SiC-2nd-paper/画图/E12/plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "515e4fab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/chn/repo/SiC-2nd-paper'"
      ]
     },
     "execution_count": 2,
     "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",
    "from scipy.signal import savgol_filter\n",
    "\n",
    "%pwd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d42fca0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.plotly.v1+json": {
       "config": {
        "plotlyServerURL": "https://plot.ly"
       },
       "data": [
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            "type": "scattermapbox"
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         "layout": {
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          "font": {
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          "geo": {
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           "lakecolor": "white",
           "landcolor": "#E5ECF6",
           "showlakes": true,
           "showland": true,
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          "hoverlabel": {
           "align": "left"
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          "hovermode": "closest",
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           "bgcolor": "#E5ECF6",
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            "gridcolor": "white",
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          },
          "scene": {
           "xaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "yaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           },
           "zaxis": {
            "backgroundcolor": "#E5ECF6",
            "gridcolor": "white",
            "gridwidth": 2,
            "linecolor": "white",
            "showbackground": true,
            "ticks": "",
            "zerolinecolor": "white"
           }
          },
          "shapedefaults": {
           "line": {
            "color": "#2a3f5f"
           }
          },
          "ternary": {
           "aaxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "baxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           },
           "bgcolor": "#E5ECF6",
           "caxis": {
            "gridcolor": "white",
            "linecolor": "white",
            "ticks": ""
           }
          },
          "title": {
           "x": 0.05
          },
          "xaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          },
          "yaxis": {
           "automargin": true,
           "gridcolor": "white",
           "linecolor": "white",
           "ticks": "",
           "title": {
            "standoff": 15
           },
           "zerolinecolor": "white",
           "zerolinewidth": 2
          }
         }
        },
        "width": 1920
       }
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 从 251019/1 读入 xzyx 的数据\n",
    "x = [None for _ in range(5)]\n",
    "y = [None for _ in range(5)]\n",
    "for i in range(5):\n",
    "  data = np.loadtxt(f'画图/拉曼结果拟合/251019/1/{i+1}.txt')\n",
    "  x[i] = data[:, 0]\n",
    "  y[i] = data[:, 1]\n",
    "fig = go.Figure()\n",
    "for i in range(5):\n",
    "  fig.add_trace(go.Scatter(x=x[i], y=np.log10(y[i]), mode='lines', name=f'{i}'))\n",
    "fig.update_layout(width=1920, height=1080)\n",
    "fig.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "75a2e608",
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 200x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize=(2, 2))\n",
    "ax = fig.gca()\n",
    "\n",
    "for i in range(5):\n",
    "  ax.plot(x[i], np.log10(y[i]), color=\"gray\", linewidth=1)\n",
    "ax.set(xlim=[740, 790], ylim=[2.1, 3.8])\n",
    "ax.tick_params(direction='in')\n",
    "ax.set_xticks(range(740, 791, 10))\n",
    "fig.text(-0.15, 0.5, \"Raman intensity\\n(log, a.u.)\", va='center', ha='center', rotation='vertical', fontsize=12, fontname='Arial')\n",
    "# ax.xaxis.set_ticks_position('top')\n",
    "ax.set_xticklabels(range(740, 791, 10), fontsize=12, fontname='Arial')\n",
    "ax.set_xlabel(\"Raman shift (cm$^{-1}$)\", fontsize=12, fontname='Arial')\n",
    "# ax.xaxis.set_label_position('top')\n",
    "ax.set_yticks([])\n",
    "ax.set_yticklabels([])\n",
    "fig.subplots_adjust(left=0, right=1, top=1, bottom=0, wspace=0, hspace=0)\n",
    "ax.set_title(\"Raman experimental spectra\\n(edge incidence, $\\\\mathrm{x(zy)\\\\overline{x}}$)\", fontsize=12, fontname='Arial')\n",
    "fig.savefig(f'画图/E12/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
}