{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "source": [
    "# Analysing simulation results\n",
    "\n",
    "With `micromagneticdata` we can analyse simulation results created with `oommfc`. This notebook summarises the available functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import micromagneticdata as md"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have a set of example simulations stored in the test directory of `micromagneticdata` that we use to demonstrate its functionality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "dirname = os.path.join(\"..\", \"micromagneticdata\", \"tests\", \"test_sample\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Data`\n",
    "First, we creata a `Data` object. We need to pass the name of the `micromagneticmodel.System` that we used to run the simulation and optionally an additional path to the base directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = md.Data(name=\"rectangle\", dirname=dirname)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Data` object contains all simulation runs of the `System`. These are called drives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>drive_number</th>\n",
       "      <th>date</th>\n",
       "      <th>time</th>\n",
       "      <th>start_time</th>\n",
       "      <th>adapter</th>\n",
       "      <th>adapter_version</th>\n",
       "      <th>driver</th>\n",
       "      <th>t</th>\n",
       "      <th>n</th>\n",
       "      <th>end_time</th>\n",
       "      <th>elapsed_time</th>\n",
       "      <th>success</th>\n",
       "      <th>info.json</th>\n",
       "      <th>output_step</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>2025-05-31T22:02:43</td>\n",
       "      <td>mumax3c</td>\n",
       "      <td>0.3.0</td>\n",
       "      <td>RelaxDriver</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2025-05-31T22:02:43</td>\n",
       "      <td>00:00:01</td>\n",
       "      <td>True</td>\n",
       "      <td>available</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2025-05-31</td>\n",
       "      <td>22:02:43</td>\n",
       "      <td>2025-05-31T22:02:43</td>\n",
       "      <td>mumax3c</td>\n",
       "      <td>0.3.0</td>\n",
       "      <td>MinDriver</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>&lt;NA&gt;</td>\n",
       "      <td>2025-05-31T22:02:43</td>\n",
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       "      <td>0.65.0</td>\n",
       "      <td>MinDriver</td>\n",
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       "      <td>&lt;NA&gt;</td>\n",
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       "      <td>00:00:01</td>\n",
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       "      <td>available</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   drive_number        date      time           start_time  adapter   \n",
       "0             0  2025-05-31  22:02:42  2025-05-31T22:02:42   oommfc  \\\n",
       "1             1  2025-05-31  22:02:42  2025-05-31T22:02:42  mumax3c   \n",
       "2             2  2025-05-31  22:02:42  2025-05-31T22:02:42  mumax3c   \n",
       "3             3  2025-05-31  22:02:43  2025-05-31T22:02:43  mumax3c   \n",
       "4             4  2025-05-31  22:02:43  2025-05-31T22:02:43  mumax3c   \n",
       "5             5  2025-05-31  22:02:43  2025-05-31T22:02:43   oommfc   \n",
       "6             6  2025-05-31  22:02:44  2025-05-31T22:02:44   oommfc   \n",
       "\n",
       "  adapter_version       driver     t     n             end_time elapsed_time   \n",
       "0          0.65.0   TimeDriver   0.0    25  2025-05-31T22:02:42     00:00:01  \\\n",
       "1           0.3.0   TimeDriver   0.0    15  2025-05-31T22:02:42     00:00:01   \n",
       "2           0.3.0   TimeDriver   0.0   250  2025-05-31T22:02:43     00:00:01   \n",
       "3           0.3.0  RelaxDriver  <NA>  <NA>  2025-05-31T22:02:43     00:00:01   \n",
       "4           0.3.0    MinDriver  <NA>  <NA>  2025-05-31T22:02:43     00:00:01   \n",
       "5          0.65.0   TimeDriver   0.0     5  2025-05-31T22:02:44     00:00:01   \n",
       "6          0.65.0    MinDriver  <NA>  <NA>  2025-05-31T22:02:44     00:00:01   \n",
       "\n",
       "   success  info.json output_step  \n",
       "0     True  available        <NA>  \n",
       "1     True  available        <NA>  \n",
       "2     True  available        <NA>  \n",
       "3     True  available        <NA>  \n",
       "4     True  available        <NA>  \n",
       "5     True  available        <NA>  \n",
       "6     True  available        True  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `Drive`\n",
    "To access one `Drive` we can index the `Data` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive = data[6]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `Drive` object has a number of properties containing information about the drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'drive_number': 6,\n",
       " 'date': '2025-05-31',\n",
       " 'time': '22:02:44',\n",
       " 'start_time': '2025-05-31T22:02:44',\n",
       " 'adapter': 'oommfc',\n",
       " 'adapter_version': '0.65.0',\n",
       " 'driver': 'MinDriver',\n",
       " 'output_step': True,\n",
       " 'end_time': '2025-05-31T22:02:44',\n",
       " 'elapsed_time': '00:00:01',\n",
       " 'success': True}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "drive = data[6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'iteration'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'drive_number': 6,\n",
       " 'date': '2025-05-31',\n",
       " 'time': '22:02:44',\n",
       " 'start_time': '2025-05-31T22:02:44',\n",
       " 'adapter': 'oommfc',\n",
       " 'adapter_version': '0.65.0',\n",
       " 'driver': 'MinDriver',\n",
       " 'output_step': True,\n",
       " 'end_time': '2025-05-31T22:02:44',\n",
       " 'elapsed_time': '00:00:01',\n",
       " 'success': True}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# MIF 2.2\n",
      "\n",
      "SetOptions {\n",
      "  basename rectangle\n",
      "  scalar_output_format %.12g\n",
      "  scalar_field_output_format {binary 8}\n",
      "  vector_field_output_format {binary 8}\n",
      "}\n",
      "\n",
      "# BoxAtlas for total_atlas\n",
      "Specify Oxs_BoxAtlas:total_atlas {\n",
      "  xrange { -5e-08 5e-08 }\n",
      "  yrange { -2.5e-08 2.5e-08 }\n",
      "  zrange { 0.0 2e-08 }\n",
      "  name total\n",
      "}\n",
      "\n",
      "# BoxAtlas for entire_atlas\n",
      "Specify Oxs_BoxAtlas:entire_atlas {\n",
      "  xrange { -5e-08 5e-08 }\n",
      "  yrange { -2.5e-08 2.5e-08 }\n",
      "  zrange { 0.0 2e-08 }\n",
      "  name entire\n",
      "}\n",
      "\n",
      "# MultiAtlas\n",
      "Specify Oxs_MultiAtlas:main_atlas {\n",
      "  atlas :total_atlas\n",
      "  atlas :entire_atlas\n",
      "  xrange { -5e-08 5e-08 }\n",
      "  yrange { -2.5e-08 2.5e-08 }\n",
      "  zrange { 0.0 2e-08 }\n",
      "}\n",
      "\n",
      "# RectangularMesh\n",
      "Specify Oxs_RectangularMesh:mesh {\n",
      "  cellsize { 5e-09 5e-09 5e-09 }\n",
      "  atlas :main_atlas\n",
      "}\n",
      "\n",
      "# UniformExchange\n",
      "Specify Oxs_UniformExchange:exchange {\n",
      "  A 1.3e-11\n",
      "}\n",
      "\n",
      "# FixedZeeman\n",
      "Specify Oxs_FixedZeeman:zeeman {\n",
      "  field {0.0 0.0 1000000.0}\n",
      "}\n",
      "\n",
      "# m0 file\n",
      "Specify Oxs_FileVectorField:m0 {\n",
      "  file m0.omf\n",
      "  atlas :main_atlas\n",
      "}\n",
      "\n",
      "# m0_norm\n",
      "Specify Oxs_VecMagScalarField:m0_norm {\n",
      "    field :m0\n",
      "}\n",
      "\n",
      "# CGEvolver\n",
      "Specify Oxs_CGEvolve:evolver {\n",
      "}\n",
      "\n",
      "# MinDriver\n",
      "Specify Oxs_MinDriver {\n",
      "  evolver :evolver\n",
      "  mesh :mesh\n",
      "  Ms :m0_norm\n",
      "  m0 :m0\n",
      "  stopping_mxHxm 0.1\n",
      "}\n",
      "\n",
      "Destination table mmArchive\n",
      "Destination mags mmArchive\n",
      "\n",
      "Schedule DataTable table Step 1\n",
      "Schedule Oxs_MinDriver::Magnetization mags Step 1\n"
     ]
    }
   ],
   "source": [
    "print(drive.calculator_script)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The initial magnetisation can be obtained with `m0`. It returns a `discretisedfield.Field` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>Field</strong>\n",
       "<ul>\n",
       "  \n",
       "  <li><strong>Mesh</strong>\n",
       "<ul>\n",
       "  <li><strong>Region</strong>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "  <li>n = [20, 10, 4]</li>\n",
       "  <li>subregions:\n",
       "    <ul>\n",
       "      <li><strong>Region</strong> <i>total</i>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "      </ul></li>\n",
       "  </ul></li>\n",
       "  <li>nvdim = 3</li>\n",
       "  <li>vdims:\n",
       "      <ul><li>x</li>\n",
       "        <li>y</li>\n",
       "        <li>z</li>\n",
       "        </ul>\n",
       "    </li>\n",
       "  <li>unit = None</li>\n",
       "  </ul>"
      ],
      "text/plain": [
       "Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=None)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.m0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Scalar data of the drive can be accessed via the `table` property that returns a `ubermagtable.Table` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "drive.table.mpl(y=[\"mx\", \"my\", \"mz\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can access the magnetisation at a single step or iterate over the drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>Field</strong>\n",
       "<ul>\n",
       "  \n",
       "  <li><strong>Mesh</strong>\n",
       "<ul>\n",
       "  <li><strong>Region</strong>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "  <li>n = [20, 10, 4]</li>\n",
       "  <li>subregions:\n",
       "    <ul>\n",
       "      <li><strong>Region</strong> <i>total</i>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "      </ul></li>\n",
       "  </ul></li>\n",
       "  <li>nvdim = 3</li>\n",
       "  <li>vdims:\n",
       "      <ul><li>x</li>\n",
       "        <li>y</li>\n",
       "        <li>z</li>\n",
       "        </ul>\n",
       "    </li>\n",
       "  <li>unit = A/m</li>\n",
       "  </ul>"
      ],
      "text/plain": [
       "Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m),\n",
       " Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m)]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(drive)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select a parts of the data and get a new drive, e.g. every second of the first 10 elements."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "OOMMFDrive(name='rectangle', number=6, dirname='../micromagneticdata/tests/test_sample', x='iteration')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_selection = drive[:10:2]\n",
    "drive_selection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive_selection.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "It is possible to convert all magnetisation files into vtk for visualisation e.g. in Paraview."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# drive.ovf2vtk()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can convert a drive into an `xarray.DataArray`. An additional dimension is added depending on the type of drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
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       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;field&#x27; (iteration: 14, x: 20, y: 10, z: 4, vdims: 3)&gt;\n",
       "array([[[[[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "...\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]]]]])\n",
       "Coordinates:\n",
       "  * x          (x) float64 -4.75e-08 -4.25e-08 -3.75e-08 ... 4.25e-08 4.75e-08\n",
       "  * y          (y) float64 -2.25e-08 -1.75e-08 -1.25e-08 ... 1.75e-08 2.25e-08\n",
       "  * z          (z) float64 2.5e-09 7.5e-09 1.25e-08 1.75e-08\n",
       "  * vdims      (vdims) &lt;U1 &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
       "  * iteration  (iteration) float64 0.0 1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0 13.0\n",
       "Attributes:\n",
       "    drive_number:     6\n",
       "    date:             2025-05-31\n",
       "    time:             22:02:44\n",
       "    start_time:       2025-05-31T22:02:44\n",
       "    adapter:          oommfc\n",
       "    adapter_version:  0.65.0\n",
       "    driver:           MinDriver\n",
       "    output_step:      True\n",
       "    end_time:         2025-05-31T22:02:44\n",
       "    elapsed_time:     00:00:01\n",
       "    success:          True</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'field'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>iteration</span>: 14</li><li><span class='xr-has-index'>x</span>: 20</li><li><span class='xr-has-index'>y</span>: 10</li><li><span class='xr-has-index'>z</span>: 4</li><li><span class='xr-has-index'>vdims</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-d7fe6f85-5d8f-461f-b54a-e4a6ed02aded' class='xr-array-in' type='checkbox' checked><label for='section-d7fe6f85-5d8f-461f-b54a-e4a6ed02aded' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>3.501e+05 6.98e+05 1.738e+05 3.501e+05 ... -3.416e-06 -6.811e-06 8e+05</span></div><div class='xr-array-data'><pre>array([[[[[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "...\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-e99cb43d-c6b7-417b-a2aa-f63fd8cee88a' class='xr-section-summary-in' type='checkbox'  checked><label for='section-e99cb43d-c6b7-417b-a2aa-f63fd8cee88a' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.75e-08 -4.25e-08 ... 4.75e-08</div><input id='attrs-0bc9229f-088f-4089-99fc-feda78b626eb' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-0bc9229f-088f-4089-99fc-feda78b626eb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0462b26e-a6ad-49fe-9f86-25fb3070dd09' class='xr-var-data-in' type='checkbox'><label for='data-0462b26e-a6ad-49fe-9f86-25fb3070dd09' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-4.75e-08, -4.25e-08, -3.75e-08, -3.25e-08, -2.75e-08, -2.25e-08,\n",
       "       -1.75e-08, -1.25e-08, -7.50e-09, -2.50e-09,  2.50e-09,  7.50e-09,\n",
       "        1.25e-08,  1.75e-08,  2.25e-08,  2.75e-08,  3.25e-08,  3.75e-08,\n",
       "        4.25e-08,  4.75e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.25e-08 -1.75e-08 ... 2.25e-08</div><input id='attrs-1b8397ad-4654-4aec-be2a-c796bd75e6ec' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-1b8397ad-4654-4aec-be2a-c796bd75e6ec' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-3956098e-e34c-40be-87db-f93c10c512f3' class='xr-var-data-in' type='checkbox'><label for='data-3956098e-e34c-40be-87db-f93c10c512f3' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2.25e-08, -1.75e-08, -1.25e-08, -7.50e-09, -2.50e-09,  2.50e-09,\n",
       "        7.50e-09,  1.25e-08,  1.75e-08,  2.25e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5e-09 7.5e-09 1.25e-08 1.75e-08</div><input id='attrs-ed201716-ac09-47ce-a10c-816a2c60f500' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-ed201716-ac09-47ce-a10c-816a2c60f500' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1d852f66-9fa6-4298-ab61-c263b5cb5f4e' class='xr-var-data-in' type='checkbox'><label for='data-1d852f66-9fa6-4298-ab61-c263b5cb5f4e' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.50e-09, 7.50e-09, 1.25e-08, 1.75e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vdims</span></div><div class='xr-var-dims'>(vdims)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-aea23ea2-a6c5-49b1-a301-5cfb1d1ca1a9' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-aea23ea2-a6c5-49b1-a301-5cfb1d1ca1a9' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e28d903-2e05-4b7b-9804-db597064cb00' class='xr-var-data-in' type='checkbox'><label for='data-6e28d903-2e05-4b7b-9804-db597064cb00' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>iteration</span></div><div class='xr-var-dims'>(iteration)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>0.0 1.0 2.0 3.0 ... 11.0 12.0 13.0</div><input id='attrs-4ab391c8-297a-4e3c-9bbf-713f6d12b9db' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-4ab391c8-297a-4e3c-9bbf-713f6d12b9db' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8b7b6e01-83b8-4624-8199-e89681f21a91' class='xr-var-data-in' type='checkbox'><label for='data-8b7b6e01-83b8-4624-8199-e89681f21a91' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd></dd></dl></div><div class='xr-var-data'><pre>array([ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11., 12., 13.])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-b824e365-f63e-4b0e-a034-d44bdfbc2ad2' class='xr-section-summary-in' type='checkbox'  ><label for='section-b824e365-f63e-4b0e-a034-d44bdfbc2ad2' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-298a39fd-6abd-46ea-8e63-3f6231581b9f' class='xr-index-data-in' type='checkbox'/><label for='index-298a39fd-6abd-46ea-8e63-3f6231581b9f' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-4.7499999999999995e-08,               -4.25e-08,\n",
       "                     -3.75e-08,  -3.249999999999999e-08,\n",
       "       -2.7499999999999998e-08,               -2.25e-08,\n",
       "       -1.7499999999999998e-08,               -1.25e-08,\n",
       "        -7.500000000000001e-09, -2.5000000000000025e-09,\n",
       "         2.499999999999996e-09,   7.499999999999994e-09,\n",
       "                      1.25e-08,   1.749999999999999e-08,\n",
       "        2.2499999999999996e-08,  2.7499999999999988e-08,\n",
       "         3.249999999999999e-08,                3.75e-08,\n",
       "         4.249999999999999e-08,  4.7499999999999995e-08],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-fde7e3d4-5a37-4c2a-98d1-4e140d23f3a7' class='xr-index-data-in' type='checkbox'/><label for='index-fde7e3d4-5a37-4c2a-98d1-4e140d23f3a7' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -2.25e-08, -1.7499999999999998e-08,\n",
       "                     -1.25e-08,  -7.499999999999998e-09,\n",
       "       -2.4999999999999992e-09,  2.4999999999999992e-09,\n",
       "         7.500000000000004e-09,  1.2500000000000003e-08,\n",
       "                      1.75e-08,                2.25e-08],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-34704351-843a-4b5d-a399-e8f8a3a2340b' class='xr-index-data-in' type='checkbox'/><label for='index-34704351-843a-4b5d-a399-e8f8a3a2340b' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.5e-09, 7.500000000000001e-09, 1.2500000000000003e-08, 1.75e-08], dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vdims</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-8cb943e3-1708-484a-9fc4-6069524ae8ed' class='xr-index-data-in' type='checkbox'/><label for='index-8cb943e3-1708-484a-9fc4-6069524ae8ed' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;vdims&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>iteration</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-2faf3176-dc2e-47cc-985d-e9a09f124a23' class='xr-index-data-in' type='checkbox'/><label for='index-2faf3176-dc2e-47cc-985d-e9a09f124a23' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([0.0, 1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0,\n",
       "       13.0],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;iteration&#x27;))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-3d28ca78-1668-4d3b-9417-d12ddf91aa5b' class='xr-section-summary-in' type='checkbox'  ><label for='section-3d28ca78-1668-4d3b-9417-d12ddf91aa5b' class='xr-section-summary' >Attributes: <span>(11)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>drive_number :</span></dt><dd>6</dd><dt><span>date :</span></dt><dd>2025-05-31</dd><dt><span>time :</span></dt><dd>22:02:44</dd><dt><span>start_time :</span></dt><dd>2025-05-31T22:02:44</dd><dt><span>adapter :</span></dt><dd>oommfc</dd><dt><span>adapter_version :</span></dt><dd>0.65.0</dd><dt><span>driver :</span></dt><dd>MinDriver</dd><dt><span>output_step :</span></dt><dd>True</dd><dt><span>end_time :</span></dt><dd>2025-05-31T22:02:44</dd><dt><span>elapsed_time :</span></dt><dd>00:00:01</dd><dt><span>success :</span></dt><dd>True</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'field' (iteration: 14, x: 20, y: 10, z: 4, vdims: 3)>\n",
       "array([[[[[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "          [ 3.50067876e+05,  6.98022376e+05,  1.73831082e+05],\n",
       "...\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]],\n",
       "\n",
       "         [[-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05],\n",
       "          [-3.41568167e-06, -6.81074074e-06,  8.00000000e+05]]]]])\n",
       "Coordinates:\n",
       "  * x          (x) float64 -4.75e-08 -4.25e-08 -3.75e-08 ... 4.25e-08 4.75e-08\n",
       "  * y          (y) float64 -2.25e-08 -1.75e-08 -1.25e-08 ... 1.75e-08 2.25e-08\n",
       "  * z          (z) float64 2.5e-09 7.5e-09 1.25e-08 1.75e-08\n",
       "  * vdims      (vdims) <U1 'x' 'y' 'z'\n",
       "  * iteration  (iteration) float64 0.0 1.0 2.0 3.0 4.0 ... 10.0 11.0 12.0 13.0\n",
       "Attributes:\n",
       "    drive_number:     6\n",
       "    date:             2025-05-31\n",
       "    time:             22:02:44\n",
       "    start_time:       2025-05-31T22:02:44\n",
       "    adapter:          oommfc\n",
       "    adapter_version:  0.65.0\n",
       "    driver:           MinDriver\n",
       "    output_step:      True\n",
       "    end_time:         2025-05-31T22:02:44\n",
       "    elapsed_time:     00:00:01\n",
       "    success:          True"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.to_xarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## `CombinedDrive`\n",
    "\n",
    "Multiple drives can be concatenated to create one \"longer\" drive. This can e.g. be convenient to analyse multiple consecutive time drives. Combining drives is done via the `<<` operator which appends the right-hand-side drive to the left-hand-side drive. It returns a new `CombinedDrive` object that behaves very similar to the `Drive` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CombinedDrive(\n",
       "  OOMMFDrive(name='rectangle', number=0, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       "  Mumax3Drive(name='rectangle', number=1, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       "  Mumax3Drive(name='rectangle', number=2, dirname='../micromagneticdata/tests/test_sample', x='t')\n",
       ")"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined = data[0] << data[1] << data[2]\n",
    "combined"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combined drive has a property `drives` that provides a list of all individual drives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(OOMMFDrive(name='rectangle', number=0, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       " Mumax3Drive(name='rectangle', number=1, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       " Mumax3Drive(name='rectangle', number=2, dirname='../micromagneticdata/tests/test_sample', x='t'))"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.drives"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'drive_numbers': [0, 1, 2], 'driver': 'TimeDriver'}"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.info"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We have direct access to the initial magnetisation of the first drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>Field</strong>\n",
       "<ul>\n",
       "  \n",
       "  <li><strong>Mesh</strong>\n",
       "<ul>\n",
       "  <li><strong>Region</strong>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "  <li>n = [20, 10, 4]</li>\n",
       "  <li>subregions:\n",
       "    <ul>\n",
       "      <li><strong>Region</strong> <i>total</i>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "      </ul></li>\n",
       "  </ul></li>\n",
       "  <li>nvdim = 3</li>\n",
       "  <li>vdims:\n",
       "      <ul><li>x</li>\n",
       "        <li>y</li>\n",
       "        <li>z</li>\n",
       "        </ul>\n",
       "    </li>\n",
       "  <li>unit = None</li>\n",
       "  </ul>"
      ],
      "text/plain": [
       "Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=None)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.m0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "290"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combined drive has one large table that contains the data for all individual drives."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 800x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "combined.table.mpl(y=[\"mx\", \"my\", \"mz\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can iterate over the drives in the combined drive or access a single element."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>Field</strong>\n",
       "<ul>\n",
       "  \n",
       "  <li><strong>Mesh</strong>\n",
       "<ul>\n",
       "  <li><strong>Region</strong>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "  <li>n = [20, 10, 4]</li>\n",
       "  <li>subregions:\n",
       "    <ul>\n",
       "      <li><strong>Region</strong> <i>total</i>\n",
       "<ul>\n",
       "  <li>pmin = [-5e-08, -2.5e-08, 0.0]</li>\n",
       "  <li>pmax = [5e-08, 2.5e-08, 2e-08]</li>\n",
       "  <li>dims = ['x', 'y', 'z']</li>\n",
       "  <li>units = ['m', 'm', 'm']</li>\n",
       "</ul></li>\n",
       "      </ul></li>\n",
       "  </ul></li>\n",
       "  <li>nvdim = 3</li>\n",
       "  <li>vdims:\n",
       "      <ul><li>x</li>\n",
       "        <li>y</li>\n",
       "        <li>z</li>\n",
       "        </ul>\n",
       "    </li>\n",
       "  <li>unit = A/m</li>\n",
       "  </ul>"
      ],
      "text/plain": [
       "Field(Mesh(Region(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']), n=[20, 10, 4], subregions: (Region`total`(pmin=[-5e-08, -2.5e-08, 0.0], pmax=[5e-08, 2.5e-08, 2e-08], dims=['x', 'y', 'z'], units=['m', 'm', 'm']))), nvdim=3, vdims: (x, y, z), unit=A/m)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined[15]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'t'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.x"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The combined drive can be converted into an `xarray.DataArray` similar to the normal `Drive`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
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       "\n",
       ".xr-section-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-section-item input {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-item input + label {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label {\n",
       "  cursor: pointer;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-item input:enabled + label:hover {\n",
       "  color: var(--xr-font-color0);\n",
       "}\n",
       "\n",
       ".xr-section-summary {\n",
       "  grid-column: 1;\n",
       "  color: var(--xr-font-color2);\n",
       "  font-weight: 500;\n",
       "}\n",
       "\n",
       ".xr-section-summary > span {\n",
       "  display: inline-block;\n",
       "  padding-left: 0.5em;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label {\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in + label:before {\n",
       "  display: inline-block;\n",
       "  content: '►';\n",
       "  font-size: 11px;\n",
       "  width: 15px;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:disabled + label:before {\n",
       "  color: var(--xr-disabled-color);\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label:before {\n",
       "  content: '▼';\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked + label > span {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-section-summary,\n",
       ".xr-section-inline-details {\n",
       "  padding-top: 4px;\n",
       "  padding-bottom: 4px;\n",
       "}\n",
       "\n",
       ".xr-section-inline-details {\n",
       "  grid-column: 2 / -1;\n",
       "}\n",
       "\n",
       ".xr-section-details {\n",
       "  display: none;\n",
       "  grid-column: 1 / -1;\n",
       "  margin-bottom: 5px;\n",
       "}\n",
       "\n",
       ".xr-section-summary-in:checked ~ .xr-section-details {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-array-wrap {\n",
       "  grid-column: 1 / -1;\n",
       "  display: grid;\n",
       "  grid-template-columns: 20px auto;\n",
       "}\n",
       "\n",
       ".xr-array-wrap > label {\n",
       "  grid-column: 1;\n",
       "  vertical-align: top;\n",
       "}\n",
       "\n",
       ".xr-preview {\n",
       "  color: var(--xr-font-color3);\n",
       "}\n",
       "\n",
       ".xr-array-preview,\n",
       ".xr-array-data {\n",
       "  padding: 0 5px !important;\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-array-data,\n",
       ".xr-array-in:checked ~ .xr-array-preview {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       ".xr-array-in:checked ~ .xr-array-data,\n",
       ".xr-array-preview {\n",
       "  display: inline-block;\n",
       "}\n",
       "\n",
       ".xr-dim-list {\n",
       "  display: inline-block !important;\n",
       "  list-style: none;\n",
       "  padding: 0 !important;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list li {\n",
       "  display: inline-block;\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "}\n",
       "\n",
       ".xr-dim-list:before {\n",
       "  content: '(';\n",
       "}\n",
       "\n",
       ".xr-dim-list:after {\n",
       "  content: ')';\n",
       "}\n",
       "\n",
       ".xr-dim-list li:not(:last-child):after {\n",
       "  content: ',';\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-has-index {\n",
       "  font-weight: bold;\n",
       "}\n",
       "\n",
       ".xr-var-list,\n",
       ".xr-var-item {\n",
       "  display: contents;\n",
       "}\n",
       "\n",
       ".xr-var-item > div,\n",
       ".xr-var-item label,\n",
       ".xr-var-item > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-even);\n",
       "  margin-bottom: 0;\n",
       "}\n",
       "\n",
       ".xr-var-item > .xr-var-name:hover span {\n",
       "  padding-right: 5px;\n",
       "}\n",
       "\n",
       ".xr-var-list > li:nth-child(odd) > div,\n",
       ".xr-var-list > li:nth-child(odd) > label,\n",
       ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
       "  background-color: var(--xr-background-color-row-odd);\n",
       "}\n",
       "\n",
       ".xr-var-name {\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-var-dims {\n",
       "  grid-column: 2;\n",
       "}\n",
       "\n",
       ".xr-var-dtype {\n",
       "  grid-column: 3;\n",
       "  text-align: right;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-preview {\n",
       "  grid-column: 4;\n",
       "}\n",
       "\n",
       ".xr-index-preview {\n",
       "  grid-column: 2 / 5;\n",
       "  color: var(--xr-font-color2);\n",
       "}\n",
       "\n",
       ".xr-var-name,\n",
       ".xr-var-dims,\n",
       ".xr-var-dtype,\n",
       ".xr-preview,\n",
       ".xr-attrs dt {\n",
       "  white-space: nowrap;\n",
       "  overflow: hidden;\n",
       "  text-overflow: ellipsis;\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-var-name:hover,\n",
       ".xr-var-dims:hover,\n",
       ".xr-var-dtype:hover,\n",
       ".xr-attrs dt:hover {\n",
       "  overflow: visible;\n",
       "  width: auto;\n",
       "  z-index: 1;\n",
       "}\n",
       "\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  display: none;\n",
       "  background-color: var(--xr-background-color) !important;\n",
       "  padding-bottom: 5px !important;\n",
       "}\n",
       "\n",
       ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
       ".xr-var-data-in:checked ~ .xr-var-data,\n",
       ".xr-index-data-in:checked ~ .xr-index-data {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       ".xr-var-data > table {\n",
       "  float: right;\n",
       "}\n",
       "\n",
       ".xr-var-name span,\n",
       ".xr-var-data,\n",
       ".xr-index-name div,\n",
       ".xr-index-data,\n",
       ".xr-attrs {\n",
       "  padding-left: 25px !important;\n",
       "}\n",
       "\n",
       ".xr-attrs,\n",
       ".xr-var-attrs,\n",
       ".xr-var-data,\n",
       ".xr-index-data {\n",
       "  grid-column: 1 / -1;\n",
       "}\n",
       "\n",
       "dl.xr-attrs {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  display: grid;\n",
       "  grid-template-columns: 125px auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt,\n",
       ".xr-attrs dd {\n",
       "  padding: 0;\n",
       "  margin: 0;\n",
       "  float: left;\n",
       "  padding-right: 10px;\n",
       "  width: auto;\n",
       "}\n",
       "\n",
       ".xr-attrs dt {\n",
       "  font-weight: normal;\n",
       "  grid-column: 1;\n",
       "}\n",
       "\n",
       ".xr-attrs dt:hover span {\n",
       "  display: inline-block;\n",
       "  background: var(--xr-background-color);\n",
       "  padding-right: 10px;\n",
       "}\n",
       "\n",
       ".xr-attrs dd {\n",
       "  grid-column: 2;\n",
       "  white-space: pre-wrap;\n",
       "  word-break: break-all;\n",
       "}\n",
       "\n",
       ".xr-icon-database,\n",
       ".xr-icon-file-text2,\n",
       ".xr-no-icon {\n",
       "  display: inline-block;\n",
       "  vertical-align: middle;\n",
       "  width: 1em;\n",
       "  height: 1.5em !important;\n",
       "  stroke-width: 0;\n",
       "  stroke: currentColor;\n",
       "  fill: currentColor;\n",
       "}\n",
       "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;field&#x27; (t: 290, x: 20, y: 10, z: 4, vdims: 3)&gt;\n",
       "array([[[[[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "...\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]]]]])\n",
       "Coordinates:\n",
       "  * x        (x) float64 -4.75e-08 -4.25e-08 -3.75e-08 ... 4.25e-08 4.75e-08\n",
       "  * y        (y) float64 -2.25e-08 -1.75e-08 -1.25e-08 ... 1.75e-08 2.25e-08\n",
       "  * z        (z) float64 2.5e-09 7.5e-09 1.25e-08 1.75e-08\n",
       "  * vdims    (vdims) &lt;U1 &#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;\n",
       "  * t        (t) float64 1e-12 2e-12 3e-12 ... 5.423e-10 5.445e-10 5.466e-10\n",
       "Attributes:\n",
       "    drive_numbers:  [0, 1, 2]\n",
       "    driver:         TimeDriver</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'field'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>t</span>: 290</li><li><span class='xr-has-index'>x</span>: 20</li><li><span class='xr-has-index'>y</span>: 10</li><li><span class='xr-has-index'>z</span>: 4</li><li><span class='xr-has-index'>vdims</span>: 3</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-62dd2896-2ad4-4e08-ba2e-2d7650ee0aaf' class='xr-array-in' type='checkbox' checked><label for='section-62dd2896-2ad4-4e08-ba2e-2d7650ee0aaf' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>-2.758e+04 6.586e+05 4.533e+05 ... 7.972e+05 -5.132e+04 4.264e+04</span></div><div class='xr-array-data'><pre>array([[[[[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "...\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]]]]])</pre></div></div></li><li class='xr-section-item'><input id='section-89a66f2c-87b0-4e5b-b60e-7433d96e0875' class='xr-section-summary-in' type='checkbox'  checked><label for='section-89a66f2c-87b0-4e5b-b60e-7433d96e0875' class='xr-section-summary' >Coordinates: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>x</span></div><div class='xr-var-dims'>(x)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-4.75e-08 -4.25e-08 ... 4.75e-08</div><input id='attrs-3a76bffc-8029-474e-98a2-10c1d00c4334' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-3a76bffc-8029-474e-98a2-10c1d00c4334' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-da860d65-bc07-4838-bc63-0b333fa04f1a' class='xr-var-data-in' type='checkbox'><label for='data-da860d65-bc07-4838-bc63-0b333fa04f1a' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-4.75e-08, -4.25e-08, -3.75e-08, -3.25e-08, -2.75e-08, -2.25e-08,\n",
       "       -1.75e-08, -1.25e-08, -7.50e-09, -2.50e-09,  2.50e-09,  7.50e-09,\n",
       "        1.25e-08,  1.75e-08,  2.25e-08,  2.75e-08,  3.25e-08,  3.75e-08,\n",
       "        4.25e-08,  4.75e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>y</span></div><div class='xr-var-dims'>(y)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>-2.25e-08 -1.75e-08 ... 2.25e-08</div><input id='attrs-8cb85a55-c711-4e5e-98e8-f322a6498ace' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-8cb85a55-c711-4e5e-98e8-f322a6498ace' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-20a3c47e-33be-429f-a4b7-04332dea9c98' class='xr-var-data-in' type='checkbox'><label for='data-20a3c47e-33be-429f-a4b7-04332dea9c98' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([-2.25e-08, -1.75e-08, -1.25e-08, -7.50e-09, -2.50e-09,  2.50e-09,\n",
       "        7.50e-09,  1.25e-08,  1.75e-08,  2.25e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>z</span></div><div class='xr-var-dims'>(z)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>2.5e-09 7.5e-09 1.25e-08 1.75e-08</div><input id='attrs-6311357d-44df-4bed-91ce-c847679a4474' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-6311357d-44df-4bed-91ce-c847679a4474' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-80ecc7f0-cfc5-4e93-981d-513f555d48b8' class='xr-var-data-in' type='checkbox'><label for='data-80ecc7f0-cfc5-4e93-981d-513f555d48b8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>m</dd></dl></div><div class='xr-var-data'><pre>array([2.50e-09, 7.50e-09, 1.25e-08, 1.75e-08])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>vdims</span></div><div class='xr-var-dims'>(vdims)</div><div class='xr-var-dtype'>&lt;U1</div><div class='xr-var-preview xr-preview'>&#x27;x&#x27; &#x27;y&#x27; &#x27;z&#x27;</div><input id='attrs-ba3e0dbe-9b6c-4498-8c07-6010e5697380' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ba3e0dbe-9b6c-4498-8c07-6010e5697380' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-de2d0a95-b19d-488e-b82d-3d7c125cffc8' class='xr-var-data-in' type='checkbox'><label for='data-de2d0a95-b19d-488e-b82d-3d7c125cffc8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;&lt;U1&#x27;)</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>t</span></div><div class='xr-var-dims'>(t)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>1e-12 2e-12 ... 5.445e-10 5.466e-10</div><input id='attrs-37ba6e5e-e564-4f50-b7d0-48bdd54f6aae' class='xr-var-attrs-in' type='checkbox' ><label for='attrs-37ba6e5e-e564-4f50-b7d0-48bdd54f6aae' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1a13f0d8-5842-44b5-8442-036b4efde9b1' class='xr-var-data-in' type='checkbox'><label for='data-1a13f0d8-5842-44b5-8442-036b4efde9b1' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'><dt><span>units :</span></dt><dd>s</dd></dl></div><div class='xr-var-data'><pre>array([1.000000e-12, 2.000000e-12, 3.000000e-12, ..., 5.423237e-10,\n",
       "       5.444648e-10, 5.466095e-10])</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-04664a53-52f9-448d-9f45-4d8986733a77' class='xr-section-summary-in' type='checkbox'  ><label for='section-04664a53-52f9-448d-9f45-4d8986733a77' class='xr-section-summary' >Indexes: <span>(5)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>x</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-38550bc6-054c-44de-aa3c-0234574fb5a2' class='xr-index-data-in' type='checkbox'/><label for='index-38550bc6-054c-44de-aa3c-0234574fb5a2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([-4.7499999999999995e-08,               -4.25e-08,\n",
       "                     -3.75e-08,  -3.249999999999999e-08,\n",
       "       -2.7499999999999998e-08,               -2.25e-08,\n",
       "       -1.7499999999999998e-08,               -1.25e-08,\n",
       "        -7.500000000000001e-09, -2.5000000000000025e-09,\n",
       "         2.499999999999996e-09,   7.499999999999994e-09,\n",
       "                      1.25e-08,   1.749999999999999e-08,\n",
       "        2.2499999999999996e-08,  2.7499999999999988e-08,\n",
       "         3.249999999999999e-08,                3.75e-08,\n",
       "         4.249999999999999e-08,  4.7499999999999995e-08],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;x&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>y</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-4c00592c-681f-4062-84aa-aeecbd133ed2' class='xr-index-data-in' type='checkbox'/><label for='index-4c00592c-681f-4062-84aa-aeecbd133ed2' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([              -2.25e-08, -1.7499999999999998e-08,\n",
       "                     -1.25e-08,  -7.499999999999998e-09,\n",
       "       -2.4999999999999992e-09,  2.4999999999999992e-09,\n",
       "         7.500000000000004e-09,  1.2500000000000003e-08,\n",
       "                      1.75e-08,                2.25e-08],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;y&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>z</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-a1428d5a-08d5-4006-8d51-dcaade4ac64a' class='xr-index-data-in' type='checkbox'/><label for='index-a1428d5a-08d5-4006-8d51-dcaade4ac64a' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([2.5e-09, 7.500000000000001e-09, 1.2500000000000003e-08, 1.75e-08], dtype=&#x27;float64&#x27;, name=&#x27;z&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>vdims</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-cfb16846-5312-417a-af1f-1f78e272c6e3' class='xr-index-data-in' type='checkbox'/><label for='index-cfb16846-5312-417a-af1f-1f78e272c6e3' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([&#x27;x&#x27;, &#x27;y&#x27;, &#x27;z&#x27;], dtype=&#x27;object&#x27;, name=&#x27;vdims&#x27;))</pre></div></li><li class='xr-var-item'><div class='xr-index-name'><div>t</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-e3523104-6af5-464e-94a0-03f5322a97b0' class='xr-index-data-in' type='checkbox'/><label for='index-e3523104-6af5-464e-94a0-03f5322a97b0' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([                1e-12,                 2e-12,                 3e-12,\n",
       "                       4e-12,                 5e-12,                 6e-12,\n",
       "                       7e-12,                 8e-12,                 9e-12,\n",
       "                       1e-11,\n",
       "       ...\n",
       "       5.274341343923693e-10, 5.295508787856869e-10, 5.316709274718813e-10,\n",
       "       5.337945031788918e-10,  5.35921453911558e-10, 5.380518487416173e-10,\n",
       "        5.40186032847568e-10, 5.423237090369723e-10, 5.444647973734903e-10,\n",
       "       5.466094681270549e-10],\n",
       "      dtype=&#x27;float64&#x27;, name=&#x27;t&#x27;, length=290))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-d5f11998-d84c-4cf5-82f3-d84d8571775b' class='xr-section-summary-in' type='checkbox'  checked><label for='section-d5f11998-d84c-4cf5-82f3-d84d8571775b' class='xr-section-summary' >Attributes: <span>(2)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'><dt><span>drive_numbers :</span></dt><dd>[0, 1, 2]</dd><dt><span>driver :</span></dt><dd>TimeDriver</dd></dl></div></li></ul></div></div>"
      ],
      "text/plain": [
       "<xarray.DataArray 'field' (t: 290, x: 20, y: 10, z: 4, vdims: 3)>\n",
       "array([[[[[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "          [ -27584.49540021,  658579.77501927,  453334.06616874],\n",
       "...\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         ...,\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]],\n",
       "\n",
       "         [[ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125],\n",
       "          [ 797212.875     ,  -51317.65625   ,   42639.36328125]]]]])\n",
       "Coordinates:\n",
       "  * x        (x) float64 -4.75e-08 -4.25e-08 -3.75e-08 ... 4.25e-08 4.75e-08\n",
       "  * y        (y) float64 -2.25e-08 -1.75e-08 -1.25e-08 ... 1.75e-08 2.25e-08\n",
       "  * z        (z) float64 2.5e-09 7.5e-09 1.25e-08 1.75e-08\n",
       "  * vdims    (vdims) <U1 'x' 'y' 'z'\n",
       "  * t        (t) float64 1e-12 2e-12 3e-12 ... 5.423e-10 5.445e-10 5.466e-10\n",
       "Attributes:\n",
       "    drive_numbers:  [0, 1, 2]\n",
       "    driver:         TimeDriver"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined.to_xarray()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also append an other drive to a combined drive."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "tags": [
     "nbval-ignore-output"
    ]
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CombinedDrive(\n",
       "  OOMMFDrive(name='rectangle', number=0, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       "  Mumax3Drive(name='rectangle', number=1, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       "  Mumax3Drive(name='rectangle', number=2, dirname='../micromagneticdata/tests/test_sample', x='t'),\n",
       "  OOMMFDrive(name='rectangle', number=0, dirname='../micromagneticdata/tests/test_sample', x='t')\n",
       ")"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "combined << data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Widgets"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "0cadee377586488482b389758ca073e0",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "BoundedIntText(value=0, description='drive', max=6)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.selector()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "editable": true,
    "slideshow": {
     "slide_type": ""
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a82b6c747f61408ea9c533cdcb41ecda",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "IntSlider(value=0, description='step', max=13)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "drive.slider()"
   ]
  }
 ],
 "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": "3.8.16"
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  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "08717bfc20a748fcb19df275fa42373d": {
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      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "BoundedIntTextModel",
      "state": {
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       "max": 6,
       "style": "IPY_MODEL_08717bfc20a748fcb19df275fa42373d"
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       "max": 40,
       "style": "IPY_MODEL_f0d1fceee33549728b7b9b254a03d9e3"
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     "d896d3721ffb4ae0b3e014122d407ba9": {
      "model_module": "@jupyter-widgets/base",
      "model_module_version": "1.2.0",
      "model_name": "LayoutModel",
      "state": {}
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     "f0d1fceee33549728b7b9b254a03d9e3": {
      "model_module": "@jupyter-widgets/controls",
      "model_module_version": "1.5.0",
      "model_name": "SliderStyleModel",
      "state": {
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    "version_major": 2,
    "version_minor": 0
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 },
 "nbformat": 4,
 "nbformat_minor": 4
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