origen-cmp.ipynb

{
 "metadata": {
  "name": "",
  "signature": "sha256:6e6cc0c206120ffc36ff1a7966fa516d815084ca9b1c08df7cbeb2613457003d"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "ORIGEN v2.2 Benchmark Study\n",
      "===========================\n",
      "This notebook attemps to benchmark the symbolic decayer to ORIGEN v2.2 results.  The basic strategy is that at $t=0$, a single kg of a pure nuclude is decayed for 0.01, 0.1, 1.0, 10, 100 times that nuclide's half life.  If the nuclide is stable then the decay calculation is run for 0.01, 0.1, 1.0, 10, 100 seconds.\n",
      "\n",
      "Throughout this benchmark I use the mean relative error as the basis for comparison. For any points $x$ and $y$:\n",
      "\n",
      "$e = \\frac{2|x - y|}{x + y} $ \n",
      "\n",
      "This may also be given in a mass-weighted form, $w$, for mass $m$.\n",
      "\n",
      "$w = m\\cdot e$\n",
      "\n",
      "The mass weighted form is useful to discrimnate against species that have a high relative error but are virtually absent in the decayed material. Let's start with some imports!"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import os\n",
      "import sys\n",
      "import shutil\n",
      "import warnings\n",
      "import subprocess\n",
      "from functools import lru_cache\n",
      "\n",
      "import numpy as np\n",
      "import matplotlib\n",
      "%matplotlib inline\n",
      "import pandas as pd\n",
      "import matplotlib.pyplot as plt\n",
      "\n",
      "from pyne.utils import QAWarning\n",
      "warnings.simplefilter('ignore', QAWarning)\n",
      "from pyne import nucname\n",
      "from pyne import data\n",
      "from pyne import origen22\n",
      "from pyne.material import Material, MaterialLibrary\n",
      "\n",
      "import sys\n",
      "sys.path.insert(0, '/home/scopatz/pyne/src')\n",
      "import decaygen\n",
      "\n",
      "exp2 = np.exp2"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 1
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load in the ORIGEN Materials\n",
      "-----------------------------"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "H5NAME = 'origen-benchmark-py{0}k.h5'.format(sys.version_info[0])\n",
      "mats = MaterialLibrary(H5NAME)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Load Origen Half-Lives\n",
      "-----------------------\n",
      "Testing the decayer is meaningless if the half-lives that are used do not match each other. This adds a function to compare the relative error of the pyne half life to the origen one."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "t9 = origen22.parse_tape9('TAPE9.INP')\n",
      "for key in list(t9.keys()):\n",
      "    if key not in [1, 2, 3]:\n",
      "        del t9[key]\n",
      "        \n",
      "def t9_half_life(nuc):\n",
      "    nuczz = nucname.zzaaam(nuc)\n",
      "    hl = None\n",
      "    for i in [1, 2, 3]:\n",
      "        hl = t9[i]['half_life'].get(nuczz, None)\n",
      "        if hl is not None:\n",
      "            break\n",
      "    return hl\n",
      "\n",
      "@lru_cache()\n",
      "def hl_relerr(nuc):\n",
      "    \"\"\"Half-life relative error.\"\"\"\n",
      "    dhl = data.half_life(nuc) or np.inf\n",
      "    ohl = t9_half_life(nuc) or np.inf\n",
      "    if np.isinf(ohl) and np.isinf(dhl):\n",
      "        return 0.0\n",
      "    hlre = np.abs(ohl - dhl) * 2 / (ohl + dhl)\n",
      "    return np.inf if np.isnan(hlre) else hlre"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import json\n",
      "t9hls = {}\n",
      "for i in [1, 2, 3]:\n",
      "    t9hls.update({nucname.id(int(nuc)): v for nuc, v in t9[i]['half_life'].items()})\n",
      "with open('o2hls.json', 'w') as f:\n",
      "    json.dump(t9hls, f, sort_keys=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Pivot Mats Keys\n",
      "----------------\n",
      "This puts the material keys into a dict mapping nuclides to the data set names (eg 'origen_922350000_3').  This filters out any data sets that contain high relative errors in the half-lives.  It also filters out species for which Origen has weird metastable behavior that seems unphysical."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "metastable_blacklist = {\n",
      "    771940001,  # have 2+ metastables that ORIGEN lumps together, or\n",
      "    340830001,  # ...\n",
      "    350830000,  # decay to metastables without reason.\n",
      "    340830000,  # ...\n",
      "    481170000,  # ...\n",
      "    461110000,  # ...\n",
      "    441030000,  # ...\n",
      "    471110001,  # missing branch in origen\n",
      "    501130001,  # ...\n",
      "}\n",
      "nuc_keys = {}\n",
      "for key in mats.keys():\n",
      "    _, nuc, t = key.split('_')\n",
      "    nuc = int(nuc)\n",
      "    if nuc in metastable_blacklist:\n",
      "        continue\n",
      "    chains = decaygen.genchains([(nuc,)])\n",
      "    maxrelerr = max([max(list(map(hl_relerr, c))) for c in chains])\n",
      "    if maxrelerr > 0.1:\n",
      "        continue\n",
      "    t = int(t)\n",
      "    if nuc not in nuc_keys:\n",
      "        nuc_keys[nuc] = []\n",
      "    nuc_keys[nuc].append(key)\n",
      "sfunc = lambda x: int(x.rsplit('_', 1)[1])\n",
      "for keys in nuc_keys.values():\n",
      "    keys.sort(key=sfunc)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Material Relative Error\n",
      "------------------------\n",
      "Unitly functions to convert materials to pandas Series and compute the relative error of each nuclide bewteen two materials."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def mat_to_series(x):\n",
      "    xmass = x.mult_by_mass()\n",
      "    s = pd.Series(list(x.values()), list(map(int, x.keys())))\n",
      "    return s\n",
      "\n",
      "def matdiff(x, y):\n",
      "    if not isinstance(x, pd.Series):\n",
      "        x = mat_to_series(x)\n",
      "    if not isinstance(y, pd.Series):\n",
      "        y = mat_to_series(y)\n",
      "    return (x - y).abs() * (2 / (x + y))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Perform the Comparison\n",
      "-----------------------\n",
      "This constructs a new data frame that compares the origen decayed material to the pyne material decay.  The comparison stores:\n",
      "\n",
      "* the parent nuclide, nuc\n",
      "* the decay time, t\n",
      "* the maximum relative error over all nuclides, relerr\n",
      "* the nuclide with the maximum relative error, child\n",
      "* The mass of the child, childmass\n",
      "* the child's mass weighted relative error, weightederr\n",
      "\n",
      "Note that this also filters by when Origen itself has messed up by gaining or lossing too much mass (1%). Furthermore, the maximum relative error is only computed for species that have a certain threshold mass (1e-3).  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "idx = []\n",
      "err = []\n",
      "for nuc, keys in list(nuc_keys.items()):\n",
      "    fresh = mats[keys[0]]\n",
      "    for matkey in keys[1:]:\n",
      "        mat = mats[matkey]\n",
      "        if (mat.mass < 0.99) or (mat.mass > 1.1):\n",
      "            # Origen lost too much mass\n",
      "            continue\n",
      "        t = mat.metadata['decay_time']\n",
      "        dmat = fresh.decay(t)\n",
      "        oser = mat_to_series(mat)\n",
      "        dser = mat_to_series(dmat)\n",
      "        md = matdiff(oser, dser)\n",
      "        md = md.fillna(value=0.0)\n",
      "        mask = (dser > 1e-3) | (oser > 1e-3)\n",
      "        mdmasked = md.where(mask)\n",
      "        maxmd = mdmasked.max()\n",
      "        child = mdmasked.argmax()\n",
      "        if np.isnan(child):\n",
      "            continue\n",
      "        childmass = max(dser[child], oser[child])\n",
      "        weightederr = maxmd * childmass\n",
      "        idx.append(matkey)\n",
      "        err.append((nuc, t, maxmd, child, childmass, weightederr))\n",
      "df = pd.DataFrame(err, index=idx, columns=['nuc', 't', 'relerr', 'child', 'childmass', 'weightederr'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Helper\n",
      "------\n",
      "A utility function for printing out nuclide summaries"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def nucsummary(nuc, i=None):\n",
      "    print('Nulcide:', nucname.name(nuc))\n",
      "    print(\"origen half life:\", t9_half_life(nuc))\n",
      "    print(\"data.half_life():\", data.half_life(nuc))\n",
      "    print('relerr half life:', hl_relerr(nuc))\n",
      "    print()\n",
      "    subdf = df[df['nuc'] == nuc]\n",
      "    plt.figure(figsize=(10, 6))\n",
      "    subdf.plot(x='t', y='relerr', logx=True, label=nucname.name(int(nuc)))\n",
      "    plt.legend(loc=0)\n",
      "    plt.ylabel('t [sec]')\n",
      "    plt.ylabel('Relative Error')\n",
      "    if i is not None:\n",
      "        okey = 'origen_{0}_{1}'.format(nuc, i)\n",
      "        fkey = 'fresh_{0}_0'.format(nuc)\n",
      "        omat = mats[okey]\n",
      "        fresh = mats[fkey]\n",
      "        t = omat.metadata['decay_time']\n",
      "        dmat = fresh.decay(t)\n",
      "        md = matdiff(omat, dmat)\n",
      "        print(omat)\n",
      "        print()\n",
      "        print(dmat)\n",
      "        print()\n",
      "        print(md)\n",
      "    print(subdf.to_latex())\n",
      "    return subdf"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 58
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "The Data!\n",
      "----------\n",
      "The data frame, sorted by relative error, childmass, and nuclide."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.sort(['relerr', 'childmass', 'nuc'], ascending=[False, False, True])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>nuc</th>\n",
        "      <th>t</th>\n",
        "      <th>relerr</th>\n",
        "      <th>child</th>\n",
        "      <th>childmass</th>\n",
        "      <th>weightederr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>origen_420930001_4</th>\n",
        "      <td> 420930001</td>\n",
        "      <td> 2.466000e+05</td>\n",
        "      <td> 1.994709</td>\n",
        "      <td> 410930000</td>\n",
        "      <td> 0.001168</td>\n",
        "      <td> 0.002330</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_420930001_5</th>\n",
        "      <td> 420930001</td>\n",
        "      <td> 2.466000e+06</td>\n",
        "      <td> 1.948262</td>\n",
        "      <td> 410930000</td>\n",
        "      <td> 0.001181</td>\n",
        "      <td> 0.002302</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_781930001_5</th>\n",
        "      <td> 781930001</td>\n",
        "      <td> 3.741120e+07</td>\n",
        "      <td> 1.629114</td>\n",
        "      <td> 771930000</td>\n",
        "      <td> 0.016067</td>\n",
        "      <td> 0.026175</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_781930001_4</th>\n",
        "      <td> 781930001</td>\n",
        "      <td> 3.741120e+06</td>\n",
        "      <td> 1.581632</td>\n",
        "      <td> 771930000</td>\n",
        "      <td> 0.001406</td>\n",
        "      <td> 0.002223</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_420990000_1</th>\n",
        "      <td> 420990000</td>\n",
        "      <td> 2.375136e+03</td>\n",
        "      <td> 1.557261</td>\n",
        "      <td> 430990000</td>\n",
        "      <td> 0.006907</td>\n",
        "      <td> 0.010757</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_4</th>\n",
        "      <td> 491140000</td>\n",
        "      <td> 7.190000e+02</td>\n",
        "      <td> 1.483783</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.033710</td>\n",
        "      <td> 0.050018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_5</th>\n",
        "      <td> 491140000</td>\n",
        "      <td> 7.190000e+03</td>\n",
        "      <td> 1.483744</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.033740</td>\n",
        "      <td> 0.050062</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_3</th>\n",
        "      <td> 491140000</td>\n",
        "      <td> 7.190000e+01</td>\n",
        "      <td> 1.483744</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.016870</td>\n",
        "      <td> 0.025031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_2</th>\n",
        "      <td> 491140000</td>\n",
        "      <td> 7.190000e+00</td>\n",
        "      <td> 1.483651</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.002259</td>\n",
        "      <td> 0.003352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_380910000_1</th>\n",
        "      <td> 380910000</td>\n",
        "      <td> 3.474000e+02</td>\n",
        "      <td> 0.803749</td>\n",
        "      <td> 390910000</td>\n",
        "      <td> 0.006907</td>\n",
        "      <td> 0.005552</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140001_4</th>\n",
        "      <td> 491140001</td>\n",
        "      <td> 4.277664e+07</td>\n",
        "      <td> 0.674001</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.075220</td>\n",
        "      <td> 0.050698</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140001_5</th>\n",
        "      <td> 491140001</td>\n",
        "      <td> 4.277664e+08</td>\n",
        "      <td> 0.673959</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.075290</td>\n",
        "      <td> 0.050742</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140001_3</th>\n",
        "      <td> 491140001</td>\n",
        "      <td> 4.277664e+06</td>\n",
        "      <td> 0.673843</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.037640</td>\n",
        "      <td> 0.025363</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140001_2</th>\n",
        "      <td> 491140001</td>\n",
        "      <td> 4.277664e+05</td>\n",
        "      <td> 0.673820</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.005041</td>\n",
        "      <td> 0.003397</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_230540000_4</th>\n",
        "      <td> 230540000</td>\n",
        "      <td> 4.980000e+02</td>\n",
        "      <td> 0.633013</td>\n",
        "      <td> 230540000</td>\n",
        "      <td> 0.001881</td>\n",
        "      <td> 0.001191</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_360790001_4</th>\n",
        "      <td> 360790001</td>\n",
        "      <td> 5.000000e+02</td>\n",
        "      <td> 0.610648</td>\n",
        "      <td> 360790001</td>\n",
        "      <td> 0.001835</td>\n",
        "      <td> 0.001121</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_561330001_4</th>\n",
        "      <td> 561330001</td>\n",
        "      <td> 1.401480e+06</td>\n",
        "      <td> 0.587649</td>\n",
        "      <td> 551330000</td>\n",
        "      <td> 0.002970</td>\n",
        "      <td> 0.001745</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_842110000_4</th>\n",
        "      <td> 842110000</td>\n",
        "      <td> 5.160000e+00</td>\n",
        "      <td> 0.531242</td>\n",
        "      <td> 842110000</td>\n",
        "      <td> 0.001683</td>\n",
        "      <td> 0.000894</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_952420001_4</th>\n",
        "      <td> 952420001</td>\n",
        "      <td> 4.449622e+10</td>\n",
        "      <td> 0.519896</td>\n",
        "      <td> 942380000</td>\n",
        "      <td> 0.001293</td>\n",
        "      <td> 0.000672</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_80190000_4</th>\n",
        "      <td>  80190000</td>\n",
        "      <td> 2.688000e+02</td>\n",
        "      <td> 0.496765</td>\n",
        "      <td>  80190000</td>\n",
        "      <td> 0.001622</td>\n",
        "      <td> 0.000806</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_741890000_4</th>\n",
        "      <td> 741890000</td>\n",
        "      <td> 6.420000e+03</td>\n",
        "      <td> 0.472665</td>\n",
        "      <td> 741890000</td>\n",
        "      <td> 0.001581</td>\n",
        "      <td> 0.000747</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_320710001_4</th>\n",
        "      <td> 320710001</td>\n",
        "      <td> 2.041000e-01</td>\n",
        "      <td> 0.462447</td>\n",
        "      <td> 320710001</td>\n",
        "      <td> 0.001564</td>\n",
        "      <td> 0.000723</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_802050000_4</th>\n",
        "      <td> 802050000</td>\n",
        "      <td> 3.084000e+03</td>\n",
        "      <td> 0.448400</td>\n",
        "      <td> 802050000</td>\n",
        "      <td> 0.001541</td>\n",
        "      <td> 0.000691</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_461110001_1</th>\n",
        "      <td> 461110001</td>\n",
        "      <td> 1.980000e+02</td>\n",
        "      <td> 0.402345</td>\n",
        "      <td> 471110000</td>\n",
        "      <td> 0.002104</td>\n",
        "      <td> 0.000846</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_40100000_4</th>\n",
        "      <td>  40100000</td>\n",
        "      <td> 4.765198e+14</td>\n",
        "      <td> 0.387555</td>\n",
        "      <td>  40100000</td>\n",
        "      <td> 0.001446</td>\n",
        "      <td> 0.000560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_781910000_4</th>\n",
        "      <td> 781910000</td>\n",
        "      <td> 2.445120e+06</td>\n",
        "      <td> 0.387555</td>\n",
        "      <td> 781910000</td>\n",
        "      <td> 0.001446</td>\n",
        "      <td> 0.000560</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_561310000_4</th>\n",
        "      <td> 561310000</td>\n",
        "      <td> 9.936000e+06</td>\n",
        "      <td> 0.346184</td>\n",
        "      <td> 551310000</td>\n",
        "      <td> 0.005383</td>\n",
        "      <td> 0.001864</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_280590000_4</th>\n",
        "      <td> 280590000</td>\n",
        "      <td> 2.398378e+13</td>\n",
        "      <td> 0.344502</td>\n",
        "      <td> 280590000</td>\n",
        "      <td> 0.001383</td>\n",
        "      <td> 0.000476</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_461090001_3</th>\n",
        "      <td> 461090001</td>\n",
        "      <td> 2.817600e+02</td>\n",
        "      <td> 0.341814</td>\n",
        "      <td> 471090000</td>\n",
        "      <td> 0.001102</td>\n",
        "      <td> 0.000377</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812040000_2</th>\n",
        "      <td> 812040000</td>\n",
        "      <td> 1.193824e+07</td>\n",
        "      <td> 0.331065</td>\n",
        "      <td> 802040000</td>\n",
        "      <td> 0.001955</td>\n",
        "      <td> 0.000647</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_802040000_2</th>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_802040000_3</th>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_802040000_4</th>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_802040000_5</th>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 802040000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812030000_1</th>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000e-02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812030000_2</th>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812030000_3</th>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812030000_4</th>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812030000_5</th>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812030000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812050000_1</th>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000e-02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812050000_2</th>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812050000_3</th>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812050000_4</th>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_812050000_5</th>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 812050000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822060000_1</th>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000e-02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822060000_2</th>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822060000_3</th>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822060000_4</th>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822060000_5</th>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822060000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822070000_1</th>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000e-02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822070000_2</th>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822070000_3</th>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822070000_4</th>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822070000_5</th>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822070000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822080000_1</th>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000e-02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822080000_2</th>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000e-01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822080000_3</th>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822080000_4</th>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000e+01</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_822080000_5</th>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000e+02</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 822080000</td>\n",
        "      <td> 1.000000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_721780001_3</th>\n",
        "      <td> 721780001</td>\n",
        "      <td> 4.000000e+00</td>\n",
        "      <td> 0.000000</td>\n",
        "      <td> 721780001</td>\n",
        "      <td> 0.500000</td>\n",
        "      <td> 0.000000</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2802 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "                          nuc             t    relerr      child  childmass  \\\n",
        "origen_420930001_4  420930001  2.466000e+05  1.994709  410930000   0.001168   \n",
        "origen_420930001_5  420930001  2.466000e+06  1.948262  410930000   0.001181   \n",
        "origen_781930001_5  781930001  3.741120e+07  1.629114  771930000   0.016067   \n",
        "origen_781930001_4  781930001  3.741120e+06  1.581632  771930000   0.001406   \n",
        "origen_420990000_1  420990000  2.375136e+03  1.557261  430990000   0.006907   \n",
        "origen_491140000_4  491140000  7.190000e+02  1.483783  481140000   0.033710   \n",
        "origen_491140000_5  491140000  7.190000e+03  1.483744  481140000   0.033740   \n",
        "origen_491140000_3  491140000  7.190000e+01  1.483744  481140000   0.016870   \n",
        "origen_491140000_2  491140000  7.190000e+00  1.483651  481140000   0.002259   \n",
        "origen_380910000_1  380910000  3.474000e+02  0.803749  390910000   0.006907   \n",
        "origen_491140001_4  491140001  4.277664e+07  0.674001  481140000   0.075220   \n",
        "origen_491140001_5  491140001  4.277664e+08  0.673959  481140000   0.075290   \n",
        "origen_491140001_3  491140001  4.277664e+06  0.673843  481140000   0.037640   \n",
        "origen_491140001_2  491140001  4.277664e+05  0.673820  481140000   0.005041   \n",
        "origen_230540000_4  230540000  4.980000e+02  0.633013  230540000   0.001881   \n",
        "origen_360790001_4  360790001  5.000000e+02  0.610648  360790001   0.001835   \n",
        "origen_561330001_4  561330001  1.401480e+06  0.587649  551330000   0.002970   \n",
        "origen_842110000_4  842110000  5.160000e+00  0.531242  842110000   0.001683   \n",
        "origen_952420001_4  952420001  4.449622e+10  0.519896  942380000   0.001293   \n",
        "origen_80190000_4    80190000  2.688000e+02  0.496765   80190000   0.001622   \n",
        "origen_741890000_4  741890000  6.420000e+03  0.472665  741890000   0.001581   \n",
        "origen_320710001_4  320710001  2.041000e-01  0.462447  320710001   0.001564   \n",
        "origen_802050000_4  802050000  3.084000e+03  0.448400  802050000   0.001541   \n",
        "origen_461110001_1  461110001  1.980000e+02  0.402345  471110000   0.002104   \n",
        "origen_40100000_4    40100000  4.765198e+14  0.387555   40100000   0.001446   \n",
        "origen_781910000_4  781910000  2.445120e+06  0.387555  781910000   0.001446   \n",
        "origen_561310000_4  561310000  9.936000e+06  0.346184  551310000   0.005383   \n",
        "origen_280590000_4  280590000  2.398378e+13  0.344502  280590000   0.001383   \n",
        "origen_461090001_3  461090001  2.817600e+02  0.341814  471090000   0.001102   \n",
        "origen_812040000_2  812040000  1.193824e+07  0.331065  802040000   0.001955   \n",
        "...                       ...           ...       ...        ...        ...   \n",
        "origen_802040000_2  802040000  1.000000e-01  0.000000  802040000   1.000000   \n",
        "origen_802040000_3  802040000  1.000000e+00  0.000000  802040000   1.000000   \n",
        "origen_802040000_4  802040000  1.000000e+01  0.000000  802040000   1.000000   \n",
        "origen_802040000_5  802040000  1.000000e+02  0.000000  802040000   1.000000   \n",
        "origen_812030000_1  812030000  1.000000e-02  0.000000  812030000   1.000000   \n",
        "origen_812030000_2  812030000  1.000000e-01  0.000000  812030000   1.000000   \n",
        "origen_812030000_3  812030000  1.000000e+00  0.000000  812030000   1.000000   \n",
        "origen_812030000_4  812030000  1.000000e+01  0.000000  812030000   1.000000   \n",
        "origen_812030000_5  812030000  1.000000e+02  0.000000  812030000   1.000000   \n",
        "origen_812050000_1  812050000  1.000000e-02  0.000000  812050000   1.000000   \n",
        "origen_812050000_2  812050000  1.000000e-01  0.000000  812050000   1.000000   \n",
        "origen_812050000_3  812050000  1.000000e+00  0.000000  812050000   1.000000   \n",
        "origen_812050000_4  812050000  1.000000e+01  0.000000  812050000   1.000000   \n",
        "origen_812050000_5  812050000  1.000000e+02  0.000000  812050000   1.000000   \n",
        "origen_822060000_1  822060000  1.000000e-02  0.000000  822060000   1.000000   \n",
        "origen_822060000_2  822060000  1.000000e-01  0.000000  822060000   1.000000   \n",
        "origen_822060000_3  822060000  1.000000e+00  0.000000  822060000   1.000000   \n",
        "origen_822060000_4  822060000  1.000000e+01  0.000000  822060000   1.000000   \n",
        "origen_822060000_5  822060000  1.000000e+02  0.000000  822060000   1.000000   \n",
        "origen_822070000_1  822070000  1.000000e-02  0.000000  822070000   1.000000   \n",
        "origen_822070000_2  822070000  1.000000e-01  0.000000  822070000   1.000000   \n",
        "origen_822070000_3  822070000  1.000000e+00  0.000000  822070000   1.000000   \n",
        "origen_822070000_4  822070000  1.000000e+01  0.000000  822070000   1.000000   \n",
        "origen_822070000_5  822070000  1.000000e+02  0.000000  822070000   1.000000   \n",
        "origen_822080000_1  822080000  1.000000e-02  0.000000  822080000   1.000000   \n",
        "origen_822080000_2  822080000  1.000000e-01  0.000000  822080000   1.000000   \n",
        "origen_822080000_3  822080000  1.000000e+00  0.000000  822080000   1.000000   \n",
        "origen_822080000_4  822080000  1.000000e+01  0.000000  822080000   1.000000   \n",
        "origen_822080000_5  822080000  1.000000e+02  0.000000  822080000   1.000000   \n",
        "origen_721780001_3  721780001  4.000000e+00  0.000000  721780001   0.500000   \n",
        "\n",
        "                    weightederr  \n",
        "origen_420930001_4     0.002330  \n",
        "origen_420930001_5     0.002302  \n",
        "origen_781930001_5     0.026175  \n",
        "origen_781930001_4     0.002223  \n",
        "origen_420990000_1     0.010757  \n",
        "origen_491140000_4     0.050018  \n",
        "origen_491140000_5     0.050062  \n",
        "origen_491140000_3     0.025031  \n",
        "origen_491140000_2     0.003352  \n",
        "origen_380910000_1     0.005552  \n",
        "origen_491140001_4     0.050698  \n",
        "origen_491140001_5     0.050742  \n",
        "origen_491140001_3     0.025363  \n",
        "origen_491140001_2     0.003397  \n",
        "origen_230540000_4     0.001191  \n",
        "origen_360790001_4     0.001121  \n",
        "origen_561330001_4     0.001745  \n",
        "origen_842110000_4     0.000894  \n",
        "origen_952420001_4     0.000672  \n",
        "origen_80190000_4      0.000806  \n",
        "origen_741890000_4     0.000747  \n",
        "origen_320710001_4     0.000723  \n",
        "origen_802050000_4     0.000691  \n",
        "origen_461110001_1     0.000846  \n",
        "origen_40100000_4      0.000560  \n",
        "origen_781910000_4     0.000560  \n",
        "origen_561310000_4     0.001864  \n",
        "origen_280590000_4     0.000476  \n",
        "origen_461090001_3     0.000377  \n",
        "origen_812040000_2     0.000647  \n",
        "...                         ...  \n",
        "origen_802040000_2     0.000000  \n",
        "origen_802040000_3     0.000000  \n",
        "origen_802040000_4     0.000000  \n",
        "origen_802040000_5     0.000000  \n",
        "origen_812030000_1     0.000000  \n",
        "origen_812030000_2     0.000000  \n",
        "origen_812030000_3     0.000000  \n",
        "origen_812030000_4     0.000000  \n",
        "origen_812030000_5     0.000000  \n",
        "origen_812050000_1     0.000000  \n",
        "origen_812050000_2     0.000000  \n",
        "origen_812050000_3     0.000000  \n",
        "origen_812050000_4     0.000000  \n",
        "origen_812050000_5     0.000000  \n",
        "origen_822060000_1     0.000000  \n",
        "origen_822060000_2     0.000000  \n",
        "origen_822060000_3     0.000000  \n",
        "origen_822060000_4     0.000000  \n",
        "origen_822060000_5     0.000000  \n",
        "origen_822070000_1     0.000000  \n",
        "origen_822070000_2     0.000000  \n",
        "origen_822070000_3     0.000000  \n",
        "origen_822070000_4     0.000000  \n",
        "origen_822070000_5     0.000000  \n",
        "origen_822080000_1     0.000000  \n",
        "origen_822080000_2     0.000000  \n",
        "origen_822080000_3     0.000000  \n",
        "origen_822080000_4     0.000000  \n",
        "origen_822080000_5     0.000000  \n",
        "origen_721780001_3     0.000000  \n",
        "\n",
        "[2802 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Relative Error Fraction\n",
      "-------------------------\n",
      "The fraction of the data with a relative error greater than 50%"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(df.relerr > 0.5).sum() / len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "0.0067808708065667384"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Weighted Error Fraction\n",
      "-------------------------\n",
      "The fraction of the data with a mass-weighted relative error greater than 50%"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "(df.weightederr > 0.5).sum() / len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Relative and Weighted Error Fraction\n",
      "-------------------------------------\n",
      "The fraction of the data with both relative and mass-weighted relative error greater than 50%"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "((df.relerr > 0.5) & (df.weightederr > 0.5)).sum() / len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "0.0"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Length\n",
      "--------\n",
      "The number of decay calculations compared"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "len(df)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "2802"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Plot\n",
      "-----\n",
      "The relative errors, grouped by nuclide"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "N = 20\n",
      "plt.figure(figsize=(10, 6))\n",
      "for i, (key, grp) in enumerate(df.groupby(['nuc'])):\n",
      "    if i > N:\n",
      "        break\n",
      "    nn = nucname.name(int(key))\n",
      "    p = grp.plot(x='t', y='relerr', logx=True, label=nn)\n",
      "    plt.xlabel('t [sec]')\n",
      "    plt.ylabel('Relative Error')\n",
      "    p.legend(p.lines, (nn,), loc=0)\n",
      "    plt.savefig('decay-relative-error-{0}.eps'.format(nn))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d7a2a208>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d7a2ac88>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJKkpE4skqan/D3Bx03Rxn3Y5AAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d79bfa20>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZEAAAEUCAYAAADqXAs8AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d79bafd0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nJhZJUlMmFklSU/8fniYR3yXIw9QAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d87ad160>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngug07ElhggAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d7d8b0f0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZIAAAEUCAYAAAABa7A/AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEhERycn/B9nuesuCpQ/fAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d7a83400>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34eb4dd668>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34de7fb470>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34d8b59908>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEUCAYAAADEGSquAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3XucFNWVwPHfYUBBQCcIhkCQAXxGgTFGJGJkUFCMIBpR\nQxQcQYIaUPNZn7tZhWSTXdzIZ8EHwRgeiiBiIigrSlhtIYkPiMAYBxDCQxiIAQUEFeRx9o/bMzTD\ndE91d1VXP87385kPXdX1OHNs+0zdW3WvqCrGGGMMQIOwAzDGGJM9rCgYY4ypYUXBGGNMDSsKxhhj\nalhRMMYYU8OKgjHGmBqBFgUR6Ssiq0RkjYjcl2C780TkgIhck+y+xhhj/BNYURCRIuAxoC/wLWCQ\niJwZZ7uxwKvJ7muMMcZfQV4pdAPWquoGVd0PPAcMqGO7UcALwLYU9jXGGOOjIItCW2BTzPLm6Loa\nItIW92U/Mbqq+vHqevc1xhjjvyCLgpfxM/4HuF/dWBsS/fG6rzHGGJ81DPDYVUC7mOV2uL/4Y50L\nPCciAC2By0Vkv8d9ERErHsYYkwJVlXhvBPKDKzh/B0qAY4DlwJkJtp8C/CCZfV34qg899JDGU9d7\nXtbVtxyEdM6RzL71bRvv/WTWF0o+U81love85CqX8pmJz2a89wr5s5lo2+h3Z53fxYFdKajqAREZ\nCbwGFAG/U9WVIjIi+v6kZPeNt31ZWVncOOp6z8u62ssbNmyIew6/JPo9/Ny3vm3jvZ/M+kLJZ6q5\nTPRefbmra1025zMTn8147xXyZzPV84jm8NDZIqKZir+8vJypU6dm5FyFwPLpL8unfwohlyISt/nI\nnmj2qLy8POwQ8orl01+WT/8Uei7tSsEYYwpMwV0piIj91PMTtkgkEnYIeSWf87lmDfz0p5k7Xz7n\n0osgb0kNlV1BxJcNRcEYr379a3jySbj1Vjj99LCjyX952XwUvTQKIaLcYPkxueLTT6FTJ7jySmjd\nGsaODTui/JCo+ciKQgGy/Jhc8fDD8MEH8MAD0KsXfPQRNGoUdlS5r+D6FEz2K/R2W7/lYz4PHIDH\nH4c77oAzznBXDK+8Evx58zGXybCiYIzJSnPmQLt2cO65bnnYMPjd78KNqRBYUciwkpISjjvuOJo3\nb06LFi3o168fmzcfNazTUfbv38/AgQPp0KEDDRo04M033zxqm/vuu4+WLVvSsmVL7r///iDC9006\nT3Sao+VjPidMgDvvPLx87bWweDFs3RrsefMxl8mwopBhIsK8efPYvXs3W7du5etf/zqjRo3ytO9F\nF13E9OnTad269VF3EE2aNIm5c+dSUVFBRUUFL7/8MpMmxR1JxJistmwZrF8PV111eF2zZjBwIEyb\nFl5chcCKQoiOPfZYrrnmGiorKwHYt28fd999N+3bt6d169bcdttt7N27F4BGjRpxxx130KNHD4qK\nio461rRp07j77rtp06YNbdq04e67787qR/ULvd3Wb/mWz/Hj4Sc/ObpTedgwmDwZgrxPIt9ymSwr\nCiGovvPniy++YNasWXz3u98F4P7772ft2rWsWLGCtWvXUlVVxc9//nNPx6ysrKRr1641y126dOGD\nDz7wP3hjAvbPf8LcuTB8+NHvnX++KxSLF2c+rkJRsLek+vH8ViqpKykp4ZNPPqFhw4Z8/vnnnHTS\nSbz66qucddZZNG/enIqKCjp27AjAW2+9xQ033MC6deuOOEa7du149tlnueiii2rWNWzYkMrKSk47\n7TQA1qxZw+mnn86hQ4eOisFuSTXZ7Be/cLee/va3db8/bhysWGHNSOlIdEtq3j7RXJ+wvhNFhLlz\n53LxxRejqsyZM4eePXuyfPlyvvjiC86tvtUCd0VR15d6XZo1a8Znn31Ws7xr1y6aNWvme/zGBOmr\nr2DiRHjttfjbDB4MP/857NoFJ5yQudgKhTUfhUhEuPrqqykqKuLtt9+mSZMmVFZWsmPHDnbs2MHO\nnTuP+KJP5KyzzmL58uU1yytWrODss88OKvS0FXq7rd/yJZ8vvABnngmdO8ffplUr6N0bZs4MJoZ8\nyWWqrCiEoLrpRlWZO3cuO3fu5Oyzz2b48OHcddddbNu2DYCqqioWLFhQs9++fftqOp5jXwMMGTKE\ncePGsWXLFqqqqhg3blzBDwFscs/48e5htfrccos9sxCYeFOy5cIP0ek440w1l5VKSkq0SZMm2qxZ\nM23evLl27txZZ8yYoaqqe/fu1X/913/Vjh076vHHH69nnnmmPvroozX7tm/fXkVEGzRoUPPvxo0b\na96/9957tUWLFtqiRQu977774saQzfkxheutt1Q7dFA9cKD+bQ8cUG3XTnXFiuDjykckmI6zYDua\nC5nlx2SjH/0IzjvP+zDZDz7o+hXGjw82rnxkYx+ZrFPo7bZ+y/V8VlXBq6/CzTd73+fmm2HGDNi3\nz99Ycj2X6Qq0KIhIXxFZJSJrROS+Ot4fICIrRGSZiPxVRC6OeW+DiFRE33s3yDiNMeGaONFdKRQX\ne9+nQwfo2tWNkWT8E1jzkYgUAauB3kAVsAQYpKorY7ZpqqqfR193Bl5U1VOiy+uBc1X10wTnsOaj\nFFh+TDbZuxfat4dFi5KfRGfmTJgyBWLuxzAehNV81A1Yq6obVHU/8BwwIHaD6oIQ1QzYXusYNkWY\nMXlu5kz49rdTm1Xt6qvhvfdg40b/4ypUQRaFtsCmmOXN0XVHEJGrRGQlMB+IvRlNgYUislRE6njg\n3eSyQm+39Vuu5lP16NFQk9G4MQwa5K4W/JKrufRLkEXBU/uEqs5R1TOB/sAzMW/1UNVzgMuBn4jI\n9wKI0RgTosWL4csv4dJLUz/GsGGuKBw86F9chSzIYS6qgHYxy+1wVwt1UtXFItJQRE5U1U9UdWt0\n/TYReRHXHHXUMFjl5eWUlJQAUFxcTGlpKWCT03tR/RdR9fjxmVwuKysL9fz5tpyr+XzwQRg1qowG\nDdI7XsuW8MgjEbp1y67fL1uWI5FIzajJ1d+X8QTZ0dwQ19F8CbAFeJejO5o7AetUVUXk28BsVe0k\nIscBRaq6W0SaAguAMaq6oNY56uxoNsZkv40bXV/Cxo1uroR0TJwIb7wBzz/vT2z5LpSOZlU9AIwE\nXgMqgVmqulJERojIiOhm1wDvi8gyYDzww+j61sBiEVkOvAPMq10QMq3Q2xn9Zvn0Vy7m8/HHobw8\n/YIArl9hwQLYXvtWlRTkYi79FOgoqao6H9eBHLtuUszrh4GH69hvHVAaZGzGmPB8/rmbLOddn55A\nKi6G/v1h+nS46y5/jlmo8nKYC2NMdps0CebP9/fBs0gERo2Cigp/5kvJZzbMhTEma6R7G2o8PXu6\nO5mWLPH3uIXGioJHhd7O6DfLp79yKZ8LF0KDBhC9ScY3IjB0aPpDaudSLoNgRcEYk1HVVwlBNPHc\ndBPMnu36LExqrE/BGJMxa9fCBRe421CbNAnmHP36wbXXugJh6mZ9CsaYrPDoo+4J5KAKArjj26xs\nqbOi4FGhtzP6zfLpr1zI52efwTPPwO23B3uefv3gww9h9erU9s+FXAbJioIxJiOmToU+faBdu3o3\nTUujRjB4sHsOwiTP+hSMMYE7dMgNjT11KvToEfz5Vq2CXr3go49ckTBHsj4FY0yo5s+HE05wncyZ\ncMYZ0KkTvPJKZs6XT6woeFTo7Yx+s3z6K9vzOX58cLehxpNqh3O25zJoVhSMMYGqrIT334frrsvs\nea+91s3XsHVrZs+b66xPwRgTqNtug69/HUaPzvy5hw93zUj335/5c2ezRH0KVhSMMYHZsQM6doSV\nK6F168yf/+23YcgQd3uqDZJ3mHU0+6DQ2xn9Zvn0V7bm86mn3HMDYRQEgPPPd3cfLT5qzsb4sjWX\nmWJFwRgTiAMH3EQ6fo+GmgwRe8I5WdZ8ZIwJxB/+AI88An/+c7hxbNsGp57qxls64YRwY8kW1nxk\njMm48ePhjjvCjgJatYLevWHmzLAjyQ1WFDwq9HZGv1k+/ZVt+Vy+HNatgx/8IOxInFtu8d6ElG25\nzLRAi4KI9BWRVSKyRkTuq+P9ASKyQkSWichfReRir/saY7LXhAlu4LtsGWKiTx/4+GM3VadJLLA+\nBREpAlYDvYEqYAkwSFVXxmzTVFU/j77uDLyoqqd42Te6j/UpGJNltm2D006DNWugZcuwoznswQdh\n1y7XrFXowupT6AasVdUNqrofeA4YELtBdUGIagZs97qvMSY7PfkkXHNNdhUEgJtvhhkzYN++sCPJ\nbkEWhbbAppjlzdF1RxCRq0RkJTAfuCOZfTOp0NsZ/Wb59Fe25HP/fnjiiezoYK6tQwfo2hXmzEm8\nXbbkMixBFgVP7TqqOkdVzwT6A8+I2HOHxuSqF15wTUdduoQdSd3smYX6NQzw2FVA7HQa7XB/8ddJ\nVReLSEOgRXQ7T/uWl5dTUlICQHFxMaWlpZSVlQGHK74fy2VlZb4er9CXLZ/5mc9f/AJ+9avw8xFv\n+cQT4b33yti4EdavDz+eTC1HIhGmTp0KUPN9GU+QHc0NcZ3FlwBbgHc5uqO5E7BOVVVEvg3MVtVO\nXvaN7m8dzcZkiXffheuvh7Vroago7GjiGzUKTjwxnAH6skUoHc2qegAYCbwGVAKzVHWliIwQkRHR\nza4B3heRZcB44IeJ9g0qVi+qq67xh+XTX9mQz/HjYeTI7C4I4JqQJk+Ggwfrfj8bchmmIJuPUNX5\nuA7k2HWTYl4/DDzsdV9jTHbassXNcvb442FHUr/SUveU88KFcNllYUeTfWzsI2NM2h58ELZvd3ce\n5YKJE+GNN+D558OOJBw2n4IxJjB790L79vDmm25u5FywcyeUlLj+j2x7niITbEA8HxR6O6PfLJ/+\nCjOfs2bBOefkTkEAKC6G/v1h+vSj3yv0z6YVBWNMylSzZzTUZFU/s2CNDUey5iNjTMoWL3YjkK5c\nCQ1y7E9MVTfPwowZ0K1b2NFkljUfGWMCMWGCu+8/1woCuFnZhg61J5xry8H/lOEo9HZGv1k+/RVG\nPj/6CF5/HW66KeOn9s1NN8Hs2fB5zNCchf7ZtKJgjEnJ44/DkCHQvHnYkaSubVu44AI3ZpNxrE/B\nGJO0L76Ak0+Gd96BTp3CjiY9L74I48a5/pFCYX0KxhhfTZ/u/sLO9YIA0K+fmxBo9eqwI8kOVhQ8\nKvR2Rr9ZPv2VyXyqug7mO+/M2CkD1aiRawabPNktF/pn04qCMSYpr7/u/r344sTb5ZKhQ+Hpp90k\nQYXO+hSMMUm58krX5PLjH4cdib8uvBDuuQcGFMDEv9anYIzxxd//Dm+9BTfeGHYk/rNZ2RwrCh4V\nejuj3yyf/spUPh97zH15HndcRk6XUdde6+5A+v3vI2GHEiorCsYYT3bvdu3ut98ediTBaNYMBg6E\n114LO5JwWZ+CMcaTRx+FRYvcE8D56u233Z1Iq1e7YTDylfUpGGPScuiQKwr5chtqPOef725RLaQH\n2WqzouCRtYH7y/Lpr6Dz+eqrrnmlR49ATxM6EejZM8JTT4UdSXgSFgURKRKRX6d6cBHpKyKrRGSN\niNxXx/s3iMgKEakQkT+LSJeY9zZE1y8TkXdTjcEYk77x491VQj43qVS79FJ46SXYtSvsSMJRb5+C\niLwNfDfZxnsRKQJWA72BKmAJMEhVV8Zs812gUlV3iUhfYLSqdo++tx44V1U/TXAO61MwJmArV0Kv\nXrBxIxx7bNjRZMa118Ill8Ctt4YdSTDS7VNYDswVkcEick305wce9usGrFXVDaq6H3gOOOKxEFV9\nS1Wr6/E7wDdrx+7hPMaYAD36qHtQrVAKAhT2MwteikJj4FPgYqBf9Ke/h/3aAptiljdH18UzDHgl\nZlmBhSKyVESGezhfoKwN3F+WT38Flc8dO2DmTLjttkAOn5UikQh9+sDHH0NFRdjRZF7D+jZQ1fIU\nj+25XUdEegFDgdhurB6qulVEWgF/FJFVqnrUPQHl5eWUlJQAUFxcTGlpKWVlZcDh/1Fs2ZZtObXl\nWbPgiivK+MY3siOeTCwDFBVBWVmEMWPg97/PrvhSWY5EIkydOhWg5vsyHi99Cu2ACcCF0VWLgDtV\ndXM9+3XH9RH0jS4/ABxS1bG1tusC/AHoq6pr4xzrIWCPqj5Sa731KRgTkIMH4ZRTYNaswpvDGGD9\nevd7b96cf01n6fYpTAFeAtpEf16OrqvPUuBUESkRkWOA66PHiQ3sZFxBuDG2IIjIcSLSPPq6KXAp\n8L6HcxpjfPLyy9C6dWEWBIAOHaBrV5gzJ+xIMstLUWilqlNUdX/0ZypwUn07qeoBYCTwGlAJzFLV\nlSIyQkRGRDd7EPgaMLHWraetgcUishzXAT1PVRck96v5K/bS0qTP8umvIPJZfRtqoYnNZSF2ONfb\npwB8IiKDgRm4u4F+CGz3cnBVnQ/Mr7VuUszrW4Bb6thvHVDq5RzGGP9VVMCHH8I114QdSbiuvhpG\njYING6Cepvi84aVPoT3wGNA9uuovwChV/Sjg2OplfQrGBOOWW1zzyb/9W9iRhG/UKGjRAsaMCTsS\n/yTqU0hYFESkITBNVW8IKrh0WFEwxn/bt8Opp7orhVatwo4mfMuXu4mF1q93dyXlg5Q7mqP9Au1F\nJM/63pNnbeD+snz6y898PvmkazYp1IJQO5elpXDSSbBwYTjxZJqXPoX1wJ9E5CXgi+g6VdVxwYVl\njAnD/v3wxBMwb17YkWSX6g7nyy4LO5LgeelTeAjXwXzEhqoaegubNR8Z469Zs1xRePPNsCPJLjt3\nuo7mtWuhZcuwo0lfouajhFcK0T6F01X1R4FEZozJKuPHw913hx1F9ikuhv79Yfp0uOuusKMJlpc+\nhZOtT8HawP1m+fSXH/lcsgS2bHGdqoUsXi6rm5DyvXHC+hSMMQBMmAA/+Qk09PKtUIB69oQvv3TF\nM5+f8vbSpzA6+tL6FIzJU1u3wre+BevWwde+FnY02etXv3LzSkyaVP+22Szl5xQSHLBRdI6EUFlR\nMMYfo0e7oaInTgw7kuxWVQWdO8OmTdC0adjRpC6l5xRE5E8xr5+p9fY7PsWWM6wN3F+WT3+lk899\n++A3v3FP7prEuWzbFi64AGbPzlw8mZaoozm2Dp5d6z2bEc2YPDFrFnTp4pqPTP3yfZC8uM1HIrJM\nVc+p/bqu5bBY85Ex6VGF885z4/pccUXY0eSG/fuhXTv3LMfpp4cdTWpSfU7hhOhczBLzmupln2M0\nxoTgL3+BXbvg8svDjiR3NGoEQ4bA5Mkwdmz92+eaRM1Hi3BzMfeLeV29XHDPO1obuL8sn/5KNZ/j\nx7u+hAZeZlYpEF5yOXQoPP20u2rIN3GvFNKYm9kYkwM2bXKDvD31VNiR5J4zzoBOneCVV2DAgLCj\n8VdKt6RmC+tTMCZ1DzwAX3zhrhZM8qZMgRdfhJdeqn/bbOP7cwrZwoqCMan54gto3x7eegtOOSXs\naHLTnj2uw7myEr7xjbCjSU7K8ymYw6wN3F+WT38lm88ZM6B7dysIdfGay2bNYOBAmDYt2Hgyrd6i\nICJNReTfReS30eVTRaSfl4OLSF8RWSUia0Tkvjrev0FEVohIhYj8WUS6eN3XGJMaVddkdMcdYUeS\n+/JxkDwvYx89D/wVGKKqZ4lIU+Avqtq1nv2KgNVAb6AKWAIMUtWVMdt8F6hU1V0i0hcYrardvewb\n3d+aj4xJ0uuvuzuO/vY3EHsMNS2qcPbZbg6Knj3Djsa7dJuPOqnqWOArAFX93ON5uwFrVXVDdJyk\n54Aj+ulV9S1V3RVdfAf4ptd9jTGpmTDBXSVYQUifSP494eylKOwTkSbVCyLSCdjnYb+2wKaY5c3R\ndfEMA15Jcd/AWRu4vyyf/vKaz3Xr4E9/ghtvDDaeXJbsZ3PwYHcH0q5d9W+bC7yMnD4aeBX4pojM\nAHoA5R7289yuIyK9gKHRYye1b3l5OSUlJQAUFxdTWlpKWVkZcPg/ri3bsi275ccfh6FDy2jaNDvi\nycblasns36cPjBkT4corw4+/ruVIJMLUqVMBar4v4/F0S6qItAS6RxffUdVtHvbpjusj6BtdfgA4\nFG2Kit2uC/AHoK+qrk1yX+tTMMajPXvcbajvvef+Nf559VX49393E/DkgrT6FETkZeBS4A1Vneel\nIEQtBU4VkRIROQa4HjjiMQ8RORlXEG6sLghe9zXGJGfaNCgrs4IQhD593HwUFRVhR5I+L30KjwDf\nAypF5AURGSgijevbKTq/80jgNaASmKWqK0VkhIiMiG72IPA1YKKILBORdxPtm+wv56fal5YmPZZP\nf9WXz0OHXAfznXdmJp5clspns6gIysvzo8O53j4FVY0AERFpCPQChgOTgeM97DsfmF9r3aSY17cA\nt3jd1xiTmgUL4Ljj4HvfCzuS/HXzzW7u5ocfhmOPDTua1HntU2gCXAlcB3wbmKeqoc/TZH0Kxnhz\n+eVw3XXui8sEp3dvGD4crr8+7EgSS2vso+jDa+fj7kB6Dlikqgd9jzIFVhSMqd+qVe7Bqo0boXG9\nDb8mHTNnuoHyFiwIO5LE0n147XdAR1UdoapvZEtByDRrA/eX5dNfifL52GPw4x9bQfAqnc/m1Ve7\nu7s2bPAtnIyL26cgIpeo6v8BzYABcvjxRwFUVf+QgfiMMWnYudMNfve3v4UdSWFo3BgGDXJXC2PG\nhB1NahLN0TxGVR8SkanU8TCZqobeOmnNR8YkNm4cLF3qCoPJjOXL4corYf16d1dSNkq3T6Gjqq6r\nb10YrCgYE9/Bg3Dqqa6d+/zzw46msHznO/DLX8Jll4UdSd3S7VN4oY51s9MLKfdYG7i/LJ/+qiuf\n8+ZBq1ZWEJLlx2czlwfJS9SncCbwLaBYRH5AtC8B93yCdVkZk+XGj7eH1cIyaJCb7nT7dmjZMuxo\nkpOoT2EAcDXQnyOHmNgNPKeqfwk+vMSs+ciYur3/vmu62LABjjkm7GgK0+DBcO65cNddYUdytHT7\nFC7IhgJQFysKxtRt+HA4+WQ3SJsJRyTiJjOqqMi+uSvS7VNYJiIjReQJEZkiIpNFZLLPMWY9awP3\nl+XTX7H53L4dXngBRoyIv72Jz6/PZs+e8OWXuTNyajUvReEZ4OtAXyACtAP2BBiTMSYNTz0FV10F\nJ50UdiSFTQSGDs29DmcvzUfLVbVURCpUtYuINAL+pKqh39NgzUfGHGn/fujY0c0Eds45YUdjqqrc\nHM6bN0PTpmFHc1i6zUdfRf/dJSKdgWKglV/BGWP88+KL0KGDFYRs0bYt9OgBs3PoJn4vReG3ItIC\n+BnuLqRK4OFAo8pC1gbuL8unv6rzaXMmpM/vz2auPbNQb1FQ1d+q6qeq+qaqdlDVVqr6m0wEZ4zx\n7q9/hU2bYMCAsCMxsfr1gzVrYPXqsCPxJtFzCv9Sx2rl8IB444IMzAvrUzDmsCFDXPv1vfeGHYmp\n7d57Xcfz2LH1b5sJKT2nICKjqWMgvGqqGvoYgFYUjHH+8Q8480z4+9+hRYuwozG1rVoFvXrBRx9B\no0ZhR5Pmw2vZLJNFIRKJUFZWlpFzFQLLp7/KyyMce2wZkybVv61JLKjP5oUXwj33ZEfzXlp3H4nI\n6SLyfyLyQXS5i4j8zOOJ+4rIKhFZIyL31fH+GSLylojsrd1cJSIbRKRCRJaJyLtezmdMIdq3z92C\nescdYUdiEsmVDmcvzyksAu4BfqOq54ibbedvqnpWPfsVAauB3kAVsAQYpKorY7ZpBbQHrgJ2qOoj\nMe+tB85V1U8TnMOaj0zBmz4dpk6FhQvDjsQksmcPtGsHlZXwjW+EG0u6zykcp6rvVC9Ev4X3e9iv\nG7BWVTeo6n7c/M5HXDip6jZVXZrgeFk2Yogx2UXVRkPNFc2awcCBMG1a2JEk5qUobBORU6oXRGQg\nsNXDfm2BTTHLm6PrvFJgoYgsFZHhSewXCLuv3l+WT3+89Rbs2AFNm0bCDiVvBPnZrG5CyuYGjrjz\nKcQYCTwJnC4iW4D1wA0e9kv31+6hqlujTUx/FJFVqrq49kbl5eWUlJQAUFxcTGlpaU0nUfV/XFu2\n5Xxd/vnPYeTIMho0yI548mG5WhDHV4Vjjilj0SJQzdzvF4lEmDp1KkDN92U8nu8+EpFmuOacPcB1\nqjqrnu27A6NVtW90+QHgkKoedaeuiDwE7IntU/DyvvUpmEK2eTN06eLmAj7hhLCjMV6NG+fmcX76\n6fBiSKlPQUSaici/RIfMvh34Atdp/AHerhSWAqeKSImIHANcz5GT9RxxulrnPk5EmkdfNwUuBd73\ncE5jCsYTT8CNN1pByDWDB7u7xXbtCjuSuiXqU3ga6AysAC4B3gZ+CvxIVa+s78CqegDX9PQabryk\nWaq6UkRGiMgIABFpLSKbosf9mYh8FL0iaQ0sFpHlwDvAPFVdkPJv6YPal5YmPZbP9Hz5pRsie+RI\nt2z59E/QuWzVCvr0gZkzAz1NyhL1KZyiql0AROQpXOdye1X90uvBVXU+ML/Wukkxr/+Bm5+htj1A\nqdfzGFNoZsyA886D004LOxKTimHD3Kx4t94adiRHSzTMxTJVPSfecjawPgVTiFShtBT++7/h0kvD\njsak4uBBN8T5vHmuXyjTUn1OoYuI7K7+ATrHLH8WTKjGmPq8+SZ89ZVrgjC5qagIysuz8wnnuEVB\nVYtUtXnMT8OY18dnMshsYG22/rJ8pm78eDekRexk8JZP/2Qqlzff7JoB9+3LyOk88/LwmjEmS6xf\nD4sWuWGyTW7r0AG6doU5c8KO5Eg2SqoxOeTuu92/v/51uHEYf8ycCZMnwx//mNnz2tDZxuSBPXug\npASWLnX/mty3dy9885uZ/2+a7oB4Bmuz9ZvlM3nPPAMXXVT3l4fl0z+ZzGXjxjBoEEyZkrFT1suK\ngjE54NAhmDDB5kzIR7fc4orCwYNhR+JY85ExOeC119w8v8uXH3nXkckP3/kO/PKXcNllmTmfNR8Z\nk+MmTHBzJlhByE/ZNCubFQWPrM3WX5ZP7z78EJYscW3P8Vg+/RNGLgcNggULYPv2jJ/6KFYUjMly\njz4Kw4cRWUbzAAAQJUlEQVRDkyZhR2KCUlwM/fu7qVXDZn0KxmSxXbvcQ07vvw9tk5m30OScSARG\njYKKiuCbCa1PwZgcNWWKG/TOCkL+69nTDYm+ZEm4cVhR8MjabP1l+azfwYOu6ejOO+vf1vLpn7By\nKQJDh7p5MsJkRcGYLPXKK3DiidC9e9iRmEy56SaYPRs+/zy8GKxPwZgs1bu3G175xhvDjsRkUr9+\nMHCg+28fFOtTMCbH/O1vUFkJ110XdiQm08J+ZsGKgkfWZusvy2dijz7qpmo85hhv21s+/RN2Lvv1\ngzVrYPXqcM4faFEQkb4iskpE1ojIfXW8f4aIvCUie0XkX5LZ15h89emn8PzzMGJE2JGYMDRq5ObL\nmDw5nPMH1qcgIkXAaqA3UAUsAQap6sqYbVoB7YGrgB2q+ojXfaPbWZ+CyTtjx7qmo2nTwo7EhGXV\nKujVCz76yBUJv4XVp9ANWKuqG1R1P/AcMCB2A1XdpqpLgf3J7mtMPjpwAB5/3EZDLXRnnAGdOrk7\n0DItyKLQFtgUs7w5ui7ofQMRdjtjvrF81m3OHDj5ZDj33OT2s3z6J1tyGVaHc8MAj51Ou47nfcvL\nyymJzjpSXFxMaWkpZWVlwOH/uLZsy7myPGYMPPhg9sRTiMvVwo6ndesIr78OW7aU0aZNeseLRCJM\nnToVoOb7Mp4g+xS6A6NVtW90+QHgkKqOrWPbh4A9MX0Knva1PgWTT957D666Ctatg4ZB/rlmcsbw\n4dCxIzzwgL/HDatPYSlwqoiUiMgxwPXAS3G2rR1cMvsakxcmTIDbb7eCYA4bNszdhZTJv30DKwqq\negAYCbwGVAKzVHWliIwQkREAItJaRDYBPwV+JiIfiUizePsGFasXtS8tTXosn0f6+GOYO9f9ZZgK\ny6d/simX55/vnlVZtChz5wz0bxJVnQ/Mr7VuUszrfwDtvO5rTL568kk3tMGJJ4YdickmIoc7nHv2\nzNA5c7lN3voUTD746isoKXHzMHfuHHY0Jtts2wanngobN8IJJ/hzTBv7yJgsNns2nHmmFQRTt1at\noE8fmDkzM+ezouBRNrUz5gPL52ETJqT/sJrl0z/ZmMtMPrNgRcGYEL39tmse6Ncv7EhMNuvTx92M\nUFER/LmsT8GYEA0aBN26wU9/GnYkJts9+KCbs3v8+PSPlahPwYqCMSGpqoKzz4b166G4OOxoTLZb\nvx7OOw82b4bGjdM7lnU0+yAb2xlzmeUTJk6EG27wpyBYPv2Trbns0AFKS934WEGyomBMCPbudc8m\njBoVdiQml2Siw9maj4wJwZQpbiKd+fZ4pknC3r3wzW/C0qXu2ZZUWfORMVlE1XUW3nln2JGYXNO4\nsbs5YcqU4M5hRcGjbG1nzFWFnM9Fi9xffJde6t8xCzmffsv2XN5yiysKBw8Gc3wrCsZk2IQJri+h\ngf3fZ1LQtSucdBIsXBjM8a1PwZgM2rDBzaq2cSM0axZ2NCZXTZwIb7zh+qVSYc8pGJMl7rkHDh2C\nRx4JOxKTy3budB3Na9dCy5bJ728dzT7I9nbGXFOI+fz8c9cW/JOf+H/sQsxnUHIhl8XF0L8/TJ/u\n/7GtKBiTIc88Axde6KZXNCZd1c8s+N1YYs1HxmSAKpx1Fjz+OPTqFXY0Jh+ounkWnn3WzdCWDGs+\nMiZkCxdCURGUlYUdickXIjB0qP9POOf8lcKQIUpRkfsfrkED6n3tdbva+6xaFaFz57LAz1Pfdvly\nG2MkEqGsgL4h+/WDq65y95gHodDyGaRcymX1oIqbN0PTpt73S3SlEOgczSLSF/gfoAh4SlXH1rHN\nBOBy4AugXFWXRddvAD4DDgL7VbVbXefo1cs9xHHokPs30evq5f373cNDyeyzZYvr6a9vu2Rep7IP\npFZ8vGz3ta9Bmzbup23bw6/btHF3OORLQcq0NWvgnXfcDGvG+KltW+jRw322ysv9OWZgVwoiUgSs\nBnoDVcASYJCqrozZ5vvASFX9voicD4xX1e7R99YD56rqpwnOUXB9CocOBVdwduxwxa+qyv1b/VNV\nBbt3Q+vW8YtG9fLxx7vLWnPYnXfCccfBf/5n2JGYfDRnjrvFefFi7/uEdaXQDVirqhuiQTwHDABW\nxmxzJTANQFXfEZFiEfm6qn4cfd++Xmpp0MD9NAz0Gu9oe/fCP/5xdNH44IMjlw8erLto1F7XpElm\n4w/LZ5+5u45WrAg7EpOvrrgCbr0VVq+G009P/3hBfrW0BTbFLG8GaveR17VNW+BjQIGFInIQmKSq\nvw0w1nrlUjtjEBo3dg/L1Dcy4+7dR15lbNkCH33kpp2MXdewYYSSkrK4RaNNG3dl0qhRJn674EyZ\n4qZSbNcu2PMU+ufTT7mWy0aNYMgQmDwZxh7VQJ+8IIuC13adeFcDF6rqFhFpBfxRRFap6lEXSOXl\n5ZREv6mKi4spLS2t+Q9a/RCKLWd++fTT3XLbtnDPPUe+37NnGS+9BJ98EmH7dmjVqoyqKliwIMIn\nn8BXX7nljz+OcPzx1BQPkQgnngg9erjlrVvd8oABZTRokF2/P8Drr0d4+GF4/vnsiMeWvS1Xy5Z4\nvCwPHQoXXBChTx/o3fvo9yORCFOnTgWo+b6MJ8g+he7AaFXtG11+ADgU29ksIr8BIqr6XHR5FdAz\npvmoeruHgD2q+kit9QXXp1BIDh6Ef/4zfj9H9etdu9xVRbx+juqfE07IbH/HvHkwejQsWWL9LCZ4\nF17ohlEZMKD+bUMZ+0hEGuI6mi8BtgDvkrijuTvwP6raXUSOA4pUdbeINAUWAGNUdUGtc1hRMOzb\nB1u3xi8a1cvV/R2JCkebNq5T2A99+rjL+sGD/TmeMYlMmQIvvggvvVT/tqENiCcil3P4ltTfqep/\nisgIAFWdFN3mMaAv8Dlws6q+JyIdgT9ED9MQeFZVj7p3I5NFIZJj7YzZLox87t59uHjULhqx65o0\nqf8uq/r6Oyor4eKL3Wioxx4b/O9mn0//5Gou9+xxfVcffOA+o4mE9pyCqs4H5tdaN6nW8sg69lsH\nlAYZmyk8zZu7n9NOi7+Nat235q5c6Z5Krl7+5z/dcx3xisazz7o7QjJREIwBNxT7wIEwbRo88EDq\nx8n5J5pzOX6Tu2L7O+pqsvryS5g5011RGJMpb7/tmis//DBxP5bNp2CMMQVA1Q178cQT0LNn/O1s\nQDwf1L5dzaTH8ukvy6d/cjmXIm58rXQGybOiYIwxeWTwYHcH0q5dqe1vzUfGGJNnrr0WLrnE3exQ\nF2s+MsaYAlI9K1sqrCh4lMvtjNnI8ukvy6d/8iGXffrAxx9DRUXy+1pRMMaYPFNU5OZXSOVqwfoU\njDEmD61fD+ed52Zla9z4yPesT8EYYwpMhw5QWuom4UmGFQWP8qGdMZtYPv1l+fRPPuUylQ5nKwrG\nGJOnrr4ali2DDRu872N9CsYYk8fuuMMN3jhmzOF1NvaRMcYUqBUroH9/1/FcVOTWWUezD/KpnTEb\nWD79Zfn0T77lsmtXOOkkN/S7F1YUjDEmzyXT4WzNR8YYk+d27oSSEli7Flq2tOYjY4wpaMXFrl9h\n+vT6tw20KIhIXxFZJSJrROS+ONtMiL6/QkTOSWbfTMq3dsawWT79Zfn0T77mctgweOopNxFPIoEV\nBREpAh4D+gLfAgaJyJm1tvk+cIqqngr8GJjodd9MW758eZinzzuWT39ZPv2Tr7ns2RP27oV33028\nXZBXCt2Ataq6QVX3A88BA2ptcyUwDUBV3wGKRaS1x30zaufOnWGePu9YPv1l+fRPvuZSBIYOrb/D\nOcii0BbYFLO8ObrOyzZtPOxbI9HlXl3veVkXxiVkOudMZt/6to33fjLrCyWfqeYy0XtecpdL+czE\nZzPee4X82axr25tugtmzE+8TZFHweltQnT3gycjEB2VDMs+Jp6iQikK+5DNbikI25zPXikI25zLZ\nfWtv27YtXHhh4n0CuyVVRLoDo1W1b3T5AeCQqo6N2eY3QERVn4surwJ6Ah3q2ze63u5HNcaYFMS7\nJbVhgOdcCpwqIiXAFuB6YFCtbV4CRgLPRYvITlX9WEQ+8bBv3F/KGGNMagIrCqp6QERGAq8BRcDv\nVHWliIyIvj9JVV8Rke+LyFrgc+DmRPsGFasxxhgnp59oNsYY4y97otkYY0wNKwrGGGNqWFFIkoh0\nEJGnRGR2zLoBIvKkiDwnIn3CjC+XxMnlGSIyUUSeF5FhYcaXa+rKZ3R9UxFZIiJXhBVbLorz+SwT\nkcXRz2jPMOMLihWFJKnqelW9pda6uar6Y+BW3J1SxoM4uVylqrcBPwQuCyey3FRXPqPuBWZlOp5c\nFyefh4DdwLG4h2rzjhUFQEQmi8jHIvJ+rfXJDsr3M9yYTQXLj1yKSH/gf3HDmxS0dPMZvXKtBLYF\nHWsu8OHzuVhVvw/cD4xJsF3OsqLgTMENvlcjmUH5xBkLzFfV/BxNy7u0cgmgqi+r6uXATUEGmiPS\nzWdPoDvwI2C4iBT6sz1p5TNmApeduKuFvGNFAVDVxcCOWqvrHJRPRFpEn8Q+J+YvilHAJcDA6ucw\nClW6uRSRniIyXkQmAW9kNPgslGI+S6vzqao/U9WfAjOAJwt9ViofPp9XR9c9DTyaydgzJcgnmnNd\nXYP1na+qn+L6Dmqo6gRgQgZjyzXJ5PJN4M0MxpaLPOezmqpOy0RgOSqZz+eLwIsZjC3j7EohvoL+\ni8pnlkt/WT79ZfmMYUUhviqgXcxyO/L0boMMsFz6y/LpL8tnDCsK8dUM6Ccix+BuNX0p5JhyleXS\nX5ZPf1k+Y1hRAERkJvAX4DQR2SQiN6vqAdwIrq/hbumbZYPy1c9y6S/Lp78sn/WzAfGMMcbUsCsF\nY4wxNawoGGOMqWFFwRhjTA0rCsYYY2pYUTDGGFPDioIxxpgaVhSMMcbUsKJgjDGmhhUFY+IQkRNE\n5LY475WIyJci8p6P5+soIstFZLdfxzQmWVYUjInva8DtCd5fq6rf9utkqrpOVUv9Op4xqbCiYEx8\n/wV0EpFl0Zn14hKRpiLyv9G/9N8Xkeui688VkYiILBWRV0WkdXT9KSKyMLr9X0WkYwZ+H2PqZZPs\nGBPffcBZqnqOh237AlWqegWAiBwvIo1ws3P1V9VPROR64JfAMOBZ4FeqOjc6MmdRML+CMcmxomBM\nfMnMZ1wB/FpE/guYp6p/EpGzgbOAhdGpkYuALSLSDGijqnMBVPUrn+M2JmVWFIzxgaquEZFzgCuA\n/xCR/8NN2/iBql4Qu62INA8jRmO8sD4FY+LbDXj6AheRbwB7VfVZ4NfAOcBqoJWIdI9u00hEvqWq\nu4HNIjIguv5YEWkSyG9gTJKsKBgTh6p+Avw52nGcsKMZ6Ay8IyLLgAeB/1DV/cBAYKyILAeWAd+N\nbj8YuENEVgB/BloH8ksYkySbZMeYFIhICfCyqnYO4Ni7VdWamEwo7ErBmNQcAE4I4uE14B9+HdOY\nZNmVgjHGmBp2pWCMMaaGFQVjjDE1rCgYY4ypYUXBGGNMDSsKxhhjavw/DjAnjb7rGMAAAAAASUVO\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f30f6cf8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEUCAYAAADa0BodAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f2805ba8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n/w989hkZOTd6cAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f4e6fe80>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f3603470>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEUCAYAAADa0BodAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEclJlYWIiOT0/wNHCmK4CtR3LQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f6ea32e8>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f3def198>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nBeWQLHcAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f23f7b38>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYsAAAEUCAYAAADa0BodAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nxqRljYUxxpi0/h+GNkCGQPDGhQAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34edc93358>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEUCAYAAAAWSSD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f3803f98>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZgAAAEUCAYAAAAWSSD2AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\noQIjIiKJUIEREZFE/H+nvFYuaC3K7gAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f12d5278>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34ed2e5b00>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAZYAAAEUCAYAAAAIgBBFAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\npkwskqSm/j/4MeO3Tf8gTgAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f6144320>"
       ]
      }
     ],
     "prompt_number": 57
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Drill Down into Problem Sepcies\n",
      "--------------------------------\n",
      "Should explain the issue with remaining high relative error"
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Mo-99\n",
      "-------\n",
      "Origen seems to have the wrong branch ratios"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nucsummary(420990000, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nulcide: Mo99\n",
        "origen half life: 237600.0\n",
        "data.half_life(): 237513.6\n",
        "relerr half life: 0.000363702491362\n",
        "\n",
        "Material:\n",
        "mass = 0.9939597000001054\n",
        "density = -1.0\n",
        "atoms per molecule = -1.0\n",
        "decay_time = 2375.136\n",
        "name = origen_420990000_1\n",
        "-------------------------\n",
        "Mo99   0.9991350755970234\n",
        "Tc99   0.000864924402870568\n",
        "Ru99   1.0604051653199704e-13"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Material:\n",
        "mass = 0.9999999336058844\n",
        "density = -1.0\n",
        "atoms per molecule = 1.0000000354067466\n",
        "---------------------------------------\n",
        "Mo99   0.9930925617347804\n",
        "Tc99   0.006907402611982175\n",
        "Ru99   3.565323735891612e-08\n",
        "\n",
        "420990000    0.000008\n",
        "430990000    1.557261\n",
        "440990000    1.999988\n",
        "dtype: float64\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>nuc</th>\n",
        "      <th>t</th>\n",
        "      <th>relerr</th>\n",
        "      <th>child</th>\n",
        "      <th>childmass</th>\n",
        "      <th>weightederr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>origen_420990000_1</th>\n",
        "      <td> 420990000</td>\n",
        "      <td> 2375.136</td>\n",
        "      <td> 1.557261</td>\n",
        "      <td> 430990000</td>\n",
        "      <td> 0.006907</td>\n",
        "      <td> 0.010757</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 15,
       "text": [
        "                          nuc         t    relerr      child  childmass  \\\n",
        "origen_420990000_1  420990000  2375.136  1.557261  430990000   0.006907   \n",
        "\n",
        "                    weightederr  \n",
        "origen_420990000_1     0.010757  "
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34e1ff8780>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYQAAAEBCAYAAAB4wNK4AAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34e2014630>"
       ]
      }
     ],
     "prompt_number": 15
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "In-114\n",
      "-------\n",
      "Origen just seems to have the wrong branch ratios"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nucsummary(491140000, 3)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nulcide: In114\n",
        "origen half life: 71.9\n",
        "data.half_life(): 71.9\n",
        "relerr half life: 0.0\n",
        "\n",
        "Material:\n",
        "mass = 0.9999699999999999\n",
        "density = -1.0\n",
        "atoms per molecule = -1.0\n",
        "decay_time = 71.9\n",
        "name = origen_491140000_3\n",
        "-------------------------\n",
        "Cd114  0.016870506115183457\n",
        "In114  0.5000150004500135\n",
        "Sn114  0.48311449343480306"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Material:\n",
        "mass = 0.999990639989361\n",
        "density = -1.0\n",
        "atoms per molecule = 1.0\n",
        "------------------------\n",
        "Cd114  0.002499989836490059\n",
        "In114  0.500004576422662\n",
        "Sn114  0.4974954337408478\n",
        "\n",
        "481140000    1.483744\n",
        "491140000    0.000000\n",
        "501140000    0.029351\n",
        "dtype: float64\n",
        "\\begin{tabular}{lrrrrrr}\n",
        "\\toprule\n",
        "{} &        nuc &         t &    relerr &      child &  childmass &  weightederr \\\\\n",
        "\\midrule\n",
        "origen\\_491140000\\_1 &  491140000 &     0.719 &  0.029356 &  501140000 &   0.006873 &     0.000202 \\\\\n",
        "origen\\_491140000\\_2 &  491140000 &     7.190 &  1.483651 &  481140000 &   0.002259 &     0.003352 \\\\\n",
        "origen\\_491140000\\_3 &  491140000 &    71.900 &  1.483744 &  481140000 &   0.016870 &     0.025031 \\\\\n",
        "origen\\_491140000\\_4 &  491140000 &   719.000 &  1.483783 &  481140000 &   0.033710 &     0.050018 \\\\\n",
        "origen\\_491140000\\_5 &  491140000 &  7190.000 &  1.483744 &  481140000 &   0.033740 &     0.050062 \\\\\n",
        "\\bottomrule\n",
        "\\end{tabular}\n",
        "\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>nuc</th>\n",
        "      <th>t</th>\n",
        "      <th>relerr</th>\n",
        "      <th>child</th>\n",
        "      <th>childmass</th>\n",
        "      <th>weightederr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>origen_491140000_1</th>\n",
        "      <td> 491140000</td>\n",
        "      <td>    0.719</td>\n",
        "      <td> 0.029356</td>\n",
        "      <td> 501140000</td>\n",
        "      <td> 0.006873</td>\n",
        "      <td> 0.000202</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_2</th>\n",
        "      <td> 491140000</td>\n",
        "      <td>    7.190</td>\n",
        "      <td> 1.483651</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.002259</td>\n",
        "      <td> 0.003352</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_3</th>\n",
        "      <td> 491140000</td>\n",
        "      <td>   71.900</td>\n",
        "      <td> 1.483744</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.016870</td>\n",
        "      <td> 0.025031</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_4</th>\n",
        "      <td> 491140000</td>\n",
        "      <td>  719.000</td>\n",
        "      <td> 1.483783</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.033710</td>\n",
        "      <td> 0.050018</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>origen_491140000_5</th>\n",
        "      <td> 491140000</td>\n",
        "      <td> 7190.000</td>\n",
        "      <td> 1.483744</td>\n",
        "      <td> 481140000</td>\n",
        "      <td> 0.033740</td>\n",
        "      <td> 0.050062</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 59,
       "text": [
        "                          nuc         t    relerr      child  childmass  \\\n",
        "origen_491140000_1  491140000     0.719  0.029356  501140000   0.006873   \n",
        "origen_491140000_2  491140000     7.190  1.483651  481140000   0.002259   \n",
        "origen_491140000_3  491140000    71.900  1.483744  481140000   0.016870   \n",
        "origen_491140000_4  491140000   719.000  1.483783  481140000   0.033710   \n",
        "origen_491140000_5  491140000  7190.000  1.483744  481140000   0.033740   \n",
        "\n",
        "                    weightederr  \n",
        "origen_491140000_1     0.000202  \n",
        "origen_491140000_2     0.003352  \n",
        "origen_491140000_3     0.025031  \n",
        "origen_491140000_4     0.050018  \n",
        "origen_491140000_5     0.050062  "
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f08eaf60>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAX4AAAEUCAYAAAAstV3AAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nUmZU+EVEysz/B3sMlFuCtt9DAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f33849e8>"
       ]
      }
     ],
     "prompt_number": 59
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sr-91\n",
      "------\n",
      "Relative error is high for Y-91, but the mass weighted relative error is low"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "nucsummary(380910000, 1)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Nulcide: Sr91\n",
        "origen half life: 34200.0\n",
        "data.half_life(): 34740.0\n",
        "relerr half life: 0.0156657963446\n",
        "\n",
        "Material:\n",
        "mass = 0.9959470702700001\n",
        "density = -1.0\n",
        "atoms per molecule = -1.0\n",
        "decay_time = 347.4\n",
        "name = origen_380910000_1\n",
        "-------------------------\n",
        "Sr91   0.9970409368550067\n",
        "Y91    0.0029589925890349494\n",
        "Zr91   7.055595834118967e-08"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "\n",
        "\n",
        "Material:\n",
        "mass = 0.9999996178756235\n",
        "density = -1.0\n",
        "atoms per molecule = 0.9999998380652019\n",
        "---------------------------------------\n",
        "Sr91   0.9930928753873625\n",
        "Y91    0.006907124612637552\n",
        "\n",
        "380910000    0.000093\n",
        "390910000    0.803749\n",
        "400910000         NaN\n",
        "dtype: float64\n"
       ]
      },
      {
       "html": [
        "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
        "<table border=\"1\" class=\"dataframe\">\n",
        "  <thead>\n",
        "    <tr style=\"text-align: right;\">\n",
        "      <th></th>\n",
        "      <th>nuc</th>\n",
        "      <th>t</th>\n",
        "      <th>relerr</th>\n",
        "      <th>child</th>\n",
        "      <th>childmass</th>\n",
        "      <th>weightederr</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>origen_380910000_1</th>\n",
        "      <td> 380910000</td>\n",
        "      <td> 347.4</td>\n",
        "      <td> 0.803749</td>\n",
        "      <td> 390910000</td>\n",
        "      <td> 0.006907</td>\n",
        "      <td> 0.005552</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "                          nuc      t    relerr      child  childmass  \\\n",
        "origen_380910000_1  380910000  347.4  0.803749  390910000   0.006907   \n",
        "\n",
        "                    weightederr  \n",
        "origen_380910000_1     0.005552  "
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34e1fd5e10>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAEBCAYAAACXArmGAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nSUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34e2a0c710>"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "... and so on"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df.relerr.plot(kind='hist', bins=10000, loglog=True, figsize=(10, 6))\n",
      "plt.xlabel('Relative Error, on range [0, 2]')\n",
      "plt.ylabel('Number of Calculations, Total = {0}'.format(len(df)))\n",
      "plt.savefig('decay-relative-error-histogram.eps')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": "iVBORw0KGgoAAAANSUhEUgAAAmsAAAGGCAYAAAAkfsYlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nRK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f34f28299b0>"
       ]
      }
     ],
     "prompt_number": 55
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": []
    }
   ],
   "metadata": {}
  }
 ]
}