verification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d9c66bfd",
   "metadata": {},
   "source": [
    "# SNES verification"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7cea2ec4",
   "metadata": {},
   "source": [
    "Each test problem is a critical assembly and should have $k_\\mathrm{eff} = 1$.\n",
    "\n",
    "Each problem is run using a diamond-difference code version (yellow circles) and a linear-discontinuous finite-element version (blue diamonds). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "a2da3e57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.rcParams[\"figure.figsize\"] = (20,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "c1d7d57e",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --out keffs\n",
    "grep 'K[ ]EFF' qa/*.outs | tr -s ' ' | cut -d' ' -f4  # Read the diamond-difference results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "2c981bae",
   "metadata": {},
   "outputs": [],
   "source": [
    "%%bash --out keffl\n",
    "grep 'K[ ]EFF' qa/*.outl | tr -s ' ' | cut -d' ' -f4  # Read the linear-discontinuous results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "5537df28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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LiBkAETEfWALclpm3NA+uxnMFZWbe0o5rjHuXJElSP5fRSZKk2qJqvyQi3tVRfwhladZcYDMl2HEZcF1EnALcTlmetrUZxBmRX2fmvzvKNwAfARawN2BRu6fj+LdSAjR3Zua2jvr1wCVVe22bWkGQZh+WVOds7Op8pR7f+RGxsqN+brWfR5mZ1HRvx/F/rfabO+rqwNRhz9OfLl3Xeazav2aCbfW5LzOfaZXV9/JQ+zln5jMR8Tj99zJuzDNzR0TcR3ku8yizlurxn9Uz/m+o9l0Buq53SZIk9TDYJEmSaq+r9hcOOG4mQGb+KSIWUmaRnErJOwTwWERcmZlXj7Bvj/eU/73aj0tg3ahrqo/7W097dfnsEfWhqR7fTw04bmZH2Y6OsrEh6g4acK22J5+nrWkTbKvPuP5m5liV67zrXuo+9N3LsM+lHv+Tqq1P1/h3vUuSJKmHwSZJklSr/9CflZn/GuaEavnWmRHxcmA+8B5KLqdvRcTOzFwzor4d2lNef3GsK0jRNbuqPq7zS2WUxON97U2mD13Xnp+Zvx1w7EtB0v+75Owp7Mewz6Xenz+JQKhfn5MkaQLM2SRJkmp3VftBuYfGycyxzNycmVcAH6qKT28cUi+bmuzsmGM7ckVBSd4N0M7Z0+dB4CngmIjoWhZ2YrXv+iLc8RHR9bvTsH2Y9Pjup7YDh7cLI2Ia/fmVXgxLOvowq+rDfylfvYMDb/wlSdpvGWySJEm1aylfd1sdEXPblRExPSIWN35eGBFds0rqsqcaZU9U+zmT7Nss4Gut/rwT+DBlxsqPh2kkM3dTkorPpJVwPCKOBM6jjMGNHae/Bfhc65z3UYIdDwObBlz+e5Rlaiuq5YfPEREvi4gThriN/cU9wJyIOLlVfgnwpinsxzkR0c6xtZLyztxcJUyvE8pvAs6IiI93NRQRb4+IQ17MzkqS9P/AZXSSJAmAzHyg+iP8u8CWiFgHPETJlTOHMiPkn5Qk2wDLgM9HxEZKsGU7cCRwGvA08M1G87+kBJ8uiIjXsjfPzjWZOWj5GcAvgE9GxLspScjfCJxJ+cfZp4dd9lf5cnUvy6tE6D8HXg98EDgYWJ6Zj3Sctw64KiLeC9xP+QLdGZTZM5/oSR6+R2Y+EREfoATG7oqIO4AtwLOU8V1EySv0igncy750JXAKsDYibgW2AccBb6YkTT9hivrxE+DOiPghJefW8dX2KOVZNy2jJIFfExHnAXdTAoCHAUcDR1Gewz+mouOSJB2oDDZJkqQ9MvOmiLgf+CJlSdnJwE7K18J+BNzaOPxmyqfqjwOOBV5J+QraLcBVmfn7RrvbI2Ip5fPyHwNeXVXdxOBcRwCPAJ8BLq/2MyhL3VZl5u0TvMdtEbEIuIgSLPoCsIsyU+cbmfnTnlPvpsyGuhRYDgQlcHFxZv5qyGvfERFHA1+iBGoWA7sp47seuG0i97IvVfdyOmXG2VmU9+RnlCDg16ewK6spAbwLqmv/B7ge+EpmPidolJl/joh3UPKKLaXMjJtGSQC+FbgG+N1UdVySpANVjP7LxJIkSaMREUdQAk03ZOZH921vJEmSNAxzNkmSJEmSJGlkDDZJkiRJkiRpZAw2SZIkSZIkaWTM2SRJkiRJkqSRcWaTJEmSJEmSRsZgkyRJkiRJkkbGYJMkSZIkSZJGxmCTJEmSJEmSRsZgkyRJkiRJkkbGYJMkSZIkSZJG5n9M9wtYsxsqPgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "snes = [float(s) for s in keffs.split()]\n",
    "snel = [float(s) for s in keffl.split()]\n",
    "n = len(snes)\n",
    "x = range(1, n+1)\n",
    "xc = [x for x in range(n+2)] # Critical curve\n",
    "yc = [1 for i in xc]         # Critical curve\n",
    "plt.plot(x, snes, 'o', color='orange', markersize=26, label=\"Diamond-difference\")\n",
    "plt.plot(x, snel, 'd', color='indigo', markersize=18, label=\"Linear discontinuous\")\n",
    "plt.plot(xc, yc, 'k--', label=\"Critical ($k_\\mathrm{eff}=1$)\")\n",
    "plt.xlim([0.5, n+0.5])\n",
    "plt.ylim([0.5, 1.1])\n",
    "plt.xlabel(\"Test problem number\", fontsize=20)\n",
    "plt.ylabel(\"$k_\\mathrm{eff}$\", fontsize=20)\n",
    "plt.title(\"$k_\\mathrm{eff}$ results\", fontsize=30)\n",
    "plt.legend(fontsize=18, shadow=True, borderpad=1.0, labelspacing=1.2)\n",
    "plt.grid()\n",
    "plt.xticks(x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "099f1870",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}