CALPHAD2015-Demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Hello, I am Richard Otis from Prof. Zi-Kui Liu's research group.\n",
    "\n",
    "# Tonight I present pycalphad, a free and open-source library for CALPHAD computations. It works on Mac, Windows, and Linux."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase Equilibria Computation\n",
    "## Al-Fe (M.Seiersten et al., 1991)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.9 s, sys: 1.15 s, total: 20.1 s\n",
      "Wall time: 13 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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BPwXuNrPfFlnWcuJuYG7tMztOqK2xHgTXWNH/WY5TpmwJnFMLNxr2UlsNqtms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\nZ7rvzDiLFpI7SnQAniDoZGc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+iyvREKp2ChUjtGoL+3sBmxCWNb5EGy+VbUO2BFZq7PND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\nc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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f0a478e9310>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from pycalphad import Database, binplot\n",
    "db_alfe = Database('alfe_sei.TDB')\n",
    "my_phases_alfe = ['LIQUID', 'B2_BCC', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']\n",
    "\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "pdens = [{'B2_BCC': 20000}, 2000]\n",
    "%time binplot(db_alfe, ['AL', 'FE', 'VA'] , my_phases_alfe, 'X(AL)', 300, 2000, steps=100, \\\n",
    "              pdens=pdens, ax=fig.gca(), mode='numexpr')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Energy Surfaces of Al-Ni (N. Dupin et al., 2001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 4.22 s, sys: 0 ns, total: 4.22 s\n",
      "Wall time: 4.21 s\n"
     ]
    },
    {
     "data": {
      "image/png": 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WlPpISpEgp2lD9CTtsLyoD+SeYNIDgD71m4W8CQ2VM8EbTekudpXo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\nM96Mdr/b9YXgZqEHvvvTJ0HOguTGH8fd7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0TK/e1ISmodu0lXNovbzTeO3+wef13Mrt+u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8aYechm4APvLqO2x5eYLXg/bpXNuG/Is8r6/FHW4FLbxeC4PuOv9+zJf+eh+uvw4O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\nYGIqSZKkJpiYSpIkqQn/DzgZu3iBzo9VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4d8cdf4d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from pycalphad import energy_surf, Database\n",
    "from pycalphad.plot.utils import phase_legend\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "db_alni = Database('NI_AL_DUPIN_2001.TDB')\n",
    "my_phases_alni = ['LIQUID', 'FCC_L12', 'BCC_B2', 'AL3NI5', 'AL3NI2', 'AL3NI1']\n",
    "\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = fig.gca()\n",
    "%time nrg_df = energy_surf(db_alni, ['AL', 'NI', 'VA'], my_phases_alni, T=1000.0)\n",
    "\n",
    "legend_handles, colorlist = phase_legend(my_phases_alni)\n",
    "group_df = nrg_df.groupby('Phase')\n",
    "for phase, phase_df in group_df:\n",
    "    ax = phase_df.plot(kind='scatter', x='X(AL)', y='GM', ax=ax, color=colorlist[phase.upper()], s=1, xlim=(0,1), ylim=(-11e4, -3e4))\n",
    "ax = ax.legend(handles=legend_handles, loc='center left', bbox_to_anchor=(1, 0.6))\n",
    "plt.title('Al-Ni Gibbs Energy at 1000 K', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/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>GM</th>\n",
       "      <th>Phase</th>\n",
       "      <th>T</th>\n",
       "      <th>X(AL)</th>\n",
       "      <th>X(NI)</th>\n",
       "      <th>Y(AL3NI1,0,AL)</th>\n",
       "      <th>Y(AL3NI1,1,NI)</th>\n",
       "      <th>Y(AL3NI2,0,AL)</th>\n",
       "      <th>Y(AL3NI2,1,AL)</th>\n",
       "      <th>Y(AL3NI2,1,NI)</th>\n",
       "      <th>...</th>\n",
       "      <th>Y(BCC_B2,1,NI)</th>\n",
       "      <th>Y(BCC_B2,1,VA)</th>\n",
       "      <th>Y(BCC_B2,2,VA)</th>\n",
       "      <th>Y(FCC_L12,0,AL)</th>\n",
       "      <th>Y(FCC_L12,0,NI)</th>\n",
       "      <th>Y(FCC_L12,1,AL)</th>\n",
       "      <th>Y(FCC_L12,1,NI)</th>\n",
       "      <th>Y(FCC_L12,2,VA)</th>\n",
       "      <th>Y(LIQUID,0,AL)</th>\n",
       "      <th>Y(LIQUID,0,NI)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-78820.96</td>\n",
       "      <td>AL3NI1</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.750000</td>\n",
       "      <td>0.250000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-42406.27</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.833333</td>\n",
       "      <td>0.166667</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-31245.28</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-96138.68</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-95724.16</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-78441.97</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.699594</td>\n",
       "      <td>0.300406</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.405684</td>\n",
       "      <td>0.594316</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-83711.23</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.666261</td>\n",
       "      <td>0.333739</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.306762</td>\n",
       "      <td>0.693238</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-52357.6</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.856900</td>\n",
       "      <td>0.143100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.811368</td>\n",
       "      <td>0.188632</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-53907.05</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.852927</td>\n",
       "      <td>0.147073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.784209</td>\n",
       "      <td>0.215791</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-94605.5</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.593467</td>\n",
       "      <td>0.406533</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.072421</td>\n",
       "      <td>0.927579</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-70472.13</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.773870</td>\n",
       "      <td>0.226130</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.513128</td>\n",
       "      <td>0.486872</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-78455.38</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.650357</td>\n",
       "      <td>0.349643</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.417235</td>\n",
       "      <td>0.582765</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-88702.35</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.584153</td>\n",
       "      <td>0.415847</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.208809</td>\n",
       "      <td>0.791191</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-45764.46</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.836801</td>\n",
       "      <td>0.163199</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.949757</td>\n",
       "      <td>0.050243</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>-85180.4</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.604807</td>\n",
       "      <td>0.395193</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.282256</td>\n",
       "      <td>0.717744</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>-86633.83</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.673257</td>\n",
       "      <td>0.326743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.216330</td>\n",
       "      <td>0.783670</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>-55505.88</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.868494</td>\n",
       "      <td>0.131506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.722351</td>\n",
       "      <td>0.277649</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>-59649.33</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.849447</td>\n",
       "      <td>0.150553</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.654595</td>\n",
       "      <td>0.345405</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>-67201.6</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.740282</td>\n",
       "      <td>0.259718</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.615686</td>\n",
       "      <td>0.384314</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>-79900.42</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.670879</td>\n",
       "      <td>0.329121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.389007</td>\n",
       "      <td>0.610993</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>-83727.73</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.649001</td>\n",
       "      <td>0.350999</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.314701</td>\n",
       "      <td>0.685299</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>-93610.27</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.577309</td>\n",
       "      <td>0.422691</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.105298</td>\n",
       "      <td>0.894702</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>-48055.96</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.863983</td>\n",
       "      <td>0.136017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.887928</td>\n",
       "      <td>0.112072</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>-45576.63</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.881772</td>\n",
       "      <td>0.118228</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.915031</td>\n",
       "      <td>0.084969</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>-91739.02</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.612108</td>\n",
       "      <td>0.387892</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.140717</td>\n",
       "      <td>0.859283</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>-67033.34</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.711129</td>\n",
       "      <td>0.288871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.622735</td>\n",
       "      <td>0.377265</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>-70011.83</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.769478</td>\n",
       "      <td>0.230522</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.531118</td>\n",
       "      <td>0.468882</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>-79884.3</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.702313</td>\n",
       "      <td>0.297687</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.368791</td>\n",
       "      <td>0.631209</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>-39460.32</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.930066</td>\n",
       "      <td>0.069934</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.967530</td>\n",
       "      <td>0.032470</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>-93850.99</td>\n",
       "      <td>AL3NI2</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.602490</td>\n",
       "      <td>0.397510</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0.087437</td>\n",
       "      <td>0.912563</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46018</th>\n",
       "      <td>-45828.58</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.050061</td>\n",
       "      <td>0.949939</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.050061</td>\n",
       "      <td>0.949939</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46019</th>\n",
       "      <td>-84954.36</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.620452</td>\n",
       "      <td>0.379548</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.620452</td>\n",
       "      <td>0.379548</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46020</th>\n",
       "      <td>-87462.71</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.503316</td>\n",
       "      <td>0.496684</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.503316</td>\n",
       "      <td>0.496684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46021</th>\n",
       "      <td>-76348.12</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.290815</td>\n",
       "      <td>0.709185</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.290815</td>\n",
       "      <td>0.709185</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46022</th>\n",
       "      <td>-51225.52</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.086494</td>\n",
       "      <td>0.913506</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.086494</td>\n",
       "      <td>0.913506</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46023</th>\n",
       "      <td>-57128.03</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.902927</td>\n",
       "      <td>0.097073</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.902927</td>\n",
       "      <td>0.097073</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46024</th>\n",
       "      <td>-72423.77</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.252032</td>\n",
       "      <td>0.747968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.252032</td>\n",
       "      <td>0.747968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46025</th>\n",
       "      <td>-83824.5</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.642015</td>\n",
       "      <td>0.357985</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.642015</td>\n",
       "      <td>0.357985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46026</th>\n",
       "      <td>-86248.2</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.588732</td>\n",
       "      <td>0.411268</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.588732</td>\n",
       "      <td>0.411268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46027</th>\n",
       "      <td>-79458.53</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.325945</td>\n",
       "      <td>0.674055</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.325945</td>\n",
       "      <td>0.674055</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46028</th>\n",
       "      <td>-71009.83</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.239095</td>\n",
       "      <td>0.760905</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.239095</td>\n",
       "      <td>0.760905</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46029</th>\n",
       "      <td>-81115.29</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.683374</td>\n",
       "      <td>0.316626</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.683374</td>\n",
       "      <td>0.316626</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46030</th>\n",
       "      <td>-38616.7</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>0.995408</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.004592</td>\n",
       "      <td>0.995408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46031</th>\n",
       "      <td>-63184.88</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.857187</td>\n",
       "      <td>0.142813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.857187</td>\n",
       "      <td>0.142813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46032</th>\n",
       "      <td>-72116.98</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.781606</td>\n",
       "      <td>0.218394</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.781606</td>\n",
       "      <td>0.218394</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46033</th>\n",
       "      <td>-87480.46</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.507160</td>\n",
       "      <td>0.492840</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.507160</td>\n",
       "      <td>0.492840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46034</th>\n",
       "      <td>-81341.86</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>0.649744</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.350256</td>\n",
       "      <td>0.649744</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46035</th>\n",
       "      <td>-47413.72</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.970456</td>\n",
       "      <td>0.029544</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.970456</td>\n",
       "      <td>0.029544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46036</th>\n",
       "      <td>-60905.8</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.156344</td>\n",
       "      <td>0.843656</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.156344</td>\n",
       "      <td>0.843656</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46037</th>\n",
       "      <td>-87482.29</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.522727</td>\n",
       "      <td>0.477273</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.522727</td>\n",
       "      <td>0.477273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46038</th>\n",
       "      <td>-76699.85</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.294551</td>\n",
       "      <td>0.705449</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.294551</td>\n",
       "      <td>0.705449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46039</th>\n",
       "      <td>-85232.33</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.614469</td>\n",
       "      <td>0.385531</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.614469</td>\n",
       "      <td>0.385531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46040</th>\n",
       "      <td>-86957.71</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.466996</td>\n",
       "      <td>0.533004</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.466996</td>\n",
       "      <td>0.533004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46041</th>\n",
       "      <td>-59245.8</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.887299</td>\n",
       "      <td>0.112701</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.887299</td>\n",
       "      <td>0.112701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46042</th>\n",
       "      <td>-58699.88</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.139826</td>\n",
       "      <td>0.860174</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.139826</td>\n",
       "      <td>0.860174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46043</th>\n",
       "      <td>-71762.15</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.784881</td>\n",
       "      <td>0.215119</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.784881</td>\n",
       "      <td>0.215119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46044</th>\n",
       "      <td>-51452.58</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.943187</td>\n",
       "      <td>0.056813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.943187</td>\n",
       "      <td>0.056813</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46045</th>\n",
       "      <td>-64214.46</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.181979</td>\n",
       "      <td>0.818021</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.181979</td>\n",
       "      <td>0.818021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46046</th>\n",
       "      <td>-45140.79</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.045538</td>\n",
       "      <td>0.954462</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.045538</td>\n",
       "      <td>0.954462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46047</th>\n",
       "      <td>-58334.11</td>\n",
       "      <td>LIQUID</td>\n",
       "      <td>1000</td>\n",
       "      <td>0.894071</td>\n",
       "      <td>0.105929</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.894071</td>\n",
       "      <td>0.105929</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>46048 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             GM   Phase     T     X(AL)     X(NI)  Y(AL3NI1,0,AL)  \\\n",
       "0     -78820.96  AL3NI1  1000  0.750000  0.250000               1   \n",
       "1     -42406.27  AL3NI2  1000  0.833333  0.166667             NaN   \n",
       "2     -31245.28  AL3NI2  1000  1.000000  0.000000             NaN   \n",
       "3     -96138.68  AL3NI2  1000  0.500000  0.500000             NaN   \n",
       "4     -95724.16  AL3NI2  1000  0.600000  0.400000             NaN   \n",
       "5     -78441.97  AL3NI2  1000  0.699594  0.300406             NaN   \n",
       "6     -83711.23  AL3NI2  1000  0.666261  0.333739             NaN   \n",
       "7      -52357.6  AL3NI2  1000  0.856900  0.143100             NaN   \n",
       "8     -53907.05  AL3NI2  1000  0.852927  0.147073             NaN   \n",
       "9      -94605.5  AL3NI2  1000  0.593467  0.406533             NaN   \n",
       "10    -70472.13  AL3NI2  1000  0.773870  0.226130             NaN   \n",
       "11    -78455.38  AL3NI2  1000  0.650357  0.349643             NaN   \n",
       "12    -88702.35  AL3NI2  1000  0.584153  0.415847             NaN   \n",
       "13    -45764.46  AL3NI2  1000  0.836801  0.163199             NaN   \n",
       "14     -85180.4  AL3NI2  1000  0.604807  0.395193             NaN   \n",
       "15    -86633.83  AL3NI2  1000  0.673257  0.326743             NaN   \n",
       "16    -55505.88  AL3NI2  1000  0.868494  0.131506             NaN   \n",
       "17    -59649.33  AL3NI2  1000  0.849447  0.150553             NaN   \n",
       "18     -67201.6  AL3NI2  1000  0.740282  0.259718             NaN   \n",
       "19    -79900.42  AL3NI2  1000  0.670879  0.329121             NaN   \n",
       "20    -83727.73  AL3NI2  1000  0.649001  0.350999             NaN   \n",
       "21    -93610.27  AL3NI2  1000  0.577309  0.422691             NaN   \n",
       "22    -48055.96  AL3NI2  1000  0.863983  0.136017             NaN   \n",
       "23    -45576.63  AL3NI2  1000  0.881772  0.118228             NaN   \n",
       "24    -91739.02  AL3NI2  1000  0.612108  0.387892             NaN   \n",
       "25    -67033.34  AL3NI2  1000  0.711129  0.288871             NaN   \n",
       "26    -70011.83  AL3NI2  1000  0.769478  0.230522             NaN   \n",
       "27     -79884.3  AL3NI2  1000  0.702313  0.297687             NaN   \n",
       "28    -39460.32  AL3NI2  1000  0.930066  0.069934             NaN   \n",
       "29    -93850.99  AL3NI2  1000  0.602490  0.397510             NaN   \n",
       "...         ...     ...   ...       ...       ...             ...   \n",
       "46018 -45828.58  LIQUID  1000  0.050061  0.949939             NaN   \n",
       "46019 -84954.36  LIQUID  1000  0.620452  0.379548             NaN   \n",
       "46020 -87462.71  LIQUID  1000  0.503316  0.496684             NaN   \n",
       "46021 -76348.12  LIQUID  1000  0.290815  0.709185             NaN   \n",
       "46022 -51225.52  LIQUID  1000  0.086494  0.913506             NaN   \n",
       "46023 -57128.03  LIQUID  1000  0.902927  0.097073             NaN   \n",
       "46024 -72423.77  LIQUID  1000  0.252032  0.747968             NaN   \n",
       "46025  -83824.5  LIQUID  1000  0.642015  0.357985             NaN   \n",
       "46026  -86248.2  LIQUID  1000  0.588732  0.411268             NaN   \n",
       "46027 -79458.53  LIQUID  1000  0.325945  0.674055             NaN   \n",
       "46028 -71009.83  LIQUID  1000  0.239095  0.760905             NaN   \n",
       "46029 -81115.29  LIQUID  1000  0.683374  0.316626             NaN   \n",
       "46030  -38616.7  LIQUID  1000  0.004592  0.995408             NaN   \n",
       "46031 -63184.88  LIQUID  1000  0.857187  0.142813             NaN   \n",
       "46032 -72116.98  LIQUID  1000  0.781606  0.218394             NaN   \n",
       "46033 -87480.46  LIQUID  1000  0.507160  0.492840             NaN   \n",
       "46034 -81341.86  LIQUID  1000  0.350256  0.649744             NaN   \n",
       "46035 -47413.72  LIQUID  1000  0.970456  0.029544             NaN   \n",
       "46036  -60905.8  LIQUID  1000  0.156344  0.843656             NaN   \n",
       "46037 -87482.29  LIQUID  1000  0.522727  0.477273             NaN   \n",
       "46038 -76699.85  LIQUID  1000  0.294551  0.705449             NaN   \n",
       "46039 -85232.33  LIQUID  1000  0.614469  0.385531             NaN   \n",
       "46040 -86957.71  LIQUID  1000  0.466996  0.533004             NaN   \n",
       "46041  -59245.8  LIQUID  1000  0.887299  0.112701             NaN   \n",
       "46042 -58699.88  LIQUID  1000  0.139826  0.860174             NaN   \n",
       "46043 -71762.15  LIQUID  1000  0.784881  0.215119             NaN   \n",
       "46044 -51452.58  LIQUID  1000  0.943187  0.056813             NaN   \n",
       "46045 -64214.46  LIQUID  1000  0.181979  0.818021             NaN   \n",
       "46046 -45140.79  LIQUID  1000  0.045538  0.954462             NaN   \n",
       "46047 -58334.11  LIQUID  1000  0.894071  0.105929             NaN   \n",
       "\n",
       "       Y(AL3NI1,1,NI)  Y(AL3NI2,0,AL)  Y(AL3NI2,1,AL)  Y(AL3NI2,1,NI)  \\\n",
       "0                   1             NaN             NaN             NaN   \n",
       "1                 NaN               1        1.000000        0.000000   \n",
       "2                 NaN               1        1.000000        0.000000   \n",
       "3                 NaN               1        0.000000        1.000000   \n",
       "4                 NaN               1        0.000000        1.000000   \n",
       "5                 NaN               1        0.405684        0.594316   \n",
       "6                 NaN               1        0.306762        0.693238   \n",
       "7                 NaN               1        0.811368        0.188632   \n",
       "8                 NaN               1        0.784209        0.215791   \n",
       "9                 NaN               1        0.072421        0.927579   \n",
       "10                NaN               1        0.513128        0.486872   \n",
       "11                NaN               1        0.417235        0.582765   \n",
       "12                NaN               1        0.208809        0.791191   \n",
       "13                NaN               1        0.949757        0.050243   \n",
       "14                NaN               1        0.282256        0.717744   \n",
       "15                NaN               1        0.216330        0.783670   \n",
       "16                NaN               1        0.722351        0.277649   \n",
       "17                NaN               1        0.654595        0.345405   \n",
       "18                NaN               1        0.615686        0.384314   \n",
       "19                NaN               1        0.389007        0.610993   \n",
       "20                NaN               1        0.314701        0.685299   \n",
       "21                NaN               1        0.105298        0.894702   \n",
       "22                NaN               1        0.887928        0.112072   \n",
       "23                NaN               1        0.915031        0.084969   \n",
       "24                NaN               1        0.140717        0.859283   \n",
       "25                NaN               1        0.622735        0.377265   \n",
       "26                NaN               1        0.531118        0.468882   \n",
       "27                NaN               1        0.368791        0.631209   \n",
       "28                NaN               1        0.967530        0.032470   \n",
       "29                NaN               1        0.087437        0.912563   \n",
       "...               ...             ...             ...             ...   \n",
       "46018             NaN             NaN             NaN             NaN   \n",
       "46019             NaN             NaN             NaN             NaN   \n",
       "46020             NaN             NaN             NaN             NaN   \n",
       "46021             NaN             NaN             NaN             NaN   \n",
       "46022             NaN             NaN             NaN             NaN   \n",
       "46023             NaN             NaN             NaN             NaN   \n",
       "46024             NaN             NaN             NaN             NaN   \n",
       "46025             NaN             NaN             NaN             NaN   \n",
       "46026             NaN             NaN             NaN             NaN   \n",
       "46027             NaN             NaN             NaN             NaN   \n",
       "46028             NaN             NaN             NaN             NaN   \n",
       "46029             NaN             NaN             NaN             NaN   \n",
       "46030             NaN             NaN             NaN             NaN   \n",
       "46031             NaN             NaN             NaN             NaN   \n",
       "46032             NaN             NaN             NaN             NaN   \n",
       "46033             NaN             NaN             NaN             NaN   \n",
       "46034             NaN             NaN             NaN             NaN   \n",
       "46035             NaN             NaN             NaN             NaN   \n",
       "46036             NaN             NaN             NaN             NaN   \n",
       "46037             NaN             NaN             NaN             NaN   \n",
       "46038             NaN             NaN             NaN             NaN   \n",
       "46039             NaN             NaN             NaN             NaN   \n",
       "46040             NaN             NaN             NaN             NaN   \n",
       "46041             NaN             NaN             NaN             NaN   \n",
       "46042             NaN             NaN             NaN             NaN   \n",
       "46043             NaN             NaN             NaN             NaN   \n",
       "46044             NaN             NaN             NaN             NaN   \n",
       "46045             NaN             NaN             NaN             NaN   \n",
       "46046             NaN             NaN             NaN             NaN   \n",
       "46047             NaN             NaN             NaN             NaN   \n",
       "\n",
       "            ...        Y(BCC_B2,1,NI)  Y(BCC_B2,1,VA)  Y(BCC_B2,2,VA)  \\\n",
       "0           ...                   NaN             NaN             NaN   \n",
       "1           ...                   NaN             NaN             NaN   \n",
       "2           ...                   NaN             NaN             NaN   \n",
       "3           ...                   NaN             NaN             NaN   \n",
       "4           ...                   NaN             NaN             NaN   \n",
       "5           ...                   NaN             NaN             NaN   \n",
       "6           ...                   NaN             NaN             NaN   \n",
       "7           ...                   NaN             NaN             NaN   \n",
       "8           ...                   NaN             NaN             NaN   \n",
       "9           ...                   NaN             NaN             NaN   \n",
       "10          ...                   NaN             NaN             NaN   \n",
       "11          ...                   NaN             NaN             NaN   \n",
       "12          ...                   NaN             NaN             NaN   \n",
       "13          ...                   NaN             NaN             NaN   \n",
       "14          ...                   NaN             NaN             NaN   \n",
       "15          ...                   NaN             NaN             NaN   \n",
       "16          ...                   NaN             NaN             NaN   \n",
       "17          ...                   NaN             NaN             NaN   \n",
       "18          ...                   NaN             NaN             NaN   \n",
       "19          ...                   NaN             NaN             NaN   \n",
       "20          ...                   NaN             NaN             NaN   \n",
       "21          ...                   NaN             NaN             NaN   \n",
       "22          ...                   NaN             NaN             NaN   \n",
       "23          ...                   NaN             NaN             NaN   \n",
       "24          ...                   NaN             NaN             NaN   \n",
       "25          ...                   NaN             NaN             NaN   \n",
       "26          ...                   NaN             NaN             NaN   \n",
       "27          ...                   NaN             NaN             NaN   \n",
       "28          ...                   NaN             NaN             NaN   \n",
       "29          ...                   NaN             NaN             NaN   \n",
       "...         ...                   ...             ...             ...   \n",
       "46018       ...                   NaN             NaN             NaN   \n",
       "46019       ...                   NaN             NaN             NaN   \n",
       "46020       ...                   NaN             NaN             NaN   \n",
       "46021       ...                   NaN             NaN             NaN   \n",
       "46022       ...                   NaN             NaN             NaN   \n",
       "46023       ...                   NaN             NaN             NaN   \n",
       "46024       ...                   NaN             NaN             NaN   \n",
       "46025       ...                   NaN             NaN             NaN   \n",
       "46026       ...                   NaN             NaN             NaN   \n",
       "46027       ...                   NaN             NaN             NaN   \n",
       "46028       ...                   NaN             NaN             NaN   \n",
       "46029       ...                   NaN             NaN             NaN   \n",
       "46030       ...                   NaN             NaN             NaN   \n",
       "46031       ...                   NaN             NaN             NaN   \n",
       "46032       ...                   NaN             NaN             NaN   \n",
       "46033       ...                   NaN             NaN             NaN   \n",
       "46034       ...                   NaN             NaN             NaN   \n",
       "46035       ...                   NaN             NaN             NaN   \n",
       "46036       ...                   NaN             NaN             NaN   \n",
       "46037       ...                   NaN             NaN             NaN   \n",
       "46038       ...                   NaN             NaN             NaN   \n",
       "46039       ...                   NaN             NaN             NaN   \n",
       "46040       ...                   NaN             NaN             NaN   \n",
       "46041       ...                   NaN             NaN             NaN   \n",
       "46042       ...                   NaN             NaN             NaN   \n",
       "46043       ...                   NaN             NaN             NaN   \n",
       "46044       ...                   NaN             NaN             NaN   \n",
       "46045       ...                   NaN             NaN             NaN   \n",
       "46046       ...                   NaN             NaN             NaN   \n",
       "46047       ...                   NaN             NaN             NaN   \n",
       "\n",
       "       Y(FCC_L12,0,AL)  Y(FCC_L12,0,NI)  Y(FCC_L12,1,AL)  Y(FCC_L12,1,NI)  \\\n",
       "0                  NaN              NaN              NaN              NaN   \n",
       "1                  NaN              NaN              NaN              NaN   \n",
       "2                  NaN              NaN              NaN              NaN   \n",
       "3                  NaN              NaN              NaN              NaN   \n",
       "4                  NaN              NaN              NaN              NaN   \n",
       "5                  NaN              NaN              NaN              NaN   \n",
       "6                  NaN              NaN              NaN              NaN   \n",
       "7                  NaN              NaN              NaN              NaN   \n",
       "8                  NaN              NaN              NaN              NaN   \n",
       "9                  NaN              NaN              NaN              NaN   \n",
       "10                 NaN              NaN              NaN              NaN   \n",
       "11                 NaN              NaN              NaN              NaN   \n",
       "12                 NaN              NaN              NaN              NaN   \n",
       "13                 NaN              NaN              NaN              NaN   \n",
       "14                 NaN              NaN              NaN              NaN   \n",
       "15                 NaN              NaN              NaN              NaN   \n",
       "16                 NaN              NaN              NaN              NaN   \n",
       "17                 NaN              NaN              NaN              NaN   \n",
       "18                 NaN              NaN              NaN              NaN   \n",
       "19                 NaN              NaN              NaN              NaN   \n",
       "20                 NaN              NaN              NaN              NaN   \n",
       "21                 NaN              NaN              NaN              NaN   \n",
       "22                 NaN              NaN              NaN              NaN   \n",
       "23                 NaN              NaN              NaN              NaN   \n",
       "24                 NaN              NaN              NaN              NaN   \n",
       "25                 NaN              NaN              NaN              NaN   \n",
       "26                 NaN              NaN              NaN              NaN   \n",
       "27                 NaN              NaN              NaN              NaN   \n",
       "28                 NaN              NaN              NaN              NaN   \n",
       "29                 NaN              NaN              NaN              NaN   \n",
       "...                ...              ...              ...              ...   \n",
       "46018              NaN              NaN              NaN              NaN   \n",
       "46019              NaN              NaN              NaN              NaN   \n",
       "46020              NaN              NaN              NaN              NaN   \n",
       "46021              NaN              NaN              NaN              NaN   \n",
       "46022              NaN              NaN              NaN              NaN   \n",
       "46023              NaN              NaN              NaN              NaN   \n",
       "46024              NaN              NaN              NaN              NaN   \n",
       "46025              NaN              NaN              NaN              NaN   \n",
       "46026              NaN              NaN              NaN              NaN   \n",
       "46027              NaN              NaN              NaN              NaN   \n",
       "46028              NaN              NaN              NaN              NaN   \n",
       "46029              NaN              NaN              NaN              NaN   \n",
       "46030              NaN              NaN              NaN              NaN   \n",
       "46031              NaN              NaN              NaN              NaN   \n",
       "46032              NaN              NaN              NaN              NaN   \n",
       "46033              NaN              NaN              NaN              NaN   \n",
       "46034              NaN              NaN              NaN              NaN   \n",
       "46035              NaN              NaN              NaN              NaN   \n",
       "46036              NaN              NaN              NaN              NaN   \n",
       "46037              NaN              NaN              NaN              NaN   \n",
       "46038              NaN              NaN              NaN              NaN   \n",
       "46039              NaN              NaN              NaN              NaN   \n",
       "46040              NaN              NaN              NaN              NaN   \n",
       "46041              NaN              NaN              NaN              NaN   \n",
       "46042              NaN              NaN              NaN              NaN   \n",
       "46043              NaN              NaN              NaN              NaN   \n",
       "46044              NaN              NaN              NaN              NaN   \n",
       "46045              NaN              NaN              NaN              NaN   \n",
       "46046              NaN              NaN              NaN              NaN   \n",
       "46047              NaN              NaN              NaN              NaN   \n",
       "\n",
       "       Y(FCC_L12,2,VA)  Y(LIQUID,0,AL)  Y(LIQUID,0,NI)  \n",
       "0                  NaN             NaN             NaN  \n",
       "1                  NaN             NaN             NaN  \n",
       "2                  NaN             NaN             NaN  \n",
       "3                  NaN             NaN             NaN  \n",
       "4                  NaN             NaN             NaN  \n",
       "5                  NaN             NaN             NaN  \n",
       "6                  NaN             NaN             NaN  \n",
       "7                  NaN             NaN             NaN  \n",
       "8                  NaN             NaN             NaN  \n",
       "9                  NaN             NaN             NaN  \n",
       "10                 NaN             NaN             NaN  \n",
       "11                 NaN             NaN             NaN  \n",
       "12                 NaN             NaN             NaN  \n",
       "13                 NaN             NaN             NaN  \n",
       "14                 NaN             NaN             NaN  \n",
       "15                 NaN             NaN             NaN  \n",
       "16                 NaN             NaN             NaN  \n",
       "17                 NaN             NaN             NaN  \n",
       "18                 NaN             NaN             NaN  \n",
       "19                 NaN             NaN             NaN  \n",
       "20                 NaN             NaN             NaN  \n",
       "21                 NaN             NaN             NaN  \n",
       "22                 NaN             NaN             NaN  \n",
       "23                 NaN             NaN             NaN  \n",
       "24                 NaN             NaN             NaN  \n",
       "25                 NaN             NaN             NaN  \n",
       "26                 NaN             NaN             NaN  \n",
       "27                 NaN             NaN             NaN  \n",
       "28                 NaN             NaN             NaN  \n",
       "29                 NaN             NaN             NaN  \n",
       "...                ...             ...             ...  \n",
       "46018              NaN        0.050061        0.949939  \n",
       "46019              NaN        0.620452        0.379548  \n",
       "46020              NaN        0.503316        0.496684  \n",
       "46021              NaN        0.290815        0.709185  \n",
       "46022              NaN        0.086494        0.913506  \n",
       "46023              NaN        0.902927        0.097073  \n",
       "46024              NaN        0.252032        0.747968  \n",
       "46025              NaN        0.642015        0.357985  \n",
       "46026              NaN        0.588732        0.411268  \n",
       "46027              NaN        0.325945        0.674055  \n",
       "46028              NaN        0.239095        0.760905  \n",
       "46029              NaN        0.683374        0.316626  \n",
       "46030              NaN        0.004592        0.995408  \n",
       "46031              NaN        0.857187        0.142813  \n",
       "46032              NaN        0.781606        0.218394  \n",
       "46033              NaN        0.507160        0.492840  \n",
       "46034              NaN        0.350256        0.649744  \n",
       "46035              NaN        0.970456        0.029544  \n",
       "46036              NaN        0.156344        0.843656  \n",
       "46037              NaN        0.522727        0.477273  \n",
       "46038              NaN        0.294551        0.705449  \n",
       "46039              NaN        0.614469        0.385531  \n",
       "46040              NaN        0.466996        0.533004  \n",
       "46041              NaN        0.887299        0.112701  \n",
       "46042              NaN        0.139826        0.860174  \n",
       "46043              NaN        0.784881        0.215119  \n",
       "46044              NaN        0.943187        0.056813  \n",
       "46045              NaN        0.181979        0.818021  \n",
       "46046              NaN        0.045538        0.954462  \n",
       "46047              NaN        0.894071        0.105929  \n",
       "\n",
       "[46048 rows x 28 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nrg_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "nrg_df.to_excel('alni_energies.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Al-Zn (S. Mey et al., 1993)\n",
    "\n",
    "Next, we show values from Mey et al.'s 1993 assessment of the Al-Zn system. We have not done any fitting here; we are just showing how arbitrary values for the parameters may be specified and easily used to plot the phase diagram. Note the optional *parameters* keyword argument specified for each call to **Model**. This feature is what makes it easy to perform arbitrary variable substitutions in our thermodynamic models."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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rJueu3OqZR+ch0pTmZjZktAZwLiBgFnBKsfFYwTbm7bl5ngUuLzAWs5ZQT9fV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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4d8779450>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycalphad import Model, Database, binplot\n",
    "\n",
    "db_alzn = Database('alzn_noparams.tdb')\n",
    "\n",
    "assessed_parameters = {\n",
    "    'LIQALZN0H': 10465.3,\n",
    "    'LIQALZN0S': 3.39259,\n",
    "    'FCCALZN0H': 7297.5,\n",
    "    'FCCALZN0S': -0.47512,\n",
    "    'FCCALZN1H': 6612.9,\n",
    "    'FCCALZN1S': 4.5911,\n",
    "    'FCCALZN2H': -3097.2,\n",
    "    'FCCALZN2S': -3.306635,\n",
    "    'HCPALZN0H': 18821.0,\n",
    "    'HCPALZN0S': 8.95255,\n",
    "    'HCPALZN3H': -702.8,\n",
    "    'HCPALZN3S': 0.0\n",
    "    }\n",
    "\n",
    "# Initialize phase models\n",
    "assessed_models = {\n",
    "    'LIQUID': Model(db_alzn, ['AL', 'ZN'], 'LIQUID', parameters=assessed_parameters),\n",
    "    'FCC_A1': Model(db_alzn, ['AL', 'ZN', 'VA'], 'FCC_A1', parameters=assessed_parameters),\n",
    "    'HCP_A3': Model(db_alzn, ['AL', 'ZN', 'VA'], 'HCP_A3', parameters=assessed_parameters)\n",
    "    }\n",
    "\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = binplot(db_alzn, ['AL', 'ZN', 'VA'] , ['LIQUID', 'FCC_A1', 'HCP_A3'], \\\n",
    "                   'X(ZN)', 300, 1000, ax=plt.gca(), model=assessed_models)\n",
    "ax = ax.set_title('Al-Zn', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ly45qwePuBewL/AxYH9gP+LWkXxSxu89v0dQ0BLR+0lHUisQ3JLaR8Cpz55yL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\nFzpmZmbWtP4felHskc6r9eoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fa4d882b0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "blank_parameters = {\n",
    "    'LIQALZN0H': 0.0,\n",
    "    'LIQALZN0S': 0.0,\n",
    "    'FCCALZN0H': 0.0,\n",
    "    'FCCALZN0S': 0.0,\n",
    "    'FCCALZN1H': 0.0,\n",
    "    'FCCALZN1S': 0.0,\n",
    "    'FCCALZN2H': 0.0,\n",
    "    'FCCALZN2S': 0.0,\n",
    "    'HCPALZN0H': 0.0,\n",
    "    'HCPALZN0S': 0.0,\n",
    "    'HCPALZN3H': 0.0,\n",
    "    'HCPALZN3S': 0.0\n",
    "    }\n",
    "\n",
    "models = {\n",
    "    'LIQUID': Model(db_alzn, ['AL', 'ZN'], 'LIQUID', parameters=blank_parameters),\n",
    "    'FCC_A1': Model(db_alzn, ['AL', 'ZN', 'VA'], 'FCC_A1', parameters=blank_parameters),\n",
    "    'HCP_A3': Model(db_alzn, ['AL', 'ZN', 'VA'], 'HCP_A3', parameters=blank_parameters)\n",
    "    }\n",
    "\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "ax = binplot(db_alzn, ['AL', 'ZN', 'VA'] , ['LIQUID', 'FCC_A1', 'HCP_A3'], \\\n",
    "             'X(ZN)', 300, 1000, ax=plt.gca(), model=models)\n",
    "ax = ax.set_title('Al-Zn with endmembers only', fontsize=20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prototyping New Models (Advanced)\n",
    "\n",
    "What if we have a new kind of functional form for the Gibbs energy we would like to experiment with?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sympy import Piecewise, log\n",
    "from tinydb import where\n",
    "import pycalphad.variables as v\n",
    "\n",
    "class MyMagneticModel(Model):\n",
    "    def __init__(self, db, comps, phase, **kwargs):\n",
    "        super().__init__(db, comps, phase, **kwargs)\n",
    "        self.models['mag'] = self.my_magnetic_energy(db.phases[phase], db.search)\n",
    "\n",
    "    def my_magnetic_energy(self, phase, param_search):\n",
    "        \"\"\"\n",
    "        Return the energy from magnetic ordering in symbolic form.\n",
    "        The implemented model is the Inden-Hillert-Jarl formulation.\n",
    "        The approach follows from the background section of W. Xiong, 2011.\n",
    "        \n",
    "        Note that this is a demonstration model; use pycalphad's builtin\n",
    "        implementation as a reference.\n",
    "        \"\"\"\n",
    "        site_ratio_normalization = self._site_ratio_normalization(phase)\n",
    "        # define basic variables\n",
    "        afm_factor = phase.model_hints['ihj_magnetic_afm_factor']\n",
    "\n",
    "        bm_param_query = (\n",
    "            (where('phase_name') == phase.name) & \\\n",
    "            (where('parameter_type') == 'BMAGN') & \\\n",
    "            (where('constituent_array').test(self._array_validity))\n",
    "        )\n",
    "        tc_param_query = (\n",
    "            (where('phase_name') == phase.name) & \\\n",
    "            (where('parameter_type') == 'TC') & \\\n",
    "            (where('constituent_array').test(self._array_validity))\n",
    "        )\n",
    "\n",
    "        mean_magnetic_moment = \\\n",
    "            self.redlich_kister_sum(phase, param_search, bm_param_query)\n",
    "        beta = Piecewise(\n",
    "            (mean_magnetic_moment, mean_magnetic_moment > 0),\n",
    "            (mean_magnetic_moment/afm_factor, mean_magnetic_moment <= 0)\n",
    "            )\n",
    "\n",
    "        curie_temp = \\\n",
    "            self.redlich_kister_sum(phase, param_search, tc_param_query)\n",
    "        tc = Piecewise(\n",
    "            (curie_temp, curie_temp > 0),\n",
    "            (curie_temp/afm_factor, curie_temp <= 0)\n",
    "            )\n",
    "\n",
    "        tau = Piecewise(\n",
    "            (v.T / tc, tc > 0),\n",
    "            (10000., tc == 0) # tau is 'infinity'\n",
    "            )\n",
    "\n",
    "        # define model parameters\n",
    "        p = phase.model_hints['ihj_magnetic_structure_factor']\n",
    "        A = 518/1125 + (11692/15975)*(1/p - 1)\n",
    "        # factor when tau < 1\n",
    "        sub_tau = 1 - (1/A) * ((79/(140*p))*(tau**(-1)) + (474/497)*(1/p - 1) \\\n",
    "            * ((tau**3)/6 + (tau**9)/135 + (tau**15)/600)\n",
    "                              )\n",
    "        # factor when tau >= 1\n",
    "        super_tau = -(1/A) * ((tau**-5)/10 + (tau**-15)/315 + (tau**-25)/1500)\n",
    "\n",
    "        expr_cond_pairs = [(sub_tau, tau < 1),\n",
    "                           (super_tau, tau >= 1)\n",
    "                          ]\n",
    "\n",
    "        g_term = Piecewise(*expr_cond_pairs)\n",
    "\n",
    "        return 1e6 * v.R * v.T * log(beta+1) * \\\n",
    "            g_term / site_ratio_normalization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 18.9 s, sys: 786 ms, total: 19.6 s\n",
      "Wall time: 12.5 s\n"
     ]
    },
    {
     "data": {
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OOoTh3+XMzBfedDoVktYgxEvdZ2ZbV1qeJEha1Mw+ydi3OPAkwbW2RAnzQjmO00Wpthih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vLS9sCZhN3i53VyeYckTQIOAX4k6eZYafPaEHgCeER5NW9B9DBhOv7tymujUmJzRZsF/PAUTrk+\n7UBcMt41VgT/hZSMl1tyGS67vgCN0Kc2q91jBrE3G41h40sYvPux4X5TL/qU1DW2Af14Lrq/GpvH\n6hrbg5W4h94YizAGRGeXann8ACYJLYgVT5nLridapEV1vawXIxnpDQsVyitCzrk4RgBn1MBFhv2z\n06t7orGQP1bTgI1qmtjQsDeSZqXnJMKehw32uq0fK/Fw0F36C2EG26To7GbAfcDjtpadmjQuF4Mx\nEpgAjE83EJeU12CL0HqlX1ccL7fkMlx2fyBsMHld2oG0p9xllw8Lto4kTHuPPQ6nlUOAYcB6kvaK\nk1B5LQ38EjiLxXsm7gxsAxyhvPrGDSbD77ssOxLY9HZuP73TK10meYuQcy6OLQEM1uxVymaj5TSZ\nPkJlW+vou0cycLMrWg43Lqlr7BssyTvR/cFcKbRh0WlHszbTWZcmmoAdgD8DxwM1wNLsyeNCH3WY\nR2t3Mqik11OFGmoatq1trNVSLDUi7VhcMj5rrA0+4t+59ugvwJ7AY2Bbpx1NGvJhW59GwoKKk3Nm\nuyTNS9KOwP3R4VZm9o+i0+bVH7iN0DW2t+XsY+W1HaGb7F3gW5azhqSxuSKJCwgrr1+LcST4d0il\n8a4x51wctwBvRrdVKZou/x6hMnRpidlNBz4k7IUYd8zVfOBu4FoWryT9FDAVuMMrQd3mHuAfwF1p\nB+KS8YpQEbzfPBkvt+QyXHbXA6sAv0s7kPZ0wxih44DlCV1Qd5eY3TPAYGBZwlT4OPYhLGVwGdC8\nKfV1wO7AScprx7jBZPh9l2X3AdtNY9qdaQfikvExQs65OL4A+i+Cpt5VOkZolb9Sf2jUGdZQV+LK\n0nvTt2W49Ugs1vT5vVmBO6mLps83jzR6jtB1CUdyltBpseLxMUKxvTfkPS37/rLMr5vvLXAVyscI\nteEr/btiaaAR49MYOawAvA+2sCwBOpca1QNXA8eCfZx2NGnJS3lg/5zZsFLzKnFl6a2AL5tXlo7O\njQWet5z9vdTYXBFEbyAPnIMxF3yMUKXxilAbWt7EYkNC328jMBzjtSJSHwv8FngdWAesqZyxOte9\ntACoA5og+WajlSwvbU1YwRlgYc6sPmlekh4AvhMd3mZm/1N02rz2B26MDre1nD2svK4CDovObWw5\nezppbK5I4lNgADAfYwnwilCl8a6xjg0DmKIp9XdfePf4i7m404rQY7DXFqFc1+gVZnPML3eQWeX7\nFiWX4bKrAzDo1SurXShTqWV7FpUr+x2PofeIy1oO60rqhhtI75a25kHsrjirda/PGjzfcrQFoXK2\n5eJAeVBobqx47qcPO1G2bsWeqJHGAUJMZ3rfUYxKOxyXgFeEOvYXYI0X7cUVLvrxRcdddPxFRbTu\n6HLgFGAKWNVWglyPNR44RfBnw45MO5i2aIcyVyIvhfxlmg/0AU4y7PykWelTDSTMGjM+YZBhRVdC\n9Lz6EmYqzQOaY/guoUI0y0bYtu2lbTfPnTNbAc+y44FxL/PynV4Rqkw+a6wjxiJgwvEc3xe4D7FM\nEYlmg50INqnza5tpRdBU0CWgHvNv4h+oyWW47LYi7De2adqBtKcbdp4XoRIEkHgNocgWhNlntcAm\nMdMuQ9j4dWNo2WtsFWAosIHyit1ll+H3XZZtASx9NEcPSTsQl0yP+dItoy0JWwqMBg4s03McRliy\n/0fAOmV6Due6QvOU7Gre2fyPBfe3LzGv2wrux52KnyNsrbEy8OPo3OVAb8L2H/FmjLmkDiR8l+6R\ndiAuGe8a69xzk5j07vZ12w/a7d7d1prClPFd/QT7wPLXwYKF8MYgeLWr809Lhse5ZF6Gy+5dYPkv\nYFG/Kh0j1HcW/GzVcH/e0jSWNEboBODC6P6P+TjWGKEDWTIaKt2IcVN09jLgEgCO5TChMbHi8TFC\nsb2y2isL15i5Rt0d/e+Ytzd7px2OS8BnjbWh9Yh/SSMNawD6AQ9iZKTQVAN8D3gN7Lm0o2ktw1/m\nmZfdstNWwETgDLBMrqTbHWWXl54ktN6umTN7q5S8JM0EFpnZmrHT5nUK8Jnl7PLoWIQ9x161nOUS\nxJLR912GiY2ACUdx1B0TbeJl4LPGKo1XhNrwtTexGA48BhiwP8Y9acX2VTqBsPu0gFXB5qQbj+v5\n1ER4vwHV+UGfl45n8craljNLPMQgqgStGh2+YGbrFZ02r3HAmdHhDyxnf1Jek4GdonOHWc7+2HZq\n12XEIsI4ryaMGvCKUKXxrrHiNAI09mqsOfnck/e7gAviDmosi8th88Ogdw001oCvV+S6jQGZnT5f\nZhv/DnaPRuQYUFLX2GrUMjO6/01WjtU1thUr8VjLUUOrW9iN3wjFbhVy8TT0aqipaarBZOrlw24r\nkrcItaGdrrHPgSWwloXUMkAi/PqbCfbvtKNpzZvZk8tu2WlD4PeErrGpaUfTlm7qGptCmC20XM7s\ni1LykvQc0GBmsX9gKa+jgbmWsz9FxyKMEXrNcvbbBLFk9H2XYWIYcM6BHHjfDXbD1eAtQpXGq6/F\nGwZMRdyRdiCLmYFNjl8J0smgRaBSd8521ec+wkzKGMtD9Cx5aUXCbLH+QKnjgy4D1gc2lvTLWGnz\nGgVcCvxReTXPNj0m+jtfea1fSmyuaJOBvQ/l0AlpB+KS8RahNrRZmxeTgF0Ms94Nvc9qrG2s2IJ7\nB45eHpZtgM97YwM6T+FcMxk0d43F2X+v5/jeQdRvdEPLOkLkjQWJM+tPHfOiMVf9aeDzgs6uztzC\nWrzE8tHRLyxnZymvp4HhAGzKp+xKVXZfdqcmNa0ihGHIJPAWoUrjY4SK93OgXuiWRbWLJqYdTGn0\nEHBmb7g47UhcxbkBOEDwtBG/K6dHuB7yN7QMGr/LsD2TZqV5+jbwCABz2dywZ4pO+5KWA54AviTs\nbwgwBvgn8AUzWMF2teSVNFesicBhQrekHYhLxrvGiiBpJKEpfEXgzXSj6Qr2d7CRYLcXd71uAj0N\nWiHOs0Tl5hLIcNm9Biwiw/8PuqnsmscF/a3EfF4EPgM+AeKO85tH2Abo0oJ4FhJmMNVH92PJ8Psu\nywYBvW7n9tXTDsQl4xWh4p0HrAUtC5dVCe0K7Edobr8y5WBc+sYRVi5O3ApS6fLSzwlrisHiPb6S\neoCwPcYg4K8x0x5JGA/0G8I4I4C/EzbG7U8Yu+LK7/tAr8EM3rLTK10meddYEcxsOuJDw5Z5esOn\nbRM2mZ52TN1lDaj7N6EPQDAlTlqffZJchstuLtC/Cawmq9PnDUT5hmcMmQHHRDutfdmfJqFZiTM7\nhYH8Jrp/OF8IjS867e6swr3UIL6gif9GZx+GaHnjXdku9tT+MpddT9SgBmqshm2Jvcety4iKGyyt\n8Gvsl8ClZnZcwfnxwBHAUsDjwFgze7Hg8XpCq87+hE0jpwLHmNnbbTxH6wUVBxI+XJ7AeKEMLyvj\ntCQwCOy/nV761XQrAdsAN4YZbq7yaUngRuAEsB6zHUxceel04IBcjAUQ2yPpbGCBmZ0VO21emwLv\nW87eKDh3HfCG5WxcqbG5IoghwL3A9zHeAB8sXWkqqmtM0paEys5zsHibC0mnACcCxwKbAXOAByX1\nL0h+IaEysz/hy3kAcK/U+W7vU5n6NnA1VTtl2D5PUAnStGnMAq6n9HEUVSfDYzU+BHYF/pN2IO0p\nd9nlpVHA2cC6eZWwmCIg6ffA6cCZkn7T2fVfSZvX9wgtQM8rr6HRuUeAQ4AzlFc+QTwj46ZxzAY2\nn870WWkH4pKpmK4xSQMJe+j8EBhfcF6ErQt/ZWZ3ROfGECpDBwJXRGkPAw41C4vASToEeAPYgdBP\n365e9OpjGF/0/WKF/vSf3sUvrUdaAnSv6BVW3mXFtONxXaYWMr6y9BRqhcq26epm51Pz3Z+2HNaX\n1DW2BcvxeHR/XfYQml902k3ZjCepBXphDATeI+xEH6zF6UKnxIpnKr0UtlFxRWqiSUB2tqB0sVVM\nRQi4ArjVzP4WVX6arQYsR0Flxsy+lPR3YKso3SaEAZ6F17wl6aXomg4rQqMYtRtwXL/5/XKGzeiq\nF9TjjdQYYB/g+LRDqTQZHiO0H3CJYKJhv0g7mDZtX+b8T4T8T/UasBIwyrBHk2alxyXgBaCBFxhu\nMbqQNUO9gcOBtyzXsqjqt4CZwMd2gA2LHdDo2CkcnAqcPopRv+v0SpdJFdE1JukIYHXgjOhU4YfF\n0Oh2dqtkcwoeGwo0mtmHra6ZTahEdfDkTALuAZ7B8EpQLHYt2PfAZsVPq7NBDaAq7Y7MrJ8AyxJm\nLFWlfPghtjrhx9U5JWY3mlB5WZ/QrR/HioRZY0cpr7ro3H7AEGAt5bVRibG54hxHmKV3VNqBuGQy\nXxGSNIwwOPogM2tsPg1FTW3oirbKXaYzvZbQ/eZiKHG8wU8ILZa7dE00lSXDYzVGRLcd/4BIUTeU\nXeFWCqVOFWpeCFHARTHT7gesQ+je3yA6Vxjb7+MGk+H3XZZ9A2A605dJOxCXTCV0jY0AlgFeKOgR\nqwG2kXQU0DxrYzm+uu/PcoQ+c6LbGkmDW7UKDSWsu/E1kq4BZo1l7OtNNK1293funnsBF0wH4C4G\nAbBHtHy9H7d9fCcIfZIk/U/6MvfcHVliJryyZvTh3NxdpOo4Hg5kKZ7m4xnTp7Pp3CYadh8djRGa\nGn2ObM+iTByfS72mqrFs+d9F7Zg9YFXgneF8qfsVPmd2Igycvj/afqOY4zvow14EV/N/QuO5glUB\nOJJZAO0eH8qn3MrHvMj73Mggwj7z45jJZQCczYZCX/JQ9IN3NGHsT0fHU+mlh6LP2WKu92NuG3Cb\nvfnZm3qi5ommURo1HqJ/H1cxMj99PhroXLiisYA/Aq8QmqVfAt4GLjazX0Vp+hC6vU4ysyujPOYQ\nBkvfGF2zImGw9M5m9mCr5wxTH8OCGpcDn2CcWs7X6UqlzQmzb84DezjtaHou7UH4P3EpWKxNQnuS\nvPQy4Qtv25zZE0nzicY7PgM0AJtZjA9k5VULHEoYIzQ5OlcHvA7Ms1yCMUIuPnEiYQLPeRhngk+f\nrzSZrwi1RdJ04PnmdYQk/Qw4jTCj7D+EsURbA8PMbF50zWXA7oQPjo8ITdIDgU1af/gUVITOA5rn\nhxyJ+crK2aV5wBLAQrD6tKPpuVr22AKq84M+Lx0NUasLWM4s8RADSdOAkdHh3Wa2R9Fp8/pfwueY\ngA0tZ68or9cI45cA7rOc7ZY0NlckFQzBMHzT1QpUCV1jbTEKxv+Y2QRJfQl77ixF2HRwx+ZKUOQE\nwh5JNxMWVJwCHNzJL7BZANOYxsQbJh57Mzcf1KWvoqe7i0Et3V1l9iHULgUsgMY+nV6dfZJGZnTm\nWCNQm+np81OpbenGKoNV74UxUfWisQZKmj5/IEtzQ3R/J4bEWll6NGsznd4YDRifR2f/S3NFaHN2\njL2y9EP0aukCckVppBEhpjOdUYxKOxyXQEW2CJXbV2rz4qjjOb7vRXbRhSmHVXG698tcSwIHADeD\nfZowj9WAD8ICkunKbkVISwK3ElaWfjntaNrSHWWXl04mLA0xIlfih6ik84H5ZnZGpxe3TpvXFsB7\nrVaWnkhYbTp+fpl932WYWAW4djd2u/Jeu/d68BahSuMVoTYUdI39H7AusAijd9pxuXLSZcDR0cGa\n1bx9RMf0KWFV9rDdmCuJpAtYPCM1Z2ZnFp02r12BOwhjJL9lOftSee0D3EZoMd/McvZkV8fsWhHP\nEZY/eA1jDfCKUKWp1K6x7rIWgGG1veg1K+VYXBm9DiusGt0XjAK8ItS2AQAGvTLbNVZJVqE/zW05\nyzJWdL7lT4u12IFXqCGsJ7QMYdZs83ITYmtuFnqr3fSuSyysXbhe70W9aVLTar2yvyKNa4NXhDp2\nBHDJLdzyuGE7pB1MpamsZnatT5iu/iZY6oPiM1x2E4ATBZMN2z3tYNqS4bL7Gr2hIYQKjDGHYYYV\nXbnUK7qEMHN2huWsucLzU8LSC5/zCDvY9i1rrxWXZwWVXWYsYgww/gK74I6f8kPqe+wAAA3JSURB\nVNNOL3fZ49XXjg0B6uupXyrtQFy52fNgg8FKXI1XI0HzQTNBPbFpfFnCOl6+eFzXGEz4QVpLmMUa\nRz3h32NIwbk+hAkjywB1bSVyXW5pYMASLDEg7UBcMj5GqA0FY4QaaG41s6JWsnZVT2+weOPLQ8M2\nIz2JCj4wfAxEqSTNAlaJDl8ys3WKTpvX+YTxRY3AmpazN5TXHwj7jwGcbjkrdQsQ1xmxkLDdShNG\nDfgYoUrjXWMdexz49sLahY311M9KNxRXCS6BgcdE9wV/TTWY8ngPGNoATXU+Rqh046jjrOj+j3g9\n1vT5XRnMJIzevMlC3o3O3gQcBsDh7Cm0Y1eG677uqQ2f+mL4s8MHvjf0vXeWZ/m0w3EJeItQGwpa\nhAYBlx7LsY9fYpfE3Qeo6lXveAOJGLuIt51Dc9lpa8JstuPha5sGp0CrEVZ2PwtsatrRtKXS3neS\n7gAWmtl+sdLlJWB7YKbl7LWC84cDcy1nNyeIpaLKLhPEcsDYUYx6fppNuxW8RajSeItQx2YBA/dl\n3wOJvyGiq1pd+uuiebuQ7wFLdmG+Sb1KGFu4HcVtfOw6IGkqYQd6JNWZ2V6dJCl0LPBrwJTXSpaz\nj5XXyUQbryqvJsvZrV0etGvtJWCpMzhjPmGNLVdhvCLUsVqA7diutNVjq5URbdfmYovKrnmJX4UB\nypmR6ZWlK+l9tzH9eCq6vybrx+oaW59v8wK9scVjU4D+LY/vxJlCY2PFU0lllxFz+84dsMT8Jdiu\nZjv/Pq1Q/g/XsfWBPwhdadhNaQfjqpH2BY4Bjkw7ksg2wFWCcwz7U9rBVLwnQdIMoMFesRFxkup5\n1RJWt/635eyD6PR4wuDpz2xL89Xwu8N81gJOqm2s/V3aobhkfPp8x24DRj/EQ9elHUglkjQy7Rgq\n1eKys9vARnfdStcSqDHM/tI9CTKYAKwd3WZSJb3vJO0PbAJsKSnuBqkjgd8D46PxQgBbAOOA3yiv\n1dtL2EE8I+OmcZwLHHEnd/r3RIXyFqGObQTwLM/23p7ts9kNkGXnUi+0IO0wKlKZym5b0PToB5Bg\npwRZbAVgMLRXVruLz2GA0Gdph1GUYSzPv6P7K3O50KZFp12BvXibQYTxYwOBTwiLwIbP9S2YqrCc\nQ/HOYEVfjTqehpqGbWsba3md10tcg8ylxStCHbseOHgOcz43bFDawVQanazxdpKNTzuOSlTesmvZ\nL+z8BImvBI4QPGbY1l0bV9fQaRpvP6+M953+rY2BfwHwJt+xGBvZ6m3dDvwBeMByLStSn0moGH3J\n4wy3neNtQKyzNd7Oqoyyy4xGzgZOfIqnnks7FJeMV4Q69gCw54d8+N+0A3Gu61jcFYwL9W1160rz\nEjAHWAS81sm1rX0E/A2YUnDuPeAJYC5QGa1ile9x4IW3eOvNtANxyfgYoY5dA/RfyMKiV3t1X7Fq\n2gFUsFXTDqAdhxCmzW+cdiAdWDXtAGJ4CBhK2Dg17gKclxP2Fru/YIzQBOC7wPeBnySIZ9UEaard\nrcCmq7DKvmkH4pLxBRXbIMkLxTnnXGK+oGLl8IqQc84556qWd40555xzrmp5Rcg555xzVcsrQs45\n55yrWlVfEZJ0jKSZkuZLmiGpw7VRJK0v6W+SvpD0lqRx3RVr1sQpO0kjJd0l6R1J8yQ9K+mH3Rlv\nlsR93xWkW1PS55I+L3eMWZWk7CSdIOllSV9G78FfdUesWZPg824nSf+Q9Jmk9yXdKWnN7oo3CyRt\nK+nu6PO+SdKYItL490QFqeqKkKT9gAuBs4HhwGPAXyWt1M71A4AHgXeBTYEfAydLOrF7Is6OuGUH\njACeJeyNtC5h6u8Vkg7ohnAzJUHZNaerA24irB1TlbMckpSdpN8CRwMnE7YH2YVQhlUlwefdasBd\nhLIaDuwA9AEmdUvA2dEPeI7weT+fTv7v+fdEBTKzqv0jLIQ1sdW5V4Bz2rn+aMIy9vUF504H3kr7\ntWS97NrJ42bgtrRfS6WUHXABcBUwBvg87ddRCWUHDAMWAsPSjj3tvwRlty9hoUcVnBsFNAFLp/16\nUirDz4EfdHKNf09U2F/VtghFv643JqweXegBov2U2jACeNjMFrS6/huSVun6KLMpYdm1ZSBhddyq\nkbTsJO0K7AocR1jQsOokLLs9gNeB70p6PeoWukbSkDKGmjkJy+4JoAE4QlKNpCWBQ4EnzKyq/t/G\n5N8TFaZqK0LAMkANMLvV+TmElV7bMrSN62cXPFYtkpTdV0Q7bY8Gruja0DIvdtlJ+gahnA4ysy/K\nG16mJXnfrQ6sQlhp+QeElbHXBu6RVE0VythlZ2ZvAjsS9i/7ktDKsS6we/nC7BH8e6LCVHNFKImq\nHJfR1SR9m7Ch7XFmNiPteCrAn4DLzexfaQdSgXoB9cAhZvaImT1CqAxtThi/4dohaSihK/ZaQlmN\nJHQN3VJllci4/HuiwlRzRegDoBFYrtX55QiD3NryHl+v0S9X8Fi1SFJ2AESzVCYB48xsYnnCy7Qk\nZTcKyElqkNRA2HG8X3T8o/KFmjlJyu5dYJGZvVpw7tUon5W7PMLsSlJ2Ywlj0U4xs2fN7GHgYGA7\nQvePa5t/T1SYqq0ImdlC4ElC02+h7xBmU7TlH8A2kupbXf+2mb3R9VFmU8KyQ9K2hEpQzswuKl+E\n2ZWw7NYDNiz4+wVh9sqGwG3liTR7EpbdI0CtpNULzq1O6Cby/7Mdl11fwsDoQs3HVfvdUQT/nqg0\naY/WTvOPMG5gAXA48C3gd8BnwErR478CphRcP4Dw6+lGQl/53sCnwE/Sfi0VUHYjgXnAbwi/joZG\nf0PSfi1ZL7s20h9K9c4ai/u+EzADmE6YAr4RYTr4Y2m/lgoou1GEVqRxwJqEwdaTgVlA37RfTzeW\nW7/ovTM8+gwbF93374ke8pd6AGn/EaY6ziQMBvwXsHXBY38EXm91/XrRB+l84G1CF0/qryPrZRcd\nNxJ+URb+vd7dcWfhL+77rlXaQ4HP0n4NlVJ2hAr3LdGX/mzCmKuqq4AnLLv9CC1Jn0dldyewdtqv\no5vLbGTB51XhZ9jVHZSbf09U0J/vPu+cc865quX9vM4555yrWl4Rcs4551zV8oqQc84556qWV4Sc\nc845V7W8IuScc865quUVIeecc85VLa8IOeecc65qeUXIOeecc1XLK0LOOeecq1peEXLOOedc1fKK\nkHPOOeeqlleEnHPOOVe1vCLknHPOuarlFSHnnHPOVS2vCDnnnHOuanlFyDnnnHNVyytCzjnnnKta\nXhFyzjnnXNXyipBzroWkOkn/kXRvNz+vJD0r6e/d+bzOOecVIed6GEmHS2qSNKmDa+6LrvnfVg8d\nD3wTOKOT53glSv9oJ9dNj67brqPrzMyAccDWkvbp6FrnnOtKXhFyrocxs6uAu4GdJR3T+nFJRwO7\nAJPM7PcF5/sBpwMPmNkz7eUvaRSwBmDACEnrdhZS9NdZ3HcDLwG/7Oxa55zrKl4Rcq5nOgJ4H5gg\naa3mk9H984APgMNapTkQGAhc00neR0a3v2513B4VEW+za4G1JG0fI41zziXmFSHneiAze59QGVoC\n+LOkGkm1wJ+BPsCRZjanVbLDgQXAne3lK2kwsBfwCvALYDZwsKT6Lgr9poJYnHOu7Lwi5FwPFXU1\nXQ1sSqi0/CK6f42Zf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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fbd4545de90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from pycalphad import Database, binplot\n",
    "db_alfe = Database('alfe_sei.TDB')\n",
    "my_phases_alfe = ['LIQUID', 'B2_BCC', 'FCC_A1', 'HCP_A3', 'AL5FE2', 'AL2FE', 'AL13FE4', 'AL5FE4']\n",
    "\n",
    "models = [{'FCC_A1': MyMagneticModel}, Model]\n",
    "\n",
    "fig = plt.figure(figsize=(9,6))\n",
    "pdens = [{'B2_BCC': 20000}, 2000]\n",
    "%time binplot(db_alfe, ['AL', 'FE', 'VA'] , my_phases_alfe, 'X(AL)', 300, 2000, steps=100, \\\n",
    "              pdens=pdens, ax=fig.gca(), model=models, mode='numexpr')\n",
    "plt.title('Al-Fe with toy magnetic model', fontsize=20)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimental Ternary support (if time)\n",
    "## CALPHAD modeling (assessment) will be covered tomorrow morning"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "# Source code available at https://github.com/richardotis/pycalphad\n",
    "# Version 0.1 available on PyPI: \"`pip install pycalphad`\"\n",
    "# Version 0.2 coming soon, with new performance improvements and features\n",
    "# Please contact me if you would like to collaborate! Thank you!\n",
    "# \n",
    "# \n",
    "# Richard Otis\n",
    "# rao140@psu.edu"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.3.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}