Data_regression_discontinuity.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import statsmodels.formula.api as smf\n",
    "import matplotlib.pyplot as plt\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data import"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['suject_id', 'timestamp', 'season', 'city', 'climate', 'building_type',\n",
      "       'heating_type', 'gender', 'age', 'ht', 'wt', 'thermal_sensation',\n",
      "       'thermal_comfort', 'thermal_acceptability', 'clo', 'met', 'ta', 'rh',\n",
      "       'vel', 'tg', 'top', 'tr', 't_out_min', 't_out_monthly', 'latitude'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('CN_DB.csv')\n",
    "print(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "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</th>\n",
       "      <th>records</th>\n",
       "      <th>thermal_sensation</th>\n",
       "      <th>ta</th>\n",
       "      <th>top</th>\n",
       "      <th>rh</th>\n",
       "      <th>vel</th>\n",
       "      <th>met</th>\n",
       "      <th>clo</th>\n",
       "      <th>latitude</th>\n",
       "      <th>tsr</th>\n",
       "      <th>p_dh</th>\n",
       "      <th>p_nv</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>HainanSanya</td>\n",
       "      <td>5</td>\n",
       "      <td>0.60</td>\n",
       "      <td>24.88</td>\n",
       "      <td>24.88</td>\n",
       "      <td>70.20</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.33</td>\n",
       "      <td>18.25</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GuangdongGuangzhou</td>\n",
       "      <td>997</td>\n",
       "      <td>-0.01</td>\n",
       "      <td>23.85</td>\n",
       "      <td>23.88</td>\n",
       "      <td>52.94</td>\n",
       "      <td>0.10</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.86</td>\n",
       "      <td>23.13</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>YunnanKunming</td>\n",
       "      <td>415</td>\n",
       "      <td>-1.15</td>\n",
       "      <td>13.62</td>\n",
       "      <td>13.62</td>\n",
       "      <td>16.95</td>\n",
       "      <td>0.28</td>\n",
       "      <td>1.08</td>\n",
       "      <td>0.65</td>\n",
       "      <td>24.87</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FujianFuzhou</td>\n",
       "      <td>7</td>\n",
       "      <td>-0.71</td>\n",
       "      <td>18.10</td>\n",
       "      <td>18.10</td>\n",
       "      <td>67.43</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.89</td>\n",
       "      <td>0.35</td>\n",
       "      <td>26.08</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>FujianZhenjiang</td>\n",
       "      <td>2</td>\n",
       "      <td>-0.50</td>\n",
       "      <td>22.90</td>\n",
       "      <td>22.90</td>\n",
       "      <td>74.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.31</td>\n",
       "      <td>26.10</td>\n",
       "      <td>0.50</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>FujianNanping</td>\n",
       "      <td>4</td>\n",
       "      <td>0.00</td>\n",
       "      <td>17.90</td>\n",
       "      <td>17.90</td>\n",
       "      <td>60.25</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.42</td>\n",
       "      <td>26.64</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>HunanHengyang</td>\n",
       "      <td>1</td>\n",
       "      <td>-1.00</td>\n",
       "      <td>16.90</td>\n",
       "      <td>16.90</td>\n",
       "      <td>66.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.66</td>\n",
       "      <td>26.89</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>HunanChangsha</td>\n",
       "      <td>1108</td>\n",
       "      <td>-0.40</td>\n",
       "      <td>17.22</td>\n",
       "      <td>17.41</td>\n",
       "      <td>48.35</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.02</td>\n",
       "      <td>0.95</td>\n",
       "      <td>28.23</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>SichuanZigong</td>\n",
       "      <td>23</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>11.03</td>\n",
       "      <td>11.03</td>\n",
       "      <td>66.52</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.59</td>\n",
       "      <td>29.34</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Lhasa</td>\n",
       "      <td>355</td>\n",
       "      <td>-1.10</td>\n",
       "      <td>10.64</td>\n",
       "      <td>10.64</td>\n",
       "      <td>71.19</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.25</td>\n",
       "      <td>29.65</td>\n",
       "      <td>0.64</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>HubeiWuhan</td>\n",
       "      <td>526</td>\n",
       "      <td>-1.55</td>\n",
       "      <td>12.63</td>\n",
       "      <td>12.91</td>\n",
       "      <td>47.98</td>\n",
       "      <td>0.09</td>\n",
       "      <td>1.07</td>\n",
       "      <td>1.88</td>\n",
       "      <td>30.58</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Shanghai</td>\n",
       "      <td>2131</td>\n",
       "      <td>-0.05</td>\n",
       "      <td>20.58</td>\n",
       "      <td>20.84</td>\n",
       "      <td>52.26</td>\n",
       "      <td>0.03</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.88</td>\n",
       "      <td>31.23</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>JiangsuChangzhou</td>\n",
       "      <td>144</td>\n",
       "      <td>0.35</td>\n",
       "      <td>21.40</td>\n",
       "      <td>20.51</td>\n",
       "      <td>41.18</td>\n",
       "      <td>0.01</td>\n",
       "      <td>1.02</td>\n",
       "      <td>1.15</td>\n",
       "      <td>31.78</td>\n",
       "      <td>0.88</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>HenanNanyang</td>\n",
       "      <td>251</td>\n",
       "      <td>-0.41</td>\n",
       "      <td>12.22</td>\n",
       "      <td>12.22</td>\n",
       "      <td>66.37</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.31</td>\n",
       "      <td>32.99</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>HenanPingdingshan</td>\n",
       "      <td>5</td>\n",
       "      <td>-0.80</td>\n",
       "      <td>17.56</td>\n",
       "      <td>17.56</td>\n",
       "      <td>57.80</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.79</td>\n",
       "      <td>33.74</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>ShaanxiXi'an</td>\n",
       "      <td>1686</td>\n",
       "      <td>0.52</td>\n",
       "      <td>23.03</td>\n",
       "      <td>23.04</td>\n",
       "      <td>21.92</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.14</td>\n",
       "      <td>0.73</td>\n",
       "      <td>34.26</td>\n",
       "      <td>0.83</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>HenanLuoyang</td>\n",
       "      <td>45</td>\n",
       "      <td>-0.18</td>\n",
       "      <td>20.16</td>\n",
       "      <td>19.04</td>\n",
       "      <td>30.62</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.05</td>\n",
       "      <td>34.62</td>\n",
       "      <td>0.93</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>HenanZhengzhou</td>\n",
       "      <td>96</td>\n",
       "      <td>0.66</td>\n",
       "      <td>23.52</td>\n",
       "      <td>22.30</td>\n",
       "      <td>28.90</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.19</td>\n",
       "      <td>34.75</td>\n",
       "      <td>0.67</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>HenanKaifeng</td>\n",
       "      <td>1</td>\n",
       "      <td>0.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>13.00</td>\n",
       "      <td>50.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.42</td>\n",
       "      <td>34.79</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>HenanJiaozuo</td>\n",
       "      <td>888</td>\n",
       "      <td>-0.26</td>\n",
       "      <td>16.80</td>\n",
       "      <td>16.80</td>\n",
       "      <td>49.04</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.10</td>\n",
       "      <td>1.19</td>\n",
       "      <td>35.24</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>ShandongXintai</td>\n",
       "      <td>3</td>\n",
       "      <td>0.33</td>\n",
       "      <td>22.83</td>\n",
       "      <td>22.83</td>\n",
       "      <td>31.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.90</td>\n",
       "      <td>0.69</td>\n",
       "      <td>35.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>ShandongQingdao</td>\n",
       "      <td>1</td>\n",
       "      <td>1.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>23.00</td>\n",
       "      <td>27.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.50</td>\n",
       "      <td>36.07</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>HebeiHandan</td>\n",
       "      <td>26</td>\n",
       "      <td>-0.38</td>\n",
       "      <td>19.58</td>\n",
       "      <td>19.30</td>\n",
       "      <td>35.81</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>1.45</td>\n",
       "      <td>36.62</td>\n",
       "      <td>0.92</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>ShanxiLvliangfenyang</td>\n",
       "      <td>9</td>\n",
       "      <td>-0.22</td>\n",
       "      <td>21.13</td>\n",
       "      <td>21.13</td>\n",
       "      <td>40.56</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.41</td>\n",
       "      <td>37.51</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>ShandongYantai</td>\n",
       "      <td>7</td>\n",
       "      <td>0.86</td>\n",
       "      <td>25.67</td>\n",
       "      <td>25.67</td>\n",
       "      <td>34.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.10</td>\n",
       "      <td>0.80</td>\n",
       "      <td>37.53</td>\n",
       "      <td>0.71</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>LiaoningDalian</td>\n",
       "      <td>1703</td>\n",
       "      <td>0.29</td>\n",
       "      <td>22.49</td>\n",
       "      <td>22.46</td>\n",
       "      <td>36.87</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "      <td>38.92</td>\n",
       "      <td>0.86</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>Tianjin</td>\n",
       "      <td>730</td>\n",
       "      <td>0.22</td>\n",
       "      <td>24.56</td>\n",
       "      <td>24.50</td>\n",
       "      <td>27.41</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.16</td>\n",
       "      <td>0.81</td>\n",
       "      <td>39.12</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>Beijing</td>\n",
       "      <td>651</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>22.95</td>\n",
       "      <td>22.07</td>\n",
       "      <td>28.61</td>\n",
       "      <td>0.07</td>\n",
       "      <td>1.06</td>\n",
       "      <td>0.92</td>\n",
       "      <td>39.90</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>Baotou</td>\n",
       "      <td>303</td>\n",
       "      <td>0.02</td>\n",
       "      <td>19.77</td>\n",
       "      <td>19.28</td>\n",
       "      <td>36.36</td>\n",
       "      <td>0.05</td>\n",
       "      <td>1.07</td>\n",
       "      <td>0.34</td>\n",
       "      <td>40.65</td>\n",
       "      <td>0.90</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Hohhot</td>\n",
       "      <td>12</td>\n",
       "      <td>-0.33</td>\n",
       "      <td>20.73</td>\n",
       "      <td>20.73</td>\n",
       "      <td>36.83</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.51</td>\n",
       "      <td>40.82</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>LiaoningBenxi</td>\n",
       "      <td>22</td>\n",
       "      <td>-0.14</td>\n",
       "      <td>19.74</td>\n",
       "      <td>19.74</td>\n",
       "      <td>41.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1.04</td>\n",
       "      <td>41.29</td>\n",
       "      <td>0.73</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Turpan</td>\n",
       "      <td>283</td>\n",
       "      <td>-0.03</td>\n",
       "      <td>18.21</td>\n",
       "      <td>19.44</td>\n",
       "      <td>33.90</td>\n",
       "      <td>0.02</td>\n",
       "      <td>1.19</td>\n",
       "      <td>1.42</td>\n",
       "      <td>42.94</td>\n",
       "      <td>0.92</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>HeilongjiangHarbin</td>\n",
       "      <td>3552</td>\n",
       "      <td>0.29</td>\n",
       "      <td>23.09</td>\n",
       "      <td>22.71</td>\n",
       "      <td>32.61</td>\n",
       "      <td>0.04</td>\n",
       "      <td>1.01</td>\n",
       "      <td>0.92</td>\n",
       "      <td>45.75</td>\n",
       "      <td>0.83</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                    city  records  thermal_sensation     ta    top     rh  \\\n",
       "0            HainanSanya        5               0.60  24.88  24.88  70.20   \n",
       "1     GuangdongGuangzhou      997              -0.01  23.85  23.88  52.94   \n",
       "2          YunnanKunming      415              -1.15  13.62  13.62  16.95   \n",
       "3           FujianFuzhou        7              -0.71  18.10  18.10  67.43   \n",
       "4        FujianZhenjiang        2              -0.50  22.90  22.90  74.50   \n",
       "5          FujianNanping        4               0.00  17.90  17.90  60.25   \n",
       "6          HunanHengyang        1              -1.00  16.90  16.90  66.00   \n",
       "7          HunanChangsha     1108              -0.40  17.22  17.41  48.35   \n",
       "8          SichuanZigong       23              -0.22  11.03  11.03  66.52   \n",
       "9                  Lhasa      355              -1.10  10.64  10.64  71.19   \n",
       "10            HubeiWuhan      526              -1.55  12.63  12.91  47.98   \n",
       "11              Shanghai     2131              -0.05  20.58  20.84  52.26   \n",
       "12      JiangsuChangzhou      144               0.35  21.40  20.51  41.18   \n",
       "13          HenanNanyang      251              -0.41  12.22  12.22  66.37   \n",
       "14     HenanPingdingshan        5              -0.80  17.56  17.56  57.80   \n",
       "15          ShaanxiXi'an     1686               0.52  23.03  23.04  21.92   \n",
       "16          HenanLuoyang       45              -0.18  20.16  19.04  30.62   \n",
       "17        HenanZhengzhou       96               0.66  23.52  22.30  28.90   \n",
       "18          HenanKaifeng        1               0.00  13.00  13.00  50.00   \n",
       "19          HenanJiaozuo      888              -0.26  16.80  16.80  49.04   \n",
       "20        ShandongXintai        3               0.33  22.83  22.83  31.00   \n",
       "21       ShandongQingdao        1               1.00  23.00  23.00  27.00   \n",
       "22           HebeiHandan       26              -0.38  19.58  19.30  35.81   \n",
       "23  ShanxiLvliangfenyang        9              -0.22  21.13  21.13  40.56   \n",
       "24        ShandongYantai        7               0.86  25.67  25.67  34.00   \n",
       "25        LiaoningDalian     1703               0.29  22.49  22.46  36.87   \n",
       "26               Tianjin      730               0.22  24.56  24.50  27.41   \n",
       "27               Beijing      651              -0.03  22.95  22.07  28.61   \n",
       "28                Baotou      303               0.02  19.77  19.28  36.36   \n",
       "29                Hohhot       12              -0.33  20.73  20.73  36.83   \n",
       "30         LiaoningBenxi       22              -0.14  19.74  19.74  41.09   \n",
       "31                Turpan      283              -0.03  18.21  19.44  33.90   \n",
       "32    HeilongjiangHarbin     3552               0.29  23.09  22.71  32.61   \n",
       "\n",
       "     vel   met   clo  latitude   tsr  p_dh  p_nv  \n",
       "0   0.00  0.98  0.33     18.25  0.80  0.00  1.00  \n",
       "1   0.10  1.00  0.86     23.13  0.90  0.00  0.00  \n",
       "2   0.28  1.08  0.65     24.87  0.66  0.00  1.00  \n",
       "3   0.00  0.89  0.35     26.08  0.86  0.00  1.00  \n",
       "4   0.00  0.90  0.31     26.10  0.50  0.00  1.00  \n",
       "5   0.00  0.88  0.42     26.64  1.00  0.00  1.00  \n",
       "6   0.00  0.90  0.66     26.89  1.00  0.00  1.00  \n",
       "7   0.03  1.02  0.95     28.23  0.93  0.00  0.58  \n",
       "8   0.00  1.00  1.59     29.34  0.78  0.00  1.00  \n",
       "9   0.02  1.10  1.25     29.65  0.64  0.01  0.68  \n",
       "10  0.09  1.07  1.88     30.58  0.49  0.00  1.00  \n",
       "11  0.03  1.10  0.88     31.23  0.92  0.00  0.18  \n",
       "12  0.01  1.02  1.15     31.78  0.88  0.00  0.00  \n",
       "13  0.00  1.00  1.31     32.99  0.71  0.00  0.62  \n",
       "14  0.00  0.94  0.79     33.74  1.00  0.20  0.80  \n",
       "15  0.04  1.14  0.73     34.26  0.83  0.70  0.00  \n",
       "16  0.00  1.00  1.05     34.62  0.93  0.00  0.04  \n",
       "17  0.00  1.01  1.19     34.75  0.67  0.04  0.00  \n",
       "18  0.00  0.80  0.42     34.79  1.00  0.00  1.00  \n",
       "19  0.02  1.10  1.19     35.24  0.98  1.00  0.00  \n",
       "20  0.00  0.90  0.69     35.90  1.00  1.00  0.00  \n",
       "21  0.00  1.00  0.50     36.07  1.00  1.00  0.00  \n",
       "22  0.00  0.98  1.45     36.62  0.92  1.00  0.00  \n",
       "23  0.00  0.97  0.41     37.51  1.00  1.00  0.00  \n",
       "24  0.00  1.10  0.80     37.53  0.71  0.86  0.14  \n",
       "25  0.00  0.99  0.98     38.92  0.86  0.80  0.00  \n",
       "26  0.02  1.16  0.81     39.12  0.95  0.98  0.00  \n",
       "27  0.07  1.06  0.92     39.90  0.91  0.76  0.00  \n",
       "28  0.05  1.07  0.34     40.65  0.90  1.00  0.00  \n",
       "29  0.00  0.96  0.51     40.82  1.00  1.00  0.00  \n",
       "30  0.00  0.96  1.04     41.29  0.73  1.00  0.00  \n",
       "31  0.02  1.19  1.42     42.94  0.92  0.00  0.19  \n",
       "32  0.04  1.01  0.92     45.75  0.83  1.00  0.00  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_city = pd.read_csv('CN_DB_CITY.csv')\n",
    "df_city"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Calcualte the distance of each city to Huai River (33˚N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_city['distance'] = df_city['latitude'] - 34\n",
    "# multiple the distance by 111 to reflecct the distance in km\n",
    "df_city['distance'] = df_city['distance'] * 111"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sharp threshold assumption\n",
    "All cities have disctrict heating systems if the distance is >= 0</br>\n",
    "No city has disctrict heating systems if the distance is < 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a new dummy variable based on the distance\n",
    "df_rd = df_city.assign(threshold=(df_city[\"distance\"] > 0).astype(int))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal effect of district heating on temperature\n",
    "Difference between the treatment and control group near the Huai river, which can be viewed as the nature experiment outcome"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>   16.4482</td> <td>    1.657</td> <td>    9.927</td> <td> 0.000</td> <td>   13.059</td> <td>   19.837</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance</th>           <td>   -0.0035</td> <td>    0.002</td> <td>   -1.444</td> <td> 0.160</td> <td>   -0.009</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>          <td>    4.9102</td> <td>    2.022</td> <td>    2.428</td> <td> 0.022</td> <td>    0.774</td> <td>    9.046</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance:threshold</th> <td>    0.0045</td> <td>    0.003</td> <td>    1.617</td> <td> 0.117</td> <td>   -0.001</td> <td>    0.010</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.wls(\"top~distance*threshold\", df_rd, weights=df_rd[\"records\"]).fit()\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The coef of the \"threshold\" is the causal effect, and coef of \"Intercept\" is the basedline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quadratic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline is:    17.1389\n",
      "Effect is:     4.4266\n",
      "pvalue is:  0.005\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>   17.1389</td> <td>    1.129</td> <td>   15.180</td> <td> 0.000</td> <td>   14.830</td> <td>   19.448</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2</th>           <td> 2.957e-06</td> <td> 1.64e-06</td> <td>    1.807</td> <td> 0.081</td> <td>-3.91e-07</td> <td>  6.3e-06</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>           <td>    4.4266</td> <td>    1.468</td> <td>    3.015</td> <td> 0.005</td> <td>    1.423</td> <td>    7.430</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2:threshold</th> <td>-2.309e-06</td> <td> 1.87e-06</td> <td>   -1.238</td> <td> 0.226</td> <td>-6.13e-06</td> <td> 1.51e-06</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_rd['distance2'] = df_rd['distance'] ** 2\n",
    "# try polynomial\n",
    "model = smf.wls(\"top ~ distance2*threshold\", df_rd, weights=df_rd[\"records\"]).fit()\n",
    "print(\"Baseline is:\", model.summary().tables[1][1][1])\n",
    "print(\"Effect is:\", model.summary().tables[1][3][1])\n",
    "print(\"pvalue is:\", model.summary().tables[1][3][4])\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "# Split the data into two parts\n",
    "df_below = df_rd[df_rd['distance'] < 0]\n",
    "df_above = df_rd[df_rd['distance'] >= 0]\n",
    "# Scatter plot for points below the threshold\n",
    "plt.scatter(df_below['distance'], df_below['top'], \n",
    "            s=df_below['records'], facecolors='lightblue', \n",
    "            alpha=0.5, label='No District Heating')\n",
    "\n",
    "# Scatter plot for points above the threshold\n",
    "plt.scatter(df_above['distance'], df_above['top'], \n",
    "            s=df_above['records'], facecolors='lightcoral', edgecolors='red', \n",
    "            alpha=0.5, label='District Heating')\n",
    "\n",
    "# Generate a fine grid of distance values\n",
    "distance_grid = np.linspace(df_rd['distance'].min(), df_rd['distance'].max(), 500)\n",
    "\n",
    "# Compute threshold values for each point\n",
    "threshold_grid = (distance_grid >= 0).astype(int)\n",
    "\n",
    "# Create a DataFrame for the grid\n",
    "grid_df = pd.DataFrame({'distance': distance_grid, 'distance2': distance_grid**2, 'threshold': threshold_grid})\n",
    "\n",
    "# Predict based on the grid\n",
    "predictions = model.predict(grid_df)\n",
    "\n",
    "# Separate predictions for below and above the threshold\n",
    "grid_below = grid_df[grid_df['distance'] < 0]\n",
    "grid_above = grid_df[grid_df['distance'] >= 0]\n",
    "pred_below = predictions[grid_df['distance'] < 0]\n",
    "pred_above = predictions[grid_df['distance'] >= 0]\n",
    "\n",
    "# Plot the smooth quadratic predictions\n",
    "plt.plot(grid_below['distance'], pred_below, color='blue', linewidth=1.5, label='Fitted (Domestic Heating)')\n",
    "plt.plot(grid_above['distance'], pred_above, color='red', linewidth=1.5, label='Fitted (District Heating)')\n",
    "\n",
    "# Add vertical dashed line at the threshold\n",
    "plt.axvline(x=0, color='black', linestyle=\"--\", linewidth=2, label='Huai River Threshold', dashes=(5, 5))\n",
    "\n",
    "# Adjust ticks and labels\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(np.arange(12, 28, 4), fontsize=20)\n",
    "plt.xlim(-1700, 1700)\n",
    "\n",
    "# Save as SVG\n",
    "# plt.savefig('fig3a.svg', format='svg')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Causal effect on thermal sensation vote"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Linear model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline is:    -0.4039\n",
      "Effect is:     0.6374\n",
      "pvalue is:  0.020\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>             <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>          <td>   -0.4039</td> <td>    0.212</td> <td>   -1.903</td> <td> 0.067</td> <td>   -0.838</td> <td>    0.030</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance</th>           <td>-3.029e-05</td> <td>    0.000</td> <td>   -0.097</td> <td> 0.924</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>          <td>    0.6374</td> <td>    0.259</td> <td>    2.461</td> <td> 0.020</td> <td>    0.108</td> <td>    1.167</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance:threshold</th> <td> 3.215e-05</td> <td>    0.000</td> <td>    0.090</td> <td> 0.929</td> <td>   -0.001</td> <td>    0.001</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.wls(\"thermal_sensation~distance*threshold\", df_rd, weights=df_rd[\"records\"]).fit()\n",
    "print(\"Baseline is:\", model.summary().tables[1][1][1])\n",
    "print(\"Effect is:\", model.summary().tables[1][3][1])\n",
    "print(\"pvalue is:\", model.summary().tables[1][3][4])\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Quadratic model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Baseline is:    -0.4314\n",
      "Effect is:     0.6441\n",
      "pvalue is:  0.002\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "           <td></td>              <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th>           <td>   -0.4314</td> <td>    0.147</td> <td>   -2.944</td> <td> 0.006</td> <td>   -0.731</td> <td>   -0.132</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2</th>           <td> 9.833e-08</td> <td> 2.12e-07</td> <td>    0.463</td> <td> 0.647</td> <td>-3.36e-07</td> <td> 5.33e-07</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>threshold</th>           <td>    0.6441</td> <td>    0.191</td> <td>    3.379</td> <td> 0.002</td> <td>    0.254</td> <td>    1.034</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>distance2:threshold</th> <td> -6.89e-08</td> <td> 2.42e-07</td> <td>   -0.285</td> <td> 0.778</td> <td>-5.64e-07</td> <td> 4.26e-07</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.wls(\"thermal_sensation ~ distance2*threshold\", df_rd, weights=df_rd[\"records\"]).fit()\n",
    "print(\"Baseline is:\", model.summary().tables[1][1][1])\n",
    "print(\"Effect is:\", model.summary().tables[1][3][1])\n",
    "print(\"pvalue is:\", model.summary().tables[1][3][4])\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Split the data into two parts\n",
    "df_below = df_rd[df_rd['distance'] < 0]\n",
    "df_above = df_rd[df_rd['distance'] >= 0]\n",
    "\n",
    "# Scatter plot for points below the threshold\n",
    "plt.scatter(df_below['distance'], df_below['thermal_sensation'], \n",
    "            s=df_below['records'], facecolors='lightblue', \n",
    "            alpha=0.5, label='No District Heating')\n",
    "\n",
    "# Scatter plot for points above the threshold\n",
    "plt.scatter(df_above['distance'], df_above['thermal_sensation'], \n",
    "            s=df_above['records'], facecolors='lightcoral', edgecolors='red', \n",
    "            alpha=0.5, label='District Heating')\n",
    "\n",
    "# Generate a fine grid of distance values\n",
    "distance_grid = np.linspace(df_rd['distance'].min(), df_rd['distance'].max(), 500)\n",
    "\n",
    "# Compute threshold values for each point\n",
    "threshold_grid = (distance_grid >= 0).astype(int)\n",
    "\n",
    "# Create a DataFrame for the grid\n",
    "grid_df = pd.DataFrame({'distance': distance_grid, 'distance2': distance_grid**2, 'threshold': threshold_grid})\n",
    "\n",
    "# Predict based on the grid\n",
    "predictions = model.predict(grid_df)\n",
    "\n",
    "# Separate predictions for below and above the threshold\n",
    "grid_below = grid_df[grid_df['distance'] < 0]\n",
    "grid_above = grid_df[grid_df['distance'] >= 0]\n",
    "pred_below = predictions[grid_df['distance'] < 0]\n",
    "pred_above = predictions[grid_df['distance'] >= 0]\n",
    "\n",
    "# Plot the smooth quadratic predictions\n",
    "plt.plot(grid_below['distance'], pred_below, color='blue', linewidth=1.5, label='Fitted (Domestic Heating)')\n",
    "plt.plot(grid_above['distance'], pred_above, color='red', linewidth=1.5, label='Fitted (District Heating)')\n",
    "\n",
    "# Add vertical dashed line at the threshold\n",
    "plt.axvline(x=0, color='black', linestyle=\"--\", linewidth=2, label='Huai River Threshold', dashes=(5, 5))\n",
    "\n",
    "# Adjust ticks and labels\n",
    "plt.xticks(fontsize=20)\n",
    "plt.yticks(fontsize=20)\n",
    "plt.xlim(-1700, 1700)\n",
    "plt.ylim(-3.5, 3.5)\n",
    "\n",
    "# plt.savefig('fig3b.svg', format='svg')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spurious correlations on thermal satisfaction rate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "      <td></td>         <th>coef</th>     <th>std err</th>      <th>t</th>      <th>P>|t|</th>  <th>[0.025</th>    <th>0.975]</th>  \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Intercept</th> <td>    0.5690</td> <td>    0.096</td> <td>    5.903</td> <td> 0.000</td> <td>    0.372</td> <td>    0.766</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>top</th>       <td>    0.0138</td> <td>    0.005</td> <td>    3.004</td> <td> 0.005</td> <td>    0.004</td> <td>    0.023</td>\n",
       "</tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.table.SimpleTable'>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = smf.wls(\"tsr~top\", df_rd, weights=df_rd[\"records\"]).fit()\n",
    "model.summary().tables[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize figure\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Scatter plot for points below the threshold\n",
    "plt.scatter(df_rd['top'], df_rd['tsr'], \n",
    "            s=df_rd['records'], facecolors='grey', edgecolors='grey', \n",
    "            alpha=0.6)\n",
    "\n",
    "# Plot the fitted lines smoothly\n",
    "top_grid = np.linspace(df_rd['top'].min(), 25, 500)\n",
    "pred_all = model.predict(pd.DataFrame({'top': top_grid}))\n",
    "plt.plot(top_grid, pred_all, color='black', linewidth=2, linestyle='--', label='Fitted (All Data)')\n",
    "\n",
    "# Adjust ticks and labels\n",
    "plt.xticks(np.arange(12, 28, 4), fontsize=20)\n",
    "plt.yticks(np.arange(0.5, 1.05, 0.1), fontsize=20)\n",
    "\n",
    "# Add axis labels\n",
    "plt.xlabel('Indoor Operative Temperature (˚C)', fontsize=24, labelpad=10)\n",
    "plt.ylabel('Theraml Satisfaction Rate', fontsize=24, labelpad=10)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      0.3311      0.113      2.928      0.012       0.087       0.575\n",
      "top            0.0273      0.006      4.570      0.001       0.014       0.040\n",
      "==============================================================================\n",
      "==============================================================================\n",
      "                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "Intercept      1.2283      0.130      9.414      0.000       0.952       1.505\n",
      "top           -0.0164      0.006     -2.785      0.013      -0.029      -0.004\n",
      "==============================================================================\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/ruijisun/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=15\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n",
      "/Users/ruijisun/opt/anaconda3/lib/python3.9/site-packages/scipy/stats/_stats_py.py:1736: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18\n",
      "  warnings.warn(\"kurtosistest only valid for n>=20 ... continuing \"\n"
     ]
    }
   ],
   "source": [
    "# Split the data into two parts based on the threshold of 16 degrees\n",
    "df_below = df_rd[df_rd['distance'] < 0]\n",
    "df_above = df_rd[df_rd['distance'] >= 0]\n",
    "\n",
    "# Fit the models for each group\n",
    "model_below = smf.wls(\"tsr ~ top\", weights=df_below[\"records\"], data=df_below).fit()\n",
    "model_above = smf.wls(\"tsr ~ top\", weights=df_above[\"records\"], data=df_above).fit()\n",
    "\n",
    "print(model_below.summary().tables[1])\n",
    "print(model_above.summary().tables[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize figure\n",
    "plt.figure(figsize=(12, 8))\n",
    "\n",
    "# Scatter plot for points below the threshold\n",
    "plt.scatter(df_below['top'], df_below['tsr'], \n",
    "            s=df_below['records'], facecolors='lightblue', edgecolors='blue', \n",
    "            alpha=0.6, label='Cool Indoor Environment')\n",
    "\n",
    "# Scatter plot for points above the threshold\n",
    "plt.scatter(df_above['top'], df_above['tsr'], \n",
    "            s=df_above['records'], facecolors='lightcoral', edgecolors='red', \n",
    "            alpha=0.6, label='Warm Indoor Environment')\n",
    "\n",
    "# Plot the fitted lines smoothly\n",
    "top_grid_above = np.linspace(16, 25, 500)\n",
    "top_grid = np.linspace(df_rd['top'].min(), 24, 500)\n",
    "pred_below = model_below.predict(pd.DataFrame({'top': top_grid}))\n",
    "pred_above = model_above.predict(pd.DataFrame({'top': top_grid_above}))\n",
    "plt.plot(top_grid, pred_below, color='blue', linewidth=2, linestyle='--', label='Fitted (Cool Indoor Environment)')\n",
    "plt.plot(top_grid_above, pred_above, color='red', linewidth=2, linestyle='--', label='Fitted (Warm Indoor Environment)')\n",
    "\n",
    "# Adjust ticks and labels\n",
    "plt.xticks(np.arange(12, 28, 4), fontsize=20)\n",
    "plt.yticks(np.arange(0.5, 1.05, 0.1), fontsize=20)\n",
    "\n",
    "# Add axis labels\n",
    "plt.xlabel('Indoor Operative Temperature (˚C)', fontsize=24, labelpad=10)\n",
    "plt.ylabel('Theraml Satisfaction Rate', fontsize=24, labelpad=10)\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}