{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Equilibrium Properties and Partial Ordering (Al-Fe and Al-Ni)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:09.190653Z",
     "iopub.status.busy": "2022-02-19T19:18:09.172558Z",
     "iopub.status.idle": "2022-02-19T19:18:10.182480Z",
     "shell.execute_reply": "2022-02-19T19:18:10.182480Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:10.201991Z",
     "iopub.status.busy": "2022-02-19T19:18:10.200454Z",
     "iopub.status.idle": "2022-02-19T19:18:12.092175Z",
     "shell.execute_reply": "2022-02-19T19:18:12.092175Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:12.099168Z",
     "iopub.status.busy": "2022-02-19T19:18:12.098168Z",
     "iopub.status.idle": "2022-02-19T19:18:13.301974Z",
     "shell.execute_reply": "2022-02-19T19:18:13.301974Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (N: 1, P: 1, T: 34, X_AL: 1, vertex: 3, component: 2, internal_dof: 5)\n",
      "Coordinates:\n",
      "  * N                   (N) float64 1.0\n",
      "  * P                   (P) float64 1.013e+05\n",
      "  * T                   (T) float64 300.0 350.0 400.0 ... 1.9e+03 1.95e+03\n",
      "  * X_AL                (X_AL) float64 0.25\n",
      "  * vertex              (vertex) int32 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 -2.858e+04 ... -1.413e+05\n",
      "    MU                  (N, P, T, X_AL, component) float64 -7.274e+04 ... -1....\n",
      "    X                   (N, P, T, X_AL, vertex, component) float64 0.25 ... nan\n",
      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 0.5 ......\n",
      "    Phase               (N, P, T, X_AL, vertex) <U6 'B2_BCC' '' '' ... '' ''\n",
      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 0.6666 nan ... nan nan\n",
      "    heat_capacity       (N, P, T, X_AL) float64 25.45 26.93 ... 42.44 42.44\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
      "    created:  2022-02-19T19:18:13.281597\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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:13.331442Z",
     "iopub.status.busy": "2022-02-19T19:18:13.331442Z",
     "iopub.status.idle": "2022-02-19T19:18:13.621652Z",
     "shell.execute_reply": "2022-02-19T19:18:13.621652Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (N: 1, P: 1, T: 1, X_AL: 100, vertex: 3, component: 2, internal_dof: 5)\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-10 0.01 0.02 0.03 ... 0.97 0.98 0.99\n",
      "  * vertex              (vertex) int32 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 -2.443e+05 ... -1....\n",
      "    X                   (N, P, T, X_AL, vertex, component) float64 1e-10 ... nan\n",
      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 9.807e-...\n",
      "    Phase               (N, P, T, X_AL, vertex) <U6 'B2_BCC' '' '' ... '' ''\n",
      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 0.009636 nan ... nan nan\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
      "    created:  2022-02-19T19:18:13.601354\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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:13.631270Z",
     "iopub.status.busy": "2022-02-19T19:18:13.631270Z",
     "iopub.status.idle": "2022-02-19T19:18:13.821371Z",
     "shell.execute_reply": "2022-02-19T19:18:13.821371Z"
    }
   },
   "outputs": [
    {
     "data": {
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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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:13.850853Z",
     "iopub.status.busy": "2022-02-19T19:18:13.831253Z",
     "iopub.status.idle": "2022-02-19T19:18:13.961842Z",
     "shell.execute_reply": "2022-02-19T19:18:13.961842Z"
    }
   },
   "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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:13.981235Z",
     "iopub.status.busy": "2022-02-19T19:18:13.981235Z",
     "iopub.status.idle": "2022-02-19T19:18:14.121543Z",
     "shell.execute_reply": "2022-02-19T19:18:14.121543Z"
    }
   },
   "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 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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:14.121543Z",
     "iopub.status.busy": "2022-02-19T19:18:14.121543Z",
     "iopub.status.idle": "2022-02-19T19:18:16.781182Z",
     "shell.execute_reply": "2022-02-19T19:18:16.781182Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<xarray.Dataset>\n",
      "Dimensions:             (N: 1, P: 1, T: 110, X_AL: 1, vertex: 3, component: 2, internal_dof: 5)\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) int32 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 0.3637 0.6363 ... 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....\n",
      "    X                   (N, P, T, X_AL, vertex, component) float64 0.25 ... nan\n",
      "    Y                   (N, P, T, X_AL, vertex, internal_dof) float64 2.229e-...\n",
      "    Phase               (N, P, T, X_AL, vertex) <U7 'FCC_L12' 'FCC_L12' ... ''\n",
      "    degree_of_ordering  (N, P, T, X_AL, vertex) float64 1.0 6.377e-15 ... nan\n",
      "Attributes:\n",
      "    engine:   pycalphad 0.9.3.dev40+g1bf356e4.d20220219\n",
      "    created:  2022-02-19T19:18:16.761326\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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:16.811073Z",
     "iopub.status.busy": "2022-02-19T19:18:16.791567Z",
     "iopub.status.idle": "2022-02-19T19:18:17.031526Z",
     "shell.execute_reply": "2022-02-19T19:18:17.031526Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1ebe70d6dc0>"
      ]
     },
     "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": {
    "execution": {
     "iopub.execute_input": "2022-02-19T19:18:17.051366Z",
     "iopub.status.busy": "2022-02-19T19:18:17.041984Z",
     "iopub.status.idle": "2022-02-19T19:18:17.481394Z",
     "shell.execute_reply": "2022-02-19T19:18:17.481394Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\rotis\\AppData\\Local\\Temp/ipykernel_7844/3149017277.py:7: RuntimeWarning: invalid value encountered in greater\n",
      "  (eq_alni.degree_of_ordering.values > 0.01)))\n",
      "C:\\Users\\rotis\\AppData\\Local\\Temp/ipykernel_7844/3149017277.py:10: RuntimeWarning: invalid value encountered in less_equal\n",
      "  (eq_alni.degree_of_ordering.values <= 0.01)))\n"
     ]
    },
    {
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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()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.8.0"
  },
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "metadata": {
     "collapsed": false
    },
    "source": []
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}