157 lines
44 KiBLFS
Plaintext
157 lines
44 KiBLFS
Plaintext
{
|
|
"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": 2,
|
|
"id": "fcd2d712",
|
|
"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",
|
|
"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": 3,
|
|
"id": "eb8e13ff",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"polarizations = [ \"zyyz\", \"xyyx\" ]\n",
|
|
"peek = [ \"E21\", \"E22\", \"A11\" ]\n",
|
|
"data = {peak: {p: np.loadtxt(f'画图/拉曼结果拟合/combined/substrate/{peak}_shift_{p}.txt') for p in polarizations} for peak in peek}\n",
|
|
"normal_dist = lambda x, mu, sigma: np.exp( - (x - mu)**2 / (2 * sigma**2) ) * 0.5\n",
|
|
"mean = {peak: {p: np.mean(data[peak][p]) for p in polarizations} for peak in peek}\n",
|
|
"std = {peak: {p: np.std(data[peak][p]) for p in polarizations} for peak in peek}\n",
|
|
"distribute_range = {peak: {p: np.linspace(mean[peak][p] - 5*std[peak][p], mean[peak][p] + 5*std[peak][p], 100) for p in polarizations} for peak in peek}\n",
|
|
"theoretical_result = { \"E21\": 0.263, \"E22\": 0.221, \"A11\": 0.188 }\n",
|
|
"color = { \"zyyz\": \"#50cfd2\", \"xyyx\": \"#9d569c\" }\n",
|
|
"peak_label = { \"E21\": \"$\\\\mathrm{E_2}$-1\", \"E22\": \"$\\\\mathrm{E_2}$-2\", \"A11\": \"$\\\\mathrm{A_1}$-1\" }"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "06852a14",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 350x250 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(1, 3, figsize=(3.5, 2.5))\n",
|
|
"for i, peak in enumerate(peek):\n",
|
|
" for j, p in enumerate(polarizations):\n",
|
|
" axes[i].scatter(np.ones_like(data[peak][p]) - 1 + j, data[peak][p], color=color[p], alpha=0.4, s=25)\n",
|
|
" axes[i].plot(normal_dist(distribute_range[peak][p], mean[peak][p], std[peak][p]) + j + 0.2, distribute_range[peak][p], color=color[p])\n",
|
|
" axes[i].tick_params(direction='in')\n",
|
|
" axes[i].set_xticks([])\n",
|
|
" axes[i].set_xlabel(peak_label[peak])\n",
|
|
" axes[i].set_xlim(-0.2, 1.8)\n",
|
|
"axes[0].set_ylabel('Raman shift (cm$^{-1}$)')\n",
|
|
"# axes[0].set_title('Experimental results', fontsize=10)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.subplots_adjust(hspace=0.4)\n",
|
|
"plt.show() \n",
|
|
"fig.savefig(f'画图/弱极性不同方向偏移/1.svg', format='svg', transparent=True, bbox_inches='tight')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 20,
|
|
"id": "1b02c147",
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"image/png": "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",
|
|
"text/plain": [
|
|
"<Figure size 350x200 with 1 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, ax = plt.subplots(figsize=(3.5, 2))\n",
|
|
"for i, peak in enumerate(peek):\n",
|
|
" ax.bar(i * 0.8, abs(mean[peak]['zyyz'] - mean[peak]['xyyx']), width=0.2, color='#1bb8df')\n",
|
|
" ax.errorbar(i * 0.8, abs(mean[peak]['zyyz'] - mean[peak]['xyyx']), yerr=np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2), capsize=5, color='#72e8b4')\n",
|
|
" ax.text(i * 0.8, abs(mean[peak]['zyyz'] - mean[peak]['xyyx']) + np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2) + 0.01, f'{abs(mean[peak][\"zyyz\"] - mean[peak][\"xyyx\"]):.3f}\\n$\\\\pm$ {np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2):.3f}', ha='center', fontsize=8)\n",
|
|
" ax.bar(i * 0.8 + 0.3, theoretical_result[peak], width=0.2, color='#7b7bc9')\n",
|
|
" ax.text(i * 0.8 + 0.3, theoretical_result[peak] + 0.005, f'{theoretical_result[peak]:.3f}', ha='center', fontsize=8)\n",
|
|
"ax.set_xticks([0.15, 0.95, 1.75])\n",
|
|
"ax.set_xticklabels([peak_label[peak] for peak in peek])\n",
|
|
"ax.set_ylabel('Difference of Raman shift\\n(cm$^{-1}$)')\n",
|
|
"ax.set_ylim(0, 0.4)\n",
|
|
"ax.tick_params(direction='in')\n",
|
|
"# ax.set_title('Experimental v.s. calculated', fontsize=10, x=0.35)\n",
|
|
"plt.tight_layout()\n",
|
|
"plt.show()\n",
|
|
"fig.savefig(f'画图/弱极性不同方向偏移/2.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": "3.13.9"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
}
|