test_hdf5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "8238552e-3b89-4b17-8c7d-fda05b18349a",
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "import numpy as np\n",
    "import meep as mp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "68cc2b83-1a2c-42b9-9d69-ae2c85b8cd1e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "     cylinder, center = (0,0,0)\n",
      "          radius 0.2, height 1e+20, axis (0, 0, 1)\n",
      "          dielectric constant epsilon diagonal = (2,2,2)\n",
      "-----------\n",
      "Initializing structure...\n",
      "time for choose_chunkdivision = 2.5034e-05 s\n",
      "Working in 2D dimensions.\n",
      "Computational cell is 1 x 1 x 0 with resolution 64\n",
      "     cylinder, center = (0,0,0)\n",
      "          radius 0.2, height 1e+20, axis (0, 0, 1)\n",
      "          dielectric constant epsilon diagonal = (2,2,2)\n",
      "time for set_epsilon = 0.0227001 s\n",
      "-----------\n",
      "creating output file \"./eps-000000000.h5\"...\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/jpazos/miniconda3/envs/mp/lib/python3.10/site-packages/numpy/core/getlimits.py:499: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  setattr(self, word, getattr(machar, word).flat[0])\n",
      "/home/jpazos/miniconda3/envs/mp/lib/python3.10/site-packages/numpy/core/getlimits.py:89: UserWarning: The value of the smallest subnormal for <class 'numpy.float64'> type is zero.\n",
      "  return self._float_to_str(self.smallest_subnormal)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "run 0 finished at t = 0.0 (0 timesteps)\n"
     ]
    },
    {
     "data": {
      "image/png": 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19TEyMmINLFkuc4qBvVxm7XCj9DFmDziC5dcOTeZ5ikajjI+PLwjDqqoq6uvrFzWfrqSsInYKRBGb7GbSlVw4w+Eww8PDHDt2jNdff52jR4/S399/ytMnzCkGG9Xp3noqFosxMjLC5OQkc3NzOBwOqquraWhooLm5ueA2brlkv5cKRcmmQBTJkn2hNDfqNX/MeW/m8mJ2kUiEwcFBBgYGrJGkhcLQ4/FYg3LMWmB2TbAc2Vf1Mc93Op3OuSJPLBbj+PHjBAIBmpqa2LRpEw0NDfj9/kWjT81l8cxpMNkbEysEpRAFosgSUqkUs7OzzMzMMDMzY23U63Q6rf0zXS4XqVSKiYkJKwhPnDjB2NhYwZqhOXUDFvZZmk2N5RaM5uAgMwztgQjkXaJufn6e0dFRhoaGaGpqoqqqimQySX19vbUeajweJx6Pk8lk8Hg8VFdXU1tbS21tLdXV1Xi93rV6mVKiFIhSsbLDJl/tIZVKMTMzw9jYGCdPnlywDqm5wozH4yGTyTAzM8Pw8DD9/f2MjIwwNTWV9/lz1UTN251OJ+l0+hRf4caz0s2SAaLRKKFQyJqoHw6HrfVQU6mUteKN2+22apNms6q5ClAuxX4OpPwpEKWiFTPa0AzEkydP0t/fz8DAAGNjY8zOzmIYBm6322r6NFdkGR0dZWJiomDt0KwB2leiMWuGG7mv8FSYr83pdC56jUvVhs1VbtxuN/Pz8wwNDeF2u62m1lQqhcPhoKamhtbWVrq7uwGorq5ecv9TjToVUCBKhco1jSHfxTCTyTA/P8/4+DgDAwO88cYbDAwMMDExQSqVwul0Llqce25ujqmpqSVreclkckEfYrk2lZqyF0e39+stda5SqRSRSIRMJsPk5CQ+n490Om2th2oukt7Q0EBPTw8Oh4NAIEBra2vBLxianygmBaJULPvFuVAznrlFkdlsaq46k6s51Ofz4XQ6Fyw8XchGHzm6WlaykXEmk2Fubs6qdRuGkXNT4vr6ehwOB42NjXR3d1v9vYXKYg6UMv9foViZFIhSsbL3M8y3BJsZcubgmomJibx9g9kXaHufmfm7UkNwOewLd5vnytwqaimRSMSaqmHW4H0+X97j7TtmrNWyd7IxKRClIuVaZ9NcKzSb3++3RpJm9/kV8zwul8tqGszeZFgWy965wuxvXM6XiGQyab2nPp8v74Aa+56WuZafk8qiQJSKlGvH+0wmU7CW6PV6rflsK31OWZ6VfnEw5yB6vd5Fc0WzH9/8UmSGr96nyqVAlIpXzEXX5XLh8XjweDzLms/mcDgW1WxWupxZpbD3LdoXMV/OknrmVBi3211wN5LTsW6tlA8FolSk7N0jlmomM1eoMecdFvscgNXMav9/Kcz+pSF7Kb1izp+9iTvfZH9gQdOsNhYWBaJUJHO1lOwd13Mxd1uYmZlZsEJNrhGOgNUXmT3SVEG4PNk7e9jDq1Bfrt/vx+FwkEgkmJmZYXJykqmpqZybDJtTZuzzEBWIlUuBKGUvu6ZhXz6sEHNz38HBQfr7+xkeHiYSiZBIJPKGp72JTiNJTz97P5/b7c4Zii6Xi3g8TiQSYXh42NoZo6uri8bGRjwez6Ljc7Hvi2k+t5Q3BaKUPft8w2KCMJ1OMzExwejoKMPDwwwMDFiLdI+OjjI3N5e3tqeL5tooVJMz5yuGQiH8fj+GYTA3N8fExATt7e20tbUVtcmw2WyrptTKoUCUspY9mtSsYRS6uE1OTnL8+PEFG/ueOHGCkZERaw5irtpf9i72mlpxemVP5s+3KXEmkyEajVoLKkxPTxMOhxkbG+OMM84gHo/jcDhoamoq+FzZnxkpfwpEqQjZzV/5JJNJQqEQx44d49VXX+X111/n2LFjjI2NMTU1RSwWWzBvLXsNTHtQKhBPL3OKRK59De3vQzKZJJ1OE4vFiEQinDx5ktHRUcLhsLXJcHV1NfX19UuOQM3ux1QtsbwpEKUiFHshMzf3feeddzh69GjBzX2dTueC/sJyXpR7o8i17qi9Gdz8smLuYWmG4tjYGNPT0wDWJsP23TByUd9h5VEgSlmzT69Y6ht+JBJhYGCAvr4+jh07Rl9f35I73dub0pazgo2cPtnLvOWqlc/Pz9Pf309tbS0NDQ00NjZSX1+fc5Nh++OqD7GyKBCl7JkXS/toU5NZi5icnOTEiRO8+eabvPnmm9Z+hkuFYXZznZpI1549rHJtK2WKxWKMjIzQ399PMBjE7/eTSqXo7Oykvr6eqqqqBTuP2KfkSGVQIEpZKbSNj72fLxaLMTs7y9TUFOFwmFAoZI0mfeuttxgaGmJycnJZzyfrw96MulSTdTgcZmBgAL/fTzqdZnp6mrGxMdra2mhqaiIQCFBdXW3NZcz3fCaFZXlRIErZyDWIJdcFy+FwEIvFCIfDnDhxgoGBAY4fP87x48cZGBhgeHiYUCiUd+K9yX7x1XJs6yO737aYTYZDoRAAc3NzTE5OMj4+Tk9PD93d3XR2duJ0OvOuf2r/jJktAgrF8qFAlLKz1O7nDoeDeDxubfj72muvWc2koVCIycnJorYZyl5eTNbHct+HSCTC/Pw8kUiE8fFxIpGINbe0qqqKYDC45P6JJoVheVEgStmwT8A3/z9fDTGdTjMzM0MoFKK/v5833niDY8eOFQzCXH2ECsKNodBCCbn+Fo/HCYVCRCIR4vE4Xq+XhoYGOjo6SKfTBZ+nmFYIKU0KRCkr9snUhbhcLmvD3/HxcUKhUN4w9Hg8CwZbJJNJTa/Y4Mwl9MzPQTqdzrnIt9mE2tXVxdzcHOl0Gre78GXRDEXtm1h+FIhSNuw1RPsowVz8fj8ej8ear2ZO2M7F5XJZF8lUKoXL5VIgbnDmyFN7uOXb9SIWi1kbCns8niX3TzTfe/umwlIeFIhSVopdHcbv91ub/tprElJ5zHmM5l6XSwXiUn3UUrq0QJ+UnWIGWLjdbmtH9WIvgmbfkvoNNz77Z8BcvSYfr9drfTkyNxUu5vH1OSg/qiFKWSl2o1eziczcD69QINrXz7TvbygbV/Y+lIXeM7/fj9frxel0Wn2N+dY4tX+uVEMsP6ohStkwl2gzN/4tdMGamJhgenqaRCKBw+HA6/XmPTadTpNKpayahmoGG58Zgul02vrJx+fzAe+OPJ2ZmWFiYiLvscv5jEnpUQ1RyoZ9Tct85ubmGB8f5/jx45w4cYJwOMzs7GzBkDMvruayYArEjW8571kmk2F2dpZwOMzQ0BC1tbWk02mam5upqqpacGwxnzEpXQpEKUnFzgXLZDLE4/FFy7QNDg7S19fH4OCgNRG/0DqY9j5EKQ3FvGdOp5NEIsHExARDQ0N4PB6SySSRSMTaSDgYDFJdXY3P58sbhpqbWB4UiFKyihntl0qliEajjI6OMjw8zNDQEIODgwwNDREKhQiFQoTD4YKLeNufS0pHMe+Z0+lkfn6ecDgMYNUUR0dH6erqoquri46ODlpbW2loaCjYtK7Rp6VPgSglJ3tFGsh/EUomk0SjUWsni7fffptjx44xMjJi9SPGYjFSqZRCrwKl02nm5uZIJBILWhDMDYXn5uaAdwfe1NXV5Q1ErWBTHhSIUpKKHfZu7mgwOjpKX18fR48e5e233yYUCuWcqG2OItSw+vKV/R4nk0mSySTz8/NMTk5aX5bi8Thut5tgMEhLS0vB/S6L/YImG5sCUUpWMU1U6XTautCNjo5aTab5uN1u62K51Pw1KT3mCFGHw2GtUpQtkUhw/Phx/H4/mzZtshb/XuqzoCbT0qdAlLJm1gBisRgzMzNEo9GCx5vD6s1v/ArE8mKOEi0mtKLRKLOzs8RiMdLptFoMKoDGD0tJWs7FyT4nrVCzF+jbvfx/5vxFc9qGArH8KRClJC0nuMxagdvtxuPxFDxWFz0xuVyuBRPw9WWp/KnJVMqa2QRazMLNsHAEq5pLy1OxmwlXVVXh8/nweDxalaZCKBClrDkcDlwuFx6PB5/PR1VVVd5NY2HpdS+ltJlbNi3VBOp0Oq3J+Pb9MKW8qclUyp69udTc2WAp6jMqT/b3dakdMDwejxWGqiFWBgWiVIxiRhhqrdLKsNR7bN8jczn9hwrN0qZAlLKni5SslD47lUWBKGXPPohCTaFSjOypFsV+ZvTZKm0KRKkI9pGjSw2mUK2g/Jmjj/Oxf3lSyFUOBaKUPTME7b+lMhX7ZcdeQ1S/cuVQIIqIiKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIsIxCHhoZWsxxFu+++++jt7cXv97Nr1y6efvrpgsc/9dRT7Nq1C7/fz9atW/nOd76zRiUVEZFSUnQg7ty5kx/+8IerWZYlPfroo9x666189atf5ciRI1x66aV88IMfZGBgIOfxfX19/Mmf/AmXXnopR44c4a//+q+55ZZb+MlPfrLGJRcRkY2u6ED8u7/7Oz7/+c9z5ZVXEg6HV7NMef3TP/0Tn/rUp/j0pz/Njh07uOeee+ju7ub+++/Pefx3vvMdNm/ezD333MOOHTv49Kc/zSc/+Un+8R//cY1LLiIiG13Rgfi5z32O3/3ud0xOTnLOOefw85//fDXLtUgikeCFF15g7969C27fu3cvBw8ezHmfQ4cOLTr+8ssv5/DhwySTyZz3icfjTE1NLfgRkcqj5doqj3s5B/f29vKrX/2Ke++9lyuvvJIdO3bgdi98iBdffPG0FtA0Pj5OOp2mtbV1we2tra2EQqGc9wmFQjmPT6VSjI+P097evug+Bw4c4K677jp9BReRkqRF3ivPsgIR4Pjx4/zkJz+hsbGRj370o4sCcbVlf0gNwyj4wc11fK7bTbfffju33Xab9f9TU1N0d3evtLgiIlIilpVmDz74IF/60pfYs2cPr776Kps2bVqtci3S3NyMy+VaVBs8efLkolqgqa2tLefxbrebpqamnPfx+Xz4fL7TU2gRESkZRfchXnHFFXzlK1/h3nvv5bHHHlvTMATwer3s2rWLJ598csHtTz75JJdccknO++zevXvR8b/85S+54IIL8Hg8q1ZWEREpPUXXENPpNC+//DJdXV2rWZ6CbrvtNvbt28cFF1zA7t27eeCBBxgYGOCmm24C3m3uPHHiBD/4wQ8AuOmmm7j33nu57bbbuOGGGzh06BDf+973eOSRR9btNYiIyMZUdCBm17TWw9VXX004HObrX/86IyMj7Ny5k8cff5yenh4ARkZGFsxJ7O3t5fHHH+eLX/wi3/72t+no6OBb3/oWV1555Xq9BBER2aDWdkTMafC5z32Oz33uczn/9tBDDy267X3ve9+qjXwVEZHyobVMRUREUCCKiIgACkQRERFAgSgiIgIoEKWCaCkuESlEgShlz+l04nA4cDgcOJ1OnE597CtVsQt2Z39m9GWqMujKIBXBvLiZP/lkMhntclABDMMgk8nk/XuxnxcpLwpEKXvmBU0XOCmWy+Va8Fkp9jOjz1ZpUyBK2VONT5bL/Mzos1NZFIhSMTKZjPWTj/qLKsNS73E6nV4QisUGowK0tCkQpayZfUWpVIpkMkkikSAejy95PzWtlif7+1pocJX5OUkmk6TTafUtVwgFopS9dDpNMpkkFosxPz9f8FiNKixv5nu71HucyWSYn59fEIoKxPKnQJSyZhiGFYiJRIJYLFbwePvAG03PKE/FDq6KxWJWIBZqZpfyoX/xUpKW823dbDY1g7EQ1QzFlE6nFzSXqoZY/hSIUpKWE1xmbc/lcuHxeAoeq4uemFwuFy6Xa8EkfSlvCkQpaw6HA4/HQ1VVFXV1dQSDwYLH20eiqpms/Czn/a2vr6empga/32/NS5TyVnIbBIuYirlAuVwuqqqqaGhooK2tja6uLuLxOKFQKOdoUzWRlTfzvXU4HHnfX7/fT1tbG93d3bS1tdHQ0EB1dTUul6vgYyswS58CUUqS/eJT6ELkdrupq6ujtbWV3t5e0uk01dXVNDU1MTExwdTUlDVwIplMKggrgP09djqduN1uqxWhtraW5uZm2tvb6e3tZevWrbS2tlJXV1cwEIv9PMrGpkCUkpN9wVkqEIPBIJ2dnTgcDmpqamhqamJoaIiRkRFGRkYIh8NEIhFSqZTCsMI4nU6qq6upr69n06ZNtLa20tHRQWdnJ11dXXR2dtLR0UEgEMDtzn+5zA5EhWJpUiBKySrmomMGosfjoa6uzrrotba20tfXh8/nA94dYj89PV3wucxmNoVmaSjmPctkMvj9fpqbm9myZQtbt26lt7fXai5tbGwkEAhQXV1dMBDN55PSpkCUklTo4mP2EcG7NYCqqiqqqqpoamqivb2d1tZW6uvrcbvdxONxIpEIY2NjS+5+4HQ6tWJJCSnmPctkMni9XhoaGujq6mLbtm2cddZZ9PT00NTUhN/vz3k/+2fMfC4pfQpEKRvmyEHz4mcOmbfz+/10dXXhcDiYnp5meHiYmpqaghc0h8Nh9R85nU7VEkuAfTUal8tV8D0zm00bGxvp7Oykt7eX9vb2nMea81ntz6EFHMqHAlHKhjkBP5PJLLnSTENDA7W1tXi9XgzDIJFI5D3WnI8Gi0NXNqbssDIMg1QqlfNYc7Sxz+ejrq6OhoaGvI9rfsYMw9ASf2VIgShlxV4TKBRaHo8Hh8NBJpMhkUgUXOPUHoiA1QwnG5cZhvYvMvnMz8+TSCSsL1KFFm8wP19qJShPqutLWSl2U1f77hdL7YCRvfalagUbX/am0IVaC+LxOLFYjEQiQTKZzFuTzH58fQ7KjwJRyk6xCzebYWj2CUnlMgyDZDJphWM+xS4MLqVJgShlwz6QYqn+nVgsRjKZxOl04vF48o4mhHdXr0mlUtY8RQXoxmdu12S+b4Xes6qqKqsJPZVKLRmIxX7GpPSoD1HKSrHbNqXTaTwez4KVSWKxWM6LobmKjZSOTCZT1EbQfr+f9vZ2mpqaqKmpwe12L9lkqlpi+VIgStkodgUbwzBwuVzU1dXR1tZGb28vyWQSn89HKBRicnKyqIup/Tk0wGJ9Lfd98Pv91NfXW0u09fb20tbWVtQSbdn9k1I+FIhSVoq5QBmGgc/no7Gxkc2bN1tLujU0NDA4OMjg4CChUIhIJFLwcezD+rU7xvqxvw/F7GxvLvTe3d3N5s2b6enpoaenh+7ubhoaGvD5fGQymbwtDRpcVb4UiFI2zGW6lrpgGYZhLdfl9XoJBAI0NTXR3NxMfX09VVVVAHmbUE3Zk7I1FH/tZc83XGqOqN/vp6OjgzPPPJMzzzyTM844g+7ublpbW61l2gr1Jy9nHV0pPQpEKStLLelmHmMu59bQ0EBLSwttbW1s2rSJ2tpaXC4XsViMaDTKyMjIks+XvYyXrC170+VSc0Sbm5vp7u5m+/bt7Ny5k+3bt9PR0UF9fT1+v3/BRH775yX7+aQ8KRCl7GXvb2jf7NXlclFbW0ttbS2BQACn08n8/DyRSITx8XEmJyfz1hLNZjVN1F5f5uox5kIL+VRVVdHe3k5PTw9nnnkmO3bsYNu2bdTV1eU83r5EW7GDtaS0KRClrJlBZd8YNp9AIMDmzZuJRCJMTEwQiUSYm5vj+PHjeVeyMS/A5gVZobj2sr+Y5FJTU2PtZrF161a2bNnC5s2b84Yh/P/PjhmGagkofwpEqQj2i1shTU1NdHZ2EolEmJ+fx+FwEAgEGB0dJRqNEovFSKVS1iAaMxDtTXbZzymnT/bITvv6tSazT9HtdlujSVtbW9myZQs7duzgjDPOoLOzk8bGxoLPpfev8igQpSIU+83e7XbT0tJi1Qirq6utDYVDoRBjY2NWzdG+ILi9JmGfAlDMqEcpjrlzRfb5zebxeBZs+tvW1mbtYmHWEFtaWgpOrzCfL9dvKV8KRClr9v4fM7SWurCZNQdz0M2mTZtoamqitrYWt9tNJpPJ2a9oD0VQM+rplt2Xl29EqdPpJBAI0NnZyZYtW6x5hps3b6ajo4OWlpaCO1qYz7Wcz4yUBwWilL3lXtCcTqc1BaO5uZmGhgZrKsb8/DyTk5N571ts06ycmmL2N9y0aRNbt27l7LPPZtu2bXR1ddHY2LhkzdD+OHovK4sCUcperguafaCNubdd9oXSbD71+/0kk0nC4TBDQ0P4fL68F+NUKoXb7VbNcJXY37d8S6yl02l8Pt+ClWh+7/d+r+BoUrM2b2/2Vs2w8igQpSKZ/U/mxdDpdOLz+XIOrQ8EAtTX11tNpul0uuDSbuaFOl/fk4KyOLnOWzF9srFYDMMw8Hq91NTUUF9fnzcMzf0wzc+A2UepIKxMCkSpSPbpGOYIxWI2FE6n0wsG0yz1HNm1jlyjImUxc6ToSs9ZPB4nnU4vueFv9mhhs5lUKpNmmkrFK6ZGYG4jZG4qXCyzOTb7RzWQ/OzLsdm3WlqOeDxubfZbaPeKXE2jem8ql2qIUpGyR59mr0uaLR6Pk0gkrDmIsnZW0h9r9jEmEomCzdv2sFVTqSgQpSLZL4RmIOa7GMZisQVNcF6vd1nPY++nzF5GThYz+wqzz9lyeL1ea8PfeDxOPB7H5/MtOi6731C198qmQJSKlV07yCcej5PJZHC73dY2UfX19Tm3h/L7/bhcLqtfKplMWhd01SyLZ+/jNTmdTjwej1WbT6fTOeeD1tfX09DQYG34aw6cyRWI5uNq8r2AAlEqWLH9RoZh4PF4CAQCtLa20tPTg8PhYHJykmQyidPpxOv14vP5cDgcxONx5ubmiEajVk0nH3OKhvk8lVKDzFUjW+pcmfMLA4EA1dXV1vQXswZojixtaGigp6eHlpYWAoEAHo+n4ONmfw4UipVLgSgVaTlb+pgX4qamJrq7u3E4HDQ2NjI3N4dhGLjdbjweDy6Xi0QiQSQSIRQK4XA4rLVP87EvRQYLRz2WYzDagzC739bpdBbs73O73dTX19PZ2cmmTZuor6/H6/WSTqetATTmZs8tLS10d3fT2NhIVVVVwf5hDagRkwJRKloxF0C3201tbS0tLS3Au/MSu7u7rUn4Xq8Xt9uNYRjMzMwwPDxsTeaPRCJ5t48yw8HtfvefoRl+Zp9mrnU6S529VmjvvwMKfnGAd5ujN23aZO1a0d7eTk1NjdVXaA56crvdBAIBGhsbaWlpsfa4XKpcIgpEqVjLWfC7trYWeHex79bWVqup1OfzWf2GyWSSyclJ6uvrMQyD6elpTp48mbOvERbPe7TXCsu1v9F8bcUun2ZXX19PW1sbvb29nH322fT29lJfX4/H41kweCaTyeDxeKiqqqK2tpaampqCcxEVhmJSIIoswe12U11djdfrJRgMLljZJnvE6dTUFG63m7m5OcLhMKOjo4yNjeXdT9FeCzSbSst5hwz7ZspmOJrNmYVqxDU1NbS2tlq7Vmzbti3ncmzm9Bj7yjMej8eqhYsUok+JSBZ7GJlNe16vt6jpFoFAgK6uLiYmJhgbGyMcDjM9PU1/f3/OptNkMkkymTyt5S8FhdYizVZVVcWWLVusnSs2b95MV1dXzuXYfD5f3tGk5vOaVDOUbApEEZtcA1mWe+FsbGykvb2dM844w6oZ1tbWMjIyQjgcztunuJRciwfkm6O32hf7fM9pn8ZyqsvU+f1+mpubaW9vZ+vWrezYsYOtW7fS0dGx5Oa++cpsltssn0JR7BSIIqeZx+Ohra3NGjFZXV1NQ0MD/f39DA4OMjIykrdfMR9zc1x7IJoX9I3SvJpvGbSVjJY1R5N2d3fT09PD1q1bOeOMM4re3HcpCkPJRYEoYmOfE3gqF8zGxkYcDocVho2NjQSDQaqqqjAMg/n5+YJTDHKVK9ccuVyhuFYX+lzPmSsQlxvafr+ftrY2zjzzTLZv386ZZ57Jli1b6OzsLGpz30LsNUMFomRTIIpkOR3z0pxOJ01NTdTX11u//X4/hmEwOztrzVUsVq6Rp/lGpC53IeyVyvW85r6C9mNWUjvs7u7mzDPPZOfOnezYsYPNmzcva3PfXBSGshQFokgOxV4wszcZzg4jl8tlbTKcSqWYnZ0lHA4zPj5ecI4isGCJsuUMQlmvKRtLrTJjBmah4/x+P+3t7XR3d3PGGWewfft2tm3bVnA/w+zNfQtREEohCkSRItknzps1M3OTYcAajZqrFhMIBOjs7GR8fNyamxiPxwmFQsRiMeu+fr8fn89nrb85Pz/P3NxcyY9ENaeu+P1+vF6vtSqNvem4qqrK2uF+y5YtdHd309HRUXCne3OKhX2iv33He1AISvEUiCLLkL2xsL2GuFRfmTmxvKenh7m5ObxeL11dXQsm+ZtTO2ZnZ5mYmCAcDpNIJEo+ED0eD/X19TQ3N9PQ0EB1dTUAiUTC2uHe4/HQ1NREb28vPT09tLa2Ul9fn/cx7e9BoT5MkWIpEEVWwD7IJd+Al2xVVVU0NjbS3d2NYRg0NDRY66F6PJ4FNadwOMzQ0BDw7mT/fBP7S0VVVZW17FpXVxeNjY0LasLJZNJah7Strc1ah9Tv9+d9zOwmUvUNyqlSIIosg/2Ca16Mi91g1uVyWU2nVVVVdHR0kE6nrTD0eDyk02lmZmYYGhrC4/FYfY6Tk5Or+8JWWW1tLa2trWzdupVt27bR2dlJXV0dDoeDZDJJPB631iGtq6ujoaGBQCCw5KLc9qko9nVSFY6yEgpEkSLZm+XMplGn07lgsvdSF+GamhqcTifBYJB0Oo3b7cbv9+P3+3G73SSTSSYmJqitrSWZTBIOhwmFQoyMjJBIJFb3Ba4Sc4J9R0cHvb29nHXWWfT29tLQ0GCtQxqLxaydQVwul7VGbCFm+NmnyKjfUE6FAlFkBYq58Gb3JxqGYS0tlq/m43K5rJpjJBJhdHSU0dFRJiYmOH78+KLjswfyrNVmxGYYuVwua4d7c4BLtra2Ntrb2xdMtG9vb7f+bgZgMBi0bjMfp9AqPKoFyummQBQ5zeyDbszaizntoNgLeHNzM21tbXR1dREOh4nH4/j9fmvTYZfLRVVVFdXV1Xg8ngULCqzFPorZzZLmRr3z8/PEYjGrjOacwt7eXrq6umhra6OpqWnJxze/MJhhax+4tNxzKVKskgnEyclJbrnlFn7+858D8JGPfIR/+Zd/yTsKLZlM8rWvfY3HH3+cY8eOEQwG2bNnD3//939PR0fHGpZcKk32jg7mbcthDsDp6upibm4Ot9vNpk2bmJ2dtcLGrG2aWxvZl0lby0A0n8vsC4zH41YZzUEyW7dupbOzc8mBMvlkn0stvSaroWQC8dprr2VoaIgnnngCgM985jPs27eP//qv/8p5/NzcHC+++CJ/8zd/w3vf+14mJye59dZb+chHPsLhw4fXsuhSYbJrTysd5BEMBq0vb8Fg0JrIb25t5PF48Hg8CzbZhdyr2pxu2TU0syZn7t5hltHv99PQ0EBraysdHR0EAoFlPc/pOpcixXAYG2Vl4AJee+01zj77bJ555hkuuugiAJ555hl2797N66+/zvbt24t6nOeff54LL7yQ48ePs3nz5qLuMzU1RTAYJBqNLvsfs1SmXLW0YlZRyTY/P8/U1BTRaJSZmRnm5uas5sNcE9HN57b/Xi3Zfaj2hQrsTcUul4vq6mrq6uoIBAIEg8Fl1xCzl39TKMpyFXsdL4ka4qFDhwgGg1YYAlx88cUEg0EOHjxYdCBGo1EcDkfByb5mk49pampqxeWWypTdt2betlw+n4+Ghgbq6uoWBE32qMpcC36vhVzPaw9k+3Jqbrcbl8u1oo167f2JCkFZTSURiKFQiJaWlkW3t7S0FL1AciwWY//+/Vx77bUFvyEcOHCAu+66a8VlFbHLdQHPrsVl79NnHzhS7MbEpcYe7sCiZlH7b5PCUFbb2iyLn8edd96Zd8UP88fs78t3YSnmH0kymeSaa64hk8lw3333FTz29ttvJxqNWj+Dg4Mre3EiOWQv/ZZOp0mlUgt+zLVRy1m+1529HJ7IWlrXGuLNN9/MNddcU/CYLVu28PLLLzM6Orrob2NjY7S2tha8fzKZ5KqrrqKvr49f/epXS/YDmiP3RNaC/aJfKQGQb7SomkRlva1rIDY3N9Pc3Lzkcbt37yYajfLcc89x4YUXAvDss88SjUa55JJL8t7PDMO33nqLX//610XNfxJZbdnLv5mLU1fKXn32ZmF7CGrZNVlv69pkWqwdO3ZwxRVXcMMNN/DMM8/wzDPPcMMNN/DhD394wYCas846i5/+9KcApFIp/uzP/ozDhw/z8MMPk06nCYVChEKhkl0CS0pf9mAYc6SoOeDEvjZnOTNft/matW2TbAQlMagG4OGHH+aWW25h7969wLsT8++9994Fx7zxxhtEo1EAhoaGrEn855133oLjfv3rX/NHf/RHq15mkVx0waciQl9KT0nMQ1xPmoco6yXXKNRSpZqfrKeymocoUqlyLQNXarRhr5QKtVuIlIH1GoiiATBSTlRDFNnAllu7Wq9wWup5FZxSChSIIhtUuTU1lsNrkPKmQBTZ4BQkImtDfYgiIiIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERIASCsTJyUn27dtHMBgkGAyyb98+IpFI0fe/8cYbcTgc3HPPPatWRhERKV0lE4jXXnstL730Ek888QRPPPEEL730Evv27Svqvj/72c949tln6ejoWOVSiohIqXKvdwGK8dprr/HEE0/wzDPPcNFFFwHw4IMPsnv3bt544w22b9+e974nTpzg5ptv5he/+AUf+tCH1qrIIiJSYkqihnjo0CGCwaAVhgAXX3wxwWCQgwcP5r1fJpNh3759fPnLX+acc84p6rni8ThTU1MLfkREpPyVRCCGQiFaWloW3d7S0kIoFMp7v7vvvhu3280tt9xS9HMdOHDA6qcMBoN0d3evqMwiIlJa1jUQ77zzThwOR8Gfw4cPA+BwOBbd3zCMnLcDvPDCC3zzm9/koYceyntMLrfffjvRaNT6GRwcXNmLExGRkrKufYg333wz11xzTcFjtmzZwssvv8zo6Oiiv42NjdHa2przfk8//TQnT55k8+bN1m3pdJovfelL3HPPPfT39+e8n8/nw+fzFf8iRESkLKxrIDY3N9Pc3Lzkcbt37yYajfLcc89x4YUXAvDss88SjUa55JJLct5n37597NmzZ8Ftl19+Ofv27eMv/uIvTr3wIiJSVkpilOmOHTu44ooruOGGG/jud78LwGc+8xk+/OEPLxhhetZZZ3HgwAE+/vGP09TURFNT04LH8Xg8tLW1FRyVKiIilakkBtUAPPzww5x77rns3buXvXv38p73vIcf/vCHC4554403iEaj61RCEREpZQ7DMIz1LsRGNjU1RTAYJBqNEggE1rs4IiKyTMVex0umhigiIrKaFIgiIiIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERAcC93gXY6AzDAGBqamqdSyIiIithXr/N63k+CsQlTE9PA9Dd3b3OJRERkVMxPT1NMBjM+3eHsVRkVrhMJsPw8DB1dXU4HI71Lg7w7red7u5uBgcHCQQC612cDUvnqTg6T8XReSrORjxPhmEwPT1NR0cHTmf+nkLVEJfgdDrp6upa72LkFAgENswHbiPTeSqOzlNxdJ6Ks9HOU6GaoUmDakRERFAgioiIAArEkuTz+bjjjjvw+XzrXZQNTeepODpPxdF5Kk4pnycNqhEREUE1RBEREUCBKCIiAigQRUREAAWiiIgIoEAsGZOTk+zbt49gMEgwGGTfvn1EIpGi73/jjTficDi45557Vq2MG8Fyz1MymeQrX/kK5557LjU1NXR0dHDdddcxPDy8doVeA/fddx+9vb34/X527drF008/XfD4p556il27duH3+9m6dSvf+c531qik62s55+mxxx7jAx/4AJs2bSIQCLB7925+8YtfrGFp18dyP0um3/72t7jdbs4777zVLeCpMKQkXHHFFcbOnTuNgwcPGgcPHjR27txpfPjDHy7qvj/96U+N9773vUZHR4fxz//8z6tb0HW23PMUiUSMPXv2GI8++qjx+uuvG4cOHTIuuugiY9euXWtY6tX17//+74bH4zEefPBB4+jRo8YXvvAFo6amxjh+/HjO448dO2ZUV1cbX/jCF4yjR48aDz74oOHxeIz/+I//WOOSr63lnqcvfOELxt13320899xzxptvvmncfvvthsfjMV588cU1LvnaWe45MkUiEWPr1q3G3r17jfe+971rU9gVUCCWgKNHjxqA8cwzz1i3HTp0yACM119/veB9h4aGjM7OTuPVV181enp6yjoQT+U82T333HMGsOQ/8lJx4YUXGjfddNOC28466yxj//79OY//q7/6K+Oss85acNuNN95oXHzxxatWxo1guecpl7PPPtu46667TnfRNoyVnqOrr77a+NrXvmbccccdGzoQ1WRaAg4dOkQwGOSiiy6ybrv44osJBoMcPHgw7/0ymQz79u3jy1/+Muecc85aFHVdrfQ8ZYtGozgcDurr61ehlGsrkUjwwgsvsHfv3gW37927N+85OXTo0KLjL7/8cg4fPkwymVy1sq6nlZynbJlMhunpaRobG1ejiOtupefo+9//Pu+88w533HHHahfxlGlx7xIQCoVoaWlZdHtLSwuhUCjv/e6++27cbje33HLLahZvw1jpebKLxWLs37+fa6+9dkMtTLxS4+PjpNNpWltbF9ze2tqa95yEQqGcx6dSKcbHx2lvb1+18q6XlZynbN/4xjeYnZ3lqquuWo0irruVnKO33nqL/fv38/TTT+N2b/y4UQ1xHd155504HI6CP4cPHwbIufWUYRh5t6R64YUX+OY3v8lDDz20YbatWqnVPE92yWSSa665hkwmw3333XfaX8d6yn79S52TXMfnur3cLPc8mR555BHuvPNOHn300ZxfyspJseconU5z7bXXctddd7Ft27a1Kt4p2fiRXcZuvvlmrrnmmoLHbNmyhZdffpnR0dFFfxsbG1v0bc309NNPc/LkSTZv3mzdlk6n+dKXvsQ999xDf3//KZV9La3meTIlk0muuuoq+vr6+NWvflUWtUOA5uZmXC7Xom/wJ0+ezHtO2trach7vdrtpampatbKup5WcJ9Ojjz7Kpz71KX784x+zZ8+e1SzmulruOZqenubw4cMcOXKEm2++GXi3WdkwDNxuN7/85S+57LLL1qTsRVvH/kspkjlY5Nlnn7Vue+aZZwoOFhkfHzdeeeWVBT8dHR3GV77ylWUNMCklKzlPhmEYiUTC+NjHPmacc845xsmTJ9eiqGvqwgsvND772c8uuG3Hjh0FB9Xs2LFjwW033XRTRQyqWc55MgzD+NGPfmT4/X7jpz/96SqXbmNYzjlKp9OLrkGf/exnje3btxuvvPKKMTMzs1bFLpoCsURcccUVxnve8x7j0KFDxqFDh4xzzz130XSC7du3G4899ljexyj3UaaGsfzzlEwmjY985CNGV1eX8dJLLxkjIyPWTzweX4+XcNqZQ+W/973vGUePHjVuvfVWo6amxujv7zcMwzD2799v7Nu3zzrenHbxxS9+0Th69Kjxve99r6KmXRR7nn70ox8Zbrfb+Pa3v73gcxOJRNbrJay65Z6jbBt9lKkCsUSEw2HjE5/4hFFXV2fU1dUZn/jEJ4zJyckFxwDG97///byPUQmBuNzz1NfXZwA5f37961+veflXy7e//W2jp6fH8Hq9xvnnn2889dRT1t+uv/56433ve9+C43/zm98Yv//7v294vV5jy5Ytxv3337/GJV4fyzlP73vf+3J+bq6//vq1L/gaWu5nyW6jB6K2fxIREUGjTEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFokjFSafTXHLJJVx55ZULbo9Go3R3d/O1r31tnUomsr60dJtIBXrrrbc477zzeOCBB/jEJz4BwHXXXcfvfvc7nn/+ebxe7zqXUGTtKRBFKtS3vvUt7rzzTl599VWef/55/vzP/5znnnuO8847b72LJrIuFIgiFcowDC677DJcLhevvPIKf/mXf6nmUqloCkSRCvb666+zY8cOzj33XF588UXcbvd6F0lk3WhQjUgF+7d/+zeqq6vp6+tjaGhovYsjsq5UQxSpUIcOHeIP//AP+e///m/+4R/+gXQ6zf/8z//gcDjWu2gi60I1RJEKND8/z/XXX8+NN97Inj17+Nd//Veef/55vvvd76530UTWjQJRpALt37+fTCbD3XffDcDmzZv5xje+wZe//GX6+/vXt3Ai60RNpiIV5qmnnuL9738/v/nNb/iDP/iDBX+7/PLLSaVSajqViqRAFBERQU2mIiIigAJRREQEUCCKiIgACkQRERFAgSgiIgIoEEVERAAFooiICKBAFBERARSIIiIigAJRREQEUCCKiIgA8P8Akae0S/Uawp4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Test: create MEEP geometry, output_epsilon, and try to read this MEEP-generated hdf5\n",
    "\n",
    "resolution = 64\n",
    "cell_size = mp.Vector3(1,1,0)\n",
    "\n",
    "geometry = [mp.Cylinder(0.2, material=mp.Medium(epsilon=2.0))]\n",
    "\n",
    "sim = mp.Simulation(resolution=resolution,\n",
    "                    cell_size=cell_size,\n",
    "                    geometry=geometry,\n",
    "                   )\n",
    "sim.plot2D()\n",
    "sim.run(mp.at_beginning(mp.output_epsilon), until=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "46ef7706-f550-4008-a051-854480c99195",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sim = mp.Simulation(resolution=resolution,\n",
    "                    cell_size=cell_size,\n",
    "                    epsilon_input_file=\"eps-000000000.h5\",\n",
    "                   )\n",
    "sim.plot2D()\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}