area51.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Area for testing Code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Try code loading weather from weather station\n",
    "This is updated code from a custom scraper"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "import csv\n",
    "import lxml.html as lh\n",
    "import datetime\n",
    "from datetime import date\n",
    "\n",
    "from util.UnitConverter import ConvertToSystem\n",
    "from util.Parser import Parser\n",
    "from util.Utils import Utils\n",
    "import pandas as pd\n",
    "from load_data import WeatherData2\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import datetime\n",
    "from visualize_data import Analyze_WD\n",
    "from load_data import SummaryData, WeatherData2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrap_station(weather_station_url=\"https://www.wunderground.com/dashboard/pws/IDRSING3\", \n",
    "                  unit_system=\"metric\", start_DATE=\"\", end_DATE=\"\", timeout = 5, savepath=\"\", output=True):\n",
    "    \"\"\"Get data from Weather Station. \n",
    "    Input: Parameters about station and start and end date\n",
    "    Return: Pandas Dataframe in format for Weather Station\"\"\"\n",
    "    \n",
    "    session = requests.Session()\n",
    "    \n",
    "    url_gen = Utils.date_url_generator(weather_station_url, start_DATE, end_DATE)\n",
    "    station_name = weather_station_url.split('/')[-1]\n",
    "    if len(savepath)==0:\n",
    "        savepath = f'{station_name}.csv'\n",
    "\n",
    "    with open(savepath, 'a+', newline='') as csvfile:\n",
    "        dfs = [] # Where the output will be saved to\n",
    "        for date_string, url in url_gen:\n",
    "            try:\n",
    "                if output:\n",
    "                    print(f'Downloading Weather data from {url}')\n",
    "                history_table = False\n",
    "                while not history_table:\n",
    "                    html_string = session.get(url, timeout=timeout)\n",
    "                    doc = lh.fromstring(html_string.content)\n",
    "                    history_table = doc.xpath('//*[@id=\"main-page-content\"]/div/div/div/lib-history/div[2]/lib-history-table/div/div/div/table/tbody')\n",
    "                    if not history_table:\n",
    "                        if output:\n",
    "                            print(\"Refreshing session.\")\n",
    "                        session = requests.Session()\n",
    "\n",
    "                data_rows = Parser.parse_html_table(date_string, history_table)\n",
    "\n",
    "                converter = ConvertToSystem(unit_system)\n",
    "                data_to_write = converter.clean_and_convert(data_rows)\n",
    "                dfs.append(pd.DataFrame(data_to_write))\n",
    "                    \n",
    "                print(f'Saving {len(data_to_write)} rows')\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "        df=pd.concat(dfs)\n",
    "        df[\"station\"] = station_name\n",
    "        return df\n",
    "    \n",
    "def to_normed_df(df):\n",
    "    \"\"\"Transform a scrapped wunderground dataframe to a normalized df\"\"\"  \n",
    "    \n",
    "    dct = {'Temperature': 'temp', 'Precip_Accum': 'total_rain', 'Precip_Rate': 'hour_rain',  'Humidity': 'humidity',\n",
    "           'Wind': 'wind_direction',  'Speed': 'wind',  'Speed': 'wind',  'Gust': 'wind_gust',\n",
    "            'Pressure': 'pressure',  'Solar': 'solar', 'Dew_Point': 'dewpoint'\n",
    "           }\n",
    "    df_new = df.rename(columns=dct)\n",
    "    \n",
    "    ### Update data column as combo of date and time\n",
    "    df_new[\"date\"] \t=  pd.to_datetime(df[\"Date\"].str.replace(\"/\",\"-\") + \" \" +  df[\"Time\"])\n",
    "    return df_new\n",
    "\n",
    "def check_valid_wunder_df(df):\n",
    "    \"\"\"Does a quick check whether dataframe is valid for \n",
    "    software here.\"\"\"\n",
    "    \n",
    "    cols = ['temp', 'hour_rain', 'total_rain', 'date', 'humidity',\n",
    "       'wind_direction', 'wind', 'wind_gust', 'pressure', 'solar', 'dewpoint',\n",
    "       'station']\n",
    "    columns = df.columns\n",
    "    \n",
    "    for c in cols:\n",
    "        if c not in columns:\n",
    "            raise RuntimeWarning(f\"Column {c} is missing\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "### Test running the scrapper\n",
    "\n",
    "d1 = date(2017, 7, 1)\n",
    "d2 = date(2017, 7, 3)\n",
    "\n",
    "df = scrap_station(start_DATE=d1, end_DATE=d2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = to_normed_df(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "check_valid_wunder_df(dft)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime.date(2017, 8, 1)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1 = date(2017, 7, 31)\n",
    "d1 + datetime.timedelta(days=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "date = datetime.date(df[\"Date\"])\n",
    "time = datetime.time(df[\"Time\"])\n",
    "combined = datetime.datetime.combine(date, time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>Temperature</th>\n",
       "      <th>Dew_Point</th>\n",
       "      <th>Humidity</th>\n",
       "      <th>Wind</th>\n",
       "      <th>Speed</th>\n",
       "      <th>Gust</th>\n",
       "      <th>Pressure</th>\n",
       "      <th>Precip_Rate</th>\n",
       "      <th>Precip_Accum</th>\n",
       "      <th>UV</th>\n",
       "      <th>Solar</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/07/01</td>\n",
       "      <td>02:03 AM</td>\n",
       "      <td>17.28</td>\n",
       "      <td>12.78</td>\n",
       "      <td>75.0</td>\n",
       "      <td>West</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1008.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IDRSING3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/07/01</td>\n",
       "      <td>02:08 AM</td>\n",
       "      <td>17.50</td>\n",
       "      <td>12.39</td>\n",
       "      <td>72.0</td>\n",
       "      <td>West</td>\n",
       "      <td>2.57</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1009.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IDRSING3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time  Temperature  Dew_Point  Humidity  Wind  Speed  Gust  \\\n",
       "0  2017/07/01  02:03 AM        17.28      12.78      75.0  West   0.00  0.00   \n",
       "1  2017/07/01  02:08 AM        17.50      12.39      72.0  West   2.57  3.54   \n",
       "\n",
       "   Pressure  Precip_Rate  Precip_Accum   UV  Solar   station  \n",
       "0   1008.81          0.0           0.0  0.0    0.0  IDRSING3  \n",
       "1   1009.14          0.0           0.0  0.0    0.0  IDRSING3  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "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>Date</th>\n",
       "      <th>Time</th>\n",
       "      <th>temp</th>\n",
       "      <th>dewpoint</th>\n",
       "      <th>humidity</th>\n",
       "      <th>wind_direction</th>\n",
       "      <th>wind</th>\n",
       "      <th>wind_gust</th>\n",
       "      <th>pressure</th>\n",
       "      <th>hour_rain</th>\n",
       "      <th>total_rain</th>\n",
       "      <th>UV</th>\n",
       "      <th>solar</th>\n",
       "      <th>station</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017/07/01</td>\n",
       "      <td>02:03 AM</td>\n",
       "      <td>17.28</td>\n",
       "      <td>12.78</td>\n",
       "      <td>75.0</td>\n",
       "      <td>West</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1008.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IDRSING3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017/07/01</td>\n",
       "      <td>02:08 AM</td>\n",
       "      <td>17.50</td>\n",
       "      <td>12.39</td>\n",
       "      <td>72.0</td>\n",
       "      <td>West</td>\n",
       "      <td>2.57</td>\n",
       "      <td>3.54</td>\n",
       "      <td>1009.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>IDRSING3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date      Time   temp  dewpoint  humidity wind_direction  wind  \\\n",
       "0  2017/07/01  02:03 AM  17.28     12.78      75.0           West  0.00   \n",
       "1  2017/07/01  02:08 AM  17.50     12.39      72.0           West  2.57   \n",
       "\n",
       "   wind_gust  pressure  hour_rain  total_rain   UV  solar   station  \n",
       "0       0.00   1008.81        0.0         0.0  0.0    0.0  IDRSING3  \n",
       "1       3.54   1009.14        0.0         0.0  0.0    0.0  IDRSING3  "
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "to_normed_df(df).head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Date', 'Time', 'Temperature', 'Dew_Point', 'Humidity', 'Wind',\n",
       "       'Speed', 'Gust', 'Pressure', 'Precip_Rate', 'Precip_Accum', 'UV',\n",
       "       'Solar'], dtype=object)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns.values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Test Loading the Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"IDRSING3.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.read_csv(\"./Data/2017/10.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test Production Code"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading: Year: 2022 Month: 5 Day: 4 \n",
      "Downloading Weather data from https://www.wunderground.com/dashboard/pws/IDRSING3/table/2022-05-04/2022-05-04/daily\n",
      "list index out of range! probably caused by an empty row in the data\n",
      "Saving 286 rows\n"
     ]
    }
   ],
   "source": [
    "wd = WeatherData2()\n",
    "wd.output = True\n",
    "\n",
    "df = wd.download_data_day(4, 5, 2022)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test Loading of whole Month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'datetime' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "File \u001b[0;32m<timed exec>:3\u001b[0m, in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'datetime' is not defined"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "month = datetime.date(year=2022, month=7, day=3)\n",
    "# Test the Class:\n",
    "wd = WeatherData2()\n",
    "wd.local_save_month(month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test plotting of clean data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 70 μs, sys: 0 ns, total: 70 μs\n",
      "Wall time: 95.6 μs\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "\n",
    "# Test the Class:\n",
    "wd = WeatherData2()\n",
    "wd.output = True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x200 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "date = datetime.date(year=2024, month=7, day=15)\n",
    "\n",
    "df = wd.give_data_day_clean(date)\n",
    "\n",
    "plt.figure(figsize=(10,2))\n",
    "plt.plot(df.index, df[\"temp\"], 'ro')\n",
    "plt.show()\n",
    "\n",
    "#df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading Rain from: 2024-05-01\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-02\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-03\n",
      "Max Rain: 5.84\n",
      "Loading Rain from: 2024-05-04\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-05\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-06\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-07\n",
      "Max Rain: 5.84\n",
      "Loading Rain from: 2024-05-08\n",
      "Max Rain: 5.84\n",
      "Loading Rain from: 2024-05-09\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-10\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-11\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-12\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-13\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-14\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-15\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-16\n",
      "Max Rain: 1.27\n",
      "Loading Rain from: 2024-05-17\n",
      "Max Rain: 6.60\n",
      "Loading Rain from: 2024-05-18\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-19\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-20\n",
      "Max Rain: 4.32\n",
      "Loading Rain from: 2024-05-21\n",
      "Max Rain: 9.14\n",
      "Loading Rain from: 2024-05-22\n",
      "Max Rain: 0.25\n",
      "Loading Rain from: 2024-05-23\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-24\n",
      "Max Rain: 8.89\n",
      "Loading Rain from: 2024-05-25\n",
      "Max Rain: 4.57\n",
      "Loading Rain from: 2024-05-26\n",
      "Max Rain: 1.52\n",
      "Loading Rain from: 2024-05-27\n",
      "Max Rain: 0.25\n",
      "Loading Rain from: 2024-05-28\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-29\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-30\n",
      "Max Rain: 0.00\n",
      "Loading Rain from: 2024-05-31\n",
      "Max Rain: 6.10\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Done!!\n"
     ]
    }
   ],
   "source": [
    "sd = SummaryData(wd)\n",
    "v_wd = Analyze_WD(wd, sd)\n",
    "\n",
    "month = datetime.date(year=2024, month=5, day=1)\n",
    "v_wd.visualize_rain_month(date_month=month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculate and save Summary Stats manually\n",
    "Do here for whole of 2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "d1 = datetime.date(2020, 1, 1)\n",
    "d2 = datetime.date(2020, 12, 31)\n",
    "sd.set_summary_statistics(d1, d2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Test getting summary data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd = WeatherData2()\n",
    "wd.output = True\n",
    "\n",
    "sd = SummaryData(wd)\n",
    "v_wd = Analyze_WD(wd, sd)\n",
    "\n",
    "month = datetime.date(year=2020, month=6, day=1)\n",
    "#v_wd.visualize_rain_month(date_month=month)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test getting period data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "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>MinT</th>\n",
       "      <th>MaxT</th>\n",
       "      <th>MeanT</th>\n",
       "      <th>TotR</th>\n",
       "      <th>TotS</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-02-01</th>\n",
       "      <td>1.94</td>\n",
       "      <td>16.39</td>\n",
       "      <td>9.105000</td>\n",
       "      <td>0.51</td>\n",
       "      <td>1.254150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-02</th>\n",
       "      <td>4.83</td>\n",
       "      <td>13.22</td>\n",
       "      <td>9.899624</td>\n",
       "      <td>1.52</td>\n",
       "      <td>0.843513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-03</th>\n",
       "      <td>7.00</td>\n",
       "      <td>12.17</td>\n",
       "      <td>8.320854</td>\n",
       "      <td>6.86</td>\n",
       "      <td>0.527267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-04</th>\n",
       "      <td>2.50</td>\n",
       "      <td>9.72</td>\n",
       "      <td>5.370418</td>\n",
       "      <td>6.86</td>\n",
       "      <td>0.632000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-02-05</th>\n",
       "      <td>-0.22</td>\n",
       "      <td>4.83</td>\n",
       "      <td>2.696979</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.010408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-01</th>\n",
       "      <td>8.22</td>\n",
       "      <td>23.78</td>\n",
       "      <td>16.383826</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.054508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-02</th>\n",
       "      <td>5.44</td>\n",
       "      <td>22.06</td>\n",
       "      <td>15.039321</td>\n",
       "      <td>3.81</td>\n",
       "      <td>6.974854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-03</th>\n",
       "      <td>10.00</td>\n",
       "      <td>26.11</td>\n",
       "      <td>17.051707</td>\n",
       "      <td>0.00</td>\n",
       "      <td>7.841808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-04</th>\n",
       "      <td>7.33</td>\n",
       "      <td>26.61</td>\n",
       "      <td>18.093902</td>\n",
       "      <td>0.00</td>\n",
       "      <td>6.790875</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-06-05</th>\n",
       "      <td>10.78</td>\n",
       "      <td>19.06</td>\n",
       "      <td>15.548624</td>\n",
       "      <td>6.60</td>\n",
       "      <td>2.524817</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>126 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             MinT   MaxT      MeanT  TotR      TotS\n",
       "2020-02-01   1.94  16.39   9.105000  0.51  1.254150\n",
       "2020-02-02   4.83  13.22   9.899624  1.52  0.843513\n",
       "2020-02-03   7.00  12.17   8.320854  6.86  0.527267\n",
       "2020-02-04   2.50   9.72   5.370418  6.86  0.632000\n",
       "2020-02-05  -0.22   4.83   2.696979  0.00  1.010408\n",
       "...           ...    ...        ...   ...       ...\n",
       "2020-06-01   8.22  23.78  16.383826  0.00  7.054508\n",
       "2020-06-02   5.44  22.06  15.039321  3.81  6.974854\n",
       "2020-06-03  10.00  26.11  17.051707  0.00  7.841808\n",
       "2020-06-04   7.33  26.61  18.093902  0.00  6.790875\n",
       "2020-06-05  10.78  19.06  15.548624  6.60  2.524817\n",
       "\n",
       "[126 rows x 5 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start_date  = datetime.date(year=2020, month=2, day=1)\n",
    "end_date  = datetime.date(year=2020, month=6, day=5)\n",
    "\n",
    "\n",
    "df = sd.give_summary_statistics(start_date, end_date)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2020-01-31    -0.470377\n",
       "2020-02-29     5.671322\n",
       "2020-03-31     6.287161\n",
       "2020-04-30    10.751573\n",
       "2020-05-31    13.313322\n",
       "2020-06-30    18.415246\n",
       "2020-07-31    20.282406\n",
       "2020-08-31    21.602537\n",
       "2020-09-30          NaN\n",
       "2020-10-31          NaN\n",
       "2020-11-30          NaN\n",
       "2020-12-31          NaN\n",
       "Freq: ME, Name: MeanT, dtype: float64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sd.get_mean_month(2020)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Test getting monthly data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "wd = WeatherData2()\n",
    "month = datetime.date(year=2022, month=5, day=1)\n",
    "df = wd.give_data_month(month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#df.tz_localize('CET')\n",
    "#df.tz_localize(\"Europe/Vienna\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#.tz_convert(timezone = \"Europe/Vienna\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from dateutil.rrule import rrule, MONTHLY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "d1 = date(2017, 7, 31)\n",
    "d2 = date(2018, 12, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2017-07-31 00:00:00\n",
      "2017-08-31 00:00:00\n",
      "2017-10-31 00:00:00\n",
      "2017-12-31 00:00:00\n",
      "2018-01-31 00:00:00\n",
      "2018-03-31 00:00:00\n",
      "2018-05-31 00:00:00\n",
      "2018-07-31 00:00:00\n",
      "2018-08-31 00:00:00\n",
      "2018-10-31 00:00:00\n"
     ]
    }
   ],
   "source": [
    "for dt in rrule(MONTHLY, dtstart=d1, until=d2):\n",
    "    print(dt)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Legacy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def scrap_station(weather_station_url=\"https://www.wunderground.com/dashboard/pws/IDRSING3\", \n",
    "                  unit_system=\"metric\", start_DATE=\"\", end_DATE=\"\", timeout = 5, savepath=\"\", output=True):\n",
    "    \"\"\"Get data from Weather Station. \n",
    "    Input: Parameters about station and start and end date\n",
    "    Return: Pandas Dataframe in format for Weather Station\"\"\"\n",
    "    \n",
    "    session = requests.Session()\n",
    "    \n",
    "    url_gen = Utils.date_url_generator(weather_station_url, start_DATE, end_DATE)\n",
    "    station_name = weather_station_url.split('/')[-1]\n",
    "    if len(savepath)==0:\n",
    "        savepath = f'{station_name}.csv'\n",
    "\n",
    "    with open(savepath, 'a+', newline='') as csvfile:\n",
    "        #fieldnames = ['Date', 'Time',\t'Temperature',\t'Dew_Point', 'Humidity', 'Wind', 'Speed',\n",
    "        #              'Gust',\t'Pressure',\t'Precip_Rate',\t'Precip_Accum',\t'UV',   'Solar']\n",
    "        #writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n",
    "\n",
    "        # Write the correct headers to the CSV file\n",
    "        # 12:04 AM\t24.4 C\t18.3 C\t69 %\tSW\t0.0 km/h\t0.0 km/h\t1,013.88 hPa\t0.00 mm\t0.00 mm\t0\t0 w/m²\n",
    "        \n",
    "        #writer.writerow({'Date': 'Date', 'Time': 'Time', \n",
    "        #                 'Temperature': 'Temperature_C', 'Dew_Point':\n",
    "        #                 'Dew_Point_C',\t'Humidity': 'Humidity_%', \n",
    "        #                 'Wind': 'Wind', 'Speed': 'Speed_kmh',\t\n",
    "        #                 'Gust': 'Gust_kmh',\t'Pressure': 'Pressure_hPa',\t\n",
    "        #                 'Precip_Rate': 'Precip_Rate_mm',\t\n",
    "        #                 'Precip_Accum': 'Precip_Accum_mm',\t'UV': 'UV',   'Solar': 'Solar_w/m2'})\n",
    "        \n",
    "        dfs = [] # Where the output will be saved to\n",
    "        for date_string, url in url_gen:\n",
    "            try:\n",
    "                if output:\n",
    "                    print(f'Downloading Weather data from {url}')\n",
    "                history_table = False\n",
    "                while not history_table:\n",
    "                    html_string = session.get(url, timeout=timeout)\n",
    "                    doc = lh.fromstring(html_string.content)\n",
    "                    history_table = doc.xpath('//*[@id=\"main-page-content\"]/div/div/div/lib-history/div[2]/lib-history-table/div/div/div/table/tbody')\n",
    "                    if not history_table:\n",
    "                        if output:\n",
    "                            print(\"Refreshing session.\")\n",
    "                        session = requests.Session()\n",
    "\n",
    "                # parse html table rows\n",
    "                data_rows = Parser.parse_html_table(date_string, history_table)\n",
    "\n",
    "                # convert to metric system\n",
    "                converter = ConvertToSystem(unit_system)\n",
    "                data_to_write = converter.clean_and_convert(data_rows)\n",
    "                dfs.append(pd.DataFrame(data_to_write))\n",
    "                    \n",
    "                print(f'Saving {len(data_to_write)} rows')\n",
    "                #writer.writerows(data_to_write)\n",
    "            except Exception as e:\n",
    "                print(e)\n",
    "        df=pd.concat(dfs)       \n",
    "        return df"
   ]
  }
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