notebook_fy_diff_jeff33_vs_jeff311.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Differences in FYs between the JEFF-3.3 and the JEFF-3.1.1 evaluations "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import sandy and other useful packages."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import urllib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture --no-stdout\n",
    "import sandy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "sns.set_style(\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load FYs from website"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For both evaluations we select the cumulative fission yields for a thermal fission in $^{235}$U."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def read_fy(url):\n",
    "    with urllib.request.urlopen(url) as f:\n",
    "        text_fy = f.read().decode('utf-8')\n",
    "    tape = sandy.Endf6.from_text(text_fy)\n",
    "    return sandy.Fy.from_endf6(tape).filter_by(\"MT\", 459).filter_by(\"ZAM\", 922350).filter_by(\"E\", 0.0253)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.oecd-nea.org/dbdata/jeff/jeff33/downloads/JEFF33-nfy.asc\"\n",
    "jeff33 = read_fy(url)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = \"https://www.oecd-nea.org/dbforms/data/eva/evatapes/jeff_31/JEFF311/JEFF-311FY/JEFF311NFY.ASC\"\n",
    "jeff311 = read_fy(url).filter_by(\"MT\", 459).filter_by(\"ZAM\", 922350).filter_by(\"E\", 0.0253)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Merge the two evaluations in one single database."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = jeff311.data[[\"ZAP\", \"FY\"]].merge(\n",
    "    jeff33.data[[\"ZAP\", \"FY\"]], on=\"ZAP\", suffixes=(\"_JEFF311\", \"_JEFF33\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calculate the ratio between JEFF-3.3 and JEFF-3.1.1."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"ratio\"] = (df[\"FY_JEFF33\"] / df[\"FY_JEFF311\"] - 1) * 100\n",
    "df[\"absratio\"] = df[\"ratio\"].abs()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot ratio for largest fission yields"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "absratio_limit = 0.9\n",
    "fy_limit = 3 / 100\n",
    "\n",
    "diff = df.query(f\"absratio > {absratio_limit} & FY_JEFF33 > {fy_limit}\")\\\n",
    "         .sort_values(by=\"ZAP\", ascending=True, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "diff.plot(kind=\"bar\", x=\"ZAP\", y=\"ratio\", ax=ax, label=\"FY > 3%\")\n",
    "\n",
    "ax.set_ylabel(\"ratio $\\\\left(\\\\frac{J33}{J311} - 1\\\\right)$ / %\", fontsize=\"large\")\n",
    "ax.set_xlabel(None)\n",
    "ax.tick_params(axis='both', which='major', labelsize=\"large\")\n",
    "ax.legend(prop={\"size\": \"large\"})\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot ratios for Nd isotopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = df.query(\"ZAP < 601520 & ZAP > 600000\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "diff.plot(kind=\"bar\", x=\"ZAP\", y=\"ratio\", ax=ax, label=\"Nd isotopes\")\n",
    "\n",
    "ax.set_ylabel(\"ratio $\\\\left(\\\\frac{J33}{J311} - 1\\\\right)$ / %\", fontsize=\"large\")\n",
    "ax.set_xlabel(None)\n",
    "ax.tick_params(axis='both', which='major', labelsize=\"large\")\n",
    "ax.legend(prop={\"size\": \"large\"})\n",
    "\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot ratios for Cs isotopes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "diff = df[df.ZAP.isin([551330, 551350, 551370])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(8, 5))\n",
    "diff.plot(kind=\"bar\", x=\"ZAP\", y=\"ratio\", ax=ax, label=\"Cs isotopes\")\n",
    "\n",
    "ax.set_ylabel(\"ratio $\\\\left(\\\\frac{J33}{J311} - 1\\\\right)$ / %\", fontsize=\"large\")\n",
    "ax.set_xlabel(None)\n",
    "ax.tick_params(axis='both', which='major', labelsize=\"large\")\n",
    "ax.legend(prop={\"size\": \"large\"})\n",
    "\n",
    "plt.tight_layout();"
   ]
  }
 ],
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