229 lines
47 KiB
Plaintext
229 lines
47 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "code",
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"id": "initial_id",
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"metadata": {
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"collapsed": true,
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"ExecuteTime": {
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"end_time": "2025-11-19T05:38:27.961047Z",
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"start_time": "2025-11-19T05:38:25.008889Z"
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}
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},
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from matplotlib.patches import Wedge\n",
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"from matplotlib.patches import FancyBboxPatch\n",
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"import matplotlib.patches as patches\n",
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"\n",
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"# 数据：每个类别及其占比（示例数据，按图中比例估算）\n",
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"labels = [\n",
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" '微博', '新闻', '自媒体', '论坛', '视频', '境外', '其他',\n",
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" '知乎', '头条', '微头条', '微信', '其他自媒体', '百度贴吧', '垃圾站点',\n",
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" '未回答', '问答', '博客', '政务', '纸媒', '网站', '热搜',\n",
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" '境外博客', '境外网站', '境外其他论坛', 'Reddit', 'YouTube',\n",
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" '境外智库网站', '境外主流媒体', '境外大型通讯社', 'Twitter', '长视频',\n",
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" '短视频', '其他论坛', '百度贴吧', '其他自媒体', '微信'\n",
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"]\n",
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"\n",
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"# 假设占比（需根据实际调整）\n",
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"sizes = [\n",
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" 25, 20, 18, 10, 8, 6, 5,\n",
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" 4, 3, 2, 2, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1, 1, 1, 1, 1,\n",
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" 1, 1, 1\n",
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"]\n",
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"\n",
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"# 颜色映射（可自定义）\n",
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"colors = [\n",
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" '#3498db', '#2ecc71', '#34495e', '#f39c12', '#e74c3c',\n",
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" '#3498db', '#9b59b6', '#e67e22', '#27ae60', '#e74c3c',\n",
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" '#f1c40f', '#d35400', '#e67e22', '#9b59b6',\n",
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" '#bdc3c7', '#3498db', '#a8e6cf', '#95a5a6', '#9b59b6',\n",
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" '#f1c40f', '#e67e22', '#3498db', '#e74c3c', '#2ecc71',\n",
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" '#34495e', '#f39c12', '#e67e22', '#3498db', '#e74c3c',\n",
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" '#e67e22', '#9b59b6', '#3498db', '#e67e22', '#3498db',\n",
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" '#2ecc71', '#e74c3c', '#3498db'\n",
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"]\n",
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"\n",
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"# 创建画布\n",
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"fig, ax = plt.subplots(figsize=(12, 8), dpi=100)\n",
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"ax.set_aspect('equal')\n",
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"\n",
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"# 内环：主要分类（大类）\n",
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"main_labels = ['微博', '新闻', '自媒体', '其他']\n",
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"main_sizes = [25, 20, 18, 10] # 对应上面的数据\n",
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"main_colors = ['#3498db', '#2ecc71', '#34495e', '#9b59b6']\n",
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"\n",
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"# 绘制内环\n",
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"wedges_inner, texts_inner = ax.pie(\n",
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" main_sizes,\n",
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" labels=main_labels,\n",
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" colors=main_colors,\n",
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" autopct='%1.1f%%',\n",
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" startangle=90,\n",
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" pctdistance=0.7,\n",
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" textprops={'fontsize': 12, 'color': 'black'}\n",
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")\n",
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"\n",
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"# 外环：子分类\n",
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"wedges_outer, texts_outer = ax.pie(\n",
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" sizes,\n",
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" colors=colors,\n",
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" radius=1.3,\n",
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" startangle=90,\n",
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" autopct='',\n",
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" pctdistance=0.85,\n",
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" labeldistance=1.1,\n",
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" textprops={'fontsize': 8},\n",
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" wedgeprops=dict(width=0.3)\n",
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")\n",
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"\n",
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"# 添加外部标签（通过 annotate）\n",
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"for i, (wedge, label) in enumerate(zip(wedges_outer, labels)):\n",
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" angle = (wedge.theta2 + wedge.theta1) / 2\n",
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" x = np.cos(np.radians(angle))\n",
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" y = np.sin(np.radians(angle))\n",
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"\n",
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" # 调整位置偏移\n",
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" dx, dy = 1.2 * x, 1.2 * y\n",
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" ax.annotate(\n",
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" label,\n",
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" xy=(dx, dy),\n",
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" xytext=(1.4 * x, 1.4 * y),\n",
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" arrowprops=dict(arrowstyle='->', color='gray', lw=1),\n",
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" fontsize=8,\n",
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" ha='center',\n",
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" va='center',\n",
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" color='black'\n",
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" )\n",
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"\n",
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"# 添加图例\n",
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"legend_elements = [\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#3498db', markersize=10, label='微博'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#2ecc71', markersize=10, label='新闻'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#34495e', markersize=10, label='自媒体'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#f39c12', markersize=10, label='论坛'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#e74c3c', markersize=10, label='视频'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#3498db', markersize=10, label='境外'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#9b59b6', markersize=10, label='其他'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#e67e22', markersize=10, label='知乎'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#27ae60', markersize=10, label='头条'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#f1c40f', markersize=10, label='微头条'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#d35400', markersize=10, label='微信'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#a8e6cf', markersize=10, label='其他自媒体'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#95a5a6', markersize=10, label='百度贴吧'),\n",
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" plt.Line2D([0], [0], marker='o', color='w', markerfacecolor='#f1c40f', markersize=10, label='垃圾站点'),\n",
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"]\n",
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"\n",
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"ax.legend(handles=legend_elements, loc='center right', bbox_to_anchor=(1.2, 0.5), frameon=False)\n",
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"\n",
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"# 设置透明背景\n",
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"ax.set_facecolor('none')\n",
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"fig.patch.set_facecolor('none')\n",
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"fig.patch.set_alpha(0.0)\n",
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"\n",
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"# 移除坐标轴\n",
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"ax.axis('off')\n",
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"\n",
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"# 保存为透明背景 PNG\n",
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"plt.savefig('donut_chart_transparent.png', dpi=100, transparent=True, bbox_inches='tight', pad_inches=0.1)\n",
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"plt.show()"
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],
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"outputs": [
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{
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"ename": "ValueError",
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"evalue": "too many values to unpack (expected 2)",
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"output_type": "error",
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"traceback": [
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"\u001B[31m---------------------------------------------------------------------------\u001B[39m",
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"\u001B[31mValueError\u001B[39m Traceback (most recent call last)",
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"\u001B[36mCell\u001B[39m\u001B[36m \u001B[39m\u001B[32mIn[2]\u001B[39m\u001B[32m, line 49\u001B[39m\n\u001B[32m 46\u001B[39m main_colors = [\u001B[33m'\u001B[39m\u001B[33m#3498db\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m#2ecc71\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m#34495e\u001B[39m\u001B[33m'\u001B[39m, \u001B[33m'\u001B[39m\u001B[33m#9b59b6\u001B[39m\u001B[33m'\u001B[39m]\n\u001B[32m 48\u001B[39m \u001B[38;5;66;03m# 绘制内环\u001B[39;00m\n\u001B[32m---> \u001B[39m\u001B[32m49\u001B[39m wedges_inner, texts_inner = ax.pie(\n\u001B[32m 50\u001B[39m main_sizes,\n\u001B[32m 51\u001B[39m labels=main_labels,\n\u001B[32m 52\u001B[39m colors=main_colors,\n\u001B[32m 53\u001B[39m autopct=\u001B[33m'\u001B[39m\u001B[38;5;132;01m%1.1f\u001B[39;00m\u001B[38;5;132;01m%%\u001B[39;00m\u001B[33m'\u001B[39m,\n\u001B[32m 54\u001B[39m startangle=\u001B[32m90\u001B[39m,\n\u001B[32m 55\u001B[39m pctdistance=\u001B[32m0.7\u001B[39m,\n\u001B[32m 56\u001B[39m textprops={\u001B[33m'\u001B[39m\u001B[33mfontsize\u001B[39m\u001B[33m'\u001B[39m: \u001B[32m12\u001B[39m, \u001B[33m'\u001B[39m\u001B[33mcolor\u001B[39m\u001B[33m'\u001B[39m: \u001B[33m'\u001B[39m\u001B[33mblack\u001B[39m\u001B[33m'\u001B[39m}\n\u001B[32m 57\u001B[39m )\n\u001B[32m 59\u001B[39m \u001B[38;5;66;03m# 外环：子分类\u001B[39;00m\n\u001B[32m 60\u001B[39m wedges_outer, texts_outer = ax.pie(\n\u001B[32m 61\u001B[39m sizes,\n\u001B[32m 62\u001B[39m colors=colors,\n\u001B[32m (...)\u001B[39m\u001B[32m 69\u001B[39m wedgeprops=\u001B[38;5;28mdict\u001B[39m(width=\u001B[32m0.3\u001B[39m)\n\u001B[32m 70\u001B[39m )\n",
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"\u001B[31mValueError\u001B[39m: too many values to unpack (expected 2)"
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]
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},
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 24494 (\\N{CJK UNIFIED IDEOGRAPH-5FAE}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 21338 (\\N{CJK UNIFIED IDEOGRAPH-535A}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 26032 (\\N{CJK UNIFIED IDEOGRAPH-65B0}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 38395 (\\N{CJK UNIFIED IDEOGRAPH-95FB}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 33258 (\\N{CJK UNIFIED IDEOGRAPH-81EA}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 23186 (\\N{CJK UNIFIED IDEOGRAPH-5A92}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 20854 (\\N{CJK UNIFIED IDEOGRAPH-5176}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\events.py:82: UserWarning: Glyph 20182 (\\N{CJK UNIFIED IDEOGRAPH-4ED6}) missing from font(s) DejaVu Sans.\n",
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" func(*args, **kwargs)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 24494 (\\N{CJK UNIFIED IDEOGRAPH-5FAE}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21338 (\\N{CJK UNIFIED IDEOGRAPH-535A}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26032 (\\N{CJK UNIFIED IDEOGRAPH-65B0}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38395 (\\N{CJK UNIFIED IDEOGRAPH-95FB}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 33258 (\\N{CJK UNIFIED IDEOGRAPH-81EA}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23186 (\\N{CJK UNIFIED IDEOGRAPH-5A92}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20307 (\\N{CJK UNIFIED IDEOGRAPH-4F53}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20854 (\\N{CJK UNIFIED IDEOGRAPH-5176}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n",
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"D:\\ProgramTools\\anaconda3\\envs\\f6\\Lib\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20182 (\\N{CJK UNIFIED IDEOGRAPH-4ED6}) missing from font(s) DejaVu Sans.\n",
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" fig.canvas.print_figure(bytes_io, **kw)\n"
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]
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},
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{
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"data": {
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"text/plain": [
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"<Figure size 1200x800 with 1 Axes>"
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],
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"image/png": 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"
|
||
},
|
||
"metadata": {},
|
||
"output_type": "display_data",
|
||
"jetTransient": {
|
||
"display_id": null
|
||
}
|
||
}
|
||
],
|
||
"execution_count": 2
|
||
}
|
||
],
|
||
"metadata": {
|
||
"kernelspec": {
|
||
"display_name": "Python 3",
|
||
"language": "python",
|
||
"name": "python3"
|
||
},
|
||
"language_info": {
|
||
"codemirror_mode": {
|
||
"name": "ipython",
|
||
"version": 2
|
||
},
|
||
"file_extension": ".py",
|
||
"mimetype": "text/x-python",
|
||
"name": "python",
|
||
"nbconvert_exporter": "python",
|
||
"pygments_lexer": "ipython2",
|
||
"version": "2.7.6"
|
||
}
|
||
},
|
||
"nbformat": 4,
|
||
"nbformat_minor": 5
|
||
}
|