analysis/enviroment factors/absolute_humidity.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The role of absolute humidity on transmission rates of the COVID-19 outbreak\n",
    "### paper: Luo, W., Majumder, M.S., Liu, D., Poirier, C., Mandl, K.D., Lipsitch, M. and Santillana, M., 2020. The role of absolute humidity on transmission rates of the COVID-19 outbreak. medRxiv.\n",
    "### data source:  https://github.com/midas-network/COVID-19/blob/master/data/data_from_papers/dataset_luo-et-al_202002/R0_absolute_humidity_Luo_et_al.2020.csv\n",
    "### code is implemented by the NSF Spatiotemporal Innovation Center according to the paper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "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>Province</th>\n",
       "      <th>R0_mean</th>\n",
       "      <th>Absolute_Humidity</th>\n",
       "      <th>Temperature_Mean</th>\n",
       "      <th>Cumulative_Cases</th>\n",
       "      <th>R0_confidence_interval_Upper</th>\n",
       "      <th>R0_confidence_interval_Lower</th>\n",
       "      <th>Country.Region</th>\n",
       "      <th>PopulationDensity_2014</th>\n",
       "      <th>Lat</th>\n",
       "      <th>Long</th>\n",
       "      <th>Capital</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Heilongjiang</td>\n",
       "      <td>3.253333</td>\n",
       "      <td>1.496135</td>\n",
       "      <td>-15</td>\n",
       "      <td>331</td>\n",
       "      <td>4.58</td>\n",
       "      <td>1.36</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>81.0</td>\n",
       "      <td>47.86200</td>\n",
       "      <td>127.7622</td>\n",
       "      <td>Harbin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Jilin</td>\n",
       "      <td>3.177333</td>\n",
       "      <td>1.691746</td>\n",
       "      <td>-13</td>\n",
       "      <td>80</td>\n",
       "      <td>5.00</td>\n",
       "      <td>1.15</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>147.0</td>\n",
       "      <td>43.66657</td>\n",
       "      <td>126.1917</td>\n",
       "      <td>Changchun</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Guizhou</td>\n",
       "      <td>2.612667</td>\n",
       "      <td>7.227437</td>\n",
       "      <td>9</td>\n",
       "      <td>109</td>\n",
       "      <td>6.33</td>\n",
       "      <td>0.91</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>199.0</td>\n",
       "      <td>26.81536</td>\n",
       "      <td>106.8748</td>\n",
       "      <td>Guiyang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>South Korea</td>\n",
       "      <td>2.580000</td>\n",
       "      <td>3.271413</td>\n",
       "      <td>3</td>\n",
       "      <td>18</td>\n",
       "      <td>6.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.56650</td>\n",
       "      <td>126.9780</td>\n",
       "      <td>Seoul</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hubei</td>\n",
       "      <td>2.493333</td>\n",
       "      <td>5.733320</td>\n",
       "      <td>7</td>\n",
       "      <td>29631</td>\n",
       "      <td>3.39</td>\n",
       "      <td>1.52</td>\n",
       "      <td>Mainland China</td>\n",
       "      <td>313.0</td>\n",
       "      <td>30.97564</td>\n",
       "      <td>112.2707</td>\n",
       "      <td>Wuhan</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Province   R0_mean  Absolute_Humidity  Temperature_Mean  \\\n",
       "0  Heilongjiang  3.253333           1.496135               -15   \n",
       "1         Jilin  3.177333           1.691746               -13   \n",
       "2       Guizhou  2.612667           7.227437                 9   \n",
       "3   South Korea  2.580000           3.271413                 3   \n",
       "4         Hubei  2.493333           5.733320                 7   \n",
       "\n",
       "   Cumulative_Cases  R0_confidence_interval_Upper  \\\n",
       "0               331                          4.58   \n",
       "1                80                          5.00   \n",
       "2               109                          6.33   \n",
       "3                18                          6.00   \n",
       "4             29631                          3.39   \n",
       "\n",
       "   R0_confidence_interval_Lower  Country.Region  PopulationDensity_2014  \\\n",
       "0                          1.36  Mainland China                    81.0   \n",
       "1                          1.15  Mainland China                   147.0   \n",
       "2                          0.91  Mainland China                   199.0   \n",
       "3                          0.91             NaN                     NaN   \n",
       "4                          1.52  Mainland China                   313.0   \n",
       "\n",
       "        Lat      Long    Capital  \n",
       "0  47.86200  127.7622     Harbin  \n",
       "1  43.66657  126.1917  Changchun  \n",
       "2  26.81536  106.8748    Guiyang  \n",
       "3  37.56650  126.9780      Seoul  \n",
       "4  30.97564  112.2707      Wuhan  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = pd.read_csv(r'C:\\Users\\44931\\Desktop\\LUO.txt')  #replace it with local file path\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array(data.Absolute_Humidity[:-1])\n",
    "y = np.array(data.R0_mean[:-1])\n",
    "fig,ax=plt.subplots()\n",
    "ax.scatter(x,y,c='r')\n",
    "plt.xlabel('Absolute_Humidity')\n",
    "plt.ylabel('R0_mean')\n",
    "anno = np.array(data.Province[:-1])\n",
    "for i in range(len(x)):\n",
    "    ax.annotate(anno[i],(x[i],y[i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda\\lib\\site-packages\\numpy\\core\\fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table class=\"simpletable\">\n",
       "<caption>OLS Regression Results</caption>\n",
       "<tr>\n",
       "  <th>Dep. Variable:</th>         <td>R0_mean</td>     <th>  R-squared:         </th> <td>   0.260</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Model:</th>                   <td>OLS</td>       <th>  Adj. R-squared:    </th> <td>   0.215</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Method:</th>             <td>Least Squares</td>  <th>  F-statistic:       </th> <td>   5.795</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Date:</th>             <td>Mon, 30 Mar 2020</td> <th>  Prob (F-statistic):</th>  <td>0.00697</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Time:</th>                 <td>23:57:34</td>     <th>  Log-Likelihood:    </th> <td> -7.3253</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>No. Observations:</th>      <td>    36</td>      <th>  AIC:               </th> <td>   20.65</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Residuals:</th>          <td>    33</td>      <th>  BIC:               </th> <td>   25.40</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Df Model:</th>              <td>     2</td>      <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Covariance Type:</th>      <td>nonrobust</td>    <th>                     </th>     <td> </td>   \n",
       "</tr>\n",
       "</table>\n",
       "<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>const</th>             <td>    0.1887</td> <td>    0.116</td> <td>    1.621</td> <td> 0.115</td> <td>   -0.048</td> <td>    0.425</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Absolute_Humidity</th> <td>    0.0690</td> <td>    0.027</td> <td>    2.538</td> <td> 0.016</td> <td>    0.014</td> <td>    0.124</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Temperature_Mean</th>  <td>   -0.0380</td> <td>    0.012</td> <td>   -3.249</td> <td> 0.003</td> <td>   -0.062</td> <td>   -0.014</td>\n",
       "</tr>\n",
       "</table>\n",
       "<table class=\"simpletable\">\n",
       "<tr>\n",
       "  <th>Omnibus:</th>       <td> 0.768</td> <th>  Durbin-Watson:     </th> <td>   0.493</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Prob(Omnibus):</th> <td> 0.681</td> <th>  Jarque-Bera (JB):  </th> <td>   0.420</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Skew:</th>          <td> 0.265</td> <th>  Prob(JB):          </th> <td>   0.811</td>\n",
       "</tr>\n",
       "<tr>\n",
       "  <th>Kurtosis:</th>      <td> 2.997</td> <th>  Cond. No.          </th> <td>    33.8</td>\n",
       "</tr>\n",
       "</table><br/><br/>Warnings:<br/>[1] Standard Errors assume that the covariance matrix of the errors is correctly specified."
      ],
      "text/plain": [
       "<class 'statsmodels.iolib.summary.Summary'>\n",
       "\"\"\"\n",
       "                            OLS Regression Results                            \n",
       "==============================================================================\n",
       "Dep. Variable:                R0_mean   R-squared:                       0.260\n",
       "Model:                            OLS   Adj. R-squared:                  0.215\n",
       "Method:                 Least Squares   F-statistic:                     5.795\n",
       "Date:                Mon, 30 Mar 2020   Prob (F-statistic):            0.00697\n",
       "Time:                        23:57:34   Log-Likelihood:                -7.3253\n",
       "No. Observations:                  36   AIC:                             20.65\n",
       "Df Residuals:                      33   BIC:                             25.40\n",
       "Df Model:                           2                                         \n",
       "Covariance Type:            nonrobust                                         \n",
       "=====================================================================================\n",
       "                        coef    std err          t      P>|t|      [0.025      0.975]\n",
       "-------------------------------------------------------------------------------------\n",
       "const                 0.1887      0.116      1.621      0.115      -0.048       0.425\n",
       "Absolute_Humidity     0.0690      0.027      2.538      0.016       0.014       0.124\n",
       "Temperature_Mean     -0.0380      0.012     -3.249      0.003      -0.062      -0.014\n",
       "==============================================================================\n",
       "Omnibus:                        0.768   Durbin-Watson:                   0.493\n",
       "Prob(Omnibus):                  0.681   Jarque-Bera (JB):                0.420\n",
       "Skew:                           0.265   Prob(JB):                        0.811\n",
       "Kurtosis:                       2.997   Cond. No.                         33.8\n",
       "==============================================================================\n",
       "\n",
       "Warnings:\n",
       "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n",
       "\"\"\""
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "y = np.log(data.R0_mean[:-1])  #Delete Tibet and use logarithm form\n",
    "X = data[['Absolute_Humidity', 'Temperature_Mean']][:-1]\n",
    "X = sm.add_constant(X) ## add intercept\n",
    "model = sm.OLS(y, X).fit() \n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loess Regression (Locally Weighted Regression)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lowess = sm.nonparametric.lowess\n",
    "x = np.array(data.Absolute_Humidity[:-1])\n",
    "y = np.array(data.R0_mean[:-1])\n",
    "z = lowess(y, x)\n",
    "plt.scatter(x,y)\n",
    "plt.plot(z[:,0],z[:,1],'r')\n",
    "plt.xlabel('Absolute_Humidity')\n",
    "plt.ylabel('R0_mean')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exponential Fit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[404.64568221  -3.51677631   1.40686777]\n"
     ]
    },
    {
     "data": {
      "image/png": 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11/mHmL/CtA9Pj9uTNz73FL1bNm23HzzXkJlZQTWd498DpgDLgY8BPwPeC5waEafmGFvDFKZ9uPFP384eQ+t456OLttsPnmvIzKygmsTxhog4JyK+CZwFDAAnR8TSfENrnMJ0EL848AhW79rP6Q/8fIfpIDzXkJlZoprEsamwERFbgMfT9cM7xoypk7jkPYfy+j1248dvOpbjHlvEv75z+7N8PdeQmVmims7xwyQVlnsV0JfermoFwHaxbTqIk/aEw+Zx0orb4bhDt7sfPNdQNTz6zKyzKVkOo70MDAzEokWL8nuBqVOhpwfyfI0OVTr6DJKa2SXvOdTJw6zJJC2OiIHRPo+nVS/n7LNh8WJYsaLZkeRm3pJBjrn0Vg684Cccc+mtdVvC1KPPzPKR1/9sLZw4ynn/+2GnneDqzpiGq1Se61979JlZ/bXamvVOHOXstReceCL8x3/Ali0jl28zedYKPPrMrP5arSbvxFHJ2WfDqlVwyy3NjqTu8qwVePSZWf21Wk3eiaNIcRvisSt35ZVx4+Gqq5odVt3lWSsoDG2e1N+HgEn9fe4YNxulVqvJ13Pp2LZWOhroifVbuO6gt/O+uXPpWbs2mXK9Q8yaNqXsyKd61Qq80p1ZfeX9P5uVaxypcm2I177pnfRs3Ag//GHD4mjEyAnXCszaS6v9z/o8jtSBF/xkx9WqIvj5tz/Bn7xpMtx5Z11frxyfA2FmefJ5HHVWtq1Q4ua3TIO77oJHH809hlYbOWH5aaUx+WZZ5Zo4JH1X0rOSHqhw/7GS1kpaml4uzDOe4VQaDTT5/I8na5E34JyOVhs5YflotTH5ZlnlXeO4Epg+Qpk7I+Lw9HJxzvFUVKkNcfr0t8C73pUkjq1bc42h1UZOWD5cs7R2l+uoqoj4haTJeb5GPVUcDXT22fCBDyT9HH/xF7m9fquNnLB8tGPN0hNXWrFW6ON4q6Rlkm6SdHCzgynrtNNgt91yP6ej1UZOWD7arWbppjUrlfuoqrTGcWNEHFLmvnHA1ohYL+kk4MsRcVCF55kJzATYf//9j3zyySfzC7qcD384GZb7hz/Arrs29rWto7Tb6LljLr2VwTK1oUn9fdx9wXFNiMhq1RGjqiJiXUSsT7cXAL2S9qxQ9oqIGIiIgYkTJzY0TiBprlq/PpkAccmSxr++dYx2q1m2Y9Oa5aupZ45Lej3wTESEpKNIEtlzzYypone8Ay68EC6/HObPhxNOgM9+Fo47Lhl1ZZZBO51dv09/X9kaR6s2rVn+8h6O+33gl8AUSU9J+oikcyWdmxY5A3hA0jLgK8CZ0SJnJO4wzn7pKrjoIvjd7+CSS2DZMjj+eHjLW5ImrA6cRdcMPHGl7chnjpdRVRv0hg3JEN05c+CRR+CNb4SZM+GAA7jrBfjmynU8tHlnxu79Ov7upIPb5telWTkeVdUZ6tXH4cRRRqbOwC1b4Ec/gssuq7jU7Lqdd2XMXhPZbcK4ZIGonXZKlqYtvS40eY10XY6by8y6wxe+AEccUdND65U4PDtuGZk6A3t64Iwz4PTTYfVq/vslN7L5mWfY4+V17DG0jj1eXsuEoXXsu2k97z5wHGzenCSb4utXXkmuAQqJvNJ1OVUm/zVDm3hm3QY2bQl6e8Trxo2lv6+3qseaWYvYsKHZEThxFCtUxysdhoftDJRgr724e+fXEfu/bse7gccv/cu6xFmLdhsCamatqxVOAGwJxSc5lVNtZ2CrntzlaS7MrF6cOFLlDqwFWcbZt+oIFI/FN7N6cVNVqtIBVDDi2bGlI05OP3IStz24uqVGoHgsvpnVixNHqtYDa2nfweCaIa5fPNhyfQfdPIGih5Ka1ZebqlK1NjG1S99Bu01zUS+eoM+s/lzjSBUOoFl/mdaz7yDvX8btNM1FvQyX2Kt9L1xjMdueE0eRWg6s9eo7KNfkNXvu8m1xtYJ2PICONrG3w+di1mhuqhqlLE1cw60z3epNXu3a5DPa4dGt/rmYNYMTRx2M7X31bezv6y3bdzDSgbfVh8u26wF0tMOjW/1zMWsGJ45RKCSDF17etG3fxs3l1yUf6cDbqicOFrTrAXS0gwJa/XMxawb3cYxClo7XkQ68rT5ctp3PAxnNoIBW/1zMmsE1jlHI8it8pF+u1f4yHq6fJE+tekZ83rp1GLPZcFzjGIUsv8Kr+eU60i/jZo7wqXW4cifoxmHMZsNx4hiFLM0Y9Tjw1uOchNHwAdTMwIljVLImg9EeeJvdQd2O53GYWf05cYxSI3+FN7OD2ifCmVmBO8fbSDM7qNv1PA5rP80aAGLVc42jjTSzg7rZzWTWHVyzbQ9OHMOYt2SQi25Yse0Ev/6+Xr5wysFN/QI3q4O6nc/jsPbR7AEgVh03VVUwb8kgs65btt1Z4WuGNjHrh8s6suo8UvNAt57HYY3lmm17cOKoYM7Ch9i0JXbYv2lrdFy7frl5tD5z7VL+cd7ybWV8Ipw1gqd4aQ9uqqpguF84nfbrp1zzQADX3PM7Bg7YY1ty8HkcljdP8dIeXOOoYLhfOJ3266dSIgzouNqVtTbXbNtDrjUOSd8FTgaejYhDytwv4MvAScDLwDkRcV+eMVVr1rQpzLpu2Q7NVb1j1HG/fip1fEPn1a6s9blm2/ryrnFcCUwf5v4TgYPSy0zg6znHU7UZUycx54zDmLBL77Z9/X29zHnvYR33pZ41bQqqcF+n1a7MbPRyrXFExC8kTR6myKnA1RERwD2S+iXtHRFP5xlXtbrll8+MqZNY9OTzXHPP7yiuX7lt2czKaXYfxyTg90W3n0r37UDSTEmLJC1avXp1Q4LrJv8041Auf9/hbls2sxE1e1RVuRaSHcfAAhFxBXAFwMDAQNkyNjrdUsMCT9hoNhrNThxPAfsV3d4XWNWkWKwKnXDA9bQWZqPT7Kaq+cAHlTgaWNsq/Ru2o3InCs6eu7ztzqT3hI1mo5Nr4pD0feCXwBRJT0n6iKRzJZ2bFlkAPAY8AnwL+Ns847HR6ZQDrqe1MBudvEdVnTXC/QF8Ms8YrH465YDrCRvNRqfZTVXWRjplHiFP2Gg2Ok4cVrVOOeB6Wguz0Wn2qCprI81cSKreumnosVm9OXFYJj7gmpmbqszMLBMnDjMzy8SJw8zMMnHiMDOzTJw4zMwsEycOMzPLxInDzMwyceIwM7NMnDjMzCwTJw4zM8vEicPMzDJx4jAzs0w8yaGNSiesQW5m2ThxWM0Ka5AXlpMtrEEOOHmYdTA3VVnNOmUNcjPLxonDatYpa5CbWTZOHFazTlmD3MyyceKwmnXKGuRmlo07x61mnbQGuZlVz4nDRsVrkJt1HzdVmZlZJrknDknTJT0k6RFJF5S5/xxJqyUtTS8fzTsmMzOrXa5NVZJ6gK8B7waeAn4taX5E/Kak6LURcV6esZiZWX3kXeM4CngkIh6LiFeA/wROzfk1zcwsR3knjknA74tuP5XuK3W6pPslXSdpv3JPJGmmpEWSFq1evTqPWM3MrAp5Jw6V2Rclt28AJkfEm4FbgKvKPVFEXBERAxExMHHixDqHaWZm1cp7OO5TQHENYl9gVXGBiHiu6Oa3gMtGetLFixf/UdKTdYkwuz2BPzbptYfjuLJxXNk4rmxaNa66nJ2bd+L4NXCQpAOBQeBM4P3FBSTtHRFPpzdPAVaO9KQR0bQqh6RFETHQrNevxHFl47iycVzZtHJc9XieXBNHRGyWdB6wEOgBvhsRKyRdDCyKiPnApySdAmwGngfOyTMmMzMbndzPHI+IBcCCkn0XFm3PBmbnHYeZmdWHzxzP7opmB1CB48rGcWXjuLLp6LgUUTrIyczMrDLXOMzMLBMnDjMzy8SJo4Sk/STdJmmlpBWSPl2mzLGS1hZNzHhhuefKKb4nJC1PX3eHoXVKfCWdVPJ+SUc0IKYpRe/FUknrJJ1fUqYh75mk70p6VtIDRfv2kHSzpIfT6wkVHnt2WuZhSWc3IK45kh5MP6cfSeqv8NhhP/Mc4vqCpMGiz+qkCo8ddgLTHOK6tiimJyQtrfDYXN6vSseGZn+/hokrv+9XRPhSdAH2Bo5It3cHfgu8qaTMscCNTYrvCWDPYe4/CbiJ5Kz9o4F7GxxfD/AH4IBmvGfAO4AjgAeK9v0f4IJ0+wLgsjKP2wN4LL2ekG5PyDmuE4Cd0u3LysVVzWeeQ1xfAP6his/5UeANwGuAZaX/J/WOq+T+fwUubOT7VenY0Ozv1zBx5fb9co2jREQ8HRH3pdsvkpyQ2E4rFZ0KXB2Je4B+SXs38PXfBTwaEU05sz8ifkFyPlCxU3l1KpurgBllHjoNuDkino+IF4Cbgel5xhURP4uIzenNe0hmVmioCu9XNXKdwHS4uCQJ+Gvg+/V6vSpjqnRsaOr3q1JceX6/nDiGIWkyMBW4t8zdb5W0TNJNkg5uYFgB/EzSYkkzy9xf7cSSeTmTyv/QzXrPXhfp7ATp9V5lyjT7ffswSU2xnJE+8zyclzZxfLdC00sz36+3A89ExMMV7s/9/So5NrTM92uYY1Zdv19eOrYCSbsB1wPnR8S6krvvI2mKWZ+2/84DDmpQaMdExCpJewE3S3ow/XVWUM3EkrmQ9BqSaWPKndDZzPesGs183z5PMnPCNRWKjPSZ19vXgS+S/P1fJGkW+nBJmaa9X8BZDF/byPX9Kj02JBWgkR9WZl9d369Kx6w8vl+ucZQhqZfkA7gmIuaW3h8R6yJifbq9AOiVtGcjYouIVen1s8CPSJoMio04sWSOTgTui4hnSu9o5nsGPFNorkuvny1TpinvW9pJejLwN5E2OJeq4jOvq4h4JiK2RMRWkolHy71es96vnYD3ANdWKpPn+1Xh2ND071elY1Ze3y8njhJp++l3gJUR8aUKZV6flkPSUSTv43PlytY5tl0l7V7YJun8eqCk2Hzgg0ocDayNVyeRzFvFX4LNes9S84HCKJazgR+XKbMQOEHShLRp5oR0X24kTQf+J3BKRLxcoUw1n3m94yruEzutwuttm8A0rWmeSfI+5+144MGIeKrcnXm+X8McG5r6/aoUV67fr3r06nfSBXgbSRXyfmBpejkJOBc4Ny1zHrCCZCTJPcCfNyi2N6SvuSx9/c+n+4tjE8lyvY8Cy4GBBsW2C0kiGF+0r+HvGUniehrYRPIr7yPAa4GfAw+n13ukZQeAbxc99sPAI+nlQw2I6xGSdu/C9+wbadl9gAXDfeY5x/W99LtzP8lBce/SuNLbJ5GM4Hm0EXGl+68sfKeKyjbk/Rrm2NDU79cwceX2/fKUI2ZmlombqszMLBMnDjMzy8SJw8zMMnHiMDOzTJw4zMwsEycOMzPLxInDWpqk0ySFpD9Nbx8r6cY6PO+Vks4Yocyxkv68xuc/R9JXS/bdLmmglucr8/wLyk2TrWRK9H9Ity+WdHy6fb6kXerx2mZOHNbqzgLuIjkzudGOBWpKHHmLiJMiYs0IZS6MiFvSm+eTnKRpNmpOHNay0knbjiE5m7k4cYxLF6b5jaRvSBojqSetRTyQLkrzmfQ5Dpd0j15dzGaHmV6VLGSzZ7o9kNYMJpOc+f4ZJQvcvF3SREnXS/p1ejlmFH/b+qLtMyRdmW5fKenrShbmeUzSXyiZoXZloUyZmD+vZEGlW4ApRWWuTJ/7UyRnC9+WPu9HJF1eVO5jkspOr2NWjmfHtVY2A/hpRPxW0vN6dTXDo0gWqnkS+CnJpHePk6xBcAhAUTPO1cD/iIg7JF0M/G+SX9/DiognJH0DWB8R/5I+5/8DLo+IuyTtTzLX0J8N8zTvk/S2ottvrO7PZgJwHMlMwzeQJM+PAr+WdHhEbFv5TtKRJEl1Ksn/833A4pK/5SuS/g54Z0T8MZ2T6H5Jn42ITcCHgI9XGZuZaxzW0s4iWSCI9PqsdPtXkSwgtIVkTqO3kayo9gZJ/5ZO7rZO0nigPyLuSB93FcnKcrU6HviqkiVL55PUfHYfpvy1EXF44QJUu4zpDZHMBbScZN2J5ZHMVLsCmFxS9u3AjyLi5Uim0h5xosGIeAm4FTg57TvqjYjlVcZm5hqHtSZJryX51X2IpCBZqjSABey4jkFExAuSDiNZae2TJCvEfabKl9vMqz+ixg5Tbgzw1ogYqiMJVuAAAAFoSURBVPJ5h1P8N5S+5sb0emvRduF2uf/ZWiac+zbwOeBB4N9reLx1Mdc4rFWdQbIE7gERMTki9iNpjnobcFQ6nfcY4H3AXWl7/5iIuB74XyRrMK8FXpD09vQ5PwDcseNL8QRwZLp9etH+F0nWcC74Gcksv0DSfzKKv+8ZSX+W/g2njeJ5fgGcJqkvrf38VYVy2/0tEXEvyfoQ76fBS7Ba+3PisFZ1FsmiMsWuJznQ/RK4lGTdgMfTcpOA29NmpCt5dRXCs4E5ku4HDgcuLvNaFwFflnQnsKVo/w0kB+WlafL5FDCQdrT/hqTzvFYXADeSNBnVvF5KJGtNX0sybfb1wJ0Vil4B3CTptqJ9PwDujmQNbLOqeVp1sy6Vng9zeUT8vNmxWHtxjcOsy0jql/RbYMhJw2rhGofZKEj6EPDpkt13R8QnmxGPWSM4cZiZWSZuqjIzs0ycOMzMLBMnDjMzy8SJw8zMMvn/Tl0Be9XG8NQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from scipy.optimize import curve_fit\n",
    "\n",
    "#the function of exponentialfit\n",
    "def func(x, a, b, c):\n",
    "    return a * np.exp(b * x) + c\n",
    "\n",
    "x = np.array(data.Absolute_Humidity[:-1])\n",
    "y = np.array(data.R0_mean[:-1])\n",
    "popt, pcov = curve_fit(func, x, y)\n",
    "pltx = np.arange(1.496135,21.497618,0.5)\n",
    "plty = [func(i, popt[0],popt[1],popt[2]) for i in pltx]\n",
    "plt.scatter(x,y)\n",
    "plt.xlabel('Absolute_Humidity')\n",
    "plt.ylabel('R0_mean')\n",
    "plt.plot(pltx,plty,'r')\n",
    "print(popt)"
   ]
  }
 ],
 "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": "3.6.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}