Chap5.Fig5.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c209a46b-f137-46d8-b3eb-a3f24e927169",
   "metadata": {},
   "outputs": [],
   "source": [
    "from aurora.solver import PWE2D\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "f7e9ae91-037e-4a26-86a7-c0e246c2f9fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 301\n",
    "x = np.arange(-N//2+1, N//2+1)\n",
    "X, Y = np.meshgrid(x, x)\n",
    "struct = np.ones((N, N, 1))\n",
    "struct[X**2+Y**2<(0.2*N)**2] = 8.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "d9479c40-bdec-47d1-8187-fa7eddc2ac1f",
   "metadata": {},
   "outputs": [],
   "source": [
    "a1, a2 = [1 , 0.0], [0, 1]\n",
    "from aurora.lattice import Lattice\n",
    "l = Lattice(a1, a2)\n",
    "path = l.bz_path([\"G\", \"X\", \"M\", \"G\"], [10, 10, 10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "cf59c9c4-3e4f-476e-904b-57ce1b5f4a33",
   "metadata": {},
   "outputs": [],
   "source": [
    "solver = PWE2D()\n",
    "te, tm = solver.solve_kpoints(struct, path, (5, 5), (1.0, 1.0))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ed5de355-afa9-414a-9ff8-6c7d2cce5240",
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'matplotlib.pyplot' has no attribute 'xtickslabels'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m/home/kaeryv/prj/aurora/Chap5.Fig2.ipynb Cell 5'\u001b[0m in \u001b[0;36m<cell line: 8>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/kaeryv/prj/aurora/Chap5.Fig2.ipynb#ch0000004?line=5'>6</a>\u001b[0m plt\u001b[39m.\u001b[39maxis([\u001b[39m0\u001b[39m, \u001b[39m30\u001b[39m, \u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/kaeryv/prj/aurora/Chap5.Fig2.ipynb#ch0000004?line=6'>7</a>\u001b[0m plt\u001b[39m.\u001b[39mxticks([\u001b[39m0\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m20\u001b[39m, \u001b[39m30\u001b[39m])\n\u001b[0;32m----> <a href='vscode-notebook-cell:/home/kaeryv/prj/aurora/Chap5.Fig2.ipynb#ch0000004?line=7'>8</a>\u001b[0m plt\u001b[39m.\u001b[39;49mxtickslabels([\u001b[39m0\u001b[39m, \u001b[39m10\u001b[39m, \u001b[39m20\u001b[39m, \u001b[39m30\u001b[39m])\n\u001b[1;32m      <a href='vscode-notebook-cell:/home/kaeryv/prj/aurora/Chap5.Fig2.ipynb#ch0000004?line=8'>9</a>\u001b[0m plt\u001b[39m.\u001b[39msavefig(\u001b[39m\"\u001b[39m\u001b[39mfigs/joa/5/2.png\u001b[39m\u001b[39m\"\u001b[39m)\n",
      "\u001b[0;31mAttributeError\u001b[0m: module 'matplotlib.pyplot' has no attribute 'xtickslabels'"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "for o in te.T:\n",
    "    plt.plot(o, 'b.')\n",
    "for o in tm.T:\n",
    "    plt.plot(o, 'r.')\n",
    "plt.axis([0, 30, 0, 1])\n",
    "plt.xticks([0, 10, 20, 30])\n",
    "plt.xticks([0, 10, 20, 30])\n",
    "plt.savefig(\"figs/joa/5/2.png\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18f58c4c-8aed-4031-8785-0fdaa126e4de",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.10.4 ('env': venv)",
   "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": "3.10.4"
  },
  "vscode": {
   "interpreter": {
    "hash": "650050455b0d877d86c5aa82b9f87099bce865684822d318fefcc7388f1de0d0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}