SiC-2nd-paper/画图/弱极性不同方向偏移/plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "01ce11e3",
   "metadata": {},
   "source": [
    "使用以下实验结果：\n",
    "* 正入射使用 250923/1\n",
    "* 肩入射使用 250923/2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "fcd2d712",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/chn/repo/SiC-2nd-paper'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "from scipy.optimize import curve_fit\n",
    "import matplotlib.pyplot as plt\n",
    "import plotly.graph_objects as go\n",
    "import matplotlib\n",
    "import pandas as pd\n",
    "from brokenaxes import brokenaxes\n",
    "plt.rcParams['text.usetex'] = True\n",
    "plt.rcParams['font.family'] = 'Arial'\n",
    "\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "eb8e13ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "polarizations = [\"zyyz\", \"xyyx\"]\n",
    "E21_shift = {p: np.loadtxt(f'画图/拉曼结果拟合/combined/substrate/E21_shift_{p}.txt') for p in polarizations}\n",
    "E22_shift = {p: np.loadtxt(f'画图/拉曼结果拟合/combined/substrate/E22_shift_{p}.txt') for p in polarizations}\n",
    "A11_shift = {p: np.loadtxt(f'画图/拉曼结果拟合/combined/substrate/A11_shift_{p}.txt') for p in polarizations}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "06852a14",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x300 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 我们要画三个非常相似的箱图\n",
    "# np.mean(E21_shift[\"zyyz\"]) 作为中心， np.std(E21_shift[\"zyyz\"]) 作为误差画箱图\n",
    "fig, axes = plt.subplots(1, 3, figsize=(6, 3))\n",
    "axes[0].boxplot([E21_shift[\"zyyz\"], E21_shift[\"xyyx\"]], widths=0.4, patch_artist=True,\n",
    "  boxprops=dict(facecolor=\"lightblue\", color=\"black\", alpha=0.5),\n",
    "  medianprops=dict(color=\"darkred\", linewidth=2))\n",
    "axes[0].scatter(np.ones_like(E21_shift[\"zyyz\"]), E21_shift[\"zyyz\"], color='gray', alpha=0.6, s=25)\n",
    "axes[0].scatter(np.ones_like(E21_shift[\"xyyx\"]) * 2, E21_shift[\"xyyx\"], color='gray', alpha=0.6, s=25)\n",
    "axes[0].plot([3, 3], [195.3, 195.3 + 0.263], color='green', linewidth=2.0, zorder=4)\n",
    "axes[0].plot([3 - 0.12, 3 + 0.12], [195.3, 195.3], color='green', linewidth=2.0, zorder=4)\n",
    "axes[0].plot([3 - 0.12, 3 + 0.12], [195.3 + 0.263, 195.3 + 0.263], color='green', linewidth=2.0, zorder=4)\n",
    "axes[0].text(3.2, 195.3 + 0.263 / 2, 'calculated shift', color='green', fontsize=10, va='center', rotation=90)\n",
    "axes[0].set_xlim(0.5, 4)\n",
    "axes[0].tick_params(direction='in')\n",
    "axes[0].set_xticklabels(['$\\\\mathrm{z(yy)\\\\overline{z}}$', '$\\\\mathrm{x(yy)\\\\overline{x}}$'], fontsize=10, rotation=45)\n",
    "axes[0].set_ylabel('Raman shift (cm$^{-1}$)')\n",
    "axes[0].set_yticks(np.linspace(195.2, 195.6, 5))\n",
    "axes[1].boxplot([E22_shift[\"zyyz\"], E22_shift[\"xyyx\"]], widths=0.4, patch_artist=True,\n",
    "  boxprops=dict(facecolor=\"lightblue\", color=\"black\", alpha=0.5),\n",
    "  medianprops=dict(color=\"darkred\", linewidth=2))\n",
    "axes[1].scatter(np.ones_like(E22_shift[\"zyyz\"]), E22_shift[\"zyyz\"], color='gray', alpha=0.6, s=25)\n",
    "axes[1].scatter(np.ones_like(E22_shift[\"xyyx\"]) * 2, E22_shift[\"xyyx\"], color='gray', alpha=0.6, s=25)\n",
    "axes[1].plot([3, 3], [203.32, 203.32 - 0.221], color='green', linewidth=2.0, zorder=4)\n",
    "axes[1].plot([3 - 0.12, 3 + 0.12], [203.32, 203.32], color='green', linewidth=2.0, zorder=4)\n",
    "axes[1].plot([3 - 0.12, 3 + 0.12], [203.32 - 0.221, 203.32 - 0.221], color='green', linewidth=2.0, zorder=4)\n",
    "axes[1].text(3.2, 203.32 - 0.221 / 2, 'calculated shift', color='green', fontsize=10, va='center', rotation=90)\n",
    "axes[1].set_xlim(0.5, 4)\n",
    "axes[1].tick_params(direction='in')\n",
    "axes[1].set_xticklabels(['$\\\\mathrm{z(yy)\\\\overline{z}}$', '$\\\\mathrm{x(yy)\\\\overline{x}}$'], fontsize=10, rotation=45)\n",
    "axes[1].set_yticks(np.linspace(203, 203.4, 5))\n",
    "axes[2].boxplot([A11_shift[\"zyyz\"], A11_shift[\"xyyx\"]], widths=0.4, patch_artist=True,\n",
    "  boxprops=dict(facecolor=\"lightblue\", color=\"black\", alpha=0.5),\n",
    "  medianprops=dict(color=\"darkred\", linewidth=2))\n",
    "axes[2].scatter(np.ones_like(A11_shift[\"zyyz\"]), A11_shift[\"zyyz\"], color='gray', alpha=0.6, s=25)\n",
    "axes[2].scatter(np.ones_like(A11_shift[\"xyyx\"]) * 2, A11_shift[\"xyyx\"], color='gray', alpha=0.6, s=25)\n",
    "axes[2].plot([3, 3], [609.7, 609.7 - 0.188], color='green', linewidth=2.0, zorder=4)\n",
    "axes[2].plot([3 - 0.12, 3 + 0.12], [609.7, 609.7], color='green', linewidth=2.0, zorder=4)\n",
    "axes[2].plot([3 - 0.12, 3 + 0.12], [609.7 - 0.188, 609.7 - 0.188], color='green', linewidth=2.0, zorder=4)\n",
    "axes[2].text(3.2, 609.7 - 0.188 / 2, 'calculated shift', color='green', fontsize=10, va='center', rotation=90)\n",
    "axes[2].set_xlim(0.5, 4)\n",
    "axes[2].tick_params(direction='in')\n",
    "axes[2].set_xticklabels(['$\\\\mathrm{z(yy)\\\\overline{z}}$', '$\\\\mathrm{x(yy)\\\\overline{x}}$'], fontsize=10, rotation=45)\n",
    "axes[2].set_yticks(np.linspace(609.4, 609.8, 5))\n",
    "# ax.legend()\n",
    "plt.tight_layout()\n",
    "plt.show() \n",
    "fig.savefig(f'画图/弱极性不同方向偏移/拉曼.svg', format='svg', transparent=True, bbox_inches='tight')"
   ]
  }
 ],
 "metadata": {
  "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": "3.13.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}