doc/tutorials/nuclide_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The ARMI Nuclide and Element Packages\n",
    "\n",
    "One of the key features that the ARMI framework offers is access to nuclide data across an application (recall: a *nuclide* is a particular isotope of an element. Iron-56, Uranium-238, and Boron-10 are all nuclides). This is specifically useful for nuclear engineers so that manual look-ups of nuclide attributes on sources like Wikipedia, Chart of the Nuclides, etc. are not needed. \n",
    "\n",
    "The available attributes for each nuclide are:\n",
    "\n",
    "- Atomic weight/mass, in amu\n",
    "- Natural abundance\n",
    "- Atomic number, Z\n",
    "- Mass number, A\n",
    "- Half-life\n",
    "- Neutron yield from spontaneous fission\n",
    "    \n",
    "Accessing the nuclide data begins with importing the nuclide bases, and optionally, the elements packages:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from armi.nucDirectory import nuclideBases\n",
    "from armi.nucDirectory import elements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One these packages are imported, there are several module-level global dictionaries that are important to know about, since these are likely what you will be working with when implementing code that requires nuclide data or just when performing data look-ups:\n",
    "\n",
    "**Nuclide Bases Global Dictionaries**\n",
    "\n",
    "- nuclideBases.byName\n",
    "- nuclideBases.DBName\n",
    "- nuclideBases.byLabel\n",
    "- nuclideBases.byMcc2Id\n",
    "- nuclideBases.byMcc3Id\n",
    "- nuclideBases.byMcnpId\n",
    "- nuclideBases.byAAAZZZSId\n",
    "   \n",
    "**Elements Global Dictionaries**\n",
    "\n",
    "- elements.byZ\n",
    "- elements.bySymbol\n",
    "- elements.byName"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Accessing Individual Nuclide Data/Attributes\n",
    "\n",
    "Here we will explore retrieving data from a couple nuclides and showing the coupling between nuclide and element definitions. For these examples, let's try to answer the following questions:\n",
    "\n",
    "- How many total nuclides and elements are defined in the framework?\n",
    "- What is atomic weight of a selected nuclide?\n",
    "- What is the natural abundance a selected nuclide?\n",
    "- Are there any spontaneous fission neutrons for a selected nuclide?\n",
    "- What is the half-life in seconds for a selected nuclide?\n",
    "- How many other nuclides for the same element exist for a selected nuclide?\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How many total nuclides and elements are defined in the framework?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of elements defined in the framework: 120\n",
      "\n",
      "Number of nuclides defined in the framework: 4706\n",
      "   - Number of nuclides of type `<class 'armi.nucDirectory.nuclideBases.NuclideBase'>`: 4614\n",
      "   - Number of nuclides of type `<class 'armi.nucDirectory.nuclideBases.NaturalNuclideBase'>`: 84\n",
      "   - Number of nuclides of type `<class 'armi.nucDirectory.nuclideBases.DummyNuclideBase'>`: 2\n",
      "   - Number of nuclides of type `<class 'armi.nucDirectory.nuclideBases.LumpNuclideBase'>`: 6\n"
     ]
    }
   ],
   "source": [
    "import collections\n",
    "\n",
    "print(f\"Number of elements defined in the framework: {len(elements.byZ.values())}\")\n",
    "print(\"\")\n",
    "print(f\"Number of nuclides defined in the framework: {len(nuclideBases.instances)}\")\n",
    "nucsByType = collections.defaultdict(list)\n",
    "for n in nuclideBases.instances:\n",
    "    nucsByType[type(n)].append(n)\n",
    "\n",
    "for typ, nucs in nucsByType.items():\n",
    "    print(f\"   - Number of nuclides of type `{typ}`: {len(nucs)}\")\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking up nuclide and elemental data for U-235"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<NuclideBase U235:  Z:92, A:235, S:0, W:2.350439e+02, Label:U235>, HL:2.22160758861e+16, Abund:7.204000e-03>\n",
      "Atomic Weight (amu): 235.043929425\n",
      "Natural Abundance: 0.007204\n",
      "Spontaneous Fission Neutron Yield: 1.87\n",
      "Half-life (seconds): 2.22160758861e+16\n",
      "\n",
      "Other nuclides for Uranium:\n",
      "    - <NaturalNuclideBase U:  Z:92, W:2.380289e+02, Label:U>\n",
      "    - <NuclideBase U215:  Z:92, A:215, S:0, W:2.150262e+02, Label:U215>, HL:7.00000000000e-04, Abund:0.000000e+00>\n",
      "    - <NuclideBase U216:  Z:92, A:216, S:0, W:2.160240e+02, Label:U216>, HL:4.50000000000e-03, Abund:0.000000e+00>\n",
      "    - <NuclideBase U217:  Z:92, A:217, S:0, W:2.170244e+02, Label:U217>, HL:1.60000000000e-02, Abund:0.000000e+00>\n",
      "    - <NuclideBase U218:  Z:92, A:218, S:0, W:2.180235e+02, Label:U218>, HL:6.50000000000e-04, Abund:0.000000e+00>\n",
      "    - <NuclideBase U219:  Z:92, A:219, S:0, W:2.190249e+02, Label:U219>, HL:6.00000000000e-05, Abund:0.000000e+00>\n",
      "    - <NuclideBase U220:  Z:92, A:220, S:0, W:2.200247e+02, Label:U220>, HL:1.11110000000e+01, Abund:0.000000e+00>\n",
      "    - <NuclideBase U221:  Z:92, A:221, S:0, W:2.210264e+02, Label:U221>, HL:6.60000000000e-07, Abund:0.000000e+00>\n",
      "    - <NuclideBase U222:  Z:92, A:222, S:0, W:2.220261e+02, Label:U222>, HL:4.70000000000e-06, Abund:0.000000e+00>\n",
      "    - <NuclideBase U223:  Z:92, A:223, S:0, W:2.230277e+02, Label:U223>, HL:1.80000000000e-05, Abund:0.000000e+00>\n",
      "    - <NuclideBase U224:  Z:92, A:224, S:0, W:2.240276e+02, Label:U224>, HL:8.40000000000e-04, Abund:0.000000e+00>\n",
      "    - <NuclideBase U225:  Z:92, A:225, S:0, W:2.250294e+02, Label:U225>, HL:6.90000000000e-02, Abund:0.000000e+00>\n",
      "    - <NuclideBase U226:  Z:92, A:226, S:0, W:2.260293e+02, Label:U226>, HL:2.68000000000e-01, Abund:0.000000e+00>\n",
      "    - <NuclideBase U227:  Z:92, A:227, S:0, W:2.270312e+02, Label:U227>, HL:6.60000000000e+01, Abund:0.000000e+00>\n",
      "    - <NuclideBase U228:  Z:92, A:228, S:0, W:2.280314e+02, Label:U228>, HL:5.46000000000e+02, Abund:0.000000e+00>\n",
      "    - <NuclideBase U229:  Z:92, A:229, S:0, W:2.290335e+02, Label:U229>, HL:3.48000000000e+03, Abund:0.000000e+00>\n",
      "    - <NuclideBase U230:  Z:92, A:230, S:0, W:2.300339e+02, Label:U230>, HL:1.74787200000e+06, Abund:0.000000e+00>\n",
      "    - <NuclideBase U231:  Z:92, A:231, S:0, W:2.310363e+02, Label:U231>, HL:3.62880000000e+05, Abund:0.000000e+00>\n",
      "    - <NuclideBase U232:  Z:92, A:232, S:0, W:2.320372e+02, Label:U232>, HL:2.17427219965e+09, Abund:0.000000e+00>\n",
      "    - <NuclideBase U233:  Z:92, A:233, S:0, W:2.330396e+02, Label:U233>, HL:5.02354704590e+12, Abund:0.000000e+00>\n",
      "    - <NuclideBase U234:  Z:92, A:234, S:0, W:2.340410e+02, Label:U234>, HL:7.74722532676e+12, Abund:5.400000e-05>\n",
      "    - <NuclideBase U235:  Z:92, A:235, S:0, W:2.350439e+02, Label:U235>, HL:2.22160758861e+16, Abund:7.204000e-03>\n",
      "    - <NuclideBase U235M:  Z:92, A:235, S:1, W:2.350439e+02, Label:U23F>, HL:1.56000000000e+03, Abund:0.000000e+00>\n",
      "    - <NuclideBase U236:  Z:92, A:236, S:0, W:2.360456e+02, Label:U236>, HL:7.39063206325e+14, Abund:0.000000e+00>\n",
      "    - <NuclideBase U237:  Z:92, A:237, S:0, W:2.370487e+02, Label:U237>, HL:5.83372800000e+05, Abund:0.000000e+00>\n",
      "    - <NuclideBase U238:  Z:92, A:238, S:0, W:2.380508e+02, Label:U238>, HL:1.40996345254e+17, Abund:9.927420e-01>\n",
      "    - <NuclideBase U239:  Z:92, A:239, S:0, W:2.390543e+02, Label:U239>, HL:1.40700000000e+03, Abund:0.000000e+00>\n",
      "    - <NuclideBase U240:  Z:92, A:240, S:0, W:2.400566e+02, Label:U240>, HL:5.07600000000e+04, Abund:0.000000e+00>\n",
      "    - <NuclideBase U241:  Z:92, A:241, S:0, W:2.410603e+02, Label:U241>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase U242:  Z:92, A:242, S:0, W:2.420629e+02, Label:U242>, HL:1.00800000000e+03, Abund:0.000000e+00>\n",
      "    - <NuclideBase U243:  Z:92, A:243, S:0, W:2.430674e+02, Label:U243>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase U244:  Z:92, A:244, S:0, W:2.440679e+02, Label:U244>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase U245:  Z:92, A:245, S:0, W:2.450708e+02, Label:U245>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase U246:  Z:92, A:246, S:0, W:2.460702e+02, Label:U246>, HL:inf            , Abund:0.000000e+00>\n"
     ]
    }
   ],
   "source": [
    "u235 = nuclideBases.byName['U235']\n",
    "\n",
    "print(u235)\n",
    "print(f\"Atomic Weight (amu): {u235.weight}\")\n",
    "print(f\"Natural Abundance: {u235.abundance}\")\n",
    "print(f\"Spontaneous Fission Neutron Yield: {u235.nuSF}\")\n",
    "print(f\"Half-life (seconds): {u235.halflife}\")\n",
    "print(\"\")\n",
    "print(f\"Other nuclides for {elements.byZ[u235.z].name}:\")\n",
    "for n in elements.byZ[u235.z].nuclides:\n",
    "    print(f\"    - {n}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Looking up nuclide and elemental data for Li-7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<NuclideBase LI7:  Z:3, A:7, S:0, W:7.016004e+00, Label:LI07>, HL:inf            , Abund:9.241000e-01>\n",
      "Atomic Weight (amu): 7.01600439548\n",
      "Natural Abundance: 0.92410004\n",
      "Spontaneous Fission Neutron Yield: 0.0\n",
      "Half-life (seconds): inf\n",
      "\n",
      "Other nuclides for Lithium:\n",
      "    - <NaturalNuclideBase LI:  Z:3, W:6.940038e+00, Label:LI>\n",
      "    - <NuclideBase LI3:  Z:3, A:3, S:0, W:3.030775e+00, Label:LI03>, HL:1.11110000000e+01, Abund:0.000000e+00>\n",
      "    - <NuclideBase LI4:  Z:3, A:4, S:0, W:4.027185e+00, Label:LI04>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase LI5:  Z:3, A:5, S:0, W:5.012538e+00, Label:LI05>, HL:3.70924971603e-22, Abund:0.000000e+00>\n",
      "    - <NuclideBase LI6:  Z:3, A:6, S:0, W:6.015123e+00, Label:LI06>, HL:inf            , Abund:7.590000e-02>\n",
      "    - <NuclideBase LI7:  Z:3, A:7, S:0, W:7.016004e+00, Label:LI07>, HL:inf            , Abund:9.241000e-01>\n",
      "    - <NuclideBase LI8:  Z:3, A:8, S:0, W:8.022488e+00, Label:LI08>, HL:8.39900000000e-01, Abund:0.000000e+00>\n",
      "    - <NuclideBase LI9:  Z:3, A:9, S:0, W:9.026789e+00, Label:LI09>, HL:1.78300000000e-01, Abund:0.000000e+00>\n",
      "    - <NuclideBase LI10:  Z:3, A:10, S:0, W:1.003548e+01, Label:LI10>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase LI11:  Z:3, A:11, S:0, W:1.104380e+01, Label:LI11>, HL:8.75000000000e-03, Abund:0.000000e+00>\n",
      "    - <NuclideBase LI12:  Z:3, A:12, S:0, W:1.205378e+01, Label:LI12>, HL:inf            , Abund:0.000000e+00>\n",
      "    - <NuclideBase LI13:  Z:3, A:13, S:0, W:1.306117e+01, Label:LI13>, HL:1.11110000000e+01, Abund:0.000000e+00>\n"
     ]
    }
   ],
   "source": [
    "li7 = nuclideBases.byName['LI7']\n",
    "\n",
    "print(li7)\n",
    "print(f\"Atomic Weight (amu): {li7.weight}\")\n",
    "print(f\"Natural Abundance: {li7.abundance}\")\n",
    "print(f\"Spontaneous Fission Neutron Yield: {li7.nuSF}\")\n",
    "print(f\"Half-life (seconds): {li7.halflife}\")\n",
    "print(\"\")\n",
    "print(f\"Other nuclides for {elements.byZ[li7.z].name}:\")\n",
    "for n in elements.byZ[li7.z].nuclides:\n",
    "    print(f\"    - {n}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exploring elemental Lithium data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<Element  LI (Z=3), Lithium, ChemicalGroup.ALKALI_METAL, ChemicalPhase.SOLID>\n",
      "\n",
      "Average Atomic weight: 6.940037501798687\n",
      "Is Naturally Occurring?: True\n",
      "Is a Heavy Metal Atom?: False\n"
     ]
    }
   ],
   "source": [
    "liElement = elements.bySymbol['LI']\n",
    "\n",
    "print(liElement)\n",
    "print(\"\")\n",
    "print(f\"Average Atomic weight: {liElement.standardWeight}\")\n",
    "print(f\"Is Naturally Occurring?: {liElement.isNaturallyOccurring()}\")\n",
    "print(f\"Is a Heavy Metal Atom?: {liElement.isHeavyMetal()}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting the Chart of the Nuclides"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import colors\n",
    "xyc = []\n",
    "for name, base in nuclideBases.byName.items():\n",
    "    if not base.a:\n",
    "        continue\n",
    "    xyc.append((base.a-base.z, base.z, base.abundance or 0.5))\n",
    "x,y,c = zip(*xyc)\n",
    "plt.figure(figsize=(12,8))\n",
    "plt.scatter(x,y,c = c, marker='s', s=6)\n",
    "plt.title('Chart of the nuclides')\n",
    "plt.xlabel('Number of neutrons (N)')\n",
    "plt.ylabel('Number of protons (Z)')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.9.13"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}