example/example_python_api.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import Series\n",
    "from ehyd_tools.data_processing import data_validation, data_availability, max_10a, create_statistics, agg_data_figure\n",
    "from ehyd_tools.in_out import get_ehyd_data, import_series, export_series, STATIONS_PRECIPITATION_HIGH_RES\n",
    "from ehyd_tools.sww_utils import span_table\n",
    "from ehyd_tools.in_out import get_station_reference_data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "outputs": [
    {
     "data": {
      "text/plain": "{'100180': 'Tschagguns',\n '100370': 'Thüringen',\n '100446': 'Lustenau',\n '100479': 'Dornbirn',\n '100776': 'Bregenz',\n '101303': 'Leutasch-Kirchplatzl',\n '101816': 'Ladis-Neuegg',\n '102772': 'Kelchsau',\n '103143': 'St. Johann in Tirol-Almdorf',\n '103895': 'Eugendorf',\n '104604': 'Schlägl',\n '104877': 'Linz-Urfahr',\n '105445': 'Vöcklabruck',\n '105528': 'Wels',\n '105908': 'Flachau',\n '106112': 'Liezen',\n '106252': 'Wildalpen',\n '106435': 'Klaus an der Pyhrnbahn',\n '106559': 'Steyr',\n '106856': 'Weitersfelden-Ritzenedt',\n '107029': 'Lunz am See',\n '107284': 'Melk',\n '107854': 'Hollabrunn',\n '108118': 'Wien (Botanischer Garten)',\n '108456': 'Gutenstein',\n '108563': 'Naglern',\n '109280': 'Waidhofen an der Thaya',\n '109918': 'Neunkirchen',\n '110064': 'Gattendorf',\n '110312': 'Karl',\n '110734': 'Eisenstadt',\n '111112': 'Oberwart',\n '111435': 'Alpl',\n '111716': 'Judenburg',\n '112086': 'Graz-Andritz',\n '112391': 'St.Peter am Ottersbach',\n '112995': 'Ried im Innkreis',\n '113001': 'Sillian',\n '113050': 'Matrei in Osttirol',\n '113548': 'Afritz',\n '113670': 'Waidegg',\n '114561': 'Klagenfurt',\n '114702': 'Wolfsberg',\n '115055': 'Kendlbruck',\n '115642': 'St.Pölten',\n '120022': 'Hall in Tirol'}"
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "STATIONS_PRECIPITATION_HIGH_RES"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {
     "name": "#%%\n"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                            Station\n100180                   Tschagguns\n100370                    Thüringen\n100446                     Lustenau\n100479                     Dornbirn\n100776                      Bregenz\n101303         Leutasch-Kirchplatzl\n101816                 Ladis-Neuegg\n102772                     Kelchsau\n103143  St. Johann in Tirol-Almdorf\n103895                    Eugendorf\n104604                      Schlägl\n104877                  Linz-Urfahr\n105445                  Vöcklabruck\n105528                         Wels\n105908                      Flachau\n106112                       Liezen\n106252                    Wildalpen\n106435       Klaus an der Pyhrnbahn\n106559                        Steyr\n106856      Weitersfelden-Ritzenedt\n107029                  Lunz am See\n107284                         Melk\n107854                   Hollabrunn\n108118    Wien (Botanischer Garten)\n108456                   Gutenstein\n108563                      Naglern\n109280       Waidhofen an der Thaya\n109918                  Neunkirchen\n110064                   Gattendorf\n110312                         Karl\n110734                   Eisenstadt\n111112                     Oberwart\n111435                         Alpl\n111716                    Judenburg\n112086                 Graz-Andritz\n112391       St.Peter am Ottersbach\n112995             Ried im Innkreis\n113001                      Sillian\n113050           Matrei in Osttirol\n113548                       Afritz\n113670                      Waidegg\n114561                   Klagenfurt\n114702                    Wolfsberg\n115055                   Kendlbruck\n115642                    St.Pölten\n120022                Hall in Tirol",
      "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>Station</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>100180</th>\n      <td>Tschagguns</td>\n    </tr>\n    <tr>\n      <th>100370</th>\n      <td>Thüringen</td>\n    </tr>\n    <tr>\n      <th>100446</th>\n      <td>Lustenau</td>\n    </tr>\n    <tr>\n      <th>100479</th>\n      <td>Dornbirn</td>\n    </tr>\n    <tr>\n      <th>100776</th>\n      <td>Bregenz</td>\n    </tr>\n    <tr>\n      <th>101303</th>\n      <td>Leutasch-Kirchplatzl</td>\n    </tr>\n    <tr>\n      <th>101816</th>\n      <td>Ladis-Neuegg</td>\n    </tr>\n    <tr>\n      <th>102772</th>\n      <td>Kelchsau</td>\n    </tr>\n    <tr>\n      <th>103143</th>\n      <td>St. Johann in Tirol-Almdorf</td>\n    </tr>\n    <tr>\n      <th>103895</th>\n      <td>Eugendorf</td>\n    </tr>\n    <tr>\n      <th>104604</th>\n      <td>Schlägl</td>\n    </tr>\n    <tr>\n      <th>104877</th>\n      <td>Linz-Urfahr</td>\n    </tr>\n    <tr>\n      <th>105445</th>\n      <td>Vöcklabruck</td>\n    </tr>\n    <tr>\n      <th>105528</th>\n      <td>Wels</td>\n    </tr>\n    <tr>\n      <th>105908</th>\n      <td>Flachau</td>\n    </tr>\n    <tr>\n      <th>106112</th>\n      <td>Liezen</td>\n    </tr>\n    <tr>\n      <th>106252</th>\n      <td>Wildalpen</td>\n    </tr>\n    <tr>\n      <th>106435</th>\n      <td>Klaus an der Pyhrnbahn</td>\n    </tr>\n    <tr>\n      <th>106559</th>\n      <td>Steyr</td>\n    </tr>\n    <tr>\n      <th>106856</th>\n      <td>Weitersfelden-Ritzenedt</td>\n    </tr>\n    <tr>\n      <th>107029</th>\n      <td>Lunz am See</td>\n    </tr>\n    <tr>\n      <th>107284</th>\n      <td>Melk</td>\n    </tr>\n    <tr>\n      <th>107854</th>\n      <td>Hollabrunn</td>\n    </tr>\n    <tr>\n      <th>108118</th>\n      <td>Wien (Botanischer Garten)</td>\n    </tr>\n    <tr>\n      <th>108456</th>\n      <td>Gutenstein</td>\n    </tr>\n    <tr>\n      <th>108563</th>\n      <td>Naglern</td>\n    </tr>\n    <tr>\n      <th>109280</th>\n      <td>Waidhofen an der Thaya</td>\n    </tr>\n    <tr>\n      <th>109918</th>\n      <td>Neunkirchen</td>\n    </tr>\n    <tr>\n      <th>110064</th>\n      <td>Gattendorf</td>\n    </tr>\n    <tr>\n      <th>110312</th>\n      <td>Karl</td>\n    </tr>\n    <tr>\n      <th>110734</th>\n      <td>Eisenstadt</td>\n    </tr>\n    <tr>\n      <th>111112</th>\n      <td>Oberwart</td>\n    </tr>\n    <tr>\n      <th>111435</th>\n      <td>Alpl</td>\n    </tr>\n    <tr>\n      <th>111716</th>\n      <td>Judenburg</td>\n    </tr>\n    <tr>\n      <th>112086</th>\n      <td>Graz-Andritz</td>\n    </tr>\n    <tr>\n      <th>112391</th>\n      <td>St.Peter am Ottersbach</td>\n    </tr>\n    <tr>\n      <th>112995</th>\n      <td>Ried im Innkreis</td>\n    </tr>\n    <tr>\n      <th>113001</th>\n      <td>Sillian</td>\n    </tr>\n    <tr>\n      <th>113050</th>\n      <td>Matrei in Osttirol</td>\n    </tr>\n    <tr>\n      <th>113548</th>\n      <td>Afritz</td>\n    </tr>\n    <tr>\n      <th>113670</th>\n      <td>Waidegg</td>\n    </tr>\n    <tr>\n      <th>114561</th>\n      <td>Klagenfurt</td>\n    </tr>\n    <tr>\n      <th>114702</th>\n      <td>Wolfsberg</td>\n    </tr>\n    <tr>\n      <th>115055</th>\n      <td>Kendlbruck</td>\n    </tr>\n    <tr>\n      <th>115642</th>\n      <td>St.Pölten</td>\n    </tr>\n    <tr>\n      <th>120022</th>\n      <td>Hall in Tirol</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Series(STATIONS_PRECIPITATION_HIGH_RES).rename(index='id').rename('Station').to_frame()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "id_number = 112086"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You choose the id: \"112086\" with the meta-data: {'Messstellen Name': 'Graz-Andritz', 'Jahr': 2017, 'Bundesland': 'Steiermark', 'Flussgebiet': 'Murgebiet', 'Seehöhe': 361}.\n"
     ]
    }
   ],
   "source": [
    "series = get_ehyd_data(id_number)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "series = import_series('ehyd_{}.parquet'.format(id_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Messstelle': 'Graz-Andritz', 'HZB-Nummer': '112086', 'Errichtet': '1946', 'Sachgebiet': 'NLV', 'Dienststelle': 'HD-Steiermark', 'Messstellenbetreiber': 'Hydrographischer Dienst', 'Höhe': [{'gültig seit': '01.01.1946', 'Höhe [m ü.A.]': '360'}, {'gültig seit': '01.01.2011', 'Höhe [m ü.A.]': '361'}], 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)': [{'gültig seit': '01.01.1946', 'Länge (Grad,Min,Sek)': '15 24 46', 'Breite  (Grad,Min,Sek)': '47 06 05'}, {'gültig seit': '01.01.2011', 'Länge (Grad,Min,Sek)': '15 24 57', 'Breite  (Grad,Min,Sek)': '47 06 35'}], 'Messgrößen,-art': [{'Messgrößen,-art': 'Niederschlag-Ombrometer', 'seit': '1946'}, {'Messgrößen,-art': 'Niederschlag-Ombrograph', 'seit': '2007'}, {'Messgrößen,-art': 'Schneehöhe', 'seit': '1946'}, {'Messgrößen,-art': 'Neuschneehöhe', 'seit': '1946'}, {'Messgrößen,-art': 'Temperatur', 'seit': '1947'}]}\n"
     ]
    }
   ],
   "source": [
    "print(get_station_reference_data(id_number))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "(datetime\n 2007-09-17 13:56:00    0.0\n 2007-09-17 13:57:00    0.0\n 2007-09-17 13:58:00    0.0\n 2007-09-17 13:59:00    0.0\n 2007-09-17 14:00:00    0.0\n Freq: T, Name: precipitation, dtype: float64,\n datetime\n 2016-12-31 23:56:00    0.0\n 2016-12-31 23:57:00    0.0\n 2016-12-31 23:58:00    0.0\n 2016-12-31 23:59:00    0.0\n 2017-01-01 00:00:00    NaN\n Freq: T, Name: precipitation, dtype: float64)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series.head(), series.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ehyd_112086.csv'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_series(series, filename='ehyd_{}'.format(id_number), save_as='csv', unix=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'ehyd_112086.parquet'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "export_series(series, filename='ehyd_{}'.format(id_number), save_as='parquet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007-01-01 00:00:00    False\n",
       "2007-01-01 00:01:00    False\n",
       "2007-01-01 00:02:00    False\n",
       "2007-01-01 00:03:00    False\n",
       "2007-01-01 00:04:00    False\n",
       "                       ...  \n",
       "2017-12-31 23:55:00    False\n",
       "2017-12-31 23:56:00    False\n",
       "2017-12-31 23:57:00    False\n",
       "2017-12-31 23:58:00    False\n",
       "2017-12-31 23:59:00    False\n",
       "Freq: T, Length: 5785920, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tags = data_validation(series)\n",
    "availability = data_availability(tags)\n",
    "availability"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(Timestamp('2007-09-17 01:55:00'),\n",
       " Timestamp('2017-09-17 01:55:00', freq='-1T'))"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_10a(availability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "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>start</th>\n",
       "      <th>end</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2007-01-01</td>\n",
       "      <td>2007-09-17 13:55:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-01-01</td>\n",
       "      <td>2017-12-31 23:59:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       start                 end\n",
       "0 2007-01-01 2007-09-17 13:55:00\n",
       "1 2017-01-01 2017-12-31 23:59:00"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "span_table(~availability)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "availability_cut = 0.9\n",
    "stats = create_statistics(series, availability, availability_cut=availability_cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'maximum': (1189.2449999991084,\n",
       "  Timestamp('2009-12-31 00:00:00', freq='A-DEC'),\n",
       "  1.0),\n",
       " 'minimum': (566.2639999999434,\n",
       "  Timestamp('2011-12-31 00:00:00', freq='A-DEC'),\n",
       "  1.0),\n",
       " 'mean': (871.1262222220123, 0.8444382177943822)}"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The annual totals of the data series serve as the data basis.\n",
      "The following statistics were analyzed:\n",
      "Only years with a availability of 90% will be evaluated.\n",
      "\n",
      "The maximum is 1189 mm and was in the year 2009 (with 100% Data available).\n",
      "The minimum is 566 mm and was in the year 2011 (with 100% Data available).\n",
      "The mean is 871 mm (with 84% Data available in average).\n"
     ]
    }
   ],
   "source": [
    "rain_fmt = '{:0.0f} mm'\n",
    "date_fmt = '{:%Y}'\n",
    "avail_fmt = '{:0.0%}'\n",
    "\n",
    "print(\n",
    "    'The annual totals of the data series serve as the data basis.\\n'\n",
    "    'The following statistics were analyzed:\\n'\n",
    "    'Only years with a availability of {avail} will be evaluated.\\n'\n",
    "    '\\n'\n",
    "    'The maximum is {rain} and was in the year {date} (with {avail} Data available).\\n'\n",
    "    'The minimum is {rain} and was in the year {date} (with {avail} Data available).\\n'\n",
    "    'The mean is {rain} (with {avail} Data available in average).'\n",
    "    ''.format(rain=rain_fmt, date=date_fmt, avail=avail_fmt).format(availability_cut,\n",
    "                                                                    *stats['maximum'],\n",
    "                                                                    *stats['minimum'],\n",
    "                                                                    *stats['mean']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 841.89x595.276 with 2 Axes>,\n",
       " <matplotlib.axes._subplots.AxesSubplot at 0x1fa0a3fe970>)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 841.89x595.276 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "agg_data_figure(series, availability)"
   ]
  }
 ],
 "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": "3.8.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}