Figure3-Plot.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as ticker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "pd.set_option('display.max_columns', 500)\n",
    "pd.set_option('display.max_rows', 500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### map 2020 date to 2019 date in Lunar calendar"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "date_dic = {}\n",
    "for i, d20 in enumerate(pd.date_range('20200101', '20200315')):\n",
    "    # print(i, d20.date())\n",
    "    d19 = pd.to_datetime('20190112')+pd.Timedelta(days=i)\n",
    "    d19 = str(d19.date()).replace('-', '')\n",
    "    d20 = str(d20.date()).replace('-', '')\n",
    "    # print(d20, ' -> ', d19)\n",
    "    \n",
    "    date_dic[d20] = d19"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'20190205'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "date_dic['20200125']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-info\"> Outflow index for nine example cities </div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "cities = ['Wuhan', \n",
    "          'Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen', \n",
    "          'Zhengzhou', 'Nanjing', 'Changsha', 'Shenyang']\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "frames = []\n",
    "\n",
    "for city in cities:\n",
    "\n",
    "    # outflow index\n",
    "    folder = 'Data/outflow_data'\n",
    "    file_path = os.path.join(folder, f'outflow_{city}.csv')\n",
    "    city_flow_df = pd.read_csv(file_path)\n",
    "    city_flow_df['date'] = city_flow_df['date'].astype(str)\n",
    "\n",
    "    date2index = city_flow_df.set_index('date')['migration_index'].to_dict()\n",
    "    # date2index\n",
    "\n",
    "    flow_df = pd.DataFrame(list(date_dic.items()), columns=['date2020', 'date2019'])\n",
    "    flow_df['migration_index_2020'] = flow_df['date2020'].map(date2index)\n",
    "    flow_df['migration_index_2019'] = flow_df['date2019'].map(date2index)\n",
    "\n",
    "    # pd.to_datetime('20200101').strftime('%b %d')\n",
    "    flow_df['date2020_str'] = flow_df['date2020'].map(lambda x: pd.to_datetime(x).strftime('%b %d'))\n",
    "    flow_df['city'] = city\n",
    "    \n",
    "    frames.append(flow_df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date2020</th>\n",
       "      <th>date2019</th>\n",
       "      <th>migration_index_2020</th>\n",
       "      <th>migration_index_2019</th>\n",
       "      <th>date2020_str</th>\n",
       "      <th>city</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>20200101</td>\n",
       "      <td>20190112</td>\n",
       "      <td>3.455298</td>\n",
       "      <td>5.052067</td>\n",
       "      <td>Jan 01</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20200102</td>\n",
       "      <td>20190113</td>\n",
       "      <td>3.524602</td>\n",
       "      <td>4.058813</td>\n",
       "      <td>Jan 02</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>20200103</td>\n",
       "      <td>20190114</td>\n",
       "      <td>5.524427</td>\n",
       "      <td>4.004834</td>\n",
       "      <td>Jan 03</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>20200104</td>\n",
       "      <td>20190115</td>\n",
       "      <td>6.099818</td>\n",
       "      <td>3.724672</td>\n",
       "      <td>Jan 04</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>20200105</td>\n",
       "      <td>20190116</td>\n",
       "      <td>5.322478</td>\n",
       "      <td>3.990384</td>\n",
       "      <td>Jan 05</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   date2020  date2019  migration_index_2020  migration_index_2019  \\\n",
       "0  20200101  20190112              3.455298              5.052067   \n",
       "1  20200102  20190113              3.524602              4.058813   \n",
       "2  20200103  20190114              5.524427              4.004834   \n",
       "3  20200104  20190115              6.099818              3.724672   \n",
       "4  20200105  20190116              5.322478              3.990384   \n",
       "\n",
       "  date2020_str   city  \n",
       "0       Jan 01  Wuhan  \n",
       "1       Jan 02  Wuhan  \n",
       "2       Jan 03  Wuhan  \n",
       "3       Jan 04  Wuhan  \n",
       "4       Jan 05  Wuhan  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "flow_df = pd.concat(frames)\n",
    "flow_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_df = flow_df.copy()\n",
    "plot_df = plot_df[plot_df['date2020'].str.contains('202001')]\n",
    "\n",
    "fig, ax = plt.subplots(figsize=(6, 3.5))\n",
    "\n",
    "ax.patch.set_alpha(0.015)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "# 2020-01\n",
    "\n",
    "text_color = '#46ac88'\n",
    "\n",
    "\n",
    "colors = ['#e6550d', '#fd8d3c', \n",
    "          '#756bb1', '#9e9ac8', \n",
    "          '#3182bd', '#6baed6', \n",
    "          '#31a354', '#74c476', \n",
    "          '#a1d99b', '#c7e9c0',\n",
    "         ]\n",
    "\n",
    "\n",
    "# Wuhan\n",
    "# ------------\n",
    "city = 'Wuhan'\n",
    "data = plot_df[plot_df['city']==city]\n",
    "x = data['date2020']\n",
    "y = data['migration_index_2020']\n",
    "\n",
    "ax.plot(x, y, '-', \n",
    "        color='grey', \n",
    "        linewidth=1.5, \n",
    "        clip_on=False, \n",
    "        label=f'{city}', \n",
    "#         alpha=0.9,\n",
    "        zorder=100,\n",
    "       )\n",
    "\n",
    "\n",
    "\n",
    "# other cities\n",
    "# ------------\n",
    "linestyles = ['--', '-',]\n",
    "\n",
    "for (i, city) in enumerate(['Beijing', 'Shanghai', 'Guangzhou', 'Shenzhen', \n",
    "                            'Zhengzhou', 'Nanjing', 'Changsha', 'Shenyang']):\n",
    "\n",
    "    data = plot_df[plot_df['city']==city]\n",
    "    x = data['date2020']\n",
    "    y = data['migration_index_2020']\n",
    "\n",
    "    ax.plot(x, y, \n",
    "            # linestyle = '--', \n",
    "            linestyle = linestyles[i%len(linestyles)], \n",
    "            # color=f'C{i}', \n",
    "            color=colors[i], \n",
    "#             color=OrRd[4][i],\n",
    "            linewidth=1.5, \n",
    "            clip_on=False, \n",
    "            label=f'{city}', \n",
    "    #         alpha=0.9,\n",
    "            zorder=10,\n",
    "           )\n",
    "\n",
    "ymax=30\n",
    "line_style = (0, (7.5, 5))\n",
    "ax.vlines(x=list(x).index('20200123'), ymin=-5, ymax=ymax, linestyles=line_style, linewidth=.75, color='grey')\n",
    "ax.text(x=list(x).index('20200123')-0.25, y=ymax/2-1, s='Wuhan\\nlockdown', fontsize=8, ha='right', color='grey')\n",
    "\n",
    "line_style = (0, (2, 2))\n",
    "ax.vlines(x=list(x).index('20200125'), ymin=-5, ymax=ymax, linestyles=line_style, linewidth=.75, color='grey')\n",
    "ax.text(x=list(x).index('20200125')+0.4, y=ymax/2-1, s='Lunar\\nNew Year', fontsize=8, ha='left', color='grey')\n",
    "\n",
    "\n",
    "N = len(x)\n",
    "\n",
    "every_nth = 1\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "every_nth = 5\n",
    "locs = range(0, N, every_nth)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "locs = range(0, 35, 5)\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "ticks = list(x)\n",
    "ticks = list(x.str[-4:])\n",
    "\n",
    "ticks = [t[:2]+'-'+t[-2:] for t in ticks]\n",
    "# string date\n",
    "plt.xticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='center',\n",
    "           # va = 'center',\n",
    "           position=(0, 0),\n",
    "           # rotation_mode=\"anchor\"\n",
    "          )\n",
    "\n",
    "\n",
    "real_legend = ax.legend(numpoints=1, fontsize=9, frameon=False, \n",
    "                         ncol=2,\n",
    "                         handleheight=1.65, labelspacing=0.01,\n",
    "                         loc='best', \n",
    "#                          bbox_to_anchor=(0.06, 0.1, 0.5, 0.9)\n",
    "                       )\n",
    "\n",
    "\n",
    "# ax.set_xlabel('Date', fontsize=13)\n",
    "ax.set_ylabel('Outflow index', fontsize=13)\n",
    "\n",
    "border_width = 1.35\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "# ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# # Move left spines outward by 10 points\n",
    "# ax.spines['left'].set_position(('outward', 10))\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=3, labelsize=11.5, length=5, width=1.5, direction='out')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=3, labelsize=12, length=5, width=1.2, direction='out')\n",
    "ax.tick_params(axis='both', which='minor', top=False, right=False, length=3,  width=.6, direction='out')\n",
    "\n",
    "plt.xlim(-0.5, list(x).index('20200131')+0.5)\n",
    "plt.ylim(0, 30)\n",
    "\n",
    "\n",
    "# plt.tight_layout(pad=0.1)\n",
    "out_dir = 'Outputs'\n",
    "\n",
    "# plt.savefig(os.path.join(out_dir, 'fig3-city_outflow_2020_Jan.pdf'), pad_inches=0.01, box_inches=0.01)\n",
    "# plt.savefig(os.path.join(out_dir, 'fig3-city_outflow_2020_Jan.svg'), pad_inches=0.01, box_inches=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### <div class=\"alert alert-block alert-danger\">Outbreak Estimates</div>\n",
    "\n",
    "* COVID-19 outbreak in other cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# real case: number of cases on 2020-02-09 excludind Wuhan\n",
    "number_base_cases = 23236"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "other_city_df = pd.read_csv('Data/negb_predicted_outbreak_size_by_area.csv')\n",
    "# this file is obtained via Negative Binomial model using R, \n",
    "# see 'Figure3-outbreak analysis by R.ipynb' for further information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city_cn</th>\n",
       "      <th>city</th>\n",
       "      <th>raw_predict</th>\n",
       "      <th>predicted_cumulative_cases</th>\n",
       "      <th>cases_increase</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>广州市</td>\n",
       "      <td>Guangzhou</td>\n",
       "      <td>43819.591973</td>\n",
       "      <td>43824</td>\n",
       "      <td>88.603891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>北京市</td>\n",
       "      <td>Beijing</td>\n",
       "      <td>43722.089364</td>\n",
       "      <td>43719</td>\n",
       "      <td>88.152006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>上海市</td>\n",
       "      <td>Shanghai</td>\n",
       "      <td>40726.771819</td>\n",
       "      <td>40737</td>\n",
       "      <td>75.318471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>深圳市</td>\n",
       "      <td>Shenzhen</td>\n",
       "      <td>40363.955435</td>\n",
       "      <td>40363</td>\n",
       "      <td>73.708900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>苏州市</td>\n",
       "      <td>Suzhou</td>\n",
       "      <td>33250.308588</td>\n",
       "      <td>33251</td>\n",
       "      <td>43.101222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>成都市</td>\n",
       "      <td>Chengdu</td>\n",
       "      <td>30492.826295</td>\n",
       "      <td>30495</td>\n",
       "      <td>31.240317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>杭州市</td>\n",
       "      <td>Hangzhou</td>\n",
       "      <td>27439.604889</td>\n",
       "      <td>27432</td>\n",
       "      <td>18.058186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>郑州市</td>\n",
       "      <td>Zhengzhou</td>\n",
       "      <td>26258.649653</td>\n",
       "      <td>26253</td>\n",
       "      <td>12.984163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>西安市</td>\n",
       "      <td>Xi'an</td>\n",
       "      <td>22668.715691</td>\n",
       "      <td>22664</td>\n",
       "      <td>-2.461697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>南京市</td>\n",
       "      <td>Nanjing</td>\n",
       "      <td>21501.896627</td>\n",
       "      <td>21495</td>\n",
       "      <td>-7.492684</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>长沙市</td>\n",
       "      <td>Changsha</td>\n",
       "      <td>21162.375040</td>\n",
       "      <td>21168</td>\n",
       "      <td>-8.899983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>天津市</td>\n",
       "      <td>Tianjin</td>\n",
       "      <td>20707.530115</td>\n",
       "      <td>20707</td>\n",
       "      <td>-10.883973</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>重庆市</td>\n",
       "      <td>Chongqing</td>\n",
       "      <td>18023.886812</td>\n",
       "      <td>18016</td>\n",
       "      <td>-22.465140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>合肥市</td>\n",
       "      <td>Hefei</td>\n",
       "      <td>16151.743173</td>\n",
       "      <td>16154</td>\n",
       "      <td>-30.478568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>沈阳市</td>\n",
       "      <td>Shenyang</td>\n",
       "      <td>12126.043472</td>\n",
       "      <td>12131</td>\n",
       "      <td>-47.792219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>南昌市</td>\n",
       "      <td>Nanchang</td>\n",
       "      <td>11134.126830</td>\n",
       "      <td>11125</td>\n",
       "      <td>-52.121708</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   city_cn       city   raw_predict  predicted_cumulative_cases  \\\n",
       "0      广州市  Guangzhou  43819.591973                       43824   \n",
       "1      北京市    Beijing  43722.089364                       43719   \n",
       "2      上海市   Shanghai  40726.771819                       40737   \n",
       "3      深圳市   Shenzhen  40363.955435                       40363   \n",
       "4      苏州市     Suzhou  33250.308588                       33251   \n",
       "5      成都市    Chengdu  30492.826295                       30495   \n",
       "6      杭州市   Hangzhou  27439.604889                       27432   \n",
       "7      郑州市  Zhengzhou  26258.649653                       26253   \n",
       "8      西安市      Xi'an  22668.715691                       22664   \n",
       "9      南京市    Nanjing  21501.896627                       21495   \n",
       "10     长沙市   Changsha  21162.375040                       21168   \n",
       "11     天津市    Tianjin  20707.530115                       20707   \n",
       "12     重庆市  Chongqing  18023.886812                       18016   \n",
       "13     合肥市      Hefei  16151.743173                       16154   \n",
       "14     沈阳市   Shenyang  12126.043472                       12131   \n",
       "15     南昌市   Nanchang  11134.126830                       11125   \n",
       "\n",
       "    cases_increase  \n",
       "0        88.603891  \n",
       "1        88.152006  \n",
       "2        75.318471  \n",
       "3        73.708900  \n",
       "4        43.101222  \n",
       "5        31.240317  \n",
       "6        18.058186  \n",
       "7        12.984163  \n",
       "8        -2.461697  \n",
       "9        -7.492684  \n",
       "10       -8.899983  \n",
       "11      -10.883973  \n",
       "12      -22.465140  \n",
       "13      -30.478568  \n",
       "14      -47.792219  \n",
       "15      -52.121708  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "other_city_df['cases_increase'] = other_city_df['predicted_cumulative_cases'].map(\n",
    "                    lambda x: (x/number_base_cases-1) * 100)\n",
    "\n",
    "# dic = {'Xian': 'Xi\\'an'}\n",
    "other_city_df['city'] = other_city_df['city'].map(\n",
    "                            lambda x: 'Xi\\'an' if x=='Xian' else x)\n",
    "\n",
    "other_city_df.sort_values(['predicted_cumulative_cases'], ascending=False, inplace=True)\n",
    "other_city_df.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 252x252 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# fig, ax = plt.subplots(figsize=(6.45, 3))\n",
    "fig, ax = plt.subplots(figsize=(3.5, 3.5))\n",
    "\n",
    "ax.patch.set_alpha(0.015)\n",
    "ax.set_facecolor('#2b3679')\n",
    "\n",
    "\n",
    "plot_df = other_city_df.copy()\n",
    "plot_df = plot_df.sort_values(['predicted_cumulative_cases'], ascending=True)\n",
    "\n",
    "x1 = plot_df['city']\n",
    "y1 = plot_df['predicted_cumulative_cases']\n",
    "\n",
    "# bar plot\n",
    "ax.barh(x1, y1,\n",
    "                height=0.45,\n",
    "#                 edgecolor='w',\n",
    "                color='#46ac88', \n",
    "                alpha=.8,\n",
    "#                 zorder=10,\n",
    "                )\n",
    "\n",
    "\n",
    "for (d, v, t) in zip(plot_df['city'], plot_df['predicted_cumulative_cases'], plot_df['cases_increase']):\n",
    "    if t>0:\n",
    "        # s = '+'+str(round(t, 2))+'%'\n",
    "        s = '+'+f'{t:0.2f}'+'%'\n",
    "    else:\n",
    "        # s = \"\\N{MINUS SIGN}\"+str(abs(round(t, 2)))+'%'\n",
    "        s = \"\\N{MINUS SIGN}\"+f'{abs(t):0.2f}'+'%'\n",
    "    ax.text(x=v+180, y=list(x1).index(d), s=s, \n",
    "            horizontalalignment='left', verticalalignment='center', \n",
    "            fontdict={'color':'#46ac88', \n",
    "                      # 'size':11, \n",
    "                      'size':8.5, \n",
    "                       #'weight':'bold'\n",
    "                      },\n",
    "            zorder=10,\n",
    "           )\n",
    "\n",
    "line_style = (0, (3, 2))\n",
    "ax.vlines(x=number_base_cases, ymin=-1, ymax=len(x1), linestyles=line_style, linewidth=1., color='#bbbbbb', zorder=-1,)\n",
    "# ax.vlines(x=22198, ymin=-1, ymax=len(x1), linestyles=line_style, linewidth=1., color='dodgerblue', zorder=1, alpha=0.5)\n",
    "\n",
    "\n",
    "N = len(x1)\n",
    "\n",
    "every_nth = 1\n",
    "locs = range(0, N, every_nth)\n",
    "ax.yaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "\n",
    "ticks = list(x1)\n",
    "# ticks = [city_cn2en[c] for c in ticks]\n",
    "plt.yticks(list(range(0, N+1, every_nth)), ticks[::every_nth], \n",
    "           rotation='0', \n",
    "           horizontalalignment='right',\n",
    "           # va = 'center',\n",
    "           position=(0, 0)\n",
    "           # rotation_mode=\"anchor\",\n",
    "          )\n",
    "\n",
    "# print(ticks)\n",
    "\n",
    "\n",
    "\n",
    "locs = range(0, 70000, 5000)\n",
    "ax.xaxis.set_minor_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "locs = range(0, 70000, 10000)\n",
    "ax.xaxis.set_major_locator(ticker.FixedLocator(locs))\n",
    "\n",
    "ticks = [f'{int(i/1000)}K' for i in locs[1:]]\n",
    "ticks.insert(0, '0 ')\n",
    "plt.xticks(locs, ticks)\n",
    "\n",
    "\n",
    "border_width = 1.35\n",
    "ax.spines['bottom'].set_linewidth(border_width)\n",
    "ax.spines['left'].set_linewidth(border_width)\n",
    "ax.spines['top'].set_linewidth(border_width)\n",
    "ax.spines['right'].set_linewidth(border_width)\n",
    "\n",
    "# ax.spines['left'].set_visible(False)\n",
    "ax.spines['top'].set_visible(False)\n",
    "ax.spines['right'].set_visible(False)\n",
    "\n",
    "# for k, spine in ax.spines.items():  #ax.spines is a dictionary\n",
    "#     spine.set_zorder(20)\n",
    "\n",
    "\n",
    "# # custom ticks\n",
    "ax.tick_params(axis='x', which='major', top=False, right=False, pad=3, labelsize=10, length=5, width=1.35, direction='out')\n",
    "ax.tick_params(axis='y', which='major', top=False, right=False, pad=3, labelsize=11, length=0, width=1.2, direction='out')\n",
    "ax.tick_params(axis='both', which='minor', top=False, right=False, length=3,  width=.6, direction='out')\n",
    "\n",
    "\n",
    "# # # plt.xlim(-1)\n",
    "ax.set_ylim(bottom=-1+0.35, top=N-0.25)\n",
    "ax.set_xlim(left=0, right=60000)\n",
    "\n",
    "\n",
    "ax.set_xlabel('Estimated cases', fontsize=13, labelpad=1)\n",
    "ax.set_ylabel('City of COVID-19 outbreak', fontsize=13)\n",
    "\n",
    "plt.tight_layout(pad=0.1)\n",
    "out_dir = 'Outputs'\n",
    "# plt.savefig(os.path.join(out_dir, 'fig3-predicted_cases_by_outbreak_city.pdf'), pad_inches=0.01, box_inches=0.01)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}