{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equilibrium Properties and Partial Ordering (Al-Fe and Al-Ni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Only needed in a Jupyter Notebook\n",
    "%matplotlib inline\n",
    "# Optional plot styling\n",
    "import matplotlib\n",
    "matplotlib.style.use('bmh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from pycalphad import equilibrium\n",
    "from pycalphad import Database, Model\n",
    "import pycalphad.variables as v\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Al-Fe (Heat Capacity and Degree of Ordering)\n",
    "Here we compute equilibrium thermodynamic properties in the Al-Fe system. We know that only B2 and liquid are stable in the temperature range of interest, but we just as easily could have included all the phases in the calculation using `my_phases = list(db.phases.keys())`. Notice that the syntax for specifying a range is `(min, max, step)`. We can also directly specify a list of temperatures using the list syntax, e.g., `[300, 400, 500, 1400]`.\n",
    "\n",
    "We explicitly indicate that we want to compute equilibrium values of the `heat_capacity` and `degree_of_ordering` properties. These are both defined in the default `Model` class. For a complete list, see the documentation. `equilibrium` will always return the Gibbs energy, chemical potentials, phase fractions and site fractions, regardless of the value of `output`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 34, X_AL: 1, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 350.0 400.0 450.0 500.0 550.0 ...\n",
      "  * X_AL                (X_AL) float64 0.25\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'FE'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 1.0 nan 1.0 nan 1.0 nan ...\n",
      "    GM                  (P, T, X_AL) float64 -2.858e+04 -2.994e+04 -3.15e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -7.274e+04 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 0.25 0.75 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 0.5 0.5 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U6 'B2_BCC' '' 'B2_BCC' '' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 0.6667 nan 0.6667 nan ...\n",
      "    heat_capacity       (P, T, X_AL) float64 25.45 26.93 28.47 30.18 32.16 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.5.2.post1\n",
      "    created:  2017-10-09T13:22:30.263862\n"
     ]
    }
   ],
   "source": [
    "db = Database('alfe_sei.TDB')\n",
    "my_phases = ['LIQUID', 'B2_BCC']\n",
    "eq = equilibrium(db, ['AL', 'FE', 'VA'], my_phases, {v.X('AL'): 0.25, v.T: (300, 2000, 50), v.P: 101325},\n",
    "                 output=['heat_capacity', 'degree_of_ordering'])\n",
    "print(eq)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We also compute degree of ordering at fixed temperature as a function of composition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 1, X_AL: 100, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 700.0\n",
      "  * X_AL                (X_AL) float64 1e-09 0.01 0.02 0.03 0.04 0.05 0.06 ...\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'FE'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 1.0 nan 1.0 nan 1.0 nan ...\n",
      "    GM                  (P, T, X_AL) float64 -2.447e+04 -2.564e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -2.312e+05 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 1e-09 1.0 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 1e-09 1.0 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U6 'B2_BCC' '' 'B2_BCC' '' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 6.28e-16 nan 1.292e-14 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.5.2.post1\n",
      "    created:  2017-10-09T13:22:33.793213\n"
     ]
    }
   ],
   "source": [
    "eq2 = equilibrium(db, ['AL', 'FE', 'VA'], 'B2_BCC', {v.X('AL'): (0,1,0.01), v.T: 700, v.P: 101325},\n",
    "                  output='degree_of_ordering')\n",
    "print(eq2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "Next we plot the degree of ordering versus temperature. We can see that the decrease in the degree of ordering is relatively steady and continuous. This is indicative of a second-order transition from partially ordered B2 to disordered bcc (A2)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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LEinS3DzurINZg/O0i/O0i/O0i01PFyRSpKioKGgFXzhPuzhPuzhPu9j0dEEiRcrLy4NW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\nCYfD4XCMyf8HgDU2pYZbMskAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x105233da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs T [X(AL)=0.25]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "plt.gca().set_ylim((-0.1,1.1))\n",
    "# Generate a list of all indices where B2 is stable\n",
    "phase_indices = np.nonzero(eq.Phase.values == 'B2_BCC')\n",
    "# phase_indices[1] refers to all temperature indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq['T'].values, phase_indices[1]), eq['degree_of_ordering'].values[phase_indices])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For the heat capacity curve shown below we notice a sharp increase in the heat capacity around 750 K. This is indicative of a magnetic phase transition and, indeed, the temperature at the peak of the curve coincides with 75% of 1043 K, the Curie temperature of pure Fe. (Pure bcc Al is paramagnetic so it has an effective Curie temperature of 0 K.)\n",
    "\n",
    "We also observe a sharp jump in the heat capacity near 1800 K, corresponding to the melting of the bcc phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4YJzukQnqKkpZ5cMaCtnM9J5uV+gKdxVSVPOK9TbLTO+wj0/I4Hd6F7JKyj8X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\nCIIgKPF/pRJp45VtpzYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f0eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Heat capacity vs T [X(AL)=0.25]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Heat Capacity (J/mol-atom-K)')\n",
    "# np.squeeze is used to remove all dimensions of size 1\n",
    "# For a 1-D/\"step\" calculation, this aligns the temperature and heat capacity arrays\n",
    "# In 2-D/\"map\" calculations, we'd have to explicitly select the composition of interest\n",
    "plt.plot(eq['T'].values, np.squeeze(eq['heat_capacity'].values))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To understand more about what's happening around 700 K, we plot the degree of ordering versus composition. Note that this plot excludes all other phases except `B2_BCC`. We observe the presence of disordered bcc (A2) until around 13% Al or Fe, when the phase begins to order."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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qCrnrvQv4zjM7eGVvJ9c8spVPnjCF9x1elZV9VPxYO0P2P7AcAV1d+g2ylm3O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\nK+VkYWZmpZwszMys1D/DpWiGjjRJSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1052de198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Fe: Degree of bcc ordering vs X(AL) [T=700 K]')\n",
    "plt.gca().set_xlabel('X(AL)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "# Generate a list of all indices where B2 is stable\n",
    "phase_indices = np.nonzero(eq2.Phase.values == 'B2_BCC')\n",
    "# phase_indices[2] refers to all composition indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq2['X_AL'].values, phase_indices[2]), eq2['degree_of_ordering'].values[phase_indices])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Al-Ni (Degree of Ordering)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (P: 1, T: 110, X_AL: 1, component: 2, internal_dof: 5, vertex: 2)\n",
      "Coordinates:\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 320.0 340.0 360.0 380.0 400.0 ...\n",
      "  * X_AL                (X_AL) float64 0.1\n",
      "  * vertex              (vertex) int64 0 1\n",
      "  * component           (component) <U2 'AL' 'NI'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (P, T, X_AL, vertex) float64 0.3637 0.6363 0.3543 ...\n",
      "    GM                  (P, T, X_AL) float64 -2.526e+04 -2.585e+04 ...\n",
      "    MU                  (P, T, X_AL, component) float64 -1.719e+05 ...\n",
      "    X                   (P, T, X_AL, vertex, component) float64 0.25 0.75 ...\n",
      "    Y                   (P, T, X_AL, vertex, internal_dof) float64 1e-12 1.0 ...\n",
      "    Phase               (P, T, X_AL, vertex) <U7 'FCC_L12' 'FCC_L12' ...\n",
      "    degree_of_ordering  (P, T, X_AL, vertex) float64 1.0 2.805e-15 1.0 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.5.2.post1\n",
      "    created:  2017-10-09T13:24:01.595109\n"
     ]
    }
   ],
   "source": [
    "db_alni = Database('NI_AL_DUPIN_2001.TDB')\n",
    "phases = ['LIQUID', 'FCC_L12']\n",
    "eq_alni = equilibrium(db_alni, ['AL', 'NI', 'VA'], phases, {v.X('AL'): 0.10, v.T: (300, 2500, 20), v.P: 101325},\n",
    "                      output='degree_of_ordering')\n",
    "print(eq_alni)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plots\n",
    "In the plot below we observe two phases designated `FCC_L12`. This is indicative of a miscibility gap. The ordered gamma-prime phase steadily decreases in amount with increasing temperature until it completely disappears around 750 K, leaving only the disordered gamma phase."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x111c4f9b0>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PTp5NVdWvicUWEI2+E5HBoj63kUo0Gi22wrQ4t+BY9rPsBrb9LLuFTbYFx1bg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\n8f8B5K8Zw/HACiEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111c4fb00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pycalphad.plot.utils import phase_legend\n",
    "phase_handles, phasemap = phase_legend(phases)\n",
    "\n",
    "plt.gca().set_title('Al-Ni: Phase fractions vs T [X(AL)=0.1]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Phase Fraction')\n",
    "plt.gca().set_ylim((0,1.1))\n",
    "plt.gca().set_xlim((300, 2000))\n",
    "\n",
    "for name in phases:\n",
    "    phase_indices = np.nonzero(eq_alni.Phase.values == name)\n",
    "    plt.scatter(np.take(eq_alni['T'].values, phase_indices[1]), eq_alni.NP.values[phase_indices], color=phasemap[name])\n",
    "plt.gca().legend(phase_handles, phases, loc='lower right')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the plot below we see that the degree of ordering does not change at all in each phase. There is a very abrupt disappearance of the completely ordered gamma-prime phase, leaving the completely disordered gamma phase. This is a first-order phase transition."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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upqWlherqahKJBN3d3X355eXldHZ2Ul5eTk1NDd3d3fT09PTlV1RUUFlZSVtb\nG2PHjmXt2rUkEom+/MrKSsrLy2lvb6euro729nZ6e3v78vO9T0uXLqWurm7AfRo9ejRr1qyJdZ9W\nrFjBrrvumrf/U9T71NLSQmVlZUG/e0Pdp6VLl9LQ0FDQ797m7FMymaSkpKTofk/Z9mnlypVUVVUV\n3e8pF6EmlRGRecCngWuCk/n2wKXAI8BS4FvAa6p6Zo7Pfw44MpUvIqcCU1T1/LQyFwc+PxCRg4Cf\nAx9X1fXp6yrGSWVWrlw5pJER48C7RoclX0uuYMu3GF37m1Qm7J3AxcA+qtoeLP9TRJ4HXlDVnUXk\n78AL/Xx+OZD+KOV4Nq3umQscBaCqi0WkAqgDVod0jI30+VOLHe8aHZZ8LbmCLV9LrhC+YXgMsEVG\n2ha4unuAJqCyn8//DZgoIjuKSDmu4fehjDLvADMARGR3oAIw0eG2paUlboXQeNfosORryRVs+Vpy\nhfB3AncAj4vIj3HVQeOBC4BfBfmfwlULZUVV14nIecAioAS4XVVfE5GrgOdV9SHg/wC3ichFuPaH\nOWpkvj5Lkd+7RoclX0uuYMvXkiuEDwJfBf6Fu4LfBngP1+XztiD/KeDp/lagqguBhRlpV6a9fx04\nOKRPUZE5QmMx412jw5KvJVew5WvJFUIGgaBx9pbglS2/J1v6cKG7uztuhdB41+iw5GvJFWz5WnKF\nQYwiKiKfAvYCNupvlH41P1yx1C/Yu0aHJV9LrmDL15IrhH9Y7EbgN8C+uF4+qdf46NTs0NTUFLdC\naLxrdFjyteQKtnwtuUL4O4GTgb1U9d0BSw5DysvL41YIjXeNDku+llzBlq8lVwjfRXQN8H6UIpbJ\nnLCjmPGu0WHJ15Ir2PK15Arhg8APgDtF5CAR2Sn9FaWcFdasWRO3Qmi8a3RY8rXkCrZ8LblC+Oqg\nm4O/x2akK67f/7CmpqYmboXQeNfosORryRVs+VpyhZB3Aqo6Isdr2AcAsNUlzLtGhyVfS65gy9eS\nK/iZxfJCT4+dxyS8a3RY8rXkCrZ8LblCP9VBIvKYqh4VvH+GLENJA6jqtIjczGCpX7B3jQ5LvpZc\nwZavJVfo/07gjrT3P8MN7ZztNeyx1C/Yu0aHJV9LrmDL15Ir9HMnoKq/hb6pIXcGrlbVDwslZomK\nioq4FULjXaPDkq8lV7Dla8kVQrQJqGoSmAf0Rq9jk8rK/kbRLi68a3RY8rXkCrZ8LblC+IbhXwHn\nRClimbZ/zOCDAAAgAElEQVS2trgVQuNdo8OSryVXsOVryRXCPycwBThfRC7FzSfQ10jsG4Zh7Nix\ncSuExrtGhyVfS65gy9eSK4QPArexYe4ATwZr164dcDLnYsG7RoclX0uuYMvXkiuEn0/gVwOXGr5Y\nmkTCu0aHJV9LrmDL15IrhB9KWkTkLBF5UkReCdKmicjno9WzgaV+wd41Oiz5WnIFW76WXCF8w/BV\nwFzgp8D2Qdpy4GtRSFnDUr9g7xodlnwtuYItX0uuED4IzAGOVdW72NAo/G/AjyKKrS5h3jU6LPla\ncgVbvpZcIXwQKAE6g/epIDAqLW1YY2kSCe8aHZZ8LbmCLV9LrhA+CCwErheRkeDaCID/Av4nKjFL\ntLe3x60QGu8aHZZ8LbmCLV9LrhA+CFwMbAO0A9W4O4Ad8G0CANTV1cWtEBrvGh2WfC25gi1fS64Q\nfj6BDlX9NO7EfyCws6qeoKprI7UzgqXI712jw5KvJVew5WvJFfofSjpbgGgOXn35qro+GjU79Pba\nGVbJu0aHJV9LrmDL15Ir9P+w2DpyzCGQwbCfXcxSv2DvGh2WfC25gi1fS67Qf3XQjrguoDsB5wN/\nAo4Cdg/+PgWcF7WgBSz1C/au0WHJ15Ir2PK15Ar9BAFVbUy9cA3Ds1X1cVX9p6o+DnwOuCTshkTk\nKBFZKiLLROSyHGU+LyKvi8hrIvLbwe5MXFRVVcWtEBrvGh2WfC25gi1fS64QfgC5amAL4P20tC2C\n9AEJJqZZAByBe9L4byLykKq+nlZmInA5cLCqtonIViHdYqekxE6NmHeNDku+llzBlq8lVxjcfAL/\nKyJni8jRInI2sChID8MUYJmqvqWqCeAu4PiMMmcBC1S1DUBVV4dcd+x0dHTErRAa7xodlnwtuYIt\nX0uuEP5O4FJgGXAi7nmB94AbCT+89La4eQhSLAcOyCizK4CI/AXX2DxfVR/LXNHq1auZO3cupaWl\nJJNJZs+ezbx582hqaqKqqoqSkhI6Ojqor6+ntbUVVaW+vp5Vq1b1De/a2dnJuHHjaG5uRkSora2l\nubmZMWPGkEwm6erqoqGhgaamJsrKyqiurqalpYXq6moSiQTd3d19+eXl5VRVVdHY2EhNTQ3d3d30\n9PT05VdUVFBZWUlbWxtjx45l7dq1JBKJvvzKykrKy8tpb2+nrq6O9vZ2ent7+/LzvU+9vb2sXLly\nwH0aPXo0a9asiXWfent76enpydv/Kep9GjlyJI2NjQX97g11n3p7e1m9enVBv3ubs0/V1dU0NjYW\n3e8p2z6VlJTQ2NhYdL+nXIhq/x2Agqqcb7EZcwyLyOeAI1X1zGD5VGCKqp6fVuZh3BSWnwfGA88A\nH1fV9CooFi9erJMmTRqKRmQsX76c8ePHx60RCu8aHZZ8LbmCLd9idF2yZMkLM2bM2C9bXqHmGF4O\nbJe2PB5YmaXMH1S1V1X/DSwFJm7GNgvGQIG0mPCu0WHJ15Ir2PK15AqFm2P4b8BEEdlRRMqBk4CH\nMso8CBwOICJ1uOqhtzZjmwWjvr4+boXQeNfosORryRVs+VpyhfBBYArwYxF5W0SeEZE/p15hPqyq\n63DPFCwC3gDuUdXXROQqETkuKLYIWCMir+OeQfiqqq4Z3O7Ew6pVq+JWCI13jQ5LvpZcwZavJVco\n4BzDqroQNxppetqVae8V9zzCxZuznTiwNJ+od40OS76WXMGWryVX8HMMezwez7AmbHUQInJGMMfw\n0uDvGVGKWaKz087cOt41Oiz5WnIFW76WXCHknYCIfB04DfgB0IgbUvpSEdlGVa+O0M8E48aNi1sh\nNN41Oiz5WnIFW76WXCH8ncCZwKdU9aequkhVf4obRO7s6NTs0NzcHLdCaLxrdFjyteQKtnwtuUL4\nIFBFMI9AGmsAWzMqR4SbbdMG3jU6LPlacgVbvpZcIXwQeAy4U0R2E5FKEZmEe3ZgUXRqdqitrY1b\nITTeNTos+VpyBVu+llwhfBA4D1gLvIybX/gloAs3z8Cwx9Ltn3eNDku+llzBlq8lVwjfRbQDOE1E\n5gB1QIufVnIDY8aMiVshNN41Oiz5WnIFW76WXCH8w2JA33zCZoZ4LhTJZDJuhdB41+iw5GvJFWz5\nWnKFQTwn4MlNV1dX3Aqh8a7RYcnXkivY8rXkCj4I5AVLE0t71+iw5GvJFWz5WnKFfoKAiHw/7f0n\nC6NjE0sTS3vX6LDka8kVbPlacoX+7wTSHwR7MGoRy5SVlcWtEBrvGh2WfC25gi1fS67Qf8PwyyJy\nH/A6MFJErspWKH0k0OFKdXV13Aqh8a7RYcnXkivY8rXkCv3fCXwW9zzA1oDgZgbLfBXXHGox0dLS\nErdCaLxrdFjyteQKtnwtuUI/dwKquhr4bwARKVVVP2poDixFfu8aHZZ8LbmCLV9LrhD+YbEzRKQG\n+A9gW2AF8LCqtkYpZ4VEIhG3Qmi8a3RY8rXkCrZ8LblCyC6iInIQ8CZunuE9gC8By4L0YU93d3fc\nCqHxrtFhydeSK9jyteQK4Z8Y/hHwZVW9K5UgIicCPwH2j0LMEpb6BXvX6LDka8kVbPlacoXwD4vt\nCtyTkXYfsEt+dWxiqV+wd40OS76WXMGWryVXCB8E/gWclJH2OVwV0bCnvLw8boXQeNfosORryRVs\n+VpyhfDVQRcCD4vIV3DTS04AJgLHRuRlitGjR8etEBrvGh2WfC25gi1fS64Q8k5AVZ8FdgZuBF4A\nbgB2CdKHPWvWrIlbITTeNTos+VpyBVu+llxhEENJq2ob8JsIXcxSU1MTt0JovGt0WPK15Aq2fC25\ngh9FNC9Y6hLmXaPDkq8lV7Dla8kVfBDICz09PXErhMa7RoclX0uuYMvXkiv4IJAXLPUL9q7RYcnX\nkivY8rXkCoMIAiJSJiKHBg+JISJVIlI1iM8fJSJLRWSZiFzWT7nPioiKyH5h1x03lvoFe9fosORr\nyRVs+VpyhfDDRnwC+CdwG/DzIPkw4PaQny8BFgBHA5OBk0VkcpZyo4GvAH8Ns95ioaKiIm6F0HjX\n6LDka8kVbPlacoXwdwI3A1eq6iSgN0j7E3BIyM9PAZap6luqmgDuAo7PUu6/gGsBU5VqlZWVcSuE\nxrtGhyVfS65gy9eSK4QPAh9jQ/dQBVDVLiDs3m4LvJu2vDxI60NE9ga2U9WHQ66zaGhra4tbITTe\nNTos+VpyBVu+llwh/HMCbwP7As+nEkRkCrAs5OclS5qmrWsE8ENgzkArWr16NXPnzqW0tJRkMsns\n2bOZN28eTU1NVFVVUVJSQkdHB/X19bS2tqKq1NfXs2rVKkaNGgVAZ2cn48aNo7m5GRGhtraW5uZm\nxowZQzKZpKuri4aGBpqamigrK6O6upqWlhaqq6tJJBJ0d3f35ZeXl1NZWUljYyM1NTV0d3fT09PT\nl19RUUFlZSVtbW2MHTuWtWvXkkgk+vIrKyspLy+nvb2duro62tvb6e3t7cvP9z4lEglWrlw54D6N\nHj2aNWvWxLpPiUSCnp6evP2fot6nsrIyGhsbC/rdG+o+JRIJVq9eXdDv3ubs05gxY2hsbCy631O2\nfRoxYgSNjY1F93vKeXJW1X4LBCfpY3FtAbcA/we4Gjes9Fmq+scQnz8ImK+qRwbLlwOo6neD5Wrc\nOESdwUcagFbgOFV9Pn1dixcv1kmTJg3oXEjee+89tt5667g1QuFdo8OSryVXsOVbjK5Llix5YcaM\nGVk724QdNuJhXKNuPa4tYAdgdpgAEPA3YKKI7Cgi5bjB6B5KW3+7qtap6gRVnQA8R5YAUKxYmkTC\nu0aHJV9LrmDL15IrDG7YiCXAl4eyEVVdJyLnAYuAEuB2VX0tmLz+eVV9qP81FDeW+gV71+iw5GvJ\nFWz5WnKF8F1ER4rI1SLyloi0B2mfCk7soVDVhaq6q6rurKpXB2lXZgsAqjrdyl0A2OoX7F2jw5Kv\nJVew5WvJFcL3Dvoh8HHgC2xo0H0NODcKKWtY6hLmXaPDkq8lV7Dla8kVwlcHnYAbOrpLRNYDqOoK\nEdl2gM8NCyxNIuFdo8OSryVXsOVryRXC3wkkyAgYIlIP2Bo4OyLa29vjVgiNd40OS76WXMGWryVX\nCB8E7gV+JSI7AojI1rgJZu7q91PDhLq6urgVQuNdo8OSryVXsOVryRXCB4ErcA+M/R3YEjfn8Erg\n29Fo2cJS5Peu0WHJ15Ir2PK15Aoh2gSCp3kPAb6mqhcG1UAtGuYps2FCb2/vwIWKBO8aHZZ8LbmC\nLV9LrhDiTkBV1wN/UNUPg+VmHwA2xlK/YO8aHZZ8LbmCLV9LrhC+OujPInJgpCaGsdQv2LtGhyVf\nS65gy9eSK4TvItoIPCoif8CNBtp3J6CqV0YhZomqqtBz68SOd40OS76WXMGWryVXCB8EKoEHg/fj\nI3IxS0lJSdwKofGu0WHJ15Ir2PK15Aohg4CqnhG1iGU6OjqoqamJWyMU3jU6LPlacgVbvpZcIWQQ\nEJGdcmR9CLwXNB4PW+rr6+NWCI13jQ5LvpZcwZavJVcI3zC8DPdswL8y3r8DfCgivxeRcdEoFj+t\nra1xK4TGu0aHJV9LrmDL15IrhA8CZwF3ArsCFcBuuOkmvwx8AndHsSAKQQtY6jHrXaPDkq8lV7Dl\na8kVwjcMfxs3gFxqAvhlInIu8E9VvVVE5uDuDIYllm7/vGt0WPK15Aq2fC25Qvg7gRHAhIy07XET\nxICbFjL0BDUfNVatWhW3Qmi8a3RY8rXkCrZ8LblC+BP3j4AnReQXuOcExgNnBOkAs4DF+dezwUAT\nORcT3jU6LPlacgVbvpZcIXwX0WtF5BXgc8A+wHvAXFV9LMh/kA3PEXg8Ho/HCGGrg1DVx1R1rqoe\nrapfTAUAD3R2dsatEBrvGh2WfC25gi1fS65QwDmGP8qMG2end6x3jQ5LvpZcwZavJVfwcwznhebm\n5rgVQuNdo8OSryVXsOVryRX8HMN5QUTiVgiNd40OS76WXMGWryVX8HMM54Xa2tq4FULjXaPDkq8l\nV7Dla8kV/BzDecHS7Z93jQ5LvpZcwZavJVfwcwznhTFjxsStEBrvGh2WfC25gi1fS64Q/jmBBHAh\n4OcYzkIymYxbITTeNTos+VpyBVu+llwhfBfRySLyJRG5HJgN7B6tli26urriVgiNd40OS76WXMGW\nryVXGCAIiON2XDXQFcBxwNeBV0TkFzKIZnAROUpElorIMhG5LEv+xSLyuoi8IiJPiMgOg9yX2LA0\nsbR3jQ5LvpZcwZavJVcY+E7gbGA6cKCq7qCqB6nq9sBBwKHAl8JsRERKcENNHw1MBk4WkckZxV4E\n9lPVPYD7gGtD70XMWJpY2rtGhyVfS65gy9eSKwwcBE4FvqKqf0tPDJYvDPLDMAVYpqpvBe0LdwHH\nZ6zzKVX9IFh8DkNzGZeVlcWtEBrvGh2WfC25gi1fS64wcBCYDPwpR96fgvwwbIsbfTTF8iAtF3OB\nR0OuO3aqq6vjVgiNd40OS76WXMGWryVXGLh3UImqrs2WoaprRSRsF9NsbQdZexeJyCnAfsBh2fJX\nr17N3LlzKS0tJZlMMnv2bObNm0dTUxNVVVWUlJTQ0dFBfX09ra2tqCr19fWsWrWqb4jXzs5Oxo0b\nR3NzMyJCbW0tzc3NjBkzhmQySVdXFw0NDTQ1NVFWVkZ1dTUtLS1UV1eTSCTo7u7uyy8vL6ezs5Py\n8nJqamro7u6mp6enL7+iooLKykra2toYO3Ysa9euJZFI9OVXVlZSXl5Oe3s7dXV1tLe309vb25ef\n73168803qaurG3CfRo8ezZo1a2LdpxUrVrDrrrvm7f8U9T61tLRQWVlZ0O/eUPfpzTffpKGhoaDf\nvc3Zp2QySUlJSdH9nrLt08qVK6mqqiq631POk3N/PT1F5APcXAG5GoD/R1Wr+t2CW89BwHxVPTJY\nvhxAVb+bUW4mcANwmKquzrauxYsX66RJkwbaZEF5//332XLLLePWCIV3jQ5LvpZcwZZvMbouWbLk\nhRkzZuyXLW+gO4HVwO0D5Ifhb8DE4InjFcBJwH+mFxCRvYFbgaNyBYBiJZFIxK0QGu8aHZZ8LbmC\nLV9LrjBAEFDVCfnYiKquC4adXoSbkvJ2VX1NRK4CnlfVh4DvA6OAe4Oep++o6nH52H7UdHd3x60Q\nGu8aHZZ8LbmCLV9LrlDAeYFVdSGwMCPtyrT3Mwvlkm8s9Qv2rtFhydeSK9jyteQKg5hZzJMbS/2C\nvWt0WPK15Aq2fC25gg8CeaG8vDxuhdB41+iw5GvJFWz5WnIFHwTywujRo+NWCI13jQ5LvpZcwZav\nJVfwQSAvrFljZ24d7xodlnwtuYItX0uu4INAXqipqYlbITTeNTos+VpyBVu+llzBB4G8YKlLmHeN\nDku+llzBlq8lV/BBIC/09PTErRAa7xodlnwtuYItX0uu4INAXrDUL9i7RoclX0uuYMvXkiv4IJAX\nLPUL9q7RYcnXkivY8rXkCj4I5IWKioq4FULjXaPDkq8lV7Dla8kVfBDIC5WVlXErhMa7RoclX0uu\nYMvXkiv4IJAX2tra4lYIjXeNDku+llzBlq8lV/BBIC+MHTs2boXQeNfosORryRVs+VpyBR8E8sLa\ntVknXytKvGt0WPK15Aq2fC25gg8CecHSJBLeNTos+VpyBVu+llzBB4G8YKlfsHeNDku+llzBlq8l\nV/BBIC9Y6hfsXaPDkq8lV7Dla8kVfBDIC5a6hHnX6LDka8kVbPlacgUfBPKCpUkkvGt0WPK15Aq2\nfC25gg8CeaG9vT1uhdB41+iw5GvJFWz5WnIFHwTyQl1dXdwKofGu0WHJ15Ir2PK15Ao+COQFS5Hf\nu0aHJV9LrmDL15Ir+CCQF3p7e+NWCI13jQ5LvpZcwZavJVfwQSAvWOoX7F2jw5KvJVew5WvJFXwQ\nyAuW+gV71+iw5GvJFWz5WnIFHwTyQlVVVdwKofGu0WHJ15Ir2PK15Ao+COSFkpKSuBVC412jw5Kv\nJVew5WvJFXwQyAsdHR1xK4TGu0aHJV9LrmDL15IrFDAIiMhRIrJURJaJyGVZ8keKyN1B/l9FZEKh\n3DaX+vr6uBVC412jw5KvJVew5WvJFQoUBESkBFgAHA1MBk4WkckZxeYCbaq6C/BD4HuFcMsHra2t\ncSuExrtGhyVfS65gy9eSK0BpgbYzBVimqm8BiMhdwPHA62lljgfmB+/vA24UEVFVzYdAbW1NPlaT\ngyjXnW+8a3RY8rXkCrZ8o3Ntbc3/1JWFCgLbAu+mLS8HDshVRlXXiUg7MBZoSS+0evVq5s6dS2lp\nKclkktmzZzNv3jyampqoqqqipKSEjo4O6uvraW1tRVXN3Z55PB5PNrq6umhvb6e3t5eGhoYBz3ur\nVq1i1KhR/a6zUEFAsqRlXuGHKcNWW23FX/7yl00K7rDDDn3va2pcJN5iiy360qKIoCkaGxs32n4x\n412jw5KvJVew5Ruta9VGXVAHOu+l8hsbG3OusVANw8uB7dKWxwMrc5URkVKgGjBRuTZQpC0mvGt0\nWPK15Aq2fC25QuGCwN+AiSKyo4iUAycBD2WUeQg4PXj/WeDJfLUHeDwejyc7BQkCqroOOA9YBLwB\n3KOqr4nIVSJyXFDs58BYEVkGXAxs0o20WOns7IxbITTeNTos+VpyBVu+llyhcG0CqOpCYGFG2pVp\n73uAzxXKJ5+MGzcuboXQeNfosORryRVs+VpyBf/EcF5obm6OWyE03jU6LPlacgVbvpZcwQeBvCCS\nrWNTceJdo8OSryVXsOVryRV8EMgLtbW1cSuExrtGhyVfS65gy9eSK/ggkBcs3f551+iw5GvJFWz5\nWnIFHwTywpgxY+JWCI13jQ5LvpZcwZavJVfwQSAvJJPJuBVC412jw5KvJVew5WvJFXwQyAtdXV1x\nK4TGu0aHJV9LrmDL15Ir+CCQFyxNLO1do8OSryVXsOVryRV8EMgLliaW9q7RYcnXkivY8rXkCj4I\n5IUHH3wwboXQeNfosORryRVs+VpyBR8E8sL9998ft0JovGt0WPK15Aq2fC25gg8CeWHdunVxK4TG\nu0aHJV9LrmDL15IrgFgbrfmJJ55oBnLPkBADra2tdbW1tS0Dl4wf7xodlnwtuYIt3yJ13WHGjBlZ\np1g0FwQ8Ho/Hkz98dZDH4/EMY3wQ8Hg8nmGMDwIhEZESEXlRRB4OlncUkb+KyL9E5O5g2kxEZGSw\nvCzIn1Bgzy1F5D4R+YeIvCEiB4lIrYg8Hrg+LiI1QVkRkZ8Erq+IyD6FdA0cLhKR10TkVRH5nYhU\nFNOxFZHbRWS1iLyaljbo4ykipwfl/yUip2fbVkSu3w++C6+IyAMismVa3uWB61IROTIt/aggbZmI\nRDLDXzbXtLxLRERFpC5YjvW49ucrIucHx+o1Ebk2LT22YztoVNW/QrxwU17+Fng4WL4HOCl4fwtw\nbvD+y8AtwfuTgLsL7Pkr4MzgfTmwJXA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      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fbc2630>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.gca().set_title('Al-Ni: Degree of fcc ordering vs T [X(AL)=0.1]')\n",
    "plt.gca().set_xlabel('Temperature (K)')\n",
    "plt.gca().set_ylabel('Degree of ordering')\n",
    "plt.gca().set_ylim((-0.1,1.1))\n",
    "# Generate a list of all indices where FCC_L12 is stable and ordered\n",
    "L12_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
    "                                              (eq_alni.degree_of_ordering.values > 0.01)))\n",
    "# Generate a list of all indices where FCC_L12 is stable and disordered\n",
    "fcc_phase_indices = np.nonzero(np.logical_and((eq_alni.Phase.values == 'FCC_L12'),\n",
    "                                              (eq_alni.degree_of_ordering.values <= 0.01)))\n",
    "# phase_indices[1] refers to all temperature indices\n",
    "# We know this because pycalphad always returns indices in order like P, T, X's\n",
    "plt.plot(np.take(eq_alni['T'].values, L12_phase_indices[1]), eq_alni['degree_of_ordering'].values[L12_phase_indices],\n",
    "            label='$\\gamma\\prime$ (ordered fcc)', color='red')\n",
    "plt.plot(np.take(eq_alni['T'].values, fcc_phase_indices[1]), eq_alni['degree_of_ordering'].values[fcc_phase_indices],\n",
    "            label='$\\gamma$ (disordered fcc)', color='blue')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:pycalphad]",
   "language": "python",
   "name": "conda-env-pycalphad-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}