{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2x2 fuel assembly\n",
    "\n",
    "This example shows the basic functionality of feign by defining a 2x2 fuel assembly (rods made of UO2 in cladding), and one detector point facing the corner. Two cases will be computed, one when the source points are the center of the pins, and one one 10 random points are sampled in each pin as source locations. In such geometry the distance travelled in the rod can be simply calculated by hand. Nevertheless, keep in mind that for this given example feign gives a fairly bad estimate of the point flux, since the buildup factor is not negligible in a case when the detector is very close to an uncollimated source.\n",
    "\n",
    "Let's import feign first."
   ]
  },
  {
   "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",
    "Then, define four Material() objects, water and uranium-dioxide, zirconium and helium."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "uo2=Material('1')\n",
    "uo2.set_density(10.5) #g/cm3\n",
    "\n",
    "he=Material('2')\n",
    "he.set_density(0.00561781)\n",
    "\n",
    "\n",
    "zr=Material('3')\n",
    "zr.set_density(6.52)\n",
    "\n",
    "\n",
    "h2o=Material('4')\n",
    "h2o.set_density(1.0)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pins\n",
    "\n",
    "In this example we assume that we are going to have only one pin type, the fuel pin. The uranium-dioxide region has 0.5 cm radius, that is followed by an 0.01 cm thick helium gap, and finally an 0.1 cm thick zirconium cladding. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "fuel=Pin('1')\n",
    "fuel.add_region(uo2,0.5) #cm\n",
    "fuel.add_region(he,0.51)\n",
    "fuel.add_region(zr,0.61)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Assembly\n",
    "\n",
    "Now we can build a 2x2 assembly from the pin. We need to set the pitch of the lattice, the source, and the coolant. And we have to define a fuelmap based on the pinIDs. (Since there is no pool in the problem, we do not need to set anything else.)\n",
    "\n",
    "Note the middle of the assembly is always considered as being placed at (0,0).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "assy=Assembly(2,2)\n",
    "assy.set_pitch(1.3)\n",
    "assy.set_source(uo2)\n",
    "assy.set_coolant(h2o)\n",
    "assy.set_pins(fuel)\n",
    "\n",
    "fuelmap=[['1','1'],\n",
    "         ['1','1']]\n",
    "\n",
    "\n",
    "assy.set_fuelmap(fuelmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Detector\n",
    "\n",
    "At least one detector has to be defined in the problem. Let us place one at (3,3), facing the corner of the assembly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "det=Detector('D')\n",
    "det.set_location(Point(3, 3))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experiment\n",
    "\n",
    "Finally we re ready to perform define our experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "ex1=Experiment()\n",
    "ex1.set_assembly(assy)\n",
    "ex1.set_detectors(det)\n",
    "ex1.set_materials(uo2,he,zr,h2o)\n"
   ]
  },
  {
   "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."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no pool in the problem, the surrounding of the Assembly is filled with coolant material\n",
      "No absorbers in the problem\n",
      "Warning: elines missing; only distance travelled in various materials will be computed\n"
     ]
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ex1.Plot(dpi=600,out=None,xl=[-3.5,3.5],yl=[-3.5,3.5],detectorSize=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One may change the color of any material for plotting."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no pool in the problem, the surrounding of the Assembly is filled with coolant material\n",
      "Warning: elines missing; only distance travelled in various materials will be computed\n"
     ]
    },
    {
     "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('#FF0000')\n",
    "ex1.Plot(dpi=600,out=None,xl=[-3.5,3.5],yl=[-3.5,3.5],detectorSize=0.4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When we run the experiment, we get some warnings, because we have not set any pool, any absorbers, and the energy vector (elines) is also missing, thus only the distance travelled can be computed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no pool in the problem, the surrounding of the Assembly is filled with coolant material\n",
      "Warning: elines missing; only distance travelled in various materials will be computed\n",
      "#0 is being calculated\n",
      "Distance travelled to detector D is being calculated\n"
     ]
    }
   ],
   "source": [
    "ex1.Run()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the results\n",
    "\n",
    "First let's just print the distance travelled by the ray to the detector. from each pin We would expect, that it travelles one radius from the pins close to the detector, and three radius from the pin farther from the detector. Indeed, this is what we see.\n",
    "\n",
    "Visualizing the data is not that interesting for this example."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5, 0.5],\n",
       "       [1.5, 0.5]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ex1.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 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.subplot(221)\n",
    "plt.imshow(ex1.dTmap['D']['1'],cmap='jet')\n",
    "plt.title('uo2')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(ex1.dTmap['D']['2'],cmap='jet')\n",
    "plt.title('he')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(ex1.dTmap['D']['3'],cmap='jet')\n",
    "plt.title('zr')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(ex1.dTmap['D']['4'],cmap='jet')\n",
    "plt.title('h2o')\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": [
    "## Computing the geometric efficiency"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to calculate the geometric efficiency, we will need to set the path to the attenuation data, set energy lines, where the efficiency needs to be evaluated at and then rerun the experiment."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "uo2.set_path(('/data/UO2.dat',1))\n",
    "he.set_path(('/data/He.dat',1))\n",
    "zr.set_path(('/data/Zr.dat',1))\n",
    "h2o.set_path(('/data/H2O.dat',1))\n",
    "\n",
    "elines=['0.5','0.6','0.8','1.0','1.5','2.0']\n",
    "ex1.set_elines(elines)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no pool in the problem, the surrounding of the Assembly is filled with coolant material\n",
      "#0 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 MeV\n"
     ]
    }
   ],
   "source": [
    "ex1.Run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "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(ex1.elines,ex1.geomEff['D'])\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.ylabel('Efficiency (1/particle)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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(ex1._contributionMapAve['0.6'],cmap='jet')\n",
    "#Same as plt.imshow(ex1._contributionMap['D']['0.6'],cmap='jet')\n",
    "plt.title('Energy=0.6 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(222)\n",
    "plt.imshow(ex1._contributionMapAve['0.8'],cmap='jet')\n",
    "plt.title('Energy=0.8 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(223)\n",
    "plt.imshow(ex1._contributionMapAve['1.0'],cmap='jet')\n",
    "plt.title('Energy=1.0 MeV')\n",
    "plt.colorbar()\n",
    "\n",
    "plt.subplot(224)\n",
    "plt.imshow(ex1._contributionMapAve['1.5'],cmap='jet')\n",
    "plt.title('Energy=1.5 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 source locations\n",
    "\n",
    "In the following the number of random source locations will be defined as well, and the geometric efficiency is compared for the the two case.\n",
    "\n",
    "We can see that for this close detector setup, the random locations result in a relatively large standard deviation, and the center source case is well aligned with the average geometric efficiency."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warning: no pool in the problem, the surrounding of the Assembly is filled with coolant material\n",
      "No absorbers in the problem\n",
      "#0 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 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 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 MeV\n",
      "#9 is being calculated\n",
      "Distance travelled to detector D is being calculated\n",
      "Contribution to detector D is calculated...\n",
      "...for gamma energy 0.5 MeV\n",
      "...for gamma energy 0.6 MeV\n",
      "...for gamma energy 0.8 MeV\n",
      "...for gamma energy 1.0 MeV\n",
      "...for gamma energy 1.5 MeV\n",
      "...for gamma energy 2.0 MeV\n"
     ]
    }
   ],
   "source": [
    "ex1rand=Experiment()\n",
    "ex1rand.set_assembly(assy)\n",
    "ex1rand.set_detectors(det)\n",
    "ex1rand.set_materials(uo2,he,zr,h2o)\n",
    "ex1rand.set_elines(elines)\n",
    "ex1rand.set_random(10)\n",
    "ex1rand.Run()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "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(ex1rand.elines,ex1rand.geomEffAve,ex1rand.geomEffAveErr)\n",
    "plt.plot(ex1.elines,ex1.geomEffAve,'r')\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
}