create_h5file_using_h5py.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "a81e3505-f2df-4cad-b016-b63d1301f985",
   "metadata": {},
   "outputs": [],
   "source": [
    "import h5py\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fc399995-ef38-431e-949e-d7249338320e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 512x384 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Create test geometry\n",
    "x = np.linspace(-0.5, 0.5, 100)\n",
    "y = np.linspace(-0.5, 0.5, 100)\n",
    "xx, yy = np.meshgrid(x,y)\n",
    "C = (xx**2 + yy**2) <= 0.2**2\n",
    "C = 1.0 + C * (1.0)\n",
    "\n",
    "fig = plt.figure(dpi=80)\n",
    "plt.pcolormesh(x, y, C)\n",
    "plt.grid()\n",
    "plt.gca().set_aspect('equal')\n",
    "plt.colorbar()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "36a59c6a-e0b6-44cc-a27a-72cd61628a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write to h5 using h5py\n",
    "f = h5py.File(\"h5pytestfile.hdf5\", \"w\")\n",
    "dset = f.create_dataset(\"init\", data=C)\n",
    "f.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "d38926f7-82d3-471c-b6b3-391d8bc1c2ec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Test with MEEP\n",
    "import meep as mp\n",
    "\n",
    "resolution = 64\n",
    "cell_size = mp.Vector3(1,1,0)\n",
    "\n",
    "sim = mp.Simulation(resolution=resolution,\n",
    "                    cell_size=cell_size,\n",
    "                    epsilon_input_file=\"h5pytestfile.hdf5\",\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
}