{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Domain wall - skyrmion conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import oommfc as oc\n",
    "import discretisedfield as df\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = 15e-12\n",
    "alpha = 0.3\n",
    "Ms = 5.8e5\n",
    "D = 3e-3\n",
    "K1 = 0.5e6\n",
    "u = (0, 0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "system = oc.System(name=\"conversion\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "system.hamiltonian = oc.Exchange(A=A) + oc.DMI(D=D, crystalclass=\"Cnv\") + oc.UniaxialAnisotropy(K1=K1, u=u)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "system.dynamics = oc.Precession(gamma=2.211e5) + oc.Damping(alpha=alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "p1 = (0, 0, 0)\n",
    "p2 = (150e-9, 50e-9, 2e-9)\n",
    "cell = (2e-9, 2e-9, 2e-9)\n",
    "mesh = oc.Mesh(p1=p1, p2=p2, cell=cell)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def Ms_fun(pos):\n",
    "    x, y, z = pos\n",
    "    if x < 50e-9 and (y < 15e-9 or y > 35e-9):\n",
    "        return 0\n",
    "    else:\n",
    "        return Ms\n",
    "    \n",
    "def m_init(pos):\n",
    "    x, y, z = pos\n",
    "    if 30e-9 < x < 40e-9:\n",
    "        return (0.1, 0.1, -1)\n",
    "    else:\n",
    "        return (0.1, 0.1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "system.m = df.Field(mesh, value=m_init, norm=Ms_fun)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "system.m.z.plot_plane(z=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018/3/26 16:33: Calling OOMMF (conversion/conversion.mif) ... [0.9s]\n"
     ]
    }
   ],
   "source": [
    "md = oc.MinDriver()\n",
    "md.drive(system)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "system.m.z.plot_plane(z=0);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "ux = 402.35  # velocity in x direction\n",
    "beta = 0.5  # non-adiabatic STT parameter\n",
    "\n",
    "system.dynamics += oc.STT(u=(ux, 0, 0), beta=beta)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2018/3/26 16:33: Calling OOMMF (conversion/conversion.mif) ... [3.7s]\n"
     ]
    }
   ],
   "source": [
    "td = oc.TimeDriver()\n",
    "td.drive(system, t=0.2e-9, n=200)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "system.m.z.plot_plane(z=0);"
   ]
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}