diff --git a/ehyd_tools/data_processing.py b/ehyd_tools/data_processing.py
index b6f2bff..62ed43a 100644
--- a/ehyd_tools/data_processing.py
+++ b/ehyd_tools/data_processing.py
@@ -5,10 +5,10 @@
 __version__ = "1.0.0"
 __maintainer__ = "David Camhy, Markus Pichler"
 
-from pandas import Timedelta, DataFrame, isna, Series, DateOffset
-from numpy import NaN
 from warnings import warn
-from matplotlib.pyplot import subplots
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
 
 
 class EhydWarning(UserWarning): pass
@@ -24,7 +24,7 @@ def year_delta(years):
     Returns:
         pandas.Timedelta: time period
     """
-    return Timedelta(days=365.25) * years
+    return pd.Timedelta(days=365.25) * years
 
 
 def data_validation(series):
@@ -41,23 +41,23 @@ def data_validation(series):
 
     first_index = ts.index[0].replace(day=1, month=1, hour=0, minute=0)
     if first_index not in series.index:
-        ts = Series(index=[first_index]).append(ts)
+        ts = pd.concat([pd.Series(index=[first_index], data=[np.NaN]), ts])
 
     last_index = ts.index[-1].replace(day=31, month=12, hour=23, minute=59)
     if last_index not in ts.index:
-        ts = ts.append(Series(index=[last_index]))
+        ts = pd.concat([ts, pd.Series(index=[last_index], data=[np.NaN])])
 
     if ts.index.has_duplicates:  # very slow an large data sets
         ts = ts[~ts.index.duplicated()].copy()
 
     if not ts.index.is_monotonic_increasing:
         raise UserWarning('Series has not monotonic increasing of the timestamps.')
-        ts = ts.sort_index()
+        # ts = ts.sort_index()
 
-    tags = DataFrame(index=ts.index)
-    tags['nans'] = isna(ts).astype(int)
+    tags = pd.DataFrame(index=ts.index)
+    tags['nans'] = pd.isna(ts).astype(int)
     tags = tags.reindex(tags.asfreq('min').index)
-    tags['gaps'] = isna(ts.fillna(0).reindex(tags.index)).astype(int)
+    tags['gaps'] = pd.isna(ts.fillna(0).reindex(tags.index)).astype(int)
     return tags
 
 
@@ -90,7 +90,7 @@ def max_10a(availability):
     avail_sum = availability.rolling(window).sum()
 
     end = avail_sum[::-1].idxmax()
-    start = end - DateOffset(years=10)
+    start = end - pd.DateOffset(years=10)
     return start, end
 
 
@@ -105,7 +105,7 @@ def check_period(series):
         series (pandas.Series): time-series
     """
     if not is_longer(series, years=10):
-        years = (series.index[-1] - series.index[0]) / Timedelta(days=365)
+        years = (series.index[-1] - series.index[0]) / pd.Timedelta(days=365)
         warn(f'The Series is only {years:.1f} < 10 years long!', EhydWarning)
 
 
@@ -120,7 +120,7 @@ def is_longer(series, years):
     Returns:
         bool: whether the series is longer
     """
-    return series.index[0] + DateOffset(years=years) <= series.index[-1]
+    return series.index[0] + pd.DateOffset(years=years) <= series.index[-1]
     # return (series.index[-1] - series.index[0]) > year_delta(years=years)
 
 
@@ -140,16 +140,16 @@ def agg_data_figure(series, availability, agg='sum', freq=None, add_mean_line=Fa
     """
 
     freq_long = {
-        'Y': 'Jahr',
-        'M': 'Monat'
+        'YE': 'Jahr',
+        'ME': 'Monat'
     }
 
     if freq is None:
         if is_longer(series, years=15):
-            freq = 'Y'
+            freq = 'YE'
             # base_delta = year_delta(1)
         else:
-            freq = 'M'
+            freq = 'ME'
             # base_delta = year_delta(1) / 12
 
     ts_agg = series.resample(freq).agg(agg)
@@ -169,7 +169,7 @@ def agg_data_figure(series, availability, agg='sum', freq=None, add_mean_line=Fa
 
     height_ratios = [1, 10]
 
-    fig, (ax1, ax) = subplots(2, gridspec_kw=dict(height_ratios=height_ratios), sharex=True, layout='constrained')
+    fig, (ax1, ax) = plt.subplots(2, gridspec_kw=dict(height_ratios=height_ratios), sharex=True, layout='constrained')
 
     # ax = dummy.plot(ax=ax, lw=0)
     fig.set_size_inches(w=29.7 / 2.54, h=21. / 2.54)
@@ -180,7 +180,7 @@ def agg_data_figure(series, availability, agg='sum', freq=None, add_mean_line=Fa
     ax1.grid(axis='y', which='minor', color='lightgrey', linestyle=':', linewidth=0.5)  # , zorder=-10000000)
     # ax1 = dummy.plot(ax=ax1, lw=0)
     # ax1.scatter(x=index, y=avail.values * 100, color='grey', clip_on=False, marker='_')
-    ax1.bar(x=index, height=avail.values * 100, color='grey')
+    ax1.bar(x=index, height=avail.values * 100, color='grey', lw=.1, edgecolor='white', width=1/12)
     ax1.set_axisbelow(True)
 
     if add_mean_line:
@@ -189,12 +189,12 @@ def agg_data_figure(series, availability, agg='sum', freq=None, add_mean_line=Fa
                 bbox={'facecolor': 'white', 'alpha': 0.5, 'pad': 2, 'linewidth': '0'})
         ax.axhline(mean, ls='--', color='darkgray', linewidth=0.7)
 
-    ax.bar(x=index, height=ts_agg.values, color='black')
+    ax.bar(x=index, height=ts_agg.values, color='black', lw=0.1, edgecolor='white', width=1/12)
     ax.set_ylabel('Niederschlag (mm/{})'.format(freq_long[freq]))
     ax.set_xlabel('Zeit')
     # ax.set_xlim(left=ax.get_xlim()[0] - 0.5)
     # ax.set_xlim(right=ax.get_xlim()[1] + 0.5)
-    fig.subplots_adjust(hspace=0)
+    # fig.subplots_adjust(hspace=0)
     return fig, ax
 
 
@@ -226,21 +226,21 @@ def create_statistics(series, availability, availability_cut=0.2):
     Returns:
         dict: of the yearly statistics
     """
-    sums = series.resample('Y').sum()
-    avail_sum = availability.resample('Y').sum()
+    sums = series.resample('YE').sum()
+    avail_sum = availability.resample('YE').sum()
 
     base_freq = series.index.freq
     yearly_index = avail_sum.index
-    delta_per_year = yearly_index - (yearly_index - DateOffset(years=1))
+    delta_per_year = yearly_index - (yearly_index - pd.DateOffset(years=1))
     avail_full = delta_per_year / base_freq
     avail = avail_sum / avail_full  # type: Series
     if (avail < availability_cut).all():
         warn('ATTENTION: only very small data availability! The statistic may be not very meaningful.', EhydWarning)
         if (avail < 0.1).all():
             return {}
-        sums[avail < 0.1] = NaN
+        sums[avail < 0.1] = np.NaN
     else:
-        sums[avail < availability_cut] = NaN
+        sums[avail < availability_cut] = np.NaN
 
     stats = {}
     stats['maximum'] = sums.max()
diff --git a/ehyd_tools/sww_utils.py b/ehyd_tools/sww_utils.py
index ea83b4e..3faa4ac 100644
--- a/ehyd_tools/sww_utils.py
+++ b/ehyd_tools/sww_utils.py
@@ -45,7 +45,7 @@ def span_table(span_bool, min_span=None):
     # first value in diff will default to NaN
     # fill value is set to double the value of the greater than operation = fixed true value
     start_bool = temp.diff().gt(min_span, fill_value=min_span * 2)
-    end_bool = start_bool.shift(-1).fillna(True)
+    end_bool = start_bool.shift(-1, fill_value=True)
 
     events = pd.DataFrame()
 
diff --git a/ehyd_tools/synthetic_rainseries_v0.py b/ehyd_tools/synthetic_rainseries_v0.py
index cb74a5f..fd0d545 100644
--- a/ehyd_tools/synthetic_rainseries_v0.py
+++ b/ehyd_tools/synthetic_rainseries_v0.py
@@ -8,7 +8,7 @@
 
 def block_rain(idf_table, return_period, duration, interval=5):
     if duration not in idf_table.index:
-        idf_table = idf_table.append(pd.Series(index=idf_table.columns, name=duration)).sort_index()
+        idf_table = pd.concat([idf_table, pd.Series(index=idf_table.columns, name=duration)], sort=True)
 
     if isinstance(return_period, float):
         idf_table.columns = idf_table.columns.astype(float)
@@ -23,8 +23,7 @@ def block_rain(idf_table, return_period, duration, interval=5):
     index = range(interval, duration + interval, interval)
     intensity = height / len(index)
     r = pd.Series(index=index, data=intensity)
-
-    r = r.append(pd.Series({0: 0})).sort_index()
+    r = pd.concat([r, pd.Series({0: 0})]).sort_index()
     return r
 
 
@@ -49,7 +48,7 @@ def euler_model_rain(idf_table, return_period, duration, interval=5, occurrence_
     r.loc[:max_index] = r.loc[max_index::-1].values
 
     # add Zero value at first posiotion (for SWMM ?)
-    r = r.append(pd.Series({0: 0})).sort_index()
+    r = pd.concat([r, pd.Series({0: 0})]).sort_index()
 
     # ---------------
     if 0:
diff --git a/example/example_design_rainfall.ipynb b/example/example_design_rainfall.ipynb
index 4c351c4..3be1baa 100644
--- a/example/example_design_rainfall.ipynb
+++ b/example/example_design_rainfall.ipynb
@@ -2,11 +2,13 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.212653Z",
+     "start_time": "2024-06-03T07:49:19.561180Z"
+    }
    },
-   "outputs": [],
    "source": [
     "from ehyd_tools.design_rainfall import (get_rainfall_height, get_calculation_method, read_ehyd_design_rainfall,\n",
     "                                        save_ehyd_design_rainfall_pdf, get_ehyd_design_rainfall_file,\n",
@@ -15,12 +17,12 @@
     "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
     "import pandas as pd"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [],
    "source": [
     "grid_point_number=5214"
    ],
@@ -28,22 +30,17 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.215668Z",
+     "start_time": "2024-06-03T07:49:20.213616Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<_io.TextIOWrapper encoding='ISO-8859-1'>"
-     },
-     "execution_count": 18,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "file = get_ehyd_design_rainfall_file(grid_point_number)\n",
     "file"
@@ -52,13 +49,28 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.477195Z",
+     "start_time": "2024-06-03T07:49:20.216358Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<_io.StringIO at 0x107d4dbd0>"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-   }
+   ],
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "outputs": [],
    "source": [
     "# save_ehyd_design_rainfall_pdf(grid_point_number, fn='new.pdf')"
    ],
@@ -66,23 +78,17 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.480659Z",
+     "start_time": "2024-06-03T07:49:20.478397Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "return period                1     2     3     5     10    20    25    30   \\\nduration calculation method                                                  \n5        MaxModN             8.8  10.4  11.7  13.4  15.8  18.2  18.9  19.6   \n         Bemessung           8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0   \n         ÖKOSTRA             8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9   \n\nreturn period                 50    75    100  \nduration calculation method                    \n5        MaxModN             21.3  22.7  23.7  \n         Bemessung           19.4  20.6  21.4  \n         ÖKOSTRA             17.0  17.9  18.5  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th>calculation method</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">5</th>\n      <th>MaxModN</th>\n      <td>8.8</td>\n      <td>10.4</td>\n      <td>11.7</td>\n      <td>13.4</td>\n      <td>15.8</td>\n      <td>18.2</td>\n      <td>18.9</td>\n      <td>19.6</td>\n      <td>21.3</td>\n      <td>22.7</td>\n      <td>23.7</td>\n    </tr>\n    <tr>\n      <th>Bemessung</th>\n      <td>8.6</td>\n      <td>10.2</td>\n      <td>11.3</td>\n      <td>12.7</td>\n      <td>14.7</td>\n      <td>16.7</td>\n      <td>17.4</td>\n      <td>18.0</td>\n      <td>19.4</td>\n      <td>20.6</td>\n      <td>21.4</td>\n    </tr>\n    <tr>\n      <th>ÖKOSTRA</th>\n      <td>8.4</td>\n      <td>9.9</td>\n      <td>10.8</td>\n      <td>11.9</td>\n      <td>13.4</td>\n      <td>14.9</td>\n      <td>15.5</td>\n      <td>15.9</td>\n      <td>17.0</td>\n      <td>17.9</td>\n      <td>18.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
-     },
-     "execution_count": 19,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "df = read_ehyd_design_rainfall(file)\n",
     "df.head(3)"
@@ -91,23 +97,134 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.495990Z",
+     "start_time": "2024-06-03T07:49:20.482859Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "return period                1     2     3     5     10    20    25    30   \\\nduration calculation method                                                  \n5        MaxModN             8.8  10.4  11.7  13.4  15.8  18.2  18.9  19.6   \n         Bemessung           8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0   \n         ÖKOSTRA             8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9   \n\nreturn period                 50    75    100  \nduration calculation method                    \n5        MaxModN             21.3  22.7  23.7  \n         Bemessung           19.4  20.6  21.4  \n         ÖKOSTRA             17.0  17.9  18.5  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th>calculation method</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th rowspan=\"3\" valign=\"top\">5</th>\n      <th>MaxModN</th>\n      <td>8.8</td>\n      <td>10.4</td>\n      <td>11.7</td>\n      <td>13.4</td>\n      <td>15.8</td>\n      <td>18.2</td>\n      <td>18.9</td>\n      <td>19.6</td>\n      <td>21.3</td>\n      <td>22.7</td>\n      <td>23.7</td>\n    </tr>\n    <tr>\n      <th>Bemessung</th>\n      <td>8.6</td>\n      <td>10.2</td>\n      <td>11.3</td>\n      <td>12.7</td>\n      <td>14.7</td>\n      <td>16.7</td>\n      <td>17.4</td>\n      <td>18.0</td>\n      <td>19.4</td>\n      <td>20.6</td>\n      <td>21.4</td>\n    </tr>\n    <tr>\n      <th>ÖKOSTRA</th>\n      <td>8.4</td>\n      <td>9.9</td>\n      <td>10.8</td>\n      <td>11.9</td>\n      <td>13.4</td>\n      <td>14.9</td>\n      <td>15.5</td>\n      <td>15.9</td>\n      <td>17.0</td>\n      <td>17.9</td>\n      <td>18.5</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "return period                1     2     3     5     10    20    25    30   \\\n",
+       "duration calculation method                                                  \n",
+       "5        MaxModN             8.8  10.4  11.7  13.4  15.8  18.2  18.9  19.6   \n",
+       "         Bemessung           8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0   \n",
+       "         ÖKOSTRA             8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9   \n",
+       "\n",
+       "return period                 50    75    100  \n",
+       "duration calculation method                    \n",
+       "5        MaxModN             21.3  22.7  23.7  \n",
+       "         Bemessung           19.4  20.6  21.4  \n",
+       "         ÖKOSTRA             17.0  17.9  18.5  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th>calculation method</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
+       "      <th>MaxModN</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>18.9</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>23.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bemessung</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ÖKOSTRA</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 20,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
    "source": [
     "df = get_ehyd_design_rainfall(grid_point_number)\n",
     "df.head(3)\n"
@@ -116,23 +233,134 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.505636Z",
+     "start_time": "2024-06-03T07:49:20.496757Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "return period   1     2     3     5     10    20    25    30    50    75   \\\nduration                                                                    \n5               8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9  17.0  17.9   \n10             13.4  15.9  17.3  19.2  21.7  24.3  24.9  25.6  27.5  29.1   \n15             16.8  20.0  21.9  24.3  27.6  30.7  31.8  32.7  35.0  37.0   \n\nreturn period   100  \nduration             \n5              18.5  \n10             30.1  \n15             38.4  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>8.4</td>\n      <td>9.9</td>\n      <td>10.8</td>\n      <td>11.9</td>\n      <td>13.4</td>\n      <td>14.9</td>\n      <td>15.5</td>\n      <td>15.9</td>\n      <td>17.0</td>\n      <td>17.9</td>\n      <td>18.5</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.4</td>\n      <td>15.9</td>\n      <td>17.3</td>\n      <td>19.2</td>\n      <td>21.7</td>\n      <td>24.3</td>\n      <td>24.9</td>\n      <td>25.6</td>\n      <td>27.5</td>\n      <td>29.1</td>\n      <td>30.1</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>16.8</td>\n      <td>20.0</td>\n      <td>21.9</td>\n      <td>24.3</td>\n      <td>27.6</td>\n      <td>30.7</td>\n      <td>31.8</td>\n      <td>32.7</td>\n      <td>35.0</td>\n      <td>37.0</td>\n      <td>38.4</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "return period                1     2     3     5     10    20    25    30   \\\n",
+       "duration calculation method                                                  \n",
+       "5        MaxModN             8.8  10.4  11.7  13.4  15.8  18.2  18.9  19.6   \n",
+       "         Bemessung           8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0   \n",
+       "         ÖKOSTRA             8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9   \n",
+       "\n",
+       "return period                 50    75    100  \n",
+       "duration calculation method                    \n",
+       "5        MaxModN             21.3  22.7  23.7  \n",
+       "         Bemessung           19.4  20.6  21.4  \n",
+       "         ÖKOSTRA             17.0  17.9  18.5  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th>calculation method</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th rowspan=\"3\" valign=\"top\">5</th>\n",
+       "      <th>MaxModN</th>\n",
+       "      <td>8.8</td>\n",
+       "      <td>10.4</td>\n",
+       "      <td>11.7</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>15.8</td>\n",
+       "      <td>18.2</td>\n",
+       "      <td>18.9</td>\n",
+       "      <td>19.6</td>\n",
+       "      <td>21.3</td>\n",
+       "      <td>22.7</td>\n",
+       "      <td>23.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Bemessung</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ÖKOSTRA</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 21,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "code",
    "source": [
     "get_calculation_method(df, method='ÖKOSTRA').head(3)"
    ],
@@ -140,23 +368,131 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.512361Z",
+     "start_time": "2024-06-03T07:49:20.506462Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "return period   1     2     3     5     10    20    25    30    50    75   \\\nduration                                                                    \n5               8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0  19.4  20.6   \n10             13.7  16.3  17.8  19.7  23.3  26.8  27.8  28.8  31.4  33.5   \n15             17.2  20.5  22.4  24.9  29.1  33.5  35.0  36.2  39.5  42.2   \n\nreturn period   100  \nduration             \n5              21.4  \n10             35.0  \n15             44.1  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>8.6</td>\n      <td>10.2</td>\n      <td>11.3</td>\n      <td>12.7</td>\n      <td>14.7</td>\n      <td>16.7</td>\n      <td>17.4</td>\n      <td>18.0</td>\n      <td>19.4</td>\n      <td>20.6</td>\n      <td>21.4</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.7</td>\n      <td>16.3</td>\n      <td>17.8</td>\n      <td>19.7</td>\n      <td>23.3</td>\n      <td>26.8</td>\n      <td>27.8</td>\n      <td>28.8</td>\n      <td>31.4</td>\n      <td>33.5</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>17.2</td>\n      <td>20.5</td>\n      <td>22.4</td>\n      <td>24.9</td>\n      <td>29.1</td>\n      <td>33.5</td>\n      <td>35.0</td>\n      <td>36.2</td>\n      <td>39.5</td>\n      <td>42.2</td>\n      <td>44.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "return period   1     2     3     5     10    20    25    30    50    75   \\\n",
+       "duration                                                                    \n",
+       "5               8.4   9.9  10.8  11.9  13.4  14.9  15.5  15.9  17.0  17.9   \n",
+       "10             13.4  15.9  17.3  19.2  21.7  24.3  24.9  25.6  27.5  29.1   \n",
+       "15             16.8  20.0  21.9  24.3  27.6  30.7  31.8  32.7  35.0  37.0   \n",
+       "\n",
+       "return period   100  \n",
+       "duration             \n",
+       "5              18.5  \n",
+       "10             30.1  \n",
+       "15             38.4  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.4</td>\n",
+       "      <td>9.9</td>\n",
+       "      <td>10.8</td>\n",
+       "      <td>11.9</td>\n",
+       "      <td>13.4</td>\n",
+       "      <td>14.9</td>\n",
+       "      <td>15.5</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>17.0</td>\n",
+       "      <td>17.9</td>\n",
+       "      <td>18.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13.4</td>\n",
+       "      <td>15.9</td>\n",
+       "      <td>17.3</td>\n",
+       "      <td>19.2</td>\n",
+       "      <td>21.7</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>25.6</td>\n",
+       "      <td>27.5</td>\n",
+       "      <td>29.1</td>\n",
+       "      <td>30.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>16.8</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>21.9</td>\n",
+       "      <td>24.3</td>\n",
+       "      <td>27.6</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>31.8</td>\n",
+       "      <td>32.7</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>37.0</td>\n",
+       "      <td>38.4</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 22,
+     "execution_count": 7,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 7
+  },
+  {
+   "cell_type": "code",
    "source": [
     "get_calculation_method(df, method='Bemessung').head(3)"
    ],
@@ -164,23 +500,131 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.519348Z",
+     "start_time": "2024-06-03T07:49:20.513428Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 24,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "return period   1     2     3     5     10    20    25    30    50    75   \\\nduration                                                                    \n5               8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0  19.4  20.6   \n10             13.7  16.3  17.8  19.7  23.3  26.8  27.8  28.8  31.4  33.5   \n15             17.2  20.5  22.4  24.9  29.1  33.5  35.0  36.2  39.5  42.2   \n\nreturn period   100  \nduration             \n5              21.4  \n10             35.0  \n15             44.1  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>8.6</td>\n      <td>10.2</td>\n      <td>11.3</td>\n      <td>12.7</td>\n      <td>14.7</td>\n      <td>16.7</td>\n      <td>17.4</td>\n      <td>18.0</td>\n      <td>19.4</td>\n      <td>20.6</td>\n      <td>21.4</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.7</td>\n      <td>16.3</td>\n      <td>17.8</td>\n      <td>19.7</td>\n      <td>23.3</td>\n      <td>26.8</td>\n      <td>27.8</td>\n      <td>28.8</td>\n      <td>31.4</td>\n      <td>33.5</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>17.2</td>\n      <td>20.5</td>\n      <td>22.4</td>\n      <td>24.9</td>\n      <td>29.1</td>\n      <td>33.5</td>\n      <td>35.0</td>\n      <td>36.2</td>\n      <td>39.5</td>\n      <td>42.2</td>\n      <td>44.1</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "return period   1     2     3     5     10    20    25    30    50    75   \\\n",
+       "duration                                                                    \n",
+       "5               8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0  19.4  20.6   \n",
+       "10             13.7  16.3  17.8  19.7  23.3  26.8  27.8  28.8  31.4  33.5   \n",
+       "15             17.2  20.5  22.4  24.9  29.1  33.5  35.0  36.2  39.5  42.2   \n",
+       "\n",
+       "return period   100  \n",
+       "duration             \n",
+       "5              21.4  \n",
+       "10             35.0  \n",
+       "15             44.1  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13.7</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>27.8</td>\n",
+       "      <td>28.8</td>\n",
+       "      <td>31.4</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>29.1</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>39.5</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>44.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 24,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
    "source": [
     "rain_height = get_max_calculation_method(df)\n",
     "rain_height.head(3)\n"
@@ -189,22 +633,131 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.527670Z",
+     "start_time": "2024-06-03T07:49:20.520225Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "73.6"
+      "text/plain": [
+       "return period   1     2     3     5     10    20    25    30    50    75   \\\n",
+       "duration                                                                    \n",
+       "5               8.6  10.2  11.3  12.7  14.7  16.7  17.4  18.0  19.4  20.6   \n",
+       "10             13.7  16.3  17.8  19.7  23.3  26.8  27.8  28.8  31.4  33.5   \n",
+       "15             17.2  20.5  22.4  24.9  29.1  33.5  35.0  36.2  39.5  42.2   \n",
+       "\n",
+       "return period   100  \n",
+       "duration             \n",
+       "5              21.4  \n",
+       "10             35.0  \n",
+       "15             44.1  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13.7</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>27.8</td>\n",
+       "      <td>28.8</td>\n",
+       "      <td>31.4</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>29.1</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>39.5</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>44.1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 23,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "code",
    "source": [
     "get_rainfall_height(rain_height, return_period=5, duration=720)\n"
    ],
@@ -212,26 +765,30 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:20.911948Z",
+     "start_time": "2024-06-03T07:49:20.528427Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 25,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "<Figure size 504x504 with 2 Axes>",
-      "image/png": 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ENqF1g38EvCmE+Jvn2HO+aoOiKEdX3+Jgc0Ft28SyzYW1bdtXhvgbGJMcxvzMWMYmh5GVZCE0oP/+MVNOTmazOdtqtW5asWJF8L333hsfHh7uyMnJCcjMzLR+8MEHB3S6Ixfguf322+NWrlxpsdlsuvHjxze+9tpr+a3nv/HGG2G33XZbSkNDg/7JJ5/MmzdvXuNjjz0W8cEHH4RZrVady+USq1at2nvdddcl7969O8DpdIo//vGPB6+44oraXvrqR+XTdd5Syr8Cfz3s8H5goi8/V1GUztxuyb6KRk9WrQXrveWNSAlCwNDoYM7OjCU7KYyxKRYGR6qsWulfdu3aFbB58+b9qampjnHjxmV8/vnnQXPnzm080vl33HFH+YMPPlgCcN555w168803Qy+77LI6AKfTKbZt27brrbfeCr333nvj582btwdgx44d5q1bt+6IiYlx/fznP0+YMWNG/TvvvJNXWVmpHz9+/PBzzjmn/qRe560oSt+qa3awufDQmurNBTXUezblCA0wkp1sYeHoeLKTLWQlWQjxV1n1ScvlhIaDUFsANfnaffvbko/BktTXrTymzMzMpiFDhjgARo4cad23b99Rly188sknwQ8//HBsS0uLrra21jBixIhmoA5g8eLFNQBTpkxpuuOOO9quc/rpp9fHxMS4AL7++uuQTz/91PLYY4/FAthsNpGbm2saO3bsUdeT9wYVvBXlJOB2S3IrGtmYX9OWWe8t1xISIWBYTDALRsczNtnC2JQwBkUEqqz6ZOJ2Qf3BzkG5Nl+71RWDdLV7g4CQeLAkQ8ppaEUw+z8/P7+2hur1epxO5xH/I7ZareK3v/1tyo8//rgzLS3NsXTp0viWlpa2PnZ/f38JYDAYcLlcbdcxm81tWbWUknfffTc3Kyur416x/YAK3ooyANVZHWwqPLSmenNBLQ02Lau2mI1kJ1k4JyuesSnaWHWQn/qnPqC5XdBQ2jkotz6vKwJ3x61OCY4DSwokTYbMZC1QW5IhLAVCEsFwcq+1t1qtOoDY2FhnXV2dbvny5WGLFi064gz0rsyYMaP+oYceinnxxRcLdDod33//fcDUqVObfdPi7lH/ohWln3O5JXvLG9rKim4sqGFfRRMAOgHDYkM4Z0y8Z121hUGRgWqTjoHG7YbGsnYBuV1grsn3BGdHx/cExWrBOGE8jLxAC8qWZC1ghyaCwa9vvksfczqdws/Pzx0ZGem6/PLLK4YPHz4yKirKmZWV1dTdaz3wwAMHb7zxxuSMjIwRbrdbJCUl2b766qtcX7S7u7wqjyqEGA2k0i7YSynf812zOlLlUZVTSa3VzqaCQ9XKNhfW0ujJqsPMRq2saIpWVnR0osqqBwS3G5rKO2bO7cee6wrBddj+TIHRHbPl1setwdkY0DffpQu+LI/aHS6Xi9GjRw9/+eWXD4wbN67Px6VP1AmVRxVCPA+MBnYArWMBEui14K0oJyuXW7KnrKHDDPD97bLqjNgQzsuOb6sDnhJhVll1fyQlNFV4MuW8zmPPdYXgPCyWmCO1oBw3GoYvPBSYW4OzydwnX2WgysvLM86aNWvolClTGk6GwH0s3vxknyylHOHzlijKKaCmya6NVefXsqmwhi2FdW1ZdUSgiezkMC4cm8jY5DBGJ4YSqLLq/kFKsFZ5suX8w8aeC6C2EJyHDYWaI7SAHDMChs07FJgtydrMblNg33yXk1Rqaqpj3759p0wZVW/+MqwRQoyQUu70eWsU5STicktyShsOlRUtqGV/pZZV63WC4XHBXDA2gexkrVpZcrjKqvuMlGCtPkJg9twch1XSDAjTAnFUBqTPaReYPTe/oL75LsopwZvg/TJaAC8FbIAApJRytE9bpigDUGG1lRVbS/h2TwVbimqx2rXlOZFBWla9eHySZ6w6FLNJZdW9Rkporuk6KLfe7IfV+vC3aEE4Ig2GzOo49hyaBP4hffJVFAW8C97PAVcC2zg05q0oikdpXQsrth5kxdYSNhfWAjAyPoTF4xIZm6KNVSeGBais2tfagnO7W/tJYfaGjuf7hWjZctggGDy9Y9ZsSQb/0D75GoriDW+Cd4WUcpnPW6IoA0hFg41PtpewYksJ6/KrkVIL2L+fl8HC0XEkhavJRj3O1tBxMtjhlcJsdR3PNwVrmXJYCgw6veNsbUsyBFj64lsoSo/wJnhvEkK8DixH6zYHenepmKL0B7VWOyu3l7J860HW7KvCLbVdtn5z1lAWjo5jcJQa4+wxzbVQuhUOboaSzVCyBaoOW15rDDzUjZ1yWucx54AwrbycovSQxYsXp65atSo0IiLC2brHeFlZmf78888fXFxc7JeQkGD78MMP90dFRbncbjfXXntt0pdffhnq7+/vfv755/OmTZvWY1uQehO8A9CC9px2x9RSMeWUUN/i4PMdZSzfepDv9lbidEtSI8zcNiONhaPjGRYb3NdNHPis1YcC9EHPfc2BQ6+HJkFcFoz+GUSme8aeU1VwVnrdtddeW/mrX/2qfMmSJYNaj/31r3+Nmz59esP999+/96677or9y1/+Evvf//63+J133gndv3+/f15e3vavvvoq8NZbb03eunVrj21veszgLaVc0lMfpigDgdXu5Itd5SzfcpBvciqwu9wkWAK47vRBLBodz8j4EDV+fbyaKttl05u1QF1bcOh1SwrEj4GxV0LcGC1oB0b2SVMV5XDz589vzMnJ6VBXduXKlZZvvvkmB+Cmm26qOvPMM4cBxR9++KHl8ssvr9LpdMyaNaupvr7ekJ+fb0xJSXF0efFu8qZIyyDgF3SusHZOTzRAUfqDFoeLr3PKWb61hFW7ymhxuIkJ8ePyycksyoonO8miAnZ3NZR1zqjriw69Hj5YK+05/jotYMeOBnN4HzVWGUjueHdL0p7Shh6dWDI0Ntj6z4uyCrv7vqqqKkNrQE5KSnJUVVUZAEpKSoypqaltZfPi4uLsvRq8gQ/QZpwvR802V04idqeb1XsrWLG1hM92lNJkdxERaOKicYksGh3PhNRwtfOWN6SEhpJDAbpks/a4sdRzgtCWW6WcpmXScWMgNlNNGFNOOjqdrtd+5HsTvFuklI9198JCiGHAW+0ODQb+grZu/C20TD4PuFhK2a2dXhTleDldbtbsr2L5loN8uqOMumYHoQFGFo6OZ1FWPJMHh2PQ6459oVOVlNomGYdn1E3l2utCB5FDtaVXcVmejDoT/NTcAKXnHE+G7CsRERHO1ow6Pz/fGB4e7gSIi4tz5OXltXWxl5SUmHoq6wbvgvejQoi/Ap/Rcbb5xqO9SUqZA4wBEELogWLgfeBOYJWU8gEhxJ2e578/rtYrihdcbsm6vGqWbznIyu2lVDXZCfIzMGdEDAuz4piWFoXJoAJ2J1JqBU3az/gu2aKVCQUQek91sdntMupRquynckqZO3du7VNPPRVx//33lz711FMR8+bNqwU455xzav/zn/9E33DDDdVfffVVYHBwsKu3g3cmWpGWmXTcmGRmNz5nFrBPSpkvhDgXmO45/hLwNSp4Kz1MSsnGglqWbznIx9tKKG+wEWDUM2t4NAtHxzN9WBT+Rn1fN7P/cLu1Gd6tXd6tgbqlVntdZ4Do4TDsbE9GnQ0xI/vVzlaK4muLFi0atHbt2uCamhpDTEzM6DvvvPPgPffcU3L++ecPSUlJiUxISLC///77+wAuvvjiuo8++ig0JSVlVEBAgPvZZ5/N68m2eBO8FwODpZT2Y555ZD8D3vA8jpFSlngelwIxJ3BdRWkjpWR7cX1btbPi2mZMBh0zhkWxcHQ8s4ZHq5KkoAXq6n2dM2pbvfa63gTRI2DkeYcy6piRp+z+0IrSavny5Qe6Or5mzZo9hx/T6XS88sorBV2d3xO8+Uu2HbAA5cfzAUIIE3AO8IfDX5NSSiFElxuKCyFuBG4ESE5OPp6PVk4BUkpyyhpYsaWEFVsPkldlxaATnDE0it/OGcrsETEE+xv7upl9x+2Cyj0dx6dLtx6q463307q6MxcfGqOOGg4G09GuqihKH/MmeFuA3UKIdXQc8/Z2qdh8YKOUsszzvEwIESelLBFCxHGEHwVSyqeBpwHGjx/fZYBXTl37KhrbAvbe8kZ0AqYMieSW6UOYOzIWi/kUDD4uJ1TsPjTju2QLlG47tBuWIUCbPDbmskNrqKOGgf4U/nGjKAOUN8H7ryf4GZdyqMscYBlwNfCA5/7DE7y+cooorLayfOtBVmwpYWdJPULAhNRw7jtvFPNHxRIZdAp16zrtULGrY0Zdth2cLdrrxkCIGw1jrz6UUUcOBZ0a51eUk4E3Fda+Od6LCyECgdnATe0OPwC8LYS4DsgHLj7e6yunhv0VjTz4WQ4fb9PWDWcnW/jLwhGcnRlHbKh/H7euFzhtULajY0ZdtgNcnmkofiFagZMJ1x/KqCOGqECtKCcxn87ekVI2ARGHHatCm32uKEdVXt/Co6v28ua6QvwNOn45M43F45NO7h27HM2eQL350ISy8l3gdmqv+4dqwXnSzVo2HTdG29JSp5a6KcqpRE29VfqdhhYHT3+7n2dXH8DhcnPFpGR+PjOdqOCTrFvc3gSl2ztWJavYDdKlvR4QrgXoKWcdyqjDUtVmHIqiqOCt9B82p4vX1hbw769yqW6ysygrntvnDCUl4iQo+mFr0CaPtS8hWrkHpKd0QmCUFqCHzfdk1FnabloqUCtKv5GQkJAZGBjo0ul0GAwGuX379l1H2hLU123xZmOSqcDdQIrnfIG2ymuwb5umnCrcbsmyLQd58LMcimqamZYWye/nZZCZGNrXTTs+jmYoWt+xhGhVLlptIyAoVgvQI849lFGHxKtArSgDwDfffLMnLi7O2fr8SFuC+rod3mTezwG/ATYAPv81oZw6pJR8u7eSBz7Zza6SekbGh/D/Lsjk9PSovm5a91mrYc+nsHsF7Pvy0PKskAQtQGcuPpRRB8f2ZUsVRelBR9oS1Nef603wrpNSfuLrhiinli2Ftfzfyt38sK+K5HAzj12azcLMuIG1i1dNPuR8DLs/gvwftLHq4HgYc7lW7zt+LAQNwB8iitLffXBbEuU7e3bmavQIK+c9ccwNT2bNmpUuhGDJkiUVt99+e+WRtgT1NW8+5CshxD+B9+jGxiSK0pUDlU08+FkOH20tISLQxD3njOTSickDY2MQKbVx690fabeybdrx6BFw+lLIWKBl2ar7W+lHmp3NlFvLO93KrGWUW8upsFbwwrwXiA+K7+um9nvffffd7kGDBjmKi4sNM2fOHDpy5MiW9q/3ty1BJ3nux7c71t2NSZRTXHlDC4+vyuWNnwowGXT8clY6N54xmCC/fj5n0uWEgh88AftjqCvQtr1Mmgxz/g4ZZ0O4mv6h9D6X20WNrUYLwk2HBeTmirbnDfaGTu8NNAYSbY4m2hzNuJhxCAbYD04vMmRfGDRokAMgISHBuWDBgto1a9YEHmlLUF/zpkjLjN5oiHJyamhx8MzqAzy7ej92p5tLJybzi1lpRAf34+Iq9ibIXaUF7L2fQnMNGPxhyEyY/nsYOg8CI/u6lcpJzOqwtgXi1iBcYa3o8LyyuRKX7DgNSS/0RAREEGOOISUkhQmxE4g2RxNjjiHKHNX2ONB4Eqzg6GX19fU6l8tFWFiYu76+XvfVV1+F/PGPfzx4pC1Bfe2IwVsIcYWU8lUhxNKuXpdSPuy7ZikDnd3p5vUf83n8y1yqmuwsGB3H7XOGMSiyn/7RaKyAPZ9oAXvfV+CyQUCYFqgzFmiBW+1TrZwgp9tJVXOVFoSbj9yN3eho7PTeYGNwW7Y8OW5y2+PWgBxtjibcPxy9qqznE0VFRYbzzz8/DcDlcokLL7yw6qKLLqqfNm1aU1dbgvra0TLv1r9Uwb3REOXk4HZLlm/Vln0VVjdz2uAI7pyfQVaSpa+b1lnVvkPj14U/AhJCk2H8tVrATj4N9P28W1/pF6SUNDoaqbBWHDVjrmypxN26tt/DIAxEmiOJNkeTZkljSvyUDkE5KkDLmM3Gk7iy4AAwYsQIe05Ozs7Dj8fGxrq62hLU1474l0lK+ZTn/p7ea44ykK3eW8EDn+xmx8F6hseF8NK1mZyRHtlrEziOye2Gkk2Hxq8rdmnHYzNh+p1awI4ZpSacKR043A6qmqs6BOWuJn81O5s7vTfEFNIWhNPD0okyR7Vlya23cP9wdGIATNhU+hWVVignbFtRHQ+s3MX3uVUkhgXwyCVjOCcrvn8s+3LaIW+1FrBzPoGGgyD0kDIFxv2fVtEsLKWvW6n0ASkl9fb6owbkcms51S3VSDruSmzQGYgO0ILv0LChTEuY1haUWwN0lDmKAENAH3075WSngrdy3PI8y75WbC0hzGzkLwtHcPnkZPwMfTzm1lIPuZ9r2fXez8BWD0YzpM2CjL9C+hwwh/dtGxWfcrgclDeXH7Mbu8XV0um9Fj9LW1Y8ImJEh4lercctfhaVLSt9SgVvpdsqGmw8/uVeXv+xAKNe2+3rhjMGE+xv7LtG1ZdoBVNyPob934DbAeZIrQRpxgIYPB2MKgsa6KSU1NnqOndhHzb5q7qlutN7TTpTh6A8PWl6h6DcGqT99CfZBjjKScmb2uYxwP1AvJRyvhBiBHCalPI5n7dO6VcabU6e+XY/z6zej83p5tKJSfxyZjrRIX2w7EtKbWOP3Su0DLt4vXY8bBBMvhmGLYCkiWpP6wHK4XKQX59Pbm0ue2v3sq92H/tq93Gw8SB2t73T+eH+4W2BeVTkKO1xQMfZ2KF+of1n/oWinCBvMu8XgReAP3qe7wHeQqt5rpwC7E43b/xUwGOr9lLVZOfszFhunzOMwVFBvdsQtxuK1kGOZ4Z4Va52PH4szPwTZCyEqAw14WwAcbldFDUWkVtzKEjn1uaSV5eHU2q1LnRCR3JwMmmWtLZsuX3GHBkQiUlv6uNvoii9y5vgHSmlfFsI8QcAKaVTCKE2KDkFuN2SFdtKePDTHAqqrUwaFM6z8zPITg7r3YZYq+HHp2D989BUDjoDpJ4Ok26GYWdDaELvtkfpNiklJU0l5Nbmarca7X5/3X5srraqyyQEJZBuSefMxDNJC0sj3ZJOamiq6spW+lxubq7x8ssvH1RZWWkUQnD11VdX/PnPfy7vt1uCAk1CiAg8+xkKISYDdd5cXAhhAZ4FRnnefy2Qg5a5pwJ5wMVSypputlvxse/2VvLAyl1sL64nIzaYF5ZMYPrQqN7tdmwohTX/hnXPg6NJK5iSuRjSzoIAS++1Q/GalJLK5soOWXRuTS776vbR5GhqOy/aHE26JZ0JsRNIs6SRHpbO4NDBai2z0m8ZjUYeeuihomnTpllramp02dnZI84+++z6Z555JrK/bgm6FFgGDBFCfA9EARd5ef1HgZVSyouEECbADNwFrJJSPiCEuBO4E/h995uu+ML24jr+b+VuVu+tJMESwMMXZ3HumAT0vbnsqyYfvn8UNr2qTTwbdSFMWwoxI3qvDcox1bbUHsqka3PZW7OXfXX7qLMd+m0f5hdGWlga5ww5hzRLGmmWNIZYhhDqN0D3aldOWSkpKY7W3cPCwsLcQ4YMaS4oKDD12y1BpZQbhRBnAsMAAeRIKR3Hep8QIhQ4A7jGcx07YBdCnAtM95z2EvA1Knj3uYIqKw9+lsOyLQexmI38acFwrpicgr+xFyd8VeyB7x6GrW9rm3+MuQym/goihvReG5ROGu2N7Kvb19bV3XqrbK5sOyfYGMwQyxBmp8xuC9JpljQiAiL6sOXKyerP3/85Kbcmt0e7adLC0qz3Tb3Pqw1PcnJyTDt37jSfeeaZjf15S1CAiWjd3AZgrBACKeXLx3jPIKACeEEIkQVsAH4FxEgpSzznlAIxXb1ZCHEjcCNAcnKyl81Uuquy0ca/v8zltR/z0esEt80Ywk1nDiGkN5d9lWyB1Q/BzmXaBiATb4Qpv1Bj2b2sxdnC/rr9ncalS5pK2s4JMAQwOHQwU+Onkh6WzhDLENIsacSYY9RMbuWUUFdXp7vggguGPPDAA4Xh4eEdat32qy1BhRCvAEOAzUDrILwEjhW8DcBY4BdSyh+FEI+idZG3kVJKIYTs6s1SyqeBpwHGjx/f5TnKiVm5vZTb39lCs8PFxeOT+PVZ6cT05rKvgrXw7YNaQRW/EG1P7Mm3qh27fMzhcpBXn9cpSBc2FLZVEjPqjAwKHUR2dDaLLYu1TDosjYSgBFWcROlz3mbIPc1ms4kFCxYMWbx4cfXVV19dC9BvtwRF28d7hJSyuwG0CCiSUv7oef4uWvAuE0LESSlLhBBxQHk3r6ucILdb8viXufzriz1kJVl4aHEWadG9tOxLStj3pZZp538P5giY+WeYeAP4q3HQnuRyuyhsKGRf7T721u4ltzaXfbX7OizD0gs9ySHJDAsfxoLBC9qCdHJwMgadquGkKK3cbjc/+9nPUoYOHdpy9913l7Ue73dbgrazHYgFSo51YntSylIhRKEQYpiUMgeYBez03K4GHvDcf9i9Jisnwmp38tu3t/DJ9lIuyE7g/gsye2dc2+3Wqp+tfhAOboLgeJj3AIy9Sm21eYLc0k1JU4kWpGsOBenDl2ElBiWSFqatlW4dkx4UOkitkVYUL3z++edBH3zwQUR6enpzRkbGCIB77rmn+J577inpV1uCCiGWo3WPBwM7hRA/AW1/CaSU53hx/V8Ar3lmmu8HlgA64G0hxHVAPnDx8Tdf6Y6iGis3vLyBnNJ6/nj2cK4/fZDvx2dcTtjxHqx+WNvFKywVFj0KWZeCQa3d7Y72y7Bal1+1dnlbnda282LMMaRZ0pgYO5G0MC1Iq2VYinJi5s6d2yil3NDVa/1qS1DgwRO9uJRyM1q3++Fmnei1le75cX8Vt7y2EYfLzQtLJnLm0CjffqDTBptfh+8fgZo8iBoOFzwLI89Xe2R7obaltkNXd2tGXW+vbzsn3D+cNEsa56Wd1xakh1iGEGIK6cOWK4rSG462n/c3rY899c0neJ7+JKVU49QDyKtr87l72Q6SI8w8e9V435Y1tTfBhhfhh8ehoUQrXTr3fhg6H3RqotPhGu2NbQG6tY53bk0uVS1VbecEG4NJC0tjbupchliGkG7RZnmrZViKcuo6Wrf5TCnll0KIi4F/oq3HFsDjQog7pJTv9lIblePkcLm5Z/kOXl1bwPRhUTx2abbvloA118K6Z2Dtf8FapZUvPe+/2m5eagkRzc5m9tft14J0uzrehy/DGhI6hNMTT++wVjraHK2WYSk9yt3UhLOqCmdlFc6qSoKmTUMXoHbdG0iO1n95BvAl2oYkE1qzbSFEFPAF2uxxpZ+qarRx62sb+fFANTedOZjfzc3wTZW0pkpY8wSse1bbNzt9Dpx+OyRP6vnPGiCKGorYVrmtw+Sxw5dhDQ4dTHZ0NheHXdwWpOOD4tUyLOW4SClxNzbirKzE1S4oH3pchauyUgvYVVXI5uYO7x+8fBl+6el91HrleBwteHv2WER3WDd5FdqkM6Wf2lVSz/Uvraei0ca/Lsni/OzEnv+QumKta3zDi+Bs0fbNPv23EDe65z+rnyu3lvNT6U/8VPITP5X+RHGjVhlRL/SkhKQwLHwYCwcvJC1MG5NWy7AUb0gpcdfVtWXIrqrKtkDsrKrEddhjae+8VSpCoA8LwxARgT4ygoCkMW2PDRGRGCIj0EdEYFSFsAaco/0FGQmsAFYKIT4F3vAcvwT42NcNU47Pyu0lLH17C8H+Bt656TSykiw9+wFV+7RJaJvfACSMvgSm/QYiT51f7bUttawrW8ePJT/yU+lPHKg7AECIKYQJsRO4asRVjI0Zy+DQwWoZltKBdLtx1dYeJUNuF5Srq8HRRSVqvR5DeDj6yEgMERH4DR6EPkJ7rAVjLSgbIiLQh4UhDOqH4snoaBPW/s9zf4cQ4kJgquelp6WU7/dG4xTvud2SR1ft5dFVe8lOtvDUFeOI7slqaWU7tcIqO94DnRHGXQNTfwmWk/8Xe6O9kY3lG9uC9e7q3YA2Rj0uZhwXpF3AxLiJDAsbhl7Xi7XglX5BOp24amq6zJA7ZcvVNeDqYrdIo1ELvhER6KMi8cvI6DoYR0aiDw1FqMmffWLx4sWpq1atCo2IiHDu3bt3B0BfbQkqul84rfeNHz9erl+//tgnnqKabFrhlZU7SrloXCJ/O29UzxVeKdqgBe2cj8AUBBOug8m3QXCXJelPCs3OZjaXb27rCt9RtQOXdGHSmRgTPYaJsROZFDeJkZEjMep6sQa80mukw4GzurpzhtxFt7WrpkarHHgY4efXFnAPBeKO3dUGz2u6kJABPSlRCLFBStnVsuBu2bJlS15WVlblsc/sG5988klQcHCwe8mSJYNag/fNN9+cGB4e7rz//vtL77rrrtiamhp9T20JumXLlsisrKzUrl7zprb5BcD/AdFos80FWllytZi0HyistnLDy+vZU9bAnxYM57ppPVB4RUrI+06rhrb/a/C3wPQ/aBuGmMN7otn9isPlYFvlNn4s/ZGfSn5iS8UWHG4HeqEnMzKT6zKvY1LsJLKis/DTq8IyA5Xbbj80aavysMlc7bPlykpcdXVdXkOYzW0ZsjElmYCxYzuNIbcGbF1g4IAOyEpn8+fPb8zJyekwFtZvtwQF/gEsklLu8nVjlO5Zs6+KW1/bgMsteXHJRM440cIrUsLez7RMu/BHCIqB2ffB+CXgF9wzje4HXG4Xu6t3twXrjeUbaXY2IxBkhGdw+fDLmRg7kbExYwk0qtKt/Zm7ubnjTOqjZMjuhoYur6ELCmoLuH5DhmCYNPHIGbJZVanrDw7e9cck2969Pfp/hl96ujX+/r93e8OT/rwlaJkK3P3PK2vzuWfZDlIizDx79QQGRZ5AkHG7YOeHWgnTsm0QmgwLHoIxV4CxF3cZ8xG3dJNbm9s2G3x96XoaHNof8iGhQzgv7TwmxU5ifOx4Qv3U5ij9hXQ6cRw8iD0vD3tePvb8fJzlZR0yZLfV2uV7daGhbRmy3/AMAtsH4vYZckQEOv+B/9+40j/0iy1BPd3lAOuFEG8BH9Cxtvl7vm2a0hW7083dy3fw+o8FzMyI5pGfjTn+wituN2x9UwvaVXshciic9yRkXgT6gTuWK6WkoKGgbYLZutJ1VLdUA9rmHHNS5zAxdiIT4yYSGaC2H+1L0u3GWV5+KEDn5WHP99wXFXWYba0LCsIYF4s+IpKAzMxDk7kOn9gVHo4wqVn+J7PjyZB9pT9uCbqo3WMrMKfdcwmo4N3Lqhpt3PLqRn7Kq+aW6UO4fc6w4y+80lgO798M+1ZB7GhY/BIMXwQDdLZ0aVNpW7D+seRHyqzajn3RAdFMjZ/KxLiJTIydSHxQfB+39NQjpcRVU9N1gM7PR7a0tJ0r/P0xJSfjl55O8OzZmFJSMA1KxZSSgj48XI0hK/1Ov9sSVEq5pDcaoHhn58F6bnh5PZWNNh792RjOHZNw/BfL/UIL3LYGWPgvGLdkwJUwrWyuZF3purYZ4QUNBQCE+YUxIXYCk+ImMTF2IikhKeoPfi9xNTS0dW9rgfpQkO4w3mwwYEpMxJSaSuBpp2FKTcGUqgVoQ0yMWgal9FuLFi0atHbt2uCamhpDTEzM6DvvvPNgv9sStJWnHOoNQGr786WU1/quWUp7H28r4bdvbyE0wMg7N5/G6ETL8V3IaYcv79Uqo0WPgKuXQ/TwHm2rr9hcNr4v/r4tu86tzQUgyBjE+Jjx/CzjZ0yMnUh6WLoqMepD7pYW7PkF2PM7Z9GuqkObqSAExvh4TCkphC5a1CFAGxMSVOEQZUBavnz5ga6O97ctQVt9CKxGq2fu84XnyiFut+SRL/bw2Je5jE228OSV44gOPs7JNVX74H/XwcFNMOF6mPM3MPb/jQgKGwp5J+cd3s99n1pbLf56f8bGjGXB4AVMip3E8IjhqtRoD5MOB/aioi4y6HycJSUdzjVERWFKSSF45gyti7s1QCcno/NTy+oUxVe8+atnllL+3uctUTpotDlZ+tZmPttZxuJxifzt/FH4GY5zPHrr27DiN6AzwCWvamPb/ZjL7WJ18WreynmL74u/Ryd0zEyeyUXpFzE+drwqOdoDpNuNs6QEe34+tsO6uB1FxR2qgOlCQzGlphA4cQLGlBT8UlMxpaZiTE5BH6SW0ilKX/AmeK8QQpwtpVT1zHtJQZVWeCW3opG/LBzBkqmpxzdua2uAj++ALW9A8hS48BkI9cEmJT2kqrmK93Pf552cdzjYdJDogGhuzrqZC9MvJCbw5K3o5itSSlyVlV2OQdsLCpG2tsUjCLMZU0oK/iNGEDJ/PqbUVPxSUzGmpGAIC+vDb6EoSle8Cd6/Au4SQtgBO92osCaEyAMa0LrbnVLK8UKIcOAttDH0POBiKWXNcbX+JPTDvkpufW0jUsJLSyYyLf04lzId3ATvXgs1eTD9Ljjj9n45k1xKyabyTbyV8xaf5X+G0+1kUuwkbp9wO9OTpqvyo15w1dV1DMztxqLdTU1t5wmjEWNysjZR7PQz2o1Dp2KIjlIT+xRlADlm8JZSnmhprRlSyva1au8EVkkpHxBC3Ol5rrrlgbfWFXDX+9sZHBnIM1eNJ/V4Cq+43bD2CfjiHq1C2jUfQcqUnm/sCWpyNPHR/o94M+dN9tbsJdgYzCXDLuHioRcz2DK4r5vX77itVi04dxGgXTXtfvvqdBgTEjClphI6btyhcejUFIxxcQh9//sBpyhK93kz21wAlwODpJT3CSGSgDgp5U/H+ZnnAtM9j18CvkYFbz7aWsKd723j9PQonrgsm+DjKbzSWA4f3KItBctYCOc83u9qkefW5PJWzlss37+cJkcTGeEZ3H3a3cwfNB+z8dQuPem223EUFmoB+kDHtdDOsrIO5xpiYjClphI8Z06HAG1KTFQFShTlFOBNt/l/ADcwE7gPaASeACZ48V4JfCaEkMBTUsqngRgpZeuU1VKgy8FMIcSNwI0AySf5RvFr9lXxm7c2MzY5jKevHHd8O4LlrvKs3a6HBQ/D+Gv7zdpth8vBqoJVvJnzJhvKNmDUGZmXOo9LMi5hdOToU6q7VrpcnpKfnYuVOIqLtZ4TD314OKaUFAKnTOkYoJOTVY1tRekDCQkJmYGBgS6dTofBYJDbt2/vs9Lh3gTvSVLKsUKITQBSyhohhLc/7adJKYuFENHA50KI3e1flFJKT2DvxBPonwZtS1AvP2/A2VVSz40vryc5wsxzV4/vfuB22uGrv8H3j0LUcLjqQ4gZ4ZvGdlNJYwnv7HmH9/a+R1VLFQlBCSwdt5Tz0s4jzP/knQQlpfSU/OwiQBcUIA8r+WlKSSFg9GhtPbSnmpgpJQV9qKqzrij9zTfffLMnLi6uV0qgHo03wdshhNCjZdGtRVvcR3+LRkpZ7LkvF0K8D0wEyoQQcVLKEiFEHFB+fE0f+IpqrFz9/E8E+hl46dqJWMzd7O6s3g/vXgcHN2qZ9tz7+3zttlu6WXtwLW/mvMk3Rd8gpeSMxDO4ZNglTE2YelIWUHHbbLTs2EHzpk1YN22iedPmDgVLhJ+fVvJz8GBtPbRnqZUpJQV9RMQp1fOgKErP8CZ4Pwa8D0QLIf4OXAT86VhvEkIEAjopZYPn8RzgXmAZcDXwgOf+w+Ns+4BW02Tn6ud/otnh4t2bp5Bg6WbQ3fo2rFgKOh1c/AqMOMc3DfVSna2OD3I/4K2ctyhsKCTcP5xrR13LRUMvIiHoBEq59kPOioq2IN28aRMtO3a0ZdOmlBSCTj8d/8xR+A0ahCk1FUNsrCr5qfRbbpcbIQTiePdJ6AOrXt6VVF3c2KNjR+EJQdZZVw0/5oYns2bNShdCsGTJkorbb7+98ljn+4o3s81fE0JsAGahLRM7z8stQmOA9z1ZhQF4XUq5UgixDnhbCHEdkA9cfNytH6Ca7S6ue2kdhTXNvHLtRIbFdmNCf4e126fBBc+AJcl3jT2G7ZXbeXP3m6zMW4nNZSM7OpvbxtzG7JTZJ0UxFelyYdu7t0NW7SjU/n0Lkwn/zEzCr76KgLFjCRgzBkN4/5ogqJwaXE43NqsTm9XhuW//2EGL1Ym93fGWdq87Wlxcfs9kLDFqHsWxfPfdd7sHDRrkKC4uNsycOXPoyJEjW+bPn9/YF23xZrZ5OFrX9hvtjhmllI4jvwuklPuBrC6OV6H9EDglOV1ufvHGRjYV1vKfy8YyaXCE928+uEnrJq85AGfeCWfcAfreLw3a7Gxm5YGVvJnzJjurdhJgCODcIedy8bCLGRY+rNfb05NcDQ00b9lK86ZN2m3Llra10vqoSMzZYwm77DLMY7PxHz5czexWeozT7jpi4G19bLc6OwTe1uNO+9FHMg1GHX5mA36BRvzMBoLD/YlMCNKOmQ2YAgZWiWFvMmRfGDRokAMgISHBuWDBgto1a9YE9tvgDWwEkoAatMzbApQKIcqAG6SUG3zXvJOLlJI/fbCdL3aVc9+5I5mfGefdG91uWPsf+OJuCIqGq1dA6lSftrUreXV5vL3nbT7I/YAGewNDQodw16S7WDR4EUGmoF5vz4mSUuIoLOyQVdv27AEpQafDb9gwQs89h4DsbAKyx2JMiFfj08oRSSlx2FzYm9tluE2HB2MntmbP46aOx13Oowdgo5/eE2y1ABwaFaAF4wBDh+N+ZgP+niBtCjDgbzaiN6phmxNVX1+vc7lchIWFuevr63VfffVVyB//+MeDfdUeb4L358C7UspPAYQQc4ALgRfQlpFN8l3zTi6PfLGXN9cV8vMZaVx5Wqp3b2qs8Kzd/rxP1m473U6+KfyGN3PeZG3JWgzCwFkpZ3HJsEsYFzNuQAUzt91Oy3ZtYlnz5k1YN23GVakNWemCgggYM4bgObMxZ2fjP3o0+qCB94NEOTFSSuwtrq67n9sH2+auu6jdrqMvjDG1BVot2IbHBWpB1hN4/TsE4UPB2GQ2oNerANyXioqKDOeff34agMvlEhdeeGHVRRddVN9X7fEmeE+WUt7Q+kRK+ZkQ4kEp5U1CCLVtkJde+zGfR1ft5eLxifx2zlDv3rTvS3jvJmipgwUPwfjrem3tdoW1gv/t/R/v7HmHcms5MeYYfj7m51w49EIiA46zZGsvc1ZW0rx5M9aNWhd4y/btbRPLjMnJBE2d6smqs/FLG6Kqj50k3G7ZIfvtqiv68GMt7bql5VHirxBg8gRWf09gDQ737xCQDw+8rY9NAQZ0A2hSmNLRiBEj7Dk5OTv7uh2tvAneJUKI3wNvep5fgrbcS4+XS8ZOdZ/uKOXPH2xnxrAo/n5+5rGzVZcDvrzPs3Y7A676AGJG+rydUkrWl63nzd1v8mXBlzilkynxU/jjpD9yRuIZ/XrrTelyYcvd5xmr3oh102YcBQWAVtPbf9Qowq66EnN2tjaxLHJg/AA51bmcbppqbdqtzk5Lo71tDNh+WOBtDcj2FqdnYWvXdDqBX+ChoOofZCQ02nwo2AYYPa8f1hVtNmL00w+oWdnKycubv8aXAX8FPkD7J/G955ieU3CmeHetz6vml29sIjPRwhOXj8V4rK6v6v3wv+uheAOMW6Kt3Tb5dhZog72BZfuW8XbO2+yv20+IKYTLhl/GxcMuJiUkxaeffbxcjU20bN3SllU3b9mCu1GbN6KPiMA8NpuwSy4hYGw2/iNHolMTy/oVKSUtTQ5PYLbTVGujsTVIex5b62w0N3Q9L1Zv1HUY6w20+BEeH3go2Aa0C86BHTNig0k3oIZ7FKUr3iwVqwR+IYQIlFI2HfZyrm+adXLYW9bAdS+tJ8ESwAvXTMBsOsb/3DuXwQe3amu3F78EI8/zafsqrBU8ueVJlu9fTrOzmVERo7hv6n3MS52Hv8Hfp5/dXa7aWhpXr8a6ceOhiWVuNwiB39ChhCxcoGXVY8diTExUf5z7kNPhagvIHYJy3aHg3FRr73KCVkCwFoiDwvyIGRRCkMWPwNZbqB8BwVoQNhxPCWFFOYl4s1RsCvAsEAQkCyGygJuklLf6unEDWUldM1c//xMmg46Xrp1IeOAxMr8d72vLwBLGwkXPg8V39dxdbhdv5rzJvzf9G5vLxoLBC7hk2CWMihzls888Hu7mZhq/+oq6FR/RuHo1OBzoAgMJyMoi+JZbtPHqrNHog0904zvFG9ItaW50tOvG7pgttwZqW1PnypEGo64tCMcMCu0YlC1+BFpMBIb6oTeoSVmK4g1vus3/BcxFq4yGlHKLEOIMn7ZqgKuzOrjm+XXUtzh566bJJIUfo9t71wqtqzxpIlz+Lvj5bpbz1oqt/G3t39hVvYsp8VO4a9Jd/aprXDqdNK1ZS/2K5TR8/gVuqxVDdDThV1xByNnz8R8xQk0s8wGH3UVTzaHsuGNQtrcF606zqQWYg00EWvwIjgggdoiFIIupY2AO9cPPbFC9IYrSg7yagSSlLDzsH57LN80Z+FxuyU2vrmd/ZSMvLZnIyPhjbC6x51N45xqIz4bL3/FZ4K6z1fHoxkd5d8+7RAVE8eCZDzInZU6/+IMqpaRlyxbqVnxE/Sef4KqqQhccTPDZ8wlduAjzhPEqYB8n6ZZYG+ydsuOmuo7HbNYusmU/vSdDNhGX3i5bDj0UmM2hJrWESVH6gDfBu9DTdS6FEEbgV0CfbYPW373+UwFr91fzj4tGMyXtGDOac7+At66A2FGejLvnu3+llCzbt4yHNzxMna2OK0Zcwa1Zt/aLoiq2/fupW76c+hUf4SgsRJhMBM2YQcjCBQSdeaaaZHYM9hZnh5nYnTNmG9Y6O253x2xZCDCHaNlxaFQA8ekWbZy5XbYcZPHD6K/vFz/uFKU/sFqtYtKkSRl2u124XC6xaNGimn/961/9ukjLzcCjQAJQDHwG3ObLRg1UFQ02/rFyN1PTIlg8LvHoJ+//Bt68HKKGwRXvQYClx9uzt2Yvf1v7NzaWbyQrKounZz/d5+VLHWVl1H/0MXUrlmPbuQt0OgInTybyllsInn2WGr9GW6fcXG/vNPu66bCbvaVzB5jJX98WgBOGhXUMyp6M2RxiRKeyZUXpFn9/f/ndd9/lhIaGum02m5gwYcKwVatW1c2aNevwidy94qjB27OW+1Ep5eW91J4B7f99vAubw8295446esaS9z288TMIHwxXftjjFdOsDitPbnmSV3a+QqApkHum3MN5aef12Xacrro66j/7jPoVH2H96SeQEv/MTGLu+gPB8+ZhjI7uk3b1BXuzs2MgrrPRVNMxOFvr7Z0KhQidIDBUy5bD4gJJHB5OYKip08Qvk3//XYuvKAOZTqcjNDTUDWC324XT6RR92TN11H/pUkqXECJFCGGSUtp7q1ED0Zp9Vby3qZhfzExjSNRRuqQLfoTXFkNoEly1DAK7sTHJMUgp+bLgSx5Y9wClTaVckH4Bvx77a8L8w3rsM7zlbmmh8euvqVuxgqZvvkU6HJhSUoi87TZCFy7AlJra623qDdZ6O5VFDdSWWQ/LmLVubYetc7bsZzZ4MmMT4fGBnbqwAy1+BASbVHUupY2UEqfNhr2lGXtLM46WlkP3zc04Wpqxt7R47tu93tyM3dZy6PVm7fVL7/0nllgv91roBz797yNJlYX5PVoAIzIpxTr3ll8fdcMTp9PJqFGjRhQUFPhdffXV5TNnzuyTrBu86zbfD3wvhFgGtDVUSvmwz1o1wNidbv784XaSwgO4bUbakU8s2gCvXQTBsXD1MgiK6rE2FDYU8v9+/H+sLl5Nelg6/zjjH2RHZ/fY9b0hXS6a1q6lfvkKGj7/HHdTE4aoKMIuu4yQhQvxHzXypBlDdbsltWVWKosaqCpqpLKwkcqiRqz1h37j6nQCs0XLjiMSAkkeGd7Wfd0+OBv91GS8k5mUEqfD7gmU7QOn1RNIW7A3Wz2vHQq4ree2BWSbdkx73MJR67i2I3Q6TAEBGP0DMPkHYPL3x+gfQHBEpOd5AHqT0cf/K5wcDAYDu3fv3llZWalfsGDBkHXr1vlPmDChpU/a4sU5+zw3HaAGJLvw7Hf7yS1v5IVrJuB/pOIRBzfDq+drXeRXL9cCeA+wu+y8sP0Fntn2DHqh547xd3DZ8Mt6rZSplJKWbduoW7GC+o8/wVVZiS4oiOC5cwldtBDzxIkDfqa4vcWpBejWW2ED1QebcDq0IiM6vSAsLpCkEeFEJgYRmRhEWFwg5mCTKqU5wEgpcTkch2Wz7YJuc7vjtqNluZ7g7AnIUnpXSVoIHUZ//3bB1h+TfwBB4eGHnnteM/ppr2nP/Tucb/TXjpn8A9AbjSfNj+ZWx8qQfS0yMtJ1+umnNyxfvjy03wZvKeU9rY+FEDogSErZZzup9DeF1VYeW7WXeSNjmZFxhLHb0u3wynngF6oF7tCEHvnsNQfXcP+P95NXn8eclDncMeEOYgN75kfBsdgOHKB++QrqPlqBI78AYTQSNH06IQsXEjT9THR+A2/PGikljTU2KgsbDgXqokbqK5rbzvELNBCZGMTIMxK0QJ0URFhsoCou0kdcTkeH7t/WwNr6uDWwtgbZLl9v15XsaGnB7fJyJawQbVnsofsAzKEWQmM6ZrmtAdbk3/6+8+sGo+mkC7Qni4MHDxpMJpOMjIx0NTY2iq+++irk9ttvL+2r9nhTYe11tBnnLmAdECKEeFRK+U9vPsAz6W09UCylXCiEGIS2yUkEsAG4ciCPp9+zfCc6IfjLohFdn1C+C14+B4xmrau8ByqnVVgr+Oe6f/JJ3ickByfz5FlPMjXB9/t7O8rKqf/kY+qXr6Blxw4QAvOkSUTeeCPBs2ejDwnxeRt6isvhprq0icrCRk9WrQXs9uudQ6MCiEoKYvhpsUQmBhORGERQmJ/64+ojDrsNa20t1rpammpr2m5tz+tqsFutHcZ23a7O69OPxOjn3ylLDQgOISQqpi2gmgK0jLZTltv6ervHBj/138KppLCw0HjNNdcMcrlcSCnFueeeW33ppZfW9VV7vOlbHSGlrBdCXA58AtyJFnS9Ct4cWhfe+pf9/4B/SSnfFEI8CVwH/Ld7ze4fPt9Zxhe7yrjr7AziLQGdT6jcCy+dAzqjlnGHDzqhz3O6nbyV8xaPb3och8vBrVm3cm3mtfjpfZfluhoaaPjsM+pWrMC69kdtpvjIkUT//veEnH02xpj+P1O8udFOZVFjh7HpmpKmtvXPBqOOiMQghoyLJioxiIjEYCISAtXM7R7gcjpprq9rC77W2kOBuKm2FmvrfW0NNmvXc38CgkMwh1oItFgICgvH5G/2BNBjZ7WmAO1co8kPoVO9I8rxmzRpUvOuXbsG1JagRk9xlvOAf0spHUIIr2ZKCCESgQXA34Glnnn1M9F2JQN4CbibARi8rXYndy/bwbCYYJZM7SIoV+2DlxYBUgvcEUNO6PPalzWdGj+VuybdRXKIb+qfu202Gr/5hvrlK2j85huk3Y4xOZnIW24hZOEC/AYP9snnnijpltRVNLeNS1cWa8G6qdbWdo451ERkYjApoyKITNLGp0OjzWomdzdIt5vmxgYtK66t9QTijlmy1fO8ubGhy4lVpgAzgZYwAi1hRCWnEjg6m0BLGGaLhcDQsLbH5hALeoP6EaUoh/PmX8VTQB6wBfhWCJECeDvm/QjwOw5NdIsAaqWUrX1dRWjFXwacx7/Mpbi2mXduPq3zNp81eVrG7bLD1Ssgauhxf06drY5HNj7C//b8jyhzFA+d+RCzU2b3eHeddLmw/vQTdStW0PDZ57gbGtBHRmL52SWELlyIf6YX+5D3IofNRVVxx0lkVQebcHqWYgmdICzWTMJQC5GJwUQmBhGRGIQ5RFVt64qUEnuztWN3dW0NTR2CsSdTrqtFujtPwDIYTQSGhWEOtWCJjSMhYwTm0DACLRbMlrAOQdloGnhzIhSlP/FmwtpjwGPtDuULIWYc631CiIVAuZRygxBiencbJoS4EbgRIDnZdztsHY+9ZQ088+1+Fo9LZELqYQVWWurg5XPB3gjXrICYI4yFH4NburWypusfpt5ez5UjruTWMbcSaAzsgW/Qrrk7d1L34TLqP/4YZ0UFusBAgmfPJmTRQgInTUL0g6xHuiXl+Q0U5VS3dXvXllu13eUBU4A2iWzElDgiEoOISgomLM6sto0EHLYWmjxd1a1d1O0Ds7WtC7sGl6Pz3tk6vd7TZR1GUHg40YOGaMHYE4jbPzYFBPSrH3iKcjLzZsLa0i4O1wkhNkgpNx/lrVOBc4QQZwP+aGPejwIWIYTBk30nopVc7URK+TTwNMD48eO9W9DYC6SU/OmD7QT6GbhzfkbnE1b+AWoLYcknEJt5XJ+xp2YPf1/7dzaWb2RM1Bj+NPlPPV7WtCVnDxUPP0zjN98gjEYCzzyD0IULCZo+HZ1/3+/l7bC5KNxVTd7WSvK2V9HsWT8dEulPZGIw6RNi2pZlBUf4n1JBw+V0eLLg2k5ZcvtgbK2rxd7c3PkCQmAOCSUwVMuIw+LitczYc2sN1oGWMPwDg9RYsaL0Q96kVeM9t+We5wuBrcDNQoh3pJT/6OpNUso/AH8A8GTet0spLxdCvANchDbj/GrgwxP5Ar3t/U3F/Higmv93QSYRQYd1/e1aAZtfgzN+B8mTun1tq8PKf7f8l1d2vkKwKZh7p9zLuWnn9mhZU8fBg1Q89jh1H36ILjiYqN8uJezii9GHHmP3s17QUN2iBettlRTn1OJyujEFGEgeGU5qZiTJI8MJCDo5u73dbhfN9fWdu6vbZ8ueYy2NDV1ewz8wqC3wxgxO7xSIW5+bQ0LRDfC196cy6ZZIuwtpd+O2uzyPPc9thz1v/7rNhdvubvf6oefRt2RhiOhi0q3Sb3kTvBOBsVLKRgAhxF+Bj4Az0Gaddxm8j+L3wJtCiL8Bm4Dnuvn+PlNndXD/x7vITrZwyfikji82VsDyX0HsaDjjjm5fu7ixmFu+uIUDdQe4MP1Cfj3211j8LT3TcMBZU0PV089Q89prAIRfu4TIG25Ab+m5z+gu6ZaU5ddrAXtrFVXFjYC2RGvUmQmkZkYQl24ZsFtOSilpaWpsGy/WZlsftvypdWJXfX2XhTyMfv6eceIwwhMSSRyRSaDF0ilLNoeGYTCqKln9iXS5tYDp0O4PD5htAbXD6x0Dbtu5NhfS4cJtc4PTu4IvrYRRh/DTI0x6dCYdwuR5HGTC2Ppc1SkYcLwJ3tGArd1zBxAjpWwWQtiO8J4OpJRfA197Hu8HJnavmf3DPz/bTXWTnZeundhxdrKUWuC2NcAFT4Ohe9nhjqod3PbFbdjddp6d8yyT4rqftR+Ju7mZ6ldepeqZZ3A3NRF63nlE/eLnGOP6po6xvcVJ0a4aDmyrJH9bJc0NDoSAuDQLUy5II3V0BJYY84DpBtcKu1RRXVREVXEh1cUFVBUXUldWRlNtTZfrkPUGg2cCl4XgyChi04Z2zJLbTewy+atsyJeklOCU3ctgW193HD2bxdWN0T7BoaDaPsAGGNCFmNqC7+GvCz89unbBudPrRp2q8tdDcnNzjZdffvmgyspKoxCCq6++uuLPf/5zeV+1x5vg/RrwoxCitXt7EfC6ECIQ6Ddr3nxtc2Etr/1YwDVTUhkZf1gX85Y3IOcjmPM3iB7eret+W/Qtt39zOxY/C8/NfY4hlhNbUtZKOp3Uvvcelf9+Amd5OUEzZhD1m1/jP/T4Z74fr/qqZvK3VZG3tZKiPTW4nRJTgIGUkeGkZEaSMioC/8D+nTW63S7qKyqoKiqguriQqqJC7b64EHuzte08v8BAIhKSSRqZSWBYuCcQH8qOAy1h+AUGDpgfJ/2JdLhx25yeLPQoAfZo2W4X76U7M2p0QguQfu0CpEmHPsiIMPm3Pdf56RFGvSeo6tCZ9B3Ob/+6zqQDg079N9HPGY1GHnrooaJp06ZZa2pqdNnZ2SPOPvvs+nHjxvXb8qj3CSFWAlM8h26WUq73PD4ltgp1uSV/+mAb0cF+LJ19WPCrLYBPfg8pU2Hyrd267v/2/I/71t7H0LChPDHrCaLMJ75RiZSShi++oOLhf2E/cICAMWNIePghzOPHn/C1veV2S8rz6tvGr6uKteIbodEBZE5PZFBmJLFpof2yO9zldFBbWkJVcaEnUBdRVVRAzcFinI5DhQADLWFEJCYx4owZhCckEZGQTERiEuZQi/oj7CXplribnbgb7bgaHbibHLgbHbga7Z577Vjrc9nFjmxHZNB1maHqLH6e7FQLop2y1S66lzs8V93Lp6yUlBRHSkqKAyAsLMw9ZMiQ5oKCAlO/Dd4eG9FmhRsAhBDJUsoCn7Wqn3l1bT7bi+v592XZBPu3yxDdbvjgVpBuOO8/oPNuEpCUkic2P8FTW59iasJUHjrzoR5ZAmZdv57yfz5I85YtmAYPJvHfjxM0a1avBBN7i5PCndrs8PwdVVp3uE4QNySUKRemMWh0JJaYHt3B74Q4bC1UFxd5sueitoy6tqykQ23rkKgYIhISSR6VRURisidQJ+EfdJRtX09h0uHSgm6jA1eTA3eDXbtvdBwK0o0OXE123E0O6Gr4VoAu0Ig+yIguyIQpMdjz2IjO39AxoB6WwWpdxXqEXv2AOplVv7snyVHa1KN/UIyxgdbwi4Z6teFJTk6OaefOneYzzzyzsSfb0B3eLBX7BfBXoAytvrlA62ga7dum9Q/lDS08+GkOp6dHsiDzsHHin56CvNVwzuMQlurV9RwuB3evuZtl+5Zxftr5/Pm0P2PUnViXccuePVQ8/C8av/4aQ3Q0sffdi+X8832+Rru+spm8bZXkba2keE8tbpfEz2wgeWQEqaMjSB7R993hLU2Nbd3c2pi09ri+sryt8pfQ6bDExhORkET6pCltATo8PhFjP1g215c6ZcetQbgtS273vMGhdUN3QZsgpQVkfbg/puRgdIHGtmO6IJN2H2hEZzaqcVql36qrq9NdcMEFQx544IHC8PDw7s0e7EHe/HX/FTBMSlnl68b0R3//aBc2p5t7zx3VMYOtyIEv7oah8yD7Sq+u1Whv5Ddf/4a1JWu5NetWbs66+YSyYsfBg1Q8/m/qPvgAXVAQUUuXEn7lFegCfDPJye2WlB041B1efVDrDrfEmBk9I5HU0ZHEDQlF18vd4VJKrHW17cahC9oy6qaa6rbz9EYj4fGJxKUPY9T0s4hITCI8IYmwuHj0hv495t6TpMOFq6Fjl7TrsKDcFqytx8qOTeiCjJjC/dEHGtEFm7T7IM9rnsc6k1qapvQcbzPknmaz2cSCBQuGLF68uPrqq6+u7Ys2tPImeBcCfbZzSl/6IbeSDzcf5Jez0hkU2a5b2+WA92/Sdgpb9Bh4EYDLmsq4ddWt7K/dz71T7uX89POPu12u2loqn36GmldfBSkJv+YaIm68AUNY2HFf80jszU4KdlaTt62S/O1VtDRq3eHx6aFMvSiN1Mze6w6XbjcNVZVUFRV0yKKriwtpaTrUe2UKCCA8IYnU0WMJT0gkIlEbkw6Jjkbn5dDGQCLdEre1NRh37J52NzlwNdjbvXaU7NjPkx0HGtGHB2BKDml7rvME6dYsWRdgUNmxckpxu9387Gc/Sxk6dGjL3XffXdbX7fEmeO8HvhZCfES7JWNSyod91qp+wOZ08acPt5MSYebW6YfNAF/9EBzcBBe/DMExx7xWbk0ut6y6hXpbPU/MeoIpCVOO+Z6uuFtaqH7lFaqefgZ3YyOh556rLftK6Nny8HUVh7rDD+491B2eMiqirViKn9n3maqUkor8A+Rv20z+1k0czNmFw3ZobkhAcAgRickMPW1ah/HooPCIAT9pzG13dZi81SkwN3V83OWMaZ0nOw40oQs24hfu3ykIt2bJukCVHSvK0Xz++edBH3zwQUR6enpzRkbGCIB77rmn+JJLLumT5Nab4F3guZk8t1PCh5sPsr+iiReumYB/+xrZxRvhm39A5sUw4txjXuenkp/49Ve/xt/gz0vzXyIjvIuSqscgnU7qPviAisf/jbOsjKAzzyRq6VL8h/XMsi+3W1K6v478bZUc2FpFTYnWHR4WayZrZhKpoyOIHdw73eEN1ZXkb9WCdcH2LVjragEIj09kxJmziEpO1cajE5Mwh/R9VThvtWXHbbOo7W1d121Bul2Alvauh9KEn/5Q4G2fHXuCcvuJXio7VpSeM3fu3EYp5Ya+bkcrb5aK3dMbDelvXlubT3p0ENOHtVu+5WiG92+GoBg4+9iF5T7a/xF/+v5PpASn8N+z/ktcUPcKo0gpafzyS8of/hf2ffsIyMoi4cF/Yp4wobtfp0sVhQ1sXVVI3rYqWpoc6HSCuHQLI6fFk5IZgSXa993h9pZmCndsI3/bJvK3bqa6WBvKModaSB6VRcrobFIyxxAcEenzthwv6Za46mw4q1pwVjfjrGrBVWvrmCUfNTs2tc2m9osI6Hoil6f7WqjNVhRFwbvZ5l/RxZ8dKeVMn7SoH9hWVMeWojruXjSiY/frqvugMgeufB8Cjjy+LKXk+e3P88jGRxgfM55HZjxCqF/3skTrhg2UP/gQzZs2YRo0iITHHyP4rLN6pDu49EAdGz7JJ29rJUZ/PYOyIrXu8BG+7w53u12U7cslb+tG8rdupmTvbtwuFwajiYThIxk1YzYpmWOISk7tVxtiSKcbZ3WLdqtqxlWl3bce61BNSy/QW/zQB5kwRAagTw3RMuJgU8fMONCosmNFUY6LN93mt7d77A9cCHSu+XgSee3HfAKMei4Yl3jo4IFvYe0TMOEGGHLk3y1SSh746QFe3/0681Pn87dpf8Ok9360wbZ3L+X/eoTGL7/EEBVF7L33YLnggh5Z9nVwbw3rP86jcFcNfoEGJp0ziMzpiT4P2LWlJW2ZdcGOLdiatG756NQhjFt4PimZY0gYNgKDqW9HZdwtzo7Zc7sA7aqzdfgJK/z0GML9McYGEjAiAn2EP4bwAAyR/uhD/FRAVhTFp7zpNj+8j/97IcRPPmpPn6tvcfDh5oOcOyaekNaCLC31WjGW8CEw++ijCM9tf47Xd7/OVSOu4rfjf+v1jmDulhbK//kgNW+8gc5sJuo3vyH8qitPeNmXlJLCXdWs/ziPktw6AkJMTLkgjZFnxGPy98068ObGBgq2bSF/2yYKtm2mrlybmBkcEUX6xCmkZI4hOXNMr49ZSylxNzq0bLnSE5Rbs+eqZtxNHX+T6oKMGML98RsUiiHCH31EAIZwfwwR/ugCjQN+UpyiKAOXN93m4e2e6oBxwMCZKdRN720ootnh4vJJKYcOrvwD1BfDtZ+B6ciV0L4s+JLHNj7G/EHzuX387V7/cbft20fx0t9iy8kh7PLLifz5bSe87EtKSd62KtZ/nEd5Xj2BFj9OvySdEVPjMfTwrGKnw0HJnl1ts8JL9+eClJgCzCSNHM24BeeRMjqbsLgEnwc86Za4am2HurSrPAG6Suve7rBMSoA+1A9DhD8BIyO1AB0egCHCE6D9fFvkRlEU5Xh589dpA1qHoUDrLj8AXOfLRvUVKSWv/lhAVmIomYme3ye7P4LNr8Lpt0PSkSeK5VTncOfqOxkZMZJ7p9zrVZCSUlL33vuU/u1v6Pz9SXr6KYLOOOOEvoPbLdm/qYL1n+RRVdRISKQ/0y8fRsbkOPTGnhlDllJSVZjfFqwLd23HabMhdDri0jM47cJLSRmdTVzaUJ/sGy0dbpw1h7Jnpyc4u6pbcNZ0Hn/WsuUA/IaEYgj3ZNAR/hjC/FWtakVRBiRvus0H9UZD+oOfDlSTW97IPy7yVH5trIBlv4TYTDjz90d8X1VzFb/48hcEm4J5bOZj+BuOXVLT1dhE6T33UL98OeZJk4j/xz8wxkQfd9vdLjd715WxYWU+NaVWLDFmZl0znPQJMT2yAUhjTTUFnmCdv31LW+WysPhERk2fTcrobJJGZOJn7tkZ6u4WJ7a8emwH6nAUNmiTxertncefI/wxxgUSMCoCQ3iANgYdEYA+xKTGnxXFw+Vy4XA4Ot1iYmIwqv3gj2nx4sWpq1atCo2IiHDu3bt3R1+2xZtu88XASillgxDiT8BY4G9Syo0+b10ve/XHAkL8DSwaHa/VvV7xa7DVw/nLj7hHt91l5zdf/4aalhpenP+iVzuDNe/YQfHSpTgKi4j85S+IvOkmxHFmqC6nm5y1pWxYmUd9ZQsRCYHMuX4kQ8ZGd9xzvJscLS0U7dreNtGssjAf0AqjJGeOIWX0GFIyxxASefw/OLritjq0YL2/TgvYBxu1QK0XGOOD8BtiacukWwO0zmxQ48/KgOZ2u7sMqj1xczqdbY/d7q7rB9x2221ERZ34roYnu2uvvbbyV7/6VfmSJUv6PKn1ptv8z1LKd4QQ04CzgH8C/wUm+bRlvayiwcbK7SVcMTmFAJMe9nwKu1fA7HshZkSX75FScs+ae9hUvokHz3yQkREjj/oZUkpqXnmV8n/+E314OCkvv3TcW3U67S52fl/Cps/yaayxEZ0SzLTF6aRmRh5Xpul2uyjfv+9QNbM9u3A5neiNRhIyRnL66TNIGZ1NdMqgHl3C5Wq0YztQj/1AHbb9dTjKmrRgbRCYkoIJnpGE3+BQrRiJqgCm9DIpZYfg56uby9WN7U49hBAYjcYub2az+YivdXULCQnxwf96J5/58+c35uTk9ItiZd4E79b/qhYAT0spPxJC/O1YbxJC+APfAn6ez3lXSvlXIcQg4E0gAm08/Uoppf3IV+odb68vxOGShyaqffcvCE066h7dL+14iWX7lnFr1q3MTZ171Os7a2oo+eOfaPzyS4JmzCDu/r8f16Q0e4uTHasPsvnzAqz1duKGhDLjigySRoR3O/u0N1vJWfMdeZs3ULB9S1t98KjUwWTPP4eU0dkkZIzAaPLrdjuPxFVvx3agFtsBLbt2llsBEEYdppQQQjJT8BsUiikpGNFDY/TKyUdKecQu4KNlnsdzOx4Gg6HLIGkymQgMDOxWYD3STa/Xn7I9Th988EFSeXl5j47RRUdHW88777w+2fDkeHgTvIuFEE8Bs4H/E0L4oc06PxYbMFNK2SiEMALfCSE+AZYC/5JSvimEeBJt8tt/j7P9PcLllrzxUwGnDY4gLToICn6EgjUw7/9A3/U40DeF3/DwhoeZmzqXm7NuPur1rRs2UPzb23FWVRFz1x8Iu/LKbv+jszU72fZVEVtWFdLS5CAxI4w5148kPt3S7WvVlB5k88oVbP/6c+zNzQRFRDJkwmStmtmoLMyhlm5d72ictTZsB+qwe7rBnZXNgLZFpCk1BHN2tJZZJwSpyWOnELfbTUtLCy0tLTQ3N3e47+pY673dbm8LqlJ2VbLu6PR6/RGDob+//wkF09aAbTAY0PWjAkPKycmb4H0xMA94UEpZK4SIA+441puk9i+rdasno+cmgZnAZZ7jLwF308fB+9s9FRTVNPOH+cO1A98/olVQG9v1Vp97a/byu29/x/CI4dw39b4jBk/pclH19NNUPP5vjImJpL7xBgGjjt61friWRgdbvixk61dF2JudpGZGMG5+KrGDu7daT0pJ/rbNbPpkGfs3rUen0zPstGlkz19E7JChPfILXkqJq7oF24E6LbM+UIerWttIRPjr8UsNJXBiLH6DQjHGByH0p2bWcLJwuVxHDbRHe81msx312nq9Hn9/fwICAvD39ycwMJDIyEhMJtMJBVe9D1Y/KL1vIGXIvuJN8I4DPpJS2oQQ04HRwMveXFwIoUfrGk8DngD2AbVSytZqGEVAl1tiCSFuBG4ESE5O9ubjjtura/OJDPJj9ogYbZ/unI+12eVdrOmubqnmF1/+gkBjII/NeIwAQ9dFVBzl5Rz83e+xrl1LyMKFxN79V/RBQV63qanOxpYvCtn2bTFOm4sh2VGMm59KVHJwt76bo6WFHd9+yaaVy6kuLsQcamHyBT8ja/Z8gsLCj32Bo5BS4qxs7pBZu+q0ERCd2YBpUChBU+K1YB0XqGZ990MOh+OYgfZIrx2rS9lgMLQF34CAAEJCQoiOju5wrP19+8dGoyqCoyhH403w/h8wXgiRBjwNfAi8Dpx9rDdKKV3AGCGEBXgf8HpLLSnl057PY/z48d3vH/NSUY2VL3PKuW16GiaDDr5/DAwBMPGmTuc6XA6Wfr2UyuZKXpj7AjGBXW8H2vjd9xz83e9wW63E/f1vhF5wgdd/iBqqW9j0eQE7vzuI2+kmfUIMY+elEBHvfeAHqCsvY9OnK9j+1WfYmpqIGZzG/NuWMvS00zEc55IQKSXOcmvbTHDbgTrcDdofcF2QEb9BofgNDtUqkkWbVbDuBVLKtgB8PBmw03n0Sscmk6lDUA0PD+8UaI8UhA09UNJXUfqTRYsWDVq7dm1wTU2NISYmZvSdd9558De/+U1lX7TFm39dbimlUwhxAfC4lPJxIcSm7nyIp7v9K+A0wCKEMHiy70SguPvN7jlv/FSAAC6dlAx1xbD1LRi/BAIjOpwnpeRvP/6NDWUb+L/T/4/MqMwur1e/8lOKly7Fb8gQEl55Gb8hQ7o873BOh4u1H+5n21dFIGHY5FjGzk3BEuP9nAwpJUU7t7Hxk2XsW/8TCEifNJWx888hfmhGtzMZ6ZY4SpsOZdZ5dW0lRPUhJvyGWNoCtiEyQGVKx0lKic1mO+4M+EjLf1r5+fl1CLCRkZFdZrtdBWHVzawohyxfvvxAX7ehlTfB2yGEuBS4CljkOXbM1E0IEQU4PIE7AM+EN+Ar4CK0GedXo2XyfcLudPPWuiJmZkSTYAmAT/8D0g2n3dbp3Fd2vsJ7e9/jxtE3cvbgrjsdGr7+muLbbycgK4vkZ59BF3jkUqrtVR1s5PPndlBV3MSIqXGMOzuVkAjva5o77DZ2rf6aTSuXU1mQh39wCBPPu4is2Wd3aytN6ZI4ShrbZdb1yBZPsA7zw39YeFuw1of7q2DdhZaWFurr62lsbOxWED7a5CshRKdAGxoaesQu5/b3fn5+avKUopyEvAneS4Cbgb9LKQ94lnq94sX74oCXPOPeOuBtKeUKIcRO4E3PcrNNwHPH2fYT9tnOUiobbdrysOZa2PAijDwfwlI7nLe6aDUPbXiIs5LP4rYxnQM7QNOaNRT/8lf4DxtG0tNPeRW4pZTs+LaY797NxeSvZ8Fto0nN9D7Y1ldWsOWzj9i66lNaGhuISk5lzs2/JGPqmV4t75JS4ihqpGVfrbbOOq8eadNWBhoi/AkYFaF1gw8OxWA5dtW4k53D4aC+vp66urpO962PjzQRS6fTdQi0ZrOZ8PDwo477tt77+fmpH0qKonTgTXnUnUKI3wPJnucH0DLoY71vK5DdxfH9wMTuN7Xnvbo2n8SwAM4YGgXfPwz2Rpj6qw7n7K/dz+++/R1Dw4by92l/73KXMOuGDRTeehumlBSSnn0GffCxJ5U1N9r58uXd5G2tJHlEODOvHk5gqHcBtzhnJ5s+Wc7en34ACWkTJpM9fxGJw0d59UfeWduCdUM5TRvLcFVps8EN0QGYx0S1jVnrQ3pubfdA4HK5aGho6DIwt95brdZO7zObzYSGhhIREcGgQYMIDQ0lJCSE4ODgDkFYTcBSFKUneVMedRHwIGACBgkhxgD3SinP8XHbfCq3vIG1+6v53bxh6F02WPskDJkFcaPbzqltqeXnX/4cP70fj898HLOx8/hz87ZtFN54E8aYGJJfeN6rwiuFu6v54oWdtDQ5mLY4ndEzEo85uctpt5OzZjUbP1lG+YF9+AUGMm7BeYyZs4DQ6K4nzrUnHS6ad1TRtL4M275akOA3OJSQmcn4DwtDH9Qvigb5hNvtpqmp6aiBubGxsVPXtZ+fH6GhoYSGhhIfH98WmFvvQ0JCVD1oRelZbrfbLXQ6nc8mKQ8UbrdbAEec0OJNt/ndaJny1wBSys1CiME90bi+9NqPBRj1govHJ8GW16GpvEPW7XA7WPrNUsqaynh+3vPEBsZ2ukZLTg4F19+APiyM5BdfwBB59C5vl9PNj8v2s+nzAsJizCz8eRZRSUfP0hurq9jyxSds+fwTmuvriEhM5qzrb2PE6TMw+h+9K1tKib2gAeuGMqxbKpA2F/owP0JmJWMeG4MhfOB3hUspaW5uPmpgrq+v7zSpy2AwtAXhIUOGdArMoaGh+PmdWr0PitIPbK+oqBgRFRVVdyoHcLfbLSoqKkKB7Uc6x6sJa1LKusO6/I4+vbWfa7a7+N+GIuaNiiPSbIAfHoP4bBh0aDvON3e/ybrSdfx92t/JisrqdA3b/v0ULLkWnb8/yS++gDG2c3Bvr7bMymfP7aCioIGRp8czdXE6xqPU6i7Zm8PGT5axZ+13uN1uBo+dwNh555CcmXXM7ldXvY2mjeVYN5ThrGhGGHUEZEZiHheD36DQAbeEy263U15eTnl5eYfx5db7w9cb63S6tsw4KSmpLRi3D8wBAWp2vKL0N06n8/rS0tJnS0tLR+FdJc+TlRvY7nQ6rz/SCd4E7x1CiMsAvRAiHfgl8EMPNbBPLN9ykPoWJ1dMSoZdy6F6Pyx+CTx/zOvt9Ty19Skmx01m0eBFnd5vLyig4JoloNOR/OILmBITj/hZUkp2rynh27f2ojcI5t+UyeDsrnfvcTkd7FnzHRtXLqc0dw+mADNj5i4ke+5CLLFxR/1O0uGmeVcV1g1ltOypAQmm1BDCzkgkYHQkOr+Bsea2ubmZkpISSktL2+4rKys7dGkHBQURGhpKTEwM6enpnbLmoKAgNcNaUQagcePGlQMDeki2t3jzF/0XwB/RapW/AXwK3OfLRvnaqz/mMzQmiImpYfDsIxA+GIYfCtLPbn2Wels9vx3/207ZmaOkhIJrliBtNpJfeRm/QUfeGc5mdfD1aznkbignYZiFs64ZQVBY113VBdu38MWzT1BTcpCwuARmLrmJkWfOwhRw5HXeUkocxY00rfd0izc70YeaCJ6ehHlcDMZI75eb9TYpJQ0NDZSUlHQI1nV1dW3nBAcHExcXx/Dhw4mLiyMmJoaQkBBV/ENRlFOeN7PNrWjB+4++b47vbS2qZWtRHfecMxKR/x0c3AQL/wU6rQv7YONBXtv1GouGLCIjvGNBOGdFBQXXLMFVX0/yiy/iP3ToET/nYG4tnz+/A2utncnnDSZ7TkqX+2tb6+v45pXn2Pntl1hi4jjvd39mcPaEo2676WqwY91UTtOGMpxlVjDoCBgZQeD4GPyGWPpdt7jb7aa6urpDNl1SUtJh9nZERASJiYlMmDCB2NhY4uLiCPRynbyiKMqpxpvZ5uOBu4DU9udLKUcf6T392WtrCwgw6jl/bAK8sxQCoyHrsrbXH9v0GEIIfpH9iw7vc9bUUHDttTgqKkh+9tkjbjAipWTdR3ms/+gAwZEBXHDHOGIGdd4rV0rJzm+/5OtXnsNubWLS+Rcz6YJLjrg+WzrdtOyupmlDGS051eAGU1IwlvPTMI+OQhfQP7JRp9NJRUVFhyBdVlaG3e6pea7TER0dzdChQ4mLi2vLqNXkMEVRFO958xf/NbRdxLYxwCeq1TU7+HBLMeeNSSCkZhfsWwWz/gJGrSt7R9UOPtr/EddnXt9hdrmrvp6C667DXlBI0lNPYR7bafk6AG635OtXd7PrhxKGTYrljEuHYvLv/D9xTUkxXzz7BAXbtxI/dDizb7iNyOTULq9pP9iozRbfXI67yYku2EjQ6YkEjovBGN2j29l2m81mo6ysrEOgLi8vb5vZbTKZiImJYcyYMW3ZdFRUlOr2VhRFOUHe/BWtkFIu83lLesF7G4tocbi5YnIK/PBbMAXB+OsALRN+eP3DhPmFce2oa9ve42psovCGG7HtzSXpP08QOHlSl9d2Odx8/vwO9m2qYPyCVCYuHNRpvNzldLDuw/+x9v23MBhNnHX9rYyeNa9TF7mryYF1cznW9WU4SppALwgYEYF5XAz+6WF9spVmU1NTp4lkVVVVba+bzWbi4uI47bTTiIuLIzY2lvDwcDVxTFEUxQe8Cd5/FUI8C6xCm7QGgJTyPZ+1ygeklLz2YwFZSRZGmWth+3sw+RYIsACwung1P5X+xB8m/oFgk7b22t3cTNGtt9K8fTsJj/yLoNNP7/LaDpuLT57aRuHOaqYtTidrVlKnc4p27+CLZ56gqqiAoZOnMeOaGzttyWnbX0vj9wdp3l0NLokxIQjLOUMIyIpCH9g7xUCklNTV1XWaSNbQ0NB2TmhoKHFxcWRmZrYF6pCQELX0SlEUpZd4W9s8A20zktZucwkMqOD944Fqcssb+cdFo2HNYyB0bRuQON1OHl7/MCkhKSwethgAabdT9ItfYl23jvh//pOQ2bO7vG5Lk4OPnthK2YE6Zl6VwfAp8R1fb2xk9esvsnXVSkKiojn/939l8NgJHc5xVjVT+/EBWnZUoQs0EnRaPIHjYzDG+nbClpSSqqoqDh482CFQt7RoJVOFEERGRpKamtoWpGNjYzGb+7a7XlEU5VTnTfCeIKUc5vOW+Nira/MJ8TewaIgJVr4Coy+BEC3Qvp/7Pvvq9vGv6f/CqNMy3Monn6Lpu++I+9t9hC5c0OU1rfV2lj26mZqyJubeOIoh2dFtr0kpyVmzmq9efJrm+nrGLTyfqYsv71AVzW1z0vBVIQ2rixF6QcjcFIKnJSKMvutqdrvdFBYWsnv3bnbv3k1NTQ0Aer2emJgYRo4c2TY+HR0djcl08pZNVRRFGai8Cd4/CCFGSCl3+rw1PlLRYOPTHaVcOTmVgM3Pg7MZpv4SAKvDyhObniA7OptZybMArexp5dNPE3LOIiwXXdTlNeurmln2yGaa6mwsvDWLpBGHusDrystY9dx/OLB5AzGD07jgD/cQM+jQvt7SLbFuLKPu0zzcDQ7MY6MJnZfqs81AHA4HBw4cYPfu3eTk5NDU1IRer2fQoEFMnTqVpKQkIiMj1d7NiqIoA4Q3wXsysFkIcQBtzFsAciAtFXt7fSEOl+SKcZHwylMw7GyI0joTXtzxIlUtVTwy4xGEEEiXi5I//Rl9cDAxf/hDl9erLmli2aObcdpdnPvrbGIHh7a9tm/Dj3z02IMgJTOuvoEx8xai0x0Kira8OmqX78dR3IgpORjLVSMxHaO++fFoaWlh79697Nq1i9zcXOx2OyaTifT0dIYPH05aWhr+x6iNriiKovRP3gTveT5vhQ+53JLXfyzgtMERDC54D5prYOqvAaiwVvDijheZnTKbMdFjAKh++RVatm0j/qEHu9whrDy/nuWPb0HoBOctHUtkYhDgWd+97H+sfuMlYgYN4ZyldxESdagb3VnbQt0neTRvqUAfaiL8Z8MIyIrq0UleDQ0Nbd3hBw4cwO12ExgYSGZmJhkZGQwaNEgt01IURTkJeFNhLV8IMQ1Il1K+IISIAoJ837Se8c2ecoprm/njvDT46npIPg2SteVeT2x+Aofbwa/H/hrQapZXPPooQTNmEHL22Z2udXBvDSue2Iq/2cg5vx6DxbPO2mm389nTj7Nr9VcMO+105t7yK4x+Wlbrtrto+KaIhm+KAAielUzwmYnojrIpSXdUVla2BeyiIu0zwsPDmTx5MhkZGSQmJqrlWoqiKCcZbyqs/RUYDwwDXkCbdf4qMNW3TesZW4vqiAnxYw4/QF0hnP0gALk1ubyf+z6XZlxKckgyUkpK/vJXhMFA7F//0ikjzttWycqntxMS4c85vxrTVqO8saaaZQ/+nZLcHKZefAWTLrhE636XkubNFdR9cgBXvZ2ArChC56disJxYV7WUkoMHD7YF7IqKCgDi4uKYMWMGw4cPJyqqZzN6RVEUpX/xpg/1fCAb2AggpTwohDjmIK0QIgl4GYhBW1r2tJTyUSFEOPAWWrnVPOBiKWXNcbXeC78+ayg3TBuE4YXpEJUB6XMA+NfGfxFoCOSm0TcBUPe//2Fdu5bYu+/utL3nnnWlrHphFxGJQSz6RRYBwdoM7LL9uXzwz/toaWrknKV3kT5pCgD2wgZql+/DXtCAMSGI8Msy8EsN5Xi5XC7y8/PbAnZ9fT1CCFJSUhg/fjzDhg3DYrEc9/UVRVGUgcWb4G2XUkohhAQQQni7+NgJ/FZKudET7DcIIT4HrgFWSSkfEELcCdwJ/P442u61wMKvoWw7nPdf0On4qeQnvi36lt+M+w1h/mE4ysop+79/YJ4wAcvFizu8d/u3xXzzRg7xaRYW3Doak6eGeM6a1az8zyMEhIRw6b3/JDp1MNItafimkPrP8tEFGQm7KB3z2Jjj2ijEbrezb98+du3axZ49e2hpacFgMJCWlsbMmTMZOnSoWm+tKIpyivImeL8thHgKsAghbgCuBZ451puklCVAiedxgxBiF5AAnAtM95z2EvA1Pg7efP8ohCTAqItwSzcPrn+QuMA4Lh9+OVJKSu+7F2m3E3ffvR1KlW5YmcfaD/aTkhnBvBtGYTDpkW43P7z7Omv/9ybxw0Zw7m/vwhxqwdXkoPqtHGx7agjIiiLs/DR0XdQ1P5rm5ua27Hrfvn04nU78/f0ZNmwYGRkZDBkyRK27VhRFUbyasPagEGI2UI827v0XKeXn3fkQIUQqWtf7j0CMJ7ADlKJ1q3f1nhuBGwGSk5O783EdFW2AvNUw934wmPh4/wp2Ve/i/mn346f3o37lpzR+sYro23+LKTW17W3bvyli7Qf7SZ8Qw6xrhqPX63C0tPDJEw+z96cfGDn9LM66/jYMRiO2vDqqX9+Ny+rAcl4agZNiuzXmXF1dzZo1a9i8eTMOh4OQkBDGjh1LRkYGKSkpav21oiiK0oFXqaEnWH8uhIgEqo51fntCiCDgf8CvpZT17YNa++74Lj7zaeBpgPHjx3d5jld2vAf+oTD2KmwuG49tfIzh4cNZMHgBrtpaSu+7D/8RIwi/5pq2txzMrWX1W3tJGRXBWUtGoNMJ6ivL+eCff6MyP48zr7yOcQvOAwkN3xRR9+kB9GH+RN8yBlOC9xPxCwoKWLNmDbt27UKn05GZmcmECRNISEhQE84URVGUIzpi8BZCTAYeAKqB+4BXgEhAJ4S4Skq58lgXF0IY0QL3a+02MikTQsRJKUuEEHFA+Yl+iaOa8zeYcB34BfP69hcoaSrhvqn3oRM6Dj7wf7jq6kh+7lmEZ/1zU62NT5/eTnCEP7Ov1QJ3cc4ulj30d5x2O+f//i8Myh6P2+qg+u09tOyuJiAzkrAL073qJne5XOzevZs1a9ZQVFSEv78/06ZNY+LEiYSEdN73W1EURVEOd7Ro82/gLiAU+BKYL6VcK4TIAN4Ajhq8hZY6PgfsklI+3O6lZcDVaD8MrgY+PP7me0EICB9MbUstz2x9htMTTmdS3CQav/ueug8+IOKmm/DPyAC0bT1XPr0Nu83FOb8ag5/ZyPavv+CLZ/5NcEQUF//l/xGRmIStoF7rJm+wYzlnCIGnxR0zU7bZbGzatIm1a9dSW1tLWFgY8+fPJzs7W41jK4qiKN1ytOBtkFJ+BiCEuFdKuRZASrnbyy7dqcCVwDYhxGbPsbvQgvbbQojrgHzg4uNse7c8tfUpmpxNLB23FHdTE6V/+QumQYOIvPWWtnNWv72H0v31zL1hFOFxZr5+5Tk2rHif5FFZLPzNnfgHBtGwupi6Tw6gDzURfUsWpsSjr5qrr6/nxx9/ZP369dhsNpKSkpgzZw4ZGRmqeIqiKIpyXI4WvN3tHjcf9toxx6CllN+h1UHvyqxjvb8nFdYX8mbOm5yfdj5pYWmU/v1+HCUlpLz2Kjo/bTOQnd8dZMfqg4ydm0zauOi2wJ01ZwEzrr4BYZdUvbKLlp1V+I+IIPyidHTmI++xXVpayg8//MD27duRUjJ8+HBOO+00kpI67/WtKIqiKN1xtOCdJYSoRwvAAZ7HeJ4PqB0tHt30KEadkdvG3IZ10yZqXn2VsEsvxTx2LAClB+r45s0ckoaHMencIWz5/GM2rHifMXMXMnPJTTiKG6l6bReuOjuhCwYTNC3+iN3klZWVfPLJJ+zbtw+j0ciECROYPHkyYV3USVcURVGU43HE4C2lPCnWJ22t2MqneZ9yc9bNRBhCOfCnJRhiY4lauhTQ9uRe+dR2AkP9mHPdKAq2bmTV808yKHs806+6nqY1JdR+tB99sImom0fjl9z1pDKn08l3333H6tWrMRqNzJo1i/HjxxMQENCbX1dRFEU5BZz0W0x9Xfg1Ef4RLBm5hKr/Pol93z6Snn4KfVAgLpc2Qc3W5OCC342jobqY5Y88QGRSCgtuvZ3at/bSvK0S/4xwwi8eesRu8ry8PFasWEFlZSWjRo1i7ty5BAf3/DafiqIoigKnQPD+5dhfctnwy9DtL6Ty6WcIOWcRQWecAcAP7+ZSklvH7GtHYA528tof78HoH8B5t/+FhvfyacmpJnR+KkGnJ3ZZ4tRqtfL555+zadMmLBYLl19+Oenp6b39FRVFUZRTzEkfvAEijBby/ngL+pAQYv7wBwBy1paw9asismYlMSjLwlt3/4Hmhnou+esDOL+upmV3NZbz0wiaFNfpelJKtm3bxqefforVamXKlClMnz5dLflSFEVResUpEbyrX36Flu3bSXj4IQxhYVQUNPDVazkkDLVw2nmDWPHo/1F2IJdzb/8T/jl6GjeWEjI7pcvAXV1dzUcffcS+ffuIj4/niiuuIC6u83mKoiiK4isnffC2FxRQ8dhjBM2YQfD8+TQ32vn4ya0EBBmZc/0ovnvrZXLXrWH6VTcQ05RA3bcHCDwtjuCZHZd0uVwu1qxZw9dff41Op2P+/PlMmDBBrdVWFEVRet1JH7yrX3kVYTAQ+9e/IN2ST5/ZQXO9gwvuGEvuT1+yfvl7ZM1ZwLCYSdS+u5eArCgsi4Z0WApWVFTE8uXLKSsrIyMjg/nz5xMaevz7cyuKoijKiTjpg3fMnb/HctGFGGNj+f5/uRTn1DDzquFYa3P54rn/MGjMOKZMuojq13fjl24hfPHQtslpLS0trFq1inXr1hEcHMwll1zC8OHD+/gbKYqiKKe6kz54C70e/2HD2LuujM2fFzDqzASiEm288ZcHiExMZs75t1H96h6M8UFEXDECYdC6wYuKinjrrbdoaGhg4sSJzJw5E3//AVWbRlEURTlJnfTBG6CyqJEvX9lF3JBQsmdH8uZfb8fo78+ia39P3Zv7MIT5EXnNSHR+Wl2affv28eabbxIYGMj1119PYmJiH38DRVEURTnkpA/eLU0OPnlyK6YAAzOvTmf5w3/VloT99u80v3cQnUlH5LWj0Adpy7y2b9/Oe++9R1RUFFdccYUqtqIoiqL0Oyd98P5p2X4aa2yc95sxfPvqvyndn8u5t90FnzcinW6ibh6NIUzrDv/pp5/4+OOPSU5O5tJLL1WlTRVFUZR+6aQP3pPPH0JqViR7f/yAvT/+wPRLryd4sz/OOiuR12dijAlESsnXX3/NN998w9ChQ1m8eDFG45F3DFMURVGUvnTSB2+Tv4HaknWsW/Y/xpy1gOSKwdhK6oi4aiR+KSG43W4++eQT1q1bx5gxY1i0aBF6/UmxJ4uiKIpykjrpg3fe1k188ex/SM0aR1bAmbRsryJs8VACMsJxOp28//777NixgylTpjB79uwjbvWpKIqiKP2Fz8qDCSGeF0KUCyG2tzsWLoT4XAix13Pv802uaw4WEZmUwplDL6VlexWhZw8icFwMNpuN119/nR07djB79mzmzJmjAreiKIoyIPiytueLwLzDjt0JrJJSpgOrPM99KnveIs45aykt6ysIOjOR4DMSaWpq4uWXX+bAgQOce+65TJ061dfNUBRFUZQe47PgLaX8Fqg+7PC5wEuexy8B5/nq81s1ri2h8csizONiCJ2XSm1tLS+88AJlZWVccsklZGdn+7oJiqIoitKjenvMO0ZKWeJ5XArEHOlEIcSNwI0AycnJx/2BhjA/AkZHEnZBOpWVlbzyyivYbDauuOIKUlNTj/u6iqIoitJX+mzCmpRSCiHkUV5/GngaYPz48Uc871j8h4XjPyycoqIiXnvtNXQ6HUuWLCE2NvZ4L6koiqIofaq3g3eZECJOSlkihIgDynvjQ3Nzc3nrrbcICgriyiuvJDw8vDc+VlEURVF8orc3o14GXO15fDXwoa8/cPv27bz++uuEh4dz7bXXqsCtKIqiDHg+y7yFEG8A04FIIUQR8FfgAeBtIcR1QD5wsa8+v5WUkuTkZC655BJV7lRRFEU5KfgseEspLz3CS7N89ZldyczMZOTIkeh0vd3JoCiKoii+cUpENBW4FUVRlJOJimqKoiiKMsCo4K0oiqIoA4wK3oqiKIoywKjgrSiKoigDjAreiqIoijLAqOCtKIqiKAOMCt6KoiiKMsAIKY97z49eI4SoQKvIFglU9nFzfE19x5OD+o4nB/UdvZMipYzqicYo3hkQwbuVEGK9lHJ8X7fDl9R3PDmo73hyUN9R6a9Ut7miKIqiDDAqeCuKoijKADPQgvfTfd2AXqC+48lBfceTg/qOSr80oMa8FUVRFEUZeJm3oiiKopzyVPBWFEVRlAFGBW9FURRFGWBU8FYURVGUAUYFb0VRFEUZYFTwVhRFUZQBRgVvRVEURRlgVPBWFEVRlAHG0NcN8EZkZKRMTU3t62YoiqIoXdiwYUOl2lWsdw2I4J2amsr69ev7uhmKoihKF4QQ+X3dhlON6jZXFEVRlAFGBW9FURRFGWBU8FYURVGUAUYFb0VRFEUZYFTwVhRFUZQBRgVvRVEURRlgVPBWFEVRlAHmpA/eX+0u597lO/u6GYqiKIrSY0764L2rtJ7nvz9AdZO9r5uiKIqiKD3CZ8FbCDFMCLG53a1eCPFrIUS4EOJzIcRez32Yr9oAMDZZu/zmwhpffoyiKIqi9BqfBW8pZY6UcoyUcgwwDrAC7wN3AquklOnAKs9znxmdGIpeJ9iYX+vLj1EURVGUXtNb3eazgH1SynzgXOAlz/GXgPN8+cFmk4HhccFsLFCZt6IoinJy6K3g/TPgDc/jGClliedxKRDT1RuEEDcKIdYLIdZXVFSc0IdnJ4WxpbAWl1ue0HUURVEUpT/wefAWQpiAc4B3Dn9NSimBLiOqlPJpKeV4KeX4qKgT22lubIqFJruLPWUNJ3QdRVEURekPeiPzng9slFKWeZ6XCSHiADz35b5uQOukNdV1riiKopwMeiN4X8qhLnOAZcDVnsdXAx/6ugHJ4WYiAk1q0pqiKIpyUvBp8BZCBAKzgffaHX4AmC2E2Auc5XnuU0IIspPD2KSWiymKoignAYMvLy6lbAIiDjtWhTb7vFdlJ1v4YlcZtVY7FrOptz9eURRFUXrMMTNvIYROCJEthFgghJgphIjujYb1tNZx700FtX3bEEVRFEU5QUfMvIUQQ4Dfo3Vt7wUqAH9gqBDCCjwFvCSldPdGQ09UVpKnWEtBDTMyBuTvD0VRFEUBjt5t/jfgv8BNniVdbTzZ92XAlRwquNKvmU0GMmJVsRZFURRl4Dti8JZSXnqU18qBR3zRIF+aNCiCV3/Mp8nmJNDPp8P9iqIoiuIzXs02F0KMEkJcLIS4qvXm64b5wuwRMdidblbvPbGKbYqiKIrSl7yZsPZX4HHPbQbwD7SKaQPOhNQwLGYjn+0sO/bJiqIoitJPeZN5X4S2tKtUSrkEyAJCfdoqHzHodczMiObL3eU4XQNinp2iKIqidOJN8G72zCh3CiFC0MqZJvm2Wb4zZ0QMtVYH6/LUxDVFURRlYPImeK8XQliAZ4ANwEZgjS8b5Uunp0dhMuj4bGdpXzdFURRFUY7LMYO3lPJWKWWtlPJJtFKnV3u6zweG9S/ACwvAs9ot0M/A6WmRfL6zjMNWwCmKoijKgODtbPMEIcQUIBmwCCHO8G2zepDbCfnfQV1h26HZI2IoqmlmV4naIlRRFEUZeI652FkI8X/AJcBOwOU5LIFvfdiunpM0SbsvWAuWZABmDY9BiG18trOUEfEhfdg4RVEURek+bzLv84BhUsqzpZSLPLeBs1QsZiSYgqHg0DB9VLAfE1LD+XDzQdxu1XWuKIqiDCzeBO/9gNHXDfEZnR6SJmqZdzuXTUzmQGUT3++r7KOGKYqiKMrxOWLwFkI8LoR4DLACm4UQTwkhHmu99V4Te0DyaVC+C5oPLQ+bnxlLeKCJV9bk92HDFEVRFKX7jjbmvd5zvwFY1gtt8Z3kyYCEwnUwdA4AfgY9l0xI4qlv9nGwtpl4S0DftlFRFEVRvHS0jUkGxG5hXkkYBzqDNu7tCd6gdZ0/+c0+3vypgKVzhvVhAxVFURTFe14tFTucEOLuHm6Hb5nMEJfVadw7KdzMzGHRvLGuELtTlUtVFOX/t3fe4XEVZ9++Z/uudrWr3ovl3nvDxvReQ00oIQFCCGkvJIEkJCHvmy8FSC8koSWQBEJJKAFCMWBwwb13Wy7qvW2v8/1xVrJktZUsycjMfV1zzZxz5pydw2L99pl55nkUitHBoMQbbSp9dFG4GCo3QSTY5fRNi4uodwd5a5eKuKZQKBSK0cGgxFtK+Z+hHsiwU7gIokGo3tbl9BnjMyhJT+L37x0kqraNKRQKhWIUkEhK0IeEEMlCCKMQ4l0hRL0Q4qaRGNyQUrBIq8u6hmXX6QT3nD+BfbVuXtlaeRIGplAoFArFwEjE8j5fStkGXAocAcYB30rk4UIIlxDiRSHEXiHEHiHEYiFEqhDiHSHEgXidMvjhDwB7BqSOhaPdc6pcPC2HaXnJ/PKd/QQj0R5uVigUCoXi40Mi4t3ukX4J8IKUsnUAz/8N8KaUchJaHvA9wLeBd6WU44F348cjQ/FSOLIKwoEup3U6wb0XTKKi2c+z68pGbDgKhUKhUAyGRMT7NSHEXmAu8K4QIgMI9HMPQggnsAx4AkBKGZJStgBXAO3b0J5CC786Mky+HEJuOPR+t0unj0/ntLFp/O69g3iCkREbkkKhUCgUAyWRlKDfBk4D5kkpw2gR165I4NljgHrgL0KILUKIx4UQSUCWlLI63qcGyOrpZiHEHUKIjUKIjfX19Ym8SwIjWgYWJ+zuHnNGCMG9F06i0Rvit+8eGJrPUygUCoViGOgrPOrS9raUsklKGY23vVLKmrgT27Q+nm0A5gB/lFLOBrwcN0UutYTaPbp4SykflVLOk1LOy8jISPyN+sJggomXwL7XIRLqdnlWgYvPLCjk8ZWH2FzW3MMDFAqFQqE4+fRleV8thFgjhPiBEOISIcQCIcQyIcStQoi/Aa8BfcUUrQAqpJTr4scvool5rRAiByBe1w3BeyTOlMsh0AqHe85o+t2LJ5HjtPKtF7YRCCvnNYVCoVB8/OhVvKWUd6N5mFcD1wI/Au4BxgN/llIuk1Ju6OP+GqBcCNEed/QctJzgrwK3xM/dArxyoi8xIErO0lKE7n65x8sOi5GfXT2d0novv1q+f0SHplAoFApFIvQa21wIsRhYK6V8DHhskM//KvAPIYQJLbXo59F+MDwvhLgNOApcN8hnDw6jBSZeCHtfg0t/Bfru2U5PH5/BZxYU8NiHh7hqdj4Tsx0jOkSFQqFQKPqir2nzzwKbhBD/FEJ8TgiRPdCHSym3xtetZ0gpr5RSNkspG6WU50gpx0spz5VSNg1++INkyhVaetAjq3rtct+FkzDodDy7Xm0dUygUCsXHi76mzb8kpZwD/BBIAf4qhPhICPGT+Nq3fqQGOeSMPQeMNtj1Uq9dXDYT503N4pWtlSppiUKhUCg+ViSyVWyvlPJXUsoLgbOBVWhr4Ov6vvNjjMkGU66EHS+Cv6XXbtfOzafZF+bdPbUjNjSFQqFQKPpjQIlJpJR+KeUbwBVSynnDNKaRYeEXIeyFLX/rtcvp4zPISjbz4qaKERyYQqFQKBR9M9iUoGJIR3EyyJ0FRUtg3aMQ7Tmiml4nuGpOPiv211Pn7jeonEKhUCgUI8JgxfvUyJ256EvQWgb73ui1yzVz84nGJC9tVhnHFAqFQvHxoK+tYvf0dgmwD89whofmQDMplh6Sl028GFyFsPaPWvCWHhibYWdOoYsXN1Vwx7IShBj9kw4KhUKhGN30ZXk7eil2tGxho4JfbvolF//7YiKxHqbGdXpY8EUoWwNVW3t9xnXzCjhQ52Hd4ZHf1aZQKBQKxfH0anlLKf93JAcyXExOnYwn7GF3425mZMzo3mHOzbDip/Dhw/Dpf/T4jCtm5fHQW/t4fOUhFpWkDfOIFQqFQqHom8GueY8aFmQvAGBddS872yxOWPJ1LeLakdU9drGa9Ny8qIjle+o4WOcZrqEqFAqFQpEQp7x4p1nTGJ8yvnfxBlj8FXDkwtv3Q6zngCw3Ly7CbNDxxKrDwzRShUKhUCgS45QXb4BFOYvYUreFQKSX7V4mG5z7AFRtgR0v9Ngl3W7mqjn5/GtzBQ2e4DCOVqFQKBSKvulXvIUQZiHEDUKI78bTg/5ACPGDkRjcULEoZxGhWIit9Vt77zT9OsiZBe/+L4R8PXa5/fQxhCIxnv7o6LCMU6FQKBSKREjE8n4FuAKIAN5OZdQwN2suBmHoe+pcp4MLfgJtlfDBgz12GZth59zJWTz90RHcgfAwjVahUCgUir5JRLzzpZTXSykfklL+or0M+8iGkCRjEtPSp/Ut3gDFS2DOZ2H1b3p1XvvaOeNo8YV57MNDwzBShUKhUCj6JxHxXiOEmD7sIxlmFuYsZFfjLtpCbX13vOCnkFIML90JgdZul2fku7hkeg6PrzpMvVutfSsUCoVi5ElEvJei5fXeJ4TYLoTYIYTYPtwDG2oW5iwkJmNsrNnYd0ezHa56FNoq4I17e+zyjfMnEIzE+N17B4ZhpAqFQqFQ9E0i4n0RMB44H7gMuDRejypmZszEorf0P3UOULAAln0Ltv8TDn/Y7XJJhp1Pzy/gmXVlHGkYVcv/CoVCoTgF6FW8hRDJ8aa7lzKqMOlNzM2ay6rKVUiZQF6VpfeALQ3W/bnHy18/ZzxGvY6H3943xCNVKBQKhaJv+rK8n4nXm4CN8XpTp+NRx/nF51PmLmNnw87+OxstMPtmLeNYa/d83pnJFr6wrITXt1ez8kD9MIxWoVAoFIqe6VW8pZSXxusxUsqSeN1eSkZuiEPHeUXnYdabebX01cRumHcrSAkb/9Lj5bvOHEtxmo3vv7yTQDg6hCNVKBQKhaJ3hjXCmhDiSNzBbasQYmP8XKoQ4h0hxIF43UOuzuHBYXJwVsFZ/PfIfwlHE9innVIEEy6EzU9BpLtnucWo58efms6RRh9/eP/gMIxYoVAoFIrujER41LOklLOklPPix98G3pVSjgfejR+PGJeNvYzWYCsfVnZ3ROuRBbeDtx72/KfHy0vGpXPV7Dz+9EEpB+tGnSuAQqFQKEYhJyO2+RXAU/H2U8CVI/nhp+WeRqollf+U9izG3Sg5G1JLYP1jvXa5/5LJJJkNfPOF7YSjPSc2USgUCoViqEhIvIUQS4UQn4+3M4QQYxJ8vgTeFkJsEkLcET+XJaWsjrdrgKwBjfgEMegMXFJyCR9UfEBLoKX/G3Q6mH87lK+Fip799NLsZv7fldPYWt7Cb5arvd8KhUKhGF4SSUzyAHAf8J34KSPw9wSfv1RKOQdtr/iXhRDLOl+U2p6tHvdtCSHuEEJsFEJsrK8fWm/uy8deTiQW4a0jbyV2w5xbwJoKK37Wa5dLZ+Ry7dx8/rDiIGsPNQ7RSBUKhUKh6E4ilvengMuJJyORUlYBjkQeLqWsjNd1wEvAAqBWCJEDEK/rern3USnlPCnlvIyMjEQ+rkeira1416/vcm5iykQmpEzgn/v+SUwmMM1ttsOSr8HBd3q1vgF+ePlUitOSuPu5rbT4QoMes0KhUCgUfZGIeIc6W8hCiKREHiyESBJCODrdcz6wE3gVuCXe7Ra0rGXDRsMjf6T89i8Q8/s7j43PTf0cB1sO8n7Z+4k9aP4X+rW+k8wGfvvp2TR4gvz0jb0nOnSFQqFQKHokEfF+XgjxZ8AlhPgCsBzo3XvrGFnAKiHENmA98LqU8k3gZ8B5QogDwLnx42EjadnpyFAI33HW90VjLqLQUcift/85sYhrCVrf0/Od3LyomBc2lXOgVnmfKxQKhWLo6Ve8pZQ/B14E/gVMBH4gpfxdAvcdklLOjJepUsofx883SinPkVKOl1KeK6VsOtGX6AvbvHkIqxXPylVdzht0Bm6ffjt7mvbwYUWC28Y6rO+f9tntK2ePI8lk4ME3lfWtUCgUiqEnIW9zKeU7UspvSSm/KaV8Z7gHNZTozGaSFizAs7K7QF869lLy7HkDs76X3g0Hl8OB5b12S00yceeZY1m+p471h4f1t4lCoVAoPoH0lZjELYRo662M5CBPlKTTTyd8tIzQ0aNdzht1Rm6bfhs7GnawpmpNYg9b+EVIHQtv3geR3p3Sbl0yhuxkCz/9757EfhgoFAqFQpEgfcU2d0gpk4HfoEVBywPy0baN/XpERjdE2JedDtBt6hzgirFXkJuUy8MbHk4sZKrBDBc9CI0HYe0jvXazmvTcfd54tpS18PqO6l77KRQKhUIxUBKZNr9cSvmIlNItpWyTUv4RLUraqMFUWIixqBDvypXdr+lN3L/ofkpbS/nLrp4TkHRj/Hkw8WL48GFoq+q12zVzC5iam8yPXtuNJxgZ7PAVCoVCoehCIuLtFULcKITQCyF0Qogbie/5Hk3YT1+Gd906YsHuCUaW5S/j/KLz+fO2P3O07WgPd/fABT+BaBjeur/XLnqd4Mefmk6dO8gv394/2KErFAqFQtGFRMT7BuA6oDZero2fG1XYl52ODATwbex5m9e3F3wbs97Mjz76UWJr1Klj4PRvwK5/w66Xe+02q8DFjQsL+euaw+ysbB3k6BUKhUKhOEYiW8WOSCmvkFKmSykzpJRXSimPjMDYhhTb/PkIkwnvh92nzgEybBn8z9z/YV3NOl7Y/0JiDz39HsidDa/dDe6aXrt964JJpCaZ+N7LO4nFlPOaQqFQKE6MRGKbZwghviuEeFQI8WR7GYnBDSU6qxXbggW433+/V8v6mgnXcFruafxs/c/YUb+j/4fqjfCpRyHsg1e/Cr0812k18r1LprC1vIUnVx8+kddQKBQKhSKhafNXACdaZLXXO5VRh/PyywiXleFbv6HH6zqh48HTHyTDmsHdK+6m0Z9AgpGMCXDe/8GBt2Fj779prpiVy7mTs3jorX3sV5HXFAqFQnECJCLeNinlfVLK56WU/2ovwz6yYcBx/vnokpNpef75Xvu4LC5+ddavaAm2cO+H9xKJJeAlPv8LMPYcePPbULGpxy5CCH561XQcZgN3P7eVUETl/VYoFArF4EhEvF8TQlw87CMZAXQWC84rrsD99ttEmpt77TclbQr3L7yf9TXr+U/pfxJ4sA6ufhwc2fDcTeDpMVEaGQ4zP7lqOruq2vjtuyrvt0KhUCgGRyLi/XU0AQ/Eo6u5R1uEtc64rr0GGQ7T+nLfycyuHHclk1Mn8+TOJ4nGov0/2JYK1/8d/E3wwue0bWQ9cMHUbK6Zm88jKw6yprRhEG+gUCgUik86iXibO6SUOimlRUqZ3Cny2qjEMmEC1lmzaHnhhT63hAkhuHX6rRxpO8K7Ze8m9vCcmXDZb+Ho6j6Tl/zw8qmMSU/ia89uoaY1MNBXUCgUCsUnnES8zYUQ4iYhxPfjxwVCiAXDP7Thw3XddYQOHcK/qef16XbOKzyPouQiHt/xeOLxyWdeDzM/A6t/A3V7euxiNxv4001z8YWifOWZzYSjav1boVAoFImTyLT5I8BijgVm8QB/GLYRjQDJF12IzuGg+Zln+uyn1+m5ddqt7Gnaw0dVHyX+Aef/PzA74LV7et0+Nj7Lwc+unsHGo8389A2VOlShUCgUiZOIeC+UUn4ZCABIKZsB07COapjRWa2kfPp62v77JoF9+/rse1nJZWTaMnl85+OJf0BSurZ9rGwNbO39B8LlM3P5/JJinlx9mGfXlyX+fIVCoVB8oklEvMNCCD0gQQvaAoyaed76Mjfb36/odj7t9tvRORzU/+rXfd5v1Bv53NTPsaFmA2ur1yb+wbNugoJF8Pb3wNv7fvH7L57MmRMz+N7LO/lwf33iz1coFArFJ5ZExPu3wEtAlhDix8Aq4CfDOqoh5PC2elY+vx+/u2vubb3TSdptt+FZsQLf5s19PuO6ideRZ8/j4Q0PJ+Z5Dtr2sUt/BcE2eOMbvU6fG/Q6fn/DHMZn2rnrH5vZV6MCuCgUCoWibxLxNv8HcC+aYFcBV0opEwz+ffIpnpEOEo7u7G79pt58E/qMdOp++cs+HdLMejN3z72b/c37eengS4l/eNYUOOu7sOsl2N57YBi72cBfPj+fJLOe257aQKs/gbziCoVCofjEkojlDWAD9PH+1uEbztCTUeggyWni8Pbue6p1NhsZd92Ff+MmvB9+2Odzzi86nzmZc/jdlt/hCXkSH8CS/4HCxfDGN6Gl93XtHKeVP900l5rWAPe+uC1x73aFQqFQfOJIZKvYD4CngFQgHfiLEOJ7wz2woUIIQfHMDMp2NxEJd5/ydl1zDabiYmr+9/+IenpPUy6E4Fvzv0VToInHdjyW+AB0evjUn7Rp85fuhD6m3WcXpvDtiybx1q5anlx9JPHPUCgUCsUnikQs7xuB+VLKH0opHwAWATcn+gFCCL0QYosQ4rX48RghxDohxEEhxHNCiGH3XB8zI51IMErl/pbu4zMayfnJTwjX1FD34IN9Pmda+jQuH3s5T+9+mgPNAwhvmlIMFz8UD97ysz673rZ0DOdNyeKnb+xhS1nvIVwVCoVC8cklEfGuAiydjs1A5QA+4+tA52glDwK/klKOA5qB2wbwrEGRN9GFwaznyLaew5Ha5swm7bZbaXnhBTz9TJ9/Y943SDYl873V3yMcG8Da9MzPaB7oHz4Ee9/otZsQgp9fM5Nsp4U7/76J2jYVgU2hUCgUXUlEvFuBXUKIvwoh/gLsBFqEEL8VQvy2rxuFEPnAJcDj8WMBnA28GO/yFHDlIMeeMAajnsLJqRzZ0dDrWnL6V7+Kefw4qu//HtGWll6flWpJ5XuLvsfuxt08uWMAac2FgEt+Djmz4KUvQmNpr12dNiOPfXYe7kCEO57eSKCH6X6FQqFQfHJJRLxfAr4LvA+sAO5Hy/G9KV764tdonurt+8LTgBYpZXuezQogr6cbhRB3CCE2CiE21tef+P7n4hnpeJqDNJT37GymM5nIffBBIi0tVN7zDWS4d6v6vKLzuKj4Iv60/U/sa+o7yEsXjFa4/m+gM8A/b4RA7/ldJuck8+vrZ7G9spV7X9yuHNgUCoVC0UEiW8Weai/Aq8CW4871iBDiUqBOStmfwPf2uY9KKedJKedlZGQM5hFdKJqWBoIevc7bsUyZQs4Pf4h3zRpq/t+P+xTM7y78Lk6Tk2+v/Db+iD/xgbgK4ZonoWE/PP/ZXrOPAZw/NZtvnj+RV7dV8evlKoWoQqFQKDQS8TZfIYRIFkKkApuBx4QQv0zg2UuAy4UQR4B/ok2X/wZwCSEM8T75DGz9fNDYkk1kj3FyaEt9n6Lsuvoq0r7wBVqee46mv/b62wSXxcWPl/6Y0pZS/u+j/xuYZTz2LLj8t3DofXj1a70GcAG468yxXDs3n9+8e4C/rz2a+GcoFAqF4pQlkWlzp5SyDbgKeFpKuRA4t7+bpJTfkVLmSymLgU8D70kpb0Sbfr8m3u0WtCn4EWHiomwaKz3UHOo7HXnG3f+D4/zzqXvooT6jry3JW8Jds+7itUOv8ezeZwc2mNk3wZnfgW3PwPu9B6wTQvDTq6ZzzqRMvv/KTt7YUT2wz1EoFArFKUci4m0QQuQA1wGvDcFn3gfcI4Q4iLYG/sQQPDMhJizIwmQ1sOP98j77CZ2O3J/9FEN6OnUPPdynVX3HjDs4I/8MHt7wMFvqtgxsQGfcB7Nv1jzQV/+m127tIVTnFKbwP//cysoDKga6QqFQfJJJRLz/D3gLOCil3CCEKAEGtAArpVwhpbw03j4kpVwgpRwnpbxWShkc+LAHh8liYPJpOZRursfb2vfH6mw20r/6Ffxbt+Jevrz3fkLHT07/CTn2HL7+3tc50nok8QEJAZf9BqZdDe/8AD56pNeuVpOeJ26ZR0lGErc/tZHVB3tfu1coFArFqU0iDmsvSClnSCnvih8fklJePfxDGxqObNvMiqePpfOcdkYeMSnZ9WH/S+2uq67CVFJC/S9/hYxEeu2XbErmj+f+ESEEdy6/k3rfACxjnR4+9ShMvhze+g5s6D31qMtm4h+3L6Q4LYnbntrAmlIl4AqFQvFJJBGHtQwhxHeFEI8KIZ5sLyMxuKGgvuwIm15/meZqTaxdmTaKpqaxa2UV0UjfmU2FwUDmN+4hdPgwLf/6d599i5KLeOScR2gKNPGl5V/CHRpAdjC9QfNAn3gxvPEt2P92r13T7Gb+8YWFFKbauPWvG1QaUYVCofgEksi0+SuAE1gOvN6pjAomLVkGQrBn1YqOc9PPysfXFqJ0S12/99vPPhvrnDnU//53fcY+B5iaPpVfn/lrSltK+e6q7w7MA11vhKsfh6xp8OKtULen167pdjPPfGERY9Lt3PbUBuXEplAoFJ8wEhFvm5TyPinl81LKf7WXYR/ZEOFITadw6gz2rFzRIaaFk1NxZdnY/OZRYrG+BVYIQdZ99xJtaKT+l7/o9/NOyzuNe+bdw4ryFTyz95mBDdaUBJ/5J5hs8Mz14O2exrSddLuZf96xiJn5Lr7yzGaeXd97xjKFQqFQnFokIt6vCSEuHvaRDCOTTz+Lltpqqg/sBUDoBAsvL6Gx0sue1VX93m+dOZPUz95M8zPP4tu4sd/+N02+iTPyz+AXG3/B7sbdAxusMw8+/Sx4auHZT0Ood2vfaTXyt9sWsmxCBt/59w5+8fY+FYlNoVAoPgEkIt5fRxPwgBCiTQjhFkL0vVH6Y8b4BadhMJnZvXJFx7mxczLIGedk3auHCPl7d0ZrJ+PrX8eYn0/1/d8jFug7WYgQgh8t+REplhS+9cG38Ib7nm7vRv5cuOoxqNwIz9/SZxQ2q0nPY5+dx/XzCvjdewf52j+3qljoCoVCcYqTiLe5Q0qpk1JapJTJ8ePkkRjcUGG22Rg7byH7PlpJNKIJoRCCJdeMx+8Os+nN/iOX6Ww2cn70f4SOHqXh97/vt3+KJYWHlj1EhaeC76z8DjHZt3NcN6ZcDpf+Cg6+Ay9/CWK932/U6/jZ1dO598KJ/GdbFTc8tpY6lY1MoVAoTlkS8TYXQoibhBDfjx8XCCEWDP/QhpYpp59FwN3G4a3HIqZlFSczcWE2294tp62h//jkSYsX47r2Ghqf/Avejz7qt//crLncO/9e3i9/n99u7jMBWy8P+Byc8wPY8QK8fk+fAi6E4K4zx/HIjXPYU+3m0t+tYtPRpoF/pkKhUCg+9iQybf4IsBi4IX7sAf4wbCMaJopmzMaa7GTPh+91Ob/oyhKEDlY8sw/Zj/MaQNa3v41pzBgqv/FNwrW1/fa/YdINXDvhWp7Y+QT/Kf3PwAe+9B5Yejds+ku/Ag5w8fQcXvryaVhNej796Fqe/uiIWgdXKBSKU4xExHuhlPLLQABAStkMmIZ1VMOA3mBgyrKzObDhI5prjjmp2VMsLLlmPOW7m9i6vO+wqQC6pCTyf/sbYoEAlXff02fqUNAs4u8s/A4LsxfywJoH2FCzYWADFwLOeUAT8U1/gdfv7lfAJ2Un8+qXl7J0XDo/eGUXd/1jM63+vsepUCgUitFDIuIdFkLoAQla0BaO5eceVcy/7Cr0BiNr//XPLuennp7L2NkZrH25lNoj/fvimceOJedH/4d/82ZqH3643/5GnZFfnPkLCh2FfOXdr7CzYefABi6ENn1++jdg01/h5Tv7dGIDcNqMPHHLfL5z0STe2V3Lxb9ZqabRFQqF4hQhEfH+LfASkCmE+DGwCug9DdbHDN+2ehqf2YOUkiRXCjPPu4g9K1fQVHUsPKoQgjNvmoTNZeLtJ3Yl5H3uvOQSUj57M81P/42mp3pPHdrR3+zk0fMfJcWSwp3L7+Rg88GBvYgQcPb34ezvwfbn4NnP9LmNDECnE3zxjLG8cOdihIBr//QRD725l1A/keUUCoVC8fEmEW/zfwD3Aj8FqoErpZQvDPfAhoqYP4J/ewOhMi1c6fzLr0ZvNLL2312tb0uSkfNvnYq7McCbj+3sN3QqQNZ99+E47zxqf/oz2t54o9/+mbZMHjv/MUw6E3e8cweHWg8N7GWEgGXf0pKZlL4LT10O3v7jm88uTOG/Xz+da+cW8MiKUq74w2p2VbUO7LMVCoVC8bEhEW/zVKAOeBZ4BqgVQhiHe2BDhW12JsKsx/ORts6d5Eph1gWXsHfVBzRVVXTpmzPOxZk3TqR8dxPvPrWnXwc2odeT+/OHsc6bS9V9307IA73AUcBj5z9GTMb4/JufZ2/T3oG/1NzPwXVPQ+1OeOzsPkOptuOwGHnwmhk8/tl51LuDXP771Tz45l61J1yhUChGIYlMm28G6oH9aKlA64EjQojNQoi5wzm4oUBn1pM0Lwv/jgai7hCgWd8Gk4lVzz7drf+UJbksurKEAxtqWfXigX49tXVmMwV/+AOm4mLKv3QXnlWr+x3TWNdYnrroKUx6E7e+dSvb67cP/MUmXwafewMiAXjifDjwTkK3nTsli+X3LOPqOXn8cUUpF/76Q7aWtwz88xUKhUJx0khEvN8BLpZSpksp04CLgNeAu9C2kX3sSVqUA1GJd30NALZkJws/dR0H1q9h30cru/Wfc0ERM88pYPt7Fez9qKbf5+udTgr/+hdMRUVUfOlLuN9/v997ipKLeOrCp3CZXdz+9u2sKF8x0NfSIrF94T1IKYJnroMPf96vJzpoqUUfumYmz9y+kHBUcu2f1vC3tUfVljKFQqEYJSQi3ouklG+1H0gp3wYWSynXAuZhG9kQYsywYZ6QgnddNTKqidv8y68me+x4lj/xR7wtzV36CyFYcvU4cse7WPX8ftxN/UcrM6SlUfTUXzFPnEjFV7+G+73+BTzXnsvTFz1NibOEr7//df6x5x8DfzlnPtz6Fky9Ct77EfzzM+Bv7v8+4LRx6bz+NW1L2fdf3sk9z2/DG+zfWU+hUCgUJ5dExLtaCHGfEKIoXu5FW/fWM4q2jNkX5xBtC+HfrWXq0un1XHjX3YQDfpY//kg3q1PoBOfcMhkpSWj9G0DvclH4lyexTJpE5Te/SWDfvn7vSbem8+QFT3Jm/pn8bP3PeGjDQwO3gE1JWjrRi38OB9+FPy+D8vUJ3eqymXjilvl847wJvLK1kkt/t4qdlcqZTaFQKD7OJCLeNwD5wMtoW8YK4uf0wHXDNrIhxjIxFX2qBc/Kyg5xTMsvZMl1N3Fww0fs+uDdbvckp1tZcs04Kvc1s+ODym7Xe0LvcJD/hz+gt9up+NJdRJr631ttM9r45Zm/5MbJN/K33X/j4Y0PD1zAhYAFX4DP/xcQ8OSFsOJBiPZvSet0gq+eM55nvrCIQDjKpx5ZzaMflvabLlWhUCgUJ4dEtoo1SCm/CiyVUs6RUn5VSlkvpQxJKQe4WfnkIXQCxxn5hMrc+Lcf214199IrKZgyneWP/Z6KPd2Dp0xZmkvh1DQ++vdBGio8CX2WMSuT/D/8nkhjI5Vf+zoyFOr3Hr1Oz33z7+sQ8F9v/vXg1qAL5sOdq2D6NbDiJ/CXi6Ahsa9pUUka//366Zw9KZOfvLGXTz+2lvIm38DHoFAoFIphJZGtYqcJIXYDe+LHM4UQo8JR7XiS5mdjzLPT+vohYkFti5ROp+eyb3yX5MxsXvn5j7sEbwFt/fvsz07CbDPwxiPb8bv7F2IA6/Tp5Pz4x/g2bqTqu/cjE3AkE0Jw3/z7uG7CdTy580l+u+W3gxNwSzJc9Shc9Tg07Ic/LYE1v4dY/9vCXDYTf7ppLg9dM4PdVW1c8OsP+fvao8oKVygUio8RiUyb/wq4AGgEkFJuA5b1d5MQwiKEWC+E2CaE2CWE+N/4+TFCiHVCiINCiOeEECMWJ13oBK7LxxJtC+F+/1gcc6vdwVX3PYAQgpd+9kN8rS1d7ktymrnoSzPwtYV489GdRKOJLfU7L72EjHvuoe2116j50Y8SEmIhBPcvup+rx1/N4zse58frfjzwdKLtzLgWvrwOxp4Nb98PT5wH1f1vSxNCcN28At66exmzC1187+WdfOaxtRyqT2zmQaFQKBTDSyLijZTy+IwdiUT2CAJnSylnArOAC4UQi4AHgV9JKccBzcBtiQ/3xDEXJWObk4l7ZQWRTmlAXdk5XHnvD/A0N/HPH36btob6LvdlFSdz1s2TqDrQwsp/7k/YIk6/4wuk3X4bLc/+k/pf/yahe3RCxwOLH+DzUz/Pc/ue4zsrv0M4NsjEIo5s+PQzmhXeUgaPngFvfheC7n5vzXNZ+fttC3nw6unsqW7jwt+s5HfvHiAYUYFdFAqF4mSSiHiXCyFOA6QQwiiE+CbxKfS+kBrtppoxXiRwNvBi/PxTwJUDHvUJ4rxoDMKgo/nfB5DRYyKcO2ESV3/3f/E2N/HPH9xLY2XX3ywTF2Yz54Iidq2sYu0rhxIW8IxvfAPXddfR+Oc/U//bxKbChRDcM+8evj7n67xx+A2+vPzLuEP9C24vD9Os8K9sgDm3wNo/wO/mwtZn+t0XLoTg+vmFLP/GGZw3OYtfvLOfC3+9kg/21/d5n0KhUCiGj0TE+07gy0AeUIlmRX85kYcLIfRCiK1o4VXfAUqBFilluwt0Rfy5Pd17hxBioxBiY3390AqF3mHCdflYgodaaX3rSJdr+ZOncf0Pf0Y0Eua5B+7rtga+6MoSppyey+Y3j7Lxja739oYQguwHfoDzmqtpeOSP1P3swYSF//bpt/N/p/0fG2o2cPMbN1Phruj/pt6wpsBlv4bb39X2h7/8JXj8HKjZ0e+tmQ4Lf7hxDk/dugCAW55czx1Pb+RIQ9/JURQKhUIx9Ii+RCS+l/tpKeWNJ/QhQrjQtpl9H/hrfMocIUQB8F8p5bS+7p83b57cuHHjiQyhR5pfPoh3bTWpN0zCNiOj67XqSp75/rdwpKVzw49+jsF0bGlexiTvPb2HvWtrWHRlCXMvLE7o82QsRu1Pf0bz3/6G69pryH7gAYTBkNC966vXc/eKuzHoDPzyzF8yN+sEI9PGYrDjeXj7+1pQl7O/B6d9FXT6fm8NRqI89uEhHllRSjga4/NLxvDls8bhtI6akPcKhWIIEUJsklLOO9nj+CTRp+UtpYwCRSfqVCalbAHeBxYDLiFEu2Llo1nzw0qsl6lh16UlmAodNL+4n3BNVwsyJSePC7/0P9QfOcQHf3+yyzWhE5z12cmMn5/F2pcPser5AwkFcRE6HVnf/Q5pX/wiLS+8SPmXv0zUk5jluiBnAf+4+B84TA5ue+s2nt719ImFM9XpYOan4a61MOECWP4A/PVSaCzt91azQc9Xzh7P+988kytn5fHYykOc8fD7PL7ykEp0olAoFCNAItPmh4DVQojvCyHuaS/93SSEyIhb3AghrMB5aGvl7wPXxLvdArwyqJEnyLp163j00Ud7FHBh0JF242SEWU/9EzsI13Xd0zx27gLmXnIFW996jQPr13S5ptMJzv38FGaclc+298p5+4ldRBIQLiEEmXf/D9k/fADvqtUcvfFGwtXVCb1LsbOYZy95ljMLzuThjQ/zrQ+/hSd0gh7gSWlw/d/hyj9qWcr+uAQ++kNC28qyki08fO1M/vOVpczId/H/Xt/DOb/4gOc3lBNJ0CNfoVAoFAMnEfEuRUtEogMcnUp/5ADvCyG2AxuAd6SUrwH3AfcIIQ4CacATgxl4ojgcDmpqati5s3sAFgC900zG7dNBQv2j2wnXdrWET7/hc2SVjOetP/2mWwpRnU6w9LrxnHb1OA5uquPlX27B09x/HHSAlE9/moI//5lwZSWHr70O77rEwpk6TA5+deavuHvu3bxz9B2u+c81bK3bmtC9vSIEzLpBs8JLzoC3vqtlKktgLRxgWp6Tp29dwD9uX0ia3cS9/9rOub/8gJe2VCgRVygUimGgzzXvbp2F0AF2KWXb8A2pOyey5h2LxfjTn/5ELBbjrrvuQqfr+fdKuM5H/WM7ICZJv3Uapjx7x7WW2hqe+d43MFksfOZHPyfJldLt/tLNdbz71B70Rh3n3zqVgimpCY0vePAgFV/9GqGjR8m4+39Iu+02RC9jPJ4tdVv49offptZXy50z7+T26bdj0CW2ht4rUsKOF+HNb2tr4QvvhLO+A+ZEfq+BlJJ399Txy3f2s7u6jeI0G3edOY4rZ+dhMiT2XgqFYnSh1rxHnkQirD0jhEgWQiQBO4HdQohvDf/QhgadTscZZ5xBQ0MDu3fv7rWfMdNGxh3TEQYddX/chndTbcc1V1Y2n7rvB3hbW3jpwf8lFPB3u3/snEyu/c48bMkmXv3dVrYuL0tofOZx4yh+4QUcF5xP/S9+ScXXvkbM3/35PTE7czYvXv4iFxRfwB+2/oEb37iR/c37E7q3V7psK/ssrH0EfjcPtj6bULpRIQTnTsnita8u5c83z8VhMXLvv7Zz5sPv88SqwyprmUKhUAwB/VreQoitUspZQogbgTnAt4FNUsoZIzFAOHFv81gsxh//+EcAvvSlL/VqfQNEPSGantlL8FArSYtzcF06FqEXAJRuWs8rD/8/imbO5opv3N/FA72dcDDKu0/tpnRzPXMvLGLhFSUIIfodo5SSpqeeou7Bh7BMn07BHx/BkJaW8Du+feRtfrzux7SF2rhjxh3cNu02TPohCF5XsRH+ey9UboK8uXDhz6BgQcK3SylZsb+eP64oZf3hJpxWIzcuLOSzi4vJdlpOfHwKheKkoyzvkScR8d6Ftrf7GeD3UsoPhBDb4pHTRoSh2Cq2Y8cO/vWvf3HttdcyderUPvvKqKT1rcN4PqzEviQX12Vjjz3nvbd5+8+/pWjGbK745v0Yzd0FKBaTfPDMPnavqmLasjxO//QEdLr+BRzA/e67VH7jmxjS0yl49FHMJWMSfsfmQDM/Xf9T/nv4vxQnF3P/ovtZlLMo4ft7JRaD7c/B8h+CpwYmXwbn/BDSxw3oMZvLmnn0g0O8tbsGvRBcMiOHzy8Zw6wC14mPUaFQnDSUeI88iYj319CczLYBlwCFwN+llKcP//A0hkK8Y7EYjzyi5VP54he/iNHY/57klv+U4lldRcp1E0iak9VxftcH7/LWH39D7sTJfOq+BzDbbN3ulVLy0UulbHm7jOIZ6Zx36xRMlsTWo/3bt1N+55eQ4TC5Dz2I46yzEnxLjVWVq/jJup9Q7i7nwuILuWfuPeTYcwb0jB4JejRP9DW/hbAf5twMy76lBXwZAGWNPv665gjPbyzHE4wwI9/JTYuKuHxmLhZj//vMFQrFxwsl3iPPgBzWOm4SwtApStqwM1RBWg4ePMjf//53Fi9ezAUXXNBvfxmN0fDEToJlbjLvnIEp/5jT1r6PVvLG735OemExV37r+zjS0nt8xvb3y1n1wkFSsm1cctcMktOtCY01XFlJxVe/RmD3btK/+hXSv/SlhB3ZAAKRAE/sfIK/7PwLALdMvYXbpt2Gzdj9h8aA8dTDhw/Bxr9oa+RzPw9L74bkgf1AcAfCvLylkqc/OsqBOg/JFgNXzcnnMwsKmZidmIOcQqE4+SjxHnkSsbx72tPdirbuvXU4BnU8Qxlh7fXXX2fDhg3ccsstjBnT/5R01BOi7vdbQULGXTMxOM0d1w5t2cDrv3kIg8nMFd+8n9wJk3t8RvmeJt56bKfmzPX5KRRNS2wtOxYIUPPAA7S+8ipJy04n96c/HdA6OEC1p5pfbf4V/z38X1Itqdwx4w6unXDt0KyHt5TBhw/Dln+AzqBZ4ku+Dq7CAT1GSsnaQ008u76MN3fWEIrGmFng4tq5+Vw2M1dFblMoPuYo8R55EhHvZ4B5wH/ipy4FtgPFwAtSyoeGc4AwtOIdCoX405/+RDQa5Utf+hIWS/9OU6FKD/WPbkfvMJHxxRnoHceEr7GijJcf+hHuxnrOvvVOpp99QY8Oai11Pt788w4aK73MOreARVeORZ/A1ikpJS3//Ce1P/0ZuuRkch/8GfYlSwb20sD2+u38evOv2VCzgTx7Hl+c8UUuHXspRt0QCGPTIVj1ay3RCRKmXwunfQ2ypgz8Ud4Q/95cwYubKthb48Zs0HHulCyump3HsgkZGPVqu5lC8XFDiffIk4h4fwhc3J4hTAhhB14HLkSzvgf+F3qADHVs84qKCp544gkmT57MNddc06f3eTvBI600PLkTfYqFjDtmoE86Jnp+j5vXf/MQR7dvYfyC0zjvjq9gdSR3e0YkFGX1vw6y84NKMgodnPO5yaTl2rv164nAvv1UfuMeQgdLSbn5ZjLv/h90Pay194WUko+qPuI3W37D7sbd5NnzuH367Vwx7oqhEfHWCljzO9j8NIR9MP58WHQXlJypTa8PcKw7K9t4YVM5/9lWRbMvTLrdxGcWFHLToiKykpWnukLxcUGJ98iTiHjvBaZLKcPxYzOwTUo5SQixRUo5e7gHORyJSdasWcPbb7/N/PnzufjiixPazhUobaHhL7swZlhJv3VaFwtcxmJsfP1lVj37NLbkZC752r3kT+k538qhLfW8/4+9hPwR5l9SzOwLitAnYFHG/H7qfv4Lmv/xD4yFheT+5MfY5g3834uUkpWVK/nj1j+ys3En41zjuG/BfUPjmQ7ga4INT8D6P4O3HjImw6I7NYvclDTgx4UiMT7YX89zG8p4d28deiG4aHoONy8qYn5xSkLfnUKhGD6UeI88iYj394FPcSwG+WXAq8AvgEdPNONYIgxXVrG3336bNWvWcMYZZ3BWgh7dgf3NNP5tN7okI+m3TsOY2dX6rT1cyuu/eYjWulouuPNrTFl2do/P8btDrHxuPwc21pGWl8SyT08gd3z3yG094V23nur77ydcUYHr2mvJ/MY96F2uhO7tjJSS98rf4+END1PpqeTcwnO5Z+49FCQXDPhZPRIOwM5/wdo/Qu0OMDu1MKzzb4P08YN6ZFmjj6c/0jzV2wIRJmTZuXFhEVfOzlNr4wrFSUKJ98iTkLe5EGI+cFr8cLWUcuiVtA+GS7yllLz66qts2bKFs88+m9NPPz0hKy5U4abhr7uQUUn6zZMxl7i6XA94PLz6y59Qvms7i6/5DIuvuaHX5x7aWs/K5/fjaQoyfl4mp109HnuKuce+nYl5vdT/7vc0/e1v6JOTyfzmN3F+6soBeaS3E4wGeWrXUzy+43HCsTDXT7yeL874IimWxH5M9IuUULYWNjwOu1+BWBiKlsCcW2DK5WBMzAO/M/5QlP9sq+Jva4+yo7IVs0HHJdNzuH5+AQvGpCprXKEYQZR4jzyJirceyAI6NipLKROL/zkEDJd4A0SjUV5++WV27NjBzJkzueyyyzAkkGM70hSg4S87iTQGcF5YjP30vC6CEY2EeefRP7Drg+WMm7+YC770dSxJPa9vh0NRNr91lC1vlWEw6Tj7s5MpmZXRY9/jCezbR80DP8S/dSuWadPI+s63sc0dXK7vel89f9j6B146+BI2g43PTv0sN0++GbspsXX5hPDUwdZ/aOviTYc0a3zaVTDrRsifN+C1cYCdla08u76MV7ZW4QlGKEqzcc2cfK6am0+ea+A/DBQKxcBQ4j3yJDJt/lXgAaAWiAICkKMpPGp/SCn54IMPWLFiBYWFhVx33XXY7f0LVswfofnF/fh3NWKZnErqtRPQ2Y5N3Uop2fT6y6x85q840jO47O7vkDVmbK/Pa6nz8fbju6gvczPz7AIWX5WgR3osRttrr1H3i18Sqa3Fcf75ZPzP1zGXlCT2H+A4SltK+f2W37O8bDlOs5PPT/08n570aZKMA1+v7pVYDI6u0raZ7X4FIn5IHQszrtdiq6cOfOy+UIT/7qjhhU3lrD3UBMDCMalcNSePC6flqGl1hWKYUOI98iQi3geBhVLKxpEZUneGW7zb2bFjBy+//DJms5lLLrmk3zCqoAm0Z00VrW8cRmczknLlOKxTu+7Frty3h9d+8yD+tlaWXH8zcy+5Ap2u50hi0XCM1f8+yI73K0gvsHPu56aQlpeY5Rvz+2l88kmanniSWCCA88orSb/rLkz5eQndfzy7Gnfx+y2/Z1XlKpJNydw05SZumHQDTrNzUM/rlUAb7H4Ztj8PR1Zq5/LmwtSrYOqnwDnw8Zc3+XhpSyUvbankcIMXk17HWZMyuHxmHmdPysRqUpHcFIqhQon3yJOIeL8PnDeSEdWOZ6TEG6Curo6XX36Zqqoqpk6dyuWXX47Z3P8adKjSQ/OL+wlXe7HOSMd1xbgu28l8ba28/effUbpxLbkTp3Dhl75OSk7vonRoaz0r/rGXoD/CwstKmHVeYcLx0SNNTTQ++hjNzzyDjMVwXnE56XfcgamoKKH7j2dH/Q4e3fEoK8pXYDPYuHrC1dw8+eahCbl6PK0VWkrSnf+Cmu3auYKFMOUKmHw5uAbmTCelZFtFK69ureI/26uodwexGvWcMzmTS6bncMbEDGymE0yjqlB8wlHiPfIkIt5PABPR9nYH289LKX85vEM7xkiKN2jr4KtXr+b999+nuLiYG264IaFY6DIaw72igrb3ytDbTaTdNBlTwbEwn1JK9qxawXt/+RPRcISFn7qOeZddhaGXZ/vdIVY8s49DW+rJLHJw5o2TyChMPGxouKaGxsefoOWFF5DhMMkXXkjqrbdindb/jEJP7Gvax192/YU3D7+JQHBe8XncNPkmZmQM0wpKw0HY9RLseQVqdmjnxiyD2TdryVEG6OgWjUnWHWrk9R3VvLmzhkZvCItRx5kTMrlwWjZnTczEaVNT6wrFQFHiPfIkIt4P9HReSvm/wzKiHhhp8W5n27ZtvPTSS0yYMIHrr78evT6xqdZQpYfGv+0m6g6RcuU4kuZnd7nubmpgxV8fY/+61aTk5HL2575I8ayencyklBzcVMfK5w8QcIeYfmY+Cy4vwWxN3FqM1NfT+Ne/0vLP54h5vdgWLCD1ls9iP/NMRILv1JkqTxV/3/N3XjrwEp6wh+np0/nMpM9wfvH5mPX9z1IMisZSzRrf8ndoOQrmZJh6Jcz8DBQsggF62UeiMdYfbuLNXTW8ubOGOncQg06wqCSN86ZkcfakTApShyAOvELxCUCJ98gzqMQkI83JEm+ADRs28PrrrzN16lSuuuqqhAU86g3T9M+9BA+0YJuVgeuKceiOE9wjWzfx3l//THN1FUUzZnP6DZ/r1aEt6Auz9pVD7PywEqvdyILLSpiyNDfhqXSAqNtNywsv0vT000RqajDm5pJyw2dwXX89esfAE4F4w15eLX2VZ/Y8w5G2IzjNTq4ceyVXT7iaMc7EU5kOiHZHt63Pao5uYS84C2H6NVoQmEGEZI3FJFsrWnhndy1v7arhUL0XgIlZDs6enMlZEzOZU+jCoEKzKhQ9osR75El0zbtbJyllz9FHhoGTKd4Aq1ev5p133mHcuHFce+21Ca2BA8iYxP1emTaN7jCRcu1ELONcXfpEwmG2vf06a//9HAGPm6lnnMuZn70dSy/e7vVlblY+v5/qg62k5SWx6MqxFE1LG9C+ZhmJ4H73PZr/8Q9869ejczpJu/VWUm+6EV3SwD3KpZSsr1nPc/ue4/2y94nICLMzZ/OpcZ/i/OLzh9ZLvTMhL+x5DXY8D6Xvg4xq0dymxR3dBhkI5nCDl3f31LJ8Ty0bjzQTiUmSLQaWjk/njAkZLJuQQY5TbUFTKNpR4j3yJCLenedzLcDVQERKee9wDqwzJ1u8ATZu3Mjrr79OdnY2N9xwA44BWKqhcjdNz+0j0uAnaUE2zguLu2wpAwh4Pax/+QU2vvYSSU4X53/xa4yZ3fO/BSklh7bUs+alUtrq/WSXJLPwirHkTxx4UBX/rl00/PZ3eD74AH1KCqmf+xwpN3xmUJY4QIO/gVdLX+WlAy9xpO0IVoOVcwrP4bKSy1iQswCDbpicwzz1msf6zn9D2UeAhMwpmpPblMu19iD2kLcFwqw+0MD7++r4cH8DNW0BAMZl2lk6Lp2l49JZWJKKw6LWyhWfXJR4jzyDzee9Xkq5oJ8+BcDTaMFdJFoo1d8IIVKB59Cykh0BrpNSNvf1rI+DeAPs37+fF154AavVyrXXXktBQeKez7FQlLZ3juJZXYnOasR58RhsszMRx0171x46yH//8EsaK8qYesY5LLvpVmzJPW/NikZj7F1TzYbXj+BtCZIzzsm8i4spmDzwCGP+rVupf+QRvB+uROdwkHLDDaTedCOGjMSCxRyPlJKt9Vv5T+l/ePPIm7hDblItqVxYfCEXjbmIGRkz0IlhmoZuq9Km1He/ekzIU8bApEu0UrAQetmq1xdSSvbXevhwfz0rDzaw/nAjgXAMvU4wI9/JkrHpLCpJY25RitqKpvhEocR75EnE8k7tdKgD5gK/lVJO7Oe+HCBHSrlZCOEANgFXAp8DmqSUPxNCfBtIkVLe19ezPi7iDVBVVcXzzz9PW1sb5557LosXLx6QUIaqvbS8dIBQmRtjnh3nxWOwjHV16RMJh1n7r2fZ8Oq/MFmsLP3MLcw454JeQ59GQlF2r65my9tH8TQHySh0MOu8AsbOyUwo4Uln/Lt20fjnR3G/8w7CYCD58stI+9znMI8f3BQ0aOFXV1as5I3Db/BB+QeEYiGybFmcV3Qe5xWdx8yMmegHIaYJ4amDva/B3jfg8AcQDYE1Rct4NuECGHu2djwIAuEom8uaWXOwkdWlDWyvaCUakxj1gtkFKZwxMYMzJmQwJSd5QL4JCsVoQ4n3yJOIeB9Gs5wFEAEOA/8npVw1oA8S4hXg9/FyppSyOi7wK/r7IfBxEm8Av9/Pq6++yp49exg3bhyXXnoprgEkBpExiW9bPW1vHiHaGsQyKZXkcwsx5Xedqm6sKGP5E49QsXsnmWPGcubNt1EwtfdtWdFIjL0fVbN1eTkttT7sKWamn5nP5CU5WO2mXu/ridCRIzQ9/TQt/34JGQhgW7SIlBtvwHHWWYgEwsf2hifk4f3y93n76NusrlxNOBYmzZLGmQVncnbh2SzMWTh8HuuBNji4HPa/BQfeBn8TCD0ULIBx58K4cyB75oA919vxBCNsONLE2lJNzHdWtgGQlmRi6Xhtiv308RlkO1U6U8WphRLvkWdEvM2FEMXAh8A0oExK6YqfF0Bz+3FvfNzEG7Qp1A0bNvDOO+8ghOCcc85h/vz5CeUG73hGOIp7dRXuFRXIQEQT8fOKMHWKqCalZO/qD1j5zFO4G+sZO28hp3/mFtLyC3t/bkxydGcjW5eXUbm/Bb1Bx7h5mUxblkfWmOQBzRREmptpefFFmp99lkhVNYasLFzXXIPrmqsx5pxYkBZPyMPKypW8W/YuKytW4ov4sBqsLMpZxLL8ZSzNW0p2Unb/DxoMsShUbISD72hCXr1NO29L1/KPjz0LSs4aVHS3durdQT7cX8+qgw2sPFBPgycEQElGEkvHpXPa2DQWjkkjJWlgP6wUio8bSrxHnkQs72uBN6WUbiHE94A5wP+TUm5O6AOEsAMfAD+WUv5bCNHSWayFEM1Sym7zlkKIO4A7AAoLC+cePXo00XcaUZqbm3n99dc5ePAgOTk5nH/++YwZM7BtUrFABM/qKtyrKpGBCPYleSSfV4TOfGwqORwKsvn1V1j/yguEAgEmnbaMxdfcQGpu3+LSWOlh54eV7FtbQzgYJSXbxuQluUxcmI0tOXHRkJEI7vffp+W55/GuXg1CkLR0Ca6rrsJ+9tnoTCcmQKFoiA01G1hRvoIPKj6g2lsNwPiU8SzNXcri3MXMyZozfFa5uxYOvQ8H39Vqb712Pm2cFhim+HSt2AfnAxCLSfbWuFl9sIHVpQ2sO9SEPxxFCJiUncyiklQWlaSxoDhVibli1KHEe+RJRLy3SylnCCGWAv8PeBj4gZRyYb8PF8IIvAa81R6RTQixj1E+bX48Ukp27tzJ8uXLaW1tZcKECVxwwQWkpaX1f3MnYv4IrW8exruuBr3LjOuysVimdHU+87W1svG1l9jy5n+IhsJMWLyUBVdcQ2Zx34k8QoEIBzfVsWd1FTWH2hA6QcHkFCYsyGbsnAwMxsTXnEMVFbS8+CKtL79CpKYGndNJ8kUX4rzsMqyzZw8qLWlnpJSUtpSysnIlqypXsbluM5FYBKvBytK8pZxfdD7L8pdhMw5TEBUpoW63tv3syEo4shpCbu1a+kQoXgpFp2klOXdQHxGKxNhe0cKa0kY+Km1kc1kzwUgMgAlZdhaMSWV+sVZyVWY0xcccJd4jTyLivUVKOVsI8VNgh5TymfZz/dwngKfQnNP+p9P5h4HGTg5rqf1tO/u4i3c74XCYdevWsXLlSmKxGBdccAFz584dsOd38EgrzS8dJFLrwzzOhevSEozZXfdKe1ua2fjaS2x757+EA37GzJrL3Es+ReH0mf1+XlO1l/3rati3vgZPUxBrsokZZ+Yz7Yw8LEmJb3mS0Sjej9bS+tJLuN99FxkIYMzLI/nii3BceCGWKVOGJK+2L+xjY+1GPij/gHfL3qUx0IhZb2ZxzmLOLjybMwvOHLrc4z0RjUD11mNCXvYRhDzatZRiLcJbYbykTxzUmnkwEmV7RStrSxtZf6SJzUeb8YaiAOS5rMwtSukok7IdKmCM4mOFEu+RJxHxfg2oBM5DmzL3A+ullDP7uW8psBLYAcTip78LrAOeBwqBo2hbxZr6etZoEe92Wltbefnllzl8+DATJkzgsssuG9C+cNDipHvXVtO6vAwZiJA0PxvHOYUYnF2njQMeD1vfeo0tb72Gr7WFtPxCZl94GZOXnoHJ2rdlKmOSin3NbF1eRtmuJgwmHRMXZjP9zPyEM5m1E/V48by7nNb/vIb3o48gGsVYVEjy+efjOO88LNOnD4mQR2NRttRtYXnZct4re49qbzUCwcyMmZxRcAZn5p/JWNfYIfms3gcRgdodcHSNVsrXHZtmNzu1vOSFizQLPW8uGAY+1R+JxthT7Wbj0SY2Hmlm49Ematu01AJWo54Z+U5mF6Ywq8DFrAKXcoJTnFSUeI88iYi3DbgQzeo+EJ/qni6lfHskBgijT7wBYrEY69atY/ny5RgMBs455xzmzZs3IIc20MKsut8tw7OuGgTYF+XiOCMfvaPrumgkHGbfmg/Z9MYr1B85hNFiZfLSM5hxzoVkjulfzBorPWx7r5z962uJhmPkjncxbVkeJbMy0BsHGDe8uRn3O+/gfvMtvOvXQySCISsL+9ln4Tj7bGwLF57wGjnEE7007WFF+QpWlK9gT9MeAHKScjg973SW5i1lQc6C4Yvwdmwg0HRIE/Hy9Vqp2w1IMFihYD4ULYXiJZqYDzChivYRkqrWABuPNLGlrIUt5S3srmolHNX+/WYnW5iR72RmgYsZ+U6m5zlx2dTauWJkUOI98iQi3mOBCillUAhxJjADeFpK2TLso4szGsW7nYaGBl5//XUOHz5Mbm4uF154IYWFvXuK90akKUDbu2X4NteCXod9QTb2ZfkYXF2tOikl1Qf2sn35m+xbs5JIOER6YTFTzziHyUvPJMnV9/RywBNm9+oqdq2spK0hgMVuZOKibCYvzhmwNQ4QbWnBvWIF7uXL8a5eg/T7ETYbSYsWYT/jDOynL8WYO7h14+Op8dawunI1H1Z8yNrqtfgiPgzCwKzMWSzOXcyinEVMSZsyfFHeOuNr0qzy9qn22p2ABL0JcmdD4WLNOi9YCLbUfh/XE4FwlN3VbWwrb2FreQvbK1o53ODtuF6YamN6npNpeU6m5SUzNddJqnKGUwwDSrxHnkTEeyswDy0i2hvAK8BUKeXFwz24dkazeIMmqDt27ODtt9/G4/EwefJkzj333AE7tAGEG/y4V5Tj21wHAmwzMrAvzeuyvaydgMfDvo8+ZNeKd6k+uA8hdBRMnc6kJWcwbsFirPbep/JlTFK+t4ldK6s4sq2BWEySUehg0uJsxs3NGpCnejuxYBDf2rW4V6zA88EHRKo0j3JTSQlJS5eQdNpp2ObNR28/cUs5HA2ztX4rqypXsaZqDXub9gLgMDqYmz2XhdkLmZ89n/Ep44cv0ltn/M1w9CNtvbzsI6jaCrGwdi19AuQv0Cz0/PmQMWlQEeAAWn1hdlS2xosm6BXN/o7rOU4LU3OTmZLrZEqOg8k5yRSk2FQQGcUJocR75ElEvDdLKecIIe4F/FLK3yXisDaUjHbxbicUCrFmzRpWr15NJBJh5syZnH766YMS8UhLAM/KSrwbapGhKKYxydgX5WKdmoYwdBejxspy9q7+gL2rP6ClphqdXk/B1BlMWLiECYuXYknq3ar2u0Ps31DL3o+qaSj3aJ7qk1IYPz+LMTPTMQ8iB7aUktDBg3hWrca7ahW+jRuRwSAYDFhnzMC2cAFJCxdinTULneXE13ObAk2sr17P2uq1rKteR4WnAgCn2cnczLnMy57HnKw5TEyZODKWedgPlZuhfC2UrYOKDVrQGACTA/JmQ948yJujTbUP0qsdoMUXYndVGzurWtlV1cauqjYO1XuIxf/p280GJmY7mBQvE7OTmZjtwGlV8doViaHEe+RJRLzXAb8G7gcuk1IeFkLslFJOG4HxAaeOeLfjdrtZtWoVmzZtIhqNMm3aNBYvXkzuIKaPY4EI3g01eD6qJtoUQGc3kjQvC9vcLIwZ3R3WpJTUHjrIgXWr2b9uNS011RhMZiaedjozz72I7HET+lwfb6zycGB9Lfs31OJuDKDTCwqmpDJ2dgbFM9IHHMnt2HsE8G/ZgvejtXg/+ojArl0QiyGMRizTp2ObOxfbgvnY5s1DZz3xrVNVnio21GxgY+1GNtZs7BBzm8HGzIyZzMqcxazMWcxIn4HdNPDlggHTvm5esUELHlOxQZtqj0W06/Zsbbo9dzbkzoKcWeDIGvTH+UNR9te62V3dxt7qNvbUuNlT3YY7EOnok+O0MCHLwYQsO+OzHEzIcjA+006SeQR+3ChGFUq8R55ExHsKcCfwkZTyWSHEGDQP8QdHYoBw6ol3O263mzVr1rBp0yZCoRBFRUUsWbKE8ePHD9hbWsYkwQPNeNZWE9jXBDEwFSVjm5OJbXp6tyxmoAl53eFSti9/kz2rVhAOBkjLL2Tamecy+fSz+lwfl1JSd8TNwU21HNxch6cpiBCQO95F8Yx0imek48oc/D7sqMeDf9MmvOvX49u4kcCu3RCJIEwmbPPnk7R0KUlLTsM8iP9WPVHjrWFL3RY21W5iS90WDjQfQCIRCMa6xjIzYyYzMmYwPX06Jc6S4YvF3plwAGp2QNVmqNykTbU37KcjQ689G3JmQs4MyJ4B2dO1rWuD/O8hpaS6NcC+Gjd7a9wcqHWzr9bNgToPoUiso1+u08K4LAfjMuyMy7QzNiOJcZl2UpNMw+vlr/jYosR75EkoPKoQwgoUSin3Df+QunOqinc7fr+fLVu2sG7dOlpbWykoKOCcc86huLh4UM+LtoXwbanDu6mGSJ0f9ALLhBRsMzOwTEpFZ+luOQV9Pvat+ZCdK96h+sA+hE5H0YzZTFl6JmPnL8Jk6d3alVJSX+bm0NZ6Dm9roKlKc5pyZdkomppG4bRUcse7BhQI5nhiPh++TZvxrlqFZ9UqQqWlABgyMkg6bTG2hYtIWrzohEO2tuMJedhev51t9dvYVr+N7fXbcYe1QC02g40paVOYlj6NqWlTmZI2hQJHwcgIV9AdF/St2t7z6u2aoEttTzjmZMiaClnTjtWZk8E8+NmDaExS1uRjf60m6AfrPByo81Ba7yEQPibqLpuRkvQkSjLslGQkUZKuCXtBqg3LCXz3io8/SrxHnkQs78uAnwMmKeUYIcQstMQkl4/A+IBTX7zbiUajbNmyhQ8++AC3282YMWNYtmwZxcXFgxIGKSXhKi++bXX4t9YTbQtpQj4+BevUNCyTUrttOQNorChn94fvsmf1B7gb6jGYzJTMmc+ERUspmT0PYz9r0K31fo7saODozkaq9rcQjcQwGHXkjneRPzmVgsmppOUmdUuHOhDCVVV4P/oI7+rVeNeuI9qkrRcbCwuxzZ9H0oIF2ObNw5g3+NjknYnJGEfbjrKzYSc7Gnawq2EXe5v2Eopp8codRgeT0yYzKXUSk1InMTVtKsXO4pFxhgv7oXa3tve8Jl5qdx+LCgeaRZ45VRPy9pI2blB70NuJxSSVLX4O1nsorfNwqMHbUde7gx39hIBcp5WSjCSK05IoTk+iOM1GUVoSBalWzAYl7KMdJd4jTyLivQk4Gy2M6ez4ObXmPYyEw2E2bNjAmjVr8Hg85OXlsWTJEiZNmjTgfeLtyJgkVO7Gv6MB/84Goi1BEGAqcGCbmYF1Zgb649arZSxG5b7d7F2zkgPrVuNrbcFgMlM8czbj5i+mZM58rI7kvt8lGKVyfzNlu5uo2NNEc40PAIvdSN6EFPImuMid4CI1J2nQlquMxQgeOIhv7Ud412/At3EjsdZWAAw5Odp6+dw5WOfMwTxuHEI/NGIRjoY50HKAPY172N24m92NuznQcoBgVBMuu9HO1LSpTEuf1lGybFkjY6FLCS1HoXaXJuR18brx4DErXeghtQQyJmoe7hmTIGOCJuqmE/P4dwfCHG7wdilHGrwcavB2WVdvF/bCVBvF6TYKUm0UpSZRmGqjMNWGcxDOkIqRR4n3yJOIeK+VUi7q7GHeHu98REbIJ0+82wmHw2zbto1Vq1bR0tKC0+lk4cKFzJo1C5tt8OvJUkrC1V4Ce5rw72wgXO0FncAyMQXbrAwsk9PQmboKXCwWpWL3Lg6sX8PBjWvxNDYghI7ciZMombOAktnzSCso6leY3E0BKvc1U7Gvmcp9zXiaNaGz2I3kjHWSM85FzjgnGQUO9D14zSf0frEYwf378W3YiG/TJnybNhKtbwBAZ7djnTEd66xZWGfOxDJjBoaUoQutGolFONx6mF2Nu9jZsJPt9ds50HyAiNQEK9WS2jHVPjltMlNSp5CdlD1ya8WRoCbgdXugfm+83qc5y7WLOoCzENLHQdp4SB+vCXr6eHDkDjplKmj/7zV5Qxxt8nG00cvhBh9ljV6ONvkoa/TR6A116e+wGChIsVGQaqUgxUZRmi1uuSeR67KiV1vcPhYo8R55EhHvJ4B3gW8DVwNfA4xSyjuHf3gan1TxbicWi7Fv3z7Wrl3L0aNH0ev1TJ06lTlz5lBU1L9g9ke4xot3cx2+rXXE2kIIow7L5FSs09KxTEztkt0M4h7rpQco3byeQ5s2UHdEW3+2p6ZRPHMuxTNnUzB1BrZkZ5+fK6WkrcFP1YEWqva3UFXaSlu9tidZb9SRWegga0wyWWOcZI1Jxp5iHvzyQUUF/s2b8W3Zgn/bdoL79kFMW681FhRgnT4Ny7TpWKZNxTJl6pDsNW8nGA2yr2kfOxt2srtxN7sad3Go9RAxqX2+y+xiYupEJqVMYmLqRCakTKDEWYJRP4JWZyQIjaXa+nnDAWjYp9WNB4/FcQcw2jRrPW0spI7t1C4Be9agneXa8QYjlDX5KGvyUR6vK5r9lDf5KG/2dVljN+pF3FLXpuALUzVxL0qzkZ+i1tlHEiXeI0+i4VHvB84HBPAW8CMpZWD4h6fxSRfvztTU1LBp0ya2b99OMBgkJSWFmTNnMmPGDFJTBxepqx0Zk4SOtOHbXo9/RwMxbxgMOiwTUrBOTsUyObXb1DqAu7GBI9s2c3jrRo5u30rI7wMhyCwqoWDaDAqnziBv0lTMCcwWeFuDVB9speZwK7WHWqkrcxOLaP+P2pwmcse7OqbbXVm2Qf9wiXm9+HfuIrBzB/7tO/Dv2N4RNAYhMBUVYZk6FcuUyVgmT8Y8efKQWuj+iJ8DzdqU+56mPexr2tdlyt2gM1DiLGF8ynjGu8Z31CNqpYM2/e6u1kS8XcwbS6GpFJqPHNvKBpqwp4yB1DHaGntKcbw9BpwFYDix6G5SSmrbghxp1KbgjzT6KGs6Zr23J3JpJzvZolnsqba49W6jIEU7zkq2KKt9CFHiPfIk5G1+slHi3Z1QKMTu3bvZtm0bhw8fBiA3N5dp06YxZcoUXC7XCT1fxiTBw634dzYQ2N1EtPXYGrllYiqWiSkYc+3dnM5i0Sg1pQc4umMLZTu3Ub1/L9FIBCF0ZI4pIX/yVPImTSV3wuR+Q7UCRCMxGis91B5uo7q0lar9zXhbtalVq8NI7jhXx1R7er4d3Qlk24o0NhLYuRP/rl0Edu8msGs3kerqjuuGrCzMkyZimTgJ88QJWCZMwFRcjDAOjYUciUU42naUfU372N+8n33N+zjQfIBaX21HH7vRTomrhHGucYx1jmWsSysjtpbemWgEWsu0KffGQ9B8WGs3HdaEPXrMaQ2h06bcU4ohpQhcReAqPFYcOaAf/P5xKSWN3hBHG49Z7EcbNWu9vMlHTVuAzn/qjHpBrstKfoqVfJeNvBQreS5rR53ttGBUmdsSRon3yJOI5T0PLRtYMdDxr0uteX98aG1tZefOnezcuZPquNjk5OQwadIkJk6cSFbWif1h71gj392If18z4Qo3SNAlGTCPS8Ey3oV5nAuDq7sXejgUpHr/Xsp3badi7y5qDuwnEtbE15WVQ+6ESeSMn0TOhEmkFxShN/T9B1xKSWudn8r9zVQfbKXyQDOepri1atKRVZxMVomT7Ph0+2DCuHYm0txMcM8eAnv2ENi3j+DefQQPHYKIZnEKoxFTSQnmceMwjx+PedxYTGPHYiooQPTzLonSGmzlQPMBSltKOdCi1aUtpTQHmzv62Aw2xjjHMMY5huLkYq12FlOUXIRZP3iP8kETi2kWe8tRTcibDsfbR7XaXd21v9BDcl5czAs0S92ZHy8F4Mw7ISe6YCRKVUuA8vg0fEWzj/J4Xdnsp66TdzyATkBWsoVcl1UrTq2d4zx2LsVmVPva4yjxHnkSEe99wLfomtoTKeXR4R3aMZR4J05jYyN79+5lz549VFRoUcOSk5MZP34848aNY8yYMVhOMNxo1BMicKCF4P5mAgeaiXm0GN36VAuWsS5MJU7MY5J7FPNIOEztoYNU7d9D1b7dVO3fi6+1BQCD0UTmmLFkjx1PVsk4skrGkZKbh66fgCjupgA1pa1Ul7ZSc6iVxgoPsXjsT0eqhawxyWQWJZNZ5CCj0IHJemKiGguFCB0+THD/foL79hE8cJDggQOEq6o6+gizWRPz4yx1/QnOiHSm0d/IodZDHG49TGlLKYdbD3O47TA13ppj40CQa8+lKLmIQkehVidrda49F6PuJHlzR4LQWqEJe2s5tJTFS7l23l0FMtb1HmuKJuTtlntKsXbcLvjmgaXd7UwgHKWqxU9VS4DKFh+VLQEqm/1Utviobg1Q3RIgFO06HrNBR47TQrbTQo7TGq8tZCdr57KTLaTZzZ+I6Xkl3iNPIuK9Skq5dITG0yNKvAeH2+3mwIEDHDhwgNLSUkKhEEII8vLyKCkpobi4mIKCAownMO0rpSRS6yNwsIXgoVaCh1qQAW3tUe8yYypOxlyUjKkoGWN2973dUkrcDfVU7d9DTekBakr3U3u4lEhQs4SMZgvpRcVkFo8la8xYcsZPJC2vANGHx3M4FKX+qJvaI23UHWmj9nAb7qa4i4YAV6aNjAI76YUOMgocpBfYBx3WtTNRj5fQoVKCB0sJ7t9PYN9egnv2Em1p6ehjyMrCPGGCJuzjx2sW+9gSdCewe+B4fGEfR9uOcqTtCEdaj3Ck7QhH245ytO0onvAx5zO90JOTlEOBo6BLyXfkk+/IH/5Uqn0RDcct93Joq9QEvbUiLvBHtTpynNtNu7i7CuN1J+s9OR+SMgbtKR+LSRq8QapbAlS3+jVBbw1Q1eKnJt6ubQsQiXX9e6rXCTIdZrKSLWQlm8lOtpCZbOk4zkq2kOkw47SObiteiffIk4h4nwN8Bs3jvGNuSUr57+Ed2jGUeJ84kUiEiooKDh06RGlpKVVVVUgp0ev1lJSUdEyx2+0nFsdbxiThGi/Bw62EDrcSPNpGzK1Z5sKkx5Rvx1SYjCnfjrHAgT65e0jNWDRKU2U5tYdLqT10kLojh6g/eoiQX/NENyclaVPt4yaSO34i2eMmYuln3H53iLqjbuqOtlFf5qa+3N0x3Q6Q5DKTnm8nLc9OWn4Sabl2XNk29Ce47imlJFJXT3D/vriVfoDA/gOESkuRoWPboox5eZjGjcVcMhbz2BJMJWMxl4wZUktdSklToIkydxlH245S7i6nrK2Mcnc55e5y2kJtXfqnmFPIs+eR58jT6njJteeSk5SDxXDiCWMGTSwG3vquYt5aHrfc43XY2/UevUlL8JKcr03DO/OPHSfnasWWNmiP+XaBr20NUt3qp7YtQE1bgJrWILVtgY7jzvvc2zEZdGTYzWQmm8l0mMlwmMl0WOK1dpzhMJOWZMY0yC2Uw4kS75EnEfH+OzAJ2MWxaXMppbx1mMfWgRLvoScQCFBWVsahQ4fYu3cvLXHrMC8vjwkTJjB+/Hiys7MHHRSmHSkl0aYAwTI3obI2QmVubV953ELROYyYcu0Y8+xanWtH38OWMBmL0VxTTfWBvVTt20PV/j00VJTR7oXkys4he+wEbcp9zDgyx5RgsvZtzQY8Yeor3DSUe2iocNNY4aW5xkssGh+bXuDKspGWm0RqbhIpOUmk5iSRnGE9cVGPRAiVlRM8eIDgwYOESg8RLC0ldOhQF1HXp6RgKinBNKYYc3ExpuJiTEVFGAsL0ZmHdi27NdhKhaeCCncF5e5yKj2VVLorqfRUUuWtIhLrKjppljRy7blkJ2WTm5RLjj2HnKQcspOyyUnKwWV2nTxrUkotDWtrhSbmrZXQFrfeWys1a76tquvedgC9GRzZ2vp7co7mSJecq9WOHO2aIxuMg0+O4w9FqXMHqGkNUOfWhL3eHaTOHaTOfazd4gv3eL/LZiTDbibdbibdYSbdbiLdbibDbiYt3m6vR2q7nBLvkSehNW8p5cQRGk+PKPEeXqSU1NbWsm/fPvbv309lZSUANpuNsWPHUlJSQklJCU5n3/u2E/68cIxQtYdwuZtQpYdQpYdIna8j34awGDDm2DDl2DFmJ2HItmHMsqE7LptV0OejpnQ/NQf3a1Puhw7gaWyIP0SQkpNHZnFJR8koGtOvh3s0EqOl1kdDhYemKg9NVV6aqr20NRybotXpBc4MKynZSbiybaRk2XDFiyXpxNaQZTRKuKoqLuSHCR3WSvDwYaKNjcc6CoEhJxtTURGmwiJMhYUYCwswFRZiys9HlzS0U97RWJR6f70m5J4qqjxVVHurO+pqb3XHNrd2LHoLWUlZZNuyyUrKIssWL0lZZNoyybJlkWJJGZkQsj0Ri4KnThPxtgpoqz4m6u7qY/Xx0/MAFldczLO0BDHtom7P0oojG+yZJ7QOH4xEafCENDFvC3S06z0BGtwhGjzBeAnhCXa35kFL95pmN5GWZCLNbiYtyURqvH3u5EyK0obm/xMl3iNPIuL9F+BhKeXukRlSd5R4jywej4fS0tKO4vVq04+pqakUFxdTXFxMUVHRkIk5QCwUJVzjJVztJVzl0eoaH7LT3l19qkWbds93YMp3YMxL6ibo3pZmag8f1KbbD5dSd+QQbfV1HddtThfphcVkFBaRXjiG9IIi0vIK+o3XHg5Gaa7x0lytiXlzjY/mGh+t9X5kp3VOi92IK9OGK9tGarZmsafmJg06wExnom43oSNHCB05SuhovJQdJXy0rMu6OoA+LQ1Tfj7GggKM+XlaOz8fY14exuzsIdve1o6UkuZgM9Weamq8NdT4arS2r4Zaby01vhrqffVEj7N0DToDmdZMMmwZZNoyO0qGNUM7Z80k3ZaOw+g4OVZ8uwXvrtGc6Ny1nepq7bynVqtjPVjKxiRNxNtLUg/tpAytPgFven8o2kXMm7xa3eAJ0uQN0dip3eQNEYlJnvzcPM6eNPi0sp1R4j3yJCLee4CxwGG0NW+BNm2utop9AojFYtTV1XH48GEOHTpEWVkZwbgzmcvlorCwkIKCAgoKCsjIyEA/RHHDQVs/jzYHCNf6OoQ9VOEmGg+pigBDuhVTnjbdbsy1Y8pN6pb+1O9xU3/kMPVHtdJQfoTG8rKOLWsIgTMzi7S8AtLiYp6am09qXj5mW99/UKPRGG31flpqfTTX+mit89Na56Opxoe/7dj0t8GsJzXb1mGtp2Yn4cqy4cywojeeuOUZbW0lVF5BuLyMUFk54YoKQhXlhMvKCdfUQLSTaOp0GLKyMOblYsztVHJyMebmYMzOHnLLHTTrvTHQSI23hjpfHbW+Wup8ddT56qj31VPrq6XeX4/3+LVqwKw3k25N72a9t1vwWbYs0q3pIxuVrjNSgq9JE/IupU4Tdm+d1vbUaj8GesJog6R0TdST0uMlA2zxOimt03H6oJPKxGKStkAYi1E/ZNPqSrxHnkTEu6in82qr2CeTWCxGTU0NR48epaysjPLycjwezYPZaDSSl5dHXl4eubm55OXl4XQ6h9xiinpChCo8hCs9hCrchKs8RFs7rRM7zRhzkzBmJ2HM0WpDmhWhPzaOWCxKS001jeVlNFQcpaG8jKbKcpqrKohGjk1BJrlSSMnNIzUnn5TcPFJy8kjJycWZmYXe0LdQBDxhmqo9NFX7Oiz2llpfRzx30HyjHOlWzVrP0mpnplbbUy3ohmCbkYxECNfUEK6oIFxZ2alUEaqqJFJb11XcAZ3TiTE7G2N2NoacbIzZORiys7TjzCyMWZnDIvAA3rCXOl8dDf6GjrreV0+9Xyu13lpqvDUdGd06k2pJ7Wa9Z1gzurTTrGkYdEOzB39QRELga9DE3Ft/rD6+eOq1frGep8QxOcCWqgm5LV1ztrOlxuu07scW1wkFwukLJd4jT6L5vJcC46WUfxFCZAB2KeXhfu55ErgUqGvPQCaESAWeQwv4cgS4TkrZy8/QYyjx/vgipaS5uZmKioqOUlNTQyweN9xqtZKTk0NOTg7Z2dlkZ2eTlpZ2wo5wxxP1hAhXeQm1T7lXe4k0+I65WOoFxkwbhixt/dyYacOQacOQepyoR6O01NbQVFVBU2U5TVUVNFdV0lRVQcBzLMWm0OlIzsjElZUTL9k4s7W2MzOrz/znoUBEs9RrfLTUxkudVkdCx/YS6/SC5HQrzgwryRlWnOlanZxuITnditE0NFaTjESI1NcTrqoiXFVNuKaacFUVkZpawjU1RKqru03Lg5bkxZClCbkhMwtDZma8ZGDMzMSQkYE+IwOd6cS34XUbs5S0Blup9dVqFruvvsOar/fXd1jyzYFmJF3/xgkEKZYU0q3pXUqaJU2rrWmkWdJIs6bhNDtP3po8aBZ9oAW8DVrxda4btdrXqBVvvI74e3+exQnWuKCf979QPDS7gJV4jzyJWN4PAPOAiVLKCUKIXOAFKeWSfu5bBniApzuJ90NAk5TyZ0KIbwMpUsr7+hukEu/RRSQSoba2lqqqKqqrq6mqqqKurq5D0A0GAxkZGWRlZZGZmdlRHI6hXdeU4Rjhep8m5nU+IjVewrU+LR1qO3qBIc2KMcOKIcOGIcOKIV0r+uOcz/zuNpqrq2iurqSltprm6ipaaqppra0m4PV06WtzuuJWumapayUPV1YOhl7ETEqJtyVEa702/d5S56Ot3k9rg5/Wej/hQFfr2JZsIjndgiPNiiPNQnKaJV5bsaeaMQyhp3EsECBSW0u4uoZIXS3h2loiNbVE6uoI19USqa0j0tDQEXmuM3qnE0Nmhibm6ekYMjIwpKVjyEjHkJamnUtPR+9y9bl/fzBEYhEa/A1drPeOY389jf7GjuNwD2vWBmEgxZLSIeipllStWFM72mmWNFIsKaRYUrAaBu+FPmSEvNoUvr9JE3p/s3bsa9TOtbfP/h7kD43eKvEeeRIR763AbGDzQFOCCiGKgdc6ifc+4EwpZbUQIgctR3i/nuxKvEc/kUiEhoYGampqqK2t7SjtznAAZrOZjIwM0tPTuxSXy4VhiEKNAsSCUSL1PsK1Pq2u8xOp9xFpDHRsYQMQVgOGdCvGNAv6NCuGNAuGNCuGVAs6e9egGgGPh5baalrramipraGlpprm6kqaqys7IshpDxU4UtNxZcet9czseDsHZ2Z2r/vVpZQEPGFa6/20NfppawjQ1uDH3ajVnqZgR1S5dqwOI45UC45UC/YUC/ZUc0ftSLFgTTYNybR8xxhjMaJNTUTq6+OiXkekvp5oQ0P8XD2ReLvzdrgO9Hr0qSmasKemok9LO1anpaJPSdUEP10TfTGEFr2UkrZQW4eYNwYaafQ30hhopCnQpLX98XagsZtnfTtWg5UUc0qHmKdaUnGZXdqxOQWXxaUdx9vJpuSTO4U/RCjxHnkSEe/1UsoFQojNUso5Qogk4KNBineLlNIVbwuguf24h3vvAO4AKCwsnHv06IgtsStGEK/XS11dHfX19V1KZ1EXQpCSkkJaWloXSz09Pf2EosMdj4xKIs0BIg1+IvV+Ig2aoEca/Zq13umfijDq0KdaMKRYMKRa0KdYMKSa0bssGFLMCKuhQ9yDPq9msddU0VKjWevNNVW01tZ0FXbAbEsiOTMLZ0YWzsxMkjOySM7IwpmRSXJGZq8OdLGYxNsSxN0YwN3ox90U0NpNATzNQdxNgS5T8gA6ncDmMmF3WbCnmElKMWN3mUlqL04zSU4ThiGanu/47ywlMY+HSH0DkYZ6oo2NRBoaiTQ0EG2KtxsbtR8CjY1If8/TwHqXC0NGOvo0TcwN6eno09M08U9LjZ/XxH8op+6llPgj/g5hbw400xxopjHQ2NFuCmrnWwItNAeb8fcxlZ1sSsZldnUIu8vsOnbO7MJpdnYtJidJxqSPVUQ2Jd4jTyLi/U1gPHAe8FPgVuAZKeXv+n14H+IdP26WUvabWkpZ3p88/H4/jY2NNDQ00NTU1NFuaGgg2sm5KiUlhYyMjG4Wu9U6tNOXMhLThL0xQLTRT6Q5SKQpQLTJT6Qp2GVLG4Aw6zGkWjCkWdCnWjGkmjGkWtGnmDG4LIi4h3nI76OltobW+lpaazWrva2+lta6Wtrq64iEulp4JquN5IxMktMzcKRn4khLJzktHUdaBo70dJJS0jD08INGSknQG8HdrIm5pymApyWIpzmAtyWIpzmItzlIJBzrdq/ZZiDJZcaWbCLJacbm7FybsCVr14wW/bAISsznI9LUfEzYG+o1i76xUfsBUF9PpFETfOnz9fgMncPRYcXrU1MwpLbXmkWvT0nBkJqCPlVrD3UAHH/ET2uwlaZAEy3Blg5Rbw220hzQ6pZgCy3Blo62L9Lzu4AW2jbZlIzT7CTZnEyyKfnYcbydbE7GYXIcO46fsxkGn0q3N5R4jzyJOqydR6d83lLKdxJ6uJo2Vwwx0WiUxsbGbtZ6Y2Njx5o6aAFm0tLSSEtLIzU1taNOTU3FPMR/mKWUxHwRos0Boi1BTeSbAkSbNLGPNAcg2vXfmS7ZhCHFgt5lxpCiWewdbacZncWgWXhtrbTV19FaX0dbQx1t9bW0NdTjbmzAXV/Xba0dtPV2e2oajrR07Clp2FM7lZRU7ClpmJO6W25SSkL+CJ6WIN6WIN6WEN5Wre1rjbdbg/jaQh051jtjMOqwOU1YHVqxJWulvW11GLW2w4TZZugW534o0IS+SZuqb+xkwTccs+SjTU1EmpuJNjd387JvR2ezoU9JQe9yaXV72+XUhN7lQu9yoXM6O9rCNrSiGIqGaAu10RLQRL0t1EZrsJXWYCttoTbtWrCFtmBbx7W2UBvukLubk16XdxM6HCYHP1n6E5blLxuSsSrxHnkGlM9bCJEONMoEb+pBvB+O39/usJYqpby3v+co8Vb0RzQapbm5ucNCb2xs7CjtW9nasdlspKamkpKSQkpKCi6Xq6NOTk4e0r3qEN+v7g5pYt4UINocINIc1OqWoDYlf9x6tbDo0TvNGFyamGvFpNXJWi3MeiLBIG2N9bgb6nE3NeBpbNTqpkY8jQ24mxq7eMm3YzCaSEpJIcmV2lHbU1KxuVwkuVJIcqZgc7mwJbu6pWltt+K9rUF87hC+1nhxh/DFxd3vDuFrCxHwhOnpr4XQCSx2I1a7URN1uwmr3YjFEa/j1yzt55OMQ7Ifvuv3EiPW1qaJfbugNzUTbW4i2txCtKVZs/hbWrTS3EzM0/3HUsc7GY3oXE70Tqcm9E4X+uTk+LETXXs7WTvWJydr55KTEUP4/1xMxvCEPR2i3i7obcF4HT++duK1TEiZMCSfqcR75OlVvIUQi4CfAU3Aj4C/AemADvislPLNPh8sxLPAmfF7aoEHgJeB54FC4CjaVrGm/gapxFtxIgSDQZqamjpKc3NzR93W1kbnfwNCCJKTk3G5XDidzm4lOTn5hFOqHo+MSWLuUFzIA0RbQkRaNCs+2hYi2hrsSLvaGWHSoU/WxFyXbNJE3aEVnePYcURE8DU3a4Le0oS3uQlPs1Z7W5rwNDfjbWki6O0eHAXA4kgmyenC1lGcJDlTsCY7teNkJ7ZkJ9ZkJyartYv1GYtpjnZ+dyhewpq4e7R2+zm/RxP6oK+XPc2A0azH6ogLuiMu/HYTls7tTsJv6uR3MFTIcPiYmLe0EGlpIdbaeuxcaxvR9uPWVqJtbURbWnpdt29Hl5SEzpmM3pHcRdT1yQ50jmT0DrtWdzl2oHc40NntQ5Y7frAo8R55+hLvjcB3ASfwKHCRlHKtEGIS8Gy75/lIoMRbMVxEo1FaW1tpbm7uUre0tNDa2tpN3AFMJhPJyckdxeFwdKuTkpKGdC+7jMQ0IW8JEm07JupRd0hrxwuR7mvWwqjTxNxuRGc3oXd0qpOOHcdMMfx+N77WFrwtTfG6OV634GuNl7ZWQv6e12P1BgPWuJBbHcmaqDuSsSYnY7VrtcXu0M45krE4HBhNx5YxotEYQW8EvztEwBvG7w4T8Ia1HwBxgffHfwxoPwrCRHt4Z9Ac8sxJBix2E5YkgybuSQYsdiPmJM2a14oBs+3Y8VBb+KDlgI+1th4T9Hg71uYm2tZGzN2mCX9bG7G2NqJud0c71suPqs4Imw29PS7o8VrnsGttuwOdPUkT+iQ7OrsdnT0Jy5QpGFL6dTlKCCXeI09f4r1VSjkr3t4jpZzc6doWJd6KTwLRaBSPx0Nra2uHmLe2tuJ2u2lra6OtrQ2Px9NN4IUQOByODmu9J7G32+1DugVOSon0RzRBd4eIucOd2lod9YSJeULEvL1YuHqBPsmILsmIzm481o6X9mNpgkDUSyDkxe9uxdemFX+n2t/Wht+tlaCvdwEymMxYHA6sdocm7PHaYrdjsTswJ9nj5+xY4qJvdTjQG4xIKQkHo11EPeiNtz3HhD/Que0Nd2SO63k8uriYa6Juthm0kmTEYjt2zmQ1YEnSLPz2PkO5t74dGYkQdbuJeTzE3G6ibW5iHjdRt0cTfbdb+xHgcRPzeLU+7X3j53qy/PMf+QOOs88ekjEq8R55+vrL0fnn7PHffOIL5QrFKEav13dMmfdGNBrF6/V2CHlnYW9tbaW6upp9+/YR6SGAic1m6xByh8OBw+HoaNvt9o6SyJY4IQTCZkRnM2LM6jt0qYzGiHkjRD0hYp4wUW+YmDtEzBvWBN6rnYs0Boh5wt286Y99KJgsBqy2ZDJsaehsBnSpRnT5BnQ2o3ZsM4BJECZMMOIjGPHiD7oJeDyauHvcBNxu/J42Ah4PDRVlBDxuAh43sV4cygCMFmuHkFvahd9x7AeAM9VBVmH7j4AULEl2zEl2dHq9JvjeMEFvhIAv3DFlr53r1PZFaGvwa8e+CJFg7+MB0Bt0mGwGzO2CbtVE3mQzYLbE21YDZqu+o60dGzBZDJisenTHpZsVBoNmIZ+AlSwjEU38vV5N2D0eTCUlg36e4uTTl+UdBbxoHuZWoH2eTAAWKeWIZQBQlrditCOlxO/309bW1iHubre7o90u+l6vt5sVD1oAG7vdTlJSUkd9fNtms5GUlITFYhmGtd4YUV+YmCdMzBcvnjBRXyR+3Kn2hon5I8i+hE5oqV911u5FWA3oLAZ0Vj0xfYwIYUKxIKGIj0BYE36/p130jxN/t5uAz0uPXnJxjBYrliS7JupxQbfY47UtCbM9XifZMduSMCclaf1sSQi9kZA/StAXJuiPEPRFCPkiBH1hAr4IIX+EoF87194O+uLn/BGiPWzFOx6DSRcXcgMmix6jRRN3o0Wvnbfoj2sfV5vba/2wePT3hLK8R55eLW8p5chkcVcoPgEIIbDZbNhsNrKzs3vtF4vF8Hq9uN1uPB5Pl+L1evF4PNTV1eHxeAgEesgzDeh0ug4hb//MnorVau2oTSZTn4IvjDoMTjM4E99mJyMxYv6IVtqF3R+J1+GOazJeh1uDHeeO31oHmqesDbDhQJiccbE3IlIM6HI7ib9FR1QXI0KIiAwRkgGCIa8m/AEPfr+boM+D3+Mm6PXQXF1J0Osh4PMSCfYcOa1jDHo9JlsSFlsSJptNE3ebDbPNjtlmw2Sz4Ui2YcpO0q5ZrZhsTsy2JExWG3qDBSkNhIMxQoFjoh7yR7U6ECEUiLf9WjsciNBS5yMciBIKan1lLLHJT4NZj8msib3RrAm+0ay1Z51XSFZxcsLfp+LjxeiPy6dQnELodLqO6fP+iEQi+Hy+DlFvbx9f19TU4PP58Pfh8azX67FarT0Wi8XS7bj9nMVi6XVrnTDoOrzfB4KUEhmOIQNRTeQDUWKBYyJ/7AfAsR8G0SY/4fYfA8dFkjMABgwk4QScoBPorHrN8k83oMuPty3a9H5MFyMqIkQIE44FCceChKLxHwAhL4Fg/AeA30vQ56Olppqgz0fQ5yUU8Pdp9bdjsloxWayYrJrga+34cfyaLd3Wcc5osXT0MVqs6PUmEGakNBCL6ggFIh3iHg5EO0Q/FIwSDkYJx4/DwSi+tlD8XO+e/YqPP0q8FYpRisFg6HCCS4RoNEogEMDn83UUv9+P3+/v0vb7/TQ3N1NdXY3f7ycc7r5NrTNGo7FD0I8vZrO5S7v9uHNtMpm6eOYLIRAmPZj06JMHHtZURmLEAnEhD0SPiXzncx3tCLFAlGiDv6PdebpfE36BFSva6mH6sXGa9egseoRLjzAb0Jn1CJMOaYCYLqYVESEiw0RkmFA0QCgSL2F/x48Bv7+NYMCHr7WFoN9H2O8nFPD3ud7fGZ3egMliwWixxkW+c9uK0WzGaLFiS7NgtFgwmrU+yelKvEczSrwVik8Ier2+Y318IEQiEfx+P4FAoFt9fPH7/Xg8HhoaGjrOJRLTyWQydYh7e+l87vjrnX8MdC4Gg0Gz+O0m9PbBxTOXMYkMRTUhD8RFPxhvB6Md4i+DUe24vQ5onv6dzxOT6AE9YMYC9BIjQCcQdj26NB3CpNeKUSANIPWSmIgRE9qMQFTGfxDEwkRiQUKRIOFogFDI3/GjIBjy4W1spilQTjgYJBzwEwoEuswKXHnv90lOzxzUfyPFyUeJt0Kh6BODwZDwVP7xSCkJh8MEAgGCwWBHfXy7p9K+rh8KhQgGgwn9CNDpdH2Kf+e6r7bJZMJkN2Fw9u0L0N+7E5HEgp2EPhTtIu4yFCUWiiKDMa0djCLDx/rgj0EwCsEoIhRFH46ij4EJI2BE8wLohbirsUjWIYzajwIMQit6SLYXDeq9FB8PlHgrFIphQwjRIYYnQvuPgFAo1Kvot19rF/v2cz6fj+bm5o7zoZ7SkfaCTqfDaDR2FXWTqeNcX/XxbaPRiMlmwmg0YzQaMRgMAw7kI6WEqOwk+lHNPyAUJRbSahmKaT8A2s+F4+dC8b7x/gbL0Mb4V4wsSrwVCsXHns4/Auy95DxPlFgsRiQS6RDy40swGOz4odC53V7C4TA+n4+WlpaOa+FwuMd9/P1hMBi6iPvxpfP1vtoGgwGjyYjBZoifM2I02jquGQwG9PrhyfqmODko8VYoFJ8odDrdkMwGHE8sFusi5n21Eyk+n6/LcSQS6dd5sC+EEF3E/IorrmDcuHFD+F9AMZIo8VYoFIohoPN6+3AhpSQSiXQIeV91b+fay0AdFxUfL5R4KxQKxShBCNExXW61Wk/2cBQnkaFPn6NQKBQKhWJYUeKtUCgUCsUoQ4m3QqFQKBSjDCXeCoVCoVCMMpR4KxQKhUIxylDirVAoFArFKEOJt0KhUCgUowwl3gqFQqFQjDJEIpl6TjZCiHrgKFoy3YaTPJzhRr3jqYF6x1MD9Y6JUSSlzBiKwSgSY1SIdztCiI1SynknexzDiXrHUwP1jqcG6h0VH1fUtLlCoVAoFKMMJd4KhUKhUIwyRpt4P3qyBzACqHc8NVDveGqg3lHxsWRUrXkrFAqFQqEYfZa3QqFQKBSfeJR4KxQKhUIxyhgV4i2EuFAIsU8IcVAI8e2TPZ6hQAhRIIR4XwixWwixSwjx9fj5VCHEO0KIA/E65WSP9UQRQuiFEFuEEK/Fj8cIIdbFv8/nhBCmkz3GE0UI4RJCvCiE2CuE2COEWHyqfZdCiLvj/6/uFEI8K4SwjPbvUgjxpBCiTgixs9O5Hr83ofHb+LtuF0LMOXkjT5xe3vHh+P+r24UQLwkhXJ2ufSf+jvuEEBeclEEr+uVjL95CCD3wB+AiYArwGSHElJM7qiEhAnxDSjkFWAR8Of5e3wbelVKOB96NH492vg7s6XT8IPArKeU4oBm47aSMamj5DfCmlHISMBPtfU+Z71IIkQd8DZgnpZwG6IFPM/q/y78CFx53rrfv7SJgfLzcAfxxhMZ4ovyV7u/4DjBNSjkD2A98ByD+N+jTwNT4PY/E/wYrPmZ87MUbWAAclFIeklKGgH8CV5zkMZ0wUspqKeXmeNuN9sc+D+3dnop3ewq48qQMcIgQQuQDlwCPx48FcDbwYrzLqfCOTmAZ8ASAlDIkpWzhFPsuAQNgFUIYABtQzSj/LqWUHwJNx53u7Xu7AnhaaqwFXEKInBEZ6AnQ0ztKKd+WUkbih2uB/Hj7CuCfUsqglPIwcBDtb7DiY8ZoEO88oLzTcUX83CmDEKIYmA2sA7KklNXxSzVA1ska1xDxa+BeIBY/TgNaOv3hOBW+zzFAPfCX+PLA40KIJE6h71JKWQn8HChDE+1WYBOn3ncJvX9vp+rfoluB/8bbp+o7nnKMBvE+pRFC2IF/Af8jpWzrfE1q+/hG7V4+IcSlQJ2UctPJHsswYwDmAH+UUs4GvBw3RX4KfJcpaFbZGCAXSKL7VOwpx2j/3vpDCHE/2hLeP072WBQDYzSIdyVQ0Ok4P35u1COEMKIJ9z+klP+On65tn4qL13Una3xDwBLgciHEEbTljrPR1oZd8alXODW+zwqgQkq5Ln78IpqYn0rf5bnAYSllvZQyDPwb7fs91b5L6P17O6X+FgkhPgdcCtwojwX8OKXe8VRmNIj3BmB83KvVhOZM8epJHtMJE1/7fQLYI6X8ZadLrwK3xNu3AK+M9NiGCinld6SU+VLKYrTv7T0p5Y3A+8A18W6j+h0BpJQ1QLkQYmL81DnAbk6h7xJtunyREMIW/3+3/R1Pqe8yTm/f26vAZ+Ne54uA1k7T66MKIcSFaMtZl0spfZ0uvQp8WghhFkKMQXPOW38yxqjom1ERYU0IcTHa2qkeeFJK+eOTO6ITRwixFFgJ7ODYevB30da9nwcK0dKgXielPN6hZtQhhDgT+KaU8lIhRAmaJZ4KbAFuklIGT+LwThghxCw0pzwTcAj4PNqP41PmuxRC/C9wPdo06xbgdrT10FH7XQohngXOREuLWQs8ALxMD99b/EfL79GWC3zA56WUG0/CsAdEL+/4HcAMNMa7rZVS3hnvfz/aOngEbTnvv8c/U3HyGRXirVAoFAqF4hijYdpcoVAoFApFJ5R4KxQKhUIxylDirVAoFArFKEOJt0KhUCgUowwl3gqFQqFQjDKUeCtGPUKIqBBiazzj1TYhxDeEECfl/20hxKz41sZE+n4tnoEsoehWQohcIcSL/fccGMP1XIVCMXyorWKKUY8QwiOltMfbmcAzwGop5QPD+JmGTjG9O5//HFrmra8k8Iy9wLlSyophGKJCoTiFUZa34pRCSlmHlq7xK/FIWMVCiJVCiM3xchpoQWNEPL94/Pj3ceFFCDFXCPGBEGKTEOKtTqEyVwghfi2E2Ah8XQhxrdByW28TQnwYjwD4f8D18ZmA64UQPxRCfLPT5+yMj+lPQAnwX6HlyU4SWt7l9fHkJt0y58Xv2xlvf04I8W8hxJtCyzv9UE//PYQQR4QQP42PZ6MQYk78nUqFEHcO9rkKheLkYui/i0IxupBSHhJaDuJMtLjU50kpA0KI8cCzwLze7o3Hm/8dcIWUsl4IcT3wY7SIUwAmKeW8eN8dwAVSykohhEtKGRJC/IBOlrcQ4oe9jPHOeIjKs6SUDUKIn6CFj71VCOEC1gshlkspvX286iy0bHRBYJ8Q4ndSyvIe+pVJKWcJIX6Fltt5CWABdgJ/OoHnKhSKk4QSb8WpjhH4fTx8aRSY0E//icA04B0tGiZ6tBSY7TzXqb0a+KsQ4nm0RB0nwvloSVzarXQLWnjOPX3c866UshVAVDViSwAAAZZJREFUCLEbKKJrOsd22nMB7ADs8fzxbiFEMP5DYbDPVSgUJwkl3opTjnjs9Cia1f0AWjznmWjLRIF4twhdl40s7bcDu6SUi3t5fIclHLeeFwKXAJuEEHN76N/b53QbNnC1lHJfb+/VA51jiEfp/d9ze7/YcffEerkn0ecqFIqThFrzVpxSCCEy0KaCfx9Pc+gEqqWUMeBmNEsatIQTU+LZk1xoWbIA9gEZQojF8ecZhRBTe/mssVLKdVLKHwD1aKkU3YCjU7cjaOlBEULMQcuH3RNvAV+NJ79ACDF7oO+uUCg+OSjxVpwKWNu3igHLgbeB/41fewS4RQixDZhE3HKOr+E+j7bu+zxaRiyklCG0FJcPxu/ZCpzWy+c+LITYEXf2WgNsQ0uROaXdYQ0tX3tqfGxfAfb38qwfoU3xb4/3/dGg/ksoFIpPBGqrmEKhUCgUowxleSsUCoVCMcpQ4q1QKBQKxShDibdCoVAoFKMMJd4KhUKhUIwylHgrFAqFQjHKUOKtUCgUCsUoQ4m3QqFQKBSjjP8PWd1AWDHaub0AAAAASUVORK5CYII=\n"
+      "text/plain": [
+       "73.6"
+      ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
-     "output_type": "display_data"
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
+   "execution_count": 10
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "fig, (ax1, ax2) = plt.subplots(2, sharex=True)  # type: plt.Figure, (plt.Axes, plt.Axes)\n",
+    "fig, (ax1, ax2) = plt.subplots(2, sharex=True, layout='constrained')  # type: plt.Figure, (plt.Axes, plt.Axes)\n",
     "\n",
     "duration_max = 120\n",
     "return_periods = [1, 2, 3, 5, 10, 20, 50, 100][::-1]\n",
@@ -257,15 +814,31 @@
     "\n",
     "ax.legend().remove()\n",
     "\n",
-    "fig.set_size_inches(7, 7)\n",
-    "fig.tight_layout()"
+    "fig.set_size_inches(7, 7)"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:49:50.311654Z",
+     "start_time": "2024-06-03T07:49:49.873081Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Figure size 1400x1400 with 2 Axes>"
+      ],
+      "image/png": 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"
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "execution_count": 13
   }
  ],
  "metadata": {
@@ -289,4 +862,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/example/example_gis_export.ipynb b/example/example_gis_export.ipynb
index b3aa75b..cc314d7 100644
--- a/example/example_gis_export.ipynb
+++ b/example/example_gis_export.ipynb
@@ -2,22 +2,24 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 11,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:57:57.665452Z",
+     "start_time": "2024-06-03T07:57:56.802268Z"
+    }
    },
-   "outputs": [],
    "source": [
     "from ehyd_tools.in_out import get_station_reference_data, STATIONS_PRECIPITATION_HIGH_RES\n",
     "from pyproj import Transformer\n",
     "from shapely.geometry import Point\n",
     "import geopandas as gpd"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "outputs": [],
    "source": [
     "lables = ['Sillian',\n",
     "          'Liezen',\n",
@@ -26,19 +28,23 @@
     "          'Graz-Andritz',\n",
     "          'Linz-Urfahr',\n",
     "          'Wien (Botanischer Garten)',\n",
-    "          'Hollabrunn']\n"
+    "          'Hollabrunn']"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:57:57.668559Z",
+     "start_time": "2024-06-03T07:57:57.666549Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
-   "outputs": [],
    "source": [
     "ehyd_stations_ = {v: k for k, v in STATIONS_PRECIPITATION_HIGH_RES.items()}"
    ],
@@ -46,20 +52,24 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:57:57.670972Z",
+     "start_time": "2024-06-03T07:57:57.669121Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 19,
-   "outputs": [],
    "source": [
     "def degree_to_decimal(degree, minutes, seconds):\n",
     "    return float(degree) + float(minutes) / 60 + float(seconds) / 60 / 60\n",
     "\n",
     "\n",
     "def convert_geo(g):\n",
-    "    l = degree_to_decimal(*g['Länge (Grad,Min,Sek)'].split())\n",
+    "    l = degree_to_decimal(*g['Laenge (Grad,Min,Sek)'].split())\n",
     "    b = degree_to_decimal(*g['Breite  (Grad,Min,Sek)'].split())\n",
     "    # https://epsg.io/4004\n",
     "    # https://epsg.io/32633\n",
@@ -71,13 +81,17 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:58:28.286244Z",
+     "start_time": "2024-06-03T07:58:28.282833Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "execution_count": 25,
-   "outputs": [],
    "source": [
     "res = dict()\n",
     "for l in lables:\n",
@@ -89,88 +103,137 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:58:32.584970Z",
+     "start_time": "2024-06-03T07:58:29.153430Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 7
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "source": [
+    "gdf = gpd.GeoSeries(res, crs='EPSG:4004')\n",
+    "gdf"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:35:50.788505Z",
+     "start_time": "2024-06-03T08:35:50.767382Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "Sillian                      POINT (12.41500 46.74694)\nLiezen                       POINT (14.23667 47.57083)\nKlagenfurt                   POINT (14.31861 46.61278)\nBregenz                       POINT (9.75667 47.50722)\nGraz-Andritz                 POINT (15.41583 47.10972)\nLinz-Urfahr                  POINT (14.27528 48.32000)\nWien (Botanischer Garten)    POINT (16.38528 48.19444)\nHollabrunn                   POINT (16.07139 48.57167)\ndtype: geometry"
+      "text/plain": [
+       "Sillian                      POINT (12.41667 46.75000)\n",
+       "Liezen                       POINT (14.23667 47.57083)\n",
+       "Klagenfurt                   POINT (14.31667 46.62167)\n",
+       "Bregenz                       POINT (9.75667 47.50778)\n",
+       "Graz-Andritz                 POINT (15.41278 47.10139)\n",
+       "Linz-Urfahr                  POINT (14.27528 48.32000)\n",
+       "Wien (Botanischer Garten)    POINT (16.38444 48.19444)\n",
+       "Hollabrunn                   POINT (16.07139 48.57167)\n",
+       "dtype: geometry"
+      ]
      },
-     "execution_count": 21,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "gdf = gpd.GeoSeries(res, crs='EPSG:4004')\n",
-    "gdf"
+    "gdf.crs"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:35:51.727177Z",
+     "start_time": "2024-06-03T08:35:51.719349Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "<Geographic 2D CRS: EPSG:4004>\nName: Unknown datum based upon the Bessel 1841 ellipsoid\nAxis Info [ellipsoidal]:\n- Lat[north]: Geodetic latitude (degree)\n- Lon[east]: Geodetic longitude (degree)\nArea of Use:\n- name: Not specified.\n- bounds: (-180.0, -90.0, 180.0, 90.0)\nDatum: Not specified (based on Bessel 1841 ellipsoid)\n- Ellipsoid: Bessel 1841\n- Prime Meridian: Greenwich"
+      "text/plain": [
+       "<Geographic 2D CRS: EPSG:4004>\n",
+       "Name: Unknown datum based upon the Bessel 1841 ellipsoid\n",
+       "Axis Info [ellipsoidal]:\n",
+       "- Lat[north]: Geodetic latitude (degree)\n",
+       "- Lon[east]: Geodetic longitude (degree)\n",
+       "Area of Use:\n",
+       "- name: Not specified.\n",
+       "- bounds: (-180.0, -90.0, 180.0, 90.0)\n",
+       "Datum: Not specified (based on Bessel 1841 ellipsoid)\n",
+       "- Ellipsoid: Bessel 1841\n",
+       "- Prime Meridian: Greenwich"
+      ]
      },
-     "execution_count": 22,
+     "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "gdf.crs"
+    "gdf.plot()"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:35:53.157135Z",
+     "start_time": "2024-06-03T08:35:52.699142Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "<AxesSubplot:>"
+      "text/plain": [
+       "<Axes: >"
+      ]
      },
-     "execution_count": 23,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "text/plain": "<Figure size 432x288 with 1 Axes>",
-      "image/png": "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\n"
-     },
-     "metadata": {
-      "needs_background": "light"
+      "text/plain": [
+       "<Figure size 1280x960 with 1 Axes>"
+      ],
+      "image/png": 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"
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "gdf.plot()"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "execution_count": 10
+  },
+  {
+   "metadata": {},
+   "cell_type": "code",
+   "outputs": [],
+   "execution_count": null,
+   "source": ""
   }
  ],
  "metadata": {
@@ -194,4 +257,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}
diff --git a/example/example_python_api.ipynb b/example/example_python_api.ipynb
index 3325c19..d6d6b1c 100644
--- a/example/example_python_api.ipynb
+++ b/example/example_python_api.ipynb
@@ -1,141 +1,508 @@
 {
  "cells": [
   {
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:52.326423Z",
+     "start_time": "2024-06-03T07:47:52.314163Z"
+    }
+   },
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ],
    "outputs": [],
+   "execution_count": 1
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:53.135792Z",
+     "start_time": "2024-06-03T07:47:52.327908Z"
+    }
+   },
    "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"
-   ]
+    "from ehyd_tools.in_out import get_station_reference_data"
+   ],
+   "outputs": [],
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "source": [
+    "STATIONS_PRECIPITATION_HIGH_RES"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:53.147804Z",
+     "start_time": "2024-06-03T07:47:53.136673Z"
+    }
+   },
    "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'}"
+      "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"
-    }
-   }
+   "execution_count": 3
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
    "metadata": {
     "pycharm": {
      "name": "#%%\n"
     },
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:53.161537Z",
+     "start_time": "2024-06-03T07:47:53.148733Z"
+    }
    },
+   "source": [
+    "Series(STATIONS_PRECIPITATION_HIGH_RES).rename(index='id').rename('Station').to_frame()"
+   ],
    "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>"
+      "text/plain": [
+       "                           Station\n",
+       "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"
+      ],
+      "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()"
-   ]
+   "execution_count": 4
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:53.171721Z",
+     "start_time": "2024-06-03T07:47:53.163143Z"
+    }
+   },
    "source": [
     "id_number = 112086"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 5
   },
   {
    "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"
-     ]
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:54.442006Z",
+     "start_time": "2024-06-03T07:47:53.172793Z"
     }
-   ],
+   },
    "source": [
     "series = get_ehyd_data(id_number)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:54.675537Z",
+     "start_time": "2024-06-03T07:47:54.442638Z"
+    }
+   },
+   "source": "series = import_series(f'ehyd_{id_number}.parquet')",
    "outputs": [],
-   "source": [
-    "series = import_series('ehyd_{}.parquet'.format(id_number))"
-   ]
+   "execution_count": 7
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
    "metadata": {
-    "scrolled": true
+    "scrolled": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:55.004225Z",
+     "start_time": "2024-06-03T07:47:54.677352Z"
+    }
    },
+   "source": [
+    "print(get_station_reference_data(id_number))"
+   ],
    "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"
+      "{'_raw': 'Messstelle:                Graz-AEndritz\\nHZB-Nummer:                112086\\nErrichtet:                 1946\\nSachgebiet:                NLV\\nDienststelle:              HD-Steiermark\\nMessstellenbetreiber:      Hydrographischer Dienst\\n\\nHoehe:\\n gueltig seit:              Hoehe [m ue.AE.]:\\n  01.01.1946               360\\n  01.01.2011               361\\n\\nGeographische Koordinaten (Referenzellipsoid: Bessel 1841):\\n gueltig seit:              Laenge (Grad,Min,Sek):    Breite  (Grad,Min,Sek):\\n  01.01.2011               15 24 38                 47 06 22\\n  01.01.1946               15 24 46                 47 06 05\\n\\nMessgroessen,-art:           seit:     bis:\\n  Niederschlag-Ombrometer  1946\\n  Niederschlag-Ombrograph  2007\\n  Schneehoehe               1946\\n  Neuschneehoehe            1946\\n  Temperatur               1947\\n', 'Messstelle': 'Graz-AEndritz', 'HZB-Nummer': '112086', 'Errichtet': '1946', 'Sachgebiet': 'NLV', 'Dienststelle': 'HD-Steiermark', 'Messstellenbetreiber': 'Hydrographischer Dienst', 'Hoehe': [{'gueltig seit': '01.01.1946', 'Hoehe [m ue.AE.]': '360'}, {'gueltig seit': '01.01.2011', 'Hoehe [m ue.AE.]': '361'}], 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)': [{'gueltig seit': '01.01.2011', 'Laenge (Grad,Min,Sek)': '15 24 38', 'Breite  (Grad,Min,Sek)': '47 06 22'}, {'gueltig seit': '01.01.1946', 'Laenge (Grad,Min,Sek)': '15 24 46', 'Breite  (Grad,Min,Sek)': '47 06 05'}], 'Messgroessen,-art': [{'Messgroessen,-art': 'Niederschlag-Ombrometer', 'seit': '1946'}, {'Messgroessen,-art': 'Niederschlag-Ombrograph', 'seit': '2007'}, {'Messgroessen,-art': 'Schneehoehe', 'seit': '1946'}, {'Messgroessen,-art': 'Neuschneehoehe', 'seit': '1946'}, {'Messgroessen,-art': 'Temperatur', 'seit': '1947'}]}\n"
      ]
     }
    ],
-   "source": [
-    "print(get_station_reference_data(id_number))"
-   ]
+   "execution_count": 8
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:47:55.015515Z",
+     "start_time": "2024-06-03T07:47:55.005166Z"
+    }
+   },
+   "source": [
+    "series.head(), series.tail()"
+   ],
    "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)"
+      "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: min, Name: N-Minutensummen-112086, dtype: float64,\n",
+       " datetime\n",
+       " 2019-12-31 23:56:00    0.0\n",
+       " 2019-12-31 23:57:00    0.0\n",
+       " 2019-12-31 23:58:00    0.0\n",
+       " 2019-12-31 23:59:00    0.0\n",
+       " 2020-01-01 00:00:00    NaN\n",
+       " Freq: min, Name: N-Minutensummen-112086, dtype: float64)"
+      ]
      },
      "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "series.head(), series.tail()"
-   ]
+   "execution_count": 9
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:05.608681Z",
+     "start_time": "2024-06-03T07:47:55.016207Z"
+    }
+   },
+   "source": "export_series(series, filename=f'ehyd_{id_number}', save_as='csv', unix=False)",
    "outputs": [
     {
      "data": {
@@ -143,19 +510,22 @@
        "'ehyd_112086.csv'"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 10,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "export_series(series, filename='ehyd_{}'.format(id_number), save_as='csv', unix=False)"
-   ]
+   "execution_count": 10
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:05.890880Z",
+     "start_time": "2024-06-03T07:48:05.609618Z"
+    }
+   },
+   "source": "export_series(series, filename=f'ehyd_{id_number}', save_as='parquet')",
    "outputs": [
     {
      "data": {
@@ -163,19 +533,26 @@
        "'ehyd_112086.parquet'"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "export_series(series, filename='ehyd_{}'.format(id_number), save_as='parquet')"
-   ]
+   "execution_count": 11
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:07.721189Z",
+     "start_time": "2024-06-03T07:48:05.891583Z"
+    }
+   },
+   "source": [
+    "tags = data_validation(series)\n",
+    "availability = data_availability(tags)\n",
+    "availability"
+   ],
    "outputs": [
     {
      "data": {
@@ -186,53 +563,65 @@
        "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"
+       "2020-12-31 23:55:00    False\n",
+       "2020-12-31 23:56:00    False\n",
+       "2020-12-31 23:57:00    False\n",
+       "2020-12-31 23:58:00    False\n",
+       "2020-12-31 23:59:00    False\n",
+       "Freq: min, Length: 7364160, dtype: bool"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "tags = data_validation(series)\n",
-    "availability = data_availability(tags)\n",
-    "availability"
-   ]
+   "execution_count": 12
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:07.900356Z",
+     "start_time": "2024-06-03T07:48:07.722021Z"
+    }
+   },
+   "source": [
+    "max_10a(availability)"
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(Timestamp('2007-09-17 01:55:00'),\n",
-       " Timestamp('2017-09-17 01:55:00', freq='-1T'))"
+       "(Timestamp('2009-12-31 23:59:00'), Timestamp('2019-12-31 23:59:00'))"
       ]
      },
-     "execution_count": 10,
+     "execution_count": 13,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "max_10a(availability)"
-   ]
+   "execution_count": 13
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:07.944777Z",
+     "start_time": "2024-06-03T07:48:07.902910Z"
+    }
+   },
+   "source": [
+    "span_table(~availability)"
+   ],
    "outputs": [
     {
      "data": {
+      "text/plain": [
+       "       start                 end\n",
+       "0 2007-01-01 2007-09-17 13:55:00\n",
+       "1 2020-01-01 2020-12-31 23:59:00"
+      ],
       "text/html": [
        "<div>\n",
        "<style scoped>\n",
@@ -264,68 +653,86 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
-       "      <td>2017-01-01</td>\n",
-       "      <td>2017-12-31 23:59:00</td>\n",
+       "      <td>2020-01-01</td>\n",
+       "      <td>2020-12-31 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,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "span_table(~availability)"
-   ]
+   "execution_count": 14
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:08.257686Z",
+     "start_time": "2024-06-03T07:48:07.945474Z"
+    }
+   },
    "source": [
     "availability_cut = 0.9\n",
     "stats = create_statistics(series, availability, availability_cut=availability_cut)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 15
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:08.267557Z",
+     "start_time": "2024-06-03T07:48:08.258422Z"
+    }
+   },
+   "source": [
+    "stats"
+   ],
    "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)}"
+       "{'maximum': 1189.2450000000001,\n",
+       " 'maximum_date': Timestamp('2009-12-31 00:00:00'),\n",
+       " 'maximum_avail': 1.0,\n",
+       " 'minimum': 564.231,\n",
+       " 'minimum_date': Timestamp('2019-12-31 00:00:00'),\n",
+       " 'minimum_avail': 1.0,\n",
+       " 'mean': 835.0704999999999,\n",
+       " 'mean_avail': 0.8777728854098718}"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "stats"
-   ]
+   "execution_count": 16
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:08.277173Z",
+     "start_time": "2024-06-03T07:48:08.268349Z"
+    }
+   },
+   "source": [
+    "print(\n",
+    "    f'The annual totals of the data series serve as the data basis.\\n'\n",
+    "    f'The following statistics were analyzed:\\n'\n",
+    "    f'Only years with a availability of {availability_cut} will be evaluated.\\n'\n",
+    "    f'\\n'\n",
+    "    f'The maximum is {stats[\"maximum\"]:0.0f} mm and was in the year {stats[\"maximum_date\"]:%Y} (with {stats[\"maximum_avail\"]:0.0%} Data available).\\n'\n",
+    "    f'The minimum is {stats[\"minimum\"]:0.0f} mm and was in the year {stats[\"minimum_date\"]:%Y} (with {stats[\"minimum_avail\"]:0.0%} Data available).\\n'\n",
+    "    f'The mean is {stats[\"mean\"]:0.0f} mm (with {stats[\"mean_avail\"]:0.0%} Data available in average).')"
+   ],
    "outputs": [
     {
      "name": "stdout",
@@ -333,65 +740,51 @@
      "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",
+      "Only years with a availability of 0.9 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"
+      "The minimum is 564 mm and was in the year 2019 (with 100% Data available).\n",
+      "The mean is 835 mm (with 88% 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']))"
-   ]
+   "execution_count": 17
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T07:48:09.273575Z",
+     "start_time": "2024-06-03T07:48:08.277985Z"
+    }
+   },
+   "source": [
+    "agg_data_figure(series, availability)"
+   ],
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "(<Figure size 841.89x595.276 with 2 Axes>,\n",
-       " <matplotlib.axes._subplots.AxesSubplot at 0x1fa0a3fe970>)"
+       "(<Figure size 2338.58x1653.54 with 2 Axes>,\n",
+       " <Axes: xlabel='Zeit', ylabel='Niederschlag (mm/Monat)'>)"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 18,
      "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"
+       "<Figure size 2338.58x1653.54 with 2 Axes>"
+      ],
+      "image/png": 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"
      },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
-   "source": [
-    "agg_data_figure(series, availability)"
-   ]
+   "execution_count": 18
   }
  ],
  "metadata": {
@@ -415,4 +808,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}
diff --git a/example/example_python_api_general.ipynb b/example/example_python_api_general.ipynb
index 7c65c0c..3dfaee8 100644
--- a/example/example_python_api_general.ipynb
+++ b/example/example_python_api_general.ipynb
@@ -2,132 +2,269 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [],
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:16.639437Z",
+     "start_time": "2024-06-03T08:36:16.125688Z"
+    }
+   },
    "source": [
     "import pandas as pd\n",
     "import json\n",
     "from ehyd_tools.in_out import (get_ehyd_data, FIELDS, DATA_KIND, get_basic_station_meta, available_files,\n",
     "                               get_station_reference_data, get_ehyd_station_frame, get_ehyd_files)"
-   ]
+   ],
+   "outputs": [],
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:16.648484Z",
+     "start_time": "2024-06-03T08:36:16.640568Z"
+    }
+   },
+   "source": [
+    "field = FIELDS.NIEDERSCHLAG\n",
+    "get_ehyd_station_frame(field).head()"
+   ],
    "outputs": [
     {
      "data": {
-      "text/plain": "                   Messstellen Name  Jahr        Bundesland  \\\nMst. ID                                                       \n107839          AbsdorfNIEDERSCHLAG  2017  Niederösterreich   \n115063          AbtenauNIEDERSCHLAG  2017          Salzburg   \n104489   Ach-BurghausenNIEDERSCHLAG  2017    Oberösterreich   \n101345       AchenkirchNIEDERSCHLAG  2017             Tirol   \n115154                    Ackernalm  2017             Tirol   \n\n                                 Flussgebiet  Seehöhe  \nMst. ID                                                \n107839   Donaugebiet zwischen Enns und March      182  \n115063                         Salzachgebiet      711  \n104489                         Salzachgebiet      473  \n101345          Donaugebiet oberhalb des Inn      906  \n115154        Inngebiet oberhalb der Salzach     1324  ",
-      "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>Messstellen Name</th>\n      <th>Jahr</th>\n      <th>Bundesland</th>\n      <th>Flussgebiet</th>\n      <th>Seehöhe</th>\n    </tr>\n    <tr>\n      <th>Mst. ID</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>107839</th>\n      <td>AbsdorfNIEDERSCHLAG</td>\n      <td>2017</td>\n      <td>Niederösterreich</td>\n      <td>Donaugebiet zwischen Enns und March</td>\n      <td>182</td>\n    </tr>\n    <tr>\n      <th>115063</th>\n      <td>AbtenauNIEDERSCHLAG</td>\n      <td>2017</td>\n      <td>Salzburg</td>\n      <td>Salzachgebiet</td>\n      <td>711</td>\n    </tr>\n    <tr>\n      <th>104489</th>\n      <td>Ach-BurghausenNIEDERSCHLAG</td>\n      <td>2017</td>\n      <td>Oberösterreich</td>\n      <td>Salzachgebiet</td>\n      <td>473</td>\n    </tr>\n    <tr>\n      <th>101345</th>\n      <td>AchenkirchNIEDERSCHLAG</td>\n      <td>2017</td>\n      <td>Tirol</td>\n      <td>Donaugebiet oberhalb des Inn</td>\n      <td>906</td>\n    </tr>\n    <tr>\n      <th>115154</th>\n      <td>Ackernalm</td>\n      <td>2017</td>\n      <td>Tirol</td>\n      <td>Inngebiet oberhalb der Salzach</td>\n      <td>1324</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "        Messstellen Name  Jahr        Bundesland  \\\n",
+       "Mst. ID                                            \n",
+       "107839           Absdorf  2017  Niederösterreich   \n",
+       "115063           Abtenau  2017          Salzburg   \n",
+       "104489    Ach-Burghausen  2017    Oberösterreich   \n",
+       "101345        Achenkirch  2017             Tirol   \n",
+       "115154         Ackernalm  2017             Tirol   \n",
+       "\n",
+       "                                 Flussgebiet  Seehöhe  \n",
+       "Mst. ID                                                \n",
+       "107839   Donaugebiet zwischen Enns und March      182  \n",
+       "115063                         Salzachgebiet      711  \n",
+       "104489                         Salzachgebiet      473  \n",
+       "101345          Donaugebiet oberhalb des Inn      906  \n",
+       "115154        Inngebiet oberhalb der Salzach     1324  "
+      ],
+      "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>Messstellen Name</th>\n",
+       "      <th>Jahr</th>\n",
+       "      <th>Bundesland</th>\n",
+       "      <th>Flussgebiet</th>\n",
+       "      <th>Seehöhe</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Mst. ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>107839</th>\n",
+       "      <td>Absdorf</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Niederösterreich</td>\n",
+       "      <td>Donaugebiet zwischen Enns und March</td>\n",
+       "      <td>182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115063</th>\n",
+       "      <td>Abtenau</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Salzburg</td>\n",
+       "      <td>Salzachgebiet</td>\n",
+       "      <td>711</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104489</th>\n",
+       "      <td>Ach-Burghausen</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Oberösterreich</td>\n",
+       "      <td>Salzachgebiet</td>\n",
+       "      <td>473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101345</th>\n",
+       "      <td>Achenkirch</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Tirol</td>\n",
+       "      <td>Donaugebiet oberhalb des Inn</td>\n",
+       "      <td>906</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>115154</th>\n",
+       "      <td>Ackernalm</td>\n",
+       "      <td>2017</td>\n",
+       "      <td>Tirol</td>\n",
+       "      <td>Inngebiet oberhalb der Salzach</td>\n",
+       "      <td>1324</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
      "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
-   "source": [
-    "field = FIELDS.NIEDERSCHLAG\n",
-    "get_ehyd_station_frame(field).head()"
-   ]
+   "execution_count": 2
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "source": [
+    "id_number = 106559\n",
+    "# f = _get_files(id_number, field=FIELDS.NIEDERSCHLAG, data_kind=DATA_KIND.MEASUREMENT)\n",
+    "get_basic_station_meta(id_number, field=field)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:16.652109Z",
+     "start_time": "2024-06-03T08:36:16.649509Z"
+    }
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "{'Messstellen Name': 'Steyr',\n 'Jahr': 2017,\n 'Bundesland': 'Oberösterreich',\n 'Flussgebiet': 'Ennsgebiet',\n 'Seehöhe': 336}"
+      "text/plain": [
+       "{'Messstellen Name': 'Steyr',\n",
+       " 'Jahr': 2017,\n",
+       " 'Bundesland': 'Oberösterreich',\n",
+       " 'Flussgebiet': 'Ennsgebiet',\n",
+       " 'Seehöhe': 336}"
+      ]
      },
      "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "id_number = 106559\n",
-    "# f = _get_files(id_number, field=FIELDS.NIEDERSCHLAG, data_kind=DATA_KIND.MEASUREMENT)\n",
-    "get_basic_station_meta(id_number, field=field)"
+    "available_files(id_number, field=field, data_kind=DATA_KIND.MEASUREMENT)"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:17.895396Z",
+     "start_time": "2024-06-03T08:36:16.652895Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "{1: 'Stammdaten-106559.txt',\n 2: 'N-Minutensummen-106559.zip',\n 3: 'N-Tagessummen-106559.csv',\n 4: 'NS-Tagessummen-106559.csv',\n 5: 'SH-Tageswerte-106559.csv'}"
+      "text/plain": [
+       "{1: 'Stammdaten-106559.txt',\n",
+       " 2: 'N-Tagessummen-106559.csv',\n",
+       " 3: 'NS-Tagessummen-106559.csv',\n",
+       " 4: 'SH-Tageswerte-106559.csv',\n",
+       " 5: 'N-Minutensummen-106559.zip'}"
+      ]
      },
      "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "available_files(id_number, field=field, data_kind=DATA_KIND.MEASUREMENT)"
+    "meta = get_station_reference_data(id_number, field=field, data_kind=DATA_KIND.MEASUREMENT)\n",
+    "print(json.dumps(meta, indent=2, ensure_ascii=False))"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:17.899746Z",
+     "start_time": "2024-06-03T08:36:17.897202Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "{\n",
-      "  \"_raw\": \"Messstelle:                Steyr\\nHZB-Nummer:                106559\\nErrichtet:                 1864\\nSachgebiet:                NLV\\nDienststelle:              HD-Oberösterreich\\nMessstellenbetreiber:      Hydrographischer Dienst\\n\\nHöhe:\\n gültig seit:              Höhe [m ü.A.]:\\n  01.01.1864               336\\n\\nGeographische Koordinaten (Referenzellipsoid: Bessel 1841):\\n gültig seit:              Länge (Grad,Min,Sek):    Breite  (Grad,Min,Sek):\\n  01.01.1864               14 25 31                 48 03 02\\n\\nMessgrößen,-art:           seit:     bis:\\n  Niederschlag-Ombrometer  1864\\n  Niederschlag-Ombrograph  2002\\n  Schneehöhe               1897\\n  Neuschneehöhe            1897\\n  Temperatur               1896\\n\",\n",
+      "  \"_raw\": \"Messstelle:                Steyr\\nHZB-Nummer:                106559\\nErrichtet:                 1864\\nSachgebiet:                NLV\\nDienststelle:              HD-Oberoesterreich\\nMessstellenbetreiber:      Hydrographischer Dienst\\n\\nHoehe:\\n gueltig seit:              Hoehe [m ue.AE.]:\\n  01.01.1864               336\\n\\nGeographische Koordinaten (Referenzellipsoid: Bessel 1841):\\n gueltig seit:              Laenge (Grad,Min,Sek):    Breite  (Grad,Min,Sek):\\n  01.01.1864               14 25 31                 48 03 02\\n\\nMessgroessen,-art:           seit:     bis:\\n  Niederschlag-Ombrometer  1864\\n  Niederschlag-Ombrograph  2002\\n  Schneehoehe               1897\\n  Neuschneehoehe            1897\\n  Temperatur               1896\\n\",\n",
       "  \"Messstelle\": \"Steyr\",\n",
       "  \"HZB-Nummer\": \"106559\",\n",
       "  \"Errichtet\": \"1864\",\n",
       "  \"Sachgebiet\": \"NLV\",\n",
-      "  \"Dienststelle\": \"HD-Oberösterreich\",\n",
+      "  \"Dienststelle\": \"HD-Oberoesterreich\",\n",
       "  \"Messstellenbetreiber\": \"Hydrographischer Dienst\",\n",
-      "  \"Höhe\": [\n",
+      "  \"Hoehe\": [\n",
       "    {\n",
-      "      \"gültig seit\": \"01.01.1864\",\n",
-      "      \"Höhe [m ü.A.]\": \"336\"\n",
+      "      \"gueltig seit\": \"01.01.1864\",\n",
+      "      \"Hoehe [m ue.AE.]\": \"336\"\n",
       "    }\n",
       "  ],\n",
       "  \"Geographische Koordinaten (Referenzellipsoid: Bessel 1841)\": [\n",
       "    {\n",
-      "      \"gültig seit\": \"01.01.1864\",\n",
-      "      \"Länge (Grad,Min,Sek)\": \"14 25 31\",\n",
+      "      \"gueltig seit\": \"01.01.1864\",\n",
+      "      \"Laenge (Grad,Min,Sek)\": \"14 25 31\",\n",
       "      \"Breite  (Grad,Min,Sek)\": \"48 03 02\"\n",
       "    }\n",
       "  ],\n",
-      "  \"Messgrößen,-art\": [\n",
+      "  \"Messgroessen,-art\": [\n",
       "    {\n",
-      "      \"Messgrößen,-art\": \"Niederschlag-Ombrometer\",\n",
+      "      \"Messgroessen,-art\": \"Niederschlag-Ombrometer\",\n",
       "      \"seit\": \"1864\"\n",
       "    },\n",
       "    {\n",
-      "      \"Messgrößen,-art\": \"Niederschlag-Ombrograph\",\n",
+      "      \"Messgroessen,-art\": \"Niederschlag-Ombrograph\",\n",
       "      \"seit\": \"2002\"\n",
       "    },\n",
       "    {\n",
-      "      \"Messgrößen,-art\": \"Schneehöhe\",\n",
+      "      \"Messgroessen,-art\": \"Schneehoehe\",\n",
       "      \"seit\": \"1897\"\n",
       "    },\n",
       "    {\n",
-      "      \"Messgrößen,-art\": \"Neuschneehöhe\",\n",
+      "      \"Messgroessen,-art\": \"Neuschneehoehe\",\n",
       "      \"seit\": \"1897\"\n",
       "    },\n",
       "    {\n",
-      "      \"Messgrößen,-art\": \"Temperatur\",\n",
+      "      \"Messgroessen,-art\": \"Temperatur\",\n",
       "      \"seit\": \"1896\"\n",
       "    }\n",
       "  ]\n",
@@ -135,107 +272,109 @@
      ]
     }
    ],
+   "execution_count": 5
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "meta = get_station_reference_data(id_number, field=field, data_kind=DATA_KIND.MEASUREMENT)\n",
-    "print(json.dumps(meta, indent=2, ensure_ascii=False))"
+    "series = get_ehyd_data(id_number, field=field, file_number=2, data_kind=DATA_KIND.MEASUREMENT)\n",
+    "series"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:17.948913Z",
+     "start_time": "2024-06-03T08:36:17.900580Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "datetime\n2002-01-01 00:00:00    0.0\n2002-01-01 00:01:00    0.0\n2002-01-01 00:02:00    0.0\n2002-01-01 00:03:00    0.0\n2002-01-01 00:04:00    0.0\n                      ... \n2018-12-29 06:56:00    0.0\n2018-12-29 06:57:00    0.0\n2018-12-29 06:58:00    0.0\n2018-12-29 06:59:00    0.0\n2018-12-29 07:00:00    NaN\nFreq: T, Name: N-Minutensummen-106559.csv, Length: 8937061, dtype: float64"
+      "text/plain": [
+       "datetime\n",
+       "1971-01-01    NaN\n",
+       "1971-01-02    NaN\n",
+       "1971-01-03    NaN\n",
+       "1971-01-04    NaN\n",
+       "1971-01-05    NaN\n",
+       "             ... \n",
+       "2021-12-28    9.1\n",
+       "2021-12-29    7.4\n",
+       "2021-12-30    2.0\n",
+       "2021-12-31    0.0\n",
+       "2022-01-01    NaN\n",
+       "Freq: D, Name: N-Tagessummen-106559, Length: 18629, dtype: float64"
+      ]
      },
      "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 6
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "series = get_ehyd_data(id_number, field=field, file_number=2, data_kind=DATA_KIND.MEASUREMENT)\n",
-    "series"
+    "print(json.dumps(series.attrs, indent=2, ensure_ascii=False))\n"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:17.951083Z",
+     "start_time": "2024-06-03T08:36:17.949523Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 15,
+   },
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
       "{\n",
-      "  \"_raw\": \"Messstelle:                Steyr\\nHZB-Nummer:                106559\\nHD-Nummer:                 HD4000152\\nDBMS-Nummer:               4000152\\nSachgebiet:                NLV\\nDienststelle:              HD-Oberösterreich\\nMessstellenbetreiber:      Hydrographischer Dienst\\nHöhe:\\n gültig seit:              Höhe [m ü.A.]:\\n  01.01.1864               336\\nGeographische Koordinaten (Referenzellipsoid: Bessel 1841):\\n gültig seit:              Länge (Grad,Min,Sek):    Breite  (Grad,Min,Sek):\\n  01.01.1864               14 25 31                 48 03 02\\nExportzeitreihe:           Schneehöhe,I,,T,1,O,Z,0,,,\\nExportqualität:            MAXQUAL (2)\\nExportzeitraum:            01.09.1970 07:00 bis 01.09.2018 07:00\\nHinweis:                   Der Intervallwert gilt bis zum nächsten Zeitpunkt mit einem Wert oder Lücke\\nHinweis:                   Messwert=0,001 - Messwert kleiner als 0,5cm\\nWerteformat:               0 Nachkommastellen\\nEinheit:                   cm\\n\",\n",
+      "  \"_raw\": \"Messstelle:                Steyr\\nHZB-Nummer:                106559\\nHD-Nummer:                 HD4000152\\nDBMS-Nummer:               4000152\\nSachgebiet:                NLV\\nDienststelle:              HD-Oberoesterreich\\nMessstellenbetreiber:      Hydrographischer Dienst\\nHoehe:\\n gueltig seit:              Hoehe [m ue.AE.]:\\n  01.01.1864               336\\nGeographische Koordinaten (Referenzellipsoid: Bessel 1841):\\n gueltig seit:              Laenge (Grad,Min,Sek):    Breite  (Grad,Min,Sek):\\n  01.01.1864               14 25 31                 48 03 02\\nExportzeitreihe:           Niederschlag,I,Sum,T,1,O,Z,0,,, und Niederschlag,I,Sum,T,1,M,Z,0,,,(Schreiber)original N-Messer-Tagessummen mit abgeleiteten N-Schreiber-Tagessummen zusammengehaengt\\nExportqualitaet:            MAEXQueAEL\\nExportzeitraum:            01.01.1971 07:00 bis 01.01.2022 07:00\\nHinweis:                   Der Intervallwert gilt bis zum naechsten Zeitpunkt mit einem Wert oder Luecke\\nHinweis:                   Messwert=0,001 - nicht messbarer Niederschlag\\nWerteformat:               1 Nachkommastelle\\nEinheit:                   mm\\n\",\n",
       "  \"Messstelle\": \"Steyr\",\n",
       "  \"HZB-Nummer\": \"106559\",\n",
       "  \"HD-Nummer\": \"HD4000152\",\n",
       "  \"DBMS-Nummer\": \"4000152\",\n",
       "  \"Sachgebiet\": \"NLV\",\n",
-      "  \"Dienststelle\": \"HD-Oberösterreich\",\n",
+      "  \"Dienststelle\": \"HD-Oberoesterreich\",\n",
       "  \"Messstellenbetreiber\": \"Hydrographischer Dienst\",\n",
-      "  \"Höhe\": [\n",
+      "  \"Hoehe\": [\n",
       "    {\n",
-      "      \"gültig seit\": \"01.01.1864\",\n",
-      "      \"Höhe [m ü.A.]\": \"336\"\n",
+      "      \"gueltig seit\": \"01.01.1864\",\n",
+      "      \"Hoehe [m ue.AE.]\": \"336\"\n",
       "    }\n",
       "  ],\n",
-      "  \"Exportzeitreihe\": \"Schneehöhe,I,,T,1,O,Z,0,,,\",\n",
+      "  \"Exportzeitreihe\": \"Niederschlag,I,Sum,T,1,O,Z,0,,, und Niederschlag,I,Sum,T,1,M,Z,0,,,(Schreiber)original N-Messer-Tagessummen mit abgeleiteten N-Schreiber-Tagessummen zusammengehaengt\",\n",
       "  \"Geographische Koordinaten (Referenzellipsoid: Bessel 1841)\": [\n",
       "    {\n",
-      "      \"gültig seit\": \"01.01.1864\",\n",
-      "      \"Länge (Grad,Min,Sek)\": \"14 25 31\",\n",
+      "      \"gueltig seit\": \"01.01.1864\",\n",
+      "      \"Laenge (Grad,Min,Sek)\": \"14 25 31\",\n",
       "      \"Breite  (Grad,Min,Sek)\": \"48 03 02\"\n",
       "    }\n",
       "  ],\n",
-      "  \"Exportqualität\": \"MAXQUAL (2)\",\n",
-      "  \"Exportzeitraum\": \"01.09.1970 07:00 bis 01.09.2018 07:00\",\n",
+      "  \"Exportqualitaet\": \"MAEXQueAEL\",\n",
+      "  \"Exportzeitraum\": \"01.01.1971 07:00 bis 01.01.2022 07:00\",\n",
       "  \"Hinweis\": [\n",
-      "    \"Der Intervallwert gilt bis zum nächsten Zeitpunkt mit einem Wert oder Lücke\",\n",
-      "    \"Messwert=0,001 - Messwert kleiner als 0,5cm\"\n",
+      "    \"Der Intervallwert gilt bis zum naechsten Zeitpunkt mit einem Wert oder Luecke\",\n",
+      "    \"Messwert=0,001 - nicht messbarer Niederschlag\"\n",
       "  ],\n",
-      "  \"Werteformat\": \"0 Nachkommastellen\",\n",
-      "  \"Einheit\": \"cm\"\n",
+      "  \"Werteformat\": \"1 Nachkommastelle\",\n",
+      "  \"Einheit\": \"mm\"\n",
       "}\n"
      ]
     }
    ],
-   "source": [
-    "print(json.dumps(series.attrs, indent=2, ensure_ascii=False))\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "execution_count": 7
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "datetime\n1971-01-01    0.500\n1971-01-02    0.001\n1971-01-03    3.000\n1971-01-04    0.000\n1971-01-05    0.000\n              ...  \n2018-12-28    0.100\n2018-12-29    1.700\n2018-12-30    6.500\n2018-12-31    5.200\n2019-01-01      NaN\nFreq: D, Name: N-Tagessummen-106559.csv, Length: 17533, dtype: float64"
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "series = get_ehyd_data(id_number, field=field, file_number=3, data_kind=DATA_KIND.MEASUREMENT)\n",
     "series"
@@ -244,22 +383,40 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:17.997899Z",
+     "start_time": "2024-06-03T08:36:17.951586Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "datetime\n1970-09-01    NaN\n1970-09-02    0.0\n1970-09-03    0.0\n1970-09-04    0.0\n1970-09-05    0.0\n             ... \n2018-08-28    0.0\n2018-08-29    0.0\n2018-08-30    0.0\n2018-08-31    0.0\n2018-09-01    0.0\nFreq: D, Name: NS-Tagessummen-106559.csv, Length: 17533, dtype: float64"
+      "text/plain": [
+       "datetime\n",
+       "1970-09-01    NaN\n",
+       "1970-09-02    0.0\n",
+       "1970-09-03    0.0\n",
+       "1970-09-04    0.0\n",
+       "1970-09-05    0.0\n",
+       "             ... \n",
+       "2021-08-28    0.0\n",
+       "2021-08-29    0.0\n",
+       "2021-08-30    0.0\n",
+       "2021-08-31    0.0\n",
+       "2021-09-01    0.0\n",
+       "Freq: D, Name: NS-Tagessummen-106559, Length: 18629, dtype: float64"
+      ]
      },
      "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 8
+  },
+  {
+   "cell_type": "code",
    "source": [
     "series = get_ehyd_data(id_number, field=field, file_number=4, data_kind=DATA_KIND.MEASUREMENT)\n",
     "series"
@@ -268,22 +425,40 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:18.041533Z",
+     "start_time": "2024-06-03T08:36:17.998559Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "datetime\n1970-09-01    NaN\n1970-09-02    0.0\n1970-09-03    0.0\n1970-09-04    0.0\n1970-09-05    0.0\n             ... \n2018-08-28    0.0\n2018-08-29    0.0\n2018-08-30    0.0\n2018-08-31    0.0\n2018-09-01    0.0\nFreq: D, Name: SH-Tageswerte-106559.csv, Length: 17533, dtype: float64"
+      "text/plain": [
+       "datetime\n",
+       "1970-09-01    NaN\n",
+       "1970-09-02    0.0\n",
+       "1970-09-03    0.0\n",
+       "1970-09-04    0.0\n",
+       "1970-09-05    0.0\n",
+       "             ... \n",
+       "2021-08-28    0.0\n",
+       "2021-08-29    0.0\n",
+       "2021-08-30    0.0\n",
+       "2021-08-31    0.0\n",
+       "2021-09-01    0.0\n",
+       "Freq: D, Name: SH-Tageswerte-106559, Length: 18629, dtype: float64"
+      ]
      },
      "execution_count": 9,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 9
+  },
+  {
+   "cell_type": "code",
    "source": [
     "series = get_ehyd_data(id_number, field=field, file_number=5, data_kind=DATA_KIND.MEASUREMENT)\n",
     "series\n"
@@ -292,13 +467,40 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:18.742723Z",
+     "start_time": "2024-06-03T08:36:18.042231Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "datetime\n",
+       "2002-01-01 00:00:00    0.0\n",
+       "2002-01-01 00:01:00    0.0\n",
+       "2002-01-01 00:02:00    0.0\n",
+       "2002-01-01 00:03:00    0.0\n",
+       "2002-01-01 00:04:00    0.0\n",
+       "                      ... \n",
+       "2020-12-31 23:56:00    0.0\n",
+       "2020-12-31 23:57:00    0.0\n",
+       "2020-12-31 23:58:00    0.0\n",
+       "2020-12-31 23:59:00    0.0\n",
+       "2021-01-01 00:00:00    NaN\n",
+       "Freq: min, Name: N-Minutensummen-106559, Length: 9993601, dtype: float64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 10
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [],
    "source": [
     "def preview(di):\n",
     "    for fn, d in di.items():\n",
@@ -308,18 +510,36 @@
     "        elif isinstance(d, dict):\n",
     "            print(f'Dict(keys={d.keys()})')\n",
     "        else:\n",
-    "            print(d[:100])\n"
+    "            print(d[:100])"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:18.745858Z",
+     "start_time": "2024-06-03T08:36:18.743465Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 11
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "source": [
+    "preview(get_ehyd_files(identifier=205641, field=FIELDS.OBERFLAECHENWASSER, data_kind=DATA_KIND.MEASUREMENT))"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:20.400879Z",
+     "start_time": "2024-06-03T08:36:18.746488Z"
+    }
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -327,34 +547,25 @@
      "text": [
       "____________________\n",
       " Stammdaten-205641.csv\n",
-      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'HD-Nummer', 'DBMS-Nummer', 'Gewässer', 'Sachgebiet', 'Dienststelle', 'Messstellenbetreiber', 'orogr.Einzugsgebiet [km²]', 'Pegelnullpunkt', 'Bundesmeldenetz(BMN)-Koordinaten', 'Messgrößen/Messart', 'Anmerkungen']))\n",
+      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'HD-Nummer', 'DBMS-Nummer', 'Gewaesser', 'Sachgebiet', 'Dienststelle', 'Messstellenbetreiber', 'orogr.Einzugsgebiet [km²]', 'Pegelnullpunkt', 'Bundesmeldenetz(BMN)-Koordinaten', 'Messgroessen/Messart', 'AEnmerkungen']))\n",
       "____________________\n",
-      " Q-Monatsmaxima-205641.csv\n",
-      "datetime\n",
-      "1954-01-21 12:00:00    63.00\n",
-      "1954-02-28 12:00:00     1.00\n",
-      "1954-03-20 12:00:00     1.78\n",
-      "1954-04-04 12:00:00    38.50\n",
-      "1954-05-06 12:00:00    63.00\n",
-      "Name: Q-Monatsmaxima-205641.csv, dtype: float64\n",
-      "____________________\n",
-      " Q-Monatsminima-205641.csv\n",
+      " W-Tagesmittel-205641.csv\n",
       "datetime\n",
-      "1954-01-07 12:00:00    0.80\n",
-      "1954-02-10 12:00:00    0.69\n",
-      "1954-03-04 12:00:00    0.84\n",
-      "1954-04-30 12:00:00    1.13\n",
-      "1954-05-05 12:00:00    0.96\n",
-      "Name: Q-Monatsminima-205641.csv, dtype: float64\n",
+      "1976-01-01     NaN\n",
+      "1976-01-02    87.0\n",
+      "1976-01-03    81.0\n",
+      "1976-01-04    77.0\n",
+      "1976-01-05    67.0\n",
+      "Freq: D, Name: W-Tagesmittel-205641, dtype: float64\n",
       "____________________\n",
-      " Q-Tagesmittel-205641.csv\n",
+      " W-Monatsminima-205641.csv\n",
       "datetime\n",
-      "1954-01-01    1.00\n",
-      "1954-01-02    0.80\n",
-      "1954-01-03    0.80\n",
-      "1954-01-04    0.84\n",
-      "1954-01-05    0.80\n",
-      "Freq: D, Name: Q-Tagesmittel-205641.csv, dtype: float64\n",
+      "1976-01-01 22:30:00    55\n",
+      "1976-02-08 16:00:00    56\n",
+      "1976-03-13 13:00:00    51\n",
+      "1976-04-19 23:00:00    53\n",
+      "1976-05-20 04:00:00    55\n",
+      "Name: W-Monatsminima-205641, dtype: int64\n",
       "____________________\n",
       " W-Monatsmaxima-205641.csv\n",
       "datetime\n",
@@ -363,25 +574,34 @@
       "1976-03-02 03:00:00     64\n",
       "1976-04-27 08:00:00    115\n",
       "1976-05-31 23:59:55    120\n",
-      "Name: W-Monatsmaxima-205641.csv, dtype: int64\n",
+      "Name: W-Monatsmaxima-205641, dtype: int64\n",
       "____________________\n",
-      " W-Monatsminima-205641.csv\n",
+      " Q-Tagesmittel-205641.csv\n",
       "datetime\n",
-      "1976-01-01 22:30:00    55\n",
-      "1976-02-08 16:00:00    56\n",
-      "1976-03-13 13:00:00    51\n",
-      "1976-04-19 23:00:00    53\n",
-      "1976-05-20 04:00:00    55\n",
-      "Name: W-Monatsminima-205641.csv, dtype: int64\n",
+      "1954-01-01    1.00\n",
+      "1954-01-02    0.80\n",
+      "1954-01-03    0.80\n",
+      "1954-01-04    0.84\n",
+      "1954-01-05    0.80\n",
+      "Freq: D, Name: Q-Tagesmittel-205641, dtype: float64\n",
       "____________________\n",
-      " W-Tagesmittel-205641.csv\n",
+      " Q-Monatsminima-205641.csv\n",
       "datetime\n",
-      "1976-01-01     NaN\n",
-      "1976-01-02    87.0\n",
-      "1976-01-03    81.0\n",
-      "1976-01-04    77.0\n",
-      "1976-01-05    67.0\n",
-      "Freq: D, Name: W-Tagesmittel-205641.csv, dtype: float64\n",
+      "1954-01-07 12:00:00    0.80\n",
+      "1954-02-10 12:00:00    0.69\n",
+      "1954-03-04 12:00:00    0.84\n",
+      "1954-04-30 12:00:00    1.13\n",
+      "1954-05-05 12:00:00    0.96\n",
+      "Name: Q-Monatsminima-205641, dtype: float64\n",
+      "____________________\n",
+      " Q-Monatsmaxima-205641.csv\n",
+      "datetime\n",
+      "1954-01-21 12:00:00    63.00\n",
+      "1954-02-28 12:00:00     1.00\n",
+      "1954-03-20 12:00:00     1.78\n",
+      "1954-04-04 12:00:00    38.50\n",
+      "1954-05-06 12:00:00    63.00\n",
+      "Name: Q-Monatsmaxima-205641, dtype: float64\n",
       "____________________\n",
       " WT-Monatsmittel-205641.csv\n",
       "datetime\n",
@@ -390,23 +610,27 @@
       "1976-03-01     3.0\n",
       "1976-04-01     8.0\n",
       "1976-05-01    12.1\n",
-      "Freq: MS, Name: WT-Monatsmittel-205641.csv, dtype: float64\n"
+      "Freq: MS, Name: WT-Monatsmittel-205641, dtype: float64\n"
      ]
     }
    ],
+   "execution_count": 12
+  },
+  {
+   "cell_type": "code",
    "source": [
-    "preview(get_ehyd_files(identifier=205641, field=FIELDS.OBERFLAECHENWASSER, data_kind=DATA_KIND.MEASUREMENT))"
+    "preview(get_ehyd_files(identifier=356980, field=FIELDS.GRUNDWASSER, data_kind=DATA_KIND.MEASUREMENT))"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:21.365659Z",
+     "start_time": "2024-06-03T08:36:20.401898Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -414,7 +638,7 @@
      "text": [
       "____________________\n",
       " Stammdaten-356980.txt\n",
-      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'Errichtet', 'PorenGW-Gebiet', 'Grundwasserkörper', 'Sachgebiet', 'Arbeitsgebiet', 'Dienststelle', 'Messstellenbetreiber', 'Höhenangaben [m ü.A.]', ' Geländehöhe', ' Messpunkthöhe', ' Sohllage', ' T-Messtiefe u.GOK', 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)']))\n",
+      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'Errichtet', 'PorenGW-Gebiet', 'Grundwasserkoerper', 'Sachgebiet', 'AErbeitsgebiet', 'Dienststelle', 'Messstellenbetreiber', 'Hoehenangaben [m ue.AE.]', ' Gelaendehoehe', ' Messpunkthoehe', ' Sohllage', ' T-Messtiefe u.GOK', 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)']))\n",
       "____________________\n",
       " Grundwasserstand-Jahresmaxima-356980.csv\n",
       "datetime\n",
@@ -423,7 +647,7 @@
       "2015-01-10 20:00:00    427.61\n",
       "2016-07-14 10:00:00    428.16\n",
       "2017-08-07 10:00:00    427.36\n",
-      "Name: Grundwasserstand-Jahresmaxima-356980.csv, dtype: float64\n",
+      "Name: Grundwasserstand-Jahresmaxima-356980, dtype: float64\n",
       "____________________\n",
       " Grundwasserstand-Jahresminima-356980.csv\n",
       "datetime\n",
@@ -432,7 +656,7 @@
       "2015-09-20 04:00:00    425.13\n",
       "2016-01-07 22:00:00    425.09\n",
       "2017-01-19 16:00:00    425.16\n",
-      "Name: Grundwasserstand-Jahresminima-356980.csv, dtype: float64\n",
+      "Name: Grundwasserstand-Jahresminima-356980, dtype: float64\n",
       "____________________\n",
       " Grundwasserstand-Monatsmittel-356980.csv\n",
       "datetime\n",
@@ -441,7 +665,7 @@
       "2013-01-01    425.90\n",
       "2013-02-01    425.55\n",
       "2013-03-01    425.64\n",
-      "Freq: MS, Name: Grundwasserstand-Monatsmittel-356980.csv, dtype: float64\n",
+      "Freq: MS, Name: Grundwasserstand-Monatsmittel-356980, dtype: float64\n",
       "____________________\n",
       " Grundwassertemperatur-Monatsmittel-356980.csv\n",
       "datetime\n",
@@ -450,23 +674,25 @@
       "2013-01-01    7.6\n",
       "2013-02-01    7.3\n",
       "2013-03-01    7.3\n",
-      "Freq: MS, Name: Grundwassertemperatur-Monatsmittel-356980.csv, dtype: float64\n"
+      "Freq: MS, Name: Grundwassertemperatur-Monatsmittel-356980, dtype: float64\n"
      ]
     }
    ],
-   "source": [
-    "preview(get_ehyd_files(identifier=356980, field=FIELDS.GRUNDWASSER, data_kind=DATA_KIND.MEASUREMENT))"
-   ],
+   "execution_count": 13
+  },
+  {
+   "cell_type": "code",
+   "source": "preview(get_ehyd_files(identifier=395855, field=FIELDS.QUELLEN, data_kind=DATA_KIND.MEASUREMENT))",
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:22.367124Z",
+     "start_time": "2024-06-03T08:36:21.368755Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -474,7 +700,7 @@
      "text": [
       "____________________\n",
       " Stammdaten-395855.txt\n",
-      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'Errichtet', 'Gebirgsgruppe', 'Grundwasserkörper', 'Sachgebiet', 'Arbeitsgebiet', 'Dienststelle', 'Messstellenbetreiber', 'Höhenangaben [m ü.A.]', ' Geländehöhe-Hauptquelle', ' Pegelnullpunkt', 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)']))\n",
+      "Dict(keys=dict_keys(['_raw', 'Messstelle', 'HZB-Nummer', 'Errichtet', 'Gebirgsgruppe', 'Grundwasserkoerper', 'Sachgebiet', 'AErbeitsgebiet', 'Dienststelle', 'Messstellenbetreiber', 'Hoehenangaben [m ue.AE.]', ' Gelaendehoehe-Hauptquelle', ' Pegelnullpunkt', 'Geographische Koordinaten (Referenzellipsoid: Bessel 1841)']))\n",
       "____________________\n",
       " Quellleitfähigkeit-Tagesmittel-395855.csv\n",
       "datetime\n",
@@ -483,16 +709,24 @@
       "1995-01-03    347.5\n",
       "1995-01-04    344.1\n",
       "1995-01-05    345.6\n",
-      "Freq: D, Name: Quellleitfähigkeit-Tagesmittel-395855.csv, dtype: float64\n",
+      "Freq: D, Name: Quellleitfähigkeit-Tagesmittel-395855, dtype: float64\n",
       "____________________\n",
       " Quellschüttung-Tagesmittel-395855.csv\n",
-      "datetime\n",
-      "1995-01-01    176.9\n",
-      "1995-01-02    158.9\n",
-      "1995-01-03    149.4\n",
-      "1995-01-04    143.0\n",
-      "1995-01-05    139.5\n",
-      "Freq: D, Name: Quellschüttung-Tagesmittel-395855.csv, dtype: float64\n",
+      "                1    2\n",
+      "datetime              \n",
+      "1995-01-01  176.9  NaN\n",
+      "1995-01-02  158.9  NaN\n",
+      "1995-01-03  149.4  NaN\n",
+      "1995-01-04  143.0  NaN\n",
+      "1995-01-05  139.5  NaN\n",
+      "...           ...  ...\n",
+      "1995-04-06  227.7  NaN\n",
+      "1995-04-07  222.0  NaN\n",
+      "1995-04-08  218.6  NaN\n",
+      "1995-04-09  214.8  NaN\n",
+      "1995-04-10  213.7  NaN\n",
+      "\n",
+      "[100 rows x 2 columns]\n",
       "____________________\n",
       " Quellwassertemperatur-Tagesmittel-395855.csv\n",
       "datetime\n",
@@ -501,20 +735,11 @@
       "1995-01-03    8.3\n",
       "1995-01-04    8.2\n",
       "1995-01-05    8.2\n",
-      "Freq: D, Name: Quellwassertemperatur-Tagesmittel-395855.csv, dtype: float64\n"
+      "Freq: D, Name: Quellwassertemperatur-Tagesmittel-395855, dtype: float64\n"
      ]
     }
    ],
-   "source": [
-    "preview(get_ehyd_files(identifier=395855, field=FIELDS.QUELLEN, data_kind=DATA_KIND.MEASUREMENT))\n",
-    "\n"
-   ],
-   "metadata": {
-    "collapsed": false,
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   }
+   "execution_count": 14
   }
  ],
  "metadata": {
@@ -538,4 +763,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 1
-}
\ No newline at end of file
+}
diff --git a/example/example_synthetic_rain.ipynb b/example/example_synthetic_rain.ipynb
index 2b1417a..ecc3bcf 100644
--- a/example/example_synthetic_rain.ipynb
+++ b/example/example_synthetic_rain.ipynb
@@ -2,28 +2,19 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 5,
    "metadata": {
-    "collapsed": true
+    "collapsed": true,
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.611913Z",
+     "start_time": "2024-06-03T08:36:50.941055Z"
+    }
    },
+   "source": "from ehyd_tools.synthetic_rainseries import RainModeller",
    "outputs": [],
-   "source": [
-    "from ehyd_tools.synthetic_rainseries import RainModeller\n"
-   ]
+   "execution_count": 1
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "<ehyd_tools.synthetic_rainseries.RainModeller at 0x7fd8c2579cd0>"
-     },
-     "execution_count": 6,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "model_rain = RainModeller()\n",
     "# bis jetzt implementiert: Blockregen, Eulerregen Typ 1 und 2\n",
@@ -33,23 +24,28 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.616213Z",
+     "start_time": "2024-06-03T08:36:51.613011Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "return period   1      2      3      5      10     20     25     30     50   \\\nduration                                                                      \n5               8.6   10.2   11.3   12.7   14.7   16.7   17.4   18.0   19.4   \n10             13.7   16.3   17.8   19.7   23.3   26.8   27.8   28.8   31.4   \n15             17.2   20.5   22.4   24.9   29.1   33.5   35.0   36.2   39.5   \n20             19.7   23.7   25.9   28.9   33.2   38.4   39.9   41.3   45.2   \n30             23.2   27.9   30.7   34.3   39.2   45.2   47.3   48.9   53.3   \n45             26.4   32.0   35.2   39.3   45.1   52.1   54.4   56.3   61.3   \n60             28.3   34.3   37.9   42.4   48.9   56.4   59.0   60.9   66.5   \n90             30.5   37.2   41.0   45.9   53.8   62.1   64.9   67.1   73.1   \n120            31.8   38.7   42.6   48.1   56.8   65.6   68.4   70.7   77.3   \n180            33.2   40.5   44.7   51.6   61.1   70.4   73.4   75.9   82.8   \n240            35.5   42.5   47.5   54.6   64.4   74.0   77.2   79.7   86.9   \n360            38.8   46.9   52.9   60.7   71.1   81.3   84.7   87.7   95.4   \n540            42.1   52.4   59.1   67.5   79.0   90.2   94.2   97.2  105.6   \n720            44.5   56.9   64.3   73.6   85.4   97.6  101.4  104.4  113.4   \n1080           50.7   65.4   74.0   84.3   96.8  109.4  113.1  116.4  125.6   \n1440           57.6   72.9   82.3   94.1  110.0  123.9  128.2  131.2  141.0   \n2880           67.4   86.4   97.5  111.5  130.7  149.5  155.6  160.8  172.3   \n4320           74.0   94.7  106.9  122.0  142.8  163.3  169.7  175.5  190.8   \n5760           80.0  101.1  113.7  130.0  151.6  173.6  180.4  186.4  202.0   \n7200           84.7  106.4  119.6  136.1  159.0  181.4  189.1  194.7  211.6   \n8640           89.6  110.8  124.4  141.8  164.9  188.6  195.9  201.9  219.2   \n\nreturn period    75     100  \nduration                     \n5               20.6   21.4  \n10              33.5   35.0  \n15              42.2   44.1  \n20              48.1   50.3  \n30              56.9   59.4  \n45              65.4   68.4  \n60              70.9   74.1  \n90              78.1   81.5  \n120             82.5   86.1  \n180             88.4   92.2  \n240             92.3   96.6  \n360            101.3  105.4  \n540            112.2  116.9  \n720            120.1  125.4  \n1080           132.7  137.9  \n1440           148.8  153.8  \n2880           182.2  188.4  \n4320           202.9  209.5  \n5760           215.3  224.0  \n7200           224.8  233.8  \n8640           232.9  242.9  ",
-      "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>return period</th>\n      <th>1</th>\n      <th>2</th>\n      <th>3</th>\n      <th>5</th>\n      <th>10</th>\n      <th>20</th>\n      <th>25</th>\n      <th>30</th>\n      <th>50</th>\n      <th>75</th>\n      <th>100</th>\n    </tr>\n    <tr>\n      <th>duration</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>5</th>\n      <td>8.6</td>\n      <td>10.2</td>\n      <td>11.3</td>\n      <td>12.7</td>\n      <td>14.7</td>\n      <td>16.7</td>\n      <td>17.4</td>\n      <td>18.0</td>\n      <td>19.4</td>\n      <td>20.6</td>\n      <td>21.4</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>13.7</td>\n      <td>16.3</td>\n      <td>17.8</td>\n      <td>19.7</td>\n      <td>23.3</td>\n      <td>26.8</td>\n      <td>27.8</td>\n      <td>28.8</td>\n      <td>31.4</td>\n      <td>33.5</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>17.2</td>\n      <td>20.5</td>\n      <td>22.4</td>\n      <td>24.9</td>\n      <td>29.1</td>\n      <td>33.5</td>\n      <td>35.0</td>\n      <td>36.2</td>\n      <td>39.5</td>\n      <td>42.2</td>\n      <td>44.1</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>19.7</td>\n      <td>23.7</td>\n      <td>25.9</td>\n      <td>28.9</td>\n      <td>33.2</td>\n      <td>38.4</td>\n      <td>39.9</td>\n      <td>41.3</td>\n      <td>45.2</td>\n      <td>48.1</td>\n      <td>50.3</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>23.2</td>\n      <td>27.9</td>\n      <td>30.7</td>\n      <td>34.3</td>\n      <td>39.2</td>\n      <td>45.2</td>\n      <td>47.3</td>\n      <td>48.9</td>\n      <td>53.3</td>\n      <td>56.9</td>\n      <td>59.4</td>\n    </tr>\n    <tr>\n      <th>45</th>\n      <td>26.4</td>\n      <td>32.0</td>\n      <td>35.2</td>\n      <td>39.3</td>\n      <td>45.1</td>\n      <td>52.1</td>\n      <td>54.4</td>\n      <td>56.3</td>\n      <td>61.3</td>\n      <td>65.4</td>\n      <td>68.4</td>\n    </tr>\n    <tr>\n      <th>60</th>\n      <td>28.3</td>\n      <td>34.3</td>\n      <td>37.9</td>\n      <td>42.4</td>\n      <td>48.9</td>\n      <td>56.4</td>\n      <td>59.0</td>\n      <td>60.9</td>\n      <td>66.5</td>\n      <td>70.9</td>\n      <td>74.1</td>\n    </tr>\n    <tr>\n      <th>90</th>\n      <td>30.5</td>\n      <td>37.2</td>\n      <td>41.0</td>\n      <td>45.9</td>\n      <td>53.8</td>\n      <td>62.1</td>\n      <td>64.9</td>\n      <td>67.1</td>\n      <td>73.1</td>\n      <td>78.1</td>\n      <td>81.5</td>\n    </tr>\n    <tr>\n      <th>120</th>\n      <td>31.8</td>\n      <td>38.7</td>\n      <td>42.6</td>\n      <td>48.1</td>\n      <td>56.8</td>\n      <td>65.6</td>\n      <td>68.4</td>\n      <td>70.7</td>\n      <td>77.3</td>\n      <td>82.5</td>\n      <td>86.1</td>\n    </tr>\n    <tr>\n      <th>180</th>\n      <td>33.2</td>\n      <td>40.5</td>\n      <td>44.7</td>\n      <td>51.6</td>\n      <td>61.1</td>\n      <td>70.4</td>\n      <td>73.4</td>\n      <td>75.9</td>\n      <td>82.8</td>\n      <td>88.4</td>\n      <td>92.2</td>\n    </tr>\n    <tr>\n      <th>240</th>\n      <td>35.5</td>\n      <td>42.5</td>\n      <td>47.5</td>\n      <td>54.6</td>\n      <td>64.4</td>\n      <td>74.0</td>\n      <td>77.2</td>\n      <td>79.7</td>\n      <td>86.9</td>\n      <td>92.3</td>\n      <td>96.6</td>\n    </tr>\n    <tr>\n      <th>360</th>\n      <td>38.8</td>\n      <td>46.9</td>\n      <td>52.9</td>\n      <td>60.7</td>\n      <td>71.1</td>\n      <td>81.3</td>\n      <td>84.7</td>\n      <td>87.7</td>\n      <td>95.4</td>\n      <td>101.3</td>\n      <td>105.4</td>\n    </tr>\n    <tr>\n      <th>540</th>\n      <td>42.1</td>\n      <td>52.4</td>\n      <td>59.1</td>\n      <td>67.5</td>\n      <td>79.0</td>\n      <td>90.2</td>\n      <td>94.2</td>\n      <td>97.2</td>\n      <td>105.6</td>\n      <td>112.2</td>\n      <td>116.9</td>\n    </tr>\n    <tr>\n      <th>720</th>\n      <td>44.5</td>\n      <td>56.9</td>\n      <td>64.3</td>\n      <td>73.6</td>\n      <td>85.4</td>\n      <td>97.6</td>\n      <td>101.4</td>\n      <td>104.4</td>\n      <td>113.4</td>\n      <td>120.1</td>\n      <td>125.4</td>\n    </tr>\n    <tr>\n      <th>1080</th>\n      <td>50.7</td>\n      <td>65.4</td>\n      <td>74.0</td>\n      <td>84.3</td>\n      <td>96.8</td>\n      <td>109.4</td>\n      <td>113.1</td>\n      <td>116.4</td>\n      <td>125.6</td>\n      <td>132.7</td>\n      <td>137.9</td>\n    </tr>\n    <tr>\n      <th>1440</th>\n      <td>57.6</td>\n      <td>72.9</td>\n      <td>82.3</td>\n      <td>94.1</td>\n      <td>110.0</td>\n      <td>123.9</td>\n      <td>128.2</td>\n      <td>131.2</td>\n      <td>141.0</td>\n      <td>148.8</td>\n      <td>153.8</td>\n    </tr>\n    <tr>\n      <th>2880</th>\n      <td>67.4</td>\n      <td>86.4</td>\n      <td>97.5</td>\n      <td>111.5</td>\n      <td>130.7</td>\n      <td>149.5</td>\n      <td>155.6</td>\n      <td>160.8</td>\n      <td>172.3</td>\n      <td>182.2</td>\n      <td>188.4</td>\n    </tr>\n    <tr>\n      <th>4320</th>\n      <td>74.0</td>\n      <td>94.7</td>\n      <td>106.9</td>\n      <td>122.0</td>\n      <td>142.8</td>\n      <td>163.3</td>\n      <td>169.7</td>\n      <td>175.5</td>\n      <td>190.8</td>\n      <td>202.9</td>\n      <td>209.5</td>\n    </tr>\n    <tr>\n      <th>5760</th>\n      <td>80.0</td>\n      <td>101.1</td>\n      <td>113.7</td>\n      <td>130.0</td>\n      <td>151.6</td>\n      <td>173.6</td>\n      <td>180.4</td>\n      <td>186.4</td>\n      <td>202.0</td>\n      <td>215.3</td>\n      <td>224.0</td>\n    </tr>\n    <tr>\n      <th>7200</th>\n      <td>84.7</td>\n      <td>106.4</td>\n      <td>119.6</td>\n      <td>136.1</td>\n      <td>159.0</td>\n      <td>181.4</td>\n      <td>189.1</td>\n      <td>194.7</td>\n      <td>211.6</td>\n      <td>224.8</td>\n      <td>233.8</td>\n    </tr>\n    <tr>\n      <th>8640</th>\n      <td>89.6</td>\n      <td>110.8</td>\n      <td>124.4</td>\n      <td>141.8</td>\n      <td>164.9</td>\n      <td>188.6</td>\n      <td>195.9</td>\n      <td>201.9</td>\n      <td>219.2</td>\n      <td>232.9</td>\n      <td>242.9</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
+      "text/plain": [
+       "<ehyd_tools.synthetic_rainseries.RainModeller at 0x107b8f550>"
+      ]
      },
-     "execution_count": 7,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 2
+  },
+  {
+   "cell_type": "code",
    "source": [
     "# nur für ÖKOSTRA Auswertungen. (KOSTRA DWD wird aktuell nicht unterstützt)\n",
     "model_rain.set_idf_table_okostra(5214, kind='Bemessung')\n",
@@ -63,22 +59,419 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.775555Z",
+     "start_time": "2024-06-03T08:36:51.616933Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "0     0.000000\n5     2.858333\n10    2.858333\n15    2.858333\n20    2.858333\n25    2.858333\n30    2.858333\n35    2.858333\n40    2.858333\n45    2.858333\n50    2.858333\n55    2.858333\n60    2.858333\ndtype: float64"
+      "text/plain": [
+       "return period   1      2      3      5      10     20     25     30     50   \\\n",
+       "duration                                                                      \n",
+       "5               8.6   10.2   11.3   12.7   14.7   16.7   17.4   18.0   19.4   \n",
+       "10             13.7   16.3   17.8   19.7   23.3   26.8   27.8   28.8   31.4   \n",
+       "15             17.2   20.5   22.4   24.9   29.1   33.5   35.0   36.2   39.5   \n",
+       "20             19.7   23.7   25.9   28.9   33.2   38.4   39.9   41.3   45.2   \n",
+       "30             23.2   27.9   30.7   34.3   39.2   45.2   47.3   48.9   53.3   \n",
+       "45             26.4   32.0   35.2   39.3   45.1   52.1   54.4   56.3   61.3   \n",
+       "60             28.3   34.3   37.9   42.4   48.9   56.4   59.0   60.9   66.5   \n",
+       "90             30.5   37.2   41.0   45.9   53.8   62.1   64.9   67.1   73.1   \n",
+       "120            31.8   38.7   42.6   48.1   56.8   65.6   68.4   70.7   77.3   \n",
+       "180            33.2   40.5   44.7   51.6   61.1   70.4   73.4   75.9   82.8   \n",
+       "240            35.5   42.5   47.5   54.6   64.4   74.0   77.2   79.7   86.9   \n",
+       "360            38.8   46.9   52.9   60.7   71.1   81.3   84.7   87.7   95.4   \n",
+       "540            42.1   52.4   59.1   67.5   79.0   90.2   94.2   97.2  105.6   \n",
+       "720            44.5   56.9   64.3   73.6   85.4   97.6  101.4  104.4  113.4   \n",
+       "1080           50.7   65.4   74.0   84.3   96.8  109.4  113.1  116.4  125.6   \n",
+       "1440           57.6   72.9   82.3   94.1  110.0  123.9  128.2  131.2  141.0   \n",
+       "2880           67.4   86.4   97.5  111.5  130.7  149.5  155.6  160.8  172.3   \n",
+       "4320           74.0   94.7  106.9  122.0  142.8  163.3  169.7  175.5  190.8   \n",
+       "5760           80.0  101.1  113.7  130.0  151.6  173.6  180.4  186.4  202.0   \n",
+       "7200           84.7  106.4  119.6  136.1  159.0  181.4  189.1  194.7  211.6   \n",
+       "8640           89.6  110.8  124.4  141.8  164.9  188.6  195.9  201.9  219.2   \n",
+       "\n",
+       "return period    75     100  \n",
+       "duration                     \n",
+       "5               20.6   21.4  \n",
+       "10              33.5   35.0  \n",
+       "15              42.2   44.1  \n",
+       "20              48.1   50.3  \n",
+       "30              56.9   59.4  \n",
+       "45              65.4   68.4  \n",
+       "60              70.9   74.1  \n",
+       "90              78.1   81.5  \n",
+       "120             82.5   86.1  \n",
+       "180             88.4   92.2  \n",
+       "240             92.3   96.6  \n",
+       "360            101.3  105.4  \n",
+       "540            112.2  116.9  \n",
+       "720            120.1  125.4  \n",
+       "1080           132.7  137.9  \n",
+       "1440           148.8  153.8  \n",
+       "2880           182.2  188.4  \n",
+       "4320           202.9  209.5  \n",
+       "5760           215.3  224.0  \n",
+       "7200           224.8  233.8  \n",
+       "8640           232.9  242.9  "
+      ],
+      "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>return period</th>\n",
+       "      <th>1</th>\n",
+       "      <th>2</th>\n",
+       "      <th>3</th>\n",
+       "      <th>5</th>\n",
+       "      <th>10</th>\n",
+       "      <th>20</th>\n",
+       "      <th>25</th>\n",
+       "      <th>30</th>\n",
+       "      <th>50</th>\n",
+       "      <th>75</th>\n",
+       "      <th>100</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>duration</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>8.6</td>\n",
+       "      <td>10.2</td>\n",
+       "      <td>11.3</td>\n",
+       "      <td>12.7</td>\n",
+       "      <td>14.7</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>17.4</td>\n",
+       "      <td>18.0</td>\n",
+       "      <td>19.4</td>\n",
+       "      <td>20.6</td>\n",
+       "      <td>21.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>10</th>\n",
+       "      <td>13.7</td>\n",
+       "      <td>16.3</td>\n",
+       "      <td>17.8</td>\n",
+       "      <td>19.7</td>\n",
+       "      <td>23.3</td>\n",
+       "      <td>26.8</td>\n",
+       "      <td>27.8</td>\n",
+       "      <td>28.8</td>\n",
+       "      <td>31.4</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>15</th>\n",
+       "      <td>17.2</td>\n",
+       "      <td>20.5</td>\n",
+       "      <td>22.4</td>\n",
+       "      <td>24.9</td>\n",
+       "      <td>29.1</td>\n",
+       "      <td>33.5</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>36.2</td>\n",
+       "      <td>39.5</td>\n",
+       "      <td>42.2</td>\n",
+       "      <td>44.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>20</th>\n",
+       "      <td>19.7</td>\n",
+       "      <td>23.7</td>\n",
+       "      <td>25.9</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>33.2</td>\n",
+       "      <td>38.4</td>\n",
+       "      <td>39.9</td>\n",
+       "      <td>41.3</td>\n",
+       "      <td>45.2</td>\n",
+       "      <td>48.1</td>\n",
+       "      <td>50.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>23.2</td>\n",
+       "      <td>27.9</td>\n",
+       "      <td>30.7</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>39.2</td>\n",
+       "      <td>45.2</td>\n",
+       "      <td>47.3</td>\n",
+       "      <td>48.9</td>\n",
+       "      <td>53.3</td>\n",
+       "      <td>56.9</td>\n",
+       "      <td>59.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>45</th>\n",
+       "      <td>26.4</td>\n",
+       "      <td>32.0</td>\n",
+       "      <td>35.2</td>\n",
+       "      <td>39.3</td>\n",
+       "      <td>45.1</td>\n",
+       "      <td>52.1</td>\n",
+       "      <td>54.4</td>\n",
+       "      <td>56.3</td>\n",
+       "      <td>61.3</td>\n",
+       "      <td>65.4</td>\n",
+       "      <td>68.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>60</th>\n",
+       "      <td>28.3</td>\n",
+       "      <td>34.3</td>\n",
+       "      <td>37.9</td>\n",
+       "      <td>42.4</td>\n",
+       "      <td>48.9</td>\n",
+       "      <td>56.4</td>\n",
+       "      <td>59.0</td>\n",
+       "      <td>60.9</td>\n",
+       "      <td>66.5</td>\n",
+       "      <td>70.9</td>\n",
+       "      <td>74.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>90</th>\n",
+       "      <td>30.5</td>\n",
+       "      <td>37.2</td>\n",
+       "      <td>41.0</td>\n",
+       "      <td>45.9</td>\n",
+       "      <td>53.8</td>\n",
+       "      <td>62.1</td>\n",
+       "      <td>64.9</td>\n",
+       "      <td>67.1</td>\n",
+       "      <td>73.1</td>\n",
+       "      <td>78.1</td>\n",
+       "      <td>81.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>31.8</td>\n",
+       "      <td>38.7</td>\n",
+       "      <td>42.6</td>\n",
+       "      <td>48.1</td>\n",
+       "      <td>56.8</td>\n",
+       "      <td>65.6</td>\n",
+       "      <td>68.4</td>\n",
+       "      <td>70.7</td>\n",
+       "      <td>77.3</td>\n",
+       "      <td>82.5</td>\n",
+       "      <td>86.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>180</th>\n",
+       "      <td>33.2</td>\n",
+       "      <td>40.5</td>\n",
+       "      <td>44.7</td>\n",
+       "      <td>51.6</td>\n",
+       "      <td>61.1</td>\n",
+       "      <td>70.4</td>\n",
+       "      <td>73.4</td>\n",
+       "      <td>75.9</td>\n",
+       "      <td>82.8</td>\n",
+       "      <td>88.4</td>\n",
+       "      <td>92.2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>240</th>\n",
+       "      <td>35.5</td>\n",
+       "      <td>42.5</td>\n",
+       "      <td>47.5</td>\n",
+       "      <td>54.6</td>\n",
+       "      <td>64.4</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>77.2</td>\n",
+       "      <td>79.7</td>\n",
+       "      <td>86.9</td>\n",
+       "      <td>92.3</td>\n",
+       "      <td>96.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>38.8</td>\n",
+       "      <td>46.9</td>\n",
+       "      <td>52.9</td>\n",
+       "      <td>60.7</td>\n",
+       "      <td>71.1</td>\n",
+       "      <td>81.3</td>\n",
+       "      <td>84.7</td>\n",
+       "      <td>87.7</td>\n",
+       "      <td>95.4</td>\n",
+       "      <td>101.3</td>\n",
+       "      <td>105.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>540</th>\n",
+       "      <td>42.1</td>\n",
+       "      <td>52.4</td>\n",
+       "      <td>59.1</td>\n",
+       "      <td>67.5</td>\n",
+       "      <td>79.0</td>\n",
+       "      <td>90.2</td>\n",
+       "      <td>94.2</td>\n",
+       "      <td>97.2</td>\n",
+       "      <td>105.6</td>\n",
+       "      <td>112.2</td>\n",
+       "      <td>116.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>720</th>\n",
+       "      <td>44.5</td>\n",
+       "      <td>56.9</td>\n",
+       "      <td>64.3</td>\n",
+       "      <td>73.6</td>\n",
+       "      <td>85.4</td>\n",
+       "      <td>97.6</td>\n",
+       "      <td>101.4</td>\n",
+       "      <td>104.4</td>\n",
+       "      <td>113.4</td>\n",
+       "      <td>120.1</td>\n",
+       "      <td>125.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1080</th>\n",
+       "      <td>50.7</td>\n",
+       "      <td>65.4</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>84.3</td>\n",
+       "      <td>96.8</td>\n",
+       "      <td>109.4</td>\n",
+       "      <td>113.1</td>\n",
+       "      <td>116.4</td>\n",
+       "      <td>125.6</td>\n",
+       "      <td>132.7</td>\n",
+       "      <td>137.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1440</th>\n",
+       "      <td>57.6</td>\n",
+       "      <td>72.9</td>\n",
+       "      <td>82.3</td>\n",
+       "      <td>94.1</td>\n",
+       "      <td>110.0</td>\n",
+       "      <td>123.9</td>\n",
+       "      <td>128.2</td>\n",
+       "      <td>131.2</td>\n",
+       "      <td>141.0</td>\n",
+       "      <td>148.8</td>\n",
+       "      <td>153.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2880</th>\n",
+       "      <td>67.4</td>\n",
+       "      <td>86.4</td>\n",
+       "      <td>97.5</td>\n",
+       "      <td>111.5</td>\n",
+       "      <td>130.7</td>\n",
+       "      <td>149.5</td>\n",
+       "      <td>155.6</td>\n",
+       "      <td>160.8</td>\n",
+       "      <td>172.3</td>\n",
+       "      <td>182.2</td>\n",
+       "      <td>188.4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4320</th>\n",
+       "      <td>74.0</td>\n",
+       "      <td>94.7</td>\n",
+       "      <td>106.9</td>\n",
+       "      <td>122.0</td>\n",
+       "      <td>142.8</td>\n",
+       "      <td>163.3</td>\n",
+       "      <td>169.7</td>\n",
+       "      <td>175.5</td>\n",
+       "      <td>190.8</td>\n",
+       "      <td>202.9</td>\n",
+       "      <td>209.5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5760</th>\n",
+       "      <td>80.0</td>\n",
+       "      <td>101.1</td>\n",
+       "      <td>113.7</td>\n",
+       "      <td>130.0</td>\n",
+       "      <td>151.6</td>\n",
+       "      <td>173.6</td>\n",
+       "      <td>180.4</td>\n",
+       "      <td>186.4</td>\n",
+       "      <td>202.0</td>\n",
+       "      <td>215.3</td>\n",
+       "      <td>224.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7200</th>\n",
+       "      <td>84.7</td>\n",
+       "      <td>106.4</td>\n",
+       "      <td>119.6</td>\n",
+       "      <td>136.1</td>\n",
+       "      <td>159.0</td>\n",
+       "      <td>181.4</td>\n",
+       "      <td>189.1</td>\n",
+       "      <td>194.7</td>\n",
+       "      <td>211.6</td>\n",
+       "      <td>224.8</td>\n",
+       "      <td>233.8</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8640</th>\n",
+       "      <td>89.6</td>\n",
+       "      <td>110.8</td>\n",
+       "      <td>124.4</td>\n",
+       "      <td>141.8</td>\n",
+       "      <td>164.9</td>\n",
+       "      <td>188.6</td>\n",
+       "      <td>195.9</td>\n",
+       "      <td>201.9</td>\n",
+       "      <td>219.2</td>\n",
+       "      <td>232.9</td>\n",
+       "      <td>242.9</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ]
      },
-     "execution_count": 8,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 3
+  },
+  {
+   "cell_type": "code",
    "source": [
     "# zum Erstellen einer einfachen Serie mit dem Index als vergangene Zeit in minuten\n",
     "model_rain.block.get_time_series(return_period=2, duration=60, interval=5)"
@@ -87,22 +480,41 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.780137Z",
+     "start_time": "2024-06-03T08:36:51.776443Z"
     }
-   }
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 9,
+   },
    "outputs": [
     {
      "data": {
-      "text/plain": "2021-05-27 16:00:00    0.000000\n2021-05-27 16:05:00    2.858333\n2021-05-27 16:10:00    2.858333\n2021-05-27 16:15:00    2.858333\n2021-05-27 16:20:00    2.858333\n2021-05-27 16:25:00    2.858333\n2021-05-27 16:30:00    2.858333\n2021-05-27 16:35:00    2.858333\n2021-05-27 16:40:00    2.858333\n2021-05-27 16:45:00    2.858333\n2021-05-27 16:50:00    2.858333\n2021-05-27 16:55:00    2.858333\n2021-05-27 17:00:00    2.858333\ndtype: float64"
+      "text/plain": [
+       "0     0.000000\n",
+       "5     2.858333\n",
+       "10    2.858333\n",
+       "15    2.858333\n",
+       "20    2.858333\n",
+       "25    2.858333\n",
+       "30    2.858333\n",
+       "35    2.858333\n",
+       "40    2.858333\n",
+       "45    2.858333\n",
+       "50    2.858333\n",
+       "55    2.858333\n",
+       "60    2.858333\n",
+       "dtype: float64"
+      ]
      },
-     "execution_count": 9,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "execution_count": 4
+  },
+  {
+   "cell_type": "code",
    "source": [
     "# oder als Zeitserie mit speizifischem Startzeitpunkt\n",
     "model_rain.block.get_time_series(return_period=2, duration=60, interval=5, start_time='2021-05-27 16:00')"
@@ -111,13 +523,41 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.787480Z",
+     "start_time": "2024-06-03T08:36:51.781785Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2021-05-27 16:00:00    0.000000\n",
+       "2021-05-27 16:05:00    2.858333\n",
+       "2021-05-27 16:10:00    2.858333\n",
+       "2021-05-27 16:15:00    2.858333\n",
+       "2021-05-27 16:20:00    2.858333\n",
+       "2021-05-27 16:25:00    2.858333\n",
+       "2021-05-27 16:30:00    2.858333\n",
+       "2021-05-27 16:35:00    2.858333\n",
+       "2021-05-27 16:40:00    2.858333\n",
+       "2021-05-27 16:45:00    2.858333\n",
+       "2021-05-27 16:50:00    2.858333\n",
+       "2021-05-27 16:55:00    2.858333\n",
+       "2021-05-27 17:00:00    2.858333\n",
+       "Freq: 5min, dtype: float64"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
     }
-   }
+   ],
+   "execution_count": 5
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
-   "outputs": [],
    "source": [
     "custom_idf_table = model_rain.idf_table"
    ],
@@ -125,34 +565,59 @@
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.789901Z",
+     "start_time": "2024-06-03T08:36:51.788115Z"
     }
-   }
+   },
+   "outputs": [],
+   "execution_count": 6
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
-   "outputs": [
-    {
-     "data": {
-      "text/plain": "2021-05-27 16:00:00     0.000000\n2021-05-27 16:05:00     3.200000\n2021-05-27 16:10:00     4.200000\n2021-05-27 16:15:00     6.100000\n2021-05-27 16:20:00    10.200000\n2021-05-27 16:25:00     2.100000\n2021-05-27 16:30:00     2.100000\n2021-05-27 16:35:00     1.366667\n2021-05-27 16:40:00     1.366667\n2021-05-27 16:45:00     1.366667\n2021-05-27 16:50:00     0.766667\n2021-05-27 16:55:00     0.766667\n2021-05-27 17:00:00     0.766667\ndtype: float64"
-     },
-     "execution_count": 11,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
    "source": [
     "# auch als Euler-Typ-2 Modellregen\n",
     "model_rain2 = RainModeller()\n",
     "model_rain2.idf_table = custom_idf_table\n",
-    "model_rain2.euler.get_time_series(return_period=2, duration=60, interval=5, kind=2, start_time='2021-05-27 16:00')\n"
+    "model_rain2.euler.get_time_series(return_period=2, duration=60, interval=5, kind=2, start_time='2021-05-27 16:00')"
    ],
    "metadata": {
     "collapsed": false,
     "pycharm": {
      "name": "#%%\n"
+    },
+    "ExecuteTime": {
+     "end_time": "2024-06-03T08:36:51.796229Z",
+     "start_time": "2024-06-03T08:36:51.790714Z"
     }
-   }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "2021-05-27 16:00:00     0.000000\n",
+       "2021-05-27 16:05:00     3.200000\n",
+       "2021-05-27 16:10:00     4.200000\n",
+       "2021-05-27 16:15:00     6.100000\n",
+       "2021-05-27 16:20:00    10.200000\n",
+       "2021-05-27 16:25:00     2.100000\n",
+       "2021-05-27 16:30:00     2.100000\n",
+       "2021-05-27 16:35:00     1.366667\n",
+       "2021-05-27 16:40:00     1.366667\n",
+       "2021-05-27 16:45:00     1.366667\n",
+       "2021-05-27 16:50:00     0.766667\n",
+       "2021-05-27 16:55:00     0.766667\n",
+       "2021-05-27 17:00:00     0.766667\n",
+       "Freq: 5min, dtype: float64"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "execution_count": 7
   }
  ],
  "metadata": {
@@ -176,4 +641,4 @@
  },
  "nbformat": 4,
  "nbformat_minor": 0
-}
\ No newline at end of file
+}