{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dk_clsQsU831"
   },
   "source": [
    "# muMAG Standard Problem #4\n",
    "\n",
    "A detailed problem description can be found [here](https://www.ctcms.nist.gov/~rdm/std4/spec4.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NOnBqo-nsvzX"
   },
   "source": [
    "## Google Colab Link\n",
    "\n",
    "The demo can be run on Google Colab without any local installation.\n",
    "Use the following [link](https://colab.research.google.com/drive/1kYudJgbuhGBrhTTFs_HzT68LxFcVkJPu) to try it out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6jerYNCbL217",
    "outputId": "e8b5be49-fdde-4ac5-f362-897f173b8dcc"
   },
   "outputs": [],
   "source": [
    "!pip install -q magnumnp numpy==1.22.4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "O_kE_ZQMWi7g"
   },
   "source": [
    "## Run Demo:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "iwW3mUynhwkJ",
    "outputId": "60f0996c-133a-4dc5-d1b9-0d9496721a59"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-06 14:23:57  magnum.np:WARNING \u001b[1;37;31mmodule 'torch' has no attribute 'compile'\u001b[0m\n",
      "2023-11-06 14:23:57  magnum.np:INFO \u001b[1;37;32mmagnum.np 1.1.3\u001b[0m\n",
      "/home/florian/.local/lib/python3.8/site-packages/torch/cuda/__init__.py:82: UserWarning: CUDA initialization: The NVIDIA driver on your system is too old (found version 10010). Please update your GPU driver by downloading and installing a new version from the URL: http://www.nvidia.com/Download/index.aspx Alternatively, go to: https://pytorch.org to install a PyTorch version that has been compiled with your version of the CUDA driver. (Triggered internally at  ../c10/cuda/CUDAFunctions.cpp:112.)\n",
      "  return torch._C._cuda_getDeviceCount() > 0\n",
      "2023-11-06 14:23:57  magnum.np:INFO \u001b[1;37;32m[State] running on device: cpu (dtype = float64)\u001b[0m\n",
      "2023-11-06 14:23:57  magnum.np:INFO \u001b[1;37;32m[Mesh] 100x25x1 (size= 5e-09 x 5e-09 x 3e-09)\u001b[0m\n",
      "2023-11-06 14:23:57  magnum.np:INFO \u001b[1;37;32m[LLGSolver] using RKF45 solver (atol = 1e-05)\u001b[0m\n",
      "2023-11-06 14:23:57  magnum.np:INFO [DEMAG]: Time calculation of demag kernel = 0.1910877227783203 s\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1e-11 dE=4.01585 E=9.19749e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2e-11 dE=0.269003 E=7.24781e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3e-11 dE=0.0524045 E=6.88691e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4e-11 dE=0.0177996 E=6.76647e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5e-11 dE=0.00863123 E=6.70856e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6e-11 dE=0.00541932 E=6.6724e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7e-11 dE=0.00400938 E=6.64576e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8e-11 dE=0.00325665 E=6.62418e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9e-11 dE=0.00278723 E=6.60577e-19\u001b[0m\n",
      "2023-11-06 14:23:58  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1e-10 dE=0.00246026 E=6.58956e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.1e-10 dE=0.00221527 E=6.575e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.2e-10 dE=0.00202292 E=6.56172e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.3e-10 dE=0.00186731 E=6.54949e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.4e-10 dE=0.00173885 E=6.53812e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.5e-10 dE=0.00163121 E=6.52748e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.6e-10 dE=0.00153991 E=6.51744e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.7e-10 dE=0.0014616 E=6.50793e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.8e-10 dE=0.00139366 E=6.49887e-19\u001b[0m\n",
      "2023-11-06 14:23:59  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.9e-10 dE=0.00133399 E=6.49021e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2e-10 dE=0.00128089 E=6.48191e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.1e-10 dE=0.00123297 E=6.47393e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.2e-10 dE=0.0011891 E=6.46624e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.3e-10 dE=0.00114834 E=6.45882e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.4e-10 dE=0.00110994 E=6.45166e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.5e-10 dE=0.00107331 E=6.44474e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.6e-10 dE=0.00103798 E=6.43806e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.7e-10 dE=0.00100357 E=6.43161e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.8e-10 dE=0.000969828 E=6.42537e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=2.9e-10 dE=0.000936556 E=6.41936e-19\u001b[0m\n",
      "2023-11-06 14:24:00  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3e-10 dE=0.000903626 E=6.41357e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.1e-10 dE=0.000870966 E=6.40799e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.2e-10 dE=0.000838534 E=6.40262e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.3e-10 dE=0.000806336 E=6.39746e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.4e-10 dE=0.000774395 E=6.39251e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.5e-10 dE=0.000742751 E=6.38776e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.6e-10 dE=0.000711464 E=6.38322e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.7e-10 dE=0.000680588 E=6.37888e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.8e-10 dE=0.000650197 E=6.37474e-19\u001b[0m\n",
      "2023-11-06 14:24:01  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=3.9e-10 dE=0.000620354 E=6.37078e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4e-10 dE=0.000591127 E=6.36702e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.1e-10 dE=0.000562576 E=6.36344e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.2e-10 dE=0.000534755 E=6.36004e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.3e-10 dE=0.000507712 E=6.35681e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.4e-10 dE=0.000481493 E=6.35375e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.5e-10 dE=0.000456131 E=6.35086e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.6e-10 dE=0.000431654 E=6.34812e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.7e-10 dE=0.000408082 E=6.34553e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.8e-10 dE=0.000385428 E=6.34308e-19\u001b[0m\n",
      "2023-11-06 14:24:02  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=4.9e-10 dE=0.000363702 E=6.34077e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5e-10 dE=0.000342899 E=6.3386e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.1e-10 dE=0.000323022 E=6.33655e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.2e-10 dE=0.000304056 E=6.33463e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.3e-10 dE=0.000285989 E=6.33282e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.4e-10 dE=0.000268813 E=6.33112e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.5e-10 dE=0.000252491 E=6.32952e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.6e-10 dE=0.000237012 E=6.32802e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.7e-10 dE=0.000222349 E=6.32661e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.8e-10 dE=0.000208472 E=6.32529e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=5.9e-10 dE=0.000195358 E=6.32406e-19\u001b[0m\n",
      "2023-11-06 14:24:03  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6e-10 dE=0.000182974 E=6.3229e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.1e-10 dE=0.000171292 E=6.32182e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.2e-10 dE=0.000160284 E=6.3208e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.3e-10 dE=0.000149917 E=6.31986e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.4e-10 dE=0.000140162 E=6.31897e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.5e-10 dE=0.000130998 E=6.31814e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.6e-10 dE=0.00012238 E=6.31737e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.7e-10 dE=0.00011429 E=6.31665e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.8e-10 dE=0.000106705 E=6.31597e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=6.9e-10 dE=9.95866e-05 E=6.31535e-19\u001b[0m\n",
      "2023-11-06 14:24:04  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7e-10 dE=9.29203e-05 E=6.31476e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.1e-10 dE=8.66724e-05 E=6.31421e-19\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.2e-10 dE=8.08249e-05 E=6.3137e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.3e-10 dE=7.53497e-05 E=6.31323e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.4e-10 dE=7.0235e-05 E=6.31278e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.5e-10 dE=6.54502e-05 E=6.31237e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.6e-10 dE=6.09752e-05 E=6.31198e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.7e-10 dE=5.67971e-05 E=6.31163e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.8e-10 dE=5.28934e-05 E=6.31129e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=7.9e-10 dE=4.92528e-05 E=6.31098e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8e-10 dE=4.58498e-05 E=6.31069e-19\u001b[0m\n",
      "2023-11-06 14:24:05  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.1e-10 dE=4.26779e-05 E=6.31042e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.2e-10 dE=3.97174e-05 E=6.31017e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.3e-10 dE=3.69585e-05 E=6.30994e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.4e-10 dE=3.4384e-05 E=6.30972e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.5e-10 dE=3.19882e-05 E=6.30952e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.6e-10 dE=2.9751e-05 E=6.30933e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.7e-10 dE=2.76699e-05 E=6.30916e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.8e-10 dE=2.57294e-05 E=6.30899e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=8.9e-10 dE=2.39234e-05 E=6.30884e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9e-10 dE=2.2242e-05 E=6.3087e-19\u001b[0m\n",
      "2023-11-06 14:24:06  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.1e-10 dE=2.06762e-05 E=6.30857e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.2e-10 dE=1.92184e-05 E=6.30845e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.3e-10 dE=1.78631e-05 E=6.30834e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.4e-10 dE=1.65992e-05 E=6.30823e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.5e-10 dE=1.54274e-05 E=6.30814e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.6e-10 dE=1.4333e-05 E=6.30805e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.7e-10 dE=1.33177e-05 E=6.30796e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.8e-10 dE=1.23722e-05 E=6.30788e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=9.9e-10 dE=1.14944e-05 E=6.30781e-19\u001b[0m\n",
      "2023-11-06 14:24:07  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1e-09 dE=1.06767e-05 E=6.30774e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.01e-09 dE=9.91608e-06 E=6.30768e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.02e-09 dE=9.21307e-06 E=6.30762e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.03e-09 dE=8.55403e-06 E=6.30757e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.04e-09 dE=7.94488e-06 E=6.30752e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.05e-09 dE=7.37734e-06 E=6.30747e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.06e-09 dE=6.8509e-06 E=6.30743e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.07e-09 dE=6.36187e-06 E=6.30739e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.08e-09 dE=5.90072e-06 E=6.30735e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.09e-09 dE=5.49059e-06 E=6.30732e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.1e-09 dE=5.09101e-06 E=6.30729e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.11e-09 dE=4.72604e-06 E=6.30726e-19\u001b[0m\n",
      "2023-11-06 14:24:08  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.12e-09 dE=4.38809e-06 E=6.30723e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.13e-09 dE=4.07336e-06 E=6.3072e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.14e-09 dE=3.7808e-06 E=6.30718e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.15e-09 dE=3.50941e-06 E=6.30716e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.16e-09 dE=3.25893e-06 E=6.30714e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.17e-09 dE=3.02325e-06 E=6.30712e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.18e-09 dE=2.80649e-06 E=6.3071e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.19e-09 dE=2.59762e-06 E=6.30708e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.2e-09 dE=2.42423e-06 E=6.30707e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.21e-09 dE=2.24335e-06 E=6.30705e-19\u001b[0m\n",
      "2023-11-06 14:24:09  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.22e-09 dE=2.08069e-06 E=6.30704e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.23e-09 dE=1.93274e-06 E=6.30703e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.24e-09 dE=1.79282e-06 E=6.30702e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.25e-09 dE=1.6626e-06 E=6.30701e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.26e-09 dE=1.54594e-06 E=6.307e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.27e-09 dE=1.43201e-06 E=6.30699e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.28e-09 dE=1.3289e-06 E=6.30698e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.29e-09 dE=1.23514e-06 E=6.30697e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.3e-09 dE=1.144e-06 E=6.30696e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.31e-09 dE=1.0623e-06 E=6.30696e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;34m[LLG] relax: t=1.32e-09 dE=9.85183e-07 E=6.30695e-19\u001b[0m\n",
      "2023-11-06 14:24:10  magnum.np:INFO \u001b[1;37;32m[LLGSolver] using RKF45 solver (atol = 1e-05)\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6e-11\u001b[0m\n",
      "2023-11-06 14:24:11  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7e-11\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8e-11\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9e-11\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1e-10\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.1e-10\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.2e-10\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.3e-10\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.4e-10\u001b[0m\n",
      "2023-11-06 14:24:12  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.5e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.6e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.7e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.8e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1.9e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.1e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.2e-10\u001b[0m\n",
      "2023-11-06 14:24:13  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.3e-10\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.4e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.5e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.6e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.7e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.8e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=2.9e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3e-10\u001b[0m\n",
      "2023-11-06 14:24:14  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.1e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.2e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.3e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.4e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.5e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.6e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.7e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.8e-10\u001b[0m\n",
      "2023-11-06 14:24:15  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=3.9e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.1e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.2e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.3e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.4e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.5e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.6e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.7e-10\u001b[0m\n",
      "2023-11-06 14:24:16  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.8e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=4.9e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.1e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.2e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.3e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.4e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.5e-10\u001b[0m\n",
      "2023-11-06 14:24:17  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.6e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.7e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.8e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=5.9e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.1e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.2e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.3e-10\u001b[0m\n",
      "2023-11-06 14:24:18  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.4e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.5e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.6e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.7e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.8e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=6.9e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7e-10\u001b[0m\n",
      "2023-11-06 14:24:19  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.1e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.2e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.3e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.4e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.5e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.6e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.7e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.8e-10\u001b[0m\n",
      "2023-11-06 14:24:20  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=7.9e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.1e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.2e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.3e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.4e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.5e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.6e-10\u001b[0m\n",
      "2023-11-06 14:24:21  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.7e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.8e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=8.9e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.1e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.2e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.3e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.4e-10\u001b[0m\n",
      "2023-11-06 14:24:22  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.5e-10\u001b[0m\n",
      "2023-11-06 14:24:23  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.6e-10\u001b[0m\n",
      "2023-11-06 14:24:23  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.7e-10\u001b[0m\n",
      "2023-11-06 14:24:23  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.8e-10\u001b[0m\n",
      "2023-11-06 14:24:23  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=9.9e-10\u001b[0m\n",
      "2023-11-06 14:24:23  magnum.np:INFO \u001b[1;37;34m[LLG] step: dt= 1e-11  t=1e-09\u001b[0m\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "====================================================================================\n",
      "TIMER REPORT\n",
      "====================================================================================\n",
      "Operation              No of calls    Avg time [ms]    Total time [s]    Memory [MB]\n",
      "-------------------  -------------  ---------------  ----------------  -------------\n",
      "LLGSolver.relax                  1    13356.2              13.3562           6.88672\n",
      "    DemagField.h              5131        1.29731           6.65649          6.82812\n",
      "    ExchangeField.h           5131        0.70471           3.61587          0\n",
      "LLGSolver.step                 100      127.107            12.7107           1.52734\n",
      "    DemagField.h              4788        1.19447           5.71914          1.52734\n",
      "    ExchangeField.h           4788        0.664887          3.18348          0\n",
      "    ExternalField.h           4788        0.0634773         0.30393          0\n",
      "-------------------  -------------  ---------------  ----------------  -------------\n",
      "Total                                                      26.1452\n",
      "Missing                                                     0.0782211\n",
      "====================================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "#TODO: read latest script content from gitlab repository, as soon as %load works with Colab\n",
    "from magnumnp import *\n",
    "import torch\n",
    "\n",
    "Timer.enable(log_mem = True)\n",
    "\n",
    "# initialize mesh\n",
    "eps = 1e-15\n",
    "n  = (100, 25, 1)\n",
    "dx = (5e-9, 5e-9, 3e-9)\n",
    "mesh = Mesh(n, dx)\n",
    "state = State(mesh)\n",
    "\n",
    "state.material = {\n",
    "    \"Ms\": 8e5,\n",
    "    \"A\": 1.3e-11,\n",
    "    \"alpha\": 0.02\n",
    "    }\n",
    "\n",
    "# initialize field terms\n",
    "demag    = DemagField()\n",
    "exchange = ExchangeField()\n",
    "external = ExternalField([-24.6e-3/constants.mu_0,\n",
    "                          +4.3e-3/constants.mu_0,\n",
    "                          0.0])\n",
    "\n",
    "# initialize magnetization that relaxes into s-state\n",
    "state.m = state.Constant([0,0,0])\n",
    "state.m[1:-1,:,:,0]   = 1.0\n",
    "state.m[(-1,0),:,:,1] = 1.0\n",
    "\n",
    "# relax without external field\n",
    "llg = LLGSolver([demag, exchange])\n",
    "llg.relax(state)\n",
    "write_vti(state.m, \"data/m0.vti\", state)\n",
    "\n",
    "# perform integration with external field\n",
    "llg = LLGSolver([demag, exchange, external])\n",
    "logger = Logger(\"data\", ['t', 'm'])\n",
    "while state.t < 1e-9-eps:\n",
    "    llg.step(state, 1e-11)\n",
    "    logger << state\n",
    "\n",
    "Timer.print_report()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "wO5hJKVCmVt7"
   },
   "source": [
    "## Plot Results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from os import path\n",
    "if not path.isdir(\"ref\"):\n",
    "    !mkdir ref\n",
    "    !wget -P ref https://gitlab.com/magnum.np/magnum.np/raw/main/demos/sp4/ref/m.dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 334
    },
    "id": "2KHqxaW4hwkL",
    "outputId": "99d3e926-e304-4620-c6ed-f6017a65366f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#TODO: read latest script content from gitlab repository, as soon as %load works with Colab\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = np.loadtxt(\"data/log.dat\")\n",
    "ref = np.loadtxt(\"ref/m.dat\")\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "cycle = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "ax.plot(data[:,0]*1e9, data[:,1], '-', color = cycle[0], label = \"magnum.np - x\")\n",
    "ax.plot(ref[:,0]*1e9, ref[:,1], '-', color = cycle[0], linewidth = 6, alpha = 0.4, label = \"reference - x\")\n",
    "\n",
    "ax.plot(data[:,0]*1e9, data[:,2], '-', color = cycle[1], label = \"magnum.np - y\")\n",
    "ax.plot(ref[:,0]*1e9, ref[:,2], '-', color = cycle[1], linewidth = 6, alpha = 0.4, label = \"reference - y\")\n",
    "\n",
    "ax.plot(data[:,0]*1e9, data[:,3], '-', color = cycle[2], label = \"magnum.np - z\")\n",
    "ax.plot(ref[:,0]*1e9, ref[:,3], '-', color = cycle[2], linewidth = 6, alpha = 0.4, label = \"reference - z\")\n",
    "\n",
    "ax.set_xlim([0,1])\n",
    "ax.set_xlabel(\"Time t[ns]\")\n",
    "ax.set_ylabel(\"Magnetization $m$\")\n",
    "ax.legend(ncol=3)\n",
    "ax.grid()\n",
    "fig.savefig(\"data/results.png\")"
   ]
  }
 ],
 "metadata": {
  "accelerator": "GPU",
  "colab": {
   "provenance": []
  },
  "gpuClass": "standard",
  "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": "3.8.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}