diff --git a/scripts/dash_timeline_static.py b/scripts/dash_timeline_static.py
index 8dd4be4..6a04ae6 100644
--- a/scripts/dash_timeline_static.py
+++ b/scripts/dash_timeline_static.py
@@ -1,9 +1,11 @@
 import time
 import webbrowser
-from pathlib import Path
+from collections.abc import Collection, Iterable
 from threading import Thread
 from typing import Any, Final, cast
 
+import pandas as pd
+
 # import dash_cytoscape as cyto
 import plotly.express as px
 from dash import (
@@ -21,20 +23,37 @@ from plotly.graph_objects import Figure
 
 import lang_main.io
 from lang_main.analysis import graphs, tokens
-from lang_main.constants import SAVE_PATH_FOLDER, SPCY_MODEL
+from lang_main.analysis.timeline import (
+    calc_delta_to_next_failure,
+    filter_timeline_cands,
+)
+from lang_main.constants import (
+    NAME_DELTA_FEAT_TO_NEXT_FAILURE,
+    NAME_DELTA_FEAT_TO_REPAIR,
+    SAVE_PATH_FOLDER,
+    SPCY_MODEL,
+)
 from lang_main.errors import EmptyEdgesError, EmptyGraphError
 from lang_main.pipelines.predefined import (
     build_tk_graph_render_pipe,
     build_tk_graph_rescaling_pipe,
 )
-from lang_main.types import EntryPoints, ObjectID, TimelineCandidates
+from lang_main.types import (
+    DataFrameTLFiltered,
+    EntryPoints,
+    HTMLColumns,
+    HTMLTable,
+    ObjectID,
+    TimelineCandidates,
+)
 
 # ** data
 # p_df = Path(r'../results/test_20240619/TIMELINE.pkl').resolve()
-p_df = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE)
+p_df = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_POST)
 (data,) = cast(tuple[DataFrame], lang_main.io.load_pickle(p_df))
+# data = cleanup_descriptions(data, properties=['ErledigungsBeschreibung'])
 # p_tl = Path(r'../results/test_20240619/TIMELINE_POSTPROCESSING.pkl').resolve()
-p_tl = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_POST)
+p_tl = lang_main.io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_CANDS)
 cands, texts = cast(
     tuple[TimelineCandidates, dict[ObjectID, str]], lang_main.io.load_pickle(p_tl)
 )
@@ -56,17 +75,27 @@ PTH_RENDERED_GRAPH = lang_main.io.get_entry_point(
     file_ext='.svg',
 )
 
-
-TABLE_FEATS: Final[list[str]] = [
+# NAME_DELTA_FEAT_TO_NEXT_FAILURE: Final[str] = 'Zeitspanne bis zum nächsten Ereignis [Tage]'
+TABLE_FEATS_OVERVIEW: Final[list[str]] = [
     'ErstellungsDatum',
     'ErledigungsDatum',
+    NAME_DELTA_FEAT_TO_REPAIR,
     'VorgangsTypName',
     'VorgangsBeschreibung',
+    'ErledigungsBeschreibung',
 ]
 TABLE_FEATS_DATES: Final[list[str]] = [
     'ErstellungsDatum',
     'ErledigungsDatum',
 ]
+TABLE_FEATS_BEST_ACTIONS: Final[list[str]] = [
+    'ErstellungsDatum',
+    'ErledigungsDatum',
+    'VorgangsTypName',
+    'VorgangsBeschreibung',
+    'ErledigungsBeschreibung',
+    NAME_DELTA_FEAT_TO_NEXT_FAILURE,
+]
 
 # ** figure config
 MARKERS_OCCURRENCES: Final[dict[str, Any]] = {
@@ -86,13 +115,15 @@ HOVER_DATA: Final[dict[str, Any]] = {
     'ErstellungsDatum': '|%d.%m.%Y',
     'ErledigungsDatum': '|%d.%m.%Y',
     'VorgangsBeschreibung': True,
+    'ErledigungsBeschreibung': True,
 }
 HOVER_DATA_DELTA: Final[dict[str, Any]] = {
     'ErstellungsDatum': '|%d.%m.%Y',
     'ErledigungsDatum': '|%d.%m.%Y',
     'VorgangsDatum': '|%d.%m.%Y',
-    'delta': True,
+    NAME_DELTA_FEAT_TO_REPAIR: True,
     'VorgangsBeschreibung': True,
+    'ErledigungsBeschreibung': True,
 }
 
 # ** graph
@@ -136,10 +167,10 @@ graph_layout = html.Div(
                             html.Img(
                                 id='static-graph-img',
                                 alt='static rendered graph',
-                                # style={
-                                #     'width': 'auto',
-                                #     'height': 'auto',
-                                # },
+                                style={
+                                    'width': 'auto',
+                                    'height': 'auto',
+                                },
                             ),
                             html.P(id='info-graph-errors', children=[]),
                         ],
@@ -186,7 +217,27 @@ app.layout = html.Div(
             ]
         ),
         html.Div(
-            [dash_table.DataTable(id='table-candidates')], style={'marginBottom': '2em'}
+            children=[
+                html.Div(
+                    [
+                        html.H5('Überblick ähnlicher Vorgänge'),
+                        dash_table.DataTable(id='table-candidates'),
+                    ],
+                    style={'paddingBottom': '1em'},
+                ),
+                html.Div(
+                    [
+                        html.H5(
+                            (
+                                'Maßnahmen sortiert nach längstem Zeitraum '
+                                'bis zum nächsten Ereignis'
+                            )
+                        ),
+                        dash_table.DataTable(id='table-best-actions'),
+                    ]
+                ),
+            ],
+            style={'marginBottom': '2em', 'padding': '2em'},
         ),
         graph_layout,
     ],
@@ -222,20 +273,21 @@ def update_choice_candidates(obj_id):
 
 
 # ** helpers to filter DataFrame
-def pre_filter_data(
+def filter_candidates(
     data: DataFrame,
     idx: int,
     obj_id: ObjectID,
-) -> DataFrame:
+) -> DataFrameTLFiltered:
+    # assert correct data type because of Dash
     idx = int(idx)
     obj_id = int(obj_id)
-    # data = data.copy()
-    cands_for_obj_id = cands[obj_id]
-    cands_choice = cands_for_obj_id[int(idx) - 1]
-    # data
-    data = data.loc[list(cands_choice)].sort_index()  # type: ignore
-    data['delta'] = data['ErledigungsDatum'] - data['ErstellungsDatum']
-    data['delta'] = data['delta'].dt.days
+
+    data = filter_timeline_cands(
+        data=data,
+        cands=cands,
+        obj_id=obj_id,
+        entry_idx=(idx - 1),  # idx in Dashboard starts with 1
+    )
 
     return data
 
@@ -258,10 +310,10 @@ def update_timeline(index, obj_id):
     obj_text = texts[obj_id]
     title_occurrences = f'HObjektText: {obj_text}'
     title_delta = f'HObjektText: {obj_text}, Differenz Erstellung und Erledigung'
-    df = pre_filter_data(data, idx=index, obj_id=obj_id)
+    df = filter_candidates(data, idx=index, obj_id=obj_id)
     # figure
     fig_occurrences = fig_timeline_occurrences(df, title_occurrences)
-    fig_delta = fig_timeline_delta(df, title_delta)
+    fig_delta = fig_timeline_delta(df, title_delta, delta_feature=NAME_DELTA_FEAT_TO_REPAIR)
 
     return fig_occurrences, fig_delta
 
@@ -293,11 +345,12 @@ def fig_timeline_occurrences(
 def fig_timeline_delta(
     df: DataFrame,
     title: str,
+    delta_feature: str,
 ) -> Figure:
     fig = px.scatter(
         data_frame=df,
         x='ErstellungsDatum',
-        y='delta',
+        y=delta_feature,
         title=title,
         hover_data=HOVER_DATA_DELTA,
     )
@@ -309,25 +362,77 @@ def fig_timeline_delta(
     return fig
 
 
+def transform_to_HTML_table(
+    data: DataFrame,
+    target_features: Collection[str],
+    date_cols: Iterable[str] | None = None,
+    sorting_feature: str | None = None,
+    sorting_ascending: bool = True,
+) -> tuple[HTMLColumns, HTMLTable]:
+    target_features = list(target_features)
+    data = data.copy()
+    data = data.filter(items=target_features, axis=1)
+
+    if sorting_feature is not None:
+        data = data.sort_values(by='ErstellungsDatum', ascending=sorting_ascending)
+
+    if date_cols is not None:
+        for col in date_cols:
+            data[col] = data[col].dt.strftime(r'%Y-%m-%d')
+
+    columns = [{'name': col, 'id': col} for col in data.columns]
+    table_data = data.to_dict('records')
+
+    return columns, table_data
+
+
+# 'table-best-actions'
 # ** HTML table
 @callback(
-    [Output('table-candidates', 'data'), Output('table-candidates', 'columns')],
+    [
+        Output('table-candidates', 'columns'),
+        Output('table-candidates', 'data'),
+        Output('table-best-actions', 'columns'),
+        Output('table-best-actions', 'data'),
+    ],
     Input('selector-candidates', 'value'),
     State('selector-obj_id', 'value'),
     prevent_initial_call=True,
 )
-def update_table_candidates(index, obj_id):
-    df = pre_filter_data(data, idx=index, obj_id=obj_id)
-    df = df.filter(items=TABLE_FEATS, axis=1).sort_values(
-        by='ErstellungsDatum', ascending=True
+def update_tables_candidates(
+    index,
+    obj_id,
+) -> tuple[HTMLColumns, HTMLTable, HTMLColumns, HTMLTable]:
+    cands = filter_candidates(data, idx=index, obj_id=obj_id)
+    overview_cols, overview_table = transform_to_HTML_table(
+        data=cands,
+        target_features=TABLE_FEATS_OVERVIEW,
+        date_cols=TABLE_FEATS_DATES,
+        sorting_feature='ErstellungsDatum',
+        sorting_ascending=True,
     )
-    cols = [{'name': i, 'id': i} for i in df.columns]
-    # convert dates to strings
-    for col in TABLE_FEATS_DATES:
-        df[col] = df[col].dt.strftime(r'%Y-%m-%d')
+    # df = df.filter(items=TABLE_FEATS_OVERVIEW, axis=1).sort_values(
+    #     by='ErstellungsDatum', ascending=True
+    # )
+    # cols = [{'name': i, 'id': i} for i in df.columns]
+    # # convert dates to strings
+    # for col in TABLE_FEATS_DATES:
+    #     df[col] = df[col].dt.strftime(r'%Y-%m-%d')
 
-    table_data = df.to_dict('records')
-    return table_data, cols
+    # table_data = df.to_dict('records')
+
+    cands_best_actions = calc_delta_to_next_failure(
+        data=cands,
+        date_feature='ErstellungsDatum',
+        name_delta_feature=NAME_DELTA_FEAT_TO_NEXT_FAILURE,
+    )
+    best_actions_cols, best_actions_table = transform_to_HTML_table(
+        data=cands_best_actions,
+        target_features=TABLE_FEATS_BEST_ACTIONS,
+        date_cols=TABLE_FEATS_DATES,
+    )
+
+    return overview_cols, overview_table, best_actions_cols, best_actions_table
 
 
 # ** graph callbacks
@@ -345,7 +450,7 @@ def update_table_candidates(index, obj_id):
 def display_candidates_as_graph(index, obj_id):
     error_msg = ''
     t1 = time.perf_counter()
-    df = pre_filter_data(data, idx=index, obj_id=obj_id)
+    df = filter_candidates(data, idx=index, obj_id=obj_id)
     t2 = time.perf_counter()
     print(f'Time for filtering: {t2 - t1} s')
 
diff --git a/src/lang_main/analysis/preprocessing.py b/src/lang_main/analysis/preprocessing.py
index 54a9f93..9130bb1 100644
--- a/src/lang_main/analysis/preprocessing.py
+++ b/src/lang_main/analysis/preprocessing.py
@@ -1,9 +1,8 @@
-import re
-from collections.abc import Iterable
+from collections.abc import Collection
 from itertools import combinations
 from math import factorial
 from pathlib import Path
-from typing import Callable, cast
+from typing import cast
 
 import numpy as np
 import pandas as pd
@@ -25,11 +24,12 @@ from lang_main.loggers import logger_preprocess as logger
 from lang_main.pipelines.base import Pipeline
 from lang_main.types import Embedding, PandasIndex
 
-# ** RE patterns
-pattern_special_chars = re.compile(r'[\t\n\r\f\v]+')
-pattern_repeated_chars = re.compile(r'([,;.:!?-_\+]){2,}')
-pattern_dates = re.compile(r'(\d{1,2}\.)?(\d{1,2}\.)([\d]{2,4})?')
-pattern_whitespace = re.compile(r'[ ]{2,}')
+# TODO removal
+# pattern_escape_seq = re.compile(r'[\t\n\r\f\v]+')
+# pattern_escape_seq_sentences = re.compile(r' *[\t\n\r\f\v]+')
+# pattern_repeated_chars = re.compile(r'([,;.:!?-_\+]){2,}')
+# pattern_dates = re.compile(r'(\d{1,2}\.)?(\d{1,2}\.)([\d]{2,4})?')
+# pattern_whitespace = re.compile(r'[ ]{2,}')
 
 
 # ** (1) dataset preparation: loading and simple preprocessing
@@ -37,7 +37,7 @@ pattern_whitespace = re.compile(r'[ ]{2,}')
 # duplicate cleansing based on all properties
 def load_raw_data(
     path: Path,
-    date_cols: Iterable[str] = (
+    date_cols: Collection[str] = (
         'VorgangsDatum',
         'ErledigungsDatum',
         'Arbeitsbeginn',
@@ -50,7 +50,7 @@ def load_raw_data(
     ----------
     path : str
         path to dataset file, usually CSV file
-    date_cols : list[str], optional
+    date_cols : Collection[str], optional
         columns which contain dates and are parsed as such,
         by default (
             'VorgangsDatum',
@@ -129,9 +129,7 @@ def remove_duplicates(
 
 def remove_NA(
     data: DataFrame,
-    target_features: list[str] = [
-        'VorgangsBeschreibung',
-    ],
+    target_features: Collection[str] = ('VorgangsBeschreibung',),
 ) -> tuple[DataFrame]:
     """function to drop NA entries based on a subset of features to be analysed
 
@@ -139,14 +137,15 @@ def remove_NA(
     ----------
     data : DataFrame
         standard IHM dataset, perhaps pre-cleaned
-    target_features : list[str], optional
-        subset to analyse to define an NA entry, by default [ 'VorgangsBeschreibung', ]
+    target_features : Collection[str], optional
+        subset to analyse to define an NA entry, by default ('VorgangsBeschreibung',)
 
     Returns
     -------
     DataFrame
         dataset with removed NA entries for given subset of features
     """
+    target_features = list(target_features)
     wo_NA = data.dropna(axis=0, subset=target_features, ignore_index=True).copy()  # type: ignore
     logger.info(
         f'Removed NA entries for features >>{target_features}<< from dataset successfully.'
@@ -156,46 +155,7 @@ def remove_NA(
 
 
 # ** (2) entry-based cleansing
-# following functions clean and prepare specific entries, not whole dataset
-def clean_string_slim(string: str) -> str:
-    """mapping function to clean single string entries in a series (feature-wise)
-    of the dataset, used to be applied element-wise for string features
-
-    Parameters
-    ----------
-    string : str
-        dataset entry feature
-
-    Returns
-    -------
-    str
-        cleaned entry
-    """
-    # remove special chars
-    string = pattern_special_chars.sub(' ', string)
-    string = pattern_repeated_chars.sub(r'\1', string)
-    # string = pattern_dates.sub('', string)
-    # dates are used for context, should not be removed at this stage
-    string = pattern_whitespace.sub(' ', string)
-    # remove whitespaces at the beginning and the end
-    string = string.strip()
-
-    return string
-
-
-def entry_wise_cleansing(
-    data: DataFrame,
-    target_feature: str,
-    cleansing_func: Callable[[str], str],
-) -> tuple[DataFrame]:
-    # apply given cleansing function to target feature
-    data[target_feature] = data[target_feature].map(cleansing_func)
-    logger.info(
-        ('Successfully applied entry-wise cleansing procedure >>%s<< for feature >>%s<<'),
-        cleansing_func.__name__,
-        target_feature,
-    )
-    return (data,)
+# ** moved to module ``lang_main.analysis.shared``
 
 
 # ** in-depth analysis of one feature
diff --git a/src/lang_main/analysis/shared.py b/src/lang_main/analysis/shared.py
index c177acd..dd66c95 100644
--- a/src/lang_main/analysis/shared.py
+++ b/src/lang_main/analysis/shared.py
@@ -1,4 +1,5 @@
-from collections.abc import Iterable, Iterator
+import re
+from collections.abc import Callable, Collection, Iterable, Iterator
 from typing import cast
 
 import networkx as nx
@@ -7,14 +8,70 @@ import numpy.typing as npt
 import sentence_transformers
 import sentence_transformers.util
 from networkx import Graph
-from pandas import Series
+from pandas import DataFrame, Series
 from sentence_transformers import SentenceTransformer
 from torch import Tensor
 
 from lang_main.analysis.graphs import get_graph_metadata, update_graph
+from lang_main.loggers import logger_preprocess as logger
 from lang_main.types import PandasIndex
 
+# ** RE patterns
+pattern_escape_newline = re.compile(r'[\n]+')
+pattern_escape_seq = re.compile(r'[\t\n\r\f\v]+')
+pattern_escape_seq_sentences = re.compile(r' *[\t\n\r\f\v]+')
+pattern_repeated_chars = re.compile(r'[,;.:!?\-_+]+(?=[,;.:!?\-_+])')
+pattern_dates = re.compile(r'(\d{1,2}\.)?(\d{1,2}\.)([\d]{2,4})?')
+pattern_whitespace = re.compile(r'[ ]{2,}')
 
+
+# ** RE applications
+# following functions clean and prepare specific entries, not whole datasets
+def clean_string_slim(string: str) -> str:
+    """mapping function to clean single string entries in a series (feature-wise)
+    of the dataset, used to be applied element-wise for string features
+
+    Parameters
+    ----------
+    string : str
+        dataset entry feature
+
+    Returns
+    -------
+    str
+        cleaned entry
+    """
+    # remove special chars
+    string = pattern_escape_newline.sub('. ', string)
+    string = pattern_escape_seq.sub(' ', string)
+    string = pattern_repeated_chars.sub('', string)
+    # string = pattern_dates.sub('', string)
+    # dates are used for context, should not be removed at this stage
+    string = pattern_whitespace.sub(' ', string)
+    # remove whitespaces at the beginning and the end
+    string = string.strip()
+
+    return string
+
+
+# ** dataset cleansing
+def entry_wise_cleansing(
+    data: DataFrame,
+    target_features: Collection[str],
+    cleansing_func: Callable[[str], str] = clean_string_slim,
+) -> tuple[DataFrame]:
+    # apply given cleansing function to target feature
+    target_features = list(target_features)
+    data[target_features] = data[target_features].map(cleansing_func)
+    logger.info(
+        ('Successfully applied entry-wise cleansing procedure >>%s<< for features >>%s<<'),
+        cleansing_func.__name__,
+        target_features,
+    )
+    return (data,)
+
+
+# ** similarities
 def candidates_by_index(
     data_model_input: Series,
     model: SentenceTransformer,
diff --git a/src/lang_main/analysis/timeline.py b/src/lang_main/analysis/timeline.py
index e010ff2..4e339a0 100644
--- a/src/lang_main/analysis/timeline.py
+++ b/src/lang_main/analysis/timeline.py
@@ -1,4 +1,4 @@
-from collections.abc import Iterable, Iterator
+from collections.abc import Collection, Iterable, Iterator
 from typing import cast
 
 from pandas import DataFrame, Series
@@ -7,14 +7,21 @@ from tqdm.auto import tqdm  # TODO: check deletion
 
 from lang_main.analysis.shared import (
     candidates_by_index,
+    entry_wise_cleansing,
+    pattern_escape_seq_sentences,
     similar_index_connection_graph,
     similar_index_groups,
 )
 from lang_main.loggers import logger_timeline as logger
-from lang_main.types import ObjectID, PandasIndex, TimelineCandidates
+from lang_main.types import (
+    DataFrameTLFiltered,
+    ObjectID,
+    PandasIndex,
+    TimelineCandidates,
+)
 
 
-def non_relevant_obj_ids(
+def _non_relevant_obj_ids(
     data: DataFrame,
     thresh_unique_feat_per_id: int,
     *,
@@ -50,9 +57,9 @@ def remove_non_relevant_obj_ids(
     feature_uniqueness: str = 'HObjektText',
     feature_obj_id: str = 'ObjektID',
 ) -> tuple[DataFrame]:
-    logger.info('Removing non-relevant ObjectIDs from dataset')
+    logger.info('Removing non-relevant ObjectIDs from dataset...')
     data = data.copy()
-    ids_to_ignore = non_relevant_obj_ids(
+    ids_to_ignore = _non_relevant_obj_ids(
         data=data,
         thresh_unique_feat_per_id=thresh_unique_feat_per_id,
         feature_uniqueness=feature_uniqueness,
@@ -61,7 +68,43 @@ def remove_non_relevant_obj_ids(
     # only retain entries with ObjectIDs not in IDs to ignore
     data = data.loc[~(data[feature_obj_id].isin(ids_to_ignore))]
     logger.debug('Ignored ObjectIDs: %s', ids_to_ignore)
-    logger.info('Non-relevant ObjectIDs removed successfully')
+    logger.info('Non-relevant ObjectIDs removed successfully.')
+
+    return (data,)
+
+
+def cleanup_descriptions(
+    data: DataFrame,
+    properties: Collection[str] = (
+        'VorgangsBeschreibung',
+        'ErledigungsBeschreibung',
+    ),
+) -> tuple[DataFrame]:
+    logger.info('Cleaning necessary descriptions...')
+    data = data.copy()
+    features = list(properties)
+    data[features] = data[features].fillna('N.V.')
+    (data,) = entry_wise_cleansing(data, target_features=features)
+    logger.info('Cleansing successful.')
+
+    return (data.copy(),)
+
+
+def calc_delta_to_repair(
+    data: DataFrame,
+    date_feature_start: str = 'ErstellungsDatum',
+    date_feature_end: str = 'ErledigungsDatum',
+    name_delta_feature: str = 'delta_to_repair',
+    convert_to_days: bool = True,
+) -> tuple[DataFrame]:
+    logger.info('Calculating time differences between start and end of operations...')
+    data = data.copy()
+    data[name_delta_feature] = data[date_feature_end] - data[date_feature_start]
+
+    if convert_to_days:
+        data[name_delta_feature] = data[name_delta_feature].dt.days
+
+    logger.info('Calculation successful.')
 
     return (data,)
 
@@ -75,7 +118,7 @@ def generate_model_input(
         'VorgangsBeschreibung',
     ),
 ) -> tuple[DataFrame]:
-    logger.info('Generating concatenation of model input features')
+    logger.info('Generating concatenation of model input features...')
     data = data.copy()
     model_input_features = list(model_input_features)
     input_features = data[model_input_features].fillna('').astype(str)
@@ -83,7 +126,7 @@ def generate_model_input(
         lambda x: ' - '.join(x),
         axis=1,
     )
-    logger.info('Model input generated successfully')
+    logger.info('Model input generated successfully.')
 
     return (data,)
 
@@ -97,7 +140,7 @@ def filter_activities_per_obj_id(
 ) -> tuple[DataFrame, Series]:
     data = data.copy()
     # filter only relevant activities count occurrences for each ObjectID
-    logger.info('Filtering activities per ObjectID')
+    logger.info('Filtering activities per ObjectID...')
     filt_rel_activities = data[activity_feature].isin(relevant_activity_types)
     data_filter_activities = data.loc[filt_rel_activities].copy()
     num_activities_per_obj_id = cast(
@@ -113,7 +156,7 @@ def filter_activities_per_obj_id(
 
     num_activities_per_obj_id = num_activities_per_obj_id.loc[~filt_below_thresh]
     data_filter_activities = data_filter_activities.loc[~filt_entries_below_thresh]
-    logger.info('Activities per ObjectID filtered successfully')
+    logger.info('Activities per ObjectID filtered successfully.')
 
     return data_filter_activities, num_activities_per_obj_id
 
@@ -129,7 +172,7 @@ def filter_activities_per_obj_id(
 ## use idx pairs to get idx values of series
 
 
-def get_timeline_candidates_index(
+def _get_timeline_candidates_index(
     data: DataFrame,
     num_activities_per_obj_id: Series,
     *,
@@ -161,7 +204,7 @@ def get_timeline_candidates_index(
 
 
 # TODO: check application for duplicate removal
-def transform_timeline_candidates(
+def _transform_timeline_candidates(
     candidates: Iterator[tuple[ObjectID, tuple[PandasIndex, ...]]],
 ) -> TimelineCandidates:
     """function to build a mapping of ObjectIDs to their respective collection of
@@ -200,7 +243,7 @@ def transform_timeline_candidates(
     return candidates_by_obj_id
 
 
-def map_obj_id_to_texts(
+def _map_obj_id_to_texts(
     data: DataFrame,
     feature_obj_id: str = 'ObjektID',
 ) -> dict[ObjectID, str]:
@@ -229,7 +272,7 @@ def get_timeline_candidates(
     model_input_feature: str = 'nlp_model_input',
 ) -> tuple[TimelineCandidates, dict[ObjectID, str]]:
     logger.info('Obtaining timeline candidates...')
-    candidates = get_timeline_candidates_index(
+    candidates = _get_timeline_candidates_index(
         data=data,
         num_activities_per_obj_id=num_activities_per_obj_id,
         model=model,
@@ -237,14 +280,52 @@ def get_timeline_candidates(
         feature_obj_id=feature_obj_id,
         model_input_feature=model_input_feature,
     )
-    tl_candidates = transform_timeline_candidates(candidates)
+    tl_candidates = _transform_timeline_candidates(candidates)
     logger.info('Timeline candidates obtained successfully.')
     # text mapping to obtain object descriptors
     logger.info('Mapping ObjectIDs to their respective text descriptor...')
-    map_obj_text = map_obj_id_to_texts(
+    map_obj_text = _map_obj_id_to_texts(
         data=data,
         feature_obj_id=feature_obj_id,
     )
     logger.info('ObjectIDs successfully mapped to text descriptors.')
 
     return tl_candidates, map_obj_text
+
+
+# ** Postprocessing
+# filter original dataset for a batch of timeline candidates
+def filter_timeline_cands(
+    data: DataFrame,
+    cands: TimelineCandidates,
+    obj_id: ObjectID,
+    entry_idx: int,
+    sort_feature: str = 'ErstellungsDatum',
+) -> DataFrameTLFiltered:
+    data = data.copy()
+    cands_for_obj_id = cands[obj_id]
+    cands_choice = cands_for_obj_id[entry_idx]
+    data = data.loc[list(cands_choice)].sort_values(
+        by=sort_feature,
+        ascending=True,
+    )
+
+    return data
+
+
+def calc_delta_to_next_failure(
+    data: DataFrameTLFiltered,
+    date_feature: str = 'ErstellungsDatum',
+    name_delta_feature: str = 'delta_to_next_failure',
+    convert_to_days: bool = True,
+) -> DataFrameTLFiltered:
+    data = data.copy()
+    last_val = data[date_feature].iat[-1]
+    shifted = data[date_feature].shift(-1, fill_value=last_val)
+    data[name_delta_feature] = shifted - data[date_feature]
+    data = data.sort_values(by=name_delta_feature, ascending=False)
+
+    if convert_to_days:
+        data[name_delta_feature] = data[name_delta_feature].dt.days
+
+    return data
diff --git a/src/lang_main/constants.py b/src/lang_main/constants.py
index 2fda715..9d0e52b 100644
--- a/src/lang_main/constants.py
+++ b/src/lang_main/constants.py
@@ -76,13 +76,14 @@ CYTO_LAYOUT_PROPERTIES: Final[CytoLayoutProperties] = {
     'isDeterministic': True,
     'singlePartition': False,
 }
+CYTO_SANDBOX_NAME: Final[str] = 'lang_main'
 CYTO_STYLESHEET_NAME: Final[str] = 'lang_main'
 # name for property, on which selection is done
 CYTO_SELECTION_PROPERTY: Final[str] = 'node_selection'
 CYTO_NUMBER_SUBGRAPHS: Final[int] = 5
 CYTO_ITER_NEIGHBOUR_DEPTH: Final[int] = 2
 
-# ** time analysis.uniqueness
+# ** time_analysis.uniqueness
 THRESHOLD_UNIQUE_TEXTS: Final[int] = CONFIG['time_analysis']['uniqueness'][
     'threshold_unique_texts'
 ]
@@ -90,6 +91,10 @@ UNIQUE_CRITERION_FEATURE: Final[str] = CONFIG['time_analysis']['uniqueness'][
     'criterion_feature'
 ]
 FEATURE_NAME_OBJ_ID: Final[str] = CONFIG['time_analysis']['uniqueness']['feature_name_obj_id']
+# ** time_analysis.preparation
+NAME_DELTA_FEAT_TO_REPAIR: Final[str] = 'delta_to_repair'
+# NAME_DELTA_FEAT_TO_REPAIR: Final[str] = 'Zeitspanne bis zur Behebung [Tage]'
+NAME_DELTA_FEAT_TO_NEXT_FAILURE: Final[str] = 'Zeitspanne bis zum nächsten Ereignis [Tage]'
 # ** time_analysis.model_input
 MODEL_INPUT_FEATURES: Final[tuple[str, ...]] = tuple(
     CONFIG['time_analysis']['model_input']['input_features']
diff --git a/src/lang_main/cytoscape_config/template_test.cys b/src/lang_main/cytoscape_config/template_test.cys
index da070f2..d245d1e 100644
Binary files a/src/lang_main/cytoscape_config/template_test.cys and b/src/lang_main/cytoscape_config/template_test.cys differ
diff --git a/src/lang_main/pipelines/predefined.py b/src/lang_main/pipelines/predefined.py
index 0951c06..f4d404f 100644
--- a/src/lang_main/pipelines/predefined.py
+++ b/src/lang_main/pipelines/predefined.py
@@ -3,15 +3,19 @@ from pathlib import Path
 from lang_main.analysis import graphs
 from lang_main.analysis.preprocessing import (
     analyse_feature,
-    clean_string_slim,
-    entry_wise_cleansing,
     load_raw_data,
     merge_similarity_dupl,
     numeric_pre_filter_feature,
     remove_duplicates,
     remove_NA,
 )
+from lang_main.analysis.shared import (
+    clean_string_slim,
+    entry_wise_cleansing,
+)
 from lang_main.analysis.timeline import (
+    calc_delta_to_repair,
+    cleanup_descriptions,
     filter_activities_per_obj_id,
     generate_model_input,
     get_timeline_candidates,
@@ -25,6 +29,7 @@ from lang_main.constants import (
     DATE_COLS,
     FEATURE_NAME_OBJ_ID,
     MODEL_INPUT_FEATURES,
+    NAME_DELTA_FEAT_TO_REPAIR,
     SAVE_PATH_FOLDER,
     SPCY_MODEL,
     STFR_MODEL,
@@ -56,7 +61,7 @@ def build_base_target_feature_pipe() -> Pipeline:
     pipe_target_feat.add(
         entry_wise_cleansing,
         {
-            'target_feature': 'VorgangsBeschreibung',
+            'target_feature': ('VorgangsBeschreibung',),
             'cleansing_func': clean_string_slim,
         },
         save_result=True,
@@ -182,7 +187,6 @@ def build_tk_graph_rescaling_pipe(
         graphs.pipe_add_graph_metrics,
         save_result=save_result,
         filename=exit_point,
-        # filename=EntryPoints.TK_GRAPH_ANALYSIS_RESCALED,
     )
 
     return pipe_graph_rescaling
@@ -247,6 +251,23 @@ def build_tk_graph_render_pipe(
 # ** timeline analysis
 def build_timeline_pipe() -> Pipeline:
     pipe_timeline = Pipeline(name='Timeline_Analysis', working_dir=SAVE_PATH_FOLDER)
+    pipe_timeline.add(
+        cleanup_descriptions,
+        {
+            'properties': ['ErledigungsBeschreibung'],
+        },
+    )
+    pipe_timeline.add(
+        calc_delta_to_repair,
+        {
+            'date_feature_start': 'ErstellungsDatum',
+            'date_feature_end': 'ErledigungsDatum',
+            'name_delta_feature': NAME_DELTA_FEAT_TO_REPAIR,
+            'convert_to_days': True,
+        },
+        save_result=True,
+        filename=EntryPoints.TIMELINE_POST,
+    )
     pipe_timeline.add(
         remove_non_relevant_obj_ids,
         {
@@ -281,7 +302,7 @@ def build_timeline_pipe() -> Pipeline:
             'model_input_feature': 'nlp_model_input',
         },
         save_result=True,
-        filename=EntryPoints.TIMELINE_POST,
+        filename=EntryPoints.TIMELINE_CANDS,
     )
 
     return pipe_timeline
diff --git a/src/lang_main/render/cytoscape.py b/src/lang_main/render/cytoscape.py
index 4df2a0d..d5201a9 100644
--- a/src/lang_main/render/cytoscape.py
+++ b/src/lang_main/render/cytoscape.py
@@ -16,6 +16,7 @@ from lang_main.constants import (
     CYTO_LAYOUT_PROPERTIES,
     CYTO_NUMBER_SUBGRAPHS,
     CYTO_PATH_STYLESHEET,
+    CYTO_SANDBOX_NAME,
     CYTO_SELECTION_PROPERTY,
     CYTO_STYLESHEET_NAME,
     PROPERTY_NAME_DEGREE_WEIGHTED,
@@ -56,6 +57,8 @@ def verify_connection():
 def import_to_cytoscape(
     graph: DiGraph | Graph,
     network_name: str = CYTO_BASE_NETWORK_NAME,
+    sandbox_name: str = CYTO_SANDBOX_NAME,
+    reinitialise_sandbox: bool = True,
 ) -> None:
     """Cytoscape: import NetworkX graph as new network collection
 
@@ -66,6 +69,12 @@ def import_to_cytoscape(
     """
     logger.debug('Checking Cytoscape connection...')
     verify_connection()
+    logger.debug('Setting Cytoscape sandbox...')
+    p4c.sandbox_set(
+        sandbox_name=sandbox_name,
+        reinitialize=reinitialise_sandbox,
+        copy_samples=False,
+    )
     logger.debug('Importing to and analysing network in Cytoscape...')
     p4c.delete_all_networks()
     p4c.create_network_from_networkx(
@@ -122,6 +131,7 @@ def export_network_to_image(
     filetype: CytoExportFileTypes = 'SVG',
     network_name: str = CYTO_BASE_NETWORK_NAME,
     pdf_export_page_size: CytoExportPageSizes = 'A4',
+    sandbox_name: str = CYTO_SANDBOX_NAME,
 ) -> None:
     """Cytoscape: export current selected view as image
 
@@ -140,14 +150,17 @@ def export_network_to_image(
     logger.debug('Exporting image to file...')
     if not target_folder.exists():
         target_folder.mkdir(parents=True)
-    file_pth = target_folder / filename
+    dst_file_pth = (target_folder / filename).with_suffix(f'.{filetype.lower()}')
 
     text_as_font = True
     if filetype == 'SVG':
         text_as_font = False
 
+    # image is generated in sandbox directory and transferred to target destination
+    # (preparation for remote instances of Cytoscape)
+    # TODO close non-necessary windows before image display
     p4c.export_image(
-        filename=str(file_pth),
+        filename=filename,
         type=filetype,
         network=network_name,
         overwrite_file=True,
@@ -155,7 +168,24 @@ def export_network_to_image(
         export_text_as_font=text_as_font,
         page_size=pdf_export_page_size,
     )
-    logger.debug('Exporting image to file successful.')
+    # TODO change back to Cytoscape 3.10 and above
+    # TODO remove if Cytoscape >= 3.10.* is running in container
+    # p4c.export_image(
+    #     filename=filename,
+    #     type=filetype,
+    #     network=network_name,
+    #     overwrite_file=True,
+    # )
+    logger.debug('Exported image to sandbox.')
+    logger.debug('Transferring image from sandbox to target destination...')
+    sandbox_filename = f'{filename}.{filetype.lower()}'
+    p4c.sandbox_get_from(
+        source_file=sandbox_filename,
+        dest_file=str(dst_file_pth),
+        overwrite=True,
+        sandbox_name=sandbox_name,
+    )
+    logger.debug('Transfer of image from sandbox to target destination successful.')
 
 
 def layout_network(
@@ -192,6 +222,7 @@ def apply_style_to_network(
     node_size_property: str = 'node_selection',
     min_node_size: int = 15,
     max_node_size: int = 40,
+    sandbox_name: str = CYTO_SANDBOX_NAME,
 ) -> None:
     """Cytoscape: apply a chosen Cytoscape style to the defined network
 
@@ -221,7 +252,16 @@ def apply_style_to_network(
             raise FileNotFoundError(
                 f'Visual stylesheet for Cytoscape not found under: >>{pth_to_stylesheet}<<'
             )
-        p4c.import_visual_styles(str(pth_to_stylesheet))
+        # send to sandbox
+        sandbox_filename = pth_to_stylesheet.name
+        p4c.sandbox_send_to(
+            source_file=pth_to_stylesheet,
+            dest_file=sandbox_filename,
+            overwrite=True,
+            sandbox_name=sandbox_name,
+        )
+        # load stylesheet
+        p4c.import_visual_styles(sandbox_filename)
 
     p4c.set_visual_style(style_name, network=network_name)
     # node size mapping, only if needed property is available
@@ -242,6 +282,7 @@ def apply_style_to_network(
         default_number=min_node_size,
     )
     p4c.set_node_size_mapping(**node_size_map)
+    # TODO removal
     # else:
     #     node_table = p4c.get_table_columns(table='node', network=network_name)
     #     nodes_SUID = node_table['SUID'].to_list()
diff --git a/src/lang_main/types.py b/src/lang_main/types.py
index 1d6ebec..3e7f21b 100644
--- a/src/lang_main/types.py
+++ b/src/lang_main/types.py
@@ -1,5 +1,7 @@
 import enum
+from collections.abc import Hashable
 from typing import (
+    Any,
     Literal,
     Required,
     TypeAlias,
@@ -7,6 +9,7 @@ from typing import (
 )
 
 import numpy as np
+from pandas import DataFrame
 from spacy.tokens.doc import Doc as SpacyDoc
 from torch import Tensor
 
@@ -33,6 +36,7 @@ ResultHandling: TypeAlias = list[tuple[bool, str | None]]
 class EntryPoints(enum.StrEnum):
     TIMELINE = 'TIMELINE'
     TIMELINE_POST = 'TIMELINE_POSTPROCESSING'
+    TIMELINE_CANDS = 'TIMELINE_CANDIDATES'
     TIMELINE_TK_GRAPH_RESCALED = 'TIMELINE_TK_GRAPH_RESCALED'
     TK_GRAPH_POST = 'TK-GRAPH_POSTPROCESSING'
     TK_GRAPH_ANALYSIS = 'TK-GRAPH_ANALYSIS'
@@ -44,6 +48,7 @@ class EntryPoints(enum.StrEnum):
 PandasIndex: TypeAlias = int | np.int64
 ObjectID: TypeAlias = int
 Embedding: TypeAlias = SpacyDoc | Tensor
+DataFrameTLFiltered: TypeAlias = DataFrame
 
 # ** graphs
 NodeTitle: TypeAlias = str
@@ -118,3 +123,8 @@ class CytoscapeData(TypedDict, total=False):
 
 # ** timeline
 TimelineCandidates: TypeAlias = dict[ObjectID, tuple[tuple[PandasIndex, ...], ...]]
+
+
+# ** Dash (Dashboard)
+HTMLTable: TypeAlias = list[dict[Hashable, Any]]
+HTMLColumns: TypeAlias = list[dict[str, str]]
diff --git a/test-notebooks/misc.ipynb b/test-notebooks/misc.ipynb
index 5fa845c..7d0193d 100644
--- a/test-notebooks/misc.ipynb
+++ b/test-notebooks/misc.ipynb
@@ -14,14 +14,62 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "af118d77-d87a-4687-be5b-e810a24c403e",
+   "id": "d46b6ce8-b51b-49e0-b494-fc24fda0f73f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import py4cytoscape as p4c"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "6fde72d3-b95b-4d37-be71-a7d3661dd3f5",
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "2024-07-24 06:14:08 +0000 | io:INFO | Loaded TOML config file successfully.\n"
+      "You are connected to Cytoscape!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "'You are connected to Cytoscape!'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p4c.cytoscape_ping()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43eeb870-6f97-4029-ac0d-210315ccaabf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "af118d77-d87a-4687-be5b-e810a24c403e",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-07-31 06:33:53 +0000 | io:INFO | Loaded TOML config file successfully.\n"
      ]
     },
     {
@@ -393,7 +441,9 @@
    "cell_type": "code",
    "execution_count": 20,
    "id": "f381b25a-6149-4a2a-876c-4cbd8bb9bd04",
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -6261,8 +6311,8 @@
           1
          ],
          "range": [
-          "2019-08-25 23:47:37.7827",
-          "2023-07-27 00:12:22.2173"
+          "2019-08-26 23:25:36.9657",
+          "2023-07-26 00:34:23.0343"
          ],
          "tickformat": "%B\n%Y",
          "title": {
@@ -6289,7 +6339,7 @@
         }
        }
       },
-      "image/png": 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",
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        "<div>                            <div id=\"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\" class=\"plotly-graph-div\" style=\"height:525px; width:100%;\"></div>            <script type=\"text/javascript\">                require([\"plotly\"], function(Plotly) {                    window.PLOTLYENV=window.PLOTLYENV || {};                                    if (document.getElementById(\"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\")) {                    Plotly.newPlot(                        \"1eafb9c2-4e4a-48cd-b91e-877cbaf8763e\",                        [{\"customdata\":[[\"2020-12-14T00:00:00\",\"2020-12-10T00:00:00\",\"Kettbaum schrauben abgerochen schrauben ausboren\"],[\"2021-12-17T00:00:00\",\"2021-12-16T00:00:00\",\"Neue L\\u00f6cher bohren und Gewinde schneiden. Kettbaum liegt vor der Schlosserei\"],[\"2022-06-22T00:00:00\",\"2022-06-21T00:00:00\",\"Kettbaum Gewinde nach schneiden (liegt vor Schlosserei)\"],[\"2022-12-13T00:00:00\",\"2022-12-12T00:00:00\",\"Kettbaum Gewinden kaputt, neue Gewinden machen bitte\"],[\"2022-02-03T00:00:00\",\"2022-02-03T00:00:00\",\"Gewinde schneiden. Kette liegt vor der Schlosserei\"],[\"2022-03-28T00:00:00\",\"2022-03-28T00:00:00\",\"Kettbaum Schraube defekt. Liegt vor Schlosserei\"],[\"2022-01-26T00:00:00\",\"2022-01-26T00:00:00\",\"2 Kettb\\u00e4ume Schrauben ausbohren. Kettb\\u00e4ume liegen vor der Schlosserei\"],[\"2021-12-16T00:00:00\",\"2021-12-15T00:00:00\",\"Kettbaum Schrauben gebrochen bitte rausbohren\"],[\"2023-01-23T00:00:00\",\"2023-01-21T00:00:00\",\"Schraube abgebrochen! 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",
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EkN8eFCGH/L5wQeQTtD3xv1hk2CgCyO9GlTuozSK//SmXt/L7pZdPyMYtO1ukbrt1lTy8bZPMmD6tjd7FS5flod37os+l3398ZEyefvZQ9N7dd66R+wbXR39Ofl59/Mj2TbJuzS2tuOn302PiNQ8eHo/mJOcjv/053LqZIL91iTHeJQHkt0varFWUAPK7KDnm2SSA/LZJl9imCCC/TZG0F+fHP+6Ti5f7ZN61E/YW8SwybU88KwjpdCSA/OZg+EoA+e1PZbyV388felEWLpgrK5cvkVg2z587pyWwFcKkhE7LcSWw1SsW3jFyNWfv174pv7PhCzJ71kx59fXTMrh9jwwPDURrqVe3uck1V624uU2Yx/GR3/4cbt1MkN+6xBjvkgDy2yVt1ipKAPldlBzzbBJAftukS2xTBJDfmiSVf+7XnMNwbQLIb21kTKiAAPK7AugsmYsA8jsXJieDvJXf6d0rGT5+9JW2291KUt+wcH40NPmeujX+3IEjHW+Kp+PGAj0ps3vJb5XHa6fOTpHqyG8n59XqIshvq3gJXpIA8rskQKY7IYD8doKZRTQJIL81gTG8EgLIb03sfSIyqTmH4doEkN/ayJhQAQHkdwXQWTIXAeR3LkxOBgUjv9NCOvlxWoyrjx/c9V4rlPj1zJM7Wje7k59/68LbsnnHE7J1cH3bze+4XYoam2xrkm6Jct28a2Rk11ZZfP2CKCw3v52cWyuLIL+tYCWoIQLIb0MgCWOVAPLbKl6CFySA/C4IjmlOCSC/NXEjvzWBFRuO/C7GjVluCSC/3fJmtfwEkN/5WdkeGYT8Vje594yMyd6d90atStK3r9PyO74RHvfxVvOHhkfbJHUMttctbzUm2RblU0sWRf3F71i7uiXK1dpjB460ckN+2z6y9uIjv+2xJXJ5Asjv8gyJYJ8A8ts+Y1bQJ4D81mfGDPcEkN+azJHfmsCKDUd+F+PGLLcEkN9uebNafgLI7/ysbI/0Xn53EtedHkipQMV9v1VPb9UOJZbfnVqbqPEqztk3zme2R4ll+hc+v2qK/E7fHEd+2z6y9uIjv+2xJXJ5Asjv8gyJYJ8A8ts+Y1bQJ4D81mfGDPcEkN+azJHfmsCKDUd+F+PGLLcEkN9uebNafgLI7/ysbI/0Wn73urGdBNOp7UmyL3csv5M3tvOK71iSxzI9fatcye+hx0Zl2z0botYnyG/bR9ZefOS3PbZELk8A+V2eIRHsE0B+22fMCvoEkN/6zJjhngDyW5M58lsTWLHhyO9i3JjllgDy2y1vVstPAPmdn5Xtkd7K73Srk14g0vI72apk5fIlko7Vq9WJktmHDo/Ll9b9ZrSkivXA8Kg8OjQQye20kE+vjfy2fWTtxUd+22NL5PIEkN/lGRLBPgHkt33GrKBPAPmtz4wZ7gkgv/WYT4hIv94URhcggPwuAI0pzgkgv50jZ8GcBJDfOUE5GOat/O7U2iT9cMmYT1pAq88rSb1xy85oSHJe3Kbk2PGTbXjjlinqk6qv98HD46330w/LTD5Qc9nSRa1+32oC8tvBqbW0BPLbEljCGiGA/DaCkSCWCSC/LQMmfCECyO9C2JjkmADy2zFwlstFAPmdCxODKiaA/FYFUP8cRr0mK64GyycJIL/9OQ/eym9/EOllgvzW4+XTaOS3T9UglzQB5DdnIgQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPyuf43rsEPkt8irJ/tk5kyRuR9Bfvt0ppHf/lQD+W24Fshvw0AdhkN+O4TNUtoEkN/ayJhQAQHkdwXQWTKTAPI7ExEDPCCA/PagCKQwhQDym0MRAgHkt8jxE/0yZ/akzJuH/PbpzCK//akG8ttwLZDfhoE6DIf8dgibpbQJIL+1kTGhAgLI7wqgs2QmAeR3JiIGeEAA+e1BEUgB+c0ZCJIA8hv57evBRX77Uxnkt+FaIL8NA3UYDvntEDZLaRNAfmsjY0IFBJDfFUBnyUwCyO9MRAzwgADy24MikALymzMQJAHkN/Lb14OL/PanMshvw7VAfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS3B0UgBeQ3ZyBIAshv5LevBxf57U9lvJffr75+WnY/tV+G7x+Q2bNmTiF38dJleWj3vujzD2/bJDOmT4v+/PyhF+XBXe99ftnSRbJ3571t81Xcwe175My5N+W6edfIyK6tsvj6BdH4x0fG5OlnD7Wt9cj2TbJuzS2tz3XLC/ntz+HWzQT5rUuM8S4JIL9d0matogSQ30XJMc8mAeS3TbrENkUA+W2KJHFMEqDnt0maxLJFoPHye1Lk9b/rk6t+TuSaOfT8tnXOisRFfhehZmeOt/L7rQtvy+YdT8ix4yc7ymuFIxbfBw+Py223rmrJ75dePiF7RsZawlvJ7LNvnG+9r8T1A8Oj8ujQQEt4J/Gq8ep13+D6KdSz8kJ+2zmoLqIiv11QZo2iBJDfRckxzyUB5LdL2qyVlwDyOy8pxlVJAPldJX3W7kYA+c3ZCIFA4+V3VKQ+6ZNJQX37dWKR3/7Uw1v5HSPqdfNbSeobFs6Pho4ffSWS2+qlboKvWnFz66Z2UnYvmHdt9P4da1fLyuVLOlail/zOygv57c/h1s0E+a1LjPEuCSC/XdJmraIEkN9FyTHPJgHkt026xDZFwJn8nhT565MiV83sl/lzJ0ylT5yaEkB+17SwNdsW8rtmBa3RdpDf/hQzWPmdFNSqxUkv+R3f1t46uF5uuvGjrRvlcRmSt8bV59JtT9ItT9QY2p74c4hNZYL8NkWSODYIIL9tUCWmaQLIb9NEiWeCAPLbBEVi2CbgTH6LyIn/3i+zZolcNw/5bbuuocdHfodewWbkj/xuRp1D3CXy25+qBSm/lex+7dTZVluSpPxWPb/THyfl95zZH27rIR63Tpk/d07HNidxb/DhoYG2m+LIb38OsalMkN+mSBLHBgHktw2qxDRNAPltmijxTBBAfpugSAzbBJDftgkTvwgB5HcRasxxTQD57Zo46+UlgPzOS8r+uCDld6cHUipU8Q1u9WfV2kT1Ao9f8UMt1cfpB2ime4SnscftVXjgpf0DWeUKyO8q6bN2FgHkdxYh3veBAPLbhyqQQ5oA8pszEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+cg5XcaX/qmd/p9dUv7689/S7Zt3iCX3nlHhh4blW33bGg97FLJ7+cOHGk9EBP57c8BdZkJ8tslbdbSJYD81iXG+CoIIL+roM6aWQSQ31mEeN8HAshvH6pADmkCyG/ORAgEkN8hVKmZOSK//al77eV3suVJ/IBLdZP77BvnOz4gU40/dHhcvrTuN6MqJR+Wufj6Ba3K0fbEn0NsKhPktymSxLFBAPltgyoxTRNAfpsmSjwTBJDfJigSwzYBZ/J7UuTU3/fLh2ZMyDXX2N4V8UMngPwOvYLNyB/53Yw6h7hL5Lc/VfNWfsfS+tjxky1ad9+5pmNf7m49vtXcuN1JUlzHfb7jtijJuOn31OLPPLmj1e87K6/Tb170p7pkokUA+a2Fi8GOCSC/HQNnuUIEkN+FsDHJMgHkt2XAhDdCwJn8Vtn29f1zk8jJ6P94QaAXAeQ35yMEAsjvEKrUzByR3/7U3Vv57Q8ivUyQ33q8fBqN/PapGuSSJoD85kyEQAD5HUKVmpcj8rt5NQ9xx07ld4iAGpTzmbN9cuWVIh+5tvq/nUB+N+jgBbxV5HfAxat56shvfwqM/DZcC+S3YaAOwyG/HcJmKW0CyG9tZEyogADyuwLoLJlJAPmdiYgBHhBAfntQBE9SOPm3fXJlv8jHP4b89qQkpOE5AeS35wVqcHrIb3+Kj/w2XAvkt2GgDsMhvx3CZiltAshvbWRMqIAA8rsC6CyZSQD5nYmIAR4QQH57UARPUkB+e1II0giGAPI7mFI1LlHktz8lR34brgXy2zBQh+GQ3w5hs5Q2AeS3NjImVEAA+V0BdJbMJID8zkTEAA8IIL89KIInKSC/PSkEaQRDAPkdTKkalyjy25+SI78N1wL5bRiow3DIb4ewWUqbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFMGTFJDfnhSCNIIhgPwOplSNSxT57U/JvZffr75+WnY/tV+G7x+Q2bNmRuQuXrosD+3eJwcPj7dIPvPkDlm5fEn08VsX3pbNO56QY8dPRh8vW7pI9u68N5qffi8OkByj1hzcvkfOnHszevuR7Ztk3ZpbplTtpZdPyMYtOyW5NvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYoA8tsUyfDjnHlD5Iq+fpn7kYnKN8MDLysvAQnkIID8zgGJIZUQQH5Xgr3jot7K76SkTorpWG5/df8Lsvmu22XG9GmiJPTQ8KiM7Noqi69fEH186vQbLWH9/KEXZfzoK/Lwtk3R+PRLvf/aqbNy3+D6lhzfOrg+kulxHvHH8dxYfKuPkd/+HOgymSC/y9Bjrm0CyG/bhIlvggDy2wRFYpgmgPw2TZR4Ngggv21QDTRmX59I9KxLHngZaAVJ2zEB5Ldj4CyXmwDyOzcq6wO9ld/xzjvd/E5T6Saok6J6z8hY6/Z3cr6aO/TYqGy7Z0MkztV6DwyPyqNDA9HH8S3zVStubsn0OKft/+FOuX94VJJinJvf1s+stQWQ39bQEtgAAeS3AYiEsE4A+W0dsbMFJmRC+qXf2Xo2F0J+26RLbFMEkN+mSBLHJAFufpukSSxbBJDftsgStywB5HdZgubm10J+p4V1Gs/jI2Ny9o3zHW9+J299x/PU+EPf/rPoJrl6JduuJNeac/XMqL0K8tvcgawyEvK7SvqsnUUA+Z1FiPd9IID89qEK5XNQdw0nJyalX1mPGryQ3zUoYgO2gPxuQJED3CLyO8CiNTBl5HcDix7IlpHf/hQqePnd6WZ2jFeJ7Qd37Wvr+Z1En771Hb+nWprs+f1vyA/f+nHU9zvu+Z0e3+nGOTe//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOb/mIb/9qgfZNIMA8rsZdQ5tl8jv0CrWzHyR382sewi7Rn77U6Wg5XcsvufPnRP16+72imR2h7Yn6oa3eiXnptusxIJ7/drV8plP3tT2IMzkenHfb+S3P4dbNxPkty4xxrskgPx2SZu1ihJAfhcl59c85Ldf9SCbZhBAfjejzqHtEvkdWsWamS/yu5l1D2HXyG9/qhSs/M4rvhXqTje8u7VKUaL8uQNH2lqkdJLkcVzanvhzmMtmgvwuS5D5Ngkgv23SJbYpAshvUySrjYP8rpY/qzeTAPK7mXX3fdfIb98rRH6KAPKbc+ArAeS3P5UJUn73anWi0Kp2JwsXzJWVy5dEpNXHYweOtD3wspvQVlJ8cPseGR4aiOYnb36vW3NLW+Voe+LPQTaRCfLbBEVi2CKA/LZFlrgmCSC/TdKsNtbExIT09/PAy2qrwOpNIoD8blK1w9kr8jucWjU5U+R3k6vv996R3/7Ux1v5HYvlY8dPtmjdfeeaqEVJLKhVP+7kq9v7y5YuahPf3dqgxLHU+xu37GyFjnt+p8uG/PbnIJvIBPltgiIxbBFAftsiS1yTBJDfJmkSyxQBHnhpiiRxbBJAftukS+yiBJDfRckxzyUB5LdL2qylQwD5rUPL7lhv5bfdbduLTs9ve2xtR0Z+2yZM/DIEkN9l6DHXFQHktyvSrKNDAPmtQ4uxVRFAfpslPzEhUpN/vGIWjGY05LcmMIZXQgD5XQl2Fs1BAPmdA5KjIchvw6CR34aBOgyH/HYIm6W0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPltuAgTIlKPzk2GweiFQ37r8WJ0NQSQ39VwZ9VsAsjvbEauRiC/DZNGfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyITb1TIAAAgAElEQVS/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyK/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJyK/DSNFfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGOABAeS34SIgv40ARX4bwUgQywSQ35YBE74wAeR3YXTGJwYrvx8fGZOnnz3UAvLMkztk5fIl0cdvXXhbNu94Qo4dP9l6/7p518jIrq2y+PoFU96/+841ct/g+ja4L718QjZu2Rl9btnSRbJ3570ye9ZMSX5evXfbravk4W2bZMb0adFY5LfxM+osIPLbGWoWKkAA+V0AGlOcE0B+O0fOgjkIIL9zQGJI5QSQ35WXgAQ6EEB+cyxCIID8DqFKzcwR+e1P3YOU30puf3X/C7L5rtsj6fzq66flgeFReXRooE1ubx1c3xLiMfKLly7LQ7v3yaoVN8u6NbdI+mM1TgnuPSNjLeGdLNfzh16UhQvmRnHjufPnzmnJc+S3P4dbN5NQ5ffEhEg/T7PXLXdw45HfwZWskQkjvxtZdu83jfz2vkQkKCLIb46BjwSQ3z5WhZzSBJDfnAlfCSC//alMkPI7jS++6R3L7vTHyfGd3lO3yNVL3f5W7w89Nirb7tkQifSsl5Lh40dfad3+Rn5nEfP3/VDl93tE+0Rk0l+4ZFaaAPK7NEICOCCA/HYAmSW0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPntQRFIoSMB5Lc/B6MW8lvd1B4aHu3a1iTZ8kShV8L6wV37RLVKuenGj7bJbnWLfHD7Hjlz7s1WlTq1RYnfTIpz9Tnktz+HWzcT5LcuMca7JID8dkmbtYoSQH4XJcc8mwSQ3zbpEtsUAeS3KZLEMUkA+W2SJrFsEUB+2yJL3LIEkN9lCZqbH7T8TorqZM/vNB4lu8cOHGm1MYnbpKhxx078jSTlthLpzx040rrJHd8UX792ddQmJfnq1B4F+W3ucLqOhPx2TZz1dAggv3VoMbYqAsjvqsizbi8CyG/ORwgEkN8hVKl5OSK/m1fzEHeM/A6xas3IGfntT52Dlt8xxl5tTtSYZCuTOVfPbLvpne7bnZbfan66tYn6XPq2eZwL8tufw62bCfJblxjjXRJAfrukzVpFCSC/i5Jjnk0CyG+bdIltigDy2xRJ4pgkgPw2SZNYtgggv22RJW5ZAsjvsgTNza+F/FY4VPuRGxbOn3I7Oy2/1ce7n9ovw/cPyOxZMyOSSbl9+twPO77/2qmzrYdadhPfKhby29zhdB0J+e2aOOvpEEB+69BibFUEkN9VkWfdXgSQ35yPEAggv0OoUvNyRH43r+Yh7hj5HWLVmpEz8tufOgcpv1XbksPfPSpf+fLaiGTc/mR4aEBWLl8S3cpWL/XntNy+9M47snnHExK3MUnf/E5/nL5V3qnVSbKcyG9/DrduJmHLb93dMj40Asjv0CrWzHyR382su++7Rn77XiHyUwSQ35wDHwkgv32sCjmlCSC/ORO+EkB++1OZIOV3LKgPHh5vkUz2/E4/tHLZ0kWtft9JWR4/1DL9QMtYeB87fjKK/8j2Ta0b5eqG+dPPHmqrYPKBmshvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CXSS3xcv98ub/zApH/ygyEeunbS5PLEhkIsA8jsXJgY5JoD8dgyc5QoRQH4XwsYkBwSQ3w4g51wiSPmdc2+VDEN+V4LdyKLIbyMYCWKJAPLbEljCGiWA/DaKk2CGCHSS32+dF/nPY/3ya78q8ull70qf9BlajTAQKEYA+V2MG7PsEkB+2+VLdDMEkN9mOBLFPAHkt3mmRSMiv4uS6zIP+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhLoJr//8D/3y+duEfnMp5HfmRAZYJ0A8ts6YhYoQAD5XQAaU5wTQH47R86COQkgv3OCcjAM+W0YMvLbMFCH4ZDfDmGzlDYB5Lc2MiZUQAD5XQF0lswkgPzORMQADwggvz0oAilMIYD85lCEQAD5HUKVmpkj8tufuiO/DdcC+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhJAfmciYoAHBJDfHhSBFJDfnIEgCSC/gyxbI5JGfvtTZuS34Vogvw0DdRgO+e0QNktpE0B+ayNjQgUEkN8VQGfJTALd5Pc3/49+Wfmv++STN/+Mnt+ZFBlgmwDy2zZh4hchwM3vItSY45oA8ts1cdbLSwD5nZeU/XG55fdLL5+QjVt2tjK6bt41MrJrqyy+foH9LDus8PjImDz97KHWO888uUNWLl/S+jiZ77Kli2Tvzntl9qyZ0fvPH3pRHty1rzX2ke2bZN2aW6ascvHSZXlo93vjHt62SWZMnybpdeNJ8frI70qOg5FFkd9GMBLEEgHktyWwhDVKAPltFCfBDBHoJL8v/7RPLlzokw98YEKunmVoIcJAoAQB5HcJeEy1RgD5bQ0tgQ0SQH4bhEkoowSQ30ZxlgqWS34r4fv9l0+0CeS3Lrwtm3c8Ib+4fIncN7i+VBK6k9XaX93/gmy+6/ZISL/6+ml5YHhUHh0aiGR8+mMlu8ePvhIJbPXa+7Vvyu9s+EIkw+N9bB1c3ybPY/F98PC43Hbrqpb8Tueq1tr91H4Zvn8giof81q2mP+PzyO+f/ETkJ//YJ5MiMvPnRK66Sv2JFwTsE0B+22fMCuUJIL/LMySCeQKd5Lf5VYgIgXIEkN/l+DHbDgHktx2uRDVLAPltlifRzBFAfptjWTZSpvxWN6j3jIy1ie940W7iuGxSuvPTeSjZ/dqpsy0pn5bhyfix5F614ua2299K+N+wcH40NBbnSrSnX/G4+OY48lu3ev6MzyO/T58R+cZzV0hfX5/c8cV35aMLkN/+VLDemSC/613fuuwO+V2XStZrH8jvetWzrrtBfte1smHvC/kddv2akj3yuymVDm+fyG9/apYpv9NyN516WjRXsTUl6IeGR1ttWFTO6hXfSO8l6ZUYH9y+R4aHBlo3v5Pzk7fG0/I7fetbrYn8ruIEmFkzj/z+u9Miz/zhFdLf1yf//svvysc+ivw2Q58oWQSQ31mEeN8HAshvH6pADmkCyG/ORAgEkN8hVKl5OSK/m1fzEHeM/A6xas3IGfntT517yu/4VvQda1e3tQRJpq/E83MHjnRtC2Jzq7G4PnPuTUn2/E4L+07yO/7cseMnJdnzOy3ze8nvTn8xgPy2WXG7sZHfdvkSvRwB5Hc5fsx2QwD57YYzq+gRQH7r8WJ0NQSQ39VwZ9XeBJDfnJAQCCC/Q6hSM3NEfvtT957yWwniocdGZds9G7o+2LLT7WfX20vLbZ2b3+m2J90eaJnu+92tHQzy23X1za2H/DbHkkjmCSC/zTMlonkCyG/zTIlYngDyuzxDItgngPy2z5gV9Akgv/WZMcM9AeS3e+asmI8A8jsfJxejaiG/FajkLWydnt9qbq/WLZ1ufnfrE65iIb9dHFs7a+SR36rn97NjV4r6QXC96vlN2xM7xSDqFALIbw5FCASQ3yFUqXk5Ir+bV/MQd4z8DrFq9c8Z+V3/Gtdhh8jvOlSxnntAfvtT1yDlt7ptfvi7R+UrX14bkUz37U4/4DIpsC+98458df8Lsvmu20X18I5vja9fu7rtgZdxiTrJ714PAUV++3O4dTPJI7/f/keRt9/uj0J/+KpJueoqen7rcmZ8MQLI72LcmOWWAPLbLW9Wy0cA+Z2PE6OqJYD8rpY/q3cmgPzmZIRAAPkdQpWamSPy25+6Z8rvzTueENUXu9dr2dJFsnfnvTJ71kwnO4tvXh88PN5aL9nzW31SCeqNW3ZG76fzS7c2Sfb8Tm8gLb97PTxTzUV+OzkCVhbJkt8XL07KT3/WJx92c8yt7JGg4RJAfodbuyZljvxuUrXD2SvyO5xaNTlT5HeTq+/v3pHf/taGzN4ngPzmNPhKAPntT2V6ym9/0gwnE+R3OLVKZ5olv8/9Q59cuNAnP3/Tu9InfeFulMyDJID8DrJsjUsa+d24kgexYeR3EGVqfJLI78YfAS8BIL+9LAtJpQggvzkSvhJAfvtTGeS34Vogvw0DdRguS36fPdcnb/1IZMknJpDfDuvCUu8RQH5zEkIggPwOoUrNyxH53byah7hj5HeIVat/zsjv+te4DjtEftehivXcA/Lbn7oivw3XAvltGKjDcMhvh7BZSpsA8lsbGRMqIID8rgA6S2YSQH5nImKABwSQ3x4UgRSmEEB+cyhCIID8DqFKzcwR+e1P3TN7fg//3tdl6He/1LWft+qBnTXGn+3azwT5bZ+xrRWQ37bIEtcEAeS3CYrEsE0A+W2bMPGLEEB+F6HGHNcEkN+uibNeHgLI7zyUGFM1AeR31RVg/W4EkN/+nI1M+e3jAy/9wTc1E+S3z9XpnVse+f2jCyKf+B9oexJulcPNHPkdbu2alDnyu0nVDmevyO9watXkTJHfTa6+v3tHfvtbGzJ7nwDym9PgKwHktz+VyZTfWbe6bd38funlE7Jxy84WqdtuXSUPb9skM6ZPk1dfPy2D2/fImXNvtpGMx1x65x1JSvtlSxfJ3p33tm6vPz4yJk8/e6g195Htm2TdmlvaYiXXT85Pr53MSwVAfvtzuHUzyZLfP/6xyMV3+mXutRPSx/MudfEyviQB5HdJgEx3QgD57QQzi2gSQH5rAmN4JQSQ35VgZ9EMAshvjkgIBJDfIVSpmTkiv/2pu7c9v58/9KIsXDBXVi5fIhcvXZaHdu+T+XPnyH2D6zvSU0L7hoXzI4mtxPWp02+0hLaKNX70lUieq9fer31TfmfDFyIZHsvs4aGBaC31UvP3jIy1CfN40WRe6nNqXfWK80J++3O4dTPJkt+68RgPAZMEkN8maRLLFgHkty2yxC1DAPldhh5zXRFAfrsizTo6BJDf7bQu/2xSfvpOf+uT0z44KR+4UocoY20QQH7boEpMEwSQ3yYomonhrfxOby8psNXt7+RLCezdT+2X4fsHOvYm7yWzY7G+asXNkSxXN9mHHhuVbfdskMXXL8iknM4L+Z2JzNsByG9vS0Ni//wXbchvjkEIBJDfIVSpeTkiv5tX8xB3jPwOsWr1zxn53V7jM2f75NB/7ZNLl/pk+gyRNf9mQq6bN1n/g+D5DpHfnheowekhv/0pfm75rW44n33jfOv2tLqJffDwuFw37xoZ2bU1lygus+30DetkrOSt705rJHNPi3Mlu1WLlK2D66Ob351aqtx955qON8473UhHfpepcrVzkd/V8mf13gSQ35yQEAggv0OoUvNyRH43r+Yh7hj5HWLV6p8z8ru9xn9/WuQPv36FXLzYJx/6kMi//9K7suA65HfV/yUgv6uuAOt3I4D89uds5JLfaUGsblI/d+BIJMJ/cOJk689psWxqm71ubve69a1uZT+4a5+ke36nxbn6OG5bktyb2k+89/VrV7f1BY/7htPz21SVq4+D/K6+BmTQnQDym9MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfiO/QzjlyO8QqtTMHJHf/tQ9t/xOtgJJ3sLOajlSdqtKRg8Nj3a8XZ5uWdJtrW7yvNON8LT8VjF7tVyh7UnZCvszH/ntTy3IZCoB5DenIgQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPxGfodwypHfIVSpmTkiv/2pey75HUvmO9aulptu/Ghbm5Bet7LLbrOX+Fax867dqY93t1YonWS+EtyvnTrbsfVJejxtT8pWvbr5yO/q2LNyNgHkdzYjRlRPAPldfQ3IYCoB5DenIgQCyO8QqtS8HJHfyO8QTj3yO4QqNTNH5Lc/dc8lv1W6yV7YcQ/suCXILy5f0lEMl9lmltjudetbyeqFC+ZGPbzVS308duCI7N15b/RAzF79w9N9vNMtX/7gPx2QWz+7otXjPC3Rkd9lql7tXOR3tfxZvTcB5DcnJAQCyO8QqtS8HJHfzat5iDtGfodYtfrnjPxur/GPLvTLP/xQZHJSpK9P5CPXTsrVs+j5XfV/CcjvqivA+t0IIL/9ORu55bfrlOOe2sl1kw/X7NWKJP3QymTP71hmHzt+sm1Lyd7d6TGPbN/U6vetpPzGLTtbc+n57fpk2FsP+W2PLZHLE0B+l2dIBPsEuslv9Wthn/3lWQECHQkgvzkYIRBAfodQpebliPzuUPOk6+aHGy/+o0B+e1EGkuhAAPntz7HwVn77g0gvE25+6/HyaTTy26dqkEuaAPKbMxECgbT8Vr8fHv72FdJ/hcivf+5dBHgIRaxhjsjvGha1hltCftewqDXYEvK7BkVswBaQ3w0ocqBbRH77U7hc8rvbben0NpI3pP3ZottMkN9ueZtcDfltkiaxTBNAfpsmSjwbBDrJ77HnrpArPiDyxduR3zaYEzObAPI7mxEjqieA/K6+BmQwlQDym1MRAgHkdwhVamaOyG9/6p5Lfqt0VRuSGxbOb7X/UJ9LPgjzU0sWyUO798mqFTe3jfFnq24yQX674WxjFeS3DarENEUA+W2KJHFsEkB+26RL7KIEkN9FyTHPJQHkt0varJWXAPI7LynGVUkA+V0lfdbuRQD57c/5yCW/1c3vocdGZds9G1oPeoy3oHpvv3bqbPTAS9UP+7kDR+ThbZtkxvRp/uzSYSbIb4ewDS+F/DYMlHBGCSC/jeIkmCUCyG9LYAlbigDyuxQ+JjsigPx2BJpltAggv7VwMbgiAsjvisCzbCYB5HcmImcDSsvvpPA+fe6Hsvup/TJ8/4DMnjXT2SZ8Wgj57VM19HJBfuvxYrRbAshvt7xZrRgB5HcxbsyySwD5bZcv0c0QQH6b4UgUswSQ32Z5Es0OAeS3Ha5ELU8A+V2eoakIueR3sr3JyuVL2tZOyu8fnDgpe0bGZO/Oe53J7zg3lVT6xrlq1fL0s4eifO++c010Oz1+vfr6aRncvkfOnHtTrpt3jYzs2tq61Z7ucf7Mkzsk3rfa78YtO1txbrt1Vdu6yG9TR9N9HOS3e+asmJ8A8js/K0ZWRyAtv1Umf3msT/r7RT5586T09VWXGys3lwDyu7m1D2nnyO+QqtWcXJHfzal1yDtFfodcvXrnjvz2p7655LdKV0nfoeHRjpJ46+D6SA6rFijjR19x1vYkFt8HD49LWkIr8a1eSeGdFN8PDI/Ko0MDU9q4xDHj3uVKkifHqj0uXDA32m88dv7cOa11kN/+HG7dTJDfusQY75IA8tslbdYqSqCT/O6TPpmUyaIhmQeB0gSQ36UREsABAeS3A8gsoU0A+a2NjAkVEEB+VwCdJXMRQH7nwuRkUG75rbJJ3paOs0veinaScWKR+CGc6lNJ6d6r93ivW+zxHpOtW9IyPL3HtPBHfrs+BebWQ36bY0mk4gSUIux0ORb5XZwpM90R6CS/3a3OShDoTAD5zckIgQDyO4QqNS9H5Hfzah7ijpHfIVatGTkjv/2ps5b89idtkeTN7rSAVh8/uGtfW7qxpE+3NFGDkrfGlThPt27pdYs8/R7y26dTopcL8luPF6PdEkB+u+XNasUIIL+LcWOWXQLIb7t8iW6GAPLbDEeimCWA/DbLk2h2CCC/7XAlankCyO/yDE1FCFJ+K7n92qmzrVYjafkd3whft+aWiFOyZYv6uNPN7rh1Sadb493kdydRjvw2dTTdx0F+u2fOivkJIL/zs2JkdQSQ39WxZ+XuBJDfnI4QCCC/Q6hS83JEfjev5iHuGPkdYtWakTPy258655bfnW5Mx9tYtnSR04dcJh9kmUQZ3+De+7Vvyg0L50ssv5OtSz7zyZva5Hcsx+Pb3n/9N3+f6+Z3px7oKhby25/DrZsJ8luXGONdEkB+u6TNWkUJIL+LkmOeTQLIb5t0iW2KAPLbFEnimCSA/DZJk1i2CCC/bZElblkCyO+yBM3Nzy2/e7X+MJdOsUid2p4kb4Yn+3zfdONHZeixUdl2z4bWwy6Tt71Pn/thx5vh8QMwY1mefvhnnDnyu1gNfZiF/PahCuTQjQDym7MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfte/xnXYIfK7DlWs5x6Q3/7UNZf8Vre+08LYny2IpOV3/GDO4aEBWbl8SdT2JNnHW4n8s2+cl4e3bYq28dDufRLL7fQDLlWsB4ZH5dGhgUiWd2p1kmSB/PbpZOjlgvzW48VoSwTU0y7VUy9TL+S3Jd6ENUoA+W0UJ8EMEUB+GwJJGKsEkN9W8RK8IAHkd0FwTHNKAPntFDeLaRBAfmvAsjy0lvJbMVOSeuOWnRG+6+ZdIyO7trZueseC++Dh8ej9u+9c0+ofrj5Ot3iJH5ap3uvUciUZH/lt+cRaDI/8tgiX0KUJIL9LIySAAwLIbweQWUKbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFIEUOhJAfvtzMHLJ71j6Jvto+7MFvzJBfvtVD51skN/5aV3+qcjfvt4vNy2eEFE3lXlZJ4D8to6YBQwQQH4bgEgI4wSQ38aREtACAeS3BaiELE0A+V0aIQEcEEB+O4DMEoUIIL8LYbMyKbf8Vu0/vv78t2Tb5g0yY/o0K8nUISjyO9wqIr/z1+6dyyJ//ud98iu/PCF9fdjv/OSKj0R+F2fHTHcEkN/uWLNSfgLI7/ysGFkdAeR3dexZuTsB5DenIwQCyO8QqtTMHJHf/tQ9l/xOtwFJp79s6SLZu/NemT1rpj87qygT5HdF4A0si/zODxH5nZ+VqZHIb1MkiWOTAPLbJl1iFyWA/C5KjnkuCSC/XdJmrbwEkN95STGuSgLI7yrps3YvAshvf85HLvntT7r+Z4L89r9G3TJEfuevHfI7PytTI5HfpkgSxyYB5LdNusQuSgD5XZQc81wSQH67pM1aeQkgv/OSYlyVBJDfVdJnbeR3GGcA+W24Tshvw0AdhkN+54eN/M7PytRI5LcpksSxSQD5bZMusYsSQH4XJcc8lwSQ3y5ps1ZeAsjvvKQYVyUB5HeV9Fkb+R3GGcgtv1XP78Hte+TMuTen7KyKtiePj4zJ088eauXyzJM7ZOXyJa2Pnz/0ojy4a1/08XXzrpGRXVtl8fULoo+T78UT7r5zjdw3uL41/6WXT8jGLTujj5P7S68bT4jXR36HcfA7ZYn8zl+76IGXf9svNy16V4Se3/nBlRiJ/C4Bj6nOCCC/naFmIQ0CyG8NWAytjADyuzL0LNyDAPKb4xECAeR3CFVqZo60PfGn7rnk98VLl+Wh3ftk1Yqb5TOfvKntwZdKBn/2lz7dJp5tb0/1IP/q/hdk8123Rw/fVGL+geFReXRoIBLcSlzvGRlr9SFPf6zk9/jRV+ThbZs6PrwzPb7XftTau5/aL8P3D0Q9z5HftqtvLz7y2x5bIpcngPwuz5AI9gkgv+0zZgV9AshvfWbMcE8A+e2eOStmE0B+ZzNiRPUEkN/V14AMOhNAfvtzMnLJbyWbhx4blW33bIgyT8peJYqfO3Ckq0h2sdX4gZxbB9dHEj4tt9NyvJf8Tu41vineaw9K/t+wcL6sW3NLNAz57aLidtZAfutxnZgUUT8Q83JDAPnthjOrlCOA/C7Hj9l2CCC/7XAlqlkCyG+zPIlmhgDy2wxHotglgPy2y5foxQkgv4uzMz1TW37PuXqmDP/e12Xod78U3XRO33w2nWCeeErADw2PtlqbxDL84wvmRlL+hW+Py2unzrbamqTbniRbnnRq75JuiRLn1GnvyO88FfNzDPJbvy6TExPS19+vP5EZ2gSQ39rImFABAeR3BdBZMpMA8jsTEQM8IID89qAIpDCFAPKbQxECAeR3CFVqZo7Ib3/qnkt+J9ueqBvOydvOWS1EbG41KarTPb9Vjn/16in53vePTen5ncwpFuXr166Obm+nb7Kn30/OTd/6Vu8hv21W3G5s5Lc+X+S3PrOiM5DfRckxzyUB5LdL2qyVlwDyOy8pxlVJAPldJX3W7kYA+c3ZCIEA8juEKjUzR+S3P3XPJb/T6cZC+Njxkz3Fsqttdmp7krzpnb4Zns5LCfx4fKc2Lp0Ef7e+4MhvV1U3vw7yW58p8lufWdEZyO+i5JjnkgDy2yVt1spLAPmdlxTjqiSA/K6SPmsjvzkDIRNAfodcvXrnjvz2p76F5Lc/6b+fSfIWdvpGdlqO95LfnVqZJOW4mpu+CZ+Mh/z28XTkywn5nY9TchTyW59Z0RnI76LkmOeSAPLbJW3WyksA+Z2XFOOqJID8rpI+ayO/OQMhE0B+h1y9eueO/PanvkHKbyWoD3/3qHzly2sjknH7k+GhgdYDL8cOHJG9O++N+pInb34vmHet/NHB78gXb/uczJg+TdJiPBbb8+fOiXqEdxLn3W59q1yQ3/4cbt1MkN+6xEQmZEL6hZ7f+uT0ZyC/9Zkxwz0B5Ld75qyYTQD5nc2IEdUTQH5XXwMymEqAtiecihAIIL9DqFIzc0R++1P3XPI72eakV+qPbN8U9c22/YoF9cHD462lOvX8fvrZQ9H71827pvUwTPWxuhkev6c+Tued3m/y/axb5Mhv29W3Fx/5bY8tkcsTQH6XZ0gE+wSQ3/YZs4I+AeS3PjNmuCeA/HbPnBWzCSC/sxkxonoCNuV3n/TJpExWv0kyCJIA8tufsuWS37EwvmHh/Da5HUvoO9aulk8tWSQP7d4nq1bc7ESA+4OwPRPkt6+Vyc4L+Z3NiBHVEUB+V8eelfMTQH7nZ8VIdwSQ3+5Ys1JxAsjv4uyYaY8A8tseWyKbI2BTfv/3vxb5+cUifX3m8iVScwggv/2pdS75rW47Dz02Ktvu2SCLr1/Qln3Ww4ORQsgAACAASURBVCL92aqbTJDfbjjbWAX5bYMqMU0RQH6bIkkcmwSQ3zbpErsoAeR3UXLMc0kA+e2SNmvlJYD8zkuKcVUSsCm//+TFfll9y4TgvquscLhrI7/9qV1p+a36Xz934Ig8vG2TnD73Q9n91H4Zvn8g6rXdxBfyO9yqI7/DrV0TMkd+N6HK4e8R+R1+Deu4A+R3Hatavz0hv+tX0zrsCPldhyrWfw/I7/rXONQdIr/9qVwu+Z1sb7Jy+ZK27JPy+wcnTsqekbHWgyb92aa7TJDf7libXgn5bZoo8UwSQH6bpEksWwSQ37bIErcMAeR3GXrMdUUA+e2KNOvoEEB+69BibFUEkN9VkWfdLALI7yxC7t7PJb9VOkpyDw2Ptj04Mv3wR9UCZfzoK9Et8BnTp7nbhUcrIb89KoZmKshvTWAMd0oA+e0UN4sVJID8LgiOaVYJIL+t4iW4IQLIb0MgCWOUAPLbKE6CWSKA/LYElrClCSC/SyM0FiC3/FYrvvr6aRncvkfOnHuzlcAzT+6Q9G1wY9l1CRTfRD94eLxrHkrWb9yyM3p/2dJFrdvosbA/dvxkW/TkmPQ+u+0xXiP5PvLbdvXtxUd+22NL5PIEkN/lGRLBPgHkt33GrKBPAPmtz4wZ7gkgv90zZ8VsAsjvbEaMqJ6ATfl99OV++VfL6fldfZXDzAD57U/dtOS3L2krgf3V/S/I5rtuj26Yp2+lK3n9wPCoPDo0ED2gM+tGevKhnenb7J1uvCsOSbmO/PblZJTLA/ldjh+z7RJAftvlS3QzBJDfZjgSxSwB5LdZnkSzQwD5bYcrUcsRQH6X48dsNwRsyu/oSZeTbvbBKvUjgPz2p6a55LcSwkOPjcq2ezZEMtm3V6f2K6+dOiv3Da6PUk3L8GT+6b2lRXl8y3zViptl3ZpbWvHUgz23/4c75f7hUdk6uL51+52b376djvz5IL/zs2KkewLIb/fMWVGfAPJbnxkz7BNAfttnzArlCSC/yzMkgnkCyG/zTIlonoBV+W0+XSI2iADy259i10J+p+X24yNjEeFYfqfleBJ/8ta3+nynW+LJeMm15lw9UzbveAL5nT7PfX0ik+H99Sjy258vTGQylQDym1MRAgHkdwhVal6OyO/m1TzEHSO/Q6xa/XNGfte/xnXYIfK7DlWs5x6Q3/7UNZf8VukqAfzZX/q08/7eWag63cxWud6wcH7rpnY3+d3pRnunW+Kx/P6dDV9ouwHfKS43v0UE+Z11bHkfAtoEkN/ayJhQAQHkdwXQWTKTAPI7ExEDPCCA/PagCKQwhQDym0MRAgHkdwhVamaOyG9/6p5bfisp/PXnvyXbNm+I+mz78IrF9/y5c1q3vGNRr/436+Z3+oZ4vCd1+/vBXfvatvjI9k3ymU/eNOWBn/GguO838hv57cN/G+RQPwLI7/rVtI47Qn7Xsarh7wn5HX4Nm7AD5HcTqhzeHpHf4dWsiRkjv5tY9TD2jPz2p0655Hd8w/nY8ZMdM1+2dJHs3XmvzJ4109nOuolvlUC6lUmn29y9+oAnN6HW2b13v3xp3W9M6XfOze8u5ebmt7P/DlioOQSQ382pdcg7RX6HXL365o78rm9t67Qz5HedqlmfvSC/61PLOu8E+V3n6oa9N+S3P/XLJb/9Sfe9TDq1OknmmBbbWX28e+2v2+1wNQf5jfz27b8N8qkvAeR3fWtbp50hv+tUzfrsBfldn1rWeSfI7zpXN9y9Ib/DrV2TMkd+N6naYe0V+e1PvbTk90svn5CNW3a2ZR+3+3C5JSW3B7fvkTPn3mxb9u4717RanSRzTd9MV+/tGRnreltdCe+nnz0UxVbtTtatuaXj9pDfLqtufy0eeGmfMSsUJ4D8Ls6Ome4IIL/dsWal/ASQ3/lZMbI6Asjv6tizcncCyG9ORwgEkN8hVKmZOSK//al7bvndSRjHEvqeu367qyD2Z6tuMqHn9/ucz57rl3cuv//xtA+IzJ//rvRJn5tiaK6C/NYExnCnBJDfTnGzWEECyO+C4JhmlQDy2ypeghsigPw2BJIwRgkgv43iJJglAshvS2AJW5oA8rs0QmMBcsnvuM3IHWtXy8rlS9oWV1L8uQNH5OFtm7x5EKYxOgUCIb/fh/atb18pf/p/v//xr/3qhNy6+l0R1RPcwxfy28OikFKLAPKbwxACAeR3CFVqXo7I7+bVPMQdI79DrFr9c0Z+17/Gddgh8rsOVaznHpDf/tQ1l/xW7T2GHhuVbfdsmPLQR3X7e/dT+2X4/gGnD7z0B2F7Jsjv93n8n4evlO/96fsf3/JrE/Ibv4789vXskpffBJDffteH7N4jgPzmJPhIAPntY1XIKU0A+c2Z8JEA8tvHqpBTmgDymzPhKwHktz+VySW/ufmdv2DIb+R3/tPCSAjkJ4D8zs+KkdURQH5Xx56VuxNAfnM6QiCA/A6hSs3LEfndvJqHuGPkd4hVa0bOyG9/6pxLfqt0nz/0oowdONL2kEh6fk8tJPIb+e3Pf95kUicCyO86VbO+e0F+17e2Ie8M+R1y9ZqTO/K7ObUOaafI75Cq1dxckd/Nrb3vO0d++1Oh3PJbpaz6e2/csrMt+2ee3DGlD7g/23OfCfL7feYv/umV8t/+4v2PV/zChHz2V2h74v5UsmIdCCC/61DF+u8B+V3/Goe4Q+R3iFVrXs7I7+bVPIQdI79DqBI5Ir85A74SQH77U5me8lv1+t6844ko27077/Wqp/fjI2Py9LOHWiSTEj7O+9jxk633r5t3jYzs2trWs1zdZn/t1Fm5b3B9a1x8m/3MuTfbqnTbrauih3ru/do329aNB8XrI7/fx/bWhT6ZmHj/Y/VAyQ/PnPD1eZfCAy/9+cJEJlMJIL85FSEQQH6HUKXm5Yj8bl7NQ9wx8jvEqtU/Z+R3/Wtchx0iv+tQxXruAfntT10zb36nRbIPN71VTl/d/4Jsvut2mTF9mihh/cDwqDw6NBDJ7TjnrYPrO95KT95gv/vONW3yu1NplGi/YeF8Wbfmlilvpx/4ifz253DrZoL81iXGeJcEkN8uabNWUQLI76LkmGeTAPLbJl1imyKA/DZFkjgmCSC/TdIkli0CyG9bZIlblgDyuyxBc/Mz5XdyqeRt6zzS2FyavSOlZXeW/I6jdbr5nV4pLbfT76fFOPLbVdXNr+Ov/O4TkUnzGyZiUASQ30GVq7HJIr8bW3qvN4789ro8JPcvBJDfHAUfCSC/fawKOaUJIL85E74SQH77Uxkt+R2nnbw5vWzpospboqh8hoZHW21N0rfVO7U8UXvJI791bn2rmMhvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CSC/bdIltikCyG9TJIljkgDy2yRNYtkigPy2RZa4ZQkgv8sSNDe/kPyOl7946bI8tHuf/O3pNyoR4Mn+3L3asSjJPXbgyJQcs+S37q1v5Le5g1lFJOR3FdRZMy8B5HdeUoyrkgDyu0r6rN2NAPKbsxECAeR3CFVqXo7I7+bVPMQdI79DrFozckZ++1PnUvLblwdiZrU5Ue8PPTYq2+7ZkPnAy7TYX7Xi5o69vtVt8z0jY1OEOje//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOebZJID8tkmX2KYIIL9NkSSOSQLIb5M0iWWLAPLbFlniliWA/C5L0Nz8QvLbt7YnCkev9iRF5Hc3ua3Wim+8dxLjyG9zh9N1JOS3a+Ksp0MA+a1Di7FVEUB+V0WedXsRQH5zPkIggPwOoUrNyxH53byah7hj5HeIVWtGzshvf+qcW37Hwvfg4fEo+yofeKnakRz+7lH5ypfXRrnE7U+GhwZk5fIlosS1eqk/q5dqbzJ+9BV5eNsmmTF9Wot+t7YnveS2mtxLjCO//Tncupn4Kr8nRKRfdzOMrx0B5HftSlrLDSG/a1nW4DeF/A6+hI3YAPK7EWUObpPI7+BK1siEkd+NLHsQm0Z++1OmTPmd7Kut0u7VW9vVttIiPp1XOuf0QzmTN9fjnJP76ibL1disFivIb1enwPw6vspv8zslYogEkN8hVq15OSO/m1fzEHaM/A6hSmHmODkp8uMfi3zo5/rkA1dOltoE8rsUPiZbIoD8tgSWsEYJIL+N4iSYQQLIb4MwS4bqKb996eldco9OpyO/neI2uhjy2yhOghkmgPw2DJRwVgggv61gJWhJAsjvkgCZ3pPAXx67Qj7+8Qm5ehbym6NSPwLI7/rVtI47Qn7Xsar12BPy2586Zt789ifVMDJBfodRp05ZIr/DrV0TMkd+N6HK4e8R+R1+Deu4A+R3Havqz56O/rd+Wbx4EvntT0nIxCAB5LdBmISyRgD5bQ0tgUsSQH6XBGhwOvLbIEwVCvltGKjDcMhvh7BZSpsA8lsbGRMqIID8rgA6S2YSQH5nImJACQLI7xLwmOo9AeS39yUiQRFBfnMMfCWA/PanMshvw7VAfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGFCCAPK7BDymek8A+e19iUgQ+c0Z8JgA8tuf4iC/DdcC+W0YqMNwyG+HsFlKmwDyWxsZEyoggPyuADpLZhJAfmciYkAJAn/xcr/ceCNtT0ogZKrHBJDfHheH1FoEuPnNYfCVAPLbn8p4K79fff20DG7fI2fOvdmitWzpItm7816ZPWumxA/jPHb8ZPT+M0/ukJXLl7TGPn/oRXlw177o4+vmXSMju7bK4usXtN5Pxk+/n147Hfvipcvy0O59cvDweBTvke2bZN2aW6I/I7/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SbfhsSdFzpztk6uvFpkxgwdeNvw01HL7yO9alrV2m0J+166ktdkQ8tufUnotvx8YHpVHhwbapLVCF8vnVStujqSzktXJsS+9fEL2jIy1RHn64/T4ZDliqb51cH0k09XcoeHRljxPr50uJfLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYrA3Kuny/m335GfvVtOopvKhzgQUASQ35yDEAiYlN//dLFPfvbTPpn54XelT/pC2D45ekwA+e1PcYKU30pe735qvwzfPxDdAk8LaXXre/zoK/Lwtk0yY/q0Njm+YN610a3tO9aubrspHpckPbdT7NdOnZX7Btd3rCLy25/DrZsJ8luXGONdEkB+u6TNWkUJIL+LkmOeTQLIb5t0iW2KAPLbFEnimCSA/DZJk1i2CJiU3395rF+O/X99snbNhHz4w/xlpK2aNSUu8tufSnstv5NtT5ItT9I3uRXOx0fGIqpKSse3tz++YG4kwF/49rjEwjrdLkXNue3WVS1Rnpbf6dhqnaefPdSqYLplCvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJbYoA8tsUSeKYJID8NkmTWLYImJTff360X8a/3y//7n95V2bNQn7bqllT4iK//am0t/I7jUhJ57NvnI8k9Q9OnJTnDhxpCeu0oI4//qtXT8n3vn+sred3t1vj8+fOicR5p5YosVjffNftU26NK1k+duBIq8UK8tufw62bCfJblxjjXRJAfrukzVpFCSC/i5Jjnk0CyG+bdIltigDy2xRJ4pgkgPw2SZNYtgggv22RJW5ZAsjvsgTNzQ9Gfiel9V//zd+39fROy28lpJOtSZJ9u9XYZMsU9XH6JnnyYZkxavVQyy98ftUU+Z3uEY78Nnc4XUdCfrsmzno6BJDfOrQYWxUB5HdV5Fm3FwHkN+cjBALI7xCq1Lwckd/Nq3mIO0Z+h1i1ZuSM/PanzkHK7/M/ertnz291U/uGhfOjh2GqV1JQ33TjR2XosVHZds+G1oM0lfxO3ySPS6R6fu/eu1++tO43ovGdYifjIb/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpgggv02RJI5JAshvkzSJZYsA8tsWWeKWJYD8LkvQ3Hxv5fcfH/m+3HTjx1qCOtnTO/0QynSrknQrkuTN71hgxy1UFEr1AMxVK25uyfIk3uS66vPpWOke4chvc4fTdSTkt2virKdDAPmtQ4uxVRFAfldFnnV7EUB+cz5CIID8DqFKzcsR+d28moe4Y2Pye1Lk3Bv9cvGdSZk7V+RD0+n5HeJ58Cln5Lc/1fBWfivJvHHLzhap5EMp1SfTD6585skdsnL5ktb45IMp0w+ljOX5wcPj0fi771wT9fuOX8m5qt1JfIM8fj/ZFiX5IE71PvLbn8OtmwnyW5cY410SQH67pM1aRQkgv4uSY55NAshvm3SJ3U6g75+v1RSTJchvzpKPBJDfPlaFnNIEjMlv0ELAMAHkt2GgJcJ5K79L7KnSqcjvSvGXWhz5XQofky0TQH5bBkx4IwSQ30YwEsQwAeS3YaCE60rgz4/2y4oVE6IUuO4L+a1LjPEuCCC/XVBmjbIEkN9lCTLfFgHkty2y+nGR3/rMes5AfhsG6jAc8tshbJbSJoD81kbGhAoIIL8rgM6SmQSQ35mIGGCIwJ98p19Wfw75bQgnYTwggPz2oAikkEkA+Z2JiAEVEUB+VwS+w7LIb8O1QH4bBuowHPLbIWyW0iaA/NZGxoQKCCC/K4DOkpkEkN+ZiBhgiADy2xBIwnhDAPntTSlIpAcB5DfHw1cCyG9/KoP8NlwL5LdhoA7DIb8dwmYpbQLIb21kTKiAAPK7AugsmUkA+Z2JiAGGCCC/DYEkjDcEkN/elIJEkN+cgQAJIL/9KRry23AtkN+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgYYIoD8NgSSMN4QQH57UwoSQX5zBgIkgPz2p2jey+9XXz8tu5/aL8P3D8jsWTOnkLt46bI8tHtf9PmHt22SGdOnRX9+/tCL8uCu9z6/bOki2bvz3tb85HtxwLvvXCP3Da6PPlRrDm7fI2fOvRl9/Mj2TbJuzS3Rn196+YRs3LKzlcdtt65qWxf57c/h1s0E+a1LjPEuCSC/XdJmraIEkN9FyTHPJgHkt026xI4JTE6KnDnbJ9fNn5S+Ak+85IGXnCUfCSC/fawKOaUJ0PaEM+ErAeS3P5XxVn6/deFt2bzjCTl2/OQUeR3ji8X3wcPjkpTQSlDvGRlrCe/HR8bk7BvnW5Jaye/xo6+0Ses4Zrzu1sH1snL5Ekl/rOYuXDA3ei9ef/7cOS1xjvz253DrZoL81iXGeJcEkN8uabNWUQLI76LkmGeTAPLbJl1imyKA/DZFkjgmCSC/TdIkli0CyG9bZIlblgDyuyxBc/O9ld/xFnvd/FZS+4aF86OhscxWf1Y3wVetuLl1W1vFeGB4VB4dGpDF1y+IboV3k9/psbHgTsZL4k/HQn6bO5yuIyG/XRNnPR0CyG8dWoytigDyuyryrNuLAPKb8xECAevyW11NL3IlPQR45GiNAPLbGloCGySA/DYIk1BGCSC/jeIsFSxY+a3Et3qpViVJAd1Jfne6vR23RFHjky1P1Mcq9qFv/5mM7NoardGr7UoyDzUW+V3qPFY6GfldKX4WzyCA/OaIhEAA+R1ClZqXI/K7eTUPccc25ffkP18OUv/fHyIYcq6UAPK7UvwsnpMA8jsnKIY5J4D8do6864JBym8lu187dbbVaiR9+zr9cVp+J2nE761fu7qtr/ee3/+G/PCtH0d9v5M9v5Nz0+1VkN/+HOwimSC/i1BjjisCyG9XpFmnDAHkdxl6zLVFAPltiyxxTRJAfpukSSxTBJDfpkgSxyYB5LdNusQuQwD5XYae2blBym912/rpZw9NIRH3/VZvqNYnqhd4/Lpu3jXRTW7V9iT9Ssr0dJuVTnJczVfie2h4dEpMbn6bPaAuoyG/XdJmLV0CyG9dYoyvggDyuwrqrJlFAPmdRYj3fSCA/PahCuSQJoD85kyEQAD5HUKVmpkj8tufugcpvzvJ6249vNVYJbS//vy3ZNvmDTJj+rSe8ltJ7ecOHGl7GGa6tUk38a0CI7/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpgggv02RJI5JAshvkzSJZYsA8tsWWeKWJYD8LkvQ3Pzay+90yxP1AMs/Ovgd+eJtn4tEePp9JcoHt++R4aEBWbl8Sev9uC1Kp1YnyXIgv80dTteRkN+uibOeDgHktw4txlZFAPldFXnW7UUA+c35CIGATfmt9j8hE9JP1+8QjoJXOSK/vSoHyXQhgPzmaPhKAPntT2W8ld+xlD52/GSLVvrBlPEb3Xp8q7md2p2k26ake3orwb1xy87Wusn3O7VcSa6B/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbImBbfpvKkzjNIoD8bla9Q90t8jvUytU/b+S3PzX2Vn77g0gvE+S3Hi+fRiO/faoGuaQJIL85EyEQQH6HUKXm5Yj8bl7NQ9wx8jvEqtU/Z+R3/Wtchx0iv+tQxXruAfntT12R34Zrgfw2DNRhOOS3Q9gspU0A+a2NjAkVEEB+VwCdJTMJIL8zETHAAwLI73JFmJiclP6+vnJBmD2FAPKbQxECAeR3CFVqZo7Ib3/qjvw2XAvkt2GgDsMhvx3CZiltAshvbWRMqIAA8rsC6CyZSQD5nYmIAR4QQH6XKwLyuxy/brOR33a4EtUsAeS3WZ5EM0cA+W2OZdlIyO+yBFPzkd+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgZ4QAD5Xa4IyO9y/JDfdvgR1Q0B5LcbzqyiTwD5rc/M1gzkt2GyyG/DQB2GQ347hM1S2gSQ39rImFABAeR3BdBZMpMA8jsTEQM8IID8LlcE5Hc5fshvO/yI6oYA8tsNZ1bRJ4D81mdma4b38vvV10/L7qf2y/D9AzJ71swpHC5euiwP7d4Xff7hbZtkxvRp0Z+fP/SiPLjrvc8vW7pI9u68N5r/1oW3ZfOOJ+TY8ZNtsZJj1JqD2/fImXNvRmMe2b5J1q25pTU+XvPg4fEp7yO/bR1V+3GR3/YZs0JxAsjv4uyY6Y4A8tsda1bKTwD5nZ8VI6sjgPyujj0rdydA2xNORwgEkN8hVKmZOSK//am7t/I7KamTYjqJLimhb7t1VUt+v/TyCdkzMtYS3o+PjMnZN863yfFkHCXKXzt1Vu4bXN+S41sH18vK5UumfByvuWrFzW1CPI6H/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbIoD8NkWSOCYJIL9N0iSWLQLIb1tkiVuWAPK7LEFz872V3/EWe938VlL7hoXzo6HjR1+J5LZ6qZvgSTmtYjwwPCqPDg3I4usXtNFTkn3osVHZds+G6L302LTsToryTmVAfps7nK4jIb9dE2c9HQLIbx1ajK2KAPK7KvKs24sA8pvzEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+dg5bcS3+qlbmsrId1Lfse3yOPb3En8nWS2in3o238mI7u2RkOTbVfUe08/e6gV4rp510TjYqmO/PbncOtmgvzWJcZ4lwSQ3y5ps1ZRAsjvouSYZ5MA8tsmXWKbIoD8NkWSOCYJIL9N0iSWLQLIb1tkiVuWAPK7LEFz84OU32lhnZTfqud3+uNu8jt96zvGGrVN+f1vyA/f+nHU9zvu+R3fAr9j7eqoJYp6qbXGDhxptVhBfps7nK4jIb9dE2c9HQLIbx1ajK2KAPK7KvKs24sA8pvzEQIB5HcIVWpejsjv5tU8xB0jv0OsWjNyRn77U+cg5Xf69nWMM+77rT5WrU/iB1Kqj9M3tNXnkrfH4xjpNiuxOF+/drV84fOrorhJ+Z0W68hvfw63bibIb11ijHdJAPntkjZrFSWA/C5Kjnk2CSC/bdIltikCyG9TJIljkgDy2yRNYtkigPy2RZa4ZQkgv8sSNDc/SPmd3n76pnf6fSW0v/78t2Tb5g2iboarV7c+4OrW93MHjrQ9HDMpyeM+4+vW3BLFSd8eR36bO5yuIyG/XRNnPR0CyG8dWoytigDyuyryrNuLAPKb8xECAeR3CFVqXo7I7+bVPMQdI79DrFozckZ++1Pn2svvbi1POt36jqX44PY9Mjw0ELU2Sd78VsJbyfGh4dFWn++0eEd++3O4dTNBfusSY7xLAshvl7RZqygB5HdRcsyzSQD5bZMusU0RqEp+T/7LBvpMbYQ4tSKA/K5VOWu7GeR3bUsb/MaQ3/6U0Fv5HUvnY8dPtmjdfeea6AGX6Ve3Ht9qbqd2J1FP75GxVp/udDz1/sYtO1ufjnt+x59Q6z24a1/04bKli9riIL/9Ody6mSC/dYkx3iUB5LdL2qxVlADyuyg55tkkgPy2SZfYpghUJb9N5U+cehJAftezrnXbFfK7bhWtz36Q3/7U0lv57Q8ivUyQ33q8fBqN/PapGuSSJoD85kyEQAD5HUKVmpcj8rt5NQ9xx8jvEKtW/5yR3/WvcZU7/PvTfXLllSLz5sb/BqVYNsjvYtyYZZ8A8ts+47wrIL/zkso5DvmdE5SHw5DfHhaFlFoEkN8chhAIIL9DqFLzckR+N6/mIe4Y+R1i1eqfM/K7/jWucocn/6ZPrvyAyMc/hvyusg6sbY8A8tseW93IyG9dYhnjkd+GgToMh/x2CJultAkgv7WRMaECAsjvCqCzZCYB5HcmIgZ4QAD57UERSGEKAeQ3h8ImAeS3TbrE9oEA8tuHKryXA/LbcC2Q34aBOgyH/HYIm6W0CSC/tZExoQICyO8KoLNkJgHkdyYiBnhAAPntQRFIAfnNGXBKAPntFDeLVUAA+V0B9C5LIr8N1wL5bRiow3DIb4ewWUqbAPJbGxkTKiCA/K4AOktmEkB+ZyJigAcEkN8eFIEUkN+cAacEkN9OcbNYBQSQ3xVAD01+v/TyCdm4ZWcr7dtuXSUP9/vG2QAAIABJREFUb9skM6ZPa30uOWbZ0kWyd+e9MnvWzOj9Xu+p9199/bQMbt8jZ869KdfNu0ZGdm2VxdcviOY+PjImTz97qLXOM0/ukJXLl7QhVPN3P7Vfhu8faK2pBiC//Tncupkgv3WJMd4lAeS3S9qsVZQA8rsoOebZJID8tkmX2KYIIL9NkSSOSQK0PTFJk1hpAqf+rk/6r+yTj86fKAWHB16WwsdkiwSQ3xbhaob29ub384delIUL5kbS+eKly/LQ7n0yf+4cuW9wfbRFJbf3jIy1Ce9470pMPzA8Ko8ODURCW8UaP/pKS56n308ye+vC2/LV/S/I5rtuj0R7eqx6f/OOJ+TY8ZOSFu7Ib83T59lw5LdnBSGdNgLIbw5ECASQ3yFUqXk5Ir+bV/MQd4z8DrFq9c8Z+V3/Gtdhh8jvOlSxnntAfvtTV2/ldxpRUmBfeucdGXpsVLbds6F1Wzs5Xo197dTZlihPCuwF866NRPoda1dPuc3dqSyx7N46uL5tPDe//TnEpjJBfpsiSRwbBJDfNqgS0zQB5LdposQzQQD5bYIiMWwTQH7bJkz8NIHJyUk59w/9MmfWhEz7YF9HQMhvzk0IBJDfIVSpmTkiv/2pezDyW7UiUS918zvZsiRGefeda1qyOzlWvZ8U2Dfd+NHWze14bqeWKvF76ob50PBoW1sU9R7y259DbCoT5LcpksSxQQD5bYMqMU0TQH6bJko8EwSQ3yYoEsM2AeS3bcLE70Rg/KUr5FOfnJCrPjSJ/OaIBEsA+R1s6WqfOPLbnxIHIb/TLU7Ux88dONJqYxLL7fVrV8u6NbdEPbtvWDg/+nNafs+Z/eG2Xt2dWqrEcjvuCU7Pb38OrM1MkN826RK7LAHkd1mCzHdBAPntgjJr6BJAfusSY3wVBJDfVVBnTeQ3Z6AOBJDfdahiPfeA/Panrt7L7043r9PyW+FMtkXZ+7VvRoTj/uDJm99p+a3G9eofTtsTfw6r7UyQ37YJE78MAeR3GXrMdUUA+e2KNOvoEEB+69BibFUEkN9VkW/2usjvZte/LrtHftelkvXbB/Lbn5p6Lb91Wo4k+3z36vk95+qZU/qFd5LpyRKlb5Kr92h74s8hNpUJ8tsUSeLYIID8tkGVmKYJIL9NEyWeCQLIbxMUiWGbAPLbNmHidyKA/OZc1IEA8rsOVaznHpDf/tTVW/nd6zZ2ulVJ+nZ28gGXi69f0HYrfMb0aVFblLNvnI/apqiXegDmqhU3R21S1NzD3z0qX/ny2ui9uL/48NAAD7z059xayQT5bQUrQQ0RQH4bAkkYqwSQ31bxErwgAeR3QXBMc0oA+e0UN4v9C4H/9wdXyE2LJ+TnZtDzm0MRLgHkd7i1q3vmyG9/Kuyt/FaC+ulnD7WRum7eNa0HT8bC+9jxk9GYR7ZvavX4Vh8reb5xy87ovWVLF8nenffK7Fkzo49jeX7w8Hj0cfJhmen31PvJnt/pddPzT7950Z/qkokWAeS3Fi4GOyaA/HYMnOUKEUB+F8LGJMsEkN+WARPeCAHktxGMBClAYHJSpK+v88T+PpG5s2fI2fP8jlsALVMcEUB+OwLNMtoEkN/ayKxN8FZ+W9ux5cDIb8uALYZHfluES+jSBJDfpRESwAEB5LcDyCyhTQD5rY2MCQUJTEyKnD0rsmB+D5vYJTbyuyB0plklgPy2ipfghgggvw2BJIxxAshv40gLB0R+F0bXeSLy2zBQh+GQ3w5hs5Q2AeS3NjImVEAA+V0BdJbMJID8zkTEAEMEfvozkfHv98mv/fKE9HW7Sov8NkSbMC4IIL9dUGaNsgSQ32UJMt8WAeS3LbL6cZHf+sx6zkB+GwbqMBzy2yFsltImgPzWRsaECgggvyuAzpKZBJDfmYgYYIgA8tsQSMJ4QwD57U0pSKQHAeQ3x8NXAshvfyqD/DZcC+S3YaAOwyG/HcJmKW0CyG9tZEyogADyuwLoLJlJAPmdiYgBhgggvw2BJIw3BJDf3pSCRJDfnIEACSC//Ska8ttwLZDfhoE6DIf8dgibpbQJIL+1kTGhAgLI7wqgs2QmAeR3JiIGGCKA/DYEkjDeEEB+e1MKEkF+cwYCJID89qdoyG/DtUB+GwbqMBzy2yFsltImgPzWRsaECgggvyuAzpKZBJDfmYgYYIiAkt8v/2Wf/OtfoOe3IaSEqZgA8rviArB8LgK0PcmFiUEVEEB+VwC9y5JByu9XXz8tg9v3yJlzb7Zt67ZbV8nD2zbJjOnT5KWXT8jGLTuj95ctXSR7d94rs2fNbBv//KEX5bVTZ+W+wfVtn398ZEyefvZQ63PPPLlDVi5fEn2cXju5pnof+e3P4dbNBPmtS4zxLgkgv13SZq2iBJDfRckxzyYB5LdNusROEpicmJTzF/rlmtmT2mDmXj1dzr/9jvzsXf252osxAQI5CSC/c4JiWKUEkN+V4mfxHgSQ3/4cjyDldyd8SljfsHC+rFtzSySoHxgelUeHBmTx9QtESe7xo690FON337mmTX6/deFt+er+F2TzXbdHEr1TrIUL5rZkuFpXvWKBjvz253DrZoL81iXGeJcEkN8uaTdjrUmZlDf+4Qq5fPm9/X7gykmZN29S+kpsH/ldAh5TrRFAfltDS2CDBJDfBmESyhgB5LcxlASySAD5bREuoUsRQH6Xwmd0ci3ktxLUu5/aL8P3D0S3u9M3utMCOybY7eZ3krCS4Zt3PCFbB9e3hHfy/bRYR34bPZ9OgyG/neJmMU0CyG9NYAzPReDb37lC/p/x94Z+Ztmk/NYX3hXpK66/kd+5sDPIMQHkt2PgLFeIAPK7EDYmWSaA/LYMmPBGCCC/jWAkiAUCyG8LUAuGrIX8Tt76VhzSt7G7Cew88lu1TxkaHpWRXVujW+TJ18VLl+Wh3ftk/tw53PwueAB9mob89qka5JImgPzmTNgg8H99+wr57vfek93/avmE3L4W+W2DMzGrJYD8rpY/q+cjgPzOx4lRbgkgv93yZrViBJDfxbgxyz4B5Ld9xnlXCF5+p299x/I7boGiPi4iv5O9vZM9v2OwcV9wen7nPWr+j0N++1+jJmeI/G5y9e3tHfltjy2R/SGA/PanFmTSnQDym9PhIwHkt49VIac0AeQ3Z8JXAshvfyoTtPyOb16vWnFz1Os7KabVn+M+3EXkdxyLtif+HFbbmSC/bRMmfhkCyO8y9JjbjQDym7PRBALI7yZUOfw9Ir/Dr2Edd4D8rmNV67cn5Hf9alqXHSG//alk0PJbtSTZMzIme3feG/X6jl8me36rmOm2KsnypW+e0/Pbn8OtmwnyW5cY410SQH67pN2ctb7z3SvkpT9/r+3JJ2+ekC/8j7Q9aU71m7NT5Hdzah3yTpHfIVevvrkjv+tb2zrtDPldp2rWay/Ib3/qGaz87nbrW6FNP+Ay/VDKbpI8nnv4u0flK19eGw2L258MDw1ED7z8g/90QG797IpW/28lxs++cV4e3rZJZkyfJshvfw63bibIb11ijHdJAPntknZD1poUOf+jfvnZz97bb3//pFx7zWSpzfPAy1L4mGyJAPLbEljCGiWA/DaKk2CGCCC/DYEkjFUCyG+reAleggDyuwQ8w1ODld/dhHbMR90K37hlZ/ThsqWL2m6HJ9+Lx8d9vWOpfvDweAt1sud3em6657fh+hAOAhCAAAQgAAEIQAACEIAABCAAAQhAAAIQgAAEChAIVn4X2CtTIAABCEAAAhCAAAQgAAEIQAACEIAABCAAAQhAoCEEkN8NKTTbhAAEIAABCEAAAhCAAAQgAAEIQAACEIAABCDQJALI7yZVm71CAALBE+j1vIPgN8cGIAABCFgmoJ7Vol73Da63vBLhIQABCNSLgGr/uWdkrK2daL12yG4gAAEI2CGQfi6hnVWI2osA8pvzERyBZF/26+ZdIyO7trYeQBrcZki48QSSzxG4+841mUIG+d34I1MaAF9DSyMkgCcE4oeSnzn3puR9Bgvy25PiBZyGOkNPP3so2kHyuUABb4nUG0igyM8CyO8GHhTDW9b9vcfw8oSDgDECuj8LIL+NoS8cCPldGB0Tqybw1oW3ZeixUdl2zwbkd9XFYP3SBNRDfF87dRb5XZokAfIS4GtoXlKM852A+mX6uQNH5OFtm2TG9Gk900V++17NMPKLxeEda1fLyuVLwkiaLCHQgYDOzwLIb46QKQJ5f+8xtR5xIGCDgM7PAshvGxXQi4n81uPFaI8IpH9YS94AU2kmb9GqX3Z/8k+X5Cc/+Sc5eHhckjfGVZzNO56QrYPrW7/AqPE3LJwv69bcIvH7x46fjHafvF0WfxH7rd/8FRn+j1+XZUsXyaeXLpLpH5zWkpjc1PXo0HicSvKHwPQ3x+Q31k8tWSQP7d4nq1bcLL/+q78w5ezyjdXjInuWms7XUHU+x4++Ildd9SH5xn/5drST+MZj1tdQNTZ5O6LT19/f+o1flmfG/msU93/+rc/JyddOt4lMhKVnh8ezdJLy+9I77/T8np48S+lzxfdrzwrrcTrpX3h1flbs9DOq+lzciif9lznq6++Du/a1aKS/9ia/fj6w5d/JyNf+izw6NNC6GIKw9PggeZBa+meB9NfF5Hn8wYmTrbYnf/KnfxH9XJD8S0e+V3tQ0EBSSMvvvD8npv+lV/zzaXwO1e9Bu5/aL8P3D8jsWTOl29fP+GfTpB/Y8b/dKQcP/1mbE+D3qkAOVEVppn8W6PX18/S5H8oDw6PR92f1iv+8+PoF0cd8r7ZfROS3fcasYIlA+oe1Pz7yfbnpxo9FP+zHInx4aCAS2uoL0aFv/1mrRUryG2XWL8rqC5F6qTjxLzfr166OxHi8zprP/1Lrl5b0N12+kFk6ADULW0R+qzPY6YfH5C/RNcPEdgwS0PkaGv/yEEuX5Nc1lVLWXyAeOjwuX1r3m1H26uvx2TfOR78wx19/P75gbusX6E5SPv0DokEMhKoBgaLym+/XNSh+RVtI/8Kr87Ni+i8Me/2yrLb3Rwe/I1+87XPRv2pQX4vHDhyJei7HX3uTXz/jr7HxBQ7+QqeiAxLQskXlt9pi8l/gIgkDKroHqSZ/f1FnMO/PiSr1+BJQ/HtQ8i9hkt/Xp3/wg12/fioxnvYDKja/V3lwOAJKoaj8Vr4qedmS79Vuio78dsOZVSwQ6PXP9NJfQHr9YpElv9OpJ2N1+0Ev+cUs+WcLGAhZEwJF5Xfyv4M5V8+kFVBNzoOLbeh8De11syZLfqf3kiXO07988E9jXZyGsNcoKr/TopDv12GfA5fZZ/1T514/K+r8spxu45P8uVN9z0//xaNikJQ/53/0dtstSJeMWCsMAkXld3yrNm7Zx/fqMOrtS5a9zkvWz4nJ79VZN7+T+03/3t7pXyrwe5UvJySMPHS+nydvfseXNeN/pcD3ajf1Rn674cwqFgikf1hLPrglXu6R7ZuiG9pl5Xfyn2Kp2HFLlW7yO/5F/N6v3CEPP/41+pJbqH/dQhaV30l5s3DB3Nx9b+vGj/3oE9D5GlpWficfcKQyVS2ikjcXk22nkvLmofvukif+4Dmhr65+fZs0o4z85vt1k06Kub12kt95f1bU+WVZye90W7+4dVQ3+Z2Mf+r0G7meJ2KODJFCI1BGfsd/0cL36tCqXn2+afmt83Oijvzu9vUzvnmrSMQtp2IqcXx+r6r+nPiegc7387T85nu1++oiv90zZ0VDBJI/rC2Yd230T6Dmz50TfQMzefM7+U/01S8heW5+x+tfePsf5ROLF2Y+xNAQEsIETKCM/I7/EkZtf+v/+m95+FbA58Bl6jpfQ8vIb/ULzdDwaKvtVNaNnuQvH3/16imZNfPncj3I0CU71vKLQBn5zfdrv2oZSjadfuGN2zll/ayo+8vy4PY9Erfxy3PzWzGMvs7+/jcinMn+36HwJU93BMrIb5Wl+r2I79Xu6lWXlZK/9+j+nJhXfqvbtN2+fvaS3/xeVZdTZn8fut/PO/b55nu1/UL9ywrIb2eoWcg0gaRAUT29kv2/0r25s/opJufGf0N8z12/PeXWePwFLpbsvfrbpXvkmt4/8epDIP2XNel+oPFtCNVvOfnAS/WvGtQrnv+3p9+IbtOqf4rKCwJZBHS+hvaS3+mvv+mvoennHnTqWZu++R3Lm41bdkr8L3iy9sP7zSWQ/B6f/nra63t6TIzv1809O0V33uufz2f9rJj+ZTn59VXlo34mVS/1XIT0TbGkJOp281vNjX+OSPcDL7pf5tWXQKfv0XEP5fR5TD7wMv5ZM/4Zle/V9T0jpneW/j6t+3NiUn6n56r3vv/yiej3ISW/k7IxLdm7PaCV36tMV7y+8dI/C+h8P+d7tftzgfx2z5wVSxCIf5g/dvxkFCV++FpSlKg/q38Seu3sD8v6/+nXM9uepP9J6W23rpKrrvqQfOoTN7Q91FI9XTqO+4u/sDS6zd1LfidvoqV7NpZAwNQaEUg+gVydu/hJ5enz/G9/+/Pyk5/8U9T6oZP8VuPpVVujg2FxK0W/hmb1VEz+s9L019B0S6pf+8Vlov5VTK+2JwpB+mGEFrEQOkACyX8iHbfRiWVMr/PY6Zddvl8HeAAqSDn9tSwp+5JnLutnxbT8TsZVczeu/zfyl6+82vqZINlOZdmSG6Odq9vcveR3Vk/yCvCxpEcEev0s0Os8dpLffK/2qLCep9Lt9x7dnxPTv/Mkv0YO/e9fku99/5gM3z8QXQbq9vWz181vfq/y/CBVnF6vnwV6ff1M/2W22gbfq90WE/ntljerNYiA+mb72V/6NC0oGlTzqraqfokZ/r2vy9Dvfolb31UVgXWNE1C/JKlX/C8cjC9AQAj8CwG+X3MU/v/27idUqiqOA/hZRS1E+oPaAzFyU0EQROAqAlcpQQSJrjJFHrbyDz60iAip91AkV4mIZotQDCQQbZMgrYQIAiHbJIXw9Im2TVvFGZjhvsvMvTO8O7+5Pj6uer7x/M75nPjde79z585yE8iB5HcXf0oHdm9NbsBYbrvbrvU4VrdrP8xm6QKuq5ZuaIThBByrh3Nq6lXC76YkjUOgIOAuCP87RApUfWN65DzUItCUQPkZpE2NaxwCZQHHa/9PLEcBnwZbjrvavjU5VrdvT8xo6QKuq5ZuaIThBByrh3Nq6lXC76YkjUOAAAECBAgQIECAAAECBAgQIECAAAECrREQfrdmK0yEAAECBAgQIECAAAECBAgQIECAAAECBJoSEH43JWkcAgQIECBAgAABAgQIECBAgAABAgQIEGiNgPC7NVthIgQIECBAgAABAgQIECBAgAABAgQIECDQlIDwuylJ4xAgQIAAAQIECBAgQIAAAQIECBAgQIBAawSE363ZChNpUiB/c+7pc1d6Qx6e2ZHe2/Rm7+f87eS7D36Vbty81fm7s8cPpjdee6nz37/89kfavmeu99rNGzekzw/sSE89+UTn7/59+F/67OiZdPnq9c7P5bGbXIexCBAgMAmBuj5Y1UP//Hs+Tc8cS3cWHnSm/urLL6YTc3vT0ytXLFrKxSs/p79u3037prdMYolqEiBAYCwCdT2w6jyy/LtRz1HHsiCDEiBAIEigrgfmaeTzx0+PnOnMqHydXpUBlM9dB52fBi1VGQIEggWE38Hgyo1fIB80T3z7Q/pw69udsKV7ETJ7aFcn4O4eVDe8/konEM+//2T2VPri0K60ft1U54C6dmrVoteuWfVML6DJB9X8Jwc23YPo/uktvfB8/CtUgQABAuMVqOqDdT00B+e35+/13nDMPfPuvX96byIWg/Wd2zYJv8e7lUYnQCBYoK4HVp1H5vPKb87/mHZ/8G7npos81qHZU+nkkf1DnaMGL1U5AgQINCpQ1wNzTzx28kLvpopiP63LAMq9OZ/rXv/190U3uTW6GIMRINAqAeF3q7bDZMYh0C+oOfr1+TT78a5OOF7+fXkOxQPjw0eP0qEvT6UDH23tXITkP8WD7jjmb0wCBAhMWqDYB+cX7qdRemj5QqW7Fnd+T3pX1SdAIEKg2ANzvVHOI+tushDeROygGgQITEqg3APzdfcLa9f0brAYdI6Z51t3jV/1bye1XnUJEBifgPB7fLZGbolA+aDZ70BXFWAXf1e+Szwv0YVHSzbaNAgQGJtAsQ+O2kMH9Ujh99i2y8AECLRIoPzmYfHThnXnkf3OO4tLcwNGizbaVAgQaFyg2AOnVj/XefRo99PbuVhVj6x787D8ycTGJ29AAgRaJSD8btV2mMw4BMoXBjm4+f7StUUfcRp08VAOefIBtnjHY91FyzjWY0wCBAhECpT74Cg9tOqiRPgduYtqESAwCYFyDxzlPNJdi5PYMTUJEGiLQLkHdn9+/523eo8brTrPHHR9331muGd+t2WnzYNAjIDwO8ZZlQkJ9HtHd9i7FsvPWcxLcOf3hDZSWQIEJiLQrw8O20PL37dQXoDweyJbqigBAkEC/XrgsOeR3ZCn+J0zxWn3681By1KGAAECYxfo1wP7vSE4KPwe5q5ujz0Z+zYqQKBVAsLvVm2HyTQpMOigV77rpt+BdNBFRf741CjPamxyPcYiQIBApMCgPjhMD60LvvM6hN+Ru6kWAQKRAoN64DDnkYLvyJ1SiwCBtglU9cBhnvk9TPCd19yvH7fNwnwIEGhOQPjdnKWRWiRQ9QzEfl+AWXz+Yt27wMWx654l1iISUyFAgMDQAqN8gVC/j/WXn2nbr7Dwe+jt8EICBB4jgVGe010+j/Sok8doo02VAIHGBUbtgeVr/qoMIJ93rp1a1XtkSv75wqVr6cTc3vT0yhWNr8WABAi0S0D43a79MJsGBLoXEjdu3lo02uaNG3rP+S6/5uzxg70DYT5onj53ZdG/fX71s+nkkf1p/bqp3jdHX756vfOawzM7et843cD0DUGAAIGJC9T1waoe2n2WYnkR3T6bg/Xte+YW/brYgye+eBMgQIDAEgTqemA33Ol3Htm9Y/zOwoNFM9i5bVPaN70l1fXmJUzbPyVAgMDEBep6YJ5gscdWXd93F9N9zfzC/TQ9cyx1+6tnfk98u02AQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQkD4HaGsBgECBAgQIECAAAECBAgQIECAAAECBAiECgi/Q7kVI0CAAAECBAgQIECAAAECBAgQIECAAIEIAeF3hLIaBAgQIECAAAECBAgQIECAAAECBAgQIBAqIPwO5VaMAAECBAgQIECAAAECBAgQIECAAAECBCIEhN8RymoQIECAAAECBAgQIECAAAECBAgnV87NAAAAZUlEQVQQIECAQKiA8DuUWzECBAgQIECAAAECBAgQIECAAAECBAgQiBAQfkcoq0GAAAECBAgQIECAAAECBAgQIECAAAECoQLC71BuxQgQIECAAAECBAgQIECAAAECBAgQIEAgQuB/vnyAlya9cVoAAAAASUVORK5CYII=",
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                       {\"responsive\": true}                    ).then(function(){\n",
        "                            \n",
@@ -8372,87 +8422,976 @@
    "outputs": [],
    "source": []
   },
+  {
+   "cell_type": "markdown",
+   "id": "453b461b-644c-4d9e-b5ff-faef6fa4698c",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Wertigkeit von Maßnahmen"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "37e08060-3b88-4215-b705-c274d04f1976",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-07-31 10:35:55 +0000 | io:INFO | Loaded TOML config file successfully.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:441: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n",
+      "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\transformers\\utils\\generic.py:309: UserWarning: torch.utils._pytree._register_pytree_node is deprecated. Please use torch.utils._pytree.register_pytree_node instead.\n",
+      "  _torch_pytree._register_pytree_node(\n",
+      "A:\\Arbeitsaufgaben\\lang-main\\.venv\\Lib\\site-packages\\huggingface_hub\\file_download.py:1132: FutureWarning: `resume_download` is deprecated and will be removed in version 1.0.0. Downloads always resume when possible. If you want to force a new download, use `force_download=True`.\n",
+      "  warnings.warn(\n"
+     ]
+    }
+   ],
+   "source": [
+    "from lang_main import io\n",
+    "from lang_main.analysis.graphs import rescale_edge_weights, get_graph_metadata\n",
+    "from lang_main.types import EntryPoints\n",
+    "from lang_main.constants import SAVE_PATH_FOLDER\n",
+    "from lang_main.analysis.shared import entry_wise_cleansing\n",
+    "\n",
+    "from pathlib import Path\n",
+    "import pickle\n",
+    "import base64\n",
+    "import os\n",
+    "from logging import NullHandler\n",
+    "\n",
+    "import numpy as np\n",
+    "import networkx as nx\n",
+    "\n",
+    "import py4cytoscape as p4c\n",
+    "#import py4cytoscape.py4cytoscape_logger_settings as p4c_logging\n",
+    "#p4c.set_summary_logger(False)\n",
+    "#p4c_logging._SUMMARY_LOG_LEVEL = 'ERROR'\n",
+    "# p4c_logging._DETAIL_LOG_LEVEL = 'ERROR'\n",
+    "#p4c.py4cytoscape_logger.detail_logger.setLevel('ERROR')\n",
+    "#p4c.py4cytoscape_logger.detail_logger.removeHandler(p4c.py4cytoscape_logger.detail_handler)\n",
+    "#p4c.py4cytoscape_logger.detail_logger.addHandler(NullHandler())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "dda237bc-9835-4db2-9825-9e97991de60e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lang_main.analysis.timeline import filter_timeline_cands, cleanup_descriptions, calc_delta_to_next_failure"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "b0de4b11-d120-4827-8668-9c101a95d051",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "2024-07-31 10:36:04 +0000 | io:INFO | Loaded file successfully.\n",
+      "2024-07-31 10:36:04 +0000 | io:INFO | Loaded file successfully.\n"
+     ]
+    }
+   ],
+   "source": [
+    "p_df = io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_POST)\n",
+    "# p_df = io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE)\n",
+    "(data,) = io.load_pickle(p_df)\n",
+    "# p_tl = Path(r'../results/test_20240619/TIMELINE_POSTPROCESSING.pkl').resolve()\n",
+    "p_tl = io.get_entry_point(SAVE_PATH_FOLDER, EntryPoints.TIMELINE_CANDS)\n",
+    "cands, texts = io.load_pickle(p_tl)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "be9d9dd7-47a6-42ea-99e3-290fe3d40844",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['VorgangsID', 'ObjektID', 'HObjektText', 'ObjektArtID', 'ObjektArtText',\n",
+       "       'VorgangsTypID', 'VorgangsTypName', 'VorgangsDatum', 'VorgangsStatusId',\n",
+       "       'VorgangsPrioritaet', 'VorgangsBeschreibung', 'VorgangsOrt',\n",
+       "       'VorgangsArtText', 'ErledigungsDatum', 'ErledigungsArtText',\n",
+       "       'ErledigungsBeschreibung', 'MPMelderArbeitsplatz',\n",
+       "       'MPAbteilungBezeichnung', 'Arbeitsbeginn', 'ErstellungsDatum',\n",
+       "       'delta_to_repair'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "756c9465-2aed-4463-ab1a-34c78742b95d",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VorgangsID</th>\n",
+       "      <th>ObjektID</th>\n",
+       "      <th>HObjektText</th>\n",
+       "      <th>ObjektArtID</th>\n",
+       "      <th>ObjektArtText</th>\n",
+       "      <th>VorgangsTypID</th>\n",
+       "      <th>VorgangsTypName</th>\n",
+       "      <th>VorgangsDatum</th>\n",
+       "      <th>VorgangsStatusId</th>\n",
+       "      <th>VorgangsPrioritaet</th>\n",
+       "      <th>...</th>\n",
+       "      <th>VorgangsOrt</th>\n",
+       "      <th>VorgangsArtText</th>\n",
+       "      <th>ErledigungsDatum</th>\n",
+       "      <th>ErledigungsArtText</th>\n",
+       "      <th>ErledigungsBeschreibung</th>\n",
+       "      <th>MPMelderArbeitsplatz</th>\n",
+       "      <th>MPAbteilungBezeichnung</th>\n",
+       "      <th>Arbeitsbeginn</th>\n",
+       "      <th>ErstellungsDatum</th>\n",
+       "      <th>delta_to_repair</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>53</td>\n",
+       "      <td>244</td>\n",
+       "      <td>285 C, Webmaschine, SG 220 EMS</td>\n",
+       "      <td>5</td>\n",
+       "      <td>Greifer-Webmaschine</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-03-19</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kupplung defekt</td>\n",
+       "      <td>2019-03-20</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>N.V.</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>NaT</td>\n",
+       "      <td>2019-03-19</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>58</td>\n",
+       "      <td>257</td>\n",
+       "      <td>107, Webmaschine, OM 220 EOS</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Luft-Webmaschine</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-03-21</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Gegengewicht an der Webmaschine abgefallen</td>\n",
+       "      <td>2019-03-21</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube ausgebohrt. Gegengewicht wieder angeb...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-03-21</td>\n",
+       "      <td>2019-03-21</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>81</td>\n",
+       "      <td>138</td>\n",
+       "      <td>00138, Schärmaschine 9,</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Schärmaschine</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>zentrale Bremsenverstellung linke Gatterseite ...</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Bolzen gebrochen. Bolzen neu angefertig und di...</td>\n",
+       "      <td>Vorwerk</td>\n",
+       "      <td>Vorwerk</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>82</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Warenschau allgemein</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Warenschau allgemein</td>\n",
+       "      <td>Allgemeine Reparaturarbeiten</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Feder ausgetauscht</td>\n",
+       "      <td>Warenschau</td>\n",
+       "      <td>Warenschau</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>76</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Neben der Türe</td>\n",
+       "      <td>0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-03-22</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Neben der Türe</td>\n",
+       "      <td>Kettbaum</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schrauben ausgebohrt . Gewinde nachgeschnitten</td>\n",
+       "      <td>Vorwerk</td>\n",
+       "      <td>Vorwerk</td>\n",
+       "      <td>2019-03-25</td>\n",
+       "      <td>2019-03-22</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   VorgangsID  ObjektID                     HObjektText  ObjektArtID  \\\n",
+       "0          53       244  285 C, Webmaschine, SG 220 EMS            5   \n",
+       "1          58       257    107, Webmaschine, OM 220 EOS            3   \n",
+       "2          81       138        00138, Schärmaschine 9,            16   \n",
+       "3          82         0            Warenschau allgemein            0   \n",
+       "4          76         0                  Neben der Türe            0   \n",
+       "\n",
+       "         ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "0  Greifer-Webmaschine              3  Reparaturauftrag (Portal)   \n",
+       "1     Luft-Webmaschine              3  Reparaturauftrag (Portal)   \n",
+       "2        Schärmaschine              3  Reparaturauftrag (Portal)   \n",
+       "3                  NaN              3  Reparaturauftrag (Portal)   \n",
+       "4                  NaN              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "  VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  ...  \\\n",
+       "0    2019-03-19                 5                   0  ...   \n",
+       "1    2019-03-21                 5                   0  ...   \n",
+       "2    2019-03-25                 5                   0  ...   \n",
+       "3    2019-03-25                 5                   0  ...   \n",
+       "4    2019-03-22                 5                   0  ...   \n",
+       "\n",
+       "            VorgangsOrt                                    VorgangsArtText  \\\n",
+       "0                   NaN                                    Kupplung defekt   \n",
+       "1                   NaN         Gegengewicht an der Webmaschine abgefallen   \n",
+       "2                   NaN  zentrale Bremsenverstellung linke Gatterseite ...   \n",
+       "3  Warenschau allgemein                      Allgemeine Reparaturarbeiten    \n",
+       "4        Neben der Türe                                           Kettbaum   \n",
+       "\n",
+       "  ErledigungsDatum ErledigungsArtText  \\\n",
+       "0       2019-03-20      Reparatur UTT   \n",
+       "1       2019-03-21      Reparatur UTT   \n",
+       "2       2019-03-25      Reparatur UTT   \n",
+       "3       2019-03-25      Reparatur UTT   \n",
+       "4       2019-03-25      Reparatur UTT   \n",
+       "\n",
+       "                             ErledigungsBeschreibung MPMelderArbeitsplatz  \\\n",
+       "0                                               N.V.              Weberei   \n",
+       "1  Schraube ausgebohrt. Gegengewicht wieder angeb...              Weberei   \n",
+       "2  Bolzen gebrochen. Bolzen neu angefertig und di...              Vorwerk   \n",
+       "3                                 Feder ausgetauscht           Warenschau   \n",
+       "4     Schrauben ausgebohrt . Gewinde nachgeschnitten              Vorwerk   \n",
+       "\n",
+       "  MPAbteilungBezeichnung Arbeitsbeginn ErstellungsDatum delta_to_repair  \n",
+       "0                Weberei           NaT       2019-03-19               1  \n",
+       "1                Weberei    2019-03-21       2019-03-21               0  \n",
+       "2                Vorwerk    2019-03-25       2019-03-25               0  \n",
+       "3             Warenschau    2019-03-25       2019-03-25               0  \n",
+       "4                Vorwerk    2019-03-25       2019-03-22               3  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "c1a4b962-8b14-4f9b-9a3f-522c52b5c325",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data_clean = data.copy()"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7dbe83c6-a8d6-48d4-a2a4-19f9ddd9f8d3",
+   "id": "cd8fdcf4-18e6-4e38-b839-fc04872a3966",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
-   "id": "c22298d2-1260-4959-a1a9-83d8f0973254",
+   "execution_count": null,
+   "id": "1960df45-058c-40f1-baed-9ab19375d404",
    "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "80fba6825e3e41d0a15729ae8bd16f57",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/6 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "id": "803555d7-57f3-4b4b-b657-caa81aca1c88",
+   "metadata": {},
+   "outputs": [],
    "source": [
-    "tk_graph_cands, _ = tokens.build_token_graph(\n",
-    "    data=filtered,\n",
-    "    model=SPCY_MODEL,\n",
-    "    target_feature='VorgangsBeschreibung',\n",
-    "    build_map=False,\n",
-    "    logging_graph=False,\n",
-    ")"
+    "string_cols_postproc = [\n",
+    "    'VorgangsBeschreibung',\n",
+    "    'ErledigungsBeschreibung'\n",
+    "]"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 20,
-   "id": "447ef344-92c1-42e4-8b32-e970c61c1d8c",
+   "execution_count": 87,
+   "id": "ebf16eef-e1f4-4ae8-a6a3-09889bb42439",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[string_cols_postproc] = data[string_cols_postproc].fillna('N.V.').copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "id": "8edea7e8-283a-499d-b7b4-00002e691a30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "data[string_cols_postproc] = data[string_cols_postproc].map(clean_strings_dataframes).copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b3e03239-52fd-41cd-8f94-c13b5236adae",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7ab104ae-518e-422f-87be-f75203b6669a",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "adc7b98c-d8ce-4c68-8a48-864ff8a64264",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = filter_timeline_cands(data_clean, cands, 1654, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "689bd3d6-2d58-4944-9c46-8548aa34f63e",
    "metadata": {},
    "outputs": [
     {
      "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VorgangsID</th>\n",
+       "      <th>ObjektID</th>\n",
+       "      <th>HObjektText</th>\n",
+       "      <th>ObjektArtID</th>\n",
+       "      <th>ObjektArtText</th>\n",
+       "      <th>VorgangsTypID</th>\n",
+       "      <th>VorgangsTypName</th>\n",
+       "      <th>VorgangsDatum</th>\n",
+       "      <th>VorgangsStatusId</th>\n",
+       "      <th>VorgangsPrioritaet</th>\n",
+       "      <th>...</th>\n",
+       "      <th>VorgangsOrt</th>\n",
+       "      <th>VorgangsArtText</th>\n",
+       "      <th>ErledigungsDatum</th>\n",
+       "      <th>ErledigungsArtText</th>\n",
+       "      <th>ErledigungsBeschreibung</th>\n",
+       "      <th>MPMelderArbeitsplatz</th>\n",
+       "      <th>MPAbteilungBezeichnung</th>\n",
+       "      <th>Arbeitsbeginn</th>\n",
+       "      <th>ErstellungsDatum</th>\n",
+       "      <th>delta_to_repair</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>57937</th>\n",
+       "      <td>24650</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>lager entfernt und neu eingebaut</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>2019-12-02</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57927</th>\n",
+       "      <td>24704</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-04</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Zylinderkopfschrauben am Kettbaumadapter ausge...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-04</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7579</th>\n",
+       "      <td>25414</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-16</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube ausgebohrt und gewinde nachgeschitten.</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-16</td>\n",
+       "      <td>2019-12-13</td>\n",
+       "      <td>3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66749</th>\n",
+       "      <td>30359</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube von Adapter entfernt</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>49908</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>Extern - Reparatur</td>\n",
+       "      <td>Kettbaum Lagerauflage ist defekt und nicht meh...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
       "text/plain": [
-       "()"
+       "       VorgangsID  ObjektID                             HObjektText  \\\n",
+       "57937       24650      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "57927       24704      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "7579        25414      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "66749       30359      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "1680        49908      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "\n",
+       "       ObjektArtID  ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "57937           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "57927           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "7579            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "66749           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "1680            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "      VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  ... VorgangsOrt  \\\n",
+       "57937    2019-12-02                 2                   0  ...         NaN   \n",
+       "57927    2019-12-03                 5                   0  ...         NaN   \n",
+       "7579     2019-12-13                 5                   0  ...         NaN   \n",
+       "66749    2020-01-12                 5                   0  ...         NaN   \n",
+       "1680     2020-07-13                 5                   0  ...         NaN   \n",
+       "\n",
+       "        VorgangsArtText ErledigungsDatum  ErledigungsArtText  \\\n",
+       "57937  Kettbaum-Adapter       2019-12-03       Reparatur UTT   \n",
+       "57927  Kettbaum-Adapter       2019-12-04       Reparatur UTT   \n",
+       "7579   Kettbaum-Adapter       2019-12-16       Reparatur UTT   \n",
+       "66749  Kettbaum-Adapter       2020-01-13       Reparatur UTT   \n",
+       "1680   Kettbaum-Adapter       2020-07-13  Extern - Reparatur   \n",
+       "\n",
+       "                                 ErledigungsBeschreibung MPMelderArbeitsplatz  \\\n",
+       "57937                   lager entfernt und neu eingebaut              Weberei   \n",
+       "57927  Zylinderkopfschrauben am Kettbaumadapter ausge...              Weberei   \n",
+       "7579     Schraube ausgebohrt und gewinde nachgeschitten.              Weberei   \n",
+       "66749                      Schraube von Adapter entfernt              Weberei   \n",
+       "1680   Kettbaum Lagerauflage ist defekt und nicht meh...              Weberei   \n",
+       "\n",
+       "      MPAbteilungBezeichnung Arbeitsbeginn ErstellungsDatum delta_to_repair  \n",
+       "57937                Weberei    2019-12-03       2019-12-02               1  \n",
+       "57927                Weberei    2019-12-04       2019-12-03               1  \n",
+       "7579                 Weberei    2019-12-16       2019-12-13               3  \n",
+       "66749                Weberei    2020-01-13       2020-01-12               1  \n",
+       "1680                 Weberei    2020-07-13       2020-07-13               0  \n",
+       "\n",
+       "[5 rows x 21 columns]"
       ]
      },
-     "execution_count": 20,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "tuple(tk_graph_cands.edges)"
+    "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 18,
-   "id": "f8781584-a992-4117-832d-c5296af83740",
+   "execution_count": 9,
+   "id": "121f0ca2-43d5-46d4-a5a0-66c4bbf1ce0a",
    "metadata": {},
    "outputs": [],
    "source": [
-    "for edge in tk_graph_cands.edges:\n",
-    "    print(tk_graph_cands.edges[edge])"
+    "df_delta = calc_delta_to_next_failure(df, date_feature='ErstellungsDatum', name_delta_feature='delta')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9bcf4372-5b94-4e74-a440-6a5cdd1ec0da",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VorgangsID</th>\n",
+       "      <th>ObjektID</th>\n",
+       "      <th>HObjektText</th>\n",
+       "      <th>ObjektArtID</th>\n",
+       "      <th>ObjektArtText</th>\n",
+       "      <th>VorgangsTypID</th>\n",
+       "      <th>VorgangsTypName</th>\n",
+       "      <th>VorgangsDatum</th>\n",
+       "      <th>VorgangsStatusId</th>\n",
+       "      <th>VorgangsPrioritaet</th>\n",
+       "      <th>...</th>\n",
+       "      <th>VorgangsArtText</th>\n",
+       "      <th>ErledigungsDatum</th>\n",
+       "      <th>ErledigungsArtText</th>\n",
+       "      <th>ErledigungsBeschreibung</th>\n",
+       "      <th>MPMelderArbeitsplatz</th>\n",
+       "      <th>MPAbteilungBezeichnung</th>\n",
+       "      <th>Arbeitsbeginn</th>\n",
+       "      <th>ErstellungsDatum</th>\n",
+       "      <th>delta_to_repair</th>\n",
+       "      <th>delta</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>117758</th>\n",
+       "      <td>272716</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2022-08-28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2022-08-29</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Schraube ausgebohrt und Gewinde nachgeschnitten</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2022-08-29</td>\n",
+       "      <td>2022-08-28</td>\n",
+       "      <td>1</td>\n",
+       "      <td>198 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66749</th>\n",
+       "      <td>30359</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube von Adapter entfernt</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>1</td>\n",
+       "      <td>183 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119480</th>\n",
+       "      <td>253945</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Störungsmeldung</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2022-04-25</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Eine seite neue Gewinde gebohrt und geschnitte...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>3</td>\n",
+       "      <td>128 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46790</th>\n",
+       "      <td>103707</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Kettbaum-Adapter schrauben entfernt und gewind...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>0</td>\n",
+       "      <td>103 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>49908</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>Extern - Reparatur</td>\n",
+       "      <td>Kettbaum Lagerauflage ist defekt und nicht meh...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>0</td>\n",
+       "      <td>72 days</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 22 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VorgangsID  ObjektID                             HObjektText  \\\n",
+       "117758      272716      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "66749        30359      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "119480      253945      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "46790       103707      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "1680         49908      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "\n",
+       "        ObjektArtID  ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "117758           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "66749            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "119480           90  UTT allgemein              2            Störungsmeldung   \n",
+       "46790            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "1680             90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "       VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  ...  \\\n",
+       "117758    2022-08-28                 5                   0  ...   \n",
+       "66749     2020-01-12                 5                   0  ...   \n",
+       "119480    2022-04-22                 5                   0  ...   \n",
+       "46790     2021-02-16                 5                   0  ...   \n",
+       "1680      2020-07-13                 5                   0  ...   \n",
+       "\n",
+       "         VorgangsArtText ErledigungsDatum       ErledigungsArtText  \\\n",
+       "117758  Kettbaum-Adapter       2022-08-29  Intern UTT - Reparatur    \n",
+       "66749   Kettbaum-Adapter       2020-01-13            Reparatur UTT   \n",
+       "119480  Kettbaum-Adapter       2022-04-25  Intern UTT - Reparatur    \n",
+       "46790   Kettbaum-Adapter       2021-02-16  Intern UTT - Reparatur    \n",
+       "1680    Kettbaum-Adapter       2020-07-13       Extern - Reparatur   \n",
+       "\n",
+       "                                  ErledigungsBeschreibung  \\\n",
+       "117758    Schraube ausgebohrt und Gewinde nachgeschnitten   \n",
+       "66749                       Schraube von Adapter entfernt   \n",
+       "119480  Eine seite neue Gewinde gebohrt und geschnitte...   \n",
+       "46790   Kettbaum-Adapter schrauben entfernt und gewind...   \n",
+       "1680    Kettbaum Lagerauflage ist defekt und nicht meh...   \n",
+       "\n",
+       "       MPMelderArbeitsplatz MPAbteilungBezeichnung Arbeitsbeginn  \\\n",
+       "117758              Weberei                Weberei    2022-08-29   \n",
+       "66749               Weberei                Weberei    2020-01-13   \n",
+       "119480                  NaN                    NaN    2022-04-22   \n",
+       "46790               Weberei                Weberei    2021-02-16   \n",
+       "1680                Weberei                Weberei    2020-07-13   \n",
+       "\n",
+       "       ErstellungsDatum delta_to_repair    delta  \n",
+       "117758       2022-08-28               1 198 days  \n",
+       "66749        2020-01-12               1 183 days  \n",
+       "119480       2022-04-22               3 128 days  \n",
+       "46790        2021-02-16               0 103 days  \n",
+       "1680         2020-07-13               0  72 days  \n",
+       "\n",
+       "[5 rows x 22 columns]"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_delta.head()"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 12,
-   "id": "3a27a6f3-f196-4f8e-bb1c-09da845c463d",
+   "id": "8de699a4-16bc-4f66-9d4d-0d9f0384db61",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]"
+       "117758    198\n",
+       "66749     183\n",
+       "119480    128\n",
+       "46790     103\n",
+       "1680       72\n",
+       "43412      70\n",
+       "100461     66\n",
+       "2774       60\n",
+       "122902     59\n",
+       "111241     55\n",
+       "83         54\n",
+       "6798       45\n",
+       "67562      38\n",
+       "78986      32\n",
+       "7579       30\n",
+       "96080      22\n",
+       "55024      14\n",
+       "98277      11\n",
+       "57927      10\n",
+       "1743       10\n",
+       "8084        7\n",
+       "4846        6\n",
+       "55952       5\n",
+       "32229       5\n",
+       "78778       3\n",
+       "46791       2\n",
+       "57937       1\n",
+       "2980        0\n",
+       "43411       0\n",
+       "87479       0\n",
+       "Name: delta, dtype: int64"
       ]
      },
      "execution_count": 12,
@@ -8461,167 +9400,1027 @@
     }
    ],
    "source": [
-    "weights = [data['weight'] for data in tk_graph_cands.edges.values()]\n",
-    "weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 20,
-   "id": "cf6c2c0f-271c-4eb0-b364-f30dbe62b119",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "weights = [20 for _ in range(10)]"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 21,
-   "id": "142c1f09-776e-4117-841f-d8b5d4c72d44",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "[20, 20, 20, 20, 20, 20, 20, 20, 20, 20]"
-      ]
-     },
-     "execution_count": 21,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "weights"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 22,
-   "id": "d995c03d-d9d7-4ad0-81a2-18dac6c9900a",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import numpy as np"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 23,
-   "id": "caba22f6-a641-40b0-b5c4-3092142a67db",
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "array([2.99573227, 2.99573227, 2.99573227, 2.99573227, 2.99573227,\n",
-       "       2.99573227, 2.99573227, 2.99573227, 2.99573227, 2.99573227])"
-      ]
-     },
-     "execution_count": 23,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "np.log(weights)"
+    "df_delta['delta'].dt.days"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "7cd4642b-0715-4b82-a7da-b75e970f870c",
+   "id": "b63b688d-29ac-4962-a49c-68417c5e16c6",
    "metadata": {},
    "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
-   "id": "5fd7759f-f1eb-4f0c-9b05-f7230f194515",
+   "execution_count": null,
+   "id": "5f30e465-6c19-4425-b3c8-7f02b6de41e0",
    "metadata": {},
    "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "1b2516c2-ec45-4ed0-8e39-1db3ad4929a2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VorgangsID</th>\n",
+       "      <th>ObjektID</th>\n",
+       "      <th>HObjektText</th>\n",
+       "      <th>ObjektArtID</th>\n",
+       "      <th>ObjektArtText</th>\n",
+       "      <th>VorgangsTypID</th>\n",
+       "      <th>VorgangsTypName</th>\n",
+       "      <th>VorgangsDatum</th>\n",
+       "      <th>VorgangsStatusId</th>\n",
+       "      <th>VorgangsPrioritaet</th>\n",
+       "      <th>VorgangsBeschreibung</th>\n",
+       "      <th>VorgangsOrt</th>\n",
+       "      <th>VorgangsArtText</th>\n",
+       "      <th>ErledigungsDatum</th>\n",
+       "      <th>ErledigungsArtText</th>\n",
+       "      <th>ErledigungsBeschreibung</th>\n",
+       "      <th>MPMelderArbeitsplatz</th>\n",
+       "      <th>MPAbteilungBezeichnung</th>\n",
+       "      <th>Arbeitsbeginn</th>\n",
+       "      <th>ErstellungsDatum</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>57937</th>\n",
+       "      <td>24650</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-02</td>\n",
+       "      <td>2</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kettbaum-Adapter-Lager defekt. (Martino inform...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>lager entfernt und neu eingebaut</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>2019-12-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57927</th>\n",
+       "      <td>24704</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Schrauben rund. Bitte ausbohren. Kette steht v...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-04</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Zylinderkopfschrauben am Kettbaumadapter ausge...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-04</td>\n",
+       "      <td>2019-12-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7579</th>\n",
+       "      <td>25414</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2019-12-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Schraube rund.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2019-12-16</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube ausgebohrt und gewinde nachgeschitten.</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2019-12-16</td>\n",
+       "      <td>2019-12-13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66749</th>\n",
+       "      <td>30359</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Schraube von Adapter entfernen. Abgebrochener ...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube von Adapter entfernt</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>49908</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>Kettbaum Adapter Lager erneuern.</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>Extern - Reparatur</td>\n",
+       "      <td>Kettbaum Lagerauflage ist defekt und nicht meh...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       VorgangsID  ObjektID                             HObjektText  \\\n",
+       "57937       24650      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "57927       24704      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "7579        25414      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "66749       30359      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "1680        49908      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "\n",
+       "       ObjektArtID  ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "57937           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "57927           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "7579            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "66749           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "1680            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "      VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  \\\n",
+       "57937    2019-12-02                 2                   0   \n",
+       "57927    2019-12-03                 5                   0   \n",
+       "7579     2019-12-13                 5                   0   \n",
+       "66749    2020-01-12                 5                   0   \n",
+       "1680     2020-07-13                 5                   0   \n",
+       "\n",
+       "                                    VorgangsBeschreibung VorgangsOrt  \\\n",
+       "57937  Kettbaum-Adapter-Lager defekt. (Martino inform...         NaN   \n",
+       "57927  Schrauben rund. Bitte ausbohren. Kette steht v...         NaN   \n",
+       "7579                                      Schraube rund.         NaN   \n",
+       "66749  Schraube von Adapter entfernen. Abgebrochener ...         NaN   \n",
+       "1680                    Kettbaum Adapter Lager erneuern.         NaN   \n",
+       "\n",
+       "        VorgangsArtText ErledigungsDatum  ErledigungsArtText  \\\n",
+       "57937  Kettbaum-Adapter       2019-12-03       Reparatur UTT   \n",
+       "57927  Kettbaum-Adapter       2019-12-04       Reparatur UTT   \n",
+       "7579   Kettbaum-Adapter       2019-12-16       Reparatur UTT   \n",
+       "66749  Kettbaum-Adapter       2020-01-13       Reparatur UTT   \n",
+       "1680   Kettbaum-Adapter       2020-07-13  Extern - Reparatur   \n",
+       "\n",
+       "                                 ErledigungsBeschreibung MPMelderArbeitsplatz  \\\n",
+       "57937                   lager entfernt und neu eingebaut              Weberei   \n",
+       "57927  Zylinderkopfschrauben am Kettbaumadapter ausge...              Weberei   \n",
+       "7579     Schraube ausgebohrt und gewinde nachgeschitten.              Weberei   \n",
+       "66749                      Schraube von Adapter entfernt              Weberei   \n",
+       "1680   Kettbaum Lagerauflage ist defekt und nicht meh...              Weberei   \n",
+       "\n",
+       "      MPAbteilungBezeichnung Arbeitsbeginn ErstellungsDatum  \n",
+       "57937                Weberei    2019-12-03       2019-12-02  \n",
+       "57927                Weberei    2019-12-04       2019-12-03  \n",
+       "7579                 Weberei    2019-12-16       2019-12-13  \n",
+       "66749                Weberei    2020-01-13       2020-01-12  \n",
+       "1680                 Weberei    2020-07-13       2020-07-13  "
+      ]
+     },
+     "execution_count": 16,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
-    "rescaled_dir, rescaled_undir = graphs.pipe_rescale_graph_edge_weights(tk_graph_cands)"
+    "df = df.sort_values(by='ErstellungsDatum', ascending=True)\n",
+    "df.head()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
-   "id": "b6ec4ffc-ab27-43de-84e7-74b14cdb05c2",
+   "execution_count": 20,
+   "id": "725bb900-7c3d-4f82-9de1-73fb127a377c",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "TokenGraph(name: TokenGraph, number of nodes: 12, number of edges: 17)"
+       "Timestamp('2023-06-13 00:00:00')"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
+   "source": [
+    "last_val = df['ErstellungsDatum'].iat[-1]\n",
+    "last_val"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "468a8c2b-59ce-48dc-ac94-41fbe12f174f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shifted = df['ErstellungsDatum'].shift(-1, fill_value=last_val)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "dc44e019-19d7-46d6-9a4a-e619ac9ccfdc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "delta = shifted - df['ErstellungsDatum']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "id": "c0aa7a33-56ba-4299-835c-245bb7d14aeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "57937      1 days\n",
+       "57927     10 days\n",
+       "7579      30 days\n",
+       "66749    183 days\n",
+       "1680      72 days\n",
+       "32229      5 days\n",
+       "83        54 days\n",
+       "1743      10 days\n",
+       "43411      0 days\n",
+       "43412     70 days\n",
+       "55952      5 days\n",
+       "46791      2 days\n",
+       "46790    103 days\n",
+       "96080     22 days\n",
+       "78778      3 days\n",
+       "67562     38 days\n",
+       "100461    66 days\n",
+       "2774      60 days\n",
+       "6798      45 days\n",
+       "4846       0 days\n",
+       "2980       6 days\n",
+       "8084       7 days\n",
+       "98277     11 days\n",
+       "111241    55 days\n",
+       "55024     14 days\n",
+       "119480   128 days\n",
+       "117758   198 days\n",
+       "122902    59 days\n",
+       "78986     32 days\n",
+       "87479      0 days\n",
+       "Name: ErstellungsDatum, dtype: timedelta64[ns]"
+      ]
+     },
+     "execution_count": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "delta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "id": "c257fdc8-c4e7-4ef9-9bc8-3ab509e91ec3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['delta_to_next_failure'] = delta"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "bd00d516-cf46-4cf2-88fc-50444c237c3b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_delta = df.sort_values(by='delta_to_next_failure', ascending=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "id": "9c791440-9aaf-4d0c-97c3-2f3fa5c9584e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>VorgangsID</th>\n",
+       "      <th>ObjektID</th>\n",
+       "      <th>HObjektText</th>\n",
+       "      <th>ObjektArtID</th>\n",
+       "      <th>ObjektArtText</th>\n",
+       "      <th>VorgangsTypID</th>\n",
+       "      <th>VorgangsTypName</th>\n",
+       "      <th>VorgangsDatum</th>\n",
+       "      <th>VorgangsStatusId</th>\n",
+       "      <th>VorgangsPrioritaet</th>\n",
+       "      <th>...</th>\n",
+       "      <th>VorgangsOrt</th>\n",
+       "      <th>VorgangsArtText</th>\n",
+       "      <th>ErledigungsDatum</th>\n",
+       "      <th>ErledigungsArtText</th>\n",
+       "      <th>ErledigungsBeschreibung</th>\n",
+       "      <th>MPMelderArbeitsplatz</th>\n",
+       "      <th>MPAbteilungBezeichnung</th>\n",
+       "      <th>Arbeitsbeginn</th>\n",
+       "      <th>ErstellungsDatum</th>\n",
+       "      <th>delta_to_next_failure</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>117758</th>\n",
+       "      <td>272716</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2022-08-28</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2022-08-29</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Schraube ausgebohrt und Gewinde nachgeschnitten</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2022-08-29</td>\n",
+       "      <td>2022-08-28</td>\n",
+       "      <td>198 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66749</th>\n",
+       "      <td>30359</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>Reparatur UTT</td>\n",
+       "      <td>Schraube von Adapter entfernt</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-01-13</td>\n",
+       "      <td>2020-01-12</td>\n",
+       "      <td>183 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119480</th>\n",
+       "      <td>253945</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>2</td>\n",
+       "      <td>Störungsmeldung</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2022-04-25</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Eine seite neue Gewinde gebohrt und geschnitte...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>2022-04-22</td>\n",
+       "      <td>128 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46790</th>\n",
+       "      <td>103707</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Kettbaum-Adapter schrauben  entfernt und gewin...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>2021-02-16</td>\n",
+       "      <td>103 days</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>49908</td>\n",
+       "      <td>1654</td>\n",
+       "      <td>WEBEREI ALLGEMEIN, Weberei allgemein,</td>\n",
+       "      <td>90</td>\n",
+       "      <td>UTT allgemein</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Kettbaum-Adapter</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>Extern - Reparatur</td>\n",
+       "      <td>Kettbaum Lagerauflage ist defekt und nicht meh...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>2020-07-13</td>\n",
+       "      <td>72 days</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>5 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VorgangsID  ObjektID                             HObjektText  \\\n",
+       "117758      272716      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "66749        30359      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "119480      253945      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "46790       103707      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "1680         49908      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "\n",
+       "        ObjektArtID  ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "117758           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "66749            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "119480           90  UTT allgemein              2            Störungsmeldung   \n",
+       "46790            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "1680             90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "       VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  ... VorgangsOrt  \\\n",
+       "117758    2022-08-28                 5                   0  ...         NaN   \n",
+       "66749     2020-01-12                 5                   0  ...         NaN   \n",
+       "119480    2022-04-22                 5                   0  ...         NaN   \n",
+       "46790     2021-02-16                 5                   0  ...         NaN   \n",
+       "1680      2020-07-13                 5                   0  ...         NaN   \n",
+       "\n",
+       "         VorgangsArtText ErledigungsDatum       ErledigungsArtText  \\\n",
+       "117758  Kettbaum-Adapter       2022-08-29  Intern UTT - Reparatur    \n",
+       "66749   Kettbaum-Adapter       2020-01-13            Reparatur UTT   \n",
+       "119480  Kettbaum-Adapter       2022-04-25  Intern UTT - Reparatur    \n",
+       "46790   Kettbaum-Adapter       2021-02-16  Intern UTT - Reparatur    \n",
+       "1680    Kettbaum-Adapter       2020-07-13       Extern - Reparatur   \n",
+       "\n",
+       "                                  ErledigungsBeschreibung  \\\n",
+       "117758   Schraube ausgebohrt und Gewinde nachgeschnitten    \n",
+       "66749                       Schraube von Adapter entfernt   \n",
+       "119480  Eine seite neue Gewinde gebohrt und geschnitte...   \n",
+       "46790   Kettbaum-Adapter schrauben  entfernt und gewin...   \n",
+       "1680    Kettbaum Lagerauflage ist defekt und nicht meh...   \n",
+       "\n",
+       "       MPMelderArbeitsplatz MPAbteilungBezeichnung Arbeitsbeginn  \\\n",
+       "117758              Weberei                Weberei    2022-08-29   \n",
+       "66749               Weberei                Weberei    2020-01-13   \n",
+       "119480                  NaN                    NaN    2022-04-22   \n",
+       "46790               Weberei                Weberei    2021-02-16   \n",
+       "1680                Weberei                Weberei    2020-07-13   \n",
+       "\n",
+       "       ErstellungsDatum delta_to_next_failure  \n",
+       "117758       2022-08-28              198 days  \n",
+       "66749        2020-01-12              183 days  \n",
+       "119480       2022-04-22              128 days  \n",
+       "46790        2021-02-16              103 days  \n",
+       "1680         2020-07-13               72 days  \n",
+       "\n",
+       "[5 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_delta.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "id": "93d28d94-cf2a-4d96-bd9a-c6898f85f883",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_delta['ErledigungsBeschreibung'] = df_delta['ErledigungsBeschreibung'].fillna('N.V.')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "403d0b49-f6e9-4d0d-92ef-45dbe81514a1",
+   "metadata": {},
+   "outputs": [],
    "source": []
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
-   "id": "1b44a944-9566-4fbd-82a6-152735a240ae",
+   "execution_count": 44,
+   "id": "21dfc078-3613-4422-97b1-4503b824f075",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "potential_successful = df_delta[['delta_to_next_failure', 'ErledigungsBeschreibung']]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "dddfd07c-51ed-4632-90cd-ffd6264404d1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "potential_successful.columns = ['Zeitspanne bis zum nächsten Ereignis [Tage]', 'Maßnahme']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "id": "e0ca4427-6744-47df-b2e0-40ea49cd6182",
    "metadata": {},
    "outputs": [
     {
-     "name": "stdout",
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Zeitspanne bis zum nächsten Ereignis [Tage]</th>\n",
+       "      <th>Maßnahme</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>117758</th>\n",
+       "      <td>198 days</td>\n",
+       "      <td>Schraube ausgebohrt und Gewinde nachgeschnitten</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>66749</th>\n",
+       "      <td>183 days</td>\n",
+       "      <td>Schraube von Adapter entfernt</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>119480</th>\n",
+       "      <td>128 days</td>\n",
+       "      <td>Eine seite neue Gewinde gebohrt und geschnitte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46790</th>\n",
+       "      <td>103 days</td>\n",
+       "      <td>Kettbaum-Adapter schrauben  entfernt und gewin...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1680</th>\n",
+       "      <td>72 days</td>\n",
+       "      <td>Kettbaum Lagerauflage ist defekt und nicht meh...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43412</th>\n",
+       "      <td>70 days</td>\n",
+       "      <td>Zylinderschraube rausgebohrt und Gewinde nachg...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100461</th>\n",
+       "      <td>66 days</td>\n",
+       "      <td>N.V.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2774</th>\n",
+       "      <td>60 days</td>\n",
+       "      <td>abgbrochene Schrauben entfernt und alle Gewind...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122902</th>\n",
+       "      <td>59 days</td>\n",
+       "      <td>Schraube ausgebohrt und Gewinde nachgeschnitten</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>111241</th>\n",
+       "      <td>55 days</td>\n",
+       "      <td>Neues Lager 6312 2Rs C3 eingebaut</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>83</th>\n",
+       "      <td>54 days</td>\n",
+       "      <td>Schraube ausgebohrt und Gewinde nach geschnitten.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6798</th>\n",
+       "      <td>45 days</td>\n",
+       "      <td>Neues Lochbild mit Schablone gebohrt, da abgeb...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>67562</th>\n",
+       "      <td>38 days</td>\n",
+       "      <td>Schraube ausgebohrt.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78986</th>\n",
+       "      <td>32 days</td>\n",
+       "      <td>Schrauben ausgebohrt und Gewinde neu geschnitten.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7579</th>\n",
+       "      <td>30 days</td>\n",
+       "      <td>Schraube ausgebohrt und gewinde nachgeschitten.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>96080</th>\n",
+       "      <td>22 days</td>\n",
+       "      <td>Zylinderkopfschrauben mit Innensechskant 3 Stü...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55024</th>\n",
+       "      <td>14 days</td>\n",
+       "      <td>Schraube von Kettbaumadapter ausgebohrt</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>98277</th>\n",
+       "      <td>11 days</td>\n",
+       "      <td>erledigt.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57927</th>\n",
+       "      <td>10 days</td>\n",
+       "      <td>Zylinderkopfschrauben am Kettbaumadapter ausge...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1743</th>\n",
+       "      <td>10 days</td>\n",
+       "      <td>N.V.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8084</th>\n",
+       "      <td>7 days</td>\n",
+       "      <td>Altes Lager demontiert und neues montiert.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2980</th>\n",
+       "      <td>6 days</td>\n",
+       "      <td>Schrauben ausgebohrt und alle anderen Gewinde ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>55952</th>\n",
+       "      <td>5 days</td>\n",
+       "      <td>Schrauben entfernt. alle gewinde nachgeschnitten</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>32229</th>\n",
+       "      <td>5 days</td>\n",
+       "      <td>N.V.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>78778</th>\n",
+       "      <td>3 days</td>\n",
+       "      <td>Kettbaum Schraube ausgebohrt und Gewinde nach ...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46791</th>\n",
+       "      <td>2 days</td>\n",
+       "      <td>Neue Bohrlöcher angebracht, Gewinde geschnitte...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>57937</th>\n",
+       "      <td>1 days</td>\n",
+       "      <td>lager entfernt und neu eingebaut</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4846</th>\n",
+       "      <td>0 days</td>\n",
+       "      <td>Schraube entfernt und alle anderen Gewinde nac...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>43411</th>\n",
+       "      <td>0 days</td>\n",
+       "      <td>TK doppelt siehe tk 67187.</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87479</th>\n",
+       "      <td>0 days</td>\n",
+       "      <td>Drei Schraubenköpfe aufgebohrt und Adapter run...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Zeitspanne bis zum nächsten Ereignis [Tage]  \\\n",
+       "117758                                    198 days   \n",
+       "66749                                     183 days   \n",
+       "119480                                    128 days   \n",
+       "46790                                     103 days   \n",
+       "1680                                       72 days   \n",
+       "43412                                      70 days   \n",
+       "100461                                     66 days   \n",
+       "2774                                       60 days   \n",
+       "122902                                     59 days   \n",
+       "111241                                     55 days   \n",
+       "83                                         54 days   \n",
+       "6798                                       45 days   \n",
+       "67562                                      38 days   \n",
+       "78986                                      32 days   \n",
+       "7579                                       30 days   \n",
+       "96080                                      22 days   \n",
+       "55024                                      14 days   \n",
+       "98277                                      11 days   \n",
+       "57927                                      10 days   \n",
+       "1743                                       10 days   \n",
+       "8084                                        7 days   \n",
+       "2980                                        6 days   \n",
+       "55952                                       5 days   \n",
+       "32229                                       5 days   \n",
+       "78778                                       3 days   \n",
+       "46791                                       2 days   \n",
+       "57937                                       1 days   \n",
+       "4846                                        0 days   \n",
+       "43411                                       0 days   \n",
+       "87479                                       0 days   \n",
+       "\n",
+       "                                                 Maßnahme  \n",
+       "117758    Schraube ausgebohrt und Gewinde nachgeschnitten  \n",
+       "66749                       Schraube von Adapter entfernt  \n",
+       "119480  Eine seite neue Gewinde gebohrt und geschnitte...  \n",
+       "46790   Kettbaum-Adapter schrauben  entfernt und gewin...  \n",
+       "1680    Kettbaum Lagerauflage ist defekt und nicht meh...  \n",
+       "43412   Zylinderschraube rausgebohrt und Gewinde nachg...  \n",
+       "100461                                               N.V.  \n",
+       "2774    abgbrochene Schrauben entfernt und alle Gewind...  \n",
+       "122902    Schraube ausgebohrt und Gewinde nachgeschnitten  \n",
+       "111241                  Neues Lager 6312 2Rs C3 eingebaut  \n",
+       "83      Schraube ausgebohrt und Gewinde nach geschnitten.  \n",
+       "6798    Neues Lochbild mit Schablone gebohrt, da abgeb...  \n",
+       "67562                                Schraube ausgebohrt.  \n",
+       "78986   Schrauben ausgebohrt und Gewinde neu geschnitten.  \n",
+       "7579      Schraube ausgebohrt und gewinde nachgeschitten.  \n",
+       "96080   Zylinderkopfschrauben mit Innensechskant 3 Stü...  \n",
+       "55024             Schraube von Kettbaumadapter ausgebohrt  \n",
+       "98277                                           erledigt.  \n",
+       "57927   Zylinderkopfschrauben am Kettbaumadapter ausge...  \n",
+       "1743                                                 N.V.  \n",
+       "8084           Altes Lager demontiert und neues montiert.  \n",
+       "2980    Schrauben ausgebohrt und alle anderen Gewinde ...  \n",
+       "55952    Schrauben entfernt. alle gewinde nachgeschnitten  \n",
+       "32229                                                N.V.  \n",
+       "78778   Kettbaum Schraube ausgebohrt und Gewinde nach ...  \n",
+       "46791   Neue Bohrlöcher angebracht, Gewinde geschnitte...  \n",
+       "57937                    lager entfernt und neu eingebaut  \n",
+       "4846    Schraube entfernt und alle anderen Gewinde nac...  \n",
+       "43411                          TK doppelt siehe tk 67187.  \n",
+       "87479   Drei Schraubenköpfe aufgebohrt und Adapter run...  "
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "potential_successful"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "cb645b89-c904-4f12-a829-30ddd9f1bbd8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_str = potential_successful.loc[55952, 'Maßnahme']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "8ad3e3da-f7ff-417f-96f6-2b2817d852c7",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "t_str = 'Schrauben entfernt \\nalle gewinde nachgeschnitten\\t\\n'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "e844ed05-b623-45e8-b3d5-46e94db052e2",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Schrauben entfernt \\nalle gewinde nachgeschnitten\\t\\n'"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "t_str"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "dbd0b2e6-b01c-40c0-a0fa-67be9f5329e1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import re"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "id": "e1568d24-56c1-4dd1-b144-339b8b58289a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "pattern = re.compile(r' *[\\t\\n\\r\\f\\v]+')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "id": "bc7bf0b4-5278-43a3-a5c1-93f9d0f2322f",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Schrauben entfernt. alle gewinde nachgeschnitten. '"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "pattern.sub('. ', t_str)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "id": "4b877452-daef-47cc-aa19-b117d301bcd1",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lang_main.analysis.shared import clean_strings_dataframes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "id": "e95e71fd-f335-4339-b587-1933b5cc5082",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
      "output_type": "stream",
      "text": [
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "------------------------------------\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n",
-      "{'weight': 0.0952}\n"
+      "C:\\Users\\foersterflorian\\AppData\\Local\\Temp\\ipykernel_14020\\1258675024.py:1: SettingWithCopyWarning: \n",
+      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
+      "Try using .loc[row_indexer,col_indexer] = value instead\n",
+      "\n",
+      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
+      "  potential_successful['Maßnahme'] = potential_successful['Maßnahme'].apply(clean_strings_dataframes)\n"
      ]
     }
    ],
    "source": [
-    "for edge in rescaled_dir.edges:\n",
-    "    print(rescaled_dir.edges[edge])\n",
-    "\n",
-    "print('------------------------------------')\n",
-    "\n",
-    "for edge in rescaled_undir.edges:\n",
-    "    print(rescaled_undir.edges[edge])"
+    "potential_successful['Maßnahme'] = potential_successful['Maßnahme'].apply(clean_strings_dataframes)"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "de68b504-d587-420d-91e3-11859ca1eb9e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {