fbzwLqeVm8PyWss/Uv9ca/n/2WsBAsp0k5iNIjevezXbP/bjWvcxc6eHay3/xTv9vtbyAV7yZ/XmxIup/z28lBW172MO99Za/v46u9byAT7bawEVZYWkOcA5wLbAemBBRHy5bZt9gO8Dd+dPXRQRJ3UrN+G2sJkBlXWTSDoLOBB4MCJ26bLdHwPXAO+NiEW979nMzGpXXZf6WuATEXGDpM2B6yVdERG3t23304g4sGyhEyqpmpnVZ7CbpGgpthDYr+uupInAyWS3JzQzs9GioqyIiBURcUP+8xPAMmBWr9Vzg9MsddV9iVwFrCrY7OPAd4F6+/HMzKxa1Z2ceL5IaS6wO3Bth9Wvl/QrSZdKelVRWe5SN0udgE1LbTlTUuvg6QURsaD0bqRZwJ8B+wJ/PJwqmplZwyrOCknTyU5AHB8Rj7etvgHYISJWSzoA+B6wU7edusFplrryVx6ujIh5PezpS8A/RMQ6ST0UY2ZmfVdhVkiaTNbYPC8iLmpf39oAjYglkr4maWZEDHnFtRucZqNBf+ZWmwdckDc2ZwIHSFobEd/ry97NzKw31VxgKuBMYFlEnDLENtsCv4+IkLQn2RDNrtNCuMFplroJ9GVapIh42eDPkhYCl7ixaWY2SlSXFXsDRwC3SLopf+5TwPYAEXE6cAjwUUlrgaeBQyOi63yVbnCapa6iyXwlnQ/sQzZ+ZwA4EZgMG75AzMxstKooKyLi6ry0btucCpw6nHILq1Z27j4zq1EF3SQRcdgwtj2q9z3aeOKsMEtAwre2LDMt0kIK5u4zsxoNdpMULWbNWoizwqw5iWdF4RnOiLgqn4fJzJpQ3d0jzGrjrDBrWOJZUdkYTknzgfkAvGT7qoo1s4rG5Zg1baOcmDO72cqYjTWJZ0VldxqKiAURMS8i5rHlNlUVa2ZQ+d0jzJrQmhPaeuumq2M29iScFY4ps9RNoOzdI8zMbLxKPCvc4DRLXeLdJGZmloDEs6KwSz2fu+8XwM6SBiQdXX+1zGwjCXeTmIGzwiwJCWdFmavUS8/dZ2Y1SLybxAycFWaNSzwrfF7ELHWJd5OYmVkCEs+KhKtmZhskPLeamZklIuGscIPTLHWDd48wMzMbSuJZ4QanWeoS7yYxM7MEJJ4VCVfNzDZIuJvEzMwSkXBWuMFplrrEu0nMzCwBiWdFPQ3Oe4Fjayn5edNVa/Grp9d/e87VM+rdx10zXlVr+QDMqH8Xf/lvZ9da/q5/VGvxTL2ngkISPmo1G4n43SSeOWarWvcxML3u8neqtXyg/u/YPnyHM7P+XZzw/k/XWv5H59RafGZ5BWUknBWV3UvdzGoyOC6nx8l8JZ0l6UFJtw6x/q8k3ZwvP5f0moregZmZ1a2irKiLG5xmqavuS2QhsF+X9XcDb4mIXYF/ARaMtMpmZtZniTc4PYbTLHUV3T0iIq6SNLfL+p+3PLwGmN37Xs3MrC98pyEz61m5T+pMSUtbHi+IiJGepTwauHSErzUzsyYk3KpLuGpmBgxnbrWVETGv591JbyVrcL6x17LMzKxPPA+nmfWkj90kknYFzgD2j4iH+7NXMzPrmbvUzaxnffikStoeuAg4IiJ+U/8ezcysUgm36hKumpkBWTdJBXOrSTof2IdsrOcAcCIwGSAiTgc+DWwNfE0SwNoquujNzKwPKsqKuhQ2OCXNAc4BtgXWk12I8OW6K2ZmuYruHhERhxWsPwY4pvc92XjkrDBrWOJ3GiozD+da4BMR8QrgdcDHJL2y3mqZ2UYmlljMmuWsMGtaBVkhaY6kn0haJuk2Scd12EaSviLpzvxmIXsUlVt4hjMiVgAr8p+fkLQMmAXcXlxtM+tZ4lcemoGzwqxx1WXF4MHjDZI2B66XdEVEtH6W9wd2ype9gNPyf4c0rKrlk0bvDlw7nNeZWQ/c4LRRxllh1oCKsqLkwePBwDkREcA1kmZI2i5/bUelqyZpOvBd4PiIeLzD+vnAfAAmb1+2WDMrIpKe6sKsVbes2CgnpjknzCpVPitK3ySky8HjLGB5y+OB/LneGpySJpN9gZwXERd12iav7AIATZ0XZco1sxJ8htNGiaKs2CgntnJOmFWq4puEFJxoVIeXdP1Ml7lKXcCZwLKIOKVoezOrmBucNgo4K8waVmFWlDjROADMaXk8G7i/W5llrlLfGzgC2FfSTflyQMk6m1mvBrtJihazZjkrzJpUUVaUPHhcDByZX63+OuCxbuM3odxV6lfT+dSpmfWDz3DaKOCsMGtYdVkxePB4i6Sb8uc+BWwPG24UsgQ4ALgTeAr4QFGhjjGz1LnBaWZmRaq7Sr3w4DG/Ov1jwynXMWaWuBCsdZe5mZl1kXpWuMFpljrBOn9Szcysm8SzIuGqmRnkR60Ty1zft772upiZWZpSzwo3OM0SFxLrJpX5qK6pvS5mZpam1LPCDU6zxAVizcRNSmzpBqeZ2XiVela4wWmWuECsZWLT1TAzs4SlnhX1NDifeQ5u/X0tRT+v7rby1JrL78c++jAl3jH17+J+XlrvDt5Yb/Gs7L2IdRX8vUs6CzgQeDAidumwXsCXyeZWewo4KiJu6HnHZp08AixquhI9mjIG9tGP9/C39e/ifrardweFN4KswPLiTYpUkRV1SbdmZgbAeiawhjLdJIUWAqcC5wyxfn9gp3zZCzgt/9fMzBJXYVbUwg1Os1FgXQXdJBFxlaS5XTY5GDgnn9D3GkkzJG1XdLsyMzNLQxVZURc3OM0SN4xxOTMlLW15vCAiFgxjV7PYuFNnIH/ODU4zs8SNzzGcZlaZQKyh1O0jVkZELyONOg36jR7KMzOzPhlGVjTCDU6zxAXqVzfJADCn5fFs4P5+7NjMzHrTx6wYETc4zRIX0K9uksXAsZIuILtY6DGP3zQzGx36mBUj4ganWeKCCZV0k0g6H9iHbKznAHAiMBkgIk4HlpBNiXQn2bRIH+h5p2Zm1hdVZUVd3OA0GwUqukr9sIL1AXys5x2ZmRGmbSEAABTkSURBVFkjRnWXuqQpwFXApvn2iyLixLorZmaZ1MflmIGzwqxpqWdFmTOczwL7RsRqSZOBqyVdGhHX1Fw3MyP9qS7Mcs4KswalnhWFDc68m211/nByvniqFLM+SX2qCzNwVpg1LfWsmFBmI0kTJd0EPAhcERHXdthmvqSl2cTTq6qup9m4NdhNUrSYNa0oKzbOiYeaqaTZGJV6VpRqcEbEuojYjWxevj0l7dJhmwURMS+beHqrqutpNq6tZWLhYta0oqzYOCe2aaaSZmNYylkxrKvUI+JRSVcC+wH/f3v3HytZed93/P1hsetiQ1CysU1Zaki8tYqc1kRokYXk0GAi7CCoFDcBy1VtuaGNivPDcSPTWgTRf5JUbWLJ1OmGEHDimjQ0jjbWxiSqg2xVtbULJq5ZTLTdOuGysZf1D7DBC1722z9m7mb27t07c/eeM+eZu++XNPKcmXOf84zRzuc75znneb7YS48kneBY41NdSCuZFdL8tZ4VU89wJvn+JOePn/9d4M3Al/rumKS/1fIwiQRmhdSClrNiljOcFwD3JtnCqED971X1iX67JWlZ63ceSmNmhTSg1rNilrvUvwBcNoe+SFpF63ceSmBWSENrPStmumlI0nBav/NQkjS8rrIiyd1JDiVZ9frrJFcleTrJI+PHbbP0z6Utpca1vnqEJGl4HWbFPcCHgI+ssc9nquq69TRqwSktgJavy5EktaGLrKiqTye5eMMNrWDBKTWu9akuJEnDm3NWvDHJXwAHgfdV1aPT/sCCU1oADqlLkqaZMSu2jlb7Om5nVe1cx2EeBl5TVd9O8lbgj4Dt0/7IglNqXOtTXUiShreOrDg8Wu3rNI9T9czE891J/kuSrVV1eK2/66ngfI5RAbzI5lGLv6Tn9h/suX3gm7f3foiD/L1+D3BFv83zZxv78y6nukhyLfBBYAtwV1X9yor3/z5wL3D+eJ/3V9XuTg4uneA54KGej9Hzd+yROeTEkb5z4qM9t89ccuIQr+r3AG/ot3kAPr6xP5/XtEhJXg18taoqyQ5GMx59bdrfeYZTalxXdx6OJ+S+E7gGWAL2JNlVVfsmdvsAowm7P5zkUmA3cPGGDy5J6lWHWfEx4CpGQ+9LwC8z/vVWVb8JvA34mSRHge8AN1ZVTWvXglNaAB1dw7kD2F9VBwCS3AfcAEwWnAWcN37+PYwuCJckLYAusqKqbpry/ocYTZu0LhacUuOOcRbPzzZMMu1C8AuBJya2lzj5goLbgT9N8h7g5YzWw5YkNW4dWTEIC05pAcz4q3XaheBZ5bWVwyA3AfdU1X9K8kbgd5O8vqqOzdhVSdJAWp7RxIJTalyHq0csARdNbG/j5CHzdwPXAlTV/07yMmArcKiLDkiS+tH6qnQWnFLjinQ1TLIH2J7kEuBJ4Ebg7Sv2+WvgauCeJP8QeBnwVBcHlyT1p8Os6IUFp9S4rn61VtXRJLcADzCa8ujuqno0yR3A3qraBfwi8FtJfoHRcPs7Z7n7UJI0LM9wStqQLr9ExnNq7l7x2m0Tz/cBV3ZyMEnS3GyagnM8h99e4Mmquq6/LklaqeUvEWmZOSENq+WsWM8Zzp8DHuNv5+iTNAetT3UhTTAnpIG0nhVnzbJTkm3AjwN39dsdSat5kS1TH9KQzAlpeC1nxaxnOH8D+CXg3B77ImkVrV+XI42ZE9KAWs+KqQVnkuuAQ1X1UJKr1tjvZuDm0dYrO+qepNanupBOLydePZe+SWeK1rNiljOcVwLXJ3krozn5zkvye1X1jsmdxkvo7QRItjuNitSR1n+1SpxWTlxqTkgdaj0rpl7DWVW3VtW2qrqY0UTRn1r5JSKpP8tfIq1elyOZE9LwWs8K5+GUGtf6MIkkaXitZ8W6Cs6qehB4sJeeSFpV68Mk0iRzQhpG61nhGU5pAbT8JSJJakPLWWHBKTWu9V+tkqThtZ4VFpxS41q/LkeSNLzWs8KCU2pc679aJUnDaz0rLDilBdDyl4gkqQ0tZ4UFp9S4Y5zV2TBJkmuBDwJbgLuq6ldW2ecngduBAv6iqt7eycElSb3pMiv6YMEpLYAufrUm2QLcCVwDLAF7kuyqqn0T+2wHbgWurKpvJHGdWklaEGfgGc7vMsqzRfaSORzjLT23v6Pn9oFv9n+IQ8/2XPO8od/mOWdjf97hdTk7gP1VdQAgyX3ADcC+iX1+Grizqr4BUFWHujiwdLLngf09H2MznFP5iX6bf9nt/bYPc8mJr9JzTvxQv813wWs4JW1IVXj+hZd20dSFwBMT20vAFSv2+QcASf4Xo2H326vqk10cXJLUnw6zohcWnFLjqsKLR2f6p7o1yd6J7Z1VtXNiO6s1v2L7bGA7cBWwDfhMktdX1RzOUUiSTtc6smIQ7fZMErD8JTLTMMnhqrp8jfeXgIsmtrcBB1fZ57NV9V3g/yV5nFEBumcdXZYkzdk6smJNSe4GrgMOVdXrV3k/jG4+fSvwHPDOqnp4WrsWnFLj6lh4/judDJPsAbYnuQR4ErgRWHkH+h8BNwH3JNnKaIj9QBcHlyT1p8OsuAf4EPCRU7z/FkYnIrYzuizrw5x8edZJLDil5oVjL278n2pVHU1yC/AAo+sz766qR5PcAeytql3j934syT7gReDfVtXXNnxwSVLPOsuKTye5eI1dbgA+UlUFfDbJ+UkuqKq/WatdC06pdQV0MEwCUFW7gd0rXrtt4nkB7x0/JEmLosOsmGK1G1AvBCw4pYVWmdeXiCRpUc2eFdNuMJ1mlhtQT2LBKbWugCOr/fuWJGls9qyYdoPpNLPcgHqSmQrOJF8GvsXomq6jG+yopPUo4OjQnZCmMyukAc0vK3YBt4wXD7kCeHra9ZuwvjOc/6SqDp9u7ySdJgtOLRazQhpCR1mR5GOM5mLemmQJ+GXGyy9W1W8yug/grYyWCnsOeNcs7TqkLrXuGHBk6E5IkprWUVZU1U1T3i/g36y33bNmPT7wp0keSnLzajskuTnJ3tGFqN9abz8kreXoDA9peGtmxYk58cwA3ZM2uYazYtYznFdW1cEkrwT+LMmXqurTkzuM73DaCZBcPPVuJUkzckhdi2PNrDgxJ37QnJC61HhWzHSGs6oOjv/3EPBxYEefnZI0YXmYZNpDGphZIQ2o8ayYWnAmeXmSc5efAz8GfLHvjkkaW/7V2ugwiQRmhTS4xrNiliH1VwEfH63VztnAf6uqT/baK0knsqBU+8wKaWgNZ8XUgrOqDgD/eA59kbSaAr47dCektZkV0sAazwqnRZJadwx4fuhOSJKa1nhWWHBKrWv8zkNJUgMazwoLTql1jX+JSJIa0HhWWHBKrSuc9kiStLbGs2LWlYYkDaXDqS6SXJvk8ST7k7x/jf3elqSSXL7B3kuS5mETTIskaUgd3XmYZAtwJ3ANsATsSbKrqvat2O9c4GeBz238qJKkuWj8LnXPcEqtK0Z3Hk57TLcD2F9VB6rqBeA+4IZV9vsPwK/R9OCMJOkE3WVFL3o6w/ki8K1+mp6b187hGA/33P4cVpX79jn9H+IrW3tt/5nLXtJr+y+es8ExjO4uBL8QeGJiewm4YnKHJJcBF1XVJ5K8r5OjSqs6Cnx96E5s0A/P4Rhf6Lf5I/+o3/YBvt3/IQ7xqn4P8Pp+m++ENw1J2pDZv0S2Jtk7sb2zqnZObOcUrY/eTM4Cfh145/o7KUkalAWnpA2Z/bqcw1W11k0+S8BFE9vbgIMT2+cy+h3/4Hh5wlcDu5JcX1WThawkqTWNX8NpwSm1bvm6nI3bA2xPcgnwJHAj8Pbjh6l6Gjh+/UKSB4H3WWxK0gLoLit6YcEpta6jYZKqOprkFuABYAtwd1U9muQOYG9V7dr4USRJg3BIXdKGdDhMUlW7gd0rXrvtFPte1c1RJUm9c0hd0oYco+lhEklSAxrPCgtOaRE0PEwiSWpEw1kx08TvSc5Pcn+SLyV5LMkb++6YpLHlYZJpD2lgZoU0oMazYtYznB8EPllVb0vyUqD/2b4ljTQ+TCJNMCukoTSeFVMLziTnAW9iPBn0eEm8F/rtlqTjGr/zUAKzQhpc41kxy5D6DwBPAb+T5PNJ7kry8p77JWlZ48Mk0phZIQ2p8ayYpeA8m9GCsR+uqsuAZ4H3r9wpyc1J9o6W1nu2425KZ7DlyXynPaRhTc2KE3NiDgtsS2eSDrMiybVJHk+yP8lqNd87kzyV5JHx419Oa3OWgnMJWKqqz42372f0pXKCqtpZVZePltbzR63UmeVhkmkPaVhTs+LEnHjF3DsobWodZUWSLcCdwFuAS4Gbkly6yq6/X1VvGD/umtbu1IKzqr4CPJHkdeOXrgb2Te+ypE40PkwigVkhDa67rNgB7K+qA+Nrse8Dbtho92a9S/09wEfHdx0eAN610QNLmlEBLw7dCWkmZoU0lNmzYuvospbjdlbVzontC4EnJraXgCtWaecnkrwJ+EvgF6rqiVX2OW6mgrOqHgEun2VfSR0r4MjQnZCmMyukAc2eFYdHl7WcUk7R+qQ/Bj5WVc8n+dfAvcCPrnVQVxqSWtf4+riSpAZ0lxVLwEUT29uAgyccquprE5u/BfzqtEZnWmlI0oCWh0mmPSRJZ67usmIPsD3JJePLY24Edk3ukOSCic3rgcemNeoZTql1DqlLkqbpKCuq6miSW4AHgC3A3VX1aJI7gL1VtQv42STXM7rv/euMF3xYiwWn1DqH1CVJ03SYFVW1G9i94rXbJp7fCty6njYdUpda1+GQ+gyT+b43yb4kX0jyP5O8pqNPIUnqU+OXX3mGU2rdMToZJpmYzPcaRheF70myq6om50r8PHB5VT2X5GeAXwN+auNHlyT1qqOs6ItnOKXWzXEy36r686p6brz5WUZ3J0qSWtf4IiE9neE8Bnynn6aP+96e2/9qz+1D/1k+h/UO57Ec8tJqU4J15/EffN30nTbgCPs33kg3wyCzTua77N3An3RyZOkk88iJV/bc/pd7bh/gtXM4Rs/mkBOHn/6+Xtt/avs8lmLt4P+ohmcscUhdWgQrp9xd3bTVI2aZzHe0Y/IORhN4/8isXZQkDWy2rBiEBae0eUxbPWLqZL4ASd4M/HvgR6rq+W67KEk6E3kNp3TmmGUy38uA/wpcX1WHBuijJGkT8gyn1LxuJlebcTLf/wi8AviDJAB/XVXXb/jgkqSetT1pswWn1Lzubq6YYTLfN3dyIEnSnM3jRrzTZ8EpNa+Yy4wDkqQF1nZWWHBKzWt7mESS1IK2s8KCU2pe0fIwiSSpBW1nxdS71JO8LskjE49nkvz8PDonCZpfPkLCrJCG13ZWTD3DWVWPA2+A42sxPwl8vOd+STqu7etyJDArpOG1nRXrHVK/Gvi/VfVXfXRG0mravvNQWoVZIc1d21mx3oLzRuBjfXRE0qm0fSG4tAqzQpq7trNi5pWGxiuTXA/8wSnevznJ3tFazs921T9Jx4dJpj2k4a2VFeaE1Ke2s2I9ZzjfAjxcVV9d7c2q2gnsBEgubHj5eGnRtP2rVVrhlFlxYk5cZE5InWo7K9ZTcN6EQyTSANq+LkdawayQBtF2VsxUcCY5B7gG+Ff9dkfS6hwyV/vMCmlo7WbFTAVnVT0HfF/PfZG0qraHSaRlZoU0pLazwpWGpOa1PUwiSWpB21lhwSk1r+1frZKkFrSdFRacUvPaXj1CktSCtrNi5nk4JQ1leZhk2mO6JNcmeTzJ/iTvX+X9v5Pk98fvfy7Jxd18BklSv9rOCgtOqXnLwyTTHmsbr299J6N5Ei8Fbkpy6Yrd3g18o6peC/w68KvdfAZJUr/azgoLTql5na0esQPYX1UHquoF4D7ghhX73ADcO35+P3B1kmz0E0iS+tZ2VlhwSs3r5lcrcCHwxMT20vi1VfepqqPA0zjNjSQtgLazoqebhg4ehg/81Tr+YCtwuJ++zM2Z+Rm+2E9HTnDVuvZe92fYsa7mT8trNvbnBx+AD2ydYceXjdaoPm7neCnBZav9+ly5vOAs+0gdWDoMv7ienIAz9Xu2Pev7DPf315FlR9Z/jHV9hleuu/nTsqmzopeCs6q+fz37J9lbVZf30Zd58TO0YTN8hpWq6tqOmloCLprY3gYcPMU+S0nOBr4H+HpHx5eOW29OwOb49+1naMNm+AwrtZ4VDqlLZ449wPYklyR5KXAjsGvFPruAfzF+/jbgU1XlGU5JOnP0khXOwymdIarqaJJbgAeALcDdVfVokjuAvVW1C/ht4HeT7Gf0a/XG4XosSZq3vrKilYJz5/RdmudnaMNm+Ay9qardwO4Vr9028fwI8M/m3S9pRpvh37efoQ2b4TP0po+siKNlkiRJ6pPXcEqSJKlXgxac05ZOWgRJLkry50keS/Jokp8buk+nI8mWJJ9P8omh+3K6kpyf5P4kXxr/93jj0H2StHGLnhWbJSdg8bPCnBjOYEPq46WT/hK4htHt9XuAm6pq3yAdOk1JLgAuqKqHk5wLPAT80wX8HO8FLgfOq6rrhu7P6UhyL/CZqrprfGfdOVX1zaH7Jen0bYas2Cw5AYufFebEcIY8wznL0knNq6q/qaqHx8+/BTzGyTPyNy3JNuDHgbuG7svpSnIe8CZGd85RVS/4JSJtCgufFZshJ2Dxs8KcGNaQBecsSyctlCQXA5cBnxu2J+v2G8AvAceG7sgG/ADwFPA74+Geu5K8fOhOSdqwTZUVC5wTsPhZYU4MaMiCc1MtoZfkFcD/AH6+qp4Zuj+zSnIdcKiqHhq6Lxt0NvDDwIer6jLgWWDhrvWSdJJNkxWLmhOwabLCnBjQkAXnLEsnLYQkL2H0JfLRqvrDofuzTlcC1yf5MqOhqh9N8nvDdum0LAFLVbV81uB+Rl8skhbbpsiKBc8J2BxZYU4MaMiCc5alk5qXJIyuB3msqv7z0P1Zr6q6taq2VdXFjP4bfKqq3jFwt9atqr4CPJHkdeOXrgYW7oJ8SSdZ+KxY9JyAzZEV5sSwBltp6FRLJw3Vnw24EvjnwP9J8sj4tX83nqVf8/Ue4KPjUDoAvGvg/kjaoE2SFeZEO8yJgbjSkCRJknrlSkOSJEnqlQWnJEmSemXBKUmSpF5ZcEqSJKlXFpySJEnqlQWnJEmSemXBKUmSpF5ZcEqSJKlX/x8RNbW9lof2eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(221)\n",
    "plt.imshow(ex4.contributionMapAve['0.662'],cmap='jet')\n",
    "plt.title('Energy=0.662 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(ex4.contributionMapAve['0.996'],cmap='jet')\n",
    "plt.title('Energy=0.996 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(ex4.contributionMapAve['1.596'],cmap='jet')\n",
    "plt.title('Energy=1.596 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(ex4.contributionMapAve['2.112'],cmap='jet')\n",
    "plt.title('Energy=2.112 MeV')\n",
    "plt.colorbar()\n",
    "plt.subplots_adjust(bottom=0.1, right=1.8, top=1.6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One can easily renormalize this plots in order to get weight percentage of the pin-wise contribution."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(221)\n",
    "plt.imshow(100*ex4.contributionMapAve['0.662']/np.sum(ex4.contributionMapAve['0.662']),cmap='jet')\n",
    "plt.title('Energy=0.662 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(100*ex4.contributionMapAve['0.996']/np.sum(ex4.contributionMapAve['0.996']),cmap='jet')\n",
    "plt.title('Energy=0.996 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(100*ex4.contributionMapAve['1.596']/np.sum(ex4.contributionMapAve['1.596']),cmap='jet')\n",
    "plt.title('Energy=1.596 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(100*ex4.contributionMapAve['2.112']/np.sum(ex4.contributionMapAve['2.112']),cmap='jet')\n",
    "plt.title('Energy=2.112 MeV')\n",
    "plt.colorbar()\n",
    "plt.subplots_adjust(bottom=0.1, right=1.8, top=1.6)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random case\n",
    "\n",
    "And the efficiency compared with the center case. We can see that in this realistic example the center source case tends to underestimate the geometric efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "#0 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#1 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#2 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#3 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#4 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#5 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#6 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#7 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#8 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n",
      "#9 is being calculated\n",
      "Distance travelled to detector D is being calculated\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.4971 MeV\n",
      "...for gamma energy 0.563 MeV\n",
      "...for gamma energy 0.569 MeV\n",
      "...for gamma energy 0.6006 MeV\n",
      "...for gamma energy 0.604 MeV\n",
      "...for gamma energy 0.6103 MeV\n",
      "...for gamma energy 0.621 MeV\n",
      "...for gamma energy 0.635 MeV\n",
      "...for gamma energy 0.662 MeV\n",
      "...for gamma energy 0.723 MeV\n",
      "...for gamma energy 0.724 MeV\n",
      "...for gamma energy 0.756 MeV\n",
      "...for gamma energy 0.757 MeV\n",
      "...for gamma energy 0.765 MeV\n",
      "...for gamma energy 0.795 MeV\n",
      "...for gamma energy 0.801 MeV\n",
      "...for gamma energy 0.873 MeV\n",
      "...for gamma energy 0.996 MeV\n",
      "...for gamma energy 1.004 MeV\n",
      "...for gamma energy 1.038 MeV\n",
      "...for gamma energy 1.05 MeV\n",
      "...for gamma energy 1.167 MeV\n",
      "...for gamma energy 1.205 MeV\n",
      "...for gamma energy 1.246 MeV\n",
      "...for gamma energy 1.274 MeV\n",
      "...for gamma energy 1.365 MeV\n",
      "...for gamma energy 1.494 MeV\n",
      "...for gamma energy 1.562 MeV\n",
      "...for gamma energy 1.596 MeV\n",
      "...for gamma energy 1.766 MeV\n",
      "...for gamma energy 1.797 MeV\n",
      "...for gamma energy 1.988 MeV\n",
      "...for gamma energy 2.112 MeV\n",
      "...for gamma energy 2.185 MeV\n"
     ]
    }
   ],
   "source": [
    "ex4random=Experiment()\n",
    "ex4random.set_assembly(bwrOrig)\n",
    "ex4random.set_detectors(det)\n",
    "ex4random.set_materials(uo2,he,zr,h2o,air,lead,alu,copper,ss)\n",
    "ex4random.set_absorbers(steelwindow,lead8mm,steel21mm,alu3mm,copper1mm)\n",
    "ex4random.set_elines(elines)\n",
    "ex4random.set_random(10)\n",
    "ex4random.Run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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.figure()\n",
    "plt.errorbar(ex4random.elines,ex4random.geomEffAve,3*ex4random.geomEffAveErr,label='Random location in pin as source')\n",
    "plt.plot(ex4.elines,ex4.geomEffAve,'r',label='Pin center as source')\n",
    "plt.legend()\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.ylabel('Efficiency (1/particle)')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}