wet_bulb.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "15.25045341880328 19.202664194612755 15.78399920915981\n",
      "77\n",
      "Firstday\n",
      "1996-06-30    13\n",
      "1996-09-30     8\n",
      "1997-06-30     0\n",
      "1997-09-30    19\n",
      "1998-06-30     8\n",
      "1998-09-30    17\n",
      "1999-06-30     0\n",
      "1999-09-30    36\n",
      "2000-06-30    17\n",
      "2000-09-30     0\n",
      "2001-06-30     0\n",
      "2001-09-30    44\n",
      "2002-06-30    10\n",
      "2002-09-30    27\n",
      "2003-06-30    25\n",
      "2003-09-30    35\n",
      "2004-06-30     0\n",
      "2004-09-30    21\n",
      "2005-06-30    22\n",
      "2005-09-30    16\n",
      "2006-06-30    49\n",
      "2006-09-30     8\n",
      "2007-06-30     9\n",
      "2007-09-30    10\n",
      "2008-06-30    17\n",
      "2008-09-30    25\n",
      "2009-06-30     0\n",
      "2009-09-30    39\n",
      "2010-06-30    39\n",
      "2010-09-30     8\n",
      "2011-06-30    17\n",
      "2011-09-30     9\n",
      "2012-06-30     8\n",
      "2012-09-30    21\n",
      "2013-06-30    10\n",
      "2013-09-30    26\n",
      "2014-06-30    10\n",
      "2014-09-30    20\n",
      "2015-06-30     8\n",
      "2015-09-30    55\n",
      "2016-06-30     8\n",
      "2016-09-30    37\n",
      "2017-06-30    30\n",
      "2017-09-30    20\n",
      "2018-06-30    10\n",
      "2018-09-30    38\n",
      "2019-06-30    28\n",
      "2019-09-30    26\n",
      "Name: duration, dtype: int64\n",
      "55\n",
      "Firstday\n",
      "1996-09-30    12\n",
      "1997-06-30     0\n",
      "1997-09-30     8\n",
      "1998-06-30     0\n",
      "1998-09-30    22\n",
      "1999-06-30     0\n",
      "1999-09-30    33\n",
      "2000-06-30     0\n",
      "2000-09-30    17\n",
      "2001-06-30     0\n",
      "2001-09-30    17\n",
      "2002-06-30     0\n",
      "2002-09-30     9\n",
      "2003-06-30    17\n",
      "2003-09-30    50\n",
      "2004-06-30    11\n",
      "2004-09-30    18\n",
      "2005-06-30    13\n",
      "2005-09-30    43\n",
      "2006-06-30     0\n",
      "2006-09-30    40\n",
      "2007-06-30     0\n",
      "2007-09-30    32\n",
      "2008-06-30     0\n",
      "2008-09-30    16\n",
      "2009-06-30     0\n",
      "2009-09-30    49\n",
      "2010-06-30     0\n",
      "2010-09-30    57\n",
      "2011-06-30     8\n",
      "2011-09-30    12\n",
      "2012-06-30     9\n",
      "2012-09-30    39\n",
      "2013-06-30     0\n",
      "2013-09-30    35\n",
      "2014-06-30     0\n",
      "2014-09-30    17\n",
      "2015-06-30    53\n",
      "2015-09-30     8\n",
      "2016-06-30     0\n",
      "2016-09-30    56\n",
      "2017-06-30    19\n",
      "2017-09-30    54\n",
      "2018-06-30     0\n",
      "2018-09-30    26\n",
      "2019-06-30    36\n",
      "2019-09-30    21\n",
      "Name: duration, dtype: int64\n",
      "76\n",
      "Firstday\n",
      "1996-06-30    10\n",
      "1996-09-30    16\n",
      "1997-06-30     9\n",
      "1997-09-30    26\n",
      "1998-06-30    18\n",
      "1998-09-30    24\n",
      "1999-06-30     0\n",
      "1999-09-30    40\n",
      "2000-06-30    10\n",
      "2000-09-30     8\n",
      "2001-06-30     0\n",
      "2001-09-30    47\n",
      "2002-06-30     8\n",
      "2002-09-30    34\n",
      "2003-06-30     0\n",
      "2003-09-30    29\n",
      "2004-06-30     8\n",
      "2004-09-30    30\n",
      "2005-06-30    21\n",
      "2005-09-30     8\n",
      "2006-06-30    10\n",
      "2006-09-30    51\n",
      "2007-06-30     8\n",
      "2007-09-30    35\n",
      "2008-06-30     8\n",
      "2008-09-30    16\n",
      "2009-06-30    16\n",
      "2009-09-30    26\n",
      "2010-06-30     0\n",
      "2010-09-30    38\n",
      "2011-06-30    24\n",
      "2011-09-30     0\n",
      "2012-06-30     9\n",
      "2012-09-30    17\n",
      "2013-06-30     0\n",
      "2013-09-30    23\n",
      "2014-06-30     0\n",
      "2014-09-30    32\n",
      "2015-06-30     0\n",
      "2015-09-30    21\n",
      "2016-06-30     8\n",
      "2016-09-30    37\n",
      "2017-06-30    19\n",
      "2017-09-30    16\n",
      "2018-06-30    22\n",
      "2018-09-30    41\n",
      "2019-06-30    14\n",
      "2019-09-30    37\n",
      "Name: duration, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#! python3\n",
    "import xlsxwriter\n",
    "from pathlib import Path\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from pandas import DataFrame\n",
    "from datetime import datetime\n",
    "\n",
    "class Data:\n",
    "    def __init__(self):\n",
    "        self.temp_av = []\n",
    "        self.hum = []\n",
    "        self.qq = []\n",
    "        self.wind = []\n",
    "        self.rows = []\n",
    "        self.columns = []\n",
    "        self.wb = []\n",
    "        self.heat = []\n",
    "\n",
    "    def reading(self):\n",
    "        # reading excel file\n",
    "        # directory below should contains only cleaned xlsx files\n",
    "        p = Path('C:/Users/kinan/Desktop/Bioinformatyka/Modern Data Analytics/cleaned')\n",
    "        files = list(p.glob('*.xlsx'))\n",
    "        files.sort  # sort in alphabetical order\n",
    "        self.hum = pd.read_excel(files[0]).drop(['FR'], axis=1)\n",
    "        self.qq = pd.read_excel(files[1])\n",
    "        self.temp_av = pd.read_excel(files[2]).drop(['FR'], axis=1)\n",
    "        self.wind = pd.read_excel(files[3]).drop(['FR'], axis=1)\n",
    "        self.rows = self.qq.iloc[:, 0]\n",
    "        self.columns = list(self.qq.columns)\n",
    "\n",
    "    def calculation(self):\n",
    "        # formula: 0.735×Ta+0.0374×RH+0.00292×Ta×RH +7.619×SR−4.557×SR2−0.0572×WS−4.064\n",
    "        for i in range(1, len(self.columns)):\n",
    "            for j in range(0, len(self.rows)):\n",
    "                sr = self.qq.iloc[j, i] * 0.001\n",
    "                ws = self.wind.iloc[j, i] * 0.1\n",
    "                ta = self.temp_av.iloc[j, i]\n",
    "                rh = self.wind.iloc[j, i]\n",
    "                self.wb.append(0.735*ta + 0.0374*rh + 0.00292*ta*rh + 7.619*sr - 4.557*(sr**2) - 0.0572*ws - 4.064)\n",
    "\n",
    "    def write_to_excel(self):\n",
    "        wb = xlsxwriter.Workbook('wet_bulb2.xlsx')\n",
    "        sheet = wb.add_worksheet()\n",
    "        cnt = 0\n",
    "\n",
    "        for i in range(1, len(self.columns)):\n",
    "            cnt_row = 0\n",
    "            sheet.write(0, i, self.columns[i])\n",
    "            for j in range(0, len(self.rows)):\n",
    "                if 3 < int(self.rows[j][5:7]) < 10:\n",
    "                    print(j+1, i, self.wb[cnt])\n",
    "                    sheet.write(cnt_row+1, i, self.wb[cnt])\n",
    "                    if i == 1:\n",
    "                        sheet.write(cnt_row+1, 0, self.rows[j])\n",
    "                    cnt_row += 1\n",
    "                cnt += 1\n",
    "        wb.close()\n",
    "\n",
    "    def percentile_90(self):\n",
    "        wb = pd.read_excel('wet_bulb_quarters.xlsx', engine = 'openpyxl') \n",
    "        # read the we_bulb file from current working directory\n",
    "\n",
    "        de_90 = np.percentile(wb['DE'], 90)\n",
    "        es_90 = np.percentile(wb['ES'], 90)\n",
    "        nl_90 = np.percentile(wb['NL'], 90)\n",
    "        per = [de_90, es_90, nl_90]  # percentiles list\n",
    "        print(de_90, es_90, nl_90)\n",
    "\n",
    "        for i in range(1, 4):\n",
    "            cnt_h = 0  # number of heatwaves\n",
    "            in_row = False\n",
    "            to_break = 0  # counter for the days after heatwave\n",
    "            start = []  # date of first day of heatwave\n",
    "            stop = []  # dat of last day of heatwave\n",
    "            first = True  # first day of heatwave\n",
    "\n",
    "            for j in range (0, len(wb.iloc[:, i])):\n",
    "                if wb.iloc[j, i] > per[i-1] and wb.iloc[j-1, i] > per[i-1]:\n",
    "                    if first:\n",
    "                        start.append(wb.iloc[j, 0])\n",
    "                        first = False\n",
    "                    in_row = True  # if True we are within heatwave\n",
    "                    to_break = 0  # in heatwave, number after heatwave = 0\n",
    "                else:\n",
    "                    # counting days with temp below percentile value\n",
    "                    if in_row:\n",
    "                        to_break += 1\n",
    "\n",
    "                    # if we have more then 7 days after high temperatures it is the end of heatwave\n",
    "                    if to_break > 7:\n",
    "                        to_break = 0\n",
    "                        in_row = False  # end of heatwave\n",
    "                        cnt_h += 1\n",
    "                        first = True\n",
    "                        stop.append(wb.iloc[j, 0])\n",
    "                        #print(\"HEAT:\"+ wb.iloc[j, 0])\n",
    "\n",
    "            date_format = \"%Y-%m-%d\"\n",
    "            days = []\n",
    "            for i in range(0, cnt_h):                \n",
    "                a = datetime.strptime(start[i], date_format)\n",
    "                b = datetime.strptime(stop[i], date_format)\n",
    "                delta = b - a\n",
    "                days.append(delta.days) # that's it\n",
    "            # print(days)\n",
    "            period = [start, stop, days]\n",
    "            df = DataFrame(period).transpose()\n",
    "            df.columns = ['Firstday','LastDay', 'duration']\n",
    "            # print(df)\n",
    "            print(cnt_h)\n",
    "            # print(start)\n",
    "            # print(stop)\n",
    "            df.Firstday = pd.to_datetime(df.Firstday)\n",
    "            df.set_index('Firstday', inplace=True)\n",
    "            df_sum = df.resample('Q').duration.sum()\n",
    "            # print(df_sum)\n",
    "            df_sum.index = pd.DatetimeIndex(df_sum.index)\n",
    "            df_summer = df_sum[(df_sum.index.quarter == 2) | (df_sum.index.quarter == 3)]\n",
    "            print(df_summer)\n",
    "            df_summer.to_excel('NumDay_quarter_{}.xlsx'.format(i), index=False)\n",
    "\n",
    "wet_bulb = Data()\n",
    "#wet_bulb.reading()\n",
    "#wet_bulb.calculation()\n",
    "#wet_bulb.write_to_excel()\n",
    "wet_bulb.percentile_90()"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}