157 lines
41 KiBLFS
Plaintext
157 lines
41 KiBLFS
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "01ce11e3",
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"metadata": {},
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"source": [
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"使用以下实验结果:\n",
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"* 正入射使用 250923/1\n",
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"* 肩入射使用 250923/2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"id": "fcd2d712",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'/home/chn/repo/SiC-2nd-paper'"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import numpy as np\n",
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"from scipy.optimize import curve_fit\n",
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"import matplotlib.pyplot as plt\n",
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"import plotly.graph_objects as go\n",
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"import matplotlib\n",
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"import pandas as pd\n",
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"from brokenaxes import brokenaxes\n",
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"plt.rcParams['text.usetex'] = True\n",
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"plt.rcParams['font.family'] = 'Arial'\n",
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"\n",
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"%pwd"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"id": "eb8e13ff",
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"metadata": {},
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"outputs": [],
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"source": [
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"polarizations = [ \"zyyz\", \"xyyx\" ]\n",
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"peek = [ \"E21\", \"E22\", \"A11\" ]\n",
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"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",
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"normal_dist = lambda x, mu, sigma: np.exp( - (x - mu)**2 / (2 * sigma**2) ) * 0.5\n",
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"mean = {peak: {p: np.mean(data[peak][p]) for p in polarizations} for peak in peek}\n",
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"std = {peak: {p: np.std(data[peak][p]) for p in polarizations} for peak in peek}\n",
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"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",
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"theoretical_result = { \"E21\": 0.263, \"E22\": 0.221, \"A11\": 0.188 }\n",
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"color = { \"zyyz\": \"#50cfd2\", \"xyyx\": \"#9d569c\" }\n",
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"peak_label = { \"E21\": \"$\\\\mathrm{E_2}$-1\", \"E22\": \"$\\\\mathrm{E_2}$-2\", \"A11\": \"$\\\\mathrm{A_1}$-1\" }"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 24,
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"id": "06852a14",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"image/png": 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NbNp5/bzPmF/sqAaVG+6/gWvvvZaTz7/L28+8jX/ET9fBLo61HKPq5irW3raO6q3VFDiWxhHbmSCpySZVVSdZnPHD7qa+l4ffxfBGo5wc8/P26CjHvKP0hi4v4SsyGNhZXsYnlpVTalp6PryZEEJw3OPjmVPnudA9RGnfKBVnvZQMBSalq7xuOdfffz1rP7gWwyKKasgGWkTjzBtnONl2ksFTFyddK7umjJU3rmTZxkqWrV9G0bKiJTfSzOhkU0NDA01NTZSVlU17fXh4mH379iVdaL4T0nV80SieqMZIJMJQOMyFcJj+UJieQJDhyOTziwzA5pJi7nLYud1hx5IjW99lGyEE0WCE0FiYoDdIwBPAPzLG2PAY3gs+vBe9uHvdrApGmHp6vKuqkAvXltJ3QzmRUitrrDqr+/qpspipNJupMJlwmIzYjUYKDYaUowAWKwaTgQ13b2DD3RvwXvDi/IWTc0fPcqn7EiNnRxg5OwI/jZ0WYLKZcKxyULKihOLlJRSWF1JQVoDNbsNaYsVSaMZoNS05ZTsdSVmkx44dY8uWLSmnmY+2//mlYU77A7MnnMDUCk18L4RAjH8mJrzXJvwf+4OI0IkIQVgXBHWdgK4R0GKfzUa11cKmwgJuLS5mc1FRxuNBhS547cCr87v3ig9E4oIYfx/7KPaBELF2QwiEHvvTdYHQdHRNR4/qaBENLaoRDWlo4SiRYIRIIJLUpsSqQaVsbRnLNixj+fUr0GrKeFuJ8M7oGCfHxhjTZt8+zqaqFBhUrKoBi6pgUlRMqoJJUVAVBaOioAKqoqAAqgIKsdcACpP9/hNVxXzURv3yypyeSAy4AwycGODCiYGEUtWTaGdFVTDZTJisJowWIwazAYPRgMFkQDWqqAYVxaCiqgrK+B+KgqIoKArjjRl/rcTfjmc+uaXnpa7HN+1OlozGkaaL+Qj51Z5zvOb2ZFiyuWMA7CYjpUYTFWYTlWYTqywWqq1W1tqsFGR5r1Bd1/nX3/yXrJY5X1SDitVuxVpipbCskILSAoqXF1OyogT7Kgf2FfarzioLIbgYjtATCNAXCjEQCjMUDjMcieKKRPDn6IbHzdeuZ2Nh/vggtaiGd8CLu8+N76IX30UfYyNj+F1+gp4gQW8wKUW74Cjwuz/8vaSTZ3Rov5Dc5bCzdpb4wmR+qeK2xvgP3wTLQ0FVSFgnqqJgAEyqikFRMCsKJlXBqqrYVAM2g0qRwYBtit94oVFQ2PobW5NKmczHl6t22VpIfK5MsCBUBVVVYpaHqqAaDagGFYNJRTUaMFqMGM0GTDYTRqsJc4EZo8U477ZTFIUVFjMrrmLdRYVgNKrhHx89BPXYX0TXiQpBJDHiEOhcHono4/ZEfKSSsMYBcaXNfgWzpagw55c/3GA0UFpdSmn19HsTCCGIhqKE/WEigQjRYIRoKEo0rKFHNbRIfHSixUYsmh4bteji8ggnMdJh8qiHy4OiywVeTdLc2Fcg5y1SiUQiyRaL1iJdjIxpGn3BEEFdx6qqrLJaKEzRFTCmaXT7A/QGg4DCKouZDYUFFBoMjGkap8f89IXCgGC11cpKixlXJDqjDPORM16WMxjAE4niMJpYZ7MmZEmWsD+Mp99DJBDBZDNhr7JjLsiOj3G6soEZ5ZnunrA/TP9b/YR8QSzFVio2VKCFNCKByOURgGDG+iXbDgvZXhKpSLNOXzDEG24PI9EICrGBSZnRxG0OO6us81sP3RcM8fNLw5zy+wnpOgpgVlU2FRRwa0kxx70+Tvn9hHUdQewZtqoqlRYzxQbDtDLMR864HG/7RnFFo2hCYFAUHCYjtxQV8ZGK8qTq6Ol303O4B787gKLEhnkFDhtrP7guodQyxXRlqwYFBOi6mFae6e6J+MN4L3oJB2Ltp0ViQ1z7ageWAjPeC7GNmEtWlmAusExbv2TbYSHbSxJj0cThhEIhHnvsMUKh3D12YUzTeMPtwadpVFusrLHaqLZY8Y1/7vL751yHMU3jZZeL034/VkVljdXKGosVq6py0u/nRxcGOTXmx6qqrLFYqbJYCGg6g+Ewo1GNFWbLJBn8mjarnP5p4oXHNI2OgQt8p/nLjAaDFBoMLLeYKTQYCGo6J8f8vDzimvbeiYT9YXoO9xAcDeFY5cCxuhTHKgfB0RA9h88Q9ofn3O7JEvaHee/V9/jH7/0TBcsKcKwupXhZEUOnhxjqHqJoWdEV8oxeGr1CXqvdysCJAXyDPuwrSnCscqAaVcLBCN4+DwFPAIPFiNFiJBKIULSs6Ir6JdsO06UrWFbAP37vn3jv1VMZba9Mkg/P80QWlSL94he/mNMN3xcMMRKNsMJsTiwHVRWFFWYzrmiEMx7vnOvQFwzRGwyhKrEoApVYaInDYCSiCy6EQ0SFwGGITfAEdR1VUbAoCp5oFFc0OkmG3mBoVjl7g1fK1xcMcdbn4xf/9I/okTA2gwEFhQKDAZXYJFBfKDTtvRPx9HvwuwOULC+JhcoQC7EpWV5CwB3A05+5CA5PvwfPJS/f+fF3iGixWOCgNxgL3zGoBL3BK+Tpf6v/Cnm9A150TWC0GBMTMnpUUFhWQHgszOilUQrsNqx2G2F/hKA3eEX9km2H6dJFtAjf+fF38F7yZrS9Mkk+PM8TkUP7LBIcH3ZPXVMffx+cR+hOUNfRxHjEwYSp99isuEAXsVnn+Cy5JkQsckFV0YUgMl7mVBnmKmdMDpFIN+lORUEgiAoxax0jgUgsqkKdEkMYC/SM+RczRLzsiWgRDVVRYjHGkcvWdFyekC94hbyRYMwHqipqIkRIAVSDAYFARMXlmMnxfKfWL9l2uFq6eKGZbC/JZRaNRZoPWFV1UqhNnPh76zxWPFlVFYMSy0OfEAoSC8ZQEoHm8eAMw7hS0MctU9N4mRNlmI+c1vFwsXi6SXcKgUIsCH62OppspljA/5TA/VjYTOx6poiXPRGDyRCrtxAYTJcny+LyWIqtV8hrsppiPlWho45bswLQNS0W9G9UxsN9RKKMqfVLth2uli6WOLPtJblMVi3SeMfxetN/4mE8z0zknS6KNQ1rIMiZ0TEqzWZUBXQBg+EwRQYDxeNHYcylDsWaRlkkzIXRUQYVlRKjEUWAR4uiC51SFKKhEBcDfuwGIyAIBoJEhE6p1YrFaMQXDCZkKCkMIWBGOUsKQ3i16BVylI4vjQ2NjuJBwWpQCWoxS9VkNFCqFFESvvLeiShFCrpZY6BngOJlxSiqgtAFviEflkIzSpGSse9YKVLQTTGr0zca25ovaowy5h8FoNRYxujY6CR5itYWMdQ/OEle1a4S1ALoPh1LpSXmFtACBC4EsVjNWIqsXLo4jAIYbUaixigDPQOT6pdsO0yXLi67ZtIy2l6ZZKGe53h5c40KzWocaW9vL9XV1dkqTiKRSObF+fPnWb06+SO7s6pIdV2nv7+f4uLinFoVJJFIJBCzRH0+H1VVVahzcLVlVZFKJBLJYkRONkkkEkmKSEUqkUgkKZJzirSrq4utW6/cxcjpdNLU1MSBAwdoamrC7XZPuqerqyuRLv76avnv27ePffv2sWvXrkn5pItM16G9vZ329nZaW1tpamqaMW2u1mEiU/NJB9noR/Op61zIxnfQ3t7OgQMHEn0q3WS6Dq2trbjd7ow8x3NC5BAtLS2is7NTTCdWTU2NcLlcQgghOjs7RUNDQ+JaQ0NDYp/murq6RLrpaG5unvS6trY2bfILkZ06OBwO0dnZKYQQYv/+/aKmpibv6hAnXk4yaZMlG/LPp665Voe2trbEvd3d3XnZj+LpJv5NfMazRU4p0jhTG76tre2KL3limv379wuXyzVrZ+7s7BQOhyPxvru7WwCiu7s7daGnkKk6xPOaeF+6fwziZLIOcVpaWiY9VOkkk/LPp67zIZN1mNrumXgOhMhcHVwul2hpaZn02UIoUSGEyLmh/XRczWyfaPI7HI5Jx0ZPR21tLU888cQV+V7tLKp0kq46ANTV1SVet7S0sHfv3lTFS4p01gFiw7L6+vo0SJYc6ZZ/LmnTRbrq4HQ6GRkZweFw0NXVhdvtpqamJo2SXp10fg8T+0+2+9NE8mKtfW1tLU6nM/E+3uAjIyNA7ItpbW0F4MiRI+zdu/eqnWJiQz/11FPU1dVl5WFIZx3i9z/11FPs3LmThoaGDEp+mXTWwe12Z10JpVv+uXxf6SJddejq6qKsrIzW1lbq6uo4cOAANTU1WVFE6arDxP7jdrsZGRnJ2o/BFSyIHTwL04nV3NycMPlbWloEkPATThwCdHZ2JuXrcblcGRtSCpG9OjQ0NFwxvEkXmazD/v37E6+zNbQXIn3yz+f7mg+ZqsP+/fsn+aZdLte0ZaWDbDwLDQ0NGXezzETeKFIhYj6czs7OxJc+0VkdJ35tNn9PQ0NDxnxCQmSnDkLE/E0T80knmapDW1vbJHmzqUiFSM93MN/va65k8juYOF8QL2vi/eki089C3ChaSPJGkU5syM7OzsQEy9QJpKlfynQ0Nzcn8svUhEGm6jD1AYhPmGXrAUhXHfbv35/4A0RjY2Pa65Ap+efT5+ZLpurQ3d29oIo0nc9zW1tbxiZckyVnFenUhnM4HInPGhoaEjPXLpdr0jCxpaVF1NfXJ953dnZO+tJaWlquem8+1KGzs3PStZaWliseiFyvw3TlZMqay4T8s6XNhzoIISaFFmUi/CkbdRAiZhjV1dVlRPZkyam19u3t7bS1tbFv3z4aGxvZvn17wvl94MABysrKEg7liTPXXV1dtLe343A46O7uprm5OXFt165dbN++ncbGRpxOJ+vXr59UpsPhwOVy5U0dIDY7GXfMt7W10dzcnFYnezbqALEJgnhAdkNDA3v37qW2tjYv5J8pbTrIRh3cbjdNTU1s3bqVzs5Ompqa8rIf7du3j+7ubvbv35822edKTilSiUQiyUfyIo5UIpFIchmpSCUSiSRFshqQLzd2lkgkuYyY58bOWVWk/f398qgRiUSS88z1qJGsKtLi4mIgJmRJSUk2i5ZIJJJZ8Xq9VFdXJ3RVsmRVkcaH8yUlJVKRSiSSnGWurkc52SSRSCQpIhWpRCKRpEhebKO31NE1nZGeYbyDPlRVpXxdOUXLimTkgyTjaELw7ugYnmiUtTYbq6yWhRYpJ5GKNIeJBCO88+zbnPjZCYK+4KRrpWvKuPXXb2Xd7eukQpVkhHdGx/jHc+e5GI4kPvugvYSHq1dhN0rVMZGsLhH1er3Y7XY8Ho+cbJqFwfcGeemfXsR30QeAucBM6ZpStLDGyNkRdE0HoLq2mrsevhtbiW0hxZUsMo54vOzrOUdUCIoMBqosZk77A+jAKouFv9mwDofJtNBipp356iipSHOQM6+f4aV/ehE9qlNYUci239rOutvWoRpiLu3QaIh3fvYOv/zxf6JHdYqXF3P///ooJStkm0pS52wgSON7pwkLwe32Ej67ZjU2g4GeQIAvOc9yKRJhU2EBf72hBuMiGw3NV0fJyaYcw/laNy/8wyH0qM6a7dfw6195gPV3rk8oUQBLkYXaXbV88vFPUVRZjO+ij2cfexbvRe8CSi5ZDER0na+dPUdYCDYXF/G5tWuwGQwArLXZeGz9OgpUlZNjfg5euLjA0uYOUpHmEL3He3npGy+BgI0f3siH/+TDmAvMV01ftqaMj//1xymtLiXg8vPc3zxHwB3IosSSxcaPLg5xPhjCYTTyx9dUY5hica6yWviDNbEVP09fHOJsIDhdNkuOOSnSnp4evN7JVs/U95L54e5188LXDyE0Qc2dNdy5566k1voWOAq4/08/SvHyYkYHfXR8rR0tomVBYsliYzAc5unBIQB+f9VKSq4yoXSnw84H7SVowHf6+pE7cSapSD0eD9u2baOmpobS0lLuv/9+fL7YJIgQgieeeILy8vKMCrqYCfvDtH+1nUggwvJNy7n7D+5BUZP3PRWUFvCRL9yPudDM4HuDvP4vr2dQWsli5d8GLhIRgpuKCrnDYZ8x7e9WrcSkKLw1OsZRry9LEuYuSSnSPXv2sHv3brq7uzly5AibN2+mtraWnp4e7HY7u3btSusu80sJIQSvHXgV74CHwvJCPvy5HRiMhjnnY6+y86H/8SFQ4L2OU3S/cjoD0koWK2cDQV5xuQH4r1UrZw2pq7SY+fiymPH0g4GL6EvcKk1KkW7fvp1HHnmEdevWUVtbS3NzM0eOHOHLX/4yPT09OBwOGcs4T9479B5nXj+DYlD40P/8UEphTKtuXc3mB7YA8NoTr+G9IN0ukuR46sJFBHC7vYT1Bcn1wV+rXEaBqtITDHLYs7T72rwnmxwOB9/+9rdpaWmho6MjnTItGTz9Hg5/9w0Atu7eRuXG5Snnubl+MytuWEk0FOXF//0CelRPOU/J4uZsIMjr44pw94rk+2CJ0cjHllUAcPDC4JL2lSalSOvq6vjKV75CeXk5Tz/99KRrjzzyCC6Xa0k34nzQozov/dOLRENRVt5Uxc2fuDkt+aqqyj3//R7MhWYudV/i+NPH05KvZPHy9OAgALfZS7jGZp3TvZ9YVo513Cpdyr7SpBTpli1beOSRR2hvb+eBBx644np9fT3d3d1pF24x85//fpxL3ZcwF5q5Z46TS7NRVFHEHb9/Z6KcwfcH05a3ZHFxIRTmVZcHgPrllXO+v9ho5KMVZUAsdGqpGlRzGtpv2bLlqtfWrVuXsjBLhaHTQwlL8fbfu4PC8sK0l1FzRw3r7qhB6IKXv/Ey0VA07WVI8p//GBxCB7YUFyXtG53KJ5ZVYFIUTvn9vDM2ll4B84SUA/J3796dDjmWDNFwlFe++TJCF6y9bR01d6TvHPGp3PF7d1BQWoB3wMPRHxzJWDmS/MQVidAxEou2eWD5snnnU2Yy8aGyUiAWpL8USVmROp3OdMixZOj8v0dx97mxOWzc8ft3ZDTawVJk4c69dwFw4rkT9P2yL2NlSfKPnwwNExGCjQU2bixMbVT0a5UVqMAx3yhO/9JbXSeXiGaR/rf7eeen7wBw12fuxlo8N8f+fKjeUs2mnZsAePXbrxAaDWW8TEnuMxrVeO7SMBDzjab6g77SYuHO8SD+1otLzyefsiItLS1NhxyLnqAvyMvfeAmA6+o2Ub0le6epbv/tD1CysoSx4TF+8eRrS3ZCQHKZZy9dIqDrXGO1srVkbge9XY0Hxyer3vB4OR9cWmvwU1akzz//fDrkWNTEVi+9hn/ET8lKOx/4Lx/Iavkmq4l7//A+FIPCmdfPcPql97NaviS38GsazwzFrdFlqGlyL11js/KBkhIE0LrEfKUpKdKOjg62b9/Oww8/TE9PDwA/+tGPePjhh9Mh26Lh3eff5eybPagGlfv+6D5M1uxviLtswzJqd9UC8Po/v4671511GSS5wTNDlxjVNFZZLNw+y5r6ufLQiphV+qrLTe8SskpTUqRtbW0cPHiQ+vp6GhsbOX78OA8++CDt7e3pki/vudQ9xJvfOwzA9k9vp6KmYsFkuflTt7DypiqioSiH/r6DSDAy+02SRYUvGuU/Bi8BsHtF5RXb5KXK+gIbHygpQQd+eGHp+EpTUqTbt29n3bp17Nixg4MHD9LW1saxY8dwOBxpEi+/CXgCdHytI7ZJ87Y13PCrNy6oPKqqct9n78PmsOHudfPKt16R/tIlRuvFIfzjvtE702yNxvnNlZUowGtuD+/7/RkpI9dI2Uf61a9+NfH6kUceYWRkRK5yIhYv2vG1DsaGxyhZaeee/35vTmzsYnPY+PAf70A1qPS8cYbjrccWWiRJlhgIhfjp+Ez971StSJtvdCprbTbuK3UA8M99A0vixzolRfrggw9esdppx44ddHZ2piRUvqPrOq9882UGT13EXGCm7vN1M+50n22Wb1rO7b93OwDHWo/xvpx8WvQIIfhO3wDR8SNEthQXZbS831q5AouqcHLMz0vj2/MtZpJSpIcOHeLJJ5/kySefnLQjfkdHB06nk8985jN87WtfS3y+lJeLCiF4/clfcOb1M6gGlQ9/bgeO1Y6FFusKrtuxiZs/Gdso5dVvv0LPmz0LK5Ako7zm9tDp9WFUFH5v1ez7jaZKhdnErvFwqH/pH8AbXdxLlJM6RbS8vJyOjg42b9487XW328369esZHh6eMZ/Ffoqoruv84slf8F7HKRRF4d7/cR81t2duCWiqCF3w6rdf4f2X3kc1qNz72ftYd/vS/RFcrIxEIvzxyffxahq7l1fyGytT364xGSK6zuffO825YIjb7SU8snZNTri3ZiKjp4ju2bMnoUR7enom/UFsb9I9e/bMWejFRDQU5cWvv8B7HadAgbs+c1dOK1EARVW4c+9drLujBl3TefEfXuDEz08stFiSNBIVgr8/ex6vprHOZuXBFNbUzxWTqvJHa6oxKgqve7z87NJI1srONkkp0onnMblcLnbt2kVra+ukNOvXr0+vZHmE94KXZ//yGXoOx2NFP8S1921caLGSImaJ3svGD29ECMEb//w6rx54lWh4cQ/FlgJCCJ7o7eft0TGsqsqfXFONKYkDFdPJ+gIbvz1uAX+nr5/ji3TP0qRadWI405YtW3jooYf4/Oc/z9q1axOf57rJngmELnj3+Xf5jy/8mOEzw1iKLXz0zz6a0R2dMoGqqtzZcBfbfmtb4syn//fof3DxlDy3PF8RQvCv/Rd4fngEBfifa1az2pr5vR2m45PLKrin1I4OfLnnLG/5RhdEjkwy/XmrU3A6nfh8vkQYg6Iok94DSyrkSQhB/y/7OPrDToadseDm5ZuWc+9n76OoIrOzoZlCURRu+dStlK+r4OVvvIS7182zf/EM6+9az5ZdtZSsWHw+7cVKQNP45vk+XnXHNmzeu7qKD2YoZjQZFEXhD6tX44tqHPON8lfOHhpWV1FXVrpoDLCkJptUVZ1UYSHEtO81bebz1PN9singDdDz+hlOdZxi5GzM32Oymdi6eyub7r8+qXPo84GgL8iR77/J+y+Oh0UpsGbrGjZ+aCOrbl2NwTT3U04lmUcXgsMeL//aP8BgOIIBeLh6FTvKyxZaNADCus7Xz55PnA+1taSY/1a1klVWywJLdpn56qikFOlnPvMZmpqaKCub/gsZHh5m3759fPvb386IkAuBruuMDo3iOudiqHuIC+8MMPT+5aMUjBYj1+24jlt+/daUTv7MZS45L9F1sJPeY72Jz4xWEytvXMmK61dQsa4cR3Up1hLrorEs8g1dCM4Fg3R6fbww4qYvFNsmcZnJxP+8ppobitJ/+kIq6ELw74ND/PDCIFEhUIDakmLucti5pbiIMlP296GYSEYV6bFjx2Y8ZiTZNPMR8uLJi4wOT+NTuZrUQsQujVdLiPHPBAhdR+gCPaqjRXX0qEY0rBENRYj4I4RGQwR9QfwuP2OXxtC1K0/gLF9bzoZ7NrDh3muxFOXOL2kmcfW6eO/Qezh/4STgunLJn8lmorC8EJujAGuxBXOhBbPNhNFiRDUZMJgMqEY1NrJRlcQfgKLE/wGFy68ncRUdXXltJcWV6dkCLhsMhcOcHJvcflO7sRjvv/E/XQg0IYgKQVgXBHSdMU3DHY0yGA7TGwwR1C/3U5uq8vFl5fx65TJshtwdOZwPBvl+/0XenBCXDlBmMlJlsVBhMmE3GikwqFhUFZOiYlQVDICqKIkuEes+kzvI1O5y9/gqq2TIqCJNF/MR8oWvH+LM62cyLNn0GEwGSlbaqagpZ/mmFVTdXJW3PtB0IHTB8JlLDLwzwOD7Qwz3DDM65Lv6j1qGuecP72XD3RsWpvB58Jrbw1d7zqU9X6uqcn1hAbc57NzlsFOQwwp0Kn3BEC+6XHR6ffQEgmnvSgrw9ObkT+idryJNarJpISmtLiXom31X90k/SgoosX/Gr122ghRFiVlHBhWDyYDBbMBoMWKymbEUmrGUWClwFFC0rIiCsoJF4/dMB4qqULF+GRXrL8ciRsNRxi6NMnppjIAnQMgXJDwWJhKMEA1F0cJawvrXdYHQBQiBPv5//MkRiEkKOZmfd5s9v1wqdqOBW2YYaifsLOVyOI2qKBhQMKkKJkXBalApNBiwG40sM5lYZbVQZbGkfRenbLHKauHTK1fw6ZUrCGgaZ4NBLoTCjESieKNR/LpGSBdEdJ2oEOjErPS4xT6p/0yDcrXhTJrJeYtUIpFIssWitUjzgbA/jKffQyQQwWQzYa+yT9qkZOL1xA+kYNq0V8v/kvNSbDNmBRyrHFTUVCTui+fvd/sJ+UJYiiyYbDGnfcAdwN3rRjWpFJYVLnn3xFJlTNPoG/enWlWVVVYLhQZD4nN3JIpPi2JSFCJCYFQVRiMakdgkA2bVQLnZSKnRlLh3prwBTo/56QuFAcFqq5X1BbZJ9y0mpCJNEU+/m57DPfjdARQlNiQtcNhY+8F12Kvsk66Hx0J4L8Sc6yUrSzAXWCalvVr+J9tOMvj+YOJseqPFSOXG5Wyq2wQIeg734O51473oJRKKoqoKQgi0iEbEH0GLaqgGFXOBGedr3dz08ZtZvXl1tppIssD0BUO84fYwEo2gEBsNlxlNrLPZOBMIcC4Y5GI4zGhUI6TrqIpCeHwoHZsABBWFMpORNVYr1VYrtznsrLJaps3bgMJYVKM3HCKs6wjAoqpcV1DA/RXlORXulC7yxgEYCoV47LHHCIVy5xTMsD9Mz+EegqMhHKscOFaX4ljlIDgaoufwGUaHRxPXi5cVEQlEMFiMGC1GIoEIRcuKEmnD/vC0+Xe/0s1Q9xBGixFHdSmFKwv54WtP0X+yj/dePEX3a934XX7CgTBGqwn7ihIigVgEQsAdIBqKUlheiKXIgmpUCXqDvP3MW9NHQuQRudgfFoLZ2mFM03jD7cGnaVRbrKyx2qi2WBmJRPjJ0CUuhkMENR2jqmIEhAJBTSMkEl5IFBRsqkpA1xmNRhmJRnjD7WEoHL4i70qzmVNjY7w1OopJUVhjsbLGasWqqJz2+3nF5cI/S7x5Jtoh0+SVIv3iF7+YUw9ObDgdoGR5yeVwHlWhZHkJAXeA/l/2J64HvUHCgQgFdhtWu42wP0LQG0yk9fR7ps3f3e9BMahY7TYURSESjfKDF3+AJjQudQ/j7vXEFHMwiq3EGrNaFQU9qqNrOopRRWgCk9WEiAqsJVYCnphs+Uwu9oeFYLZ26AuGGIlGWGE2JzZyVhUFi6rijkYI6wK/rmMCIgrYFJWwEChCIFDQAWV8qK8Cbk3HrKi4ohF+6Ru9Im9vNOYO0BCYlPHJXRTsJiMGBXqDsZCtbLdDppFD+xSIBCIoCgklGkdRYxEDIV8wcV2LaIk4yXhqLaIl0kYCV56fFAlE0DUtFnUwNVZOUdAiUXTNiB7VE3nrWmxoJvSYNaGqauz1+P3xucWQb+kcTLaUCeqxvjF1N/xovB+M/68Tc9+LCZEuCRQFIWKf6eNKEsAX1a7IOyLiMa0xJRxHJdaHo0JMintdLOSNRZqLmGym8UD/yYEPsRAfsBRbE9cNJkNioUBcmRlMhkTa+OTQ1PxVgyEWpD0luEIIgcFkRDUYUA1qIm/VoKILkVDuuq7HXk/YJwFiskkWP1ZVTQT2T8QY7wdxK5XYQF6JhxNNTC/EuP9fJMKxAIqNhivyNilxlSImKRedWB82KgrWRRhSmFWLNP7leKesZkiG+D3zuTdTKEUKulljoGeA4mXFKGrMEvQN+bAUmilaV8RQ/yADPQMUOGxElDCXLvpRAKPNSNQYZaBnAEuhGaVIuaJuSpGC0WFg7MIowXAAi91KIBhbGTMaGKV8fTkGkwGPx0OYEIHBACabEX9oDE3XiGpR8IdQCkEPxhRqaCiEpdhK0bqinGrLuZKL/WEhmK0dijUNayDImdExKs1mVAV0Ae5gEGsojKZFUSMao0JHj0TxCR00nTCgIlBQ0BQFg6qCAqUmEx49SqnRxDqrhf4peRt1HX1sDC0SZTQSxmwwIRTwRqNEhM66oiJKwiG8Wnq3aUxXf4jfP9eo0KzGkfb29lJdXZ2t4iQSiWRenD9/ntWrk49syaoi1XWd/v5+iouL5SYXEokk5xBC4PP5qKqqmtOqxqwqUolEIlmMLD6vr0QikWQZqUglEokkRRYkjrSrq4v29nYAjhw5whNPPJE4F8rpdNLa2kpNTQ1Op5OGhobEtfg9brebI0eOsHv3bmpra6ctY6Z8coFstEG8nD179tDZ2ZnR+syXbLTDTGXkCtloh7n2nYUgW89FnKamJh599NHU+4NYAJqbmye9rq2tTbyf+Lq7u1vU19cn3jscDtHZ2SmEEGL//v2ipqbmqmXMlE8ukI02aGlpEZ2dnWKBvuakyEY7zFRGrpCNdphL2oUiG+0QJ/5suFyulOXO+hPW2dkpHA5H4n13d7cARHd3t+ju7r6ik09M29bWlni9f//+qz4Qs+Wz0GSjDSaSq4o0G+0wUxm5Qrb6w3z6TjbJ9nPR0tIiampq0qJIs+4jra2t5Yknnki8d7vdAJSVldHe3n7FuVBlZWV0dXUBUFdXl/i8paWFvXv3TlvGbPksNNlog3wgG+0wUxm5Qrb6Q673nWw+F62trdTX16dJ8gXykU6swFNPPUVdXR0OhyPRcFMZGRlJvO7q6uKpp55i586dNDQ0TJs+mXwWmky3Qb6QjXa4Whm5RLb6Q673nWy0g9vtTvv3v6Cz9m63m9bWVlpaWmZNF6e2tpZHH32U7u5uWltb51xerpHtNshVstEOyZaxkGS6HfKl72SyHQ4ePDjJgk0HC6pIm5qaaGtrS/w6OByOK6zGkZGRK349HA4Hu3btYteuXdMqx2TzyQUy1Qb5RjbaYWoZuUg22iEf+k6m2qG9vZ2HHnoo/QKn7GWdJ83NzQmHv8vlEi6X66oOZZfLJdra2qZ1RMdn6iYyUz65RCbbYCIL+DUnRTbaYboyco1MtsN8+85CkOl22L9/f+IPEI2NjSm3w4JYpK2trdTW1lJTU4Pb7ebgwYM4HA5qamompXM6nWzbtg2Hw0FZWdkkc7yrqwuHw5GIFevq6sLpdALMmE+ukOk2mEquWh7ZaIerlZFLZLodZkubK2S6Herq6mhoaEj8Aezduzf1dkhJDc+D+K/FxL+pvyaNjY2ipaVFNDY2TrIcWlpaEr8k9fX1k0JY6uvrJ8WgzZTPQpOtNmhraxONjY2JX92Wlpas1C9ZstEOs5WRC2SrP8yUNhfIVjsIEbN0m5ubBSAaGhpStkjlpiUSiUSSInKtvUQikaSIVKQSiUSSIlKRSiQSSYpkdWWT3CFfIpHkMmKeO+RnVZH29/fLM5skEknOM9czm7KqSIuLi4GYkCUlJdksWiKRSGbF6/VSXV2d0FXJklVFGh/Ol5SUSEUqkUhylrm6HuVkk0QikaSIVKQpEAmEiQQjCy2GRJI3BDSN6CJcA7Qg+5HmO2MjY7x24FV6j/WiKAprb1vLHb9/J5Yiy0KLJpHkJJoQ7O/to33YRZHBwB+uWc0H7IvHvSct0jniHxnjmT/7Cb3HeoFYuMSZ18/w7F88Q2gstMDSSSS5ycELg7QNuxCAT9P4as85eoPBhRYrbUhFOgd0XefQ3x9ibHgMe5WdB772IJ/8209SUFqAu8/Nq996Bbl1gUQymUvhCE8PDgHw36tXcWtxEREh+G7/hQWWLH1IRToHTradZPC9QUw2EzubPoJjtYOK9cuoa9yJYlA4e+QsZ9/sWWgxJZKc4tlLl4gKwQ2FhewoK6VhdRUKcNTr4/wisUqlIk2SsD/MsZbYQVtbf3MbJSsu+3cqaiq45VO3AnDk346gRbUFkVEiyTUius6hYRcAv1ZZgaIoVFksCf9o23DunKOWClKRJsmJn71DyBfCXmVnU92mK67f8qlbsDls+C76OP3S+wsgoUSSexzzjeLVNEqNRmpLLge57ygrBeAllxttEbjD0qZIn3766XRllXNEghHe+dk7AGyu34JquLLZTFYTN3/yFgB++R+/RNf1rMookeQir7k9ANxVascwIch9S0kxRQYD3qjGO6NjCyVe2kiLIvV6vRw5ciQdWeUkp18+TcgXonh5MetuX3fVdNftuA5LsQXfRR/njp7LooQSSe4R0XWOerwA3OGwT7pmVBRuGx/ev+HxZF22dJOUIt22bRsGg+Gqfw6Hg3379mVa1gVBCMGJcWv0xl+5ccYdYUxWE9eND/tP/PSdrMgnkeQqJ8b8+HUdh9HIxoKCK67H/aRHPL68j3ZJSpE+8cQTHD16FE3Tpv3TdZ1HHnkk07IuCANvD+Dp92C0mrj2vmtnTX/9R65HURUuvHsB17nF4UiXSOZDpzdmjW4tKUadZu36LcVFmBWFS5EI54L5HYOdlCLdsmULZ86cmTHNzp070yJQrnGy7V0ANtyzAZPNPGv6wrJC1my7JnZv+8mMyiaR5DJHvT4gpkinw6Kq3FxcNJ7WmzW5MkHSPtIHHnhgxus7duxIWZhcw+/2c/boWQA27bxypv5qxNOefvm0XIsvWZIMhEIMhMIYgFvHleV01I5vV3fMO5olyTKDDH+agfdfeB+hCSo3VlK2pizp+6puqqJ4eTGRQIQzr89syUski5Hjvphi3FRYSIHBcNV08ZCok2Nj+LX8jb9OWZHu3r07HXLkHEIXnDp0CojNxs8FRVUS95zqkMN7ydLj2PiwfkvJ1a1RgBUWMystZjTgLV/+WqUpK1Kn05kOOXKO/rf6GB30YS4ws+72mjnff+29G1EMCkPvDzFyVk46SZYOEV3nrfHY0C1J7DQfH953LWVFulg51RGzRtffvQGjZe67DdocNq4Zn3SSVqlkKfHumJ+grlNqNLLOZp01fdxqPebN3zColBVpaWlpOuTIKfyuCZNMdXMb1k8kHlMqJ50kS4mu8WH9rcVFSR3ZcVNRESZFYSgSoTeUn2FQKSvS559/Ph1y5BSnOk7FJpmuW07pHCaZplJ1UxUlK0uIBCI4X+tOo4QSSe7S5Zs57GkqFlXlxqJCADrHlXC+kZGh/fHjxzORbVbQoloi/vP6j1yfUl6KqiQ2ODnx3Im8HbZIJMlyMRTmfDCECmyew0mccaV71JOfijSlo0aupjCfeuopNm/enErWC0bPGz0EXH5spQWsvW1tyvlde99Gug524TrnYuCdAapuqkpdSIkkR3lzPLD+hqJCioxXD3uayraSEr7TN8C7Y2P4olGKjfl1ClJK0v7whz/E4/Fgt0/ekODYsWMpCbVQCCF4+5m3ALh+5yYMc+gIV8NSZGHDvddy8vl3efuZt6QilSxqDrtjinT7HI9bX2Exc43VytlgkCNeHx8uy6+5l5QU6aOPPsrRo0evWNXU0dGRklALRf9b/QyfGcZoMbIpxWH9RG762E2cajtJ77FeRs6OUHbN/P2uEkmu4o5EODEWC3u6zTH3g+1us5dwNhjkDbcn7xRpSj5Su90+7dLQfFwuKoTgWGvMkt744Y1Yi2cP20iWkhUlCTfB8R/lp7UukczGa24PAri2wEalefZ9KaYS32rvmG8UXzSaZukyS9omm5588sl0ZbUg9B7vZfDURQwmA7eMb9CcTjY/sBkU6DncwyXnpbTnL5EsNC+63ADcXeqY1/1rbFbWWq1EhUhsCJ0vpEWRHjp0iP3796cjqwVBj+oc+f6bAFz/0RsoKCtMexmla8pYf9d6AN78P4flDL5kUdETCHDaH8AA3O1wzDufD40P6duHXXn1jMxbkR46dIiHH36Y8vJy6urq8nqp6NvPvo27142l2MKtv3ZrxsrZ+hvbMJgNXDhxgdMvn85YORJJtnl2aBiIbdbsMM1/6uW+MgcmRaE7EOCU358u8TLOnBTpVOXZ3t7OgQMH0HWdL3/5y5mSMaO4zrsSp4Nu//QHsBRZMlZWUUURmx/YAsDh777B2HD+n1UjkQyHI4lh/ceXVaSUV4nRmHANPH1xKEXJskdSivQLX/gC5eXl7Ny5E6fTmVCejY2NPPjggwDs2bMno4JmgtBYiEN/14EW0Vi9ZXVSO+Cnys2fuJmK9RWEx8Ic+rsOouH8cqpLJFP54YWL4+fWF3BDUepusQcql6ECR7w+To7lh7GRlCLdvXs3u3bt4pFHHqG5uTmhPJNZR5urhP1h2prb8PR7KCwv5O6H78lKfVSjyn3/40OYC80MnR7iha+/IJWpJG85MTpGx0js3PrfXrkiLXmusloS4U/fPt9POA9O5FXEHD26HR0dHDt2DCEETqeTb33rW0BsldNsq5m8Xi92ux2Px0PJHAN204mn382hv38B17kRzAVmfuUvf5XyteVZlWHgxADP/+3P0SIaldct574/uo+iipn3bpRIcomhcJgvvN/NSCTKh8tK+eya1WnL2xON8kfvvodX0/hQqYM/XLN62nOf0s18ddScFelEJirVgwcPznok80Ir0tEhHyd+/i7vPncCLaJhtVv5yBfup6ImNb/OfBl4u5/2r7YTCUQwWk3c9PGb2LRzEwWOK09clEhyiXdGx/j62fNcikSotlpovnY9thl2wp8Px7w+/sbZgw7cbi/hM9WrKMnw0tEFUaQT+chHPjLrTlDzETI0FkKPXGnaCwQIQIjYf7pA6AI9qhENa0SCEUK+EGMjY3j63Ay+N8jwmeHE/atuWcVdD99NYQZCneaCZ8DDK998mcH3BoGYu6RiQwWV11Zir7JTUFaIpdCCyWbCYDKgGlUUVYn9xW4ABcbfTcJgNmAumHtgtGTpENJ1AhOO+BAT/hcCNAQRXRDQNVyRKOeCQTq9Pt4di82or7JYeGz9WirmEYCfDC+NuPjHc71ogFVVuc1ewvWFhaywmCkxGrCoKiZFQUGJPwpXPAkOkynp8hZckXZ0dMy6omk+Qr7w9UPpO/dIgZU3ruSmj93E6i3VOePjFbrgzOEznPjpOwmFmg5u+JUbuO2/3p62/CSLj0PjimquqEBdeRm/U7WCwjRbolN5b8zP/t4+nIHgnO9VgKc335x0+vkq0rTZyVlZFqpMfaskPldUBdWgohpUDCYDRqsRS7GVAoeNkpV2yteVs/LGlTk5bFZUhZrba6i5vYbRS6MMvDPASM8w3os+Ai4/obEQkWAEPaKjRbWE9R03HwRTfgvzJ45ZkkMoU14rgEFRMCoKNoMBu9FAlcXCdYUF3Ga3U2FO3tJLhY2FBXx14wbeHfPT6fVxJhBgMBxhVNMI6TpRIdDH7cGJXT+bj0HaLNJkWGgfqUQikczEgluki4mwP8wl5yXcvW60SBSD2YilyMLYyBhjQ7EDuuxVDlbcuAItpBEJRDDZTNir7AmfZNgfxtPvSVyzOWx4L3hx97pBAccqBxU1FdKHKVlUjGka3f4ATr8fd1SjQFUpNBgwqypD4TAGVaHYYKDCbCKo6fQGQxgVhXKziVuKi1g27msd0zT6giGCuo5VVVlltWTchZAKUpFOwdPv5mTbSQbfHyQ0GiLsDyeG0tFwFEVRxid8VEz/z0jZ2nKKK4sQAgocNtZ+cB0g6Dncg98dQFEg6AsxOugjGoom1g8bLUYqNy5nU90m7FX2mYWSSPKAvmCIn18a5q3RUdyRKBEhiAqBCuiAUQEVJTZBikARIBQwoFBgUHnV5eETlRUsN5t5w+1hJBohlhLKjCZuc9hZZc3cysNUkIp0AmF/mO5XuhnqHsJgNGAwGTDZTAR9QSKBCIqqYLKZUFWVaDRKcDSIu89F5XWVmG1mvBe9dL92GgREQlEcqxxoUQ3PgBffkA+DyUBFTQWqQcXvCTB0ehCTzchNH7tZWqaSvGZM03jZ5eLU2BhBTcdmMKBqUYSAoBAoxGbddQFC6AT0mIJdaTajE/PFeqJRfjw4yAarDV1RqLZYURUFXQguhMO84fbwK8vKKchBy1QexzwBT78Hd78HxaCimlS0iI7BYEBoAnRQVRVFUdAiGggwmo2xIXyvG0VVKFlegrvPg7vPQ8nyEhRVIeAO4B328vSxf0fTY24AFIUCuw3VoOLu8+Dpz68twzJFKBTiscceI5SnJ0lmk1xrq75giN5giIgQKIqCAdAUBVVREMQUTVgX6EBUF4mJrSACq6oSFYISg4FL4SinA0FWmM2JAHxVUVhhNuOKRugNzq2+2WonqUgnEAlE0DUNRVEQ41+2ELHZcUVVrpgdV1QlZn2OH7WsqAp6VIvlocY6gRbRCEfCtL7ZSlSPomvjMbGKgqLE0kcC8qhmiHX6L37xizmjHHKZXGuroK6jjT8eCrEhe3xYrhCLj05Eg48rSEVR0MYVrxhPqwtBROhXrGKKvw/OcblottpJKtIJmGwmVIMBIcS44hzfT0BhXLFO/nKFLkABk9WUeK8aDbE89FivMpgMCaUqhEA1jDe5ELH3xpj7QCLJZ6yqimH88RAQ839yWZmKcV9pLMF4qJIQGBQl9ryNp1UVBZOiJsKZ4sTfW9XcVFm5KdUCYa+y46iyIzQdPaJjMKlomoZiUEAFXddjX77JAApEw1HMBWbsqx0IXeC96MWxyo5jlR3vRS9CF9gcNqwlsWNLdKHHlKYQ+D0BdE3HscouJ5skec8qq4XVVgumccWoAYbx+E6F2GSTWVVQAeO4kQJgRSGo6xgVBa+mUWE2ssFm5UI4nFCecR9pqdHEajnZRGLG2jt+ZGsusmxzJW6Xm6HuIcKjYUKBEEIHDIJoJErIG0rM2hstBswVZi71D4EAq91K1S2xU0LPHT1L/+k+UEA3xpbgRdUoAz0DMf+qxciyDZUsu6WSYDRI0Dv3VRuLjXi/yOX+kSvkYlttMRgY0XVOBPy4I1GiQqBNmLUPxJcyKwqm8SXeFwN+DCgUGlTKTWbqlpVTaTZxxOPlfe/luQOH0cSN9hKiY2PMpcZzbad4urmG12c1IL+3t5fq6upsFSeRSCTz4vz586xenfxuVllVpLqu09/fT3Fxcc6sc5dIJJI4Qgh8Ph9VVVWoc/DHZlWRSiQSyWJETjZJJBJJisiVTXOkvb0dp9NJTU0NAHV1dQA4nU5aW1upqanB6XTS0NCAY/xYWqfTyf79+1m/fj3d3d08+uijiWtT6eqKHcRXW1uL0+nE7XZTW1ub8Xplgky31Uxl5BOZbqfW1tZEnjO1Za6T6XZyOp20t7dTVlaG0+mkvr4+UdasCEnStLW1iYaGBiGEEN3d3aKmpiZxrba2NvG6u7tb1NfXJ97X1NQIl8slhBCis7Mzkcd0NDQ0xGOTRV1dXeK+fCMbbTVTGflCNtop3p8m/jU3N6e5JpklG+00tU1mSjsVqUjnwMQvRYjYlxb/f+KXKYQQDodDCBHrAFMf8Jl+v/bv3y9cLlfeKtA42Wirq5WRT2S6nVwul2hpaZn0Wb4pUSGy05+m5jMXRSp9pEnidDoZGRnB4XDQ1dWF2+1OmP3x4cBEysrKEummIz6Enw6Hw5HXQ7BstNVMZeQL2epT9fX1idetra2T3ucD2WqnsrIytm7dmhji79y5M2kZpSJNkq6uLsrKyhK+mAMHDtDa2gpw1S9sZGQk4eucmE/82nS43W5aW1tpbW2lqalp0r35QjbaaqYy8oVstNPEH2S3283IyEje/eBk69lraWkBYP369bS0tMzpB0dONiXJyMgITqeTuro6HA4HDQ0NlJaWzrgCIv7L2dzczIEDB3jooYcSX+zUX9E4Ex3lNTU17Ny5k+7u7rTXJ5Nko63mU0auka0+FaepqYnm5ua01iEbZKud2tvbaW5uxul0snfvXgD279+flIzSIk2SmpqaSUPu+P9dXV04HI4rfuXiQxGAxsZG6urqEp0hnt90TPwFjc9C5ptVmo22mqmMfCFbfQpiiqW9vT0vXUbZaCen08mRI0eoq6ujoaGB7u5uDh48mPSzJxVpkszUSa8WcrNt2zaARMhGfKhRW1s7bYfu6uqa9hDB2SyNXCMbbZVvw9PpyEY7xTl69GheKlHI3rO3ffv2SWU++uijV3UdXEHS01KSSeFIs4Vg1NXVJd47HI7EfQ0NDaKtrS1xrbOzMzED6XK5xP79+xPXWlpaJoVy5BOZbqvZysgXstFOQsRm6ifen29kup26u7tFY2PjpDKnvp8JuUR0Drjdbpqamti6dSudnZ00NTUlfi3jgb/bt2/nyJEjkwJ/Dxw4QFlZWcLRP/FXdNeuXWzfvp3GxkYg9ssYH4J1d3fnpU8LstNWM5WRL2SjnQD27dtHd3d30j6/XCMb7dTe3p5wF0DM2k22P0lFKpFIJCkifaQSiUSSIlKRSiQSSYpIRSqRSCQpIhWpRCKRpIhUpBKJRJIiUpFKJBJJikhFKpHkCPFYyXxa5iqJIRWpZFESX1ue9BK/ae7PNkePHl2QciWpIxXpIqKrq4u9e/eiKApNTU3s27ePffv2sXfv3rzb+CROe3s7W7du5cCBA1dNs3Xr1klb6Lndbvbs2UNNTc20exfMxoEDB6661Vomie9uNJV9+/ZlXRbJHEl6MakkL3C5XAKYtJt4W1vbpDXH+UZjY+OkPQim0tbWNqluLS0tiV3g458nW/fOzs4rdpTPJo2NjaKzs3PSZy6Xa07rviXZR+5HugSoq6tLDHXzbXd0gPLy8hmvT90ByOl0TtpyLb7jeUNDw6xlPf7444kNftNJa2vrtKOCZA5Ym3iQW77tJbBUkIp0CTDxZNKlSHNzM1u3bp01XSaPK0n1B2z37t20trZO2ohEkjtIRbpIie8g1dXVxfDwMN3d3ZOURGtra8Jam7jLVHt7O01NTezduzexsXRbWxvNzc0Jq3bqLkJzzetqFp/b7ebgwYPU1NTgdrs5cuRIIq942VPz6OrqYs+ePezdu5eGhga6urpoa2tL5FlTU8PRo0cTPs+ZdvQ5ePDgpD0pYfLOQiMjIzz00EMcPXp0zm2U7Hc2ccZ+4g9fbW0tTU1NUpHmKgvtW5Ckl6k+0s7OTlFbW3uFjxBI7MXY0NAwyS/Y2Ng4aR/Uurq6SSdPTj3Rca55TfUBxmlubp60X2TcL9rc3DxjHs3NzZN8qM3NzZPknc3HOjHdxHxdLtekujY2NibynWsbpYOpp1xKcgdpkS5yamtr2bZtG01NTZMsJJfLlbAi42fixCkvL5/kl3Q4HJOsuPjxDnHf3VzzutqMeH19PVu3bqWmpobdu3dP8mlOtBRnyiMV3G73pFnzuHUc/+zRRx9NXJtrG0kWNzL8aQngcDhob2+f9Nnjjz+eCKtJxi84k0KYa15Xo6ysDJfLxRNPPMHw8DC7du2ad15XY6Y4TYfDMen6VMU68dygq90vWZpIRboEWL9+fcJKjO/A39XVRWNjY8IfCVyhbCdyNQU0n7yuxuOPP544V6e5uXlOiinZQPaZ5JrYThCzkKeuMppPG6WLfDu7aykhh/aLiK6uLp566inq6+s5cOAAdXV11NbW0tDQQFtbG/v27cPhcPDQQw9NslJ37drF/v37qampSeQBJE5fnDjrH5/waW5uprm5mW3bts05r/j1qdZreXk57e3tiaMhdu/ePWsebrebp556irKyMurr6ye9r6mpob6+nr179yaO5b3aYWnx/Pfv35+YYa+pqWH//v00NTUlXAt1dXVzbqN0WKpdXV3s3Lkz5XwkmUEeNSKRTGDXrl0ZiSNNlYlRApLcQw7tJZIJ7N27d9Jy01wg7jKQSjR3kYpUIplAXV0dIyMjObV5yOOPP563p8kuFeTQXiKZhqkz9hLJTEhFKpFIJCkih/YSiUSSIlKRSiQSSYpIRSqRSCQpIhWpRCKRpIhUpBKJRJIiUpFKJBJJikhFKpFIJCkiFalEIpGkiFSkEolEkiL/Pz/6yrdrbmq3AAAAAElFTkSuQmCC",
|
|
"text/plain": [
|
|
"<Figure size 350x250 with 3 Axes>"
|
|
]
|
|
},
|
|
"metadata": {},
|
|
"output_type": "display_data"
|
|
}
|
|
],
|
|
"source": [
|
|
"fig, axes = plt.subplots(3, 1, figsize=(3.5, 2.5))\n",
|
|
"for i, peak in enumerate(peek):\n",
|
|
" for j, p in enumerate(polarizations):\n",
|
|
" axes[i].scatter(data[peak][p], np.ones_like(data[peak][p]) - 1, color=color[p], alpha=0.4, s=25)\n",
|
|
" axes[i].plot(distribute_range[peak][p], normal_dist(distribute_range[peak][p], mean[peak][p], std[peak][p]) + 0.1, color=color[p])\n",
|
|
" axes[i].tick_params(direction='in')\n",
|
|
" axes[i].set_yticks([])\n",
|
|
" axes[i].set_ylabel(peak_label[peak], rotation=90)\n",
|
|
" axes[i].set_ylim(-0.05, 0.7)\n",
|
|
"axes[2].set_xlabel('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": 36,
|
|
"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",
|
|
"ax.invert_yaxis()\n",
|
|
"for i, peak in enumerate(peek):\n",
|
|
" ax.barh(i * 0.8, abs(mean[peak]['zyyz'] - mean[peak]['xyyx']), height=0.2, color='#1bb8df')\n",
|
|
" ax.errorbar(abs(mean[peak]['zyyz'] - mean[peak]['xyyx']), i * 0.8, xerr=np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2), capsize=5, color='#72e8b4')\n",
|
|
" ax.text(abs(mean[peak]['zyyz'] - mean[peak]['xyyx']) + np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2) + 0.005, i * 0.8, f'{abs(mean[peak][\"zyyz\"] - mean[peak][\"xyyx\"]):.3f} $\\\\pm$ {np.sqrt(std[peak]['zyyz']**2 + std[peak]['xyyx']**2):.3f}', va='center', fontsize=8)\n",
|
|
" ax.barh(i * 0.8 + 0.3, theoretical_result[peak], height=0.2, color='#7b7bc9')\n",
|
|
" ax.text(theoretical_result[peak] + 0.005, i * 0.8 + 0.3, f'{theoretical_result[peak]:.3f}', va='center', fontsize=8)\n",
|
|
"ax.set_yticks([0.15, 0.95, 1.75])\n",
|
|
"ax.set_yticklabels([peak_label[peak] for peak in peek])\n",
|
|
"ax.set_xlabel('Difference of Raman shift (cm$^{-1}$)')\n",
|
|
"ax.set_xlim(0, 0.4)\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 (ipykernel)",
|
|
"language": "python",
|
|
"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.13.9"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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