{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cowley short range order parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [Cowley short range order parameter](https://doi.org/10.1103/PhysRev.77.669) can be used to find if an alloy is ordered or not. The order parameter is given by,\n",
    "\n",
    "$$\n",
    "\\alpha_i = 1 - \\frac{n_i}{m_A c_i}\n",
    "$$\n",
    "\n",
    "where $n_i$ is the number of atoms of the non reference type among the $c_i$ atoms\n",
    "in the $i$th shell. $m_A$ is the concentration of the non reference atom."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can start by importing the necessary modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pyscal as pc\n",
    "import pyscal.crystal_structures as pcs\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We need a binary alloy structure to calculate the order parameter. We will use the crystal structures modules to do this. Here, we will create a L12 structure."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "atoms, box = pcs.make_crystal('l12', lattice_constant=4.00, repetitions=[2,2,2])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In order to use the order parameter, we need to have two shells of neighbors around the atom. In order to get two shells of neighbors, we will first estimate a cutoff using the radial distribution function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys = pc.System()\n",
    "sys.box = box\n",
    "sys.atoms = atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "val, dist = sys.calculate_rdf()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can plot the rdf,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.0, 5.0)"
      ]
     },
     "execution_count": 7,
     "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": [
    "plt.plot(dist, val)\n",
    "plt.xlabel(r\"distance $\\AA$\")\n",
    "plt.ylabel(r\"$g(r)$\")\n",
    "plt.xlim(0, 5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this case, a cutoff of about 4.5 will make sure that two shells are included. Now the neighbors are calculated using this cutoff."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "sys.find_neighbors(method='cutoff', cutoff=4.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we can calculate the short range order. We will use the reference type as 1 and also specify the average keyword as True. This will allow us to get an average value for the whole simulation box."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.33333333,  1.        ])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.calculate_sro(reference_type=1, average=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Value for individual atoms can be accessed by,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "atoms = sys.atoms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.33333333333333326, 1.0]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "atoms[4].sro"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Only atoms of the non reference type will have this value!"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "pyscal-test",
   "language": "python",
   "name": "pyscal-test"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}