{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2024-07-02T11:40:36.914661Z",
     "start_time": "2024-07-02T11:40:36.892058Z"
    }
   },
   "outputs": [],
   "source": [
    "from idf_analysis.idf_class import IntensityDurationFrequencyAnalyse\n",
    "from idf_analysis.definitions import *\n",
    "import pandas as pd\n",
    "from os import path\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.style.use('bmh')\n",
    "\n",
    "import matplotlib\n",
    "matplotlib.rcParams.update({\n",
    "    'figure.figsize': (10, 8),\n",
    "    'figure.dpi': 100,\n",
    "    'figure.constrained_layout.use': True,\n",
    "})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    }
   },
   "source": [
    "# Intensity Duration Frequency Analyse - EXTENDED"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_balance_parameter_change\n"
     ]
    }
   ],
   "source": [
    "# sub-folder for the results\n",
    "output_directory = path.join('ehyd_112086_idf_data')\n",
    "\n",
    "# initialize of the analysis class\n",
    "idf = IntensityDurationFrequencyAnalyse(series_kind=SERIES.PARTIAL, worksheet=METHOD.KOSTRA, extended_durations=True)\n",
    "\n",
    "# reading the pandas series of the precipitation (data from ehyd.gv.at - ID=112086)\n",
    "# You need to install `pyarrow` or `fastparquet` to read and write parquet files.\n",
    "series = pd.read_parquet('ehyd_112086.parquet').squeeze('columns') # ['precipitation']\n",
    "\n",
    "# setting the series for the analysis\n",
    "idf.set_series(series)\n",
    "\n",
    "# auto-save the calculated parameter so save time for a later use\n",
    "idf.auto_save_parameters(path.join(output_directory, 'idf_parameters.yaml'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Columns: ',\n",
       " Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n",
       " '| Number of events: ',\n",
       " 1776)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events = idf.rain_events\n",
    "\"Columns: \", events.columns,  \"| Number of events: \", events.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Columns: ',\n",
       " Index(['start', 'end', 'duration', 'rain_sum', 'last_event'], dtype='object'),\n",
       " '| Number of events: ',\n",
       " 311)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reduce number of event by limiting the minimum sum of rainfall\n",
    "events = events[events[COL.LP] > 10].copy()\n",
    "\"Columns: \", events.columns,  \"| Number of events: \", events.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "291e23e444b34c2c9faa6e3938b81b5e",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "calculating rainfall_sum data-frame:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "4e39778f99e242ac885d3e3ee6b725c4",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "calculating return_periods data-frame:   0%|          | 0/21 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "('Columns: ',\n",
       " Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n",
       "        'max_return_period', 'max_return_period_duration'],\n",
       "       dtype='object'),\n",
       " '| Number of events: ',\n",
       " 311)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# add the maximum return period to the events and at which duration this period occurs\n",
    "idf.add_max_return_periods_to_events(events)\n",
    "\"Columns: \", events.columns,  \"| Number of events: \", events.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('Columns: ',\n",
       " Index(['start', 'end', 'duration', 'rain_sum', 'last_event',\n",
       "        'max_return_period', 'max_return_period_duration'],\n",
       "       dtype='object'),\n",
       " '| Number of events: ',\n",
       " 21)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reduce number of event by limiting the minimum return period of an event\n",
    "events = events[events[COL.MAX_PERIOD] > 2].copy()\n",
    "\"Columns: \", events.columns,  \"| Number of events: \", events.index.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "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",
       "      <th>duration</th>\n",
       "      <th>rain_sum</th>\n",
       "      <th>last_event</th>\n",
       "      <th>max_return_period</th>\n",
       "      <th>max_return_period_duration</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>2008-06-23 19:50:00</td>\n",
       "      <td>2008-06-23 23:16:00</td>\n",
       "      <td>0 days 03:26:00</td>\n",
       "      <td>34.8</td>\n",
       "      <td>2 days 21:52:00</td>\n",
       "      <td>5.443256</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>2008-07-17 14:14:00</td>\n",
       "      <td>2008-07-18 06:27:00</td>\n",
       "      <td>0 days 16:13:00</td>\n",
       "      <td>74.6</td>\n",
       "      <td>0 days 04:09:00</td>\n",
       "      <td>6.296477</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>2008-07-20 17:43:00</td>\n",
       "      <td>2008-07-21 03:55:00</td>\n",
       "      <td>0 days 10:12:00</td>\n",
       "      <td>27.7</td>\n",
       "      <td>2 days 02:48:00</td>\n",
       "      <td>2.831020</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>2009-07-18 08:55:00</td>\n",
       "      <td>2009-07-18 13:19:00</td>\n",
       "      <td>0 days 04:24:00</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2 days 01:56:00</td>\n",
       "      <td>3.379566</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>279</th>\n",
       "      <td>2009-08-21 19:49:00</td>\n",
       "      <td>2009-08-21 20:29:00</td>\n",
       "      <td>0 days 00:40:00</td>\n",
       "      <td>34.2</td>\n",
       "      <td>4 days 03:04:00</td>\n",
       "      <td>3.975974</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>282</th>\n",
       "      <td>2009-08-28 23:41:00</td>\n",
       "      <td>2009-08-29 00:42:00</td>\n",
       "      <td>0 days 01:01:00</td>\n",
       "      <td>49.3</td>\n",
       "      <td>1 days 03:44:00</td>\n",
       "      <td>19.719853</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>2009-09-04 00:31:00</td>\n",
       "      <td>2009-09-04 18:18:00</td>\n",
       "      <td>0 days 17:47:00</td>\n",
       "      <td>69.0</td>\n",
       "      <td>5 days 11:06:00</td>\n",
       "      <td>2.605674</td>\n",
       "      <td>1080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>544</th>\n",
       "      <td>2011-08-03 19:53:00</td>\n",
       "      <td>2011-08-04 07:44:00</td>\n",
       "      <td>0 days 11:51:00</td>\n",
       "      <td>54.7</td>\n",
       "      <td>1 days 23:11:00</td>\n",
       "      <td>12.733190</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2012-07-14 16:54:00</td>\n",
       "      <td>2012-07-15 13:06:00</td>\n",
       "      <td>0 days 20:12:00</td>\n",
       "      <td>59.8</td>\n",
       "      <td>1 days 01:15:00</td>\n",
       "      <td>3.719740</td>\n",
       "      <td>8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>772</th>\n",
       "      <td>2013-05-05 20:47:00</td>\n",
       "      <td>2013-05-07 12:40:00</td>\n",
       "      <td>1 days 15:53:00</td>\n",
       "      <td>119.5</td>\n",
       "      <td>0 days 17:31:00</td>\n",
       "      <td>30.573431</td>\n",
       "      <td>2880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>819</th>\n",
       "      <td>2013-08-27 17:10:00</td>\n",
       "      <td>2013-08-27 23:48:00</td>\n",
       "      <td>0 days 06:38:00</td>\n",
       "      <td>43.3</td>\n",
       "      <td>0 days 13:58:00</td>\n",
       "      <td>2.617407</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>2013-08-28 11:42:00</td>\n",
       "      <td>2013-08-28 16:21:00</td>\n",
       "      <td>0 days 04:39:00</td>\n",
       "      <td>28.5</td>\n",
       "      <td>0 days 11:54:00</td>\n",
       "      <td>3.542479</td>\n",
       "      <td>2880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>921</th>\n",
       "      <td>2014-05-11 10:21:00</td>\n",
       "      <td>2014-05-11 23:17:00</td>\n",
       "      <td>0 days 12:56:00</td>\n",
       "      <td>55.6</td>\n",
       "      <td>0 days 13:17:00</td>\n",
       "      <td>2.082202</td>\n",
       "      <td>2880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>922</th>\n",
       "      <td>2014-05-12 17:22:00</td>\n",
       "      <td>2014-05-13 17:26:00</td>\n",
       "      <td>1 days 00:04:00</td>\n",
       "      <td>20.6</td>\n",
       "      <td>0 days 18:05:00</td>\n",
       "      <td>2.797981</td>\n",
       "      <td>4320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1111</th>\n",
       "      <td>2015-07-08 11:50:00</td>\n",
       "      <td>2015-07-09 00:25:00</td>\n",
       "      <td>0 days 12:35:00</td>\n",
       "      <td>77.8</td>\n",
       "      <td>8 days 13:53:00</td>\n",
       "      <td>5.981488</td>\n",
       "      <td>720</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2016-06-05 11:03:00</td>\n",
       "      <td>2016-06-05 14:56:00</td>\n",
       "      <td>0 days 03:53:00</td>\n",
       "      <td>40.4</td>\n",
       "      <td>0 days 19:56:00</td>\n",
       "      <td>6.794841</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1269</th>\n",
       "      <td>2016-07-21 22:29:00</td>\n",
       "      <td>2016-07-21 23:21:00</td>\n",
       "      <td>0 days 00:52:00</td>\n",
       "      <td>37.2</td>\n",
       "      <td>5 days 00:05:00</td>\n",
       "      <td>2.448124</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1273</th>\n",
       "      <td>2016-07-25 10:18:00</td>\n",
       "      <td>2016-07-25 15:47:00</td>\n",
       "      <td>0 days 05:29:00</td>\n",
       "      <td>33.8</td>\n",
       "      <td>0 days 19:01:00</td>\n",
       "      <td>3.399509</td>\n",
       "      <td>5760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1275</th>\n",
       "      <td>2016-07-27 15:25:00</td>\n",
       "      <td>2016-07-27 20:24:00</td>\n",
       "      <td>0 days 04:59:00</td>\n",
       "      <td>12.2</td>\n",
       "      <td>1 days 01:05:00</td>\n",
       "      <td>4.663455</td>\n",
       "      <td>8640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1531</th>\n",
       "      <td>2018-06-12 17:00:00</td>\n",
       "      <td>2018-06-12 17:31:00</td>\n",
       "      <td>0 days 00:31:00</td>\n",
       "      <td>21.6</td>\n",
       "      <td>3 days 16:11:00</td>\n",
       "      <td>3.070451</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1532</th>\n",
       "      <td>2018-06-13 14:58:00</td>\n",
       "      <td>2018-06-13 22:27:00</td>\n",
       "      <td>0 days 07:29:00</td>\n",
       "      <td>63.5</td>\n",
       "      <td>0 days 21:27:00</td>\n",
       "      <td>11.429391</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   start                 end        duration  rain_sum  \\\n",
       "104  2008-06-23 19:50:00 2008-06-23 23:16:00 0 days 03:26:00      34.8   \n",
       "124  2008-07-17 14:14:00 2008-07-18 06:27:00 0 days 16:13:00      74.6   \n",
       "126  2008-07-20 17:43:00 2008-07-21 03:55:00 0 days 10:12:00      27.7   \n",
       "271  2009-07-18 08:55:00 2009-07-18 13:19:00 0 days 04:24:00      58.0   \n",
       "279  2009-08-21 19:49:00 2009-08-21 20:29:00 0 days 00:40:00      34.2   \n",
       "282  2009-08-28 23:41:00 2009-08-29 00:42:00 0 days 01:01:00      49.3   \n",
       "284  2009-09-04 00:31:00 2009-09-04 18:18:00 0 days 17:47:00      69.0   \n",
       "544  2011-08-03 19:53:00 2011-08-04 07:44:00 0 days 11:51:00      54.7   \n",
       "662  2012-07-14 16:54:00 2012-07-15 13:06:00 0 days 20:12:00      59.8   \n",
       "772  2013-05-05 20:47:00 2013-05-07 12:40:00 1 days 15:53:00     119.5   \n",
       "819  2013-08-27 17:10:00 2013-08-27 23:48:00 0 days 06:38:00      43.3   \n",
       "820  2013-08-28 11:42:00 2013-08-28 16:21:00 0 days 04:39:00      28.5   \n",
       "921  2014-05-11 10:21:00 2014-05-11 23:17:00 0 days 12:56:00      55.6   \n",
       "922  2014-05-12 17:22:00 2014-05-13 17:26:00 1 days 00:04:00      20.6   \n",
       "1111 2015-07-08 11:50:00 2015-07-09 00:25:00 0 days 12:35:00      77.8   \n",
       "1236 2016-06-05 11:03:00 2016-06-05 14:56:00 0 days 03:53:00      40.4   \n",
       "1269 2016-07-21 22:29:00 2016-07-21 23:21:00 0 days 00:52:00      37.2   \n",
       "1273 2016-07-25 10:18:00 2016-07-25 15:47:00 0 days 05:29:00      33.8   \n",
       "1275 2016-07-27 15:25:00 2016-07-27 20:24:00 0 days 04:59:00      12.2   \n",
       "1531 2018-06-12 17:00:00 2018-06-12 17:31:00 0 days 00:31:00      21.6   \n",
       "1532 2018-06-13 14:58:00 2018-06-13 22:27:00 0 days 07:29:00      63.5   \n",
       "\n",
       "          last_event  max_return_period  max_return_period_duration  \n",
       "104  2 days 21:52:00           5.443256                          15  \n",
       "124  0 days 04:09:00           6.296477                         120  \n",
       "126  2 days 02:48:00           2.831020                        5760  \n",
       "271  2 days 01:56:00           3.379566                         240  \n",
       "279  4 days 03:04:00           3.975974                          20  \n",
       "282  1 days 03:44:00          19.719853                          20  \n",
       "284  5 days 11:06:00           2.605674                        1080  \n",
       "544  1 days 23:11:00          12.733190                          20  \n",
       "662  1 days 01:15:00           3.719740                        8640  \n",
       "772  0 days 17:31:00          30.573431                        2880  \n",
       "819  0 days 13:58:00           2.617407                           5  \n",
       "820  0 days 11:54:00           3.542479                        2880  \n",
       "921  0 days 13:17:00           2.082202                        2880  \n",
       "922  0 days 18:05:00           2.797981                        4320  \n",
       "1111 8 days 13:53:00           5.981488                         720  \n",
       "1236 0 days 19:56:00           6.794841                           5  \n",
       "1269 5 days 00:05:00           2.448124                          30  \n",
       "1273 0 days 19:01:00           3.399509                        5760  \n",
       "1275 1 days 01:05:00           4.663455                        8640  \n",
       "1531 3 days 16:11:00           3.070451                           5  \n",
       "1532 0 days 21:27:00          11.429391                          15  "
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
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    "pycharm": {
     "name": "#%%\n"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "start                         2008-07-17 14:14:00\n",
       "end                           2008-07-18 06:27:00\n",
       "duration                          0 days 16:13:00\n",
       "rain_sum                                     74.6\n",
       "last_event                        0 days 04:09:00\n",
       "max_return_period                        6.296477\n",
       "max_return_period_duration                    120\n",
       "Name: 124, dtype: object"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets pick one event\n",
    "event = events.loc[124]\n",
    "event"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, caption = idf.event_plot(event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
    },
    "pycharm": {
     "name": "#%%\n"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1200x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# you can also reduce the displayed duration steps\n",
    "fig, caption = idf.event_plot(event, durations=idf.duration_steps[:11])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.11.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}