{
 "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 0.6666 nan 0.6665 nan ...\n",
      "    heat_capacity       (P, T, X_AL) float64 25.45 26.93 28.47 30.18 32.16 ...\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.7+5.g20149e02.dirty\n",
      "    created:  2018-04-18T19:27:07.389851\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:             (N: 1, P: 1, T: 1, X_AL: 100, component: 2, internal_dof: 5, vertex: 3)\n",
      "Coordinates:\n",
      "  * N                   (N) float64 1.0\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 700.0\n",
      "  * X_AL                (X_AL) float64 1e-12 0.01 0.02 0.03 ... 0.97 0.98 0.99\n",
      "  * vertex              (vertex) int64 0 1 2\n",
      "  * component           (component) <U2 'AL' 'FE'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (N, P, T, X_AL, vertex) float64 1.0 nan nan ... nan nan\n",
      "    GM                  (N, P, T, X_AL) float64 -2.447e+04 ... -1.949e+04\n",
      "    MU                  (N, P, T, X_AL, component) float64 -2.714e+05 ... -1.444e+05\n",
      "    X                   (N, P, T, X_AL, vertex, component) float64 1e-12 ... nan\n",
      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 1e-12 ... nan\n",
      "    Phase               (N, P, T, X_AL, vertex) <U6 'B2_BCC' '' '' ... '' ''\n",
      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 3.029e-16 nan ... nan\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.8.3+10.gfd19517e.dirty\n",
      "    created:  2020-10-27T14:48:18.391819\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": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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[2] 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[2]), 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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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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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2p8jf6OgMenrr6Ax6eo/Fec2aNZXjOaZWj6Fyc8c06V5ao6Mz6OmtozPo6e2FsyuZhYj8VkQaRSTleEhi8BMxpmd8S0QOcyIdpaWlTuzW1+joDHp66+gMenp74exWyeL3GEMYD8VZGO2f52IMGPgLJxLR3KzfHO06OoOe3jo6g57eXji7klkoYxz14eZ5PRf4ozJYDUw057awleAORB909NbRGfT0zuSSxUhMxZhKMM52c5mtRKOjGRo/M9DRGfT01tEZ9PT2wtkvraGSJ76BISZ3aWxsZNmyZeTk5BCLxVi6dCnLly+nvr6ewsJCsrOzaW9vp6qqitbWVpRSVFVV0dDQQCQSITs7m87OTqqrq2lqakJEKC8vp6mpiZKSEmKxGF1dXdTU1FBfX09ubi6lpaU0NzdTWlpKNBolEokMrg+FQhQXF9PS0kJZWRmRSISenp7B9fn5+RQUFNDW1kZFRQUdHR1Eo9HB9QUFBYRCIcLhMJWVlYTDYfr6+gbXj+RUVFQEMKRTJBIhKysro5ysxKm5uTnjnEaKU7JzJjhZiVNTUxOlpaUZ5TRSnOLHGo3TuC/Sbg33YQ7i9bBS6kMp1v0PxiTtfzHfrwNOVkrtM1bJqlWr1Fibzvb29qJbHw0dncEbb6UUW1p7mFaaRyjH/UJ7EGt9GIvzmjVrXluyZMkRYz2mXx5DPQhcaLaKOhoIp8ooxkt9ff3IG2UYOjqD+967Onr55opNXPbPtVx63/u8vrNj5A/ZTBBrffDC2ZXHUCLyF4wJNipFZDvGBBy5AEqpuzEmXPkwxpj03RjTONpOKBRyYre+RkdncM87NqD457tN/OG1XfT2DyDAzvYoVz+6kTPmlXPpUVMpznPnaW8Qa33wwtmVs1gp9akR1iuMaUkdpbi42OlD+A4dncEdb6UUP3nxAx5b1wLAybMncslRU3lifSt/fr2eFetbWd/Uze1nz6XIhQwjiLU+eOHsl8dQrtDS0uJ1ElxHR2dwx/v3r+7isXUthLKFG06bxXWnzKKqMMSnD63h7qULmFaax5a2Hq5/YjO9/QOOpyeItT544axVZlFWVuZ1ElxHR2dw3vu+dxr5y5sNZAl8a8ksjq2duNf66RPzue2s/aickMs79V3c+vRWYgPONiYJYq0PXjhrlVlEIhGvk+A6OjqDs95Pb2zl7tU7APjaiTM4ekbqDlKTikJ876w5FOdls2pbmDte2IaTrQ+DWOuDF85aZRY9PT1eJ8F1dHQG57zf2NnBj57bBsCli6Zw2tyKYbevLSvg5jPmkJeTxYr1rdzzunOtWIJY64MXzlplFjU1NV4nwXV0dAZnvLe0RrjxyS30DyjOO6CKjx9Ubelz+08q5LrFM8kS+NOaelasd+Z5cxBrffDCWavMImiPrQ92e7d09fGtFZvoisY4fmYplx41utFojqktZfkx0wC44/ltvLq93db0QRBrnfDCWavMIj8/3+skuI6OzmCvd1c0xjdXbKSpq4+Fkwq5+uSZZGelGqFmeD66sIpPHDSJmIKbntrChuZu29IIQax1wgtnrTKLgoICr5PgOjo6g33e0dgANzyxmc3mMB43nj6bvHEM5XHxkVNYPKeMSN8A31qxiV0dvbakE4JY64QXzlplFm1tbV4nwXV0dAZ7vAeU4vbntvHmrk7KC3K45cw5lOaPr3NdlghfP3EGh04poi3Szzcf30S4p3/caYUg1jrhhbNWmUVFxfAtVzIRHZ1h/N5KKX717x08s6mNgtwsbj5jDpOL7RmsLjc7i+tPnc3s8gK2h3v59opNRPpi495vEGt98MJZq8yio8P9wd28RkdnGL/3395q5B/vNJGTJVy/ZBb7VU6wKWUGhaFsbjljDtVFIdY2dXPTU1voi42vl3cQa33wwlmrzCKYJEUfxuP92LoWfvPKTgS46qRaDp9WYl/CEqgozOV7ZxmPtl7dbvTfGBhHp70g1vrghbNWmUXQHlsfxur94tbd3PmC0elu+bHTOHmOs8MqTCvN55Yz51CQm8Uzm9q4e/WOMffyDmKtD0E/C4cJ2mPrw1i81+xo59antzKg4IJDazhnYZUDKduXeZUTuOG02eRmCfe/28Sf1owtZkGs9SHoZ+EwQRM7fRit97sNnXzniS30DSjOXVjJZw9z987t0CnFXHuK0cv7ntfr+b+3Gka9jyDW+hA0nXWYYJIUfRiN96aWbr61whhG/LS55XzhmGmIjL7T3Xg5fuZErjxhBgC/fHknj6xtHtXng1jrgxfOWmUW4XDY6yS4jo7OYN17a1uEax7bM4zHlSfMIMuDjCLO6fMqBocF+ckLH/DkhlbLnw1irQ9eOGuVWVRWVnqdBNfR0RmseW8P93D1oxsJ9/RzxLRirlk8tmE87ObcA6r43JGTUcCPnqtj5SZrHbCCWOuDF85aZRbBHYg+jOS9s72Xqx7ZSFukn0OnFPGdU2cTyvbPz+GTB9dwwaE1DCi4beVWXti6e8TPBLHWh6Bk4TB9fX1eJ8F1dHSG4b13tfdy1aMbaO7u48CaIm44bXzjPTnFZw+r4ZMHVzOg4Nant/JS3fAZRhBrffDC2X+/EAcJ2mPrw1DeO9t7+fojG2jsNEaQven02RTkZrucOmuICBcfMZmPHziJ/gHFzU8Nn2EEsdaHoJ+FwwTtsfUhlXc8o4gPNX7rmXOYEPJnRhFHRLhk0ZTBDOOmJ7fw4hCPpIJY60PQz8JhCgsLvU6C6+joDPt6bw/38PWHN9Dc1ccB1emRUcRJzDBiCm5+agvPbdm30juItT544axVZpGdnR4XBzvR0Rn29t7aFuFrDxt1FB+qLuSWM9Ino4gTzzDONzOMW5/eylMb925WG8RaH7xw1iqzaG+3fypLv6OjM+zx3tTSzTfMVk+HTCniljQqUSQjInx+0RQ+Y7aS+sHKOh5bt2c+b91jrRNeOGuVWVRVuTPWj5/Q0RkM7/cbu/jGI0Y/iiOnlXDT6XN8W5ltFRHhPw+fzMVHGP0wfvz8Nv75TiOgd6x1wwvn8U37lWa0trYyYYK98xL4HR2dAZ5fX8/P3uigp3+AY2tLue6Umb7qRzFePnVIDXk5Wdy9ege/WL2Drr4BTqrs0zLWOp7jXjhnzq/HAmMd+jmd0dH5pbrd3PlamJ7+AU6dW863l8zKqIwiztIPTeKrJ8wgS+CPr+3ir2s7tYx34OwOmfcLGoaguJr5rFjfwnef3EK/gnMXVvL1E2f4YggPpzhrfgXXLZ5JTpbwr7oe/vv5bcQG9Lp46naOgzfOWmUWDQ2jH/Y53dHFWSnF395s4PbntjGg4COz8rn8mGmeDgroFifOLuPG02YTyoIV61u58UljBF1d0OUcT8QLZ60yi6KiIq+T4Do6OA8oxS//vYNfm1OhXn7MNC44ZJInw4x7xZHTS/jWCdUU52Wzels71z62kY7efq+T5Qo6nOPJeOHsWmYhImeKyDoR2Sgi16RYXyoiD4nImyLyrohc7FbaAtKXaGyA257Zyj/eaSInS7hm8Uw+doB+jyUA5pbn8eOz51FZmMs7DV1c+dAGGjv1m586wBlcySxEJBv4GXAWsBD4lIgsTNpsOfCeUupg4GTgdhGxdYaPzs5OO3eXFmSyc2dvP998fBMrN+9mQm4WN50+m8XmnNmZ7D0UnZ2dzCjL546PzqO2LJ+63T18+cH1bG6JeJ00R9E11m7jVsliEbBRKbVZKRUF7gXOTdpGAcViPDsoAloBW8vR1dXVdu4uLchU58bOKFc+vIE3d3VSXpDD7WfP5fBpJYPrM9V7OOLOk4pC/PfZczmwpoiW7j6ufHg9a3Zkbsc1nWPtJm5lFlOBDxLebzeXJXIXsD+wE3gb+LJSytZauqamJjt3lxZkovOG5m6+9OA6trb1ML00jzvOmcecir3bnGei90gkOhfn5fC9M+dw0qyJdPcN8M3HN/F4Qm/vTEL3WLuFW53yUtU0JrfvOwN4AzgFmAM8ISLPK6X2uiVqbGxk2bJl5OTkEIvFWLp0KcuXL6e+vp7CwkKys7Npb2+nqqqK1tZWlFJUVVXR0NBAb28vLS0tdHZ2Ul1dTVNTEyJCeXk5TU1NlJSUEIvF6Orqoqamhvr6enJzcyktLaW5uZnS0lKi0SiRSGRwfSgUori4mJaWFsrKyohEIvT09Ayuz8/Pp6CggLa2NioqKujo6CAajQ6uLygoIBQKEQ6HqaysJBwO09fXN7h+JKd4RddQTtFolObm5oxxevK9Hfzq7S56Y4p5ZTnccNpMulrr2dmxt1NXVxd1dXVp4WTXudfZ2bmXc35+PsuPKCd/oIcVZrPatdsbuey4WTQ0NKSFk5U47d69m0mTJqVNnOw49xKdrTqN+yLuRucOETkGuEEpdYb5/loApdT3ErZ5BLhNKfW8+f5p4Bql1MuJ+1q1apVasGDBmNLR3d2tXU/PTHFWSvHAe83cvXo7AwqW7FfGV0+YMWRnu0zxHg3DOT/8fjN3vfQBAwpOmj2Rr59Y68sJn8ZCEGtrrFmz5rUlS5YcMdZjunW2vALMFZFZZqX1J4EHk7bZBiwBEJFqYD6w2c5EBMXV9CQ2oLjrpe38fJWRUVxwaA1XnVQ7bK/sTPAeLcM5n71/Jd89fTYFuVk8u3k333hkA63dmTHDXBBrd3Als1BK9QNXACuA94G/KaXeFZHLROQyc7ObgGNF5G3gKeBqpVSznekoKSkZeaMMI92dO3v7+daKTTz0fjO52cLVJ9dy4eGTR+xDke7eY2Ek50XTS7njo/OoLgqxtsmo98mEllJBrN3BtYEElVKPAo8mLbs74fVO4HQn0xCLxZzcvS9JZ+ft4R6u/9dmtod7mZifww2nzWZhtbVJX9LZe6xYcZ5VXsBPzpnHDU9u5v3Gbr7y0HquPrmW42ZOdCGFzhDE2h0y46GlRbq6urxOguukq/Or29v50gPr2R7uZXZ5Pj85d57ljALS13s8WHUum5DLDz88l1PmlNHTP8CNT27hz6/Xp+2AfEGs3cFSyUJEUmYqdjdtdZpgYnf/o5Tin+828ct/72BAwbG1pVx9cu2o56FIN287GI1zKCeLq0+uZVZ5Ab99ZSd/eG0XW1sjXHnijLSb8yOItTtYLVn0A33JfyLSKyJbROR2EfH9AC3BxO7+prd/gB8+t427VxsZxacOqeb6U2eN6eKVTt52MVpnEeETB1dzw2mzmZCbxbNbdvPVhzZQ39HrUAqdIYi1O1jNLL4IPI1Rp7A/Rp+Ip4CrgC8AxwJ3OJFAO8nNzfU6Ca6TLs5NXVG+9vAGntzQSl5OFt9aMpOLj5gy5lFj08XbTsbqfExtKXeeM4+pJXlsbo1wxf3reH1nh82pc44g1u5gtYL7SuAwpVTYfL9eRF4FXlNKzTFbML3mSAptpLS01OskuE46OL+1q4ObntpKuKefmuIQN5w6m9kVBePaZzp42814nGvLCvjpufO49ZmtvLq9g2sf28jnF03l/32oyvej9waxdgerJYsSILkHyAQgnuJ6YHy/bhdobra1JW5a4GdnpRT3vdPIVY8a82QfOqWYu86dP+6MAvzt7RTjdS7Ky+Gm0+fwiYOrGVDwy3/v4HvPbCXS5+/WRkGs3cFqyeKPGMNv3IkxxtM04MvAH8z1pwPr7E+evQR3IP4h0hfjjhc+4JlNbQB84qBJXHTEFNtmtfOrt5PY4ZydJSw7cgrzKifwo+fqWLl5N3VtPVx/6mymlubZkEr7CWLtDlYzi28AGzB6Xk8BdmEMOf4rc/0zwEq7E2c30ah+Y/v70Xl7uIfvPrmFrW095Odk8fUTZ3Di7DJbj+FHb6ex0/mEWROZMTGPG5/cwpa2Hq54YB1XnVTLMbX+uzAHsXYHS4+hlFIDSqm7lVJLlFL7K6VOMd/HzPU9SinfdwWNRHyfRNvxm/OLW3dzxf3GiLHTSvP46bnzbM8owH/ebmC3s1GPMZ/jakvpisb4zhOb+d0rO303x3cQa3ew3INbRE4HDsGYa2IQpdT1difKKYL22N4RG1D87tWd/O2tRgCOnzmRr504g8KQM236/eLtJk44F4ayuf7UWfz97UZ++8pO/vJmA2uburh28UwmFvijFVIQa3ewVLIQkbuAe4DDgekJf9OcS+o53NAAACAASURBVJr9BO2xvaG1u4+rH93I395qJEvg0kVT+PaSmY5lFOAPb7dxyllE+I+DqrntrP2YmJ/D6zs7ufyf63i3wR8z1AWxdgerJYtPAYcopT4YcUsfEwrZOktrWuC181u7Orj16a20Rvopn5DDN0+ZxYE1zvff9NrbC5x2PmRKMT8/bz63PL2Vdxu6+PrDG7jkqKmcd4C3zWuDWLuD1aazLcBuJxPiBsXFxV4nwXW8ch5Qir++2cBVj26kNdLPQTVF/PxjC1zJKCCItVNUFob44UfmsvRDVcQU3L16Bzc9tZWuqHfNa4NYu4PVzOJ24M8icoyIzE78czJxdtPSkpnTSg6HF87tPf3c8MRmfvPKTgYUfPLgar7/4f0on+DeM+4g1s6RkyVcdvQ0vr1kFhNys3hh626W37+OTS3drhw/mSDW7mD1MdQvzP9nJy1XQNqMOlZWZn+rG7/jtvO6pi5ufmorDZ1RikLZXHVyLUfPcL+5ZRBr5zlh1kRmlxdw01Nb2Nwa4csPrmf5sdM5c165q4+lgli7g9Wms1lD/KVNRgFBEzsnUUrx4HtNfPWhDTR0RplfNYGfnzffk4wCgli7xdTSPO48Zx5nza8gGlP8+Plt/Oi5bfT0uzcgdRBrd3Bt8iM/0NPT43USXMcN5+5ojDte2MbKzUa11rkLK7nkqKnDTnvqNEGs3SMvJ4uvnjCDA6oL+emLH/DEhlY2NHfz7SWzmD4x3/HjB7F2hyEzCxF5XCl1pvn6eYxHTvuglDrRobTZTtAe2362tEa46aktbA/3UpCbxVePn8HJc7x/LBDE2n1On1fB3MoJ3PSU0Tv/igfWceUJMzjJgU6XiXjt7QV+62fxx4TXvwZ+M8Rf2hC0x7aXJze08qUH1rE93MvMsnzuOne+LzIKCGLtFbPKC7jr3PmcNHsikb4Bbnl6Kz97aTt9MeceS/nB22181c9CKfW/ACKSDcwBblFKpdesKEnk5ztfJPYbTjhH+wf4xertPLLWaJFx6txyvnTcdPJz/DNLbxBr75gQyua6xTM5sKaZu1fv4IH3mljX1MW3lsxiUpH9/QP84u0mXjiP+Os2x39ajjE7XlpTUOD7UdRtx27n+o5evvrweh5Z20JutvCV46fzjRNn+CqjgCDWXiMinLOwitvPnktVYS5rm7q5/J9reXV7u+3H8pO3W3jhbPUX/gfgMicT4gZtbW1eJ8F17HR+dXs7y+9fx4bmCDXFIe746Dw+vKDSl5PjBLH2B/tPKuQX5y3giGnFtPfG+Objm/jLG/UMKPsGI/Sjt9N44Wy1NdQi4IsichXGfBaDkU6nCu6Kigqvk+A6djgPKMVf3mjgj6/tQgGLppdw9cm1FOf5tzFdEGv/UJKfw81nzOGeNfXc83o9v3t1F+83dnHVSbUU2XAO+dXbSbxwthqpX7Fn7oq0paOjg6Iid4ab8Avjde6KxvjBs3WsqgsjwIWH1fDpQ2vGPDe2WwSx9hdZIlx4+GTmV03g+yvrWL2tnS8+sJ4bTptFbdn4Hqn42dspvHC2lFkopf4w8lb+J5gkZXR8sLuHG57YzAfhXopC2VyzuJZF0/03+U0qglj7k6NmlPKzj83nxic3s7m1hy89uJ5vnFTL8TMnjnmf6eBtN76d/EgMLhGRp0XkLXPZiSLyH84mz16C9tjWefmDMF98YB0fmM1if3ru/LTJKCCItZ+ZXJLHjz86j5PN5rXffXILf3xt15jrMdLF20781s8ike8Cy4BfAjPMZduBq51IlFME7bFHRinF399q4NsrNtPdN8AJsyZy5znzfDv/8lAEsfY3BbnZXLt4JpcsmkKWwD2v13PzU1uJ9I1+9Np08rYLX/WzSOIi4FClVLOIxAcV3AKk1aizQRO74YnGBrjzBWO4BoALD5/MZw6p9mVrp5EIYu1/RITzD6pmZlkBtzy9hRe27mZXRy83njZ7VP0x0s3bDvzcdDYbiE+LFS8rFiUsSwuCSVKGpr2nn2sf28QTG1rJy8ni20tmccGhNWmZUUAQ63TiyOkl/OTc+UwpyWNTS4QvPbiODc3WhztPV+/x4OfJjx4F/ltE8sCowwBuAh5yKmFOEA6HvU6C61hx3tney1ceWs/b9Z1UTMjlx2fP5YRZY69w9ANBrNOLGRPz+ck58zh4chGt3f187eENrN5mzSedvceKF85WM4srgSlAGCjFKFHUkmZ1FpWVlV4nwXVGcl7b2MWXH1zP9nAvs8vz+cm589ivcoJLqXOOINbpR0l+DreeOYdT9yujp3+AG57YzEPvNY34uXT3HgteOFudz6JdKfUxjAziaGCOUuo8pVSH1QOJyJkisk5ENorINUNsc7KIvCEi74rIs1b3bZXgDmRvXtvezlWPbiTc088R04q5/ex5VBVmRpE+iHV6kpudxTdOquWzh9UwoOCnL23nT2t2oYZpKZUJ3qPFC+fhhihPlZE0mX+D65VSIw4naQ5G+DPgNIxWVK+IyINKqfcStpkI/Bw4Uym1TUQmjUbECn19aT+81agZynnlpjZ+8Gwd/QOKU+eWc+UJM8jJSs/6iVQEsU5fRITPHjaZygm53PniB/xpTT27I/1cfsw0slOco5niPRq8cB6uNVQ/Q8xhkYSV2fIWARuVUpsBRORe4FzgvYRtPg3cp5TaBqCUarSw31ERtMc2eGxtM3e88AEK+H8fquKSo6b6vkf2aAlinf6ctaCS4vwcvvfMVh56v5nOaIyrTqrdJ8PING8r+K2fxSyMprGzgS8CzwJnAvub/58BrrB4nKkYY0rF2W4uS2QeUCYiK0XkNRG50OK+LRO0x4aH32/mx2ZG8bkjJ3NpBmYUEMQ6Uzh+5kRuPWMOBblZPLOpjdtWbiU2sPc9bCZ6j4Sv+lkoperir0XkSuAIpdRuc9F6EXkVeBX4RarPJ5HqapRcaskBDgeWAAXAKhFZrZRan7hRY2Mjy5YtIycnh1gsxtKlS1m+fDn19fUUFhaSnZ1Ne3s7VVVVtLa2opSiqqqKhoYGBgYGaGlpobOzk+rqapqamhARysvLaWpqoqSkhFgsRldXFzU1NdTX15Obm0tpaSnNzc2UlpYSjUaJRCKD60OhEMXFxbS0tFBWVkYkEqGnp2dwfX5+PgUFBbS1tVFRUUFHRwfRaHRwfUFBAaFQiHA4TGVlJeFwmL6+vsH1IznFx4cZykkpRXNzM11dXbwWDnH3y8ZJ9rlDqzhmYi/hcDjtnKzEqb+/n7q6uoxyGilOfX19ezlnglMkEmFiXw/fXTKDbz+xlWc376Y3uoH/nB+iuqqSjo4Ouru76e3tTSun8Z57ic5WncaLDFdxNLiRSBNwsFJqZ8KyqcCbSqkRq+VF5BjgBqXUGeb7awGUUt9L2OYaIF8pdYP5/jfA40qpvyfua9WqVWrBggUW1Palra2NsjJ/zOTmFnHnh95r4qcvbQfg8mOm8bEDqjxOmbPoHOtM5f3GLq59bOPgyALXLZ5JdpZkvHcqxuK8Zs2a15YsWXLEWI85mvksnhSRS0XkLBG5FFhhLrfCK8BcEZklIiHgk8CDSds8AJwgIjkiMgE4Cnjf4v4t0d5u/8Qrfqe9vZ1nNrVyl5lRXHFs5mcUoG+sM5n9JxXy/Q/vR2Eom+e37ObOFz5AKZXx3qnwwtnqcB9XARuBT2D0t9gF3IXFYcuVUv0icgVGBpMN/FYp9a6IXGauv1sp9b6IPA68BQwAv1ZKvTMqmxGoqsr8i2QyddECfvBcHQpYduQUzlmox3egY6x1cJ5fVchNp8/m2sc28vj6Fkrys/n0hzLfOxkvYj1iZmE2e/0Oxhzcd4/1QEqpRzF6gicuuzvp/Q+BH471GCPR2trKhAnp3+HMKu82dPKD53cQU3D+gZP4xMHVXifJNXSLNejj/KGaIr596iy+86/N/O2tRlRvF5ecMM/rZLmKF7HWag5uK/UzmUJ9Ry83PLGF6ACcMa+czy+a4nWSXEWnWMfRyXnR9FKuOrkWAf6+rosXtu4e8TOZhBex1moObh2K6QCRvhjf+ddmwj39HDK5kK8cPyNtBwQcK7rEOhHdnBfPKedzRxo3QT9YWcemFuuDD6Y7XsTaamaxCLhTRLaKyPMi8lz8z8nE2U1DQ4PXSXCcAaW4bWUdW9p6mFaax0Xzc1P2es10dIh1Mjo6/8dBkzh6coie/gG+88Rm2iJp/wDEEl7EWqs5uHWYp/eeNfWsqgtTFMrmu6fPpqC/y+skeYIOsU5GR2cR4QtHVrN7VQtrm7q56ckt/PAjczP+BsmLWGs1B3em8+bODv78ej0CfPOUmUwrzaelRc/MIkAfQtnCDafNZvn963inoYt7Xq/nPw+f7HWyMg6rj6EQkYvNObjXmf8vdjJhTtDZmVZzNY2K9p5+vr/SaCL76UNrOHxaCZDZzsOho7eOzmB4l0/I5Rqzwvt/X6/nrV2WB8ROS7yItaXMQkS+CVwD3At8yfx/lbk8baiuzsymo0opbn9+G83dfSycVMgFh+4ZZCxTnUdCR28dnWGP98FTivnkIdUo4LaVdbT39HubMAfxItZWSxafB05XSv1SKbVCKfVLjMEEL3UuafbT1DTyRCrpyCNrW1hVF6YwlM01i/celTNTnUdCR28dnWFv788eNpmFkwpp7urjjhe2ZWxzYi9ibTWzKMScxyKBFowB/9KGTGw+2twV5Vcv7wDgy8dNp6Y4b6/1mehsBR29dXSGvb1zsoRrFtcyITeLF7aGeXFrZk6M5EWsrWYWjwN/FpH5IlIgIgsw+l6scC5p9lNeXu51Emzn56t2EOkb4NjaUk6es+/AYpnobAUdvXV0hn29a4rzWGb2v/j5qu10R2NeJMtRvIi11cziCqADeBNj/u03gC6MeS7Shkwrpv97W5gXtu4mPyeLy4+ZlnKbTHO2io7eOjpDau8PL6hkftUEmrv7+MOaXR6kyll8+xjKnIP7QmACMBmYoJS6MGF+i7SgpKTE6yTYRk//wOBIshceVsOkotRzZ2eS82jQ0VtHZ0jtnZ0lfPm46WQJPPBuExubM6t3txexttx0Foz5tpVSjVbm3fYjsVjmFEf/9/V6GjqjzC4v4LwPDT1deSY5jwYdvXV0hqG996ucwLkHVDGg4M4XP2Aggyq7vYj1qDKLdKerKzM6qLV09XHfO8YU5V86bvqwvVUzxXm06OitozMM7/2fh02mYkIu65q6eX5LWj0IGRYvYq1VZpEpE7vf+2Y90Zji+JmlLKwuHHbbTHEeLTp66+gMw3tPCGXzGbPf0Z/W1O8zf3e64kWsh8wsROSHCa9PcSc5zpIJE7s3dkZ5dG0LgtGmfCQywXks6OitozOM7H3GvHKqi0Js293Dys1tLqXKWbyI9XAli8QOd/c7nRA3yM3N9ToJ4+Yvb9TTN6A4cfZEZpWP3M0lE5zHgo7eOjrDyN652Vl82ixd3JMhpQsvYj3cQIJvisj/Ae8BeSLy3VQbKaWudyRlDlBaWup1EsZFfUcvj69rIUuslSog/Z3Hio7eOjqDNe/T5pbz1zfr2dHey1MbWzl9XoULKXMOL2I9XMni4xj9KSYDAkxP8Ze6cb9PaW5u9joJ4+J/X28gpuCUOWXMmJhv6TPp7jxWdPTW0RmseedkCRccatxg3fN6Pf1pXrrwItZDliyUUo3AzQAikqOUSrtRZpNJ5zuvtkgfT25sJUsYrLCzQjo7jwcdvXV0Buvei+eU8b9v1LM93MtLdbs5cda+Ix6kC34rWQyilLpYRMpE5EIRudb8n3ZjC0SjUa+TMGb+tb6V/gHFouklTC21VqqA9HYeDzp66+gM1r2zs4RzFhrTkT7yfnqXwryItdUhyo8BNmHMw30Q8F/ARnN52hCJRLxOwpgYUIpH1hon99n7V47qs+nqPF509NbRGUbnfep+ZeRlC6/v7GR7uMfBVDmLF7G22s/iDuBypdSxSqlPKaWOA74A/MS5pNlPurZDX7Ojg/qOKNVFIQ6fOrpu/unqPF509NbRGUbnXZSXMzjg5qNrW5xKkuP4qp9FEvOAvyUt+z9gP3uT4yzp2g79YbPI/OEFFaOeWzhdnceLjt46OsPoveOl8xXrW4j2p+XIRb7rZ5HIBuCTScvOx3g0lTaEQqkH2/MzTV1RVm8Lky1wxhia+6Wjsx3o6K2jM4zee17lBParKKCjN8ZzaToEiBextppZfAW4S0RWi8hfReTfwM8xplhNG4qLi71Owqh5fF0LAwqOmzmR8gmj74iTjs52oKO3js4wem8RGSxdxOsC0w0vYm21NdRLwBzgLuA14KfAfubytKGlJb2eUQ4oxWPrjDR/ZJQV23HSzdkudPTW0RnG5r14ThkTcrN4t6GLurb0axjgRayH68G9F0qpNuAeB9PiOGVl6dWuel1TN81dfVQV5nLI5KIx7SPdnO1CR28dnWFs3gW52ZwwayIr1rfywtYwtWVpNUO0J7HWatTZdGtauLrOmD/42NrSMc+5m27OdqGjt47OMHbvY2snArB6W/rN0+3nprMZQU9PerWrfsk8iY+eMfbemunmbBc6euvoDGP3PnRqMXnZwrqmblq6+mxOlbN4EWutMot0aoe+s72XurYeJuRmcdAYH0FBejnbiY7eOjrD2L3zc7I4zOy3tCrNShd+7meBiOSKyAki8gnzfaGIDD/zzt6fP1NE1onIRhG5ZpjtjhSRmIh83Oq+rZJO7dBXmY+gFk0vITd77Hl6OjnbiY7eOjrD+LyPqTVK7fHfW7rg234WInIgsB74FfAbc/FJwG8tfj4b+BlwFrAQ+JSILBxiu+8DK6zsd7Tk51sfU8lr4idv/GQeK+nkbCc6euvoDOPzPmpGCQK8sbOD7mj6zGHuRayt3rL+ArheKbUAiD/cexY43uLnFwEblVKblVJR4F7g3BTbfRH4B9Bocb+joqAgPVo8tPf0805DJ9kCR04b3fAeyaSLs93o6K2jM4zPu6wgl/0nFdI3oHh1R7uNqXIWL2JtNbM4gD3NZhWAUqoLsJriqcAHCe+3m8sGEZGpwHnA3Rb3OWra2tJjSsWXP2hnQMFBk4soyrPcujkl6eJsNzp66+gM4/c+Ng0fRXkRa6tXoq3A4cCr8QUisgjYaPHzqdp9Js8+cgdwtVIqNlwz0cbGRpYtW0ZOTg6xWIylS5eyfPly6uvrKSwsJDs7m/b2dqqqqmhtbUUpRVVVFQ0NDeTk5NDS0kJnZyfV1dU0NTUhIpSXl9PU1ERJSQmxWIyuri5qamqor68nNzeX0tJSmpubKS0tJRqNEolEBteHQiGKi4tpaWmhrKyMSCRCT0/P4Pr8/HwKCgpoa2ujoqKCjo4OotHo4PqCggJCoRDhcJjKykrC4TBPvd8KwPziAZqbm4d1KioyKr+HcsrNzaW5udlzp76+vsH1I8VpJCcrccrKyqKuri6jnEaKE7CXcyY4WY1Tb2/vmJ1m5RkPS1Zt3U1jUzPZWeILp+HilOhsNU7jRZQaecYoETkbo67ibuBrwC0Yw5VfopT6l4XPHwPcoJQ6w3x/LYBS6nsJ22xhT6ZSCXQDlyql9pr/e9WqVWrBggUjm6Vg165dTJ5sbTpSr+iLDfDxe94m0jfAnz5xANXF4xsDJh2cnUBHbx2dwR7vz/39PbaHe/nRR/bjoMn+HzZlLM5r1qx5bcmSJUeM9ZhWh/t4GKNyugqjrqIWWGolozB5BZgrIrNEJIQxKOGDSceYpZSaqZSaiTGi7eXJGcV4SYfJYba09hDpG2Baad64MwpID2cn0NFbR2ewx/vwqUYG8W5D17j35QZexHo0w32sAS4fy0GUUv0icgVGK6ds4LdKqXdF5DJzvWP1FImkQzv0tU3GybqgaoIt+0sHZyfQ0VtHZ7DHe35VIdDM2qbu8SfIBXzbz0JE8kTkFhHZLCJhc9npZgZgCaXUo0qpeUqpOUqpW8xld6fKKJRSFyml/s/qvq2SDu3Q1zaamcUky11YhiUdnJ1AR28dncEe7/0nGTdn6xq7sPJo3mt8288C+DHwIeAz7KmYfhdjtry0IR2aFsbvbBZU2ZNZpIOzE+joraMz2OM9pSSP4rxsWiP9NKXB0B9+bjp7HvBppdQqYABAKbWDpOavfsfvk8N09PazPdxLbrYwq9yeTjd+d3YKHb11dAZ7vEWE+eaj33jp3s/4efKjKEn1GyJSBaTVAPrhsL/bUa8zSxVzKyaMa4iPRPzu7BQ6euvoDPZ5x0vz6VBv4UWsrV6R/g78QURmAYjIZIyJkO51KmFOUFk5tgmE3CJ+ks6fZE/lNvjf2Sl09NbRGezzXjApfUoWXsTaamZxHUbHvLeBiRhzcu8EbnQmWc7g9zuvdfHKbZvqK8D/zk6ho7eOzmCf93zzd7ehuZvYgL8ruX1ZshCRLIwxoK5WShUB1UCxUuqr5jhPaUNfn38rrpRSeyq3bSxZ+NnZSXT01tEZ7PMuzc9hSkmI3phiq8+nWvUi1iNmFkqpAeABpVSv+b5JpUPbshT4uR16fUeUcE8/pfk51BTZV3nlZ2cn0dFbR2ew1zteuni/0d/1Fr7tZwE8JyJHO5oSF/BzO/TEznhjnUI1FX52dhIdvXV0Bnu9451h1zX5u97Ci1hb7cFdBzwmIg9gjB47WLJQSl3vRMKcoLDQvroAu1nbGK/ctjeNfnZ2Eh29dXQGe73jnWHX+rxk4UWsrWYWBUB8nKZpDqXFcbKzs71OwpDYPcxHHD87O4mO3jo6g73ecyoKyMkStu3uoSsaozDkz+/Ui1hbyiyUUhc7nRA3aG9vHxzK2U/0xQbY2GJUqNmdWfjV2Wl09NbRGez1DmVnMaeigHVN3axv6ubQqf4cgdaLWFvKLERk9hCreoFdZiW476mqqvI6CSlp7uqjL6aYVJQ77smOkvGrs9Po6K2jM9jvPbdiAuuautnaFvFtZuFFrK1WcG/E6FuxIen1NqBXRP4hItXOJNE+WltbvU5CSiJ9Rl5bmGt/0dKvzk6jo7eOzmC/d405NUBDp397BngRa6uZxSXAn4F5QD4wH2Oa1cuBAzFKKD9zIoF24tcWv5F+Y6L4AgcyC786O42O3jo6g/3e8XlkGjr8m1l4EWurzzxuBPZTSvWY7zeKyBeA9Uqp/xGRizBKGr7Gr8X0eMkiP9ee8aAS8auz0+joraMz2O9dXeT/koWfH0NlATOTls3AmMgIoJNRTKTkFQ0NDV4nISXxzKIgx/7Mwq/OTqOjt47OYL93dRo8hvIi1lYv8HcAT4vI7zD6WUwDLjaXA3wEWGV/8uzFronL7SbSZz6GcqCZnl+dnUZHbx2dwX7vifk55GULHb0x3zaf9SLWVufg/gHwOaAGOBeYAixTSn3fXH+/Uuosx1KZ4fT0O1eyCAgIGB0iwiTzUVSjj0sXbmP56qSUelwptUwpdZZS6nNKqcedTJgTdHZ2ep2ElHTHSxYO1Fn41dlpdPTW0Rmc8Y4/iqr3aSW3F7F2bQ5uP1Bd7c/WvXsquO0v7vrV2Wl09NbRGZzxrinKA/xbb+FFrLWag7upqcnrJKSkx8wsJjhQsvCrs9Po6K2jMzjjHS9Z+PUxlBextlrBfR5G09kuERmcg1tE0moObjtHc7UTJ1tD+dXZaXT01tEZnPGO11n49TGUF7HWag7u8vJyr5OQknhrKCceQ/nV2Wl09NbRGZzx3tOLu9f2fduBF7HWag5uvxbTI/3BYyi70dFbR2dw6DFUkb97cXsRa63m4C4pKfE6CSkZfAzlQGbhV2en0dFbR2dwxrusIIdQttDeGxss+fsJL2JttZ9FVCn1lXSfgzsW81/QwdnHUH51dhodvXV0Bme8E/ta+LFFlBexttp0dqGI/JeIXAssBfZ3NlnO0NXlz6kSnazg9quz0+joraMzOOft50dRXsR62NZQYlS5/wb4T2A7xqOnqcAUEfkT8DmVRkNd+nVC+/iosxMcKFn41dlpdPTW0Rmc8/bzGFFexHqkW9lLgZOBo5VStUqpY5RSM4BjgBOA/3I4fbbi1wntexwcddavzk6jo7eOzuCcd7WPm896EeuRrk6fBb6klHolcaH5/ivm+rQhNzfX6yTsg1JqTw9uBx5D+dHZDXT01tEZnPOu8XHHPC9iPdLVaSHw7BDrnjXXpw2lpaVeJ2EfevoHUEBetpCdZX9HGz86u4GO3jo6g3Pefq7g9iLWI2UW2UqpjlQrzOVpNUxqc3Oz10nYh57BZrPODIPsR2c30NFbR2dwzjs+PpQfH0N5EeuRhvvIFZHFwFC3vJYnPBKRM4E7MSZM+rVS6rak9Z8BrjbfdgJfUEq9aXX/VvDjnVe8Q54TfSzAn85uoKO3js7gnHfZhBxys4RwTz+RvphjN3RjwYtYj3SxbwR+O8L6ERGRbIw5uk/DaFX1iog8qJR6L2GzLcBJSqk2ETkL+CVwlJX9WyUa9d8dQsTB4cnBn85uoKO3js7gnHeW2ddiR3svTZ19zCjzT2bhRayHzSyUUjNtOs4iYKNSajOAiNyLMYnSYGahlHopYfvVGLPx2UokErF7l+NmT+W2MyeiH53dQEdvHZ3BWe/qYiOzqO/sZUZZvmPHGS1exNqtebOnYkzHGmc7w5calgGPpVrR2NjIsmXLyMnJIRaLsXTpUpYvX059fT2FhYVkZ2fT3t5OVVUVra2tKKWoqqqioaGB/Px8Wlpa6OzspLq6mqamJkSE8vJympqaKCkpIRaL0dXVRU1NDfX19eTm5lJaWkpzczOlpaVEo1Eikcjg+lAoRHFxMS0tLZSVlRGJROjp6Rlcn5+fT0FBAW1tbVRUVNDR0UE0Gh1cv6vDeMInsShdXV2Ew2H6+voG14/kFJ9ecSingoICmpubXXUqKCggFAoRDoeprKy03clKnHJzc6mrq8sop5HilJOTs5dzJjhZiVMsFqO3t9cRp0KMO/gPWjupHtjtyTUilVOis1WnXZtvRwAADq1JREFU8SJu9KkTkfOBM5RSnzfffxZYpJT6YoptFwM/B45XSu0zqu2qVavUggULxpSOuro6amtrx/RZp3huSxs3P7WV42dO5PpTZ9m+fz86u4GO3jo6g7Pef3xtF/e8Xs8Fh9Zw4eGTHTnGWBiL85o1a15bsmTJEWM9plsli+3A9IT30zB6g++FiBwE/Bo4K1VGMV5CoZDduxw3PQ4OIgj+dHYDHb11dAZnvUvzjUtkuKffsWOMBS9i7VbT11eAuSIyS0RCwCeBBxM3EJEZwH3AZ5VS651IRHFxsRO7HRfdDmcWfnR2Ax29dXQGZ71LzMyi3WeZhRexdiWzUEr1A1cAK4D3gb8ppd4VkctE5DJzs+uBCuDnIvKGiLxqdzpaWvw3V9Oe1lDOVHD70dkNdPTW0Rmc9S7NN36X4V5/ZRZexNqtx1AopR4FHk1adnfC688Dn3cyDWVlZU7ufkz0ODjiLPjT2Q109NbRGZz1LvVpycKLWKdVD+zx4semhU53yvOjsxvo6K2jMzjrXTJYZ+GvuUK8iLVWmUVPT4/XSdgHJyc+An86u4GO3jo6g7PeJXl7ShZ+mo3Bi1hrlVn4cbz/eKc8J+bfBn86u4GO3jo6g7PeeTlZ5Odk0TewZ3RoP+DH+SwyCj+O9+/k/NvgT2c30NFbR2dw3nuw+ayPKrn9OJ9FRpGf75/u+nHis+Q5NdyHH53dQEdvHZ3Bee8Ss0WUnyq5vYi1VplFQUGB10nYB6dLFn50dgMdvXV0Bue9/dgxz4tYa5VZtLW1eZ2EfXC6zsKPzm6go7eOzuC8955Kbv+0iPIi1lplFhUVFV4nYR96HG4N5UdnN9DRW0dncN7bjyULL2KtVWbR0ZFy0j9PGexn4VCnPD86u4GO3jo6g/Pefhzyw4tYa5VZ+G1ymAG1pzlefjD5ka3o6K2jMzjv7cfWUF7EWqvMwm/t0Hv74xMfZZElQ81cOz785uwWOnrr6AzOe/uxNVTQz8Jh/NYO3emWUOA/Z7fQ0VtHZ3Chn0We/4b8CPpZOIzfmha6kVn4zdktdPTW0Rmc9/ZjnUXQdNZh/DY5zOC4UA51yAP/ObuFjt46OoPz3iU+bA2VyZMf+YJwOOx1EvYi3hLKqT4W4D9nt9DRW0dncN67JM+ss+jtZ8Angwl6EWutMovKykqvk7AXe0acdS4MfnN2Cx29dXQG571zs7OYkJvFgIKuqD/qLbyItVaZhd/uvPbMv+3cYyi/ObuFjt46OoM73n6bBCkoWThMX1+f10nYi26HZ8kD/zm7hY7eOjqDO95+mwTJi1hrlVn4rR260/Nvg/+c3UJHbx2dwR1vvw35EfSzcBi/tUPvcXhKVfCfs1vo6K2jM7jjPdh81ie9uIN+Fg5TWFjodRL2wo1+Fn5zdgsdvXV0Bne8S/P81Yvbi1hrlVlkZzv3uGcs7Oln4VwY/ObsFjp66+gM7nj7rWOeF7HWKrNob2/3Ogl7MTiXRci5wPvN2S109NbRGdzxLvVZBbcXsdYqs6iqqvI6CXvh9PDk4D9nt9DRW0dncMe7xGcjz3oRa60yi9bWVq+TsBdudMrzm7Nb6OitozO44+23fhZexFqrzEL5pKt+nD1Tqjr3GMpvzm6ho7eOzuCO956RZ/2RWXgRa60yC78V091oDeU3Z7fQ0VtHZ3DrMZS/WkMFj6EcpqGhwesk7EVPv/OjzvrN2S109NbRGdzxLs7LQYCO3hixAe9LcF7EWqvMoqioyOsk7IUbJQu/ObuFjt46OoM73tlZQlFeNgro9MFggl7EWqvMwm90u5BZBAQE2IPfhvxwG62uUp2dnV4nYZDYgKK3fwAB8hxsOusnZzfR0VtHZ3DPuyTPPy2ivIi1a5mFiJwpIutEZKOIXJNivYjIT8z1b4nIYXanobq62u5djples49Ffm4WWSKOHcdPzm6io7eOzuCet59KFl7E2pXMQkSygZ8BZwELgU+JyMKkzc4C5pp/lwK/sDsdTU1Ndu9yzLjRIQ/85ewmOnrr6AzuefupRZQXsc5x6TiLgI1Kqc0AInIvcC7wXsI25wJ/VEYD4tUiMlFEJiuldo334N3RGL95ZSedXZ0UbftgvLuzhfiMW04OTw4gDpZa/IyO3jo6g3ve8ZLFivWtbGyJuHLMZM5ZWEltWYEnsXYrs5gKJF6ltwNHWdhmKrBXZtHY2MiyZcvIyckhFouxdOlSli9fTn19PYWFhWRnZ9Pe3k5VVRWtra0opQgVl/HQ+83mHnpsVhsfxbmK3bt3Ew6HqaysJBwO09fXR01NzbBOVVVVNDQ0DLaK6OzspLq6mqamJkSE8vJympqayMvLo7m5ma6ursF95ubmUlpaSnNzM6WlpUSjUSKRyOD6UChEcXExLS0tlJWVEYlE6OnpGVyfn59PQUEBbW1tVFRU0NHRQTQaHVxfUFBAKBRyzKmkpIRYLDasE0BdXV1GOY0UJ6XUXs6Z4GQlTj09PfT29jrulBs16gnea+zivcYuV68TcQ6ZlEdupG0vZ6tO40Xc6AkoIucDZyilPm++/yywSCn1xYRtHgG+p5R6wXz/FHCVUuq1xH2tWrVKLViwYFTH7+0fYMX6FlpbWykvLx+njb0sml5CTXGeY/uvq6ujtrbWsf37FR29dXQG97yj/QM8t2U33X3eNZ09anop1cWhMTmvWbPmtSVLlhwx1mO7VbLYDkxPeD8N2DmGbcZEXk4W5yysoq0th7KyMjt2mTaUlJR4nQRP0NFbR2dwzzuUk8Wpc/1xs+lFrN1qDfUKMFdEZolICPgk8GDSNg8CF5qtoo4GwnbUVyQSi3nfmcZtdHQGPb11dAY9vb1wdiWzUEr1A1cAK4D3gb8ppd4VkctE5DJzs0eBzcBG4FfA5Xano6vLm+eMXqKjM+jpraMz6OnthbNbj6FQSj2KkSEkLrs74bUCljuZBh0ntNfRGfT01tEZ9PT2wlmrHtw6TmivozPo6a2jM+jp7YWzVpnF/fff73USXEdHZ9DTW0dn0NPbC2etMov77rvP6yS4jo7OoKe3js6gp7cXzlplFv393nfTdxsdnUFPbx2dQU9vL5xd6ZRnJ0899VQTUDeWz7a2tlaWl5c3j7xl5qCjM+jpraMz6Ok9BudmgCVLlpw51mOmXWYREBAQEOA+Wj2GCggICAgYG0FmERAQEBAwIhmZWfhhoiW3seD8GdP1LRF5SUQO9iKddjOSd8J2R4pITEQ+7mb6nMCKs4icLCJviMi7IvKs22m0Gwvnd6mIPCQib5rOF3uRTjsRkd+KSKOIvDPEenevY0qpjPoDsoFNwGwgBLwJLEza5sPAY4AARwP/9jrdLjgfC5SZr89Kd2er3gnbPY0xgsDHvU63C7GeiDFXzAzz/SSv0+2C83XA983XVUArEPI67eP0PhE4DHhniPWuXscysWQxONGSUioKxCdaSmRwoiWl1GpgoohMdjuhNjKis1LqJaVUm/l2NcaovumOlVgDfBH4B9DoZuIcworzp4H7lFLbAJRS6e5txVkBxWLMClSEkVmkdZtapdRzGB5D4ep1LBMzi6EmURrtNunEaH2WYdyRpDsjeovIVOA84G4yAyuxngeUichKEXlNRC50LXXOYMX5LmB/jGkN3ga+rJQacCd5nuHqdcy1gQRdJNV8g8ntg61sk05Y9hGRxRiZxfGOpsgdrHjfAVytlIplyLSjVpxzgMOBJUABsEpEViul1judOIew4nwG8AZwCjAHeEJEnldKtTudOA9x9TqWiZmFpxMteYQlHxE5CPg1cJZSqsWltDnJ/2/vbkLkKMIwjv8f3GBYXBRcNxfRVcR4ERU0ntScAsZLUBG/wiKIgp4EP27JQZDoRRSJgkG9REUWUdAgQYh4U8RDJK6CaAweFASVGDUw+ORQtdCOC927MzvZGZ4fNAzVNb1V7NBvd03P+3aZ9/XA2zVQzAI7JfVsj2tCoa6f719tnwJOSfoUuAYY12DRZc4PAPtcFvO/k/QDcBXw+WiGeFaM9Dw2ictQG6LQ0oi1zlnSJcC7wO4xvsLs1zpv25fZnrc9DywCj4xxoIBun+/3gZskTUmaptS7XxrxOIepy5xPUO6kkLQF2EqpjzPJRnoem7g7C9s9ScuFls4BXnMttFT3v0J5KmYnpdDSX5SrkrHVcc57gAuB/fUqu2d7zfV4N4KO854oXeZse0nSR8BR4F/ggO0VH78cBx3/z08Db0j6irI885TtsU4BIuktYDswK+knYC+wCc7OeSzpPiIiotUkLkNFRMSQJVhERESrBIuIiGiVYBEREa0SLCIiolWCRcQqSNohaeDfaUjaImlJ0rnDGFfEekuwiKgknSfpuKR7G20zkk40Ups/A+zre58kfS/p6xWO+YmkB/vbbf8CHAEeGu4sItZHgkVEZftPysn7BUkX1ebngC9sL0q6ATi/ZvhsuhmYAy6vfbo6CDw86LgjRiHBIqLB9mHgQ+BFSduBu4BH6+5bgZUKCS1QUmwcqq+7+owSYC5d84AjRiTBIuL/HqOkWVgEHm/k27ka+LbZseZeupNyl3AQuLvmL2plu0dJ1TARVQtjsiVYRPSpRaKOAdOU5IvLLgBO9nW/HTgNHAY+oORbu20Vf+5kPW7EhpZgEdFH0v3APPAx8Gxj12/ATF/3BeAd2z3bpynBZTVLUTPA72sfbcRoTFzW2YhBSJoDnqd8V/ENcEzSm7XE5VFKFbrlvhdTiu1sk3RHbZ4GNkuabct6KmkKuIJSUzpiQ8udRcR/vQS8Z/tI/a7iSeDV+nuIQ8Atjb67KQWFtgLX1u1KSlGaexr9piRtbmybavs24LjtH9d3ShGDS7CIqCTtopSbfWK5zfYBysl/j+0vgT8k3Vh3LwD7bf/c3Cj1vptLUS8Dfze212v7fUxObfCYcKlnEbEKknZQqu3tGvA4c5THcK+z/c9QBhexjhIsIiKiVZahIiKiVYJFRES0SrCIiIhWCRYREdEqwSIiIlolWERERKsEi4iIaJVgERERrc4A7h+zP46Yr8wAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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",
    "# Select all points in the datasets where B2_BCC is stable, dropping the others\n",
    "eq2_b2_bcc = eq2.where(eq2.Phase == 'B2_BCC', drop=True)\n",
    "plt.plot(eq2_b2_bcc['X_AL'].values, eq2_b2_bcc['degree_of_ordering'].values.squeeze())\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:             (N: 1, P: 1, T: 110, X_AL: 1, component: 2, internal_dof: 5, vertex: 3)\n",
      "Coordinates:\n",
      "  * N                   (N) float64 1.0\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 320.0 340.0 ... 2.46e+03 2.48e+03\n",
      "  * X_AL                (X_AL) float64 0.1\n",
      "  * vertex              (vertex) int64 0 1 2\n",
      "  * component           (component) <U2 'AL' 'NI'\n",
      "Dimensions without coordinates: internal_dof\n",
      "Data variables:\n",
      "    NP                  (N, P, T, X_AL, vertex) float64 0.6363 0.3637 ... nan\n",
      "    GM                  (N, P, T, X_AL) float64 -2.526e+04 ... -1.944e+05\n",
      "    MU                  (N, P, T, X_AL, component) float64 -1.719e+05 ... -1.816e+05\n",
      "    X                   (N, P, T, X_AL, vertex, component) float64 0.01427 ... nan\n",
      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 0.01427 ... nan\n",
      "    Phase               (N, P, T, X_AL, vertex) <U7 'FCC_L12' 'FCC_L12' ... ''\n",
      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 2.396e-15 1.0 ... nan\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.8.3+10.gfd19517e.dirty\n",
      "    created:  2020-10-27T14:48:26.640842\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 0x7faec02c0208>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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[2]), 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": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "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[2] 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[2]), 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[2]), 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:espei]",
   "language": "python",
   "name": "conda-env-espei-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": "3.6.2"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}