{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd, numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "ro=['CDO7252998062998','CDO5064618063001','CDO3042698063020','CDO7893378063026','CDO4604228063028','CDO7821968063031',\n",
    "   'CDO5072238063046','CDO4981038063054','CDO4725178063056','CDO5209078063060','CDO699718063062','CDO4894288063064',\n",
    "   'CDO1632508063066','CDO8765068063068','CDO9993348063070']\n",
    "hu=['CDO5941998062972','CDO5285728062974','CDO3021588062978','CDO9675788062981']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "p='C:/Users/csala/Onedrive - Lancaster University/Datarepo/szekelydata/klima/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "stations=pd.read_csv(p+'stations.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDO5941998062972\n",
      "CDO5285728062974\n",
      "CDO3021588062978\n",
      "CDO9675788062981\n"
     ]
    }
   ],
   "source": [
    "dfs=[]\n",
    "for i in hu:\n",
    "    df=pd.read_csv(p+'daily/raw/hu/'+i+'.txt',dtype={' FRSHTT':str,' YEARMODA':str})\n",
    "    dfs.append(df)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CDO7252998062998\n",
      "CDO5064618063001\n",
      "CDO3042698063020\n",
      "CDO7893378063026\n",
      "CDO4604228063028\n",
      "CDO7821968063031\n",
      "CDO5072238063046\n",
      "CDO4981038063054\n",
      "CDO4725178063056\n",
      "CDO5209078063060\n",
      "CDO699718063062\n",
      "CDO4894288063064\n",
      "CDO1632508063066\n",
      "CDO8765068063068\n",
      "CDO9993348063070\n"
     ]
    }
   ],
   "source": [
    "for i in ro:\n",
    "    df=pd.read_csv(p+'daily/raw/ro/'+i+'.txt',dtype={' FRSHTT':str,' YEARMODA':str})\n",
    "    dfs.append(df)\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs=pd.concat(dfs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "year_fixer={'199710':'19971001'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "dfs['time']=pd.to_datetime(dfs[' YEARMODA'].str.strip().replace(year_fixer),format='%Y%m%d')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "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>STN---</th>\n",
       "      <th>WBAN</th>\n",
       "      <th>YEARMODA</th>\n",
       "      <th>TEMP</th>\n",
       "      <th></th>\n",
       "      <th>DEWP</th>\n",
       "      <th>.1</th>\n",
       "      <th>SLP</th>\n",
       "      <th>.2</th>\n",
       "      <th>STP</th>\n",
       "      <th>...</th>\n",
       "      <th>.5</th>\n",
       "      <th>MXSPD</th>\n",
       "      <th>GUST</th>\n",
       "      <th>MAX</th>\n",
       "      <th>MIN</th>\n",
       "      <th>PRCP</th>\n",
       "      <th>SNDP</th>\n",
       "      <th>FRSHTT</th>\n",
       "      <th>Unnamed: 22</th>\n",
       "      <th>time</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>127560</td>\n",
       "      <td>99999</td>\n",
       "      <td>20150101</td>\n",
       "      <td>24.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>16.9</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1036.1</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1016.1</td>\n",
       "      <td>...</td>\n",
       "      <td>5.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>999.9</td>\n",
       "      <td>27.3</td>\n",
       "      <td>20.8*</td>\n",
       "      <td>0.00I</td>\n",
       "      <td>999.9</td>\n",
       "      <td>000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>127560</td>\n",
       "      <td>99999</td>\n",
       "      <td>20150102</td>\n",
       "      <td>25.6</td>\n",
       "      <td>8.0</td>\n",
       "      <td>23.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1030.7</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1010.9</td>\n",
       "      <td>...</td>\n",
       "      <td>6.0</td>\n",
       "      <td>7.8</td>\n",
       "      <td>999.9</td>\n",
       "      <td>31.8*</td>\n",
       "      <td>21.2</td>\n",
       "      <td>0.02E</td>\n",
       "      <td>999.9</td>\n",
       "      <td>000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>127560</td>\n",
       "      <td>99999</td>\n",
       "      <td>20150103</td>\n",
       "      <td>34.4</td>\n",
       "      <td>8.0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1023.9</td>\n",
       "      <td>8.0</td>\n",
       "      <td>1004.6</td>\n",
       "      <td>...</td>\n",
       "      <td>8.0</td>\n",
       "      <td>15.5</td>\n",
       "      <td>999.9</td>\n",
       "      <td>40.5</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0.00I</td>\n",
       "      <td>999.9</td>\n",
       "      <td>000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>127560</td>\n",
       "      <td>99999</td>\n",
       "      <td>20150104</td>\n",
       "      <td>33.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>30.4</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1016.6</td>\n",
       "      <td>7.0</td>\n",
       "      <td>997.3</td>\n",
       "      <td>...</td>\n",
       "      <td>7.0</td>\n",
       "      <td>13.6</td>\n",
       "      <td>999.9</td>\n",
       "      <td>40.8</td>\n",
       "      <td>28.4*</td>\n",
       "      <td>0.04A</td>\n",
       "      <td>999.9</td>\n",
       "      <td>000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>127560</td>\n",
       "      <td>99999</td>\n",
       "      <td>20150105</td>\n",
       "      <td>27.2</td>\n",
       "      <td>12.0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1022.6</td>\n",
       "      <td>12.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>...</td>\n",
       "      <td>10.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>999.9</td>\n",
       "      <td>37.4</td>\n",
       "      <td>18.3*</td>\n",
       "      <td>0.00G</td>\n",
       "      <td>999.9</td>\n",
       "      <td>000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2015-01-05</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   STN---  WBAN    YEARMODA    TEMP           DEWP    .1    SLP      .2  \\\n",
       "0  127560  99999   20150101    24.2   6.0     16.9   6.0   1036.1   6.0   \n",
       "1  127560  99999   20150102    25.6   8.0     23.9   8.0   1030.7   8.0   \n",
       "2  127560  99999   20150103    34.4   8.0     30.0   8.0   1023.9   8.0   \n",
       "3  127560  99999   20150104    33.6   7.0     30.4   7.0   1016.6   7.0   \n",
       "4  127560  99999   20150105    27.2  12.0     24.0  12.0   1022.6  12.0   \n",
       "\n",
       "     STP    ...    .5   MXSPD    GUST     MAX      MIN    PRCP    SNDP   \\\n",
       "0   1016.1  ...   5.0     7.8   999.9     27.3     20.8*   0.00I  999.9   \n",
       "1   1010.9  ...   6.0     7.8   999.9     31.8*    21.2    0.02E  999.9   \n",
       "2   1004.6  ...   8.0    15.5   999.9     40.5     28.0    0.00I  999.9   \n",
       "3    997.3  ...   7.0    13.6   999.9     40.8     28.4*   0.04A  999.9   \n",
       "4   1003.0  ...  10.0    11.7   999.9     37.4     18.3*   0.00G  999.9   \n",
       "\n",
       "    FRSHTT Unnamed: 22       time  \n",
       "0   000000         NaN 2015-01-01  \n",
       "1   000000         NaN 2015-01-02  \n",
       "2   000000         NaN 2015-01-03  \n",
       "3   000000         NaN 2015-01-04  \n",
       "4   000000         NaN 2015-01-05  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dfs.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x2274de14f88>"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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": [
    "stn=151700\n",
    "d=dfs[dfs['STN---']==stn]\n",
    "d.set_index('time')['   TEMP'].plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "154700\n",
      "154750\n",
      "154770\n",
      "154800\n",
      "154810\n",
      "154820\n",
      "154900\n",
      "154910\n",
      "154931\n",
      "154980\n",
      "154990\n",
      "150090\n",
      "150400\n",
      "150410\n",
      "150520\n",
      "151400\n",
      "152120\n",
      "152410\n",
      "152730\n",
      "152820\n",
      "152980\n",
      "153550\n",
      "154230\n",
      "154290\n",
      "150550\n",
      "150890\n",
      "151320\n",
      "151340\n",
      "151580\n",
      "151740\n",
      "150750\n",
      "152590\n",
      "152870\n",
      "152990\n",
      "153210\n",
      "153560\n",
      "154430\n",
      "150320\n",
      "151590\n",
      "152670\n",
      "153630\n",
      "154760\n",
      "150235\n",
      "151205\n",
      "151455\n",
      "153355\n",
      "154930\n",
      "154470\n",
      "154580\n",
      "154510\n"
     ]
    }
   ],
   "source": [
    "for stn in dfs['STN---'].unique():\n",
    "    d=dfs[dfs['STN---']==stn]\n",
    "    d.to_csv(p+'daily/export/'+str(stn)+'.csv')\n",
    "    print(stn)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Determine most frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'127720',\n",
      "'128050',\n",
      "'128120',\n",
      "'128220',\n",
      "'128250',\n",
      "'128300',\n",
      "'128390',\n",
      "'128430',\n",
      "'128510',\n",
      "'128600',\n",
      "'128820',\n",
      "'128920',\n",
      "'129100',\n",
      "'129150',\n",
      "'150040',\n",
      "'150100',\n",
      "'150140',\n",
      "'150150',\n",
      "'150200',\n",
      "'150230',\n",
      "'150800',\n",
      "'150850',\n",
      "'150900',\n",
      "'151080',\n",
      "'151200',\n",
      "'151450',\n",
      "'151500',\n",
      "'151700',\n",
      "'151970',\n",
      "'152000',\n",
      "'152300',\n",
      "'152350',\n",
      "'152470',\n",
      "'152600',\n",
      "'152800',\n",
      "'152920',\n",
      "'153100',\n",
      "'153350',\n",
      "'153460',\n",
      "'153500',\n",
      "'153600',\n",
      "'154100',\n",
      "'154200',\n",
      "'154210',\n",
      "'154500',\n",
      "'154600',\n",
      "'154700',\n",
      "'154800',\n",
      "'154810',\n",
      "'154990',\n"
     ]
    }
   ],
   "source": [
    "for i in np.sort(dfs.groupby('STN---').count()['time'].sort_values(ascending=False).head(50).index):\n",
    "    print(\"'\"+str(i)+\"',\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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