diff --git a/src/test.ipynb b/src/test.ipynb
deleted file mode 100644
index 0f08952..0000000
--- a/src/test.ipynb
+++ /dev/null
@@ -1,148 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import transbigdata as tbd\n",
-    "import pandas as pd\n",
-    "\n",
-    "data = pd.DataFrame([\n",
-    "            [34745, '20:27:43', 113.80684699999999, 22.623248999999998, 1, 27],\n",
-    "            [34745, '20:24:07', 113.809898, 22.627399, 0, 0],\n",
-    "            [34745, '20:24:27', 113.809898, 22.627399, 1, 0],\n",
-    "            [34745, '20:22:07', 113.811348, 22.628067, 1, 0],\n",
-    "            [34745, '20:10:06', 113.81988500000001,\n",
-    "             22.6478, 1, 54],\n",
-    "            [34745, '19:59:48', 113.820213,\n",
-    "             22.674967000000002, 0, 23],\n",
-    "            [34745, '20:11:06', 113.82048, 22.6423, 0, 57],\n",
-    "            [34745, '20:13:46', 113.82676699999999,\n",
-    "             22.630899, 0, 66],\n",
-    "            [34745, '19:43:18', 114.828217, 22.7069, 0, 62],\n",
-    "            [34745, '19:42:18', 113.83161899999999,\n",
-    "             22.716998999999998, 0, 69],\n",
-    "            [22233, '14:41:40', 113.878571, 22.571199, 0, 0],\n",
-    "            [22233, '14:42:00', 113.879135, 22.571617, 0, 7],\n",
-    "            [22233, '14:45:40', 113.886253, 22.573217, 0, 25],\n",
-    "            [22233, '14:49:00', 113.8899,\n",
-    "             22.56956700000001, 0, 0],\n",
-    "            [22233, '14:55:15', 113.91093400000001,\n",
-    "             22.552383, 1, 60],\n",
-    "            [22233, '19:02:12', 113.927116,\n",
-    "             22.543948999999998, 1, 41],\n",
-    "            [22233, '14:32:47', 113.92831399999999,\n",
-    "             22.556867999999998, 1, 57],\n",
-    "            [22233, '14:57:35', 113.934036, 22.555267, 1, 65],\n",
-    "            [22233, '20:54:11', 113.942467, 22.507566, 0, 21],\n",
-    "            [22233, '18:51:30', 113.964569, 22.541849, 0, 0]],\n",
-    "            columns=['Vehicleid',\n",
-    "                     'Time',\n",
-    "                     'slon',\n",
-    "                     'slat',\n",
-    "                     'OpenStatus',\n",
-    "                     'Speed'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Amount of data\n",
-      "-----------------\n",
-      "Total number of data items:  20\n",
-      "Total number of individuals:  2\n",
-      "Data volume of individuals(Mean):  10.0\n",
-      "Data volume of individuals(Upper quartile):  10.0\n",
-      "Data volume of individuals(Median):  10.0\n",
-      "Data volume of individuals(Lower quartile):  10.0\n",
-      "\n",
-      "Data time period\n",
-      "-----------------\n",
-      "Start time:  14:32:47\n",
-      "End time:  20:54:11\n",
-      "\n",
-      "Sampling interval\n",
-      "-----------------\n",
-      "Mean:  1422.7222 s\n",
-      "Upper quartile:  596.75 s\n",
-      "Median:  210.0 s\n",
-      "Lower quartile:  125.0 s\n"
-     ]
-    },
-    {
-     "data": {
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eI+nLGc1fc87FMtrKdayN5v9BjGOUqn9K6pL3rf8fSZrtO3eHpLc459ZmuY7X5OyxgYIzs20l/dTX9N1RzJLHWM4eGyiUPfTmvwenJN0g6W3yPgSukfd6/D/yXp/9vmJmn85oYxxnjw0UypSM425Jf5BXvFE3xLVTJf3ezL6R5RxjOXtsoFi+lHGcOYtHLozl7LGBikFxB1C6ohnH8WFen9m/ahS5APAUelz645dTbKDozOwoeX/QMl/zH51z12TpXq5jbTT/D2Ico1Rtl6O9Td764ZbjPK/J2WMDBZWeHvo3kiakm56U9KZv8g8DYzl7bKBQ9sw43iDpWOfcac65u5xza5xzMefccufcb+Ut4fKzjGsuNrN5vmPGcfbYQKFkfmD5NUmnpvfXSvq8pHnyCrWaJR0vKXOJwwvN7MMZbYzl7LGBwJnZvpIW+poW5TNrRxpjOXtsoGJQ3AGUrnDGcWqY12f2Z7wDo1focemPX06xgaIys6Ml/VkD1x1eKumMHJeU61gbzf+DGMcoOekPibfNcbpJ3h+ql5rZN9J9/XhNzh4bKLRzJR2V3u+VtxzLcP/w6sdYzh4bKJQ3JN0k6TF5HwKf5Jz7V67OzrmUc+48Sbf4miOSvu47Zhxnjw0USuYHln0fAD8gab5z7mLn3MvpQq0Nzrm/OueOlfSFjOt+bmbNvmPGcvbYQDF8IuP4J8O4lrGcPTZQMXhyA6UrkXGc+aI8lEjGceaU9ACGr9Dj0h+/nGIDRWNm75N0pwZOP7tG3jcQt+S4rFzH2mj+H8Q4RimKSHqXvCngqyW1yFtX+J++PlWSLtSbvzXMa3L22EDBmNlOkn7ga/p/zrnMJRuGi7GcPTZQEM65PzvnTnXO7e+cm+6cuy/PS8/XwG/EvtfM+gqrGcfZYwNBWi7pXc65Dbk6OOd+LOkqX1ODpM/5jhnL2WMDgTKzWm2djUfy/sb1x2GEYCxnjw1UDIo7gNLVlXFck7VXbpn9eTEDRq/Q49Ifv5xiA0VhZudJulHeh8J91kh6q3PulUEuLdexNpr/BzGOUXKcc3Hn3D/TU8DHnXPr0h86vV3SpzO6f8rMjvUd85qcPTZQEGYWlvQ7bS2mfFTS98cgNGM5e2ygpDjnVkq629dUI+mQ9D7jOHtsoFB6s7R90zm3KY9rv55x/Xt8+4zl7LGBoL1TXvFVn9875zILNgbDWM4eG6gYFHcApWtjxnHTMK/3909J2jyqbABI0hZJSd/xaMal5K1v7Ocf94WM3ZhlevvRxAYCZWZhM7tM3rSU/vezyyW9xTm3ZIgQ5TrWRvPegHGMsuKcu0zSjzKav+Tb5zU5e2ygUL4o6aD0fkzecizJQfrni7GcPTZQih7OOJ6TvmUcZ48NFEpbxnFS0q35XOicWy3pEV/Trr6lWRjL2WMDQTsl4/h3w7yesZw9NlAxKO4AStdaSc53PHWY17f49jeM0R/egHHNOeckrfM1jWZcKiOW5M04EETssKRJYxgbCIyZNUm6Q9K5Gaeek3Soc+7lPMKU61hbk3E8nNwZxyhH35LU7Ts+zMzqJV6TB4kNjDkz20Pe8kh9vu6ce2EsYjOWc8YGStHajONmiXE8SGygUDKfa8udc5kFH4N5JuN4psRYHiQ2EBgzq5J0jK/pRefc4uHEYCznjA1UDIo7gBLlnItJWu1r2maYIfz9Xxt9RgDS/ONpNONynXMuc5o8f+zG9IfYI4nt5M1gkCt2Zv/hxM4WCwiEmc2QdL+kYzNO3SuvsGNVnqHKdawxjjGuOOfaJT3kawpL2sl3zGsyYxnBOEkDl0D7gZm5wTZJ38iI8VpGn9n+c759xjJQujK/Peuf7pxxzDhGcJZlHHcO8/rNGcf+D08Zy4xlFNehkhp9x38aYRzGMmMZFYziDqC0+asy55lZXmPWzKZp4BvzYVV3AhiUfzxNMrPMquCs0uPX/4FUtnGZ2bbrMPLaxbf/mnMu85f7sYrdKymfmRGAMZX+EOhBSXtknLpe0tudc5uHEa5cx9pYxc4WCyi49JJKw103N/NbwrW+fV6TeU1GZWAsM5YREDNrMLO5Znawme01zMunZRy3+vYZx4xjBMQ5t0ED3yNPH2aI+oxj/7hgLDOWUVxHZxz/ZYRxGMuMZVQwijuA0uZfz7Re0m55XndgxvF/xyYdAHrzOsP753ndfEkNvuNs43JEsc1sgqSdCxTbMvo+5pxL5HMtMFbMbFtJ/5E0O+PU9yR9MD3b1XCU61h7St4vqMOKneZ/b/CGc27lMK4FRszMdjKzu83sVUk9Gri0Qz4mZxz7p1blNZnXZFQGxjJjGQEws90ktUtaKmmRpEuHGSLzOf6Eb59xzDhGsPzP5+Z8P7hN2yHj+FXfPmOZsYziOsS336U3P7fzxVhmLKOCUdwBlLZ/Zhwfn+d1J2Qc/2sMcgHgyRxPIx2XmeNb8v7A5p/qLt/Yx2nga/qbYjvnXpX0yghiL1R6LeVcsYFCMrMGSX+XtL2vOSXpXOfcV9JriQ5XWY4151y3vGVp+hxtZtGhApvZTEl7DxYbKKANko6UNEdSRNI7870wvd7wQl9ThyR/YRKvyUAAnHMXOudsOJukb2aEmZPRZ5nvHGMZCMZLGriUygFmltda9mY2RdJbfU1rJT3vO2YcA8H6m2/fJJ2cz0VmVq2BHx6/7Jzb6DtmLANFYmZhDSxQeMQ5Fx9hOMYyUMEo7gBK24Ma+Afss9NvwnMys2ZJp/maHsj4wxmAUUh/2/1BX9MH0n/oyin94evZvqYVGvgBbV/smKQ/+pqOMbOdM/tl8Wnffpek23P0u9G3v4uZZU71l81n/ClKui6Pa4CxdJkGzlyVlPQR59zPRxqwzMeaP/YESR/OI/anNXCN9N/ncQ0wJtJ/LPZ/s3e3PMeEJH1M3vO8z9+cc/2z1/CazGsyKgNjmbGMYKS/wfp3X1O1pLPyvPx/NXBptCv9RdaMY8YxAneLvFnx+pxvZnV5XHe6Bs6Md6v/JGOZsYyi2lmSfxyPeDldxjJjGZWN4g6ghDnnUpIu9zXtIOniXP3Ta6L9VlKjr/mnBUkOGN9+5ttvknR1evzl8hMNXE7i0vT4zsY/NW5I0rVmVpujr8zsS5IO8jX9xjnXlqP7FRq4pMOvzGzqILHfL+n9vqa/OueW5uoPjDUzO1HeH5/8PuecG4tf0sp1rF2vgeub/8DM5g0S+zBJ5/uanpX071z9gQL5ZcbxZekpWXMys/0k/dDX5DKO+/CaDFQGxjIQjF9lHH/VzHbJ2jMt/Z7c/35ys7wC7EyMYyAgzrkNkn7ta5oj6dLBxpyZ7aiB76d7JWX70gRjGSiO3TOOXxhlPMYyUKmcc2xsbCW8SaqXtFzeH7T7tssk1WT0myiv2trf7/5i58/GVsqbvDes/jFzYZ7XhSQ9knHtLZImZvSrkfeLsr/fUknRIeLfknHNvZJmZfQJS/qavOUp+vptlDRtiNg/zoj9rKRds/T7mLxvgfT1i0maX+zHjG38bPJmmngq4/l66xjfR1mONUmfyoi9QtLBWfqdKGlTRt+3FfuxZRt/W3ocPZbxXHwi23M93fdj8pZg8ff/WY7YvCazsZXgJunCjOf37CH6M5bZ2ALa5C3n4H/OrpZ0TJZ+VZK+KCme0f/0HHEZx2xsAW6Spkh6PeO5e5ukbbL0PTI91v19v5MjLmOZja0Im6SvZjx/TxxlPMYyG1uFbuacE4DSZmaHyFsnzV/92CrpDklrJG0vb/3yBt/5DZL2dc4tDypPoNyY2WxJr/mavumcuzDPa+fJW2PQv55fh6S/yCvImiFvnUL/lHc9kg5zzj02ROypkh6SNNfXHJN0p6SX5U2h+Q5J2/jOpyS9xzn35yFi18h7s32Arzkpby3CxfL+P3KkvKkA/T7tnMv27SygIMzsGEn/GKt4zjnLbCvXsWZmJm/6yhMzTt0n7wP0KnnrKO+Tcf4i59wFg8UGCsXM5sib0nWWr9ml2x6X9xq5jaSjJM3MuPzPkk5yziVzxOY1GSgxZnahpG/4mua4IZYLZSwDwUiPh0WSdso49ZSkByS1S9pW0tslZX5T9lvOua8PEptxDATIzPaR9zz1j6m4vL8jL5H3werBkvbPuPReeYX/8RxxGctAwMzsV/KKFfosdM49PMqYjGWgAlHcAZSJ9Npit2ngkiu5rJF0rHPu6cJmBZS30RR3pK/fQ96Hz9Pz6N4m6b3OubvyjL2tpLsk5VxuwadX0lnOuWvzjD1B3re1Dhqqr7w33f/nnPt+PrGBsWJmP9XANTNHJVtxR/p+ynKsmVm1pJv05gKPXC6X90sub/5RNOmpoG+StHeelzhJP5I3NhJDxOY1GSghIynuSF/HWAYCYGbbSLpBXkFwPmKSvuGc+0EesRnHQIDMbL6kP0raMc9L7pD0fudc5xBxGctAgMzsdknv9jUtcM49NwZxGctAhRlsfSUAJST9grqLpOvkVU9m0yVvTfP5FHYAheece0bSrvI+NG3P0S0u749mu+f7xjgde6WkPSV9S95MPFm7SfqrvFl68npjnI69RdJhkj4rbwrPXB6QdARvjFEkc4fuMnrlOtacc73OuXdL+rC8b0Tk8rSkdzvnPkVhB4rNeeveLpR0ngYWV2bqlVfUvJ9z7ktDFXakY/OaDFQAxjIQDOfcKklHSDpb0ouDdO2SdLOkPfMp7EjHZhwDAUp/+LtA0lfkLdmZy/OSPuyce+dQhR3puIxlIFgNGce5PgMaFsYyUHmYuQMoQ2bWIO+X8O0kTZJXUfmipIecc7leoAEUkJlFJR0uaY68qey6JL0iaZFzbuMoY4clHSpv2typ8r41tSIde/UoY5u86TnnS2qRlJD0hrz/nwz2wRtQccp5rJnZ7vJmQ2iRZJLWSnrEOffCaGMDhWJmu0raT9I0SdXy/hj0mrwx1zWKuLwmAxWAsQwEx8zmSjpQ3mtyraR1klZJup/XZMYxyouZ7StpN3nf0g9LWi3pUefc86O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",
-      "text/plain": [
-       "<Figure size 2400x900 with 1 Axes>"
-      ]
-     },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "tbd.data_summary(data, show_sample_duration=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Obtaining city id: 深圳市success\n",
-      "1号线\n",
-      "2号线\n",
-      "3号线\n",
-      "No such busline\n"
-     ]
-    }
-   ],
-   "source": [
-    "import transbigdata as tbd\n",
-    "data, stop = tbd.getbusdata('深圳市', ['1号线','2号线','3号线'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "py38_native",
-   "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": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}