{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Defining micromagnetic system\n",
    "\n",
    "A micromagnetic system is the main entity of the micromagnetic model. It consists of three main components:\n",
    "- Energy equation\n",
    "- Dynamics equation\n",
    "- Magnetisation configuration\n",
    "\n",
    "In this tutorial, we will assemble a micromagnetic system, which can then be \"driven\" using different drivers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import micromagneticmodel as mm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Firstly, we create a the energy equation:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "energy = mm.Exchange(A=1e-11) + mm.Zeeman(H=(1e6, 0, 0))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we define the dynamics equation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "dynamics = mm.Precession(gamma0=mm.consts.gamma0) + mm.Damping(alpha=0.1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, magnetisation configuration must be defined as `discretisationfield.Field`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import discretisedfield as df\n",
    "\n",
    "p1 = (0, 0, 0)\n",
    "p2 = (10e-9, 10e-9, 10e-9)\n",
    "n = (5, 5, 5)\n",
    "Ms = 1e6\n",
    "mesh = df.Mesh(p1=p1, p2=p2, n=n)\n",
    "m = df.Field(mesh, nvdim=3, value=(0, 0, 1), norm=Ms)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Using these three parameters we can assemble the system object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "system = mm.System(energy=energy, dynamics=dynamics, m=m, name=\"mysystem\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now check some basics properties of the assembled system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "System(name='mysystem')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$- A \\mathbf{m} \\cdot \\nabla^{2} \\mathbf{m}-\\mu_{0}M_\\text{s} \\mathbf{m} \\cdot \\mathbf{H}$"
      ],
      "text/plain": [
       "Exchange(A=1e-11) + Zeeman(H=(1000000.0, 0, 0))"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.energy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$-\\frac{\\gamma_{0}}{1 + \\alpha^{2}} \\mathbf{m} \\times \\mathbf{H}_\\text{eff}-\\frac{\\gamma_{0} \\alpha}{1 + \\alpha^{2}} \\mathbf{m} \\times (\\mathbf{m} \\times \\mathbf{H}_\\text{eff})$"
      ],
      "text/plain": [
       "Precession(gamma0=221276.14872118403) + Damping(alpha=0.1)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system.dynamics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "system.m.sel(\"x\").mpl()"
   ]
  }
 ],
 "metadata": {
  "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": "3.10.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "4faa142ab2ce48799e61a693d182626f": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
     },
     "67947f150d7d4307b6b03ad39887a66e": {
      "buffers": [
       {
        "data": "",
        "encoding": "base64",
        "path": [
         "attribute",
         "data"
        ]
       },
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