diff --git a/pdm.lock b/pdm.lock
index f8a7afd..d5f3208 100644
--- a/pdm.lock
+++ b/pdm.lock
@@ -5,7 +5,7 @@
 groups = ["default", "notebooks", "trials"]
 strategy = ["cross_platform", "inherit_metadata"]
 lock_version = "4.4.1"
-content_hash = "sha256:fc88dc465a3d04eb53b847d7b58db1e55ce8adb004489102cceea01fb52527dc"
+content_hash = "sha256:7574154c6728ede3eaf76a8b1a3b5d4339fcc8f2dc8c41042401004b6583e151"
 
 [[package]]
 name = "annotated-types"
@@ -182,6 +182,17 @@ files = [
     {file = "bleach-6.1.0.tar.gz", hash = "sha256:0a31f1837963c41d46bbf1331b8778e1308ea0791db03cc4e7357b97cf42a8fe"},
 ]
 
+[[package]]
+name = "blinker"
+version = "1.8.2"
+requires_python = ">=3.8"
+summary = "Fast, simple object-to-object and broadcast signaling"
+groups = ["trials"]
+files = [
+    {file = "blinker-1.8.2-py3-none-any.whl", hash = "sha256:1779309f71bf239144b9399d06ae925637cf6634cf6bd131104184531bf67c01"},
+    {file = "blinker-1.8.2.tar.gz", hash = "sha256:8f77b09d3bf7c795e969e9486f39c2c5e9c39d4ee07424be2bc594ece9642d83"},
+]
+
 [[package]]
 name = "blis"
 version = "0.7.11"
@@ -220,7 +231,7 @@ name = "certifi"
 version = "2024.2.2"
 requires_python = ">=3.6"
 summary = "Python package for providing Mozilla's CA Bundle."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "certifi-2024.2.2-py3-none-any.whl", hash = "sha256:dc383c07b76109f368f6106eee2b593b04a011ea4d55f652c6ca24a754d1cdd1"},
     {file = "certifi-2024.2.2.tar.gz", hash = "sha256:0569859f95fc761b18b45ef421b1290a0f65f147e92a1e5eb3e635f9a5e4e66f"},
@@ -265,7 +276,7 @@ name = "charset-normalizer"
 version = "3.3.2"
 requires_python = ">=3.7.0"
 summary = "The Real First Universal Charset Detector. Open, modern and actively maintained alternative to Chardet."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "charset-normalizer-3.3.2.tar.gz", hash = "sha256:f30c3cb33b24454a82faecaf01b19c18562b1e89558fb6c56de4d9118a032fd5"},
     {file = "charset_normalizer-3.3.2-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:802fe99cca7457642125a8a88a084cef28ff0cf9407060f7b93dca5aa25480db"},
@@ -306,7 +317,7 @@ name = "click"
 version = "8.1.7"
 requires_python = ">=3.7"
 summary = "Composable command line interface toolkit"
-groups = ["default"]
+groups = ["default", "trials"]
 dependencies = [
     "colorama; platform_system == \"Windows\"",
 ]
@@ -331,7 +342,7 @@ name = "colorama"
 version = "0.4.6"
 requires_python = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,>=2.7"
 summary = "Cross-platform colored terminal text."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 marker = "platform_system == \"Windows\" or sys_platform == \"win32\""
 files = [
     {file = "colorama-0.4.6-py2.py3-none-any.whl", hash = "sha256:4f1d9991f5acc0ca119f9d443620b77f9d6b33703e51011c16baf57afb285fc6"},
@@ -386,6 +397,61 @@ files = [
     {file = "cymem-2.0.8.tar.gz", hash = "sha256:8fb09d222e21dcf1c7e907dc85cf74501d4cea6c4ed4ac6c9e016f98fb59cbbf"},
 ]
 
+[[package]]
+name = "dash"
+version = "2.17.0"
+requires_python = ">=3.8"
+summary = "A Python framework for building reactive web-apps. Developed by Plotly."
+groups = ["trials"]
+dependencies = [
+    "Flask<3.1,>=1.0.4",
+    "Werkzeug<3.1",
+    "dash-core-components==2.0.0",
+    "dash-html-components==2.0.0",
+    "dash-table==5.0.0",
+    "importlib-metadata",
+    "nest-asyncio",
+    "plotly>=5.0.0",
+    "requests",
+    "retrying",
+    "setuptools",
+    "typing-extensions>=4.1.1",
+]
+files = [
+    {file = "dash-2.17.0-py3-none-any.whl", hash = "sha256:2421569023b2cd46ea2d4b2c14fe72c71b7436527a3102219b2265fa361e7c67"},
+    {file = "dash-2.17.0.tar.gz", hash = "sha256:d065cd88771e45d0485993be0d27565e08918cb7edd18e31ee1c5b41252fc2fa"},
+]
+
+[[package]]
+name = "dash-core-components"
+version = "2.0.0"
+summary = "Core component suite for Dash"
+groups = ["trials"]
+files = [
+    {file = "dash_core_components-2.0.0-py3-none-any.whl", hash = "sha256:52b8e8cce13b18d0802ee3acbc5e888cb1248a04968f962d63d070400af2e346"},
+    {file = "dash_core_components-2.0.0.tar.gz", hash = "sha256:c6733874af975e552f95a1398a16c2ee7df14ce43fa60bb3718a3c6e0b63ffee"},
+]
+
+[[package]]
+name = "dash-html-components"
+version = "2.0.0"
+summary = "Vanilla HTML components for Dash"
+groups = ["trials"]
+files = [
+    {file = "dash_html_components-2.0.0-py3-none-any.whl", hash = "sha256:b42cc903713c9706af03b3f2548bda4be7307a7cf89b7d6eae3da872717d1b63"},
+    {file = "dash_html_components-2.0.0.tar.gz", hash = "sha256:8703a601080f02619a6390998e0b3da4a5daabe97a1fd7a9cebc09d015f26e50"},
+]
+
+[[package]]
+name = "dash-table"
+version = "5.0.0"
+summary = "Dash table"
+groups = ["trials"]
+files = [
+    {file = "dash_table-5.0.0-py3-none-any.whl", hash = "sha256:19036fa352bb1c11baf38068ec62d172f0515f73ca3276c79dee49b95ddc16c9"},
+    {file = "dash_table-5.0.0.tar.gz", hash = "sha256:18624d693d4c8ef2ddec99a6f167593437a7ea0bf153aa20f318c170c5bc7308"},
+]
+
 [[package]]
 name = "debugpy"
 version = "1.8.1"
@@ -459,6 +525,24 @@ files = [
     {file = "filelock-3.14.0.tar.gz", hash = "sha256:6ea72da3be9b8c82afd3edcf99f2fffbb5076335a5ae4d03248bb5b6c3eae78a"},
 ]
 
+[[package]]
+name = "flask"
+version = "3.0.3"
+requires_python = ">=3.8"
+summary = "A simple framework for building complex web applications."
+groups = ["trials"]
+dependencies = [
+    "Jinja2>=3.1.2",
+    "Werkzeug>=3.0.0",
+    "blinker>=1.6.2",
+    "click>=8.1.3",
+    "itsdangerous>=2.1.2",
+]
+files = [
+    {file = "flask-3.0.3-py3-none-any.whl", hash = "sha256:34e815dfaa43340d1d15a5c3a02b8476004037eb4840b34910c6e21679d288f3"},
+    {file = "flask-3.0.3.tar.gz", hash = "sha256:ceb27b0af3823ea2737928a4d99d125a06175b8512c445cbd9a9ce200ef76842"},
+]
+
 [[package]]
 name = "fqdn"
 version = "1.5.1"
@@ -550,12 +634,26 @@ name = "idna"
 version = "3.7"
 requires_python = ">=3.5"
 summary = "Internationalized Domain Names in Applications (IDNA)"
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "idna-3.7-py3-none-any.whl", hash = "sha256:82fee1fc78add43492d3a1898bfa6d8a904cc97d8427f683ed8e798d07761aa0"},
     {file = "idna-3.7.tar.gz", hash = "sha256:028ff3aadf0609c1fd278d8ea3089299412a7a8b9bd005dd08b9f8285bcb5cfc"},
 ]
 
+[[package]]
+name = "importlib-metadata"
+version = "7.1.0"
+requires_python = ">=3.8"
+summary = "Read metadata from Python packages"
+groups = ["trials"]
+dependencies = [
+    "zipp>=0.5",
+]
+files = [
+    {file = "importlib_metadata-7.1.0-py3-none-any.whl", hash = "sha256:30962b96c0c223483ed6cc7280e7f0199feb01a0e40cfae4d4450fc6fab1f570"},
+    {file = "importlib_metadata-7.1.0.tar.gz", hash = "sha256:b78938b926ee8d5f020fc4772d487045805a55ddbad2ecf21c6d60938dc7fcd2"},
+]
+
 [[package]]
 name = "intel-openmp"
 version = "2021.4.0"
@@ -651,6 +749,17 @@ files = [
     {file = "isoduration-20.11.0.tar.gz", hash = "sha256:ac2f9015137935279eac671f94f89eb00584f940f5dc49462a0c4ee692ba1bd9"},
 ]
 
+[[package]]
+name = "itsdangerous"
+version = "2.2.0"
+requires_python = ">=3.8"
+summary = "Safely pass data to untrusted environments and back."
+groups = ["trials"]
+files = [
+    {file = "itsdangerous-2.2.0-py3-none-any.whl", hash = "sha256:c6242fc49e35958c8b15141343aa660db5fc54d4f13a1db01a3f5891b98700ef"},
+    {file = "itsdangerous-2.2.0.tar.gz", hash = "sha256:e0050c0b7da1eea53ffaf149c0cfbb5c6e2e2b69c4bef22c81fa6eb73e5f6173"},
+]
+
 [[package]]
 name = "jedi"
 version = "0.19.1"
@@ -670,7 +779,7 @@ name = "jinja2"
 version = "3.1.4"
 requires_python = ">=3.7"
 summary = "A very fast and expressive template engine."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 dependencies = [
     "MarkupSafe>=2.0",
 ]
@@ -1038,7 +1147,7 @@ name = "markupsafe"
 version = "2.1.5"
 requires_python = ">=3.7"
 summary = "Safely add untrusted strings to HTML/XML markup."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:629ddd2ca402ae6dbedfceeba9c46d5f7b2a61d9749597d4307f943ef198fc1f"},
     {file = "MarkupSafe-2.1.5-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:5b7b716f97b52c5a14bffdf688f971b2d5ef4029127f1ad7a513973cfd818df2"},
@@ -1203,7 +1312,7 @@ name = "nest-asyncio"
 version = "1.6.0"
 requires_python = ">=3.5"
 summary = "Patch asyncio to allow nested event loops"
-groups = ["notebooks"]
+groups = ["notebooks", "trials"]
 files = [
     {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
     {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
@@ -1974,7 +2083,7 @@ name = "requests"
 version = "2.31.0"
 requires_python = ">=3.7"
 summary = "Python HTTP for Humans."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 dependencies = [
     "certifi>=2017.4.17",
     "charset-normalizer<4,>=2",
@@ -1986,6 +2095,19 @@ files = [
     {file = "requests-2.31.0.tar.gz", hash = "sha256:942c5a758f98d790eaed1a29cb6eefc7ffb0d1cf7af05c3d2791656dbd6ad1e1"},
 ]
 
+[[package]]
+name = "retrying"
+version = "1.3.4"
+summary = "Retrying"
+groups = ["trials"]
+dependencies = [
+    "six>=1.7.0",
+]
+files = [
+    {file = "retrying-1.3.4-py3-none-any.whl", hash = "sha256:8cc4d43cb8e1125e0ff3344e9de678fefd85db3b750b81b2240dc0183af37b35"},
+    {file = "retrying-1.3.4.tar.gz", hash = "sha256:345da8c5765bd982b1d1915deb9102fd3d1f7ad16bd84a9700b85f64d24e8f3e"},
+]
+
 [[package]]
 name = "rfc3339-validator"
 version = "0.1.4"
@@ -2229,7 +2351,7 @@ name = "setuptools"
 version = "69.5.1"
 requires_python = ">=3.8"
 summary = "Easily download, build, install, upgrade, and uninstall Python packages"
-groups = ["default"]
+groups = ["default", "trials"]
 files = [
     {file = "setuptools-69.5.1-py3-none-any.whl", hash = "sha256:c636ac361bc47580504644275c9ad802c50415c7522212252c033bd15f301f32"},
     {file = "setuptools-69.5.1.tar.gz", hash = "sha256:6c1fccdac05a97e598fb0ae3bbed5904ccb317337a51139dcd51453611bbb987"},
@@ -2240,7 +2362,7 @@ name = "six"
 version = "1.16.0"
 requires_python = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*"
 summary = "Python 2 and 3 compatibility utilities"
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "six-1.16.0-py2.py3-none-any.whl", hash = "sha256:8abb2f1d86890a2dfb989f9a77cfcfd3e47c2a354b01111771326f8aa26e0254"},
     {file = "six-1.16.0.tar.gz", hash = "sha256:1e61c37477a1626458e36f7b1d82aa5c9b094fa4802892072e49de9c60c4c926"},
@@ -2806,7 +2928,7 @@ name = "typing-extensions"
 version = "4.11.0"
 requires_python = ">=3.8"
 summary = "Backported and Experimental Type Hints for Python 3.8+"
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "typing_extensions-4.11.0-py3-none-any.whl", hash = "sha256:c1f94d72897edaf4ce775bb7558d5b79d8126906a14ea5ed1635921406c0387a"},
     {file = "typing_extensions-4.11.0.tar.gz", hash = "sha256:83f085bd5ca59c80295fc2a82ab5dac679cbe02b9f33f7d83af68e241bea51b0"},
@@ -2839,7 +2961,7 @@ name = "urllib3"
 version = "2.2.1"
 requires_python = ">=3.8"
 summary = "HTTP library with thread-safe connection pooling, file post, and more."
-groups = ["default", "notebooks"]
+groups = ["default", "notebooks", "trials"]
 files = [
     {file = "urllib3-2.2.1-py3-none-any.whl", hash = "sha256:450b20ec296a467077128bff42b73080516e71b56ff59a60a02bef2232c4fa9d"},
     {file = "urllib3-2.2.1.tar.gz", hash = "sha256:d0570876c61ab9e520d776c38acbbb5b05a776d3f9ff98a5c8fd5162a444cf19"},
@@ -2923,6 +3045,20 @@ files = [
     {file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
 ]
 
+[[package]]
+name = "werkzeug"
+version = "3.0.3"
+requires_python = ">=3.8"
+summary = "The comprehensive WSGI web application library."
+groups = ["trials"]
+dependencies = [
+    "MarkupSafe>=2.1.1",
+]
+files = [
+    {file = "werkzeug-3.0.3-py3-none-any.whl", hash = "sha256:fc9645dc43e03e4d630d23143a04a7f947a9a3b5727cd535fdfe155a17cc48c8"},
+    {file = "werkzeug-3.0.3.tar.gz", hash = "sha256:097e5bfda9f0aba8da6b8545146def481d06aa7d3266e7448e2cccf67dd8bd18"},
+]
+
 [[package]]
 name = "widgetsnbextension"
 version = "4.0.10"
@@ -2933,3 +3069,14 @@ files = [
     {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
     {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
 ]
+
+[[package]]
+name = "zipp"
+version = "3.18.2"
+requires_python = ">=3.8"
+summary = "Backport of pathlib-compatible object wrapper for zip files"
+groups = ["trials"]
+files = [
+    {file = "zipp-3.18.2-py3-none-any.whl", hash = "sha256:dce197b859eb796242b0622af1b8beb0a722d52aa2f57133ead08edd5bf5374e"},
+    {file = "zipp-3.18.2.tar.gz", hash = "sha256:6278d9ddbcfb1f1089a88fde84481528b07b0e10474e09dcfe53dad4069fa059"},
+]
diff --git a/pyproject.toml b/pyproject.toml
index 2388983..7218e7c 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,4 +32,5 @@ notebooks = [
 ]
 trials = [
     "plotly>=5.22.0",
+    "dash>=2.17.0",
 ]
diff --git a/src/lang_main/__init__.py b/src/lang_main/__init__.py
index ced02b3..c6ae768 100644
--- a/src/lang_main/__init__.py
+++ b/src/lang_main/__init__.py
@@ -1,5 +1,8 @@
 from typing import Final, Any
 import inspect
+import sys
+import logging
+from time import gmtime
 from pathlib import Path
 
 from lang_main.shared import (
@@ -11,7 +14,6 @@ from lang_main.shared import (
 from lang_main.analysis.preprocessing import Embedding, PandasIndex
 from lang_main.analysis.graphs import TokenGraph
 
-
 __all__ = [
     'save_pickle',
     'load_pickle',
@@ -21,6 +23,15 @@ __all__ = [
     'TokenGraph',
 ]
 
+logging.Formatter.converter = gmtime
+LOG_FMT: Final[str] = '%(module)s:%(levelname)s | %(asctime)s | %(message)s'
+LOG_DATE_FMT: Final[str] = '%Y-%m-%d %H:%M:%S +0000'
+logging.basicConfig(
+    stream=sys.stdout,
+    format=LOG_FMT,
+    datefmt=LOG_DATE_FMT,
+)
+
 USE_INTERNAL_CONFIG: Final[bool] = True
 
 # load config data: internal/external
diff --git a/src/lang_main/analysis/graphs.py b/src/lang_main/analysis/graphs.py
index 98eafb2..0c524a2 100644
--- a/src/lang_main/analysis/graphs.py
+++ b/src/lang_main/analysis/graphs.py
@@ -1,7 +1,6 @@
 import typing
 from typing import Any, Self, Literal, overload, Final
 import sys
-import logging
 from collections.abc import Hashable
 from pathlib import Path
 import copy
@@ -12,14 +11,12 @@ from networkx import Graph, DiGraph
 import networkx as nx
 from pandas import DataFrame
 
+from lang_main.loggers import logger_graphs as logger
 from lang_main.shared import save_pickle, load_pickle
 
 # TODO change logging behaviour, add logging to file
 LOGGING_DEFAULT: Final[bool] = False
 
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.graphs')
 
 def get_graph_metadata(
     graph: Graph | DiGraph,
diff --git a/src/lang_main/analysis/preprocessing.py b/src/lang_main/analysis/preprocessing.py
index adbf142..feecfb6 100644
--- a/src/lang_main/analysis/preprocessing.py
+++ b/src/lang_main/analysis/preprocessing.py
@@ -1,7 +1,5 @@
 from typing import cast, Callable
 from collections.abc import Iterable
-import sys
-import logging
 from itertools import combinations
 import re
 from math import factorial
@@ -19,6 +17,7 @@ import sentence_transformers.util
 from tqdm import tqdm
 
 from lang_main.types import Embedding, PandasIndex
+from lang_main.loggers import logger_preprocess as logger
 from lang_main.pipelines.base import BasePipeline
 from lang_main.analysis.shared import (
     similar_index_connection_graph,
@@ -27,10 +26,6 @@ from lang_main.analysis.shared import (
 #from lang_main.analysis.graphs import update_graph, get_graph_metadata
 
 
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.preprocess')
-
 # ** (1) dataset preparation: loading and simple preprocessing
 # following functions used to load a given dataset and perform simple
 # duplicate cleansing based on all properties
@@ -436,6 +431,7 @@ def merge_similarity_dupl(
     similar_id_graph, _ = similar_index_connection_graph(similar_idx_pairs)
     
     for similar_id_group in similar_index_groups(similar_id_graph):
+        similar_id_group = list(similar_id_group)
         similar_data = merged_data.loc[similar_id_group,:]
         # keep first entry with max number occurrences, then number of 
         # associated objects, then length of entry
diff --git a/src/lang_main/analysis/shared.py b/src/lang_main/analysis/shared.py
index 5011324..9165e96 100644
--- a/src/lang_main/analysis/shared.py
+++ b/src/lang_main/analysis/shared.py
@@ -19,16 +19,17 @@ def similar_index_connection_graph(
         # inplace operation, parent/child do not really exist in undirected graph
         update_graph(graph=similar_id_graph, parent=idx1, child=idx2)
     
-    graph_info = get_graph_metadata(graph=similar_id_graph, logging=True)
+    graph_info = get_graph_metadata(graph=similar_id_graph, logging=False)
     
     return similar_id_graph, graph_info
 
+# TODO check returning tuple
 def similar_index_groups(
     similar_id_graph: Graph,
-) -> Iterator[list[PandasIndex]]:
+) -> Iterator[tuple[PandasIndex, ...]]:
     # groups of connected indices
     ids_groups = cast(Iterator[set[PandasIndex]],
                       nx.connected_components(G=similar_id_graph))
     
     for id_group in ids_groups:
-        yield list(id_group)
\ No newline at end of file
+        yield tuple(id_group)
\ No newline at end of file
diff --git a/src/lang_main/analysis/timeline.py b/src/lang_main/analysis/timeline.py
index 766de1a..3f67bb4 100644
--- a/src/lang_main/analysis/timeline.py
+++ b/src/lang_main/analysis/timeline.py
@@ -1,6 +1,4 @@
 from typing import cast
-import sys
-import logging
 from collections.abc import Iterable, Iterator
 
 import numpy as np
@@ -12,16 +10,13 @@ import sentence_transformers
 import sentence_transformers.util
 from tqdm.auto import tqdm # TODO: check deletion
 
-from lang_main.types import PandasIndex, ObjectID
+from lang_main.types import PandasIndex, ObjectID, TimelineCandidates
+from lang_main.loggers import logger_timeline as logger
 from lang_main.analysis.shared import (
     similar_index_connection_graph,
     similar_index_groups,
 )
 
-# ** Logging
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.time_analysis')
 
 def non_relevant_obj_ids(
     data: DataFrame,
@@ -42,6 +37,8 @@ def non_relevant_obj_ids(
             data.loc[(data[feature_obj_id]==obj_id), feature_uniqueness]
         )
         # check for uniqueness of given feature for current ObjectID
+        # ignore NaN values
+        feats_per_obj_id = feats_per_obj_id.dropna()
         unique_feats_per_obj_id = len(feats_per_obj_id.unique())
         
         if unique_feats_per_obj_id > thresh_unique_feat_per_id:
@@ -56,7 +53,7 @@ def remove_non_relevant_obj_ids(
     feature_uniqueness: str = 'HObjektText',
     feature_obj_id: str = 'ObjektID',
 ) -> DataFrame:
-    
+    logger.info("Removing non-relevant ObjectIDs from dataset")
     data = data.copy()
     ids_to_ignore = non_relevant_obj_ids(
         data=data,
@@ -65,7 +62,9 @@ def remove_non_relevant_obj_ids(
         feature_obj_id=feature_obj_id,
     )
     # only retain entries with ObjectIDs not in IDs to ignore
-    data = data.loc[~data[feature_obj_id].isin(ids_to_ignore)]
+    data = data.loc[~(data[feature_obj_id].isin(ids_to_ignore))]
+    logger.debug(f"Ignored ObjectIDs: {ids_to_ignore}")
+    logger.info("Non-relevant ObjectIDs removed successfully")
     
     return data
 
@@ -80,14 +79,13 @@ def filter_activities_per_obj_id(
 ) -> tuple[DataFrame, Series]:
     data = data.copy()
     # filter only relevant activities count occurrences for each ObjectID
-    #relevant_activity_types = list(relevant_activity_types) # TODO: check deletion
+    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(
         Series,
         data_filter_activities[feature_obj_id].value_counts(sort=True)
     )
-    
     # filter for ObjectIDs with more than given number of activities
     filt_below_thresh = (num_activities_per_obj_id <= threshold_num_activities)
     # index of series contains ObjectIDs
@@ -97,6 +95,7 @@ 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")
     
     return data_filter_activities, num_activities_per_obj_id
 
@@ -109,7 +108,7 @@ def generate_model_input(
         'VorgangsBeschreibung',
     ),
 ) -> DataFrame:
-    
+    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)
@@ -117,6 +116,7 @@ def generate_model_input(
         lambda x: ' - '.join(x),
         axis=1,
     )
+    logger.info("Model input generated successfully")
     
     return data
 
@@ -133,16 +133,17 @@ def generate_model_input(
 def get_timeline_candidates_index(
     data: DataFrame,
     num_activities_per_obj_id: Series,
+    *,
     model: SentenceTransformer,
     cos_sim_threshold: float,
     feature_obj_id: str = 'ObjektID',
     model_input_feature: str = 'nlp_model_input',
-) -> Iterator[tuple[ObjectID, list[PandasIndex]]]:
+) -> Iterator[tuple[ObjectID, tuple[PandasIndex, ...]]]:
     # already sorted ObjIDs (descending regarding number of activities)
     obj_ids = cast(Iterable[ObjectID],
                    num_activities_per_obj_id.index)
     
-    for obj_id in obj_ids:
+    for obj_id in tqdm(obj_ids):
         data_per_obj_id = cast(
             DataFrame,
             data.loc[data[feature_obj_id]==obj_id]
@@ -220,7 +221,58 @@ def candidates_by_index(
         yield idx_pair
 
 
-"""
-next part:
+def transform_timeline_candidates(
+    candidates: Iterator[tuple[ObjectID, tuple[PandasIndex, ...]]],
+) -> TimelineCandidates:
+    """function to build a mapping of ObjectIDs to their respective collection of
+    timeline candidates (as tuple), each candidate group is separated as distinct 
+    tuple within this outer tuple
 
-"""
\ No newline at end of file
+    Parameters
+    ----------
+    candidates : Iterator[tuple[ObjectID, tuple[PandasIndex, ...]]]
+        Iterator provided by ``get_timeline_candidates_index``
+
+    Returns
+    -------
+    dict[ObjectID, tuple[tuple[PandasIndex, ...], ...]]
+        dictionary: ObjectID -> tuple of candidate groups
+    """
+    
+    candidates_by_obj_id: TimelineCandidates = {}
+    
+    obj_id_target: ObjectID | None = None
+    collection: list[tuple[PandasIndex, ...]] = []
+    
+    for obj_id, cands in candidates:
+        if obj_id_target is None:
+            collection = []
+            obj_id_target = obj_id
+        elif obj_id_target != obj_id:
+            candidates_by_obj_id[obj_id_target] = tuple(collection)
+            collection = []
+            obj_id_target = obj_id
+        collection.append(cands)
+    
+    if collection and obj_id_target is not None:
+        candidates_by_obj_id[obj_id_target] = tuple(collection)
+    
+    return candidates_by_obj_id
+
+def map_obj_texts(
+    data: DataFrame,
+    obj_ids: Iterable[ObjectID],
+) -> dict[ObjectID, str]:
+    obj_id_to_text: dict[ObjectID, str] = {}
+    
+    for obj_id in obj_ids:
+        data_per_obj = cast(
+            DataFrame,
+            data.loc[data['ObjektID']==obj_id]
+        )
+        # just take first entry
+        obj_text = cast(str, data_per_obj['HObjektText'].dropna().iat[0])
+        obj_text = obj_text.strip(r' ,.:')
+        obj_id_to_text[obj_id] = obj_text
+    
+    return obj_id_to_text
\ No newline at end of file
diff --git a/src/lang_main/analysis/tokens.py b/src/lang_main/analysis/tokens.py
index b1436b1..02c05e9 100644
--- a/src/lang_main/analysis/tokens.py
+++ b/src/lang_main/analysis/tokens.py
@@ -1,6 +1,4 @@
 from typing import cast
-import sys
-import logging
 import re
 from itertools import combinations
 from collections.abc import Iterator
@@ -12,6 +10,7 @@ from spacy.lang.de import German as GermanSpacyModel
 from pandas import DataFrame
 from tqdm.auto import tqdm
 
+from lang_main.loggers import logger_token_analysis as logger
 from lang_main.analysis.graphs import (
     update_graph,
     TokenGraph,
@@ -19,9 +18,9 @@ from lang_main.analysis.graphs import (
 
 
 # ** Logging
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.token_analysis')
+#LOGGING_LEVEL = 'INFO'
+#logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
+#logger = logging.getLogger('ihm_analyse.token_analysis')
 
 # ** POS
 #POS_OF_INTEREST: frozenset[str] = frozenset(['NOUN', 'PROPN', 'ADJ', 'VERB', 'AUX'])
diff --git a/src/lang_main/loggers.py b/src/lang_main/loggers.py
new file mode 100644
index 0000000..f33302e
--- /dev/null
+++ b/src/lang_main/loggers.py
@@ -0,0 +1,24 @@
+from typing import Final
+import logging
+
+from lang_main.types import LoggingLevels
+
+LOGGING_LEVEL_PREPROCESS: Final[LoggingLevels] = 'INFO'
+LOGGING_LEVEL_PIPELINES: Final[LoggingLevels] = 'INFO'
+LOGGING_LEVEL_GRAPHS: Final[LoggingLevels] = 'INFO'
+LOGGING_LEVEL_TIMELINE: Final[LoggingLevels] = 'DEBUG'
+LOGGING_LEVEL_TOKEN_ANALYSIS: Final[LoggingLevels] = 'INFO'
+LOGGING_LEVEL_SHARED_HELPERS: Final[LoggingLevels] = 'INFO'
+
+logger_shared_helpers = logging.getLogger('lang_main.shared')
+logger_shared_helpers.setLevel(LOGGING_LEVEL_SHARED_HELPERS)
+logger_preprocess = logging.getLogger('lang_main.analysis.preprocessing')
+logger_graphs = logging.getLogger('lang_main.analysis.graphs')
+logger_graphs.setLevel(LOGGING_LEVEL_GRAPHS)
+logger_timeline = logging.getLogger('lang_main.analysis.timeline')
+logger_timeline.setLevel(LOGGING_LEVEL_TIMELINE)
+logger_token_analysis = logging.getLogger('lang_main.analysis.tokens')
+logger_token_analysis.setLevel(LOGGING_LEVEL_TOKEN_ANALYSIS)
+logger_preprocess.setLevel(LOGGING_LEVEL_PREPROCESS)
+logger_pipelines = logging.getLogger('lang_main.pipelines')
+logger_pipelines.setLevel(LOGGING_LEVEL_PIPELINES)
diff --git a/src/lang_main/pipelines/base.py b/src/lang_main/pipelines/base.py
index e77ac09..8273c4b 100644
--- a/src/lang_main/pipelines/base.py
+++ b/src/lang_main/pipelines/base.py
@@ -5,14 +5,9 @@ import logging
 from collections.abc import Callable
 from pathlib import Path
 
+from lang_main.loggers import logger_pipelines as logger
 from lang_main.shared import save_pickle, load_pickle
 
-
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.pipelines')
-
-
 # ** pipelines to perform given actions on dataset in a customisable manner
 
 class NoPerformableActionError(Exception):
@@ -94,8 +89,9 @@ class BasePipeline():
         self,
         filename: str,
     ) -> None:
-        target_filename = f'Pipe-{self.name}_Step-{self.curr_proc_idx}_' + filename + '.pickle'
+        target_filename = f'Pipe-{self.name}_Step-{self.curr_proc_idx}_' + filename
         target_path = self.working_dir.joinpath(target_filename)
+        target_path = target_path.with_suffix('.pkl')
         # saving file locally
         save_pickle(obj=self._intermediate_result, path=target_path)
     
@@ -104,7 +100,7 @@ class BasePipeline():
         saving_path: str,
         filename: str,
     ) -> tuple[Any, ...]:
-        target_path = saving_path + filename + '.pickle'
+        target_path = Path(saving_path + filename).with_suffix('.pkl')
         # loading DataFrame or Series from pickle
         data = load_pickle(target_path)
         
diff --git a/src/lang_main/pipelines/predefined.py b/src/lang_main/pipelines/predefined.py
index 367fefd..e440646 100644
--- a/src/lang_main/pipelines/predefined.py
+++ b/src/lang_main/pipelines/predefined.py
@@ -22,15 +22,6 @@ from lang_main.analysis.preprocessing import (
 )
 from lang_main.analysis.tokens import build_token_graph
 
-"""
-# ** config parameters
-SAVE_PATH_FOLDER: Final[Path] = Path(CONFIG['paths']['results'])
-DATE_COLS: Final[list[str]] = CONFIG['preprocess']['date_cols']
-FILENAME_COSSIM_FILTER_CANDIDATES: Final[str] =\
-    CONFIG['export_filenames']['filename_cossim_filter_candidates']
-THRESHOLD_SIMILARITY: Final[float] = CONFIG['preprocess']['threshold_similarity']
-"""
-
 # ** pipeline configuration
 # ** target feature preparation
 pipe_target_feat = BasePipeline(name='TargetFeature', working_dir=SAVE_PATH_FOLDER)
diff --git a/src/lang_main/shared.py b/src/lang_main/shared.py
index 428a353..e54286b 100644
--- a/src/lang_main/shared.py
+++ b/src/lang_main/shared.py
@@ -1,16 +1,11 @@
 from typing import Any
-import sys
 import os
 import shutil
-import logging
 import pickle
 import tomllib
 from pathlib import Path
 
-# ** Logging
-LOGGING_LEVEL = 'INFO'
-logging.basicConfig(level=LOGGING_LEVEL, stream=sys.stdout)
-logger = logging.getLogger('ihm_analyse.helpers')
+from lang_main.loggers import logger_shared_helpers as logger
 
 # ** Lib
 def create_saving_folder(
diff --git a/src/lang_main/types.py b/src/lang_main/types.py
index 5f53c66..85d032a 100644
--- a/src/lang_main/types.py
+++ b/src/lang_main/types.py
@@ -1,9 +1,19 @@
-from typing import TypeAlias
+from typing import TypeAlias, Literal
 
 import numpy as np
 from spacy.tokens.doc import Doc as SpacyDoc
 from torch import Tensor
 
+LoggingLevels: TypeAlias = Literal[
+    'DEBUG',
+    'INFO', 
+    'WARNING',
+    'ERROR',
+    'CRITICAL',
+]
+
 PandasIndex: TypeAlias = int | np.int64
 ObjectID: TypeAlias = int
-Embedding: TypeAlias = SpacyDoc | Tensor
\ No newline at end of file
+Embedding: TypeAlias = SpacyDoc | Tensor
+
+TimelineCandidates: TypeAlias = dict[ObjectID, tuple[tuple[PandasIndex, ...], ...]]
\ No newline at end of file
diff --git a/test-notebooks/dashboard/app.py b/test-notebooks/dashboard/app.py
new file mode 100644
index 0000000..c190b0d
--- /dev/null
+++ b/test-notebooks/dashboard/app.py
@@ -0,0 +1,159 @@
+from typing import cast
+
+from dash import (
+    Dash,
+    html,
+    dcc,
+    callback,
+    Output,
+    Input,
+    State,
+    dash_table,
+)
+import plotly.express as px
+import pandas as pd
+from pandas import DataFrame
+
+from lang_main import load_pickle
+from lang_main.types import TimelineCandidates, ObjectID
+
+#df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/gapminder_unfiltered.csv')
+
+# ** data
+data = cast(DataFrame, load_pickle('./data.pkl'))
+cands = cast(TimelineCandidates, load_pickle('./map_candidates.pkl'))
+texts = cast(dict[ObjectID, str], load_pickle('./map_texts.pkl'))
+table_feats = [
+    'ErstellungsDatum',
+    'ErledigungsDatum',
+    'VorgangsTypName',
+    'VorgangsBeschreibung',
+]
+table_feats_dates = [
+    'ErstellungsDatum',
+    'ErledigungsDatum',
+]
+
+# ** graph config
+markers = {
+    'size': 12,
+    'color': 'yellow',
+    'line': {
+        'width': 2,
+        'color': 'red',
+    },
+}
+hover_data = {
+    'ErstellungsDatum': '|%d.%m.%Y',
+    'VorgangsBeschreibung': True,
+}
+
+
+app = Dash(prevent_initial_callbacks=True)
+
+app.layout = [
+    html.H1(children='Demo Zeitreihenanalyse', style={'textAlign':'center'}),
+    html.Div(children=[
+        html.H2('Wählen Sie ein Objekt aus (ObjektID):'),
+        dcc.Dropdown(
+            list(cands.keys()),
+            id='dropdown-selection',
+            placeholder="ObjektID auswählen...",
+        )
+    ]),
+    html.Div(children=[
+        html.H3(id='object_text'),
+        dcc.Dropdown(id='choice-candidates'),
+        dcc.Graph(id='graph-output'),
+    ]),
+    html.Div(children=[
+        dash_table.DataTable(id='table-candidates')
+    ]),
+]
+
+@callback(
+    Output('object_text', 'children'),
+    Input('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_obj_text(obj_id):
+    obj_id = int(obj_id)
+    obj_text = texts[obj_id]
+    headline = f'HObjektText: {obj_text}'
+    return headline
+
+@callback(
+    Output('choice-candidates', 'options'),
+    Input('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_choice_candidates(obj_id):
+    obj_id = int(obj_id)
+    cands_obj_id = cands[obj_id]
+    choices = list(range(1, len(cands_obj_id)+1))
+    return choices
+
+@callback(
+    Output('graph-output', 'figure'),
+    Input('choice-candidates', 'value'),
+    State('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_timeline(index, obj_id):
+    obj_id = int(obj_id)
+    # title
+    obj_text = texts[obj_id]
+    title = f'HObjektText: {obj_text}'
+    # cands
+    cands_obj_id = cands[obj_id]
+    cands_choice = cands_obj_id[int(index)-1]
+    # data
+    df = data.loc[list(cands_choice)].sort_index()
+    # figure
+    fig = px.line(
+        data_frame=df,
+        x='ErstellungsDatum',
+        y='ObjektID',
+        title=title,
+        hover_data=hover_data,
+    )
+    fig.update_traces(
+        mode='markers+lines',
+        marker=markers,
+        marker_symbol='diamond'
+    )
+    fig.update_xaxes(
+        tickformat="%B\n%Y",
+        rangeslider_visible=True,
+    )
+    fig.update_yaxes(type='category')
+    fig.update_layout(hovermode="x unified")
+    return fig
+
+@callback(
+    [Output('table-candidates', 'data'),
+     Output('table-candidates', 'columns')],
+    Input('choice-candidates', 'value'),
+    State('dropdown-selection', 'value'),
+    prevent_initial_call=True,
+)
+def update_table_candidates(index, obj_id):
+    obj_id = int(obj_id)
+    # cands
+    cands_obj_id = cands[obj_id]
+    cands_choice = cands_obj_id[int(index)-1]
+    # data
+    df = data.loc[list(cands_choice)].sort_index()
+    df = (df
+          .filter(items=table_feats, 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
+
+if __name__ == '__main__':
+    app.run(debug=True)
\ No newline at end of file
diff --git a/test-notebooks/dashboard/data.pkl b/test-notebooks/dashboard/data.pkl
new file mode 100644
index 0000000..ec76da0
Binary files /dev/null and b/test-notebooks/dashboard/data.pkl differ
diff --git a/test-notebooks/dashboard/map_candidates.pkl b/test-notebooks/dashboard/map_candidates.pkl
new file mode 100644
index 0000000..77c1a48
Binary files /dev/null and b/test-notebooks/dashboard/map_candidates.pkl differ
diff --git a/test-notebooks/dashboard/map_texts.pkl b/test-notebooks/dashboard/map_texts.pkl
new file mode 100644
index 0000000..49bdc55
Binary files /dev/null and b/test-notebooks/dashboard/map_texts.pkl differ
diff --git a/test-notebooks/timeline_analysis.ipynb b/test-notebooks/timeline_analysis.ipynb
new file mode 100644
index 0000000..86d4127
--- /dev/null
+++ b/test-notebooks/timeline_analysis.ipynb
@@ -0,0 +1,2335 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "6b2c6dad-a9b0-4463-a129-7b991d51487d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "id": "165ff3f2-fd3c-48ce-ad93-af237909d8d3",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from pathlib import Path\n",
+    "\n",
+    "from sentence_transformers import SentenceTransformer\n",
+    "\n",
+    "from lang_main import (\n",
+    "    save_pickle,\n",
+    "    load_pickle,\n",
+    "    SAVE_PATH_FOLDER,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "id": "1013213d-d651-4ec7-a8ac-321c83b2a344",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "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": [
+    "model_stfr = SentenceTransformer('sentence-transformers/all-mpnet-base-v2')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "id": "d2c78879-ba2c-4b36-9fa2-6a9a138f04fc",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "WindowsPath('A:/Arbeitsaufgaben/lang-main/test-notebooks/results/test_new2')"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "p = Path(os.getcwd()) / SAVE_PATH_FOLDER\n",
+    "p"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "id": "b9e65423-bc49-49d5-8bd0-875fcb014f7e",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[WindowsPath('A:/Arbeitsaufgaben/lang-main/test-notebooks/results/test_new2/Pipe-TargetFeature_Step-3_remove_NA.pickle'),\n",
+       " WindowsPath('A:/Arbeitsaufgaben/lang-main/test-notebooks/results/test_new2/map_candidates.pkl'),\n",
+       " WindowsPath('A:/Arbeitsaufgaben/lang-main/test-notebooks/results/test_new2/map_texts.pkl'),\n",
+       " WindowsPath('A:/Arbeitsaufgaben/lang-main/test-notebooks/results/test_new2/data.pkl')]"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "folder = list(p.glob(r'*'))\n",
+    "folder"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c0679937-d669-4eaa-aec6-85d12edc4a0a",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "\n",
+    "# Gather timeline candidates"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "id": "e4abc1cc-e69d-43a3-b328-2ef3f08a9c90",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from lang_main.analysis.timeline import (\n",
+    "    remove_non_relevant_obj_ids,\n",
+    "    filter_activities_per_obj_id,\n",
+    "    generate_model_input,\n",
+    "    get_timeline_candidates_index,\n",
+    "    transform_timeline_candidates,\n",
+    "    map_obj_texts,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "369838fc-3e77-4a9a-ac1f-e71a380e0353",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shared:INFO | 2024-05-22 13:45:11 +0000 | Loaded file successfully.\n",
+      "len(data)=123457\n",
+      "timeline:INFO | 2024-05-22 13:45:11 +0000 | Removing non-relevant ObjectIDs from dataset\n",
+      "timeline:DEBUG | 2024-05-22 13:45:12 +0000 | Ignored ObjectIDs: (0,)\n",
+      "timeline:INFO | 2024-05-22 13:45:12 +0000 | Non-relevant ObjectIDs removed successfully\n",
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 122958 entries, 0 to 123456\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column                   Non-Null Count   Dtype         \n",
+      "---  ------                   --------------   -----         \n",
+      " 0   VorgangsID               122958 non-null  int64         \n",
+      " 1   ObjektID                 122958 non-null  int64         \n",
+      " 2   HObjektText              122953 non-null  object        \n",
+      " 3   ObjektArtID              122958 non-null  int64         \n",
+      " 4   ObjektArtText            122880 non-null  object        \n",
+      " 5   VorgangsTypID            122958 non-null  int64         \n",
+      " 6   VorgangsTypName          122958 non-null  object        \n",
+      " 7   VorgangsDatum            122958 non-null  datetime64[ns]\n",
+      " 8   VorgangsStatusId         122958 non-null  int64         \n",
+      " 9   VorgangsPrioritaet       122958 non-null  int64         \n",
+      " 10  VorgangsBeschreibung     122958 non-null  object        \n",
+      " 11  VorgangsOrt              0 non-null       object        \n",
+      " 12  VorgangsArtText          122958 non-null  object        \n",
+      " 13  ErledigungsDatum         122958 non-null  datetime64[ns]\n",
+      " 14  ErledigungsArtText       122465 non-null  object        \n",
+      " 15  ErledigungsBeschreibung  115562 non-null  object        \n",
+      " 16  MPMelderArbeitsplatz     5709 non-null    object        \n",
+      " 17  MPAbteilungBezeichnung   5709 non-null    object        \n",
+      " 18  Arbeitsbeginn            119459 non-null  datetime64[ns]\n",
+      " 19  ErstellungsDatum         122958 non-null  datetime64[ns]\n",
+      "dtypes: datetime64[ns](4), int64(6), object(10)\n",
+      "memory usage: 19.7+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "ret = load_pickle(folder[0])\n",
+    "data = ret[0]\n",
+    "print(f\"{len(data)=}\")\n",
+    "data = remove_non_relevant_obj_ids(data, thresh_unique_feat_per_id=3)\n",
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0afe2888-ff74-4be8-b7e6-374546942070",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3d22728c-2fd9-4742-a4be-61909f14cfd7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "id": "79973397-4df2-43ca-9f40-182644592337",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Reparaturauftrag (Portal)', 'Wartung', 'Störungsmeldung'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "data['VorgangsTypName'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "id": "b10fbf48-9e1a-4d2b-9b41-b6711b366a34",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timeline:INFO | 2024-05-22 13:45:12 +0000 | Filtering activities per ObjectID\n",
+      "timeline:INFO | 2024-05-22 13:45:12 +0000 | Activities per ObjectID filtered successfully\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "ObjektID\n",
+       "1       283\n",
+       "1654    243\n",
+       "7       223\n",
+       "151     189\n",
+       "140     140\n",
+       "       ... \n",
+       "1698      2\n",
+       "683       2\n",
+       "1135      2\n",
+       "2170      2\n",
+       "297       2\n",
+       "Name: count, Length: 366, dtype: int64"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "activity_types = [\n",
+    "    'Reparaturauftrag (Portal)',\n",
+    "    'Störungsmeldung',\n",
+    "]\n",
+    "\n",
+    "(data, num_activities_per_obj_id) =\\\n",
+    "filter_activities_per_obj_id(data, relevant_activity_types=activity_types)\n",
+    "num_activities_per_obj_id"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "id": "39207835-e7b2-43c8-b6a6-8d685ac488a6",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6104 entries, 0 to 123456\n",
+      "Data columns (total 20 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   VorgangsID               6104 non-null   int64         \n",
+      " 1   ObjektID                 6104 non-null   int64         \n",
+      " 2   HObjektText              6103 non-null   object        \n",
+      " 3   ObjektArtID              6104 non-null   int64         \n",
+      " 4   ObjektArtText            6087 non-null   object        \n",
+      " 5   VorgangsTypID            6104 non-null   int64         \n",
+      " 6   VorgangsTypName          6104 non-null   object        \n",
+      " 7   VorgangsDatum            6104 non-null   datetime64[ns]\n",
+      " 8   VorgangsStatusId         6104 non-null   int64         \n",
+      " 9   VorgangsPrioritaet       6104 non-null   int64         \n",
+      " 10  VorgangsBeschreibung     6104 non-null   object        \n",
+      " 11  VorgangsOrt              0 non-null      object        \n",
+      " 12  VorgangsArtText          6104 non-null   object        \n",
+      " 13  ErledigungsDatum         6104 non-null   datetime64[ns]\n",
+      " 14  ErledigungsArtText       6079 non-null   object        \n",
+      " 15  ErledigungsBeschreibung  5812 non-null   object        \n",
+      " 16  MPMelderArbeitsplatz     5672 non-null   object        \n",
+      " 17  MPAbteilungBezeichnung   5672 non-null   object        \n",
+      " 18  Arbeitsbeginn            5915 non-null   datetime64[ns]\n",
+      " 19  ErstellungsDatum         6104 non-null   datetime64[ns]\n",
+      "dtypes: datetime64[ns](4), int64(6), object(10)\n",
+      "memory usage: 1001.4+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "b848d8ee-cdf3-4b92-ac5b-e5a74e562e92",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# VorgangsTypName, VorgangsArtText, VorgangsBeschreibung\n",
+    "prop_interest = ['VorgangsTypName', 'VorgangsArtText', 'VorgangsBeschreibung']\n",
+    "prop_interest = ['VorgangsArtText', 'VorgangsBeschreibung']\n",
+    "prop_interest = ['VorgangsBeschreibung']\n",
+    "\n",
+    "target_feature_name = 'nlp_model_input'"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "8d981bee-2fc9-4ea4-88a5-5589b1d76684",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "timeline:INFO | 2024-05-22 13:45:13 +0000 | Generating concatenation of model input features\n",
+      "timeline:INFO | 2024-05-22 13:45:13 +0000 | Model input generated successfully\n"
+     ]
+    }
+   ],
+   "source": [
+    "anlys_data = generate_model_input(data, target_feature_name, model_input_features=prop_interest)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "id": "616dc58f-f7d7-4787-b662-3900c66c3b64",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 6104 entries, 0 to 123456\n",
+      "Data columns (total 21 columns):\n",
+      " #   Column                   Non-Null Count  Dtype         \n",
+      "---  ------                   --------------  -----         \n",
+      " 0   VorgangsID               6104 non-null   int64         \n",
+      " 1   ObjektID                 6104 non-null   int64         \n",
+      " 2   HObjektText              6103 non-null   object        \n",
+      " 3   ObjektArtID              6104 non-null   int64         \n",
+      " 4   ObjektArtText            6087 non-null   object        \n",
+      " 5   VorgangsTypID            6104 non-null   int64         \n",
+      " 6   VorgangsTypName          6104 non-null   object        \n",
+      " 7   VorgangsDatum            6104 non-null   datetime64[ns]\n",
+      " 8   VorgangsStatusId         6104 non-null   int64         \n",
+      " 9   VorgangsPrioritaet       6104 non-null   int64         \n",
+      " 10  VorgangsBeschreibung     6104 non-null   object        \n",
+      " 11  VorgangsOrt              0 non-null      object        \n",
+      " 12  VorgangsArtText          6104 non-null   object        \n",
+      " 13  ErledigungsDatum         6104 non-null   datetime64[ns]\n",
+      " 14  ErledigungsArtText       6079 non-null   object        \n",
+      " 15  ErledigungsBeschreibung  5812 non-null   object        \n",
+      " 16  MPMelderArbeitsplatz     5672 non-null   object        \n",
+      " 17  MPAbteilungBezeichnung   5672 non-null   object        \n",
+      " 18  Arbeitsbeginn            5915 non-null   datetime64[ns]\n",
+      " 19  ErstellungsDatum         6104 non-null   datetime64[ns]\n",
+      " 20  nlp_model_input          6104 non-null   object        \n",
+      "dtypes: datetime64[ns](4), int64(6), object(11)\n",
+      "memory usage: 1.0+ MB\n"
+     ]
+    }
+   ],
+   "source": [
+    "anlys_data.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "5e147157-5710-440e-a787-5f6a6d66bc76",
+   "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>nlp_model_input</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>52136</th>\n",
+       "      <td>262888</td>\n",
+       "      <td>315</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>32</td>\n",
+       "      <td>Flurförderzeuge / Putzmaschine / Rasenmäher</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Reparaturauftrag (Portal)</td>\n",
+       "      <td>2022-07-11</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Flurförderzeug</td>\n",
+       "      <td>2022-07-12</td>\n",
+       "      <td>Intern UTT - Wartung</td>\n",
+       "      <td>Räder gereinigt</td>\n",
+       "      <td>Versand</td>\n",
+       "      <td>Versand</td>\n",
+       "      <td>2022-07-12</td>\n",
+       "      <td>2022-07-11</td>\n",
+       "      <td>Laufrollen verstopft (Garn)</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       VorgangsID  ObjektID HObjektText  ObjektArtID  \\\n",
+       "52136      262888       315         NaN           32   \n",
+       "\n",
+       "                                     ObjektArtText  VorgangsTypID  \\\n",
+       "52136  Flurförderzeuge / Putzmaschine / Rasenmäher              3   \n",
+       "\n",
+       "                 VorgangsTypName VorgangsDatum  VorgangsStatusId  \\\n",
+       "52136  Reparaturauftrag (Portal)    2022-07-11                 5   \n",
+       "\n",
+       "       VorgangsPrioritaet  ... VorgangsOrt VorgangsArtText ErledigungsDatum  \\\n",
+       "52136                   0  ...         NaN  Flurförderzeug       2022-07-12   \n",
+       "\n",
+       "         ErledigungsArtText ErledigungsBeschreibung MPMelderArbeitsplatz  \\\n",
+       "52136  Intern UTT - Wartung         Räder gereinigt              Versand   \n",
+       "\n",
+       "      MPAbteilungBezeichnung Arbeitsbeginn ErstellungsDatum  \\\n",
+       "52136                Versand    2022-07-12       2022-07-11   \n",
+       "\n",
+       "                   nlp_model_input  \n",
+       "52136  Laufrollen verstopft (Garn)  \n",
+       "\n",
+       "[1 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anlys_data.loc[anlys_data['HObjektText'].isna()]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ce40cc45-a2bc-4d84-9688-a4cf2797eecf",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3141fb4b-b861-41fe-a887-fe3a9dbbab9c",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "id": "a08d8f39-d814-4583-b77c-51e660cba6ec",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ObjektID\n",
+       "1       283\n",
+       "1654    243\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = num_activities_per_obj_id.iloc[300:302].copy()\n",
+    "test = num_activities_per_obj_id.iloc[:2].copy()\n",
+    "test"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "03b9ef0f-0fc9-4e16-bd1c-0774c8c295e8",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "366"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test = num_activities_per_obj_id.copy()\n",
+    "len(test)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "133ad574-7f55-421f-927b-969d26a80ffc",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "tl_candidates = get_timeline_candidates_index(\n",
+    "    data=anlys_data,\n",
+    "    num_activities_per_obj_id=test,\n",
+    "    model=model_stfr,\n",
+    "    cos_sim_threshold=0.8,\n",
+    "    model_input_feature=target_feature_name,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26085123-1336-4e7f-8b90-68b7c5cb6711",
+   "metadata": {
+    "collapsed": true,
+    "jupyter": {
+     "outputs_hidden": true
+    }
+   },
+   "source": [
+    "for (obj_id, cands) in tl_candidates:\n",
+    "    print(f'{obj_id=}, {cands=}', flush=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "14e2d02b-e24b-4a29-b410-17ef7a0d503d",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "id": "e690e6ff-92a5-48a8-b721-cf78dfe25ab7",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ec2ac9d40ab545d5b6d27b339eeac98e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/366 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "candidates_by_obj_id = transform_timeline_candidates(tl_candidates)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "id": "04b23a46-a349-4ee2-8910-17e92bf62835",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys([1, 1654, 7, 151, 140, 20, 21, 1652, 1656, 138, 137, 141, 1808, 22, 1139, 144, 306, 345, 346, 1662, 347, 1663, 405, 337, 336, 69, 1657, 65, 515, 73, 84, 250, 1809, 79, 325, 90, 63, 96, 326, 72, 193, 315, 59, 136, 67, 195, 91, 74, 51, 77, 60, 2166, 186, 76, 100, 121, 78, 2165, 70, 407, 66, 109, 132, 94, 2480, 115, 80, 114, 111, 92, 110, 317, 68, 180, 54, 1126, 318, 108, 58, 107, 47, 98, 113, 81, 181, 119, 309, 127, 53, 75, 95, 218, 120, 149, 123, 183, 135, 116, 255, 152, 57, 388, 319, 44, 148, 316, 89, 625, 220, 64, 338, 145, 175, 698, 52, 129, 241, 182, 895, 122, 41, 259, 404, 214, 167, 153, 2562, 1348, 223, 894, 168, 112, 256, 312, 423, 157, 126, 213, 1659, 184, 432, 216, 281, 170, 1705, 232, 171, 279, 632, 626, 1124, 1152])"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "candidates_by_obj_id.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "id": "49cb06c0-57c7-4bdc-81fd-1462c0735a63",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj_id_to_text = map_obj_texts(\n",
+    "    anlys_data, \n",
+    "    obj_ids=candidates_by_obj_id.keys(),\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "id": "da4a1d84-296d-4918-a667-f4937c88c962",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{1: '00001, Ausrüstungsanlage 1',\n",
+       " 1654: 'WEBEREI ALLGEMEIN, Weberei allgemein',\n",
+       " 7: '00007, Ausrüstung 2',\n",
+       " 151: '00151, Bandschneidmaschine 4 / Kitamura',\n",
+       " 140: '00140, Schlichtmaschine 4 - Sucker',\n",
+       " 20: '00020, Lasercutter 2',\n",
+       " 21: '00021, Lasercutter 3',\n",
+       " 1652: '01652, Vorwerk allgemein',\n",
+       " 1656: 'GRUNDSTÜCK UND GEBÄUDE, Grundstück und Gebäude',\n",
+       " 138: '00138, Schärmaschine 9',\n",
+       " 137: '00137, Schärmaschine 7 - Matthys',\n",
+       " 141: '00141, Schlichtmaschine 5 - Sucker',\n",
+       " 1808: '01809, Labor allgemein',\n",
+       " 22: '00022, Lasercutter 4',\n",
+       " 1139: '01139, Ausrüstung 2 / Spannrahmen',\n",
+       " 144: '00144, Zettelmaschine 1',\n",
+       " 306: 'B04, Ketteinlegewagen',\n",
+       " 345: '00348, Warenschautisch 4',\n",
+       " 346: '00349, Warenschautisch 5',\n",
+       " 1662: 'AUSRÜSTUNG 2 ALLGEMEIN, Ausrüstung 2 allgemein',\n",
+       " 347: '00350, Warenschautisch 6',\n",
+       " 1663: '01663, Bandschneiderei allgemein',\n",
+       " 405: '00405, Assembliermaschine 8',\n",
+       " 337: '00337, Bändchenablängmaschine 1',\n",
+       " 336: 'B22, Ketteinlegewagen',\n",
+       " 69: '448 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 1657: 'WARENSCHAU ALLGEMEIN, Warenschau allgemein',\n",
+       " 65: '434 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 515: 'B52, Elektro-Kettbaumhubwagen',\n",
+       " 73: '444 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 84: '451 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 250: '141C, Webmaschine, OM280SOS',\n",
+       " 1809: 'LASER ALLGEMEIN, Laser allgemein',\n",
+       " 79: '456 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 325: 'B41, Niederhubwagen',\n",
+       " 90: '466 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 63: '517 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 96: '474 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 326: 'B38, Niederhubwagen',\n",
+       " 72: '445 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 193: 'B18, Hochhubwagen',\n",
+       " 315: 'B15, Niederhubwagen',\n",
+       " 59: '514 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 136: '00136, Schärmaschine 3 - Benninger',\n",
+       " 67: '432 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 195: 'B36, Niederhubwagen',\n",
+       " 91: '467 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 74: '443 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 51: '507 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 77: '458 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 60: '515 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 2166: 'A061, Webmaschine Jacquard',\n",
+       " 186: '245 J, Webmaschine Jacquard',\n",
+       " 76: '441 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 100: '478 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 121: '217 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 78: '457 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 2165: 'A060, Webmaschine Jacquard',\n",
+       " 70: '447 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 407: '00407, Einziehmaschine Delta 110',\n",
+       " 66: '433 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 109: '422 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 132: '411 C , Webmaschine, DL 240 EMS',\n",
+       " 94: '472 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 2480: '00352, Ultraschallschweißanlage 1',\n",
+       " 115: '415 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 80: '455 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 114: '427 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 111: '424 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 92: '468 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 110: '423 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 317: 'B35, Niederhubwagen',\n",
+       " 68: '431 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 180: '207 C S SI, Webmaschine, DL 280 EMS Breite 220',\n",
+       " 54: '504 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 1126: '01126, Ausrüstung 2 / 0900-Streichmaschine',\n",
+       " 318: 'B45, Niederhubwagen',\n",
+       " 108: '421 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 58: '513 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 107: '437 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 47: '608 , Webmaschine, DL 190 SOS',\n",
+       " 98: '476 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 113: '426 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 81: '454 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 181: '221 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 119: '215 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 309: 'B08, Niederhubwagen',\n",
+       " 127: '624 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 53: '505 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 75: '442 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 95: '473 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 218: 'A004, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 120: '216 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 149: '00149, Bandschneidmaschine 2',\n",
+       " 123: '620 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 183: '223 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 135: '408 C, Webmaschine, DL 240 EMS',\n",
+       " 116: '212 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 255: '105, Webmaschine, OM 220 EOS',\n",
+       " 152: 'A001, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 57: '512 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 388: 'H31.1, Deckenlaufkran',\n",
+       " 319: 'B47, Niederhubwagen',\n",
+       " 44: '605 , Webmaschine, DL 190 SOS',\n",
+       " 148: '00148, Bandschneidmaschine 1',\n",
+       " 316: 'B34, Niederhubwagen',\n",
+       " 89: '465 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 625: 'R08 , Schnelllauftor',\n",
+       " 220: '271 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 64: '435 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 338: '00338, Bändchenablängmaschine 2',\n",
+       " 145: '00145, Zettelmaschine 2 - Mayer',\n",
+       " 175: 'A032 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 698: 'R13, Sektionaltor',\n",
+       " 52: '506 C , Webmaschine, DL 280 EMS Breite 280 Bj. 04-07',\n",
+       " 129: '626 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 241: '294 C, Webmaschine, SG 240 EMS',\n",
+       " 182: '222 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 895: 'U25, Ballenpresse',\n",
+       " 122: '610 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 41: '602, Webmaschine, DL 190 SOS',\n",
+       " 259: '109, Webmaschine, OM 220 EOS',\n",
+       " 404: '00404, Assembliermaschine 6',\n",
+       " 214: 'A010, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 167: 'A024 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 153: 'A002 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 2562: 'TECHNISCHE ENTWICKLUNG ALLGEMEIN, TECHNISCHE ENTWICKLUNG ALLGEMEIN',\n",
+       " 1348: '01348, Filteranlage für Lasercutter 2',\n",
+       " 223: '275 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 894: 'U22, Kippgerät (Versand)',\n",
+       " 168: 'A025 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 112: '425 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 256: '106, Webmaschine, OM 220 EOS',\n",
+       " 312: 'B12, Regalbediengerät',\n",
+       " 423: '00423, Knotex 2',\n",
+       " 157: 'A015 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 126: '623 C , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 213: 'A009, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 1659: '01659, Laser 3 allgemein',\n",
+       " 184: '224 C, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 432: 'B02 (ALT), Gabelstapler',\n",
+       " 216: 'A012, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 281: 'A051, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 170: 'A027 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 1705: 'EINZIEHEREI ALLGEMEIN, EINZIEHEREI ALLGEMEIN',\n",
+       " 232: '291 C, Webmaschine, SG 240 EMS',\n",
+       " 171: 'A028 , Webmaschine, DL 280 EMS Breite 280',\n",
+       " 279: 'A049, Webmaschine, DL 280 EMS Breite 280',\n",
+       " 632: 'R17 , Schnelllauftor',\n",
+       " 626: 'R09 , Schnelllauftor',\n",
+       " 1124: '01124, Ausrüstung 2 / 0700-Kühlaggregat',\n",
+       " 1152: '01152, Ausrüstung 2 / Strukturdetektor SD 1C Serie 210'}"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "obj_id_to_text"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "id": "d801c7ac-250e-4d22-8a3b-df302f161487",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "p_data = (p / 'data').with_suffix('.pkl')\n",
+    "p_cands = (p / 'map_candidates').with_suffix('.pkl')\n",
+    "p_texts = (p / 'map_texts').with_suffix('.pkl')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "id": "dda24ec0-e9ac-40dc-8d1b-c65ff57d21fe",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "shared:INFO | 2024-05-22 13:53:32 +0000 | Saved file successfully under A:\\Arbeitsaufgaben\\lang-main\\test-notebooks\\results\\test_new2\\data.pkl\n",
+      "shared:INFO | 2024-05-22 13:53:32 +0000 | Saved file successfully under A:\\Arbeitsaufgaben\\lang-main\\test-notebooks\\results\\test_new2\\map_candidates.pkl\n",
+      "shared:INFO | 2024-05-22 13:53:32 +0000 | Saved file successfully under A:\\Arbeitsaufgaben\\lang-main\\test-notebooks\\results\\test_new2\\map_texts.pkl\n"
+     ]
+    }
+   ],
+   "source": [
+    "save_pickle(anlys_data, p_data)\n",
+    "save_pickle(candidates_by_obj_id, p_cands)\n",
+    "save_pickle(obj_id_to_text, p_texts)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "401d6b0a-e880-482f-807b-9655ccb64b4b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "0009cec0-5793-4d4a-991e-3c5268b7eaec",
+   "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",
+       "       'nlp_model_input'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "anlys_data.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "d4f59a20-d8ba-4d90-a182-56028ddc3515",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dashboard_feats = [\n",
+    "    'ErstellungsDatum',\n",
+    "    'ErledigungsDatum',\n",
+    "    'VorgangsTypName',\n",
+    "    'VorgangsBeschreibung',\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "6c5459ec-986e-4971-a243-bbb048322f3f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "test = anlys_data.iloc[:20].filter(dashboard_feats, axis=1).sort_values(by='ErstellungsDatum', ascending=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "id": "bd86d16e-da54-4c93-a7aa-ea2a32b45600",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0     2019-03-19\n",
+       "1     2019-03-21\n",
+       "2     2019-03-25\n",
+       "6     2019-04-01\n",
+       "8     2019-09-09\n",
+       "10    2019-09-20\n",
+       "12    2019-09-24\n",
+       "11    2019-09-25\n",
+       "13    2019-10-02\n",
+       "14    2019-10-04\n",
+       "19    2019-10-27\n",
+       "20    2019-11-06\n",
+       "21    2019-11-08\n",
+       "22    2019-11-13\n",
+       "24    2019-11-27\n",
+       "25    2019-12-16\n",
+       "26    2019-12-19\n",
+       "27    2019-12-19\n",
+       "36    2020-02-06\n",
+       "42    2020-03-10\n",
+       "Name: ErstellungsDatum, dtype: object"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test['ErstellungsDatum'].dt.strftime('%Y-%m-%d')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b31aa36e-2d88-4c6c-9820-03a882da70fa",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "88ef77af-ee1b-40a7-bace-16fdd0a28ee7",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "id": "badcab3e-0a87-4623-932c-d4e52a086016",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[{'ErstellungsDatum': Timestamp('2019-03-19 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-03-20 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Kupplung schleift'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-03-21 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-03-21 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Gegengewicht wieder anbringen'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-03-25 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-03-25 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'da ist etwas gebrochen. (Herr Heininger)'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-04-01 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-04-08 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'KBK tauschen\\nUrsache vermutlich mechanisch'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-09-09 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-09-09 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Lager defekt.'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-09-20 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-09-20 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Obere Umlenkwalze Lager Defekt'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-09-24 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-09-24 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Abflussrohr beschädigt'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-09-25 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-09-26 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Bitte DU überprüfen, Verbindung zu Cyclop nicht in Ordnung'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-10-02 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-10-04 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Rad von der Gabel defekt.'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-10-04 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-10-04 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Cyclops scheibe putzen bitte ..'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-10-27 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-10-28 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Zahnriemen erneuern'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-11-06 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-11-06 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Not aus Störung WM:'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-11-08 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-11-11 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'beim Verfahren der Scanner kommt ein lautes Quitschgeräusch, bitte kontrollieren und beheben'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-11-13 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-11-15 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Gewinde defekt'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-11-27 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-11-27 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Kugellager an der walze tauschen und walze polieren\\n( am Trittbrett unten)'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-12-16 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-12-23 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Gummi Puffer defekt Bitte neuen bestellen\\n\\nBestellt am 17.12.2019'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-12-19 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-12-27 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Docke walze neu überziehen'},\n",
+       " {'ErstellungsDatum': Timestamp('2019-12-19 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2019-12-20 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Klemmbrett an neuer Ablage anbringen'},\n",
+       " {'ErstellungsDatum': Timestamp('2020-02-06 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2020-02-07 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'docken belag neu überziehen( gleicher belagtyp)'},\n",
+       " {'ErstellungsDatum': Timestamp('2020-03-10 00:00:00'),\n",
+       "  'ErledigungsDatum': Timestamp('2020-03-11 00:00:00'),\n",
+       "  'VorgangsTypName': 'Reparaturauftrag (Portal)',\n",
+       "  'VorgangsBeschreibung': 'Bitte Rau und Kontrawalze überziehen ( gleicher Belag \"Grau Weiß \"'}]"
+      ]
+     },
+     "execution_count": 38,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test.to_dict('records')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "32d39e61-4908-4994-8d64-dabc6d73c443",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b431760b-ac75-4160-903c-65007a25def3",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 228,
+   "id": "8748c6a3-6196-42d8-a12c-a92ae7081e30",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "obj_id = 1654\n",
+    "cands = [6696, 113620, 50223]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 229,
+   "id": "cee34ab6-6c10-45c7-afaa-9117e8680020",
+   "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>nlp_model_input</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>6696</th>\n",
+       "      <td>144239</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>2021-11-07</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Verkabelung / Verdrahtung</td>\n",
+       "      <td>2021-11-08</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Knotex Netzkabel wurde getauscht. Maschine läu...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2021-11-08</td>\n",
+       "      <td>2021-11-07</td>\n",
+       "      <td>Knotex Netzkabel hat einen Wackelkontakt. Kabe...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50223</th>\n",
+       "      <td>508708</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>2023-01-23</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Verkabelung / Verdrahtung</td>\n",
+       "      <td>2023-01-23</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Kabel an Stecker ab, gekürzt alles OK.</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2023-01-23</td>\n",
+       "      <td>2023-01-23</td>\n",
+       "      <td>Knotex Netzkabel hat Wackelkontakt.\\nHerr Bric...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>113620</th>\n",
+       "      <td>141606</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>2021-10-06</td>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>...</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>Verkabelung / Verdrahtung</td>\n",
+       "      <td>2021-10-06</td>\n",
+       "      <td>Intern UTT - Reparatur</td>\n",
+       "      <td>Kabel repariert (beide Seiten Wackelkontakt). ...</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>Weberei</td>\n",
+       "      <td>2021-10-06</td>\n",
+       "      <td>2021-10-06</td>\n",
+       "      <td>Knotex Netzkabel hat Wackelkontakt. Kabel lieg...</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3 rows × 21 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "        VorgangsID  ObjektID                             HObjektText  \\\n",
+       "6696        144239      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "50223       508708      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "113620      141606      1654  WEBEREI ALLGEMEIN, Weberei allgemein,    \n",
+       "\n",
+       "        ObjektArtID  ObjektArtText  VorgangsTypID            VorgangsTypName  \\\n",
+       "6696             90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "50223            90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "113620           90  UTT allgemein              3  Reparaturauftrag (Portal)   \n",
+       "\n",
+       "       VorgangsDatum  VorgangsStatusId  VorgangsPrioritaet  ... VorgangsOrt  \\\n",
+       "6696      2021-11-07                 5                   0  ...         NaN   \n",
+       "50223     2023-01-23                 5                   0  ...         NaN   \n",
+       "113620    2021-10-06                 5                   0  ...         NaN   \n",
+       "\n",
+       "                  VorgangsArtText ErledigungsDatum       ErledigungsArtText  \\\n",
+       "6696    Verkabelung / Verdrahtung       2021-11-08  Intern UTT - Reparatur    \n",
+       "50223   Verkabelung / Verdrahtung       2023-01-23  Intern UTT - Reparatur    \n",
+       "113620  Verkabelung / Verdrahtung       2021-10-06  Intern UTT - Reparatur    \n",
+       "\n",
+       "                                  ErledigungsBeschreibung  \\\n",
+       "6696    Knotex Netzkabel wurde getauscht. Maschine läu...   \n",
+       "50223              Kabel an Stecker ab, gekürzt alles OK.   \n",
+       "113620  Kabel repariert (beide Seiten Wackelkontakt). ...   \n",
+       "\n",
+       "       MPMelderArbeitsplatz MPAbteilungBezeichnung Arbeitsbeginn  \\\n",
+       "6696                Weberei                Weberei    2021-11-08   \n",
+       "50223               Weberei                Weberei    2023-01-23   \n",
+       "113620              Weberei                Weberei    2021-10-06   \n",
+       "\n",
+       "       ErstellungsDatum                                    nlp_model_input  \n",
+       "6696         2021-11-07  Knotex Netzkabel hat einen Wackelkontakt. Kabe...  \n",
+       "50223        2023-01-23  Knotex Netzkabel hat Wackelkontakt.\\nHerr Bric...  \n",
+       "113620       2021-10-06  Knotex Netzkabel hat Wackelkontakt. Kabel lieg...  \n",
+       "\n",
+       "[3 rows x 21 columns]"
+      ]
+     },
+     "execution_count": 229,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "res = anlys_data.loc[cands].sort_index()\n",
+    "res"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "id": "3801daba-6927-44c4-ba22-f0033411372a",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import plotly.express as px\n",
+    "import plotly.io as pio"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 267,
+   "id": "cbc277b4-f77f-4e52-9d3d-845a8dab6169",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "customdata": [
+          [
+           "Knotex Netzkabel hat einen Wackelkontakt. Kabel liegt in der Elektrowerkstatt."
+          ],
+          [
+           "Knotex Netzkabel hat Wackelkontakt.\nHerr Brich weiß bescheid"
+          ],
+          [
+           "Knotex Netzkabel hat Wackelkontakt. Kabel liegt in der Elektrowerkstatt auf der Werkbank."
+          ]
+         ],
+         "hovertemplate": "ErstellungsDatum=%{x|%d.%m.%Y}<br>ObjektID=%{y}<br>VorgangsBeschreibung=%{customdata[0]}<extra></extra>",
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+         "type": "scatter",
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+          "2021-11-07T00:00:00",
+          "2023-01-23T00:00:00",
+          "2021-10-06T00:00:00"
+         ],
+         "xaxis": "x",
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rwMueF/e1Ym/v4j5x+iknLL1ZVQa8xf2l+CRHdf/vcbe3GPbYstJV7StdAV59PBr3Ma84ZtwtPsYJwIc9Xk6yArzfWFYagE+ypU4/ewH4mC8y3IwAAQIECBAgQIAAAQIEZiYgAB+TdiUBeHnMN267c7tQubjsoHMOCgz6hWzjhCqDhlgPNsrw4qd/8seWha3D9qWtnrtf6DVJkFQPk/7sL77cd1uWfg5jtnHozcoA8ede8JzYY7cnLzMo9z8uVuP2C2nHCWbLi48TgJeri0fNu3FCu7IHd91z74q/uLMMQ8fdA3xU3fWQetTty3GO41ye60t/+3+3e6Oi2oPqWMa9fnH8uHN63gH40uPNP94Rd22+L6pfRLjS+0f5mFB8QuPe+7+77HGs/KLYZ//EM+Irf/+1Zb8bp0+jLKuBb3Hbcd8IGhaAT6OuquWwebOSFeDD5lY9AJ90LCsNwCe5bwjAV3pPcxwBAgQIECBAgAABAgQIzFJAAD6m7qgQYFBwMejj8YNCyTIEvvJzf7UsvCvPU+wzXOzBW/70u+6wALAIZYqfYm/rfiv7BoXgg0L5Kt+gVZ9lUFNfzV1cv/jSwOLLA4svl+y3wrjfdhbDAq7Sb9C2H8PaXd2yo773+LDfFeccN4wqbjsoAO/Xt1HzrjqeYeFZv3k4Sc1l3eMG4KN6VB/XuAHnuDVX+9VvX/Zp7LVe/7LR+tyadwBenYfF/6/P4UGPRcPuE9VPENTvv8N+N+x+WPTwz//qb5btJV7f5qXfY8GwN+jOueCSOPHYl/ZWp4+ae/Xel+PvV9eoHg+6P5f1T7oH+KjzlVu0lPvHTzKWlQbgRU1N7K0AH/NFhpsRIECAAAECBAgQIECAwMwEBOBj0o4KIketCu23X2u/7T3K4KgIwPfZc/e48eZ/WKqw3xdCjtpCoAgsqz/9Apl6MFKGjNX9uetbk9SDmDK4ufTya/qudh+0H3Q5pjIwGhQ8D1q9XI6tGnIOCoVGtXrYdiHDflcPHvtdp+pVjnXU7Yrfj7Onezn2UXOw3td7Nt8XxRY9g37q+yHfeNOtSzft1//qecbdh7vcymbNfvv0Vvhe8dlr+5ZTztuv/sM/Dq25Pn8G7UE/LMwd5DGuc3n8oAB80BiL48pr9Jsj43xhZ3W+DLLo9zgy7L5R3p/6HTfsd4PuF9XHlaVtmLZu7a1aL/f47vfdAMPuD9V+jgrAJ6lrnAC8fOyrPtYWVsVP9fFwkk/r1PtfnK/4AtJ+X27cb473M24SgDexF4CPeubxewIECBAgQIAAAQIECBCYtYAAfNbCMz7/qNBzxpd3egIEOiIwzqc4OjKUhStz3G2oFg7GgAkQIECAAAECBAgQIECAwBgCAvAxkFK+ybhbQqQ8BrURIDBbgUm+yHC2lTj7KIHqVizlbWf1vQejavF7AgQIECBAgAABAgQIECCQg4AAvINdLD/iX24XMOmWBh0cspIJEGggUH6J6wVnndrbb99PugL9tjSpbl2VbuUqI0CAAAECBAgQIECAAAECaQoIwNPsi6oIECBAgAABAgQIECBAgAABAgQIECBAoKGAALwhoMMJECBAgAABAgQIECBAgAABAgQIECBAIE0BAXiafVEVAQIECBAgQIAAAQIECBAgQIAAAQIECDQUEIA3BHQ4AQIECBAgQIAAAQIECBAgQIAAAQIECKQpIABPsy+qIkCAAAECBAgQIECAAAECBAgQIECAAIGGAgLwhoAOJ0CAAAECBAgQIECAAAECBAgQIECAAIE0BQTgafZFVQQIECBAgAABAgQIECBAgAABAgQIECDQUEAA3hDQ4QQIECBAgAABAgQIECBAgAABAgQIECCQpoAAPM2+qIoAAQIECBAgQIAAAQIECBAgQIAAAQIEGgoIwBsCOpwAAQIECBAgQIAAAQIECBAgQIAAAQIE0hQQgKfZF1URIECAAAECBAgQIECAAAECBAgQIECAQEMBAXhDQIcTIECAAAECBAgQIECAAAECBAgQIECAQJoCAvA0+6IqAgQIECBAgAABAgQIECBAgAABAgQIEGgoIABvCOhwAgQIECBAgAABAgQIECBAgAABAgQIEEhTQACeZl9URYAAAQIECBAgQIAAAQIECBAgQIAAAQINBQTgDQEdToAAAQIECBAgQIAAAQIECBAgQIAAAQJpCgjA0+yLqggQIECAAAECBAgQIECAAAECBAgQIECgoYAAvCGgwwkQIECAAAECBAgQIECAAAECBAgQIEAgTQEBeJp9URUBAgQIECBAgAABAgQIECBAgAABAgQINBQQgDcEdDgBAgQIECBAgAABAgQIECBAgAABAgQIpCkgAE+zL6oiQIAAAQIECBAgQIAAAQIECBAgQIAAgYYCAvCGgA4nQIAAAQIECBAgQIAAAQIECBAgQIAAgTQFBOBp9kVVBAgQIECAAAECBAgQIECAAAECBAgQINBQQADeENDhBAgQIECAAAECBAgQIECAAAECBAgQIJCmgAA8zb6oigABAgQIECBAgAABAgQIECBAgAABAgQaCgjAGwI6nAABAgQIECBAgAABAgQIECBAgAABAgTSFBCAp9kXVREgQIAAAQIECBAgQIAAAQIECBAgQIBAQwEBeENAhxMgQIAAAQIECBAgQIAAAQIECBAgQIBAmgIC8DT7oioCBAgQIECAAAECBAgQIECAAAECBAgQaCggAG8I6HACBAgQIECAAAECBAgQIECAAAECBAgQSFNAAJ5mX1RFgAABAgQIECBAgAABAgQIECBAgAABAg0FBOANAR1OgAABAgQIECBAgAABAgQIECBAgAABAmkKCMDT7IuqCBAgQIAAAQIECBAgQIAAAQIECBAgQKChgAC8IaDDCRAgQIAAAQIECBAgQIAAAQIECBAgQCBNAQF4mn1RFQECBAgQIECAAAECBAgQIECAAAECBAg0FBCANwR0OAECBAgQIECAAAECBAgQIECAAAECBAikKSAAT7MvqiJAgAABAgQIECBAgAABAgQIECBAgACBhgIC8IaADidAgAABAgQIECBAgAABAgQIECBAgACBNAUE4Gn2RVUECBAgQIAAAQIECBAgQIAAAQIECBAg0FBAAN4Q0OEECBAgQIAAAQIECBAgQIAAAQIECBAgkKaAADzNvqiKAAECBAgQIECAAAECBAgQIECAAAECBBoKCMAbAjqcAAECBAgQIECAAAECBAgQIECAAAECBNIUEICn2RdVESBAgAABAgQIECBAgAABAgQIECBAgEBDAQF4Q0CHEyBAgAABAgQIECBAgAABAgQIECBAgECaAgLwNPuiKgIECBAgQIAAAQIECBAgQIAAAQIECBBoKCAAbwjocAIECBAgQIAAAQIECBAgQIAAAQIECBBIU0AAnmZfVEWAAAECBAgQIECAAAECBAgQIECAAAECDQUE4A0Bb7t7S8MzODxngV132TF22GFV3PfgIzkP09jmLPDkXXaMHZ+wKu79rnk1Z/psLrfLzk+IJz3xCXHP/Q9nMyYDaV/giTvtELuu3inuvu977RejgmwEzKtsWpnsQPbZ44m911SPfP+xZGtUWPcE9tn9iXHflkfi4UfMq+51L92K9979ifHAlkfie+ZVuk2aYWVr9l49w7Pnf2oBeMMeC8AbAmZ+uAA88wa3NDwBeEvwGV1WAJ5RMxMaiqAyoWZkVIp5lVEzEx2KADzRxnS8LAF4xxuYaPkC8EQbM6eyBODNoAXgzfxCAN4QMPPDBeCZN7il4QnAW4LP6LIC8IyamdBQBJUJNSOjUsyrjJqZ6FAE4Ik2puNlCcA73sBEyxeAJ9qYOZUlAG8GLQBv5icAb+iX++EC8Nw73M74BODtuOd0VQF4Tt1MZyyCynR6kVMl5lVO3UxzLALwNPvS9aoE4F3vYJr1C8DT7Mu8qhKAN5MWgDfzE4A39Mv9cAF47h1uZ3wC8Hbcc7qqADynbqYzFkFlOr3IqRLzKqdupjkWAXiafel6VQLwrncwzfoF4Gn2ZV5VCcCbSQvAm/kJwBv65X64ADz3DrczPgF4O+45XVUAnlM30xmLoDKdXuRUiXmVUzfTHIsAPM2+dL0qAXjXO5hm/QLwNPsyr6oE4M2kBeDN/ATgDf1yP1wAnnuH2xmfALwd95yuKgDPqZvpjEVQmU4vcqrEvMqpm2mORQCeZl+6XpUAvOsdTLN+AXiafZlXVQLwZtIC8GZ+AvCGfrkfLgDPvcPtjE8A3o57TlcVgOfUzXTGIqhMpxc5VWJe5dTNNMciAE+zL12vSgDe9Q6mWb8APM2+zKsqAXgzaQF4Mz8BeEO/3A8XgOfe4XbGJwBvxz2nqwrAc+pmOmMRVKbTi5wqMa9y6maaYxGAp9mXrlclAO96B9OsXwCeZl/mVZUAvJm0ALyZnwC8oV/uhwvAc+9wO+MTgLfjntNVBeA5dTOdsQgq0+lFTpWYVzl1M82xCMDT7EvXqxKAd72DadYvAE+zL/OqSgDeTFoA3sxPAN7QL/fDBeC5d7id8QnA23HP6aoC8Jy6mc5YBJXp9CKnSsyrnLqZ5lgE4Gn2petVCcC73sE06xeAp9mXeVUlAG8mLQBv5icAb+iX++EC8Nw73M74BODtuOd0VQF4Tt1MZyyCynR6kVMl5lVO3UxzLALwNPvS9aoE4F3vYJr1C8DT7Mu8qhKAN5MWgDfzE4A39Mv9cAF47h1uZ3wC8Hbcc7qqADynbqYzFkFlOr3IqRLzKqdupjkWAXiafel6VQLwrncwzfoF4Gn2ZV5VCcCbSQvAm/kJwBv65X64ADz3DrczPgF4O+45XVUAnlM30xmLoDKdXuRUiXmVUzfTHIsAPM2+dL0qAXjXO5hm/QLwNPsyr6oE4M2kBeDN/ATgDf1yP1wAnnuH2xmfALwd95yuKgDPqZvpjEVQmU4vcqrEvMqpm2mORQCeZl+6XpUAvOsdTLN+AXiafZlXVQLwZtIC8GZ+AvCGfrkfLgDPvcPtjE8A3o57TlcVgOfUzXTGIqhMpxc5VWJe5dTNNMciAE+zL12vSgDe9Q6mWb8APM2+zKsqAXgzaQF4Mz8BeEO/3A8XgOfe4XbGJwBvxz2nqwrAc+pmOmMRVKbTi5wqMa9y6maaYxGAp9mXrlclAO96B9OsXwCeZl/mVZUAvJm0ALyZnwC8oV/uhwvAc+9wO+MTgLfjntNVBeA5dTOdsQgq0+lFTpWYVzl1M82xCMDT7EvXqxKAd72DadYvAE+zL/OqSgDeTFoA3sxPAN7QL/fDBeC5d7id8QnA23HP6aoC8Jy6mc5YBJXp9CKnSsyrnLqZ5lgE4Gn2petVCcC73sE06xeAp9mXeVUlAG8mLQBv5icAb+iX++EC8Nw73M74BODtuOd0VQF4Tt1MZyyCynR6kVMl5lVO3UxzLALwNPvS9aoE4F3vYJr1C8DT7Mu8qhKAN5MWgDfzE4A39Mv9cAF47h1uZ3wC8Hbcc7qqADynbqYzFkFlOr3IqRLzKqdupjkWAXiafel6VQLwrncwzfoF4Gn2ZV5VCcCbSQvAm/kJwBv65X64ADz3DrczPgF4O+45XVUAnlM30xmLoDKdXuRUiXmVUzfTHIsAPM2+dL0qAXjXO5hm/QLwNPsyr6oE4M2kZxKAX3fDzfGGt5y1VNkB++0dG88+LQ4+cE2zahM8+ra7tyRYlZJSERCAp9KJvOoQgOfVzzZGIwBvQz3/awoq8+9xGyM0r9pQX6xrCsAXq9/zGq0AfF7Si3UdAfhi9bs+WgF4s/5PPQB/78ZL44s33BwXnHVq7LnHbr3qNt97f5x85nnxgkMPibeuO75ZxYkdLQBPrCGJlSMAT6whmZQjAM+kkS0OQwDeIn7GlxZUZtzcFodmXrWIvyCXFoAvSKPnPEwB+JzBF+RyAvAFafSAYQrAm/V/qgF4sfL73I2XLgu/y/LKEPy0dcfH8w89pFnVCR0tAE+oGQmWIgBPsCkZlCQAz6CJLQ9BAN5yAzK9vKAy08a2PCz9wG3qAAAgAElEQVTzquUGLMDlBeAL0OQWhigAbwF9AS4pAF+AJg8ZogC8Wf+nGoAXq7+f/tT949ijDu9b1WVXfj6+9s3bs1oFLgBvNgFzP1oAnnuH2xmfALwd95yuKgDPqZvpjEVQmU4vcqrEvMqpm2mORQCeZl+6XpUAvOsdTLN+AXiafZlXVQLwZtJTC8C3PPRwvOOcC+O4Y44YuMK7WCH+scuviXeeflKs3mXnZpUncrQAPJFGJFqGADzRxnS8LAF4xxuYQPkC8ASakGEJgsoMm5rAkMyrBJqQeQkC8Mwb3NLwBOAtwWd+WQF45g0eMTwBeLP+Ty0AL7Y4Wf+eTXH6KScM/LLLW75+W5xz/iWx4W1rl/YHb1Z++0cLwNvvQcoVCMBT7k53axOAd7d3qVQuAE+lE3nVIajMq5+pjMa8SqUT+dYhAM+3t22OTADepn6+1xaA59vbcUYmAB9HafBtBODN/EIA3hAw88MF4Jk3uKXhCcBbgs/osgLwjJqZ0FAElQk1I6NSzKuMmpnoUATgiTam42UJwDvewETLF4An2pg5lSUAbwYtAG/mJwBv6Jf74QLw3DvczvgE4O2453RVAXhO3UxnLILKdHqRUyXmVU7dTHMsAvA0+9L1qgTgXe9gmvULwNPsy7yqEoA3k55qAH7ymefFjTfdOrSi5zzroLjgrFNtgdKsb47uiIAAvCON6liZAvCONSzBcgXgCTYlg5IElRk0McEhmFcJNiWzkgTgmTU0keEIwBNpRGZlCMAza+iEwxGATwhWu/nUAvBmZXT3aFugdLd386hcAD4P5cW7hgB88Xo+7RELwKct6nyFgKDSPJiFgHk1C1XnrAoIwM2HWQgIwGeh6pwC8MWeAwLwZv0XgDfzswVKQ7/cDxeA597hdsYnAG/HPaerCsBz6mY6YxFUptOLnCoxr3LqZppjEYCn2ZeuVyUA73oH06xfAJ5mX+ZVlQC8mbQAvJmfALyhX+6HC8Bz73A74xOAt+Oe01UF4Dl1M52xCCrT6UVOlZhXOXUzzbEIwNPsS9erEoB3vYNp1i8AT7Mv86pKAN5MemoB+OZ7748Nv/ORWP/mEwfu7z3ObZoNZ/5H2wJl/uZduqIAvEvd6k6tAvDu9CrVSgXgqXam23UJKrvdv1SrN69S7Uw+dQnA8+llSiMRgKfUjXxqEYDn08uVjEQAvhK1Hx4z1QDcl2A2a4aj8xMQgOfX0xRGJABPoQvdrkEA3u3+pVq9oDLVznS7LvOq2/3rQvUC8C50qXs1CsC717MuVCwA70KXZlejALyZ7VQDcCvAmzXD0fkJCMDz62kKIxKAp9CFbtcgAO92/1KtXlCZame6XZd51e3+daF6AXgXutS9GgXg3etZFyoWgHehS7OrUQDezHZqAXizMrp7tC1Qutu7eVQuAJ+H8uJdQwC+eD2f9ogF4NMWdb5CQFBpHsxCwLyahapzVgUE4ObDLAQE4LNQdU4B+GLPAQF4s/5PPQAv9vle/55NcfopJ8TBB65ZVt11N9wcH7v8mnjn6SfF6l12blZ5IkcLwBNpRKJlCMATbUzHyxKAd7yBCZQvAE+gCRmWIKjMsKkJDMm8SqAJmZcgAM+8wS0NTwDeEnzmlxWAZ97gEcMTgDfr/1wD8Fu+flucc/4lseFtawd+UWaz4cz/aAH4/M27dEUBeJe61Z1aBeDd6VWqlQrAU+1Mt+sSVHa7f6lWb16l2pl86hKA59PLlEYiAE+pG/nUIgDPp5crGYkAfCVqPzxmrgH4ZVd+Pq69/itWgDfrmaM7JCAA71CzOlSqALxDzUq0VAF4oo3peFmCyo43MNHyzatEG5NRWQLwjJqZ0FAE4Ak1I6NSBOAZNXMFQxGArwCtcsjUAvBidfe6M86Nb91x98CKDthv79h49mnbbY3SbAjtHm0FeLv+qV9dAJ56h7pZnwC8m31LqWoBeErdyKcWQWU+vUxpJOZVSt3IsxYBeJ59bXtUAvC2O5Dn9QXgefZ13FEJwMeV6n+7qQXg5emH7QHerNQ0jxaAp9mXVKoSgKfSibzqEIDn1c82RiMAb0M9/2sKKvPvcRsjNK/aUF+sawrAF6vf8xqtAHxe0ot1HQH4YvW7PloBeLP+Tz0Ab1ZO944WgI/o2WOPxarvPhBbd9u9e82dQsVNAvBV39kcW5+y5xSqcIrcBATguXV0/uMRgM/ffBGuKKhchC7Pf4zm1fzNF+2KAvBF6/h8xisAn4/zol0l1wB81f33xdYn7xqxww6L1tKJxisAn4hruxtPPQAvVoDfeNOtcfhhz+1b2eev/et4zrMO8iWYzfrWjaMfeyz2POVXYsf/+3/irk9+Jrbuums36p5ilSsNwHe44/b4kWOOjId+/uVx7385e4oVOVUOAgLwHLrY7hgE4O3653p1QWWunW13XOZVu/6LcHUB+CJ0ef5jFIDP33wRrphjAL7qvntjn1cfFd//sZ+Izef/gRB8yEQWgDe7l88kAD/5zPPitHXHx/MPPWRZde/deGl88Yab44KzThWAN+tb+kcX4fevrY3VH7u4V+vD//wFcfcf/cnCheArCcCL8HufV74sdvzq3/fsHjjl38d9v70h/Z6rcG4CAvC5UWd7IQF4tq1tdWCCylb5s724eZVta5MZmAA8mVZkVYgAPKt2JjOY3ALwVQ88EPu8+uWx05ev7xlvOf5fx+bf+30h+IAZJwBvdlecegBelFN+IeaG9WuXQvAi/L7yc3/lSzCb9asbRxfh96//aqy+9L9HPCEiit1PNpch+OWxddfdujGOKVQ5aQC+LPzeLyLu2FbEA28+Le77z++aQkVOkYOAADyHLrY7BgF4u/65Xl1QmWtn2x2XedWu/yJcXQC+CF2e/xgF4PM3X4Qr5hSAr3rg/tj7l46Jnf/3FyOKnV/vi4hHIx583b+N7/zXC4TgfSa0ALzZvXwmAXhR0nU33BzrN2zqBd6fvOoL2a38LtntAV6bgI9ve7L6jy6J2DEiPhERT4+IF20LwR953vPjrj++YmFWgk8SgJfbnjzh1lsiDoyIv/iB22cj4g0RsTXigX//H+K+t/92s3u8o7MQEIBn0cZWByEAb5U/24sLKrNtbasDM69a5V+IiwvAF6LNcx+kAHzu5AtxwVwC8N7K79ccHTtdf13EPhFxzQ9yj69FxKsi4vsRW37pBNuhCMCnfp+eWQBehuBveMtZvT2/57HtSbH/+Pr3bIrTTzkhDj5wzTKsclX6t+64Ow7Yb+9lK9GL1ekfvPjKZbd/1xknxbFHHb7s34pQvxjPRe87c2lluwC8QlQNv3eKiI//4J2Qox///d/94EHtiIi4a7FC8HED8B3u+nb8yFEvjmXh948+bveHQvCpP/J1/IQC8I43MIHyBeAJNCHDEgSVGTY1gSGZVwk0IfMSBOCZN7il4QnAW4LP/LI5BODbhd9fiIifeLxxV9RC8As+GLFqVeZdHX94VoCPb9XvljMNwMsQ/GOXXxPvPP2kWL3Lzs2qHXD0locejnecc2Fc8dlrtwu3i0OK8Ps3N2yKd69fu10wXvy+CMCLn7euO35gfWX4XdxAAN6HqR5+Fw9cR9Zu938i4ucWKwQfJwAvwu99jvn53peFLq38LsPvklAIPpPHjq6eVADe1c6lU7cAPJ1e5FSJoDKnbqYzFvMqnV7kWokAPNfOtjsuAXi7/rlevesB+NDwu2xaJQR/8MTXx3fed74Q/HEbAXize/ZUAvBi5XXxxZc33nTryGpmuRq83wrwMhw/7pgjtvtSzrLYUQF4EaCfc/4lccavvS7etmHTsi/4tAI8IsYJv0vsegj+8U/H1ic9aeS86eoNRgXgY4XfQvCutn9mdQvAZ0a7MCcWgC9Mq+c6UEHlXLkX5mLm1cK0urWBCsBbo8/6wgLwrNvb2uC6HICPFX5XQ/BXR8QjEULwH043AXizu95UAvBmJUzv6H4BeL9w/uiXHLZsRXp9C5Tq9ifV1eN7PWW3XtB/2rrjbYFStm3r1tjz5DdFb8/vYtuTfiu/6y2uhOAPH/azcfeln8o2BB8WgC8Lvw+KiM9HRH3ld93OSvDpPWB0+EwC8A43L5HSBeCJNCKzMgSVmTU0keGYV4k0IuMyBOAZN7fFoQnAW8TP+NJdDcCXhd/7RcT/rGx7MqhfVz++pa4QfElIAN7szp19AF6u3t7wtrWx5x67RbkifP999+q75Um5V/iG9Wvjmc/40WV7ipdhejUAv3/L95t1oMtHb90au/z6ybHThz4YUexu88mIeNmYA7o5Iv5lRNwZ8fdrfi7e98rL4+Gdnjzmwd2/2a4Pfjv+42UvjQPuuSnixx5/AjhgzHEVIfjrt932ky98e1z+wrePeaCbESBAgAABAgQIECBAgAABAgTmI/DEhx+IUz95dDzzW38Zsf/j2cePj3ntynYoj7xpbTz0O+9f6O1Qdlu945hwbtZPYCYBeL89udfst09vn+7Dnvfs7b5cclqt6bcCvB6AF9cq9vM+d+OlA7+Ys1gR/vSn7h/P/afPjHVnnBvFF2fWf8p9wO9/8JFpld+985QB+EUXRhT3w8snDMBfGRF/H3Hr/i+Ic191VXxv5127Z7DCiosA/IzLXhprigD8+Y+/eTBuAP7/RcSbV0V8Z2t8+nmnxx//i3evsAqHESBAgAABAgQIECBAgAABAgRmI7BdAP5nEXHImNcqAvCTVkXcuTUe+Te/HA99YNNiB+BPKrZd8LNSgZkE4GWA/PIXHxbnXHBJnHjsS3tfPlkEz7P8QsxBW6Csf8+mOP2UE5a+AHNUHWX9xx51+DLXfivAF34P8IZboDzynOfGXZ/6TGzdbfeVzuGkj5vlFijf/ZV/F/ee9d6kx6+42QjYAmU2rot0VlugLFK35zdWW1XMz3qRrmReLVK32xmrLVDacc/9qrZAyb3D7Ywviy1Q9omIL0y2BcqWY4+LzRsvWujwu5hxtkBpdr+begBeDaGLVd/VALzfauxm5S8/ul8AXtyiCLRvv/Oe3r7fxU91JXpxzJWfvTZOPPbI3u+qe34XoX31RwA+oFsr/RLMzMPvQmtWX4Ip/J7mI0f3ziUA717PUqtYAJ5aR/KoR1CZRx9TG4V5lVpH8qtHAJ5fT1MYkQA8hS7kV0NXA/CiEyv9Ekzh9w/nsQC82X16rgH4qJXXKx1KdcuV8hzVL7qs//5Nrztqaf/vfseW25vU6xGAD+lQPQT/+ONfWFA95O8i4oiIuCsi95Xf5bBHBeDF7Yovw/yRo14cT7j1logDI+Iv+nwZZuXLL4XfK32kyOc4AXg+vWxrJALwtuTzvq6gMu/+tjU686ot+cW5rgB8cXo9z5EKwOepvTjX6nIAPnYIXtn3W/i9fG4LwJvd16cegBflXHbl5+Pa678S6998YvzuhR/vbYGy11N2i5PPPC+OP+aIme0B3oxiZUcv/BYoVbZqCF7sCf6JSghehN8viojNixN+FzTjBOC9EPyO2+NHjjmyfwgu/F7ZnTPjowTgGTd3TkMTgM8JesEuI6hcsIbPabjm1ZygF/gyAvAFbv4Mhy4AnyHuAp+66wH4yBBc+D10dgvAm935ZxKAFyUVq73f8JazllU3aGV1syG0e7QAvObfLwR/+mKG35ME4AND8CsjYt0P9u3ZGmHld7v39ZSuLgBPqRvdrEUA3s2+pV61oDL1DnWzPvOqm33rUtUC8C51qzu1CsC706suVZpDAL4Ugr/yF2Knv/5yRLEn+DUR8bWIeFVEfD/Cyu/+s1IA3uzeOrMAvFlZ3TlaAN6nV0UI/mtrY/XHLo7YISKKL6r9XsQjh/503HXZFbF19z260+CGlY67Ary8TLESfJ9f/PnY8ZavRjwpIh7c9psHfu0tcd8739OwGofnIiAAz6WT7Y1DAN6efc5XFlTm3N32xmZetWe/KFcWgC9Kp+c7TgH4fL0X5Wq5BOC9EPy+e2OfY4+OnW74UsTO24LveCxiy/H/Oja/f9PCf+FlvzktAG92TxeAN/MLAfgAwOpK8Ih4+J+/IO7+2Kdi6267NxTv1uGTBuDF6Ha4/Vuxzyt/YVsIXoTfv35q3Pdb7+7WwFU7UwEB+Ex5F+LkAvCFaPPcBymonDv5QlzQvFqINrc6SAF4q/zZXlwAnm1rWx1YTgH4Ugj+mlfETl++vucq/B4+vQTgze5+UwvAyy+IfONrXxYf+uhVceNNtw6t7DnPOiguOOvU2HOP3ZqNoOWjBeBDGvDoo7HX2l+OJ3zzG3HXxz8dW3fdteVuzf/yKwnAeyH443uCP/TiI+Pes947/8JdMWkBAXjS7elEcQLwTrSpc0UKKjvXsk4UbF51ok2dLlIA3un2JVu8ADzZ1nS6sNwC8KUQ/NVHxfcPfmZs3niRld9DZqgAvNndd2oBeFlGEYSvf8+mOP2UE+LgA9cMrK78osx3nn5SrN6l+LxDN38E4CP69uijserB7y7cyu9SZaUBeO+JYPM9sXXPvbp5x1D1TAUE4DPlXYiTC8AXos1zH6Sgcu7kC3FB82oh2tzqIAXgrfJne3EBeLatbXVgOQbgZQje2y1g1apWfVO/uAC8WYdaC8Bv+fptcc75l8SGt63t9CpwAXizCZj70U0C8NxtjG/lAgLwlds5cpuAANxMmIWAoHIWqs5pXpkDsxYQgM9aeDHPLwBfzL7PetS5BuCzdsvl/ALwZp2cegA+bjnX3XBzfOzya8IK8HHF3K6LAgLwLnYt/ZoF4On3KPUKBeCpd6ib9Qkqu9m31Ks2r1LvUPfrE4B3v4cpjkAAnmJXul+TALz7PWwyAgF4E72I1gLwZmWnc7QV4On0IsVKBOApdqX7NQnAu9/DtkcgAG+7A3leX1CZZ1/bHpV51XYH8r++ADz/HrcxQgF4G+r5X1MAnn+Ph41QAN6s/zMLwIs9vt9+9oVL1R2w396x8ezThu4L3mwo7RwtAG/HvStXFYB3pVPdqlMA3q1+pVitADzFrnS/JkFl93uY4gjMqxS7kldNAvC8+pnKaATgqXQirzoE4Hn1c9LRCMAnFVt++5kE4EX4fenl18QFZ526tL93sef3ujPOjQ3r18bzDz2kWdUJHS0AT6gZCZYiAE+wKRmUJADPoIktD0EA3nIDMr28oDLTxrY8LPOq5QYswOUF4AvQ5BaGKABvAX0BLikAX4AmDxmiALxZ/6cegG++9/44+czz4rR1x28XdOey73eVXADebALmfrQAPPcOtzM+AXg77jldVQCeUzfTGYugMp1e5FSJeZVTN9MciwA8zb50vSoBeNc7mGb9AvA0+zKvqgTgzaRnEoCvf8+mOP2UE7bb7qRYBX7O+ZfEhretXVoZ3qz89o8WgLffg5QrEICn3J3u1iYA727vUqlcAJ5KJ/KqQ1CZVz9TGY15lUon8q1DAJ5vb9scmQC8Tf18ry0Az7e344xMAD6O0uDbTD0A3/LQw/GOcy6M4445YrsV4ALwZs1ydPcEBODd61kXKhaAd6FLadcoAE+7P12tTlDZ1c6lXbd5lXZ/cqhOAJ5DF9MbgwA8vZ7kUJEAPIcurnwMAvCV2xVHTj0AL046aKuTYm/wr33z9njruuObVZ3Q0VaAJ9SMBEsRgCfYlAxKEoBn0MSWhyAAb7kBmV5eUJlpY1selnnVcgMW4PIC8AVocgtDFIC3gL4AlxSAL0CThwxRAN6s/1MJwMt9v2+86daR1TznWQct+3LMkQckfgMBeOINark8AXjLDcj08gLwTBs7x2EJwOeIvUCXElQuULPnOFTzao7YC3opAfiCNn7GwxaAzxh4QU8vAF/Qxj8+bAF4s/5PJQBvVkK3jxaAd7t/s65eAD5r4cU8vwB8Mfs+zVELwKep6VylgKDSXJiFgHk1C1XnrAoIwM2HWQgIwGeh6pwC8MWeAwLwZv0XgDfzCwF4Q8DMDxeAZ97gloYnAG8JPqPLCsAzamZCQxFUJtSMjEoxrzJqZqJDEYAn2piOlyUA73gDEy1fAJ5oY+ZUlgC8GfRMAvD3brw0Pnjxlcsqe9Prjspq7+9ycALwZhMw96MF4Ll3uJ3xCcDbcc/pqgLwnLqZzlgElen0IqdKzKucupnmWATgafal61UJwLvewTTrF4Cn2Zd5VSUAbyY91QC83Av8aWv2jXeeflKs3mXnXnVbHno43nHOhfGN2+7Mav/vYmwC8GYTMPejBeC5d7id8QnA23HP6aoC8Jy6mc5YBJXp9CKnSsyrnLqZ5lgE4Gn2petVCcC73sE06xeAp9mXeVUlAG8mPdUAvFj5Xfy8dd3xfasa9ftmQ2nnaAF4O+5duaoAvCud6ladAvBu9SvFagXgKXal+zUJKrvfwxRHYF6l2JW8ahKA59XPVEYjAE+lE3nVIQDPq5+TjkYAPqnY8ttPLQAvV3kfd8wR8fxDD+lb1XU33Bwfu/yaZavDm5Xf/tEC8PZ7kHIFAvCUu9Pd2gTg3e1dKpULwFPpRF51CCrz6mcqozGvUulEvnUIwPPtbZsjE4C3qZ/vtQXg+fZ2nJEJwMdRGnybqQXgxfYn69+zKU4/5YQ4+MA1fa94y9dvi3POvyQ2vG1t7LnHbs0qT+RoAXgijUi0DAF4oo3peFkC8I43MIHyBeAJNCHDEgSVGTY1gSGZVwk0IfMSBOCZN7il4QnAW4LP/LIC8MwbPGJ4AvBm/Z9aAG4FeLNGODpPAQF4nn1te1QC8LY70P3rC8C738MURyCoTLEr3a/JvOp+D1MfgQA89Q51sz4BeDf7lnrVAvDUOzTb+gTgzXynFoAXZYza43vU75sNpZ2jrQBvx70rVxWAd6VT3apTAN6tfqVYrQA8xa50vyZBZfd7mOIIzKsUu5JXTQLwvPqZymgE4Kl0Iq86BOB59XPS0QjAJxVbfvupBuDFNignn3lePG3Nvsv2+S5Xh3/jtjvjgrNOzWb7k4JSAN5sAuZ+tAA89w63Mz4BeDvuOV1VAJ5TN9MZi6AynV7kVIl5lVM30xyLADzNvnS9KgF41zuYZv0C8DT7Mq+qBODNpKcagJelXHbl5+PtZ1+4rLI3ve6oeOu645tVm+DRAvAEm5JQSQLwhJqRUSkC8Iya2dJQBOAtwWd+WUFl5g1uaXjmVUvwC3RZAfgCNXuOQxWAzxF7gS4lAF+gZvcZqgC8Wf9nEoA3K6lbRwvAu9WveVcrAJ+3+GJcTwC+GH2e5SgF4LPUXdxzCyoXt/ezHLl5NUtd5y4EBODmwSwEBOCzUHVOAfhizwEBeLP+C8Cb+dkCpaFf7ocLwHPvcDvjE4C3457TVQXgOXUznbEIKtPpRU6VmFc5dTPNsQjA0+xL16sSgHe9g2nWLwBPsy/zqkoA3kxaAN7MTwDe0C/3wwXguXe4nfEJwNtxz+mqAvCcupnOWASV6fQip0rMq5y6meZYBOBp9qXrVQnAu97BNOsXgKfZl3lVJQBvJi0Ab+YnAG/ol/vhAvDcO9zO+ATg7bjndFUBeE7dTGcsgsp0epFTJeZVTt1McywC8DT70vWqBOBd72Ca9QvA0+zLvKoSgDeTFoA38xOAN/TL/XABeO4dbmd8AvB23HO6qgA8p26mMxZBZTq9yKkS8yqnbqY5FgF4mn3pelUC8K53MM36BeBp9mVeVQnAm0kLwJv5CcAb+uV+uAA89w63Mz4BeDvuOV1VAJ5TN9MZi6AynV7kVIl5lVM30xyLADzNvnS9KgF41zuYZv0C8DT7Mq+qBODNpAXgzfwE4A39cj9cAJ57h9sZnwC8HfecrioAz6mb6YxFUJlOL3KqxLzKqZtpjkUAnmZful6VALzrHUyzfgF4mn2ZV1UC8GbSAvBmfgLwhn65Hy4Az73D7YxPAN6Oe05XFYDn1M10xiKoTKcXOVViXuXUzTTHIgBPsy9dr0oA3vUOplm/ADzNvsyrKgF4M2kBeDM/AXhDv9wPF4Dn3uF2xicAb8c9p6sKwHPqZjpjEVSm04ucKjGvcupmmmMRgKfZl65XJQDvegfTrF8AnmZf5lWVALyZtAC8mZ8AvKFf7ocLwHPvcDvjE4C3457TVQXgOXUznbEIKtPpRU6VmFc5dTPNsQjA0+xL16sSgHe9g2nWLwBPsy/zqkoA3kxaAN7MTwDe0C/3wwXguXe4nfEJwNtxz+mqAvCcupnOWASV6fQip0rMq5y6meZYBOBp9qXrVQnAu97BNOsXgKfZl3lVJQBvJi0Ab+YnAG/ol/vhAvDcO9zO+ATg7bjndFUBeE7dTGcsgsp0epFTJeZVTt1McywC8DT70vWqBOBd72Ca9QvA0+zLvKoSgDeTFoA38xOAN/TL/XABeO4dbmd8AvB23HO6qgA8p26mMxZBZTq9yKkS8yqnbqY5FgF4mn3pelUC8K53MM36BeBp9mVeVQnAm0kLwJv5CcAb+uV+uAA89w63Mz4BeDvuOV1VAJ5TN9MZi6AynV7kVIl5lVM30xyLADzNvnS9KgF41zuYZv0C8DT7Mq+qBODNpAXgzfwE4A39cj9cAJ57h9sZnwC8HfecrioAz6mb6YxFUJlOL3KqxLzKqZtpjkUAnmZful6VALzrHUyzfgF4mn2ZV1UC8GbSAvBmfgLwhn65Hy4Az73D7YxPAN6Oe05XFYDn1M10xiKoTKcXOVViXuXUzTTHIgBPsy9dr0oA3vUOplm/ADzNvsyrKgF4M2kBeDM/AXhDv9wPF4Dn3uF2xicAb8c9p6sKwHPqZjpjEVSm04ucKjGvcupmmmMRgKfZl65XJQDvegfTrF8AnmZf5lWVALyZtAC8mZ8AvKFf7ocLwHPvcDvjE4C3457TVQXgOXUznbEIKtPpRU6VmFc5dTPNsQjA0+xL16sSgHe9g2nWLwBPsy/zqkoA3kxaAN7MTwDe0C/3wwXguXe4nfEJwNtxz+mqAvCcupnOWASV6fQip0rMq5y6meZYBOBp9qXrVQnAu97BNOsXgKfZl3lVJUliWfAAACAASURBVABvJi0Ab+YnAG/ol/vhAvDcO9zO+ATg7bjndFUBeE7dTGcsgsp0epFTJeZVTt1McywC8DT70vWqBOBd72Ca9QvA0+zLvKoSgDeTFoA38xOAN/TL/XABeO4dbmd8AvB23HO6qgA8p26mMxZBZTq9yKkS8yqnbqY5FgF4mn3pelUC8K53MM36BeBp9mVeVQnAm0kLwJv5CcAb+uV+uAA89w63Mz4BeDvuOV1VAJ5TN9MZi6AynV7kVIl5lVM30xyLADzNvnS9KgF41zuYZv0C8DT7Mq+qBODNpAXgzfwcTYAAAQIECBAgQIAAAQIECBAgQIAAAQKJCgjAE22MsggQIECAAAECBAgQIECAAAECBAgQIECgmYAAvJmfowkQIECAAAECBAgQIECAAAECBAgQIEAgUQEBeKKNUdbiCrx346W9wb913fGLi2DkBAgkJXDL12+L39ywKd69fm0cfOCapGpTDAECiyPgNdLi9LoLI9187/1x8pnnxWnrjo/nH3pIF0pWY8IC5lPCzVEaAQJZCAjAs2ijQYwjcNmVn4+3n31hvOl1Ry2Fy8ULjfXv2RSnn3JCMqGOP+7G6WY3blOEhuvOODe+dcfdcfRLDot3nn5SrN5l524Ur8pkBbY89HC845wL4xu33RkXnHVq7LnHbr1ayz+civ9f/fdpDEQAPg3Fbp6jjfnWTSlVTyqwkudIr5EmVe7O7avzoay6+pp9pSMpXv9fe/1XZvIaTGC50q7M97jyeeyKz14bF73vzKU3K6674eb42OXXzGRurGSE5tNK1NI9pphfb3jLWb0Cp/FYlu5IVUagOwIC8O70SqUNBcoXwPc98OBS4C0Ab4jq8LEEUnuBPVbRbpSsQPmH3L33fzd+4Yjnx7FHHd6rtXiM+8w110Xx7wLwZNvXucLamG+dQ1JwI4FJniMF4I2okz24X/BXPPZc8OFPxBtPePnSG70rGYAAfCVqeR1TPo/tv+9ecfud9ywF3pM89sxDRAA+D+X5X6N4DPraN2/36e7507sige0EBOAmxcIIlE8+T3/q/ktPQv0C8OKPqw9efGXPpXy3tt/qx+JF07kbL10KmsoV5sVx1dW+xb//j//5v3vn+/O/+pve/xarD4r/X16nuhqhuH7x4qxY3XnjTbfGAfvtHRvPPm1phXr54qj4XXmu4mOXZY2vOPJnY8PvfiSe86yDph6CLcxkmfJA6y+wq3Ol2sOyt6946c/ERZdetd3K8fo8LF/QH3fMEb3VLPXVU9XVBuUfgLvu+qT46Cc/F//62JfEjTf9w7KP7VrlO+XGz+h0Zd9f8qKfjs/++Zdi/ZtP7F1pw+98JIp/+9BHr1q67w+bE8VjzQMPPhQPPPBgFKui3nXGSb0wvbpipXz8Kc5fbIFSPr5UHx9nNEynTURgGvNtnOfQRIarjBYEqs+RD33ve9ttKVE8VhWv3YrHp2oAXg/Dy7l62POevfTGYAvDcckVCNRfU/c7xaDXv8NeO912x11Ln8Qrzll9bTzodXu/1+Hv++1fjz+89DO958rq6/Ly2i849JCl1/T1T/xVn1Or1/e6fQUTZYWHlI8NrzjyZ+JPrv7LKF8311+fD5pj/d5EqT7+VFeYFyWWr6eK/z/t+TTs783ikw7l63wrjlc4WWZwWDUAr8+x6uNF/TGh/lq7Pg+L259z/iWx4W1re28SDvr7spyH1df8v/GmY+Oav/jysq0Nx3kcngGPUxKYq4AAfK7cLtamQPnkU6wkKbc92espuy3bAqX6xFLUWmwzUKwWKPbjrv4BVj6RlH+QFcddevk1S6FT9bbF787/8CeXQuzyyakMvetPZsWxX7zh5mXBevnRzfIPw+OPOaL3x131ie+e79zfe5F/1Itf6B3mNidan2tXX2AXv/7jK/5nvObof9nbDqU6d4rfFXtJPm3Nvr3VKeUcLP+YHxWAf+aaL8Yzn/FPem+WlMHnhvVre+F4fd4V566vSLCyLrGJM6Cc6hsf37ztzt6tnrpm3yj+f/G/1Tfmhs2Jot9Xfu6vlr3BVszV9Rs2Lf1bMY+2PPS9WL3LE5c9vlil1I25Mo0qpznfyudMQeU0OpPPOVYagNf/+PfHe3fnRPmaZdBr2PI5p9/r31GvnfqFl8Net9dfh9efK6uvlaoBePG3Qv2xrf6cWn3dNWrM3e1mepVXn8eK6sptT/725luX/n+/19zF31XF6+hnPuNHl/29WF1AtWa/fZb9vVhfXDXN+TTq781iq8/qoqr0OrGYFVXv98VjQvFT/G3W73Gt+rd8/bX2sAB8lyc+ceDfl0U43u81fzWv8LpsMefmIo5aAL6IXV/QMVeffPqF4eULmOrKoeofU1/9h39cepFUBNFliD7ouPLF1ac/d+2yvQfrf6DV/7seQlZDz3s237cs3Kq+oNtrz919SV2ic3vYRyyr/S3ekKl/mVL1xcmoALw6/PoLmX5/AFZfpNffDEqUUlkRS39gFyuYyj/Kdt/1Sb2V4MXjVDUAHzYn+r3hMehNkEnmniblJVB9nmky3+pv2FZXLeUlZjSTCqw0AC+uU18dXr7JMmkNbt++QL89wMswr/5auf64VH/tVH3NU38d3i/oqc7BYtuV4qf8Mvr666fqf/f7xEL19/Vz1R8Hfbn0fOZddb785CEH9QLr4jVUNQwvPi1Q70f9Eyfl40t1vgw6rt+nVorrrXQ+nfkb/ybO+t3/FvW/Uwf9vTkfWVcZR6C+4Kh6THWOjXqtPWoFePW89XP1e33vddk43XOb3AQE4Ll11HgGCtQ/flQE2MXHw4ptSIovwSyD7PJjccWJqk8MxX+XoXcRRJcvOIp/L15IFR+LrP6UH2lqGoBXQ8riuuWXaVSvVfyBIABPd/LXA/D6H3nlx2mbBuD1j2AWIuXHMPsF4NXwoFg5nNIXAaXbzfYrq299Uw2ABoUE1cenck4MCsD7BUijXpS3r6KCWQlMa77VV5LbD3NWHeveeZsE4OWxp/7qcfHO9344qS81714n0qq4+gnKYa9/izfm6gF4dU4NCsAHvW5vGoDXw/Ryu8NSt9wGpfjkpgB8PnOu/jxW9qjcEqX41GURZNffmK2v3C1fJxdzpHyt1O+Nm+rr7/prrUkD8LLWMgAf9+/N+ci6yjgC/T5xW31cGLTlan3ejgrAB/19WXwyuN9rfq/Lxume2+QmIADPraPGM1YAXtyofBIpvxRz1Arw8uND5R7iL3rhP+t9fKn+5FQvoP5k1XQF+KCQ0v7N6U7++kqR8iOV1b3b371+bTQJwMsVLeWWPeOsAC/EynlT/P/T/t1re3PaT9oCwx5zqo8vxcchq9s41eeEFeBp9zmV6qY134rx9ObnBz7aG1rxmFf8UeaHQJMAvJyfxZf//sTBT7UFXEbTqfrx/2JYg17/9tuSa5wV4NUFL1W2poFlfQX4oE8leN0+v8lafx4r/7v6pZijVoCXC5KKsLKYi8Wn7oq/DetbMdVHNa35VAbgg+btoIUu81N2pUEC1QC83BO+eNOl2ApzWivAy61Qy60vx1kB7nWZObuIAgLwRez6go65/u5r+YL5rnvuXbY/d7mXdz08KsPCs99/caxatWrpCyfKML26B3jxwqrc57npCvB+ew2WeyCWT1zF/1oBnu7EHvbipro/5KgAvP5HXvnFSsUnAMoAvPxoZH1fuUEvjMs/AoovXb3grFN7L+b9pC0waSA5aE70C8Dr+5WWexXWH19GvfGXtqDqJhGY1nwrrlk+LpXfc1D88eeHQPWxqP5GXbmi7ZTXv3K7L8Es5YrnN3vfdnseFc81xZfDl9uOLAUzj3/ZfPHfxSrvfq9/6yvA+71Wqm8NVt9Lufq6vckK8H7Xrn6vRjGOj1x2dRz1ksPCCvD5zdl+z2Pla+jyE7tFNdVFA/Xv0in/3vvMNdcte7OtGqaX87c49qv/8P/iF454wXYrbydZAd5vD+hx/96cn64rDRMYtvikPndGfdqy37ap5feG1R9P6q/nB21x6HWZ+btoAgLwRev4Ao+33/5b1Y9XlivRiieI8mNJ9W/Q7vcip/4HWPnfg7aeGGcFePVjUfVvk69/e7SPUqY5qcsX1kV1ZY/KcLk6x55zyDN6AxhnBXj5B2G5Dc5rX/nieOCBB5d9m335u2JblX323D2O/8V/1QsNhq0MqW6hkaamqqoC4waSxXyrzsP6nBj0YrgMk4prltvzFP+/+lFtAfjizMlpzbdCzLxZnHkzaqTDniOrH+MuXgPtuuuT4id/4ukDA/Bh37Mxqg6/T0Og/tq232unQa9/y3D8xptuXRpM+Rq8+rhTbB1RfT1Wfa4rbjdoe7BxAstB166/biv+e9B2B2l0Is8q+j33lP9WjLhcjVufY/UvlOwXitfnWPW1U7+tJ5rOp0Hz1grwtOZutU/Vv+Wrz2/l6/IX/NSzem/+jQrAixFW/4Zc/xsnxhe+eOPSorxBf18O2gLF67K05oxq5iMgAJ+Ps6sQIEAgSYHixf6G3/nI0kc5kyxSUQQIZCFQ/HH3kcv+NE4/+YTeR3/9EJiGQPFHf7kt3TTO5xzdEui3BUq3RqBaAgQItCPgdVk77q7anoAAvD17VyZAgEDrAsO+mbz14hRAgEBWAj5tklU7kxjMqP13kyhSETMVEIDPlNfJCRDIWMDrsoyba2h9BQTgJgYBAgQIECBAgAABAgQIECBAgAABAgQIZCkgAM+yrQZFgAABAgQIECBAgAABAgQIECBAgAABAgJwc4AAAQIECBAgQIAAAQIECBAgQIAAAQIEshQQgGfZVoMiQIAAAQIECBAgQIAAAQIECBAgQIAAAQG4OUCAAAECBAgQIECAAAECBAgQIECAAAECWQoIwLNsq0FNW2DLQw/HO865MK747LVLp77ofWfG8w89ZOm/r7vh5njDW87q/fdznnVQXHDWqbHnHrv1/rv4huUPXnzl0m3fdcZJcexRh29Xpm9innbn0j7fLOfVOOdOW0d1BAgQIECAAAECBAgQyE/glq/fFuvOODe+dcfdffOD4h8vu/Lz8fazL+z9/uiXHBbvPP2kWL3LzjHq77zN994fJ595Xtx4060Dz52fqBERGC0gAB9t5BYEongS+dAln46TX/+q3pNOEXav37ApNp59Whx84JoonsB+c8OmePf6tb3/Lp6srr3+K70nqeLngg9/It54wst7gXj5ZLdh/dqlAL365DYoHNeG/ARmOa9GnTs/TSMiQIAAAQIECBAgQIBA+gJFnvDN2+5cWhRXLIS7/c57lkLu4vfnbrx0aVFd8fvi563rjh+ZTdTPXc0miizDD4FFFRCAL2rnjbuRQPmu6mnrju+F2MWTyte+eXvvCan4qQfi1YuV79ge9rxnb7cK3ArwRm3p/MGzmlcFTP3cnccyAAIECBAgQIAAAQIECGQg0C/wfvpT91/KC+q/rw551N95w47NgM4QCIwtIAAfm8oNCfxQoB5wV9+RHRU2DnuCEoAv9iyb1bwa9abMYqsbPQECBAgQIECAAAECBNoTqH+CvNh+tbpgbtgCu2G/K0ZUX13e3ihdmUC7AgLwdv1dvYMC/VZw14PrUSF3MexytXiVQADewQkxpZJnOa+GfepgSuU7DQECBAgQIECAAAECBAhMKFAPsMu/3Y475oilLVMHhdzD/s4rt1mtfz/ZhOW5OYFsBATg2bTSQOYhUD7B7L/vXssC7HFXgI9691UAPo8upneNWc6rQedOT0FFBAgQIECAAAECBAgQWByBft8P1i/U7heAj/t3ni1QFmc+GelwAQG4GUJgTIFhTzDj7AE+KvwuyhCAj9mMjG42y3k17ouijDgNhQABAgQIECBAgAABAskL9Au/y6LruUA9xJ7k77zi0+nr37MpTj/lhDj4wDXJuyiQwKwEBOCzknXerARGbSFRf0e2/k3L9RXig3AE4FlNm5GDmeW8GnXukcW5AQECBAgQIECAAAECBAhMXWDUvt39vhSzKKLYRnXU33lFFvHUNfsubZ9S/Pell18TF5x1auy5x25TH4sTEuiKgAC8K51SZ6sC5buz37rj7mV1vOl1Ry1thVI8Sb3hLWf1fl/dZ6vcD/zGm25dduzRLzks3nn6SbF6l52j3J+rvMEB++0dG88+zTu0rXZ99hef5by67Y67Yt0Z58awOTv7EboCAQIECBAgQIAAAQIECFQF6n//l7+76H1nLguu3372hb1fVbODUX9D1n9vD3Bzj8A2AQG4mUCAAAECBAgQIECAAAECBAgQIECAAAECWQoIwLNsq0ERIECAAAECBAgQIECAAAECBAgQIECAgADcHCBAgAABAgQIECBAgAABAgQIECBAgACBLAUE4Fm21aAIECBAgAABAgQIECBAgAABAgQIECBAQABuDhAgQIAAAQIECBAgQIAAAQIECBAgQIBAlgIC8CzbalAECBAgQIAAAQIECBAgQIAAAQIECBAgIAA3BwgQIECAAAECBAgQIECAAAECBAgQIEAgSwEBeJZtNSgCBAgQIECAAAECBAgQIECAAAECBAgQEICbAwQIECBAgAABAgQIECBAgAABAgQIECCQpYAAPMu2GhQBAgQIECBAgAABAgQIECBAgAABAgQICMDNAQIECBAgQIAAAQIECBAgQIAAAQIECBDIUkAAnmVbDYoAAQIECBAgQIAAAQIECBAgQIAAAQIEBODmAAECBAgQIECAAAECBAgQIECAAAECBAhkKSAAz7KtBkWAAAECBAgQIECAAAECBAgQIECAAAECAnBzgAABAgQIECBAgAABAgQIECBAgAABAgSyFBCAZ9lWgyJAgAABAgQIECBAgAABAgQIECBAgAABAbg5QIAAAQIECBAgQIAAAQIECBAgQIAAAQJZCgjAs2yrQREgQIAAAQIECBAgQIAAAQIECBAgQICAALzhHLjt7i0Nz+BwAgQIECBAgAABAgQIECBAgAABAgQI9BdYs/dqNA0EBOAN8IpDBeANAR1OgAABAgQIECBAgAABAgQIECBAgMBAAQF4s8khAG/mJwBv6OdwAgQIECBAgAABAgQIECBAgAABAgQGCwjAm80OAXgzPwF4Qz+HEyBAgAABAgQIECBAgAABAgQIECAgAJ/VHBCAN5S1BcpwwJ2+fH3seOtXY8trXttQevEOf/KFvx8P/csXx6MHP3PxBm/EBAgQIECAAAECBAgQIECAQPYCq//4o/H9g38sHjn0p7Mfa5MBWgHeRC9CAN7MzwrwIX473fCl2OfYo2PVA/fH5g98KLYce1xD7cU5/MkX/G7s8fb/GI/tt398+/Kr49GDDl6cwRspAQIECBAgQIAAAQIECBAgkL3A6j+6JPY8+U2xdbfd466PXxmPPPensh/zSgcoAF+p3LbjBODN/ATgA/x64ferj4pV99+37RY77BCbf//DseVVr2konv/hT/7A78Ue/+mMpYE+tu9+8e0/+VMheP6tN0ICBAgQIECAAAECBAgQILAQAqs/8cex59pfjti6tTfeXgj+iU8LwQd0XwDe7G4hAF+B35FHHrndUR++5FMrOFOehyyt/L7v3ojDI+LHI+IPHg/BN14UW179S3kOfAqjWhZ+/6eI+GRE3Bjx2AFr4tuf+h/x6DMOmsJVnIIAAQIECBAgQIAAAQIECBAg0I7A6ss+Fnv+uzdGPPZYxNqIuDki/jxi6x5Pibsuu0IIXmnL60/4xe2adPXVV7fTuA5fVQC+guYVAXh1shX/LQDfBrnTX39528rvIvz+2Yj404jYJSLeEBF/GBFPeELcs+kP46FffPUK5PM+5Mkf2hR7nP7vtw3ynIj4DxGxOSJeFBF/F/Homh+Nu4qV4E87MG8IoyNAgAABAgQIECBAgAABAgSyFNjlUx+PvYqV348+ui38/v2I2BIRL42I/xWxdfc94tt/cnV8/9k/meX4Jx1UEYDXM0gB+KSKtkCZXCwiBOD92ZaF30dExJURsfrx2xafaFkXEZuE4P30+obf5Q2F4Cu6nzqIAAECBAgQIECAAAECBAgQSEegb/hdllcJwR/bc8+465OfEYJHhAB8OvPXCvAVOArAt0fbLvy+KiKe2Af3Vysh+IcviYdedvQKOpDXIUPDbyF4Xs02GgIECBAgQIAAAQIECBAgsIACQ8NvIfjAGSEAn86dRQC+AkcB+HK0scPv8rAyBN9xx7jnoosXOgQfK/wWgq/gXuoQAgQIECBAgAABAgQIECBAIAWBZeH3r0fE7w6pqlgJflREXBNhJbgV4NOavwLwFUgKwH+ItuPf3Rj7vOplscPmzREvjogrHt/ze5Trm7c94G3d4Qnx+dM+Et984StHHZHd73/i0xfE8z/41m3jOjsiTh9jiMV2KC+JiC9HPLjPP4nP/Pafxnf3tSf4GHJuQoAAAQIECBAgQIAAAQIECMxZ4Gn/64/j8Pf98rYvvDw1It47RgFFCF5sGPBnEY/ttVfc9YmrFnY7FCvAx5gvY9xEAD4GUv0mAvDHRR57LPb9mUNjx1u+GvHkiPh/EfGUCUAP3/Ytv99/ws5xxhtvifuetN8EB3f7pgfc/ZV410cO3TaIEyPiv00wnq9HxE9t+4LMv1/zc3H2L31ugoPdlAABAgQIECBAgAABAgQIECAwe4Hdv3t7nPOhg+MJjz2yLcf40gTXvCci/llE/GPE9w9+Ztz5lzdE7LDDBCfI46YC8On0UQC+AkcB+A/Rdvzbv4kfOebnY9X99237xt4/GbD3d925+MjL+7f94xfe8uH42s8dv4JOdPuQH79qY7zgD96ybRCTrAAvnL8U8dAe+8Zn/stn4/4DntltCNUTIECAAAECBAgQIECAAAECWQo87S8vixed929jVbEC/Nci4vfGGOb3Ht8G5XMRW3fdLe765FXxyHOLBH3xfgTg0+m5AHwFjgLw5Wgr3gN81arYvPGi2HLscSvoQh6HrHQP8Mf23S++feXn4tGnPyMPCKMgQIAAAQIECBAgQIAAAQIEshQY6wswy5FX9gDfutvucdenPhOPPOe5WbqMMygB+DhKo28jAB9ttN0tBODbo40dgpdfgCn8XkIcKwQv9v5+UUT8XYTwewV3WocQIECAAAECBAgQIECAAAECrQmMFYIX4Xfxqff/FSH83tYqAfh0pqwAfAWOAvD+aNuF4FdGxOrHb7s1ItZFxKaIEH5vBzg0BBd+r+Be6hACBAgQIECAAAECBAgQIEAgJYGhIbjwu2+rBODTmcEC8BU4CsAHoy0LwX82Iv40InaJiDdExB8Kv4dNtyd/4Pdij/90xrabnBMR/yEi7oqII6z8XsHd1CEECBAgQIAAAQIECBAgQIBAYgK9EPxX/m1EsSf42oj4/YgQfg/skgB8OhNYAL4CRwH4cLSdbvhS7HPs0bHqvnsjDv/Bqu8fjYiLtx2z+QMXxpZfOmEF6otxyLIQ/M0/2Pbks4+H3/vtH9++/Op49KCDFwPCKAkQIECAAAECBAgQIECAAIEsBVb/0SWx58lviti6NeLEiPj6D7Y++ULE1t33iLsuuyIeOfSnsxz3SgYlAF+J2vbHCMBX4CgAH42205evj31e84ptIXjxs8MOsfmCD8aW17x29MELfotd3/9fY/d3rF9SeKwIvz/1P+LRg5+54DKGT4AAAQIECBAgQIAAAQIECOQgsPpjF8eep/zKthA8hN+DeioAn85sF4CvwLEIwOs/H77kUys4U96H7HT9dbHPsUfFqi1bYvPvfzi2vOo1eQ94iqPb9fzfid3/85nx2I/sG9++4rNWfk/R1qkIECBAgAABAgQIECBAgACB9gXKleBbn7xr3PWJT1v53aclRQBe/7n66qvbb17HKhCAN2zYbXcXGxX5GSRQhOA7fv1rseXY4yBNKPDkP/hAPPTiI4XfE7q5OQECBAgQIECAAAECBAgQINANgdWX/vf4/o8fIvwe0a41e6/uRkMTrVIA3rAxAvCGgA4nQIAAAQIECBAgQIAAAQIECBAgQGCggAC82eQQgDfzCwF4Q0CHEyBAgAABAgQIECBAgAABAgQIECAgAJ/RHBCAN4QVgDcEdDgBAgQIECBAgAABAgQIECBAgAABAgLwGc0BAXhDWAF4Q0CHEyBAgAABAgQIECBAgAABAgQIECAgAJ/RHBCAzwjWaQkQIECAAAECBAgQIECAAAECBAgQIECgXQEBeLv+rk6AAAECBAgQIECAAAECBAgQIECAAAECMxIQgM8I1mkJECBAgAABAgQIECBAgAABAgQIECBAoF0BAXi7/q5OgAABAgQIECBAgAABAgQIECBAgAABAjMSEIDPCNZpCRAgQIAAAQIECBAgQIAAAQIECBAgQKBdAQF4u/6uToAAAQIECBAgQIAAAQIECBAgQIAAAQIzEhCAzwjWaQkQIECAAAECBAgQIECAAAECBAgQIECgXQEBeLv+rk6AAAECBAgQIECAAAECBAgQIECAAAECMxIQgM8I1mkJECBAgAABAgQIECBAgAABAgQIECBAoF0BAXi7/q5OgAABAgQIECBAgAABAgQIECBAgAABAjMSEIDPCNZpCRAgQIAAAQIECBAgQIAAAQIECBAgQKBdAQF4u/6uToAAAQIECBAgQIAAAQIECBAgQIAAAQIzEhCAzwjWaQkQIECAAAECBAgQIECAAAECBAgQIECgXQEBeLv+rk6AAAECBAgQIECAAAECBAgQIECAAAECMxIQgM8I1mkJECBAgAABAgQIECBAgAABAgQIECBAoF0BAXi7/q5OgAABAgQIECBAgAABAgQIECBAgAABAjMSEIDPCNZpCRAgQIAAAQIECBAgQIAAAQIECBAgQKBdAQF4u/6uToAAAQIECBAgQIAAAQIECBAgQIAAAQIzEhCAzwjWaQkQIECAAAECBAgQIECAAAECBAgQIECgXQEBeLv+rk6AAAECBAgQIECAAAECBAgQe5llYQAAC1pJREFUIECAAAECMxIQgM8I1mkJECBAgAABAgS6JbD53vvj5DPPi9PWHR/PP/SQuO6Gm+PcjZfGBWedGnvusVu3BqNaAgQIECBAgAABAgR6AgJwE4EAAQIECBAgQKAVgS0PPRzvOOfCuOKz1253/aNfcli88/STYvUuO09cWz3IHvcEXQnA37vx0vjgxVcuG9YB++0dG88+LQ4+cM24w+3d7rIrPx/XXv+VFVtPdDE3JkCAAAECBAgQINCCgAC8BXSXJECAAAECBAgQiCgD8P333Sveuu74qZEsQgB++533LAuti9Xqb3jLWfGm1x01kaUAfGrTzokIECBAgAABAgQSFRCAJ9oYZREgQIAAAQIEchcYJwAvtyEptiVZv2FTfOuOu+NdZ5wUxx51eNRXQhfh78mvf9V2q8qf86yDlrYxueXrt8W6M87tnaf4qQbGo1aA9wuL69uklLd5xZE/09tOpfjptzq7uN3bz75wWYvLcRX/WAba5Q2qYyjGXQ/Ai9uVYzvl9a/s+RQ/9etUa6lfo7h9UcO/+hc/tWwrmOLfy14d9rxn985dWr3xtS+LD330qrjxplt71yuOf+4/feYy44ved2ZvSxk/BAgQIECAAAECBNoQEIC3oe6aBAgQIECAAAECY60AL0Pa+pYo9TC6CGgv+PAn4o0nvLwnW93Lu6QuAuLf3LAp3r1+bW+rkHoAP60AvAi2q8F6PbAuar/08muWQvl6uFyvswyyn7pm316QPCgAL27X71rlceV5qtfuF+r3W0E/KAAvzlnukV72qhrW20fdHZ0AAQIECBAgQKBtAQF42x1wfQIECBAgQIDAggqMswf43958a98voiyC3uKn39Ypg7ZAKY55+lP3X1odXRxfDWjrwfmg1d3VvcknvU2/cL4eLo8KjYcF4PVwvT61Cpv179kUp59yQu9NgKYBePmFocV1+rmvdDuaBb1LGDYBAgQIECBAgMAMBATgM0B1SgIECBAgQIAAgdECk2yBUq4yLs9a3b6juuJ4UBA7LGwvj59HAH7Pd+5ftgq9uOag1dXltiL1LUQmDcD7fWlmeU4B+Oh56hYECBAgQIAAAQLdFhCAd7t/qidAgAABAgQIdFagSQA+LAivB9n9QuZ+aNPaAuXa67+y3RdUnrvx0t5WIUUAfs75l8SGt62NPffYrVdGPQAvayvrqQfh426B8tD3vtfbCuZpa/Zdqqc+RgF4Z+8+CidAgAABAgQIEBhTQAA+JpSbESBAgAABAgQITFdgGgF4WVF13+w1++3T+yLM4445YtmXLw7bNqU4z7wC8Oo+5OOE8/WAfNSXYG5Yv7Y37sKkHrb3G+PHLr9mWWA/yR7gtkCZ7n3C2QgQIECAAAECBKYvIACfvqkzEiBAgAABAgQIjCGw0gC8OO6cCy6JE499aW8f6+Knum/2Lk98Yi8A33/fvZbtEV5um/KuM05a2ge8CHs/dMmn4+TXvyrKFdNlqFvfi7v47/UbNsXGs0/rXbcMiovrl1u09FtRPaq2el3FOYqfY486vPe/9UC6XwBenqP65ZtFAL7ujHOjDMSLc5XboZRboNTHVA3kq35FTcWXe5Z24+73bQ/wMe4IbkKAAAECBAgQIDBTAQH4THmdnAABAgQIECBAYJBAky/BLAPZ8twH7Lf3UjBd/FsZ/n7rjrujukd49d/LYweFuv2+jLJ63eK8b3zty+JDH71q7AC82PakPu7XvvLF8cADD8Zhz3t2L/QeVmM1xK661sdf/q66V3rv2N86pVdvdeV2dY/wukW5/cr63zgx/uYrtyzVKAB3vyZAgAABAgQIEOiKgAC8K51SJwECBAgQIECAQJYCZSBe37Ily8EaFAECBAgQIECAAIE5CwjA5wzucgQIECBAgAABAostUKwif+qafZf2J++30nyxhYyeAAECBAgQIECAwPQEBODTs3QmAgQIECBAgAABAiMF6tu3VLdoGXmwGxAgQIAAAQIECBAgMJGAAHwiLjcmQIAAAQIECBAgQIAAAQIECBAgQIAAga4ICMC70il1EiBAgAABAgQIECBAgAABAgQIECBAgMBEAgLwibjcmAABAgQIECBAgAABAgQIECBAgAABAgS6IiA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WEBEREI ALLGEMEIN, Weberei allgemein\"},\"hovermode\":\"x unified\"},                        {\"responsive\": true}                    ).then(function(){\n",
+       "                            \n",
+       "var gd = document.getElementById('64a5454a-ea90-4b06-8393-a5434fc4c859');\n",
+       "var x = new MutationObserver(function (mutations, observer) {{\n",
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+       "        if (!display || display === 'none') {{\n",
+       "            console.log([gd, 'removed!']);\n",
+       "            Plotly.purge(gd);\n",
+       "            observer.disconnect();\n",
+       "        }}\n",
+       "}});\n",
+       "\n",
+       "// Listen for the removal of the full notebook cells\n",
+       "var notebookContainer = gd.closest('#notebook-container');\n",
+       "if (notebookContainer) {{\n",
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+       "    x.observe(outputEl, {childList: true});\n",
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+       "\n",
+       "                        })                };                });            </script>        </div>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "title = 'HObjektText: TEST'\n",
+    "\n",
+    "markers = {\n",
+    "    'size': 12,\n",
+    "    'color': 'yellow',\n",
+    "    'line': {\n",
+    "        'width': 2,\n",
+    "        'color': 'red',\n",
+    "    },\n",
+    "}\n",
+    "hover_data = {\n",
+    "    'ErstellungsDatum': '|%d.%m.%Y',\n",
+    "    'VorgangsBeschreibung': True,\n",
+    "}\n",
+    "fig = px.line(\n",
+    "    data_frame=res,\n",
+    "    x='ErstellungsDatum',\n",
+    "    y='ObjektID',\n",
+    "    title=title,\n",
+    "    hover_data=hover_data,\n",
+    ")\n",
+    "fig.update_traces(mode='markers+lines', marker=markers, marker_symbol='diamond')\n",
+    "fig.update_xaxes(\n",
+    "    tickformat=\"%B\\n%Y\",\n",
+    "    rangeslider_visible=True,\n",
+    ")\n",
+    "fig.update_yaxes(type='category')\n",
+    "fig.update_layout(hovermode=\"x unified\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8642ade9-30c1-4ac6-8257-9437e2e5e5a1",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
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+   "execution_count": null,
+   "id": "5f909534-8a08-42c1-a640-453b0c76d3c9",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
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+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
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+   "version": "3.11.8"
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+ },
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+ "nbformat_minor": 5
+}