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SiC-2nd-paper/画图/E1减小/plot.ipynb
chn 3488cf33ee
2025-09-15 13:59:52 +08:00

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"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",
"\n",
"def fano(x, A, x0, gamma, q, b):\n",
" epsilon = (2 * (x - x0)) / gamma\n",
" return A * ((q + epsilon)**2 / (1 + epsilon**2)) + b\n",
"def lorentzian(x, A, x0, gamma, k, b):\n",
" return A / (1 + ((x - x0) / gamma)**2) + k * x + b\n",
"def double_fano(x, A1, x1, gamma1, q1, A2, x2, gamma2, q2, b):\n",
" return fano(x, A1, x1, gamma1, q1, b) + fano(x, A2, x2, gamma2, q2, 0)\n",
"def lorentzian_fano(x, A1, x1, gamma1, A2, x2, gamma2, q2, k, b):\n",
" return lorentzian(x, A1, x1, gamma1, k, b) + fano(x, A2, x2, gamma2, q2, 0, 0)\n",
"def double_lorentzian(x, A1, x1, gamma1, A2, x2, gamma2, k, b):\n",
" return lorentzian(x, A1, x1, gamma1, k, b) + lorentzian(x, A2, x2, gamma2, 0, 0)\n",
"\n",
"%pwd"
]
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"source": [
"offset = [0, 0.3, 0]\n",
"fig = go.Figure()\n",
"for i in range(1, 4):\n",
" # 从 ../stuff/42/20250422/1. 测试斜切角度/1.txt 中读取数据，第一列是x, 第二列是y\n",
" data = np.loadtxt(f'../stuff/42/20250422/1. 测试斜切角度/{i}.txt')\n",
" x = data[:, 0] + offset[i-1]\n",
" y = data[:, 1]\n",
" fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name=f'{i}'))\n",
"fig.update_layout(width=1920, height=1080)\n",
"fig.show()"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "1f813749",
"metadata": {},
"outputs": [],
"source": [
"def fitting_peek(x, y, x_range, function, guess, bound):\n",
" mask = (x > x_range[0]) & (x < x_range[1])\n",
" x = x[mask]\n",
" y = y[mask]\n",
" popt, pcov = curve_fit(function, x, y, p0=guess, maxfev=10000, bounds=bound)\n",
" return popt\n",
"\n",
"def fitting_line(x, y):\n",
" shift = []\n",
" height = []\n",
" width_of_half_peak = []\n",
" integration = []\n",
"\n",
" result = fitting_peek(x, y, fitting_range[0], double_lorentzian, fitting_init_parameter[0], fitting_bound[0])\n",
" shift.append(result[1])\n",
" shift.append(result[4])\n",
" height.append(result[0])\n",
" height.append(result[3])\n",
" width_of_half_peak.append(result[2] * 2)\n",
" width_of_half_peak.append(result[5] * 2)\n",
" integration.append(result[0] * result[2] * np.pi)\n",
" integration.append(result[3] * result[5] * np.pi)\n",
"\n",
" result = fitting_peek(x, y, fitting_range[1], lorentzian, fitting_init_parameter[1], fitting_bound[1])\n",
" shift.append(result[1])\n",
" height.append(result[0])\n",
" width_of_half_peak.append(result[2] * 2)\n",
" integration.append(result[0] * result[2] * np.pi)\n",
"\n",
" result = fitting_peek(x, y, fitting_range[2], lorentzian, fitting_init_parameter[2], fitting_bound[2])\n",
" shift.append(result[1])\n",
" height.append(result[0])\n",
" width_of_half_peak.append(result[2] * 2)\n",
" integration.append(result[0] * result[2] * np.pi)\n",
"\n",
" result = fitting_peek(x, y, fitting_range[3], double_lorentzian, fitting_init_parameter[3], fitting_bound[3])\n",
" shift.append(result[1])\n",
" shift.append(result[4])\n",
" height.append(result[0])\n",
" height.append(result[3])\n",
" width_of_half_peak.append(result[2] * 2)\n",
" width_of_half_peak.append(result[5] * 2)\n",
" integration.append(result[0] * result[2] * np.pi)\n",
" integration.append(result[3] * result[5] * np.pi)\n",
"\n",
" return shift, height, width_of_half_peak, integration"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "0dd0e315",
"metadata": {},
"outputs": [],
"source": [
"fitting_init_parameter = [\n",
" [300, 196, 1, 2e3, 204, 0.8, 0, 900],\n",
" [50, 266, 5, 0, 100],\n",
" [1e3, 611, 5, 0, 100],\n",
" [1e4, 776, 1, 1e3, 798, 1, 1, 100]\n",
"]\n",
"fitting_range = [\n",
" [180, 220],\n",
" [250, 280],\n",
" [600, 617],\n",
" [700, 850]\n",
"]\n",
"fitting_bound = [\n",
" ([0, 192, 0, 0, 200, 0, -np.inf, -np.inf], [np.inf, 198, np.inf, np.inf, 206, np.inf, np.inf, np.inf]),\n",
" ([0, 260, 0, -np.inf, -np.inf], [np.inf, 270, np.inf, np.inf, np.inf]),\n",
" ([0, 605, 0, -np.inf, -np.inf], [np.inf, 617, np.inf, np.inf, np.inf]),\n",
" (-np.inf, np.inf)\n",
"]\n",
"# A1, x1, gamma1, A2, x2, gamma2, k, b):"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "5317702a",
"metadata": {},
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],
"source": [
"for i in range(1, 4):\n",
" data = np.loadtxt(f'../stuff/42/20250422/1. 测试斜切角度/{i}.txt')\n",
" x = data[:, 0] + offset[i-1]\n",
" y = data[:, 1]\n",
" result = fitting_peek(x, y, fitting_range[3], double_lorentzian, fitting_init_parameter[3], fitting_bound[3])\n",
" fig = go.Figure()\n",
" fig.add_trace(go.Scatter(x=x, y=y, mode='lines', name='data'))\n",
" fig.add_trace(go.Scatter(x=x, y=double_lorentzian(x, *result), mode='lines', name='fit'))\n",
" # fig.update_yaxes(range=[1, 4])\n",
" fig.show()\n",
" print(result[3])"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "de30c5c7",
"metadata": {},
"outputs": [
{
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",
"text/plain": [
"<Figure size 400x150 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig = plt.figure(figsize=(4, 1.5))\n",
"ax = fig.gca()\n",
"color = [\"gray\", \"orange\", \"green\"]\n",
"\n",
"for i in range(1, 4):\n",
" data = np.loadtxt(f'../stuff/42/20250422/1. 测试斜切角度/{i}.txt')\n",
" x = data[:, 0] + offset[i-1]\n",
" y = data[:, 1]\n",
" ax.plot(x, np.log10(y), color=color[i-1], linewidth=1)\n",
"ax.set(xlim=[770, 810], ylim=[2.5, 4.7])\n",
"# fig.text(-0.02, 0.9 - i * 0.2, title[i], va='center', ha='center', rotation='vertical', fontsize=12, fontname='Arial')\n",
"ax.tick_params(direction='in')\n",
"ax.set_xticks(range(770, 810, 10))\n",
"fig.text(-0.05, 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(770, 810, 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(normal incidence, $\\\\mathrm{zyy\\\\overline{z}}$)\", fontsize=12, fontname='Arial')\n",
"fig.savefig(f'画图/E1减小/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": "3.12.10"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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