{ "cells": [ { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'/home/chn/repo/SiC-2nd-paper'" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "import pandas as pd\n", "from scipy.optimize import curve_fit\n", "import matplotlib.pyplot as plt\n", "import plotly.graph_objects as go\n", "plt.rcParams['text.usetex'] = True\n", "plt.rcParams['font.family'] = 'Arial'\n", "# get current working directory\n", "import os\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"gridcolor": "white", "linecolor": "white", "ticks": "" } }, "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, "yaxis": { "range": [ 22, 30 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "for m in range(0, 24):\n", " fig.add_trace(go.Scatter(x=[*range(0, 101)], y=data1[m,:], mode='lines', name=f'{m}'))\n", "fig.update_layout(width=1920, height=1080, yaxis_range=[22, 30])\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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"standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 }, "yaxis": { "automargin": true, "gridcolor": "white", "linecolor": "white", "ticks": "", "title": { "standoff": 15 }, "zerolinecolor": "white", "zerolinewidth": 2 } } }, "width": 1920, "yaxis": { "range": [ 22, 30 ] } } } }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = go.Figure()\n", "for m in range(0, 24):\n", " fig.add_trace(go.Scatter(x=[*range(0, 101)], y=data2[m,:], mode='lines', name=f'{m}'))\n", "fig.update_layout(width=1920, height=1080, yaxis_range=[22, 30])\n", "fig.show()" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "# 设置颜色\n", "# A-Gamma 的颜色\n", "color1 = ['gray'] * 24\n", "color1[23] = 'green'\n", "color1[19] = 'magenta'\n", "color1[18] = 'magenta'\n", "# Gamma-K 的颜色\n", "color2 = ['gray'] * 24\n", "color2[23] = 'red'\n", "color2[19] = 'blue'\n", "color2[16] = 'green'\n", "\n", "# data1 为 A-Gamma 的数据，data2 为 Gamma-K 的数据。\n", "# 第一个下标为第几个声子线，第二个下标为倒空间中的坐标\n", "\n", "# 取 A-Gamma 的长度为 100，那么 Gamma-K 的长度应该是 378。将它上采样。\n", "new_data2 = np.zeros((24, 379))\n", "for i in range(0, 24):\n", " new_data2[i, :] = np.interp(np.linspace(0, 1, 379), np.linspace(0, 1, 101), data2[i, :])\n", "\n", "# 将单位从 THz 转到 cm^-1\n", "data1 = data1 * 33.35641\n", "new_data2 = new_data2 * 33.35641\n", "\n", "# 横坐标每一格为 0.0004937 A-1\n", "x1 = np.linspace(-100, 0, 101) * 0.0004937 * 1000\n", "x2 = np.linspace(0, 378, 379) * 0.0004937 * 1000\n" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(1.9, 1.6))\n", "ax = fig.add_subplot()\n", "for i in range(0, 24):\n", " ax.plot(x1, data1[i,:], color=color1[i], linewidth=0.5)\n", "for i in range(0, 24):\n", " ax.plot(x2, new_data2[i,:], color=color2[i], linewidth=0.5)\n", "\n", "# 重绘几条线，让它们在最上面\n", "for i in [18, 19, 23]:\n", " ax.plot(x1, data1[i,:], color=color1[i], linewidth=0.5)\n", "for i in [16, 19, 23]:\n", " ax.plot(x2, new_data2[i,:], color=color2[i], linewidth=0.5)\n", "ax.set(xlim=[-49.37, 186.61], ylim=[0, 1000])\n", "ax.tick_params(direction='in', labelsize=8)\n", "ax.xaxis.set_ticks_position('top')\n", "ax.set_xticks(np.arange(-50, 160, 50))\n", "# ax.set_xticks(np.arange(-50, 160, 100), minor=True)\n", "ax.tick_params(axis='x', which='minor', direction='in')\n", "ax2 = ax.twinx()\n", "ax2.spines['right'].set_position(['axes', 100 / 478])\n", "ax2.set_yticklabels([])\n", "ax2.tick_params(direction='inout')\n", "# 将左侧标签稍微往右移动一点\n", "ax.tick_params(axis='y', direction='in', pad=0)\n", "ax.set_ylabel(r'Frequency (cm$^{-1}$)', fontsize=8)\n", "ax.xaxis.set_label_position('top')\n", "ax.set_xlabel(r'$q$ ($\\times 10^{-3}\\textrm{\\AA}^{-1}$)', fontsize=8)\n", "fig.tight_layout()\n", "fig.savefig('画图/声子不连续/整体图.svg', format='svg', transparent=True)" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "image/png": 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QqhOAQXzXpfRumu4a8XxnQ7iVD+DaHb/AqfPH8BGtFjB+RFlebnkSKPtyON37f0XLFEDL9Z85gOJbRHvXATfIxkvvew3QF9FjsY4Y/00n9IEAUPYlYNNDmB2jZcS/6yXklIjPTd46UYcd9DxKHTE+fQ343a/hI7pl1REA6Hex1BH3vwFA5uhUJKWOob6+ftHfzWazYnaQIAgLOgUAKNhkDU+xLJa1OnUGZeFfswEl2+8H0EEjkOLwTZ3xz2EGZUCu+Na24Tr6YRTfFD7W6pTp5pXSD6Noh3RICJSyuWsBk2wjIFZwGKZdMtkS5f+ygsNYKxskzimKkN2qlDV+JHysL1DK5m+mH9DZSU6nE1VrNtOV5fVrImy4KfwgA/hwSIuiDbeH15iKsDfWyBbUYw8qQCsfuWxM9jbRD4NVFoxY7L32zvBxTnGEDW+IkP2oTLYwwobUIWu1J2nEYLwj/Js+P0L2evphlOwMH88rsxvpR5K9VSabG2HD9WHHBQDFN4ePtfoI2XX0w2DOBaAOYgl7hwLG8LnJghnyNgiZAnKNGAttQwhiV03hdiiIwd7+NbdgMvgOjRii2Zs1ggDAeHv4eIkyG83e2tyLNEoQy2xgiEYburU7AbYawjLKbBBUlsyz4cJ1BDSaGMvsHUgUaf0WksVigSAIaG1tRUNDA+x2u2ppsw20I99+VnudpEylrKwM09PTC64nJYcQgt7eXj5NVYZOp1t0E5vVQjImVbA8kvGiaG5urmK6Ktu3JdZFNTN9a8+YIwa1trtcLmxGUiJmJmm12nlvPweDQdXXScqkN58Z7O3lnp4erF+/flFZt9uNqakp7hhkSFs7JmFLynQiEe8xpNIxBAIBaDSamJf8zvR7HrP2iy21DcS3AFuy0ev181YSDYVCWbmAXiQ5OTnYunXrsu7n5cuXodFosHXr1iVlswVWkaz2qCEZayUl0zHk5OTMixhycnJizluKGDJomroc1d2aWm8mJ4Pc3Nyoc5aLiopUzysTC8j27dvR29u7pO59fX3YuHEj74KTIY8YsoGFIgU1KvNkLYkB0DpBvu3v7Ozsil5uk+5/trzHEI0333wTjz32GEwmEw4fPqxGkknBYDAoWgdshpTajiETu5IA6hhmZmYwMjKyqBzbmIcTht3zbHEMjESU9XSIGLKNmBzD6dOncfr0aQC0++CZZ56ByWRCfX09TCYT3G432tvbE6FnQlizZo3i7VS2HIbaC+hlKlu2bIFOp1u0O8nr9cLr9fLxhQiyLWKIhtrLbidrjCEyYojnTf5M7CkAYnQMY2NjGBsbw6OPPgqr1QqNRgOHw4HOzk5UVlYCAHbt2rVEKulDQUGBtCwGAIyP05ef0nWDoWSj1+uxZcsW9Pb2LijT10ffvuURgxLuGNQjmRGDXq9HKBSSGoxzc3Mrihi06T3hc0li1l6j0eDw4cNoamqC2WyW1u7PxO4S1mXElt9mjiHZM6/SmW3bti06ztDb24t169ahoKAgyZqlN8wxZGqLUQ0ycVYScwJsyfS5ubkVRQyZfv9jumL5iqps+qjX68WRI0fQ2dkJi8WC9vZ2PP744+pqmSCYY3C73SgsLJSW4VY7Ysi0RfTkbNu2DSdOnMDo6Ki0V7ac/v5+PhspCtk2xsCuN5GVd7IiBoA6hLy8vJU7hmyLGCIpKSnB7t27sW/fPng8HjQ1NamhV1JgYwlsbIFFDnx10DBbtmyBVquNOs7g8/lw9epV3o0UhUxvMS6XZE5XTQYsYmDjDCt1DIxMvf+qurVdu3bhueeeUzPJhMIiA/aSm8/ny/gXU9SG7d/c398/77eBAbpQH3cM88n0PX/TiWSPMQDhrqRgMBhXV1Kmorr2ydrARw3YOkzMMfj9/oy/oYlg69at0iCznL6+PuTn5/MxmShkW1dSIkmFY5APPsf61vNqYEW1IOtWuHz5Mg4cOKCmPkmFdSWxsYVAIJCVhWAptm7dCq/XKw3OMwYHB7F169aMnHiQaLKlKykamXzN7PmXRwzx1AmZ+oJbzDHSM888A5fLhR07dsDj8WDHjh342te+hp/85CeJ0C+hsIeXbf4db3/iaoUNLg8ODkrOlBCCwcFB3HfffYv9a9aTyZVkupDMiIE5ARYxrNQxZPrgc8y1YFVVFSoqKnDw4EHs2bMHAHDkyBHVFUsWGo1G4RgMBkOKNUo/ioqKUFxcjIGBAdx2220A6Dstfr+f7++8AHyMYRHSeK2kSMcQ7yKImXr/Y3YMLpcLPT09klN48803M7orQavVSjMQQqFQQiKGTLYPY9OmTRgeHpbO2fGmTZsW+pesJtscw0LTVTOt7Ee+mBgKhbKyeznmWnDPnj2KPQw8Hg+qqqpUVSqZaLVaqT8xFApl5booy2Hjxo04efIkCCHQaDQYHh5GcXEx8vPzU60aJ0tIZsQgdwzZOCFlRc1j+QtgSy3Dne7IHQMhJGGtg0xvOa5fvx4zMzOYnp5GQUEBRkdHl9ynIZvJtohhtRA5mywUCmVc1KMGcfWbHD16FI888sjSgnHQ2toKgL6dbDabpTWZ1EKj0UiFgBDCB58XoLSUbkfodrtRUFAAt9uN7du3p1YpDkdlImeTEUJWFDFkepQRVy3odrvV0iMqLpcLdrtdepu6qqpKdceg1WoVjiHTb2iiYO98CIKArVu3QhAE/v7CIvCIQT2SaUO1I4ZM3dozrWvB9vZ2qUICaOWk9rLekesYJcIxrIZQNDc3V5rBFQqFMDs7y2dwcTirlLTuN+nu7pa6MADAZDJBEIR5cqxiHxoaUnyfl5e35K5iHo8HhBCMj4/D5/Ohr69v3otc8TIyMoJgMKh6usnG5/PhzJkzuO666+Dz+XDlyhXs2LEj1WqlJZcuXYLP58PIyMiqXnl2YGAAPp8PFy9eRHl5OYaHhzE7O4vP/b/P4Z2ed/AVfEVR7j8J4K88wNdjeBRYHgMDA4r6IBF4vV74fD50d3djy5YtGBsbw6VLl2C1WmNKxzfnA3xAYDqA8ddeA770JeDVV4G/+AvVdJ2YmACQoIiKxMHBgwfj+fclqa+vJzabTTqvra0lTU1N8+S6uroI6Oxo/uEf/uGfrPp0dXWpXvfGFTEkuo95x44digiBDUBHUlhYCAA4efKkYl79ciKGH/7whwgGg3jyySexb98+mM1mPPbYY+pcgMhLL72EqakpfOtb31I13WTz4osvwmq14s4778SLL76I6upq3HTTTalWKy3p7u7G4cOH8Y1vfGNV7wg4NjaG5uZm3HvvvfjTP/1THDx4EIIg4K3r38LpvtP4G93foL6+XpIvKQE+8xngF79Yfh69vb345S9/ibq6OphMpgRcRRifz4cXXngBn/vc53DrrbfiRz/6ET760Y/ik5/85MoTPXQIqK0FmpsBFeuWwcFB7Ny5U6r/1CQux5DoqaqVlZVoaGiQzl0uV9TBZ9aHv2nTJmzZsiWmPAwGA+bm5lBcXAyDwYA1a9ao/iCzLUQzvYIwGAwoKCiQbFVYWJjx15QoioqKYDAYUFxcvKptFAgEFOVizZo18Pl80K/RQ2PQwKAzzLv+nBwgFpMwW7I38BNJbm6uomzn5eVJ17Zi2Ls++fmxXfgSsC66RIxhxuUY3nzzTQiCILXkH3zwQbX0AgCp9d7a2gq3243GxkZV008mhM9O4XCymwya8bgix3D8+HHYbDYYjUYptDt27BgaGhrQ2Nio6rsNbKe4RBHpbRNRga+GWUmc2OANAY5EBpaFFTmGnp4eHDt2LOpvBw8ejEuhVMAfYk6i4I2C+OE2TD4rim0WG3RO9HSyRMOdBIfDyXZWPMbwxBNPYO3atQpH0NHRofqMnkQjb41EvuzG4XBWBn+OMpsVOYbdu3fDYrGgvb0d3d3dAOjU0ueeew5lZWWqKphokuEMeCicfWRrxcjL+iKs9sFnACgrK5P2ZFhNJGrweTVVFKvpWjjqwMvEImTgvt+qu7CjR4+qnWRCiay0E7F5+2ptRa3W61IDksRdxziJJe57yOqXDCoLK4oYDhw4sOBvdrs94Utxq4ncMSSqZb9aIobVcA3JZrU7hsgywc4TsapoMsrfQtcTFxkYMazIMRBCMDY2FnUBNfkmPplA5GqqPGJYnNV0LYkkWyKGTN/KM6ms9jGG2tpatLe3R10SI9paRulMZGueRwwcNeD3m5PJrMiFlZSULLhOUkVFRVwKJZvIriQeMSwNr/SWJlsihmSQ8TZkdUoGXUfmxDYJgi+JwUkk/N5nJgm5bxnUlRSXppcvX17Wd+kMH3zmJIJsu9/RBm01ULdyTYVNVckzA8tCXI7BbrcrpqceOXJE9a03E41Wq034dNXVuo80bw0vTLZ0JbHrS+R1ptqGcefP6pQMqgfi0nTPnj0ghODll1/G0aNH4Xa78fjjj6ulW1KQV9qJHGPIthZktpMtjoETAxlUFuLe83n37t144okn4PF4cOjQITV0SiryiCFRFfhqiRikOercyS1JtjsG9h6DmmUlFe8xqJSo+mkmmLhrrAMHDqCmpga1tbWLvvi2UmpqauB0OuF0OhW7uamFTqcDIPaJJihiYI5hNVSokYsOcqKT7Y5hMTLBJKret2CQ/s2gBmJcEcPzzz8Pi8UiTVE1mUxobGzEvn37VFEOoNt5VlRUoLy8HC0tLaqly2CV9tzcXMIdQygUkhxRJrMaHFyiyTbHkMgykUobsgZjXGTgGENcjqG2tlbxpvOuXbtUf8GtsbExobu4sYo6GAxCq9Um1DEEg8FV4Rg4S8MqytXSjZgOZGyDJFvWSmJEW/5C7SUxOjo6AAButxsAdUYLMTExIW2QDQB5eXnIy8tbNH1WUbOIIcjCPhWROx9OdpCxldgKiVw9QM3rT2bEkNC1kjKokZD2mtpsNlRXV6O2thY2mw2CICwo+9mKnSgpKUFJSQnu2VmCHz3/DP0h6APcTmB2gp7PjACe9wDQSrs0ZxTBCRe0Wi00ZI7KBrxU1ncNcJ8KZzJ+EZjsocehoCjroef+MXrOCtNENzDRLbYaCciYg8oA9H/cTpoGQNMcvxjOx32K5g1QXdxOIDRLz6d6gfEPw7Ke9+g1AfQa3U56zQAw1Q94u8KywhlgZogez01R2bkZej49CAhnZbJngekBUXYGG3IGoSNUtlA3gbwZmQ7ec8BUn2hvv2jvcZm9T8ts+CEweVm04axoQ0Fmb2eEvV1Ke/vdor3dor3FB2/SBUxcktnQCfhGRRsye8+Jsj3A+AWZDU8DvquiDcdFGwZEG/YB3vMy2feBmWFRdlJp7+kBwNsldUFovGeB6SuiDadFe0+LslcA4QOZDbvC9pbK7KRow2GaryR7XmbvgNLevqsR9r4gK7NzyjLrG1Xae+JS2N4kFN3eUpl1QTdNZTUaDeB2wqChz1gh5nBnXghaMNnLEWX2tKzMMnv7ZfY+J4nqJ7tQqJtQ2lsqswNRyuygaO8ZUXZKtOEQLf9ye0/1R9ib5qOfG5PqCI1GQ3Wf6hVtOBtbHaHvB/JBHcMCdQS1NxHtvcw6wiMrOyqzIsew3KW1X3755ZUkL9Ha2qoYcDYajXC5XAvK27+9Hl6vF16vF2d+cjv+T5X4YE8PAG1WwO2g5z2vAscfAEAdw8MbXkfehf3QaDTI00xQ2WtvU9m+w8Cxe8KZdHwNeO/v6HFwisoOi+9uDL5Bz4l4I51PAc6nqMNBCPknPkllAPo/bVaaBkDT7PhaOJ9j99C8AapLmxXwi5XcB88C73w5LHv8AXpNAL3GNmu4gjn3PPCHz4dl3/pz4GITPfZ2UdkJsbBd+CFw4uGw7O8fAT78Pj2edOF/bXgRBbPU/ruKndh0/ith2T9+Aeiy0eOZIZru2El6fvnfAPv9Ydl3vwqc2UuPAwKVvXqCnvcfBX57d1i285vAKbEMhHxUdui39PzKf9Bz5jCdfws4ngz/b5sVGHidHo+8Rc9Z5fn+PwAnZdHnsfuA3tfo8bV3RHuLjuLsPwPvfDEs+2YF4PoZPfacprKs0jj3r8DbNQiFQrRC+d1fAhd/TH8bP09lx0Unc/HH9HfG2zX0/wGaXps1XMG7fkbzZbzzRaoXQPVss1K9AXodx+4Ly56spdcL0Otvs1J7ANQ+bdawrONJakeA2rXNSu0MULu3Wel9AIBTDSjokk0I+e3dMOdQfe/UjuPEDVMwaEXZM3vpfWfY76flAqDlpM0abrB02Wh5Ell35q+xq9hJW+/CGSrLnNeH36fllHHiYVqOAVqu26zhhtHFJlr+GX/4PH0+AKmO0HppBV889iupjqD2/jJ97gD6HMZSR6z9F+AOUMewQB0BgH7XZl1+HXHi00gYZAW4XC7S0NBAjhw5Mu+3np4e0tzcTJ555hni9XpXkryEw+EgdrtdOjebzVHl+vv7CQDS/0FYlghnCZnso8dzM4SMOQgJjNPz6WFC3KcJIYT8+te/Jj/452+SEddJ8r3vfY9895++TWX9ApWduUrImDOcrvcCIRMuehycE2Xd9Nw3Ss9DIXo+fomQ8UvkV7/6Fdm79x+Jx3WcyhBC/2fMQdMghKbpvRDOZ8xJ8yaE6jLmICQYoOeTlwnxng/Luk/TayKEXuOYg14zIdQGwtmwrOd9Qqav0OPZSSo7O03PpwYI8Xwgk/2AkKl+UXaa/Oxf/jf544ljxOPxkAPPfov0vf/vMnt3ETLZK9rbJ9rbK7P3KZkNzxMy0SPaMCDa0COztyPC3t1Ke/vGRHuPifYOijbsJmT8osyGDkJmrok2ZPaeFWVdhHg/lNnwFCEzI6INvaIN/aINewkRzslk3yNkekiUnVDae6qfEOEseffdd8l3vvMdQjxnCJkaFG04Jdp7SpQdpL9LNjwbtrdUZidEGw7RfCXZczJ7+5X2nhmJsPeHsjI7qyyzM9eU9h6/GLZ3KBjd3lKZ7SaevpNk79695NixY4SMOcgrP7GRZ599llS9fBf55L4C8k/f+UdRtocQ73kCEFJdLdpbKrPM3j6ZvbsklYbPHyMHnv0WGRoaCttbKrP9UcrsgGjvaVF2UrThFVr+5faOqCMmPUNk79695OKZtwlxnyY2m438/ve/p2V28rJow0BsdcT3nyIkH4QcP75gHUHtHRLtvbw6ov/9/6T1Xn8/UZsVjTGUlZXhueeew5EjR/DEE0/A7XZDEARoNBqYzWbU1dWpsrubxWJBa2srWltb0dHRAbvdvvg/lNwqO94ZPtYZAJMlfL5mA/0A0Ov1GJtdB79+I7RaLeZCGqWs4Tr6YRTfFD7W6pSyeaX0wyiiy5LrdOcBaBAouCP8e+5awLQ2LFsYsSWqaVf4OLdEmU/BNqXs2o+Fj3OKImS3KmWNHwkf6wuUsvmb6UeSvV0muwYjs5txkzYfADAZLMJswW3h30tkx7q8Be0NACi+JXyszYnD3ib6YRRGTHyQyy5l77V3ho9ziiNseEOE7EdlsoURNtwCACDkXdqFaLwj/Js+P0L2evphLFpmN9KPJCsr67rcCBuupx9G8c3hY60+QnYd/TCKbgwfa7RL2jsUMIrXSwCTBT7yLoAxTEKPs34dqrTiZIvC7VAQg72DRbdjMvhHmkc0e4s2BzCvzCptuIl+GFHsTSZp110wZx2w9hYQ0kZ/j6fMzm4GpgHo9QvWEQDo4HRMZfYOJIq4Bp9379694CqrasFmJCVqZlJOTg4AYHZ2FjqdLqGzkubm5lRPOxWQLBtYXQlSV1KWomYZSbUd484/A99jyBxNE4ReT33j7OysYlqpmjDnsxocA3/BbXkQQrJyqiorEwQLL6K30mKTzAaJdB1qzkrKoOcl+0puBKzSDgQCiqmrapKodDnpSygUygrHkIzGQaobIDxiyELkXUkseggEAgnLI5NhrSfelbQ0vCuJqL7vczLKXULyYGlmk2M4ffq0CmqkDtaaT2TEwBzOaosYsrniW4ps7UqSo9Z+DKleEiNuWMSQQasexF1yX3rpJdx4442466670NjYiKNHj0pvH2fCpj3RHIPaEQNLN9MjBgaPGJYmW7qSGJFlQu1oIVoeiUTVsbRsjBisVisuXbqE9vZ2mM1mfPe730VFRQVuvPFGVFVVqaFjQmEPbyAQSFjLfrVEDESNBcWyBG4r9eBLYiSfuPdjYEtUlJSUYM+ePTCZTNIU1ueffz7e5BNONMeQqIhB7XRTBXtYeMW3MNkSMUSbvUNUXitJnm4qyMaIIW7HUF1djfLyclRVVWHHjh2KtYyefvrpeJNPOOzh9fv9iqmrichjtXQlcZYmWxwDI1rludAYQ6xmUXXqaIx5qgIbYxAnoWQCcZfcsrIyHD9+HGazGYQQVd54TiasNe/3+6XZQ36/X9U85FHJaoJHDAuTLY4hstJebWVCFWfE0tDH3Q5PGqpoyrqRMpFojkHtCny1OAY+XXX5ZMt01YX65NXc2jOZEUO0PPh7DFlItFlJb7zxhqpRQ7yOwe/3Y+/evapHMitB/pC88soraaGTnHSxlXy3vnTRKZJE6KWYzbOC6arRdEqFg5U7I41GE5+tmGNQOWJg9UkiGpzcMcgeXp1OB41Gg9/97ndp5xi+/e1vp03FwlpVP/3pT9NGJ0a62EoeMaSLTpEkQq94o8rFdErlC25x2SpBO7gxXRJRrrLeMcgrbeYkcnNzE5JHpg8+s9YT70pamtWyv/dykY8xLFU+Yq0fUzn4rMq0YxYxqFyvJJKsdwzRupKW2g40VlaLY4iEO4iFyZbB58VYDW8+q0I2vseQDrClsoeGhmL+39HRUXi9Xmg0GoyOjkIQBMzNzWFwcFCxf3Q8jIyMwOv14urVqxgYGIj5/ycm6HaDauq0Etg15Obmwuv1qm4nNUgXW127dg0zMzMYGBhIG50iUUMvQRDg9XoxOjqKgYEBeDweeL1e+HP9CHlD8Gq888r89DSw0GMQTSe2K+OVK1ekCSKJgl3P0NAQcnNzIQgCRkZGUFhYOE+vZcPkh4cBFccDhofp9rKJ2CpAQ1ZBs6+jowN333330oIcDoezynj77bdx3333LS0YA6vCMczNzeHUqVPYsGFD1ofvHA4nOwiFQhgZGcGuXbukl3PVYlU4Bg6Hw+GoB29eczgcDkfBqhh8jpXW1la43W44HA7U1NSgsrJS+h4A3G43zGbzkt+rrZPZbEZnZycAoLa2NuU6RepnNBrTwlY1NTVobGwEABw6dAg2my3lOjH2798Ps9kMILxPeaptdfDgQRiNRsX3qS7rctLBTgDQ3NwMk8kEl8uFyspKWCyWpOslCAKam5sBAPX19dL3seoQt24ky3A4HKSlpYUQQojH4yFGo5EQQkh3dzepra2V5CorKxf9Xk08Hg+xWCzSMbstqdQpmn7MbqnWy2KxEKPRSCorK4nH40kLnVjaTB92P1OpV3d3NwFAjEYjMRqNBACx2WwpL+s2m006Z/ml+v45HA5SXV297PwTpVdLSwupr69X2ChWHdTQLeu6ktxuN+x2OwDAaDTCZDLB6XSivb1d0aoyGo1ob29f8Hs1MRqNcDgcACC1VgCkVCc5hw8fxmOPPSadp1qvxsZGeDwe2O12Kb9U6+R0OqV8nE6ndD9TqZfL5YLH45E+TU1NqK+vT3lZb2pqgtPplM6B1N8/tp+MnFTUC9XV1dixY8c83WLRQQ3dsq4rqbKyUhFWud1uWCwWHDp0CKWlpdL3JpMJgiCgu7s76veJoLm5GXa7HS0tLQCwYN7J1MnpdKKyslIR/qdar46ODgD03gG02y3VOnV2dsLlcsHlcgEA6urq0NTUlFK95OW8ubkZjz76KIDU3z+bzQar1QqLxYLjx4+nhU5ms1kqVwCke5lqvYDYbaOGblkXMcipq6vDwYMHF/ydVTzL/T5eamtrUVdXh4aGhrTRyeVyzWtJxZJ/IvSy2Wyorq5GbW0tbDbbgoU+mToJggCTyQSLxQKLxYLOzk6pVZxKvQB6DwVBmDfOkCqdOjo64HA4YDKZUFFRkRY6sXEOQRCk1vVC9kr2/VNDh1h1y1rH0NraiqqqKqlARIZvbNBmoe/VhlVulZWVOHz4MNrb21Ou0/79+wFQW3V0dMBut8PpdKZUr9bWVoXjNBqNcLlcKbeV2WxWpMsGMVOtFwA0NTVJA6lAass6e+4sFgvsdjvKy8vToqwDQEtLC1wuF8rLy2E0GlFeXp4WesWqgyq6xTwqsQqw2+3EbrcTQuigU3d3N+nu7lYMPskHD6N9ryZNTU2kvr5eOjebzZJeqdIpkvr6esXgc6r0cjgc0r0jhNoq1ToRQgdV5YN8ZrOZeDyelOvFdOnu7pbOU13WHQ6HdN7S0pIWZd3j8Uj5yAdvU6FXU1PTvMHnWHRQQ7ese8HN5XLBarVK54IgSIvByad4mUymqNPo5N+rBQtfjUYj7HY7SktLpalqqdJJTnt7OxoaGmA2m2Gz2WA2m1OqF8ujo6MDdXV1Umso1bZi06AFQYDZbF4y/2TpZbVacfz4cUXXSCp12r9/v6TLcvJOlp3k01UXmiqaaL3a29vR1NQEQRBQV1e3YtvEq1vWOQYOh8PhLE7WjjFwOBwOJzrcMXA4HA5HAXcMHA6Hw1HAHQOHw+FwFHDHwOFwOBwF3DFwOBwORwF3DBwOh8NRwB0Dh7NKaW1tVbzMyeEsF+4YOGmB0+lETU0NrFYrWltbpTWRItd9yXRcLte8jWoSRXV1NUwmk3QuCIK0/hWHsxhZt+w2Jz2xWCyoqqqCw+GQXt+vrq5WLB+8GmhqapJ2nEs2bAe+1tbWhC6hwsl8uGPgpCXy1WaXWjI6U2htbcVdd92lanqRyykvtS6OxWJBU1MTdwycReGOgZOWHD58GOXl5XC73aipqZFa2i0tLTAajWhoaMBdd92l2J+XdZOYzWZpqWGTyYSamho4HA643W7U1dUpFieLTAcAKioq0NLSAkEQcOjQIWnjJEBZGbNumj179qClpQWVlZWoqamRFhuMxG63o6mpSfFdZHpms3le/o2NjXC73WhpaUFdXZ20hHY8lfty99ngZCfcMXDSis7OTmknu/LyclRWVkpbsDKn0NzcjNLSUqlirKqqQl1dHbq7u6WKt6amRqpEWQVoNptRVVUl5RUtHZavyWRCZWWlNCZQXV0Np9MpOQqXy4WGhga0tLRIW8WyNGpra6NeW2TrfqH0IvNvb29HfX09TCYTmpqa5jmXhWhvb1foz7BarXA6ndwxcBaEOwZOWlFeXo7a2lrFtpQAFBvNOBwOxT62NpsNTU1Nihk4y6n0oqXDiNZ1dejQIcmxmM1mKZKoq6vDvn37UFlZqRjsjSRyp7mF0ovMf6XdaJWVleju7p73/WI6cjgAn5XESVMid0STwxyAfP9u1tpnyCtho9Eotda7u7ul42jpMKJVnqWlpYqKluVhsVjgcrnQ3Nw8L53FWCi9yPzVrsgTudsYZ3XAHQMnLXC5XLDb7ejs7FRM53Q6nVKly6itrUVpaSmam5sl2erqapjNZuk7l8slydfV1aG1tRXt7e1wu92w2+0QBCFqOvL8BEGA3W7HoUOHIAiCtHnL/v375+Xx2GOPSRHIQpjNZsX/REtvsfwPHTqEzs5ORRorobu7mzsGzqLwjXo4q5KGhgZUVVXF1IKPB6fTCbfbvWh+TqcTnZ2dC45BJIuamhpFtxWHEwmPGDicOGCRRmdn55JOyGKxRO3zTybt7e1obGxMqQ6c9Ic7Bs6qg83kWe7snXjzam1tRXl5+bLkGxsbFd1iyUQQBAiCoBjI53CiwbuSOBwOh6OARwwcDofDUcAdA4fD4XAUcMfA4XA4HAXcMXA4HA5HAXcMHA6Hw1HAHQOHw+FwFHDHwOFwOBwF3DFwOBwORwF3DBwOh8NR8P8B47lI+SB1AKYAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(4, 1.5))\n", "ax = fig.add_subplot()\n", "for i in range(0, 24):\n", " ax.plot(data1[i,-21:], x1[-21:], color=color1[i], linewidth=1)\n", "for i in range(0, 24):\n", " ax.plot(new_data2[i,:21], x2[:21], color=color2[i], linewidth=1)\n", "for i in [18, 19, 23]:\n", " ax.plot(data1[i,-21:], x1[-21:], color=color1[i], linewidth=1)\n", "for i in [16, 19, 23]:\n", " ax.plot(new_data2[i,:21], x2[:21], color=color2[i], linewidth=1)\n", "ax.set(xlim=[150, 1000], ylim=[-9, 9])\n", "ax.invert_yaxis()\n", "ax.tick_params(direction='in', labelsize=8)\n", "ax2 = ax.twiny()\n", "ax2.spines['top'].set_position(('axes', 0.5))\n", "ax2.set_xticks([])\n", "ax.axhline(y=-5.1, color='orange', linestyle=':', linewidth=1)\n", "ax.axhline(y=5.1, color='orange', linestyle=':', linewidth=1)\n", "ax.set_xlabel(r'Frequency (cm$^{-1}$)', fontsize=8)\n", "ax.set_ylabel(r'$q$ ($\\times 10^{-3}\\textrm{\\AA}^{-1}$)', fontsize=8)\n", "fig.tight_layout()\n", "fig.savefig('画图/声子不连续/部分图.svg', format='svg', transparent=True, bbox_inches='tight')" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "fig = plt.figure(figsize=(2.5, 1.5))\n", "ax = fig.add_subplot()\n", "for i in range(12, 20):\n", " ax.plot(data1[i,-21:], x1[-21:], color=color1[i], linewidth=1)\n", "for i in range(12, 20):\n", " ax.plot(new_data2[i,:21], x2[:21], color=color2[i], linewidth=1)\n", "for i in [18, 19]:\n", " ax.plot(data1[i,-21:], x1[-21:], color=color1[i], linewidth=1)\n", "for i in [16, 19]:\n", " ax.plot(new_data2[i,:21], x2[:21], color=color2[i], linewidth=1)\n", "ax.set(xlim=[740, 780], ylim=[-9, 9])\n", "ax.invert_yaxis()\n", "ax.tick_params(direction='in', labelsize=8)\n", "ax2 = ax.twiny()\n", "ax2.spines['top'].set_position(('axes', 0.5))\n", "ax2.set_xticks([])\n", "ax.axhline(y=-5.1, color='orange', linestyle=':', linewidth=1)\n", "ax.axhline(y=5.1, color='orange', linestyle=':', linewidth=1)\n", "# ax.set_xlabel(r'Frequency (cm$^{-1}$)', fontsize=8)\n", "# ax.set_ylabel(r'$q$ ($\\times 10^{-3}\\textrm{\\AA}^{-1}$)', fontsize=8)\n", "fig.tight_layout()\n", "fig.savefig('画图/声子不连续/放大.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": 2 }